pax_global_header00006660000000000000000000000064147415704630014525gustar00rootroot0000000000000052 comment=6ad7f5e69e53b0cdcc2a0650d28befbced7e6e02 sourmash-bio-sourmash-bc3c0f8/000077500000000000000000000000001474157046300164665ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/.cargo/000077500000000000000000000000001474157046300176375ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/.cargo/config.toml000066400000000000000000000002521474157046300220000ustar00rootroot00000000000000[target.x86_64-unknown-linux-musl] rustflags = ["-C", "target-feature=-crt-static"] [target.aarch64-unknown-linux-musl] rustflags = ["-C", "target-feature=-crt-static"] sourmash-bio-sourmash-bc3c0f8/.envrc000066400000000000000000000001231474157046300176000ustar00rootroot00000000000000#use flake PATH=~/.pixi/bin:$PATH watch_file pixi.lock eval "$(pixi shell-hook)" sourmash-bio-sourmash-bc3c0f8/.git-blame-ignore-revs000066400000000000000000000001061474157046300225630ustar00rootroot00000000000000# Pre-commit updates (#2427) fee62922d8857ce93f1d4e90fd7240629d606997 sourmash-bio-sourmash-bc3c0f8/.git_archival.txt000066400000000000000000000000301474157046300217320ustar00rootroot00000000000000ref-names: tag: v4.8.14 sourmash-bio-sourmash-bc3c0f8/.gitattributes000066400000000000000000000004321474157046300213600ustar00rootroot00000000000000.git_archival.txt export-subst tests/test-data/*.sig linguist-detectable=false tests/test-data/*/*.sig linguist-detectable=false tests/test-data/*/*/*.sig linguist-detectable=false tests/test-data/.sbt.v2/*.sbt linguist-detectable=false include/sourmash.h linguist-language=C sourmash-bio-sourmash-bc3c0f8/.github/000077500000000000000000000000001474157046300200265ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/.github/PULL_REQUEST_TEMPLATE.md000066400000000000000000000012431474157046300236270ustar00rootroot00000000000000Please replace this text with: * a brief description of your changes in this PR * which issues this fixes in the issue tracker, if any ("Fixes #XXX") * which issues this PR is related to, if any ("Ref #XXX") Please also be sure to note here if file formats, command-line interface, and/or the top-level sourmash API will change because of this PR. If you are a new contributor, please add your name and [your ORCID](https://orcid.org) to the `pyproject.toml` author list (maintaining alphabetical order by last name). If you don't have an ORCID, please [register for one](https://orcid.org/register). Once the items above are done, and all checks pass, request a review! sourmash-bio-sourmash-bc3c0f8/.github/dependabot.yml000066400000000000000000000011021474157046300226500ustar00rootroot00000000000000version: 2 updates: - package-ecosystem: pip directory: "/" schedule: interval: weekly open-pull-requests-limit: 10 - package-ecosystem: cargo directory: "/" schedule: interval: weekly allow: - dependency-type: "direct" open-pull-requests-limit: 10 ignore: - dependency-name: "byteorder" - dependency-name: "wasm-bindgen" - dependency-name: "once_cell" - dependency-name: "chrono" - dependency-name: "js-sys" - dependency-name: "web-sys" - package-ecosystem: "github-actions" directory: "/" schedule: interval: weekly sourmash-bio-sourmash-bc3c0f8/.github/workflows/000077500000000000000000000000001474157046300220635ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/.github/workflows/asv.yml000066400000000000000000000013151474157046300233770ustar00rootroot00000000000000name: asv benchmarks on: push: branches: [latest] pull_request: schedule: - cron: "0 0 * * *" # daily jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - name: make latest branch available run: | git checkout latest git checkout - - name: Set up Python 3.10 uses: actions/setup-python@v5 with: python-version: "3.10" - uses: dtolnay/rust-toolchain@stable - uses: Swatinem/rust-cache@v2 - name: Install dependencies run: | python -m pip install --upgrade pip pip install tox - name: Runs benchmarks against latest run: tox -e asv sourmash-bio-sourmash-bc3c0f8/.github/workflows/build_wheel.yml000066400000000000000000000046051474157046300250760ustar00rootroot00000000000000name: cibuildwheel permissions: write-all on: push: branches: [latest] tags: v* pull_request: schedule: - cron: "0 0 * * *" # daily jobs: build_wheels: name: Build wheels for ${{ matrix.os }}-${{ matrix.arch }} runs-on: ${{ matrix.os }} strategy: matrix: build: [ linux-x86_64, macos-x86_64, macos-arm64, ] include: - build: linux-x86_64 os: ubuntu-20.04 arch: x86_64 macos_target: '' - build: macos-x86_64 os: macos-latest arch: x86_64 macos_target: 'MACOSX_DEPLOYMENT_TARGET=11.0 CARGO_BUILD_TARGET=x86_64-apple-darwin' - build: macos-arm64 os: macos-latest arch: arm64 macos_target: 'MACOSX_DEPLOYMENT_TARGET=11.0 CARGO_BUILD_TARGET=aarch64-apple-darwin' fail-fast: false steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - uses: actions/setup-python@v5 name: Install Python with: python-version: '3.10' - name: Build wheels uses: pypa/cibuildwheel@v2.22.0 env: CIBW_ENVIRONMENT_MACOS: ${{ matrix.macos_target }} CIBW_ARCHS_LINUX: ${{ matrix.arch }} CIBW_ARCHS_MACOS: ${{ matrix.arch }} - uses: actions/upload-artifact@v4 with: name: wheel-${{ matrix.build }}-${{ matrix.os }} path: './wheelhouse/sourmash*.whl' build_wasm: runs-on: ubuntu-20.04 steps: - uses: actions/checkout@v4 - name: Build wheels uses: pypa/cibuildwheel@v2.22.0 env: CIBW_PLATFORM: pyodide CIBW_BUILD: 'cp312-pyodide_wasm32' - uses: actions/upload-artifact@v4 with: name: wheel-wasm path: './wheelhouse/sourmash*.whl' release: name: Publish wheels runs-on: ubuntu-20.04 if: startsWith(github.ref, 'refs/tags/v') needs: [build_wheels, build_wasm] steps: - name: Fetch wheels from artifacts id: fetch_artifacts uses: actions/download-artifact@v4 with: path: 'wheels/' # if it matches a Python release tag, upload to github releases - name: Release uses: softprops/action-gh-release@v2 with: files: | ${{steps.fetch_artifacts.outputs.download-path}}/wheel-*/* sourmash-bio-sourmash-bc3c0f8/.github/workflows/build_wheel_all_archs.yml000066400000000000000000000040231474157046300271000ustar00rootroot00000000000000name: maturin wheels permissions: write-all on: pull_request: # use for testing modifications to this action push: branches: [latest] tags: v* workflow_dispatch: schedule: - cron: "0 0 * * *" # daily jobs: linux: runs-on: ubuntu-latest strategy: matrix: target: [aarch64] #target: [aarch64, ppc64le] #target: [aarch64, s390x, ppc64le] steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - uses: actions/setup-python@v5 with: python-version: '3.10' - name: Build wheels uses: PyO3/maturin-action@v1 with: target: ${{ matrix.target }} args: --release --out dist --find-interpreter --zig --strip sccache: 'true' manylinux: auto container: 'off' - name: Upload wheels uses: actions/upload-artifact@v4 with: name: wheel-${{ matrix.target }} path: dist windows: runs-on: windows-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: '3.10' architecture: ${{ matrix.target }} - name: Build wheels uses: PyO3/maturin-action@v1 with: target: x64 args: --release --out dist --find-interpreter sccache: 'true' - name: Upload wheels uses: actions/upload-artifact@v4 with: name: wheel-win64 path: dist release: name: Publish wheels runs-on: ubuntu-latest if: startsWith(github.ref, 'refs/tags/v') needs: [linux, windows] steps: - name: Fetch wheels from artifacts id: fetch_artifacts uses: actions/download-artifact@v4 with: path: 'wheels/' # if it matches a Python release tag, upload to github releases - name: Release uses: softprops/action-gh-release@v2 with: files: | ${{steps.fetch_artifacts.outputs.download-path}}/wheel-*/* sourmash-bio-sourmash-bc3c0f8/.github/workflows/codspeed.yml000066400000000000000000000015431474157046300243770ustar00rootroot00000000000000name: codspeed-benchmarks on: # Run on pushes to the main branch push: branches: - "latest" # Run on pull requests pull_request: # `workflow_dispatch` allows CodSpeed to trigger backtest # performance analysis in order to generate initial data. workflow_dispatch: jobs: benchmarks-rust: runs-on: ubuntu-22.04 steps: - uses: actions/checkout@v4 - name: Setup rust toolchain, cache and cargo-codspeed binary uses: moonrepo/setup-rust@v1 with: channel: stable cache-target: release bins: cargo-codspeed - name: Build the benchmark target(s) run: cargo codspeed build -p sourmash - name: Run the benchmarks uses: CodSpeedHQ/action@v3 with: run: "cd src/core && cargo codspeed run" token: ${{ secrets.CODSPEED_TOKEN }} sourmash-bio-sourmash-bc3c0f8/.github/workflows/dev_envs.yml000066400000000000000000000035011474157046300244160ustar00rootroot00000000000000name: "Dev env instructions" on: pull_request: push: branches: [latest] concurrency: group: ${{ github.ref }} cancel-in-progress: ${{ github.ref != 'refs/heads/latest' }} jobs: nix: runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest, macos-14] steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - name: Install Nix uses: DeterminateSystems/nix-installer-action@v16 - name: Run the Magic Nix Cache uses: DeterminateSystems/magic-nix-cache-action@v8 - run: nix run .# -- --version - run: nix develop --command bash -c "tox -e py311" pixi: runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest, macos-14] steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - name: set up pixi uses: prefix-dev/setup-pixi@v0.8.1 with: pixi-version: v0.39.5 cache: true frozen: true - name: run tests for 3.10 shell: pixi run bash {0} run: tox -e py310 mamba: runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest, macos-14] steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - name: cache conda uses: actions/cache@v4 env: CACHE_NUMBER: 0 with: path: ~/conda_pkgs_dir key: ${{ runner.os }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles('environment.yml') }} - name: setup conda uses: conda-incubator/setup-miniconda@d2e6a045a86077fb6cad6f5adf368e9076ddaa8d with: python-version: "3.10" miniforge-version: latest environment-file: dev.yml activate-environment: sourmash_dev auto-activate-base: false - name: run tests for 3.10 shell: bash -l {0} run: tox -e py310 sourmash-bio-sourmash-bc3c0f8/.github/workflows/draft-pdf.yml000066400000000000000000000012221474157046300244520ustar00rootroot00000000000000on: [push] jobs: paper: runs-on: ubuntu-latest name: Paper Draft steps: - name: Checkout uses: actions/checkout@v4 - name: Build draft PDF uses: openjournals/openjournals-draft-action@master with: journal: joss # This should be the path to the paper within your repo. paper-path: paper.md - name: Upload uses: actions/upload-artifact@v4 with: name: paper # This is the output path where Pandoc will write the compiled # PDF. Note, this should be the same directory as the input # paper.md path: paper.pdf sourmash-bio-sourmash-bc3c0f8/.github/workflows/hypothesis.yml000066400000000000000000000013751474157046300250130ustar00rootroot00000000000000name: Hypothesis tests on: push: branches: [latest] pull_request: schedule: - cron: "0 0 * * *" # daily jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - name: Set up Python 3.10 uses: actions/setup-python@v5 with: python-version: "3.10" - name: Install dependencies run: | python -m pip install --upgrade pip pip install tox - name: Run Hypothesis tests run: tox -e hypothesis,coverage - name: Upload Python coverage to codecov uses: codecov/codecov-action@v3 with: flags: hypothesis-py fail_ci_if_error: true files: .tox/coverage.xml sourmash-bio-sourmash-bc3c0f8/.github/workflows/metadata.yml000066400000000000000000000007451474157046300243740ustar00rootroot00000000000000name: Metadata checks on: push: branches: [latest] pull_request: schedule: - cron: "0 0 * * *" # daily jobs: archive: runs-on: ubuntu-latest steps: - name: Checkout sources uses: actions/checkout@v4 - name: Trigger new archival in software heritage on new tags if: startsWith(github.ref, 'refs/tags/') run: curl https://archive.softwareheritage.org/api/1/origin/save/git/url/https://github.com/sourmash-bio/sourmash.git/ sourmash-bio-sourmash-bc3c0f8/.github/workflows/python.yml000066400000000000000000000041661474157046300241360ustar00rootroot00000000000000# note: to invalidate caches, adjust the pip-v? number below. name: Python tests on: push: branches: [latest] pull_request: schedule: - cron: "0 0 * * *" # daily jobs: test: runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-22.04, macos-latest] py: ["3.12", "3.11", "3.10"] fail-fast: false steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - name: Set up Python ${{ matrix.py }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.py }} - name: Get pip cache dir id: pip-cache run: | echo "::set-output name=dir::$(pip cache dir)" - name: pip cache uses: actions/cache@v4 with: path: ${{ steps.pip-cache.outputs.dir }} key: ${{ runner.os }}-pip-v4-${{ hashFiles('**/pyproject.toml') }} restore-keys: | ${{ runner.os }}-pip-v4- - uses: dtolnay/rust-toolchain@stable - uses: Swatinem/rust-cache@v2 - name: Install dependencies run: | python -m pip install --upgrade pip pip install tox tox-gh-actions - name: Set up Pandoc uses: r-lib/actions/setup-pandoc@v2 - name: Set up IPFS if: startsWith(runner.os, 'Linux') && (matrix.py == '3.10') uses: ibnesayeed/setup-ipfs@master with: ipfs_version: 0.6 run_daemon: true - name: Start Redis if: startsWith(runner.os, 'Linux') && (matrix.py == '3.10') uses: supercharge/redis-github-action@1.8.0 with: redis-version: 6 - name: tox cache uses: actions/cache@v4 with: path: .tox/ key: ${{ runner.os }}-tox-v4-${{ hashFiles('**/pyproject.toml') }} restore-keys: | ${{ runner.os }}-tox-v4- - name: Test with tox run: tox env: PYTHONDEVMODE: 1 - name: Upload Python coverage to codecov uses: codecov/codecov-action@v3 with: flags: python fail_ci_if_error: true files: .tox/coverage.xml sourmash-bio-sourmash-bc3c0f8/.github/workflows/rust.yml000066400000000000000000000142601474157046300236060ustar00rootroot00000000000000name: Rust checks on: push: branches: [latest] pull_request: schedule: - cron: "0 0 * * *" # daily jobs: check: name: Check runs-on: ubuntu-latest steps: - name: Checkout sources uses: actions/checkout@v4 - uses: dtolnay/rust-toolchain@stable - uses: Swatinem/rust-cache@v2 - name: Run cargo check run: | cargo check test: runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: build: [beta, stable, windows, macos] include: - build: macos os: macos-latest rust: stable continue: false - build: windows os: windows-latest rust: stable continue: true - build: beta os: ubuntu-latest rust: beta continue: false - build: stable os: ubuntu-latest rust: stable continue: false steps: - uses: actions/checkout@v4 - uses: dtolnay/rust-toolchain@master with: toolchain: ${{ matrix.rust }} - uses: Swatinem/rust-cache@v2 - uses: taiki-e/install-action@nextest - name: Run tests run: | cargo nextest run test_all_features: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: dtolnay/rust-toolchain@stable - uses: Swatinem/rust-cache@v2 - uses: taiki-e/install-action@v2 with: tool: cargo-all-features - name: Run tests for all feature combinations run: cargo test-all-features --no-fail-fast --all coverage: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: dtolnay/rust-toolchain@stable with: components: llvm-tools-preview - uses: Swatinem/rust-cache@v2 - uses: taiki-e/install-action@cargo-llvm-cov - uses: taiki-e/install-action@nextest - name: Collect coverage data run: cargo llvm-cov nextest --all-features --lcov --output-path lcov.info - name: Upload Rust coverage to codecov uses: codecov/codecov-action@v3 with: flags: rust fail_ci_if_error: true files: lcov.info lints: name: Lints runs-on: ubuntu-latest strategy: fail-fast: false matrix: build: [beta, stable] include: - build: beta rust: beta - build: stable rust: stable steps: - name: Checkout sources uses: actions/checkout@v4 - name: Install Rust toolchain uses: dtolnay/rust-toolchain@master with: toolchain: ${{ matrix.rust }} components: "clippy, rustfmt" - uses: Swatinem/rust-cache@v2 - name: Run cargo fmt run: | cargo fmt --all -- --check - name: Run cargo clippy run: | cargo clippy --all -- -D warnings wasm-pack: name: Check if wasm-pack builds a valid package for the sourmash crate runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: dtolnay/rust-toolchain@stable with: target: wasm32-unknown-unknown - uses: Swatinem/rust-cache@v2 - name: Install wasm-pack run: "curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh" - name: Prepare node for running tests uses: actions/setup-node@v4 with: node-version: 16 registry-url: https://registry.npmjs.org/ - name: run wasm tests continue-on-error: true ## TODO: remove this when tests works again... run: wasm-pack test --node src/core - name: run wasm-pack build run: wasm-pack build src/core -d ../../pkg - name: Prepare package for NPM publishing working-directory: pkg run: npm pack - name: Publish to NPM if: startsWith(github.ref, 'refs/tags/r') working-directory: pkg run: npm publish env: NODE_AUTH_TOKEN: ${{ secrets.NPM_TOKEN }} - uses: actions/upload-artifact@v4 with: path: 'pkg/sourmash*.tgz' publish: name: Publish (on tags, dry-run otherwise) runs-on: ubuntu-latest steps: - name: Checkout sources uses: actions/checkout@v4 - uses: dtolnay/rust-toolchain@stable - uses: Swatinem/rust-cache@v2 - name: Check semver uses: obi1kenobi/cargo-semver-checks-action@v2 with: crate-name: sourmash version-tag-prefix: r feature-group: default-features features: branchwater - name: Make sure we can publish the sourmash crate run: | cargo publish --dry-run --manifest-path src/core/Cargo.toml # Login to crates.io on tags - name: login to crates.io if: startsWith(github.ref, 'refs/tags/r') run: | cargo login ${{ secrets.CRATES_IO_TOKEN }} # Publish to crates.io on tags - name: Publish to crates.io if: startsWith(github.ref, 'refs/tags/r') run: | cargo publish --manifest-path src/core/Cargo.toml minimum_rust_version: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: dtolnay/rust-toolchain@master with: toolchain: "1.71.1" - uses: Swatinem/rust-cache@v2 - name: check if README matches MSRV defined here run: grep '1.71.1' src/core/README.md - name: Check if it builds properly run: | cargo build --all-features check_cbindgen: name: "Check if cbindgen runs cleanly for generating the C headers" runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: dtolnay/rust-toolchain@stable - uses: Swatinem/rust-cache@v2 - uses: taiki-e/install-action@v2 with: tool: cbindgen@0.26.0 - run: make include/sourmash.h - name: check if headers have the same content run: | git update-index --refresh git diff git diff-index --quiet HEAD -- include/sourmash.h || echo "::error file=include/sourmash.h::Header doesn't match committed file, did you forget to rerun cbindgen?" sourmash-bio-sourmash-bc3c0f8/.gitignore000066400000000000000000000005611474157046300204600ustar00rootroot00000000000000*~ *.fa *.fastq.gz *.abundtrim *.keep *.pyc *.sig *.png *.labels.txt distances *.npy dist build sourmash.egg-info .ipynb_checkpoints .cache *.so .coverage .pytest_cache **/*.pyc .python-version src/sourmash/version.py *.DS_Store .tox src/sourmash/_lowlevel/* .env target/ .eggs .asv pkg/ wasm-pack.log .hypothesis # IDEs .vscode/ # nix .direnv/ result # pixi .pixi/ sourmash-bio-sourmash-bc3c0f8/.pre-commit-config.yaml000066400000000000000000000013071474157046300227500ustar00rootroot00000000000000repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v5.0.0 hooks: - id: check-ast - id: check-builtin-literals - id: check-docstring-first - id: check-merge-conflict - id: check-yaml - id: check-toml - id: debug-statements - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.9.1 hooks: - id: ruff-format - id: ruff args: ["--fix", "--unsafe-fixes", "--exit-non-zero-on-fix"] # - repo: https://github.com/tox-dev/tox-ini-fmt # rev: "1.3.1" # hooks: # - id: tox-ini-fmt # args: ["-p", "fix_lint"] - repo: meta hooks: - id: check-hooks-apply - id: check-useless-excludes sourmash-bio-sourmash-bc3c0f8/.readthedocs.yml000066400000000000000000000007171474157046300215610ustar00rootroot00000000000000# .readthedocs.yml # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details version: 2 build: os: ubuntu-22.04 tools: python: "3.10" rust: "1.75" apt_packages: - llvm-dev - libclang-dev - clang # Build documentation in the docs/ directory with Sphinx sphinx: configuration: doc/conf.py python: install: - method: pip path: . extra_requirements: - doc sourmash-bio-sourmash-bc3c0f8/.sonarcloud.properties000066400000000000000000000000011474157046300230220ustar00rootroot00000000000000 sourmash-bio-sourmash-bc3c0f8/.zenodo.json000066400000000000000000000041771474157046300207460ustar00rootroot00000000000000{"creators": [{"name": "Luiz Irber", "orcid": "0000-0003-4371-9659"}, {"name": "N. Tessa Pierce-Ward", "orcid": "0000-0002-2942-5331"}, {"name": "Mohamed Abuelanin", "orcid": "0000-0002-3419-4785"}, {"name": "Harriet Alexander", "orcid": "0000-0003-1308-8008"}, {"name": "Abhishek Anant", "orcid": "0000-0002-5751-2010"}, {"name": "Keya Barve", "orcid": "0000-0003-3241-2117"}, {"name": "Colton Baumler", "orcid": "0000-0002-5926-7792"}, {"name": "Olga Botvinnik", "orcid": "0000-0003-4412-7970"}, {"name": "Phillip Brooks", "orcid": "0000-0003-3987-244X"}, {"name": "Peter Cock", "orcid": "0000-0001-9513-9993"}, {"name": "Daniel Dsouza", "orcid": "0000-0001-7843-8596"}, {"name": "Jade Gardner", "orcid": "0009-0005-0787-5752"}, {"name": "Laurent Gautier", "orcid": "0000-0003-0638-3391"}, {"name": "Tim Head", "orcid": "0000-0003-0931-3698"}, {"name": "Mahmudur Rahman Hera", "orcid": "0000-0002-5992-9012"}, {"name": "Hannah Eve Houts", "orcid": "0000-0002-7954-4793"}, {"name": "Lisa K. 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Joshua Swamidass", "orcid": "0000-0003-2191-0778"}, {"name": "Connor Tiffany", "orcid": "0000-0001-8188-7720"}, {"name": "Pranathi Vemuri", "orcid": "0000-0002-5748-9594"}, {"name": "Erik Young", "orcid": "0000-0002-9195-9801"}, {"name": "Nick H", "orcid": "0000-0002-1685-302X"}, {"name": "C. Titus Brown", "orcid": "0000-0001-6001-2677"}]}sourmash-bio-sourmash-bc3c0f8/CITATION.cff000066400000000000000000000145221474157046300203640ustar00rootroot00000000000000cff-version: "1.2.0" authors: - family-names: Irber given-names: Luiz orcid: "https://orcid.org/0000-0003-4371-9659" - family-names: Pierce-Ward given-names: N. 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Titus orcid: "https://orcid.org/0000-0001-6001-2677" date-published: 2024-06-28 doi: 10.21105/joss.06830 issn: 2475-9066 issue: 98 journal: Journal of Open Source Software publisher: name: Open Journals start: 6830 title: "sourmash v4: A multitool to quickly search, compare, and analyze genomic and metagenomic data sets" type: article url: "https://joss.theoj.org/papers/10.21105/joss.06830" volume: 9 title: "sourmash v4: A multitool to quickly search, compare, and analyze genomic and metagenomic data sets" sourmash-bio-sourmash-bc3c0f8/CODE_OF_CONDUCT.rst000066400000000000000000000031771474157046300215050ustar00rootroot00000000000000Contributor Code of Conduct =========================== As contributors and maintainers of this project, we pledge to respect all people who contribute through reporting issues, posting feature requests, updating documentation, submitting pull requests or patches, and other activities. We are committed to making participation in this project a harassment-free experience for everyone, regardless of level of experience, gender, gender identity and expression, sexual orientation, disability, personal appearance, body size, race, age, or religion. Examples of unacceptable behavior by participants include the use of sexual language or imagery, derogatory comments or personal attacks, trolling, public or private harassment, insults, or other unprofessional conduct. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct. Project maintainers or contributors who do not follow the Code of Conduct may be removed from the project team. Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by emailing `titus@idyll.org `__ which only goes to C. Titus Brown. To report an issue involving Titus please email `Judi Brown Clarke, Ph.D. `__ the Diversity Director at the BEACON Center for the Study of Evolution in Action, an NSF Center for Science and Technology. This Code of Conduct is adapted from the `Contributor Covenant `__, version 1.0.0, available at http://contributor-covenant.org/version/1/0/0/ sourmash-bio-sourmash-bc3c0f8/CONTRIBUTING.md000066400000000000000000000005351474157046300207220ustar00rootroot00000000000000We welcome contributions! We use [GitHub Flow style development](https://guides.github.com/introduction/flow/). Please set up a pull request against our `latest` branch with any changes you want us to consider merging. 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sourmash-bio-sourmash-bc3c0f8/LICENSE000066400000000000000000000035411474157046300174760ustar00rootroot00000000000000======= License ======= Files: * Copyright: 2016, The Regents of the University of California. License: BSD-3-Clause Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of The Regents of the University of California, nor the names of contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Files: third-party/smhasher/* Copyright: none. License: public-domain Quoting from the file header: "MurmurHash3 was written by Austin Appleby, and is placed in the public domain. The author hereby disclaims copyright to this source code." sourmash-bio-sourmash-bc3c0f8/MANIFEST.in000066400000000000000000000003561474157046300202300ustar00rootroot00000000000000include LICENSE Makefile README.md requirements.txt include Cargo.toml include include/sourmash.h prune .eggs global-exclude *.rlib global-exclude *.orig global-exclude *.pyc global-exclude *.so prune tests/test-data global-exclude *.git sourmash-bio-sourmash-bc3c0f8/Makefile000066400000000000000000000023131474157046300201250ustar00rootroot00000000000000PYTHON ?= python all: build .PHONY: build: .PHONY $(PYTHON) -m pip install -e . clean: $(PYTHON) -m pip uninstall -y sourmash rm -rf src/sourmash/_lowlevel cd doc && make clean install: build dist: FORCE $(PYTHON) -m build --sdist test: .PHONY tox -e py310 cargo test doc: .PHONY tox -e docs include/sourmash.h: src/core/src/lib.rs \ src/core/src/ffi/mod.rs \ src/core/src/ffi/hyperloglog.rs \ src/core/src/ffi/minhash.rs \ src/core/src/ffi/signature.rs \ src/core/src/ffi/nodegraph.rs \ src/core/src/ffi/index/mod.rs \ src/core/src/ffi/index/revindex.rs \ src/core/src/ffi/storage.rs \ src/core/src/errors.rs \ src/core/cbindgen.toml cd src/core && \ RUSTC_BOOTSTRAP=1 cbindgen -c cbindgen.toml . -o ../../$@ coverage: all tox -e coverage benchmark: tox -e asv cargo bench check: cargo build cargo test cargo bench last-tag: git fetch -p -q; git tag -l | sort -V | tail -1 wasm: wasm-pack build src/core -d ../../pkg wasm-test: wasm-pack test --node src/core wasi: cargo wasi build FORCE: sourmash-bio-sourmash-bc3c0f8/README.md000066400000000000000000000135451474157046300177550ustar00rootroot00000000000000# sourmash Quickly search, compare, and analyze genomic and metagenomic data sets. [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active) License: 3-Clause BSD [![Documentation](https://readthedocs.org/projects/sourmash/badge/?version=latest)](http://sourmash.readthedocs.io/en/latest/) [![Gitter](https://badges.gitter.im/sourmash-bio/community.svg)](https://gitter.im/sourmash-bio/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [![DOI](https://joss.theoj.org/papers/10.21105/joss.06830/status.svg)](https://doi.org/10.21105/joss.06830) [![pyOpenSci](https://tinyurl.com/y22nb8up)](https://github.com/pyOpenSci/software-submission/issues/129) [![Bioconda install](https://img.shields.io/conda/dn/bioconda/sourmash.svg?style=flag&label=Bioconda)](https://anaconda.org/bioconda/sourmash) PyPI [![Conda Platforms](https://img.shields.io/conda/pn/conda-forge/sourmash-minimal.svg)](https://anaconda.org/conda-forge/sourmash-minimal) ![Python 3.10](https://img.shields.io/badge/python-3.10-blue.svg) ![Python 3.11](https://img.shields.io/badge/python-3.11-blue.svg) ![Python 3.12](https://img.shields.io/badge/python-3.12-blue.svg) [![Build Status](https://github.com/sourmash-bio/sourmash/workflows/Python%20tests/badge.svg)](https://github.com/sourmash-bio/sourmash/actions/) [![codecov](https://codecov.io/gh/sourmash-bio/sourmash/branch/latest/graph/badge.svg)](https://codecov.io/gh/sourmash-bio/sourmash)

Usage: sourmash sketch dna *.fq.gz sourmash compare *.sig -o distances.cmp -k 31 sourmash plot distances.cmp sourmash 1.0 is [published on JOSS](https://doi.org/10.21105/joss.06830); please cite that paper if you use sourmash (`doi: 10.21105/joss.06830`):. The latest major release is sourmash v4, which has several command-line and Python incompatibilities with previous versions. Please [visit our migration guide](https://sourmash.readthedocs.io/en/latest/support.html#migrating-from-sourmash-v3-x-to-sourmash-4-x) to upgrade! ---- sourmash is a k-mer analysis multitool, and we aim to provide stable, robust programmatic and command-line APIs for a variety of sequence comparisons. Some of our special sauce includes: - `FracMinHash` sketching, which enables accurate comparisons (including ANI) between data sets of different sizes - `sourmash gather`, a combinatorial k-mer approach for more accurate metagenomic profiling Please see the [sourmash publications](https://sourmash.readthedocs.io/en/latest/publications.html#sourmash-fundamentals) for details. The name is a riff off of [Mash](https://github.com/marbl/Mash), combined with @ctb's love of whiskey. ([Sour mash](https://en.wikipedia.org/wiki/Sour_mash) is used in making whiskey.) Maintainers: [C. Titus Brown](mailto:titus@idyll.org) ([@ctb](http://github.com/ctb)), [Luiz C. Irber, Jr](mailto:luiz@sourmash.bio) ([@luizirber](http://github.com/luizirber)), and [N. Tessa Pierce-Ward](mailto:tessa@sourmash.bio) ([@bluegenes](http://github.com/bluegenes)). sourmash was initially developed by the [Lab for Data-Intensive Biology](http://ivory.idyll.org/lab/) at the [UC Davis School of Veterinary Medicine](http://www.vetmed.ucdavis.edu), and now includes contributions from the global research and developer community. ## Installation We recommend using conda-forge to install sourmash: ``` conda install -c conda-forge sourmash-minimal ``` This will install the latest stable version of sourmash 4. You can also use pip to install sourmash: ``` pip install sourmash ``` A quickstart tutorial [is available](https://sourmash.readthedocs.io/en/latest/tutorials.html). ### Requirements sourmash runs under Python 3.10 and later on Windows, Mac OS X, and Linux. The base requirements are screed, cffi, numpy, matplotlib, and scipy. Conda will install everything necessary, and is our recommended installation method (see below). ### Installation with conda conda-forge is a community maintained channel for the [conda](http://conda.pydata.org/docs/intro.html) package manager. [installing conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/), you can install sourmash by running: ```bash $ conda create -n sourmash_env -c conda-forge sourmash-minimal $ conda activate sourmash_env $ sourmash --help ``` which will install [the latest released version](https://github.com/sourmash-bio/sourmash/releases). ## Support For questions, please open an issue [on Github](https://github.com/sourmash-bio/sourmash/issues), or ask in our [chat](https://gitter.im/sourmash-bio/community?utm_source=share-link&utm_medium=link&utm_campaign=share-link). ## Development Development happens on github at [sourmash-bio/sourmash](https://github.com/sourmash-bio/sourmash). sourmash is developed in Python and Rust, and you will need a Rust environment to build it; see [the developer notes](doc/developer.md) for our suggested development setup. After installation, `sourmash` is the main command-line entry point; run it with `python -m sourmash`, or do `pip install -e /path/to/repo` to do a developer install in a virtual environment. The `sourmash/` directory contains the Python library and command-line interface code. The `src/core/` directory contains the Rust library implementing core functionality. Tests require py.test and can be run with `make test`. Please see [the developer notes](doc/developer.md) for more information on getting set up with a development environment. CTB Jan 2024 sourmash-bio-sourmash-bc3c0f8/asv.conf.json000066400000000000000000000010451474157046300210760ustar00rootroot00000000000000{ "version": 1, "project": "sourmash", "project_url": "https://github.com/sourmash-bio/sourmash", "repo": ".", "branches": ["latest"], "dvcs": "git", "environment_type": "virtualenv", "env_dir": ".asv/env", "results_dir": ".asv/results", "html_dir": ".asv/html", "build_cache_size": 8, "build_command": [ "python -m pip install 'setuptools_scm[toml]>=4,<6' milksnake maturin", "PIP_NO_BUILD_ISOLATION=false python -mpip wheel --no-deps --no-index -w {build_cache_dir} {build_dir}" ] } sourmash-bio-sourmash-bc3c0f8/benchmarks/000077500000000000000000000000001474157046300206035ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/benchmarks/README.md000066400000000000000000000003461474157046300220650ustar00rootroot00000000000000# benchmarks for asv ([airspeed velocity](https://asv.readthedocs.io/en/stable/index.html)) The code in here is run by GitHub Actions during continuous integration. To test quickly, run: ``` asv run --show-stderr --quick ``` sourmash-bio-sourmash-bc3c0f8/benchmarks/__init__.py000066400000000000000000000000011474157046300227030ustar00rootroot00000000000000 sourmash-bio-sourmash-bc3c0f8/benchmarks/benchmarks.py000066400000000000000000000143301474157046300232730ustar00rootroot00000000000000import random from tempfile import NamedTemporaryFile from sourmash.sbt_storage import ZipStorage from sourmash.minhash import MinHash RANDOM_SEQ_SIZE = 3000 RANDOM_SEQ_NUMBER = 300 MINHASH_NUM = 500 MINHASH_K = 21 GET_MINS_RANGE = 500 ADD_HASH_RANGE = 10_000 ADD_MANY_RANGE = 1000 SIMILARITY_TIMES = 500 COUNT_COMMON_TIMES = 500 MERGE_TIMES = 500 COPY_TIMES = 500 CONCAT_TIMES = 500 SET_ABUNDANCES_RANGE = 500 ZIP_STORAGE_WRITE = 100_000 ZIP_STORAGE_LOAD = 20 def load_sequences(): sequences = [] for i in range(10): random_seq = random.sample( "A,C,G,T".split(",") * RANDOM_SEQ_SIZE, RANDOM_SEQ_NUMBER ) sequences.append("".join(random_seq)) return sequences class TimeMinHashSuite: def setup(self): self.mh = MinHash(MINHASH_NUM, MINHASH_K, track_abundance=False) self.protein_mh = MinHash( MINHASH_NUM, MINHASH_K, is_protein=True, track_abundance=False ) self.sequences = load_sequences() self.populated_mh = MinHash(MINHASH_NUM, MINHASH_K, track_abundance=False) for seq in self.sequences: self.populated_mh.add_sequence(seq) def time_add_sequence(self): mh = self.mh sequences = self.sequences for seq in sequences: mh.add_sequence(seq) def time_add_protein(self): mh = self.protein_mh sequences = self.sequences for seq in sequences: mh.add_protein(seq) def time_get_mins(self): mh = self.populated_mh for i in range(GET_MINS_RANGE): mh.get_mins() def time_add_hash(self): mh = self.mh for i in range(ADD_HASH_RANGE): mh.add_hash(i) def time_add_many(self): mh = self.mh mh.add_many(list(range(ADD_MANY_RANGE))) def time_similarity(self): mh = self.mh other_mh = self.populated_mh for i in range(SIMILARITY_TIMES): mh.similarity(other_mh) def time_count_common(self): mh = self.mh other_mh = self.populated_mh for i in range(COUNT_COMMON_TIMES): mh.count_common(other_mh) def time_merge(self): mh = self.mh other_mh = self.populated_mh for i in range(MERGE_TIMES): mh.merge(other_mh) def time_copy(self): mh = self.populated_mh for i in range(COPY_TIMES): mh.__copy__() def time_concat(self): mh = self.mh other_mh = self.populated_mh for i in range(CONCAT_TIMES): mh += other_mh class PeakmemMinHashSuite: def setup(self): self.mh = MinHash(MINHASH_NUM, MINHASH_K, track_abundance=True) self.protein_mh = MinHash( MINHASH_NUM, MINHASH_K, is_protein=True, track_abundance=True ) self.sequences = load_sequences() def peakmem_add_sequence(self): mh = self.mh sequences = self.sequences for seq in sequences: mh.add_sequence(seq) def peakmem_add_protein(self): mh = self.protein_mh sequences = self.sequences for seq in sequences: mh.add_protein(seq) def peakmem_add_hash(self): mh = self.mh for i in range(ADD_HASH_RANGE): mh.add_hash(i) def peakmem_add_many(self): mh = self.mh mh.add_many(list(range(ADD_MANY_RANGE))) #################### class TimeMinAbundanceSuite(TimeMinHashSuite): def setup(self): TimeMinHashSuite.setup(self) self.mh = MinHash(MINHASH_NUM, MINHASH_K, track_abundance=True) self.populated_mh = MinHash(MINHASH_NUM, MINHASH_K, track_abundance=True) for seq in self.sequences: self.populated_mh.add_sequence(seq) def time_get_mins_abundance(self): mh = self.populated_mh for i in range(GET_MINS_RANGE): mh.get_mins(with_abundance=True) def time_set_abundances(self): mh = self.mh mins = self.populated_mh.get_mins(with_abundance=True) for i in range(SET_ABUNDANCES_RANGE): mh.set_abundances(mins) def time_set_abundances_noclear(self): mh = self.mh mins = self.populated_mh.get_mins(with_abundance=True) for i in range(SET_ABUNDANCES_RANGE): mh.set_abundances(mins, clear=False) class PeakmemMinAbundanceSuite(PeakmemMinHashSuite): def setup(self): PeakmemMinHashSuite.setup(self) self.mh = MinHash(MINHASH_NUM, MINHASH_K, track_abundance=True) #################### class TimeZipStorageSuite: def setup(self): import zipfile self.zipfile = NamedTemporaryFile() with zipfile.ZipFile( self.zipfile, mode="w", compression=zipfile.ZIP_STORED ) as storage: for i in range(ZIP_STORAGE_WRITE): # just so we have lots of entries storage.writestr(str(i), b"0") # one big-ish entry storage.writestr("sig1", b"9" * 1_000_000) def time_load_from_zipstorage(self): with ZipStorage(self.zipfile.name) as storage: for i in range(ZIP_STORAGE_LOAD): storage.load("sig1") def time_load_small_from_zipstorage(self): with ZipStorage(self.zipfile.name) as storage: for i in range(ZIP_STORAGE_LOAD): storage.load("99999") def teardown(self): self.zipfile.close() class PeakmemZipStorageSuite: def setup(self): import zipfile self.zipfile = NamedTemporaryFile() with zipfile.ZipFile( self.zipfile, mode="w", compression=zipfile.ZIP_STORED ) as storage: for i in range(ZIP_STORAGE_WRITE): # just so we have lots of entries storage.writestr(str(i), b"0") # one big-ish entry storage.writestr("sig1", b"9" * 1_000_000) def peakmem_load_from_zipstorage(self): with ZipStorage(self.zipfile.name) as storage: for i in range(ZIP_STORAGE_LOAD): storage.load("sig1") def peakmem_load_small_from_zipstorage(self): with ZipStorage(self.zipfile.name) as storage: for i in range(ZIP_STORAGE_LOAD): storage.load("99999") def teardown(self): self.zipfile.close() sourmash-bio-sourmash-bc3c0f8/binder/000077500000000000000000000000001474157046300177315ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/binder/README.md000066400000000000000000000001161474157046300212060ustar00rootroot00000000000000# binder directory Configuration files for [mybinder](https://mybinder.org). sourmash-bio-sourmash-bc3c0f8/binder/environment.yml000066400000000000000000000002761474157046300230250ustar00rootroot00000000000000channels: - conda-forge - bioconda - defaults dependencies: - python>=3.10 - sourmash>=4.8.4 - screed - matplotlib - pandas - pip - pip: - matplotlib_venn - mmh3 sourmash-bio-sourmash-bc3c0f8/codecov.yml000066400000000000000000000004671474157046300206420ustar00rootroot00000000000000# config file for codecov, https://app.codecov.io/gh/sourmash-bio/sourmash/ # use path fixing to properly report code coverage on source code # per https://docs.codecov.io/docs/fixing-paths fixes: - "sourmash::src/sourmash" ignores: - "src/core/src/ffi" codecov: token: 66273971-681c-44a5-b93a-f60249a2a70c sourmash-bio-sourmash-bc3c0f8/codemeta.json000066400000000000000000000010041474157046300211350ustar00rootroot00000000000000{ "@context": "https://raw.githubusercontent.com/mbjones/codemeta/master/codemeta.jsonld", "@type": "Code", "author": [ ], "identifier": "", "codeRepository": "https://github.com/sourmash-bio/sourmash", "datePublished": "2016-06-07", "dateModified": "2016-06-07", "dateCreated": "2016-06-07", "description": "Generate, compare, and manipulate MinHash sketches of genomic data", "keywords": "MinHash, k-mers, Python", "license": "BSD 3-clause", "title": "sourmash", "version": "v1.0.0" } sourmash-bio-sourmash-bc3c0f8/data/000077500000000000000000000000001474157046300173775ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/data/GCF_000005845.2_ASM584v2_genomic.fna.gz000066400000000000000000052070761474157046300252430ustar00rootroot00000000000000nͮկQB$:H@rAE"%eg>r +{|ߜQʾ}s/_/o?o???__W?x'3"~_Gן?gW~$?u????m=֟_?yU^9'y ߷_;(y~'=/8}%3? 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The following files are used by the doctests in doc/ & may also be useful for developers who are benchmarking: ``` GCF_000005845.2_ASM584v2_genomic.fna.gz GCF_000006945.1_ASM694v1_genomic.fna.gz GCF_000783305.1_ASM78330v1_genomic.fna.gz ``` CTB 6/16 sourmash-bio-sourmash-bc3c0f8/data/eschericia-sigs.tar.gz000066400000000000000000045724341474157046300236130ustar00rootroot00000000000000Y[usOA:VgrWm/ 4%V6A{o?+W\or(eDzgq^7(X"bQ=:V(Doj/T?{?7ޟ{V_uz_+o?]yg_{˿|ûW߽շ:J>Yy߿J_Wo^p__5_x]_cU7{gyo~"/?{W/,W_M~o?|?w.D'M]#'M~_.|]O{'[.']? io%.[.|]?|?]w.|o\u]~f^|]6];x]wg<>or]w.|]'-z|w.|]w.|]qo.˟?hރ],?|I,q]-:㵱w.)?;]w.\o}O||?{|?[w.|]v7w.._/u] /zy? 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Note that ignored advisories will still # output a note when they are encountered. ignore = [ #"RUSTSEC-0000-0000", ] [licenses] unlicensed = "deny" allow = [ "MIT", "Apache-2.0", "Apache-2.0 WITH LLVM-exception", "BSD-3-Clause", "Unicode-DFS-2016", ] copyleft = "warn" allow-osi-fsf-free = "neither" default = "deny" confidence-threshold = 0.8 exceptions = [ { allow = ["Zlib"], name = "piz", version = "*" }, { allow = ["ISC"], name = "libloading", version = "*" }, ] [bans] multiple-versions = "deny" wildcards = "allow" highlight = "all" workspace-default-features = "allow" external-default-features = "allow" [sources] unknown-registry = "warn" unknown-git = "warn" allow-registry = ["https://github.com/rust-lang/crates.io-index"] sourmash-bio-sourmash-bc3c0f8/dev.yml000066400000000000000000000003631474157046300177710ustar00rootroot00000000000000name: sourmash_dev channels: - conda-forge dependencies: - clangdev >=16.0.6,<16.1 - cxx-compiler >=1.7.0,<1.8 - git 2.41.0.* - libclang >=16.0.6,<16.1 - pandoc 3.1.3.* - python 3.10.* - rust >=1.80.0,<1.81 - tox >=4.11.0,<5 sourmash-bio-sourmash-bc3c0f8/doc/000077500000000000000000000000001474157046300172335ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/doc/Makefile000066400000000000000000000176251474157046300207060ustar00rootroot00000000000000# Makefile for Sphinx documentation # all: html # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = _build # User-friendly check for sphinx-build ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) $(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don\'t have Sphinx installed, grab it from http://sphinx-doc.org/) endif # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . # the i18n builder cannot share the environment and doctrees with the others I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . .PHONY: help help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " applehelp to make an Apple Help Book" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " epub3 to make an epub3" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" @echo " text to make text files" @echo " man to make manual pages" @echo " texinfo to make Texinfo files" @echo " info to make Texinfo files and run them through makeinfo" @echo " gettext to make PO message catalogs" @echo " changes to make an overview of all changed/added/deprecated items" @echo " xml to make Docutils-native XML files" @echo " pseudoxml to make pseudoxml-XML files for display purposes" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" @echo " coverage to run coverage check of the documentation (if enabled)" @echo " dummy to check syntax errors of document sources" .PHONY: clean clean: rm -rf $(BUILDDIR)/* .PHONY: html html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." .PHONY: dirhtml dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." .PHONY: singlehtml singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." .PHONY: pickle pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." .PHONY: json json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." .PHONY: htmlhelp htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." .PHONY: qthelp qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/sourmash.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/sourmash.qhc" .PHONY: applehelp applehelp: $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp @echo @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." @echo "N.B. You won't be able to view it unless you put it in" \ "~/Library/Documentation/Help or install it in your application" \ "bundle." .PHONY: devhelp devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/sourmash" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/sourmash" @echo "# devhelp" .PHONY: epub epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. The epub file is in $(BUILDDIR)/epub." .PHONY: epub3 epub3: $(SPHINXBUILD) -b epub3 $(ALLSPHINXOPTS) $(BUILDDIR)/epub3 @echo @echo "Build finished. The epub3 file is in $(BUILDDIR)/epub3." .PHONY: latex latex: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @echo "Run \`make' in that directory to run these through (pdf)latex" \ "(use \`make latexpdf' here to do that automatically)." .PHONY: latexpdf latexpdf: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through pdflatex..." $(MAKE) -C $(BUILDDIR)/latex all-pdf @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." .PHONY: latexpdfja latexpdfja: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through platex and dvipdfmx..." $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." .PHONY: text text: $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text @echo @echo "Build finished. The text files are in $(BUILDDIR)/text." .PHONY: man man: $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man @echo @echo "Build finished. The manual pages are in $(BUILDDIR)/man." .PHONY: texinfo texinfo: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." @echo "Run \`make' in that directory to run these through makeinfo" \ "(use \`make info' here to do that automatically)." .PHONY: info info: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo "Running Texinfo files through makeinfo..." make -C $(BUILDDIR)/texinfo info @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." .PHONY: gettext gettext: $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale @echo @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." .PHONY: changes changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." .PHONY: linkcheck linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." .PHONY: doctest doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." .PHONY: coverage coverage: $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage @echo "Testing of coverage in the sources finished, look at the " \ "results in $(BUILDDIR)/coverage/python.txt." .PHONY: xml xml: $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml @echo @echo "Build finished. The XML files are in $(BUILDDIR)/xml." .PHONY: pseudoxml pseudoxml: $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml @echo @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." .PHONY: dummy dummy: $(SPHINXBUILD) -b dummy $(ALLSPHINXOPTS) $(BUILDDIR)/dummy @echo @echo "Build finished. Dummy builder generates no files." sourmash-bio-sourmash-bc3c0f8/doc/README.md000066400000000000000000000010431474157046300205100ustar00rootroot00000000000000# Documentation on the docs We use [MyST](https://myst-parser.readthedocs.io/en/latest/sphinx/intro.html) to generate Sphinx doc output from Markdown input. ## Useful tips and tricks: ### Linking internally between sections in the docs For linking within the sourmash docs, you should use the [auto-generated header anchors](https://myst-parser.readthedocs.io/en/latest/syntax/optional.html#auto-generated-header-anchors) provided by MyST. 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Load a comparison matrix into R

sourmash_comp_matrix <- read.csv("ecoli.cmp.csv")

# Label the rows
rownames(sourmash_comp_matrix) <- colnames(sourmash_comp_matrix)

# Transform for plotting
sourmash_comp_matrix <- as.matrix(sourmash_comp_matrix)

Make an MDS plot

fit <- dist(sourmash_comp_matrix)
fit <- cmdscale(fit)
x <- fit[, 1]
y <- fit[, 2]
plot(fit[ , 1], fit[ , 2], xlab = "Dimension 1", ylab = "Dimension 2")

Make a tSNE plot

library(Rtsne)
tsne_model <- Rtsne(sourmash_comp_matrix, check_duplicates=FALSE, pca=TRUE, perplexity=5, theta=0.5, dims=2)
d_tsne = as.data.frame(tsne_model$Y) 
plot(d_tsne$V1, d_tsne$V2)

Make an unclustered heatmap

heatmap(sourmash_comp_matrix, Colv=F, scale='none')

Make a clustered heatmap

hc.rows <- hclust(dist(sourmash_comp_matrix))
hc.cols <- hclust(dist(t(sourmash_comp_matrix)))
heatmap(sourmash_comp_matrix[cutree(hc.rows,k=2)==1,], Colv=as.dendrogram(hc.cols), scale='none')

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C227-11, complete genome","NZ_MKJG01000001.1 Escherichia coli strain SF-596 Contig_10_consensus_sequence, whole genome shotgun sequence","NC_017651.1 Escherichia coli str. 'clone D i2', complete genome","NZ_MOZC01000010.1 Escherichia coli strain 3.3-R2 NODE_10_length_194691_cov_23.1887, whole genome shotgun sequence","NZ_MOFN01000001.1 Escherichia coli strain 461 BN4_461_1_(paired)_contig_1, whole genome shotgun sequence","NZ_JHJO01000001.1 Escherichia coli O157:H7 str. F6751 contig1, whole genome shotgun sequence","NC_027387.1 Escherichia phage CICC 80001, complete genome","NZ_MIWP01000001.1 Escherichia coli strain K6412 contig_0001, whole genome shotgun sequence","NZ_JHRJ01000001.1 Escherichia coli strain 303289 303289_1, whole genome shotgun sequence","NZ_MIWF01000001.1 Escherichia coli strain AF7759-1 contig_0001, whole genome shotgun sequence","NZ_AVRP01000001.1 Escherichia coli T924_01 gecT92401.contig.0, whole genome shotgun sequence","NZ_JHRE01000001.1 Escherichia coli strain 302014 302014_1, whole genome shotgun sequence","NZ_KB732756.1 Escherichia coli KTE66 acEnR-supercont1.1, whole genome shotgun sequence","NZ_GG774190.1 Escherichia coli MS 196-1 Scfld2538, whole genome shotgun sequence","NZ_AEKA01000453.1 Escherichia sp. TW10509 contig_453, whole genome shotgun sequence","NZ_JNLZ01000001.1 Escherichia coli 3-105-05_S1_C1 e310505S1C1.contig.0_1, whole genome shotgun sequence","NZ_GG749254.1 Escherichia coli FVEC1412 supercont1.49, whole genome shotgun sequence","NZ_JHGJ01000001.1 Escherichia coli O45:H2 str. 2009C-4780 contig1, whole genome shotgun sequence","NZ_KE701326.1 Escherichia coli KOEGE 58 (171a) acYAA-supercont1.1, whole genome shotgun sequence","NZ_KE699066.1 Escherichia coli HVH 26 (4-5703913) acXtt-supercont1.1, whole genome shotgun sequence","NZ_JH704579.1 Escherichia coli PA33 PA33.contig.0, whole genome shotgun sequence","NZ_MBNU01000001.1 Escherichia coli strain 1409150006 contig1, whole genome shotgun sequence","NZ_JHNB01000001.1 Escherichia coli O103:H25 str. 2010C-4529 contig1, whole genome shotgun sequence","NZ_MKJO01000001.1 Escherichia coli strain USVAST184 Contig_10_consensus_sequence, whole genome shotgun sequence","NZ_APWY01000001.1 Escherichia coli 178200 gec178200.contig.0, whole genome shotgun sequence","NZ_CCQJ01000001.1 Escherichia coli strain E. coli, whole genome shotgun sequence","NZ_LVOV01000001.1 Escherichia coli strain swine72 swine72_contig_1, whole genome shotgun sequence","NZ_KE701477.1 Escherichia coli UMEA 3053-1 acYxz-supercont1.1, whole genome shotgun sequence","NZ_JSRD01000001.1 Escherichia coli strain blood-09-0856 blood-09-0856_ctg_605, whole genome shotgun sequence","NZ_LQWB01000001.1 Escherichia coli strain GN03624 GCID_ECOLID_00149_NODE_1.ctg_1, whole genome shotgun sequence","NZ_KE700241.1 Escherichia coli HVH 147 (4-5893887) acYxy-supercont1.1, whole genome shotgun sequence","NZ_JSGR01000003.1 Escherichia coli strain upec-73 upec-73_ctg_2796, whole genome shotgun sequence","NZ_KB733029.1 Escherichia coli KTE46 acASD-supercont1.1, whole genome shotgun sequence","NZ_JMGW01000001.1 Escherichia coli 1-176-05_S4_C2 e117605S4C2.contig.0_1, whole genome shotgun sequence","NZ_LEAD01000071.1 Escherichia coli strain 1935, whole genome 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Suppose we have a 35 base DNA sequence, and we break it into four 31-mers: ``` >>> K = 31 >>> dnaseq = "ATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGC" >>> for i in range(0, len(dnaseq) - K + 1): ... kmer = dnaseq[i:i+K] ... print(i, kmer) 0 ATGCGAGTGTTGAAGTTCGGCGGTACATCAG 1 TGCGAGTGTTGAAGTTCGGCGGTACATCAGT 2 GCGAGTGTTGAAGTTCGGCGGTACATCAGTG 3 CGAGTGTTGAAGTTCGGCGGTACATCAGTGG 4 GAGTGTTGAAGTTCGGCGGTACATCAGTGGC ``` sourmash uses a hash function (by default MurmurHash) that converts each k-mer into 64-bit numbers. These numbers form the basis of everything else sourmash does; the k-mer strings are not used internally at all. The easiest way to access the hash function is via the `seq_to_hashes` method on `MinHash` objects, which returns a list: ``` >>> from sourmash import MinHash >>> mh = MinHash(n=0, ksize=K, scaled=1) >>> for i in range(0, len(dnaseq) - K + 1): ... kmer = dnaseq[i:i+K] ... print(i, kmer, mh.seq_to_hashes(kmer)) 0 ATGCGAGTGTTGAAGTTCGGCGGTACATCAG [7488148386897425535] 1 TGCGAGTGTTGAAGTTCGGCGGTACATCAGT [3674733966066518639] 2 GCGAGTGTTGAAGTTCGGCGGTACATCAGTG [2135725670290847794] 3 CGAGTGTTGAAGTTCGGCGGTACATCAGTGG [14521729668397845245] 4 GAGTGTTGAAGTTCGGCGGTACATCAGTGGC [15919051675656106963] ``` Note that this is the same as using the MurmurHash hash function with a seed of 42 and taking the first 64 bits. Because DNA is double-stranded and has no inherent directionality, but computers represent DNA with only one strand, it's important for sourmash to represent both strands. sourmash does this by building a canonical representation for each k-mer so that reverse-complement sequences match to their forward sequence. Underneath, sourmash DNA hashing does this by taking each k-mer, building the reverse complement, choosing the lexicographically lesser of the two, and then hashes it - for example, for the first and second k-mers above, you get: ``` >>> from sourmash.minhash import hash_murmur >>> from screed import rc >>> kmer_1 = "ATGCGAGTGTTGAAGTTCGGCGGTACATCAG" >>> hash_murmur(kmer_1) 7488148386897425535 >>> hash_murmur(kmer_1) == mh.seq_to_hashes(kmer_1)[0] True >>> kmer_2 = "TGCGAGTGTTGAAGTTCGGCGGTACATCAGT" >>> hash_murmur(kmer_2) 6388498842523164783 >>> kmer_2_rc = rc(kmer_2) >>> kmer_2_rc 'ACTGATGTACCGCCGAACTTCAACACTCGCA' >>> hash_murmur(kmer_2_rc) == mh.seq_to_hashes(kmer_2)[0] True ``` where the second k-mer's reverse complement starts with 'A' and is therefore chosen for hashing by sourmash. This method was chosen to be compatible with [mash](https://mash.readthedocs.io/. ### Protein-based encodings By default, `MinHash` objects work with DNA. However, sourmash supports amino acid, Dayhoff, and hydrophobic-polar (hp) encodings as well. The Dayhoff and hp encodings support degenerate matching that is less stringent than exact matches. The simplest way to use a protein `MinHash` object is to create one and call `add_protein` on it -- ``` >>> K = 7 >>> mh = MinHash(0, K, is_protein=True, scaled=1) >>> protseq = "MVKVYAPAS" >>> mh.add_protein(protseq) ``` This creates three 7-mers from the sequence and hashes them: ``` >>> list(sorted(mh.hashes)) [5834377656419371297, 8846570680426381265, 10273850291677879123] ``` As with DNA k-mers, above, you can also use `seq_to_hashes` to generate the hashes for protein k-mers, if you add the `is_protein=True` flag: ``` >>> for i in range(0, len(protseq) - K + 1): ... kmer = protseq[i:i+K] ... print(i, kmer, mh.seq_to_hashes(kmer, is_protein=True)) 0 MVKVYAP [5834377656419371297] 1 VKVYAPA [10273850291677879123] 2 KVYAPAS [8846570680426381265] ``` In this case, the k-mers are always hashed in the forward direction (because protein sequence always has an orientation, unlike DNA). sourmash also supports the [Dayhoff](https://en.wikipedia.org/wiki/Margaret_Oakley_Dayhoff#Table_of_Dayhoff_encoding_of_amino_acids) and hydrophobic-polar encodings; here, amino acids are first mapped to their encodings and then hashed. So, for example, the amino acid sequence `CADHIF*` is mapped to `abcdef*` in the Dayhoff encoding: ``` >>> mh = MinHash(0, K, dayhoff=True, scaled=1) >>> h1 = mh.seq_to_hashes('CADHIF*', is_protein=True)[0] >>> h1 12061624913065022412 >>> h1 == hash_murmur('abcdef*') True ``` ### Translating DNA into protein-based encodings. If you use `add_sequence(...)` to add DNA sequence to a protein encoding, or call `seq_to_hashes(...)` on a protein encoding without `is_protein=True`, sourmash will *translate* the sequences in all possible reading frames and hash the translated amino acids. The k-mer size for the `MinHash` is used as the k-mer size of the amino acids, i.e. 7 aa is 21 DNA bases. ``` >>> dnaseq = "ATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGC" >>> len(dnaseq) 35 >>> K = 7 >>> mh = MinHash(n=0, ksize=K, is_protein=True, scaled=1) >>> mh.add_sequence(dnaseq) >>> len(mh) 30 ``` Here, 30 hashes are added to the `MinHash` object because we are starting with a 35 base DNA sequence, and using 21-mers of DNA (7-mer of protein), which give us 15 distinct 21-mers in the three forward frames, and 15 distinct 21-mers in the three reverse-complement frames, for a total of 30. If a dayhoff or hp `MinHash` object is used, then `add_sequence(...)` will first translate each sequence into protein space in all six frames, and then further encode the sequences into Dayhoff or hp encodings. ### Invalid characters in DNA and protein sequences sourmash detects invalid DNA characters (non-ACTG) and raises an exception on `add_sequence`, unless `force=True`, in which case k-mers containing invalid characters are ignored. ``` >>> dnaseq = "nTGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGC" >>> K = 31 >>> mh = MinHash(n=0, ksize=K, scaled=1) >>> mh.add_sequence(dnaseq) Traceback (most recent call last): ... ValueError: invalid DNA character in input k-mer: NTGCGAGTGTTGAAGTTCGGCGGTACATCAG >>> mh.add_sequence(dnaseq, force=True) >>> len(mh) 4 ``` For protein sequences, sourmash does not currently do any invalid character detection; k-mers are hashed as they are, and can only be matched by an identical k-mer (with the same invalid character). (Please [file an issue](https://github.com/sourmash-bio/sourmash/issues) if you'd like us to change this!) ``` >>> K = 7 >>> mh = MinHash(n=0, ksize=K, is_protein=True, scaled=1) >>> protseq = "XVKVYAPAS" >>> mh.add_protein(protseq) >>> len(mh) 3 ``` For the Dayhoff and hp encodings on top of amino acids, invalid amino acids (any letter for which no encoded character exists) are replaced with 'X'. ``` >>> K = 7 >>> mh = MinHash(n=0, ksize=K, dayhoff=True, scaled=1) >>> protseq1 = ".VKVYAPAS" >>> hashes1 = mh.seq_to_hashes(protseq1, is_protein=True) >>> protseq2 = "XVKVYAPAS" >>> hashes2 = mh.seq_to_hashes(protseq2, is_protein=True) >>> hashes1 == hashes2 True ``` ### Extracting both k-mers and hashes for a sequence As of sourmash 4.2.2, `MinHash` objects provide a method called `kmers_and_hashes` that will return the k-mers and their corresponding hashes for an input sequence -- ``` >>> mh = MinHash(n=0, ksize=31, scaled=1) >>> dnaseq = "ATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGC" >>> for kmer, hashval in mh.kmers_and_hashes(dnaseq): ... print(kmer, hashval) ATGCGAGTGTTGAAGTTCGGCGGTACATCAG 7488148386897425535 TGCGAGTGTTGAAGTTCGGCGGTACATCAGT 3674733966066518639 GCGAGTGTTGAAGTTCGGCGGTACATCAGTG 2135725670290847794 CGAGTGTTGAAGTTCGGCGGTACATCAGTGG 14521729668397845245 GAGTGTTGAAGTTCGGCGGTACATCAGTGGC 15919051675656106963 ``` This works for protein `MinHash` objects as well, of course, although you have to provide the `is_protein` flag, since `MinHash` objects assume input sequence is DNA otherwise -- ``` >>> K = 7 >>> mh = MinHash(n=0, ksize=K, is_protein=True, scaled=1) >>> protseq = "XVKVYAPAS" >>> for (kmer, hashval) in mh.kmers_and_hashes(protseq, is_protein=True): ... print(kmer, hashval) XVKVYAP 3140823561012061964 VKVYAPA 10273850291677879123 KVYAPAS 8846570680426381265 ``` For translated `MinHash`, the k-mers and hashes corresponding to all six reading frames are returned. ``` >>> dnaseq = "ATGCGAGTGTTGAAGTTCGGCGGTA" >>> K = 7 >>> mh = MinHash(n=0, ksize=K, is_protein=True, scaled=1) >>> for (kmer, hashval) in mh.kmers_and_hashes(dnaseq): ... print(kmer, hashval) ATGCGAGTGTTGAAGTTCGGC 16652503548557650904 CGAGTGTTGAAGTTCGGCGGT 9978056796243419534 TACCGCCGAACTTCAACACTC 2748622134668949083 CGCCGAACTTCAACACTCGCA 4263227699724621735 TGCGAGTGTTGAAGTTCGGCG 14299765336094039482 GAGTGTTGAAGTTCGGCGGTA 18155608748862746902 ACCGCCGAACTTCAACACTCG 14490181201772650983 GCCGAACTTCAACACTCGCAT 17205086974168937105 GCGAGTGTTGAAGTTCGGCGG 13354527969598897281 CCGCCGAACTTCAACACTCGC 16506504121672505595 ``` In all cases, the k-mers are taken from the sequence itself, so the k-mers will match to the input sequence, even when there are multiple k-mers that hash to the same value (e.g. in the case of reverse complements, or DNA k-mers that are translated to the same amino acid sequence). Note that sourmash also provides a `translate_codon` function if you need to get the specific amino acids - ``` >>> from sourmash.minhash import translate_codon >>> kmer = 'ATGCGAGT' >>> for start in range(0, len(kmer) - 3 + 1, 3): ... codon = kmer[start:start+3] ... print(codon, translate_codon(codon)) ATG M CGA R ``` ### Summary In sum, * `MinHash.add_sequence(...)` converts DNA sequence into DNA or protein k-mers, and then hashes them and stores them. * `MinHash.add_protein(...)` converts protein sequence into protein k-mers, and then hashes them and stores them. * `MinHash.seq_to_hashes(...)` will give you the hash values without adding them to the `MinHash` object. * `MinHash.kmers_and_hashes(...)` will provide tuples of `(kmer, hashval)` for an input sequence. * The `dayhoff` and `hp` encodings can be calculated on amino acid k-mers as well, using `MinHash` objects. Note that this is the code that is used by the command-line functionality in `sourmash sketch`, so the results at the command-line will match the results from the Python API. ## Set operations on hashes All of the hashes in a `MinHash` object are available via the `hashes` property: ``` >>> mh1 = sourmash.MinHash(n=0, ksize=3, scaled=1) >>> seq1 = "ATGGCA" >>> mh1.add_sequence(seq1) >>> seq2 = "AGAGCA" >>> mh1.add_sequence(seq2) >>> list(mh1.hashes) [1274996984489324440, 2529443451610975987, 3115010115530738562, 5059920851104263793, 5740495330885152257, 8652222673649005300, 18398176440806921933] ``` and you can easily do your own set operations with `.hashes` - e.g. the following calculates the Jaccard similarity (intersection over union) of two ``` >>> s1 = set(mh1.hashes) >>> s2 = set(mh2.hashes) >>> round(len(s1 & s2) / len(s1 | s2), 3) 0.571 ``` However, the MinHash class also supports a number of basic operations - the following operations work directly on the hashes: ``` >>> combined = mh1 + mh2 >>> combined += mh1 >>> combined.remove_many(mh1.hashes) >>> combined.add_many(mh2.hashes) ``` You can create an empty copy of a MinHash object with `copy_and_clear`: ``` >>> new_mh = mh1.copy_and_clear() ``` and you can also access the various parameters of a MinHash object directly as properties -- ``` >>> mh1.ksize 3 >>> mh1.scaled 1 >>> mh1.num 0 >>> mh1.is_dna True >>> mh1.is_protein False >>> mh1.dayhoff False >>> mh1.hp False >>> mh1.moltype 'DNA' ``` see the "Advanced" section, below, for a more complete discussion of MinHash objects. ## Creating MinHash sketches programmatically, from genome files Suppose we want to create MinHash sketches from genomes -- ``` >>> import glob, pprint >>> genomes = glob.glob('data/GCF*.fna.gz') >>> genomes = list(sorted(genomes)) >>> pprint.pprint(genomes) ['data/GCF_000005845.2_ASM584v2_genomic.fna.gz', 'data/GCF_000006945.1_ASM694v1_genomic.fna.gz', 'data/GCF_000783305.1_ASM78330v1_genomic.fna.gz'] ``` We have to read them in (here using screed), but then they can be fed into `add_sequence` directly; here we set `force=True` in `add_sequence` to skip over k-mers containing characters other than ACTG, rather than raising an exception. (Note, just for speed reasons, we're truncating the sequences to 50kb in length.) ``` >>> import screed >>> minhashes = [] >>> for g in genomes: ... mh = sourmash.MinHash(n=500, ksize=31) ... for record in screed.open(g): ... mh.add_sequence(record.sequence[:50000], True) ... minhashes.append(mh) ``` And now the resulting MinHash objects can be compared against each other: ``` >>> import sys >>> for i, e in enumerate(minhashes): ... _ = sys.stdout.write(genomes[i][:20] + ' ') ... for j, e2 in enumerate(minhashes): ... x = e.jaccard(minhashes[j]) ... _ = sys.stdout.write(str(round(x, 3)) + ' ') ... _= sys.stdout.write('\n') data/GCF_000005845.2 1.0 0.0 0.0 data/GCF_000006945.1 0.0 1.0 0.0 data/GCF_000783305.1 0.0 0.0 1.0 ``` Note that the comparisons are quite quick; most of the time is spent in building the minhashes. ## Plotting dendrograms and matrices If you're interested in building comparison matrices and dendrograms, please see the notebook [Building plots from `sourmash compare` output](plotting-compare.ipynb). ## Saving and loading signature files Signature files encapsulate MinHashes in JSON, and provide a way to wrap MinHash objects with some metadata (the name and filename). To save signatures, use `save_signatures` with a list of signatures and a Python file pointer: ``` >>> from sourmash import SourmashSignature, save_signatures >>> from tempfile import mkdtemp >>> sig1 = SourmashSignature(minhashes[0], name=genomes[0][:20]) >>> tempdir = mkdtemp(suffix = "temp") >>> with open(tempdir + '/genome1.sig', 'wt') as fp: ... save_signatures([sig1], fp) ``` Here, `genome1.sig` is a JSON file that can now be loaded and compared -- first, load it using `load_one_signature`: ``` >>> from sourmash import load_one_signature >>> loaded_sig = load_one_signature(tempdir + '/genome1.sig') ``` then compare: ``` >>> loaded_sig.jaccard(sig1) 1.0 >>> sig1.jaccard(loaded_sig) 1.0 ``` There are two primary signature loading functions - `load_one_signature`, used above, which loads exactly one signature or else raises an exception; and the powerful and more generic `load_file_as_signatures`, which takes in a filename or directory containing a collection of signatures and returns the individual signatures -- for example, you can load all of the signatures under the `tempdir` created above like so, ``` >>> loaded_sigs = list(sourmash.load_file_as_signatures(tempdir)) ``` Both `load_file_as_signatures` and `load_one_signature` take molecule type and k-mer size selectors, e.g. ``` >>> loaded_sigs = load_one_signature(tempdir + '/genome1.sig', select_moltype='DNA', ksize=31) ``` will load precisely one signature containing a DNA MinHash created at k-mer size of 31. ## Going from signatures back to MinHash objects and their hashes - Once you load a signature, you can go back to its MinHash object with `.minhash`; e.g. First, load two signatures: ``` >>> sigfile1 = 'tests/test-data/genome-s10.fa.gz.sig' >>> sig1 = load_one_signature(sigfile1, ksize=21, select_moltype='DNA') >>> sigfile2 = 'tests/test-data/genome-s11.fa.gz.sig' >>> sig2 = load_one_signature(sigfile2, ksize=21, select_moltype='DNA') ``` Then, get the hashes, and (e.g.) calculate the union: ``` >>> hashes1 = set(sig1.minhash.hashes.keys()) >>> hashes2 = set(sig2.minhash.hashes.keys()) >>> hash_union = hashes1.union(hashes2) >>> print(f'{len(hash_union)} hashes in union of {len(hashes1)} and {len(hashes2)}') 1000 hashes in union of 500 and 500 ``` ## Advanced features of sourmash MinHash objects - `scaled` and `num` sourmash supports two basic kinds of signatures, MinHash and modulo hash signatures. MinHash signatures are equivalent to mash signatures; they are limited in size, and very effective for comparing genomes and other data sets that are of similar size. The key parameter for MinHash signatures is `num`, which specifies the maximum number of hashes to be collected for a given input data set. ``` >>> signum = sourmash.MinHash(n=500, ksize=31) ``` Because of this parameter, below we'll call them 'num' signatures. Modulo hash (or 'scaled') signatures are specific to sourmash and they enable containment operations that are useful for metagenome analyses. The tradeoff is that unlike num MinHashes, they can become arbitrarily large. The key parameter for modulo hash signatures is `scaled`, which specifies the average sampling rate for hashes for a given input data set. A scaled factor of 1000 means that, on average, 1 in 1000 k-mers will be turned into a hash for later comparisons; this is a sort of compression factor, in that a 5 Mbp genome will yield approximately 5000 hash values with a scaled factor of 1000 (5000 x 1000 = 5,000,000). ``` >>> sigscaled = sourmash.MinHash(n=0, ksize=31, scaled=1000) ``` Note also that with a scaled factor of 1, the signature will contain **all** of the k-mers. ---- You can differentiate between num signatures and scaled signatures by looking at the `num` and `scaled` attributes on a MinHash object: ``` >>> signum.num 500 >>> signum.scaled 0 >>> sigscaled.num 0 >>> sigscaled.scaled 1000 ``` The MinHash class is otherwise identical between the two types of signatures. You cannot calculate Jaccard similarity or containment for MinHash objects with different num or scaled values (or different ksizes): ``` >>> signum2 = sourmash.MinHash(n=1000, ksize=31) >>> signum.jaccard(signum2) Traceback (most recent call last): ... TypeError: must have same num: 500 != 1000 ``` However, you can make signatures compatible by downsampling; see the next sections. ### A brief introduction to MinHash object methods and attributes MinHash objects have the following methods and attributes: * `ksize`, `num`, and `scaled` - the basic parameters used to create a MinHash object. * `hashes` - retrieve all of the hashes contained in this object. * `add_sequence(seq)` - hash sequence and add hash values. * `add(hash)` and `add_many(hashvals)` - add hash values directly. * `similarity(other)` - calculate Jaccard similarity with the other MinHash object. * `contained_by(other)` - calculate the Jaccard containment of self by other. * `copy_and_clear()` - make an empty copy of a MinHash object with the same parameters. * `__len__()` - return the number of actual hash values. Note you can also do `len(mh)`, where `mh` is a MinHash object. ### Downsampling signatures Num and scaled signatures can always be downsampled without referring back to the original data. Let's start by loading 50kb of genomic sequence in to memory: ``` >>> genomes = glob.glob('data/GCF*.fna.gz') >>> genomes = list(sorted(genomes)) >>> genome = genomes[0] >>> record = next(iter(screed.open(genome))) >>> sequence = record.sequence[:50000] ``` Now, suppose we make a num signature limited to 1000 hashes: ``` >>> larger = sourmash.MinHash(n=1000, ksize=31) >>> larger.add_sequence(sequence) >>> len(larger) 1000 ``` We can downsample this to 500 by extracting the hashes and using `add_many` to add them to a new MinHash like so: ``` >>> hashvals = larger.hashes.keys() >>> smaller = sourmash.MinHash(n=500, ksize=31) >>> smaller.add_many(hashvals) >>> len(smaller) 500 ``` Also note that there's a convenience function that does the same thing, faster! ``` >>> smaller2 = larger.downsample(num=500) >>> smaller2 == smaller True ``` The same can be done with scaled MinHashes: ``` >>> large_scaled = sourmash.MinHash(n=0, ksize=31, scaled=100) >>> large_scaled.add_sequence(sequence) >>> len(large_scaled) 459 >>> small_scaled = sourmash.MinHash(n=0, ksize=31, scaled=500) >>> small_scaled.add_many(large_scaled.hashes.keys()) >>> len(small_scaled) 69 ``` And, again, there's a convenience function that you can use: ``` >>> small_scaled2 = large_scaled.downsample(scaled=500) >>> small_scaled == small_scaled2 True ``` ### Converting between `num` and `scaled` signatures (Beware, these are confusing techniques for working with hashes that are easy to get wrong! We suggest [posting questions in the issue tracker](https://github.com/sourmash-bio/sourmash/issues) as you go, if you are interested in exploring this area!) The hashing function used is identical between num and scaled signatures, so the hash values themselves are compatible - it's the comparison between collections of them that doesn't work. But, in some circumstances, num signatures can be extracted from scaled signatures, and vice versa. We haven't yet implemented a Python API for this in sourmash, but you can hack it together yourself quite easily, and a conversion utility is implemented through the command line in `sourmash signature downsample`. To extract a num MinHash object from a scaled MinHash, first create or load your MinHash, and then extract the hash values: ``` >>> num_mh = sourmash.MinHash(n=1000, ksize=31) >>> num_mh.add_sequence(sequence) >>> hashvals = num_mh.hashes.keys() ``` Now, create the new scaled MinHash object and add the hashes to it: ``` >>> scaled_mh = sourmash.MinHash(n=0, ksize=31, scaled=10000) >>> scaled_mh.add_many(hashvals) ``` and you are done! The same works in reverse, of course: ``` >>> scaled_mh = sourmash.MinHash(n=0, ksize=31, scaled=50) >>> scaled_mh.add_sequence(sequence) >>> hashvals = scaled_mh.hashes.keys() >>> num_mh = sourmash.MinHash(n=500, ksize=31) >>> num_mh.add_many(hashvals) ``` So... when can you do this extraction reliably? You can extract num MinHashes from scaled MinHashes whenever the maximum hash value in the num MinHash is greater than or equal to the `max_hash` attribute of the scaled MinHash. You can extract scaled MinHashes to num MinHashes whenever there are more hash values in the scaled MinHash than num. Yoda sayeth: *When understand these two sentences you can, use this code may you.* (You can also take a look at the logic in `sourmash signature downsample` if you are interested.) ## Working with indexed collections of signatures If you want to search large collections of signatures, sourmash provides two different indexing strategies, together with a generic `Index` class that supports a common API for searching the collections. The first indexing strategy is a Sequence Bloom Tree, which is designed to support fast and efficient containment operations on large collections of signatures. SBTs are an _on disk_ search structure, so they are a low-memory way to search collections. To use SBTs from the command line, we first need to create some `scaled` signatures: ``` sourmash sketch dna -p scaled=10000 data/GCF*.fna.gz --outdir data/ ``` and then build a Sequence Bloom Tree (SBT) index with `sourmash index`, like so: ``` sourmash index foo.sbt.zip data/GCF*.sig -k 31 ``` Here, sourmash is storing the entire SBT in a single portable Zip file. ### Creating an on-disk SBT in Python Let's start by using 'glob' to grab some example signatures from the test data in the sourmash repository: ``` >>> import glob >>> input_filenames = glob.glob('tests/test-data/doctest-data/GCF*.sig') ``` Now, create an SBT: ``` >>> import sourmash.sbtmh >>> tree = sourmash.sbtmh.create_sbt_index() ``` Load each signature, and add it to the tree: ``` >>> from sourmash.sbtmh import SigLeaf >>> for filename in input_filenames: ... sig = sourmash.load_one_signature(filename, ksize=31) ... leaf = SigLeaf(sig.md5sum(), sig) ... tree.add_node(leaf) ``` (note, you'll need to make sure that all of the signatures are compatible with each other! The `sourmash index` command does all of the necessary checks, but the Python API doesn't.) Now, save the tree: ``` >>> filename = tree.save(tempdir + '/test.sbt.zip') ``` ### Loading and searching SBTs How do we load the SBT and search it with a DNA sequence, from within Python? The SBT filename is `test.sbt.zip`, as above: ``` >>> SBT_filename = tempdir + '/test.sbt.zip' ``` and with it we can load the SBT: ``` >>> tree = sourmash.load_file_as_index(SBT_filename) ``` Now, load a DNA sequence: ``` >>> filename = 'data/GCF_000005845.2_ASM584v2_genomic.fna.gz' >>> query_seq = next(iter(screed.open(filename))).sequence >>> print(f'got {len(query_seq)} DNA characters to query') got 4641652 DNA characters to query ``` and create a scaled signature: ``` >>> minhash = sourmash.MinHash(ksize=31, n=0, scaled=10000) >>> minhash.add_sequence(query_seq) >>> query_sig = sourmash.SourmashSignature(minhash, name='my favorite query') ``` Now do a search -- ``` >>> for similarity, found_sig, filename in tree.search(query_sig, threshold=0.1): ... print(query_sig) ... print(found_sig) ... print(similarity) my favorite query NC_000913.3 Escherichia coli str. K-12 substr. MG1655, complete genome 1.0 ``` et voila! ### In-memory databases: the LCA or "reverse index" database. The LCA database lets you work with large collections of signatures in memory. The LCA database was initially designed to support individual hash queries for taxonomic operations - hence its name, which stands for "Lowest Common Ancestor." However, it supports all of the standard `Index` operations, just like the SBT. First, let's create an LCA database programmatically. ``` >>> from sourmash.lca import LCA_Database >>> db = LCA_Database(ksize=31, scaled=10000, moltype='DNA') ``` Now, let's load in all of the signatures from the test directory: ``` >>> for sig in sourmash.load_file_as_signatures('tests/test-data/doctest-data', ksize=31): ... hashes_inserted = db.insert(sig) ... print(f"Inserted {hashes_inserted} hashes into db.") Inserted 493 hashes into db. Inserted 490 hashes into db. Inserted 525 hashes into db. ``` and now you have an `Index` class that supports all the generic index operations (below). You can save an LCA Database to disk with `db.save(filename)`, and load it with `sourmash.load_file_as_index`, below. ### The `Index` class API. The `Index` class supports a generic API for SBTs, LCAs, and other collections of signatures. To load an SBT or an LCA database from a file, use `sourmash.load_file_as_index`: ``` >>> sbt_db = sourmash.load_file_as_index('tests/test-data/prot/protein.sbt.zip') >>> lca_db = sourmash.load_file_as_index('tests/test-data/prot/protein.lca.json.gz') ``` `Index` objects provide `search`, `insert`, `load`, `save`, and `__len__`. The signatures can be accessed directly via the `.signatures()` method, which returns an iterable. Last but not least, `Index.select(ksize=..., moltype=...)` will return a view on the Index object that contains only signatures with the desired k-mer size/molecule type. sourmash-bio-sourmash-bc3c0f8/doc/api.md000066400000000000000000000013651474157046300203330ustar00rootroot00000000000000# `sourmash` Python API The primary programmatic way of interacting with `sourmash` is via its Python API. **Please also see [examples of using the API](api-example.md).** ```{contents} :depth: 2 ``` ## `MinHash`: basic MinHash sketch functionality ```{eval-rst} .. autoclass:: sourmash.MinHash :members: .. automethod:: __init__ ``` ## `SourmashSignature`: save and load MinHash sketches in JSON ```{eval-rst} .. automodule:: sourmash.signature :members: ``` ## `SBT`: save and load Sequence Bloom Trees in JSON ```{eval-rst} .. automodule:: sourmash.sbt :members: GraphFactory, Node, NodePos, SBT, Leaf :undoc-members: ``` ## `sourmash.fig`: make plots and figures ```{eval-rst} .. automodule:: sourmash.fig :members: ``` sourmash-bio-sourmash-bc3c0f8/doc/classifying-signatures.md000066400000000000000000001004271474157046300242560ustar00rootroot00000000000000# Classifying signatures: `search`, `gather`, and `lca` methods. sourmash provides several different techniques for doing classification and breakdown of genomic and metagenomic signatures. These include taxonomic classification as well as decomposition of metagenomic data into constitutent genomes. ```{contents} Contents :depth: 3 ``` ## Searching for similar samples with `search`. The `sourmash search` command is most useful when you are looking for high similarity matches to other signatures; this is the most basic use case for MinHash searching. The command takes a query signature and one or more search signatures, and finds all the matches it can above a particular threshold. By default `search` will find matches with high [*Jaccard similarity*](https://en.wikipedia.org/wiki/Jaccard_index), which will consider all of the k-mers in the union of the two samples. Practically, this means that you will only find matches if there is both high overlap between the samples *and* relatively few k-mers that are disjoint between the samples. This is effective for finding genomes or transcriptomes that are similar but rarely works well for samples of vastly different sizes. One useful modification to `search` is to calculate containment with `--containment` instead of the (default) similarity; this will find matches where the query is contained within the subject, but the subject may have many other k-mers in it. For example, if you are using a plasmid as a query, you would use `--containment` to find genomes that contained that plasmid. `gather` (discussed below) uses containment analysis only. See [the main sourmash tutorial](tutorial-basic.md#make-and-search-a-database-quickly) for information on using `search` with and without `--containment`. ## Analyzing metagenomic samples with `gather` Neither search option (similarity or containment) is effective when comparing or searching with metagenomes, which typically contain a mixture of many different genomes. While you might use containment to see if a query genome is present in one or more metagenomes, a common question to ask is the reverse: **what genomes are in my metagenome?** An alternative phrasing is this: **what reference genomes should I map my metagenomic reads to?** The main approach we provide in sourmash is `sourmash gather`. This constructs the shortest possible list of reference genomes that cover all of the known k-mers in a metagenome. We call this a *minimum metagenome cover*. From an algorithmic perspective, `gather` generates a minimum set cover for a query metagenome, using the reference database you give it. The minimum set cover is calculated using a greedy approximation algorithm. Essentially, `gather` takes a query metagenome and searches the database for the most highly contained genome; it then subtracts that match from the metagenome, and repeats. At the end it reports how much of the metagenome remains unknown. The [basic sourmash tutorial](tutorial-basic.md#whats-in-my-metagenome) has some sample output from using gather with GenBank. See Appendix A at the bottom of this page for more technical details. The `gather` method is described in [Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers, Irber et al., 2022](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2). Our benchmarking in that paper and also in [Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets, Portik et al., 2022](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-05103-0) suggests that it is a very sensitive and specific method for analyzing metagenomes. ## Taxonomic profiling with sourmash sourmash supports two basic kinds of taxonomic profiling, under the `lca` and `tax` modules. **As of 2023, we strongly recommend the `tax`-based profiling approach.** But first, let's back up! By default, there is no structured taxonomic information available in sourmash signatures or collections. What this means is that you will have to provide your own mapping from a match to some taxonomic hierarchy. Both the `lca` and `tax` modules support identifier-based taxonomic mappings, in which identifiers from the signature names can be linked to the standard seven NCBI/GTDB taxonomy ranks - superkingdom, phylum, class, order, family, genus, and species. These are typically provided in a spreadsheet _separately_ from the signature collection. The `tax` module also supports `lins` taxonomies, for which we have a tutorial. There are several advantages that this approach affords sourmash. One is that sourmash is not tied closely to a specific taxonomy - you can use either GTDB or NCBI as you wish. Another advantage is that you can create your own custom taxonomic ranks and even use them with private databases of genomes to classify your own metagenomes. The main disadvantage of sourmash's approach to taxonomy is that sourmash doesn't classify individual metagenomic reads to either a genome or a taxon. (Note that we're not sure this can be done robustly in practice - neither short nor long reads typically contain enough information to uniquely identify a single genome, especially if there are many genomes from the same species present in the database.) If you want to do this, we suggest running `sourmash gather` first, and then mapping the reads to the matching genomes; then you can determine which read maps to which genome. This is the approach taken by [the genome-grist pipeline](https://dib-lab.github.io/genome-grist/). ### Using `sourmash tax` to do taxonomic analysis We recommend using the `tax` module to do taxonomic classification of genomes (with `tax genome`) and metagenomes (with `tax metagenome`). The `tax` module commands operate downstream of `sourmash gather`, which builds a minimum set cover of the query against the database - intuitively speaking, this is the shortest possible list of genomes that the query would map to. Then, both `tax genome` and `tax metagenome` take the CSV output of `sourmash gather` and produce taxonomic profiles. (You can read more about minimum set covers in [Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers, Irber et al., 2022](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2).) The `tax metagenome` approach was benchmarked in [Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets, Portik et al., 2022](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-05103-0) and appears to be both very accurate and very sensitive, unless you're using Nanopore data or other data types that have a high sequencing error rate. It's important to note that taxonomy based on multiple k-mers is very, very specific and if you get a match, it's pretty reliable. On the converse, however, k-mer identification is very brittle with respect to evolutionary divergence, so if you don't get a match it may only mean that the specific species or genus you're searching for isn't in the database. ### Using `sourmash lca` to do taxonomic analysis The `sourmash lca` module supports taxonomic classification using single hashes, corresponding to single k-mers, in an approach inspired by Kraken. Briefly, you first build an LCA database using `lca index`, which takes a taxonomy spreadsheet and a collection of sketches. Then, you can use `lca classify` to classify single-genome sketches or `lca summarize` to classify metagenomes. The `lca` approach is not published anywhere, but we're happy to discuss it in more detail; just [post to the issue tracker](https://github.com/sourmash-bio/sourmash/issues). While we do not recommend the `lca` approach for general taxonomic classification purposes (see below!), it remains useful for certain kinds of diagnostic evaluation of sequences, so we are leaving the functionality in sourmash. ### `sourmash tax` vs `sourmash lca` Why do we recommend using the `tax` module over `lca`? `sourmash lca` was the first implementation in sourmash, and over the years we've found that it is prone to false positives: that is, individual k-mers are very sensitive but are often misassigned to higher taxonomic ranks than they need to be, either because of contamination in the reference database or because the taxonomy is not based directly on genome similarity. Instead of using single k-mers, `sourmash gather` estimates the best matching genome based on combinations of k-mers, which is much more specific than the LCA approach; only then is a taxonomy assigned using `sourmash tax`. The bottom line is that in our experience, `sourmash tax` is as sensitive as `lca`, and a lot more specific. Please let us know if you discover differently! ## Abundance weighting By default, sourmash tracks k-mer presence, *not* their abundance. The proportions and fractions reported also ignore abundance. So, if `sourmash gather` reports that a genome is 5% of a metagenome, it is reporting Jaccard containment of that genome in the metagenome, and it is ignoring information like the number of reads in the metagenome that come from that genome. Similarly, when `sourmash compare` compares genome or metagenome signatures, it's reporting Jaccard similarity *without* abundance. However, it is possible to take into account abundance information by computing signatures with `-p abund`. The abundance information will be used if it's present in the signature, and it can be ignored with `--ignore-abundance` in any signature comparison. There are two ways that abundance weighting can be used. One is in containment queries for metagenomes, e.g. with `sourmash gather`, and the other is in comparisons of abundance-weighted signatures, e.g. with `sourmash search` and `sourmash compare`. Below, we refer to the first as "abundance projection" and the second as "angular similarity". ### Projecting abundances in `sourmash gather`: `sourmash gather` can report approximate abundance information for containment queries against genome databases. This will give you numbers that (approximately) match what you get from counting the coverage of each contig by mapping reads. If you create your query signature with `-p abund`, `sourmash gather` will use the resulting k-mer multiplicity information to calculate an abundance-weighted result, weighting each hash value match by the multiplicity of the hash value in the query signature. You can turn off this behavior with `--ignore-abundance`. The abundance is reported as column `avg_abund` in the console output, and columns `average_abund`, `median_abund`, and `std_abund` in the CSV output. For example, if you have a metagenome composed of two equal sized genomes A and B, with A present at 10 times the abundance of B, `gather` on abundance-weighted signatures will report that approximately 91% of the metagenome is A and approximately 9% is B. (If you use `--ignore-abundance`, then `gather` will report approximately 50:50, since the genomes are equal sized.) You can also get count-like information from the CSV output of `sourmash gather`; the column `median_abund` contains the median abundance of the k-mers in the match to the given genome. Please see Appendix B, below, for some actual numbers and output. **Buyer beware:** There are substantial challenges in doing this kind of analysis on real metagenomic samples, relating to genome representation and strain overlap; see [this issue](https://github.com/sourmash-bio/sourmash/issues/461) for a discussion. ### Computing signature similarity with angular similarity. If signatures that have abundance information are compared with `sourmash search` or `sourmash compare`, the default comparison is done with [angular similarity](https://en.wikipedia.org/wiki/Cosine_similarity#Angular_distance_and_similarity). This is a distance metric based on cosine similarity, and it is suitable for use in clustering. For more information on the value of this kind of comparison for metagenomics, please see the simka paper, [Multiple comparative metagenomics using multiset k-mer counting](https://peerj.com/articles/cs-94/), Benoit et al., 2016. **Implementation note:** Angular similarity searches cannot be done on SBT or LCA databases currently; you have to provide collections of signature files or zip file collections to `sourmash search` and `sourmash compare`. sourmash will provide a warning if you run `sourmash search` on an LCA or SBT with an abundance-weighted query, and automatically apply `--ignore-abundance`. ### Estimating ANI from FracMinHash comparisons. As of v4.4, `sourmash` can estimate Average Nucleotide Identity (ANI) between two FracMinHash ("scaled") sketches. `sourmash compare` can now produce a matrix of ANI values estimated from Jaccard, Containment, or Max Containment by specifying `--ani` (optionally along with search type, e.g. `--containment`). `sourmash search`, `sourmash prefetch`, and `sourmash gather` will now output ANI estimates to output CSVs. Note that while ANI can be estimated from either the Jaccard Index or the Containment Index, ANI from Containment is preferable (more accurate). For `sourmash search`, `sourmash prefetch`, and `sourmash gather`, you can optionally return confidence intervals around containment-derived ANI estimates, which take into account the impact of the scaling factor (via `--estimate-ani-ci`). For details on ANI estimation, please see the paper ["Deriving confidence intervals for mutation rates across a wide range of evolutionary distances using FracMinHash"](https://pubmed.ncbi.nlm.nih.gov/37344105/), Hera et al., 2023. ## What commands should I use? It's not always easy to figure that out, we know! We're thinking about better tutorials and documentation constantly. We suggest the following approach: * build some signatures and do some searches, to get some basic familiarity with sourmash; * explore the available databases; * then ask questions [via the issue tracker](https://github.com/sourmash-bio/sourmash/issues) and we will do our best to help you out! This helps us figure out what people are actually interested in doing, and any help we provide via the issue tracker will eventually be added into the documentation. ## Appendix A: how `sourmash gather` works. The sourmash gather algorithm works as follows: * find the best match in the database, based on containment; * subtract that match from the query; * repeat. * when the number of shared hashes between the _remaining_ query and the best match drops below `threshold_bp/scaled` (or is zero), break out of the loop. The output below is the CSV output for a fictional metagenome. The first column, `f_unique_to_query`, is the fraction of the database match that is _unique_ with respect to the original query. It will always decrease as you get more matches. The sum of `f_unique_to_query` across all rows is what is reported in by gather as the fraction of query k-mers hit by the recovered matches (unweighted) and should never be greater than 1! This column should be used in any analysis that needs to avoid double-counting matches. The second column, `f_match_orig`, is how much of the match is in the _original_ query. For this fictional metagenome, each match is entirely contained in the original query. This is the number you would get by running `sourmash search --containment `. The third column, `f_match`, is how much of the match is in the remaining query metagenome, after all of the previous matches have been removed. The fourth column, `f_orig_query`, is how much of the original query belongs to the match. This is the number you'd get by running `sourmash search --containment `. ``` f_unique_to_query f_match_orig f_match f_orig_query 0.3321964529331514 1.0 1.0 0.3321964529331514 0.13096862210095497 1.0 1.0 0.13096862210095497 0.11527967257844475 1.0 0.898936170212766 0.12824010914051842 0.10709413369713507 1.0 1.0 0.10709413369713507 0.10368349249658936 1.0 0.3134020618556701 0.33083219645293316 ``` Where there are overlapping matches (e.g. to multiple *E. coli* species in a gut metagenome) the overlaps will be represented multiple times in this column. A few quick notes for the algorithmic folk out there -- * the key innovation for gather is that it looks for **groups** of k-mers in the databases, and picks the best group (based on containment). It does not treat k-mers individually. * because of this, gather does not saturate as databases grow in size, and in fact should only become more sensitive and specific as we increase database size. (Although of course it may get a lot slower...) ## Appendix B: sourmash gather and signatures with abundance information Below is a discussion of a synthetic set of test cases using three randomly generated (fake) genomes of the same size, with two even read data set abundances of 2x each, and a third read data set with 20x. ### Data set prep First, we make some synthetic data sets: * r1.fa with 2x coverage of genome s10 * r2.fa with 20x coverage of genome s10. * r3.fa with 2x coverage of genome s11. then we make signature s10-s11 with r1 and r3, i.e. 1:1 abundance, and make signature s10x10-s11 with r2 and r3, i.e. 10:1 abundance. ### A first experiment: 1:1 abundance ratio. When we project r1+r3, 1:1 abundance, onto s10, s11, and s12 genomes with gather: ``` sourmash gather r1+r3 genome-s10.sig genome-s11.sig genome-s12.sig ``` we get: ``` overlap p_query p_match avg_abund --------- ------- ------- --------- 394.0 kbp 49.6% 78.5% 1.8 genome-s10.fa.gz 376.0 kbp 50.4% 80.0% 1.9 genome-s11.fa.gz ``` * approximately 50% of each query matching (first column, `p_query`) * approximately 80% of subject genome's contents being matched (this is due to the low coverage of 2 used to build queries; `p_match`) * approximately 2.0 coverage (third column, `avg_abund`) * no match to genome s12. ### A second experiment: 10:1 abundance ratio. When we project r2+r3, 10:1 abundance, onto s10, s11, and s12 genomes with gather: ``` sourmash gather r2+r3 genome-s10.sig genome-s11.sig genome-s12.sig ``` we get: ``` overlap p_query p_match avg_abund --------- ------- ------- --------- 0.5 Mbp 91.0% 100.0% 14.5 tests/test-data/genome-s10.fa.gz 376.0 kbp 9.0% 80.0% 1.9 tests/test-data/genome-s11.fa.gz ``` * approximately 91% of s10 matching * approximately 9% of s11 matching * approximately 100% of the high coverage genome being matched, with only 80% of the low coverage genome * approximately 2.0 coverage (third column, avg_abund) for s11, and (very) approximately 20x coverage for genome s10. The cause of the poor approximation for genome s10 is unclear; it could be due to low coverage, or small genome size, or something else. More investigation is needed. ## Appendix C: sourmash gather output examples Below we show two real gather analyses done with a mock metagenome, SRR606249 (from [Shakya et al., 2014](https://pubmed.ncbi.nlm.nih.gov/23387867/)) and three of the known genomes contained within it - two *Shewanella baltica* strains and one *Akkermansia muciniphila* genome ### sourmash gather with a query containing abundance information ``` % sourmash gather -k 31 SRR606249.trim.sig.zip podar-ref/2.fa.sig podar-ref/47.fa.sig podar-ref/63.fa.sig == This is sourmash version 4.8.5.dev0. == == Please cite Brown and Irber (2016), doi:10.21105/joss.00027. == selecting specified query k=31 loaded query: SRR606249... (k=31, DNA) -- loaded 9 total signatures from 3 locations. after selecting signatures compatible with search, 3 remain. Starting prefetch sweep across databases. Prefetch found 3 signatures with overlap >= 50.0 kbp. Doing gather to generate minimum metagenome cover. overlap p_query p_match avg_abund --------- ------- ------- --------- 5.2 Mbp 0.8% 99.0% 11.7 NC_011663.1 Shewanella baltica OS223... 2.7 Mbp 0.9% 100.0% 24.5 CP001071.1 Akkermansia muciniphila A... 5.2 Mbp 0.3% 51.0% 8.1 NC_009665.1 Shewanella baltica OS185... found less than 50.0 kbp in common. => exiting found 3 matches total; the recovered matches hit 2.0% of the abundance-weighted query. the recovered matches hit 2.5% of the query k-mers (unweighted). ``` ### sourmash gather with the same query, *ignoring* abundances ``` % sourmash gather -k 31 SRR606249.trim.sig.zip podar-ref/2.fa.sig podar-ref/47.fa.sig podar-ref/63.fa.sig --ignore-abundance == This is sourmash version 4.8.5.dev0. == == Please cite Brown and Irber (2016), doi:10.21105/joss.00027. == selecting specified query k=31 loaded query: SRR606249... (k=31, DNA) -- loaded 9 total signatures from 3 locations. after selecting signatures compatible with search, 3 remain. Starting prefetch sweep across databases. Prefetch found 3 signatures with overlap >= 50.0 kbp. Doing gather to generate minimum metagenome cover. overlap p_query p_match --------- ------- ------- 5.2 Mbp 1.2% 99.0% NC_011663.1 Shewanella baltica OS223, complete... 2.7 Mbp 0.6% 100.0% CP001071.1 Akkermansia muciniphila ATCC BAA-83... 5.2 Mbp 0.6% 51.0% NC_009665.1 Shewanella baltica OS185, complete... found less than 50.0 kbp in common. => exiting found 3 matches total; the recovered matches hit 2.5% of the query k-mers (unweighted). ``` ### Notes and comparisons There are a few interesting things to point out about the above output: * `p_match` is the same whether or not abundance information is used. This is because it is the fraction of the matching genome detected in the metagenome, which is inherently "flat". It is also reported progressively: only the portions of the metagenome that have not matched to any previous matches are used in `p_match`; read on for details :). * `p_query` is different when abundance information is used. For queries with abundance information, `p_query` provides a weighted estimate that approximates the number of metagenome reads that would map to this genome _after_ mapping reads to all previously reported matches, i.e. all matches above this match. * When abundance information is not available or not used, `p_query` is an estimate of what fraction of the remaining k-mers in the metagenome match to this genome, after all previous matches have been removed. * The `avg_abund` column only shows up when abundance information is supplied. This is the k-mer coverage of the detected portion of the match; it is a lower bound on the expected mapping-based coverage for metagenome reads mapped to the detected portion of the match. * The percent of recovered matches for the abundance-weighted query is the sum of the `p_query` column and estimates the total fraction of metagenome reads that will map across all of the matching references. * The percent of recovered matches when _ignoring_ abundances is likewise the sum of the (unweighted) `p_query` column and is not particularly informative - it will always be low for real metagenomes, because sourmash cannot match erroneous k-mers created by sequencing errors. * The `overlap` column is the estimated size of the overlap between the (unweighted) original query metagenome and the match. It does not take into account previous matches. Last but not least, something interesting is going on here with strains. While it is not highlighted in the text output of gather, there is substantial overlap between the two *Shewanella baltica* genomes. And, in fact, both of them are entirely (well, 99%) present in the metagenome if measured individually with `sourmash search --containment`. Consider a few more details: * `p_match` for the first *Shewanella* match, `NC_011663.1`, is 99%! * `p_match` for the second *Shewanella* match, `NC_009665.1`, is only 50%! * and, confusingly, the `overlap` for both matches is 5.2 Mbp! What's up?! What's happening here is that `sourmash gather` is subtracting the match to the first *Shewanella* genome from the metagenome before moving on to the next result, and `p_match` reports only the amount of the match detected in the _remaining_ metagenome after that subtraction. However, `overlap` is reported as the amount of overlap with the _original_ metagenome, which is essentially the entire genome in all three cases. The main things to keep in mind for gather are this: * `p_query` and `p_match` do not double-count k-mers or matches; in particular, you can sum across `p_query` for a metagenome without counting anything more than once. * `overlap` _does_ count matches redundantly. * the percent of recovered matches is a useful summary of the whole metagenome! We know it's confusing but it's the best output we've been able to figure out across all of the different use cases for gather. Perhaps in the future we'll find a better way to represent all of these numbers in a more clear, concise, and interpretable way; in the meantime, we welcome your questions and comments! ## Appendix D: Gather CSV output columns Note that order of columns is not guaranteed and may change between versions. | `Gather` column header | Type | Description | | :------------------------------: | :-------------: | :----------- | | `unique_intersect_bp` | integer | Size of overlap between match and _remaining_ query, estimated by multiplying the number of overlapping hashes by scaled. Rank/order dependent. Does not double count hashes. | | `intersect_bp` | integer | Size of overlap between match and query, estimated by multiplying the number of overlapping hashes by scaled. Independent of rank order and will often double-count hashes. | | `f_orig_query` | float | The fraction of the original query represented by this match. Approximates the fraction of metagenomic reads that will map to this genome. | | `f_match` | float | The containment of the match in the query. | | `f_unique_to_query` | float | The fraction of matching hashes (unweighted) that are unique to this query; rank dependent. Will sum to the fraction of total k-mers (unweighted) that were identified. | | `f_unique_weighted` | float | The fraction of matching hashes (weighted by multiplicity) that are unique to this match. This will sum (across the rows) to the fraction of total _weighted_ k-mers that were identified. Approximates the fraction of metagenomic reads that will map to this genome _after_ all previous matches at lower (earlier) ranks are mapped. | | `average_abund` | float | Mean abundance of the weighted hashes unique to the intersection. Empty if query does not have abundance. Rank dependent, does not double count. | | `median_abund` | integer | Median abundance of the weighted hashes unique to the intersection. Empty if query has no abundance. Rank dependent, does not double count. | | `std_abund` | float | Std deviation of the abundance of the hashes unique to the intersection. Empty if query has no abundance. Rank dependent, does not double count. | | `filename` | string | Filename/location of the database from which the match was loaded. | | `name` | string | Full sketch name of the match. | | `md5` | string | Full md5sum of the match sketch. | | `f_match_orig` | float | The fraction of the match in the full query. Rank independent. | | `gather_result_rank` | float | Rank of this match in the results. | | `remaining_bp` | integer | How many bp remain in the query after subtracting this match, estimated by multiplying remaining hashes by scaled. Starts at `query_n_hashes`. Unweighted. See `sum_weighted_found` for the related weighted value. | | `query_filename` | string | The filename from which the query was loaded. | | `query_name` | string | The query sketch name. | | `query_md5` | string | Truncated md5sum of the query sketch. | | `query_bp` | integer | Estimated number of bp in the query, estimated by multiplying the sketch size by scaled. | | `ksize` | integer | K-mer size for the sketches used in the comparison. | | `moltype` | string | Molecule type of the comparison. | | `scaled` | integer | Scaled value of the comparison. | | `query_n_hashes` | integer | Number of hashes in the query sketch. Unweighted. See `total_weighted_hashes` for weighted value. | | `query_abundance` | boolean | True if the query has abundance information; False otherwise. | | `query_containment_ani` | float | ANI estimated from the query containment in the match. | | `match_containment_ani` | float | ANI estimated from the match containment in the query. | | `average_containment_ani` | float | ANI estimated from the average of the query and match containment. | | `max_containment_ani` | float | ANI estimated from the max of the query and match containment. | | `potential_false_negative` | boolean | True if the sketch size(s) were too small to give a reliable ANI estimate. False otherwise. | | `n_unique_weighted_found` | integer | Sum of (abundance-weighted) hashes found in this rank. | | `sum_weighted_found` | integer | Sum of the hashes x abundance found thus far, i.e., running total of `n_unique_weighted_found`. The last value divided by `total_weighted_hashes` will equal the total fraction of (weighted) k-mers identified. | | `total_weighted_hashes` | integer | Sum of hashes x abundance for the entire dataset. Constant value. | ## Appendix E: Prefetch CSV output columns Note that order of columns is not guaranteed and may change between versions. | `Prefetch` column header | Type | Description | | :----------------------------: | :-------------: | :----------- | | `intersect_bp` | integer | Size of overlap between match and original query, estimated by multiplying the number of overlapping hashes by `scaled`. | | `jaccard` | float | Jaccard similarity of the two sketches. | | `max_containment` | float | Max of `f_query_match` and `f_match_query`. | | `f_query_match` | float | The fraction of the query contained by the match. | | `f_match_query` | float | The fraction of the match contained by the query. | | `match_filename` | string | Filename the match sketch was loaded from. | | `match_name` | string | Full name of the match sketch. | | `match_md5` | string | Truncated md5sum of match sketch (8 char). | | `match_bp` | integer | Size of match, estimated by multiplying the sketch size by scaled. | | `query_filename` | string | Filename the query sketch was loaded from. | | `query_name` | string | Full name of the query sketch. | | `query_md5` | string | Truncated md5sum of query sketch (8 char). | | `query_bp` | integer | Size of query, estimated by multiplying the sketch size by scaled. | | `ksize` | integer | K-mer size for the sketches used in the comparison. | | `moltype` | string | Molecule type of the sketches. | | `scaled` | integer | Scaled value at which the comparison was done. | | `query_n_hashes` | integer | Number of hashes in the query. | | `query_abundance` | integer | Median hash abundance in the sketch, if available. | | `query_containment_ani` | float | ANI estimated from the query containment in the match. | | `match_containment_ani` | float | ANI estimated from the match containment in the query. | | `average_containment_ani` | float | ANI estimated from the average of the query and match containment. | | `max_containment_ani` | float | ANI estimated from the max containment between query/match. | | `potential_false_negative` | boolean | True if the sketch size(s) were too small to give a reliable ANI estimate. False if ANI estimate is reliable. | sourmash-bio-sourmash-bc3c0f8/doc/command-line.md000066400000000000000000003203471474157046300221310ustar00rootroot00000000000000# Using sourmash from the command line ```{contents} Contents :depth: 3 ``` From the command line, sourmash can be used to create [FracMinHash sketches][0] from DNA and protein sequences, compare them to each other, and plot the results; these sketches are saved into "signature files". These signatures allow you to estimate sequence similarity and containment quickly and accurately in large collections, among other capabilities. sourmash also provides a suite of metagenome functionality. This includes genome search in metagenomes, metagenome decomposition into a list of genomes from a database, and taxonomic classification functionality. Please see the [mash software][1] and the [mash paper (Ondov et al., 2016)][2] for background information on how and why MinHash sketches work. The [FracMinHash preprint (Irber et al, 2022)](https://www.biorxiv.org/content/10.1101/2022.01.11.475838) describes FracMinHash sketches as well as the metagenome-focused features of sourmash. sourmash uses a subcommand syntax, so all commands start with `sourmash` followed by a subcommand specifying the action to be taken. ## An example Download three bacterial genomes from NCBI: ``` curl -L -O https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/017/325/GCF_000017325.1_ASM1732v1/GCF_000017325.1_ASM1732v1_genomic.fna.gz curl -L -O https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/021/665/GCF_000021665.1_ASM2166v1/GCF_000021665.1_ASM2166v1_genomic.fna.gz curl -L -O https://ftp.ncbi.nlm.nih.gov/genomes/refseq/bacteria/Escherichia_coli/reference/GCF_000005845.2_ASM584v2/GCF_000005845.2_ASM584v2_genomic.fna.gz ``` Compute sourmash signatures for them all: ``` sourmash sketch dna -p k=31 *.fna.gz ``` This will produce three `.sig` files containing MinHash signatures using a k-mer size of 31. Next, compare all the signatures to each other: ``` sourmash compare *.sig -o cmp.dist ``` Finally, plot a dendrogram: ``` sourmash plot cmp.dist --labels ``` This will output three files, `cmp.dist.dendro.png`, `cmp.dist.matrix.png`, and `cmp.dist.hist.png`, containing a clustering & dendrogram of the sequences, a similarity matrix and heatmap, and a histogram of the pairwise similarities between the three genomes. Matrix: ![Matrix](_static/cmp.matrix.png) Here, the two genomes that cluster together are strains of the same species, while the third is from a completely different genus. ## The `sourmash` command and its subcommands To get a list of subcommands, run `sourmash` without any arguments. Please use the command line option `--help` to get more detailed usage information for each command. All signature saving commands can save to a variety of formats (we suggest `.zip` files) and all signature loading commands can load signatures from any of these formats. There are seven main subcommands: `sketch`, `compare`, `plot`, `search`, `gather`, `index`, and `prefetch`. See [the tutorial](tutorials.md) for a walkthrough of these commands. * `sketch` creates signatures. * `compare` compares signatures and builds a similarity matrix. * `plot` plots similarity matrices created by `compare`. * `search` finds matches to a query signature in a collection of signatures. * `gather` finds the best reference genomes for a metagenome, using the provided collection of signatures. * `index` builds a fast index for many (thousands) of signatures. * `prefetch` selects signatures of interest from a very large collection of signatures, for later processing. There are also a number of commands that work with taxonomic information; these are grouped under the `sourmash tax` and `sourmash lca` subcommands. `sourmash tax` commands: * `tax metagenome` - summarize metagenome gather results at each taxonomic rank. * `tax genome` - summarize single-genome gather results and report most likely classification. * `tax annotate` - annotate gather results with lineage information (no summarization or classification). * `tax prepare` - prepare and/or combine taxonomy files. * `tax grep` - subset taxonomies and create picklists based on taxonomy string matches. * `tax summarize` - print summary information (counts of lineages) for a taxonomy lineages file or database. `sourmash lca` commands: ```{attention} We do not recommend using the `lca` subcommands for taxonomic analysis any more; please use `sourmash tax` instead. See [taxonomic profiling with sourmash](classifying-signatures.md#taxonomic-profiling-with-sourmash) for more information. ``` * `lca classify` classifies many signatures against an LCA database. * `lca summarize` summarizes the content of metagenomes using an LCA database. * `lca index` creates a database for use with LCA subcommands. * `lca rankinfo` summarizes the content of a database. * `lca compare_csv` compares lineage spreadsheets, e.g. those output by `lca classify`. See [the LCA tutorial](tutorials-lca.md) for a walkthrough of some of these commands. Finally, there are a number of utility and information commands: * `info` shows version and software information. * `index` indexes many signatures using a Sequence Bloom Tree (SBT). * `sbt_combine` combines multiple SBTs. * `categorize` is an experimental command to categorize many signatures. * `watch` is an experimental command to classify a stream of sequencing data. * `multigather` is an experimental command to run multiple gathers against the same collection of databases. Please use the command line option `--help` to get more detailed usage information for each command. ### `sourmash sketch` - make sourmash signatures from sequence data Most of the commands in sourmash work with **signatures**, which contain information about genomic or proteomic sequences. Each signature contains one or more **sketches**, which are compressed versions of these sequences. Using sourmash, you can search, compare, and analyze these sequences in various ways. To create a signature with one or more sketches, you use the `sourmash sketch` command. There are four main commands: ``` sourmash sketch dna sourmash sketch protein sourmash sketch translate sourmash sketch fromfile ``` The `sketch dna` command reads in **DNA sequences** and outputs **DNA sketches**. The `sketch protein` command reads in **protein sequences** and outputs **protein sketches**. The `sketch translate` command reads in **DNA sequences**, translates them in all six frames, and outputs **protein sketches**. The `sketch fromfile` command takes in a CSV file containing the locations of genomes and proteomes, and outputs all of the requested sketches. It is primarily intended for large-scale database construction. (`fromfile` is a new command as of sourmash v4.4.0.) All of the `sourmash sketch` commands take FASTA or FASTQ sequences as input; input data can be uncompressed, compressed with gzip, or compressed with bzip2. The output will be one or more signature files that can be used by other sourmash commands. Please see [the `sourmash sketch` documentation page](sourmash-sketch.md) for details on `sketch`, and see [Using sourmash: a practical guide](using-sourmash-a-guide.md) for more information on creating signatures. ### `sourmash compute` - make sourmash signatures from sequence data **Note: `sourmash compute` is deprecated in sourmash 4.0 and will be removed in sourmash 5.0; please switch to using `sourmash sketch`, above.** The `compute` subcommand computes and saves signatures for each sequence in one or more sequence files. It takes as input FASTA or FASTQ files, and these files can be uncompressed or compressed with gzip or bzip2. The output will be one or more JSON signature files that can be used with `sourmash compare`. Please see [Using sourmash: a practical guide](using-sourmash-a-guide.md) for more information on computing signatures. ______ Usage: ``` sourmash compute [ ... ] ``` Optional arguments: ``` --ksizes K1[,K2,K3] -- one or more k-mer sizes to use; default is 31 --force -- recompute existing signatures; convert non-DNA characters to N --output -- save all the signatures to this file; can be '-' for stdout. --track-abundance -- compute and save k-mer abundances. --name-from-first -- name the signature based on the first sequence in the file --singleton -- instead of computing a single signature for each input file, compute one for each sequence --merged -- compute a single signature for all of the input files, naming it ``` ### `sourmash compare` - compare many signatures The `compare` subcommand compares one or more signatures (created with `sketch`) using estimated [Jaccard index][3] or (if signatures are created with `-p abund`) the [angular similarity](https://en.wikipedia.org/wiki/Cosine_similarity#Angular_distance_and_similarity). The default output is a text display of a similarity matrix where each entry `[i, j]` contains the estimated Jaccard index between input signature `i` and input signature `j`. The output matrix can be saved to a numpy binary file with `--output ` and used with the `sourmash plot` subcommand (or loaded with `numpy.load(...)`. Using `--csv ` will output a CSV file that can be loaded into other languages than Python, such as R. As of sourmash 4.4.0, `compare` also supports Average Nucleotide Identity (ANI) estimates instead of Jaccard or containment index; use `--ani` to enable this. Usage: ``` sourmash compare [ ... ] ``` Options: * `--output ` -- save the output matrix to this file, as a numpy binary matrix. * `--distance-matrix` -- create and output a distance matrix, instead of a similarity matrix. * `--ksize ` -- do the comparisons at this k-mer size. * `--containment` -- calculate containment instead of similarity; `C(i, j) = size(i intersection j) / size(i)` * `--ani` -- output estimates of Average Nucleotide Identity (ANI) instead of Jaccard similarity or containment. * `--from-file ` -- append the list of files in this text file to the input signatures. * `--ignore-abundance` -- ignore abundances in signatures. * `--picklist ::` -- select a subset of signatures with [a picklist](#using-picklists-to-subset-large-collections-of-signatures) * `--csv ` -- save the output matrix in CSV format. * `--labels-to ` -- create a CSV file (spreadsheet) that can be passed in to `sourmash plot` with `--labels-from` in order to customize the labels. **Note:** compare by default produces a symmetric similarity matrix that can be used for clustering in downstream tasks. With `--containment`, however, this matrix is no longer symmetric and cannot formally be used for clustering. The containment matrix is organized such that the value in row A for column B is the containment of the B'th sketch in the A'th sketch, i.e. ``` C(A, B) = B.contained_by(A) ``` **Note:** The ANI estimate will be calculated based on Jaccard similarity by default; however, if `--containment`, `--max-containment`, or `--avg-containment` is specified, those values will be used instead. With `--containment --ani`, the ANI output matrix will be asymmetric as discussed above. ### `sourmash plot` - cluster and visualize comparisons of many signatures The `plot` subcommand produces two plots -- a dendrogram and a dendrogram+matrix -- from a matrix created by `sourmash compare --output `. The default output is two PNG files. Usage: ``` sourmash plot ``` Options: * `--pdf` -- output PDF files. (defaults to PNG) * `--labels` -- display the signature names on the plot (default) * `--indices` -- turn on index display on the plot. * `--vmax` -- maximum value (default 1.0) for heatmap. * `--vmin` -- minimum value (default 0.0) for heatmap. * `--subsample=` -- plot a maximum of samples, randomly chosen. * `--subsample-seed=` -- seed for pseudorandom number generator. Example command lines for label and index display - * `--indices` will show only numbers; * `--no-labels --no-indices` will remove all labels! Example output: ![An E. coli comparison plot](_static/ecoli_cmp.matrix.png) ### `sourmash search` - search for signatures in collections or databases The `search` subcommand searches a collection of signatures (in any of the [formats supported by sourmash](#storing-and-searching-signatures)) for matches to the query signature. It can search for matches with either high [Jaccard similarity](https://en.wikipedia.org/wiki/Jaccard_index) or containment; the default is to use Jaccard similarity, unless `--containment` is specified. `-o/--output` will create a CSV file containing all of the matches with respective similarity or containment score. `search` makes use of [indexed databases](#loading-many-signatures) to decrease search time and memory where possible. Usage: ``` sourmash search query.sig [ ... ] ``` Example output: ``` % sourmash search tests/test-data/47.fa.sig gtdb-rs207.genomic-reps.dna.k31.zip ... -- loaded 65703 total signatures from 1 locations. after selecting signatures compatible with search, 65703 remain. 2 matches above threshold 0.080: similarity match ---------- ----- 32.3% GCF_900456975.1 Shewanella baltica strain=NCTC10735, 5088... 14.0% GCF_002838165.1 Shewanella sp. Pdp11 strain=Pdp11, ASM283... ``` `search` takes a number of command line options - * `--containment` - find matches using the containment index rather than Jaccard similarity; * `--max-containment` - find matches using the max containment index rather than Jaccard similarity; * `-t/--threshold` - lower threshold for matching; defaults to 0.08; * `--best-only` - find and report only the best match; * `-n/--num-results` - number of matches to report to stdout; defaults to 3; 0 to report all; Match information can be saved to a CSV file with `-o/--output`; with `-o`, all matches above the threshold will be saved, not just those printed to stdout (which are limited to `-n/--num-results`). The `--containment` flag calculates the containment of the query in database matches; this is an asymmetric order-dependent measure, unlike Jaccard. Here, `search --containment Q A B C D` will report the containment of `Q` in each of `A`, `B`, `C`, and `D`. This is opposite to the order used by `prefetch`, where the composite sketch (e.g. metagenomes) is the query, and the matches are contained items (e.g. genomes). As of sourmash 4.2.0, `search` supports `--picklist`, to [select a subset of signatures to search, based on a CSV file](#using-picklists-to-subset-large-collections-of-signatures). This can be used to search only a small subset of a large collection, or to exclude a few signatures from a collection, without modifying the collection itself. ### `sourmash gather` - find metagenome members The `gather` subcommand selects the best reference genomes to use for a metagenome analysis, by finding the smallest set of non-overlapping matches to the query in a database. This is specifically meant for metagenome and genome bin analysis. (See [Classifying Signatures](classifying-signatures.md) for more information on the different approaches that can be used here.) `sourmash gather` takes exactly one query and one or more [collections of signatures](#storing-and-searching-signatures). Please see [`sourmash multigather`](#sourmash-multigather-do-gather-with-many-queries) if you have multiple queries! If the input signature was created with `-p abund`, output will be abundance weighted (unless `--ignore-abundances` is specified). `-o/--output` will create a CSV file containing the matches. `gather`, like `search`, works with any of the [signature collection formats supported by sourmash](#storing-and-searching-signatures) and will make use of [indexed databases](#loading-many-signatures) to decrease search time and memory where possible. Usage: ``` sourmash gather query.sig [ ... ] ``` Example output: ``` overlap p_query p_match --------- ------- -------- 1.4 Mbp 11.0% 58.0% JANA01000001.1 Fusobacterium sp. OBRC... 1.0 Mbp 7.7% 25.9% CP001957.1 Haloferax volcanii DS2 pla... 0.9 Mbp 7.4% 11.8% BA000019.2 Nostoc sp. PCC 7120 DNA, c... 0.7 Mbp 5.9% 23.0% FOVK01000036.1 Proteiniclasticum rumi... 0.7 Mbp 5.3% 17.6% AE017285.1 Desulfovibrio vulgaris sub... ... found less than 50.0 kbp in common. => exiting found 64 matches total; the recovered matches hit 94.0% of the abundance-weighted query. the recovered matches hit 45.6% of the query k-mers (unweighted). ``` For each match, * 'overlap', the first column, is the estimated number of base pairs shared between the match and the query, based on the number of shared hashes. * 'p_query' is the _percentage_ of the query that overlaps with the match; it is the amount of the metagenome "explained" by this match. It is typically a lower bound on the percent of metagenomes reads that will map to this genome. * 'p_match' is the percentage of the _match_ that overlaps with the query; it is the "detection" of the match in the metagenome. It is typically a lower bound on the number of base pairs that will be covered by read mapping. Quite a bit more information per match row is available in the CSV output saved with `-o`; for details, see [Classifying signatures: how sourmash gather works](classifying-signatures.md#appendix-a-how-sourmash-gather-works). The "recovered matches" lines detail how much of the query is explained by the entire collection of matches. You will get two numbers if your metagenome sketch has been calculated with `-p abund`, and only one if it does not have abundances. The abundance-weighted number should approximate the fraction of metagenome reads that will map to at least one reference genome, while the unweighted number describes how much of the metagenome itself matches to genomes. Here's another way to put it: if the metagenome could be perfectly assembled into contigs, the unweighted number would approximate the number of bases from the contigs that would match perfectly to at least one genome in the reference database. More practically, the abundance-weighted number is less sensitive to sequencing errors. See [classifying signatures](classifying-signatures.md#abundance-weighting) or [the FAQ](faq.md) for more information! The command line option `--threshold-bp` sets the threshold below which matches are no longer reported; by default, this is set to 50kb. See the Appendix in [Classifying Signatures](classifying-signatures.md) for details. As of sourmash 4.2.0, `gather` supports `--picklist`, to [select a subset of signatures based on a CSV file](#using-picklists-to-subset-large-collections-of-signatures). This can be used to search only a small subset of a large collection, or to exclude a few signatures from a collection, without modifying the collection itself. Note: Use `sourmash gather` to analyze a metagenome against a collection of genomes. Then use `sourmash tax metagenome` to integrate that collection of genomes with taxonomic information. #### Alternative search mode for low-memory (but slow) search: `--linear` By default, `sourmash gather` uses all information available for faster search. In particular, for SBTs, `prefetch` will prune the search tree. This can be slow and/or memory intensive for very large databases, and `--linear` asks `sourmash prefetch` to instead use a linear search across all leaf nodes in the tree. The results are the same whether `--no-linear` or `--linear` is used. #### Alternative search mode: `--no-prefetch` By default, `sourmash gather` does a "prefetch" to find *all* candidate signatures across all databases, before removing overlaps between the candidates. In rare circumstances, depending on the databases and parameters used, this may be slower or more memory intensive than doing iterative overlap removal. Prefetch behavior can be turned off with `--no-prefetch`. The results are the same whether `--prefetch` or `--no-prefetch` is used. This option can be used with or without `--linear` (although `--no-prefetch --linear` will generally be MUCH slower). ### `sourmash index` - build an SBT index of signatures The `sourmash index` command creates a Zipped SBT database (`.sbt.zip`) from a collection of signatures. This can be used to create databases from private collections of genomes, and can also be used to create databases for e.g. subsets of GenBank. These databases support fast search and gather on large collections of signatures in low memory. All signatures in an SBT must be of compatible types (i.e. the same k-mer size and molecule type). You can specify the usual command line selectors (`-k`, `--scaled`, `--dna`, `--protein`, etc.) to pick out the types of signatures to include when running `index`. Usage: ``` sourmash index [ ... ] ``` This will create a `database.sbt.zip` file containing the SBT of the input signatures. You can create an "unpacked" version by specifying `database.sbt.json` and it will create the JSON file as well as a subdirectory of files under `.sbt.database`. Note that you can use `--from-file` to pass `index` a text file containing a list of file names to index; you can also provide individual signature files, directories full of signatures, or other sourmash databases. As of sourmash 4.2.0, `index` supports `--picklist`, to [select a subset of signatures based on a CSV file](#using-picklists-to-subset-large-collections-of-signatures). This can be used to index a subset of a large collection, or to exclude a few signatures from an index being built from a large collection. ### `sourmash prefetch` - select subsets of very large databases for more processing The `prefetch` subcommand searches a collection of scaled signatures for matches in a large database, using containment. It is similar to `search --containment`, while taking a `--threshold-bp` argument like `gather` does for thresholding matches (instead of using Jaccard similarity or containment). Note that `prefetch` uses the composite sketch (e.g. a metagenome) as the query, and finds all matching subjects (e.g. genomes) from the database - the arguments are in the opposite order from `search --containment`. `sourmash prefetch` is intended to select a subset of a large database for further processing. As such, it can search very large collections of signatures (potentially millions or more), operates in very low memory (see `--linear` option, below), and does no post-processing of signatures. `prefetch` has four main output options, which can all be used individually or together: * `-o/--output` produces a CSV summary file; * `--save-matches` saves all matching signatures; * `-save-matching-hashes` saves a single signature containing all of the hashes that matched any signature in the database at or above the specified threshold; * `--save-unmatched-hashes` saves a single signature containing the complement of `--save-matching-hashes`. Other options include: * the usual `-k/--ksize` and `--dna`/`--protein`/`--dayhoff`/`--hp` signature selectors; * `--threshold-bp` to require a minimum estimated bp overlap for output; * `--scaled` for downsampling; * `--force` to continue past survivable errors; * `--picklist` will select a subset of signatures to search, using [a picklist](#using-picklists-to-subset-large-collections-of-signatures) #### Alternative search mode for low-memory (but slow) search: `--linear` By default, `sourmash prefetch` uses all information available for faster search. In particular, for SBTs, `prefetch` will prune the search tree. This can be slow and/or memory intensive for very large databases, and `--linear` asks `sourmash prefetch` to instead use a linear search across all leaf nodes in the tree. #### Caveats and comments `sourmash prefetch` provides no guarantees on output order. It runs in "streaming mode" on its inputs, in that each input file is loaded, searched, and then unloaded. And `sourmash prefetch` can be run separately on multiple databases, after which the results can be searched in combination with `search`, `gather`, `compare`, etc. A motivating use case for `sourmash prefetch` is to run it on multiple large databases with a metagenome query using `--threshold-bp=0`, `--save-matching-hashes matching-hashes.sig`, and `--save-matches db-matches.sig`, and then run `sourmash gather matching-hashes.sig db-matches.sig`. This combination of commands ensures that the more time- and memory-intensive `gather` step is run only on a small set of relevant signatures, rather than all the signatures in the database. ### `sourmash multigather` - do gather with many queries The `multigather` subcommand runs `sourmash gather` on multiple queries. (See [`sourmash gather` docs](#sourmash-gather-find-metagenome-members) for specifics on what gather does, and how!) Usage: ``` sourmash multigather --query --db ``` Note that multigather is single threaded, so it offers no substantial efficiency gains over just running gather multiple times! Nonetheless, it is useful for situations where you have many sketches organized in a combined file, e.g. sketches built with `sourmash sketch ... --singleton`). #### `multigather` output files multigather produces three output files for each query: * `.csv` - gather CSV output * `.matches.sig` - all matching outputs * `.unassigned.sig` - all remaining unassigned hashes As of sourmash v4.8.7, `` is set as follows: * the filename attribute of the query sketch, if it is not empty or `-`; * the query sketch md5sum, if the query filename is empty or `-`; * the query filename + the query sketch md5sum (`.`), if `-U/--output-add-query-md5sum` is specified; By default, `multigather` will complain and exit with an error if the same `` is used repeatedly and an output file is going to be overwritten. With `-U/--output-add-query-md5sum` this should only happen when identical sketches are present in a query database. Use `--force-allow-overwrite-output` to allow overwriting of output files without an error. ## `sourmash tax` subcommands for integrating taxonomic information into gather results The `sourmash tax` subcommands support taxonomic analysis of genomes and taxonomic profiling of metagenomes. See [taxonomic profiling with sourmash](classifying-signatures.md#taxonomic-profiling-with-sourmash) for more information. The sourmash `tax` or `taxonomy` commands integrate taxonomic information with the results of `sourmash gather`. All `tax` commands require one or more properly formatted `taxonomy` files where the identifiers correspond to those in the database(s) used for `gather`. Note that if using multiple databases, the `gather` needs to have been conducted against all desired databases within the same `gather` command (we cannot combine separate `gather` runs for the same query). For supported databases (e.g. GTDB, NCBI), we provide taxonomy csv files, but they can also be generated for user-generated databases. As of v4.8 and 4.8.6, respectively, some sourmash taxonomy commands can also use `LIN` or `ICTV` lineage information. `tax` commands rely upon the fact that `gather` provides both the total fraction of the query matched to each database matched, as well as a non-overlapping `f_unique_to_query`, which is the fraction of the query uniquely matched to each reference genome. The `f_unique_to_query` for any reference match will always be between (0% of query matched) and 1 (100% of query matched), and for a query matched to multiple references, the `f_unique_to_query` will sum to at most 1 (100% of query matched). We use this property to aggregate gather matches at the desired taxonomic rank. For example, if the gather results for a metagenome include results for 30 different strains of a given species, we can sum the fraction uniquely matched to each strain to obtain the fraction uniquely matched to this species. Alternatively, taxonomic summarization can take into account abundance weighting; see [classifying signatures](classifying-signatures.md) for more information. As with all reference-based analysis, results can be affected by the completeness of the reference database. However, summarizing taxonomic results from `gather` minimizes issues associated with increasing size and redundancy of reference databases. For more details on how `gather` works and can be used to classify signatures, see ### `sourmash tax metagenome` - summarize metagenome content from `gather` results `sourmash tax metagenome` summarizes gather results for each query metagenome by taxonomic lineage. Here is an example command to summarize a single `gather csv`, where the query was gathered against `gtdb-rs202` representative species database: ``` sourmash tax metagenome --gather-csv HSMA33MX_gather_x_gtdbrs202_k31.csv \ --taxonomy gtdb-rs202.taxonomy.v2.csv ``` The possible output formats are listed below, followed by the file extension used when writing to a file rather than stdout. When using more than one output format, you must provide an output basename (`--output-base`) that will be used to name the output files. If an `--output-dir` is provided, files will output to that directory. - `human`: ".human.txt", - `csv_summary`: ".summarized.csv", - `lineage_summary`: ".lineage_summary.tsv", - `krona`: ".krona.tsv", - `kreport`: ".kreport.txt", - `lingroup`: ".lingroup.tsv", - `bioboxes`: ".bioboxes.profile", #### `csv_summary` output format `csv_summary` is the default output format. This outputs a `csv` with lineage summarization for each taxonomic rank. This output currently consists of six columns, `query_name,rank,fraction,lineage,query_md5,query_filename`, where `fraction` is the fraction of the query matched to the reported rank and lineage. example `csv_summary` output from the command above: ``` query_name,rank,fraction,lineage HSMA33MX,superkingdom,0.131,d__Bacteria HSMA33MX,phylum,0.073,d__Bacteria;p__Bacteroidota HSMA33MX,phylum,0.058,d__Bacteria;p__Proteobacteria . . . HSMA33MX,species,0.058,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria; o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli HSMA33MX,species,0.057,d__Bacteria;p__Bacteroidota;c__Bacteroidia; o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri HSMA33MX,species,0.016,d__Bacteria;p__Bacteroidota;c__Bacteroidia; o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola vulgatus ``` The `query_md5` and `query_filename` columns are omitted here for brevity. Note: When using `--lins` with a `--lingroup` file, the `csv_summary` file will report summarization for each specified `lingroup`, rather than all possible `lin` ranks (v4.8.12+). #### `krona` output format `krona` format is a tab-separated list of these results at a specific rank. The first column, `fraction` is the fraction of the query matched to the reported rank and lineage. The remaining columns are `superkingdom`, `phylum`, ... etc down to the rank used for summarization. This output can be used directly for summary visualization. To generate `krona`, we add `--output-format krona` to the command above, and need to specify a rank to summarize. Here's the command for reporting `krona` summary at `species` level: ``` sourmash tax metagenome --gather-csv HSMA33MX_gather_x_gtdbrs202_k31.csv \ --taxonomy gtdb-rs202.taxonomy.v2.csv \ --output-format krona --rank species ``` example krona output from this command: ``` fraction superkingdom phylum class order family genus species 0.05815279361459521 Bacteria Proteobacteria Gammaproteobacteria Enterobacterales Enterobacteriaceae Escherichia Escherichia coli 0.05701254275940707 Bacteria Bacteroidetes Bacteroidia Bacteroidales Prevotellaceae Prevotella Prevotella copri 0.015637726014008795 Bacteria Bacteroidetes Bacteroidia Bacteroidales Bacteroidaceae Bacteroides Bacteroides vulgatus ``` #### `lineage_summary` output format The lineage summary format is most useful when comparing across metagenome queries. Each row is a lineage at the desired reporting rank. The columns are each query used for gather, with the fraction match reported for each lineage. This format is commonly used as input for many external multi-sample visualization tools. To generate `lineage_summary`, we add `--output-format lineage_summary` to the summarize command, and need to specify a rank to summarize. Here's the command for reporting `lineage_summary` for two queries (HSMA33MX, PSM6XBW3) summary at `species` level. ``` sourmash tax metagenome --gather-csv HSMA33MX_gather_x_gtdbrs202_k31.csv \ --gather-csv PSM6XBW3_gather_x_gtdbrs202_k31.csv \ --taxonomy gtdb-rs202.taxonomy.v2.csv \ --output-format lineage_summary --rank species ``` example `lineage_summary`: ``` lineage HSMA33MX PSM6XBW3 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola vulgatus 0.015637726014008795 0.015642822225843248 d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri 0.05701254275940707 0.05703112269838684 d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli 0.05815279361459521 0.05817174515235457 ``` To produce multiple output types from the same command, add the types into the `--output-format` argument, e.g. `--output-format summary krona lineage_summary` #### `kreport` output format The `kreport` output reports kraken-style `kreport` output, which may be useful for comparison with other taxonomic profiling methods. While this format typically records the percent of number of reads assigned to taxa, we create ~comparable output by reporting the percent of k-mers matched to each taxon and the estimated number of base pairs that these k-mers represent. To best represent the percent of all reads, we use k-mer abundance information in this output. To generate this properly, query FracMinHash sketches should be generated with abundance information (`-p abund`) to allow abundance-weighted `gather` results. Note: `sourmash gather` makes all assignments to genomes, and then `sourmash tax` integrates taxonomy information and uses LCA-style summarization to build assignments. For species-level specificity, our current recommendation is to use use our default k-mer size of 31. standard `kreport` columns (read-based tools): - `Percent Reads Contained in Taxon`: The cumulative percentage of reads for this taxon and all descendants. - `Number of Reads Contained in Taxon`: The cumulative number of reads for this taxon and all descendants. - `Number of Reads Assigned to Taxon`: The number of reads assigned directly to this taxon (not a cumulative count of all descendants). - `Rank Code`: (U)nclassified, (R)oot, (D)omain, (K)ingdom, (P)hylum, (C)lass, (O)rder, (F)amily, (G)enus, or (S)pecies. - `NCBI Taxon ID`: Numerical ID from the NCBI taxonomy database. - `Scientific Name`: The scientific name of the taxon. Example reads-based `kreport` with all columns: ``` 88.41 2138742 193618 K 2 Bacteria 0.16 3852 818 P 201174 Actinobacteria 0.13 3034 0 C 1760 Actinomycetia 0.13 3034 45 O 85009 Propionibacteriales 0.12 2989 1847 F 31957 Propionibacteriaceae 0.05 1142 352 G 1912216 Cutibacterium 0.03 790 790 S 1747 Cutibacterium acnes ``` sourmash `kreport` columns: - `Percent [k-mers] contained in taxon`: The cumulative percentage of k-mers for this taxon and all descendants. - `Estimated base pairs contained in taxon`: The cumulative estimated base pairs for this taxon and all descendants. - `Estimated base pairs "assigned" (species-level)`: The estimated base pairs assigned at species-level (cumulative count of base pairs assigned to individual genomes in this species). - `Rank Code`: (U)nclassified, (R)oot, (D)omain, (K)ingdom, (P)hylum, (C)lass, (O)rder, (F)amily, (G)enus, or (S)pecies. - `NCBI Taxon ID`: Reported (v4.7+) if using NCBI taxonomy. Otherwise blank. - `Scientific Name`: The scientific name of the taxon. notes: - `gather` assigns k-mers to specific genomes. To mimic the output of other tools, we report all results as "assigned" to species-level, which summarizes the k-mers matched to each genome within a given species. Hence, column 3 will show all estimated base pairs at this level, and 0 for all other ranks. Column 2 contains the summarized info at the higher ranks. - Since `gather` results are non-overlapping and all assignments are done at the genome level, the percent match (first column) will sum to 100% at each rank (aside from rounding issues) when including the unclassified (U) percentage. Higher-rank assignments are generated using LCA-style summarization of genome matches. - Rows are ordered by rank and then ~percent containment. example sourmash `{output-name}.kreport.txt`: ``` 92.73 64060000 D Bacteria 0.44 11299000 D Eukaryota 6.82 284315000 U unclassified 60.23 30398000 P Proteobacteria 21.86 22526000 P Firmicutes 10.41 5250000 P Bacteroidetes . . . 3.94 6710000 S Escherichia coli 4.56 6150000 S Pseudomonas aeruginosa 0.71 5801000 S Clostridium beijerinckii 2.55 5474000 S Bacillus cereus 21.95 4987000 S Escherichia sp. XD7 28.57 4124000 S Cereibacter sphaeroides 0.25 4014000 S Acinetobacter baumannii 7.23 3934000 S Staphylococcus haemolyticus 0.09 3187000 S Phocaeicola vulgatus 0.61 2820000 S Streptococcus agalactiae 0.20 2499000 S Cutibacterium acnes 0.03 2339000 S Deinococcus radiodurans 10.31 2063000 S Porphyromonas gingivalis 9.24 2011000 S Streptococcus mutans ``` #### `lingroup` output format When using LIN taxonomic information, you can optionally also provide a `lingroup` file with two required columns: `name` and `lin`. If provided, we will produce a file, `{base}.lingroups.tsv`, where `{base}` is the name provided via the `-o`,` --output-base` option. This output will select information from the full summary that match the LIN prefixes provided as groups. This output format consists of four columns: - `name`, `lin` columns are taken directly from the `--lingroup` file - `percent_containment`, the total percent of the dataset contained in this lingroup and all descendants - `num_bp_contained`, the estimated number of base pairs contained in this lingroup and all descendants. Similar to `kreport` above, we use the wording "contained" rather than "assigned," because `sourmash` assigns matches at the genome level, and the `tax` functions summarize this information. example output: ``` name lin percent_containment num_bp_contained lg1 0;0;0 5.82 714000 lg2 1;0;0 5.05 620000 lg3 2;0;0 1.56 192000 lg3 1;0;1 0.65 80000 lg4 1;0;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 0.65 80000 ``` Related lingroup subpaths will be grouped in output, but exact ordering may change between runs. Note: this output format requires a single sample only. For a similar output with multiple query samples, provide the `lingroup` file and use the 'csv_summary' output format. #### `bioboxes` output format When using standard taxonomic ranks (not lins), you can choose to output a 'bioboxes' profile, `{base}.bioboxes.profile`, where `{base}` is the name provided via the `-o/--output-base` option. This output is organized according to the [bioboxes profile specifications](https://github.com/bioboxes/rfc/tree/master/data-format) so that this file can be used for CAMI challenges. This output format starts with some header information: ``` #CAMI Submission for Taxonomic Profiling @Version:0.9.3 @SampleID:SAMPLEID @Ranks:superkingdom|phylum|class|order|family|genus|species|strain @__program__:sourmash @@TAXID RANK TAXPATH TAXPATHSN PERCENTAGE ``` and then provides taxonomic profiling information in the tab-separated columns described by the last header line: - `TAXID` - specifies a unique alphanumeric ID for a node in a reference tree such as the NCBI taxonomy - `RANK` - superkingdom --> strain - `TAXPATH` - the path from the root of the reference taxonomy to the respective taxon - `TAXPATHSN` - scientific names of taxpath - `PERCENTAGE` (0-100) - field specifies what percentage of the sample was assigned to the respective TAXID example output (using small test data): ``` # Taxonomic Profiling Output @SampleID:test1 @Version:0.10.0 @Ranks:superkingdom|phylum|class|order|family|genus|species @__program__:sourmash @@TAXID RANK TAXPATH TAXPATHSN PERCENTAGE 2 superkingdom 2 Bacteria 13.08 976 phylum 2|976 Bacteria|Bacteroidota 7.27 1224 phylum 2|1224 Bacteria|Pseudomonadota 5.82 200643 class 2|976|200643 Bacteria|Bacteroidota|Bacteroidia 7.27 1236 class 2|1224|1236 Bacteria|Pseudomonadota|Gammaproteobacteria 5.82 171549 order 2|976|200643|171549 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales 7.27 91347 order 2|1224|1236|91347 Bacteria|Pseudomonadota|Gammaproteobacteria|Enterobacterales 5.82 171552 family 2|976|200643|171549|171552 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Prevotellaceae 5.70 543 family 2|1224|1236|91347|543 Bacteria|Pseudomonadota|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae 5.82 815 family 2|976|200643|171549|815 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Bacteroidaceae 1.56 838 genus 2|976|200643|171549|171552|838 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Prevotellaceae|Prevotella 5.70 561 genus 2|1224|1236|91347|543|561 Bacteria|Pseudomonadota|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae|Escherichia 5.82 909656 genus 2|976|200643|171549|815|909656 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Bacteroidaceae|Phocaeicola 1.56 165179 species 2|976|200643|171549|171552|838|165179 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Prevotellaceae|Prevotella|Prevotella copri 5.70 562 species 2|1224|1236|91347|543|561|562 Bacteria|Pseudomonadota|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae|Escherichia|Escherichia coli 5.82 821 species 2|976|200643|171549|815|909656|821 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Bacteroidaceae|Phocaeicola|Phocaeicola vulgatus 1.56 ``` #### `lingroup` output format When using LIN taxonomic information, you can optionally also provide a `lingroup` file with two required columns: `name` and `lin`. If provided, we will produce a file, `{base}.lingroups.tsv`, where `{base}` is the name provided via the `-o`,` --output-base` option. This output will select information from the full summary that match the LIN prefixes provided as groups. This output format consists of four columns: - `name`, `lin` columns are taken directly from the `--lingroup` file - `percent_containment`, the total percent of the dataset contained in this lingroup and all descendants - `num_bp_contained`, the estimated number of base pairs contained in this lingroup and all descendants. Similar to `kreport` above, we use the wording "contained" rather than "assigned," because `sourmash` assigns matches at the genome level, and the `tax` functions summarize this information. example output: ``` name lin percent_containment num_bp_contained lg1 0;0;0 5.82 714000 lg2 1;0;0 5.05 620000 lg3 2;0;0 1.56 192000 lg3 1;0;1 0.65 80000 lg4 1;0;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 0.65 80000 ``` Related lingroup subpaths will be grouped in output, but exact ordering may change between runs. ### `sourmash tax genome` - classify a genome using `gather` results `sourmash tax genome` reports likely classification for each query, based on `gather` matches. By default, classification requires at least 10% of the query to be matched. Thus, if 10% of the query was matched to a species, the species-level classification can be reported. However, if 7% of the query was matched to one species, and an additional 5% matched to a different species in the same genus, the genus-level classification will be reported. `sourmash tax genome` can use an ANI threshold (`--ani-threshold`) instead of a containment threshold. This works the same way as the containment threshold (and indeed, is using the same underlying information). Note that for DNA k-mers, k=21 ANI is most similar to alignment-based ANI values, and ANI values should only be compared if they were generated using the same ksize. Optionally, `genome` can instead report classifications at a desired `rank`, regardless of match threshold (`--rank` argument, e.g. `--rank species`). If using `--lins` taxonomy, you can also provide a `--lingroup` file containing two columns, `name`, and `lin`, which provide a series of lin prefixes of interest. If provided, genome classification will be restricted to provided lingroups only. All other options (`--rank`, `--ani-threshold`, etc) should continue to function. If you specify a `--rank` that does not have an associated lingroup, sourmash will notify you that you eliminated all classification options. Note that these thresholds and strategies are under active testing. To illustrate the utility of `genome`, let's consider a signature consisting of two different Shewanella strains, `Shewanella baltica OS185 strain=OS185` and `Shewanella baltica OS223 strain=OS223`. For simplicity, we gave this query the name "Sb47+63". When we gather this signature against the `gtdb-rs202` representatives database, we see 66% matches to one strain, and 33% to the other: abbreviated gather_csv: ``` f_match,f_unique_to_query,name,query_name 0.664,0.664,"GCF_000021665.1 Shewanella baltica OS223 strain=OS223, ASM2166v1",Sb47+63 0.656,0.335,"GCF_000017325.1 Shewanella baltica OS185 strain=OS185, ASM1732v1",Sb47+63 ``` > Here, `f_match` shows that independently, both strains match ~65% percent of this mixed query. The `f_unique_to_query` column has the results of gather-style decomposition. As the OS223 strain had a slightly higher `f_match` (66%), it was the first match. The remaining 33% of the query matched to strain OS185. We can use `tax genome` on this gather csv to classify our "Sb47+63" mixed-strain query: ``` sourmash tax genome --gather-csv 47+63_x_gtdb-rs202.gather.csv \ --taxonomy gtdb-rs202.taxonomy.v2.csv ``` > This command uses the default classification strategy, which uses a containment threshold of 0.1 (10%). `sourmash tax genome` can produce the following output formats: - `human`: ".human.txt", - `csv_summary`: ".classifications.csv", - `krona`: ".krona.tsv", - `lineage_summary`: ".lineage_summary.tsv", #### `csv_summary` output format `csv_summary` is the default output format. This outputs a `csv` with taxonomic classification for each query genome. This output currently consists of six columns, `query_name,rank,fraction,lineage,query_md5,query_filename`, where `fraction` is the fraction of the query matched to the reported rank and lineage. The `status` column provides additional information on the classification: - `match` - this query was classified - `nomatch`- this query could not be classified - `below_threshold` - this query was classified at the specified rank, but the query fraction matched was below the containment threshold Here is the `csv_summary` output from classifying this mixed-strain Shewanella query to species level: ``` query_name,status,rank,fraction,lineage "Sb47+63",match,species,1.000,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Shewanellaceae;g__Shewanella;s__Shewanella baltica ``` >Here, we see that the match percentages to both strains have been aggregated, and we have 100% species-level `Shewanella baltica` annotation. We have omitted the `query_md5` and `query_filename` columns for brevity. #### `krona` output format `krona` format is a tab-separated list of these results at a specific rank. The first column, `fraction` is the fraction of the query matched to the reported rank and lineage. The remaining columns are `superkingdom`, `phylum`, ... etc down to the rank used for summarization. This output can be used directly for `krona` visualization. To generate `krona`, we must classify by `--rank` instead of using the classification threshold. For the command, we add `--output-format krona` and `--rank ` to the command above. Here's the command for producing `krona` output for `species`-level classifications: ``` sourmash tax genome --gather-csv Sb47+63_gather_x_gtdbrs202_k31.csv \ --taxonomy gtdb-rs202.taxonomy.v2.csv \ --output-format krona --rank species ``` > Note that specifying `--rank` forces classification by rank rather than by the containment threshold. Here is the `krona`-formatted output for this command: ``` fraction superkingdom phylum class order family genus species 1.0 d__Bacteria p__Proteobacteria c__Gammaproteobacteria o__Enterobacterales f__Shewanellaceae g__Shewanella s__Shewanella baltica ``` To produce multiple output types from the same command, add the types into the `--output-format` argument, e.g. `--output-format csv_summary krona`. **Note that specifying the classification rank with `--rank`, (e.g. `--rank species`), as needed for `krona` output, forces classification by `rank` rather than by containment threshold.** If the query classification at this rank does not meet the containment threshold (default=0.1), the `status` column will contain `below_threshold`. ### `sourmash tax annotate` - annotates gather output with taxonomy `sourmash tax annotate` adds a column with taxonomic lineage information for each genome match in the gather output, without LCA summarization or classification. This format is not required for either `metagenome` or `genome`, but may be helpful for other downstream analyses. By default, `annotate` uses the name of each input gather csv to write an updated version with lineages information. For example, annotating `sample1.gather.csv` would produce `sample1.gather.with-lineages.csv`. This will produce an annotated gather CSV, `Sb47+63_gather_x_gtdbrs202_k31.with-lineages.csv`: ``` sourmash tax annotate --gather-csv Sb47+63_gather_x_gtdbrs202_k31.csv \ --taxonomy gtdb-rs202.taxonomy.v2.csv ``` The `with-lineages` output file format can be summarized with `sourmash tax summarize` and can also be used as an input taxonomy spreadsheet for any of the tax subcommands (new as of v4.6.0). ### `sourmash tax prepare` - prepare and/or combine taxonomy files `sourmash tax prepare` prepares taxonomy files for other `sourmash tax` commands. All `sourmash tax` commands must be given one or more taxonomy files as parameters to the `--taxonomy` argument. These files can be either CSV files or (as of sourmash 4.2.1) SQLite databases. SQLite databases are much faster for large taxonomies, while CSV files are easier to view and modify using spreadsheet software. `sourmash tax prepare` is a utility function that can ingest and validate multiple CSV files or SQLite databases, and output a CSV file or a SQLite database. It can be used to combine multiple taxonomies into a single file, as well as change formats between CSV and SQLite. The following command will take in two taxonomy files and combine them into a single taxonomy SQLite database. ``` sourmash tax prepare --taxonomy file1.csv file2.csv -o tax.db ``` Input databases formats can be mixed and matched, and the output format can be set to CSV like so: ``` sourmash tax prepare --taxonomy file1.csv file2.db -o tax.csv -F csv ``` **Note:** As of sourmash v4.6.0, the output of `sourmash tax annotate` can be used as a taxonomy input spreadsheet as well. ### `sourmash tax grep` - subset taxonomies and create picklists based on taxonomy string matches (`sourmash tax grep` is a new command as of sourmash v4.5.0.) `sourmash tax grep` searches taxonomies for matching strings, optionally restricting the string search to a specific taxonomic rank. It creates new files containing matching taxonomic entries; these new files can serve as taxonomies and can also be used as [picklists to restrict database matches](#using-picklists-to-subset-large-collections-of-signatures). Usage: ``` sourmash tax grep -t [ ...] ``` where `pattern` is a regular expression; see Python's [Regular Expression HOWTO for details on supported regexp features](https://docs.python.org/3/howto/regex.html#regex-howto). For example, ``` sourmash tax grep Shew -t gtdb-rs207.taxonomy.sqldb -o shew-picklist.csv ``` will search for a string match to `Shew` within the entire GTDB RS207 taxonomy, and will output a subset taxonomy in `shew-picklist.csv`. This picklist can be used with the GTDB RS207 databases like so: ``` sourmash search query.sig gtdb-rs207.genomic.k31.zip \ --picklist shew-picklist.csv:ident:ident ``` `tax grep` can also restrict string matching to a specific taxonomic rank with `-r/--rank`; for example, ``` sourmash tax grep Shew -t gtdb-rs207.taxonomy.sqldb \ -o shew-picklist.csv -r genus ``` will restrict matches to the rank of genus. Available ranks are superkingdom, phylum, class, order, family, genus, and species. `tax grep` also takes several standard grep arguments, including `-i` to ignore case and `-v` to output only taxonomic lineages that do _not_ match the pattern. Note: `tax grep` only searches taxonomic ranks, not identifier strings. Use `sig grep` to search for identifiers in sketch collections. Currently only CSV output (optionally gzipped) is supported; use `sourmash tax prepare` to convert CSV output from `tax grep` into a SQLite taxonomy database. ### `sourmash tax summarize` - print summary information for lineage spreadsheets or taxonomy databases (`sourmash tax summarize` is a new command as of sourmash v4.6.0.) `sourmash tax summarize` loads in one or more lineage spreadsheets, counts the distinct taxonomic lineages, and outputs a summary. It optionally will output a CSV file with a detailed count of how many identifiers belong to each taxonomic lineage. For example, ``` sourmash tax summarize gtdb-rs202.taxonomy.v2.db -o ranks.csv ``` outputs ``` number of distinct taxonomic lineages: 258406 rank superkingdom: 2 distinct taxonomic lineages rank phylum: 169 distinct taxonomic lineages rank class: 419 distinct taxonomic lineages rank order: 1312 distinct taxonomic lineages rank family: 3264 distinct taxonomic lineages rank genus: 12888 distinct taxonomic lineages rank species: 47894 distinct taxonomic lineages ``` and creates a file `ranks.csv` with the number of distinct identifier counts for each lineage at each rank: ``` rank,lineage_count,lineage superkingdom,254090,d__Bacteria phylum,120757,d__Bacteria;p__Proteobacteria class,104665,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria order,64157,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales family,55347,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae ... ``` That is, there are 254,090 identifiers in GTDB rs202 under `d__Bacteria`, and 120,757 within the `p__Proteobacteria`. `tax summarize` can also be used to summarize the output of `tax annotate`. ## `sourmash lca` subcommands for in-memory taxonomy integration These commands use LCA databases (created with `lca index`, below, or prepared databases such as [genbank-k31.lca.json.gz](databases.md)). ### `sourmash lca classify` - classify a genome using an LCA database `sourmash lca classify` classifies one or more signatures using the given list of LCA DBs. It is meant for classifying metagenome-assembled genome bins (MAGs) and single-cell genomes (SAGs). ```{attention} We no longer recommend using `sourmash lca` for taxonomic analysis; please use `sourmash tax` instead. See [taxonomic profiling with sourmash](classifying-signatures.md#taxonomic-profiling-with-sourmash) for more information. ``` Usage: ``` sourmash lca classify --query query.sig [query2.sig ...] --db [ ...] ``` For example, the command ``` sourmash lca classify --query tests/test-data/63.fa.sig \ --db podar-ref.lca.json ``` will produce the following logging to stderr: ``` loaded 1 LCA databases. ksize=31, scaled=10000 finding query signatures... outputting classifications to stdout ... classifying NC_011663.1 Shewanella baltica OS223, complete genome classified 1 signatures total ``` and the example classification output is a CSV file with headers: ``` ID,status,superkingdom,phylum,class,order,family,genus,species "NC_009665.1 Shewanella baltica OS185, complete genome",found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Shewanellaceae,Shewanella,Shewanella baltica ``` The `status` column in the classification output can take three possible values: `nomatch`, `found`, and `disagree`. `nomatch` means that no match was found for this query, and `found` means that an unambiguous assignment was found - all k-mers were classified within the same taxonomic hierarchy, and the most detailed lineage available was reported. `disagree` means that there was a taxonomic disagreement, and the lowest compatible taxonomic node was reported. To elaborate on this a bit, suppose that all of the k-mers within a signature were classified as family *Shewanellaceae*, genus *Shewanella*, or species *Shewanella baltica*. Then the lowest compatible node (here species *Shewanella baltica*) would be reported, and the status of the classification would be `found`. However, if a number of additional k-mers in the input signature were classified as *Shewanella oneidensis*, sourmash would be unable to resolve the taxonomic assignment below genus *Shewanella* and it would report a status of `disagree` with the genus-level assignment of *Shewanella*; species level assignments would not be reported. Here, the assigned rank is the rank immediately *above* where there is a taxonomic disagreement, and the taxid & lineage refer to the name at that rank (the lowest common ancestor at which an assignment can be made). For another example, if you saw this line in the CSV file: ``` TARA_ASW_MAG_00029,1224,disagree,phylum,Bacteria;Proteobacteria ``` you would know that TARA_ASW_MAG_00029 has k-mers that are shared between different orders: 'Pseudomonadales' and 'Rhodobacterales'. Therefore, the classifier status is `disagree`, and the classified taxid is at rank `phylum` - just above `order`. (This is the approach that Kraken and other lowest common ancestor implementations use, we believe.) Note: you can specify a list of file names to load signatures from in a text file passed to `sourmash lca classify` with the `--query-from-file` flag; these files will be appended to the `--query` input. ### `sourmash lca summarize` - summarize a metagenome's contents using an LCA database `sourmash lca summarize` produces a Kraken-style summary of the combined contents of the given query signatures. It is meant for exploring metagenomes and metagenome-assembled genome bins. `sourmash lca summarize` also weights output with hash abundances, so that output percentages are weighted by the number of times a k-mer is seen; this can be turned off with `--ignore-abundance`. ```{attention} We no longer recommend using `sourmash lca` for taxonomic analysis; please use `sourmash tax` instead. See [taxonomic profiling with sourmash](classifying-signatures.md#taxonomic-profiling-with-sourmash) for more information. ``` Usage: ``` sourmash lca summarize --query query.sig [query2.sig ...] --db [ ...] ``` For example, with the data in `tests/test-data/fake-abund`, the command line: ``` sourmash lca summarize --query query.sig.gz --db matches.lca.json.gz ``` will produce the following log output to stderr: ``` loaded 1 LCA databases. ksize=31, scaled=10000 finding query signatures... loaded 1 signatures from 1 files total. ``` and the following example summarize output to stdout: ``` 79.6% 550 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica;Shewanella baltica OS223 79.6% 550 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica 79.6% 550 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella 79.6% 550 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae 79.6% 550 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales 79.6% 550 Bacteria;Proteobacteria;Gammaproteobacteria 79.6% 550 Bacteria;Proteobacteria 79.6% 550 Bacteria 20.4% 141 Archaea;Euryarchaeota;unassigned;unassigned;unassigned;Aciduliprofundum;Aciduliprofundum boonei;Aciduliprofundum boonei T469 20.4% 141 Archaea;Euryarchaeota;unassigned;unassigned;unassigned;Aciduliprofundum;Aciduliprofundum boonei 20.4% 141 Archaea;Euryarchaeota;unassigned;unassigned;unassigned;Aciduliprofundum 20.4% 141 Archaea;Euryarchaeota;unassigned;unassigned;unassigned 20.4% 141 Archaea;Euryarchaeota;unassigned;unassigned 20.4% 141 Archaea;Euryarchaeota;unassigned 20.4% 141 Archaea;Euryarchaeota 20.4% 141 Archaea ``` The output is space-separated and consists of three columns: the percentage of total k-mers that have this classification; the number of k-mers that have this classification; and the lineage classification. K-mer classifications are reported hierarchically, so the percentages and totals contain all assignments that are at a lower taxonomic level - e.g. *Bacteria*, above, contains all the k-mers in *Bacteria;Proteobacteria*. The same information is reported in a CSV file if `-o/--output` is used. The proportions reflect the query signature construction, where the metagenome contains a 1.5 Mbp Archaeal genome and a 5.4 Mbp Bacterial genome. The Archaeal genome is therefore only ~20% of the distinct k-mers in the metagenome (1.5 Mbp divided by 6.9 Mbp). If `--with-abundance` is given, the output changes to reflect the proportions of the query metagenome based on k-mer/read abundances: ``` 56.8% 740 Archaea;Euryarchaeota;unassigned;unassigned;unassigned;Aciduliprofundum;Aciduliprofundum boonei;Aciduliprofundum boonei T469 56.8% 740 Archaea;Euryarchaeota;unassigned;unassigned;unassigned;Aciduliprofundum;Aciduliprofundum boonei 56.8% 740 Archaea;Euryarchaeota;unassigned;unassigned;unassigned;Aciduliprofundum 56.8% 740 Archaea;Euryarchaeota;unassigned;unassigned;unassigned 56.8% 740 Archaea;Euryarchaeota;unassigned;unassigned 56.8% 740 Archaea;Euryarchaeota;unassigned 56.8% 740 Archaea;Euryarchaeota 56.8% 740 Archaea 43.2% 563 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica;Shewanella baltica OS223 43.2% 563 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica 43.2% 563 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella 43.2% 563 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae 43.2% 563 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales 43.2% 563 Bacteria;Proteobacteria;Gammaproteobacteria 43.2% 563 Bacteria;Proteobacteria 43.2% 563 Bacteria ``` Here, the changed proportions reflect the query signature abundances, where the 1.5 Mbp Archaeal genome is present 5 times, while the 5.4 Mbp Bacterial genome is present only once; when weighted by abundance, the Bacterial genome is only 41.8% of the metagenome content, while the Archaeal genome is 58.1% of the metagenome content. Note: you can specify a list of file names to load signatures from in a text file passed to `sourmash lca summarize` with the `--query-from-file` flag; these files will be appended to the `--query` input. ### `sourmash lca index` - build an LCA database The `sourmash lca index` command creates an LCA database from a lineage spreadsheet and a collection of signatures. This can be used to create LCA databases from private collections of genomes, and can also be used to create databases for e.g. subsets of GenBank. See [the `sourmash lca` tutorial](tutorials-lca.md) and the blog post [Why are taxonomic assignments so different for Tara bins?](http://ivory.idyll.org/blog/2017-taxonomic-disagreements-in-tara-mags.html) for some use cases. If you are interested in preparing lineage spreadsheets from GenBank genomes (or building off of NCBI taxonomies more generally), please see [the NCBI lineage repository](https://github.com/dib-lab/2018-ncbi-lineages). You can use `--from-file` to pass `lca index` a text file containing a list of file names to index. As of sourmash 4.2.0, `lca index` supports `--picklist`, to [select a subset of signatures based on a CSV file](#using-picklists-to-subset-large-collections-of-signatures). This can be used to index a subset of a large collection, or to exclude a few signatures from an index being built from a large collection. As of sourmash 4.4.0, `lca index` can produce an _on disk_ LCA database using SQLite. To prepare such a database, use `sourmash lca index ... -F sql`. All sourmash commands work with either type of LCA database (the default JSON database, and the SQLite version). SQLite databases are larger than JSON databases on disk but are typically much faster to load and search, and use much less memory. ### `sourmash lca rankinfo` - examine an LCA database The `sourmash lca rankinfo` command displays k-mer specificity information for one or more LCA databases. See the blog post [How specific are k-mers for taxonomic assignment of microbes, anyway?](http://ivory.idyll.org/blog/2017-how-specific-kmers.html) for example output. ### `sourmash lca compare_csv` - compare taxonomic spreadsheets The `sourmash lca compare_csv` command compares two lineage spreadsheets (such as those output by `sourmash lca classify` or taken as input by `sourmash lca index`) and summarizes their agreement/disagreement. Please see the blog post [Why are taxonomic assignments so different for Tara bins?](http://ivory.idyll.org/blog/2017-taxonomic-disagreements-in-tara-mags.html) for an example use case. [0]:https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2 [1]:http://mash.readthedocs.io/en/latest/ [2]:http://biorxiv.org/content/early/2015/10/26/029827 [3]:https://en.wikipedia.org/wiki/Jaccard_index ## `sourmash signature` subcommands for signature manipulation These commands manipulate signatures from the command line. The signature commands that combine or otherwise have multiple signatures interacting (`merge`, `intersect`, `subtract`) work only on compatible signatures, where the k-mer size and nucleotide/protein sequences match each other. If working directly with the hash values (e.g. `merge`, `intersect`, `subtract`) then the scaled values must also match; you can use `downsample` to convert a bunch of samples to the same scaled value. If there are multiple signatures in a file with different ksizes and/or from nucleotide and protein sequences, you can choose amongst them with `-k/--ksize` and `--dna` or `--protein`, as with other sourmash commands such as `search`, `gather`, and `compare`. Note, you can use `sourmash sig` as shorthand for all of these commands. Most commands will load signatures automatically from indexed databases (SBT and LCA formats) as well as from signature files, and you can load signatures from stdin using `-` on the command line. ### `sourmash signature cat` - combine signatures into one file Concatenate signature files. For example, ``` sourmash signature cat file1.sig file2.sig -o all.zip ``` will combine all signatures in `file1.sig` and `file2.sig` and put them in the file `all.zip`. #### Using picklists with `sourmash sig cat` As of sourmash 4.2.0, `cat` also supports picklists, a feature by which you can select signatures based on values in a CSV file. See [Using picklists to subset large collections of signatures](#using-picklists-to-subset-large-collections-of-signatures), below. ### `sourmash signature describe` - display detailed information about signatures Display signature details. For example, ``` sourmash sig describe tests/test-data/track_abund/47.fa.sig ``` will display: ``` signature filename: tests/test-data/track_abund/47.fa.sig signature: NC_009665.1 Shewanella baltica OS185, complete genome source file: podar-ref/47.fa md5: 09a08691ce52952152f0e866a59f6261 k=31 molecule=DNA num=0 scaled=1000 seed=42 track_abundance=1 size: 5177 sum hashes: 5292 signature license: CC0 ``` Here, the `size` is the number of distinct hashes in the sketch, and `sum_hashes` is the total number of hashes in the sketch, with abundances. When `track_abundance` is 0, `size` is always the same as `sum_hashes`. ### `sourmash signature fileinfo` - display a summary of the contents of a sourmash collection Display signature file, database, or collection. For example, ``` sourmash sig fileinfo tests/test-data/prot/all.zip ``` will display: ``` path filetype: ZipFileLinearIndex location: /Users/t/dev/sourmash/tests/test-data/prot/all.zip is database? yes has manifest? yes is nonempty? yes num signatures: 8 ** examining manifest... 31758 total hashes summary of sketches: 2 sketches with dayhoff, k=19, scaled=100 7945 total hashes 2 sketches with hp, k=19, scaled=100 5184 total hashes 2 sketches with protein, k=19, scaled=100 8214 total hashes 2 sketches with DNA, k=31, scaled=1000 10415 total hashes ``` `sig fileinfo` will recognize [all accepted sourmash input files](#loading-signatures-and-databases), including individual .sig and .sig.gz files, Zip file collections, SBT databases, LCA databases, and directory hierarchies. `sourmash sig fileinfo` provides optional JSON and YAML output, and those formats are under semantic versioning. Note: `sourmash signature summarize` is an alias for `fileinfo`; they are the same command. ### `sourmash signature grep` - extract matching signatures using pattern matching Extract matching signatures with substring and regular expression matching on the name, filename, and md5 fields. For example, ``` sourmash signature grep -i shewanella tests/test-data/prot/all.zip -o shew.zip ``` will extract the two signatures in `all.zip` with 'Shewanella baltica' in their name and save them to `shew.zip`. `grep` will search for substring matches or regular expressions; e.g. `sourmash sig grep 'os185|os223' ...` will find matches to either of those expressions. Command line options include `-i` for case-insensitive matching, and `-v` for exclusion rather than inclusion. A CSV file of the matching sketch information can be saved using `--csv `; this file is in the sourmash manifest format and can be used as a picklist with `--pickfile ::manifest`. If `--silent` is specified, `sourmash sig grep` will not output matching signatures. `sourmash sig grep` also supports a counting mode, `-c/--count`, in which only the number of matching sketches in files will be displayed; for example, ``` % sourmash signature grep -ci 'os185|os223' tests/test-data/prot/*.zip ``` will produce the following output: ``` 2 matches: tests/test-data/prot/all.zip 0 matches: tests/test-data/prot/dayhoff.sbt.zip 0 matches: tests/test-data/prot/dayhoff.zip 0 matches: tests/test-data/prot/hp.sbt.zip 0 matches: tests/test-data/prot/hp.zip 0 matches: tests/test-data/prot/protein.sbt.zip 0 matches: tests/test-data/prot/protein.zip ``` ### `sourmash signature split` - split signatures into individual files Split each signature in the input file(s) into individual files, with standardized names. For example, ``` sourmash signature split tests/test-data/2.fa.sig ``` will create 3 files, `f372e478.k=21.scaled=1000.DNA.dup=0.2.fa.sig`, `f3a90d4e.k=31.scaled=1000.DNA.dup=0.2.fa.sig`, and `43f3b48e.k=51.scaled=1000.DNA.dup=0.2.fa.sig`, representing the three different DNA signatures at different ksizes created from the input file `2.fa`. The format of the names of the output files is standardized and stable for major versions of sourmash: currently, they are period-separated with fields: * `md5sum` - a unique hash value based on the contents of the signature. * `k=` - k-mer size. * `scaled=` or `num=` - scaled or num value for MinHash. * `` - the molecule type (DNA, protein, dayhoff, or hp) * `dup=` - a non-negative integer that prevents duplicate signatures from colliding. * `basename` - basename of first input file used to create signature; if none provided, or stdin, this is `none`. If `--outdir` is specified, all of the signatures are placed in outdir. Note: `split` only saves files in the JSON `.sig` format. ### `sourmash signature merge` - merge two or more signatures into one Merge two (or more) signatures. For example, ``` sourmash signature merge file1.sig file2.sig -o merged.sig ``` will output the union of all the hashes in `file1.sig` and `file2.sig` to `merged.sig`. All of the signatures passed to merge must either have been created with `-p abund`, or not. If they have `track_abundance` on, then the merged signature will have the sum of all abundances across the individual signatures. The `--flatten` flag will override this behavior and allow merging of mixtures by removing all abundances. `sig merge` can only merge compatible sketches - if there are multiple k-mer sizes or molecule types present in any of the signature files, you will need to choose one k-mer size with `-k/--ksize`, and/or one moltype with `--dna/--protein/--hp/--dayhoff`. Use `--set-name ` to set the name of the output sketch. Note: `merge` only creates one output file, with one signature in it. ### `sourmash signature rename` - rename a signature Rename the display name for one or more signatures - this is the name output for matches in `compare`, `search`, `gather`, etc. For example, ``` sourmash signature rename file1.sig "new name" -o renamed.sig ``` will place a renamed copy of the hashes in `file1.sig` in the file `renamed.sig`. If you provide multiple signatures, all will be renamed to the same name. ### `sourmash signature subtract` - subtract other signatures from a signature Subtract all of the hash values from one signature that are in one or more of the others. For example, ``` sourmash signature subtract file1.sig file2.sig file3.sig -o subtracted.sig ``` will subtract all of the hashes in `file2.sig` and `file3.sig` from `file1.sig`, and save the new signature to `subtracted.sig`. To use `subtract` on signatures calculated with `-p abund`, you must specify `--flatten`. `sig subtract` can only work with compatible sketches - if there are multiple k-mer sizes or molecule types present in any of the signature files, you will need to choose one k-mer size with `-k/--ksize`, and/or one moltype with `--dna/--protein/--hp/--dayhoff`. Use `--set-name ` to set the name of the output sketch. Note: `subtract` only creates one output file, with one signature in it. ### `sourmash signature intersect` - intersect two (or more) signatures Output the intersection of the hash values in multiple signature files. For example, ``` sourmash signature intersect file1.sig file2.sig file3.sig -o intersect.sig ``` will output the intersection of all the hashes in those three files to `intersect.sig`. The `intersect` command flattens all signatures, i.e. the abundances in any signatures will be ignored and the output signature will have `track_abundance` turned off. The `-A/--abundance-from` argument will borrow abundances from the specified signature (which will also be added to the intersection). `sig intersect` can only work with compatible sketches - if there are multiple k-mer sizes or molecule types present in any of the signature files, you will need to choose one k-mer size with `-k/--ksize`, and/or one moltype with `--dna/--protein/--hp/--dayhoff`. Use `--set-name ` to set the name of the output sketch(es). ### `sourmash signature inflate` - transfer abundances from one signature to others Use abundances from one signature to provide abundances on other signatures. For example, ``` sourmash signature inflate file1.sig file2.sig file3.sig -o inflated.sig ``` will take the abundances from hashes `file1.sig` and use them to set the abundances on matching hashes in `file2.sig` and `file3.sig`. Any hashes that are not present in `file1.sig` will be removed from `file2.sig` and `file3.sig` as they will now have zero abundance. `sig inflate` can only work with compatible sketches - if there are multiple k-mer sizes or molecule types present in any of the signature files, you will need to choose one k-mer size with `-k/--ksize`, and/or one moltype with `--dna/--protein/--hp/--dayhoff`. ### `sourmash signature downsample` - decrease the size of a signature Downsample one or more signatures. With `downsample`, you can -- * increase the `scaled` value for a signature created with `-p scaled=SCALED`, shrinking it in size; * decrease the `num` value for a traditional num MinHash, shrinking it in size; * try to convert a `scaled` signature to a `num` signature; * try to convert a `num` signature to a `scaled` signature. For example, ``` sourmash signature downsample file1.sig file2.sig --scaled 100000 -o downsampled.sig ``` will output each signature, downsampled to a scaled value of 100000, to `downsampled.sig`; and ``` sourmash signature downsample --num 500 scaled_file.sig -o downsampled.sig ``` will try to convert a scaled MinHash to a num MinHash. ### `sourmash signature extract` - extract signatures from a collection Extract the specified signature(s) from a collection of signatures. For example, ``` sourmash signature extract *.sig -k 21 --dna -o extracted.sig ``` will extract all nucleotide signatures calculated at k=21 from all .sig files in the current directory. There are currently two other useful selectors for `extract`: you can specify (part of) an md5sum, as output in the CSVs produced by `search` and `gather`; and you can specify (part of) a name. For example, ``` sourmash signature extract tests/test-data/*.fa.sig --md5 09a0869 ``` will extract the signature from `47.fa.sig` which has an md5sum of `09a08691ce52952152f0e866a59f6261`; and ``` sourmash signature extract tests/test-data/*.fa.sig --name NC_009665 ``` will extract the same signature, which has an accession number of `NC_009665.1`. #### Using picklists with `sourmash sig extract` As of sourmash 4.2.0, `extract` also supports picklists, a feature by which you can select signatures based on values in a CSV file. See [Using picklists to subset large collections of signatures](#using-picklists-to-subset-large-collections-of-signatures), below. ### `sourmash signature flatten` - remove abundance information from signatures Flatten the specified signature(s), removing abundances and setting track_abundance to False. For example, ``` sourmash signature flatten *.sig -o flattened.sig ``` will remove all abundances from all of the .sig files in the current directory. The `flatten` command accepts the same selectors as `extract`. ### `sourmash signature filter` - remove hashes based on abundance Filter the hashes in the specified signature(s) by abundance, by either `-m/--min-abundance` or `-M/--max-abundance` or both. Abundance selection is inclusive, so `-m 2 -M 5` will select hashes with abundance greater than or equal to 2, and less than or equal to 5. For example, ``` sourmash signature filter -m 2 *.sig ``` will output new signatures containing only hashes that occur two or more times in each signature. The `filter` command accepts the same selectors as `extract`. ### `sourmash signature import` - import signatures from mash. Import signatures into sourmash format. Currently only supports mash, and can import mash sketches output by `mash info -d `. For example, ``` sourmash signature import filename.msh.json -o imported.sig ``` will import the contents of `filename.msh.json` into `imported.sig`. Note: `import` only creates one output file, with one signature in it. Note: `ingest` is an alias for `import`. ### `sourmash signature export` - export signatures to mash. Export signatures from sourmash format. Currently only supports mash dump format. For example, ``` sourmash signature export filename.sig -o filename.sig.msh.json ``` ### `sourmash signature overlap` - detailed comparison of two signatures' overlap Display a detailed comparison of two signatures. This calculates the Jaccard similarity (as in `sourmash compare` or `sourmash search`) and the Jaccard containment in both directions (as with `--containment`). It also displays the number of hash values in the union and intersection of the two signatures, as well as the number of disjoint hash values in each signature. This command has two uses - first, it is helpful for understanding how similarity and containment are calculated, and second, it is useful for analyzing signatures with very small overlaps, where the similarity and/or containment might be very close to zero. For example, ``` sourmash signature overlap tests/test-data/63.fa.sig \ tests/test-data/47.fa.sig ``` will display the detailed comparison of the two files like so: ```text loaded one signature each from tests/test-data/63.fa.sig and tests/test-data/47.fa.sig first signature: signature filename: tests/test-data/63.fa.sig signature: NC_011663.1 Shewanella baltica OS223, complete genome md5: 38729c6374925585db28916b82a6f513 k=31 molecule=DNA num=0 scaled=1000 second signature: signature filename: tests/test-data/47.fa.sig signature: NC_009665.1 Shewanella baltica OS185, complete genome md5: 09a08691ce52952152f0e866a59f6261 k=31 molecule=DNA num=0 scaled=1000 similarity: 0.32069 first contained in second: 0.48282 second contained in first: 0.48851 number of hashes in first: 5238 number of hashes in second: 5177 number of hashes in common: 2529 only in first: 2709 only in second: 2648 total (union): 7886 ``` `sig overlap` can only work with compatible sketches - if there are multiple k-mer sizes or molecule types present in any of the signature files, you will need to choose one k-mer size with `-k/--ksize`, and/or one moltype with `--dna/--protein/--hp/--dayhoff`. ### `sourmash signature kmers` - extract k-mers and/or sequences that match to signatures Given one or more compatible sketches and some sequence files, extract the k-mers and/or sequences corresponding to the hash values in the sketch. Because the sourmash hash function is one-way, this requires FASTA or FASTQ sequence files in addition to the sketch. For example, ``` sourmash sig kmers --signatures sig1.sig --sequences seqfile.fasta \ --save-sequences matches.fasta --save-kmers kmer-matches.csv ``` will search `seqfile.fasta` for matching sequences and k-mers, and produce two files. The file `matches.fasta` will contain FASTA sequences that match the hashes in the input signature, while the file `kmer-matches.csv` provides the matching k-mers and hash values, together with their originating filename and sequence name. If the sketch is a protein sketch (protein, dayhoff, or hp), then the input sequences are assumed to be protein. To search DNA sequences for translated protein hashes, provide the `--translate` flag to `sig kmers`. `--save-sequences` and `--save-kmers` are both optional. If neither are given, basic statistics on k-mer matching are given. Please note that `--save-kmers` can be very slow on large files! The input sketches are the source of the input hashes. So, for example, If `--scaled=1` sketches are provided, `sig kmers` can be used to yield all the k-mers and their matching hashes. Likewise, if the sketch is built from the intersection of two other sketches, only the k-mers and hash values present in both sketches will be used. Likewise, the input sequences are used for matching; they do not need to be the same sequences that were used to create the sketches. Input sequences can be in FASTA or FASTQ format, and either flat text or compressed with gzip or bzip2; formats are auto-detected. By default, `sig kmers` ignores bad k-mers (e.g. non-ACGT characters in DNA). If `--check-sequence` is provided, `sig kmers` will error exit on the first bad k-mer. If `--check-sequence --force` is provided, `sig kmers` will provide error messages (and skip bad sequences), but will continue processing input sequences. ### `sourmash signature manifest` - output a manifest for a file Output a manifest for a file, database, or collection. Note that these manifests are not usually suitable for use as standalone manifests; the `sourmash sig collect` and `sourmash sig check` commands produce standalone manifests. For example, ``` sourmash sig manifest tests/test-data/prot/all.zip -o manifest.csv ``` will create a CSV file, `manifest.csv`, in the internal sourmash manifest format. The manifest will contain an entry for every signature in the file, database, or collection. This format is largely meant for internal use, but it can serve as a [picklist pickfile](#using-picklists-to-subset-large-collections-of-signatures) for subsetting large collections. By default, `sourmash sig manifest` will rebuild the manifest by iterating over the signatures in the input file. This can be slow for large collections. Use `--no-rebuild-manifest` to load an existing manifest if it is available. As of sourmash 4.4.0, `sig manifest` can produce a manifest in a fast on-disk format (a SQLite database). SQLite manifests can be _much_ faster when working with very large collections of signatures. To produce a SQLite manifest, use `sourmash sig manifest ... -F sql`. All sourmash commands that work with manifests will accept both CSV and SQLite manifest files. ### `sourmash signature check` - compare picklists and manifests Compare picklists and manifests across databases, and optionally output matches and missing items. In particular, `sig check` can be used to create standalone manifests for a subset of a large collection, using picklists. For example, ``` sourmash sig check tests/test-data/gather/GCF*.sig \ --picklist tests/test-data/gather/salmonella-picklist.csv::manifest ``` will load all of the `GCF` signatures and compare them to the given picklist. With `-o/--output-missing`, `sig check` will save unmatched elements of the picklist CSV. With `--save-manifest-matching`, `sig check` will save all of the _matched_ elements to a manifest file, which can then be used as a sourmash database. `sourmash sig check` is particularly useful when working with large collections of signatures and identifiers. With `-m/--save-manifest-matching`, `sig check` creates a standalone manifest. In these manifests, sourmash v4 will by default write paths to the matched elements that are relative to the current working directory. In some cases - when the output manifest is in a different directory - this will create manifests that do not work properly with sourmash. The `--relpath` argument will rewrite the paths to be relative to the manifest, while the `--abspath` argument will rewrite paths to be absolute. The `--relpath` behavior will be the default in sourmash v5. Standalone manifests created with `-m/--save-manifest-matching` will use the paths given to `sig check` on the command line; we recommend using zip files and sig files, and avoiding directory hierarchies or path lists. You can use `--from-file` to pass in long lists of filenames via a text file. ### `sourmash signature collect` - collect manifests across databases Collect manifests from across (many) files and merge into a single standalone manifest. Standalone manifests can be used directly as a sourmash database; they support efficient searching and selection of sketches, as well as lazy loading of individual sketches from large collections. See [advanced usage information on sourmash databases](databases-advanced.md) for more information. For example, ``` sourmash sig collect tests/test-data/gather/GCF*.sig -o mf.sqlmf ``` will load all of the `GCF` signatures and build a manifest file `mf.sqlmf` that contains references to all of the signatures, but not the signatures themselves. This manifest file can be loaded directly from the command line by sourmash. `sourmash sig collect` defaults to outputting SQLite manifests. It is particularly useful when working with large collections of signatures and identifiers, and has command line options for merging and updating manifests. The standalone manifests created by `sig collect` will reference the paths given on the command line; we recommend using zip files and sig files, and avoiding directory hierarchies or path lists. You can also use `--from-file` to pass in long lists of filenames. Standalone manifests produced by `sig collect` work most efficiently when constructed from many small zip file collections. As with `sig check`, the standalone manifests created by `sig collect` in sourmash v4 will by default write paths to the matched elements relative to the current working directory. When the output manifest is in a different directory, this will create manifests that do not work properly with sourmash. The `--relpath` argument will rewrite the paths to be relative to the manifest, while the `--abspath` argument will rewrite paths to be absolute. The `--relpath` behavior will be the default in sourmash v5. ## Advanced command-line usage ### Loading signatures and databases sourmash uses several different command-line styles. Most sourmash commands can load sketches from any standard collection type; we primarily recommend using zipfiles (but read on!) Briefly, * `search` and `gather` both take a single query signature and search multiple signatures or databases. In this case, there has to be a single identifiable query for sourmash to use, and if you're using a database or list of signatures as the source of a query, you'll need to provide a selector (ksize with `-k`, moltype with `--dna` etc, or md5sum with `--query-md5`) that picks out a single signature. * `compare` takes multiple signatures and can load them from any sourmash collection type. * the `lca classify` and `lca summarize` commands take multiple signatures with `--query`, and multiple LCA databases, with `--db`. `sourmash multigather` also uses this style. This allows these commands to specify multiple queries **and** multiple databases without (too much) confusion. The database must be LCA databases. * `index` and `lca index` take a few fixed parameters (database name, and for `lca index`, a taxonomy file) and then an arbitrary number of other files that contain signatures. None of these commands currently support searching, comparing, or indexing signatures with multiple ksizes or moltypes at the same time; you need to pick the ksize and moltype to use for your query. Where possible, scaled values will be made compatible. ### Selecting signatures (sourmash v4.3.0 and later) sourmash is built to work with very large collections of signatures, and you may want to select (or exclude) specific signatures from search or other operations, based on their name. This can be done without modifying the collections themselves via the `--include-db-pattern` and `--exclude-db-pattern` arguments to many sourmash commands, including `search`, `gather`, `compare`, `prefetch`, and `sig extract`. In brief, `sourmash search ... --include ` will search only those database signatures that match `` in their `name`, `filename`, or `md5` strings. Here, `` can be either a substring or a regular expression. Likewise, `sourmash search ... --exclude ` will search only those database signatures that _don't_ match pattern in their `name`, `filename`, or `md5` strings. ### Using picklists to subset large collections of signatures (sourmash v4.2.0 and later) Many commands support *picklists*, a feature by which you can select or "pick out" signatures based on values in a CSV file. This is typically used to index, extract, or search a subset of a large collection where modifying the collection itself isn't desired. For example, ``` sourmash sig extract --picklist list.csv:md5:md5sum ``` will extract only the signatures that have md5sums matching the column `md5sum` in the CSV file `list.csv`. The command ``` sourmash sig extract --picklist list.csv::prefetch ``` will extract only the signatures found in the output of `sourmash prefetch ... -o list.csv`. The `--picklist` argument string must be of the format `pickfile:colname:coltype[:pickstyle]`, where `pickfile` is the path to a CSV file, `colname` is the name of the column to select from the CSV file (based on the headers in the first line of the CSV file), and `coltype` is the type of match. An optional pickstyle argument, `:include` or `:exclude`, can be added as a fourth parameter; if omitted, the default is `:include`. The following `coltype`s are currently supported for picklists: * `name` - exact match to signature's name * `md5` - exact match to signature's md5sum * `md5prefix8` - match to 8-character prefix of signature's md5sum * `md5short` - same as `md5prefix8` * `ident` - exact match to signature's identifier * `identprefix` - match to signature's identifier, before '.' * `gather` - use the CSV output of `sourmash gather` as a picklist * `prefetch` - use the CSV output of `sourmash prefetch` as a picklist * `search` - use the CSV output of `sourmash prefetch` as a picklist * `manifest` - use CSV manifests produced by `sig manifest` as a picklist Identifiers are constructed by using the first space delimited word in the signature name. One way to build a picklist is to use `sourmash sig grep --csv out.csv` to construct a CSV file containing a list of all sketches that match the pattern (which can be a string or regexp). The `out.csv` file can be used as a picklist via the picklist manifest CSV format with `--picklist out.csv::manifest`. You can also use `sourmash sig describe --csv out.csv ` or `sourmash sig manifest -o out.csv ` to construct an initial CSV file that you can then edit further and use as a picklist as above. The picklist functionality also supports excluding (rather than including) signatures matching the picklist arguments. To specify a picklist for exclusion, add `:exclude` to the `--picklist` argument string, e.g. `pickfile:colname:coltype:exclude`. For example, ``` sourmash sig extract --picklist list.csv:md5:md5sum:exclude ``` will extract only the signatures that have md5sums that **do not** match entries in the column `md5sum` in the CSV file `list.csv`. In addition to `sig extract`, the following commands support `--picklist` selection: `index`, `search`, `gather`, `prefetch`, `compare`, `index`, and `lca index`. ### Storing (and searching) signatures Backing up a little, there are many ways to store and search signatures. `sourmash` supports storing and loading signatures from JSON files, directories, lists of files, Zip files, custom indexed databases, and SQLite databases. These can all be used interchangeably for most sourmash operations. The simplest is one signature in a single JSON file. You can also put many signatures in a single JSON file, either by building them that way with `sourmash sketch` or by using `sourmash sig cat` or other commands. Searching or comparing these files involves loading them sequentially and iterating across all of the signatures - which can be slow, especially for many (100s or 1000s) of signatures. ### Zip files All of the `sourmash` commands support loading collections of signatures from zip files. You can create a compressed collection of signatures using `sourmash sig cat *.sig -o collections.zip` and then specifying `collections.zip` on the command line in place of `*.sig`; you can also sketch FASTA/FASTQ files directly into a zip file with `-o collections.zip`. ### Choosing signature output formats (sourmash v4.1 and later) All signature saving arguments (`--save-matches` for `search` and `gather`, `-o` for `sourmash sketch`, and `-o` for the `sourmash signature` commands) support flexible saving of collections of signatures into JSON text, Zip files, and/or directories. This behavior is triggered by the requested output filename -- * to save to JSON signature files, use `.sig`; using the filename `-` will send JSON to stdout. * to save to gzipped JSON signature files, use `.sig.gz`; * to save to a Zip file collection, use `.zip`; * to save signature files to a directory, use a name ending in `/`; the directory will be created if it doesn't exist; * to save to a SQLite database, use `.sqldb` (as of sourmash v4.4.0). If none of these file extensions is detected, output will be written in the JSON `.sig` format, either to the provided output filename or to stdout. All of these save formats can be loaded by sourmash commands. **We strongly suggest using .zip files to store signatures: they are fast, small, and fully supported by all the sourmash commands and API.** Note that when outputting large collections of signatures, some save formats require holding all the sketches in memory until they can be written out, and others can save progressively. This can affect memory usage! Currently `.sig` and `.sig.gz` formats are held in memory, while `.zip`, directory outputs, and `.sqldb` formats write progressively to disk. For more detailed information on database formats and performance tradeoffs, please see [the advanced usage information for databases!](databases-advanced.md) ### Loading many signatures #### Indexed databases Indexed databases can make searching signatures much faster. SBT databases are low memory and disk-intensive databases that allow for fast searches using a tree structure, while LCA databases are higher memory and (after a potentially significant load time) are quite fast. SQLite databases (new in sourmash v4.4.0) are typically larger on disk than SBTs and LCAs, but in turn are fast to load and support very low memory search. Commands that take multiple signatures or collections of signatures will also work with indexed databases. One limitation of indexed databases is that they are all restricted in to certain kinds of signatures. Both SBT and LCA databases can only contain one "type" of signature (one ksize/one moltype at one scaled value). SQLite databases can contain multiple ksizes and moltypes, but only at one scaled value. If the database signature type is incompatible with the other signatures, sourmash will complain appropriately. In contrast, signature files and zip collections can contain many different types of signatures, and compatible ones will be selected automatically. Use the `sourmash index` command to create an SBT. Use the `sourmash lca index` command to create an LCA database; the database can be saved in JSON or SQL format with `-F json` or `-F sql`. Use `sourmash sig cat -o .sqldb` to create a SQLite indexed database. #### Loading signatures within a directory hierarchy All of the `sourmash` commands support loading signatures (`.sig` or `.sig.gz` files) from within directory hierarchies; you can just provide the paths to the top-level directory on the command line. However, this is no longer recommended because it can be very inefficient; we instead suggest passing all of the sketch files in the directory into `sig collect` to build a standalone manifest, or using `sig cat` on the directory to generate a zip file. #### Passing in lists of files sourmash commands support `--from-file` or `--query-from-file`, which will take the location of a text file containing a list of file paths. This can be useful for situations where you want to specify thousands of queries, or a subset of signatures produced by some other command. This is no longer recommended when using large collections; we instead suggest using standalone manifests built with `sig collect` and `sig check`, which will include extra metadata that supports fast loading. ### Combining search databases on the command line All of the commands in sourmash operate in "online" mode, so you can combine multiple databases and signatures on the command line and get the same answer as if you built a single large database from all of them. The only caveat to this rule is that if you have multiple identical matches present across the databases, the order in which they are used may depend on the order that the files are passed in on the command line. ### Using stdin Most commands will take signature JSON data via stdin using the usual UNIX convention, `-`. Moreover, `sourmash sketch` and the `sourmash sig` commands will output to stdout. So, for example, ``` sourmash sketch ... -o - | sourmash sig describe - ``` will describe the signatures that were just created. ### Using standalone manifests to explicitly refer to collections of files (sourmash v4.4 and later) Manifests are metadata catalogs of signatures that are used for signature selection and loading. They are used extensively by sourmash internals to speed up signature selection through picklists and pattern matching. Manifests can _also_ be used externally (via the command-line), and these "standalone manifests" may be useful for organizing large collections of signatures. They can be generated with the `sig collect`, `sig manifest`, and `sig check` subcommands. Suppose you have a large collection of signatures (`.sig` or `.sig.gz` files) in a location (e.g., under a directory, or in a zip file). You can create a manifest file for them like so: ``` sourmash sig collect

-o manifest.sqlmf ``` and then use the manifest directly for sourmash operations, for example: ``` sourmash sig fileinfo manifest.sqlmf ``` This manifest contains _references_ to the signatures (but not the signatures themselves) and can then be used as a database target for most sourmash operations - search, gather, etc. Manifests support fast selection and lazy loading of sketches in many situations. The `sig check` command can also be used to create standalone manifests from collections using a picklist, with the `-m/--save-manifest-matching` option. This is useful for commands that don't support picklists natively, such as commands in plugins. Note that `sig collect` and `sig check` will generate manifests containing the pathnames given to them - so if you use relative paths, the references will be relative to the working directory in which the command was run. You can use `sig collect --abspath` to rewrite the paths into absolute paths, or `sig collect --relpath` to rewrite the paths relative to the manifest file. **Our advice:** We suggest using zip file collections for most situations; we strongly recommend using standalone manifests for situations where you have **very large** sketches or a **very large** collection of sketches (1000s or more), and don't want to make multiple copies of signatures in the collection (as you would have to, with a zipfile). This is particularly useful if you want to refer to different subsets of the collection without making multiple copies in a zip file. You can read more about the details of zip files and manifests in [the advanced usage information for databases](databases-advanced.md). ## Using sourmash plugins As of sourmash v4.7.0, sourmash has an experimental plugins interface! The plugin interface supports extending sourmash to load and save signatures in new ways, and also supports the addition of sourmash subcommands via `sourmash scripts`. In order to use a plugin with sourmash, you will need to use `pip` or `conda` to install the plugin the same environment that sourmash is installed in. In the future, we will include a list of available sourmash plugins in the documentation, and also provide a way to list available plugins. You can list all installed plugins and their versions with `sourmash info -v`. Below are some useful plugins that the sourmash team uses regularly and supports! ### The `branchwater` plugin - multithreaded and optimized sourmash operations (Installable via conda and pip as `sourmash_plugin_branchwater`.) The [`branchwater` plugin](https://github.com/sourmash-bio/sourmash_plugin_branchwater) provides faster and lower memory versions of `search`, `gather`, and `sketch`, as well as large-scale metagenome search (used for [petabyte-scale sequence search](https://www.biorxiv.org/content/10.1101/2022.11.02.514947v1)) and large-scale clustering. Read [the branchwater plugin docs](https://github.com/sourmash-bio/sourmash_plugin_branchwater/tree/main/doc/) for more information, and ask questions on [the sourmash issue tracker!](https://github.com/sourmash-bio/sourmash/issues) ### The `betterplot` plugin - improved plotting and visualization (Installable via pip as `sourmash_plugin_betterplot`.) The [`betterplot` plugin](https://github.com/sourmash-bio/sourmash_plugin_betterplot/) provides a variety of new plotting outputs for sourmash, including improved distance matrices, MDS plots, tSNE plots, upset plots, and Venn diagrams. It also supports cluster-cutting and extraction, as well as improved labeling and coloring by category. Read [the betterplot docs](https://github.com/sourmash-bio/sourmash_plugin_betterplot/) for more information, and ask questions on [the sourmash issue tracker!](https://github.com/sourmash-bio/sourmash/issues). sourmash-bio-sourmash-bc3c0f8/doc/conf.py000066400000000000000000000243331474157046300205370ustar00rootroot00000000000000#!/usr/bin/env python3 # # sourmash documentation build configuration file, created by # sphinx-quickstart on Sat Jun 4 16:35:43 2016. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os import sourmash print("sourmash at:", sourmash) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath("..")) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.doctest", "sphinx.ext.coverage", "sphinx.ext.viewcode", # 'sphinx.ext.napoleon', "nbsphinx", "IPython.sphinxext.ipython_console_highlighting", "myst_parser", "sphinx_design", ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # source_suffix = ['.rst', '.md'] source_suffix = [".rst", ".md"] # The encoding of source files. # source_encoding = 'utf-8-sig' # The master toctree document. master_doc = "index" # General information about the project. project = "sourmash" copyright = "2016-2023, C. Titus Brown, Luiz Irber, and N. Tessa Pierce-Ward" author = "C. Titus Brown, Luiz Irber, and N. Tessa Pierce-Ward" # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. from importlib.metadata import version release = version("sourmash") version = ".".join(release.split(".")[:2]) # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: # today = '' # Else, today_fmt is used as the format for a strftime call. # today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This patterns also effect to html_static_path and html_extra_path exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # The reST default role (used for this markup: `text`) to use for all # documents. # default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. # add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). # add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. # show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = "sphinx" # A list of ignored prefixes for module index sorting. # modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # CTB: suppress warnings about circularity in ToC. # see https://github.com/sphinx-doc/sphinx/issues/7410. suppress_warnings = ["toc.circular"] # -- Options for HTML output ---------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = "alabaster" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = { "logo": "logo.png", "logo_name": True, "description": "Quickly search, compare, and analyze genomic and metagenomic data sets", "sidebar_collapse": False, } # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] # The name for this set of Sphinx documents. # " v documentation" by default. # html_title = 'sourmash v1.0' # A shorter title for the navigation bar. Default is the same as html_title. # html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. # html_logo = None # The name of an image file (relative to this directory) to use as a favicon of # the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. # html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. # html_extra_path = [] # If not None, a 'Last updated on:' timestamp is inserted at every page # bottom, using the given strftime format. # The empty string is equivalent to '%b %d, %Y'. # html_last_updated_fmt = None # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. # html_use_smartypants = True # Custom sidebar templates, maps document names to template names. html_sidebars = { "*": [ "about.html", "navigation.html", "relations.html", "sourcelink.html", "searchbox.html", ] } # Additional templates that should be rendered to pages, maps page names to # template names. # html_additional_pages = {} # If false, no module index is generated. # html_domain_indices = True # If false, no index is generated. # html_use_index = True # If true, the index is split into individual pages for each letter. # html_split_index = False # If true, links to the reST sources are added to the pages. # html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. # html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. # html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. # html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). # html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'h', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'r', 'sv', 'tr', 'zh' # html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # 'ja' uses this config value. # 'zh' user can custom change `jieba` dictionary path. # html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. # html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = "sourmashdoc" # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, "sourmash.tex", "sourmash Documentation", "C. Titus Brown", "manual"), ] # The name of an image file (relative to this directory) to place at the top of # the title page. # latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. # latex_use_parts = False # If true, show page references after internal links. # latex_show_pagerefs = False # If true, show URL addresses after external links. # latex_show_urls = False # Documents to append as an appendix to all manuals. # latex_appendices = [] # If false, no module index is generated. # latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [(master_doc, "sourmash", "sourmash Documentation", [author], 1)] # If true, show URL addresses after external links. # man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ( master_doc, "sourmash", "sourmash Documentation", author, "sourmash", "One line description of project.", "Miscellaneous", ), ] # Documents to append as an appendix to all manuals. # texinfo_appendices = [] # If false, no module index is generated. # texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. # texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. # texinfo_no_detailmenu = False autodoc_mock_imports = ["sourmash.minhash"] myst_heading_anchors = 3 myst_enable_extensions = [ "html_admonition", "colon_fence", ] sourmash-bio-sourmash-bc3c0f8/doc/databases-advanced.md000066400000000000000000000267401474157046300232600ustar00rootroot00000000000000# sourmash databases - advanced usage information. sourmash uses a variety of different mechanisms and formats for storing, organizing, and searching signatures. Some of these mechanisms, "collections", just store the signatures; others ("indexed" databases) provide indices on the signatures for fast content-based search. _Most_ of the mechanisms now use manifests that permit fast selection and loading of signatures based on metadata. Below we refer to "databases" generically as any on-disk storage mechanism for sourmash signatures. Which database type is best to use depends on what you're doing - which is what this document is about! In general, however, sourmash should be fast enough that database choice will only impact performance when searching thousands of signatures, or doing thousands of searches. The recommended file extensions below are conventions used to signal the output format when using `-o` with `sourmash sketch` and the `sourmash sig` subcommands; so, for example, `sourmash sketch dna *.fa -o xyz.zip` will output signatures in the .zip format. sourmash will automatically detect and load the database, based on the database _content_ and not the database extension, in most cases. Unless noted otherwise, the below database formats are supported in all release since sourmash v3.5. ## How are signatures actually stored? sourmash signatures are typically serialized into JSON for on-disk storage, with rare exceptions (SQLite and LCA databases). The internal sourmash code automatically detects and properly handles compressed (gzipped) JSON data. ## Storing JSON in `.sig` and `.sig.gz` files: the original format. Multiple signatures can be stored in a single JSON file. However, this file will be loaded in its entirety by sourmash, even if you only select one for later analysis. This is the least efficient way to store multiple signatures, because all of the JSON must be loaded before any signature can be selected or searched. But it is the oldest format and so a lot of our documentation describes it! ## Storing signatures in `.zip` files: the **recommended** format. **This is our recommended format for storing collections of signatures. It is supported as of sourmash v4.1.** Multiple signatures can be stored in a single .zip file. The best way to construct that zip file is from within sourmash, by specifying `-o filename.zip` when outputting signatures. Zip files created from within sourmash will automatically have manifests; this enables rapid subselection and direct loading of signatures via e.g. picklists. Zip files are not indexed by content, so they can be slow for searching. But they are small, and provide a good compromise between disk size (small), flexibility (can store any mixture of signatures), and speed (good for `gather`, not good for `search`). Zip file collections can contain any number of signatures, of any type (`num` or `scaled`, DNA/protein/dayhoff/hp). You can create your own Zip files by simply zipping any number of `.sig` or `.sig.gz` files into a .zip file, and sourmash will read this. However, since this zip file will not have a manifest, it will not be fast for certain operations that rely on manifests for speed, such as picklists and `sourmash sig summarize`. So we recommend using sourmash to create zip file collections with manifests. ### Storing signatures in SQLite databases As of sourmash 4.4, we support storing signatures directly in a [SQLite](https://www.sqlite.org/index.html) database (`-o .sqldb`). This is a fast, low-memory, on-disk format that is suitable for use with `search` and can support multiple simultaneous queries. However, the resulting file is also rather large, so we do not distribute databases in this format. SQLite databases are implemented as an [inverted index](https://en.wikipedia.org/wiki/Inverted_index), with hashes stored directly in a table. SQLite databases are limited to scaled signatures, and can only contain sketches with the same scaled value across the entire database. They *can* store multiple molecule types. While SQLite databases are a new format, they seem promising, especially when disk space is not a concern and/or when memory is limited. We particularly recommend them for use as LCA databases (see next section) where they are a considerable improvement over the legacy JSON format. ### Other Indexed collections - SBTs and LCAs. We provide two other indexed collection formats, Sequence Bloom Trees (SBTs) and LCA databases. SBTs implement our version of [Sequence Bloom Trees](http://www.cs.cmu.edu/~ckingsf/software/bloomtree/), a fast tree-based index that support rapid `search` for matches; they are particularly effective when searching for *best* matches across large databases. They are relatively low memory and typically about twice the size of .zip files on disk. They can be constructed with `sourmash index`. LCA databases are [inverted indices](https://en.wikipedia.org/wiki/Inverted_index) that support individual hash lookup. They provide fast `search` and `gather`, and also support all of the `sourmash lca` subcommands for hash-based taxonomic analysis. There are two LCA database formats, JSON and SQLite; JSON is small on disk but JSON LCA databases consume a lot of memory when loaded, while SQLite LCA databases are large on disk but low-memory and fast. JSON LCA databases do not support multiprocess queries. LCA databases can be constructed with `sourmash lca index`. Both SBTs and LCA databases can only store homogenous collections of signature types - all signatures must have the same molecule type and scaled or num value. Furthermore, LCA databases can only store scaled signatures. We recommend SBT and LCA databases for use only in specific situations - e.g. SBTs are great for single-genome "best match" search for SBTs, and `sourmash lca` commands require LCA databases. ### Standalone manifests Manifests are catalogs of signature metadata - name, molecule type, k-mer size, and other information - that can be used to select specific signatures for searching or processing. Typically when using manifests the actual signatures themselves are not loaded until they are needed, although the efficiency of this depends on the signature storage mechanism; for example, JSON-format containers (`.sig` and `.lca.json` files) must be entirely loaded before any signature in the file them can be used, unlike zip containers. As of sourmash 4.4 manifests can be *directly* loaded from the command line as standalone collections. This lets manifests serve as a catalog of signatures stored in many different locations. Sketches can be selected by name, k-mer size, molecule type, and other features without loading the actual sketch data. Standalone manifests are preferable to both directory storage and pathlists (below), because they support fast selection and direct lazy loading. This means that sourmash operations that support streaming or online search (such as `prefetch` and `gather`, among others) can avoid loading everything all at once. Standalone manifests are the most effective solution for managing custom collections of thousands to millions of signatures, as well as working with multiple large sketches. They can be created with `sourmash sig collect` and `sourmash sig check` (sourmash v4.4 and later). Sourmash supports two manifest file formats - CSV and SQLite. SQLite manifests are much faster and lower-memory than CSV manifests. ### Directories Directory hierarchies of signatures are read natively by sourmash, and can be created or extended by specifying `-o dirname/` (with a trailing slash). To read from a directory, specify the directory name on the sourmash command line. When reading from directories, the entire directory hierarchy is traversed and all `.sig` and `.sig.gz` files are loaded as signatures. If `--force` is specified, _all_ files will be read, and failures will be ignored. When directories are specified as outputs, the signatures will be saved by their complete md5sum underneath the directory. We don't recommend loading signatures from directory hierarchies, since the implementation is not particularly memory efficient and most of the use cases for directories are now covered by other approaches - in particular, standalone manifests. ### Pathlists Pathlists are text files containing paths to one or more sourmash databases; any type of sourmash-readable collection can be listed. The paths in pathlists can be relative or absolute within the file system. If they are relative, they must resolve with respect to the current working directory of the sourmash command. We don't recommend using pathlists, since the original use cases are now supported with picklists and standalone manifests, but they are still supported. Loading sketches from pathlists is also not very efficient. Pathlists are not output by any sourmash commands. Many commands support `--query-from-file` or `--from-file` as a way to pass in a file containing many paths to sketches or collections. The internal implementation of sourmash simply adds these to the command-line arguments, and this is an effective and efficient way to provide long lists of files to commands like `sig check` and `sig collect` that create standalone manifests to support efficient lazy loading. ## Storing taxonomies sourmash supports taxonomic information output via the `sourmash lca` and `sourmash tax` subcommands. Both sets of commands rely on the same 7 taxonomic ranks: superkingdom, phylum, class, order, family, genus, and species (with limited support for a 'strain' rank). And both sets of subcommands take lineage spreadsheets that link specific identifiers to taxonomic lineages. Lineage spreadsheets can be provided in two on-disk formats, CSV and SQLite. CSV is the original format, and consists of separate columns for identifier and each taxonomic rank. SQLite taxonomy databases are typically built from CSV using `sourmash tax prepare`. They contain a single table, `sourmash_taxonomy`, with columns for `ident` and each taxonomic rank. Only the `sourmash tax` command supports SQLite taxonomy databases. ## Appendix: SQLite complexities The SQLite implementation of signature storage, metadata manifests, and LCA databases is all bundled into a single SQLite database. Beacuse of this, sourmash must examine the database tables to decide what kind of sourmash structure the database is - the logic is roughly this: * does the database store both sketch information and taxonomy information? It's an LCA database! * if it has sketch information but no taxonomy information, it's just a regular index. * if it only has manifest information, it's a manifest! * if it only has taxonomy information, it's a taxonomy! This is complicated by several other details - * we can treat SQLite databases with sketch information as read-only manifests, but because the sketch information is tightly coupled to the manifest table, we cannot insert new manifest entries; * we can treat SQLite databases with sketch information as read/write taxonomy files, since the taxonomy information is not tightly coupled to the sketches; Last but not least, the hashes in SQLite are stored as signed 64-bit integers and must be converted to unsigned 64-bit numbers internally by sourmash; negative numbers in the SQLite table represent unsigned ints that are larger than 2**63 - 1. Please see [this blog post](http://ivory.idyll.org/blog/2022-storing-ulong-in-sqlite-sourmash.html) for more information. The SQLite schema itself is not very complicated and can be used for lineage and manifest querying by other scripts. However, we recommend doing hash value querying/search via the Python code. sourmash-bio-sourmash-bc3c0f8/doc/databases.md000066400000000000000000000425571474157046300215210ustar00rootroot00000000000000# Prepared databases ```{contents} ``` We provide a number of pre-built collections and indexed databases that you can use with sourmash. ## Types of databases For each k-mer size, three types of databases may be available: Zipfile (`.zip`), SBT (`.sbt.zip`), and LCA (`.lca.jzon.gz`). The Zipfile and SBT databases are built with scaled=1000, and then LCA databases are built with scaled=10,000. We recommend using the Zipfile databases for `sourmash gather` and the SBT databases for `sourmash search`. You must use the LCA databases for `sourmash lca` operations. You can read more about the different database and index types [here](https://sourmash.readthedocs.io/en/latest/command-line.html#indexed-databases). Note that the SBT and LCA databases can be used with sourmash v3.5 and later, while Zipfile collections can only be used with sourmash v4.1 and up. ## Taxonomic Information (for non-LCA databases) For each prepared database, we have also made taxonomic information available linking each genome with its assigned lineage (`GTDB` or `NCBI` as appropriate). For private databases, users can create their own `taxonomy` files: the critical columns are `ident`, containing the genome accession (e.g. `GCA_1234567.1`) and a column for each taxonomic rank, `superkingdom` to `species`. If a `strain` column is provided, it will also be used. As of v4.8, we can also use LIN taxonomic information in tax commands that accept the `--lins` flag. If used, `sourmash tax` commands will require a `lin` column in the taxonomy file which should contain `;`-separated LINs, preferably with a standard number of positions (e.g. all 20 positions in length or all 10 positions in length). Some taxonomy commands also accept a `lingroups` file, which is a two-column file (`name`, `lin`) describing the name and LIN prefix of LINgroups to be used for taxonomic summarization. ## Downloading and using the databases All databases below can be downloaded via the command line with `curl -JLO `, where `` is the URL below. This will download an appropriately named file; you can name it yourself by specify `'-o ` to specify the local filename. The databases do not need to be unpacked or prepared in any way after download. You can verify that they've been successfully downloaded (and view database properties such as `ksize` and `scaled`) with `sourmash sig summarize `. ## Sketches for human and animal genomes These sketches are of the latest releases of a number of animal genomes. Among other uses, they can be used to detect host contamination in microbial metagenomes. Each file includes sketches at k=21, k=31, and k=51, at a scaled of 1000, and is under 50 MB. * Human (hg38) - [hg38.sig.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/host/hg38.sig.zip) * Cow (bosTau9) - [bosTau9.sig.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/host/bosTau9.sig.zip) * Dog (canFam6) - [canFam6.sig.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/host/canFam6.sig.zip) * Horse (equCab3) - [equCab3.sig.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/host/equCab3.sig.zip) * Cat (felCat9) - [felCat9.sig.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/host/felCat9.sig.zip) * Chicken (galGAl6) - [galGal6.sig.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/host/galGal6.sig.zip) * Mouse (mm39) - [mm39.sig.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/host/mm39.sig.zip) * Goat (oviAri4) - [oviAri4.sig.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/host/oviAri4.sig.zip) * Pig (susCr11) - [susScr11.sig.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/host/susScr11.sig.zip) ## Sketches for plant genomes These sketches are for the plant genomes available in GenBank as of 2024-07. | K-mer size | Zipfile collection | | -------- | -------- | | k21 | [download (7G)](https://farm.cse.ucdavis.edu/\~ctbrown/sourmash-db/genbank-plant-2024-07/genbank-plants-2024-07.k21.zip) | | k31 | [download (8.8G)](https://farm.cse.ucdavis.edu/\~ctbrown/sourmash-db/genbank-plant-2024-07/genbank-plants-2024-07.k31.zip) | | k51 | [download (11G)](https://farm.cse.ucdavis.edu/\~ctbrown/sourmash-db/genbank-plant-2024-07/genbank-plants-2024-07.k51.zip) | Lineage spreadsheet for sourmash `tax` commands: [download](https://farm.cse.ucdavis.edu/\~ctbrown/sourmash-db/genbank-plant-2024-07/genbank-plants-2024-07.lineages.csv.gz) ## GTDB R08-RS214 - DNA databases [GTDB R08-RS214](https://forum.gtdb.ecogenomic.org/t/announcing-gtdb-r08-rs214/456) consists of 402,709 genomes organized into 85,205 species clusters. The lineage spreadsheet (for `sourmash tax` commands) is available [for the genome database (402k)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214.lineages.csv.gz). ### GTDB R08-RS214 genomic representatives (85k) The GTDB genomic representatives are a low-redundancy subset of Genbank genomes, with 85,205 species-level genomes. | K-mer size | Zipfile collection | SBT | LCA | | -------- | -------- | -------- | ---- | | 21 | [download (2.2 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-reps.k21.zip) | [download (4.4 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-reps.k21.sbt.zip) | [download (189 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-reps.k21.lca.json.gz) | | 31 | [download (2.2 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-reps.k31.zip) | [download (4.4 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-reps.k31.sbt.zip) | [download (221 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-reps.k31.lca.json.gz) | | 51 | [download (2.2 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-reps.k51.zip) | [download (4.4 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-reps.k51.sbt.zip) | [download (230 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-reps.k51.lca.json.gz) | ### GTDB R08-RS214 all genomes (403k) These are databases for the full GTDB release, each containing 402,709 genomes. | K-mer size | Zipfile collection | SBT | LCA | | -------- | -------- | -------- | ---- | | 21 | [download (12 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-k21.zip) | [download (23 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-k21.sbt.zip) | [download (406 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-k21.lca.json.gz) | | 31 | [download (12 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-k31.zip) | [download (23 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-k31.sbt.zip) | [download (438 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-k31.lca.json.gz) | | 51 | [download (12 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-k51.zip) | [download (23 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-k51.sbt.zip) | [download (460 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs214/gtdb-rs214-k51.lca.json.gz) | ## Genbank genomes from March 2022 The below zip files contain different subsets of the signatures for all microbial Genbank genomes. The databases were built in March 2022, and are based on the assembly_summary files provided [here](https://ftp.ncbi.nlm.nih.gov/genomes/genbank/). Since some of the files are extremely large, we only provide them in Zip format (which is our smallest and most flexible format). Note that all of the sourmash search commands support multiple databases on the command line, so you can search multiple subsets simply by providing them all on the command line, e.g. `sourmash search query.sig genbank-2022.03-{viral,protozoa}-k31.zip`. Taxonomic spreadsheets for each domain are provided below as well. ### Genbank viral 47,952 genomes: [genbank-2022.03-viral-k21.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-viral-k21.zip) [genbank-2022.03-viral-k31.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-viral-k31.zip) [genbank-2022.03-viral-k51.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-viral-k51.zip) [genbank-2022.03-viral.lineages.csv.gz](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-viral.lineages.csv.gz) ### Genbank archaeal 8,750 genomes: [genbank-2022.03-archaea-k21.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-archaea-k21.zip) [genbank-2022.03-archaea-k31.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-archaea-k31.zip) [genbank-2022.03-archaea-k51.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-archaea-k51.zip) [genbank-2022.03-archaea.lineages.csv.gz](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-archaea.lineages.csv.gz) ### Genbank protozoa 1193 genomes: [genbank-2022.03-protozoa-k21.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-protozoa-k21.zip) [genbank-2022.03-protozoa-k31.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-protozoa-k31.zip) [genbank-2022.03-protozoa-k51.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-protozoa-k51.zip) [genbank-2022.03-protozoa.lineages.csv.gz](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-protozoa.lineages.csv.gz) ### Genbank fungi 10,286 genomes: [genbank-2022.03-fungi-k21.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-fungi-k21.zip) [genbank-2022.03-fungi-k31.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-fungi-k31.zip) [genbank-2022.03-fungi-k51.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-fungi-k51.zip) [genbank-2022.03-fungi.lineages.csv.gz](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-fungi.lineages.csv.gz) ### Genbank bacterial: 1,148,011 genomes: [genbank-2022.03-bacteria-k21.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-bacteria-k21.zip) [genbank-2022.03-bacteria-k31.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-bacteria-k31.zip) [genbank-2022.03-bacteria-k51.zip](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-bacteria-k51.zip) [genbank-2022.03-bacteria.lineages.csv.gz](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/genbank-2022.03/genbank-2022.03-bacteria.lineages.csv.gz) ## GTDB R07-RS207 - DNA databases [GTDB R07-RS207](https://forum.gtdb.ecogenomic.org/t/announcing-gtdb-r07-rs207/264) consists of 317,542 genomes organized into 65,703 species clusters. The lineage spreadsheet (for `sourmash tax` commands) is available [for the species database (65k)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.taxonomy.reps.csv.gz) and [for the genome database (317k)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.taxonomy.with-strain.csv.gz). ### GTDB R07-RS207 genomic representatives (66k) The GTDB genomic representatives are a low-redundancy subset of Genbank genomes, with 65,703 species-level genomes. | K-mer size | Zipfile collection | SBT | LCA | | -------- | -------- | -------- | ---- | | 21 | [download (1.7 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic-reps.dna.k21.zip) | [download (3.5 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic-reps.dna.k21.sbt.zip) | [download (181 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic-reps.dna.k21.lca.json.gz) | | 31 | [download (1.7 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic-reps.dna.k31.zip) | [download (3.5 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic-reps.dna.k31.sbt.zip) | [download (181 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic-reps.dna.k31.lca.json.gz) | | 51 | [download (1.7 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic-reps.dna.k51.zip) | [download (3.5 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic-reps.dna.k51.sbt.zip) | [download (181 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic-reps.dna.k51.lca.json.gz) | ### GTDB R07-RS207 all genomes (318k) These are databases for the full GTDB release, each containing 317,542 genomes. | K-mer size | Zipfile collection | SBT | LCA | | -------- | -------- | -------- | ---- | | 21 | [download (9.4 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic.k21.zip) | [download (19 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic.k21.sbt.zip) | [download (351 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic.k21.lca.json.gz) | | 31 | [download (9.4 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic.k31.zip) | [download (19 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic.k31.sbt.zip) | [download (351 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic.k31.lca.json.gz) | | 51 | [download (9.4 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic.k51.zip) | [download (19 GB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic.k51.sbt.zip) | [download (351 MB)](https://farm.cse.ucdavis.edu/~ctbrown/sourmash-db/gtdb-rs207/gtdb-rs207.genomic.k51.lca.json.gz) | ## GTDB R06-RS202 - DNA databases All files below are available under https://osf.io/wxf9z/. The GTDB taxonomy spreadsheet (in a format suitable for `sourmash lca index`) is available [here](https://osf.io/p6z3w/). ### GTDB R06-RS202 genomic representatives (47.8k) The GTDB genomic representatives are a low-redundancy subset of Genbank genomes. | K-mer size | Zipfile collection | SBT | LCA | | -------- | -------- | -------- | ---- | | 21 | [download (1.3 GB)](https://osf.io/jp5zh/download) | [download (2.6 GB)](https://osf.io/py92w/download) | [download (114 MB)](https://osf.io/gk2za/download) | | 31 | [download (1.3 GB)](https://osf.io/nqmau/download) | [download (2.6 GB)](https://osf.io/w4bcm/download) | [download (131 MB)](https://osf.io/ypsjq/download) | | 51 | [download (1.3 GB)](https://osf.io/px6qd/download) | [download (2.6 GB)](https://osf.io/rv9zp/download) | [download (137 MB)](https://osf.io/297dp/download) | ### GTDB R06-RS202 all genomes (258k) These databases contain the complete GTDB collection of 258,406 genomes. | K-mer size | Zipfile collection | SBT | LCA | | -------- | -------- | -------- | ---- | | 21 | [download (7.8 GB)](https://osf.io/vgex4/download) | [download (15 GB)](https://osf.io/ar67j/download) | [download (266 MB)](https://osf.io/hm3c4/download) | | 31 | [download (7.8 GB)](https://osf.io/94mzh/download) | [download (15 GB)](https://osf.io/dmsz8/download) | [download (286 MB)](https://osf.io/9xdg2/download) | | 51 | [download (7.8 GB)](https://osf.io/x9cdp/download) | [download (15 GB)](https://osf.io/8fc3t/download) | [download (299 MB)](https://osf.io/3cdp6/download) | ## Appendix: database use and construction details Database release workflows are being archived at [sourmash-bio/database-releases](https://github.com/sourmash-bio/database-releases). Some more details on database use and construction: * Zipfile collections can be used for a linear search. The signatures were calculated with a scaled of 1000, which robustly supports searches for ~10kb or larger matches. * SBT databases are indexed versions of the Zipfile collections that support faster search. They are also indexed with scaled=1000. * LCA databases are indexed versions of the Zipfile collections that also contain taxonomy information and can be used with regular search as well as with [the `lca` subcommands for taxonomic analysis](https://sourmash.readthedocs.io/en/latest/command-line.html#sourmash-lca-subcommands-for-taxonomic-classification). They are indexed with scaled=10,000, which robustly supports searches for 100kb or larger matches. ## Appendix: Memory and time requirements The detailed memory usage of sourmash depends on the type of search, the query, and the database you're searching, but to help guide you here is a range of numbers: | Search type | Query | Database | Max RAM | Time | | -------- | -------- | -------- | -------- | -------- | | gather | Bacterial genome | GTDB complete (280k) | 1 GB | 6 minutes | | gather | Simple metagenome | GTDB reps .zip (65k) | 2 GB | 6 minutes | | gather | Real metagenome | All Genbank (1.2m) | 100 GB | 3 hours | lca summarize |Simple metagenome | GTDB reps .sql (65k) | 400 MB | 20 seconds | | lca summarize | Simple metagenome | GTDB reps .json (65k) | 6.2 GB | 1m 20 seconds | Please see [sourmash#1958](https://github.com/sourmash-bio/sourmash/issues/1958) for detailed GTDB numbers and [gather paper#47](https://github.com/dib-lab/2020-paper-sourmash-gather/issues/47) for detailed Genbank numbers. ## Appendix: legacy databases Legacy databases are available [here](legacy-databases.md). sourmash-bio-sourmash-bc3c0f8/doc/dev_plugins.md000066400000000000000000000075031474157046300221010ustar00rootroot00000000000000# sourmash plugins via Python entry points As of version 4.7.0, sourmash has experimental support for Python plugins to load and save signatures in different ways (e.g. file formats, RPC servers, databases, etc.) and to run additional commands via the command-line. This support is provided via the "entry points" mechanism supplied by [`importlib.metadata`](https://docs.python.org/3/library/importlib.metadata.html) and documented [here](https://setuptools.pypa.io/en/latest/userguide/entry_point.html). ```{note} Note: The plugin API is _not_ finalized or subject to semantic versioning just yet! Please subscribe to [sourmash#1353](https://github.com/sourmash-bio/sourmash/issues/1353) if you want to keep up to date on plugin support. ``` You can define entry points in the `pyproject.toml` file like so: ``` [project.entry-points."sourmash.load_from"] a_reader = "module_name:load_sketches" [project.entry-points."sourmash.save_to"] a_writer = "module_name:SaveSignatures_WriteFile" [project.entry-points."sourmash.cli_script"] new_cli = "module_name:Command_NewCommand" ``` Here, `module_name` should be the name of the module to import. * `load_sketches` should be a function that takes a location along with arbitrary keyword arguments and returns an `Index` object (e.g. `LinearIndex` for a collection of in-memory signatures). * `SaveSignatures_WriteFile` should be a class that subclasses `BaseSave_SignaturesToLocation` and implements its own mechanisms of saving signatures. See the `sourmash.save_load` module for saving and loading code already used in sourmash. * `Command_NewCommand` should be a class that subclasses `plugins.CommandLinePlugin` and provides an `__init__` and `main` method. Note that if the reader function or writer class has a `priority` attribute, this will be used to determine the order in which the plugins are called. Priorities lower than 10 will get called before any internal load or save function, while priorities greater than 80 will get called after almost all internal load/save functions; see `src/sourmash/save_load.py` for details and the current priorities. The `name` attribute of the plugin (`a_reader`, `a_writer`, and `new_cli` in `pyproject.toml`, above) is only used in debugging. You can provide zero or more plugins, and you can define just a reader, or just a writer, or just a CLI plugin. ## Templates and examples If you want to create your own plug-in, you can start with the [sourmash_plugin_template](https://github.com/sourmash-bio/sourmash_plugin_template) repo. Some (early stage) plugins are also available as examples: * [sourmash-bio/sourmash_plugin_load_urls](https://github.com/sourmash-bio/sourmash_plugin_load_urls) - load signatures and CSV manifests via [fsspec](https://filesystem-spec.readthedocs.io/). * [sourmash-bio/sourmash_plugin_avro](https://github.com/sourmash-bio/sourmash_plugin_avro) - use [Apache Avro](https://avro.apache.org/) as a serialization format. ## Debugging plugins `sourmash info -v` will list all installed plugins and their versions. `sourmash sig cat -o ` is a simple way to invoke a `save_to` plugin. Use `-d` to turn on debugging output. `sourmash sig describe ` is a simple way to invoke a `load_from` plugin. Use `-d` to turn on debugging output. `sourmash scripts` will list available command-line plugins. ## Semantic versioning and listing sourmash as a dependency Plugins should generally list sourmash as a dependency for installation. Once plugins are officially supported by sourmash, the plugin API will be under [semantic versioning constraints](https://semver.org/). That means that you should constrain plugins to depend on sourmash only up to the next major version, e.g. sourmash v5. Specifically, we suggest placing something like: ``` dependencies = ['sourmash>=4.8.0,<5'] ``` in your `pyproject.toml` file. sourmash-bio-sourmash-bc3c0f8/doc/developer.md000066400000000000000000000270701474157046300215500ustar00rootroot00000000000000```{contents} Contents :depth: 3 ``` # Developer information ## Development environment You can get the latest development branch with: ``` git clone https://github.com/sourmash-bio/sourmash.git ``` sourmash runs under Python 3.10 and later. We recommend using `conda` for setting up an environment for developing new features, running tests and code quality checks. Here are some suggestions on how to set them up :::{note} You only need one of these, no need to install them all! ::: ::::{tab-set} :::{tab-item} conda Follow the [installation instructions](https://github.com/conda-forge/miniforge#install) for installing `Miniforge3` (a conda distribution that uses the [`conda-forge`](https://conda-forge.org/) channel by default). Once `conda` is installed, run ``` conda env create -n sourmash_dev --file dev.yml ``` to create an environment called `sourmash_dev` containing the programs needed for development. Once the environment is created, you can activate it for development with ``` conda activate sourmash_dev ``` ::: :::{tab-item} pixi Follow the [installation instructions](https://github.com/prefix-dev/pixi) for installing `pixi`. To activate the new environment, run ``` pixi shell ``` and proceed to the ["Running tests and checks"](#running-tests-and-checks) section. ::: :::{tab-item} nix Follow the [installation instructions](https://zero-to-nix.com/start/install) for setting up Nix in your system (Linux or macOS). Once Nix is installed, run ``` nix develop ``` to start an environment ready for [running tests and checks](#running-tests-and-checks). ::: :::{tab-item} General instructions As long as you have `tox` and a Rust compiler available, you can skip `conda`, `pixi`, or `nix`. For Rust, we suggest using `rustup` to install the Rust environment: ``` curl https://sh.rustup.rs -sSf | sh ``` And for `tox` you can use [pipx](https://pipx.pypa.io/latest/installation/) for installing it without interfering with other Python environments: ``` pipx install tox ``` We suggest working on sourmash in a virtualenv; e.g. from within the cloned repository (and after installing `tox` and Rust), you can do: ``` tox -e dev . .tox/dev/bin/activate ``` Finally, you can also explicitly install all the Python dependencies for sourmash by running ``` pip install -r requirements.txt ``` (but they are already installed in the virtualenv created with `tox -e dev`). To update rust to the latest version, use `rustup update`. To update your Python dependencies to the latest required for sourmash, you can run `pip install -r requirements.txt`. ::: :::: ## Running tests and checks We use [`tox`](https://tox.readthedocs.io) for managing dependencies and running tests and checks during development. `tox -l` lists available tasks. You can run tests by invoking `make test` in the sourmash directory; `tox -e py310` will run the Python tests with Python 3.10, and `cargo test` will run the Rust tests. ## Adding new changes We use [`pre-commit`](https://pre-commit.com/) to run automatic checks and fixes when developing sourmash. You can run it with ``` tox -e fix_lint ``` which prints a "hint" at the end of the run with instructions to set it up to run automatically every time you run `git commit`. ## Automated tests and code coverage calculation We use [GitHub Actions][2] for continuous integration. Code coverage can be viewed interactively at [codecov.io][1]. [1]: https://codecov.io/gh/sourmash-bio/sourmash/ [2]: https://github.com/sourmash-bio/sourmash/actions ## Writing docs. Please see [the docs README](README.md) for information on how we write and build the sourmash docs. ## Code organization There are three main components in the sourmash repo: - Python module (in `src/sourmash/`) - The command-line interface (in `src/sourmash/cli`) - The Rust core library (in `src/core`) `pyproject.toml` has all the configuration to prepare a Python package containing these three components. First it compiles the Rust core component into a shared library, which is wrapped by [cffi] and exposed to the Python module. These steps are executed by [maturin], a modern [PEP 517]-compatible build backend for Python projects containing Rust extensions. [cffi]: https://cffi.readthedocs.io/ [maturin]: https://www.maturin.rs/ [PEP 517]: https://peps.python.org/pep-0517/ A short description of the high-level files and dirs in the sourmash repo: ``` . ├── benchmarks/ | Benchmarks for the Python module ├── binder/ | mybinder.org configuration ├── data/ | data used for demos ├── doc/ | the documentation rendered in sourmash.bio ├── include/ | C/C++ header files for using core library ├── src/ | │ ├── core/ | Code for the core library (Rust) │  └── sourmash/ | The Python module and CLI code ├── tests/ | Tests for the Python module and CLI ├── utils/ | ├── asv.conf.json | benchmarking config file (for ASV) ├── Cargo.toml | Rust definition for a workspace ├── CITATION.cff | Citation info ├── codemeta.json | Metadata for software discovery ├── CODE_OF_CONDUCT.rst | Code of conduct ├── CONTRIBUTING.md | Instruction for contributing to development ├── LICENSE | License for the repo ├── Makefile | Entry point for most development tasks ├── MANIFEST.in | Describes what files to add to the Python package ├── matplotlibrc | Configuration for matplotlib ├── flake.nix | Nix definitions (package, dev env) ├── shell.nix | Nix config for creating a dev env (backward-compatible) ├── paper.bib | References in the JOSS paper ├── paper.md | JOSS paper content ├── pyproject.toml | Python project definitions (build system and tooling) ├── README.md | Info to get started ├── requirements.txt | Python dependencies for development └── tox.ini | Configuration for test automation ``` ### The Python module (and CLI) ``` src/sourmash ├── cli/ | Command-line parsing, help messages and overall infrastucture ├── command_compute.py | compute command implementation ├── command_compute.py | sketch command implementation ├── commands.py | implementation for other CLI commands ├── compare.py | Signature comparison functions ├── _compat.py | Py2/3 compatibility functions ├── exceptions.py | Mapping from core library errors to Python exceptions ├── fig.py | Plotting functions ├── index.py | Index base class and definitions ├── lca/ | LCA index and utility functions ├── logging.py | Logging functions (notify, error, set_quiet) ├── __main__.py | Entry point for the CLI ├── _minhash.py | MinHash sketch implementation (calls the core library) ├── np_utils.py | NumPy utils ├── sbt*.py | SBT implementation ├── search.py | search functions for indices (search, gather) ├── sig | signature manipulation functions │   └── __main__.py | implementation for `sourmash sig` commands ├── signature_json.py | signature parsing code (to/from JSON) ├── signature.py | signature class and methods ├── sourmash_args.py | convenient shortcuts for CLI usage └── utils.py | Convenience functions to interact with core library ``` ### The Rust core library This is completely defined in `src/core` to avoid mixing with the code of other components (and trying to make it easier to reason about changes). If you're only working on the core, you don't need to change any files outside this directory. This is also published to [crates.io] (the Rust package repository) and [NPM], after it is compiled to Webassembly. The GitHub Actions workflow publishes new versions automatically to these repositories. [crates.io]: https://crates.io/crates/sourmash [NPM]: https://www.npmjs.com/package/sourmash ``` src/core ├── benches/ | Benchmarks for the core library ├── Cargo.toml | Crate definition and metadata ├── cbindgen.toml | Configuration for cbindgen (the C header generator) ├── examples/ | Examples using the crate API ├── README.md | Containing links to CI, docs and general info about crate. ├── src | │   ├── cmd.rs | High-level commands (search, index, compute...) │   ├── errors.rs | All the errors generated by this crate │   ├── ffi/ | FFI-related functions. They are exported to a C header by cbindgen. │   ├── from.rs | Conversion methods for other crates │   ├── index/ | Index methods. An index is a collection of signatures, optimized for searching. │   ├── lib.rs | Entry point for the library, control the exposed public API. │   ├── signature.rs | Signature methods. A signature is a collection of sketches. │   ├── sketch/ | Sketch methods. A sketch is compressed representation of data. │   └── wasm.rs | Webassembly API. └── tests/ | Integration tests (using the public API of the crate) ``` ### Exposing new functions on the FFI If you change anything in `src/core/src/ffi` (where the boundary between Rust and C is defined) you need to regenerate the `include/sourmash.h` header, and potentially fix any differences in the Python CFFI layer (which reads the C header file and expose functionality to Python). To regenerate the C header, run ``` $ make include/sourmash.h ``` This requires `cbindgen` (and technically a nightly Rust compiler, but we cheat with `RUSTC_BOOTSTRAP=1`. For more info check [this post]). `cbindgen` can be installed by running ``` $ cargo install --force cbindgen ``` [this post]: https://fasterthanli.me/articles/my-ideal-rust-workflow ### Changing code touching all layers: an example PR Luiz wrote a [blog post] describing a PR that changes code at the Python API down to the Rust code library, including some tools for evaluating performance changes. [blog post]: https://blog.luizirber.org/2020/01/10/sourmash-pr/ ## Versioning Versions are tagged in a `vMAJOR.MINOR.PATH` format, following the [Semantic Versioning] convention. From their definition: > Given a version number MAJOR.MINOR.PATCH, increment the: > > MAJOR version when you make incompatible API changes, > MINOR version when you add functionality in a backwards compatible manner, and > PATCH version when you make backwards compatible bug fixes. [Semantic Versioning]: https://semver.org/ The Python version is not automated, and must be bumped in `pyproject.toml` and `flake.nix`. For the Rust core library we use `rMAJOR.MINOR.PATCH` (note it starts with `r`, and not `v`). The Rust version is not automated, and must be bumped in `src/core/Cargo.toml`. ## Common errors and solutions ### Cannot import name `to_bytes` from `sourmash.minhash` If you are getting an error that contains `ImportError: cannot import name 'to_bytes' from 'sourmash.minhash'`, then it's likely you need to update Rust and clean up your environment. Some installation issues can be solved by simply removing the intermediate build files with: ``` make clean ``` ## Additional developer-focused documents ```{toctree} :maxdepth: 2 release requirements storage release-notes/releases dev_plugins ``` sourmash-bio-sourmash-bc3c0f8/doc/faq.md000066400000000000000000000402301474157046300203230ustar00rootroot00000000000000# Frequently Asked Questions (FAQ) ```{contents} Contents :depth: 3 ``` ## How is sourmash different from mash? [mash](https://mash.readthedocs.org/) is an awesome piece of software that inspired sourmash - hence the name we chose for sourmash! But are they the same? No, they are not! mash is based on [MinHash sketching](https://en.wikipedia.org/wiki/MinHash), a technique for randomly selecting a very small set of k-mers to represent a potentially very large sequence. MinHash sketches can be used to estimate Jaccard similarity (a distance metric that lets you find closely related sequences) with very high accuracy, under one condition - that the two sequences be of similar size. So, MinHash sketching is great when comparing bacterial genomes, which are all around 2-10 MB in size. But... MinHash sketching doesn't work when comparing _metagenomes_ to genomes, because metagenomes are usually _much_ larger than genomes. So we built sourmash around a different kind of sketch - FracMinHash - which can estimate Jaccard similarity between sets of very different sizes. FracMinHash sketches also support overlap and containment analysis, and are convenient in a variety of other ways - see our paper on FracMinHash, [Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2). There are some drawbacks to FracMinHash sketches - read on! ## What are the drawbacks to FracMinHash and sourmash? There are two drawbacks to FracMinHash relative to MinHash. Neither one is a showstopper, but you should know about them! One drawback is that FracMinHash sketches can be a _lot_ bigger than MinHash sketches - technically speaking, MinHash sketches are bounded in size (you pick the number of hashes to keep!) while FracMinHash sketches can get arbitrarily large. In practice this means that when you're sketching large metagenomes, you end up with large sketches. How large depends on the _cardinality_ of the metagenome k-mers - if a metagenome has 1 billion unique k-mers, a FracMinHash sketch with scaled parameter of 1000 will contain a million hashes. The other drawback is that FracMinHash sketches _don't work well_ for very small sequences. Our default parameter choice for DNA (scaled=1000) works well for finding 10 kb or larger matches between sequences - some simple Poisson matching math suggests that about 99.98% of 5kb overlaps will be found with scaled=1000. ## How can I better understand FracMinHash and sourmash intuitively? Please see [the k-mers and minhash tutorial](kmers-and-minhash.ipynb). ## What papers should I read to better understand the FracMinHash approach used by sourmash? I would suggest reading these four papers, in order: [Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2), Irber et al., 2022. This is the fullest technical description of FracMinHash available. [Mash: fast genome and metagenome distance estimation using MinHash](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0997-x), Ondov et al., 2016. This is the original paper that inspired sourmash. It discusses sketching with MinHash and does a great job of showing how well Jaccard estimation works for comparing genomes! A good contrasting point to take into account is that _MinHash cannot do overlap or containment estimation_, which nicely motivates the previous paper and the next two. [Mash Screen: high-throughput sequence containment estimation for genome discovery](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1841-x), Ondov et al., 2019; and [CMash: fast, multi-resolution estimation of k-mer-based Jaccard and containment indices](https://academic.oup.com/bioinformatics/article/38/Supplement_1/i28/6617499). Both papers discusses containment and metagenome analysis extensively, and use an approach that can be usefully contrasted with sourmash. There is a nice blog post on [mash screen](https://genomeinformatics.github.io/mash-screen/) that is worth reading, too! If you want a nice chaser, please see this section at the end of the blog post above: >It would be great to see additional methods developed to process containment scores, reduce the output redundancy, and report accurate compositional estimates for metagenomes. One easy approach is a “winner take all” model, like sourmash implements. ## What k-mer size(s) should I use with sourmash? The short answer is: for DNA, use k=31. Slightly longer answer: when we look at the k-mer distribution across all of the bacterial genomes in GTDB, we find that 99% (or more) of 31-mers are _genome_, _species_, or _genus_ specific. If you go lower (say, k=21), then you get a few percent of k-mers that match above the genus level - family or above. This is useful for fuzzier matching, e.g. if you have a metagenome with a high fraction of unknown k-mers. If you go higher (k=51), a higher percentage of k-mers are genome-specific. This can be valuable in situations where you have highly specific reference genomes (e.g. isolates, single-cell genomes, or MAGs) that should match very closely to this metagenome. For the core sourmash operations - search, gather, and compare - we believe (with evidence!) that (a) the differences between k=21, k=31, and k=51 are negligible; and that (b) k=31 works fine for most day-to-day use of sourmash. We also provide [Genbank and GTDB databases](databases.md) for k=21, k=31, and k=51, so choosing from those k-mer sizes for your own sketches will allow you to directly use those databases. For some background on k-mer specificity, we recommend this paper: [MetaPalette: a k-mer Painting Approach for Metagenomic Taxonomic Profiling and Quantification of Novel Strain Variation](https://journals.asm.org/doi/10.1128/msystems.00020-16), Koslicki & Falush, 2016. ## What scaled values should I use with sourmash? We recommend scaled=1000 or scaled=10000 when working with bacterial and archaeal sketches and DNA. We have quite a bit of experience with this, and even some [published benchmarks](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-05103-0) showing that this works very well. You may need to use lower scaled values with smaller query and target sequences, such as viral genomes or genes, but we do not have systematic advice on this. That having been said, you can always use a lower scaled value - the only consequence is that memory and compute requirements increase. Also, sourmash will automatically use the larger of two scaled values when comparing two sketches with different scaled values. So if, for example, you use [the precomputed databases](databases.md), you will always end up using your query sketches at a minimum scaled of 1000, even if you created them with a lower scaled value. Please also see [What resolution should my signatures be?](using-sourmash-a-guide.md#what-resolution-should-my-signatures-be-and-how-should-i-create-them). ## What threshold-bp value should I use with `sourmash prefetch` and `sourmash gather`? The parameter `--threshold-bp` sets the minimum estimated overlap for reporting a match, in both the `gather` and `prefetch` commands. The default is 50kb, and this works well for microbial-genome-scale work, where the genomes are often quite large (one or more megabases). In case you need more sensitivity, setting `--threshold-bp=0` will return any match that shares at least one hash. This will also increase potential false positives, however. We have found a good intermediate threshold is 3 times the `scaled` value, e.g. `--threshold-bp=3000` for a scaled value of 1000. This requires at least three overlapping hashes before a match is reported. If you are using a lower scaled value (a higher density sketch) because you are looking for matches between shorter sequences, then setting threshold-bp to 3 times that scaled value will take advantage of the increased sensitivity to short matches without introducing more false positives. ## How do k-mer-based analyses compare with read mapping? tl;dr very well! But it's a bit one sided: if k-mers match, reads will map, but not necessarily vice versa. So read mapping rates are almost always higher than k-mer matching rates. ### Mapping reads to reference vs k-mer "detection" or containment Let's start by looking at a simple example: suppose you have Illumina shotgun sequencing of a new isolate, and you want to compare it to a reference genome for a member of the same species. You calculate k-mer containment of the reference genome in the isolate shotgun sequence to be 65%, using e.g. `sourmash search --containment ref.sig.gz isolate.sig.gz`. You then map the reads from the isolate data to the reference genome. What should you expect to see? What you should see is that 65% or more of the reference genome is covered by at least one read. This is known as the mapping-based "detection" of the reference genome in the read data set, and k-mer detection typically *underestimates* mapping-based detection. (If you want to know _how many of the reads will map_, you need to use a different number that is output by `sourmash gather` - although, for single genomes with only a few repeats, the percentage of reads that map should match the k-mer detection number you calculate with containment. Read the section below on metagenome analysis for more information on read mapping rates!) ### Why do k-mers underestimate read mapping? K-mers underestimate read mapping because k-mers rely on exact matches, while read mapping tolerates mismatches. So unless you are looking at entirely error free data with no strain variation, mismatches will sometimes prevent k-mers from matching. For some math: consider a k-mer of size 21. It will only match to a specific 21 base sequence exactly. If you are matching two isolate genomes that align completely but have a 95% average nucleotide identity, then one out of every 20 bases will be different (on average), and, on average, every 21-mer will be different! (In practice you'd expect about 1/3 of the k-mers to match if the variation is random, since there will be many 21-base windows that don't have any mismatches.) However, alignment-based approaches like read mapping will happily align across single-base mismatches, and so even at 95% ANI you would expect many reads to align. In practice, the numbers will differ, but the intuition remains! ### How do read mapping rates for metagenomes compare with k-mer statistics? Shotgun metagenome sequencing adds several complicating factors when comparing metagenome reads to a reference database. First, each genome will generally be present at a different abundance in the metagenome. Second, metagenomes rarely contain the exact strain genome present in a reference database; often the genomes in the metagenome differ both in terms of SNPs _and_ accessory elements from what's in the reference database. And third, metagenomes often contain complicated mixtures of strains - even if one strain is dominant. The `sourmash gather` method is built for analyzing metagenomes against reference databases, and it does so by finding the shortest list of reference genomes that "cover" all of the k-mers in the metagenome. This list is arranged by how many k-mers from the metagenome are covered by that entry in the output: the first match is the biggest, and the second match is the second biggest, and so on. We call this a "minimum metagenome cover" and it is described in the Irber et al., 2022, paper below. When we construct the minimum metagenome cover, it correlates well with mapping (per Irber et al., 2022), with one caveat: you need to assign reads to the reference genomes in the rank order output by gather. This is needed to properly assign reads that map to multiple genomes to the "best" match - the one that will capture the most reads. `sourmash gather` is still a research method but it seems to work pretty well - in particular, it is both highly sensitive _and_ highly specific in taxonomic benchmarking. Please ask questions as you have them! ### Further reading and links on k-mers and mapping: * The paper [Biogeographic Distribution of Five Antarctic Cyanobacteria Using Large-Scale k-mer Searching with sourmash branchwater](https://www.biorxiv.org/content/10.1101/2022.10.27.514113v1), Lumian et al., 2022, shows that k-mer detection almost always underestimates mapping, and k-mer abundance analysis is always more conservative than mapping-based analyses. * The paper [Deriving confidence intervals for mutation rates across a wide range of evolutionary distances using FracMinHash](https://genome.cshlp.org/content/33/7/1061), Rahman Hera et al., 2023, shows how to translate between average nucleotide identity (ANI) and k-mer statistics. * The paper [Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2), Irber et al., 2022, describes how `sourmash gather` assigns k-mers from metagenomes to a set of reference genomes, and shows that read mapping correlates pretty well with k-mer overlap. Note that it is focused on *systematic decomposition of metagenomes against reference databases*, so it tackles the question of analyzing an entire metagenome against all available references, not just a single matching genome. ## Can I use sourmash to determine the best reference genome for mapping my reads? Yes! (And see the FAQ above, [How do k-mer analyses compare with read mapping?](#how-do-k-mer-based-analyses-compare-with-read-mapping)) If you're interested in picking a single best reference genome (from a large database) for read mapping, you can do the following: * sketch all your reference genomes with `sourmash sketch dna`, and/or download one of our [prepared databases](databases.md). You can use default parameters for `sourmash sketch`. * sketch your read collection with `sourmash sketch dna`. * run `sourmash prefetch reads.sig -o matches.csv` * sort `matches.csv` on the `f_match_query` column and pick the highest value - this is the k-mer detection - and pick the match name from the `match_name` column; * use that reference genome as your mapping target. If you want to map a metagenome to _multiple_ references, consider using `sourmash gather` and/or [the genome-grist workflow](https://dib-lab.github.io/genome-grist/). (This is also known as "read recruitment.") ## How do I get the sequences for a particular reference genome from a metagenome, using sourmash? If sourmash reports that a particular strain or genome is present in a metagenome, how do you retrieve the reads using sourmash? The short answer is: you have to use a different tool. You can do read mapping between the metagenome and the relevant reference genome (which can be automated with [the genome-grist workflow](https://dib-lab.github.io/genome-grist/); or, if you are interested in retrieving accessory elements, you can try out [spacegraphcats](https://spacegraphcats.github.io/spacegraphcats/02-spacegraphcats-use-cases/). ## How does memory usage for sourmash change with k-mer size? sourmash hashes k-mers into 64-bit numbers, so the size of what is stored is independent of the k-mer size. The only impact of k-mer size on sourmash behavior is then more on the biology side - how many matches do you gain (or lose) with that k-mer size? And do you have a lot of new k-mers that pop up with a longer k-mer size (e.g. because of included variation)? These questions must be answered by experimentation and may be data-set specific. ## Can sourmash run with multiple theads? sourmash is currently single-threaded, but the [branchwater plugin for sourmash](https://github.com/sourmash-bio/sourmash_plugin_branchwater) provides faster and lower-memory multithreaded implementations of several important sourmash features - sketching, searching, and gather (metagenome decomposition). It does so by implementing higher-level functions in Rust on top of the core Rust library of sourmash. As a result it provides some of the same functionality as sourmash, but 10-100x faster and in 10x lower memory. Note that this code is functional and tested, but does not have all of the features of sourmash. Code and features will be integrated back into sourmash as they mature. sourmash-bio-sourmash-bc3c0f8/doc/funding.md000066400000000000000000000011571474157046300212130ustar00rootroot00000000000000# Funding Sourmash development was initiated under award GBMF4551 from the Gordon and Betty Moore Foundation to C. Titus Brown. Further development was sponsored by: * [NSF DBI-2018911](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018911&HistoricalAwards=false), BBSRC-NSF/BIO:Collaborative Research: genomeRxiv: a microbial whole-genome database and diagnostic marker design resource for classification, identification, and data sharing. * [NIH 1R03OD030596-01](https://reporter.nih.gov/search/aktj48mYz0ObXo62p_Tx7Q/project-details/10112077), Large-scale annotation-free disease correlation analysis of the iHMP. sourmash-bio-sourmash-bc3c0f8/doc/genomes/000077500000000000000000000000001474157046300206705ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/doc/genomes/akkermansia.fa000066400000000000000000017205431474157046300235020ustar00rootroot00000000000000>CP001071.1 Akkermansia muciniphila ATCC BAA-835, complete genome AAATCTTATAAAATAACCACATAACTTAAAAAGAATTATGCGGATTTTTAATCCGTCCCCGGCTACGGCTTCTCCGAGCCTTTCGGGTTTAGCTGCTCTGATGATGCGCCGCAGCATCTAAAACCCATTTCCCCCCCCTCCGCCGGATTTCCAAAAACAATGCGGTTTTTTATTTTCCAGTACCTGTCCGGTATCGGAGCCTTCACTTCATGCGGCATCCTGAACGGCAAAATCCTGAGAAAAGAAAACGCAGCTTCTCACAAGCGAGATTCCAATCCAACATTCATATCCCCGTGTGGATATGTGCACCGGAATAACAGTACTCCACGGTTTTCCAGGGCGGAAAGTGCCGGGGGGAGGCTTCCAATTGTCAATCAAGCTGTGCATCATTGTGCATGCTTTTTTGACCATATGATACCCTTTTTCACTGATTTCCAGCATGATGTGCAATATATGCACTAAAAGTAACAAGCCCCTTAAACCGTTGGATTTAAGGGGCTTTGAAGATGGTAGCAGGGAGGTGATTTGAACACCCGACCAAAGGCTTATGAGTCCTCTGCTCTACCACTGAGCTACCCTGCCGTTAAGTGGTTGATGCCGTAAGGCGGAGAAATATTTAACCCGGATCGGAAAGAATGTCCAGATTTTTTTATGGATCAGGAGCATTTTATCACGGCAACCCGCCTTCCCCTCCAGAGAAATCATTACGGATGAAGCATTTTTCACTCAACCGTTTCATACAGCCTGTGTCCCATGCCGTTTCCTACATGCGGGCATTTACGGCACAGGACATTATGATGCCTTCTGTCCCATGCCATTGCCGCAGCAGGACAGGAAATAGAGGCCCATAAAGATTTTCGGGTCCACCACTTTTCCCTGTCCCATCTGCTCCATCAGCCAGTCATGCACCTGAGGAAGAGGAATGCGGTGCACAGTAATATTTTCGTGGGCCACACCGCCTCCTGCCGATATTCGATGCAGGTCTTCCGCCAGATAGAAGGATACCATTTCCGTAGTCAATCCTGGGGAAGAAGGCCCCGCAAAAAGATACGTCCAGGAGTCCGCCCGGTATCCGGTTTCCTCTTCAAGTTCACGCCGGGCGGATTGCAGCGTGCTTTCCTCCCCTTCATCCCCGGAAATGCCCGCAGGCAGCCCTACGGTCCGGGCATGCAGGGGAACGCGGTATTCTTCCACCAGCAACAGTTCTCCTTCTTTCGTCACGGCCACGATCATGACAGCTCCGCTGGCATTAACCCGGCGCACGTATTCCCACCTCCCCTCTTTCAGAAGTTCCAGAAATTTCCCCCGGTAGAGAGACAGGCATGCTTCCCCTGGCTGTTCCATTTGTTCCATGATGCAGCCACGTTAGGTTTCCGGACGGCCGTTGTCAACCATGCGGACCGCCTCCCTCCTGCATCTTCCCGGACAGTTCCGTTCCGGTCCCTGTAAGTGGTTGATTTCCGGCGGCGTAATCATGCACTTTTCGTCACCCAGGCCAGTAACAAAACAACAACACATTGATGATGAGTAATTAACAATGTCATACAGAAAATATACTCAGGAGGACTAAATTATCGGAGGAAGCAAGAACAAAATATTTTTTCATGTGGAACCCCCGTGGAACGTAGAGCCGCAGAACGATGACTCGCGCTGTTTCAAAAAGATATTTTTTCCTCAGACGCAAATCAGTAATTTGGCGCTCCCGTCAACCGGGGTGTTGAGCTGAAAGCGGATGAGTTCGCCTTCTCCACCCCGATGGACGCCACGAGCTGTCACGGCTGCGGATGAACCTGATCCGGTTCCATCCGCGGGCGGCCTCGTAGCGGGACGGAACGCCTTACGGCTATTTCTAACCAGCAACATCTTTTATTTCCAATAACAATGACTCCGCCATCAGAACTGTCAGCCACCTGGTCCAAAATCACGGGAGCCTTGCAAACAATCATGTCCCCGGAGGTTTATGGATTGTGGTTTCCCAAGTTTTCCCTGCTGGAGGATTCAGGGAAGGCGCTGACGCTGGTATGCGACGATCCTATGGCCGCCCTCTGGGTGGAAAACAGCTATACGCCGGAGCTGAAGCAGGCCGCCATGCTGGCTCTGGGCACGGAACGCCAGATCAAATTCGTCTGCACAGATGAAGTGGCTTCCATGCCCGCTGCAGAAGGCCAGCCGCAAAAAGCTTCTTCCCGGGCCAAAACGCCGCAGGCGGCGGATTCCCCGGCCGCACCGGTCAAAAAACAGCGTACATCGGCCGCCAGGGCATGCCTGAATGACTTATATACGTTCGATTCGTTCGTGGTGTATGAGGACAGCCGTTTTGCGTACCAGGCCGGACTGTCCGTCGCCCAGTCGGAACGGGCCCTGTTCAATCCACTTTTCCTGTACGGTAAATCCGGCGTTGGCAAAACCCATCTTCTCCAGGCCATTGGCCATGAGGTGCTCCATCAGGATTCATCCACAAATGTGGTGTACGTCACGGGCGAACAGTTCGCCAATGAATTTATTGACGCTTCCCGAACCCAGAACGGACAAAGCTTCACCAAACTGCGCCGCAAGTACCGCAAGGCAGACGTCCTGCTGGTGGACGACGTGCAATTTATCTCCGGCAAGGAAAAAACGGTGGAAGAATTCCTGCATACGTTTGACGAACTGTTCCATGCCCACAAAACCATCGTGATTTGCGCGGATGCCGCCGCATGCGATATTTCCAATCTGGACCCCCGGCTGGCGGCGCGCCTGGAATCAGGCCTTACGGTAGAATTGAACCTTCCGGACGACAATGCACGGCTGGAGATCCTGCGCAGCAAGCGCGACCGGGCCGGCATGAACGTATCCGACGAAATTCTGGAGTTTCTGGCAAGCCGCATCCAGAAGAGCGTGCGCCGCCTGGAAGGAGCACTGCTCCGTGTAGCCACCTTTACTTCCCTGTCCGGAGACATGCCTGACATCGCCAAAATTGAGCAGCTTTTACGCGACATCCTGCGGGAAGAAACAAGCCGCATCCTGACCGTAGATTCCATCCAGAAGCGCGTGGCGGATTTTTACGAACTCAAGGTCAGCGACCTGACCGGCAAGCGCCGCCCCAACAGCATCGCCTTTCCGCGCCAGATCGCCATGTACTTGAGCCGCCGCCTGACGGAACGCTCCCTGAAGGATATCGGCCAGGCGTTCGGCGGCCGGGATCACGGCACCGTCATCCATGCGAACAAGCTGGTCGCCTCCCGTATGGAAGAGGATGTGCGCGTGAGGGATATCGTCCAGAGGCTGGAAGAGGAGTTGAGGGATTGATTTTCCTTCCCTCCAGCCCTGCTGGAAAAGCATGGTCCCGTTGAATGCCCATGGCTTTTCAAGCGGAAGAAGCGCTTCCGCGGCGCAGGGGAGAATAAGCTTGAGGCGAAAGCGGTTTGGATTAGAATCACCCTGTTCCCATTGATTCCATGATCCACGCCGACCAGCTTTCCAAAGAGTTTGGCCGCTTTCAAGCGGTAAAGAATGCCTCCTTCCAGATTGAAAAGGGGGAAATCGTAGGGTTCCTGGGGCCGAACGGCGCAGGCAAGACCACGACTTTGCGCATGCTGACCGGCTATCTGCCCCCCACTTCCGGCACAGCCACGATTGCCGGGTTCGACATTGTGAAAAATCCCCTGGAGGCTCGCAAACATCTGGGCTACCTGCCGGAACACGTGCCCCTTTACGACGACCAACGCGTCACGGAATACCTGAAATTCCGCGCCAGGCTTAAAGGCATCTCTTCCAGGCAGATCTGGCCTGCCGTCTCCAAGGTTGTAGAACAATGCGGCCTGGATCCCGTGCGCCGCAAGATGATCCGCACCCTTTCCAAAGGCTACCGCCAGCGCGTAGGCCTGGCGGACGCCCTGCTGGGGGAACCGGACCTGCTGATCCTTGACGAACCTACCAACGGTCTTGACCCCAACCAGATACGCCAGATACGCGAACTGATCAAAGGTCTGGCGGCCAACCATACCATTATTCTTTCCACCCACATTCTCAGTGAGGTGGAAATGATTTGCAACAAGGTCATCATCATTGACCAGGGAACTATCAAGGCAGCGGATACCCCCTCCAACCTGACGGCCAATCTACGGGCAGCCGGCAAGATTACTCTGGAATTCCGGGGAGACCTGGCCCTGATTACGCGGAAACTGGAAAACCTGGAACATGTGAAGAAGGTCATCCATGAAGGAACGGACGCCGACGGCTGGCATACGCTGACCGTACGCGCGGAGGCCGGGACGGATACCCGTGAAAAGGCTGCGCGCCTCCTTGCGGAACAGGGCTGCCCCCTGCGCCATATTTACCGCCACACCCCCACGCTGGAAGAAGTGTTCGTGGAAATGACCCGTAAAGATTAACCCCCCCTTTTTCCATCCCCGCCTATCCACCACCGCCCATTTCCCTTTTCTCCCCATGTCTCCAGTACTTACCATTTTCAGAAAAGAACTCAGAAGCTACGTAATGACTCCGTACGGATGGGTTATCCTGGCTTTCGTCATGGCCCTTCAGAGCGTCTCCCTGTCCGGCACGCTGAAGGCTTTTCAACTGGCCCCGCAGAAGGAAGGCATCCTGTTCTTCATCCTGCATTCTCCCATGTTCTGGTTTTACTTCCTGTTCATCTTCCCTCTGCTCACCATGCGTTCCCTGGCGGAGGAGGAAAAGACGGGAACACTGGAATCCCTGCTCACCACGCCCATCAAGACATGGCAGGTGGTTCTGGGAAAATACTTCTCCGCATACGCCTTTTACATCATCCTGTGGCTGCCCATGCTTCTCTATCCCATCCTGGCGGACTGGTCCAACCTGATTGTGCAGTGGATTTACGGATATGACGCGGGCATGGTTCTGCCTTACCGCCTGGCGGACTGGGCCGGGGCATACGCCATTCTGCTGCTGGTCGGCGCCTGGTTCACGGCCATCGGCATTTTCGCCTCCTCCATGACGGGCAGCCAGATTATTTCCGGCATCATCACCATCGGCCTGCTGGTCCTGATTTTCTTCATGGGTCTGATTCCCGTGGTGTGGGGGGAATTCCCCGCAGCGGGGATTTTCCACTACATGTCCTGCTCGGAACATCTGGACCGCTTTTCCGCCGGGCTGGTTGACACGCGCCCCGTCGTGTTTTACCTGACCATGACAGTCCTGACGCTGGCCGTCACCATCCGCATCATTGACCACCGCCGCTGGAAACACTGACTCATCCCTCCTTCACCACTTTTCATTTTTATCTATGAGCGAAACACCAGACACCCCCGCCGCGGCCGCAGAAGCCTCCCAGCCCGCCGCCCGCAAGGTCAAACGCCCCTGGATGACCTGGATCAAGCTTTTCCTGCTGTTCCTTATCGTCGTATGCCTGAATTACGTAGGCTGCCACGAGTATTACCGCCGGGACCTGACGGAAGACCAGCGTTATGAAATTTCCCGTCAGAGCATCAACATGCTCCAGTCCCCGGAAATCCAGAAGCGCAAAACCCCCGTCAAAATCACGTTCGCTTTCCTGCGCACCACGCAGAACTACACCCGCATGCGTTCTCTGCTTGAGGAGTACGAACGTTATTCCAACGGCAAGGTGAAGGTGGAGTATGTGGATCCCCTCCGCCAGCCGAACAAGGCCCGTGAAATCTCCAATATCTACGGAATTGAATTCAAGAAGAACCTGGTCATCATTGATGCCCGGGAGGATACGGAAAAAGCGCTCAAGACGTTTGAAGGCACCCAGGCGGACGCCGCCCACGTGCGCATCCTGCCCGGAGACGCCTTCGTAGTATACGCACCCGGGCCGGACGGCAAAAGCATGAAGGCAGTGGCGCTCCAGATTGAAGACATGATGACTGCCGGCATTTACGGAGCGGCCAACGGCGAACCTCGTAAAATTTATATCGCGGCGGATAAGAGCAACTTCAACGAGTCCCTGAGCAACAACCAGGAAGAAAGCATTTTCACGACGCTGGGCAAAATCTGCCGTTCCGTCAACCTGCAGCTTGTTCCCATCCGCATGAGCGGTCTGGAAGAAATTCCGGAAGACGCCGCAGGATTCATGATTATCGGTTCCAAATATGATCTGTCCCCGCAGGAGGCGGAAGTGCTCCAGTGGTACTGGGCGCGCCCGAACGCCGCCATTCTAATCATGCTGGAACCCCAGAATGACACACCCAAACAGCTTTACCGCTTTCTCCGCGAACAGGGGCTACGGCCCCAGAATGACCGCGTGATGCTCCGCAACAGGGGCAACCGTTCCGTTTTTGAAATTAACTCCATTTTCGCCCCCTCCCTGAATTGCACCCGTGAATTCTGGAATTCCAGCACCGGACTGGAAGGGGAGAGCATCTCCCTCATTCTGGATTCCGACAATGCGGCCATGGAACAGAAGCGCATTACGCCATACCCCCTCCTGGTCACAACGGAGGATTATTACGGAGAAACCAAATACAACCAGTTCCCTGCCCAGTTCGACGCAAGGGAAGACAATCCGGGCCCTCTGATGATCGGCGCGGCCCTCATCCGGGGGAATGCCGGGGACGTGAACCAGAACAAGACTACCGGGCGCCTGGTTCTGCTTGGCAATACGGACCTGCTCCAGCCCCGGCAAATCAAACCGGAACAGAGGGATTTCATGCGTACGCTGATCGGCTGGATGACGGACCGTGAAGAATTGCGTGGCCTCGGCTCCCGCCATGACCTGACCGTCAAGCTGAATCTGGATCGCAACGCCCTGGGCGTTTTGGAACTCCTGACGAATATCGGACTCCCCCTGCTGGCGCTGCTGATCGCCCTGATTATCTGGAACACGCGCCGTCATTAACTCCACCCACGCCCATTACCGGAAGCACATGCGCACATTCCGTTTTATTCTTCTCATCCTCATCACCCTCGCTTCTGTCGGAGCAGCGGTGCTCCTCATGATTGACGGCAATCTGTCCCGCATCATCGGGCGCACCGCTTTCAGTTCCGGAGAACGGCTTTTCCCGTACACCCGGGAAGAAATGAACGAGGTTTCCTGGATGCGCATCAATTGCGTGGGAGATGTCGCGGAATTCCGCCGCAAACCCAACGGCGTCTGGTGGTGCGAAAAGCCCTGGAACGACCGTATGGATCCTCGCGCGGCGGCGGCCATTCTCCAGTACACGTACTCCACCAGCATCGTGGACGCATTGCCCCTGCACAAGATTGATTCCGCATCCCTGAAGGAATTCGGCGTGAAAACCACTCCCGTTACCATCACTCTCAAGGAAATGAGCGACGACGGAAAGCGTTCCTCCACTGTGGCGCGCTATACGCTGGGTTCCCCGGCGCCCTGGCTGGTGGACGATCCGGAGAACAAGACCACGGATGACACCACCTATATGCAGACGGATTTTTACGGCCGGGACACCCGCATTCTGGTGGGCACCGGCAACATCCTCCCTCTGTTCAAATCCGGCATCCGCCAGCTTCGGGACCACCGTCCCCTGCTACTGCATCCGGCCATGCCCGCCTCCATTGAAATCAACAACCGGGGCCAGCGCATCGCCCTGGAGCGCAAGACTCCTGGCTCCCCCTGGAAAATCACGTATCCCCTCTCCCTGGATACGGACCCGGCCATGATGGATGTGCTGCTGGGCACGCTCCAGAAGCTGACCGCCGTGCGCGTGTACAACCCGGAGGAAACGAGCGTGCCGGATATGACGGATGATCAAATCACCTCCGTCTCCATCAGGAATTTCACAGGCCGCCTTTCAGGGGACGGCAAATCCCTGCAAATGGAGGAACAGCCCGTCACGCTCCGGATTTATCCTCCCTCGGACAACGGAAGCCTGTCCGAACTGGTCAAAGCCACCGTTTCCGACCGTAAGGCCGTCTTTGAATTGGCGCAAACGACGGAAACCAACAAGGAAGTTCCCGGCGTACGCAATATTCCGCTGGATCTGGGCCTTCTGCGTTCCAAGCAATTGACGGACATCGGGGATTACAAGATTACCGGCCTTTCCATCCGTAAAAGTTTTCAGGATTACCCTACCATCGCCCGCTTCATTCAGGGGGATGAGAAAAAAGGCATCAAGCCCACCTGGCTGTACACGGTAGAGGGCGCCCGTTACCAAGAAGCTAATCCGGAGCATCTCATTTCCCTGCTGAAAACCGTTAAACAGGGAAAAGTGGCCGGCTTCGCTTCGGACAAAGTTACGGACCTTTCCGTGTACGGGCTGGATAATCCCAGAATGACGCTCACCATGTCCCTTCTGCCCAAGCCCGGTGAAGAGCCACGCCCCCCCGTGACGGTGTTTTTCTCCAAGGGTGCGGACGGTTCCTGGTATGCCCGCCAGGCCGGAAAACCCACCGTCGCCATACTGGACAACGCCTACATGAAGGATTTGCTGACGGATGCACTGGCCTGGAAAAAAAAGAGGCTTTTTTCCTTCAGCAGGTTTGACCTCAGAGAAATGCACCTGGAACGCATCGGTTCCGGAGGCCCCCTGGTTCTGAAATTCGACCGTCTGGACGATTCTTGGACAGCCTCCAAAGACGGGAAGGATGAAACCTTGAATATCAACCCGAACCGCGCCAACCGCTATCTGGATGAACTGGAAAAAATGGAGGTGAGCGCCTGGCTCCCCTATACGGACGCCACAGCGCATGAAGCCCTGAAAAATCCCGTTTTCCGCCTGAAACTCATCCTCCAGTCTTACAAGGAGGCGGCCCCCGTGCAGTCCCAATCCGGTTCCAGCGACATTACCTTTGCCCCGGAACCGGAAACGGAGGACAAAGTCATCACGCTGGAAATCGCCCCGGCGGGAGAGGCCGGATACAGCAGATTTTATTACGGCAGAGTAAATACAACCCCGAATTATTTTATCCTGAACATGGACGCCGTACGTCTGCTCGGCGCCAGCCTTTCGGAAGACAACTAACCACTCCTCCCGCATGAGCAAGATTGAGCTGCACACCGCCATCTCCTTTCTGGACGGAATCCTCTCCGTGAATTCCATTCCGGACGCCCCCCGCGCCCTGAACGGCCTTCAACTGGAAAACGGGGGCACCGTTTCCAAGGTGGCCGCCGCCGTGGACGGCTCAGAAAAGGCCATTCATGCGGCCTTGGAAACAGGTGCGGACCTGCTCCTTCTTCACCACGGCATTTTCTGGCAGCCCATGCAGCCCATTACCGGCATCGCCTACCGGAAGCTGAAAGCCGCCATGGACGGGAACCTGGCGATTTACGCCGCCCACCTTCCTCTGGACGTCCACCCCGCATACGGCAACAATGCCCTGCTGGCGAAGGCCTGCGGCCTCCGGCCATGTTCCCATGATGGGCTGGACTACCACGGCGTTTCCCTCGGTACGCACGGAGAATTTCCAGGTACATGCGCGGAATTGGAGCAAAAACTGGAAACCGTCCTCGGCGCGCCCGTGCAGGCCTTCTGGAAAGATTCCCCGGAGGCTCCGGCCGGAGATATCTTCATCTGTACCGGAGGCGCGGGAGACGATCTTGCCCAGGCAGCGGCCCTCGGCTGCCGCACCTACGTGACCGGGGAAGGCTCCCACTGGAACATCCCCCTGGCCCAGGAACTGGGCGTCAATCTGGTCTTCGGCGGCCACTACTTTACGGAGACGTTCGGCGTAAAGGCCCTGGGGCTTCTTCTCAAGGATGTTTACGGGCTGGATTATACCTTTATTGACCTGCCGCCCTCCGCCTACAGCCATTGACCAAACGCCACGGAACATGAATCTGACTACGGACGACGCCGACGGGAAAATCCGCCTGGATCAATACCTTGCCGCCCACCTTCCGGAGCTTTCCCGCTCCAGGATCCAGAGCCTGATCAAAAGCGGTGACGTGTTGGTCAACGGCTCTCCCGCCAAGCCTAAAAACCCCGTCTCCCGCGGTGATTCCATCACCGTCCGCATTCCGGAACCGGAACCGGCGGAAGCCCAGCCTCAGGATATCCCCCTGGACATCCTGTATGAAGATGAAGACGTGGTAGTCATCAACAAGGAAAGCGGCATGGTCGTGCATCCCGCGGCCGGCAACCCGGACGGCACCATCGTAAACGCCCTGCTCCACCACTGCGGGGACCTCTCCGGCATCGGCGGAGTGGAACGCCCGGGCATCGTCCACCGCCTGGACAAGGACACCTCCGGCTGCCTGGTCATCGCAAAAAATGACGGCGCCCACCAGTCCCTGACGGCGCAGTTCGCGGCCCGAAGCACGGAAAAGCGTTATCTGGCCGTCGTCCAGGGTATTCCTTCCCAAAGTTCCGGCACGGTCTTCACCCATATCGGCCGCCATCCCGTCAATCGCCTGAAAATGGCAGTAGTCAATCCCGGTTCCGGGAAAGCCGCCATTACGGACTACGATTTGCTCTGCGCAGACCCGTCCACGGACTCCTCCCTGGTTCTGTGCACGCTGCACACGGGCAGAACCCACCAGATACGCGTGCACATGCTCTATCTGGGCCATCCGCTCATCGGGGATCCCATTTACGCCAAACCCGCCCGGCAAAAGGCAAAACCCGGCAGGCTCATGCTCCACGCGTGGCGGCTGGGCTTCGACCACCCGCGCACGGGAAAGCGCATGGATTTCGAAGCGCCCGTCCCGCCGGAATACACGCCGTGGCTCCAGCTCTTCCCCAACGGGCTTTACGGGGCCATCCCGGAGCTGCGCCCGGTACAGGAAGACGGAGGACGGAACTAAACCTCCCCGCCATTTCCCGGCAGCGGAAGCCGCTTCAACGGACAGAATACGGGAAAGCGCCATATTCAAATCCCAGAGCCGGCCCGGCCTTCGTCAGCAATCCGCCGTCATCCGGCGCCCCCATCCTTGCGGACAAAGATGCGGCAATAGACCCGTCGTTCACCTTCTTACGCCCCCTTTCCGGCAGGAGCGTCTACGGTCACAGTCAGCCGGGAACAATATTCTTTTCATCAATCAAGGAGAAATGAACCTGCCCGGCATCTTCCGCGCATGCAAGAAGAATCCGCGGCGGAGACCTCCCCTTTAATCTTGATTTGCCCCCCCGGAACCGCTACCCTGACACGGCTTTAACCACACAGCTTTCACAGAGACATGGAGACCACGCACCACGGGGAGGACGCCTCTCCGGACAATCCCCCCAGGCAGCAAAAAAGCGGAAACACGGATGAACAGACCATCGCCCTGACAAAGGCGCGTCTGGCCATTGACGAGGTGGACGCCCGGATTGTAGAGCTGTTAAAAAAACGCGCCGAATGGGTGCATGAAGTCGGCCGCATTAAAAAGGAAAAAAATTCCCCCATCTTCGTTCCCGAACGGGAAACGGCCCTGCTCAACAAATTGAACCGCCTGAATGCGGGCGTGCTGCCGGAAGCCTCCCTCCAGGCTATTTACCGTGAAATCATTTCCTGCTCCTTCTTTCTGGAAGGCGGCCTGACCATTGCCTACCTGGGCCCCAAAGGAACCTGGAGCCACCAGGCGGCCCTCAAGCAGTTCGGAAAAAGTTGCGAACTCATTCCGTGCCAGAGCTTCAAGGACGTATTTGACATGGTGGACCGGGGGAAGGCCCAGTACGGCGTAGTTCCCGTGGAAAACTCTTCGGAAGGTTCCGTCACCGCCGTGATGGATCTTTTCGTCACCTCTCCCCTCAAAATCTGCGCCCAAATCAATCTGAACATCCGCAACAGCCTGATGGCGGATATTCCGCGGGAACACATCCGCATCCTGTATTCCCACCCCCAGGTTCTCGGCCAGACGCGGAACTGGATCCAGCGGCATTTCCCAAACGCGGAACTCGTGGAAACGTCCTCCACCACGAAAGCCAGCATTCTTGCCAAGGAGAACGCCGCCATGGGCGCGGCATCCCTCGGCTGTCCGCTGGCCGCGGAATTGTTCGGCCTGAACATCCTGGAAGAAGACGTGCAGGACCAGTCCTGCAACACCACCCGTTTTGCCGTCATCGGACGCCAGGAAACGCAGCCCAGCGGCAGGGACCGCACCTCCCTGCTCATCCGCATCCAGCATAAACCCGGCACCCTGGCGGAAGTAGTCAACTGCTTCCAGCGGCACAACAATAACCTGATACGCATTGAATCCCGCCCGTCCAAAGTCATCAACTGGGAATACGTCTTTTACATAGATGCCGCCGGCCACATTCAGGAATCCCCCTTACGGGAAACCCTTCCGGAGCTGGAGCAGCACTGCTCCATGCTGAAGATTCTGGGCAGCTACGCGGATACGGACGTCATTTAAGCAGGTTCAGCCTTCCCCCCCAGCCTCACACCATTCTGAACAACCATGAACATAGCCACGATTGCCGCCCTGGCCGTCACTCTGGGACTTCCCGCCCCGGCCCAGCAGTCCGCCACTAAAATCATTACGGAAAACGGACAGCCTGCGCTGGCCCTGGACGAACAGTTCGCAGCCCGGCTGCGCGACGGCGGAATGAAGCAGGGCTCCCCGGCCACCGTTCCCGCGGAATTTTCGGCAGCAGCGCCCGGCCTCCCCCTCCCTGCGGATGCCAACCATGCCGCTGTCAGGGAAACGGCGGGGGAACGCAATCGGCGCATGGAATGGTGGAGGGATGCCAAATTCGGCATGTTCATCCATTACGGCCTGTATTCCGGATTGGCCGGGGAATGGAAAGGCAGGCCCGGAGGTTCCGAATGGATTCAAAAAAACGTGGAAGTGGATACGGATACCTACGCCGCGGAAGCTCTGCCCCTGTTCAAGCCCAGGGAAGGCCTGACGGAAGAATGGGCCCAGCTCGCCCGGGATGCAGGCTGCCAATATGTAGTGCTCACCAGCAAGCACCATGAAGGCTTCGGCCTGTTTGACAGCGCCTTGACGGATTACGACGCCAAAAGCGCCGTGAACCGGGATATCATCCGCGAATATGTGGACTCCTGCCGCAAGCGCGGCCTGAAAGTGGGCCTCTACCACTCCGTTATTGACTGGCATCACCCGTCCTATGACAACACCATCTGCCCGGACCTCTGCTACCCTGCGGGACAGGCGGCAATGCTTAACAGGAAAAACATTCCCCGCGACCACGCCGCCTACCAGAAATACCTGCACGCCCAGGTCCGGGAGCTGATGACCCGTTACGCGCCCATCGACATCATGTGGTGGGATTACTCCCAGGGAGCCATGGAGGGGGCCAAAGGCTGGAAGGCGCCGGAACTGATGGACATGGTGCGCTCCATCAATCCCGGCGTCATCATGAACAACCGCCTGTACGCTTATTCCGGCCTGAACAAAAACCAGGCCGGCACGCTGGACCTGCGCTGCGGGGACTATATTACCCCGGAACGCTTCATCCCCCGGCGCGGATATCCCGGCGTGGACTGGGAATCCTGCATGACGGTAAGCGACAAATGGGGCTACAACCGCTACGACACCAACATCAAATCTCCGGAAACCATCATTGAAAAACTGGTGGAATGCGTTACCAAGGGAGGCAATCTTCTGCTGAACGTCAATCCCATGGCGGACGGCACCATTCCGGAAAAAGTGGCCGCCACCATGCGCGGCGTAGGCCGATGGCTCAACATCAACGGTGAAGCCGTGTACGGAACACGGGCATTCATGCAGCTGGACCAGCCTGCCACCATCAATCGGCAGGGGGATATCTTCATCTTCCTGATTCCCCCGCCGGACAAGGAGCCGGCCCAGCCGCCCAGTGAAGGAACCATGAAAGAATTGACGGCGGGAGCGGAACACGCCCGCATGCAGCCGCTGGATGCCACCGGCTACGAAAATGACGGGGGAACAGCCGTCACCCTGCCCGCCGGCTATTCCAAAGCATTGCTTCTGGGAGACAACACCGCCCTTCCCGTCGTCAACGGAACGGTCCTGTTCAAGGCTCATGAACATTCCAAAACTCCCTGTTCCGTCATCAAACTGAGCAAATAGCCATTTTCCCCACTCATTTTTCCTATGGAGGAAGACCTTTTTTCCCTGGCAGCCGGCAAACAGCCTTCACAGCAGGCAACAAATGAAACGGCACCCCGGGCCGGGGAGGCGCGGGAAAATGCCGGCACCGACCACCCCGGAAACGCGGAGGACCCCGCTCACCGCCGCATAGACTATTTGCGTTCCGAACTGCGCCGCCACAACCGTCTGTATTACGAACAGGCGGAACCGGAGATTTCCGACGCGGAATATGACGCCCTGTTCCTGGAGCTGGAAAAATTGGAAAAAGCCCATCCCGAACTGTCGGATCCGGACTCCCCCACGCGCCGCGTAGGCGGCGCCCCTCTCCAGGGCTTCAACCAGATCAGACATGCGGTGCCCATGTTGTCTATTGACGACATTTTTGAGCAGCGGGACGCCCCCGTACCGGATGAAGAACTGGCGGAATTCTACCATAAGCTGTCCAGGGCGCTGCAAACGGAGAACGTGCCCGTCTCCGTTGAGCCCAAGATTGACGGAGTGGCCCTTTCCATCATGTACCGCAATGGGAAGCTGGCTTATGCGGCCACGCGCGGAGACGGAGACGTGGGGGACGACGTAACGGCCAACGTCCGCACCATCCGCAGCGTCCCCCTCACCCTCCCCCCCGGCGCGCCGCCCGTGCTGGAAGTGCGCGGGGAAGTCTTCATGCCCAATGAAGCCTTTGCCAAACTGAATGAAGAACGGGACGCCGACGGGCTCCCCGCATTCGCCAACCCGCGCAACGCCACCGCCGGAACCCTCAAGCAGCTGGACCCTCGGCAAGTGGCCGCCCGGCCGCTGGCCTTCCTGGCCCACGGATTGGGCGCGTATGAAGGCCCGGAACTCAGGGACGCAAAAGACTTCTGGAACATGCTCCGCCATTGCGGCATCCCCTGCAATGAGCCGGTGTATTACACGGACAGCCTGGAATCCACCCGCCAGGCCGTGCGGGATATTGACAGGCTCCGCCATACGCTCCCCTACGGCACGGACGGAGCCGTTATTAAAATCAGCTCCACGGCTACGCGGGAAGCGCTGGGAGCTACGGCTCGCGCGCCCCGCTGGGCGGCGGCGTATAAATTCCCCCCGGAACAGAAGGAAACCACCCTGCTGAATATTGTCGTGCAGGTAGGACGCACCGGGGTGCTGACACCTGTGGCGGAATTGCAGCCTGTGCTGCTGTCCGGTTCTACGGTAGCCCGGGCCACCCTCCACAACCAGGATGAAATCGACCGGAAGGACGTTCGCATAGGCGATACGGTGCTGGTGGAAAAGGCGGGGGAAATCATCCCGGCCGTTCTGAAAGTGAACCTTTCCAAACGCCCGCAGGACGCCAGGCCCTACAGCATCCTGGAAGCCACGAAAGGGCTGTGCCCCGCCTGCGGCAACCCCATCATGAAGGAAGAAGGGAAAGTAGCCTGGCGCTGCACCAACTTCACCTGCCCCGCGCAGGCCGTCACGGGCATCACCCATTTCTGTTCCCGGTCCGCTCTGGATGTGGAAAGCATCGGCTCTTCCGTGGCGGAAGCGCTGAGAAGCTCCGGACTGGCGTCCTCCGCGCTGGACCTTTTCTCCCTGATGCCGGACCAGCTCGCCAACCTGAACCTGGGCACGCCGGAAGAACCGCGCCGTTACGGGGAAAAGAATGCCCGGAAAGCGCTGGACGCCCTGCAAAACGCGCGAGGGCTTCCTCTGGAGCGCTGGCTCATCGCCTTCGGCATTCCCCTGGTGGGGGAAGTTGTCGCCAAGGCGCTGGCGGACACGCATCCCGACCTGAAGCATGTGGCGGATTCTCCCTACCTCCGGGACATCGTCCGCCTGGATGAACTGGTGGAACAGGCCGCCAAAACCAACCCCAACACCCGCGAAAACAAAAAGGCGGTGAAGGAAGGCGCGCTTTCCGCAGAGGCGGTACAGGAGCGCCACCAGGAACTGATGGAGGAAATCGACCGCCTGACCGCTCCCTATCTGGCAACGGGCTACCTGCGTAAAAACACGGCCAAATTCAGCTATGGCTCCGAAATCGGCGTGGCGGCGGCCAAATCCCTCCGGAGCTTTTTCGCCTCCGCAGCAGGGAACCATACCATGGACGTCCTGCACAGACTGGGCATCAATCCCCAATCCCAATCTTACCGGGCCAGCCTTCTCGAAATCCCGGCGGGCTCCCTGTCCGGCAAAACCTTCGTCATCACAGGAACGCTCAGCCATCCGCGGGATTACTTTGAACAGCTTATCGCCTCCCACGGGGGAAAAGCCACCGGAGCCATCTCCAAATCCACCTCCTGCCTCCTGGCAGGCAACGGAGGAGGCTCCAAGCGGGACAAGGCCCTCAAACTGGGCGTGCCCGTCATTTCCGAGGAAGACTTCTACAAAATGATCGGGAATTAATTGCCGCAAAACAGCGGAAACGGGAAGCAGGAACTGAAACGCACGCATCATGCGCCCGGCTGCCCGTATCTTCGTATTCCCTTCGCTTTTTTCCGTGCGCCGTTTTCCGGAAATTTCATTCCCGAATCCAGCAAAAAAGCTTGCTACGGGCAGCCATCTGTCCCCTAATAAACCGGTATGCTGAACCTGCCCAATGCCATTACCCTGACGCGTATTGCCCTGGTAGTCGTCTTTACGGCAGCAGTCAGCGTCGCGGACCGGTATTCCTGGGGATACCTGGCTGCGCTGGTCACGTTCATTCTGGCTGCCTGCACGGACTGGCTGGACGGCTACCTGGCCCGCCGCCTGAATCAGGTCACCACCTTCGGCAAGCTGATTGACCCCCTGGCGGATAAAATAGCCGTCTCCGCTGCCTTCATTTACCTGACGGGCGAAGGGCTCTGCCCCGCCTGGATTACCATTCTCATCATCAGCAGGGAATTCTTGGTCACCGGACTGCGCCAGATTGCGCAGGATCATGGCGTCATCATCCCTGCGGGAACGTCCGGCAAATGGAAAACCGCCTTCCAGCTGGCCTTCTGCATCGCCTGCCTGCTGGCCCTGACATGGACGCATACGCCGGAATACCTGCCTTCCATCACGGACCGGCTGGCCGCGCTCTGCCTGTGGCACGGTTCCGGCGCTTCCAACTTGCTGTACCAGGTTACACTGTGGGGTTCCGTCATCCTGACCGTTTATTCCGGAGCCGTTTACTGCGCGGGCGCCCGCAAATTCCTGCAGCGTTGAAAATTGCCTTTTCCCCCATCCGTCCGGAGACAGAGGCAACATTGTTCATTCTCCGGGACATTTCATATACCGGGAGCATGATTTCATTCCGCCGGGTTCCTGTATGAAAGAATGCATGCGGAATCTTCCGCTGTCGCCCTTCCTGCACATGCTGAAGCCTTGGAACGTTCAAGCTGTCCATCCTCTCTTCCGGAAGATGAACGCCTCCACGGCTCTCAAGCCCGCCTTACCTGTCCTTGACATTGCGTTCCGCAAAAACATTTAAAGGATCATCATGTTTGCCCGAAGGTTTGGCATTAATATAATGCCCCCTCTTTTCCACGCCATCCCACAGCGTTTCCAAATCCATAAGAAAGCCGTTCCTATCGTATCTCCCCAAAGCAACAGCTATGCTGTATACTGAATTAAAAACAAGCCTGTCATTATTTCTCGGATAAGCAAAAACATCTTTAGACCCCCTGTTTTTGGGAACTGTGGCAAAAAACAGGAATGAGCCTTCTTCGGAATTTCTGATTGTTATTTTTCCATTTTCATCAACCAAAATTTTTAAAGTATAGAAAATTCCTTTCTTCGTATCGTGAGCACATAAATGCAGCCATTCCTCTCTATTCATTCTGTCTATGACGAACCTGCCGTCATCAGATGAATTCACGCAAGCTTCCAACCGGTACAAATAATCGTTGCTGTCTTCCGTATACCAAGCTTTATAGTTTTCAGCATTGCGCTTTTTCTTATTAATCAGAATACCGAATTCCACCATCTTTTCCTTATTTTTTGCATACAGAGTATCTTGCTGTTTAAGTCGGATAAGATCTCCTTCTATGACAAAATTTATTTTCTGATCCACCCATTCCCAACGAGGTTCATTACCATAGGAAGGAGTAAATAAATGTTTTCCCGTAAAAAGATTTACGCGCTCAGAAATTCGTTTTTTCCTACCGTGAACGCTTTTTAAATATTTCATATTGAACAAACCATCTTCCTTAATAAAATCTTTCGGATACCCGAATTCATGTTTTTTCATTTCTTCATAAAGAGCATCAATCAGATTCTGGTAATATTCCACACCTTCTTCTCCATCAAGGCGTTTTTGTTTCGCTTCAGCAAGAAATCCGACAAATGGATGCTCCTTGCCATTCCTGCCGGAATCCGGAAAGCCAGCTTCCCCGCCCCGGTTTTCCTCCCGCGTCAGATGCTCCAATAACCGCTGATCAGCTTCTGAAAGTTTTTCCAGTAAAATGGTTTTTATTTTTCCACCATTTGCAAGCAACCGGATTTTGTTGCCATCTCTTCCCTCATACGCTGCCTTTAATTGATGGCCTGTCACCGCCGTCCAAATCCGGGGTTTGCTCAAATCCATCCCCTCTTCCGGCTGAAGCCCCGCAAAAAGAGGGCTGCCGCAACATACGGACAACACGACAAGAAAAATGCCAATTCCATTCTTTACCTTTGTTTTAAGGGACATAACTAATTCAACATCGTCTATTCAAAGGAAAGTATACGGATGAGGAATCCGCTGATTGGGACATGCGGCCTCTAACATGTCGGCCGGCTCCCTGATGCAGATAAAACCTGTACCGGAACAGGAAAAGAGCCTCTTGGGTATTCCTCCCCTAAATTGTCCGCCAGAAATTAATCAGTCATTCATATTCAGAATTTAGGGTTGACGGCGGATTCCTCACCCGCTTCATCCGGTAATTTATTCATATTCAATTTCTATAAAAAAATCATGACAAGAACTGTTTTTGAAGCGGAGATACGGAATACTAACAGGACAAACGTTTTTATTTTCCTTGACAGGAGATTTTCGCCGTGTTTTGATAAGTTCGTTCCCGCTGAATGGTGTTTGGTTGTTGATTGTTGATAGTCATTTGCGGTTGGCGGAATACGGGCGTCTCCGTATTGATCTTTTCAGTATCGCTCCTGCGGACTTTTTTGTTCCGTTATTGCCGTGATTCCGTCAGTTCATTGCAGCCGAGGCGCATTCACAGACACATCCACAGACATGGTATCAACTACATTCAGGGAGCTTGGCTTGTCGGCTCCCATTCTCCGCCAACTGGAGAATCTGGAGTACAAGACCCCCACTCCCATCCAGGCAGCCTGCATCCCCCTGTTGCTGCAGAACAAGGACCTGATGGGGCTGGCGCAAACCGGAACCGGAAAAACGGCCGCGTTCGCCCTGCCCCTTATTCAACAGCTTTCAGAACGTTCCGGAAAGCCGCCGGGCCGCCGGGTAAGGGCGCTTATTCTGAGTCCCACCCGGGAACTGGCTGCGCAAATTCATGAAAATATCCGCGCCTACGCCAGAGGGCTGCATCTGTCCACCGCCGTCATTTTCGGAGGCGTGGGGTATGCTTCCCAGTTCAAGGAACTGGCCGGAGGCCTGGATATTCTGGTAGCCACGCCCGGCAGACTGATCGACCATCTGGAACGCCGGACCGTCAGTCTGGACGGCGTGGAAACCCTGATTCTGGATGAGGCGGACCATATGCTGGATATGGGATTCGCCCCAGCCCTGAAAAAAATCGTCTCCAAAATCCCGAGGAAACGCCACACGCAGATGTTTTCCGCCACCATGCCGGACAATATCCGGCAACTGGCCCAGACCCTGCTTCAGAAACCGGAAACGGTGAGAGTCTCCCCTCCCTCCTCCACGGCGGACAGGGTAGAACAGTTCCTTTGCATGGTCGGCTCCCAGTCGGAAAAAAGGCTCCTTACCCTGGAACTGCTGAAACAGCAGGAGCTCCCTGGCCGCACGCTTATCTTTACCCGGACCAGGCATGGAGCAGACCGCCTGGCCTCCTTCCTGTCCGGAAAGGATTATCCGGCTTCCGCCATTCACGGGGACAAAAGCCAGGGCAGGCGCGAACGCATGCTCCGTGAATTCCGTTCCGGGAAAACGCCTGTCCTCGTCGCCACGGACATCGCCGCCCGCGGCATTGACGTAAAAGACGTGCGGCTTGTCATCAATTATGATTTGCCGGAAGAACCGGAGGTCTATGTGCACCGCATCGGCCGCACGGCGCGCGCCGGAGCCGCCGGGCAAGCCATCGCTCTCTGTTCCCCGGAGGAAATCAGGAAAGCCAGGAATGTGCACAAGCTGTTGGGACGCCTCCTTCCCGTACACGCTTCCAGCGCCTCCGTCCCGGCTGAATTGCGCGCAGTCCCAGAAGCGCGCAGGAAAAGGAGCAGCCCCATGCAGGAAAAACGTTCCGCTCCCCGCAGGGAGCCGCGGAAAGGGTACGGAAGGAACGGCAGCCGTGCGGAAGCATGATTTTAATCATTCAAAAAACTTTTGAACGGAATTGAAGTCAGCGCGGTCTCACTTGCTGCAAACGGTTCTCCTAATCGTTCCAGCCGGGAAGCTTCAACCGGAGTTTCAATCATGAAAATAACCTCGTTCATCATCAGGACACTATGCGCGGGCACCGTCGGTTCCCTCGCCTGCCAGGCCGCCATGCAGGCTTACGCGGATGACGTTCCCTCCACCTTCGTGGAAGGAAATTATTCCCCGGAAGTAGCCCGCTACGTGCAGAGCATCGCAGGCGTGGCGCTGGCCTCGGGCATTCATGATTATGACGAAAACGCCACCAGCAGTTATCAGGTTCTGGCTCAGGTCCTTAACAGCGGCAATGTAACGGAATTGCCGGAAGATCTGAAACGGGCCATTGGAATGCTGATCGGCTACGTCCCCGACCCGAATGAAATCATCCAGCCGATCATGTCCAGAAAACAGATTGAGAGGATGAGAAAGATGGCGGACCGCATCAAGAACCTGATGGAGGAAAAATACGGAGTGGACCTGGAAGACTGCACGAACATCATGAAGCGCACTCTGGCCCCGTCCATCTCGGACATCCTCCGCTCCTGCGGCATGGAAGGCACCCAGCTGCCCTCCACCACCGCTTTCCGCCCCAGGCTGGGCAAGGCTCTGAATGAAGCCTATGCCAGGCTTGCGGCGCCAAAACTTCCCGCCGCCGGCATGGCGCAATAACCGGACCATCTACGGATGATCGGGGAGATGATCCCGATTCCACGGCACGCGGCCCCCTGCCAGGGTCCCCTGAATAGAAGCCGCAGGAAACGGAATGATCTTCCCCGTCGCCGGGAACGGAAACGGCAGCCAAGCGCCGGCGGCAACAAACAACACCCTCCGGATGTCCTCCGATGCAGGGCCGATCCTTCGGAACGCGGAGCGGCGATACGCTCGCTCCCCTGTTTTATTCCCAAGCAACGGCATGCAACATCGGCACAACCGGATGATGCCCTTACAGGCCCCGTCCTTCCCGCGCTGCGGCAGGACTGGGGCTTTTTTCATGTCCGGGATTTTCAAAAGGCGGCGGCATTCCCTCCTGATTCCTTCCAGGAATCAGGGATATTCCGTTTTTACAGAGGAATGCATTTTTTTCACCATCTGTGAAAGAAAGATTCTCAAGCCTCAGTATCTATGGCAAACTGGGGATTCAAATCCTTTATATAATTACCATCCATGGATAATTCATCATCACGCCGTCGTTTCCTTCAGACCCTGGGCCTGGCCACTGGCGCCCTGGCTGCCGGTTCCTTTGCCAACGCCCAGGAAGTAGCCCCCCTGGCTCCCAAGAAAATCACCATTCCGGACCCGAATAACATCGGCCCCATGACCACGTGGCCCCCGCGCAAGCCCGGCGCCATCTACATGGGCGGCTTCAGGGCTCCCAAGCTGGACAAGGTGCGTGTGGCCTTTGTCGGCGTGGGTGAACGCGGTTCCATGCACGTGGGCCAGATGGCCGTTATAGAAGGTGCGGAAATTGTCGGCATTTGCGACCTGTATGAAGACTGGGCCAAGCGCAGTGCGGACGTCGTGGAAAAGAAGACGGGCAAGCGCCCCCCCATTTTCACGAAAGGACCGGAAGACTACAAGCGCATGATGAAGGAAGTCAAGCCGGACGCCGTCATCGTCTGCCCCAGCTGGGAATGGCACTGCCGTGTTACCTGCGACGTGATGAAGATGGGCGCCCACGCCTTTGTGGAAGTGCCTATGGCCGTCTCCATCAAGGAACTCTGGGAAATCGTGGATACCTCCGAAGAAACCAGGAAGCACTGCATGATGATGGAAAACGTCAACTACGGACGTGAGGAACTCATGTACCTGAACATGGTGCGCCAGGGCGTCATTGGCGACCTGCTGTACGGAGAAGCCGCCTACATCCATGAACTGCGCGGACAGATGAAGCAGGTGGAACGCGGCACCGGTTCCTGGAGAACCTATCACTACGCCAAGCGCAACGGCAACGTGTATCCCACGCACGGCCTCGGCCCCATTGCCCAGTACATGAATCTGGCCCGCAAGGACGACTGCTTCGGCAGGCTCGTCTCCTTCTCCAGCCCGGCCCTGGGCCGCGCCGCGTATGCCAAGAAAAATTTCCCGGCGGACCACAAGTGGAACAAGCTGGACTTTGCCTGCGGCGATATGAATACCTCCATCATCAAGACCACCATGGGCCGCACCGTCCTGGTGGAATGGGATGAAACCAGTCCGCGCCCCTACTCCCGCCTGAATCTCATCCAGGGCACCCTGGGCACCTTGGCCGGCTTCCCGACCCGCGTAGCCGGGGAAAAGCTGGGCAACGGAAATTATCATGAATGGATTGAAGGCAAAGAAAAACTGGCCCCTATTTTTGAAAAGTACGATCACCCGCTCTGGAAGAGAATCGGGCCGCTGGCCCTGAAGATGGGCGGTCACGGCGGCATGGACTTCGTGATGCTCTTCCGCATCATCGAATGCCTCCGCAATGGCGAACCGATGGACCAGAACGTTTATGAAGGAGCTTTCTGGTCCTCCGTCTCCGAGCTTTCCGAATACTCCGTGGCCCAGGGCGGCATGCCCCAGGTATTCCCGGACTTCACCCGCGGAGACTGGAAAACGACTGCTCCGCTGGGCATCGTCCAGTAACAGCCGGCAATCTTCCCCGGCCCGAAGCTTTTGCGCCTCCATGGCGTGTTTCCGGGCCGGGGCAAAATGCCTCCTCTTCCAAAAACAGGCCCAGCTTGCAGGCGGCCTGTTTTTTATTGCTCCAATGACACCCTATTCCGGAAGTTGAAGGGCTCCGCAGGCGCTCCACTTGCCGGGGAAAGCTCCCGGATGACGCCAAAAAGCGGAACTCCATGAAATTTTCCTTCTGCGCGGAACCGTATTTAGGCAAGAAAAATATCCTGCAGAAAAACGCCGTTCCCCTTACATCACACACGCCTTCATGCTTTCCTATTGTACCAATATTCATCCCGCAGAATCCTGGGCGGAAACCAGGGAGGCTCTTTTCACCTGCGTTCCCCGCATCAGGCAGGAACTCGCGGCGATGGACTCCCCTCTGAAGGATCTCCCCCTGGGCATCGGCCTGCGCCTGTCCGCCAGGGCGGCAGCGGAGCTTCTGGCAACGCCGCACGCCGCGGAAACCCTGAAATCATGGCTGGAAGACCAGGGCGCGCGCGTAGAAACCCTTAACGGGTTCCCTTACGGAAATTTCCACGGGCAGCGCGTGAAAGAACGCGTTTTCCAGCCGGACTGGACTACACCGGAACGTTTTGAATACACCTGCAACCTGTTCCGCATTCTGGCACTCATTGGTGACGAACAGGCTGACAGGCTGACCGTCAGCACGCTCCCTGCCTCGCACAGCTGGTTTCATGCGGATGAAGAACGCATCTTCTCCCGGCTGGACGCCATGAGCGGATTCCTGGATGTGCTGGGCAGGCAGACCGGCTGCCTGATGCAGCTGGGGCTGGAACCGGAACCCTTCGGCCATTTTCACGATACGGATGGAGCCATCCGTTTTTTCAACGGCCTCCGCAACCGTTCCCGCCGTCCCGAACTTATCGAACGCCACCTGGGGCTGACATACGATACCTGCCATTTCGCCATTCTCCGGGAAGAACCGGAATTCACCCTCTCCGCCTGGGAGGAAAACAACATCGCCCTCTGCAAAGTGCAATTTTCCAACGCCCTGGAATGCCGCATATGTGGGGAGGAAGACCTGGAACGCCTCCGGCAGTTTGACGATGGCGTTTATTTCCATCAGACCAGCATCCTCCACCGGGAAGGCGCCATGCTTTTCCCGGACCTGCCCAATGCCCTGGCCTATGGGCGGGATTATGCAGAGGAAATACGTGATTCCCAATGGCGCATTCATTACCACATTCCCCTGTACGCTTCACCGGAACCACCCTTGAAAAGCACGGAAGAATTCATCCAGAAAACGCATAATTTCCTCCGGAGCCGCAAAGGCCCGCAACCGCATCTGGAGGTGGAAACCTATACCTGGAGCGTCCTGCCGGACCACATGAAGATCCCCCTGGCAGCCCAGATTGCCCGTGAACTGCATTATATTGAAACCCTGTAAAGCCATGATGCCCCGCATGCATCCCTCCGCCTTCCTGTCTTCCGGATTCCTGCTGCTGTCCACCGGTCTTTCCTCCTGCACATTTCTTCCCATGTTTTCCTCCGGAGGGCAGGAAACGCCGCGGACCCTGGGCGCACGCATGGAAAAACACACGGAAGTCCTGCAAAAAGACATCGGGGAAAGAAACGCCAGCCAGCCCAAATCCATGGAAGCCGCCGCCAAATACATCGCCGGGAATTTCTCGGACATGGGGTACGCCGTCACCATGCAGCCCGTATCCGGAGGAAACGTGAAAAAAGGAGCGGCCATTTACAACATCCTGGTTTACAAGCCCGGCCTGTATTCCAACAATCAAAGCATCGTCGTGGGAGCCAATTATGATTCCAGCGGACAGCGCAACAACGGCAGCGGAACGGCCCTGCTGCTGGAGACGGCGAGAGGCCTGAAAGACATCCCCACCAATCACAATATCTATTTTGTCGCATACGCGAACGGAGCGGAGCCCGCAGGGAAATCCATCTCTTCCGGAGCCTGCGTGCATGCCAGCACCCTAAGCGGCAGCATTGGAGCAACCAACATCCTGGGCATGATCAATCTGGAACCCCAGAAGCTGGATGCCGGAGGGGAACGAAAACAGCCGGGCCCGGAATCCTCCCGGGAAGCCGTTCTGTTCACGACCACCCCGCACGGCAAGAAATTTGCTGGCCAGTGCGCCGCCCCCTTCGCCAAATCCTGGGAGCAGGCGCCCGCTACCTTCCACCGGGAACCTTCCGCCCTCACAGGCAATGACCGCCATTACGCGGTCCTGGGCATTCCCACGGTCTCCTGCCGGAGCAAGTATCCCGTCACGGATGCCCGTACGGAAGAGTATGTGCAGGCGCTGACGGACATGATTCACCAGGTAGCGGACAACGACGCGCCCGAACCGGCAAAAAACGCCGGCCAGGGCGAGAAAGCATCCTGACGTTTCCCTCTCTCCCCGTTACCGGGACAGCTTTCTCCTTCCGTGGCCGCCGCCCAGCATCAGCACAGGCATGAAGGAAAGGGCCACCAGCGCAATAACCATAGGCATCCAATTGTCGGAATACCTCTGCATCAGGAACGAAGTCAGGGAAGGGCCGGCCAGCGTGCCCAGGGAATAACTCAACAGCAGAAGCTGGTTCATGGTCACCAGTTCATCCCGGGAAGCTTCCTCGCATCCCCAGGCCATGGCAACAGGGTACAGGGAAAATCCGGCCAGCCCCAGAGCAATCAGGGAGGGAGCCATCAGCCCCCCCTTCAGCATCAGCCCGGCACAGGCCGCCGCCACCAGCAGGGACTGGCATTTCATGACGAAAGCGCGGCCGTACCTGTCCGCCAGCCGCCCCATGGGCCACTGCCCCAGAATGGCAGCGGCAATCAGCAGGGCCATCCAGTATCCCACGGAAGAGTGGTCCATTCCCTGGTGCTTCAGGAACAGGGGCATCAGGCAATAAATAGTACCCAATACCACGCCGGAAATCACACATCCCACAACACCCAGAAAGACGCTGCGGCGTCTCAGCAGTGTCCGAATCTCTATATGGGAGGAGGATGAAACCTGTCCATTGCCCGGCTCCAGACGCGCAAACATCAGCGGAACCATGCCCGCCACTGATAAAATGCAGACTTCCGTCACAATCAGGGGCATATCGCTGGGGAACCAGCCCAGAAGCAACTGCCCCAGCACCGTACCCAGATAATAAACCACCATGTAGGAAGCCAGCAGAATGCCGCGGGTTTGCAGCGTTCCGGCCCTCAGCAGGGCGCTTTCCACCACCACCCAGACCAGGGCGCAGCTGATCCCCGCCAGCAGCCGGAGGCCGCTCCAGGCCGGCACGCCGGGAAACACAGGCAGCAGAACGGTGGAGACTGCGCATAAAAGGCATGCATATTGGTAACTTCCTTTGAACCCGGCCCGGCGAATCACGCCGCCAGCCAGCAGCGTTCCGGCCAGGTTCCCGAGAAAAAAGAAAGCCCCCACCAGCCCTGCCTCCCAGGTGGCGGCATCAAATTGCTCCATCCACAGGGGAACAACCGTATTCAACACGGCAAACGCCACTGTCTGGAAAAGAAGCCCCGCCAGCAGCAGGGCAATAGGCCAGGTAAATGTACGCATCACGATAAAGCGGGGACTATAGCACCAAAGCCGCATCAGTCAACCGCCTCTTCCGTCGGAAAGAAAAACAAAAAAAGCGCCTTATCAACAAAAGACACAACACATTGATTGTTAAATTATTAATAAACAATTCAAAATATAGGGGACTGTCATATTCCCCGGCGCAGGGAAGCGGGCAGTATCTTCATCTGCTCCCGATATTTGGCCACCGTCCTGCGGGCGATGTTCACCCCTTTTTCCCGCAGCGCGGCCGCCAGCCCGGCGTCACTGAGGGGTTTGGAAGAATCTTCCTCCGCAATCAGCCGCCGCATCAGCTCTTGGACGGCGCCGGCAGCCACGGCTCCGGAAGCCGCCCCGTCCGGGGAAACGCCGCCTTCAGACGGAACTCCCGCACTGAAAAAGGATCTCAATTCTTTCATCCCCCACCTGCACAACAGGTACTTGTCCCGGCACGCCCGGGAAACCGTGGAAACATGAACCCCCATCTCCTCCGCCACTTCTTCCATGCCCATGGCCTTCAGGCGGGACGGGCCGGACCTGAAAAAATCCTCCTGCCTCCGCACAATGACACGGGCCAGGCGCAAAACGGTCTCCTGCCTCATATCCAAAGCCCGCAACAGCTCCTGTCCCTCACGGAACGCCCTGCGGAGATATTGCCTCAGCTCCCGGCTTCCGGAACCCTCAGCCATTAACTCCATATACCGTTCATTCATGGACAGGCGCGGAAGATACTCGGAAGTGAGGATAACCTCCAGCACGCCGGAAGGCCCCTCCTCCACAATGACGTCAGGCAGCAGATGAGGGTTGCCGCCGGGGGCGTAGGCCCCCCCTGGATCAGGAGTCAGGCTCCTAATCACCTCCAGAGCCGCCGCCACGGCCCCCGGTTCCACGTCCAGCAGGCGTGCGGCTTCCTCATACTTGTGCGCCGCCAGTTCCCGCCAGCAGCGCTTGACCAGGCGGAAAGCAAGCGAACGCTTCAAACCGCGCTGTTCAAGCTGGATCAGCAGGGAATCCCTTAAATTCGCCGCCCCCACTCCAGGCGGGTCCATTTCCCTCAGCGCAGCCAGAGCTTCCTCCATGTCCCGCATGCTGAAACACCCCTGCTCTTCCAGCTCCTGCGGAGACTCCGTCAGAAACCCGCGTTCGTCCAAAGCGTCAATCAGCAGGAACAGGGCGTCCCGGGCGCGGCCCGTCAATGCGGAATGCTGCGCCTGATCCATCAAATGGGTTTTCAGGGATTCCGGAGCCACGACGGATTCATACATGAAATCACGGCGGGCGGCCGCGTCCCCAGACAGGCGGCCTTCCGCCGTAAACTCGTTCCACCCGTCCTCCCGCTCATTCCAGGCATCCCCGTCCGGACCGTCTTCCAGCGCTTCCGGGGAAGCGTCCGGCAACTCCTCAAGCATGGGATTGGTTTCCAGCGCCTGACGGATAATCTGCCTTAATTCCAAAGCAGAAGCCTGAAGAATCTGCATCCCTGTCTGCATGGACTGGGAGACGGCCATCTGGCGCGTCATTCCATGCTGCAGGGATACCTCACTCATACCGCCCACAAGCTACACCACCCCCCGGAATCTGGCAAGGGGGAACACCCTGCAGCAAAAGCTGCCCGCACACCGCAGCAGCAAAAAAGGCCACCACTTTCATGTGGCGTTTTTGGAAAACGTTCATACAATAAATGCCAATGAAGATTTTGCACATACTTGCTTTCTCCCTGCTGGCCTCCCTTCTCTCAACGGCTGCGCCGTCCCCTCTGACTCCGCTGCCACGGGAACAGCCCCAATGGTGGAGGAAGCAATATGAACAGCAGGTGGAACAAATAAAAAACAACCCCTGCGGCGTCCTTTTCCTGGGAGATTCCATTACGGACTACTGGAAAAGGGAAGGAAAACCAATATGGGAAAAGGCGTTCTCCCCCTATCATCCCTGCAATTTCGGCATTTCCGGGGACCAAACCACCAACCTGATTTACCGCATCACGGATTCCGGCATCCCGGCCCAAACGGACCCCAAACTCTGCATCGTCATGATCGGCACCAACAATACCGGTCATTTCAAAGGAGGGGAAGCCCCGGAAAAAACGGCCATGGGAATTCTGGGTGCCGTTGAACACCTGCTGGTCAGGTTCCCGGACACCCACGTCCTCCTGCTGGGCATTTTCCCGCGCGGCACAGGTCCCCAGGACAAGCTGCGCCAGCACAACGACCGCATCAACGCCATTCTGGCCCAGTGCAAGCTGCCCCGCACCACTTACGCCAACATCAACAGGCGCTTTCTGGACTCCTCCGGCAAACTGCTCCCCGGCATCAGCAGGGACAACCTGCACTTTACGGAAAAAGGCTACGCCATCTGGGCAGACGCCGTGCTGCCCTATATCAAAAAATACTGCAAATAGCTTTCTTTCCCATCCTCGACGCATGACTGTCGGAATGCCCCCTTCCGCCAGTCATGCGTGCGGTTCGGGAACATATTTGTCAATCAATGATGAAGCTTACGCAGCAACAACTGTCCAGCCTTCATTTTGAAACTGCGGTTATGGAGCCTGAAAAGCACCGCTCATGTCCATCAATTCATTTTTCCCGGCGGCTCCGCCCTGAATGCTTATGAGTAACGTCCGGACTTTGGGGAGCCCGCTTTAATAAACGGTTAAGATACAAAAGCAATACGGAGCATGGAAAGTATTTACCGCCAAGCCCGCAAAAAGCTGCATGGATGCCAGGGCCCGCGTCTGAAAATTTTGCGCAGAACCGAAGTTGCAGTTGATTTGGAATAAATATATTATAATGAAACAATATATCATAGACGCCTTTACTGACAAAGTTTTTCATGGAAATCCCACGGCGATTTGCATTTTAGAACAATGGCCGTCCGGGGAATTGATGTTAAATATGGCCGTTGAAAACAATCTTTCAGAAACGGCCTTTGCTGTCAAAAAGGGGGGGAGCTATCATCTGCGGTGGTTTACCCCCGGTGGAGAGATTGACTTATGCGGTCATGCGACATTAGCCTGTGCCTATGTTATTTTGAACTTCTATGAAAAAGAAGCGGCAAAAGTTGTTTTTCAAACTTTAAGCGGTGATTTGGTGGTAGAACGGCATGGTGATCTTTATGAAATGGATTTCCCCGCCTATGAATTAAAACCAGTTCCCGTAACGGAGCAAATGATCGACGCTATCGGCGCTACCCCCAGAGAAGCGTATATGGGGCGTGACCTTTTATGCGTGTTTGACGATATAAAAATAATCGAACATCTCAATCCGGATCTATCAAAAGTTCATGCCCTGAACGGTCTTTTGCTCCATGTAACCGCAAAAGGTACGGAAACTGACTGCGTTTCCCGTACCTTTGCCCCCAAATGCGGAGTGCCGGAGGATGCCGTTTGTGGTTCCGGTCATTGTCACATTATACCTTACTGGGCGAACAGATTGGGAAAAGACGACATCATAGCTTATCAGGCTTCCAAACGCGGTGGGACACTTTATTGCCGCATGGATGGAGCACGGGTAAAATTAGCAGGAAAAGCAGCCTTATATTCCATTGCCGAAACACAATCAAGATGAAGCGTCAATAGGTTAAGAACATATGCCGGGCGACGGAAAAAACGTCGCGCGACAGAAATTATACATGCCCAATGACACAAACGGCGGTTTTGAATAATCAAGGACGCCGTTCTTCTATTCCCTAAAAAATAATGCAACTATGCTTTCAAACATGGCGGTCAACGGGAATAACTGCCATGAAGCACAAAGCATGCCGACAAATTCCGACGAGGTCTATATCAAAAAATACATCCGCCGCAGAATATCATATCTCCAAACGTGAATCATACCATGTAGCAAAAGTGAATATTTTTTCAAACTTCCATCCGGTATGCTTGTGTCTTTACAGGTGTGTTTTTTATTTTTCTTTATGGGAAATACAACAAAAAAAATCATGAAACAACCAAATCCCCCGGCCGGAAGCAGGAATCCAGCCGACCCGCCTGTGCCGCATTTGGAATCAGGAAAAAGCGCAAGGAACAAAAAAATTGCTTTTTCCCGGGGATATCCTAGAGTGCCTCTCCGCAATGAGCATGCCGCTTGCCGACTTGATCTTCCTGGCTTTTGCCCTCTCCGTGGACGCTTTCGTCGTGGCGTTCTCCTACGGCCTGCTCATTAAGCAAAAGCGTTTGAGCAACGGAATCAAGCTCTCCCTGTCCACCGGGGCCGGTCAATTCCTGATGCCCGTCCTGGGTTTTCTGCTTACAGGCTCCGTGCACCGGTATATTGCGGCCTGGGACCACTGGCTGGGCTTCGCCGTGTTCACCTTCCTCGGGGTCAACGTCCTCCGCGAAGGCTGGAATCACCGCAACGAAGAAGAGGATATGCCCCACGTCACCAGCCTGACGTTTCCTACCCTTATGGCCGTGGGAATCGCCACCAGCATCGACGCGCTGGTAGCGGGCATCAGCATTTACCTTTCATCCGCCCAGTGTGGAACCGACCCTACCCTCCACGCGGTGCTTCTCCCCGCCGCCGCCATCGGCTTCACCACCTTCCTGTGCACGGCGGCCGGCTTTTTCCTGACCAGACGGCTGCACCGCTTTCCGACCTTTCATCTGGAAGCGGGAGCCGGGCTCATCCTCATCGGCCTGGGCGTCAAAATGCTCTGTGACCATTTGTGCTGAAATATGCCGCGAAGGATGATAAAAACGTCCTTCTGGCATCGTAATAAGTCGAAAAACGCACTGGTTCCCTCCCACCTCTTTCTGCTATTATCAAGTAATCCCTTCCACATGAATATTTCACATCTTCTTCTCTGCACTTCCCTGCTCGCGGCTCCCGTCTGCGCGGCACAGGGCCTGACGGAAACTGAAACGGAAACGCCGGCCTCCTCCAGCCGCCTGCCGGCGCCCCTGGCACAAAAAATTGCCTCCGGAGACTTTGATGGCCTGCAGACGGAACTGCGCTCCACCCTCCTGAAGGCTGGAGAACAAACGAAATCCGAACAGAAACTGCTCCAGAACAAGCAGTACCGCCATTTGCTGGACATTCACGAACTCCTGCGCGTCACGGGACCGGACAAGGTGAAAGCCGTTTTCTCCAAAAGCGCGCAAGATGCCGCTTTCATCAAAACCTTTCTACAGGACCCGGTGTGGATGGAGCTTTACCTTGGAGCAGGCCTGATTCCGGAAAATTCACCGGAAGGCCTTCAAATCCTCTCCGACATCTGGAAAGCGGACGGCAAGAGCCCGGATTTCCGGAATTACCAATCCCTGGCCACCGGGCTGGCCTCTGTCTTTTCCACCGGCCCCATGGCGGGAAGGCTCAAAACCAACTCCGCCAACAGCAATCCGGTGCGCCGCTACCGGATTTTCAAAAAACTGCATCAGGAAAACAGGCTGCATCCGGGATTCATCAAACTGCGCCCATGGGAAATGCGCTTCGTCGTGGGAAGTCCCTGGGACGATAAATCCTATGAATGGAGCAATGAACACGTCAACCTCCCCTGGCGGCGATACACGGCCGCCTGCTGGGCCGCTCCCTATACGGGCCACAATTTCTTTGGCGATACCATTCAGGGGCCCCTTTTCTACGTGCCGTGGAGGGATCTTAACACGACCGCGGAAAACACGCAGATCATCGGCGGCGTTTGCGGCGGCCTCTCCTATTTTGGCACCATGGCGGCCCAGGCCCACGGCATCCCCGCTTATCCGGTAGGACAACCGGGCCACTGCGCGTACGCGGTGCGCGTGAAACGGGGGGAATGGAAAGGCGGCTTCGGCGGACCAGACGGAGGCATGCACAACCATATCTTCGGCTCCCAGGCTCCTACTTCCTACCTGCTGATGGAAAATGTCTTTGCTGACAACGATAAGGCGGACCAGGCCTACCTGTGGGCCGCCCAGGCCCGGCTGGACGAGGCTTCCGGCAACAAGGACAAGGCCATTCAGGCATGGGAGGAAGCGCTCAGGCAAACACCGCTGCACCCGTTCTTCCGCACGGAGCTTCAACGCCTGCTGATGGAAAAGGAAGGCATGCAACCTGTCGACTGGTACGTTTACGCCAAGGACGCCCTTTCCCACTATCAGGGCAACGGCTTTGCCGCATTCGACATCCTCAAGGACGTGCAGAATAAATTCCTGATGGATATTCCGCCCGCAGACCGGATTGCCTGGTTCCGGGACCTGCATGAAGCCATCGCCACCACTCCCACATCATGGGCGGTGAAATTCCAGCCCGTGCTGGACTCCCAATCTGCCTTCCTGGCAAATCCGCAGGAAAAAGCGGCCTATCTGGAAACAGTCCTCTCCACCCACCTGAAAACGGGGGACGGCACCAACTTTGGCCAGGCGTTGGAATGGGGCGTGAAAAACTTTGTGGAAAACGGACAGGCGGACGTGTTCTCCAACGCATTCGCCAAGGTAGCCCAGCAAACGGGAAAAACCGGGACCTCCGGGAAAGCCCCCGACCCCAAAAAGCTGAAGGAAGCCTACGGCAAAGCCATCTATGCCACGGAAACGGCCCGCTCCATTCCGGCCTTCCAGGCCTTGAGCAAAGCGGCGGCCTCTTTCTCCGGCGCCAATGCGACGAACAATACGGTCAAGGCGTCCATTCCGCAGGGCTGGAAACTGGTGCCCGCAGACGGCATGGTCCGGTGCTCCACCACGAGCCAGTGGGACAGCCCCTGGGACCATATCAACCTGCTGCGTCCCTGCGGCGGAGCCCAGCATACGGATAAGGAAGCCAATCCGAACGTCATTGTAGAACTGAAAAACGGAGTGAACCTGGCCGGGCTGGTAGTCACCAAGCGCGACGGCAATGAAAACCGGATGAAGAAAATGGAGGTCTCCACGTCCACGGACGGAGCCACCTGGTTCCCCCTGGCCGCCACGGAAAACATGCCCAAGGAATGGGTCATCACCGCACCGGAAGGCACCAAAGCCAAGTGGATCAAGGTGGAAGCCAAAAACGCACAGCCGGAATTCATGCACCTGCGCCATATCCTCGTTTACGAAAAATAAATTCTCCATTAACGCCATGCTTCATCACATCAATCCCGTTTCCGGCCTGCTGGCCGCAGCCCTCTTCCTGTCCGCCCCTGTTCAGGCGGCCCTCCCCGTTTACAGCGCCGTGAAAGACGAGGCGAAAACCGTCAATAAATACATGATCGTCGTATGGGCCGGCACGGACTGGTCCCCAAAATCACGTGAAATCACCCGCGCTGTGGAACACCTGGCCAAGAACTCCCCGGAACCCGTGCTCTGGTGTATCCAGGACGAGCGTGAGGAAATGACGGAAGAAGAGCAGAAACTGCCCAAGCCCCCGGGGGAAATCTGGAACATCCCCGCCATTCAGGTGGTCTCCCCCACCGGAAACATGGTATTCCTGTCGGAAGGTGTCTCCAGGGAAACATTGCCAGCCGTCATGAAGCAGGCAATGGAAGCCGTAAAACAGCAAAACAAGGCCAATGCCCTGTGGGAAAAAGCCGCCGCTTCATCCGGAACGGCCGCGGCCCTTCTTTACGGAGAAGGGCTTCAGCAGCTTCCTCCTTATGCGGCCTCTTCCAGAAAGGATATTCTGGAAAAAATCAAGAAGGCGGATCCGGAAGACATCAAGGGAGTGCACTTCAAATACACGTTCAGGCATCTGCCCTACATCGAAAAAGTGCAGAGAATGGTGAATGATTCCGCCAAAGACGGCGGTCCAAAGGATTACAAAACCGCCCATGCCTATGTAAACAAACAGCTCAAAACCCCCGGCCTGACTCCCCTTCAAAAACAACAGGTCATGGCGGCCAGGTTCTGGCTCTACCGCAATGAAGGGAAAAAGGACCAGGCGCTGAAGACCCTGACGGACATCGCCAGGATATCCCCTAAAACCCTCATGGGAATAGGCGCCCAGAACTACTACCGCTACCTGACGGAACCCGTCACCCTGAAAAGTCCTCACTTCACAGGCTACGACCTGCGTCCGGAACTGACGCCTACCCGCGTCAACGTCAGCAGCATGCTTGACGGCCCCGGAAACTACAAGATTACCTTCAAGATGAACAGCGGCGGCTGCAATATCCGCAACCCCCGCTTCATGAAGGGCAACCGCGTAGTCAGCGAACTGCCCAAGGACCGGCAGGACAAAAACGGGCGCGAATTCACCCTGCGCCTCTCCGGTTCCGAAAAGCCGGACTTGGTTTTCGACTGCCAGGGCCAGGGATGGTTCGATGCGGACTGCGACATCATCGTGACCAAGGAATCCTGACTCCTGCCGGCAAACGAACAAAACCGCCATTCACAACCTTCACGAAGGCAGGACATTCCCAAGGAATTTCCTGCCTTTTTTCCGATGGGAGAAGAATTTCCAGCCAGCGCCCAAGCGGAAGCATCCCTCTCTCCCTGCTTCTTGCCGGCCAAAGCCGAACAACATTCCCGGCAGCTTCAAAGGAGCAATAAGGGAAACCTTCTTCTCATTCCCGCGTTTTCAAGGAAGAATTACCCCTTCTTCCCGCCTTCCCCGGCGTTCTCCGCCAGTTGACGGCGGTATTCCATGCGTTCCCTGATCCTCAGTTCCAGCCCGTTTCCGGTGGGGGTGTAATACACGCGGCCTTCCGGGAGGTAGGCCTGGGGTACGTAATGTTCCGGATAGTCGTGGGAATACAGGTATTCCAGCCTGGCGGCATCCGCGCCTCCGGCGGCAGCCAGCTTCTTGCGCGTTGGCGTCCGCAGGTGTTCCGGCACTGCCAGCGTTTTCCCATTGCGCACGTCATCCATAGCGGCATTGATGCCCATGTAGGCGGAATTGCTCTTGGGGGCCGTCGCCAGGTAAACCGCAGCATGCGCCAGAGGAATGCGTGCTTCCGGCATGCCGACCATTTCCGCCGCGCGCGCCGCGTCCAAAGCTACGCGCAGGGCGTTGGAATCCGCCAGCCCTACATCCTCCGACGCGGCGATGACCAGCCTTCTTCCTATAAAGCGGATATCCTCCCCGGCTTCCAGCATCATCCCCAGCCAATACAGGGCGGCGTCCGGGTCAGAGCCGCGCATGGATTTGATGAAAGCGGAAATCGTATCGTAATGGGAATCCCCCAGGCGATCGTACCTGATAGCCTTGCGCTGGATGGATTCCTCCGCTACGGAGATGTCCACATGAATGACGCCGTCTTTCCCCGCAGGAGTGGACAGAACAGCCACTTCCAGAGCCGTGAGCGCCTTCCTGGCGTCTCCGTCCGCTTTGGCGGCCAAATGGTTCAGGGCTTCCGCCTCCATATCCACGCGGGATGCGCCCAGCCCGCGCACCTCATCCGCCAGGGCGCGCTTCAGCAGGGCGGCCAGCTCCTCCTCCGGCACCGGCTCCATCGGAAAAACCTGGGAACGGGACAGGAGGGGGGAGTTAATCGCAAAATAGGGATTTTCCGTGGTCGCCCCGATAAACCTGACCGTACCCTTTTCCAGATGGGGAAGCAGAACATCCTGTTGAGCCTTGTTGAACCGGTGCAGCTCATCCACGAAAAGAACGGTTTTCCGCCCGTGCATGCTCATCCTCATCTGCGCCTGGGCTATTTTTTCCCGGATGTCCGCTACATTGGATTCCACGCCATTGAGCATCATGAAATGGGCATTCGTCGTCCGGGCGATGACGGCGGCCAGAGTAGTTTTTCCGCACCCGGGAGGCCCGTAGAAAATCAGGGACGTAAACCGGTCCGTCTCAATTGCCCGTCTCAGCAGCTTACCCGGAGCCAGCAGATGCTTCTGCCCGGCCACTTCATCCAGGGTGCGCGGGCGCATGCGCGCGGCCAGCGGCATGGCGGAAGTGTCCGCCCCGGGCTCAAAGCCTTCCCGCGCTTCCTCTTCCTGTAAACTGAACAAATCCATCGGAAACACTTTACCCGGTTGAAAGGCCGGAGAAAAGGCCAATCCCCGGAATCCTGAAAGTTTGCGGAAGCGACACCGTTCCCCCCCTCAGGACGGAAAATGCACCTTTATCCAAACATGGACCAGCAGGACAGCATGAAAACCATGCTGACAGGAGCCAAAATGCTGAACAGCAGCCCCGCAACAGCCAATCCCCTGCGGTTCACCTCCCAGTAATGAAGGGAGGCGATCAGCATCCAGATGGGAACCACAACCATGTGGACGAGAGCCAGGAGCCCCCAGCCCGCCAGCACCAGCAAAAAGAGCCATGACTGCGAGCCGTCCGCATTATTGCTCGGCATGACAAGGCCAAAGAAGCGGTACACAAGCAAAAAGAAGACGAAGATGGCAAAAGGTATCCATCCCCAGCGGGCTTCCTCCTTCCGGCGGGCCATGGCCAATTCAAAACAGGGATCTTTAAACGCAGGCAATTGCTTGGCAGAAGAATCTCTGAAGAAATACATATCGCCCGAAGTCTCTATTATTACGTCTTTCTTCCGTCAATAACATTCAATGCCAGTCCGGATTTTTTCCGCAGAAAATTTTTCCCTCTAACAAATATGGGATATCGGCTTCCGACCATTACCTTATTGTCAAAGCAAAGTTTTGTGAAAATTCATGAAACGCTCTTGATTATAAATAATTTTCCCTTTTTTTCGGATACTTGCTCTTCGTCTTTTCCCATGATAAAAAAGTCTTTTTGCATCATCCGAATTTCAACACCCGTTCAATTTGTCATACACAGCCCCAGTTTCCCATAAAAAAGACTGCCTCCGCTGCAGGAGCGGAGGCAGTCCGGGGGTAATATAAAACCGGAAATTACAGAAGGTTACCTGCCCAGAATGCCCTTGAAGTATTCCAGCGTCGTCGCCAGGCCTTCCGGCAAAGTATGCCTGGGCTGCCATCCGGCATTGCGGAGCTTGTCCGCACAGGCGCGGGAATGCTTGACGTCTCCCGGACGTTCCGGGGCATGAAGCACCTTGGAGGAAGACCCGGCAGCCTTGATGATGTTCTGCGCCAGCTCTTCAATGGTGATCTGGCCGCCGTAACCGGCATTGAACACGCCGGTGACTTCCGGGTGTTCCGCCACAAAGGTGAGGGCTCCTACAATGTCTTTCACATAAATGAAATCGCGCGTCTGGGAGCCGTCCCCATACACGGTGATGTCTTCTCCTTTGACAGCTTTTTCAATGAAAATGGGCACGGCTGCGGCATAGGCGCCCTTGGGGTCCTGCCGGGGGCCGAAGACATTGAAGAAGCGCACGGCTGCCGTATTAATTTTTCCTTCCGCCCGGAACATGTTGAGGTAGTATTCCCCATCCAGCTTGGTAATGGCATAGGGACTCTTGGGTTCCGGGTACATGGTTTCCAGTTTGGGCACCGTGGGATTGTCTCCGTAAATGGCGGCGGAAGACGCCAGCACGATTTTTTTGACTCCGGCGGCGGAAGCTTCCTCCAGCACGTTCAGCAAACCGTTGACGTTGATGTCGATGCATTCGCTGATCTTGCTCATGGATTCCGGCACGGAGACGAGCGCGGCCATGTGGAAAATATAGTCCACTCCCTGCACCGCCTGGCGCACCAGCTCCCGGTCGCAGATGGAACCTTCGATGAACGTGTGCCTGAGCCCTTCCAGGTTCTTGAGATAGCCCGTGCGCAGGTTGTCCAGCACACGGATTTCCTCCGCCTTATCCTGATAGTGTTCCACAATGTGGGAACCGATGAATCCGGCGCCGCCGGTTACGAGAATCTTCATGTTTTTCCGATGTTGGTAGATATTGGTTAATAGCGGTCCACAATGGCCTTCATTTCATCCATCTGCGCCTCAATGGATTCTACCAGGGCCATCTGCGTGCGGCAGCGGTCCAGGTTGTGTTCCGGACGGTGGATTTTAAAATACACGTCACCGGACAGGTAGTCCGTCAGGAAGCGGATGCCGCATTCCATCGTGAGCAATTTGCCGGAGAACGGCAGCAGGCTCTTTTCCAGAGGCGTCAGGAAACTCTTGGCTGAACTCAGGTAACCCTGAACCAGAGCTTCAAACATGAACATGCGCATCGTGACCTTGGAGACGTCCTTTTCATCTTCCGCGGCGGGAGACGTAGCCGTCCGGATCATGTCGCCGAAGTCGTAAATGGCGGAACCGGGCATCGTGGTATCCAGGTCAATCACGCAAATGCCTTCTCCGGTTACATCGTCCAGCATCACATTGTTCAGCTTGGTATCATTGTGCGTACAGCGCAGGGGCAATTCCCCGGATTTCAGGTAATCCACCACCAGATGGCAGTCCGACTCACGGGAAAGGTACCAGTCTATTTCCTTCTGCACTTCCTTCACGCGCCCCAGGGGGTCGGACTGGATGGCCTCTTTCAGAGCCGCCAGGCGGGAAGTGGTATTGTGGAAATTGGGAATGGTTTCAAACAGGGGCTCTCCGGGCAGGTCTGCCGTGAGCTTCTGGAATTCGCCGAAGGCGCGCGCCGCTTCCTGGCACTGCCTGGTAGTTTCAATCTGGTCATACGTGCGTGCGCGTTCAATGAACGGGTACACGCGCCAGCAGTTGCCGTCCGAATCCAGGGCGTACGGCTTGCCGTCCACGGAGGGAACCAGCGTGAGAGTGCGGCGGTAAGCTTCCGGGCACTGTTCCTCCTTCAGGCGCTTCAGGGCGTGGTCCGTCACGCGCTTTACGTTCTCCATCAGAGGGATGGGCTGCTTGAACACGTTATGGTTCACGCGCTGGAGGATGTAGCGGATGCGCTGTCCCGCCTGATCCACCCAGATACAGTAGGTGTCATTGATGTGGCCGCTGCCGTAGGAATAACCGTGGACAAAATCGCCGCGGAGATCGAACAGGGTTGATACAGCCTTGAGATCGTGCATAGGGAAAAAGGCTTAGTTGAGCTTTTCAGGGTATACGCCCTGCTGGATAAGTTTGTTGATGCCTTCCACAACCATGGGCTTGTCCCCCGGATAGGTCATGCCGAACCAGGAGGAAGAAGTGGGAAGAACGGCGCAATCCGCCCGGTCCGCATGAATCAGGGCGTCCACCACGGTGGGAATAAAGCATTCGCTCTTGGGTTCCTGACCGTGTTCTTTCAGGAAGTCAATGAAATGATCCTCCGTCAGTTCGATGAAGGAGGGGGAGAACAGCCAGAAATTCATGGAAACCAGTTCTGCAGGATCCACCGGCAGGCGTTCGCCCTTCAGGTTGTTGCCGCGCACTACGCCGTCTTCGTCAGCCTGGATTTTCACGAATTCCTCCACGCCATCCAGAAGGCCGCCCTTGATGGAGCAAATGCCGCGGTTCACGTCCCCGTTGTCGGAGAGGGTGTTCTTCAGCGGATAGCCGATCATGCCGTAATGGGCCTTGCCGGGTTCATCCGTAGTGGAGGTCAGAAACTTGGCGGCCTGCACAAAAGCGTCCGCACCGTAAAAGTCATCCGCATTCACTACGGCAAAGGGTTCCTTCACCACGTGGCGGGCGGCACGCAGGGCGTGGGCGGTGCCCCAGGGCTTTTCCCGGCCTTCCGGCACGGAGAAGCCTTCCGGCAGGTCGTCCAGGGACTGGAAAGCGTAATCCACCTCAATCTTGCCGGCAAAACGGCTGCCGACCTTTTCCTTGAAAATATCTTCAAAGTCACGGCGGATAATGAATACCACTTTTCCGAAACCGGCCCGGATGGCATCATAAACAGAGTAGTCAAGAACTACTTCCCCGTTGGGGCCCATTGGGTCAAGTTGCTTGAGACCGCCGTAACGGCTGCCCATGCCAGCTGCTAAAACAAGAAGTGTAGGCTTCATCATACGCAGGCCATTATGACCAAGCCTCCCCGTTTGGCAACAGGAAAATGCCGGAAAACCGGAGAAATCCGCCCGTTAAAAAACGTAAGAGCACCAAAGCCCCAGCACTACTTTTTTGAAATCGTCCCGCCCGTAGCCGCAATACCACCCCAGGAAAGGCTCCACCCGCAGACCGCCGGAAACGGCCCACAGCGCCCCCGTGCGCACCATCAGCTCTTTCAAGCCGTTCCTTCCCAGATAATCGTGGGCAAAACCCAGGGAAACCGTATTCACCATGGCGACGGATTCGCACAGCAGGGGCTGCCAGGTAACGCCCCACTGGGAATATGCACCCTCCTGTCCGGAATCATACAGGAAGGAACCGCGGAAAGACCAGGAAGGGGTGGGATTCCATTTAAGGGACAGGCCCGGCTCCGCCCCGTTCTTCAGGGGACAGGAATCGTACCACTGCCCGTTGACAAAAAGCCCGGCCGTGCATTCGTCCGCCAGATAATACTGGACTTCCCCGTGTAAAGTGGCCTGACTGAAATGGCGGCCCTGCCAGTTGCGGATAGCGAAAAGTTCCCCGCTCACCGCCCAGGAATCAGACAGGGAGGCTCCGCCGGAAATCTGGCCCTCCGTGGAATCAGCCCCCATCTTTTCCCCCCGGTGGACATAGTATGAACGGTAGCCGCCAAGCATCTCCCCGCCCATGCGGATGGGGGAACGGCCGTCCACCTCCGTCAGGGGCGGCGGCCCGTCATACCCCAGGACGGCCTGCACGGCACAGCACAGGATGATGGAAAACAGCCTCATGAAAGACTTTTAACATAATTCGCTGCGCCGCTCCAACGCCAAAACGGTGCATGCACATTCTCCGGGCTTGGCAAAACGGGAATCTGCCCTTACCCTCTCGCGCATGGTTCATTTCACCCTGTTCGGGATACCTGTTTACATCCGCCCCAGCTTCTGGGTGGTGCTGGCCATTTTCGGCGGCGCCCTCAGCATCAGCAGTGTGGAAGATCTCATTTATCCGGCCCTGTTCGTCATTGCCGGCTTCATCGCCATCCTGTCCCATGAAATGGGACACGCGCTGGTGGGCCGCAAGCTGGGCGGCGGCCAGCAGACGATTGTTCTGGAACTCTTCGGCGGCCTGACAAGCAGCCATGGCATGCAACTGACGCGCGGAGGGCGGGCCCTCATGATTCTGGCAGGCCCCATGATGACCCTGCTGCTGGGCATCATCAGCCTGTGGCTCACCTGGAATATCGTCGCTCCTGTCATGGCCTCTTACAACCTGAACTTCTGGGACCTGGCCATCAGCCCCTTCACAGCAGCGCTCATCTCCCCCAAGCTGTACATTCTCTCCTGCCTCATCATGATTGGAGAATGGTGGACTATCCTGAATCTGCTGCCCATCTACCCCCTGGACGGCGGCCAGCTGATCGCCCAGTACATCCGCTCCCCCAGGAAAGTATTCATGACCGGCTTCATCACGGCCATCCTCATCGGACTGCTCAGTTTCCAGCTTTTCCACGGCTACTTTATCCCCATCTTCATGGCCCTCTTCGCCTACAGCAATTACCGGGAATACAAAAACGCCCCCTTTTAATATTATTCCGAGTTTCCTTTGCGGGCCCCGCCCGTTCTATTCAACAAACCATCCAATATGTCTCAGCAAACCGCAATCACCCCTACCCGCGCCCAGGATTTCCCCGAGTGGTACCAGCAAGTCATCAAGGCCGCCGACATGGCGGAAAATTCCGAGGTCCGCGGCTGCATGGTCATCAAGCCATGGGGCTACGCCATTTGGGAACTCATTCAAAAGGACCTGGACCAGCGCTTCAAGGACACCGGCCATACGAACGCCTATTTCCCCCTCCTGATTCCTATCTCCTATCTGGAAAAGGAAGCGGAGCATGCTGAAGGCTTCGCCACGGAATGCGCCGTAGTCACCCACCACAGGCTGGAAGCGCAAAAGGATGAAGCCACCGGCAAGACACGCATGATTCCCACCGGGGAGCTTACGGAACCCTTCGTCATCCGGCCCACCTCGGAAACCGTCATCGGCGCGGCTTTCGCCCGCTGGACCTCCAGTTACCGGGACCTGCCCCTCAAAGTCAACCAGTGGTGCAACGTGATGCGCTGGGAAATGAGGCCCCGAATCTTCCTGCGCACGGCGGAATTCCTGTGGCAGGAAGGGCATACGGCCCATGAAACCCGCGAGGAAGCCATTGAGGAAACCCTCACCATGCACAAGGTTTATGAAGAATTCCAGCGGGACGTGCTCGCCATCCCCACCATTCCAGGGGAAAAGACGGAGGCGGAACGCTTCCCCGGAGCGGAACAAACCTACACGGTGGAAGCCATGGTGCAGGACCGCAAGGCCATCCAGGCAGGGACCTCCCACTTCCTGGGGCAGAATTTCTCCAAGTCCCAGAACATCTGCTTCGCCGGAAGGGACAACACCCAGCAATTCGCATGGACAAGCTCCTGGGGCGTTTCCACCCGCATGATCGGAGCGCTCATCATGATGCACTCCGACGACGACGGACTCGTCTGCCCGCCCCGCGTCGCTCCCCAGCAAATCGTCATCATTCCCGTCACGCCCAAGGAAGAAAGCCGCCAGGCCGTTCTGGACCACTGCGAGGAACTGGCGCGCACCCTCCGCGCCAAAACCTTCCACGGCCAGCCGCTGCGCGTGCTGGTGGACAGGCGCGACCTGGGCGGCGGCGCCAAGAAATGGGAATGGGTGAAAAAAGGCGTGCCCGTGCGTCTGGAAATAGGCCCCCGGGACCTGGAAAAAGGCTCCGTCTGCCTTCAGCGGCGCGACCGGCCCGCCAATGAAAAATCCTTCGTCCCGGAAACGGAACTGATCGATACCGCTGCGGATATTCTCCAAAGCATCCAGGACACCCTGCTTCAGCGGGCCATTGCCTTCCGGGACTCCCATATCCGCCCCGCCTCCACTCTGCGGGAATTGGAAGAAAACTTCTCCGGAGAGGGAGATGCAGATTGGCTCCAGGTGCCGTGGGACGGGTCTCCGGAAGAAGAAGAAGAACTGGCTAAACGGCTGCGCATTTCCATCCGCTGCATCCCGCTCGGCGAGCTGGGCCGCGGCGAACCGGCACCCTGCATCCTCACCGGACGTATGACGAAACGCCGTGTTCTCTGGGCCAGAAGCTACTGAACCCTGACACCCCCTTTCAACGGGATTGTTCCAAGACAGGCAAAACGGGCCTTTTCGGAACAATCCCGTTTTTTACGCCTCTCTGCTTTCCCCAAAAACGGCATGAACAAGAAACGGAGCGGCATACCCCACAGGGCAGAATTCTCCCCTCTCCTGCCCTATACGCTGGCAACAACGCACACATGTCGGCAAAACAGGATTTTTCTCTAAAAAAATCTTGCCAAGGCAAAAAAACCTTATATACTTTTGGCACACGGACGCCGTAAGGCTCCAATCGTTCCAAGCGGATGTAGCTCAGGGGTAGAGCGCAACCTTGCCAAGGTTGATGTCGTGGGTTCGAATCCCATCATCCGCTCCAATTTTTTATAGCAAAAGGCCCGCACAGATGTTGATGATGTGCGGGTTTTATATTATGGTTCGTACAGAAAAACCTGCCCCCCTATTGCAACACCGTTCCAGGAAACAATTTTCATCTGCTCTGATGAGGCCCACCGGACGTTCCTCCTCCATGCATTTCATGGAAATGTTGCCGAAAGGCGGCCTCCGTCAACGTTGCAGACGTTGCCGGTTATCACAGGCGCATCCTGTATTATTGCTTAATGATTCAGTATTTATCCTCCGGGACACAGGCGGACTTAATGATCACCGGATCAAGAACCTGCCGCCATCTGTCATATGCGGAATGATATAACGCAAATCAGGTTTCCTCATCTAAGCATTCGAATATTCGCTGAAGAATCAGCGACAGTTTCATTCCTGCCACGTTCTATTCTCTCTTGACGGCCAGACTGACCATAAGAAGCGTATTCGTGCAAAGCACACTGTCCTGAACGATTCCCCAGATAGAGCGTTCGTGTTCTTGAACGTGCCGTGTGTATAGTACAACTCTTCCATATGGGTTTCCGTCTCATCGTAGACAGAAAAGCGAATGTTTTTGTCGAATAGCGTTACACAAACTATAGGAAACAGGATCAATGATGGCACATGCTTAAAATAATCTTATAACGCTTCGTTCATCAACTGCCGCAGAAAGTCGGTCGGCAACGATGAGAATCTGGCTCCCTCCATCAAGAATTAGAAAAAGATAAGACAAGCAGAGCGATTTCCGTAGAAATAGGGAACATAATTCCGCATTTATTTAATAGAAAAGTCTCCAAGGAAAATATATTTTTTCGCAACAAACCCTGTAAGGCTCTTCTTTTGATTCAGGGGGAATTTTGATTTGCACTTCGATACGGCAGCCTTTCACGAGGTCAGGGTTGTTTAACAGATACTCCTCGACTTCCGGAGGAATCCGGGCATTTGTTCCGTCTTCGACAACGAACCAGATCTCACAAGTTTTACCTCTCCAGGAAGCTCCATCGCCTGTTAATTTCATTCCATGCCGGGCAAATATCTTATGGAGTTCCAAATCCTTTTTCACTGTTTTCAACAGAGTTACACTCCTCTCCATACCGTTTCCCTTATCTATGCCCGACCAGAAGAGGGAGAACGGTAAATAAATGAAGTAAAGAGTGGCTATTCCTATTACAATCCATATTTTCTTTTTCATTGTAAATAATTTATCGGCATTATTTACTTTAGTCTTGTTTCGTTGACCTTCCCGATTTTTTATCGAAGCAATTTACTGAATATCAACATCAAGAATATTCTAGAAATCTTACAGCAAGATTATTCCACATAATTCCGAAATTAAATAACTGAATCAGAAAAAAAATATTGAGCATGTCTACTCAATTAAACAATTGGCATAAATTTATATTCATGCCAATTATTGTTTGGGATGATATTTCCCTTCAAAAGGAATAAATGTCTTACCCGACACCCATGTCCCCTTTATAGGAGATATGTTTATATAATATGACCCAGGCGAAAGGAAGACCTTCTCTTCTTTATCTCGTTTCGCGTATAAAATACCTTTACGCAGAGCAAAATCAATGCCAAAGAATTTCATGTGCTGATTTTCTTTAAGTATAATTTCTTGTGTTCCTCGGGAGGAATGACCAAATGTTACAATGACTTGTAAATTTTTAGTCAGATCTCTCAAGATAATTCGATCTGGCTCCATATGTTGATTTTTATTTTCTTCAGATATTGCACAATCAATCAACAATATCGAAAATAAAATGATAAAGAATATTGAAATATATTTATTGATTTTCATCGCATGGACATTCGATATTTTTTAAAATAGCATCTATTTCATGTTGATTGGCTTTTCCTCCATATTCCCCCATTATATGTCCTTCATGTCCCGCGTTAGGAACGGAAGTTAAGGAGCCATCTGAATTGGCGACATCTTGATAATCGTCAGAAAGCCCCATAAGATGGCCGGCTTCATGCGCATAAACCCATGGGACGGCCTGAGAACCCCATACTCCATATCTGCCTCCAACTCCGTTAACATATGAACGATGAGATGAATCTGATGTTATTTCTATATTATTCTCATCACTATTGAGAAATCTATAAAGCCATCTGCTTTTCCTGCTCTGAACAGACACATCCGCTGTCACATTAACTACACAGCATCCTTTCTCATATCCATTCCAATTGCTTTCAATCGATTGTTTTATTCGCTGTGCTGTAGTACGCATTTCTATATTATTAATTGAATCTCCACCTTTCGGATATATCACGATATTCAGGCTCACATTGATCGAACAATCATCCTTATTTGTAGTTATTGTACGCGCTAATCCTAATTCGTCAGAATATAAGGTAGGCATATTATTAACATATCCATACAAAAGATAGCTCTCTTTTTCTGTCATAGGATCTCTGCTGATCCACCTACCATTTTGGGGATTATAATAGCGGTAATTGTAGTAAACCAACCCCAGCTCGTCATCAGCATACTCGCTGGAGAACCGGAACGGATTCAACTCCGCCGCATTTCCTTCCATCTTCACGGCAGAGCCGTACGGGCCGTATTCGTACAAGGCTCGGCGCCCCGCCTTGATGCCGAAGAGCGCCGTCGTGTTCTTCAATGCGTCATGCGTGTAGTAAAGATCTTCCACGTAGGCTCCCGTTTCATCAAAGACGCCCATGGCCAGAATGCGCGTGGCCACCGGTTCCTGCGGATCCCACAGGTACGTTTTACGCAGGACGGGCGCCACAGACTCCAAAACCTCCGTGGCATCCAGTTCCGCGATTTGCAGGTACCCGTGATAGATGAACCGTTTCCTTGACATAAGCGATTCTCCCTCATAGACGGATTTTTCAACCCGCCTGTTCAGATAATCGTATACGCATTCAATACGCCTGTCCCCCTGTATGAAGCTGACCGCCTGATTCAACGCGTTGTAGGAGGCTTCCCATTCCCCGGTGGCGGTCCGGAGCTTCGTCTGGTTGCCGTCGGCGTCATAATTCGGCACAAAAGGAGCCTCCTCACCTTCGGTGATATCCGTATATTGATTGACCCGGTTGGCAACGTATGTAGTGGGAAGGGAATCCGTTCCCTCCAGGGACGTTTTGCGGTTGCCTATGTTATCGTAGGAGTAGCTGTATGTGCCGCCCCGGCTCATCGCATCGCTCACAAGTTCATTCCTGTCATCGTAGGCGCAGGTGTGCGTCAGGTCGGGTGCAGGCGCGTTAAAGTAGTCTTTCTTCGTGACCGGGCGCCCCAGTATGTCGTAAGAGTAGTCGGTTTTAGCCGGGTAGTTGGCGCTGCCGGGGCGCAGGTAGTCAATCTTCACCGGGAGGTCGCGCTTTTCTTCCAAAGTGCGCCACCGTTTCAGGGTGTTGGGGTAATCAAGAGTGTCCAGCAGGCCGGTTTCCTCATTGTAGCCATAGGTGAAGGGCTTCCCGACGGCGTTCAGCGAAACGGAGGATAGCCTTCCGTAGGCGTCATAGGCCCATGCCGTCTGCAAGGCGGGGGAGCCCGCATAATCCAGACAGTAGCCCGCAGGCCTCCCCACTCCGTCACGGCGGAGGGTTAAGACGCTTGCCGCAAGGCCAGCCGTCGTTTCCGTTTCCTGTTCGTTATACTGGTTGTACGTGAACATGCGCGTTCCGGAAGCGTCGTCAATCTGCGTCAGCTGCCCCAGATGATTGTAGAGGCTGCTGATGGAGGGGGTAAAACCGTCGTCATTAAAGGTAATGGAAACGAGCTCCCCAGTAAGGGGGGCGTAGGAGCGCGAAGCAACGGTTCCTCGCGCGTCCGTCGTAGCGGCAACGCGGTTGAGCATATCGTAAACGGTATCCACATGGGTGGCGTCGGCATAGGTTTTGCGGATAACCAGCCCCGTAGCGATATCATACGTCCATGTGGTGATGTCCCCATCCGTCCGGAGGCGGGGATCAGTGGTAATGACATCTCCGGGAACACGGAAAGTGGAAAGGCTAACCATATGGCCGGCGGCGTCATAAGCATATACGGCGGGCTGTACGGCTGTCCCCCATTCGGCTTCCTTGCGGCCCCGGATGTCATAGAAAGAACAGGCCGTTTTGCCAAGAGGATTAGTAACGCAGGTAGGCAGATCAAAAGGCTGGCCATAAGCGTAAGTGGTCGTGTTGCCCGCGGCATCCGTGCCGGAGATGATGCGGCCGGCGATGTCGCAGACGGAGGTAAAGACATTCCCGCGCACATCTGTGTCCGCGTAGGTGATGCCCGTTTCCGTATAGGTGCGCCGCCGAAGGACAGTTGCACCGTCAAATCCCGTAACCGCCGTCGTGAATCCGTCCACGATGCGCGTGAGGAAGACGCTCTCGACGCCGGGCACGGTTTTCTTCCGCAGGCGAACAGAACCGCCGCCATATTCCATCCATTCCACGGCATCATGGCCGCGGGAATCCGTCATGACGGTTTTGCCGGCAAGGACAGGGCTCAGGGCAGAGAGCAGGGCCGCCCGCTTTTGAAGAACAGGAGTACCTGAGGAAGTATAGCTTGTTATGGAAACAAGACTATAGACTCCGTCTTCACGGCTCTCGGTCACATAAGCATAGTCAGTAATGCGGGAGTTCAGGATGGTCGGGTCATCCTCCAGGGCAACGATTGCCCTGGTCCTGTTCCCAAAAGCATCATACGCGTAGAGGGCGGGCGCCATATCCGCCAGCTGTTCCCTGATGAGGCGCCCCTTGTTATCGTATGCAAGCGTGCGCACGTTTTCCCCATTCAAGGTATTGGCGGCAGCGACCCGTACAAGGTCGCCAAAGCCGTTGACAGTGGAGCTTCCCGTCACGACGTGGGGATCGCCCCCCTCCCCGCCCGGCACAGCCTCCGTCGTGCGGATCCCTTCTTCCGTGACCTCAACAGTATAGAGGATATCCTGCTGCCCGGTGCCGTAACGCCGCAATATTGTTCCGGAAGCACTTTTCCGGGTGATGAGGGTAGCGCCGGCGGGGGTGGTAACAGTTTCTACCAAGCCATCCCCGCTATAGGAGCGAACGGTGGAGCGCCCCAGAATATCGGTTTCTTTTACCAGACGCCCCAGCAAATCGTATTCCCGGCTTTCCACCGTCGTCATTGGGCCTGTATCCCTGCGCAGGGAGGTAATTTTACCGAGGGCATCACGAGTGTAACTCGTGATGGTTTCCGGAGTGATCATGGTTTCCCCGTCTGCAATGGCTGAACGGATGGTTTCTGTCAGCTGGCGGGCCGTATTGTAGGAATAGGAAGTCGTCACGCCATTCTCATCCTTTTCCCATAAAGGGCCGCAACAGGTCATCATGCGCTCGTAAACGCGTCCATTGGCGCGGGTGCGCTTTACCCAACGGTTCTCAACATCATATTCATAAGAAGACGCATCCGTCATTGACCAAACACCATCGGCAAGCTGTACGTATTGCTCATAACGCATGTTATTGCCCTGGCCGGAAACGTAAGTAACCTTCCGTGAACTCAGGCCGGGCACGCACGCACCCGCTATCCGCGTTTCCGCAGTCACCTTGTAAAGGGCTCCGTACTGGTCCGTCTCTTCATAAGCGTAATGCGTCTGAATTCCGCCCATGTCCTGTTTCATTTTCAAGCGTCCGCGGGCATAAACATTGGGAGCCGTGCCGAGCCAGGATTCCTTCACTTCCAGCCGTACATTTTCTTCCCCCAATGCGGTGGTGCGTTTTTCCACGCGCCGCACATGATTTTCCTCAATGTAAGTATAATCCGTTCGCCAGAGTTCCGCCGCAGTAGCCGTCAACGTCACCCATTGGGTGAGAATATCCGGATCCGCCTGCGTCTGATCCCGATAAGTGGTGTAAGTGACTTTATCTCCAGCTTCCGCCCAGGGGACACTGGACACGATTTCACGCCCAAAACGGTCGTACGCCCATGTTTTGACACTTCCGTCAGGAGCAGTTTCCTTCGCGATTTTTCCCATGCCTGTGTACTCGTAGGTCGTCTCCCGGGCGTAATCCGTTCCGTAGCCTTCAATACGGCTGGTGCACAAATTGCCATTGCGGGTGACGGCATAAGTCTCACAGACACGGGAGATAGGGATACCGTTTTCCCCTCTCTGGATGGTAGTGATGAGTTTCCACGTTCCGGGAGAGACAATCTGCTTTTCCTTCAGAGTGAAGATGGAATCTTCTCCTTCCCCCTGAGACATGTTCCATGCTCCGTCAGTTCCCTGCCAGTAGCGGGTGGTGAAGGGCGCGCGCCCGGCAGTTTGTTCGGTGACGGCAAGCCTGCCCGCTGCCGCGTCCCCGGTGATCGTGAATGTTTTAAATGGGTCGCCCGTAACGGGATAAAGGCCGGTGGAGCTGTTTTTCCCATCCACCTGTTCGGGGAGGTAGAGGGCGATGCGATATCCGGATTCCGTAACATTTTCGATGTTGGCAAGCCCATCCCACAGGTTCCATATCTGCCGGATGCTTCCATCCGAAGCCCGCACGATATCCAGCTTTCCGTCAAATTCTTCTCTTGTGTAGGACGCGCCATTTTTAGCGGTGTAGCCGGTGGAGTATTTCAAGGAAGAGGCCACACCTGCGGTATAGTTAACAGTATTGCCCGCGGTTGAACGGATACTGAGGCAGAATTCTCCCGCCTTTGCTGAGGAAGCGGACCTCCTCCCGAACCACGCAGACCCTCCGCGCTTGGCGGATCCCGCAATGGGGGCAACACTGTCAACCCCGCTGCCGCCACCATCCGCGTAGCAGTAGTAATTAATGTCAGTTCCGCCGCTCCTGATTTGGAAAGCGGTATTCGCTCCAATTCCCGATAGCGGCGCCGAAATGATTTCATGAGTCATCGGATGCCAGTAATGGAGCGCACGAGGGGACCAGAGTTCAGACGAGAATGTGTACCCTACTATGTCCAGCAACCCCTGCGGCAACCCCGTCATGCCCCGGAAAGAGCCAAAATTGGTACGCCAGACCATTTCCGTCTTGAGCGTCCGGGCGACCACGCGCCTGCCGGCAGCCGACGAACCATAAAGGGTGGAGGAACCGCGCACGCCGTGCGCCGAAGAGCTGCTGCTGCCGCCCTCATTGTTTTCACAGGTGCAGGGGGCCTCATCTTCACCGTTATCCCCTTCTCCGTTGTCTTCCTCCGGACAGGGAGAACTGGAACTGCTTTGGGAGCCATTGCCGTCCCCCCCGCATTCACACGGCACGTCTTCTCCCTCATCTTCCGTACTCACAGGTTCCGGATAACCTTCATCCGGCTGATAGGAGGATGAAGAGGTGCCATCCGAGACCATGACTTCAATGGAATAATTAAGGATTGCCAGATTAGTCCATTCAGGAGGAACAGCAACGTTCTCATAAGTCATGTGTACACGGTGCGGTCCCGGTTCAATGGGAAAGCTCAAGCTTTTTTCCCTATAAGTGCCTCCATGGTATTTACCCTCCCCTGGAATGAAAATTCCTTCACCGTCAACGGTCAGAGTACCGTTATCATCTACCCCCAGCATTAAACGGCACTCTGCCGTTTCCCCCGGACCGAGCGGATCAACAATAATCCCCCAGTCCCAGCTGCCGCTAAACGCCCCGGCCTCCTTGTCAAACGGAGCCGGCCCACCTCCGGACACTGTGAGAGCATTATCAGGCAGAGAATCCACATGATGCTGAAACTGCCGTTTTTTCATGAAATCCAAATAGTTAGATATTAAGGGACGAATATCGGGAAGCCAATGCTTCCCCGTATTTATTACTCTTTTCTATTTCAGGAAATAAAACAGAACAAGGATTTATTTTTGAATAATTGTTATTAATAAGAATAAATTTCTATCAATTAACATTTTATAAAATATATATCTTTCTTGAAAATTCATAAGGGATAACCATGTGTTGACGCGTTTCCCAACTCCCGGCCATTCACTGCCGGCCAGGATATCCCATCCGGAAGGTCTAAAAATTTCCCGGCCTCCATTAAGACAAACTCCGGCGGTTCTATCTGCATCATCCCTTCAACGAAAGGGTCTCCCGCCTGGTCGTCCAGATAAGGCCGGTGTCGGCACTTTCTTTACGCCGTCCCAATTCTCAACCCGGAATAAACCAATGAATAATACGACTCAACCAGGGGCTGCTCCAACAGGCCCCGCAACAACCAATAACGCTCCGGCGGTAAAATCCGTCCTCCGCATGGGAGTATTCACTCTCGCCATGATCAACGTTTCCGCCATTGTCAGCCTGCGCGGCATGCCTGCGGAAAGCACTTACGGACTGAGTTCCGTTTTTTATTACATTTTCGCCGCGGTATTCTTTCTGGTGCCTGTTTCCCTGGTCGCCGCGGAGCTTACTACCGGATGGCCCCAAAAGGGCGGCGTTTACCGCTGGGTAGGCGAGGCATTCGGGAAAAAATGGGGGTTCCTGGCCATCTGGCTGCAATGGATTGAGAGTACCATCTGGTTCCCTACCGTTCTGACATTCGCCGCCGTTTCCCTGGCTTTCATGGGGCCCGGACAAAGATGGGATGAAGCGCTTGCCGCCAATAAATGGTATGTTCTCATCGTGGTGCTGTGCGTGTACTGGGCGGCCACCCTGCTTAATCTGCGCGGCATGAAGACTTCCGCAGGCGTCACCAAATGGGGAACCATCATCGGAACCATTATTCCCGGAGCCATCCTGATCCTGCTGGGCCTGGGCTATTGGGCCGGCGGCAACCCGATCCTGCTGGATATGAGCTGGGACAAGCTGGTGCCGGACATGAGCAATTTCAACAACCTCGTTCTGGCAGCCAGCATCTTCCTGTTTTACGCGGGGATGGAAATGTCCGCCGTGCATGTGAAGGATGTGAATAATCCCGGACGCAATTATCCGCTGGCCATTCTGATTTCCGCCATCATTACGGTGCTTATTTTCGTTCTAGGCACGCTGGCCATCGGCTTCATCATTCCCAATTCCCAGATTAATCTGGTGCAGAGCCTGCTGATTACTTATGACAGCTATTTCAGCTTCTTCGGCCTCGGCTGGATGAACTGGATTCTGGCGCTTGCGCTGGCCGTCGGCGTTCTGGCCCAGGTAACCGCATGGGTGGGAGGCCCCTCAAAAGGCCTGTACCAAGTGGGCCTGGCCGGCAACCTTCCGCCTGTCATGCAGAAGCGGAACAAGAACAACGTCCAGATGGGCATCCTTTTTATCCAGGGGGGAATCGTCACCCTGCTTTCCATCATGTTTGTGATCATGCCTTCCGTGCAATCCGCCTACCAGATTATTTCCCAGCTGACCATCATTCTGTACCTCATCATGTACATGCTGATGTTCGCGTCAGGCATTTACCTGCGCTACCGGGAACCGAATACGCCCCGTACTTTCCGCATTCCCGGCGGCAGAACCTTCGGCATGTGGATTGTCGGAGGGCTCGGCTTCCTGGCCAGCCTGGCGGCTTTCCTGGTGAGCTTCATCCCGCCTAACCAAATTACCGTAGGCAGCAGTACCATGTACATTCTCCTTCTGGTGGTGGGTACCTTCATTTTCGCGGGTATTCCCTTTATCATCCATGCCATGGCCAAACCCTCCTGGAAACGGCCGGTGGATCCCGAAGACGCCTTTGAACCCTTCGGATGGGAGAAAAACAATGATTCCCATTCCGCAGCAACCCCTAGCCATTCCATATCCCATGAGTGACATTCCTTCCAATGAACAGATCCGGACGGCCCTGGATAACGCATACGCCTACGCCAAAACCGTCCAGGGAGGCAAAAACGCCTCGTACATTCCCGCCCTGGCCCAGGTGCCTTCCGACCTGCTGGCGATTGCCGTGGTCACCGTCAACGGAGACCTGCTGACCGCAGGCTCCGCGGACGCGCCCTTCGCCATTGAATCCATTTCCAAGGCATTCAACCTGGCTTATGTCATGGATCTGATCGGCATGAAGCAGCTGCGCGCGAAAATCGGAGCGGACCCCACCGGGGAACCCTTCAATTCCGTAATGGCCATTGAGCTGCACGGCGGCAAGCCCCTCAATCCCCTGGTCAACGCAGGGGCCATGGCGACGGTCAGCCTGGTCAACGGTTCGGATTCCGATGAAATATGGGGAAACATGATCCATAATTTCAATAACTTCGCCAACACGGCCCTGACTGTGAACCAGGAGATTTACAAGTCGGAAAGCGCTACCAACCAGCACAACCGCGGCATTGCCTGGCTGCTGGACAGCTACGGCTATTTTTACAATACGCCGCCCATGATTGTGGATTTGTACACACGCATGTGTTCCCTGAATATCACCGCTTCCCAGCTGGCCCTGATGGGCGCCTGCTACGCCAACGGCGGAGTCAATCCCGTCAGCAAGAAGCGTGTGGTGAAAGAGGAGAACGTTCCCCCCATTTTGGCGGAAATGTGCATGTCAGGCCTTTATGATTCCACAGGGGACTGGATGTACAAAGCCGGCCTTCCGGCCAAGTCCGGCGTGGGAGGCGGCCTGGTAGCCGTAGCTCCCGGCAAGCTGGCCATGGCGGCTTTTTCGCCTCCCCTGGATCCCGCAGGGAATACCGTCAAGGGGCAGGCGGCACTTCAATCCATCATCAGGGAACTGAATCTAAACCTTTTCCGGAGTTGATCCGGGATGAGAAGGTAAACCCGCGGAGCCAAAATCCGAAAGGATTCCCGCGGGTTTACTGTATCCTGTCCAGCCCCCCGAGGCAGACCGCAGTTTCTCGACTGCGGATTTCAGCCATAAAAAACAGCCTTCCGGCACAGTATGCTGAAAAACGGATATATCCGGCGGACAACGGCCGCATCGCCTGCGCCCCGGCAGCCGCAGACAACCTCTCCCCGGCGCCCCGCATCAGCTCCTGTTTGAAAAGTTGCACAGCTGCATGGCTACGCCGTCTTCCATAACCCGGAAGCCGTGCTTCTCATAAAAAGCGGCGTTTCTGCTCTCCTCCGGCATGATTTCAAGATACAGGTAGTCCCTGTATTTTTCCTTAACCATTTCCACCATTTTTCCAGCAATCCCCCTGCCCTGATAAGCGGGATGCACCAGAACATAATGCATGTAGGCAACCATCTCGCTGTCGTCCAGCACCCGCACCAGCCCTGCCAGGCGGTCACCGTCCCAAGCCGTAACAACAGTGGACGAATTCATGAGAGCCTTGAACAGACGCCCCGGATATTGTCCGGAAATCCAGCCAACGGACAGGAACAATTCTTGAACATCTTCCCTGGAAAATTTCTTCTCTTCCGTAAAAACAATCATTTTTTCTTTAATTAATTATTGATTATCCGCCTGTACCGTCACGGCCACACAACCGCCTGAGCGGGAAACGCCTTTCCCTCGGCCGGATTGACACCCTGTTTCAAGGTTTTATCTGCTCCCACCGCCGCAGTCAACACCGTCTTGTCCGAAGGTTTTAACCATTCCAAGGACATGGCGGAGCCTGTGATAAGAATGCGCGCGCCGGAAACAGGCAACCCTTTCCACAGTCAGCAGCTCGTCCGCACCTGCCTCCTCCGCTTCCGGGGAAGGAAGAGCCGCACCAGGAACACACATCTTCTTTTCAGGGGAACGATACTCGAATACGGGAGACGGCATTTTTCCTGGAACCGATTCCCCAAAAACGGAAAACTCCGGGCCCAGGCTCATGACATTGTGAAAAACCTTTCTCCGTCATTTGTTTAAGGACTTGTCACCACGCTTCTTTTCCCTAGAATCCCCCGGTATGCTTGACGCCCTCCAAAATGCCTTTTTCCAGACCGGGGACACGCTCACATCCTGGCTGTCCGCTTTCAGCAGCTTCCTGTGGGGATGGCCCCTGCTCATCATGCTGCTGGGCACGCACCTGTACCTCACCGCCATCCTGCGGTTTCCGCAGCGTTATCTGCTTAAGGCCCTGAAATTGTATTTTGTGAAAGATCATACGGACAAGGGAGATATCTCCCCGTTCAGCTCCCTGATGGTGGCCCTGGCAGCCAACATCGGCGCCGGGAATATCATCGGCGTGGGCGTGGCGATTGCAGCCGGAGGCCCCGGCGCCGTCTTTTGGTGCTGGCTGACGGGAGTTCTGGGAATGGCAACCCGTTATTCGGAAGGATTGCTCGCCATCAAATACCGGGTGGAAAACAAGGACGGCAATATGAGCGGAGGCCCCATGTTCGTGCTGGAACGCGGTCTGAACAGCAAATGGCTGGGGGCAGCCTTCGCCGTCTTCACGGCCATTGCCGCCTTCGGGATCGGCAATCTGACGCAAGGGAACGCAGCGGCGGAACAGCTCCACCACGCCTTTTCCATTCCCTCCTGGGGAACAGCCGCCGTTCTGACGGCATTGACGGCTCTGGTGATGCTGGGAGGCATCCAGGGCATTGCCAGGGTATGCGCTTTCTTCGTGCCGTTCATGGCCGTCATCTATATCATGGGATGCCTGTATATCCTGACCGTACAGGCAGAGTATATTATTCCCGCCATCCGGTATATTCTGGACTGCGCCTTCACGGGAGAAGCCGCCGCCGGGGGCGCAGCAGGAGCGGCCGTCATGGCGGCAATGAGAACCGGTGTGGCCCGGGGCCTCTTTTCCAATGAGGCCGGCCTGGGATCCGCAGCCATCGCCTCCGCCGCAGCGCAGAACCGCAACCCCGTCAGGCAGGCGCTCATCTCTTCCAGCGGCCCCTTCTGGGATACCGTCATTATCTGCGCCCTGACGGGAATTGTGCTGACCACCAGCATCATCGCGCATCCGGACATTTCCTGCACGGACGGCCCCAGGCTTACTACGCTGGCATTCAGCAAAATTCCTTACATAGGCTCCCCTCTGCTCACGCTCAGCCTGGTTACGTTTGTAGTTTCCACCATTCTGGGCTGGTCCTACTTCGGAGAAAAAGCGCTGGAATACCTGGGAGGCATCCGGCTGATCATGCCTTACCGCGTCATCTGGGTGGCGGCGGTCTTCATCGGCTGCGTCTCTAAAATAGAACTGGTATGGATTTTTGCAGACTGCGCCAACGGCCTGATGGCCCTGCCCAACCTCATTTCCCTGCTGGCACTTTCCGGCGTGCTTGTCCAGCAAACGCGGTACTACCTCTGGCAGCACAGGCTGGACGATTACGACGAATCCCACATACCGGAAGGAAAATAACGATTTTTTTAACTCGGATTCAAATAATTCTTGCCTTTCGGCTTCCGGTAGTATTTACTTCGCCGCGCAGTCCCTGCAAACCTAAAAGTCGCGTTCGTCTAGCGGTCCAGGACTCCCGCCTTTCACGCGGGCAACACGGGTTCGAGTCCCGTACGCGATGCCAGCTTTACCGCCGCGGCAGTTACGATAATTCCGCTTTTCCCCTTTTCAGGGAAGCATATTGGAAAAACGTAATGGCTGCGGTTTTCTTTTGGCCCGGTTTTCCGGATTCAGGAAAGCGCGGCAGAGGCGCCGGAAGCGGAATCCCTCCCTGCTTTTAAATGCCGGAACATGTCTTACATCCCCCGGATTTGGCTTTCAGAAAGCCCCGCCATCTTTTAAAATCCTTTACACGCAAACGCCATGGATTACACACCTCTCATTGAAAAACGCCGCCAGCGCCTTGAAGAATTGGAAACAGTCATTGCCGAACCGGACTTCTTCAATGACCAGAAAAAGGCCTCGGAAATCATGAGAGAGCACCGCCGCCTGAAGGAACTCATGGAAACATGGGATTCCCTGAACGCCACGGAACAACAGCTGGCGGACAACCAGGAACTGGCCAAGACGGACGATCCGGAATTAGCGGAACTGGCCGCCCTGGAGATTCCAGAACTGGAAGCCGCCCTGGAAAAACTGCGGAGCGACGTGCAGTACAGCCTGCTACCCCGCGACACGACGGAAGACCGGGACGCCATCATTGAAATCCGCGCCGGAACGGGCGGGGATGAAGCCTCCCTGTTTGCCGGAGACCTGCTGCGGATGTACCAGCGTTTTGCGGAAGAACGCGGCTGGCGCTTCGAGCATCTGGAAAGCAGCCCGTCGGACGTGGGCGGATTCAAGGAAGTCGTCTGCCGCATTGCCGGGGAAGAAGTGTTCCGCTTCCTGAAATATGAAGGCGGCGTGCACCGTGTGCAGCGCGTTCCCGCTACGGAAACGCAGGGGCGCATCCATACTTCCACCGCCACCGTGGCCGTCATGCCGGAAGCGGAGGAAGTGGACATAGAAATCCGCCCGGAAGATCTCCGCATCGAAGTATGCCGTTCCGGCGGAGCCGGAGGCCAGCATGTGAACAAAACGGAATCCGCCGTTCAGATATGGCACATACCCACGGGCGTTTATGTCCGGTGCGAGGAAGAGCGCAGCCAGATGAAAAACCGAGAAAAAGGCATGAAAATCCTGCGCGCCAAGCTCTTTGAAGCCAAAAAACGGGAAGAGGCGGAAAAATACTCCGCCGCGCGCCGCAATCTCATCGGCTCCGGCGGCCGTGAAGAAAAGATACGCACATACAACTTCCCGCAGAACCGTCTGACAGACCACCGCATCGGCTACACGTCCCACAATCTGGACGGTATCCTGATGGGGCAGCTGGAAGACCTGATTATGGCCCTCCAGCATGCGGAAATGCAGGAACGCCTGGCGGAGGCCGGAATGTCCTAACTTCCTTTTTCCTTTCATGAAAACTTTACTGGAAGTCCTTCAGGCCGGCACGGATTATCTGGCCCGCCAGGGGTGCGACGAGGCACGCGCCACCATGCAGCATTTGCTGGCCCATGTCCTGCACTGCAACCGCACGGCTCTTTACTCCCAGTTTGACAGGCCCGTGGAAGAAGCGGAGCTGGCACCCCTGCGCGAACTGCTGAAACGCAGGGCAGCGGGAGAACCGCTGCAGCATCTGCTGGGGGTCACGGAATTTTTCCGGAGAGATTTCCTGACGGATGCCCGCGCCCTGATTCCCCGCCCGGAAACCGAGGAACTGGTGGAAATGGTGTTAAAGAAAATCCCGGACCATCCGGTACGCATCCTGGACATGGGCACGGGCTCCGGCGTTATCGGCGTCACGCTGGCGCTGGAACTCAAGGAGCGCGCCGGAGAAGTCGTGCTGGCGGACATTTCCCCGCAGGCTCTGGATCTGGCCCTGGAAAATGCCATGCGGCTGGGAGCCAGAGTCTCCACCATCCAGACGAATCTGTTCGCCAACATTTCCCAGGAAAAAACGGATCCGCATGCGGAGGATGCCGATTCTGCTCCGGAGGGAAAAAAAGGGGAAAACGGGCGGAACATGCTCTTTGACGTTATCGTGGCCAATCTGCCATACATTGCAGAGGGGGAAAAACTTGCCCCGGAAGTCATGAAAGACCCCCATACAGCCCTGTTCGGAGGCCCTAAAGGCTGGGAAATCATTGAACGCTTTCTGGCCCGCGCCAGGGATTACCTGAACGAAGACGGCTTTGTAGCCCTGGAAATCGGGTACGACCAGGCATCCGTCGTAACGCAAATCATGGATGGATACGGCTACAATTACATTGAAGTTCTGAAGGACATGAGCGGCGTCTCCCGTTTCCCCTTCGGCTATCGTTAATGTTATCCTCCTTTTCACTCCATGCAGGCGGCGCGCATTCTGGTTGACGGACAAAGCGATCTGGTACTGGATTACGGTATTCCTCCGGAAGCGGGAGACGTCAAGCCGGGCTGCCGCGTACAGGTGCCCCTGCGCAACAGAACGGCTACCGGAACCGTGCTTACGCTGTCAGAACCGGCCCCGGCCTGGAAGGACAGGCTCAAGCCCATTCTGAAACTGATCGATCCGGAGCCCCTGATTTCCCCGGTGATGATGAATCTGGCCTCATGGGCGGCGGACTATTACTCCGTGGCTCTGGATCAGATGATCCGGTGTCTGCTCCCGGAAACCGTTCGTCAGGAAAATACGGCGGAAAAAATGCGCAAAATGGTGTATCTGGAAAAAACTCCGGCACGGGAGGAACTGGACGCCCTGTACCGCAAAGCACCCCGGCAGGCCCAAATGCTGGATTATTTCTCATCCGCGAAACAACAGTCCGCCCCCCTGGCGGCATTCGGCGCCGGAGCCCTGAACGTTGCCCGCAGCCTGGAAGCCAAAGGCTTCATCTCCCTGAAAGAAGAGGCCGTGCACCGGGACCCCAGCACCGGGGAGCAATTCGTCCCCACCCAGCCCATGAAGCTGAACAGCCAGCAGCAGAAAGCGCTGGAAGAAATCACGGCCATGTGCGCGGCAGAGCGCAAAAAACCGGTCCTGCTGCAAGGAGTTACCGGTTCCGGCAAGACGGAGGTTTACTTACAGGCCGTTTCCCAAATAGTAAAATCCGGAAAATCCGCCCTGATCATGGTGCCGGAAATTTCCCTGACGCCCCAGACGGTTCAGCGTTTCAAATCCCGTTTCGCAGAACTGCCTTCCTCCGTGGCAGTGCTGCACAGCCTCCTCTCGGACGGGGAGCGCTTTGACGAATGGCATGCCATCCGTTCCGGAAAAGCCCGCATCGTCATCGGTCCCCGTTCCGCCGTCTTCGCCCCCCTCCAAAATCTGGGCCTGGTGATTGTGGATGAAGAACACGACGCCTCTTACAAACAGGAAAGCTCCCCCCGCTACCACGGGCGGGATCTGGCCGTGCTGCGCGCCCATCTGGAAAATTGCGCCGTGCTTCTTGGCTCTGCCACTCCCTCCCTGGAAAGCATCCATAACGCCCTGATTGGAAAATATTCCCTGGTAAAACTGACGGAAAGGGCGGACGGCCAGCAGCTCCCGCTCATTCGCATCCTGGACATGAAAACGGAAGGAAGGAACAAATCCGGTCCCAACGTTATCTCCGAACGCCTCAGGATGTCCATTGACCGGCGTCTCGACAAGGGGGAACAGGTCATCCTGCTGCTCAACAGGCGCGGATTCGCACGCTCCATCCAATGCCCGGACTGCGGCCACGTAGTCACGTGCCTGCACTGCTCCCTGCCCCTGACCTACCACCGTACGGAAGACCGCCTCATGTGCCACCTGTGCGGATTCAAAGCCCTCCCGCCCCGTTCCTGCCCGGAATGCCGGTCCGCCAACATCCTGCTCCAGGGGTACGGAACCCAGAAAGTGGAGGAACTCCTGCGCCGCACCTTCCCTGCGGCGCGCATCACGCGCGTGGATGCGGACGTGGCCCGGCGGAAAAACGCCGTCCGGACCATTCTGAACCAGTTTCGCGCCCATAAGATAGATATTCTCCTGGGCACCCAAATGATCGCCAAAGGTTTGGATTTTCCCAACGTGACGCTGGTAGGCGTACTGAACGCGGACCTGGGGCTCCACATCCCGGATCCCCGTGCAGGGGAACGCACTTTCCAGCTCCTGACGCAGGTAGCGGGCCGCGCCGGCCGCGGCGATCTTTCCGGAGAAGTCATTATCCAGACTTTTACGCCCCAGTCCCCCTCCCTGCAATACGCCCGGCACCATGACACGGACGGCTTTGCAGCCCAGGAACTGGAAATGCGGCGTACTTTCGACCTCCCCCCCTTCACCCATATCGCCGTCTTGACCATACGTTCCCAGCATGAAAGCATGGCGGAATTCGCCACGCAAACCCTCGCGGCCCGCCTGCGCGGCATGCTCCCCCCTCCCGCCACGATGACCGACCCCATGCCTGCCCCCATCCCCCGTGCGCACGGACAGTTCAGATTCCAGATTACGGTCAAGGGGCCATCCGCCCGCATCCTCTCCCGCACTCTCCGGAAACTGGTGCAGGAAGCCGGCCTGGGGGAAGACTTGACGGCTGTCATTGATGTGGATGCCATGTCATTCATGTAAGCCGCCTTGCCAAAACCGGCCGGGAACATTAGACTTTCGGCACAACACAACTTACTATTATGCTGCTTCACTTCTACAAGATGACGGGTGCCGGCAACGACTTTGTCATGGTGGACAATCGTGACCTGAGCCTCTCCTCCGTGCTGGACAGAGAAACCATAGAAGCCCTGTGTGACCGCCGCTTCGGCATTGGGGCTGACGGGTTGATCGCCGTGGAACCTTCCCAGGGCAAGGGGGGAATTGTTCGTATGCGTTATTACAATGCGGACGGCGGAGAAGCGGAAATGTGCGGCAATGGCGCCCGCTGCTTCGGCAACTTTGCCGCCGCCCTGCTTCATCACGACAAGACAGCCCCCCTTCCTTTTGAAACCATGGCCGGCGTCGTCACCGCAACCTTTGAAAAAGACGGCAACGTCACCGTAAACCTGACCAATCCCCACTCCATGCAGCTCTTCGTGCTGAATGCCGATCCGACTGTGGGGGCAGATGTCCATTTCCTGAACACGGGCGTCCCCCATGCCGTTGCGTTTCTGGACAAGCTGGAGGGCCTGGACATTTCCAGGCTGGGAGCCTATCTGCGTTACCATGAGGCCTTTTCACCCAAAGGCACCAATGCCAACTTCGCCAGAATCCTGTCTCCGGGGCACATTGCCATACGTACTTACGAACGCGGCGTAGAGGATGAAACGCTTGCCTGCGGAACGGGAATGACCGCCTGTGCGCTGCTGCATGCCGTATTGACGGACGCCCCCTCCCCCATTCAGGTGGACGTGGCGGGCGGAGACACGCTTAAAGTCGGATTCCAACGCACAGGGGACCGTTTCACTGACGTCACGCTGACCGGACCGGCAGACCTTGTGTTCACCGGAGACATTCAGATCTAAACAGCTTCGACCTGAAAAGGCGGACTCCTTCCCATCAGGGAAATTATCTGTTCAGCAGGGAACATTCCGCCCGCACATAACCGCCGTAATTATTGCAGAACGCCTTGTACACGGCAGGGCCCATATTCGTTTCGCAATAGTACTTTACGGTAATACAGCCGTTTTCGCGTTCCATATTAAGAAAACGGCTGGACAGCAGTACCAGATTGCTCTGGTAATTCCACAAAGCCATGTCACGCTCCATTTCCTCATGAATCCTGTCGCATATAAAGCTCTCCGGAGGAGGAAACAGAATGGCGCTGACAACACCGATCAGACAGATTCCCGGAATGACCCCTACGGCAAGAACCCCGTGACATTTCCTGACCAGGGGAAGGTGATTCCTGAATTTCCGGAAAATGGTGAAACGGAATAAATAACCCGCCAGCAGCGCCACCGGGAAGAACACATTCACGTACTCAAAAGCCAGGCTCATTCCTCCAAGAACGAAAAAACCGATGCCCTTCCATACCTTCCGGTAAACAGGAATGCAGGTACAAAGAAACACGTACAGAAGATATCCCAGCAGGCAAAGGAAAGGCAGAAGCGTTCCCGTAAAAATATGCTCCACCATGCAGTCCCACGGATATTCCCTGCTGGGAGCGAAGGCTTCATGGAAAAACATGCATACCATGCTCAAAAGCACCAGCGCGGCAAGAAGGATGGTCTTTACTTTTTTTGACTCATACAAAAGCCACAGCAAAACAACTCCGAACGGCAATAAATAGCAACCTATATTGTTGAATACAGTGGACAACATGGGAATATGAGGAAAGAAATTCATGGAGCGGATACGGATGAATGAGCGGTTAACAACTAAAACGCAGCAGCGGCTTCAGCAACAATAATTGGAAATAAAATAAATTCAAACACTCGTTTTCAAATAACCCATAAAAAATCATATTCTTTTCACGAAAAGACGTTCAGCCGGGCTCTCCCACTTGACAAAACAACCCTCTGAAAAGGATAGTAACGCTCTTGTGAAACACTACCTTTTCATCCAGGCCGTCCTCCTTCCGGTTCTCTTCTGCCTTCCCCAATGCAGCCGCAAACCGCTCCTTCCGGAATATACGCCTCTGTGTTCCGGAATGGTGCCGGGAGAACACACTGCCCACCGGGCCCTCCGGGCACGGGAAAACAGAACCTACCGGGTCGGCCCGAAAGGCGGCGTATATTACATCAATTCCCATAATAAAAAAGTTTACATCAAATCCAGGAGGGTGCAATAGAAGACCACACGGTTTCTCCGCAATTTTCCAAATGGGAAACAAATAACCGTCCTATCATGTTCTCATTGTATAATTGAGAAAATATTCCGTCCAAAAAACTCATTAAACTCATTTTCTTCTTGCCAAAATGGAAGAGGACGGGTAATGTCTGCCGCACCACAACACTAAATGGTGTCGGTAGTTCAATGGTAGAGCCCCAGATTGTGATTCTGGTCGTTGCGGGTTCGAGTCCCGTCCGACACCCCATCCCCTTTCCTTCATAAAAAGAGCACTTATAGTGCTCTTTTTTACTATCCCCCAGCCCTTTCCGCATGCGTAATCCAGAATTCGTCAAAGCCGCTTTTTCCGCTATTGCCCCGCGCTATGTGGCTACCAACCATGTGCTGAGCATGGGTGTGGACCTGCTATGGCGCCAGCGCGTCGTTCAGCTTGTCTCCGAATGGAAACCGGAAAACCTTCTGGATCTGGCGACTGGAACGGGAGACCTGGCCCTGGCCATCCTGAACGCAATGCCGGAAATCCGCCTCACCGGGTCCGACTTCTGCCAGCCCATGCTGGACATAGCGGCCAGGCGGGGACTGGATCATCTGGTTTGCGCAGACGCCATGAACCTGCCCTTCCCCTCCGCCTCCTATGATGCGGTCACTGTCGCCTTTGGCCTGAGGAACATGGCCAGCTATCCGGACGCCCTGCGGGAAATGGGCCGTATGCTGCGTCCGGGGGGCCATCTGCTCATTCTGGATTTTTCCCTGCCTGAGAGCCTGTTGAAAAGCCCTTACCGGTTTTATCTGCACCGCATCCTTCCCGTCATCGCAGGGTGGATGACCGGCCACCGGGAAGCATACGACTACCTGGCGGACAGCATTGAAGCCTTCCCCTGCGGGCAGGCCATGAAGACTCTGCTGCGGGAATGCGGCTACAGCAACATCACGGCGGAAGCTCTGAACGGGGGAATTGCCAGCATTTACACAGCCCAGCGATGAGTGCGGGGGACGCAAGAGAAGGCATCATACGCGGCAAGGCGGCACGCGCCCGCCTGGGCGGAAAACTTGCCGGACTATCCCTGCCCCGGCAAATCGCCGTGATTGCTTTCTGGCCATTCCTGGAACAGCTGCTGAGCTTTTTCGTCACCTCGTCCGACCTTTTCATCGCCACTAAAATAGGGGTGGACGCCCAGGACACCATCAACATCTCTGACGGCATGGGCGCCGTCGTCTTCATCATGTGGTTCGGCTTCGTCATCCAGGGGTCTATCATGATGGGAGCCACAGCCATCGTTTCCCGCATGACGGGCGCCAGGGACTATCCCCAGGCCCAGCATGGCCTGCACCAGGCCGCCATGCTGGGGCTCCTGGCAGGAACCATCTCCTGCGGACTTCTCTTCACATGCAGCGATTTCCTGGTCACGCACGTACTGACCATGAATGAAACCGCCCGGGCCTACGCCCTGCAATACGTTTATATGGCCGCATTTGCGGCCCCGTTCAGCGGCGTGGTCTTTGCCATCAATGCCGCCCTGCGAGGCTCCGGAGACACGCGGCTTCCCTTCTGGATCATGATGAGCGTAGGCATCCTCAATGTCATCTTCAGCGTCATCTTCGTCTTTGCGGACGCTCCTCTGGGCGGCTGGCGCATCGGAGGAATTGCGGCAGGAACGGTCTGCGGCTACGGCATCAGCATGTGCATCCTGCTGTTCATCATGCTGCGGCGCAGAAAAAAAATCTTCACGGGCAGGAAAAATGAATCCCTGGAAGAACTTGTCCGGGAACGGGGAGAACACTATGCCCCGCCCCTGTACCTCAGCTTTTCCCATCTGCGGCCGGACATGGGCATGCAGAAGCGCATCCTGAAAATCGGGCTGCCGCAGGCCGTGGAAGTCTTCGGCATGTGGGGCATCCAGATGTTCTGCCTTTCCATCATCAGTGAACTGCCTGTTAAAGGCGTGCTGGGCGTCCACAATATTGCCGTCCGCATTGAATCGCTCAGTTTCCTGCCCGGCTTCGCCATCGGCATGGCCGCTTCCACGCTCGTGGGGCAATACCTGGGCGCCCGGAACGCGCTTATGGCCCGCATCACCATCTGGAAATGCATGCGGTACGCCGTCATCTTCATGACAGGCCTGGGAGTGCTCTTCAGCGCCTTTCCCGCCCTGTTCATGGAGATATTTTCCAACGGCAGCACGACCCTCATTGATACCGGAATCCCCGTGTTGCGCACCATGTTGGTGGTGGAACCCTTCTTCGCCGCCTGCATCGTGATGAAAATGTCCCTGCGCGGCGCGGGAGACACCCGGAGAGTCATGCTTATTTCCTACGGCATTATGGGCTTTTTCCGGGTCGTCTGCACCTGGGTATGGTTCAGGCTGGCACCGGAAACCATGACTTTGTGGGGCATCTGGCTGGTTTTCGCCTTTGAAATGGCCGTTCAATCCGCCATCCTCTATAAAATCGTCAAGGGGCGGAGCTGGACGAAGCTGCAAGTCTGAACCTTTCTCCGGCCTCTCCCGCCTCCCGGCCCATTCGCAAATCCAAAACGTTTACTGCAGGATAAAAACTTTGCTTTCCTCCTCCAATTCTGCTAGCATCCTTTCACGTTGTTCTGCAACGGGCTCGTAGCTCAGCGGTTAGAGCAGGGGACTCATAATCCCTTGGTCGTAGGTTCGAACCCTACCGGGCCTACCAGATAACCCCCTGCAAGCTTTTCACTTGCAGGGGGTTTCCTTTTCCAGCGCAGAAGAAAAAGGCTCCTGTTGAAGACGCCTCTGCTTCCCCTCCTGCATATGACGGAGCTTCAAAACGACTGCCGGCAAATTCCCCGGTCGGCAAGTCCATGCCGAACACATCCCCATACAAATACAACAACCATCCGGAAATTCTGTGGGGAATTTCCGGGTAGTGGAAATTCATGCTTAAAGGGAGTCCACGAGGGCTGCCAAATCATCATCATATACGAACTTGCATCGATCTCCCCGTTTCTTGAACAGTTCAAACAGGCGCGGCATGTCCGTATCCGAAACGGAGAGGCCCAGCGCACTCAGGCGCATTTTCACGGCGTGCCTTCCAGAATGCTTGGTCAAAGGCAGTTCCGTAGCGCCCCAGCCGATTTCCTCCGGGTCCATCACTTCATACGTGCTGCGGTTTTTCAGCACGCCGTCCTGGTGAACAGCCGAATGCGGTCCACATTGGGAACATGGGATTTCAGATAGGAAATCAGGCGGAAATATTCCTCCGGCATCGTGAACCCCACCGTATCCGGAATATTGATGACGGCAGCGCCCGCGTCAATTACAGACTCTACAATCTCCGCCAGGAATTCCGGCTCCGTGCGGGAAGCGTCCTCTGCGGAAAACTGCACTTCCCCGCACAAATCGGAAGCCATGGCAACGGACTTTACGGCCATGTTCCTGATTTGTTCCCGGCTTTTTCCCAGCTTGTATTTCCGGTGGATGGGGGAGGTGGCCACCACCAGATGGATGCGCTCCCGTTCCCCGGCTGCCTTCAACGCTTCATGGGCGGCGATGTCCTTTTCCACGCAGCGGGCCAGCCCGCAGACACGGCTTTTTCCGTGCGTTCCGCCACGGCCCGGACAACTCGGAAATCGCCGTCTGAAATAACGGGGAAGCCCGCTTCAATAATATCCACGCCCAGCGTTTCCAGCTGCATGGCCACCTGTATTTTCTGCTCCACGTTCATCGCCGCGCCGGGGCACTGTTCCCCGTCTCTCAGCGTCGTATCAAAAATATGTATTTGTTCTTTCATTTACAGTAAAAATGTTAAGAGGTCGTTCCTTCGGGAACTGCGCACGGAAAACGTTCCTGTTACGGAATCATTCCTTTGGTGCAAATCAAGACAGCCGTTTACTTCCCTCGCCCCGGAACGGCTCACCGGCGCATATCAAGATATCTTCACGGACTTACCGTCCGAGAAGCAGCAATAGACGACCTTGCAGAAACAGGGTATTCATCTTGCTTGTTTTTACTATAAATCCACCCTGAATGGGGAACGGGACAGACAATGCTCTTTAAAACCTGCTCTGTCAAGACACCTCAAATACATATGACATTGTTAGAGTTGTCTCATCCATTGAAATTTTGAAAGAGAGTGCCTTCCTTCACAAAAAAACTGCCTGTTAAACAAGGTTATAATGTACGAATAAACATAGAATTCCAATAACTGGAACTCATTCCTGAAATCAGTTCCAGATGAGAAAATTATGTAACCCCAAACTGGATAATACGCCTAATAACAATCTATTAAGATTACCATAGGATAAGGTTTGACACATTATTCACAACTCAACGGCCTATTGCTATTCACAGCAATAGGCCGTTTTCTACATTTCCCGGTGGGAACGTATTTCCTGAATGGAAATGGACAGGGAGGCGCGGCCGCGGTACACGTTGCGGTCAATCGTGAAAGCGATATCCCAGGGAGGATTGGGAAGCTCCCGTTCCGCTCCGTTGAAGAAAATAGCGTCACGCTCCACGATGCCCTGGCGCATGAAAAGCTTCAGATGATTGGTTCCCACGCGCTTGGGCGGTTCCGTGGGAAAAACGTCGGAACTCATGAAGAGAGGCTGTGGATTGGAGTTGCCGAAAGGTTCCAGCTTTTCATAGCTGTCCAGCAAATCCAGCGTCAACGCCTGGAAGGACACTTCCATGTCTATGTTGAGCACGGGGCTGCGCTGTTCCTCCGTCGTGGTTTCAGACACATAACGGTTGAATGCCCGGCGGAAATCGTCCATGCGGGATTCTTCAATCACCAGCCCCGCCGCCATGTCATGGCCGCCGCCGGAAACCAGCGTATCCGCACAATGGTGAATGGCCTGCACCAGGGACACGCCGGGAATGGAACGGCCGGAGCCCTTCCCCACGCCGCTTTCGTCAAAGGCGATGACGAAGGTCGGCTTGTGGTACCGCCTCATCAGCTGGGAGGCTACAATGCCCACCACGCCGGGATGCCACGCGCGGGAACCCAGCACAATGACGTTATCCCTCTCCGGGTCAAAGGAGTTATGAAGCATTTCCACCGCATCCGTCCGGATGGCCTCCTCTTCCTCCTGCCGCTTGCGGTTGTGGGAATCCAGCATTTGGGCAAGCTGCACGGCCCTCCTGGCATCCATGGTCAGCAGGAGTTCCAACGCATCCATGGGGGAATCCATGCGCCCGGCGGCATTAATGCGCGGGCCTATCCTGAACCCCACGTGGGCGGCGTTCAGGAAGCCCGCGTGGTTGACGGAGTCGGAAGGTCGGATGCCCGCTATTTCCGTCAGGGTTTTGAGGCCCGTATGGCGGCTGTGCGCCAGCCTGCCCAGGCCGTGGCGAACCAGAATCCTGTTTTCCGCCACCAGGGGAACAATGTCCGCCACGGTTGCCACGGCCACCAGGTCCAGATAAAGTTTCAGGTCAAAGGTTTTCAGTTTCCGCTCCTTCAGGAGGGCATGCGCCAGCTTGAAGACTACGCCCGCGCTGCACAGATAAGTATATGGGCTGTTTTCCTCAATTTTGGCATTGACCACCGCCGCTGCATCCGGACGCCCCAAAGGGCCCGCCTCATGGTGATCCAGAATGATTACGTCTATCCCCAGACTGTTAAGCATGTCCACCTCCTTCACGGAGGAAGTGCCGCAGTCCACCGTAATAAGCAGGCTGGGAGGGTCCGCACATTCGCAAAGGCAACGCTTGATGCCGGCCTCGCTGAGTCCGTAGCCTTCCCGGGAACGGACGGGAATAAAATACTGGGGATCCAGGTCATAAGCCATCAAAATGGCCCGGAGAAGCGCCACGGAGGTGACACCGTCCACATCATAGTCCCCATAAATGCACACGGTTTCCCCTTCATCCACGGCCCGGAAAATGCGGTCCACGGCAGCCCTCATTTCCCCCATCAGGAAAGGGTCGCTCAGGTGGGAGAGCCTGGGGTCCAGAAAGAGTTCCGTTTCCATTCCCCCGGTGAAGCCGCGCTGCAGCAGGAGCTGCTTGACCAGCAGGGGCAGTTCCGCAGGAAACGCTTTCAAGACCGGATCATCCTCCTTCACGGAAGGGCGTAGAGTCCAATGAAAACCCGGCGTCATGGCAGTATTTTAGCCCCGCATCCAACAGGGTCAAGGTTGTTTCCGCAACAGACGGCCAAAGCGGAGAAAAGAAGCGTCCGCTTCCGTTTCCGGAAACAGTCCGCACGCCGCGCGCCCGGCCCTTTCAAAAAAAACTCCGCCCGGTTCGTTCCGGACGGAGTTCGGCGTAAGGCGCCAGCGCCTGAGACGGTTATTTCCTGGAGGAAAAAATCACTTCATTAATGATTGCATCCGGCTGGGGAGCCTTGTAGGTCAGGCGCACGGCGGAAGTTCCGGCAGGAATGGCAAATTCGGAGACTCCGGGACCGGCGTTTCTCTTGCCGACCAGCGTCCAGGCACCCTCGCCGCGGCGCGCCTGGATGGCGAAGGCGGCGGATCCCACTACGACTACCTTGGAAGCCCTGGGGTTGTCCAGATTGGGAACGACTACGTCCAACGGCTTATCGGCACGGTAAACCGTCTTCAGGTTTCTGTCGCTCAGGGAGACCAGGCTGGTGCCAGGATCAGAAGGAGGAACGTCAAATTTAAACATGTTCAGAACAATGTCCTGTTCCTTCCCGCTTTTATTGACCAGTTTCATGCCTTTGATCCCCTTGGGGAGCTGATTTCCCCTGGCAATGAAGTTCTTGCCCTGTTTGTCCAGCTTCTGGACCACGGGTTTGGAGGAACCTTCCACATCCAGCACTACCTCAGCCCAGGAGTTGACGTCATCCCGTTCCAGGTTGACTTCCAGCCAGGTGGCGTCCGTGGGGCCATCCAACCGCAGGGAAATAAATTCTCCGGGTTTCATCTTGTGCGTTTCCATTACGCGGTTGATTCGCACGATTTTGTCGGATTTCTGCACGGAAACGCCCTCCAGCCCAGCCACGTTGGAGGAAGCCATAGCGGAAGGAGGCGTGTTGACGGCTATTTCCCTCACGGCAGTCCAGACGGCCCGTCCGTTGCCCGTTTTCTTCGGCTCAATGACGCGGTAGCGCACGGCACGGAGCAAAACCGGCTTGGGAGCCTTCCAGGCTACCTGCATTCCGGCGGTCTCCGGCCCCAGCGGCTTCCAGGTCTTGAGGTCGCGGGAGCCTTCCAGCTGCCCTTTCTCTACATAGTCACCGTCCTTGTCGTTGCGGCCCATCAGCACTTCCACGCTCCGCACGGGAATGGGCATTCCGTAGTCCACGCCATACCAGTCTCCCGGCTGACCATAAGCGCCGGAGTGCCAGAAGGAGCCGCGGTCCCCGTCACAGAAGAGTTCCGCCTTATCCATGCTGCCCCCCTTGACCAGAGGCTTGGGGGAAAGAGCCGGAGCGCCGGCAATGGCCGGGAAAACTTTTTTGGAGACAATATCCGCCAGTTCATTGACGGCGGGGGCCATCACCCGGGAACCGGTCTTTACCGCGGAACGGTACAGCTGCCCTTCCTGGTTATGGCGGCGGGAAATTCCATCCATGGCCGCCAAAGCCTGCGTGGCCTGCACCAGGTGGGTCGCGGCCTCTCCGGCGTTGTTTTCCTCCAGCGCCGCCACGGCATGCTGGCCAGCACGCCCCAGTTGTTCAAAGGCGTCCACCCACGCGCCGATTTCCTTCATCAGCCGGGGATTGTCGGCCTTGGCGCGGATGACAGGCGCAGCGGAAGCCATGCGAGCGAATTCCTTTTTAAGCAGAGCCGCATCAGCCTTCTCTATCCTGCCTTCCCGGGCGGCTTCCAGCACGCGCTTGACCACAGGTTCTATTTCCACGGATTCCTCACGCCGGTAACCGTGGCCGTTAGGCCCCTGGTCGGAGTTGTGGTTCACAAAAACCTGCATGGCTTCCGCCGCCTTCGGGAACAGGCGCCTGACTCCCTCCTTCCAGGATTCCTCCGACTTAAAGCCGTTGATATTCCAGGAGTAGTCCGCAAGACCGAAGAGGGAAACCTTGGAGGCCTCCGGCTTATCCATGGGGTTGGAGACAAATCCGCCCATGGATTCCCTGGCTCCCGGTTCCGTGGCAAGTCCGTACACGCGGCCCATGCACAGGTTGGAACGGCAGTAGTCCGTAACGGGGAAATTCCACCAGACGTAGGAAGGGCGCTTGATTCTCCTGTTCACCCACTGCTGGCCTTCCAGCGTGATGTCATGGCAGACGGAATTCCCCGTCCACATAACGTGGATGGAGGGGTCCAGGCGGTCGCCCAGAATATCCAGATAAGTTCCCGGCTTGGGATTGGCCCAGCCGCGGTTGTACTCCGTAGGGCACATGACCAGGGGAGTGACGTCTTTCTTCACCTTGACAAATTCGTCATTGATTTTGTTCAGCAGAAGGGCCTGCATGTCCCCCCGCTTGCCCTCTCCGAAGATGTCGTCAAAAAACACGGCAAAGGAACGGACGCCCAGCTTGTACATCATCTCAAACTTTTTGATGACGTTGTTCATGTCTTCTTCCGTCCACTTAATGTCTTTGCCGGGGTGGATGGCCCAGACAAAGTCCACATGGTTTTCCTTCGCCACCTTCACCAGTTCCCTGATCTGGGCGGCCTGGTCTGCGGGATAGGGATCCCGCCAGTGGGGGGAGGAATGGTAGGGATCGTCCTTCGGCCCGTAGATGTATGTGTTCATTTTGTTCTGCCCGTAAAAGCGCAGCTGGCTCAGACGGGCTTCATGGCTCCAGGGAGTGCCGTAAAAACCTTCCACCGTTCCGCGGAATTCAATGTCCGGCCAATCCGTAATTTCCCCAACGGGCAAAATCACGCCTTCTCCGCCGGTCTTCGTTCCCAGCTGCCGCAGCGTCTGCATGGCGTAAAATGCCCCCCGTTCGTCATGGGCTCCTATGCCCACCTTTCCCTGCGGAGAAATCACCAGCTTGTAGGCTCCGGATTTTTCCGGAACCCCGTCCAACAGCTTGCTCCCGCCCGCCTTGGCAGAACGTATTGTCACGGAAGGCTTCCCGGAGAAAGCAACCGTCTGGGAGGTGAATTTGGACTGCTGGGGGGACGGATATACGGCAGGAGTCGCCGCCAGAGCGGAGCCGGCCAGAAAGGCCGCCACGCTCAAGCCACATTGGAGAAGTTGAAAACCATTCATGATGATGCGCGATAATGGAACCAGAAAAAACTTTCCACCTCAAGAAAAAACCGTCCTCCGCCATCAGGGGAAGAACACGGGAAAAGACTTCAAGAAGAAACTTCCCCGCCTTCTCTTCCCTCTTCAGGAAGATCCCCCCGCTCCTCATGGAATATTCGGGAACTGTCATCACTCCATCACCCGGCTCATCCGGAGACCGCCCGGCGCAGCCGCCTCCCTGCAGGAAGCGGCATGTTGATGGATTTCCTGTGAAGGGCAATCGTCCCGCACGGAAATCCTGGTGGGGCTGGATGCCGGGGGCGCCGCCTTCTCCCTTCTTGACCAAAAATTCACCGTAACGCTGAAAAACAAAAAAGGGCCGTGCAGAATCCCTGAAAAGTCAGCGTCATCCCATTTCAGAAGACAAATTTGACCATTGCATCACTTGAAGGGAAGGTTCATCATGCGGAACATGCTCCGGCGCATTTTGATTCCTTTTCTGTGTCTTCTGGGCCTGTTCTGCTCTCTTCAGGCCCAGGATGCCCCAGTATTGAAAATCGCGGTTCTGCACCCCGTCTTGGGAGACATGGCCCGCGCCATCGGCGGCAGCCGCGTGCAGGTGGCGGACCTGCTGCGCCCCAACGGCAACCTGCACAGCTTTGAACCCTCGCCGCAGGACATTGCCGCCGCAGGACGGGCGCGCCTGGTGCTGGCCTCCGGCAAAAACCTGGAGCCGTACCTTCCCAAATTAAAGGACGCCCTGGGAGGCAGCGCGCAAATTCTGGATCTCGGAGCCTCCGTCCCGGATGTTCCCGTGGAGGAAGACACCGCAGACCATGACCACGAACACGACGGAGACTGCTGCGCCCACGGTCCCAACGATCCCCACTGGTGGCACACACCGGCCAATATGAAACGCGCCGCCCGCACCCTGGCCGCAACGCTCACGCGGCTGGACCCGGACCATGAACAGGACTATAAAGTCGGCTTGTCCCGGTGGAACAGAAAGATGGACCAGCTTTCCTCCTGGGCCAGAAAGGAACTTGCGGACATTCCGGAAACGGACCGCGTCCTGGTTACGGGGCATGCGGCCATGAACCATTTCTGCAGGGAATTCGGCTTCCGCAGCATCAGCATCCAGGGGATAAGCCGTGAAGATGAAGGCAACTCCGCCCAGCTGGCCTCCACGCTGAAAAAGCTGAGGTCAGCCGGGGTAAAAGCCCTGTTCCCGGAATATTCCTCCAACCCCAAGAGCCTTACGGAAATCGCCAAATCCCTGAACATCCCCGTTGCCAGACCAATCAATACCGACGGGCTGGCCCCTGAGGGCCACACGTTCGAATCCATGTTCAGACAAAACACAGGCATCATCAAGGAAGCCCTTTCCCCGTCTCCCAGGCCATGACCAGCACACCCCTGCGGCAGTTTTTTCCCACTCTGGGAGTCGTCATCCTGCTGGGATTCTTCCTCTACAACATGACGGCACCCACCACCGGATCACAGGAAGCGGAGCATTCCGCCCCCACGGCGCAAACCGCGGATGTTCCCACGGTCATCACGGTCAGATGCGCCCACCGTCCGGAAGCCCTTTCCATCTCCTGCGGCGGCCGTCTTCTCTGGCAGCCCGAAACGCCCGGGCTGCATGAAGAAACGGAATGCGGCCTGCCGTTGAAAAACGGTTCCGTAACGCTTACCGTTTCCGCCCGCTGGCCGGAAAACACGCCGGACACCCCCGTCACGCTGGAACTGGAACCGGAGGGGAAAACCACCGTCTCCGCCACCCGCTGGTCCTTCGGCCCCTCCCTGAACGACAGCTACTCCTTCTCATGGAAATGAACAGCCCAGCCCCTCCCTGCACCCAGGACCACATCTGCTGGGGCGCGCACGCCAGCCACCCGGACCGCCACCGACTGGAAGTGGACAGCCTCAGCGTTTATTATGGAAGCCTTCTCGCGCTCAACGGCATCAGCTTTTCCATCACCTGCGGCCACACGCTGGCTCTGATGGGTCCCAACGGTGCAGGAAAATCCACGCTGATCAAGGCTCTGGCCGGGCTGATCCGCCCCGATTCCGGAAAAATCCTGTGGAACGGATGCCCCCTGCACGACACGCCGGGAGAAATAGCCTACCTGCCCCAGCGTTCCGACGTAGACTGGTCTTTCCCCATCACCGTCCGCGCGCTGGTGGAAATGGGCCGCTATCCGTCCCTGGGCCTGTGGAAAAAATTCGGGAGGCATGACCGGGATATTGTGGAAAAATCCCTTCACGTTCTTGGTATGGAATCCCTGGCGGACCGCCAGATTTCAGAACTCTCCGGCGGCCAGCAGCAGCGGGCCTTTCTGGCTCGGGCCCTGGCGCAGGAGGCCCATGTCCTCCTGCTGGACGAACCCTTCACGGGCCTGGACGCCCCCGCCAGCCAATCCCTGGGGAGGCTACTGGATTCCCTGTCGGCGGAAGGGCGGCTGGTCATCGCCTCCCACCACGACCTGAACACGGCGGCAGACATCTTCGACACCATCCTGCTGATGAACAGGGAGCTGGCCGCCTTCGGCCCCCCGAAGGAAGTGCTTACCCCCGCACGCATCCGGGAGACATACGGAATGGAACCTCAACCGGAAACCCAAGCTTCATGATGGATGATTTTCTCCAATTCCTCCGCGAACCCATCGCGCAGCGGTCCCTGCTGGCCTGCGTCATGATCGGCTTCGCCAACGGCTTCGTCAGCGGGCTGGTAATCCTGAAAAAATCCGCCCTCCAAATCGGCACGCTTTCCTGCGCCCTGCTGCCGGGCATTGCGGTGGCGGTGCTCCTCTTCGGCCTTACCCGCTGGAGCCTGCTGACGGGGGCTGTGGTGGCCGCCCTGCTGGTGGGTCTGGGTTCCCTCTTCGTCTCCCGTACATCCCGGCTGAACCAGGACACCGCCCTTTCCATCCTGCATACCACGGCTTTCGCTGCGGGCTTCATCGTGCTGGTCAGGCTAGGCCTGCAGCAGAAAATTGACGACTGGCTGTTTGGCTCCATCATGAGCCTGTCCGACTCCGACCTGTGGATCGCCTTCGCCATCAGCTCCGTCAGCGTCCTGATCCTGCTGCTCTTCCGCCGCCCCATCCTGATCTGCCTCTTTGACCCGGACATCGCCACCACGCTGGGAATCCCCTCCCGCCTCATCAGCTACGGCATTTTCACCCTGATCATTCTGGTGCTCGTTTCCTCCCTGCAGGCCGTGGGAGCGTTCCTGACGCTGGGGCTGCTTGTCGGCCCCGCCGCCACGGTGTACCTGCTTACCAACAAGCCGTCCCATCTCTTCTGGGGGGGAGGAATCATCGGAGGGCTGGGCTCCCTGCTGGCTTTTTACCTGTCCTTCCCCCTGGGCTGGCATCTGGGCGCCACCATCATTCTGGTGCTGGGCGTCATTTTTTGCACAGCCTATTTGTATTCATCACGGTGCGGACGTTTGAAACAACGCGGGAAGAATTCTTCTCAAGTACAATAACAAAGCCCATTTTCATGACAATCCATGACACGCAGACTACTAGTCCGCCACAGGGAGCGCCATTATAATGCGGGTTGTGAAAAAATGTGAGATATGCTCTGCACCGGCAACGGTGTTCCTGACACAGATCATCAACGGGAAGTCCACCAAATTCTGCCTTTGCGCCAAATGCGCCCAGGAGCGGGGTCTGCTGGATCCAGATGCCTTCGATCTGGCAGAGAAACTGTTCCCGAACCTTCAGGGCCAATTCGGCAAGGAAGGCGCGGCGGGGCCCGCCGCTCCCATTCCGGCCCTGCAATCCCTGCCCCTCACAAGCTGTCCGATCTGCTCCTTCACCCTTCAGGATTACAAAAACGTAGGACGCCTGGGCTGCAGTGAATGCTACAACGTCTTTGAAGAAGAAATCCTCCCTCTTCTTACCCAGATCCAGCCTGATTCCCACCATCACGGGAAAACGCCGGAACGGGCCGAAAAACGCGAAACGGAAACCCATACCATCAGCGATCTGGAACAACAGCTTTCCCTGGCAGTGGCAAGAGAGGATTATGAACGCGCCGCCAAACTCCGCGATCAAATCAAACAATTGCGCACGCCGACCAATTAACATACATGCTCTTTGACGACTTACTCAACAACCCCGCCAAATGGATGGTGGAATCAAGGGATGAGCACGACATCGTCCTCACCTCCCGCATCCGCCTGGCGCGCAACCTGACCGCCACCCCCTTTCCCGGCTGGGCCACACGCCAGCAGAGGGAGGAAACGCTGAAGCTCACTTCCGGAGAAGCGCGCCAAATCCCCGTCATGAAGGGAGGCTATTATGCGGAGCTCTCCGGACTTACCCAGCAGCAGAAACAGTTGCTGGTGGAACGCCACCTCATTTCCCGGGAACTGGCGGCGCGTTCGGAAGGCTGCGCCGTCCTCATCTCCCGCAGCCAGAATGCCAGCATCCTCTTTAATGAGGAAGACCATCTGCGCCTGCAGTATATCCTGCCCGGCATCCAGCTTAAAAAAGCATGGGGGGCCATCTCCAAAATAGACTCCGAACTGGAGGCCAGGCTCCCATACGCCTACAATACGCGGCTGGGCTACATTACCGCCTGTCCCACCAACCTGGGCACGGGCATGAGGGCCTCCGTGATGATGCATCTGCCAGGCCTCGTCATCTCCGAACAGATGCAGCAGGTCGTTCAGGCGGCCGCGCAGCTCAACATTACCGTCCGCGGCCTTTACGGGGAAGGGACGGAAGCCACCGGTAACCTTTTCCAGATATCCAACCAGTCCACGCTGGGGGACAGTGAAGACCAGATTGTGGAACGCATGACCCGCTTCACCTCCGACCTGGCCCATCAGGAATGGAACGCACGCAGGCGGCTGCTCCAGTCATCTTCCCTGCAGGTGAAGGACCGCGTTTCCCGCGCCTACGGCCTTCTGACCAATGCCACTCTGCTTTCCACGCAGGAGGCGCTGGCCCTGCTCTCCTTCCTCCGGATGGGAGCGTCTCTGGACATCTTTTCCCACCAGGCATTAAAAAACGTCAATAAAACCATCATGAACATCCAGCCGGCCCATCTGGCCCGCCTGTCCACCACGGACCAAACCACTCCGGAACACAGGGACCAGATCCGCGCTGATATTATTCGGAAAGAACTTTCCGGCAACTAATGCAACCAAACAATTTTTTTGACCATCCATGAATAATTTTACGCCTAGGGCGCAACAAGTGCTGGCGCATGCGCGCCGGGAAGCGGACCGCTTCAATCATCATTACATAGGAACCGAACACCTTCTGCTCGGCCTGCTCAAGCTGGGCAAGGGCGTGGCCGTCACCATCCTGGAAAATCTGGGCGTGGAGCTTACCGCCGTGCGCAAACAGGTGGAGGAACAAATAGGCCGGGGCACGGAACCCCAGGCGGAGGGCAACATCCCGTACACCCCCAGGGTGCGCAAAGTCCTGGCAATGGCAAACAGGGAAGCCCAGGAACTCAACCACACGTATGTGGGCACGGAACACCTGCTCCTGGGCCTCATCCGTGACGGCGACGGCGTGGCCGGACAGATTCTGCGCCACTTCGGCGTGGATTTGGAACAAGCGCGGCGCGAATTGCTGGACGTGCTGACCCCCAAATACCAGATGGATGCGGATGAAGACAACATCATCCCGGACGACGATGACGACGAGGAGGAAAATGAATCCCCCGCCATCCCGACGGACGAGCCTTCCTCCCCCTCCTACTCCAGGCAGCAGAAATCCAAGACACCCGCGCTCCAGGCCTTTGGCCGGGACATGACCCAGCTGGCCCGGGACGGCAAGCTGGATCCCGTCATCGGCCGCGCCTCGGAAATTGAGCGCGTCATCCAGATTCTGTGCCGGAGGAACAAAAACAACCCCGTCCTGCTCGGTGAAGCCGGGGTGGGCAAAACCGCCATCGTGGAAGGGCTGGCCCAGGAAATAGCCCAGGGCCATGTTCCGGAGCTCCTGCGCAGCAAGAGGGTCATCTCCCTGGACCTGGCGCTGATGGTGGCTGGCACCAAATACCGCGGTCAGTTTGAGGAACGGCTTAAGGCCGTAATGGATGAAATACGCCGGGAAGGCAATGTCATCCTTTTCATTGATGAACTGCACACCATCGTAGGCGCCGGCTCCGCGGAAGGCTCCATGGACGCCTCCAACATCATCAAGCCCGCCCTCTCCCGGGGTGAGCTCCAGGCCATAGGCGCCACCACGCTCAACGAATACAGGAAACACATTGAGAAGGACGCCGCCCTGGAACGCCGCTTCCAGCAGGTGCAGGTAGGGGAACCCTCCGTGGAGGACACCATCCAGATTCTTGCCGGGATCCAGCCCAAGTATGAGGAACATCACAAGGTGCATTATACGAAGGAAGCCGTTGAAGCCGCAGCCAAGCTCTCCCACCGCTATCTGACGGGCCGGTTCCTGCCGGACAAGGCCATCGACATCCTGGATGAAGCGGGCGCGCGCAAGCGCGTCTCCCAGATGACCCGCCCGGACCACATCAGCAACATGGAAATCCGCATTGCGGAAATCAAGGAACGCAAGGACAAGGCTGTGGAAACCCAGCTTTTTGAAGAAGCAGCCCGCCTGCGCGACGAGGAAAAACAAGCCAAGGCGGAACTTCAGAACATGCTGGAAACCTGGCGCAACTCCTATGAGACCAACTACGTTCCCGTGACGGATGAAGACGTGATGAGCGTACTCGCCAAATGGACGGGCATCCCGCTGGCCCGGATGGAGGAAAAGGAAACCACCAAGCTTCTGCGGATGGAGGAAGAGTTGAAAAGCAAAGTCATCGGCCAGGACGAGGCGGCTTCCGCCATCGCCCGCGCTCTGCGGCGCAGCCGTGCGGATATCAAGGATCCGCGCCGCCCGATCGGCTCATTCCTCTTCCTGGGCCCCACCGGCGTGGGCAAAACCTATCTGGCCCGCAATCTGGCGGAAATCATGTTCGGTACCTCGGACGCCCTGATTCAGGTGGACATGAGCGAGTACATGGAAAAACACACCACGTCCCGTCTCATCGGTTCTCCTCCCGGCTATGTAGGCCATGACGAAGGCGGACAGCTCACGGAAGCCGTCCGGCGCAGGCCCTACTCCGTCATCCTCTTTGATGAAGTGGAAAAGGCCCATCCGGACGTCATGAACCTGCTTCTCCAGATTTTGGAAGAAGGCAGCGTAACAGACTCGCTGGGACGCAAGATCAACTTCCGCAACACCATCATCATCCTGACCTCCAATGTGGGGGCCGCTTCCGCCAAACGGCAGAGCACCATGGGCTTCGGCGCCATGGCCGCCGACAACGCGGACTACGCCGCCATGAAGGAAAAGATTCTGGAAGCGGCGCGCAAGCAATTCCGGCCGGAATTCCTGAACCGATTTGATGACATTTCTGTCTTCCGCATGCTGGAACGGGACGACCTGGAACGCATCGTGCATCTGGAAGCGGACAAGCTCATCTCCCGTCTCAAGACCAAGAATATCACGCTTGCCCTGAGCCAGGAAGCTCTGAGCCTGATCATCAAGAACGGCTATGACCCGCAGTACGGCGCCCGCCCGATGCGCCGCGCCATCGAACGCCTGCTGGAAGACCCGCTGGCGGAATCCCTGCTGAGGGGAGACGTGAAACCCGGCGATAAAATTGAAGCCGTGGAAACGGACGGGACGGAGACGCTCACCTTCCTGCACATCAAGGACAAAAAACCAGCCAGGCCCAAAGCGCCGCGCAAGAGAACGCCGCGGAAACCCAAGGCGGAATAAACGCTTTTAGGCTTTTCAACCAGGGCCCCTGCATGTTGACATGCAGGGGCCTTTTCATTGGGCCGGATTCCGCGGAACTGCCATTTCCATCCCCTGAAAGGACAATGACCCTCCCTGCGTATTTACGGGCATGATCTCATCCTTTCCCAGAATTCTGATCTTTCTGATGATGGCAAGCCTGCATGTATTCGCGGAAGACATGCAGAAAAACAGGGAACGGGAAGCCGCCCGCGCAGTCATCATGCGGACCGTGCCGTCTCTGGCCGCCTCCCCGGAAAAACTGCACCTGGAAACCATTGAAAAAGAAGACGGATATGATGTTTTCGAGACGAAAGCCTCCGGAGGAGTGCTGACGATCCGGGGAAGCAGCGGCACAGCCCTTTGCCGGGGATTCTATGATTTTCTGAAAACGAACCGTCTGGGAATGGTATCCTGGGAAAACAAAGACATTCGCTGGCCGGCACAGCTGCCGGACACCGCGCCGCGCCGCATCGTTTCTCCTTTCCGCAACCACTATTATTTCAATGTGGTCACCTATGGATACACGATGCCCTACTGGACCTGGGAACGCTGGGAAAAGGAAATAGACTGGATGGCTCTGCACGGCATCAACATGCCCCTGGCTCTGGTTGCCACGGAGGGGATCGCCGTCCGGGTCTGGAAGCAACTGGGGCTGACGGAAAAAGAAATAGAAGAATTTTACACGGGGCCGGCCCATCTTCCCTGGCAGCGGATGGGAAACATCGTCAATCATGACGGCCCCCTGCCCGCCAGCTGGCACAAGGAACAAATCGCCCTGCAGCACCGCATCCTGCACAGGATGAAATCCTTGGGCATGACCCCCATTTGTCCCGCTTTTTCCGGTTTCGTTCCCCGGGGAATCCTCCGCCTTTATCCGGAAGCGAAGCTTCACCGGCTGGGGTGGGGAGGCTGGCCTCAAAAAAACCACGCCCATTTTCTTTCTCCGGAAGAGCCCCTTTTCCTTAAAATAGGACGCCTGTACATGCAGGAATGGCAGAAGGAATTCGGAAAAAACACCTATTTTCTTGCGGACTCCTTCAATGAAATGGAACTTCCCGAAAACAAAGGAGGCGTCGAAGCCCGCAACAATATGCTCAGCTCCCTCGGGGAACAAATCTACCGCTCCATTTCCTCCACCAACCCGGATGCGGTATGGGTCATGCAGGGATGGATGTTCGGATACCAGCGCAATATCTGGAACGCAGATACTCTGAAGGCTTTGCTCAGCAAGGTTCCGGACGATAAGATGCTCCTTCTGGACCTGGCGGCGGATTACAACAAGACTTTCTGGCGCAACGGCATGAACTGGGACGTCTTCAAGGGTTTTTTCAATAAACCGTGGGTTTACAGCGTGGTTCCCAACATGGGCGGCAAATGCGCCATGACCGGGGTAATGGATTTTTACGCCAACGGCCATCTGGAAGCCTTGAACTCTTCATCCAGGGGGCGCCTCTCCGGAATGGGCATGGCCCCGGAAGGAATAGAAAATAACGATGTGATCTACGAACTGATCACGGATGCCGCATGGAGGAACCGCCAGGAAAACGTGGAACAATATCTGGAAAATTATTGCCGTGCCCGATACGGAAATTATCCGGACAGCATGAAGGAAGCCTGGAATCTGTTCCGCCGCACAGCCTATTCCAACTTAAAGGACCATCCCCGTTTCAACTGGCAAATGAAACCCGGCACCCGCGGCTGTTCCGTAAACACAAGCGAAGATTTTCTGAAAGGCCTCTCCCTGTTCGTCAACACCCGCGGCCTGGAACAATCCCCCCTGTTCCGCCAGGATGCCGTTGAGATGGCAGTCCATTATCTTGGCATACGCATGAATGAAGCCATCCGGGCCGCCCAGGAAGCTCTGGACGAACAGGATCAGGAAAACGCGGAAAAATGCATGGCCTATTTCCGCAAATACGCCCTTCTGGCAGATTCCCTTCTGGAAGGGCACCCCACCTGGAGGCTGTCGCGCTGGATATCGTTTGCCCGGTCTCACGGAACTTCTCCTGAGGAAAAAAACAAGTATGAACAAAACGCCAGAAGGCTGGTGACCAGATGGGGGCCTCCCGTAGACGACTACGCCGCCAAAATATGGAGCGGCCTTATCAGGGACTACTATCTGCCGCGCTGGGAACACTTTATTCAGAGCCGTCTTTCCGAAAAAAATCCGGATATGGGAGCATGGGAGGAAAAATGGGTCCGTTCCACTGGCGTAAGCGCTGCGCACGCTCCCGGAGATCTGGTCAACGCCTGCCGCCAGGCTATCCGGGAGGCGCCGCCCCTGCCTTCTTCCCTGAAAAGAAAAGCAGCCGGATCAATTATAGGAAACTGGACCTCCGCGACGGTATCCACGGAATGGAGTTATCTGGACTGGCCCGTTTCTTCCGCTGATCTTGAAAAACTCCGGGGGGTGCGCTTCGTCTTCACTTCCGGCAGCCATGCCCTAGAAATAGACGGCGTCGAACTCCTGGAAAACGGCAAAGTAATCGCCCACGACCGGCACGCCGGATTGGCAGGCAAGCCTTCCCGCGGGAATTTCTACAAACTTTCCCTTCCCAAGGGAATCCATGCCAACAACGGATGCTCCCTCAGAGCCAGGGTCCGCTCCGTGGGCGGAAAATCCTCCAATGGGAAACTGGAACTGATTAAAGAATGAACGGCTTAAATTCAGTTAGAATTATCCACTTTAGGTTTGACAAATATCACCAAAATTAACTTATAAATAGTCATAAATTATTTTGAATTAAAATAGATCTATATTTCAACCATTGTAAGTCAGATCTTCTACATCAAGGGAACGATTTTTGAAAACATCTATCTTGACTTATGTATGTTGTTGTATGCAGATAAATCATCTACACGAAGTATATTGCACTAGATACACAAAGCAATGGAGAAAGAGGAATACAAGTAAATATTTGAAGCTATTATTAAAGTGAACGAAAACCATGTAGTGGATAAACCTTGCTAAGACAATAAACATGGAGAAACAAATGGATAAGGATGACGAAAACAAACCAAAGACAAAACGAGGCAAGAGGACATACACAACTACTTTGATTGGGGCGAAAGAATATAATACCGAAAAAATCCATGCAGTAATTTCAAATTTTAACAAACCCGACAATCTATTAAGTGGAAAACAATATCTCAATATTTCCAAAAAAGTTCGCAAAAAAATAACATCCATAATAATTGACTAATCTTCAGTCTACAACATACTTGATAAAAAAGGAATGTGCTTTTATGTAGTTGAAAATTGACCATTCCGTAACTTTTTCTCTTGAAAGCAGGATACACATGAATGGAATTGAGAGCTTTTGAATACTGTTCACAATGGTTTTTGATAAATTGGCCGAGTTGTTCGTGGCTTGATAATTTTAGAAGATGGTATTATAATTCTTGACATCTATTTTCGTACTAACTAATCACACGTCCATGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAGCAGGAAACGCTTCCTCTCCAATGTGATGATTTCGAATTAACTAACGAATATCTAGATTATTTATCAGAGCAAATAATCACATATATAGGAAACAAGCGATCCCTGCTTGATTTTATAGGAAAAGGGATAGAAGTGGTTTGTAAGAGGTTGGGAAGGAACAAAATAAGCAGTTTGGATGCTTTTTCTGGTTCTGGGGTGGTTTCCCGATATTTGAGACGCTTTTCTTCAAGCCTCCATTGTAACGACTTGGAAGATTATTCAAGGGTTATCAATGAGTGCTACATGACCAATGAATCTTCTGTAGATATTATCTTGCTTAAAAACGAGCTATCTTGCCTTCATAAAAGAATTGAAGAATCTTGGCATGAAGGAGTTATTACAAAAATGTATGCTCCTGCCGATGAAGATAATATAACACAAAATGATAGGGTTTTCTACACAAAAAGGAACGCTATGTACATTGATACAGCAAGGCAAGCGATTGATGAACTTGCTCCTGATCTAAGAAAGTTCTTCTTGGCTCCACTGTTGTATGAAGCCTCTGTGCACAACAATACATCAGGTGTTTTCAAGGGGTTCTATAAAAATCACGAAGGGATTGGGCAGTTTGGGGGACATGGGAAAAATGCACTAAAACGCATATTGGGAAATATTGAACTGAAACTCCCTGTTTTTTCTCGATTTGATGTTGACTGCTACATTCACCAACAGGATGCACGACAACTAATGGAAACAATACAGGCGGTTGATCTTGCATATTTTGACCCTCCTTATAACCAACACCCCTATGGCTCAAATTATTTCATGCTCAATTTAATAGCAAATAATAAAAAACCACTGTCATTTTCCCGAGTTTCTGGAATCCCTGACGACTGGAACCGTTCTCTCTATAATAAACGCCAATCAGCTCAGAATGAATTGTTTTCAACCGTCCAAGCCTGCCCCGCAAAGTTCATCCTAATATCTTATAATTCAGAGGGATTTGTGAAATACAACGACTTTATCAACTTCCTCTCCAAAATAGGTAAATTGCAATCCTTACAAACTGATTATAACACATTCAGAGGGTGCAGAAATCTTAACGAACGCCCAATAAAAGTTAAGGAATTCCTCTTCCTTGTCGAAAAATTCTAATAGCCATCTCACTATGTCTCATAAAATTGCATTGCGTACCCAGCGGGAAGGTACAATTATTAATACTACTTCATCCAAGCAGGAAAAATTATTACGTCAAGCTGTGAAATCTGTTTCTATGCGTCTGAAAAAGAAATTTCCCGGAATCACCCTTGAATGGGAACCACAAGCTAAATTGATAGATATTATTGTCCACCTTCGAACCCAATTCCCTGATGTTGACTTTGCAGCCCCCTCGTGCGCTTCTTCAATGAAACCAGACGGTGGAATTCTTTATATGAAAAGTGAAGATGAACGGCGATTTCCTATCCTCATATCAGAAAAGAAAAACCAAGGTACAAATGATATGAGGGCAAAGGAAGGAAAAAAAAGACAAGCTCAGGGGAATGCCATAGAAAGATTAGGAAAAAACGTCATTGGTTTCCGAGCTTCCATGATTAGAGAGTCAATTTTTCCCTTTGTTTGTTTTGGAGATGGTTGTGATTTCGTTGAAGGTTCATCCATAAGAGACCGTGTCATCACCATAGCCATGTTCGGTCAGCTCAACAAAGAATATGTTTATGCTCAAGGTCGAGGAGAACTTTTTAACAGGGGATCTTTCTATTTCCGAGAAAAACAATGGACTGAACAAGAAATGGCAGACATCTCGTTCTCTATAGCCAGCAAAGCCATCTTCTATTATTTTTCCAGATATGGAGAATCAACTTTTTTCGATAATTGATTGGAGCACATTTTTAAAATTATTCAAAAAATATTCTAGATTTTAATATTTAGCAAATTACTCAGATGAAAAAACAGAGAAGCTCAATAAAACTAGACTAAAATAAACAATCTCGATATTTTACAGAGAATTTCGACTCAAAACGTTCAAAATCAGTCGATTTTAAATTAAATAATTCTGAATTTAATTTGCTTATTAGGAAAAACCACATCCATTCCGTGCCTTTTACGTTAGAAAATTCCGCCGTCCGAACCAGGAAACAGCATGCCCGGCGCCTTGCGGAATCCCCGCACGGAACAATCGGGCGGAAAGTTACATTTCGAACCGGTGCAGGTTTTCCACATGGGAATAACCCATCTTCCTGAGCGCTTCCAGCGCCTTGTCCGCTCTCTTTCCCGTACGGCAATACAGCATAATCACGGCGTTCTTATCGGGAAGAAGCCGGGGGGCCAGTTGTTCCACCTCATCTGCCGGAATATTCAAGGCGCCCTCCTTATGTCCCGTGCGGTACTCATCCAGAGTCCGGACATCCACCAGCACAACAGGACGCGCGGCGGCGCCTTCCGGAGACACAACCATGGCGGAGGAAGCAGGGTCACGTTCCGCATTCCTGCCTTTCAGCCAAACGAAGAGGGCCGTCAGCCCGGTCACCATGACCACGTACAAAACCACGTTTTTCACGCGGCCACTTCACTAAACCGCGGCGAAGTAATCAAGAGAAAACAACGCCCTGCCCACATGCGCAGGCGTCATTTGCCGCCCACGCAGCACCGGCGGCTCCGTTCCACGGGAAAACAATGCGCCGGATACAGAAGAAATACGCAGAAGCGGGAAGGGACAGAGCTTTCCCTTCCCCCTCCACATTCAACAAGGCCGGAGAACCATTCCCCTACCCCTCATCATCAATTACCACAGCCTGGGAAGCGAAGCCAAAAGCGGGGCTTCCCCATTCCACCACATCCCCGGGTTTCAAATATACCGGAGGCTTCATCCCCATCCCCACGCCTCCCGGAGTGCCGGTGGAAACCACATCTCCGGGCCATAAAGTCATGAACCGGGAAACATAGGCAATCAGACGCGGAACAGGCCAGATCAAGCCGTCCGTATTGCCGCTCTGGCGCACTTCCCCGTTCACTTTCAATTCCAGAGGAATGCCGGAGCCGTCCCCAAGTTCATCAGCGGAAATGAACGTGGGCCCCATCGGGGCAAAGCTGTCATAGCTCTTCCCCTTGGTCCACTGCCCGCCATGTTCCAGCTGGTTGGCCCTTTCCGAATAGTCGCACATCAGGGTATAGCCGCTGATAGCCCGTGCGGCCTCCTCTGGGGAAGCGTTTTTCAGCACATCCTTAATCACTACGGCCAATTCAATCTCATAATCCAGCTTGTCGCAGCCGGGCGGATACAAAACAACATCACTGCAGGAAGTAATGGCGGAAGGCGACTTCAGAAAAACAGCCGGTTCCTGCACGGAATCCCCGTCAAACTCCTTCGCATGGTCTGCATAACTTTTTCCCAGGCACACCAGCTTGGAAGGGGAACAGGGCAGCGCGGTATCAATCATCCATTCAGGGTTGACAGCTTTGGCGGCGGCACTATCCAGAAACGCGACAATGCGCTTCATTTCCTCTCCGCAGGAACCCGTTTCTCCGCAAATGCAGGCATCAGCCTCGGCAAGCACGGAGTCCGCCGCCAGGTATTCCCCGCGGCTTTCGCTCCACAGAGCCATGATGCGCTCCGCCTGGTCATTCAGGTAAAATCTGATCTTCACCCTTCCATCCTACAAAGCTCCTCCTTTTAAGCAAAGAGCGGTTCCGTCATTCTTCTCTCCGGCGGCCCTCTTCCGCCGCCCGGACGCTCTGTGAAAGCATGCAGCGCGTAGGGCTTGCGTTACTGGAGCAAAACATTATCATGCCACTGCTTAACATCATTAACCTATGTCCGATCAAATCTACTTTTTTGACACTACCCTGCGCGATGGAGAACAATGCCCCGGAGCATCCATGAACCTACGCGAAAAGCTGGAAGTGGCGCGTCAAATGGAACGGCTGGGCATGGACGTGATTGAAGCCGGCTTTCCCTGCATCTCCGACGGCGACTTTGAAGCCGTGCACACCATCGCCCGCGAAATCAAAAAATGCCGCATCGCTGGTCTGGCCCGCTGCGTAAAGGCGGACATTGAAGCGGCGGCCAAAGCCCTGGAACCCGCCGGGGAACGTGCCCGCATCCACATTTTCCTGGCCACCAGCAAGCTGCACATGCAGTTCAAACTGAAAAAAGCGGAATCCGAAATCCTGCGCATGGCCGCGGAAGGCGTATCCTATGCCAAACAATTTGTCCAGGACGTGGAATTTTCCCCGGAGGACGCTTCCCGCACAGACCTGGACTTCCTGACCAAAGTCGTGGAAACAGTCATCGACGCCGGCGCCACTACCGTCAACATTCCGGATACCGTGGGCTACACCACACCGGACGAGTTCTACCGCATCATCCGCCACCTGAAGGAAAATGTGCCGAACATCGGCAAAGCCGTCATCTCCGTGCATTGCCACAATGACCTTGGCCTGGCCGTAGCAAACTCCCTGGCAGCCGTCCGCGCCGGAGCCCGCCAGGTGGAAGGAACCATTAACGGCATCGGCGAACGCGCCGGAAACGTGGCTCTGGAAGAGGTCATCATGGCCCTGCGCACCCGCGCCGGACAATTCGGAGACGTCACGGACAACATCAACACGAAGGAAATCGTCCGCACTTCCCGCATCGTGGCCCGCATGAGCGGCATGCAGGTGCAGCGGTCCAAAGCCATCGTGGGGGAAAACGCTTTCGCCCACTCTTCAGGCATCCACCAGCACGGCATCCTCAACTGCCGGGAGACCTATGAAGTCATGGACCCGCAGGCGGTCGGCTGGGGAGCCACGGAACTCCCGCTCACCAAGCATTCCGGACGCGCTGCCGTCAAATCCAGGCTGGAACAGCTGGGCCATGTCCTTACGGAAGAGGAAGTCAACACCGTCTTTGAACGCTTCAAGAAAGTGGGCGACTCCAAAAAATTCGTTTATGATGATGATCTTTCCGCCATTGTGGACGACTCCCTGCATGCCTCCACCGGTCTATGGGAACTGGACTTCCTGCAATTCGTGGCAGGCAGCCACGCGCGGCCCACGGCTACGGTGGGACTGCTCAAGGAAGGCAAGAAATTTGAAGACAGCTCCACGGGCAATGGCGCCGTGGACGCCGTTATCAAAGCCATTGAGCGCATCACCAGGCGCAAAGGCACGCTGAAAGGCTACAGCGTCAACGCCGCTTCCGAAGGGAAGGACGCTCTGGGAGAAGTAACAGTGCATGTGGACTTCGGCCAGCCCAAGCCCATTGTGGGCAAGGGAGCCTCCACAGATGTCATTGAAGCTTCCGCCCGGGCCTACCTGAACGCGGTCAACCGTTCTATCCGCATGGAAAACATGCCCCAGCCCAATAATCTGGATGCAGGAACCATCTAACACGGGAAGCGCCCCGTTTGCCCGAACGGGGCGCAACTCCGCCCATCAGCCTGCCAGCCTCTGCGGGGCTTGCTTTTCCATTGCTCCCATGCCATAAGGTTCAAAACTCTTACACCACCCCATGAAAGCGACATTACTCCTTCTTTGCCTGGCGTGCAGCGCGCTGGCCGCGCCGCCGACAAAAAAACAGCCGGCCCCCGTTGAACCGCTGCCCGCCCTGGAAAAAAACACCATTGCCATTGACGGCCATCCGGAAAGCGTTGCAGCGGATGCCGAGGGCAATATCTATTTCACCTGCATCGGCTCCAGGCTCACGCCTACGGAAAAAGACAAGGACGGCTACCTGGGCGTCATTCCGCACGGTTCCACAGAGCCGAAAAAAATCACGGACGTAGATACCCTGGACGCGCCGAAAGGGTTGCTGTATCAGGACGGCTTCCTTTACTGCACGGACGTGGACATGGTATTCAAAATAAACGCCAAGACCGGCGCTATTGAAGGATACGTGGACTTGTCCCCATCCCGCATGAAATTCCTCAATGACCTGGCGTTCATCAATGGCAGGCTCATGGTTTCCTCCACGGATACCAACCAGATCTTCTATGTGGACACGAATACCAGCTCTTATGGGGAACTGGTCACCAAACAACCCATCTACAAACCGAACGGCCTGGCCTGGGACCCGGAAAGAAAGGTCATTTACCTCTGCGAATACGCAACGGATGAAAAAGGCAAACCCAGCGGACGCCTGCTCTCCATTAACCCCGTTTCCCGGGAAGTTACGGAACTTTCCAAGGAACGGGGACAGTATGACGGACTGGTTTACCGTGACAACGCCCTTTACTACAGCGACTGGTCCAAGGATAAAAAACCGGAAGCTGTCCGCAGGCTGGATTTGAAGACGGGGCGTTCCGCCCCGGTAGCCACTGGCCCTGTTGAAGGGGCGGCGGACTTTATCCTGTATGACTCCATGATGGTGGTTCCGGGCATGACGGAAAAGAAAATCCACATCATGCCCATTGGCGGAAAAAAATAAACACGGCTGTTCCGTTTTTATTCCCCTTATCCGGGCCGCCGGCGGCATTCCCCGCGCTGGCGGCCTTTTTCAATTTACGGCTTGAAGTACACCTGGGGATGCGCTTAGTTAATGGAGATATGAACACGATTTCCGACCAAATCATGGAGGGGATGAAAACCGCCATGAAAGCAAAAGACTCCGTTACCCTCAATACCCTGCGCGCCCTCAAGACGGCTCTCACGAACACGGCCATCGCCAAGGGAGGCTTGGGAACCCGGCTGGAAGAAGCGGAAGAACTGGCCGTGGTCCGCAAGCAAATCAAGCAGCGTGAGGATTCTGCGGAACAATTCCGCGCCGCCGGACGCCCGGAACTGGCGGAAAAGGAAGAGGCAGAAATCACCGTTCTCAAGCAATTCATGCCCGCAGAGCTTACAGCAGAAGAAACGGCGGCCATTCTGGAAACCGTGATGAAGGAAACGGGAGCCTCTGCCAAAAAAGATATGGGGCAGGTCATGAAACTCATGCAGGAACGCACTGCCGGCAGAGTCAACGGCAAGGAGTTGGCCCGCATGGTGTCAGCCAGACTGTCATGAAATGCTGCGCCGCCATCATCGTTGCGGCCGGGTCCTCCCGGCGTGCCGGATTTGACAAGCTGCTGGCCCCCCTGCACGGCGTCAGGGTGCTGGAACGCAGCATCCGCGCCTTTGCCAACTGCCGGGAAATCACGGAAATCGTAGTCGTTTGCCCGGAGGAACGTTTTCATGCCATTAACGGCGCAAACCTGGAAACGGAAATACCCGTTACCCGTGTGGACGGAGGGGCGGAACGGCATGAATCCGTGCAGAACGGACTGGCAGCCCTGCTTTATACCCCGGAATTCGTGGCCGTGCATGATGGAGCCCGCCCGCTCATTACGGTGGAGCAAATCTCCCGCTGCATTCAAACCGCCAGGGAATATGGGGCGTCCGCTTCCGCACATCCCGTAACGGACACCCTGAAGCGTGCGGACAAAGAACACTTTACGCTTCCGGAACAGGTGGAACGGGACGGGCTGTGGTGCATGGAAACCCCGCAGGTCTTTCAATATCCTCTTCTCCTGGACGCCTATGTGGAAGTCACGGAACGGAATATTCAGGTAACGGATGAAGTAACCGCCCTGCAACTCATCGGCCATCCTACGCGCCTGGTGCATAATCATGAGCCCAATCCCAAAATCACATGGCCGGAAGATATTTCCCGCGCTGAAATGCTAATGGAACTCAAGCACCTCCGCAATGACTCATGACCGGCCTTCTGTGTAAGAATCCCCGCATCGTTCTCATTCTGGGAACAGGGTATCTGGGAAAAGCCCTGGCGGAAAGCCTGCGGAAAGCGGGACATACGGCCCTGACGGCGGATATTGACCCGCAAAAAGCCATTTACGAAGCAGACGTTGCAGACCAGGCCTCCATGCAGGGGCTGGCCGCCCGCATCCCCTCCCCCCAGATCATCGTCATGTGCGCCAGCACGAGGGGAGGCGGAGAGGAAGCCTACCGCAACCTTTACGCTCATGGAACGCGGAACACGCTGGAAGCCTTTTCCGGAACGCCGGTCATCTTCTGTTCCAGCACAGCCGTCTATGGAATCACGGACGGACGCTGGATTACGGAGGAACACAACGTTTATCCCTCCTCTGGAAAAAACGGGCTGCTTATTCAGGCGGAACAGGCGGTTTTGGCAGCGGGGGGCACGGTCGTCCGGCTGGGAGCCCTGTACGGGCCGGGCCGCTGTGTTCTCGTCTCCCAGTACGTCACGAAGGGGAAGGCCCTCCCCGGAGCCATGAACAGATGGCTCAATTACATCCACCGGGATGATGCCGCAGCCGCCCTGCACCTGCTCTGCACCCTGCGGGAACCGCCTGCCGGCATTTACAACCTGACGGACCGCACCCCCATGCAAATGGGAGAAATATACTCCTACCTTTCCAATTTGCTGGGGAAACCCGCTCCGCAGCCTGAACCGCTCCCGGCGGAAGCGCGCCGGGGCTTCTCCAACCAGAGGATTTCCTGCTCCCGCCTTCTGGCCCTAGGCTGGGAACCGCTCTACCCAAGCTTTGCGGACGGTGTGCACAATGTGCTGGAGGCACTGGAAGAGTAAATTTCACACCCATCCTTTTCCTCGCTGACCGGCCCTTTCCGGACATTCCATAAACAAGAAGAGGCACCCCTGCTGAACGCATCTCGTGAGGCAGGCACATGCCGGAAAAAAATCCAGCCGGCGGCCCCGCCTGTCTTTGACTCTCCCCTTTCTTGACTTGCCGGGCGCGAACAGGCAAAATCCCGCGCGCAATGCGTATCATCACAGGTCTTCAACCCAGCGGCAAGCTGCATGTCGGCAACTACTTCGGAGCCATGGAACCGGCTGTCCGCATGCAGGAAAGCGGGGAATCTTTCTACTTCCTGGCGGACTACCACGCCATGAGCACCGTTCACGACGCGAACGCGCTGAGGGAAAACTGCAGCAACCTCGCTACGGACTTTCTGGCCGTGGGCCTGGATCCCGAAAAATGCGTCTTTTTCCGCCAGTCCGCCGTGCCGGAAGTCAATGAACTGGCCTGGATACTCAGCACCGTCTGTCCGATGGGCCTGCTGGAACGCTGCCATTCCTACAAAGATAAAATCGCCAAGGGATTCTCCCCCAGCAACGCCCTGTTCACCTACCCCGTGCTGATGGCTGCCGACATCCTGATGTATGACTCCGACCTGGTGCCCGTAGGCAAGGACCAGAAACAGCATCTGGAAGTCACACGCGACCTGGCGGGCAAAATGAACGAACAATTCGGAGAAGGCACCTGCAAACTTCCGGATGCGCTCATTGCGGAAAGCACAGCCATTGTTCCGGGGCTGGACGGGCAGAAAATGAGTAAAAGCTATAATAACACGCTCCCTATTTTCGGAGAGGAAAAAGCTGTCCGCAAACTCATCATGAGAATCCCCACGGATTCCACCCCCGTGGAAGATCCCAAACCGACGGAAGGTTCCGTCATCCTGCAGCTTTACAAACTCTTTGCCTCCGCGCAGGGTTATGAAGACATGGTGCGCGGTTTCCAGAACGGGGGATGCGGCTATGGAGACTTCAAAAAGCGCCTCTTTGACGCTTACTGGGAATACTTCCGGCCCGCGCGCGAACGCAGGGCGGAACTGGAAGCCAATCAGGACTATGTGCGCCGGACGCTGGAAGAAGGCGCCCGCAAGGCGCGCGAGACGGCCTCTGTAGTGCTGGACAGGGTGCGCCGCGCCGTGGGGCTGGCCTGAAACGGAATAAAACGGAACACCCGGGAAAGCCGGGGCAGCACACGTTTTTTTGTGGCATGGAAAAGTACGGAATGTTATACAGAAAGATATGAAGAATATTCTGGTAGCTATTGATTTCTCCAACGCCACGGACGCCGTCATCCACCAAATCGGCAAACTGGCCTGCCCCAACGACAGCATCATCCATTTGCTCCACATTGTGGAACCCAGCATTTGCTATGAAGTCTCCGGCGTTCTGCCGGATGAAGTGCCCGTTCCCGTGATGGACCAGACGGAAGAAAACGAAGTCATTTCCATCGCCAAAAACCATTTGAAACAGACGGCGGAAAAACTCTCCCGGCTGACGGACGCCACGATCATCCAGGCGGTGGAAGATGAATTTGAAATCAGCGAAGCAGTCATTAATTATGCTGAAAAACACCACATCGACATGATCGTGGTGGGCAAGCATAACCACGGCTTCCTCTCCACCGTCTTTCTGGGCAGCGTAGCAAGCTCCGTGATGCGGAAATCCCCCGTGCCCGTTCTGGTAGTGCCCGTCACCAGGGAAGAACAGTAAAAACCGCCATCTGCGCCAGCTCCGTCCCAATCCATGATTACCATACTCTTTATAGGCGATGTGGTCGGCGAACCCGGACGCACCGCCGTGAAACACATGTTGCCGGAACTCAAACGGGAACACGGGGCGGACTTCATCATCGTCAACGGGGAAAACGCGGCTGCCGGGAGAGGTATCACGCCCCGTCTGGCACAGGAACTGCTGCACGCCGGAGTGGATGTCATCACAACGGGAGACCATGTCTGGGACCAGGCGGAGCTGGCCCCGTGGATGGAATCCGAACCGCGGGTGCTGCGCCCCGTCAACTATCCGCAGGGAACACCGGGGCACGGAAGCGTAGTCGTGGAAACTCCCAAGGGGAAAATAGCCGTCATGCAGGTTCAGGGGCGCTCATTCATTCAGCCGCCGCTGGAAAACCCCTTCCTCATCTCGGAAGCGGAAGCCAAAAGGCTCAGGGAAGAAAAAGGCGTGCGGGTTATCTTTGTGGACATGCACGCGGAAACGACCAGTGAAAAAATCTCCACCGGCTACAATCTGGACGGCCTGGTTTCCGCCGTCGTCGGGACACATACCCATGTGCAGACGGCGGATGAAACCATTCTGGAGCGCGGCACGGCCTACCTGACGGATGCCGGCATGTGCGGCCCTGCCGTCGGGGTGCTGGGACGGGAACGGGAACCTATTCTCCAGCGTTTCCGCACCTCCATGCCGGCCAAATTCCCCGTAGCGAACTGGCCCGTGCGCCTCTGCGGAGCCGTCGTGCAAATAGATGAAGCCACAGGCAGGGCGCTCAGCATCGCCCGCATCAATAAAATCGTGGAAAAACCGGCCTCCTGACATGCATGCTTCAGCAGCATTCCTCTTCCCGTGCCCGGTGGCGGCTCTCTGCGGATGTTCCGAACATGGCGGAAGCCTCGTGAAGCGCGACCAGAACGCGGATTATAAGAAAAACCTGTTTCAACGGACGCCCTCCATCACACTCGCGGAAGGAAAAAGCTGCCGGCGTCCCGCCTCCCCTTCCCGGAATAACGGGAAGCTGGCTGTGGCGTCCGGAATCCAAGGATTTCCCGCGTTTCCATATTTTCAGGATGAAGCAACGGACAGAAACCCGGGCGCCCTTATTCCCCTTAATCTCCCATACTCTGGAACAGACACGTGGTCATGAACTATGAAGCCAGATTCGGCGGTATAGGCCGCCTGTACGGAAACAGCGGACTATCTCTTCTCAGAAGCGCCCGCATAGCCGTCGTCGGCATTGGGGGTGTAGGTTCCTGGGCGGCGGAAGCCCTGGCTCGGTCAGGCGTAGGCACTATCATTCTGATGGATATGGACGACCTCTGCATTACCAACACCAACCGGCAAATACATGCGTTGGCGTCCACAATCGGGCAATCCAAAACGGAAACCATGGCCACACGCATCCGGGAAATCAACCCGGAAGCGGAAGTCATTCCCATCAGCAATTTCTACACGGCCTCCAACGCGGAAAAACTGCTGTCGGCGGAACCGGACGTCATCATTGACGCCATCGACTCCCTGATACCCAAGGCCCACCTCATCGCCTCCTGTTACCGCGGCAAGCAACCGCTGGTAACCTGCGGCGGCGCGGGAGGGCGCATCAATCCCGCCCGGATAGAAATAGACGACCTCTCCCGCACCAGGGGGGATCCCCTGCTCTCCAGCTTACGCTACAGGCTGAAAAAAGATTATGCCCTTCCGCTTGGAGAAAAAGCCCGGAAACTGAAAATCCCCTGCGTTTTCTCCCAGGAAACGCCCGTATACCCCACCTGCGACGGAAAAACCTCCTGCACACGTGATCCGGAATTTCAAGGGAAAACGGGTTGTGACGCCGGATTCGGCTCCGTCACCCATATCACGGGAACCTTTGGCTTCTTTGCGGCTTCCGCCGCCATTCAAATGCTCTTAAATAAGAAAAAAACACCATCGTCATCATGAACAAACTCCTGCTTTCCCTGCTGGGGGCCGCCCTCCTTCCCGCCTTGACCCAGTGCAACACCCTGGAAAAAGACATTGCCGGCATCAACGCCCGCAATGCGGAAATAGCCTGCGAACCCAGGGGAAACTACTTCGTCGGCCGCCGCTACCATGTACCGGCAACGCGCTTCTGGGGATACCTGCGCCAGCCGGGCCAGACTTGGAGAACGGCCCAACTCGTCATCATGGATGAAAGCGCCTGCCGCACCCCGGACCGCCTGCCGGAATACACCGGAAATCCGCGCTACGCCTACGACAACAACTATGAATACAGGGTATACGGCAAATACACGGGCAAATACGGTTACGAACCCAATTCCAATCTGAAGCTGCCCATCTTCAAACCGACCAGGTTTGAAGTGGCCAACGCCAACCCCGGCTGGCTCTTCAAACCCTCGGAAAAATATGATACCGGAGCCGTTACTCTGCGCCCTGCTATCATGCCCGCGACAACCTCCCTCCCGGTCCGGTAAGAAAAACCTGTCCGCAAAAACGGGCCCGGAAAGATAAAAAAGAAGAAAAAGCTTGATATTTTCATTTCTCTGCGCTAAAAACTCTCTGCCTTAAGGCCTCCATGCTCAGGTGGCGGAATTGGTAGACGCGCTAGACTAAGGATCTAGTGGTGATAAACCGTAGGGGTTCGAGTCCCCTCCCGAGCACCATCCTCTTTCATACCGCAAGTTGCTGGTTTTCAGCTCTTGCGGTTTTCGTTTGCCGGCGGCGGGAAAATAAGCCGCAACACATTTTTTATCAACAGGGGAGGGCATGCGCTATAAAAAGAAAGGCATTTCCCCACTTCCGGCCCCCCCGAACTCTTACTTGCCGGAAACAATCCGGACGTAATCAGTGAAGGATTGGCCCTTACGGCATCCGTTAAAAACCCTCCGCCATCAGGGCCACGGCTTCTTCACGGCTCGGCATGGAACGGATGGCTCCCTTGCGTGTCGTCACAATGGAAGCGGCGGCATTGGCGAACAAAAGCATGTCCGCCAACTGGCCTTCCGTCAGGTTTTCCAGCCCGGTTTCCAGAATCCGGTCCAGGCAGCATCCCAAAAAGGCGTCTCCGGCTCCTGTTGTGTCAATAGTCTTGACGTTCAAGTACGTAGGCTGGTGAATAAGTTTGCTTTCATAGGCAGCCCAGCTTCCTTTTCTGCCCCCCGTCACCAGAATAAGGCGCAAATTGTCGAACTCCCTCTGTAAAATCCTCAATCCTTCTTCCATGGTGGCGCAGCCGGTGAGGAAGAGAAGTTCCTCCATTTCTATTTTCATGACGCTGCATGCGCCGCATCCGTAGAGCATTTGTTCCCGGGCCAGTTCCATATCAGGCCACAGGGGCGGACGGAGATTGGGGTCAAAAGAGATGAGTGCTCCTTTTTTCCGCGCATGGCTGACGGCATGCCGGGTAGCCCTCCGAACATCATCATGCGTCATGGAAATCGTCCCGAAATGGAAGACGCGCGCGTGCTCCACCAATTCAAGGGCCACTTCTCCCGCAGTCAGCTTCATATCGGCGCCGGGATTACGGTAAAAGGAAAATTCACGGTCTCCGTGTTCGTCAATCTGAACGAAAGCCAACGTCGTATTGACTTCCCGGGATGCGACGAGACCTGAATCTCCAATGCCTTCTTCCTGAAGCGTGCGGCGAAGCATTTTCCCGAACATGTCGTGGCCCACTTTGCCAATGAAGGAGGTTTTGCGCCCGAGCCTGGACAGCATGGACAGGACGTTGCAGGGAGCTCCGCCCGGATTGGCCTGATAAACGGGCAGTTCCCGGTCATTTATTCCGCAGGGAGTGAAGTCTATCAGCAATTCTCCGAGAGCTACGACGTCATACATGCTTTTGCAAATGTTGAAAGTGAACCTTCCGGCGAAATATAAACCGGGAGAGATAAAGAAGCATAGAACGCGACGGCCTTGCGGGAAAAGGAAAAAGAGATTCATTCCAGTCATGGCGGAAAAGGAATTTCCCCTTTTATCCCGCCCTGCAGAAGCCTCGGAGGTTGAAGGAATGCCCAGATGTGAACAAGTATGGCATATATCCCTAACGCCCTCCACTCAGGAAATGCCCATCAAGATGAGACGGCAAAAACAGTCCTGCGCGAGGCACGCGCGGAACGCAGGAGAGAGTGTCCCCAACGGGGCAAATGCCCCCTTACGGGCATTTATGGGCGCGACTTCTTCTGCTCCCTCCCATTCCTCCGCAACAAACACGCATTTTCAAGGCATTTCAAGCTTCTTTAAGAGAATTCAACCCTTTTCAAGCATGTGTGATGGTCACAATATTCCGCATTTTTCTTCATGATTCCCTTCCCATCCTGTAATATGCGCCCATGCAAGAAAAAAGCAGCGGCTTTCAATGGCCCGCACAGTGGACCCCCGATATTCTCGCCACGTTGATGTTCGTGGTGCAGAAAGGAAGGATTCTCCTGATACGCAAAAAGAGGGGAATCGGCGCGGGCAAGGTGAACGGCCCCGGCGGAAAGTTTGAACCGGGGGAAACGGCCCTGCAATGCGTTTTGCGGGAAGTTCGGGAAGAGCTTCACATTGATATTGAGGATGCCCGGGAAATGGGCGTGCTGAATTTTTCCTTTGCCTGCGGCACCATTCCGGAAATCCGCTGCCACGTGTTTATGGCGACGTCTTTTACAGGCACCCCCACGGAAACCCCCGAGGCGGAGCCCTTCTGGTGTCCGGTGGACGGGATTCCCTATGACCTCATGTGGCAGGATGACCGGTTCTGGCTGCCCGCCATGCTGGACGGGAAACGGTTTGAGGCGTTTTTCACGTTTGAGGGGGACCGAATGCTGGAATTTTCCCTGAAAGCAGAGGATTAAATCGCCAAGAAACCCCAGGTTGCGGCCTCCTCCCTTTAAGCCGGATGGGGAACGCTGAACTTTTTTTATTCCGCCCTTCCCCCCGGAAGCAGCAGATTCAGAAGGAGGGGGAAACAGCACTGCCCGGCGCTCCAGAAAAGATTCCCTGAAATTCACCTGAAGGTGGGGGGCAAAGAGGGGGCATTTGCCCTTGAAGACCATTCTGAACAGCAGCTAACGTTGAAGAACCGGCTTCCGAAAAAAATGGACCTTCCCCCTTGTACCGGGATACATCGTATTCATCCCCTTACCTCCAGGTATCCTGAAACGGATCATAGGAATTGCTCCGGACGCCTGCATCGCAGAGGTTTCCCCATGGCGTCCAGAAAAAGGAAAGTTCTCCCTGCCGGAAACCGGTTCCGGCTTGAGGTTCATGCGAAGGGCTGGAATTCAGCCAAATCCGCGGCTCCGTAAGAAAACGGGGGGATGGTTGCATAACCGGAACCGGTAATGGAGGTTCAAGGGAGCAAATCAGCTCCGGCCTTTATTTTTTCCAAGCGCATCCGAATATCCCCTGGTGCATCAAATCCCTTTTCCGTAAACGGGAGGGGGCTTTTCTTTTTGCCCGGTATCCGGGAAATGCTGATTTTTCCGATGTTTTGCCTTCTTTTTCAGGTAATGCGCGGATGCCTACGCCCTTGCCGCTTTTTTACTTTGCACGGGGAAAGGACGGCTCCCGCCTCCGGGCATCAGGCCGTATTCTTTAAGCTTTTCCTCCCGCTTCCGTTGTGATAATGATGCGGGAGCAACCGTGTGATCCTTGATCGTTCACTTGCTGACTATTTCAATTCATATTCCATTATTCAACATGTATACCCCCGAATTCAAAAACATCCTCACTTCCACCCTGGACGGACTGCGTGCGGAAGGACTTTACAAGGAAGAACGCTTTATCGCCTCCCAGCAGTACTCCCAGGTGACGCTGAAGGACGGACGCTCCGTCATCAACATGTGCGCCAATAATTATCTGGGCCTTGCTAATAATCCGGAAGTGATGGAAGCCGCCAAGAAAGCCATCGACCAGTGGGGTTTCGGCATGGCGTCCGTGCGTTTTATCTGCGGCACGCAGACCCTTCACCGTCAATTGGAAGAACGTCTTACCCAATTTCTCGGCACGGAAGACACGATTCTGTTTCCCTCCTGCTTTGACGCCAACGGCGGGCTGTTTGAAGGCCTCCTGACTGCAGACGACGCTATTATTTCCGATTCCCTGAACCACGCTTCCATTATTGACGGCGTGCGCCTCTGCAAAGCCAAGCGCTTCCGTTATGCCAACAACGACATGGCAGACCTGGAAGCCAAGCTCCAGGAAGCGGACGCCGCCGGAGCGCGCGTGAAGTTGATTTCCACGGACGGCGTCTTTTCCATGGACGGCATCATTGCTCAGCTGGACAAGATTCACGAGCTGGCAGCCAAGTACAATGCCATTGTCCACTTTGACGACTGCCACGCCACGGGCTTCCTGGGCGAAAAGGGCCGCGGCACGCATGAATACCGCGGCCTGTTCGGCCATATAGACATCACCACCGGCACCCTGGGCAAGGCACTGGGAGGCGCTTCCGGCGGCTACGTCTCCGGCCCGAAAGAAGTAGTGGATGTCCTGCGCCAGAAGGCGCGCCCGTACCTGTTCTCCAACAGCGTGGCCCCGGCTATCGTAGCCGCTTCCATCAAAGTACTGGACCTGTTGGAACAGTCCACGGAAGCGCGCGACCGCGTAGAAGCCAATACCAAGTATTTCCGTGACGCGATGACGGGAGCCGGATTCACCATCGGCGGCAAGGACCACCCCATTTCCCCGGTCATGCTGGGGGATGCCGTCCTGTCCCAGAAATTCTCCGCCCAGCTTCTGGACGAAGGAGTGTACGCCGTCGGCTTCTTCTATCCCGTCGTTCCCAAGGGACAGGCCCGCATCCGCACGCAGATTTCCGCAGCCCATACCCGCGAACAGCTGGACAAGGCGATTGAGGCCTTCTGCAAGGTAGGTAAAAACCTGGGCGTTATTTCCTGATTCCCGCGCTTTCCATGGCTTCGGACGCATCACCCATGCCCGGCGGCAACCTGCCGACGCTGCCCGTCAAGCGGGTGGTGCGTCCCACTCCCTCCATTCCGGACCATGAAGTTGTACGCCAGATAGGGAGCGGCGCGTACGGAGAAGTATGGCTGGCAAAATCCCTGACCGGGGCGTGGCGCGCCGTCAAAATCGTATGGAGGGAGGATTTTGAAGACGAGCGCTCTTTCAACCGGGAATTTGAGGGCATTCTCCAGTATGAACCCATCGCCCGCAACCATCCGGGGCTGGTCCATATCCTGCATGTAGGGAGGCATGACCAGGATTCCCCGTTTTATTATTACGTCATGGAACTGGGCGACGACGCCCGCACCGGAGTTCATATTACACCGGACGAATATATTCCGCGCACGCTCCAGACGGACAAGAAGTTTTCCGGCAACAAGCCTCTTCCCCTGGATTACTGCCTGGAGGTAGGCAGCCAGCTGGCCCACGCCCTGCTGTACCTGCACAGCAAGAACCTGACGCACCGGGACATCAAGCCGGCGAACGTCATCTTTGTCAACGGGCGCCCCAAACTGGCGGACATAGGCCTGGTGGCCCATCTGGACCAGCGCAGCTTTGTAGGGACGGAGGGATTCATCCCCCCCGACGGCCCCGGCACCCGGCGGGCGGACGTTTACGCGCTGGCCAAGGTTCTGTATGAAATCAGCACGGGAAAGGACCGCATGGATTTTCCCGAACTCCCGGATGACTTGCCGGAAGGCACCGTGCGCAAGAAATGGCAGGCCTTCAACACCATCATTTGCCAGGCGGCGGAACCCCGCATTGAAGAATGCTCCATTGATTCCGCAGAGGAGCTGGCGGAAAAAATAGACGCCCTCCGGGGGTATGAAGTGCCTTCCCGTTTCCGTCTTCAGAAGAAAAAGAAACGCCGCAAACTCAGACGGGCCCTCCAACTTCTGGCTGCAGCCGCCGCCGGAGCCCTGACTGCCTACTGGGGGGCTTTATGGCTCAACAACAGCCAGCATCCGCCGGAGGAACCTGCCTCGGATATTCATCCGGATATCCACTCATCCGCTCCGGAAGAAGAATCCCGGACCGGATACGTGCTGGTGACCAGCTCCCCGGCGGGAGCGTCCGTGTATGATGCGGACGGCAACTATCTGGATGAAACTCCCTACGGCCCCATTGAACTGCCTTCCGGCTCCCCGGTAGCCTATACCATCAAGAAATCGGGGTTTGCGGACAAGGAAGAGGCGGGAACCGTCAAAGGAGGGTCCACGCTGGCCCTGGGCGGCGTGCTGAAAGAGTACCATCCCCCTACGGACAGCCAGCCCTGGAAGGATACGGAGGGTGTTACGTACCTGCCCGCAGAAACGCGCCATGTGGCGCAAGGCCCCCTTACCGCCGCCCTGTTCAACAAATTTCTGAAGGAGGACCGCCAGAAAGGGAACTTCCAGATGAAAAGGGAACAGACGGAACCGGGGCATCCGGAGAAGGATGTGGCCCTGCTCACGCAGGACGGCATCACGGCCTACCTGGCGTGGCTGAACAAGAAATGCGAGCGGGAGGGGCTGCTGGGCAAGGAATTTTCCATCAATGCGGATCCGCTTCCGCAGGCTTCCGGCAGCACGGAAAACCACAATGCCTACGTCCTGAACGTCACCCGCGTGTTCCAGGTGCCCATTACCGTCACCACCAATCCGCCGGGAGCCAGCGTTTTTTTCAATAACAGGCTCATCGGGCGTACCCCCATTGAAGAATACGTCAACCAGGTTCCCTACGTCATTGAAATCAAACTGCCGGGGCACGCCACCATGCGTCGCAGGGGGCTGGATCCGCAGGACTTATACCTTTCCCTGCAGTTGGAACCGGACAAGTCTGTCGTTTTTGGCTCGCCCTGGACCAACAGCCTGGGCATCAAACTGGTCCCTGTGGGTGGGAATGCTCTGGTCATGACCCACGAAGTGCGCGTAAAGGACTTTCAGGAATTCCTGAAAGCTACCGGAAGGAAAGCTCCGGCCCGGCCCGGCTTTCCCCAGGGAGAAGACCACCCGGTGGTGAATGTCAGCAGGCAGGATGCGCGCGCCTTCGCCAGATGGCTTACCAGCAAAGAGCGGGCCCTGGGCCTGATTGATCAGCACGACTCCTACCGTCTTCCCAAGGATGAGGAATGGAGTTCCTGGGTGCGTCTGACGGATGAACAGGGAGCCTCCCCCTATGAAAAGACGCTGCCTCATGAAAACTCCCGGGAAGCCTTCCCGTGGGGGTATTCATGGCCGCCGCCTGATAAAACGGGAAATTTTGCGGACCAGTCCGCCCTGATTTACCTGCCGTCCTCCCGCGTTATCGTAGGATATAACGACGGACAGCCGTATACGGCCCCCGTAAAAACCTTTCCGCCCAACCACCTGGGCCTGTACGACCTGGAAGGAAACGTCATGGAATGGGTAGACGATTCCTACGGGGGGCCGGAATCCCTCCCCATCCGCAACTACGGCGTCGCCCGCGGAGGCAGCTACCTGTCCTTCCGCCCCAAACAGCTCACCACTTCCATCCGCACTCCCCTGCCGGAAAATACGAGGGATAACGCGCTGGGCTTCCGTCTTATCCTGTCTTCCGAACGCCCCGCCATTCCAACAGCTCCCTGACGCCGTATCTGCGGCAGCGGTTTTCCCCGCTCTTTCCCCCTCTGAAACACACCGCCCCCCAAGGGGGAAAACCCTGAGGCTTGAGAGAGCTTCCGGCTCTCCGGGAAAATGATTGAAAGGGCCTCCCGTTATGCTACCATTTTTCCGCATCCTTTTCCTGAATGAAAATAATCGCCATCGCCAACCAGAAGGGGGGAGTGGGAAAAACCACTACGGCTATCAACATGGCTGCCGCCCTGGCTCAGCTGAAAAAGAAAATCCTTGTCATTGACCTGGACCCGCAGGCCAATGCCACCAGCGGCATGGGCATTGACGGAAAAGACATGCCCAGCCTGTATCCGGCCCTGCTCGGCCAGCTTCCGGCGGAAGAGTGCATCCTGCCTTCCGGCAGGAAACGGCTGGATATCATCCCCTCCACCATGGACCTGGCCGGCGTGGAAATAGAATTGGCCCGTTCCGGCAACCATCTGACCCGCCTCCGGGAAACGCTCGCCCCCCTGCGGGAGTCGGACACCTATGATTACTGCATCATGGACACGCCACCTTCCCTGGGCGTTTTAATGACCTCCGCCTTGGCCGCCTGCGACGAAGTGCTGACTCCCCTCCAATGCGAATGGTTCGGTTTGGAAGGGCTGGCCAAGATTCTGCACGTGACGGCGCAAATCTGCGCCAGCGGCGCCAATCCCCGCATTCGGCATGAAGGAGTACTGATGACCATGTATGACGGCCGCACCAATCTTTCCCGCCAGGTCATCGACCAGGTGGAACGGCATCTGCCCCAGATGCTGTACAAAACGGTCATCCCCCGTTCCATCCGGCTGGGGGAGGCTCCCAGTTTCGGCCACACCATTTTTGAACACGATCCTTCCGGAGCTGCGGCGGACGCCTACCTGAAAGCAGCCCGGGAATTCATCAAACGCCATAAACCCTGAACATTTGAAGAAACAAGCTTCCCGGAATGCCGCAAACCTCGGCGAGAGGAACAGGCAGAAAGGGCACCGCAGAAAACTTCCTCCTTCATTTTGCCTTCCGGGTTTGTTGCGTCCATTTAACGTCAGGAAAACATCTTCCATACCGCCCCCCCGGAGACGGAGGAAAACAGGCATTCCCCTTGACTTGACTTCCGCGGCATATAAGAATGCTCCAGCTCCGTTTCAGGAACTTTCCCCTCATGCCAGCAGAAGAAGGCCAGCCAGACGACCAGCCCCGGGTGACAGGGCAAGTCCCGCCTCCCGCTCCGACGACAGGCCTGGAACAGATTCTGCCCGGACGTCCGCCCCTGCCCCATGCGGAAGAAAAAGGGGCCTTTTTCGTTTACCAGCGGGAGCTGGCTCCCGGCACGCGCCTGGAACAATATGAAATCATCCGCGTCCTGGGCAGCGGAGGTTTTGGCATCACATACCTGGCGAAGGATCTGTTCCTGAACCGCAACGTGGTTATCAAGGAAAACTTCCCTTCAAGATACTCCTACCGGGACCCTCTGACCGGGCATATCCGGCCCAATAATGAACACGATCTGGAAAATTACACCTGGGCTCTGAAAAGTTTTCTGAGCGAGGCTCAGACGCTGGCGGAGCTCAACAACCCCGGCATCGTCAGAATCCTTTCCGTCTTTGAAGCCAACGGCACGGCCTACTTCGCCATGGAGCATATTACCGGCCTTTCCCTGGATTATCTGGGGGAAAAACTTCACAGCACCGGACACCGGTATACGGAAGACGAACTCAAGGGGCTGCTGACGCGTCTGCTCCGGATTCTGGACTATCTCCATTCCCGGCACATTTACCACCGGGATATCAAACCCGGCAACATCCTGCTGACGGAGGAAGGCACGCCCGTGCTGATCGACTTCGGAGCGGCACGGCATGCCCTGAAACTTCATGCCGCCACCGTCCTGACCACCCAGGGGTATTCCTCCCCGGAACAGGCGCTGGGCAAAACGAATATCGGCCCGTGGAGCGATCTCTATTCCGTAGGGGCCACTTTTTACGCCCTGCTTACAGGAAATCCTCCGGACCGTGCGGAAGCGCGCCTGGCGGAAGATGAAATGCAGCCGCTGCACAGCGACCCCGTGCTCACCCGCTATTACTCACGGCAGTTCCTGCTTTCCCTGGACAAGGCGCTGGCCCCCCAGATCAATTGCCGCTACCAGACTGCCGGCGAATGGATTCACGACATTTGCGCCATTTCCGGGGAAGAATCCTCCGCCACTATTCAAGTCCTCCCGGCAGACCTCCGCCATGCCCATCCGGAAGGAATGCCAACTCCCGGCGCCGGGAAAAACATCGGCAGCAGAAAGGCCGCCACCGGAACGGACGGTCTTCATTCCGCCTCACGCAGGCAGTCCGCACGCGCTCTTCGCCGCAACCGCCACATGATGTCCCTGCTGGTAGGAAGCGCATGCACCATCCTGCTGGGAGCGGGAGCATTTTATCTGCTGGATTACATGAAAAAAGCCCCTATGGGCACTACCATCAATACCATCAAGGAAGCCATCCAGCAGCCCGCGGCAAGCTCCAACGTGGGAGAACCGGAACCGCCGCCACGGGATCTGGCATCCATCCGCATTCCGGAACTCAAAATAACGGAAGACGCGGCGCTGGAACCGCGGTCGCTGACGGAGTTCCGTTTGCGCCTTGACGACACCATCCTGACCCGTTCCGGGCTTCCTCCCATCCTGCCGGAAAAACTGCGCATCTCCTGCATTTATCTCCAAATCATTCCCCCGGCAGCAAACGGGGAAAAACTGTACCTCACCATCCGGGACTCCAACGGCGTCCTGGTGGACCGCTCCATCAATGCCGTCCCCTCCTTCACGGTGCCGGGGCAATCCACCTCCACCTTTTTCTTTCCCGCACTTCCGGAACTGTCCACGGACCGCACCTACACGTACTCCTTTGAAAATGCCGCCCACCAGGCTGTTCCCATGCAAATGGCATGTTTCAAAAACGAGACGAAACGCACGGAGGAAGCCTTCCCGAAAATCCGTTTCATCGCTGCTCCTCCCGTGCCGGAACCAGCCCTGCTCCATGACCCGGCCTACAAAAATCTTGTGGCGGCAATCGCCGCGCCCTCCCCGGAGAACAAAAATATTCTCGACCAGGCAAGTTCCATGCCGGGCATGCTGCCGGCCCTGGAGCAACTTTCCAAAGCCGGCTACCCGATAGCCCAGCAGGCACTGGGGAAAACCCTCATGTCTCCGGACTGTCCCGGCGGTTCCAGGCCGGAAGAAGGCGTGGAATGGCTTTACCGTTCCGCCACAGGAGGGTGCTATGAAGCCATGAAAGAGCTGGGCATCCTGCTCATGGACATCCCCTCCTATTTTCCCCGCCTTCCCCAGCAGCCTCCCCTTGCCTCCCGGGATTACGCCCAGGCGACGCGTTTCCTGAGAATGGCGGTCCAATACCGGGACCAGGAGGCCCTGTACCTGCTTTCCCTCATGTACTCCCAGGGCTGGGGCGTACCCGCCTCCCCGGAACTTTCCAGCCTGATGATGGAACGCCTCAATGGATCCTCCTACTCCACGGACAGCCTGAATCCCAACGCGCCTATCGCGGCGTACTGGCTTCCCCTTCCTCTCGCCGGAGAAAAAAACACCATTCTGCGCTTCACTATTCCAGGGCCGTTCGCCCCGTCCCAGCTCAAGGGCGTTACTCTGCACAACACAAGCCTGAAGGATGCCTTCTCCATCAGTAAAATCAATATCCTCCAACATGGGCGCCTCATTCTGGACATCATTTCCCCCCAGGAAGTGCTGCCTGGCAAAAGCTCCGCTCCCATAGGCATATCCATCCCGGAAGGGCTGCTGGCGGATACGACCCTGCCTCTGGAAGTGGAAATTTTGCTGACCCCCGGAAACAGCTGCGGCGTGGTGGAAATGCCCCCTCCGGTGGAAAAGAAAAACTCCGGAATTGAAATCAATTAAGCTTCACCGCTTTTCAACGTGAAACAGCAACAGGGGAAACGGAAAACACCCTTGATTCCCGCTTGGGGCATGCTACAAAAGATAAATGAATGCCTCTGTTTTCCTGCGGCGCATGCTCCCTATGCTGGCCGCGTTCCTGATGGCCTCCTGCGGTGATCCTACCGTCTCCTACTGGAAAGAGGTGGCGGACATCAATACGGAATACTCGGAAAAAAGCGACGCACTGGTGCAACACCTCCTGAAACTGAAAAAGAACCCTACCCTTCCGGGCCTGGAAGAAAGCAGCAGGGATGCGGCTGACCTGTTGCGGGAAAGGGATGAAGAACTGGCGGACCTGAGCACGAAAAACGTGGATCCCGCCGTGACGGCCTATGTGGAAGAAGACCGCAAGCTTTTTGCCAGAGGAAGGGAATTGGCGGAACGCTACCAGCAATACTTTGAAAAATATCTTAAGGGAGGACCGGATTTTACTCCGGATCCTTCCCGGGCCGTCGCCCATATAGGCCGGGGCCGGCAGGAAATACGCAAAATTCTGGCAGAAGCCCGAAAACTGGAAGCTCGGGCTGAAATGCTGAGAAAAGAAAAATCAGCTGAGCTTGAGCAAGAACTTCCTCCGCTGCACTTTCGCCTTCCGGAGTTGAAACAACTTCTATCCAGCCGTTAGAAAACGCGCACGAACCACTTGCATCAATGCGGAGTCTCGGCCAGTCTGACAGCGTCCTTTCCTTTGCACGGGCGGACACAACCGGTTCCTGAAACTTTCCCCGTCCATGTTACGGAAATATCCTTTCCCTGGATTTACAGGAAAAACGAGTCCCCCTGCTGTCCATCGTCGTTTATTTCAGCATTCAAATTTCATTCCCTGAATACCCCTTAGCACAGGCTTCATAGTTTAAAATTCCAAAGAAGGAGTATGCCTCCCCTCCACAAAAAAGCCCCGTTCATATTTCATGAACGGGGCTATATTAATTTTTCAAATTCACCTGGGAGTTTGATCAATGACGATAAAATCCTTTTTCCCCACATACTCCCTCCGGGCACCCGGATCTCCATATTCGTCCCGGAAACGTACTTCCAATTCATATATTTTCCCGCTGGCCAGAGGTTGTAAACAGGTCAGCAGCAAATACGCATCCTTGACGGGCACTTCATGTTTCTCTCCGGGGTTTAGAGTAATAAAGTGCATAAGATTCGCGACACGCTCCATTTGATAGGAGGGAAGATACCATGCCGTTGGCCTTTTTCCCTTCTTTCCGTTTCCCTTACTCCATATCTTGATGATCATATTCGGCCCGCGTTCCTTGAAAGGATAGAAGTCGTCGAAAAGACGCACAGCCTTTTCTCCCTTATTGGCAAACACCAGCTTGAAGTCCGGAAAAGCGGCTCTTGGATCGAACGAAGCCTTTTCCATCACCACGGAACATTCAATCCCGTTATCGGCCATATCCCCCCATGCAGGAGCATGGAAGAACAGGCTGACCAAAAGCCCTAAAATTTGGAAGATTTTCTCATTGCAGCGCGAGCTTTTGGGAGAAGGCGAGAATTCGCGCTTCATCGTCGAGTTCCCTTCGTACTTCCCTTCCTAGATAGGTTCCCTGTCCATCGTGACGTTGAACGCGACCGCATTGCCACTAGAGGGATCGTAAGCGTTATTCTCATACTTTAAAGCCACCTTGTAATAAAATGTCTCACTATCTCCAGGGCGATTGATCGACCACGGAGAAATCTTTTCTTCCTACATACTTCCTCCGAACACCGGGATCTCCATATCCGTCATGGAAACTAACTTCCAACTCGTAGTTCTCACCATTGATAAGACGGCGAAAAGGTTTAAGATAGAGCATTAGCAGGTACGCATCCTTAATGAGCACTTCATGCTTCTCTCCGGGCTTCAGCGCGATGAAGTTCAAGAAACGTCCTCGCTCAATTTGATACCTGGGGTTGTACCACGCAGCTTCTTTCTTCTGCCCTCGTGTTTGTTTTTGAAAAATCTTTATGAAAATATTCGGGCCGTTATCCTTGAGAGGATAGAAGTCATCGAAAAGACGCACGGTCTTTTCGCCCTTGTTCGTGAACACCAGCTTGAAGTCAGGGAAGGCAGCCCTCGCGTCAAATTCGGTTTTTTCCATCACCACGGAGCATTCCACTCCGTTGTCGGCCATGTCCCCCCACGCAACCGCATGGAGGGACAGGCTTGCCAGAAGTACTAAATACCGGAAGATTTTTTTCATTGCAGCGTGAACCTGGCCGTTGCCCGGAGAACCCCCAGGAAACAATCCTTACCCTGCTGATTATAGTAGCGCGTTTCCAGTTCACAAGAACATTGTTTCCAGCCTTCCAGATCGACTTCATCCAGACACGCCGGAATCGTGACGCTGTATTCCTGACCGGGTGCAAGCTCCCGGTAGTTCAAGGGGCGCTGGAAGGTCATGGTGCCAATTTCCCTGTTCAGAACGCGCATTTTCCCCTGTTGCGTCCTCAACACCGTCACGAAAAAGAAATCGGACGTTTCGTCGTTGATTTCATCGGGAATGCGGACAGGCTCGTTTCCTTCGTTGCAGAAGCGGACTGTGATTTTGCCCTCCGTGAATTGGGATGTGTCCACAATGGAGATCTGAAGAGGTGTCGATTCCTCTGCAATTGCCATGCTGCGGGTTTTACCTTCGCTGACCGGAGGCATGAAACAGGGAGGCTCGATCGTTCCAGGTTTCGTGATAAACCTGGGCACATTGTTCCACCCCATCGCGTCGGCCAGACGAATGACATAGGCGGTTGCCTCGTTCAGGACTTCCTGTCCCGCGCAATAGGGTCTGTCATTGGCATCATCCGGCAACGCCAGTACATAGTCTGACGTTTCTTTCCAAAGGTCCAATGCCATTTCATTGGCAAGAGCCTGCATGAACTTCACGGCTTCATCCATGTTCGGTTCTTTGTCGCAGGAGATAGTCTGATTTTCAAGCATTTCCTGTATTTTGGAGTCCTTCCAGTAGAACTTGTAGGCATCCTCCCATTGACGGCGGTGGTGTTCTTCGTGGGCGAGGGAAGCCTGCGGCGTGTGCCATGTTCCAACTTCTGCACGAGATTGATAACCGTTCATTTCGTTGACCGCTTCGGTGGCTTCTTCTTCATTGAGAGGGGGATCAACATAGGGATTCACGTAAATGCCTTCATACATAGTTAGCTTGGCTCCGGCAGAAACGGAAACAACACGACACCTCCATACGTTTTCCGATTCATCTTTGCAAACCGTTACGCAAATCTTAGGTCTCAAGCTACTTACATTGCAGTCACCATTCCCTTCCAAATAGACGGGGTCCGGTTTCTCAAATTCCAGTCCCACTTCCTTGGATGGCTCACAGGTGATAGCGGATTCCTTCTCAATCGTCCACAATTTGATTTTGGGGGACGACGAGAATTCGCGCTTCATCGTTGAGTTGCCTTCATACTTCCCTGCCTGAATGGGTTCCCTGTCCATCGTGACGTTGAATGCAATGGCATTGTTGCTGACGGGGGTGTAGGCATTATTCGTGTAAGACAAGGCCACCTTGTAATAGCCAGCTTTCAGGTACCTGGAATCGGTTTCAATATTCAGTTTGCCGTTGGGTGGTATCCCATCCTTTTTTATAGCTTGCTCACCGGCAATCGTCAGGCTGCCTGTGTCGTCGGCTTCCACGCCGAAATAGTACAGGCCGTCTTCCTCGACCTTGATGAACATTTCCGGCGCTCCGGTGCAAGTGGCGTGTTCGCACGCCGAACCGTCAAAGTTGAACGTCATCGCGTCAACTCCGAGCGTCTTGCTGATAAGAGGCTCATTCCCTTGATAGATGTAGACGGGATTAACTGCCTTGTTGATTGGTATATAATTGTTCATTGCTATTGTTGTTGTTTACTTTTTGGAAAGACCGAACCATCAGCCTTCCAGTATTCTGGCGGCGTTCCCTGAACCCATCAACACGTTTAACGCGTAGTCCCCCCTCATTCGGAACGAAGAGGAACGACCTCCCCTCTAATTTTACGTGCTGCCGTGAACTTGCAAAAAAACTCCTGGTGCATTTTTATATCCTAATTCACGCCATTTTGGAACAAAGTTGCTCCATATAGATCTGACAACAGGCACGGGCGGCATTCCGCCGCACAATCATGATGCCTTAAAAAACAAGCATTCTCCTCCTTCTCTGTCCGTTCCGCCAAAGAAAAAGGCTGTTTCCAGAGAGTCCTTCACTTAAAAACAAAGCATCTTTTCCAAAAACGAGATTGACACCCGCGGTTCTTTTCCCTAGTATCCGCGCACAGCATCAGCGGCGGGGTAGCCAAGTGGTAAGGCGACGGTCTGCAAAATCGTTATTCGCGGGTTCGATTCCCGCTCCCGCCTCCAAGATGTTGTCAAAGCCGGTTCAAGTCGCGTTCGTCTAGCGGTCCAGGACTCCCGCCTTTCACGCGGGCAACACGGGTTCGAGTCCCGTACGCGATGCCAGCTTTTCTTTTTTCCAACTTGCACCGGAATCTTTTTTACGCCATCCCCGCGGGATGATTCTGGATTTTTTCTCCTGTTGTGGTACAGTAGCTTCCGTCATGGCAGCCCAGAACGATATTATGCTGAACGGAGCCCCGTACGAACTGGCGGAACCCTGTTCCGTCTCCCGGCTTCTGGAACTTTTGAACATGAAAGGCAAACCCGTCGTCGTGGAACACAACGGGGCCGCGCTCCTGCCGCAGGATTTTTCCCGGGTGACGGTTTCTCCCGGCGACCGGGTGGAAATCGTCTCCATCGTCGCCGGCGGCTAGGGTTCCGCTTTCGGGAACAGTCTTTTCCAATCCGTCATCGCCATGCCATCCATCCACGTCATGTCTCCCACCCTTGCCAGCCAAGTGGCGGCAGGAGAAGTAGTGGAGCGCCCCGCCTCCGTTGTGAAGGAACTGGTGGAAAACAGCCTGGATGCAGGAGCCAAATTCGTACGGGTGGAAATACGCCGCGGAGGCGTGGGCATGATCAAGGTGACGGACGACGGCAGCGGCATGTCACGGGCAGACGCCGAGCTGTGCACCAAACGGCACGCCACCAGCAAACTTTCTTCCCTGGAGGAGCTTTTTGAAATCACACACCTGGGATTCCGCGGAGAGGCTCTGCCCAGCATTGCCAGCGTTTCCCGCTTCAAACTCTGTACCAGGCAGCAGCAGGATCTGGAAGGCTGGGAAATACGCATTGACGGAGGCCTGGAACACGAACCGAGAAGCTCAGGCGTCTCTCCCGGCACCGCCATTGAGGTGGCCGACTTGTTTTACAATACTCCAGCGCGCCGCAAGTTCCTCAAATCCGCAGAAACGGAAGCTTCCCATGTGGAGCACCAGATACGCCTGCATGCCCTGGCTTATCCCCAAGTACGGTTTGCCTATAAACGGGACGACCAGCTTGTTTTCGACCTTCCCGCTACAGCGGATCTGCGCGTCCGCATTTCCGCACTGACGGATGCTGCCACAGCGGCGGCCCTGATTCCGATAGAAACGACCATCGGCCCCGGTATTTCCATCACGGGATTCCTGCTTCCTCTTTCCGAAGCCAGACGCACCAGAAAAGGGCAGTACGTCTTTATGAACACGCGCCCCGTGGAGGACCAGCTCATTAACAGGGCCATTCGGGACGGCTACGGAGGCTTCCCCACCGGACTGCATCCGGCGCTTTTTTTGTATATGGAGGTGGAACCCGCTCTGGTGGACGTCAATGTGCACCCCGCCAAGAAGGAAGTGAGATTCCGCCGTTCCGCAGACGTGGTAAACACCATTGTGGAAGCCATAGCCAACACTCTTCAAAAGCATGCCCGGCAGGAAATCCACGCTGCCGCCGCGCCGGAGCCGGAGAGAACTCTTCCTCCTGCCCATTCTACCACCGCGCCATATGGGGAAATACCCGCCCGTTCCACCAACCCCGGTTCTGCTTTCCCGGCGGCCGCCAGACCGGCCCCTGCTTCTTCCGCCGCCCAACCGCCGCTTTCCTCCTCTGCCAAACAATCCCATGGGGCTGTCCCTCCCCCCACCCTCCGGGCCATTCCCCTGAAACAGGTTCCCGCCACCCAGGGAAAACTGGATTTTCACCGTCAGGAGGATGAAGAGACGGCACGAAACGCCCATGAAAACGCAGCTCTGGAAAGGGATGCCTCCGCCGGATTTTCCTATCTGGGAACACTCCGCCAGCAATTCGCCCTGTTTGAAACGCCGGAAGGCCTGGTTCTGATGCATCCCAAGGCGGCCCGGGAACGCATCATATTTGAACGGCTGCGCGCACGCCGGGAAGCCCCCATGCCGTCCCAGCAGCTTCTGGATCCGGTGGTGCTGGATCTGGACCCGCGGGATTTTGCCGTCATCCGGCAGTTCGCCCCGCATTTTGACCAGGCCGGCATGGCCGTTACGCCCTTTGGACAGAATACAATCAGAATAGAATCCATCCCCGCACTGCTGGAACTGGAAAACGCACGCGCTTTTCTCCTGGAGCTGGTGGACCGTCTCACCCAGTCCGAATTCAGCCGAAATGCCAAACGCGTGGCTTATGAGACCTTCATTGGGGAATTTGCCAGAAAATCCGCCTGGAGGGAGCGCATTTCCCCTCACCGGGCCCCTGCCATCCTAAAGGATTTGCTTGCCTGTGAAGTGCCGTACTGCACCCCGGGTGGCAAACCCACGCTGGTGAATTATTCCGTTCCGGAAATTAAACGTAAATTCGGCCTACAGGCATAAGGATAAGGGAACCTTGTTTTCCGGAAAACAGGCCGAAACGCTGCAAGATTTTACGGCAAACGGAAAATATGCAGCCAAAGACGGATTGACGCCCGCTTCCGCAGTATATTTATTTTCCCTTACCGATACTCAGTTTTATTTCAGCCGCAGCAAGGGGCAGAAACTCTCCCTGGCAACAGGGAGCATTTTACCCTGTCCGGCTACGGACACCTTTTCCGTCTTGTCCACCAGAACTCTGGCATTCGGACGCATCCTCCCACATACAACAGGGAGCTTGCTCTTGGCAAGGAAGTTTCCCGCGTTTATACTGCAGCCTTCCAGATAAATTCCCAGCCATGCCAGACTCTCCCCCCAGCACCTCCCCCGCTCCGCGCATTGCTGAAACTGCCGCCGGGAAAATTCTGGCGGGCCTCCTGGCCCTTTTTTACATCTTCCTGGCAACGACGGCGGCAGGCGGGGAATGGCACACCCTTTTCCTTCCCGCCTGCTTTCTGGCGGCAGCCCTCCTGCTGTTCTGCTTCTGTATTCTCCGGGGATACAAGATTCCCAGCCCCGGACTGCCGGGCTGGCTGGCCCTGGGCCTGGGAGGCGGCTATTTCCTTGTCCGGGCATGGTTTTCCCCCTGGTTTTATTATGAGAGCGTAGCGGATCTGGGCCTTATCGCCACGGCCATCGTCATGTTCGCGGCGGGAACGTATGCCGGAGCCGGAAACGGGGAAAAGAGCATTCTTCCCGTACTGGCTGCGGCATTGGGGCTGCTGAATGCCCTGCTGTGGGGGTATCAGAACATAACGGGAACGGAAGCCTCCTGGTTCAGACCGGATTATTCCCTGTTCGGCACGGAAATCCGCAATATCGGATTGTTCGGATACAAAAATTTTTCCGCCCATTTCCTGTCCGTCACCGGTTTTTTCCTTTGCGCATACAGCATGGCTTCCGCCAGAAAATGGGGCATCCGCCTGTTCACGGGATTGGCGCTCATCCTCGTTTCCTTCACGTGCGGCTCGCGGTCCGCCTTCCCAAACGCCCTGGCTGGCGTAACCCTGTGCTTCTTTATTTATACCTCCAGCGTTTTCCGCAATAACAGAAAATTTTACACGGCGTCCATCCTCTTTATTATCCTGCTTTTCCTGGGCACGTCCTATGCCGTGCTGGATTTGTCCCGGGGGGCGGGCAGGCTTGCCGCCCTTCTGGATACGTTTTCCTTCGGCAACCGGCTGGACCTGTCCAAACTGGCCTGGGCGCTGGCGGACCAGGCTCCGCTGTTCGGTCACGGCAGCCGGATGTACACCAATCTTTCCACGGAGTTTTTCTCCGGAGCCAACCTCCCCAATTTTGCCCACCACGAATACGCACAAGCCGCCTGCGACTACGGCTATGCCGGGCTGGGGCTGATGCTAGCTCTGCTGGCCCTGTTTCTTATTTTCGGACTCCGGAGCGTTCTGAAATTGTCAGGAGAACATCAACGGCCCAATCCTCTGGGACCGGCGGCGTTCTGCGTATTGTGCATCGCCGCTTTCCACGCCTATGGGGAATTCATCTGGCATAACCCTGCCTTGCTTGGAGCCAGCGCCCTATGCGGCGGCATTACCTGCACGGCCCCTCTTTCCAGGGTGAAGGCTTCGCGCCAAGCCGGCCGCTGGCTTCAAGCGACCGCCGCACTGCTGATGGCCATATTGGCGCTGTGTTATGCGTTTCTGGCTTTTCCGGTCTGGAAAAACTCCCTTCAAGCCGTCCCGGCTTCCTCCGGCAACCGGCTGCCCATGCTGGAAGCGGCCGCCTCCTGCAGCCTGGACCCGGATTTGGTGCGGCGCAACATTCTGCATGCCGCAGGCAGTTCTCCCCCCCCAAACCCGGCCCGGCTCAAAGCATTGGAGCATCAGGAAGAAAAAGCGGAATTACTGAGCCCCGGAAACCACGGCCTGACGGCGGCTAAAAGCCTCCTTTACATCCTGCAGGGACGCCTCACGGAAGCGGAGCAACTGCTCCGCCCATACGTAGAGAGTCCCGGCAGGTTTGATGACCGAATGTTCGCCTGGACTACCATTTACAATAATATGCTGTATTCCTGGAGTACGGCCATTGCTGCCCAGTCCCCCGGACGCGCCCTGTCCATGGCCATGACGGCCCAGCGCCTGATGTCCGCCCAAACGGACCGGTGGCTGTATTATGGGGCCCTGGATCCCGAAGTCAGGAAAAAACACTACTCGCGCCTCAATGAGCTCAAGATGCTCATCATGATGCTTCAGGCCCGCGGAACGACGCCCGACCCTTCCTGGAGGAAATAGTATTCCGGAGAATCGGGAAAAAGGAATATGTGCAGCAGCCGTTATATCCTTTTTCCAGAGAAGCCGCTCCATACAGAAGTTTCCCCGTCTTATCCGGTTTCTTTTTTACCGGATTCCACGTCAGCTTATTTGTCCACGGAACACAGAGCGTACTGCAGCCAGAAAGATGAAAAGCCGACAGTTGGCGCAGCAAGTCCGCACAGGCGTTACGCCGGAATTTTAAGCGGATTCAGCTATCTTCCGCCGGAAGCGGCAGCCTCTCCGGAACAGAATCGTTTGTTTCTTTTTCCCCCAATCGCGGACTGCCCCGGTTGGCGGTCCCGTAAAACGCATGTCCCGACGCAGCTCAACAGAAAAAAACTTTCCCGGCTCTATTCCTTTATCGAATCTTTTTGTATGAACATAACCATCAAACGAATTTATGACCCCAAGGAACCCGGGGACGGATACCGCGTCCTGGTGGACCGGCTCTGGCCCCGCGGCATTTCCAAGGAAAAAGCTTCCTGGGACGAGTGGCTGAAGAACATAGCTCCATCCACGGCGCTCCGCCAGTGGTTTGCTCATGATCCAGTCAGATGGGACGCATTCCGCAAAAAGTACGACGAGGAACTGGATGGAAACGGAGAAGCCGTTTCCCATCTGCTCCGGCTGGCGCGGAAAGGGAAGGTTACGCTGCTGTACTCCGCCAGAAATACGGAGCACAACGAAGCTGCGGCTCTGAGGGATTACCTTTTGCAACGGTTTGCTTCATAAATAGAAGCATATTCCTGCACAAACTCCATCCGCAAGCCCCAAATGCTGTCACGGGAACCTATGATACGGCAGGATTTCTTCTCTCGTTCATGAACAAGCGAAGAAAATGGAAAGGAGGTGAATACAATATGGCCAGCAGTTCCCACATGGGTGGAAGTCATAAGGGCAGTTCCAAAAAACGCTCCGAGACTACCACAAGCAAAACCGGGCGGCGCATCAAACGCGATGCTTCAACCGGGCAGTTTATGAGCGATAAAAGCTCAGGCTCCAAGGAAAAACATTAATGCCCGCGAGTTTTTCGCCTGATTCAGGCTCCGGATCCGGTCCGGAGCCTTTTTATTTTTCTCCGCATCCGGAACACGAGGAAGTCTTCCGCCATCCACTCAGGCCCAATTCTTCAAAAGCCCGAAGGCCCAGCGCTTCAGGGGCTTTCCGGTCTCCGCGAATCACAACGGCAAAAGGCTCTTCCCTGCCCGGAACGTAATACGCAGCCCGCAGAACCAGTTCCTCTCCATCCGGATGGGCGTAAACACCCACGGGCGTGGAACAGTTCGCACCCAGACGTACCAGGAAAGCCCGTTCCGCCAGCGCACAGGCAAAAGACTCCGGATCATTGATGCCTTCCAGCATTTCCGCCGTCTCCCTGTCCGAGGAACGGCATTCCATTCCAACGATGCCCTGCCCCACAGCAGGAATAAAAACTTCCACCGGCAAGGGACGCATGTAAAGTTTTCTTCCTTCCACAAGGGTTTCCGGCGCGTACAGCCCCAGGCGTTCCAAACCGGCCCTGGCCAGAATCACGGCATCCCAGCCGGGATGTTCCACCAGCTTTCCCAAACGCGTAGGCACATTCCCCCGCAGGTTTTCAAACCGCAGGCCGGAACCCCAGTACGTGCGCGCCATCAGGCGTCGGCGCACGCTTCCGGTAGCCAGCGTTCCAGAACCGTTCCAGTCCGGTTCTTTCGTGATAAGCACATCCTCCACGGCAGCACGGGGCAAGACGGCGGCCAGCGTGAAGCCGGCTGCAAGCTCGCTCGGCACATCTTTGAGGCTGTGCACCGCTACGTCAATGCGGCCATCCCGGAGGGCGGTCTCCAGTTCCTTAATGAAGATCCCCTTGTCCACCATGCCGGAAACGCGGGCCACATCCGCCAGAGGAACATCCGTGCGGCGGTCCCCAATAGTATGGATGATTTCACAGCGCACGTCCAGTTCCGGATAACGGAGGGAAAGGGCAGCACGGACCATATCCGCCTGGGCCAGGGCCAGGGCGCTCCCGCGGGTGCCGATGATGAGGGTATTTTTACTGGTCATAAAGAGCGGAATCCGGAAAATATCTGGAGATGCTGTCCCGGATGATGGCCTCGCACCGGGAAACCTCCTGCTTCCGGCTGCGCTTGGCTTCATCCGCCAGCTGGGTGAGGGTATCAATATCGTAGAGATACACTTCGTCAATATCCGCCACGTCTGGAGAAATGTTGCGCGGCACGGAAATATCAATCAAAAAGAGGGAGCGGTATTTACGGGAGGCACGCAAAGGCAGAAGCGTTTCCCGGGTGATGATGCAGTGGGGAGCAGCGGTGGCGGCCACCACCACGTCAATATCCCTGAGATAGTTCCCCCACACGTCATACCGGATGGCCTGGCCGCCGATCATGCCCGCCAGCTCTACCGCACGGTCAAAGGAGCGGTTGGCCACATAAATGCCCTCAGCACCCCGGGCATGAAGGGCGCGCCCCGTAACGCGGCTCATCTCGCCCGCCCCCAGAATAAGGACGCGGGTGCCGGAAAGATCCCCGAAAATTTGTTCCGCCAGCTCTACTGCCACGGATCCCACGGAGGTTGCCCCGGCATGAATCTGGCTTTCCGTCCGCACTTTTTTACCGATAGTAAAGGCTTTCTGAAAGGTTTTGTTGGCGCAGGCCGCAGTTACTCCGGCATCCAGGGCCGTCTGGTAAGCCGTTTTTACCTGTCCGAAGATTTCCGTTTCCCCCAGCACCATGGAATCCAGGCCGCTCAGCACGCGGCACAGATGGCCTAGAGCATCTTCTCCCTGATGGCTGTAAAAATAAGAAGCCATATCCAGTTCCCCACGCCCATCACCCAGAAAATGGGATAAAATGTGCTCCTGCGCATTTTCCGGAGCGTTGGACCAATAATAAATTTCCATGCGGTTGCAGGTGGAAAGCACAACGCACTGGTCCACTCCAGGAAGGGAACGGATGCGCTGTCCTTCCTCCCGGAGTTTATGGGACGGCACGGCAAACCGCTCCCGTATTTCCACGGGAGCGGTCCGGTGATTCAAGCCTAAACAAACCATTCTCATCAACTTACCAGGAAAATGCTCATGGATGCAATAAAGAGGGCCACGCAGCAGCAGGCCAACATCCTTCCCGGAATGCCGCGGCGGTAGATGAACCACAGCAGGACGCTGTAGCCTGCCGTGAGAAGAATGACAATGGAGGTCTTGGCCGCATTAATGGGGAGATTCCCCCGCCACCCAAGCCATTCCCCAAGCAGAAGAAGGAGGAATGCCACAAGAACCAGGCGCTTCATGCTGGCTTCCAGTGTACGGATGGGAGGAAGCACACGGCAGGTACCGGGAATTCGGTGTCTTTTAAGACATTCATTCTGCATGCAGAACGCCGCCCCCGCGGCAGCGGCCAGGCCGCCGGCTCCGTAGGACATCATGGCTATGCCCACATGTGCCGTCACCCAGAAGTCATGCGTGCCCTGCACGTCCGCTCCGGAACGGCCTATCAGCAGGGAAAAAACCGTCAGCACGGCAATAGCCGGAGCCGTAAAGATGCCAAGCACGGACATTCGGTAAGCACGTCCCAGCATCAGATAAAAGAGATTGATGCTCCAGGAGAGGAAGAAGAGCACCTCTGAAGCGCCGCGGATGGGACACATTCCCGTCTGGTACGCTTTGACGCATAGCGCCCCTGTCTGGAGAACAAAGAGGCACGCCATCCAAAGATGGGCATATAAAAACCGCCGACCGGAAGCCAGCAGGCGCCATCCCAGCCCTGTGAAAGCCAGGGAGAGGATGAGAGAGGCAATGGCCCACCAGTTATCGTTCATAAATCCGGCCCGGAAATGGTTCAGGAACGCAAATCGCGCTCAATGACGGCGTATGCCTGGTGGTTATGGATGGATTCAAAATTTTCCGCTTCCACCCGGTACCAGCTGAAGGCGGAATGACTCTGCGTTTTCAACGCCACGTTGCGCACCAGATCTTCCACAAAGACAGGGTTTTCATAAGCCTTTTCCGTCACCCATTTTTCATCCGGCCTTTTCAGCACGCTGAACAGGGAGCAGCTCGCGCAGGATTCCACCAGGTCAATCAGGTCTTCAATCCAGACGGGCCTGGAGGCAAAACGCACGGAATAAGTAACCAGCCCGCGCTGGTTGTGGGCTCCGTAGGCACTCATGGCCTTGGAGCAGGGGCAGAGCGTCGTCACGGGAACGATAACTGTCAGCTTGAAATCAGCCAGTTTGTTGTTCGCTTCCGCCTCCATCTCAAACCGGACAACGTAATCCATGAGCCCTTCAATTCCGGTCACAGGAGCGTTTTTGGAGCGGAAAAACGGGAATTCCATTTCCACATGGGAACGGCGGGCGGACAGACGGCGGAGCAGTTCCCGCGGAATGGCCAGGGCCGTATGCACATCCAGCAAAGGGCCATGCTGGTGCAACGCCTCCACAAAACGGCTCATATGGGTGCCCTTGAATTCTTCCGGCAGATCCACCGCCAGAGAAACCGTCGCCACAGTGGACTGGATGCGGTTGAGCTTGTCCTGTATCTGGATGGGGAAGCGCAACCCTTTTACGCCCACCCGGTCAATGGAAATATTGCGCGTATCCGCCTCGGACTGGGTATCTTTCAGTTCCTGCATGGGGAAAAAGGATGATGGGTGGAAATAAAGCGGGAGTCTGCCCTGTTACCGGACAAACTCCCGCCTGCAAAGACAATTCTACCGAAAGAGATTTTCCTTGGGGCTTGTTACGGCAGAAGGTTGATGGAACGGGCCCGCTTGATTTCACGGGTTACCTTGCGGTGCATCTTGGCGGACAGCCCGGTCACACGGCGGGGAAGAATCTTGCCGGTTTCAGAGGTGAACTTGGACAGAAAATCAGGATTGAGCAGATTGACCTGATCCATCGGGATGTCGAGACGGCGGCGCGGCATCTGGCGATTGCACGTGCGGAAACGAATACGGCGTTCTACAGTCTTGGGTTCAGTGGACATAATAATAAAGATAATCGATAAGCCGAACCAGTTAACGCATTTCACGATGAAGCGTGCGGCGCTTCAGGAAAGGATTGTACTTAATCTTTTCCAGGCGGCCGGGAGTACGCAGGCTTTTTTTATTGCGCGTGGTGACGTAGCGGGACGTAGGCTTTCCTTCGGCTTTGGCCTCGGTGCATTCGAGGATGATGATGTCTCTTGGCATGATGAAAATGTGTTACGGGGGAGGATTTATACACTATTCTTCGTCATCGTCAAGAGAAACAGATAAATCTACATCATTTTGTCCTCCGGCAAAGCCCAGGGCCACTCTGGGACGGAACCTGGCATTCTGGCCTTTTTCCTTTTCCCACTGGGCCTGGCATTCCACGGTAAGCCGAGCAAAGGGGATGGCCTCCAGACGCAGAATGGGAATGACCTTGTATGACATTTCGCAGATGCCATATGTTCCGTTGTCAATGCGGGCGAGCGCCTGTTCGATTTCATACAGGCCGTCCTGCTCTTTGGAAAGCAGGCTGAGCGCAAAATCACGGTCATAGGCGTCACTGCCGGCATCCGCCTGGTGTTCCCCGGAGCCGGAAGCCTCACTGCCTTCCGGATGATTCCGGAGAGTATCGCGAGCCACGCCGCTCATGTTGTCCAGAGCCCTGTCGCGCAAGTCCAGCAGATGCTGGCGCTGCATCTGCACAAAAGGCTCCCATTCGGGGTTGGCGGCCAGTTCCGCCCTGGCGGCGGCAATGGCTTCCGCCTGTTCATCCGTCAATTCATTTTTCGCGGGAGCGGGAACCGGGGCAACGGGAGCAGGAGCAGCTTTTTTAACCGGAGCCTTTCCGGCCGTTTTCGGCGCAGGCTTCTTTTCCATTTCAGCCGCGGCGGCGGCAGCCTTTTTGGCGGGCTTGGCGGCTTTTTCCGGTTCAGCCGCCTTTTTCCTGCAGCATTTCTTCTTAGCGGGAGCCTCCAGGTCTACAGCGGTCTTGCAATCGGACTTCCCGCAGGTCTTTTTCTTGGAAGCCGGAACTTCCTTGGCGACGGTCTTGCTATTGGTTTTCTTGGGAGTAGGCATAGGAGGTGTCTCTATGGGCTGAGGTTGACAATATTTCACGAACAGAGGGCGGAACACGCCCCGCTGACTAGACTATGAGACATTACAAACGCCCAAATGTTCATTCAAGAAAATTCAGTGATGAAACAAGTCATTCCCCCCCCTTTTGCACCCTCTATTTTCCTGTCGGGATTTTACCGGGAACATACTTCTTTTAACCTTTTCAACGCCCGGTAAAATTCTATATTGGCGTTATATGACAGCTTCAACCATTCCCTCCATCGGCATTGACTTCGGAGGCACCTCCATCAAGATGGGGGTCGTCAAGGGCGCGGAAGTAATCGCCCACGCTCCTTCAATAGCCACCCAGGAATACGGCAATCCCGATCAACTGATTGAAGCCATAGCCCAGTTCGTGAATATGCTGCGGCTGAATCACCCGGAAGTGCAGGCCATCGGCATGGGAATGCCGGGGTTCGTCAATTTTTACCAGGGGACCGTTTATACGCTTACCAACGTGCCCGGTTGGAACAACGTTCCGGTAAAGGACATGCTCCAGGCTGCCTGCGGACTGCCCGTATACGTGGAAAACGACGCCAACTGCATGGCTTATGCGGAATGGAAGCTGGGCGCCGGAAAAGGAAAAAGGCATCTGGTCTGCCTGACCCTCGGCACCGGCGTTGGCAGCGGCCTGATCGTGAACGGAGAACTTCTGCGCGGGGCTACCTGCTCCGCCGGGGAGCTGGGGCAGACGAGCATCGACTACCGGGGGCGTCTGGGCCATTACGGAAACCGCGGCTCCCTGGAAGATTATGTAGGCAACCGGGAAATAGCTGCGGATGCACGCACGCTGTATGCCAGCCACGGCATTGACAAGGCCATTGTGGACTGCAACCCCATCTCTTTGGAACGGGCCGCATTGGCTGGTGATGAAGTAGCCGAGCAAGTATGGCGGGACCTGGCCGTAAAACTCTCCTGCGCCCTGATGAATTGCTGCTATCTCCTGAACCCGGAAGCCATCATCATCGGCGGGGGCGTGGCCAAGGCCAGAACCCTGCTTTTCCAGCCCCTTCAGGAAATCATGAAAACCCAGCTCGCCGCCCCTCTGGTGGAATACCTTGAAATCCTTCCCGCCCAGTTCGGTACGGAGGCGGGCATCCTGGGGGCCGCCCATCTGGCTCTCAACACCCACTTCGGAGAAACATTCCGGGCCTGATCTCGTGTCTTCCTCCCAAATACTCAAACTCGCCCGCAAACTGGAATTGACGGCGGAACAGGAACGGGATTTCCTGGACGCCATGCAGGCCGGGAACCGTTCCAGAAGCGCGCTGGTCATCACCCCGAAGGCACCGGAAGGCTACAGGCCGCCCTTGCCGGCAAAGGACACGCCGCAGGAGTGGGGGCATTCCTCCATCCTGGCGCTCCCGTCCGGAGAGGCGGAAACCAAACCCGGCTCCCTGCCGGATTACGAATGCGGCTATTACTATCCCCTGGATCTTTCGTCCGTCTGGGAAACGGCCCCTCTGGCGCATCTTCCGTTTCGTCCGGAACGCTGCCTGGACATGTGCGCGGCGCCGGGAGGGAAAAGCATTCTGGCGCAGACGCGGGTATCTCCGCAGGAGCATGTTTCCAACGAAGTGAATCCGAAGCGCCTGGGCATTCTGCGCCACAACCTGCTTCGCTGCGGCTTCACCAATCTGTACACCCAGCGCCTGCGCCCGGACCAGTGGGCGGAACTGGCCCCCGGGTGTTTTGACCTCATTCTGGCGGATGCCCCGTGCAGCGGGCAGTCCCTGCTGGCCAAAGGCATTCCCAACCCCGGCTGTTTCAATTCCTCCGTCACCGGAGGCAACGCCAAGCGGCAGCGGGGCATTTTGCTGGCCGCCCTCCGGTGTCTGGCTCCCGGCGGCTTCCTGCTGTACACTACATGCACTTACGCTCCGGAGGAAAATGAACGCAATGTCCTGTACCTGCTGAAACGCCATCCGGACCTGCACACCATAACCGTGCCGGAGCTGGAGCATTTCCGTTCCTCCCTGACGCAGGAAGCCTGCTACCGGCTCATGCCCTTCCACGGAGCAGGAGCGGGCGGTTTCACCTGCCTGCTGCGCAAGGAGGGAGGCCATCCGCTCCCGCTTTCCCTGCCGGATGAATTGAAGGCATGGCCCATTCATGCCATGAATTGTTGAAAAAAGATGAAGATAGGCATTTTTTCCGACTTGCACAACCGCACAGACCATCTGGAGCCAGCCATGCACCGGATGCGCCTGCTGGGCTGCGGACATTTCATTTTCCTGGGCGACTGCACCACCCCGGAGAGTTTCCGGCGCCTGATTGAACTGACGGAGGACTTTCCTCTGGATGCCGTTCCGGGCAACAACGACTACGATGTGGAAACCATGCGGCGGATGGCCGCCAGTTCCCCGGCGGCGCGCCTGCATCCGGAACATGCCCTTATCACCCGGTACGGCATGAAACTTTCCCTCTCCCATTACCCAAAATACGCCTTGCGGGAAGCGCGCAGCGGAAACGCAGACGCCGCCCTGTACGGCCATACCCACCAGGCCGTGAGCGAAAGATGCGGCGGCTGCCTGCTGGCCAATCCCGGAGAACTCCAGGGCAGGACCGGACGCATCGGTTTCGGCGTCCTGGATACGGACACCCGCATCATCAACCTGCATAACCTGGACTTTTACGCCCCATGAACACGCTGGACGTCTGCTGTGCCCTGATTGAACTGCCCGCCGCACGGGGCCCTCTTCTTCTGGGCGCCAAAAAGAAAACGGGGCAGTCCAACGGGCTTCTTTACGAATTCCCCGGCGGAAAAGTGGAACCAGGGGAGAATGCCCGGCACGCCGTCATACGGGAAATACGGGAAGAGCTGGGATGCACCGTCTTCCCCGTCCGCATGCTCACCCCCGTACGGCACCGGGAGCCTGAACGCATTATCCGCCTTATCCCCTTTCTGTGCCGTCTGGAACTTTGCGCCCTGCCCCGGCCTCTGGAACATGAAAACCTGGGGTTCTTCTCCCGCCGTACGCTGGAAGAACTGCCCTGGGCGCCTGCGGACCGCCTTGTGTTAAAGGAATGGCTGGAAGAACACCGCTGAAAGCAGCATCCCTTTCAAAATCAATCGCTCCACGGGAAAGTTCCCTCCATGTCTGAAAAGATTGCAAAAACACCTTGGAACATTCCCTACCATGAGGTATATAAGGTGGTGGCTGCTGCACTCGTCATCGCCTGGCAAAGTGGCCCGGACCGATATTACAACCTAATGGGGGCAGCCCCGACGCAGGGCTGACTACGTCCTATACCGGAAAAGTCAAAACAGCGGCGGAAGTCCCCGCTCTATTGAGGCACGGGAACGTGGCTCACAGCCAGAGACGGTGCAGCGGTGTCTTTTTTCCATCACCTGAATCCAGAAAGCTCGTTCTTTCAACGTTTTTCCCCTCCTTGAGCCCGCAGGTCAGCCGGGATTGCCCTCTTGGGCAACATTACTCCTGTCGGGATTCACCTCCTTTCCCCGCTCTTCTCACCCTCGCAGCATTTACAAATACGGAGAAGCCAATTCCCCAAAAGATCGAATGCGGCCACCGGCAACTGCCGCCTTCAGATATTTCGTGGCCCTCCATTCCCGCTCCTTCCGTCACTGGACGAGAAGGAAGAAAGGAAATATTATTAGTTAATAAACAGCAACTGTTAATAAACATAATAATCCCTTAGACATTTCCGGAAATCATGATAGGATCATGCTCCGTTGCGGAAAAGTTTTTAACATCCGTGCAACTTTTTCATTATTGCGAAGTATAATATAGTCGTGAAGGGATTCATCAAACTCATCATCGTCATTGCCGTTCTCGGAGGCATCTGGTTTGCCTGGGAACAGTGGAAAGGAAATGAAGCCATCCAGGTGGACTACCAGACAGAACCTCTTGAAAAAGGAAACCTGATGATCACCATCGAAGCAACCGGCGTCACGGAGCCTGATGAACTGGTAGATGTAGGCGCCCAGGTCAGCGGAATCATCATGGAGTTCGGCAAGGACCTGAACGGCAAGGTGGTGGATTATTCCAGCCCTGTAAAAGCCGGTCAGATGCTCGCGGAAATCGACAAGCTTCCCGTGCAGCTGGACGTTCAGAGGGCGGAAGCCTCCAAAGCGCAGGCCATTGCCGGAATCGCCCGCTCCAAGGCGGACATCCAGCAGGCCAAAGCCAAGCACCAGCAGGCCAGACTGGACCGGGAACGCGCGGAAAAGCTGGGGCCTGGGGATGCCCTGTCCAAATCCAGCTACGACCAGTATATTGCGGATGAAGAAACCGCCCGCGCCAACGTAGCCGTGGCGGAAGCCTCCCTGCAGGAAGCGGAAGCCTCCCTGAAACAGGCGGAAGCCGCGCTGAAAAAGGAAATGCGCAACCTGGAATACACCACCATCAAGTCCCCCGTGGACGGCGTCGTGGTCAAACGCCTGGTCAACATAGGCCAAACAGTGGTTTCCAGCATGAGCGCCTCCAGCCTGTTTTACATCGCCACAGACCTTTCCAAGCTGAAAATTTGGGCCGCTGTGAACGAGGCGGACATCGGCAGCATCCGCAAGGGGCAGGAGGTACTGTTCACCGTGGACGCTTTCGCCGGCCGCAAATTCAAGGGAACGGTGGACAAGATACGGCTGGACGCAACCATGACGTCCAATGTGGTCACTTACATCGTGGACATTGATGTACCCAATCCGGACAAGCTTCTTATCCCCTATCTGACGGCAAATGTCCAATTCGTGGTGGAGGACATACGAAACGCTTTCCTCGTTTCCAATGCGGCCCTCCGCTTCCGCCCGGAAACAGAACTGGTCAGTGCTGAACAAAAAGCGGCCTTTGACCGGATGCAGCCGGAGCTGGCCGCCCCCGTCCGGGACGGACAAAAGAAAAAAGCCGTGGTTTGGGAATTGCGGAACAATCTTCTTTATCCCCACCTGGTCACCAAAGGGGAAAGCAACGGCATGCTGACGGCCATTGCGGGAGAAACGCTCCGGGAAGGAATGGAAATCGTCTCTACGGCTCAGATACTGAACCGCTCCGACAGTTCGGGGGACGGTTCTTCCGCCTCCGCCGGCGGGGAAAACCCGTTCGCGCCCAAAATGCCCCCGCGCCGCAAGCCGAGCACAGGCAACACGAAAACGGCAGCCTCGGCTGGCAATGACGGGCCGCCCCCGCCTCCCTAACCCGGGGAACCGCCTTCATCCGACCGCTCAATCACTCTTTTTTACCGCATCAAGCATTCCAACAACGTGATCCGCTTACGTGACATAACCAAGGTGTACCACTTGGGAGAAATAGACCTTCAAGTACTCAAGGGCATCACCCTGGATATCAATGAAGGGGAATTCGTCTCCCTCACGGGGGCATCCGGCTCAGGGAAATCAACCCTGATGAATATCCTGGGCTGTCTGGACCGCCCTACCTCAGGCCATTACTATATTGCCGGGGAAGACGTTGCCAAATTCAACGCCGCAGAACGCGCCACTCTCCGGAACAAGCGCATCGGATTCGTCTTCCAGAACTTTAACCTGCTCTCCCGTACCACAGCCCTGGAAAACGTGATGATGCCTGCCGTATATGCCCACCCCGCCCTCAGCATGAAAGCCATGCGCGAACGTTCCGTGGAATTGCTGAATTTGGTAGGCCTTTCCGACCGCATGGACCACACCCCGGCCCAGCTTTCCGGCGGACAGCAGCAGCGCGTAGCCATTGCCCGTTCCCTCATCAACAACCCCAGCATCCTGCTGGCGGACGAACCAACGGGAAACCTGGACTCCACCACTTCCAAGGAAGTGCTGCACATGTTCAAGGACCTGAACCGCCAACAGGGAATCACAGTCATCCTGGTGACGCACGACCCCAAAATAGCCGCCTTCACGGACAGGGCCATCAACATGGCGGACGGCCTTATCTGCGAAGGGATTTCCGATATGCTTAGCAAGGACGACGTATGAAATTCCTTCCTCTCCTTAAAACCTGCATCAGGGCCCTGGTGCGCAATCCCATGCGCGCAGCCCTCACCATCCTGGGCATCATCATCGGCATTGCCGCAGTAATCGCCATGGTGGAAATCGGCCAGGGATCTACGCTCCAGATTAAAAATACCATCGCTTCCATGGGGGCGGACACGCTGAATATCCGCCCCGGAGCCATCTCCAAAAGCGGCGTCAATACCGGCGCGGGCGGACGGGCCTCCCTGACCAACGCAGACTGCGAAGCCATTGCGAAGGACTGCCCCATGGTTCTGCGGGCTACCCCCGTGGTGCGCGCCAGCGGCCAAGTCATTTACGGCAACAAGAACTGGAGCCCGGAAACTGTAGAGGGCGGTTCCGTGGAATACCTGAAAATCAAAAGCTGGTATGACATGGCGCGAGGCCAGCCCTTCTCCGAGGAAGATGTGGAACAGGCCAGGCGCGTGTGCGTCATTGGCCAGACGGTGGCCAAGGAACTTTTCGGGGACGAAGACCCGCTGGGCAAGGATATCCGCATCAAGAATGTCATGTTCAAAGTCATCGGCATCCTTCAGAAAAAAGGGGCCAACATGATGGGACGCGACCAGGACGACTCCATCATCCTCCCGTGGACAAGCATCCGCTACCGCCTCCAGGGCCTGGGCGGCGGTTCCACCACCACTTCCACCGGCAACAGCACCACCACCTTCAACCGGGCAGATAAATACACCGCCAGTTCCGTGGATTACTACACGGAAACTACGGACCAGCCCTATACGGACGCGCCCCATCCGCGGCGCTTCAACAATATTGATTCCATCATGGCTCAGATTTCAGACCCGGAACGCTCCTCCGAGGCCATTGACCAGATTACGGAAGTGATCCGTGCCAAACACAACCTCAAGGACGGCCAGCTGGACGATTTCCGGGTATGGGACATGGCGGAAATGTCCCGCGCCATGAGCAGCACCACGGAAGTGATGACCAATCTGCTGATGATCGTGGCCATGATCTCTCTGGTCGTCGGCGGCGTCGGCATCATGAATATCATGCTCGTTTCCGTCACGGAACGGACCAAGGAAATTGGCCTGCGCATGGCGGTGGGCGCCCGTCCGCAGGATATCATGCGCCAGTTCCTGCTGGAAGCGGTGCTGCTCTGCGTGGTGGGCGGCGCGCTGGGCATCATGCTCGGCAAGGCGATCTCCATCATCGTCAGCCGCACCATGAACTGGGCCACGGCCTCCTCCCCGGAAGCCATGGCTCTGGCTGTAGGCGTCTCCGTATTCATCGGCCTGGCCTTCGGATGGTACCCCTCCTGGAAGGCGTCCAAGATGGACCCCATTGATGCCCTTCGCCACGAATAACACCTTACTTTTTATTTTTCCATGTCATCCACGTTCAAACATCTCCCCCTTCTGGCCGCCTGTTTCCTTGGCTCCTGCATGGTAGGCCCTGATTTCCGGACGCCGACCAATGACCTCCCCGCCACGTGGGAGGCGAACAGGCCCCCCCACAGTTCGGACAAAGATTTGCGTTCCTGGTGGAATATCTTTGGAGACCCCCAGCTCAACCGCCTGGTCTCCACCTCCCTCAACAACAATCCGGACATGAAGGTGGCTTTGCTCCGCATCCGGGAGGCCAGGGAAAAGCTCCGCGTCTCCCAGGCATCCCTGCTTCCCTCCGCAGACGCCAGCGCCGGGTGGAGCCTGTCCCCGGACAGAGGTTTTCGCAGCTCCACTTCCCAGAACTTCTCCCTGGGAGCGTCCACCTCCTGGGAGCTGGACCTCTTTGGCGGCAACCGGCGTTCCATCGAGGCCTCCCGCGCCTCCCTCATGTCCACAGAAGCTTCCGCAGGAGCCGTGCGCACAGCCCTGCTGGCGGACGTCGCCACGGCCTACTTTGACTGGATTACCGCCTGCGAACAGCTCCGCATCGCCAGGGAACAACTGGAAATCCAACGCAGCACCCTCACCATTGCGGAAAAACGCTACAAAACGGAATTCGCCCCCCGTCTGGATGTGGAGCAGGCCACCTCCACCGTCGCCTCCACGGAAAGCCGCATCCCGCAACTGGAAGCACAGGTGGCCTCCTCTAAAAACCAGCTCGCCGTGCTGCTGGGAACATACAATTCCCGTGTAGAACTGACGCTGCCCAAAGCTTCCGTCTTTGAGAAAACGCCCACCGTTCCGGTAGGCCTCCCCTCCGAACTCCTGCGGCGCAGGCCCGACGTTATCGCCGCTGAGGCGGACCTGCACGCGGCCGTGGCCAACGTGGGCGTAGCCGTAGCGGATCTGTACCCGCGCTTCAGCCTGACGGGCTCCCTTTCCAGCCGCGGCGGAGATTTCGGCCAGCTCTTCAGGGAAAACAACAACGCTTGGTCCCTGGGAGGGAATCTGCTCCAGCCCCTGTTCCGGGGCGGAGCCCTGAGAGCCACGGTACGCGCCCAGAAAGCCGCAGCAGAACAAGCTGCGGAAACTTACCGCAAAACGCTCATTACCGCCGTCTCCGAGGTGGAGGACGCCCTGATCGCCTATGGCAGCTATACCTCCCAGATGCAATACCTGCACAAGGAAAACAACGCCAACAGAGAAGCGTTCCAGCTCTCCCGCACCCTGTACATCAACGGCGAGACGGACTTCCTGAACGTGATCACCGCCCAGCGTTCCTGGCTCAGCTCGGAGGAATCCCTGGTCACCATGAAGCAGAACATCCGCAAGGCCGTGGTGCAGCTCGCGCGCGCCCTGGGCGGAGGGTGGTAACCAGCCGCTCCCTCCAAAGGCCGGGGTCGCCATCCCCGGCAAACGGAACCCTGATTTTTCAACAGGGGACACGGCAACAAGGGAATGACTGAAGAATCATTCTTTGTCCCCCCACATTCAGATTAAAAAAGGCCGCAAGAATAGGAACGAATAAACTATCCGGAAACTTCCGATGGAATTGAACAGATAGCCGGGGCTCAGGTTAAGAACACTCTCCAGCCACTTCTTTCATGACTGCGCTGATAAGGTTCCGGGTCTTCACGCCGGGCAGCACGTTCAAGTCCATGCGGTTCATCGCGTAGCCGAAGGAAAGGCCGTGGGCCGGATCCGCAAAGGCATGGGAACCGCCCGCCCCGGCATGACCGAAGCCGCCGGAGCCGAACAGATGGCGCAGCTTGCGGCCCGTGGCGGGGTCTGCGGGATCGTGCATGAAGCCGCAGGAAAAGGCGGTGGGCGTTTTCAGCGTCAGGTCCGGCCCGTCAATGACGACGTTCCGCATCCAGCCGCGCACGTCGGACGGGAATACCCGGAGTTCATCCAGCCCCAGGCAGGCCTGGTAGAACTGCGCCATGCCGCGGGCGGAAGCCACGCCGCCAAAGGCCGGAACTCCGTACACCCACGCTTCCGGCGTGTTCATCTGCCGCACACTGTTCAGCCCTATCAGAGTATTGAACGCACGGTTTACGGGCGTGCCCTTGTTCATGTATTCCAGATAAAACGGCGTACCCATGTTGGACATGTCCATCTTGCCGGGATACAGCGTCGCCACGCGGTTCCATTCGGATTCCGGCAACCCGATGTAAAAATCCAGATTCAACGGTTTGCGGATGCATTCCTCCCAGTAACGCCACAGGCGTTCCCCGGTCAGCCGCAGCATGAGTTCATCAAGCATCACTCCGTAAGTATGCGGATGGTAACCGTGTTCCGGCGGCTGCCACGCCGGAACGGTTTCCTCCAGGGCGCGCACGCAATCCTCATGGTCAAACACGCTTGCCGTCCGGGAGAAAGCCGCCAAACCGCATTGATGGGACATCATCTGCGCGATGGATGCATAGGGCAGCGGGAATTCAGGCCACAGTTCCCCCATGCTCATCTGCGGCGAGGCTCCCTGCTGGTGCAGGGCCAGCAGAAGGGTGGCGGATGCGGCTCCCTTGGTGGCGGAGTAAACGGGCACCAGCGAATGTTCCGTCCATGGACGCGTTTTATCCGCTGCGGCAAACCCGGAATGCAGGGAAACGATTTCCCTTCCCTCCTTCCAGACAGAAACGGAGGCGCCCAATTCTCCGTAGTTACGGAAGTTTTCCTGAAAAGCTTCAGTTATCTGCCGGTACTCATTCATTCCCTTAAAATGACCCGGCGTTACTCCCGGCGTCCGGACAAGAAAATACTCCCCTCCGGTTCCGAAAGCAAGCTCCGGCGCTGCCCTGGATGGTCAGCGCCGGAACAGGACAATCTGTAAAACATCAGTCCTTCGTATGGGCGGCCAGGTATTTCCTGGCTCCCACTTCATCCGGATCCTCCTTCAGATCATTCAGGGAGAATTTAATACGGAATAATCTCATGATGAACACATAAGAACATGGAATCAGAAAAGCGCCCACCAGTGTAGCCACCCCCATGCCAATCACCACCACCGTACCGATAGCGTTGCGCGCCAGAGCTCCGGAACCGGAGGAAAGCATCAGGGGGATACATCCCAGAACGAATGCCAGAGAAGTCATCAAAATGGGACGCAGGCGCAGACGCGCGGCAACCAGCGTAGCCTCCATCAGGCCCTTGCCGCGCTGCATCTCCAGATTGGCGAATTCCACGATGAGGATGGCGTTCTTGGCCGCCAGACCCACCAGCATCACCAGACCGATCTGCGCGTAGAGCGTCAGCTCCATGCCCTGCCAGAACAACCCTGCATAGGCCCCCAGCACGGCAATGGGAACCGTGAGGAAAACCGCCAGGGGAAGAGACCATTTCTCATAAAGCGCCACCAGGATAAGGAAGGCGAAAACGGCGGACATGGTGAAGATGGCCCCCAGCCCAATACTGTTGCGCACTTTATTTTCCTGGAAGCTCATGTCCTGGTAGTCGAAGCCAACCTTCGTAGGATCCATCGTCTGATGGAACACTTCCTCCAGCGCGTCCATCACCTGCTGGGTGGAGTGGCCGGGCCGGGGCATCACGTTCAATTTGGCGGCGTTGTAGATGTTATGGTGCATCACGAATTCCGTATCCTCTATCTCTCTGATGTTTACCAGGGCGGAGAGAGGCACGCGGGCTCCGTCCGCGTTCGTGACAAAGAAGCCGTCCATCTTCTCCAGGCTGGCGCGGTCTTCGCCCTGCGCCTGGATATACACTTGCCACTGCTGGCCGAAGGCATTGAAGTAATTGATGAAGGAGGAACCGTTGTAGGAGGCCAGAATGCCGTTGGCCACGTCAATATCCACCTTGTGGAATTTGCATTTTTCCTTGTCCAGGTTCACGTACTTCTGCGGCATGTCCGCCATCATCATGTCTATGCACAAGGCTATTTCCGGACGCCGGGAAGCCGCTTCCTGGAACCGTTGCACCTGTTCATGCAGATAAGCTACGCCCTGCCCTTCCAGATCCTCCAGTACCATCGTAACCCCGTTGGCGGTTCCCACGCCGGGGATGGCGGGAGGTTCCAGCACCATCGCCATACCGTCCAGGCCGGCCTGGGCCAGCTTTCCCTGAAGCTTTCCGGCCAGCTCCGCGGAGGTTTCCGGCCGTTCGCTCCAGTCCTTGAGCTCAATGTAGGCAATGCAGGCGCCGGGAGTCTGCACGCTGTTGAGGATGTTGAGCCCCACCACGGTAGTAAGGTTCTTCACGGCGGGATCCTGGAGGGCCGCCTCAAATTTTTTGTCCTGTTCATAGGCAACCTGCAGGGAGGTATTCGGCTTCAGGATCAGGCAGGCCAGCAGAAAGCCCTGGTCCTCATCCGGAAGAAAGCCGCCGGGAACGCGCTCCGCCACAGGAGCCACCGCCCCCCAGAATAACAACAGGAGGGGGATGGAAATCGCCAGCTTGCGGGCCAGGCTTCCGCACATGCGGGTATAGCCGTTGGAGACACGTCCGTAACACCAGTTGAACAAACGGTGAAATGGGGAAAAGAGGCTCTTCCTGCCCGCATTCCGGGGTTTCAGCAACAGGGCGCACAAGGCAGGGGAAAGCGTCAGCGCATTAAAGGCGGAAATGAGCATGGAAATGGCGATGGTCACGGCGAACTGCTTGAACAGCGTTCCCGTGATGCCCTCCAGCAGCAGGGAAGGCAGGAATACCGCCGCCAGCACCAAGGCAATGGCGATGACGGGGCCGGATACCTCCTGCATGGCGGCGAAGGAAGCCATGCGCGGATTGAGCCCCTTGTCTATGTGCTCCTGCACGGCTTCCACCACCACAATGGCGTCATCCACAACCAGGCCGATGGCCAGCACCATGCCAAGAAGGGAAATGGTGTTCAGAGAAAACCCCAGGACGGGAAAGAGGCAGAACGTGGAAATAAGGGAAACCGGAACGGCGATGAGCGGAATGAGGGTGGCGCGCCAGTTTTGAAGGAAAACATACACGACCAGGGCCACCAGGATGACGGCTTCCATGAACGTGTGCTTGATTTCATCAATGGAATAGCGCACCGCCGTAGTCGTATCCAGGGAAACGCGCCCGGTAATTCCTTCCGGCATCAGCGGAGCCTTGGCTTCCAGCAGTTTTTCCACGCGGTCCGCCGTAGCGATGGCGTTGGAACCCGGAGACTGGAAGATAACGATAGCCGCGGAAACTTCTCCGTTCAGGCGGCCGGTGGCGGAATAGTCTTCAGCTCCCAGTTCCACCTTGGCAATATCTTTCAGGTACAGGATTTCATCCCCATTCTGGCGAATGATGATGTCTTCAAATTCATGGGGCTCCGAAAGACGCCCCTGAGTGCGCACGGTAATGGTCTGCTCCTGGCCGTCCGGCACGGGGTCCGCCCCAATCTTGCCGCCGGGGTTGGTGGTGTTCTGGGCGGAGATGGCGCCCCGCACTTCATTGACGGAAACGCCGTAATGGGTCATTTTGGACGTATCCAGCCAGATGCGGATGGCATAACGCCCCGCGCCGTACACCTGAACTTCCCCCACGCCCTCCACGCGCTTGAGTTCATCCACCAGCTTCACCTGGGCATAGTTCGCCAAATCCACGGAATTATAACTGCCGTCCGGGGATGTCAGGGAATAAATCATCAGCGGAAGCCCCGGCTGCTGCTTGATGGTCACGCCCGTGCTCTGAACTTCCTGCGGAAGCTGGGAGGAGGCCTGCCCATAGCGCATATTGGTCAGCACCTGGTCAATATCCGTATCAGAACCCGGTTCAAAAATGACGGAAAGGTTATACACGCCGTTGTTGGAGGAAACGGACGTCATGTAGTCCATGCCCTGCACGCCGGAAATCTGGCGTTCCAGGGGAGTGCCCACGGAATCCGCCACCGCCTGCGCGCTGGCCCCCGGAAACATGGCCTGAAGCTGGATGGTGCGCGGCGTGATTTCCGGATACTGGGAAATAGGCAGGTTGAACATGCAGACCAGCCCCAGCAGCGTGGTCACAATGGCAATCACGGCTGCGATTGCGGGATGCTTGATGAAAAACGCACTCATGGCTTATTTGGCAGTTACGGAAGGAGTGGTGGAAACAGGGGCGGTATAGATGAAGGGAACGGGGACAACCGTCCCGAACCCTTCCGTGGCACCATGTTCCCGCATCAGGCTGTTCGCCTTGGAATACTGGGCGGCCATCATGCCGCCTTCCACAATGACGGGAGCATCCGCCGGCTTTCCGAATCCTATTTCCTTCAGAGATTCCGGAATGGGCTTGACCGTTCCCGTCACTATCTGCATCAGCATGGGGGCCGTTCCGCCCTTGCCGTCAGGCATGTCAAGGACTACGGTTTCCCCCGGCTGCACGTCAATGCGGCGCGGCGTCCCGTCCGGAGCCACCACCATGATGTTGCGGTGGTTCATGGAAGAAAGCAGGGCGCGGGCGGGAACCAGCAAGGCATCCTTCTCCGTACCCGTCACGGCGCGCACGCGCACCGCCATTCCGGAACGCAGTTTCAGATCCGGATTGGGAATATGGCCGATGAAATTGACGGTGCCCGTCGAACGGCTCACCTCGCTGTCCACGGAAACCACCTTCCCCTTCTTTTCAAAGACGGAGCCGTCTTCCAGAATCAGTTCAAAATCCGGCATGTCTCCCATGCGCCCGGTTTTCACATTCACGGTTCCATCCATCAGACGGCTGAGAACCTGTTTTCCCGTCACGGAAAAATCCACCCGGATGGGGTCGATGGAAGACATGGTAGTGAGCGGCGAGCCGCTGGCGGAAATCAGTTCCCCCACGCTGGCCGTCGCCTTGGAAGCCAGTCCCCGGAACGGCGCGCGTACTTTGGTGCGGTCCAGATTGATCCGGGCGTTTTCCAGGGAGGCTTCCGCCTGCTTGATGCCCGCTTCCGCCTCTTCCAGAGCAGCCTGCATCTCCTTAAGGTTCTGTTGCGCGTCCGTCAGGTTCTTGACGGAAATAGCCCCCGTCCTGATCAGTTTGTCATAGCGGTCCAAATCCTTCTGCACCCTGTTCAGAGAAGCTTCCGCCTGAAGTTTGACGGCACGAGCCGTAGCCAGGTTGGCCGCCTCCCTGTCCACGGCGGCCTGATAGGTAGCCGGGTCAATTTCAAAGAGCAGTTCTCCCTTGTTGCAAATGGAGCCGTCCCAGTAAACCTGGCGTATCAGCGTTCCGGAAACTTGCGGCCTGATGTCCGCCTGCTCCACCCCGCGCAAATGGCCGAACCAGGAAGATTTCAACTCCACCGTTCTCTGCTCCATATTCATCACGGAAACCTTGAGCGGAGGCATTTCCCTCCGGTTTGCAGCATCCGCAGCGCCGTTGTCTTCCCGGCACCCTGCCAGCCCGCACAGGGCCGCTATCAGTATATAGTGTGCTTTCATGTTGTTGGTTAACAATATTATTGAAGCATGCTTCATATTTTTCCAAACCCGTTCCCCGGAAAAAAGCACGGGCTTTTATTATTTGAGATTGTCGTACATGCGCCGGAGCAGATCCCTCAGCATGCTGCGGCTTTCCTCCGGCACCCCGTCCAGCAGCGTATTTTCAATCACTCCTATCTGCTGGCAAATGGACTCATGAACTCCCCGGCTCATATCCGTCAGGTACACATTCTTCAGCCGTCCGTCTTCACTGTCAAAAGCCGTGGTTACCCAGCCCTTCTGTTCCAGCCTCTGGACAATTCCCTTGGCCGTGGTATGGGAGACTCCCAAATGCCGCTCTATGTCCCTCTGGGAGACGTTTTCTCCGTCACGGGACATCAGGTAAGCCAGCACCCGGCCCTGGGAGGCCGTAACACGCTCCAGAGTCAAGCCGCGCGTAAGCATCATATCCACGCGGTCCGTAATCATTTTCATCAAAAACGAAATCGTAAAAGGGTCGGCCATAAAACAATGAAGCATGCTTCAAAAATGTACCGATAGGTACTCTTTATCCGGACGCAAGTCAAGCGGAATCAGAATGACAGAGCGCGTGAAAAACACGTTGGCCTCACCAGTAAAAATAAATCAAAGGCCGGAACCGCTTCGCCTCCTTTCCAGGGACAATACGCACAGCCGCTTTCCCTCTACCTTGAATTTCACCTCCCAGCCAGAGAACAAAAGGCCGTAAACCCGTTCCGGATCATCCTGATACTGGGGACGGGGATCCAGGGACAGCGTATCTTCCAGTGCACTCCTCTCTTCCGGAGACATGGAATCGCCAAAAGATTCGGGAATTTCCACATCCAGCCGTTCATAGGGGACAGCCGTAAACCCTTCCGACGCCTCCGGAATGCAGTCCGCCAGAGGAACATAGGGCTTGATATCCAGAATGGGGGTGCCATCCACCAAATCCACGCCGGAAAGATGCAATACCGGGCCGTCCAGAGGATGCAATTCCACCCGTTCCAGCCTGACGGCAGACAACCCGATGGGATTGGGACGGAACGGCGCACGCGTGGCGAACACCCCCATGCGCACATTGCCCCCCAGCCGCGGAGGCCGGACCGTAGGGCTCCACCCCTTATCCACGTTTTCACTGAACACCCAGACCAGCCACAGATGGGAAAACCCTTCCAGACCACGAACGGCATCCGGATGGCGGAACGGCGGCTCAAATACCAGCCGCGCCCGCGCATCCGGAACCAGGCCGCTCTGGCGCGGCGCACCGAATTTGTCCGGATAACACGTGCTGACGCGGGCGACGGGCTGCAAGCGGAGACTGATAGTTAATAATTAATAGCTGCCGGCCTTTCTCCGTGACGCTTTCATCAGAAGCGGTGGAGCTGTTCTCCGAACACCACGGCATCCACTTTTCCCTTCAAGGTCTTGCCCAGGAATGGAGTGTTGCGGGCGCGGCTGCGGAGCGTTTCATCCGTGACGGGCGTTTCTCCTTCCGGGTCAAACACCACCAGATTGGCCGGAGCCCCTTCTTCCAGGGAGACGGGGGCCAGCCCCATCAGCCTGCGGGGAGCATCGCTGCAGGCGCGCACCACCGTCTGCCAGGACATTTTTCCAGGTTCCACCAGATACTGGTAAATAGCGGGCAGGAACGTATCCAGCGCAATCATGCCCTGCGGGGCGACAACGAAATCCTGAAGTTTGGAAAACGGCGTGCAGGGCGTGTGATCGGAAACAATGCAGTCAATGGTGCCGTCATTCACGGCGGCAATCAGGGCATCCACATCCGACTGGTCGCGCAGGGGCGGCAGCGTCTTGTAGCAGGTATCGTACCGGCCCACGTCCTCATGCGTGAACAGGAGATGGTGAAGCGCCGCTTCCGCGCTCAACCCGGCGGTTTTCTGCTTGCAGCGGCGCAGGATATCCACGCTGCCGGCCGTAGAGACGGTCTGCACATGCAGGGAGTTGCCCGTATCCACGGAAAGGCGGATGATGGTTTCCAGCCCTATTTCCTCCGCACAGGAGGGGGAGCCCATCAACCCCAGACGGTAGGAAGTAGTGCCCGGGTGCATGGCCGCCCTGGCCGTCAGGGACGGCACGTCTCCGCGCAGGGCGAAAGTAAGGTTCAAATTGCTGGCGTACTGCATGGCGCGGTACAGCAGCAGCATGTTGTCCGGCGCACGGTCCCCGTCCGTGATGAAACGGACGCCCTTGGCGCGCAGGGAGTCGTAGGAGGCCTGCTCTTCCCCGCCGTCCCCCTTGGTCAGGCACCCGGAGGGGATAACCTCCACCAGAGCATCCGTCCGGCATATTTCCATGAACGTGGTAATCTGGGCGGCATTGTCCATCACGGGTTCGGTATCCGGCATCGCCATCAGTCCGGTCACTCCTCCATGCAGGGCGGCGGCGGTAGCCGTAGCCACACGTTCCTTGTCCTCCCGGCCGGGCTGGCACAAATGGGCATGGATGTCGAACAGGCCGGGCAGAACAAGCTTCCCGGAAGCATCCAGCGCCTTTTCCGCGGCGCTCTCCAAAAGCGTTCCGGCAGGAGCTTTCGCTGTAATCACCCCCCCGGAAACCAGAAGGTCCGCCGGGGCGGAACCGTCCGTCAGCCGGGCGTTGCGAATAAGTACGGAAGTTTTCATGAACAATCAGTGCTGGGTTTGCGGAGTGAGATGGTAAAGGATGGACATGCGGCAGGCGATGCCGTTTTCCACCTGGCGGTTGATCAGGCAGCGCTCATAGTCCATCGCCAGGTCACAGATTTCCACGCCGCGGTTGACCGGCCCCGGATGCATGATGTACAGGCCTTCACCGGAAATGCGCTTCAGGCGTTCTTCCGTGACGCCGTAAATCTTGTTGTACTCCCGGGCGCTGGGGAAGAAAGGGGCGTCCTGCCGCTCTTTTTGAACGCGCAGCAGGTAAATTACGTCCGGCTTCCAGGCAAACGCCTCATTGAAATCGGAAAAACGGGGAATGCCGGAATGTTCGGTACGCGGCACCAGGGAACCCGGCCCCAGGAAGGCGACCTCCATCCCCAGCCTCTTCATCAGCAGGGAGGTGGAACGGGCCACGCGGGAATGCAGGATGTCCCCCACAATCAGAACGCGCTTTCCCCTCACATCCGGAAACACCTCCCGGATGGTAAAGGAATCCAGCAGCGCCTGAGAGGGATGGGCGTGGGCTCCGTCCCCGGCGTTCACCACACTGGCGCAGGTATGGCGCGCAATCACGTTCGGCAAGCCGCTGTTGTAATGGCGCACGATTACATAATCCACTTTCATGGCCTGGAGCGTGGCGATCGTGTCCTGTACGGATTCCCCTTTCACCACGGAAGAGGTGGACACCGTGAAATTGGTCACGTCCGCAGACAGGCGTTCCGCCGCTACCTCAAAAGAGGAACGGGTGCGGGTGCTGGGTTCATAAAAAAGCGTCAGAACGGACTTGCCTTTCAGGGCGGGCACCTTTTTGACGCTTTTGGTAAAAAGCTCCTTCATCGGGCCGGCCGAATCCAGCAGCGTGTGAATTTCCTCGTCAGTCAATGAGGAGATATTAAGAAGATCCTTGCGGGGATGATTCATGGGAAACGGAGAATGGATTGGGAAATTTTCAGTGGGAGGGAATGACCACGCTGTCCTCGCCGTCAGCCTCCTTCATGCTTACGATGACCTTGGCTCCGGCATGGTCCAGCACGTGCCCCGTATAATCCGGCTGGATGGGAACTTCCCGCCCGCCGCGGTCAATCAGGCACGCCAGTTCAATACGGGCTGGACGGCCGTAGTCGAAAAGGGCGTTCAGGGCTGCACGTATGGTGCGCCCCGTATAAAGGACGTCGTCCACCAGCACGACCCTCATGTCATCCGTGGAAAACGGCAAATTGGAGGAACGGAGGGAAGGCCGGGCTTCCAGCTTGAAGATGTCATCCCGGTACAGGGAAATATCAATAGAGCCGTACTGGGCTTCCACTCCCAGCTCCTTTACCTTGTCCACCAGACGCCGGGCAATCACATCCCCCTGGCTGAGCAGGCCGACGATGGCGAGAGGTTCTCCGGGGAAACGGGCCACAATGCCGTGGGCCATGCGCTCAAGCGCACGGGAAATTCCCGGACCGTCCAATACGATTTCCTGTGAAGGAGGAGTCATTTGCCGGATGGGGGAAAAAGTTCACGTTACAGCCCCGCCAGTTTTAAAATGCGGTGGGCGGCCATGGCGGCATTGACGGGGGTTTTCTTATGCAAGCCAACCGTAGTGCAGGGCACGCCGCCGGGACACTGGGCAATCGCCAGCAGGGCGTCAATGCCGTTCAGGGGGCCTCCGGGGACCGGCACGCCGATAACCGGGAGGGAACAGCTCATCACCACCACGCCGGGAAGGGCGGCGGACAGGCCGGCCACAGCCAGGATAACCGCTTTTTCTCCGCTGGCTTCCAAATCCCCCAGGAACCTGATCAGCTCCGGAAGGTCGCGGTGGGCGGACAGAACGGTTTCTTCATAAGGAACATTCTCCTGAGCAAAATACTCGCGGGCCGGTTCCATAAAGGGCTTATCATTCGGGCTGCCGTAGATGACGATGACTTTCATGGCTGTGTGTGCGGTTGTGAAACTTGCAGATTATGCCCCCAAGCCCCCTGCCCTGGCAAGACTTAACCTGCAAAAAAAGGAAGGCCCCGGGTGGAAATGGACAACGGGAGAAAGGAAACACAGGAGTTCCGGAACCGTTCCGGGAAATTGGCCCGGCATCCCGGTCATTCCTCCAACCCAGATTCGCCCATCCCTCCGCCCTCTCCGGGAACCTTCAGTTTTCCGGAGAGGATATCCTTCCTGATACGGTAAAGAGCCTTCAACCTTTCCTTATCATCCAATCCTTCCGTTTCCCGCATCAGGTATAAAGAAAACTGTACGGAACAATTCTCCAGCCGATATTTTTCCAGAATGCTCCCCCCCATTTTCATGTATTGGCCTCCGTACTTTCCGTAAGCGTTCTGAATTTCATCGGCAGTCATCTTCTCTCTTTCCACCCGTTCCAGCAATTTCCCGGACTCCCTAATCATCTTCAGATGCATGGCGCGGTAATCGGAAGGGCATTTATCCAGAATCCCAGCCCATTCGGGAGATTGAATATATTTCAGCGTGGCCACCAGCACCGCACGATTATCCTTTTCCTCCAACTCCACAATGCACATCCAAATAATGCCCAGCTGCAAATGCCTGGCCATGGCCTCCCTGTACGCCGCTATTTTCTTTTCATCCTCCATTTCCAAATCCACATTAGCCATGGAGGGCGCCGCTATATCCTGGGACATGCCGGAAAAAGTCATCAAAAACGCAGGGAGAATTCCCCACCGGAACAGATTTGACATGGAACCACGTTACAGGAATCTTTTGCGCAGGGTCAACAATATTCTTCCCTTCCGCCGCATCCCGCAGCATTGACGGCCGCCGCCACGGCGGACTGTCTTTCCTTCCCGGGAAAAACAAGCCTGCCCCCATTCCCGGCTTGATGGCATCCGGTTCCGGCATAACAAGGCTCGCAGGGGCGTATCTTCATCCAGAAACGCAGAAAAACTGCATCGCTCCGGCGGCCGGACGCTTGAAAAAACCTAGCGGGTCGCCTGCTGCGCCATGGCCTCCGCACAACGGCGTCCGTCCAGGGCGGCGGAAACAATGCCGCCGGCGTAACCCGCCCCTTCGCCGCATGGGTACAGGCCCTCCAGACCGGGATGCTCCAGCGTGGCGGGGTCCCTTGGAATGCGCACGGGGGAGCTGGTGCGCGTCTCACAGCCAATGAGCTGGGCGTCCGCTCCGGCGAATCCCCGCCATTTACGGTCAAACTGCCGCAGCCCCTCCCGCATGCGCCAGACAATCCCTTCCGGGAGCAATTCATGAAGGGGATGGGATACGATGCCGGGATGATAGCTGGTAGGCAGCAGGGAAGAAGAAACGCGCCCTTTCAGAAAATCCTCCACCTTCTGGGCCGGGGCTTTCTGCATGCCTCCTCCGGCACGGCAGGAAGCCGTCTCCAGCGCTTTCTGGTACGCCACGCCCGCCAGCACCCCGTGTTCCCTGCAAAACGGTTCCGTATCTTCCGTATGCACGCTGACTACAAAACCGGCATTGGCAAATGGAGAATCCCTCCGGGCCAGAGACATGCCGTTGACCACTACCTCATCGTTCTCCGTGGCGGCGGGCACGATGAAGCCGCCGGGACACATGCAGAAGGAATGGACGCCGCGGTCCTGGATGGTGGTCGTCACGGAATAACGGGCTGCCGGAAGCTGCTCCGGGCGCCTCTGCTCCGGATTCAGGTGGTACTGGCGCGCATCAACAAACGCCTGGGGATGCTCAATGCGCACACCCACGGCAAAGGATTTCTGTTCCAGAGCCAGCCTCTCCGCCAGCATCATGCGGTACACGTCCCGGGCACTGTGCCCGGTAGCCAGCAAAACGGAATCCGCCGCAAATTCCCGGCCATCCGCGCAGGCCACGCCGCGTATGCGGCGGCCGTCCGCAGAACGGAGCAGATGCTCCACACGGGCATTGAAATGAATTTCCCCGCCCGCGCGAAGGATGGATTCCCTGATGGCTTTGACAACATTGGGCAACAGATTGGAGCCGATATGCGGGTGGGCGTCCGTTAAAATTTCCCTGGGCGCTCCGTGGGCTACAAAAATCTCGTAAACCTCCCGGACGGGGCCTCTCTTGGTGGCGCGCGTAAACAGCTTCCCGTCGGAAAACGTGCCTGCCCCTCCCTCTCCAAAACAGTAATTGGAATCCTCCATCACCGTGCCGCGGCGCAGGATGGGAGCCAGGTCAAACCGGCGGGAGGAAACATCCTTCCCCCGTTCCAGAACCACAGGCTTCATACCCAGTTCCAAACAGCGGAGCGCGGCGAACATCCCGGCCGGTCCGAATCCAACGATCAGCGCCACGGGGGAGCCGGGACGGACGGACGGATAATCCCGGGAGGGAAGACGTTCCGGCGGCAGCGGGCTATCCACCCCGACCAGCAGCCGAAGCTGGAAGAGAATTTTTTTCTGCCGGGAGTCAATGGACTCCTTCACCAGCCGCACATCTTTGATACGGGAGGGGGAAATACCCAGCTTCCGCGCCACGGCAGCGCGGCGCACGCGCTCGCTGCCGGCCTTTTCCATCGGCAGAATCACATCCACCGTTATTCCTTCCCCTCCGCTCACAGTCCCTCCTTCCCCGGCCGAGGCTGCTGCTGGGTGATGCAGTGGATGGCGCCGCCTTCCAGCACCAGCTTGCGGGCATCCACCCCTATTACCTGTTTTCCGCTGAAACATTCACGCAAAATGCCCAGGGCGCGATCGTCATTCCGGGGCTGGTTGAATACGGGAACAATGACGGCCTCATTAACAATGAGGAAATTGGCGTAACTGGCAGGGAGCTGATCCAGGCGCCAGTCCTCCGCATGGAGGGGATCCGGCATCGGCAGGGGAATCACCTCCACCCCGGAACCGTCCACGCATCTCAAATCCTGAAGGCGTTCATTATTCTCCGCCAGAGCGCGGTAATGGGGAGAGGAAGAATCCGTTTCCACGATGGAGACTACGGCATCCCGGCACACAAAACGCACCATGTCGTCAATATGGCCGTCCGTATCATCCCCTTCAATGCCGGAACCGAGCCAGAAAACGGCTCTGACGCCCAGCATGCGCTTCAATTCCTCCTCAATCATGGCCCGGCTCCAATCCGGATTGCGGTTGGGATTCAGCAGCACGGACTCCGTCGTCACCAGCAAGCCGTCCCCATTGCCCTCAATCGCCCCCCCTTCCAGAATCAGGGAAGAACTGCGCACGGGAAGCCCCAGCGCCGCCCCAATCAGGGCGGGAACGCCGTTGTCCAGGTCCCACGGGGCAAATTTGCCGCCCCACGCATTAAATTGCCAATCCGCCAGCATCAGGGAACCGTCCTTCACATCCTGGACGAAAACGGGGCCGTGGTCCCGGCACCATACGTCGTTGGTCGGGTGGTTGAACAGGCGGAATTGCTCCTCACTCATTCCGGAGGCCAGCATCGCCTGCCTGACGCCGGGATGAAGGGGAGCTGCGGCATTCACGCATACAAGGGCATGCGGAGCAATGGCAGCGGCCACGCTCCCATAAGTCTGCTGCAACTCGTCCAGCCCCCCTTGCCATAAATCCCTGCGGTGGGGCCAGGACAGCCAGACAGCATCCTGAGGCTCCCATTCAGCGGGCCAGCGTACATCGGGTTCCTTGTTCATGGCGGTATCGGGCGGTTCAACTGGGCTGGTCCTGAAATTCATAAGGCTCTTCAAAAGCCTTGCGGAAGCTCATGAAGCCCTTGAAATCATCCAGGGAATCCTCCGGCTGTTTGGAAAAAACGTCTTCCTCAATCAGTAAAAAAACCATCTCCCCCACATTCTTCCCGTTCCTAAGCCCCCATTCCTTCATCACGGTGGAAGCCATGGGCCCGAACTGGGCGAGGGCGTAATCCCGGAACCCTTCCAGCAGTTCCTGCCCGCTGACATGCCTCTGAGAGCCGTTTTCCCTTTCTTCAACGCGCTGCATGGTAAAATCCAGCGCATCTTTCAGAAAAGAATATGCCCGTTCCGCAAAACGGGGATCCTTCCGTACGATACGGCTCACGGCATCTTCAAACGTAGGGGTGGTCATAGCGGCTCACCGCTACCCTATACGGCTCCTTGTGAAAATCAAGATTTTTGGGAGCGCTCCAGCTTCAGCCCTCCCTTGGGGACAGCATGGGGAAAATGAAGGCAGCCGCCCCCTCGCTGGAACGGAACGCCGGACGCGGCAGACGGCAAAATTCCCGTCCATGATATAGATATAGAGCCTCAAAAAACGCACGCGCCCGCATTTTGAATATCATGCTTCTGCATGAGCGACCCTCCGGCAGGCAGCGGTTTTTACGTTCTGATGCCGTCAAAAAGCCCTGTAAGCAGGAAGGAAACCTTGCCAGCCTATGGTCTGTTTGTTATGAACGGAATACTTTATGTTCCGTTCCTCCATCATGCTTCTGGCAACCATGCTGTGCGTTTCCTGCGTATCGCACCGGCCTATCCAGGACAGCAGCTCCCCGCCCATTGACGCGGCCAATCCCCTGGACGGCACCCCCGTGGCGCTGGCGTGGAGCTCCGGAACGCAATTAATGATGGGCGTAGACACGGGAGCAGTCCAGACATCCCTGCTTTTCTCCCCGGCAGTGGAATCCATCGGCGCACGCCTGCGCGGCAGGGGCGCCATGCGCACGGCCAACGTGCCCGTTTCCCTGAAGGATGACGGGGAACCCATTTCCCGGAAACAGGACGTAGTAATGGCAGACCAGGCCCCGTATGACGGTTTGCTGGGCTGGGAATGCATCCGGAAATATGTGTGGAACATCAACTATCCCAAACGCTCCCACCGTTTTTTCAATAAACTTCCCTCCAGAATAAAAAGCTGGAACAAGCTTTCCCTGATTCCCGGATCCGACTATCCGCAAATCGCGGACAGGCACGGAAGGCGCATCATTCTGGACACGGGAGCCCCCCACGCCGTTTACATCTCCAAAAAACGCTGGAATGCCATTAAGCAGGCCTACCCGGATGCGTTCGTCAGCGTCTATTCCGGCTATAGCCCCGCCGCAGGCGGCTTTTACGCCCACGAATGCATGCATGTAAGCTCCTTCCAGCTCGGTCCACTGGAATTAAAAAATATCCTGCTCTGTGAAAGCTTCGCCAACCCGGAAGTGATGGGCATCCCCGATGACATCGACATCATCCTGGGCTACGGCGCTCTGGCCGCACGCCAGTTCTGGCTGGACGGCCCGGGGAACGCCCTTTATTTCAGCTCCACCAGCCACCGGATGCCCGCCCCCGCCTCCTTCAACCTGATGGGAGGCACCTTTATCCAGGACAGCAACGGGAACGGCCCCATGAAAGCTTACGTGGCAGAGTGGTCTCCCGCATGGGACGCCGGCCTCAGGACGGGAGATGTGCTTATTTCCATCAATGGAAGAAAGAATCCCTATCCGGACCTCGTAGAATATGTTACCACCCAGCGGGGGGCTCAGGCCAGCGTGGTGGTCCAGCGCAGGAACAGGCTGGTGCGCATCCAATGGGAAGTTCCGGCCGCGCCCCCTGCCGGGGATTATTACCCCACGCCCCAGGCCATTACGGAACAGGAATTCGAAAACCACGTCAGGCAGCAGGAAAAAAAAGAACAGACCCAGCCCTCCGCAGACGGCCAGCAGCCTCCGGCTACGGCCGGAGAAACTCCGGATGAAGCCTCTCCCGCAGCTGACGGGAAAACGGACAAGGCCTCCGCTGCCTGAGCCGGGCCAGCAACCAGCCATCTCTCTACACCGGCATGAACAACGAACAACATCAAAATACCCACACTTCCGCCGCCCCCGCCATTCCCTCCCCCTTCCAGAAGAAAACCTGCTGGCATGCCCTGACAGGCGTATCCATTCTGGTGATGCTGGGCATTGCGGCCTTCGTCATTTTTGAAGTAGTGGAACTTCTGGGGTTTCTGGAACCGGTGCTCCTGCCCATCCTGATTGCCGCCGTCGTGGCGTATTTGCTGGAACCCATCGTTTCCTGGCTGGTGCGTCTCAAATTCTCCCGTCCATGGGCCGTAGTCACGGTCATGTTTGCGGCTCTGGCCGTTCTGGTAGGCTTTGGAGCCACGATTCTCCCTCCCCTGATCAGGCAGACGGATGAACTGATCGACAACCGGATGGAGCTATGGGACAAAACCTCCGAACTGATTGACTCCACCATTGAAATCCCCTTCATTTCCCGCACCATTGACAGTGTTTACAGCACCAGCCTGCGGGAACTGAATGCCAGCCATTATACGGAAGCGGAAGTCCATGACCTGAGAAACGCCCGGACCGCCCGGGAAAAGCTGGGAGCCTACATGACCATCAATTCCTCCTTTTACCAGGACAAGCTGATGAGCTGGCTCACTTCCGGGGGACGGGCCCTGTACAGCACCATAGGAATCATGGTCAGCATCCTGATCACGCCCATTTTCGCTTTTTACTTTCTGCTGGAGGCGGATAAAATCAAAGAGAAATGGCCCAGCATCCTGCCCCTGAAAGTCTCCAAATTCAGAAAAGACGTGGTGGACACCATGGAGGAAATCAACGGCTACCTGATTTCCTTCTTCCGCGGCCAAATGCTGGTAAGCATCATTGAAGGCATTCTGATTGCCATCTGCCTGAAACTGATAGGCCTGCCGTACGCCATCACCATCGGCGCCGCGGTCTGCGTGCTGGGCATCGTTCCCTACCTGGGCATCATCACCGCCTTTATCCCTGCGGTGCTGCTGGCCTGGTTCACATGGGGGGATTTCCAGCACGTGCTGATTGTTTCGGGCATCTTCCTGGCCGTCAACCAATTTGACGGATGGATCATCCAGCCGAAAATCGTGGGGGATTCCGTGGAACTCCACCCGCTCACGGTCATGTTTTCCGTGTTGATCTGGACACTCATCCTGGGCGGCTTGATCGGCGCCCTGCTGGCTGTCCCCCTGACAGCCGCCATCAAGGTCCTTTACAAGCGGTACATCTGGCAAAATGCCAGCATGCGCCCCATGACGGACCCCGTGCTTCCGCCGGAACATCCCGGCGAACAGCCTCCGGACCCCCCCGAAGGTTCGGCCCACGCTTAAACAACGGCATCCTTCCGACCATCTTTTCCGGAAAATTATAATCACACACACCTGACAACATGAATAAATCCGCATTACACCTGATGCTTGCCATGGCGGCGGCAGCGGCCTGCCCGGCCGCAGTCACGCCAACGGTCAAGCCACCCAAGGCCATCGTCATGATTTACGCTGACGACCTCGGCTATGGAGACGTAGGCTGCTATGGAGCCAAGGGAATTCCCACCCCCGCCATCGACAAGCTTGCCGAGCAGGGCTGCCGCTTCACGGACGCCTATTCCACCACATCCGTCTGCACCCCTTCCCGCTATGCCCTGTTCACAGGGGAATATCCGTGGCGCAAGGAAGGCACGGGCATCCTGCCGGGAGATGCCGCCCTCATTATCGATACCAAGAAGCCCACCCTGCCCAAGATGCTCCAATCCCACGGCTACAAAACCTACATGATAGGCAAATGGCACCTGGGCCTGGGGGAAAAGGGGAAGAAGATTGACTGGAACAAACATATCTCCCCCAGCCCGAACGAAATCGGATTTGACGAGAGCTTTATCTTCGCCGCTACGGGCGACCGCGTTCCCTGCGTGATTCTGGAAAACGGCAATGTCCGCAACCTGGACCCGAACGACCCCATTGAAGTATCCTACAAGCACAACTTCCCCGGGCTTCCCAATGGCAAGGATAATAAGGACCAGCTCAAACTCATGTGGAGCCACGGACACAACCAGGCTATTATCAATGGGATCGGACGCATCGGGTTCATGAAGGGCGGCAGAAGCGCCTTGTGGAAGGATGAGGAAAACGCCGATATCATTACGGATAAGGCCATTGAATACATTCAAAAAAGCGCCAAAGCCAAGGAACCGTTTTTCCTGATGTTCGCCACGCATGACATCCACGTGCCGCGCTGCCCGGAAAAACGCTTTGTGGGCAAGAGCCGGCACGGCGTGCGCGGTGACGTGACCGTGGAACTGGATGACTGTGTGCGCCGCATTACAGAGGCTCTGCAACAGGCCGGTCTGGAAAAAGACGCCCTGGTGATCTTCTCCAGCGACAACGGTCCCGTGCTGGATGACGGCTACAGGGATTTCGCCGTCCGGGACAACGCCACCCATTCCCCCGCCGGCCCCTTCCGCGCAGGCAAATACAGCATTCTGGAAGGAGGTTCCCGCATTCCGTTTATCGTCAAATGGCCCGGCGTGATCAAACCCGGAACCACGAGCAAAGCCCTGCTCAATCAAATGGATTTGGGGGCCTCCCTGGAACAGCTGCTGGCCCCCGGCAAGGCCAATTCCTTCCGCGACTCTGAAAACGTGATGCCCGCCCTTCTGGGCAAATCCGCCAAGGGGCGTGACTACCATGTCATCAACAGCACCGGCAAGGCATTGGCGATTCGCCACGGCAAATGGAAATTCATTCCCGCCGGCGTGGCCATTCGCGACGGCATCAACGGAGCCTCCGCAAAAATGAGCAAGTCCCCGGAAGGAGGAAGCCTCTTTGACCTGGAAAAAGACCCGAAGGAACTTGACAACGTAGCCTCCCAGCATCCGGACATTTGCGAACAGATGAAAGCCAAGCTTGAGGAAATCCGCCAGAGGCCCGAAACCAAGGCTGACCAGGAGGACCTGCTTCCCTTGGACGACTAACATCCATTCGGGCCAGATCATGAGAGAATCCGCCACATGCTCCCAATGCGGCCACGGGTACACCTACCCGGCCGGACTGGACGCCAAAACCGGCATGCGCTGCCCGGCGTGCGGCCTTCTGATTCACGTTACAACGGGGCTTCCCTTCGGGGAAGCCTCCTCTTCGCCATCCGGCCGCAAACCGGAAAACGCCATGAAAACGCACCCCGCCCTGCCTCCCGTCAGAAAAAAGCGGACGTCTTTCTCCGGACGCGATACTGACGAACGCGGCATCCGGCAGGAAAACCTGCCGCAGCGGCCAGCCGCAGCATCCGGCCGCCGGATTCCCGCTACATCATGGCTGGCATGGGGGCTGTCCCTGATCGCGTTGATTCTGGCCTTTATTTTCCCCTCCTACGGAAACGGTTCCACAGATCCGCTGTACATGCTTCAGTGCCTGCCCTCCATGGTATTCGGCTGCACTGCGGCCATCCTTTTCATCCAGGGCCTGGCGGCTATGCGCCGCTAAAAAACAAAGCCTGCGGCCCCATCCGGGGCCCATGCGGCAGCCCCGCCCCAAAAACGGCCGGAATGCCTGGTTTCCATGCCCCTTTCCGCGGAACCGGGCTTTTAGGCTTGCCTGTCAAATGAACAGATTTTAAGTTACGTCCCGGTAAACGTTATGGAACGGGAGGCAGAGGCGAAGGACAACCTGTGGAGGGCCAGACTGGATGCCGTCCTGATGCACAGGATATGGGGTTCCCTTATTTTCCTGGGCATCGTTTATATTATTTTCTTCCTGAGCTTCGCTATCGGGGATCCTCTGGTCAGGCTCATCCAGACGGGTACCCAGCTGTTCAGCGTCTGGGCCTGCCGCATGCTGGAACCGTGGCCCCGCCTTCAATCCCTGCTGGGGGAAGGCGTGGTAGGAGGCGTGGGCGGCGTACTGGCCTTTCTGCCCAACGTGGTTCTCCTCTTCGGGGCCATTACCGTTCTGGAAAACAGCGGATACATGATGAGGGTTTCCCGCCTGATGAGCCGCATCATGAAAATCATGGGGTTGAACGGCAGCAGCTTTGCCCCCCTCCTGCTGGGATTCGGCTGTTCGGTTCCCGCCATCCTGTCCACCAGGAGAATTGAAACGCGCAGCGACCGCCTGGTCACCATCGCCGTGCTGCCCATGATGAGCTGTGCGGGCCGCCTGCCGATCTACATGATGTTCGTCTCCGCCCTGTTTCCCTCCCGCCTGCAGGCCCCCGTGCTGTTCGGGATTTATGCCAGCGGCGTCCTGCTGGCGCTGTGCTGCGCCCGGCTTCTGAAAAACACGCTTTTCAAGGCGCCCCAGAGGGATTCCCTCCACCACATGAAGCGCCTGCGCCTCCCCTCCCTGCGGAAGGTGGGCATCCTGATGTGGTCACGGGCCTTCATGTACGTCCGGAAAGCAGGCACTTTCATTCTTGGGGCCTCCATCATCCTGTGGTTTCTGAACACCTACCCCAGGCCGGAAGAAAACACGGCGCCGACAAACGCAGCGGCCATGGAACATTCCTACGCCGGACAAATAGGCCATTGGATGGAACCCGTCACGCAAGTGGCCGGATTTGACTGGAAAATCAATTCCGCCCTGGTAGGAGCCTTTGCCGCCAAGGAAATTTTCGTCACCCAGATGGGTATCCTTTATGCGGTGGAAGACGGAAATTCGGTCCCGGCTGCCCAGCAGACATTGAACGCCCGCCTTAAAGCCAGCTACACCCCTCTCCAGGGCATCAGCATCATGATATTCTGCCTGATTGCCCTGCCCTGCATCGGCACCGTCACCGTCGCCAAGAGGGAAGCGGGCACCTGGTGGTTCGCTCTGGCTCAATTCGGCGGATTGACCCTCCTGGGTTTTGCAACGGCTACGCTGATCTACCAGGCAGGGCTCCTTCTGTAAGGGCCAGCCATAAAGAAACCGGAAGCAGGGAATACCCCCCGGAGGAAGAAAAAATGAACCTCGTGGAAAAAGAGGCATTTACGATGAACTTCCGCAAGTTCTCCTGCTCTACGGTCAAGTTATCACAACCTCGCCATCATCCGGCCACGAAAGCCGCAGCATAACACCATGCATGGCCGTGCAAGCCTCCCCTCCGCAACAATCCGGAATCAGTATGCATCGGCTGAAGCCTTTCCTGTTTTTTTCAAGCCGCCCGGGTATCCTTTTCCGTTTGCAGAGGGGGGGAACAACCTGCGGCAAAAAAAGATCATGCAGCCATTTCCGGAAAATCAATGCCCTTTTGACAGGGAGTAATCAAAAAAGGGGCACCAATTATCCATTCGGTAATCTTTGCCCTGGATATGGGCCTTAACCTCATCTCCCTGGGAATCCCTCAGACAGCCCCGCGCCCCCGTCCGCGGACCGGAAAGACGGTGCTCATTAATCGTCATCCCATAGCGTAGACGCCCTTTCCCCGGGTCCTTCTCCGTCAACAGCTGTACCGCATTCTTCCCCACCAGCCGGACAGACTTGATGGCAATATCCTCTCCCTCAGCCCCCACAAGGGAAAAACCATAATTGCCAGGATCGTCAACCGTCTTCGTGTCCAGAACCAAAGGGGGGCAGGGAGCATAAAATGCCACTTCCACCGTCCAACCCTTTCCGGACTTCCTGATTTTATGGGTCAGGGGATGCACGGGTTTCCAGTCCGCCCCATCCAACAGGACTTTCTTCATGACATAGCCCACATACTCCCCATAAAGGCGGGACATGGGCGCCGTCAAATGGACTCCGTCGCTATACTCGAAATGATACATCGGGGTCGTCATCACATACCGGCTGTCCTTCTCCAAAGCCATCTCCAGCTGTTCCAGGGCAATAGCCGGATAATCCGTAGGATTAAACTGATAATAATTCAAATGGGAACCAACCTGATAGCCGAAACAGACCACATCGTTTTTCTGCCCGGTCACGGCCCTGGCGTCATGATTTATGTCCCTGATCAGCTTTTCCATTTTTTCCCGGTACCATTCCCTCGTCTTTCTGTCCTGGTGATCCGTCTCTCCCTGGGTCCAGATAAACGCAGGCATGGAAAGGGTCTTCCCTTCCCGGTCAGCCAGTATTTTGGCGGCCTTCAAATCATCCAAAATGCGGTTGTAGGCTCCAGTTCCCCTGCCCAGGGAACCAAGGCTCGCCCCACCCACTCCCGTCGCAGAACAAATCAACCGGATACGGGAAGCCGTAACGGGAGAATAATGCTGCATCAGGCGGAACAAACGACAGGCAGCCCCGGATACGGGAGTTTCGCCGCGCGGACCGTTCACTCCTTCCTCCAGAGGAACCAGCATCTTGCGGTCACTGCCGTTCTCATACGCTCTCACCCCTCCCCTGAACATATACAAGCCCCCTTCCTCCTGCTTTTCCGTTACAACGGGCTTAGCCGTCCAGCCCACAGAAAGGGATTGACCGTAAGAAGTTAAATAATTCCAATCCGCCCACTCTGTTTTCCGGGAAGCACAGATGGATGCGGAAGCCGCCGCAGCCATGTGGAAAGCAAGCAAAATCAAGGTAACAGGAATATTCATCAGATAATTCAACGGACTCTGTCCGTGAACATACGTTAGCCGTTCCCTCTTTTTATCAATTCCTGAACACAAAGCTGTCCATCCATTGCTGCAGTACCATTACGCCAGGAAATACGGAATTGCCGTTCCAGCCGGGTCCACGCCATGTTTCGGTACCATGCCGTACCGGCAGGAAAACGCATTCAATATACCAACGGGATTTTTTATCCTCCAAAAGAAAGGCGGCAGAAACATTCTCCGTATTGAATAAACAACATGAACCATTTCCCGTCTATCAACGCCATATCCGCCTCCATCTGCCTTCTGGGATGCGGACTCGCCCTGCCTGTTTTCTCCCAACAGCCGGAAAAAGGAAAACCCGATGCCGCCCACATTTCCAAAACAAAAGCTGACTGGTGGAGCCAGAGGCACGCCCTGTTAAAGCAGACCCTGGCGGAAACACCATGCCAACTGCTGTTTATCGGCGATTCCATCACCCACCGCTGGGAAACCGACGGCAAAAAAATATGGTCTCAATATTTTTCCCCCTATGCTCCTGTCAATTTCGGCATAGGAGGTGACAGGACGGAACATGTATTGTGGCGCATTGACGATTCTGCCCTTAAAACGCCTCATTCCCCCCAAGTATGCGTTATCATGGTGGGCACCAATAACACAGGGCAATACAAAGGCAGGCAGACCCCGCAGGAAACGGCGGAGGGTATCCGGGAAATAGCTTCCCGGGTCCATCGGCTCCACCCCGCCACTGAAATCATTCTTCTGCACATCTTTCCCCGGGGGAAAACGGCGGAAGACCCCCTGCGCATCCAAAATGAAATGATCAACAGGGAGCTGGACAAAACAAATATGCCGAGAGTCCATGTCGTCAACATTAATTCCGCATTCCTGGACAAGGACGGCACTTTCCTGCCGGGAATTACCGGGGACCTCGTCCACCTTACAGAAAAAGGCTACCGTCTCTGGGCGGACGCACTGCTGCCGGAAATCAAAAAATACATGAAGTAAGTCCGCTACCCCCTTCCGCCCGGCTCGACGGCCAATTCGGCCTCATCTGCGCAGAAGCGGCCAGCCAATCATGGTTGTCACTCCCGCACATAAAAAACCGGAGGGATTATTTTCCCTCCGGTTTTCGGATACGGCCGTTAAAACCGTTTAGATTGTCTTAATCACGGAAGAGTCGTCATCATCCCTGTCCTCGGCAGAAGCGGCCTTCCTGGTGGGAACAGGCGTCTTCCTGACCTTTTCCAGAGCCTTGTCCACCGCCGCAATCACCTTGTCTTCCCCGGTCTTCTGACTGGACAACTCATTATAAATCTTTTCCTTGGCCTCATAATCGGCCTTGGCTGCCGCATCGCCGGGATTGGCCTTGAACTTGTCCTCCGCCTTGAACAAGGCCTCCTCCGCAGCCTGGAACTTTGGTTCCACCGCGTTTCTCTGGTCTTCCGCAGTCTGCTTGATGCGGCCCAGCAGGCGAATCACATACGGGCGGTTGCTGAGTCTCTTGAATGCGGAAACCAGAAACTGGCGGCGTTCGCTGGTTTTGGCATCCGCAATAAGGGGGTCAAACAACTTGAGCGTGGCGTTGTCATCCCGGTCACGGTCCACGGAGAGTACGCGGAGAATCGAATCATGGGCCTGGGAACGCACGCGTTCATGCAGGGATTCGTCTTTCCAGGCCTTGAACAGGTACGGAACGGCGTCATCCGTATTCCAGTCCCCCCAGGCGGTAATGATGGCAAGCGCCTTTTTGGTATCCTCCTTATAGCTCTTTTCCATCATGTCCAGCACCTTGGGATCACCGGTGCGGGCCAGCAGCTTGTAGAGCATTTTGATGTTTTCCTCCGGCATATCTTTCTGATACTGGAGCAGTTTGGAGGAAAGCGCCTTGCGCTTGGCTGGAGGAGCTTCGTCAATCAGGTAGCGCGCCGTACGGAAAGCGGAGGAAATCAAATCCGGGGAAGCATCCTTCTTCTGGAGGATCTTCATCACGTCGTCAAGGTCGTCCAGCTTCATGGCAAAACGCATGTACTTGAGCACCACGGCCTGCTTGTCCTGGATCTTCTTATTCTTGCTTTCGCTGATGGAAGCGTACAGGTCTTTCAGCATGTCGCGCATGGCGGGGCTGGAAGACTTGGCCAAAAGGGTCACCATCATGGAATATTTTTCCTTACTGTACCTGCCGACATTTTTTTTCATGTCTTCCACCACCTGGCGGGCAATGTTGTCATCCATCTGGGCCATGATGGAAACCAGGTGGGCGGCGCCGGGCCAGTTTTCCCTGTTGCCCTTGACTCCCCTCACATTGGCGAGCAGCAGAGCGGCATCCTCCGCGTCGGGAACGACTTTCACATCCGGGACACGGCTGAGATTCTTGGAGTCAAACAGACGGCTGCGGCCCAGTTTCCCGCCTTCCACGACCAGCGCATTCAAGCGGGCGTTCTCACCTTCCACCTTTTCTTTCTTGCTGTTGGTGCTCCATGCATAAATCCCTACGCCGGCAGCGATGACAACCAAAGCGCATACCCCATAAATAACACCCCGCAGCAGGGATTTGGATTTATCCACGGAAGCGGCCATATCCCGGGCGGATTCCGTTCCGGCGGGAGGCTGAATACCGCCGTCGCCCGCCGGAGGAGCGCCAGCGGCAGGGGAAACTCCCTCGGCGACCACAGGCTGGGGAACCGCAGCGGGAGCCACGGAAGAAGCTGGTGCTGCCCCCACGGGGGATTTTAATACTTTCAGCTTGGGAACGGCGCTTTCCGGAGCGGCGACCGGGGATTTTTCCGCCGCAGCGGGAGCCGCGGCCGTCTTGAAAACTTTCAATTTGGGAGCCCCCCCCGCAGGAGCAGGCTCCGGAACAGACGGTTCCGGGGTCTGCACAGCCGCAGGCGCTACCCCTGCGGCCTGGGCCAGAGCAGCGGCCTGAGCCTCCGCCAGGGCTTTCTGGGCCTCCGCTACCTTGGCAAGGGCATCCTGAATCTGGGCGTCCACGGAAGGCTCGGGCTGCGGAGCGGAAGCGGCTTCCTTTTTCTCCAGCTCCGCCGCAAGGCGCGCTCTTTCCTCTTCGGCAGCTACACGGGCGGCCTCCTTTTCTGCGGCAATGCGCGCGGCTTCGGCAGCCTCCTGCTCCGCCTTGATTCTTGCGGCTTCCTGCTCGGCAGCTATTCTCGCCGCCTCCTTTTCGGCAGCAATACGGGCGGCTTCCTGCTCGGCAGCCGCTTTTTCCGCAGCAATCCTGTCCGCTTCCGCCTGTTCCGCTGCGCGGCGGGCGGCTTCCTCCTGCTCCTGAGCCAGCTTGGCCTGCAACTCAGGAGTGAGAAAACGCGGCTTGACAACCTCCACGCCTTCCGCGGCGGGAGAAACAAACTTTACCTTGGGTGCGGCCGCAGGGGCTGCCGTTACCAAACGGCGTCGCAATTTGGGAGGTTCCGGCGAAGAAGGGGTTCCTGTCTGATCGGTCATCGTGATAAAATGGCTGGGAAATTGGAGTGATTTTATACGATTCCGCGGCCCCCTGCAACCTCAAAATACCGTCTGGAAAACGGGCGGGGAACAGCCGCCGTGCGTGGTTCAACAGGCAGATTGCGAAAAAAGGCTTCACCCGGGATATAACATGGAAAAGCGTTTAAATATCCCGCGTATTCTCCCGCAAAATCATAAAAAACACAATATGTTAACAACAACAATCATGAATAAAGTAAAGCGCCCGTAATAGAACTTCAAGAATACGTACCAGTCATTTAAGAATCCTCTTAAAAAATGTTATAACACATATACTTTTTTCTTGCCAGACCAAGGATGATTAGTTAAATAAATAACCACTCGTCAACATCATGAATTCCAACGACAGTACCAAAAATAAGAGGCGCTACACGCCTGAAGAAAAACAATCCGTTCTTGATTTCATTGCTGAACGGAAAACCCAAAACAAAAGGGGTGCCCAGAAAGAAGCCGCCGAACGCTTCGGCATCAGCACGGTGACCATCTCAAGCTGGATGAAGGCATCCAAGGCCAAGCGCGGCCGCAAGCCCGGCACCAAAAATACAACTGCTCCCAAAACACAGCCTCTCGCAGCCGCCCAACCCGCCGACCTGCGCCGCCTGGCGGCCGTTCTGGAAGAAATCGCCGACCTTGAAGCCCAGACAGCCAAACTCGACGCCCTGCGCGCTGAAGCGGAGGAACTCAAGAACAGGCTGACTGCCAAGCAGGCCTGATTGCTTTTATTCCTTCCTTTTCCGGAAAACCGGCTTCCCCTGACAAGGGAAGCCGGTTATTTTCTTTACGGAGATCGCGCTTGCCCCCCCGGAACAATTCCTTTAATATCCGCCGTCGTGACTCAACCCGCCCACATTGCTCCCGCGAGTCAGTCTGTGGAAGGCATGTATGCCGACTACTTTCTGGATTACGCCTCGTATGTCATTCTGGAACGGGCCGTACCCAAGATCAATGACGGGTTTAAACCTGTGCAGCGCCGTATTCTGCACGCCATGGACCGCCTGGACGACGGGCGTTATAATAAAGTGGCTAATATCGTGGGAGACACGATGAAATTCCATCCGCACGGCGACCGTTCCATTGCAGACGCGCTGGTGGGGCTGGGACAAAAAGGGCTGCTTATCGACACGCAGGGGAACTGGGGCAATATTCTGACAGGCGACCCCGCAGCGGCTTCCCGCTATATTGAAGCCCGCTTTACCTCCTTTGCGCGTGATGTAGTATTCAGCCCCAAGGTCACGGAATGGCAGCTCTCCTATGACGGCAGGAACAAGGAACCGGTCAGCCTTCCCGTCAAATTTCCCCTGCTTCTCGCTCAGGGAGCGGAAGGCATCGCCGTGGGGCTTTCCAGTAAAATTCTTCCCCACAACTTCAATGAGTTGATTGAAGCCTCCATCGCCTACTTGCGCGGCCAGCCCTTCCAGCTTCTGCCGGACTTTCCGACCGGAGGCGTGATGGATGCCACCAACTACCGTGACGGAGAACGAGGAACGGGCCGCGTCCGCATCAGGGCGCGCATCCTCACGGAATCGAAGAAACTTCTCCGCATCACGGAAATCCCGTTCGGCGTCACCACGGAAATCTTAATTGATTCCATTGTCTCCGCTGCGGAAAAGGGAAAAATTAAAATCGCCCGCATTGAGGACAATACGGCCCAGCATGTGGACATCCTGGTCCATCTCCCGGCCGGAGCGGATCCGGAGCAGACGAGGAAAGCCCTGTTTGCCTTCTCCGCCTGTGAAGTCAGCATCTCCCCGAATGCCTGCGTCATTGTGGAGGAAAAACCCAGGTTCATGTGTGTTAGCGACATCCTGCGCTACAATACGGACTCCACCAAGGAAATCCTGCGCCAGGAGCAGGAAATCCGACTCAAGGAACTGAACGAGGCGTGGCACCAGGCCAGCCTGGAAAAAATATTCATTGAAAACCGCATCTACCTCTCCATAGAAGATTCCGAAACGTGGGAAGAAGTGCTCGGCACCATTGACCGGGAATTGCAGCCGTTTGCATCCCGGCTGCGCGCCCCCATTACCAGGGACGACCTGGTAAGGCTGACGGAAATCAAAATCAAGCGGATTTCCAAATTTGACGCTTTCAAGGCGGACCAGCACATCCGCCAGCTTGAAGAGGACATCGAACAAACGCAGAAGAACCTCAACCAGCTTACCAAATTCACTATCCGCTGGTTTGAAGCCCTGCGTAAAAAATACGGCGCCGCCTATCCGCGGAAAACGGAAATTTCCTCCTTCGGCTCCGTAAACCGCGCGCAGGTGGCTGTTGCCAATGAAACATTGTATATTGATGACGAAGGCTTTGCCGGTTACGGTGTCAAGAAGGGAAACCCTGTCTGCAAATGCTCCACGCTGGATGACGTGTTGATCATTGACAATGCAGGCGTGCTCAAAATCGTGCGGATTCAGGACAAATTTTTCGCTGGTAAAAATCCCCTTTATATTTCCGTCATCAAAAAAGGGGACGACCCCGTGTTCAACCTGATTTACCGGGACGGAAAAGACGGCCCCGTGTACGCCAAGCGTTTCCGCATAGGAGGGTTCACCAGGGACAAGGAATATCCGCTTACCCGAGGCGCAAAGGGAACGCGCATCTTCCACTTCTCCGTGCATGAAACGGAGGAAAACAGTTCCCAGATAAGCGTAAACGTTTATCTGAAAGCCGTTCTGAAACTCCGTAACCTGATCAGGCCCTTCCATTTCGCGGACCTGAGAATCAAAAACCGCGGTGCCCAGGGAAATATCATTACCAGGCATCCTGTGGAACGCGTCTCCCGCATCATGCCTCCGGCCAAGTCCGGAAATGAGGAAACGGAAGGGCCAACAACCGCTCCTTCCGCAACAGCGGAACGGACGGAAGGCTCACCCGCCCCCTCCGCGGAAACGCTGCATCCTGAAACAGCCCCACATTTGGAGGAACCGCCTGCCGATCCGCCGCTGGAACAGGGCTCCCTGTTTGACTCCTGAAACGTTTTCAATCTCTTAAAATGGAAATATCCATGCCTGACTATCCATTTTCGGCTTGCCACAACGGGCCATCTTCTGTAGATATGGGCCACCTCATTACCATGCCGCTTTAGCTCAGTGGTAGAGCACCCGCCTTGTAAGCGGACGGTCGTCAGTTCAAATCTGACAAGCGGCTCCATCATAACCCGCTCAAGGTCAACCCTTGAGCGGGTTGCTTTTATGAGGCGTGCGCCATTACTCGTTCCCATGAATTTCCGGCGTGCCGCTTTTGAACATTCATCATCCATTCATATCTCCGGCGGAATGCGGCAGCCGCAGGAAGCAAAGTGGGACGCAACATCTGGCAAGAGGACATTCCACCCACAGGCATCCCCGTCAACTTCCGGCAGCGAAGGGCCATTCCCGGAGCGGGAAGAAGGTTCCGTTGGCGATAACGCCGCCTCAGCAAGCCGTTTCCCCCCTTGAATACGCCTCCATTGCCATTTCGACCAAGGGAACCATGATTCAAGCCAATGGATACTACAGAAAGAAAGATGGCCGCGGCTATCCTCCGGTTTGAAGACAGCCGCGTTACCGGGCCGTATTCCCTGCGCGTTTCCCGCCTTCCCGCCGCCGACAAGGGCGGCAAGTGGGAGATTTGCGGTATTTGCGACGGCATTGAACCGGCCGTGTTTAACAGATTGAAGGCCCTGCTGGATGCCGGAAGGCGTGAAGAGGCCTGGGAAGGTTGCCTGCAATACGTCCTGGACAATACCGCAGCCGTGCGTTCCTGGCTGGGTTCTGACGCTTTTCCGGCCACGGAGTTTATGTTGCGGGATCATTTTTTCAATTCCGGGAGCAGGAATACCGGGAAGATTTTGCAGCGCGCGCTGAATGTCCACGGCGCCGGGCTTGTGGTGGACGGCATCCCGGGAAAACAAACCCGGCAGATGTTGCAAACCGTTCTCGCCCAAACGGGAGAGTCCGAATTTCTCTCCACTCTGAACGAACGGCGCAAATCATTTTACCATTCTTGCAAACAGTTTCCCGTGTTCGGCAAAGGATGGCTGAACCGCAGCAATGAGGCATACCGGTTCGCACAATCCCTGGTTTAGGGAAGTTCCTGCAAAAAAACTCCCGGAAAAGGAGTGCAGGAGAATTCAACCATTGTTGCAGCAACCTGTTTCCATGCGCCGGATGCCATGAAAGAGCCTCCGTGCGGACATCCGCACGGAGGCTCTTTCATTAACACGATCAGGAAAACGCTTCGTCCCCCACAAACGCTCCCAGCAGACGGCAAAACCGGGAAGCAGGTAATTTTACCAATCTCCCGTCCCCCCGCTTGCCATGCTGGGTCAGAATCCAGGGCAGGGAAAGAATCTCCCGTTTATAAGCCAGCAATTCCCGTACATCTCCCGCCAGAAAATGGAGATGCCAGGCATCACACCGGACCGGCTCAAACCGGAAGCGGACATCGACAATCTTTCCGAAAGATGCGGAGGAATCCACCCTGCGCGCCATCAGGACAAGCTCCGGAGTACGGACATAGACATCCTGGCGGCGGCCGTCTTCCTCCATTTCCTCCACCCTCCGGCCTGCAGCCAGATAATAGGAAAAGGCGGCAGATAAAGCGTTCATACCCAGTTTTTCTTATGACGCCTCAACCGCTGGCGCACCGGACGATTCCCGTCGGAACGGCTCCTGAAAAACCGTTTTTCCCTAAAACCGCCGGAGCAAAAGATCGGACGGCACCCAGTCATTTGCCCTGCCGGATTTTCTGGATAACCGGAGGCATCTCCGTTTTCGGCTGTTTCTGGAAGCAGGACAAATGCCCCGGTTCCAGAACCAGGCAGGAATCGTCCCTGCATATCTCCGCCCGCTCTGCAGTCACGTCTACGGTATACCCGCAGCCGGAGAGGAGAGAAGACGCCGCATAAGCCGCTCCGGCCAGAACCACCCACAAGAGACGTTCCCACCAGGTCAATCCGGAACGTGTTCCGCTTTTTTCATAAGCGTTTTTCATGCCCTGTTTTCCTGCCTCCAAAGCCGCTTTCTTTTGTTCTTCTGTCAAATTGAAGTTCATTTGAGAGAGGGGGTAAGGGATTCCTGGAATTCTCGCGCATGTTGTGCCGCAACGGTTCCGCGGGAAAAAATGGCGCTGCGGCAGGACTGTTTATTGCTATTCATTTTGTTCATTTTTTTGTTTATTGCCCGGAAACTGGCCTTTACCGGCATGGAACCGGCGGTGACATCAGACCGCTCCCAGACACAGACATCCTTGCCCTGCCCTTGGGGCGGGAGCAACACGCCTGATACGTCCGGGAACAAGGAATATTCCGCCAGAGACAAACGCCGGAATTTCCGCCGCGGATAACAAATACCCTGTTGAAAACGGAACCGGTTCATGCTAGTCTCTTCCCTGCTATGGCAAAACCGGCTCTGGGGAAAGGCTTTGACGCCCTTATCAATCAAAATCTGTCTCGTGAATCCTTGGCGGCTCCCCAGGCGGGAGACGTTGTGCATCAACTATCCCATGCCGCCATTATTCCCAGCTCCCTGCAGCCGCGCGCCATTTTCACGCCGGAACAACTGGCGGAACTTGTAGATTCCATCAAGGAACACGGCATCATCCAGCCTTTAATCGTCCGTAAAACGCAAAGCGACAAATATGAACTGATTGCCGGGGAAAGGCGCTGGAGAGCCTCCGGCATCCTGGGGCTGTCCACCGTGCCCGCTATCATCCGGGAAGCTTCCGATAAGGACGTGCTGGAACTGGCCCTGATTGAAAATCTTCAGCGAGAAAACCTCAGCCCGCTGGAAGAAGCCGCCGGCTACATGCGCCTGAAAACGGAGTTCCGCATGAAGCAGGGGGACATCGCCAAGCGCGTAGGTAAATCCCGCGCCGCCGTGGCCAACAGCATGCGGCTGCTCGACCTGCCTCAACCGGTTCAGGACATGCTGGGCAATACCTTTATCAGCGTAGGGCATGCCAAGGTACTTCTCTCCCTCAAAAACAAGGACCAGCAAATCCAGCTTGGGCGCGACATCGTCAACAAGGGCTACACCGTCCGCCAGACGGAGAAGGCTATTCAGAAGATGCTCAACCCGCCGGAGCCGGCTCCCGTTAAAAAACCGTCCTCCCCTCAATATAAGAAAATTTCCAGCATCCTCGCCAAACAATTCGGTACTCCGGTGAACATTTCCGGCCAGGGAAGCCGGGGGGCCATTGAAATCACCTTCTCCAGCAAAGCGGAATTCATCCGCATCCTTGAGCTTCTGGGCCAGGATGAATGGCCTGATTCCAAGTAAACTTCCCCCCCCTTTTTCCATGACGCCCCGCATCGGACTTCTCCTGTTCACGGCCCTGCTGATGCAGTGCGAAAACCCCGTGGAAACGCCCGTGCCCGGTAAGGAAACCGTTCCCGCGCAAATCCGGCAAACGAATTGTTTCAACGGAGGAAACGCTCTGTTCCACGCCGCGCGGATAACGGAAATGGGGAATCGTGCTTCAGGCACTCCCGGCTACCGCAGGCAGATGGACTACCTGAAGGAGGAACTGGCCAAATGCGGCTGGACCTGCCGGGAACAGGCTTTTGAAAAGGAAACGCCCCAGGGCCCCGTCCGGTTCGTCAACCTGAGGGCCCGTTTTGGCAAGGCTCCAAACTTCCTGGATCCGGTTCGGGGCCTGCTGACCTGCCACATTGACACCAAGCGGGGCATCGACGGATTCACCGGCGCCAATGACGGGGCATCGGGAGCTGCGGCCATTCTGGAGACAGCCCGCATTCTGTCCGGAGACCCCGCACGCGCCGGAAATTTGGAGCTTGTGTTTTTTGACGGGGAAGAAAGTTTTGCGGAACATATGGACAGCGACGACGGCCTGTACGGCTCCAGGCATTACGCATCCTCCATGCGGCAGCCGCTTCCCGAATGGATGATCAATTTGGACATGGTGGGGCGGCAGGGTAAAAAGATACGCATCCCGGCCATGACGCCCCAATCCATGTACCGGGTTTATTCCCGCGCCATCCGTGAACTGGGTTATTCCCCGGAGGAATGGGGAGTATCCGGCTATGCCATCCTGGATGACCACGTTCCTTTCATGGAGCGCGGCGTGGACACGTTGAACCTGATTGACGACTTCCAGGACGGCAACTGGTGGCACACCTCCAAGGATAACATCGGCATTCTGGGCGAAAAATCCTTCCAGCAAACCGGGGAAATGACCCTTCACATCCTCCGGCAACTGCTTCCCGGCCCGCCCTCCGCCTGACCAGGCAGCTCTGACCCTCCAATTATCCCTGTCCCGCCATGTCCTTCAACTCTCCCTGGCTGCTGTGGGCTCTCTTTGCAGCCTCCGTTCCCATCATCATCCACCTGGTCAACCGGTGGCGGCACCGTTCCGTCCCCTGGGCGGCCATGGAATTCCTTCTCCGTGCGGCCAGGGAAACACGTGGAACAAAAAAACTGCTGCACTATCTGATTCTGGCGCTGCGCGTGATGGCTGTGGCGGCACTGGTAACGGCCTTTGCGCGGCCCCTGCTCGGAAGCTTTTTCGGCTGGGGGAGCTCCGGATTGAATGAAGTCCTGCTGGTTCTGGATCGTTCCGCTTCCATGGATGCCCGGCCGGACAAAACCAGCTCCCTCCGGGATGCCATCCCCCCGCTGGTGGAATCCACGTTCGCGCAGCTCGGCCACTGCCGCCTGTCCCTGCTTGATTCCGCCACCGGAACCGTGACCCAGATTCCCGCTCCGGAAGCCCTGGCGGATCTGACTGTAAGCAAGACAACGGATGGAGGCGCGGATATTCCGGCACTTCTGCAAAAAGCCATCCCGTACCTGGAAGAATCCGGCGCAGGAAAGACGGAAATCTGGATTGCCTCGGACATGCAGGCCTCTTCCTGGAAGCCAGACTCCCCTCTCTGGCCCGGTGTGCGCCAGAGGCTGGCCGCCCTTCCCCTGCCCCCTTCCATCCGCATCATGGCCCTCCGCGACAGGCCGGAAAGCAACCGCGGCATCAGAGTCCGGCAGGCTCAGGTAAATAACGGGAAACTGGTGCTGGACCTGGAAGTGCTGCGGCAGGGTTTCAACCCCAACCAGCCGGAGGACGTGCCGGTGCAGCTTTCCGTGGATAACGCCTCCTCCTCCGCCACGCTGCAACTGGCAGGGGAAAACACCTCCATCCGGAAAGAAATACCCCTGCCGCAGGGCAAAGAATCCGGCTTCGGCTTCGTTGCGCTGCCTCATGACGACTTCATCAGGGATGACATTTCCTTCTTCACCTTCGCCCCCAAGCCTCTCGCCAACATCCTGATTTTCGGTCCTTCGGGAGAAGTGGGAAAAACCCTTTCCCTGATGTCCGCCCCTCCCGGCCTGCCCGGCAGGAAAGCAACGATGCCCGGCAATACGCGGGAAGCTCAGGCTAATCTGGCCTCCCAGTCCATGGTCGTCTGGTACGGGCCGCCGCCGCGCGCGGAAATGGAAAAAAAACTCCAGACATTCATTGAAGAAGGAGGAATAGCCCTTTTCCTGCCTGACGACACGGAACACGGAACACGGCATTCATTCCTGGGCGTTTCCTGGGGCGCGACGGAGACGGCTCCGTCCGAACAATACTACCGTCTGGAAACGTGGGACCGGCAAAGGGGATTCCTCCGCGACGGTTCCGATCAGACACCCATCCCCGCCAACCGTCTCCGCGCCGTGCGTAGAAAACCCCTGCTGGGCAAATACCGCACTCTGGCCTCCTGGGATGACGGTTCCTGCGCTCTGGCCCAGGTCAGGGCCGGAACGGGGTCCGCTCTGTTCCTGGGAACGCTGCCCAGATATTCCTGGTCCAACCTGGCGGACGGCCACCTGCTGCTCCCCCTGCTCCAGCGCATGGCGGACCGGGGGGCGGAACGCTTTTCCACCTCCATTTCCCTACGGGTGAATGACGCCTCCCTGCCGCAATCCGCCACGGACATCCCCGTGCGCCTGGATAATGCCCAGGGAAGCCACCCCTCCGGTTCTCCGGCCGATACGGCGGGCGTGTACCGACTGGGAGCACAGACCTACGCCGTGAACCGCCCCTGGCCGGAAGACATGCCTGACCAGATTACGGATGAAAAACTACACCTCCTTCTGCCGGAAGCTTCCATCAGTTCCATGCAGAGTGCAGAGGAAGCCCCCTCCCTGGTGCAGGAAGCATGGAAGCTCTTCCTGTTCTTCGTCCTCGCCTGCCTGCTGATGGAAGCCCTCCTGTGCCTGCCCGGCCAGACGGCCAAACGCCCCTCCCCCACCACCCGTCCATGAACCTGAGCCTTCAACCTACTGCGTCAAGCGTCATCCTGACCGCCCTGGTTATGGCGGCCGGCATCTGGATGAGCTATATCTCCTGCAAGCGCAACCGCCGGCCGCTGACATTCAAACTGGAAATTCTTCGCCTGGCAATCCTTGGCTTCATTTGCTTCCTGCTCTTCCAACCGGAATGGATCATCACCTCCGCCCCGCAGGAAAAACCCAAGGTTTCCATTCTGGAGGACCGCTCCGGCTCCATGGAAACGCAGGATGTTGAAATCTCCCCCCAGCATGTCGTCACACGCACGGCCTACGTGCAGGAACTGCTCAAGGGCAACAGTACGGAATTCCTTAAAGACACGCATGTAGTGGAATACGCCTCCTTCGGCGCGCCACCTGCGCCGGACTCTCCGGATTATGCCATGGCCGGCACAGACCTGGCCAAGCCGCTGGAGGACGCCATGCAATCCTCCGACAACCTCCGGGCGGTCATCATGTTCACGGACGGCAGCCACAACGCCTCATCCTCCGTGCTGACGCAGGCCCAGCGCATGCGCACCAGGGGCATCCCCCTGTTCATCATTTCCGCAGGCAGCCCGTATCCCCTCCCGGACCTGGCCCTTCAGGACGTGAAAGCCCCCACTTACGGCATCATCGGGGAAACGGTGCAAATACCCTTTACCATTAAAAGCACGCTGGGCAAAGAAACGCGCACCACGCTCATCATGACTTCCAGGGATACAGGAAAAACCGTCACCCGCACGGTCACCATTCCGGCCCAGGGGGAGGTATCCGACGCGGTTCTGTGGAAAATAGAAAAAGAAGGGGCGGAAACGCTGGAACTCAAACTCCCGGTCCAGCCCCAGGAACGGATGCACAATAATAACGCCTCTTCCTTCAGCATCTCCGGCAGGAGGGAATCCATCAAAGCACTGGTCATCGATACCCTGCCGCGCTGGGAATACCGCTTCATCCGGAACGCCCTGTACCGGGATCCCGGCGTGGACGTGCATACCCTGCTCTTTCATCCGGAACTGGAAGAAATGGGGGAAGGCCCCGGGTACCTGGTCAAATTCCCGGACAGGATGGAAGACCTGGCCCGGTATGACGTCATCTTCATCGGAGACGTGGGGCTGGGCTCCAAAGGGCTGACGGAAGAACAGGCCTCCCTGCTCAAAGGCATGGTAAAAAACCAGGCCAGCGGCATCGTCTTCCTCCCCGGCTACCAGGGCAGGCAAATGGAACTCCTGAAATCGGAACTGGGGGACCTGATGCCCGTTACATTCCTCAACACCAAGCCGGAGGGGACAACCCAGCCTTCCCCATCCCCCCTTATCCTGACGCCGGAAGGCCGCGGCTCCCTGCTTACCATGCTGGCGGATACGGAGGGTGAAAACGAAGAAGTATGGCGCAATCTGCCGGGCTTCAACTGGTACGCCCCCGTGGAACGGCCCAAAGCGGGAAGCACTGTCCTGGCCGTGCATGCGGCGGATAAGAATGCCTATGGCCGCATTCCTCTTATTGTCACCCAGTCCTGCGGCAACGGAAAAGTCCTCTTCATGGGAACGGATTCCGCCTGGCGATGGAGGCGAGGAGTAGAAGACAAATACCATTACCGTTTCTGGAGCCAGGTGGCCCGCTGGATGAGTTACCAGCGCAACATGGCCGCAGGGGAAAGAATCCGTCTGATTCCCACCCCGGAACGGCCCCGGCCGGGAGACACGCTGACCGTCACCGCCATGGTCTCCGACAAACTGGGGGCCCCTCTCCAGAATGGAGAGGTTTTTCTGGACATTACAACTCCGGAAGGAAGCTCCAGCCGCGTTCAAATGGACACTATGGACCATACATGGGGCTCCTTTACCGCCTCCGTCAAAATCAACCGTCCCGGCACCTGGAAGCTCACAGCTTCATCCTCTGCGGAACCGGATAAATCCGTTACGCTTCCCGTCATCACCATGAGTGAAACGCTGGAAAAAATAGGCAGCCCCGTCAATACCGCGCTAATGGAGGAAATGGCCTCCATCACACGCGGGCGCATGGTAAAAGCTGAGGAAATACAGCGGCTCATTAAGGAAATCAGGGATCTCCCGGTGCCGCCGCCCATGGAAACGCGCATCCTGCTCTGGTGCAACCCGTATACGCTCTCCATCCTCATCCTTCTGCTTACCCTGTTCTGGATAGGGAGAAAACTCAACGGCACCATTTGAGACGCAGGTTTCCCACACCTTCCCGGCAGGAGTTTTTCTTCCCTCCCTTCCCGTTATTTTCCCGCAGACTCCCTGTCCGCGGCAGCAGAAGCCGGCTGAGAAAATCTCAGCCTCAATCCCATTTTTTTCCAGGCCGCGACCATTTCCCCCTGTACATCCTCCGGAGTCTTCCCATCCAGCAAAACAGCACGCGCCTTCCCGCCGCCCTCCGCATCACGAACCCTCAAATAGCTTCTGAGGCGCTCCGCCTTTCCGCTCCCTTCCAGGTGGCTCCAGTAAACCATCCACAACAAATCGGCCCAATACTGAAGCGTTCCTCCCCTGGAGGAAACGCCATGCTCCGCGGCGGAATGTGCTGAATCCCTGGACAGCATGTCCGCCAGGGCCGGCAGAGCTTCTCTGCCCGTGCGGACGGCGCGCGGAACCATCGCGGCCAGCCTTTCCGGAATCTTCCGGAATACCGGCGCTTCCTTCTCCATGGGAAGAAAAGCCATGTACGCGGAAAAGCCGTCCACCAGCCAGTGATCCCAATCCATGGACTGCGCCGCCCAGTGCGTGACGGCAAAGCGCGGATCCAGTCCCGGCTTCAGCAGCTTTCCCTTTTTATCCCGCAACAGCAGAGCCGTGAAACTGGTAGGGGAAAACTTGAGATGCCCGTTCCGCAGGCCCGGAGCCCTGTTGAAATCCTCTTCCTGGAAACATACCCTCACCAGATACTTGCGGGAAGGCCGGCGGGCCACCGCCAGATTCAACGGAACAGCTGCCAGGGCGGACAGAATGGTTTCAAAACGCCGGGCAAGGATCATACGCTCCGCTTCCGGCAGCTTTCCGGGAGACTCCATGATGAAATGATCCGTTTCATACACACTCAGCTCCTCCGTCTCCTGCACGGTTTTCACCAGCAGGAAACCGGGAACAACCACCCTCTCCGGCCAGGGAGCATCCCAATTCGGCTCCAGGGGAACGTACCGTCTGCCGGAATCCCCCTGCACAGGAGGATTCTCAGCCCCCACATCAGTATCGGGGAGCGTCCCGCCAGACTTTACCCATTCCAGAATATATTCCTGGTCGGCTCCGGAAAAAATATTCTTGGGAACAGCCACCACGCGACCATCCTGAAGCTTCAAATCCACCTCCGCATCACGGACGGCCACCAGCACGGCTACGACGGTAGCGCCCCGGTCATTCGTCCAAATGCGCGCCCGGGCGCTTCCCGCCAGGCTCAGCCCTGCCAATACGGACAGAATCTTTACAGGTAAGGTCATAGGGTTCCCTTTGCCCGCACAAGCTACCACATTGTCCCGCGGACGCAACCCTTTTTCCGTCTCCCGTTCTTCCGGAACAGGTTCCTTGAAAAGAATATGGGGAAGGAATGCATCATCGCCTGTCCCCCGGAGGGGTTGACACGCTCCGGCAAAACCCATAATCTTATGGAAGAAAGGAAAAGAAGGCATGCATATCAGTCCCCTCAGAAAAAATATTCCGCTGACTGTCGTCACCCTGGCGGCTCTGGGCATCATGTTCTTCCTGTTTTTCATGCATCCCGCCCCTCCGGAAGAATCATTGGAATTCTATGCGCCGGACGGCAATATCGCGGGTTCCTTCCGGGTGGAAATCACGAATGCGCCTTCCAACCCCTCCTACACGGAAGTAAAAATCATTCCGGAAGACATGGAATCCTTCCAGCGGTTTTTGGCCCAGTTCCGCAACAAACCCGTTACTGCAAGAGTTCCCTCCCGCGGCAACCACATCATTGTTCCGGGCCGGGACATGCAATACGACGGAGTCCTGCGCCTGACCGGACATTGAGAGGTCAGGCAAACAGAAGCCATCTTAAAGTTGTTCTCCCTTTCCCACGTCCTTCGCCCACTTCCGGGCCAGGAAGGAACAAACCATCAACTGGAGCTGGTGGAACACCATGATGGGAATCACCAGCAGCCCGATGGGGGCTCCGGCAAAAATCACGTTCATCATCGGAATGCCGGTAGCCAGGCTCTTTTTGGAGCCGCAGAAAATAATCGCAATGCGGTCCTCGCGGGAAAAACGGAGCCTGCGTGAAAGGAAAGCCGTCAGCCACAGGGCCGCGGCCAGCAGGAGGCCGCAAAAAGCCAGCACCTCCACCAGGGAAAGCCAGGGCAGATTTTTCCATACGCCGTTAATCATCGCATGGCTGAATGCCGTGTAAATAACCAGCCAGATGGTGCTGTGGTCTGTCCAGGAAGTGAGGTTCCGGTGCGTGACTACCCATTTGCCAATCCAGCGCTGGGCGAACTGGCCGGCCACAAAAGGCACCAGAATAATATAGCAGATGTTCAGGAAAGTGGAAAAATCCACGCCCCCTTCCCCTCCGCGGGACCACACCAGCCCCACCAGCAAGGGGGTGAGGAACACCCCCAGCAGGCTGGAAACGGAAGCGGAGCAAATGGCCGCGGCCACATTGCCGCCCGCCATGGAAGTGAACGCTATGCTGGACTGCACGGTGGAAGGCAGCATGCACACATACAGCATTCCCATGCACAAATCCGGCCCCAGCACACTATTCCACAAGGGCATGCTGAATATACCCAGCAGGGGGAAAATAACAAAGGTGCTCGCAGCCACCAGCCCCTGAAGGCGCCAGTGCATCAACCCTTCCCATACGGAACGGCGGGAAAGCTTGGCCCCGTACAGGAAAAAAAGCAGAACGATGGCTGCATCGCTCAGGCGGCTGAACACTGTATCCCATACTCCGGAACAGGGAACCAGAATTCCCGCGCCCACACTGACGATGAGGCCGATGGTGAAGCGATCCAATTTGGCAAGGAATCCCGGCATGGAGAAGGAAAAAAGGTAAATGCCGCCTCCGGCGTTCTATTTGAAACGCACAGCATGCTTCTTGCTCAGGCGCAGGATGCTGCCCGGAGCCAGTTCCAAAACGGCGGAGGGGTCTGTCATTTTCACATCATTCAACTGGATAGCGCCGGGCGTGAAATGCTCCTTGCGGAGAACGCCATTGGATTTTTTCACCTGGAAAACATTCTCAAACAGGAAGGAAACCAGGGCCAGGGCATTCATGTCGGCAGGCAGGGAGGCAATCTCCACTTCCGGCAAATCCGCGGCAGCCAAATCCCTCTTGGAAAAACGGGTTTCCCAGTCAGAACGCGCGGCATCCGCAGCGGCGGAATCATGATAGCGTGCCGTGATTTTCCAGGCCAGGAGCTTTTTGGCTTCCATCGGATGCAATCCCATGTCCCGCTTCTCACCCAGCAGCACCAGGTAATAACGGTCCATCAGTTCGTCGCTGGCGCTCATCATCTTGCCGAACATCATCTCCGGAGACTCATCCACGCCCACGTAATTCCCGTAGGACTTGGACATCTTCCGAACGCCGTCCAGACCTTCCAGAAGAGGCATCGTCATGGCGATCTGCGGCAACATGCCTTCCTCCTTCTGAAGGTCGCGGCCCACCAGGATGTTGAAAAGCTGGTCCGTCCCGCCCAGTTCCACGTCCGCACGGATTTCCACGGAATCCCAGCCCTGCATAATCGGATACTGCATCTCATGCAGGCGCACCTCCTTACCTCCCTCCACACGGGCCTTGAAATCCTCCCGGGCCAGCATCTGCTGCATGGTCACGCGGGAATTAAGCTTCAGCACCTCCTCATACGTCATCTTGCGGAACCAGTCCCCATTATACACGATCTCCGTCTTGTCACGGTCCAGAATCTTGAACGCCTGCTTGGTATATGTCTCCGCATTCTCCAGCACCTGTTCACGGGAAAGGGGGGGGCGGGTCACGGAACGGCCGGAAGGGTCGCCGATCGTGGCGGTGAAATCCCCGATGAGCAAAACAGCCTGGTGGCCAAGTTCCTGGAACTGGCGCAATTTCTCAATAGCCACGGTATGGCCCAGGTGGATGTCCGGCGCAGTAGGGTCCACGCCCAGCTTCACGCGCAGGGGGCGGCCCAGCTTGAGACGCTCCTTCAGCTCTTCGCGGCTGATCACGACGGCGGTACCGCCCATCAATATGTCTAATTGCTCATCTATAGTCATTGATCTGGCGCATAGAGTGCCGGGGGCACCGCGCCTTTTCAAGCCTATATCCTGTGCGCGGCTCATCGAACGCGGCCAAGAGTCCCCCGCCACCGGTCCCGACGTGAGGGAACAGGCACGGAATCCGTCCCGAAAGGCGGAATAAACGGCACGCCGGTGCTGGCATTTTCCCCGCATGGCCCTTCCTCCCGTTCCATGTCACGGACCAGCCCTGCTCCCTTCCGGATGCAGAGAAACGTCCCCGCAAATCTACAGAAGCTGGTACAGGCGGGGATAGGAAGCATGATTGGCGGTTATGTAATGCGGGCGCGGCTTGGGGACCGGCTTTGTCCGCCTTTGATCCCAGCCGTCACGGTCATGATGATAATTATCATGGCGCGGCGGCCCGGAACGGTAATCATCACGATCGTGGTGGCGATGATGGCAGCCTGCGGCCAAAACGATTGAGGCTGCTGCGGCAAAAACAGGAATTATGTTCATCTTCATCTTTCTTCAGGGTAAGACAGTCCGGCGGGAGGAATGCGAACGGAAAAAGTCTGTCACCCGAACAAACGCTCCCTGTTGACGAAGAAAACGGCTCCCAATACACATAAAAACGCCCACAGGTAATCCCACTTCCATTTTTCTCCGATGTAGAACAGGGCGAAAGGAATAAACACGCACAGGGTAATCACCTCCTGAATAATCTTGAGCTGGGCCACGTTAAGCCCGCCCGCACTGCCCAGGCGGTTGGCGGGAATCATGAAGCAATATTCAAAAAACGCCAATCCCCAGCTCATCAGGATAATTACCAGCAGGGGCTTGCCATCCGCCCCGGACCCCTTCTTCAGATGGCCGTACCACGCCCACGTCATAAAAACGTTGGAGACCACCAGCATGGAAATCGTTAACGCAATCTTCATGATTTTTATTTAAAAACATCAGTCCTTGGAGGACTTGGGATCCAGGGCGTCCCGCAGGCCGTCCCCCAGGAAATTCAGCGCCAGCAGGGTAGCCGAAAACAGGAGCGCCGGGAACAAAAGCAATTCCGGACTTACCTCCATGCGGTCCGCCCCTTCCTTGATCAGCGTTCCCCATGAGGAATTGGGAGCCTTCACGCCCAGCCCCAGAAAGGACAGGACGGCTTCCAGCAGCATGATGCCGGGAACGGCCAGCGTCGTGTACACGATGACGGCCCCGAACAAATTCGGAGCAATATGGCGGAACAGAATCTTCATGTGACCGATGCCCAGGGAACGGGCAGCTTCCACAAACTCCTGCCCTTTCAGCTCCATCGTCTGCGTCCGGACAATACGCGCCAGCGTCAGCCAGCCCAAAGCGCCGATGGCGATGAACAGGGGAATCAGCCCGGTCATGGGGCTCACCATTTCCACAGACCAGCCCGTCATGCCGGAAACCCACTGCGTCAGCCTCCGGGCCGGCTCCTCCACGGACAAGGAAAAAATAATCACCAGCACAATGAACGGCAGGGCAAAAAGCACATCCACCAGGCGCATCATCAGGGCATCCAGCCTGCCTCCGGCATAACCGGAAACCAGTCCGTACGCCACGCCAATCACCAGGGAAACGCCCGTAGCGACCACTCCCACCAGCAGGGAAATGCGGCCGCCGTACAGGACACGCGCCAGCAGGTCCCTCCCAAGCTGGTCCGTGCCGAACCAATGGTCCCAGGAAGGGCTTTCCGCAATCCGGGACAAATCCTGCACATTAGGATGAGGCAGCCACGGCAGCAGGGGACCCAGAAAACAGGCCGCCGCAATCAGCGCCAACGCCAGCAGGGAAAACATGGCCGCCCGGTTGCGGGAAAGACGCAGAAAAGCGTCCTTCCACAGGGAGGAACCCTGTTCCTCCATCCTGTTCCATCCTCTTTTTCTCATGATTCCTTTCTCAATCTCGGGTTGACGGCCATTTGCACCAGATCCACACCCAGGTTGGCCGCGACGATCAGAATGCCGTAAAACAGGACAAGCCCCTGGATGAGGAAATAATCCCGGTCCGTGGTTGCGTTCACGAAATGCTGCCCCATGCCGGGAACCTGGAAACAGGTCTCCACCACAAAAGAACCGGTAATCATAGCGGCAAAAGCAGGCCCCAGATAGGAAAGGGCCGGAATCAGGCCGCCTCGCAAAGCGTGCTTCCAAACCGTACGGAAAGGCCCCGCCCCCTTGGCTCGAGCCGTCCGGATGAAATCCTGCCCCAGCACTTCAAGCATGCCGCCGCGCGTCAGGCGCGCCAGATATGCGGCATTCACCAGCCCCAGCGTCAGCGCCGGAAGCACCACACAACCGGGAGAATCCCATCCGGCCACGCTCAAGCCGGGCACATGCATGCCCAGCGCCACGCCAAGCAGCGGAGCCACGACGAAAGCGGGAATGCAAATGCCGGCCATGGAAACGAGCATCACCCACCAGTCCATCCACCGGTTCTTGTACAGTGCCGCCACAATTCCGGCGGGAATCCCCGCCAGAATGGAAATAGCCATGCCTATGACTCCAAGCTGGAGGGAAACAGGGAATGACTGGCCGATCATATCCGCCACGCTGACGCCTTCCTTCACCAAAGACGGCCCCAGGTCGCCATGAACCAGCATGTTCCACCAGTAACGCCCGTACTGGACCAGGGGAGCCTGATCCAGCCCGTAACTGGCCTGGAGCTGCTGGAGGACATGCTCCGGAAGTTTCTTTTCCCCCATGAAGGGGTGCCCCGGCAACAGGCGTATCAGGAAAAACGTCACAGTTTCCAGCACCAGCAGGACCAGAAGCCCCTGCCCCAGGCGTTTCAGAATCATCCTGGTCAAGGCGCGGCCTCCTTCCCATGTTCCCGGGCGTCGTCCAGGCTGATATCCTCAAACAAATGGTTATCCAGCAGCAGGGGGTGCCATCCCCGCACCTCCGGCCTCTTCAAATAGGACCGTTTGGCCCAATACAGGGGAATCACCGGGGATTCGGAAAGCATCAGCGCTTCCGCACGGGCCAGCAGGGCCATGCGCTTCTCCTGGTCTCCCGTGGCGCGGCTCTCCGCCAGCAGGCGTTCATACTCCGGATGAAACCAGCCCGTATTATTGTTCCCGGAGGCGGAACCCCACAAATCCAGAAAACTGGAAGGGTCCAGATAATCACCGGACCATGAGGAGGAACTAAAATCATACTGCATGGAATTCTGCGCGAACTTGTAGGCCGTCCACTCACAGGAACGGATATCCACATGAATCCCCAGATGCTTTTTCCACATGCCCTGGATGGCTTCCGCCATCGTCTTCTGAACTTCGCGGGATGTAGTCATCAGCTCCAGCCGGGGAAAACCCTTCCCGCCGGGAAAACCGGCCTGGGCCAGCAGGGAACGCGCTTTCTCCGGATTGAACTCCACCCCATGCGGCGTCTTGTATCCGGCGCCGTCCGGCGTAAAGCCGAAGCACGGCTGGCCGCCGCCGCACACCACGTCCCGCACCAGAGCCTCCCGGTCTACCGCCAGGGAAAGGGCCCGCCTGACCCGGGCGTCATTCAGCGGCGCACGCGACGTATTCAGACGGTAAAAAATAGTCACGTAATAATCATCCTGGCAAAAATCATCGCCGCCGCGCTCCCGGGCGTAAGCCGTCATCTCCGGAGGCACGTTGTTGGTGACATGAAGCTTGCCGTTGAAAAACATGCGGGTTTCCGTAAAGCCGTTCACGGTGGGAAGAAAACGGACTCCATTCAGCCGGACGGCGGAAGCGTTCCGGTACCGGGGATTGCGGCGCACCTCCACATAATCATTGAAACGGTGCTCCGCCATAAGAAAAGGTCCGTTCCCTACGGCCGCTCCGGGCCGCGTCCATTTCCCCGTCCTGTCCAGCATGCCGCCGTGGGCTTCCACGGCATGGCGGGGAACCGGAAACCAGGTGCAGTGAAGCAACAGCAGGGGAAGCTGGGGCATCGGAGAACGCAGCTCCATCTCCAGGGTATAATCATCCGCGGCGCGCACGCCCACGCGGGCTTCCTCCCATAAATCCGGCTTTCCGGCCCGGACATCCTCCAACATCCGGGCCGCAATCTTCCGCGCTCCTTCCTCCGGCATCCCGTCCGGCCACTCCATCAGGGTAGGATCATCCCGCAGAGCCTCCAGCTCAGCGGCTCCCATACCATCCAGCCTCTCCCAATCCACCCGGGCCAGAACCCTCTCTTCCAGCCCCTCTTCCCCAGCCAGCCGGGAACCGGGACCGCACAGCAGCAGGCTGCGCCTGTTCCGGTTATAGGCTTCCGCATTCTTCAGCGGATAAAGCATGTCCGCGTACCTCCCTCCAAACCCCGGATGAAGCAGGCGGTGATAGGCATACACGAAATCATGGGCCGTCACAGGAACGCCGTCGCTCCACACGGCGTCCTTCCGGAGGGAAAAACGCCACTTGTCCGCCTCCGGACTATGCGTCCACGACTCCGCCACAGCGGGATGAAAAACGGCGTCATCCCTGGAATCCCCGCGCAGCAGCCCCTCCATGACGGCATTGATCATCTTGAAATCGGAAACGGAAGTGGCGATGTGGGGGTCAAAGCTCTGGGGCTCCGCATTATTGCCCACGATGAGCACCCCTGCCTCCTGCGCCCGTTCCACGCTCGTGCGCGTATCGCATCCGGCGAGCAAAAGCATCACACAGAACATCATGAACAGGCGCATGGCGGGATTCTGTCAGGTTTCCTTCCCGTACGCAAGGCTTCACGTCAAGAAATACGGCGGCAGAACCACAACGACCTGAGCGCCCGCCCAGAAGCGGAAGGCTCCTCCCAGAACCTTTATCCCACCCGGCTCCCAGTTCTGCGGCTTAACAATATATTTTCCTTCGTACGCTCATGATACATATAAAATTTCATAGAAAGAAAAACACCCCTCTTCCTTACTGGTCTTTTACAAAACGGAGGGGGTTTATCCTGTTCTCCCGGTTATTCCTCCTCTAAATTTTTCCCATGTTTAAATTCAAACACCGGACATCCTGTATCTTGACCGTACTCGCGGGGATAATCTCCATTCAGGTAGGGAATGCCATCACCGTCCCGTGGCTCAGACCTTATATTAACGGTGAAGAAGTGGTCGGAACTACGCTGGAAAGCCGTTACAGCACACCTGACGGAGAATCCGCCAATCCTGTGATCTGGTGGGAAAAAGCAGATGTCCGAAACGGAAAAACCGTAGTGGTTCAGCAGAGCCGTGATGCGGAATCCTACACCCTGACGGACAAGGATGCAGGAAAATATTTCCGGATTATTGTAGGCTGCGGAACCAAAGACGAAGCTGCCAGCCAGTGGATCGGCCCGGTCATCACGGAAAAACAGGCGGAAAGCATCAATAACCGTTTCCATCCCGGCAGGAGCTACCAGGAAAATGTGAACGAACTTCAAAAGGAACTATCTGAAAAACTTCGAAACGCCGTGTACTTTACCGTAGATACGGACAAGCTGCATGGCGCACCCTGCGTGATGGCCAGAAATAAACGAATCCCTCTGCCCGAAGATATCAGACCTTTCCGGGAAAACGGAAAAATCTATATCAGCCAACCCTTTGTACAGGCCATTTTCAACAGGGAACTGCCTGATGAAATCATGGAGGAAGCAGCCGGACAGAAAGCTTATGAAATAAGCAATGTGGCCAGGGTTCTGCAACTCAACCTCTGGCAGGGAGACAAGGAAAAATCTCCCGTACCGAACTTTCGCATGCAACCCATGGCGGAAGGACTGGTGATTCTCTCTCCTGAAAAAGACATCTTCTCTCCAGTAGAAGACCGTGACCTGATTAATGAAGCCGTCAACGGGCTCTTCGATTTCAAGGCCGATAATAAACAGTTAAAATGGTTTCGGGATGCCAAATTCGGCTTGTTTGTTCACTGGAACCCTTCTTCCCTGCTGGAACGGGAAATAAGCTGGGAACGCAATGCGGCGCGTCCCAAAGACAGCGCGAGCGGTCATAAGAATACCGTAGACTTTGAGTACGACTCCGCCCACCGCCGCTTCAACCCCAAGCAATATGACCCCAGAAAATGGATGTCCGTCGCCAAAAAAAGCGGTATGAAATACGCCGTGCTGACCACCAAGCATCACGACGGCTTCTCTAACTTCCCCTCCAACTACGACACCTTCACCATTGCTGCCACCCCCTACGGCAAAGACATCGTGGAACAATTCGCGGCGGCAGTTCATGCGGCCGGGCTCAGACTGGGATTTTATTATTCTGGAAGAGACTGGTACAACCCCTACTACCTGACCGACCAGCACTACCGTTATCTGGAATACTACTTCGGCCAAGTCAGCGAACTGCTCACCCGCTACGGCCAAGTGGATGTGCTCTGGTTCGACAGCCTGGGCAATAGCTCCCTCAACCAGTGGGATCCCCGCACCATGATACGCCGCATCAAGCAGTACCAGCCGGACATTCTGATCAACAACCGCATGAACAGCACCCGCGGAGGCAATAAGGAACCGCTGCCCGATGATTTGAAAGGGGATTTCCTGACGCCGGAATGCAAGCTAGGCCCCTTCAACACTCAAACGCCTTGGGAAAGCTGCATGACCGTGGCGGATATCCCCGGCACCCGCTGGACGGGCAACTGGTCCTACACATCCAACGCAACAACCAAACCCGTCGAAACCTCCGTCAAATTCCTGATCAATAACATTGTCAAGGACGGCAACCTGCTTTACAACATCGGTCCCACTCCACTGGGAACATTTGATCCGAAACAGGCTGAAACTTTTCTGTCCATGGGAAAATGGATTGCTCTCTACAAGGAGGCCATTTACAACACTCGCGGAGGCCCCTACAGGGACCAGCCATGGGGCGGCAGTTGCTACAGAGCGGATCACAACGGCAAAAAAACCGTTTACCTGCACGTCAGCCCGCTGATTGCCCGACAGGGAAAAGAGCTGAAGGGAAATACCCCCCTGTTCATCAAAGATATCGGGGAAAAATTCACGAGAGCGACCCTCATTGTGAACGGTACCGGAAACGGAAAATTCGTCAGGCTTGAAAAGGAAGGAGACCAGTACAAAATCACTTTGCCCGAGGGTGTCACCTGGGACAGGCTGGATACCGTCATCAAACTTCAATAACCCGTTCTTCCAAGAAAACAGCATGCCTTTCCCGCACTCTCTCCTAGACCGGATTTTTATTACCGGAGAATAGTGTTGCCGCCACACGGTACTCTTGCCTATCCCATATACCCGGCCTGACGGAACCGGCATCAAATCCTGCCTTTCGCCACTCCCGCAGCAGAAGCGCCGAAAACACGCGCCGCAGAAGAACAGTGCCCCTCATAAGCCACAGTGCCTCCTTTTCATCATCCCTTTTGAGTAACCGGAGAAACAGGGGGAAAGCAAAACCCCGCAACGGCAGAAAACCGTTGCGGGGCTGTTTTCCCAAAGCAAGGGAAAAGAAAAGCGGCTTTAATTATTTGCCCTGCGCCTTTTTGGCAGCGTCAATCTGGGCCTTGATCTTATCCTTGTTGGCAAGAATGCCGGCCTTGATGCGGGGGGCCTGCATTCCGGCTTCCGTTCCCGGCTTCAACTCGGCCACCTTATCCAGAATCAGAACGGCGCCGTCCACGTCACCGGTGGAATTCAGGTAGGTCCCGAACTCCATCATCAGCAACTCCTGCTGTGTTTCCGGCAGAAGCTTGTCGCGGTCGGGATATGCCTTAACCACCTGAAGGGCTTCCGCAGGCGTAGTCTTGTCCGTCAGCTTGCCGGCAAGATAATTTCGAACATCTTTCCGTTCCTTTTTAAGCTGGTCGGAGAGGGCGTCTGCAGCTTTCAGGCCGGACTTGTCATCCTTGTCCAGCTTCTTGATTTCCGCTTTCACGTCACCATAAAAATTGTCCACATATTCCTGAGGAGCCAGCTTGAGCACTTCCATCAGAGCGGCAACTTTCGCTTCATCCGTAGCAGCCTTGGCAACATTCGCTTCCGCGGCATTAAGAGCTTCCTTGTTCTTCAGGGCATCCTGCATGGCTTTCATGACATCATCCCCGGAGCGCCCTCCAACAAATCCCCCAAAAGGCTTGCCGGAAGCGTCCGCAAACACAACGGTCGGGAATCCCCGCACGCCGTACTGCTGGGCAAGCTTTTCATTGGCGGCTTTCATTTCCTTGGACTGTTCCTTGCCGCGCGGATAATCCAGCTCCACCAGCACGAAATTTTTCTGCGCTTCCTTCTGGAAGTCAGGCTTGCTGAATACGTTGGCGCGGAGCTGCATGCAAGGAGGACACCAGTCGGAACCGGTGAACTCAATCATTAAATCCTTTTTCTGTTCGGCGGCCTGTTTTTTGGCGGCATCCATATCGGTAATCCACCCTTCCGCGGCAAAGCAGGGAGCGACGCAGGCGGCAACAACAATTGGCAGAATTTGTTTAATCATATCTACAATTCAATATTGTAGAATCGGACAGCCCGCGTCAATCCCCTGTTCAATCATTTTTAGAAAATCTCCAAAAGGGTGCGTCTTCCGCAGGGAAGTCCGCATCCGGCCGATGCCGGAAAAACTGATGCCCCATGGGAAGACGGTGTCATTATTGAAGCAAATCGGGAGTTGCCTTTCAGCCTGTTTTCACTTATGTACAGAACCTATCATGAACACGGTTTTAATCATTGGAGCAGGCGGCGTTGGGCATGTGGTAGCCAACAAATGCGCCCAGCTGCCGGATATTTTTCAAAACATTCATCTGGCCTCCCGCACGAAGAGCAAATGCGATGCCGTTGCGGAGGACGTACGCTCCAGAACGGGCGTCAGCATCACTACCCATTCCGTAGATGCGGACGACGTGCCCGCCACCGTAGCCCTCATCCGGGAAATCAAGCCGCAGCTGCTGATCAACGTAGCCCTGCCCTACCAGGATCTCACCCTGATGGAAGCCTGCCTGGAAACCGGAGTCAACTACCTGGATACCGCCAACTATGAACCCCGTGACGTAGCCAAATTCGAATACTCCTGGCAATGGGCGTACCAGGAGCAATTCCGCAAAGCCGGGCTTTTTGCCCTGCTGGGTTCCGGCTTTGACCCAGGCGTCACCAATGTATTCACGGCATGGGCTCTCAAGCATCACTTTGACGAAATCCATACGCTGGACATCATTGACGTCAATGGCGGCAATCATGGCAAGGCCTTCGCCACCAACTTCAACCCGGAAATCAACATCCGGGAAGTAACGGCGCCCTGCCGCCACTGGGAAGACGGAGCCTTCCGGGAAACCGCTCCCATGAGCATGCACCAAAGTTTCACCTGCCCGCAGGAAGTGGGCACCTATGAAATCTACCGCATGTACCATGAAGAAATGGAAAGCCTGGTCAAGCACATCCCCACCATCCGCCAGGCTCAATTCTGGATGTCCTTCTCACCGAACTACCTCAAGCACCTGGAAGTGCTTCAGAACGTGGGTATGACCCGCATTGACCCGGTCATGTACAACGGAGTTGAAATCATCCCGCTGCAATTCCTCAAGGCCGTTCTGCCGGACCCCGGGGACCTGGGCAAGACGACCAGGGGAAAAACCTGCATCGGCAACGTCATCACCGGAATCAAGGACGGCCAATTCAAGGCTGTGTACATTTACAACATTTGCGACCATGAACAATGCTTTAAAGAAGTAGGTTCCCAGGCCATTTCCTATACCACAGGGGTTCCGGCCATGATCGGCGCGGAAATGATCCTTACGGGCAAATGGACTGGCGCAGGAGTCTTCAATATGGAACAAAATGATCCGGATCCCTTCATGGACGAGCTCAACAGGCGCGGCCTACCCTGGCACTGCGTGGAACTCACGGAAGAACAGGCCAAAACCCTGAAGGTCAACCAGCAATAAATTTCAGCAAGCGCTGTCGGCATCATCAAAGCCGGACATTGGAAATTCAATGTCCGGCTTTTCCGTTTCCAGCGGGGAACAGAATTCCCCAGGACAGCACGTCAACGCCTGCCTCCGGGAAAAAGAAAAAGCCCCGGGCGGCGTCTTTCCGCCTCAGTCATAATTTTCTGGCGGGAAGAGCAGACAGGTTGTAAAAACTCAGTGCCATGAAATATCTTGCGTTATTTCTCATGTTCCTGCTTCAGCCGGTACTGGCGGAAGATACAGCCCCATGGAAAATAGCGATCATCGGCGATACGCACGACTCCCCCAAGCGTATGGAAGGAAGCGAGGGAGTAGCAGTCAACTTCATCAAAACCCTCTATGGGGAAATCCTGAAACACAACGTGGACATGGTCGTCCAGGTAGGGGACATGGCGGATATTGAAGGAAGCGCCCCCGTCAAGGGACTCGCCAAACGCAAGGAACTCAACAAAATTCTGAGAGAAAAAGGCATCCCCTTTTATGCCGTGCGCGGCAATCATGAATCCCTTCCTTTCCGGGCGGAACAATTCCGGGAACTCTTCCTGCCTACGCGGAAGCAGGGCACCAAAGGGCTGGCTACCAGAAAGTTGAACTACGGCATCCGCCATAAAAACGCTTCCCTGTATTTCATGGATATTGACCTCACCCCTGACCAGCTCGTGGACTTCAGTGCGTGGGTCAAACGCAACAGAAGCAAGGCCAACACCGTTCCGAGGCACTGCCTGGTCTTCACCCACCGCACCCTGCAGACGCCCATGCAATTCCGGGAATGCCTGTGGGGCCGTTACAACGACAGCGCGGCGGAACAGCAAAACATCTTTTACCGCAACCTCCGGGATGCCGGAGTGCGCTTCGTCGTCACCGGACACCTGCACGCCCATGACCTGTATATGATTACCTCCCCGGACGGACAAAACACCCTTACCTCCCTGATCTGCGCCCCTGCGGGGAACAAGGTTCTTCCCATCCCTTTTCTGCTGCCTGCAAAATCCCGGGTAAAAACCCTGCAATACCGCTCCGGCATTACGGCCTACTATATCCTGACCATTTACCCGGATTCCATGACCCTGGATACCTACGCCGCCCCCAATAACGGCACTACTGACGAAGGCCCCCAAAGCGGAGAGTTCAACAAATTGAACTCCTATACAATTCCGATGGATTAGAACATACTGCGGCCCCCGTTTCTTCCATAACGCAGCATCCCGGAATCTGCCTGCTCCCGTCCGCTATACACCCCTGGCGTCCCTGGCGGAACAATGGAATTTTCCGGGAAGTTCCCAATCCCCCATGTTATTTCATATGCCCGGAGCCACGGGACTTTATCTTTCTATCGTGCTGGCGCTCCCGGTCGCCCTTATATAAAAACTCCGTTCTTCCATCTCGGCCTACAGTACGGTCAATCAGGTCTCCGATTTCCTGGTCAGCGTATCCGGCCTTCTTAAGATACTTAACCACTTCACCAGCCTGCCCCGGATAAAAACGGACCAGCATCCCCACAAACGCAGGTTCCAATCTCTTTTCCAAATGATACTTACAGTTGTTATTTCTGTCTTCATTAGACAGCAGCTTATTGTTGTAATAAATTGATCGGGGATCAGCCCCGCGAAATAAACTCAGTTTTCGTTCCTGAACCAGTCTCAAGGTTCTTTCGGCACTCATTTTATCGTATTCCTCCTTCATCCTGGCCTGTTTCTGCTTTAATTCCGGATCATACTGAGTCCTGAATGTTTTCAATACGGCCGCTCCATATTCTGCCCAGATTCTGCACAGCTCCTTTCTCTTTACGCCGCATCGGGAAACGATTCCGGATATATCCCAGGATAAACTGCTCATCCACCTCACAACTCCGGTCTTCAATTCATAATCCATGTTTCCATAGAGGCCGTCTTTGGGGGTATTTTCATCATAATCCCATGAAAATGCATCCGCCGAAACTACAACATAAAAAATCCATGACAACACCCTGACGTCTTCTGCGTTCACATCTCCAGCTCCCTTGGAACTAAAGGCGCCAAGTTTCTTCCGCGCCTCCCGGTAACAATCCGGCAGCTCTTTTTCATGCAACAGGGAAAGAAACTCCTTCTTTTCCTGAGGAGACGTTTTGAATGCGGAGATGCTGTCGGGCAACGGAAGCCGGAGCTGGGCAGGCTTTCCTTCAGCAGTATTTCCCAAATATATATCAGCGGAACATTCCTCTGTTTCCGTTTCAATCCTCAACGTGCCTAAATCCTCTTCCTCCACGTCCGCAAAGGAAAAACCGCACAGGGAAACAGCGCAACTTATTAAAAGGATTCTTATATTTTTAACAACAACCGCCATCAGACGCAGATGATATCTTATTGCCATTTCCCATTATTCCATAGCAAAAAATTCTAACATTCCGCAGCTTTTTCCTGCTAAAAGATTCTGATGCTCTTCACGCCATAATACATCGCCGCGCCTCTGTTGACGTAGTCTTCCGGGTTTTGCGACAGGTCTACCCAGAAAATGAAAGAACACCAGGGGGAGGGAGGAGAGAGGGGAGGAACAACGATTTCCTGAATACCGGCCTTTTTCTGCTCCAGAACCAGGCGGACGCGCTTTTCATGCTGCCGGAACAGAAGCAGATGATCATGAAGGGTTGAAAAAACGCATATTCCGGACAACACCAGAAAAACGGCCACAGTTATCTGCGGACGGGGCATGGCACTAGGGATGCCGTACCACATACGAAGAAGAACCAGCACGCCGACGCCGAACAGCAGGGTGGCGGAGAACATGGCATGGTCCGCCGGAATGACGGCGGCGGCGAAAGAAAGAGCCATGGCATAAGACGCCAGAATATATGCCGCCGCTATTCCAAGGGAAATTTTCCATGCCCGGAAATAGTTCCGGTCCTTGCGAAAACTTTTCCAGAGAACCCAGGCAGCCGCCATGCCCAGCAACACCGGAAGAGCCAGGTATTCATGCATCCGGAACAGGAGCAAAGGGAGAGCTTCCCATCTGCTCCAAAAACCGGAAAGGGGTTCCGCGCAGCCGGGAGTGGCGGAATGCATCCGCGCGGAAATGCCGGGCGCAAGCAGCATGCACAGGGAACCTGCCACCTGGAACACAAGGCCCGCATAAAACCACAAAGGAAGTTTTTCCTTCCGCACCAGGCGGACAAAAACAAACAAGGCTCCCAGCAAAAGCCAGGTTCCCGGAATATTGTTTTCATTGGTCATGCCTGCCGGAAATCCCAGAACAGCCGCAGCGGCAAACTTTCCCCAGCCACTGTTTCTTCCGGAATCTCCCTTTCTCCCGGATTCCGCGTCGTCCAGAAAACCGCGGTACAGACACAGAAAGCCCAGCCAGACGGCGGCGCCCCAGGAATAGTTGGTGGCGCCGGAGAGCCAGTAAATCGTGACGCCGGGCCGGGCCGTGCACGTGAACAGCAGGAGAAGCCCCAATCCGAACAGACGCACGTCCGGCCAGGAACGGGGATCCACCCTGCGCCCGGAAGCCAGATAAAACATCATCAGCGCAAGACCGACCTGTACAAAGGGGTTGAGCAGCATGAAGAGCCATTTTCCCGCCGTCGCTACGGCAAAGGCCAGGTATTCCCCGATTCTGGGATTCCAGGTCATGTAGGAGGCTACGCAGCGCTCCCAGACCAGGCTGAAAGAGAAGCAGTGCTCCATGCCCGTCAGGGCATGATGGTACGTGTCGGAAGTGAGAGGACTCCACCAGCTGAGCAGCGCCGTATACAATAAGGCGGCTGCGGTCAGCGCCCAAAACCACACCCTGGACGTGCGGGGGGACATCATCAGCTCGGATCCGGTTTCCTTTTCCGCAGTGCGGGAGGCAGATCCAGGTCTTCCCCATCCAGAATGAGGGGGCGCACACGGGCAAAAAGGCCGTGGCCCGCACGCTCTTCCTCTCCTTCCTCCGGAGGCAAGCCTTCCTCCAGCTCCTTGATGTCAGGCATCGGGACTGACTCCAATTCCGGCTCCGCCTCCGTTTCGGAAATTTGCGCAACAGACGCGGACTCCCGGAGCGTCTCCTCTTCATCCGAAATCCCGGGAACGGACTTTACGGGCACCAGTTCCTCTTCCAATTCACGTTCATCCCTCCTGGGGACGGGAACAGGGGCGGAAAGATCCGGCTCCGGATGCCGTTCCGGTTCGTCAACCGGGGCAGGAGGTGCAGGCTGAATAATGGTTTTCCCCTCTCCGAGAAAAGATTTTTCCTCCGGTACAAGCACTGTCTGCGATGCCACTTCCCGCTGAAGCACAACAGACGGGGAATCAAATGTCTCTACCACAGGCACGGCGCCCAGCACGGTCAGGGACAGGCGGTCTTCCGTCTGCTGCTCCACGGAAGCGCCGATGTGGAGGCGCACCCCCTTCCCCAGGATTTCCGCCACCCCGTCCACCAGGGCTTCCAGGCGCTGCAGTGTCAGGGATGCTCCGCCGCGCACCAGAACCAGGACATCATCCACTTCCCCCAAACGTTTTTCCAGAAAGAGCGGGGAATGCCGGACCTGATCCAATATGTCCGCCACGGAAGCATCCGCAGACGCTTCACCCACGCCAAAGGAGCAAATTCCTTTTCCCGTTCCGGCTACGCTGAGCAGGTCGTCCAATCCCACCCGGAGCAGGTTTCCGGAAGAATTCAACAGAGACGGCACAATAAGCACGGCACGGGCTATAAGGGCGTTAACTACGGAAAAGGCTTCCAGCACTCCCCTGTCGGGGTCTATGAGACCTTCCATGGCGTCATTGTCAAACCGCAGCACCATGTCGGAATACAGGGCAAGGCGGGACAGCGCTTCATCCGCCTGGACAGCGCGCCGTTCCCCTTCAAACCTGAAGGGACGGATGACAACGCTGACGACATAAGCCCCCTGTTCCTTGGCCAGACGGGCCGCTTCCGGAGCCACGCCGGACCCTGTTCCCCCGCCCAGCCCGGCGACAAGAACAGCCAGGGCGCTGCCTTCCAGAGCACGGAGAATGGAAGACTCGCTTTCACAGGCCGCCTGGGCTCCTACGGAGGCGTCTCCGCCGGAGCCGAGCCCTCTGGTAAGCCTGGCGCCCAGATGGACCTTGCACGGAACGGCTGAAGCGTTCAAGAGCCGGGCATCAAGATTCATGGCACAAACGGAAGCTGGCTGAGGGGACAGCAACAACACTTCTTCCATTACCTTGGTGCCTGCGGCCCCTATGCCGCACAGGCAGATTTTTCCGGGCAGGACGGACGGCCCCTGGGGAGAAATTTCCAGCATGGTTGTTAATTTGGATAAAGGTTGATCAGGAGTTCTTCTTTCCGAAGAAACCCAGCATCCGGCCCAGCCAGCTGCGCTGTTGCGGCAGCTCGGCATCCAGAATCTGGGCATACCGTATCAGGCCTATGGCCGTGCAGTAGCGCGGATCGTCCAGGTAGGAAGGCGCTCCGTTTTTGACCAGCGTAGGCCGGCTGATGGCCACGCCGAACACGTGGGAGGCCAGTTCCCCCACGCCGCGCATCAGGCTGGCGCCCCCGCACAGGTACACGCCGTGGCAGCGGTTGCCCTTGAAAGTATCCTTGGGCAGGGAGGATTTGACCAGATTGAAGATTTCCAACAGACGGGAACGGATGATTTCATTCAGCACGTTGCGTTCGATGGCGGCATCCTTCATATGCCCCTCTCCGCGCACGCGCACCATCTCGTTGGTTTTCCCGGAAAAGCTGTTGGCGTCCCCTTCCGTTTTCTTGAGCAGCTCCGCCTGGGCCAGCGGAATGCCGGTCATCAGCGTGATGTCATTGGAAATGTGGTCGCCGCCCAGCGGCACGCATCCGGAGGCCACAAGCTGGCCGTCCAGGTAAAGCACATAGTCCGTCGTGCCCGCTCCCATGTCAATAAGCAGAGCGCCCGCCTGCTTGACCTGCCTGTTGAGCACAAACTGGGCCGTGGCAATGGGGGCAAAAACCACATCCGCAATATCCAGCGGGACTTCCCGCACGCAACGGAAGGAGTTGGTGATGCGGGATTTGATTCCGTGGATGATATGGCAGTCGATGTCCAGGGTTCTTCCGGTCAATCCGGCGGGATTGGTCAGGTTCTCCTGCCCGTCCACGGAAAAGAGGCCGGGCACGCGGTGCAGGACGAATTGTTCCGGCCCCAGGGCGACGTCCCGGGCAATTTCCGTCACCTCGTCCATATGCTCCTGGGAAATAATGCTTTCGTCCGGGGGGAGACGGAACGTCCCCCGGTTGTTCTGCCCCACAATATGGGCTCCCGTCACGGACAGATAGACGGTCATGATATCCACGTCCGCATGGTCCTGGGCCATGTTCCACGCATCGTAAATGCACTGGATGACCTTGGAAGTGTCTTCAATCTCTCCGCGAACCACGCCCTCGCTAGGAACCTCACCCACGCCGATGATTGTCACGGTCGCATCCGGCTTCACTTCGCCTACGACCATGCAAGTCTTGCTTGTCCCTATCTCAAGCCCTACGTGAATTTTTGTCTTAGCCATGAAAGTCAAGGAACTATCAACGTCCTCGCACCTGATAGCACATTACTTTCCATTATTCAAGAAAGCCTGCGCGAACATACGCAGAAAACGCGGTTTTCCTCCGCGCAGCCCCGGTTCAAAGGCCTGTAGGATGACGCACCAGGAAGTTTCTGGTATTGCGGCCGTCCACTCTCCAGCGGGGACCTTCGCCGCGGCGCCCGCTTCCCGTGGTGTCAAACCGGAGGCCGCATATGACCATGAAGACGTGTCCGTTTTTGGCATAAATGGAAATCCAGTCCCCGGCGCCTTTCTGCCCGTAATTCAGGAAAAGCCTGGAATGGCGGGTGGACGTGAGCAAACCGGCTTCCTTCAGCACAAAGGAAGTGCTGCCGGAGCAGTCATAATGGGTATCATAATGCCTCCTGTGCCCCCCGCCCAGCCGGTACGGCTTGTTGACCAGGGAATTGGCTGCCGCAATGGCGCGCTTGACCTGCGAAGGGGCATTTCTGGGGGGAATGGCTTTCCCGTTGTGCAGCACGGCCGTCTTGCCGGGCACAAAGTGGTAGGCGGGAAGACGCGCAGGAGCTTCAACTTGTTGCTGGGAGGAGCAGGAAGAGAGAAAAAGGCCGGCCAAAAGGGCACCAGCCACGGAGAGAGTTCTGTGGGCCGCAAATGTCATAAGAATTATAAACTGCGGAATTCCCGCCCCCTTGGCAAGAAAATAAATTTCCCCCGTCCACATTACATGGTAGACGGGGGAAAGAAATCAGCAATATTTAGCCCTAGGAACGGCTGTGGGCATCCTTCTCCACAGCCGCCCACAGGGAGTCCAGGGAGGCCTGGACCTCCGTCAATGCTTCCTTCACAGGGGCGCCCGGTTTCCCGTGCCCGAAGAGGTAGTATTTAATCTTGGGTTCCGTTCCGGAGGGGCGCACCGCAAAACTGCGGCCGTCCGCCAGATCCACAAAAATCATTTTTTCCGCCGGAATGGGATCGCCTTCCGCGTCCACCAGGTCGCCTCTGGAAAAATCCCGGATGCCGCTGACGGTTACGCCGTCCACTTCCGTGGGAGGATTGGAGGAATAGGAGGCGGCAAGGGCGGCGATTCTGGCCGCGCCGTCTGCGCCTTCCATCACCAGTGATTTGCCCATTTCCAAATACACACCGAACTGCTCAAAGAGTTCATGCAGCAGTTCTAGAAGGCTCTTGCCGATGCTTTCCGCATAAGCGGCCAATTCCGCAAAAATGATGGCGGCGGAGTTGGCATCCTTGTCCCGCACAAAGTCCTGGGCCAGGTAGCCGTAGCTTTCCTCCCCGCCGAAGACAAAGTAACGGCTGTATTCCAGGCGCAGGGCACGCGTTTGTTCCTCGCTCATCCTGCGGTAGCCTTCCCGTTTTTCCGCCGGAATGGCCTGTTCATATTTACCGAGTTTCTGGGCAATATACTTGAACCCCGTGAGCACGTTGACCACTTCATAACCGAAGTGGTGGCCGATGGCATCCTGAAGCCCCGTCGTTACAAAGGTTTTCACCATCACGGCACGGGAACGGTTGGAGTCATTGATGATGCCAAGCTCGGACATGCTCATGCAGCGGTACCACGCCAGCAGGGAGCCGATCTGGTTGCCGGTCAGCAGGTGCATTTTGCCGTCTTCCCCGCGCACAGCCACCCCCATGCGGTCCGCATCCGGGTCCGTGGCGATGACGATATCCGCCCCAGAAGCATCCGCCTGTTCAATGGCCAGGGCCAGAGCCGGCGGATTTTCCGGGTTGGGGGAAGCAACCGTCGGGAAACGGCCGTCCTGCACATCCTGCGCCGCCACGGTCTGCACGTTGCAGCCCAGTTCCTTCAACAGGGGCACGATGATATGGCCGCCCGTCCCGTGGAGGTTGGAATAAACGATCTTAGCCCCTCCCCTGTTGAAAAGCTCCGGACGGAGCAGCACGGTTTTCAAGCGGTCCATGTAAATACGGTCAAAGGAGGAATCCAGCACATGAAGGGTTCCGCGCCGGTCTTCCGGCAACGGCTCATATTCTTCGGAAGTCAGGGAATTGACCTCCTTGATCACGGCCTTGTCATGCGGAGGCACAAGCTGGGCCCCATCATTGAAGTAAGCCTTGAAACCATTGTCATGGGAGGGGTTGTGGCTGGCGGTCAGCACCACACCGGAATCCGCATGGAGTTCCCGGATAGCAAAGGAGATTTCAGGCGTGGCGCGAGGACTGTCGAACAAGTAAATGTCACAACCCAGGTCCGTGCCGATTTTTGCGCAGAATTCGGCAAAGTCACGGGAAAAGTGGCGCGTGTCGTGTCCGATGACCAGGCGGGGCTTCCTGCCCGGGTCCGTAGCCTCCACATGCTTTTTCACATAAATAATCAGGCCCCGCATTGCCCGCCCAACATTGAAGTAGTTCATGCAGGCGGTTCCCACGCAGGGATATTCCGGGCGGCCGTTCACGCCGCCGTTCCCCTGCTCGGCCCGGGTTACGATGCTGCCGATGGTGCGTCCCCGCAGCCCCCCCGTTCCGAAGGCCAGCGTTTTGTAAAAACGGTCGTTCAACTCCTGCCATTCCCCGGCGGCAGCCAGTTCCTCTACGGCCTCGCGTGCTATGGGGGATTTGGTTCCATCCAGCAGAAGGCGGATATTGGTCCGGGAAGACTCCAAAAGCTTCCCTTCTTTAACGGCATTCATCAGGGATTCGTCCAGTATATTCATGCTGGCGGAATAGTACATCATGCCCCTTGTTTGACAATAGCTCTTTCCATCATGGCCTGAGAAAAAAACGCCATATTCATCCCGGTGCGGGCGCAGGGAACGCTTTCCTTCCCGTCCCCTTTTTATTTAAAGGGACTGGCGGTCATACAGCTCCCGGTACAACGGGCATGAGGCATAAAGCTCTGCATGGGAACCGTCCCCGACGATGCGCCCGCTCTCAAACACCAGAATGCGCGTCGCCACGCGGATGGAACTGAAACGGTGGGCCACAATAAGCGCCGTCCTTCCCTCCACCAGCCGGTCCAGTTCCTGCTGGATCAGGGCCTCGCTTTCCGCATCAAGGGAAGCGGTGGCTTCATCCAGAATCAGAATGGGAGCATCCTTCAGGAAAGCGCGGGCAATCGCTATACGCTGCCGCTGGCCTCCGGACAGTCCGCTTCCTCCGTCGCCAAGCATGGTCTGGTAGCCGTCCGGCATGCCCATGATAAAGCCGTCCGCAGCTGCGGCGCGCGCCGCAGCCACCACCTCATCATCCGCAGCGTCCTGCCGTCCCAGCCGGATGTTTTCCATAATGGTGCCGTGGAACAGGACGGGGTGCTGGGAAACGAAGGCGATATTTTTGCGCACATCGTGCAGGGAAGCGTCCCTCACATCCACGCCATCCACGCGCAGGGCTCCGGAACTAACGTCATAAAACCTGGGGAGAAGGCTGGCAAATGTTGTTTTTCCGGCTCCGCTGGGCCCTACCAGCCCCACTACTTCCCCGGGGCGGATATGCACGTTTATGCCGTCCAGCACCTTGCGGGCACCGTCATAGGAGAAGGAAACGCCGTCATAGTCCAGACGACCGGCGGCGCGTCCCATAGGAACGGGGTCAGCGGCCTCCGGCATGGTATCCGGTTCTTTCAGGAGGTAATTGATGCGTTCCAGGGAAGCGTAAGCGCTCTTCAAATTGGTAAGCGTCACACCCAGCCGTTTGAGAGGGTCGTAACACATGAACAGGGCGGCGGCCAAAGCAGTAAAATCAGCCTTGGTGATGCCCATTTCATGCCCCCTTACCAGCAAAACGCCCAGCCCCAGCGCCGTCACCATTTCCAGCACGGGAACCAGGAAGTGCCTGTACTTCACCGTTCTGAGATTGATTTTGAAAAAACGCTGAATCAGCCCCAGGAAAAGATTTACCTGCTGTTCCTGCATTCCGTAGGAACGGATTTCACGCTGGGAGGCCAAATTTTCCTGGGCTGCGGCCGTAATATTGCCCAGTTCGTCCTGAGCCTGTTTGGACTTCTTCATCACGCGGCGGCCGAAAAAGCGGATGGGCCATATGGCCAGCGCAATCAGCCCCAGATTCATCAGGACTACTGCCGTATTCGGATCAACAAATACCTTGTACACCAGGAAGGCAAGGGCGGTCAGCAGAGTCAGGGGCTGCTTGATGATGTCATTGGCCGCTACCACCAGCCCCCCCTGCACGTTGGCGGCGTCATTAATTAGGCGGCTGATCAGGTCCCCCCGTTTCTGGCGGTCAATGAAGGAAAGGGGCAGCGTCTGCAATTTGGAAAAGGCGTCCAGCCTGATTGCCTCCAGCACTTTCATGCTGACGATGCTGATCCAGTACACGTTGAAGAACGTGCCTATGCCACGGACGGCGAAGACCAGAGGCAGCAGAGAGCATACGGCCAGCAGCACCGCCAGGTGCATGTGTTCCGGGGCCACCAGCCGAGCGATCATGTCCTGAAGCTCCGGTGGAATCTGTGTATTGTCAAACACCACCGGAAATACTTTGGCAATCATCAGCGGAAAGCCGAAACCGCTGGCGGCGGCAGCCAGAGCCCCGGCAGCCAGAGCGGCGATGAACAGCTTCATCACGGGCTTCAAATACCGCGCATATTCAAAAAACTGGCTTAACATGGAGCGCCTCCTTCCTCCTCCGCAACCATCTGCTGCTTATTGTAAAGTTCCCGGTACAGGGGAGAAGAAGCCATCAGCTCCTCATGCGTGCCGTCCGCAACCACACGGCCTTTCTCCAGCACCAAAATGCGGTCCGCCATGCGGATGGTGCTGAAACGGTGGGCAATAATGAACGTCGTGCGGCCGGAAGCCAGTCTCTCAATCTCCTTCTGAATCTGCTCCTCGCTCTTCATATCCAAGGAAGCCGTGGCCTCATCCAGCACCAGGATGGGAGCGTTTTTCAAAAAGGCCCTCGCCAGGGAAACGCGCTGCCGCTGGCCGCCGGAAAGGCCCTCCCCCATTTCCCCGATCATGCGGGAATACCCTTCCCCCGTCTCATTGATAAAACTGTCCACGGCGGACATGCGGCCGGCTTCCTCCACCTCCGCGTCGGAAGCGTCCGGACGGCCGATGCGTATATTGTCCGCCACGGAGCCGCGGAACAGAACGGGATACTGGGAAACCAGCGCTATGTGGGAAAGAAGTTCCTTTTTACTCATCTCCCTCACGTCGATGCCGTCCACCTTCACGCTGCCGGAAAGGACATCATAAAAACGGCACAGCAAATTAATGAAAGTCGTCTTCCCCGCTCCGCTGGGACCGACCAGGGCCACCACCTGCCCGGACGGAATGCGGAGATCAATGTTCCTCATGACAGGAGAGTCCTGCTGGTAACCGAAGCAAACGTTGCGGAACTCCACTTCCCCCTTCCATGCCGCCGGGATCCTTGGGTTTTCCGGTTCGGGAACGTCATCAGGAGCATTCAGCACGTGGTTGATGCGTTCCAGACCGGCATTAAGGGTTTCCGCCTTGTTATGGACGGCCCCCAGCTTCTTGACCGGCTCATAGCAAAGATACATGGCCGTAGCCAGCGCGGCAAAATCCCCCATGTTCATGCCGTTCATGTTGCCAACCAGCAGGGTGGCGGCCATGGCTATGGCGCTGACGAACTCAATCAAAGGGGTCAGCAGGCTCTGCCACCTGACCGCGCCGATGGATGCCTGGATGTAACGGCGGATCCTGTCCCTGAACAGCCTTATCTGCTGCTGTTCCAGTTCAAAGGCCCGGATATCCCGCTGGGAAGAAAAATTCTCCTGGAGCGTGGAGGAAAGGTCCCCCTGGCTGGACTGCACTACCCGGGCGCGGGCAAGCATTTTCCTGCCGATGTACCGGATGGGAATCACGCAAACGCCTACCAGGAGCATATTCGCCAGCAGAATAAGAAACTGCTCGCTGACGGAGGCCCTGTAAATCAGAAACCCTAACGCGGCCAAAAGAGTCAGGGGCTGGATAATAAGGTCATTGGCAATCTGTACCAGGCCGCCCTGGAGAAACTGGGTATCCTGCATCAGGCGGCTCAGCAGGTCCCCCTTCTGCTGCCGGTCATGAAAAGCCAGGGACAAATCCTGATACTTTCTGTACACCTTGAGCCGCAATGTCTCCAGCACTCCCAGCCCTATTTTGGAAAGCAAATAGACATTGACAAAAGAAGACAGGCCGCGCACCACCACCACCAGCGGGAGCATAGCCGCCGTGGCCCACACCGTCACCAGGGGCAGGTTTTCCGGGCTCACATACCACAAGAGCACCCGTTCCGCCCACGGAGGCAGCGGTTTCCCGTCAAAAATCACGGGAAACATCTCCTGTAAAATCACCGGAATGCCAAAACCGCTGGAAACAGCGGCCACAATGCCTCCGACCACCGCCCACACCAGCGTGCCCTTATAAGGAAGAAGATACTCGCGCCACAATGCCCTGGCGCGCTGGATGGAGGATTCCTGCCCCTCTGTTGACTGTTTCCTCATAAGTATTCCGGATGGCCGCGCCCGGGAACGGACGGCGCGCAATCGGCAGGGATCATACCATGAAGACCGTGAATGACAAGCTCCTTGCGATAAAAACTGCATGGGTAAATGCCTGGCGGGCAGACCTCCGGGCATCCCGGCTTGCCCGCCACCCCGTAATGGTCTACCTTTCCGTCGCATGCTGAAGACAGGATTAATCCAACCGGAAACCATTCCCGGCCACTTTCCCCGAAATCTCAGAACCATCGTGGAACTGTACCGTTCCTGCCTGGACGCAGGCGCGGAGCTCGTCCTCTGTCCGCCGCTGGCCCTCAGCGGCGTGCATACCGGGGAACTGGCTCTGCGCAGCGGCTTCCGGACCCAGCACCGGGCGACCCTGGCCTATCTGGCACGGGAAATAGCGGACGTCCCCCTGCTGCTGGGAGCGACGGACGCGGAAGGCATAAGATTCCACCTGTTGAGGAACGGCCTTTCCTTTCCCCGGCAGGCCGTTATCCCTCCCTCCCCGCATGGAAAAGAACGCGTTCCCGTCTTTGGCATCTGGAGAACGGAGGATGAGGCAGTTTTTTCCATCGCCCCGTGGGGAGAAATTCCTCCTCCGCACGTTTCCGCCACATGCCTGCTGCTCCGCACGCCTGCCAATGCATGGCATGAGGGATTGCTGGAACAGGATGAGGAAGAAGCCCACCTGACGGCGCGGGAAACAGGCCTGCCCGTATTCACGGCCCGCCTGGCCGGGGGAGAAGGTCCGTTTCTGCTGCCCGGCGCATCTTCCGTATGGTCCGGCAATGAAAATCTTCTCAAACGGCTGCGTCTTTTTGAACGGGATTCCGCCGTCATCTCACCGGAAAACTCCACAGGAACGGACTCCCCCCTGCCCTCCCCGGAACAACAGCTCCGGCACGCCCTCCGCAAAGGAATGGAAGATTTCATTCTGAAAAGCGGGTACGGGGCCGTCTGCCTGAATTTGCTGGAAAATTCCGCATCCCTCCTGTTGGCGGAACTATTGAAGGAAACGTGCCCCTCTCTGGAAGTGACGGGGTTCCTCCCCTCCCTGCCCGGCATTCCGGAAGAAACACGGGAAAGAGTTCAAGCCTTCGCCGGCACGGCTGGCATTGGCGTACGGACGGTTTCTTTCCCTGCCAAAGCCGGAGGGCTGGACGAAAAAGCTGCAATCGCGTGGCTCATGCGCCAATGGGCGGAAGAAGAAGGCGCCCTGCTGCTCTCTTCTCTGACGGGGACGGATATCATGACGGCGCCCCGCTTTCTTCCAGCCGCTTTAGCCGCGGATTTCATGCCGCTGGGGGATTTATACGAAACGGAACTGGCAAACCTGTTCCCCGGTTTTATTTCTCCCGCTCCGGAGGCCGCCAGGCGCGACGGGTTTCTCATCCGCCTGCACCGGGAACATGAATCCGCCACGGAGCTGGCGAATCGGTTCCCGGAATCGGAAAGGGAAATCCGCCGTCTGCAGCGGCAGGCCCGCGCATCCGAATGGACACGCCTGAAACTGCCGCCGCGCCTCATGCTGCGCTCCATCCCCGGAACGCAGGAAACGCCTTATATCCATCGGCTGGCAGACTGACGCGGAACCTCCCTTTTCCTCCCGTGGCATACATTGCGCTTGCTCTTGAAGAGGAAGAAGTGATTAGTAGAAGCCATGCAATTCCATGTAGCAACCATGATCGGCCGCGCCGCTTTAAGGCTGGTTGACCGGTTGGGGTCAATCGGATTGCTGCTTTATCAAGTGGCTTCATGCATGTGGTCCGGCCAATTCCGGATGCGCGTGCTTGTGGAGCAAATTGCAAAAATCGGGGTACAATCCCAGCCGGTGGTCATCATCACCGGCGCGTTCACCGGCGCGGTGCTGGAAGCCCAGACACTATTCCAGCTTCAAACGGTGAGAATGGAAACCATGGGCGGGGCCGTCGTCGCCGTGGGCATGTTCCGGGAACTGGGGCCCGTCATCACCGGGCTGATGCTTGCGGGGCGCGTCGGATCCGCCATGGCCGCGGAAATCGGCACCATGAAAGTCACGGAACAGGTGGATGCCCTTAAATCCATGAATGTGGATCCGGTAGACTACCTGGTGAAGCCGCGGATACAGGCCATGCTTATTTCCATGCCCATCCTGATGATGGAAGCCGTTCTGGTGGGCATCGCCTCCGCGTACATCGTCAGCGTCACCGCCTTTAACGTAGACCAGGCCTACTGGATGCACTTCATGAGCAAATTCGTCACCATGGGGGACATCATCGTAGCCCTGGTCAAGGCTCTTGCTTTCGGCCTCATCATTTCCACCATCTCCTGCCGGGAGGGGCTGAACACCACGAACGGCGCCGTGGGCGTAGGCAAATCCACCATGCAGGCCATGGTGTACGCTTCCGTGGCCCTGCTCATCGCCAACTTTATCCTGACCATGATCCTCAACTCTTTCTTCCCCATGGGATTCATGAGATAGCCTTTCCCCTCCGCCATGCAGCCATTCATCCGCGTCCATCAGCTCAGACAAAGCTTCGGCACCCAGAAAGTGCTGAAAGGCGTAAGCTTTGACGTGGGCAAGGGAGAACTGATGGCTTTAATAGGAGGTTCCGGAGCCGGGAAAAGCGTCATTCTGAAACATCTGGACGGCCTGATGGACCCCTTGGACGGCTATGTGGAAATAGACGGCAGGCGCATCAGCGGCGCGCCGGAAAAAATCAGAAAGCAAATCCGCAGCAACATCGGATTCATGTTCCAGCAGGGAGCCTTATTCGATTCCTTGAGCGTGGGAGAAAACGTAGCCTTCCCCCTGCGGGAGGCGGGGATCAGGGATGAAAACGAACTGGACACCCGTATCTCAGCCGCTTTGGATTCCGTAGGACTGCTGGGACAACAGGAAAAAATGCCGGCCAGCCTCTCCGGCGGCATGATCAAGCGCGTCGCCGTAGCCAGGGCAATCATTACCACTCCGGAATGCCTGCTCTATGACGAGCCCACCGCGGGCCTGGACCCCATTGTTACGGACAGCATCAGCTTCCTCATCCGGCAAATCTGCAAGGACAAAGGCATCACCACCGTCATTGTCTCCCATGACATGCCCAGCGTGATCCGCATCGCGGACAAAATCGTCTATCTCAGAAACGGTGAGGTTTACTGGACGGGAACTCCGGAAGAACTGTTGCACTCCAAAGACGAAATCCTGCAAAAATTCCTGTACGGGGACTCCGGGGAAAACTGGGCAGCCCTGTCCGGCAAAAATGAAAACTTCCAACGGGTTCTGCTGGAACGGGCGGAACGGGAACGCAAACAATAACCATCTTGCCACAGAGCCATGAAACAGAAACTTAAACCGGAAACCTGGGTAGGCATCTTTCTGATCGCCGGGATCCTGATGATTATCGGAGTCATCCTGGGATTCGGCAACATCAAAACATCCAAGGAGCAAACCTACCCCATCAACATCATTTTCAAAGATGCGGCGGGGCTCATCAAGGATTCCCAGATACGCCTGGGCGGCGTCACGGTGGGGAAAGTCACCAAAGCCCCTGAACTCCTGCCTTCCGGCAATGAAGTCATGCTGGAAGCCAGCATTCAGAGCGACGTGAAAATCCAGCAAGGGTCCGTCTTCCGGGTGGACATGCAGAACATCCTGGGCGACAAATACATCGATATCGTCCCTCCGGCCCAGCCCACGCATGAATACATCCTTCCCCATGCCACCATCATCGGTCAGCCGGAAAGCGATTTCAGCAAAATCAAAAACAATGCCGTGGGCGCCACAGAGGAAATCCTGAAAATCCTCAAGCAAATCGAGAAAAATTCGGACAACATTGACGACGCCATCCTCAATATCGGAGAGGCGGCCAAAGGGCTGGCCCAGACGACCAGGCTGATCAACGAGGGCATCCTGAACCGGGAGAACATCCAGAACCTCGGCAGCGTACTGTCTCAAATAAACCGGGCGGGAGAACAGCTCCCCGGCCTCATGGAAGAAACGCGCTCCTCGGTCGCGGCCATGAAAGACACCGTCCGGGATGCCCGCAAACTCATTGCCGGAGCTGAAGAAAAGCTCAACACTCTGGACCCCGCCGTCAAGGCAATCCCCCCTACGCTGGCCGCTCTGAGGAAAGCATCTGAACAAATCTCCTCCTTCACGGCTGATGCACGTAAAAACCAGGGCTTTCTTGGGCTGCTTATGTACGATGCTCGCTTCCGGGCCAACGCTCAGGAATTCATCCGCAATCTGAGGGATTACGGCATTCTGAGGTACCGGAATCCTAACGAACCCCAAGTCAAACCGGACCCCCGCGGCGGTTTTTCAGGAAGCCGCCGCTGAGAGACCGCACAATTCCGGGTGAAGCCGCTTTACACACTTGCCAATAGTGGGAAGCGTGCTAAATAATTTCCAATGTGAGAGGCGAAAACATGTAAGTCGACATGTTTTTCAACAGGAACCAACCCTTAATACTCTAATCAACCATGCAAGACAACCAAACCGAACTTGAAACGACCGCAGCAGCAAACGACACTTCCTGCTCCTGCCCTTCCGCCCGCGAGCTTGCCGTCACCAGATTTCAACAGGCCAGGGCTTTCGCCGCAGCTAAAGTAAACCAAATCCGCAAGGCGGCTACGGAACAGGCCAGCACCATCTGCGACTACGCCCAGGAAAAGGGAGAAGTCATCCGTGACAAGGCCAAGAAGTTCCACGAAGCAGGCGAAGAATACGTGAAGGAAAAGCCCACCCAGTCCGTGCTGATTGCCATGGGCGTGGGCCTTCTCATCGGTCTGCTTATCCGCCGTCGTTAACCCTCTCCCTTCATCCCGCGCCATGCGTGCCATGCGCATGGCGTCCTCCTTCCCCTTTCAAACCGAACCATGGGACTGATCGACAAACTCAAGCGAACCTTCGTCACCCCCCTTGTGGAGGAAAAGGAAGAAGGGATGGCCATGGTCCGGTCCCTCCGGGAAACCGGGACGGCAGCCCTGGCCCATGCGGAAGCCCTGGCCGCACTGCTCAAACTGGAACTGAAGGAAGCGTCCAACAGGCTGGGCAAAAAAACGGCCTTTCTCCTGATGGGCGCCTTCATGGCCTTCTTCGGCTATCTATTTCTGTGGTGCCTTCTGACTGTGGTCATGGCGTATTTCTGGGGAATCATCGCTGGGCTTGCCGTAACCACGGGCTTTCATCTCCTGGCCGCCGCAGCCGGGTTCATCCTTTTCAGCCGAACCCATGTAACGCCTATCGCTCCGGCGACGGCGGAAGAACTTAAAACGGATTTATCATGTCTGCAAATGGCTCTCAAGAAAAGCTCGCACTCCTGATGCGGGTAGCTTCCTCCCGGCTGGACATCGTCAATGAGGCGGACAGCCTGGTGCAAAAGACACAGCAGCAGGCCGACCACTTCCGTAAATTGAAAAAAAAGGCCATGTCCCTGCCTGTAATCGGCTCTATTGTCGGAACGGTGGGAGGCGTTCTTCTCATCCGCATGTTTGCGGGGAAAAAAAGCAATCCGGCCCCTCCCCCGCAGCCGCGCACTTCCGGAAGCTGGGTTAATTCCTCCATCTTCCGTTTCATCGTTGAAATTCTGATTACCCTGGCTTTCCCTTCCCTGAAAAAAATGGGGATCGGCCTGGCCGGCAAAAAATTCCTCAACCTGTTCAAATAACAGGCTTTTCAGGCCAATACAGCGGTTTTCTGCCACTTCTTGAAAAAGGTTCGGGAACTCCGCTGCCGTAAAAAATGGGGATGTTCCCGCGCCGTTAAATGAGAACCGCCCCATATCCTTCCCGTCTTCCAGGGAACAATAAATCATGCCTGGCGTAAAAAGCCGGCCTGCCGCGCCAGCCCCCCCGTTCCATCATCCTCCCCTTCCCCGGAAACAAACGCCCGCAGCTCTTCCGCAAAACTTCCCCTCTCGAAATTCCTCACCCGTCGCGGAACAGGGCAAAGCCGCTTTCACGGAAATCGGTACCCAGACATTTTTTGCTGGTCAAAAAAGCTCTGTTTTCGTTAGTATGCAGCTTTATCAGTTTGCTGCATTCACAACATGAGCGAATACATTTTGCCCCACGTCGACGGATATCTCAAACTTGGACAGGATTATGACTGTTCCGATATCCACCTTGCCGTCAACAGCCGTCCCACCTGGCGCCGGTTCGGCCAGCTTCTCCCCATCTGGGAGGAAGCGCCCAATCTCACGCCCCAGGACACGGAAGTGCTGGCGAGGGGCTTTCTGGAAGACCCGGAATGGAACCGCCTTCAGCAACGGGGGGACGTGGACTTCGCCTACGCCAATGACTTTGGCCGCTACCGCGCATCCGTGGTTAAACAGCGCCTGGGTTATGACATCTGCTTCCGCATCATCAACACGCGGGTGCGCACCATGGAGGAAATAGGCCTGCCGACAGAATACGTGGTGCCCCTCACCCGCTACACCAACGGTCTCATTTTAGTCACGGGCTCCGTAGGTTCCGGTAAATCCACCACGCTGGCCTCTATCGTGGATTTCATCAACCAGGACCGCCACGACCACATCATCACGCTTGAAGACCCCATCGAATACCTGATTCCCTCCAAAAACTGCCACGTCAACCAGCGTGAAGTGCATAAACATACGGAATCCTTCGCGCGGGCCCTCCGCGGAGCCCTCCGTGAAGACCCAGACGTCATCATGGTGGGCGAAATGCGCGACCTGGAAACCATCTCCCTGGCGCTCACGGCGGCAGAAACCGGCCACCTGGTGCTCGGCACCCTGCATACGGGTTCCGCAGCCCGTACGGTGGACCGCGTGCTGGACGTCTTCCCGGTGGAACAACGCGACCAGATACGCATCATGGTCAGCGAATCCCTGCGCGGCATCATCTCCCAGCAGCTGGTTCCCAGAGCAGACGGCAACGGCCGCGCTCTAGCCATTGAAATGCTGGTTAATACGCCGGCTATCGCCAACTGCATCCGCGAAGGCAAGACATTCATGATTCCCGGCTGCATCCAGACAGGGAAAGCCCAGGGCATGATCCTGATGGACGACTCGCTCCGGGCCCTGCACCAGGCGGGCCTCATCACCACGGAAGACTGCATCTTCCGCGCGGAAGACAAAACCATCATGAAATCCTTCCTGGGAATCAAGTAACCTTTAATTTTGAAACACTCCTGTTATGGCTGTCATCGACCAATACTTCAAATACCTCGTAGAAGCGGGCGGTTCCGACCTTCACCTCAGCGAAGGCCAGCCTCCTAAAGTCCGCGTTAACGGGACCATCTCCGCCATCTCCGACGAAAATCTGGAAGGCCAGGCGTTCAAAAACCTTCTCGCGGAAATATGCGATCCCCAGGCCTTCCAGAAATATCTGGAAGAAGGGGACCTGGACTTTGCCTATGAAATGGATGAAACATCCCGTTTCCGCTGCAACTACTTCAAGCAGCAGCACGGCCTGGGGGCTGTCTTCCGCCTCATTCCCACCAAAATAGCCACGCTGGAAGAACTGGGCGTGCCTACGGTCGTCAAGGAATTCGCCCATATGAGATCCGGCCTTGTACTGGTCACAGGGCCCACCGGCTCCGGTAAATCCACCACGCTGGCCGCTATCATCGACTACATCAACAGCAACCAGGCGCGGCACATCATCACCGTGGAGGAACCTATCGAATTCGTCCATCCCAATAAAAAATCCATCGTCACCCAGAGGGAAGTGCCTCTCCAGACGCCTTCCTTCGCAGACGGCCTCCGGGCTTCCCTTCGTGAAGACTCCGACATCGTGCTGGTGGGTGAAATGCGCGACCTGGAAACCATCTCCCTGGCGCTCACGGCGGCGGAAACCGGCCTTCTGGTATTCGGCACCCTGCACACGAACAACGCCAGCAAAACCATTGACCGTATCATTGACGTATTCCCGTCTGACCAGCAATCCCAGGTGCGCACCATGCTTGCGGGTTCCCTCCGCGGCGTCGTGGCGCAGCTCTTGATGAAGCGCAGCGACAAACCGGGGCGCGTAGCCGTCAATGAAATTCTCTTTGCCACGCCGGCCGTGGCGGCCATCATCCGTGAAGGCGCCACGCAGAAAATCCCTGACGTCATCGTCGGCGGCAAGGCCATGGGCATGCAATTCATGGATGACGCCATCTGGCAAAAACTCCAACAGGGCATCGTCTCCCCGGAAGAAGCCTACATGAAGTGCATTGAAAAGAAGAGATTCCGCAACTTCCTTCCTCCGGAATCCGCCCGCCTGGCAGACTCCGGCGGCGGAAACTAAGCTCTTTGCCCTCCTTCTCCTGCCATGTCTCCCACTGGAAAATTCACGCTCAGGCTTGTGATTTATGGAGCCTTCCTGCTCTATCTGGTGGGAGACTTGTTTGTCTGGCACGGCTTTCTGGCCGGCAGGATGGACGCGTACCTGAAACCGTTGCCGGGGCCGCCAGGAGACAACTCCGCCCGGATAGCGGAAGTGTACGGGGAACCCGTCACCGCCAACCAGCTTTCCCGAAGAATGACGGAACTGAAAATGCTGCGCCAGCCTCCCGTGCTGGATATGGAAGGCGGGATCAAACTTACTCATGAACTGGACATCCCCGGCGACCTGGCTCCGCGGGCCAGGTATGACCTGATCGGCTCCTCCCTTCTGCGCCTGAAAACCAGCGTCAATGACCTCCAGCTCCCCAACCGCAACGCGGAAGCGGCACGCGCCGTGGAACAAATCCGGTCCCGTTTTGACGGAGACACGGAACAATACCTGAAAACCCTGCATGGTCAGAAATTGACGCAGGAACAATTTCAAAAAAAAATAGCGGCCAGGCTCAAACAGACGGAACAGCTCTACCGGGCAACCGCCCAGGCGGCGGAAGCGTCCGACGAAGACCTGAAAACCTACTACAACCTGATCCGGGACCAGCTCACCCCTCCTGATCTGCGGAAAACCAGGCACATCTTCCTGGCTACCCTGAACAGGGAAGAAGCCCAGGTCCGGCAAACCGCGGAAACGCTGCTGGAGCGGCTGAAGGCAGGGGAATCCTTCTCCCGGCTCGCCAGGGAATTCAGTGAAGACGAACGTTCCGCTCCCGCAGGCGGGGAACTCGGCTGGATAAGCCCCGCCCGCGCCAAGGAAACCCTGGGTCTGGCGTTGGCAGACGTCCCGGACAACAGGCCGGTACTTCTCAAAAGCCGGTGGGGGTGGCACCTTGTGGAAGCATCCCCTGTCAAAAAAGGGAAAACGCCATCCTATGAAGAAGCGCTTCCGGCCCTGAGGGATGCAGCCCGGAGCCTCAGAAAAGCTCAAGCCGTGGGATTGTACATGGACGGCCTTTTTGAAGAAGCCCATCTCAGAAACCGCATTAAAAACAAGCAGGGCCGCTGACGCCCCAATTCCGACAAACGGGCCTTCGCAAAAATCGGAAACATCAGGCCCGCCTGCCTTTACACCTGAACATTCCCGCAGGCCCTCCGGCTTCCAGACCGGGGCGTTCCTCCTTTCCCGTAAAAGCCGGGCGTTCCCGGAAATAGCCATCTTCCCTGCGGACGGCCTCCGGCAGTCATGAAACACCCGCACGAGATGAGGAAAGGCGGCATCTTTCTTCTTATTTGCCTTTCCTGACGAATTCCGCTTGCTTTCAAACGCGGGATGAACTAAATTCTTTCCGGTATTTCCCACGGGGTATTAGCTCAGTTGGTAGAGCGTCTCGTTCGCAATGAGAAGGTCAGGGGTTCGAATCCCCTATACTCCACCATCCATCTCTCTTTCCCGCTACTGTTTTTTATTCGGGAATAACGGGTAGCCTTATGAATGGTAACTTATTTAAAATTGTACTGGTTTCAGTCGTTGCCATTCTTTTAGCCATCATAGGCGGCATCATGTCGGCGGACGGCGATCCCTTTTCTATAGTTCTGGCTGTTTCCCCCTTTATCCTGGCCGCCTTATTCCTGATGAAGGGAAAAGTGTGGTATCTGTGGATACTGATACCCATTCTTTTCCTGCCCATCCCCTCCTTAAGGGATTACGCTCCCCTTCTTGCTTACGGAATAACCCTTCCTTGTTACTTGTGGAACGCCATGCTCAGGCGTTCAAGCTTGACGTGGAATTCCGCCCCCCTTCTGGACGCTGTCGTCCTCGTGCTGTTCATGCACGTGGGCTATATCTTCCTGAGCCACCCTTTCGCTCTGGGGCTGGATGTGCTGGAAGACTACTACGGAGGCAAGGGATACATCCTTTTCCTCCAGGCCCTCCTGGCCTATCTCTGCCTATCCTCGTTAAAAACGACCAGCCATGAACTGGGCAAGGTGCTTCAATGGGCTGTTTTCCTGACCATCGTTTTTACCCTTATCCTAACCGCCCGGAGCCTCCTTTCTCCGGATAGCGCCGGGGCGGATCTTGCCGCGAGCGCGGGCTCCGCAGGCACACTCTCCGAAAACTCCCGGAACTCTTCCTTCCTCAGAATTTCTATCTTGGTGATGCAGCTGCTCATCATCAACTACTCCGTGTGGCAGATGATCAAGCGTCCCTGGTGGGGGGCGTTGCTTCTTCTTGGAACCGTTGGCATCCTGTTAAGCGGATTTCGCTCCGCCATGGCCCAGGTTCTGTTCCTGTTCTTCACCATATCCCTAATTTACAGAAGATGGTTCTTCTGCCTTCTGGCTCCCGTCCTCGGAGTGCTATTGCTCTTGCTGCTCTCCTCTGCGGGCATGCTCCATTCTCTGCCTTTCGGCATCCAGAGAACCCTCTCAGCCGTTCCTTTCCTGGACGTCAGCGCGCAAGCCAAGGCAAACGCCGAAGACTCCATCAACTGGCGCTTTGAAATGTGGAGCTGGGCTCTGGATGACCGTGAACACTTTATTCAGGACAAAATATTCGGAGACGGCTTTTCACGGGACATCAGCATCGTGAAAGCCAATGTATATGAAGAAGCCTACAACCTGAGCAAAGACCAAAGCGCCTTTGCGTGGAACGGCCAATGGCACAGCGGCCCCATTTCCACGATTCAAACGCTCGGCTACATAGGGATTTCCCTGTACCTTATCCTGTCCGTCGTTGGCATGACCTACGCATGGATCGTCAGTAGAATTTACCGCAACCATCAATACAAGCTCGGCATCCTGTATGTTTCCGCCGTCTATTTCACCAAACCAATCTTCTTTTTCCTGATCTTTGGGGAAAGCATCACTATTGCGATGGACATCATATCCCTGGCCATCATCAAAGTACTCTACTCCTGCGCCAAGAGGGAGGGCCTCTATGTATCTCTCCATGTCCGCAAGGAATACATGCCGCTCATGATCCGGAAAACACAGGAAAAACGGCAAGCCGCCGCAACGGCATCCATTTCCGGCTGACTGTTCCTTCCCGGGAAAAACAGGCGTCCCCTATGCTTGAGGCTGGACTAATTCAAAGGGAGTTGCTACCTTTCGGGGCGGTTCCTTTTCCCATGGCAGCAGATACTTCCCATCATATCATCGGCCTCATTCCGTCACGCTGGGGTTCCTCCAGATTTCCGGGCAAGCCCCTTCATCCCATTGCAGGAAAACCTCTTGTCCAGCACGTATGGGAACGCGTCTGCCGCTGTTCCCGCCTGGACGCCATCGCCATCGCCACGGACGACCAGCGCATTTTTGACGCCGCCGTATCCTTTGGAGCCAGGGCCATCATGACCTCTCCGGACCATCCCAGCGGCAGTGACCGCCTGGCGGAAGCGGTACGTTCCTTCCCTGCCGCCACCCATGTCGTCAATATTCAGGGGGACGAACCTCTCATTGACCCGGCCCTGATTGACAGGCTGGCGGAAGCCCTGGTCTCGGACGAAGCCCTCTCCATGGCCACCGTCGCCTGCCCCATCAGCACCCGGGAGGATCTGGACAACCCGAACATTGTCAAAGTGGCCCTTGCCCGTAACGGAGACGCCCTGTACTTCTCCCGTTCCGTCATTCCCTATGCCCGCCACCCCCGCGTAGCACCGCCTTTGCGCCATCTGGGTATTTACGCCTACCGCCGCGACTTCCTTGAAAACTATGTGCGCTGGGCCCCGACCCCCCTGGAACAGACGGAATCCCTGGAACAACTGCGCGCACTGGAAAACGGGGCCAGAATCCGGGTCATCCTGACGGACCACGTCAGTGTGGGCGTGGATACGCCGGAACAGGCGGAACAGGTGGAACAAATCTTATTAAACATACACTAGGAAGCAAATCATGAAATATATCTTCGTCACAGGCGGTGTTGTCTCCTCTCTCGGCAAAGGACTGGCGGCGGCATCCATCGGCACGCTGCTGGAACGCTGCGGACTCAAGGTAACCCTTCAAAAATTCGACCCCTACCTGAATGTGGACCCCGGAACGATGAGCCCGTTCCAGCACGGGGAAGTGTACGTCTTGAACGACGGAGCGGAAACGGACCTGGACCTGGGCCATTACGAACGCTTCGTCCATTGCAGCCTCTCCCGGCTCAACAACCTCACTTCCGGACAGGTCTTTGAAAGCGTGCTGCGGAAGGAACGCCGGGGAGACTATCTGGGAAAAACGGTTCAATACATTCCGCATGTCACGGATGAAATCAAAAACCGCCTCTATGAAGTCACGGAAAAATCAGACGTGGACATCATCATCACGGAAATCGGCGGCACGGTCGGGGACATGGAAGGCCACATTTTCCTGGAAGCCCTGCGCCAATTCGCCCTGGAAGTGGGCCGTGACAACGTCTGCTTCATTCACGTCACCCTGCTCCCCTATATCAAGGCCGCCGGGGAAATGAAAACGAAGCCCACCCAGCAATCCGTCGCCAAGCTCCGGGAAATCGGCATCCAGCCGGACGTGATCATCTGCCGGACGGAATACGACATGAGTGAAGACGAACGCCGCAAAATCGCCATGTTCTGCAACGTGGAAGCCAAAAACGTCATTGCCTTCCGCGACGTCAAAAACACCATCTACGAATGCCCCCTGGATCTCAGCCAGGATAAAATAGACCGCATTGTCACCAGGCGGCTGGGACTGGACGTTCCGGCGCCCAATCTGGCGGACTGGCAGCGTTACGTCGGCAGGGTCATCAGCCCCAGCCACTCCATCAGGATTGCCGTAGTAGGTAAATACATCGCTCTCCAGGACGCCTACAAATCCATCTATGAATCCTTCACTCACGCCGGTGCGGAAAATGACGCCCGCGTGGAAATTCTCCGGATAGACGCGGAAGAAATTGAGGAAAAGGGCGCGGAGGCCATGATTGGCTCCGTGGACGGCATTCTGGTGCCGGGCGGCTTTGGAGACCGCGGCATTGAAGGAAAAATCCAGACGGTGGAATACGCGCGCACCAAAGGAATCCCGTTCCTGGGCATTTGCCTGGGCATGCAGGTGGCCGTCATTGAGTACGCACGCCACATCTGCGGCATGGATGACGCCAACTCCACGGAATTCGACCAGAAAACCACCCACCCAGTCATCAGCCTCCAGGAAGAGCAAAAAGGCATCAAGGCCATGGGAGCCACCATGCGCCTGGGCGCCTACAAGGCCCTGATCCAGCCCGGAACGCTGGCGCACAAGCTTTACGGCAAGGACAGCGTTACGGAACGCCACCGCCACCGGTACGAATTCAACCCGGCCTACCGCGACGAACTGGAAAACGCGGGGCTGGTCATCAGCGCCGTCAATGACGAACACGGACTGGTGGAAGTCGTGGAACTTCCCGATCATCCCTTCTTCATCGCCTGCCAGTACCATCCGGAATTCCAGTCCGCTCCCAACCGGGCCCACCCGCTCTTTTCCGGCCTGGTTTCAGCCGCGCTGGAGCACAAGAGCCACTCCTGACCCTTTCATTAAGCCCGGGGCCTTTTCCGGAATTTCCATTCCGGAAAAGGCCTTCTCTTTCCAGGCATGCTCACGGACCTTTTCGTCATCGTCATTTACTTTCTTGCTATCTTCTGCATTGGCATTTATGCGGGACGAAAGCAAAACTCCCTGACAGACTATGCCCTGGGCAACCGCTCCCTGCCCTGGTGGGCTATTCTGGCCTCTATCCTGGCGGCGGAAATCAGCGCGGCCACCTTCCTGGGGGCTCCCGGAGAAGGGTACCATACCCGCAACTTCACATACGCCCAGCTCTGCATCGGCACCATCCTGGGCCGCATCATCGTCGGCAGGCTCTTTCTCAAGCCCTATTATGACTACAAGGTTGTTTCCATCTATGAATACCTGGAAAAAAGATTCGGGCTCCTGACGCGGCGGACAGCCTCCATGGTCTTCCTGATCAGCCGGGTGCTCGCCAGCGGAACCAGGCTCTATTTCGCGGGCATTCTGCTGGTCATTGCCTACCAGTTCCTGACAGGCGTCACGGCAGACGCCGACCAGATTGTCCTGCTCTACATTGCCGCCCTGGTTGCCATCTCCGTCGCCACGACAATTTATACCGCGATCGGAGGATTGAAAGCCGTCGTCTGGACGGACGTCCTCCAGGCCGTCGTTCTGGGAGTCTCCATGCTTTCCGCCCTGTGGGTACTGTTCTCCCATATCCCCGGAGGGTGGAGCTCCATCTCCGCCGCCATGAATGGCGGAGACGATTGGAAATTCTTCTCCTGGGGAACCGGAGAAGGCCTGGACTTCCTCCAACAAGGCGCCCGCGTGCTGGGCCAGGAATACACGGTGTGGGCAGCCTTCCTGGGAGCAACCTTCATCACCATGGCTACGCATGGCACGGACCAGGACATGGTGCAGCGCATGCTGGCGGCAAAAAACAGCAAGGCGGGAACGCGTGCAGTCATCGTATCCGGCTTGCTGGACTTCCCCATCGTCATCATTTTCCTCTTCACGGGCATTCTTCTCTATGTCTTCTACCAATACAACCCGGCAAACCTCCCTGCAGACACGCCGCAGCTGCATGTGTTTCCCTACTTCATCATCCATGAACTTCCCAACGGCATCCGCGGGCTGCTGATCGCCGGACTTCTGGCGACGGCCATGGGCTCCCTCTCCACGGCTCTAAACTCCCTGGCGACTACCGCCACCAAGGACTGGTACCAGGGGATATTCAAACCGGAAGCCACGGAACGGCAGCTGCTCCGGTGCGTGCGCTGGGGAACGGCCGTTTTCTCCCTGCTGCTCATCCTCGTGGGTTCCATCACAGCGTGGTATGTGGTGCATCACCCGGAAGTCCGCATCATCCAAATAGCCCTGGGCATTTTCGGCTATACCTATGGCTCCCTGCTTGGCATTTTCCTGTTGGGAATGCTGACCCGCACCAGAGGCAGCGACTCAGGAAACATCATTGCCATGGCGGCAGGATTCTTTGTCATCGCTTTACTGACGGAACTCATCCCTCTGCCCTCCGGCTGGCAACAATATGTTCCGGAAATAGCATTTCCGTGGCGCGTTACCATCGGAACTCTGGTTACTTTTACCGTCGGCTTCTGTTTCAGAAAACGCCGCCTTCCCATGCGCTGAACGGTGAACGCCGTTACTTGGCCACGTTCATGTTCCGCATTCCCCTTCAAACCGCCCCGTCAATGGGAGTAAAGGTGCAAGCGAAAAATAGGGGATTAGGGGATAAAAATCCCTGAGGGGGGGCGCTCCCTGTCCAACGGGTGCGCCGGAACTCCTTCTCCCGGACACAAAAGCGCCGCTTTCCCGGAAAGGGAAAGCGGCGTGAAACGGAATGGAAACACGCTCCCTCCGCTACACTTCTTCGGGGGACAGCCAGCGGAAGGAAACGATGTTGAACTTTCTTCCAAACGCCTTGTTCACGGCGCTCAGCTCCGTATCCTCCAGCGCGCCGCTCTTCATGTTGACCTGCCGGGCCGGAGTTTCCGGGCTGTTCACCGGATCCACGTAGTTGATCCCCCTCTGCACCACCGCTTCGCACCACGCGCGCGACAGCACCACTCCTTCGCGGTTGGCCAGCTCTCCGTAGGCGCGCACCGTAAACGTATCGTCCCTCGTCGTCAGGATGTTGCCCAGCACCGCCAGCACGTCAGACTGGATCAGGTAGCCGGGGGCGGCCGTATACACCGATCCCCTGGCCGCGTCCTGGTTCGGGTAGCCTCCCTTGGGCGCGATCACCATGTCGGAAAGTTCGTCAAACGTGCTGTTGATGGAGCTCTCGTCAATAGCCGCCTGAAGCGCTCCCTTCACCCCCATTTCCCCGCTCTGCAGACGGCGGTTGACAAAGTCGGACATGTTCAGGAAGGGGCCTCTCTTTTTGACCTCCTTGACCATGTTCCGGGCCAGGGACCTGATTTGCGTGTCGCTCAGGGTTCTCAGGTCCGACCACGCCATAGCCTCCCCGTCCGGGATGCCCTGGGTCACCCCTATCGAACCATAGTCGTCCACGTGGGACTTGTCGCTGCTGGCCACTCCGAAACGGGAAAAGGACGTCTGGGAGTTGTTGAGCACGGAGGGCCTGCCGCTCCTGGAATAGAGAATCTTGCGCTTCTTGAGCCCCAGGAGGGTGGCTTCCCAGGCGCGCTGGGAGGTGGAGTTGACATTGAATCCTCCGGCGACGGTGAGGTACTTGGAGGCATGCTTGTAGCCTTCGTCCGTTCTGGCGAGTTCTTCGACGACAGCTTCCGAGGGTTTGCCCTGAAGGTCGGGCAGGAAGCGGCGGTTGGCGATACCTGAAGCCCTGTTGGATGAAGCGTCCGTAGAGAAAGCCTGCTGAAGCACGGTTTTAAGTTCTTCCCTGCCCGTGCCCCCAAGGCTGGAAGGACGGGCGGCCAGGGAGGAGGCGAACCAGGAGTCCCAGAGGGCGTCGTTGATCATCAGGCCGTGGTCCCAGAAGTCCCCCAGGGCGGCGTCCCCCATGATTTCGTTGTGGGAGAAAACCTTGTCGGCAGGAAGCATGGGATCGGCAAAAGCATTGCCGATGCCTACGCCCGGCACGCCGGACTGGTAGGCGAAGCGTTTGGCGATAGCCGCCCTGGAGTCGCTCCTGTACCATCCCGGGGTGAGGCGGCATCCGGCAAAGCCGGCCAGGCTGGAGGGCTGCTGGAAGGGAAGCTCCGCGGCGACAATCTTGTTGACCTGCTCGGCTCCCGGTCCTCCGAAGGGGGAGAGCCTGGTGCCGTCATTGGAGAGGATGTTGGAGATGGTAATCGGGAAGAAATCGCTGTTGGCGTTCTTGACTTCAAACTGGTACGGGCTGTAGAGGCGCCCCAGTTCGGAGGCGGTGGGCATCTGGCCTCCCCAGAAAAGGGGGCTGGAGTGCTGCCATGTCTTGGCCCGGTAGTCCTTGCCCTCCGGATAGGCTCCTATCACGGGGGGCTTGCCCCATTTTACCGATACTCCATAGTAGGCGACAAACGTGGAGTCCAAAGAGGGCGTGCCGTATTCGCCCGCTTTTTTAGCTTCTTCATTGGTCCCTTCGTTGCGTTCCCACATTTCTGCGGGAAGTTTAATTTGGTCAACAAGAGGTTTTTCCCAGGCACCCCAGTTCAGGTTGAGGATGGCCGGCATGGTCTTGGAGAAAATTTCTATTTCCGCCGTGCTTTTCCCATTCAGGGAAGAGGAACTGTTCATTTTGTTGGGATCCATCAGGCCGGCCCCCATCACGAAGGTTCCCAGGGCTTTGGAGGCACCCTTGTCTCCGTATTTGACGGCGAGAACATGATTGTCCGGCGTTCCGTTGAGCCGGCTGGTATCTTCCGCGAAGATAACGGAAGCCTTGGCAATACCTACCTGGTTATATCCGCTGCCGTCCAGAAGTTCATAACGGACGCCGTCCGTCAGGTTGACGGCATAAGAGCTGACTTGTCCCGGTTCCTGGGACCATCCCTCTTTGAGCGGGAATCTGTCCGGTTTGAAATCACCGCTGTTGGGCTTTGTTTTGTAAACTTCGTTACCGTTGGCGATATTGGAAACGACCGGCTGGGCAAAGACGACGATCTCGCCTGCTTTCAAGGTGGAAGGCTCTCCCTTGACGGTGGAACCGTCACTGTTAAGTTTGGCCGTAGTTCGGAAAGAGGCGCCGAAATCCGCCACCTGGCGGTTGTTATAAGAAGCGTTCAAATAGGGGATCAGGCAGGATTCTCCATAATTGGAGAGTGCATTCTCATCCAGTTGGTTCCATGCGAATTTAACATTATGGGAAAGCAGCAGGGGCATGAAGCGGTGCTCGCCGAAGTAGGCGCCCCAGACACCTCCGTTGGCCCCGGTCAGCTTCATATCCACATTATAAGGATTCCACCAGACGTACACGGGAATGACGGCAATACGGAAGTCGATCCATCCTCCACGGTTGGGATGCTTGTTGCCGTGTTTGACGTAGTTAAGTCCTACAAAGGAATAAAAGCGCAGCAGGATGGGATGGCGCATGTACGTGTAGCGGTTGTCCATCATTTCCATGCTGGCTGCGGAGTCCGCCGCAATCAGCGTGCTGGGAGCGCTTCCATTCCATTGAAGGGTGGCGGAGGCATCCTGGCCTTCCCTCTTCGTGGAGGAGTCCCACACGTTCGCCCACAGGTAAAGCTGGTTCCAGGAGCCGATCGGCCGGTTCTGGTCCGACGGGTTGCCGTCCTCGGACGTGTACGGACGCAACCCTATGTCCGCCGTATCGCCGCGAAATTCTTCAGGAAGTTTTTCCTGGGAAAACATAATGTTGAGGTCTTTCTTAAGACCGCCGAATTTAGAATCCGTCAGCAAACTGGAAGAAACCAGAGAGATGTCGTGGTAAACGTCACCCAAATCCTTCAAACGTCCGTTTTTATTGTTGGCCAGCAGAGAATCAAGAGAGATAAGCTTTCTGACTGCGGACATGTCGTTCCCGGATCCCGCCGTCATTACGGAAGAAAGATTGAGAGACTCCAGATCGGGGCCGGGCGTATCCCAGGTGGAACGCAGCACCGTCAGGGCATCCGTACTTTCTTCAGAAGCATGCGGCATCAAATTGATACGCGCTTTCTGGTTTTCCCCCGTGACCCACCAGGCTATATATTTACGGGAGTTAGCATTCTTTTTCAGTCCTGTGGTCTTGGAGGAATCATCTACGCGCACCATGCGGGCGTAGATTTCCTTTTTATCCTGATTCCTGGCTCCAACGGGTGTCCCTATCGTGCCTGGTCCCACCAGGGCGACGCTGCGGAAGTTCTGGTTAGAGAGAGAACGTTTGCCCAACAGGTTTTTACCCGCTTCCAGAGAACTGAGCCTGCGCAAATCAGGGTCGGAAATGAGCCAGCGGCGGAACATGGATTTTCTTCCTGTATCATAGGTGGCCGAGATGTTCATACCCTCGGAGTTATTCGTATTGAGCCAGGAATCCCAGCTGTTCCAGACACCCAGAATATAAGGGGCGTTTCCTTCGCTGGTTCCGGAAGAAAGAATGCCGGAATTGGCGGAAACACGCTGGTCAGGGCCCAATTCACACTGAAGCTGGCCCAAAGCTACTTCCAGGGAAAATCTGGCGAGGGATTTGGCCTCCGCCGCAAAAAGCTGTTTCTCGGAAATACGGACCTGTGAAGATGCAATGGTTAAAAGGCCGACGCTGAGGAGGGCTAAAAGCATCATCAGGGTAATGGTGGCAATCAGGGCAAACCCCTTGCTGACATGCCTGACAGGACGTTTAGTGAAAAGTTTGATCTTCATGAAAATGATGGGTTTCGGCAGGCCCCTTTCCGTTCGAGTGTGAGGAGGAAAAACCTCTGCTGTCACAAAGGTGCGAAGAGCCAATTCTTAACTCTAGCCAATACAAAAAAGAGGCAGTTCATGCAAGCTCAAAGTTGGTTCAACAGGGCACACGTTGAGCAGAGCAAACAGGCTCCGGGACAATTGGAAAGAAAAATAAGGCAAGAAAAATCGCATTTTTATCCAATTCCGTTCTTAAAGGTTTATATTATGATTTTTATAGAAAAATCGGATTCTAAATTCGGTGGATTAAAAAAAGACTTGAATATCATATTTTCTCAAGAAAAACTGCCTGCGGAATTTCGTGGCGATACGGCGGACATAGGGTTGCGTCCGTACACGTCCGAGGACGGCAACCCGTCGGACCGGAACCGGCCGATTGGCTCCTGGAACCAGCTTTACCTCTGGGCGAACGTGTGGGACTCCTCCACGAAGAGGGAAGGCCAGGATGCCTCCGCCACCCTTCAATGGAACGGAAGCGCTCCCAGCACGCTGATTGCGGCGGATGCCGCGGCGAACCTGGAGATGATGGACAACCGCTACACGTACATGCGCCATCCCATCCTGCTGCGTTATTACAATTTTATCGGTTCTTATTTAACAAAGAATGATCGGCAATCTCAAAACGGAGGCTGGATTAACACAGCTGTATCAGTCGTTCTTGTTTATGTCTGGTGGAATCCGTATAATGTGGACATGAAATTTCAGGGAGCCGGAGGAAATCCATGGGGCTCTTACTTTGGCGAACATCGTTTTAAACCCATGCTTTTTGCGGATAACATCGCTTCCTCTGGACCTCTTTCCAGCTTAAAAATGTATACGGAGCATCCTTTGACGCCATATACGCAAGGGGCTTTCGGAGCTATACATGATCGGCAGGTAGCCGATTTCGGTGCCTCTTACAGGAAAACTTCCCGGCTGAACGACGGTACAGGAGAAGTAGTGAAAGGAGAGGTATCTGTTCTTAAAGCAGGAGAAATCGTTGTTTTTTCCCAACCATCTACAACTCGTACCAACAAAATTCAAAATGGTGCTGTATATAAATCTAAAAGTGGGGGATATGATATTCGAACGGATAATTTTGCTTTTAGAGAAGGGTGGCCGCAAGACATGAGTGAGATGGTTTCTTATGGCGTTAATATATACACAGGTCTTCAATATCAATACTTTGAAAGATCTGGCTATCCAACAGAGAATAAGTACACGTTTTGGGCCCGCTTTGCGGAAACGGATAATGACCTGATAGGGAATGGAGGAAATACCTCCTTGGGAACTCCTCCCAATCATGTTCTGTGCCATAAGTATACGGACGAATACAACTCCAATTCCAAAAAATTGGGAACTTTCATGATGGGTGCTGGCATTATGGACGCCAGCCAAATTGGAAAAGATACACGTTTAAGCGGGGCTTCCACACCGGACCCTGTCGTTTTTTCTAAGATGTCCCCAACGATGCTCAATCTGAACTGGGGATTTGAGGAACAAATCATGCAGGCGACAAAAATGCCAGTAGATGGATGGATTAACTTCAAAAGAAATGACGATGAAGATGGCACGAAACGCGGCAAATACGCAGAACCTGAGCCGAACGGTCCATTCCCCTATTTCATCGCCTACTATGGAGTATCGGTAAAATGGGGCAAGCCCCCCGTGATAGGAGCCTATCCGGAGGGCAAGGACTACCGGGCCAAGACATGGCAGCACTCCAGCCCCCTTTTCTGGGGAGGCCAGATGCCCACCGCCTCCGAACTGGGGCGCCTCTACAGCCCGTACCAGTTTGAAGTCAAGAACGCCAACAGCGATTTCTTCCCGATTACCATCTCCAACATCCTCTCCAATGACGGCACCAGGCTCTCCCCCTTCGGAGGACCGGGAGCCGAGCAGGTCAACAAGATTGTCGCCGCGGAGCTTCCCTTCCAGCAGCCCTCCAGCCTGGCCGGCTTTGCCGGATGCCGCCTCACCCCGGGATGGTACAGGAGCGACTCCAGGGCGGCTATCGCCAAACGCTTCGCCTACCAGTCCGGCGTGCCGGGCGTAGGCATCGGCAATGCTTTTGCCGATCCCATGCTTCCTGCCGACAAGGTTTTCTCCCACAACGAAATCATGGGGGACGCCGCCCTGGGGGACTTCTGGGACCACGGCCTGATGATCAACGACGCCCTCTGGGACTCCTGGTTCGCCTCCTCCCTGGCCGCCCGTCCTTCCAGCCTTGGGGGCACGGGCAGGGAAGAACTTAAAACCGTGCTTCAGCAGGCTTTCTCTACGGACGCTTCATCCAACAGGGCTTCAGGTATCGCCAACCGCCGCTTCCTGCCCGACCTTCAGGGCAAACCCTCGGAAGCTGTCGTCGAAGAACTCGCCAGAACGGACGAAGGCTACAAGCATGCCTCCAAGTACCTCACCGTCGCCGGAGGATTCAATGTCAACTCCACCTCCCAGCGCGCCTGGGAAGCCACCCTCCTGGGGCTCAAGAAGCGCAAGATTCTCTATTCCAGGAGCGGCAGGCCCTCCGTGCTCAACAACTCCCAGACGTCCTTTTCCCGTTTCGGAGTGGCCAGCAGCGACAAGTCCCACGTGGACGACTATGGTTCGATAGGGGTGACCCAGGGCATCCCGGACGGGGAGGCTATGGCGTGGTCGGACCTGAGAACCCTGAGCGACACGCAAATCAGGTCCCTGGCCCGGAACATGGTCAAGGAGGTCAAAAAGAGAGGCCCCTTCCTGAACATGTCCGACTTTGTCAACCGCCGTCTGCAGAGCGGGGAAATGGGGGTGAAGGGAGCGCTTCAGGCGGCTATTGACGAGAGCTCCATCAACAGCACGTTTGACGAACTTTCCGACATGGTGATCGCGCCCAAGGGAGGCTACCCGAACCAGGACGCGGCCAGGGGATCGGTGTATACGGCCGCCCCCGGCTACCTGATCCAGTCTGACGTGCTGGCGGTGCTGGGCAACATCCTGACGACGAGGGACGATACGTTTACGGTGCGCGCCTACGGAGAGCTGGCCAACCGCGAAGGAGTGGTGCTGTCGCGCGCGTGGTGCGAAGCGGTGGTGCAGAGGGGGATCAACTACGTGGATCCGGTGAACAGCCCGGAAACTCCGGCCCGGCAGGTCAACATGAAGAGCGGCGCGCTGGAGGATACGGAGCTGAGCGCCGTGAACAAGGCGTTTGGAAGAAAGTTCAACATCGTTTCCTTCCGCTGGCTGTCCCCCGAAGAAGTGTAGCGGAGGGAGCGTGTTTCCATTCCGTTTCACGCCGCTTTCCCTTTCCGGGAAAGCGGCGCTTTTGTGTCCGGGAGAAGGAGTTCCGGCGCACCCGTTGGACAGGGAGCGCCCCCCCTCAGGGATTTTTATCCCCTAATCCCCTATTTTTCGCTTGCACCTTTACTCCCATTCATGTACTTCACAAGGGATAAAAGCCCGTCAGGGGCCTGCCATCGCGCTCCGCTGCGCGGGCCGATACCCATGCCCTTCCGCATTTCCTCCGTGCGGTTATCCGCCAAAGGGTTATGCAATGTATCAAAACTTTTTCCATTCCCCAGAACCCAGAATATATGCGTATTTTACCCATCCTGGCCTTTACCTGCCTTTTCGGAAGCATTTCCTTCGCCCAAGGACAGAGCCGCACCCCCTCCAGGCCGGAACCGGCCCAACCGGCAGCAAATGCTCCGGCGGCCGATACCGTGGACAAGTCCCTGCCGCCAGCCGCCGTCAACGGCATCCGGTTCGTTCTGAAGGAGCCCATGTCTCTTCCTTCTCCCATTTACATGCCTATTGATTCCAAAAGGCTGGAAAAAGTGGAAATCCGCGCCGCCCTTCCGGGGCGCCGCAGCGTTTACCCCAAGAGCAAGATCATTACCCTGTTCGGGGGCCTGGACCAGAAAGGCATGCCGACCAATAAACTCGTCAGCAAGCCCCTGCCGCCCGATCTTGGCTCCAAAACCCTGGCTCTGGTCGGCAAAAACAGCAAAGGGGAATTGACGCTGGATTTTATTAATGAGTCGGAACTGCCGCTGAACTGCGTTTACATCCAGAACCTGACCGGACGCACGTTCACTTTGGAACTTCCCAAACCTCCCTCGGGAGAAAAGAGCGCTATCGAACTGCCCAGCAAATCCACTTACATTTTCGGCAAGAACTCTACAGACAGCAACATTTCCCGCACCACCCCCGCCAATTTGACGTACATGACCCGTTTAAAGAACGGCAAAGTCGTCAAGGTGAAGGACAGAGGCATGATGCTGACGACCTACCCGGGACGAAAAGTGGTTATGATTATTTCCCCGGATTCCACCGGCCGCACCGTCGTTTTGACGGAACTGATGATTTTCAAGGAAAGGCCGGGAGCGGCTTCCTCTGACAAGTAAATTTTCTTCCGATATCCGGGAAGCCGGAAGCTGCTCTGGAGTATATCCGGATTATTCAGAAAAACGGGCATTGCTGCCCGTTTTTTTATTTTTCTTCTCCTGACGCCATGGATTGAGGGAATAAACCACTCCGGACATTTTTCCGGCACGGGAACAATGTCCACCCTGTTTCCCGCGGCTCCAATGCGGCAGGCGGAGAAAGAGGTCTCGAAGCCATGCGGTTTGCCGAAAAAGTTCACCTGCCGCCTTCCGGCATCCGGGCAGGCTTAATAAACTTTGGGCACCAGTCCCTGACGGCTCATCTTATCCTCCACCTTTCGCCGGTTTTCCTCCAGACGCCGGGCGTCATAGGAATCATCCACAGGCTGGGCCTCCGGAATGTTTTCCCCGCGTCCCGCCCGGTATTGCTGAACCTGCGGGAGCACTCCTTCCACCAGTTCCCTGCATATCCAGGAAGGCAGGATATAGGTCTCCGTGCGTCCGGCCCGGCTCAGAGCCAGCCCAACAAATTTTCCGTTGACGTCAATGACCGGGCATCCAGCCCGTTCCGGGAACAAGGTCATGTCCGACTGGATAACCAAAGGGAATTCATCCCTTTTCAAGCTCATCCGGTTGCCCATGGAGTTCATCATGTCGAGACGCTTTTGCGGGAATTTCATCACCCTGGGCCTGGGCCCCGTGAGGAGTTTTCCTTCTATCACCTGGTTCCGCCGCCTTACTTCCACGGGAACCGTCTCACCGGGACGCACCCCGACCATGGCGGCACGGATGGAGTACATTCCATCCACCGGCTTTCCATTCAGCTTCATCAGCACATCGCCGGCCGTCAATCCGCTGCGGTGCGCGCCGCTTCCGGCTTCCACGCCGCCTATCATGACCCCCTCCCCGTCCCAGCGGGGATCGGAAACAATGCCGAGATAAGGCTGGTCATCCGCCCGCAGGCTGCGCTGAGCCACGGAAACGACTCCGAAGTCGCTCACATGCCCCGTGGGCGACACGGCTGCAATCACGTCCCCTTCCTTCGCCTGCACATAGGAATCCAGGTCAATGGGAGGAGCGGGAAGCCCCGGCACATCCATCATCAGCAGATCATGCTCCGGAAGGGCAAAGAGCACCTTGGCGTCATAAACCCGCCCGGAAGAATCCACCAGCATGACCGGACGCCGTTCCTTCTGGAGATCGCTCAGCTTGGTGAGGACTTTTCCCTTCCCCACCACTGTTCCCAAAGCCACCTGGGTATTGCCGGCAACCAGAGCCACTACGGACTGCCCCACCGGAACAGCCACCTTGTCCCATTCCCGGAAAATCTCCTTGGCCTGGCTGTCCAGCACGCTTTTGTCCTCCGGGGCAATCATCTGCTCCGGAGGCAGCGGGGAAGCTATTTCCTGGGCATACGCGGAAGCCGCCGCAAATGCCGCCCATAAATATTTCATCATCATCCTTGAGTTTGTTCCTGGTATTTCAGGCCGGACATCAGGTCGCCGCGCCTGCCGAATGTAAGATCCGCCTTGTAAGCCGTACCTTTCCTCACCACCACCAATGGAACGGTTTCCCCCGGACGGTGCCTGCCCAGTTCACGGAGCATATCCCGCACGGAACGCAGGCTCCGGCTGTCCATATGGGTAATGACGTCCCCCGGCTGGATGCCGGCGGTATCCGCCGGAGAATTCACCACAACGTCTTCCACCACCACGCCGTCCACGCCCAGCACATCCATCATGGCAAACCCGAGCACAGGAGATTCGGGGTCCGCCATGGGATGAAGGCCCAGCTGGCCCCAATGCCTGCCGGAGAGCAGTTTGTCCCAGTCTCTCCTGAAAACGGAAACAGGGACATGGTTGTTGATTTTCAACATAGGGCCAATGGAGGAATGGATGCCAACCAACTTGCCGGACAGGTCAAAGAGGGGACCGCCGGAATCTCCGCCAATCAGCGTACATTCCGAAATAAGGAAGCGCTGCTTTCCGTCCGTGCACAATCTTCCCATGCGGATGGGCGCGCGGCGTTCCGGGTCAAATCCCTTGGAATGGCCCAGAGCCACCACAAAATCCCCCACATGAAGACCGTCGGATTCCCCCAGTTCCACATGAGGATAGTTTCCTCCGTCCATGATTTGCACCATGGCGGCATCCCGCGTGAAGTTGGCGCCCAGCACCCGTCCCTTTACCACGCGCCCGTCGGCAAAGACCACACGCATGATTTCATCGCCGCCCACGACGTGGGCCGCCGTCAAAATCAGGCCCCGCGGAGAGACAATGACGCCGCTGCCCGTCTCTCCCCCGTCGGAAACCAGAGCCACCGTAGCAGCCGTAACCCTGGAGGCCACCTCATGCACATGTTTCTGCAAATCCGCCATGGAAGAAACATTCCCCGCAAGGCTCGCCCCCCCTGACAGCAGGGACACAAGCCCCCCAACGACCAAGCAACAAAAACTCGTCATTCTCAATAGTGATATCATGAAATTACCGATAATCGAATGATAGGACATACCCTACTGTATAAACGTTCAAAACGGCAGAATTTTGTCTATGGATTGCGACATGGCGCCCCATCTTTTGCTTAGACCATTTCGGTTATTTATGATAAACTCCGGCCCACGGATGGGTTTAAACGACAAATCAAGGAAACGAAATGCCGCCTCCTGGAGGGAACGCATCCAAGATGGAGAAATGTCTTCCCGTTCCCAGACGGAACGCAAAAAAAGGCCCCTTCCCCGTCCATCCTATCCCGGGTTGCCGCAGGAAGCTCCTGATCCGGCTGTCATTCCCGGCCCTACCCGCAGAACGGGGAATTCCCGCAACGGTCCTTCCCGGTATGACACGCCGCCCCAGCGACGGACCTCCCGCCCGTCTCCGAAGGAAGAGTACGAAGGCAATCCGGAAAGAGAAGAAGAAAACCGCCCCGCACGCGCAGGAACCATGCGCAGGCCGCGTTTCACGATTTTCGGAGCCCTGCTGTTTGTCCTGCTGATGCCGTTCCGCTTCATCGGCTCCCTGACGCGGAATATCCGCTGGTTCATTTGCTGGCCCCTGCGCATCCTTCTGGGATGCTGCTTCGTCGGCATTGTGATCGGGGCCATCCTGGTGTTTCTTTACGGCACCATTTCCAACCGCTATGACATTTCCGAGGTAAAGAGCAACATCCCGGAACGCACGGTGATTCTGGACCGTAAAAACCGGACCATCGGAACCCTGCACGGGGAAAACCGCAAGCGGGTATCCCTTCAGGAAGTTCCTCCCGTTTTTATTGATGCCCTGCTCCTGCGCGAAGACAACCGCTTTTACGACCATGGCGGCGTGGACTGGATAGGCGTGGGCCGCGCCTTTGCGCAGGTGCTCAAGCACAAGAGAGCTACGCAGGGGGCTTCCACCCTGACGATGCAGCTTGCTAAAATTACATATAACCACCGGGAGCGCAACCTCCATTCCAAACTGACGGAAGTGGCCCTGGCGAAACGCATTGAAGCCACTTATACGAAGGACGAAATTCTGGAGACTTACATCAACCGTATTTTCTGGGGGCACACCTTCCTGGGGATAGCGGCGGCGGCGCGGGGATATTACGACAAGGAACCCCGGGATTTATCCCTGGCGGAATGCGCCACCCTGGCCGGCATTATTTACGGCCCAAATGATTTTTCCCCCATCAAACATCCGGAGGAAGCAAAAAAAGTGCGCGATATTGTCCTTGGCCTGCTGAAAAACGAAGGCAAGATTACAGAAGTGCAGTATCAGGCGGCCCTGGCGGAGCCTATCGTCACCCGGACGCCCCAGTCCCGGTCTGAGGAAAATTACGCCATGGGTCTCGTGCGCCGGGAGCTGGACGCCATTCTGGAGGAAGAAGACATCCGCCTGGGAGGGCTGGTGGTGCATACCACATTGGACCTTGATCTGCAGAACGCCACGCTGGACGCCATCAATAAGCACATGGACGCGCTGGAAGCCCGCAAGGATTTCAAGAAGCACCTGGCTTCCCTGCAGGCCAAGAGGGAGAAGAGGGGAATTCTGAAAAACAAGCTGACCACCAAGGCGGAATATGAGGCCTCCCTGGCCGCCTGGAAGGCGCTCCCTGCGGAACAGAAGGAAAAGACGCAGCCGCCGATGCCCAATTACATCCAATCCGCCGCCGTAGTCATGGACAATGCCACGGGGGCTCTTCTGGCCGTCGTCGGCGGCCGGGACGCGGAAGAATCCAAGCTGAACCGCGCCATTCAGTCCAGGCGGCAGACCGGCTCCCTGTTCAAACCTTTCATTTACGCCACGTTTTTCCAGCAGGGCAATTCTGCGGATACCCGCATTTCGGATGACAGGATTGCCTACGGGGAAATACGCGGAGCCGCCAACTGGTCCCCCCGCAATTCGGACGGAACGTACCGAGGCATGAAGCCGGCCTCCTTCGGCCTCATCCTTTCCCGCAACACCATGTCCGTGCGCATCGGCAACCGCGCCGGTTTGTCCAATGTCATTCAATCCGCACAACTGGCGGGCTTCCACGGAAATATTTCCCGCACCCCGGCCCTTTATCTGGGCACGTGGGAGGCGTCCCCTCTGGACATTGCCAGCGCCTACAGCGTTTTTGCCAACGGGGGCGTACGCCCCACACCGTACATCATTGACCATATTACGGATTCCCAGGGCCAGCCGCGCTTCGCCATCACCAAGAGCAAGCGCACCGTCTATTCCCAGCGGGCGGCGAACATTACCTCTTCCATCCTCCAGCAGGTCTGCAAGCCCGGAGGCACGGCAGGCAAAATTACCACGCTGGGCTTTAAATCCCCCTGCGGCGGCAAAACTGGAACCACGAATAATTATACGAACGCATGGTTCGCCGGATATACGTCCAACCTCACCTGCAGTGTCTGGGTGGGGTTTGATTCCTCCACCAAAATTCTGGAAAAAGGCTACGGAGGCACCCTGGCCCTGCCCGTCTGGGTGGACATTATGCTTGCCGCCCAGAAGGAAGGCTATCCCGCCAACGCCATCCGTACGCGTCCCGGTTCGGAAGGGCAGGCCGTACTCGTCTGCCGGGAATCCAATCAACTGGCCCATTCCGGCTGCCAGTACGCCAAGACTGCGTATTTTGAAACCTCCGCAGGCTACCAGGCCCCCGCCAACATGTGTGAACAGCACATTCCCATGGCGGAACCGGATTCGGAAGAGAGCATTCCCTATGCGGAACCGCTGGACGGCAGCGACGATAACATCCCCCTGGCCGAACCCGTGGAAGAAACGGACGGCATCCCCTACGCCACCCCCATCTGATTGCTTATTTTTTCCTTATGATTTCCCCATTTTTTCCCTGGTTCCTGGCCTTTTTGCTGCTGATCGCCTGCGTGGCGCTGCTGGTTCTGCTGCTGCGGTACCGGGACCGCTCCGTCCGCCTCGAAGCGGAATCCCGCTCCTGGCAGGAACGGGCGGAGGACAGAACCCAGATGCTCCGCCGGGCGGAAGAGGTGCTGAAAGATTCCTTCGCCGGCATCAGCGCGGAAGTTCTCCGGAACTCGGAGAAACAGTTTCTGAACCTGGCGGAAACGCGGCTGCAAAGCCAGCGCCAGCAGGCCAACCATGACCTGGATTCCCGCAAACAGGCCATCGAGTCCATGCTGAAGCCGGTGAGCGAATCCCTGAAACTGGTCCAGTCCCGGCTGGGAGAGATGGAAAAGGAACGCGTGGGCGCCTATACGGACCTGAAAACGCAAATCCGCTATATCCTTTCCGGCAATGAAGCCCTGAAAAATGAAACCGCCAGGCTTTCCCAGGCTCTGCACCACAACAGCGTTCGCGGCCAATGGGGAGAGCTCCAACTGCGCCGCGTGGTGGAACTGGCGGGAATGGTGGAGTACTGCGATTTCACGACTCAGGAAACCCGTTCCAGGGAGGAGGAACGGGTGCGGCCGGACATGGTCATTCACCTTCCTGGAGGCCGTTCCATCATCATTGACGCGAAAGCGCCCATGGCATCCTACCTGAATGCCGGGCAGACGGAGAATCCGGAAGAGCGCAGCCAGTGGATGGCCCGCCATGCCGCGGACGTCAAGAGGCATTTGCAGCAATTAAGCGCCAAGAATTACTTTGCCCAGTTTTCCCAATGCCCCGAATTCGTCATTATGTTCCTGCCCGGGGAATCCTTTTTCCAGGCGGCTCTGGAAGCAGACCCCTCCCTGATTGAATTCGGCGCGGAGAATAAAGTCATTCTTTCCACCCCATCCACCCTGATTGCCCTGCTGAAAGCCGTCGCTTACGGATGGAAGCAGGAGCAGCTGGCGGATAATGCCAAGAAAATTTCGGAAGCGGGAGCGGACCTGTACAATACCTGCTCTATCCTTTCCGGTCATTTTTCCTCTCTGGGCAAGAGCCTGAACCAGGCGGTAGCCCAGTACAACAAAACCGTTTCCTCTTTTGAACACCGGTTCATCCCCCGGGCCCAGAAGTTGAAAAATCTGGGCGTCACCTCCACGAAGGAACTTCCCCGCTCCCTGGAAGACATTACCGAACAGGCCAAATCTGCGGATGTTCCGGATACTACGTCCTGAAAGAGCGGCAACAATACGTCTTTTTCCCTTCCTGCCCATTTCCGAGCCGGGAAAATACAGCTCTAAGGCATCCCCCGCAAACGCTTTCCTGCGCTTGAAAGCATCTTGGAGATGTAGAGGAATTCAGCCTCCGGCGGCCCTGCACTTCCCGGAATTTTCAACAATTCCTATGCGCGGCAACGGGAAACGGCAGGCTGCTTTCATCATGCAGCCGGGCAGATAATTCCCCCGCGGCGGCGCACACCGGCACCCGGAAGGCATCCAGAGCGGAACGGGAACTGTAGCCCCACCCCACCAGAATAAGCCGTGTTCCGGCGTTCACGGCCGTGTTGCCGTCATGGGCGGAGTCCCCCACCATGCACACCTGCGCCGGCTCCCTGCCCCAGGAACGGACGATGGACAGCAGGGAGTCAGGTTCCGGCTTGCGCGGAAAGCGTTCGGAAAAACCCATCACCTCCCGGAAGGGAATTTCAGGCAGGATATGGCGCACCAGCGGCACCGTAACCGCATGGGGCTTGTTGGATAAAACGCCCAGAGGATGGCCTTCTCCGGCCAGGGACAACAGCATTTCCCGGATGCCGGGGTACGGAACCGTGCCCTCCTGCCAGGTGTGCGGGTATTCACGCATGAATCCCCGGTGCACCGCTTCAAAAGCTTCCTCCGGCACGTTTCCGTTGAAATAGCCTTTCAGGGAAGCCAGGCAAAGCTCCCGGGCCCCCTTGCCCACCATTTCCCGGATGGCGTCCACGGAATGCTCCGGATACCCCAGCGCTTTCAAGGCCAGATTCAGGCCGCGGGCGATACCGGGAATAGAGTCCACCAGCGTCCCGTCCAGGTCAAAGACAAAACCCGTTTTCATGGATTCAATGGCAGAACCCCCGTTCGGAAGTTTCCACGAATTGGATGATTCTCCTCAGGCAGGCATTTTCCGCATACCCCGAATGCCGCAGTTCCTCCAGAGCTTCATGAACGGTCAGCTTTTTATTGACGAAAAGCTTTTTATAGCACATGCGCACGGCTTTCAGGTCTTCTTCGGAAAAGCCGCGCCTCTGCATGCCGATGGAGTTAACGGAACGCGTTACGGCCGGGTGGCCCTCCACGATCGTGTACGGCAGGACATCCTGGCTCACGCGGGCCATTGCTCCCACCATGGCATGTTCCCCGATGCTCACAAACTGGTGTACGGCGCAACATCCCGCCAGGATGGCGTAATCCCCCACCGTAACATGGCCGGCGGCGGTAGCGAACCCGGAGAAAATCACATGATTGCCGATCTGGCAATCGTGCCCGGCATGGCAGGAAACCAGGAAAAGGTTGTTATTCCCTATCCGCGTTGCGCCGCCAATGTCCGTAGCCCGGTTGATGGTGGCGTTTTCACGGAAGACGTTGTTATCCCCCACTTCCAGATAAGTCGGCTCTCCCTTGTATTTCAAATCCTGGCTTTTCAGGCCGATGACGGAAAACGGGAAAAATTCGTTGCCGCTGCCGAAGGAAGAAGGACCGTCTATAACCACATGGCTGTGCAGCACGCAGCCGGGCCCCAGTTTGACATGTTCCCCGACAACGCAAAAGGGACCGATTTTAACATCATCCGCGATCTCTGCAGCAGGATGCACTACTGCCGTTGGGTGTATTTCTGGCATGGCTGAAACAAGTATGAAACAAATTCAGGTGATTAAAAGCCTAAACCCCACAGGGCCGTCTTTTTATCACATTTGGCCGGAAGCGGCCGCATAAGCCTCCATCTCAGGCAGCCAGGGAATCAGCAGAACGTAGATGAGTTGAAGGCACAGAATGATCAGCACCAACGTCATCACCACTTTACGGAGCGTGAGGGAGCCGTTCAGCACATCCGTAGTCCCCAGCTTCCTGTCCCAGGGAGTAAAACCGAAACGGCGCACCCACCACCAGGAAAAAAACAGGGCCAGCAGCATCAGGGAAGGAGCAAAACACCCCAGACCAAGCACCATGACGAACAGGGAGCGCCGGAATGCCATGGGGAAAGAAAGGCGTCTTCCCCGGTAGTCCCTGACGGAAACGCCCAGCATGGCTTTTCCCGGGGTTGTACCCAATGTTCCAAGAAATGCAGTTTCCATGAGAACCATCGGGAGGCAGAGGTAAAGCAGAGCTTCATAGGAACCAGGAAGAAAACCGGGATTGAAAGCAGCGCCGGATACCCTAAGCACAGCCAGGTACAGCGTCATGTGCATCATTACATCAGCCATTCTGCCAAGAAAACGGACGTACGCATACGGAACCACCAGCGGCACTCCCTCCTCCGCGTCCTCCCCTCCTTCTTCCGGTAGGCAATCCGCATCTGCGGAAAGCGCCGGGACATCTTCTCCCCCTGCGTTTTCCAGCCTCTCTTCCCTCCCGTTCCGCCGTTCCCCTTCTTCTCTGCTGACGGCACCTGCTCCTTTCAATGCCGGAAGGTCACGGATGCATACCCACTCCGGGCATCCGGCATGCCACGCCCGGGCGTTTTCCGGAATGAGGCCCGCCTCCAGCATGGAAATGACCTCCACTTCCGGCAGAGGCCCCTTCCGTTCATGATCCTGAATCCAGTAAATATCCATAGGCGATTCCTGTTACATACTGCGCAGGGAGCGGGCCGGATCCTGGCGCGCCGTGATCAGGGCAGGAATGATGGATGCCACCACGACCATGACAAAAGCTTTCACCGCCTGCCATGCCAGCTCCGCCCAGTCAATCACCACCGGGATATTCGCCGTGCCGTGGAAGGCCACAGGGAACGGGTCAAAGCCTATTCCCGCAAGAAAACCCTGAATCTGCATGCGGTATTCCAGCACCAGAAGCCCCAGCCCTACACCCAGGAGAGCTCCCACAAAACCGATGATGACGCCCTGCCACAGAAAAACGCGCACCACCTGGAACGGCGTGGCTCCCAAAGCCTTCATCACGGCGATTTCCTTTTTCCTCTGGATGGAAACGGTAAACATCACCGCCATGATGCAAAAAGCGGAAATCAGGGAGATGAAGGACAGGACAAAGCTCATCATCATGCGCTCCTTCTGCATCAATTCAAACCACGCGTTAAAGCGGTCATGCCAGGTTTCCAGCGTCCATGCGGAAGAAGGCTTCTCCTTTTCCAGCGCCTGCATCACGGGGGCCAGCATCGTTTCCACATGGTAAGGGTCTTCCACGCGCAGGGCCACGGCCTGCACCACGTCATCCTCATAACCCAGCAATTCCTGACCGATGACCAGCGGAATGAAAAGATCCGGGCTGGGCGTATGCTGGGAGGCCCGGTAAATTCCGATCACCTCCAGGTCTTTCGGCATCACCAGCTCCTTGATTTTCCGGAAGCATTCCATATCCGCCTCATTTTTTGCCCCGGCCTTGAAACCATTCAAAACGGTTTCCCATTCCTTCCGCGTTCCTTGCGAAAAGGCCACCCTGCCGTTGTCCGCAATTTCCCCGTCGTTCAGCACGGCAAGCAGATCCATCAGGCTGGAACGTTCCGCATCCCTGCGCGGGACGGACAACAGCCCGTTAAGCAGGGAAAACGCCCTGTCCACGGAGGCTCTGCCGGCCATTTCACGCTCTCCCTCCGTCTTCAGGTTCTTTCCCCACTCCTTCAAATCCTTGAGTTCCCGCGCGTTTTTTTCCGCCAGCATCGGCAGCTCCGTCTGCTGGTAGGCGTGGGAAATTTCCTGAAAATTGCGGGTCGTATAAACCCGGACAACATCTCCCACATTCAATCCCATATTCTCCGCCACGATGGAGGAAACCACAGCCTTTTCCCCCATGTCCAGTTCCGCATCGCCAGCTACCACCAAACGCTTCAAATCCTCCAGCTGGACGGCGTTTTCCGTATCAATGGCCCGGAAAAAGCAGGGCCTCTGCCGCCCCTGCACATCCACCAGCGCATAGTCTTCTATCAGGGCGTAGGTGGACTGGACGCCGGGAACGTGCCTGAGTTTCTCCCCCAAATCCATCCAGTCCGGAATCACTTCCCTCACGCCGAAGTCGTTCCGGTAGCATACCTGGACGTGCGGGGAGTGCGCAAACAGATTCCCCTGAATTTCCTTCTGGAGGCCTCCCATGACGGACAGCACGACAATGAGCACCATTACCCCCAGAGATACGCCCAGCAGGCAAATCAGGGTAATAATGGAAAAGAAAGTCCGCAACGGGTTCAAATACCGCAGCGCCAGACTCAGGCTGATGTACCGGCTCAATAGGTTCTTCATGAATAACGCGGCACACCTTGCCTGATCCCGCTCCCCTTTCCAAGCCTAAAACATGTTCCGGGGAAGGAGATCGTTTACCTGCCTCCGGCACCCAGCCATTCCAGGCACAGCCAGCGGGCCATGGAGGCGATCACCCGGTCACAAGCGGATTCAGAATCCTTTGAATCCCTGATGAACACGGAAACCGCCATTTTCCGGCCGTTGGGAAGAACAATGATGCCGACGTCATTGCGGGCGGTAACGCCACCCCCCGGCAGCGTGAATCCGGATCCCGTCTTATGCGCCACCACGTAATCCCGCGGCAGCTGCCCTTTCAGACGTTCAGGACCTGTAGAACATCCCGCCATGATGTTCCAAAGAACACTCTGTGTGCCTTTTTTCAAAATTTTACCTTCGTCAAACGCCCGGAGAAGGGAGTTCATGGCCGACGGACGGCTTGAGTTGACATATTGCAAATCATGATTCCGGTGTATTTCTTCTTCCGTAAAACGGACATTAATGCCGTCAATTCCCCATTCCTTCAAATCCTTCTGCACGGCCTGCGGCCCTCCAATAAGGCCAAACAGGAGGTCGCAGGTATTGTTGTCGCTTTCCTGGACAGTCACTCTCAGCAGTTCCTTCAGAGAGAAATCGCCGCCTTGCGGGAAACGCTCCCGGAGGGGGCTCCAGGTATCTTTCACCAGTTGTTCCGGACGTATGCGGATGATCTGTTCCAGATTCATGGCGCCCTGGTCCACCCGCTTCAAGACGGACAAGGCCAGCACGAATTTGACGACGCTCTGCATGGGATAGGCCGCCTCATCCCCCACCATGACGGTTTCCCCCGTATCCAGCATTTCCGCAGCCATGCCCACACGGGCCTTCAGGGAATGGGCCAGTTCGCGTACGGCGGAGGAATCCGCTGCGGAAACGGCTGGCGGAGAAGACGTGGCCCCCTGAGAAAAAACGCAAAAGAACATGATGCAACCGCATAGATAAACACCGGTGGAAGTCATAGGCGCCATTCCATATCACGCCCGCAGCAACCGCAATAATAATTTCTCCCCCTTTTTTCCTTTCTTCGTCATCCGCAAGCCGGGCAGAAACCTTGCCACCTGTCCGGCTTCCTGATAAAAAGGAAAGCATGCCCCATCTCCTTTCGGAAACCCTCGTCTGGTGCGTCACCCTGCTGTTATTCGGCATGGGGCTGATCGGCACGCTCATCCCCATGCTTCCCGGCATTATGATCATTGCGGCCGGGTGCGTCTGGCAGGGAGTCATGGGCAGAGAAAACCTGGCGTGGTGGGAATGGACCGTCCTGGCGCTGCTGGTGGCGGGAGGCATGGTCATTGACAAGCTTTCCGGAGGCATGGGCGCCAAAAAATTCGGCAGCACTGCCGCCGGCATCTGGGGAGCCATCATCGGCGCCGTCGCGGGGAGCCTCCTTTTCTCCCCCATTACGGGCCTTTTGTTCATGCCGTTCCTGGGGGCGCTGCTGGCGGAACTTATCTTTGCCAGAAAGGATATCATGAACGCTTTCAGGGCCGGGTCCGGAGCGGCGCTTGGGATGCTTGCCGGTCTGCTGCTGGAATTTACCTGCGGCCTGCTCATCATCGCCTGGTTCTGTTCCTGCTGCTTCCTGTTTTGATGCCGCGCCCGGCGGAAGTTTCCGGCAGGAAAAGCAGACATTTTTCTGCTGCTGAAAAAAACACAGTTTCCCCAACGTATTCAGTTAAACGCCGCTCTACACGCGCATCCGTCCGAACGGACGGGTTCTCCCATTCCCTCATTTTACCAGCTTATGGATGCACCTGTTCCGATACCCGTCGCCATGATGGCAGGTTTCATGATTCTTGGAGCCATTCTTTCCATGGGGAAATGTTCCTTCCTCATCGCAGGCTACAGCATGATGAACAGAGGGCAGAAAAAGCAATATGATGAACAGGCGCTGTGCCGGTTCATGGGGAAGATCATGTACTGCCTCGCCTTCGCCATGCTGCTCTGGCTCTCCAGCATCATTCTCCAGAACTCCGTGCTCCTGTCCGCCTCCCTGTACTTTCTGGTGGGAAGCATTGCATTTGCGGTCATTTACGCTGGCGCCGGAAGCCGGTTCAAAAAATAGCATTTTTTACACCAGCTTTCTTTTACTTTTATTATACGCAACATTATCTTTTCCCATGAACGTCCCTGCAACCTTGAAACATTCCTTCCTGCTCTGCCTTTTCGCGGCAGCCGTCCCCCTCCGGGCCATTCCGGCCCCCATGCAGGTCACCGCCTCCACCCCCGCCCGGGTATGGACGGAAGGATACGGCACGGGCAACGGAAGGCTGGGCATCCTCTCTTTCGGCGTTTTTCCGAAGGAAACCGTCGTCCTCAATGAAGGAAGCATTTTCGCAAAAAAAAATTTCCAAATGAGGGAAGGCGCCGCGGAAGCTCTGGACAAGGCGCGCGAACTTTGCAAGGAAGGAAAATACCGCAGTGCAGACCAGCTATTCCGTAAGAATATCCTTCCCCCCGGCAACATTGCGGGGGATTACCAGCAGGGGGGGCGTCTTCAGGTGGAATTCCAGGGGCTTCCTTCACCTTCCTCCTATCAGCGTACACTGGATATGCGACGGGGCAAGGCAACCACCCGCGCCCAATTCGGCACGGGGGAATTGACCACAGAAATCCTGGCGGCCCCGTCCAGCGACTGCGCCGCCTACCACATTGCCTGCACGATGCCGTCCGGATGCCGCGTTTCCCTGAATCTGGAACACCCGGATCCGTCCGCCCGAATTGTTGCGCAGCCGAATGGCTGGGTGCTGGAAGGACAGGGAAGCAACGGAGGCACCCGGTTTGAAAACACGGTGGTCATCCTGGCGCCCGGAGCCTCCGTTACCCGCAAAGGCTCAACAATTATTCTTGATTCCGCCCGGGAAGTGATGGTGCTTTCATCCATTTCCACAGATTACAATATCCGGAAGCCGGAAGCTCCCCTGACGCATTCCCTGGCGGCTAAAAACGCGAGAATCCTGGCAAAAGCGCAAAAGGCGGGATGGAAAAAACTGGCGGCGGAAACGGAAGATTATTTTTCCCGGCTCATGACGCGCTGTCAGGTGGATCTGGGGGATTCCCCGGCCGGGGTTTCCGCCATGACCACCGCCCAAAGGCTGGAGCGGGTCAAACAGGGAAAGAAGGATCCGGATCTTCTGGAACAGCTTTTCCAGTTCGGCCGCTTCTGCACGATTGCCCACACCAGGCCGGGGCAGCTTCCCTGCGGTTTGCAGGGATTATGGAATCCGGAGCTGAGGGCGGCGTGGATGGGCTGCTATTTCCTGAATATCAACAGCCAGATGAACCAGTGGCCCTCCCATGTCACCGGGCTGGGAGAATTCCAGAGCTCCTATCTTGATTTTGTCCGCAGCCTGCGCCCCCACGGAGAGGAGTTTGCGCGCTTCATCAAACGGGACGGCTTCTGCTTCGGCCACTATACGGATTGCTGGAAGCGCACCTATTTTTCAGGCAACAATCCGGAATGGGGGGCCAGCCTCATGAACGGGGCATGGGCATGCGCCCACCTGGTGGACAGTTACCGCTTTACCGGAGACCGGGAAGACCTTAAAAAATCGCTGCCCATTCTGGAATCCAACGCGCGTTTCATCATGTCCTGGTTTGAAGACGACGGCGAGGGCCGTTACCTCTCCGGCCCGGGGGTGTCTCCGGAGACCGGATTTTACGCGCCGGACGGCACAGGCCCCAACGTGCTTTCCTATGTTTCCAACGGCACGTCCCATGACCAGCTTCTGGGCAGGGAGGCTCTCCGCAATTATATCTACGCCTGCGGAGAGCTGGGCATAAGGACTCCCACCCTGCTTAAAGCCGTCCAATTTCTCAGGAAAATCCCCCAGCCTGCCATCGGTCCCGACGGAAGGGTGCAGGAATGGAGGCAGCCTTTTGAAGAAATGCAGAAGGGCCACCGGCACATCAGCCACCTTTACGGCCTGTTCCCCGGCACGGAATGGGATGTTCTCAATACTCCGGAATATGCGGAGGCCGTACGCAAGTCAGCCGATTTCCGGCGCAAGTACGCGGATATGGGAAACAACGGCATCAGGACGGGATGGAGCACGGCGTGGCTCATCAATCTTTATGCCGCCCTTGGCGACGGGAACGCCGCAGAGGACAGGATGTACACCATGCTGAGGCACTACATCAACTCCAACCTTTTTGACCTCCATCCCCCGTTCCAGATAGAAGGGAATTTCGGCTTTTCCTCCGGCGTGGCCGAATGCCTGATCCAGAGCCGGATCATGCAGGACGGGTTCCAGGTTATCCTGCTGGCTCCGGCCCTGGCAGACGACTGGAAGAAGGGGTCTGCCACGGGATTGCGCACGCGGGGAGGGCTTAAGGTGGACTTGTCCTGGCAAGACGGCCGGGTGCAGGCCACGGCTACCGCTGCGCGTCCGGGCAAATTCCGGTTCATGCATCAGGGCCGGAAAAAGGATTTGTCCATGAATAAGGGAGAAACGGTGCGTCTTGATTTTCCTCCGCTTTCCCATTAGGACTCCCCTTTCTGCGTTACCTCTTCAATTTCCGCTATGCATTTCTTTTCCATGACTTTCAGGCTCGGTCTGCTCATAGTGGTGGTCTGCATCGCCCTCAGCTCCTGTTCCAAGCTTTTTTCTCCCCGCGGAAGCGGAGAAATACCGGACACGGACCAAACGGAAACGCTCCTGAAAGCGCATGTATTCCACCTGGCGGATACCATCGGTGAACGGAACGTCTACCACCCCGGCTCCATGGAGCGTTCCGCCCGCTACATTGAACAAAAACTGGAGGGAATGGGTTATGCCGTCACGCGCCAGGCTGTTCATATCCCGCCCTCCGGAGAATTCGGCGCCGTAAAGGACTGGACGGCATATAATCTCATCGCCATCAAGAAAGGAACCTCCCCCCAGCCCAAAATGCTCATCGTCGGCGCGCATTACGATACCAAAGTGGGCATGGACAACTGGCACGACCACGGTCCTGCCCGGCCTTCCCGCACCGGAACTCCGGGAGCCAATGACAACGCTTCCGGCGTAGCCGCCCTGCTGGAAACGGCACGTGCCCTTGCAGCAACTTCCACCCTCCACGATGTTTGTCTGGTGGCCTACGCCAATGAAGAGCCTCCTTTTTACCAGACGCCCGCCATGGGCAGCGCAGTACATGCCAAATCCGTCTCCCATCATTCGGGCAGGGAAAAAATCATTGGCATGATTGCCCTGGAAACGCTGGGCTGCTATTCCCCCCGGGTGAATAAAAAACGCCAATCCGCCGTGGTTGCCGGATTGGCCGGCCTGCCGGACCGCTGCGACTATGTTGCTTTCCTCTCCACCAACACGGGCCGCAAACTGGCACGCTCCTGCGCGGAGGAATTTTCCGCCTTGAGCCGCTTTCCGGTGCGTTCCGCCGTGTTCCCGTATTACACCCGCGGCGTTTCCTGGTCTGACGACTGGGGATACATGAAGGAAGGCATCCCCTCCTTTGCCGCCACGGATACGGCCTTCCTCCGCTGCGACGATTACCATGAAACCAGCGATACGGCTGAAAAGCTGGACTATCCCCAGTTTGCAGAAGTGGTGCAGGGCCTTTCCAAACTCGTCATTTCACTGGCCAACAAACCATGAATAAGAAACAGATATTACTCTCCGCTCTTTTTCTGACATTCGCCTGTGCTTCAGCCGGGGCCGCTCCGCCTGAGCCGTCCGCGCTGGGCGCCTTCTGCCCTCCCTCAGAGGGAAAAGCCTTTCACTCCGGATGGATTTCCCTTGGGCTCAAAGCGGCGGACCTGAAAGAAAAACAAGCCTCCGCCGGACACATTACGGACGTAGGGGAAGCGCAAATCCAGCTTCACTTTCCCGCCGGATGGAGTCCGCAGGACAGGCGCACGGCCCTGTGTATTTTTCCGGGAGGGGGGTATGCCATCCAGGCCATTGAAAAGGAAGGGAGCCGCATTGCCGGCTGGGCGGCGGAACACGGCATGGTGGGGGTTGTCGTGAAATACCGCGTTTCAGGAACCGGCAATGCCCTGGGGAAGTTTCCCGGTCCCCTGCTGGACGCCCGCCAGGCTCTGAGGACCGTGCGGAAAAACGCCTCTTCCCTGGGAATTGCCCCTCTTCGCATCGGCGTCATGGGCTTTTCCGCTGGCGGCCATCTGGCGGCCATGGCCTCTACGCTGTGGAACCGCACCCTGCCGGAAGAAGCGGAAAATCCGCTGAAAAACATTTCCGCCCGTCCGGATTTTTCCATGTTTATTTATCCCGTCATTACCATGGCGCCCCATACTACCCACGGGGGAACGCGCAACAAAATATTGGGGGCCCATCCATCCCCTGCCCTGGAGGAGATGTGTTCCGCGGAGAGACAGGTGACGGAGCAGGCGCCGCCAGTGTTCCTCGTCCACGCTCTGGATGACGGCGTTTCCTGCGCCAACAGCAGGCTGATGGAACAGGCTTGCCGGGAAAAGGGGGTTCCCGCCAGTCTGAACTTCTATCCCACGGGAGGCCACGGGTATGGCATGGAAAAACGCGGACATCCCACGGATCAATGGCCTGAAGCCGCCGAACAATGGCTCAGGGAACAGGGATTCCTTTCTTCCCCGTCTTCTGCCCGCGGGGGTGCGCCCTCCGGAGCGGGCACTTCCTCCGGAACACGGCGGGAATGAATGTGCTAGGACCGGCGTGTGCCGCACCGCAGGGTTGACCGGAACCGGGCCCGGTGGTACTTTTTCCGGGCATGAGTGCGCCCGAAAAGAATTACAAAGACACCATCCTGTTGCCGGAGACCGGTTTCCCCATGAGAGGGGATCTGACGAAAAACGAACCCGTACGCCTGAAAAAGTGGGAAGACACCGGGCTGTACGAACGAATTTTGTCCCGCAGGATGGCACAGGGCGCACCCCGGTTCCTCCTTCATGACGGCCCCCCCTTCGCCAACGGGGACGTGCACATGGGTACGGCCCTGAACAAAATTCTCAAGGACCTGGTCCTGAAATCAAAAACCATGGCCGGTTATGCCGCCCCCTATATTCCGGGCTGGGACTGCCACGGCCTCCCCATCGAATTCAAGGTGGTTCAGAAGGCCCGTGACCTGGATGCGGCGGAAATCCGCCGACGCTGTGCGGAATTCGCCAAAGGCTTCATTGACATTCAGCGCACTTCCTTCCGCCGCCTGGGCGTGTTCGGCGACTGGGAGAATCCCTACCTGACCATGAAGCCGGCGTATGAGGCCAACATCCTGCGCGTGTTCGCCAAACTGGTGGAAGACGGAGCGGTGTACCAGAGCCGCAAACCCGTCCAGTGGTCCTATGGGGCCTATACGGCGCTGGCGGAAGCGGAAATCGACTACAAGGAAAAGGTCAGTTCCTCCGTTTTCGTGCGCTTCCCGCTTCTGGACAATCCCCTGGGCCTGAAAGCATCCATGGTCATCTGGACCACTACTCCGTGGACGCTGCCCGCCAACGTAGGTCTCGCACTCCATCCGCGTTTCACCTACGTGGCAGGCAGGTTCATGAAAAACGGCCAGACGGAAAACCTCATCATCGTCAAGGATCTGCTGGATGCCTTCGCTCAGAAAACGGGCTGGGCTCTTGCGGAAACCATCCGTGAATTCAAGGGAGCCGAACTGGAAAACTGCGAGGCCCGCCATCCCTTCCTTTCCCGCACGTCAACAATCATCCTGGCGGACTTTGTCACCACGGACACCGGAACCGGCATCGTTCACATCGCGCCCGGCCATGGCGCAGACGACTATAACGTGGGGCGCCAATACGGGTTGCCTGTCCTCTCCCCTGTGGACGATGACGGCAAATACACGGAGGAAGTGGGCGTGCCCTCCCTGGTGGGCAAACATGTCTTTGACGCCAACAAGGACATCATTTCCATGCTGGAACAGGGCAACAACCTCTTCGGCGTGGAGGAATACCGCCACCAGTACCCGCACTGCTGGCGTTCCAAGACGCCTATTATCTTCCGGGCCGTGGAACAGTTCTTCATCTCCATGGACAAGCTCCGCCCGCAGGCCCTGGAAGAAATAGACAAGGTGCAGTGGCTGCCCGCCTGGGGCCGCAACCGCATTTACGGCACAGTGGAAGCCAGGCCGGACTGGTGCATCTCCCGCCAGCGCACCTGGGGCGTGCCGCTGCCCGTCTTCTTTGATGAAAACGGCAAAGCCGTTCTGGATGCCGCCCTGGTTCGCAAAATCGCGGATATGGTGGAGAAACACGGTTCCAACATCTGGTTCGAACTCTCCGATGAAGACCTGTGCGAACGCCTCGGCCTGCCGAAGACCTGGAAGAAGGGCAAAGATACGCTGGACGTCTGGATTGATTCCGGCAGTTCCCATATGGCCGTCATGGACAGCCGCCCCGGGCTGGATGCTCCTGCGGACCTGTACCTGGAAGCTACGGACCAGCACCGCGGCTGGTTCCAGAGTTCCCTGATGCTTTCCGTCGCCTGGCGCGGCAAGGCCCCGTACAAGGCCGTGATGACCCACGGCTTCGTCGTGGACAAGGACACCGGGAAAAAAACCTCCAAATCCGACGCCAAGAGCGGCAAACCCATTGACGCGGCCTATTACTACGACAAATACGGCGCAGACATCGTGCGCCTGTGGGCCGCCTCCGTGGACTGGCAAAATGAAGTCCCCTTCGGGGAAGATCTCTTCAAACAGGTGACGGAACCCTACCGCCGCCTCCGCAATACCCTGCGCATCCTGCTGGGCAATATCAGCGGATTTGACTTCGCCGCGCAGGCGGTCGCCCCGGAACACATGCCCATCCTGGACCGCTGGATTCTGGAACGCCTGAACGCCGTCATCAGGGAAACGCTGAAGGCCTATGAAGCTTATGACTTCCGCAAAGCGTTCAGTGCCATCAACCAGTTCTGCACCAGCGACCTGTCCGCTCTGTACGTGGACACCACCAAGGACCGCCTGTACTGCGACGCTTCCTCCTCCGTGCGCCGCCGCGCCACACAAACGGCCATGACCATTATCTTCAAGGCTCTGTGCCGCCTGCTGGCACCCATCCTGGCCTTCACGGCGGATGAAGCCTGGGAATACGCGGGATATGAAGGCAGCGTGCATGAGCAGGACTTTCCCGCCCCCATGCCGGAATACGATACCCAGGAGGCGTCCTCCGTCATTTCCCGTCTCCAGGAAATCAGGTCCGTCATCCAGGTAGCCATTGAAGAACAAGTGAAAGCCAAGGTATTTTCCAAAAATAATGAGGCCTCCATTACCCTGACCGTTCCCGTGAATGAGAGCGAAGACGTAGTGGCGCTGCTGGAAGACCGGGCGTTTGCCACGGAATTTTTCATCATTGCGGACCTTGACGTGAAAACCGGTCCGGAACTGGCGGCCTCCGCTGCAAAAACGGAACATGCTATGTGCCCGCGCTGCCGCCGGTATGAACCGGCGGCGGAAGGCGGAGACGTATGCGAACGCTGCGCGGAAGTTCTTTCCTGAACCGCATCCACTCCATTTCCCGACCTTTATATGGACCCCCAGACGGAAAAAGAACCCGCCCCCCATAAAAAGACGCCTCCCGGCTGGCTCTGGTGGAGCTTGCTGGGCGCAGGGCTGTATGTGCTGGACCAGGCCAGCAAGCTGTGGATTATCCGCCGCTTCCCGCTGAACTACCTGAACGAAAAGCACCATAAGTACGCCTACCTGCCGGACTTTCCGGGCTTCTCTCACATCAATGCGGACGGCACGCTGGACTTCGCCACGAACGGGCGCATGCCGGAAGCGGAACTGGCGAGGCTCCCGGATCTGAATCCAAACACGGTGGACGCCCTGAACAAACTGAGGGAACTGGAACCCATCTCCTTTCTGGACGGAACCATGAACATTACCCGCGTGCACAACACGGGCGTTGCCTTCGGGCTGGGGAACGGCACCGTCTGGTCCAGCTATCTTTTCCTGGCAGTTCCCGTGCTCGCCATCGTAGTCCTGGTCGTGCTTTACCGGAAAAACCTCTTCCACACGGCATGGCTCAAGCTGGCCTATGTCCTGCTGCTGGCGGGCGTAGCCGGCAACCTGACGGACCGACTGATTCAGGGGTTCCTGATTCCGTATGAGCAACAGCACGGTTTCTTCACCAAGCTGATGAACGGTTACGTGGTGGACTTCATTGACGTCACCATCCCCCTCTTCAACTACCGCTGGCCCGCCTTCAATGTGGCGGACTCCTGCATCTTCGTAGCGGCAATTATTTTCTTCGTCGCCAGCATTTTCTCCGCCAAGAATAAAGAAGAAAAAACATCCTGAAACGCCCATGACCACCCGGCTCGTTATTCTGTCAGGACTGCTTTTGTGCGGAAGCCTCGCCTCCGGCAAAAATGAATTGTCCTTCAAGGAAACGGTGGTTCCGGTAAAGGTGGCTCCGGACCAGGACAGCATCACGGCATCCTTCCCCTTCACCAACACTTCCGGCACCCCGGTCACCATCAGTAAAATCCACGTTTCCTGCGACTGCACCACCGCCGGAGCGAAGGATAACAAGCTGACGTATGCCCCGGGGGAATCCGGCGTCATCAGCGCCGTAATGAAAACCGGCAATTTTTCCGGCACCGTGGACAAGGACATGACGGTGCACGCCAACGGCTCCGCCTACAAGCTGGTAATACGCGCGCAGATCCCGGACATCATCCGGATGGAACCCCGCAAGCTGGAATGGGCCAGAGGAGAGGCCGCCGTGCCCAAAACCATAAAAATCACCATCTCCAAAGAATTGCCCGTCAACCTGACCACGGTGGATTTGACGGGAGACGCCTTTGACTACGAGCCTGTAACCGTGAAAAAAGGCAGGGAATATAAAATCATCGTCACTCCCAAATCCACGGCCAGGCCGGCCTTCAACACCATCTGGGTGCGCACGGATTCCGCCGTGCCGCGCTACAAGCGCCAAATGGGGTTCCTGGCCATCAAGGAAAACTGACATCCGCCCTTCTTTCCATGAGAGACGCCTTTTCCACCGCTCTGAAACTCCTGCTGGCCGTATTTCTGCTGGCCGTAGGCGTCGCCGCGCTGGATCTGCGCGTCATCCAGCCGTTCCGCACGCCGCCCTGCAACCCGGAAACTCTGGAGGAAGGGCACGTGTGCCTCTCCCAGGTATTAAAGGAATGGCCGGGAAAAATCCTCTGGATAGATGCCAGAAAGCAGGACGACTTTGAACGCCATACCGTCACCCAGGCTCCCGTTTACCCCATCCGCCCGGCGGATGCCAATTACCAGGAACTCCTGGCAAATGCCATGGAAGCCCTGATGACGGCAGAGGACAAGGGATTCTGCATCGTCATCTTTTGCAGCAGGGACTGTACCTCCAGCGCGGCCGTGGCCAATGAACTGAAAAAGCCGGAATACGGCATCCGCGCGCCCATTTTCATTCTGGAAGGAGGCTGGGATGAATTGCGCAAAGAACCCTCACTAGTACCATAGCCATGCTTGACACAGCAACCATTCGCCCGCAGTTCCCCATTCTGGAAACCAGCGTGCACGGCAAGCCTCTCATTTACCTGGATAATGCGGCCACTACGCAAAAACCCCTGGCCGTGCTGGACGCCATCCGTCACTACTACGATACGGAGAACGCCAATGTGCACCGCGGCTCCCACTACCTGAGCCAGCTCGCGACGGAAGCGCATGAAGAATCGCGGGAAACGGTGGCGCGGTTCATCAACGCGCCGGAAACGGCGGAAGTCCTGTTCACCTCCGGCTGTACGATGGGCATCAACCTGGCGGCGGATACCATCGCCGGGTCCGGCATGGTCAAACCGGGAGACGAAGTCATCGTAACCGCTTCCGAACACCATTCCAATATCGTTCCCTGGCAAATGCTGTGCGAACGCACGGGCGCCGTCCTGAAAGCGGTTCCCCTGACGCCGGGCCAGACCCTGGACATGGAAGCTTACCGGAACATGCTTTCCCCACGCACCCGCATCGTAGCTGTGGGACACGTTTCCAATACGCTGGGAACGGTTAATCCCGTGAGGGAAATGGCCGCGCTCGCCAAGGAGAACAGGCAGGAAACCATCGTGCTGATTGACGGAGCCCAGGCTGTTTCCCATATGAATGTGGACGTTCAGGAACTGGGCTGCGACCTGTATGCCTTTTCCGGCCACAAGCTGTACGCACCCACCGGCATCGGCGCGCTGTGGGGAAAAAGGGAGCTGCTGGAAAAACTGCCGCCGTGGATGGGCGGCGGGGAAATGATCAAGGAAGTCACCTTTGAAAAAACCGTTTACAACGACATCCCGTTCAAATATGAAGCGGGAACGCCCAACATTGGCGGGGCAGTGGGTCTGGCGGCCGCCATCCGCTACGTCTCCGGGCTGGGTCTGGACAACATTGCCGCCCATGAACAGAAACTGACGGATATGGCGGTGGAAGGCCTGAAGGCCATGCCGCGCCTGACCGTACTGGCCCCGGACGTGCCGCACAGCGCCGTGGTCTCCGTCCTGGCGGAGGGCGTCCACCACTATGACCTGGGTACGCTGCTGGACCAGATGGGAATTGCCGTAAGAACCGGGCACCATTGCTGCCAGCCGCTCATGTGCGCCCTGGGCACCACCGGGACTACCCGCGCCTCCTTTGCCCTGTACAATACGGAAGAGGAAGTGCAGACCTTCCTCAAATCCATGAACCGGGCGCTGGACATGCTCTCCTGAATCCCCCTCCCTTCCCTGCTTATTGACACCGCGCATCATGAAAACGGTTTCCATCCTTCTCTTAGGACTGGCCATAGGCCTTTCCGGCTGTTCCCTGCACCAGAGGCAGGAAATGGAGTACGCCCATTACCAGGATGACGCCGCGGCTATCCGGGAGGCCCATGCCGCATGGCTCATTCTTTCCTCTCCCAACCGCAGCCGGGAATGGCCGGAAGCCCGGAAAAAATACAATGCGTGCATTCGGGAGCTGGCCGCCCACCTGAAGGAAGCCAAACGCAAAGGCGGCATGGATGAGGAGAAGCGCCTGAACCTTCCCTTCGTCATTGAAAAATCTCCCTATGCCAAGGACAGCAATCCGTGGTTCTATGAGGCCATTTTCATGTCCGATGAAGTGGACCCCACCTTCCGCCTGCGCGAAAGCGTGACGGTGGAGGGAATGGGCATACCGCTGGCGGGGCTGGCGCCCCGGGGTGGTTCCCGCCCCCATGCCAACGTGCTTAAGGACAACGGCAACGTGCACACGCTGACGGCCATTCTGGACTTTGACCGCATGGTGGACGGGAAACCTACCCTCCGCACCATTCCGCGCCTGATGAATGAGCACATCTTCATTGGCAAAAACAAGGTGCGGCAGCCTCTGGCGGCCAATTTTTCCGTCCCCATCGCTCTCTTCTGGAAGCTTTCCGACGCAGACGGGACGGAACTGCTGGGGGCATTCCGCCCCAAAAAGGCCATCAATACGATGGGGCTTTATTTTTCCGAACCCTATGACCCCCGGAAAATACCCGTGGTTTTCACCCACGGCCTGATGTCCGGCCCCGCCACCTTCGCTAACCTGACCAACCGTCTCCTGGTGGATCCGGTCATCCGGGAAAACTACCAATTCTGGTTCTTCGGCTATCCGTCCGGGCTGGCCTGGACCATTCCGGCCAACAGGCAGCGGCAGGCCCTGAAGGAGCTCATGCAGGAATACAATCCCCGCGGGACATCCAGGGAAATGAACAACATCGTCATGGTGGGGCACTCCATGGGCGGCCTCATCACCCGCTTCAATAACTCCACCAAACCCTGGACGCTGATGAAGGGACTCTTTGAACTTCCTCCGGAAACCTTCGCAGGCATGACGATGGGAAACTGGAAAAACGGACTGGCGCCCCTGAACTGTGACGAACATACGCTTGAACAGCTCCAGAACAATTTCATCTTCTCCCCGTCCAGCAGCGTCACGCGCATCGTGTACATGGCCACCCCGCACCGCGGTTCCACTTTCGCGGACAACTGGATAGGAAGGCTGGGACAGCACCTGATCGACCTCCCCTCGGACATGCTGGAAGAAGTCACCCGCATCGCCACTCTCAGCCGCGGCATGTTCCTCCTTAACCCCCTGCAACTGAAGGACGAGCTGACCAGCATCCGCCAGCTTTCCCCAAATTCATCCCTGGTCAAATACATGTCGGAGCTGCGCGGCTCCCCAAATGTTCCCGTTCATTCCATCATTGGAGACAGGGGAAGGAACGACACGCCCAATTCCTCCGACGGAGTGGTCAAGTACCACTCATCCCACCTGGACTGGAGCGCCAGTGAAAAAATCGTTCCGTCCGGGCACAGCGTGCAGGATGACCCGGCCTCCGCCGTGGAACTGCGGCGCATCCTCCGGGAACACCTGGTCAAGGTCAAAGGCCGTAAAACGCTCGAAGAAGCGGACGCGAAAGCCGCCACTCCCGTATGGCAGGCCAATCCCTCGCCGCCTATCATCCTGAAAAGGCCCTGATGCAGGGGGCTGATTCTCCTTGTCCATGCAAGGCCTCCTTCCCTTCCCCCTTCTGTTGTTCCTGCTGATAGCGGCAGCCATCTTCCAGTCAAACGGCCACGCCTCGGCGGTCCAGTTTCCGGGTTCCTGTATGTCCCGGCTTTAACGGTTGGAGCGCCCTCGTTCATCCGCCCTCCCTGCCGCTTCCGGGAAAAACAGGAACCGGACAGGATTTACCTTGCCGCGGGCATCTGCGGCAACATGCCGCCGCGGTTGAAACAACTGAAATTTTCCCTGCTCATCACGGCCTGTTCCTGGCTGCCCTGATTTGACTGGCCGGTTACGGAAAAGGGCGCGCTCCATCCGGGCATGATTGGCGTCCGTTCAGCATCAGGGCGTCCCGCCATCAAAATATGCCCGCTTATAGAGTTGCTTCCGTAGGGGAATTTAAGGTAGAGTGGGAATCAATACTATTCCACCTCTCTTTTCCATGTCTGCTGACGATTCCTTTCCCATGGACCAGATTTCCTCTCTCTTTCCCCAATTGGAAAATATCCAGCTTGTCGGAGCCAAAGGCCCCCAGCAAATTTTCACAGCTACCCTGAAATCAAACTTTGCTCCCGTCATGCTGCGGGTGGTTCCCACGGAAGAAGCCGGCATCTTCGGCTGGGATCCTGAATTCATCGTCCGCTCCCTCACCATCGTGGAACAATCCCACCAGGGGCTCCTGCGCGTTTATGAAGTAGGACAGGCGGGGCCGTTTACTTTCATCATCTCCGAACACTCCCCCTATCCGCGTCTGGCGGATATGAATACCCTTCCCCAAATATCTCCCCAGGCGGCGCTCAACCTGGTGCGCAACATGGCGGAAGGACTGCTGACGCTGCACCGGAAAAACATTTTTCACGGGGGCATCACGCCCAAGCTGATTTGCCTGCGCGAAGACGGCGGAGACGCCCTACTGCTGCCCATCAACATTTATCCCGCCCAACCTCCGGTGGACATGGGGGACTTCGCCTCTCCGGAGTGGGTCACCGGAGCGGAAACCACGTTCACGCCGGGCATGGATATTTACGCACTGGGGCTGGCGCTTTACATTCTGCTTACCCGCAAAACGCCGATGGAAGCGGGCTTTGCCATGCCCTCCTCGCTAATCAAATGCAGCGATGCCGTGGATTCGGCAGTCTCCCGCGCCATCAATCCGGATACCCGGGAACGCTACCGGGACCTGGGGGATTTCATCACGGATCTGGATAAAGCCATTGCCAATCCTGCGGGAAGAAATGCGGCCTCCATCCCTCCGCCGCCATCTTCCCCGGCCATTCCGGCCCTGAACATGCAAAAAGGGCAGTCCAACATCTATTACTATCTCATTCCGGCCCTGATCGTGGGCATTGTGCTGACCTATACCTGCATCCTGTATAAGAAGGATGTCGTTAGAATGCGCAATGATTATAATGAACAGGTGCAAAAGGAAAATAACGCGAAAGCGGAAGCGGCAAGACAAGCGAACAGGGACGCGCGCCGCCACGCTCCGGCCACCGCGCAAAACGCCGCTGCGGCCATTCCTTCACCTGTTCCGGCCACGCGTCCGGCGCAGGCGCCGGCAGTTCCCAAAGTTTCCGCGGAACCCGGCAAAGTCAATTGGAGCCTTCAACCCGGCGTCAAAGTTCGCCAGAGTTCCAACCGCAACATGCTGGGAGCTTACGGTCCGGAAAAGGCCGTTGACGGCAATACCAGCTCCAGGATTTCCGACGTTTCAATCAGCGCCACAGGCGTGGTTGAAGGGAAAACCGCCTGGTTCGGAATTGACTTCGGCAGGGAGACCAACCGCACCATAGAAAAGGTGGTCATTTACACGCCGGCCAACCTGACGCTGCTGGGCACCATGGAAAAATTCAAGGTAATCCTCTACGGCAATGACAAAAACGTGCTGGCGGAAAAAACCTTCAGCACCACCCCTTCGGAAAAATCCACCAACGTCACTTCCTGGAAACTGGACGCTCCGGTCAAGGCGCGCGCCCTGCGCGTGGAATCCGCAAATCCCTCCCAGCCGCTGGCGTTGACGGAAGTGGAAGCGTACGGACCGGAAGATGTGGAGGACACACCGGCTCCCGCACCTGCGGAAAAATAATTATCCACGCAGCCCTGCTCCCGCTTACCCCCCTCCGGGGGCCATGCTGAAAACTACCGTTTCCTTTCCCCTTCCCGGAAAAGCAGGACGGGACAGGGCCTGATGGAATGTCACGCGCTAAGTTCCGGGATTGGCTTCGCGTATGAAATTTTCCGGTTTCTGCTCCATTCTCCGGGAGCCATACAGGCCCGGACGGAGGAAAATCCGAAAAACGTATTTGCTCCGGACCGGAAACAGCCGCCCTTCTCCACCTTCCCCACCCCGGAAAAACGCTCTCATCCCCATATGGCTCCCGGGGAAATATCCTGAAGCGGCTGTCGTCCCTGTGGACCCCTTTCCCATTCAGGAAAAACAATACTCCGGAAATAAAAAACCCGGCGCCGCCTCACGGCGGGCCGGGCCAGCAAAATAGCAATTCTTCCGATTACCGGTTGGCAGACTTCACAATCATGGCATCCTTGCCGATGCGGCCGCGCAGATAGTGAAGTTTGGCGCGGCGGACTTTGCCGCGGTTCACGACTTCAATCTTGGCAATGCGGGGAGTATGCAGGGGGAATACGCGTTCCACGCCTTCACCGTAGGAAATTTTGCGGACGGTGAAAGCTTCATTGATGCCGGAACCGCGCTTGGCGATCACAATGCCCTGGAAAATCTGGATACGTTCCTTGCCGCCTTCAATAACACGAGTGTGAACCTTGATGGTATCACCTACGTTAAATGGGGTCACATCCGCCTTGAGTTGCTCTTGCTCGATTTTGTTGATAATGTTCATCTTGTGTTATCTATTGCTGGTGAATATCAGTAAAAGGAAAACTTACGGGACGCAGAATATGAGCCATAATCCCCTAAAAGGCAATCCAAATTTGCCGTACGTCCTTAATTTATTTTTTCGGCGGGGCCGGCGGCGGAGCCACCCTGGCAAGCCATTCCTCCAGCGCAGGAAGAAAAGCGGAGGAAGACATAAGGCCCCGGATGCCGTCCGCCCGCTGCAGAAAGGCCTTGTTGCCGCCCCGGGCCCGTTCCGCCATCAGGATTTCCAGCAGCAGGCGCGCGCGGGTGCGGGCGTCAGCCGCGCCGTCCAGGGCGGAGGAAAGGAGCTCTCCGGCGCGGGCCGGTTCCTTCCGGTACAGGCATTCCAGCGCCAGAGCTTCATAAAGGCAGGAAAAAGGGGCCTTCCGGGCGGACTCCATCAAACGCGCGGAACGGTCCTTCCAGCGGTCTCCGAACTGCACGGAGGAAGCCCACCACCCCTGGAAATAACGGTCTTCTTCCGTCGGCTCCACGTCCATATCCTTCATGCGGGCGTATGGACGGTACAGGGGGTCTCCGAACACCACCCCCTGCCAGGACAGCACAGGCATGCTCATCCAGGAAGCCTCCGCCACGGTATAGCCGTCCAGGAGGCGGTCCACAAAAATATCAAAGCGGTGGCAGGCGCCCAGGAACGGCTCATACACATTTCCCACCGTCACAGCAGCCCCCTTCTCCAGCAGGGCGGAACTCCACCCCCTGCCGGGGACCTTGAAATCGGCGGCGCTGAAGGAATGCAGATGCATGGCTATCGCCCCGGGCCGGAAGCGGAACGAAGGATCCGAAAACGGTCCGTTCGCATTCCCCGCATACCACCCGCAGTAAAGAGCCGTATCCCGGCTCAAGGGGAATTTTTCCGGCAGGGTCTGGGGCCAGTCATCCAGATAAGCGGGAATGCCCGCCTCCCTCACGCGCTCGAACACGTCGTCCAGCCACCGGTCCCCCTGGGCATAGGGACCGCCCCGGTCCACAACGGCCCACCCCCATAAACCTTTTTTCTCCACGTTCGCCGGTTCCGCCACCAGCCGCATGCAGGTATCGGGCGTCAGTCCGTCCAGGCGGCATACCAGAAACGAAGGAAGGCCGGAACCCACAAAATCCTCCCTTTTATTAAAATAGGGATTCACCTGCCAGGACTTCCTTTCATAACCGCCCACCGCCAGCAGAGCCAGTTCGGAATCCACCGCAGCGCGATCCGTCTGCATCTGGTTGACGCCTTTCCCGGGAAGGGGAGCGGGCGTTTCATGCCGTATCTTCATCGGCAGGTCCGCCATCAGCACCAGCACAAAAATCTTCCGGTTCATCAAGGGGGAGGACGCAATGCCGGAAGGCACCCACCACCGTTGTTTCCGCGCCGCCTCCAGCAGCGGGACGCGGATCGTATCCTCATATTCCTTCCGCGAAATCTCGCTGGTCATGGGGCAATCCAGGGAAACGAGATTCCCTTCTGGAACGCCGCGCACCCGCGCATACTCCCTGGCCATGCGGCGGCTCAGCTCGGATTTTCCGTTATAAACCACGGCCACATGAGCGGGCAGCAGCTCATAGGCACACGCGGCGGAAAACCACGACGCCAGCAGAAATAACACGGCTAATCCACTCTTGAACATGCCCCTACCATACCTCATCTTCCGGGGCAATCAAGCAGGTGAAAGGGACGGCCACGAATTCGCCATTTTTCCGCTTTTCGCTTCACATCCTAAAAAAATACGCTTTAATAACTCTTGATGTATTGCAGGCCTCCCCCCTGCATTCCCTATAACCATCACACGCATATGATTCAGCAAATTGACCATATCGGCATCGCCGTCAAGAGCCTTGACGCCACCGTCCCCTATTACCGCGATGCGCTCGGGCTCGGAGAGCCCCACATTGAGGAAGTTCCCACTCAAAAGGTCCGCGTCGCCATGTTTGACGTTGCCGGAGTCCACATCGAACTGCTCGAACCCACCTCTCCGGAAAGCGCTATCGCCAAGGCTATTGAGAAAAAAGGGGAAGGCATCCACCACATCGCTTTCAAGACCAATGACATCGCCGGAAATATCTCCCAGGCCAAGGAAGCAGGCCTTACCGTGCTGAACGACCCGCCCGTCCCCGGCGCTCACAACACGCAGGTCACCTTCCTGCATCCCAAGTGCACCTTCGGCGTCCTGACCGAATTCTGCCAGCACCCCGGCAGCTGCGAATGCGGCAAGTAACATTCTCCCTATTCACCATACCACTAGACCAATGGCTATTGATCCCAAATTGATTGACGATCTGAACAGCCGCCGCAAGAAGGTCATCCTCAGCGGCGGTCAGGAAAAGATCGACAAGAGACATGAAAAGGGCGAAATGACAGCCCGCGACCGCATGGGGTACCTCTTTGAGGAAGGCACCTTCTCGGAAATCGGCATGCATGTGCGCCACAACTGCCACAACTTCGGCATGGGTAAGAAGGAAATCCCCGGCGACGGCGTCGTTTCCGGTTTCGGCCTGGTGGACGGCCGCCCGGTAGCCTGCGCGGCCTCCGATTTCCTGGCCCAGGGCGGTTCCCTCGGCTACATGCACGCCATGAAAATTGCGGATGCTCAGAAGTATGCGCTGAAAGCCGGCATCCCGATGGTGACCGTGAACGACTCCGGCGGCGCGCGCCTCCAGGAAGGAGTGGCGGCCCTTTCCGGATACGCCCAGGTATTTTACAACAATGTGCTTGCCTCCGGCGTGGTTCCGCAAATCTCCATGATCCTGGGCCCCTGCGCGGGCGGCGCGGCCTACTCCCCCGCCCTGACGGACTTCATCATCATGCGCAATTCCGGCAACGCGGGCATGTACATCACCGGTCCCAAAGTGATTGAACAGGTCACGTATGAAAAATGCACGATGGACGACATCGGCTCCGCCGCCATTCACGCCACCGTGTCCGGCAACGTCCATTTCGTAGCGGACAGCGACGCGCACGCCATGGACATCCTGAAGAGGCTTCTTTCCTTCCTTCCTTCCAACAACACGGAAGAACCGCCCCACAAACTGAACACTCCGCTGGACCTGAGCGCTGACGAAGGCATGAGCGACCTGATTCCCGGCGACAACCGCACGCCGCTGGACGTCCAGCCCATCATCAGCCGTCTGGTGGACAACGGGGACTTCCTGGAAGTACACAAGGACTTCGCCAAAAATGTCGTCGTCGGATTCGGCCGCATCTGCGGCGTGGTGGTCGGCATCATCGCCAACCAGCCCAATGTGAAAGCCGGCTGCCTGGATATCGACTCCTCCGACAAGGCCGCACGCTTCATCCGTTTCTGCAACGCGTTCAATATTCCGTTGGTGAACCTGGTGGACGTGCCCGGCTTCCTGCCCGGCAAGAACCAGGAACGGGGCGGCATTATCCGCCACGGCGCCAAACTTATCTTCGCCTATTCCCAGGCCACGGTGCCCAAAGTCACCCTGATCATGCGGAAAGCCTACGGCGGCGCCTACATCGCCATGTGCTGCAAGGACCTGGGCGCTGACGCCGTCTTCGCCTGGCCTGGTGCGGAAATTGCCGTTATGGGTGCGGAAGGCGCCGTCCCGGTACTCTACGGCCGCGAACTGAAGGCTGTGGAAGACCCGGCGGAAAAAGCCAAGCGCCAGGGCGAACTTCTGGAAGAATACCGGGAAGCCTTTTACAACCCGTATGTGGCGGCCGGCATGGGGCAGATTACGGAAGTCATCAATCCGGAAGAAACCCGCGCCAAAATCGCCTTCGCCCTGCGCACCCTGCTGAACAAGAAGGAAGTGCGCCCGGCCAAGAAACACGGCAACATTCCGCTCTAACATCCATCCCAACGGATTCATATGTTACTTACAACACTCGCTTTTGCCTTCGCACAGGGCATCGCCAACGTTATGAACAATCTGAACTATCAGGTTGTCGGCATCATGGTGGTTATGCTGTGCCTGGGAGGCATGGCCGTCATTCTGACCGTCAGCGGCTCCATCGCCGCCGCCATTCAGAACAGGGCCAAAGCCAAAACAGCCGCTCCCGTAGCCGTTCCTGCCGCCGCTCCCGCTGCGGCGGGCAAACCGGGCATGACTCCGGAAATGCTCGCCATCCTTTCCGCCGCCGTGGATTCCGCCATGACGGAGCTTACGCCGGAAATGGTGGCCGTCATCTCCGCCGCCGTGGACGCAGGCCTGGACGGCATGAGCCACCGCATCGTGGAAATCAAGCAATCCCCGGGTTCCGGCTATGCGGCCGCGGGCAGGGCGGAAATTTTCGCCTCCCACCGCATCAGGCCTTCCCGCTGAACTACTGCATTATTCACCCTTACCCCGCCCAGGCGGAATCATTTAACTTAATATCCCATACCACACACTATTATGAAGAAACTTCGCATCACCGTAGACGGCAAAGTATTTGACGTATCCGTAGAACTTCTGGACCAGGTGTCCTCCACCACTGCGGCGCCGGCCCCCGCACCTGTGGCCGCTCCCGCTCCTGCGGCTTCCGCCCCCGTGGCCGCCCCGGCCCCGGCTCCCGCACCTGCGCCGGCTCCGGCCGCCGCCGGCGCAGGAGACGTTCCCAGCCCGCTTGCCGGGAAGGTCGTCTCCCTGGACGTGGCAGCAGGCGCACCCGTGAAGGCCGGTGACCAGATTCTGACGCTGGAAGCCATGAAGATGAACACCATCATCTACGCTCCCGCCTCCGGCACGCTCACCGCTTTCTGCGTGAACCCGGGCGACACGGTTCAGGAAGGACAGGCACTGGCCAAAATTGGTTAATCCCGGCGGCGGCAGCCCGGAAACGCCAACAGTACCGGGCTCCGACTCAAATAATAAACCGCTACAAAAATATTCCCTATGAGTCTTTTTGATTCATTAATCACCTTTTTACAGGGGATGGGCGTCTTCTCCCTATCATGGCAGATGGTAGGGATGTGGGGCATTGCCATTCTCCTCCTGTATCTGGGTGTAGCCAAGCAATACGAACCGCTCCTGATGGTTCCCATTGCTTTCGGCGCCCTCATTGCCAACATTCCGGATAACGGAATGCTCATCACCCAGCTGAACCAGCAGGTCATCTCCTCCAATGAACAGGGGGAAGTAACCGCCACGTCTCTGAACAACGTGGGGTACCTGCGCGTTCACGTGGCTCCGCTTCAGCAGGCTCCCTCCAAGGTGCCGGCCAATCTGACCACGCCTGAATCCCGTGCCCAGTATCAGGAAATCATGCAGCAGCCCATGCAGGTTTACCCCGGCAGCCAGCTGACCGTTTCCAAGATCAAGTCCGTGCGGGAATCCCAGGAAAAAGCAAAAGCTGACGCGGCCCGCCTGGGCGACGACAGCCTGACGGTGGACCCCAACCTGAAGGATTTCCAGAACGTGACGGAAGACAACGGCAATGAACCGGTCTTCCTGCTCACGAACGGAGAAGGGACCACCGTCGTCCGCCAGCAGGGCGTCAATTACTTTGACACCAGCGGCAACCGGATTCCGGTGGATCTGAAAACCCAGAAGCTGGAGCCCCTGGTGGTTTCAGCCGCAGGCAAATACGTGGCCGTAGGCCAGCATACCCAGGAACTGCTGGTCACCTCCATCCACGGGGGCCTGTATGACTGGATCGGGCTGGGCATCAAGGCTGAAATTTTCCCGCCCATCATCTTTCTGGGCGTAGGAGCCCTGACGGACTTCGGCCCGCTGCTTGCCGCTCCGCGCACCCTGCTTCTGGGCGCAGCGGCCCAGGTGGGCGTAGCCGCCACGTTCTTCATGGCCCTGTTCATGGGGTTCACCCCGAATGAATCCGCGTCCATCGGCATCATTGGCGGCGCTGACGGCCCTACCTCCATCTTCCTGACGATGAAGCTGGCCCCGCACCTGCTGGGCGCCGTGGCCGTGGCCGCATACACGTACATGTCCCTGGTTCCGCTGATACAGCCGCCCATCATGGCGTTGCTGACGACCAAGAAGGAACGCCTCATCCGCATGAAATCCCTGCGAACGGTCAGCAAGAGCGAAAAACTGTTTTTCGCCGTGCTGGTAACCATTGTCACCATTTTGCTGATTCCGGACGCATCCCCGCTGATCGGCATGCTGATGCTTGGCAACTTCCTGCGCGAATGCAAGGTGACGGAACGCTTGGTTCAGGCCTCCCAGAATGAAATCATCAACATCGTCACCATCTTCCTGGGAACCTCCGTGGGGCTCACGATGCAGGGCGACCGCTTCCTGCAGTCGGAAACGCTGCTGATCATCCTTCTGGGTATCGTCGCCTTCGGCGTGGCAACAGCGGGCGGAGTGATTGCCGCCAAGATTATGAACCTCATCTGGCGCAAGAATCCGGTCAACCCCCTGATCGGGTCCGCAGGCGTCTCCGCCGTCCCGATGGCAGCCCGCGTCTCCCACAATGTGGGCCAGAAGTACGACCCCTCCAACTACCTGCTCATGCACGCCATGGGTCCGAACGTTGCAGGCGTGATCGGCACCGCAGTCATCGCGGGCTACTACATCGCCACTCTGGCGAAATAACCAGAATTGAAAATTACTCCCGGCCTCGGCCCGTCCCATGAATTCCGCAGCCTCATTTTCCACCCCTCCGGAGGAGGAAGGACCATGCGGCGACTGGACTTATCATGAATGGGCTGAGGCTTTTTCCACCAATGATCTGGCCCGCACCCTCCCCCCGGGGCATATTGCCGTCTGCCGCGTTCAGACAGGAGGCCGCGGCAGGTTCAACCGCAAATGGATAGGGGAGGAAGGCGGCCTGTGGGCATCCTTCACCGTCCCCCTGTCCGCTCCTGAGGATACAGCGGCCGGCGTGCACTGGGGCCATTTGCCCCTGGTTGCGGGGCTGGCCCTGTTCAACATGCTCCGCGGCATGGGGCTGGATTCCGTGCGCCTGCGCTGGCCGAACGACCTGCTGGCGGGCAAATCCAAACTGGCGGGAATACTGGTGGAACGCCCGGCGGAGAACATGGCCGTCATCGGCATCGGCATCAATGTTTTCAATGATGTATCCTCCATCGCCGGGGAAATCAAGGACCCTCCGGCAAGGCTGGCGGATCTTGTTCGGGACTGCCCTTCCCTGCACGCCGTCATGGCTTCCCTGGGCAGTCATTTGAACCAGGTTCTTTCCGTCTTTTCCCACGGCGGCGTCCCCGCCCTTCAGGAGGGTCTGTCCCGGGCATGGGCCGGAGAAAGGCCGGTTTCCATTCTTACGGATGACGAAACCGTCCGGGGCATTTTTACAGGGATTGACGATCAGGGCAATCCTGTGCTATCCCTTCCGGCCGGCACCGTCCGCACCATTCCGGCGCACCTGATCAACAGGCTCCTCGAAGAATGAGCCTGCCGCTCCGTTTTTACACGGGGTTTCTTTCTTTTTTCATCCAACAAAAATCACTGACATGAGTTACAAGGTACTGACACCAGAAGAAGCAGCAAGCCTGATCAAGCATGGCCACCATATTGGGCTGAGCGGATTCACGCCCGCCGGAACGGCCAAGGCCGTTACGGCTGCACTGGCAAAAATCGCAGAAGCGGAACATGAGAAGGGGAACCCTTTCCAGGTGAGCGTCTTCACCGGTGCTTCCACCGGCGATTCCTGCGACGGGATCCTTTCCCGGGCCAAGGCAATCCGCTACCGCGCTCCCTACACCACCAACGTTGATTTCCGCAAGGCGGTGAACAACGGGGAAATCGCCTATAATGACATCCATTTGTCCCAGATGGCCCAGGAAGTGCGCTACGGGTTCATGGGCAACGTGGACATCGCCATTATTGAAGCCTGTGAAGTCACGGCCGACGGCAAAATTTACCTGACGGCCGCGGGCGGGATCGCTCCCACCATCTGCCGCCTGGCAGACAAAATTATCGTGGAATTAAACGCCGCCCACAGCAAGAGCGCCATGGGCCTGCACGACGTTTACGAACCCCTGGATCCTCCCTACCGCCGCGAAATTTCCATCTTCAAACCCAGCGACCGCATCGGCAAGCCGTACATCCAGGTTGACCCGAAAAAGATTGTGGGCGTTGTGGAAACCAACTGGCCGGATGAAGCCCGTTCCTTCGCCGCAGCGGATCCCCTGACCGATAAAATCGGGCAGAACGTGGCCGATTTCCTGGCTGCCGACATGAAACGGGGCATCATTCCCTCCACGTTCCTGCCTCTCCAGTCCGGCGTAGGCAACATCGCAAACGCGGTGCTGGGCGCCCTGGGCCGCGACAAGACGATTCCCGCGTTCGAGATGTACACGGAAGTCATCCAGAATTCCGTCATCGGCCTGATTCGGGAAGGCCGCGTGAAATTCGGCAGCGCCTGCTCCCTGACCGTGACGAACAACTGCCTGGAAGGCATTTACAGGGATATGGACTTTTTCCGCGACAAACTGGTGCTCCGTCCCTCGGAAATATCCAACAGCCCGGAAGTTATCCGCCGTCTCGGCGTCATCTCCATCAACACCGCCATTGAAGCCGACATTTACGGGAACGTCAACTCCACGCACATTGGCGGCACGAAGATGATGAACGGCATCGGCGGGTCCGGCGACTTCACGCGCAACGCCTACATCTCCATTTTCACCTGCCCGTCCGTTGCCAAGGACGGTAAGATCAGCGCCATCGTCCCCATGGTTTCCCACCATGACCATACGGAACATGACGTCAATATCATTATTACCGAGCAGGGGGTCGCGGACCTGCGCGGCAAGAGCCCGAAAGAACGGGCCCAGACCATTATTGAAAACTGCGTCCACCCGGATTACAAAAACATCCTCTGGGATTACCTGAAGCTGTCCGACGGCAAAGCCCAGACACCGCAATCCATCAGGGCCGCTTTGGGCATGCACGCCGAACTGGCCAGAAGCGGAGACATGAAGAATGTCGACTGGGCCCAGTATAAGTAATTCCGCATGCCGTCATCCGCATTGGCATCCATGAATTCACGTTAACCCCTCAGGACCGCTTCCGCAAAGGGAGCGGTCCTTTTCCTTTTTCCCATCCCCTCTACTCCTTCTCTTCACCATGAACTGGATGATTTACCCGGCAGCCAGGGCTTTCATCAAAGTCTGCGCCGTTCTTTTCGGCGCGGGCGCAGCAGCGGCAGTCCTCATCCATCTGTTCATGCCTTTTTTCCTCCGCTCTCCCCGGACAGCCGCACCCTTCCTGCTTTTGGGCACAGGGGCGGAATTGCTCTCCATCCTTTCCCTGTCCTTCCTCATGTTCTGGGGCGCCAGAGTCCTGCTGGCCTCCCGCGGAAGCAGAATAACAAGAAATCTGAGCATGCTGGCCTTCCTGCCCGCCCTGTACGCCCTCCTCATTCCGTTTGCGGGCATGCCCGTCCCATCGCAATATACGGAAATGGACAGGGTTTGGGAGGCCGTCCTCCTTCTGGCGGCCGTCATTTCTTTCTTTTCCTTCCCTTATACGGCGGGATACGAAGCCGCAGGCAGGAGATTATTCCTGGCCTGGGGCATCGCGGCGGCAGGCTGCGGTTTGCAGAGCGCCCTGTTTTTTCTTGCCATCCTGTTGCGTCAGTTCCTGTCCACCGCAGTTCCCTCCCTGGCAGTGCCTGTTGCATGGCTGGCCGTTCTTCTGTCCTGCGGCCTGGAAATCATGCTGGCGGCCCTTTCCGCTATTCTGGCGCAGCGCCTGTTTAAAGAGGTCATGTCCATAGCATCCATGCCGGAACCGATTGATCCTTCCATGCCGGGCAACCGTCCCTGACTTTCCTCCCCTCCGTTTTTACCGGTCTGCCGGAAAGCACCCTCATTCCCCGGCATGAAAGGCTTTTACGGCTTTGCGCAAAAAGCCGTCCGGAAATTTCCGCCGGTGGAACGGAATCCGTATGGAAAGCATTGCCGGAAAGCAACCCGCCCCACCCCCGGAAAGTCGCGGAGTCGCAGGGACACCCCTCCCGTTTCCCTGCCGGCGGCATACAGCCCTATTTCACCAATTCATCCAGATAACGGCCATAACCGTTATTATTCATGGCTCCGGCCTGGGAGCGCAGCGTTTCCTGGGAAATCCAGCCGTTATTAAAGGCTATCTCCTCCAGGCAGGCTACCTTGAACCCCTGGTGATGCTCCAGAGTACGAACGAACATGGAAGCCTCCAGAAGGGTATCGTGCGTGCCCGTATCCAGCCACGTATAGCCGCGCGCCAGTTTTTCCACGCGCAACAGGCCCTGCTTCAGGTATTCCTGATTCACAGAGGTGATTTCCAACTCGCCGCGGGAAGACTTGGGAATCCGTTTGGCTATCTCCACCACCCGGTTGTCGTAAAAATAGAGGCCCGTCACCGCAATATCCGATTTGGGCCTGAGCGGTTTTTCCTCAATGGAAACCGCCTTCCCCTCCTCATTGAATTCCACTACCCCGAAACGCTGCGGGTCATGCACCTGGTAACCGAAAATGGTCGCTCCGCTCGTCAGTTCGGCGGCCCTCCTCAGCTTCTCCGTCAGGCCGCGCCCGTAAAAAATATTGTCCCCCAGCACCAGGGCAACGGAATCCTTTCCTATGAACTCCTCTCCAATAATAAAGGCCTGAGCCAGCCCCTCCGGCCTGGGTTGTTCCCTGTATTCAAAGGAAACGCCGAACCTGCTGCCGTCCCCCAGCAAATCCCGGTACAGGGGCAGATCCCGGGGCGTGGAAATAACAAGGATTTCCCGTATTCCGGCCAGCATCAGCACGGAGATGGGATAATAAACCATCGGCTTATCGTAAACGGGCAGCAATTGCTTGCTGACGGAAAGCGTAACGGGATGCAGACGCGTGCCGCTTCCCCCGGCCAGAACAATACCCTTCATATCAAATTCCTAGCCATTGGTTCCAAGACGTTCCAGGCGGTATTCCCCGGACAGGATGCCCCGCCACCATTCCTCATGGTCCAGATACCACTGAATGGTCTTTCTGAATCCGGAAGAATGGTCCTGGCGCGGAGTCCACCCCAATTCCCTCCGTATTTTGGAAGCGTCAATCGCATAGCGCAGGTCATGGCCCGGCCTGTCCTGCACAAAGGAGATTTGCTCCCGGTAGCTCCGGCCGTCCGGACGGGGCCGCAGTTCATCCAGATGGGAACACAGCAGCTCCACCAGCTCCAGATTGGTTTTCTCGTTATTGCCGCCGATGTTGTACGTTTCCCCCGGTTTTCCGCAGGAAACGACCGTCAACAGCGCCTCGCAGTGATCCCCCACATACAGCCAGTCCCGCACGTTTTCCCCTTTGCCGTATACAGGAATGGGTTCCCCGCCCAGCGCTTTCAGGATAACGACGGGTATCAGCTTTTCCGGGAACTGGTAGGGGCCGTAATTGTTGGAACAGTTGGTGATCAGCACGGGCAGCCCGTACGTATCGTGCCAGGCGCGCACCAGGTGGTCGCTGGAGGCCTTGCTGGCGGAATACGGGGAATGGGGGCGATACGGCGTGGACTCCGTGAACAGGCCTTCCTTTCCCAGGGAGCCGTACACTTCATCCGTGGAAATGTGATGAAAGCGGAAACGCTCCTTTTTCTCTCCCTCCAGGGAACGGTAATATTCCAGGGCGGCCTGAAGCATGGAAAAAGTGCCCATGACGTTGGTGGTCATAAAATTTCCCGGATCGTCAATGGAACGGTCCACATGGGATTCCGCCGCCAGGTGCATCACCCAGTCCGGAGCGTACTCCCGCAAAGTGCGCCGCATCAGGGAAGCGTCCCGGACGTCGGCGTGCAGAAAGCGGTATGCCGGAGAATCCGCCGAGCCGTCCAGTGAATGGATGTTGCCGGCATAGGTCAGGGCGTCAATATTCAAGACTTCATGGCCGCGCTCCAGCAGCAGCCTGACCAGATTGGAGCCGATGAATCCGGCGCCGCCCGTAACGATAACTTTCATAGCACTGTTTTCAGGGAAAGATGCGGCGCCATCATACAGCAGCGGCGTTTTTTTTCAAAACAAATCCGCCCATGCCGCGGGAACGTGGCGGCATGGGCGGGAAAAAGCGGGTTCCGTAACCGCTGAGACGGCTAGCAGCTCAGCTCCCGGCAAAGGTTGATGATATCCTGGTTCACGGAGTACTGCTGCCCGGCTACGGAATCCAGGGAGGCATAGCCGTACTGGCCGTCATGATACACCATGATCTGGTTGATGTCCCCGTTGTTCAGAGCTTCCACGGCGCGGGTGGCAAAACGGGTGCCCATCAGGCGGTCACGGGCGGTGGGGGAACCGCCGCGCTGCACATGGCCCAGCACGGTCAGGCGGGTATCCATGCCGGCGCGTTCCGTCAGCCAGCGGGTCAGGTACTGCGCCACGCGGGTGCCTTCCGCCACCACGGCAATGATGCAGCTTCCTTCCTCATTGATGACAGGCTGGAGTCGCTTGTAAATGGATTCCAGGTCATACGGAATTTCAGGGACAATGCAGATATCCGCCGCGCTGCAAAGAGCGGTGGTCATCGCCAGATAGCCGCAGTCCCGGCCCATCGTTTCAATCACGAAAGCGCGGTGATGGGAACGGGCCGTATCACGAACGGAATCAATGCAATCCAGAATCACGTTCTGGGCCGTATCCACGCCCAGGCAGTAGTCCGTGCCGGCAATGTCGTTGTCAATCGTGGCCGGAATACCGGCAAAAGGCACTTCAAAATCATTGAAAAAGTCGTTCAGGGCGCGGAAGGAACCGTCCCCGCCCATGATGATGAGCTTGTCGATGTTGCGTTTCTTCAGATGTTCGTAGGCCTGCTTGCGGAATTCGTAGTCGAAAAAGCGCTTGGATCGTGAGGAACGGAGGAGAGTTCCGCCCCTGTAGATCATATCGTTGGTCAGTTCCCGGGTGGCTTCCTTGATTTTTCCATCGATCAGCCCTTCCAAGCCGTCATAAACAACGTACGGGTTCATCCCAAGCTGGCGGGCATGATCCACTGCCGCCTTGATCGCCGGGTTCATTCCGGCGGCATCGCCGCCAGATGTCATAATGGCTATTCCACTCATAAGTAATAAGGTTAACTTGTCCGGGGGGAAATGTTACCGGGCGGAACAAATAATGCAAGCGCGGAGTTTTTACTGGCGCAATAAACGGCAACGCTTCATGATGCGGAGGCATGAACGACACTGCAGCAGCCATTCTGAAAGCCACGGCCGCCCTCCGGGACGGCACGGAGAAGCTCCGGTTCGAATCCCCGGTCCATGTTACCTACAACCCTCTCACCTATGCATGGGGGCCGCATGAACAGTATGTGCGCACCTACGGAAACGGGGAAAAGGGCCATCTGTTCCTGGGCATGAATCCCGGCCCCTTCGGCATGGCCCAGACAGGCGTCCCCTTCGGGGAAGTGGACGCCGTCGTCAACTGGCTGCACATTCGCGGAGAAGTCGGGCGGCCGGAACATACCCACCCCAAACGTCCCGTGGAGGGTTTCGGCTGCCCCAGGTCGGAAGTCAGCGGCCGCCGCCTCTGGGGACTTTTCGCGGAAGCTTTCGGAACTGCGGAAAACTTCTTCCGTCACAATTACGTGGCCAATTACTGTCCGCTGATCTGGATGGGAGCCACCGGAGCCAACATTACGCCGGACAAACTGCCGGCGGAACAGCGCGCCGCCGTGGACGCAGTATGCATGGAACACCTGCTTTCCCTCATCACCATCCTGAATCCCCACACGCTGGTGGGTGTGGGTGCCTACGCCACGCAGAAGCTGCGGGACGCCGCCTCCCGGCTCCCCGGCAAAAGCTTCACCATCGGCACGTTGCTCCACCCAAGCCCCGCCAGCCCCATCGCCAACAAGCTCTGGCCGGAACGCCCCATCCAGCAGCTTAAGGAACTGGGCATTTTATAAAAAGAGCGGAGGAGGTAGAGTGGAAAGCTCCGTGACATGGAAGGAATCACCAGAGCGACAGGCCGGAAATCGGCAGTTCATCTTGTTCTCCTCCAAATCTACATCGTTTTGAAGGAATATCCGGCCCCCTTTACTGGCTTGATTTGCCCAGGGACACGGCCTTCCTGACGGAAGAAAAGCACGCCGGCATCTACCGGGACGGAGAAGAACTTAAACGCCTCCCCACTTCCACCACAAAAATCAGCCTCCCTCCAAATCAATCATCATTCTTAGACTGAAAACCCTCCGCTCTCCCCCGCAGGCCTTTTTACTGGACAGGGGGAAAAGGGTTCCGTACAGTTTCCGCGATCTTCAAGCGTTCCGAATTTATGGCAATTTCAGATTACATCGTGACCATTGGCCTGGAAGTCCACTGCCAGATCAAGACAAAGAGCAAAATGTTCTGCTCCTGCGAGACCGGCTTTGGCTTTGAACCCAATACGCGCGTGTGCCCCACCTGCCTGGGGCTGCCGGGCGCCCTGCCCGTCCTGAATGAGTTCGCCATTGAACGCACGCTGCTGACCGGCCTGATGCTGGGATGCTCCAGCCCGGAGATTTCCCAATGGGACCGCAAAAATTATTTCTATCCGGACATGGCTAAAAATTACCAGACCTCCCAGCTGGATCTTCCGCTGTGCCTGGGAGGGGAAGTGCCCCTGTACCCCTGGTCCTATCCGAATGACGTGATCAAAGCCGGCATCCCCCCTTTCCGCACCGTGAAGCTGACCCGCATCCATCTGGAAGAAGACGCGGCGAAGATTACCCATCATGCCAATTATTCCCTGATCGACTACAACCGCGCGGGATCCGCCCTGATGGAAATCGTTTCCGAACCGGACATCGACACCCCGGAAGAAGCCTACGCCTACCTCAAGTCCCTCCAGCAGATTTTGAATTACGGGGATATCTCCGACGCGGACATGGAAAAGGGCCAGATGCGGTGCGATGTGAATATTTCCCTGCGTCCCCACGGGCAGAAGGAATTGGGCGCCAAGGTGGAAATGAAGAACATCAACTCCATGTCCGCCGTCCGCAGAGCCCTTCATTATGAAATCCAGCGCCAGGCGGAAGAACTGGACATGGGCATTCCTCAAATCCAGTCCACCCGCCGCTGGGACGACGAACGCGGAGAAAGCGTCGTCATGCGAACCAAGGAAGACGCGCACGATTACCGTTATTTCCCCTGTCCGGATCTGGTTCCCGTACGCACGGCCCCGCTGGTGGAGAAAGTGCGCGGGCAGATTCCCGAACTGCCTCAGGAACGGCAGAAACGCTTCATGGAAGAATACGGCCTGAGCGAGTACGACGCCAACGTGCTGGTGGGAGACCTGGAACTGGCACGCTTTTTTGAAGAGGCGGCGCGGGGAACCGCCAGCGGCAAAAAAATCGCCAACTGGGTGATTAACAACATTTCCGCCGTGCTGAATGAAAAAGGCATGCGCCCGTCTGAATGCCCGGTGAAACCCGGCGCCATCCGGGAGCTGATCGCCATTATTGACGACGGCACCGTTTCCAACAACCAGGCCAAGGACGTATTCGCCAAAATGTGGGATGAGCCGGCCCTCGCCGCAGCGCAGGCCGCCAAGCTGCTGGGCTTTGAAAAGGCGGATTCCTCTTTTCTGGACGGCATCGTGCGGGAAGTCATTTCCGCCAATCCGGACAAGGTGGCGGAAATACAGGGGGGCAATGAAAAGCTCCTGAACTGGCTTACCGGCCAGGTCATGAAGGCCGCCAAAGGCAAAGCCAACCCGAAAATCGTGACGGAAGCGCTGAAAAAAGCCCTTTTCATGGAGTGACCATAAACAAAGAGCCGCTTCCCTTGCCGATGCCGGCTCCTTGCCCGCAGGGCATCACGGTTTGAACAGCTTCCTGTTTCCGGCCAGGGTGTTCCATACCTGCTTCACTTCCCCGGCCAGCGTCTGCTTCATCATGGCAAGGGAAAAGTCCGCCGCCTGGCCCATACTGATGTAGGGAGGCAGGGCAAGGGCGTCCCTGTCCACATGCGCGTCCAGCAGAGCGGCGCCCGGATAATTCAGGAACATGTCCACCATCTGGTCCACCTGGCTGGATTTGTCCAATAAAAACCCCTTGATGCCGACGGCATCCGCCACGGCGGCAAAAGAGGGGTTGTTCAGGTTGATCTGCCACGGAATCAGCCCGGCCGCCTGCATTTCCAATTGGATGAAATCCAGACAGTCATTGTTATACACCACGATTTTCACCGGCAGCCTGTACTGGACAATCGTGATGAGATCCCCCAGCAGCATGGACAGGCCGCCATCTCCGCACAGGGCAATTACCTGCCGTTCGGGGTATTCCATCTGCGCCCCAATAGCCATCGGCATGGCATTGGCCATGGAGCCATGATTGAAGGAAGCCAGCGTCCTCCTTCCCGGCGTGGCGCACAGGAACCTGGCGCTCCAGATACAGGGGGTGCCGGTATCTACCGTAAATACGGCGTCGGATGCCGCATGCCTGTTCAGGGCGGTCGTGAGCTGTTCCGGCCGGATGGGAGACTCCTTGTCATTTCCTTCCATATAGGCGTTCATCTCCTTCACGTCCCTGGCGTGGCGTTTCAGGGCGGCGTCCAAATGCCCGGAATCCGTCTTTTCCTGCACCATGTTCAGCAGAGCTTCCGCCGTGACGGAAACATCCCCGCAAATACCCAGGTCCAGGCGGCAGCGGCGGCCCAGCACTTCACCGCGCCTGTCAATCTGCACGATTTCAGGTTTGGTGGGAATGAAATTGAAATAGGGGAAATCCGTCCCCCACAACACGAGCATGTCGCTTTCATGCATGGCCTTGTAGGCATCTCCCCATCCAAGCAACCCCGTCATGCCGATTCCCAGGGGATTGTCATGTTCAAAATAATCCTTTCCCCTCCATGTGTAGGCAATCGGAGCTTTCAGGCGGTGCGCGAGCCGGACGACCTTGTCTTCCGCTCCCGCGCAGCCGGCGCCGCAGAAGAAAGTAATGCGGCGGTGGTCGTCAAGCATCCGTGCCAGAAGGGATACGGATTCATGCTCAGGAACAACATTCTCCCGCGTGTAGGAAGGAGGATGCACCAGATTCTTCATCTCCGCAGGGGCGTCCGCAATGTCTCCGGGCAAGACGACGACGCCTACGTCGCGCCTGGCCCACGCGTGCTGAATGGCGGACATCAAAATGGACGGGGCCTGGGAAGCTTCCGAAACCAGTTCCGCATATGCACTGCATTCCTTGAAAAGCTGTTCCGGATGCGTGGCCTGGAAATATTCCATGCCTACCTGGCTCTGCGGGATGTGGGAAGCAATGGCCAGCACCGGGGCATGGTTCCGGTGGGCGTCAAACAGGCCGTTGATCAAATGGAGGTTCCCGGGGCCGCTGCTGCCGCAGCATACAGCCAGATTCCCGCTCAGCTGGGCTTCCGCGCTCGCGGCAAAAGCCGCCACTTCCTCATGCCGCACATGAATCCATTCAATGGTCTTCCTCCTCCTCAAAGCGTCCGTAACGGGGTTCAGGCTATCCCCCACAATGCCGTAAATCCGCTTAACTCCCGCCTTCTCCAAAATTTCAACAATCTGATCGGCTACCCTGTGTATGCAGATTAGACACCAGCCAACCTCCAACGGATTCATTCTTATGTGTCATCCCCTCCATCTTTTCCGCACCGGAAAAAATGCCTGAGGATTTCCCCTGATATCATAAGGCCGCCGCCTTTTCAGGCAGCGGCCTATGAATTACCAGTTCTCTGGCGGAAAACCGTCAGGAACGGCGGCGGCGCATCATCAGAACGGCCAGGCCCAGGATTCCCAGAGAAGCCGAGGCCGGTTCCGGAACCTGAAGGCCGGCAAAACCGGCAATCTGTTCCGCGGAAAGTTTTGTATCCGTCCACTGAATTTCACCCCATGTAAGATTGGGATTAATCCATCCTTGGATATCAGTGCGATTTCCGTTCATGTTGCCGTTGTAGGACAACGTGTCAAGCAAGGAACCATTCACATAGACCTCCACAGACTGGCTTTGGTCTGCGCCAGTAACGGCTAGAGTCACGTTAAGAAGACCTCCCTCCAAAACAACGGGAGTTTCAGAAACGTCTCCACGGTTTTGGGAAAAAGAGCCGGTAGAAGTATCCAAATTTCCATCTCCCATGCCAAGCTTGGCATCTGTCGTCAGATACAAGGCATTTGTCCCGTAACTTTGGCCCGCATTGGTGCCATAATACCCAAACAGCGTGGTCTGGGTTCCTTCCGTTAAGGAAGAGGTATCCAGCCAGAGGGAGATCGTGTAATTGGAAGCATAAAAATTACCGCCGTAAGGAATGAAACCGCCAGGAAGGGCACTCTGTGAGAGACTCCCAAACGTCATGTCTTTTCCCAATGCAATACCTTCAGTTGTCGTCGCCGTACCGGCAGCGTAAGACGTTCCGGCCGATGGTTCTAAAGAGCCGGGAGACGCCAACGCCGGAACGGAACAACACGCCGCAATGGCCATTAATGCTAATGTTTTTTTCATTTTCTGTGAATGATGGACTGAGTAGCAATCAGAGAGATATTCCCCGCCCTCTGTATATCGGATTCCCAATCCGCTCTTTTTCTCCCCCACAGGAAAGATGAGAACAAGCTTTTTCAATCCGCTAAAAAATATAATATAATTACCTTCTATCACTTATGAATATAAAAAATAAAACGCAGAATAGATTTTTTGTGGAGGTTCTGAAGTTGCTGTATAGGAACAGCAATCCCTTTTCGCAAAATAGAAAAAATTGTAATAATAGTTTCATAATTGAAATGTTAGATTGACCTCGGATTGGGAATCCGGTTGTTTTCCCTCAGCCATCAAAAAAATCAACATAATGTTTTCATATTCAATAAAAATTAATTTTCTTTCCTTCCATTTCCAGAACCTTCTTCTTGAAAACGAGCCTTCCCCAAATTATCCTCCCGCTAGCTGTAATATTATTTCATCATGAAATTTTTCAAGACAACCATTGCCATGGCAACCCTGCTGGGCTGCCTGCCCACATCCCCCGCAGAAGTCGCCTTTCCGGGGCCTGCTCCCGGAACAGCCAGCGCGGAACAATCCGCTTCCGGCTATACTCTTTCCAATAACATCCTGACGGCCAAATTTATCCTTCGGAACGGAAGTCTTTCCTTCGGAGGAATGACTTCCAAACTGGGCCCCGTCGCCAAGGCAGGGGATGAATTATTCATCATCAATCTTCAGGACGGACGGAGCATTAAATCCTCCGAGCTGAAAGCGGGAAACGTCAAATTCGTCTCCCTTCAGCCCAACCCCAAGGCCTTCAGGCTGGCTGACCGCTTCCCGGGAAAAGCCGTCAGCGCAACATTCCAGGCTCTGAACGGAACCCTTCGCATTTCCTGGCGCGCCGTGCTCAGGGACCATTCCCATTACCTGAGGCAGGAACTCCGCCTGGCAAGTATGAAGCCCGTCCAGATGAAAAACATCGTAGCGATGCAATATGACCTGGCAGAAGGGAAAGCCGGGGCACCCTCCGTTTCCGGAAACGCCAGAGGAGCACTCATCGTCAATGACCTGGCGTTCACCGCCCTGGAAACACCCATGGGCATCAATACCGTGGGAGGCGCCGGAAATATGGAAGGGGGAGAAACCGCCATCTGGAGTGCGGACAAGTGGTCCACAGCTTCATGGACCGGAAATTTCAATGTTCCCCAGGAACTGAAAAAGCAGTACGGAGAACAGGTTTCCAGTGCGGAAGGCCCGGTTTCCATTGACGGCAAAGGAGCCTGCACCGTCACCTTCCAGTATAAGGGAGGGGACAAGGGCAACAATAAGCTGAACCTTGCCGGCGTGCAGCTCCTCTCTTCCAAGGGGGCCGTTCTTGACTCCGATTTCCACAACGGCAGCACCGGAGATTCCAATGCCGGCAATACTTACACGGTGCAGGTTCCCTCCAGGGGAAATTACATTCTGCGCTACTGGGCCCAGACCAAGAGCGAACCGATTGCCAGCAAGGGGGAAATCACCTTTTCCCTGCCCGTGCGCACGCTGGAGGAAAAGCAGGAACCCCAGGCGGACGAATCCCGGCTGGCCCAGGGACTCTGGAGCCGTAAAACCACCTTGCAGCCCGGGGAAATATGGGACGTATCCTCCGTTCTCGGAATTTTTGCCCCCGGCCAGAAACGTCGCTCCTTCCTGGCCTATATGGAACGGGAACGCGTGATGCCCTACCGCCCCTTCATCCATTATAATTCCTGGTATGAACTGAACATCAACCGCAACAATGATTCGGACCCCGCCAAGCGCATGACGGAAGAACAATGCCTGGCCGTCCTGAAAGACTGGCAGGAACAGTTTTTCCAGAAGCGCGGCATGTCCATTGACGCCTTCGTCTGGGACGACGGCTGGGATGAATTCAACAGCCTGTGGGATTTCCACAAAATGTTCCCGCAGGGCTTCAAGCGCATTGACGCCGCCGCCGGCAGGCAAAAGGCCGGCATCGGCACGTGGCTGGGTCCGGTGGGGGGCTACGGAGCCTCCAAGGGCAAGCGTCTCGCTTACTGGAACGTGAAGCATCCGGACAACAAGATCGGTAATTTCCAGCTCTCCAACAAGGAATATTTTGACGCCTTTGTGGGCCGCTGTTCCCAGATGGTGAAGGACTACAATATGAAGTACTTCAAATTCGACGGCATCAGCACTCACTTCCATGCCAAGGGGCCCGGCAACGAGGAGGATGCGGAAGGCATCATCCGCGTGCTGAACGCCCTGCGCAAGAAAAAGGGGGATCTTTACATCAACTGCACTGTAGGAACCTGGGCATCCCCCTTCTGGTTCCGTTATGCGGATTCCGTATGGAGGCAGGAGAATGACTTCGGCACCATCGGAGCGGGCGACAACCGCGACAAATGGATCACCTACCGCGACCGCCTGGTGCATGAAGTTTTTGTGCAGGGTTCCCCGCTGATGCCCATCAATTCCATGATGACTCACGGCCTGATGGTCACTAAATTCGGTCCTCCGGCCTGCATGCCCCGCGATCCGGAAAACGTCAAGAAAGAACTCCGCTGCGCCACGGCTTGCGGCACTTCCCTCCAGGAGCTCTACGTGGATCGGGACCTGATGAGCGCCAACGGCGGAGTTCTGTGGGACGAACTGGCCAAGGGGATCAAGTGGATACGCCGCAATGCGGACGTTTTGGACGATGTGCACTGGGTGGGCGGCAATCCGTGGAACAAGGAAACTAACGAAGGGGCCGTGTACGGGTGGGCCGCATGGAATAAAAACAAGGCCACCCTGGCTCTCCGCAATTCCTCCGACCAGGAAAAGAGCCTGACCGGAACCCTGCGCAGCATTCTGGATATCCCTGCGAACGTCAAGGGCTCCATCACCTTCAAGGATTCCTATGACGACCAGCGGACGCTGGACGGCTTCTCCGGCAGTTCCGTAAACATTGACAAGGAAATCTCCTTCACGTTGAAGCCCTTTGAGGTGCTGGTGTACGAAGGGGGAAAGGTCAAATAAATCAGCCGGCCCACACGCCCGGTCAAAGCCGGAACGGCGTTGGAAAAAACATATTGGGGGAGGCGGGATTCCCGCCTCCCTTCCTTGTTCCCGTGCGGACAGAATCCGGGTCAGAATGTTTTTTCAAACGTCACCGCTTCCAGCGGATCGGCAGGGTTGACCGGCATCACCAGGCATTCCGCACCTTCCCCGCCCAGGTACAACATCAGGCTCCTGCCGGGGGAATAGGCGATGGAGACACCGTAATATCCCCCTTTTTTCACCAGGGCCCAGGTTCCGCGGCAGGAAGAAACGAGATCTTCCTCCAGCACGGAAAGGTCCTCCCTCTCTTCCGGACGGGCAATCCACCTGCCCCATGCCTCCGGACGGGGAAAGGAAGAAATCTCGCACGTCCCGTCTTCATTCAGGAGGAGCGTGGAAGAACTGACCTGGAAAAAACGCTTCAACTCCGGAGGGCGGAAAAACCGCCTTTCTCTGGGAGATGGTTCCACCTCATACTTTCCGGAAAGGGCCTGCACATCCGGCTCATGAATCCACAGGAAAGCTTTTTGACACACCGCCTGGCGGCACGCCAGCCCCACCACGGCCAGCAGGGAAAGGAACGCCAGGACAAACGCCCACAGGGCCAGCCGCTTTCTCCACCACGCATAAACGGCGGCCAGCCCCGCCACACTGCCCGCGCAAACCCAGAACCCCCAGGAACGGGAAAGGAAGGCCAGGGCATCCGCCAGCACGGAAAATCCATGAGAAAGATCATTCCACATAGGTGCTCCAGAATCTCCGGAACATAGTCAAAGTCAAGACAACAATGCCGCCTTTCAAGGCACGGGCCTCAGGCTGTGCCCCGATAAAAAACTCCCGTCATCACTCCTGAAAATATTCCTCCGCGCTTTTCTCACAGGCTCCAATCAGGTAAGAAGCATCTGAACCATGAAAAGCGCAGCACATCATCAGGGGCTTTCGGAACAGCAGGTTCTGGAAAACCGGACCAGATACGGGATCAATATCCTGACTCCCCCGGAAAAGGAACCCCTGTGGAAGCAATTTCTGGAGAAATTTTCAGACCCCATCATCAAAATTCTGCTGGTGGCCCTGCTCCTGTCCGTAGGCGTGGCTTTCTACCAGTTTTTCACCGGAACGGAATCGGCCAGCGTCTTCCTGGAGCCCGCGGGCATCCTGGCGGCCATCCTGCTGGCCACCTGCGTGGGGTTCGCCTTCGAGGTAAGCGCTAACAAGAAGTTTGAAATCCTGAACCAGGTCAATGACGACACCATGGTCCAGGTCATCCGCGGCGGCAATATCTGCGAATTGCCCCGCCGGGACGTGGTGGTGGGCGATATCGTCATCCTGAACACAGGGGAGGAGGTACCGGCGGACGGCGTCCTGCTGGAAGCCGTTTCCCTCCAGGTCAATGAATCCACACTGACGGGGGAACCCCTCATCGGCAAAACCACGAATGAAGCGGAATTCAAGAAGGATGCCACCTACCCCTCCAACCACGTCCTGAAAGGAACCACCGTGGCGGACGGCCACGGCGTCATGGAAGTGACTTCCGTGGGTGACAAAACGGAATACGGCAAGGTTTATGAAGGCTCCCAGATCGACAACAAGGTGCAGACGCCCCTCAACCGCCAGCTTTCCAGGCTGGGCGATCTCATCACCTGGGCCAGCTACGCCATCGCCGCCCTCATCGTCATCGGGCGCCTCACCCTGTATTTTTCCCATCTGCCGGGCCCCGTGGAATGGCTGAGCGCGGGAACCTACATTCTGAATACCGTCATGATTGCCGTGACCGTCATCGTAGTCACGGTGCCGGAAGGGCTGCCCATGAGCGTCACTCTGAGCCTGGCGCTGAGCATGAAGAGCATGCTCGCCAACAACAACCTGGTGCGCAAGATGCACTCCTGCGAGACCATGGGGGCCGCCACCGTCATCTGCACGGATAAGACGGGCACCCTGACGCGCAACCAGATGAACGTGTACAAGGCGGATTTTTACGGGTTGGGGAACGCCGTCCCGACGGAAGACGAGCCGGGCAACCTGATCAGGGAGGGAATCGCCGTCAACTCCACCGCTTTCCTGGATTACGCAGACCCGGAACATATCAGGGCCCTGGGCAATCCCACGGAAGGGGCGCTCCTCCTGTGGCTGCACGGGCTGGGCGTGAATTACCTGGACCTGCGGGAAAACGCCCGCGTGCTGGAACAGCTCACTTTCTCCACGGAACGCAAGTACATGGCTACCGTAGCGGAATCCCCCCTGCTGGGCAAAAAAGTCCTTTACGTGAAAGGCGCGCCGGAAATCGTGCTGGGGCTCTGCTCCACCGTCCTGACTCCGGAAGGCCAGGTACCCGCCGCGGACATGCGCCCCGCCATTGAGGAACAGCTCCTGGCCTACCAGAACCAGGCCATGCGCACGCTGGGCTTCGCGTACCGCATTCTGGAGGATGACGCCCCGGTGTTCGAGGACGGCCGCATCATCCGCAAAGACCTGGTTTACCTGGGCATCACCGCCATTTCCGACCCCGTCCGGGACGATGTGCCCCAAGCCGTGAAGGACTGCATGGACGCGGGCATCCGCATCAAAATTGTCACGGGGGACACTCCCGGCACCGCACGGGAAATCGGCCGCCAGATCGGCCTGTGGACGGAGGAGGATTCTCCGGACCGCCTGATGACGGGAGTGGAATTCGAGCAGACTCCGGACGCGGAACTGCTGGACCGCGTGATGGATCTGAAAATCATGTGCCGCGCCAGGCCCATGGATAAGGAACGGCTGGTCAAACTGCTCCAGAAGAAGGACCAGGTAGTGGCCGTCACCGGGGACGGCACCAACGACGCCCCCGCTCTGAACGCCGCGCAGGTGGGGCTTTCCATGGGAGACGGCACCACCGTAGCCAAGGAAGCCAGCGCCATCACCATTCTGGACAATTCCTTCATGAGCATTTCCCGCGCCGTCATGTGGGGGCGTTCCCTGTACAGGAATATCCAGCGCTTCATCGTCTTCCAGATGACCATTAACGTGGCGGCCTGCCTCATTGTGCTCATCGGCGCATTCCTGGGCACGGAATCCCCCCTCACCGTCACTCAGATGCTCTGGGTGAACCTCATCATGGACACCTTCGCCGCGCTGGCGCTGGCCTCCCTGCCGCCGGACGAACGCGTGATGCAGGACCCGCCGCGCAAGACCACGGACCACATCATCACCCGCCCCATGGGCCGGAACATCCTGGGCGTGGGCATCCTGTTCGTCGCTTTCCTGTTCGGCCTGCTGCAATATTTCAAGCATGCGGACATTACCAGCCTCACGCAGTTCAGCCTCTGCGGCTACTTCCGCAGCTACCTGGATTTTTCCGCCCCGGAACATGAACTGACGGGTTATGAACTGTCCCTGTTTTTCACCATTTTCGTCCTGCTCCAATTCTGGAACATGTTCAACGCCAAAGCATTCAACACGCACCAGAGCGCCTTTGCACGCATGGGCGCCAGCATTGGCGGCTTCGGTCTGGTAGCCCTGCTTATCCTAGCCGGGCAGTACCTCATCGTCACCTTTGGCGGAAAGATGTTCAACGTGGTGCCCCTGAGCTGGACAGACTGGGGCCTCATCTTCGCAGGCACCTCCCTTGTCCTGTGGTTCGGAGAACTGGCGCGGGCATTCACTCCGGATAAATCCGACGCCCGCTCCTGAACCGTCCGGGGGAATTCCCCCCGCGCATGACGCGGACGGCGACGCCGCCCGGAAAACCTTCTTTCAACAAAGGCGCCCGCAGGCGTGTCATTCCCAGCATGGACTTGACACGCCTGACGGGCTCTATCTCCAGCCCCGCCGTTTTCCTCTTCCTGCTTTCCCTCTTTTTTGCCGGGTGTAAAAAGACGGAACAGGTTCCGCATGAAACGCCCACGCTTATTTATGAACACCCCGTCACCATGGATATTCCGGTGACGGGCAGCTGGGTGGGCCACCTGGACGGCGTGGACAACGCCTCCATCGTCCCCCAGATTACGGGCTACCTTCAAACGCAGAATTACGCCAATGGCGCTATTGTGAAGAAGGGGGAAGTACTCTTCCGCATTGACGATTCCACGTTCAGGGACCAGGTATCCAAGGCCAAAGCCACCCTGGCCCAGGCACAGGCCCAGCTACAGCAGTTGAATTACGACGCGGAAATTTACAGGCCCCTGGCTGCGGAGAATGCCATTTCCAAGCAGAAATACGAGGATACGCGCCTGTCTGCTCTTGCCGCGGAAGCACAGGTGCAGCAGGCGGCGGCGGCCCTGGCCCTGGCTGAAAAAAATCTTTCCTATACGGTGCTGTACGCGCCTTTTGACGGAATAGCCGGCATCTCCAGGACAAACATCGGCGACCTGGTCAGCCCGGAAAGCGGCCCGCTTGCCGTCGTCTCCTCCGTCAATCCCATCCGCGTCAATTTCGCCGTCACCCAGCAGGAATGGATTCAGCAGGCGGGGAAAAACGGGAATGAAGGCGTCCAGACGGGCAGCAAGCTGGACATTACGCTGAAAGACGGCACCAAATACCCGGAACAGGCCACGGTAGTTGCCATTGACCGGGCGTTCAATCCCCAGACAGGCACCATCCGCGTACAGGCCAACCTTCCCAATGCGGACTATCTGCTGCGCCCCGGCATGTTCATCATCGCCACGGCCCGGCTGGCCACCGTCAAAAACGCGCTGACCGTTCCGGCAAAATCCCTTCTTTCGACACAGGGGCGCTTTTTCGTGATTGTTCTGGACGGGAGCAACCACCCTTCCATCATCCCCGTACAGGCCGGGACGCAGCTTGGAGACCGGCAGCAGGTTATTCCTCTGAAACCGGATTCCCTCACCCCCCAAAGCAAAGTGGTCGTGGACGGTCTTCTCCAGGCGGAAGCAGCCTCCAGAAATCCGGCCGCCCGCCTGAAAGCGGTACCCGCCAGCAACCAATAAGCTCCTCCCGCCATGGCTGATTTCTTCATCAAGCGCCCCATCCTTTCCATCTGCCTCTCCGTCATCATTGTCCTCCTGGGGGCCTTTTCCATTCTGCGCCTGCCCATCTCCGAGTACCCGGACATCATCCCCCCCTCCATCCAGGTGACGGCCACTTATCCCGGAGCGGACTGTGAAACGGTGGTCAAGTCTATCGCATCCCCCATCGAACAGCAAATGTCCGGCGTGGACGGCATGTCCTACATGACTTCCGTCAACACCAACAACGGGCAAATGAGCATGATGATCCTTTTTGAAATCGGCACGGAAGCCAACATGGACCAGGTACTTTCTTATCTGCGCTACGGGCAGGCCACCTCCCAGCTGCCGTCGGAAGTCAGCGCGCTGGGAGTCTCCCTGCGCAAGACAAGCGGTCTGCCCGCCCTGGTCGTCAGCCTGTATTCTCCCCAGGGGACTTATGACGGCCTCTGGCTGGCCAATTACGCCTATATCAATATGGTGGACGCCATCAAGCGCGTGCCCGGAGTAGGAGACGTGCAGGTGTTCGGGGCAGGCCGCTACGCCATGCGCATCTGGCTGAACCCGGAAAAGATGGCCGCTCTGAATATTACGGCCAGGGACGTGATGCTGGCCGTTCAGGCCCAGAACGCCGTGAATCCTGCGGGCAAGATAGGCGCACAGCCCGCTCCGCCGGACCAGCAGCTTTCCTTCACCGTGAAAGCTCCAGGCCGCCTGACGAGCGTGAAAGAATTCGAAAATATCGTGGTACGCGGGCAGGACAGTTCCATCGTAAGAGTGGGAGATATCGCCCGCGTGGAACTGGGCTCGGAAACCTACAGCCTCAGTTCTTCCGTAAACGGCATGCCCGCCGCTTCCATCGGCATTTACGAGGCGCCCGGAGGAAATGCCATCCAGCTGGTGGACAATATCAAAGACCTGCTGAAGGATACGGATATGCCTCCCGGCATGGATTACCTCGTTTCCCTGGATTCCACGCTGGCCGTCCGCGCCGGGATTGAAGATATCGTCAGCACGCTGGTGATTGCCCTGGGCCTGGTGGTGCTGGTGGTTTTCATTTTCCTCCAGGGCTGGCGCGGTACGCTGATTCCGGCCTTTGCCGTTCCGGTGTCTATCGTGGGCGCCTTCATCGCCTTTCCATTATTCGGTTTTTCCATCAATACCATCTGCCTGATGGGGCTGGTGCTGGCCATCGGCCTGGTCGTGGACGACGCCATTGTGGTGGTGGAAGCAGTAAAGAACCATATTTCCAATGGAGAAAAGCCCCTCCAGGCCACCATTGCGGCCATGAAGGAAGTATCCGGCCCCATCGTCAGCACGGCGCTGGTGATTTCCTGCGTCTTTATCCCCACGCTGCTGTTGCCCGGCGTCACAGGCAAACTGTTCGAACAGTTCGCCGTGACCATCGGCATGTCCATCATCATTTCCGCCTTCTGCGCCCTGTCCCTCAGCCCCACCCTGTCCGCCGGGCTGCTGAGAGGGGGGAAAGCTGACTCCATTCCCCTGCTGGGGCCCTTTTTCAAATTGTTCAACAAGGGTTTCAACAAGGCGCGCGACTGGTACGTGGAAATCTGCGCCAGGCTTATCCGGCACATGTGGCTGTCCATCCTGCTGCTGGCCGCTATGACCGCCCTTCTCTTTCCCCTGGCCAAACTTATTCCCAGCGGATTCCTTCCCAATGAAGACCAGGGCTACCTGTTCGGCGGCGTGGAACTGCCGGATAATACATCCCTGAACGTCACGGCAGACACCGCCGCGCGGATTGAGAAAATCATCAGGGAGGATCCGGGCGTGGACGTAGTGACTACCGTCAACGGCTTCAACCTCATCAGCTCCGTCCAAAGCTCATCCAACTCCTTCTTCTTCATTTCCCTGAAACCGTGGTCCGAACGGACTGCTCCGGGCATGACGGCGGACGGCATCGCCGCACGCCTGAAAAGCAGGCTGAACGCGGAAATTTCCTCCGGCGTGGCGTATGTGGTTCCGCCGCCGCCCCTGCCTGGCGTAGGAACGTCCGGGGATGTCACATTCCTGCTGGAGGACCGCCAGGGAATCGGGGAAAAATTCCTGGCCGCCAACACGTCCAAATTCATCGAGGCAGCGGAAAAGAGGCCGGAAATAGCGGACATCAGCAATTTCATGTCTCCCTCCAACCTTCAGTACAACCTGAATGTAAACACGGAGCAGGCCACTCTCCAGAATATGGACGTGGATGAAATTTACGCCACCATCCAGGCATACATGGGAAGCACCTTCCTGAACAATTTCAATATTTACGGGCAGGAATGGCAGGTGTACATGCAGGCGGACGCACCGTACCGGGACAGCATCGACAAGCTTTCCATGTTCTATGTGCGCAACAATGAAGGAGACCCGGTTCCCCTGGATTCCGTAATCAACGTTACGCACGGCTGGGCCCCGGAATTCCTGATCCGCCAGAATATGTTCAACAGTTCCCAGCTCAACGTCACCCCGGCTCCGGGCTATTCCTCCGGCCAGGTCATGAACGCCCTGGAAGAAGTTTTTGCCCAGACCATGCCGTCCGGCATGGGATACGACTATTCCGGAATGAGCTACCAGGAAAAACAGGCGCAGAAAGGTATCACCATCGGCATGATCTTTGCGGCATCAGCCGTGTTTGTCTTTTTGATTCTGGCATCCCTGTATGAGAGCTGGTCCCTGCCCGTAGCCGTCTTCATGACCGCCCCCATTGCCATCCTGGGAGCATTTCTGGCCCTGTGGATAACCGGGCTGGACCTGAACATTTATTCGGAAATCGGCCTCATCGTCCTGATTGCTCTGGCGGCCAAAAATGCCATCCTCATCGTGGAATTCGCCGTGCTGGAACTCCGTCAGGGCACGGACCTGCTCACGGCCACCCTGGATGCGGCCAGAATCCGCCTGCGCCCCATCCTGATGACCAGCATCGCCTTTATCATGGGCTGCCTTCCTCTGGCCGTAGCCACGGGAGCGGGCGCAGCGGCGCGGCAGGTGGTAGGCATCGGGGTCATCGGCGGCATGATGACAGCCGTCTTCATCGGGGTATTTTTCATTCCTTCCTTCTTCTACCTGATTGCCAAACTTGCCAGGCTGGACAAAAAAGCGGCGCGGAAACTCCAGGAAACGGAACAGGGAACTCCCACCCCTGCGAAGCAGGCCGAATGACACTTCCGAAACGGGAAAACGGCTCCCTCACAGCTTCCCATTCCCCCTACATCCAGAATATCCAGGCCCAGAAAGCCTTCACGAAAACGAGCACCACATTAATCAGCAGCCCCACCTTCACCATGGTCATCTGAGGGAATTTTCCCGTCGCGAAGGCCAGGGCGTTGGGAGGAGTGGAAACGGGGAGCATGAAGGCGCAGGAAGCGCCTATGCCTACCAGAAGCACCAGCGGCGTAGCAGACATGCCCATGGCGGAGGCTACCGTGACCATCAGCGGGGCCACCAGGGCCGCACTGGCCGTATTGGAACAGAATTCCGTCAGCGACGTAATGAAGAGGCTGATGATGAGCAGGATGACGATGGGGCTCTGCCCCATGGCGAGAGAAGATACCTGGTCCGCCAGGAATCCCGCCGCCCCCGTCTGGACGAGAATGCTGCTGAGCGTGATGCCTCCCCCAAACAGGAGCAGGACGCCCCAGTCCGTATTTTTGGCGATGCCGCTCCAGTTGATAACCCCGCACAGCGGCAGCAGCACCGCAGCACTCAGCGCGATGAGCGTGTCCATGGACGGAATGCCGCCCAAAGCAGAGGAAAGGAAACTGCTGCACATCCAGCTCCCGGCCACCAGCACGAAGAGAATAAGGACACGCACCTGCCTGGCATTCAACCTGCCCTCCGTTTCTCCCCCTTCCGCCGGCGTCATATCCACATGCATCCCCAGACGCGGACGGAAGAAAAGATACATCAGGCAGAAAACGATCACTCCGAACACCAGGACAATGGGCATGGCAATCCGGAACCATTCCGCAAACCCCATGCCGAGTTCATGCGCCGCAATGGCATTGGGAGGAGACCCCACCAGCGTCCCCATACCGCCGATGGAGGCGCTGTACGCCACACCCAGAATAGCAAAAGGAGCCGTCGTCTTAAGTTTTTCCGCCGGAATGCGGTCCAGAAGGCCGATCACCAGAGGAAGCATCATCGCCGCCGTGGCCGTATTGGACATCCACATGGAAAGAAACGCCGTAGCCAGGAAAATCAGAATCAGAGCCATCCCCAGACTCCCGCGGGCCATTCGGAGGATTTTTCCCGCCAGCCAGGTATCAATTTTCTGTTCGTGGAGAGCCCCGGCCAGGGCAAAGCCCCCGAAAAACAGAAAAATGGTAGGATCCGCAAAACCGGACAGGGCCTTCGCCCCCGGCAGAATCCCCATCAACATGGCAAGCACCGGCACCAGCAGGGAGGTTACGGTTACGTGCAGGGCTTCCGTCAGCCACAGGATGCCGATAAAGGTAAGAACAGCAAGACCCCTGGCCACCTCCGGCTCTACGGGAAGCCACTTGAGCATAGCCAGGAAAAGAAGCACGTCACACGCAATGATGATGTAATTGCGGTGCCCTTTGGGAGTGTCTGTAGTACGGTGGCGCATAAAGCAAAGATAAACGACGCTATTTTCTTTGTACGCATCAGAAAAACAGTGTCAATGCTTTTCCATAAAAATCCCGGAAAAGGCCTTTCCCGGAAACAGGGAAACACGGACCCGCGCACCGGCTTGAGGATACGCACGGCGCACATTACCGGAAAAGCAGAGCCTCTTCTTCCTGCTTCTGAAAAATTCAGGAAGCCCATACACGCAAAAAACCGCCATGCAATCGCACGGCGGCTTGTAACAACTGTTTGACTGGCGCGCGGAGGGGCAGCCTCCGCCCGGGCAATTATTCGGCGGCGGGTTCAGCGGCCTTCTTGGCAGCCGGCTTTTTAGCGGGGGCCTTCTTGGCGGCGGCAGCCTTGGTTTCAGTCTTTTTGGCCGGGGCCTTGGCCGCAGTCTTCTTGGCGGGAGCTTCCTTGGCGGCAGGCTTGGCATCCTTGGCAACGGGAACGGCCAGACGGATAGGCGCATTGGAATTCAACTTGGACTGTTCCTTCTTACGCTTGAGGTATTCAATACGTCTGCGGCGCTTGGTGACTTTGCGGATTTGCTGTCCCATAGTGATAATTGCTATGATATTTACTAATTCTTACGAGCTCCATGTGGCACATGGGCGTGGATACAACTACCAACCCCACCTCCCTTACGCAAGCACAAAGCGTTATTAAGGCACAGAAAGGCGGAGGCAGTCCCCGGTTACCCCAACCCGCGCGGAAGAGGAAAACAACTTGGATGAAGGAGGCTTTACCACAATTTCAGCGCATCCACCCGGACCGGCTGTTCCCAGCGCAGATGGTTCCGGATTGCGGAAGCGATGAATTCCAGGCCGCGCTCCACCGCCGCAACCAGTCCATCCCCGGCAGCCAGCCGTGCCGCGACGGCGGCGGACAGGGAACAGCCCGTTCCGTGAGTACTCACATCCTGTACGCGCGGACGCTCCCATTGCCCCAGCAGGCAGCCGTCCGGGCCGGACAGCACGTCCCGGCAGTCCCCTTCCAGATGCCCGCCCTTCAGAAGAACGGGGCATCCGTACCTGACAGCGAGGCGCGCGGCGGCTTCCGGCAGTTCGTCCCTTCCCGGATTAACGGAAGAGCGGAGCAATACGGCAGCCTCATCCAGATTGGGCGTAAGCAATGCCGCACCCGGCAGCAAAAGCTCTTCATAAACAGCCACCGCTTCTTCCCGCATCAGGCGGTCGCCCGCCGTGGCGATCATGACCGGGTCCACCACGACGGGAATATCCGCCCCGTCCAGCACCCCATGCACCGCACGGACGATAGCCGGGGAATACAGCATCCCCGTCTTCACGGCGCGCACGGGAAAATGTTCCAGATTGATGCGCACCTGATCGGCCACCAGCGAAGGGTCCACCTCCTGAATGCCGCGCACAGTCCCCGGCGCCTCGGACACGACGCAGGTGACGGCCGTAAGCGCAAACGCCCCCATGGCATGGGCCGCTTTCAGGTCAGCCTGGAGCCCGGCCCCCGCGGAGCAGTCGGAACCGGCAATCGTCATCATTACGGGAATACTCATGGCCTTGTTTTTAATCGGGAAAGAAGCCCTTGAACATCCCAAAATCCGCATGTGCCGCCTCAATGTCACAAATCCGGGTGGGAAAACAAGGAGGGAATGCCACAATCATCCCATGACGCACGCCTCCCCAGCCCCGGATGAACAGGAATTCTGGCTTTTCATTCAGGATGCCCCTCTGCCCTCCGCCGAGGAATTGCAGAAGAACATGTTCCGCCTGGACGGCTGTTTCCGGCTGAACCTTTCCGGTGAACCGGAAAACGCTCCGGACCATGTGATGAAAGGAGAATATCTGGATGAAGAGGGAGAATCCCAGCTGGACGTATTCTGGGTCATGGAAACGGACGACGCGCGCCAGGCTTTCAAAAAGGACGGCGCGGCTCTCCGGCAGATAGGGAACCGCACCAAAATGCTGCGCTTCATTCTGGAAGACGAATATGCCCTGGGCCATGTATTCGCCATGATTCACGCCATACTGGAACACCGGGACGGCCTGCTGGTTATTCCGGACACCCGGGGAAACATTATTCTGGAACGGAGGAAAGCTTTGGATTTTCTGAACAGGGAAATTCGGGAAAGTTAACGTTTTTGCGTTTGCGGCGCAGAACTTCCCTCGCGTTCCGCATTCAGTTGAAGCGCCTTCTTTTTCCAGACTTCCGCCATATCCGCATTCTGAGGGGTCCCTTTTCCTGTGCTGTAGCAGACGGAAAGCATCAGCATGGCGTTGACGTGGTCCTGCCCGGCGGCCAGCCGGAGCCAGCTGAACGCCTTGCCCTCATCCACAGGCACGCCGGAACCTTCCAGACAGGCCAGTCCCACAATATACTGGGAATGGGCGTCTCCGGCTGAAGCCGCCTGTTCATACCACTTCATGGCACGCTCCATATCCCGCTCCACCCCCTGCCCCTTTGCATAAATGTTCCCCAGATAGACGGACGAAGGCGTATTCCCCCCGTTGGCGGCCTTCTCCAGCAGCGCCAGGCCGGAAGCGGCATCCTTCTCAACCCCCATCTTCCCCTCCATGTAAGCCCGGCCCAGCAGCCCCATGGCTGCCGGATGTTCCTCATCCGCTGCCTTGTGCCACAGCCCCAGCGCCTTATTCAGGTCCTGATCCGCGCCATCCCCCTTGAAATACATCATTCCCAACATGTACAGAGCGTTTCCCCGCTGGGTCCCCGGGCGGGCAAGCCCGTACTCAAACCACTCCCTGGCCGCTACGGCATCACGCTTGATTCCCGTCCCATACAGGAACATGGCGCCGATGACCGTTTCCCCGGAATACCCGGAACCGGCCTCCGCCACGGAATCACACCAGGCCCGCACATCCAGCGGATGAACCCAGCCATTCTGCAATCCCTCCGCATACAGGGCAGCCACATCATTGAGGGCTTTCACATGCTCGCTACCATTTTCCTTCTCGCGGACGGAGCCGGCAACAGCAGCGTCCAGACGCTCCCTCCATGTCTCCGCCTCTTTCCGCACCGCAGACGGCTTTTCCCGTTCCGCAGAAGAAGCAGCCCGCTTTTCCCCGGAAGAAGGGGCGCGGTCACATCCGCACAAAAAAAAGAAGGCGGAGGAAGCCGCACATAAAGAGACAACTTTTTTCATCATGGCCATTACCATAAGACAGCCTCATCAGGCTGCCAAGAAGAGAAATGACGGAAACGCCGCATCCGCACGCTATGGAAGAGCGAAACCGAAGAGCCGGATTCCCTCTTTTTGCATTGAATATTTTCCTCTTTTTGCTCTACTGAAACCAGTTCAACCTCTGATGAAATTCGTTCCGCCCCTGTATCTCTTGACATGCCTGGGCCTGGCGTTCCTGGGAAGTTTTTCCAGCCGGGGTTCCGCAACGGAACTTCCCGTAAAATACATTTTTGAGCTGTCGGATGAACCGGTGAAAGTGCACCCTGGCAAAATAGACACCAAGAGCGTTTTCTTCCCCAAATACGCACGCGGAACCTCTCCGGAAACCCTGAAGCGGCAGGCAGCCCTCTTCCAATCCCATACTCCCCACGAGGCCACCAAAAGCAAACCGGCCATCAGCATCCACATGAATGACGACAAAACGGAAAGGGACGTCCGTGAGCGGTCAGGCTATGAGTTTCACCATGCGGAAAACCAGGAAGAAATTGTCATCACAACCGAAACGCCGGATGAGGAAATCTGGCTGAACGACGGGGATCCGGACAGCCCCTTTTTCCATACGCCCCGTTCCGCCGCCAATACGGAAATTCGTGAAATCCCCACGTCTTCCGATTCCTGGCCCGCCTGGAATGAAGAGGAAGTCAACCTGCACATGAATCTTCCGGAATCGGCGCCGCGCCCCCTGGTAGTGCAGCCGGCTCCGTCCGGAGCCATCCGCACCATCATCAAATCCTTTAACGGAACCGTTTTTGAGGCGGATGCCCAGCCCATGCATCCCGTCTGGGATCAGCTCCCGCCCGGATACGTTCCTATCCCGGCTCTCCCGGCCTATCAGCCCACCTTATTGAAGAAATTACAATAGGAGGGGGCAAAAGCCTCAGAACTTGAAGCGGACAGCCCCGAAAACAGACCATCCATTGAAAGCTTTGGCCGTTGCCGAATCCACATGGCTGGTGACATACTCTACAGCCAGCATCAGCTTGACCGCATTGACGGCCTCCGGACACAGATAGCAATTCAGCCCCAGGTAAAAGGAATGCATGCTGTCCACCCATGCGGGATAATGGGTGACGCTGGGCACATAACGGGTATTCAGCTTCACAGAACGGTTTCCAAAGGAACACTGGTATTGAAAGACGCCCTCAAAATGCGGGGAAATCCGGTAAACGGGCTGCAGAACCAGGCCGTACACATTCTTTGCACCGGGCTGGCCTACAATTCCCACGCCCGCCAGCAAATTTCCCATGATGGAGAAAGCGCCGCGGCTGGCGTCCCAGCTCAGGGAAATCACGTCCTGCGCCCCCGTTCCGCAGTAATCGGACGTGGAAGGAATTTTCCTTCCCCGCCATTCCGTAAAATTATGGGCATACTGGTACCCCAGAAACTGGCTGTCCCACATGGGAGAAGCCACCTTCCACTTCATGGCGTTCAGGATAAACAGGTTGTCCGCAGAATGAAACTGGATCTCATCCTTCAAATCCGTACCGTTCGCATTCAGGTAAATGCCGTAGGAATGGTACAGGCTCTTGGCATCCTTCTGAAAATTGGCCTCCAGCCCCCAGTTGGAAATGGGAATCAATTCATTGCACAGGGCGGATCTCTCTACTGTTTTAATCCGGGAGGAAGCCAGGCAATACTCCGACGTCAGGTGAGGCGTCAGCTTCCCGGCCCTGAGCTTGACGCCGGGCATGGTCTTTTCCAGATAAAGTTCATACAGGGACCACTCCGTATGGCTGCCGATCCATTCTCCCCGCACTTCACGGTGCCGGCCTTCCAAATCCCCCACATTCGTCAGATTGGACAGGCGCCAGGAACCGTCCCCCATCAGGATGTCGCCGCCCAGGTAAGCGCGCCTCCACCCGTTGTTATGGCCGCCGGAAGACGGGCAGAATTTATTGGCCCCATTGGGGCTGACGGCTGCTGCCTGATACTGGCCAATCAGCGTCAGCTTCACCTTCCTGATCCATGAGGAATCATTCCGGTACAGCACCGGACCGCGATCCAGCACACGGCACAGGGACGGCAGAGAGCCCCCGGAAACCGGCTGGGACTGAACTGCTCCCGCGCCAGCTTCCGCCGCCGGAAAGGCGGCATACCAACAAGCGGCCAACAGGAACATTCCTGGAAAAACAAGCTTCATCATAGGCTAAACAAACAAATTGGGCGCACATCTTAAGGAGCCCTCTCCGAAAATCAAGCCCGGATGCTCCTTTCGACATTGTTGTTTTCTGAAAGGGTTGTTACGAAGAACGGCAATGATGGACGCACCAGTGCGCCCATCCGGAACGTCTCCATCAGAAGGCCAGGGACGCAGGTTTCAGCACGGCTTGGAGTTACGGCTACATCGGCCTGTTGTATCTCCGGCCTGATGAACCGGGCCATTTCAATCACTTCCTGTACGATGAAGAAACAACGATGCAGCAGCCTGGCATCATCGCGAACAGTTACCGGCACCAAATCATCACTCCGGCAAGCTTTCCCTCCCGGCCCGCATGGGGGAACATGCCTGAATAATTATTTCCCGCTCCTGCGGGGAAAGGCCGTAAATGTCTTCCATCAGCCCATTCATCCGCTCATCCCCTTCCGGGCAATAGCGGGCCGCGAGAGCGTCCGCCAGCCCTTCCACTTCCCGCAGCAGGGACAGACCGGGGACCCGGATGGGAGCCCGGAGGACAGGGGCGGTATCCATCTGGAAAAAATCCCCCTGCATTTTTCCCCGGCGGAGAAACCAAAACTGCATCAGGCGCGAATTGAACAGGGCCGCCAGATATTTCATGCTCACGCGTTCCGAGCGTATCACGTTGAATGCCATCATCACATAAGCTTCCTTTTCCGTATAGGAGAAGGTTGGACGGGCACACTTCCTGACAGCCAGAATTTTGGGGCCGGGCCGGAAAAAACGTTCCTGCCGGGGCCAGTGCACATGGTAATATTTCATGCGGCCCATGCGTGTTTCCCGCCGGGCCTCCATCAGGGGACGGAATTTTTCCAAATGGCGCAAAAGCGTAACGGCCTGCTCCGCTCCGTTGGACGGGGTCAGATATAACAGCACCTTCTCCGGAGCTTTCAGTGCATGCCGCCCTGCCTGGACAGGTTCATACAGAGGTTTGAGAAAACGCCGCTCCCGTTCCGGCAACAGGCCAAAATACCCCTGAGGCAAGACAAATACCCCATCCCCCCTCCGGATTCCGTACCGCCGCACTGTTTCCGGCGCCAGATTCTCCAGAGCTCGGGAAGAAACGACGTCAGGATTCGGCACAATCCCCTGCGTCATTTCCTTTGCGGCATCCAGCCTGAAATTCCGGCAGGCATCCATCTTTTCCAGAAGATGCCGCTCTGCTGCAGAGCAGAAGGACAAACCTTCCCTGACGCACGCTCCGGCATCTTCCGGCAAGGGAAACCGACGGTACGGCTCATGTTCCAGAAATGATTCCACGTCATTCAAGGAGCCGCCAAAACGGCGGTATTCCGGAACCAGGCGTCCGCCTCCCTCTCCCTTTTTCTCCGCCAGAACAGTCATGGTATGGACGCGGGCGGATTCAAACACGCGGCAGGCTCCGAAATCCGACAGGCGCAGCAAACCGCAATCTTCCAGCAGCTTGCGGCGGAGAGAGGCGGCTCCCGCATTAGACATCCACTTGTTGGGAACGACCAGATGCATTACGCCCCCCCGCTTCAGGGCATCCAGCCCCACACAGGCAAACACGTACCAGTAATCCATGCGGGAAGAGCAGTAGGAAGCAAGGGAAGAACATTTCAAACGGTCAAATAATTCCTTGTTCCCTTTTTCTCCGACAAAAGGGGGATTGCCGATCACCAGGTCAAAGCCTCCTTCCTCCATCACCGTTGAAAGACCCTGACGCCAGACGCCGGACGCAGACATATCCAGACTGTCCCCGCACAACAGGCGATCCCGTAAATCCCCCTTTCCTCCGGCGGCCAACCATGCGCAACGAAAACGGAAACGGGCCACCTCCAATGCCTCCGCGCAAACATCCACTCCGTAAATATTCTCTTCCAAAACGGCGCGGATTAAATCGGGCTCCGGGCAATCCGGTTCCAGAAGCTTTCTGCGGGCGACAAGCTCCCGGAGCATTCCCATGGTGAATGCTCCGGCCCCTACGGATAAATCCAGCACACGGATACTTCCCAGCAACCCGGCTTCTTCATCACCGCTCCTGGGAGAAGGCAATCTGGATTCCAGCCAGGAAGATACCGCTTCCCGGGCCATCCAGCGAACAAGAAAACCGGGGGTATACACCGTCCCGGAACGGCGGGCGTCCGTCAGGAAACGCCCCAGCAATCCTTCCAGCACGGAAGGAGTAACAGCCCGGCAGGCATCCTCTTCCCTCAAGGAAAAAGCGAATCCCCCCGGCAGCCGGACCTCATGGCCTGCCTGCCGTTTCTGCAAGTTGTCCATCAAAACCTCTTTCAATCGTTCCCAGGTTTCAGGACGGCCCCCGTTGCCCTTCTCCACGGAGAAATACTGAGCTATAAAACAATCCTGAACGTTCCGCTCCTTCACTGCGGCATTCGTACACTACTGGAACAGGCCGCGCAACCAAAAGGAGAAATTGTCCAGAAAGCCAGCTTCAGAACATGAATGCCCGGAACTCCCTTTTCTGAACATTCCCGGCGCCCCAAAACCGTATCAACCACCGGGAAGGGGTCATTCGAACAGATTCACCTATATGCGCGCATATTGAAAAACTTTAACCGCCCGATAAACATTTGAAAAATATCCCCTATTATTATTTTCCGGCGCAGCGCTGGATGAAAAGTCAATCATGGCCATGATGAATCAATCGAAAAGCGCTTCCGGCAGAAAGGGAATTTCAAGCATCGATGAGACAGGAGGCTAAGGCAGGAAATGCCCCGCCAGCCTCCCCATGCTGACGTTGTCACCTTCTTCAAATTCAGAAGAATTTCCTCAATTCGTCTCTCTATTCCCTTATTTTGCAATAAATAAAATCCGGCTTTGCCATCAGCAAGCCGGACTAGAAAAGAAATGGTACCCCGTCACGGGCTCGAACCGTGGACAAACTGATTAAGAGTCAGCTGCTCTACCAACTGAGCTAACGAGGCACAGGAATGATGGCGTTCCTTGCAGGAACAACCGGATGGTACCCCGTCACGGGCTCGAACCGTGGACAAACTGATTAAGAGTCAGCTGCTCTACCAACTGAGCTAACGAGGCATGTATCCGGCGATTCCCCCGCGACAAGCACCACCCATGAAGATGGAGCCACTGGTCGGAATTGAACCGACGACCCACGCATTACGAATGCGTTGCTCTACCCCTGAGCTACAGTGGCATCGCGAGAAGCGCACGAGCAAGTAATCATAAGCAACCCCTTTTAGCAAGTCTTTTTTGCAATAAGACCGTTTTTTTACGCCTTGAATGCGGAATAAAAGCCAAGAGCGGCAGGTGACAGTAGAAGAGCTTGCATAGGCGGGGGAAACATGGTGGAGTGCATGTCCTCAAAGAGCTTTTCTTTCCCTACTTATCATGAAGGCATATTACATTTTCCAGCAGGCGGACGAAGAAACAATCAGCAACATTCTGGACTGGATGCGCAACCAGGAACGCGCCATTTACCGCGCTGCCGTGCGTGAACTTGGAGCCCTGAAAAAACTGCGCCCCGAATTCATCCAGCGCAAACCTCTTCAGGAACAATTTTCCTTCATCAAGAAAATGCTGTCATGGAAACCCAGCAATGAAATCGGTGACCATCTGCTTCAGGTCTGGCTGTTGCGCAAACACCAGGACATGCTGATTACGTTCCTGAATACCCTGGGCATCCCCCATGACGGCAACGGCATTGTCAATGAACTGCCGGAAACGCTGGACAAGGAGAAACTCGCCAAAGCTGTGGACGAACTGTTCGAAAAATACCCTGCCGGCGTAGCGTCCGTCTACCTCCAGATGTTCCAGCTCCAGACGGAAGACGGCTGGGAAGAACTGGCCGAGGTTCTGGCGAACGATCCCCGCGTTACCATCCGTTGACGTTATTCTCCTTCGGGACATAAACAAGGGCCACTCCGTTCCGGGGATGGCCCTTTTTATGGAGGCAAACGGACGCTCCTGCCGCTGTTATTCCAGTTCCAGGCGGAACAAGCGTCCGGACGGCACGGGACCTGTTTGAAACAGCTGCAATGGAAAGCATGCCGGAAGCGCAAGCCCGTCAGGAGGGAACGCTGTCCGACTGTTCCGGATGGAATGACCACCCCTCATCTCCGTGGTACACCATCATACGGGACATGCCGCCGTCAATGACCAGATTCTCCGCATTGACAAAGCTGGAGCCGCATAGAAAGAAAACGGCCTTCACGATATCTTCCGGCAAACCGACGCGCCCGGAAGGATGCTGAAAAGCATCTTCCGGAGACAGGACGCCGAACACCCCCGTGTCAATCCAGCCTGGACTGATACTGTTCACACGAACGCCGAAAGGGGAAAGGCTCATGGCGAGAGCGTGAGTTAAAGAAGTGAGGGCGCCCTTGGCCGCACTATAGCTTTCCGTATCCTTCTGGGACATGGAAGCGCGGGTGGAAGCAATATTCACAATGGAAGCGCCGCGGGAAAAATGGTCGCGAAATAAAAGGCTCAGCAGATAGGGCGCGGAAACTCCCACGCGCAAAACTTCATTGAATTCCTCGAAGGAACAGCCGGAAAGAATGCCGCGACGCGTGATGCACGCATTATTTACCAGCACATCCACACGGCCAAAACATTCCAACACCTTTTCCGCAAAAGACCGCACGTCCTCTTCCACACCCGCGTTGCCCGTCACGCAAAGATGCCCCTCTCCCGGCAATTCCTCCAGCAGGGAGGCCGTTTCCTCATGCTTCACATCCAAAACCGCCACCCGGGCTCCCGCCGCAGCGAAAGCTTCCGCAATGGCGCGGCCAATGCCGCAGGCTCCTCCCGTCACCACCACGGTACTGCCGGAAAAATCAACATCCTTCATAGGCTGATTGTCATATTCCACTCAAGAAAGTCAATCATCGCCTTCCGGCATACGGTACTTAACGTGCATACATTACATCGCGGTAAAGCCCCATGGCTCTGGAAAGGAGAGGAGAACGGGTTACCACGTCCGTTTCCACAATAAGACGGGCCATGGAACCGGAAACGGCCATTTCCGCTTCCTCCGCACGGCGGCTGATACGCTCCATATCCCTGGTTTTCACCAGATTCATACGTCCGGATTTTTGGAAATCATCAACAACATAGCGCAACCTTTTCAACTTGGCCACCACTTCCACCAGCTCCATGGCGCACTGGTCAGCCGTTCCGGCATCCTTGATGCTGCGCAGGAGAAAGTCAATTCTCTCCAGGCACATGCAGAACTCTTCCATGGCCGCGACAGGATCGGCCCAGAACTCCTGAAAATCGATTAGCCTGGGTTGCTCTATTTTGAGCCCCTCCACTGCCTTTTGCATGGAAGGGGAATCATACATCCTGGCCGTCGTGAGCTTCAGGAGCGCCCGGAAAGCGCGTTTGCGCTCATCCTCCGCCTTCTTTCTCATCTTTTCCAGCCCATCATGGAAATGCCGTTCAAAACCGGGAATCCAGTCTCCATACTTCTTCTCCATTCTGGCAATGTTGTTCATCTGATCCCTGACCATTCGTCCCTGAAGGGCTACAGATACGGCATAGGCTTCCGCCGCAACTTTATTGTCAATCCCCTCCAGCATGTCCACCATGGTGGAATACTGTTCCAGATAAGCCTTCAGAAACGGCTTTATCTTGTTGTTCAAATACAGGGCCACCCGTTCGTCCGTCAGGCTGAGACCGCACTTTTCCAGATCATTCCTGATGACATCGGAATCATAATACCTGAATTCATGGAGCTTCAGTATTCCCGACATCATCCGGTCGCGGGCCTTGTCCACGCGCTCCCGGAGAATGGAATGAAAGGCGGGATGCATCTTACGCGATTCTTCATGCACGCGCGGATTCAGAGCCATCAGGCGCCCGCAATTCATATCGTAAAATTTAAACTTCTCCGTCAGGCGCCCAAGTTCATTCATCAGGGCCTCTCCTGCGGTACGGTCCTGGATGGAAACGACCAGATCCGTCATCTGTTCCACTTCCTTCAGGCGCAGGGAAAGCAGCTCTTCATAAGATTCCACCGGGGAAAGCCTTTCTGACTTCCCTGCTTCCGCAGCCCCGGCCTCCTTTTCCTTATCTCCGCAGGAACACAAACACAGCGCCAGCAGGAGCGCGCCTCCCATCAACGGCAAATTCGGGAACAGAGAAAAACAGGAGCCAAGCTTCATGAGAACGTAGCGTTGAGGAGGGAGAATGAAACTCCGGCCCGCATCGCGGTACCGGAACTACCGTAAAAAAGCGTCATCAAAGTTCAAGGCCTTTTCATTTCATCCGGGAAAAACATTTCCGTTCCTTCGACGGAAAAAATATTCATTTAATATAATGAACACTCTTCTGAATCGGGATGTCACACTCTTTTATCCATATCATCCTGTCTGCCGTGATTCTTGATATTCTTCAATACGGACATCCTCTTTTGAGAGAGGAATGCGGGCCCGTGGTTCATATCAACAGGGATATCCTTTCCTTCCTGGACGATATGCAGGAAACCCTGGCCCAGGGAGGCATAGGCCTGGCCGCGCCGCAGGTGGGCAGGCCCATCCAGCTGGTCACCATCAACATCCCTTCCACAGACGCAACCACTACCTGGCTGGAAGTGGACGGATGCCCAACTACGTTGTCCCGAATCATGCCCTTGAATTTTATCAATCCCATCCTGCATCCCTTCGGCAAAAAAGTGCCGTACCGGGAAGGGTGCCTGAGCATCACCAAAGTATATGCGAACGTGATGCGCCGCTCCTGCGTAAGGGCTGTTTTAACCATGATGGACGGACGGACTGTGACCGTGAAATGCAACGGACTGCTGGCGCGCTGCCTGCAGCACGAGGTAGACCACCTTCACGGCGGCCTGTTTACGGATCTGGTTTCCTCCGGTGACCATGACAAGGTGATACGGAGGCTCAGATTGACCCATCCGGACGCTTTTGAAGAAGATGACGACAGCTACGCTCGCCGCATGAAGGAAAAACGCCGCGCCCAGGCACGGAAAGCCCGGATGCCGCAGCCTCCCGGAAAAACAGCCTGACGTTCAGGCAACAAACCTATCCCCCCTTTTACTGCCCTTCCCTAAAAAACGCCCCGGTCTCCCTCCAGGAAACCGGGGCATCCATAATCACATGGAAAGAAAAAGAGAGAATCAAACAATCCAGTCCAGAACAACCTTGCCGGATTTTCCCGAATTCATGGCTTCAAACCCTTCCCGGAATTCCGTGTAGGGCAGACGGTGCGTGATGATGGGCGCCACATTCAAGCCACTGCGGATCATGGAATCCATTTTATGCCAAGTTTCAAAAATTTCACGGCCATAAATGCCTTTCATCTTCAACCCTTTCCAAATGAACTTATTCCACTCGATGCTGGAACCATCGGGCTGAATCCCCAGCAGGGAAATGTTGGCTCCGTTGCGGGAATTGTCGATGATGTCCTTCAGGCAGGACGGAGCGCCGCTCATTTCCAGGCACACGTCAAATCCCTCCGTCATTTCCAGTTCCTTCATCACGTCTTCCAGCTTTTCACGGGTCACGTTCACGGTTCGGTCCGCCCCCAGCGTTTTGGCAAGACCCAGGCGGAAATCATTGATGTCCGTAATAACCACCGTCTTGGCTCCGGCCTTCTTGCAGACGGCGGCAGCCATGCAGCCGATAACCCCGGCTCCCGTAATCAGTACGTCCTCGCCCACCAGATCCCAGGACAAAGCCGTATGGACGGCGTTGCCCAGCGGGTCAAAAATGGAGGCGATTTCCATCGGAATGCTCTTGTGGATGCGCACCACGTTATCCTGCGGAATGGAAAGGTACTCCGCAAAGCAGCCGGGCCTGTTGACTCCCACACCCTTTGTGTTGGGGCACAAGTGCTTCTGCCCTGAACGGCAGCTGCGGCAATGGCCGCAGACAATATGCCCCTCACCGGAAACAATCTCCCCGGGCTTGTATTCCGTCACGGAAGAACCTACGGACTCAATCACGCCGCAGAACTCATGGCCCACATGCATCCCTACCGGAATGGTCTGCTGGGCCCATTTATCCCAATTCCAAATATGAAGATCCGTGCCGCAAATGGCTGTTTTATGAATTTTGATCAGGACGTCATTCGGGCCGACTTCCGGCATAGGAACATCCATCAATTCCAAACCGGGGCCAGCCTGCGTTTTTACAAGAGCTTTCATGCCCCCCATTCATGCTCTTTTTCCCCTGCGTCGGCAAGCTCAAAATTCATTTCGGAGCGCCGAAAAAGCCTAGGGAACGGAGGCTTCCGCCCCCCCCTTCCCTTCCCATGGAAACGCCCGGAGCAGCGGCGGGCGACAAACGCCGGAAAGACATTCCTGTTCAATCGACAATTCAGACACACACGCAATTTGCCCTTTCCAACAGGAAGGAATCATGATACCAAGGCGTGCGAGTTCTCCTTTTGAACACTTTACTCTATTTACAGAACCATGGCGAAATCATTTTTACCCACCATTTTCGAGCACCCGTATGAAATAGACCCCAAATATGGGAAAAGCGTTGCGTACTTCTCCATGGAATACGCCATTGATAATTCATTTAAAATCTATTCCGGCGGCCTGGGCTATCTGTCCGGCTCCCATATGCGCAGTGCGCATGATCTGCGCCAGAACCTCGTAGGCGTCGGCATTCTGTGGAGCTACGGCTACTACAACCAGATCCGTGCGGAGGACGGATCCATGGCCACGCAATACATGCGTAAAAACTACCCGTTCCTGGAAGATCACAACATCAAATTCCTCATTCATGTCTGCGGAGCGCCCGTCTGGGTAAAAGCCTACTTCCTGAATCCGGAAACATTCGGCACTGCGCCCATGTTCTTCCTTTCCACGGACCTGGAGGAAAATGACGAGGAAAGCCGCAATATCTCCCGCCGCCTGTACGACGCCAACGGCTTCACCCGCATCGCCCAGTACGTACTGCTGGGCAAAGGAGGCGCCCGCCTTTTTGACGAACTGGGCATTGAACCGGAAATCTACCATCTAAACGAAGCCCACGGCCTGGCTGCGGCCTTCCACGTGCTTGCCAAGACGGGCAGCGTGGAAGAAGTGCGCAAGCGTTTCGTTTTCACTACCCATACCCCGGAAGAAGCCGGCAACGAAAAGATGGACGTAAACACCATGAATACGTTCTCCTTCTTCGACGGGCTCTCCATGGAACAGGTGCGCGCCGCCGTAGGCATGACGGACAATACGTTCAACTACACGCTGGCCGCCCTGCGCCTCTCCCACATCGCCAACGGCGTTTCCAAACTCCATGGAGAAGTATCCCGCCAGATGTGGAAGGACTATGAAGGCATATGCCCCATCATCCATATCACGAATGCCCAGAACCAGAAATACTGGCAGGATCCGGAACTGGCGGAAGCTTTCCAGGCCCGCAACAAGGAAGCGTTCATCCGCCGCAAGCGGGCTCTGAAAAAAGCCCTCTTCCGCATGGTGGGTGAACAAACGGGCCGCGTCTTTGACCCGAACTGCCTCACCATCGTATGGGCTCGCCGTTTTGCGGCCTACAAGCGTCCGGACCTGGTCACCGGCAATCCCACGATGTTCGAACGCATGCTCCAGCGCACCAACTATCCGGTGCAGTTCATCTGGGCCGGCAAACCCTACCCGATGGATCATGGCGCTATTGAAATCTTCAACCGCCTGAATGACTTGACGGCCAAATATCCCCGTTCCGCCGTGCTCACCGGTTATGAACTGGGCCTGAGCCGCTACCTGAAAAACGGCTCCGACGTATGGCTCAACAACCCCGTGGTAACCCGGGAAGCATCCGGCACCTCCGGCATGTCCGCCGCCATGAACGGCTCCATCTCCGTCTCCACCAATGACGGCTGGATCTGTGAATTCGCCAAGGACGGAGAAAACTGCTTCGTCATTCCGGAAGCGCCCGCCCATCTCTCCCCGGAAGCCCGCGACCGCTCCGACCGCGATAACTTCTACAACATTCTGGACGACAAGATTCTTCCCCTCTACTACGATCACAACGACAAGTGGATGGACATCGTTTTCAATGCGATGACAGACATCTATCCCGAATTCGACTCCGACCGCATGGCGGATCAGTACTACACGGAAATGTACAACAGCTAAGATACAGCCTTGTCCCTCTTACGCAACGGCCGCCTTTCTTTGGGAAAGGCGGCCGTTCTTTTTGAAACCGGTGTCAAAAATCCACGGGAAGCGGGCCAAACCTACTTGGGTACTTCCTCCTCCCCGGCCTCCGCCGCCACCTTGTCTGCGTCCCGCTTATGACGGTGCGCAAAAACATTCTCCTGCTGCCATACGCGGGAAAAAGCAGACGTGATCAGCCCTGTGGAAATAGCAATGATGGACACGGCAATAAACATAATCACCCCGGTAAAAATACGCCCCATGGGCGTAATAGGCACCAGGTCTCCGTACCCGACCGTACTCAACGTTTCCACGGCAAACCAGAGCGCGTCAAAAAAGGAACCGAATTTCTCGGGCTGGGCATCGTGCTCTGCAATATAAATTCCCAGGGAAGTGACGTACAGCATGAACAGGATCACGGAGAAAAACAGAAAAAACTCATCCCGGATCAGGTAGAAAGCCTTTTTAAAACTGGCGGCGGTTTCATTAAACCGGGCCAACTTGAAAATAGCCAGTATGCGGACAATCCTCAGGGAATAAAGAATCTGCCAGTCCGTCCCGAAAAACATGCCCAGGAACGGCACGCAGACAATAAAATCCAGAATACCCATGCCGCTGAAAATATAAGCCTTCCGCGGTTTGGCGCACAGCACCCGCAGGATATACTCAAACATGAAGAAATAGCTGATCCAGTAATTCACCTTGGCCGCGATACGGCCTACCAGAGGATGAATATCCGGAATGGCATTCAACGTGTACAGCATGAACCATACCAGAATCAGTACCCTGACAATGTTCTCCCAGGGACCGAAATTGGCAGCGCCCAGCTTGGTGCGGGTAATTTGGGGCATGGCGGGAATTAGGAACGATGGGCGGCCTTCCACTCGTCAGCCTTGAAGCCGACGCATATCTTTCCATTGGTAATAAAAATGGGGCGTTTAACCAGCATGCCGTTGGAAGCCAGCAGGGCTATCTGCTCATCCAGGCTCATGTCGGCAAGCTGATCTTTCAACTTCATGCTCTTGTAAAGCAATCCACTGGTATTAAAAAACTTCTTGGCCGGCAATCCGCTCCCTTCAATCCAGCTCCGCAGTTCCTCTGCCGTGGGATTCTGTTCCACAATATGGCGATCCTCATAAGAAATTCCCTGCTCATCAAGCCACTTCTTTGCTTTCTGGCAGGTCGTGCATTTGGGATATTCAATAAATAATGAAGACATATCCGCACGCTAGCCCCGTAAACACACCGGGTCAAGCGGCCAGCGCAGCCATCCCGACGTCATGACTTGATTGGAAGGCTGCCGATGTACCCGCCGTTGAACACAAAACCTGAGAACGAAAGCCCGGAATGCGTGCGGATACCGCAGAAATTCTATATTCCGGAACATTAAAGCACCATGGCGCTTCCCAACTGCGCGATGGAAACGGGATTGGCCTGACGCTCCCGAAGTGCCCCCGCCAGAGCCTGGGTAGCCTTCTCCTCCCCATGCACCAGAATGATATTCCTCTTGGGCCCCCCGGTCCTGTCAAAATACTCCAGCAGTTCTCCATGGTCCGCATGGCCGGAAAAGGAATCCATCTCCCGTATTCTGGCCCGGACAGGATATTGTTTACCCAGGATGGGAACCTCTTTCCACCCATCCATAATCTTGCGGCCAAGTGTATGTTCCGCACAATAGCCTACAAAAAGAACCGTATTTTTCGGATCGCCGATGTTATTCGCCAAGTGGTGAAGAATGCGCCCTGCTTCACACATTCCTGAGGCGGAAATAATAATAGCCTGCCCGTTCATTTTATTCAAAGCCTGGGACTCGCCCACGGAACGGATCAAACGCAGCTGTTCAAAACAAAACGGATCCTTCTCCCGGAACAGGAAATCATAAACCTCCTTATTGAAACACTCCGGATGAATGCGGAAAATCTCCGTCGCACCCACTGCCATGGGACTGTCCACATATACGGGCAGCAAGGGCAGAGTGCCTTCATGGTAGGCCCTGTTCAGCAGATAAAGCAGCTGCTGGGTGCGTTCCACCGCAAAAGCCGGAATATAAACGCGGCCTCCTAATTCCAGAGCCTCCTTGATAGCCCGGCAGAACGTCTCGTCATCACGGGAGGGGGCCTCATGGAAACGGCCGCCGTAAGTGCTCTCCATCAGCAAAATATCCACATCCGGAACAGGGACGGGATCGCGCAGTAATTCGTTATCTCCCCGCCCCACATCCCCGGAAAACAGGAAACGCTTCTTCCGCCCGTCTTCCCTGTCCTCAATATCCAGGCACACTTGCGCAGCGCCCAGAATATGGCCCGCATCATAAAACGTGAGTGTCACCCCCGGAACAACGGGAATGGGCCGGTCATACCCGATATTCACGAACTGGCGCATGCACCGCTCCGCATCCTGTTCGGAATAAGTAGGATAAACATCCGGCTGATGAATACCGCGGCGCTTGTTCATCTTATTGATGAATGCGCAGTCGTGCTCCTGAATGCGGGCGGCGTCCGCCAGCATGAGCTGGCAAAGATCTCTGGTTGCAAATGTACTGTAAATATTTCCCTGAAACCCCTGTTTCACTAAATTGGGAATATTGCCGGAATGATCAATATGAGCGTGGGACAGAACTACCGCATCCACCCCCGCCGGGGAAAAATAGGGGAAATCCTTATTGATATGCCATGCCTCCTCACGCCGCCCCTGATACATGCCGCAATCCAGAAGAATCCGGCACCCGTTCACCTCCAGCAAATGCTGGGAGCCCGTCGTCGTTCCCGCTGCGCCGCAAAAATGAATTTTCATTAATACATGTGTACAACAAGCCAGCGGGCAACGCAAGAAATCAATACCTGAAGCCTTTCATTCAATATACCCTTTGAAAGACGGGAAACATCAGCCGCAAAATCCACGCCTTTTCTTTTTTCCAGGCCATAAAAAGAGAAAAGCCAATAAACTGCGCCATTCCTCCTTCTGTACAGTATTCCTGTGAACATGAAATTGCACTTCTTTCCCGCCCGGGTTGCGGTTTCCGTAAAAATGCATCCGTCAAAAACTTCCCCTTCGCAAGGCACATGCTCCATCACGGATGCACGCCGGAATACCCGCATCCAGGAGAGCCCTCCGGTATCACTGCCAAAAATAAAACCTGCAGGTTATTCTGAAAACATTCCGTTCTGATTGCAACCCGCCTCCTGAAAAAAGCGGCCGGCAAAAAGAGTCCCGCCTAATCTTAATGAAGCCTTTCAGCTAAAATGAACAGGACAGGAAATTCCCGCCCTGTTCATAATGCCGTTCAATTACAAAAAATTACTTGTCCTCCGGTTTGGAGTCCGCTGTCTTCTCCACCTCCACGTCAATGGGCTGGGAAAGCTCGGGAGAAGGCCGGACCTCCGTTTTGGAGGCGGAAGCATCACTCATGGTCTCCTTCTCGGTCTTCAGCAGTTCTTCTTCAAATTCAGATTTGGCCTTCTTGAATTCTCCCAGGCTCTTGCCCAATCCGCGGGCAAATTCCGGAAGTTTCTTTGCGCCGAACAAAAGGAAAACAATCACCAGAATGGCGATAATCTCCGGTGTTCCCAGTCCAAAAATAGCTAACATGTTCATCATGGAGTACTACAGTATTTTTAACAGATTTTTTTCATGCCGGATACCTCATAATACGCTTCATGCGTCATCAGAGGCCTCCCGTTCCTGTTTCTGCTGTTCCGCAAGCCTTCTGGCGGCAAAACGCTGAAAACGCCAGACAATAATAAATATTGCCAGCAAAAAGATGCCTGAGCCGCCCCACAACAGCCAAAAAGCGGCGGAAGCCGCCTTTTCATCCCCCATTTCTCCAGCCTGGTTCCAATAATAATAGGATAAAACCGGATTCCAGGGCAGTGAACTGTCCGGCAGAAGATGGAGACGCGCCATGCGGTTCATGGCCGGCAAATAGGCAGCCTGGGCGGATGCTTTCATCAGGGAATCTCCCTGAGCCTCATCACCCCGTGTATAATATACCTGGGCCAGCATGTACATGGCCTTGGGATCCCCTGTAGCATCCGCCACACGCCGCAGCAGCCTGTAGGCCTCATCCGCATCCAGTTCCCCGCCCTTCCCGTTCTGGAGCAGCTCCGCGGCCTTTAGTGCCGCAGGGAAATAACCGGCGGCGGCAGCCTCACGATACAGAGCCAGGGCCCGGGCCGCCTGTTCTGCGGAAGGAGTTCCCTCCAGCAGCATATCCGCCAGGGCAGCCATGCTGTACGGATCCTTCCATTCCGCCCCTTTTTCAAACCAATGGAAGGCCTTTTCCCGGTCAGGGGGCAAATACTTGCCGTCCCGGAACATTATGCCCAGTTCCATCACGGCTCCCAAATCTTTACGGGAGGCCGCTTTCTCAAACCATTCTACGGCCTTGGCCGCATCCTGCGGCACTCCCAGCCCATCCCGGTACTTAAATGCCAGAAGGCCTTGTGCTGTCAGGTTGCCGCTTTCCGCTGCCTGAAGAAGATATGGGACTGACTTTTCCGGAGAACTGTCCGGAACCAGCTTTCGTTCATAAACAAGGGCAGCCCATAACGCCGCCTTGGAATCACCGTCATCCGCCTCTTTCTTCCACGTCTGGAGAGATTTTTTATAATAAAGGGCTGCCGTTGCTTCATCCTTCGGTCCTCCTGCTCCCTGGGAAGCCATTACAGCCACCTGGTACTGTGCCGCGGCATTACCGCTGTCAGCAGCCCGGGTAAACCAGTACCGGGCCCTTTCCAGGTTCTTTGGGTACAGGCGGCTGCCTGTATAAAACAGCTTGCCCAAATCCAGCATGGAATCCACATCTCCGTTATTGGCCCTTTTCTCTGCCAAGGAAACATACTCGGACATCCATTTGGACGTTTCCGCAGCATTACCCTCCACGTCATAATGCACAGACAGCTCCCGGGCGGCAGCTGGATCCCCATTCAGAGCTCTTTCGCGGAGACCGGACAAGTAAGCCTCAAAATTTCCCTCCCCGGCAGGCTCCGGAGAGGAAATATTCTGAGAAGCAAGAAGTGGATGCTGAACGAGGGACAGTCCCCACAATCCGGCAAAAAGCCACCATCTGCAATGTTTCTTCATACCGGAACAAGTCATTCATTAATGGCCCCTGTTTCCGCGGGAAAAACTGCTGGAGGACCTGCCTCCAAAGCCCGGCTTACGAAACCCGCCTTCACGCCTCTGAAAACCTCCTCCGCGGCGGTCTCTCCCAAAACCGCCTTCACGGCGTTGGAACCCTCCTTCACGACGGTTTTCCCTGAAACCTCTCTCCTGATGCTGGAAACCTCCTTCGCGGCGGTCTCTCCCAAAACCGCCTTCACGGCGTTGGAATCCTCCTTCACGACGGTTTTCCCTGAAACCTCCCTCCTGATGCTGAAAACCTCCTTCGCGGCGGTCTCTTCCAAAGCCGCCTTCACGGCGTTGGAACCCTCCTTTACGTCGCTCTTCTTTGAAACCGGCCTCGCGGCGCAGGGCCCCATCCCCGCGGCGGCCGCGGAAGCCATCTCCACGACGATGAAAATCTCCGCGGCGGTCACGGGAACGGAAGCCGGAATCTCCCCGGAAATATCCATCTTCCTTGACTTCACGTTCATCCTCCGTCTCCACAAAACGAGTCTTCATTTCCGGAGTAGGTTCATAAGAATCGCCAGCTTCAAAATCTTCCGGATTAAACACATAATCCTCATCGGAATCAACGCCTCTTCCGCCCTGCTCACCCTTGGCGCCCCCCCCGGCGACAAGCTGGGAAGCACCCAGGTATTTACGCTGGCGGGGAGGATTTTTCAGTGACAGAGCCACGTCCGCATCATCCAGGAATTTCCAGCCCCCGGGTTCCAAGTCCCCTCCCCACAGGGAGCCGATGCGCACGCGCACCAACTTGCGGACACGAAGGCCCAGGCACTGGAACATTTGACGCACCTGACGCTTCAACCCCTGTTCCAAAACAATGCAGGCACGGCGCGCGGAAGCGCGGCAAACATATTTGGCCTTGGCATTTCCCTCCGGAATGCGTACTCCGCGCAAAAACTGCATCAGTACGGAATTATCAAAATTCTGATCTACCGTCACCCAGTACTCCTTTTCTATGCCGCCGGTAGGATGGGAAAGAACCTGGGTCAATTCCCCCTTATTCGTCATAATCAGAAGACCTTCGGAATCGGCATCCAGCCGTCCCACATAATTCAGATGGCGCAGGCGCGGCGGCAGCAGGTCGTAAACAGTACCTATGGCTCCCTGCGCTTCGCGGCTGCACACATACCCGCGGGGCTTGTTCAACAGCACCACAACCTCTTCCATAGGGGTCATATGGCGGCCGTCCACCTTCACGAAATCCTTCTCCGTCACCCTCATCCCAGGCGTATCGATAACCTTCCCGTTCACTTCCACACGTCCTTCCCCAATCAGCCTGTCGGCCACCCGGCGGGAGTCAATGCCGCAGGAAGCCAGGAATTTATTAATGCGGATACCTCCGTTTTCTTCGGAGCCATGTTGTTCTTCACTCATATTGGTATAATGTCCGCTATCTCAGCGGATGATTTTGTATTGTTTATATTACGGCAATTCACATCCTCAAAAAGAGGAGGCTTTCTTTTGCCGGAAGAAAAAAGCCTCTCGGTTCATAGACATCCGAGAGGCTTCCTGTATTAGGAAAATGACTTGCCGTGCAAACTCCGCCGGGAAACTCTCCGGAGAGGATAAAGACAAGCTTAAGCTTCATGCTTGGTCTTCGGCAGGCCGAGAGGGCTTTGGCCCTTCTTCCATTCGCCGCGTTCCTTCAGAAGCTTGACACGTTCAAAACGCTTCAGGACGGAACGCTTGCCGCCTACGGTACCGGTTGCTTTGAGGCTGGAATGTTTGGACATGATCTTGTGTTTGTAAAAGGTTCAGAAAAATACTTTACACCACCCTTTCAGTGGCAGGCGGGATTTTATAACGGAAGGTTCCGAACTTGGCAAGAGAAATTTGGCGCAATAGCCAAATATATCACTCTGTGGCATCCCCCGGATGCGATTCCAGCCACTTCCTCCACAAATCCGGACGGTTCAGGCGCGTACGCTCCAGAGACTTCCCGTGCTTCCATTTTTCAATGGCGGGGTGATTGCCGCCCAGCAAAATCTCCGGAACAGCCAGGCCCCGGAACACGTTCGGCTTAGTATAGGCGGGAGCCTCCAGCAAACCGTTGGAAAAAGATTCCTCCACGGAAGAACGCTCATCCCCCAGCACGCCGGGAATCAGGCGCGCTACGGCGTCAATTACCACCACAGAGGCAATGGCCCCGTTCGTGAGAATATAGTCCCCGATGGACAATTCCATATCCACCAGCTCTTCTACTACACGATGGTCCACCCCCTCATAATGACCGCAGAGGATAATGAGATGGCCTCCGGAGGCAGCCAATTCCGCGGCCAATGACTGGTTGAACGTACGGCCCTGAGGAGTCATCAGCACAACGCGGGTTTCCCGCCGGCGAAGTTCCTCTACGGCGGCAAAGATAGGTTCCGGCTTCAGCAGCATGCCCTGCCCCCCTCCGCAAAGGTAATCGTCCGTTTTGCGGTGCTTGTCCGTCGTCCAGTCACGGATATTATGGCAGCGTATCTCCAACAATCCCCTGTCACGGGCCTTTCCCAGAATGCTGCCGCCAAGAGGGGCTTCCACCATATCCGGGAACAGGGACAAAACGTCAATCATCAGTTTGGCGGCCATAAGAAAAAAACATCAGCGTTCGCGCTTATGGCCGCGCAGCATGGCCTCAATGAACGGATCCAGGTTGCCGTCCATCACGTCCTGAATATTGCCGGACTCTACGCCTGTGCGCAAATCTTTCACCATTTGATAAGGCTGGAAAACATAGGAACGGATCTGGTTGCCCCACCCGATGTCTCCCTTCTCACTGTACTGGCGGTCGGCTTCCGCCTGCTTTTTGTCTTCCTCAATCTGGTATAGCTTGGCACGCAGCATATTCATAGCCTCTTCCTTGTTACGAAGCTGGCTGCGCTCATTCTGGCAGGCCACGATCACACCGGAAGGAATATGCGTGATACGCACGGCGGTTTCCACCTTGTTCACGTTCTGGCCGCCCTTGCCGCCGGAACGGTAGGTATCCACCTTCAAATCCTTGTCCAGGATCTCAATATTGATGGAATCAGAAACTTCCGGCGTCGCGTCCAGGGAAGCGAAAGAAGTATGTCTCTTCCCGGCGGAATCAAAGGGAGAAATGCGCACCAGCCGGTGGATGCCGCGTTCATTCTTCAAATATCCGTAGGCATACTCTCCATCCACCTTCAATGTCACGGAACGAATGCCGGCGTCATCCCCGTCCGTACTCTCCAAATACGTCACGGAAAATCCCCGGCGCTCACACCAGCGGATATACATGCGCAATAACATGGATGCCCAGTCACAGGCTTCCGTGCCGCCGGCTCCGGCGTGGATGGTTACATAACAGGAAGCCTGGTCCTGCGGTCCGTTCAGCAGGGTCAGCAATTCAAAATCCGCCAGAGACTTCTGCCATCTGGCAAACTCTTCCACAGCCTCCCGGCCCAGGTCATCGTCATCCGCCTCCTGAGCCAGCTCAATAGCAGCGTCAATATCCTCCAGACGGGACTTCAGCGCGGAAAACGCCTCCATCCTGTGTTTCAGGGGATTCACCTCCGCCATCAGCGCCCGTGCGGCATTCTGGTCATCCCAGAAATCCGGGGCGCTCATGCGTTCATCCAATTCGGCTACTCGTTGTTGAATGCCGTCCAGGTCAAAGATAGCTCCCGAGCTCGGAAAGACGGCTATGCAGGACAGCCGTGTCGAGCGCCGAGAGATCTGCAGCGATATGGGGATCCATGCGCGGGAACCTATCACGAATTACCTGGACGGAAAAGGGCAAAATGCCGTTTCTCCCAGTCATCCTCTTTTACTCCGACTCGCACCGGGCCATTTCCAAGAATAATAACACTCCGCACGCTGAACAAAACCGCTCCTCTTCCGCAGGAAGAACATCTAACAGGAAATGAATATTTCCGAAACCAAGCCCACTTCCCCATTTCAATGCCCCATGGGAAAAACAGCAGACAAAAACCTTATTTCATTCCCTTACCTTACATCACCGTCTTCTGCCCTTCCCCCTCTTTCCGGAATGTGGCTCGGCCATCCATATCCCCGGCTTCCCATAAGAGAGCCCGGAGCCATGCTACTTGGCGGAATTTTCCTCTCCACCCTCAAAAGGGCGGATCCGGCAGGACCTGAGTTTTTCAATCTCCTCAAAAGTCATGATGTGTTCTATCTGGCAAGCCACTTCATTGGCGCGTTCTTCCGGGAGGGCCAATTCCTCCCGCAGGAAATCAAACAGAATACCGTGGCTGACAATAATTTTCCCGGCAATTCTGCGGCCTTGGGGAGTCAGCTTGACGGGAGCATACTGCGTATACTCCACCAGTCCCATATCCGCCAGTTGCTTGACGGCAGATGTAACGGAAGGCATCTTTACCTTAAGCATCTCTGCAATATCCCTCACCTGGGCCGTCCCGTTCTTTTCAGACAGCAACCCGATAGCTTCCAGATAATCCTCATTGCTCTTGGTCAGCTCCAACATGGTATAAAATGAAAGCTAATACTTTAGTGGAAACTAACTCAAGATTTTTTCCAGACTTCTTTGGAATGAGAAAAAAACCGCCGCGAATATGACCATTCGCGGCGGTTAAATTAATCAGCGTAATACGAAAACGGGCTTACTTCTCTTCAGAAGGGCTCCACTCTTCGTTTTCACGGGTAGCCAGATTGAAGGCGTGTTCCAGACCCACCATATCGCTGCTCGGGCGGAGGGCTTCAAAGGAAGCGGTTTCGCGGCGGAGCTGTTCGGTGTCGTAATTGACGGCCTTGATGGAAAGGCCGATACGGCGTTCGATGCTGTCCACTTTGATCACGCGGGCAGTGATTTCATCACCCACCTTGATCACATCCTTCACGCGTTCCACATGGTCTTCGCTCAACTGGGAGATATGAATCAGGCCGTCAATATCGCCGTCCAGATTCACAAAGGCGCCGAAGCTGGCAATCTTGGCCACCTGGCCAGTCACCATATCGCCCACCTTGAAGCGGTCATTGATGGATTCCCACGGATCGGACTCAAGCTGCTTGATACCAAGGGAGACGCGCTGATCCTCCTTCTTGATTTCCAAAACGATGGCTTCCACTTCGTCGCCCTTCTTGAGAACTTCGGAGGGATGATTGATCTTGCGGGTCCAGCTCATATCGGACACGTGGATCATGCCGTCGATGCCTTCTTCCAGTCCCACAAAAGCGCCGTAGGGAGTAAGGTTGCGAACCTGGCCCTTGATGACGGTACCAATCGGGAAACGGGATTCGATATCCGCCCAGGGATTGTCTTCCAACTGGCGGACACCGAGGGAAATCTTCTGTTCCTTCACAGAAATGGAAAGAACCACGGCTTCGATTTCCTGGTCCAGCTTCAATACATCGCTCGGACGGGTGATTCTCTTGACCCAGGACAATTCGGAAACGTGCACTAGGCCTTCCACGCCCTTTTCCAATTCCACAAAGGCGCCGTAAGGCAGGAGCTTGGTCACGCGGCCCTTGACATGGGAATTGATCGGGTATTTGCGTTCGATATCCGCCCAGGGGTTGTCTGTCATCTGCTTCAGGCCCAGGGAAACGCGTTCCTTTTCGCGATCCACTTCCAGAATCACGACTTCCAGGGACTGACCGATATGGAGCATTTCGCTCGGATGGTTCACGCGGCCCCAGCTCATATCCGTGATATGGAGCAGGCCGTCCATGCCGCGGAGGTCGACAAAAGCGCCGAAGTCCGTGATATTCTTCACGATACCTTCCACCTTGTCGCCTTCCTTGACGGTTTCAAGGAAGCGCTGGCGCTGGTCGGCGCGTTCGGCTTCAATCACCTCACGGCGGGAAAGGACGATATTTTTACGGTCGTCATTTACCTTGACGATCTTAAATTCGTAAACTTTTCCAACATACTCGTTCAGGTCGCGAGGAGGAATAATATCCACCTGGGAACCGGGCAGGAAAGCTTCCACGCCAACATTGACCATAAGACCGCCCTTGACGACGCTCTTCACTTTACCCTTAACCAGGCCGCCATCGCGGTACACGCCCACGATCTTATCCCAGTTCTGCTTATGGGCGGCCTTTTCCTTGGAAAGGACGACGATGCCTTCGTCGTTTTCGAGGCGTTCCAAAAGGACTTCAATTTGGTCCCCCACTTCGATTTCCTCGTCTTCAAACTCGGAAATGGAAATAGCGCCTTCGGACTTGTAGCCGATGTCCACCAAAACGACTTGGGGACGGATTTCTTGGATGGTTCCGGTAACAATGGAACCTTCACGCAATTCGCGGAACTTGCTGTCAATAAGTTCCGCCAGTTCAGTTGTGCTCATTTAAATGGTTGATGAGTTAGTATTAACGATCTTTCTCATGGCCCCTTATTGACCGGCGGGCAATCGGGGCGCCTTCCCCCGCCGGGCGAAAAATCGGTTGCTTTCAATATCAGTTTCACACAAGAACTCAAGGCTTTTCTTGAAAAACTCCTCCCGATTTTTTACGGAACCTGTGGAGACCCTCTGGACGGCGGCAGAAAACGAGCGCACAAAAACCTTGTAGCACTTCCGTCCCATGTTAAATTCCGGCTAATGGGAAAATGCAGGAATACCTGCATCTATCAGCCACTCCTGCCGCAAAACCATGAAAAAAAACCTCCTTTACCTGCTCGCAGCGGGGCTGTCCGGAACAGCCTTCCTTCACGCCGCGGATACCGTCAAACTGGACCAGCCGGACCTGGAACGGGGAGCTCCGATCATGAAAGCCCTGTCCGACCGTCAATCCATTCGGAGCTTTTCTGAAAAAGCGCTATCCCAAAAAGATTTATCAGACCTGCTCTGGGCGGCCAATGGCATCAACCGTCAGGAATCCGGCAAACGTACAGCCCCGTCCGCCATGAACCGCCAGGACGTTAAAATTTATATATGCACTAAAAACGCCTCCTACCTCTATGACCACAAGGCCCACGCCATGATTCCCGTAAACGATGGGGACGTGCGCCCGGCGGATGCCCCCATATGTCTCATTCTTGTAACGGATACGGCGGAACCCTGGGCTGCCATGGACGCTGGCATTGTCTCCCAGAACATCTCCCTGTTCTGCTCCGGGACGGGACTGGCTACCTATCCGCGCGCCAGCATGAACAAGGATGCCCTGGCAAAGGCCCTGAAACTAACCTCTCCCCAAACACCCATGCTCTGTCATCCGGTAGGATATAAAAAATAACTACTTCCGGCTACATTCTCCGGACAGTCCGGAAAAGAAACCATCAGAAATGGAAATAAATTCCACAACAGGCGGAATTATGGTTCCGGCAAAATATCCATGTAGATGCCGGGAGAGCTCCCTTCAATAGGCATCGCGCCCACAGCCCGGGCATAAAAATCAGCCGGACCAACCCCGCCGATGACACCGTATGCATACCCCAGCTCCCGCAAACCTTTCAGGGCGGAAACCAATAGCACGGTGCCTAATCCGCTTCTGCGCACCTCTTCCGCCACTCCCATGGGGCCCAAAAAACCGCGAGCCGTTACATCGTAACAGGCAAAACCAAGAATCTTCTTCTCCCGTGTGGCAATATAACAGGAAATGGGCTGGCGGCCAAACGCGACTTTCGCCTCACTTACCCACTTGGGAGAAAAATTCCGGCCAACCCAGTCAGCTACCAAATGACGTTCGTAAGCACCCGGACGGCGAATCAAAAAACCTTCCCGCTCCAGCCTCCTCAGGTCTTCTTCAATATCGGGCAAATCATACAGCCGCACCAGCATATCCATCATAACCCCGGCACCGTATCACAACTAAGTGCCGTGGCGAATAAAAAAAACTGGACAAACCCATCTTTCAACCTTAGACTTTGCCGCACCACAAACAACGAGCTGTAGCGCAGGCTGGTAGCGCGCTTCGTTCGGGACGAAGAGGTCGCAGGTTCGAATCCTGTCAGCTCGACCAATTTCCAAGCCGCCTCCGGGCGGCTTTTTGATTACTTATTTACTGATATAAAACAATTTTACTCATTAATAACTCTGCACTTCCCCTATTTTGCGCCAAGGCAAATAAACAGCATCATACTGATTGAATTTACCCTCCTCACAGGAACAAAATCAAACGGGCAAATCTTCTCAATTAAGCCTGGAGGATGTCGGGGGTATTTACCGATGCAGAGGAAAAATTATCTATGCCCAATTCTCACCCCTGTGCCAGGCAGCATCACAGCCGCTGCGGAAGTACTCTATAACATCCCCCTAAACAAAGGAGAATGAACCTCCTCCACAAAAAAGCCCCGTTCATATTTCATGAACGGGGATGAGTCAAACGAAGTCGAAGCAGGGAGATAAAACCGGGTTACTTGCCGGATTCCGGTTTGAAACGAGCTGCTTCCGATTCGGCGGTGATTTTTGCAGGCTCGATATGCAGGAGCTTTTTCATGGCACGGCCCGGGTCGCTCTCCGTCATCGGAAAATATCTGACGTGGTCGCCTAGGCTGTAGGAATCCAGTCCATAAATGTTGTCAGCCGGAGTAAAATCTTCCGGTGTGTTCAGCAGTTTTTTTACCTCCTTTGAAGACGCAAGGACATAACTCAGATTACAACTGGAATACAATTCCGTTTTAGGAGCGCTTCCCTTGGGAAGCTTATTGGCGTGCCCGTTCCACTTGACCAGAGCTTCTACAGGAATATCTCCCCGCAGCGACTGAACGACGCGGGCATAGTGCGTCGTTCCCTGGTCAGTGGAGACTATTTTGTAAATGATAACAGACAGGACCACATCGCTTTTTTCTAAATTTCGGGCTGTACCCCTGAGAAAACTATCCTGTTCTACTTCGGCCTGCGTAGCAACATGGGAATAACGGTCTGGCTCTCCAAGGGCTACGGTTGAAAGTAATTCTGAAAACAGAATGCAAAGAAAAGAAACATACTTCATATGTATATATTAGATACTGATTTTTTGGTTTATATTCAATAAAAATAGGGCAAACACAAAAAATGTCCACCCTATGAATATATTATTTAATATCAGTTATTAGTCATCCAGTGTCGATCTTTGGAGCAGCCACACGGGGCCGGAAATAAAACTGCCCACGCCAATGTTATTTCCCGAACACATTCCCACATGCAGACTATCGCCAAATACGATAATGTCTCCAGCCTGGGGAGTGTTGTACCCATTTTCTCCATCATCCGGATAATCAGGAGCCCCCAGCAGGATACGGAGATAAACCGCCATTTGAGCTGCGCTGATGATAACGTGCTTTTTACCCCCCAACGCCTGGGAATTACCCTTTGCTCCAATATTATTGGCTCCCGGAGCTCCATTAAGGTCAATACCATAACTACTAAGAGCAATGCTCAGACGCAAGGCGCAACTGTTAATGTAATTGGCATCTCCCGAAACGTGGCTATCGTTCAGCCATCCTCCAATATAGTTCCACACATCATCTGTAGTCATGGTGGGATAATCCACTACATTATAGCATCCCAGTAGACGTTGTGCATTGGCAATATTCCAAAGATTTTTTGGAGCATCGATTTCCCAAAATTCCAGTTCGGAATCTCCCATCTTGGGAACAAACTCCTCCGCATTGGGGTAAGGAGCAATTTCTTCCGGGACGGCAAGGTTCTCATGATGCAGTTTCGCATAGTGGAAGCCCTTTTTAAGTTCCTTGCTTCCAGAAACGGATGTGGACGAATGTGAGCCAGTCAATTCCGCTACCTTTTCACCATTGATTTCCAGATAGCCGTTGTCGTCAATCTGAATCGTGAAATTATAGGTTCCATCTTCCGGAACGCGGATATATCCTTCCCAGTCATGTCCCCGGTTTCTTCCGTCTTGTGCCATCCAATCTTGGGGGACTCTTTATTCGTTACCTTGTAGCGGCGGTTGTTTTCCACATCCAACGGGTGGAACGGTTTTGTAGATATAGCCATAATATTATGTTTCGTATCGTTTACTACTGAATATTCAGTTCATGGAAACCTTGAAAGATTTCTCGCTCCTTATCAACACGTTTAACGTGTAATCCAACCCTCATTCGGAACGTGAAGGAAAAGGTTAAAACGGCACGACAATCAAGTGGAAAACAAAGGATTTGAAAAGGGCACAAGAAACACAATCATTGATTATCAATAGTTACATTTCAAATTTTCTTCATTCGGGACGAAGAGACCGCTGGTTCGAATCCTGTCCTTTCGATTATTTTCTAACAACCTCTTCCGGACGGTTATTTTATTTTCCGGTAAAATCACTTCCCCTGCAAAAGCCAGTCAGAAACCCACTAGCAGCTGATTCTTCTATCTGAAAAAAGCCCCTGCTGGGATACTTTATCAGTCATCCGTCAAATTCCCATTGCCCCGCGCATGTTTCTTTCTCTAAAATACAGGTAATGAGTGAATACTTCCTGAAACTTCCCGGCGATTCCCAGCCGAGAGCCTATTCCGAATACGAAGTCCGTGAATTTCTGGGACAGGGCGTGATCGACTCAAAAACGCTTACCTGGAAACAGGGCATGGAAGGATGGCAGCCCGTAGCCCAAGTTCTTCCTCCGATGTCACCTGTATGGGAGGAAACACAGGATCAGGAGGGCAAAGATCAGCCAGAAGAGCAGCAGTACCGCTTGCGTTATCCTCTAAACGGGCTAGCCAAATTATCCATTTTGGCATGTTTCGTATTGCTGCCCTTCATGCTCTGGAGCAGCTGGATTGTTTTTTCCAACAATGTCGTCAATTATGAAGAAATTCAGACACTCATCCAGCAGAATATGGTTCACATGCCATCTTTTGCAGTACCATTGGTCTTCTTCCTCAGCATCCTGTTCATTCCATCCCTGCTTATTCAGCTGATCTGGCTGTACCGGGCATCTGCCAATATTCAGGAATTTCAAGTTCAGGGAATTCGGTTCACTCCATTCCTTAGCGTTCTTCTAAGCTGTATGCCGGTAGTCGGCATGGTGCTCAATGCCCTGGTTCTTCAGGAAATATATAAGGCATCCAAAAACCCGGCAGAATGGATGTTGCAAACACCCTCCCGTTCCCTGCGCTTATATCTTCTGGTGACTACGGCTCTAACTCTGTGCACCCTTTTCCCTTTCGGGGCGGAACATTACGTGACGGCCTTTCTGCTCACCGGCGTACTGATGACGGCAGCCAGCATCCTGTGGCTGGTCTGTGTCCTGCAAATCACCAGAAAACAGGAGGAACTGGTCTCCAAAAATCAAAACTCCCGTTCCTGATCGAAAAAACCGTAATCAGCGTTTTTCCTTTTCTTCCGTGGGCGGCTGACTGTCTGGGAGGATTTTATCCAAAGCTTCCATGCTGTTATCCTTATACAGATTCTCCAGCAGTTTTTCCCACCCCGCCGCCATCTGAACCTGTTTCAAGGCAATAATAGGATCAGGCATCATCATTTCCGGGTCGGGAGCATATTGCTTTAGCACCTCATCCGGATTGATTCTGTTAAACGTTGTCTTCCCATAGCCCGGCATTCCCGCCTCATGAGCGATGGATTCAATCATCTCAAAATGCAAATGCGCCAAATAATTGCCGTGCGCAGTTCCGACTGAGCCGATTTGCTCTCCGCGTCCCACCAGAGTCCCCAGCGGAATATCGCTGACGGTTTTCAAATGGGCATACAGACTCTGCACAAAACGCCCGTCCGGCAAGCGGTGCAGCAGCACGACAACATTGCCCCAATCTGGAGAAGGTTCTCCTGCATAAATCAGCAATCCGCGGCCGGCGGCCCGCACGGGCAGTCCCTCATCCGTGTTTTCTCCTCCTATTCCATTTAAATCCTGTCCTGCATGCCTTCCCTTACGGGCGGCATTCATATCCCCCACACCCTGGGCAACATAGGTAAACGCTCCATTCTCATCTCCGAGGGGCGATGTAAACACATCGGCCAGAGGCGCCTTTGCCAGCTCCTGGGGCGTCAACAAAACCATCCGGTAATCTACAGGAACCGGAGCAGACCGCGGAATGTCTTCGTCATAAATATTATGGTGCATCCCGGCCTCCGGAACGGCGGCATATTTCGTCTTGTACCAGGCAAATGCCGTAATCAAGACACAGGCTGTTCCCAAAAGCCCCATAATGACGCGGGATGTCGTCTTATCTATCATCGTGCCGACTCTATACCCACGGAACCGGATTTACAATCACAAGATAGTCAGGCCAGCGTCTGCGGAAATTTTTCCATCCACAATTTGCGCGCCGCACGGATATCTTCTGAGGAAACACGGCTGGAGAGCTCCCATACGAGCGGAACTCCATGCGGGAAAAAAGGAAGAAGGCGTTCAAAGGGAACTACTCCTCCCAGCAGGGGAACCTGATGATCCCGCGCGGGCCATTTGACGTCGTTCACATGTCCTCCAATCAAATGGGAGGACATTCGGCGAAGAAACTGCTCATGATTTAATATAAGCAAATTATGCTTTCTTTGAATATGCCCGAAATCATGCCAGTAACCAATAAAGGGATTGTCTCCAAACTCCTTCATCAACAAATTCATTTCATCCTCACTCGGCACCTGCTCATAATGGCTGCGCCCTTCAATGCCCAGCTTGACGCCAAATTGTCCGGCTTGCGGAAGGAGCTGCCGGAGAGCTTCACGCGCCCGTTCCAGATAAACGGGAGCCAGGCGGTTGCGGCGGCGTACAAATTCGCCCTTGGTGCCGGCAAACGATTCCGTTCCCACCATTCCCTGACGCGCCAGGTTTTGCAAACGAGCCGTATCCGTGCGTTTCGCCAAGGGTTCCACCGTTCCCATATGCAACACGATATATCCGGCCCCCAGTGCTGCGCCCTGTTCAATAGACTTTTTCGTCAATTCAATAGCCTTGGAACGCACCTGCAGCAAATCCGCCGTAAAAGGCCTGCTGTCTGGAGAATCCCCCACTTCGTCAATCGGAGCGGGAAAAAAATTATGCACCCCGGCCACCTGCACCTTTCCGGCTTTTACCGCCCTCATGATACCGGGGAGAAGGGATAACTTGATGCCGTGGGAAAGTTCCACATGGTCAAAACCCAAAGAAAGAATCTCATCAATCATTTCTTCCCCGTCCTGATGACGGTGGGAATTCCAGCACGTAGAAAATACCAGCATAAAAAAATCAATTGTTATGAGCTGATGGAGCCGGGTTCCGTGTAGCAATCTTCCATCCACTCCTTGACAGCGGCAGTCATGCTTCCAGCGACATCCGCACGAGGTTTGACGAAAGCCGGAATAAACCACGGAAATGCGCCCACCAGATCATGAATCACCACAACATCGCCATCACATGTATGGGAGCCGGACCCAATACCCAACGTGGGAATGGAAGAATGTACGGTAACTTGTCGGGCCGCATGAGGCGTTACGCCTTCCACAACCACAGCACAAGCCCCCGCCTCTGCCACAGCCTCCACATCCCTTACCAAGGCTTCCTCCTCCGCGGAGGATTTCCCTTTGATCTTATATCCTCCCTCCTCCAGCACTTTCTGCGGCAGGAGGCCGATATGACCCACTACGGGAATACCGGCCTCCACAATAGCACGGATATTTCCTGCCTGGGCTGCCCCCCCTTCCAGCTTGACAGCATCCGCTCCAGCCAAAAACAATTTCTTTGCGGATTGCACGGCCTGTTCCACCGTATCATAAGAATGTATGGGCATATCTCCCACCAGCATGGCGTTCTTCACTCCGCGCGCAGTAGCTTCCACATGATGAAGCATATGATCCAGCGTCACATGAGTGGTATCCGGAAAGCCCAAAACCACCATCCCCAAGGAATCCCCTACCAGCACAATATCTACGCCGGCCTCATCCAAAAGACGGCCTGTAGGATAGTCATAAGCAGTAACCAGAGTCACATGCCGCCTGTTCTTGCAGGCGCGGATTCGTTCCGCTTTCTCAAAAGATTGATTCATGGCGTTCAATAAACATACAATTTACCAGCGTTCCGATACCAGGAGCGGATCAGCCTCATCCGTATCCAATTCCGCCAACAGGCAGGCAACCGTTTTATGCTGGCGCGGCAGGATAAGCTCCGGCCTGATATCGGAAAGAGGTTTTAAAACAAATTTCCTCAAATGGGCTCTGGGATGGGGGAGAATCAGACGAGGAGGATCATTGACCACCATGTCCCCCACATAGATAATATCCACATCCGCCGTGCGGGGTTCACAGCGAATCCCGGAACGAATGCGCCCCAGAACCCGTTCTATACCCTGGCAATGCACAAGCAATTCCTCCACCGTACCTGGCCAGGTAAATTCCACCACGGCATTCAAATAATCATCCGCACCGTACGGACATCCCTGAGGACTGGTGGCATACAGGGAGGATTGCAAAAAAGGGTCATCAGACAGGCTCATCTGCAACAAATGATCCCTGGCCTGAACCATCAGGCTGTTTTTATCCCCAAGATTGGACCCCAGTGCAATGCCTGCTCTCTTCATCAGCAAAAAACATCGTGGCCGTTCCGGAAAAGAAGAACGGCCACGATGCCGAAAAAGTTATAATTGGGACAACCCCAGAACGTCTTCCATGGTATACAGACCCGGTTCCCTGCCTTGGAGCCAGAGAGCCGCACGAACTGCTCCGGAGGCAAATGTCATACGGCTGGACGCCTTATGGGTAAGTTCCAGCCGTTCACCATCGGAAGCAAATATTACAGTATGGTCCCCCACCACATCCCCTCCCCGCAGGGAATGCATGCCTATTTCTTTGGCGGGTCGAGGCCCTACCAACCCTTCACGGCCAAAAACCACACTATCTTCATAATTCCTGTCAATGGCGCCGGACAGGATTTCCGCCAGTGTCCGGGCAGTACCGGAAGGAGCATCAATTTTGTGACGGTGATGCATCTCCATTACCTCTATATCGCAGCTGCCTCCCAGAATCTGTGCAGCCTTGCGCGTCAGCCAGAACAGGGCGTTCACCCCTACGGAATAATTGGAAGCGAACACAATCGGAATGGATGCCGCAGCGTCCACGATCTCCTGGCGTTCCAGCTCCGTATGCCCCGTAGTGCCAATCACCAGAGGTTTGTTATTGGCAACAGCTTCCGCCAGCAGAGTGCTGGTGAAAGCGTGATGGGAAAAATCAATGGCTACGTCGCATTTGGCGAGGGCGGGATAAAGCTCCTGTCCCTGATCATGAGTAGAACCCACGCAAGTATCCGGATTCTGCGTCACCGCCTCTTGAATAGCTTGGCCCATGCGGCCCGAAATGCCTGTAACAAGAATGGAAATCATATCACTAAAATGTTAAAGAATATTGAAACGCTGAAGAAGAGCGGAAAGCTGAGCTTCCTTCTCTTCCGCCAATGGAACCAGCGGAAGACGTATGCCGGGCTGTATATCTCCCCTCATCGCCAAAGCCGCCTTGATCGGAACAGGATTGGAATCCAGAGTCATTAGCCCTTTCATCAACGGATAAAATTTCTTCTGGAGGGAAAGCATTTCCCCCATATCCTGATTCAGGCCAGCTTTGACAATGGAATTCATGATGCCCGGAACCAGATTGGATGTTACGGAAACCAATCCGCTGGCGCCACAGGCCATAAAAGGCACCGTCAGACCGTCATCGCCACACAGAACAGTGAAATTTTCCGGAACCACCTGCATCAGCTGGTTCACGCGGTCCACATTGCCGCCGGCCTCTTTCACACAAACAATATGCGGGCAATCCTTCGCCAGACGGCTGATTGTTTCCACACTGATTTCAATACCGCAACGCCCCGGAATGCTGTACAGCATAATCGGCAGACGGGTAGCGTCCGCAATAGCCTTGAAATGGGCATAAACTCCATCCTGAGAAGGTTTATTGTAATAGGGGCACACCTGGAGCGTTCCGTCTACTCCCATCTTTTCCGCTTCCTGGGTCATATGGATAGCTTCTGCGGTGGAATTAGCGCCCGTTCCCGCAATAACCTGAATACGGCCAGCCGCGATCTCCACGGCCAATTCTATTACCCTCAAGTGTTCTTTTACAGTCAAGGTGGGGGATTCTCCCGTGGTACCGACAGCCACAATGCCGGCCACGCCGGCAGCTATCTGCGCTTCCACAAGCATTCTAAAAGCCTCTTCATCGACCCGGTTGCCGGCAAACGGAGTCACAATTGCTGTATAAAGACCTTGAAACTTCATAACCTTACTTGGAAATATACTATCTTAACAGACACTTTTTGTCACGACAGAATACAGAAATATGGCACGCCTGTCAGGCATCCTGAAATACCATTCGTGAACAAATGGAGCAAAACTCCAGGCAGGAATTAAACAGGGACAGGAATGATTCCGGCCGCCTGCCGGGCCGCCAGGCAGCTTTCCTTCCATTGACCACGATATATTCCCGGTGAATATGGCGACCTTCGTTCCCACATAAAAACAACGCAGCCGCACAGGCGGAAGAAGCCAATCGAGACTGGGAAACATACCGAAATACCGGACGGTGAATTACGATCTGGCTCATTCTGACATCTCTCCGGTTCAGGCAATTTTCCAAGCCCGCGATATCTTCCACCCATACTCCCTTTTTTCCAGAACGGGAAACCATATTCACAATCATGGCCCCAGAAACAAAGGCATGTTCCAGCAAAACGGACAGACGGACACGGGAAGAACAACTCAGTCCCTGCACCTCGACTGAGGCATCTTCACAAATAATCAAATAATCAACACGTCCATAAAGAGTATGTACCAGACCAGCAAAAGAAGCCAAATCGGAAGAATGGTTCAGGCGGCCTGACATGACCAGGTGGCTGCTGCCCGGAAGACAATCAACGATATGGCAAGATTCTTCACCGGCCTCATGAAGCATGGCAACCACAGTTCCCTGACAGGCAAATGCCTTGGCTGCCAAATATCCTATATTGCCGGCGTTGCCCAGAATGACGACCACAGGTTTCTGAATGACTTGGGATTTCATATGCCGCCATTCTGACGAAAGAGTGTAATCCGTCAATACCCCACACATACTTAAGTCATTAAGAAAGAAAAAGGAATAGTTAAGAAAAAATTATATGGCCACGACATCTTACAAGAAGGAAACTAACTAAGAGAATGTAGGGGGAGAAGAACGGGACCGAGCAAGAAAAGAACAGGCCCCGCGAGTCAAGTGACTGGCGGGGCCTGATGGAAAAAAATCCCGGCGGCGACCTACTCTTGCGGGACCTGTCGTCCGACTACCATTGGCGCGGCAGCGTTTCACTTCCGGGTTCGGAACGGGACCGGGTGGGACCACTGCGCTCTGGCCACCGGGCTTCAGGGGGAAGAGGTGATTTTTTCTTCATCCTGCCTGCCCGGGAGCCCTGCTTTTTTGGGGGCTTCTGAGTGAGGGATTTTTTTCAGGGTATGTGTGTGGCGGCTAAGAAGATTTCCGTCCTTTGTGTGGGAAGGGATTCAGCGTAAACTGACATCTGCGTGGCTGGTGGTTGATGAACATGCACAGGTAAATGAGACAAGAGCAAGTTGTCATTCACGACGGGGAGTGATGTAATATGATGAAGTCAATTGGTAATTAGTATCGCTCGGCTCAATACATTGCTGCACTTACACCTGCGACCTATCAACGTGGTGGTCTTCCACGAACCTGTAGGGAAAACTCATCTTGGGAAGGGCTTGGCGCTTAGATGCTTTCAGCGCTTATCCCGTCCGCACTTAGCTACTCGGCCATGCACTTGACAGTACAACCGATGCACCAGTGGTGCGTCAGACCCGGTCCTCTCGTACTAAGGTCTGAACCCCTCAATTTTCCAACGCCCACAGAGGATAGAGGACCGAACTGTCTCGCGACGTTCTGAACCCAGCTCGCGTGCCGCTTTAACCGGCGAACAGCCGGACCCTTGGGACCTTCTCCAGCCCCAGGATGCGACGAGCCGACATCGAGGTGCCAAACCACGCCGTCGATATGAACTCTTGGGCGTGATCAGCCTGTTATCCCTAAGGTACCTTTTATCCGTTGAGCGACGGCAATTCCACTTTCTACCGCCGGATCACTTGGGCCTGCTTTCGCATCTGCTCGACTTGTTGGTCTCACAGTTAGGCGGGCTTATGCCCATGCACTCGACACCCGGTTGCCAACCGGGCTGAGCCCACCTTCGCGCTCCTCCGTTACTCTTTGGGAGGATACCGCCCCAGTAAAACTGACCGGCTGACACGGTCCTCTGGCCGGATTCACGGCTCAGGGTTAGATCCTCCAGTTTCAAAGGGTGGTGTCTCATTGATGACTCGGACGCCCCCTAAAGGGCGTCTTCAGCGTCTCCCACTTACTCTGAGCATTAAAACCGAAGAAACAATGACAGCTTACAGTTAAGGTCTATAGGGTCTTTCCGTCCTTCTGCGGGTAGGCGGCATCTTCACCGCCACTACAATTTCACTGAGCGCCTGGTTGAGACAGTGCCCAACTCGTTTCACGATTCGTGCAGGTCGGAACTTACCCGACAAGGAATTTCGCTACCTTAGGACCGTTATAGTTACGGCCGACATTCACCGGGACTTGGGTTTAGAGCTTCGCCTTGCGGCTAACCCCTTGCCTTAATCTTTCGGCATTGGTCACGTGTCACACCCTATACTTCGACTTGCGTCTTTGCAGAGTGCTGTGTTTTTGATAAACAGTCGGTTGGGCCATTTCTCTGCGGCCTGCATCGCTGCAGGCACCCCTTCTTCCGAAGTTACGGGGCTAATTTGCCGAGTTCCTTAACCAGGTTTCACTCTTACGCCTTGGTATATTCTACCCACCCACCTGTGTCGGTTTGCGGTACGGGTCGCTTAGCATACGCTGGGGCTTTTCTTGGCACTCGATCCAATGATGCGCTGAGTCCGTGGACTCAACTCGGAATTCAATAACCTAGTCATCTTTTCTCATGCGTCCCCCCAGTTTAAGGGTTGCGAGCTCAGGATTATTAACCTGATGTCCATCGCCTACGCCTCTCGGCCTCGGCTTAGGTCCCGGCTAACCCAGGGACGAAACACGTTGCCCTGGAAACCTCGGGTTTACGGCGGCCGGGGATTTCACCCGGCTTAACGTTACTCATGTCTGCATACTCACTTGCATGTGCTCCAGGGTCAGTCGCCATCGCCCTTCGACGCGCATGCAACGCTCTTCTACCACTCTGCCTTGCGGCAAAGTCCAGAGCTTCGGTATAACGCTTGATCGCCAATCATTTTCGGCGCAGGATCACTCGATGAGTAAGCTATTACGCATTTTTTAAATGGTGGCTGCTTCTAAGCCAACATCCTCACTGTCTGTGTAATCCCACATCCTTTCCACTGAGCGTTATTTAGGCACCTTAGCTGCTGATCTGGGTTGTTTCCCTCTCGACAATGAAGCTTATCCCCCACTGTCTCACTGCCACGTTCCATTGCACGGTATTCGGAGTTTGATAAGGTTTGGTAAGCGGGTGTGCCCCCTAGCCTTGTCAGTGCTCTACCCCCGCGCATCAGCACGTGACGCTGCACCTAAATGCATTTCGAAGAGAACCAGCTATCACGGGGTTTGATTAGCCTTTCACTCCTACCCTCAGCTCATCAGAGAACTTTTCAACGTTCACCTGTTCGGTCCTCCACATGGTTTTACCCATGCTTCAACCTGGCCAAGGGTAGGTCACCTCCGCTTCGGGTCCAGTACCTGCGACTTATTGCGCCCTGTTCGGACTTGCTTTCGCTGCGACTCCGGGCCGGAAGCCCTTAGTCTTGCCACAAACACTGACTCGCAGACTCATTAAACAAAAGGCACGCCATCGCAGATTGCTCTGCTCTGACACTTTGTAGACATACGGTTTCAGGTTCTATTTCACTCCGCTCACAGCGGTTCTTTTCACCTTTCCCTCGCGGTACTTGTTCACTATCGGTCGCCAACTAGTATTTAGCCTTGCGCGGTGGTCCGCGCGGGTTCATACATCGTTCCACGTGTGATGTATTACTCAGGATACCACTAATTCCCACTCTGGATTTCGTCTACAGGGCTTTCACCTTCTCCGGCCAGCCTTTCCAGGCTGTTCTACTATCCAGTATGGTCGAATATGACGTGGTCCTACAACCCCGAAGGAATACCTTCGGTTTGGGCTCCATCCGCTTTCGCTCGCCACTACTTACGGAATCGATTTCTCTTTCTTTTCCTCCGCTTACTGAGATGTTTCACTTCAGCGGGTTTCGCGTTTCATACCCTATGTATTCAGGTATGAACGATCGAGTATTGCCTCGATCAGGTTACCCCATTCGGATATCCCCGGATCATCGCTTGCTTGCAGCTTCCCGAGGCTTTTCGCAGCTTGCCGCGTCCTTCTTCGCCTGTTGGCACCAAGGCATTCACCGTACGCCCTTACTTACTTCATCATATTGCAAAAGCTGTTACCACTTCCACAATACCACAAAGCCCTTTCGTGACTTACTGTATTGTCTCATTGCTTGGTATGCATGTTTATCTTGTGTTTTGTAGTTGTTGTTCAACTACCCACTTTGTTGGAAAGCGATTGCTTCTCAGCAATCGTTTTCTTTGCCACGCAGATGTCAATTTTCGCTTTCCCTCAATAATCGATTTTGCTGGTGGGCCTGACAGGACTTGAACCTGTGACCCCGCGCTTATCAAGCGCGTGCTCTAACCAACTGAGCTACAGGCCCGGCTTTGGGTCTCTTTCCCGAAACTGGTGGAGGCACGGGGACTCGAACCCCGGACATCCAGCTTGCAAAGCTGGCGCTCTACCAACTGAGCTATACCCCCAGTTGCGGTTCTCGGTGAAAAAAGGTCGTCACGCACGTCGTGCCAGGTTTCGCTCCAGGCGTCTGGCTGTTTACACATCTTTGGTTCGTTGCGGGCCCTCGCGGGTTCCGCTTTCTCTCCCCTCGGGAAGAGATCCGACCTGTGCACTTCCGTGCACTTGCTCCATAGAAAGGAGGTGATCCAGCCGCAGGTTCCCCTACGGCTACCTTGTTACGACTTCATCCCAGTTACCAGTCTCACCTTAGGACCCTGCCTCCTTGCGGTTGGCTTCAGATACTTCGGGTGCGACCGGCTTCCATGATGTGACGGGCGGTGTGTACAAGACCCGGGAACGTATTCACGGCGCCGTAGCTGATGCGCCATTACTAGCGATTCCGGCTTCGTGTAGGCGGGTTGCAGCCTACAGTCCGAACTGGGCCCAGTTTTTAGGATTTCCTCCGCCTCGCGGCTTCGGCCCCCTCTGTACTGGGCATTGTAGTACGTGTGCAGCCCTGGGCATAAGGGCCATACTGACCTGACGTCGTCCCCACCTTCCTCCCAGTTGATCTGGGCAGTCTCGCCAGAGTCCCCACCTTCACGTGCTGGTAACTGGCAACAGGGGTTGCGCTCGTTGCTGGACTTAACCAAACATCTCACGACACGAGCTGACGACGGCCATGCAGCACCTGTGTAACGCCTCCGAAGAGTCGCATGCTTTCACATGTTGTTCATTACATGTCAAGCCCAGGTAAGGTTCTTCGCGTTGCATCGAATTAAGCCACATACTCCACCGCTTGTGCGGGTCCCCGTCAATTTCTTTGAGTTTTAATCTTGCGACCGTACTCCCCAGGCGGCACGCTTAACGCGTTAGCTCCGGCACGCAGGGGGTCGATTCCCCGCACACCAAGCGTGCACCGTTTACTGCCAGGACTACAGGGGTATCTAATCCCTTTCGCTCCCCTGGCCTTCGTGCCTCAGCGTCAGTTAATGTCCAGGAACCCGCCTTCGCCACGAGTGTTCCTCTCGATATCTACGCATTTCACTGCTACACCGAGAATTCCGGTTCCCCCTCCATTACTCTAGTCTCGCAGTATCATGTGCCGTCCGCGGGTTGAGCCCGCGCCTTTCACACACGACTTACGAAACAGCCTACGCACGCTTTACGCCCAGTGATTCCGAACAACGCTTGAGACCTCTGTATTACCGCGGCTGCTGGCACAGAGTTAGCCGTCTCTTCCTCTTGTGGTACTATCTTTTTAATTTGCTCCCACATGACAGGGGTTTACAATCCGAAGACCTTCATTCCCCCACGCGGCGTCGCACCATCAGGGTTTCCCCCATTGTGAATGATTCTCGACTGCTGCCACCCGTAGGTGTCTGGACCGTGTCTCAGTTCCAGTGTGGCCGGACATCCTCTCAGACCGGCTACCCGTCATCGCCTTGGTGAGCCGTTACCTCACCAACTAACTAATAGGCCGCGAGCCCATCCCCAAGCGCATTGCTGCTTTAATCTTTCGATACTATGCGGTATTAATCCCAGTTTCCCAGGGCTATCCCGCTCTCGGGGGCAGGTTACTCACGTGTTACTCACCCGTGCGCCACTAGAGAATTATTAGCAAGCTAGCAATTCTCTCGTTCGACTTGCATGTCTTATCCACGCCGCCAGCGTTCGTTCTGAGCCAGAATCAAACTCTCCATCAAATTAATTTTTGAAATTGCTGGACCAAATGGAATCTTGCGATTTCATTTGCGCCATGCTTCAGCCAGCTCTTCCTCCAAGTTGTGTCTCTTGAAGGCCTCTTCGCTCTGCCTCGCACGGCGCGCGTCACGACCTTTTTTCTTTCCCCTTGCTTCTACCAAACCACCCCTCTCAGAGCTAGGTCGCTCCTTCGGGCTTTTACCCCTCAGACACCTCTCGGCACCCTCGGAAAACCCTCGGCGGTTTCGGTAGTCATTCCCTTGAAATCTTCTTATCGCAACCCCTTCCGGGGTTTTCTCCGGGTGGCCGGCTCCAGCCGCTTCCAGCGCTCTTCCGAGCCCTTCAGCCGCCGGGCTTTTCATCCCGTCGCCGTGTTGGCGTGCCGAGAAATCTATCATACCACACCTTTTTGTCAATTTTTTTCGTGCTCTTTTTTGCGATTTGTTGAGATCAAAAACAAAAAAAATATCTGATTATCAACGTATTACATTTTTATTTTTTTCTAATAAAAAAGCCTCTTTCACCACGAAAGAGGCCTAAACGTTTCAGAAGTCCTAAGTTTCTCTCTACTTTTTGCCTCCTCCACTCTTTTTCTTCATGACTGCGTATTGATGAACCAGAGGCAGTTCCCGGAGGGCGTTTGCAGCATCCTTCCTTGCCTGGTCATAATCTTTTTTCTCCATTGCCATATTCATCATCACTTCATAGCAGGCGGCATTGTATCCGCCATCATCCTTCGTATTAGAATCCCTCATAATCTGGAGAGCGCGCCGAGCCGCCTTATAGCTGTATTTACGCATTTCCTCCGAGCCGTTAAGGATTTTCAGGATATAGGAAGCAAGCGAATCTTCCCGGGGGAAACGCTCCAAGACGTGGTCAAAGATATCAACCATTTTCCGATAATTTTTCTCCTTCCAAGCCTTTTCCGCCCGCACTTCCGCCACAGTAGACGCAAACCATCCGTTATCGGAAAACTGCCCTGCATTTTCCTCAATCAACTTCTGGTATTCATCAAATTTCTTTTCCCGCCTCAATTCAAAGGATTTTTTTATGACAGCATACATGGCCTGCTGGCGGGCCTTCCGTTTCGCATCTTCTTCCGCAAACCTGTTTTTGTCAAAAGAGTCCAATCTGGCAGTTTCCGCCACCCATTCGGGCATCTTCTTAATTTCTCCAGCCCACAGCAATGTTCCGTCACGAACGACGACCGCGGCGGGAAATGAGTCAAGTTTGAGCAAATCACAAAATTCAATGACCGAACGATCCGATGGAATACCGACAGGATAAGGAGTCCATCCTTCCGGCCCGGATAAATAGTCCCGCATCTGTTTTTCCGCCGACGGATTGAGCCCCAATACCACGGCTTTTACTCCCGGATGGTCTAAGAGCCCGTGTTTTTTAAGAACGGCATCAAGAGCTCTTCCAGGAGCTGTTTCTCCAAGTATATTATTGTGGCCTCTCCAGAAGCTGATGAATACGGTTTTGCCGACAGGAAGTCCTGTTAGACCGGGATGATTGAACCATTTTAATTTGCTGAACAGGGGAGTGTACTGTTTGTCTTCCTGCACTACTCCCATTTCCTGCATCTTCCTCGGAGGTTCCGGTTTTATTTCAGCAACCGCCTGATTTACAAGATTCTGAAAGGATTCCTTATTGCCTGAAAGCTGCTGCAACTTCGTCCAGGCTTTGCTGATGGACGTTAAGGAAAGCGCCTTTTCCATATCCTGCAACGCTTCTTCCATATTTCCCTGCCGTTCCCTAAGTTCCGCCATCAGTTTCCATGGCAAGGAAGCTTCCTTGTTATTCCTTCTGGAGATTTCAATACAACGGTTCAGACATCTTTCCACCAGGGCGGCATCCATTTTTCCGGCAGGCACGGATTTTCCATTTAGAAGCGTTCCCGCCACATCAATCTGTACACGGTAATTGTCCGGATACTCTTTGCACAGGTCATCCAGTACGGCCTGTGCTTCCTGGAATTTTTCCAATTCCGCCAAGACTCTGAATGGAACCATTTTCAGTTGAATGATCTGATCCTGCTGAAACTGTCCCGAATCCTGAATTTGCCTTAAAAAGGCCTGCGCTTCCCCCTTTCCTCCCTTCCGGGCAAGCTCTCCCGTTCTCTCAGAAACCTGGCGGTAGAGCTTCCATGCACGGGCGTTTGCGCCGGGATTATCCCCCGGAAGAGATGCTGCAGAAAAATCAGGCTTCAACATGGCCCGCATCATTTCTTCAGAAAGTTTTCCAGGATGCCCTCTCCAAATAAGCTTGCCGTTTTTAACGGCAAAAGCATGGGGGATGCCGTTAACCCCCATTGGAGACAGCCATTTATCCCTGGTTTTTTTCCCATCCACATCCACGGCCATGGCATAAGTAAGGGGCGATGGACGGTTTTTTAAAAATTCTTTCAGAGATTCCGGAGATTTCCTGTCCATGACGTTAACTCCGATAACCTGCATATGAGGATTATTCTTAAATGCCTGATGCAATTGTTCCATATGGGGCATAGCAGCCAGGCACGGGCCACACCAGGTAGCCCAAAATTCAAAAATGTACACTTTGTCCTTTTCCCATTCCTTTACCGGAGTTCCCTGTATCCATACATCCGGCAAACCATCCAGAGCAAGTGATGGAAGGGCATCCTCCGAAGCAACCGTTTTGTTTTCCTTTTTCTCAGAAGAAGCTGCGTGCAATGGCATTCCGGCAGACACCGCAAAGATCATGGAACACCCTAAAACCAACAGAGAGGAATTTAACATGGTAACACGGTACTATCGTAACCTTCTGCGTCAAGCCAATAGAAAAAGAAAAGGAAAGATAGAAGAGCCGTTATCGTGTTCTTTCCACCAGAATAAAATGCAAAATAATCCCAAAAAGAAAAGATATTAATCATGGAGTTTTTCATATGGAATAAGAAAACAAACACGCTCAGCATCCACACTTTTTTGAGTTTCATTGGATTTAGGAAAAATGATTTACCTTTTTGGATACACAAAGACAATATTATTTTCTAAAATAGAAAATATGCCAGAAATGGCTTGCCAGATTTTTCTGAATGCGAAAAATGCGCTCATGGATGAAATGAAGTCCAGTATCAGAAAGTTTCTCGCTCTTACGAAAATGACCCGGGACGAATTTGCCGACCTTTGCGGCGTCAGCAAAAGTCAGGTCGATAAATGGCTTTCCACTGTTCCAATTCCCCGTGCCAGACAACGCCTTATCATCAGAATCATGAAGGAAGAGTACGCCAAACAGGCACGCCTGGCTCAAACAAAAAATCCGAACAGCATCCACGTTCCCGTTACTCCTCAAAAGTATGAAAAGTTCAGGAATGAAGCTGAACGCCATGGCCTGACCGTTCCGGAATGGGCGAGTGAAGCACTGGATGCTCTTTCCAGCATCAAAAGCAGAAGCTGAAAAGAGGATCCGATATTCCGGATCGATTCCAGAGAAAGAGAAAATAGTGTTGTTATCGCCTCTTCCCTATCTTTTCTTTCATTACAAATCTGCACAAATTCATGTCTGACGACGATATACGCATTTTGGTGAAGGAGTGGCTACAAAGCCAAAACCGCAAGTCCTATCAATTGGCGGAGGAGATAGGCATCCGGCCGCAAACTTTTTACGCACAGATGAGCGTTCGTAAAATATCGGCCAATACCAAAAAAGCGCTTTCCCGCATCATTCCAGGCCTGGTTTCCGATTCAGATCTGAATCCACCTTCCAATACGGAAGAATGTGACCCGAAAAAACTCAGAATCAAGGAATGGCTCCGGGCTCACGGCAAAACACGCCGATGGCTTGCGGAACAATGCCGCGTTTCCGTCCGCACCGTACATACCTGGTTTACTGCACGCGGTAACATTCCTGTCACCCAATATCTCTTTATCGATAAGCTGATGAGTGAAGGCGAGTCGTGGAAAGTTCAGCTTCCCAATACCATTCCCGTGAATTTTCCGGAAACGGAAATGGAAATTGTTCACAAATTCCAGAAACTACATCCGGACATAGATCTTCCTATGTACGGCATGCATAAAATCCTGGAGTTATGCGTTAATGTACTCACTCAGGATGAATATTCAAAGACGTCTCCATACTCCCGTATTACCAATACCGGAGAGATTCGCCACCGCCGGATAAAATAAGAATTATTCAAAAGTTCAAATTTTCTTTTCCACGGGAGCATTTCTGAAAATGCACTGTTTGCATACCCATGCCGGTATTTTTCTAAAACATCGCAGGTTCTCCGCCAGCCTGGCCTTCCGGGCCATTCTTCTTAATCTTTCACGGAACCCTTTCATTTTTTTGTTCCTTTTACCACGACGGCCACAGACTATCCTATCTCTCAACACAGAGAAAAAATCTTCAGCCACATTACATCATCCCTCCAGTTTCAAAAATCAATCACTATTGATTGGAAAAACAGTCCGAACCATGCGTAATAGTGCTTCTATGCAGCCTGTTCACCAGAGTTAACCATACTTACCTTGCCTCTCCATTTTTAAGAAAGCCGGCTTTTGGTGAAAAAGCTCTTATCGCATGAAAAACCCCGTATTCTTAAAAAGAATACGGGGTTAAAAAATGGCACGTCCAACTGGATTCGAACCAGTGACCCACTGCTTAGAAGGCAGTTGCTCTATCCAACTGAGCTATGGACGCATGATGCGGCAAAAGAGTAACAGAGTTCATCTCAACTTGGCAAGCCCGGATCTTTGAAAAGCGCGCTTTAAGCGCCGGAGCAGCGGTCATGCGTAAAAGGTATGTCCACCAGGGTCGCTCCAAACTTCCGCACCAGAGGCTCCTGACAAGAAAACATGCGTTCCCGTTCCTCCGGTTCCGTATCTATATATCCGTGCAGATGCAGAAGACCATGCACCATGTACCGGAAAAGTTCTCTGAAAACAGGTTCTCTGAATTCCGAAGCCTGCCGGATAGCGGTATCAAAGCTGACGATGATCTCTCCTATTCCGTCTCCATGCGGAAAAGTAATGACATCCGTCGCCGTCGGATCATTCAAAAAACGGGCATGCACATCCCGGATGACGTCATCGGAAACCACGCTGATTTCTACTTCATCCAATCCGGAAAGTACGGGGACAGGACCTTCAGGCAACGCCAACACGGACGGAAGACAACTCTCCAAGGCTCCAATCAGGGCATCCGTAACGGATGAACTGAGCCTCCCCTCCCCCAAATGATTGTAGAGTTCAAGATTCGGTTTCATTTTCCTCCGCATCCGCTCCGTTCTTCATCTCCGTTTCCGTGGATTTTCCCTCTTGAAGGGTTCCGGCATCGGGCAACTCCATGATTTCAGGAACGGCTTTTCCTTCACCTTCCTTTTTCACCGCTTTCTCTTCTTTTTCCCTGACGGACACATCAGAGAAAGCATTTTTCTCAATTTTTGCCTCCGGATTGTGAGAAGGATAGGCAATGCGGTTGTGGTGGATGCTTTTCAGCGTCTTAATGAAACTGTTGCGAATCTTTTTGAGATCTCCGAAGGTAAGGCCGCAATCGTCCAAATGGCCGTCCACCACCCGTTGCTTCAAAAGTTCATCCACCCTTTTCTGCATTTCCTCACAGGAAATTTTACCCATGGAGCGCGTGGCGCTTTCCACGATGTCCGCCAGGCTGACGATTCCCGTTTCCTTGCTCTGCGGGTTGGGCCCCTTGTAACGGAAGTTGGATTCCACCACCTCCGGGACGTCATCCGGAGACGCCAGGCCGCTTTCCACGCGGCTCAGAATCTCATCACGGTATTGGAGGGCTTTCCGGTAAAAGAAGTAAGCCAGTGACGTGCCGTGATGCTGCTCTATAACGTCCACCAAAGGACGCGGAAGGTTGTTCTCCCTGGCCAATGCCACCCCGTCCTGAACGTGGTCGATGATGATACGGGCGCTCATGCTGGGCGTCAGCTCATCATGGGGATTGGGGCCGTCCATGATATTTTCAATGAAATAAAGGGGGTTTTGCATCTTGCCAATGTCATGGTAGTAGGCCTCCACCCGGCATTCAATGGGATTGGCCCCAATGGCTTCCGCCGCAGCTTCCGCCAGCTGGGCAACCATCAGGCAATGGTGAAAGGTTCCCGGAGCTTCCATTTGCAGCCTTTTCATGAGAGGGCGGTTCATATCCGCCATTTCCAGCCAGGAAATGGGCGTAATGATTTTAAAAGCTCCTTCTATGATAGGCAGGACACCGCTGATCAGCACGCTGGTCAGCATGCTTACGCCAAAAGCCACGGCCAGGCAAACCAGAACTCCAGTAAGGTTGTAGTCCCATGCCTGGAGATTGATGACACCCATGATGCAACACAGCACCATGACAAGCAGCCCCACAAAGAAGCCCGCTCGCAGCAACTGAGCACGATTCCGCAGGTTATGGGTAAGCAACACGGTAAGCATGCCGGAGAGAGAGCTCAGAATCCAGAATTGGACCTGCTTTTCCGGAGCATATTGCTCCGGCAGAATGAAAAATCCGCCAAGCATGCAGATGGAAATAGTGGCAAACATGCCCAAAAGAGGTCCCAGCAGCACCGTGACAACCAACGGAGCCATCATATAGGGAAGGTACAAAAGTTCATTGCCTATATCGGAAGTAATGTCAGCCGTTACATGCCGGATGAGACCAAAAAACAGGAGCTGGACCAGAACCGCCCCCCACGTGACGACAAAGGTTTTGTTTTTCCTGCGCAGGGAACCGGCGCATAACCAGTACATAGGCATCAGCGCCAGCGTCACGGAAAAAAGCAGAAAAGCTTCCGACACATGATGCCATATGGATTTTCCCCAGTGGGGACTGTAACTGCCCATCCATTGAAGCACGATGAAAACCAGCACGCACAACCCGACGGATACCGCCAGGCTGGAATCCAAACGGTCACCGGCTGAACGGGACACGGAGGCAGCCCCTTCCGGGGAGGCACTTTCTTTTGCCGCTGCGGCGGCATGCTTTTTCAGAAAATGAAACATGATAATTATTTGGGGTACACTTCGGCAACCTCCGGAATAGTGCCCAGCCGGGCCATCTCCTCTTCTATGAGGGGGTTGACGCCCTGCTGCACCATTTCCATGGAAACTCTCAGGGCATTGCGAATGGCTACAGGGGTAGAACTGCCATGCGCAATGATGGTCACGCCCCTGACGCCCAGAAGAGGGCTTCCCCCTACGGAGTCCGCAGAAAGGCGCGCTTTCATGGCACGGAATGCTCCGGAGGCGCAGGCAGCTCCAACCATACGGAACGGATTTGCCTTAAGTTCCTCTTTCAGCCATTTGCTGAAAGCCTTGGCGGTAGCCTCGCATGTTTTCAACAGGACGTTTCCGGTAAAACCGTCTGTCAGAGCCACATCTATTTCATGTTCAAAAAGATCGTGGCCTTCCACATTTCCTACGAATTTAAAAGGGAGCGCACCGCGCTCCTCCAAATGCTTCAACAGACAAAAAGTTCCCTTGGTAAAATCTGTTCCCTTTTCATCTTCAGAGCCATTGCTCATAACTCCCACCTTAGGCATCTGTTTGCCGTAAACAGAACGCGCCAGAATGCTGGCCATAACGGCATACCCTACCAAATGCCGGGGCTTGGCATCCGGGTTGGCTCCGGTATCAGTCACATTGCAGACTCCGAATTCATTCGGAAGCTGGGTCACAATGCCCGCACGTTCCACACCATCCAGCAACCTCAGTTTGACGGTGGCAGCCGCTACAGCCGCCCCCGTATTGCCCGCGCTGACCACGGCATCCGCATCTCCGGACTTGACCAGATCCACGGAAATGGACATGGATGAATTTTTTTTCTTTCTCACCGCCAACAATCCGGATTCATTCATTTCCACAACTTCCGCAGCCGGAACGATCTCCACGCGCGGGCCGGAAAGCCCCCAGCGGTCACAAGAGCTGCGCACCGTTTCTTCCCTTCCCACAAGATAAATTTTTTCAATGAGAGGAAAATCCTGCAGGGCCCGCTTCGCCCCGTCCACGTTAATATCGGGCGCGTTGTCGCCGCCCATCACATCCAGTGCAATCTTCATATTATCTGTTGAAGAAATGTGTTAGCAGAAAAAACAGTCCGCTTCAAGCAAGTTCAGCCGCTTTGCGAAGCAAAAGCGGCTGGAAAGTCTATATGATAGCGCCGCAATACGGCGATAATTTTCAAGGCAAATGCGGCGCCCGCGGAACTTACGCTTCCGCTTTCACCACAATTTCCTTGCCGGAACGGGTCGTGTACGTACCGCAATTGGGGCAAGCGGTATGGCCGGGGGTGCTGCTGCCGCAATTCTGGCACTTGCGGAGCTTCGGCGCGCGCCATGCCTGAGCGGCAAGGCGGGTACGCTGCTTCATCTTGGACGTTCTGCGCTTAGGTGCTGCCATAATTTTAGTTGGTAAAAAGGTGATTAATGATGATCTCCCAGTTCATTGAGGGCGTCCCACACGCCGCTATTCCTGATCGGGGCAGAACTGTTTACACCCGTTCCCCCCTCTTTGTCCACTCCAAAATACGGAGTTTTCGCCATACAGTCGTTTTCCATGCCTCCGTCAGCACACTTCGGGTATGCCGGAAAATCCAGAACTATTTCTTCCCTCAAGGAATCTGAAACATCCACAACATTCTGTGAATCAATCTGGAAAGAAGCCGCGAAATCACAAACTTCCACCACGTAGTCAAAGTAGCCCAGGCAACGCACACAGCGGAATTTAAAGGGGGCGGAAATATTGCCTCTTACAAACAATTCATTTTCAAATCTCTGCACATGCAGATCAAAAGCAAGAGGACCAATGGAAGTAACCTCTTCATCATCAATGCCGAAAATTTCAGAATCCAGTTCGCCGCTGTATTTTTTCCCTGCCTCCGGCAAGTTCTCCAGTTCTACCAGCAATCTTTTCATGAGTATATCTTACAGGCATACTTAATGCCTGACAAGCAGGCAGGGCGGCACCATTCATGGAAAATGACACATCCGTCCCTCTTTCAGGCAAATGCCCGAATTCGCTCCGGCCTAACCGGGAGGCCATTCCAGCTTGCGGCCTGCCAACAGATGCATGTGCAAATGGGGGACAGCTTCCCCGGCGTCATGCCCATTGTTGATGACCAGGCGAAAGCCACCTTCATCCAGATGAAGAGTCCGGGCAATCCGCCCGACTGCCAGCATCATGTGTCCCAGAAGGGCGGCATCTTCCTTCCCGGCCTCGGAAAGGCGGGGAATGGGCTTGCGCGGAACAAGCAGCAAATGTTCCGGTGCTTGTGGACTAATGTCCCGGAAGCAAACACACAGGTCATCCTGATATACGATGTCCGCGGGGATTTCCCCTGAACAGATTTTTTCAAATAACGTAGCGGCCATAAAATCAAAAAGGAATCTGCAATGACTTTAACATGTTTTGCAAACCGTGGGCAAGTTATTCCCTGACGGCTGAAGCGCGTCGCAGGCGATCCATAATAGCGCGGCCCAGGCCGGATTCCGGAACGGATTCCGCCACAATGACGTCAATAGCTTCATTTTCATCCATCTGTCTCATCAGGGCAAACAGGCGCACGGCAGCTTCTGCCAGCCGCCCGCTGCCGGGACTCATGGCTACCACTTCCGCCCAGTCCCCTTCCCGGGCAAGAGCGGACGTTCCTTCATAGGAAAGCAGGCCGTACTTCACCCCCTCCACCGGGGCAAAATTTTCCCCCGGTTCCAGCAACAGCATAGGTTTGCGCGGGGCGTAATGGCTGGTGAGCTGCCCCGGCGCATCCGCCGGAGATTCTTCCGAAACAGGACGGCAGGAAGGCTTGCGGCGCACCACCTTGACCAGGTTGCGAAATTGTTCCCGGGTCACCGGCCCCTCACGCAGCAATTCCAGAGAGGGCTTGCCCTTCTCGTCAAGCACAGGGCGCACAATCGTGCTTTCCAGCCCCTCGGAACAGGCTCCGGCATCCAGAATCATTTCAATACTGCCCCCCAATTCCTTCTGTACGGCAGAGGCGGAAGTAGGGCTGATATGGCCGAAGCGGTTGGCGCTGGGCGCAGCTATAGGACGCCCCAGCGCTTTCGCCACCCCCCGCATGGCAGGATGAGCGCTTACGCGAACGGCCACCGTAGGAAGGCCGCTGGTTACAATATCCGGAATGATATCCGCCTTTGGCAACACCAAGGTCAGCGGCCCCGGCCAGAATCTGGAGGCAAGCGTATGCACCAGATCTTTCAGCCCCTCCGGAACGGCGGTATATTTTTCCACCTCACTCCCATGATGCACATGAATAATCAGCGGATCAAAAGAAGGCCTTCCCTTGGCTTCAAAAATCCTTGCTACCGCTTCCGGGTTCAAGGCATCCGCCCCCAGGCCGTATACAGTCTCCGTGGGAATCCCTACCAGCGCGCCCGCACCCAGAGCTTCCGCCGCACGGGCATACAGTTTGCGGTTGTCTGACAGGGGATCTACCTCGAACAATTCGGTTTGCATGGCTTTATCCTTGCCTGAAGAAGCTCCTGCGTCAAGCAAGACCTCCTTCCCCTGAAAGGAGAAAACAGACAAATGAATAAGAGCACGTCCTGCGGCCTCATACTTCCCCTCTCTTTTTCCAACTGCCTTTACTTCACTCCACCCTTACGGACTTCCCCGAGTGAAAGCTGGCGCAGGAAAAGGCCATTCCGCTAAAAAGTTGTTTTCTCACGCTACCGGATTGAGTACAATCAAGCCATGAATAGGGCCGCATCAACATCCTTGCCAAACATCATCCTGATCGGGCTGATGGGATGCGGCAAAACTACCATCGGAAAGGAACTGCACCGGGAAACGAACCTTGGTTTTACGGATACGGACCAGTTAATCGAACGCCAGACGGGAATGCACATTCCGCAGATTTTCAAAATTCATGGGGAATCACATTTTCGAGACCTGGAAACCGGAATCCTGCAACAGCTTCAGACGGCCGCCGCACAAAGCCGCATTATTTCCACAGGAGGAGGCATCATTATCAGACCGGAAAACCGGCTGCTACTGAAAAAACTGGGCTTCGTCGTCTGGCTCCATACAGACGTAAACATCCTGTACCAGCGTATTTCCAGATGCACCAACCGCCCCCTGCTCCAACAACCCAATCCCAAGGCCGTGCTGGAACATCTCATGAATGAACGGCATGATTTTTACCAGGAAACGGCGCACTTGACCATTGATACGGCCAATCTCCACATCCATGAAATCGCATTCGGCATTCTGGAATCCGCCAGAGTATTCCGGTCCCGACGGCAATAATGCCGACCCGTTTTTCCGTGAGCGGGAAATTTTCTCTTTCTGATTCATCCTAACGCAAGCGCTTCCGGGGAAGCCCTTTTTCCCGCTCCACGCACAAGAAACAACAAAAGGCGATCCCCATTAAAGGAACGCCTTCAGGAAAATAATAATGCACCGGAAAAGGCTTAAGCCTTACGGGCCTTACGGATCATTGAATTCACCGTTTCAGAGGGCTGTGCTCCGTTGGCAAGCCATTTGTCAACCTTTTCCAGATTGATGGTATAGTTGACGCCTTCCTTCATGGGGTCATAGGAGCCCACTTGCTCAATGTAACGGCCGTCACGACGCGCGCGGCTGTCGACAACGACGATTTTGTAGTAAGGACGGTCCTTGGAACCCTGACGGTTGAGTCTGATTGCTACTGCCATATCAATTATAAATAAAAGGTGTTAATGGAAAACTTGCGCGCCTTGCCACTCACATGCGTGCGCGTTCGGCTTTCCACAGCGGGTGCAGATGTATGCGATATAATGGAGTCATTGTCAATACCAGAGTTATTTTTACAGGAAAAAAGCCTCTCCTCCATTTCTGCATCAGGAAAAATTTTCCACCGCCATGCGGCCAAAAGAATACAGCATGCTGAATCCGAACCCCGCCAGCACAAGGGACAATATTCCATCCAGTATTCCGGCATACCGTCCGTAAAAGGAACGAATGGGCTGCCACCGGAAAACAAGAGCCCACAAAATCCAGCCCGCCGTTCCCGTCGCCACGATTAAAAAACAGATAAAAAGAGCATCGGAAGGATCATGATTTTTTTCCAACAGCGGAGCGGAAAGAGCCACAAAGAAAAACGTGGCCTTGGGATTCATCAGGTTAGTCACCAGAGCATCACGGTAAATACAGGAAGCCGGGGAAATATCCCCGCACCCGGCAAGGAAAGCATTCCTCCCCCTCGGCCATATTTTCTATGCCAAATACAGCATCCAGCAGGAAGCGACGCAAAACAGCCCCACCCCCAGGAAACTGTGAAAAACGGATGCTCCTACTGTACAAGCCAGTGCAGCATGGGCAACTATTCCGGTACCAATGCCCAAAGCAGCCATCACTCCCGCGCCAAAGCCGTAAGCCAGCGTGCTCCGGGTAACAAAAGCCTGGTCGGGCCCCGGAGATGCCTGGGAAAGAAGCAGAATACCTGCGAACATAAGTTCTTCCTTCATGAAACAAAAAAACCCTTATGCCCTGGGATGCGCCTGTCTGTAAACTTCCGCCATTCTGGCACGCGTCACATGCGTGTAAATCTGGGTAGTGGATAAGGAAGCATGGCCCAGCAGTTCCTGGACGGAACGGAGATCCGCCCCTGCCTCCAGAATGTGCGTGGCGAATGTATGCCTGATCTTATGGGGAGAAATGGTAAAAGGAATGGAAGACAGCTTCACATATTTGTTCAGCATCATCTGCACGGAGCGAGCGCTTAACCGGGTTCCTATACGGGAGACAAACAAGGGGGAATTCTTCGGCAGACAGGCCATGGAAACATAAGTTTCCAGAGCAGCCAGAGCAGGAGTTCCCACAGGCAGGATGCGCTCCTTGCGCCCCTTCCCCATTACCTTCACGCCCCGGAAACGGTGGTCCACACTGCCGACATCCAGCCCTACTAATTCACTCAGGCGCATCCCGCAGGAATAAAAAAGTTCCAATATGGCGGCATCACGGTAGGGAAGCCATGCGGGAGCATTGGCGGGAACGGCCGTCTTGTAGGGAAGCTCCAACAATTCCAACATCTGGTTGAGTGTCAGGAAAACGGGAAGCGTTTTTTTCCTTCTGGGAAGGGAAACGCCCGTCATCGGATTCGTCTCCAGCCCGTGCCTCCGCATCATGAAACGGTAAAAACTGCGCAAGGCGGCAAACCTCAGCCGGATGGAAGACGTGGCGGCTTCATCCTTCAGCTCCTGGAAAAGCCAGTCCCTCATCTGGTCCGGCGTACAATTCTCCCACCCTGGGAATGAATCAGCCGCCCAGGCCCTGAACTGGCGCAAGGCGCGGGCGTACACCTCCACCGTATGAGGAGAAGCCTGCTTTTCCACCTCCAGATATTGAAGAAAGTCCTGTTCCGGTTCCAGAGGAATCCTCATCATGAAGAATAAATTACTCCGTACCTTTATCCTTGTCATCCCCGGCATCCACCGGCGACTCCTTCAGCAGCACTTCACTAATATTGGCCCGGATGAACAAATCAATATCGTCCGGTTCTTCCATGGCATACAAAGGGCTGTCTTTATGGCTGTCCGCCAGAGCCTGCTCTCTCTCCACCGGGCTGAGCACACCGTCCTGGTCCAAATCGTAAAGCTCCATCATTTCCTTCATTTTGGCCTGGTACAGAGCCGCGGCATCTTTCATGACAACGGCATGTTCCTCCGGATCAATGCGGCCGTTCCCGTTGGCATCATAATTCTGCAGCAACATGTTACGAGTCAGCAGAAACAATCCGGGAGCAACCATGAAGCGCTTCCGTCCCACCGTGCGAATTTCCACCATGGGCATTTCCACCCCCTCTGCCGGAGGGGGGAGCGGAGGAATTTCCTTCCTCTCGGCGGGAGGGTAAAACGGAATTCCGTCTCTCTTGTCCGGCCCGGCATCCGGCCTGCGCACCCCGCGCCGCTTCTCTACATGTTCCCGGAAAGCCCTGCACTCTTCCGGGGAAAGCCTTCCGTCCCCATCCTTATCAAACTTCCTCAGGAAGGCTTTTCTGGCCTCCCTCCGCGCAGAGCGGGCATCCTCCACCAACCGGGCCTTGTCCTGTTCGGACAAAATTCCCGTACTCGTGCTGTCATAGCGGTCCAAAACCATTTGGCGGATGAGCAAAGTCACCTGAAGGCATTCCAGTCCGGGCCTGGGGACAGTTTCCATTTTCCCGCGGTCACCGGAAGCCACGGATTGCCCTGCCGCAAACGGGAACAGACCGAACGCCAGCGCTGCGGAACATGACAAGATAAACATGTGGAGCAATCTCATAACCACTTTTACTAACCCCGCCGGAAAAAGACGGTTTCAAAAATAAACATCCGCCCCTGCCGGCGGGCGACACGGGCGGATGCAGGAAACGTACGGAAAGCGGAAAAACTTACTTCTCCACTTGAACGATCGCGTTCTTTTCCAATTCAATCACCACGCCTTCCGCAATCTTGAGAGAAACGGTGCGCTCATTCACCTTCTCCACAAAACCGTGCAGCCCTGCGTTTGTAATCACCTTGTCCCCGCGCTGCAGGGCTGCGATGCGCGCCTGCTGCTCCTTCTGCGCCTTCCTCTGGGGACGGATCAGCATCACCCAGAACAGGACGATGATAATCACAAACATGAACATGGGGCTGGCGAGAATCTGCTGAAACATATTTGCGGGTTCCTGCCCGGCGGCGCCCGCCGCATCCTGAGCCTGTGCTAACATAAAGATATTCATAAAATGTCGTTTGCTTTATACCGCGCGATAAACGAATCCTTGAAGGGACCGAACGTGCCGGCGGCTATGGCCTCGCGCGCCTGGGCCATCAGCCGGAGATAGAATTCCAGATTCTGAAAAGAAAGCAATCTTAAAGCGAGTATTTCACCGGCTTTAAACAAATGCCGCACGTAAGCGCGGGAAAATTGCGTGACATGCGGATGTCCCTCCGGGTCAATTGGGCGGGAATCCGTCGCCCAGCGCTGATTCTTGATATGCATGGGGCCGTCCGGAGTCAGCGCCACGCCGTGGCGAGCCAGGCGGGTAGGCATTACGCAGTCAAACATATCCACGCCGCGGGCAACCATTTCCAGCAATTGGGGGGGGGTGCCCAGCCCCATGGCGTACCGGGGCTTCTCCTCCGGCAGCCAGGGAGCGGAATGATCAATGGCGCGGAGCATTTCATCCTCCGGCTCTCCCACGGAAACGCCGCCGATGGCGTACCCGTCAAAATCCATGGCGGCCAGTTCTTCCGCGCACTTCTTTCTTAAATCCGCATACACGGAACCCTGCACAATACCGAAATGGTGCTGACGCCCTTCCCCGGAACGGGGGCGATGTTCCTGCACCCACGCCTTGCAGCGGCGGGCCCAACGAAGCGTGTACCCCAGGGAGGCTTCCGCGTACTTCCTGTCGCAGGGATAAGGGGGGCACTCGTCAAATAGCATGGCGATATCACTGCCCAGGTCCGCCTGGATCTCCATGGACCGCTCCGGGCTCAGCATCATGTACGCCCCGTCCAAATGGTTCTGGAAACGCACCCCTTCTTCCGTAATTTTACGGAGCTTGGCAAGAGACCAGACCTGGAACCCGCCGGAATCCGTCAAAATAGGGCGGTTCCAGGAGGAAAATTTGTGTAATCCGCCCATTTGCCTAATTAAAGCGGAACCGGGGCGCAGGGACAAATGATAGGTATTTCCCAGAATAATCTCAGCTCCCAGCATCTCCAAATCCTGCGGATGCATGGTTTTCACGGAACCCTGCGTTCCCACCGGCATGAAAATGGGGGTGGGAACCCGTCCATGCGGCAACTCAAGCGACCCCAGGCGCGCAGCCGTGGAGGAATCTTTCTGATGAAGTATAAAAGGCATCGGAATCAGACTTTCTGCAATTTGATAAAGCGGGCGCGGTAAATCGGCTTTCTCATGGCCTCCCATTGAAGCTGGAAATCCGTACGGGGATAAAAAAATTCGTCCTCATCCCAACTGGCACGGCTAAACAGACCGCTGCGCGCCACATGGTCCAGCACCCACAGAAAATATCCTTCATGATCCGTCTTGAACAGGAACTCCCCGCCATCGGACAGGGAACGGTGAAGCACGGGAAGAAAATCCTCCTGCACCAGCCGGTTCTTATGATGCCTCTCCTTGGGCCAAGGATCCGGACATAAATAATGCAGCCGGGAAATGCAGCCGGGCTGCATCATCCATTCCAGAAAGTAACGGCTTTCCACGCGAAACACCTTCACGTTATCCAACCCGCGGACGGCCGCCCGGGAACAAACCCCCCGTACGCGGCCCAAAAGCCGTTCTATTCCCAGAAAACGGCGCTCCGGATAATGGGCCGCCATCTGAAGCAGGAATCCTCCATCCCCACACCCCAAATCAACCTCAAACGGACCATCCGCGCCAAAAATATCAGAAGGCGCCAGCACCTTGAAAAAATCGTCTGGAATAAAGGCCGGATTTATCATGGGAATCAACGTGCGGATAAGATGCGTTACAAAACGGACTCTGTCAAACGAGAAGACCCTCTCCCCGCAAGCATGGCGGGAAGAGGCCCGGAAAAACGGAAAAAATAAGCCAGAACGGCCCGGAACCGTCAGCTCCGGCAATAAAGGCGATTATAAATTTTACGGTCAAAACGGTAATAGCGCGCGGCGCGGTGGGGAACCCCCTCCTCCTTCTCATCCAGCGGCACAATGTAGGGGCGGGAAGCGATCTTTTTGTGGAAATTACGCACATCCATGGGCTTGCCGTGAATCTCCTCATTCAGCCGCCGGAGCTGAAGAGCCGTAAACTTGGCAGGCAGCATGTCATAAATCAGCGCAGGCTCCTTCTTCACGGAAGAACGCACTCTCTCCAGCGCCAGGTCAATAATATCCCGGTGATCAAACGCCAGTTCCGGCAAATCATCTACAGGAACCCAGCGGGTCTTGTGGCTTTCCATCAGTTTGCGCAGCTTGGTGGAAATCCTGAGCATGGCCATGTAGGCCACCGTCACCAGGCGTCCTATCTTCTGGCCGGAAACAGCCTCCACCCATGCCCTGTCCTCCGGATTCTTGATGCGCGATGGCGCACCGAAGGTATGGAACTGTTCCAAATGGGGGGAAGACATGCCGGTCATGTCAAACAAAACGCGGCGGGCAGCAGCATCCAGTTCTTCCTCCTCATAAATCAAATCGCCGGGCAATTTGGAAATGGTGCCGGTATGGCCGGGTTCAAATTGCGTCTTCTCAACCAGAAGAACTTTCAAACCTTCTTCATCGAACCCGATCAACACACAATCCACTGAGAGATGCGGATAAAATGCCTGCCTGCTCATTATGTTATCTATTATTGTAGTAAGTTCCCAGAATCGCACCAACGCTACGCAAGCGAACCAGCCAAGTCAACCAAAATCAAAATAGAAACGGCAATGAAAAGCCGGCCCGCTTGGCCCCGGGAAACATCACGCCACTTCCGGAAGACTGCTTTTCACGGGCGGAAAAACGCGCCCGCGTCTTGCATTTCATCATCTCCCCGTCCATATAATAAAATCATGCAGACAGGTATCCTCAGGGGAATGTCCGTGCTGGCCGGGCTGCTATGGCTGGCATCCTGCTCGTCCTCTCCCAAAAGCCGGGATTATCCGGGCTACATGACCCGCCCCTATACCATCCGGGGGCACCGCTACCATCCCATGAACGTGGAACAGGCGCTGGCGTATGAACAAACGGGAATTGCCTCCCATTACAATGAATGCGCCCTTTGGGGGCTCGTCAGCGGAAAAACGGCGATCGGAGAAAACGTGCGCCCCTGGCACCTGCACGCAGCGCATCCCACCCTTCCCCTCCCGTGCGAAGTGCTCGTTCAATCCCTTCGAACAGGCAAAACCGTAAAAGTCCGCGTAAACGACCGGGGCCCCTTCATCAAAAACAGAATCATAGATCTTAGTGAAGAAGCCGCAGAACGGCTGGACATGAAGCACCATGGGCTGGACAAAGTCAGAATCACCGTCCTTTCCGTAGGAGACGGGAAATGGAAAAAAGAAGCGCCCCCTTGCGCCACTCCGGCCTGATTCCCTACTTCAACACATTCCGTGTGGTCTTAAAATGCATCTTGGCCACCTTGGGCAGCATTTCTTCCCCATGCAGGGCCACCAGCTGGCGCGCAGCTTTCACCACTTGGCCGGAAGCTCCCAGCGGCAACTTCATGCCGGCGCCCTCTCCCGCCGCATCCATCCAGTTCACAAAACGTTCGCGCGCCAGAATAGATGCCGCAGCCACAGCAATATCACTCTCCGCCCGGACACGCTGTTCCAGCCGGACACACACCTTTTTGGCGGCAAGCGCCCTCTTCAGAACAAACGGATTGGCGAACTGGTCGCTCAATGCCATGGGACAATCCGGCACCTTGGCCGCCAGCCCCTCAATCACCGTAGCATGCCCCCAGGCGAGAAAACGGTTCAAATTCCCGAACTCCGGATAAAGCTGGTTATACCGTTCCGGGCCAATGCTTACCAGATGAAACCGGATGCCCGGCACCCTGCGGATCCCCTCCGCCAGGCAACGGATTCTGGATGAAGACCCTACCAGCTTGCTGTCACAAACGCCCAGCTTGCGCAATGCGGCGCCGATGCCCCCGTCCACATAAACGCCGGCCACCACCAGCGGGCCGAAGTAATCTCCCTTTCCGCTCTCGTCAATCCCGAAGTGGGGAACAAAACCGCATCCTTTTTCCATCTCTTCCGGCTGGGCCAAAACTCCGGTCACCTGCGGTTCCAGCGTAAACTCTATAAAATCATCCGTTCCCTTTCCCTGCAGAAGAAGCTTGTTCTTCTTCTCGTAAACCGTCGCCAGCAGGGAAGGCCCGGCAAATGAAAAACGCGCATAGGGAATCTCTCTTCTTTCAAACCCCTTTTCCTCAAGAATGGACTCAAGCCTTCCGGCCTGGGCGTCAGTCAGCATGGTTGTAAACGTGGTTCGCATATATCCTTCCCGGAGCCGGAACACTCCGGGGCAAGCAATGGAAGCATCCCAACCATTCCGGTCAAGCAAAATTCCCGCATCACGCGGTTGAGGACGTAAAAATCAAGCATGCCACGGATTATGTGATTTTTTAGGAGACAAATCATGCACGCTGAACTATAAAACTCATACATTCATATCATTCTGCACTCCATATGGCTAAAACATTAACAAAACGAGACCTCGTTAACAAAATCAGCGCCGAAACCAACTTCACTCAGATTGAAGTGTTCGACATCGTACAGCGTGCCGTGGACATTATCTCCAGCACCCTTGCTGAAGGCGACCGTGTGGTGATTCGCAATTTCGGAACCTTCCAGGTGAAGGAAGTGAAGCCCAAAGTAGGCCGCAACCCCAAAAATCCCGACCAGGATGTGCCGATTCCGGCCCGCTCCGTTGTCAAATTCAAGGTTGGCAAGGAATTGAAGGATCAGGTCGCCAAGCTGACCGCCTCCAAGTAATCCCCACAGGGATCAGACAGATTACTCCGGGCGCCTCCATCAAACAATGGAGGCGCCTTTTTGTTTGCAATTTGCGCGGCATTCGGGAATATAAGGGAACGTGAGCATGTTCCGGATACAGCACCTTCTTCTGATCACCGTAGCAGCACTTTGCTCCTGTGCTACAGAACGCACGGTAGCTACAAACTTTCAGCCCCGGCAACGCCCTGATTCGGAAAAAACGGACGCAGAGCTGATGGATGCCCTGGAAGCGAAATTCGCCGGCGGCTTCGGCCAGCAGACGGACAAGGAAGGAAACACCAGGATGGTGTCCCAAAAACGGAGTTCCTTTGAAGGGCTCCGGTACAGCGGCAACACGTCTCAAATCGAAAAAAAGGCTTTTGAAACCGCATCCTTTGAAAAAAAGAAATTTGACGGTGCCTCCACCCGGTTTGAAACGAAGAAATGGGACGGCATCAAATCCTTCACGGATGATAAACTGGAAACACCGGACTTCATCGCCCGCGCCAGGGGAGTAAATACCCACTCCTGGCAGGATGCCTCCAAACAATACGCCACGCAGCAATCCGACTTTCATGGACGCTCCTGGAAAGATGCGGACAAGCGCCTCGGCCACACTGTCAACCGGGACATTGAAGCCAAGAGACAAAGCTTTGGACAGCCAACTATCATGAGTGCGCGGCAGGCCCAGGCGCATACTATTCAGGAAACGCGGGAAATGATGGGACGCACGGACTGATGCGCCGATGTTTCGGACGTGATCCCATGCCACATTTCAGATTCGACATTGTTTTCAGCCGCCATCTATAATTCTGCCCGTGAGTGCCAAAGACGTTCCTCCTTCCATCACCCTGCCTACCAGCGACTACTATACCATTGTCAAAATGAGCAATCATGCCGTCGTGGGAGTATTCCGCCAGCATGTCTTTGCGCGCCGCAGCACGCGCCGTTATGCGCCCCCCATCCCTGAAGAACACGACTCCTATTGCGTTAAAAGAACTCCGGAGCGTGTCATGGTCCAAATCTTTCATGACGGCAACGAAGTTTACCGTTGCATTTTTGTGCCGCCTGCCGATTATTGACGGCAGGACAACCATGAAAGCTCCTTTTTTCAGTCCGGCCTTCTGCCTCATTACAAGCCTGATTACAGCCTGCTCTTCTCCCTCCGGCCCCAATGCGTGGACGCCGGTCACTTCCGGTCCTCCGCGTGCGCCCATCCCCCAGAACGAAGTTCTGGTCATTGACGAATACCCCATGGGGGAATACACCAACGTGGGGCATTTTAACGGGCCGGAAGGGCGCATTGTCCATGTCTCTATGGAAGACAAGGAACTAATCGACTACTTCACCAAGGAAGCTGCGGCCATGGGGGGCAATACCGTCGTCATCAGGGAACCGCGCATCCGCTACCGTTCCGGCGAAGGCAAGAGCAGCCGGGTGGACGTCATCTATGTTCGGGAAGAAATGGGAACACACGGTGCGGAAGACCTGGGCATGGACTCGCTCCCCATTGATGAAGAATCTCTGCGGATTTATTAGAGCCGCCGGAAACGTCCCAGGTGCCATTTCCTGCCCGGAGTTTTTTCAGCTCTTTTTATTGGCCGCACCGCAATGTGGGCACTCCGCACGGGAATTCCCCTTCTCCATATAAAACCGGAATCCGCATATCCGGCACTGGAACTTGCGCCTCCGATCCAGGAAAGATCGTATCCATCCCACAGCCAGGCAAACAGACACCACAGCGCAAAAACCGCCGGCGCCCAGTAAAAACAGGCGCATCAAAAAATCTTGGAGGGTCATATTCACGGCTCTTCCTCCTTCCATTCGATGGACATGGTGCCGCTCCGCGGCTCCCGGGACGGAACCCAGCGCAAAGACCGGGCGAACCTCATTGCGGACTCATCCGCTCCATTACTTCCGGAAGACTCCAGTAACAAAACTTGAGAAGGAACGCCCCATACATCCACCACCACAAAAAAATCACTCCTCAGCCCCGTGTGATCTTCACTTCTGGAAAAACCGGACAGGGAGCCGGCTGTAACAATCCGTCCATCCAGCCCCGGGGAACAAAAAGACAAATAGGGAATGCATGTTCCGGAAATCTCCAAGGAAGGATTCATCATGGCGCGTATATGAAAATCCGGATGCTGCCACATACGCCAGACCTGATCCGCCACAGTGGGCACGGACACGGGGGGGAGAGGGGCCAGAGGCGTATCTGCGGAACGCGGAGAAGGCAAAGTCACACCCATTTCCCCGGTCGGGCCGGCCGTCACCGGATCAGCCCAAACGGGGCCTCTCACAGGCAGAGGAGTATTGCGCAGCACATACTGATGCAGGCGCGGATTATCTGCAGGCAGCAGCACCACGGCACCCGGCTGATCTCCCCTGCTGCGGATTTCCGGCACCTCCACATGGACAAAAAGGCCGGCGGCAAAAGCGCAAACGCCAAAAAAAAGCATCCACAGAAAAATACGCCAGCGCAACAGGCCGGTTTCCCTCCAGTCAAACGTCAATCCCAAATGAAGGTCGAAAATTTTATCCCGGCTCTTTTTATTCAGCTTCATGAGGTAACAAGGGAAAAAATCAATCCGCGGGCTCGGCGGCAATATAACACGTCATCCCCTTGGCAAGCACCATATCCACCAGCTGCTGTTCCAGGGCCCGTGAAACGGAATCATCCAAACGCAGGGCAATCCCCATACGGCTCCCATTCTCCCCATGCTCCCGGGAAATCCGGTCCAATTCTGAGGAAACGCCGGGCAGCCCGCCTTCAATCAGGCGGTTTCCGATATAAAAAGCGGGCTGGTCGCCAGCCGTAACTGAAATAAACTCAACATCCCCCACAATATTCATCAGAAAATGGGAAGAAGGAGCCTTTACCTCATACCCGTACGCCATTCCCACACGGGAAGTCATGATCGTCAGCACGCAGACAAGAACAACCAAATCCAGCAGGACGACCACCTGAATGAAAAACGCCCCGGCGGGCAATGTTGAACGGGTCTTGATCTGCATGCCTTTCCTCCTTCCCTGTTGCACTCACACGCCCCGGCCGTTCATGCTCTCTCCGGCCTCGCGCCTCTGCCGGGCATCACACACCAGGCAAACGGCCTCCGTGCCGGCCCGTTCCAGGGAATGTACAATCTGGCGGGAGCGGGAGGCCAGGTAGGAATAAAACAAATAAGCGGGAATAGCAATGCTCAGCCCCAGGGCGGAACTAAGCAGGGCCTGGTAAACGGCATCCGACATGGCAAGAGAGGGAGCCTTCCCTTCCAGAACCCCCGGCTGGGAATAAAATCCCACCAGCCCCTGGATCGTCCCCAGCACGCCGACTAGCGGGCTCACCGTAGCCACTACCAGCAATCCGCGGATATTCTTTTCAATCTGAAACACTTCCATCTGAACGGAATCCTCTGCCACAGTCCGTAGTTCTTCACGCCTCAATCCGGCATGCGCCAGCACGGAAGAAACCACGCGGGCCGCAGGTCCCGGTAAAATGGAAGCCTCATGCCGGGCTTCATCCGTCTTTCCGGCATTTACCAGCTTGGAAATACCTCTTAAAAAATCTCCAGTATTAATTTGAATCCTGTGAAAATACAAAATCCGCTCCGCGACAACCGCCAGCGCATAAACGGCCAGAACGCTCAAAACCCAGAACAACGGACCTATCTTGATGATCAATTCATTCATAACCCTCTTTCTCGCGTTAGATCATGCACACTTCCCATGTAAATTCAACTCAAACCTATGTGGCCCGGCAGCCGAGCTTCTGCAGCAGCCGCGTTCCCGCCCATAATAAGACGGAACCAAGCCACAGCAAGGAACCGAAACTCAGGAGCCATACCGCATCAAACAGGCACATGCCGGAAAGCAGCACCGTCACAGCCCTTTGAACCCGGGGCCTGCCCGCCTCACGCCGTGCTACGGCGGAAATCACGCAGGTAAACAAAAACAGGCCGAAAGCATACCACAGCAAGGCCGGAGGAGGAACGGACTCTCCACCCGAACGGGCAAATGCCAGGCCGGCGGCCAGCACCCACACCCCGCGGCACAACCCCATCAAAACAATGGAGCCGCTCCACAGTTTATGGAACACATTATACAGGGAAATCACAATCAGCAGCAAAGCCCCCACGGCAAATTCCCCGCCCAGCGCCATGTTCAGCACCAGGCCGGACAACCCGGCCGCCAAAGACAACAGCCTCAGTACGGAAACGCTCACCGCGCCGCACGGCACGGGGCGGTCCGGATAACGGGCGGCATCCCACCTGGCGTCCACACGGTCATTCTCCCACATGCCATACAAATAAAAGCACAGCCCCATCAGCACTACGCCCCCATACAGGGCGGGAGGCGGCAGCTCCGCCCCACGGACGGAAGCCCACGCCGCCGCCGCGTTGGTAAACAGCGTGGGAATATTGGCCGGACGGGAGGAACGGAGCACTCCTCTCAATAAACGGCAATCATGCATCGCCGCATACTGTACAGGGAAAAACGGCGATGACCAAGCACATCCACAGTCAGCAAAAGTGCAGGAACCGGCATCCCGCCCATGCCGGGCAGCATCCATACATACCCAGAAATAGATTGTCTTTGCAGGGCTGTCCCCTATCATGCCGGAGGCATTAAAAATACTCTTCTCTCCCCCCATGAATATCAGGACCCACATCCAGGCGCTTCTCATCTGTACCATGCCCCTGTCCGCACAGGGGCAAAGCATCGTGGATTCACGGGCCTCCCTTCCACGAGCCGTCCAGCTTCACGGGGATGAAGGCAGGGAAACGGTGCGGCCAGACGAATCCAAGCGCATCTCTCCGAAAGAAGCGCCGGCATCCCGTGTTCAGCCCCAGCGGATCGTCAACCGGATTGCCGCCACAGTCAACGGCCGTCCCATCACGGCCAATGAGGTCAGCGTGCGTCTCATGCCCATCGGAGCACAGCTCGCGGCCCAGTATCCCAAACAGGGGCCCGAATTCTACAAGCAACTGGCCCTGGCGAAAAAAAACATCATTGAAGACCTGGTGGAGCGAGAACTGCTCCGCAATGAATTTGAAGGCATGGGCGGCGTCATTAGGGACTCTCTCATTGACCAGGAAGTCAACCGCACCATCCTGACCACCTTTAACGGAGATCGTTCCGCCTTCCTGAAAAACCTGAGCCTCTCCGGCATGACCATCCGCGCTTTCCGGGAAATGACGAAAAAACAGCTTCAGGTTCAAATCATGCGGGCTTCCAAATACGACCAGGAAATACCGCCTACTCCGGAGGAAATACAGCAGGAATACGAATCTACCAAAGAACAATACCGGGACCTGACCAAAGACAAAATCAAATTCAAGAAAATCTTCATTCCCATGCTGGGGGATGACTCCGCCTCCACGCCGGAAGTACAGCTGAACCTGGCGGAACTCATCGCCAAGGAAATCAAATCCAAAAACGCCACCTTTGAGGAAATGGCCAAACGCTACTCCAAGGATCTATATGCGGAAAAAGGCGGTGACTGGCCCGTTACGGAACGCTCCACGCTCTCCCCGGAATCCGCCGCCATCATCTTTGGCGCACAGCCCGGCGAAATCATCGGACCGTTGGTGGACTCCACCGGCTTCACCATCGTATTGGTGGAGAAAAAGGAACTGGCGCCGCCGCCCCCCCTCTCCGCCATCAAGGAGCAGATTGACATCATGGCGCGCAACAAGCGCAGCAATGAACGTTATAAAAAATGGGTGGAACGCCTCCGGAAAAAAGCCATCGTCAAAATATACATCTGATTCTTCTCCTTTCCCCATTGCCGGACCGGCCCATTCAACAAGGCCGGTCCGGCGTTCTCTCTTTCATTTCCCCTGTTTTTCTTTCCGCCAACCGTTCCGTCCGCTCTCCTTCTCCTGCCGGGGGCATCTTCCTCTCGCCCATATCTCTGGAAGACGCCTTCATACATAACTGGACGGCGGCCGCAAGTCATCCCGCCTGACGCGTGGAACCCTGCTGACGTAATCCGGGCAGAAACCGTATCCTGCTGAATAAAAATCTTGCAAGATGGCAGATTTCCAGTCGTAATATCACAGATATGTTCACCAATATTACACGCACGACCCTCTGCATTACTGCGTTCAGCATCGCCAGCCTCATGGCGGCGCCTCTGAATGCAACCAAAACGGAGAGCCTGGATTGGAACTGGAAATTCGCCCGTTTCGGGAAAATGCCGGATGGCAGTACGCAACCGGAACCGGGAAAAGCCATGGGATTCGCCACTGCCACCAGTGAAGAATCCGGCAATCCGGCGGACAATGCCGTGGACGGGGACAAGTCCACCCGCTGGTGTGCCGCCAGTGGCAAAAGCGGAGAAAAAATCACCGTGGACATGGGACGCCCCGTAGATGTAAAAACCGTTAACATCCTGTGGGAAAAACAAAGCAACCATCTTTTCAAGCTGGAAGGCTCCGGTGACGGAAAACGCTGGGCAACTATTGAAGACAAAACTTCCGGGCAAAACGACTCCAAGGAAGACACGGTAGAAAACAAAACCGGCAAACCGCGATACTTCCGCATCACCGTCACGGGCAACAACCAGAGCAACTGGGCCAGCATCCGTGAAATCACCTTTAAAAACGACAAGGGGGAAATTATCCGCCCTCAGGCCGCCGCCGGAACCAGTAAGGCGGACAATCCCTCCAGCCCCTCTTTCAACGACAAAAACTGGCGTTCCTTGAACCTGCCGCACGACTGGGGCGTGGAAGGACCCTTCCGGATGGAAATTGAAAACAGAACCGGAAAACTCCCCTGGGTCGGCATTGGCTGGTACCGCAAAACGCTGGAAATCCCGGCGGACGCCAAGGGCAACCAATTCTATCTGGACTTTGACGGCGTTATGTCCCGCCCCAAAATTTATGTGAACGGACACCTGGCCGGCGAATGGAAATACGGTTACAGCTCCTTCCGCGTGGACATCACGCCCTTCCTGAAATTCGGGCAGCAAAATACCATTGCCGTCAGAGTGGACAATCCCCCCAGCACCTCCCGATGGTATCCGGGCGGCGGCATCTACCGCCATGTGTGGCTCACGGAATCCAACCCTGTGCACATCGAACACTGGGGCGTTTTCGTCAAAACTCCGGAAATCACCAAATCCGCCGCCAAGGTAGAAGTGGACACCACGGTGAAAAACACCACGGACAAAGCCGTCATCCCCACTGTTACTGAAGAAATCCTGGACGGAGGTAAAATCGTAGCCTCCACAACCACCAAAGGGGAAGAAATTCCCGCCGGGGAAAAGGGCAAAATCACCAGTACGCTGACGCTCAAAAACCCCACTCTGTGGACGCTAAACGCTCCCCATCTGTATAAGATGAAAACCACGGTCAGGATGGGAGACAAAGTCATAGACCAAAAATTCACCAACTTCGGCGTAAGAACCGTTGAATGGAAACCCACGGGATTCTACCTTAACGGGGAGCGCGTGCAGCTCAAGGGCGTTTGCCAGCACCATGACCTGGGACCGCTCGGCTCCGCCGCCCACACGCGAGGCTATGAACGCCAGATTGAAATCCTGAAGGAATTCGGCGTCAACTCCATCCGCACGTCCCACAACCCGCCTGCGCCGGAAGTGCTGGACCTGTGCGATAAAATGGGCATCCTGGTCATTGACGAACTTTTCGACGTATGGCAATGCTCCAAAGAAGGCGTCAACAACGAATCCTTTAACGAATGGCATGAACGGGACGTGGTTAACCTCTGCCACCGGGACCGAAACCACCCCTGTGTCATTGCATGGAGTTCGGGAAATGAAGTTCCGGAACAGGGAATGAAAAATCTGCACCATATCTCCCAAACCCTGACGGATCTTTTCCACCGGGAAGACCCCACGCGTAAAGTGACTTCCGGCTGCAACAACGCCAATGCCGCACGCAACGGCTTTGGGGACACCCTGGATGTTTACGGCTATAACTACAAGCCCTGGGCCTACAAGGACTTCGCCAAGGACCGCCCCCACCAGCCGTTCTATGGTGCGGAAACCGCCTCCTGTGTCAGCTCCCGCGGAGAATACTTCTTCCCCGTGGACTGGAACAAAGGCAAGGGATTCTACCTCTACCAGGTCAGTTCCTATGACCTGTACGCCCCCGGCTGGGCCAACCGTCCGGATGTGGAATTCGCCGCTCAGGAAGACAATCCCAACAGCGCGGGAGAATATGTATGGACGGGCTTTGACTACATTGGGGAACCCACCCCGTACAATCTGGACGCCACCAACGCCCTGAACGTGCCGGAAGGGCCGGAACGCGAAAAGCTGATGGCGGAACTCAAAAAACTGGGAGACCGCGCCCCCTCCCGCAGCTCCTACTTCGGCATCGTGGACCTGTGCGGCTTCAAAAAGGACCGCTTCTACATCTACCAGGCCCACTGGAGGCCGGATCTCAAGATGGCGCACATCCTGCCGCACTGGAACTGGCCGGAACGCAAGGGGCAGGTAACGCCCGTGCATGTCTACACCAGCGGGGATGAAGCGGAACTCTTCCTGAATGGGAAATCCCAGGGCGTCCGCAAAAAGGGCACCGGGGAAAAGGACCGCTACCGCCTCGTGTGGGAAGACGTTAAATACACGCCCGGCACCCTCAAAGTAGTCGCCAAAAAGGACGGTAAAATCTGGGCTACGGACACGGTAACCACTACCGGAAAACCTGCGGCGCTCACCCTCAAGCCGGACCGCAATGAAATCAAGGGAGACGGCTATGACCTGTCTTATGTCACCGTAGCCGTCCGCGACGCCCAGGGCCGTATGGTGCCCCGAAGCAAAAACCAGCTCACCTTCAAGGTAAGCGGCCCCGCGGACATCGCCGGCATCTGCAACGGTGATCCCACGGACTTCACCACCATGGCGAATCCGGAAAACAAGAAAATCATGAAAATCAAGGCCTTCAATGGTCTTGCCCAGGTCATTCTGCGCTCCCGCAAGGGAGAATCCGGAAAAGTGACGCTCCAAGTCATCTCCAACGGACTCAAGCCGGCTCAGACAACTGTGACGGTCAAATAAAAAAACAGGAAAAACTCCGTTCCAGCCGCCGGGCTCCATCAGGAACCCGGCGGCTTTTTCATCACATTATTCCGGGGAAAAAGAGCCATCTTTCCACCCTCTCCCGGACAGGTAGGGATTGAAAGAAAAAAACTTTATGCTACCCTTTTGCCCATGTCTGATCTGCCCAAAGCATACGATCCCTCCCTGGTGGAAGAAAAATGGCAATCCCGCTGGATTGAAGAAGGTTGTTTCAAGGCTGACCCGGCTTCTGAAAAGCCTGCCTACTCTGTCGTCATCCCTCCCCCCAACGTCACGGGAGTGCTGCATCTGGGCCATGTACTGAACAACACCATCCAGGACATCCTGGTGCGCCGCGCGCGCCAAAAAGGATATGAAGCCCTCTGGCTGCCCGGAACAGACCATGCCGGAATCGCCACGCAGGTAAGGGTGGAAAAAGACCTCAAACAGACAACGGGCCGGAGCCGCCACGATCTGGGCCGAGAAGCCTTCCTGGAAAAAGTCTGGGAATGGAAAGAAAAACACGGAGGCATCATCATCAACCAGCTCCATAAGCTGGGTTGTTCCTGCGACTGGGACCGCGAACGCTTCACGATGGATGAGGAATACACCAAAGCCGTTGGACAGGTCTTCATTGAACTCTTTAAGGAAGGACTCATCTACCGGGGCCGCCGCATGGTCAACTGGTGCCCTGTATCCCTCACGGCCCTCTCTGATGAAGAAGTCATCATGACTGAGCAGAAAAGCAAGCTCTACACCGTTCTTTACAAACTGGAAGACGGGTCCGGAGCCCTCCATGTAGCCACCACCCGCCCGGAAACCATCATGGCGGACGTGGCTGTGGCCGTCAACCCGAAGGATCCGCGGTACGCCCACCTCATCGGGAAAAACGTCATGCGCCCGCTCAATCCCACCCCTATTCCTATCATCGGAGATGAATATGTAGAAATAGAATTCGGCACGGGCGCTCTGAAAATCACTCCGGCCCATGATAAGGCCGACTTTGAAATAGGCCGGAAATTCAACCTGGAAATTATCGACATCCTTACCCCCGACGGTCATATCAACTGCCCGGAAGTGCCCGAACTCCACGGCATGGACCGCTTTGACGCACGCCGCAAATCCGTGGAAATGCTGGAAGCCTCCGGCCTCATGGTCAACGTTGAAGACTATGATAACAAAGTCGGGTTCTCCGAACGCGCCAATGTCCCGATTGAACCGCGCCTCTCCATGCAGTGGTTCCTCAAATACCCCTGCGTGAAAGAAGCCGCGGACGCCGTAGCGGGAGGGGATATCACTTTTCGCCCCACGCGCTGGGCGAAAACCTACGCCCACTGGCTGGAAAACATTCAGGACTGGTGCATCTCCCGACAGCTCTGGTGGGGCCATCGCATCCCGGTCTGGTACCGCAAAGACAAGGCGGAAGAACTCAGGAACGCCCCGGCGCTGGACGCCTCCGCACTGGAACAGGGCTTCCTCTACGTGGGAACCGAACCGCCCCAGGACCCGGACAACTGGATTCAGGACAACGACGTAATGGACACATGGTTCTCCTCCTGGCTGTGGCCTTTCTCCACCATGGATGAGGAAACCCGCGCCAAATTCTATCCCACTACGGATCTTGTCACGGGACCGGACATCATCTTCTTCTGGGTGGCGCGCATGATTATGGCCGGATACCGTTTCCAGCATGACAAACCGTTCAGCAACGTCTTTTTCACGTCTATCATCCGTGACAAAATCGGGCGTAAAATGAGCAAATCCCTGGGCAACTCCCCGGATCCTCTGGACCTGATCGCCAACTACGGAGCGGACGGCCTCCGCTTCGGACTCATGCGCATCGCCCCCACAGGAACGGACGTGCGCTTTGATGAAAACCAGATCAGCGAAGGCCGCAACTTTGCCAACAAGCTGTACAATGCCACGCGCTTCCGGCTGATGCAGGGAGATGCCAGGGGAGAAACGGCCCCTCACTACTCATCCGTACATATCTCCATTATCTCCAAGCTCAAGCAACTGCATGCAGATGTAGAAAAAGCGCTGGCGGACTACGAATTCAACGCCCTCATCCAAACCCTGTACCAATTCTTCTGGAACGAATACTGCGACCGCTTCCTGGAAGCCGTCAAGGGTGACCTGAAGGACGGGGCGGACCCAGCGGCACAGGCAGCCACGCTCACCACGATGGACACGGTGCTCAGGCACTACCTGGCCCTTCTGCATCCGGTCATGCCTCATATCACGGAAGAACTCTGGGCCTCCCTGGGCTTTGCGGAAAGCAACGGCGGCCTGCCGCTCATGCGCACTCCTCTCCCCTCTGCCGAAAACCTCCTGGCAGGACTTGATGAAAGCCGCATCACCCTGGCGAATACCCAGGCCGCAGCCCTTTATGAAACGGCCAACAAGGCCCGCAACCTGAAAGCGGAATACGACCTTTCCAACAATAAAAACGTCAGCTTCATCCTGAAAACTCCCCACGACGTACCCCAGGATATCCTCTCCCGTCTGGCCATTTTGGCGAACGCCAAATCCGTCATCCGGGACGCAGCTTACTCTTCCCCCAAGGGAACGCCTGCGGCCCTCACGCCGCTTGGGGAACTCTTCCTGCCCCTGGAAGGCCTTATTGACGTGGAAGCGGAAAAGGAACGCCTGGGCAGGGAACTGGACAAAATAGCCAGGGAAATCGCCAAATCCGCGGCCAAGCTGGGTAATGCGGGCTTTGTGGAAAGAGCTCCGGCAGAAGTCGTGAATCAGGAAAAAGCCCGTCTGGCGGACTGGGAGGCAAAACAATCCCAGTTAAAAGGGATGCTTGATTCCCTTTCCTAACCATCTATCATTCCGGGAATGAATAATATTCAAAAGTACTTTGCCGGCCGCGAGCAATTCATTGAATTGCTTGAAGCGGTGGGCATACGTACTCTTGAACAATTCGCTTCCGCGGACCCCTCCACCGTTTTGCCTGAACTTCACCAGGCAAAACGGATGCTGAAACTGCAGACGGAAATTCCTTCCGCTCCCGTTTTCAGGGAATGGGTGAATCAGGCTCTTTCCTCTCCTACTGCGCCGGAACCGGAGCTTCTTCCCATCCATGAGGGCGACGGCCTCCCCCTTGCCACTCCCGTTGACCCTCCGATTACTGATTCTTTAGAAAACAGAAGGGAACGTGGGCGTTCCCATACGGACGGACCTCCAAAACATCTCTCCGCTAAGGCCAGGCTGCAGGAAGAACACAGCTACCAGCCCGCAAAACATGTTCACACCCACAAGGAGCCGAAAACCTACCTTCGCAAAAAAGGCATCAAGCACATGACCTCCTTCCGGACATGGATGGGAGCCGTTATCGTCCTGCTCCTGTTCATCTGCATCCTCTTCTCCATTTCGGTCACCACCCTTGTGCTTCTCAATGGAGAACGAGGCTGGCCGTTAATCAGCCTCTGCTTCGGCCCCTGGATTCTGGCGCTGGTTCTCTATCTTTCCCTGGCGCTTCCCCGGAAATGCAGCGTGTGCAGGGCCCATGTCTTTTCCTTTAAAAAATACACCCGCAATAAGGCGGCCCACCACATTCCCCTGTTCGGCTACGTATTCGCCACGGCACTGCATATATTCCTCTTCCGCTGGTTCCGTTGCCCGGCCTGCGGTTCCTCCCAGCAACTGGGAAAACCCCGTACCGAACCGCACAGGCATTAACGAAGCTTAATTCCTGCGCCAGGCTTCACGGTATCCCGGCCTCCTGCCTCCTTTCTTCCCCTTACCTGGCATTCGGAAGGCGGTTCCTCCATCTGTCCGTCCCTTCCATTATCTGCCCCTATTCCCTGCACTTCAACCCCACGCTCTGACGGGACAGGAAGCCCCTGCCCGCTTCCGTATCTCATGGAACGGCTCCCGGCAACGAATCCCCCCTTCCCGGTAACTTATTCTATAAAGAGCCACCATTAGGAACCGCCTCAAAACCGGATCGGAAAAATCCGGCAAGAAGGCCGTTTCTCCGTCTCTTCCGGATAAATACGTTTCCTCCTTCCTCCGGATATCCCAGCAACTCAAACATGCGTTCAACGTCATGGAAAAGCATATCCCTGAAAACGGCAGTCCAGGAAAACAAGCGGACATTTCAACAACCGGAATTCCCCCTTCATTAACCTATTCCGGATTTGCCGGCCTCGTGCGCCAGGCAAGGGCTGCGCTCCGGCAACCTTCTCCCTATATGGGAAGCACACCGTAATGAAAACTTATCAGAAAAACGGGGACAATAAAAAACGCCATCCTCCTTTTCGGAGGATGGCGTCCAAAATCCTGAGGCGTCTCGGTTAGAGCACTTCCGGAGCAAGGGAAACAATCTTCATGAACTTCTTTTCACGGAGTTCACGAGCGACCGGCCCGAAAATACGGGTGCCGCGCGGGTTGTTGTCCTTGTCGATGATGACGACGGCATTGCCGTCAAAACGAAGCACGGAACCGTCATCACGGCGAATCGGGGCGGCAGTGCGCACTACCACGGCCTTCACCACGGTTCCTTTCTTCACGGAAGCGGTGGGAATGGATTCGCGGATATGGCAGGTAATAATATCGCCAATGTGAGCCTGACGGGTGCGCTTGCCGATAACGCCAATCATCTTGGCGGAGCGGGCGCCGGTATTGTCGGCTACCTGGACTAGGGATTCCATCTGGATCATGGTATCAAATCTCCTTTACTTAATGGTTGATTCCTGATTAATGGGTAAGCACTTCCACCAGTTCCCAGCACTTCAGCTTGGAAAGCGGGCGGGTTTCAACGATACGTACCTTATCGCCTACCTTGGCCGTGGAATTTTCGTCATGGGCATAGAACTTCTTGCTCTTCTTGACGATCTTCTTGAACTTGGGGTGCGGAACGCGGGCAACGTATTCCACAACGATGGTCTTGTCCATCTTGGTGGAAGTCACCACGCCGACGCGCGTCTTGCGAAGGCCGGGCTTCTTGGTTGTTTCGGTTTGTTCGGACATTGGATTCTAAAAGTGTTAGAATATTCTGTAATTACCGGAAATCCTCAATTAAACCTCCCCGGTTTCACCCTTAATGGTAAGGGCGCGGGCAAGGTCCTTGCGGACGGTGCGAATTCTCTGGTTGTTTTCCAGCTGGCCGGTGGCCTGCTGGAGGCGGAGATTGAAAAGTTCTTCACGAAGGCTGCGCACCAGAGCGGAAAGCTCCTTGGCGGACATAGCACGAAGTTCTTTGGCGGAGTTCTTATCGGACATGGTGATGAGTAGCTTTAGTGTTGAACGCCTTGACGATAGACGAAGCGGGTGGATACGCCCAGCTTGTTGGAAGCCAGTCGAAGGGCTTCACGCGCCTGGGATTCGGTCACGCCGCCTACTTCAAACAGAATGTTGCCGGGGCGGACAACGGCTACCCAGCCTTCCACGGCGCCCTTACCCTTACCCATACGAGTATCCGGGGGACGGGACGTAAAGGATTTCTGCGGGAAAATGCGGATGAACACCTTACCTTTACGTTTCAGGTAACGGTTGATCGCAATACGGCAGGCTTCAATCTGGTTGTTGGTGATCCAACCGCGGTCGAGCACCTGAAGGCCGAAGTCACCAAAAGCAACATAGGTACCGCTGGTGGCATTGCCGGAACGGCTGCCGCGGTGCATCTTGCGGTGCTTCACTCTCTTGGGCATTAAAGGCATATCTTATTCCTCCTTGTGTTAAGTGTGATGAACAAATGTGGTAGCGGCTTTCTTAGGCGCGCGGACGACGGGGTCCGCGAGGGCCGGACGGACGGGAATTCTGCTGGGAGACGACTTCATCGCGCTTGTTGACCCAGCACTTCACACCGATGATGCCGTACAGGGTACGGGCTTCGGCAAAGCCGTAGTCAATCGGAGTGCGGAGCGTCTGCAACGGCACCTTGCCTTCGCGGTACCATTCGGCACGGGCAATGTCAGCCCCGCCAAGACGGCCGGCGCAGCGGATGCGGATGCCGTCGGCCCCGCGTTCCATGGCAACCTGCACGGCGCGCTTCATGGCGCGGCGGAAGGAAACGCGGCGTTCCAGTTGAACAGCCACGTTCTCTGCAATAAGCTGGGCGTCCGTTTCAGGGGAACGGATTTCCACAATGTCAATGTTGACCTGGGTGGAAGCGCCGCACAGGCCCTGGAGGTACTGGCGGATCTTTTCAATTTCCTCACCCTTGCGGCCAATGACAAGCCCCGGACGGGCAGTGTGGACGGTGATGCGGACACTGTTCCAGGCGCGTTCAATCACGATGCTGGAAACGGCGGCGGACATCAACTGTTCCTTGATGTACTTGCGCATCACCAAGTCTTCATGCAGCTTGGTGGCGTAATCCTGGCCCGTGGCGTACCACTTGGAGCGCCAGTCCTTGTTGACGGCGAGGCGGAACCCGATTGGATTTACTTTCTGACCCATGATATTAGTTCAGCTATATGTGTGTTAGATTGGTATGGCCTAGCCTTCTTGATCGGCCAGGATAACGGTGATATGGGATGTACGCTTGCGGATCATGTTGGCGGATCCGCGGGCGCGGGGCATCGTGCGGCGCATCGTGGGCCCTTCATCGACCATGACGGACTTGACCACCAGCGTATCAACGGACAGTTCCGCATTATTCTCGGCGTTGGCCAACGCGGACTTGAGCGTCTTATTCAGCAGGTAGGCACCTTTCTTGGGGGTGTAGGACAGGATGTCGGTCGCCTGGGAGACGGACAAGCCTTGGATTTCTTGAGCAACATCGCGCATCTTCTTGGCGGAGATGCGGGCGTATTTGTAAACAGCTTTCACTTCCATGGGTTTTTCCTCGTAGTGCAGGTTATTATGAAGATGATTTAGTCGTTGGTTTTTACTGAGGCAGAATCTCCGACGCGCACCGGATCGGCTGGCGTCTCAAGTTTCTGAACAAGCGCGCCACATACTTCCTTGCGGACGTCCGTGACTACGGCTTCTCCGATGGTTTGAGCTCCCCGGGTTAACCGAACCGGCATCCCGACTTGCATTCCTGCCGTTCTGCCGGTATTCAGCACCACCACTCCGGATTCGGAATCAATGCTGAGCACCTTGGCTTCCGCCAGGGTTCCCGCGCCATCTCGCACGGGCTGTTGCCTGTATCCCAGAACGGATTCCAGCAGGCGCAGAGAACTCTCCACAGCCGTCCTGGCATTCGCATCTTCGGATATGGCTTGTTTCATGTAGGCAAGGATGGCTCCGGAGAGCTTGACGGATGCCTGCTCCAGCCGCTGGACGCGGTCATTAAAAGCTTCGATGTCCGCCATCGCCTGAATCAGGCGTTCCTCGCTGTTACCTAGCGCAGCGCCCCCCAAAGCTTCCAGCCTGGAACGGATGTCCACCAGCTGTGCGGCCGCCTTGTCGGCGTCGCTGCGGGCCTGGACATAGGATGCCTTGAGGGCCGCATTCTGCTTTTCGAGGCGCTCGATTCTGCGCGTCAGTGCCGCCGTATCCTCTTCCGCTCCCTGCAACGCCCCGCCCAGCCCTACCAGGGTGGTAAGAGAGTATGCAAGGATGGAAGAAGAATTCGACATGTTGATTGTTAACTTACTGATTACTTCTTACCGATACCGCCGTGCTGTTTGAAGACACGAGTCGGGGCAAATTCACCAAGCTTGTGGCCTACCATGTTTTCCGTGACGTACACGGTGGCAAAGTTCTTGCCGGCGTGCACCAGGAAAGTCAGGCCGACGAAGTCAGGCGTAATCATGGATGCGCGGCTCCAGGTCTTGATCGGCTTGCGGTCGCCGGATTCAAGCTGAGCGTCGATCTTGGCGAGAAGCTTTTGGCTGACAAAAGGGCCTTTTTTGAGAGAACGTCCCATGTTATATAATCCTAGTTAAAGTTATAATATATTGATTACTTGGCATTGCGGCGCTGTACGATGACGGAGTCGCTGGGCTTGCGGAGACGGCGGGTCTTCTGGCCCTTGACGTGGCCCCACGGAGATTTGAGGTGCTGGCGACCGCCACCGGACTTGGACTTGCCTTCACCGCCACCGTTCGGGTGGTCGACGGGGTTCATCGTCATACCGCGCACGGTAGGACGAACGCCCATCCAGCGGGTACGGCCGGCCTTGCCGGACATTTCGTTCATGTGCTGGGTATTGCCTACCTGGCCGATCGTGCAGTAGCAATCTTCATTGAAACGGCGGATTTCACCGGAAGGCATCTTGATCAGCGCGTAACCCGCTTCACGGTTGGAAACGATGGCCTGCTGGCCGGCAGCGCGGGCTACCTTGCCGCCGGAACCCGGACGAATTTCAATGTTGTGAACGGAAGTACCCAGAGGAACGTTTTTCAAGGGCATGGCATTGCCTACCTTGGGAGCAACCTTCTGGCCGCTTTCCACCTTCATGCCCACCTGCAGCCCCGTGGGGGCCAGAATGTAGGATTTTTCACCATCCTTGTACTCAATCAGAGCAATGCGGCAGGTACGGTTGGGATCGTATTCGATAGTAAGGACAGTAGCGGGCACGTCAAACTTGTTGCGTTTGAAGTCCACCAGGCGATATTTGCGCTTATGGCCACCGCCAATGTGGCGGGTGGTGATGCGGCCATTGTTATTGCGGCCGCCGGATTTCTTGAGGGGTCTGCAGAGACTCTTTTCCGGGGTAGAGGTGGTGATTTCGTCAAAGGACGGCCACACCTTGTAACGGTTAGAGGGAGTAACTGGCTTGAATGACTTGAGGGACATGATAGCTTTCCTTCCTAATAGGGGCTTTAAACGAGGTCAAGGGTTTCACCGTTGGCAAGCTTGACGTAAGCTTTTTTCCAATGAGCGGTGCGACCTGCATCAGAGCGGCGCTGGCGCTTGAGCTTGCCGTCATAGTTGGCGGTACGCACGGATGCGACCTTCTTGCCGAAAGCAACTTCGACAGCCTTTTTGATTTCGATTTTGTTGGCGTCACGATCAACTTCGAAGACCAGCTCGCCGGTAGTTTCCTGGAGCATGGTGGCCTTTTCGCTGATGCGCACCTTCTTGATGACTTGGTAAATGTCTTTCATGGCTTCTTAAGCAGTACGGCTGGCGAGGGTTTCTAAAGCGTTGTCAACGAGGATCACTGCATCAGCATTCATGAGCTGTTCAATGTTCACTTCCGCAGCGGTCATCAGGAGAACTTCCTGAACGTTGCGGGCGGCACGGTAGGTGGTTTCGTCAAAGGAAGCTGCCACAATGAGCACCTTCTTGGCATCCGTCAGGTCTTTGACGGCTTTGATGAAAGATTTGGTCTTGCCGTCAGCCACGGAAAATTCGGAAACGGTGCTGACCTTGGAAGCGGCAATCAGGTCACCCAGTACGCGGCGCAGAGCCAGGCGTCGGGTGCTCTTGTTCACCTTCTTGCTGTAGTCGCAGGGGCGGGGACCGAAGACCACGCCGCCGCCCACAAAGATGGGAGCGCGCTTATCGCCGTGACGGGCATTACCGGTGCCCTTCTGGCGGAATATCTTTTTGTTATTGCCGCTGACTTCTGCGCGGGTCTTGGAATTGGCGGAACCGGTGCGGCGGTTCGCCTGGTAAGCCACGATCAGGTCATGCACAGCCTGGGAGCCCTTGTCACTGCCTACAACCTGGATGTTGGCGGCGGCGGCGGCTTCTAATGTAAAGGTATTTGCGGACATAGATAGGTTTTCCTATAATCAATGTGAGGTTAACTAAAGCCTTACTTCTTCTTGGCGGGGCGAATCACGAGGTAGGAACCGCGCGCGCCGGGCACAGCCCCGGAGATAAGGATGACATTATCTTCCGGACGAACGGCGACAACCTTCAGGTTCTGCACCGTGCGCTTGGCATTGCCCATCTGTCCGGGCATCTTCTGGTTCTTCCAGACGCGGCCGGGGGTGGAACAACCGCCTACGCCGCCGGTACGGCGGTGCATCATGGAACCGTGGGCGGCGGGAGAGCCGTGGAAGTTGTGGCGGCGCATCGCCCCCTGGAACCCTTTGCCTTTGGAAGTGCCGATCACATCCACCCACTGTCCGGCGGAAAAGAGATCCACACCGGGATCTTTTGCGCCTTCAGCAGGCAATTCGGATGCTTCTACGCGGAATTCCTTGAGGAGTTTGGTAGGCTGGATGCCCAGTTTCTTGAAGTGGCCGGCCTGAGGCTTGGCCACGCGGCTTTCCTTCTGCGCATCAAAAGCAACCTGGATAGCATTGTAGCCATCCTTGTCTTCTGTTTTAATCTGAGCGAAGGTGTTACCCTTGACGTCAATGACGGTCACGGGAACCATAGCGCCGGATTCCTGGTCAAACAGACGGGTCATACCGACCTTTTTTCCGATAAGTCCTAGAGCCATTGTAGTGTGTCTTTCTGTTTTTTAATTACTTATCCGGGTGCCAAACTAGATGCGGATCGTGATGTCCACCCCGGCCGGAAGATTAAGCTTCTTGAGCTCATCAATCGTGCGCGCAGTCGGGTCGACGATGTCGAGCAGACGCTTGTGGGTGCGGATTTCAAACTGTTCAGCCGATTTTTTGTTGACGTGAACGGAACGGTTCACAGAAAACTTTTCAATACGGGTCGGCAACGGAATCGGGCCGTGAACTTTGGCTCCGGTGCGCTTGGCGGTTTCAACGATCTCCTGGGAGGAGCGGTCGATGGCGCGGTAGTCGAATGCGCGGAGTCGAATGCGGATTTTTGGACTTTGCATGGACGTGCGGTCTATTGGTTATACTTCAAAGGTTTGTTTATTTAGCCTTCTCGCTGACAATCTGGTCAACGAGGTTCTGGGGCACCTGTTCAAAGTGGGAGGGTTCCATGGAATAGGAAGCACGGCCACTGGAAAGGGTTCGGATGGCGGTGGAATAACCGAACATTTCGGACAAGGGGACCATAGCTTTCAAAATGCAGGCGTTGGCCTTGTTTTCCATGTTGCTGATCTGGCCGCGGCGGCGGTTCAGGTCGCCCATGATGTCGCCCTGGTAATCGGTCGGCGTGGAAGCTTCAACGGACATGATGGGTTCCAGCATGATCGGCTTGGCTTTTTTGAAAGCATCCTTCATGGCGAAGATGGCAGCCATTTTGAAAGCATTTTCATTGGAGTCCACTTCGTGGTAGGAACCGTCCACCACTTCAACGTGCACGTCCACCACCGGATAACCGGCGACGACGCCCGTGGTCATGGCTTCATTAAGGCCGGCATAAACGGCGTTCATGTATTCCTTGGGAATGGCGCCGCCTACGATCTTGTTTTCAATGGTAAGCCCCTTGCCGCGTTCGTTCGGCTTCACATCGATGACCACATGGCCGTACTGGCCGCGGCCACCGGACTGCTTGACCAGTTTGCCGTCGCCATGGGCAGGGGCAGTAATGGTTTCGCGGTAGGCAATCTGGGGCTTGCCGATATCGGCTTCCACCTTGAATTCACGCATCAGGCGGTCAATAATGATTTCCAGGTGAAGTTCACCCATGCCGGAAATCAGGGTCTGACCGGTTTCTTCATCAGTCTTCACCCGGAAAGTCGGGTCTTCTTCAGACAGACGGCCCAAGGCATTGGACATTTTTTCCTGGTCAGCCTTGGTCTTGGGCTCTACCGCCATGGAAATAACGGTTTCCGGGAAGGTGGGGGGTTCCAGGCAGATATCATTGTCCGGGCTGGTGATGGTGTCGCCCGTGGTCACGTTGCGCAACCCAACGATGGCGGCAATGTCACCGGAGTACACGGCATCAATATCCGTATGCTGGTCAGCCTGAATCTGAATGATGCGGCCCACGCGTTCCGTCTTGCGGGTGCGGGGATTGTACACGGTGTCACCTTTCTTGAGAACACCGGAGTACACGCGGATGAACACCAGCTTGCCGACGAATTTGTCAGCCCAGAGCTTGAACGCCAGCACCATGGGCTTGGCATCGTCCGTAGCGGTGATTTCGAAGGTCTTTTCCGGATCCAGCGTGCTTTCCGCGTGGGCGGGAACGGTTTCTACCGGAGAGGGGAGGTAATCCACCACGGCGTCAAGCAGGAACTGGACGCCTTTGTTCTTGAAAGCGGAGCCACCGGCGACAGGCACAAATTTGTTGGCGATGGTGGCGCGGCGGATAGCTTCCTTGAGCTCTTTGGGCGTGAAGGGTTCTTCCATGAGCACCTTTTCAGCAAGCTCATCGTCCACGTCAGCCACACGGCTCACGAGTTCATGGTAGGCCAGTTCGGCTTCTTCCTTGAGTTCGGCGGGAATATCCTCAATCGTGTAGGTGGAGCCCAGGCGGTCGTTGTCGGCGTAAATGACGGCTTTCTGGTTCACCACGTCAATCTGGCCGCGGAGCTGGTCTTCAGCGCCGATGGGGATCAGAATGGCAGCGGCATTCGCGCCCAGCTTGTTGTGAATATCGTCCAGCACGTTGTTGAAGTTGGCGCCGGTACGGTCCATCTTGTTGACGAAGCACATGCGGGGCACGCTGTACTTGGTGGCCTGACGCCACACGGTTTCCGTCTGGGGCTGCACGCCTGCCACGCCGCAGAACACCACGATGGCACCGTCAAGCACGCGGAGGGAACGTTCCACTTCGGCAGTGAAGTCAACGTGCCCGGGAGTATCAATAATGTTCAGCTGGAAATTTTCGCCTTCGAACACTTTGCTGATACCTTCATTCTTCACCTGCTTCCAGTTGGTGGTAACAGCGGCGGAGGTAATGGTGATACCGCGTTCGCGTTCCTGCTCCATCCAGTCGGTCGTGGTGGAACCGTCGTGGGTTTCACCAATCTTGTGAATCATGCCGGTGTAGAAAAGGATACGCTCGGACAACGTGGTTTTGCCGGCGTCAATGTGAGCGGAAATACCGATATTGCGGTAACGCTCAAGGGGAGCCTTGCGATTGGGATTATTTACGTGATCGGACATAGTGTCTGGAAAGTATCTGTAGTGATCGAAAGTGAATGATTCGGCTGACAGTCGCAGAAGGAATTACCAGCGGAAATGGGCAAAGGCGCGGTTGGCCTGAGCCATTTTGTGAACGTCGTCGCGCTTGCGAACGGAAGAGCCCTGATTGTTGGCGGCTTCCTTGATTTCGTTGGCGAGAGCCTTGTGCATCGGCACACCCTTGCGGTTGCGGGCGTAGTTCACAATCCAGCGCATAGCCAGGGATTCGGAACGGGCCGGATCCACTTCAAGCGGCACCTGGTAAGTAGCGCCACCGACACGGCGGCTCTTTACTTCCACACGGGGTTTGGCATTTTCAATGGCGCGGGTGATTACTTCCAGAGGATCAATGCTGTCCGTGCCTTCGTTAGCCATGTCAATAGCGGCATAAACAATACGTTCCGCAAGGGAGCGCTTGCCATCCAGCATAACCTTGCTGATCAGTTTGCCAACAAGAGCGCTGTCGTAACGGGGGTCACGACGTTCGATTTTTCTATAAACGCGTTTACGGCGAGCCATAATATATAGTAGGTAAGATGTTTGGTTTACTTGGCAGCAGCCTTCGGACGCTTGGCGCCATACTTGGAGCGGCCCTGGCGACGCTTGTCAACACCGAGGCAGTCAAGAGCGCCGCGGACGATATGGTAGCGAACACCCGGCAAGTCTTTCACACGACCGCCACGAACAAGCACGATGGAGTGTTCCTGAAGGTTATGACCTTCGCCGCCAATGTAGGCGATCACTTCTTCACCATTGGTAAGACGGACTTTAGCCACTTTACGAAGAGCCGAGTTCGGCTTTTTGGGCGTACGGGTCATCACCTGGAGACAAACACCGCGGCGTTGCGGGCAGCTATGAAGAGCGCGTGATTTGGACTTCTCTTCCGGAGTAATACGTCCCTTGCGGACGAGCTGATTAATGGTCGGCATGTTTTCCTGCTTTGGTTGTGGTTCGTCGGTCTGATCCGATATTCAAGGCGAACATTCCTTTTTATAAGCAGGAAGCGCGCCTTTTTTCCGGAGGCAAACCCGATAGAAAACGTATTTCTTGTGCCTTTCGGCACGTTTGGGGACGGCCAATATAACGTGATTTTATATCATGACAAGTCTTTTATTCATCTTTTCCTAAATATTTTATCCGTTCCATTTTCCCCGCTCCTTTCAAAACTCCATTTCGGGCTCCCGGAGTCCTTTTTCCTCCCCGTCCAGCAGTTCTCCACTTATGGAGGATGAAACAAAAAATCTTCTTCACAGGCAAAAAAAACATTTCTTCATGTCCGACATGTTACCGCAGAAAAAAATGCATGAAGCGCCGGAGAGGCACAAAAAACAAGCTTTCCATCAGAATTCACGGAAAATTTTTTCATAAAAACGGCAGCCGGTCTATTATTTATTGTTAACCTGCCTGCTTTCCCATTTTGCCCCGTCCGGAAAGCATGACATAGAAAAATCCGTTTCCGGAACGCATCCGGAAACGGATTTCATCAATGAACTCAGCAGCCCGCTATTTCCCAGGCGTCACCAGGGAATGTCCGGACATTTCGGCAGGCTGTTTCAGCCCCATCAGGCTCAGCAGCGTGGGAGCGACATCCGCCAGAATGCCATCGGAGAGAGTTACCTGATCCTTGTCGTCCGCCACGTAAATGAGATCCACAAGGTTGGTCGTGTGGGCTGTATTCGGAGAACCATCCTCATTGCGCATGTGTTCGGCATTGCCATGATCCGCCGTAATAAGCAGCTTGCCGCCAAGTTCCAACACCTTCTTCACACATTTTTCCAAGGCGCTGTCCACTGCTTCGCAGGCGGCGATGCCGGCTTCCACAAATCCGGTATGGCCAACCATGTCCCCATTGGCGAAATTCATGATTACCACATCATACTTGTTCACAGCGTCCACAAATTTATCCGCCACTTCTGCCGCGCTCATCTGGGGCTTCAGGTCATAAGTAGCCACTTCCCGGGGGGAAGGAACAAGGATGCGGTCTTCTCCGGGGAACTGGGTCTCCACACCTCCGTTAAAGAAGAAAGTAACGTGGGCGTACTTTTCCGTTTCCGCTATGCGGAGCTGGGTTTTCCCGGCTTCCGACACAATCTGCCCGAAAATATTGTTCAGCTGTTCCTGTTCAAAAACAATGGGAGAGGGATACTTGGCATCGTATTCCGTCAACGTCACATAGTGCACTTTGGGCGTTACTTCACGGTCAAATCCGTCAAATTCGGGGTAAAGGAAAGCGTCGCTCATCTGCCGGGCGCGGTCCGCACGGAAATTAAAGAAGAACACCACATCGTTATCCCTCACGCGCTGTTCGTTGCCATAGGCAAAAATGCCGGGCTTCAGGAACTCATCCGTCTCCCCCTTCGCATAACACTGCTCCACATATTCGGCAGGGGAGCAGGAGCACTGTTCGCCGCGCCCCAGAACGATGGCATCCCATGCCAGCTTGTTGCGGTCCCAGCGCTTATCCCGGTCCATCGCATAGAAACGGCCGACAACCGTGGCAATTTTGGCGCCGTACGGCCTGACGGCTTCCTCCAGCTGGCGAATAAAGCCGACCCCACTGGTTGGCGAGCAGTCACGTCCATCCGTAATGGCATGGATGCAAATATCGCGGACACCCGCTTCATAAGCATATTTGACGATGCCGATCAAATGGTTGATATGGCTGTGCACGCCGCCGTCACTCACCAGGCCAAGCAGGTGCAGACGGGAAGAAGCGGCCTGGGAAAAAGCGGTCTTCAGCACGGCGTTTTCCCCCATGGACCCATCTTTGATGGCATTATCCACCCGGCAGAGGTCCTGAAAGACAACGCGGCCCGCACCCAGGTTCAAGTGTCCTACTTCGGAATTACCCATCTGGCCGTCCGGAAGGCCCACGTCCTCCCCGGAAGCGCCCAGCAGGCTGTGGGGATAGTTGGCAAGGAGGTAATCAGTAAAGGGGGTATCAGCCAGAACAGTGGCGTCACCGTACTCTTTCGCGACATCAGGCCCCAACGGATTGCGGCCCCAGCCGTCGCGAATAACAAGAACGACAGGCTTTTTAGAAGACATAGGCTTTATTTACAGGCATCCCCTCCTTTTGAAAAGCCTGAAATCAGGCATTCCCTCTCCGATTCCGGAATTGCCTGAGGTTTGCCGCCCTTCCCCACACGGACGACTACATATCTTCCCCTCATCACCGGACGCTCGGAATGAAATACAGCAAATTTCCATTCCAGAGAACTGGTTCCCACCGTTCCCAGGCTTAATTCTATGCTCAGCCAATCTCCCAGCCCCGCAGAGCTGGAGTAATCCGCCTCCACATGCACGCGGGGCCAGCCGCAGTCTGGGGAAAGAACCTCCACCCCCCGGGAACGCAGGTACTCGTGCTCCACTTCTTCCACCAAACAAAGCAGCCGGGAGAAATGGGCCACCCCGGAAGCATCCGTATCATGAAAAGCTACCTGGCGCGTTCTGACAAAGGGCATGCAGGGAAAATCTTCAGGCATGCCGTCATCCTGCCTCCTCCCCCGGAACCATGTCCAGCCGCAAATGCGCGACAATGTGTCACATCAGCTTGATTTGTGACATGCCGCTCTTTATATATATAAGAGCAAGCCGCACGGCAACGGACCCGCCGCCATGAAAAAAGACACCATTGACAAGCTGATCGGCCGCATTGACCACATCAAGGAGGAGGATCTCCAGCGCTTTTTTGTCAAGCTGGCGGAACAGCAGGGTTTTTTCCAGCAGGTATTTGAAGCCATTCAGGAAGGCCTGATCCTGCTGGATAACAAGGGAAAAATTCTGTTCGTCAACAAGGCTGCCCTTAAACTCTTTGACAAGGAACGCGGGCAAATAACCCCGGATGACTTCTGTATTTTCCTGGGCAGGGACTGCTCATGGGACACGATCCAGCAGAGCCAGACCGCCGTCTCCCGGGACACGGAAATCTTTTACCCGGAACACAGATTCCTGAACATTTTCATTTCCCCCATCGGCAGCAAAAACCAGGGCCACCTGGTCCTGATACGGGATGAAACGCCCCGGCATAAAAAAAACGCGGAAAACCTTGAGGCAGAACGCCTGAATGCCTTGACGCTGCTGGCCGCAGGAGTGGCTCATGAAATAGGCAACCCCCTCAATTCCATAGGCCTCCATCTCCAGCTCCTTGCCAGGAAGGCAAAGCAGCTGCCTCCAAAGTACCGGACGGACATGGAGGAATTGCTGAAAACAGCGGAGAGCGAAACCACGCGGCTGGACGTTATCCTGAAGCAATTCCTCCAGGCCATCCGCCCCACCAGGCCCATCCGGGAACCCTACAACATTGAAACCATCCTCATGGAAGTGCTGAAACTGCTGGAACCGGAAATCCAGCAGCGCGGCATCCAAATCAACACGGACCTCCAGCCCAGTCTTCCCATCCTCAGCCTGGACCCCGTCCAAATCAAACAGGTCTTTTACAACCTGATCAAGAATGCCTACCAGTCCATCCCTCCGGAGGGAGGCACCATTCTGCTCAAAAGCGGTTATACGGATGACAGCGTATTCGTCACTGTGGCGGATACCGGCTGCGGTATTTCACCGGAGGTCATGGGCAGCATTTATGAACCTTTCCTGACCACCAAGTCCACCGGCTCCGGACTGGGGCTGCTTATCGTTCGCCGCATCGTGAAGGAGCACGGCGGGTCCATCACGCTGGCCAGCCAGCCGGGTCAGGGAACCACCATCACGGTCTTCCTGCCGCGCGTGGAACGCACCATCAGGCTCCTGCCATCCTCCATTCCCTCATGAAGATGCCTGTTCTGCTGATTGTAGATGATGAAAAACCCACGCGCGACGCTCTGCGCATGGGTTTTCAGGATGATTATGAAGTGTACACCGCCGCCAATCTCTCCCAGGCTTCCGCCCTCCTGCAGGAAGAATCTCCGGACCTGGTTCTGACGGATCTGAGACTGGGCGGGGAAAGCGGCATGGACGTGCTGAAAGCCGCTGCCTCCCTGCCCCACCCTCCGCAAAGCATCATGATGACTGCCTACGGCTCCGTGGATGCCGCCGTAGCAGCTATGAAAGAGGGAGCGTACGACTTCGTCACCAAACCTCTGAATCTGGACGCCGTGGAGCTGGTGCTCAAACGGGCGCTCCATACCCGGAACCTGGAAACGGCCAACCAGGAACTCACCACCCGCATCCAGGCGGACTCCGGACTGCAAAAACTCCTGGGCAGATCCGCCGCCATGGAACACGTATTCTCCATTATTCGTCAGGTGGCCCCCAGCAAAACGACTGTGCTGATTGAGGGGGAAAGCGGCACCGGGAAAGAACTCGTAGCCCAGGCCATCCATTCCCTTTCCGGAAGGCCGGAAAATAAATTCGTGGCCGTCAACTGTGCGGCCCTTTCCCCCCAATTATTGGAAAGCGAGTTGTTCGGCCATGAAAAAGGCTCCTTCACCGGAGCCGGACAACGCCGCATCGGCCGCTTTGAGCAGGCGGACGGCGGCACCATCTTTCTGGATGAAATCGGGGAAATAGACGCCGGAACACAAGTCCGGCTGCTGCGGGTGCTGTCTGAACGGACCATTGAACGCGTAGGGTCCAATGCATCCATTCCCGTCAATGTGCGCGTTATCGCGGCCACCAACAAATCATTGAAAAAGCTCGTGCAGGAAGGAAAATTCCGGGAAGACCTCTACTTCCGCCTCAATGTGGTGCATATCCAGATGCCCCCCCTCCGGGACCGCCGTGAAGACATCCCCCTGCTTGCCACCGCCTTTCTGAAGGAATTCGCCAGGGAAAACAACAAGGAATTCAAGCCCCTCTCCCGGGACGCCCTTGAAGCCGCCCGGAATTACCAGTGGCCGGGAAACGTTCGTGAACTCCGCACGGCCATGGAACATGGCGTCGTCATGAGCAATTCCGCCAGCATAGGCCTCCATCATCTTCCCCCGCAACTGCAGGATACTTCTCCGGAAGCCGCTCCACAGGAACGGCCGGAGGACAATGCACCCAACACGCAGAATGGGCTTGTCCCGGCCGGGGTTTTGAATTTATCCTTGCTGGAACGGAATGCCATCCAACAAGCGCTGGCCCAGTCAAACGGCAATAAAACGGCAGCAGCCCACCTGCTGGGCATCAGCCGGCGCACTCTTCAACGCAAACTACAAGACATCCTCCATTCATGAATGAGCCCTCCTCGGCATCCCGCACCCCTTTTATCATTATCCTTATCCTGCTGCTGGCAATCACGGGGGTGCAGTACTTTTTCAACTTTAACGGGCTGGGCACTGATGCCGCCATGGACCACGCCCAAATCGCCCGCAACGTAGCCAGGGGGCAGGGGATGACCACCAACTGGCTGAGGCCCATCCAGATGGTCTCCGGAAGCACACGCGCCGGCCTCAACCCTTTTCTTTCAGACGCCCAGATACACGAACAGGAAGCCATCCGGGCTGGACAAGGCGAAACCCTCGTCGATCCGGAAAAATTCAATCCCTATGCCCTCCGGGACACACGCAACGCTCCGCTGAACATCCTGACGGAAGCAGCTGTCTTCAGGATGGCCGGCGTCCACAAATTTGACCTGTGGAGCATGACGGGCTCATCCATGATTTACCTGCCTGACCGGATTGTGGTGGGCATTTCCTGCATGTTCTTCATCCTGTCCGTCCTGAGCTGCTATTACATCCTTCTCCGGATGTTTGACGTCACCATCGCCTGCTTCACCTGCCTGACGATGATACTCAGCAATCTTTTCCTGCAGTACGCTACCAGCGGCCTCCCGCAGATGATGATGCTCTTTTTCTTTGCCTGGGGCATCCACTTTTTGTACACGGCCCTGCAAAACAAGGAGGAAAACCGCGCATTCCTGTGGCCTCTCATCGGCAGCTCCATCTGTTTTTCATGCGTCTGCCTTACCGGATGGATCGGGCTGTGGCCCATGGCCGGTTTTCTGATTTTTGCAGGCATCCGGTTCAAACCGCACGGCCTGTACTGCATTCCGGGCCTTATCACTCTCCTTCTTTTCCTGGCCTACCCCATTTACATTAACCGCTCCCTCAGCGGAGGCTTCTTCGGAACGGCTTATTACACCATCTTCACGGGGCTGACCGGCAATGAAGAAATAGCGATGAACGCCCTGGTTTCCGGGAGCATTCCCATCGCTGCCCAAAAAGTAGTCACTTCCGTCATCAACAACATTCTGACACAGGGTGACCTGCTCTATGAAAACCTCGGCAATCTCCCCCTGGCCATGGTATTTCTGCTGGCCCTCCTGCATAAATTCAAACGTCCGGAAGTCAACCAGTCCAAATGGGGCATCTTTGCCATGTGGGTCCCTGCCGTCATCGGCATGGCCATTTATACCTCCAATAAAACCGGCATTTCCTTAGGGCAAATTCAAATCCTCTTCGCTCCCTTTTTCACGGCCTATGGCACGGCATTTGTTCTCAACCTGATAGCCAGGCATTCCAACAAAGAGCTAATCTCCATCCTTCGGGGAGGCGTCCTGCTGCTCTCCCTTCTCATCACTTCCCTTCCGCTTCTGCTCAACCTTCCGCACATTGTGCGGGTGGGCATCCTGACGGCCGGCCGCGGCATTCCGGCATGGCCGCCCTACTATCCCCTGGGCCTTAACCGGGATCTTCGCACCCAGACGGCGGAAAAGGATTTCATTCTCACGGACCAGCCGTCCGCCGTCGGGTGGTATGCAGACCGGAAAGCCATCGGCATCCCCAAAATGGTGGAACAATTCATGGTGCTGGAACGCATTTTGAAATTCCACGGAGGCAAGGTGGGCAGCATTCTGGTGACTCCTTCTTCCACGTCAAAAATGAACCTCCGCACCATTTCCTCCTCCTATGGAGAATTCACTCCCCTCGTTCTGGAAGGCACGGTTCTGTTCCAGACCAAGGATAAAAACCCCGTGTACATCTTTGACCACAGCCGGGCGCTTGCCCCCCTGGCGCAGCGCTTTGGAACGCCTGACTCCCGCCAATTCATTCAGGGAGCGGAAATGATTCTTTACAAAGACCTTCAGAAAACCGCCCCTCAACCTTAATTCCCATATGGCCAAACAACGGAAAAACGCTCCCGGATTTGAAGAATCCGTAGCACGCCTGGAAGAAATCATACGCCTGACGGAAGCCCCCGTCACCGAGCTGGAAGACATGATAGCCCTGGTGGAAGAAGGCAATAAGCTCATCCGTCACTGCCGCAGCATCCTGCATGATGCGGAACTGCGCATTCAAACTCTCAGCAACCCGGAAACCGTTCAGGATAAAACGGATACTGATGAACCAGACAGCAATGAATTCTCCCTCACCTGAACTTCCGGAACTGCTGGCAAGCATCCGCAGTCACGCAGATCTGATGGAAATCCCTGAGTCGGAACTGCCCAGGCTTGCGGAAGAAATCCGGAATACTCTCATTCACTCCCTTTCCCTCACCGGAGGCCACCTGGGCCCCAACCTGGGCGTGGTAGAGCTGAGCATTGCCCTTCACCGCGTCTTTGAAACGCCGCGGGATAAAATCATCTTTGATGTCTCCCACCAGGCCTATGTCCATAAAATGCTGACTGGGCGCGCTCCCCTGATCCACACCATCCGCCAGCACGGGGGGCTTTCCGGATTTGCCAAAATGTCCGAATCCCCTCATGACTCCTACGGAGCGGGGCATGCGGGCACGGCGCTTTCCGCCGCCCTGGGCATGTGCGCGGCCAGGGACCTGAAAGGAGAAGACTACCATGTGGTGGCCGTAGCCGGGGATGCGGCATTCACCTGCGGCACCACTCTGGAGGCGCTGAACAATATCAGCCAGACCACCAAACGGTACATCACCATTCTGAATGACAACGAATGGGCCATCGACAAAAACGTAGGAGCGCTGGCAAAATACTTCAACTCCCTGCAAACATCGGAAACGTTTTCCTGGTTAAGGGATAAAACGGCTTCCTTCATTGAAAAGCTGGGCGGCACGCAGGCCAGGGACTTTGCTTTCAAGCTGGAAGGCACCACCAAAAACCTTATCTTCCCGTCCCTGCTGTTCAACAAATTCGGGCTGCGCTATTTCGGCCCCCTGGACGGACACGACATTCCCACCCTGATACGCACCCTTGCCTACATCAAGGATCTCAACGAACCCGTTATTCTGCATGTGGTCACTCAGAAGGGTAAGGGCTACCAGCCCGCTCTGGACAACCCCACCAAATTCCACGGCCTGGGCTCCTATTGCGTGAGGGACGGGGAAACGCAGGCCGCCCCCACCCCCACATTCTCCCATATCTTCGGCTCCACGCTGGTGGAAATGGCGCGCGAGGATGAATCCATCACAGCCATCACGGCCGCCATGGCCAGCGGCACCAAGCTGGACTTATTCAAGGAAGCCTTCCCCAAACGCTATTTCGACGTAGGGATCGCGGAAGAGCACGGCGCCCTCTTCGCCTGCGGGCTGGCGGCGGAGGGCATGAAACCCTACATCGCCATTTACTCCACTTTCATGCAGCGCTGTATTGACATGATTCAACATGACGCCGCGCTGCAAAAACTGCCCGTGCGTTTCTGCATGGACCGCGCCGGGCTTTCCCCGGACGACGGCCCCACACACCACGGGCTGTTTGACATTGCCATGATTCGCAGTATCCCGAACATTGTCTTCATGCAGCCCAAGGATGAAGCCGAATTCGTCCACATGCTCCGAACCATGAACCATTACCAGAACGGCCCCACCGTTATCCGCTATCCCAGAGGATGCGGCGCCGGAACCCCCGTCCCCGCCAAGGCGGAAATCCTCCCCATCGGCAAGGCGGAAGTACTTCAGGCGGGACAGGATGTAACCCTCGTTTCCCTGGGCACCATGATCGGCATCGCCCGGGAAACCGCCTCGCTGCTGGAAGTCCGCGGCTACACCGTCACCCTGATCAATGCCCGTTTCATCAAACCTCTGGATGATGAATGCATCCGCCGTCACGCAGCACGCAGCCGGGTGGTCTGCACCTTCGAAGACCACTCCATCAGCGGCGGCTTCAACTCAGCCGTTCTCGAATCCCTGGAAACCGGAGATGTCAAGACGCCCGTGGAAGCCATCGCTTGGCCGGACCAATTTATTGAGCATGGTTCGGAATCCATCCTCAGAAAAAAATATGGACTCACGGCGGAAGCCGCGCTACAAAAAATCATTCCCCACCTGAACTCCTGATCTGTTTATGAAAACACGCACCTTCCTGCTGGCAGCCCTTGCGCTTGCCCTTCTTCCCGTCTCCGCGCGGCAATCCCTGCCGGCCTCCCCTTCCCCTGCCTGGATTCAGGAATTCAACAACCTGCCGGAAACAACCAGAAAAAGCTATATAGCCCAATTCCGCAAAGCGGAACAGCTCTTTGCCCAGAAAAGAATCATGGAATGTCTTTTTTCCCTCATGGAGCTGGAAAAACTTTATGCCGGAAATCCGGGCTTATACAACCTCCGCGGCGCATGCTACATTGAAATCCGCAATATCGAAAAAGCCCTGGAAAATTTTGAAAAAGCCCAGAAACTGGATCCCTCCAACCTCACCATCCAGTTCAACCTGGCGGAAGCGCATTACGTCAATCACGACTATTCCAGGGCTCTGAAAGCGTTCACGGAACTCCTTCTCCCCTTCAAGGACAATCCCGGCATGACGCCCCTGCTGCAATTCAAGCGTTATATCTGCGCCCGCAAACTGGATAACCAGCCCCTTGCTGCGGAATTGGAAAAGCTGTACGGCCCGATGGATGATACGCCATACTACTACTGCACCCAGGCCATCCTCAAACTGATGGAAGGGGACAAGGAAGCCGCCCAGGGACAGCTCCTTTCCGCCATCCGCATCTACGGAGGCACCAGCGCCATCCAGGCCTTCACGGACGCCATGACGGAGGCTGGCATCCTTCCCTCCCCCTATGGGCAAACCGTCCCAACCGAACAGCCGGTAAAAACCGGACTGGAAAAACGCCGTTAATGAATGCGGAAGCGACGGTCCTGAAGCTTTATCCCCTGGGAGAAAACGGCCTGATTGCCGTCTGGTGCACGGAAGAAGGCCTTATCAGGACGGCGGCAAAAAGCGCCAGAAAACCCGGCAGCCCCTTTGCCGGGTGTCTGGACATTTTCTATCAGTGCCGCATGCAGTGGACGCAGGCAAAGAAGGGGGATTTGCACACCCTGACCTCCGCAGACCTGCTTTCACCGCGCCTGGCCCTCCGGAAAAGCTACCTCCGACTCAGTGCGGCAGGCTATTTCGCGCGCCTGTTTCTCCAAATGCTGGAACCGGACACGCCCATCCCGGAATTTTACGACTTGCTGCAGAGGGCCTACACCTACCTGGAAAACAATGATCCCACGCTGCGCGCCGTCCTGCACTTCGAACAGGAACTCGCCCGGCTGCATGGAATAGCCCACCCAGGCATCCCGGCGCACGTCATTCTGAAATCCCACTTCGGCAAGCTTCCCCCGCAGCGGGAAAAACTGCTCGGAAGCCTGGAACAAAAAGAAAGCTGAGCAACATGCCTATCCCGCTTTCATCCTCAGCAGGGGATGTTCCCCGCCCCGCTCAACACTTCCTTCCTCCGGGACGGGATGCGCATTGTTCCGTGCGCGAGATTGACTTGTCCGCAGCCTTCGGGCAGAATATCCGGCACGGTTTTACTGCATGAAGATCCTTGACAGATACATTGCCCGCCAGCTGCTGGGTGTCACGGCCCTGGGGGTAATGGCCCTGAGCGCCCTGCTCCTTCTGGGCAACCTGTTCAAGGAACTGCGTCCCCTCCTGGTGGAAAACAAGGCTCCCATCAGCATCGTTCTGGAATTCATCTTCCAGGTCATCCCCTTTTCGCTGATGTTCTCCATTCCGTGGGGGTTCCTGACGGCCGTTCTTCTGGTGTACGGCCGTCTGGCCTCGGACAACGAACTCACCTCCATGCGCATGTCCGGCATGAGCCTGTGGCGTCTGAGCGCCCCCGCCATCGCCATCGGCATCGCCCTTTCCGGCCTTTGCTACTGGATTAACATAGACATCGCACCGCGCGCCAAGCAATCCATTTCCGAGTTGCTCATCAAAGCCGCATCCATCAACCCGAAGGGGCTTCTCAATGAAGGCCAGGCCATTACCAAATTCGACAACCTGGAAATCTACATCGACAAACGCGTGGACGACGTCATCCACGGCATGCATATCTACCAGAAAGCGGACGACAAATCCCCGTCTGTGGCCATGCACTCGGAACGCGTGACCATGGATTTCTCCCCCGAGAAAAAAATCCTCACCCTGCATCTCATCAATCCCCTCATCACCACGCAGGAAGAAGGTTCCATCTCCCAAAGCGTGACCATGGACGAGATGCCTCTGAGCATCAACTTGGACAAATCCCGCTCCCGGCGCATCAAGGCCAACCGCTTCACCAACCGGGAAATCCGGGAAGCTCTGGACACGCCCGGCTATCTGGATAAAAAGCAGACCACGGAATTCGCCACGGAACTGCCCAGGCGCGCCTCCTTCTCCCTGGCCTGCATCGTCTTTGCCCTCATCGGCGTGCCACTGGCCATCAACACCCGCAGAAAAGACACCTCCACAGGATTTGCCCTGGGCATCCTGATCGCCTCACTCTACTTTCTGGCCCTGATCTTTGCAGACTTGTCCCGGAAAAACGACACGATGCTCCCCTATATTCTGCTGTGGCTTCCCAACATCATCACGGTTGCCGTGGCGTTGCACCTTCACAAACGGGCCAAGCACAAAGGATAAGCCCCCGGCGGAAGCCCTTCCCGCTGGCGTGGAATTAGCTTTCACAGCATGGGAGCTTCGGGTAACATGACGTGCATGCAGCACGTCCCCACCATCGCCATCGTAGGCAGGCCCAATGTCGGGAAATCCGCCATTTTCAACAGAATGGCAGGCAGGCGCATCGCCATCGTGCATGATGAACCCGGCGTCACCCGCGACCGCCTTTCCGCCCCCTGCAAGATTACGGACCGCGCCTGTAAAATCATGGATACGGGCGGTATCGGCGCGCGCCTGAGCGACGGCTTTGCAGAACAGGTAGAGGCAGAGGCGGACATTGCCATAAAAACGGCGGACCTCATCCTCTTCGTGCTGGACTGCCGGGATCACCTTACACCTATCGACCAGAGCATCGCGGACCACCTCCGCAAATCGGACATTCCCGTCATCCTCCTTCTTAACAAAGCCGACCACGAAAAACAGGACCTTAATCTGGGCGAATTTGCCGGACTGGGTTTTGACGACCATATCTTTCTTTCCGCCGCCCACGGCAGAGGCTTTTCCGAATTAGCCTCCCGGCTGGACGGTTTTCTCAAGCAAAAAGGGGCTCCGCTTAAAGAAGAACTGGAGGAAGAACCGGAAAACGGAGAGGAAACGGCCCTTCCCATCAAGGTAGCCGTAGTGGGACGCCCCAACGCAGGCAAATCCTCCCTGGTCAACGCCATCCTCCGGGACAGGCGCACCATTGTCTCCAATGTGGCGGGCACTACCCGCGATGCCATTGACGTCCCCTACCTGCACGACGGCCAGCCCTACGTGCTGATTGACACCGCCGGCATGCGCCCACGCTCCCGTCGGGACACCTCCGTAGAAGTTTTCTCCGCCATGCGCAGTGAAAAAGCCATCCGCCGGGCGGATATCTGCCTGCTGGTCATTGACATTGCGGCGGGCATCACGCAGCAGGACCGCCGCATTGCCGGCATCATTGCGGAGGAAGGAAAACCCTGTATCATCATCGTCAACAAATTCGACCTCTTCCATCCGAATGCCTCCCGCAAGGACCGCATGGCGGAAGTGGAGGAACAGGTGCGCAGGGAACTCTTCTTCATCAGCTACGCGCCTTTCATCGCCACCTCCGCCAAAAAAGCGGAAGGCGTGGAAATCATCTTCAAAGTCATCACGCGCATCCGCCGGGAATCCCATAACCTGCCCACTACCGGCCAGCTCAACCGCCTGATCCAGCTTGCCCAGCAGATGAATCCCCCCGGCGCCGCCAGCGGCTCTGCCAGAAGGTTGAAAATATATTACGCCACCACAGCGGTGGACCCCAAGTACAATACTATTCCCGTCCCGCGCTACGTTCTTTTCGTCAATGACAAAAGCCTGCTCACGGACAGCTATTCCCAGTACCTGCGCAACAAAATAAGGGAAGCCTACCCCGCTCCCGGCATTCCGGTCATCTTCTCCGCCCGTTCACGCGTGCGCAACGACTGATCATCCCTCCTTCTCCGCATGCTCTCCGTCAGGAACCTCAGCGCCTCCTTCCACACCAGGGCCGGCATTGTCCGGGCGGTAAGGAACGTGTCCTTTGATGTAGCCCCGGGGGAAACGCTTGGCATCGTGGGTGAATCCGGATCAGGAAAATCCGTCACCTGCTACTCCATGATGGGGCTCATTCCGATGCCTCCGGGCCGCATTGAAAGCGGTTCCGCCATGCTGAACGGCACGGATCTGCTCCGTTGCCCGGAAAAGAAACTCCGGGCCATCCGCGGCAAACGCATCTCCATGATCTTCCAGGACCCCATGACCTCCCTCAACCCGTACCTGACCATCGGGGAGCAGGTGGCGGAACCCCTTGTCATTCACGAAGGGGCCGGCAAAAAGGAAGCGCGGGCACGCGCGCTGGAACAGCTTGCCCTGGCAGGCATACCGGATGCGGAACAGCGCATGGACGCATATCCCCACCAATTCTCCGGCGGCATGCGCCAGCGCGTGATGATTGCCATGGCCCTCATCACCAGGCCGGAAATACTCATCGCGGATGAACCCACCACCGCCCTTGACGTCACTGTCCAGAAGCAGGTGCTGGATCTCATCAGAAAACTTCAGCAGGACATGGGCACCTCCGTCATCCTCATCACACATGACCTCGGCGTGGTGCGCCAATACGCGGACCGCATCAATGTCATGTATGCAGGCCGCATCGTGGAAAGCGCCCCGGCGAAGGAACTTCTGGAACATCCCCGGCATGCCTACACCAGAGCCCTGATGAAATCCATTCCCGGGCTGCATGCGAAAGGCGCCCCCCTCTATACCATTCCCGGACTGCCTCCCAACATGACTCAGGAGCCATGCGGATGCAGCTTCCGCCCCAGGAACACCCTCGGAAACCCGGCACTCTGCCTCACGGACCGGGAACCGGAGCTGGTGGAAATATCTCCGGGGCACTCAGTCCAGAATTGTCCGGGCTGCCTAGCTTAGCTCCCTTTCTTTCCTGCCTGTTTGCACGCAGATAATGCCGCGCACGATTGATTGCTTCTCGCTTTCGGGAAAAGCCGGGCGGCCGTTGCGGTTACGGTTCTTCAGATTTGACTTGGCTGTTGTCTTGGGTTTCATTGGCGGTGGGGATTTGGGGTGGTTGAAAAATGGAGTGGCTGGCGATGGGCGGTACACTGGTGTACCGTTGCACCGGGTGAACTCTTCTTCAATCATACGCTTCTTTTCTTCCTTGAATGATTTTGGCACCCACTTGTCGATGTCAAAGTCATAGTCCATATCTGGGGGAAGGAGCATCCTCCCAAGGATTGCCATCCATGGCACCTTCGGGAACAATGCTCTTGATGGCTATCTCTGCGTCTTGACGCGCTCTCATTCGGGCAATATACTCATTCGCATCAAAACTACTTGTATATCTGTCTATCATAATAATTTAAATTGTGGTCACCGAGAAAAACTCAGCAACCGGGGTTAATGTTGAACTTGATAATTTGATGAAAAGTAGTGGTAACTATATGTGAAATAGTATGTTATTTTTTCATCGGTATTTCATCAAAATTTTTCGACCAATGAAAAGAAGAAGCAACCGGGGCAAAAGAGAAGTCCAATCCTGATATTTTTCTTTTCAGTGGTTTTATCCTTGTTGAGCCGATGATGAAAGGGCGATGAAAAGAAAAACTTCTGATAACTATTCTTTTATGTTCTCATTCATCAATTCATCAAATTATCAAGAGCAACGAGAAAAAAATTAAAGGTTCTCCAGCATCTGCCGGGTGACGGTGTAGAAGCGTCCGACTTTCCGACTCTCGACGTATGAGGGTTCGTTGAGAAACACGCCCATCTGATAGGAGGTATAGCAGAGCGAGTTCGGTGCCGGATGCAATTTCCAGCATTCCTGTACCACTTTTCTCAGTGCCGTGCGGTCGGCCTTGACGCGCTGATAGTCGAAAAGGTTGAGGATATCGTTCATGCAGAACGAAACGGACTCAACATTCTTCGTCACCATTATGTCCATTAGCAGCTCGGCGAGTTCTATCTCCACACGGTTGCGGTTGGCGCGGATTACGCGCTGCAACGCCTCGGTGTCGGTAAGAGCAGGGTCGAACCACATACGGCTTTTCTCGGTGGTGGATAGAGGTCGCTCCGAAAGATGATGCAGAAATGCTGGAATCTCGGCTCTCACTTTGTCGAGAAATGCGGTGTCATCGTGTTGCAGCCGCTCAATCTTCCTGACCCAATAGCGAGTTTCGCCGGGATCAATGACAACGGGAAGATGCTCGTTGTTGGAACACAGCACGAACTTGGCGAAGAAACTCACTTCCTCGCGGTCGCGTCCTTGCGCCTCCATCTTGTAATTGTAGGTGATTGAAAGGTTCTTCAGACGCTCGGAGTCCTCACGTTTATTCAGAAGCACCTCATCAACGACAATTACCAATTTCCCGTCCCAATCGGAATTGAACTGACTGCGGAAGCTCTCGTTGGTGTTGAAGGTCACGTTGGCCTCAAACATCGCTTTGAGAAAGTTGAGGAGCGTAGATTTCCCGGTGTTGCGTTCTTCCGATAAAGCAGCAGAATCGGGAGTTTCTGCAACGGTTTCGTATAGAGCAGTTGCAGGTAGTCAAGACCGAGTTCGTACTGACTGACGAAGATGTGACGACAAGGGCTTCGATTGTCGGCCATTCTCCCGGCGTGGGGATATGGCTTATGGGCGCATATCGGTTGAGGAAATTGCCTACAACGGGCTTGTAGTCTATGTGAGCCGGAACACAGCAGAAGCCATCATACTTGGGTACATTGGCGAAAAAATCTTTGCCATAGTCCTGACGGATGGCCTCAATGCTCCACGGCATACGGTATTCCACAAGTTCGCCTGTGGCGTTGGGACGCATCACAATCTTGTAGTAGGTCGTGGCGATACGCACAAAGCGGTCTTCGGGTAGGTCAGTAGATTGTGCATTGTTTTCTTTCATTGGCTCAGTCATTTTTAGAGTTGAGAATATCGGATACCTCACTCTGGCGATAACGGACACTGTTGTTCACTTTGACAGGGGTGAGTTTACCGCGTTTGGTGAAATTCCAGACGGAGGTTTCTCCTATGCCGAGTTGGGCGTACACCTCTTTCTTGGTGAGCAGACGGTCTTCCTCGGCTTTCATGAGGATGGGGAGAAGACGCTCGCGAGTAGCGTCGATGGTGTCAACCACAAGCTGATGGAGGTCAGCGAGGGTGACATTGAGAACAATGTTGGGAGAGTTGGGGTCGGAGAGAGTTGCTTGAAGAAGGGTATATAGGTTCATTGGCTGTATGCTTGAATCTATTGAAATGAATCGCCTGATTATCTGCACTGCCAAACCTCCGGCGACGATTGAAGGCATGGCGGTGTGTCCCGGAAAGGACATAGCTGTGGCTGTCGGCCCCTTTTTCGTTGAAATCAACACAAGGGGTGCTACCGACCCTTTTGAGGTCAGCAGACGGACGCACTGCCGATTACGGGTGCCTCTGACAACCGGGCACAACCGTGGCTGTCCGCCTTGTCGGGGCAGACTTGCGTCTTGCAATGTCGCATGGGTGCAACATACGCTGAAATGTTAAGATGGAACATCGCGCGTACCGCGCTTCATAGGATATATTCCTCTACCTGCATCTTACCATATCCTATTGTCAGTCATCGCTGCCACTAAATCCACAATTATCAGCGTGCTGACAGTCTGATATAATCATCTGCGTACCAAGGAATATCCTTTCTTTCATTGAGCCATATACAGCAGCCATTCAATCCTTTGCAAACCGTTGAGCATCGCTTCCTTAGTGCGGGTTGTTATCGGTCTGCGTCTGCCGTTATCTTAGGCTCTCTCATAGTATTCCGGGCGGCTTCAGCCGGTCGTGTTGGTAAGTATGAACACAAAAAAGTGTAGCACAGTCAGGAGCATCATTCCAATCCGATATATAAAAGTTGAATGTGTCGGACGGTGGCTCTGCCATCTTGTGTTATATAAAGATACGAAATATTTTTCGTATTTCCAAATTTTTAGATCCCTCGACCGAAGAAAGTGCGATTTTGCCGCATTTTCCGCATCATACGACACGGCGGCAACTGCCGACAATATGCCTTGAACCAAAGATGATAACGGTGCAATGTCGCAGGTTCGTTCCCGCACACCATATGTTTCGCTCGGCAAGCGTCGGCGCACTTTGAGCAGTGCGTGAGTCATTTGTCCTCGAAGCCGGGGACTTGGGTTATGGTCTGCCCTTGTACGGGACTTTTTAGGCACAATCCATGCTGTTTGACCGTCACCGCAGAGCTTCGATGTATAATTCCATCCGTTGATGATGGTGTGTAATTGCTCCGCTGGGTCGCATGATGTGCCATCACAAAATACAAAACAGAGGAATGTTTCAAAAAGTTCAGCCAATCAGAAAGATTTTTCTTTCGGCGAAAAAATCTGATTACCGATACAGCCTGACATTTGAAACGCCCCGGAATTGCGATGCAAAGATAGTAGCGATAATCGAAGCCTCCAAATAATCAGATGCCCGGTATCGCTTCAGGTTGTACAAGTATTGCCTAATATCGCTTTTAGCTCACCATCTCCACTGTACCGCAAAGATAATATGTCTGGATCTAAACCACCAAATTTTTAGTCCCACTATCCGTGAATCTCCGTTATACTTCCGAGGTTCAGCCAAAAACTTCCGACCCATTTCCGAATACCGCTTTCTTCCGGCAATTCTAAAACGAATATGTCCTTCACAATATCTTGCGGAGTACGGATCACTTGTCCCTTTTCAGCCAACTCCAAAACGCCTATTTTTATACTGACTTTGAGCAAGCCGCATAAAAAGGCCTGAATCTATATGTCGTCGGAGAGTTCGCCCCCCCAATGCTGTGATAAACATTCATTTTCATAGAATTGATGTTCAAGCCTATCATCCAGCTCTAAAAGCTCCCACTAATGAGACCAATATCATTAACTTTACTTCCAAATTTATATTCAAACTAGATCTGGAAGTAAATCACTGGTAATCAATCACAATTACACTTTCTCTTAGTTTGCCGATTTTATCACTTTGCTGTGGGCTGGTTTTTAAATCCCTCGCTTTTCTTATTTCGCGAACCTTAAATCCACAAGATTCTGCTATGTCAGCAATGATATAATCAACAGGAACTTCAACTCCATAGTAAGCAGAATTAGCCACATTGAAATATATGACACCCCCTTTATGCATTTTCTTTGCAAATTCCGAGAATAAAAATTGCATATCCTCAAAATAGGCACATATCATATTTGTAATATCTGTATTCCATGTATTTAACTCTTTATTAGACATCTCTAGAAGGCTATTCTTAAGTCTTTCATTTGCTATCGGAAAGACTTTTCCATATGGAACTTGAACATGAGAACGTAATGTGTTTTTACGTAATTCACGAAGTTCCTCATAAGAGTTAATTAATTGTAAAACTTTTAGCTCTAACATATAAATATCCGTGTAATCTCTGGAGTTTAGATAGGGTGGAGAAGTAATAATCAAATCCACTTCTCTATCTGGAACCTGTTGTATGTTTTTCCGAACATCTCCCAAATAGCATATCTCAGAATTTTGTACAGATGGATTCTGTTTGGAAATGATTCTAATATCTTCTGTAAAAATTGAATTTAATCTGTCCAGAAAAAAATTATGAATATCTTTTCTACTAAAGATTCTAGTTTCCCATCCTTTCTTGTACGACAAGCACTTGCCATTACGAAACATATTGCTTGCTTGAATGATAATCGAAGCCAATGCGATTGTAAATAAATTCTTATAATCATCATTATCAACAATCGTTCTAATGGCATGTCTTACATCAAGTATTCCATCTATAGCAGTGTCGTGAAAATTCCACTTCTTACTCTGCTCTTTTTTTACAATCGTATCGCCAAAAGGTAATGATTCTATTTTACGTATATTTCGACTTGTACGTGACAGCTTCTTTGTTACGGCTTTAACATAATACGTAAAGGTATCGACATTATATTTTCTACCCAATTTGACCGTTGATAGGAGATGCATAAAAGGACTGACCTCAAAAGAATAGCACTTTATTCCGTGGTTTTGCAGTTCAACAGGAGTTGTTCCGCTACCACAGAAAGGCTCTAATGCACAGGTTGGCGCAAAAGGTAATTCGCCTAGTATGTCTTCAATAAACTCCTTAGAATATCCTTCCACAAAAGGGTACCAACGATGTACAAAAACATTCTTGTTTTTGTCAAATTGCATAACCGAATGGTATTTTTCTGTGCAATCTATGAATTTATAGTCTTTGTACATAGTTGTTAATTATTCAGTTGGGATGTTCATTTATTACTCTAAAATCATCTAATTTAATGTGTCTGTAATAAGCTGGTCTATCTTTCTTAGGTATTTGATCGATGGTCATGTCTATTCCTGTGTCAGGAAGTTCTTCGTCTCTAGCATCAAATACTGCTAAATACCCCAACTTGAAATCTTTTTCCTCGGCCAACAATTCTGTTATGTAAGAGGTTGATTGTTTAAATGCATCAGGGACAATACGCGGTTTGGCACTATATGTGGTTCCTATTTTCTTGCCTAAATGATGAACACTAGTACCTACCCACTTGACTTCTATAAAATACAATCCCTCGCCATCTTCATCAATCATAGCAATATCCAATCTCTTCAAACTTTCTAACATGACCTCTTTCTGCACGAAGAAAACTTTTAATTTTTCCTTTAAGAACATACGTAAATCATCCCTAAAACAATCTTCTGGAGTATTTCTGAGTAATTGCTTATTGGCTTTGATAAACTGTTCTTCATTAAGTTCTGATTTCAAATCGGTTCGTAAAGATTTTAAATGACTTTTCTCAACAAAGAATTTAGAATAGGTATCTCTGAGTTTTAAGTGTTTTCTATATTCCTCAAGTACTCTCGGCAATTCTGAAATAGGCTTTTTACTATTGAATGAGTGTAAGGCCGAGCTTTCATAAAAAATGGAATCACCACCGTCGCGGCCGTTAACAAAATAATTAATAATGCCTTCTGAAGTCACCATAAATACAACGATACCATTCTTTGACATTTGTGTGAAAATACGTCTAGTTTCGTTGTTATCTTTGTGCAATTCGTTATCAAATTCTTGAATATTAAAATTAGAATCAATGACAATGTGGCGATTCTGAACAAACGTTGGGTCTGTAAACAACTGTTCAATAGCACTATCTGCATTATTTGTAAGATTCTGTAAGTCTTCTTCTGATATGACAATATACTTAAAACTATTGTCTGGGTGTTTATCGCACATTGTTTTTATGAGCGACTGCATAAGCTTGAAATTGTCAGTAGCTTCCAATCCATTCAATGCCGCGTCCACTATAGCTTCTTGATTACGTTTTAGCAAATCTTTAAAGTTTGCATTTTGGCTGAATCTTGTTTTCATAACCTGCTACTTTATAATTTCGCTAAAAACATACTCAAAATATAACTCTATGTATTTTTCATCGCTGATAGCTGATGCGTCGTCAAGAGTTTGTTCAAAAGCGATTTTGAAATCTTTGAGGACTTTTAGTGTTTCGCTATCTGGTGATGATGTATCTATGCCATCAATATTGTTAGTTTGAGATGCGTTTTCATAAGATGGGTCATTAACGATGAAACCAAATAATCGTTTTTCGCCGTATTTGCCTAACATATACATAATAGTTTTCAGCCATTCAACTTTTTCTTGATTCAAAGGAATCTCCTTCTTAGAACATAAAACCAATCCACCCTCCTCTTCACGTAATTTTAAAGAACTTCTTAGGAAAACTAAACTCCGTTCAATCAAATCTGAATATGGACCGAAAGTTGTTACTGTAAAATGGTAATATGAAAATATATTTTCTCCTTCATGCTTGAATGAATAGAGAACTTGTACCAAATACAATATTCTTTGGAAAGATTGTTCTGAAACCGCCCCCCAACCCATACTTTTGGCTATGGTTGAGACTGTTACAATATCTTCATTTGCTCTTATCAAATTCTTTTCTAGCATAATTCCAATCTGTTTTTAATATGATGATAGATTGATTTTAATTGCTCACTTGTGTTCTGTATAGTAGATTGTTTTGCAATATCCAATAACCCAACAATACCAAGATCAACATTTTCCTGATCCAATAAATGCTCAATAAATAAACTATTGTTCAGGTTTGATGTTATGATTAATGAGTTGGGCTTCTTTAGGAATTCAGCAAAAACGCTTGCAGCATTCCTTTCGTATGATATATCCAAACTACTGTCCGGTGTCTCAACTATATAAAAAGACGGAGTCCTATAGAAGAATGTAAGAATACTCATTCTAAAAGAATGATCCACAAAGAATCTTTGTGATTCTGACATTTCTTCTTCGTAAAACCGGACTTTCCCATCAATAACAGGATAAAAACGCTTGTTTGTATCTCCAGTTTGTTTGATGTATGTCAGCTCGCAAGGTACACCTAAAAACTTTCCTGCGTATTGGGCAAAAATCGAGGAAAACGCATGAACATTAGCTGTTATAACATCTTCTATTTCGTTTGAAATAGAAGTCACGATATTATACTGCTCGTCACGTTGTTTTTGGTATGCCTCTTTTTGTTGGGTCAGACGATTTACTTCATCTAGGAATGGTTGAATGGAATTTTGACTATTATCAGACATAGCATTTTCATACTCCAGTTCCCTGATTTGTTGCTGAATCGTTCTTCTTTCCAATTCAATCTTAGTAAACTCTGTATCTGAGTTAATGATTTGTTGCTCTATATTTTGTATTGTCTGCTTCTTGGTATTAATAGACTGATATAATGTTTTTCTATGTGTGCTTATCTCATCAAAACGTTTTCTTAATATTGTATCCGTTTCTGTGTTTTCATTAATTGAAGAGCCACAAGCAAAACAACTATTTGAGTCATTTGCAACTTTTTCAACCAATTCATCATTTGGCTGATTGCACATCGGACAGATGTGGTTCAGCTGAATGTTCTTTATGAAAACATGATATGAAGGATGTAGAGTCTCCCATATATGGTTATTTAGTTGCTCCTCTATTTGTTTTAACTGTTTATCCATATCATTTGCAGATAAACTTATTTTGTTGATTTCATTCTGCAATAGTGATATGTCTTGACTTTTAGAAGCACGTTCTTTTTGTATGGATGCCAGTTTGGCGTCCATTCCTGCAAGCTTTCCCTTTAAATCAATAAGAATAAGTGCAGTGTTCCCTTGATTAGCTGTTGAGTGGTCGGCTGACGGAGAAGTATCCTTCATTTTGCCAAGAACCTTATTAATAGCTCTCATGTCTTCTGAACTATGACGAGACAAACTATCAAAATATTTAGCTTTTCTAATAGCTTCTTGCCGTTTCTTGTCGAGAACAGGGTCATTGAAATATTTGTTAAATAGTTCGCTTTGTACATCAGGGAAGAAGCCACCATCATTCCAAAGGATGGTCTTGTGGTCTTCTCCGAAAAAAAGTACCTCGTTTACGAAAAATATTAAATCATCAAATTCTAAATTTGTATTAGTCTTAATGATTTTCTCATATAGATAAGGCAACATATTAACTTTTGCAGCATCAGATAATTTTTCGTAGCTAAACTGATTGATTAAGGTGCCAAATACTTCTGTTCCATCAACCTTAAGTTTGGTTAAGGTTATGGAGGATAAATCTCTTGTCAAATCAACTGTAACATTGTTTATCTTCATGCTAATTGATACGGTAGGAGAAGCATCCGTGTGAATGCTGTCATCCATGCGATTGCTGAAATACTTTTGCGGAAACAGTTGGCGTTTTTCTATAATATTTCCTTGGTATGTACGAGTATATCCGAAAGGCTTTTTGTATAACCCAAGAATTGCATATCGAATAATGTTGACAAAAGTAGTTTTCCCCATGCCATTACCACCTAGCACAAGATTGACGCCTTTAACAAAATCAAAGGTGTAATCCAAACCGTTGGGGTATAAAGTATAATTCTTAATATTTATACTCAATAGCTCTGGCAGATAAATGTGTCTACTCATAATACTTTCTTTAAAAAGATCGGCTATATATACAACAGATCCAATATAATTACAAAAATACTTTTTTCTTCAATTATAAAGTTGTAGCACGGTATTATTAAAAACTTTTGTTCTTTATATAATATCCTCCGTTTTTTTTGCTCCACGATATTCTATCAAGCCTAGACTGATTAGATTCTTCAATGTGCGAGACAACGTAGGGACACAGACTTCTATCATTGTAGACAACTCATTGGTCCTTAGACCAGGAGTTTTAGATAAAATCAGAAGAACTTGTTTCTCTCGTGAATTTATCCTATCATTTAATCTATCATTTAGTTTATCATCGGTGGTTCTTAGGGACTTGGTTCGCTTCATAAAAGTAACGGTAAAGAAGCCCTTGTTGTCAAATTCGGGTTCAGGCAGACCAGCTTCTTTCATAAGGCGGCGCATACGGGGAATGCCGGAGCCTACTTTCTCAACAAGCTGCATTCTTGTGAATAGTCCGAAAATAAGTGGATTCCGGCTCATACTCTTTTCTCCGAAGTTTTCTGCAACAATGGGAAGAAGACCTCCGGGATTGGAAATCTCAACCCGGTCATCATATACTTCAACCATCACTGTCGCACCCTCTTCATACAGGTCGCGATGACAGATGGCGTTCATAACCGACTCCTTGAAAACATCCAGCGGTATCTCCCATATTTCTTTTCGTGGCCCCGTACCTTCGATGATGTACTCTACTTCAAGTTTGCTCTCAATCCACGAGAGAGTATTGAGATACTGCTGATATAGTGGTCCTCCAAAAGTCTTGTCATCTATGATGTGGACCTTGTCAAAACCTTTGAATCTCAAACATCTTACGACAGCATGAGCAAACTTCCTCTCCGGCTCTTTGCCGAAGAAAAGAGCAGCGGCATTCTTCACTCGTCCATCATCGGTTGTTAGTTCCAGGTTGTCGAATAATTGTCGAATCGGTAAATCTTCAGATAGGTGAGATTTTTCAAGAAAGGAACGGAAATTATGGCTGTCTATGTCCTCGTCAATGCCAAACCATTTGCATGGAATGGCATCATAAAATATCTTTGAACATTCGGCAAAGAAAGCCCGTATTTCTTCAGCCGTTCTCAACTTTTGGCAGTTGGCACCCTCTCTTACATATATCACGCCTCCGGCGATGTAAGGTTTGTCCTTACCGCTTGGCACATCAACAACCCATATCTTCTTACCATCAACATCCATCGGATAAAAGACGCAATGCAGGGCAGGAGAAATTTCACCGATGGTGTCTTGGATTGCTGAACGCTTATTATTGTCTATCTCGGCGCCTATAATCTTGTTGTCATCACTAACACCAATGAGAACATAACCTCCGGCGGCATTAGCAAAACCAGCCACTTCCTCGAATAGTTCTCTTACTTTTGAAGGCACACTGCGTTTGAAGTCCACATTGTAGCCTTCGCCTCCTTTTACAAGGTCAAGTATTTGAGATGCCGTTAACATAATGCAAAATTACGAAATTATTTTGAAATGACATAATGATAAGGGCTGTGTGTCACCACATCTCACACTCTTTCTTCGACATTGCGGCGATTTCATCGGCTATCTCTTCGGAGGAGAGTTTGATGTAGTCCATGAAGGTCTTTTGGGTTTTGTGGCCGCTGACGTGCATCATCTGGAGCATCGTGTATTTGTGGGAGAGGTACATATTGGTGATTCCTGTTCTTCGGGCGGTATACGAAGTTACACAAGCATAACGCGGAACAATAACGTTGCCGTTGAGGTCGGTCTCAGCTTCCTTGCCTTCTTTTTTCAGTGCCTCCTTCTGCTTCATAGTCAATCGGGTCGGCACTTTCTCAGCAAGGGTCGGCATAATTTCCGACAAATCTTTGAGAATGTTCTTGATGTAGCGGTTCAACACCTGCTCGTTGACTTTCGGCACATTGTAGCCGTATTTCTCGCAGATGGCTATTAGATTCTCATTAAGAACCGGAATTGTCACCTCGGTCTTGGTCTTTTGCTGGACTATACGGATGATGCGTGTGCCTTTGCGTGTAGTCTCAAAGCAGTCGGCATGAGATTGTTGTAGTCACTGACACGTTGGCAAGTATAGCAACCGATGAGAAAAACATCGCGTATAAGGTCGTTCTTACCGGTCAGCGGCATTTCATAGAGAACTTGCAGTTCATCTTCCGTCAGATAGATTTCGACAGCCTTGTCGCGGTCTTCAATCTTGTTCTTCACAATCAGCGAAGCGGCACGTTGGTTGTGTCATGCACACCATCAATGAAAGCGGCGTTTATCAAAGCTTTCAGGTTGGTCAGATGCTTGTTGACAACCTTCTCCATATAGCCGTTCGCGCCTTTTCTCTCGCCTCTTCACGCTTGGTAATCGCCTTCCGCTCGGCTGCGGCTGCTTCCTCTTTTGCCCTGCGCTCTGCATCCACCTTTTCAGACTCGGATATGTAGCGCACATCCATGTCGAGAGCCTCACGATCGAAATCAATTTTCGCCATGTGAGCCTTTACGATGCCTTCAATCTGCGTTAGCTTGGCATGAAGTTGGTAATTCTTCTTCTGATGCGCCAGCCACGCACGTGGCGAAGCGGTGGCTTTCAGCCATTCGTTCACCTCCACTTGGAGCAATGTCGAAATAAGCACATCAATCTTTCGGGTATGGATGCGGATAAACAGAGTCGCGGTATCCTTTTTCAAGTCCTTGTTGCGAACATGAAACGGTGTCAATTTTTTCGGAGAGGGTGTTGTTGCCATATATTTGTTTGTCGTTAAATTTGAATGTCGGCTTTCACAGTTGTTCCCTGTCAGCCACTTTGGTTCTGCAAATATAACAACTCAAACTGCCATTTGTCAAATCGGGTCACCACGCAGGTCACCACGTTTTTTGAATGACGGTGAAATTCCAAAATTCAGTCGAAATTTATATTGCGTTATTATTCAGTATTTTGCATCCAGTTCAAATTCATCTTGATTCACCTCAATTCACAAACTCAAGTTCTGTTGGAACCACGCCACGGAAAAATCCTTTTAACAGAATTGATTTCCGCACAATTCTTCCAGAATACCGTAAAAACGGCCCACATGCACCCCGTCCACCAGCACATGGTTTGCCTGAATGGCCACGGGCATCACCATCCTGCCGGAAGCATCCGCCTCCATCTTCCCCCAATGGATGAGGGGAAAGTATTGTCCCCGAAACACTCTGTAGGCGCTGGCCACCTGTGTGAATGGAATCCACGGCAGGCAGCTGAATACGGCGATATCCGCCCTATGGCACGGATTCGCCTGGGCGGGAGCAAGTTTGGCAGCCTGCACAAGAGGAACGGCATGTTTCAGGAAATCAGACAACTCCATCCGGTATTCCACTAAAATCTGAGTGAATTCTCCATCCGCAGCCAGCACTGGAACCAGGGCATCCACGGACTCATACTCCCATACATCCCCTTCCTCCACCCGCTGGCGCAGCTGGGGCACCCTGTTCAACGCCTTCAGCAGCACAAACAGGCACAAAATGAAAAAGGAAACCCCCTCCCGTTTCGCATAACGGTACAGGGCATCCACCCGGACCGGGGACGTAATGGAAAACATGGGGCAGTCAAACGTCTGGAAATACTCATAATGAGACTTTCGAGGCCAGGAAGAGATTTCTATTTTCTTTTTCATGCACTTATTTTCTATTCAGGGAAGGCTCCCCGTTTCACAACAGCTTCTTTCCTTCCATTATTCCCCGGCTCTACCGGAAATTCCAACCCGGCTGTTCCCAGCGCCAAAGCATTGATTAATGCTTCTATTTATTCAATTATCAACTATGTTGCAACAAAGAACATCCTGAATGCCCCCTACCCCTCGCGAACGGAAAGCAAATCCGTTACGGACGCATGCTTCATGCGCCACGCAGGCAGAAGGCACGCCAAGGATGAGAGGGAAAGCACCAAAAGCACAGCACAGGCCAGATGCGCCCAAGGCATCGTGACAGGAGGAGTAACGACGCCGAAGTGATAGCCGTATTCCAAAATCTGGATGGAGCACCAGGCGCCCAGGACTCCCAGCACAAGACTCATGACAACGGAGCACAGGGAAACCATCAGCGTTTCCGCCCAGAGCATCCGCATCACCATGCCGCCCGGCACGCCCACCGCACGCATCAATCCAAACTCCCGGCGGCGGGACTGCACGGAAGCCAGGACGGTGTTAAGTACGGCCAGAACGGCGATCACCATCATGATGATGGGAAGCTTGCTCATCGTAAAAATCACATCGTCCCCGCGGCTGGTAACGCTTCCTCCCAGGCTTTCCCGGGTCAGGGCCTTCACCAGAGGCTTGGCGCCTTCCCCTCCGTTTTCCTGCTCCCGCACCTTCATCAGGGCGTATTCCCCCAGGTCGTTCTGAAGTTCCACGTTGCTGATCCCCGGAGCAGTATCCCCCCAGATAAACTGGAATCCCTGATGAGCGTATTCCTCTTTGAGCCAGCGTTCATCGGCAATCGCCAAAGCGGCAACAAAACCCCCGCGGCGCACGCGCATTCCGCTGGTCTTGGTCAACCAATGCCAGCCGGGGAAGGAAACTATTCCCGCCACCTTCCAGGGTTCTCCCCGTACGGCGCCACCCCTGCCCCGGCCGCGAATACCGGCAGAGGGCTCATTTCCGGAACGGCGTTCCCGGGAAGACGGATTAACCAGCATCAAGTCATCCCCGACCTTCAACCCCACCGTCCGTGCAAACGTGTCCGGAATCAGGAGAGAACGGGTGGATTCCAGCATGGCATAAGCCTCCTGCGGATTTCCGGAAACAAACACGGGGTTGAACAGCGGATGGCTCCCCCCGGCCATCTCCGCCACGGGAATGCCGGCCAGTACGATGGAACGGTTGCGCATCCCGGAAAAACCGGGGCTTTTCATCTGTGCCGGAGCAATATCCGGTTCATCCACGTAGATGGGATACATTCGGGAATTCCGCAGGCTGGGGCGCGCCATCAGCTCCGGAACATCCGCAGATTTGAACTCCGTATGCAGGAAGGAAACCAGGGTTCCGGGTGTTGACGTATCCGGGGAAAAAGGAACCAGCATGGAATAGCCCCATGTCTGCACCCCCACAAAAAGAGAGAGGCCCACAGACATGGATACGGCAGTTCCCACGGAACGGCTGAGGTTGCGGCTGAGCTGCATTTTCAGGAAAGAATGCGGAACGTGCAGCAGAAATCCGGTAATCCATGCCCCGGCCCACTCCGTCACGCGCACGAAGGTGGGGGCCAGAAACAGCGCACCGGCTACCAGGCCGGGGTAACCCAGCCAGAAAAATATCCATTTGCGCGTTTCTACCTCCAGGCCCGGCAACAGCAGGGCCGCAGGCTGAAGACACACGCATGCGAGGCCCGCCACGACAGACCACACAGGAACACGGCTCACCTTCCCGATGAATCCGGAGGAAGGAACGGCCGCTTCCAGCGGAGACTGGCGGGAAGCGCGCCACGCAGGAATAATGGAAGCCAGCAAGGCCCCGCCCACAGCGCATACGGCGGCGGTTAATACTGTGGACCAGGTCAGCACGGGTACAGAGGCAGAGCCTTCCTCCAGCAGGTACACCAGGAAAAAGCCTGCGGCCAACCCTCCCAGCAATGCCGGAATGCACAGAAAAATCCCCTCCCCTGCAATCAGCAGTGCAATTTGCATGCGCCCCAGCCCCAGAGCACGCATCAGGGCCAGTCTGCGGGCACGTTCGCTGACGCCAATGCTCAACGTTGTGAAAATAATGAAAATACAGGAAAACAGGACAAGCCAGACGGACATTTCCGCGCTATCCTTCTGTGAACGGACGGCCCGGTCGCTGGAAAGCCGCCGGATGATGGAATCCGTATCCGCCACCGCAGCGGAAGCCTGGACCAGCTCCTGCCGGAAATTCTCAGCAAATTCCTTTTTGTCCACCCCTTCCTTGAGCTGAACGTAAATCAAATTAGGGGCAAAAGGCTGCCCCGTAATTTTCTCGCATACCTTCACGGGCACAAAAAGGGAGGAAAAAGCCGGGCCGGACATGCCGCCCGGCCCCATAATGACGCCGGGGGTGGCCTTGGCCTGCTTGACGATGCCCACAATCTTTACGTCGTACACATGCGTTCCCACGCGCACGTTCATGACAGTCCCCACTCCCGCGCTGAAATATTTGGCGCTTCCGCTGCCCAGCACCCCCTCCATGGCTGAGGAGGAAGCCATATCCGGCCAAATTCCCTCTTTCAATTCATAAGGACACTCCACAGCATGGTTTCCCACCAGGATGGGGCTCTGGGGAGGAATTCCCATGCGGTCGCGCGTCTGCTCGTCAAAGCTGCCCCGCTCATTTCCGCAGCCGATTTGGAGGCGCGGCACCTGGCACGCGGCGTTCACCGTTTCCACAAGGGGAGAGGCGGCCAGACGGGCGGGCAGCTCCGGGTCACGGAACTGCGGGTACTGTCCGGGAGGAAGGGGCCCTTTGGGAGACTCCGGCACCAGGCAGAGGTCATAATTCCCCATATAGGCCTCCGCATCATTGTCAAACTCCCTGACCAGCGTATCATAGCTCCCCATCATCCATACAATGAGGCTGACGGAGACAAGGATGGCGAACACGCTGACAGCCATTCGGCCGGGGTTGGCGCGCAGATCCCTCCAAATCAGTTTGAACATCAACGTCATAAGGCGTCATTCAGCGTACGTTCATAGAAGGAGGACAATTCCGCAGGACCGGCAAATTCCGCTGTGGATCTGTCGTCCACAATGCTGCCGTCCTTGAGTACGATTACACGATCCGCCCACATGGCTACGTCCGGCGCATGCGTCACCAGGACAAAAGCACAACCTTGCGCACGATGCAAATCCCGGAACAAATCTCCCATAAGCCGGGTATTTTCAGAATCCAGGTTGCCCGTAGGCTCATCCGCCAGCACCAGAGCGGGACCGGAGACCAATGCACGGGCAATCGCCACGCGCTGCTGCTCCCCGCCGCTGAGCGTATCAGGCATGGCATGCCGACGGTGAGACAGTCCAACCTTTTCAATCATGGCGTCCAGGGCGGCGGGAGCCACCCTCCTGCCTCCCGCCAGGGACGGCAACAGAATGTTTTCCTCCACGTTCAAGGTGCCCACCAGGTTAAACATCTGGAAAATGAACCCTACACGGTCCCGCCGGTACACCGTCAGGGAGGCATCCGACATGGAACCGAGATCCATCCCATCCACCGTCACCGTGCCGGAATCCGGAGCCAGCAGCCCTCCCAGCACGTTCAGCAGAGTACTTTTTCCGGAGCCGCTTGCGCCCATCACGGCGACGAACTCTCCCCGGTTCACGGTAAGGCTGATATCTTTCAGCACAGTTATACTCCCGGAACCGGATTTGAAACCGCGCTTGAGATGGCTGACGGAAATGACGGGGGAACTCATGGCGGAAACGCAAGGTTAAAACGTGGTCTTCAGGCCTACTTCAAACGTGCGCGGCTCTCCCACCGTGCACTGCACGGCATGGCGGGTGGACTGCACGTACTTTTTATCGAAGATATTATACACAGCCAGCCTCAGCGTGGCATCTTTCAGCCATTTGGATTCCGGCAGGGGGATTTCCACCGTAAAGTCGAATACGCTATAGGAGGGGATGGTGTAGTTGTCCGCAATTTTTGCGCCCCGGAACGTAGCGTAATAGGAATCCTTGCAGCGGTAGCCCAAGCCCAGCACCGTACCGTTCAGCAACCCTCCGTCAATGCGGTACTTCTGCCAGAGGGCCAGAGCATTCGGAGCAATCGTGGGATACACTTCCCCGGTGGTCCGGTTCTTGGTGCGGGTGTAGGTGTAGGAAAGATAGGAACTCCAATTCTTGGTTATTTCCCCGTTCAGGGAAAGTTCCACGCCTTCCGCCCTCTTGGAGCCGTCCGAATAGTACCGGTTGGTATATCCGTCTATGGCAACAGGCGTATTATTCTGGATGATGTCAAACCAGGAAGCGGAAAACCACACCTTATCAACCGGGCTGACCCGGAACCCGAATTCCATCTGGTCCGTACGCCAGGAATCGGTCAATTCCTTCCCGTTTTCATCCAGATACCCGAAGTTGGGGGCGGAGGTACGGGCTGCGTTGGCGAACAACGCTACGCGTTCGCCGAACATGCGGGTAATTCCGAAGCGGGGGCTCCACGCAAACGCATAATTATTGTCCAGGCTGAAATGCGCGTCACCGCGAACGCCGGCCAGAAAACGCCACTGTCCATAACTGAGGACATCCTGCAGCAGAAAACCAGCCCTCTGCACCACGGTATCCGTCGCATTGGCGCCGGAATAATCGCGCCCTGCAGGGAAAAAAGGAGGAGGATTGTACAGGGAAAAATAGCCGTTCGTATTTGCGGTGGCGTTTGTCACCAGGCTGGAACCGTCTCCATAAGTGCTGCTCCCCGTGTAAGAAACGCCCATTAGGGCTTCATGCTTCACCTGCCCGGTCTTAAATTCCGCCAGTGCGTTGGAATAAAAATTCCAGTTGATATTCCACTCGTCGCTCCAGGCGGCTTCATATTTGGCCTTTCCGGAAGCAATCATCTGGTTGTAATAATCCGCCGTGCTGGTTCCTCTCCCAGCGGAAGAGGAAATGCCCCAGACATTATAGTCCACATCGCTGTACCCAATACCGCCGCCAATGCGGATCGTCCATATCTTTTCCAGCTTGCGTTCGAAATCCAGCATCGCCAGCAGGGATTTGGAATTAAGCCTTCCGCTCGGAGAACCGTACCAAGCGTCATACGGCCCCACAAAATGGCCGCCCAGCACAGGGATGCCCATGGTCGTCGGAGAATTCTGATACTGGAAACTGGTCGTCAGCACCGTCTTGGTGCGGGAATCATGCTGCCAGCGGAAAATGGGGGACACCGTGTACTTCTGGCCTCCGTTGGCTCCGTTGCTCAGCCAGAACGGGCGTTCATACTCGGCGGCTACCACCGTGCGCAGGGCAAATCCATTCGTTTCATCCCCCCTGTACCGGGTATCGTCAATCGTTGCGCGGTATTTCTGCCCGTGATGGGAAAGGCGGGCGTAAGCCGTCAACTCCGTCCGTTCCAGGAAATCAGGCTCCTTCAGGATCAGGTTGATGGAACCGCCGGCGCCGTATGCCCCGAGCGTACTGGTCTGACCGCCGGAAATGGAACCGATGGGGCCTTTGACAATCTCAATATTTTCTATCAGGGACGTATCCATGCCATACCCCATGCCTCGCGGGAGGGGCATATTGCCGATCTGAACATCACTGCCGGCAAAGCCGCGGATGGTGTACTGGTCCGCCGTACGGGACAAAAGCATGTCCCCACCCGTATTGACGGAGGAATCCATACGCAGCGCTTCCACCAGGGAATCGGACCCCTTGGAATCCATCAGGTCCCTTGTAATGACGTTTACCGTTTGCGGAACTTCCTCCGTCTTCATGTTCGTGAGAGTAGCGGAACTGACCGTTTCCGCCCGCAGGGACCTGGAACGGATGGTCATCACGCGTTCCGGCATTTCCTGCACGCCTTCCTCTGCGGCGGCGGCCTTTGTCTCGGCATAGGCAGATTGTCCGAGAACAAGCAGGCTCCCAATGGCGGCAGCACGGTGCAGGCCGCGCCCCTGGCGGCGATGGCGGGGGGTATTCATGTTGGATAAACGCATAGGGGCAAGTATAGGACGGAACAAAAAACCGTCCATTCCCGTTTGTTAACAATTTGATAACAGACCGTTCCTTTTCCTTCCGGCAAAAGCGCATCTGATGGCAGAACGGCCCCGCCCTTTCACAAACCCAATGCCTGCGCCCGGCTCTCCGCCGCGGCCTTCAGCCATTCCCGCAATGCCTCCTTATCCCCTGTAGCCAGCCAATTCCTCACTTGTCCCAGCTGGGACAGGGCTTCATCCAGACGCTCCAGAACAGCAGGAGCATTTTCCTCCAGAATTTCAGCCCACATGGACGGTTCCCCCATGGAAACGCGGGTAGTATCACGGAAACCTCCGGCGGATACCAGCCCCAGCAGCTTCACATCCCCACCGTCCAGGGCGCTGTGGACGCACAGGGCAGACAAAGCATGAGGTATGTGGGAAATGCGTGCCACGGAAGAATCATGATCCGCCGCTTTCATCCGGATGCAATGGCAGCCCACACGCTCCCAGAACCTCTGGAGCAACAGCAGCACATCTTCATGCACATGCTCGTCATTCGTCAGGATGCAGGTCGCATCCCGGAAGAGGTCCCCGGAAGCATGCTCCATACCCTGCTTTTCGGAACCGGCCATGGGATGGGAGCCGATAAACGCCACGCCAGCTTTCTTCAGCACGGAGCCCACGGCCTGGTGAACACACCCCTTCACGGAACCCACATCCGTTACCAGTACTCCCGGTTTCAATAAAGGAAGCATATCGGAAACAAGGCGGGGCATCACGCCAACCGGGGTAGCCAGCACAACCAGATCCGCCCCCTCCACCACCTCTTCCATCCGGGTGGAAGCGACATGGGCGGCTCCGGAAGAACGGGCATATTCCAACGGTTCTTCCCTCCTGCCCCACAAGCGCACGTGCACATGCGGCAACTGCTTCCTGACGGCCAGCGCCAGGGACCCGCCCAGAAGGCCGGGGCCGAGAATGGCGACGGAGGAGAAAAAAAGCTGCTTTCTGGAATATTCTCTCATGGATTTACAGGGAAATACATCAGCGCCCCGGAACATCGGCATGTTCAGGGGCGCGGCGTGATCTCGTTGCGGCGGCCGCATCAGGGCACGCGGAACTTCTTGATCTCGTTGGGGAAGTTGGTGTCCCGCAACACCGTCCCGGAGGGATACGGCTTATTGGTGCTCCTGTTCAAAATCTTGATCTTCTTGGTACGGTCATACGGGCTGACCACCACCGTGGGATCCGCCGTAGGCCACGCCACCGGGATGGAAGAAGAAGCGGGAGGTTCCACAGGCCTGGGGGCGGCAATGCCGGAACCGGAGGAGTCACCCACGGGATTAGGAACGACGGGCTTGGGAACAACGGGAGCCGGAGGCGTGATATTGTTGGCCCCGCCCACCATATTGCTGATGGTTCCGGAATTGGTCCGGGCGGGGTCCGGGTTAAACCCGGAATTCTGCCCGGCCATCGGGTCCAGAGAATTTTCACGCTGAATATCATTCAACAGGCTATTGCCCTGGACGCCCATAGCGCCGGACGGATAGACATTTTCCGTAGGATAAATGCCGATGGGGCGTTCATTCACGCGCTCCGTTTCACAGGAAGGCAGAAGAATTGCACCTGCGGCCAGCAGCACGCCTATCGTCGGGGAAGTATTCATATAATCTCTCTTCAGGTGATAGCTTTCTGCCAGCATCCTACGGTCCCGCAACCTCATGTCAACCATAAAGCCTGTTTGCCGAACCTCTTCATGACAAGGAATCTTCGGCTTCCGTCCCCGCACGCGGGCAAAGCCGCCCGAACAAACGCGCTCGCCGCATTTCTTTTACCCATCGGCAGCGCCGGAACAGCCCTGGACGGACTCAACAAAAGGAAGAGCCGGAAAACGGCACAAAGACGGAACAACCATCACTCTTTATCAACAAGCGCTCCAACAGGTACTCCTGCCGCAGCCTTATTCGGAAGAAGCCGGAGCTCCATTCCGTTCGCCAGCGGGAAGAGACGCTTCCCTCCCGAACATGCCGCACCGGCCTCCGGCCCTCCCCTGCCCGCCCTCTTCAAAAAACCGGAAAAAACGCTTCCCCCTCTTCCCGGGAACGCGCCTGTACGGGAACAGATTATCCTTCCCTACCCCCCATGATCTGCTACAATCCGCCCCACATGGATGACGTACAGACATCTAAGAAAAAAATGCCCTCAAAAAAGGAATGGAGGGGATTTTATTCCCTGATTCTCATTCAAGCCCAGAACGCTTTCAATGAAAAAGCCGCCCAGTTCCTGCTGATTCCGCTGGGCGTCTGGCTGGCTTCCACCAACGCCGCATACGGACCGGATTCCTGGGTCAACTCCCTCCAGTACATTCTGGCCTGCATCTTTGTTCTGCCCTATATTCTGTTCTCCCCTTTCGTCGGATGGCTGGCGGACTGCTTCTGCAAGGCCCGCATCATCCAATTCATGTCTTTCCTACAGATTCTGGTACTGGGGGCCATGCTTCTGTGTTATAAATACGAAAACATTGAAATGGCCGTTTTCTGGTTCTGCGTCTTCTCCGTCCAGGCGACCATCCTGAGTCCGGCCAAAAAGGGAGTGGTCAAGGACATGGTAGGCTCCCGGCAGCTGGGCTACGCTTCAGGGCTGATGGAAATGAGCCTCATCCTGTCCATGCTGGCGGCCCAGATAGGCATCTTCGCCTGGTTCGACATCCTGCAAGTCTCCTCCAATGACGGCTGGGAAGCAGCCGCTTTCCCCACATTCATCCTCACCTGCATCGCCGTTCCCGTAGCCATTGCGTCCCTTTACCTCCCGCGGTATCCGGCCAACCAGACCAGAAAATTCGAATGGAAGCTCTTTTACGAACATTTCGTGCAGCTCAAGTATCTCTGGAGCCAGCGGGATTTGAGGCTCAGCGAAATCGGTGTTTCCTACTTCTGGTTCCTGGCCGGAGCCCTGATGCTCATCTCCCTCCAGATCGCCCAGGAACACCCCATTGACGGCACCGGATTCAGCATGTCCGCGGCCATTCTCATGGCATGGCTCAGCGGCGGCACGGTGGTAGGCGGCGTCATCGCCTCCATCATCTGCCGGAAAAAAATTGAACTGGGGCTCATCCCCCTGGGCGCAATCGGCTTTACTATCGGGTGCATGTTCATGTCCTTCTTCGCTCCCGGCTCCCTCCCCAGCAACATCGGGTTCGGCATCACCGGGGCTTTCGCCGCCGCGTACCTGGTGCCGTTAAACGCCCACCTCCAGGACAATTGCGACCCCTCCAACCGCAGCACCGTAATCGCCGCCGGCAACCTGATGGACTGCCTGATGGGGCTGGTGGCCGTCGGCTTCCAGCTCATGCTCAGGAACATCTTCTCCATCCAGAACCAATTCTGGGTGCTGGCCGCCCTGGGCGTCGTCATTACCATTGTGGCATTCCGGCTTATTCCCCGTGAATTCATCCGCATGATGGGCCTCTGGATCATGAGGATTGTCTACCGCTCCCGCATCATCCACCAGGACCGCATTCCGGAAGACGGCGGGGCCGTTATCGTCTCCAACCATGTCACCTACGGGGATGCGCTGTTCCTTTCTCTCATTTGCCCGCGCCCCATCCGGTTCATCGTGGCGGAGGAATTCGTAGCCATCCGCTGGCTCGGCTGGATTCTGGAACTCTTCAACTGCCTGCCCATTTCCTCCCGTAATCCGCGGGAATCCCTGTCCAAGGCCATCCAGGCCCTGAAGGCCGGAGAAGTCATCTGCATCTTCCCGGAAGGCCAGCTCACCCGCACGGGAACCCTGTGCGCCGCGCGCCGGGGGCTGGAAATGCTGGCGAAAAAATCGCGCTGCCCCATCGTGCCCATTTACATGGACGAACTATGGGGCAGCATCTTCTCCTACTCAGGCAACCGTTTCTTCTCCAAGGCGCCTCTCCGCGTTCCCTACCGTTTCACCGCCGCCATCGGAGAGCCCATTGAGCCTGATGCCGTCAATCCCCGCATGGTCATCAACACGCTGCGGGAGCTATCCTCCACATGCCTGGAAATTGCAGCCAGCATCGGCAGGGATGCCATCCTGAACCATCTGGAGCGCATCGCCCACAAACCTCTGGTCTTCTCTAAAAACGCGCGGCTCTCCGGGTATGAAATAGCGGAATGCCTGATGAACGACACCGTGGAAGCAGAGCATCCGGAACTCAGGAAATGGCTGTCCACCCTGCTGGACAGCACCCGCTCCCAAAGCCGCCTGTGCGACTTCTGGATGAACGCCCAGCAGCTGGAACGGGTGAACTCCCTCCAGCCCCGGGAACTGCTGCTCACCAGCGTAGGACGAGAAGAAGTCCACGAAACGGTGGCCGCCGTCCTGTGGCCCATCCTCACCAGCACTCCCGTTTACCTCATCAGCGACGGGGACCACAGCATGCCGGAAGGCATCAGGCAGATAGCCGGCTCCGATTTCCTGCGCCGCAGGCTCTACAGCCTGGTTCCGAATACGCGCACGCCGTTTTATGACTTCAGCGGTTCGGGCGACCTGGTTCTCCCCAACATCGGATGGAGGCCCTGCTTCGCCACGGACCGCGGCATTATCCTGGCCATGTCCATGAAGAGAAGCGTCTTCAAGCTGGATGACGGCACCGTGCAGCTCGGCATGCGGGCCAGGACAAGAGGTCGCCTCCTCCCCGGCTTTTACCTGAATCCCCCTCTTTCCAACGTCATCGCCGGGGCTACCCTCTCCACCCCTTATTCCCTGCCGCCCAACCTGTATCTGGACGAGTCGGGCTTCCTGGCGGAACTTCAGTCTTCCAACCATGAACAATAACTCCTCGTCAACCCGTCTTCCCATCCCGCAGTATGTAATGACGCTGGCGCACGCGGCAGCCCTGCGGTCGGAGGACCCGTACCGCAAGGTGGGAGCCGCCGCCCTGGATGCGGACAACCGCGTCATCGGCACCGCCTACAACGGACTGTATCCCGGATTCAAGGCTCAGGACACCTTCTGGGCCAGCCGGGAAGAACGCCAGAAATACATGCTGCATGCGGAAATCAACCTGTGCAGCCTGTTCCGGAGGGGGGAAGCCAAGGTGGTGGCATGCACTACGATGCCCTGTACCTCATGCATGCAGGCGCTGTGTGCCCATGGCGTAAAAACCATTTATTACTGTGAGCCTTATGACAAATCCGAAGCTCCTGCCATCGCCAGCCTTTACGGCGTGGAACTGATCCAGGTTACGGACTATCCTCTCAGCCGGCACTTCCCCCTTGTCCTGGACAGCTGATGCCCGGAAAATCAATCCCGGTGTACGCGGCATGTCCAACCAGCCATGGAGCGGGGAACGCCGTCAATGGCAGGGTCCAAATCAAGACCGGTTTCCGTAATGGTGCCCGAAGTAATGACGCCCGCATTCCGCCCGGCGTCGTCAAAGGCAATGTTCACATTCGCCATGGTACAGGCGGAGCTGCTGTTGGCTGCCGGAGCAATATTCAATACGCCGGCATTCTCCCCGCTATTGAACCGGTACGTATAAACCCCGTCGTACACGGCTCTTGTATCCATTCTGATAACATCCACGTCCTGCCCGCTGAACTGAAGCTGGAATAAGATGCTTCCTCCCTCAAACAGGCTTACCTGCTGTCCGCTCAGGGAAGTGTTCCCGTCGTCTCCGGATTTTCCCGAACCTCCGCCGCACCCGGACAACAATCCGCACACCGTCACAGGAATCAGCAACAAGACAGGGAACAACTTTTTCATTCCCGGAAAAATGCACAACAGAAAACCACAAGTCAAGTATACTCAACTATTATTGCATTTTTCCAACATTTTTTAACATCACAAAAAAAACCGCCTGTTTTCACAGGCGGTCTCAAGAAAACACGGAAGCGGTCAGCCAACCCACTTCCAGGTAATGGGTATTTCAATATCAGGGTGCAGGCTGTGCTTGACCGGGCAGCTCAGAGCAGCCTGTTCCAGCAGCTTCCTGTCCGGATAATCGGACGGAAGAGGAACGGTAATGACGACTTCCAGCTTGCCGATACGGCGGGGATCCGCATTCATGTGTTTGGCCACTTCCACCGTCATGCCTTCCAGATTAATCCCCTTACGTTCGGCAACAATACCCATGATCGTGCTCATGCAGCCGGCAAGCGCCGCTCCTACCAGGTCCGTGGGGGAAAAGGACTCGCCCTTGCCGTTGTTGTCCACGGGGGCATCCGTGGAAAGCGTGGCCCCGGAAGGTTCATGAACCATGGAACAATGGAGGCCGCCCTCGTACGTCGTTTTAGTTTTTACCATGGCCTTATTTTATACAAGGCTCCCCGCCATGCAACACACAAACATTGAATCCCCCCGTTCTTTATGGTAACCGCCTTTGCTATGAGAAACTTTTTGAAAGCAGCCGTCGGCTGCCTGGGCATGATCGCCGGGTGTTTCAGTACGGGGGAGGCCCAGGACTTCGGAGGCATGAGCTTCGGGGAAGCGGGAAACTTTGGTCCGCCCCAGACATCCGGTTCCTCCAAGGCCACTGTCACCTCCTATGGAGAAGCCTCCTTCGTCATCGTGACGGAGCTGGCGCTGCCGGACCACTGGCACGTTTACTATAAAAATCCGGGAACCGTGGGCCTTCCCATGGAAGCCGCTCTCCAGCCTGTGCCCGGCTTCCGGGTGGAAGGCCCCTTCTGGCAAATCCCGGAACTGGAGAAAGGGCTTGTGGATTTTTACGGATACAGCGGAAAAGCCAAAATGGCGTTCCGGGTGACTCCGGAAAAGGATGCCCCCCCGGAAGCGACGTTCATCACCACCATGACATGGCAAATGTGCGCGGAACAGTGCGCCGCCCCGGAAACCAAAAACTTCAGCGTCACACTGAAGCGCGGAGACGGGCAGACAGCCCCTGACGCGGCGGAATTGACCGGAAACATGGCAGGGCTTGCCGCCCCCGACTGGGCGGAAGGGCTGAAAGCCCGGATTTCCCAGGAAGGGAAAACCGTCACCCTGCACCTCAGGACAAACGGACGCCCCGTTCCGCAGGATTCCGTCTATTTTTTCTGCAATCAGGGGGAAATCAACCCTACCACTCCCCAAATCTTCAAAAAACTGGATGACTCCAATTATGAGCTGTCCATGCAATTCAATGACACCACGGACGGCCTTTACCCCAACAACCTGCCGGATGCAGACAGGGGAAAACCGCTGACCAGCCTTTCCGGCATCCTGCGCGCCGGCAGGGAAGGCATCATCATCACCGCGGACGACCGCCCCTTCTCCGGGGAATCTCAGTCAACAGCCGCCGGAACGGAATCCGCCCCGGAACCTTCCGCTTCCATCCCCGCTCCTCCGCTGATGGGCCTGGGGGAAATCATGTTCTTCATGTTCATCGGCGGCATCATCCTCAATGTCATGCCGTGCGTCTTCCCGGTAATCGGCCTCAAGATTATGGGATTCGTCCAGCTGGGAGGGGGGGAACGGAAAAAAGTGCTGGCCCACTCCCTTACCTTCGTACTGGGCATCCTGATTTCTTTCTGGCTCATTACTGCCATTCTGATTGCGCTGAAAGCGAACATGTTTGACTGGAGCGCCCCAGCCGGCCCCGGCGTGTTCAGCGGAGACTTCTGGCTGGGCCGCGGCGCGGAGGGCGTTGTCAACTGGGCGTTCTGGTTTGAAAACCCCTGGGTGAATTTCTGCCTGCTGGGCCTGATGCTCGCCATGGGGCTGAGCATGTTCGGCGTCTTTGAAATAGGCGTCAAAGCCACGACCATGGGCAACGACCTCCAGCACCGGAAGGGTTATGCCGGCTCCTTCTGGTCCGGCGCCCTGGCTACGGTCATCTCCACCCCGTGCAGCGCTCCGTTCCTGGGCCAGGCTATCGGCGCGGCCATGCTCCAGCCGCCGCTGGGCATCGTGCTCTGCCTGACCATGATGGGGCTTGGAATGTCCCTTCCCTACATCATTCTGGGAGCCTTTCCCGTCCTGACCAGATACCTGCCCAAACCCGGCGCATGGATGGAATCCTTCAAGCAGTCCATGTCCTTCCTGATGTTCGGCACCGCAGCCTACTTCCTCTGGATTTACATGGCCTTCTTTGATGCAGAAAACCATCCTCAGGACATCCTGTTCCTCTTCTTCGGGCTTGTGCTGTTTTCCATGGCCTTTTGGGTATACGGCAGATGGTGCCCCATGTACCGCAGCAGAAAGTCCCGCATCACGGGAGGCATCTTCTCCGTAATCTTCCTGCTGGCCGGCCTGTACTACATGCTCCCGCCGGAAGGAGCAGCCTGGTTCGGCCGCGGTTCCGCCCCCGGGGCGGCGGAGTCCGCCGCCGCAGCACCCTCCCTTCAGGAGGAAGGAAACATCTGGATACCCTGGAGCCCGGAAGCCATGCAGGCCGCCCTGGACGGAGGGAAACCCGTTTATGTGGACTTCACGGCCCGCTGGTGCTCCACCTGCCAGGTCAACAAGGCCTCCTATACGGATGAAGTGCTGGCCGCTTTCAAAAAATATGGCATCGTCATGATGAAAGCGGACAAGACCCGCACGAACCCCGCCATTGACCAGGAACTTAAAAACCTGGGGCGCACGGCCGTTCCCGTCAATGCCCTGTATCTTCCCGGCAGAAAACCAGCCGTCACCAGGGAACTCCTGTCCCCGGCCTACCTGCTGGAATTCCTGGAAAAGGAAATGGAACGCTAGGACGTCTTGTCATGCGGGCGGCAATCCGCCGCCCTCCCTCCAGGCCATGCCGGGCAGCAGGAAAACGGGGAAAGCTTACCCTTCCCCGTTCAACAGCGGCAGCAACGCGTCCGCCCATGCCTGATAACCCTTCTCCGTTAAATGGACATGATCCTTGAACATGCCCTCCCTGAACGTCCCGTCCGGCTTCAGCAGGCTGCCGTTAATGGATAAAATAGTGGTCCTCGGAAGCTCAAGCCTGCGCAGCAAACGGTTAATCTCCTCCTGCCTCCTGCGCAATGGGCTATCGGGATCGGGGCCGTAGGGAGTCACCTCCATCAGGATCAAACGGGTCTCCGGAAATCTCTTCAGCAGGCGTACGGCTATCTCCCGGATGCCCTTCACCGTCTGCTCCGCGGGCTGCCGCCCTTTCCACAGGCCGATATTATTCGTCCCGGCCAGCAAAATGCAATAGCGCGGAGAAGTTCTGACTGCCAGTCCCGTATCTTCCATGCGCCACAGCATGCTGTCCGTCCTGTCCCCGCTCACTCCGAAGTTGACCGCCTTCATCGGCACGAACAGCCTCTTCCACAGCGCGGAACCTTCCCGTTCCCAGCATTCTGTAATGGAATCCCCCACGAACAGAATGCCGCATTCCTTCCCCAGCAAATTTTCCCGGACCGCCTTGTAACGGGCCGGAACCCACGGCTGGGTATTCAGTTTGGGAACATCGGGCGGATCCGGAACCTCATCTGAAGGTGCCGCATACACGGCAGAAGAAGCAAGGGACAACAATAAAAAAGCGGCGGAAAAAACAGAAGCATTCATCTTTTTCCGAATACGCGGCCCGGAAAAGCATTCTTTCCCCTGCCGGGAAAATCATGTTCTCACCGCCTTGCCCAATACATCCAGAGGCTGGCCGGACAAATTCAATTCCTTTACCAGAAAACGGACAATCAGCAGGCTTGCCAGCGCATCATACAAAGCGTCATGCCATCGGCGCGCAGGAAGCAGAGCCTCCACGGAACAAGTCAGCCCCAGGGCGCCGCACAAAGAAGACAGGGAATAATCCGCCAGACCGGGAACGCACATCCGGCCCAGGGCCAGCGTATCCAGCCACGGGCCGAAGCCGTGGCCCGGAAACCGGCGCAGGAACTTCCGTTCCGTGGCCGGATTATGCCCCACCACCACGGCCTGCCCCAGCCGGGAACGTATCTCCGGCCACAGGGAGGGAAAAGACGGGGCATCCGCCAGCATCTCCGTAGTAATGCCGTGCACCTGGGAGGCGGACCATAAAACCGGCTTCTCCACCGCAATATAGGAAGTCCACAACTCAGGCTCGTCCTCCAGGCGGGGACATGCGGCTATGCCTATCTGGACGGGCTGGTCCGTCTCCCCCCGGGCCGCTCCGGCGGATTCAAAATCAATGGCGGCGAAATGCAGGCCGCCGATTGTATCCGAAAGCAGAGGCATATCAGAACAACTCCAGCTGAGCATATTCCCCGGCAGGCATATCGTCTTCCATATCCGTATTCTTGGCTCTCGGTTCCGCCAGCCGGGAGCGCTCCTTCCTCCGGGGGCGCGTGGAATTCATCTCCAGATGGGACAGGATGGCCTTGGCGCGGTCCACAATGACGGCAGGCATGCCGGCCAGTCGGGCCACCTGGATGCCGTAGGACTTATCCGCCGCCCCCGGCACGATCTTGCGCAGAAACACGATTTCCTCCTTCCACTCGCGCACGGCCACATTGTAATTCTGCACGCCCTGCCTGGAATTGGCCAAATCCGTCAGCTCATGATAATGCGTGGCGAACAGGGTGCGGGACTTCAACTCGTCATGCAGGTACTCCGCCACGGCCCAGGCAATGGAAAGCCCGTCAAAAGTGGCCGTGCCTCGCCCGATTTCATCCAGAATAATCAGGGAGCGCTCCGTGGCATTATTCAGAATCAGGGAGGTTTCGCTCATCTCCACCATAAAGGTGGACTGGCCGCGCGCCAGGTCGTCGCTGGCTCCCACGCGGCAGAAAATGCGGTCCACCAAACCGATATGGGCGGACTCCGCCGGCACATACGCTCCAATCTGGGCCATCAGCGTAATCAGGGCCACCTGGCGGATATAGGTGCTCTTGCCCGCCATATTGGGCCCGGTCAGCAGAATAAGGCGGTTTTCCTCCGGTTCCAGAAAAGCGTCATTGGGAACGAACACATCGCCGGAAACATTCTGCTCAATAACGGGATGACGGCCATTGACAATGCGCAGGGTCATGGAATTGTCCAGAACGGGGCGGCAATACCGGTACTGCTGGGCCCCCTCCGCCAGCCCCAGCAGCACGTCCAGGTCCGCCATGGCATCCGCCGTAATCTGGATATCGTCAATGTGGCGCCCCACTTCCTCGCGCAACAGGAGGAACTGCTCATACTCCACCTGGCGGGAACGCTCGTCCGCCCCCAAGATGGTATTCTCCATCTGCTTGAGTTCCGGGGTAATAAAGCGCTCCGCATTCACCAGCGTCTGCTTGCGCTGGTAATCCGGGGGCACTTTATCGTAATGGCTCTTCGTTACTTCAATGTAATAGCCGAAGACGTTATTGAAGCGGATTTTCAGGGAATCAATTCCCGTGCGCTTGCGCTCCTTCTCCTGCAGCCGTGCCAGCCACTCCTTCCCGTCGCGGGAAGCCAGACGCAATTCATCCAGACCGGCATGGTACCCTTCCCTGATGATGCCTCCCTCCTTGATGGTCACGGGAGGTTCATCCACCAGAGCGCGCTGGAGCAAATCCACCAGCTCATCAAAGCAGCCCATGCGGGAACGGATGCTCTCCAGCATGTCCCCGCCGCCGGGCAGGGATTCCAGATCATCCCTGAGCGCGGGAATGCGCGCCAGGGAGGAAGCCAGCGCCTGGAGGTCGCGGGCATTCCCGGCGCCCTGGGAAATGCGCCCCGTCAGCCGCTCCATGTCCCGCACATTCTTCAGGCTCTCCCTCAGCTTGCTCATCAGGTAGGGTTCCTGAAGCAGAACGGCGATCACCTCCTGCCTCGCCAGGAGCTTTTCCAGATCACACAGGGGGTGGAGCAGCCAGTCCCGGAGTTTGCGCGCGCCCATGGGGGTGCTCGTCCTGTCCAGGGTCCCCAGCAGGGAAAGCTTCACGCCGCCGCGGGAATCCACCAGGTCCAGATTCCTCTGGCTGGCCTGGTCAATCAGCACGGCGTTCTCCGTGGCGCGCACGGAAATGCGGCGCAGGTGGTCCGTGGGGCGGCGGAGCTGGTAGCCCAGATAATGGAGCACCGCGCCGGACGCCCCCAGAGCGGCGGTCATCTCCCCGCAGCCGAAACCGTCCAGGGAATGCACCCGGAAATGGTTCAGCAAATTGGGAATGGCCGTGGAAGGCAGAAAAGTATATCCGTCGTAATAAAGCGTGGGGTGGGTTCCGGGGAAGCAGTCCGTCTGCTCGTCGCTGACCAGGAGTTCGGAGGGATTGATGCGGGACAGCTCGTCCAGAAGCAAATCCATGTGTTCGAACTGGGCCACGGAAAATTCCCCCGTGGTATGGTCCACGCATGCCAGGCCGAAGCGCTTCTTGTCCTTGTACAACGCCACAATGTAATTATGGCGGGAGGAATCCAGCAAATTCATATCCGCCAGGGTTCCGGCGGAAATCACGCGGGTCAGTTCCCGCTCCACCAGCTTGCCCGGCTGCGGAATGGTGGTCTGCTCCGCAATAGCCACCCGCTTTCCTCCCTTCACCAGCCGTCCGATATACCCTTCCGCGCTGTGGTGGGGCACCCCGCACATGGGAATACCGTGGCGCTTGGTCAGCGTCAGGCCCAAAATGGCGGAGGCCTCCTTGGCGTCCTCAAAAAACATTTCGTAAAAATCCCCCAGCCGGAAAAACAGCAGCACGTCTTCCGGCAAGCCCTTCTTCATGCGAAGGTACTGATCCATCATCGGGGTTGAGGCAGGTCCGGAACTACTCATAAATAACGGTGAAAGAGTAATCCGATTGCGGCTTCAAGGCAAGGTTGGAATCTTGTTAGAACCGGGGCTCCACTCCCCTTTTCCAAGGAAAAGCCAGGGAAGAATCATCCAAAGCCCCGCAAAGAAAATGCGTTGCCTCTGCCATTATCGCGTCTTTTCTCTTCCTCTCCGGAAACAGGCCCTCATGCGGAAGAACCCGGCCATGCAGACCGCCATCTTTTTGCGAAGCGCGGCAAACAACAGGCCATTTATTCCAGAAAAGAAAAAACCGTGCCGCAACCGGATGCGGCACGGTGTGAAGTCAGATTGGAGGAATTAATATTCCACCACGGGGGCATCCTTCCACAGGGTGTCCATTCCGTAGAATTCGCGCGTTTCCTGCCCCATGATGTGAACCATCACGTCAATGTAGTCCACTACGGACCACAATGCCACAGGCGTATCTTCCACATAAGTGGGGAGGGCTCCCGCCTTTTCCCGGACGGCTTCTTCCAGCTCCCGGATGACGGCGCGCAGATGGGGGACGGACAACCCCGTGCACACTACCATGAAATCCGTCAGGGAAGACATGCCCCGCAAGTCGTACACCCGCACATTTTCGGCCTTGGCGTCATCCGCGGCGCGGGCGCACATCCTGGCCAGTTCCATCGCATCGTTGGCTACCATATTCGATTCGTTTCTATTGAATTATTTCTTTTCTCCGCCGTCCTTGCCGAACTCATCAACGGGATTGTAGGAGAAGGGGGCCCGTTCCGCCTGTTCCAGTTCCTCTTCCATCTTCCGTTCTTCAGCGCCGTCCGCGCGGGTCTCTTCAGCTTCTTTCTTCTCGTTATGGCCGGAATCATCCTTCCCGGGCTGTTCCTCCACTTCGGAAGGCATGGGGGGCGGAGTCACCCTGGCGGGAGGGTTTTTCATTTCCCCGTATTCCAGAATATCCATGACCTGGGAACCTTCCAGCGTTTCAAACTCCATCAGGGCCTCCGTCAGAATGTCCAGCTTGTCCCGGTTTTCCGTCAGGATGGCCATGGCGCGTTCATAAGCGCTGTCCACCAGGAAGCGGACTTCCGAGTCAATCAGCTCCGCCGTGGATTCGGAATAATTGCGGGAACGCGTCCCCAGGTCGCGGGCAATATAAACTTCTCCCTGGTGTTCTCCGTACTCAATCAGCCCCAGTTTTTCGCTCATGCCGAATTCGCACACCATGCGCCGCGCCAGGTTGGTGGCGCTCTTGATGTCTCCGGTAGCACCGCTGGTCACATCACCAAAAACGATTTGTTCGGCGCAGCGCCCGCCCATCGCCACGACGAGCTGGTCCAGCATTTCGGACCGGAGCTGGTGCATCTTGTCGTCGGAAGGAAGCCACATGGTCATGCCGAGGGCCCCGCCGCGGGGAACAATGGTCACCCGGTGCAGCGGCTCGCTGTGCGGTGTTTTCAACAGGCAGATGGCATGCCCCGCCTCATGCACGGCAGTAATGCGGCGCCCCCGTTCGTTAATCGCCAGGCTGCGGCGTTCACGCCCCCAGCTGACCTTGTCGCGGGCTTCTTCCAATTCGGCCTCCGTAATCTCCTTCAGCCCCTTGCGGGCGGCCAGCAGGGCGGCTTCATTGACCAGGTTGGCCAGCTGGGCACCGGAAAAGCCGGACGTGCCGCGGGCAATCCGCTCAAAGCTGACTCCGGGCGCCATCTTCACTTTTCTGGCATGCACCTGCAGGATCTGTTCGCGCCCCCGGACGTCCGGCAGGTTCACCACCACCTGCCGGTCGAAGCGGCCGGGACGCAGCAGGGCCGGGTCCAGAATGTCCGCACGGTTGGTGGCGGCAATCACGATTACATTGGAGTTGTTTTCAAAACCGTCCATCTCCACCAGCAGGGCGTTCAGCGTTTGTTCCCGCTCGTCATTGCCGCCGCCCATGCCGTAACCGCGCTGGCGGCCCACGGCGTCAATTTCATCAATGAAAATCAGGCTGGGCGCCGTCCTTTTGGCCTGTTCAAACATATCGCGGACACGGCTTGCCCCCACCCCCACGAACATTTCCACAAAATCCGAGCCGCTGATGGAATAGAAGGAAGCGTTGGACTCCCCGGCAATGGCACGCGCCAGCAGAGTCTTGCCCGTACCGGGCGCCCCGACCATCAGCACGCCGCGGGGAATGGTGGCGCCCAGATCGCGGAATTTTTCCGGATTGCGCAGAAACTCCACCAGTTCCCACACTTCCTCCTTGGCTTCGCTGATACCGGCCACATCCTTGAACGTCACCTTGTTCTTGTCCGGGGAGATGAGGCGCGCCCGGCTTTTGCCGAAGCTCATGGCCCCCCGGGCCCCGCCGCTCTGCGCGCGGAACATGAAGAACAAAATCACCAGAATGAGCACGATGGGCAGCAGATTCAGCAGAATGCCGCCCCAGGAACCGGATTCACGCTTGAACTTCGTATCCGGCCCCAGCAGCTGCTTGACGCGGTCTCCCTGGAACTCCAGGGTAAAAGGCACTTCCACACGTTCAAAACGCTGTTTGTCCACGGAGGCGTCTCCCGTAGCCGCGGGCCAGATGCGGGTGACAATCTGTCCGACCAGCACGTTCTGGTTGCCGTCTTCCGCCAGGATGATGCCGTCCTTTCCTCCGGCGATGCGGCCTTCCAGGGCCAGGCGGTTGAATTCTGGCACGGAAAGCTTCTGAGCTCCCTCAGGAATGAGGGAAACATTCTTTCCCTCTTCCGTCCGGTACGGGCTCTCCACCACCCTGTAGCCGGAAAGTTCATTCAGCAAAGGCTTGTCGCGGTCCGGAAGGGACATGGAATAAGTCAGCGCAAAGGGGGTCATTTCCACCTTCGGCTGAATTTCCTTCCTGTACACCAGCGCGTGAATAACGCCTTCGGAACCGTTCTCGCTCAGCACCACTTCCACGGGGGCCTTGGGGTCGTTCAGCACCACGCGCCCCGCCTTATACTGGGCTTCAAAGGATTCCAGATTTTCCTTGCGGGGGCCCAACCCGAAAGATTCAGGGGTGAAAAAGCCAAACGCAAGGACGCCTACAATGAGAAGTACCATGACCCACACGCCCCAGTTGGGGGATTCGGGCCTGCCGGAGCCGGGAAATTTGGGAGGACGGGGAGGACTGGGTGGCATTCTGTCAGACATAATGTGGTTAGCACACAGAAGTTAATGAATAGGGCGGCATGATACGCGAGTCCTTGTAAAATAAAACTTTTTTCCGAGACATGCCATACAGCCGCCGCAGAACGGGCCCGCACCGTTCCGGGCCGGGGCTCTTCCCGGCAATAAGGCCTTAACCGGCGCAGGCCTCATTCCCGCTTTCTTTTTCTGCGCCGCCCCTCATGGTTCGCGCTTTCTCCGTTCATGCGCTGCCGGAGACGCCGCTTCCACTGGGGCATCCTTTCTTCCGCCGGGGCCGCATGGGAACCGGCATCTTCCCGGCTCTTTTCCTGCCAGGCCCCCTCCTCCTGCGCGTTCAGCCGCTCCCGCCGCCTCTGCTCCTGGATTTCCGCCAATTTTCTGGCGCGGCGGATTTCCACCCGCTTGCTCACCTCCCCGTGGGCGGAGGTCACGCGCCAGGGGCCGATGCGGCGGGGATAGGTAATGGGTGCGCAAACCAGTTCCGCATCCAGAAAACGCATGGACCCCACCAGCAGGTTCTTGACATTCGCCAGCAGGCCGCCGTCCGTCCAGACGCCGCCCATGCCCATTTCCAGCAGAAAAGCCAGATGCAGCGTCTCCGCTCCCGAGGCCTGAATGGAAAAATCCCTCAGGGAAATGCCCTCCGCCGGCTCCTCGTCCGGAGCGGCCACGGAAGCGGCCCGGAGGAGGAACATCGTATCCCGAACCTTGCGGCTCAGGGCGCAGACCAGTTCCCCCACCTTGGGCCGAGCGTCAAAACGGTAATCCTTCACTTCCACCAGCCACAGATCCCGGCGTTCCGGATGATACAGCAGAAAATCCATTTCCTTGCATCCCCCGCAGAAATTCTGCGCCTGCCGGGCATAATAATCCGAACATTCAATGCGGCACACCACGTAGCCTTCATCGATCCAGAAACGGTGGACACCCTCCACGTAATAAGCTTTTGACGGCTTATGACTCATAACTCAGCTTCAAATACCTGTCCGCCTGTTCGATTTCCGCTTCAAGAGACTCGACAGGTCCCACGTCCTCCAGGGATTCCCCCCAGGACACCCTTACCCCGGAACGTTCCCCCCGCGGCACGCTCATCCCGAAAAACTTTCTCTGCACCATGGCATTGCGGGGTTCGGAAAGCTGAATCATCAGCTCCCGCAGCAGGAACAGGCTGTGGGAGGACAAAATAACCTGCACCCCCGTCTTGGCAAGCCCCGTCAGGGTTTTCACCAGCACGGGCAGGTGGGAGGCGTTCAGATTCATTTCCGGTTCATCCCAAAACAGGACGGAACCTTTTTTCACGGAACCGTTCCGGATCAGGTAGCTCAGCGTTCCCAGCCTTTTGAATCCTTCCGCCACCAGGCTCATTTCTATCGGCTGCTGTCCGGGACGCTGCAAAAAGAACCGCCCGTCACGCTTCACCACCTTCCCCCCGATGATTTTTTCCAGCCTGGCAACCACGCGCCCGGCATCCGTACTGATGGGCTCCGCTTCCGCCTCCATATCCAGATAACGGCAGAGGTCCCAACTGCCGCCGTCCAGAAATTCGGGGAATCTGCTTCCCACCTGCACAAAACTCGGATAAATGGTCAGCACCTCCCGCGCTGCCAGGAACACCACCGGCACGTTCAGAAAACGCCGGGGCATTTCCCGGATGCTCAGCCCCTCCTCTTCATGCCCCGCCTGGAAATCAAACACCAGGTTCCCCATGCCCTCCGGCACGCCGTCCCCCTCCATGGAGGCCTCCACGCGCGCATGGGCGTTTCCCCGGTTCCGGGCCGTCAGCCCGGCCAGCCCGCGGGACGCGAACACCCGCATCAGGTCTTTTCTGAGCCGCTGCTCCTCCGCCCATTTTTCCGGCAGGGATTTTCTCCCCCCGTCAGCGCTCCACTTGGCCACGGTATAGCATAATTTCAGCAAATGGCTTTTGCCGGAATCATTGCCTCCCACCACCACATTGATTCCCGGAACAAACTCAAAATGCTCTCTCCCGGGAAACACCGTCACGCCGCTCACCAGTAGGTCATGTATCATACCGTTCCCAATGTAGCACAGAAGCGCAGGCCGTAAATGACAAAAGCCGCCAGCTGCCGGCCACCGGGGAGCCTGCCGGAAAAACACCGGAAACAACGGGGAAAACGCATACGGCCGCAGCACCGCCCCATCCCCCGCAGGTTCCGGACATACCCGGAAGGCGGCGCATTTTAGGCTTCCCATTGCCGGGCGCCTTATTAAAGTAATCCGTATGAGCGCACAATGGATCACCGCCAAGGAATACACGGACATCAAGTATGAAACAACGGACAACGGCAGCATCGCAAAAATCACGATCAACCGCCCCCACGTCCGCAATGCGTTCCGTCCCCTGACCGTGCATGAAATGCTCAACGCCCTGAACGCCGCTCATGAAGACCCCAAGGTGGGCGTGGTCATCCTGACCGGGGAAGGCCCGGACGCTTTCTGCTCCGGAGGCGACCAGAAAGTGCGCGGAAATGCCGGCTACATCGGTGAAGACGGCGTGCCGCGCCTGAACATTCTGGACGTGCAGCGCACCATCCGCACCATGCCCAAGCCCGTCGTCGCCATGGTGGCCGGATACGCCATCGGCGGAGGTCACGTCCTCCACGTCGTCTGTGACCTCACCATTGCCGCGGACAACGCCAAATTCGGACAGACAGGGCCCAAAGTGGGCTCTTTTGACGGAGGCCTCGGCTCCTCCTACCTGGCGCGCATTGTGGGCCAGAAAAAAGCGCGGGAAATCTGGTACCTGTGCAGGCAGTACGATGCCCGGCAGGCGCTGGACATGGGCCTGGTCAATACCGTGGTTCCCCTGGAGGACCTGGAGGAGGAAACGCTCTCCTGGTGCCGCCAGATGCTCCGCCACAGCCCTCTGGCCCTGCGCTGCCTGAAGGCATCCCTGAACGCCGACTGCGACGGGCAGATGGGCCTGCTGGACCTGGCCGGCAACGCCACCCTGCTCTATTATATGAGCGAGGAAGCGAAGGAAGGCAAAAACGCCTTCGTTGAAAAACGCGCTCCGGATTTTTCCAAATTCCCCAGACTTCCGTAACCTCCCCCTCCTCCGCACCATGGCACCCAGCTCCGACAATGAAGGCGCCCCCCGGAACGGCACCGCCGGCAGGGAATCCCTGCGCGACCTGCCGGAACATGAAAACATCGTGGAGCATTTCTACGATCCGGAAGATCAGGGCTCCCGGGAAAAACCCGTCCGTGAAAAAACGCCTTTCTCCCTGATAGGCAATCTCATCTTCCTGCTGACCATAGCCATTCTGGCCGCTATTGCCTGGCTGGTTTACTCCTCCTGGTGCCCGCAGAAAACGGACGACCTGCCCGGCTTCCGCCAAAGGGAAAACGCCCCGGATATCCCCAGAATCCTGAAACAGGCCATCAACAGGGACGCCTCCGTCTCGTTCTCGGAAGAAGACATCAACCGCTACCTGGCTTCCTCCATCCATCCCCAGCAGCACGGCACCCTGGCCATCTTCGCCACCAACCCGGCGGCTGGCATCCGTCTGCACGGCGGCAAGGAACGGCCGGACGGAACCATCGGGGAAGGATACATGGAAATCATCATCGAACGCTATACGGGCATCGACTCCCGCCAGACGATTTCCCTGTTCCTGACGCCCTTCCAATCCCTGGATCCGCACAATTACATGGCGGTGCAGACCCGCTTCGAATTCTACAATGACGAAACGCTGCCGGGAGGAATCCACGTGGGGGGCACCATCGGCAGCCTTTCCGTTCCCCAGGGTTACATGATTTTTCTCCTGCCCGCGTTTGAAAACCTGCTCCAGGCCTACCTGCCGCTTATCCACATGATTGAAGAAAGCGGCATGGGCATCCATATCAGTGAAGGCAGGCTGAACCTGACGCCTCCGCAAAAGCGCACGCTGTAGCGGAAGACCGCAGGGCCTTCCGCAATCACCGGATGCCCTGCCGGGCAATGCCCCCTTCCGGCCCCGGCATTCAATTTTTCTCCAGATCCCTGACCATCAAAGTCATATACTCCTCAGGCCAGGAATGGGTAATCTGACCCGCATCATTGAAAACCGCCACCAGGCGGATGGAATCCCTCCCATCTGAAAGCACCAGCACGGTTTCATCCTTTTCAAAATCATAACGGGGCTTGCCCAATTTGAATGCCGCACTCCTGCGGTCCGTCCTCAGCCACGCGCGCGCCTGCTCCATCGTCAGGCCACGCAGCGCCCGGGCATCCCTGAAAACGCGCCGCGCCGCATTCATCGCCGCAGGGCTGGAAACCACCATGCCATCCCGGAACGGGCGGGTTCCCCACATGCGGTCAATATCCCGGTCCAGCGCGCCGCCCATCCCCCCGGAAAGGAAAAACACGGCTGCGGGCCAAGCCAGGAAAATACCCGCAGCCCATATTGCCAGCCGTTTGCGGCTTCTGGAAAGAGCCGCTATTCCCTGGGCTATTTTCCCCCATTTTTCTACCAGTTCCCGGTCCGCATGCCCGAACAGGAAATTCACCTGCCTGCGTTCTATGCCGCCCCGCAGTTCAGCCACCGTCTTTCCCTTTTCGGGCGCCAGATCCTCCAGCGTAGGGCTTCCTTCCACGAAGTGGCGGAAAATCCTGGGCCACAGCACGGGAATAACCGCAAACCGGATTGCAAGCAGAACCAGGCTGCCCACCAGGCAGCCGCTTATCAACAGAATGTCAAACGAACGGCTGAAAAAATAGTCCGGATTCCACATCCAGATAAAAGGAGCCGCCAGATACATCCCCAGGGAAAATAGGCACCATGACCAGACTATGGCGGAAAAACGGCTGGCCGCCACAACCACCCCCAGCAGGAACAAAAAGGCCCCCAGGGCAATTCCCCACGGAAACGCCCCGTAAATGGCGGCCGGAAAAGCCAGAATCATCAATGCCGTACCCGCGGGCTGCCGAACTTTTTGCATATCCGGAACATAGCACAAGCCCTTGAAAGCATCAAGAAAGGAAGCCCCCGCAGTCACACAGGGACTGTTCATCCTCCCCCGCCTTACTTCAGCCAGCCATATTCCATGATGAGGGCCGCTTCCACCTCCGCATTCTTGGAAGCTTCATTATCCAGGTCAGCATACATTTTCCTGATATCCTTCCTCTGTTCCAGATATTCATCTCCGGAAACGGATAATCTGGACTCCTTGAACGCGATTTCCTTCATCAGCACCCCCTGCCAGAGCTCAAACCTCCTGCGGCTCTCCAGCAACGCGGCTATCTTGATACGCCGCCGCTGCAGTTCCATCACCACTTCGCGCTGCAGCAGTTCGTGGCTTTCTTTGGCCGACTTATACTGGAACCAGGATGTCAGGCGCGTATCCAGTTCTTCCCGCAGGCTCATGTTCACCTTCATATCGCCCGCTCCCGCAAAAAAACCGCCGTACGTCCCGCCCGTGCTGGAAAACAGGGTTGGGCTGTAAAAATTAATATCCACGGACGGGAAGAACTTCAGTTTGGCATTCAGCACCTGGAGGCGGGAAGCTTCCAGCTCCATGGCCACCATCGTCACCACCAGCAGATCCAGCTGCCGGGACGCCGCCCTGTACCTGCCCCAGTCCAGCCTGGGCATCGTTTCAGGGTTCACCAGCCACCGGGCGTCCATATTCCCCAGCAACACGGCAAACCCCTGGGAAATCTCATTCAAATTCCGCTCCCGTTCCAGGTCCAGCTTCTTTTTCTGGACGCCATCATCGTCATAAGGCAGGGAGCTCAGCTGGTTATTGTAGCTGATCTGCGCGTTCCGGTAGGAAAGCACCTGCTGGTACAATCTGGCCACCAGCTCCCTTTTTTTCATCTCCAGCGTCTTTTCCGCCGTATAAACAGCCGCCTTGGCGGAATAATAATCAAACGGGATCTGGGTGAGGGACGGCATGTTGAACAGAATGTTCGTATTGTATTCCACATCGCTGGCCGTCACCCTGGCCAGGTCGCTCAATTCCTTGGTCAGCATCCAGTCCAGATTGACTCCGGGGATAATCTGGGTAAAAACATTAACGACGGAACGTTTCGTCGTCTTGAGGGACTGCTCCGATTTTTTCAAGTCAATGTTCCGCTCCATCATCATCGCCAGGGCCTGGTGCCATGAGATTTCCTTCCGCGGCAGCTGCCGCCAATCGGGAACGGCATCCATCCTGGCCATCAATTCCCCCGCCTTTTTTTCCATGTGCCTGTCCACGGAGCAGGAGGCGCATACCGCCGTACAGAGGCAGAAGCACAACGCAGCCCTCAGCCATGCCCTTTCAAACAGGGAAAACAGGAAAGAACGGACAACAGGAGGAAACGGGAACATACAGGCCCCTTGTTACATAGCTTCTGCCCCTGAAAAGTCAAGCATCAACACGGCAGGAGGGGAAAGAAGGAAAAAGAAGTCCTGGAGCGCAGGGAATTCGGTTTTTCCTTTCCCCGTGTAAAAAATACCTCCCTCCTGATTCCTATCTTGACAAATCATGTGTGATATCACAGTTTATCGGAAAATTATTATCCTTTTATGATGCGTCCTTTTCTTACCTCTTTGTCCGCGGCCTGCTGCTCCCTCGGCATTCTTACGCTGGCCCAATGCGGCGGAAGCAGTTCCGATGCCGAGGGCCTGGCGCCCTCCACCATCGGCAGCAAGGTGTTAAACATGCAGGTAACAGGCATTACGGATGCCGCCATAACGCTTCCCGCCATTTTTGACATGACCACCGGTGACAACGGGGCATTCATCATCCAGACGCAAGGGGAAAACATGAACATGTGGAACGGGGCAGGCATTTATTCCTACACCAAAACCGGGAAAAACACTGCGGAGCTGAGCCTGTCCTACAACAGAAACAGCGGTTCGTCCTCGGAACCCAACACCCTCATGAGCACCATCAGGATTCCCCAGTTAACCTTTGAAAACGCCACCCATGCTTCTTCCGGGGAAGGCGGCGCCAGCATTTCCTACATGCAAGCGCAAGACAGGACACCCATCCCCATCGACGGCATCACCATCAGCGTCAACATCATATCCCAGTCATAAAAAAATTCTTTCCTTTATCCATCAAATCATGAATTCCCGCCTTTATATTCCCGCTTTAATTACTCTCGGGCTTGGGCTCCTCTCCGGCTGCGGCGGATCCAGTTCCAGCGAACAGGCCTATCTTCCCATTGGTCTTCAGCGCACGTTGGACATCACGCTTCAGGACGGCAAGCCTTACGTCTTCTCCTTCATCATCGCCTCAGAAAGCACCGCCACCATTTACACCGGAACCAGCCCGGCCGCCTCAACGGCATCCCTTCATGTCGTCAACTACAAACCAGCTTCCGACTCTGCGGAACAAATAGGCTTTTACTGGTCGTCCAATGACGGGGAAACAACCAGCAATCTGGATGCCCTCGTTGTCCTGTATAATCCCCAGAATATTTCCAAAGGCATCGGTTCTGCCACCTGCAGCGTCAGAGACGCCGAATATACTGAAACGGAAACCCAGCAGGATAACTGCCAGGCGGTCATCAGGGATTTCAGAGTGCCCCCCAGCTCTGATCAATAAGCCCTTTCTTCCTACCTCCCTTCCCGCCCCCATGGCGGGAAGGGATTTTTTTATCCATTATCTCTTCCTGCCGGAACAGAGACGGCCTTTCCCCCAGACCGCCGGCAAAGGCGGACATGCCGGAACGCAGACAAAACCGGGAACAGCCCCGAAGCCCCATCACTTACTTAAGCACTTTCCCTATGGGGCGGCAGGCGGAAAAGGCAACCGGAATGGAGGAAAGAAGAACACACCCCGCCAGGGAAATTCCCAGCAGCCACAAATCCTGCCGTATATCGGCATAAGTCAGGACGGAGTTTCCCAGCTTAAAGAATTCCCCCAATACGATTCTCTGGGTTTTCATGAACGCCCAGACGGCTCCCGCAAAGGAAACGCCAATCAGAAACGCGTTTTCCACGATAAACGCCATAATCAGCAGTACAGGACGCACCCCGAAGCTCTTCATCAGCGTATAGACATATTCATTCTGACGGAATTCCATGGATGCCAGGGCTGTAAGCAGAATGCCGACGATCACGGCGATGCCGAGGGAAAACCCAGTCCTGCACTCCGTCTGGTTGCCCATCAGCACTTCCAGCCTGTCAAGCAGCCCTACGTGAGTCTGAACCATGGTCATGCTGCCGTCATAACGCACCATGTTGTTCAGCACTTCTTCTATGCCCCGAATGCGGGCCGCCGTCATTTCATGCACCCGGATGATGTAGCGGCAATTTTCCACGGGGACCTCCTGTCCGGAACTGAAAGTTCCTTCCGGGGCCAAAATGACTCCGGAAGGATACAGCCTGCCCAGCAAATGACCCTCCGGCAGAGGGGAGGCATTCAGGGAAAAATCAAAATCCGCTATGGTAACCGTACAAGGCCCCGCCTGCAACGGGCTCCGGGAGGGATTGTACAGGAGGACGGTAGCCACGTCCTCCAACCCAAGCGCGGAAAGAAAGGAACAGGACTCCATGTTGTATTCCAGGATGGGGAATACTGTCTTGCCCACGCGCGCCACGCCCGCACGGGCATCATTCAGCACCAGGACGTCGCAGCCATACAGGGAAGGCAGTTCATGCTGCAGCAAATTTTTCTTGGAAGGCTCACCGTCGCGCACGGTATCAAAAACAATAATCAGGTCCGCCCCGTTCTTCCTGATTTCATCATGCAGCATTTTGGCGGAGAGGACATAATTGGCGAGAAAAGAGAGCGCGCACAGGGAAAGGAAGAAGGCCACCAGAATCCGTGAAAGGGGGCTGGCCGGGCGTTCCTTCCAACGGTGGAGAGTATCTTTAAAGAGATACAGACAGGTTAAAATCAATGATTTCATGGGTCAGGTCAAACAGCCGGGAATCATGCGTGCTCAAAATGCAAATTTTGTCAGCAGGGTACTCCCTGATCAAATTTTTAATGATTTCCGTATTACCGTCATCCAGGCCGGAAGTAGGTTCATCCAGCAGCAGCACATCGGAATCCTTCATCATCGTGCGGGCAAGAGCCGCCCGCTGGGCCTGGCCGCCGGACAAGCTCCCCGCCTTTTTATGCCTCAGAGATTCCAGGCCCAGGCGGGCAATCAAATCATCCGCCCTCCGCGTCCACTGCTTTTTAGGCAGCATAGCCATTTCCGCGCATAATTGAAGGTTTCTCTCCACCGTAGCGAGGGGAAGGAGATTGAGCCCCTGGAACATGTAACTTACCTCCTTGCGTCGGTAGAGGGAATCCGCCCGAACCTTACGGCGGGGGGTAATCACGCGCCCGCGCTGCGGGCTCAGATAACCCGCCAGAATCTTTAACAGCGTCGTTTTCCCGCATCCGGAATACCCCTTCAAAATGGTGATTCCGGAAGTGAACACCCTGTTGTAATTTTCAAAAATCCACTGCTTGCCTGAATAACTGAAACAGACTGATTCGCACAGCAATTTCATTTTTTTCGAACAATCAATTCCGTTTCCCCATTCACCACCAGCTCCAGGTCCGGATGCACTTTTTCCAGAAGTTCCTGCCAGGACGAACATTTTTTTCCCTCCGGCAGGGAAGCCAGCCTCACCTTGTTGAGATGAAACACTTCTCCCTGCGGCATGTAATAAAGACGGGCCGTGCAGTTGGAACCCAGCATGGAATGGGCGCGTGCATGCTCCTGACGGGGCAAACGGAAAAAATACGCCAGCAATTCCCCGCCTCCGGAAACATTCTTCTCCACCCGCTTGAAGGCAAAAACCCCCTTCAGGGCGGAACCGTCGCCCAGAGGGACGGACAGGGAAAGGGATTCCGGCGGCAGATTCGTCAAATCAGGGTCCGAAATGGAAATGGTCAGATAATAAGAGGAGTCATCGCACACGGTCGCAATCGGGGACGCGGCGTCCATGTAAAGGGCTACGCTGTTATCCGGCGGAATGGAAAACTGGTACTGCAGCTTCCCGTCAAAAGGCATTCTGAGGATATATTTTTCCTCCTTCTTGCGAAAATCCAGACGCGGCTTGTCCTCCACCAGGGACAACTCCCTTTTGGAAAGCTCAATCTTGTCTTTCAAAGCCCGGATGGCCCTTTCCCCGCCTTCCGCCCGCTTGCTGGCATATTGCCGTTCCTCCCTCGACAGGGAAGAGTAGAATTCTATCTCTTCAAGCTGTTTTTCCAGCTTTGACAACTCTTCCTCCTTGGCGATGCGGTCCTTCAGGATTTTGACCTCCAGCTCCTTGCGCTCCAGCTCAATTTCATCTTCATTGACCGTTGCAATAATGGTATCCTTCTGAACGCGCCCGTCCGCAGGAATCCAGTTGCTGACAACTCCATCGGCCGGCATGACAAAGGAGCGCAGCCTTTCCGGGACGATCTTGGCGCGGAAAGTCTTGATGAAACAGGCATCTTTCGATGCCGCCATATCAGCCCCCCCGCCGGCCGATTCCGCATTCCAGACACAAAGAGCCAGCCCCGAGCACAGCCAGATGCCCCAAAGACATTTCTTTGCCCTTCCGACCATAATTTCAAACAGGCCTGCCCCCCCCGTTTCACGGCAGCAAAGCCCCGTCATAAATGATCAGAACTCAGGATACCGTGTAACTCACCGTTCCTTCCGTAGCGGTCCAGTCCTGCGCTTCAACCTCGCCTTCATCATTCTCAACGGGAACCTGGTAAGTGATGGAACCGGTGAACGTCCCGCCGCAGCTCTCACGTTCATTGGAGGAACTGAAGGAAATATTAGCATCTATAAAGGCCTTGTCCCTGTTTGTCACGACAATCGGGCCCGGAGCCGTCCACTGCCCGGAAGTAATCGTGAAGTTCCCCCTCAGAATAGCGGATGATCCCGTCCTGCGGTACACTACGGAAGCAATGTTCATAGGAGCACTTTCATTGCTGCTTGCCGCATTAATGCTTACCTGGGGAAGGTCCGAACCATTGCCCGTCAATTTGACGATAATATTCCACGGCCCTAGCCCATCTTCCGTGGAAAGAGAAGCGCTCAAGTCCAGCATGGTGCCGGAAGGAAGGCTTTCCCCGTCACCGCTGCTGCCGCTGCCTCCGCAGGAAAAGAGTCCAAGGGACAAGGCTGCCGCGGAAAGGGAAGATAATACGCACCTGTTCGTCAATCGGGAAAACATAAAGAAAAAGATTTTCTTCAAGGATAGTGATTCCGTCCGGCGCGTCAACACATAAATAAAAGAATATTTGGAGGGGCCTTCCGTTTTTTTTGCGGGATGCACCGGGGCTTACGGCCCGCTGCCATTCCCCGGACGTGCACGCCGGATTTTCCGCTTTGCGGCGGAAAATCCAGACGCCAATCCCCGCAGGCCGCAAACGTCATCCGGCCTTTTCCAGCCTGCGCTTGAGGACGCGGCCCGTGACCAGCAGGGGGGCAGCCCCCATCAGGAACAGGAACAGGGAATCCTTCCATGGGGTGGGAACCGTTCCGTCATCCGCGCTGTATCCGGAATCCCCCGGGGACAGGCCGGAGCCTCCCCAGGGCAGAGGCTGGCGGCGGGCCAGAAAAATTTCCGGCCTGTCCAGGGCATCCCGGCTGACACGCTGGATTTCGGCAGATTCATCCAGCATCACAACGCGGTCATTGACATAGAAGGATTTAACGGACGGACGGTCCTTACCCAGGAGTTCCAATTCCGCAATAGTCCGCCTGTAGCTTTTTTCCGAACCGCCAAGGGCTGCCCTCCTCACCTGCCGGACAGCGGCTTTGGCCTGTTCCGGCCCCTCCGCTCCAATAGCGGCAATGTGGAGCAGCGCCATCTTGAGCAGCTTGAATTCTTCAAATTCCCTGTCGTCCAGATGAGGGCGTTCCTTCAGGTCGTCCACTTTCTCCTGGATGGTTTCACGCTCCCTCTCCCATACATTTTTGGACGCCTGGTCCACCACCATCGTTTCAAAAGAGTAGCGCAAAGGGCAGAATTCCGCCACCAGCGGCACGCGCCCCGGCTTAAGACGCCCGTGTTCATCCGTACGGTGGGCGGACCATGGGATGAACTGGTTCATTTCATCAAAGCGGACAATGGCTCCGGCCATCAATATCTGGGGAATCAGAAGCAGAGGCACCATGTTAAGCGCCGCCCTCTCCGTCCTGGCGAAGACGGAAACCAGCAGGGACAGGCTCACGCCGACAAAGGCCGTCAGGGTCATCGTCCCCAGGTAAATCAGGAACATTTCATGGATTTCCAGAATGGAGTTCCCCACCCAAAGAAACAGGGCGGACTGCACGGCGGAAAGCCCCGTCAACACCAGCGCCTTTGCCAGCACATAACCGGTCACAAACACCTTGGCATTGCTCTCCCTCTTGAGCAGGGCACGGTCCTTGAGCACTTCGCTGGCCGCCCCGGTCAGGCCGAAGAACATGGCCACAATGGTCGCCAGGAACAGGTAGGAAGGGATGTGGAGGGCCGTGGCAAACGTATATTCCGGGCTGGAAGAGGCGCGCAGGGTCATGGAAATCAGCAGGGCCAGCACCGGCCCTTCCAGCAGCATGGTGTACAGCCCCATACGGCTCCGCACGCGCCCCAGGAAGGTGCGCACGGCCCAGATGCGGAAAAGCCGCCAGAGCTGTATGACGCTGCGCCTGGGGGCGGGGGGAAATTCCAGCGTGCCTTCATACAGCGTTCTGGGGGCGCCCCCTTCATGATGATGCCCCATCACGTTGCGGAACCGGTAGCCTTCAAAGCGCTCCTGCCACAGGCGGGGGTGCTGCCGCCTGCGGCGGTCGTGCCAGGAGAGGGGGGCTTCCAGCACCTCAAACACGTAGTCCGCGCCTCCGGCCGTTCGAGCCTCCTCGGATACGTCAATAGCCATGTCCGCTGCCGCCTTCCGGAAATACCGCATCATGCCCGGCACGTCCCCCCAGTAAGCCATCTGGCCGCCGTGGTCCAGCACCAGTACTTTGTGGAACTGGTTCAGAATAGCCGTGCTGGGCCGGTGCATGGTGGCGATTACCATTTTGTCGCGGCTCATGTTCTGCATGGTCTGCATGACCAGCTTGGCATCTGACGTGGACAGGCCGCTGATAGGTTCGTCCAGCAGGAAAACATCCGCAATGCCCGCCAGGTCAAGCCCCAGGTTCAGGCGTGTGCGCTCCCCGTCGGAAATGGTGCGGGCGTTCATCTCACCCACGCGGCGGGAGGCGATGTGCGTCAGGCCCAGAAATTTCAAAATAGCGTTCACGCGCCTGACGCGTTCCGACCGGCTCAGCCGCGGACGGCGTATCATGGTGGCCTGAGAGATGTGTTCGGAAACGCTCATGGCCGCATCCAGAATATCTTCCCGGGGAATGAAGGCAATGTAGGGCCGGATAAGATCCGGGGCGCTGTACAGAAGCTGGTTGTTGTAACGGATGCAGCCCCGGGTGGGCGGCAGCTGGCCGGCCAACATGGAGAGCAGCGTGCTTTTCCCGGATCCGCTGGGCCCCAGGATGCACACCATTTCCCCGCGTTTCACGGTCAGGTCAATATTGTCCAGCACGCGCCCGGAGCGCAGGAATTCCTTGGTCACTCCTTCCACGCTGAGGGTTCTGATCGCATGGTATTCTTCATCCAGCACCCCGGCGGAAAAACGGCAGCGCAGCGCGTGGTAGGCATTCAGGTGCACCACGTCCCCATCCCTCAGGGAAACCCTTCCCCGAACAGGCCGGCCATTGAGCAGCAGAGGCATGGAGGCCTCCACCACTTCCAGCCAGCCGGAATTGGTGGCGCTGGAAAAAGAAACCTCCAGCACCGCTCCGGGGGCCAGCCCGGGAGTCAGCAGCATGTCTCCCTTGCGCGCCTTTTCCGGCAGGTTGGTCACCCGCAGTTTGCGGTTTCCGGGATCCAGGCGGAAACGGCGCCCGATGTCCGACATGGAATTCTGCACCTCTCCCATGGAGAAAGGCCCTTCCCCGTCCAGCGTGAAGGAGGAGTAATAAGCCATGCGCACGCTTTCACCGGGGTACAGCCGTCTCCCGTCAACCGCGAATTCCACTTCCGGCCTCAGCACCTCCACCTCGCACGCCAGCCCCATGTTCACGCGCACCTCGCTGGCGCGGGAACGCACCCGGCTGATTTGCAGGGAGCCGCCGTCCAGGGAAATATAGCATACTTCCTGCTTGCCTGAACGCTTGCAGTCCAGAAAAAACGCGAAGTCTTCAAAACTCAGCGGACCGGACGGCAGCAGCACCACCTGCCCGTTGGTCAGCGGCATCACCCCGCCGGGCTGCAAATGGCGCCCGCGCACCACAATGGGGCGGGTGCCGTCATTCACCACCAGCACCAGGCGGGAACACCGGATCAGGCGGAAGGAAATGCCCTTGTCCTGCTCCGGAAGGGATACCTCCGCACCGATGCCGGAGGAAAAACCGACGCTGTACGCCGGTTCCTGGGCTTCCACGTCCGGCGTCATCAGCAGCTGCCTCAGGTGGTTTGCCGTGCCCGGCAGTTCCAGACCGTACGTCACCTGGTCAAACAGCTCCCCTGTCATGCGGGAATCTCCCGTATTGATCAGCAGGGAAAGCACTTCCAGAGCTATCGCCATGCGTTCCGTCTCATCCCTGCCGGAAGCTGCGGAGGCCAGGACATCCGCAACCGTCAGGCTGGCCCTCATGGACCGCTCAAAACGGTTGGAAAGCCAGCGGTGCTCCACTTCCGGGAAAGTATAGCGCAGAATATCCAGAGCGGCGTCCGCATCACGGGCGCTCAAATGGCGGCGTCCGCTTAAAATAGTGGCAAACAAGTCCACCAGCGTTCCGGCATGCACCTTCATGGAGCCGTCGTCACGGCGCAGCCACGGCACGTAGGCGCCCAGCGCCCTGACCAGATTCAGCCTCCAGTGAATCAGCTTGGAAGGAATGTCAAAAAACCATTGGATCATGCCCGGAAACGCGTTGCCGGAACAGTAGTACAGGGATTCCCGCCCCATCTCAAGCGCAACCGGAATTTCACGGAGAAATGACAATATTTTCTACAGCTTTATCTTGCAATAGCTCCGGAGGAAGTATAGTTCTGTCCAATGTTCATGCTTTCCGCGTGAAAGCTAGCCAAATACATCAACATTATCACAACATGGATATTTTAGTTTCTACTCTTTGTGACTTCGCCGCCGACTACCAGGGCAAGCTCTGCATCCAGGGCGGCTTTGACTCCCTGGTCGCCCGCCAGTTCCCGGTCGTGCATCCCGTCTGCGCCGTTGCTCTGCGCATCTGCCTCACTCCTGAAGACGAAGGCACGCACGAACTGGGCCTGAGCATCGTTGATGCCGACGGCACGCCGCTGGACAAGGAACGCATGCCCATCAAGATCAATTTCCCGGTTCCCGCTTTCCCGGAAGGAGCCTCTTTCTTCACCCGCAACCTGATCATGAACTTCCAGGGCCTGCGTTTTGAAAAACCGGGCAACTACTCCATCGACCTGACGGTTGACGGCGAACTCGCCTCCCGCGTACCCTTCCGCGTAGTGCAGGTGCAGGAAGAAGCCGCCCAGGCCTAAGAAACCATCCCCTCTTCCATCAAACCGCCTCCCGTTCATCCGGCAGGCGGTTTTTTATTGGAGCGGCCGTCCGCCCGGAAGTGCGGAACAGGCCGGCCCCGGTTCCATGCACCTGAAAACCTCTGCGCCTGGCCCCGGCCTAAAAAAGGGAGACCGGCTTTTTGCGTCCTCCTTCCCCCTTTTTCATTGCGGTTCCGCACCGGATTGACCATGATACGGGCTGCTCCAACCCGCCATGCACACCGCACGCCTCCAATACGACAGGATTATCATTACGGGCGCATCATCCGGCTTCGGGGAAGCGTTTGCCGGAACGCTGGCCCCCCATACGGCGGAATTGGTGCTCATCGCCCGGAATGAAGCCGCCCTGCGCCAGCTTGCCGCCGCCCTGGAAAAACACCATTCAGGCCTTCATGCCTCCGTCCTTCCCTGCGACCTGGCGGACGAAGCCTCCCTGAATATGCTCATTTCCCGCCTGGACAGCCTTCCTCCGGGAAGAACCCTTCTGATTAACAACGCCGGTGCCGGTGATTACGGAGATTTCTCGGACGGCCGCTGGGAAAAAATTCGTTTTCTACTGCGCCTGAACGTGGAAAGCCTCACCCGCCTGTGCCATGCGCTGGTCCCCTCCATGAAGAGGAACGGAGGAGATATCATCAACCTCAGCTCCCTGGGAGCCCTCCTGCCCATTCCGGATTTTGCCGTTTATGCGGCCACCAAGGCTTATGTTTCCAGCCTGTCGGAAGCGCTGCGGCTGGAACTGCGCGAACACGGCATACGCGTGCTGGTCGTCTGCCCGGGCCCGGTTTCCACGGGTTTCGGAAAAGCGGCGCGCCGCCCCGGCTTTACCGGAAACATGATGCCGGGGCGCAACGCTTTTGACACGTCCGTTGAAACCGTCGTGAAAGACAGCCTCCGTGCCCTGGCCCGCGGACGCGCCCGGGTCTTTCCCGGCATGAAAATACGACTGGCCGCCATACTGCTGGCCGCCGCCCCTCTGGGGCTGATACGCCTTTTCATGGGCAGGCGCCCCCGGAAAGTCCTGCCGGCATCCAATGATTCTCCTTCTTCAGCATCATGAAAGCGCACCGCCGCAGAATACCCCGCCTCACCTGGTTCAAGCTCGCAGTCTGCATCTGCCTGGCAGCCATGGCAGCCTCCCTGTTCACGCCGTACCCCGCCCAGATCATACGCGCCCTGAAAGGGGCGCCGGAACAGGCACCGCCGGATTCCGGAACCTGTCCGCCCCAGAAATCCCCCCCCGCGGAACAACCCGGGCAGGAAGAACAGCCAATAACCGCTGCACCATCCGCCTCCGGAAAGGAACCCGGGGATGAACGGCTGTACACTCCATGGACGCCGGAGCGCACAATCGCCCTCCCCCCGGTGCAGTACCCCCCATTTCCCCCCGTGCTTCCCACAAGGCCTGCGGAAGGGGCCATGACCCATATTTATGAACTGGCCCGCGGACTGAACCTGAAAACGCAGGTTAATTTCCAGCCCGGCACCCTGGCCTCCAGGGACAGGGAAACCAAAAGCAATTACCAGATGACCCTTTCCCTGAACGTCAAACAGCCCAAAGCATTGACGAAAAAGGAAGATATGCTGAAGCTCAATCCGAAACTGGAACCCATGCTTCCCGGCCTGTCCACCCTGTTCAGGCATGCGCGCGTCTCCCCTTACTACGGGCAGATTTACGTCCGCAAACAGACGGAAATCCGTAAAAACCTGGCCTCCCTGCTCAAACTTCTGGACCGCCATAATTATTACGATACGGAGACGATTCTGGAAACCACCTATCCGGACACGGGAAGGAAGCTTCTCTGGCTGCAAAGTGAAATGGATGTTGTTTCCGACGGCTCGGACGGGGACCGGCTGGCCGCCATGCCGGATAAAATCCTGAAAAGCTCCTTCTATCAGCCCAGCACTTCCTACCGCTGGAAAAAGCGCACAGACAAGCCCAATCCCCTGCTCAAACCCTGGCAGCAGAGGCTGGCTTCCTACAAGAAGACGCTGGAAAAAGCCCCCGCCGCGGAAAAAACGGCTCTGCGCCGCAAAATAGACCATGCGGAACGGGTCATTGAGGAACTGAAGCGGTACAGCTTCCTGATCTCAGAATACGATCCGTTCATCGTGGTTCCGCTGGGAGTCGTCAACCAGTCCACTCCCTTCTCCCCGCAGTTCGGGGACTATGCCGTCGTCATCGTGGGAGACAAGTTGTACCCGGCCCTGGTGGGAGACGCCGGGCCGCGCTACAAAACCGGGGAGGGTTCCCTGCGCCTGAGCCGTGAAATCAACCCCAAGGCGGGCCCTTACAGCCGCCCCGTCTCCGACCTGAAGGTAAGTTACCTGATTTTCCCCGGTTCCGCAGAGCCCGAGGCCGGGCCTCTCGACTATGAAAAAATTACGGACAGATGCCGGGAACTTCTCAACGAAATAGGCGGCGTGGGGAAAGGATTCAACCTCCACCAGTGGGAAGACCTGCTGGCGCCCAAGCCACCGCCCGCCCCAAAACCGGAAAGCGGCGGCACAGCGGAAACACCTGCAGATAAGCGGAAAAAGGAGGAAGCTCCCGGAAACAAGCCTGCCCCTGCCCCGGCTCCGCCGGCTTCCCCGCAAACTGCTCCGGCTTCATCAGCCGCAAAAAGCGCCGCATAAGCTACAGAGCCTCTTTCCAAGAAAAAAGAAAGGCTTTCCACATCCGTAAATAAACGTCTCCGCGCGCAATCTTCACCTTCCCGGAAGACGGATGCCGCCGATCCGCCCGCTGGAGCAGTTCCAGCGTGTCCAGCCCCAGCCAGGCGGGCAGGACGGTGGAAAAACGCAGCCGCCCGTTCCGCAGAGATTCCGCGTAAAACACGCCTTCCTTCAGCCAGCGTTCCGCGCGGTCCAGCCATGGACGGTCATCTGGGGCAGGCAAATAGCTTCTTCCGCGGGCGAGGTCTTCCTCCCGGTCACGCAGGATATTCACCAACTGCAGCCCCATGCCATAGTGCCTGCCCCAGCGCAAAAGCTGCTCCTCCGGCTGCGTGCTGAAGCCCGGCCCCTGGGCCAGCAAGCCCAGCCTGGTCCAGAATTCCCCTACGCATCCGGCCACCATGTAAGTATAATGTTCCAGCTGTTCCGGGCATGAGACGGCAGCATGGCTTTCAAAATACATCAAATCCCACGCCTGTCCCTCCACGATGGAGGCCAGCACCTGCCGCACCAGCATCCGTTCATGCGGGGAAAGGTTTTCCAGCAGCGCCAGGCATTCGGGGAAACGGGCCAGCAATTCCGCCTCCCCCCGGTGCGTCTGGATTGTGGAAAGAACGCCCAGACGGCCAAAACAGGTGGCGGCAACCTCCCGGGAGGCAGAGCCGGCCACCGTCTCCCCCATCATCCTCAAAAGCGCCAGGCGCTCCCGGCAATCCATGTCCACGGCATCCGCCACCGTATCCGTGGCACGCGCCAGCAGATAGCCCACGGCCACGCCGGGGCGCATTTCCCGGGGCAGGAAGCGCATGCTCAGATAAAAGGACCGGGAGACGGCCGATAAAAGATGGTCAAGGCCTTTCATGATTTCTGCAGGCCCAGCCTGTGCATCTTATAATGGAGTACCCGCGGGGAAATGCCCAGTTCCCGGGCCGCGGCGGACATATTGCCCGGAGAACGTTTCAATGCGGCTACAATCAATTCCCGTTCAAAGGAATCTACCATGGTCGGCAAATCGGCGGGAGCCTCCCCGTCACGGGAATAAGGGACGTCATGGCTGGGCATCTGGAGGGAGGCGGGAAGGTTGTAACCGTAAATGCAGTCGTCCTGCGCCGTAATGACGGCACGTTCCATGCAGTTTTCCAGCTCCCGGACGTTGCCGGGCCAGTGGTAAGCCATGAGCATGTTGATGGCGGGGGTGGACAGCCGTTTGATATCCTTTCCGTATTTCAGGTTGAACTTGGAAAGAAAATGTTCCGCCAGCAGCATGACGTCCCCCTTGCGTTTGGAGAGGTCCGGCATAACGATGGGGAAAATGTTCAACCGGTAGTACAAGTCTTCCCGGAACTTGCTCGCCGCCATCAGTTCCTCCAGATTGCGGCTGGTGGCGGCGATGAAGCGCACGTCTGCATGAAGCTCCCTGTTGCTTCCCACGCGGGAAAAGGTCTTTTCCTGAAGAAAGCGCAGCAGCTTCACCTGCATCTGGAGGCTTAAATCCCCGATTTCATCCAGAAACAGGGTGCCGCCGTCCGCCGCCTCCGCCCGGCCGATGCGGCGGCTGGTGGCCCCGGTAAAAGAACCCTTTTCATGACCAAACAGCTCGCTTTCCAGCAGGTTCTCCGGCATAGCCGCACAATTCAGCGTGACCAGCGGCTTATCCTTTCTGCCGCTCAGATTCACCACGGCGCGCGCCACCAGTTCCTTCCCCGTCCCGGAACTTCCCCGTATCAGCACCGTGGCATCGCTGGGCGCCACCTGGCGGATCTGTTCATACACCTGGAGCATAGTGCTGCAATTGCCGATCAATTCGCCCGGATTAGTCGTCAAAAGCTCCCGCAGTTTTTCATTTTCCGCGACCAGGGCGGCACGTTCCTCATGCGCCTGGAGGCTGGCAGCCAGAGCATCCCCAACAATATTGCCTACAATCTCCAGCAGTTTCTGATCCCGTTCCAGGTCGGTGTCCCGGTCCACGGCACGGTCAATGCTCAACGTGCCGATGACCTGCTGCAAATGAATGATGGGAACGCAGATGAAGGCTACTTTTTCACCGCTTTTGCGCGTGCGGGTACGGTTCAGGAACCTGGAATCCCGGGAAATATCCTCAATCACATGGGGCCTGCCCGTTTCCGCCACATGCCCCGTAATGCCTTCCCCCACATGGTAATGGCCGCGTTTCATTTCCTGCTCGTCCAGGCCGTGGGAAGCTTCAATCATCAAATCGTTCCCGCGGCGGATGGTGAACGTGCCGCGCAGCATTTTCATGCGGCGGTAAAGCACGTCCAGAATATGCCGGAGCAAAGCCGTGCCGTTGCGTTCATTGACAATGGCGGAGCTGATTTCTTTCAGCACGACCATCTCCAAATCCGGTGACCTGTTTATTTCATTCATGCAATCCGGGGCTGGGCGAATATTAACCGCACGCCCCCGCCATACCAACTACTTTTTCTTCCACCACCATGCAGCCGGGCAAAAAAAAACGGCACGGGGAATATGGATAACATCCCAGCAATTAATGTTGATGATATTACGAACGGGGAACAAAAGATTTTTACAAAAATGTAATATACATCCCTTGTTTCGTTCCGTTTTACATGCCCACGTCTCCGCCGGACTCCCGTTTCATCATCTCCGTGATAACCTGGAACAACGCGTTGCGGTACAGCACGCCGTCTAGCCGACCGGTATTCTTCCAGACCACGGGAAAGGCGGGCAGATGGGTTTGTTCAAACAGCTTCAAGGCATCCGTCAGATGGGTGCCGGCGTCCAGCACGGGAGCCCCCGTACGAACCAAGGACCCGGCATCGGCCGGAACCGCCCCCTTCCGCTCCTCCATCTGCCGGCGGACCGCCAGAAAGTCCTCCGGATAGATGGCCCCTTCATAAATTCCATCCTTATTCACCACAAAAACCAGATCTCCGGGATTCCGCAGCATCAGGGAGGAGACATCATCCAGGGAAGCATCCGGGGCCACCTGGGGCACCACGTGCCGCGCCAGGTCATGCACATGGACTTGCGCCATGGGCAGGTTGACGACGGACGCCGGACTGCCGGCCAGGCTGGCGGCATACATGGAACGCGCGCGAATCAGTTTTCCCGTGCCATAAGCGCTTACGACACCGATCAGCAAGGGATAAATCATCTGTCCGCTCATGGAAAACTCCACCACCATCACCAGGGCCGTCAGGGGGGCCTGGGCAGCTACGGCGAAAAAGGACGCCATCCCCACCATCGCATATGCCACCGTATGGTTGCCCTCCATTCCCAGGGACGTCAGAACCGTACTGAAGATAAAACCCACCATGCTCCCCACGTACAGGGTGGGCGTCATGGCTCCGCCCACCGTACCGCAGCCGAACACCACCGCCACGGCAGACACCTTCACCAGAAGCAGGACGAGCGCCTCCCGCGTGCCGTAATCCTCATGGACCAGCCCGGTAATGAGAGCCTGACCGTTGCCCACCACTTCCGGATAAAAAACGGCCAGAATCCCCACGACCAGCCCGGCGGCCGCCAAACGCAGCGGCAGAAGGGACTGGCGGCCATTCAAATATCTGGCGGCCGCATCCAGCAAACTGACCCATCCCCTGGCCGCCACGGCCGCCGCCGCCCCCAGGGCGATGCACCAGAGAGACTGGGAAAAGACAGTAAAGCTTTCAAACGGGGAACTGTACAAAGGGGAGGGATCCCCCAGCAGATGCAGCATCACGAACCCGGTACAGGAGGCAATGAGCAGGGGGGCCAGAATATCCATGCTCACGGTACCGATGATGATTTCACTCACGAACAGGCAGCCGGAAAAAGGGGCATGAAACGCCGTCGTGAACCCGGCTGCCGCTCCGCAGCCGACCAGCAGACGGAGCCGCGGAATGGATACGTGCAGACGCTGCCCCACGGTAGACGCAATCAGGGCGGAAGACTGGATGATAGGCCCTTCCTTGCCGATGGCCGCGCCGGAAGCGATGCTCCATGCCGCGGAAAAAACTTTCAGGGCGGAGGCCCGGAACTTGATCAGCCCGTTCCCGATGGACATGGCCTCCATGTATTCCATAGCCTGCCCCGGCATCCTTTTCTTCAGGAACAGCAGAATGCAGCCGGCCACCAGGCCGCCCGCCACCGGAATAAACATGCGCCATTCCGGCTCCACCAGGGAAAAGGTTCCTACGCGGGAATCCGACCAGATGCCGGTGAGCATCCACTGGATCACTTCCACCATCCACTCGAAAAACAGAGACATGCAGGAGCCCACCATGCCGACGAGAACGGCCCATACATAAGTAACCTGCCTTTCCCCCACCTTCCAATGATGGAACAGTTTTAAAATCCATCCGTACCAGACGGAATCCGGCGGCTGGGACTGGGGGAGTGGAGAATTCATAGAAAAGAAGGCAATACTATTCCATTGTGACAAAAAAATACCTCCGGCTGTTCGATACGGAAGAAAAAACTGCCCAGCCCCATGCCCCTCCGGAGCCAGAGAAGATTTTCAACGGATACGCAGACGGCTCTTACGGCCCCCCCTTCGGGGCCTGGCCCCCGCGCCGGTAGTCAGAACGTATATTTCAAATTGACGCCGTAATAGAAATCATCCCCCAGCCCCTTTTGCTTTACCACGGTCAGCGCCTGGCTGTAATTGGTGAACAATTCCACAGTCAGGCGCCCGGTAGCACGGAAAGTAACGCTCAGATTATAATCCAGAGCATTGGAACCATGGTGCACCACCGGGCCTACGTACCCCCCATTGTAACCGTAGCGCACTACGGCATCCACCCGCACGCAATCAGCTATTTCTTCATGCATCCCCGCAAATACGGCATAATAACCGCGGAACGTATGGTTGTTATACTGCCAGTACAGGTCCGCGCCGCCAAACCAGCGGTCGGAAAAATGGTAGGTCGTTTTCAATCCGGGACGCGGGATTCCCTTATCTCCCGGAAAAACAAAGCTGTGCTCATACCAGGGCATGACCGTCCAGTTGCCCAGTTCCCTGACGTAGTCCAGCCGGTATTTCAGCTCCGCATGATTCCGGTGGTCCGTGGAAACGCCGTACCATATTTTTCCCACCCACTCTTTATACTGTATTTCAGGAACAAACATGCAGAGAGAACCGTCCCACAAGTCCAAGCCCTCGCACTCGTACTTGCTGGCCCACCCCGCCTGAAATCGCAAATAAACACGCTCATCCGCAGGACCGGCCAGGGAAAGGGGGTGGGGGCATGCCGTCTTCAGGTGGTAGGACTGTTCCTGGCAGACCGCCGTCGCCCAGGCTCCGGGACACGCCAGCATACCGGTGTAGGCCGCCAGCGACAACAGAATTACGGTATTCATTTTCATATTGGTCATGGCTGGAAATTTTTCTGAAACGGCATATCCGGAAGCGGCGCCATCCATGCGTACCCGCATCCCGCTTTAGCCGTAAACGGATACTACGCCCGCACCCGGACAAATACCTTCTAAAGATGAGACCAGCATGAAAAAACAATATTCAGTTCCAAGAGCCAAAAAAACGAATTCCCCATGAAAGATTGTGTCAGCAGTCTTTGTACTTATAACCGTGTCCATGAAATTCATTATCCCCGCACTCGCCCTGTCCGCCTGCCTTCCCGCAGCCTTCTCACAAGAACAAATCATCCCGAAACCGGCGGAAATCACATTGTTCACAGGAAGTCCGGCCCGTCTCACACCGGATTCCCTCATCATCACGGAAACACAGGACAAAGCTTTCCTGGATCAGGCAGGGCAATTGCAGCAGATGCTCAGCGCAGGGACGGGACTCCCCCTTCCCCTTAAACCGGCCGGGCAAGCGTCGAAAAAAGCCGCCTGCATTGTCATCAAAAAAGACCCAGCCCTGGCCGCCAGGGGAGAAGAAGCCTACTCCATCCAATCATCCCCAAGTGGAATCATCCTTTCCGCAGCCGATGCCAGGGGCATCTTTTACGCGGGGCAAAGCCTGGTCCAGATGATGCCCTCCGTCTTCCACGACCGGACGGGGGATAAATCCGCCGTCCGGTGGAATATTTCTGAAACTCCGTTCCGCATAACGGACTACCCGCGATTCTCCTGGCGGGCGCTGATGATTGATGAAGCACGCCACTTCTTTGGCGAGAAAACCATTAAACAGATCATCGACCAAATGGCTCTGCTGAAAATGAACATCCTGCACTGGCACCTGACGGACGACACAGGATGGCGCATTGAAATCAAGAAGTATCCGCGCCTGACCTCCATCGGCTCCAAACGCAGGGAATCAGAAATCGGCACATGGAACAGCGGCAAGTCAGACGGAACGCCGCATGAAGGCTTTTATACCCAGGAACAGATCAGGGATATCGTACAATACGCAGCCCGCCGAAACATCACCATCGTTCCGGAAATTGAAATGCCGGGCCATGCCAGCGCCGCCGCCGTAGCATACCCCTTCCTGAGCCTGAAAACTCCCGGGGAAGTGCCCACAACATTCATCGTCAATACGGCCTTCGATCCCACCTCGGAAAAAACTTATGCCTTCCTGTCCGATGTTTTGGATGAAGTCACGGCGATCTTCCCCGGCAGAATCATCCACATTGGCGGAGATGAAGTGCGCTATGACAAGCAATGGAAGGGGGTTCCGGAAATTGAGGAATTCATGAAAAAAAACGGCATGAAAAGTTATGCGGACGTCCAGATGCATTTCACCAACCGCATGTCCGGCATTATTGCCCAAAAAGGGCGCCGCATGATGGGATGGAATGAAATTTACGGACATGACGTCAATGGGGACGGAGGAGGAAAAGCCGGCGCCAAACTGGATACAAACGCCGTCATCCAGTTCTGGAAGGGCAACACCAGCCTGGCTAAAAACGCCATCCGGGACGGGCATGACGTTATCAATTCCCTCCACACCTCCACCTATTTGGATTACAGCTACGGCAGCATTCCCCTGCAAAAGGCATACGGGTTCGAACCCGTTTTCCCCGGGTTGGAGAAACAGTACCATTCCAGAGTCAAGGGACTGGGCGCCCAAGTATGGACGGAATGGATTTCCACACCGGAACGCCTGCACTACCAGGCATTCCCCCGTGCCTGCGCCTTTGCGGAAGTCGGCTGGACTCCCGCTGGTAAAAAGGATTTTCCGGATTTCAAAAAACGCTTGAAAGCGTATAGCGAGCGTATGGATCTGATGGGGATCAAGTTTGCCCGGAACGTCATCAGCCAAATAGACAAATCTGACTTTTTCAATACGCCCAGGATCGGCACATGGACGCCTGCCACCCTGACCCGGGAGGAACATTCGTTTGACGTTACCAAACTGGTCAAAGCGTCCGGCAAACACACCGTCACCCTGCTGTACGACAAAGGCGCCCACGCCATCGAAATCGAATCCGTAGCCCTGTATGAAAATTCCCGGGAAGTCTCCCGGGACGCCCATGCAGGCAGAAGCGGCGCCCATAAGGAAAATATCCAGTACATTCTGAATGCCCCGGCCCCTAGGCAAGGAGCAACCTATACGGTCAAAGCAAACTTCAAAGGGGCCGGAGGCCGGGATTCCCACGGGACAGTGTATTTTGAAACGCCATAAAAACACCGCCCCCGCCCCATACCAGAAACAGCAGGGGGGGCTTCACACAGAGCCCCCTTTACTCCAACAAACGGAGCACAGCATCCGCAGACTCATGGTTACGGACACGAGCCTTTCAGTATCATTGGCCTCCCAAGGGCGCCGGCATGTCTTTTCATAGAGGAAGACGCCCCTTTCCATCAGGTGGCACGAAATTGATACTTTAACCCACAGAAAGGCTTACCCTGAAATCAGCCTTGCCTTAACAAGGCTCCGGAGGTCGTTGCTTGCGAACCTCCGGAACCTGAACAACCGTGAATCGGCGGAAGATGATTTTCATCCGGGAAAGCCAGACGGAATTTACATGTTATCATCTACCACTTTTTCCACATACTGGAAAAAGATGCTTCCGTCCTCCCCGCTGATTCCATAGTCTCTGACATCCTCAATATTCGCATTCTTGATTCGGGTAATAATATAGACTTTGGAGTACAAAATCCCCCATTCCCCATACTTTTTCTTATCAAACCAGATATGGTGGTTATAGTATGGTTCAAAATAGATGGTGCCTCCGTCTATCGGATCGGAAATCGGGCCGGAATATTTGACAACAATATGGCTGATTCCCTTGTAACACCATTCCCTGCCGCATTGAGGATTTTTGGAAAGGGTGGTGCTCATTTCAATGGTGGTGTCCATGACATCATAGGACAACCTATCCTCATCATGGTATTTGATTCCTTTTTGAGGCAGATTGAACTGATTTTTGATGATGACTCCATCATAAATGAGACGTCCCCCTTTCCATTCCGTCTTCTGCGGCACTGTTTCCCCTTTCAAGAGACAGATTCCTGAGAGAGAAACCATTATTGCACAGGCAATTTTATTCATTGTTATTTTCTACTTTGTTGTTGTTTCAGCAATTCCTTATTGAGGAATTCTTTCATTTCCTGAACAGTTTTTTCAGGAGGGGTCTTCGCATCGTCCCGATACTGCCTCCAGTCGATGCGCAGCTTGGCCATCAGGCGCAATTCGCTGAGAGAAAAAGGAACAGTACCCGGGGCATTCGGCATGAAATCCCGGGACCTGTTGCCAATATAATAAATCTGGCTCTGCTCCCAAGAACGCTTCATGGTCTCATCGAAGGAAGCTCTCACACGGTCGTCGCTTACTCTGGCTTCCCACTGGAGGCAGTCCGCCAGACAACCACCCTCGGCCCGACTCTTCCGGGCCTGCCGCCACAGGGAAAGAATATACCTGTTCTGAAGACTGCACGCAGCCGGAGTAAGGTACTTGCGCGTATACTGCAACTCATACAGAGGCTTGCACACAGCCTTTTCCCTCTGTCCCAGAGTAGCTCCACTCACCCTTTCCGGAGTGGGGACAATACCGGCCAGAGCACCGGATACGTAATAAGGCAGTTCCTCAGGCATTCGAATTTCCCACGGTATTTTTTCATTCACAAACACGCCGTTTAAAATGGTATTAAAGGAACCGTTCCGGCAAATACGGAACAGGTAAATCCCCCCTTTCCTGCTCATGAACCTTTTATAAACCCCGGACCCCGCAAATCTGGAAACGCGTGTGCGCGCCTCCACCGGGTAGGAATACCACTGCTCCATGGATGCAGCAAGTTCCTTCCCCCAATTATTCTGTTCTCCTATCGCAAGATCCCCCTCAATATTGAACTGGAACACAAGGGGAGCGGTGGAAGGGTGCCTTCTGGCTTCCACCACGTAATCCCGCCTGGATTCATTGGTGAGATAACCGTTGGGATTGTAAAAATACAGGGCTATTTCATGGACACCTTCCGGCACCTCTACGGCTACCCATATATCGGGTCCGTCCACAAAGCCCGGATACACTTCTCCGTGGTCATCCCACTCCGCCTCTGTCCGCACGGAATCCGTAGGGTCGAAAAGAACGTTGCGGTTATCATTTTCCTTGATGCTGTGCACCCACCAGCGCAAAGCGTCCCCTTTCTTGCGGTTGAGCCCCATCAGCCCCTGAATCCAGTAGGGAGACAAGGCCCCCTTATATCCGGGATGTCCGGGAACGGAAGAAAGAGTACGGAACTTAATAGGCTTGGCTTTGAATTCGGAATTGCTCATGGGGGCATTCGTGGCGCACAGCGTGGCACGCGTGTGCCCGTACTTCCCGCACCAGTCCCCTTTTGTAGTCCAGTCCTCCCCCCGGAACACAATATTCTTCCCCGCATGCGCATCGGACCCCAGCCTTTCCAACCTTCTGAGCACCACAGAAGGCTCCTCCATCTGCGCCGGACGAGGGAACGGGACTTCATTGTCCATCTGTTCCCTGTCCAGTTTATAAGCCAGAGTCCCCTTTTTCAGACGAACAAAGCCCCGTCCGTATGTCGCCGCCCAAACACTCCCGTCCTGAAGCGCCAGCAAGGAAGGAACATCCACGTTTTCCAGCCCCTTCCTTATTCTCTGATTCACCGTCATTTTTTCAGCATCCAGAACATCCACTCCCTGCATCCGGTAGCCGATGAAAATTTCCTTTTCCTGCTGGATAATGGAAGAAATAAAATCTTCAGACAACATTCTGGAATTCGACTCCCCCGGCACCGCAGTCTTCCGTGCGGCAGAACCATACAGATGGTTGTTTTTCTGGGCATAATCACGCCCTCTCATATAACGCCAAGAACTAATGCCGTTACTGTACGCCAAGCCTGAGCAGGTACCTACCAACACCTGGCCCTTATCCGTACAAGTCAAAACATTGCAGATATTGGAAGGCAAACCATCACCATACGGCTGGTACGGCTGACGGGCGAATTGCCTGTTATCCCAGTACCAGGGAGCCTGAACATTTTTCCACTGCATATACTTTGACTGGCGCGAAGAGCAGGATACCCCGCCGCAAGCATAGGCCAGCCACAAATTCCCCCTGGTATCAAAACCTGCGGAAGCCGCCTGGTCTTCCACCAGGCCGGTGGACCTGTCCAGCGTCTTCCATGAATCGCCTGCAGGATCGTAAATGGATACGCCTCCGGATGTGGTGACGGCCACCTCCCCGGAAACGGGAGAAATGGCCAAAGCATAAACATGCTCTCCATTCAGGGCATTGCTGCGGTCATACATCTTCCATTCCCTTCCGTTAAAAACGGCTACGCCGCTGTCATCCGTGCCCGCCCAGATGCGCCCCTGCCTGTCCTCCGCGATACAGGTGAAATTCTTTCCTTTGGGGAAACCGGAATAATAATCCATATTAAGCCACGATTTTTCATATGTCCGGTCCCCAATCTGAAGGCGGTAAATGCTTTCTTTTTCTCCTACGGCCCAGATGGAGCCGTCGCCAGCTACCAGCGTGCCGCGAATAAAACGGACCGGACATTCCGGAAGTTGCTCCGGTATCTGGACGGTTACAACCTTTCCTGCCGCCCACGCCCCCAACAAAAGGAAAATCAACACAAATGTCCCTACGAAGTGTTTGAATTTAATCAACATAACCAACTATCTCCTGATTATTACAGGATAATATTATCGGTTTTTATCACTGTCAACACCATTTTCAATTTATTGTCAAGTTTATCCAAATAAAATACGGCATAATCCGTTTAGAATTATACACATAAACCATTTAATATATGTTAGTTATATTAAAAACATCAATTCCAAGAAAATCCTCACTAATACTTTTTTATGCTTGGATGGGAAAATACAGGGAACAGGCCTGATAGCAACGTCCGGCATTCCGTTTTTTCAACATGTTTTCCCCGGTTATTCTCTGATTCAAATTTCAAACCTACCATGCGTTTCCCCATATAAAGTTATATTTACCGGCCTTTTTTGAGGCAGGAGAATAAACACAGCTCAAGATCCGAAGGAGAAGCCATCTACTCTTCCGAAGCGGCAATAGGAGGCAATATGGACGTTCCCGGAGGAGGGCTTCCCGATGATGGCCAAAGAAAAAGAAAGTGTTGTCCTCACCAACACGAACAATTCTTCCGGACCTGATTCCAGCAACCAGTTCCGGCATAGTCACGCAGCACCCTTCCGACCATGAATGAGGTATTGCTTCATAAAGCGGGACTGACGGTCAACTGTATATTAAAGCCACCAAAGGAAAGGAGGGAGGAACCAGTCCGACAGGCGGAAAAGAAGAAAGCGAAGACAAAAAAGATGGAATAAAGCCCCCGGTTTTCTCCCTAGATGGCCTATCCGGGCGATGATAATAGCGGTCTGCAGGTTTCTGTTGAGCCCCAAGCCGGGGGTATGAAACACCGCCTCCCTAAAAGATACGGCCAAATTCACGACAGATAGAATTATGCCGTCCCGTACGTTCCTTCCTCATACCCAGGTGCCAATTATTACTTCTAGATAAAAGGGAAAAACGAACCTCCCTGACCTATCCAACCCCTCCGGATTATTTCTGGAGAGGAAGGTGTTCCGTTCCACAACAAAGAAAAGGAACATATCATTCTGCATCCGGCCCATAGCAGAACAGCCTTTCCGCCCCGGATTACCTCCCTGGGGCTGTTGAATACCCTGCTGAAATATCGAAACGTATCAAATAAACACTTATTAAAGCTCCGCAGCCGTCAGGTCCACGCACAAACCTTGACAACTGCGGAGCTGCGCTTACACACACTTATTCATCCAACAAAAATCGCTCAGCAGAGCTTCTTCCATCAGGAATGGTGGAAAGCCGTTCCTTCTTCCGCCGTCATCGGAGAAGAGACCGGGTGTTTCTCGAAGAAAGAGAGCATGCGCTTGTAGTCTTCCAGGAACAGGTCTGCCAAATCTTTGGAGAAGCCCTGGCGAACCATGACGCGCATCACCACCGTATCCTGACAGTTGGCAGGGAGCGTGAAGGCGGGCACCTGCCAACCGCGGGTTCTCAGCTTGTCCGAAAGGTCAAAGAGCGTGTAGTTCGCACGGGCTCCATCCTTGATGAAGAAGCAGACGGCGGGAATACCTTTTTCCGGATCGCCCGTACAAATGAATTCATACGGCCCCAGTTTGGAAAGCTCATCTGAAATGTACTTCGCTACTTCGTAGGCCTGTGTATGGATGTTCGTATAGCCTTCCTTGCCCAGTCGGATGAAATCATAATACTGGCAGATAATCTGTCCGGCCGGTCGTGAGAAGTTAATGGCAAAATTGGGAATTTCCCCGCCAAGATAGTTGACATTGAAGATCAACCCTTCAGGCAGTTCGGAAATATCTCTCCAAACGACCCAGCCCACTCCGAGCGGAGCCAATCCGTATTTATGGCCGGAAGCACTGATGGACTTGACGCGGGGAATGCGGAAGTCAAAGACAAGATCCGGGGCGCAGAACGGAGCAAGGAAACCGCCGCTGGCTCCATCTACATGGATGTCAACGCTGATGCCGGTCTTGTTTTCATAATCATCCAGGGCCTTGGCAATCTCCGCCGGCAATTCATAAGCACCCGTATAGGTCACTCCGAACGTGGGAACCACAACGATGGTGTTCTCATCAATCTGTTCCAGCATCCGGTCAACATCCATCTTCCATTTTCCGGGCACCATCGGGATTTCACGCATTTCAATATCCCAATAGCGGCAGAACTTGTGCCAGCAGATTTGGACAGGGCCGCATACCAGGTTGGGCTTGTCAATGGGTTTGCCGGCGGCTTTCCGGCGGGCTCTCCAGCGCCACAGGGCGGCCATGCCTCCAAGCATGCATGCTTCACTGGAACCGATAGTGGAACAACCAATAGGCTTTTCATTCTGGCTGGCATTCCAAAGATTGGCAATAATGGCAGCGCAACGCATCTCAATAGCGGCTGTCTGGGGATATTCATCCTTGTCAATCATGTTCTTGTTAATGCTGAGATTCATCAGCATCCTGACCTGCTTTTCATCCCAGGTCTGGCAGAAGGTTGCAAGATTCTGGCGGGCATTGCCGTCCAGCATCAATTCATCGGACACCAATTGGAAAGCGTCCTCCGCGCGCATTGCATAATCCGGGAACTCATCCTTGGGGAGAAGGTTATCTGCTTCCGGAGAACCGAAAATGGAATTTTCCGCCCGGGCTGTATCGGGTTTTTGATTTGGATCAAACATGACGGTATCTATTGTTTCATCGTTTCCGGGCCAACCGTGAAAGGCAACCCGCATGAAGGATAGAAGCACCCGAGAATAAAAGCGTTCATATTTCCATTGTCATTATCCTAGGTATAATTTTCCCGGAAAAACACAGAGTCGAGATTTATTTCATCATTCATTGATGACTAAAAATTATTTAATTGCAAATATCTTTATAAAAACTCGCCAATATCTCCAGATAACTTTATGTATGACATCTCCATATTATGACAGTTAAGAGAAACGGACGTTTTCGGATGCAATTCCCAGACGGGATAATGTGTTACCCCCTATCTTAACAATACAACATCCATTGGAAAAAACTGTGTCACGGACTTTTACGGTTCCTGATGCTGAGGCGTGCATGCGCATTTTCAGAAAGGGAAAAATACGGATTAACCCGAATTTCCATCAAAAAAACTGCTGCACAATAATAAAACCAACATCGTGGAATCTTCCCCGCTTTTCCTGATGAGCAACTTCTGGTGCAGACAGGGGGAAGAAATACCCAACCTATACCGGGTTAATGCCGTGGCAAGTTACACCCTTTTAAGCCACGGCTGGCAGCAGAGGTACCTTCTCCCCCTCCCTGCACTTTTTACCCGGTCAACAGGTGGCATTCCTTCAAGATTACTCCCGTTCAATTTTCCGGAAGGTATCCTTCACATAAAGTACATGCGGATAAAGAATGGAATCCGGAGCTACTTTGGGATGATTGAATTCCGCTATTTTTCGCATTCCAACACGTTGCATCACAGCTTGTGAAGGGAAATTCACGCAAGCAGTAAAAGAATATACCCTGTCAAAGCCGAATCGTGCAAAACCATGTTTCAGGCATGCACGGGCGCCTTCCGTAGCGTATCCGTACCCCCAGGCTTCCGGCACAAGCCTCCAGCCTATTTCCACACACGGTGTAAAGTCCGCTTCAAAATCAGCCCAATGAAAGCCGAGAAGTCCGATCAAGGAACCGGAATGTTTCAATTCCACTGCGTACAATCCATACCCTCTTTCATCCAGTTCTTTCCGTATACGGTTCCACATGGAAAGAGAATCCTTTTCCGTCATGACACCGGGAAAAAAACGCATCACCTGGGGATTGGCGTTCATAGCCGCAAAAAAAGGCCAGTCTTTTTCCCGCCACTCCCGTAACACCAGGCGTTCAGATTCCGGCAATGACAGTTTTCCTCCCGGCATGGCCTTAGTTGTACATGGAAAGCTCTGTTTCCAGCTCCGGGCAGCGATTCACGCGAAACCGTTCATCCAGTTCCAGAATTTGGCTATGGCCCAGGCTGTCCTTGATGGTGAGATTGACTTTGGAACGACCGGGATACCTTTTTAGAATATCCCGTATCTGTTCCAGATTTTTTCTCGTGTGGCGGAGGGGACTGACAATGATATTCAGAGGCCCCTGATCCCCGGATTTGGATTTGCGCTTTTTGCCTCCAAGTGGGTCAATGCTCTGGGCAGCCACGCTTTTGGAGGATGTACGGTCATCCTCCCGGATGTTGGCTCGCACGGAGACGAAAACGCCCGGCTCCAAGCCTCCCTCCATTTCCTGAGCCTTTTCATAAACATCCGACCACAACAACACTTCTATGCTGCCGGTAAAATCTTCCAACGTCAGAATGGCGAATTTTCTACCGGCCTTGCTGATTTTGGGGGTGACGGAACGAACCATGCCGGCAATCTGGTGCTTCTGCTTGAGTTCGTGTGGTTCCAGCGTGTCAATGAGTCCTATGCGGGTATATTTTTCCGCATCAATGACACCGCGCATGGAGTCCAGGGGATGGCCGCAAAAATAAGCGCCCAACAGATCCTTTTCATCTCCCATACGCTGCTCCTTGGACCACTCTGGAACGGCAGGTTCATCCTTCACCCTGGGCGCTTCAAAGGTCATGTCAAACAGAGACCCCTGCCCCAGAGCCTTATCCTTATGTACCTGGGAAGCTCCGCTGAGGGCCAGGTCCACACGTTCGAACAGAACACAACGCGGCTCCAGCGTCCAGTCCAGCGCTCCCGCACGAATCAGGGATTCCAGAATCTTTTTGTTGACAGATTTGGAATCCAGCCTGTTACAAATGTCCTCCATACTGGAAAATTCCCCATTCTTCTCTCTTTCTTCCAGAAGAATCTGCATAGCCCCCTCTCCCACGTTCTTAATGGCCGCCAGCCCATAACGCACGGCCAGCTTGCCGGAGGGCATTTTCTCAGGAGTGAACTTGAGCATGGATTTATTCACATCGGGAGGCAGGATCTCAATACCCATGCGATTGGCTTCCTGAATGAACACTCCGATTTTGTCATTGTTCGCCTCATTAGACATCAGCCCGCAGAGGAATTCCACAGGGAAGTGGGCCTTGAGATAGGCCGTCCAGTAGGAAATATGCCCGTAGCAAGCGGAGTGGGATTTATTAAAACCATACCCGGCGAATTTTTCAATCTTGTCAAAGATAGCTGTAGCCGTTTTTTCATCAATCTGGTTTGTTTTCCAGCATCCTTCCACAAAGTGGGCCTTTTCCTTGGCCATCTTCGCCGGGTCTTTCTTGCCCATGGCGCGCCGCAGAAGGTCGGCCCCGCCAAGCGTATAGCCGGCCAGCAGCTTGGCCGCCGCCTGCACCTGTTCCTGGTAGATCATCACGCCGTAGGTTTCCCCACTGACCTGTTCCAGCAAAGGATGCTCATACTCCACCTGGCGCAGTCCCTTCTTCACTTCAATCATTTCATCCATAAACTGCATGGCTCCCGGTCGGTAAAGAGCCAGCAAGTCAATGATGTCTTCAATTTTCTGAATCCCGTAACGGCGGCAAGTGTCCACCATACCGCCGGATTCCAATTGGAAAACCCCCATCGTATCCCCCCTGTTCAGCAAGTCCAGGGTTTCCCGGTCATCCAGAGGGACGGAATCCAGTTTGAACTCCGGCACGCGCCAGCGGGCGTATTGCTCCGCATCATGCATGACGGTCAGGGTTTTCAAACCCAGGAAGTCCATCTTCAAAAGCCCCACTTCATAAATCGCGCTCATGTCGCACTGGGCGACGACAGCGGCATGAGGATCGGACAGATCGTCACGGGTAAGGGCCACATGCTCGTCCAGGGGACGGTCACCGATCACAACACCTGCCGCATGTACCCCCACGTTGCGGATCAAGCCTTCCAGGCGGGTCGCGTACCCCCACAATTCCGCATACCCCTCATCAGAGGCAATCAACTCCCTCAGCTCCTCGTTACTCTTGTAGCTGGACTGAAGAGTCACCTTGGGGCCGGTTTCGATAAGTTTGGAAATACGGTCCCCGTCCGCATAGGACATATCCATCACACGGGCTACATCCCTGAGTACGGACTTGGCCCCCATGGTGCCGTACGTAATAATGTGGGAAACAGCGCGCTCTCCGTATTTCTGCCGAACATAATCAATTACTTCCGGCCGGCGTGTCTGGCAAAAGTCAATATCAATATCCGGAGGGCTGACGCGTTCCGGATTTAGGAAACGTTCAAAGATTAATCCGAATCTCAGAGGATCTATGTTCGTGATTTTCATGGCATAAGCCACCAAGGAACCGGCTGCAGAACCGCGACCGGGACCTACGGGAATGTCATGATCCTTGGCCCAGTTGATGAAATCCGCCGTAATCAGGAAATAGGAGGGAAATTTCAACTGGTTGATAATTCCAAGTTCATAATCCAGACGATCGCGCAATTCCTTATCTGCAGCTACACGTTCCTTTCCGTACCGTTCTTCCAGCCCCTTATAGCATACTTTACGCAGGTATTCTTCGCGCGACGACCCGTCCGGCGTGCCGAATTCGGGGTATTTCTCTGTACTGGTGGAATCCAGTTTGATGGCAACATTGCACCGTTCCGCAATTTTCAACGTATTTTCCACGGCCTCCGGATAATCCTTGAACACCTCCCGCATTTCCTCTGGAGATTTCAAATACACTTCCGTGGAATAGCGCATGCGTTTTGGGGAAGCCAGTTTTTCGTTGGTTCCGATGCAAATCAGAACATCGTGCATGTCGTGATCTTCCTTCCTGAGGAAGTGCACGTCATTGGTGGCAACAATGGGGGTATTGGTCTTGCGGGCGATACGCACCAGCTCCGGCAGGCATTTTTTCTGTTCCTCCATGCCATGGTCCTGAAGCTCTACAAAAATATTGTCGTCCCCAAAAATATCCTTTAGCGCCAGAAGGGCTTCCTCTGCTTTTTCAGGCTGGTCATTGAGCAGCCACTCATTAAGCGGTCCGGCAATGCATGCTGTACAACAGATGAGGCCCTCATGAAATTCCCTCAGGGTTTCCATATCCACGCGGGGTTTATAATAAAAGCCTTCCAAGTGGGAACGGGACACCAGTTTAACCAGATTCTGCCACCCGGTTTCATTTTCCGCCAGCAAAAGGAGATGTGTGTATTTGCGGCGGCCGGGAAGCTGTTTTTTCACGCTGATGGGCGTCGGGGAAAGGTAAACCTCGCAACCAAAGATAGGCTTTACCTCCTGCTTCTGCGGTCCTTCCGGATGCTCCTTGTTCCAGGCGGCCACGGATTTATTGAGGCTGTTTGTCTCCATGACCAGTTCAATGGCTCCGAACAGATTGCCGTGATCCGTAACAGCAACGGCGGGCATCCCCATGCGGGAACACTCAGCAATGAGGTCCTTGATATGGACTGCGCCGTCCAACAACGAATATTCCGTGTGATTGTGAAGGTGTACGAAGGAACCGGGCATGGATGGATCCTGTAAGCGGTTAATAAAGGAAATCAGCGGTGAACAGGGCATTCGGCCTTCATGGCTTCCACGCATTCCCGGATAAGGTCCGGGCCGCGATAAACGAAGCCGGTATAAAGCTGCACCAGGCTGGCTCCAGCCTTGATTTTGGCGACGGCGTCCACCCCGTTCATAATGCCTCCAACGCCAATGATCGGAATACGTCCTTTCAATTCGGAAGCAAAAGCTCCAATAACTTCCGTGGACCGTTCCGTCAGAGGAGCGCCTGACAGGCCGCCTGCTTCTTCATGCCGAGGATTGGCCTCTACCCCGACGCGGGAAAGAGTCGTATTCGTAGCAATAAGACCATCCAGTCCCTCATCCAGAAAAACGCGGCTGAGCTCCGCAATATGCTCATCTGTCACATCAGGAGCCACTTTCATAAAAATGGGCACATGGCGCCCCGTTTCCGCAGCCAAGTTGGATTGCTCCGATTTCAGGGAAGCCAGCAGACGGGCAGCGGGTTCCGCCGCCTGAAGGTCGCGCAGGCCTGGCGTATTGGGAGAAGAGAAATTAATGGCTATATAATCCGCCACAGGCCATGCCGCACGCAGACAAGTAAGGTAATCCTGGGCGGCATCCTCATTGGGAGTTATTTTATTTTTCCCGATATTGACCCCCAGAACACCGTGAAAACGAGTGGCGGAACGAATGTTTTCCACTCCGGCGGCAATACCTTCATTGTTAAATCCCATCCGGTTGATAATAGCTTTCTGGGGAATCAGCCGGAACAGGCGGGGCTTTTCATTGCCCGGTTGCGGCCGGGGCGTCAGCGTACCCACTTCCACAAAACCGAATCCGGCCTGGCCAAAAGCGCTTACCGTATCCGCTTCCTTGTCCATTCCGGCGGCAAGTCCCACCCGGTTGGGGAATTTCATCCCCAGGATTTCCACAGGATCAGAAGGAACTTCCCCCATTACTAGAGGTAGTACACGCATCTTTTCTGCCAGGCGCAACCCCCACAAGGTCACCTTGTGGGCAGTTTCCGGATTCATTTGGAATAATACACTTTTAGCGGCGGAATATAAGGCGGGTGACACAATGCGGGGAGTGTATCACACCCTCTTTTAGGGTCAAGAATGGGAAGCGTCTTGCCGGGATGATTTTATCCGGTACAATTCCTTGCCATGAACAGGAAGGAATGCCTCTCATCCTGCCGTCTGTACGGTATTGTAGATATGGGATATACAGCTGAACATCAACTACTCCCTGTAACAGAAAAACTATTGGCAGGCGGCCTGCGCATTCTCCAGCTAAGGGCAAAAAACCATAACCCGGAACACATTGAAAATATGGGGCTCCAGCTTGCCCCCCTGTGCCGTAAATACGGCTGTCTTTTCATCATCAACGATTATCCTGAAATTGCCCTGAACATCGGCGCAGACGGCGTCCATCTGGGACAGGATGACGGAGACTTGGCATCCGTAAGGGGGCTGCTGGGAAAGGATGCCGTTATAGGACGGTCCACGCATAGTCCTGAACAGGCCTTGGGTGCATGTGGAGAGCAGGCGGACTATATCGGGTTTGGGCCCTTGTTCCCCACGGGTACCAAACCGGGAAGACAGGCCATTGGCCTGGAAGACATCGCAAGCGTACAGCAACAGCTACCGGAAAATTTCCCTGTCTTCTGTATCGGCGGCATCAATGGGAACACACTGCCCTCCGTACTGGAGGCGGGGGCAAACAGGGTAGTCATCGTTTCCTGGCTGCTTACACACCCCGACATCACGGGAACAGTCAGAACTCTCAGAAAAGAACTGGGAGAAGCGTAACGAAAAACGCACACCCCCAGCCATGTTATCCCTGCCTTTCCTCTTCCTCCGGAGACAGAGCCGGACGGGAGATCAGAAAAACGGCGGTATTCACAATCATCTGCAAAATGTGCTCCTGATCCTTCATCCACGGTTCCTTTTCTTGAAGATCCATGCACGCGACATATTTCCCCAACGTAGCATGGAAGATGGAACTCATGATATGCGCATGCCAAGATAAGGAAAGCTCATCATCCCACGGACAGAATCTCCGAATTCTTGCTATCATCTCCTGGATAGATGGAGCATAAAATTCATCATAGATTTTTCTTCCCAGATATTCCGGCTCCTGCGGCAAACGCCACAATAAACGGACATGATCCGGCCCTCCCAGGCTCTCATCCAGAAATAGCCGGAAAGCAGGTTCTATAAATGCACGGGCATACGCCCTCAGGGACGGATGAAGATCCGCCTCTTTCAGCATCTTCATGCGCGCTTTCATCAGAGGTTCCGCATATTGTCTGTAAACAGCCAGATACAGTCCCTCCTTATTCTCAAAATAGTAAACGATAGAAGCCAGATTAACTCCTGCTTCAGAAGCGACCGCCCTCATGGTCACGCCATTAAATCCCCTGAGGGCAAAAAGCTTCTGTGCAGCACGGAGGATTTTGCCGCTGGTTTCCGCAGAACGGTCGGACTGGGATTTGCGGGTAGAAGACATCTCCTCAACAATCAGACAATGCGCCGCCTAGCGGGAAGAACGGCTTGAGCCGATGGCTTTTACCAACTGTTCCATGTTTTGGGAGGACTGAATAGGCTTGCCGTCCTTGTCCACCAGAATCATGGTAGGGATACCGCGTGGAGCCACCTTCTTGGCAAGCTCCGTCAAATCCTCTTTCAACAGGATAGGCCATGGCATGTTATTGGCAGCAGCCCATTTTTGGGCGGCATCCAGGTTCTGGTCCAGATTGCACATGATTACTTCCACATCCGGATTATCCTTTACCACCCTGTTATAGGCCTCCACGCTGTGAGGCGCATTCCGGCAGCAAGGACCGCACCAGCTTGCACTGAAATACAAAATGAAATGCTTGGGATTCAATTTCCCGTCCCTCTTTTTAAGTTTGCCGTTTTCAAGCATGTAAGTCTTTCCCTTCATCTCCTCCGCCACAGGGCCGGACGGCATGGACGACCATTCTGCAATAGGCTTGCCGATAACTTCCTTGTGTTCCTCCAGCCATGCGCGATCTTCATCGGAAAGCTTTTCCAGCGGCATCGTAATGGGCTTTTTAGCCGTCGTCACCAATTTGACGGTCTTCATATTCAGGGCTACTGGACGGGCTTCCACCGTACTGCCCGTATTTTTATTAGTCCAGGTGCGAAGGGAAGGATCATCCGCTACTCCGGAAGAATCCGCCCAACAGAGAGAGGACCCCATCATGAATGTGGATGCCAATAAGATACACCAAGATGAAAATTTCATACACATAATGCTAATATACTATTTCTTATCTTCCATGTCGAGAAGCTGATTCCAAAATTTGCCTGGCCCGGGCATGTCCCGGGTCGCGGCGCAGGGCTTCCGCGGCAGCGGCAGCGGCCTCACGGTACCGCCCATTGCGGTAAAGCATCACCGCAAGAGTGAAAGGAATATCCGCATTTCCCTTTTCCAGGGAAGAACAGTTTTCCAAATCCTGCAAAGCCTGCTCCAGCCGGCCCACTTTTTCATTCAACAGAGCACGGTTAAACAAAGCACGAATAAAGGAAGGCTCTATTTTTAAAGCTTCGTTAAAGGATTCCAAAGCTCCCGACTCATCATTATTTTCAATCTGGCCCAGTCCCAGGAGATACCACAGATTGGCATCCCTGGGAGCCAGGCGTACAGCCTCTTGCATCTGATCCACCGCCTCCCGGCTGCGGCCTTGAGAAGCCAGGAAAACGGCATAATCACGGTGCACCACGGATGAAGTGGCATCCCATTTCAAAGCTTTGGAAAACCACAATTCCGCAGTTTTCACATCCTTACGGCTAATGGCTATCTGGGCCAGCTTCATAGCTCCGGAAGGCTGGTCCGCCTGGTGCAAAGCGGCAGCAGTAAGCTCCTTCATTCCCGGACTGTCCGGAGGCAGGGAATCCCGGAGAGCCCATTCCGCCTGCAAGCGCACGACCCTGAACGGATCATTAAGAAGTTTGCCCGCAGCAGGATTACCCACTCGCCCCATCACTTTGGCGGCGGCGGCTCGCGTCAGAGGATCCGGGTCCTTAACCGCCGCCGCGGCCACTTCCTGGACGGCGGGTTCATGAGCCCATGCATCCATCAAATCCAATAACGTAGCGCGCCATGCACCCACTTCCTCCCTCTTAAAACATTCCATAAGCAGAGGCAAAACATCTTCCTTTCCTTCGTAGGCATGCTGAACGGCCCTGGTCCGTTCCCTGTACTGCGCCATTTTGGGTTTAGCTCCGTAATATTTCTTTACCGCCTCCGCAGCCCATTCATTGCTTTTTTCCTCGTGGCACATGGTACAGGCATTCGGAATCCCCAACTCTACGCTCAGCAAAGGATCAGGAGAATTGAAAGAATGATCCCTGCGGGGGTCCCGAGCCATATACAGGCTTTCCGGCATATGGCATTCCACACACCGGGCACCCATGCTGGACTGAGGACATGGCGTATGCGTAGCCATGTCAATAACGGGAGCTTTTACGCCGTTTACCGCTTCTCCAGTACCGTGACACCTCAAGCAGAGCGTATTATCTTCTTGAGGAAGCTTCAGTGTTCCCGTATGCGGATCATGACAGTCCAAACAGGTCACTCCCGCCTTGCCCATACGGCTTAACCTCAGACCAGTCTCGCAATAATCCTCATCCCTTTGCATGCCATTGGGCCAGAAAACGCCGGGCTGCACGGGAAGCACCAGTTGGAAATGATTGTCAAAACGGTCACCTACGGCAAAATCATGATCAAATTCATCCCTCCGGGCATGGCAGGAAGCGCAGTTATCATGAATCTGCTGTGGCGTCAGCTTGTTCCTGGTGGAAATCATGCAACCCGTCCCCGCCTCCGGCTTGTCCAGCAGGGGGCCGTGGCATTGAATGCAAGTCACTCCGGGTTCCGTCCAGGTAGAAGCATACCGATCTTTAACAGGATCATAATTCTTATGGAACAAAGACATGTGGCACCACGCGCACTGCGTGTTCCAATTCATGCCTCGTCCTGTCCAGTGCCCCCAGTCTCCCGGCTGGCGGGACTCCTTGCCGAAAATATCAAACCATTCTTTGCGGTTCACGTCCCATGCAGCGCTCAAGGTATGAAAACCGCCGTCCTTGGCAGGAACCAGATACTGCACCAGGGGAATTCTCCCTGTGGCCCATTCCGCGCGCCAGGAGGTTGAAGACTGACCGTCATGGACCATGCGCCCGTGACCATTCGTAGAGAATTGGAGAATGCTGCCATGAGCGTCCAGCTTCATGTTATGGAACGCTTCAGATTCCCATTGATCTCCCAGTTTGCGGAATGCCCAGGCATGGTCTGAACCGGCCCACTGACGGAATTCCTTTTCATGACATGCTCCGCACTGCATGGAAATAGGCACCTCATTCTGGTCCCAGTGCTCACTGCCTCCTGAAAAAACAACCGGCTGCGCTGCCGCTCCAGAACGTCTGTCCATCCAGGCCTTCACCATTGTGCCCAGTCCGATCAAAGCCCCAAGAACAACGAGGAGAACCACCCATTTCTTCATGGAAGGGCGGTTCACAGGGGCATCAGCAGATTCAGGGGCGGACATGGCGGAAATATTCTGCCATATCATACGCGCTCCACCAAGGAACTTTTACATCAGGCAGCACAGTCCTTGACATGCCCAGTCCGTAAACGGCAGATTATTCCTCCTCTTCCAGGACCTCCTCCCGCATCAGGGGAGTAATCCGGGTCACGGTACAGCGCTCCACACCGCCATTCTCCGTCCGGGTCACCTTGAATTCCAGCCCTCTATAGCCGACAGTATCTCCTTCCTCCGCAGGAGCTTCCAGCAAATTAAGAATCAGGCCCCCTACGGTGTCTATTTCCCCGTCCTCCAAATCCATGTCCATGGCCTCCCCCAATTCCTCAATGCGTGCCAGACCGGATACGTCACAGGAGAAGCCCACCGCATCCCTAGACAACTCCGTTAGCACTTTAATGCGGCCTCGGTCCCAGCCCATTACTTCGGAAAAAACATCCGTCAGGGTCAAAAGTCCGGAAGTCCCCCCGTGCTCATCCAGAATAATGGCAAGGCGCACATTGTCCTTCCTCATGATTTCCACCACATTGTCAAACTTCACCGTTTTGGGAACCTTGGGTATGGCATGAATGTATTCAGGAGTCAGAGCCTTTCTGCGGAACATGATACGGAACAGATCGCGGGCATGCACCATGCCCACCACATGGTCCAGATCACCGCGGTAGACAGGGTAACGCGTACGGCGAGTGCGACGCACAATTTCCCGTATCTGCTCATGTTCCGCCCCTTCTGGAATGGCGTCTATCCTGACGCGCGGGGTCATGACCTGGTGTACATACAGATCATCCAGGTCAAACAAGCCGTCCATGATGCGGCCGGATTCCTGCGGCAACGCCCCGTTCTCATGGCTCTCTTCAATAATATACTGGAGTTCTTCCGCAGAATGGTAGTGGGACTTGGATTCATTACGGTCCACACCCAGCAACCCCAGAAAGAAATTGCCCAGGGCATTCATACCCACTACCAGAGGATACAGGCAGAGCTGGATCACGTACATGGGCATCGCAATGATGAGGCAAAGCTTTGCGGCGTACTGTAGGGACAGCGCCTTGGGCACCATTTCCCCAAGAACAATATGCAGGTACGTCAGCACCGCCACAGACAGGATGCTGGCTGCGCCATGAGCCACCAGCCAGGAAGCCCAGCCGTACTGCGCCAACCATTCATGAAGCCATCCGGCTACCGCATGCTCTCCGTACATACCCAACCCCAGACTGGCGAACGTGACGCCAAGCTGCGCCGTAGCAATGTAGCGGTCCTGTAACCGGGGCGTTTTCAATATCTGGGAAACGCGGCGCGCCACCATGTTGCCGGACACACCCATCTGCTCCAATGCCGCACGCGGAGCGCCAAGCAACGCGAATTCTGCAGCCACAAACAGGCCGTTCAGCACAATCAGCAACAGAATAATGAAAACCGTCATCATGCATCCACCCCGCTTTCCATGGCAAATTCATTTTCCTTTCTCTCCGGAGGCAGCAGCAGGATAGAGGAAACACGGTTATGGTCCATCTTTTCCACCACAAGCACGCGTCCGGCAATAATCAGGCGGTCCCCATCCTTCGGAATGCAGGACATGTGCTTGATCACGCAGCCGCCCACAGTAGTGGCGCCATTCATCAAAAAGGGTACCAGGCATTCCGCCTTATGGGCACGCATCATGCCGGGGATGCGCACGGAACCATCCGGAAGGGTTTCCACCTTTTCCCCGGAACGGATGAATTCCTCATCTATATCTCCCACCATTTCTCCCAGCAGACGTTCCAGCGTCACCAGCCCCACAAACTTACCGTATTCATCCAGAACAAAGGCTTCCTTGGCCCTGTCTTCCCGCAAACGGGCCATCAGCATCTCTACCGTCTGCATTTCCATCACGCATGGCACCTGCCTCAGCATGGAACGGAGAGGAGGCAGAATTCCCTTTTCCGCATAAGCGGATACCATATCTTTAATATGCAGCATGCCAATGACAGATTCACGGTTCCCTTCATACACGGGAATATGGGTATGAGGCCTATCTGCGATCATATTCAAAATATCTTCCTGTGTGGCATCCACATCCAGGCAAACAAGCTGAAAACGGGGAATCATAATTTGTTCCACCGTTCGTTCCGCCAGGGACAAAGCGCTGTGCAGCCGTTCATGCTCATCTTCCTCCAACATCCCGCCCTGGTGGCTTTCATCCAGCAGCATATTAATCTCCTGCTGGGAGTGGATGTGCTGATGGCCGCCAGGTGGGAGCCGGAACAGTTTAAGAAGCAACAAACCGCTTCCATTAAGAAAGTCAATGAACCATGCGAAGAAAGCCAGTGTCCATCGCATGGGATAATAAGTATACAAGGCCGCCTGCTTGGGAAACTGAAGAGCCAGGGATTTGGGCATCAATTCTCCAAGAATTACCTGAAAAACCGTCAAGCCTATCAGTACAACAAGAGCGGCTGCAGAATTGGCCATTACGGAGTCCATCCCACCAAACCGCTCAAACAGAGGGAACAGATAGGCGGCCAGTTCAACCTGTCCGTATGCGCCCAGAACCAGGCTGGAAATGGTAATGCCGAACTGGCAAGCCGCAATGTAGCGGTCCAGCTCTTTTGTATTTTCCAAAATCTTAAGCAGATGAACAGCCACCCTGCTGCCGTTCTCCGCCATTTCCCGGATCATGTTGCGGCGTACGCTCACTGCGGCGAATTCTGCTGCAACATAAAAGGCGTTGACCAGAATCAGAAAAAGAATAACGAAAGTGACAAAAAGAATCATGTATGGCAGGGGTGTCCGTAAAACGCAACGGGCATCCGCATCATAGGAACCGAAAAAATCCTGTCAAGAACGGGAAACAAAAAAATCTGCCATGCGGAAAGAGCTCCATTCCTCCCGTCCCTTTCCAGCATTCCAAACTTTCTCCTTCCGGCACAGGTAAAAAAATTCCGGCGATCACGCAAGCAGTTGAAGCAACTGCAAAACATCTTTCCAGCAACCGGAAAGCCCCGTTCCCGGAATAACCGGAAACGGGGCTGAGAGTTTAAAGGCTCAACAAAAAACGTTATTCCGTAATGCCGGCGGCTTCTGCTTCGGCAGCCTTGGCATCTCGCAAAGCGGCACGTATGGACATTTTCACGCGTCCCTTGTCGTCAATACCAATGCACTTGGCGGTCACCACGTCTCCGACGCTCACGACGTCCTCCGTCTTGGCGGTGCGGCCTTCGGCCAGTTCGGAGATATGAATCAGGCCATCCTTGCCGGGCAGCACTTCCATAAACGCTCCAAAGGTGGTCGTGGAAACAATCTTACCGGTGTACAGTTCCCCGATTTCAATGGTCTTGAACATGCGGGTGACCAATTCCAGAGCACGGTCCAGGCCTTCCTGCTTGGCGGCGTAAATATGCACGGTGCCATCTTCCTCGATATTGATATCGGCGCCGGATTCGGCCTGAATAGCCTTAATGTTCTTGCCGCCGGGGCCGATCAGTTCGCCAATGCGGTCGGCGGGAATTTTGGTGGTCTCAATACGGGGAGCATTCGGGCTCATCTGGGCCGGAGCTGCAATGGCTTCATTCATGACCTTCAGAACATCCGTGCGGCCGGCTTTCGCCACGTGAATGGCTTCCTCCAATATGGAGAGGGGAATGCCGGGCAGCTTGAGGTCCAGCTGGTAGCCTGTCACGCCTTCCGACGTGCCGCAGAGCTTGAAGTCCATATCACCGTAAAAGTCTTCGGAACCGATGATATCCAGCAGAGTTTTGTAAGAGGTGATTTCATGCTGGTCATTCTGTTCCGTCACCAGGCCCACGGAAATACCCGCTACAGGGCGCTTGAGGGGCACGCCGGCCGCCAGCAGAGACATCGTGCCCGCACAGACGGAAGCCATGGAGGTGGAACCGTTGGATTCCATGATTTCAGAAGAGACGCGAATGGCGTACGGGAATTCCTGTTCGCCAGGCACGACCGGAGCGATGGAACGTTCCGCCAAAGCGCCGTGGCCGATTTCCCGGCGGTTCTGACCGCCGAAACGGCCGGTGTCTCCCACGGAGAAAGGCGGGAAATTGTAGTGCAGGATGAAACGCTTTTCATTCACGCTGCCCGTGTAATTGTCCATGTACTGGCGTTCTTCCATCGGAGCGAGCGTCGCCAGGCAAAGGGACATCGTTTCACCGCGGGCGAACATCGCGGAACCGTGCACCACAGGAGGCAGCACATTGACTTCCGCAGTCAGAGGGCGCAGCTGCTTGAGAGCCCGGCCATCGGCCCGCTTGTCCTTTTCCATGATGGAGATGCGGAAAGCCTTTTTCTGAATATATTCAAAAACCTGTTCCACATCAAAGTCAGTAGCTTCCGGATGGCGTTCCTTGATGGCGGCCTCCACTTCGTCACGCAGGGCCCCCACTTTTTTCTGACGCTCCACCTTGGAAGGGGCGTAAATGGCTTCCTCAATACGATCGCCGGCAATTTCATAGCCGATTTCCAGCAATTCCGGCTTGGCAAGGCAAAGTTCATAAGCGCGCTTCTCCTTACCGCAAACGGCGCGGAGCTCTTCCTGCTTTTCGCAAATGACCTTCACGTTCTCCTGGGCGACACGCAAAGCGGCGATAAAATCTTCTTCCGGCAGTTCATTAGCGGAACCTTCAATCATGATGACCTGATCCTTCGTGCCGGCAAATACCAGATCCAGCTGACTATGTTCACGCTGGGAGTTGGTCGGATTGATAACAAACTGTCCGTCAATACGGCCCACACGCACAGCCCCTACGGGTTCGGCAAAGGGAAGATCGGAAACGACACAGGCGGCGGAAGCGCCGTTAATGCTCAGGATGTCCGGTTCGTTTTCACCATCGGCGGAGAGCAGAAGCGTAATTACCTGCGTATCGTAGAAGTAGCCCTTGGGGAACATCGGGCGCAGGGGGCGGTCCGTCATGCGGCATGTCAGGATTTCCTTTTCCGTGGGACGCCCTTCGCGTTTGAAATAGCCGCCGGGGAACATACCCGCCGCAGCGGCCTTTTCCTTGTACTCCACGGACAGGGGGAAGAAGGTTTGCCCTTCCTTGACTTTGGTGGCGCTTACCACGGTCACAAGAACGACGGTATCGCCGCTGCGGACAACAACAGCACCGTCCGCCAAACGGGCCATCTTGCCGGTTTCAATCGTGATAGGATTCGTACCAACGTTGCATTCAACTGAATGTATGCTCATTATATTTTTTGTCTTTCAATCACCCGCAGCCTCATCAGCCCATCCAGGCCGGCTCCGGGCGGGAAGCCTGAATGGCGTCAGGTACGGGCAGTGCCTCAGGAAGATGTTATCCGCGACAGTCCTTTCCCAGAAACAGAGGGGGGAATACTGTCCACGCGGCAGGACGCGCATCCCAGATGGATCAAACGCTCCGGCCCAGTCTTACCGGCTTGACAGCCTGTGAAAATCCGAAAATTTCTCAAGGCATGCCCGACAGAAACGTTTCGGGAAACGAATCCCCGAACGTCGAATGAGAGCATAATCCCTGTCAAGGATAATGCAAGCAAATAGTTTGATAACCATAGCAGTCCATTTCCTTCCAGGGCTTTAAATATTCACCCGCATGGGGAGAAAAGGTATTTTTCCCATCTCACCTTCATCCATTCAATATTCTTCGCATTCCTGATTGAAATGCATTACTCCAAGCAAAATATCCGGAAATGTCCGCCTTCCCTTCTTCACAGGAGCGGAAATGCAGAATTTAAGGGCGGGCTGAGTATTCCTGCCTGACTCCCATAACAGCCGCCCACTGCTTTTTCATCCATATTTTCCTATTCTTTGCGCGGCTGCATCATACCCGCAATACCGGACTTTCCGTCCCCAGCAAACATTTACGCAGGAATAAAGGGGCAGTCATGCCAACAAAAACAATCTCCACACTCCAGAATATCTTCTTTCACAAAAAGACAGGCCTTTCAAACTCTCCTGACCAAAACACAAATCCACAGTCACCCGCCATGCCCCCCTTCCATGACAACGCTACCATGCCAGCCCCTCCCATAAAAACAACGGCCGCTGCGGAAAACCGCAACGGCCGAATCAAACGGGAATATGCTCCGCCTTTTAGCGGCGAAGGCCAAGACGCTGGATCAGACTCTGGTACTGGGCCATATCTTCACGCTTGACGTAATCAAGAAGCTTGCGGCGCAGAGCCACCATCTTCAACAACCCGCGGCGGGAAGAAACATCATGTTTATTCGTGCTCAGGTGTTCGGTCAGGTGGGCGATGCGCTGGGTCAGGCGGGCAATCTGGTAGCTGGAACTACCGGAATCCTTTTCATGCATCTTGAATTCCTGCAAATTGATTTCGTTACTCATAAGTGTTTTCCTTGAATGTATTTAGCTCGTGGCAGCGGCTAATCGACACTGTGAGGGCGCCAACTATGCATCATCCAGCCCCAAAAGCAATTCAAATTTCGTCATCCCGGGGAATTTGCCTTCGGTCCCTGGGAAAGCGGCCCCTCTCCATTTTCCCCGGTGTCCCAGACTTCGCGGTCCTTTTCCGTCACGGGGCGAACCACACGGCAGGGATTACCCACGGCCACTACATTGGCGGGGATGTCCTTCGTCACCACGCTGCCCGCGCCTATCACGGAATTTCTCCCAATACGCACGCCCGGGGCAATCACCGCATGACCGCCTATCCATACTCCATCCTCAATGGTAATGGGCAGAGCAAATTCCAGCCCCTGCCGTCTCAGCCCCGGGTCTACGGGATGCCCGGCAGTATAAATGCCCACGTTGGGACCTATCAGCACGTCATTGCCAATGGTAACGGCAGCGCAGTCCAGCACCACGAGGTTGAAATTGACATACACGCGGTCCCCCAGCCGTATATTGGAGCCGTAATCACAACGCAGCGGAGGTTCCAGATAACACCCCTCTCCCATTTGGCCGAAAAGCTCCCGGGCCAATTCCCGGCGCAGGCTTTCATTTTCCGGAGCGGACTGGTTATAACGGAAAACAAGCCGGCGCGCCCTGAGCCGGGCGGCAGCCAATTCAGAATCCTGGGCATGATATAAACGCCCGGACAGCATGTTATTCCAGTCCTTCGAGGCCATTGGATGAAGAGGGGGTCACAGCGTCCGCCTCAGAAAAAGGCAGCTGCCCGTTTTTCTGTTCTCCAACAGACGGCATGGCGGCGGCATCTTTTTTTACGGACGTTTCCGATTTTACAAGTTCCTTCTTTCCAGAGGAAGCTGAATTTGCGCCAGCCACCGGGGCAGCAGGCTTGTGCACCGTCCGGAACATTCCGGACGGCTTTTGTTGAAGCTCGTGAATATTCCAGTTTCCATCCCTGACAACCACACGGGTTCCTACGGGAGCTATTTCAAAGAGATTTTTCGCAATCAAGTGGGGAACACGGATACATCCATGGGAGGATGGATGACCGGGCAAGTACCCTTCATGAATTCCTACAGCCCCGTTCACTCTCATGAAATAGGGCATCTTGGCAGGTTCAAACCTCTCGCCGGGACGGGGGGAATCCCTTCGTATATCCCCATCCCGCAATTCTCCGTCAGCGTTAATAAACGCACCGTAATAGGAATGATGGTCTTCCTCCTTGGAAGCAATGAGGAAGTAGCCGGAGGGCGTCATGCCGGAACTTCTTCCGGAACTGATGGGGGCATACGCAATGCGGTGCGTCCCCCGGTAGATATACATCATCTGCTTTTTCTTATCCACTACGACCAGCAGCTTTCCGGGCAGCGACGGATCATCTTCCCACTTGAGCAGGTGCCCCGGAATAGTTCCCGTTTCTTCAAAATACCCCGTCATTTCTTCTACCGGAATATGGGAAGAGGGGACAGGGGAAAGCTCCCTTCCCGTGGTACCGCAGGACGAAAACAAAAACGGAACACACAGGCAGGAAACCAACGGATGGAAAAGGGATAATTTCATCTCAATAAGTCGCATCACTGCGTTAGACAGCTGGAGGCCGGAAAAAAGAACCGCTTTTTGACAAAACTGTCAACTATCAATCAAAAAAATCTCTTTTCCTTTACTCCTGCCCTCTGTTACCACATCCGGGCGCGCCTGCCGCGCTCCCGGCCGTTCCCGGTTCCGTGGAGCAGGCCATGGGACCAAGCCTCCGCAAAGGTTCGTGCGGCATCCGCCGTATGGGAATGCCCGTCATGCACGGGGCTTTCCGCAATGGCTCCGCCTACGCCGGGAGGCCTGGACCTGTACAACTCCAGATGCTCCACGCCTCCCGGCTCCTCTTCCCCACAGAAGCCTCGCGCCTTTTCCTGGGTGCGGGCATGGAAAAAACTGCGCTCCAGCAATTCCCTCAGCGTATTAATGCCACGCCAGACATCCGTGGTTCTGGGGACGACGCGCACATTCACCAATCCCAGCCTCCCCATATTCTCCACATAGGAAATGCCGTGCGCATCCCTCCGTGCAGCATCATGAGGCAAAAGATGGAATGTTACCGCAACACCATGATTTTTCTCCCAGTCCTTCATTTTTTCAACGTAATGGGCCAGGGGCTGCTGGCTGGCGGCATAATGGTCCAGCCAGTGGAAACTGTCTCCCATGATCTGCACAAGCCAAATGGCCGTATGGTCACTCAGCCCCAGATCCCATGCCGTGAAAGTGGGAGCGTCCGGATCAGCCTCAAACTCCACCCCTACCTGCCCCCGCTCCCGCAGGGCCATGATACGGCTGCCGTAAATACTGCCTTCTTCCCCTGTAGCAAAAGCCTCCTGCGGCGTTCCCGGATACTCCTGCTTCATGGAGGCGCCCATCACCCGGGCCATCCGGGCATACCATCTCTTCTGCCCCGCATCCAGAACCACTCCGGTCTCCCGTTCCAGAGAAAGGAAATACTCATCCAGTTCCCTGCTCCATCTGCCGCGCCCCGGCAAAGTGTACTCATCCTGGTCAAACCAGCTGAAAAAAAAGAATCTGAAATCCAAGGGGGAAAGCTCGCTTTTGCCCATGTTTTCCATGGCCTGGCGCGTCAGTTCATAATTCACTCCGTACCGTCCGCCCTCATGAGTGCTTTCCTTCAGAATAAAACCTCCGGGGGGAACTGTATTGATTGCCCCCGTACGGATTTCCCTGGCTCTCCACGGAGCATGCACGGAAACATACGCCAGCTCGGAAACATGGAGAAACTGCATGGTTCCCCCTCGCAGATTAGTCCCCACCCGCACATCGCTCCCATTGGAGAAAGCCAGGCGGCTGCGCGCCAACGTACGGGGTTGCCATTCCCCCTTTTTTTCCACGCCGGACAGGCTCTTGATTCTCTCTCCCAGCCGGGCCAGGGCCACATCCATCGCAGAGGGGTTGGGCGGCACGTAATCCAGATGGTCCCAGGCAAAATGCAGCTTGGCCAGTTTCTGCTGGGCGTCCGGCAGGCTTTTATCAATGATTCCGCAATGGAAATTGGACCGGAACAGGCTCATATCCAGCATCAGCATGGCCACATACGTAGAAATGCCCAACTGGCGCGCCTTCAGGATGTCATTCCTGTACCACAGCCGCTCATGCAGCCGCCGCTGCGCCCTGTTCAGGGAAAAACGCTGGAGCTGACCGGCTTTATTTTCTATCCAGTACAGGTGTTCCAGCCTCCAAAGCTGGCTGGACAGGACGGACTGCCACGGCAGGCTCCATGATTCCGTCAAATGATTAGAATTTTCCATACTCCTCTATTAATTCTTTCAACAGGGCTCCTTCCCTGGTCATCCCCTTCTGGGCCTCCGTCCCCTTGTCCAGGGGAAGGCGGTATGCCAGTGGATCGTTCAGCACGTGGGAAACGAACCGGAGTGCCTGCACCATCACGGGATCAAGCAGATCAAGTTTTCCTTTCAAATAACTCTCACGCACGCCCAGTTCTTCATACATCCTGATTACATCCTCCATGAAGCATCCCACAGCCTGTCTTCTGTTCCACCACCATCGTTCCTGAGCATCCGCATCCCCCAGCGTTTCCAAAATCCGGCCCAGGAACCGGGTACGGCTCGCCCGTGAATCTTCCGGGGAAGGTTCCTCTCCGGCTCCCGTAAAACACTCCAGCGGAATATAGGAATCCCGTTCGGACACCGCAGGCGCTCCCCGGCTGTCCAGATAAACGCCGTTCCATGCATGTACCACGTAGGGCGCGCGTGCCTGCCGTACGGGACTTTCCGGAATGCCCGGCATCAGGCGCACGGTATGCCCCGGGTCGTCCGGGTCACGGTAAATCACGCAGTTGGCATACATCGCGCGCGCTTCCTCCGGCTCAGGAAGAAAGGGAAGCATGCCGCCTCTCAGTTCCTCCTGCCTGCGGTCCAGAACACCAATCAGGGAAATGGCCTCATCCAACGGGAGTTCCCGGTTCCAGGCAGACGTCACCCCCCCGGGAGCCGCTCCGGAATCAATCCGGTAAATCTTCCCTTTCCAACTAACCCCGGATTCCGTAGCTTCCGGTCGGCCTGCAAAATCCCGCCTCACCGCATCCAGCGTCTCCACAGCCCGGACCACATCCGGACGGCTACGCCAGGCGGCAGGGGCGGGAACGCCACGCCTGACGGAAAAATCTTCTTCCACATACAGGGGCGCTTCCAGAATGGGTTCCGGAAGATGTTCTCCCAAAGCCTCCAGGGAAACATCCTTCGCGCGCAGAGGTCCCAATCCGGTCCGGAACGGCTTTTTCACCAAATGCAGGTACAGGCCGGGATGTTCCGGATCCGGACGCCAGGAATTCAGCTGGGGGAACTCCTGAAGCGCCCGGGACAGTTCCCTCATGCGGCTTTCCGCCAGCCGCTCCATCCTGCGTACGGAACCGCCCGCGTATCCGTCCAGCGCGGACAGGGAACGCACCAGTCCCTCTTTCACGTGATCCGGAACGGTAAACAACAGCCGTCCGGACATCAGGCTGTGAGAGTACCGCTCCACCATGGGGAGGCGGACCTCCCCTCCGGCCGGATCCCAGGCGCGCTCCGCCCGTATGCGTTCATTCATACCAACCGTCTCTCGGAGCATTCCCATAAACTCGGCAGTGTCCGCATCCTCCCTCAGCGTTTTCTTCAGCAATCCGGCCCGGCGCACCAATTCCTCAAGAGGTTCCGGAACCCTGTCAGGCATTGCGGCCCCGTAACGCGCCCAGGAAGCCACCGTTTCCGGTACGGTGTCATGATCCAGATGGGCAAAAAAATACTCCTTGGACAGCTGGGACAACTCTTCAATCAGTTCCGTTTCCCCTGGAAGCTTCCTGCGGCGCGGCACACGTTCCAAATGTTTCAACATCTCCCAAGCGCCTTCCGGGGAAAGCTGATCCGTGCGCATCAGCCGGTCCCATACGACTTCCGCCTTGTCTTCGATCAAAGCCAGGGGATTGGCCGTATTGTGAAGCCTCAGCCGGTGCATTACCAACCCCTTGGCTGAGGAAACGGGCAAATTATCCGCATCCACGCCCAGCCTCTGTACGGAACCCACATACTTTTCGTGAATATTCATGCGCTCCAACAGGGCATCACGGCTCATCAGCGTTTCAGCGGCAAGGCGCCCCCGGCCCGTCACCTCCAGACGATCCCCGGACCCCACCGCTCCGCGCACGCCGTAACCAGCTTTCAGCATATCCGCCACCGCCATGGCCGTCAGGCTGTCATACAGGCACGGGAGTCGCCCCCCCTGCACGTTTTCCAACCCGGTGGTCAGCCTCAGATAATTGGTATCCCATTCCGGCCGCCCGTTGCGGGCATGCCATTCCCAGCTACGGATCAAATCCCTCTTAGCCGAGCAGGCGGCAGAAAACATCATGGAAACATTGGCCATCAGGTCCGCTTCCGCCGCCTCCCGAGAAGGATGCCAGTCCGACCACCTTCCGTTGGGATACTTTACTCTCCACAGCCTTTCCGCACCTTCCCCTCCGGGTGAGGACTGGAACAAGCGCGGGGTCAGCAGGAGAAGGCTCTCCACCGCTTTCTGCGTCCCCGGATAAATACGCGGCGAAACCTGTTCCGGACCGGCATGCAGCCGCGGAGCATCCAGCACAGAGGCATAACGGGCCACGTTCTCCGGCGCCACCTCCAGAAACCGGGCCAGCAACCGCCCGTAACTCTCCACGGGCGTATAGCCGCGCCCCACATGCACGTCAAATTCCTTCATGTTGGGAAGTACGTGGTACAGGCTGTTCACATCCGCCTGGGTTCCCCATACCATGCGGTTCATCTCCCGCAGCTCAGGGCTTACCCCATGGCGTTCCTCTCCGCCAAGAGAAGCCATCATGCCGCTCATCCTGGCCAGGGCGGAGGGAAGGCCGTCCGGATTAAAAGATTCAGACAACAAATCCGGAGCATTCAGCAACGCACTCCCTTCCTCCAGCCACCGGCGCGCCCTGCCGCAGGCGCCTTCATCCCGGCAAAACGACTTCCATAAACGAAAAGCCACATGCCCGGACGCCTCTTCATGCGGCACGCCGTGTACCCGTTCCATCACTCCTTCATAAACATTGGCTCGTGTCCGCAGATCCATATCCTCCGTAACGGCGTCCCAGTCCCTGCCGGAAAAACCCGCACGGCGCCCCTCCTCTTCCGGATAAGCCAGCAGCAACATCCTGTACGGCCGATAGGAGCGCTTCTTCAGCCTGTCCGCCACAAACGCTCGGCGCAAAGCATCCGCATGAATATCATATTCCTCCGCATTCCTTAAACGCCATGGGATTTCCCGTCCGTTCCTGTTCCGGACCAGACCCAAATCTTCCTCTTCCATACGGCCCAGAGTCTCATACCAGGAAGAAAGAGCCTCCTCTACGGAAGGCCGGCCCTGCGTTCGCTTCATCTCCACACCGGGAGCCGGTTCCGCCGCATAAACAAGCTGCCCGTCCGCTGCGGCGGCTTCACCCACGCGGGGCAGACCAGCACGCAACAGCCACTTGGTTCTGGCCGTAACATAATCCGGATGAGGAAAAAGCTTTTTCAACACCGCTTCCGATTCGGGGAAATCCCGCATCTTCCAAGGCTTTGTACGTACAGGAGAAGGCAAATCCAAAAAATCTCTCACATCCTGTTCCGTCTTCAAAAAAGGCTCTCCGGCTTCATCCAGGGCGCGAGACCACTCCAAAGCCTTCCTGGAAATGTACAAAGACCCCCACAGCGGAGAATTACGCAGGGACTTCTGCAACAATTCCCCGGCACGGTTTACATCCTTATCCTGCAAAATCCTGGAAACCGTTTCATCCGGAATTCCGAATACTTTCAGAGGGGAACCGTCTCCAAGCAGCATTCCGGCATGGCGGAAATGGTTCAGAGCGACCTTTCCCGCGCCTATCAGCAGGAAAGGCATCATCATGACGGCCTCCCTCAACTGGTCATCCGCGGAAGCCTGTCTGGCCTTCCATCCCTCCCAGTCCACACCGGACGCCTTATTCGTAAGAGCTCCCACGCCTTCCTGAACGACCATGGGCGTCAACTCCATCAATTTATTTTCCGCCTCCATCTTCACGGCATCCGCCGCAACGGCTCCCATTCCGGGAAAAAACGCACCAGGATTCCCTGATGCGGGCCTCATGGCCCGGAACGCCCGCATTCCCTGCCCCAACATCTTCTGGCCCAGTTTCGTCATTCCGAAGGAAAGCAAGGTATTCACCCCGCCGGAGACGACAGCTCCCCCCATCTGGGCATCAAACACGCCGTCCGGGTTCATGCTCCGGGCCTCCGCCATATGGCGCCCCGTCTCCCCCGCCATCAACATGCCAAGCCCGGCAGGGCCGCAAAACGCCAAAGCCGTGGACGCTCCCTGCTGGGCCATGTCCACCAGCATCTCCCGGAACCAGGGGGAATCCTTTCCCCCACGCAGCGGGGCAAACTCCATCCTGGCAAAATTCCTCAGTTCTTCAAAACTCCGGGAATAATTGTCCAGCGCCTCCTTTTCCCGTCCCTCCGGCAGGAGCCATGCTGCGCCGTTTACGGCCAGCTGGGCCGCATTCTCCGCAACCTCTGCCGCACCGCGTTTAAGCGCCCCCCCCAGGGCATCCCGAAAGGCGGGACGGGAACGCAAATAGGGAGCCATCATGCGGAACACCTTCTGACGGTCTCCATCCCCCAGGGAAGCCAGGCAATCCACGGCGCGTATGACGTCCGGAACTCCGGCAACGGCTTTCCGGTTCAGCTGCATGGCGGGAAAACCTTCCTTCACCGGCTCCTCACTGGCTACCAGCCCCTCCAATCCCTTGCGGATGATCCCGGCGGCATCCGCCAGACGGTTCCTGTCATCCAGAGCATCGGATACGGCCCTGCCTTTCAATTGTCGCGCCACAGCCTGCAAATCTTCCGGCCATGCGGAAATGGCTTCCCTTCCCCGTTCGTCAGGAACCGTTCCGCCACCGGACAGAGCACGTTCATACTCTTCCTGGTATAATTCAAACAAAGCATCCCTCCGGGAAAGGCGTACCTGATTGTGCATGGAAACTGCCACGAACAATTCGTCATCAGACTTGCCGGACGCTCCATACTGCCGCGCCACCTGTTCCCGGATACTCTTCCAGTCCCTCCGGATGCGTTCACGGGGAATCGCGCCAGAATCCGCCACCCAGGACTGCATGACGCACGCGGCAATTCTTTCCCGGTCCGCCGTCCCTTCCGGATACATCGCCTCCGCTTTCAGCCTCACTTCCCGCGGAATGGTCATCACATCCCCCGTCAATACGCTATTCCAATATTCTTCCCTTTCCGACTCCCGTTCCGGGAATTTTTCCGTGCCGGAAGGTACGGAAAACACTGCGTTCCCTTTCCGAATATGGCTTTCCCCCACAGTTCCGGGAACCGCTTCCGGAAAACTCCCTTCCAGCGGGAGCGCCCCTGTCTGTTCCCTGGTTCCATTATCTTCCATCATCACTTTCCTTCTTTTCCGTTATTCATATCTGAAAACCCCACCCAGCCGGCATCCTGGAGGCGTTTCTCTTTCTCAAAAACCTTTTCCATCTCCGCAGCCGGCTGCTCCGGGGCCCTCTTCACCGCCTCTGCCAATTCCGTCTGGACACGCTTCCATTCCTCTGCCGCACGGGCATCCTCAGGCGGCCCCCATGCGCCATTCCGGAACATGGCGGACAGGCGACGGGAAGCAGCGCGGCGCAATTCGCGGGGTATGCCCAACATCACCTCGCGTCGCCGGAGAAGCCTTTCCACCTGAACGGCAGGCAAACCTGAAGTGGCCACTTCTATCAGGGCGTCAGCATCTTCCTCCCCTCCGCAATCCTCATCAATCAGCGAGGTCATGCGGCACAACAGCATATCGTCCTCCGGAATGCTGATACCGGAAAATTCCGCCGTCTGCCTCCGGTCACGGGAAGCCGTATAACGCCTTAGCTGCTCGGAACTGATCAAGCCATGCTTTTCAGCATTCGCCAATCCGTCATCCCGCAGCAACCCGCCGCCGGACACGGTGGCCGCCAGAGCATCCGCATAACGCCGCTTCAACTCCCCATACGCTGCCGCAGCCTCTTCCGCCACCTTCTTTTCCTCCGGAGTTTCCGGCATCTTCTCCTGCCGGGAAACGATGCTTACAGCGGCCGCCGGATCACGGCGGACCTCCTCCACCGCCCGGTTCAGCGCTGCGCGGGAATGCGCTTCTTCCGCCATGCGGGAAGCCTCCTCTCCCGTTACAAATATTCCGCTGCCCTCCTTAATCCTGCGTTCGGCCAGAGCGGCCTCTCCCTGCTCCACGGCAGACTCCACCCCCCCGGCCCAATACCGGCGCGCCTCCTCCAACTGCCCCAGATGGGACATCTTCTGCAAATAAATGCTCCCGGCAGTCTCCAGATTCTCTCTCGCCATGGCCACGCGCTCCCTAACCGGGCCGGACATACGGGCAGGCAGATAGGAAGGAAGCCGTTCTGACAGCGCCTTCTTCCATCTCTCCTGAAGGTGTTCCGAATCCCCTCCGGCTTCCAGCTCCCGCATCACGTCCTGTTCAAAACGATACATCCCGTCACGGGCCTGCGCCCATTGGCCGGCATCCCTCACCTCCTGCATTTCCTTCAGCGCGGTTCCCGCCACTCCGGACACATCCATCACGCCCGCCGCCAGATCCCGCACACTGGCGGCCTGTGCTTCAAAAGCATTCAACGGAATCACGCCGGACAAATCCGGAGCCACATGCAACATTCCAAAATCCTGTATTCGTATAGCCATCTTTAACGGCTTCCAAGCTAAAGAGCGGATGGAACGCACGGCATCCATTCCAAACCGTCACCGTCCGGCACTTGCCGCTTGCCAAAACCGGGCCGTTCCAACACAATCACCCCTATGACCCGCATTCGTTTCACCGCCGCATATGACGGACGCCCTTATCTGGGCTGGCAAAGCCAGCCGGGAGGCCGTACCGTCCAGGACGTGCTGGAACGAGCCTTTTCCGGACTCTTTGGAACCACCTGCCGTATCCACGGTTCCGGCAGGACAGACGCCGGCGTTCATGCTCTGGGCCAGGTTTTTCATGCAGACGCTCCGGATACCCACCGCATTCCTGCGGACAAATGGCCTGCCGCGCTCAACACGCGCCTTCCCCGAACCATCCGGATTACCCATGCGGAATACGTTCCTCCCGGTTTCCATGCCCGGTTCAGCGCCACGGGAAAAACCTACCGTTACTGCATTTCACGCGCACCCATCCTCAACCCTTTTGACGCCGGGCTGGCCTGGCACCGCCCGCTGGCATGGAGCGTGGATATTCTGGAACAGGCGGTACACCTATTCCGGGGAACGCATGACTTCACTGCTTTTGCCGCCCTGCGCGGCAATGAACCGCGCCCTATTCCGGAGGGTTACTTCCGCCGAACCATCACGCAGACACAAGTGGCACAGACAGGGGAACATGTCTTCATCACCTTTACCGGAACGGGCTTCCTCTACAAAATGGTGCGCCTCATGACGGGAGCCGCCCATGAAGCCGCACGTGGAAAAATAACGCTGGAAGAACTGGCTCGCCTCATCAACGCTCCGCTCCCGGATGACAAAAGCCCGTTTTGCGCCCCGCCGGACGGACTCACGCTGATGAGGGTCCATTACCCGGAAGAAACGTTCGGAGACAAAAAGAAAATAAACTGAAAACAGCCGTCCTTCTAGACGCGTGCCGTACCGTACAGCCCTTCCCTCCTGATCAGCGCCTCCACTTTCCGGTTCAGATGGGGAACCGGACATACGCCCGAACGCAAATCATGCCGTATGCGGGTGGAAGAAGCGGGTTCATCCCCTTCTATGAAAAGGGCGCGCACCCCTTTCCTGGGTGATGGAACCCCGCCGCGGCGGTAAACAATGAACGTCACCAGGCTGGACAAATATTCCCAGCGTCCCCACTGTTCCAGAGAATCCCACTGATCCTTGCCCATCAGCCAGAACAATTCTGCACCGGGATAACGCTCCGCCGTCCTCTCAGCCACCCTCCACGACCACGAAGGAGGCGGAAGTTCCAAATCCGTCCGGTCAAGCATGGCCCAATCCAGCCCTTGAACAGCCAGCTCAATCATCCGGCAGCGCTGGTCATCCGTCACGGAGGGGGCCTGCTCCTTCAGCGGAGACAGGGAACACGGCATAAACAGCACGCGGTCCATTCCGCAATATTTCCGAGCATGCTCGGCCATGCAAACGTGCCCCTCATGCACGGGATCAAACGAACCGCCGAAAATACACAGCTTCACCGTCATCCACCAGAAGTCAGAGTCCTTCCGTTCCCTTGTAAGGCAGAAACCCGCCGATGGGAACGCGCACCATCCTTTTCTCCAGAGGCAGGCGGCGGAAGCGGGTAATAAAATCCGTCAGGCGGCCGTAATTGCGCTCCGTCTTGGGAATCCCCTGATGGCCGCAATTCACATCCGGATTGTAATGCAGCTCCACATGAAGATGGGCCGGATACATGCCGCGGTTCGTGCCGATAGTGCCCACCTGGGTGCCGCGCAGCACCAACTGCCCCAAAACAACGTTAATATCGTTCAAATGGGAATAAAGCGTCTGGCAGCAAAGAACCTTTCCGGACTTGGGTTCGCGGAAAGCATGGCGCACAATCACCACCTTTCCCCACGCGCCCCGGGCGTCCGCCGCATAAGTTACCACCCCGTGGCCTACGGAATAAACGGGATCGCCCAAATCGGAATTGCCTCCTTTATTGCCGTTCCAATCCTCACCCATGTGACGGGGGCTTTTCAGACGCAGACCGCGGGACCGGTAATAGCCGTTGCCGTCAGGCTTTCCTACCGGAAAATCAAAACCGTCACAAAGAGGAACAAGCGCAAACTTGCTGTCCCGCCGGGCCATGTAAACGGGAGCGCTCTCGCTGATCAACGCGGAACCGGTCAGAAAAACCAGCGCCAGCAGTAAAAAACGGAAATAGGACATGGGAACGAAAGTCCTTTGTATGCTAAACGGCTCCCTACTCCGTATCAAGCGGAATTCCCCGCATGTCGGAGTTAGAATTCACTAACACGCTCCCCCATGTTTCCCACTTCCTTTCAACTTGACAGCAAAGCCTCCGTATGAAAGAAGGGAGGAGCGATTTTCGTATTTTTCAACACACTTTTTTCCGCATGAACAAACTAACCACACTCTTCCTCGGAACGGTTCTCTCTTCCTCCATGCTGCCCGGCTGGACCGCAGCACCACCCAAACCATACGGCGCGATTCCAACACCCGCCCAAATCAACTGGCAGCGCATGGAATTCTACGGCTTCATCCACTTTGGCCTGAATACGTTCACGGGACGGGAGTGGGGTTACGGCGATGAAGACCCGAAAATTTTCAACCCCACGGATTTCAATGCCTCCGATATCGTCTCCACCTTTAAAAAAGGCGGAATGAAAGGAATGATTTACACGGCCAAACATCATGACGGCTTCTGCGCCTGGCCCACCAAATCCACGGACCACAACATCACCAAGAGCCCTTGGAAAAACGGAAAAGGAGATGTAGTCCGGGAATTCGCCCTTGCCTGCAAAAAGCACGGCATCAAATTCGGCACCTACCTCAGCCCCTGGGACCGCAACAACGCGGATTACGGCAAGGATGGCTACCTGGACGTTTACTATAAACAAATCCGGGAACTGCTGACCAATTACGGGCCTGTCTTTGAAATCTGGTTTGACGGAGCCAACGGAGGAGATGGCTATTACGGAGGAGCCAGGGAAAAGCGCAATATCGGTGACGCGGAAAAATACTACAACTTTGAAAAAATCGTGGAAATGATCCGCAAAATCCAGCCCAGCTGCATCATCTGGGGAGCGGGCCACTACGGAGACGCCCGCTGGGGAGGCTCCGAAAAAGGCCACGTCAATTATCCCCATTGGAGCACTGTAGGGCTGAACGGCGGGGGCGGAGGCACCGGAAAACGCGGCGGAGAACGCTGGGTGCCTGCGGAAGGAGACACCACCATCAACCATGCCGGCTGGTTCTGGCACCAGGGACAGGCATCCCGCGTCAAATCCCCTGAAGAACTTATGCAGGTATGGTTTGATTCCGTAGGCCGCGGGGCCAACCTCATTCTGAACGTGGCGGCGGACAAAACGGGCAGACTGGATCCTGCGGATGTCAAATCCCTGCTGGAATTCAAAGAACTGCGCGACAAGCTATATGCCAGGGACTACGCGTTGGGAGCAACCGTCACAGCCAGCCAGACGCGCGGCAACGACAAAAAATTCTCGCCCTCCAACATGACGGACGGCAACATTGAAACTTACTGGGCCGTGGAAGATGACAACCTGACCCCCACCGCCGTTATTACGCTGCCCAAACCCGCCACTTTCGACGTCATCCGGCTGCGCGAACAAACACGTCTGGGACAACGCGTGGACTCCTTCAATATTGACGCTTTCGTCAATGGCAAATGGGTTTGTATCGACAATGAAGGGAAAACCATCGGCAACCAGGTCATGCGCCGCCTGAACCGCCCCATCACTACCCAGAAACTGCGCCTGCGCATCACGGGCAGCCAGGCCACTCCCTGCATCTCGGAATTCTCCCTCTTCCGCCAGCCTGCGGGCGCCGTGCGGCCCTCCATCTTCCGCCGCGGAGACAACCTCGTCATCATTGCGGACGGCAAAAATAAAATCCTGTACACGACGGACGGCAGCGAACCCAAAGCAGGATCTCCCGTTTACAGCCAGGGTGCTAAATTTACGGAAAGCGGCATTGTCAAGGCTCGCTGCCAATTCTCCAATGGTAAATTGGGCCCTGTCAGCCAGGCCAAATTCGGCATCAGCAAAACCGGATGGAAAGTGAAAACCGCCACATCCGGGAATGCGGCGGCGGCCATAGACGACAATCCGGAAACCTCATGGCTTGCCAAGGCGGAAGCGCCTCAATCCTTTGTGGTGGATATGGGAAAACCGTACCAGGTTTCCAGCTTCTCCTACCTGCCCAGACAGGATGGAAAAACCTCCGGCATGACGGATAAATACCAATTTGAAGTAAGCGCGGATGGGAAAAACTGGACGAAAGTGGCGGAAGGGGAATTCTCCAACCTGCGCGCTAACCCCATTGAGCAATCCGTTAATCTCAAAAACATGAACGGACCTGTGCGTTACTTCCGCTTCATCGGCACCAGTTCTCTGGACGGCAACAGCGCTTCCGCTGCGGAAATAAACGTTTTCGGCACTCCCGCAGGCAAATAAAAACTTCCCTGACTTTTCTCACCAGGGCATTCCGGTTTTTTACGGCCGGAATGCCTTTTTTGCGGCACGAACCCATTTGCAAAACAGGCTCCGGCAGACAAACCGGCATCAAGGAAAACGGGAAAGGACAGGCCAGAATTCCGTCCAACTCCCCCTGCAGCGCTTCAATAGGCTGGGAATGTCCCGTATATCGCCGCAACGGCCAAAAGTCCAAAGGGACGGCTCCTGCATGGCTGCCTTTTTCAGCCACCGCCGGTATGCGGAACATATTGAAATCGGAAAAAGCCCTGCAACCCATGCCAACGGCAGAACCCCGGGAAAAACGCCTACCGACTGAAATCAGCCACGATATCATCTCCGGAAGGCTTCTGTTCCATTTCCTGTTTGTACGAACAATCTTCTCCCTGCCGTCGGAAAACCTGGCGCTTTTCCTTACATCGTAAAATCTATTTTAAGCAAACAACGGGAAGACCCGGTCAACTCAGCGAGGGATTTCCGCAAAACAGAAGCTTTCCTTTTCAAAAATCAGGTATGAAAAAGGGCGCATCCTTTTGGATGCGCCCTGTGCAAATCATGCCGGCGGCGAATTTACTCCTGGCCGCTGAAATCAGGCGTGAGGATCAGGAACTGGTCGCTGATGTCCTTGCCCCTTCTCTTGGGAATGAAGGTATCCTGAAGGTTGTCGTTGTCGTCCAGTTCCAGGGTCTTGGCACTCTGGGCAGTCGTGAAAATCAGAACCTTGCCGCGGAAGCTTTCCGGGTCAAAACGCACGGCATTGCCAGCCACAACGGTGCTGCCGTCTTCACCAGGAAGCACGGAAGTGACCATCAGGGGATATTCCCCCACGGAACTTCCAATACCCACTTTCTTGTTATTGTCACCCTTGCGCATGACGTAGGAAATACCCACTTCACCGGGGGTCAGGGCCTGACCGTCGTAAATTTCACCGTTGGGAGTAACGGTGGAACCGCCGGAAGGAGTCTGAACTTTGGCGTAAAAGTTGCTTTCGGAAACAGACTCATTGCCCAGAAGCTGACGGAAATAGTCATTGGAAGTATCGCCCTGAAGTGCGCCCAGGCCGCTCATATAATTATGCTGGGCAACAACGCGGTCGGCAGTGATGTCATCGGGGAAATTGCCCAGTTTGCTGTTGGATTTGAACTCGTTCATCGCTACGCCCACGTTCTTCATGTTGGTCAGGGCCTGCATCTGGTCGCCTTTGTTGATCTGGTTCAGGATAGGACCATAGGCCACGGAAGCCAGCAGGGCGATAATGGCAATGACAACCAAGAGTTCAATCAGGGTAAACCCCCTGCGTAACTGGTGTGTTGCAAAAGATACTTTCATTGTTGGATATGATGGAATGTTTACAGTTTAAGAAAACCGTACGTGGTTATCTATGTTCGTTTCTACTATTAACGCGATTTCCCAGTCAGGACAAGCATATTCTCATAAAAATCTTGGTCCTGCAAGGAGTGAAAACTCTGCCGCAGCCCCCCTGCAAAAGGCCCGGAAAATACCGGGCCATGGGGAAAAATCCTACCTGCCGCTGCCGAAAAAGGAACTCAACCCTTCCAAAGCTCCGGCGGACGGCCTGTCCTCCTCGGCAATCACCTGCCGCTGGAGTTCCGCGATCTCTTCCAGCCCCTTGCGGCCTAATTCCAGCATCCGGTTCATCTGATCCGCCGTGAACACGGCCTCTTCACCGGACCCCTGCACCTCCACAAACTCTCCCTGGTCGGTCATCACCAGATTCATATCCACTTCCGCATCCTTATCCTCCACATAGCAGAGATCCAGCAGAGCCTCTCCCTCCAGTATTCCCACGGATACGGCGGAAACCAGCCTCTTCATGGGAGACTCCGCCAGCTTTCCTTCCGCGACAAGCTTGTTCACGGCAATGGCCAGAGCTACGGAAGCCCCGGTAATGGAAGCGGTGCGGGTACCGCCATCAGCCTGTAAAACGTCGCAATCCACCCAAATCGTGCGCTGGCCGATCTTGCCCAGATCCACGGCCGCCCTCAGGGAACGGCCGATCAGACGCTGGATCTCGCTGGAACGTCCGTCCAGTTTCCCCGCCGTAATATCCCGGCGTTTACGGTCAAGCGTGGAATAGGGAAGCATGGAATACTCCGCCGTCAGCCATCCTCCCTCCACACGCTGAACCTTCATCCAGCGGGGTACATCCTCCTCAATCGTAACGGCGCAAATCACCTTGGTGCGTCCGAAAGTCACCAGCACGGAAGCCGTGGCATTCGGGGCGATACCCGTTTCAAAGCTGATCGGACGCAACTGGTCCACAAGTCGTTTATCCTGACGTTCCATGCGGGGAATCATGACGGATGCCGTCATCTTTTTCAAATGGAAAGATGGAATTCCCGTTCCAAACCGCTAATATCCCTCCCATGAAACACCCTCGCACCCGTGTGGCCGTCATTGACGTGGTGGCCCTTTCCCGCCAGATGATGGAACACATGCCGCGGCTCTCCGCCTGGGCGGAGGGGCGGAGCGTTTCCTCCTTCCCCCCGGCCTTTCCGGCCCTCACCTGCTCTGCCCAGAGCACCTACGTGACAGGGCTTTCCCCGCGGGAGCACGCCATTCCCGGCAACGGATGGTACAACCGGAATATGTGTGAAATCCAATTCTGGAAGCAGTCCAACAAGCTGGTGCAGGGCCCGCGCCTCTGGGAGAAACTGAGGGAACGGTACGGTTCCGGCTTCACCTGCGCCAAACTTTTCTGGTGGTACAACATGTATTCCACGGCGGACTGGACCATCACGCCGCGCCCCATGTACCCGGCAGACGGCCGCAAGATCTTCGACATTTACACCCAACCCATGGAACTCCGGGAAACCATTAAAAAGGATCTGGGAGAATTCCCCTTCCCCACCTTCTGGGGCCCCATGGCAGGGATTCAGTCCTCCCAATGGATAGCAGACTCCGCCCGGTGGGTGGAACGGAAACATCGCCCTGACCTCAGCCTCATCTATCTGCCCTATCTGGACTATGACCTTCAGAAATTCGGACCGTCCTCGACCCAGGCTGCCCACGCGGCAGAGGCTATGGACGGTCTTCTCTGCGACTTGATCGACTTTCTGGAACGGGAAGGCGTCACCCCCGTCGTCCTCAGTGAATACGGTATTTCCGACGTATCCCGCAGCATTGCCCTCAACCGCCTCTTCCGGGAACGGGGCTGGATTACCGTCAAACCGGAAATGGGTACGGAAATGCTGGACTGCGGCGCCTCCCGCGCCTTTGCCGTGGCGGATCACCAGACTGCCCATATCTACATCAATGATCCTTCCGTAAAAGAAGAAGTGAAAGCACTGCTCTCCGCCACACCCGGAGTGGAAGAAATCAGGGAAACGGACTTCTCCGGCCTTTCTTCCGCGGCTCTGGAACGCCTGCCGGAATTCACCGCCGTCGCAGCCCCGGATGCATGGTTCACCTACTATTACTGGCTGGATGACACCAAGGCGCCGGACTTCGCCCGCTGCGTGGACATCCACCGCAAACCCGGCTATGACCCCGCGGAAATGTTCTTTGATCCGGGCCTTACCCTCCCCATGTTCCATGCCGCCGCCTTTCTGCTGAAAAAAAAGCTGGGGTTCCGCGCCCTGATGAAAGTTATCCCCCTCAATGGCGACCAGGTGAAAGGCTCCCATGGCAGAGACCGGGTGCCTGCAAACCAGCAGCCCGTATTCATCGGCCCGGCCTTCCTGCCGGAAATCCATGCTGCTGAGGATGTCCATCAAGCCATCCTCTCCGTCTTTGAAAAAGAATAATAGCGGTTCCCCTCAGCGGGCATCCGGACGGGGGTCATATTCCAGGCCGGGAACCTCCCGGTAATGAATGCCGTCCCACAGGACGGGAATGCAGCAGAAAAACCAGCTTCTCTGGCCGTCGTCTCCCTTCCAGTGCCCGAACACGCTGCGGATCAATACACGGTTCAACCCCTTGCGGAGCTTGATCTTTACAGGAGGGCGGAAAAAATACCCTTCCCAGGTCAGAGGAGCTTCTTCAATGCGCCCCTTTCCCCACCCCGTCCAGGGCAGGCTCTTGAAAGGCCACCTGGGAGGGTTCACGCGCCTTCCGTTCAACCATACGTCCCCGCCGGAAAAATCCCAGCTTCCCTGTTCAGGGGCGCGTGCGCTGCGGTACCCGCCTGAATGCCCCCACATGCCGTTCAGGCCAAACATCAGCCACACCTCCTGCTCCCTGGGGCTGCGGATGAAAGTCAGAGCATAGCAGGTTCCATCCTCCTTTCCAACCTCCCTTGACATCAGCGTGGGCCAGTGGTCCGTCCGGTACTGGTTCCGGTGCATGTTGAACATCGCAAAAAGATGCCTGACATGTACGGCGGCGCCGTAAACAGGAGTCTTCTTCCAGGCCAGTATCCTGTCCCCGTCCCTGTAGGAAGGGGCTATTCTTCTTTCCGGTTCAAAAGAGGTATCATTCTTCCCCCGGTGGTCGAATGGCCCCACCAGGCTCCAGGCAACGTCCGCCTGTTTCACATAGGCAAAAGGAACGCCTTGGAAAAAATGATCACGATGGAAAGCCAAACGCTGTTCAAACTCCCGGAATTCCTTCCAGCCGTCCGTTCCCCTGGGGGGAAGCTGGGCCATATAATCCCTCCTCTTGGTAGCGCTTCCGCGCCAGACACGCTCTGCAAACGTCAATGCACAAGGATAAAACGGATACTGTTCCAGCACCCGCCTCTCACTGCTCAGGTTTCCGTCGCACCACACGGGCATGATGGCCCCCAACGCCTTATCATCCCCTTGAGGCACCTCGCAGGGCTGCTGGAAAAACACCTGGTACACGCCGGACTGGGAATCCGCCCAGTCAATGTAAAAACCATTGCTGTCGATGCGCTTCATATTCTTATCCATCCTGGCGCCGGCCTCGTTCTCTCCCCAGCACATCAATACGGAATCCTTATCGTGCGGGCCGCCCGGAGACCACACGACCACCTCCTTGCCCTTCCCGCGGATATGCTCCGCCATACGTGGGATAAAATCCTTCATGGAAATGTGCGCCTCGTCGGATCCCATGTGGAAAAACCTCCCGGGAAACAGGGAAACGGCCTCGTCCACGACATCCTTCAATATGGAAACACCCTTCTCCGTCTGCATGCCGGTCTTCATGGCCCTGGCAAAAGGCTGGCTGTGTCCCGGCATGTCAATCTCCGGAATCACCTGGATATTCAACCGGGCGCAATAATCCGTCAGATCCTTCAATTGAGCCTGTGTATAAAACCGCCCCGGGAGCCGGGACTTCCAATGAAAAGCCCCATCCGTCAGGGCCGGATACTTCTTCACTTCCAGCCTCCATGCGGGATCATCCGTCAAATGAAGATGCAGCGTATTAATCTTATAGCGGGAAAGACGGCGCATCACCTCCTTGATCAAGGCAGGAGACATGTAATAACGCCCCACATCCAGCATAATTCCCCGCAGGACGAAAGCGGGTTTGTCACTAATACGCACAACAGGCATGGCAAATTCCCCTTCTCTATTGAAAACCAGTTGCCGCAGCGTTTGAAGAGCGTTAAAAAAACCGCCAAAACTTCCTGCCCTGACGGTTGCTTTTCCCAATCCGACGGACAGGTCATACCCCTCTCCTTTCATCCTGGCGTCCGGAAACAATTCCCATGTCATGGCACCTTCAGCAGCCCCCCCACTATTCAGGGAACCCTGAATGCCGGAAGTCCTGAATATGTCCTTGATCTCCTTCATCAAATCATCCCTGCCAGGAACATCCGAACCAACCTTCACATGAACGCTCCTGAAACCTGCCTCGCCCGCGCCCCTGACGACTTTGGAAGGATATGGAATAAGCGCGGGGGAAGAAAACTCCGGCACAGGCGCGGTAACCACCCCTCCGGCCAGCGGCCCCCATGAAAAATAAATCAGCAAGGCAATAAAAAACCGTACGGGAATGCTGAACACTCTGAACAAAAGCATTCCTTAACTACGGCATTCCATCTCCGCTCCTGTCAAAACAGGGCTTTCTCAACTGGGAAACCAAATGCTTTTCCCATCCTCTCCAACACCGACATGCTTATTCCTCCATATCGCTGTTTCCCTCCGCAAATCCGGCAATAACGAACGGGCGTCTTCGCTATCGTCCCGTTCATTCGCCAGACAGACTCCTCCAGTCAGTGCAAACGTTCCAGCAGGACGTCCATGTCCATGTAATCCTGCCCGTGAATCAATTCGCCGAGAATGGGCGCATCGGAAAACTCCTCCACCATGCTCCTGTTCGTCACGCAGGCAGTATCCCATTCATCTTTCACATTATTGAACACGATGCCCAGGCATTCCAGGCCGCGTTCCTTGATTGCATTCAGCGTTAACAGGGCATGGTTGATGGCTCCCAGCTTGTTGCCGATCACCAGAAGAACAGGCAGCTTGAAATCCGCAGCCATATCGCTGAAATTCCTTCCGGGGCCAATCGGGACTTCCCATCCTCCCACCCCTTCCACCACCACGCACTCATGTGCGGCGGACAAAGCGTCATAAGCCCGCAGGATAACCTTTTCATCCACTTTCGTATTCTCCAGCCGTGCGGCCACATAAGGACACGTGGCATTCCTCAAAAAAACGGGATTCAGTTCATCCAGCGTCAGATCCTTCGGCCCGGCTTCGCGCAAAAGGCGGGCATCCTGCCTGTCTCCGCAGGCCACGGGTTTGAACCCGGCGGCATTGATGCCCGCTTCTCTTAGAGATTTGACGATCAGGCAGCTCACATAAGTTTTACCGACTTCCGTATCGGTACCGGTAACAATGAAATTCCTCATAACGGTAAATATTCAGGGATGGGATCTTCTAATCTCCTCAAGCTGCCCGCGGGAAGGCACGCCGTCATTCGTGGTCACCCAGTGATTGATGAAAGACCAGATAATCACGGCCAACACCAGACAGAACAACTTGGCAGGCCAATTAAGCAATAGCAGTTTTTTCATTGGAAGAAGAAATATTCAGCAGCTCTTCAAGGCGTCCCTTCAACTGTTCCGGAGTCAGGTTGCGTTCCAGCTTCCCGCCTACGGCCAACGCGATAGCCCCCGTCTCCTCGGACACCACCACCACCACGCAGTCGCTCTCCTCGGACATGCCCACGCCCGCGCGGTGGCGCAGCCCCAAGGTACGGTCGCTCATTTCCTTCTGGGAAACAGGGAACACGCAGGCAGCCGCAGAAATGCGGTCGCCGGAAATCACCACGCCCCCATCGTGGAGAGCCGTCTTGGTATGGAAAATCGTCAGGGCCAGTTCCGGAGAAAAAATGGCGTCCAGCTTCACGCCGGAATCCTCAATAGGCTTCATGCTGATGCTCCTCTCAAAAGCGAACAGGGCGCCGAACCGCTGGTTGGAAAGCTTGCTTACCGCCTTGCAGAAATTATCCAGGAAATCCACCCGCTGAAGCTTGGCGAAGGAAGAAAAAAACGGGTGGCTTCCCAGCTTTGCCAGCCCCACGCGCAGCTCCGGCTGGAAAATCACCACCAGAGCCAGCGCCAGGCCGGGGGCGAAAATGCGCGTCAGTATCCACGAAATAACATTCAGCTGAAAGAAAAAGGCCAGCACCACCAGGGCCAGAAAGCAGGCGAACAGCCCCACCATGATGCGCGCGCCGCGCGTTGCGTGGAAAGCACGGTAAATCTGGTACAGGAACACCCACAGGATGAAAATTTCAACAACCGCCCGGAGCGCATCCTTGATCACAATCCAGTCCCACATGCCCAACAGGCTACACAACAACCCGGCCGAATGCAATACCGATCCTCTCGCGTTGCCGGCAGGCTCCGGGAATTTACCGTTCATCTCAGTTCCCGAATTTGAAGACATCTCTGTACGTTCCGGAGACAGATAGAAGTTCTTAAAAAGAAGAGCAGTTCTTTGGAAAATAAATAATAACAATAAATAAAAATTGGATTAAATAAACAATGTTAATCCGCATAAAGCAAGGCTTCAGAAAACTGAAATTCTCTCTGGATTTACGGCATGAACTTGCGGCGGATACACTGAAAAACCAAACAAAAAATAATCACCAGATGATAGCAATCGAACAATTAAAAGAAGTAGGGAAAATCTGGCTCGCCTGGGTAAGGCTTCAGGATCATGATCCTGCCGAAGTCACGCAAATATTTGAAAAACTTAAAGGAGAACTGGATGTAAAATTAGGAAGAGGCTGGCAAAAATATGACCCGGTAATAAAAAAAGGAAGGCCCCAATCCTATAAAAAACTCTTTCAAGAATTGGATAGTGACAGTCAGGAATCCGCAAATGCATTGATCAAATATTTCAATGAGGGAATACCGGAAGACGCTCCTGTCTCGCCAGTCATACACCTCATTCTCATCTTATGCGTCTGTGAAGTAGGACGTGGATATGGCCAGGCGCCGGACAATATGCTGGCTTATGCACATGATCTCATTGATTATCCCAATCTCACATGGAGCTCACTGCTGGACACAGCGGGAAGTTCCCCGGAATTCGCATTTCTATGCAAGGGTGATTCCGATGAAGACTATAAGGAATAATGCATGAGCACCGTCCGCAACCGACTCACCGGTGAGCCTCCCGCACGGCATGGGTTCCCTGTATCCGGGCACAGGGAACTTCACCCCCATCGTCGGCAAAAGGGACAGCCGCAGGCGGGCTCGGGCAATTCAGAAACATAGGCGGCCCTTCCTGCCTTCCATCTGTCTCAATCCTGCGGACATCAAAAGTTGGTGCATCAACTTTTCTCATCACCTCAATTTCCGGGCCAAAAACCTGGCCGCCGTCATGATAACGGACACCTTCCTTTTCCGGAAACCGAACTGCCATCAACAGGAAACCTTCCGGAAACGCTGGATTACCGCCGCATGGGGCAGATGCCCCAGAGCGGCACCAGGCCGTCCTGCATCTGACGGCGGGCATCAAATAAAACGCAGGCGGCCCCTACGCCCGGCAGCCTCAGACGGCCTGCCCGTCCATAATCGTAATAATGCGGTCCCCTCTCCGTGCCAGATGTTCGTCATGCGTCACAATCACCAGCGTTTTGCGGGACTCGTCCGCCAGGCCGAACAAAAGGTCCAGCACCTCCGCGCCGTTCTTCCGGTCCAGGTTCCCGGTAGGTTCATCCGCAAAAATAATGGGGGCGTCGTTCGCCAGGGCACGGGCGATTGCCACGCGCTGCTGTTCCCCGCCGCTCAGTTCGCTGGGCAGGTGGTTCAGGCGGTGGGACAAACCCACGCGCTCCATCAGTTCCCTCACATATTCCACGCGCGGCCTCCTGCCGATGGAGGAAGCCAGACAGGCGTTTTCCAGCGCCGTCAATTCCGGCATCAACAGATAATTCTGGAAAATAAACCCCATGCTCCGGTTCCGGAACACGGACCGGGCCGTGGCGGACAAGGCGTAAATATCCGTGCCGTCAATCGTGACCTTTCCCTCTTGCGGCGTTTCCAATCCGGCCAGCGTGTACATCAGCGTGGTCTTTCCGGCCCCGCTGGGCCCGCACAGGAACACCTTCTCTCCGGCGGCAATGTGCAAATCCACCCCATGCAGGATCTCAATGGACTTCTTGCCGATGGAATAGCTGCGGTGGAGGTTGGAGGCGGTAATCACGGGCGCAGTATAAGAAAATCCTTGCCGTATTAAAGTTCTATGTTGTCTATAAGACGCACGTCGCCGAAATAGACGGCGGCGGCCATCAGGGCCGGCTTCCGGTTCTCCGCCACCGGCTGCATGGTCTCCGCATCCACAATCTCCAGATAATCGATGCGCGTGCCGGGCACACCCTCAATCATGGCGGCGGCATGCTCCTTCACCTTGGAGGCGTCCGCCCCGGCCTGGAACTCGTCCCGGGCCTGAAGCATAGCCTGCCGTATTACCACGGCCTGCTCCTTCTGCTCCGGCGTCAGGCGGGCGTTCCGGGAGGAATAGGCCAGACCGTTGCCATGCCTTACAATCTCCGCCCCGTGAATGCGCACCGGAAAATCCAGGTCGCGCACCATGCGGCGGATAATGGCAAGCTGCTGGTAATCCTTCTTTCCGAAAATGGCGTCCGTGGGCTGTACCAGGTTGAACAGTTTAGCAAGCACCGTGCAGACACCGGAAAAATGCCCCGGGCGGGACGCACCGCACAATGTTGCGGACAAAAAGCTTTCTTCCACGGCAATGCTGCGTTCTCCGGAATACATTTCTTCAGGAGCAGGGGAAAACACATAATCCACGCCTGCACCTTCGCACACCTCCAAATCCTTTTCAGGAGTCCGGGGATAAGTCTGAAGGTCGGAGGAATTGTTAAACTGGATAGGATTGACGAAAATACTGGCAACCACCACGCCATCCTCCCCGGCCAACTTGCGCGCCTGTTCCAGCAAAGCGCGGTGTCCGGCGTGAAGGGCCCCCATTGTGGGGACGAGAACCACATGGTCATGTTTGCGGTGGTGCTTGAGGAGGGCAGCCCTCAACTGGGCCTTGGTGGAAAATGTTTGCATGCGCCTAAAGTGGCCCCTTGATGACAAAAGTTCAATCCTGAAATATTGCTAGAAGGTTGACGCATGCCATTCCACATGTAAACTGGTAAAGTCAGAAGACGCTAAAAATTATGAGATTCTTCAAAACAGCATTAGGTGTATCAGCAGCAATGGCATTAAGCGCCACGGCCAACGCTGAACTTAAGGTAGCGACGGTGGATGTGCAGAAACTCTTCGCCGACTACTACAAGACCCATGAAGCCCAGGCGGAAGTAGACAAGGCTGCCCAGACGGTGCAGGAAACCAACAACACCCGCGCGGAAACCATCAAAAAGATGGAAGCGGACTTCAATGACATGGTCAAAAGGCTTCAGGATCCCATGCTGAATGAAAAGGACAAAAAGGAACTGGAGCAAAAGGCCCAGATCAAGCGTCAGGAAGTCATTGCGCTGGAACAGGAACGCCGCGGATTTGTGGAACGCCAGCTTAAATCCCTTCAGGAACAAATGAAAGTGCGTTCCGCCAAAATCATGGGAGAAATCACAAAAATCACGGAAGGCATCGCCACCAAGGGCAACTATGACCTCGTTCTGGACAAAAGCGCCCAGGCCCTTCGGTCCAACCAGGTATTCGTCTACACGAAGCCCGGCATGGACATTACTCCCACAGTGATGAAAGAACTCAACAAGGACGCCCCTGAAGGGTTTGACCCCTCCAAGAAAAAGGCTCCGGCCGTCCCCGCCGCTCCCGCGGCCCCGGCCAACTAACCCTTGACATTACCCCCGGGAAGGCGGAATCATATACCCATGAAGCTTACACTTGAAGCCGTGGCCGCCCTCACCGGGGGAAAAATCCTTTCCGGCGACCCCGAACTGACCGTATTCGGGGTCGCTTCCCTTCTTGACGCATCCCGTGAAGAAGCCTCCTTCCTTGGCAATGAAAAATACTTCCAGGACTTCCTGCACACCTCTGCCGGAATCGTGCTGGTGCCGCCGGGCCTGCCTGCGTATCCAGAACACGCAGCCTTCGTGGAAGTGGACAACCCCTCCATGGCATTCAACGCCCTGGTGAAATATTTCATGGCTTCCGCCTACCGGTTCACCCCCGGCATTCATCCCACAGCCATCATCGACCCCACGGCCAGCTTCAACCCGGACAAAATCCATGTAGGAGCCTATACCTGCATCGGGGCCCACTGCATAATCGGAGACGGGACGGACATTGGCAACGGCTGCGATATCGGAGACGGCGTCACCATGGGGGAAAACTGCCGCCTGCATGCCCATGTTACCATCAGGGAACGCTGCAAACTCGGCAACAGGGTGACCATCCAGCCCGGAGCCGTCATCGGTTCCGACGGATTCGGCTTCCTGATGGGAGACAATGGACGCTACGTGGGCATTGACCAGGTAGGCATTGTGGAACTGGGGGATGACGTGGACGTAGGGGCCAACACCACTATTGACCGCGCCAGATTCGGGCGCACCATTGTGGGGGAAGGCACCAAAATAGACAACCTGATCCAGCTCGGCCATAACGTCGTTGTCGGCAGGCATTGCATCATCGTCGCCCAATCCGGCATAGCCGGCAGCACCAAAGTGGGGGATTATGCCACCATCGCCGCCCAGGTCGGCATCTCAGGACACCTCAACATCGGTTCCAAATCCACGCTGGGCGCTAAAACGGGAGTCCTCTCGGACATTCCGGAAAACTCTACTTACTGGGGCATGCCGGCTTTCCCCTACAAGGACGCTACCCGCCAATACGCCGCCCTCAAAAAACTTCCAGCACTGATTAAGGAAGTCCGCGCGCTCAAAAAGGAGCTGGATTCTTCCGGCAAATAACGGGCACCATATCGCCGGAACGCTCTGCAGCCCCTCTCCGTGTTTCTGTTTCCCGCCGCTTCAGGCCTGTCCTGTCCCCCAGCGGCGGTGCAGAAACCGTTCCAGAGGAGAAAACAGAATCTTGTCGGACAGTAGCCCAATCACCACAATCACGAACATGATGCCTACGACCTGGTTCATCGCGTTCAGTTCGCGGCCGAAATGCAGCAGCTGGCCCAATCCGAACCCGGTCAGGATGGGAACAAAAATTTCCGCAGCCATCAGGGAGCGCCAGGCAAAAGCCCATCCCTGCTTCATTCCGCTCACCACAAACGGGGCGGAAGCGGGAAGCGTCACATGAATCAGGCAATAAATGGGTCCTGCTCCCATGGTGCGCGCCGCACGGGCATAAATGGGCGGCACGTTCCGCATGCCGTTGGCCGTAGCCAGGATTACGGACCACAGCGTCCCCATAATCACCACGAACAGCATAGCCCCTTCCGTCTGGCCGAACCACAGGGTTGCCAACGGAACCCAGCACACGCTGGGCAGAGCCTGGAACCCCAGGGAAACCAGCCCCAGAGTATTCTGTATAAGCTGGAAACGGGAGCACAGCATGCCCAAGGGAATCCCCGTCACCAGGCCGATTCCATACCCCAGCACCAGCCTTTTCATGGTAATCCATGAAGCCTCCTCCAGAGACCCGTCCTGAAATGAAGAGCGCAGATACTCCCATACTTCCAGCGGAGAGGGAAACAAATAAGGCTTCCAGACCCTCAGGTCGCTCAAATACTGCCATGCCCAGACCACGAAAATGAAAAACACGAGCGAACAGGCGTGCGCCCCCCACTTGGACCATTTGTTCCTGTTCATGCTGTGGCTCCTTTCAAATCACGGGTAATGCGGGAGGACAATGCCGCCAAATCACTGCTGTTGATATCCCTGGGATGAGGGAAATCCACGAAATATTCCTCACAGACGCGCCCCGGATGCGGAGTGAGCAACACCACGCGATCCCCCAGGCACACGGCCTCGCGCATATTATGCGTAACGAAAACAATCGTCATGCCGCAGCGTAAATGAATCTCCTGAATATCCTGATAAAGCTGTTCGCGGGTCATGGCGTCCAGGGCGGCAAAAGGCTCGTCCATCAGCAAAATGCGGGGGCTGGTAGCCAGGGCGCGGGCAAGCGCCACGCGCTGCTTCATTCCGCCGGAAAGTTCATGAATATGAGCATGGGCACACTCTTTCAGCCCCACCAGTTCCAGATTCTTCTCCGCCATGGCAAGCCGGTCTCCCCGAGTCAGTCCGGGCACCAGATTCAAGCCGAACATCACGTTCCCCATCACATCCAGCCACGGAAACAGGGCGGACTCCTGGAACATCACAGTGCGGTCCCGTCCGGGTCCCGTCACAGGCTCCCCGTCCAGCTCCACCACCCCGGAAGAAGGAAACTCCAGCCCTGCAATGATATTCAGCAGGGAAGTCTTCCCGCAGCCGCTGGCCCCCACCAGGCACACGAACTCGCCTCCTCTGATATTCAAATTAATGCCCTCCAGAGCCACCACCTTTCCGCGCCTCCCGTCAAACACCTTGCTCACGCCGGCAATACGGAGCTTGCAGCCTCCGGGGCCGCAATGTTCTGCACCCGCAATCATCTTCCGCCCGCCTCCTTCGCCATGGTCAACTGTTCCTCCGCCGCCTCCGGCACCACCAGGCCGCCTATATCCGGAACCTCTTTCATAAATCCGGCCTGATGGGCGTCCTGCACAAACTGCCGGAGCTTGGGAATGGAAATCCTGTCCTTGATGACAATGCTTTTCCAGGCCTCCGCAATCAATGCCGGGTCTATTCTGGAATGCGTCAGCTCCTCCAGCTCCCTGACCACGATTAACTGGGCCTCATCCGGATGCAGCCTTATCCATTCGTTCAGTTCCTCATGCGCCTGCACCAGGGCTTTCGCCACCTCCGGCTTTTCCCTGAGAAACTCCGCTCCGCATACCAGTACGGTCGCGATGGACTCCTTCTCGTCCACCAGCACCCTGCCTCCCGCCGTCAGCACCAGGCGGCTCACCCAGGGTTCCACCGTCCACACGCCGTCCAGCTTGCCCTGCCGGAACAGGCTGAGCTGTTCGGGGTTGGGAGTCGGCAGAATCGTCACGTCCCCGCCGCGCTGCGTCACATGCAGGCCGCCGCGGGAAAACCAGGCGCGGGCGGAAACATCCTGCGTATTCCCCAGTTGGGGAGTAGCAATCACCTTGCCGCGGAAATCCGCAGGCTCCTTCAGCAAGGAATCCTTCGGAACCACCAGGGCGGCGCCTCCTTCCACAGCTCCGGCAATCATCCTGATATCCTCCCCGCGGGAACGCACAAACGCATTGATGGCCGGGCTGGGCCCCACATAAGCCAGTTCAATGGACCGGGCGAACACGGCTTCCATGGCGCTGGGGCCCGCATTGTACACGTACCAGTTGATCCTGACATCCTTTCCGGTAGCCTCCTTCAGCCGTTCTTCAAACCACCCCTTCCCCTGCCGGGAAAAATGATGCGCCACCAGGCCCTGCACATGTGTCACGTTGGGAAAATGACCGAAGTTCAACTCCACGACATTGGGATCTTCCTTCTCCTTCTTTCCGCAGGAAACAGCCAGCAGGCATAAAAACAGGGCCGGTAAAATCATCAGGAATCTGGAACGGGTCATAAGCAAAAAGAATGGGATACTGAGGCACAGCCGTTTTTCCGCGGCCTTTCAGGCACCGACTAGCAGAAGAAGCCGCCGGAGGCAAGAAGCCAAAGGCAACAACTTTGTCACAATGTGGACAGCAAAAATGCCCGTTTTGTTGAAAAACAATCCCGGTTCCGGATCTGAACCGGGCTTTATTGACAAACCTGCACGTTCCCGTTACAAAAGCACCATTCCCAACCCGCCATGCAATCCTCCCCCGACCAGCCCTCATCCGGAGCATGGCTGGGCCTTCCTCCGGATGAAGAACGCCCGGTAAGCCGGGGCGCCAGGTACCGTTGCATCTTTCTGATGCTCCTGTACTGGGCCGTTCTTTCCGCCATTCCACAAGGCGCGGACCGCCTTGCAGAAATAGGAACAAATCTCCCTCTCTGGATGGGAGCCCTGGAACTGACTCCCTTCCTGCTGGTGCCGGCCTTCCTGTTCCTGGCGGTGCGAACAGGCGCCCAATGGAGGGCCTTGATGATAACCGTATATGCGGGCCTGTTCCTGAACCTGCTGATGAACATGGCGCTTCCCTCAGGCGCCATGTGGGGAGACCTTCCCGCAGCCGGCCCCTACGGTACGCTTGTCTGGCCCGGAGGGGACCACCGGGAATGGCTGGTCACGCTGCCCCGTTTTCCCCAATTCTGGGCTCTCGTCATCTATGTCTTCCTCTCCGGCAGCGGAACCCCGCCCCTGCTTCGCCTGCTGGCCCTGGCCTGGTGGTTCCTGCTTTGCGTGGCCCCCGTGAACACGGGCATGGTGGGGTATGCGGACATCCTAGGGCCTCTGGTCATCATCGCCCTCGTCCTCGGCTGCATGCACCTGGCGGAAAAAAGAGCCTCCCGGCAAAATGACCGCTAACGGGACAAAATCGGCCGTTCTCCCCGGGGAGCGGCCATTCCCGCCTCTGTCAGCCTTTCCTCCATCACTCCGCCGCTACGGATCCAGCCGGTGGAACATCCGGGAAGGCGCCGCGCCGATTCCTCGGATTCGTTTTTCCGGGAACACGCTCCTGAGTCAGTTCATTCATCATCCCTCCCCTGGCCTTTGACCCGGTTCCGGAAAAAATCCGTCTGAACTGGTAGACAAACTCTACAGGCGGCCGAACCTCACCGCGGAAACAATCCACCGCGCCGACCGGCAAAAAACGTGTCTGAACCTCCGTTTTACAATCTTAAAAAACAATGGTACTCAAAATCTTACAACCATTTTTCATTTTCCTGACGGATTCCCTATCCGAAGTCAAGTTTTGATTTTAAGGGCTAATAACTTGCGTTGGCATTCCCTTGAATATCAAAGAGGATTTTCATGGAATTAAAATCTGCAGGCGCTTTCTGATTTTAATGAAAATTTTGAAATTTTTATGGTATTGCAACCTGCGGTCAAATTTTACTAACTCTATTCCCATGAAACAGTTACATGCGGCAAAAGTTTATCATTACATGACCGTAAAGATGTGTAACTTTCCTGAGTCAAACAGAATTTTCTTCGGTTTCAGCGCTATAGCGGATAGGGAATCCGCCAGAATCAGAACACCTTAGACAGGAGGTTGGACTTGATGAATTTTAAAACTATCAGAAAAGTAGGGATTCCTGCGTTGCTGACCGGCACTTTTGTATTGTCGGCAGAAGATTCATCGGGTGCTCCCACTTATAAAGAAGCGACGATTGCAACTTTGACGGCCAGTCGGGCGAACCTCCAGATGAAAAGAACTTTCGCGGAACAGGAAAAGCTATTGACCGCCGAACAAAAAAAATTGTGGAAAATATTTAAATCCAGTCCTCATTTTACTTCTATCTTACCTGAAATTAAATTAAACATGGATATTTCTGGAATTTATGAGTCCACGGGTGGGATTTACATTCCATTTGCCCGTTGTGAGGATATCGCGGAAACCGACATTACCATCAAAGATCGTTACAGAAAATTTCCTTATAAATCGATTGTCTTTGATCCTGCGAACCGTGGGTATTATTCCGTTTGTGTCCACTATTTTATAAATAATGATTACATTTTACCAACCTATGATGTCCCTGATTTTATCAAATACCATCATGACTGCTATTTTTACACCTCAAAGTCTGAACTAGATAAATTATCGGAACAAGAAAAGAAATATATTCAAACGCTGGACAAGGCATTAAATACTATTTACCATAGCATTTATGGGATTGGATACGCAAAAATTGATACTGATAAATTAAAAAAAGGCCACCTGGCTGCTTATCTTTATAATCATTACAGACTTATTGGTGGAACGGAGTATATAGATAAAAGCTCACTCCCAAAAGTCCTATGCGCCAATGCTCGACAACAGAGGAAAAAAAACAAGGAAATAATGGCCTCGTGGAAGCGACATGTGAAGAAGTTGGAGACAACAACTCCTCGAGAAAGAAATAAAGGAGTAAGGCGCAAGTTTTCTCATTGGATAAAAGAGCGTTCTGAATTAGAAACCTCTTTCTATGATATTTTGGGGTATCCGATTTCAGTATATGACCTTATAAAATCTTTTGCTGGAAATTTCTCTTCTTCCCACTATCATATCTGCGGCGGTTGCTTCGGGGAAATTTACTATTCTTCGGGATCAAGTTGCTTACCTGGATTCACTGGGATACCTTTCCCTGAAGACCTTTCAAAGGACTTTAAAGCGCTTAATGCGATACTTGACCATATTAGAGAACGACAAGCGATGAATCTTCACATCAGATGTTGGTCGCTTTCTAAAAGAATAAAATATTTGCTGAATGAAAATTTGCCGCACGGAGGGACCAGACATGAAGCTTATCTGAACGGCTCCCCAGAATTCAGACAAATTTTAGAAGACAAAGTAAATACCGTCCTCCTATACAATAAGAGACTGTGTTTAAATGCAGATAGAATGACTAAAGAAATGCCTAAATGTATAGCCGATACCAGGGCATTGGATTCGTATATCAAAAAAAATTATGCCCTGAAAGTGTCGGATCAAAATGCGGCCCAAGCTCAAAATCCCGGCAAATAAACTGTAGGGGTGCGATGTACGGGAAAAAATTTACAATCAGGGAGAGCCATTGAATGAAAGCAGATCTGTTTGCCCACAGGAGGACAGCCAAGGGAAGGCATGTCATCAAGGGCAGCAAAAGAACGACCTCCCCTATTTCCCACGATTGAAAAATCCGGAAATATCGAAATAGACGGACAGCTTCTTGCGTCCTGACCGTAACCAGGTCATAATATTTGCCTCCTTTCTTGACGAGTCCCTGTATGAGTACTTCGTAATCTCCGTACTTCTTTTTGAACCATTTCTATTCGGCAGGGACGCCGTCCATCGAACTTTTCGCCCCGACAATCACGACGGCCTTCTCAAAGCTGGAGCCGTCTCCACCCTGATAGGTAATGCGGACAGCCCTCTCTCCTTTTTCCAGCCGCTCGGGCATGGCTGTCTCTAAAGTTTTGGCACAGGAACACTCATCATCCCAACCCGGCAGTCGGAACAAGGGGAGGGCTGCCAGGCCAATCCTGAACAGCCATTCCCAAACTCACAGCCTTGCCGCCACTTCCTCCAACGTCTCCACACGGGCCGTGGCGAGGGTGGGGACATCTTCCGCAGAAAACAGGAAATTGCGGCCTACTCCGGCGGCAAGAGCCGCCCGGATGTCCCGCTCCCGGTCACCTACGGACACGCAGGCTTTCATATCCAGCCCATGCGCGGCGGCAGCCTTCAAAAACAGGCCGGGATTCGGTTTCCGGTCGGGATGTCCGTCATCCGCGCTGGGACAGTGAAACACACCTGCAATCTCCGCACCGGACGAACGGAACACCCCTCTCATATATTCCGTCAACCGCTCAAAGTCATCTTCAGAAAACATGCCGCGTCCTATGCCGGACTGGTTGGTCGCCACCAGCACCAGATACCCCTTCTCTTTAGCCTGGCGGCAAAGATTCAGAATTCCGGGAACCAGCCGGAAGTCCTCTATGCGGTGCACATACCCGCCATCCACATTAACCACGCCGTCCCTGTCCAGAAACAAAGCCTTACCCGCCATATATCTCCCGTTCAACCAGTTCGCAAACCAGATGCCCCACTGCAATGTGCATCTCCTGAATGTTGTTCGTGGCATCGGAAGGAACGGCAATGCAAAACTCCGCAACTTCTTTCATCTCTCCGCCTCCGCGGCCTGTCAAGGCCACCGTCCGGACGCCCATCCGGCGCGCCAGCTCCATCGCCAAAACGATGTTCCGGCTGTTGCCACTCGTGGAAAGCCCCACCAGCAGATCTCCGGGACGGCCCACTCCCTCCAGCTGCCTGGAAAACACCGTCTCATAACCGTAATCATTGCCCACGGCGGTCAGGATGGAAGTATCCACCGTCAGGGCCAGCGCATTCATGGCGGGGCGGTTCAGCTTGTAACGCCCCACCAATTCGGCGGCCAGGTGCTGGGAATCCGCAGCGGAGCCTCCGTTGCCGCAGAACATGATCTTATTCCCGGCCTTAAGGGTATCCGTACAAATGCGGGCAACCTGCTCAATATCCCCGGCCATGGAAGCCAGGGCTTTGAAATTGTCCGCTATCCCTAAAATCTGGTTCCTGATGTATTCGCTCATTCCGGGCTGCTTTTCATGCGGTTGATGATATTGGTGGAGGAAAAGCCCTCCAGGCGGGGCAGTTCAAGAGCCTGCCCTCCATAAGATTTCACGAACTGTCCTTCCGGCCAGAATTCCAGGGGATACCCTTCTTTGGCAATCACGTCCGGACGCAGCTTCTCAATCAGCGGCAGAGCCGTATCTTCATCAAACGGCACTACATAATCGACAAAATCCAGGGATGCCAGCAGCAGGGAACGCATCTCCTCATTATTAATCGGCCTGTCCTCGCCCTTCAGCCGCTTGATGGAGGCGTCCGTATTCAACCCTACGAAAAGCACGTCACACGCTTCACGCGCCTTCATAAAGGAATGCAGATGCCCCAGGTGGAGCAAATCAAAACAACCGTTTGTAAATCCAACCACCTTCTTCTCACGGCGGAAACGTTCCGCCAGCTCCGCCGCGGCCTCCGGCGTCAGGATATTGTCGCGGTGACGCCAGGAGGGCGTCTGGATATCCTTCTCCTTCAACGCCTCCTTCAATTCCGTTCCGGTCACGCTGGCCGTTCCGAACTTGCCCACTACGATGCCGGCGGCCACATTGGACACGACCAGAGCATCCGGCACTGCGGCTCCGGCGGCCAGCGCCGCGCCCAGACTGGCGAGGGAGGTATCACCCGCACCGGACACGTCAAAAACATCCCGGGCCTCCGTAGGAATGCAGACAAAATCGGAAGGGCTGGAGGAAGGAATGAAAATCATGCCGTGTTCGCTCAAAGTGACAATCAGATTCTCAATGCCGAATTCGGAAAACAGCCTCTGCGCCCCCGCCACGGCATCCTGTTCCCACCCGTCCATGGCAGGATTGAGCGCCACGCCGGTAGCCTCCTGGAACTCCTTCATATTGGGTTTCACCAGCGTGGCCCCCCTATAGCGGGAATAATCCGCTCCCTTCGGGTCCACAATAACGGGCTTGCGCGCCGCACGGCACCGGGCAATCACATCCGGGGCGGTTCGTCCCTCAAACAGGCCTTTCCCGTAATCGGACAAAAGGACAAGATCCGCCTCCGGCAAACAGGCATCCACTCGTTCCAAAAAACGGGAAGCCAGCTCCGGCTCCATCCTCAGCGGCTGTTCCTGATCCGCTCTCAGCAAATGGTGCTTGCCCGCCACAAAACGGATCTTCACGGTCGTCTCATACCCCTCGCACTCCACCAGCTCCGGAACGCAGTGCGTCTCTTCCAGAAAACGCCTTACCTGCCGCCCATGGCCATCATCCCCGACCACGCTGATAAAAGTGGTGCGGCATCCCAGCGCGCACAGATTGGACACGACATTGCCCGCGCCTCCCAGCATGCGCGTCTCCCGGTCCATCTTCATGATGGGAACCGGAGCCTCCGGCGAAATACGGCTGACGCTCCCATACAGGAACTTATCCAGCATCACGTCCCCGATGCAGAGAATCCGCACCCGGGAAAATCCGTCAATCAATGTATGCAGCCGGTTCATGGGAGACGTCTCTTTCTACCACGCCCCCTGTTATAAGGCAAGGACAGTTGAACCGCGCTCCGGAACCGGAAAACGGGGCAACGCGGCCGCTTCCCGCCGTTCCGGACGGCAGAACACTCATTCAGCGGCAAAAAATAGGTCAGTCATGATAAAAATCTTGACCTGTAAGCAGGAAAGCACTACCTATTGTCTCCCGTTCCCGGACGAATAGAAAAAGGAAAACAAATCATGCGTGAAGTTACCGTAAGAAAAGGTGAACCTATCGACCGCGCTCTCAAGCGCCTCAAAACAAAACTGGATGTTGAAGGCATTCTGGATGAAATGCGCCGCCGCCGCGCCTTTGAAACCCCGATGGACGAACGCCGCCGCAAGGCCCGTTCCGCCAGCAAGCGCAACAAGGTAAAATGGCGTTACAGCAACAAGAGCGAAGAAACCGCTTCCGAAACTGCTGAAACACCCGCTTCCGCTCCTGAAGCTTAAAACAGTATTGGTTCGGGAATAAGAACCCAATTTCCATGAGGGCAACCCTTTCTCAAGGATTGCCCTTTTTGCTTCCCTCTGCACATGAATCAGTCAGCCCAGCTCTTCTCCCGCGCGCGCTCCGTTATCCCCGGCGGAGTCAACTCCCCCGTCAGGGCCTTCCGCAATGTGGACGGGGACCCCTTCTTTGTTCAATCCGCCAAAGGAGCCTACATCACGGATGCGGACGGGCGCCAGCTTATTGACTACATAGGCACATGGGGCCCGGCCATTCTGGGCCACGCTCCGCAACCCGTTCTGGACGCAGTACATGCCGCCGTGGACAGGGGGCTGGGCTATGGAATTCCTGCGCCGGCGGAAGTGGATATGGCGGAAATGATCACGGACATGGTTCCCTCCGTGGAAAAAGTGCGCATGGTCAACTCCGGAACGGAAGCAACCATGTCCGCCATCAGGCTGGCTCGAGGCTATACCGGACGCCGTAAAATCATTAAATTCATCGGCTGCTACCACGGCCATGTAGACTCCCTTCTGGTGGCGGCGGGTTCCGGAGCGCTCACCTTCGGAGAACCGGACAGCGCGGGCGTGCCCAGGGAAATGACGCAGCTCACCCTCACCGTGCCCTACAATGACCGGGAAGCAGTAAAAAAAGCCTTTGAACTCCATGGAAGCGACATTGCCGCCGTCATCCTGGAACCCTTTCCTGCCAATGCCGGGCTCTACTTTCCGCAAAACGACTTCCTTCACTTCCTGCGGGAAATAACGCTCCGCCACGACACCCTTCTCATCTTTGATGAAGTCATGACGGGCTTCCGGGTCGCTCCCGGAGGCGTGCAGCAGCTTTACGGCATCACTCCAGACCTTACCTGCATGGGCAAAGTCATCGGCGGAGGCCTCCCCGTGGGAGCTTTTGGCGGCCGTTCGGAAATCATGGACTGCCTTTCTCCGCTTGGCCCCGTGTATCAGGCGGGAACCCTGTCCGGCAATCCCGTTGCCATGGCGGCGGGATTGGCCCAGCTCCGGGAACTCCTGAAAGGGAACGCCTATGAACGCCTGGAACAGCTCGGAGCACGCATGGAAGAAGGCATCCGCGAAGCCCTCAAAAAACACGGCAGAAATTACACCTTCCACCGTGCGGGCTCCATGTTCTGCCTCTTCTTCACGGAAGAGGAAGTCTATAATCTGGAATCCGCGCAAAAAGCTTCCAAAAAACTCTTCAAATCCTTCTTCTGGAACATGCTGGAACAAGGCGTCTACTTCGCGCCTTCCCCGTACGAAACGGGCTTTATCTCTACAGCCCATACGGAAGAAGACATTGACCGCACGGTGGAAGCTGTCCGCATCAGTCTCTCCAGGCTGGGTTGACATGCCCCTGATTTGAAGTGACAGCTCACGGCCCTTCCCCTATAAGGACTTCATGAGACAAATGACAGGAACCATGCTGCTTCTGTCCTCAGCAGTCGTTATGGCCGCTCCCTGGGCGCATGCGGAGGAAAAGACAATCCAGCTCACGGAAGCGGAACAACAGGAAATCAAGACGGCAAACGAAAAGCTGCTGGGCCTTACCCTCCGTTTCCTGCATGACTCATGGCCGCTGGAAATCATGTTTCCCGGAGAAGTGCAGGAGGAATTCCACTCCATTCTTCAATGCCATCAGATGCTGGAGCAATTCCGCCAAACCGGCAACCTCCTGCTCCAGACGCCGGACCGTACCACGCCTCTGCACCTCTGCATTGCCCTGGGATTAAACCGGCTGGCCGTCCGGATGGTAGAAGCGGGCGCTCCCGTCAATGCTCAATCCATTTTCATGCATGACGGCACAAAAGAGCCGGGAGACACCCCCCTTACCTGGGCGTGCCTCTCAGGCCTTTATATGAATTCCACCGCGGAAGAAAGGCTGCCGCTGGTGCACGCCCTGCTCAAACACGGCGCCGAGCCGGATCAGCCGGGGCCTTGGGGCGTCACCCCGTTCATGTATTCCGCCGCCCTCAATGACTCCGATCCGGGTCAGGAAAAAATAGCGCTGGCGCTCCTGGACGCCGGCTCCCCGGATCTCAAGCGCAGAATGAACGCTCAGGCGCGCGGAGTCGGCTTCCTCAGTCTGTCCCCCGCCATTTACGAACGGCTCATCAAAGCCGGCTGCGATGTTAACGAACGCTTTTTTGAAAGCAAGCAATCACCCCTCCACCTGGTCTGCACCAAGGAAAAACCGGCGGAACGCCTCATTCCTCTCATTGAACTTCTCATCAACGCGGGAGCGGATCCCAACCAGCCGGACGTGGATGGCCTGACTCCGCTGATGGCCTGCAACTCTCCGGAAATAGCCGTTTGCCTCATGAATCACGGCGCCAATCCCTCTCTCCGCAACGATGACGGCCAGACAGCCTATGACTTCCATATGAAAAACGGGTATCCCCCCATTGCGGAAGCCATCAAGCACTGGCAGTCCAAGCAGAAAAAAGGGGAAACCCGCTAAATAGCGGACGCCATCGCAGGCTTTGCAGCCCTCCTTCTCCCACTGGTGCGGGAAAGAAAAGCGGATACCGTCACGGCCCGGGAACAGGCGGCCGGAACCCTCTCCCCGGAACAGCCGTGCTTCCGAAGCCATGCAGGCAGCCCACCCTGTTTCCTGTTATAAAAAAGCCGCTTCCCCAATTCAGGGGAAGCGGCCTTGAAAACCACAGGAAGAAAGAATTATTCCTCCGCTTCTATGCCTTCGCCGATCATCTTGGGATCTTCCGGCATCTTGCCTGTTGTGAAGTAACCTTCAATTTCCTCCAGGGAACGTCCCTTGGTTTCAGGCAGGAAGAAGGTCACCGTAATGAAATACAGCACCGTAATGCCGCCCAGCGTAAAGAACACGCCGGAATAACCGCAGCTGCCCACCCACGGGAG sourmash-bio-sourmash-bc3c0f8/doc/genomes/shew_os185.fa000066400000000000000000017205301474157046300231150ustar00rootroot00000000000000>NC_009665.1 Shewanella baltica OS185, complete genome AAAGAGGCCGAATTGTAATCACAATACTTAGTCACGTCAACAACGAAAGTAAGTAAGTGCTTTATTTCTGCCAGCGTATCCTTTGATCAATCCCCAGAATACACCATAATATGTGGATAACTCTGTGTACGGACACTACATATAGTAGTGCAGGGGATCTTCGAGCCGATCGAAGGGGCGCCATTATAGATCAATGTGGATCGCTTAATAAAGCAGGATTTGCGGATAGTGCAGAATAAAAGGATCGTTTTGGATCTTTGATGATCGAGGATATCCACACAGAATGATCTCGCTGGGGATAAATATTTTTTAAGGATAGCGATCCTTGAGATCTCGCTATAGAATACACCTCTTTTTTGATGATCTTTTGGGGATAACTAAGTGGCTGTTTCACTTTGGCAACAATGTATCGGACGATTGCAAGATGAGCTTTCTGCTCAGCAGTTCAGTATGTGGATCAGACCGTTACAAGCCGAAATGGATGGAGATACTCTGGTGCTTTATGCGCCCAACCGCTTCGTGCTTGATTGGGTCAGAGATAAGTACATCAATATCATTAATCAGTTCTTTACTGAGCAGATGGGCAGCGATGCACCTAAATTGCGTTTCGATATTGGTAGTCGTCCTTCGGCGAAAAAGCCAAGTGTGCCAGCACCTATAGCGCCAACTCGTGTAGCGAATACCCAAACTAAAGCGACTGTAGGCACGACGTTTAACGTCCAAGCCGAGCCGATGGCGAACGCGAATCACCGCAGTAACATCAATCCTAGCTACCAGTTTGATAACTTCGTAGAAGGTAAGTCGAACCAATTAGGTAAGGCTGCGGCGTTGCAAGTGGCTGAAAATCCAGGTGGTGCTTATAACCCATTATTCTTATATGGCGGAACAGGTCTAGGTAAGACTCACTTGTTGCACGCTGTCGGTAACGGGATCATTAAAAATAATCCGAATGCCAAAGTGGTTTACATGCATTCCGAGCGTTTTGTCCAAGACATGGTTAAAGCGTTGCAAAACAATGCGATCGAAGAATTTAAACGTTACTACCGTAGCGTCGATGCTTTGTTTATCGATGATATTCAATTTTTTGCCAATAAAGACAGATCCCAAGAAGAATTCTTCCATACCTTCAACGCCTTGCTTGAGGGTAATCACCAAATTATTTTGACTTCAGATCGTTATCCAAAAGAGATCGACGGTGTTGAAGACAGATTGAAATCGCGTTTTGGTTGGGGTTTAACCGTGGCTATTGAGCCACCAGAGTTAGAAACCCGTGTCGCGATTTTGATGCGTAAAGCTCAAGAGAGTGGCATTAACTTGCCCGATGAAGTGGCCTTCTTTATTGCTAAACGTTTACGTTCAAACGTGCGTGAGCTAGAAGGGGCGTTAAACCGCGTTATCGCTAACGCTAACTTTACTGGTCGACCAATTACCATTGATTTTGTTCGTGAAGCTTTACGTGACCTCTTGGCACTTCAAGAAAAATTAGTCACTATAGACAATATTCAGAAAACCGTAGCTGAATACTACAAGATCAAGATGGCTGATATGTTGTCTAAGCGCCGTTCACGCAGTGTCGCGCGGCCAAGACAAGTGGCAATGGCGTTATCCAAAGAATTAACTAACCAAAGCTTGCCGGAAATTGGTGATGCTTTTGGTGGTCGAGATCACACCACAGTGTTGCATGCCTGTCGTAAGATTGCTCAGCTACGGGAAGAGAGCCACGACATTAAAGAAGATTATGCTAATTTGATTAGAACCTTATCTTCTTAAAACAAGGAATTTGGACACTATGAAATTCTCAATTGATAGGGATGCCCTATTAAAGCCGTTGCAATTGGTGTGTGGTGCGGTAGAAAGACGCCATAATTTGCCCATTTTGTCTAACCTCCTTGTAGAAGTAAGTGGTCACTCACTTAAGCTGACCGGAACCGATTTAGAAGTCGAACTTGTGGGCCAAGTTGTGGTTCACGGTGATATCGAAGAAGGTCGTACTACGGTTCCGGCTAAAAAGCTGCTCGATATTGTTAAGTCTTTGCCAGAACAAAGCGAACTGAAAGTCGAACAACAAGATAATAAATGGTTGCTGCGTTCAGGCCGTAGCCGTTTTTCATTAGCGACGTTGCCCGCAGAAGAATATCCCAACGTTGAAGCCTTCCAAGCTGAGATTGAATTTAGCTTGAAGCAGGGTGTGTTGAAGTCGATTATCGACTCAACGCAATTTTCTATGGCTAACCAAGATGTGCGTTATTACCTCAACGGTTTATTGTTTGAAACTGAAGGTAATGTGTTGCGCGCGATTGCGACCGACGGCCACCGTTTGGCCTTAAGTCACCGCACAATTGAAGCGCAATTACCTGAAAAACAAGTCATTGTTCCACGCAAAGGCGTAATGGAAATGGCGCGTTTATTGGAGTCGGACGATCTCGATATCGCGATTGCCATTGGTGATAATGCCATTCGTGCTACTACGGCTAACACTGTATTTACCAGTAAATTAGTCGATGGCCGCTTCCCTGATTATCGCCGTGTATTGCCAAAGGGCGGTGATAAAATTGTGATTGCGAGTCGTAATCACCTGAAGCAAGCGCTGACCCGCGCATCGATTTTATCGAACGAAAAATTCCGTGGTGTGCGTATTCAACTCGAAAATGGTCTGCTTAAGATCACCGCTAACAATCCTGAACAGGAAGAAGCGGAAGAGATCATCGATGTGGAATACAACAACCAAGCCTTAGAAATTGGTTTTAACGTCAGTTATTTACTCGACGTGCTCAACAACCTTAAGTCCGATGATGTGCGTATCACGCTCATTGATGGTAACTCCAGCGCCTTAATCGAAAACCATTTGGAAGAAGATTCCATGTATGTGGTTATGCCGATGCGCTTATAAACCATCGATATTTACGTTACCCGTGTCTGTACCTCTGTCTTCGATGTCTATGCCGCTTAACGGCGGCATAGTGTCTAATACGCAGCATTGGCCATTCCTTCACTCATTTACCGATAGCCTTAGCCTGTGGCAGCGATTGAGTTGCCTGTTAAGTGTTATGAATCCTTGCCTTCGCCGTTTACGCGGCTTAATGAGTAATGCATGAGCCTGACCCGTCTCAATATCGAAGCGTTTCGCAACATTCAGTCCGCACAGTTAATCCCTGCACCTGGGATTAACTTGATTTATGGCCAAAATGGCAGCGGCAAAACCAGTATTCTTGAGGCGATTTACTTCCTCGGTATGGGGCGTTCGTTTCGCAGCCATTTATCCCAACGAGTGATTAATAACGATGACGATAAGTTAACGCTCTTTGCAACCTTAAATTTAGCGCGCGGTGACAGTAAAATCGGTTTGCGCCGATTTCGTAGTGGTGAGACTGAAGTGAGAATCGACGGCGAAAAAGTCAAACGTCTGTCGACATTAGCCGAAACCTTACCTATTCAAGTGATCACGCCTGAAAGCTTTTCTTTACTGTTTGAAGGCCCAAAATCCCGCCGACAATTTATCGATTGGGGCGCATTTCATGCTGACCCACTGTTTTATGGTGCTTGGACGAATGTTCGCCGCGTTCTTAAGCAAAGAAATCAATTACTTAGAAATGGCTCGTCCTATAGTAATATTCAATTTTGGGACCAAGAGTTTGTGCGTTATGCCGAGCAGGTTACTGAAATACGAAACCATTATGTAGACTCGTTAAATGAGCTACTTAAGGGTATAATCGGGGAGTTTTTACCGAGTGTAGATGTGAAGGTTTCATTTACGCGCGGCTGGGATAGTAAAACGGATTTTGCAGAACTACTCGAAAACCAATATTCCAGAGATTTGGCCACAGGTCATACCGTCAGCGGTCCCCATAAAGCTGACCTTAGACTTCGTGTAGGCACTTTGCCGGCGCAGGATGCCCTGTCCCGTGGTCAATTAAAATTATTAGTCTGTGCACTGCGTATTGCACAGGGAAAGTTGCTCAAACAACAAATAGATAAACACAGTATTTATCTTGTGGATGATCTTCCGTCCGAGTTGGATGCACAGCATAGGCAGCTATTGCTTAAGCAGTTAACGGATACTGGAGCACAAGTTTTTGTGACCGCCATCGATCCTGCTGCAATAGTCGATTCGTTACACACGCCTCCCAGTAGGATGTTTCATGTGGAACAGGGACGTGTAACGGTAATTGAATAACCATTGAGAGAATAATATGTCAGAGAATAGTTACGATTCTTCGAGTATTAAGGTCCTTAAGGGCCTTGATGCGGTACGTAAGAGACCTGGGATGTATATTGGTGACACCGACGACGGTACGGGTCTACATCATATGGTGTTCGAAGTAGTCGATAACTCTATCGATGAAGCCTTAGCCGGCCACTGCAGCGATATTACCATTACTATCCATGTCGATGGTTCGGTTTCTGTTAAGGATGACGGCCGTGGTATTCCGGTCTCTATCCATGAGGAAGAAGGCGTATCGGCAGCAGAAGTTATCATGACGGTATTGCATGCCGGCGGTAAGTTCGACGATAACTCCTATAAAGTGTCTGGTGGTCTCCACGGTGTGGGTGTATCGGTTGTAAACGCTTTATCTAAAAAGCTGCAAATGACCATTCGTCGTGCTGGCAAAGTGTACGAGCAGTTTTATACTCACGGTGTGCCTGATGCGCCAATCAAAGAGATTGGTGACGCGACTAAAACCGGTACCGAAATCCGTTTTTGGCCAAGTGAAGATACCTTTAGCGATGTTGAGTTTCACTTTGAAATTTTGGCAAAGCGCGTGCGCGAACTGTCATTCCTTAACTCAGGCGTAGGTATTCGTCTGGTTGATGAACGCGACAATAAAAATGAATTTTTCAAATACGAAGGCGGTATTAGCGCATTCGTAGATTATTTGAACCGTAATAAAACCCCCGTCAACAAAGACGTATTCCACTTTATGCAAGAGCGTGAAGATGGCATTACCGTTGAAGTGGCTATGCAGTGGAATGACGGTTATCAAGAAAATATTTTCTGTTTTACCAACAACATTCCACAGCGTGATGGTGGTACTCACTTAGTCGGTTTCCGTAGTGCGTTGACACGTAACCTCAACAGCTATATGGAAAACGAAGGCTATAACAAGAAAGGCAAAACTAGCGCGACTGGCGACGATGCCCGTGAAGGTTTGACTGCCGTTATCTCTGTTAAAGTGCCTGATCCTAAGTTCAGCTCGCAAACCAAAGATAAACTGGTTTCTAGTGAAGTGAAAACCGCGGTTGAACAAACCATGGGTGAGAAGCTGAACGATTATCTGCTGGAAAACCCAGCCGATGCGAAGTTGATTGTCGGTAAGATTGTCGATGCAGCCCGTGCCCGTGAAGCGGCGCGTAAAGCCCGTGAAATGACCCGTCGTAAAGGCGCACTCGATTTAGGTGGTTTGCCAGGTAAGCTGGCCGATTGCCAAGAAAAAGATCCCGGTCTTTCAGAAATTTACATAGTGGAAGGTGACTCTGCTGGCGGTAGCGCTAAGCAGGGACGTAACCGTAAGAACCAAGCGATTCTGCCACTGAAAGGTAAAATTCTAAACGTTGAGAAAGCCCGCTTCGATAAGATGTTGTCTTCTCAAGAAGTGGCCACACTGATCACGGCGCTTGGCTGTGGTATTGGTCGCGACGAATATAACCCAGATAAAACCCGTTATCACAACATCATCATCATGACGGATGCTGACGTCGACGGCTCGCACATTCGTACCTTGCTGTTGACCTTCTTCTTCCGTCAAATGCCTGAATTGATTGAACGTGGTTATGTGTATATTGCTCAGCCACCACTATTTAAAGTGAAAAAGGGCAAGCAAGAACAGTATTTGAAAGACGAGCCAGCATTGACTCAATATCTCACGACTCAAGCATTGGATGGCACTATTGTGTACCCAACGCAAGGTGCGATTGGTATGTCAGGTGAGCCGTTAGAGCGTTTAGTGACTCAGTATCGTGAAGTGGAAGCCATTGTTGCCCGTTTAGAGCAGCGCTATCCAACGCACATTACTAACCGTATGCTTTACCACCCAATGATCACCAACGAGATGTTGGCCGATGAAGGCAAGATGAAAGAATGGATCGATGCCTTTATCCATGAGCTGGTGGAAATGGAAAGCAGCGGCGTACTGTATTCTGCTGAGCCGATTCTCGATCCTGAGCGTAAAGTGTATCTGCCTAAGATCACCATTCGTAAACACGGTATCGATACTAACTATTTGTTTACTTATGACTTCTTCCAATCAAGCGATTATCAACGTATCGGTAAGTTAGGTGCGGCCTTGGATGGCATGATTGAAGAAGGCGGCTATGTACAACGCGGCGAACGTGTTAAAGAGGTCGGAAGCTTCCTTGAAGCCCTAGATTGGACGATTTCTGAAGCCAAACGCGGCTTATACATTCAACGTTATAAAGGATTGGGTGAGATGAACCCTGAGCAATTGTGGGAAACTACAATGGATCCTGAATCACGCCGTATGTTGCAAGTCACGATTGATGACGCCGTGGGTGCCGATCAGTTGTTCACTTGTTTGATGGGTGACCAAGTTGAACCTCGCCGTGAGTTTATCGAAGCGAACGCATTGAACGTGGCTAACTTAGACGTGTAGTGCTAGATGCGCAATGCGTGTTAGTACGTGAATGATTAAGGGAAAGCCAAGGCTTTCCCTTTTTTATCTCTATGATTTATCTCTTCTATCGATCAATACCCGCTTCATCCGCTGCAACAATTTCAGTCTCAGCTTTAGCCGCACTCATCCACGCCTGCATTTCCGGACAGCTGATCACATGCTGCATATAGGCACTGCTTGCCTGCGAGACTTCAACACCGTAGGTGAGAAAGCGCATGACAACAGGCGCAAACATCATGTCGGCAATCGACCATTGGCCAAATAACCAAGTACCTTCAGCATGTGATGGGGCAAACTCGGCCATTTGTGCTGACCAAATCGCATCGATGCGCTCAATGTCTTTGCGGACCGCATCAGTGAGCTCAACATAACGGCTCGCGCGTATGTTCATTGGTAGAGCGTTACGCACGGCATTAAAGCCAGAATGCATTTCACTGGCAATCGATCTAGCCTTGGCTTTTTGTTTAGGATCTTGCGGCCATGCACTATCGGCAAGATAGGTGTCGTTGAGGTATTCGCAAATCGAGAGTGAATCCCATACCGTGATATCGCCATCGACTAATGTCGGTACTTTTAACGTCGGCGAAATGGCTTTGAGTTGCTGATAAAAGCTGTCGGTATCGAGTTGCAGCAATACTTCGTTGAACTGCACGCCAGATTTCGCAGCCATTAACCAAGCCCGCAGTGACCAGCTAGAGTAGTTTTTATTGCCAATATAAAGATCCATTGTTTGTCCTTGAGTGAAATGCCGAGTGTTTCTGCAAAGCTACTATATGGGCTCATTTCACCTATGACTAATGAATTAAATCTAGCTTCTTGATTTCTATATACCTAGTTGAGACTGCGTTGTAAGCGATAGAAGTAGTCATGCCATTCTGCCTGCGCCGTTGCGGCTTGTTCATCGTCGACTAATTCGAATGTTACTGTTTGCCCTGGGCGAGTTTGGGCAAGCTTCCAAAGATCGGCTTCAATAATTACGGCTATTTGCGGATGACTGCTGGTGGTCTGCGCATCGGCAAGTAACACGAGTGGTTGCCCTGAAGCGGAGACTTGAACCACTCCGGGCGTGACTGTGTGTGATTGCACAGGGAATGGCTTCGCCAATGTTAACGTATCACCTTTGAGTATTGCACTCGCACGATCACTTTCAGCTGAAAGTTGCCACTGTGTTTGCCAGAATGTTTGCTGAGATTTCAACGAGAAGGCTTGAGTGTGCTTGGTCGCGAGTGCGCGAATATGCCCATCCCAACTTTTTTGAATTGCGCCAACTTGCCTTCTTAGTGGATGAGCAGAACCTAAGGTCAGGCGATCGCCCGCTTCTAATGCGCGGCCATGAAATCCACCGATACCTGAGGTTAAATCGGTAGCAGCAGAATTCATCACTAGCGGTACTTTAACGCCGCCATCAACGGCAATATAAGCACGCATGCCTTTATTTCCCCCTCTAAGACTGAGTTTTTGCCCTGCCTTAATGGAGTAACGCCAGCCGCAAACTATGGGCTTATTGTCTATACGAGCATCGAAATCGGCACCGGTTAAGGCAATCCAAGCGTCGCGTTTAAACAAAAAAATCGATTGGCAGAAGGTCAATTCTAATACGGGCGTGTTGTCTGGGTTTGATAATAGGCGGTTTGCTAAACACAGGGCAGGTCTATCTAAGGCGCCAGCTTGGGTTACACCTAAATGTCGGTATCCCATGCGTCCGAGATCTTGAACTGTGGTTAGTGCACCCGCTTTTATTACCTCGATCATCAAATATTCCGTCGTTAGCTGGCGAGTATGTCGTGAATGACTTAAAGGGAGACGAAATACGTATCACTTTATGAAGGGTGGACATATTGTCAGCTAAATGCTGCCAGAATGTAAGTTTTTTGAGGTTTCAAGTTGTAACCAAGATTGTCTTTATAAAGGCAGCAATAGCGGGATTGTAGACGTCAGAAATGAATTGAATATGTTACACTTAAGCAAGCCATTAGCATGTCTGCAACGTGTTTATATATAGCTTTAGCGGATCAAAATGGATTTATTATGGGCAATATTTAACTGACTTTTTAACAATTATTTATAATGACAGGTTGATGCTCCTTGATTAAATCACTTTTTAATAAGCTCTTTAATATTCGCGATACAGCGACGGTTGAGAGGCCCAAAAGTTTTTCAACCACTGACTTAGGTAAGTCGCTTGAACCTGCGCGAAAGCCAGCTAAACCTCTCGAAGACATTAGTGTGGTATCTGTGGATTTAGCCGCACTCTTTTATAGTTTGTTGTTTCCAACACGGGTTAAGGACACTGGCGGTGTAGCTAATAATCTTGAACGCCGAGTCATGGCGGAGATAGAGCAGGCCTTGGCATCGCCACAAGTGATCGCCGAAAAAGTGCTAAAGCTACCGTCAAAGGTATTGGAGCTAGATCGTAAGCTGGCCGATCCTCAGTTTGATATTAAGGATCTATTAGCGCTTATTGAGCGAGATCCTTTACTGAGTATTGAGGTACTTAAGCTATGTAATTCCCCTGCGTTTAAACGTAGCGATAGGGATATCACCAGTTTACAACAGGCTTTGGTACAGCTTGGTCGTGAACAGTTGAGAAAGTTTGTCACCACTTGCCTAGTGCGTGAAATGTTGGATATCAAGCCAATCTATTTTCGTCGTTTCGGCGCTGAAATTTGGCGCCACTCGATGCAGGTCGCCTTTCTCGCCAGTGAGTTAAGCGACGAAGATCAAGATACGGCTTTTTTATTGGGTTTACTGCATGATGTCGGCAAGCTAGCCATTTTCAAAATGCTACTCGATGCGTTTGTGCAGGCGGAGCCGGGAGAGCAGCCTAATTCTGGGTTATTTAGACAAGTGATGACGACGAAATCGTTAACCTTAAGTTCATTGCTCGCTAAGCATTGGCAGTTACCCAATACCTTTGAAACAGAATTAGACAAGCTCGCCAGTGTGAACTCGCGCCCGCAAGTTGGGCTCGCCGCTGTGATATGGCGGGCGAATGTGATCAGCGAGATTTCTATGCTGCATCAAGCAGGGCAATTGTCTCCTGAAATACTGGCGAATTTACTGCAGCAAGTTAACTTAGATCAAACTCAATTTGATGGCTTACATCAAAAACTGACGCAGTTCTGATTGGGCTGTTTAAGAAATCAGGGCAATAAAAAACGCGCTATTTAGCGCGTTTTTTATAAAGCGATAATTTAAAAAACGTTAGTATTGTTATTGCAGTAGTGATATATCCGCGACGTGCAGGAACTGCTCGCGTAGATTATTCAACAATGCTAAACGATTGTTTTTCAACGCTTCATCATCCGCCATTACCATCACATCTTCGAAGAACTGATCCACGCTTTCACGTAGGCCAGCTAACAAGGTTAAGGCTTGTTGGTAATCGGCATTGGCAAACAAAGGCGCCAGTAGTGGTTGCAGCTCGTTCAACTTGGCCGCTAATGCTTGCTCTGCGGCTTCAGTTAACAGATTCGCATTGATGGTTGTTGGTAAAGCACCTTCAACCTTAGCCAGAATGTTGGATACACGTTTGTTCGCTGCCGCAAGTGCGCTAGAAGCTTCTAGGCTTCTAAAGTGTGATACCGCATTGATACGGCTATCAAAATCAGCTGGGCGTGTTGGGCGACGTGCTAGCACGGCTAAGATCACATCAACACCAATGCCTTTATCTTGATACCAAGCGCGGAAGCGTGCCATTAAGAACTCTAACACTTCGTCGCTTGCATTGGCGTTGCTTAAGTTAGTGCCATGTAGAGCTTGTGCTTTAGCAATAAGATCAACTAAATCAAGTGGTAGCTTGTTTTCTACTATGATACGTAGCACGCCAATTGCTGCGCGGCGTAGGGCAAAGGGATCGGCTGCGCCTTTAGGAGCTTGGCCAATACCAAAGATACCGACTAAAGTATCGAGCTTATCTGCTAATGCGACAGCGCAAGATACGCCAGCACTTGGCACTGTATCGCCAGAGAACTTAGGCTTGTATTGCTCTTCCATCGCAACTGCAACGGCTTCGGTTTCACCATCGAGGCGGGCGTAATGCATGCCCATAGTGCCTTGAGTGTCGGTAAATTCCATCACCATATTGGTCATCAAATCGGTTTTAGATAACAAACCTGCACGGGCTGCATCAACCGCATTCGCACCAGTTTGCTCGGCAATAAAGGCGGCTAGGGCAGAGATACGATTTACTTTGTCTTTTAATGTACCCAGCTGCTGTTGGAACAATACTGTTTCTAAGCTAGGTAAACGTGATTCCAGCGTGTGTTTTTTATCGGTATTAAAGAAGAATTCTGCGTCGGCAAGACGTGGACGAACGACTTTCTCGTTACCAGAGATAATTTGCGCAGGATCTTTAGATTCTATGTTGGTCACAAAGATAAAGTTTGGCAGTAACTTGCCCGCTTCATCAAAGACTGGGAAGTACTTTTGATCGCCTTTCATGGTGTAAACCAAAGCTTCTGACGGTACGTTCAAGAACTTTTCTTCAAAGCTTGCAGTTAGTACCACAGGCCATTCAACCAAAGAGGCGACTTCTTCAAGTAGGCTATCTTCGATATCGGCTTTACCGCCAATTTTCGCGGCTGCTTTTTCGGCATCGGCTTTTATCAAGGTTTTACGCACTTCGTAATCAGCGATGACTTTGCCTTTTTCTTTTAAGTCGGCAAGGTAGTGATCGGCATGTGCTAGTTCAAAGCTGGCTTGGCCCATAAAGCGATGGCCACGTACTGTGCGTGCTGATTTAATACCGAGCAATTCGCCTTCAATCAACTCGCTGCCAAGCAACATAGTTGCTGTATGAACAGGGCGGATAAATTGGGTTTTATTATTACCCCAACGCATTGGCTTAGGAATGGGCAATTTATCGAGTGCACGTTGAGCCATAGCAGCGATCAAACTTTTCGTTTCGACGCCTTCAACTTTAGCGTTATGGACTAACCATTCGCCTTTATCTGTCACTAAACGTTCGGCTTGTTCAACAGTAATACCATTCCCACGCGCCCAACCTTCGGCGGCTTTAGTCGGCTTACCTTCAGCATCAAAGGCTGAACTCACGGCGGGGCCACGTTTCTCAACTACTTTGTCGGCTTGTGCAAGCGCAAGTTCAGTGACGTTAATCGCCAGACGACGTGGCGCTGCGTACCAAACGGCAGAGCTAAATGCTAAATCGGCTTTGGTGAGTTCTTCGGTAAAGTTAGCTAAAAAGGATTCAGCCAGTTTACGCAGTGACTTAGGTGGTAACTCTTCTGTGCCTAACTCTATGAGTAAGTTTTCAAAATTCATGTTTTTACCTCTACTTACACATTGGGAAGCCAAGCGCTTCGCGTGCCTGATAATAAGATTCAGCGACCGCTTTCGCCATAGTACGAACGCGTAAGATGTAGCGTTGGCGCTCGGTCACTGAAATGGCATGGCGGGCATCGAGTAAGTTGAAGGCGTGCGAGGCTTTCATCACTTGCTCGTAGGCTGGCAGAGGTAAAGGTTTTTCCAGTGACAATAAATGCTGACACATTTTTTCGCACTGATCGAACAGGGTGAACATAAAGTCCACATCGGCGTGCTCGAAGTTATAGGTCGATTGCTCGACTTCGTTTTGATGGAACACATCGCCATAGGTGATGCGCCCCAGTGGACCATCAGTCCAAACGAGATCATAAACGCTATCAACACCTTGGATATACATAGCCAGACGCTCTAGGCCATAGGTGATTTCACCTGTGACTGGGCTGCATTCGATGCCGCCAACCTGTTGGAAGTAAGTAAACTGAGTCACTTCCATACCGTTAAGCCAGATTTCCCAGCCTAAGCCCCAAGCACCTAGTGTTGGTGATTCCCAGTTATCTTCTACAAAACGGATATCGTGGATTTGCGTATCGATACCCAGCGCTTGCAGTGAGCCTAGATATAGCTCTTGAATATTGTCAGGTGAAGGCTTCAACACGACTTGGAATTGGTAGTAGTGCTGCAGACGGTTAGGGTTTTCACCGTAACGACCGTCCGTAGGGCGGCGCGATGGCTGCACATAGGCACTGCTCATAGGTTCTGGCCCTAGAGAACGTAGGAAAGTTTGTGGGTGGAATGTTCCCGCGCCGACTTCCATATCTAGAGGTTGAACGATTGCACAGCCTTGCTGTGCCCAATATTCCTGCAGGGTGAGAATGAAACCCTGAAATGTTTTTACGTCGTGTTTTGTCGTCATGTCTACTTGTCTACTGCCGTCAGCCTGAATAGAAAATGGGTTCGATTATACCTTGTAGATATCTGCTTATGTAGGTTATTTTTTACCTTCTTAATTAAAACAATAGGGTTATATACCATGGAAGAGATCCGTTGTGGCTGGGTAAGTGATGATCCCTTGTATCGTGAGTATCATGACAAAGTGTGGGGGCGGCCGGTTTATGATCCTAAGGAGCTGTTTGCTAAGTTGTGTTTAGATGGTCAACAGGCCGGATTATCTTGGATAACTATCCTGAAGAAACAACAAAATTACGAGCAGGCCTTTGCCAATTTTGAACCCGAAGTGATTGCTGGCTTTGATGAGGCGAAAGTGGTGGAGCTTATGTTGAATCCCGGCATAGTGCGTAATCGCTTGAAGGTGAACTCTATCATCAAGAATGCCAAAGGATATTTGGCCTATACTGCCGATGGGAAGGATTTCTCTACATTCTTATGGAGCTTTGTTGGTGGTAAGCCCATAGTTAATCATTTTACTGCTATGTCGCAGGTTCCAGCGCAAACGCCGGAATCTGAAGCCATGTCAAAAGCGTTAAAGAAGCTTGGATTTAATTTCGTCGGCCCAACAATTTGTTATGCCTTTATGCAAGCGGTGGGTATGGTGAATGATCACCTTGTAGAGTGTATTTCCTATCAGGCCTGTGTTGATTCCTGTGCAGGAAAGCATCACCGCGAACATCAGTAATCTCTCATCTTGCTGATGTAGGACAGTACTCATAGCAATATTCGAGTTCTTTTCCATATGCACAATAAACAATGCACAAAATGTAAAAAAGCATGTTCCACGTGGAACATGCTTTTATCTTCATTGGTGATATCTGGTTAAACCAAATAATTAAAACAGGAAAGCGACTCTCCCATTCTAGCCTTGAGGATAGAGTTTAGATTTTCTCAAAGAAAAATGGCTTACGTTTTTCCTTATCCCCATTAAGCTCATTCATCGTCTCAGCATCAAAAAGCTTACCATTGACCATGGTATAAGTGACTTTATCTGTGACGCGTATGTCGGCTAATGGATCACCATCAATAACGATTAAGTCTGCGAGCTTACCTTCTTTAATCGAGCCTAGCTGTTGTTCCATACCAAAGGTCGTCGCCGGATTAATGGTGGCCGTTTTCAATACTTCCATATTGCTCATGCCGCCTTGGGCGAACATCCACATTTCCCAATGTGCCGCTAAACCTTCACGCTGACCGTGGGCGCCAATATTTGGGTGGACTCCTAACTTATTGAGCTCGTTAGCGACTTTTGCAACATTGAAATGATTATAGTGGCCATCTGGAGCATGCGGGCGTCGCATTGATCTCGCCTGCAAGATATCGGCGGGGACATACATGGACAGGCGTGGATGCGCCCACACATCGGTTTTGTCGTACCAGTAGTTTTCGCCTGAAATGCCGCCATAGGCGACCACTAAGGTTGGGGTGTAACCGACTTTAGTCTGGCTCCAGAATTGTTTGATATCATTATAAATCGCTGCTACTGGCAGAGAGTGCTCAACCGTTGTGTGGCCATCGGCGACCATAGTGAGATTGTGCTGTAGCAAACTGCCACCTTCAGGCACGACCATCATTTCGAGTTCACGTGCGGCAGCGATGACTTGCTGCCTCTGATTACGTCGCGGTTGGTTGTAGCTTTTAACGCTAAACGCCCCGACTTTTTTCAGACGTTCAAGATGAAATTTGGCATCATCGAGTGAGTCTATGTGCGATGTGTAACCTGGCGCATTGGCACCATACAATATGGTACCAGTAGAGAAGATACGTGGGCCAACTATGTGTCCTGCTTTTTGTTGTTCGGAAGCGGCAAAAATCTCTGTCGTGTCATTCGAGGGATCGTGAATAGTGGTGACACCCAAAGATAAACCGGAATAAAGCGCCCAGTTCTGTTGTGGCACAATTTCATCATCTGCTTGAGGTCCATGGGCGTGGGCATCGAATAGCCCTGGCATGATCGATTTTCCTTTGATGTCGATGATCTGTGCATCTTTTGGAATCGCGATATTGGCATCACCTACGGCGACAATATGATTGTCTTTGACTATGACCACGCCATTGTCGATGACTTGGTCATTTTCCATTGTAATTACTTTGCCACCGATGAAGGCGATTGTTCCACGTGGAACATCGGCCTTTTCGGTAAAGCCTAAATTGATAATGCTTGGCTGAGCCTTTTCATCTTTATTAAGATATTGAGTATCAACCTTGGCTTGATAAAGTTCAGGGCCAAGCGTCCAATACAGTTGGTTGCTTTTACTGTTCCAACTAATACTTTCACCGGCGTGGATACTCAATTGGCTTACGGGTAGATTGTTGGCATTGGGTCCAATCTCTACAGTTTCGCTGTGTTTAGCAAAAGGAGTCACCCAAACCTTAAAGCGTTCGGCAAAGGCGAGTTGCTCACCATCGGGTGAAACTCTAAATTCAGTCGCATGCTTGCTGCTGTAATGAACCCGTTTATCGAAGCCATCTAAGTTGATAGATGATAACTGTGGCGTTTCATCTTCGCTGCTATTCATAAAAAAGACGCGATCGGCATTGGCACCGAATTGAGCTTGGTAGCCATCGGCACTGATTTTCTTATTTTGTTTGCCTTTGATATCGACTTTATATAGTCCAGGCTCCTGCGACCAAGTTCTTGGTGTGAGGTTACCGCCATGGGTTTTGCGATACACCACAAGTTCACCATCGGGTGAAAAGGTGGGTTCTACGTACTTTCCTGGTTCAGATGTTAGCTGCTTAGGTTTGCCACCTTTGGCGCTAATGACTTGTACTGTTCCTTGGTCTTGGTCATTCCAAGTGGTGAAAACAATATTTTTGCCATCGCGGGACCATTGGGGATACAGTTCTTCAATCCCATTATTGAGTTCAGTTAAGCGCGACATTTTTCCATCGGGTAATGATTTTAACCAAATCTTACCCAGCGCTTCGAAGGCCACTTTACTGCCATCGGGAGAGACCTGCGCCATACGTAGCATTTTGACATCGAAAACATCTTTATCTATATCTTGCTTAAAGCGCACTGAGGGTTGTACTGCTAACTCAGTTTTTACGCTAAAGGGGATATCTGTGAGTGTTTTGCTGGCAACATTCAGGCGGTTAATCTTTCCCTTCGCCCAGAAGAAAATATCCTTGTTATCTTGAGTCCACGCCATTGTCGGGTAGACGCCATGAATCGCCCAAGTCTCTTGCATATCACGGTCTAGTTCGCCAAACAGTTTAGTGGTTTCACCAGACTTGAGGTCGAGCAGGTAGAGTGACGAATTAAAATCATCGCGCTTTATATAGGCGAGCTTAGTGCCATCAGGACTTGGTGTTGGTCGAATTGCACCACCGGTACCTTCAATCAAAATTTCAATGTCACCGGTTTGGGTATCATAACGTTTGATTTTGTAGATACCGTTAACTGAGTCCTTTGAATAGTGGAATGTTTTGCCTAGCGTATCATCTTGGCTAAAGTAGATATAACGGCCATCGGGCGAGTACGCGGGTTCGCCTAAATCTTTCTGATCATTTGGGCGTTCAGTTAGTTTGACGCCTTCGCCGCCCGCTACGTGGTATAGCCACACTTCACCAGCACCTAAACTGCGGCTGGCGGTAAAATGCTTACGTGCAACAAGATACTGAGAGTCAGGGCTCCAAGCGGGACTGTTTAGCAGCCTGAATGTTTCTTGGGTGACAGTTCGTGGATTGCTACCATCGGCATCCATGATCCAAATGTTATCACCACCATCGCCATCCGAGGTAAATGCGATGTGTTTGCCATCTGGGCTATAGACGGGCTGCATCTGCCAAGCAATGCCTTGGGCTAATGGCTTAGCTTCACCGCCGGTTACTGGGATTTGGTAAATGTCGCCGAGCAAGTCGAAGACGATATGCTTGCCATCTGGACTGACACTAAGGTTCATCCAAGTGCCTTCATTAACATCAATTTTGACTTGCTCTAAGGGAGCATTTGCTGGAGCGTTAACATTCCATTCGGCCTCCTTCGTTTCTGCTGCGTTAACGGCGAGGGTATTCATACCTAGAGCGAGGGCGATATAAAGCGGGGTAAGTTTAGATTTCAACATGTCACTTCCTGTTATCGTTCTTATGGACTGCTTGAGTGGTATTAATTTTAAGATGAACTTTTGTTAATACTTAGTCTAGTTATCGCATAAATAGGGAAGAAGGAACACGTAAATACTGTTTCAATTGATGACCGAGTGAAGGATAAGTCATGGCGATATGGATAAGAGAGCTGGCATAGCAGAAATAGAACCAAGTCACTTGGTTCTATTTACTGAGGAGTATGGAGAATTAACTAAGAGTGGCCGAGTAGCTGGGAATAAAATTAGCTTGGATTAACCAGAGTGATCTCATCGCCTAAATGGATCATGGCATCATTCAATACTCGGCAGGTTACACCACCTCGCCAATCTGGCGTGAGCGCGGCCTCTAAACCGATATGCGCTATTTCCATCTTTTTACAGGGATCGGTTTCACCCGTGATTTCAAGTCGTAACTCACCGATCTGTAAATGTTTACCAACATCGGCACTGCTAAATACGAGATCATCCACCAGCAGATTTGCTCTGCGAGTCGTCCAAGGTAAATCTGCCTCGATACTATTACAGGCCTGTTGCCACTGAAGTTTGGAAAGCACAGTGACTTGGCGTTTGCCTGGGCGACCAAACACATCTTTCTCTACGCCACTGAGTTGGGTGACATTGGCACAGAGAACTTCATTCATTGGGCCGCGTTTTACAGTTTTATAGGCAATCGCCAGCAGTTTTGGCATAGTTTCTCATATCCTTTGTTTTATGAGTAAGTCGTTAACGATACGACTTAAAGTCCTTTTTTGATCAAATAATGATATGGCGTTTGCGTGGTTTCACTGGCAATTAAGGTGTGATCCATAAACTCACAAAAGCTCGGAATATCGCGCGTCGTCGCAGGATCATCGGCAATGATCAGCAATGTCTCGCCGGCAGCCATTTGTCTTACGGTTTTACGCACCATCATTACAGGCTCTGGGCAGCGTAAACCAAGAGCATCGAGTCTATGCTGTGCGGTCGAGAAAGCGTCGTTCATATTACCTCTACTGAACGGTTAAGCAGAATTAGCTAATGTTACTCCAGCCCCCATGATAAACCAAGTTGGCGGTTTCAATTTTGATCTGGTTTTATTGATGTGTTCTATCAATACGAATGTTAGATATTTAATGTTCCACGTGGAACATGATGACGAATGGGTTTAAAAATTACTCGACGGCTTTATCAACGCTTTGAGATTGAGAGATTGAATGGACATCTGCGGTACTATTCTCAGGTGCTAAATCATTTGCTGCCGCCATAGTTTGTTGCTGCTGTTTGGATGCACGTTTGTAGCTATACATTCGCTGATCGGCTTTATGAATTAAGCTATTGGCTTCTTCGTACTCATAGGGTCCAGTATCAATAATGCCAAAGCTTAAGGAAATATTGTTTAAACTGAGTTTTATTTTTTGATTGAATCGAGTGATGAAATTATCCCGAGCTTGTTCTTCACGGCAGTTAGGCATGATGATACAGAACTCATCTCCACCATATCGAAAGCAATGATCCTCGGCACGGGTGATTTCTTTAATCGCTTTTGCTACAGCACGCAGTACTTCATCCCCTGATCTATGGCCTTGGGTGTCGTTGAATGACTTAAAATCATCTACGTCTAAGTAGATCACGCTGACGGGTTCGGCACGCCTTTGGGCAGCCCTGAGTATGCGCGTTGCCGTTTCTTGTAAATGCCTAACGTTGAGCAGGCCTGTTAAGGGATCGGTTTGCGACAGTTCTTCTAACTGCTGTGTCCGCTCTTTGACTTTGCTTTCCATGCTTTGGGCATATTTTTCGGCTTTATCCTTAGCGTTCTCAATCTCGGATACTAAGCTGCGAATATAAGTATCGAATACTAACGTGACATCAAACAGCACCAGTTTATCAATCGCGACACGGATACATTCGCCTTGATGTGTATCTTGCACTGAACAGTTGATCACGGCGTAGATAAGCTCTTTCAGTGTGTGTACCGCAGACAGGTAAAGCTTAGGCTCAACACCAATGCGTTTATGGACCAACCCAATCCGCAGGCGATTGTTTACGTACTCAAGATCATAAACACCATTAAATAAATCAAGCACATATCTTCTCTGTGCGGTACGTAAGCGGGCGAGGGTGTCTGAGTCTCCGATCAGCAGCGCAATTTCTGACACGCTGGTTTGTAAGCCATAAAAGTCATCGACGATTTTATCTATGTTTTCTTCAATGATGGGCTTAAAGGATTGCAGGGCCTTTACGTCCGCCCCAGTAAAAGAGAAGAGCGCCTTACGAGACTCGATCTCCAGCTCAGTGATCCGCATTTGTTCGAAAAGGGTCTGTTCCGTTTGTAGCATCAGGCATTCCTCTTGTAAAACAACGAATTGCATCCTTGCACTACACTGTCTAGATTTGGCTGATGATTGCTTGCCTGTTGTCGTCCACAATAGCCAGTGATGCCCTTGTAGTTAAGTAGATAACGGACTGATTTTCAAATGAACAGCAGAAGAAATGAGAAATATTAATATTTACGCTGGCAGAAAATTTGGCATAAAAAAGCCGGAACAAGTTCCGGCTTTGAAATTATTCGAGTAACGAATTAAACGCGTTCGAATACAGTGGCGATACCTTGGCCTAAACCAATACACATGGTCGCTAACCCTAAGGTCGCGTCTTTGTCTTCCATTAAGTTGATCAGCGTCGTAGAGATACGAGCGCCTGAACAACCCAGTGGGTGACCTAGTGCAATTGCGCCGCCGTTGAGGTTGATTTTGTCATCAACCACATCTGCTAAACCTAAATCTTTCACACAAGGTAAAGACTGAGCAGCAAACGCTTCGTTAAGTTCAATCACATCTAAATCGGCAACTGTGATACCAGCGCGAGCCAGTGCTTTTTGCGTGGCTGGCACTGGGCCGTAACCCATGATAGCCGCGTCACAACCTGCAACCGCCATGGAGCGAATGCGTGCACGAATTGGCAAGCCAAGTGCGCGCGCTTTAGATTCTTCCATGATCAACATGGCTGAAGCGCCATCAGACAGTGCAGAAGAAGTACCAGCAGTCACAGTACCGTTTACTGGATCAAATACGGGACGCAGTGCAGCCAAAGATTCCATTGTGGTTTCTGGGCGGATCACTTCGTCTTGCAGGACTTTAATTAAGGCGCCGTTAGCATCGTGACCTTCAATGCCATAGATTTCCTTGGCAAAGCGGCCTTCAACTGTCGCAGCATAAGCGCGTTGGTGTGAACGTACCGCAAACGCATCTTGCTGTTCACGGGTAATACCGTGCAGTTTACCCAGCATTTCAGCCGTTAAACCCATCATGCCAGAGGCTTTTGCCACGTTGTTAGCAAGACCTGGGTGGAAGTCGACACCATGGCTCATTGGCACGTGGCCCATGTGTTCTACGCCACCAATAATAAAGGTGTCGCCCATGCCAGTCATAATCGCACGCGCAGCTTGATGGATAGCATCCATTGAGGAACCACATAGGCGGTTAACCGTCACAGCGCCAGCGGTCTTTGGAATGCCCGCTAATAGCGAAGCGTTACGGGCGATGTTGAAGCCTTGCTCTAGGGTTTGTTGCACACAACCCCAAATCACGTCTTCAATCAGGTTTGGGTCGAGTTGCGGGTTACGCAGCAACAGACCTTTCATTAATTCGGCAGACAGAGTTTCTGCACGCACATTACGAAATACGCCAGCCTTTGAGCGGCCCATCGGAGTACGGATGCAGTCTACGATAACAGCTTGTTTCATGATTAAATTCCTTTCCCGCTAATTAGGCTTGGTAGTAGCTACCGTTATTGGCTGCTAATGTGCGCATCGCATCTGTGACTTGATACAGACCGCCCAAGTGAGCATATTTGTCTGCTAAGGCCACAAAATTTGCGACACCCATGGTATCCAAGTAACGGAACACGCCGCCTCTGAATGGCGGGAAACCTAAGCCATAAACTAGGCCCATATCCGCTTCGGCAGGCGATGCGACAATACCTTCTTCTAAACAGCGCACGGTTTCGATGATCATAGGGATCATAGTACGGGCAATAATGTCATCGGCTTCAAAGGCTTTTAACTCGCCAAAGGCATCTTTCAATAGGCCGTAGCTCGTTGGATCGACATCTTTCTTCGGCTTGCCACGGCTATCGACTGAATAGACATAGAAACCTTTACCATTTTTCTGGCCAAGACGTTTGTTCTCAAACATCACATCAATCGCGTCAGTGCCTGATTTACCCATGCGATCAGGGAAACCTTCAGCCATAACAGCTTGGGCGTGATGACCTGTGTCTAAACCGACTACGTCGAGCAGGTAAGCTGGACCCATTGGCCAACCGAATTGTTTTTCCATCACTTTGTCGATTGCAGCAAAGTCGCCACCTTCGGCAAGTAAGCCGTTAAAGCCTGCAAAGTAAGGGAAGAGTACGCGGTTAACGAAGAAGCCAGGGCAATCATTAACAACGATTGGGGTCTTGCCCATTTTGCTGGCATAAGCCACAACAGAGGCGATGGTTTCTTCAGAGCTATGCTCGCCACGGATAACTTCGACCAAAGGCATTTTGTGCACTGGGTTGAAGAAATGCATACCGCAGAAACGTTCAGGTTTCTTCATGCTCTTAGCGAGCAAGTTGATTGAAATCGTTGAAGTGTTTGACGCGATAATTGCATCTTCACTCACATAGCCTTCAACTTCGGCTAAAACCTGTGCTTTGATTTTTGGATGTTCAACAACGGCTTCAACGACGACATCACTGTGTTTAATCGCGGCATAATCTAATGAAGGCGTGATGTTGTTCAGCACTTTGGCCATTTTTTCTGGCGTTGAACGGCCACGGGCGACTTGGGCAGATAGCAGTTTAGCGGCTTCATTGAGGCCTAAATCTAATGCTGGCTGGGCAATGTCTTTCATGACAATCGGTGTGCCTTTGCTGGCGCTTTGGTAAGCGATGCCGCCGCCCATAATGCCTGCGCCTAATACGGCCGCGTTGTTGACTTCTTTCGCTAACTTGCCGGCTTTCTTCGCTTTACCTTTAACAAATTGATCATTAAGGAAAATACCGATAAGTGCTTTAGCTACGTCAGTTTTAGCCAGCTTGATAAAGGCTTGATGTTCGATTTGTAATGCTTCAGCACGACCTTTAGTCGATGCTTGCTCAATTACGCTTACAGCAGCCATTGGTGCAGGGTAGTGTTTACCCGCAACCGCAAACACCATGCCTTTGGCTGTGGTGAATGACATCATAGCTTCAAGTTTTGGCAGTGTTAGCGCAGACAGTTTACGGTTACGACGTGCTTGCCAATCCAGCTTTTCAGCAACAGCATCTTTCAGCATTTGAATCGCAGCAGCTTCTAACGCTTGTGGTGCAACCACGGAATCAACAGCGCCGACTTTCAGTGCATCTTCAGCGCGTTGGTCTTTACCGGTCGTGATCCACTCAAGTGCGTTATCTGCACCAATCACACGTGGCAGACGAACTGTGCCGCCAAAACCTGGGATGATGCCGAGTTTAGTTTCAGGTAAACCGATTTTGGCTGTGGTGTCTGCGATACGGAAATCGGTTGCCAGAATGGTTTCACAGCCGCCACCTAAGGCAAAGCCTTTGATTGCTGATGCAGTTGGGAAAGGCAAGTCTTCGAGTTTGTTGAAGACTGCGTTAGCTTGTTCAACCCAAGAGAGCAATACGGCGTCATCTTGTGCGAACAGGCCTAAAAATTCGGTAATGTCGGCGCCCACAATAAAGGTATCTTTACTTGAGGTCAGCACTAACGCTTTGATATTGGAATCTTGCTTGATGCTGTCTAATGCCGCATCGAGCGAGGCGAGGGTTTCTCTGTCGAATTTGTTCACCGAACCGGGTGCGTTAAAGCACAGCTTGGCAATATTATCCTCGAGTAACTCAACCTGAATTGTAGGACTTTGGTAGATCATTGCTTGCTTCCTTTTGGCTTAAAAGTGATACCCACCATTATGTGTTGGTGCTAGTACGACTCTCTATCTCACAGCAGCTTCGGCCATCATTTGACCAGTTGTGTGCCAGTGTGCTTCGAAATTCGATAAATTACAACACCCAATTTAAACGACTGTTTAATTTCTTGTCGGCTAGAGTGATTTTTCACCTTTGTGATACACTGCACAGAGTGTAGAACACCTAGAATGCGTTAAATCACTTTGCGGATAAATAGGTCTTTGTGGCTGTTTACCTTAGACGTTTAAACGCTGCAGCCATGCTAGGGTTGAACATAACTCTTAAAGATAATCATGATGTTATCGCTTTAATGTTCTTTGTTCCGCGGCCATGTATTTTAGATTGAGTGACAGTTTAGTGTTTTGATTGAATCTGTTGTTCATGATCAGATTAAGAATATGTGAGCAAATTCGGCTCATATTATTAACTTTTATTCATTGCAGGAGATGACAAATGGATCAGTTGGCTCATCACTATCGTGCCCATATTGCCGAGTTAAACCGTCGAGTCGCAGAGATTTTGTCTCGAGAAGCCTTGTCTGGTTTAGTGATCCATTCGGGTCAGCCGCATCGGATGTTTTTGGATGATATCAATTATCCCTTTAAGGCAAACCCGCACTTCAAGGCATGGTTGCCAGTGCTAGATAATCCGAATTGCTGGTTAGTGGTTAACGGCCGTGATAAGCCGCAGCTGATTTTTTATCGTCCTGTGGATTTTTGGCACAAAGTGTCTGATGTGCCTGATATGTTTTGGACTGAGTATTTCGATATTAAGCTGCTGACCAAGGCTGATAAGGTCGCTGAGTTTTTACCGACAGATATCGCCAATTGGGCCTATTTAGGTGAGCATTTAGATGTGGCCGAAGTGCTGGGTTTTACCAGTCGTAATCCCGATGCTGTGATGAGTTATTTGCATTACCACAGAACCACTAAAACCGAATATGAGCTGGAATGCATGCGCCGCGCGAATCAAATTGCAGTGCAGGGACATTTGGCGGCTAAAAATGCCTTTTATAATGGTGCGAGCGAGTTCGAAATCCAGCAGCACTATTTATCTGCTGTTGGCCAGAGCGAGAATGAGGTGCCCTATGGCAATATCATCGCCCTTAACCAAAATGCGGCGATTTTGCATTACACCGCACTTGAACACCAAAGCCCTGCGAAACGTTTGTCATTTCTTATCGATGCGGGCGCGAGTTACTTTGGCTATGCCTCTGATATCACCAGAACCTACGCATTCGAGAAGAATCGTTTCGATGAATTGATTACTGCGATGAACAAGGCGCAGCTAGAGCTTATCGACATGATGCGTCCGGGTGTGCGTTATCCCGATTTACACTTGGCCACCCATGCTAAAGTCGCGCAAATGCTATTGGATTTTGATTTAGCCACAGGTGATGCCCAAGGTTTGGTCGATCAAGGCATAACCAGTGCTTTCTTCCCCCATGGCTTAGGCCATATGTTAGGCCTACAAGTGCATGATGTTGGCGGCTTCTCCCACGATGAACGCGGTACTCATATCGCGGCGCCAGAGGCCCATCCATTCCTACGTTGCACCCGCATTTTAGCGCCAAACCAAGTGCTAACTATGGAACCCGGGTTATACATTATCGATACTCTGCTTAATGAGCTTAAACAAGATAGTCGTGGCCAACAGATCAACTGGCAAACGGTTGATGAGTTAAGACCTTTTGGCGGTATTCGTATCGAAGATAACGTCATTGTGCATCAAGATAGAAACGAGAACATGACCCGTGAACTCGGTTTGACCGATTGATCATAGGGTAAAGATTAGGGCGGATGTGTGCTCGAAAGTTATAGCGTTCCCTGTGCAGATATCGTGATTGAAGAAGAGATAAAGCATAGCCGCTTTATCTCTTTTCTGTTTCATTGCGATAGCTTTGATAAATTTAAGTTAGTACTCACTGACATAAAGCGTGACTATCCCGGCGCAAGCCATTATTGCTCTGCTTTTGTGGCTGGCGCACCGGATGATAGCGTCTTGATTGGTTCGAGTGATGACGGCGAACCTGCCGGCAGTGCTGGTCGCCCTATGTTAGCTGTGTTGCAAGGGGCGAATATCGGCGAAGTGGGTGCTGTTGTGGTGCGTTATTATGGCGGTACTAAGCTAGGCGTAGGTGGACTGGTTCGGGCTTATACGTCGGGCTTAAGGCAAGGTTTGGTTCAATTGCCGACGCGACTAAAACAGTTACGTTATCCCGCCAAGTTGCATTGTGATTATGCGCAGCTGCGCGATGTGGAACATCTATTACAGCAAGTCGATGCCGTGATTATCGACAAGCAATTTGCCGAAGCGGTCGATATTGTGTTCGAGATAGGCAGGCAGCAACAGACATTACTGTGTGAGTCGCTGGCGACACTCAGTCAGGGAAGTTTGAGGCCTGAGTTTGAGCTGTGATGCCTGCGTAAATTATGCCAAAATAGCCCAAATGCTGAAAATACCTTTGCAGCCAATGGAATGATTGAACTCGCTTAATGCAATATAAAACCATAATACGAATCATAGGCCTGCTGATTGGTTTATTTTCAATCACTATGCTGCCACCAGCACTGATCGCTGTTTGGTATAACGATGGCGGCGGCACGGCGTTTATGCAGGCATTTTTTGTCAGCCTATTTATTGGTTTCTGGCTCTGGTATCCCAATCGCCACTGTAAAGAAGAATTGCGTACCCGAGAGGGTTTCTTGATTGTGGTCCTGTTCTGGACCGTACTGGGTTCTATCGGTGCTTTACCGTTTATCTTCTCGAGTCAACCCGACCTCAGCTGGACCGATAGTTTCTTCGAATCTTTCTCTGCGTTAACCACCACCGGAGCCACAGTGATTGTCGGGCTCGACTTTTTACCTAAAGCCATTCTGTTTTATCGTCATATGCTGCAATGGCTCGGCGGTATGGGGATTATTGTGCTCGCCGTGGCGATTTTACCTGTGCTTGGCGTCGGGGGAATGCAGCTCTATCGCGCTGAAATTCCGGGCCCAGTGAAAGATAGTAAGATGACACCGCGGATCGCCGAAACCGCTAAGGCACTGTGGTATATCTATTTATTGCTAACTATTTCCTGTGCGACCGCTTATTGGCTGGCGGGCATGAGTGTGTTCGATGCGGTTTGCCATTCATTTTCAACTATCGCTATTGGCGGTTTTTCGACCCACGATGCCAGCATGGGGTATTTCGATAGCTCAGTGATTAACCTTATTTGTGTGTTCTTTTTAATCGTTTCTGCGGTTAACTTTAGCGTGCATTTTGCGGCATTTTCGCGGCGAGGAATTAACGTTAGAGTGTATTTTAAAGATGCCGAATTTAAGATGCTGGTGGCGATTCAACTCATTCTTACCGCGATTTGTTTCCTCACGCTTTATCATTCGGGCATTTATGATTCGCCGGAAGAAACCTTAGATCATGCCTTATTTCAGGCGGTTTCCGTGGCCACTACCGCAGGGTTTGGTACTGAAAGCTTCCACATGTGGCCGCTGTTTTTACCTATTCTATTGATATTTTCGAGCTTTATCGGTGGCTGTGGTGGCTCAACTGCGGGTGGGATTAAGGTGATTCGGGTCATTTTGCTGCTGTTACAGGGCTCGCGCGAATTAAAGCGGCTGGTTCATCCTAAGGCGATGTTCTCTATCCGTATTGGTTCTAAAGCCTTGCCCGATCGCGTTGTTGATGCCGTGTGGGGATTCTTCTCTGCCTATGCGCTGGTGTTTGTGATCTGTATGCTTGCACTTATGGCGATGGGATTAGATGATATTACTGCCTTTAGTGCCACCGCAGCTTGCTTAAACAACCTAGGCCCAGGCTTAGGCGCGGTAGCCAGTAACTACGCCAGTATTGGCGATGGCGCCAAATGGGTATTGGTCGCGGCTATGCTGTTTGGTCGCCTCGAAGTATTTACCTTACTGATTTTGTTTACGCCAACTTTTTGGAAGAATTAATCATGCAGACGTTAATAATCTATTCGACGATTGATGGCCAAACATTGGCAATTTGCCAAAAAATGAAGACGATTGCAGAGCAGGCGGGAGAACAGGTTTCGCTGGTCACATTAGAGCAGGCTGATGCGTTGAGCTTAGCGGATTTTGATAAAGTGCTGATCGGCGCCAGTATTCGTTATGGTAAGCATAGACCTGAACTTTACCAGTTTGTGAACCGCCACCATGCGGTGCTCGATACTAAGATTAATGGCTTTTTTACCGTTAATGTCGTGGCGCGCAAACCCTTAAAGAATACGCCTGAAACCAATCCTTATATGCAAAAGTTTCTGCAGCTTTCACTGTGGCAACCTCAGCAGTTAGAAGTGTTTGCGGGCAAGATTGATTATCCTAAATACGGTTTCTTCGATCGCACTATGATACGTTTCATCATGTGGATGACTAAAGGGCCAACCGATATCACAGGCACCTTTGAGTTTACCGATTGGGAAAAAGTCGACGCCTTCGGTAACCGCTTTAGCCAGCACTAAAAACCATTAAGCCCTAATCACTTGCAAGCGATTAGGGCTTAAATTTAAGCTAGCTTTACCGTGTTACACTGCGCAGTAGCGGCTATGCAGAATCGAAATCACTTGGGTGACTTGTTCGTTCTTGAGCTTGTAGTAAACAATTTGCGAGCTCTTACGGGTATCCACCAGATCTTCAGCGCGTAATACGGCTAAATGTTGGGATAGTGCCGACTGGCTTAATGGCACAGTGGCATTGAGTTCAGTTACGCTCAACTCTTTGTCTAGTAATAGGCATAAAATCATTAGGCGATAAGGGTTCGCTATGGCCTTAAGCCATTTAGCCGCACTTTCCGCATTAGTTACCATTGCAGCTACATCTATGTCATTTTGCATAAGGTCACTCACTTCATCGCTTTTGATTAGATAATTCTAATTGAACACCTGTGTTTACGCAATCATACTGCTCAGCCATGCAGATCTCAATTTTTGCATCTCAGAATATGAGATACGGCTGATTTGTGATCCTATCAGAGGTTTATTTAACGTAACCTTGCCACAATGGTGACATTAACTCAGTGGATCGGTAATTTCCTATGACACGCGTGCTCGTACTTTATTTTACCCGTGGTGGCCACACGGCCAAGATTGCCAATGCAATTGCCGACCAATTAACCTTTCGTGGCGCTAAGGTCGACCTTGTCGATATCAACAGTGCTGCCGCGACTTGTATCAATTGGCCTGATTACGAGGTTGTCGCCTTAGGTGCCTGCGTACTCTATGGCACCTATGATAAGAGTGTGTTCAAGTTTATCGAGCAACATGCAGAGGCTTTGGGTGCATTGCCAAACAGTTTCTTCTGCGTCAACGTGGTAGCGAGAAATCCAGAAAAACGTATTCCTGAGAATAATAAATATCTGCAAAAGTTCATCGCCTTATCGCCGTGGACGCCAGCCGATCTTAAAATTATTGCGGGGAAAGTGGATTATCCTTCATGGCCTTGGTACGACAGGTGGATGATCCAGTTGATTATGAAGATGACCAAAGGCCCAACCGATCCTAAGTCGGTTATCGACTACACCGACTGGGCAGATGTAAAGGTCTATGCCGATCATCTGCTTGAATTAGTCGACGTTACCGCTATTTAGGACGGGTCAGGCTTAATAAAAAAGGCGCTTAACAAGTTAAGCGCCTTTTTAGTATCCAAGCTCTACAGAATTAATACTTCCAGAAACTCGGGTGGAATAACACTGCTACGGTCAGAATTTCTAAACGACCGAGGAGCATGCCGAGTGACAACGCCCATTTCGCCACATCGGGCAGGGTTGAGAAGTTACCCGCAGGGCCAATAATCGGCCCTAAGCCAGGACCTACGTTCGTGACCGCCGTGATAGCGCCCGTGAAACTGGTCATAGGATCGAGCCCTGTGAGCACTAAAATTACCGACAAGCCTACAATCACCATTACAAACAACAGCACGAAAGTGATCAGCGAGCGCACGATATCTTCACTGATGATGCGATTGTTGTAGCGTTCCCTAAAGATACCGTTTGGATGACATTGCTGTTTCAATTGCTCACGCATAATGGCGCCAGCGATTTGAAAACGGAAGATTTTAATCCCGCCAGAAGTTGAGCCAGAGCAGCTACCGACAAACATTAAGAACATAAAGGCGATGTTGGCAAAGGCGCCCCAAGCACCGTAATCCGTTAAGCCATAACCTGTAGTGGTCACCACAGAAACCACGTTAAAGCTGGATAAACGCAGTGCATCGAGGAGTTCAATATCGCGGGTTTGCCACAGCCAAAAGCCAATAGAACAAGACACGAAGGTCAGGAAAAACAAGAAACCTTTGACCTGAGCATCGTTCCAAATCCGCAGATCCCTTTGCTGTATGGTGTGCACAAACATGAGTAGCGGTAAGCCACCCGCCGCCATAAAGAACACGCCAACCCAGTGAGCCGAGTGGGAGAACGCGGCCATTGAGCTATCAGATGTTGAGTAGCCGCCGGTTGATAGGGTGGTCATGGCATGGTTGATGGCTTGAAACCAAGTCATCCCCGCCAGATGATAGGCAACGCAGCAGGCAACAGTCAGCAAAATATAAATAAAGAATAAGTACTTAGCCATGCTCTGGGTGCGTGGTACGGCTTTATCGCTCCAATCCGAAGATTCAGTGCGGAACAGCCGCATACCACCGACGTTCAAGAAGGGCAGAATGGCCACCGCCATCACGATAAAGCCAATCCCACCTAGCCACTGCAATAAAGAACGCCAGATTAAAATGCTGTGATCCATGGTATCCAACCCCGAGAGCACAGTAGAACCTGTGGTCGTGATGCCGGACATAGTCTCAAAGAAGGCGTCGGTATAGCCAATCCCGTGATATAGGGTAAAGGGCATGGCGGCAAACAGGCTTACGATCAACCACGTTAGGCTGGTCAGCAGGAACATATCGCGGATATTGAGGTTAATAGTCTTGCTCTGGCCATTGTGTAAACACAAGCTGGCACTAATGCCTGTCACTAACGCCGACAGCATGAAAGAGCCCACGGTTTCCTCGGCATTGAACACGGCGAGGAGCAGTGGGATCAACATGAAGGCTGTCAGCATGGACAAAAACAGCCCTAAAATAAACAATAGTGGTCTAAAGTTCAGCATAAGTTGGCTTACTTAGGTTCTTAGAAGAAGAAAGCACTTGGTTGGAAAAGCTTCTCGACTTCGCCGACAAACTTTTTATTCACTAGGAATAAGATCACGTGATCCCCTTGTTCTATCACGGTTTTGTCGTGAGCCATTAATACTTCGTCGTTGCGGACAATAGCACCAATGGTGGTTCCCGGTGGCAACTTAATATCGCCAATGGCTTTGCCAACCACTTTAGACGTGCTGGCATCCCCGTGGGCAATCGCTTCAATTGCCTCGGCGGCGCCGCGACGTAGGGAGTAAACGTTACAGATATCACCCTGACGGATATGGGTTAACAGTGCCGAAATGGTGGCTTGCTGGGGTGAGATAGCAATGTCGATATTAGCTTCTTGGACGATATCCACATAGGCTTCGCGCTGGATCAGCACCATGACTTTTTTCGCGCCCATACGTTTTGCCAGCAATGCCGACATAATATTGGCTTCATCGTCATTGGTCACCGCAATAAATACGTCGGTTTGATCTATGTGTTCTTCGAGCAGTAATTCTTGATCCGAAGCGTCACCGCAGAACACGGTAGTATTTTCCAGTTGCTCCGATAAGGCTTCAGCCCGCTCATACTTGTGCTCAATCAGTTTAACTGAGTGGGTGCGCTCGAGGCGTTTGGCTAAACCTAAGCCTATGTTACCGCCACCGGCGATCATAATGTTGCGGTAGGAGTTATCGAGCTTTTGCATTTCGCTCATAACAGCGCGAATGTGTCGACTGTCGGCGACGAAGAAGACTTCATCATCGGCTTCGATAATCGTGGTACCGCGAGGCATGATAGGGCGGCCTTGGCGGAAAATCGCCGCAACCCGGGTATCAATATTCGGCATGTGTTCGCGCAGTGCCGCTAACGCATTCCCCACCAGTGGACCACCATAATAGGCACGAACGGCCACTAGGCTTAGCCGTCCTTCGGCAAATTCGAGTACTTGTAGCGCGCCGGGATATTCAACGAGTCGCTGAATGTAAGCGGTCACTAACTGTTCAGGGGCGATTAATTCATCGATCACGAAGCCGCCGCGGGGGCGACCGTCGCTATTTTTGGTTTCACTGTCGATAAAGAGTTTATCGCGCATCGCCAGATAGGGTTCTGAGCGAATACGGGCAATTTTTGTTGGCGTGCCGAAAAGAGTGTAAGCAATCTGACAGGCGGCCATGTTGCATTCATCGCTATTGGTTACCGCGATCAGCATGTCGGCATCTTCTGCGCCCGCTTCTCTGAGCACATCGGGGTGGGCTGCATGGCCCGCCACAACGCGAAGGTCATATTTATCTTGCAGGGCGCGTAGTCTGGATTTGTCATGATCGACTATGGTGATGTCATTATTCTCACCTACCAAATTCTCGGCCAAGGTTCCCCCGACTTGACCCGCACCTAATATGATAATTTTCATGGCCTTGCTTACTTCCTAATCGGTCGCTTTTAATAAACGGGCGTAATAGAACCCATCCATATTGTCTTGTCCTGGCGTGATTTGCCAACCTATATCCTGTGGCGAATTTTGCTGGGAGAGTGTCTCTAGCTTAGCATCTGCCGTTCTCTCTAAAAACGCACTGATCTGGTCGCGGTTTTCCTGCGGTAAAATCGAGCAAGTAGCATAAAGAAGCGTGCCACCGGGTTTGAGCCACTTCCAGCAATGCTCGAGAATTTGCTGTTGTAGTTCAGCCAGTTCTTCGATGTCATGGTTTTTACGTAACCATTTAATATCGGGATGGCGACGGATCACTCCAGTGGCCGAGCATGGCGCGTCCAGTAAGATACGATCAAATTGTCCACCTTGCCACCATGAGTCAATATTGGCCGCATCGCCATGGATAAGCTCCGCTTTTAATGACAAGCGGTCAAGGTTCTGTTGTACGCGCTCGAGGCGTTTGGCATCAAAGTCGACGGCAACCAGTTTGATGCTTGGCTCAAGTTCGAGTAGATGACAGCTTTTACCACCGGGCGCCGCGCAGGCATCGAGGATTAATTCATTCGCTTGTGGCGCCAGTAATGTCGCCGCCCATTGCGCCGCGCCATCTTGCACTGAGGCCGCGCCTTCATGGAAGCGTGGCAGTGTGGCGACATCTTTTGGATGCGCTAAAAGGATGGCATCGGCACTGCTGCCTTCGCTCGCTTCAATCTCGAGTTCGGCTAAAGCAGCAAGGTATTCGGCACGGCTCTGGGATAAGCGATTGTTACGCAGCCACATAGGTGGGCGTTCATGGCTTTGTTGAATGATGTCTTGCCACTGCTCGGGATAGGCTTCTTTCAAACGCTTAATCAACCAAGCGGGCGTGTTGTAGCTTAAGGTATCTGACTCAGTGCTTAGCGGCGAAAGCTGACGCTGAATGTTGCGCAGTACACCGTTAACGACCTTAACCATGCCTTCAAATTTTAACTGGCGACAGGCTTCGGCGGTTTCTGAAATCGCGGCGTGGCTTGGAATGCGAGTGAAGTAAAGTTGATAGCAACCTATGATCAGCAACTGGTGGATAATCCTTTGCTTACCTTTGAGTGGTTTCGCTAGGCATTCACTGATGCGTTTCTCTATTTGCGGCAGAGTGCGCATCACGCCGTAACAGAGCTCGGCGAGTAGGGCTTTATCTTTGCCACTGGCAAGGTGCTTTTGTTGATCGGGCAATGCCACTGAGAGTGACACGCCTTTTTCTAATACTTCGAAGATGGCTTTCGCAGCCAGCGCGCGCAAGTTCATACTATTGTGCCTCGTTTGCGAGGCGAGTATTCGGGCTAAACCATTCACCTCGAGCATTGAGAATATCGGCAACATTGAGCGGCTTTTTCCCTGGCAGCTGCATATTCAGCAGGGTTAACACGCCGTCGGCGGTGGCGACTTCTATGCCTTTTTTGCTGGCGCTGATAATAGTGCCCGGAGCGGCTGTGCTGGTGGTTTCGCTAACTTGGGTCTGCCACACTTTAATGGTATTGCCTTGATGCTCAAAGTAACTCACTGGCCAAGGATTAAAGGCACGGACTTCTTGCCAGAGTTGCTGGGCAGATTTATTCCAATCGAGTCTGGCTTCTTCTTTACTGAGTTTTTCTGCGTAGTTCGCTAGGGCTTCATCTTGTTTTTCAGCGGCTAAGGTGCCGTTTGCTAGACCTTCGAGGGCTTGCAATAGGGCAACAGGACCTTGTTCCGCCAGTTTCTCGTAAAGGCTGGCTGAGGTGTCGCTATCTTCAATCGGCAGAGTGGTTTTCAGTAGCATATCGCCGGTATCTAAACCCACATCCATCTGCATAACGGTCACGCCGGTTTCTTTGTCGCCGGCCCAAAGCGCGCGTTGGATCGGTGCGGCACCACGCCAGCGTGGCAAAATAGAACCGTGAACGTTAATACAACCTAGACGTGGCGTATTTAATACGACTTTTGGCAGGATAAGGCCGTAGGCAACTACCACCATAATATCGGCATTCAGCGCGGCAAGTTCTTGTTGCGCGGGTTCTTTACGTAATGAACCAGGTTGATACACAGGAATATTGTTGCCTACGGCGAGTTCTTTTACTGGGCTGGCGGTCAGTTTCTTGCCGCGGCCAGCGGGTCTATCGGGCTGAGTGTAAACGCCAATCACATTGTGGTGTGAGTTCAACAGTGCTTGTAAGTGGCGAGCGGCAAAATCCGGTGTTCCGGCAAAGATGATGTTGAGTGGTTTCAAATCTGATCCTATGCTTCTTTGGCGTCCAATCGGGCCGCTTTTTCAAGTTTTTGTTTGATCCGCTGACGTTTTAGCGGCGACAGATAATCGACAAACAATTTGCCTTTGAGGTGATCCATTTCGTGTTGAATACAGATAGCAAAAAGCTCATCGGCCTCAACAATAAATTCGTTGCCGTGCCTGTCTAAGGCTTTTACCGTAACAAATTCGGCGCGATCGACTTTGGCATAAATGCCCGGCACCGACAGGCAACCTTCTTCATTGCAGAAGTCGCCGCTGCTGGCGATAATTTCGGGATTGATAAACACTTTAGGTCGCTCGACTTCATCTTGTAAGTCCATGACAATAAGTTGTTTATGGTAATCCACTTGGGTCGCGGCCAGACCGATACCTTTTTCTTGGTACATGGTTTCGAACATATCATCAATTTGAGTTTGCAACTCGGCATTAAACTCTGTGATGGGTGTGGCTTGTGTCCGCAATCTTTCGTCAGGAAATTGTAATACTTTTAATTGTGCCATACATAAACTCTTAACAAGTCTGTCAAATCTCTGCCAGTTAGTTATACTGACTGATGCTAATGGGTTAATTTTAATCTTTAGTGCCTATCAATAACAGTAAAAGCTCAAACTAAGAGCCAAACAACATGGAAATACCCATGAAACGGTTAATTTTACTCGCGTTAATGACATTCAATTGCACGTTTGTTGCCGCCGATACCCTTACGCTGAAAGCGGGGCATCCTGAGTCATATGTGGTGAAAAAGGGCGATACTCTTTGGGATATCTCCGCAGCGTTTCTAAATGATCCATGGAAATGGCCACGTCTTTGGGATGTAAACCCACAGATTGCCAATCCCCATCTTATTTATCCTGGCGATCAACTCACACTCGTGTTTGTCGATGGTCAGCCTCGGCTTGTGCGCAATGGCGACATGGTCGACAGTGCTAAACCCCATGTGAAGAAAAGTCCTGAAGGACGGATCATTGCCAAGAGCAATGCCATTCCTGCCGTCGACTTGGCTTTGATTGAACAGTATTTAGTGCAGAATCGCGTTGTCGCAACCGACTGGCTTGCCGAGCAACCTATGGTATTAGCAGGTGAAAGCCCATCGCGTCACCATATTGTGGGTGATGTGATTTATATTGATGCTGAACTGCCGATGAATCAAAAGTACGGTGTTTACGAACGTGGTCGCGAGTTTTTCAATAAAGCGTCAGGCGAAGCGATAGGACAAGAGGCCATTTTAGCATCAACTGGGCAAGTGGTTGAGTCTGGTAAGGTTTCAAAAATTAAGTTATTAACCAGCTTTCGTGAAACCAAAGCGGGTTTCAGGGTCATGCCGATGGAAAGTGATTCTTTGATGTCAGCTTACTTCACCCCAAAACCGGCAGAGTTAAAGACACCGGCCGTCGTGCTTGCTATCGAATCAAAGATGCGTGAAGCGGGTAAGTTGGATGTGGTGTACTTAGATAAAGGCATCCAAGATGGGGTTGAGCCCGGAGTTGTGTTCTCTATTTACCGTGATGGTGAAGTCATCGTCATCAACAATGATGGTATGCCAGTGCCTGCCACTGAGCGCACCGCCTACGATAACCTGATGGCATCTGTCTCATCGGATCGCGCTATTCAAATGCCAGATATCTATCATGGTAAATTGTTGGTTTTCAAAGTATTCGACAAAGCGAGCTTAGGTTTGATTGTCTCCACCGAGCGGCCTGTGCGTGTCGACGATAAGTTAATTGCCCCTGAGTCTTTAGCCTTAAGAGGTCAGTAATATCTGAACATTTGGTCGATTGGTTAGTTGTTTGCGCTGTATCTGGGCTGGGACCCGCTCGGATCCAACAATTATTAACGCACATGGAAGTGGACGACCTCAGGCAAAGGTTAGAGCACGAGAGAGAAGCGCTACCTTTATCTGACCATCTGCTCCATAGTCTTGCTATTGATTATCAAAAAGTCGATGTCGCCCTCGAATGGCAGCAGCAAGCCGCTGACCATCATCTCGTTTGTTTCTCCGATCCGCTTTATCCGCCGTTATTAAAACAACTCTCAGATCCGCCCGCCGTCCTGTTCGTTAAAGGTTGTTTGGAGGCGTTAGCCCTTCCTTATCTTGCAATTGTTGGCAGTCGTAATGCTTCACCAGGCGGATTGCAGGTCGCTTATCAGCTGGCTGTGGAGATGTGCTCAATGGGATTTGGTATCTGCAGCGGTATGGCGATGGGGATTGATGGCGCTGCCCATAAAGCCTGTGTTGAACATCAAGGGCGAACCGTTGCTGTGCTTGGGACTGGCATTGAGACTGTTTATCCACGAAAACATCGGCAGCTTTATCATGATATTCAGCAGCAAGGCTGTATACTTAGCGAGTTTTGGCCAGATGTCGGTCCCTTTGCGGGGAATTTCCCGAAACGAAATCGGATTATTAGCGGTCTTTCTTTAGGGACACTTGTGGTTGAAGCCTGTCGTAAAAGCGGTTCTTTAATCACAGCTAGGTTGGCGATGGAGCAAGGGCGTGAGGTGTTTGCCGTTCCGGGCTCAATCTTGGGCGGTTACCATCAAGGCTGTCATGATTTATTACGTGATGGGGCAAAACTTGTGGAAACAGCCGCCGATATAGTGGAGGAGTTGGCAAGTTTAACCGCTTACCACCTTGAAGAATTGAAGTTATGCCACCATATACAGCAAGGCGAGATTTGTAATTTGCCATTTTCGTCTCTGTTAGCTAGTGTAGGTTATGAAACTACATCCATTGATGCAGTAGTTGAACATAGTGGGAAAACGATAGATCTGGTGTTAGAACAAATGCTTGAACTTGAGTTGCAAGGTTGGGTGATTGCAGTACCCGGTGGTTACGTCAGAGTAAAGAGGAGCTAGCCATGTTTGATATCCTCATGTATCTATTTGAAAACTATGTTCACAGTGAAGTTGAACTACTAGTTGATGAAGACGAGTTAACCAAGGAACTCACTCGTGCCGGATTTCATCAATCCGAGATTTTAAAAGCGTTAACTTGGTTAGAGCGCTTAGCAGAATTACAAGAAGGGGATAAACCTTATCTTTGTAATCACGATCAGCATTCGTTTCGTATCTATACAAAGGACGAAATGGACAAGCTGGATGTGGAATCCCGCGGTTTCTTGCTGTTTTTAGAGCAAGTTAAAGTGCTGAATGTCGAAACCCGTGAAATGGTGATCGATAGAGTTATGGAGTTGGATGAGCCAACCCTAATTTTAGAAGATCTCAAATGGGTTATCTTAATGGTGTTGTTTAACGCTCCTGGTCATGAGTCCGCTTATGAGCAAATGGAAGATTTAATCTTCGAACAGCCAGAAGGCCGACTACATTCTTAATCGTTGTGAAAGATGAATTATGAGAGAAAGGAGGCTAAGACCTCCTTTTTTGATGCCATAAATTCCTTGATGGCACTGCGCTTGATATCCTAATGCGCTACGACAGGTGGTGAATGATTATGTCTAAGATTGATGAACAATTATTTAGTGCCCACGAACACGCACTGGAAAAAGAATACGAACTTTGCCCTGAATGTGGCAGCGAGTTATCGGTCAAACACAGTAAGCACGGCGGTTTTATTGGCTGTAATAATTACCCTAGCTGTAATTACACTCGTCCCTTAGTGCAGCACGAATCCATTGAAACTCAAGTGATTGAAGGTTCTGTATGTCCTGAATGCGGCCACGAACTGGCGGTAAAGTCTGGTCGATTTGGGATCTTTATCGGTTGCACTCAATACCCTAGCTGCACGCATATTGAAAAACAGGATCAAGCGAGCGACAAGCCTGATATCCCTTGCCCAGAATGTAAAACGGGTAAACTGGAGCACAGAACGAGCCGCTTCGGAAAGAGTTTTTATGCCTGTAGCGCTTACCCTAAGTGCAAGTTTATTGTGAATTATCCTCCGGTTGCTGAAGCTTGCCCGAGCTGTGGTTTTGGGATCTTAGTCGAGCGTAAAGGTGCGGCGGGGATGCGTTTAGAATGTCCGCAAAAAACCTGCAAGTACAAGCGCCCCTTGTAGCGAGGCCATTGCATAATATTTGTTGCCTTAGTGCGGCTTGAGTATAATCCCCAACCGACCGTTAGATGGGGCGGCGAAGATTCGGGTAATGACAAAAACTGACAAGACGCATAAGGTGATTTGATAATGTTGCAGCTACACCCATCCGATATAAAAGACATCATTCTTCAAGGTGGCGTTATCGCTTATCCAACCGAGGCGGTTTATGGCCTAGGTTGTGACCCTGATAACGACACTGCTATCCAAAAGTTATTGGCGGTAAAACAGCGCCCATGGCAAAAAGGACTGATCTTAGTCGCCAGTGATTTTCAGCAGCTTTTAGCTTATGTTGATGAATCACAATTAACGGCTGAGCAGCTCGAGTTTGCATTTTCCAAGTGGCCCGGGCCTTTTACCTTTGTAATGCCAATCAAAGCACATGTTTCAAAATACCTCTGTGGCGAATTTGACTCTATCGCAGTGCGAGTGTCGGCCCATGCTGGGGTACAAGCCCTGTGCCGCGCACTTAATAAACCTTTAGTATCGACCAGCGCTAACCTTGCAGGTGAAGATCCCGCATTAACTGCCGCTGAAATCCTCGCCGATTTTACCGGTAAAATCGATGCACTGGTATTAGGCGAACTCGGCGAACAACGTCAACCCTCGACTATCATAGATGCGCGAAGCGGCAAAATTTTACGAAATGGACAATGAGCAAACCAATAATAAGGATAACAAGATGAGTGTGCCTGATGCTGCAGTGGTAAAAGCGTTTTTACTCGATTTACAAAACCGAATTTGTGCCGGTTTACAGGCTCTGGATGGCCAAGCAAGGTTTGCCGCCGATTCATGGACCCGCGCCGAAGGCGGTGGTGGCACGAGCCGAGTATTAACCCAAGGTGCAGTATTCGAGCAAGCTGGAGTTAACTTTTCCCATGTGACTGGCGCCGCTATGCCGGCGTCGGCAACGGCGCATCGCCCAGAATTGGCGGGTCGCAGTTTTGAAGCTATGGGTGTGTCTTTAGTGATCCATCCTAACAATCCTTATATCCCGACGACTCACGCCAATGTGCGCTTCTTCATCGCGCAAAAAGAAGGTGCCGATCCTGTGTGGTGGTTTGGCGGTGGTTTTGACTTAACGCCTTATTATCCATTTGAGGAAGATGTGCGTGAATGGCATCAAACGTCTAAGGATATTTGTGCGCCTTTCGGGGATGAGGTTTATCCCAAATATAAGAAATGGTGCGATGAGTATTTCTTCCTGCCGCATCGTAATGAAACCCGCGGCGTGGGCGGACTGTTTTTCGATGACTTGAATCAAGCAGGATTCGAGCAAAGCTTTAGCTTTATGCAAGCCGTGGGCAATGGCTTCTTAACGGCCTATGCGCCGATTGTTGAGCGTCGTAAGGATACTGAGTTCGGTGAGCGTGAGCGTCAGTTCCAACTCTATCGCCGTGGTCGTTATGTTGAGTTCAACTTAGTCTATGACAGAGGCACGCTGTTTGGTCTGCAGACAGGTGGTCGTACCGAGTCTATCTTAATGTCGATGCCGCCATTAGTGCGTTGGCAATATGCTTATACGCCAGAGGCGGGAAGCCCAGAGGCAGATTTGTACGATAACTACCTCAAACCACGCGACTGGGTTTAGCCGTTAATCTTAAAAAGTGGATGCCATATCCACTTTATTGATTACGCATATGATCGGCCAGAGTTGAAATCGCCTCAAAAATGGCGGCTCGATGCTCTGGCTTTTTAATATTATCTTCAATCGCAAACTTCATACAAAACAGCCATTGATCGCGCATCGCTTCATCAATATCGACATGCATGTGTCGTGCTCGTAATGCCGGATGACCATATTTTTGCTGGTAGAGCTGTGGTCCGCCGAGCCAACCACTTAAAAATTCGAATAGCTTCTGCTCAGACTCCGCAATGGGCGCCCTATGAATGGCAAGCAGTGTTGTCGTTTCTGTTGAGCTTGCCATCTTCTGATAAAAACATTTAGTGATAGCGCGGATCACTTTTTCGCCGCCGATTAAGTCATAGGCGTTAGATTGACTCGGATCGCGATCATCTTGCGGAGTGTGTTTACTAAAAATCTTTTTAAGCCAGTTCATTGGAATAGTCGTTCACTAAATAGAGTGTATTGAGCCGATGGCGCATTATAGCGTGAAACCTAGCGTGAAATTTAGTTGAGCATAATTCGTCTTGCGTGGGCTCAAAAGCGCGGTAGACTGCAAAGCCATTGAACCTAAAATTTCATATCGCTATGACAGACAGATACGCAGTGTTTGGCAATCCAATTAGCCACAGTAAATCGCCGTTCATCCATGGACAATTTGCCGCGCCCACCCAAGAGTCATTAACCTATGAAGCCATTCTCGCTCCGGTCGATGGTTTTGAAGCCTCATTGACGGCGTTTTTCAATGCGGGCGGCAAGGGCGCCAATGTGACTGTGCCTTTTAAGGAGCAAGCTTTTGCGCTGTGTGACAGTATCAGCCCCGAAGCCAAACTCGCTGGCGCAGTCAACACCTTAAGTTTATTGGCCGATGGCACTATTCGCGGTGATAACACCGACGGCTTAGGGCTGGTGGCCGATCTCATCGCTAACTTAGGTAGCTTGCAAGATCAGCGCGTGTTACTCATTGGCGCTGGTGGCGCGGCGCGCGGTTGTATTCTGCCGTTATTAAATGCGGGAATTGCGCAGCTGACCATCAGCAATCGCACCCACACAAAGGCGCAGCTTTTGGTCGATATCTTTACTTCTGTGGATAACGGCGCTTACGTGAGTAAAGTCACCGCCGTAGAAATGAGTGAGCTTGCGGGCGAGTTCGATATCATCATCAATTCAACCTCGGCTAGCCTTGCGGGCGAGTTGCCACCTTTGCCAGCACATATCATCACCACCCAAACCGTTTGTTATGACATGATGTATGGCGCCTCGGTCACCGCGTTTAACCAATGGGCACTCTCCCAAGGTGCTGCGAAAGTCATTGATGGGCTAGGTATGTTAGTCGGACAAGCGGCTAAGAGTTTTACCTTATGGCGAGGTATTGAGCCCGACACCCAAGTGGTGTTGACGCTTTTGCGGGACAAACTAATGGCAGAGCCTAAGTAGGAAGCATGAATCAAAGTATTCTTTTCCCCGATCTGCAATATTGGGACGATGCCCAAGGGCAAATCTGTTTTATGGCGCAATCGCAGGGGATGAACATCAAGTGCTATATCAGTGCCGCTAAATTACGTGAGCTGAATGATTTCTCTGAACAGCCGAGCACCGATGAAGCGGCTGCTATGCTGGCATTGTTTGATGCGGTGAGATTTGACGCCGAAGAAATGGCGGAAGACTTAATCGAAGCCGAAGAGTTTGATGAGTTTGGTGCAGTACGCCTAGGCTAGCTTCATCGCGAGTTTCAGTGGACCAGTTTTTGAATCAATAAATAGCAAAAGGCGACAGAGACTGAATGAGTCTCTGCCGCCTTTCTTATATAGCGATACAAAGCGACTAACTATAAGAGATTAGCGCTAAGTGACTAGAGGTTTTGTAATTCAACTAAATACTCGTTTTTCAGTCTGACATAGTTGTCGGCTGATTCTGGCAAAAAGGCCATTTCGGCAGGTGTTAATGGACGAGCCTGCTTAGCTGGGCTGCCTACATATAAATAGCCGCTCTGTAATACCTTGCCCGGTGGCACTAAAGAGCCCGCACCTAAGATCACATCATCTTCTAAAATCGCACCGTCAAGAATAATCGCGCCCATGCCCACTAATACTCGATTACCGACTTTGCAACCGTGAAGCATCGCCTTGTGACCAATAGTCACATCATCACCAATAATCAGTGGATGACCATCGGGACGAGCAGCTGACTTACGCGTCACATGCAATATGCTACCGTCTTGCACGTTCGAGCGCTTACCAATACGAATATGGTTAACATCACCACGCGCCGCCACCATAGGCCAAATACTAGCATCAGTATCTAACGCAATATCGCCAACAAGCACCGAAGCTTCATCTACATACACATTATTGCCTAATTGAGGATGAATACCTTGGTAAGTTCTGAGTGGCCCTGACATAGAAAAGTCCCTAATTGAGTGGTTTTTTGCATTATAAAGGTTAAAAACTAGCTGGTCAGGTCAAATATCCAACGAACAGTGAGAAAATGACAATTATCTTAAAAATAGGGGTTGCGCACTTTCTCAATCTCCCTATAATGCGCATCCACTGACACGGCAGATAGCGAAAGCAAGCTGGCGAGTTAGATTGAATTGAATGGCTTACTCTCTGTTGAGAATGGCACTTAATTCAATCGCAGCAAGAAGTTGAAAAAACTCCTTGACGCGAAACGGGAAATGCGTAGAATACGCAGCCCTAGCCAACTGGAAGCGTTCGGCGCTCAGTGTGGTGCTCAAGTCCTCTTACGGATATTGAGTTGCTCTTTAACAAGATAAAACAAGAAATCTGTGTGGACACTCACAGGTGTTGAGTTAATCGAAACTGCTTAACCTTCGGGTTGGCAGTCAAAAATTTAAATCAATGTAACAATGAGTGTTCATAGCAATATGTACAGTTTGTTTCAGCCCTTCTTTTATAAGGAGTGCGAGAAACAAAAAATCAGAATTCATTGAGCCGCTGAAGTCGCAAGACGGAAGCAAAAAACTTTAATTGAAGAGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGCAGCGGGAAGATAGTTTACTATCTTTGCCGGCGAGCGGCGGACGGGTGAGTAATGCCTAGGGATCTGCCCAGTCGAGGGGGATAACAGTTGGAAACGACTGCTAATACCGCATGCGCCCTACGGGGGAAAGGAGGGGACCTTCGGGCCTTCCGCGATTGGATGAACCTAGGTGGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCCTAGCTGTTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGTAGGGAGGAAAGGTAATAACTTAATACGTTATTGCTGTGACGTTACCTACAGAAGAAGGACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTCCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGCGAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTCGAACTGGCGAACTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCTACTCGGAGTTTGGTGTCTTGAACACTGGGCTCTCAAGCTAACGCATTAAGTAGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCACGGAATTCGCTAGAGATAGCTTAGTGCCTTCGGGAACCGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCCTTATTTGCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGATAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCGAGTACAGAGGGTTGCAAAGCCGCGAGGTGGAGCTAATCTCACAAAGCTCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTGGATCAGAATGCCACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGCTGCAAAAGAAGTGGGTAGCTTAACCTTCGGGGGGGCGCTCACCACTTTGTGGTTCATGACTGGGGTGAAGTCGTAACAAGGTAGCCCTAGGGGAACCTGGGGCTGGATCACCTCCTTACCTATACGACTAACTTAATGTTTGCTGAGTGTTCACACAGATTGCTTGTTTATCTTCTCTTAGGAGAAGTCAGAGCTGAATGCGCCGCTAGCCGGTACGCATTGTTCTTTAACAATTTGGAAAGCTGATAGTATGTAACGTGATGATGTCTGTCGTCATGTTACTTACAAACCGATGTAAACGTGGAAACGTTTGCATCATGAGTTCTCAAACACTTTATTAAGTGTCTTGAATATTCAAAAGTAAATTATTCTTAGTTGAATAGTTTACGTCTAAGGCGCGTCCACTTCTTTGGTCAGAAGTGAGACAAGTAAAACCAAGCTGGTCGCAATGCGACTCAGATGAGTGAAACTCATTTGGGTTGTATGGTTAAGCGACTAAGCGTATACGGTGGATGCCTTGGCAGTCAGAGGCGATGAAGGACGTAGTAACTTGCGAAAAGCGTTGGCGAGCTAGTAACAAGCATTTGAGCTAACGATGTCCGAATGGGGGAACCCAGCAGCATAAGCTGTTATCGCAACATGAATACATAGTGTTGCGAGGCGAACCCGGGGAACTGAAACATCTAAGTACCCGGAGGAAAAGAAATCAACCGAGATTCCCCTAGTAGCGGCGAGCGAACGGGGATTAGCCCTTAAGTCAGTGGGGTGTTAGTGGAATGTGTTGGAAAGCACAGCGGCACAGGGTGATAGCCCCGTACATGAAAACTAACCATTGATGAAAACGAGTAAGGCGGGACACGTGACATCCTGTTTGAATATGGGGGGACCATCCTCCAAGGCTAAATACTCCTGACTGACCGATAGTGAACCAGTACCGTGAGGGAAAGGCGAAAAGAACCCCTGTGAGGGGAGTGAAATAGAACCTGAAACCGTATACGTACAAGCAGTGGGAGCGGTTCTTGAGACCGTGACTGCGTACCTTTTGTATAATGGGTCAGCGACTTACATTTTGTAGCGAGGTTAAGCGAATAGCGGAGCCGTAGGGAAACCGAGTGTTAACTGCGCGTTTAGTTGCAAGGTGTAGACCCGAAACCCGGTGATCTAGCCATGGGCAGGTTGAAGGTTGAGTAACATCAACTGGAGGACCGAACCGACTAATGTTGAAAAATTAGCGGATGACTTGTGGCTGGGGGTGAAAGGCCAATCAAACCGGGAGATATCTGGTTCTCCTCGAAAGCTATTTAGGTAGCGCCTCGAGCGAATACCATTGGGGGTAGAGCACTGTTAAGGCTAGGGGGTCATCCCGACTTACCAACCCTTTGCAAACTCCGAATACCAATGAGTACTACTCGGGAGACAGACGGCGGGTGCTAACGTCCGTCGTCAAAAGGGAAACAACCCAGACCGTCAGCTAAGGTCCCAAAGTACTAGCTAAGTGGGAAACGATGTGGGAAGGCTTAGACAGCTAGGATGTTGGCTTAGAAGCAGCCATCATTTAAAGAAAGCGTAATAGCTCACTAGTCGAGTCGGCCTGCGCGGAAGATGTAACGGGGCTAAGCTAGTCACCGAAGCTACGGGTGCACAGTTTACTGTGCGCGGTAGAGGAGCGTTCTGTAAGCCGTTGAAGGTGAAGGGGTAACCCACGCTGGAGGTATCAGAAGTGCGAATGCTGACATGAGTAACGATAAAGGGGGTGAAAAACCCCCTCGCCGAAAGACCAAGGGTTCCTGTCCAACGTTAATCGGGGCAGGGTGAGTCGACCCCTAAGGTGAGGCCGAAAGGCGTAATCGATGGGAAACAGATTAATATTTCTGTACTTCCGCTAACTGCGATGGAGAGACGGAGAAGGCTAGGCTAGCGCGGCGTTGGTAGTCCGCGTTTAAGGTGGTAGGTTGATTTCTTAGGCAAATCCGGGGAATCGCACTTTAGTGTGCAGACTGAGAGCTGATGACGAGGTCCTAAGGGACTGAAGTAGTTGATGCCATGCTTCCAGGAAAATCTTCTAAGCTTCAGGTTAGTGGGAATCGTACCCCAAACCGACACAGGTGGTCGGGTAGAGAATACCAAGGCGCTTGAGAGAACTCGGCTGAAGGAACTAGGCAAAATGGTACCGTAACTTCGGGAGAAGGTACGCTCCTGTTGGTGATGAGACTTGCTCTCTAAGCTGACGGGAGTCGCAGATACCAGGTGGCTGCAACTGTTTATCAAAAACACAGCACTGTGCAAACTCGCAAGAGGAAGTATACGGTGTGACGCCTGCCCGGTGCCGGAAGGTTAATTGATTGGGTTATCGCAAGAGAAGCTCATGATCGAAGCCCCGGTAAACGGCGGCCGTAACTATAACGGTCCTAAGGTAGCGAAATTCCTTGTCGGGTAAGTTCCGACCTGCACGAATGGCGTAATGATGGCCACGCTGTCTCCAGCCGAGACTCAGTGAAGTTGAAATTGCGGTGAAGATGCCGTATACCCGCGGCTAGACGGAAAGACCCCGTGAACCTTTACTATAGCTTGGCACTGAACATTGAACCTACATGTGTAGGATAGGTGGGAGACTTTGAAGTTGTGACGCTAGTCATGATGGAGTCGTCCTTGAAATACCACCCTTGTAGTTTTGATGTTCTAACCTAGGTCCCTTATCGGGATTAGGGACAGTGCCTGGTGGGTAGTTTGACTGGGGCGGTCTCCTCCCAAAGAGTAACGGAGGAGCACGAAGGTTGGCTAAGTACGGTCGGACATCGTACGGTTAGTGCAATGGCATAAGCCAGCTTAACTGCGAGACATACACGTCGAGCAGGTACGAAAGTAGGTCATAGTGATCCGGTGGTTCTGAATGGAAGGGCCATCGCTCAACGGATAAAAGGTACTCCGGGGATAACAGGCTGATACCGCCCAAGAGTTCATATCGACGGCGGTGTTTGGCACCTCGATGTCGGCTCATCACATCCTGGGGCTGAAGTCGGTCCCAAGGGTATGGCTGTTCGCCATTTAAAGTGGTACGCGAGCTGGGTTCAGAACGTCGTGAGACAGTTCGGTCCCTATCTGCCGTGGGCGTTGGATGATTGAAGGGAGCTGCTCCTAGTACGAGAGGACCGGAGTGGACGAACCGCTGGTGTTCGGGTTGTCATGCCAATGGCATTGCCCGGTAGCTACGTTCGGAATCGATAACCGCTGAAAGCATCTAAGCGGGAAGCGAGCCCTAAGATGAGTCATCCCTAGGAATTTAATTCCTCTAAAGAGCCGTTCGAGACTAGGACGTTGATAGGCATGGTGTGTAAGCGTTGTGAGGCGTTGAGCTAACATGTACTAATGACTCGAGAGGCTTAACCATACAACCCAGATGGGTTTTACTGAAAATGTTCCAGACATTTTCTAGCACTTCCTCCATCCATGGAGGTCGTACGAAAAGCCTTAGACAGAATATCGACACTTGATGAAGTGTGAACTCAAACAAAGAAAATCAGCTTTCCGAATTATTATTAACGTGGTCCAGCGATGGACACGGTAATAAACAGAATTTGCTTGGTGACAATAGCCTTGTGGAACCACCTGATCCCATCCCGAACTCAGAAGTGAAACGCAAACGCGCCGATGGTAGTGTGGGGTCTCCCCATGTGAGAGTAGGTCATCGCCAAGCGCCTAATTTAGGTTAACGTGCAAACGTTAGACTAAAGGAGCGGTAGTTCAGTTGGTTAGAATACCGGCCTGTCACGCCGGGGGTCGCGGGTTCGAGTCCCGTCCGCTCCGCCAACATAAAGATGAAAGCCTCTACAGCAATGTAGAGGCTTTTTTCGTTATGCAATTTATTTCTATTGTCGCGGCTTTTTTGCAAGGATCTAGCCTAGCTTCGTGTTCATAAGTTCATCGTTTCCTACCTGCATAGAATGAAAGCCTTTTCAAAGGTTGCAACTCATTCGTAGTAAATTTAAATCTCAGCTACTTTTGTCTAAGTTTGTCCCACTTTATTCATATTATGTCTTTCACGCACAGAATGGCGCTTGTGCGCGACTTGGCAGTATTGACCTGTGACACTGATCTCGGACAAATTAAACGGTATAATGCCTTATGTCTTTCATGAGTAACGAAAATGAATTGTCGTCTTGGGTGTGGTGCTTGCTGTATTGCGCCTTCAATTAGCAGCGGTATTCCTGGTATGCCAAATGGTAAGGCTGCGGGTGAGCGTTGTGTGCAGCTGAGTGATGCCAATTTATGTTTGATCTTCGGCTCGCCCGATCGTCCCGCGGTTTGTAGTGATTTTGATGCATCCGAAGATGTATGTGGTCATTCGAACGAAGAGGCGCTTTGGTTGATTACCCAACTCGAAGCTCAAACCTCTCATTAAGTTTATCGCTGACAGGTGAAATATGCAGTTAACTCGTTATACCGATTTTGGCGTTCGTACCTTGATGTATTTGGCGGTTCAGCCAGACAGAACCACTTTATTTAGAATTGCTGAAATTACTCAGGTATTTGATCTGTCTCCAAATCATGTCTCTAAGATAGTGCATCATTTAGGCAAGATGGGTTACCTCGAAACCATTCGCGGTAAGATGGGCGGTTTCCGTTTGGGTAAGCCTGCGAGTGAGATTAACTTAGGTCAGTTAGTTCGGGTATTAGAAAACTCGTTGGCGCCGATTGATTGCAGCAAACCTTACTGCCGATTCACGCCTGCCTGTAAGCTGAAAGGCGTGTTAGCACAAGCAGTTGAAGCGTATTTGAATGTGCTCGATCAATATACTCTGCTGGACATTGTGAGTAATCGTGATGAGTTACTTGCTTTGTTGCCTGACATGTCTATCTCAGTGTTACAGTTGGATTAGTTCCATTTTTGTGTGCAATGCCGATGGGGAGCAAGCTTGATGCAGGTACTAGATCAATCCCCTCGGTTTTTAGTCTTTGTAGATTGGCCTTTAAGAAGCGCATGGTTTCTGGATAAGGATGGGCAATCGCCACTAGGTTACCTTGAGTCTGTGCCTGACTTATCATTAAGCTAAATTGTCTTTCGAGTGCTTTAGCACTGATATCATTGTCTAGAAACAGTTGGCGTTTTAATAACGGCACTCCTAACTGATCCGCTGTCTCTCCCGCCTTAGTGAACCTTGTTGTCATACTATCGATAAAATACAGATGCTTTTGTTTGAGGGTTTCCATCACCCACAGCATAGGATCATCGAGTTGAGTCAGTAAGCTCCCCATGTGGTTATTGGCACCTTTAGCAAAAGGAACGCTGTTAATTGCATCGACAACACTGGCGCGAATTTGTGCCTCAGTCATAGTATTGGTCAATCCACCCAAGCCTAACGCCTTACCATTAAGCGCTTGCATAGGCAGATGCAGCATGATTTCGTGGCCCTTTTTATGGCCAGCATTGGCGAGTTCTTTGCCAAGAGGTGTATGAGGCAACACTGATAATGTGACTGTGCTGGGCAGTGCCAGCACGGCTTGATCGGTATGACGATAACCTATGTCATCAATAATGAGGGCAATTTGAGCAGCATGACTTTGAGAAATGCTTAGGGCCAATAGGGCCAAGGAATAAAGTAAGCGCACAGTTTCTTCTTATTAGTCATGATGCTGTATCCATGCAATGGCTGAGGTCACTTGATGATCTTGGGCCAGAGATGCATGGGCGGATAGGGCGTCGATTATAGCCAAATTACCCGCGCCGGAACCAGTTTCTGCGGCAATTTTTATGTCTGGGACGATTCCTATGTTGTGAATGTTTTCACCTCTGGGGGTATTGTACTGAGCAATGGTGAGTTTTATCGCGTTACCGTCTTCTAAGATGGGGATCAGGCTTTGTACTGTGCCTTTACCAAAACTGGTTTCACCTAACAGCTTAGCCCGACCATTTTCTTGCAGTGCGGCGGCGAGCACTTCGGATGCAGAGGCCGAGCCTTTGTTGATCAACACTAGCATGGGCACGTTAGTCAGCATGGTTTGTGGTGAGGCGTAGTAGTCGGAGTTGGCATCGAAGAAGCGGCCAGAGGTTGAGACTATGCGCCCCTTGGCGAGGAATAGGTCGGCAATTTTAACGGCTTGGTCGAGCAGGCCACCGGGATTATTACGTAAATCTAAGATGAGTCCGGCGAGCGGTTTATTTTGCCATTGGCTAAGTGAGCGCACCATATCTTCGGTCGAGTTTTCTTGGAAGCTACTTAATCTGACATAACCAATATTGTCATCAAGCAGTTGAGTGGTGACAGATTCGATAGTGATAGTGCTCGGCTTGAGTGACACCTCAAAGGTCGTATCGTCATTGCTGTGCTTGAGCACTAGCTTAATACTTTGATTGCTGAGGCTATGTTTTTTTATCTCGGCAAGGATATTGGCGAGGTTAGTCTCGGTCGCCAGTGTGTCGTTGAGCTTAACAATAATGTCGCCTGCGCGTATTCCCGCTTGTTCTGCTGGGGAATGGGCGAAGGGGGCGATGATGTTGATGTGGTCATTTTGGGTTGCGACTTCGAAGCCGAAACCAAAATATTCGCCGCGATTGGAGTCTTTGAGATCGAGTAATTCTTGGTGATTCAAAAAGCTGGAATAGGGATCTAAGTGCTCAAAAATACCTTTGATTGCCGCTTGTACTAACTCTTCTTTCGAAACGGTTTTCACATAATAAGTTTCAACGGTTTCAAGCACATCTTGCAGTAACGGATAGTCAAAATCACTGCGATAAGACTTAGCGTTCTCTTGACTAGAGAGGCTGATGGATAGGCCCAAAGTCAGGCCTAATCCAACGCAACTGATATAGCGGATCATGTGTTTCATAGCCGTCCCCTGTGATGAGGGGACCGTTATGATTAGCGACAGTATTTCGCTGGATCGACGGCTTGCCCCTTGTGCCTTATTTCAAAGTATAGGCCAGCCTCGCTCTGTCCACCCGAACGTCCGACTAAAGCTATCGCTTCGCCGCTTTTTACCATGTCGCCAGGGCTTTTTAATAGGGTTTGAGCATGGCCGTACAGGCTCATATAACCTTTGCCATGATCGATAACCATCACTAAACCAAAACCGCGTAACCAGTCGGCATAAATAACCTTACCACCTGAAACGGCTCGAATATTTTGCCCTTCTGGCGCGGATAACATAGTGCCTTTCCACACGACTTGGCCGGAACGTTGGCTGCCAAAGCTGGCACTGACGCGACCTTTAGTTGGCCATTTGAGTTTGCCACTTTGTTTACCAAAACCTTCCATCGAAGGATTGTCTTTCATGGCCCGCAGCGCTTGTTCGACCACACGCTTTAAGCTGGCCTCCTCAATTTGCAACTGCTCCAGCTCCGCGCCCTTAGAATTTAAGGTGCGTTGCAGCTCTTTGATGGTCAGTTGTCGCTGATCTTGCTCTTGGCTCAAACGTTTGGCTTGGACCTGTTGTTCGGCCACGAGTTTGGTTAATTGCTTTTGCTTACTCGTTTGCGACACCTTGATCTCATCTAGCTCGGTCAAGGTTTGCTTTAATTCGTTGATCGACTTCATCCGCGCTTTATTGAGGTATTGGTAATAGGCCAACATACGCTCGATAGTGGCAGGACTTTGCTGATTGAGCATCATCTTAGTGTAATCGTGATTACCGGCAAGATAGGCGCTCGACAGCTGTTTCGACAGTGTTTGTTGCTGACTCACTTTAAGCGTTTCTAACTCATCTTGACGTTGTTTCAGCTCGGTCAGCTTAGTATCGATTTGCGCCAAGCTATTTTTGGTGCTGTTGACTTTTTTGGCCGCTGACGCAATCGCTTCCTCATCGCTACGCAGCAATGACAGCAATTTTTCTCTTTGTTTACTGGTGGTTTTTAACGCGCTTTGTTGTTGATTTATTTGCGATTGGATTGATTTCAACTCGGACTGACGCTTCTCCAAATCAGAAGCGTTGGCCGAGAAAGATAACATCATAAAGCCAGCAAGAATGCTGGCTTTAACAAGGAGTCGAGTGCTCACTAGGTAACGGTTACTCTTTAATGTGGATTAAGGGCTTGCCCGTCATCTCTGCTGGAATGGCTTCGCCAATCAAGGTCAAGATAGTCGGTGCGACATCGCTGAGTTTACCACCTTCATCTATGGTCGCATTGCGGCCCACAAAGACGAAAGGTACCAGTTCACTGGTGTGCGCTGTGTGCGCCTGACCTGTGGTTTCGTCCGTCATTTGCTCTGCGTTGCCGTGGTCAGCGGTAATAATGCACTCACCACCCACTTTCGCCAGTGCATCCACTACACGGCCGATACAAGCGTCAACCGCTTCACAGGCTTTAACGGCGGCATCGAAATTACCCGTGTGGCCAACCATGTCGCCATTCGGATAGTTACAGATGATCACGTCGTACTTGGTCGACTCAATCGCCGCCACTAACTTGTCGGTCAGCTCGGCTGAACTCATCTCAGGCTGTAGATCGTAAGTCGCCACTTTAGGTGAGTTGATTAGGATGCGATCTTCACCTTCGAATGGCTCTTCTTTGCCGCCGTTGAAGAAGAACGTCACATGGGCGTATTTCTCAGTTTCAGAGATACGCAGTTGAGTGCGACCTTGTTTCTGCAGCACTTCACCTAAGGTATTTACCAAGTTTTCAGATGGATACGCAATAGGTGCTTTGATGTCGCCGGCGTACTCAGTCAGAGTGACAAAGTGCATTTTCGGCACCACAGCGCGTTCGAAACCATCGAAGTCTGGGTTGATAAAGCTACGGGTGATTTGGCGGGCACGGTCGGCACGGAAGTTCATAAATATCAGTGCATCGCCATCTTGCAAGGTCGCAACTTGGCCGTCTGCATCTGTAATTGCCGATGAAGAGACGAACTCGTCGTTCTCGTTACGCTCGTAAGCCGCTTCTAATGCAGTAACCGCATTGTCGTATTGGAATTTTGACTTGCCTTGGGTGATCAAATCGTAGGCTTGCGAGACGCGATCCCAACGATTATCCCTGTCCATTGCAAAGTAACGACCAATAATCGAGGCGATACGGCCATGGCCTAATGTAGTAAACAGATCATCAAAATGGCTCAAGCTGGTCTTAGCACTACGCGGTGGTGTATCGCGGCCGTCGAGGAAAGCGTGCAAGTAAACCTTAGTTGCGCCACGGGCAACGGCCATACGGCACATCGCTTCGATATGCTCTTCATGGCTGTGAACACCACCGGCAGAAAGCAGACCCATAATGTGTACTGCGCCGCCGGCTTTAATGGCCGAGTCAACCGCATCGCACAGGGCTGGATTTTGTTCAAACTCATGATCTGCAATGGCTTTACTGATGCGAGTGAGTTCTTGATAAACCACACGGCCAGAGCCTAAGTTAATGTGGCCAACTTCAGAATTTCCCATCTGGCCATCAGGTAAGCCTACATCTAAGCCAGAACCTGAAATCAAACTATGGGGATATTGAGCGTTGAGACGGTCGAGTACCGGAGTGTTGGCGTGGAAAATCGCATTGTTATGCGTGTTTTCACGGTAGCCCCAGCCATCGAGGATCAATAACGCGAGCGGACGTTTAGCTGTCGTCATAGTGATACCTTTAAGTTTTATAGAAGCGATAGAAATCTGAAATTGGTCAAATATTACTACGTTAGCGGGGTATGAAAAAGCGCTTTACCCCGATTAAGGTCATCGAATCACTAATTCGCCCGATTCTGTGAGTGGCTTGTGACAAGTGCTGGACCAATTGCGCTAAAAGTGCGATCTCGAACTGTGACTAAACTGCTGCATTCTGCCAAGGCAATCGGTATACTCTGTGCCCTTATCGAATTTTCGCCATTTATGCGGGCAATCATCATGCAAGAATATATCGAATTTTTTAAAGCTCACTCAATGTTAAGTCTGGCCTGGGTGGGATTATTTGTTGTGCTTGTCGCGAGTATCATCAAATCGAGCATGTCAAAAATCAAAAGTATTTCTCACCAAGAACTGACAATTATGGTGAACAGACAGGACGCTAAAGTGGTTGATGTGCGCTCGAACGACGAGTTCCGCAAGGGGCATATTGTTGATGCAATTAACGTTACGCTAGCAGATATTAAAAATAATCAGATATCTGCCCTTGAAAAGTATAAAGCGAGTCCCATTATATTGGTATGCAATGCTGGCATGACCTCGTCTCAAGCCGCTCAGCTTTTGAGTAAGCAAGGTTTTGAAAACCTGTACAACTTGAAAGGTGGTATGGGTGAATGGCAGGCAGCAAATATGCCTGTTTCAAAAAGCAAAAGATAAGTTGTCGGCGGCAGTTATGATTTAAGCTGCACTCAATCCACCGGATAGTGCAGCAATACAGTCATCATTGTTTGGCATACTGATCCTGCGGTCGCTAAGGGCTTAATGTAAGTTTTTAGACTGGTTCGATCGATACCCGAATATGGGTGAAAGTTGGGATGATACCGAGATCTGATACAGGCCTTGAGTAACATAAGCCCCGGGCCAATTTACATTCAAAGATGAATTGGCTTTCAAATAATAATTGGCTTTCAACGAAAAGCCGATACAACATTGATAGGATAGGTAGGAAATTATGGCTGAAGTAGCAAACAACGAACAACAAGCCCCACAATTCAACATCCAACGTGTTTACACTAAAGATGTGTCTTTCGAAACGCCTAACAGCCCAGCTGTATTCCAAAAAGAATGGAACCCAGAAGTTAAGTTAGATTTAGACACGCGCAGCGCTAAATTAGCTGACGATGTGTATGAAGTTGTCCTGTCTTTAACGGTAACTGCACAAAACGGTGGCGATACCGCTTTCCTATGTGAAGTACAACAAGCTGGTATCTTCTCTATCACTGGTCTGACTGAGCCACAACTGGCCCATTCATTAGGTGCATACTGCCCTAACATCCTATTCCCATATGCTCGCGAAACCGTGGGCAGCTTAGTCGGTCGTGGTACTTTCCCACAACTGAACTTAGCACCAGTTAACTTCGACGCGCTGTTTGCCCAATACGTGCAACAACGTCAAGCTGCTGCGACTGCACCAGCTGCCGAAGAAGCTAACGCTTAATTACATGAAAAACTCTGCCGATATCACGGTATTAGGGGCGGGCTCTTATGGCACCGCCCTTGCCATTTCTTTAGCCAGCAACGGTCATAAGACCTTGTTATGGGGACACGATCCTGTCCACATGCAAACGCTCGCTCAAGACAAGTGCAACCAAGCGTTTTTGCCTGGTATTGCGTTTCCTGATTGTTTACAGATCGAAGCCGATTTAGCCAAAGCCTTAGCTGCCAGTAACAATGTGTTAGTGGTAGTACCAAGCCATGTGTTTGGAACTGTGCTTGAGCAAGCTAAACCGTTACTCCGTAGCGATGCGCGCATCGTATGGGCAACGAAAGGGCTCGAGCCTGAAACCGGACGTTTGCTGCAAGATGTGGCCCGTGATGTGTTAGGTGAGCAATATCCGCTGGCGGTGTTATCTGGGCCGACCTTCGCGAAAGAATTAGCCATGGGTTTACCTACGGCCATTTCGGTTGCGGGGACTTGTCCTACGTTCACCAACGATCTCGTCGAGCTGTTACACAGCCCTAAACGTTTGCGGGTTTACGCCAACGACGACTTTACCGGACTGCAACTTGGCGGCGCGGTAAAGAACGTGATCGCGATTGGTGCGGGCATGTCTGACGGTATCGGCTTTGGTGCCAATGCCAGAACAGCCCTGATTACCCGTGGTTTAGTTGAATTGACGCGTTTAGGCGAAGCCTTAGGGGCCAATGCGGCCACTTTTATGGGCATGGCGGGGTTAGGCGACTTAGTGTTGACCTGTACCGATAACCAATCCCGTAACCGTCGTTTCGGTTTAGCCTTAGGTAAAGGCTGTGATGTGATGACAGCACAGGCTGAAATTGGCCAAGTGGTTGAAGGTTATCGCAATACCAAGGAAGTGTTTACGCTTGCGAAACGCCTTGGGGTTGAAATGCCAATCACTGAGCAGATTTACCAAGTCTTGTACCAAGGTAAATCGCCCGTCGATGCTGCGAAAGAATTGCTCAGCAGAGAAAAGAAATCAGAAACGCCCGCGCAATAAACCGCGAGCCGTGATGATGCTGAATAAAGGAAAGGGTGCTAAAATAGCGCCCTTTTTTGTGCCTAACGTTTGAGCTTATCTTTGCTCCGTTTAGCGGTTGCTGGCTTGCCTGCTAAAGGTTCGTGAGTCAGCAAACTGCCCGTTGGCAGCCAATACCAATCAACTTATCTGTGTGGAGTAAGAGCGTGAAACATCATGATGTCATTATTATCGGAGCCGGCGCCGCGGGATTGATGTGTGCAGCGACCGCGGGTTACCGAGGCCGTGATGTACTCGTACTCGACAATGCCAAGCAAGCCGGGCGTAAAATCCTCATCAGCGGCGGCGGGCGTTGTAACTTCACTAACCTTAAAGTCGAGCCCGCTAACTTCCTCTGTGGTAATCCCCACTTTGTTAAGTCGGCACTCGCGCGTTATCCGTCGCAGCAGTTTATTGAACTGGTCGAGCGCCACGGTATTGAATACCACGAACGCGATCACGGCCAATTGTTCTGCAATGACTCGGCTAAAGAGATAGTCACTATGCTATTGACCGAGTGTGAATGGGCGGGCGTGACCATTAAGCTGCGCACCGATATTCTGGCCGTTAAAAAGACCGAGGCAGGGCGCTTTGAGCTCAATACCTCTAACGGCGAACTGAGCTGCGACTCACTGGTTATCGCCACGGGCGGCTTATCCATGCCTAAACTCGGCGCCACGCCTTACGGTTATCAACTGGCCGAACAGTTTGGCCTCAAAGTATTACCCACTCACGCGGGTCTGGTGCCGTTTACCTGGCACAGTGAAGATAAAATTCGTTTCGAACCGTTGTCGGGAATAGCCGTACCGAGCCGCATTACCGCCAAAGATGGCACTGCGTTCAGCGAAGCGTTGCTATTCACTCACCGCGGCTTATCTGGCCCCGCAATTTTGCAGATTTCCAACTATTGGAAAGCGGGCGAAACCATAGAAATCGATCTCTTGCCGAATATGGATTTGGCGCAATTGCTAGAGCAACAATTCGCCGCGCACCCTAAGCAAAGCCTGCGCAACACCCTCAGCCAGTGGTTACCTAAACGTTTGGTTGAAGTGCTCTTCGATGAAGCCTTGTTGAACAAAGCCTTAAATCAGCTAGTGCACGCCGAACGGGCAAAACTGGTCGATGATATACACCGCTGGACATTATTGATGAATGGCACCGAAGGCTACCGCACCGCCGAAGTGACCTTAGGTGGTATTGATAGTAATGAGTTGTCATCCAAAACTATGGAAGCCAGCAAAGTCGCAGGACTCTACTTTATCGGTGAAGTCATGGACGTAAGTGGTTGGTTAGGCGGGTTTAATTTCCAATGGGCCTGGGCATCCGGCGTCGCCGCAGGGCAAGCGGTGTAGTTTAAGTAGGGAATTAGAACGAAGACAAAGTAGATTTACTTAGTTGTAATTCCGCCTATTGATGTTAGAGATATAAGCCTACTTATACTAGAAGTCGATTCCTAACGTAAAATTAGTCCGCCGTGGTATACCCCTGCTTTGGGGAATTAGGCTTATGAGGAAACGCCATTTTTAGCAACCCTTCTGAAATCATAGGCTTCAAGTAATTTTGTCGTAAGCTCTGAGGCTTACGATCTAATATCATTTCCAAAATAGAAACAGAGATGTAATGTTCTTGGCAGAGGCCAGAAATCAGTGACTTCATCGTAACTGTATCTAATCGACGCTGTTCTCTTGCAGGAGCAGCTAAAGCGTTAAGTTCTTGTCGAAATGATTCGCTTAACACATCAAGGCTATCGACAAATGGTTTGTCAATTAAAGAGCTGATAAACCTGCCTTTATCATCTCTAGTTTCGTTCGAGTCAATAGCATTACTGTTGTTATGCGTTAAGTTACCAATGCTATGCGTTAAGCTTCTGGCGTTATGCGTTAAGTTATTTATAGGAGTTAACGCATTAGAAAAAACTTCCTCTGGGGATGGCAACTCCATTCCTGGTAAGCTGTATGACTTATCTCTTTTCTTTCCACTTGCGACAAGAAAGCCCTTATCGACTAGTCGAGGTAAAGCCAGCGTAACATCACGAGAATGCTTACTGGTTAGCTGACAAGCTCGTTCGTGGTTTACCCAGCCTTCAATTGCTGCGGTTACAACAATCATCTGCTCAAGTTCGTTGAGTGAATAAAAGGTGGTGTTGCCAAACTTAACCGCTAACAACTGAGTTACTTCTTCTGGGATAAGGCTTGCGGTTGATAACTCTAAGAGGGTTTGCTCTGACGGTTCATATTTTTCGTACAACTTCGGGGTTCGCCACTTTGCCCATTCCCATCCACTATATATTTTGGGAATTCCTGAACCAGCACGCTCTCCCGCACCAATCATCAGAAACATTTGATGCATCCTGCGGTTTCTACAATCGCTTTCACCACCTTTTACTGCTATTTCTGGGGGGACACGCATCAACCCTGGGTTGCGAAAACCAAATAAATCAGGCCGCTTTACGATAAGGATTGAGCTTCGTCCTGTGTAATCTGCATGAACAAGCGTATTCACTAATGCTTCACGAATAGCTTCGTGGGCTTTTGAATCATCCAAGCGAATACCATTTTGAAGTTCAAATGGTACTTTTAAGTCCGTGACCAATTTCCGGTATGTACGACGATAGAAATCAAACACGTTTCCAGACCAAGATCCATCGGGGTAAAGGCGGTCCACCCAACGCTCAACACTGGCTTCATCTCGTTCGCGATAGTCAACAAAATAATTCGGAACAGCGTCTTGTATGGCATCCCAAGTACCAAACATCAATACTCCAGCTAACGTTATACCTTCAACGTTCGTCTGACGATCTTTTCTCCATCCACCCAGCTTTTTAAACAGTTCAAAAAGGTCTAATTCCAGCCAAGGATGTTGCGGTTTAGCAACTGAAAATAAGTTCTTGTATGCTTTGAGGCTGTCCATGTCGATATCAGAGAAGTCAAAACCAGTGAGAATTTTATCATCTCGGCTATCTTCTACCTGCTCTGCCATCATCCGCTTAACCTGTTCTTCACTACAGCGGTGATCACCTTCGTGTAGACGAGTGTAAGTCTCCTTCATAGGCTGATTTTTCAGATAAATAGGCTTTTGGTCACGACGAGCTGTTGGAATTTCTATAACTAGTAGTGATTTTCCTTCAATGTTCACCGTTTGAACACTATCATTTGTCAGTAAGTTTATGTTTACTTTGGCATTATTTCCCGCGATATCAAACAATTGTTTGTGAACAATCTCTATATCTTCAATCCCAGCTAATGTCAGTTGTCCTTTTTTCTCACGCACACCGAGAACAACATAGCCACCACGAGAATTGGCCATTGCGCTGTAAGTTGGCCAAAAATCATCAGGTAATTTACCTTTACCATCTTGTCCTTGAGCCAATTTAAATTCCAACTCCGCTGACTCTGATAAGGCTTGAAGGTCGACTAAATCCTGTATTTGAAACATATCTATCGCTTACCACCCGACTTTTTAGTTAATAAACATACTTATTAGCTTCAGTTATTGATCAACTCAAAGTTCTTGCTTAATTAGCAACAGTCTTCATTCAAACCTTAGCTTTTTTGTTCTACTAATAGGTTTAACTGCTACAAATAAAAACAAAGCCACTGATAGTATGGAATATAACCTACAGTATCTTTCGTGCTATCACCAAGCGTATTTCTGCTAAGGTCTAAAAGTCGAACTTTGATAGAGCGAAGGTGAGTTTTTTATTCATTGGCAATGAGTTAGCTGGAGTTTGGTCAATGGAATATTATGCAACATCCACTATAGCGGATAAAGGTTGAAGTGGATAATCAATGGATAGCTTATGCCATCAGATGTTTAGATATATGGCGGGAGTGCTTTCGTCGTTTTTCAAGGTGATCACAATGGGTGGCTTAGAAAGAATGACTCAGTTGGGGGAATTCTGAGATATAGAGTGCCACTGATATGGTTTTCGATGGCATGAAGCCATCGAATAGCCTATCGGAATAGTTCTTTTAAAAGAAACGGACGGGTGTTATAGCGGTGAACAGACTCGAAGGGCGGCACTGACTTGCAGTGCCTGCCGTCCAACTAATTAATTTAACCAATCGCCAGTTTGGTTTCTGCATAGCGCAGTTTTTTCACCCTTGGATTCGCGAGGAAGTAGGCGAGCATGAGTGGGCCGAGGCGTCCCATGAACATCATAAAGATGATGATGGCTTGGCCTAGGCTAGTTAAGTTACCTGTTAAACCTCGGGATAAACCGACTGTACCCAGTGCGGATATGGCCTCGAAGACGATATCGATTAACGGCGCTTTCTCACTTAATACCAGTATGAAGATGGCCAGCCAAGATACGACCAGAGAGATGATGGTCAGTGCTAAGGCTTTGCTGACGGTATCTTTGGGGATTTCCCGTTTAAACACATAGATGGCTTCGTCGCGGCGTAAATAACCATAAGTGGCCAAAATCAGTACCATAAAGGTGACGACTTTAATGCCGCTGGCGGTACTTAATGAGCCGCCGCCGATAAACATCAGCACGAGTATGAGCAAGGTACTGGCGTCTTCGAGTTGGTCAATCGCTAGGGTATTAAAACCTGCGGTGCGCGGTGTAACGGCTTGAAACCATGAGGCTAACCATTTGCCGAGTTCGCTTAAGGGCGCCAAGGTGTTGGGGTTGTTGTACTCGATAAGGTAAATCGCAATCACGGCGACCGTGTTGATGACAATAGTGCCAGTAATCATCATGCGGCTGTAAACGGTGAGTTTTGACCAACGTTTGTTCCTACGCAGATCTATCCACACCGAAAATCCTAAACCACCAATAATAAACAAAGCTGTGATGGTCAGATTGATCACGGGATCGGCCACATAGGGCATTAAGCTATCTGGACTTAGGGCAAAACCTGCGTTATTAAAAGCGCTAATGGTATAGAAAAAGCCGTGGAATAAGCTAGTTTGCCAGCCCAGCTCGCCGCTCCAGTAGACAGATAAAATCAGCATGCCGACGGCTTCAACCAATAGCGAAAACACTAGCACGGCTTTTGCGGTCGAGACTAATGTCGAGGTATCGGTTTGATTAAACGCCTCTTTTGCAACGGTTTGCTGCAAGAAGCCAATCTTACCGCCAAGGGCGATAAGGGTGACAATCGCAAAGGTCATCAAGCCTAAACCCCCGCATTGAATTAACAGCGCGATTATCACTTGGCCAAAGGGCGTGAACACACTTCCAGTATCGACGACCACAAGCCCTGTTACTGTCACCGCCGAGGTGACGGTAAACAGGCTTTGCAGCCAAGTAATGGGTGACACAGTGGCGATGGGCAGTTTAAGTAAACATGTGCCGACGAGGATCAACAGAGCAAAGCTTGCACTTAAGATAAAGGGCGGCGCGCCAAAGAGTTTTTTGCCAGTTTTAGAAGTGTGCTCTAGGGTGAGCGAAGGATGCCATTGAACCACCTTACACCAACCTTGGCGCGAGCTGTTTTAACTCGGCACGGCTACCGCAAAGGAATAAGGCATCCTCTGTGTGCAAGGTAAAATCGCTATCAATCTTGCTAAATACGCTCTGCTCACGTTTGACCATTAACGGTGCGACTTTGCCCTTAGGGCTGCGTTGCACGTTAGTTTGCTCATCAGTGTTTTTCTCTTGCAAGCTCCCCAGCAGTTGGCCAACTGTGACTTGATGTAAATGGACTTTAATATGGATTTCAACAATATAGAGCCCATCGCCGATAGCGAGAAAGTTATTAACCATAGGGTAGTTTAGCGACTGGGCGACGCGGACCCCCATATCTTCTTCAGGGTGGATAATCCGAGTAACATCGAGTTTAGACAATATGGTGTGGTGCGCCTTATTGCTGGCTTTAACCCAAATATTTTGCACGCCGAGATTTTTCAGTGCCAGTGTGCACAGCAGGCTCGATTGCATATCAAAACCGATAGCGACAATCACAACTTCACTACTGGCAAGATCGAGTTCCCTTAGTGACGCCTCATCGGCGCAATCGCAAATTACGGCTTCGGTAAGAACTTCTACATATTTTTCAACAGCTTTATGGTCATTATCGACACCTGTAACCGTATGACCTAAGTGGATCAGTTCTCGGCTCACGGCGACGCCGAAGCGGCCTAATCCGATGACAGTAAAATGTGCCATTTCATATCCTTGTGTGGTTGAACTAAAGAGTGAGGCGAGCTGGCCTAGGGCGGGCCGAGGTCAGTGAGCCGATACCCTAAGCCAGGCTCGGTTTTGATCAGTTGTTGTTCGTCACTATCGTTAAGCTTTTTACGCAATTGGCTGACTAAAATCCGCAGATAATGGCCATCTTCCTGGTGAGTTTCGCCCCAGATATTGCGCAGCAGGTCGCTCTGTTTCAGCAATTGCCCAGGGTGGGACATTAACTGAACGAGTAGGGCAAACTCCTTTTTGGTGAGGGCGATTTCCTGCTGGTTAAGCCAGCATTGATGGCTGCTCTTTTGTAGAGTGAGCGGGCCAAATTGCAGCATATCGGCGCCGCTTGAGGTATCCACAAGGTCGCGAACCAACACCTTGATGCGAACGATTAATTCCCTGATCCCAAAGGGCTTACTCAGGTAGTCGTTAGCACCGGCCTCGAGTAGGCGGATTTTTTCTTCCTCCTGATCGCGGGCGGTGAGTACTAGCACTGGGGTCTTATCCTGCTGGCGCAGCGCATGGAGTAACTCGATGCCATCACCATCGGGCAGGCCGAGGTCGAGCACGATAAGGTGCGGTTGATGGCTGCGGTACTGTTTGAGCGCCGTGGCTATGCTGGTGGCGCTTATGTATTCAAAGCCTTCCGCCTCGAGTGAGATCCGCATAAAGGTATGAATTTGCGGCTCATCATCCACGACTAACACCTTGTAAGCCATTTATACATTTTCCTTCGTTATCGGTAACGCGATGCGGATCAGACTGCCTTGAGCAACATTTTCGGCACTGAGACTCCCGTTATGGGCAATAATAATGCCTTTGGCGACAGGAAGGCCCAATCCTGCGCCGCCATCGGTAGAAGGATGTTGGCGGTAAAACAGTTCGAAAATCGCCTCCTTTTGAGATGGGGCTATGCCTTGTCCTAAGTCTTGAATATCGATGCGCAGCATATCTTGGTGGCGATAGAGATTTACTGTTACAGGTTGGCTGGTGGGTGAATAGTGCAGGGCATTGTCGAGCACATTAAAAATCGCTTGTTCAATCAAAGAGCTGCAAATCATTAATGGTGGTAGCTCTGGCTCACTGTGAATTTTTATCCTAGGCTGCGCGGTTTGAAAGCGGGCGATAGTGCGCTGTAACACTTCGCGCAGATTGGCTTCGTCTAGGTTAAACTTGAGTGCGCCGTGTTGTAATTTAGTGGCCTGTAACAGGTTTTCGATATATTGATGCAGACGATGACTCTCGGCGGCCGCGCTATCAATTAGCTCAGTCTTTTGCGACGCACTGAGCTTAGGCATGTATTCCTTCAGGGTCGTCAGGGTGCCTATGATGGTTGAGAGCGGGGTGCGTAAGTCATGGGAAACCGACAGTAAGATGCTGTTACGTAACTGAGCCTGTTCGAGTGCTGCTTGTTGGCGGCGATAAAGTTCGGCCAATTTACTGGTGGTAAGGGCGACAAGTAAAAACACAGTTAAATTGAGAATGTCCTCAAGATTGAACATCTGTAGCGAATAGCGCGGCGTGGTAAACAGAAAATTAAAGCTAGTCGCCTCAAACACAGCTGCAAAATAGGCAAAGTTAGCGTTGCATTGCAGGGCCACAGCGACCACGGCCAATTGTAAGATCAGCAGCACGGCTATGGTGGAACCAGCAAAATAATCGATAAAAGCACTGCTAAAAATCGCTATCACCAGCACTGTGCCGGTAAAAAATGTTGGGTGCGAACGCCAGTATTGAGTCATGGGAGCCGCTATGGATGAAGTCATGGATAGAAAGACGCTTATAAAGAAAGTGTCTGTACTCTAGCAAAGCCATTTTAAGATAGCGCGTAAAAAATCCGTAAAATTTATAGCCCTGTGAGTGGCTTAGCGTGCAGTTTGCTCGGGCTGTTCTCGCGCACTCATATCAAGCTGCGAGCTAATAATCAGGCTTACCCCATAGACTCATGGGGTAAGCCATTCTAGGCGTGGGCCGTTATCGGATTGACGCTAATGGGCTAGCTATCAAGTATTTGCTTGCCATTATCTAGCTGAAGATCACCAAGGTATCGGCGGTGGCACTAAGCGCTTGCCGTTATAGCTGGGCACAGTATCAAAACGTGTCGAGTGGGCCTCCCAATCGGCTTGGGCTTTAGCGATTTCGTCTTTGCTGTGGCCGACAAAATTCCACCAAATACTGACAGGGTCAGCCAGTGGTGCGCCGCCAATCAAGAGTATGCGACAACCCGCGTCGAGTGCGACTGTGACGCTTTCACGTAACATGCCCAGATAGGCGAGGTCGTTATTGTTGAAGCTTTGGTCATCGATGCGGAAAAAGCCTTCGAGTGGCATGAGGGCGTATTCAAAACTCGGGTCGAGAGTCAGGGTAAGCTCAGTGTTTTCGGCGGCAAATAAATCCATCGCCACTATCGGCGAGAAGTGCAATGTGGGCGCCTGTTGCTGCTGCCAAGTGCCAATCAGCAGAGTGAAATCAACGCCCGCATTGTGCCACGTCGGGAGTTCGGGATAGTGATCGAATCTCGGAGTTGTGTCTTTATGCTCAAGCGGCAGGGCAATCCACAGCTGCGCCGCGTGCATAGTGCTATGGCCTGCGACGGATTCTTCGGTGTGGGCGATACCGTGTCCGACGGTCATCAAGTTAACTTGTTTTGGGCGGATCACTTGGGCGCTGCCGAGACTGTCCTTGTGCATGATTTCGCCTTCGAGCATCCAAGTAAAAGTTTGCAGCCCAATGTGCGGATGCTGGCCAACGTTTAGCTCAGGGCCGTCGGTAACTGGGCCAATATGGTCGAGGAAACACCAAGGGCCGATAAGACGACGTTCTTTTTGTGGGATAGCCCGTGCGACGGGAATACCGCCAACATCACTGATTTTGGGGGAAATTCGTTGAACTTGAATGCGGCCATTTTCAATAGGGCATTCCTTAGGGCCACAATAAAACTGTGCTTGAACTTCACTCATGTTCGCTCCTTACTTCCTTTGCGAGGACACACTTAGTTAGTGTTAGTCTGGCATGCTGAACTGGTTTCAGATCCCATCTTGCATTCGAGCCGAAGGTTTTAGCTTCGGGTATTCCCGTGGATTTTGCAAATCGACGCAGTCATATGACTGGGTAATTGAGCTGTTAAAATCGCCCTTGGATAACTCTGTTAACCCAATTCAAACGTGAATCTGTGACTGAGCCCGCGTGTATGGGAGTTTTTGCTGTCCTTTACTTTAGCAAGCCTGTATTTATGTGGGGCATTAGCGTTTGATGTTTTACTCGAGCCGTAATAGCGGACGAGTTCTGGGTTAGTCTCGATGATTTTATCCTTCACGCGTTAAGTCAGATCTGCAAAGGGGAAAGCGTGCTCGAAAATCAAATGATGTTACTGGCGGTATTTGAACGGGCGGCCTTGATGCTGATGACCTTGTTCTTTTTAACCCGCACTCGACCGTTCCAGCGTTTATTCCAAAAGCGTAATCACACTCCCGCAGAGCTTGTCTCAGTGGCGGCGATATTTTGCTTATTTGCGGTGTTTAGTACTTATACCGGCATTGAGGTTGAAGGCGCGTTGGTCAACGTGCGGATTATCGCGATTATTTCCGGCGGGATATTATTTGGCCCTTGGGTTGGTATTCCCGCGGGCGTGCTGTCGGGGCTGCATAGATATCTGATTGATATGGATGGTGATACCTCTATTCCGTGTTTAATCGCCAGCATTATTGCCGGTCTAATGGCGACCTGGATTTACTACAAATGCCCCAAATCTAAGTTATGGATTTATGGCATTGTGGCGGGCATGTTGTGCGAAACGCTGACCATGTTGTTGATTTGGTTGCTGACAGAGCCACACGAGGTGGGCGTTGAGATAGTCAGTCATATTGCCTATCCCATGATTGCGGGCACTGTGTGTATCGGACTGATTATTAAGCTAGTGCAGGACCTCGACGATGAGAAAGAGCTCATTGCCGCTAAGCAGGCCAAGCTCGCGCTGGATATCGCCAATAAAACCTTACCTTTCTTCCGCAAAATTGACCGCCATGCCTTAGAGCAAGTGTGCGCCGTTATCCGTAATGAGATCCATGCCGATGCCGTGGCCATTACTGATACTCGCGATGTATTGGCCTATATGGGCGTTGGCAAAGATTACTACGAGGTCGATGGTCATCAAGCGATTAGCGAGATGACTCAAAGGGCGATAGAGCTAGATCAAAACATTATTAACAACGACTTACGCCAATATCATTTATCGGATTTTCATTCGGTCATCATAATCCCGCTGCGTGAAAACGGCACTGTGAGCGGTACGCTGAAGATTTATTATCGCAATAACTATCGGATTACCAGCTCGCTGCGAGAGATGGCGGTGGGCTTGTCGCAGTTGATTTCGACCCAGATGGAAGTGTCGCGTATCGAGCAACTGCAGGAGATGACCCGCAAGGCGGAGTTTACTGCGCTGCAGAGCAAGATTAACCCGCACTTTTTATTTAATGCCCTCAATGCGATTTCTTCTCTGATCCGAATTCGGCCGCAGCAGGCGCGGGAGTTGATTGCCAATCTGGCCGATTATCTGCGCTATAACCTAGCCAAGGGCGATGAGTTGATTGATATCCAAGAGGAAGTCAAACAAGTACGCGATTATGTGGCGATTGAGCAAGCGCGCTTTGGCGACAAGCTTGAAGTGGTGTTCGATGTCGACGATGTGCATTTTTGTGTACCTTGCCTCTTGTTACAGCCGCTGGTGGAAAACGCCATTTTGCACGGCATTCAGCCGCGTAGTGCACCGGGCAGAGTGACCATAGAAGTGAAAAAACTCGATTCTGGCATTCGCGTGGCCGTGCGTGACACAGGTTATGGCATCAGCCAAGAGGTGATAGATGGCGTCGCCGCGGGTCGCATAGAAAGCAGCAGTATTGGGCTGACGAATGTGCACCAGAGAGTGAAGTTATTGTACGGCGAAGGTTTGCAGCTTAAACGGCTGGAGCCTGGGACTGAAGTCAGCTTTTATTTACCGGAAAATGAGGCGCAAGTATGTTAAAAGCCATCATAGTTGAAGATGAATATCTGGCCCGTGAGGAGCTGGAGTATTTAGTAAAGAGCCACAGTGAAATAGACATAGTGGCAAGCTTTGAGGATGGCTTGGAGGCCTTTAAATATCTGCAGGATCATGAGGTTGATGTGGTGTTTTTGGATATCCAAATTCCGTCCATCGATGGCCTATTGCTGGCGAAGAATCTGCATAAATCGACCCATCCACCCCATGTGGTGTTTGTGACTGCCTATAAAGAATTTGCGGTAGAAGCGTTCGAGCTCGAAGCTTTTGATTATATTCTCAAGCCCTACAATGAGCCGCGGATCATCAGCTTATTGCAGAAGATTGAACAGGCGGGGCGACAAGCACCAAGGCCGCAACATGAAGCGACGAGTAATGCCAGTCGTACCGTGAATTTAGTCAAAGGGGAGCGGATTATCGTTACGCCCTGCGAGCAAATTTATTACGCCGAGGCTGATGAAAAACTGACCTATGTCTATACCCGCACCGACCGTTATGTGATGCAGATGACCATTAGCGAGTTTGTCAGCCGCTTGCCCGCCGAGGGATTTTTCCGTTGCCACCGTTCCTATTGCGTCAATATCAACAAGATCCGCGAAATCGTGCCTTGGTTCAACAGTACTTACCTGATTAGGTTGCATGATTTGTCGTTCGAGGTGCCCGTTAGCCGTAGTAATATTAAGGCTTTTCGACAGCTGATGAGGCTGTAATTGTCATTCATTCCTGCAGATCTGTATTTCATGCCTTAATTGATCGGCGCGAATAGCATCTCTCTTATAGTGGACTCAATTCCTGATCCCTAAAGCGAGATGCATCATGACTAAAGAGATGAACCGTACTCGGTACCTGACCTTAGTTGGTACTATTATCACCCAGTTTGCACTCGGCTCAGTTTACACTTGGAGCCTGTTCAATGCGCAGTTGGCCGCTAAGCTCGACGAACCTGTCAGCCAAGTGGCGTTTGTATTTGGTTTGCTGAGTTTGTCTTTGGCCGTGGCTTCTTCCATGGCGGGCAAATTACAGGAACGCTTCGGCGTGCGTAATGTCACCTTAGGCGCAGGCCTATTACTCGGGCTGGGATTCTTACTCACTGCCCAAGCCAGTAACTTGATGATGCTTTATCTGTGTGCCGGGATTTTAGTGGGCTTTGCCGATGGCACTGGCTACTTAATGACTTTATCTAACTGCGTGAAGTGGTTTCCCGAACGCAAAGGGCTGATTTCGGCGTTAGCGATTGGCGCCTATGGTTTAGGTAGCCTTGGATTTAAGTACATCAACGTGCTGTTACTCGAAAATACTGGCCTTGAAACTACCTTCCAACTTTGGGGCTTAATCGCCATGGCGCTGGTGTTATGTGGCGGCATGTTGATGAAAGATGCGCCTGCACAATCAGCCGCAAGTCAGCAAGCCGAAAGCCGCGACTTCACCCTCGCAGAAGCCATGCGTAAACCACAATACTGGATGCTGGCGCTGATGTTCCTGTCTGCCTGTATGAGTGGTTTGTATGTGATTGGTGTAGCGAAAGATATCGGCGAGAAGATGGTCGACTTGCCAGTACTGGTGGCTGCTAATGCGGTTGCGGTAATTGCCATGGCGAACCTCAGTGGTCGTTTGGTGCTGGGTATCCTGTCGGACAAAATTCCTCGCATTCGGGTTATTTCGCTGGCGCAGATCATCACCTTAGTCGGTATGGTATTACTACTGTTTGTACCTTTGAATGCTAACCTGTTCTTCGTTGCCGTTGCCTGCGTTGCCTTTAGTTTTGGTGGCACTATTACTGTTTATCCCTCACTGGTGAGCGACTTCTTCGGCCTGAATAACCTGACTAAAAACTATGGGGTTATCTACTTAGGTTTTGGTGTCGGCAGCATTATCGGTTCGATTGTCGCCTCGTTATTCGGTGGCTTTATTGCGACCTTCAACGTGATTTTAGTGCTGCTGGTGGTGGCGTTAGTGATGTCATTAACCATACGTTTACCAGAACCTAAAAACCCAGTGTCAGCCAAGAAAGCGACTAAGAGTACACCGCGCTTAGTGAACATGACGGCAGAATCGGCTTAAGCACACACACTTAGTCCGGCTTAGCCAAAAGCTAAAACAAAAATCCGCAGCTAAATGGCTGCGGATTTTTTGCTTTTTTATCACCTCAACAACAGGTAACTTGATAGCCATAAAGCGTTATTCAGGATGTGTTGATGTTAACGGACCCTCTTTTTTGGTTGGTGGCTATACCCGCAGTGTTGATTACGGGGATTTCTAAATCCGGTTTTGCGGGTGGCGTGGGCGGCTTAACTGTCCCGCTATTGGCGCTGGCCATTAGCCCTACCACAGCGGCGGCGCTTATGTTGCCGCTGCTGATTTATATGGATTTTTTGAGTGTGCGTTCTTGGTGGGGGCAACATAATCCGCGTCATTTATGGATATTACTGCCCGCGGCGATAGTGGGTATTTTTATCGCTTATCTGTTGTTTGATAGGTTGAATGAAGAATATCTTCGGGCGATTTTAGGTTGTGTGTCCCTTGGTTTTGGTTTGTATGGTTTGATTCTGGGGGATAAAACCCAAGCATCACCTTCACCTCTGGTAGGGCGCTTGTGTGGCTTAACTGCGGGATTTACTAGCTTTGTTGCCCATGCGGGCGGGCCGCCACTCAACGCTTATTTGTTACCGCTGCGTTTGGCTAAGGCAGAATTTTTAGCCACGGCTGTGGTATTTTTTGCTGTGGTAAATCTGGTCAAGCTTATCCCCTACAGTTTGCTTGGGCAGATTAACCAAGAGAATATTTTAGTTTCTCTGTTACTGGTGCCGCTGGCATGGTTAGGGGTAAAGCTAGGTCTGTTTATTCAAGATAAAATTAACGACAGGTTATTTAAGCGCATCATATTGATTTTAATGGTGCTGGTGGGCGTGCGTTTATTGTGGACGGCGTTATAGTTTTAAGCTTTGGGTTACATTAGTCTGCTAGAGTGTGCGGGTGTTGGGGCATTTTGATCCCCAGCCGCCCTGAATTTACCTGATAAAAACTCAAAATTACGTTACTGTGAAATCTGCCTGCAATGAGTTTGAAGCTATGAAACGATTACTGATAATTATCTTAGCCTTAGGCCTTGGTGCTTGTGCTAGCGCGCCAGAACCTAAGCCTGTGGTAAAGCAAGTTGAACCCGCCACGGTTTGGAACGATAGCAATATCTCCGAACTGCATTCTGAGTGGCGCGGTGTGCCCTATCGTTTAGGCGGTGGCACTAAGCGGGGCATAGATTGCTCGGCCTTTGTGTCGGTCGCCTATCAGAAGATGTTGGGTATGACCTTGCCGCGCACGGTTGAAGAACAACAAGCACTAGGAAAACCTGTGCCGAGAAATCAACTGCGCAAGGGCGACTTAGTATTTTTCAAGACAGGTTGGAGCACACACCATGTTGGCATTTATGTGGGAGACGATAATTTTCTCCATGTTTCCACTAGCCAAGGGGTGAAGATTTCGAGCCTGCATAACAGTTATTGGGCGTCTAAGTATTGGAATGCACGGCGAATTTAGCACATCTTCTTTATGCACTTTCTTTTTGCACTTTGGCTGATGAGTTAGCGGAGACAATATGTCCTTAGAGCCTGCGTTATCCCAGAGTGACCCTTTACCGACTTCTGTTTTTCTCGCCATTTTCTCGGCCGTATTCTTGCCCATGTTTTTGGCGGCGGTCGATCAAACCTTACTCGCAACGGCGACGCCAGCCATAGTTGAAGATCTTGGCGGCCTGCGCCAAGCCTCGTGGATCACTATCGGTTATATGCTGGCAATGGCGGCCAGCGTGCCGATTTATGGCTGGTTAGGGGATAACTTCGGTCGCGCGAAAATCCTGATGATCGCCATAGTGATTTTTGCGCTGGGCTCCATCGTTTCGGCGAGTGCGGGCACTATGGATCACATGATCGCCGGACGTATTTTGCAGGGCATGGGCGGTGGCGGCTTGATGAGTTTGTCTCAATCTTTGATTGGTGAGTTAGTTCCCATTCGGCAGCGGGCGCGTTTTCAAGGCTATTTTGCTGCTATGTTTACCCTCGCGAGTGTTGGCGGCCCTGTCATTGGCGGTATTGTGGTGCACGCTTATTCGTGGCATTGGCTATTTTGGGCGAATATTCCACTGGCTATGCTAGCGGTATGGCGACTCAATGGCCTGCATAAGCGCAGCGTTAAACCCGTACGCCAAGGCAAGTTTGATTTAGTGGGCGTGGTCTTGTTTCCGACCATTATCACCGCACTGTTATATTGGTTATCTGTGGCGGGGCAAGAGTTTGCTTGGCTTTCTACGACCAGCTTAGGCTTTGCCGTCTTTGTGGTGTTCGGCATTCTAGGTCTGCTGTTGTGGGAACGGCGATTGGCGAGCCCGTTTCTGCCGTTAGATTTACTGGCGAAGAAGGCCGTGTATATGCCACTACTAACGGCGGCGCTGTTTGCGGCCTGTTTGTTTGCGATGATCTTCTTCCTGCCGATTTATCTGCAAGTGGGCTTACACACCAATCCCGCCAAAACCGGCTTGCTGCTCTTGCCTATGACCTTTGGTATTGTCACTGGCTCGACGATTGCGGGCCGACTGTTAAGCAAAGATGTCGCGCCTAAGTGGTTGCCGACCTTTGGTATGGGCTTAGCTTTCATCGGCTTGTTATTGATTAGCTTTGTGCCGCCCAATGCCAATGTTATCGGCGGCTTAGGTGTGTTGGTCGGAATAGGATTAGGCACTGTGATGCCGAGTGTGCAGCTTGTGGTGCAGAGCGTGTCTGGCAAAGCGCGACTCAGCCAGATCACGGCCATGGTGTCACTGTGTCGTTCTATGGGCGCTGCTATTGGTACTGCGCTGTTTAGCGTGCTGCTTTATAGTTTGTTGCCACTCACGGGTTCAGAGCTTGGCATCGCTGCGATTAAAACATTGCCGACAGAGGTAGTGCATCACGCCTTCCAATATGGATTTATGGCCGCTGCGGGTGTGGCATTGTGCGCCGCGATAGTCGGTTTTCTATCCCCTGCGACGGCATTAAAAGATCACGATAGCGAAACTGCGCATTAAGCCTAGATTTGCAGGTGCACGACCCATTTAAGCCGTGTACTATTTGCGCTGAGCCGTATTTTTGTGGCCTTATTCGATTCCAGGACATAAGCATGACAACAACAAAACACTGGACCCTAAAGAGCATAGCCAACGAGTTGGGTGTCTCTAATGCCACTGTTTCTAACGCGTTTAATCGCCCCGACCAATTATCGGAAAAACGTCGTAACGACATTTTAGCTGCCTGCACTAAGTTAGGATATTTCGGACCGAATAAAGCGGCTCAATCCCTACGTAAGGGCAAGTTCGATACTGTCGCCTTGGTACTGTCGGACAGCGTGGAATACATGGTGTCCGATCCTGTCGCCAGTAAATTTATGAAGGGCGTGGCCTCTGTCTTGGAGCAAGAGAAACTCAATTTGCTACTATTTTCGGGCAGTTCAGACAGTGTGAATGCGGTGGCCGACTTTGTGGATGGTTTTATCTGTTATGGTCGCCCGCGCAATGCCCTGTTAGCCGAGCAGTTAAAGCAGGTAAAGAAAAAGGTCGTCACGGTCGATTTTGATATCCACCGCAATGCATCTGTGAGCATAGACAATAAGTTAGCCGCCTATGAAGTGGCTAAATTGGCATTGCAATCACCCGCGGACAATGTCGCTATTTTAGGGTTACGTTTGCTCGATACCCACTTAACTTGCCGCGTGTATGATTTGAATTTGTTAGAAATCGACACCTCTGTTGCCCATCAAAGATTACAGGGTTACTTGCAAGCGATTGAAGAAGTGGGCGTTGTACTGGGGCAGGACCGCATTTGGAATATCCCCGAGAGCAATGCCGACTATGCGGCGATAGCGGCAAAAGAAGCGTTGAATTCGACTCCACGGCCGAATGTATTTTTATGCATGAGTGACTTAATCGCCTTGAGTGTGCTCGGTGAGGCGCAGGCCATGGGGCTGAGTGTTCCTGAAGATGTGCGAGTAGTGGGCTTTGATGGTATAGATGAAGGCACACGTTCAAATCCGCCGTTAACGACCGTCTATCAATACTCTGAGCAGAAGGGCCGGAAAGCGGCGCAAATGTTTGTTAGTGATGCTGTGCATGCGGAAGTCTTGGGCTATGAATTACGCCTCGGTAAGAGCTGCTAATCGCTACATCTTTTAACTAAAAAAGGCGCAATGAATGCGCCTTTTTGCTATCTCAAAACTATGCCGCTTAGCTTGAACTTAAACCTAAGCTCAAGCCCGAATAGGAACGCAAGCTTATGCTTGAGCCAACAGTTTTGATAACGACACTTGTGTCTCTTTACCTTGCTTATCTAAAAGGATGATTTGGCCATCTTTATTGGCAAAGTCGAGGGTGAGACTTGCCTGCGAATCGGCCGCTGTCCAAGATAAGGTTAACGTGCGTTCTGCTGATGCCATCGTAAAGCGGCCATCGAAGGCTGGGCTGCTATTACGCAGTTTAATCAGTTTAGTCAGCGCCTTAACGACAGGTTTCTGCATGGCTTGCTGCACTTTTTCACGGTTCAAGTAAGGGCGATTGATATCGCGACCGACATGGGTGTTGTTGAGCAGATCCATATCGTTTGGAATCGCCAGCAGACCCGCGTAATACACCTGCGGAATACCGGGTGCGAAAAACTGAATCGCCCGCGCCATCAAATAATCTAAGTCATTCTGGCCGAGGGCATCGTAATAGGTGCAGTTGATTTGATACAAATCGACGTTGCTGGCGGCCGCGCCTGTGGCTTGTAGACTCTCGCCCTTACTGTTGCCATGTATGGTTTCAACCAGATTATCGATTTGATTTTCGGTCAATAATCCGCGCAAGCCATCCATAGGGCCGACGTCGATAATGCCGATGCCGTCGTGGGTGTCGAGTACGGTAATGCAGTTGCGCGGCGCCATTTCCAGCCAATGGGTCAAGGCTTGGCAATCTTGGCTAAACAGCGTGTGCAAGATGAGCGGTGGCAGGGCGAAGTCGTACACCATGTCGACCCGTTTGGCGATCTCGACCTGAGTCATGTGGTGCGAATGAATTTCGGCCAGCGTTGTCATGCCTAACGCATTGGCTTGCTTGGCTAACTTATCCACGAACTCGAAGGACTCTTCAATCATAAAGCAGCTAGTGCCGGGCTTTTTAATCGCATAGCCCGCAGCATCTAAGCGGATCAGATTCACTTTACTGTGGTCGAAACGCGCCAGCACTCGGTTTAAATAATCTTTACCCGCGGCGGAATGAACGTCGATATCGATTTGATTGCGGGTAAAGGTGGTCCAAAACTCTTCGGTCGAGCCATCGGCTAAGGTGTAAGTGCTGAAGCAACTGCCCGGACGTGGACGATAGATAGCGGCTTGATCTTGCTCGCTTAACCCCTCAGGAAATACCTTGTCTTTAGTTAGAAACAAATCCCAGTAAGCCGATTGTTTGCCGTGCTTGAGCACGTCTTTAAATTCTGGCGATTCGGCGGACATGTGGTTAACGATAAGGTCGGCCATGATGTCGTAGTCGTCACCAATGCGTTTAACGTCATCCCAATTGCCTAAGCGTGAATCGACTTGCACATGGTCTATCGGATCGAAGCCCGCATCGCTGCCGTCAATGGGGAAATAGAAAGGCAAAAGATGTACGCCGCCGAATAGACCTTGCAACTCGTCATCGAGTAGCGCTTTGAGCTCCTTGAGCCCTGCATTGGTAATTCTGTCGACATAGGTGATCAGTTGCACTTGGTTTTTCATACTTTGCCCACGCTTGAGTTGTCTTTTTAGAGTAAAGGTTTATGGCAATCCTTTAATTGATTGCACAACTAAACTTAATCGATTAAGTTGTATATTTAACCAATATGAAACATTATTCAAGTGGTTTGTTGGATTTTTGAGTTAGTCTCAAGTTTTGGGGGGGGCTGGCTCACTGCTGTACACAAAAGCCGAAACCCTTTCAGCAAGCTGTCACTGAGCTGCTGAGAGCGAGTAGGTATAATAACGATAAGGTGAAATGGTATGGTAAGTGATTCAAAAGGTTGCGATAACCGCAATCACACCCGTATTCGGGTACTCACATATTTAATGTTTTTCATGTTCGCTATGACCTCAGATGCCGTGGGCGTCATTATTCCAGAGCTGATATCCCAGTTTGGTTTGTCCATGTCGCAGGTCAGTGCTTTCCATTATATGCCGATGATATTTATCGCGATGAGTGGACTATTTTTAGGCTTCTTGGCGGACAAGATTGGGCGTAAGTTAACCATTCTATTCGGGTTATTGTTATTCGCGCTCGCCTGTTTTATGTTCGCGCTGGGGGAATCGTTTTACTATTTCTTGTTCCTGCTCGCGTTTGTCGGCACGGCGATTGGCGTCTTTAAAACCGGCGCACTCGGGCTTATCGGCGATATCTCGACTTCCTCAAAACAGCATTCGAGCACTATGAATACGGTAGAGGGCTACTTTGGTGTCGGCGCCATGATAGGGCCGGCCATCGTCAGTTATTTACTGATCAGCGGCGTCTCTTGGAAGTATCTGTATTTTGGGGCGGGTTGCTTCTGTTTAGTGCTGTGTTGGTTAGCTTATCGGGCGGATTATCCGCAAATCAAACGTTCCTCGACGGATGCCATTAACCTTGCCAGCACCTTTAAGATGATGAAAAACCCTTATGCATTAGGGTTCTCGCTCGCGATAGGTTTATATGTTGCCACCGAAGTGGCGATTTACGTGTGGATGCCGACCTTGCTGCAAAGCTATCAAGGTGATTACACCACCTTGGCCGCTTATGCCCTGACGATCTTCTTTACCCTGCGTGCGGGTGGGCGCTTTTTAGGGGGTTGGGTGCTCGATCGCTTCCCATGGCAGCAAGTGATGTTCTGGTTTAGCTTCGCGATAAGTGCTTGTTATCTCGGGTCTATGATCTACGGCATTGAGGCAGCTGTTATCTTGTTGCCCTTGTCCGGGCTGTTTATGTCCATGATGTATCCCACGCTTAATTCTAAGGGGATTAGCTGTTTCCCCGTGGATCAGCATGGCTCTGTGGCTGGGGTGATTTTGTTTTTTACTGCCGTGTCCGCCGCCGTTGGCCCCTTGTTGATGGGGTTCGTGGGCGATATTTTCGGCCATGTTAAATATGGCTTCTACCTCGCCACTGGTTTTGCCATTTTGTTATGCCTCCTGAGCGGATTTAATCTCGTTAAAGATCCTGCCCGTCATTTGTTAACGGCTGAAGCGCACTAATTTGGGTTGTGACAGAAAATATTTTTCTGTCACATTTCTTCAATTAATCAATACTTTGACCTAGTGCACCTAGAGTGAGAACCAAAGTTGTGCAAAAAACGCACTTGCTTTTTGTTGCCAAGTTGCTAACTTGTTAACTTAATCGATTAAGTTGATTCAAGATTAAGTTCAATATGACCTTGAGCAATCACTAAGATGGTTCTTCGATTAATCACAATTCGGTAGAGGCAGCCATGGGGAGATAGGGCTGCATCTCAAATATGGGTGGGGACATAAAAATGATAAAAAGCACATCACAGAAATTCTTGTTAAGTACATTGGCCGTGTTGGTGAGTTCTCACCTTTGGGCAGCCGAAGCACCGCAACAGGAAGAAGCCAAGAAAGACGCGTTAGGTTTAGAAAGAATTACCGTGACTGGGGTTGCCCGCGGCACTCGTGTTATGGACTCGAGTGTGTCAGTCAGCAGTGTGTCACTGGCTGAGCTTGAAGTGAGCTCACCACGTTCATCGGCCGAGGCATTTCGTATTATTCCGGGGATGAAGGTCGAGTCTACTGGCGGTGAAGGTAACGCTAACATTGCCGTGCGTGGTTTACCTGTGGCGTCTGGTGGTGCTAAGTTCCTACAAATTCAAGAAGATGGTTTGCCAATCCTGCAGTTTGGCGATATCGCCTTCGGTAACGCAGACATCTTTTTACGCTTAGATTCAACAGTGCAAACCATTGAATCTATCCGTGGTGGCTCGGCATCGACTGCAGCCAGTAATGCGCCCGGCGGCATTATCAACGTGATCAGTAAAACGGGCACCTCGGATTCGGGCAGTGTTGCTACCACCTTAGGGCTGGATTACGACACTTTCCGTACCGATTTTGAATATGGCACTAGCATCAACGACAGCCTGCGTTTCCATGTGGGCGGTTTTGTGCGCACGGGCGAAGGCCCACGCGACGCTGGCTATAACGCCAACAAAGGCGGCCAGATCAAAGCTAACTTAACCAAAGAATTCGATAAGGGTTATGTTCGTCTGTATTTCAAACATTTAGACGATAAAAGCATTGGTTACTTACCTATGCCTATGTACTCGGATGGCAGTTCAATCCCTGGTTTTGATGCTAAATCCGATGCGATTCAATCCGCGTATTTCCAATCAACCTTGAGCTTAGGTGCCGATGGCGAACGTCGCCGTGGCGATATGCGCGATGGCATGAATCCTGTGGTTAATTCTGTCGGCTTAGAAGCCTCCTTCGATTTAGGTAACGATTGGCAGGTAGAAAACCGTTTCCGTTTCTCCGATGTGAGCGGTAACTTCATCGCGCCATTCCCCGCCGAAGTGGCCAACGGTAGCGACATCGCGACCAGTATTGCAGGCACTGGCGCTAAGTTGATTTACGCCAATGGACCTAATGCGGGTAGTCAGATGGACGGTTTAGCCATGCGTATCAATACCTTTGATGTGCAGATGAATGACTTTGGCTCAATCGTAAACGACTTCAAATTGACTAAAACCTTCGACGATACCAGCGTGACCTTAGGTTATTACAATGCGGTGCAAAACATCAGCATGACCTGGATGTGGAACTCTTATCTGATGGAAGTAAAAGGCGATAATGCCGCGCTATTAGATGTGGTTGCCGCCGATGGTACTGCGTATTCTGACAATGGCCTCTATGGTTATGGCGTACCTTACTGGGGAAATTGTTGCCAACGTAATTACGATACCGACTACACCATTAAAGCGCCTTACCTCGCGTTAGCCTCGAGTTTTGGTGACTTATCGCTCGATGCCAGTGTGCGTTATGACAGTGGTGATGCGAGCGGTAACTACGCCGGTAGCGTCCAATCGCAAGTGGATATGAATCTCGATGGGGTAATTTCAATCCCTGAGCAGAGCGTGTCTTCCATCGATAATGCGAACCCTCAGCCAGTGAACTACGATTGGAGCTACACCTCTTACTCATTAGGTGCTAACTATCAGTTTGCCAGTGACTTAGCTGCTTTTGCACGCTTGAGTCATGGTGGCCGCGCCAATGCCGACCGTCTGTTATTTGGTAAGGTGCGTGCCGATGGTTCAGTGGCGAAGGAAGATGCGGTTGATATCGTTGACCAGTATGAATTAGGTGTTAAGTACCGTTACGATGATCTGTCTGTGTTTGCGACGGCGTTCTACTCAGAAACCGAAGAGCAAAACTTTGAAGCCACCAGCCAACGTTTCTTCGACCGTAAGTACAAAGCCAAAGGTATCGAAATCGAGTCTGCCTATTTTATCGGTGACTTCGACTTTAGAGGCAACCTAACCTGGACCGATGCTGAGATTGCTAAAGATGCATTAACGCCAGAAGTGGTGGGTAACACCCCAAGAAGACAGGCTGACTTCATCTACTCTTTGATGGGTCGTTACAACTTTGACCAAGGTTCAGTGGGCGTTAACTTAATCGGTACCACGGATTCTTATGCTCAAGATAATAACGACTTAAGTTTCGACGGTTACAACCAAGTGAATGCCTTTGCAACTTACAATCTGACCGAAGCCATGAGTGTGTCCTTAAACGTCAACAACTTGTTTAATGCCACGGGTATTACTGAAGCGGAAGAAGGCTCAATCCCTGATAACAATATCATCCGTGCCCGTACCATTAATGGCCGCACCACGAGTGCGACGCTTAAATACGAGTTCTAAGCGGATCTTGCTACAGAAACTGTTTTTACAGAAAAAATGTTAAGCCCAAAACAGCCCATGAAAGTGGGCTTTTTGCCTGCTACAGAATACAGAATATAGAAAACAAAGTTGGAGTGAAAATGACGGTATTACTCAGTTTCGGTGAGGTGTTAGTGGACTTGCTGCCAACCGATGCCACGGGCAAGCATCATCAGCCGATTGCCGGTGGCGCGCCCGCCAATGTGGCCGTGGGTTACGCGAAACTCGGCGGTCAGAGTTACTTTGCTGGCGGCATCAGCGCCGACCATTACGGCGTTATGTTAAAGCAAGCCTTAGCGGATCAGGGCGTGGCGACCGATTATTTGGCCGAAGTACCGGGCGCCGCTACCGCGACTGTGCTGGTGAATTTAGATGAGCAGGGTGAGCGCACCTTTGAGTTTAACCGCACTGGCACTGCCGATATGCGCTACAGCAATCAGCATTTTGACAATATCCCATGGCAGGGGATTGATATCTTTCACCTTTGCTCGAATACCTTTACCGAAACCGCCATCTTTAACACTAGCCTTTATGGCGCGCGTTGTGCCAATGCATTCGCGACTTTAGTGAGTTTCGATGTGAACCTGCGTTTATCCCTGTGGCAGGATACGTCTTTACTGGCCGAGCGGGTTGAGCAGTGTTTCCGTTACACCCAAGTGCTGAAAATGAGCCGCGAAGAAGCGGACTATTTAGCATTAGCGCGGGAAAAGAGCTTTGAAGATTATCTGCAGTTCTGCCTCGCCCAAGGGGTTGAGGTGATTTTGGTCACCGATGGCGCCAACCCAGTGCAATGCATCACAGCGCAGCAAAGGTTTAGCGTAACCGTCCCCAAAATCTCTGCCGTCGATACCACGGCTGCAGGTGACAGTTTTATCTCAGGCTTCCTGTTCGCCTTAGGCTTAGAGCTGGAATACGATTTAGGCAATTTGACCTTGAAACAAAGGCTCGAAAGCAAAGCGCACTTAGAGGCGGCGGTGGTATTTGCCACTCGCTGCGGTGCGATTACCTGCGGGCAAAAGGGCGCGTTTCCTTCTTTGCCCACGCTTTCGCAGGTGAGCTAAAGCTGACTGCATTAACGATAACATCCAAGGCTGGCCTGTTGAGTTTGCCTTGGATTTTTTGTCTTTTAGATTTTCCGCTCGCCATTTTTCAATGGCAGAGCGCGAACCCCATCACGTTTTAGTCGCCCCTTTTCCTGAGCATTTATGCTATTTTAGCTGCGGCTAAAGGTCGAAGCTGTAGCGGCTTCTTCCATGCTTGATGTCGATATTTTTTATTTCGGCATGTTTCATTTCAACATCACTGGGGATTTCCTATGAGCAAAGCAAAAATCGGTATTGTGACGGTAAGCGATCGCGCCAGCGCGGGGATTTATGAAGATATCTCAGGCAAGGCGATTATCGACACGCTCAACGACTACCTCACCAGCGAGTGGGAGCCGATTTATCAAGTGATCCCCGATGAGCAGGATGTGATTGAAGCGACCTTGATCAAAATGGCCGACGAGCAGGATTGCTGCTTGATTGTCACCACGGGCGGCACTGGACCTGCGAAACGCGATGTTACCCCAGAAGCCACCGAAGCCGTCTGCGATCGCATGATGCCAGGCTTTGGCGAGTTGATGCGTGCTGAGTCTTTAAAGTTTGTTCCTACCGCCATTTTATCGCGCCAAACCGCAGGGTTACGGGGCGATTCTTTGATTGTGAACTTGCCAGGCAAGCCAAAATCGATTCGCGAATGTTTAGATGCCGTGTTCCCCGCCATTCCGTACTGTATCGATTTGATGGAAGGCCCGTACTTAGAGTGCAACGAAGCCGTGATCAAGCCATTCAGGCCTAAGGCTAAGTAATATATTGAGATGTTAGGTTAAACTTGGTTGCTGTCAGTCTAGGCAGACGCTGCGGCGGAGCTGTGGATGGAAAGGCATAGCCCACGCAAAATCGTCTATGGGTTGTTACATCTAGCCCCATTCACCTCGGTTGATGTGGGTATTTATTTAATTCATGTTGGTCATACTGCCATTGGATGCATCCGCGGGGATGTAGGAGAAATTAGGGATGAAGACTCTGTTAGCAAATATTTTTGGTCATTGGCAATCACCACCTTGGATGCGCTGGTGCAAAGCTCATCCAGGCAAAAGTGTCGGTAGCCTATTAGCCCTATTATTCGTTGCAGCAGGCCTATGGAAAGCGTGGGATTGGTATCAGCATTTACCTAAACCACAGACAGTGCAGTCTAGAATCTTCGCGCCGTCATTGACTAATTACGAGGCTGAGACGACTCAGGTTGCACCGCTCGTCGTTCGTTTTAGCGATTCAGTTGCCCCCCTCGATAATATAGGCAAACCCTTGAATAAGGGCGTCCATTTGAGCCCCGCTATTGCAGGTCAATGGCGCTGGGCGAGCGATAAACATCTACAGTTTCAACCGGATGGTGACTGGCCAGTGGATCAAGCCTACGATCTCACACTGGATAGTGACACATTATTAGCCAGTCATATCCTACTGGATAAGTATCGCCTCGAGTTTAAGACCGCTCCCTTTAGTGCCAATATTAGCGATGCCCGCCTTTACCATGATCCGAATCAGCCCAATATTCAAAAGTTTGTCGCAACCGTTCATTTTAGTCATCCCGTCGCAACACAAACCGTTAAGCCAGCTTTGTCTGTACTGTTAAGCCCAGGTTTAACCTATAGCCAAGACAGTGATTATGAGCTGACCTTCGATGATAAACACCTAAATGCCTATATTCATTCGGCGCCTTTAGCGACGCCATTGGAACCGAGCTCGGTTAAGTTAGAACTGAGTAAAGGGATTAAAGCGGAAACAGGGGGGAATGCGGCCGCAGAAGGCAGTACGCAGGTTAGTGTACCGGGACGTTATCAGTTGAGCTTTTCCGCGGCACAAATTGGTTTTGCCTATAACGATAAGGAAGAACCTGAGCCTGTACTGACCATGGAGAGTTCTCGTCCCGTTGGAGATGAGGCACTTAAAGGCAAGGTCGAAGCTTGGTTGTTGCCCCAGAAAGACCCCCAAGGGAATACGTACTGGAATACTCAAAATGTCACTGACTCAGTGTTGCTGGCAGCACCTAAAGTGTCTTTGAGTCAAAACCCTGGAGCGGAAGGCCTTAATCAGTTACATAGTTTTAAATTTAAAGCACCTGTTGGTCGTTATCTTGCCATCAAGGTGGCTAAGCATGTGGAAGGTCAGGGTGGCTATTTGTCCCGTGATGCCGCTTTTTCCGTGGTGCGTATGCCGGAATATCCCAAGGCGCTTTCGTTCCTCGGCGAGGGTGCGCTACTAACGTTAAATGATGATCAACGTTTAGGTTATCTCGTGAGAGGTGTTGCAGATGTCAGGATTGAGGTCGCTCAACTGTTGCCCGATCAACTTCATCAGCTGGTGGATCAGAATTCGGGTATTTTCACAGCCCAGTACTTTAACAATGCCGCCTTCGATCGACTGGTGACGCGCACTGAAATAACCCAAAGCTTTGCATCGACTGATCCCGCTAAAACCTCCTATGGTCAGGTAGATCTTAAATCCTATTTCGCGGATAAGCGCGGTATTTTTGTGCTTAAGTTAACGCCCGCCGAAGAGAATAATTACGGTACCTTCGACGATTATCCACGTGAGGCCCATGATTTACGTTTTGTGGTAGTCACTGATCTGGGGATTTTGGCTAAGCGCAGTGTCGACGGCAGTTTGGATATATTTGTGCAGTCATTATCCAAGGGCTTAGCAATTGCCGATGCCAAGGTGGAAGTGGTCGGACGCAACGGTCTGCCAGTGGCGCAAGGACTAACTGATCCACAAGGCCATGTGCATTTAGCTCAGTTGGGTGAATTGCGTCGTGAAAAGACGCCGCTTTTCTATCGGGTAGAAAAAGCCGGAGATGTGTCTTTCTTGCCGATAGGCGATTGGCACCGTCAGCTCGATATGTCCCGTTTTGATATTGGCGGTAGCTACGAGAGTGAAGATCCTAATACGCTGAGTGCGTATCTCTTTACTGACCGTGGGTTATATCGCCCAGGTGAAACGGCCCATGTGGCGAGTTTAGTCAGGAGCCAGAGTTGGGCAGCCTCTTTTAAGGGCTTGCCGGTCAAAATGGTGGTGACAGATCCGCGTGGCATTAATGTATTAGATCGCACATTTAATTTAGATGCGAGCGGTTTTAACAGTTTGGACTTTAGCAGTTCCGAGGCCGCAGTGGCTGGAGAGTACACTGCCAGCTTATCTTTAATCCGCGATAAACATCGTTTGACTCGTTTGGGTGAGGTGAGCTTTAAAGTTCGCGATTTTGAACCTGATCGTATCAAGGTCAGAGTAACGCTAGGTACGACGGATGGCGGCACTGGTTTAGGGTGGTTAAAACCTGCGGACGTGTCGGCTAAGGTTCAGGCCGAGCAGCTTTTTGGTGGACCCGCGGGCGATCGACGCGTGACGGGTGAAATTGTACTGTCGCCCACGGATATTAGTTTTAAGGAGTGGCCAGGTTATTACTTTGGTGTGTTAGGTGAGTTGAAAGAATCCTTCCGTGAGACCTTAGCGGAAGTGAACACCAATGAAGAAGGCGTTGCAGGGCTAAATCTCGCCTTAGATCGTTTCGGACAGACGACGTTCAAGTTGCATCTGCTTGCTAAAGTGTATGAGGCAGGATCCGGTAGAGGTGTGAGCGCTCAAGATCAGGCACTGGTCTCGAGCGCCGATATGCTACTGGGTTATCAAGCCGATGGTGATTTGGGCTTTATTAGCAAAGGTGGAGAACGGCATCTTAAGCTAGTGACTGTGGGTCAAGATTTACAACCTCGAGCCGTACAACTTAAGGCTGAACGTATTGCTCGACGTTGGGTCTCTGTGTTGGTGAAGCAAAATGATGGCACGTATCGTTATGAGTCACGTCGTAAAGATACGCTATTGGAAAGCACAGATTTACAGCTTGAGCAGGGTCAACTCGATTTGCCTCTCGATACGCGTGAGTCTGGTGATTACCGATTAAGGATCATCGATGCCGAAGGGCGCCAGCTTAACCAAATTGACTATAGTGTTGCGGGTCAGGGAAATGCGAGTCGTACCTTAGAGCGTAATGCTGAACTCCAGCTCAAGTTAGACAAAGATGAGTATCAACCCGGCGAGACGATCAAACTCGCGATTCAAGCTCCCTACACTGGTGCGGGTTTGATCACCATTGAGCGAGATAAAGTTTATGCATTTAAATGGTTTAAAGCAGATAGCAGTCGCTCGGTGCAATCGATCCAATTGCCAAAGGGGATAGAAGGCAATGCCTATGTAAACGTGCAGTTCTTGCGCGCGCCAGACTCAGATGAGATTTTTATGAGTCCGTTATCCTATGCGGTTAAACCCTTTAAGATAGATTTAGGCGAACGTCGTCAGCCCTTGTCGTTAAGCCTGCCAGAGCAGATGAAGCCGGGTAATGATCTAAAGATTGCCTTGAACTTGTCTACAGCCTCAAAAGTCGTGGTCTTTGGGGTGGATGAAGGCATATTACAAGTCGCCCGTTATCAAACTCCCGATCCGTTAGGACACTTCTTTGCTAAAAAGGCGTTGACGGTAAACTCGAGCCAAATCTTAGACTTGTTATTACCAGATCTTAACGTGTTAATGCGTTATGCCGCACCGGGCGGCGATGCCGAAGCTTTGCTCGCTGCAAATTTGAATCCGTTTAAGCGTAAACGTGCATCGCCTGTCGTATATTGGTCTGGACTACAGGAGCTCAGCGCCGGTGAACAGCAGTTTAATTACAGTGTCCCCGATACGTTTAATGGAAAGATTCATTTCTTTGCCGTCAGTGTGACTGACAGCAATATGGGTGTTGCCGAAGCTTCAGTTCACGTGAAGGCGCCAGTGGTGATGAGTCCAAATGTGCCCGCGTTTTTGGCTCCAGGTGATGAGGCCGAAATCACGCTTGGACTCTACAACACTGAGCCTACATCTACCCATGTCCAATTGGCTTTATCTTTGGAGGGTGGATTGACCCAGCTAGAGGGTGCTGAGCAATCGTTTGATATTCTACCGGGACAAGAGGCGACGGCGCGTTTCAAACTCAAGGCGATTGAGCCTCTGGGAGAGGCGAAGTTGCATTGGACGCTCAAGGGTGAACAAGGAGATTTCCAGATTGGAGAGAGTATTTCAATCCGGCCTCTAACCACGAATCGCGTGCAGTTGCAGACGGCAGTGTTGGATAGTGCAGAGAAGCAATTATCGCTGGAGCGTAAGCTATTTGAGCCTTTTAGCCGCAAAGAGGCGGGACTGCAAAGTAGCCCCTTGGTCTGGGCGCAGGGATTAAGTGCTTATCTCGATAGTTATCCCCATGCCTGTACTGAACAACTGGTATCTAAATCTGTTCCGGCCCTGATATTAGGAAAACCGCAGGAGAACTTACCCGCATTTAATCAATTGATTGCGCAACTCCGTAGCCGCCAGAACAGTGAAGGTGCCTTCGGTCTATGGGCGGCAAATCCCGTTGTTGAACCTATGGTATCCCTGTATGTCGTGGATATGCTATTGGACGCCAAAGGCAATGGCTATGCTGTACCGCAGGACATGCTAGAGAGAGCCAATGGCTATTTATCTGTGCTGAGCAGTGGCCCGAGTAGTGGTTTGAGCGAGTTGCGTCAGCGCGCCTACGGCACTTATCTCTTGGCTCGCCAAGGCGTGCAGGTCAGCGGTGCTCTGGCTGATATTCGCGATCGATTACAGCGTTATTATCCGAAAGATTGGCAGTCTGATATTGCGAGTGCTTGGCTAGCGGCGAGTTTTACACTCATGCAGCAACAAACGCTGGCGAAGCCGCTGTGGGAAGCACAAACATGGGAATTAATACAGAAAACGCCGAAGCGTTTCGATCTATATTTAGATCCTTTGGTTCATGATGCTCAATTGCTAACCCTAGTCGCCCGCCACGCACCAGAACGCCTTGAGAAGCTACCCGAGGGATTGTTAGAACGTGTGGGTAATTGGCTATCGGCACAACGTTATACTTCTCTATCTGCAGGAACCTTAGTACGAGCACTGGCGGCTTACGATCAGCAAGCAAGCAGTGGTGAGTTGACGTTAACCGCTCAAGTGGCGGACAAGTGGCTGCTGCAAACTTTGCCCAAGACTGAGTTACCGTTTACTACCGATAAGGTCAAAGTCCAAAAGACGGGTGCTGCCGATGCATTCTACATGTTGATGGAAGCGGGTTTTGATAAGCAGCCCGTTGCCGCTTTCAGTCACGGTTTAGAAGTGAGTCGCGATTATCTCGATCTTAAGGGCGAGCCGCTTAAGGGATTGAAAGTCGGAGAGGAGTTCTTAGTTCGATTGCGACTGCGTGCGACGTCGCTCGAACAGTTGGAACAAGTCGTTGTTATCGATCTGCTCCCAGGTGGTGTTGAGCCAGTATATAGAGACAAAGGTGAAGAATTCAGTGGTTGGCAGCCGCCAATAGGCGTTGGCGAACGGAGTGATTGGTCACCTTCTTGGCTCAATGTCCGTGAGGATAGAGTCGTATTGTATGGCACGGCTAGCCGTGATGTGGGCACCTTTACTTATAAAGCTCGCGCAACCAATGTTGGCCATTTCCAAGTGCCAGCTGCCTATGCTGAAGGGCTGTATGATCCTCTGCAACAAGCACTGGGGGCGGGCAGTGAATTAGTGATCTCTGCTCCATGAGATCCGTTTATCTAGCCTTAGTCGCCCTAGCCGTTATATTGCTAGGGCTTAGACTTTGGCCTGCGCAGCCATTGAGTCAACTGATGCCCAGTTCACGTGAGGTGTTGGCTGCCGATGGCTCTTTAATGCGCCTGACCTTAGCCACTGATGGCCAATATCGACTCTGGCAAGATTTAGAGGATATCTCCCCCATCATGATTAAGGTCATGTTGTTAAAGGAAGATCGCTATTTCTATTATCACCCAGGTGTTAATCCAGTCGCATTAGTACGTGCCATGTTAGCCACTTATGTGGGCGGTGATCGTCAAGGTGCCTCGACCCTGACTATGCAGTTAGTTCGTCGAGTTTATGATATTAACAGTCGCACTGTGTCGGGCAAATTAGAGCAGATCTTGGCGGCATTTTGGTTAGAGGCTCGTTATAGCAAGCATGAGCTATTAGAGGCTTATCTTAACCTCGCTCCCATGGGGGGCAATATCGAAGGGGTTGGTGCTGCAAGTCAGATTTATTTCCATAAAATCGCGGGTGAGTTGTCTTTGAGCGAAGCCTTGAGCTTAGCGGTTATGCCTCAGAGTCCGGCGCGTCGGGCTCGATTTCAGGCAGACTTTCAAGCGGCGAGGGATAGGTTGGCGCTATTTTGGCAGCAGACCTATCCAGATGATCCTCGTAATCAAGGCTTGTTGGTGTTGACGGCCCTAGGGGCTGAACGCAACGAGCTGCCTTTTAAGGCGCCCCATTTTACTCGTCGACAACTACGCCAAGACGCGACCATTATTCACACGACAATAGAGCCTAAGTTACAAACGCTGCTGGAACTGCGAATTCGTCAGTATGTTGCGGGTAAGCGCCATTTAGGCGTGAATAATGCCACAGCGCTATTGATCGATACCCGTGATCAGGCGATTAAGGCTTGGGTAGGGTCCGCTGATTTTTTTAATCCCATGATTTATGGTCAGGTTGATGGTGTTGTCGCCCGTAGATCTCCGGGTTCAACTCTGAAACCCTTTCTGTATGCCTTAGGGTTAGATCAAGGGATTATTCATCCCCAAAGTATCATTAAAGATGCGCCTACCGCTTTTGGGAGTTTTGAACCGGAGAATTTCGACCATCGCTTCACGGGCCCTATTAGTGCCCACGATGCTTTGATCAAGAGCCGCAATCTGCCAGCCGTCTGGCTGGCCGGACAGTTGACTCGCCCTAATTTGTACGGCTTTTTACGACAAGCCCAGATACTTGGTCTAAGGGGAGAAAGCCATTATGGGCTGGCACTTGCCTTAGGGGGTGGCGAGTTGACTATGATGGAACTTGGCAGACTCTACCTAATGCTAGCGAGTGAGGGACGTTATTATCCTTTACGGGAGAGGACAGATCAGCCCATTAATCCGCCGAGCACTCAGTTATTGAGTCCAGCGGCGAGCTTTCTTGTATTGGATATGCTTAAAGACAATCCGCGCGCCGATGGTTTACCTAAGCAGGGCTGGCGTGTGGCTTGGAAGACGGGCACCTCTTGGGGATTCCATGATGCCTGGAGCGCTGGCGTCGCTGGCCCCTATGTATTGGTGGTCTGGGTAGGGAATTTCGATGCAACGCCAAATGCTGCTTTTATTGGTGCTGAAATAGCTGGGCCATTATTTTTTAGCATCAGTGATGCTTTGCAGGCAAGTCTGCCGAGTACCGCCGATAAAGCCTTAGCTCAACCCAGTACAGTGAAAAAAGTGAACATTTGCGCCGAATCAGGTGAATTGCCTAACCTGTGGTGCCCTAAAGTGGTTGAGGGTTGGTTTATTCCCGGCGTCTCCCCCGTCAAAGTGTCTACGCTACATCGTCCTGTTTGGGTTGATAATCAAACGGGTATGGCTGTGTGCCCACCCTATGATACTCAAACCCAGCACCAAGAAGTCTTCGCGTTCTGGCCTTCGGACATGGCGAGTTTATTTGAGCAGGCGGGGCTACCGCGCAAGTTGGCGCCACAGTTGCCCGAATCGTGTCGTAACACTATTCAGCTCACTAACATAGATCCTGTTATTCGCTCCCCCCTGAGAAATGTCACTTATAGCTTGAGACAAAGTCATTTAAATGAACATATTGCATTGAAGGCCGATGCCGCATCCGACGCGAGTATGTTGTACTGGTTTGTTGGGCGGGATCTCATCGGCCAAAGCTTACCGAAACAGACCTTAGAGTGGCGGCCCTCGACGGCGGGTGACTATAAGTTATCTGTCACTGACGACAAGGGGCGTAGTGCGAGTTTGAATCTTAATGTCAGCATCTTGCAGCAACCCTAATGGTTTTAGGCCTGACTCTTATTTTAACGCGCTAATCTGTTGCTTTAGCTCGTACTCTGGCTCTGTCACTATTTTTTCAAGCACTTGCACTCGCTCGATTAGCGCTTGGTTTTGTAGGCTGAGTTGAGTCACTTTATCCATAAGGTGGCTATCGTCTGTGGCGCCTACTCCAAGTTGGCGCGCCTTTAGATAGTGTTTAAACATCTCAGTGGCACTGATTCCTATGATGAAGGTGACCATAATCATCATTAAGACATATTCGTTTTCCATAACAAACTCCCGTAGCTATTGAGGTTTTCAACTTGGGAACGACAGGATAGTCGTTCCCAAGTTTGTCGGTTAAAGTTTATTGCCCCAAGTTGATCAATCGAAGCATCCTACAAACCGAAGATCACTTTTTCACTCTTGAGCGATCTTTCCATGCCAAACGCCAGTAATACCGCAATGGCGAGTGTGCCTAAGGTCGAAACGAATAATTGCAACATGGGCAAGTCTTTACCTTTGATAAAGTCCATTAATGCTTGTTGCTGACCCGAGACCGGCAGCCACTGCAGCATATCTGGCGCTATGTTGTAGCTGGCGGCCATCGACAAGGCTAAGGGCACAAACAGCACCATAGTCAGGTAGGATTGTGCTTCCTTAAAGGTTTTCGCCATAAAAGACACAAACAATTGCAGGCAAGCGGCCATTAACGCAACCGGGATACCGACCACTAACATCAGCGCCATAAATTCTGTGGTGATGTTGACGCTAAAGCCCAACTCCTGCCAAGGTACAAAGGTGTAGGCGACTTTAGATACCAGCAAAATCAGCACTAAGCCTAAGAGGGCAAACAGACTCACTGCGATGATTTTCGACAGCACTAATTGGCGTGTGGTCAATGGATGACTGAGTAACAGAGCTAGCGAGTTACGTTCGCGTTCACCCGCGCTGGTATCTATGGCGAGGTTCATGCCCGAGATAAATACCGAGTAAATCATAGTGAAAATCGCGATACCTAAGATCATCCCGCCCTTCGAATCTGTTGTCGCTTGGTCGTGGACACTGACCTTCAGTGGCTGCATCACACGAGGATCGATACCGCGGGCAATCAGTCGTAGGCTGCCCATTTCTGTGCTGTAGGACTGCAGTTGCTTCTCGAGTCTTCGAATTGAGTTTTGCAGTTTTTCATCGGAATTATCGGCAACAATCGTGATCTCGGCGCCTTTGCCTTGATTCATCTGCGCGGCATAGTCGGGGCTGATGATAAGCTCTATGGCTTTAAGCTCACTGTCCTTCACTTCACCGCTGCTGATGCCTTTATTCGATAAAAAGCGAACTAAATCCGGTGCTTTATCGGGATTGGTGATAGTGATTTTTAGATCGTCTGGACTCGTTAATTGGCCAATTAACACCATAAATAGGCCGCACATGATCAGTGGCGTGCCGATGGCGTAGTAAAGCCCCGCCATCACAGAGCGCTTATCGCGGGCGGCGTCGATGAGTTCTTTACGAACCATAGCCATTATTTTATTCATGCTGTCATTCCTTTAACCATGTTCTTAAATGCCACGCTTTTAGATATCAAGATCTTAAAGGTTAAAACCTGTGGGCGTGATCTCTGCTGAGTCATCATGCTGCTATCCCTTCATCTGTGCCGATCAGTTGAATAAAGGCTTCTTCGAGCGAGTCCTTACCCGTTTGGGTGCACAGTTCAGTTGGGCTACCAATGGCGACGACTCTGCCCTGGGCCATGACAATCACTTGGTCACACAGTGCGGCGACTTCTTGCATCACATGGCTAGAGAACAGCACGCAATGTCCTTGATTTTTAAGATCGATAAGAATGTCACGCAGCAGGCGAGTGCTCATCACATCGAGTCCACGGGTTGGTTCGTCGAGGATAATATTGCTCGGGCTGTGGACTATGGCTTGTGCGAGGGCGGTTTTCATCCTTTGGCCTTGGGAGAACCCCTTGCAGCGGCGATCGCTAATGTCTTCTAGACGTAATTTCGCAATCACTTGGCTGGTGGCCTCCTTGGCATCCTTAGAGGATAAGCCGCTTAGCTCGGCAAAATAACGAATGTATTCCCGTGGCGTTAAGCGTTCGTACAGACCAAAAGGGTCGGGGAATAATCCGAGTTGCTGTTTAGCGCCGATAGGGTCGATGGCGACATCTATCCCTTCGATTTCTGCTTTGCCGTTATCGGGTTTCAGCAAGCCAAAAATCGTGCGTAAACAGGTAGTTTTACCCGCGCCGTTGGGGCCGAGCAATCCTGTTATTTGGCCGTTTTGTGCGACGAAGCTTAAGTCATTGAGCGCCTGTACGTCACCGATACGCTTGGAAAGATTGGATACTTTGATCATGGCTTACTCCTTGGCCCCAGTGCTGGCGTTATCATCTGCGGTAGTTTTTGTGCTGGTCGCGTTAAGCGGTTCTACCGAGCTGGCATTCAAATAAAAGCTGCGGCGCACATCTTTCTTTAAACATTCGCCGTCGAGATCCTTAACGGAACCCGAGCGAACGAGATCGGCGATCAGGTTATTGGCGCAGGATTGATAGGCCACGCCGTGGGTCGCATAGGGGGCGACAAAGTGCTTGGCATTGGTGAGTTTTTCCATCGCTAACTCGCCCCAACTAGGTGGTGTCGCAGGGTCGATTTCGCCGGATAACAACAAGGTCGGAATATCGCTGCTGATGGGCTCGCTAAAACTTTTATCGACCGCGGGGACTTTCCATACTTCACAGGTGGCTTCTAAGCCTTCGAGCATAGTTTTGCCCATAAAGGAGTGCTGAGCCTGCTCGCGCATGGCGGGCGTAATACGGTGCATATCTTCGCCACAGACCACGGATGCGTGCATACCCATCGCCATGCCAGCATTGTCGATAGTGAGGGAATACAAACCGAGTATGGGTTGGAAGTTGTGTTTCGCGGCTTGGTGAATCGCATGGGGCACTAAGGCGCGCACATTGGCTTGATACAGCGCCATGCGGATGGAGCCGTAGAACTTACCGCGCGTCATTGTCAGCATGGTCTTTTCACCCGTGACTGGGTCGTAGACGTTTTCCATCACAGGCGCTTTGGCGAGTGAGGCCGCGACTTGGTCGAATTCGGCTTTAAGCTCAGGGAACTGGGCATGACAAGCGGCGGTTTCTTGGCAATCTTTAAAGAGTAAATCAAAACCCCGATCAATCGACGCGCCGATTTCTAATACGCTTTGTTGCATCGGCACTATGCCGTCTAAGGTCACTGTGGCTAAGTGTGCCGGGTATAAACGCATGTAAAGCTGCGCCATACGCGTGCCGTAGGAGATGCCGTAAATATGCAGTTTTTTGTAGCCTAAATGCTGACGTACGGCTTCGAAGTCTTTAATCGCGTTGAGGCTACCGTATTGGGTGACGTCGGCATCGATTTTGGCTAAGCATTTTTGGGTTTCGACTAAAGTGTCGGCATTGCCATCATCGAATGACAGTGGAGATTGCGCGCCTTCATCGCAGGTCAGCAGATTCGAGCGGCCTGTGCCCCTTTGGTCGATCAGCAAGATATCCCGCTGTTGGCGGACTTTGCTCAGCATGGCATCGAAACCCGCGGCATTGTCTATGGCCGATTGTCCTGGACCGCCAGCAATGGCGAGTAAGGCTTCTTCATGGTTGACGTTTTTCACCGCGGGCAATACGACGTAATGCACTTGGATCTGTTTGCCATCGGGTTTGTTAGGATTTTCTGGAACAGTCACAAAACCGCAGTTTAGGCGATCGGATACGCCTTCCACATAACAAGTGTCGGTGTTATTCGCTATATCAGCCGCCGCTTGTGTCTGTTGGCTTTTCACATCACTCGCCCACGTCGCCGTACTTGCCATCGCCAGCATAAGTGCGCTCATGCTTAACATGCGCCATGTGGCATTAGCGCCATGCCGTGGCCGAGTTGGTGAAGGGCGTTGAGCGGTTTTGCTTAACTTCCTGTGTGTCATGCTATCTATCATGCGCGCTTTCCTTTTTGCATTTCAGGACGAGTGTGTTAATTTAACTACCAGTGTATCGGTGTATCAATGTATTAATACAGTGTGTAGAGGCTAATGTTAGAACTTTTAAATGTCAACCCCAGTAGTGGTGAACCAATTTACAAGCAATTACACGAACAAGTCGTGCGTTTAATTGTGGGCGGTCAGCTACAAGTGGATGATGTCTTGCCCTCTGTGCGCCAGATTGCCGAGTATCTTGCGGTGAATCCTATGACTGTTTCCCGCGCAATTCAGCAGCTTGTCGATCAGGGGTGGCTCGAACGCCGCCGCGGCCAAGCGACAAAAGTCGCGCTGCGCACTGAGACTATGGCATCGGGGGCGAGCATGCTCGAACCCCAACTCGATGCTTTGTTGTCGCAGGCTAAACAGTTAGGCGTCAGCCTGCCCGAGTTAATGCAATTGATAAATGAACGCTGGCAAGACTAGCGTTAACCTACTGTCACTTATCGATAAAATAGAATAAAAAAGGATGATATTCGATGGACCAAGCGTGCACACCAATACTTGAATTTAGCCAAGTGAATAAAGTCTTTCGCGGTAAAGGCGCAGGTGAAAAGTGGGCGCTTAAGGATTTATCGTTAACCTTGTCGGCGGGCATGGTGGTCGGGTTATTAGGGCAAAATGGTGCGGGTAAATCGACCCTAATGCGCTGCGCTTTGGGTGTGCTCATGCCGGATTCAGGCAGTATTGTTACCTTAGGCGAAACGCCCGAAAACTTAACCTCAAAGGCAAAGGAGCGCTTAGGTTATGTGCCGCAGCAACCTTTTGGTTATGAAGGTTTTACCATTGAACGGGCTCTCGATTTACACCGCAGTTTTTATCCCAATTGGGACATGCAATTAGAGCAAGATTGGCTAGTACGGTTCGAGTTAGATGTGACCCAACAGGTGCAACGTTTATCTGTGGGGCAAAGGCAATCGCTAGCCTTGATTATGGCGATGGCTTATCGCCCTGAGCTATTGATCCTCGATGAGCCCGTCGCCAGTCTCGACCCGATTGTACGGCGTAAATTTATGGTCGATTTGTTCGATCTCGCCCTCGAATCGGGTTCCGCTGTACTGTTTTCATCCCATATCACTTCAGATATCGAGCGGGTTGCCAGTCATGTGGCGCTGATTAAACAGGGCGAATTAGTGCTGTTTAAAGAAATCGATGCCCTACGGGAACAGGTGCGTTTACTCACACTCGCACCCGAAACGGTTTTGCCTGAGTCGGTCACTGTGTTGAGTCGTGTCAGTGATTCAGTGCTGGTGGATTTGGGCGAACACAGTGCGCTAATCTCGGGCGTGTTAAAGAACGAAGCGCTTAATCTGGAACAGCTTTTTATGGAGCTACACAGATGAGTGCGCCAGAACTTAACATCACGCCGACAGCTAAAATGAAAGGCGCGCGGTTGATGAATCCATTTAAAGGGATGCTGCGTCTTTGGTGTTTTGACGTCGGCAGTGTGAGCTTTTTAGGTACCGGCTTACTGGGAATGGTTTTAGGTTGCATTACAGGGTTGCAGGGCAAAAGCGACAGTGCTGAACTGTTGTTTTGTATGGGAATCGTTTCAAGCTCGGCGGCGGTAGCATGGCAACTCATACGTTTGATGGCGAGCGAGTGCGCCCAGTTAACTCCCCATTATCGCCGCCATATTTATATCCAAAGCGCAGTGGTGTTAACCAGCGTATTTGGCATAAGCGTATTGTTTTGTTTGGCTCTGGGTTCAACGTCTAGCCTACCCACCTTAGTGTTGGCCTTGGCGATGAGTCTTGCCTTTATTTGCCTGTGTTTACTCAGCACTCAGTGGTTTTACGCCTCCTTTTTATTGTTTGTTTTAGTGCCGTTTATCTCCTTGATTACAGGTTATATCCCCCTATGGTTAAGCGCGATTGCACTGGTGGCGTTAGGTGCTTTCATTATCCGTATCTGCCGCGCTTTACCTTGGCGTGCTGAGGCGAGAACTGTGTACCTCAATGGCCTCGAAATGGGCTGGTTCTGGCTGCCCAATTTGCAATCGATGCGTATCTTGAGCCGTTTTGAGCGTTATCTGCATCCCACCAATTTTTTTATCGGTCCTATGCTGACGATTTTACTGCTGCTCATACCGGCAGTCTGCCTAGTGCTTGGGGTGTTGTCGCACCAATTCCATCAGAATTTCCCCGTGTTACTGCTGTTGGCACAGTTCAGCGTGATTATGTGTTCGCTGGTGCATTGGAGCCGAGTGCAGCGTTCGCGCTCGACAGAAACCTTATTGCTGATGCCAGGATTTGATGGTCGAAAAGGACTCATAGCGGCTTTTTGCCGTGGCCAGCAAAGACTGCTGAATGTGGTCGTTGGCATTATGCTGGCGTGTAGTCTGCTGCTGGGTTGGCTCAGTGGTGATCTGAATATGCAGTTAGTAGGTCATCTGGTGCTTAGCACTTACTGGGCCTGCGCTTTGACCTTAGGTTTTGGCTGTATGTGTCGCCGAGTGTTGCATGTCACGCTCACTATGACGGTTGTTGCGGGGCATTCCCTGTGGGTGTCCATCAGCCTTGCATCGTTACGCGATGGCGGCAATCTAGGCGATTGGATTATCTGGGATATGCTCTTAATGATACTTGGGAGCATAGTACTGATTTGGGGCAAGAAAACCCTCTGGAAGGGAGATGTCATTAGCGGCTAAACGGATATAGATTGCAATATTGGGACTCAGCTTGCACAAGATTAGAGCAAAAACTCTTAGTTTTTGACTGGCCTTCAGGTAGAATGGCCAAAAAGCGGTACACTTGTAAGCTAAAAGGTGAGTCTCTTATGTCTCTCGATCAATATCATGTTATTCGACTATTACAGCAGCAGAGCGAGTCTTTAAAAGACGCGATAGCGCTGGAAGGTTTTGAAATGGCCGCTCCTTGGTATCAGGTGAGCTGGCAAGCCTTTGACCAAATTAGCAGTAAAATTGCACAAGTGTTGATCGAACTTGGCGTACAAGTGCAAGACCGTTGCGTTATTTTGGCGCAAAACTGCCCACAATGGACCTGTGCCGATGTCGGGACGCTTAAAAGTCGCGCCGTTGTGGTGCCGATTTACCCCACCAGTACCGTCGAGCAGGCGAGCTTTATCGTTAACGATGCGGCGGCGAAAGTCATTTTTGTCGACGATGCTAAACAATACGCTATGGCCTGCGAGCTTCAAAGCTTATGCCCAAGCCTAGAGCATGTGATTGTTTTTGATGCCAGCGTGGCACTTGCTGCTGACAAAGCTGAGCAGCATTGGCATTTAGATGCGCTGCTAGCGGGCGATGAGAAAGCGATTAAGCAAGAAGTTGAGCTTAACCAGCGTTTACAAGCGGCCAATCTCGATGATTTACTGACCTTAATTTATACCTCTGGCACCACGGGCGATCCTAAAGGCGTGATGCTGGATTACCGCAATATGGCCTCGACCATACGTCAACACGATCAAAAACTGGCTTTCCATTCTGGCGATGTGTCTTTGGCATTCTTGCCTTTGAGCCATGTATTTGAGCGCAGTTGGAGTTTTTACGTTTTGTGCCGTGGCGGTCATAACGTCTATTTGCAAAATACCCAAAGAGTGAAAGAGGCGATTAGCGCCGTGCGTCCGCATACCTTGTGTGTGGTGCCGCGTTTCCTCGAAAAAGTGTACAGCGCCGTACAAGATAAAGTCGCCAAGTCAGCCGATGGTCGTAAGAAGTTGTTTGCTTGGGCTATGCGCGTTGGTCAACGCCAGTTCGAAGTTGGCCAAGGTCGTGCTAAGGGCGGATTATGGTTGTCGCTGCAATGGCGTTTGGCCCACAAACTTGTGTATAGCAAGCTGCAAGCCGTGCTTGGCGGGCGTTTGAAGTTTATGCCCTGCGGTGGCGCGGCATTAGATGTGAATGTCGGTAGCTTCTTCCATGCGATTAATATTCCTGTGCTGTGTGGTTACGGCATGACGGAAACCAACGCGACCGTGACTTGCAATACCTTAGGTAATCGAGTGCCAGGTTCTAATGGTCAACCTCTGCTGGAAACCGAAATCAAGCTAGGCAAGGACGATGAGATTTTAGTCCGCGGCGCGACTGTGATGCGTGGCTATTACAATCGACCAGAAGATACCGCCGCCGCCTTCGAAGACGGTTGGTTAAAAACCGGTGATGCGGGACGTTTCGATGCGAACGGTAACTTATTTATTACTGACCGTATCAAAGAGTTAATGAAAACCTCCAACGGCAAATATATCGCGCCGCAACGGGTAGAGGGCGCTGTGGGCTGTTGTCCTTTTATCGAGCAAGTGGCGATTATTGCCGATGCGCGTAACTATGTGACCGCCTTAATTGTGCCAGCGTTTGAGTCACTGGAAGCGTGGGCGAAGGACAAAGGGCTGAAGTATGAGTCGCCTTTAGAATTGCTACGCCATAGTCATGTGGTTGAGCATTTTGAGCAGCGCTTAAAGCAGTTGCAGCATGAATTGGCGGGTTTTGAACAGATCAAAAAGTTCACACTGTTGCCAGAAGCCTTCTCGATGGAAGCGGGTTTAATCACCCCAACGCTCAAGTTGCGCCGCAAGATGATTTATCATAAATACGCCCACGAAATTAATGCTATGTACAACAACTGATTGTTAAGTCATTGTTAAAAAAGGCCGCGGTTGCGGCCTTTTTTGTTTCTGCTACTTCCTGAAGCGCTTAACTTAGCTTAAAATTCGCTCCGTTTACTGGTGCGCACGAGGCGCAAGTTATCGGTATAAAGGGCGTAGTTTAAATGGAACTTTCGATTCGTAGTCTTTTCGCAGCCACTGCGGTGTTGTTGTCTGCTTTTTCTGTCACTCCTGCTGCTATCGCTGCGGATTACGACAAAATCGTCCACGGACCTGTGGATTTACCCCATGGCCAATGGCTTAATTTCTATCAAAAAGATCAGCAAGTGTGGGGCGAAATGCTCTACGGCGATGCGCCCGGCGTGCGTATTGACGATTATGGCCGAGCCGAAATTGTGGCGGTTTTTTACTATGACTTTGATAAAGGCGGCACTAAAGAAGTGATCGTCATGCTCAAGGATGCCGATGGTCAGCACCTGCGCGGCTATGGTTTTGATGGGGAAGCTCTGACTAAACTACCGCGCCTACAAGTTATGCTCGATGAAGTGGCCCCAACCTTAACCACGTTTACCGTGGGCGGCGTCCGTAAAGTACTAAGCCAAATTCCGCCACAGCAATACCGTATGACCTATGATGTTGAAGCAATTGAAGATCCTGCCATTAAAGCGATTGTCGATGGCTCAACTAAGCTCAGGCCAACCTTAGTGGGTTATCGAAATTCCGAAGGTTCACCCGTCGATGTAAAAACCGCCGTCGAGTATAAATTACGTTATCCACTGACTCGCAAAGATAAAGATGCTCAAGGCATTGAGCGCCAATACAGCTTAGTGACCACCTTTGATCGCAGTGGTTATAGCGAAGAAGACGGTAGTTTTGTATTGGTGAGTGTGGCCTTTGAGGCCGATCACTTCGACTGGCTTAAAAGCGGCGGCGCTGAAATCCCCCGCACTGGCCCATTCTACAATTTTATGAGCATGGGCATGGGTAGCACTTGGGCAGGGCTTGCGAGTGAAAGTCATTATGCCCAAGGTCTGCTCGATGGTGCCTATGTTGCTTACGATGGCCGCAATGGCAATGTGATGTATTCGGGCGATTACAAGCAGGGTAAAAAAGTCGGTAAGTGGATGGAAGCCGATAGCCGAGAAACCTATTGGGAAGGCGAATACCTTGACGGTAAAAAACAAGGTAAATGGATTGCACAGTCAATATTCGATGAGGCCGATAACTTCGGTTTTGCTCACTATAATCAGGATGTGCTCGACGGCCCCTACGAAATTTATGCACCAGATTATGGCTCCAGCACCGAAAGCGCTAAGCAATTAATGGAAAAAGGCATTTATCGCAACGGAGTCAAAGACGGTGAATGGTTAGAGCAAGATGGGACGAAAGGTCACTATGAACAAGGCATAAAACAAGGGCCTTGGACTGAAAAGATATCGGTTGGGCATTACCGCGATAGTCTAGGTCAAGGGAGTTACCTTGAGGGGAAACGCACTGGCCCTTGGCTGTTTACTTTTGCTGGGGGCAGTAAAGCCGAGGTGAGTTATCTGAATGGCTTGCGCCAAGGCGAGGCTAAGTCATTCAATAAAGATAATGTGCTGCTTGTTACCAGCCATTATGAACAGGGCAGGCTCGATGGTCAGAGTATTTGGTACTCAAGCCCTAACGTAGTGAGTAGTATCTCTAATTATCGCCAAGGTGAGCTAGATGGCCCGCAAATGCATTTTGATACTAAGGGTGAACTGACAGGGTTAATGAGTTTTAAATTAAATGAAACCGTGACACTTAAGCCCTCGCAAGACGAATGCATTATGCGTAAAAATCCCTACGCAGCGGATTGTGCAGGAGTAATCAATCAAAAAAATACCGAGACCTCATCGATAAAGCACGGTGAACAGCGTGAGTATCATAGCAACGGCAGCTTGTACAAATTAGCTTACTACAGCAATGGCCTGCTAGATAAAACATACCAGTTTGATTCCAATGGCCGTTTGTTAGAGATGACAAGTTACCAAGCGGGTAAAGAATACGGTCCAAGTCTGCGTTATTCGACCACGGGCGGTTATGCTCTGCGTATTTATGCTCACAATGTCAATAATCGCCTCTCAGGCCCACGTTACAGTTTCTATGCTAACGGCCAATTAGATAAGCTGTGGAACTATTGCCAGCAAGAAGGGGAAACCTGGAATGGTGCGCCTTACTACTGGGATAACGCCATTGCGCGCTGTGGTATACAGCGGGAGTACTTTGATAATGGTGCTGTGAATTGCATCGAAGAGTTAGATAGCAATTATGCCGTAGATAAAGTGTGTTACGACATCAATGGCAAGTTATCGAGTGAAATGCTGCGCATCGATGAACAACATGTGGTCCATAAACGCTATATCAATGGGGTGATTTATCAGGAAGAGCCGGGTTTTTCCGATTATTCGCATGTGACCAAAGGGCGCAAAATTTATCACTTAGAAAACCCTAAAAGCCATGGTGTGTTTAAGTCCTACCGCAGCGGCAAGTTAGAATATGAAAAAATTTATGATATGGGTAAGGCAGGATGCTTTAAAAAGTATGATGCCAATGGCAAGCAAACGGCAGAAACCGCCAGTTGCCAATTCTAAGGTCTAGGTTTGTTTAGCCTTTAGGGTCTGGTAGGCTTGGGATAAAGGATGAATTTGGTGCGGGTAGCTTAATCGCGATAATCATTAAATTCATCAGCTTATAGAATAACAATAAAAGGGAAGTCCGAATGTCAGTTACCGAATACAGCCTCGAAATCCACCCAAGATTCACAGAAACCGATGGTCTAGGCCATATCAACAACACAGTGATCCCCGTATGGTGCGAAGCAGCGCGCACGCCAATTTTCGAGATTTTTAATCCAGAGTTGGATTTGCAGCAATGGAACTTAATTGTCGCGGGCTTTACCGTGGCGTTTATCGCGCCAACCTATTACGGCAAAAGCGTGACAGTGAAAACCTTTGTCAGCCGCATCGGCAACAGCAGTTTCGAACTGACTCAAACCTGTTGGCAAGCCGGCAATAAAACCGCTGAAGTGAAAACCACATTGGTACATTACGACTACAGCTGCGAAAAAAGTCGCCCGATTCCTGAAGATATCCGCGAAATGCTCGCTAATCTGAATGGTGAAAGCGCCTAATCTGCCCTGTCTCTCGGTCAGCGCAATTGCAATTGCGCTGCTGAAGCGTTAATTTGTCGACCCGCTAACTGTGATGCAGATCACTTACCTGTATCGCCTAATTTTTATCGGTTCGGATTAATCATGAGATTCTATTTTACCCGCATGCGCTCGAAGGATATTTGTCCACCAAGACAGCCACTTAAAAAGATTGTGTGGTCATGGGTAGGGGCTTTCTGTGGCATTTACTTAGTCGCGAGTCTCGCAAATTATATGAGCGAAAACCTCGTTGGCACTATGTTTGTGATTGGCTCATTCGGCGCGTCGGCTGTGCTGGTATACGGCGCACCGCTGGCGGAGTTTTCTCAACCGCGTAACTTGATTGGTGGCAGTGTGTTATCGGCCTTAATTGGGGTCGCCGTGTACCAAGTGTGTGGCGATTATGTGGTGCTGGCCAGCGCCTTAGCGGTATCCCTGTCTATCGCATTAATGTACTTGACCCGCACTCTGCATCCTCCGGGCGGCGCAACGGCATTGATCGCTGTGATTGGCGGTGAAAACGTGCATCAACTCGGCTTTCTCTACGCGCTAATGCCAGTATTTCTCGGCTCAGTATTACTCTTACTGGTTGCGCTGGTGGTCAATAACCTCTCCACCGATCCCAAGCGCCATTACCCTGTGTATTGGATTTAGCTAACTGGTTGTGAAATAGGCTATTTCAAAAACTTCTGCACATACCAGCAGCTGGCTTGGATAGTAAAATCTTGGCTTTTGGCCCACCTTAAGCCGTGGCGAACTAAGCGTTCGGCAAGTCCTTTACCGCGCAGTTCGTTGGGGACAAAGGTGCGGCTAAAATCGATATCCTTGCCTGATAATTGGTATTCCAACACGGCTTCATAGCCATCGACAGGGATAATAAAACGCTGTCTATCCTGTTGATGTTCAATAGCTATCTCAATATTTTCGTTATCTGACATACATTCTTCCCTCAAGGAGTGGCTTAAAGTGTGCTGGCAATTTAGCTATCGATTTGGTTAGCTTAGGCGACTTCGTGTTGTTTGAGAATCGCCAGCAGTTCTGCCGGTAACTCGGCCTTTTTGCCGGTTTTAAAGTTAAACATCACCGCGACTGAAGTACCTAAGGTGGTTACTGCCTGCTGCTGTTTACTAAAAGCCTGATAATGCATGGTAAATCTATCGCTGTGAATATCGCTAATGCTGACACTCACTAATAGAGTATCGGGGAAGGTCACTGGGCGTTTATAGCGGGCCTGATTTTCGCTGATCACCGGGCCAATACCTGATTTATACAGGCTATCTAGCGGGAAAAGGCGATTGAAAAAGTCGATACGTGCTGTCTCAAAATAACGGAAATAGACGACGTTGTTGACGTGTTGCAACGCATCCATTTCGCCCCAAGCCACGGGGATTTCGGTCACTATAGGGTGCTGTGCGAGAAACTTTTCCATTGTGTTTTCCAGTGTGTTTGGCATTGAATTGCCGATCTAAAAATGCGAGTTGAAACGCTCGTTTAAGATGGTCAGACTAGCAGCATAAAGAACAAGCGACAAGCCATGGCGTTATAGGCATTTAGCGATAGCATTAAGCGAAACAAGGAGCAGTGCATGATTAAACGAGTCGGAAACACCCATATTCAAGCTGAACACACAGCCTCTTGCCATTGCGGCGCTGTGGTTTTGCAATTATCGCTGCCCAATGGCATCGAAAATCCTCGCCGCTGCGATTGCTCTATCTGCCGCCGTAAGGGCGCGATTGTCGGCTCTGTAACGCTGGCGGGGATCAAAATCCTCAAGGGTGAAGATGTACTGAAATGCTATGAATTCAATACCAAAACTGCCAAGCATTACTTTTGCTCCAACTGCGGTATTTACACCCATCACCAGCGGCGTTCCAATCCCCATGAATATGGTTACAACATTGGTTGCCTCGAAGGGGTCAATCCATTCGATTTAGACAATGTGCCCACGAATGATGGCGTTAACCATCCTGCGGATCGTTAGATTATGAACATAGGTCAGTTTGCTGTACTCACTGGATTGTCGGCGCACACGCTCAGATATTACGAGAAAATTGGCCTGCTGCAGTCTGTGGTGCGAAATGCGAGCGGGCATCGCGACTATGCCGCGAAAGAAGCAGAATGGATTGGGTTTATCAATCGCTTGAAAGAAACGGGCATGCCACTAAAGCAGATTTTGACCTATGCCGAACTGCGTGCTCAAGGCGATACAACCGCAGGTGAGCGACAGCAATTACTTGAGCAACATGCCTCACAATTGGCTGAGCGTATTCGCCAAGAGCAGGCGCACCTGATCGCCCTAGAGCAAAAAATCCGCTATTACCAAACGCATTTCAGTGATTAAGCTTAGCGATTCGGTTCAGTCGCTCAGTTCTGCATTTAAATCCTAATCGATTAATATTATTCGCTTTTATCTTTAGCTGCTGTCTTCCTGCTTCAATCACAGGGCTACCTGCATTATTCATCAATCAGCCGTTGACTTAGAGTTAACTCTAAGGCGTAAGCTGACTTCTGTGGTCAAATCCTAAGGAGTCAAAATGAGTAATCAAAGGTATGTGCAGGGCTTAGCTAAGCTGACTGAAATCGACGGCGAGGCGGGCGAGAAAGTGATCAGCTGTCTGGCAAATATTTGCCCCGACTTGGGCAAGTACATTATCGAATATCCTTTTGGGGATATTTATCAACGTGAAGGACTCGATCTTAAGACGCGGGAGCTAGTCACGGTCGCGGCGCTCACGGCGTTGGGGCATTGTCAGCCGCAGCTTAACGTCCATATCAACGGCGCGTTGAATGTCGGTTGTGCACCGCAGGAAATCGTTGAAGTGATACTGCAAATGTCTGTTTATGCTGGCTTCCCCGCAGCACTGAATGGCATGTTCGTCGCGAAAGCTGTCTTTGCCGAGCGGGAGTTAAATGTCGTTTGAGTCTATTCTGAGTCTATTTTGAGTTAGGTTCATAACACTTCATTGAGGCGATCTTAAGGTCGCTTTGATTTTACTCATAGGTATTACTTTCAAATCCATTTAAACGGTATTTCTGTTCCGCATCATCGACATAGAATCTCGCGGGAGATTCATTTCGCCGCTAAAGGACGATTCGGCAAGTTTTGTTAGTGAATTGCCAAAGTTAGTCGCTACACTGGCATAGGTGACGCGAGTGTCGCCATAGATTTTGCAGCTTACATCGGCAACTCAAGCGTGTCTTAATCGTAACCGGAATTTAAAATTATAATGAATTTAGGTCGTTACACTGAATCTGCCCGCCAGCACTTACGGCAAGTCATCAAGCCTTTGCCTAGCGAGCTCCAGCTAGGGTTGAGCAAGTTGTGGCGCTACTTAAGCCAAGGGTTTGCTAAGTTATCCTTTGCGCAAAAGCTGTATCTTATTGCCTTGATATTGGCCTTTAGTACCGACTTGATGGGTGTGGTCGCCGTGATTGCGGTTATCGCGATGGCCATCGAGTTTTGGCCAGTGTTCGAGCGGGTCTGGCACAGTTTGGCAGGCAAGGCTGTGTTGTTACTGTTTTACGCTATTATTGCCAACTTTGCCTTAGGTTGGTCTGGCGCCATAGTCAATGAAGTGGTTGGGGTGTCGGCAAGTCATCTGGATTACACCCATAACTTAGCGATTTTACTCTATATGCCCGCTTGGTTTTTAATGGTGAGCGCAATTGTGCTGCTGGCATTGCAGTTGATTATCCCCATCTATTTAGTATTAATTTTTTTGCTTAAACCTTTGGGTGTCAAGTCGTTACGGATCACCTCACATACATCATTTCGTAAAACGACCTTTACGATCCGTATCGTGTTGGCGGCTGTGGTCTTATATCACTTGAGCTTACTGATTGATTTAGAAACCCGCCTTCAAACCTCGTTTGCGGACTTTATTACAACAACTCAAGAAGCCTCTGAACATAATCCCGTCGAGCCAGCATTAGGGCTTATGCCGCATACGACCGATGTGCTGACGGCACCGATTGCTGAGTTAGCTAAGATTAAGCCCGAGCTTGAAGCCGACAAACCGGCTGACAAACAAACTGCCGATGAGCACGAAGAAGCGGAACGCAAGCAAACCTATGCGTCGGTGCGGGCGCAATATCAGTATCTCGTGCGGCAATGGTTAGCGACGTTTGCCTTTGCGCTTGAAGCTAATAGCCGTTCGCGTTGTGAAAAGCCCGCCGAATCGAATGTGATCGAGTTGAATGACTATGAGATTTTGCAGATCACGCCGGATAAGAAAGCGGATTATGGTTATCGTTTTGAAGTGAAAAAGTGTATCTCGGCCGCGTTTCCGCTGACAGAAAAGGGTAAATAAGCAGACCCATAAAAAAGCGACCACAAGGTCGCTTTCATTTTTGGCTAAATCAGTTTGGCTGAGTTCGTCGACTATTGCGTTCTTCAACAATCACGTTATTCAATCAGCAACTCATCAAAGCTGCTGATTTTACGGAACTCACCAGTGCGCTGTGTGCTATCGCGTACCATTTGACAGGTCATCATGCCTGCAGCTTCTGCAGCCTTTAATTCTTCAATCACATCAGACACAAATAGCACTTGCTTAGGGCTTAAGCTGATAGTGTTGAGGATATTGCAGTAAGCCTGTTTATCTAATTTGTTGCCTGTACGGGTATCAAAATGACCGTTGAACATCTCGGTTAAATCGCCGCCGTCACTGTGGCTGAATAACAGTTTTTGCGCATCAACAGAGCCCGATGAAAAACTGTAAATACGCAAATTTTGCGCGCTAAAACGTTTCACCGCTTCAATAAAGTCAGGGAAGATATGGCCCTTAAATTCGCCATGGCCGTAGCCTTGCTTCCAGATCAAGCCTTGCAGGGTTTTGAGTGGTGTCGCTTTACGATCTTCGCTGACCCACTGCTGCAAAATTTCAGTCACGCGTGCTAAGTCCGCATCTGGCTCTAGGGCGATATCTCGGGTGTCGCAAATACAGTTTTCAACCAATACATTGTGTTGGTTCTGTTCTAAAAAGTCTGGCAAGGCTTTGACAGAATAGGGGAATAGTACATCTTGGATAAAGTTTAGGTCTGTCGTGGTGCCTGCTGTGTCTACGACTATGGCTCTGATACCCATAATGTTTTGATACTCCACAATATGTTGGCTTAAAAATGCAACTCCGATCCTAAGCGTTATTGGCGAAATTGGCTAGTCTATTTCGCGAGTCTTACCACAGCTATCACAGCCTAAATGGGAATACTTAGGGTTTGCGATATGCTTGGCAGAAGGATTAGGCCGTGAGATCGGATTTACTTTCAATAATACGGGTGTTAACTTCGGCTGATACTTATATAGTTGATTTTCAAGGAATGAAATTTATGCGTACGCTATTAATTCTGCTGGTGATTTACAGCTTTGCATTCTTCAATGTGTCAGCAAATGCCGATGATACTTTTCCCGCTCAGCCACTGATGTTAAAGGTCAGTTCGAGCCTGTTAACTGAGCCTGCTGACTTTAAGGTGACATTGCCGGCCAGTTACCAGTCCAATACTGATAAACGTTACGTGGTATTGGTGGATTTACATCCTCGTAGTCATGGCTATCTCGCTGGAATGGAAGATTGGATGAGTCATAACGGTGGCTGGCCTTGGCTGGAAACCATTATAGTGACGGCGCCAGATGGTCATGCAGGCTTGGGTGAGTTAAAAAGCTTAGCCACTAAGCAGCAAGAAAATCAGCAGTTTTTAGATTTTATGGAGCAAGATCTACTGCCCGCCATTGATAAACAGTATCGCACAAATGGTTTTCGGATCCTCAATGGCTTCACTGGCTATGCCACTTTTAGCCTCTACACGCTTTTGAATCGCCCAAGCCTATTTAATGCTTACATCGCCGCAAGTCCCGTACTGGATGATAACTACGCTTTTGTAATGGCAGATGCTGCAAAGAAGCTCGCAGGTATGAAAGGGAAGCCAAGATTCCTTTTTATAAGCACTAGCGACAGTGATTTCGAACAAAATCAATTAGCTGACTTTGCCAAGCTTGAAGCCATTATCAAAGCGAACGCTACGAATTTACTCGATTATCGCATCAAGCGCTTCGATGGCACTTACTATATGACGCAACCTATTTTGGCGACGGCTTATGGTATTGAAGCTATTTTTAACGATGTACACCAAGTGCTTAAGCCTGACTCGGCAATATCACGTCAAGGTGTAGCCGCTATTTTGGCGCATTATAAAACCCTGTCGAACGACAAATATGGTTTTGACGTACCCGCGACAGATTCGCTTATCGCGTTGGCAAGTTCTTTCGAGACGACTGATCCCACCAAAGCTATCGACATTTATCAGCAAACCTTAAAAGCGATCCCAACAGCATTCGAGGCCAGCCATGCATTGGCAAGCGCCTATGCGAGCCAGGGTCAGCTAGTTGAAGCGATTAAGTATGAAAAGCAAGCGCTTGAATTAACTGACCATCCATTCTTCCAAGACAAATACAGTAAACAGTTAGCTGAGTATCAGGCGAAGCATTAAGTGTAAACGCTCACGTTTTTGCCTGTCGTTAACTGTACTCATATGTATAAGTTTGTGGCTACCATAAGACTATGGTCTTAATAGCTAAGGGAAATTTAGCCTGCTATTGTAGCTAAAGTCCCTTTTCTATTCTGTGGCTTGCATGCAGTGGTTTTTCGGGAGATGAGACTGCCTATTTTCAGCTGCAAATAGAGAGACCTTAAATGAAATATTTACCCGCTCTTGGCTTTGGTATCTTATTAGCTTTACTGAGTTTTATCAGTTTCTCCTTAGTCGCTAGCGCGGGTTATATGCTGGATATGTTAAGTGCAGTGCCCAAGATTACGCCGAACAGTGTGGAGTACCTGTTGTTAGGGGCTCACGATGCGAGTTTGCTGATATTGTTAGCAGGTTTAGTGCTTTACGCTTATCACAGGATTTTTCCTAAACTTCCCTTCGATTGGTTCACCGCTGTGTTCATCCAAATGCCACTGGGTTTAGCTGTGCTGGCGTTAGATGGTTTTAGCTTAAACCTATTGTCATTCAAAGGTTTTGCGCTGACGCTGACTACCTTCGCCGCTTCCTTTGGAGTGTTAGTCATCTTCTGGTTACTGCAGCGTCGAGCACGTCGTTTGTCTTTAGCCACGGTTAACGATTAGTTCAGCATATACCGCAGGCTGCTTAACTGTTTTTCGGTCACTAAATTATGTAAAATCATTGGCCTAAATGGATTTGAATCGTAAGAGTAGGAGCCAATGAGTTCGATTGAATTTGATATTCCCGAAGATCACCGTATTGCCTTTCAGAAGTATTACAAACAAAAAGTCGCCCCTAAATGCGGTCCGTTTGAAGCAAAGCGTGAAGGCGTAAACCAAAGGCGGCGTGAAAATGCCAAGTTCACCACCCCAATCTACTACATATGCATCACGGTGATTTTGGGTGCATTCGCCGGGCTGCTCGGGGTCCAGTTTATGCTGGTGCCTATGGATTATTTTGTGCCTTTGATGATTGTCGCGTTCCTGCCCGCTCCCGTGATAGCGTTAGCGGTCATTGCCTCGGGTAAGGAAAGCGATGAACTCTACAATGCCGCCTTTGAAAAACTCTATCCCATTAGCCTTAAGTATTTTGGCGATGACTTTGCGCTAACGAGGGCGCCTAAGGTCGATTTTTCGACCTTTATTGAGTTGGGGCTACTCCCTAAAACGGATAAGCAGTACGCGCAAAATTGCCTTAAGGGAACCTATAAAAAGGTGCCGTTCCAGTTTTATGAGGTCGATGCTTACAACACTAAATCGAACGATAATGGCTCGGTTCAGCGGGAGACCATCTTCAATGGTGTGCTGCTGGTTTGCAATATTCCTAAGAGATTCAATAGTACTACGCTGTTAGTCACTGATAAGGGCAGTTTAAAAAATCGCCTTGAGGGTTTTAAGACTAAGCTATTGCGCGTCAAGCTTGAGGACGTAAGGTTCGAGCGGAGTTTTGAGGTTTATAGTAGCGACCAAGTCGAGGCGCGCTACTTACTCAATCCCTTGATGATGGAAAGTCTGTTGGCTTTGAGCCGCGAGTTAAATGCCGAAGTTGAAGCCTGTTTCTATCAAAAACAGTTGCTGATTAAGATCCCGACAAAACACCACTTCTTTATCAATCAACTCGACTTAGATCGCCCCATCAATTTTAAGCGTAATATCGAGCAGATGTTTACAGAGTTAGGTTATACCTTCTCAATAATCGATACTTTAAAACTTGATAAGCATACTGGGCTCTAGAGTGTTAGGTTATTAACGAATAAGTGCATGGTATCTATTGGCCCTAGTGTTCTACGGCGATTTAGAGTCAGTACAACTTTGCTGCGGTAGGTATGTTTAGCGCCTACTGTGAGTGGTGAGTCAAACTGCATAGGCATGGAAGTTGGTACTCAAGTTGCTGAACACGCTGTTGATCGCTAATGGGTTGGGTCTACGCTAGCCGAGCTTTTCCTTCGCTTGAATCTTATCCATCACAATCTTCAATATAGCCTGTCGAAGCTTTCTACAGGCTTGAGGCACCATAACAATGAATCCCGAAACTCCCCTTTGATTGGTTCACCGCTGTGTTCATCCAAATGCCACTGGGTTTAGCTGTGCTGGCGTTAGATGGTTTTAGCTTAAACCTATTGTCATTCAAAGGTTTTGCGCTGACACTGACCACCTTCGCCGCTTCCTTTGGGGTATTAGTCATCTTCTGGTTACTGCAGCGTCGAGCTAAGCGCCTTGAGGCTATGCCCAGCTAATCGTTGCGGCATTTACAGCACATAACGTAGACGCAGTGCCGTCTGCTCAACGGGTGACAGTGATAGCCATTGTTCTCGTATTTCCACAGCTTCCTGTGGATAGTCGGTGATCAGTACATCGGCTCCCAGTGCTAACATGCGTAGCATTTGCCGTGGATTGTTCACTGTCCATACATGCACTTGCTTGCCGCCCTTATGGGCTCTGGCGATAAACTTCTCATTAGTAATCGACTGATTTATCGCTAAGAATTGTGCATTCGACTTAATCGGGTCGCCGATAGATTGGGTGACAATCAGCCCTGTTTGGATTGAAGGGTCTAGTCGCTGCACTTCTTCAATGGCTCTCGCTTCGAGCGAAGTAATCACCACTTGTTGTGTCATTTTTTGTTGTTGCAGCAGGGCAACAACAGCCTGCGCTAAGGTGGGATCGGCTTGATTATATTTGAGCTCAATATTCAAACCTATTTTATTGCGCGCCTCATTGAGCATTTGTAGTAGCGTCGGCACGGATTCACCTGCAAACGCGGCACTGTGCCAGCGACCCGCATCGAGTTGTTGTATTTCACTGGCGCTGAGATTAGCTACTCGCCGAGGATCGCCCGCGATACGCATTAAATCCGCATCGTGCAGTACCATTAAGGTGCCGTCAGCGGCGGTTTGCACATCTATTTCAGCATAATCGGCCTTTTCTGCAATCGCTGAACGCAAAGCCGCAAGGGTATTTTCTGGTGCCCTAGCTGCCCCCGCTCGGTGGGCTGAAATGAGGATTTCATCCCTTTGTGGCTCACTGTTTAGGGCATACACCCCTAGTCCCAGCATAATTAAAAAAGTCGTGACCAAGACGCCACTTAGAGTGTAAATCGACAGCTCTGAAGTCTTTTTTACTGTCTCTCGTCCAAGGCGTTCTAACCCTGCAGGTTTCTCTCCCAGTTGGCGGTATAACAGCAAGATGATAAAGGCTTCGCCAAACATGCCGAGCAAGGACCAAGTTGCACTCAGCATAAAAATCAGTGCAGAGACGAGGGCCAACATGGCTAATTCTGCCGAAAGTGTCAGTGAAACCCGGTCGAGCAAGCTATCGCCCCCCCATGAGATCAACAGCATTAAGGGATACAGCAAAGCCCCTATCCTTAACCACCAACCAGCTGCGTGCCACATAATCGCGCGTTCATTACCTGCACTTAACTGCCAACTGCGTTTGAGTGCCGTTAATGCATCGGTTGTTTCACTGACAATTATCGTCAGGGAAAAGGCCGCTTTGGCCCACAGATATAAGCTAACAAGCAGATAGAGTAACGCTAAGAAGGCCATGCTCGATAAGGCTAAATACCATTCTACTGGCTTGAAATACAGGTAGTAATTGATGTCGTGTCCCTGTAGAAACACGCTGAAAATGAGATACCCTAATCCCATAAAAGGTACGCTGAGCAGCAGTAAGCAAGCCGCATATATGCTACCAAGTCGAATGAATACGGGTAAGCGCGCCGTGGCGAAAATCAGTGCCCGCACTGGGCCAATGGGAATATCGTATAACCCTGATGCGGCCAGAATAGTGACCCCAGCACGCTGGGTAAAAAACACGGTAAAACTGCTGAATAGGGTAATGATCATAAATAGCAAGCCATAGGGAGACATGGCGAAGGCGATCAAGTCGTAATTGGTTATGGTGACATTGCCCGTTTTGGCGATCAGATGGCGCAGTAGCCAGCCAAAAGATGGGACGATAATACCCGCCGACACTAAGGTAAATAGCAGTTCAAATTCAATGAGTTTTCTACCGCAGCGACGAATGGCTTTAAGCGTGTCATGCCAGTAACTGTGTTCAGGATTGAGCATGCAGCTTCCTTATTCGTGCGAGTTAGGACTGTAATTTAGCAGTGTTTCAGGTTGGCGGTTAGTCATTGGTGCAATGTTTTAGATATGGGCAGGCTGTTAAAGTCTTCTTATCAATGAATTTACAGAAGACCTATGGGTACCTGATTACATTCGACAATGATTAACAGTGCTGTGTGACTGTGAGTGGCTAATCAAAGTATTGGCTTAGCTGCATGGGCTCATCCATTACCTTTGCCAATATCTCGTTGATTCATATAGTGGGCTAATTGCGCTGCATCGGCATGGGGGCTGGCACTTAAGCCGCTGAACACACCCTGTTGATCGCTAATGGATCTATGCTGGCCGAGCTTTTCCTTCGCTTGAATCTCATCAATCACAATCTTAAAGCCGACTATGGCTTGGCGCAGCTTTTGCTGATACTCATCCGGCATAATTTGTTTATCTTGCAATAGCTTGGGTTCGTACTTTGTGACTAGCTTGTCCAACATGTCTTGGGTTTCTGCATCACTCAACAGGCTTGCGACACCATAACAATGCACCGCCGCATAATTCCATGTAGGCACCGCAGGTTTAGCATCATACCAAGTTGGGGAAATGTAAGCGTGTGCGCCGCTGAAAACCACTAATACCCGCTGTTTTTCAAGCTCTTGCCAATGTGGGTTCGCCCGTGAAACATGGCCATACAACACACCTTTCTCTCCCGCTGTTGCATCGAGTACAAAGGGAATATGGGTCGCATTAAGTGAGCTCGAAACCAGCAAACCAAAGCTGTTTTGCGTGATAAAAGCTGTGATGGCCTGCTGATCTGTCATCGCCATATTCTTAGGGGTATACATTGTCTTCCTTGGGTTTCAACATGAGTGAAAGCGTTATATTACAAGGAAAAAGTTGGGAGTCGGTAGGGGATTTAGAATGTTAGTTTGTGGCTTATTAAAATCAAAATCGTATGGACTTGAACTCGGACTCGGAGATTAAACACTCGCATTACTGTGACTCGCAGCAAGTACATCCGTGTATGCTCGAACGTGGCATCCATGCCATGGACAGTCACATACATGCGAGTGCTTAATCTAGTTAAGACCTGCGTATACATCTAGCCTCGTTAAAATCCGTGAATCAGACTTGCCTAGTTTGGTTCACGCGCAGAACTCAGCCATTTGGCATTCGTTGTTAACTAACGCGTTTTTGCCCCAAAGTGAGCTAAACCCATCTAGGATAGATCTTCCCCGTTGACTAAATTTTAGTCATGATACAAAGTGACCAATATTACTGGCTCAGAGCAATGCGCTGTTAGCTACCACAATGGACTTCAGATGAATACAGAAATGAAAGAAGCTCTAGTTAAAGTCCCACAGATTACGCTTGGCTTTTGGGTAATTAAAATTCTCGCAACTACCTTAGGTGAAACAGGCGGCGATGCCGTTTCCATGTCAATGGATCTAGGTTACTTGATTGGAACAGTCATTTTTATGGCTTTTTTCATTCTTGCGGTTGTGTTCCAAATCCGTACGAAGAAATTCAATCCGTTTGTTTATTGGGTCGCAATCGTCGCATCAACTACTGTAGGTACGACGCTCGCTGATTTTGCAGACCGTTCCTTAGGTATTGGCTATCTGGGCGGCTCAACACTCCTCGCAGTGTTTTTAGCAAGTACGTTGTTTATTTGGTATAGAACACTAGGCAGTATTAGTGTTTCGTCAATTCACACACCTAAAGCAGAAGCGTTCTACTGGCTTACCATAATGTTTTCTCAGACGCTCGGTACAGCGCTAGGTGACTGGACCGCAGGTTCAGCTGATTTGGGTTATGTCGGCGCAGCTTACATTTTCAGCGGCGGGTTAGCATTAATCGTCGCAGCTTACTTCTTCACAAAAGTATCTCGAACAATTTTATTCTGGTCAGCGTTTATTCTTACCCGTCCACTTGGAGCCGTGGTTGGTGACTTTTTGGATAAACCTATCGCTCAGGGTGGTCTAGAATTCAGCCGCTATACGGCTACAGGTGTACTTTTGGTCGCCATAGTAATTCTGGTTATTGTACTGCCACAGCGCGCAGCCAGTAAATCACACTAATGTGTTGGTGGTGCTTAACGGGACGGGCTAAAGTCCATTCTCCTAAACCAAACGTTAATCATAAATTTCTCATGATCGTTTAGTTAGCAATCGTGCCAGTGAATCACTCTACACCAGACTGATTCACGCTCCCCACTAAAACCCGCTCTCACGGCCTGTTTGCGGCTATTGGAGGGTATTTTTTTACCCTAAAATGAATCAAATCTCTTAAGAACTGATTCAAGCTCACTTCATGACGGCACATTATCAACATAACGAGTTTGTGATGGTGGCGCAGTTTCCTGTAACGCAAATTGCATCAGGCTAATAGCTAAATTAGGTCATCAATATGCACGGGAGTTGATGAGATTAAGCACTGGTTTGTAAGTGACCGTTCGTGACAGGGATGTCACGGCCGAGCCCTCAAGGATGAGTTTATGGCGTGTCACTGGAAAGCCAGTGCTTAATCATTTTCTGATTATTTACACATTGCCGCACACTCAGTACATGGTTGCTTAGTTGACACAATCAACCAATCTCGATATTGTTGATACAGTCAACTATATCGAGAGCCTTCTATGCCTTCATTGTCACTCAGTGAAACTTTGCACAGATTAGTGCATGCCTACAAAAAGCAATTGCGTAGCGATATTGCTGCACAAGAGATTGATTTACCTGTCACTCATATCCGTGCCTTGAAGGGCGTGTGTCGCAACCCTGAATCGACGGCCCAGTCTATCGCCCTGCGAATGCAGCGGGATAAAGCGCAAATTACCCGTGTTCTTAATGAGTTACAGCAGGGTGGGTTGATCACTAAAATCGATAACCCTAAGGATGGCCGCAGCCAGTTACTGCGACCCACGGCAGAGGGGGAAAAGATCATGACCCAGATCAGTGCCTCAGAACGTAAAACCGTGGCGCGCATGACGCAGGCGCTGAGCTCAGCAGAAGTCGACACCTTTATTAAACTTGCCAATCGAATCAGCAAAAGTGTGGATGAGGCCGCTGCCGCTAAAAGCGGTGCGGATTGCCATGCTCAAAAAAGACCATCTAGTAAGCCAAAAGACCCGATTGGCAAAGGAGCCGTTGAGGCACGCTTAATGGAAGAACACGCCATAGGAGAAAATCATAATGAATAAACCTGCACCACGTGAGCTGGAAGTGATCCGCTCGACTTATATCACCCCGCATATGTTACGTATGACCTTAGGCGGTGCGGGATTCGCTGGTTTTCCTATCGATCAAGAGAGTGCCTATATCAAGTTGTTATTTCCACAAACGGGTGAACGTCCATTGATGCGAACCTACACGATTCGCCAGCAACGTGCTGATGAAATTGATGTGGATTTTGTATTGCACGATACCGACGGCCCTGCATCGAGTTGGGCGAAAACTGCGCAGGTGGGTGATGTCATTCAAATTGGCGGACCCGGGCTTAAAAAACTGATCAACTTTGAGGCAGATTGGTTTTTACTCGCAGGTGATATGAGCGCCTTGCCCGCAATTAGCGTGAATCTGGCACAATTACCAAACAATGCAGTGGGTTACGCTGTGATTGAAGTATTGAGCGAAGCCGACATTCAACCTCTTGTGCATCCTGAAAATGTTGAGCTGCATTGGGTGATCAATCCTGAGGCCGATCCAGAAGGTCGCCCTTTGGTTGAGCGAATAGCCCAATTACCTTGGCTTGCTGGCGAGCCCGCCATGTGGATTGCTTGTGAATTCAACAGTATGCGTGCGCTAAGAAGACACTTCAAGCAAGCGCATTCATTACCTAAGAGTCATTTTTATACCTCGAGCTATTGGAAAATTGGTTGTAACGAGGGCGAGCATAAGCTGGTAAAACAAGAAGATGAACAGTTAGAAAATGGTGCCAGTGCTTAAGCTTAAATCCTGTTGTTAAACAAACCGAGCTATTAAACAAGATAAGGTCGCCAAGTTTGATTTTGCGACCTTATTTTATTTGCTACTTATGCCACATTTGCGGCGAGTACGCGCATTCTATTGATGTACTAACTTGCCATTCTTTACCACATCTTTGCAGGGATTTACGCCATAGCTATAAGCCAGCTGCGCAGGCGTGGCGATATCCCACAGACAAAAATCGGCTTGTTTGCCAACAACTAAACTGCCGACGTTATCTTCAATCCCGAGTGCCTTGGCGGCATTTAATGTGAGGCCTTTTAAGGCTTCTTCTGGTGTTAAGCGGAACAAGGTGCAGCCCATATTCAGCATCAACAGGGTTGAGCAAATGGGGGATGAACCTGGGTTGAAATCGCTGGCGAGCACCATAGGTACGCCGTATTGACGCAGTAAATCGATAGGTGGTATTTGGGTTTCCCGCAAAAAGTAAAATGCGCCCGGCAGCAGTACGGCGCAGGTGCCACTTTCACTTAAGGCTTTAACGCCTGCTTCATCTAAGTACTCGATATGATCCACAGACTTAGCGCCAAGGCGTGCGGCCAGAGCGCTGCCGCCCATATTGGTTAACTGCTCGGCGTGGAGTTTAATCTCTAAGCCCGCCGCTTTGGCTGCGCTGAGTACCCGTTCGGTTTGCTCAAGATTAAAGGCGATATTCTCGCAGAACACATCCACCGCATCGGCAAGGTTTTCGGCGATCACGGCGGGCAGCATCTTATTGATAATTAAGTCAACATAGCCGTCGCTGTTGTCTTTATATTCTGGCGGAATCGCATGGGCGCCAAGGAATGTGGTCTTCACATCTACGTGGTGATGCTTGCCGAGCTCGCGGGCGACACGCAGGATTTTCAATTCAGTTTCAGTATCTAAGCCGTAACCTGATTTGATCTCAACCGTAGTCACCCCTTCCTTGGCCAGCGCGTTTAAGCGTTGACGGCCGAGTTCAAATAACTCTGCTTCATCCGCTTCGCGGCAGGCCTTAACGGTCGAAATAATTCCGCCACCTGAGCGGGCAATTTCTTCGTAACTCGCGCCTTGTAGGCGTAACTCAAATTCATTGGCACGATTACCGGCAAAGATTAAGTGGGTGTGGGCATCAATCAATCCTGGGGTGATCCAGCCGCCTTTGCCTCTGTAAACGGGAATGGACAACACATCGAAGGCGGGCAGTTCGCTGCGGGGGCCAAGCCAAGCAATTTTGCCATCTTTAACTGCGATAGCCGCATTGGTGATCGCGCCATAAGGTGCTGATATGGAAGGGTCCATTGTGGCTAAGTTAACGTCAATCCAAACCTGATCCCAAGACATGTATTCCCCTCGCTGTGCTGGCAGACGTTAACCAAGTGATTAACGGTTTATTAAATTTATGTGAGCTAGCTTGTATTTACTTGTATATACAAGCTAGGATTGTAGCCGCTATTTTCACCGTTTGAAAGTGGCATGACAGACCCGTGTCTTTGTCGCCTTCGTCTGTGCCCCTTTAGCAAGCATGACTTTCGCAAGGGCACAGGAACAAGAAGCTGAATTTAAGAGGATGTGTTTTGGCTATTCCTAAGTTTGCAGAAATTAAGCAATACATTATCTCCTCTATCGAATCTGGCGAGTGGGAAGAAAACGCCCGCGTGCCTTCCGAGAATCAGCTGGCCGACTTGTTTACCTGCAGCCGAATGACGGCGCGACGTGCATTAACCGAGCTGACGGATAACGGCGTACTCGAACGCTCCCAAGGACTCGGCACCTTTGTGGCTGGGCGAAAATCCCAGTCATCTATGTTGGCGATTCGCAATATTGCCGATGAGATTAAAGACCGCGGCCACGGCTATAGCGTACAGCAATGGGTGCTAGAACAAACCGTTGCCAGTACAACTATTGCAATTTCCCTTGGGCTTGAGCGCGGTGCAAAGGTATTTCACTCGATCTTAGTACATTGCGAGCAGGGTTTACCGCTGCAAGTCGAGGAGCGTTACGTGAATCCCGAGTTGGTGCCCGACTATTTATTGCAGGATTTTACCTTGCAGACGCCCCACGAATATTTATCCCAAGTGGCACCGTTGACCGAGGCGCATCACACGATTGAGGCGATTATCGCCAGCGCCGAATTACAGCAAAGGCTCGAAATCCCAGCCACTGAGCCCTGTTTACAGATTTTACGCCGCACTTGGTCGCGCCAAGGTGTCGTGAGTTTTGCCAAGTTAGTTCACCTCGGTAGCCGCTTTAAACTCGGCGGCCACTTAACTTTTAGCAAGTAGCGCCAGTGGCGTTATTTACCGATGTAAATCAGTCACTCCAATAGCTGTACCCTTATAAGTTAAAAGATAAAAACACCATAAAAACAATAGTGTTAACCCGTATTAGCGACTCAGCAAATACGAGTTAGCACCACAGGTTGAAAGAGGAAAATTCAATGGACAAGCGACATGACCCAAGCCGCCGCATTATTGCACCGCATGGCACACAATTAAGCTGTAAGAGTTGGCTCACCGAAGCGCCAATGCGCATGTTAATGAACAACTTGCACCCAGATGTGGCCGAGCGCCCAGAAGACTTAGTCGTCTACGGCGGTATCGGCCGTGCCGCCCGCGATTGGGATTGCTACGACAAAATTATCGAAGTCTTACAACGCCTCGAAGATGACGAAACCTTATTAGTGCAATCGGGCAAACCTGTGGGCGTATTTCGCACCCATGCCGATGCGCCGCGCGTGTTAATCGCTAACTCCAACTTAGTGCCACATTGGGCTAACTGGGAGCATTTCAACGAGTTAGACAAGCTAGGTTTGGCCATGTATGGCCAGATGACGGCGGGTTCTTGGATCTACATTGGTACTCAAGGCATAGTTCAAGGTACCTACGAGACCTTTGTGTCTGTAGCGAAACAGCACTTTGAGGGTATCTCCAAAGGCAAATGGATCCTCACTGGCGGGTTAGGCGGCATGGGCGGCGCGCAAACGCTGGCGGGCACTATGGCTGGCTTCTCGGTGTTAGCCTGTGAAGTCGACGAGACTCGCATCGATTTCCGTTTGCGCACTCGTTATGTCGACAAAAAAGCCACTTCGCTCGATGAAGCATTGGCGATGATTGAAGCGGCAAACCAAGCTGGCAAGCCTGTATCTGTGGGCTTACTGGCAAATGCCGCCGATGTGTTTGCCGAACTGGTTAAGCGCGGCGTTACCCCTGATGTGGTGACCGACCAAACCTCGGCCCACGATCCATTAAACGGTTATCTGCCGCAGGGTTGGACTATGGCAGAGGCCGCAGCTATGCGTAAAACCGACGAAGCGGGCGTAGTGAAAGCCGCAAAAGCCTCGATGGCGGTGCAAGTACAAGCCATGCTCGATTTGCAAACTGCCGGCGCAGCTACCTTAGATTACGGTAACAACATTCGCCAAATGGCGTTTGAGATGGGCGTTGAAAACGCCTTTGATTTCCCTGGCTTTGTACCTGCTTATATTCGTCCGCTGTTCTGCGAAGGTATTGGCCCGTTCCGCTGGGTGGCCCTGTCTGGCGATCCAGAAGATATCTACAAAACCGACGCCAAAGTGAAAGAACTGATCCCAGATAATCCACACCTGCACAATTGGTTAGACATGGCACGCGAGCGCATCGCCTTCCAAGGTCTGCCTGCGCGTATTTGCTGGGTCGGCTTAAAAGATCGCGCCCGTTTAGCGTTAGCCTTTAATGAAATGGTCAAAAATGGTGAGTTGTCGGCGCCCGTGGTGATTGGCCGCGATCACTTAGATTCTGGCTCAGTTGCCAGCCCGAACCGCGAAACCGAATCTATGCTGGACGGCTCAGATGCAGTATCCGATTGGCCATTATTGAACGCATTACTCAACACCGCCAGCGGCGCAACTTGGGTGTCGCTGCACCACGGCGGCGGCGTCGGCATGGGCTTTAGCCAACATTCGGGTGTGGTGATTGTGTGTGACGGTACCGATGCGGCGGCAAAACGAGTTGGCCGTGTGCTGTGGAATGACCCAGCGACAGGCGTGATGCGCCATGCCGATGCGGGCTACGAGATTGCGAAAAACTGCGCCAAAGAGCAGGGGCTCGACTTACCTATGCAAGAGTAGCCGATGCTAACGACTCAATAACAATAATAACTAAAAGCTTGGAGCTAAAAATATGAAATCTGTGAATCACTTAGTGTTAACGCCGGGCAGCTTGAGCCTAGCGCAATTACGCGAAATCAGCCGCCATAAGCTGACACTCGAACTCGCGCCAGAGGCCATTACCGACATCAACACCAGCGCGCAAATCGTGCAAAAAGTGCTGGATGAAGGCCGCACCGTTTACGGCATCAACACGGGTTTTGGCTTGTTGGCCAATACCAAGATAGCGCCCGAAGATCTGCAATTGCTGCAACGCTCTATCGTGTTGTCGCACGCTGCTGGTACGGGCCAATACATGCAAGACGCCACAGTGCGCTTGATGATGGTGTTAAAAATCAATTCATTAAGCCGTGGCTTCTCGGGCATTCGTTTAGAAGTGATTAACTTCTTAATCCGTTTAGTGAACGCCGAAGTGTATCCCTGCGTGCCAGAAAAAGGCTCAGTGGGCGCATCGGGCGATTTAGCGCCACTGGCGCACATGTGTTTGCCTATGCTGGGCGAAGGCGAGATGAGCTATCGAGGTGAGTTAATTTCTGCCGCTGAAGGTTTAGAAATCGCCGGCCTTAAGCCAATCGATTTAGCGGCCAAAGAAGGGCTAGCTTTGCTTAACGGCACACAAGCCTCGACTGCGCTGGCGCTAGAAGGGTTATTCCACGCCGAAGACTTGTTTGCTGCAAGTTCAGTGATCGGCGCCATGAGTGTTGAAGCGGCGATGGGCAGTCGTAGCCCGTTCGATCCTCGCATCCATGCAGCTCGTGGTCAGAAAGGTCAAATCGATTCGGCCATGGTTTTCCGTCATCTATTGGGTGAAGAGTCTGAGATCAGCGTAAGCCATGCAAACTGTGAAAAGGTGCAAGATCCTTACTCACTGCGCTGTCAGCCACAGGTACTTGGCGCTTGCTTGACCCAAATTCGTCACGCGGCCGAGGTGTTAGGCACTGAGGCCAACGGCGTAACCGATAACCCACTGGTGTTCCAAGACACGGGCGATATCATTTCAGGCGGTAACTTCCACGCTGAGCCTGTGGCTATGGCGGCGGATAATCTGGCGATTGCGATTGCCGAATTGGGCTCTATTGCCGAGCGTCGTATCGCACTGTTAATCGATTCTAGCCTGTCGAAACTGCCGCCTTTCCTGGTGAAAAATGGCGGCGTGAACTCAGGCTTTATGATCGCTCAAGTAACAGCGGCAGCTTTGGCGTCTGAGAATAAAACCTACGCCCATCCAGCATCGGTAGACAGTTTACCGACATCTGCTAACCAAGAAGATCACGTCTCTATGGCGACGTTTGCGGCCCGTCGTTTGCGCGATATGAGTGAAAACACCCGTGGCGTATTGGCGATTGAATTGTTAGCGGCGGCGCAGGGTTTAGATTTTCGCGCACCTTTAGCGCCAAGTACTGCGGTGGCTAAGGCCAAAACTGAATTGCGTGAAGTCGTCGCCTATTACGATAAAGACAGATATTTCGCGCCAGATATCGAAGCCGCGACAGACCTGTTATATACAGCGAGTTTTAACGCTTACCTGCCAGCAGGAACGCTAGCGAGCTTTTAAGCGCTTTAGCTCTTAGTGTGTAACGACTAAGCCGATGTTGACTCGGTTGGGTTGGGTGCATTGGGGCGCTTTTGCTGAAATACTCAGGATTCACTCCCCAATTTGCTAAACATAACTGCTGAAATAAAACAAAACGAAGGCATAAGTGGATTGCTTATGCCTTTTTCTTGAGTGTTTTGCCTTTTTAGCTCGATCCTCATCTTTGCCCTTTTGGTGTGTTTAACACCGCAGACTCTGCACTTTTTGCGCTGATCTTTACATTGTCAATTTCAGTGGCTACTTACCCCTCAATAAACCGTCACTTTTGTGTTTTTATCCGCCATCGTTATACACATTTTAATATATAAAACTATTTTTAATAACCATATGGTTAGGTTGTGGGACAATATGGACCTGCATTGCTAGACAAATTGTCCTTTTGGAATTATTGGGATTTAACTCTTAAATTAAGTGTTTTTACATTATTTAAAGTTTTTAATTTCAATATTATTTATTTATTTAGATTTAAGTTTTGTGATTGGTTATTTGCGATCTCGATCTCATGTCATTATTTGTTATCTATTTCCTCTTTAAGGATAATCTTAAATTAAGTGTGGTCTTTAAGTGGGCCCACTCGATCGGTTGCAATCTTTACCACCGCAACCACTAGACTCATAACGACAAGAGTGTGAGAGGACGCAATGAACAGACGCCAGTTTTTTAGACTGTGTGCTGCCGGAGCTGCCACCTCAGCAATTTCTGCGTTGGGATTGATGTCCGAAAAGGCCTATGCGGCCGTTCGGGAATTCAAACTCATTAGCGCGAAAGAAACCCGCAATAACTGCTGCTATTGCTCAGTGGGTTGCGGATTGCTGATGTACAGCAAAGGGAGTAATGGCAAGAACGCCGAACAGAGTATTTTCCATATCGAAGGGGATGCAGACCATCCAGTCAACCGTGGGGCCTTGTGTCCTAAAGGCGCTGGTTTAGTGGATTATGTCAATAGTCCCAATCGTCTTAAATACCCAGAGTACCGTGCACCAGGCAGCAATGAATGGACACGTATCAGCTGGAGCGAAGCTTACCAACGCATCGCCCGCCTGATGAAAGATGATCGTGATGCTAACTTGATTGAAAAGAATTCTGCCGGGACGACGGTTAACCGTTGGCTTAGCACTGGGATGATGACCTCGTCGGCGATGCCGAACGAAGGCGGCTATATCACCCAGAAATTTGCACGGGCACTGGGCCTAGTTGCAATCGATACTATTGCGCGTAACTGACACTCTCCAACGGTAGCAAGTCTTGCTCCGACATTTGGACGTGGTGCCATGACCAACCACTGGATCGATATTAAAAATTCTAATTTGATCATTATCATGGGCGGTAATGCTGCAGAGGCTCACCCTGTCGGATTCGGCTGGGTTACAGAAGCAATGCAACATAACAACGCTAAACTTTTGGTGGTCGATCCTCGCTTCACCCGTAGCGCCGCTGTGGCCGATTATTATGCGCCGATCCGTTCGGGCACAGATATCGCCTTCCTGCTCGGGGTGATCCGCTATCTTATTGAAACCAAACAAGTGAACTACGACTATGTGAAGGCTTATACCAATGCCAGTTACATAGTGCGCGAAGACTTCTCTTTTAGTGAAGGTCTGTTCAGTGGTTTCGATGAAGAAACAGATAGCTACGATAAAGAGTCTTGGTACTACGAGTTAGACGAACAGGGTTACGCGAAAGTCGATCCTAGCTTCGAGCATCCACGCTGCGTGTGGAACTTAATGAAGCAACATGTAGATCGCTATGACTTTGAAACAGTAAGCAATATTACCGGTACGCCGATTCCGGACTATGAGATGGTTTGCCAGCAAATTGGCAGCACTCATACCCACGATAGGGCTGCCACTTTTATGTATGCCTTAGGTTGGACTCACCACACTAAAGGGGCGCAGAACATTCGCTCTATGGCTATGGTGCAGTTGCTGCTGGGTAACATTGGGGTGTTGGGCGGTGGCGTTAACGCACTGCGTGGTCACTCTAACGTGCAAGGCGCGACCGATATGGGTTTATTGTGCCAAAGCTTGCCTGGTTACTTGAAGTTACCGAGTGAAAAAGACACTGATCTTAAGACCTATCTCGATCGTTACACGCCTGTCGCCTTGCGCCCGGGTCAAACCAACTATTGGCAGAATTACCCTAAATTCTTTATTTCGCAAATGAAGAGCTTCTGGGGCGATAACGCCACGGTTGAAAATGACTTTGGTTATGACTGGGTACCTAAGTGGGATAAGCAGTACGACTTTACTAAGCACTTAGACATGGCTTTCCACGGTGAAGTGAATGGCTACATTATCCAAGGGGTTAACGCCATTAATTCCATGCCGAACCGCAATAAGGTACTCAAAGCCTTGAGTAATCTTAAGTACATGGTGGTGCTGGATGCCTTGGCAACGGAAACGGCAACCTTCTGGCAAAACGCCGATGGCTTCAACGAGGTGAATCCCGCTGAAATCATGACGGAAGTGTTCCGCTTACCGACGACGTGTTTTGCTGAGGAAGAAGGTTCCATCGTCAACTCTGGACGTTGGATGCAGTGGCATTGGAAGGGCGCTAACCCGCCGGGTGAAGCCAAGCCTGATGCTGAGATTCTCTCTGGTGTCTTAATGGCCATGCGTGAACTGTACAAACAAGAGGGCGGCAAGTTGCCTGAGCCAGTGCAAGCGATAAGTTGGGATTACCACAATCCTTACTCGCCGCATGCCGAAGAAGTTGCTCGCGAGCTTAACGGTAAGGATCTCGTGACTCAGCGTCAGCTGAGTAGTTTTGCTGAACTCAAAGCCGATGGAAGTACTGCTAGCGCCTGTTGGATTTATGCGGGTTCTTGGACCGAAGAGGGCAACCTGATGGCTCGCCGCGACAACCATGATCCTTCGGGTAAAGGTGTCACTCCGGGCTGGGCATTTGCTTGGCCAGCCAACCGTCGCGTGCTGTATAACCGCGCTTCTTGCGATATCAACGGCAAACCATGGGATCCCAAACGTACTATCGTCGAGTGGGTTGATGGTAAGTGGCATGGTATCGATGTGGGCGACTTCAATATGAAGTTAACACCACAGGAATCCGCAGGTCCCTTCATCATGCAACCTGAAGGTGTGGGTCGTTTCTTCGCCCTTAAATTATTGGCCGAAGGTCCGTTCCCTGAGCACTACGAGCCGATGGAGTCGCCTATTGGGTTGAACCCGTTACACAAGGTCACCAGTAACCCTGCGGTACGCATGTTACCGGGTGTGAAGGAAACCTTAGGTTCCCATAAAGACTTCCCTTATGTGGCAACCACTTACTGTGTAACTGAACACTTTAACTTCTGGTCTACTCATGCCCGTTTGGCAGCGATTTCTATGCCGGAAACCTTCGTCGAAATCGATGAAATGTTGGCAGCAGAAAAAGGTATCGCCAATGGGGATTGGGTCACTGTCAGCTCTAAACGTGGCAGCATTGAAACCAAAGCCTTAGTGACTAAACGTCTGCAGCCGTTAAAGGTGAATGGTCAGTTAGTTCATACCGTCGGCTTACCGCGTCACGGCAGCCATAATGCGCTCACTCGTAAGAGTTACTCCTGTAACGTGCTCACCACGGAAGTGGGGGATGCAAATACACAGGTGCCTGAGTTCAAAGCATTTTTGGTTAACATCACTAAAGCGAAGGGGCTCTGATTATGGCATCACAAGACATTATCCGCCGCTCCGGTACTTCAGCGGTGACGCCTGCGGCAAAGGGGCGTAATTCGTCAGAAAAGGTCGCCAAGCTGTTCGATGCCACTAAGTGTAATGGCTGTAAAGCCTGCCAAGTGGCGTGTTCTGAGTGGAATGATTTACGGGAAGAAGTGGGCACGTTCCAAGGTAGTTACCAAAACCCTATGTCGCTATCGGGCCAATGTTGGACCCTGATGGAGTTCCATGAGGTGATGGAAAGCAACAAGCTGCGTTGGCAATTTACCCACTCGGCCTGTATGCACTGTGCCGACCCTGCCTGCTTAACTGCCTGTTCGACTTCGGGCGCGATTATCCAGCACGCCAATGGTGTGGTGGACTTCGATTCGGACAAGTGTATCGGCTGCGGTTATTGCGCCAGTGCTTGTCCCTTCGATGTGCCCAAAATCGATCCCAAGGATAACAAGGCCTACAAGTGCACTATGTGCTCTGACCGTTTGGCCGTGGGATTGGAACCTTCCTGTGTTAAGTCCTGTACCACAGGGGCGCTGCGTTTTGGCACCCGTGAGGACATGTTGTTTTACTCGCAAAAACGGGTCGAAGAACTGAAGGAAAGGGGCTTTGCTAAGGCAGGACTTTATAACCCTGAGGGCGTAGGTGGTACGGGTATGATGATGATATTGCACGATATCAATCAGCCCGAAAGCTACAACATGCCTGATAACCCGCAGATCCCGCTGTCACTCACTCTGTGGCAGGACTGGGTCAAACCTTTAGGCACTATGGGCATTCTTGCCACTGCCGCTGTCGCTTGTCTGCATAAGATCACCGTCGGTCGCAACATAGTGGAAGAGGACCAGCCGGGTTATCAACCGCCAGAGGAAGAGGCTAAGCAGGAAGAGGAGGGCGACAAATGAGCAAGCATGATAAGCAAGAAATGATAGTGCGTCACAAGCTGTTCGATCGCATCTGTCATTGGTTCATTGTAGCGGTTGGCCTAGTGACCTTTCTCACTGGCTTTTCCTTCTTCTATCCGTCTTTTCAATGGTTGGGGGCGATAGCGGGTACTCCGCAATTGGCGAAGTTTATTCACCCCATTGCGGGCTTGATGATGTGCTTACCGCTGATGTTGATGTTGGTGCGTTACTACAAACACAACAAATGGGAAAAGCACGACCTCGCTTGGATGCTCGCCATTAAGGATGTGATGTTCGAGAACGAGGAGAAAATCCCGCCTATCGGCCATTACAACCCTGGGCAAAAGGTATTGTTTCGCGCCTTCGTGCTGACATCTATCACTTTAACGGTGACGGGAATCATCATGTGGCAGCCTTACTTTGCGCCCTATTTCTCAGCCACTGTGGTGGGTTGGGCCATTCTGTTGCACGCCATTTGTGCGCTCATCATGTTGATTTTTGTGATGGTGCATTTCTGGATGGCGACATGGGTGGAAGGGTCGGTTACAGGCATGCTTTACGGCAAAGTCTCTAAGGCTTGGTGCCGCAAACATCACCCTGTGATGTTGGATGAACATTCGGACGTGGAGAAACATTAATGAGTCATACAGCCGAGATACCTCTGGTCCCCGGCAGCGAATCGCCGCTGGAACTAAAACCGTTAAAAGCGGCAGATCCCCAAGCTGTGTATCACAGAAGGGCGCATCGTTTGCTGTCATTGGCCAAGGACTCGCCGCTGGCTGACTACTTTGAGCTATGCCGCCGTCTGGTCTCTATACAGGCCAAATTGGCGGAGGAGGCTGACTTCGGTCAGCTTCTCGCTTGGGGGAAAGATGAGGCGACTCCGCTTTCTCTGTTGGGCTCAGAGGCAGATAGCTATTGGCAAGGACTGCTACAGCAACTGTTAAGTGATTTGTTGCCGCAAGTGGATGAATCCATTGCTCGGGTAATGCGTTTGCTGATGCAGCAATCTCCCGAGCAGCTGAGTTCGTGGGGGCGTTCACTGCGCCAAGGACATGTCAGTGAAGTGCCGGCCCATTTTAGCTTATTCATCTGGGCGGCGATGGGCGTTTATTGGTCGCATTGGGCACCTATGGTCATCAAGCGGATGGATCAACGTAAGGTGGCACAACAGAGTATGTGCCCCGTATGTGGATGTCATCCGGTGGCTAGCGTGATTGTCGATCAGCCCCGTGCTGGTTTGCGCTACCTGCATTGCAGTTTGTGCGAAAGCGAGTGGCATTACATTCGCGCTCATTGCACTAGTTGCGGTCAGGACAAAGAGATGACGATTTGGTCGCTAGATGATGCTCAGGCTCAAGTGCGGATCGAAAGCTGCGATGAATGTCATGGCTACACCAAGATGATGTTCGTCGAAAATTCACCTTCTATGGATGTGGCGGCTGACGATCTCGCAACCTTGATGTTAGACAGTGAGCTAAATGCTAAAGGCTTTGGGGCGACAACATTGAACCCACTGCTAATGGCCCACGAAACCACGTAAGCCAAATAACTTAAGTCAATGCAGGAGGGAGTCGGGTTGCTGATCCCTTCCTGCTTTTTATCCAACAGATGACAATAACTTAAACAAGATGACACAGATGATGACCCAAGCACCCGAAACGCCACAAGCCTTGTACCGCGCTTTGCCCTCAATGGACAGCTTACTGGCGGAGCCTAGCTTTGTGGTATTGCAGCAGTCTTACGGCAAAGTGGCAGTAAAGACGGCGCTGGATAGCCAGTTAAAGGCGGCGCGAAGCTTAATTAGCAGTGAGCGCCGTTTGCCGACATGGTGTGGCGAGCTTGCGCAATTGCACGGATGTTTGGTTGATCAACTGTCTAAAGATTACAGCCACAGCTTAATACCTGTGTGGAATCTTACTGGCACCATACTGCATACCAACCTTGGCCGAGCGCAGCAGTCAGAAGCGGCAATTCGAGCCGTGACTTCTGTTATGCGCTACCCCACACCGCTGGAGTTTGAGCTTGCGGCAGGTGAACGTGGTCACAGGGATAATGCAATCAGTGCTTTAATCCAGCGCTTAACGGGTGCCGAGGCTTGTTGTGTCGTGAATAACAATGCCGCAGCTGTGTTGTTGATGCTGTCAGCTGTGGCTGCGGGGAAAGAAGTCATAGTCTCCCGTGGTGAGTTGGTGGAAATTGGCGGCGCGTTTCGCATCCCTGACATTATGCGCCAAGCGGGCTGCACCTTAGTCGAGGTCGGCAGCACCAATCGGACTCACCTTAAGGATTATGAGCAGGCGATTACTGAAAATACCGCTGCAATAATGAAGGTCCACACCAGTAATTATCATATTAGTGGTTTTACTGCCGCAGTGGAGGAGGCAAGGCTGGGTCAGTTATGCCGTGAGCGTGGCATATCACTCATTTCGGATCTTGGTAGTGGCTCACTGACGGACTTGCGCCGCTTTGGCCTAAAACAAGAACCTACGCCCCAAGCCATGCTGGCTGATGGGGTCGATTTAGTGAGTTTTTCTGGCGACAAATTATTGGGCGGTCCTCAATCGGGTTTAATTGTCGGCAAACAGTCGCTGATTAAGCAGTTACAGAGTCATCCACTAAAGCGGGCCCTACGTTGCGATAAGCTGATTTTGGCGGCGTTAGAGGCAACATTGATCCACTATCTTAACCCTGAAACCTTAGACTCTGAGCTGCCCATTATGGCGAAATTTGCCCGTAGCCAAGAGACGTTATGGCAGATTGGTCATCGCTTACAGCAAGCTTTGGTTCCACACTTTGAACCTTGTTATCGTATTGAGTTAATTGAGTGCCAAACACAGGTCGGCAGTGGCTCGCAGCCCGATACTTTTTTACCCTCAGTGGCGCTGTGTTTTATCGCACTCGATGGCGGCAGTTTAAGTTTGTTAGAACAAAAATTTAAGCAGGCGGCACGCCCTATCATAGGTCGCATGAGTAACGATCAACTCAGATTGGATTTACGCGGTCTTGATAGCGAAGCCGAGCTACTCGCCGAGCTGAGCATATTAGGTGTGCAGTTGTGATCATTGTTACTGCGGGTCATGTCGATCACGGCAAGAGTACGCTTATTCGCGCCTTGACTGGGATGAATACCGATCGTCTGCCGGAGGAGAAGCGCCGCGGTATGACGATCGATCTCGGCTATGCGTTTATGCCGCTTAGGGATGGTTCGCGCCTCGCTTTTATTGATGTGCCTGGGCATGAAAAATTTATCAACAACATGCTGGTGGGCGTGAGTCATGCTCGCCATGCCTTACTCGTGCTGGCCTGTGATGATGGCGTTATGCCGCAAACCCGCGAACATCTGCAAATCCTAGCGCTCTTACCGCTTCACAGTTTGACCTTAGTGCTGACTAAGCGGGATCTGGTTGATAGTGATATGGCTGAGCAGCTTGTCGCCGATGGACTCACTTTGCTGGCCGAGTATGGCATTGAGGCCAGTGGTTTTTTTCAGGTGTCGGCAAGTGAAACTGCCGACTTAGCATCCAATGGTGTGGCCGCACTTAAGGCGCATATTCTTGATTTAGCTGAGCAGGAGTCTAAGCGGGCTGAGGATGCCAGCAGTTTTAGGATGACGCTCGATCGCGCCTTTAGTGTGAAGGGTGCAGGTTGTGTGGTTACGGGCACAGTGATCAGCGGCAATGTGTGCGTGGGTGATAGTCTTTTTAGCTCAGGACAAAAGGCCAAGTTGAGAGTACGAGGCATCCATTGTCAAGGACAAGAAGCCCAAAGGGCCGTCACGGGGACACGTGTAGCACTCAATTTAACCGGAGTTGATAACCATAGGTCGCCCGAACGCGGCGATTGGCTAAGCAGCTTGCCTCAAGCTGAGCACTGTAGCCGCCCTGTGGTGCATATTCGAGTCTTCGATTTATTAGGTATTGAGCCCTTGGTACATTGGCAAAATGTTCACTGTCACCATGGCGCTGATCATACTTTAGGGCGAGTTTCGCTGTTAAATGAAGCTGATGCTGAAGGTTTTTATCTGGCGGAGTTGGTACTCGAAAAGCCGCTACTTTTATGCCAAGACGATCCCATCGTCCTGCGCCACATTGGCGGTAAACAGACTTTAGGTGCAGGGCGAGTGTTGGCGCTTAAAGTCCCTAATCGAAAGAAGCGCACGCCTGAACGAGTCGAGAGATTACAGCAAGTAGCGCGCTTGGATAATCCTGCCCATGTGGTGGCTTTACTCGGCCAAACTCAGGCGCTAGAACTTGCTGAAATTGCGTGGCGTTGGCAATTAACCGACGAAGGGCTCGCTATGCTCTTAGCCCAAGCGGGTTTAGTCCGTCTTGGCGAGTTTGCCGTGGCCACACGTTTACTTGAGCAAGAAAAACAACAGTTTATTACTTTACTGAGCGAGTTCCATCAACAACATCCCGACCAACTTGGGCTTGGGCGTAATCGCCTGCAACGTATGAGTCACAGCGCTTTACCGACCGAATTAGCCAATAAAGTGCTCGATACGCTGTGCCAAGAGGGGCGCATGGTATTGCGTGGCGCCTTGTTGCAATTGGCCGAGCACAGAATCGTGCTTGATGCTGAGGCGCAGCATTGTTGGCAGGCACTTGCGCCTTGGCTTGCCGAGCAAACCGCCCCCGTGTGGGTCACCGAAATGGGCGAATATCTTGCGCTAGAACCTGCCAAATTAAGACTGCTGTGTTTCCAGCTGGTGCAACAGGGGTTTATTACTGCAGTGGTGAAAGATAGGTATTTACTCAGTGAACAACTTTGCTATTACGCCAGCTTAGTGCGTACTCATATTGCAGAACATGGCAAGCTCGAAACCGCTGAATTTAAAGACTTAATAGGGCTTGGTCGCAAGGTCAGCATTCAGCTGCTTGAGTTTTTTGACAGAAGTGGTTTTACCCGTAGGAAATATCGCAGTAATAGCCGGGAAATTCGGGATGCGGAGCTTTTTTTCTCCCATGAGAATTGGCGGAAGAACATAGGAGTCGAACCTACCCAGGACTGCTGGCAGCCCTACCCGGATTTGAAATCCGGACGCCTCACCGGAGACGACGTTCTTCCAATAGAAGACCTAGGGATAGTAGAAAATGCTTAAAGATGATTCAAGGCGATTCGTATTTAGTTTATGGGTTTGTGATACAAGTCTTAAATTGTCATCAAACTAATTGTTTTTATTTGATTTTATAGTTTTAGAGGGGCGCTATGGAATATCATTCACATGAAGTGGTTACTGAAAGAAACGTGGTCCTGTTAGAGCATGGACACGCATCTGAGCTGACTGATTACTTAGCCAATGAAGTACGGGTTGCGCTGGTCTATAACGGCATCGCCCATACCGTTATGTTGGCGAGCCCCCAAGATTTAGAAGCCTTTGCCATAGGTTTTACGCTGTCTGAACGTATCGTTTCCCATATCAATGACATCAAAGGGATTGAACTCGAATTCACCCGTGAAGGCGTGTTGATCCAAGTGGAAATCAACCAACGATGCTTTATGGAACTCAAACAACAAAGGCGCAATATGGCGGGGCGCACGGGTTGTGGACTGTGCGGTGTCGCCCAGCTCGAAGAGGCGGTCAAGCCTGTTATGCGGGTAGACTCAGATGCCAGATTCGACATCAGCCACTTACAGGTAGCACTTGGGCAAATTAAAGATAATCAACAGCATTTTAAGCTGACGGGTGCGACGCATGCGGCTATGGGGCTTAACCATCAAGGACAGATCATCGCCGCCTATGAAGATATAGGCAGGCATATCGCACTCGACAAGTTAATTGGCGCCTTTTCCGCTCACAATGTGGAACGTCCAGTGGCCGTATTACTTACCAGCCGTGCGAGTTTTGAAATGGTGCAAAAGGCCGCCAGTGCCAATATCCAAATTCTGTTTGCTATGTCCGCCGTGACTTCACTGGCGTTGGAATTAGCAGAGAAAAGCAATATCACGCTTATCGGTTTTTGCCGCAATGGCAGGGCAACGCTTTACACCCACGGTTATCGACTGTTGGGACAGCACTGCGCTAGCTTAGCCAAAGCCATTTAAGCGTGAGGCCACTTAAGATTGGCGTTGTATATAACGACCTCAATCGCTGTGGCATAAAGGTTATAAGCGCATTGTGCTTATTTTTTTATAGTCTCGTTCATTAGGGTAATGACATGACATGGCAACAATTTAAACAAGAATACTTAGTTCGCTTTTGGGCTCCTATGCCAGCTGTGATCGCTGCGGGTATCCTTTCTACTTACTATTTTGGTCTGACTGGAACCTTCTGGGCTGTTACTGGGGAATTCACTCGTTGGGGCGGGCACTTGATGCAGTTTTTTGGCGCAGAGCCTGAAACCTGGGGATATTTCAAAGTGATAGGTCTGCAGGGGTCTCCTTTAGATCGTATCGACGGCATGATGATTATCGGTATGTTTGGTGGTTGTATTGCTGCTGCGCTCTGGGCCAATAACGTCAAACTGCGTATGCCACAGAGCCGTATCCGTATTGCTCAGGCGCTGATCGGCGGCATTATTGCTGGCTTTGGTGCCCGCTTGGCTATGGGCTGTAACTTAGCGGCCTTCTTCACGGGTATTCCGCAGTTCTCTTTGCATGCTTGGTTCTTTGCTCTGGCGACCGCTGCGGGCTCTTACTTTGGTGCAAGATTTACCTTACTGCCTATGTTCCGTATTCCGGTTAAGCTGAAGAAGGTAAATAAAGCGACTTCGATTAAGCAGGATGAAAATCAGGCTCGACGTCGTTTCCGCATCGGTATGTTAGTGTTTGTCGGCATCGTCGGTTGGGGCTTATTGACTATGTTCAATGCGCCTAAACTCGGCATAGCTATGCTTTGCGGAGTCGGATTTGGTCTGTTAATTGAACGGGCGCAAATCTGCTTTACCTCAGCGTTCCGCGATATGTGGATTACTGGCCGTACTCATATGGCGAAGGCCATTATCTTAGGTATGGCGGTCAGTGCGATAGGCATTTACAGCTATGTGCAACTGGGTGTGCCACCTAAAATTATGTGGGCGGGTCCTAATGCGGTGATCGGCGGCTTGCTGTTTGGTTTTGGTATTGTGCTTGCTGGTGGTTGTGAAACGGGCTGGATGTACCGTGCAGTTGAAGGCCAAGTGCATTTCTGGTGGGTCGGTTTAGGCAACGTGTTAGGTTCGACTTTACTGGCGTATTACTGGGATGACTTAGCTCAACCCTTAGCGACCAACTGGGACAAAGTGAATTTATTGACCAGCTTTGGTGATCGAGGCGGCTTATTGCTGACCTATGTGCTGCTGGGACTTTCCTTTGGCGCTATGTTGCTATGGGAAAAACGTTTCTTTGCTCGCAAGGCGAAGCGTGATGAACAATTGCTAGCGGAGGCGGCGTAATGAATCAATATGTTCCAGATTATCAACTCGATATGGTCGGTGAACCTTGCCCTTATCCTGCAGTGGCCACCCTTGAAGCTATGCCTAAGTTAAAGCCAGGTGAAATTCTCGAGGTGATCAGTGACTGTCCGCAGTCCATCAACAATATTCCCCTCGACGCTAAAAATCATGGTTATAAAGTGTTGGAAATTGAGCAGAATGGACCAACGATCCGTTATCTGATCCAGCGTTAGTGACTGATTTTCGCTAAGGTGTAAAACTGAAAAAGGCACAAAATTATGTTTGTGCCTTTTTACTATCTCCTTTGGAAATACAATTAACTTCCTCAGGAAAGCTTCTTTCAGTCGATGTCTAATTGAGTTAACTCAGGTTTAGCCTTGGCAATCTACTGGCAGATATTCGTCGGGAATGTCGCTACTGCTGCATACCCAAGTGATATTGCTTTGCTCATCGACTTGTGGCGATAGATAGACTTCGTAGCCTTGATAATCACCGAGGGCTTCGCCTCCAACAAAGCCGACTATGCCATCTGTGTACAAATTAAATGTCGCAATCTGCTCGTCCGTCAGCTTTAGGGCATCTAATTCAGTTGGCCATGCTTGGTTGGTTTGATAATAGCCAACTAAGATGTCGGTGATCGGCGTCAGTGCTTGGTGCTGCTGATCGATACTGGCAATGGCGAGTGTTGCGTCAGGCACAGTCATAGCTGCTGGTGTCGCTTCGGTTGCGGCCGGGGCTTCATAACTCGTGGCTGCAGTCATCTTTTCTTGATATTGCTTGTACGCTGGCAACGCAACTGCGGCGGCAACACCAATAATCGCAGCAAAAATAATGAGTCCACCGCCAGCTGTGCCCATGCGTGGCATAAAGAGGCTAAATACAAATGCCCATGCACTGCGTGATGGCGGCGTGTATTCCGTTCCCTGCGCTTTGGCGTCGACTCTTGCCATACGTTTAGTCAGCCAAGGATAGCTGCCGTTAAGCTCGTGCAGTGACATCCAAAAACCACTCGATTCGCGAGCTTGGCGGATATATTGATCCACATTCATACGTTTCCATTGCTCTACGCCAGCGGCTAATACGGCGACGGCATTGGTGGCAGAACGTAAGCTGTTGCAGCAACGTAACCCGTGGAGATCGCAGGTATATTCACAGGCGCGAGAATAAGCCGCACCCACTAAAGGCAACCAAGTGGCGAAGACTAAAAAGGGGGCCTTGCCAATATGATTGCGGCGAATATGGCCTAATTCGTGGCCGATATAGAAGTTAAGTGCATCTTTATCGGACTCAAGCGCATCCACAATCGATGAAAATAGTACTATATAGTTACGGCCGAGGAAGCGGGTTGCCAGCGCGTTGAGCATGCCATCGGCGGCCAACAAGTAAGCTCGTGGTGGCTCTTTCATTTCTAAACGATCGCAGCATGCTAAATATTGTTTATGTAATTCAGGGAATTGCTCAGCATTGATTTCGACAGCTGTACCTTTTAGGTAGCTAATAAAAGCGGAGTGGCTGAAAAGATAAATAATGAAAAACATCAACACATAGAGCAGGGCCATGCCCAAAGTACCTATGATTAATGCTGCCCAGACGATCCCTGAGATAATCGCTAACAGAGTGAACAGCGATTTTTCCTTCGAATAGACAATATCTGTCATTTGGATGGGATTATCTTTCCCTGCGGTTGGGGTAATAACTTGTTCCATGTTGATCCTTTTCCTAGTGAAGTGACAAGCGGCGTGAAAATAGTCAATTTCGCTCGAAAATTCAACCACTATCACGCGTAATGCCAAAATTAGCCGTTTTGCGATTCGGTTAAATCAGCATTAAGCGCAACTGGGGTAGGAAGCTTGTGTCAGGATCTTGCGGCTTTTAGCCTGTTGGCAAATCCCGATGGCTGTTTTCAAAGGCGGTTAAGGTGCCTTTATCGCCGTGCCCGCTTTAATGGCTGCTTGAGCGAGGCTGACAATTTGCTTCTCTGCGGTATTGGAAAGGCTTTCATGAACTTTGCTTAGCTCATCCATTTGTTGACTAAGCATATCCATTGATGTGCTCAGGTTGTCGATCTCCATCTGTATCTGAGGATCTTCTCTATCGGCCGCAATATTGAGTAGAAGTTGCTGAGTTTGGCTGTTGATCGCGTCTCCTTTTTGCTGCATCTGTTCGCCTAACACTTTCATTTTCTCACCCTGTTGCTGCATTGGCATGATGGCGGTTTTTATTTGCGCCACTATGGCGGGATCTGTGATGACATAAGGCGTGCCTGCTTTTTTCACCCATAGGTAATCGATATGATAAGGATTACCTTGGCGCTCCAGCTGTTCCCAATCGTCACTGTGGCCAGCACCGATAGTCATCTCGCCATCCTGTTTAACATAAATCCATGAAAAATCTGGCTGACTTTGGCTGAGCACTGATTGGCTGAAGACTGATATACCTCCATCGGCACTGTTGCTCGTTGCTTGGCCCTTTGTTTGAGTCGCCCAAATGGCGGTTGGGATCAGAGCGAGAATAAACAGACCGCCCATAAGTTGAGTGCGCGTATTCATAGTGTTTCCTTGTATGGCTGTGTGGATCGGCGGGTTATGCCAGAGTTGCATTAACCTGCCACAGCAGAATCTGTGCCATAAAAATTTAATCTTAATTATCAATGTAATAATTGTTTAAGTGTCCGCTCACTGTTGCCGAGAAGGCGTAAGAGTGTCCGCTAATGTGTCCGTTCAGGCTGGTGGACACGGGTGGCTAAGTCGACGTATTTCTTTCGCCGAGTTTGATTGTTAGCACTAGAATGAAGTGCTCGTGAGGTTGAGATAAAGGTGAATTTAATCACGATTTGTACACTATTTTGAGTGCAATTTAAGGCATGACTCTTTGAGTCAGGTTAGGTTAAGCCACTATAGGCTTAGCTATGGCAAGCGTTGAAGTAACCTACAGAAAATAAAGGAATTGAGATGGCAAAATTAATCAATAAGATGTTGAATAATCGGCTGGGCTTCACTCGGTATTGCGCCGCCGTTGTGATGGTGAGTTCGTTAAGTACAGGAGCGATGGCGGCCGATGTGCCGTCCTTTGACTGCACAAAAGTCGATGCGGGCAGTATCGAAGCTATGGTCTGTAAGGATGCGGGACTGAGCCAACTCGATAATAACTTAGCTGAGGTTTACAGCCAAGCGCTTGAAAAAGCAAAGAATGAACAGCCACCGACTTTAAAAGCGATGCAGCGTGGTTGGGTGAAAGGCCGTAATGAGTGTTGGAAGAGCGACGATAAACACGCGTGTATTGAAGCTAGCTATCAAACCCGTATTGCCGAACTGCAGGCGCAGTACCGTTTAGTCGATATGACCGGCCCAGTGTTTTATGCCTGTAACGGCAATCCTGCTAATGAAGTCGTGGTGACTTATTTTAAAACCGAACCCGCGACTCTAATTGCTGAACATGGTGATAGTACTTCATTGATGTTTGTACAACCCAGCGGCAGTGGTGCTAAGTATCAAGGCCGCAATGAAAGTCTTTGGGAACATCAAGGCGAAGCGAAAGTGGTTTGGGGCTTCGAAGCGCCAGAAATGACGTGTAAACTGGTGACCAAATAGCGTTAATTGAAGGTCGCCATGAAACAAGCCCTTGTGCATATCGCCTTGGTCGTACGAGATTACGATGAGGCGATTGATTTTTATGTGAATAAATTAAAATTCGAATTAGTCGAGGACACTTATCAAGCCGAGCAAGATAAGCGCTGGGTCGTCGTTGCGCCGCCGGGTTCTAAGGGCGCGTCTATTTTGTTAGCCCGGGCTTCTAAGCCAGAGCAGTTCGATTTTATCGGTAATCAGGCGGGTGGTCGGGTGTTCTTATTCTTGAATACTGATGACTTTTGGCGTGATTATCGCCGCATGGTCGCCGATGGCGTCGAATTTGCGCGCGAGCCACAAGAGCAAGACTACGGCACAGTCGCTGTGTTTAAAGATCTCTACGGTAATTTATGGGATTTACTGCAATTGAATGATGGGCATCCTATGGCGTACAGGTTAGCGTATCAGGAGTGAGGATTAGAAATTTACCCCTAATCCTAAGGTGTAACTGGTATCTCTGGTATTACCTATCACTGTTTGAGTGTCGTTGAAGTCATATTTAAATGACAAATAAATCCGCTGAGTGAGCAGATATTTAAGTCCAACGGTCGAGTTTGAGATGTAATTAATCGCAGGCATATCTGGAAAGGCAATTTGCAGATCGGCATAGGATTCTAAAATGCCACCAAACCAATACTGTTTATAGTTAAGCGATGCAGCCCAAGCATTTAGTTCTAGTTTAAAGGAATGTTGTTCAAGCCAAAAATGGTTGTAGGTTACGACGAAGTCTAATTTATCGCGCCCAGCACCCCAAAAACGATAACCAGGACCGAGCGCTGCATAATAGCTATGGCTATCTATGCCGAGCTTATCTTGTTGATAAAAATCTTCCTGACGTAACAGCCAATGTTCATTGATCAGATAGTCTAATGAATAATGTCCTTCGAAGGTGTTGCGCTTTGTCACGTCATCTTCGGTCTCGTAGCGCACTTCGGTCTTGAGATTGTGTCGCCATTGCAGGGTTTCGGCTGTGATATCCCCTTTGACATGCAGCTTCTGGCTATTTTTCTTGTTTTTAGTGACATCGAGTGAGCTTTCTACGCTGCCGTTATAATTCCAAGCTGAGTCCGCATAGTGTTGGGAAAATCTCAGCTCATCCACTGGGGTTTGTTGGTGATTAATCAATACCATGCCATGTTGTTCAGAAGCCTGAATACTGATGTCCTGTATTGAGCGGCCGACACTCCAAGGTTTAGGCGTTGAGACTTCAAAACTGAGAATAGCACTGCGCTGAATGGCAATCTTAGGGGCATAGCTGGTTTTGATGATTAAATCTTGATCGTTGAATTCTTTGATTTCACCGCTAATTTTATCGCCATTGAGCAAGAAAACCGTATCGGCTAAGCACAAGGGGCTGAGACTACAGAGCGTAGCAGCAAGGTACAATGCTGGATTGGATATCAGATTATGCATCTTGCTTGTGAGCTCAAAATAAGGGCGTTAGTGTGTGGTTATTCTACCTGAACTCTAGCACTACAAAGCAATAATATTCGCTAAAAGTACTGATAAGGGATTGAAAACTGGCTCGCAATCCCTTACTTATTCGCCGTGTTAGCGGGAGTTAATTTCGGTGATTTTGAGGTTAATTAACTGCTTCTGCCATTGAGCTTTATCTATGCCTTCGACCCAAATCACGCTGCTTTTGCGCTGCTCGCCCTTACGCAGATAGGTTTCAGGCATGCGGAAAATGGCTTCCCACACTTCGAGTTTTTCGGTTTTGAGCAAAGTGCCATTATCGGCGAAGAAGCTGGCTTCCACTTCGACTAAGGTCACGTGTTCATTGCTGGTACTGGCAAGATCGAAACTGAGTCCAAGTTTATCGCCTTTCCATTGACCTTCGGCAAAGCTGACTTTGATGCCATCTTTACCGGTTGAGCCTAGTAGCTCAGGTTGGGCAATTGCGGTGGCAGTCAAGGTGGTGACTGGCGCAGCCACAGTTTGAGTGGCGAGAGCAACACTTGGGACGCTGGCAATATTGCTTGAGGTGCTCACTGCCGTCGCTGCTAAGGCATTGGGCGCAGCTTTGGTTTCTGTGATCACATATTCCCAGGTAAAGTCGTCTTTAAGTCTGACTTGGGCGCCATTTTCTAGGGTGATTTGAGCTACGTCGGCGGCCATAACCGATGATGTTAGCAATAACAATGAGCTAAGTGCGCTAAGGCGTAATGAAGTTTGCATTAATTTTCTGGCTCCGATATCCGTCTGAGTCAATATTCTGCCCTAACATAAACAATTTGCCGCATATTAGTCACTGTCTAAGTCGTGTTTTGACGAATAAAGTGGATTTAGCTGGAAGATACTGACTCGAATACAGATTGAGTTCACTGAGTGTGGCACTATATTGATAGCGCGGTGACAGGACACTGCCAGATAGCGGCAATGATCAATGAATTAAGCGAGAGGAGATTAATATGCAGATTAAACATGGAATTTATGGCGTCGTTATTAGCTTGGCATTGAGCGCGGCAACATGGGCCGATGAAGCCCCTAAAGTGGGCTGCGCGGCTAAGTTAGAAGCTATCAGTGCCGAACTTGCCCAAGCGAAAGCGGCGGGCAATAAACATAAAGTCGATGGGCTCGAAAAGGCCTATTACGAAGTCTCAACCCATTGTGATGACGACAGTTTATATGCCGAGCGCGTTGCCAAAGTTTCGGCCTTAGAAGAAAAACTGACTGAGCGCCAAAACGAGTTGGCTAAAGCCATAGAAGAAGGTCGTTCTATGGATAAAATCAATAAGAAACGCAATAAGGTCGCCGAAGCGGAATTGGAGCTTGCTAAGGCGCGAGCCGAGCTAACCCAATAAATCGCTCAAGCCCAGTCGCTAGGAGCTTAGCCCGCATAACCTATGCTCAAAACTGTGGATTCTGTACTGGAGTTATAGCCCGCGCTCAGGATGAAATGTTCTGTAATGCGGTATCTTAGCCCTGCTTCTGCGCCCCAACGGGTGGTTTTATCTTCTTCCCATGTGGGTTTGCCATCGACAATTTTGGGTGAAATGTTGTTCGTGTAAGACACAGTGCTGATATAGCCGCTGCCGCCCGCATAAAAGCTTAAGTCGGCAGTGAGGCTGTAGCTTAACCCTAAGCGAAAGAGGCTTTCTTGGCGATAAGACAGCGCGGGTTCTGGCACTATTACATCGGTTTCTCGGCTGCGGGCATAGCCTAGGTAATAGCCCCATTGCTGGTAATTATTATCGAAATGATAGGGTGCTAATGTAATGCCGTAGAGATTGTCTGCGGCGGGAACGTAATCCAGCATGAGGGCGATATAAGAAGGATTGGCGGTTTTCTCTGATTTATCTGACATCACCAATGGCTTCGAAGTGCTATATTCGGCAGCTTGAAGTGGTTGGCTTAGGCTTAATAGTCCAAAGCTAAGACAGGCTAGCATAGATGAATAACTACTCATGGGATGATTTCCTTGGGAACAGGTTGAAGATTGTTCATTTTCCTTTGTGCTCGGTCAATTCAAAATGCTAAAATCTCTTTTAAAATAAGTGTTTGTAAATTTATTGTGTTTGGGTGTCAGTATTTTCTAGGGTGACATTAACCGAGGTCAACTCGGCGGTTACCTTTGTATGGGTTTGTTGCGCTAGCGCCTACGTTGGAGTGGAATTGTGGATCAGGAAATAAGGTTCGAAATTTCGGCATGGTTAGCGGGCTTTGGGATTGATAGCCAACCCTCGGACGGCATATCAACCAGCATCATGATCATTGCCTGCTTACTGCTCGCGGGCATCGCTTATTTCATTGTTCGACGCGTAGTCATACGTGCGGTCAACATGGTGATCCTCCGCTCCAAAGTCACTTGGGACGATGTGTTCATGCGTTACAAGGTGCTCGAAAAGCTGGCCATGCTAGTGCCTGCAATCGTGCTCAATCTATTAGTGCCCATCGCCTTAACCGAACATCAAATCTTAAGTAATTTAGTCGATCGGCTACTGAGTATTTGGCTGGTGGTGCTGATGATCCGCGCTATTTATGCGGGTTTAGATGCGGTCGACGAAATTTCAGATGTGAATCTGGTGAGCCGGCGTTTACCCGTTAAAAGCTTTGTGCAGTTAACTAAGCTGTTTTTATTCTTTGTCGGTATTATCGTTTCTATCTCAATCTTGGCCGATCAGTCGCCTGTGTACTTCCTCAGCGGCTTAGGTGTGGCGACGGGTTTTGTGATGTTAGTGTTCCGCGACACTATCTTAGGTTTCGTGGCGGGCATTCAGTTAGCGGCTAACCGTATGGTGAGCAAGGGCGACTGGATCCAGATGGACAAATACGGCGCCGACGGTGCGGTAGAAGAAGTCTCGCTGACCACGGTCAAAGTGCGCAACTGGGACAAAACCATCACTATGATCCCAGCCTATGCCTTAGTGTCGGATGCGTTTCGCAACTGGCGGGGTATGTCTGAGTCGGGTGGTCGTAGGATCAAACGCGCCATCAATATCGATATCAACAGCATTAAGTTTTTGAGCGAGGAAGAGCGCGAGCGTCTGAGTAAGATCAATTGCTTAAAAGAATATTTCCCCGCCAAAATCAACGAAATCCGTGAGTCGAATGCCAGAGTGTCTGACCTCGACATGAAAGTAAACGGTCGCCATTTGACCAACGTCGGTACCTTCCGTGCTTACCTGCAGGAATACCTACAGCGTCACGATAAAATCCATAAGGAAATGACCTTAATGGTGCGCCAATTAGCGCCGACCACTGAGGGATTGCCGATCGAGGTTTATATCTTCACCAACGATACTCGCTGGGCATTTTATGAAGCGATACAAGCGGATATCTTCGACCATATTTTTGCGATATTGCCTGAGTTTGGTCTGCAGGCATTCCAAGCGCCAACGGGCAACGATATTCGTAGCTTAAAGTCGGTTAAGGTTGACTAGTGTTAATGACTGTCGACGATTATTGATTTAATTTCGAGAGATAAGAATCGCGGCTAAGGTAAGAAAGACACCGCCGCTGGTTCTGTCAAACCAATGCAGCTTGTTGCTCGCCTTTAGGCTGGGCGCAAGCACATTGGCCATGCTGGCGTAAATCATCACAAAGCTAAAATCCACCACGGCCCAAGTGGCTGCTAGTATCATAAGTTGCGGTAATTGCGGCGCGGCAAGATCGATAAATTGCGGAAACAGCGCGGCAAAAAACAGTAAATCCTTTGGATTACTGATCCCCACCAGAAACGCCTGCTGATAAAGTTGCCTTGGCGTGCCTTTGCCTTTTAATTGGCTAACCTCAAGGGTTTGACCTTGGTTTTTGGTCAACAGCAGCTTGATCCCTAAAAATACTAAATAGGCCGCACCGCACCATTTTAGCAGGGTAAAACCGTATTCTGAGGCGCTGATAATGGCGCCTAATCCCGCTGCTGACGCCATCATCAAGACTAAAGCGGCACTCACACTGCCAAGGCCGGTGGCCATGCTGCGCATCTTGCCGAAATGGATCCCGTGGGACATAGACAACATAGCGATGGGGCCTGGTGAGATCCCAATCAATACAATGGCGAAGAGATAGAGTAACCAAGTTTCGGGTTGCATGTGTGCCGCTCTAGATGGGGGAAATATTAATGCTTCGCGCTCGCCACAAAATCAGGGATCACCTTAGGATGGCGACCTTTAATGGTGCGATAAAAGCTTAAAATCTCGTGCATGTCGGTCGCGATATCACCACTGGGTTGCAGTGGCGTGCGGATAACGACAGTGCGGCGGCTAAAGTCTAAGCCAACGGGAGTGATAGGCACGTGAGCCTTGCAGGCAATATGGTAGAAGCCGCACTTCCAGCGTGTTACTGGGCTGCGAGTACCTTCGGGCGCCAACGCTAGCTTATAATCGGTTTTCGACTCAAACAGCTGCACAGCGGCATCGACCAAGTTGTTGTTTTTGCGTCTATCCACTGGGCTGCCGCCAATCGCGCGGAAGAACCATCCCCAAGGCGGGATAAAGAGTTGGTGTTTACCGAGAAAGTGAATTCGAGTGCCAAGCGCCCCACGAGCTAAGATACCCACGATGAAGTCCCAGTTACTGGTATGCGGTGCGACTATGATGATGTATTTAGCGCAGTCGGGCAGTTGGCCTTCTATCTGCCAGCCTGAAAGCTTAAGTAACCAACGGCAAAGTGGTGTAAACATAGTTCGGTGCCCAGAGTCGAAAAATTAATAATATCATCAGCTTTTTTAGATTTTAAATTGGCTTAAGCAAGATAGTCTGTGCTGAATTCGCTATAGTACAGTTTAACCTGATTGTGGGGGAACAATACTATGTTGAAAAATGGACTATTAAACAAGGTTAGCTGGCAGCGTGTTTTTGCGGCACTTTTACTCTTAGTGACTTGCCATGTGAGTGCGGTGCCGACGGATGAAATTACGCAGATGCTCAAAGGCCAAGAAGACGCCTGGAATCGCGGCGATCTCGATGCTTATATGCAAGGTTACTGGAAGAGCGAGCAACTGCGTTTTGTCTCTAACGGTAAGTTCCGTTTTGGTTGGGAGGATACGCTCGCTGCCTATAAGAAAAATTATCCCAATAAAGAAGCACTTGGTGAGCTGAAGTTCACGATCAAAGAAATCAAAATGCTCAGTAATTACGCCGCTATGGTCGTTGGGCGCTGGGATTTACGGCGGATAAAAGATGCCCCAACGGGCGTGTTTACCCTGTTGATTGAGAAAATTGACGATCGCTGGGTGATCACTATGGATCACAGTTCGGACTAAGCCACGGCTTTAATTGAACAACAGCGCTGACTAAAGTGCGCCGTTTCAATACAAGGCTGCACAGTTGTTCAGTTTAGATTTGCGTTAATAAACAGTCTGGCTTAGCGGGTGGCTCGGCGGGCAGGGTTAAATGGAAATCCTGCTGTCCGAGCTGTCTACCTTTGCCGTCGTATCTCACTTCGGTGAAATGCATGCCGCCCTCAGCATCTTCAAGCAGAATACTGGTGGAGCGCGTGCCATAGTCGGGGTGACGAATATAAATTGCGGCTAAGCGCCGTTCCCACTCGATCCCCACTCCCGTGTTAGGTAATTCATTATCTTGTGGTTGGGAGTCGTCTTTCATCAGCTGTAACAGCGCTTGCACTTCTAGGTTGTCGGACTGATTGATCACGGCTTCTAGCGCTTGCTGTCCCTTCGCCATTTTCGGCCAAATATCATCGAGCGCCCCATTGCTAATGGCATGAAACCCATCGGCGAGTTTGACTGTGTCTTTATTGATACTGTTAAAACAATATAAGTCAGTGCCTTGGCCGAAGACGAGATTGAATGGCTGATAATTGTCACTGTGCTCAATGAGCCAATTTGGGCAAATGAGCGAGCCTGAACTTAGGGTCATAGTGATGAGTTCACCGCGGCTACGCATGGCTTCTGGGTTCTTTTGCGGTACGCGCAGATTCGTCACAGCGGCGACTTGTCCCTGTTTATTCACCCCAAACCAAGTGCCGCCCGCCTGCAGATCTTTACCTGCAAGTATATTTTCTTCTGGTGACCAAAAGTGCGCTGGCGCGGTCGGTCTATGGTGAAACTCATCGCGGTTGGCACAGATGATTAAGGGATACTTAGGATGGGCATTAATGGCGACAAAGAGTATGCACATAGCAGGCTAAATTAATCCAAAGGTAGATTAACTTAGCCTGTCTGCTCATAAATGAACAGTGTTAGTGGTGCGAGTGCTTATCCGCTTTTACTTTATCGTGTGTATGTTCATCGGAATGATCATGGGCACTAGGCGTTTCAGCAGTGCTCTGCACAGATGTGTGTGAAGCGCTAGGTTCATGGCTATGTGCATGTTCGTGGCTGTGTCCATGAGCTGCTGGTTTACTCAGCATTAAGCGGCGTAGAAATTTACGTGGGCCTAAGCGTAGTAAGCTGGCAGCAAATAATCCCGACAGGAACAGCAGCGCGATTAAGTTCACCACTTCCGGTAAAGTGAACAGCGAGAACCACAGTGGTAGACGCAGTGCAGCGACTAAACTCAGCGCAATAATACTCAGTAATATTGCCCTTTGTGGCCATGTCATTAACTTAAGCTGCGCTATGTTAAGCACAGGTGCGGCAATCAGCGGCATGATAATCGCGATAGGGCTCCAGCCACTATAGGCCAGTGCAAGAGCCAGCACCGCTGCGCCTAAGTTGCAGAAACGCATAGGTAAAAATACGAGCAACAGCACTATAACCTGCAGCAGCGGATTGCTTAATGGCACTGAAGGATGGCCGATTAAATTCACTAGTACTAGGCTGAGTAAAATCCACGGTGCGCTGCGATCGACTAAATGTGCAAAGCCGAAACGTAGGGAATTACTGGGTAATTTAGTATGCGGATCGGTAATAGTAACGTTAGCGTGGCTTAAATAAGCGCTAAGAATAAAAACCACAAGTAACTGGAATAACGCTAGCCAAGGACCGAGTAACAGGGCGGTGATGACGAGAGCTTCTGGCCCCGCTAAGCGTTGGAACCAGCGACGGGCTAAGCTGCTATCCTGCGGTGTCAGACCTAATTTAAAGCGCAGCGACGCCGCACCATAGCTCAGCAGTAATATGGGCGCGAGTGTCAGTAGCCAAGTCATTAATTGTTCTGTGCTGTGATCATGGTGGGCATGTTCGTGGCCGCCAGAATCCATCAAGAGCAGCATCACGAGCAGGCCTATGCCGAGCAAACTACCGATACCCGCTTGGTACTCGTATTTGCCTTGCTTGTCGGTATCGTGCTGGCCGTGGGGTTGATGTAGCACCACATGCAAAATAGAACCGGTAACAAAGGCTTGTAAATAAACGGTATTTTCTAAACTGAGCTGAGTGATTAATTGCTCGCCTGCAAAGTAACCAACACCCGTAAGTAGCATCATAGCGACCAGCACTAAGCTAGCCCAGCGGGTGCCGACTTGAGGTTTGAGTAACCACCAAATGGCGAGGCCGACGGGGAGGCGATGCATAATGACCCCGAGGGCAAGTAAGATCGAACTGCCGTCTTGCTGCGCCAATACCATAGCGCCACCGTCGGTGATGGTGTGCAGTAGCAAGCCACCGATCCCTAGGGACAAGGTGAGATTATGGGTGATTTCTGAATAACGGTGGAATAGGCGTTCGCTGGCGGTCGGTCCCCAGATACCTAGGAATACAAAAACAACGGCTAACAAACCGCCATGCTGCAGTAGCTCTGGCAAGATGTGGATCAGCACTAATCCGCCTAATGAGACGAATATAAAGCCATCGAGCCCCTTCTGTAATCCGCTGCCCGATGAAAAGTAGCGATAAAACAGTGGCCCAATTAAGAGTGCGATACAGCTAGCAAGAAGATAGAGCATGGATTCCACGGAAATTTGCTGAAAAAACGCCATAGTATACCAGCTGTAAGGCAAAGTGAGCAGTGACTTGCTGGCAATTTACCAAACTGACATTTTGCGCTGGGGTTTTCGGGGACGAATTTTATTCACTTTGGCCATTTAGCGGAGAAATTTTCAGATCTAAGCCGAGAAAAGGTACTATACCCAAGCTACCTGAAGATACGAGTTTCAGATCGCCTAGAGTGCTTCAATTCAAGGCGCAGCAATGACAGAATGTCGGCTACCTTTTGAATTGATGCAACACGGAAGTGAAGTGCCCTAGGCGCTCCGTCCCGGCGGGTTTTAACGCACTTTATGCTGCGTTGGTTATTTTTAACTTAGTCCACTAGGTCCGCAAATAACCGCAATCGCCATGGATGGCGTGAATGCCATTTATGCAGGAGCAATTAAGGGCCTTGCCTAAAGTGCGTTACATTCCCGCTGAATCCTGCATCTTCAGGTTGTTTGGGTATATAATATGGACTTATGTTGGCTTGGGTGTTGATACGTTAAATGGATATATTTTCTGCTGCGGTAATGTTGTTTTTGATCATGGACCCCCTGGGGAATTTGCCGATTTTTGCGTCGATTCTACGGCATATCGATCCCAAGAAACGTCGTAAGGTATTGATCCGCGAGTTACTCTTCGCCTTAGTGATCATGCTGTCTTTCCTTTATGCGGGTGAGGCCATCTTAAGTTTCTTAAATCTTCGCTCTGAGTCTGTGAGTATCGCGGGCGGGATTATTCTGTTTCTTATCGCGATTAAAATGATTTTCCCCCAACCCGGCGGTGTCGTTGGATTAGCCGCGGGAGAAGAGCCATTTATCGTGCCTATGGCCATTCCCTTAATGGCGGGTCCATCGATTCTTGCCGCTTTGATTCTACTGGCGCATACCGATAGCACACGCATGGGCGATTGGACCATAGCATTAGTCTCTGCTTGGGGGGCAAGCGCAGTGATCTTAATGTTTTATAAACTCTTCACCCGTGTGCTGGGTGAAAAAGGCCTCACTGCGGTTGAGCGTTTGATGGGCATGGTATTGGTCATGATCTCAGTACAAATGTTCCTCGACGGTATTTCAAATTATCTAAAAACCGCCGGCTAAATTTATCTCGCTCGCTTAAAACGAAACCGCATTAACGCTGTTAATGCGGTTTTTTTATGGCTGGGATTTGGCTGGAATGCCGTCAGCATAGAGGTTTGCTGAATAAACGCTGACAAAAGTGTAAATCGTGCCAGCGAAGCAATGTATCAAGTTGTTACTTCAACGCAGTGCTGATAATGTCAAAAACTCTGTCTCTCTATCTCGAGGTAGATAAACCCTGAGTAGATGACTCATAAAGGCAAATTTGTCTTAATACAAAGAGACACTAGTCTTAATAAATAGAATGGCGTTGGCTGTTACAGTAAGGATGTTAAAAATGCGAAGTGCAAAAGGCGTGACAGCGTTTGGACTGATGTTGGCATTGATTAGCGGCGCGAGTATTGCGGCTCCGAATCATACGCCTGCTGATGCGGGTGTGATTAATAAAGATCGAATTTTATATTGGTTGATTAAACGTGGCGAAGTGGCCGCCGATGCCAGTGATAGCGTAAAGCAGGAAGCGATAGAAGCGTATATTCATCGTGCCACCTTGACTCAACCTAAGGCTCCGCGCATCGAAGTTGAAGCTGAACATCAGCGTCTGCAAAGAGCTAAGTCATCGCAAATGATGCGATCACCTGCACAGCGTTTACTGGTTGATGCGGATGTAAAAAAAACCGTTAAAGTACTCACGGTATTAGTGGATTTCCCTGATTTAAAACATGATAACAATGGCTTAAGTGCGGGCGATACGGCCATGTATTACCCAAGTTATCCTGCGTCCCACTACAAGGCATTACTCTTTTCGACGACTGGCTTTAATGGTCCACAGGGGCAGACGTTAGATTCGGCTTATCAATATTATCAAGCGGTTTCAGGGCAAACCTTCTCCTTTACTGGGGATGTAAAGGGTTGGTATACCGCCTCACAAAATGCCGAATACTATGGTGCTAATGATCCAGATACTCGTAGTGATTCACAGGTCGAACAATTGGTAAAAGAAGCCGTATCTCAGGCCGTCGCGAATATGTCTCCTGCAGAGTTGGCTAGCTATGATGTTGAAGACCAAAATGATTTGAACAACAACGGTAATTACAACGAGCCCGATGGCATTATCGACCATGTGATGATTTTCCACTCTAGCGTAGGCGAAGAGGTCGGCGGCGGAAAATTAGGTGCGAGTGCAATTTGGTCGCATCGATTTTTTGTCGACCAAGCAACCAATGGCTATGCGCTTCCTGGTACGGATAAAAAAGTATTCGGTTATACCATTCAGCCTATCGATGCGGCCACGGGCGTGTGTACCCACGAGTTTGGCCATGATTTAGGTCTGCCAGATGAATACGATACCACTGAAAATGATAATAAAGACGGCTCGCCTGTAGGGTTATGGTCTTTGATGTCTGGTGGAAGTTGGGCTGGCGCTATCGCTGGTGCCCAGCCGAGTGGCTTTAGTCCCTATGCGCGCTCTTACCTACAGGAAAGATATAAAGGTAAGTGGTTGAATGAACGTGAAATCAGCTTAGATAGTATTCCAAAATCTGGCATGGATGTGACCTTGAATGAGGCGGTTAATCATCAGGCGGTTAACCAGATATCCATTCCACTTCCTCCAACGCCAATCCCCTTTAAAGCACCTTATCAGGGCAGTTATCAGTATTATTCTGGTCAAGGGGATATGCTGAATAACAGCATGAATTTCAATGTTACTTTGCCCGCAGTCACGACAGATAAACTGATCCTGAGTATGAAAGCCAGTTGGAGTATTGAGCGTGACTATGACTACATGCAAGTGAAAGTGGATGGTGAGGCTATTGCGGGTAATCACACTAAGTTTAACAATGGGGTAAATAGCGCTCGCCATATCATTACAGGCGAGTCGTCTGCGATTGCTCAGGCGGAAGGCAATGATGCTTGGGTAACGTTAGAGTATGATTTATCTGCCTATTCAGGGAAAACGATTGCGGTCGAGATCAATTATGTCACCGATGACTATACCGGAGGGTCAGGGATCACTCTCGATAACATCAGCGTTAAACAAAATACCACTGATTTCTATAGTGATAATGCTGAAGTTGCGGATAAAGTGACATTCTATGGTTATAGCCGGATTAAGGACTCACGTCCTGATCCAAAATCCCAGTCTCGTTATATTATCCAATTGCGTAGCCATAACGGTGTCGATGCGGGTTTGCAAGCAAAAAGCTTTGATCCTGGCGTATTAGTCTGGTTAGAAAATTTGGGTTACAGAGACAATAACGTTTCAGAGCATGCTGGCCATGGCTTGATTGGCGTGATTGATGCTGACCAAAACTTGATTGGCACTCGCTCTACCGAGGTACAAATCCGCGATGCAGCCTTTAGCACAGTAAAACAAACTTTCTATGCAGGTGACAATAATCTCGATGCTGTTAGCCTGTTTGATGATAGTTTTGATTACAGCGCGCCGCTACAACCCCAGTCAGGCATAGTATTGCAGCAGTTAGGTTTGACGATGCAGGTACTATCGCAAGCCAGCAATAACAGCACTGCGGTTATTCGGTTAGCAAATGCAAATTCTGATGCTGTAGCGCCATTAAAAGTCAGTTTTACTAATGTGGTCAATCAATTAGTTGTAAACTTCACCAGTAATATTAATGGTGGTATGGGGGCATTAACCTACGCTTGGGATTTTGGTGATGGCACCACAAGTACAGCGGCTTCGCCAACCCATACCTACGCAAGCGCAGGCACTTATACTGTGACGCTTACAGTAACTGATGCGCAAAATACTAAGGATATTAGTAGTTTGACAGTGACTGTTACTGCACCAGTTACGCCCCCTGTCACACCTCCTGTGACTCCGCCGGCGAATGAGAGTTCTGGCGGTGGTAGTCTAGGTTGGTTGAGCCTTGCTTTCCTTGGCTTATTGACTTGGCGTCGCCAGTTAAATATTCGCTAAGTTAATCGCGTGATAGCAGATCAAAAATGCCTCTAATTTAGAGGCATTTTTATTTGTCATTAGTCATTAAAGCAAGCTTTGGCTAAAGCTCGAGTTCATAACTGACGGATAGCTTGCTCGGCCGCAGGCAGAAAGATTTCACTGACGATCATTTCGTGTTCACCGTGATGAAAATAGCGGCGGCGTCCCCAGAGTTCATGTTCGACACTCTGATCTAAACTTTCGGCTAAATGAGCGAGGCGGCTACAGGTCTCAAAACTGGCGACTTCAATTCTGCCTGGGATAAAATGATCTTGGCTAAACAGCAGTTCACCTAAGGGGCGGGTGCCCAGACCAAGGAAATCGGCTTGGCGTTCGGCCATTAACTTTTCGGGGATCAAGGTTCTGGCAAATACCCAAGGGATTGAGTCGAGGCAAAGTAACACTTCGCGGATCCATACTGAGCCTTGCTGGGGATATTCGCCATCTAAGGGCGCATGCAGTCCTTCGCCAAGGACTTTGACTTCAAATTGAGTGCAGCAAGTTTTGAGTTTTTGAGTTAGGCTGCCAGTGGCAAGTAACCATTCTTTCAAGGGCGATTTTGGGAGTTTGTTGGCGCTTTCGGCACAAAACCACTGAATAGATTCACCATAGGGGAAGCTTAAGCTAGTCACATTCATTAAATACTCGTTACAATATAGGCAAACGTCGGCAGTCTAGCATGTCGCGACGCGAGCAGATAGATTGGGAAGTTTCTTCCATTACCATTATTGTTTTAGGGAGCTGTAAGCTGCTATGAAAAAATTACTCTCAGTCTGTCTGATCCTGTTTACCACTGTTTTCAGTGTTTATGCGGCCGATGAAAAAGAAGCGGCACCTGCAGCGACGCCTACGGGTGAGTACGCCTACTATGGTTTTGAGCCCGAAATCGTCACTAACTACATTTCTAACCGCAAGAAATTAGGTTTTGTGAAGATCAGCGTCGAGTTAATGGTTAAGAGCCCAGATGATTTGGTCACAGTTGAGCGTCACGATCCGCTATTGCGCGCCGCGATTGTTGAAGTGTTAGGTAATCAATCAGAAGAAAAGATTAAGTCACTCACTGGCCGTGAAGAAATCCGTCGTGAGTGCTTCGATATGGTCAATAATCTGCTCACCAAAGAAGCGGGCAAGGCCTTGATTGTAAACTTACTCTTTACCTCTTATCTGTACGATTAATTCATTGTCTTATTCTATGGGCTGACTTATTTCGGCCCATTGCGTTACAAGAGCTGCTATGTCAAAACCTGCCGTGAAATCCGTTCAATCAGCGACTGCGAAAACCGCAACCCGTGCAGTTAACATCCGCCAAAAAGTTAAAGCACCAAAGCAGGCTAAACCTGAAGCTAAAGGCAGAGTAAGGCCAAGCAAAGACAAACCAAGAGTAGAAATCAAAAAAGCCCTGCATCCGCGTAACGCCCACTTGAATGGCTATGATTTTCCGGCGCTAATCAGTGCATTTCCTCAGCTGAAAACCTTTGTGCGGCCCACGCCCTATGGCGCGCTATCGGTAGATTTTGCCGACCCGCTGGCAGTTAAAACCCTCAATGCGGCGCTACTCAAACATCACTATGGAATTGGTTCTTGGAATATTCCTGAAGGAGCGTTATGTCCTCCGATCCCCGGTCGCGTCGATTATGTGCATTACGTGGCAGATCTGTTAGCCGAAGGCGATAAGTCATGTGCCATGGATAAGGCGAGGGTGCTGGATATTGGCACAGGCGCGAACGGCATTTATCCCATATTGGGCTGCCAAGTGTACGGCTGGCAATATGTTGCCTCGGATATCAATGCCCATTCACTCACCAATGTGCAGAGTATTATCGAGCAAAACCCAGTGCTGCAAGGGCGGATAAGCTTAAGATTACAGCCAGATGACAAGGCGGTATTCAAGGGAGTTATTCAAGCCGAAGAACGTTTTGAGTTAACCCTGTGTAATCCTCCGTTTCATGCCTCTATGGCCGAAGCGAGCGAAGGTACAAAGCGTAAGGTGAATAATCTACAGCTTAATCGGGGGAGCTCAGTAAAGGCTGCACCTAAGCTGAATTTCGGCGGCCAAGCGGCAGAGCTTTGGTGCCAAGGCGGAGAGCGACAGTTTTTGGCGACCATGATCCGTGAGAGTCAGATGTTTGCAGACCAATGCCTGTGGTTCACTAGCCTAGTGTCGAAGCAAGAAAACCTGAAACCTTGTTATCAAGCCTTAGCACAGTTGAACGTCGATACAGTGAAGACGATTGAAATGCAGCAAGGCAATAAAATTACGCGGGTCTTAGCGTGGAGCTTTCAGTCGGCCGCTAAGCGCAAACTCTGGCGCGCTGAACACTTAGCCAATTAAGATTTTCAGTTTACTTAATAACAAAACAGGCGCCAATGGCGCCTGTTTTGATTAAAGGTAAAAATAACCACTGTTACTGCGGCTTTTCTTCCTGATGATCACGGCGGAATAACGCCCATTCTTCAATGGCTTCACCGTTTGGCAAGGTACAGATGCCATGCTCACCTTCGGCGGTTTTCTCTATTGCCAAGCTACCACCGAGGGAGATGCAATACTCTGAGGCTGGATTGGCAATTTTAACTTGCGCAGCAGGCTTTTCAGCTTCGCCGCAACCCACGAGTGCTACAGTTGCCAGCAGTAAAGCTGCAGCAAGCGGTAATAGGATTTTCATAGATACTCCTTGAACACTTATTTACGCCCTAAAATTCGCAGAATACGGAGGAATAAGTTGATGATATCCATATACAAAGCGGCTGCGCTATCGATGGCATTATCTATGGTTTTTGGGATTTGATTCGCCCGTCCCCAGTCGTAACCAATATACCCGCAGAAAATCAGCACGACGATCCAATCTAAAATCCCGTGGTGGGTATTAAAGATAAACACTTCAATAAGTTCGACGACGATGACGAGTACCAAAGCAATCGTGAGTGCGCCGGAAATTTTTTGGAAAAAGGCGGGAAAGAGTGTGCCTAAACACATCATGGCAATGGTTACTAGGCCAGTAATGCGAATGGCTTCCGTCACTAACTCAGGGTCGTATTGACTGACTACCATATTGATAATCAAGCCGAAGGGGACGACTACAAAGTTGTAGCCAATAAAGCTGACCACGGGATTGCTCGATTTTTGAAACAGATAAATCCCAAAAAAACAGGAGGCAAAATAGCCGATAAAGAAAATCCAAGGATTCACGCTGGCAATCGCTTCTGGGTCGATATTGGTGACCATCCAATAGTTAACCGCAAAACCCCAAATCAGCGTGAGGCCGATAACGAGGTTGTACATACCTGCGCCAACGATGGGATCGTCTGTGGTCATACGATCAAATACACTGTTTTCCGAATCCATATATTTTCCTTTTTATTTCCGTATGTTAACTGGTCGTTTAGCCTCACCAGCGTCGGGCTAACTTAAGGCAAGTAAAGCTGTTTTTCAAGCGAGAATATCTTAAGCTTGAGTACCACAGACACTGCAGTTGGCGTGTTTCGGGAGTTTCATCTCACGAAACTCCATCGTCATCGCATCGATCATCAACATGCGGCCCGCTAAGGTTTTACCCATACCAGTGATGACCTTGATGGCTTCAACTGCCTGCAAGCAACCTATCATGCCGACCACAGGCGCGAGTATGCCTGACTCGACGCAGCTGAGTTGTTGCTCGCCAAATAGGGCGCTAAAGCAGTGATAACAAGGTGTGTCGCTGTGATAATCAAACACTGTGACCATGCCTTCCATCCGAATGGCTGCCGCGGACACTAAGGGCACTTTATGCTTAAAACAGCTGAGGTTGAGTTGTTCGCGCACACTGACATTATCGGTGCAGTCGAGCACTAGATTGTGATTGGCGACAAGGGCGTCAATCTCATGATCATCTAATACTGCATTGATAATGTTGATCTTAATAAAGGGATTGAGCTGACTCAGGCTCTGTTTGGCCGATTCGACTTTTGGCTGGCCGACATTGGCATCTTGATGCAGCACTTGGCGCTGCAGGTTAGAGAGTTCAACGTGATCAAAATCGACCAAGGTCAGCTCGCCAATCCCTGCAACTGTGAGGTATTGGCTGGCGGCGCAGCCTAATCCGCCTGCACCTATCATCAACACTTTCGCTTGTTTTAGGTGTTCTTGGCCGTCGATATCCATGGCTTTGATGGAGATCTGACGGCTGTATCTTAAGAGTTCGCTGTCGCTGAGAATATCGTCAGGCACATTCATAGAGATTTTCCTAGCAGAGCACGCTGTTAAAGGGCTCGACCGTCACTTGCGTGCCAGCGGCAGTATCCCCTTGGAATTGTTCCAATAAAACGAAACAGTTCGCTAGGCTCATTGAAGTCAACATGCCAGAACCTTGGCTACCAGTAATGGCAACCTCGAGTTTGCCTTGTGCATTACGGCTTAAAATGCCCCGTTGATATTCGACGCGGCCCGGTTGTTTACGCACCGGTGTGGTGAGTATGGCGTCTAACATCAAAGGTTCAACTGGGGTTAAACCCTGCATCTTGTTCAGGATAGGCCAAACGAGCTTATAAAACGTCACCATAGAAGACACGGGATTGCCCGGTAAACCACAGAATACCGCCTTGCCAATTTTTCCCATGGCGAAGGGCTTGCCTGGTTTGATGGCGAGTTTCCAGAAGGTGATTTTGCCTTCTTCATCGAGAATTTGTTTCGTGAAATCCGCTTCGCCGACCGACACGCCGCCCGAGGTCAGCACCATGTCTGCTTGACTGGCGGCTTGGCGAAATGCCTGACGTATGGCTTCAGGATCATCGGCGATTACGCCTAAATCGAGCCATTCCACATTAGCGCGACTGAGTAAACCTTGAATCGAATAACGGTTCGAATCGTAAATTTGTCCCGGTGCGAGTTCGCTGCCAACCGGACGCAGTTCATCACCCGTGGAGAAAAACGCGACCTTAAGTTGACGATAGGCTCGCACTTGGCTGATACCAATAGTGGCTAATACGCCGAGCTCGGCGGCGCCAATTTGAATACCAGCAGTAAGTACTTTAGTGCCTTGGGTCAGCTCTTCGCCACGACAACGCACATTGGCACCTTGCGCCTTAGGGTGGCGAATATGAATAATCTCACCTTCAACTTCGGCTTCTTCCTGCATTTGTACTGTGTCATAACCTGCAGGCACAGGCGCGCCCGTCATGATCCGTATGCAGGTATTAGGTTTGGCTTCACCTTCAAAACCATGGCCGGCGAAGGAACTGCCGATAAGGCGTAATGAAGTGCGATTGGTTTGCGTATTCAAGTCGACAAAACGGAAGGCGTAACCGTCCATTGAGGAGTTGTCGAAGGGGGGCAGGTCGATGCAGGAAGCGAGATCTTCCGCAAGGACGCGGCCAAGGGCGTGGGGGAGCAATACGACTTCTGTGTCTGACACTGGGCTAACTTGTTCGAGCAAAAGGGGAATAGCTTGATCTGGATGCATTAAGCTAGGTTGCGAACAAGGATCGGCTTTTACGGACATGAGGGTCTGCCTTATATGCAATGGGTCGTGATGACGCTGAATGAATTTCCACCTGACTATGATTGGCTATTATGCCACGGTGATCGGCGATGGCTATGATTTGCATGTGAGTTTCTGTGATAGGCCAGTCAGTTTTTCCTGCCACTTTGGATGATAACCTAGCAAGAAGTAACGCAATTTGCGCAGCTAAGTCGAAGGCTGAACGCGAGTCGCTGGACAAATGAAAATCATTTTTATCAATGAATCATTAACTTAGCTAAAAATCTAAAGGTAAATTTTGTTGCTTTTGTCGACCCCCTTGATGACAATGTGCCCCATGATTCAGTTCGAATGCAAAAGAGGATTTACTATGCTGTCAGCCACTATGATCGAAAAGTTGAATGAACAAATTAATATGGAGTTCTTCTCCTCAAATTTATACCTGCAAATGAGCGCATGGTGTGAAGATCAGGGATTTGAAGGTGCAGCTAAGTTTATGCGTGAGCATGCGGACGAAGAAATGGGCCATATGCGTCGTCTATTCACTTATGTGAGTGAAACCGGTGGCTTACCTTTGTTAGGGGCGATCGAAGCGCCTCAGTCACAGTTTAGTTCTTTACTGGCTCTGTTCGAACTTACCTATGAGCATGAGCAGTTGATTACCAGTCAGATCAATGCCTTGGCCCATGCTGCCTTTTCTAACCAAGACTACTCAACCTTCAACTTCCTGCAATGGTATGTGGCCGAGCAGCACGAAGAAGAAAAGCTGTTCAAGTCGATTGTCGATAAGATACGTTTAGTGGGTGAAGATGGTAAGGCACTGTTCTTTATCGATAAAGATTTAGCTAAGTTAGCCTCTAAGGGCGGCGAAAGCATAATGAACGGCCAAGGCCAACCACAGGCGTAAACTTGTTGTATTCGCTACAAAAAAAGCAGCCTAGGCTGCTTTTTTTGCTATTTATTCTTCAATTCTTTTTGCTATTAGTTGCTAGCTTTCAGCATCTTAGATATTCACGCATCTATCTTGGTAATCGTGGTTGAGCAGTGTTGTGTGCTTGATGTGCAGCAAGGGGGTGATCTCGTCGAAGGGCAGTGGTTTAGAGATCAAATATCCTTGGGCAAAGTGGCAACCTAGATCCCGCAGGAAGTGCCACTGGGCCTCGGTTTCTATGCCTTCGGCCACTAACTCCATTTCGAGACTTTGCCCCATCATGACGATAGCTTTGACTAAGTTAGCATTAGCGTTACTTTCGAGGGCATTTTCCACAAAGCTACGATCGATTTTAATCACTGAAATCGGGAATTTTTTCAAATAACTCAGGGAAGAATAACCCGTACCAAAGTCATCGATATAGATTTGCGAACCTAGATGGCGAATCGATTCGAGCGTGCTCAAGCAGTGGCTGGCATCGCCCATCAGCATGCTTTCTGTGATCTCAATATGCACATTGCGCGGGTTCACATGGTGGCGGTTAAAGCATTCCTGCAGCACTTGGTCGAATCCCATGCCACGGGTGTTGATACATTGGCGACCGGAAACATTAATCGCAACATTGATTTTTAAGCCACTTTTCTGCCACTGCCTGAGATCATGCATGGCTTGGTTGAGCACCCATTCGCCCATAGGTTCTATCAGTCCGGTTTCTTCGGCCAGCGGGATAAATTGATCTGGTGGGATCAGGCCAAAGTCGGGGTCTTGCCAGCGGATAAGGGCTTCGACGCTGCAGACCACTCCAGTTTGCATATCCACTATGGGTTGATAATGCAGGGCAAATTCATTTTGTGCCACGGCGCGGCGCAGGCGTTGCTCTAATTCCATCCGGGCGAACATGGCTTGATTCATACCCGCGGTGAAGTATTTGAAATTGTTCCGTCCCGCGTCTTTGGCTTGATACATGGCGGTTTCGGTATTGCGGGTGAGTGAGCTGACATCGAGGCCATCTTCGGGATAGACGCCGATACCAAAGCTGGCCGAAATCTGGATCTCGCGGTCATTTAAGTCAAACGGCATGCTTAAGGTTTCGATAATACGCGTTGCCATGTGTTCGATGGCGAGTTCGTTGGTCAGTCGCGGCAGAATAAGTACAAATTCATCACCCCCGATGCGGGTGATAAAGTCGGTTTTACCCGCAATGCCTTCTAAGCGTTTGGCCACATCTTTAAGTAATAAGTCGCCCGTGGCATGGCCTTGTACATCGTTGATGTATTTGAATTGGTCTAAATCTATCAGCAGAATCGCCAGTTTGCGTGAGTCATGCTGCGCCACATGGATTTCGTGCTGTAGGCGCTCATTAAACAACTTACGATTGGGTAGTCCCGTTAACGCATCGAAGTTGGCTTGATACCAAAGATCTTGCTCATATTGCTTACGCTTACTGATATCCATAAATAAACCAATGTGTTGGATTATTTGTCCTTGCTCATTTCGCACCACTGTAATGGCTAGATACTCGGGATACACTTGGCCATTTTTACGCTTATTCCAAACCTCGCCCTCCCATTTTCCTTCCGTTTGCAGCGACTCCCACATGGTGGTGTAAAAGTGCTGCGAATGACGGCTAGAACTGAGGATGGATGGACTGCGGCCTTTCACTTCTTCCAATGTATAGCCAGTAATATTGCAGAAAGCCGGATTGACCAGTTCGATATGGTTATCGGCATCTGTCACCATTATGCCCTCGGCAGAATAGTCAAATACCGCAGCGGCGCGGGTGAGTGCATGGTTCGCCTTCTGCCGTTCGCTGATCATCCGGTTCATGGCGCGGGCCATGCGAGCGAGTTCATCAGTACCTGAGACGCGGATATTGATATTGAGCTTGGGATCTTCCGCCATTTGCTGCATGTCATTAGCAATCTTAGTGATTTTATCCATAAAGTTGCGAATGATCTTCTGTCCGAGCCAGAGTGTAACGGTTGCGGTGAACAGGGCTGATAACAAGTACAAGCCGAGGAAGGTCAGGGCTTGGTTTTGTTGATACTCGCTCTGCTGCGCCATGGTCTGGGTGATGCCTTTGGCGAGTTGATGCAGTTGTTCAATACGCTGGGTGGTGAGCGCCCACCATTTTTGGCTGTCAATCGGATGTGGCGGCATTAACAGCTGTGTCATCGCTTGGCTTAACTTGTCCTCACTGACTGGGACTTGCGCGATGGCGTTGATTTCCGCCATCGGCAAACCTTGCTGCCGCAGTTGCTTTAGCTTGACCATGCTGAATTGGTATTGATCTACGCTGTCCTCAATGGTTTTCAGCGCCGACTTAAATTCGGGGAGAAAGCGAATTTCGTGATTGAGTTCATCTAAGTTGAGGTGAATGTTCGCCAGCGCATCATTAAATTTTTGCAGTGAAACGTCATCGCCCTTCAGCAGATAGTCCTGAAAACTCCCCATTAAGCCACCGTAACCTAAGTTGGCATTAATATTCTGTGCCAGTGCGCTAATGCGGGTTTGCTGCTCAATTTGTTGGCTTATCTGCAGAATACGTTGATTCTCTGGCGAATCTTGTAGGTTTTGCAGTAACGATTTTCTATCGGAAATCGCTGCATTGCTCGCGTGCATCACAGCTCGGCCATATTCATACATGAAGGAGCCTATGGTGCGGTAATCATTCAGATTGAGTGAGTCGCTGAGCAGCAAATGATTAATTGCGCTACGTTCTTTTACGGCGAGTTCCTGCACCATTAGCAGGTTTAATAAGTCGGTATAGGCGCGGGATTGGCTGATTTCGTTGGTTTGTAACTGCAATTGTTGAATGAGCTGTAATAGCTGAGTGTGCAGTTGACTATAGCGGTCAGTTAACGGGCTTTGTGTTTCGCTCGCAAGGGTTTGCCGTGCGTTGGCGAGTCTGTGGCCCGTGAGTGTGATGGCATCTAATAATGCGTTCATCTGACTGGCTTCAATACTGTTAGGATCGAGCGACTCACTGAGGTGGGCTAAAGGTTCACTGTTAAGTAACGTATTGAATGCGTTGGAGGTCGCCTGTTGTTGCGCGACTAGCGCGTTATCGGCGGGCAGTTGATTGGTGCTCAGGCCATATTCGTTTTGCAACTGATACAGTAATTCAGTGATTTGTACCGTCGTCTGCACCGCAAGATGGTTGTGCTTGGCCGAGTGTGATTCATTTATTAAGCCATGCATGCGAGTCAGAATAAAAAAACTCATCATCAACAAGGGGACAAGCGAAAACAGAATGAGCTTTTGCTTCAAACTGAGGTGGTGTAGCGCTGACGGCATTAAAGATCACTCCACTCGTTCGCCTAGCTAAAAAAGAAATTCAACGTCATCACGTTGAACTTGAGTTGAAAATGGAACTAGGCACAGGGTGAGAATATTTAACATAGGCGGCCAGTCACGGCGACCTTTAGTTTATGGATATCTAATCTAGGTCACGAAATTAAGCAGATTGTTATATATCGAGCTAAGCAACTATTTTTATTAGTGTATTTTTTGCCTATCAAACAAACATTTCATTTGTCTGTGATGTGACACAGTATTTTGTCATTCGAAAGAATGGCTTTATTTATTGCCCTTATGGGCGTTGATTCAGGGAATGTTATAACCGGTGGGTTAAGCGATGTTGGACTGCAAATAAAGCCCAATAAAAAGGACGTTTGCCGCAGCGAAACGTCCTTTATTTTTTAGCTAATGACCTAAGTTTAGGCCATTGAGGTCTGACTTAACCGACTTTCGCCAGTAGCGATTTACGGTAGACCACTATGTCTTCAATCGTCAGCACAGGCATATTGTGCAGTGCGCCGAAGGCGATAATTTCGGGTAAACGCGCCATAGTACCGTCGGGATTAGTGACTTCACACAACACGCCAGCGGGTTTTAAGCCAGCGAGTTGCACTAAATCGATCGTGCCTTCGGTGTGGCCGCGGCGGGTTAGCACGCCGCCCGGTTGGGCGCGAAGTGGATACACATGGCCTGGGCGAGCGAGATCGCTTGGCTTAGCGTTGTCGGCAATCGCCGCTTTAATGGTGGTCACGCGGTCAGCGGCAGAAACCCCAGTCGTTACACCGACAGTTGCTTCAATGCTCACAGTAAATGCCGTGCCGTATTGGCTCGAGTTGTGCTCGACCATCGCAGGCAGTTCGAGGGCTTTGATTTTTTCATCGGGTAAACATAGGCAAACAATACCGCTACATTCACGGATGAGCATGGCCATTTGGGCGTTAGTCAGTGTCTCGGCAGCGAAAATTAAATCGCCTTCGTTTTCTCTATCTTCGTCATCGACAACTAACACACCTTGGCCTTGACGCAGGGCATTAAGACCCGCTTCAACGCGTTCGATAGCAGTACCAAAAGGAGCAAGTAAAGACTGATTCATGGTTAATATCCTTAAAAAATACGACATTGGAATATCCAGAATCAGGGCGTGCAGAAATACAACTCAATCAAACCTAATGGCTCGATTTTGCTGTAGCAATATCACTTCACTTGGGGTGAAGTTATTGCAAAACATGAGGCTAACACTGCAATACATAAGCGCGTTGCACTTAAATAACACAATGTTGTCTTACATTCTCTTCCATCCGGACTCAGCACTGTCGCCATTAAAGCAAACAGTCCATTACCGTCGGCTCTGGAATAGTCAGTTTTGCACTAGGCAAAATCTAAGGCACCAGATCTGCTGACCCCGAAAGTGATTTCGGGCGCTCGCGGGCTTTGGGGATGCTCAGTAAAATAATCACTTAGCGTTACCCATTTACCGCCGGTGGGGAGTTTCACCCCGCCCTGAGAATTTGCTTTTGAGCTGTTTGCGCTCAAAAACGCGGTTATGTTGCTCTTATTTAGCGCGAATGGCAACTTCTCGCGTTCAATGTTTGCGCTAGTGCACACCTGACCACTATCATACTAATTTATTTGCCGTCGCATTTTTCGACGGTCGTTATGGACGTCACAAGGACGTATAAATGCGATCTCTGCTGCTAACTTTAGGTATTGCCATGTTGTTATCCGGTTGTTCGACCTCAAAAGGGCAAGTACCCGCCCCAGTTGCTGAAAAAATTCCCCATGTGATGACTTTGCATGGCGTAACGCGCACCGATGATTACTATTGGCTGCGTGACGATGAACGCAAAGATCCTAAAGTGTTGGCCCATCTCGAAGCCGAGAATCGCTACACTGCGGCCTATTTCAAACCCTTAAAGCCGCTGCAAGATGGCCTTTTTAAAGAGTTAACTGAGCGCTTAGTCGCCGATGAGTCGAGCGTGCCATACCAATGGCACCAGCACAGTTATTACAGTCGTTATAAAGAAGGCGGCGAGTATCCGCTAATCAGCCGTAAGGGCGCCGATGGCGTTGAGCAAGTGATGCTGGATGTGAATGAGCGAGCTAAGGGCCATGAGTTTTATGGCTTAGGCGGCGCCAGTGTTAGCCCAGATGAAACCATGTTGGCCTTTGGTGAAGACGTCCTCAGCCGCCGTGTGTACCACATCTATTTTAAAGACTTGGCCTCGGGCGACATGATCGTCGACGTGCTTGAAAACACCGAAGGTCGAGTGGTGTGGGCGAACGACAACAAACATGTGTTCTATATCGCCAAAGATTTGCAGACCTTACTTGGCTACCAAGTGTATCGCCATGAGCTGGGGACTAAGCAATCGAGCGACGTGTTAGTGTATGAAGAACAAGACGATTCTTTCTATATCTCGCTCGGCAAAACCTTAGATGAGTCGCAAGTTGTGTTGTTCCAAGAGAGCACCACGACGAGCGAAGTGTCAGTGCTCGATGCCAGCGAACCTCTGAGTTTATTCAAGCCAGTGTTAGCGCGGGAAGAAGGCCACGAATACAGCGTGTCTAAGTTGGGCGATACTTATTACATTCTCACCAACTGGCAGGCGACTAACTTCCGTTTGATGAAAGTTGCCATCAAAGATGCGGCGGACAAATCTAAGTGGCAAGAAGTGGTTGCGCATAACCCCAATGCCCGTATTGAAGACGAGTTAGTACTCAAGGATTATCTGATTATTCAGACCCGCGAAAACGGCCTGACGCGAATTAAAGTGATGCCGTTCAATGGTCAAAAGCCTTTCGAACTGAGCTTTGATGAACCTGCCTATGTGATGGGTTTAGATGTTAATGCCCAGCAAGACAGCAATAAGTTACGCATTTTCTACTCCAGCCTGACAACGCCAGAAGCCGTATACGAATATTCGCTAGTGAATCCCGATAGACGGGATTTATTAAAACAAGATCAAGTCTTAGGCGGCTTCGATGCCAGCCAATATCGTGCCGAGCGTGTATTTATTACAGCTCGTGATGGTGTGAAAGTGCCCGTTTCTTTGGTGTATCGTAAAGATAAATTCAACAAAGATGGCACTAACCCTCTGTATCAATACGGTTATGGATCTTACGGTTACACAGTTGAACCCGATTTTTCTTCATCCGTAATCAGTCTGCTCGATCGCGGTTTTGTGTACGCCATCGCCCATGTACGCGGCTCTGAAATGCTTGGTCGCCCTTGGTATGACGACGGTAAGCTGTTGAATAAGCAAAACACCTTCAATGATTTTATCGACGTAACAACGGCATTAACGGCACAAGGTTATGGCGATAAAAACAAGGTCGTCGCCTCTGGCGGCAGCGCGGGCGGTTTGCTAATGGGCGCAATTGCTAACCAAGCGCCGGACAAGTACTTTGCCATCGCCGCCCATGTGCCGTTTGTTGATGTGGTGACCACTATGCTGGACGAGTCGATTCCGTTGACGACCAATGAGTACGACGAGTGGGGCAACCCGAACGAGAAAACCTATTTCGATTACATGCTGAGTTACTCGCCCTACGACAATGTTACCGAGCAAGAATACCCACACATGTTAGTGACGACAGGTTTGCATGACTCGCAAGTGCAGTATTTTGAACCCGCCAAGTGGGTCGCTAAGCTGCGCGATGTTAAAAATCAATGGTATAAGCTCGACGATAAAGTCTTATTACTTAACGTGGATATGGAAGCCGGCCACGGTGGTAAGAGTGGTCGCTATCGCCAGTATCAAGACACAGCCCAAGAATATGCGTTCTTCTTGAGTCTGTTAGGTATGGCAAAGTAGGTCATTCGCAAGTGACCGAATCAGCCGTCAGCCTAGGCAATACGGACATAGAAGCTGTGCAGCAACATGAGTTACTGGGGGCAGACACACTTGCCCCAGTGACTAGCGCTGTCGCTACCAAGAATGTTGTTACAGAAGCTTCGACTATAGAAGAAACCCCAATAGCAGAATTAGTGACAGAAGCTTCGCTGAATCCCGTACCATTAAATCCTGCACCTCTGAAGCCTACACCCATAAAGTCATCATCTCTCTGGTACTTGTATCTAGTCCGCTGCGCCAACGGGCATTTATACACAGGTGTCACCACCAACGTGGCGCGACGCTTTAGCGATCATCAGTCGGGTGGCATCAAATCCGCTAAGTATCTGCGCGGTAAAGGTCCGTTAACGCTGATGTATCAAGAGCAGGTTGGTAGTCACGGCGATGCACTGCGCCGTGAAATTGCCGTTAAAAAGCTGAGTCGCAGTCAAAAGCTGGCATTGATTGAGTCAGGGCGTGGATTACTAGAGCGTTAACACTGCATCATACTTGTTCTGTGACGAGGAATGATTAAGCACTGTCATTCCAGTGACTCGCAGCAAGTACGTCCTTGTATGCTCGATCGTGACATCCATGTCACGGACGGTCACTTCCATGACAGTGCTTAATCCAGTTAATCATAGAGTATCCTGTCAGCCAGATCATAATCCTGTGTCCATTGCCGAGACAGAGGTGCCATCTGCGCAGGCATTACATAACAAGGCATTCCAGTTCGTTCCAGACCTTCTGGTCTCCACAGAACGTGCTCAATCGCGCCACTGAATTTTCGCGTTAGGCATTAATTATGAAAGCATTAGAGTTAAAAGAAAAATTGCTCGCCGAAGGTTGCAATGCAAGTAACTTTGCCATCTTGGGCAGAGGTGATGGCGCTTTCTGCTTAGATAAAAAAGGTGCTAAATGGGCCATTTTCTATTCTGAGAGAGGTTGTGATTCTGAGCCTATCTTTATATCAGAAAACGAAGAAGAAGCTTGCGGATATTTTTTCAACTATGTGTTAAAGCAAAAACATTGGCACATAGTCGGCTTTTTCAAATATGAATCTGATGCAATTGCATTAGAAGATAAGCTTGTTTCAATTAGCATCAAATCGATAAGAAATGATATTTCTGCCTACAAAATGGCCAATGACCTAAGGTACAGGGTCTTTGTTATAGGCAAAGATATTTTTAAAGTCAGGGAGCGCTTAGGTCATGTACAAGTCTGCTATATCTGATTAGCATATGCTAATTAGACTTGTTTTCCGCTGGCACTCAAAGCCAGCCGATTAAGTGGCGTTATATTTATTAAGGAAGCCATGTGTTTAAAAAATTTATGACCGTAATTACAACATTACTTTTTGTTACTTCTATTCAGGCAAAGGATATGAATTTAAATTTTACCCCAGATCCAAATGTATCCAAGCTTGCAGAGGCTTTTTCACTAGATTGTGTTGATTTTGTTGGGCGGAAATTCAACTTAATATTAGATTGGTCAGATGAAAGTATCTTTAAATTAGAGCCAATTCTTGCAAACTTAAGCAATTATGCACGTGCGAGCAATATGCCTGCGGAGCAGGTAAATGATTATGCAAAAATATTTGGTTTTTATATTGGCGAGGTTTACCGTAAAAATCACGCTGATGTTGTATGGGGTGGCGTTGAAATTGATGGGCGGCAACATTTCGCACTAGGTACAAAAGAAAACAAACCAATAATCTGGCCAGTTACAAATGTGAGTAAGCGGATTTATCTTGGTGAAGAAGCTAATATCGCAACATACTATTCAGCAGTTGTTGGTAAGCAATAACAAGACATTCGGCATCCAACCTTAATAGTCCAATCGTTAGCCTCCAACTAAGATTTGTTGTTTTAAATTAGTAGGATTTATTTTAGGAGCCACAATGAGACTAAGCCTTAAAATCTCTTCTTGGGCGATAGCTATCGTTGTGATGCTGCTATCTGTAAGTTTTGCAATTTATGATATTTTTTGCTTTCAGCCTCACCGTGGGGAAATAACTCGACTCATCCAAGTGGCTACTACAGAAGAAAGTGAGCCTCCAGCTTTAATCGTGCAGCTCGTTCGTGCATCCGCAGGCGAGAATCTTGATTGGCTGGTTACAAGGTTAATTATTGATAATTTAAATATCAGCCATAAACCCGAGTCATTGGTTGGCTGGCATATTGAGTCATTTCTGTGGCTACAGTTGGTCAGGATTCACCTTACAGAGCAAGAGCAATTAACTCTTTATTTGGCTCAAGTGCATACTGGCAAAGACTTAAAGGGCTTTTCCTCTACCGCTCAGGTAATATTCAATAAGCCTTTTTCTGCATTAAGCCCTCAAGAGGCTGCCACAATTGCTGCCTTGCCATATGCCCCAAGCATCTTTGCAAAGAGCTCAGAGCGTCTTGCAAAGCGTCGTGAATATTTGCTTTCTAAGCTGGAGAATGGCTCCTAGCCAGTCATTCAAGCGAACACCTTCGGCGCAAAAATTAAATGAGTTACTCACGATTAAAGTTTTTGTATTTATGTATGTAAAATTCGGGCATTAAGGTGTGAGTGGATTAAAGGAATTAAGCACTGTCATGGAAGTGACCGTTGGTGGCATGGCCGCTCTTTCACATCCATGTGATCGCAGCATTCGCATTTCCGTATGCAGCAGATGCCGCCGTCGAGCCTATAGGGACGCTTCTTGTTAAACGAAAGGGCGGCGAGTCACTGGAATGATAGTGCTTAATACCTTAACGATTAACTTTTATAATAAATCAATTAGTTAAGCTTTGGTCGTCACCAAAGACTGGCCTGCATTGTGGTTGGAGAAGTAAGAATAGAGAGCTAAAAAAATGCCACTACAAGGGGTTAGTGGCATAAATGCATAAGTCGGGCACAAGCTTAAAAGGGTGGGTAGAAGACTGACATTTTGGCTGGGAACCGTTGCAACATCAGTCTCTACGCCGGGAAGTAGGTAAAACCTTTGGGTGTAGGGTGGTAAAACGGCGCGTTATTCCATGCTTACTGGGCCGTCGATTTTGTTAAATTTCACATCGCCAGAACCTGCCGCAAGAATCGTTAATCCCTTAGTGTTAAAGACATTAATGTCGCCTGAGCCATCTGTGATGGTCACTGGACTCTGCACATTGGCGACTTCAATGTTGCCTGAACCATCATCGATGGCGATATTGCCGCGAATATCAGTCACCTTGATTGCGCCAGAGCCATCGTCAATCGTGAGGTTGCCGTTGATTTGACTGACTTCAATGTCCCCTGAACCATCATCAATGCTGACATTGTTACCGCCGTTAATGATGAGTTCACCTGAACCGTCTTTGACATTGATATCTGCTGTCATCTTGCTGATCAAAATGGCGCCTGAGCCGTCATCGATATCCAGTGCTAAATTCGCAGGCACTTGCAGTTTTAGGTCGATGTAGGGTGAGTAATTATTAAAACCGCTTTGTTCAAAATCGGCTTTGAGCTTGGCCTTGTTGGCCTTTTTCTCAAGGGTAAGAATGAGTTTGCTGTCGTTGCTGTCTTTATTGCTATAGATGTCGGCGACGAGTTTAATTTGGGTTAGGCCTTCGACACCGATAATTTCGAGGCTGCCTGCGCCAGTCTCGGCAATCAGGCCTTGCAAGTCTTGGCTATCGAGTGTGAGTTCGCGTTGCTGGTGGTCTAAATCCGGCATGCCAGCGGCGTTGACATTAATAATGCAGCCAGTGAGCGATACCCCTGCGGTAAACACCAATATAAAAGCGAGGGTGAATATAAATGGGCGTAGTGACATGTTAGCTTCCTTGTTTATCTGGCACTCGTTATTCGGGTGAGCGCTGAGTCGTCTTGCGATGGACAAGACATTGCACAGCCCATGCCAATTTTAAGTTTCATTTATAATCAATGGTTTAAGCTGAAAGTGTCCGCGCTTGGTTGTGCGGACATGTCCGCCGAATGAGGGATTTACTCATAGATTAGTGAAATTGCTCATTTATTAAGTTAGGGTGAGCTTCATAAGGCTGGCCGGGCAAACATACAGACAATACAAAAAATACAAAAAATACAAAAAATACAAAAAATACAAAAAAGATAAGAAGGGATGCAGATGGATTATTTAACGACGAACAAGACGGCTTGGGATGCGCGTACTCGGGTACATTTAACCTCGGATTTTTACGATGTGGCGGGCTTCCTGCGTGGGAATACTTCATTACGGGAAATCGAGCTTAGCGAGCTGGATGTGGCAGGGAAAAGCTTGCTGCATCTGCAATGCCATTTTGGCTTAGATACCCTGTCGTGGGCGCGCATGGGCGCTAAAGTACTGGGTGTCGATTTGTCTGAGGTGGCGATTGATGAAGCACAAAAGTTGGCGCAGCAGACACAATTGTCCGCCGAGTTTATCTGTAGCGATGTTTACAGTGTGGCGAGCAAGGTTGAGCCGCAAGATATTGTGTTTACGTCCTATGGTGCCATTGTGTGGCTGCCGGATTTAACCCTGTGGGCGCAGACGGTTGCTGCTTGTTTAAAGCCAGGCGGACAGTTTTACATGGCCGAGTTTCATCCGGCGCAGCAGTTGTTCGATGGTTATAGCTATTTTAATCGGGGTGAGCCAGATATAGAGCAGGAAGGCACTTATACCGAAAATGCCGGTGATGATCAGCAAACTTTAATGTGTTGGTCACACAGTCTATCTGAAGTGATCAATGCGTTATTGCAGGCGGGCTTAGTGTTGGAGTTCTTCCACGAATTTGATTTCAGCCCGTACAACTGTTTCGAAGGGCTCGAAGCGCAAGCCGATGGTCGTTATGTGTTAACACACCAAGGGCAGAATGTGCCCTTGGTGTATAGCATCAGCGCTCGTAAACCTGCGTAAAGCTTGCGGTTTGTGATGGCGAGGATTAACGACTACGGTAAACCGGATACTTAAGGTAACGGTTATCGTAGTAAGGGCTGCGTTCATAGAACCAGCGCAGGCGTGCCTCGGGATCGGCGGCAAATTCGGCGTTGGTCAGTGCTTTGTCGAACTCAGCTTGCAGCTTAGGATCATCTTTTAGCATCTTGGCCGCCAGCGGCTCAACCGCATAATCTTCAATGTATTCAGTGCGGGTGAAGATAGGATTAAAGAAACCCCATTGCAGCAAGGAATCTGGTGATTGCGGCTCCAGCAGTAAAACCGCCAGATCGCCCAGCGGTTGATCTGTGCTGATTTTAATGGTGCCAGCGGGCAGAGTGGTGCTGACTTTCGTCAACTCTGACTCGGCTTTGACCGTTTGATGGCCCTCAAAATCCTTAGTGTTAAACACTGGGTTACTGAGTTTGTATTGCTGTAATTTAAGCTCTGTCGGTTTGGTCAGGCGCGTCATGCGGATGCCATGTATGTTCAGGCGATCAATCACTTGCGTCCATTGCGCCGGAATATAGTAAGCGCTCGGACGAGTGACTTTAATATCTGGCACAGTCTCGGCGATCACGGGCAAGTTGGGGTACAGCTTAGGTTGACCGTTCCATCGCACCACCTCAGTGCCACTGATGGGGCTCTTTTCGAGTTGATAGTCGATGCCCTTAAAGTCCCAACCTTTTGCCAGCGGCGCCGATTTCCACGTGAGGGTGATCAGCGGTGAGGCGCGGTATTTATCTTCCTGAATCGCACTCTTCAGTTTAGTCGCCTGTTCGCCCACGGTTTTGAGGGTTTGCTCCAGCATCACATAGGTGCCGAGCACTCTTTGTTTAAAGGGTTTTAAGCTGTGGTTTTCAATCAAAATAGTCGGTAAATGGCGGGCATCGCCATAACCGTTAGAGAAGCGCGGACTTGGATTCCACAGCGACATGCCTTTGGTGATATCGGCGTTATCCACGGCAAAAATCAGCGGGCCGGGAATATGGCCTTGCTCGGTTAATGCCGCTTCAACTGCGGGTCTGTAGCTGTTTTCGAGCCAGCGGTAACTGGCTGGGCTTAACCCTTGGGCAAGGTTATAACCAAAGGTGACATCGTATTGATAATCTATGCCGTCGGTGACATGCACGTCGATATACAAGTCTGGCTGCCACACGTTGATTGCGCGCAGCATGTGCTGCATTTCTAAGGTATCGGCCTTGGCATAATCGCGGTTAAGGTTAAGATTATTTGCCGTAGTGCGCCAGCCCATATTCACAGGGCCACGTTGATTCACGCGATTAAATTCGCCGCTGCGCTCATGGGCATCGACACTAAACATAGGCACAAACAAGAGGTTGGCTTTATCCAGCAGGGCGCTTTTATCGCCTTTAACGATGTCACGTAGCAGCATCATGCCGGCATCTTTACCGTCAATTTCTCCGGCATGAATCCCTGCTTGCACTAAAACCGTTGGCTTAGTGTTTTGCTTGAGGCGTGCCGACTCATTAATGCCTTCACTGCTGGCGATGTACATCCAAATATCGCGCCCCTGTGGGCTCTTGCCTAAGCTCACCTTTTGTAATTTATCGGTTTCGCCCGCAAGCGTGTCGAGCCAAGCGATAGTGGCCTCGTAGCTCGGACTTTCAATACCACTACTTTGTTCAAACGGCGTCGCCCACTCGTTATCGACACTCAACATGAGCGATTCGCTGGCGCCATGCCAAGTGATACTCGGCGGTAAAATGGCATCGTTGACAAAGGTGTTGGCTCGCTCAGGGATCGGCGCGGGCTCTGGCGCTACAGTGGTGGCATCGGTTGCGACAGTCGCAGCGGGTTCGGTTGCTTGGGTATTCGCAGCAGGGCTTACATCCTGTGGCGCTGTTGCTGGGGTTTCAGCTGGCGTTTTGTCGGTGACGGTTTGCGCCGCAGCGACTGGAGTCGAGGTCGGCAAATCTGTAGATGCTTGAGCTGTTTTGGCTAAGGTTTTTGCCTCGTTGGCAACAGCATCGACTGAGTAGAGACTGCCCGCAGAAAGGCAGGCAATGGCGATAACGGAGAGGGAAAATAAGCGCATGAAAAGAGTCCATATATCGAATTTTTATTAGGACGGTATCTTTTCATTAAACGCCCCGTTAACGCAAGGGCGGTCAAGGGTTTATCTGGCATTCAGGCGGAGGGTTAATACGATTGGTATAAAACCCTGAATGACATAACAGGCTAATCCGCTTTGCCGGACGTTATCTGCGATGCGGGCTGCTTGTTATAGGCAATTTCGATAATGGCAATGTCTTCGTAAAGTGCTTCGACGGCAATGGCGAGCTGATAAATAAGCCAGCTATATCCTGTGAGCGCAAATCCCTGCGGGACTTCGCCGTCGATTGCGGCGGCATTTGAATCGGTTATTGATGCCGTTGTTGATGTCACGATTGTTGATGCAATCGTGGCTGAGGCTAGCGCAAGCTGGAGTCTGTGTTGTAACTCTTGTTCTAAGCATTCCGGCAAGGTCGGTAATTGTTGGGCTTTACCCGCCACAAACTCTGCAAGCTGTTGTAAATAGAAGGCGAGCTCAGAGGCAACCGCATTAATCTGCAGATAGGCGTCTTTATCTTCTTCCTGCCAGCGGGTTAAGGGCAAGAGTTCGAGCAAGGTGATTAGACGCACTTGTTTATTGGCAAGTTGCGACAACAGTACTTTGTGTTTTTTGAGGGTTTTACTTTCCCATTCTAAGGAGAAAAACAGCTTCTTCTGGGTGAGTACGGCTTTCATCGCGGCTTCGAGTTTAGCGCGAAAATCGAGGTCGTGATTAGCTGGCGCCTTAAGATGCTGGGCTAGTACTTCGGCCATATTGTCGATGGCATGAGTGAGTTGAGAGCGCCAATCTTGTTTGGCTTTGATTGGGAAAATAAACAGCGAGACCAAAATAGCGATCACGCAACCAGAAAGTATATTGGCCGTGCGCCACAGTGCCTCGGAGGTATCGTGATTTGCATCGCCGACCACTATGATGATGGTAAATCCTGACACTAAATAGGCGTAGCTGTAGCGTCCGGCCGAGATAAAACAAATCAAACTGACGGCCAAGATCAGCAGGCTCATGATCAGCCAATAACTGTCAACTGTGGCAACCAGCATCACCCCATAAGCCGAGCCCAAACAGGTGCCTATCGCTCTTTGAAGTGACTTTTCAATCGCGCCGCCGACCTGCGGCAGACTCATCATGATGACAACAATCGTCACCATGCTCCAAATAAAATGCGGCGGCGCCCAAATAGCATTAATAGTGACCGCAACGAGCAGGGCTAAACCGAGTTTTAAGGCGTGGACTGAACGGAAATGACGATAGATAAAGTCGGCAAAGGGTGTGGCTGGGGCAAATACAGACATAGGGCAGAATTCCTTTGGGCGTTAGCCAAGCGACATTACGTAGAGCCAGTATCGCAGAAAAACCGTGTCACTTGCGGCTATTGCGCCAGAAAGAAATACGAATTGCGAAATCTAGCAAATTGAGCTTAGTGGATAGCGCGGGAGAGATCACATTTAAATTCAGATATTTGAATTGGATTGGCTGAACAGTCGTGTGCAGAAGCGGGTTGTTATGCCGTGCAGTAGGTATAAAAAAGGCTTGAATAAGGGTTGCCTATTCAAGCCGGTATTTGAAGCGTTGAGTGACTGTATTTGCTAGCTTAAGCTGCGCTCGCTATCCGCTTTAACGTCAACTTCTGTGTACTTATCCATGATCAAGGCACAAGCCGCATCGCCAGTGATATTAAGTGCGGTGCGGATCATGTCAAACACTCGGTCTAAGGCAAACAGCAAAGGTAAGCCATCGATAGGGATGCCAGCCGCTAACAGCACTGCCACCACTAAGAAAGACGGACCGGGCACACCCGCTTGACCAATTGCGCCTAAGGTTGAGGTGAAGATAATGGCCGCATAGGCCGTCATCGACAGGTCGACGTTATACATTTGAGCGAAGAACATGGCCACTAAACCATAATAAATCGCGTTGCCGCTCATATTGATCGTGGCGCCTAACGGCAGCACGAAGGCTGTGGTTGCCTTAGAGACTTTAAGATCTTCCTCGCAGGTTTCCATATTCACCGGCAGCGTCGCCATTGACGAGGCGGTCGATAACGCCATCACCTGTGGTTTCTTCATTGCCGAAATAAACTGGCGCGCCGACACTCTTGAGAAGAGCTGCACCACCAATGGGAAGAAGATAAAACCGTAGATCAGAATCGCGGCCACAAACACGGCGAATAACTTAAATACCACTTCTAACGCATCGAAACCGAAGGTGCCCACGGAATCGGCCATCAAACCAAATACACCGATAGGCGCAATGATCATCACGCAGTTGATCATCCACACAAAGGCATCGACGACAGTGTTGAGTGCAGCCAAAATAGGCTTAGCGCCATCGCCTTTAACCTTAGTCAGCGCGATACCAAAAAAGATACTAAAGACTAAGATTTGTAAGATGTTACCGCCGTTCAGCGATTCGAACACATTGGTTGGGATCATACCGATAAAGGTATCCATCACCCCAGGCAATGCGCCGTGTTCCTTGGTTACTTCCATCAAATCTGAGCTGCTGTGGGCTGTGAAATCAACACCCGCGCCCGGTTGAAACAGATTGCCCATCACCAACGCCAGCACCACGGCGATGCCAGATGTCACGATAAAGAAGCCGAAAGTGCCGATGCCAATCTTACCCGCCGATGGGCTATCGCCTAAGCTCGCCGCGCCGCTGATGATAGAAACTAACACTAGTGGGATCACTAGCATTTTGATTAAATGGATAAAAATGGTTCCGAGTGGTGCAAACACGCTTGCACTCTCGCCCATGATAAAACCGACTAGCGCGCCGATAATCATGGCAATAACCACTTGCACACCAATATTGCCTAATAAACTCTTGCTCTTCATTCTTGCGAAACTCCCCAGTTGCCGCAGCATTTTGTGTGATGTAGGCGATTTGAATCTAGATGCCGTAAACCTTAGTGATTAAGGCTTACTCTAGGCTAAAAGCCAATGCCTAACACTTTTTTTGTCCGGCTAAAGTACCACAATTGCATAGCTGGGGATTGTGAAACATTACCAACCTTGGTTTCAAAATAACCAAAGTCTAAATCACGTTAATGTCAAATGAAGAATACTTAAATTTATTAGCTTAAAAATGAATAAAGTTTATTTAATGGTGCGATTTTAGCTGCCGGATTGGTGAATTGTTATTTTTGGGGCTGTTGCAACATCTGCTATATAAATTTAATTCGGATGCCCACCAATATAGTTGCCGTCGCAATGATGCCGTTGGGAGATATTAAGCGTCCATCCATCTTTAATGAGGGAGTCGCTGAGCTTTAAATGGTCGTGCAGCATTGACTCTGTGGGATCTTAATAGCATCAAGACTAAGTATTTGCCTTTGGCACGACAATAAAAAGCGCCGACTCATTGCTGGGTCGGCGCTTTTTTACAAGTAAGGCTATGGCTTAATACTTAAGCGTTTATGCCTTTGGACCAGCGTTGCGGATAGCGTCTGACACTTGATACTTAGCGAAGTTTTTAGTGAAAGCTTCGGCCAGCTTCTGGGCATATTCGGCGTATAGCGCTTTATCGCTCCAAGTGTTGATTGGGTTGAGTAAGTTGCTGTCTACGCCTGTGACTGCCACAGGAACGGCGAGGTTCAATGTGTCTAAGTGGACGGTTTCAACATCTTTCAACTCACCGCTGACAATCGCATCGACGATGGCGCGGGTGGTTGGGATGTCAAAACGTTTACCTACACCGTGTGGGCCGCCAGTCCAGCCGGTGTTGACTAAGTACACTTGGCTACCGAATGACTCAATGCGCTTCATCAACAGTTCAGCGTACACGCCAGCAGGGCGTGGGAAGAAAGGTGCACCGAAGCAGGTTGAGAAGGTCGATTGGATCGCTGAGGTTGAGCCGATTTCAGTCGAGCCCACTTTAGCGGTATAGCCAGACAAGAAGTGGTATGCCGCTTGTTCTTTGGTCAGGATAGACACAGGTGGTAATACGCCAGATACGTCGCAGGTTAAGAACACAACGGCATGTGGCTCGGCGCCGCAGTTGTCTTCTTTACGTTGGGCGATGTGCTCCAGCGGATACGCCGCGCGGGTGTTTTCCGTCAGGCTGCTGTCTTTGTAGTTAGGTACGCGGTGTTCGTCCATTACGACGTTTTCAAGCACAGTGCCGAAACGAATCGCATCCCAAATAACTGGTTCATTCTTTTGGCTTAAGTCAATACATTTGGCGTAGCAACCGCCTTCAATATTGAATACGCCGCCAGGTGCCCAGCCGTGTTCATCGTCACCGATCAGGAAGCGTTTTGGATCGGCAGAGAGTGTGGTTTTACCTGTGCCAGATAAACCGAAAAATAGCGTAGTGTCGCCATCTTTACCTACGTTGGCCGAGCAGTGCATAGGTAGCACGCCTTGGGCTGGCAGTAGGAAGTTCTGCACTGAGAACATAGACTTTTTCATTTCGCCGGCGTATTTCAGGCCCGCGAGCAGCACTTTGCGTTCGGCGAAGTTTAAAATCACGGTCGCATCAGAGTTAGTGCCATCACGCGCTGGATCGCAAACAAAGTCAGGGGCATTGATGATCTGCCACACTGGTTTGTCTTTACGATTAAATTCTTCAGGAATGATAAATAGATTGCGGGCGAACAGTTGGTGCCAAGCGTATTGCGTGGTCACACGTACTGGCTGGTAATGCTCTGTATCGGCACCGACTTCGAGATCAGATACAAACAGTTCTTTATCGGCTAGGTAAGCTTCAACCCGTGCCCATAAACCTTCAAAAGCACCAGGGTCGAAAGCTTGATTGACAGGGCCCCATTCAATCTCTGCTTCTGAACTGGGTTCTCTTACGATAAAACGATCGCCGGGTGAGCGACCACTGCGAGCGCCTGTTTTAGCGACTAATGCGCCATTGGCTGTCAATTCACCTTCGCCGCGTAGCAGGGCAAATTCGACAAGTTGTGCTGTTGAGGGGTTAAAGTGAACGCGGTTTAATCCATCCGCCATAGTGAGGTCTCCATTTGATTTGTAAGGTTATTTTGTTATCGATCGTCTGCCGATTTTATTTTATTTATCACCTAAGCTTTGCTAGGTAACAAAGTGAGGCGATTGTAGCCTAAGCCGAGTGCAATGTGTGAGGGGATTGTGGGGATGAATAAAAATGGGCGCCGAATCGGGCGCCCATATTGTTTAGCTTTTATGATCCGTTTAACCGATTATTGCTGGGTTGTTGACGTCTGACCGTTCGCAAATAATGCCGCTACGTCTATGGCATCAAAAATGTATTTTGCGTGGCAATATTCGCAGCCCATTTCGATCTTGCCGTCTTCGGCGAGAATGGCTTCGATTTCAGCTTGATCTAAGGTCTTGATCGCGCCGGCACTGCGTTCATGGGAGCAAGTACACTTAAAGCTCACATCGATAGGATCGAATAGGCGCACTTCTTCCTGATGATATAGGCGATGTAACACGCTTTCAGCGTCGAGTGTGAACAGCTCTTCGGCCTTGATAGTCGATGTCAGTTGGCTCAAGTGTTCGAAGTCTGGGTTGTGATCTTCTTTGCTTGGCAGAATTTGTAGGAACATGCCCGCCGCTTGTTTGCCGTCGGCAAATAACCAAATGCCTGTTGGTAACTGTTCAGATTGATTGAAGTATTCTTCAACACAGGCCGCTAGCGTTGGCTTGTCGAGTGCGACCACACCTTGGTAGCGTTCACCTTCGTCGGGTGTTAAGGTGATGACCATATAGCCCTGACCGAAAAGATCGGTAAGGCTGGCATCATCGGCGAGTTCTGCATTCCAGCGAGCCACGCCGCGTAGCTCTTGTAGATTGTTACCGTTGATCACAGCTAAAGAAACAGGACCATTGCCCTGTAACTGCACGCTGATATCGCCGCTAAATTTCAATGTCGCCGTTAATAGTGAGGTGGCCGCCATTAATTCACCCAGTAATACTTGCAGTACGGCTGGATATTCTTGGGCGCTGATCACTTGTTGATAGCTGTCTTGTAACTGCACCAGTTCACCACGCACATCGGCATTGTCGAACAAGTAGCGGTGTAAAGTATCTTGGTTCATCGTGTATCTCACTGTTAAAGGTCTTTAAAGCGCATGAGCTGGCGTCGTTGCTTTTTATCTGGTTTCTGATCGGGTATCGGGTTATTGAGCATGTTGAGACGTCGTTGCTCAGCATTAATGGCCCGTTTAGCCAGACTTGTCGCGGTTTCTTCGTACAATTCTTGTGCAATCGCACCACTTTGGCGTAATTGCGATAATTTTTTTATGACGATCTCTTTATCGTCATATCCTTGGCGTATCTTAAGGACGGCGCCTATCTCGGCATTCCTACTAGATTTGGCGCGCTGGCCGTTGTAATGTACTTTGCCGCCGTTAATCATTTCTTTGGCGATGGCACGGGTTTTATAGAAACGTGCAGCCCAAAGCCACTTATCGAGTCGAATGTCTGTTTTGTCGCTTTGACCTTGAGTCATAGTGTCTCCAGCTAAAAGATCGATGAGTTCACATGCCGAGCAAGAAAATGCGTGAAGTGCATTTCTATTATGTTAAAGGGTGGCGTGCTTCACACCCTTTTGTGGCGGGCAAAGTTAGCATAATTGGCCTATTTTCGCCAATTTGAATGCTGTTAAGGGCTTGTTGCCAAATGCAGCCTAGGTTAGCATGAGGCGGTTATTGCCTAATTATTATAATCAGAACAGAGACCTGTATCGAGGGTCCACATAAAATATGGATTTATTAGATAAAGTTATCGCAAAAGCCATTGGCATGCCCCATAAACCCCTAAGCCGGATCGTCTTCTGGTTGGGCTTTATTGTGATCATGTTGTTAGCTGCGCAAATTACATGGAAATTAGTGCCTACTAGCTCATCAGCCAGTGCTTGGTCCCCAACGCCCGTAAGCGTGAATGGCAAAGGCGCCGGTCAGGTTGATCTCGCAGGTCTGCAGCAGCTTGGACTCTTTGGTAAGGCGGATGCGAACTCTGATAGACCTAAAGTCGAAGCGGTCGAGACTGTGACCGATGCACCTAAGACTACACTTTCAATTCAGCTGACTGGGGTTGTCGCATCGACAGCGGACCAGAAAGGCCTCGCAATTATTGAGTCAAACGGCAGCCAAGATACTTATAGCCTCGGCGATAAGATCAAGGGCACATCTGCATCACTCAAAGAAGTCTATGCCGACCGTATTATCATCACTAACGCTGGCCGTTACGAGACCTTAATGCTCGATGGCTTAGTGTACACCAGCCAAAGCCCAGCCAATCAGCAGTTACAACAAGCTAAGAGTAATAAGGCTGGCTCGGCAGTGAGCCGTGTTGATCAACGTAACAATGCCGATATTTCCCAAGAACTCGCCGAATCCCGCACCGAATTGTTAGCCGACCCCAGTAAGATCACCGATTACATTGCCATTTCACCCGTGAGACAGGGTGATTCTGTGGCGGGTTATCGTCTGAATCCGGGTAAAGACGCCAATTTGTTCAAGCAGGCGGGTTTTAAAGCCAATGATTTAGCCAAATCGATTAATGGATACGACTTGACCGTAATGAGTCAGGCGTTAGAAATGATGAGCCAATTATCCGAGTTAACCGAAGTATCAATTATGGTTGAACGGGAAGGACAATTGGTTGAAATCATGTTTAGTTTGCCGCAATAACACGCGTTAGAGGAAAGAAAAAGAATGAATAACAAAAGGATTCGACGCAAGCTGATTGCAGGGATTGTTGCGGGTGCAGCCATGTTCTCCTCCCAATTCGCTTGGTCTGAACAGTATGCGGCCAACTTTAAGGGCACGGATATTCAAGAGTTTATCAACATAGTTGGTAAAAACTTGAACAAGACCATCATAGTCGACCCGACGATCCGCGGTAAAATCAACGTTCGCAGTTATGATCTGCTGAACGATGAACAGTATTACCAGTTTTTCCTCAACGTCTTACAAGTCTACGGTTATGCCATCGTCGAGATGGAAAATAACGTCATCAAAGTCATCAAAGACAAAGACGCCAAAACTGCAGCAATCCGAGTGGCTAATGATGCCGAACCTGGGATAGGCGATGAGATGGTCACGCGTATCGTGGCGCTGTATAACACTGAGGCCAAGCAATTAGCTCCGCTGTTACGCCAATTAAATGATAACGCGGGTGGCGGTAACGTAGTGAACTACGATCCCTCTAACGTCTTGATGTTGTCTGGTCGTGCTGCTGTGGTGAACAAGTTAGTGGAAATTGTCCGCCGCGTAGATAAACAGGGCGACACCTCAGTACAAGTGGTGCCGTTAGAGTTCGCTTCCGCCGGTGAAATGGTACGAATTATCGATACCCTTTATCGCGCCACGGCTAACCAATCTCAAATGCCTGGTCAAGCGCCGAAAGTGGTTGCCGACGAACGTATCAACGCTGTGGTTGTTAGCGGCGACGAAAAGAGTCGCCAACGTGTGGTCGAACTTATTCATCGTCTCGATGCCGAGCAAGCCAGCACGGGCAACACTAAGGTGCGTTACTTACGTTATGCCAAGGCCGAAGATTTAGTTGAAGTGCTGACAGGGTTTGCCCAAAAATTAGAGGGTGATAAAGACCCAAGCGCTCAGGCTGCTGGTGGTAAACGCCGTAATGAAATCAATATCATGGCGCATACTGAAACCAATGCCTTAGTGATCAGTGCTGAACCCGATCAAATGCGCACCATTGAGAGCGTGATTAATCAGCTCGACATCCGCCGTGCTCAGGTACTCGTAGAGGCCATTATTGTCGAAGTTGCCGAAGGCGACAGTGTCGGCTTTGGGGTGCAATGGGCGGCGAAAGCGGGTGGTGGCACTCAGTTTAATAACTTAGGCCCAACCATTGGTGAGATTGGCGCTGGTGTATGGCAGGCGCAAGATAAAGAAGGCACATTCATCACTAACCCGAGTACTGGTCAGGTGATAGGTCAAAACCCGAGTACTAAGGGCGATGTTACCTTACTTGCCCAAGCTTTAGGTAAAGTGAACGGTATGGCATGGGGTGTGGCCATGGGTGACTTTGGTGCGCTGATCCAAGCCGTATCCAGTGACACTAACTCAAACGTACTGGCGACACCTTCTATTACTACCTTGGATAACCAAGAAGCATCTTTCATCGTCGGTGATGAAGTGCCTATTCTGACGGGCTCTACCGCCAGCTCTAACAATAGCAACCCATTCCAAACCGTTGAACGTAAAGAGGTTGGGGTTAAGTTGAAAGTGGTGCCGCAGATCAACGAAGGCAATGCGGTTAAGCTGACGATTGAGCAGGAAGTATCAGGAGTTAACGGTAATACAGGTGTGGATATTTCCTTTGCGACACGCCGTTTAACCACTACCGTTATGGCTGATTCAGGGCAAATTGTGGTTCTGGGGGGCTTGATCAACGAAGAAGTGCAAGAAAGTGTGCAAAAAGTGCCTTTCTTGGGTGACATTCCTATTTTGGGACATTTATTCAAATCTTCTTCGAGCAAAAAGACCAAGAAAAACCTGATGATCTTCATCAAACCAACCATTATCCGTGATGGCATCACAATGGAAGGGATTGCTGGGCGTAAATATAACTATTTCAGAGCATTACAACTTGAGCAGCAAGAGCGTGGCGTTAACTTAATGCCAAATACAAAAGTGCCTGTGTTGGAAGAATGGAATCAATCAGAGTATTTGCCGCCAGAAGTTAACGCGATTTTAGAGCGTTACAAAGAAGGCAAAGGTTTAGATGCCCAGATGCGCCAAACGGATCCGACTTTAAAGTCACTGGAAGAAAACAAGAATAAAGATAAAACCAATGAGTGAAATACAATTATCCCAAGTCGATGACCTCAGCCAAGTCTCCAATGAGCTAGGTCTGGAAGCTGAAGGTGATGAGGTGTTTCGTTCGAGCAGTAAAGAGCGTTTACCCTTTGCCTTTGCCCATCGCCACGATGTGGTGCTTGCGCCGGGTGAAACGGGCGAGTTGAGCTTGTTTTATACCAGCAAAACACCGTTAACGGCCATGCTCGAAGCACGGCGCTATTCGGGTGTCGATTTGCCTTTGGTTAAGCTCGAAGTCGCGAAGTTTGAGGCCAAACTCACTCAAGCGTATCAGGCGAACTCGTCTGAAGCGCAGCAGTTGATGGAAGATATCGGCAACGAGATGGATTTATTCACCCTTGCCGAAGAGCTGCCGCAGACCGAAGACTTGCTCGAAGGTGATGATGACGCGCCTATCATCAAACTGATTAATGCCTTGTTATCTGAAGCGATTAAAGAAGAAGCCTCGGATATCCACATCGAAACCTACGAGAAACAGCTCGTCGTGCGTTTCCGTATCGATGGTGTACTCAAGGAAGTCCTCAAGCCAAACCGTAAGTTGTCGTCGCTATTAGTGTCGCGGATTAAGGTGATGGCGCGCCTCGATATCGCCGAAAAACGTGTGCCACAGGATGGCCGTATTTCACTGCGTATTGCAGGTCGTGCCGTGGATGTGCGGGTATCGACTATGCCATCGAGCCACGGCGAGCGTGTTGTGCTGCGTCTGCTGGATAAAAATACCGGTAACCTCGATTTAGAGCAATTGGGGATGACCGACGGTATTCGGGTTAAGTTTGAAGATCTTATTCGTCGCCCCCACGGCATCATTCTGGTCACCGGCCCAACGGGTTCGGGTAAAAGTACCACCTTGTATGCGGGTCTGACTGAGATCAACTCAAAAGACACCAACATTCTTACCGTGGAAGACCCAATCGAATACGAGCTAGACGGTATCGGTCAAACCCAAGTTAACACTAAGGCTGACATGACCTTCGCCCGTGGTCTGCGCGCAATTTTGCGTCAAGATCCCGATGTGGTGATGATAGGTGAAATCCGTGACTTGGAAACCGCACAAATTGCGGTGCAAGCATCACTCACTGGTCACATGGTAATTTCAACCCTGCATACCAACACAGCGTCTGGCGCGATAACGCGTCTGCAAGACATGGGCGTTGAGCCATTCCTCGTATCATCGAGTTTGCTGGGCGTGTTGGCGCAACGTTTAATCCGTACTCTTTGTCCAAAATGTAAAATCGAGCACGTGCCCGATGCGCGTGAGCGTGAGTTACTGGGCATAGCAGCCGATGACGACAGGCGTATTTTTCGTGCCAATGGCTGTAAATCCTGTGGTAACAATGGCTACCGTGGTCGAACGGGTATTCATGAGTTATTGCTGGTTGACGACAATGTCCGCGAATTGATCCATGGTGGCCGCGGTGAGTTAGCTATAGAAAAGTATATCCGTCAGTTTGTGCCGAGCATTCGTCACGATGGCATGAGCAAAGTGCTCGCGGGCATCACCACTCTCGAAGAAGTGCTACGTGTGACTCGCGAGGAGTAATTTATGCCAGCATTTGAATATAAGGCACTGGATGCCAAGGGAAAGCAGCTTAAGGGCGTAATCGAGGCGGATACCGCCAGACACGCCCGCAGCCAGTTGCGCGATCAGCGCATGATGCCCTTGGAAATTCTGCCTGTTTCCGAAAAAGAAGCCAAAGCTAAGAGCAGCAGCTTTTCGTTTTTCAAACGGGGTATCTCGGTCGCTGAATTAGCCTTGATCACCCGCCAAATCGCGACCTTAGTGGCCGCGGGTTTACCGATTGAAGAATCCCTTAAAGCCGTTGGTCAGCAATGCGAGAAAGATCGCCTTGCGAGTATGATCATGGCGGTACGTTCGCGGGTGGTTGAAGGTTACAGTCTGGCCGATTCCTTGGCTGAATTTCCGCACATTTTCGATGATTTATACCGTGCCATGGTGGCCTCGGGTGAAAAGTCCGGTCATTTAGAAGTGGTGCTCAATCGGCTGGCGGATTATACAGAGCGACGCCAACAGCTTAAATCTAAGTTGACGCAAGCCATGATTTATCCTGCGGTATTGACCACAGTGGCGATTGGGGTAATTTCCATCCTGTTGGCGGCGGTTGTACCTAAGGTGGTCGGGCAGTTTGAACACATGGGCGCCGAGTTACCGGCTTCGACACGCTTCTTGATTTCGGCCTCTGACTTTGTCCAAAACTACGGTGTGTTTGTGGTCATTGCCCTAGTGATGCTGTTCGCGCTCTTTCGGCGCATGTTAAAGTCGCCTGCGTTTCGAATGAAGTACGACAATTTTTTATTGAGCATGCCAGTTGTTGGCCGCGTGAGTAAGGGCTTAAATACGGCGCGTTTTGCACGGACCTTAAGTATCTTATCCGCAAGCTCTGTGCCTTTGCTCGACGGTATGCGTATCGCCAGTGAAGTATTGCAGAATGTGCGAGTACGGGCCGCAGTGGATGATGCGACTGCGCGGGTGCGTGAAGGAACTAGCTTAGGTGCGGCATTGACTAATACTAAGTTGTTTCCGGCAATGATGTTATACATGATTGCCTCGGGCGAAAAGAGTGGCCAGCTTGAGCAAATGTTAGAACGCGCCGCCGATAACCAAGACAGGGAATTCGAGGGCAACGTCAATATAGCGTTAGGGGTATTTGAACCTATGCTGGTGGTGAGTATGGCGTGTGTGGTGCTGTTTATCGTGATGGCGATTCTGCAGCCAATCTTAGCCTTGAATAACTTGATTAGCGGTTAATTTTTTGAATTTATGGATTATGACCAAGTCGATTGGCATAGGATTTTTTAGGAGAAGTAGGCGATGCAAATGAACAAGAAACATCAAGGTTTTACCTTACTCGAAGTGATGGTGGTTATCGTTATTTTAGGGATTTTAGCCTCTATGGTGGTGCCTAACTTAATGGGTAACAAAGATAAAGCTGACCAACAAAAAGCAGTATCTGACATAGTCGCACTCGAAAATGCCTTAGACATGTATAAGTTAGACAACGGCGTCTATCCAACGACCGAGCAAGGCTTAGAAGCCTTAGTGCAAAAACCAACGATTTCACCTGAGCCACGTAATTACCGTGAAGATGGTTATGTCAAACGTCTGCCAGAAGATCCATGGCGCAATAAGTACTTGCTGCTCAGCCCTGGCGAAAACGGCAAGCTAGATATATTCACTGCAGGCCCAGATGGTCAACCAGGTACCGAAGATGACATCGGTAACTGGAATTTACAGAATTTCCAATAAGATGCTAAAGCTGCGCCACGCTGGTTTCACCTTAATGGAGGTGATGCTAGTGATCTTGTTGATGGGACTCACGGCAGCGGCTGTGACTATGTCTATCGGCAATTCGGGCCCACAACAGGCCCTAGATAGAACGGCGCGGCAGTTTATCGCTGCCACTGAAATGGTGCTCGATGAGACCGTTTTGAGTGGCCAATTTATTGGGATTGTGATTGAGAAAACCAGTTATCAGTTTGTTTTTTATAAAGACGGTAAGTGGGAGCCTTTGGATAAAGACAGGCTCTTGTCTGAAAAGCAGATGGAGCCGGGTGTCGTGATGAACTTAGTGCTCGATGGTTTGCCCTTGGTACAAGACGATGAAGAAGACGATTCTTGGTTTGAAGAACCACTAATTGAGCCCTCTGCCGATGACAAGAAGAAACATCCTGAGCCACAGGTCATGCTATTCCCGAGCGGCGAGATGAGCGCTTTTGAGCTGACGTTTATCGCCAAAACTGACAAAGGTCAGCAGGCCGAAGCGTTAGTGGTCGGCGATGCGCTGGGCAGATTAACCATAGGGCGCCCCGATGAAACGCGTTAAGGGCATGACTTTACTCGAAGTGATTGTGGCCCTTGCCGTGTTTTCGATTGCGGCCGTGTCGATCACTAAAAGTTTGGGCGAGCAGATGGCCAACATGCCGATTCTGGAATCGCGCACTTTGGCACAGTGGGTGGCCGATAATCAGATGGTCGATGCCAGACTCGAGCCGCAGTTTCCTGAAATCGGTAAAAAAGAAGGCCAAGTTGAACTCGCAGGAAAGGATTGGTATTGGCGTAAAGAAGTGGTGAAAACCACGGACGATAATTTTCGTATGATCCGCATAAGCGTGAGTGAAGACGAACGATATCAGCGTATTGCAGCACAAGTGAGCAGTTATGTCCTTAAAACTGACTAGTGCGCAGCGGGGATTTACCCTACTAGAAATGCTGATCGCCATCGCGATTTTCGCCATGATAGGTTTGGCTTCTAATGCCGTGCTCAGCACTGTGCTGACTAACGATGAAGTGACGCGCACTTTCTCTACTCGGCTTAAAGCCCTGCAGCAAGGCTTTGGTGCGATTGAGCGTGATCTCGCCCAAATGGTGGCGCGTACACCCAGATTACTCGAAGGGGGTCGAGGTTCGACCGTCTTCCAAACGGGCAATGATATCTTAGACTCGGAATCAGAAGCCTTAGTGTTTTACCGTTTAGGTTGGTTAAATCCCGACGGTTTATTACCGCGGGGCAGCTTACAGTCTGTGGCGTATGTGGTGCACGAAGGGCGACTGGAGCGTTGGTATTTTCCTTATCCTGAGCCTGAATTTGGTGCCGAGCCGATTAAGACAGTGGTCATAGATAAAGTATTGTCGGTGGAATATTCCTTCTTTATGGATGACAAGTGGGAGCGCAAAGTCGAGGCGACTAAATTGCCTAAAGCGATCGCCATGGAAATTGAGATTGAAGGCTTAGGCAAGATCCAACGTAAGTTCTTGTTACCGCTAGGCGCTGCTGCACCTGACAAGTCCAAAGATGAAGACAATAAAGACGAAAATCCGGACGATGCTAACAAAGATAAAAATGACGACGGCAGTGGAACTGGCAATGGTTCCGGTGACCCAGATTCTGGCGATTCCGGAGACGGTGATCCAGACGCAGAGGGCCGCGAATGAGAACCCAATCTCGGTCTAAACAGCGCGGCGTTGCACTGATCGTCGTGCTCTTGATTGTGGCTATGGTGGTGATCATCGCGACAAACATCACTGGCCGTAATCAGTTATCTATGCGCCGAACCTTAAACTTGGCCCAGTACGATCAAGCTTATTGGTATGCGATTTCCGCCGAAGAATTGGCGAAGAAGATTTTAAAACAAGATTTAGATGACTCTGAAGGTCGAGTCCACAGGCAACAATATTGGGCCATGGCCGATGTGGTGTTTCCAGCGGAATACGGTGAAATCGCGGGCAAGATCACCGATATGCGCGCCTGTTTCAACTTAAATGCCTTGTCCGCGACCACGAAAGAGGTGGAAAACGGTCAACCTAAGCTGCCCTTGGCGGCGAAGCAATATAAGGCCTTGTTGGTGAGTTTAGGGATGGACGACTTTAGTGCGGATCGCCTGACTCAGACCTTAAAAGATTATATCGACGAAGACATGACGGCCAGCCCCTACGGCGCCGAAGATGCAGAATATGAATCGCGTAATGTGCCTTACCGTGCGGCCAATACGCTGATGAATCATCGCAGTGAGTTACGCGCAGTGATGGGATACACCCAAGATATCTATTTGAAACTCTTGCCCTATGTTTGTGTGATCCCAGGCAATGACACGCAATTGCTCAACGTGAATACCGTGGAAGTTGAGCAAGCGGCGCTGTTAGCTGGCATGCTCGATAATCAGATTTCGGTCGGCGAAGCAGAAAGCATTATCAACCAACGCCCCAGCGATGGCTTTGCCAAAATCGAAGATTTTTATGAAAGCTCGTCTATGGGCTCGATCAAGTGGGAAGGTGCCATGAAGTCGAGTTTTGTCATCGATAGCCAATATTTTCTGTTGGCCTCGGGGGCGAAAGTCGATAACGCCGTGTTTAGAATGGAAAGCGTGCTCAAAAAGGGCGGCGGCAACAAAATGGAAGTGCTCACCCGTCAGTTTGGTGGCCAAAAATAATGGCAGTGCTCACCCGGCAGAATCTGGTGGACAAAAATAATAGCAACGAGCTCAGTCACGCTTTCTTGACTGGGACTGGTGGAGAAAAACTGTGAGTGAACGGCTATTTATTCGCTTAGGCAGAACAGCAGAGCAAGCATGCTCTTGGCTCGTTTGGTCTGAGCAAGAACAGGAAATTATCGCCTCTGGCGAGCTCGCCAATGCCCAAGGTTTGTCGACCTTAACTGAGCGTGCGGGTAATCGTCCCGTCGATGTGTTAGTGCCCGCCGCGGCAATGACACTCACTAGCGTGCATTTGCCCGAAAAAGGCCAACGCCAAGCCTTGAAAGCCTTGCCCTTTATGCTCGAAGAAGCCTTAGCCGAGGACGTCGATGCCATGCATTTTACCGTCGGCCCGCGTGATGGCGATGCCTTAAGCGTGGTCGCCGTGGCCCATGAGCAAATGCAGACTTGGCTGAGTTGTTTGACCGAGGCGGGTTTAAAAGTGAAACGCATCGTACCCGATTGCTTAGCCTTACCATTGCAAGAGTGCCAATGGGCGGCAATGAATTTTGGTAATGAACTATTACTGCGTACCGGATTAGCCACGGGCCAGAGTTTACCTTTGCCTTGGTTACCGATTGCGCTGGAGCAACTTAGGCCCGCACAGGGCGAAGTGTCTGTCGCCAGTTATACCGACATGCAACTCGAAGGTGTTGAGCTTAAGCCACAATCACTCGAGCTGCCTATGTTAGTGTTAGCCCGCGGTATTTTGCATGCGCCAGTTAACTTACTGGGCGGCGTTTACACGCCTAAGCGTGAATACAGCAAACAGTTGATGATGTGGAAGAGTGCTGCCATCGTTATCGCCATCGCTTGTGTGCTGGCCTTAGCGAATAAAGGGCTGACCATACATCAAGTCAATGCGCAAACTGCGGAATTGACGGCACAGAGTGCAGATATTTATAAGCAAGTGATACCAGGCAGTACGCCGCAAAGTGGCATTTTACGACAGCAAATAGATATAGAACTGCGATCTTTGCAGGGACAGGGTAGTGGTGCCGAGTTTTTTGCCATGCTCGATGGGCTACAGGATGCCTTTAAGCAAGTACCTGAACTTAAACCTAATAGCCTAAGATTCGAAAGTGCGCGTAACGAGATCCGCATGCAAGTGAGTGCGAAAAATTACGCGCAGATCGAGAAGTTTAAAGAGATCGTTGGTCGTCGTTTCCAACTCGATGGCGGTACTATGAATAGCGGTGAAGATCAAGTCACCAGTACCTTAACCTTGAGGAGCAAATAAGATGGATAATTTGCGGACTTGGTGGCAAGGGCTCGCATCGCGCGAGCAGCAGTTAGTCGGTTTTGGTTCGGTATTCTTAGTCATAGGTATTTTTTATTGGGGTGTTTGGACGCCAATCGCCACTGCCGAAGCCGATGCATTACGTAACTTCACTGTGCAGCAACAGACGTTGAACCATGTAAAACAAACGGCGAATAAAATTGCGAGTTTGAAGCAGAATGGTACTAAACCGACTATGTCGGGCAGTTTAAGCTCAGTGGTTAACCAAACTGCGGGCAATTATGGGTTAGTGATTACTCGCATGCAGCCCCAAGGCGATAAGATCCAAGTGTGGATGGACGATGTTCCTTTTGATGCCTTGTTAGGTTATTTGAGTGAGTTAGTGCAGAAAAAAGGACTTTCACTCGAAAGTGTTGATTTGGCAGAGGCCGATGCCCCTGGCTATGTCAAAGTTAGACGTATCCAACTGTCGCAATAAATTAAGGTGTAGCTGTGAGTTTGATAAAAAAAGTCATCATAGGCGTACTGATTTATTTAGTATTTTTAGTCGTTCTATTTCCCGCTAAGATTGCGATAGCGTTAGCGCCATTACCTGCAAATATCAGTGTTTCGGGTGTGAGCGGCAGTATTTGGTCTGGCAGCATAGAAACCTTAAGTATGCCGCAACGCCAGTTAGAACAAGTGCGTTGGGATCTCAGCCCTTGGGCCTTGTTGTTAGGTAAAGTGAAAGTTGATTTTCAAGTGGGCAGCCGCGCAACGCCGGTTAGCGCTAAGGGATTAGTCTCTTGGTCCATGGGCGGCTTAAGTGCGCAGGGTTTACGTTTCGAAGCGCCAGATAGCTTTTTACTCGGCAATGCAAAACTGCCCTTTAAAACCGAAATATCCGGTGAAGTTAGCCTATTAGTCGAGACGTTAGAGCAAGGTAAACCTTGGTGCGAGCAGTTATCTGGTAAGCTGTTTTTGAATCAAACCAATGTTAAAAACCAGTTTGGCAACTATCCACTAGGCAATATCGAACTCGGCCTGAGTTGCGTAGACGGCAAAGCGCAATTAGCCACAGATGAAGCCAAAAACCAGCTCGGCATCGTCGGCACCTTGCAGTTAGATGAAGGCAATATGGTCAAAGTAGCAGCTAAGATTAAAGAAACGGCTGAGCAACCAGAAGATATGCGTAAGTCACTGGGTATGCTAGGCAAACGCGGCAGCGACGGTTATTTCCCCGTGGTATATCAGGGTCGTATTCCTGGGTTATAGTGCTGTTTCAGCGCGCGATGATAGATCTGCATTATGAATGAAAACGAAGGCACCTTAGGTGCCTTTTTTTATTGTATTTTTATCGTGTTAGATCATTGAGTAGCGAGTGATAGTCGGTAATCGCGGGGAAATCTTGAAAGACTTTGACTGGCTTTTTACTGTCTGGGTTAGCAATCCCCAATTGATGGCCTACGCCGGCAAGTTTAGCGGCCTTGAGTATGGCTTCGCTGTCGTCGATAAAGAGACAGCGACTTGGCTCAAGTTGAAACTTTTCAAATAAGGTTAGCCAAAATTGCGGATGCTCCTTGGGGTAACCTGTTTCGTGGCTGGAGATCATGGCATCGAGCCCTGTTGCTAGTTCGGTATGTTCAAGCTTTAGGGCGAGACTCTTAGGATGCGCATTGGTCACCAAAATCCGCTGTTTACCCGCAGCGGCTAAGGCAGCTAAAAACGGCATACTGTCTTGGCGCAGCTGAATGCGATCCACTAAAGTGCGATGCAAACCCATAATGTCGAGTTGTAGCTGCTCCTGCCAATAATCGAGGCAGTACCAATCTAACGTGCCGACAACTCTCTCGTAGGCGTTGACAACTAACTGCTGCGCTTCGTCTTTGCTCAGACCCCGTTGCTTGCTTAATTCCTGTGGCACTAAACTCAGCCAAAAATGATTATCGAAGTGCAAATCCAATAGTGTGCCGTCCATGTCGAGCAGAACCGTATCGATAGTCTTCCAGTTAAACATTGGGTATTTCCTATTCTTTGCATGGTGAACAACGCCTGCGATTGACGTATGGGCAGGGTAATTCTCAGTGGTCGCTATGATGATTATTCCCGTAACTTGTATGCTTACATCTTTGTGCTAGTCTTGGATGACTAATTATTTTTAGTCTGAAAATTAAGCGGTTGGATATGGATATCTGTTCTTTTGCCCTATTAGCTCAAGTTCCAAACTCCCTTAGTTTACAGCAAGAGCTGAATGCTTTGCTGCTGCAAGACTGGCTACCCCACGTAAATCAGCGTGATTATCGTCTAGGGGATCAGGGCGGTTGGGATGTGTTGCCTTTGCGCTGCGCCGCTGAGTATCAAACAGCACATCCTATCTTGCAAGCCTTCTCAATCTCTACACCGGATCACTGGCAAGATCTGCCTAGTCTCAAATCGAGCCCTGCATTATGCCGTTTTCTGCAATCCCTAGCCTGCCCAGTGAAATCCGTGCGTTTGATGCGCTTACATGCGGGTGCAGAAATCAAACCCCATAGAGATCAAGGACTCTGCCTAGAGCAAGGTGAGGCAAGACTGCATTTGCCACTGCAAACGAATGAACAACTTAATTTTTACGTGGACGATAAACGTGTACCTATGCAAGCCGGCGATCTTTGGTATATCAATGCCGACCAAGTCCATTGGGTTGAAAATAAAGGGCAGGATGCGCGGATCAACTTAGTGATTGATTGCGAAGTGAATACTTGGTTAAGGGAGTTAATCGATGCTGCAAAGTTCAGAGCGTGAATTAGAACAGAGTGTGAATGTCGTCGCGTTAACTCAGTTTGCTAACGCGCTATTGCTGTCAACCTCGGCAGGGGAGTCTAAACGGCTAATCGATCCCATATTGGAGCAGTTATGCCAAATAACCGCGGTGGAATTGCACCCCGAGTATTTTTTGGATACTGAGGCGAGTATTACGCTTTATGGTAAGGCCGTTTCGCTCACTACTGCGGCGCAGTGTGCAGAAGATCCTGAGCGGGGGCGAGTGTTTATTCAGGGCATTTATCAGGTCATACAGGATAAGTTGGCGCTAAATCCGCAACGGCCAATACAGATTTTGTATGCGGGTACAGGACCTTTTGCCTGGTTGTTATTACCATTACTGCCGCTGTTTTCTGCGAGTCAGATTCAAGTCACCTTGCTCGACATTCACCCTGCCTCGTTAGATAAAGTCACCAAGCTTATTGAACACTTTGATTTAGCAGATCGTGTGGCAACGTATGTCTGCGCCGACGCCACAGTTTGGCAACCAGAAGCTGCAGTAAAGTTCGATATGATAGTCTCTGAAACCATGAAGCATCTGCTGCAACAGGAGCCTCAAGTCCAGATATTCAGCCACTTACAGGCGTATCTTGCGGATGGCGGCGTGCTGATTCCGCAAAACATTGAACTAGACGCTTGGCTTGAGTCTTGTACTGTGCAGGATTTAGCTGAGACGCATTATTTAGGGCCTTTGTTTGCACTCAACCTGCAAACTGCGCGCTTACTTGCCGATGGAGATCGTAGCTTCTTGGCTGGTACTTTACTGCTCCCCGATTTCAGCCCGAGTGCGGTAACGCTTAAGTTCACGACTTCCATTCAAGTCTATGGTAATTCCACGCTGAGCGAAAACCAATCCCAACTCACTCTACCTCGATATCGACGCGAGCATTGGCTTAAGCCTCTTTCATGCCTTGCTTTTCGATATGAGCAGGGCGCTCATCCCGATTTTGTTTTTGATGTTATAGAGCAAAAACCAGTGCTGGTGTCGAGTGACGATCTGAGCTGCTTAGGCATTTACCATTTGCAGCGGCTTTGGCAAAAGATACAGCTACGTAAGCGGGGAGTGCCATTCACTGAACGGGCAAATGAATGGCACTTAGATAAAACCTTGCTGGATCTTTGTGGCATAGGGCTAGAGCCGGGTCTAAGAGCTTTATACCAACACGATCATCAGTCAGCTTTTGGCACTTACATTCAGCAAACAGCCAAGCTGACAGCCGCGGATATTGCTCGGATTAATCAGCAATTAAGCACTATTTCCCATGGATTAACGTCATCGGTCACAATGCCAGAAGTCGATGATTTAAATTCAATAGAGGTTGAAGACTCAAATCCGGCTGCATTACTCAGCGAGTCACAACTCAATTTTTGGCGTAGTGAAGGCTACTTAGTGATCCCGCATGTGCTTACGGCAGAGCAATGCGCCGCAACTCGAGATTTTATTTGGCAGCAGTTAGGGGCAAATGAACAAGATCCCGCCACTTGGTATCAAGCTCATGAGTTTATGCAGAAAATAATGCTACAACTGTTTCGCCATCCGCAACTGGACGCGAATCGACAAGTCCCCAAAATTCGTCAAGTGTTTGAACAGCTATGGCAGCGAACCGATTTAGTCATGACAACCGACAGAGTCAGCTTTAATCCACCTGAAACACCCACTTGGCGTTTTCCCGGGCCAGATATGCACTGGGATATGCCACTGCAGCTACCCGTTGAGTTTGGCACTCAAGGGTTAATTTACTTAACCGATACCAGTGTTGACCAAGGCGCATTTTGTTGTGTCCCAGGATTTCATCTCAAGATAGAGGAGTGGCTCCGTAGCTCAAAAAAGCCTGATATCGAGCTGCAGCAACAGGATTGGAGCAAGTGGCCTGTTAAATCCATAGCCGCAAATGCAGGTGATTTAATCATTTGGCACCATGCCTTGCCCCATGGTGCGAGTCCGAATCGAGCCAAGTTACCGCGTATGGTGCAATACATTAACTTTTATCCTATGGCTTGCTAATGGTTGATTTTAAAGAGCAGGAGCAACGCTATTGGCAATTGTTACATCAACCAGAGTCATTGTTTTTACTGCATCGAGCGAATGATGTGGAGCATTTTAGCGCGCTGATGGCTGATTTTCTTGCTTGGCCTGAGCTTAGTTTGGAACACATGCTTGCATTCATTGCTTTGCAGCAAGAAACCTTTATTCCAGATTTAGATCGCTTCAGCCAAGCCTGGTTCCCTTGTCGTTATCACTCACAACATAAAACGGTCAGTTGGGTTCCCGCACTTCATAAGTTATCTAAGTCTTTCCTAGAGGACGATATCAGTGATGCTCGTCGTTGTTTACTCGCCAGTTTTATTCAACCTAAAACCTTGCTCGAACCCCTTTTATCCCAACGCGAAAGTTTGCCTATTGTAAATCCAAGTTTGATGATTTTTCACTGGTCCCGTTGTGGTTCGACGCTATTGTCGAGCAGCTTTGCCCTGCTCGAAACTTGCCGAGTGATCTCTGAATCTATGCTATGCAGCGATGTTATGCATGATGACCATTGGCCTTTGGCACTAAAACCTCAGCTCGTCGACTTATGCTTAAGACTACAGGGGCGATTGAGGCTGGGCGAGCGTGAGTTAGTGATCAAGTGGAATGCATGGGATCTGGCTTGCTGGCCGATGTTGCTCGAACTTTACCCTAAGAGCCGAGTGTTATGCTTGATACGCAATCCTAAGGATATTATTGCGTCTCATCAAAGAGTTGCTGGGAGGCATATGGCTGGAGGGCAAAGTTTACTTCCAACTGAACTGCATACTCAACAGATGGAAAATACGGATACTGAGCCTAGCTTAGATGAATTTCGCACCACCGTTTTACAGTATTTAGCGCAATTATCTCTCGCTTTACATCAATCTTCTCGAGTTATTCTACTGGATTATCATCAATTAACAGATTTTAAACCCACAACATTCTCTACCATCTTAGGATGGCCATTATCTGAAGGTGAGATTGAGCGCTGGCAACACCATTGGCGTTTTGATGCGAAGCAGCAAGGGCAACTATTTACACCTGTTGTGCTAAATGAGCCCGTCCCAATCCGTGAATGTCAAAGCCCATCGTGGCAGCAATTATTAATCACCTATAACCAACTTTTGCAGCGTTTATATTGGGATAAAAAGGCATGATGCGAGGTGAGTTAAATCTACCAATAGGCTTGAGTGTTAAAGCTGCTAATACAACTGACGACCCATTTATGGCACAGCTTTTTTATTCTACAAAAACGCTTTTTTATGAACTTGGTTTACCCAGTGAAGTCGTTGAAATCATGCTGGAGCAACAATACCGACTACAGCAAGCCTCTTATCGGGAACAATATCCCAATGCCAACACTTATATTCTGTTTTATCACCAGCAAGCCGTAGGGAAGGTGATGCTCGATATCAGTGAGTATAGGATTCATCTTGTTGATTTTATTATCATACCAAGTATGCGGGGACGCGGTTTTGGTTCGGCCATTTTAGCGGCCATTAAGCAAGAAGCGATGAAACGCCATTTACCAGTCGGTTTATCTGTGGAGAGTGAAAATACTCAAGCTAAAAAGCTCTATCTACAGCATGGGTTTAAGCTCGAAAGTTACTCTGGCGCCTATGAATCTATGCTATGGCGTTAAGTGGAGAGTGATAATTTACACTGATTCATTTTTGTATAATTGTGTTTATTGATTTTTAATGTATGTTTAATTGTAATCAATTTGTTTTGTTTTTGATTTAGGTTATCTATATAGTCCTCTCATCGTGTGATGTGGATGATGTTGTAATCAGCAAAAGGAACTCGCTGTCGATTAAATGTCGTGAGTAATCATTTCGTTGAAAAACGTTAAATAGACAAAATCGTGATTTTAAGGAGAAAAACAATGTCAGATCCATTTATCGGTCAAATCTCTATGTTCGCTGGTAATTTCGCTCCCCGTGGTTGGGCCTTTTGTGATGGGCAATTATTGTCCGTCTCGCAAAACTCTGCGCTTTTTGCAATTCTAGGCACCACATATGGTGGCAATGGCCAAACGACTTTTGCACTGCCTAACCTGCAAGGGCGTGTGCCTGTGCATCAAGGCCAAAGCCCTGGCATGAGTCAATATAACCTGGGACAAACGGGTGGGACTGAAAATGTGACATTGACTGCCAATGAATTACCCGCGCATGTTCATTCCGTGGCACTAAATGGAACAGGAAATACGAGCGTTGCATTGGGAGCCTCCTCCGCTAACGGTAATACCGCTATTCCAGGGCCAACCACGGTACCCGCCAAAGTGGTTAGTGGGCTAACTGCTCTTAATGCTTTTAGCACTACCGCACCAGACACCACACTGTCGCCTGCGACTGCCACCACAACGGTTAACGTAAATGGAAATACTGGTATTGCCGGTAATAATATGCCAGTGCATATCGTACAACCTTACCAAGTTGTTAATTTCATTATTGCTCTTGAGGGGCTTTTCCCTTCTCGTAATTAAATCTGAAACAATATGGTTTAAATCTGTAAGTGCAAATGCGCGCAAGGATTGTGCGCTACTTGTATTTAAAACAAGCTAATTTAGGGGATGTGTCATGCTGCAACATGCTTACAAAATGATTCATGCTTTTAAGCATATTATGACTAGATTCTGTCATTCTTATTTGAAAATGTTCATTGTAGGCAGTTTTTTTGCGATGACCTCCCTCTATGCTAGCGCACAAAATCGTTCTTTTGACTTTACAACTGGTGATACCGTAACGCTGACAAATGCGGGATCTGCGCTATCTAATCGAACAACATTTACACTGGAGTTTTGGGCCAAATTTACTAACGTCAGTGGGACCATCAATTTAGTGGATTTCACTGGCGGTGCCGATGCCGGTGGCCTCATATTTAACTCAAGTAAACTAACTGTCGATCTTGAGTGTGATTTTGGTTGCCTGACAGAAAGTAATGTGTTGTCGTTAAGCACAGGAACCTGGTATCACATTGCAGTTGTATTTGATAATGGAACTTGGGATTTTTATGTTGATGGCGTCGCACAGGGAATAAACGTTTTAGATCAAGGCGCCCGTAGTAGCGTTCCTAATTATACGGGATTTGGAGTGACCAATCTTGTGTTTGGGTTGCAGAACCTCGGAGCGGTAGATGATTTCGTCGGTAGTATCGATGATATACGTATGTGGAGTAGTGCGCGCACGCAAGTGCAGATTCAAAATAACCGCAGTATCGAGTTGGTAGGCAATGAATCCGGCCTGCTGGGGTATTGGAAGCTGAATGAAGCCAGCGGTACGACAGTCAACGATAGCCAGACTAATTCCTCCATGTTAACAGGTACTAGCAGCGGTATCGGGTATAACGCAACGGGTGCATTTGTTACCGATTTTGTTGCCCCTACGGTAAGTTCTATCACAGCCAGCACTGCAAATGGTACTTATAAAGTCGGGAATGTTATTAGTGTTCAGGTGAATTTTGATGAAGCAGTATTGGTTACTGGCACACCTCAACTCACACTTGAAACAGGCACCACTGATCGCACCATTAACTACGCCAGCGGTAGTAACTCCAGTACGTTGACATTCAGTTACACCGTTCAGTCTGGCGATACTTCGGCCGATTTAGATTATGTTGCTACCAATTCATTGATGCTAAATAGTGGCACTATTCGCGACGCGGCAAGTAACAACGCCACCTTGACCTTGCCTTCTCCCGGTGCCGCAAACTCTTTGGGGGCGAATAGAAATATCGTCATCGATGGCGTGGCCCCGACGGTTTCATCTGTTAATGCCAGCACCGCGGACGGTACTTACAAACTGGGCGATATCATTAGTATCCAAGTTAACTTGAGCGAAGTGGTGAGCGTCACTGGTACCCCGCAACTAACGCTGGATACGGGTACAACGGATAGAACGATTGACTACGCAAGTGGTAGTGGTTCCAGTACGCTGACATTCAACTACACCATTCAGTCCGGCGATACGTCCAGTGATCTAGATTATGCCGCCACTAACTCCTTGGCATTGAATAGCGGAACCATCCGCGATGCCGCTACTAACAATGCGACCCTCACATTGGCGTCCCCTGGTGCAGCTAATTCACTGGGCAATAATAAAGCGCTGGTAGTGGACGGCATTGTGCCCTCGGTGACGAGCACCGCACCTGCGGGTGGTGCAGTATCAACAGATACCAGTGTTGATTTCACCGTTAACTTCAACGAATCAGTCAACAATATTTCTATCGATGATTTTGCGCTTGGTACAACAGGGAGTGCGACCGGCACCATCGCCAGTGTCTCAGCAAGTTCGGGCTCCGGTGTCACTGTAACGGTGAATGGTATTACGGGTAACGGCACCATCAAACTCAACCTGAATGCATCGACCAATATCAGCGATGCAGCGGGTAACGCTGGCCCTGCAGCCTATACCAGCGGCTCAATTCATACAGTGGCTATCCCGACAGCCCCAGATGCTCCTACTATTGGCGCTGCAACGGCAGGTATTGGGCAGGCCTCTGTGGCTTTTAGCGCACCTGCAAATAATGGAGGCGCTGCCATAACGGGTTATACAGTAACATCTAACCCTGATGGCATTAGTGCTGGTGGTAATGGATTTACAACTTCTCCGATAACGGTAACAGGCCTAACAAATGGTACGGCTTACACCTTTACTGTGACGGCAACTAACACCATTGGAACAAGCTCTGCATCAGGTGCTTCAAATTCAGTAATACCCAAAGTTAACCAGACAATTACTTTTGCTAATCCGGGGGCGCAAAATTTCGGCACTAGTCCGACGTTAACTGCAACATCAGATTCATCGTTAATTGTCAGTTTCAGTTCATCGACACCAGCAGTTTGTACGATTACGAGCAGTGGAACCCTGACCTTTTTGTCAACGGGTTCCTGTACTATCGATACAGATCAGAGTGGTAACGCGAGTACCAATGCTGCAAGCACTGTAAGTCAAACCTTTATGGTGAATGCCGTAGTGCCAAGTGCCCCTACGATTGGCACCGCAACGGCAGGGAATGCCCAGGCATCGGTTAACTTTACTGCGTCCGCAAGTAATGGTGGTGCTGCAGTTACTAGCTATACAGTTACGTCCAACCCCAGTGGTTTTACCGGTACGGGCGTGGGTTCACCCATTTCGGTGATAGGATTAACCAATGGAGTGGCTTATACCTTCACTGTTACCGCGACTAACTCGGCGGGTACAGGTGCTGCATCAGCAGCATCTAATTCAGTCACACCGGCATCACCGCAGACAATTACCTTCGCCAATCCAGGGGCGCAGAATTTTGGTACCTCGCCAACCTTGACGGCGACATCAGACTCAGGATTAACCCCGACCTTTACTTCATCAACCACAGGAGTGTGTACTATCTCTTCTGGCGGGTTATTAAACTTCGTTTCCGTGGGTACTTGCACTATCAATGCCGATCAAGCGGGCGATAGCAGCTATTTGGCAGCGACGACTGTCAGCCGCTCATTTAGTATCAGTGCGGTTGTACCGGGTGCACCAACTGCAGCGAGTGCGACTGCTGGCGATACTCAAGCCAGTGTGACCTTTAGTGCGCCAATTTTTACCGGTGGTGCAGCTATTACTGGTTATACAGCAACGTCAAATCCCGGTGGCTTCACTGGGGCGAGTGCTAGCTCACCGCTGACAGTAACTGGTCTTACTAATGGTGTAGCATATACATTTACCGTCACGGCTACTAACTCGGTGGGGACGGGAGCCGCTTCTACTGCAACTAGCTCTACCACGCCTAAAGCGATACAAACCATCACTTTTGCAGACCCTGGTGCTCAAAGTTTTGGTACCGCACCGACGCTCATTGCGTCAGCAAGCTCAGGCTTAACAGTGACTTTTACATCATCTACTACAGGAGTTTGTACTATTACCGCTGGTGGCGCCTTAACGTTCGGTAGCACTGGCACCTGTACCATTGATGCTAATCAAGCGGGAGATGGCAGTTACTTAGCTGCGGCTCAGGTGAGTCGCTCATTCACTGTGAATGCTGTCGTTCCTGGCGTGCCAACCATAGGAGCGGCTAGTGCAGGTGACAGCCAAGCGAGTGTGAGTTTTACCGCTCCGATATTCAATGGTGGTTCGTCAATCACGGGGTACACAGTGACCTCAAGCCCAGGTGGTTTCCACGCTTCGGGGGCAGCTTCCCCTATTACAGTAACAGGGCTTGCAAACGGTAGTCCTTATACCTTTAGTGTCGTAGCAACGAACGCGATTGGCAGCAGTAGTGCGTCAGTGGCGAGTAACTCGGTAACGCCAAATGGCGCCCCAGTCATTAGTGGTACGCCGACCTTAAGTGTTAATCAAGATGTGTCGTATCAGTTTACGCCTACAGCGTCTGACTCTGTGGGAGATGTACTTAGCTTTAGTATTGCTAATAAACCTGCATGGGCGACATTTAACACAACAACAGGAACGCTGTCAGGTACTCCCTCTAATCAAGATGTTGGTGTAACTAACGGGATTATTATCAGTGTTTCTGATGGTGCTTTGTCTGCTAGTTTACCCGCATTTAATTTAAGTGTAGTGAACATCAACGATGCGCCGACTATCAGTTCAACAGCCATCACAGCTGCCACTCAAGATGCGGCGTACAGCTACAACTTTGCGGCCGCAGATACTGATGTGGGCGATGTGCTGACATTGAGCGCGGTGACTAAACCAAGCTGGTTGAGCTTCAATGCGGCTACGGGCGTGTTAAGTGGTACGCCAAGCAATGCCGATGTGGGCAGCCATGCGGTGACACTGAAAGTCACCGACACGGACGGCTTAACAGCAGAGCAAAGCTTCAGCATCACGGTCACTAACGTCAACGATGCGCCGACCATCAGTTCAACGGCCATCACAGCTGCCACTCAAGATGCGGCGTACAGCTACACCTTTGCGGCTGCGGATACCGATGTGGGTGACACCTTAACCTTAAGCGCCGTGACCAAACCGAGTTGGTTGAGCTTCAATGCGGCTACTGGATTGTTAAGCGGTACGCCAAGCAATGCCGATGTGGGTGCACACCCCGTGACATTGCGGGTGACGGACACGGACGGCTTAACGGCAGAGCAAAGCTTCAGCATCACAGTGACCAACGTCAACGATGCACCGACCATCAGTTCAACGGCCATCACAGCTGCCACTCAAGATGCGGTGTACAGCTATAGCTTTGCGGCGAGTGATACCGATGTGGGCGATGTACTGACATTGAGCGCGGTGACTAAACCAAGCTGGTTGAGCTTCAATGCGGCTACGGGCGTGTTAAGTGGCACGCCCAGCAATGCCGATGTGGGCGCGCACCCCGTGACACTGCGGGTGACGGACACGGATGGCTTAACCGCTGACCAGTCCTTTAGCATTACCGTGACCAACGTCAACGATGCGCCGACGATTAGTTCAACGGCCATCACTGCCGCCACTCAGGATGCGGCTTATAGCTACAGCTTTGCGGCTGCGGATACCGATGTGGGCGACACATTAACCTTGAGCGCGGTGACTAAACCAAGTTGGTTGAGCTTCAATGCGGCCACTGGATTGTTAAGTGGCACGCCAAGCAATGCCGATGTGGGCAGCCATGCGGTGACACTGAAAGTCACCGACACGGACGGCTTAACGGCAGAGCAAAGCTTCAGTATCACAGTGACCAACGTCAACGATGCACCGACCATCAGTTCAACCGCCATCATTACCGCCACTCAAGATGCGGCGTACAGCTACACCTTCGCGGCCGCGGATACCGATGTGGGCGACACATTAACCTTAAGCGCGGTGACCAAACCGAGTTGGTTGAGCTTCAATACGACTTCAGGCGTGTTAAGTGGCACGCCAGGCAATGCCGATGTGGGCAGCCATGTCGTGTTACTGCGAGTGACGGACACGGACGGCTTAACAGCAGAGCAAAGCTTCAGCATTACCGTGACCAACGTCAATGATGCGCCGACCATCAGTTCAACGGCCATCACTGCCGCCACTCAAGATGCGGCGTACAGCTACACCTTCGCGGCTGCGGATACTGATGTGGGTGATGCGCTGACATTGAGCGCGGTGACTAAACCAAGCTGGTTGAGCTTCAATGCGGCCACTGGATTGTTAAGTGGCACGCCAGGCAATGCGGATGTGGGCAGCCATGTCGTGTTACTGCGAGTGACGGACACGGATGGCTTAACCGCTGACCAGTCCTTCAGCATTACCGTGACCAACGTCAACGATGCGCCGACCATCAGTTCAACGCCCATCACTGCCGCCACTCAGGATGCGGCGTACAGCTACACCTTTGCGGCGAGTGATACCGATGTGGGCGACACCTTAACCTTAAGCGCGGTGACTAAACCAAGTTGGTTGAGCTTCAATACGACTTCAGGCGTGCTAAGTGGCACACCAGGTAATGCGGATGTGGGCAGCCATGTCGTATTACTGCGAGTGACGGACACGGATGGCTTAACCGCTGACCAGTCCTTTAGCATCACGGTCACCAACGTCAACGATGCGCCGACCATCAGTTCAACCGCCATCACTGCAGCTACTCAAGATGCGGCTTACAGCTACACCTTTGCGGCGAGTGATACCGATGTGGGCGATACCTTAACCTTAAGCGCGGTGACTAAACCAAGTTGGTTGAGCTTCAATGCTGCCACTGGATTGTTAAGTGGCACGCCAGGCAATGCCGATGTGGGCGCACACCCCGTGACATTGCGGGTGACAGACACGGACGGCTTAACCGCTGACCAGTCCTTTAGCATCACGGTGACCAACGTCAACGATGCACCGACGATTAGTTCAACGGCCATCACTGCTGCCACTCAAGATGCAGCGTACAGCTACACCTTTGCGGCGAGTGATACCGATGTGGGCGATGTACTGACATTCAGCGCGGTGACTAAACCAAGTTGGTTGAGCTTCAATGCTGCCACTGGATTGTTAAGTGGCACGCCCAGCAATGCCGATGTGGGCGCACACCCCGTGACATTGCGGGTGACGGACACAGACGGCTTAACGGCAGAGCAAAGCTTCAGCATCACGGTGACCAACGTCAACGATGCACCGACGATTAGTTCAACGGCGATGACTGCCGCCACCCAAGATGTGGCGTACAGCTATACCTTTGCGGCTGCGGATACCGATGTGGGCGATGTACTGACATTCAGCGCGGTGACTAAACCAAGTTGGTTGAGCTTCAATGCTGCCACTGGATTGTTAAGTGGCACGCCCAGCAATACCGATGTGGGCGCGCATCCCGTGTTACTGCGGGTGACGGACACAGACGGCTTAACCGCTGACCAGTCCTTCAGCATTACCGTGACCAACGTCAACGATGCACCGACGATTAGTTCAACGGCCATCACTGCAGCCACTCAAGATGCGGCGTACAGCTACACCTTTGCGGCGAGTGATACCGATGTGGGCGACACCTTAACCTTAAGCGCGGTGACTAAACCAAGTTGGTTGAGCTTCAATGCTGCCACTGGCTTGTTAAGTGGCACGCCAGGCAATGCGGATGTGGTCGCACACCCCGTGACATTGCGGGTGACAGACACGGACGGCTTAACCGCTGACCAGTCCTTTAGCATCACGGTGACCAACGTCAATGATGCGCCGACCATCAGTTCAACGGCCATCACAGCTGCCACTCAAGATGCGGCGTACAGCTACACCTTTGCGGCGAGTGATACCGATGTGGGCGATGTACTGACATTGAGCGCGGTGACTAAACCAAGTTGGTTGAACTTCAATGTTGCCACTGGATTGTTAAGTGGCACGCCCAGCAATGCCGATGTGGGCAGCCATGTCGTGTTACTGCGAGTGACGGACACAGACGGCTTAACGGCAGAGCAAAGCTTCAGCATCACTGTCACTAACGTCAACGATGCGCCGACCATCAGTTCAACGGCCATCACTGCCGCCATCCAAGATGCGGCGTACAGCTACATCTTTGCGGCGAGTGATACCGATGTGGGCGACACATTAACCTTGAGCGCGGTGACTAAACCAAGTTGGTTGAGCTTCAATGCGGCCACTGGATTGTTAAGTGGCACGCCAAGCAATGCCGATGTGGGCAGCCATGCGGTGACACTGAAAGTCACCGACACGGACGGCTTAACGGCAGAGCAAAGCTTCAGTATCACAGTGACCAACGTCAACGATGCACCGACCATCAGTTCAACCGCCATCATTACCGCCACTCAAGATGCGGCGTACAGCTACACCTTCGCGGCCGCGGATACCGATGTGGGCGACACATTAACCTTAAGCGCGGTGACCAAACCGAGTTGGTTGAGCTTCAATACGACTTCAGGCGTGTTAAGTGGCACGCCAGGCAATGCCGATGTGGGCAGCCATGTCGTGTTACTGCGAGTGACGGACACGGACGGCTTAACAGCAGAGCAAAGCTTCAGCATTACCGTGACCAACGTCAATGATGCACCTGTAGCGACAAGTTCGACTGTGACGTTAGAGGAAGATGGTTCGGTGACTATCATCCTTACGGCGGAGGATGTGGATAACGACCCGCTTACCTATGAAGTGGTGAGTCAGCCAGATTCCGGCACCTTAGAGCAACATGGCACTGTTTGGTTATATACCCCTGAGAAGGATTTCAATGGAACAGACGTGTTTAGCTTTATTGCTAAGGATGCCGAGCTGAGTTCAGAGCCTGTAACTGTCACGATTAACGTCACGCCAGTGAATGATGATCCGCAGGCGGTTGATGACGACTACACATTAACAAGTACCGCCAACGATATTTATGCGCTTGCGGTGCTCGCGAACGATGTTGATGTCGATGGTGATACCTTAACGATTGATGGTGCGGCTGCGGATATCGGCAGTGTGCAAATCACCAGTGATGGCTTGAGCTTTACTGCGCCTGAAGCGTATGTCGGGCCTGTGGCGCTTCGCTATACCATCAGTGATGGTAACAAGGGGCGTGCAAGTGCCAAAGTGAATGTACTGATTGAAGGCGCTGAGTCGGCAAATCAGCCAGTGATCACTTTGCCAGACGATGTCGATGTGAATGCAACGGGGCTATTTACTCGCGTTAAGCTTGGTTTCGCGAAAGCAGTCGATCGTAATGGCCATCCATTGCCAGTTTCTCTGGTGAATAAGAGTCTGTTCTTCGCACCAGGCAGCTACTTAGCTTACTGGCAAGCTGTAGATCGCGATGGTAATAAGGCGATCAAGGCTCAAAAAGTGAAAGTACATCCACTGATTTCCTTGAGTAAAGATCAGCTTGTGGGTGAGGGCAATGAGGTGGTGGTGAGTGTGCACCTTAATGGCGTCGCACCCGTATATCCCCTTTCGGTGCCTTATACCGTTTCAGGCTCTGCAGGTAGCAGTGATCATGATTTGGTTGATGGGGTGGTCGAGGTGACATCGGGTCAACAAGCTGAGATCCGCTTTAGAACCTTTGAAGATGGTGAGGTCGAAGGGGATGAGGATGTGCTGATTAGTCTAGACCCAAGCTTGAATTTGGGCAGTAAGCAGCAAACTCAGGTGCTCATCACTGAAGCGAATATTGCGCCTAAGGTGACGTTGGATGTTACCCAGGCGGGAGCGCACCAATTTATCGTGGCGCAAAATGGCGGTGATGTTCGCATTAATGCTGATATCAGTGATGCCAATATGCAGGACAGCCTAGCGTCAACATGGGCAAGCGGTTCGCTGAACTTACAATCCGATGATGCGGGTATGTTCTTCTCTCCAGTCTCTGTCCTGCCGGGCATTTATCCTGTGAGTGTGACGGTCACTGATAATGGTTTGCCAGCGCTGTCTACCACAGAGAAAGTCTACATTGTGGTGCGTTCAAGCTTACCTGTATTGACTGGCGCAGACACGGACGGTGATTTGATCCCTGATGTTCAAGAAGGCTTTACCGATACGGATGGTGACGGTATTCCTGATTATTTGGATGCGGTCAACGACTGTAACATTATGCCTGAAGGTGAATTGCAGCCTGTGTACTTCTTAGCTGAGGGTCAGGCTGGGGTATGTTTGCGTTTAGGCAATATTGCTCTCAGACAAGGACAAACGGGTATGCAATTGTTACCTACTTTGGTGCCTGAAGACAGCATCGCGGCCAATGTTGGCGGTATTTTTGACTTCATTGCGACGGGATTACCACAACAAGGGCAGAGTTACAGTCTAGTGATGCCACAGCGCTCACCGATCCCTGCGAATGCGGTTTATCGTAAGTATCAAGCACAAAATGGTTGGAAAGATTTTGTGATTGATGCGCGCAACAGTGTAGCTAGTAGTGAAGGTGAGCGTGGTTTCTGTCCGCCACCGGGAGACAGCCGTTGGACGGCGGGATTGACTGAAGGTCATTGGTGTTTGCAGTTAACCCTAGAGGATGGCGGTCCAAACGATGACGATGGTGTCGCTAACCGTACTATCGTCGATCCAAGTGGCGTGTCCGTGATACTCAATGGCAACCATCTACCTGTGGCTCATCCAGATACAGCCGCTTTATTCTGGAACCAAAGTATCGATGTCGATGTGCTCGCCAATGATACCGATAGCGATGGTGATACGCTGACGGTCACGCAAGTGACCAGCGAGTTCGGCACCGCCGTCATCTTGGCTAATCAGCAGCTGAGTTACACGCCAGCAAGTGATTTTATCGGTACGGATGTACTGATATACAGTATTACCGATGGCAAAGGAGGCACGGCAAGTAGCGAGTTGACTGTGATAGTGAGTGGCAATACCGCACCAACGACAGTCAACGACAGCGCCGCAACCGACGATAGAACGAGCTTGCTACTCGATGTGCTGAGTAACGACAGTGACCCAGACGGTAATGCTTTAACCTTAGTCAGCGCTACCGCGCAGCAAGGGGCAGTGTCAGTGGAGTCGAACAAGCTGCGTTATATCCCTAAAGCGGGATTCGATGGCGTTGATACTGTGAGTTATCAGATTAGCGATGGGTTGGGCGGTGAGGCAACCGGGCAAGTGCTCATTACCGTTAAGGCCTATCAAGAGGTGGTGATTGATAACAAATCGGGTGGCGGCAGTCTGTCCTTGTGGGTATTAGGATTGCTCATTACGACGGGTTTAATCCGTCGCCGTAAATTACATAAAGTGGTCTTGGGGGCTGTGCTGCTCGCAGGCACAGTCAGTCAAGCGAATGCGGCTGATAACTGGTATGTCGAGGGCTTTGTCGGGCAAGCTCAGGTTGATAGCAGCAGACGAGATTTACAGCCACAGACTGCAGCTGGCGTAGTGACATCGGTTGATGACAAAGATACCGCGTTTGGCTTAAGTGTGGGTTATCAATGGACTCCCATGGTAGCAATCGAATTAGGCTATGCCGACTTTGGTAATGGGAGTGCGCGGATTGAAGGCGCGAGTCTGACTCCGGCGCAATACCATGAGCAGGTAAAAGCCGTGACTCCCGTATTGGCCGATGGCGTGATGCTGGGATTGCGTTTTACCTTACTGCAGCATGATGCTTGGCGGTTTGAAGTGCCTATCGGATTGTTTAGATGGCAAGCGGATATCAGTAGCACTATGGGCAATAACCGTTTAACCACAGAACTCGATGGCACTGATTGGTATGCCGGAGTGCGGTTTAGCTATCAAGTGAGCGACGCTTGGTCAGTTGGCTTGGGGTATCAATATGTGGATATTGATCCGAATGACTTCCTCAGTTATCAGCTCAATTTACGTTATCAATTCTAAGCGTAAACCAAACAAAAAGGGGCTTTGCCCCTTTTTACTATTTATAAGCTACAGAATTTTTTAGGCATACTTTGATGCAGTTCACTTAGGCTGAGCTTGGTTTGTGATGTATCTCGCAAAGGATTTATGGCTTGTCGTTAGGTGTATGCAATAAATTCAGGTTGTAGATTAAAGACAGGATAGTAAGATTGTCACTTATCTTATTTAATTTATCGCTTTTTTATCCAGAGGCAGTTATGTCGCAGCGGCAGAATAAGCCGGAAATATTGCATACCGAAGTTGTCGCGAAAAGCCGATTATTTCAAATTGAGCAAGTGCATCTTAAGTTCTCAAATGGTGTCGAACGCCAATACGAGCGCATGAAAGGTGGCAGCCGTGGCGCTGTGATGATAGTGCCTATTCATCAAGGGCAAATGCTACTGGCGCGCGAATATGCTGCCGGAACCGATAATTACGAGCTCGGTTTTCCTAAAGGTCTTATCGACCCTGGTGAATCGCCAATCGAAGCCGCTAACCGTGAATTGCAGGAAGAAATTGGCTTTGGGGCGAATAAGCTCACGCTATTGAAAGAACTTAGCCTGGCGCCGGGTTATTTCTCCAGTAAGATGCAAATTTTTCTTGCAGAAGATCTGTATGAGAGTCGCCTTGAAGGTGATGAGCCAGAGCCCATTGATGTGGTGCCTTGGGCGCTCGCCGATTGGGAGGCCTTGCTCGATGATGTCGATTTTTCCGAATCCCGTAGTGTGAGTGCGTTGTTTTTAGCGCAAAAATATTTATACCTAAACAGTCAGAAGATGCAGGGTTTAGCGGGCATTTAACGCACTGAAACTCGTGTATTCTGGTAGCTTGGGTATATACCTTAAATAATCTTTTTCCTTTTAAGCGTTCAAAGCTGGAACGCTTGCAAAACATTCGCGAACTTTGCGATTGGAGTTGTTATGAAGCCAGAAGAGTTGATCGACGAAGTTATTGCGATTGCGACCGATGCCGGGATAACTATCCGTGACATTTATCTCAAAGGTAATTTTGAGCGAGAAATGAAGTCTGACAATACCCCTGTCACTTCCGCAGATTTAGCGGCGAACAAGATCATTTGTGATCGATTAACTGGTTTAACGCCTGATATTCCTGTGTTATCTGAAGAGGCGGCCGACATTCCGCTGAGCGTGCGTGAATCTTGGAAGCGCTATTGGTTGGTCGATCCACTCGATGGCACGGGTGAGTTTATTGCCGGTAGTGGTGATTTTTCAGTGATTATTGCACTCGTTGAGCACAATCGTCCTGTAATGGGCGTGGTGTATGTGCCTATGACGCAAGTGTGTTATTACGCGATTGCAGGTTTAGGGGCATACAAACGCACGGACAAACAGGAAGTGCGGATCAGCAGTCGCCAAATCGAGCACCGTGAAAAGGTCTCCCTAAGATTGGCCGTGAGTCGCCGTCAGGATCCTCAATCTGTATTGACCCTGTTCAACCACCCTAAACATTGCGAGCTCGTGGTGATGGGCGGCGCAGCGTTAAAGAGTTGTCTTGTCGCCGAAGGTCGAGCCGATTGTTATGTGCGAGTTGGCCCAACGGGCGAGTGGGATACGGGAGCGGCGCAAATCATAGTCGAGGAAGCGGGTGGACAGCTGATGGATACTGAGCTACAACCGTTAACCTACAATGAGCGCGAGACGTTAGAAAACCCTAACTTTATCGTAGTGGGCGCACCCAACTTAGAGTGGGATAAGATTTTAATAGGCGAATGATTTTCGAATGCAGTCTATACGTCCGGTATTAAAGTCTGAGTTATTCGCGGTTTATCAGCTTGAGCAGGCCATTTTTGGCGAACATAGTTATCCCGATTTTTTCTTCCGTCAGGGATTTGATTGCTGGCCAGAGCAATTTTTAGTGGCGGTGGATGAAGATAATCTTCTGCAAGGTTATTTGCTCTGTGCCCAGAGTGGCGATCCTACTTGTAGGTGGATTTTATCTGTTGCAGTAAGTGAAGATGCTCGTGGAAAGGGATTAGGCAAACGTTTGATGCAGCAGTGTTTAGCTGAGTTACCCGTCTCTGTTGAACGTGTGTGTCTAACGGTTGACCCGAGTAATCCTGCCCATGGCTTATATCAGCATTTAGGTTTCGTTGATTCTGCTTTTGAGGCCGATTATTTTGGCGCTGGTGCAGATAGAGTGGTGATGACTTATCGCAGGCCTTAAATTCCATGGCCACTTTTAATTAAAAACATCTTTGAATTAAGAATGATTTTTAAACCAAAATAATGGACGGCGTGCTGACTGAGCGCGCCGTTTTTTATGTTATTTTGTGTGGTGTAATCCCACATATATAGTTCAGTTTTACTGAGTGCATCGATTTCGAGTGTCTATCTTTATCTATCATCTTCAATGATATCCTTCACTATTTATCCGCAGCTGTACGTAAAACAAACTTATTTTGAACGTTGTTTTTTATTTTGACTGCTGTTAGTCTCTTGCTATCCAATAGGGAGTGAGAGCTATGGCTAGACGAAAAGAACACAGCCATGATGAAATTCGCGTGATGGCAATACAAGCGGCAACTGAGTTACTGACCGAGCAAGGTGTTGTCGGGCTGAGCTTGCGCAAAGTGGCCAGCCAAATTGGCTATGTCCCTAGTACCTTGATCAATATTTTCGGCAGTTATAACTATTTGCTACTGGCCGTTTCTGAGTCAACGCTTAGGACTTTACATGACAGATTAGCGGGTGTTTGCGCGGTTGACAGTTTGCGTAAAATCATTGCAATGGCGTGGGAGTATAGTCAATTCGCCCATGAACAGCGCCAAGCTTTCAAACTGGTTTTTGAGTTGCAATTACTCGATTCTGAGCCGCTGCCGCAATCACAAGGACAATTGATTGCAGGGTTATTCACACTGATTGAACGTGAGTTAGCGCAGCTCTTCCCAGAGGCGACACCGGAGCAGCATCTCAGTATGAGCCGTGTGTTGTGGGGGGGGATTCATGGGTTAACTGCTTTGTCCCTCGATAATAAGCTCTTTGCTGAATCGTTATCACTGCGGGATTTACTCGAGAGCCATGTGCAGGCTTATCTACAAGGAATGGGTTACCAGAGGGAACTATCATGTTGCTAACAAAACGTTTTTTACCTTATTTTGCGACTCAGTGCCTCGGCGCCTTGAATGACAATATCTATAAAAATGTGTTGCTGCTGTTAGTGACCTTCAGCCAAGTCAAAGATTTACCCATAAGCGTCGATATGTTTGTGAATTTAGCCGCTGGGGTGTTCATCCTGCCGTTTTTCCTGTTTTCGGCCCACGCAGGTGTGGTTGCCGATAATATGGATAAGGCCAAACTTATCCGTCGATTGAAATTACTCGAAGTTATTATCATGTCCTGCGCCGCTATCGCGATAGTGAGCGAGAACTATATGATGATGTTATTACTGTTATTTTTAACAGGTAGTCAGTCGGCTTATTTCGGCCCGGTCAAATATTCCTTGTTACCCCAAGCGTTGAAAGAGGACGAGTTAGTCACAGGCAATGCTTGGGTTGAAATGGGCACTTTTCTCTCTATTCTGGTCGGCACGTTAAGTGCGGGGATCTTAGTTTCTGAAGTCAACGGTACACTTTGGGCTGCCATTACCGTCGCCGTATTGGCCTTGGCGGGATATCTATCAAGTCGGGCTATTCCGGCTTTGCCCCCGCAGGGGACAGTACAAAAGATTCAGTTTCGCCCACTTTCTGGTACTTGGCGCAGTATTAAAAAAGCGCGTCAAACACCTTCAATCTGGATGGCGATCCTCGCTATCAGTTGGTTTTGGTTTTTAGGCGCGACCTATCTGACTCAATTCCCTAACTTTGCCAAGCTGCATCTGCATTCAGGCGCGACTGTGGTGTCGTTATTGTTGGGCATGTTTTCCATCGGGATTGCCGTTGGCTCCTTCGTCTGTGAACGTTTTTCCTACGGTCATGTGGAACTCGGTTTGCTGCCATTTGGGGTATTGGGGTTAACTATCTTTGGTGTCGATTTGCTTTGGGCTATTCCACCTACGCCAGTGGATGCTGGTTTTGTCTATGGATTAAAAGCCTTTATTGCCGAGTCGCAACATTATCGGGTGATGCTAGATTTCTTTATGATCGGCGTTAGCGGCGGGCTGTTCATCGTCCCCTTATACGCCTTTATTCAGTCCCGTTCGGCTAAGGGTGAATGTGCTCAGGCGATTGCTGCCAATAACATTATGAACTCTCTGTTTATGGTGGCTTCGGCGCTGTTATCTATGTTGCTGCTGGGCGCAGCGGGTTGGACGATTCCGCAGTTATTCTTGTTGCTTGCCGTAATGAACTTTGTGGTCGCCCTTTATGTTTATTCGCAAGTGCCAGAATTTACTCAGCGATTTATCAGTTACTTACTTAGCCATGTTATGTATCGCGTGACTGTTACAGGCCGTGAAAAAATTCCAAAGGAAGGGGCTGGCATCATAGTCTGTAACCATGTGAGTTATGTGGATGCATTGATTATTATGGGAGCCTCGACACGGCCGATCCGCTTTGTGATGGATAAATCCATTAGCGAAATTCCTCTGCTGAAGTATGTGTTCCGCCACGCTGGGGTGATCCCTATCTGTTCGCCTAAGCAAAGTGAAGAGACCTATAATCGTGCCTTCGAGGCAATTCATGAGGCGCTCGCCAATGGTGAGTTGGTGTGCATTTTCCCTGAAGGGCGACTCACACCTGACGGTGAACTTGGCGATTTTCGTCCCGGAATCGATAAGATCTTAGCCAGAGATCCTGTGCTTGTTATTCCAATGGCATTACACGGATTATGGGGGTCTTACTTTAGCCACAAGGGCGGACATGCCTTAGCTAAAATGCCCAAGCGTTTTTGGTCAAGGGTGTCGGTGGATTTAACGGATGCCGTCGATGGCAAGCTTGTGAATAAGGATTCTTTGCGAGTCCAAGTGGCGAGCCTGCTGGGTAAGTCTTAAATCCGTTATGGCAGTATGAAATAAAATGATAACCGCGAAGCTAATGTTCGCGGTTATCTTATTTTAGGGTGGCGATTTATCGTGTCTGATAGCTATCCAGCCACTCATGCATGGCTTGGTGCGAACCTTTAAAGAGCACCCGTTCCGACTTTTTATCTAATTGGTATAGGTACATAGGGTCGTAATACTTATGTAATAACAGGTTTATCCAAACTAAGTGCAGCGAAGTGTCATTCTGGTTGATTTGCTGATCGAGTGCTTGTTGCATCAAATCTTGCAGTTCTTGATGTTGTTTCCCGCCTAAACGTTTACGAATACTCACTAAACTTTGCAATAGATAGTCGCTAAATGCCGCAAAACCCGCTTCTGGCCCTAGGCGCTCACAGAAGCCACTATACATTTTATGCACATATTCATTGCGTAAGCGTTCGAGCCTCGCATCGTCTGACTCTTCTAGCACTAAGACATTGGCGGCTTGCATGGCATTGTAAAATGTCTGCGGCAGCGCCGAACGCCCAATCAGGAAACTTTCATCTTCGAGCAGTAAACAGGCAACACCGCTCGTTTGGTGCTGTAGCAAGGCAATGGCAAGCTGATTTTCAAAGTTAATCTGCGTCGGCTGTGGGTCGATATTCTTCCCAAAGCTAGAGCCTCTATGATTGGCTATCCCTTCAAGATCAACCGCTTCTTTACGCTGTAATAAGAAATCGGTTTTACCGCAGCCCGTCATCCCGCTTAAACTCAATAACGGTTGCTGAATCGGTGCCGCTTCAATCACGCCAATCAGATATTGGCGCATGGCCTTATAGCCCCCTTGAATATATGGGACTTCTACACCCGCTTCTTTAAGCCATTGTTGCGTGAGTTGCGAGCGTAAGCCGCCACGAAAGCAATAAAGGTAGGCGTTGGGGTTGGCTTTCACATAGCTTGTCCAAGCCTCGATTCTCTGTTGCTTGATGGGGCCATTAACCAGTGAATGACCCAATGCAATCGCGGCTTGCTGACCTGATTGTTTATAACAAGTGCCGACTTTTTCCCTTTCACTATCCAGCATCAAGGGTAAGTTGGTCGAGTTGGGAAACGCACCTCGAGTAAACTCTATGGGTGCTCGCACATCCATGATGGGATGTTGATTGAGGAAAATTTCCCGATATTGTTCTGCAGCGACGATCGCGTTTGGCATCAGTACAACACCACATTCGCTTCAGTGGTGGGTGTTTCCAGTGATCCAATGCAAATTGGGGCGATGTGATGCGCATCGAGTAGAGCAATCAATTCCGCTTCGGCTTCGGCCGAGACGGCAACTAACAGACCACCACTGGTTTGCGGATCGCACAGAATCGCTTTTTGATGCTCGCTGAGCTCGGGGAGATGCTCGCCATAACTGTCGTAATTTCGGTGAGTCCCGCCAGGAACGCAACCTAAGGCTAAGTAATCTAAGGCGCGTGGTAGTAACGGCACCGCATCAAATTTGAGTTTTGCCGTGAGCTTGGCACCTTGGCACATTTCGAGTAGGTGTCCCGCCAGTCCAAATCCTGTGACATCGGTCAGGGCGTTTACACCGCTAATTTTGGCAATCGTCGTGCCTATAGTATTCAGCTGGCACATAGCATTAACCGCAATGTGACTGTCTTCATCTTGGAGTTTTTTCTGTTTTTGCGCCGTGGTTAAAATGCCAATGCCGATAGGTTTAGTGAGATATAAACGATCGCCCGCTTTAGCAGTGTCATTTTGTTTTAAGTCAGTTAACGCAATTTGTCCTGTCACTGCGAGCCCAAAAATCGGTTCAGGCGCATCTATGCTGTGGCCACCGGCTAGCATGATCCCCGCTTCCATACAGGCTTGTCTGCCGCCATCGACCACTTGTTGTGCGACTTCTGCAGGTAGCTTATTGATTGGCCAGCCTAAAATAGCGATCGCCATGATAGGCGTGCCGCCCATGGCATAAATATCACTGATCGCATTAGTCGCAGCGATGCGGCCAAAGGTGAAGGGATCGTCAACAATCGGCATGAAAAAATCGGTAGTGCTGATGATGCCTATGTCGTCATTGAGTTTATACACTGCGGCATCGTCACGGCTTTGATTGCCGACCAGCAGATTAGGATCAGTAAACACGGGTAACTGCGAGGCCAAGATAGTGCTGAGCACTTTAGGGGATATCTTGCAGCCACAACCTGCACCATGACTATATTCTGTTAGTTTGATTGATTCTGGGAGTGTAACTGGGGAATCTGACATGATCTCAACAGCCTATTTAAGTGATTAAGGCATCATCATAATCAGATACGGCCACCATGCAAGGTGGCCGTATGACTTTCGGGCAGAATTATGGTTTAGCTATAAGTTGCGGTTAGGCTTCGCCAATGTTTTTGCTGGACTAAGAAACGCGCTTTTAGCTCTTCAACTTGCTGCAATAGCTGCTGATCGACAAGTTGTTTACGCTTGGCCTCAAGCAAGGTTTTCTTCGCTTGGTAGTAATTCATTAAGTGCAGTTTGAGTAATTCATACTCAGCTTGGACTTTTTCAATAATTTCATCGGCATTGGGTAAGTGCACGACTTTATTCTTGGTCCGCAGCAGCTGCATCTGCAAGCGTGCGCTCTCGATACGTTCCTGTGGACTCGTACGCAAGTCCTTGGCTAACCCCAACCAAGATAATGACTTAATCAACCATTTAGTTGGATCGTAGTGCCACCATTTAATGCCATTACGATAGTCATTTTCGAAGATGTGATGGAAATTATGGTAACCCTCACCGTAGGTCAGCATGGCGAGAAAGCCATTATCACGGGCAGTGTTCTTATCGGTATAAGGCTGGCTGCCCCACACGTGCGCTAAAGAGTTGATGAAGAAAGTGCAGTGATGCACCACCACTAAACGCAGTAGACCTGCCATCAATAACATAGACAGTACATCGCCGTTCAGCCAGCCGAGCAGGGCAGGCAGTCCAATGTTCATCAGTAATACGAGTGCGAGGTAATGTTTGTGTTGCCACATAACGATACGGTCATTTTGCAGATCGCGCACATTTTGATAATCATGATAGCGTTGCGACTGATACTCACGCAGCATCCAACCGATATGGCTGTACCAGAATCCCATTTTGGCTGAGTACGGGTCTTTGTCGTTGTCGTCTACGTGCTTGTGGTGAATTCTGTGATCGGATGCCCAATGTAATGCACTGTTTTGTAATGCTAAAGCGCCACCGAGTGCATACATAAACCGCACGGCTGGATGGGCTTTATAGGCCTTGTGTGACCACAGTCTGTGATAACCTGCGGTGATCGACAGGCCACTGGCAAAGGCTAAGACAATAAAAGCGATCCATTCACTGGCGCCATAGCCGTGAACAATACCGCGCCATGGAACTAAGATCACCGCACTGAGGAAAGTGATGGCAAACAGACTGACATTAAGCCAGATGATAGGGGGTTTTTTCATTATAGGTTTCCAAAATTCGCTTTTGAGCGTACATCTGTAAGCTAATTTAGCCTAGCTATATTGTTTGGTCAAGGACGATGGGGCTGTGTTTAATGCCTAAAAGCGGTATTATCGCGTCATTACATTCAGTTAGCGGATTTAAGAATGGGTATTCGAGCACAGCAAAAAGAGAAAACTCGTCGAGCTTTAGTCGATGCGGCCTTTAATCAGCTAAGTGCTGAGCGCAGTTTCTCAAGTCTGAGTTTGCGAGAGGTTGCTCGGGAAGCCAATATCGCGCCGACATCTTTTTATCGCCATTTTAAAGATATGAACGAACTCGGTCTCACTATGGTGGACGAGGGCGGATTAACACTCAGGCAGATGATGCGAAAAGGCCGTCAGCGAGCCGAAGCCGGTGGCAGTGTGATCCGTATTTCTGTTGATACTTTTATGGAAGTGCTTGATTCTAATCCAAACGTATTCCGCATCTTATTGCATGAACGTTCAGGAACCTCAGCAGCGTTCCGTGCGGCGGTTGCCCGCGAAATTGAACATTTTATTTCTGAATTGGCTCACTATACCGAAGCGAAAGCGGGCCGAACCCCGTTATTGGCCCGTGCACAGGCCGAAGCCTTAGTGACTCTAGTATTTAACGCCGGCGCTTCAGCATTAGATATGAAACGTGCCGATCGTAAAATCCTTGCCGATCAATTAGTCATGCAACTGCGTATGGTCGCCAAAGGCGCCGAGGCTTTGCAGCATAAGGTTGAGCACCGCTAATCTGAGTGATATGCACATCAGTTGCTAAAAATAATGGAGCCTGAGGCTCCATTATTTTTTGCGAATTTATGTTTGAGTACTGTGCAGCGTTTAACTGCTACGCAGTCGAGCTCAATGCATTTGAGGTGACTAACGACGCTCCAGTAGAACACCTGTTTCCATGTGTTCGGTATAAGGGAACTGATCAAACAAGGCGAAGCGAGTGACTTTATGGGTTTCGTTCAGTAGCTGCAGGTTGTCTTTTAAGGTTTCAGGGTTGCAGGAAATATATAAGATCCGCTCGTAACCTTGTACTAATGCCAACGTATCAGGATCAATCCCTGCGCGTGGTGGGTCGACAAAAATGGTGTTGCACACATAGCTGTCGAGATCGATACCTTCTAAACGTCTAAAGCTGCGTTTCTTCGCCATGGCATCGCTAAAGTCTTCGGCCGACATGCGAATAATCTGCAGATTCTCAATACCATTCACTTCGATATTGTATTGTGCCGCATCGACTGAAGGCTTAGCGAGTTCGGTGGCCAATACGCGATTAAAGTTTTGCGCCAGTGCGATAGAGAAGTTGCCATTACCACAGTAGAGTTCGAGTAAATCACCTTCACTGTCCTGAGTCGCATCGATAGCCCACTCCAACATTTTTACAGAAACTTTAGCATTGGGTTGAGTAAAGCTGTTCTCGATTTGCTTATATAAGAAGGTTTTATCATTCACTTGTAATGACTCGACCACAAAGTCTTTATCTAAATCGATCTTTTGCTTACGGGCACGGCCGATGATATTGACGTTGAATTGGCTGCTTAACTTGGTTTTTAGTGCAGCCGCATCGGCGCGCCATTGGTCATCAAGCTGTCTGTGATAAAGCAAGGACACTAAAATCTCGCCGCTTAAGGTCGACAAAAAATCCACTTGGAACAGTTTATGTCTTAGACTAGGATTCGGTTTTAGTTCGGCGATTAAGGCCGACATCATTTGATTGATGAGCACGCTGGCTGGCAAGTATTGATCGCATCGCACTTTCTCATTCAATACTTTATCGAACATGTAATAGTATAAGTCGTCACCGTCATGCCACATGCGGAATTCAGCACGCATACGGTAGTTGGCAGGCTCTGAGGCAAAAACTTCAACACTCGGTGTTTCAAATTGGGCAAAGGCTTGTTCTAGCTTGATACGCTTGTGTTCTAACTGCGTATCATAAGTCTGAGGATCCATTGCTGCTAAGTTCATTGTTTGATTCACCGTTGCTGTAAATTGGGGCGCAAATATTATACTAGGTGACACGAATGTCCAGCATCTTGCTTTTGCTGTTTACGCAAAGCTGTGGGAAAATCCGCGCGCTTGAGTGTGATTCTATGGATATTCAATGAGTATTTGGTTGGATTCATCCAATCATCAAACATCGAATCACTTATGTCGCAATGCGTTCAATCTGTATAGCCAATTTGATATAACCGAAACGGGAGACTCGCTTGTCGCGACCTATATTAGTGTTTGATTCAGGGATTGGTGGCCTATCCGTATTGGCTGAAATCCGCAAGAGTTTGCCCCATAGCGACTATTGTTATCTTTTTGACAATGCGAGGTTGCCCTATGGCGAGTTAGAAGAGCAGGTATTAATTGCTGGCTGTGTCGCATTAGTATGTGATCTTGTGGCTCGCACCAATGCAATGATAGTCGTCGTCGCCTGCAATACCGCGAGCACTGTTGTGCTACCGGCATTACGTGCAAACCTCAGTATTCCTGTTGTGGGCGTTGTACCCGCTATCAAACCCGCGGCACAGATGTCTAAGAGTAAACGTATTGGACTCTTGGCCACACCAGGCACAGTTAAGCGTCATTATACCCATGAGCTCATCAGTCAATTTGCCGATGATTGCCATGTCGAACTCTTTGGTTGTTCTGAACTTGTCATGATGGCGGAGCAGAAAATAGCGACAGGTGAAATGGACATGCACAGACTGGCAGATTTACTCGCGCCAGTTGTTGCCGCGCAATTAGATGTTCTCGTACTGGGTTGTACCCACTTCCCTATGATCCAAGCTGAATTACAACAAGTGCTCGGTGCGGGCGTAACGCTAATGGATTCCGGCGCTGCGATAGCTAAGCGAGTCGTGACTCTTTTAACGCAACAGAATCTTATTGTGGAACAGCGCAGAGTAACAAATGAGAGAGAAGCAGTAGGGCAGTCAGCAATGCAGGCGTTTTATACTAAAGCGGAAATCAGTGAAGGCTTAACAACAACATTAATTGATTGTGGCTTTTCAACTATCGAACGAATCACCACAACCAACTAAATTTACAACGTCAAACAAAACAGTTTAAACGGCAATACTACTCGTCAGTACCAGTACGGTCGGCAGCTTCCGAATCTTGTGTTTTTGCTTCTCTTACTTTTAAGGTTCTTTCTTGGAAGCTATAGTCATTCAGTTTAACCATGGCTTTCTGAGCGCCAGACTCTGACATCTCCACAAAACCAAAACCTTTACGACGACCAGTCTTACGATCTCTGACTAAACGCACAGAGTTTACTGGGCCAAACTCACCAAACAATACTTTTACTTCACCTTCATGGACACGATAAGGCAGGTTACCTACATATAAGGTCATAGTAGGACCAACATATTGTTCTTCTGTAGCATGAGATGATGAAGTCTCTGGAACAAGTTTAAAGATAAGGCTAGTCACAATAACGCCTGCAACGAAGGCAATATAAGCCGGCAGCGTAGGTGCAAACTGAAATAGCACAAAGGCGCCAACAATGGCGACAATCAAAACAATAAGAAATGACTTTTGCATAAGAATGCTCTCTGATGAATGGAAAATTGATAAATAACTGTTAACAGCAAGTATATGGTAATGAATTATTTACTTTTACACTAGACCATTGCTGAAATATTGAGCGTGAATTATCAATTATTAGCTAAAAAAGCTGACTTATCTCCGATAAATAAGGTCTACAAGCTAAAAAATGGTCATAACGTTTAAAATATCGACGTACGATTCGATGATCGCTAAAAAGCCCTTGCGGACTTTCTCAATCTCCCTATAATGCGCATCCACTGACACGGCAGACAGCGTAACGCAAGTTAAGCAAAGAAAGCCAAGCAGTTCATCGGTTAGCAAGCTAAACGATGAGTGTTGAGAAAGTTGAAAAAACTACTTGACGCACTAACGGGAATGCGTAGAATACGCAGCCCTGACCCGCTGAAAACTCAGTGTGTGGTCAACGCTCTTTAACAATTTATCAAGCAAATCTGTGTGGACACTCACAGGTGTTGAGTTAATCGAAACTGCTTAACCTTCGGGTTGGCAGTCAAAAATTTAAATCAATGTAACAATGAGTGTTCATAGCAATATGTACAGTTTGTTTCTTTTAGCGAGAAACAAAAAATCAGAATTCATTGAGCCGCTGAAGTCGCAAGACGGAAGCAACAAAACTTTAATTGAAGAGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGCAGCGGGAAGATAGTTTACTATCTTTGCCGGCGAGCGGCGGACGGGTGAGTAATGCCTAGGGATCTGCCCAGTCGAGGGGGATAACAGTTGGAAACGACTGCTAATACCGCATACGCCCTACGGGGGAAAGGAGGGGACCTTCGGGCCTTCCGCGATTGGATGAACCTAGGTGGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCCTAGCTGTTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGTAGGGAGGAAAGGTAATAACTTAATACGTTATTGCTGTGACGTTACCTACAGAAGAAGGACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTCCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGCGAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTCGAACTGGCGAACTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCTACTCGGAGTTTGGTGTCTTGAACACTGGGCTCTCAAGCTAACGCATTAAGTAGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCACGGAATTCGCTAGAGATAGCTTAGTGCCTTCGGGAACCGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCCTTATTTGCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGATAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCGAGTACAGAGGGTTGCAAAGCCGCGAGGTGGAGCTAATCTCACAAAGCTCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTGGATCAGAATGCCACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGCTGCAAAAGAAGTGGGTAGCTTAACCTTCGGGGGGGCGCTCACCACTTTGTGGTTCATGACTGGGGTGAAGTCGTAACAAGGTAGCCCTAGGGGAACCTGGGGCTGGATCACCTCCTTACCTATACGACTAACTTAATGTTTGTTGAGTGTTCACACAGATTTGCTTGGTAGAAGAAAGAGTAAAAGATGGGTCTGTAGCTCAGCTGGTTAGAGCGCACCCCTGATAAGGGTGAGGTCGGTGGTTCAAGTCCACTCTGACCCACCAATCCTTCCTTCTCAGCGTTGGAAAGTGACTCGTTTAGTGAACTAAACGTCGCCACTGTCCGCCTTGATAAGAAATGATTGGCAAACCAATCCTTTTAGAGGTTTGGAACATCTTACTTACTCGCACTGCATGTAAATGGGGCTATAGCTCAGCTGGGAGAGCGCCTGCCTTGCACGCAGGAGGTCTGCGGTTCGATCCCGCATAGCTCCACCATTTACACCTTATTCTGACTTTATTGCAAAGTAATAAGGCATATGCACTATGGATAGAGATGCCAAAGATAAACTTAAAAATTATCTTTGGCTTTTTTAAGCCCGCTCTTTAACAATTTGGAAAGCTGATAGTATTTAATTGCATGAGTCTGTCATGTAATTAATTACAAAACGTTCAATGCGAAAGCATTGAACATGAGTTCTCAAACACTTTATTAAGTGTCTTGAATATTCAAAAGTAAATTATTCTTTGCTGAATAGTTTACGTCTAAGGCGCGTCCACTTCTTTGGTCAGAAGTGAGACAAGTAAAACCAAGCTGGTCGCAATGCGACCCAGGTGAGTGAAACTCATTTGGGTTGTATGGTTAAGCGACTAAGCGTATACGGTGGATGCCTTGGCAGTCAGAGGCGATGAAGGACGTAGTAACTTGCGAAAAGCGTTGGCGAGCTAGTAACAAGCATTTGAGCTAACGATGTCCGAATGGGGGAACCCAGCAGCATAAGCTGTTATCGCAACATGAATACATAGTGTTGCGAGGCGAACCCGGGGAACTGAAACATCTAAGTACCCGGAGGAAAAGAAATCAACCGAGATTCCCCTAGTAGCGGCGAGCGAACGGGGATTAGCCCTTAAGTCAGTGGGGTGTTAGTGGAATGTGTTGGAAAGCACAGCGGCACAGGGTGATAGCCCCGTACATGAAAACTAACCATTGATGAAAACGAGTAAGGCGGGACACGTGACATCCTGTTTGAATATGGGGGGACCATCCTCCAAGGCTAAATACTCCTGACTGACCGATAGTGAACCAGTACCGTGAGGGAAAGGCGAAAAGAACCCCTGTGAGGGGAGTGAAATAGAACCTGAAACCGTATACGTACAAGCAGTGGGAGCGGTTCTTGAGACCGTGACTGCGTACCTTTTGTATAATGGGTCAGCGACTTACATTTTGTAGCGAGGTTAAGCGAATAGCGGAGCCGTAGGGAAACCGAGTGTTAACTGCGCGTTTAGTTGCAAGGTGTAGACCCGAAACCCGGTGATCTAGCCATGGGCAGGTTGAAGGTTGAGTAACATCAACTGGAGGACCGAACCGACTAATGTTGAAAAATTAGCGGATGACTTGTGGCTGGGGGTGAAAGGCCAATCAAACCGGGAGATATCTGGTTCTCCTCGAAAGCTATTTAGGTAGCGCCTCGAGCGAATACCATTGGGGGTAGAGCACTGTTAAGGCTAGGGGGTCATCCCGACTTACCAACCCTTTGCAAACTCCGAATACCAATGAGTACTACTCGGGAGACAGACGGCGGGTGCTAACGTCCGTCGTCAAAAGGGAAACAACCCAGACCGTCAGCTAAGGTCCCAAAGTACTAGCTAAGTGGGAAACGATGTGGGAAGGCTTAGACAGCTAGGATGTTGGCTTAGAAGCAGCCATCATTTAAAGAAAGCGTAATAGCTCACTAGTCGAGTCGGCCTGCGCGGAAGATGTAACGGGGCTAAGCTAGTCACCGAAGCTACGGGTGCACAGTTTACTGTGCGCGGTAGAGGAGCGTTCTGTAAGCCGTTGAAGGTGAAGGGGTAACCCACGCTGGAGGTATCAGAAGTGCGAATGCTGACATGAGTAACGATAAAGGGGGTGAAAAACCCCCTCGCCGAAAGACCAAGGGTTCCTGTCCAACGTTAATCGGGGCAGGGTGAGTCGACCCCTAAGGTGAGGCCGAAAGGCGTAATCGATGGGAAACAGATTAATATTTCTGTACTTCCGCTAACTGCGATGGAGAGACGGAGAAGGCTAGGCTAGCGCGGCGTTGGTAGTCCGCGTTTAAGGTGGTAGGTGGTGTTCTTAGGCAAATCCGGGGACACGTACTTTGATGTACACACTGAGAGCTGATGACGAGGTCCTAAGGGACTGAAGTAGTTGATGCCATGCTTCCAGGAAAATCTTCTAAGCTTCAGGTTAGTGGGAATCGTACCCCAAACCGACACAGGTGGTCGGGTAGAGAATACCAAGGCGCTTGAGAGAACTCGGCTGAAGGAACTAGGCAAAATGGTACCGTAACTTCGGGAGAAGGTACGCTCCTGTTGGTGATGAGACTTGCTCTCTAAGCTGACGGGAGTCGCAGATACCAGGTGGCTGCAACTGTTTATCAAAAACACAGCACTGTGCAAACTCGCAAGAGGAAGTATACGGTGTGACGCCTGCCCGGTGCCGGAAGGTTAATTGATTGGGTTATCGCAAGAGAAGCTCATGATCGAAGCCCCGGTAAACGGCGGCCGTAACTATAACGGTCCTAAGGTAGCGAAATTCCTTGTCGGGTAAGTTCCGACCTGCACGAATGGCGTAATGATGGCCACGCTGTCTCCAGCCGAGACTCAGTGAAGTTGAAATTGCGGTGAAGATGCCGTATACCCGCGGCTAGACGGAAAGACCCCGTGAACCTTTACTATAGCTTGGCACTGAACATTGAACCTACATGTGTAGGATAGGTGGGAGACTTTGAAGTTGTGACGCTAGTCATGATGGAGTCGTCCTTGAAATACCACCCTTGTAGTTTTGATGTTCTAACCTAGGTCCCTTATCGGGATTAGGGACAGTGCCTGGTGGGTAGTTTGACTGGGGCGGTCTCCTCCCAAAGAGTAACGGAGGAGCACGAAGGTTGGCTAAGTACGGTCGGACATCGTACGGTTAGTGCAATGGCATAAGCCAGCTTAACTGCGAGACATACACGTCGAGCAGGTACGAAAGTAGGTCATAGTGATCCGGTGGTTCTGAATGGAAGGGCCATCGCTCAACGGATAAAAGGTACTCCGGGGATAACAGGCTGATACCGCCCAAGAGTTCATATCGACGGCGGTGTTTGGCACCTCGATGTCGGCTCATCACATCCTGGGGCTGAAGTCGGTCCCAAGGGTATGGCTGTTCGCCATTTAAAGTGGTACGCGAGCTGGGTTCAGAACGTCGTGAGACAGTTCGGTCCCTATCTGCCGTGGGCGTTGGATGATTGAAGGGAGCTGCTCCTAGTACGAGAGGACCGGAGTGGACGAACCGCTGGTGTTCGGGTTGTCATGCCAATGGCATTGCCCGGTAGCTACGTTCGGAATCGATAACCGCTGAAAGCATCTAAGCGGGAAGCGAGCCCTAAGATGAGTCATCCCTAGGAATTTAATTCCTCTAAAGAGCCGTTCGAGACTAGGACGTTGATAGGCATGGTGTGTAAGCGTTGTGAGGCGTTGAGCTAACATGTACTAATGACTCGAGAGGCTTAACCATACAACCCAGATGGGTTTTACTGAAAGCCTTAGACAGAATATCGACACTTGATAAAGTGTGAACTCAAACAAAGCAAATCAGCTTTCCGAATTATTATTAACGTGGTCCAGAGATGGACACGGTAATAAACAGAATTTGCTTGGTGACAATAGCCTTGTGGAACCACCTGATCCCATCCCGAACTCAGAAGTGAAACGCAAACGCGCCGATGGTAGTGTGGGGTCTCCCCATGTGAGAGTAGGTCATCGCCAAGCGCCTAATTATCTCGTAGAGATGTAACGAAGCCAGCTGAATAAGCTGGCTTTTTTGCGTCTGTAATTTGTGAAATTCAGCGTAATTTTTCGCCTAAATACCCACTCCACGATGACTATTTCATCAATACGTGTCCTCCGCGTTAGCTGCAATCCTCGCTTTTAGCAGTTACAATGTGTTTTTGTCGTCATTGATCAAACACATGTCTCTAGCTCATAGCCTTAAATCGTTTAATTCCTTTGGTTTAGCCCAGTCTTGCGCCGATCTTGTTGAGGTGCACTCAAAAGAAGCTGTTCAGGCGATGTGTTTGCCACTATGGCAACAGCAATTGCCTATGTTGGTGCTTGGTGGCGGTAGCAATCTGGTCTTCACAGAGGACTTTAACGGTACAGTTGTCAGGATTTTATCGAAGGGGATAAAAGTAACTGAAGACGCAGAAGCATTTTATCTCGAGGTTGAGGCGGGTGAGAATTGGCACGAGTTAATTCAGTTTACGCTCGAGCATGGCATGTTTGGGCTGGAAAATATGGCGTTAATTCCTGGAACGGTTGGCGCAGCACCTATCCAGAATATTGGCGCTTATGGTGTTGAACTCTGTGATGTGTGTGATTGGGTTGAGTATCTTGATTTACCCTCAGGTGAGTTCGTGCGTATAAGCACAGCTGAGTGTCAGTTTGCTTACCGTGAGTCTATTTTCAAAGACAAATTACGCAATCTTGCCGTAGTTACAGCAGTCGGACTGCGCTTAGCTAAACGTTGGCAACCGCGTTTGGCCTATGGTCCACTGCAATCGTTTGATCCAGCAACCGTCACTGCGCGTGAAATTTTTAATCGCGTATGCCAAGTTCGCAGTGAAAAGCTCCCAGATCCAGCAGTGCTCGGCAATGCAGGCAGTTTCTTTAAAAATCCTATTGTTAGCGCGGCCTGTTATCTAGATTTAGCACAGCGTTTTCCGACGATAGTGGGTTACGCACAAGCGGATGCGACGGTGAAATTGGCGGCAGGTTGGTTGATTGAACAAGCGGGACTGAAAGGTTTTGTGCTTGGTAATGCGGCTGTGCATGACAAACAAGCTTTGGTATTAGTGAACCGAGGCGGGGCAACGGGGCGCGATATTTGTCGCTTAGCGCTGCATGTTATTGCGCAAGTACAGGATAAATTTGGTGTTGTGCTAGAAGCAGAACCTAGAATCATGGGCGCTAACGGTGAAGGAGATTTGTATGTCTGATAATTGGGGAAGAAAGCGCCAGATATTAGCGTTATTGTCTAGCGGTCAATTTGTTTCAGGCGAGCAGTTAGCCACTGAACTTGGGATTTCGCGCGCCGCTGTAAATAAGCACATTGATGCGTTAGAAACCTATGGCGTAGCAATTTATAGCGTTAAAGGTCGCGGCTATAAGCTAGCCAATCCCATCTCTTTGATTGATGCTTCACGTTTAGTGCAGTCAATTGATAACCGTTGTTTTTATTTTGATGAGATCGCAAGCACCAACGGCTTTATGCTGAGCCATACCACTGAGCTAAAGAATGGCGATGTGTGTGTGGCAGAGTATCAATCTGCAGGTCGCGGTCGCCGAGGTCGCACTTGGGTGTCGCCCTATGGGCATCACTTGTACTTCTCATTGTTTTGGACATTCCCGCAGGGAATGGCACAGGCCATGGGTCTAAGTTTAGTGGTGGCGTGCACTCTAGTTGAAGTGCTTAAATCGTTTGGGGTCGAGAATATTGGGGTTAAGTGGCCGAATGATATCTATTTGGATAACAAGAAGCTTGCCGGGATCTTGATTGAAATGTCGGGACAGGCGGATAGTCAGTGTCAGCTGATCATTGGTGTTGGCGTTAATATGGCGATGTCAGAGGAGCAAGGCAAAGGTATCGATCAGCCTTGGAGTGACCTGTCAGATTTGGTCGATATGCCAGATAAGACTGCGCTTGTTATCGAATTACAGAGGCAACTGAAGCGCGATATCCAGCTATTTGAACGTGAAGGATTAGCTGCATTCAAGGCTCGTTGGCAAGCGGCAGACTTATTTTATGGTCGTGAAATACGTTTGCTTATGGGGGAAAAGTCTGTCGAAGGGATTTGTCGCGGTGTTGATGAGCAGGGTGCGGTATTGCTCGAAACCGCCGACGGTGTGCAAGCATTTATCGGCGGTGAAATTAGCTTAAGGGCACGCTAAGCACCCAAGAGACTATTTTACAGAAGACTATTTGCGCATCAGTACGTGGGACATCAAATGATCCTGACCTTTCTGTAGGATCAAATGCGCCCTTTCACGGGTCGGCTGAATATTCAATTGCAGGTTGGGACCGTTAATCGTGTCCCAAATATTGGCGGCAATGGTATTGGCTTCATCATCGGTTAGCGACGAATAGTGATGGAAATAGGATTTCTTGTCACTGAAGGCGCCAGAGCGGAATTGCAAGAAGCGTTCTTTGTACCATTGTTTGAGTAACGGCTCTTCCGCATCGACATAAATAGAAAAGTCGACAAAATCGGAGACAAAAGGACGACGGGTATCGACGGGGGAGTCTAGCCCAGTCTGCAGTACATTCAAGCCTTCCAAAATTAAGATGTCTGGCTGAGAAACGGTTTGGTGCTGATTCTTAACACGGTCATAGGTGACGTGTGAATAGATAGGCGCTTTCACATGGGGTTGGCCAGATTTTACTGCTGAGATAAACTCAACCAGCATTTTCATATCGTAGCTTTCCGGAAATCCCTTACGCTGTAACAAGCCTTTACGCTTCAAGTCTGCCAGCGGATACAAAAAGCCATCCGTTGTGACTAGATCGACCTTAGGGTGCTCAGGCCAATGCCGTAGCAAGGCTTGCAAGATACGCGCTGTGGTACTTTTGCCGACGGCGACACTGCCGGCAATACTAATAATGTAAGGGCTTCTTGATGGTTTTTGCCCAAGGAACTCATCAACCACTAAGCCACGTTGCTGCTTAGCTTTGACAATTAAATTGAGTAGCCGGCTAAGGGGAAGGTAAATATCAGTGACTTCCGATAAGGATATCTTCTCATTTATGCCTCTAAGGTTTTCCAAGTCTTGCTCACTCAGCGTCAGCGGCACTGAATCTCGTAACTCAGACCATTGCAGGCGCTCAAAGGCGAGATAGAGTGCTTTTTGAATTGAATTTTTAGAAGTCATTGGCTTTCCTCAGCTTAGGAAAGGCACAGTACACTATGAGATTATTCGGAACAATACTAAATGGGTGATTGCAGTCGTGTTTCCGTCGTCGTTTGATGTATTCGCTGTTTAATTGGTCGCCAAAGTTGGATAACGTGTAAAAAAACACCGTTCAAGCGCTTTTTTTACGTAAATGGACGTTTTTTACTTGCACTTGAGAGCGCATTTACCTAATATCCCGTTCCGAAATGATATGCCGGCATAGCTCAGTTGGTAGAGCAACTGACTTGTAATCAGTAGGTCCCGAGTTCGACTCTTGGTGCCGGCACCATTTTCTGTGGAGGGGTTCCCGAGTGGCCAAAGGGATCAGACTGTAAATCTGACGGCTCAGCCTTCGAAGGTTCGAATCCTTCTCCCTCCACCAATTTTTTCTAGGTAGTTAGGTAGCCTTAGTTAGGTTGCGCGGACATCGTATAATGGTATTACTCCAGCCTTCCAAGCTGATAACGCGGGTTCGATTCCCGCTGTCCGCTCCAAATTAGTTGCTGATATGGCTCAGTTGGTAGAGCACACCCTTGGTAAGGGTGAGGTCGGCAGTTCGAATCTGCCTATCAGCACCAGCCTCTCTAAATTTAGCTCCTACGCCTAAACATAATCGGTTCGATGTCATTTTAATGCATCGGATTTTTTCTATATGTTAGTTTTCATCACCAAGATTCTTGGACTGAGGCATTACCATGGCTAAAGCTAAATTTGAACGTATTAAGCCCCATGTAAACGTGGGCACCATTGGTCACGTTGACCATGGTAAAACCACACTGACTGCAGCTATCTCTCACGTACTGGCTAAGACCTACGGTGGCGAAGCTAAAGACTTCTCTCAAATCGATAACGCTCCAGAAGAGCGTGAGCGCGGTATTACAATCAATACCTCTCACATCGAATATGACACGCCTTCACGTCACTACGCACACGTAGACTGCCCAGGCCATGCTGACTATGTTAAAAACATGATCACTGGTGCTGCACAGATGGACGGCGCGATTTTAGTAGTAGCTTCAACAGACGGTCCAATGCCACAGACTCGTGAGCACATCCTGCTTTCTCGTCAGGTTGGCGTACCGTACATCATCGTGTTCATGAACAAATGTGACATGGTAGATGACGAAGAATTACTGGAATTGGTAGAAATGGAAGTTCGTGAACTTCTGTCAGAATACGATTTCCCAGGTGATGACTTACCAGTAATCCAAGGTTCAGCTCTGAAGGCACTAGAAGGCCAACCAGAGTGGGAAGCTAAGATTATCGAATTGGCTAACGCACTGGATTCTTACATTCCAGAACCACAACGTGACATCGATAAGCCGTTCCTACTGCCAATCGAAGACGTATTCTCAATCTCTGGCCGTGGTACTGTAGTAACTGGCCGTGTTGAGCGTGGTATTGTTAAAGTGGGTGACGAAGTTGAAATCGTTGGTGTTCGCACTACGACTAAAACAACGTGTACTGGCGTAGAAATGTTCCGTAAGCTGCTTGACGAAGGTCGTGCAGGCGAGAACTGTGGTGTGTTATTACGTGGTACTAAGCGTGATGACGTAGAACGTGGTCAAGTATTGGCTAAACCAGGTTCAATCAACCCACACACTACATTTGAATCAGAAGTTTATGTTCTGTCAAAAGAAGAAGGCGGACGTCACACTCCATTCTTCAAAGGCTACCGTCCACAGTTCTACTTCCGTACAACTGACGTAACCGGTACTATCGAACTGCCAGAAGGCGTAGAGATGGTAATGCCTGGCGACAACATCAAAATGGTAGTTACTCTGATTTGCCCAATCGCGATGGACGAAGGTTTACGCTTCGCAATCCGTGAAGGTGGTCGTACAGTAGGTGCTGGTGTAGTAGCTAAAATCATTGCTTAATAGCGATAGAGATTAGTTTGAAAAGGAAGCTTCGGCTTCCTTTTTTGTTTAAGTGGATTATTTCATAGGTTTTGTGGTTTGCATGGGATTCGCATCCTTGCTTACAAAGTCGAATAAGAATACAATCTATGGCCGATTTTTGACCCTGAGCTTATGCTATTTTGCGTTTCTAAGGGTGTTCGGAAATGCAGAGTATGATGTGGGAATTATGCCCGTCAATTAATGTGCTCTAAGCTCAGGCCGTATTGAGTAACGGAAAAACCGATGACAACAAATACTGAAAACCAGAACAATTCTCTGGATATCGTGAAGTGGGGTTTAGTGATTGTATTGCTAGCCGCTGCTGTTATTGGCAATCAAATGTATAGCGAAGCCAGTGCCGTCATACGTGCGCTTAGTGTTATCGTTGCATTCGCTATTGCTGGTTTTATTGCGCTCCAGACTGTAAAAGGTAAAAAAGCGTTAGCTTTTGCCCGTGAAGCGCATATCGAAGTGCGCAAGGTTGTATGGCCGACGCGTCAAGAAGCGCTAAACACCACTTTTATTGTCCTTGCAGCAACAGGTATCTTAGCTCTTATCCTTTGGGGTATGGATGCTGTGTTAATGCGAATTGTCAATTTTATCACTGGCGTATAGGCACCTCGAATGACTGAAGCTAAAGAAGCTAAAAAAAGATGGTATGTAGTGCAGGCATTCTCAGGCTATGAAGGCCGAGTTTGTAAGTCACTGATTGAATACATTAAGATGCACGGCATGGAAGAGTACTTCGGTGAGGTATTGGTTCCGACTGAAGAAGTCATTGAAATGCGTGCAGGTCAGCGTCGCAAGAGCGAACGTAAATTTTTCCCTGGCTATGTTTTAGTCCAGATGGAAATGAACGATGACAGCTGGCATTTAGTCAAAAGCATCCCGCGTGTTTTAGGCTTTATTGGCGGAACGTCTGATCGTCCTGCCCCAATTTCAGATAAAGAAGCGGATGCAATTCTGCGTCGCTTACAAGAAACGACAGCGTCACCGACTCATAGAACCATATTTGAGCCAGGTGAATTGGTTCGTGTGTGTGATGGTCCATTTGCTGACTTTAACGGTACCGTTGAAGAAGTGGATTATGACAAGAGCCGCGTAAAAGTATCAGTAATGATCTTTGGTCGTTCTACGCCTGTTGAACTTGACTTCAGTCAAGTTGAAAAAGGCTGATTAAATAAAATACAAAATCCGTTTGGCAACGGGGGCGAATTTCTATATAATTCGTCCCCGTTGTTTTCGGGGGAGCACGTCAGCATGTCGCTGATGCGCGTTTGAACCCAAACTGAGGAAATGTCAGATGGCAAAGAAGATTGATGCTTATATTAAGCTACAAGTAAAATCAGGTTCTGCGAACCCGTCACCACCAGTTGGTCCAGCTTTGGGTCAAAAAGGTGTGAACATCATGGAATTCTGTAAAGCGTTTAACGCCCGTACAGAAAAAATGGAAAAAGGCATGCCTATTCCAGTCGTGATCACTGTATACAGTGACCGTTCATTCACTTTTGAAACCAAGACTTCTCCAGCGTCTTACTTACTGAAGACTGCTGCAGGCCTGAAATCAGGTTCTCCACGTCCAAACACTCAGAAAGTGGGTACTATCGCTCGCGCTAAAGTTCAAGAAATTGCTGAACTTAAAGCTGCCGATATGACTGGTGCTGACATTGAAGCGATGACTCGCTCAATCGAAGGTACTGCGCGTTCAATGGGTTTGGTTGTAGAGGATTAATATAATGGCAAAGCTAACTAAACGCATGCGCGTAATTCGCGAGAAAGTTGACGGTACTAAAAGCTACGATATCAACGAAGCTGTTGCTCTGTTAAAAGAATTAGCAACTGCAAAATTCGTTGAAAGTGTAGACGTAGCAGTTAACCTGGGTATCGACCCACGTAAATCTGACCAAAACGTTCGTGGTGCAACAGTGTTGCCACACGGTACTGGTCGTGACGTTCGCGTTGCAGTATTTACACAAGGTGCAAATGCTGAAGCTGCTAAAGCAGCTGGCGCTGAGCTAGTTGGTATGGATGATCTGGCTGAGAAAATCAAAGCCGGTGAAATGAACTTTGACGTGGTTATCGCATCTCCAGATGCAATGCGCGTTGTTGGTATGTTAGGTCAAATCTTAGGCCCACGTGGTTTAATGCCTAACCCTAAAACAGGTACAGTAACGCCAAACGTTGCTGAAGCTGTTAAGAATGCCAAGGCTGGTCAAGTTCGTTACCGTAACGACAAGAACGGTATTATCCACACTACTATCGGTAAAGTGGATTTCACACCAGTTCAATTGAAAGAAAACTTGGAAGCGTTAGTGTCAGCACTGAAAAAGGCTAAGCCTGCAGTTGCTAAAGGCATTTTCGTGAAAAAGATCAGCATCTCTACCACTATGGGTGCAGGTGTTGCAGTTGATCAAGCTACGCTTGAAACAACCGTATAATTTTACAAAGCGCGTGCATTAGTCTATAATGCGCGCACTTTGTGGGTTGAAGCCTGTTTTATCCTCCGTTTTAGCGGTGTTTAACTCAGGTTTCCGTCCAAGACCGCAGGTGTTTTTTGTAAAAATAACTTAATTTCCTGCGTAGACGGTGTCAGGCCCCAGCTAAATTTTTTTACTGGATAATCTGCACCGTAAGTCACTAAATACATAAGTATTTAGTATGGTAAATACTAGGGAATACCCTAGGTAGAATCCAGGAGTAAAGCCAATGGCATTAAGACTCGAAGACAAAAAAGCGATTGTTGCTGAAGTCAACGAAGCTGCCAAAGGTGCGCTATCTGCAGTTGCCGCTGATTCTCGCGGTGTGACTGTAGGTGCTATGACCGGTCTGCGTAAAAAAGCTCGCGAAGCTGGTGTATATGTACGTGTAGTACGTAACACATTGGCTCGTCGTGCTGTTGAAGGTACTGCTTTTGAGTGCCTAGCAGAGACGTTCACTGGCCCAACTTTGATCGCTTTTTCTTTAGAGCACCCAGGTGCTGCAGCACGTCTGTTAAAAGACTTTGCTAAAGAACAAGCTAACTTCGAAGTTAAAGGCGCAGCCTTTGAAGGGAATTTCATCCCTGCAGCTGAAATTGATCGTTTGGCGAAACTGCCAACATACGAAGAAGCACTAGCTCAGTTAATGATGACTATGAAAGAAGCATCTGCTGGCAAGTTCGTTCGTACACTGGCCGCCCTGCGCGATCAAAAACAAGAAGCCGCTTAATTTCAAGCTGGCTGCTTAATAAAATTGAATTCAGAACAATAGGAAACATTTGTTATGTCTATCACTAAAGACCAAATCTTAGAAGCCTTTGCAGCTATGTCTGTAATGGAAGTTGTTGAACTTATTGAAGCAATGGAAGAGAAGTTCGGCGTTTCTGCTGCTGCTGCTGTTGTTTCTGGCGGTGCTGAAGCTGCTGTTGTTGAAGAACAAACAGAATTCAACGTAGTTCTGACTGCTCACGGCGACAACAAAGTTGCTGTTATTAAAGCAATCCGTGGCGCAACAGGTTTAGGCCTGAAAGAAGCTAAAGCTATGTCTGAAGCTGCTCCAGTAGCAGTTAAAGAAGGCGTTTCTAAAGAAGAAGCTGAAGCTCTGAAGAAAGAACTTACTGAAGCTGGTGCTTCTGTAGAAATCAAGTAAGCTATTATTTAGCTTGCTCACCCAAGCCACTTGGGTGGAGGCTGGCGGTTTTTTAACCGTCGGCCTTTTTTGCGCTATATGCGCTGGCGATTTTTTTTCACCGTTTGTCGCCAAATTTTGCAGTCCCTAGCAGAGATTACTGGTTAGGTTCTTGCCATCAACGGTGATGGGCAAACGTGCTGAATTTTAATAACTTAAGTTATTTATTCAGTCTTGGTCACATCTCGTAAGCAGAGGAAACCCATGGTTTACTCCTATTCTGAAAAGAAGCGTATTCGCAAAGACTTTGGTAAGCGTCCAAAAGTTTTGGATATCCCTTATTTATTGTCTATCCAGCTAGACTCTTTTAAGAAGTTCACCGATCAAGATCCTACCGGTGAGCGCGGTTTAGAAGCCGCCTTCCGTAGCGTTTTTCCCATCAAGAGCTTTTCTGGTAATTCAGAACTGCAATACGTCAGCTATAAGCTTGGCGAGCCTGTTTTTGATGTGAAAGAGTGTCAGATCCGTGGTGTTACGTACTCAGCTCCACTACGCGTTAAATTACGCATGGTGTTGTATGACCGTGAAGCAGCGGCGGGCACAGTAAAAGATATTAAAGAACAAGAAGTCTACATGGGTGATATCCCATTGATGACGGATAACGGTACTTTTGTTATCAACGGTACTGAGCGTGTAATCGTATCTCAATTACATCGTTCTCCAGGCGTGTTCTTTGATCATGACCGTGGTAAAACCCACTCATCAGGTAAGGTGCTGTATAACGCACGTATTATTCCTTACCGTGGTTCATGGCTTGACTTTGAATTCGATCCTAAAGATGCACTATTTGTGCGTATTGACCGTCGCCGCAAATTGCCTGCGACCATCATGTTACGTGCATTAGAATATTCTACTCAAGAGATCCTCGATCTGTTCTTTGAACGCGTTGAGTTCAAGATCAAGAAAGATACTCTGGTTATGGCTTTGGTTCCTGAGCGTCTGCGTGGCGAAACTGCCAGCTATGACATCAAGGATGCTGAAGGTTCTGTATTGGTTGAAGCCGGTCGTCGTATCACTGCGCGCCACATTCGCCAATTAGAAAAAACCAATACCACTGAACTCGAAGTGCCAGTTGAATATATCGTGGGCAAATATGCTGCTCAAGATTATATCGATCCGGACACGGGTGAAGTTTTAGTCTCTGCCAACAGTGAAATTAGCCTAGAAGATCTGGCTAAATTATCATTGGCCGGTATTAAAGAACTCAGCACGTTGTATATCAACGAGCTAGATCATGGTGCTTATATTTCAGATACGCTGCGTATCGATTCAACCACTAACCGCTTAGAAGCGCTGGTTGAGATCTATCGTATGATGCGTCCTGGCGAGCCACCAACCAAAGATGCTGCCGAAGCACTATTCCAGAACTTGTTCTTCAGTGAAGAGCGTTATGACCTGTCTAAAGTAGGTCGTATGAAGTTCAACCGTCGTCTCAGCATTCCTGATGACGAAGGTAGCGGTGTATTGTCTAAAGAAGACATCGTCGCGGTAATGAAGAACATCATTCATATCCGTAACGGTTTCGACGAAGTCGATGATATCGATCACTTAGGCAACCGTCGTATTCGTAGTGTCGGTGAAATGGCTGAAAACCAATTCCGCGTAGGTTTAGTCCGTGTTGAGCGCGCCGTGCGTGAACGTCTATCTTTAGGCGATCTGAACGAGCTGATGCCACAAGACTTGATCAACGCTAAGCCAATTTCTGCTGCAGTGAAAGAGTTCTTCGGTTCTTCTCAGCTGTCACAATTCATGGACCAAAACAACCCGCTGTCAGAAGTAACGCATAAGCGTCGTATTTCTGCTCTTGGCCCAGGTGGTTTGACTCGTGAACGTGCAGGCTTCGAAGTCCGCGACGTACATCCAACGCATTATGGTCGTTTATGTCCAATTGAGACCCCTGAAGGTCCAAACATTGGTCTAATCAACTCATTAGCAAGTTTTGCCCGTACTAACTCATACGGCTTCCTAGAAACGCCATACCGCAAGGTTATCGACGGCGTGATTACTGACGAAGTGGAATACTTGTCAGCAATCGAAGAAGGTCGTTATGTGATTGCACAGGCGAACATTGAAATTGATGCGAACGGTCGTATGGCAGAAGAGCAAATTGCTTGTCGTCATAAAGGTGAATCTACCTTTATGCGCGCTGCTGACATCCAGTATATGGACGTTTCGCCACAACAGATCATTTCTGTTGCTGCGTCGCTCATCCCGTTCTTAGAACACGATGATGCTAACCGTGCATTGATGGGTGCGAACATGCAACGTCAAGCCGTACCAACGCTGCGCTCTGAAAAGCCGCTAGTAGGTACTGGTATTGAACGTACTCTCGCTGTTGACTCAGGTGTTGTGGTTGTTGCTAAGCGTGGTGGTTTTGTAGACTACGTTGATGCGAGCCGTATCGTTGTTAAGGTAAATGAAGATGAGCTACGCCCAGGCGAAGCCGGTATCGACATTTATAACCTGACTAAGTACACCCGTTCTAACCAAAACACTTGTATCAACCAACGTCCTTGTTGTTCAGTTGGTGAGCCAGTGGTTCGCGGTGACGTGTTAGCAGACGGTCCGTCTACTGACTTAGGTGACTTAGCCCTTGGCCAGAACATGCGTATCGCGTTCATGCCTTGGAACGGTTACAACTTCGAAGATTCGATCTTAATTTCTGAGCGCGTAGCGCAAGAAGACCGCTTCACTACTATCCACATTCAGGAGCTGTCTTGTATTGCTCGTGATACTAAGTTGGGTAGCGAAGAAATCACGGCTGATATTCCAAACGTAGGTGAGTCTGCTCTGTCGAAACTCGACGAGTCAGGTATCGTTTACATTGGTGCAGAAGTGAAGGGCGGCGATATTCTGGTTGGTAAAGTGACGCCAAAAGGCGAAACACAGCTAACGCCAGAAGAGAAGTTATTGCGAGCTATCTTCGGTGAGAAAGCGTCTGACGTTAAAGACAGTTCACTGCGTGTACCTAACTCTGTTAAGGGTACTATCATCGACGTTCAGGTATTTACCCGTGACGGCGTTGAGAAAGACAAGCGTGCAGTTGAAATCGAAGAGATGCACATTGCCCAAGCTCGTAAAGATTTAGGCGAAGAGTTCAAGATCCTTGAAGAAGGTGTATTGAGCCGTGCGCGTAACCTGTTAATCGGTGCTGGTTTCACTGACGCGCAAATTGCTGCCTTGCCACGTAAAGATGTTTTGATTCAAGTCATTGACGATGAAACTAAACAAACTGAACTTGAGCAATTAGCTGAACAGCATGAAGAGCTGAAAGCTGACTTCGATAAGAAGTTTGAAATCAAACGTCGCAAGATCACCCAAGGTGATGACTTGGCTCCAGGCGTACTCAAGATCGTTAAGGTTTACTTAGCGGTTAAGCGTACTATCCAACCTGGTGACAAGATGGCGGGTCGTCACGGTAACAAGGGTGTTATCTCTAAGATTTGCCCAATCGAAGACATGCCGTATGACGAACAAGGTAACCCAGTAGACATCGTATTGAACCCACTAGGTGTTCCATCACGTATGAACATTGGTCAGGTACTTGAAGTCCATATGGGCGCCGCTGCCAAAGGCATTGGTAACAAGATCACTGCGATGCTTGAAGAACAGCGTGAGCTGGCTGAAGTTCGTGGCTACATCAAACAAGTTTATGAATTAGGTGACGAAGTGCAACAGCGCGTCGATATCGATTCATTCACTGATGATGAAGTATTACGTCTTGCGACTAACCTGAAAGGCGGTATCCCGATTGCAACTCCTGCATTCGATGGTGCTAAAGAGAAAGAGATCAAGCAGATGCTTGAACTTGCAGGTTTGCCAACCTCTGGTCAGTTGAAGTTGTTTGATGGTCGTACCGGTAACGAATTTGAGCGTCAAGTAACTGTTGGTTACATGTACATGCTCAAACTGAACCACTTAGTTGATGACAAGATGCACGCCCGTTCTACCGGTTCGTACAGCTTAGTGACTCAACAACCACTGGGCGGTAAAGCTCAGTTCGGTGGTCAGCGTTTCGGTGAGATGGAAGTGTGGGCACTAGAAGCATACGGTGCCGCTTATACGCTTCAAGAAATGCTCACCGTTAAATCGGATGACGTTAACGGTCGTACTCAGATGTATAAGAACATCGTCGACGGTAACCATCAGATGCAACCTGGCATGCCAGAGTCCTTCAACGTGTTACTGAAGGAGATCCGTTCACTCGGTATTAATATCGAGTTGGATCAGGCATAAGCGCAGGCCTTTAGCCGCGTTTGGCAAACAGAATGGTGCTCCGTGCGAGCGGAGCACCCGGTTTAACTCCTTCAGGAGAGAAACGTGAAAGACTTATTAAAGTTTCTGAAACAGCAAAGCAAGACTGAAGAATTTAACGGCATCAAAATTGGTCTGGCATCGCCTGATCTGATCCGTTCTTGGTCTTTTGGTGAAGTTAAGAAGCCAGAAACCATCAACTACCGTACCTTCAAGCCTGAGCGTGAAGGTCTGTTCTGTGCGCGTATCTTTGGCCCAGTCAAAGATTACGAATGTTTGTGCGGTAAGTACAAACGTTTGAAGCACCGTGGTGTTATTTGTGAAAAGTGTGGCGTTGAAGTAACCCAAACTAAAGTACGTCGTGAGCGTATGGGTCACATCGAACTGGCTAGCCCAGTTGCGCACATTTGGTTCTTAAAATCACTACCGTCCCGTATCGGCTTAATGTTGGATATGACGCTGCGTGATATCGAACGCGTACTGTATTTTGAATCATTTGTCGTGATCGAGCCTGGCATGACCAGCCTTGAACGTGGCCAAATGCTGACAGAAGAAACTTATCTGGATGCATTAGAAGAATACGGTGATGAGTTCGAAGCTAAAATGGGTGCCGAAGCAGTGCTTGAACTGTTACGTGCTATAGATTTAGCGAAAGAAATCGAACAAATGCGTGAAGAATTGCCGTCAATCAACTCTGAGACTCGTCGCAAGAAAGTGACTAAACGTCTTAAGTTGATGGAAGCATTCTACACTTCAGGCAACAAGCCTGAGTGGATGATCTTAAAAGTACTGCCTGTGTTACCACCTGACTTACGTCCGCTGGTACCGCTCGATGGCGGCCGCTTCGCGACTTCAGATCTTAACGACCTGTATCGCCGCGTGATCAACCGTAACAACCGTTTGAAGCGTCTGTTAGATTTAGCTGCGCCAGACATTATCGTACGCAACGAAAAGCGTATGTTACAAGAGTCTGTTGATGCGCTATTAGATAACGGTCGTCGTGGTCGTGCTATTACCGGTTCTAACAAGCGTCCTCTGAAATCTTTGGCTGATATGATCAAAGGTAAGCAAGGTCGTTTCCGTCAGAACTTATTGGGTAAGCGCGTCGACTATTCTGGTCGTTCGGTAATTACCGTAGGTCCTACTCTACGTCTGCACCAATGTGGTCTTCCTAAGAAGATGGCACTGGAACTGTTCAAGCCATTCATCTATGGCAAGCTTGAAGGTCGTGGCTTAGCTACTACCATCAAAGCGGCTAAGAAGATGGTTGAGCGTGAAGTGGCGGAAGTATGGGACGTTCTGGATGAAGTGATCCGCGAACATCCAGTGATGCTCAACCGTGCACCAACACTGCACAGACTGGGTATCCAAGCGTTCGAGCCAGTACTGATTGAAGGTAAAGCAATCCAGTTACACCCACTCGTTTGTGCGGCATACAACGCCGACTTCGACGGTGACCAAATGGCGGTACACGTACCGTTAACACTGGAAGCGCAGCTTGAAGCCCGTGCCCTGATGATGTCTACCAACAACATCCTGTCACCAGCCAACGGCGAGCCTGTTATCACCCCGTCTCAAGACGTGGTATTGGGTCTGTACTACACCAGCCGTGAGCGTATTAACGGCCGTGGTGAAGGTATGGCATTTATGTCTGTTGCTGAAGTTGAAAAAGCTTACGCAACTGGCGCTGCTGAACTGCATGCCCGCGTTAAAGTACGTATCACTGAAACCATTATTGGTGACACTGGTGAACGTACTGAACAACGCCGTATCGTTGATACGACTGTGGGTCGTGCACTGCTGTCACTTATTCTGCCAGCAGGTTTGTCATTTGATCTGGTTAACCAGAACATGGGCAAAAAGCAGATCTCTAAACTGTTGAACACTTGTTATCGTCAACTGGGTCTGAAAGATACTGTTATCTTCGCTGACCAATTGATGTATACCGGTTTCCGTTTTGCAACCATCTCTGGTGCCTCTGTAGGTATCGATGACATGGTTATTCCAGACGAGAAATACACCTTAGTTGCTGATGCAGAAGCGGAAGTTCTTGAAATTCAAGAGCAGTTCCAATCTGGTCTAGTTACAGCCGGTGAGCGTTACAACAAAGTTATCGATATCTGGGCAAGTGCCAACGAAAAAGTTTCTAAAGCTATGATGGAAAACCTGTCAACTGAGACAGTGATTAACCGTGATGGTGTTGAAGAGAAACAAGCCTCGTTTAACAGCATCTATATGATGGCTGACTCAGGCGCTCGTGGTAGTGCCGCACAGATCCGTCAGTTGGCGGGTATGCGTGGTCTGATGGCTAAGCCTGATGGCTCAATCATTGAAACGCCAATTACCGCTAACTTCCGTGAAGGTCTAAACGTACTCCAGTACTTTATTTCTACTCACGGTGCGCGTAAAGGTCTAGCCGATACGGCATTGAAGACAGCGAACTCGGGTTACCTGACACGTCGTCTTGTAGACGTTGCACAAGATTTAGTGGTTATCGAAGACGATTGTGGTACTCACGAAGGTCTAACCATGAAGCCGCTGATTGAAGGTGGTGATGTGGTTGAGCCGTTACGTGAACGCGTACTGGGTCGTGTGGTTGCGCTTGATGTGTTCTATCCAGGCACTGAAGACGTACTCGCACCACGTAACACGCTACTCGACGAAGCCTGGTGTGACAAGTTAGAAGAATATTCAATCGACGAAGTGATTGTACGTTCTGTTATTTCTTGTGATACTGATTTCGGTGTGTGTGCAGCCTGTTACGGTCGTGACTTGGCACGTGGTCATATCATTAACCACGGTGAAGCAATCGGTGTTGTGGCAGCACAATCTATCGGTGAACCAGGTACACAGTTAACGATGCGTACGTTCCACATCGGTGGTGCGGCATCAAGAGCGTCTGCAGAAAACAATGTCCAAGTGAAAAACTCGGGTTCATTGAAACTGCATAATGCTAAGCATGTTACTAACAGTGACGGCAAACTGGTTATCGTTTCTCGTTCTTCTGAACTGGCCGTTATTGACGAGCTGGGTCGTGAGAAAGAGCGTTATAAAGTGCCATACGGTACTGTGCTCGAGAAGCTAGAAGAAGCGGCTGTTGAAGCGGGCGATGTTATCGCTAACTGGGATCCACATACTCACCCAATCATTTCTGAAGTGGCGGGTAGTATCAAGTTCGTAGACATGATTGACGGTGTCACTATGACACGTCAAACAGACGAACTAACGGGTCTGTCATCAATCGTGATCCTTGACGTTGGTCAGCGTGGTACTGCGGGTAAAGAAATGCGCCCAATGATCCGTCTGCTTGGCGCTAATGGTGCTGACTTGATGATCCCTGGTACTGAAGTACCAGCACAGTACTTCTTACCTGGTAGTGCGATCGTTAACTTGGAAGATAACGCGCAAATCAACGTGGGTGACGCTTTAGCTCGTATTCCTCAGGAATCGTCTAAAACACGCGACATCACCGGTGGTCTGCCACGCGTTGCTGACTTGTTCGAAGCGCGTAAGCCAAAAGAGCCTGCTATTCTGGCGGAAATCTCTGGTACCATCTCGTTTGGTAAAGAGACCAAAGGTAAGCGTCGTTTGGTGATTACACCTGCTGATGGCGGCGATCATTATGAAGAGATGATCCCTAAGTGGCGTAACTTAAACGTGTTCGAAGGTGAAAAAGTCGAACGTGGTGAAGTTATCGCTGACGGTCCAGAAGCGGCACACGACATTCTGCGTCTTCGTGGTATCCACAACGTAGCAAACTACATTGTGAACGAAGTACAGGACGTTTACCGTCTGCAAGGCGTGAAGATCAACGATAAGCATATCGAAGTGATCATCCGTCAAATGCTGCGTAAGTGTCTTATCACTTCTGCCGGTGACACTGACTTCCTCGAAGGGGAACAAGCTGAAGTGTCACGCGTGAAGATCGCTAACCGCGAACTGATCGCCCAAGGTAAAGTGCCTGCGACCTTCGAACGTGAACTACTGGGTATTACTAAAGCATCTCTGGCAACAGAATCCTTTATCTCAGCAGCATCGTTCCAAGAAACTACGCGCGTACTGACCGAAGCCGCCGTAGGTGGTAAGAGCGATCAATTACGTGGTCTGAAAGAAAACGTTATCGTTGGCCGATTGATCCCAGCCGGTACTGGTTACGCTTATCACAAGACTCGTAATGAAGCGCGTGCGAAGAAAAACGAACCTGTAGTAGTGAATAAGATCACTGCAAGTGAAGCCGAACAGAACTTGGCCGATCTCTTAAACCTGGCTGGTAGCCAAGATTAAGTAATCAGTTTGTTATAAAAAGGCGCCTATGGCGCCTTTTTTCTTGTAATATGCCGCAAAAGTTGGCTATTTCTTGACAGTCAGGCATTACCTTTCTAAAATTCCGCGTCCCACCATTGTGGGATATAGATTTTTCACACCTTATCGTTGAGTTTGATTCAACTGACTCGGAGCTATACATGGCAACTGTAAACCAGTTGGTACGTAAGCCACGCGCGCCAAAAGTCGACAAGACTAACGTGCCTGCGTTGAATGCGTGCCCGCAAAAACGTGGTGTTTGTACACGTGTGTACACAACTACCCCTAAAAAACCTAACTCTGCACTACGTAAAGTAGCTCGTGTGCGTCTAACTAACGGTTTCGAAGTGACTTCGTACATCGGCGGTGAAGGCCACAACCTGCAGGAACATAGCGTGATCTTAATCCGTGGTGGTCGTGTTAAAGACTTACCAGGTGTGCGTTATCACACTATTCGTGGCGCATTAGACTGTGCAGGCGTGACTTCACGTCGCCAAAGCCGTTCTAAATACGGTGCTAAGCGTCCTAAGTCTTAACTTATCCGTTAAGTAAGGCCAAGCTAGTTTTATTTTGAGAATTCCAGTTTTGGAAATCCCTGAAGCATACGGAGAATTGTTATGCCAAGACGTCGCGTTGTAGGACAACGTAAAATCCTACCAGATCCAAAGTTTCACAGTGAGTTGCTGGCTAAGTTCATCAACGTCATTATGCAAGACGGCAAAAAGTCGACTGCAGAAAAAATTATCTACAAGGCACTAGATGTTGTCGCTGAAAAGAAAAGCGAATCACATCTGACTATCCTTGAAGCAGCCCTGGACAACGTTCGCCCATCAGTCGAAGTTAAATCTCGTCGCGTTGGTGGTTCTACGTACCAGGTACCATGTGAAGTTCGTCCTGTGCGTCGTAACGCACTGGCGATGCGCTGGTTAGTTGAAGCTGCTCGTAAGCGTGGTGAAAAATCTATGGCTTTACGTCTGGCAGGTGAAATGCTGGATGCGTCTGAAAACAAAGGTACTGCTGTTAAGAAGCGTGAAGACGTGCATCGCATGGCTGAAGCTAACAAAGCCTTTGCTCATTACCGTTGGTAATATGATGGTGTGGGCTTAGGCCCACACCATAGTTGTTGATATTGCCAAGGCTTAGGTCTTGGTGGAGAGGGTATAATCGTGGCTCGTACAACCCCAATTGAGCGTTATCGTAATATCGGTATTTGTGCTCATGTTGACGCAGGTAAAACCACCACAACAGAACGTGTTCTGTTCTATACCGGTTTGTCTCATAAAATCGGTGAGGTGCATGACGGCGCCGCTACCACAGACTGGATGGTTCAAGAGCAAGAGCGTGGTATCACTATCACCTCGGCTGCGGTAACCACATTCTGGCGTGGTATGGATGCTCAATTCACTGAACACCGCATCAATATCATCGATACCCCTGGTCACGTTGACTTTACTATTGAAGTTGAACGGTCTCTGCGCGTCCTCGACGGTGCTGTTGTCGTTTTCTGTGGTTCATCTGGTGTTGAACCTCAGTCAGAAACAGTGTGGCGTCAAGCTGATAAATACCGCGTTCCACGTTTGGTATTTATCAACAAGATGGACCGTGCAGGTGCAGACTTTGAACGTGTAGTGAAGCAAATTAGAACTCGTCTTGGAGCCACTTGTGTGCCTATTCAATTGAATATTGGTGCAGAAGAAAACTTCAAAGGCGTGATCGACTTAATCAAGATGAAAGCAATTAACTGGAATGAATCAGACCAGGGTATGACTTTCACTTATGAGGAGATCCCTGCAGAGTTAGCAGCTAAAGCGGCTGAAATGCATGAGTATCTGGTTGAAGTGGCCGCCGAATCTTCGGATGAACTGATGGATAAGTACCTTGAAGAAGGCACGCTATCAGAAGACGAAATTAAAAAGGCGCTACGTCAACGTACTATAAATAATGAAATCGTTTTAGCGACTTGTGGTTCTGCATTTAAGAACAAAGGCGTTCAAGCGGTACTTGATGCGGTTATTGAATTCTTGCCAGCGCCAATTGATGTGCCTCCAATTAAGGGCATCGATGAGAACGACCAAGAAGTAGAGCGCCCATCAGATGACAATGCGCCTTTTGCTGCATTGGCATTTAAGATTGCTACAGATCCATTCGTAGGAACTTTGACCTTTATTCGCGTGTACTCAGGCGTACTCGAATCAGGTTCGGGCGTTTACAACTCTGTCAAACAGAAGCGTGAACGCGTAGGTCGTATGGTGCAAATGCATGCAAACGATCGCACTGAACTGAAAGAAGTCCGTGCTGGTGATATCGCAGCGGCGATTGGTCTAAAAGAAGTGACTACGGGTGATACGCTTTGTGATAACGATCATAGAGTGATCTTGGAACGTATGGAGTTCCCAGAGCCAGTAATTACCATTGCCGTTGAGCCTAAGTCAAAAGCCGACCAGGACAAAATGGGTATCGCGTTGCAAAAGCTTGCAGCGGAAGATCCATCATTCCGAGTTGAAACTGACGAAGAATCATCACAAACTCTGATTTCGGGTATGGGTGAGTTACACTTAGACATCATCGTCGACCGTATGCGTCGTGAATTCGGTGTTGAATGTAACGTAGGTAAGCCACAAGTGGCCTACCGCGAAACTATCCGAGCATCAGTAGAAGCTGAAGGTAAATTTGTACGCCAATCAGGTGGTCGTGGACAATTCGGTCACGTTTGGTTAAAACTAGAGCCTAATGAAGAAGGCGCTGGTTACGAATTTGTCAATGCAGTTGTGGGAGGTGTGATTCCTCGTGAATTCATTCCTGCGGTTGATAAAGGTATCCAAGAACAGATGAAGAATGGCGTTCTCGCTGGCTTCCCTGTGTTGGACGTGAAGGTCACTCTGTTCGACGGTTCATATCATGATGTGGACTCGAATGAGATGGCGTTCAAAATTGCAGGTTCTATGGGCTTCAAAAAGGGTGCGCTTGAAGCGAATCCGGCGTTGCTCGAACCTTGCATGAAAGTAGAAGTAACTACCCCTGAAAATTATATGGGTGACGTCGTTGGTGATTTAAACCGTCGTCGTGGCTTGATTGAAGGGATGGATGATGGCTTCGGTGGCATCAAAATAGTCCATGCTGTAGTGCCTCTTTCTGAAATGTTTGGTTATGCAACTGATTTGCGCTCTGCTACACAGGGTCGCGCTTCATACTCTATGGAGTTTTTGAAGTACTCTGATGCACCGCAAAACATTGCAAAAGCGATTATTGAATCTCGTAGCTAATATCCAGTTACGACATAAATGTTATTATGGTCCAGCACTGACTGGACTATTCTGAAAAGAAAGGAATATATCGTGGCAAAAGCTAAATTTGAACGTATTAAGCCTCACGTAAACGTGGGCACCATTGGTCACGTTGACCATGGTAAAACCACACTGACTGCAGCTATCTCTCACGTACTGGCTAAGACCTACGGTGGCGAAGCTAAAGACTTCTCTCAAATCGATAACGCTCCAGAAGAGCGTGAGCGCGGTATTACAATCAATACCTCTCACATCGAATATGACACGCCTTCACGTCACTACGCACACGTAGACTGCCCAGGCCATGCTGACTATGTTAAAAACATGATCACTGGTGCTGCACAGATGGACGGCGCGATTTTAGTAGTAGCGTCAACAGACGGTCCAATGCCACAGACTCGTGAGCACATCCTGCTTTCTCGTCAGGTTGGCGTACCGTACATCATCGTGTTCATGAACAAATGTGACATGGTAGATGACGAAGAATTACTGGAATTGGTAGAAATGGAAGTTCGTGAACTTCTGTCAGAATACGATTTCCCAGGTGATGACTTACCAGTAATCCAAGGTTCAGCTCTGAAGGCGCTAGAAGGCCAACCAGAGTGGGAAGCTAAGATTATCGAACTGGCTAACGCACTGGATTCTTACATTCCAGAACCACAACGTGACATCGATAAGCCGTTCCTACTGCCAATCGAAGACGTATTCTCAATCTCTGGCCGTGGTACTGTAGTAACTGGCCGTGTTGAGCGTGGTATTGTTAAAGTGGGTGACGAAGTTGAAATCGTTGGTGTTCGCACTACGACTAAAACAACGTGTACTGGCGTAGAAATGTTCCGTAAGCTGCTTGACGAAGGTCGTGCAGGCGAGAACTGTGGTGTGTTATTACGTGGTACTAAGCGTGATGACGTAGAACGTGGTCAAGTATTGGCTAAACCAGGTTCAATCAACCCACACACTACTTTTGAATCAGAAGTTTATGTTCTGTCAAAAGAAGAAGGCGGACGTCACACTCCATTCTTCAAAGGCTACCGTCCACAGTTCTACTTCCGTACAACTGACGTAACCGGTACTATCGAACTGCCAGAAGGCGTAGAGATGGTAATGCCTGGCGACAACATCAAAATGGTAGTTACTCTGATTTGCCCAATCGCGATGGACGAAGGTTTACGCTTCGCAATCCGTGAAGGTGGTCGTACAGTAGGTGCTGGTGTAGTAGCTAAAATCATTGCTTAATAGCGATAGAGATTAGTTTGAAAAGGAAGCTTCGGCTTCCTTTTTTGTTTTAAGGGCTGGTTTTATCCACAAATTTGTGTAAAATGCTCGCCACTCTGAAGTTAAATAGTTATTTGATGACTATTTACACAGCGGGCTTGATATCCGGATTTAGATAGGTATAATGGCCCCCTCGTCGACCTAGGTTGACGAATTAAATGATTAGTCGCTTAAGTTAATCATTATCATAGCGGCTCCGATTGGGAGTCGAACGGTTAAATCATCTCGCTCTGCTTTTCTATATAGAAGAAGCTAGAGGGTGATTTTTTATATGTCCATTTTAGGAGCTCTGGTCAATGCAGAACCAAAGAATCCGTATCCGCTTAAAGGGATTTGATCATCGCTTAATTGATCAATCTACAGCGGAAATCGTTGAAACTGCTAAGCGTACAGGCGCTCAAGTACGTGGTCCAATTCCACTACCTACGCGTAAAGAGCGTTATACCATTTTGATCTCTCCGCACGTTAATAAAGATGCTCGTGACCAGTACGAATTACGTACACACAAGCGCCTGGTTGACATCGTAGAGCCAACTGAAAAGACTGTAGACGCGTTAATGCGTTTAGATCTTGCTGCTGGTGTCGACGTACAGATTAGCTTGGGTTAATTGAGATCCTAGTAAGAAGAGGTTTGAGAGATGGCTATCGGTCTTATTGGTCGTAAAGTTGGTATGACTCGCATCTTCACTGAAGATGGTGTTTCAATACCAGTTACTGTAATTGAAGTTGCAGGCAACCGTGTTACTCAAGTGAAAACTTTAGAAACTGACGGCTACCGTGCACTTCAAGTTACAACTGGCACCAAAAAAGCCAATCGCATCACTAAACCAGAAGCAGGTCACTTTGCCAAGAGCGGCGTTGAAGCCGGTCGTGGTTTGTGGGAAATGCGTTTGGTAGACGGTGAAGGCGAGGGCATTGAAGTTGGTGCTGAGCTAAATGTTGATATTTTCGCAGACGTAGCGAAAGTAGATGTTACTGGTCAATCAAAAGGTAAAGGCTTCCAAGGCGGCGTTAAGCGTTGGAACTTCCGTACTCAAGATATGACTCACGGTAACTCTTTGTCGCACCGCTCGAATGGTTCTATCGGTCAGAACCAAACGCCTGGTCGCGTATTCAAAGGCAAAAAAATGTCAGGCCATATGGGTGCCGAGCGTGTTACTACTCAAAATCTAGTTGTAGTTCGTGTAGATGTTGAGCGTAACCTGCTACTAGTCCGTGGCGCTGTTCCTGGCGCTACCAATGGCGACTTGATTATCAAGCCTGCCGTTAAAGCTTAAGGTCTGAGGAGATAGTAATGGAATTGGTATTGAAAGACGCGCAAAGCGCTCTTGAAGTTTCCGAAACTACCTTCGGCCGTGACTTTAACGAGGCACTGGTTCATCAGGTAGTTGTAGCATACGCTGCAAACGCGCGTCAGGGCACTCGTGCTCAAAAGACTCGTGCGGAAGTAACTGGCTCAGGCAAAAAGCCTTGGCGCCAAAAAGGAACTGGCCGTGCTCGTGCTGGTTCTGTTAAAGGCCCAATCTGGCGTGGCGGTGGCGTAACATTCGCTGCTAAAACCCAAGATCATAGCCAAAAAGTTAACAAAAAGATGTACCGCGGCGCGCTGAAAAGCATTCTGTCTGAATTGGTACGTCAAGAGCGTCTGGTTGTTGTTGAATCTTTCGGCGTTGAAGCTCCTAAAACTAAAGAGCTGAAAGCTAAATTGAAAGCAATGAACCTTGAAGACGTTCTGATTGTGACTGCAGAAGTTGATGAGAACTTATTCTTAGCAGCTCGTAACCTGTACAAAGTTGACGTTCGTGACGTAGCGGGTCTTGACCCAGTTAGTCTGATCGCGTTCAACACTGTGCTTGTGACTGCTGACGCAGTGAAGCAAATCGAGGAGATGCTAGCATGATGATCCGCGAAGAACGTTTGCTAAAAGTTATTCTCGCTCCACACATCTCTGAAAAGAGCACTGTGAACGCTGAGAAGCACAACACTGTTGTTTTCCGCGTAGCTATCGATGCAACTAAAGCTGAAATTAAAGCTGCTGTTGCTAAGCTATTTGAAGTTGAAGTTGATTCAGTTCGCACTTTAGTAAGCAAAGGCAAAACTAAACGCACTGGTGGCCGTACTGGTCGTCGTAGCGATTGGAAAAAAGCCTATGTAACTCTAGCTGCTGGTGCTGACATCGATTTCGTCGGCGGCGCTGAGTAAGCAAAGGAGAATTATCATGGCAGTTATTAAGTGTAAGCCAACCTCTCCAGGTCGTCGCCACGTTGTTAAAGTGGTGAACACTGACCTGCATAAGGGTAAACCCTTTGCTGGCCTGTTGGCGAAAAAATCTAAAAGTGGTGGCCGTAACAATACTGGCCGTATCACTGTCCGTCACGTAGGTGGTGGTCATAAGCAGCATTATCGTCTAATCGACTTCAAACGCGATAAAGATGGTATCCCTGCAAAGATTGAGCGTCTGGAATACGATCCAAACCGCACAGCTAACATCGCGTTAGTACTGTATGCGGATGGTGAACGTCGTTATATTCTTGCTGCCAAAGGCATGCAAGCTGGCGATAAGATCCAGTCTGGTGTTGCTGCCGAGATCAAAACCGGTAACGCAATGCCACTGCGCAACATCCCAGTAGGTTCTGTAGTTCATGCAGTTGAAATGAAGCCTGGTAAAGGTGCTCAGATCGCTCGTTCTGCAGGTGCTTATGTACAAGTTGTTGCTCGTGATGGCGCATATGCCACTCTACGTCTTCGCTCTGGCGAAATGCGTAAAGTGCCAGTTGATTGCCGCGCGACATTTGGTGAAGTTGGTAATGCCGAACACATGCTACGCCAGTTAGGTAAAGCAGGTGCTAAACGCTGGAGAGGCATACGCCCTACAGTGCGTGGTGTTGCAATGAATCCAGTAGACCATCCACACGGTGGTGGTGAAGGCCGTACTTCTGGTGGTCGTCACCCAGTGACTCCATGGGGTGTGCCAACTAAGGGTTATAAGACTCGTAGTAACAAGCGCACTGACAAGTACATCGTACGTCGTCGTAATAAATAGTAAGAGGATTCGCCATGCCACGTTCTCTCAAGAAAGGTCCCTTCATTGACCTGCACTTGCTGAAGAAGGTAGAGAAAGCGATGGAAGCCGGTGACAAAAAGCCTATTAAGACTTGGTCACGTCGCTCTATGATCATTCCAAATATGATTGGTTTGACCATCGCTGTCCATAATGGTCGTCAGCACGTTCCTGTGTTCGTAACTGACGAAATGATCGGCCACAAACTTGGTGAATTTTCACCAACTCGCACTTATCGTGGCCATGCCGCTGATAAGAAAGCGAAGAAGCGTTAATACGGGAGGAATAAGATGGAAGTTTTAGCTAAACATCGTTTTGCCCGTACGTCTGCGCAGAAGGCCCGTCTGGTTGCTGATCAAATTCGTGGTTTGCCTGTTGCTAAGGCACTCGAAATTTTGACTTTCAGCCCCAAGAAAGCCGCCGTACTGGTTAAAAAAGTACTTGACTCAGCTATCGCAAACGCCGAACACAACGAAGGCGCGGATATTGATGAGCTTAAAGTAGGAGCCGTCTTCGTAGATGAAGGCCCGACTATGAAGCGTATCATGCCACGTGCTAAAGGCCGCGCTGATCGTATCATGAAGCGTACCAGCCACATTACTGTGGTTGTATCAGATCGCTAGGAGAGAGCAATGGGACAGAAAGTACATCCTAATGGTATCCGTCTGGGTATCACCAAGCCTTGGATCTCTACCTGGTACGCAGATAAATCAGATTACGCAAGTAACCTGAACAGCGACTGGGAAGTGCGTAAGTTTCTTGTAGAGAAATTACAAGCTGCTTCAGTATCTAAGATTGTTATCGAGCGTCCAGCGAAAAGCATCCGCGTTACCATTCACACTGCCCGTCCAGGTGTTGTGATCGGTAAGAAAGGTGAAGACGTTGAAGTATTGCGTGCTGCAGTGTCAAAACTTGCTGGCACTCCTGCTCAAATTAACATCGCTGAGATCCGTAAACCTGAGCTAGATGCCAAATTAGTTGCTGACTCAATTGCACAGCAATTAGAGCGTCGCGTAATGTTCCGTCGTGCTATGAAGCGCGCAGTTCAAAACGCAATGCGTATTGGTGCTCAAGGTATCAAAGTACAAGTTAGTGGCCGTTTAGGCGGAGCTGAGATTGCGCGTGACGAGTGGTATCGTGAGGGTCGTGTACCTTTACATACTTTGCGTGCTGATATCGACTATTCTACATCTGAAAGTCACACCCAATACGGTGTGATTGGCGTTAAAGTTTGGATCTTCAAAGGTGAAGTTCTAGATGGAATGCTGCCACAGATTGAAGAACCGAAGCAGCAGCAACCTAAGCGCAAGCCTCGTGGTAAATAGGAGAGCCGGCAATGCTGCAACCTAAACGTATGAAGTTTCGCAAGATGTTCAAAGGCCGCAACCGCGGTCTAGCGAACGGTACCGAAGTTAGCTTTGGTACTTTTGGTCTGAAAGCAGTCGGCCGTGGCCGTTTAACTGCCCGTCAGATCGAATCTGCACGTCGTGCCATGACACGTCACATTAAGCGTCAGGGACAAATTTGGATTCGAGTTTTCCCGGACAAGCCGATTACCTCTAAGCCTCTTGAAGTGCGTATGGGTAAAGGTAAAGGAAACGTTGAATACTGGGTATGTCAGATTCAACCTGGTAAGGTTCTTTATGAGATGAATGGTGTTTCTGAAGTGATCGCTCGTGAAGCGTTTGCTTTGGCTGCTGCCAAGCTTCCAATTAAGACTACCTTCGTAACTAAGACGGTGATGTAATGAAAGCGAGCGAACTGAGAGAAAAAAGCGTTGAAGAACTGAACGCTGAACTACTTGGTCTGCTGCGTGAGCAGTTTAACCTGCGTATGCAACACGCCACTGGTCAGTTGACTCAGACTAATCAACTGAAATTGGTGCGCCGTAACATTGCGCGCGTTAAGACCATTATTACTTCTAAGGCGGGTGCATAATGTCTGATAAAATCCGTACTTTGCAGGGTCGTGTAACTAGCAACAAAATGGACAAGTCCATTACTGTTGCTATCGAACGCCAAGTGAAACACCCAATTTATGGGAAATACATTAAGCGTACGACGAAGATCCATGCACATGACGAAACTAATCAGTGCAATGAAGGCGATTTAGTAGCGATTCGCGAATGTCGTCCTTTGTCTAAGACTAAGTCTTGGACCCTGGTTGAAGTAGTATCAAAGGCCTAAGTTAATTAGGTATTTAGGTAAACGGCTCCAGAATGTGGGGCCGTTTGTTTTTTAATACTATACATTCCACTTTTATGGTGTTATATTTGCGCGCCATTTTTGACTAAATTAATAGCAAAAATGAGTGTTTTTATTGTCCCATAGTGGGAATTGTGTAGTAACGATAGCGGAGCACTTAAAATGATCCAAATGCAATCGACTCTTGACGTTGCTTGTAACAGCGGCGCACGCAGAGTTCAGTGTATTAAGGTCTTGGGTGGCTCTCATCGTCGTTATGCCGGTATCGGCGACATCATCAAAGTTTCTGTTAAAGAAGCGATTCCTCGCGCTAAAGCGAAGAAAGGCGATGTGTATAACGCGGTGGTAGTCCGTACTAAGAAAGGCGTACGTCGTCCAGACGGTTCTGTCATTCGCTTCGATCGGAATGCAGCTGTTCTTTTGAACAACAACCTGCAGCCGATTGGTACTCGTATCTTTGGACCAGTGACACGTGAATTGCGTAATGAGCAATTTATGAAAATTGTCTCGCTGGCACCAGAAGTACTGTAAGGAGCTTCAAAATGGCAGCTAAAATACGTCGTGAAGACGAAGTGATTGTATTAGCAGGTAAAGACAAGGGTAAACGTGCAAAAGTTTCTCAAGTCCTTCCTACTGGTAAATTGATTGTTGAAGGCATCAATCTTGTCAAAAAACACCAAAAGCCAAACCCACAATTGGGCGTAGCTGGCGGTATTGTTGAGAAAGAAGCACCGATACAAGCATCAAATGTAGCGATTTTCAACTCTGCCACTGGCAAAGCTGATCGCGTTGGTTTCCGCTTTGAAGACGGCAAAAAAGTTCGTTTCTTCAAATCGAACAGTGAACTCGTTAAGTAATTGGAGTAAACGATGGCGAAACTGCATGATAAATACCAAGAGACTGTTGTCGCTGAACTTGCTAAAAAGTTCGGTTATACCAGTGTCATGCAAGTCCCTCGGATTGAGAAAATCACCCTGAATATGGGTGTTGGCGAAGCTGTTGCAGACAAGAAAATCATGGACCATGCTGTCCGTGATATGACTGCAATCGCTGGTCAAAAACCAGTAGTGACTGTAGCTCGTAAGTCAGTTGCTGGTTTTAAAATCCGTGAAGGCTACCCTATTGGCTGTAAAGTTACCCTACGCGGTGAGCGTATGTGGGAATTCTTAGAGCGTTTAGTCGATATCGCAATCCCACGTATTCGTGACTTCCGTGGCTTAAGCGCTAAAGCGTTTGACGGCCGTGGTAACTACGCAATGGGCGTGCGTGAGCAGATCATCTTCCCAGAAATCGATTACGATAAAATCGATAAGATTCGTGGTATGGATATTGTTATCACTACTACTGCGAAGAATGATGAAGAAGGTCGTGCTTTGTTAGACGCTTTTAACTTCCCATTCAAGAAATAAGGGTAGCGTAATGGCAAAAACATCAATGAAAGCACGTGAAGCTAAACGTGCACAGCTCGTGGCCAAGTACGCTGAAAAGCGTGCGGCTCTTAAGACTATCATTGCAAGTCCAGCTTCTTCTGACGAAGATCGTTGGGATGCGGTACTTAAGCTGCAAGCTCTACCACGTGATTCCAGCGCTGCGCGTCAACGCAATCGTTGTAATCAAACTGGTCGCCCTCATGGCTTCTTACGTAAATTCGGCTTAAGCCGTATCAAATTACGTGAAGCAACCATGCGTGGTGAAGTTCCTGGCCTGCGTAAGGCCAGCTGGTAAGCACTTGTCACGGAGTAAGCTAATATGAGCATGCAAGATCCTATTGCGGATATGTTAACCCGTATTCGTAACGGCCAAGCTGCTAACCACGTATCGGTGAAGATGCCTTCAGCTAAGTTGAAAGTCGCAATTGCGAAACTACTTAAAGATGAAGGTTATATCGCTGATTACGCCGTAGCAGATGAAGCCAAGCCTGAACTGGAAGTTACTTTAAAGTATTTCCAAGGCCAACCAGTCGTTGAGACTATCCAGCGTGTAAGTCGCCCTGGTCTTCGTATTTACAAAGGTAAAAACGAACTTCCAAAGGTGATGGGCGGTCTGGGTGTCGCAATTGTGTCCACTTCTAAAGGCTTGATGACTGATCGTGCCGCCCGCCTTGCAGGCATGGGTGGCGAGGTTATCTGCTACGTAGCTTAAGGAGCTAGGAATGTCTCGTGTAGCAAAAGCACCAGTATCTATTCCAGCTGGCGTAGAGGTGACCTTGAACGAACAGACCCTTACCGTCAAAGGCGGAAAAGGTAGTCTGACTCGAGTGATCAACAATGCGGTCAATGTTGTTATTGAAGCTGGCGTAGTTAAGTTCCTCCCTGTTGAAGGCGTTTCTAACGCTTGGGCACAGGCTGGTACAACTCGTGCATTAGTAAACAACATGGTTGTTGGTGTATCTCAAGGTTTTGAGCGTAAATTAAAGTTAGTTGGCGTTGGTTACCGTGCGAAACTCGTCGGTTCTGACATTGACCTTACTTTAGGTTTCTCTCATCCGTTAGTACACAAACTGCCCGCAGGCGTTACTGCAGAGTGCCCTAGCCAAACTGACATCGTCCTACGTGGCGTTGATAAACAGTTAATTGGCCAAGTCGCTGCTGAGATTCGCGGATACCGTCCACCAGAGCCATATAAAGGCAAGGGTGTTCGCTATGACGACGAAGTAGTACGCCGTAAAGAGGCTAAGAAGAAGTAGGTAACGCGATATGGATAAGAAAACATCTCGCTTACGTCGCGCTACTCGCGCTCGTAAGAAGATCCAAGAGCTGGGCGTGAACCGTCTGGTTGTACATCGTACACCGCGTCACATTTATGCTCAGGTGATCAATCCTGAAGCTCAGGTGTTGGCAGCTGCTTCAACCGTAGAAAAAGCGGTTAAAGAGCTACTGAAGAGTACCGGTAACGTAGACGCAGCGAAAGCAGTAGGTAAATTTGTTGCTGAGCGCGCGATCGAAAAAGGCGTAACTTCAGTTGCGTTCGATCGTTCTGGTTTCAAGTATCACGGTCGTGTAGCTGCTTTAGCAGATGCAGCTCGTGAAGCTGGCCTGAAGTTCTAAGGGGTTATAAAATGGCTAAATTAGAAGCTCAGCAAAAAGACGATCTGCAAGAGAAATTGATTGCAGTTAATCGTGTTTCTAAAGTAGTTAAGGGCGGTCGTATCTTTAGCTTCACAGCACTGACAGTAGTGGGTGACGGTAACGGTAAGATTGGCTATGGCTATGGCAAAGCACGTGAAGTTCCAGCAGCGATTCAAAAAGCTATGGAAAAAGCTCGTCGTAACATTGTGACCGTTGAGTTGAATGCAGGTACTCTGCATCACCCAGTTAAAGGTCGTCATACTGGTTCTCTTGTGTACATGCAACCTGCATCACAAGGTACTGGTATTATTGCCGGTGGCGCAATGCGTGCCGTATTGGAAGTAGCAGGCGTTCATAACGTTCTGTCAAAAGCATACGGTTCTACTAACCCGATCAACATCGTTCGCGCGACTGTCGATGCTTTGGTGCACATGAAGTCACCTTCGCAAATCGCAGCTAAGCGTGGCCTGAATGTTGACGAAATTCGGGGGTAATGCACCATGGCAACTAAAACTGTAAAAGTTACTCAAACTAAAAGCGGTATCGGTCGTTTACCGAAGCACCGTGCAACCCTAACGGGTCTTGGTTTGCGTCGCATTGGTCACACTGTTGAATTAGAAGATACTCCTTCAGTTCGCGGTATGATCAACAAGGTCTACTACATGGTTAAGGTGGAGGATTAATAGATGCGTTTAAATACTCTATCTCCAGCTGCAGGCTCTAAGCATGCACCAAAGCGTGTAGGCCGTGGTATGGGCTCAGGTCTAGGTAAAACAGCGGGTCGTGGTCATAAAGGCCAAAAGTCTCGTTCTGGTGGCGGTGTACGCCCAGGATTTGAAGGTGGTCAAATGCCACTTAAAATCCGTTTACCTAAATTTGGTTTTACCTCGCGTCGCGCTATGGTAACAGCTGAAGTTCGTGTACTCGAACTGGCAAAAGTTAACGGTGATGTTATCGACTTAAACGCTCTGAAAGATGCGAACGTTATAACTCGCAACATCCAGTTTGCGAAAATTGTTCTTTCAGGTACCATTGAGCGCCCTGTGACTGTTAAAGGTCTGAAGGTAACCAAAGGTGCACGTGCAGCTATTGAAGCTGCCGGCGGTAAGATCGAGGAATAATACGTCGATGGCAAAACCAGGACTTGATTTAAAAAGCGCGAAAGGTGGACTTTCAGAATTGAAAGCTCGCCTCCTGTTCGTGATTGGTGCGATTATCGTCTTTAGAGCCGGTTCGTTTGTGCCAATTCCTGGTATTGACGCAGCTGTATTAGCAGAGCTGTTTGCTCAGCAAAAAGGGACCATCCTGGGCATGTTTAACATGTTCTCGGGTGGCGCCCTTTCGCGTGCCTCTATCTTTGCATTAGGTATCATGCCGTATATTTCGGCATCGATTATCATGCAACTGTTGACTGTCGTACATCCTGCACTCGCTGAATTGAAAAAAGAAGGCGAATCAGGTCGTAAGAAAATCAGTCAATATACCAGATGGGGTACCTTAGTGCTGGGTACATTCCAGTCGGTCGGTATTGCAACCGGGTTACCAAACTTAGTGCCAGGCCTTGTGGTCAACATTGGATTTGGGTTCTACTTTGTTGCGGTTGTGAGCTTAGTGACGGGAACGATGTTCCTTATGTGGCTAGGTGAGCAGATTACCGAACGTGGTATAGGTAACGGTATCTCGATTTTGATTTTCGCAGGTATTGTTGCTGGATTACCTTCCGCTATCGGCCAAACGGCTGAGCAGGCGCGTCAAGGTGACTTGAATGTGTTAGTATTGTTGTTGCTCGCGGTAATTGTGTTTGCAGTGACTTATTTTGTAGTGTTTGTGGAACGTGGTCAGCGTCGTATCGTCGTTAACTATGCTAAGCGTCAGCAGGGCCGTAAGGTGTTCGCTGCGCAAAGTACTCACTTACCGCTTAAAATAAACATGGCAGGTGTTATTCCGCCAATTTTTGCGTCAAGCATCATTTTGTTCCCTGGAACACTGGCTCAGTGGTTCGGTCAAAATGAATCCATGTCATGGTTAAGTGATTTTTCACTGGCTGTGTCACCTGGACAACCGCTTTACTCATTATTATATGCAACAGCGATTATCTTTTTCTGTTTCTTCTATACTGCGTTGGTATTTAACCCACGCGAAACAGCAGATAACTTGAAGAAGAGTGGTGCATTCATCCCTGGGATCCGTCCTGGAGAACAGACTTCGCGTTACATTGATAAAGTTATGACCCGTTTGACATTGGCAGGCGCGTTATACATAACCTTTATCTGCTTAATTCCGGAGTTCATGTTAATCGCGTGGAAAGTACAGTTCTATTTTGGCGGTACTTCACTACTAATTATGGTAGTCGTGATCATGGACTTCATGGCTCAGGTTCAGACGCATATGATGTCTCATCAGTATGAGTCTGTGATGAAGAAAGCTAACCTAGTAAACAAAGCGAATTTAGATCGCTTTGGTCGCTAAGTAGCTTTACGGAGTGATGAAATGAAAGTTCGAGCTTCCGTGAAGAAGATCTGCCGTAACTGCAAGATCGTCAAGCGTAGTGGCGTTGTACGCGTTATCTGTGTTGAACCAAAACACAAACAGCGTCAAGGCTAAAAAGTAATTTGTTCAGCCCATGAATTGGGCTGAACAAAATTTAGTTTGCAAATCTGTTGACTGTCGAGTATCCTTTCGGGCTTTTCGCAGTTGGCCTTTAACTTTAAGGAGTGCATAGTGGCCCGTATCGCTGGCATTAACATTCCTGATCAAAAGCATACAGTCATCGCATTGACTGCTATTTTCGGCATCGGCCGTACTCGCGCTAGAGCAATCTGCGCGGCTACAGCAATCGCTGAAACTGCTAAGATCAAGGAATTGAGCGAAGCTCAAATAGATATACTACGCGAAGAAGTCGCCAAATACATTGTAGAAGGTGACTTACGTCGTGAGATTTCAATGAACATCAAGCGTCTTATGGATCTTGGTTGTTATCGTGGTCTCCGCCATCGCCGTAGCCTGCCTCTCCGTGGGCAACGTACTAAGACCAATGCGCGTACGCGTAAAGGTCCACGTAAACCTATTAGAAAGTAACGGGAAGGTAAAGCAATGGCTAAAGTTCCGTCACGTTCTCCGCGTAAGCGCGTACGTAAACAGGTTGCAGATGGCATGGCTCATATCCATGCTTCTTTCAACAACACTATTGTCACCATTACAGATCGTCAAGGTAATGCGTTGTCATGGGCTACTTCAGGTGGTTCAGGTTTCCGTGGTTCACGTAAATCTACTCCATTTGCTGCACAGGTGGCTGCTGAGCGTGCAGGTGCTGCTGCTCAAGACTACGGTTTAAAAAACCTTGAAGTGTTTGTGAAGGGTCCAGGTCCAGGTCGTGAGTCAGCCATTCGTGCGCTGAACGCTGTTGGTTATAAAATTACCAACATTACCGATGTGACGCCGATCCCTCATAATGGTTGTCGTCCTCCTAAAAAGCGTCGTGTATAACGCCGTTTCGTTAGGATAGTTGGAGAAAGATCATGGCAAGATACTTGGGTCCCAAGCTCAAGCTCAGCCGCCGAGAAGGTACAGACCTTTTCTTAAAAAGCGGTGTGAGAGCAATCGATTCAAAGTGTAAACTGGAAACTGCACCTGGACAACATGGCGCTCGTAAGCCACGTTTGTCAGAGTACGGTACTCAGTTACGCGAAAAACAAAAAGTTCGTCGTATTTACGGTGTGTTAGAAAAACAATTCCGTAACTATTACAAAGATGCTGCACGTACTAAAGGCAACACAGGTGAAAACCTGCTTCAGCTTTTAGAAACCCGTCTTGATAACGTCGTTTATCGTATGGGCTTCGGTGCAACTCGTGCAGAATCACGTCAGCTAGTAAGCCATAAATCAATTATGGTTAACGGTCGTGTTGTTAACATTCCATCATTCAAAGTGTCTGCGAATGATGTTGTGAGCATCCGCGAGAAGTCACGTACTCAAGCTCGTATTAAAGCGGCTTTAGAAGTGGCAGCTCAACGCGAGAAGCCTACATGGGTTGAAGTCGACAATGCGAAAATGGAAGGTGCTTTCAAGCGCGTTCCAGAACGTAGCGATTTGTCTGCGGAAATTAACGAACAGCTGATCGTCGAGCTTTACTCTAAGTAAGGCTAACAAACAAGAGAGGACACAATGCAGGGTTCTGTTACAGAATTTCTTAAACCGCGTCTCGTTGATATCGAGCAGGTTAACTCAACACGTGCCAAGGTTACATTGGAACCACTTGAGCGTGGTTTCGGCCACACTTTAGGTAACGCGTTGCGTCGCATCCTATTGTCGTCTATGCCCGGCTGCGCGGTTACCGAAGTCGAGATTGACGGTGTACTGCACGAATACAGCAGTAAGGAAGGCGTTCAAGAAGATATCCTTGAGATCTTGTTAAACCTGAAAGGGTTAGCAGTGACTATCGAGGGTAAAGACGAGGCTATGCTTACGTTGAGCAAGTCCGGCGCAGGCCCTGTCATCGCAGCAGATATCACGCATGATGGTGATGTCACTATCGTGAATCCTGATCATATTATCTGTCACCTGACAGGTAACAATGATATCAGCATGCGTATTCGCGTTGAGCGTGGTCGTGGCTATGTACCAGCATCTGCTCGTGCACAGACTGAAGACGATGATCGCCCAATCGGCCGCTTGCTGGTTGATGCTTCTTTCTCGCCAGTTGCACGTATTGCCTACAATGTAGAAGCAGCACGTGTAGAACAGCGTACTGACTTAGATAAACTCGTTATCGATATGACCACAAACGGTACTATCGATCCTGAGGAAGCTATCCGTCGTTCTGCAACTATTCTGGCTGAACAGCTAGATGCGTTTGTTGAATTACGTGACGTGACTGAGCCAGAGCTGAAAGAAGAGAAACCGGAATTCGATCCGATTCTGCTGCGTCCTGTCGACGATTTAGAGCTAACTGTACGTTCGGCTAACTGCTTGAAAGCCGAAGCGATTCATTACATCGGAGATCTGGTACAGCGCACTGAAGTTGAGTTGCTGAAGACCCCTAACTTAGGTAAGAAATCTCTTACTGAAATAAAGGACGTTTTAGCTTCTCGCGGACTGTCGTTAGGTATGCGTTTGGAAAATTGGCCTCCAGCCAGTTTAGCAGACGACCTATAAGTCTCAGGTTTGTACAGATTTAGGTTATAAGGATTAGGTCATGCGCCATCGTAAGAGTGGTCGTCAACTAAACCGTAACAGCAGTCATCGCCAAGCCATGTTTCGCAACATGGCCGGTTCACTAGTTCGTCATGAGATCATCAAGACAACTGTGGCTAAAGCGAAAGAACTGCGTCGCGTAGTTGAGCCTCTGATAACACTTGCTAAGAGTGACAGCGTTGCAAACCGTCGTTTAGCGTTTGCACGTACTCGCGACGCTGAAGTCGTAGGTAAGTTATTTACTGAGTTGGGTCCACGTTACCAGGAACGTCCTGGTGGCTATACCCGTATCCTTAAGTGCGGTTTACGTGCTGGTGATAAAGCCCCTATGGCTTACATCGAGCTAGTTGGCCGTCCAGAAGCTGCTCAAGCTGTTGATGTTGAAGCTGCTGAGTAATTAAGGTTACACTTTTAAAAAGCCGGGCGAATGCTCGGCTTTTTTATATCTTTTTTTCGTTACTTCACCCAGAGTGTATTTTTCTCGATTTCTTTCGGTAGGCTAATATTCCATTTGGCCAAACCTTGCTGACTAAACATGTATTTCCCAAGATCATCTGTCACTGGAAAGATGTTTTCCGGTTTCTCGCCAGCCAGTATTTTTAACGCCATATTCGCTGCTATTTCGCCATGATGATAGCCATCGAGTACGTAGCCTCCAATATTGCCCTCGGAGGAGACGCTAAATCCCCAAAATCCGAAATTCGGTACCGGTGTATGTTCTGATATCCATTTGAGGAGCTGATCCGCATTCACAGAACGATTTTCTGCATCCCTCACTGTTTGGTGGAGCGCCACGACAATGGCGTTATAGCCTTGCTCCTTCGCCGTCAGTACCTGTTGCTGCCAATCTGCGAGCTGTGACAATTGCACCAAATTCACTTCGATATCGCCCAAAATACTTGAGTGTTGTTTGCTGATGTATTCTGCAGCGGAGTCTGAGGTAACACCTTGGTCAAATAGGACTAGTATTTTGCGGTGCACAGTCTGTGGGAGTAAGCGTTGAATGAGGAGGAGTGAGCGTCGTAGCAGCGGCCTTTCTATGACACCGGTAAATCGCTTGTGCTCATTGAGATGATACTCTCTAGGGTTCATATTCACCCCAAGATAGATCACTGGGATTAAGCTGTCATTGAGGCGCTGATGTAACAGGTTAACCGCATTATCATCGGCGAGAATAACCAGTTGAGGTGCTTGGTTTTGGATGTACTTCCACGCTAAATCAGCTCTTTCGGCAAATTGCTCCTTTGGTAAACGCTTGGTATCCATTTCGAAATAACTCACATCGTGCAGGGGGCTAAGCGTGTCATTAATCGCTCGGTAGTATTCTTGGTCCCATTGATATCCATGGTGATAGCTTTCGATCACGATCACTTTAGCTGCATTGGCGTTCTGCGAGAGACTCACAAGGCATAAGAGTAGGGCAACACAAAGCAAGTTTATGTTTAATGGTGTTATTTGAATCATACGCAGTCTTAGGTTTTGAGGCTGCCTTGAGTATAGTTCACCTTTAATTATCTAGATTGAGATAATAAAAAAGCCTCACTAGTTATTAGGCTAGGAGGCTTATTGTGTATGCTGTTGCTACTTCATCAATCGCTTACTTAGTGTCTGGTGCCTTTTGTTGGCTGTAATCCAACTTTTCGTGCTTTTGGCCCATGGCTTCGGCAACTTCTGGTGGCATGTAGTTTTCATCATGCTTAGCCAGTACTTCAGTCGCTTCTAGCTTACCATCAGCACCGAGTACACCTTGCGCCACAATACCTTGTCCTTCACGGAATAGATCGGGCAGTAGATCGTCATAGGTGACCAGTACTTCGCCGCCTAGAGAGTCATGCACTGCGAACTGCACATGTAAGCTCTTAGGATCTCGCACCATAGAACCGACTGTTACCATGCCGCCCACACGAATGCGTTGGCCAATTTCTGGTTTGACGCCAGTATCTGTTTTGCCGTGGACAATTTCAGACGGTGTATAGAATAAGTTTAAGTTGGAGTTCAGCGCATAAAGCAAAAGCGAAGTAATCGCGGCAACACCACCGATTAATGCCACTGCTAACGTCAGTCTTTTTTTGCGTCTTGGGTTCACGATTTAGTACTCCTAGTTTCTTTTAGGCGTTCTTCTCTATCCATCTTCTTCGCGATTTCAATTAACACCTTACGCTTTTGGCGTGCGCTGGTAATGATAAGCGTTGAGAGGCAGAAGAAGGTCACTCCGTAGGATAGCCACACGTAAAATGCATAGCCGCCCATATTAAAAAAATCACTGATTGAATCGAATTGCATTATTTGACCTCCTCAGCCTTAGCTAGTTCACGCACCCAAGGGCGCATGCCATTTCTTGCTAAAATTTCTGCTTTGAATCGCACAATAGTGATGGCACCAATCATGAGACCGAAGCCAAGCATATTGATTAACAGTGGATAGAGCATTTCCGTCGACATAGTCGATTTCTCGGTAATGCGGATAGTTGATGGCTGATGTAATGAGCTCCACCATTCAACTGAGTATTTGATGATAGGGATGTTAATTACACCCACGATCGCTAGGATCCCTGCAGCACGAGCCGCGAGCACTTTATCCTCAAATGAGGCATAGAGTGAGATAACACCTAGATATAGGAACAGTAATACTAGCTCAGAGGTTAAGCGTGCATCCCATACCCACCAAGCGCCCCACATAGGTTTACCCCATGCAGCACCGGTAAAGAGGGCAATAAAGGTGATTACTGCACCGATAGGCGCAATCGAAGCCGCTGCCCAGTCGGCAAGTTTTATCTGCCACACTAAACCGATAAAGGCTGCTGTAGCCATACCCATGTAAGCTGCCATCGACATAGAGGCTGCGGGTACATGGATAAAGATAATGCGATAGCTATCACCCTGTTGGTAATCCGTCGGCGCGAAGGCTAATCCCCATGTTGTGCCTACAGCAATAAACAGACCTGCTAAGATCGCAAACCATGGAAATAATGTTCCTGATAACTTATATGCGCGTTCAGGATCCGCGTAAGGGTGTAACCATTTCCACATTTTAGTTAGTACTCACTCGCAGAGATGCACCAATTGCAAAAGGTGCTAAGGTTAACGATCCGACCAGCATAGCGCCTATTATAGCGAGCTGACCGTCATAGGGTAAATTCATTCCTGCCGCGTCTATGGCACTCGTTGCAAAAATGAGAACCGGAATATACAAGGGCAAAATAAGTAAGCTGAGTAACACGCCACCTTTTCTAAGGCCGACGGTTAATGCCACACCAATGGCACCTAATAGCGATAATACTGGCGTGCCTAGCGCAAGGGTCGATATTAACGCACCATAACTATTGCCATCTAAATTGAGTAGCACCGCTAACAGTGGCGCAATAATAATCAGTGGCACACCGGTTAAAATCCAATGGGCTAACACTTTGGCTAATACCAGAATGGCTAATGGCTGTGGGCTCAATAACATTTGCTCTAAGCTGCCATCACTAAAGTCCGCTTTGAACAGTCTTTCAAGTGACAACATAGAGGCTAATAACGCTGCAACCCAAATTATCCCAGGAGCCACACGGGCTAACATTTGTGGCTCTGGGCCAATCCCCAGTGGGAATAAGGTCACAACCATAATAAAAAATAGCAGTGGGTTAAAAATGTCGCCGCGGTGACGAATGGCAATTTTAAGATCTCGCTGCAGCAAGGTGAAAAATGCCTGGGTAAAACTGATGCCTCTTTTCATTTAGCCTGACCTTATATGAAGCGATAATCTAGACGAATTTTACGAAGCCTATCATCTTTGATAATGCCCATATCTTGGTGAGTTGTGAGGATCACACAACCACCATTATCCGCATGCTGAATAAATAATTGCTCCAGCTCTTCGACGCCTTTTTTATCGATCGCGGTAAAAGGTTCGTCAAGGATCCAAATTTTACAATTGCTATGCCACAATCTCGATAGCGCGGTTCTGCGATGCTGGCCTGCGGATAAATGTCCGGCAAGCGCCTCTTCAAAACCTGTTAAGTTTACTTTGGCTAAAATGGCTGAGGTATCAAAGTCATCATAGCCACTGATTCTTAAATTGAAATTAAGGTTTTCTTCGGCGGTAAGCTCGCATTTCACTCCCGCTAAATGGCCGAGATACAAGAGATCTTCATTGTATTCATCACGGCAGCGGTTTATGTCTTCATTTCTGTAAAAGGTTTGTCCGGCATAGGGGCGAGAAAGACCTGCCAATATGCGTAATAAACTGGTTTTGCCCGCGCCGTTAGGGCCTTCAATTTGAACAATATCACCGGCATTAATATCAAAACTTAACTCATCAAACAGGATGCGTTCTTCGCGGATACAGGTCAGCTTGCTGGCTGACACTAATGTGGTTTCTTTAGTTATATTTGTCACTGAACCCTCTATCTCAAGCCGGAAGGAAACACAAGTTATTCTAACACAAACGTTATTGGGGGTTTACTCTTGGTTTGCACTAGATGTCAGCAATATGATGTGGTTCCAATAACCGCAATATTTATCGAACTTTTTTACGACTTGCTAATATATTAACGAATTTGTCACCCTTGTCGCCTGATTCATTTTGTGTTGAGATTCTAATCACAAAAAGATGAGATTTCTTGAAGTTTGTAGTAATCTTAGTCGGCTATAATGAGTCTGCCTAAGTAAGGGCAGCGCCGAGCTTTGTACGTATTTGCATTAGGAACATTGAACATGAAAAAACTGTTAGCAATGACTGCAGTCGCTGCTTTGACTATGTCAGTCAACGTATCAGCTCAAGAAGCTGAAGCTATTTTCAACAAGGCATGTACCGTATGCCACAGCATGGGTGTTGCTGGCGCTCCAAAAGTGCATAACGCTGCGGAATGGGAACCACGCTTAGCTAAGGGTATTGATGCTCTACTGCACAGTGTGAAAACTGGTTTGAATGCAATGCCACCAGGCGGTATGTGTACTGATTGTACTGACGAAGATTACAAAGCAGCTATCGAGTTTATGTCTAAAGCTCAGTAATTTTTTCAGTGTTAATAAAAAACCGGCCAAAGCCGGTTTTTTTATGTCTGCAATAAACTGGGTTTATTGCACTTGGGTATCAAGCACTAGGTTAGCGGTATCGCCGACAGCCACTGTGCTGATCTTACCTTGAAGATCGCCCGCTTGTGGTTTGATATTACCGTGCTTAGAAAGCACTGCAATTACTTCCACATTGGTAGCGTCACTTAATTTCTTATCGCCACCCATACCGCTGCTATCATCTAATGTCACCGTAATTGGGAACGACTTAGCACTGACTTTGGTTGCCGCTAATGGGACTTTTGGTCCTTCAGTTGCACGGGCAAAGATAAAGATAGTGTCTTCAGGGCTGGCTTTAGCGGCAAGTTCTGCAGAGATAGATATAGTCACATTGACCGACTTAGTCGCACTCGCCTTCACTTGCTTGTGGGCATCATCATTTGGCATGCCAGCCGTTTCAGACTGCATACGTAAGTTAGCCGTTTCGATAGCATTCATTAGGGCTGCGCGATCAACATCAGCACGATCGCTATCAAGAATGATTTGCCATGATGCGATAGCTTTTTTGTAGTCCGCGGTAAAGAAAGCATCCATTCCTACGAGCAATAGTGTTGAAGGGTCTTGAGCATCCATCGCTAAAGATTCATCAATGATCGCTTGGATTTGCGGTGTCATTTGCTGGCCCGCTTTGTAATACATGGCTGTTGCTTTAGGACCTAATAATTCAGCGTGTTTTCCGACTAAATCCATTACTTTATCGAAAGCCGCAATCCCTTTGTCGTATTGGTTTGCCGATACATAAGCGTGGCCTAAGCTGAACCATACTTGGCTGTTCTCTGGCTCGGCTTTTACTTGGGCTTCCATCATCTGTACGCGCTGCGCCATGATTTGAGCCGTATCCATTCCCTCATGTGGGTTGGCAGGTTGTTGGGCAGTTTCAATATTCTCGAAGGCACCTAAATGCTGATAGAGATAGCCTGAGATAACAATCAAACAAACTGACATGATCGACGGCCACAGAATCGTCTTTGGCTTGATTTGGTTGACCAGTGAATCATCGCTCGCTTCTTTGATGTCTTGGAGTAGACTGATTTCAAGCTCTTTTTTCAATGCATCGAATTCGGCTTGATCCAGTAACTCTTCGCTTAATTCTTTTTCAAGAACGCCTAGGCGCTCATTGAACAATTCAAGGTTCGTTTGTTTACGAACTCCAGCTTCTTCAGTTTTCAGCAACTTCTGCTGTCTGAAATGCGGGATCCAAATCAGCATCAGGCCGACTAACATAACAAGCGCAATAAAAATCCAAAATGTCGTCATTGTTTCTATATTCACTTTAGTTTGCAGAAAGCGGAGTAACCGCTAGGCTTGGAAGCAAGATTATACGTAAAGATTGTTCATTGAACAGTAATATAAGGTAATCCCAGTACCGAAAGCTTATAGTTGGGCACTGGTTCACAAATTGATTCTTCTTGTAAAAATTGTATGAAACTTAAGTGTCATTTGTCGGTCACATGCAGAACTGAGCCTCTTCCGTGACTTTCATCAGCCTCTAAAATTGAGAATGAGACAAGTAACCCAGTTTTGTACGAAAGAGGCAGACAGGAACAATAGCAAATACTAAAATGAGCAACATTTCGTGCATTTGCGTGAATAACATGTTGTGCGTTGAGATAGAGTCTTTCTAAATCAACATATTATTGAAGACTGAGGAAAACCCATGATCCCAGAACTTGGACACTTTGCGCTGATAATAGGGGTGGCTTTTGCATTCTTATTAATCAGCGTCCCCCTAATAGGTGTTGCCCGTAAAGATCAATACCTTGTTAGGTATGCTTGGCCGTTAACGTACGGAATGTTCTTTTTTATCACTGTATCAGTTGTCGTGCTGGCCTATAGCTTTGCCGTCGATGATTTCTCAGTCGCTTATGTGGCACATCATTCTAATTCTCAGTTACCTATCTTCTTTAAGATTTCGGCCGTATGGGGCGGTCATGAAGGGTCACTGTTGTTCTGGGTGTTTGCTTTATCGACTTGGGCTGCAGCGGTTGCCTTATTTAGTAAAGGTTTAGAGGAAGTGTTTACTGCGCGTGTACTGGCAGTATTAGCACTGATCGTGATTGGCTTTAGCTTATTCATGCTACTCACCTCTAGCCCATTCGAACGTTTGTTCCCGATCCCTGCCGAAGGTCGTGACTTAAACCCTATGCTGCAGGATGTGGGGTTAATCTTCCATCCACCTATGCTGTATTTAGGTTATGTGGGTTTCTCGGTGAGCTTTGCATTTGCGATTGCCGCGCTGATGAGCGGTCGTCTCGATTCTGCATGGGCTCGTTGGACTCGTCCTTGGACCTTAGCCGCTTGGATCTTCCTAACTGGAGGTATCGCGCTAGGTTCTTGGTGGGCATATTATGAATTAGGCTGGGGCGGTTGGTGGTTCTGGGATCCTGTTGAGAACGCCTCTTTCATGCCTTGGTTAGTGGGTACTGCGCTTGTTCACTCTTTGATTGTGACCGAGAAACGCGGCGCCTTCCGTAATTGGACCGTATTACTGTCTATATTCGCTTTCTCTTTAAGTTTGCTTGGTACCTTTATTGTTCGCTCAGGCGTATTAACCTCAGTACATTCATTCGCAGCCGATCCAAGCCGCGGTATGTTTATTCTGTTGTTACTTGGCTTAGCGATTGGCGGTTCATTAACACTGTTTGCCTTCCGCGCCAGTGAAATGAGCAGTCCAGCCCGTTTCGAGCTGAAGTCTAAAGAAACCATGCTATTGGTCTGTAACGTGTTACTGACTGTTGCCGCAGGTACTGTGCTATTGGGTACACTCTATCCATTGCTCATCGATGCACTCGGCATGGGTAAAATTTCTGTAGGACCTCCATACTTCAACGCTGTATTTGTGCCTATCGTATTAGTCTTATTTGGCTTTATGGGTATTGGTCCAATTATCCGTTGGAAAAAATCGAAAGCAGGCGAGCTTAGGCGTCAGCTATTGATCCCTGCAATGATTGCTGCGGCTATCGGTATTGTGACGCCATTTATCGTCGATGGTGTATTCAATACTTGGGTTGCCTTTGGTATTGCAGCGGCTTCTTGGATTATCCTAGCGACAGCAAGAGCGGCCTATAACATGGTCAAGTCGAAAGAAGGTGATATCAGCATCAGCCGTATGGGCCGTAGTCAGCTGGGCATGATCTTCGCGCATCTTGGTATTGCTGTGTCTGTGATTGGTGCCACTATGGTGTCGAATTACTCGATTGAGAAAAGTGTTCGCATGGGACCTGGTATTAGCCAAGAGCTTGCGGGTTATACCTTTAAGTATCTGGAGACCAAAAACGTAGTGGGTCCTAACTACACTGCAATGCAGGGACAGATAGAAGTCTACAAAGGTGATGAGCTGCTGACATTACTCAAGCCAGACCGTCGTCAATATAATGTGCGTACTATGGACATGACAGAAGCGGGTATCGACTGGGGTCTATTCCGTGACTTGTATGTGACCATGGGCGATCCAATTACCAGCACTGAATTTGCGGTTCGTTTGAACTACAAACCGTTCGTGCGTTGGTTATGGTTTGGGGCAATTTTCATGATGATTGGTGGATTCTTCGCGGCATCGGATAAACGTTACCGTGCGAAAGTCACTGCAGCTGTCGCGCCTTCGGCCGAAAAGGCAAAATTGGCAACGGCTCAATAATTGGGGAATGTATGAAGAAGTTGGTTCTGTTTATCCCATTGGTCATATTTCTCGCCATGGGGGTGTTTCTATATAAAGGGTTATTCCTGAATCCGCAGCAATTGGATTCGGCACTGGAAGGAAAGCCTATTCCGGCTTTCCAACTGGAACGCTTAGAGACACCAGCTGAGATCATTACGAATGAGCAATTAAAAGGTAAGGTGTCATTACTTAATGTGTGGGCGACTTGGTGTCCATCTTGTAAATATGAGCATCCCTTCTTAGTGATGCTAAAGCGTAAAAATCTTCTGCCAATTTACGGGATCAACTATCGCGATGAGCGTGCTCCCGCATTAGAAGAGTTGAAGCGTCAGGGCGACCCCTATAACGTCAATATCTATGATAAAGATGGCCGTTTAGGATTAGACCTTGGGGTCTATGGTGCACCTGAAAGTTTTGTGGTTGATCACAACGGTATTATCCGCTTCCGTTATGCGGGTCCAATCGACCAACGCGTATGGAGTGAAACCCTTTACCCTATGATCCAACAATTACAAGCTGAAGCGGCGAAGGATGGCGCATCATGAGAACGCTGACAAAAATCATTGGTGGTCTAGTGCTGTTATTCAGCATGACGACTGCCGTTATGGCAACACCGGTTGATACTTATGAATTCAAAACGCCTGAAAATCAAAAGCGCGGTTTGTCTTTGGCCCATGAGCTTCGTTGCCCACAGTGCCAAAATCAAAACTTGATCGACTCAAACTCACCGGTTGCGCGCGATCTGCGTCTTGAAGTCTTCAAGATGGTAGACGAAGGCAAAGGCGATGATGAGATCATCGAGTTTATGACCAGCCGTTATGGTGAGTTCGTACTCTATAACCCTAAGATGGAAACCAAAACCTACATTCTTTGGTTAGGTCCTATCGGTTTGTTACTTATGGGTCTAGTGATTGGTTTTATCTTCATTCGTAAACAACGTATCAGCGGTAATGTGCCGAACGAAATTAGCGAAGAAGATCAGCGAGAGTTAGATACCTTGCTAAAGCGTAATACTAAATGAAGCATAAAGTCATAGCGGCATTATTGTTGTCATCCCTCTTTTCGTTACTTGCTGGTGGGGTCGCGCATGCGTACCCTGGCATGCAGCAAGCGACGAAACCGATGGAATCAACCGTTGATTTGATCAATGTCTTGTCAAAGACTTTCCCGATAGAGCCTGTCGCCTTTAATGACGTCGATGGTAAAGCCATCGACTTCAGTCAATTTAAAGGCAAGATCATCATGGTGAACATGTGGGCGACCTGGTGTCCACCCTGTGTAAGGGAGCTGCCAGCAATCGAAAGACTCGCGACCAAGTTCAAAGCTGAAGACTTTGTACTATTACCTATCTCTATTGATGCAGAAGGTAAGCAACAAGTTCAACCGTTCTTAAACTCGTTAGGTATGCCAAATTTCAACTCTTACTACGATCAGTCACAAAGTCTTGGGGATGTTTTCCCGCTCGATACGATTCCAGCGACATTTATTCTTGATCAACAGGGCCAATTGATAGCTTTTGTTCGATCTTATGTCGATTGGGACGATGCCAAAGCTGTCACGCTAATCCAAGGATTTATCGATAAAGGCAGTAAAAAGCCCAATTAATCGGCACTCTGCACGTAAAAGAATCACCATCGTTTAAAAGATCGCCATGCGATCTTTTTTCATTAAAAAAGCCCTTGCACAAAATCTCCTGCTCCCTATAATGCGCATCCACTGACACGGCAGACAGCGTTAAGCAGCCTGATGTGACAGTTCATCAATGAGTTTACTGATTGATGAGTGTTGAGAAAGTTTGCAAAAACTCCTTGACGCGAAACGGGAAATGCGTAGAATACGCAGCCCTAGCCATCTGGAAGCGTTCGGCGCTCAGTGTGGTACTCAAGTCCTCTTACGGATATTGAGTTGCTCTTTAACAAGATAAAACAAGAAATCTGTGTGGACACTCACAGGTGTTGAGTTAATCGAAACTGCTTAACCTTCGGGTTGGCAGTCAAAAATTTAAATCAATGTAACAATGAGTGTTCATAGCAATATGTACAGTTTGTTTCAGCCCTTCTTTTATAAGGAGTGCGAGAAACAAAAAATCAGAATTCATTGAGCCGCTGAAGTCGTAAGACGGAAGCAACAAAACTTTAATTGAAGAGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGCAGCGGGAAGATAGCTTGCTATCTTTGCCGGCGAGCGGCGGACGGGTGAGTAATGCCTAGGGATCTGCCCAGTCGAGGGGGATAACAGTTGGAAACGACTGCTAATACCGCATACGCCCTACGGGGGAAAGGAGGGGACCTTCGGGCCTTCCGCGATTGGATGAACCTAGGTGGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCCTAGCTGTTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGTAGGGAGGAAAGGTAGCAGCTTAATACGCTGTTGCTGTGACGTTACCTACAGAAGAAGGACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTCCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGCGAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTCGAACTGGCGAACTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCTACTCGGAGTTTGGTGTCTTGAACACTGGGCTCTCAAGCTAACGCATTAAGTAGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCAGAGAATTCGCTAGAGATAGCTTAGTGCCTTCGGGAACTCTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCCTTATTTGCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGATAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCGAGTACAGAGGGTTGCAAAGCCGCGAGGTGGAGCTAATCTCACAAAGCTCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTGGATCAGAATGCCACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGCTGCAAAAGAAGTGGGTAGCTTAACCTTCGGGGGGGCGCTCACCACTTTGTGGTTCATGACTGGGGTGAAGTCGTAACAAGGTAGCCCTAGGGGAACCTGGGGCTGGATCACCTCCTTACCTATACGACTAACTCGATGCTTTTGAGTGTTCACACAGATTGCTTGTTTATCTTCTCTTAGGAGAAGTCAGAGCTGAATGCGCCGCTAGCCGGTACGCATTGTTCTTTAACAATTTGGAAAGCTGATAGTATGTAACGTGATGATGTCTGTCGTCATGTTACTTACAAACCGATGTAAACGTGGAAACGTTTGCATCATGAGTTCTCAAACACTTTATTAAGTGTCTTGAATATTCAAAAAGTAAATTGTTCTTAGTTGAATAGTTTACGTCTAAGGCGCGTCCACTTCTTTGGTCAGAAGTGAGACAAGTAAAACCAAGCTGGTCGCAATGCGGCTCAGGTGAGTGAAACTCATTTGGGTTGTATGGTTAAGCGACTAAGCGTATACGGTGGATGCCTTGGCAGTCAGAGGCGATGAAGGACGTAGTAACTTGCGAAAAGCGTTGGCGAGCTAGTAACAAGCATTTGAGCTAACGATGTCCGAATGGGGGAACCCAGCAGCATAAGCTGTTATCGCAACATGAATACATAGTGTTGCGAGGCGAACCCGGGGAACTGAAACATCTAAGTACCCGGAGGAAAAGAAATCAACCGAGATTCCCCTAGTAGCGGCGAGCGAACGGGGATTAGCCCTTAAGTCAGTGGGGTGTTAGTGGAATGTGTTGGAAAGCACAGCGGCACAGGGTGATAGCCCCGTACATGAAAACTAACCATTGATGAAAACGAGTAAGGCGGGACACGTGACATCCTGTTTGAATATGGGGGGACCATCCTCCAAGGCTAAATACTCCTGACTGACCGATAGTGAACCAGTACCGTGAGGGAAAGGCGAAAAGAACCCCTGTGAGGGGAGTGAAATAGAACCTGAAACCGTATACGTACAAGCAGTGGGAGCGGTTCTTGAGACCGTGACTGCGTACCTTTTGTATAATGGGTCAGCGACTTACATTTTGTAGCGAGGTTAAGCGAATAGCGGAGCCGTAGGGAAACCGAGTGTTAACTGCGCGTTTAGTTGCAAGGTGTAGACCCGAAACCCGGTGATCTAGCCATGGGCAGGTTGAAGGTTGAGTAACATCAACTGGAGGACCGAACCGACTAATGTTGAAAAATTAGCGGATGACTTGTGGCTGGGGGTGAAAGGCCAATCAAACCGGGAGATATCTGGTTCTCCTCGAAAGCTATTTAGGTAGCGCCTCGAGCGAATACCATTGGGGGTAGAGCACTGTTAAGGCTAGGGGGTCATCCCGACTTACCAACCCTTTGCAAACTCCGAATACCAATGAGTACTACTCGGGAGACAGACGGCGGGTGCTAACGTCCGTCGTCAAAAGGGAAACAACCCAGACCGTCAGCTAAGGTCCCAAAGTACTAGCTAAGTGGGAAACGATGTGGGAAGGCTTAGACAGCTAGGATGTTGGCTTAGAAGCAGCCATCATTTAAAGAAAGCGTAATAGCTCACTAGTCGAGTCGGCCTGCGCGGAAGATGTAACGGGGCTAAGCTAGTCACCGAAGCTACGGGTGCACAGTTTACTGTGCGCGGTAGAGGAGCGTTCTGTAAGCCGTTGAAGGTGAAGGGGTAACCCACGCTGGAGGTATCAGAAGTGCGAATGCTGACATGAGTAACGATAAAGGGGGTGAAAAACCCCCTCGCCGAAAGACCAAGGGTTCCTGTCCAACGTTAATCGGGGCAGGGTGAGTCGACCCCTAAGGTGAGGCCGAAAGGCGTAATCGATGGGAAACAGATTAATATTTCTGTACTTCCGCTAACTGCGATGGAGAGACGGAGAAGGCTAGGCTAGCGCGGCGTTGGTAGTCCGCGTTTAAGGTGGTAGGTGGTGTTCTTAGGCAAATCCGGGGACACGTACTTTGATGTACACACTGAGAGCTGATGACGAGGTCCTAAGGGACTGAAGTAGTTGATGCCATGCTTCCAGGAAAATCTTCTAAGCTTCAGGTTAGTGGGAATCGTACCCCAAACCGACACAGGTGGTCGGGTAGAGAATACCAAGGCGCTTGAGAGAACTCGGCTGAAGGAACTAGGCAAAATGGTACCGTAACTTCGGGAGAAGGTACGCTCCTGTTGGTGATGAGACTTGCTCTCTAAGCTGACGGGAGTCGCAGATACCAGGTGGCTGCAACTGTTTATCAAAAACACAGCACTGTGCAAACTCGCAAGAGGAAGTATACGGTGTGACGCCTGCCCGGTGCCGGAAGGTTAATTGATTGGGTTATCGCAAGAGAAGCTCATGATCGAAGCCCCGGTAAACGGCGGCCGTAACTATAACGGTCCTAAGGTAGCGAAATTCCTTGTCGGGTAAGTTCCGACCTGCACGAATGGCGTAATGATGGCCACGCTGTCTCCAGCCGAGACTCAGTGAAGTTGAAATTGCGGTGAAGATGCCGTATACCCGCGGCTAGACGGAAAGACCCCGTGAACCTTTACTATAGCTTGGCACTGAACATTGAACCTACATGTGTAGGATAGGTGGGAGACTTTGAAGTTGTGACGCTAGTCATGATGGAGTCGTCCTTGAAATACCACCCTTGTAGTTTTGATGTTCTAACCTAGGTCCCTTATCGGGATTAGGGACAGTGCCTGGTGGGTAGTTTGACTGGGGCGGTCTCCTCCCAAAGAGTAACGGAGGAGCACGAAGGTTGGCTAAGTACGGTCGGACATCGTACGGTTAGTGCAATGGCATAAGCCAGCTTAACTGCGAGACATACACGTCGAGCAGGTACGAAAGTAGGTCATAGTGATCCGGTGGTTCTGAATGGAAGGGCCATCGCTCAACGGATAAAAGGTACTCCGGGGATAACAGGCTGATACCGCCCAAGAGTTCATATCGACGGCGGTGTTTGGCACCTCGATGTCGGCTCATCACATCCTGGGGCTGAAGTCGGTCCCAAGGGTATGGCTGTTCGCCATTTAAAGTGGTACGCGAGCTGGGTTCAGAACGTCGTGAGACAGTTCGGTCCCTATCTGCCGTGGGCGTTGGATGATTGAAGGGAGCTGCTCCTAGTACGAGAGGACCGGAGTGGACGAACCGCTGGTGTTCGGGTTGTCATGCCAATGGCATTGCCCGGTAGCTACGTTCGGAATCGATAACCGCTGAAAGCATCTAAGCGGGAAGCGAGCCCTAAGATGAGTCATCCCTAGGAATTTAATTCCTCTAAAGAGCCGTTCGAGACTAGGACGTTGATAGGCATGGTGTGTAAGCGTTGTGAGGCGTTGAGCTAACATGTACTAATGACTCGAGAGGCTTAACCATACAACCCAGATGGGTTTTATTGAAAGCCTTAGACAGAATATCGACACTTGATGAAGTGCGAACTCAAAAAAATCAGCTTTCCGAATTATTATTAACGTGGTCCAGAGATGGACACGGTAATAAACAGAATTTGCTTGGTGACAATAGCCTTGTGGAACCACCTGATCCCATCCCGAACTCAGAAGTGAAACGCAAACGCGCCGATGGTAGTGTGGGGTCTCCCCATGTGAGAGTAGGTCATCGCCAAGCGCCTAATTTAGGTTAACGTGCAAACGTTAGACTAAAGGAGCGGTAGTTCAGTTGGTTAGAATACCGGCCTGTCACGCCGGGGGTCGCGGGTTCGAGTCCCGTCCGCTCCGCCAACATTTCGATAAGCCCTAACAGCAATGTTAGGGCTTTTTCGTATCTGGAATTTATCTATTTCTATTGTCGTGGCCGTCCCCGTCCAACAGCATCCCTGCTTAACAGCCCAGCTCGGCATCAATGCCTCTCCTTCATGAGCTTATCGCTGCACGATATTTCACCCGCCATCATAAAGATGAAAGCCTCTACAGCAATGTAGAGGCTTTTTCCGTTGTGGAGAAATTAACATTATTGGTGATATCTCAAGCTGTTATTCGTTCAATTGTCCAAATTTCGTACCGTCTATTACTGTTTTTCTGATTGAATATCAACCTTCTTTCGCTTGATAAATACTCTGCCTCACAAGATTTCACCTGTTTCTTGTTATTTTTAGCTGCTTAATAGGGCTTATTTAGGTGTGTTTATTGTTAAGCAAATACTTAAATTCCAAGAATGATTGAAATTGAGTTATTTGCCCCAAGATAGTCTTAAGTGCTGCAGTTTTGTAGCGTCCATCACTGAGGTCAATGCCATGCTTTATGATTTAACGGTTGTACAATTTAGCAAGATGCTGAAAAATTTAAGCGTTATTCTTGAAAAGGGCGAATGCTTCGCAAACCTCAAGAAAGTCGATATGGCGGTGCTATTAAACTCACGTTTAGCGCCGGATCAATTTAACCTTATTCGCCAAGTTCAGATTGCCTGCGATACTGCCAAAATTGGCGTGGCACGTTTGACCGATAACTTAGATACAGTGCCAAAGCACGATGATAGCGAAACGACTTTAGCTGATTTGCAAGCGCGTATTAGCTCAGTATTAGATTACTTAGCGACGTTCAAAGCGGATGATTTTGCTAATGCGGCCACGATCCATGTCTCACAACCGCGTTGGGAAGGAAAATATCTTACGGGTTATGAGTTTGCTATTGAGCATGCGATTCCGAACATTTACTTCCATGTAACAACAGCCTACGCGATTTTGCGCCACAACGGTGTTGAAATTGGCAAGAAAGATTACCTTGGCGCTATGCCATATAAGTCGTAAATCTAGGCTAGATGTAGTACTGTTGCGCAGCCTAATTGTTGCGCAGCAGCGCTTTGCCGCTCGAGCTTACCAACCTTTCTGCATTACCACCTGTTCTTTCTCTACCTATGTTATGCCGCTTTGTCGCTGGAAATTTAGGGCTCGTTAGGGTTCTGTTACGCCGATGAAGTAAATCAATACTCAGTTTTTCATTTCGACCTTACGTCTCGATTATTTTTATCCTGTTAGCATGTAATTTAATTTTGTTGGCTTAGGTAAATGATATGGGCCGTAAGTTTATGAGTCGCCAAATTATCTATTACTTAAATAGTGAATGGCGAATTGACTCATTATAAAGATGACCTGTTTGTTCGTTTTGAGCAGGGATTTACTGGAAGAGCCTATTTAAGGTTCTAATTAAAGGAGTTAGCTATGTCTATCGAATCTCAAGCCGTCGAGAGTCCAACGGATGCAACGACAACTAAGAGCAAGAAAGCAAAAAAAGCCGCCATTACTAGTGGTTTGCGCCTCAAAGACAAGTTGACCCAAGACACTCGCCGCCGAATTGAAATTCTGCACGAGCAACGCCAACTATCCAATCGTTTCGGCGTGGATATAGAAATTGACTAAAGCCATTGGCTTGTCGCTAAGTATTGGATATCACATCTAGATATTTAGCCAGCATGTACAGCTTCACGACGCACGTCGTGGGGCTTGCTCACTCTCCTTAAACATTCACTTCTTAAATTCATATTCCATTATCGTGACCATCTCGCCCATATAATCTTCCCGTGAAATCTTTGACCAACCAAGTGTCTGATAAAATTCTGCTTTGTCTGCGGTAAACAAATAGATTTTTTGTAATCCGAGTGTTTTGGCATAATCAACGACGGCATTGACTAATAATTTCCCTAAGCCTTTATTTCGATAATGTGCATTAACATACACATTCGCCAGCCAAGGGCTTAAGTCGGTTCGGGTATCCATATCTGCTGCGGTTAATGAGGAGGAGCCCATGACTTGGTTAGCGTCTTCACAGATAAACATCTTAGGAATAGCGGCGTCAGAGAGGTACTCGCTCATCTGCTCAATGAGTGTGGTGACTGTGGCGTCCGCGGAACTTAAATGTGCCCATTCTTGATGATGCCATTGTGCCAGCTGTGGAATGTGCTGCGGGGCCGATTTTAGGTCGATAACTTTCATATAAATCTCTGACGTGTGTTATGCAAAGGGCTTAATGGTGCGACATAGTGGCACGAATACTTAAGTCTGTTCGCTGGTCTGAATGATGTGGTGGCAGGTTTGATTGAGCATGGTTTGGCTGAGTATACCGCTTAACTTGGGATCGGCAGAAATACGCTGGCTTCGAGACCCCCTTGTTTCGCGTGGCTAAAGCTAAGTTCGCCCTGATAACTGTCGACGATATCTTTACAGATTGATAATCCTAAGCCATGGCCTTGGATGCTTTCATCTAGTCGAATGCCGCGCTCGATGATAATCGACAATGCGGCATCGGAGACGCCTTCGCCATCATCGCTGACTATGATCCCATAACCTGCGGTGGCAGCCGTTTGATGCACTATACGGATCTCAATGTTCTTGCGGCTATGTTTGCAGGCGTTATCCAGTAAATTGCCTAAGAGTTCGAGTAAGTCATCGCGGTCAAATGGCATCACTAAGCCACTCGCGTAATGACTATGAATAATGCGGTCTGGGTAGATACGCTGCATCACTTGAATCAGCGGCGCGAGGTCGTCATCGAGCACGGTATAACGCCCCGGAGTAGACAGGCCGACTATCTTAGTGCGCTTAAGCTCTCTCTCAATGATGTGGTGCAGGTTTGCTAGCACCTTGCTGCCCTTACTCCTCTGTTCCGGTGGAAGGCTTTCTAGGTACGACTGCAGCCCTTGTAGCGGACGTTTCATTTCGTGGGCTAAGTTACCCAAGGCATTACGGGAGCGTTGTACTCTTTGGCCCAATTGATTGACTAAACGATCTATCTCCTCAATCAGCGGGATGATTTCGCTCGGCATTTGGTCTGGATTGATTTCTTGCCCTTGGGTGCGCATCTGCCGTATGGCTTCAGGCATCTGCTCTAAGGGTTTAAAGCCGCGTTTAAGTAGCTGATACTGCGCCAGTAATAGAATAATAATCGTGAGCAACACCGCCGCACTGGCAAAGGCTAAAAATTGCACTTGGGTCTGTTCGAGCTCGCTGATGTCTTCTGTTACCCACAGGGTAACTAACTGATCATCTTTGATTACGGCCTGACTGAACATCAGCCAATGTTCTTTACCAATAAACCCTGTGGCTTGGTGGTGCTCACCTGCGGCCAGTTTTGGCATGTTCACGTTATGGTCGAATAGGGAGCGTGAGCGCAGGGTTTGCTCGCCGATTTGAATCGCAAAATAATGCCCAGAATTAACCCTGTGGTACACATTGGGCAAGCGGTCGGTGGTGAGCGCCCAGATGCCATCGGGATTGTTATTCAGGGCGGAGATCAGGCTGTCGGCATCGTGTTGCAATCGCGAGGCAACAAAATCTTGGGTCAATATTTGGATACCTTGGTACAGAAAATACAGTAACAGCACCATGGCAAAGGTGAGCGTGATCAGCAGATTGCGCGTCAGATAACCCTTGAGTGAATACACGCTTTTTACTCCTTGAGTCGATAACCTTGGCCGCGACGGGTTTCGATATAATCCTTGCCTAGGCGTTGGCGCAGGCGGTTGACGTACACTTCGATGACATTGCTGTCTTTGATTTGATCTTCTTCGTAAATGCGTTCACTGAGCTCTGACTTAGAGACGATTTTGCTGGGATTGACCATTAAGTAATGCAGCAGCCGATATTCAATCTTAGTCAGCTCTATCACTTCACCGTCGACATTGGTGACGGCTTGTTTGTCGACATCGAGGGAAATGCCCGCATGTTGCAACACATTGTCGGCGCGGCCAAAGTGGCGGCGGATCAACACTTCGAGCCTTGCGAGTAGTTCTTCAATATGAAAAGGCTTGCCGAGGTAATCATCGGCACCCGCCTTGAGGCCATCGACCCGCTCGTGCCACGCATCGCGAGCGGTGAGGATCAGCACTGGCATGGCTAAGCCTTTTTGTCGCCATTGGCCGAGGACTTGTAGACCGGGTTTGCCGGGTAAACCGAGATCGAGGATCACGGCTTCGAAGTTTTCTTCTTCGCCTAAAAAGGCGCCATCAATACCGTTATCGGCGTGTTCGACGGTATAACCCGCCTTGTTAAGGGCGGGAATGAGGCGCGCGACCAGATCGGTATCATCCTCAATCAATAGCAAACGCATCAGTCGACCTTTTCCTTTAGCCACTCGCCGGTTTTGGCGTCAATCTTGATTTCATACACTATGCCATCGTCACGAAGGATTTCGAGTTCGTAGATGATCTTTCCATGTTTTTGCTCGACTTCGAGATCGAGCAACCGTCCCTTTAGCCGTGATTCATAGCGTTGCATTATGGTGTCGAAGGGCAAAACCTTGCCCTCCTTAACCCACTCGACCACCTTGTTTGAAAGATCATCGTCGTGGGCATTGCAGCTGGGGCTGCTAATCATCCCAACGGCCAAAGCCAGGAATATTAACGGTATCTTCATCGAATATGCCCTTTTCTGCGAGGTTGTGACAATTGCTGCATTGACTGAATGAGCCGACTTTGGGGTTATCTTGCACCATACGTCTTGGGATCTCATCGTGTTTGCGAATAAAAAATGCCTGTTCTGTGATCTTCTGCGGCCCAGCGCCTGCAAGCAGTGGCGTGCTGTTTTTCATCACCTTACCATTTTGCGCCGCATGTTGAACCAGATATTCCTCAATTCTAGCAGTGACTTGAGGATCTAGGCTGGCGTTGTCACCAAAGTGATTCTCAAGATTTGCGGTAATGGCGCGCCACTTGTCGGCGGGGAGTAAGTTCGCGGGGTAAGCCATGTGGCAGCTGCCACATTCTGCCGTATATTCCGCATTTTGTGGAATGGCGCGGCTGAGCCCGCGGCCATCGTCCGCGTAGCCTGTGCCGGTTAAGGTTAGGCTGACACCGATAACGCCAATGGCTAATGCCGCAGCTGTGAGTGATGAACCTAAGAACCAGCTAATAGGGTGTGCTTGTTTAGTCATGACATCTTCCTTTTTGTGCCGTTACCTTCTGTGGAAGGTATTGAAATTAATTGATCTAATTGAATGAGTTATAAGGTCGAAATTTGGCGGTTATTGCAGGCTTAACCAGAGTAAGGCATCGCCTTTTTCCTGCGGCGTGCAGTCGCGTTTAAAGGTCCAGTTACAGTTACGGGTAAACCATTTCTCGATTTTGGCTGAGTCGGTTAACCGATCTGCGGTAATCGACGGCGCCATAGGGTCAATGGGTTTACGGGTGTTTTGGTGCATCCCGATGTCGGTGACTTTAGCGGTATGGCAACTGGCGCAGCTACGACCGTCCATGTCCTGTAGCCAAAACTTTTGTCCTGCGGCGGCGGTAAAGGGTCCAGCACCTTGGGCTTGATAGCTTTGCAGTTGCGTGCCGATGCGCTCAGCGCTCAGTGGCAGGCTTTGCACTTTACTGTTGGCGGCGCTAACGCTAGTCAGCAGCAAGGTGGAAATCGCGACGATAGCAAGGCGACTTGTATTCGAGTGTCTGGTTAATCTGTTCATAGGGCATTCCTTTTGGGCTGGATGTCTTGTTGCTAAGCAAAATGGCTAATGCATTAATCTTCTTGTTCCACAGGGCGTTGTTTCTTGCCCGTGACCATGGCTCTGACTAAGTTTTCTTTGTGTAACAGGCTGCTGACGATGACGCCGCCAACGTGGATGACCACAAGGAACACGGTGAAGTTGGCAAAGAATTCATGGACTTCTTTTATGGCGCCTTCAGGCCAAGTGGCGAAAAAGCTCGTGGCTAATGGGCCGTGTCCGTCGGTGGCGTACAGTGCCATGCCTGAGAATCCCGTGATGGCGATGCTGGCAATCAAGGCCACTATCATCATTGCTCCAGCGGGGTTATGGCCTATGTAGTCTTTGGCTTTGCCTGTGATGAGCCCTTTGAGGTACACCAGCACTTCTTTAGGGCGAGTGATAAAGTTGCTAAAGCGCGCATTGTGGGTACCAATCACGCCCCACAGTAGACGAAACACCAGTAAAATTAAGATGCTGTAACCTGCGTAACTGTGGATGGTCATCCACTCGTCTTCGCCTTCAGTGAGGTAGGCTAAGGTGAAAAAGCCGACGAGAGACCAATGGAATATGCGAATCAACAGGTCCCAGACTTTGATGGTTTGCAGTGTGTTTGATGACATGGTGTGACGCTCCTAAGTTGGTTTAGGTGTAGGCTACTTAACCCAAACTTAAACCTCGCTTAAATTTTGAGGCGCGAACATTCATTGAGTTGATGGGGTTAAGTGGCGCGCTTGCCTAGGTGGGGAGCTTTAAATGTCAACGAAACTCGCTTGTTAACCTATTGGTATTACGGTAGATTCTGTCACCTCAACCGAGTGAAGTTAGGGACTAACTTATCTATGGCTACCGCCGTTATTCGCGCTTTCATCAGCCGCAATCCGAGTCGTAAATCCTTTTTTATTTATGCCGTTTTATTATTTTTGACTGAAGTCTTAATCGCTTTATATGCGCCTGCGGGTTTTATCCGTGGTTTTGTGGGCGATGTATTGGTGGTGATCTTGCTGTTTTGCATGGCGCGCGTTGTCGTTCCTGTCACTCATCCGGTAGAAAATACCGCAGCAAAAAACGCCGGCGATGGCATTAAGCGCTTATTTCAAACTCCTTGGCTGGCGTTTGCGGTATTGCTGTTTGCCTTTGCTATCGAGTTTGGCCAGTATTGGGGTTTAGTCGACAAGCTAGGCTTGGGCGGAAATCGCTTGGCGCGGATAGTGATAGGTAGCCATTTTGATCCACTCGATCTCGTGGCATATTTTGTCGGTTACTTGATCTTATTGGGCTGTTATTGGAAAAGCCGCCAGTCCTAGTGATTTGAATCCGCAAGAGCTGCGTGACATGTGTTTTCGCGGATAGGCGAGACAAGTGGGGTAAAGGATGCAACCGATAACGGCATTTTCACTGCTAACAGTACGTACTGATGGACTTGAGCTTAATCCACTGAAAATGCCTTTGTATTTCAATGGCCAACATACTCATACCTTTATTGCTGGCCTAGTGATTGAAGGCCAATATCGCTGCGTTTTACCCAATAAAACCTCGGGGTATCTGGTCATCACTTCTTTTGATTGTCCCTTCGAAGAATCAACCGAGTTTAGCCTGCTTGATGAAGCGTTTAAGCTCATTGCGACGACATCTTTGGCGCAAATGTATGATTCGTTTTTACTGCACTCACATTGGCCCATAGCCGATAACCGCTTGCGACTGCACTATTACGGTCAGTTTGTGCTGGATTTAGTCATTAGTGCGGGCAGCTCTTGGTTAACGGCACGGCCGAAACTTAAGCTGATTGAAGTTGTTAATCCCCAAAGCGATCCGCAAACGGCCGCCGCTATGGCCGAATTGGCTCAGCGTTTAGCGGCCATCGAAAAGTCTCTAATGTGGGACTAAAAACGGCGTCATCTGCTGATTTAACCATGCCATAAAGACTTGCAGGCGTTTAGATAGGTGCCGCCTGTGGGGATAGAGTAAGCTTACAGACATAGGCGGCGCAGGGTAATTTGGTAGGATCTCGACCAACTCTCCAGTGTGTAAATACTTAGCCACCCCTATGCTAGGGACTTGAATGATCCCCAGCCCCGCCAAGCAGGCTGAGCTATAGGCATCGGCATTATTTACCGTAATCAATTTAGGCAGTTGGATAGTAAGGTTCCCTTGATGAGTGTGGGGATCTTGATATTCAAAGCCTGAATCAGTGCCGCCTAATTGGCTTACGTAGTGGACGAGTTTGTGGTTTTGTAAGGTGAGATCCTCAAGGCTTTCGGGTTTACCGTATTTGGCAATATAGGCTGGGCTGACACAGTTCAGCATCTTAAAATGCCCCAGTTTTTTCGCTATTAATCCTTCTTGCTGCACACTGCCGACCCTAATCACACAATCGAACCCTTCGGCAATGACATCCACCTTACGATCTGTGCTGCTCAGTTCAAGCTCGATATGCGGATATTGCTCAATAAATTCAGCCAGTTTAGGGAGCACTAAGTTGCGGGCAATGCCTGTCGACATATCGACTCTGAGCCGCCCAGATACTTGCTCGGCTTGCAGTTGGAACAGGCTTTCTAATTCTTGAAATTCCGCCAGTAAACTTTTACAGCGCTCATAACAACTCTGGCCATCTTGGGTTAAGCGTACCGTCCGCGTGGTGCGTTGCAACAAGCGCGCACCCAGAAATGTTTCGAACTGCGCCACCACATTCGACACATGGGTTTTCGATAGACCTAAGCTGACACTTGCTTGGGTAAAGCTTTGTAATTCAGCGACCCGCATAAAAATTTGCATGGCTTGCAGTCTATCTTGCACGGCGATTGTCCTTTATTGGTTAACAGTCTTAGCATTTATAGTGTGTTTATCTCGTTAATTGCAAGTAATAAGCTGTGTGCAGTTAATTAGGCCTCAAGGCTTAGGTTTCACTTCCAGCGACGATTATAAGGACTGACCATGAACACGACTCCAATTGCACTTATCACTGGCGCTAGCCGCGGTTTAGGTAAAAACACTGCCCTGAAATTAGCGGCGCAGGGGATAGATATTATCCTGACCTATCAAACGAATGCTGCAGCGGCCGCTGAAGTGGTTGCCGAGATTGAATGGCTGGGACGCAAAGCGGTAGCATTGCCGCTGGATGTGAGCGATAGCGGCTCATTTGCCGAGTTTGCCACCCAAGTTAGCACTGTATTAGCTCAGACTTGGCAGCGGGAGTCGTTTAACTATTTGATTAACAATGCTGGTATCGGGATTCATGTGCCTATGGCGGAAACCAGTATCGAACAGTTCGATACCTTGATGAACATCCACGTCAAAGGACCGTTCTTTTTGACTCAAACCTTATTGACGCAGCTGATGGATGGGGGAAGCATTGTCAATATTTCCACCGGCTTGACGCGTTTTGCGATTCCCGGATTTGGCGCCTATGCCACTATGAAGGGCGCGGTCGAAACTATGACTAAGTATTGGGCGAAGGAGTTAGGCCCACGGAGTATTCGGGTCAATGTGCTTGCCCCTGGGGCGATTGAAACAGATTTTGGCGGCGGTGCTGTACGGGATAATGAACAGATGAATCAGTTTTTAGCGCAGCAAACTGCGTTAGGACGTGTGGGCTTGCCGGACGATATCGGCGGCGCGATTAGTGCTTTGCTATCGCCTGCGGCCGCTTGGATTAATGCCCAGCGTATCGAAGCTTCTGGCGGTATGTTTTTGTAAGTATTTTTTGTGTAGAAATTTGATTTATTAATATATGAGGCAGAAACAAAGAAAGGGCGAATCGCCCTTTCTTATTTATGTGATTTTTACACAGCAATCACTGCGAATGCACCATCTTCAACAACGCTTAGCTGCAACCGCTAAGAACAACTGCTGTAGCTGGTTAGAGATGTCCTTTATAGCAAGCCTCTTAGATAAAACCATTCTTCACAAAGCTTCTTAGAAGAAACCCAGTGGATTCACATCGTAACTTACCAAGAGATTCTTGGTATTTTGATAATGGTTGAGCATCATCTTATGGGTTTCGCGGCCAATACCGGATTTTTTGTAACCACCAAAAGCAGCATGGGCAGGATAGGCATGGTAACAGTTAATCCACACGCGGCCCGCTTGAATGCCGCGTCCCATACGTTGAGCCGTGTTCATGTCTCGCGTCCACACACCAGCGCCTAAGCCGTACTCAGTATCGTTCGCGATCGCTAAGGCTTCGGCTTCATCTTTGAAGGTGGTGACCGAAATCACAGGGCCGAAGATCTCCTCTTGGAAGATACGCATCTTATTGTGGCCTTTCATAATCGTCGGGCTGATGTAATAACCTTCGCTTTGGCCGCCATCGAGTTGGCAAAGTGTGCCGCCCAGTAGCACTTCTGCACCTTCAGCCTTACCTATGGCTAAGTAGCTTAAGATTTTATCGAACTGTTCTTTAGAGGCTTGAGCACCTACTTGAGTGTCGGTATCTAAGGGATCGCCTTGCTTAATGGTTTGCGCGCGGGCGAGCACTTTTTCGATAAAGCGATCGAAAATCGACTCTTGGACTAGCACCCGTGATGGGCAAGTACAGACTTCTCCTTGGTTAAAGAAGGCGAGTAACATGCCTTCGACGGCCTTATTTAAATATTCGTCTTCATGGTCCATTACATCGGCAAAAAACACGTTAGGCGATTTGCCGCCAAGCTCAACGGTCGATGGAATTAAGGATTCGGCGGCGCATTTTAAAATGTGATAGCCAATTTCGGTGGAGCCCGTAAACGCAAGCTTAGCGATGCGTTTGCTGGTGGCCAGTGCTTGTCCTGCTTCGGCGCCAAAGCCGTTGACTACGTTAAGAATGCCCGCGGGTAATAGGTCTTCAATCAGCTCGAGTAATACCAGAATCGATACCGGCGTTTGCTCCGCAGGCTTGAGTACCACACAGTTACCCGCCGCCAGCGCAGGAGCGATTTTCCATGCCGCCATCAAGAGCGGGAAGTTCCAAGGAATGATCTGGCCGACTACTCCCAAAGGTTCTGGGAAGTGATAGCTGACTGTGTTGCCGTCGATATCGGCGGCGCTGCCTTCCTGTGCGCGGATACAACCGGCAAAGTAGCGGAAGTGATCGACAAATAAGGGCAAGTCGGCGTTTAAGGTTTCACGCACCGCTTTACCGTTTTCCCAGGTTTCGGCGACGGCTAAATATTCGAGATTTTGTTCGACCCGATCGGCAATGCGCAGTAACAGGTTTGAGCGTTCGGTGACTGAAGTTTTGCCCCATGCTTCTTTGGCTGCGTGGGCAGCATCTAACGCTAATTCAATATCTTGTGCATCGGATCGCGGGATTTGGCAAAAGTTTTGGCCATTTACGGGGGAGCGATTGTCGAAGTATTTGCCATTGACTGGGGCGACCCATTTACCGCCGATGAAGTTGGCGTATTTTTCTTTGAAGTTAACGATTGCGCCTGCGGTTCCGGGTTGAGCATAAATCATAGAATCATTCCTTCTTGTTGATTAGCTTAGTGTTCTCTAACGTAACACTTGAGACTTGCATACCTAAGGTGCATCGCAAGGAGAGTGCCAAACTGGCAAGAGTTGTACACTTGTTGCGCTAACAGTATGATGAAAAGCTAATTTTGCAGATTTGAAACAGTGTGGGCACTCAGGGAGAGAGGTGTGTCCTGTGGTCACTTTGTCATCGAATGGCACATCGCAACTGTGCCGAAACGGAACACAGCTCGGGGGAGCACGGCTATGGCAGTTAAACCGCATTTACCATCGACACAGGCGTGGTTGGCCGATTCGTGGCAGCGCAGCATTGGCGCTGGCTTGTCAGAATTTCAGTTACCCCAAGAGCTTAGACTCGACGCCAGCGAACTAAAGCAGAAACATGAGCAATATCAAATGCTCATTGCATTAGTGCAATCCCATGCTTTGCCTTTGTTCAATCAGTTGATGGCGCATTCTAATAGCCGACTCTTGCTCTCCGATGCCGAGGGTTATGTGCTTAGGCACTGGGGCGTGAGCCGTTACTCCACAAAACTGGCGGACGTGGCTTTGGATATCGGGGTGAATTGGCTCGAGCAGCACAAAGGCACCAATGCAATAGGGACGGCACTCACAGCCAAACAAGCCGTATCTGTAGTGGGTGAGCAGCATTTTATTCGTCAGAATCGTTTTATGAGTTGCACCGCCTGCCCGATATTTTCTCCCCAAGGTGAGATGCTCGGGGTGATAGATATCACCAGTGAGCAGCAAAGACACACACAGCAGACGTTGATGTTAGTATCGAGCTTGGCGCAGCAGGTCGAGACCGCGCTCTTGTGTCATCTGCCCGATAGCCGTTATCGAATCGATTTAGCGGCGCAGCCAAACCTACTGAATTCGGGTTGGCAGGGCATAGTGATTGCCGACAGTGATGGTCGCATTGTGGGGTGCAATCCCATGGCGAAACAATTGCTTAGTAATGCCAAGGTTGGCGATTCAGTCGAGCAGTACTTAGGCAATAATTGGAGCCGTGCGGGTGGCTTTAATCAGCATTTAGACTTGCATCTACTGACTCAGCCCTTAAATATTCCCAGCATCAAGAGTCGGGTAGCGACTAGTAATAAATCGCTTAATCAACTGGGCGTGCGGTTTCGCGACCCTCAACTTGAGCGCGCATGGCAACATGCCAATAAGGTGATCACCAAGCAAATTCCGTTGTTAGTGCTCGGTGAAACCGGCGTCGGTAAAGAGCAGTTTGTTAAAAAGCTCCACGCCCAGAGTGCGCGGCGCAGCGAGCCCTTAGTGGCGGTGAACTGTGCGGCATTGCCTGCAGAGTTAGTCGAATCTGAACTCTTTGGTTATCAGGCGGGCGCGTTTACTGGGGCGAACCGCACTGGATTTATCGGCAAAATTCGCCAAGCCCACGGCGGTTTTTTGTTTTTAGATGAAATAGGTGAAATGCCGCTGGCGGCACAGAGTCGTTTACTCAGGGTATTGCAGGAGCGCGAAGTGGTGCCCGTTGGCAGTAATCAAAGCTTTAAAGTCGATATCCAAATCATTGCCGCGACCCATATGGATTTAGAAAAACAGGTGACTCAAGGGCTGTTTCGACAGGATTTATTCTATCGGTTAAATGGCTTGCAAGTGCGTTTGCCTGCGCTGCGGGAGCGGCAGGATATCGAGCGCATTATCCATAAATTGCATCGTAAACATCGCATTGCGCTGCAGGCGATCTGCCCTGAACTGTTAGGACAGATGATGCAACACGACTGGCCCGGCAATTTGCGTGAGCTGGATAATCTTATGCAAGTCGCTTGCTTGATGGCTGAAGGGGATGACACCCTAACTTGGCAGCATTTACCCGATTATCTTGCGGCCAAGCTAATGTGCGAGCCGTTATGTGAAGAGGCTAAAACCTGCCAAGATGCTGCATCACACCCGCTAAGTAGCGGTATAGCACCCCAGCCCAATGCTCTATTTTTAGCCGCAGAGACTGCAGATATCAATTCTCTCCATGGGGCGATTTATAGCAATGTACTGCAGGCATTTCAGGCCTGTAATGGCAATGTGAGCCAATGTGCCAAGCGCTTAGGAATTAGCCGTAATGCCTTATATCGTCGATTAAAACAGATGGGACTTAAGGGTTAGTGTTAGCTCGCGACGCTTATTATTAAGTGTAATAAACAACGCTAAGCTCGCTTATCACGCCCATGACTAACCAATTCGCAATGGACTAAAACTGTGAGATTTTAGTCCATAAACTCAGATTTCGGAGTCAGGTAAAATGAGGTCTTGTTGTAGCTTATTCATGCCTAATAGGTTTTATTGATTCATATAATCGATAAAGATCCTTTCTTTTAATTTTAAATTCAGAACTATACTGCGGGCAGGCAGAAAAGAGCTTGGCGTTATGCATAGAGAGGAACTGTTATGCATCTGACTAAAAGCCTTTTAATTATTTCTATGGGTCTGGCATTAAATGTACAGGCTCAAACTTTACCAACAAATACCCTGACACGTGACAATGGCGCCCCTGTGGGTGATAACCAAAACTCGACCACAGCGGGCAGTAATGGCAGCGTGTTATTGCAAGATGTGCAGCTTATTCAGAAGCTACAACGCTTTGCCCGTGAACGTATTCCTGAGCGGGTTGTTCACGCCAGAGGCACAGGTGTGCACGGTGAGTTTGTATCGAGTGGTGATTTTAGTTATCTGACTCAAGCGGCACCCTTTGCACAGAAAGGCAAAGTTACGCCTGTGTTTGTGCGTTTTTCTACTGTTATCCACTCGAAAGGTTCGCCAGAAACGCTGCGCGACCCCCGTGGATTTGCGACCAAATTTTATTCGGACCAAGGTAACTGGGACTTAGTGGGTAATAACTTACCCATATTCTTTATTCGCGATGCGATCAAGTTTCCTGACATGGTGCATTCGCTCAAACCATCGCCTGTGACCAATATGCAAGATCCGAATCGTTTTTTCGATTTTTTTAGCCACGAAGCGGGCTCGACCCATATGTTGACTCAGGTTTACAGTAACTTAGGTGTACCTGCCAGTTATCGTGAAATGGACGGTTGGGGCGTGCATGCTTACAAGTTTATTAACCAAGACAAGCAAGTGAAATACGTTAAGTTTCACTGGAAGAGTCAGCAAGGTGTAAAAGGCTTGCGCCCTGATGAAGTGGTTAAGGTACAAGGTCAAAACTTTAATCATTTGACCGATGATCTCTATGGCGAGATCAATAAAGGTAATTTCCCTAAATGGGATTTATACGTCCAATTACTCGACCCAAGTGAGCTGAATAAGTTTGATTACAACCCGTTAGATGCCACTAAAGTATGGCCAAATGTGCCTGAACAAAAAGTCGGTACTATGACGCTTAATCGCATGCCAGCAAACTTCTTTCAAGAAACTGAGCAAGCGGCATTTGCGCCATCGAATTTGATCCCTGGGATTGAGCCTTCGGAAGACAGATTATTGCAAGGTCGTATGTTCTCCTACGCCGATACTCAGCTGTATCGACTCGGCGCGAATTTGTCGCAATTGCCGATTAATAAACCCTTAGTGACGGTGGCAAACCACAACCAAGAAGGCGCGGGCAATATGGGGCATACCCAGTCCGAGGTGAATTATCAACCGAGCGTACATTTAGCCTTGGCCGAAGATAATCGTTATCGTGCGGTAACGACTGCGCTCAGTGGCACAGTGCAACAAACGGCTATTAGCAAGCAGGATAACTTTAGCCAAGCGGGCGTTTTTTACCGCAGCTTGAGCAAGCAAGATAGAGCCGATTTGATCACTAACTTGGCCGGTGACTTAGGCAAAGTGAACGATGCTAAGGTGAAACATCAAATGCTGAGTCACTTTTATCGTGCAGACCCAGAGTTTGGTGAGCGCTTAATCAAGGCCGTAAAAGGTGATTTGGCCGACGTTAAACAGCGTTCAATTTAACGCATCATTTTAGAGCGATTTGGTGCAGATTCGCGTTAAACCGCTCAAACCCAGTAAGTCCCGCAAGGCGTAATCAAGCTCTTTTCTGCCCAGTTTAAGAGGGCGTCTTGCGGGCATTTCGTCACCTCTTATTGTTTAAATCTCTATTTTTGCTGCGCGGGAGTTATTGATGAAACACAATATTACTATCTTATTATTGATGATTGCGGCACTGAGTATGTCGGTCGTGATGGGCGCAGGTTCGGCTCAGGCATCCGCAACTGTCAGCCAAGCGGACGAGCCCGATGCTGCACAAGTGCAATTGCTGGTGGACTACTTTTTTGCTGCCGCGCGTACTGGCGATATCACAGTGCTTGAGCATTTTCTCGATGCGGGCTTCCCTATTGACCAGAGAAATAGTCAATCCTATACCGCGCTAATGGTGGCGGCTTATAACGGCCAAGAGGCTGCAACAGATGTATTACTTGAGCATGGTGCTAATGCTTGTTTGCAAGATAAGCGTGGTAATACGGCAATCATGGGCGCTGTAATTAAGGCCGAATTCAATATCGTAAAGCGGCTATACGCGAAAGAATGTGATGCGAACCTAACGAATAATGCCGGAATGACCTTGGTTGAGTTTGCCCGCTACTGGGGTCAAAGTGGCGCTTTGCAGCAGGCGAGTTTGAAGCCATAAAGGCATCATTTTTTGCCATCTTTTGCGCTCGATCATTCTGGGCCATTCTCTTTAATCTGTTTAGATCTCAGCGTTTACTATTAGCCATAAGTCTGCTTGTTAATGGCGTCGATGCCTTTTATGGTAACTTTAGGAATATCATTTAATGCGATTTGCATGGCCTTCGGAGAGCAAGTTATGTTTGATGCCTTACTACTGACACAAGAAGATAAACGTACGTTTGCTCAAGTTTCACAGATTACAGAAGCCGATTTACCAGAAGGGGAAGTCTTAGTTGAGGTCGACTATTCCTCGCTGAATTATAAAGATGGTTTAGCTGTGACGGGTACGGGCAAAATCATTCGCCAGTTTCCTATGGTGCCAGGCATTGATTTTGCGGGGGTCGTGCGCGAATCAACAGATGCGAGATACCAAGTTGGCGATCAGGTGATTTTGACAGGCTGGGGCGTGGGTGAAAATCACTGGGGTGGTATGGCGCAAAAAGCGCGCGTTAAAGCCGACTGGCTGGTGCCTATGCCGCAAGACTGTGATGCCGCTAAAGCCATGATGATTGGCACGGCTGGACTTACGGCTATGTTGTGCGTGCAAGCGCTGCAGCAGGCTGGGGTAACACCGCAAAGTGGGGATATTTTGGTCACAGGTGCCAGTGGTGGTGTGGGTTCTGTGGCGGTGACTTTGCTCGCCAATGCAGGCTATCGCGTGGTTGCCTGCAGTGGTCGGGTCGAGCAAAATGCACCTTTATTGAAGGAGCTTGGCGCCGCTGAAGTGATTGATCGCAGTGAGCTTGAACAGGATGCTAAGCCACTTGAGGCGCAGCGCTGGGCGGGTGTGGTCGATACTGTTGGCAACAAGATCCTCGCCAAAGCATTAGCGCAAATGCAATACGGTGGCGCCGCCGCTATTTGCGGTTTAGCGGGCGGTTTTGCATTACCCACAACCGTAATGCCATTTATTTTACGAGGAGTGAGTTTGCTGGGAATCGATTCTGTTTCTTGTCCATTTGAGAAACGAAAAGCCGCGTGGGAAGCTGTGATTAAGGCACTGCCTGCACATTTCTATCAGCAAGCTTGCCAGCAGATATCATTGGCAGAGGTTCCTGATTTCGCGAATAAAATTATCCAAGGCCAAGTCACAGGGCGTGTATTAGTTAAGCTGTAATCTAGCTTCTTATACTTTAAAATGATGGCAACACTGAGGTGTTGCCATCTACGACTATGCCATCCGTTTTACCAAGTTTACTTGTCTTCATATTGGCTATTTCGGCAGTGTAATAGTCACTTCAAGACCGGTTGGTACACGGTTCTCGGCTTTAATCTTACCTTTGTGGGCGGTAATTGCCGCTTCAGTGATTGCTAGTCCAAGTCCCCAGCCACCGCTTTCTCTTTGTCGTGCTGAGTCAGGGCGGTAGAAAGGTTTAAAGATGGCATCGAGTTCGGCGGCATCTATGCCTGGTCCATCGTCTTTTATCGTGATTTTTACTTGATCAGCTGTTGCACTTGCCTGTAACTGAATGTCGCTTGCGGCATAGCGAATTGCATTACGCAGCAGATTTTCAATCGCGCGGGAAAGTGATTTAGGAAAATGCGCTAACTCAATTTCTTCATCGATATCGATAGTAATGCTTTTACCTTGCTGTTCTGCTTCAAATTCCGCATCGTCTAAAACTTGGCTCAGGGATTCTGCCAAGCCTAAATGCACTTTGGTTTCGTTAGTGCTGAGCTTGACCCGCGAGAGTTCTAACAATTCACTGATAAGCTTTTCGAGTTGCTCGGCTTCATAGCCGATACGATCCGTTTCTGCCGTTTGCTGGCCCTTTTTACGGGCGAGGGCTAACGACAATTGTAAACGGGTGAGCGGGGTACGTAGCTCATGGGAAATATCGCCCATCAATCTCTGTTGATTGTTCATCATAGATTCGATGGAGTCGGCCATGCTATTGAAGGCCTTAGCTAATTGGCCTATTTCATCTTTACGCTGTGTAGTGGATTTATCGACACGATGACTTAAGTCACCCTCTGCGAGGGCATTGGCACTTTTTTTAAGTGAATTTAATGCCTTACCTAGATGCCAAGCGAGCAGGCCACATAATAATCCTGATAGGAAAATTGCTAGGCTTAAAGTTAATAACTTATTTTCGGCGAAGAAGAAGAACCAAGGCCGCGGATGGTTATCAGGCAGCCGGCCGTAGAGTGAATACGTCTCGGCGCCCAGATTGAATTGAAAGGGGCCAAACACCAACTCATCTTTAAATTGATGGCTAATCGGTTGTTTTTCTTCATCTGCCATCAGCATAAAGCTGCGCACACCGCGACTCACCCTATGGGTGTTGATCACTTGGCCTTGGCTGTTGACGAGATAAACCCGCAGAGGTTTGCCTTCCATGTCTCTGTGTCTTTCCCAGCGCCGAAGAAAATCAGACTCCAGTAAGGCGGGGTTTTCTTGAATGCGCTTAGCTATAGTCGATAGGACTTTTTCAAGGTGAGGTGGCAGCGGCGCACGATCGTGACTCTGTTGCAGTAAGGGCAACAGGCCGACCAAGGCAATGATCAGCGAGCTGCAGAGCCAGAATCCGAGCAATAATTTGATAAATAAACGATTAGGCACAGTCGATACCGCCGTTATGGAAGCCAAATATAACCTTTGCCACGGATGGTTTTCACCCTTGGCCGCCCATCGCTACGCTCGGGGAGTTTTTTACGTAGATTCGATAAATGCATGTCTAAGCTGCGATCGAAGGGCATGAGTTTTTTGCCGAGCACTTTCTCGTTTAACTCTTCCTTGTTCATCAACTCGCCAGCGTGCAGTGCTAGGATGTGCAGCAGAGTGAATTCTGTGCCTGTAAGTATGATCAACTGCTCGTTACAGTAAGCCTCTTGGCGCGATGGGTCTAAGCGTAAATCGCCAAATTCTTGTGCGGGCGCGGCATGGATTTCTTGGGTTGTTAAGTTCGAACGACGAATAATGGCGCGGATACGGGCGATTAACTCTCTGTCATTAAACGGCTTTGGCAGATAATCATCGGCGCCTATCTCAAGCCCAACAACGCGATCGATTTCATCGCCGCGAGCGGTCAGCATTAATACTGGGGTTTGCTTGTGCTGACGCAGTGCACGCAGCACTTCGAAGCCGTTCAATTTAGGCAGCATGACATCGAGTAAGATCAGATCGTAATCCGAACTCAGTGCCAGATCTAAACCTTGTTTACCGTCGTAGGCCAAGGTTAATTGAAACCCTTCTAACTCGAGCAGTTGCCCTAGCAGCTCAGATAAACCCAGATCGTCATCGATTAATAATATCCGACTCATACTGTTCCTTAATCTCTGTAATATGGCCTTATGCGCCCATGTAATACTGGCACTTTTAGCGCTTTGGCTGAGTATACCTGTTTTGATGTCAATTGCTGTGTAGCAATGTAAGCCAAAGTAAAGAATGGAGAATCTGTTATACCCAAGCAGCCTCAGGATAAGAGTTTCAGAGCGTCTAGGGTGATTCAATTCAAGGCGCATCAATGACAAAATGTCGGCCACCTTTTGAATTGATGCAACTTTACAGTGATTTACTCAGGCAACGTCCTCACTTACATATAGGGGTTTAGACTGGAAGGGTCGAAAGTGACTCGGCTTAAAAAGGTCGTCACTGTATCTTTTATTTGCCATATTAAAGTTGAACCACTGAGGTTAACACTATGAAAACATTATCACCTTTGAAAGCGAGCCTGTTTGCCATTCTAGCGAGTTCTGCGGTTTTAGCGACAACAGTCAATGCCGAACCGACAGACGGTGGCTGTGATTTCTCCCGTTCTGAATTCCACAAAGGCGACCGCATGATGGGCGATCGTATGGAACATGGCGGCCGACATATGGATCATGGTGGCATGCATCGTATGTTTGAAGGGCTAGATTTAACCGATGCCCAAAAAGCTGATATCAAAAAGCTGTTTGCCGATCAGAAAGCTGCGCGTGCCGATAGCCGCCCAACGAAAGAGCAGCGTTTGGCCCATCGCACTGAAATGCAGGCCTTGATGACGGCCTCTAATTTTGATGAAGTGCAAGCCAAGGCACTGATGAGCGCGCAGCAAGAACAGCGTCAAGCTCAAGCCATTGAACGTATGAAGCTGCATAATCAAATCTACAATCTGTTGACACCAGAGCAGCAAGCTAAGTTTAAAGCGCGATTTGAAGCACAAGCTGGTAAAGAACCTCGCGGTTAAGCGTCTTAGCTGATATACAATAGGGTCATAGCTTCCCAAGATAGATCCCTTATGACTCAGACTTCCCAATACGATTTTTGGGTCAAACTGGCTAGCCGCGCTTCCGTGGCTACCGCTTTGATCCTTATTCTTATCAAGCTGTTCGCTTGGCTTTATTCCGGCTCTGCCAGTATGTTGGCATCTTTGACGGATTCCTTTGCTGATGCTCTCGCTTCTATTATCAATTTTATTGCCATTCGTTACGCGCTTGTGCCGCCGGATCATGATCATAGATATGGCCATGGTAAGGCTGAGCCGTTAGCGTCTCTAGCGCAATCCGCCTTTATTATGGGCTCCGCATTTTTATTACTTTTCTATGGTGGCGAGCGTCTATTAACACCTGTTCCGGTTGAAAATGCCACCATAGGTGTGGTGGTTTCTGTCATAGCGATTGTGATGACGTTAGCCTTGGTCATGTTGCAGAAACGCGCATTGGCGGCAACAAAAAGTACTGTGGTTGAAGCGGATTCGCTGCACTATAAATCAGATTTATTCCTTAATGGTGCGGTATTGCTAGCACTCGTCCTATCCCAATATGGTTGGTGGTGGGCTGACGGATTGTTTGCAGTAATGATTGCATTCTATATTGGTCATCAAGCCTTTGGCTTAGGTTATCGCTCAATTCAAGCCTTGTTAGACCGAGAGTTAGACGAAGAAACTCGCCAGCAAATCACGCAAATCGCGATGGAAGATCCACGGGTGCAAGGGCTGCATGATTTACGCACTCGTCAGGCTGGCAAAACCGTTTTTATCCAATTTCATTTAGAGCTTGATGGCAATTTGAGTTTGAATGAAGCCCACAGTATTGCCGATACGACAGGAATAAGAGTCAAAGCTGCCTTCGAAGATGCCGAGGTGATTATTCACCAAGATCCGGTAAGCTAAAAGCCGCTTGATGGTCTAATTCAAGGATGATAAAGCGATATTAATCGCTTAGGAGTAGGGATATGGCGTTATGGATAGAGAAGCAAAGCCATCGATTAAGTGAGAGTACTCAGCTCATTTGCCGTTACCATCGCTTATCGACTTGCTTTGAATTGGTATTACAGCAAGACGAAAGCCCGTTGGTTTGTCAGGGAGATATGCAATTAGAGATAAAAAAGATGCAGCCAATACAATGGTATAAACGACCTCTTTTTTATCTTCCAAGACATATCAAGCTTAATCATGAGGATAGGCAATATCAGCTGAGAGTTTTGCCTTTTCCCTTCTTTTGGCGTGCACGTTTGCGTTGTGCTAAAACAGGCAACATTGTGATTGAAGAATGTTTGGTGGGCCGCAAGCGTAGAAGTATCGTGCGTGTTATTTATTTGGGCATAATCAATTTACTGCGAGTAATGCTGCAAGTTATTACTTAATATGTTTGATACTAGGAGTTGCCAAAGGCAACTCCATTACGCTAGTTTACTCCATCGACAGTTCTTTTAATTTCCGTGTTAAGGTATTTCGTCCCCAGCCTAAACGTTTCGCCGCTTCTTGTTTATGGCCTTGGGTATGGCGCAGCGCAGTTTCTAGCAGTATGCGTTCGAATGCGGGTTGCACCTCTGTTAACAGATCGCTGTTTCCTTCAGAGAGTTTTTGATCTATCCACTCAGTCAGGGCCGATTGCCAATCTTGGCTGCCTTTCGCCGTGTGGGTGACACTCACAGGATCTTTCAGTAATTCTGGCGGTAAATCTTGGGGTAAAATTTCTTGCCCCGATGCCATTACGGTTAACCAGCGGCAGGTGTTTTCGAGTTGTCTGACATTACCGGGCCAAGGCAATTGGGAGAGTTTTACCGCGGTTTCCTTGGTCATGATCTTAGTTTCTACACCAATTTCCTTTGCTGCCGAGGCGAGGAAGTGGGTGGCGAGTTGGGGAATGTCTTCCCGTCTTTGTGACAGTGGCGGTAAGTGCACGCGGATCACGTTGAGGCGATGGAATAAGTCTTCCCTAAAGCCGCCTTTTTGCACTAATAATTCAAGATCTTGATGGGTCGCCGCTATGATACGCACATCCACTTGCACCGCATTATGACCACCGACGCGGTAGAATTGACCATCGGCGAGCACTCTGAGTAACCGTGTTTGTACATCTAACGGCATATCGCCGATTTCATCTAAAAAGAGCGTGCCACCGTTTGCTTGTTCGAAGCGACCTTGGCGCACATTGGCTGCACCAGTGAAGGCACCTTTTTCGTGGCCGAATAGCTCAGATTCGATCAAATCCTTAGGGATCGCAGCCATGTTCAGCGCGATAAATGGCTTATCTTTACGTGGGCTGTGTTTGTGTAATGCACCCGCCACGAGTTCTTTACCTGTCCCAGATTGGCCATTGATTAGCACGCTGATGGATGATCGTGAAAGCCTGCCTATGGCGCGAAACACTTCCTGCATCGCTGGCGCTTCACCTATGATTTCGGGTGTTTTGACTTGGGCTTCCTGCGCGGGTGCGGGGCTCTGTTCAGTGGCATGGGTGAGTGCGCGCTCGACCAGAGAGATCGCCTCATCGATGTCGAAGGGTTTAGGCAGATACTCAAATGCGCCCGCTTGATAGGCACTGACTGCGCTGTCTAAGTCTGAGTGCGCCGTCATAATGATCACGGGAATATGGGGGTAATGTACTTGCAATCGTTCGAGCAGGCTTAAACCGTCTGTGCCTGGCATACGGATGTCCGACACAATCACGTGCGGTTGCGATATTTCTAAGGCCTGCCACAGGGATTCGGCGGCGGCAAAGCTGGCGGTACTGAGCTTGGCACCTTGAAGTGCCTTTTCGAGTACCCAACGTATCGAGCTGTCATCGTCGAGGATCCACACTTGTTCACTAATTCGCATCTTGCTTCCTCATACGGTATGGGTAGGTACTGTCGAAATTATTTAGCGCCTAAAATCGGTAATGAAATGATGAATTCGGTGTGCCCAGCGCTGGAGAGGCAATCGATTCTTCCCGAGTGTAAACGGGCAATATTATGGGCAATTGAGAGGCCTAAACCTGAACCTTGCTCACGGCCTGTCACCATTGGATAAAATAATGTGTCCATCAACTCAGGCGGTATGCCGGGGCCGTTGTCGATAATCGATAGGGTTAACACTAGCTTGTGGCGCTGTGAACCTATAGTGACTTGGTGTTGAGTGCGAGTGCGGATCAAAATCTCGCCACCGGTATGCTCAAGCGCCTGCACCGCATTTTGTAAAATGTTCAGCACGGCTTGCTGCATTTGGTCTGGATCCATCTCGATATCGGGAATGGATGGATCGTAATCTCGTTTGAGTTGGATGTTAGCGGGCAGTGCCATTTCAACTAACTTATAGACCTTTTGCACCACTTGATGGATGTTGTGCAGACTGTGCTGTGTTGGCCTTTGGGGGCCGAGCAGGCGATCCACTAAATTACGCAGCCGATCGGCTTGCTCTATGATGAGGGTAGTGAATTCCTTCAGCGCCGGATCATCCAGCTCCCGCGATAACAATTGCGCCGCACCGCGAAGACCACCTAAAGGATTTTTGATTTCATGGGCTAAGTTGCGCACCAGAAACTGCGCCGCTTGTTGCTGTGCATCTTGGCTCAGCTGCTGATGAATGCGTCTTTGTTGATCGACTTGGCGCAGTTCGAGCAGGCTGAGTTCAGCCTCGTCATCGAGGGGAATAAGCGTTAAATCAACGGTATGGTGCTGACCATCGAGCGTGACTAGGGCCGCCGTATTGACCGTTAAACCTTGGCCAGCTGCTACCGCATCCCTCAATAACTGGGCTTCGACACCGAGGATTTGATAATGCTCAGCCAAAGCTTGTTCTACGAGTCTATGGCTGCCAACACCTAATAATTGCTCTGCTGCTGCGTTGGCATAACAAGGCTTAAGATCCTTATCGATGACAAGTACAGCGGTAACTAAATGATTGAGCAGTGTCTCTTTATCCATCGACCGTTCCTAGAAATAGGGTTGCACCACCATGGTGCACCAATCTGGTGCAAGCTGCAACTTGTCTTCATCAAACGGTTTAGTCTGGGGCGGTTAATCTAACTCTATGTATTTAAAGTTTTTATACTAACTCTAACTATACAAATAGCGACATTAACGCTTAGCAGCTTGATGAAGAAATATCTTTCTTGGTGAACTTGATGCAAAGACTTTGCCGTTTTGAGTCAAAGCGTCAACGACTAAGGTATGCTCACCTCGGTCGATGTTTTTGAGTTTGAAGGTACCGCCAATTTGCGGCTGCCCGACGGGAGTGCCATCGAGTTTGAGGGCCATTAAATACTGGCTTTTTAGCTCTGGGGTGATAGTCGCAACGACGTCGAAATCACCATTATTATCTCTGACGGTTTCTTCTTCTGCTGGGGAGGTGATATTAACTTGATATTGAATGGATTCAATTGCTTGGTTTGCGTTATTTGAGTCTGTTTTAGGTAGGGGAAGGGCAATTTGATTTAAGGTCTTCTCTTTTAGCTCAACGATTTGGGCATTTGGCTGCGGCTCATCTGAGTAATGGACTTTACCATCTTTATCGACCCATTTATAAACCGTCGCTTGCGCTAACATGCTAAATAGCACTAAGCCGATGAGTGTGAGTGAACGCATGCTAAAGTCCCTGTAGCCTTGAGAGAGAGTTAATCAAGATTAGTCTGTTTTACATCAAAAATCATGGTGTTTGTTATGCGGCGTCGCCACATGGAAGGCGTAAAACGCTACAGGAAAGGGGGATCTAGAAATCGGACTGGGTTGAACGAACTGAAGTTCGGGCACACTCATGTACCCGAACTCGAGTTGTAAATTACACGCTTGCGTCGAGTTGTTTCTCGATCTCACTCGCTTTAACCCAAGCGGCATGCTGGGATAATTGAGTGGCATAGCGTTGAATGTGTTCGAATTTTTCGAGGTCGCCACTATTAGCCAGTAGCTCCACCACGAAGGACATCATGATATCCGCACCTGTGAGCTTGTCTGCAACCAAGTAGGTTTTACCGGCCAACATATCATTGAAGTAGTGGCTTATCTTACTGACTTCTATCGCGGCATAACCTTCAAGGAAGTTGGTTTTACAGCCATCCTTTTGCACGAACATGCGCAGTAACAAAGGTAGCATGGCTGAGCTTTCGGCAAAGTGTAACCATTGCAGATAATCGACGTAGTCAGCGCTGTCTCTTGCTGGCGCTAAGATGTCTGCCGCATATTTTTCAATCAGATACTCAGTGATCGCGCCTGACTCGGCAATCACTTGACCGTCCATTTCGATAACGGGTGATTTGCCCAACGGATGAATTTGCTTGAGTTCGGGTGGCGCGAGAAAAGTCTGACTATCACGCTGATAACTCTTAATTTGATAAGGTTGACCGAGTTCTTCTAGCAACCAGATGATACGTTTAGAGCGTGATTTATTGAGATGGTGCAAGGTGATCATTGGCTTCCCTTTCGATTGTGAGAGTACATTTTTAGCTATCGCACCATTATGGTGCGATAGCTATTTCTACCGCATTTAGCACACTCGAGCTTGTCATTATTCACTTCAAGCTGAGTGTGTCGCGTCATTTATCGCTTAGCCTCTAGCGTTCAGTTGCGCGACGATGGCCTGCGCAACTGCTTCGGATGATCCTGGGTTTTGACCTGTGATCAAATGGCCATCTGCGACAACAAAGCTTGTCCAATCATCACCTTTGACATAAGTGCCGCCGTTTTTCTGCAGCATGTCTTCGACTAAAAATGGCACGATATGAGTAAGTTGCACGGCTTCTTCTTCAGAATTACTGAAACCGGTAACGCGCTTGCCGCTGACCAATGGCTTACCATCACTGGCTTTAGGGTGCAGCAGTGCCGCAGGCGCATGACAGACAAGTCCTAGGGGTTTATTCGCTTGATAGAATTGTTCAATCAAGGCAATAGAATGTTTATCTTCGGCAAGATCCCACAGTGGGCCGTGGCCGCCTGGATAGAAAACCGCATCATAATCAGCTGCGTTGATTTCACTTAAGCGAGTGGTATTTGCTAGCAGTGCTTGTGCCTCAGGATCTTTACTGAAGCGCGCAGTGGCTTCAGTTTGAAAGTCTGGCTCATCACTTTTTGGGTCGAGCGGTGGTTGACCGCCCAATGGCGAGGCTAAGGTAATGTCAAAACCTTTATCTTTAAACTCATAAAAAGGTGAGGCGAATTCTTCGAGCCAAAAGCCGGTTTTTAAACCTGTGTCGCCGAGTTTATCGTGTGAAGTTAGTACCATTAATATATTCATTTTCATACCTTTACCTAAGTTGCTCACATCTATACGTGTGAGCTTTGGGTTTAGTTCAATCAGGATACGGTTTGGCGTTATAGGGCTAAGGTCAAAATGTCACGGCTCATTGCTGGGTTAATGTTGCCATGTTCGCCGAGTGCCACCATGCCGTGCAGTTCAAGCTGTTTCACTAGGGTATCAACGTGGCTGGCATCGAGATCATAGGCAGATAAATGTGTCGGTATGCCCATGGCTTCGAAGAAAGCCTTAGTTTTTGCAATAGCGGCATCGATGCGCTCGTCATCGGTACCTGTGTTGATGTGCCACACTCGGTCTGCGTACTGGAGTAACTTTTCGCGCTTAGCCTCTTTGGTACATTGCAGCAGTGCAGGCAATACGATTGCCAATGTACGCGCATGGTCGATATCGTAAAGCGCAGTCAGTTCATGGCCTATCATGTGGGTTGCCCAATCGTGCGGAACGCCTACGCCAATCGTGCCGTTGAGTGCCATAGTGGCAACCCACATTAAGTTAGCGCGAATGTCGTAGTCTTCGGGTTGAGCCAGTACCTGTGGACCAAGCTCAATCAAGGTTTGTAACAGACCTTCGGCAAATCTGTCCTGTACGGCGGCGTTGACTGGGTAAGTGAGGTATTGCTCTGTGATATGCACAAAAGCATCGACGACACCGTTGGCCACTTGGCGCTCTGGCAGAGTGAAGGTTTTCGTCGGATCGAGTATTGAGAATTGTGGATAAACTAAGTCATTACGGAATGGCAGCTTAGCTTGCAGTGATTTACGGGTCACTACGCTGGCGTTGTTCATTTCTGAACCCGTCGCAGGCAAGGTTAATACTGAGCCAAAAGGCATAGCTTGAGTTACTTTTGCGCCCCAGCTAGTGAGAATATCCCAAGGTTCACCTTCGAATACTGCTGCAGCGGCAACAAATTTAGTGCCATCGATAACCGAACCACCACCCACTGCAAGGAGGAAGTCGATCTTCTGTTCGCGCACTTGGGCGACGGCTTTCATTAAGGTTTCATAGGTTGGGTTTGGCTCAATGCCATCGAACTCGACGATGAAACGATTGCCCAATGATTGTTTAACTTCATCTAGCGTGCCGGTTTTTCTGGCGCTGCTGCCGCCAAATAAGATCATCACTTTGGCATCGCTCGGTACTAAAGTATCAATTTCGGCGATAGTGTCTTTACCAAAACGGATGCGGGTTGGGTTGTAATAGTTAAAATTTAGCATGGATGATTTATCCCTTATTACTTTAAGTGAGCTACTTTAAATTAGCAGTGTTAAATGCGCTGCGTTAATTAATCGCCCGCTAAATTAGTAGACCAGTCGTCTAATTCTGGGGCGTGAAAATGTATTCTTTAAAAATCTAGGCTGACTGATATCTGCAAATATCGGTCTTGAATCGGTATTAAGTTAAAGGGCCGTTTTAGGCGTTAAAATAGCTTTAGTCGTCACGAGCGCACGTTCGAGTGCCGCGGGGCTGTGGCCTAACTTGTTCATTAAGCTGGCACCTAACCACATGTGATACAACATTTCAGCGGTCGATTGCGGATCTTGCTCGGCGATTGAACTGTCGTTTATGCCAACTTGAACACAAGTGGTTAACCTGTCTATGATGCCGGCGGAACCTTTAAGTAAGGCAACTCGCATGGCTTCGGATAAATCGGCCACTTCAGCACTGAGTTTCACGACGAGGCATTTTTGATCTACGCAACCATCAGCTTGTACGTGCAACCATTGTTGCCAGTACTGCATCAAGCGCTGATAGCCTGTCAGTGTCGAGTCATTGAATAATGCGTCTAAATCTAACTGATATTTCTCAAAATAATCAGTTATGAGCGCTTCGCCAAATTGTTCTTTGGATTTGAAGTAATGATAAAACGAGCCTTTAGGCACTTCGGCGGCCTGCAGCAGTAGCGATAAGCCGACGCTGGAGAAGCCTTTTCGTACGATCAGCTTATAGCCGATGTCGAGAATGTGCTGGCGAGTGGATTGAGTTTCAATTTTCATGTGGTCTAGGATAAGCTAAAATAGACCGGTCGTCTAGTGTTGTTTTTATCGGCTATATCAACACGCCCGATTTAGTCTTCTTTAAGCCGCGAGGTTTAAACTGTCTTGGGTCTGCCTAATAAAGCTATCAAATTGATAAAAAGCCAAACGGATAAAGCATGCAGAGGGTGAAATTGTGGCAAGGAGAAATGGTTCGGTAGGGCAAAATACCCACAGTGCAGAGATAAACCGTCACTGTGAGCATTAGATTCATAATTGATATTGAAACTTTTACTCAATCAAGATGATTACAGGTAGCTGCAACAGTACTCTGCAATGGCTGTGTTACGAATTTTAAAGCTGACATTTGCAGCCAGTTCAAACTGAGTTCCTTCGCTAAATTCTTGCCAAGTGGTCGTTTCTGGTAGTAACACCTCAAAACGGCCGCTAATCACTTCCATGATTTCGCCTTGTGAAGTCGAGAACTCATACTCTCCAGGCTGTACTACGCCTAAGGTTTGCTTACTGCCATCGGCAAAAAATACGCTACGACTGGCAACTCTGCCATCAAAGTAAATATTAGCTTTTTTAGATACAGACACTTGCTCGAGTAAACTCATTTTTAATCCTCTATAAATTGGCCTGGGATGAATTTATATGCATATTTAATAAATTTCAATGCATTATTTGTCGTGTGGATATGCGTACTGAATATAAGGTGATGCTAACGTTTGGTTTATAAAGTGTTTTCCACTATATCATTTTGTTTGAAATGGTCGGTATGGCTTGGCTGTCATTTAAAGTTATTAATCATTTTTCTATTTAAAAATAATGAGCTAGCAGTATTTCGGTTAACTTTTGATGAAAATAGTCTGCGTCGCTTTGTTTAGCTGAGATTCTTCTCCTATAGTTTTAGGGCAGATTTAATTTTGCAAAAAGTGAATGAATGGAATTCGAAGCGCGACAAGGAGTCTCACCATGGGTTTTACTCGTAAAATTCAGCTGATCAGTTTATTGATTGTCCTACTACCCCTCATATTTGCTACCGCGATAGTGACTTACATTGCTCGGGATGAGTTATTTGCCGAGGCTCAGTCTCGTTTAGTGGCGGTGCGCGAGATCAAGCAGCGACAGATTGTCGGTATGTTTCAGGACTTTTCAGATAACCTGCAGGCCGTCAGTGCGGTGATCGCCAGCCAAAAAAGCCTCGATACTATCTCCGACCTTGATGAGACCTTAAAGTCGTTAAATAAGTCTTTAGGTTTCTATGATCTGTTCATCATTCGGGATGACGGCACTGTGTTTTACACCGCCGCGAAGGAGTCGGATTTCGGCACAAATCTTCGCACTGGGCCCTACCATGATTCGGGGTTAGCGCAGTTATTCAACAAGGCATTGGGGTCGACTGGCACTGTATTATTGCAAGACTTCTCCGCATACGCTCCTTCAAATGGTCAACCGGCAGCATTTATTGGTCAGTCGATTCAATTCAACCAGCAAAGATTAGTGGTGGCGGCTCAAGTATCAATCGACCGCATCAACCGAGTTATGCAGATCCGCGAAGGTATGGGACAAAGTGGCGAGACCTATTTAATTGGCCCCAATAATCGCATGCGTTCCGATTCTTTTCTTGACCCGAACAATCGCACTGTGACCGCCTCCTTCGCTGGAACGGTTGCCCAGAATGGTGTTGACACACTTGCGGTGAAAGAGGCGCTGGCGGGTAAGACTGGTGTGATGCAAGTGAATGACTACAATCATAATCCGGTGTTATCTGCCTATTCTCCCGTTGTTGAACTTGGCTTACGTTGGGGCTTGATGGCTGAAATTGATGTGAGCGAAATCGCTGCGCCAGCCTATCGAATGCTATTTATCGGCTTATTTGTTTGTGTGGCGGCCTTAGTGTTGGCATTCGCTGCCGCTAAGTTAGTCACAGGATTTGTGTTGAAGCCACTCGGCGGTGAGCCTGAGGATATGTGTAACTTGACCAGCATGATCGCTTCTGGCGATCTCACCCATAATCTGCCCCATGTTCAAACGGATAATCATCTGATGAGCTGGTTGGCCCGGATGCAAACAAAGCTCAAAGAAATCATTAGTCAGCTGGTGGGCGTTGGACATGAACTCGAATCCGCCGCTGAACAAAACTCGGCCGCTATGACCCAAGCCGATTGCAGTATTCAGATGCAGGCGAAAGAGACCGATATGTTAGCAACCGCAGTCGAAGAAATGAGTTATGCCGCCGCGGAAATCAGCACGAATACGCTTAAATCCTCCGATGAAGTGTCTGCCTGTACCCATTCGAGTGAAATTCTTTCGCAAAATTTAGCCAGCACACGTAAAAGCTTGAAAGTGACCTTGAGCAGTTTTGCGACCATACATCAACAGGTGGGCAGCCTTGAGGCTGACAGTCAGAAAATCGGTTCGGTATTGGCTGTGATTAATGCGATTGCTGAACAAACGAATCTGCTGTCACTTAATGCGGCGATTGAAGCGGCGAGAGCTGGTGAACATGGTCGTGGCTTTGCCGTAGTTGCCGATGAGGTGCGCCAGTTAGCAGTTAAGGTTCAGTTGGCGACTCAGGATATTGGCCAAGTGTTAAAAGGCATTCAGCAGCAGTCTGGCGTGTTGGCACTGCATAGCGTGACTTGTACTACAGAAGCCTCTAAAACCGCAGAAGATGCCGATGGCATGCAAGCCGCAGTGGATGACATCGCTTTACGTTTGGAAACCTTAAAGGCATTAATGATCCAAACCGCCACCGCCGCGGAGGAGCAAACAACGGTTTCGGCAACGATTGCACAGGGGATTGCGGGCTTGAGCGTGGCGGCGGAAGAAAATAGCGCGGCGATCAGCCAAGTGGCAGCGAGTACTCGCAGTTTGTTAGGGTTAGCGAATCAGCTTGGTTTAACAACGGCACAGTTTAAGGTGTAGGTATTTGCGCTGTATTTGCCGTGTTCAACTTTAGTTGATGCTGCAAAGATGACTGAGCTGGATGTTACACATCCGCCAGAAATGGATTTCTGGCGGATATTTTTATTGAGACTTCAGTTGTTTATCCCATGAGTGAGCATTAACGCTTACGCCAAATTGTCATTTCTGAGATGGAATGTTGGAATTTACGTTTAGTCTCGCGGATCACGAAGGGGATTTCCTTCACTGCGACTAGCTCAAATTGGCCGATCAAGGTTTCGGTCAAGCCATCTAAGGTGGTGAAGTTCTCTCCTTTGACTTTAACGCCACCGAGCCATTTATCCTTGGGCGTATAATCCTCCAGCCAAGTGTACGGCGAAGTAATAACAAGGTATCCACCTTGATTAATCCGCTGGGCAATACTGGTTAAAAAGTGCTTAGGATCGTTCAGTCTGTCAATAAGGTTCGAGGCATAAACCAAGTCATAACCTGTATACATTGGCTTTAAATTACAGGCATCACCTTGCATAAAGTTGACTTTTTTCGCCTCAAGGCCATAGCCAAGTTTAGCTAGGCTCGCGCTCTTAAATTCCTGTAAGTCGCCCTCGGTACGTATGGTGTAACGTTTTTCACCTTGTTCGGTCAGCGCATAGGCCTGTTGAATAAAGCGCGCCGAAAAGTCGATACCATCAACATGATCAAACACTTTTGCCAGTTCAAAACTCGCTCGGCCTACGGAGCAGCCGATGTCGAGGGCTTTGGCCGTATGGGTGAGGTGAATTTCACTTAGCGCTTGCTGTACGCCATTGACGCAGAAGTTAGCAACCCCGAAATATTCGGCGCCATAGTGGAATTCAAGGTATTGGGAAATCAGTTCATCAGTTTCGTACACATTGACCTCGGCCATTTGGGCGGGATTTTGCGTGGATTCCACATAGCGGAAGCCAGCGTGTTGATAAAAATGTCGACGAAACGCATAGCGTGACGAAGCAATGGCTTCATTACCTGTGGAGATCCACGAGCCACCTTTGATGAGATTATGCTTACCATCGAAGGTTGGAGTAGAAAAATCATCGTAGAGTGGATGCACGGCAAAACCGTTAAAACCGTCGATTGCGGTTTCTGTCCATTGCCAAACATTACCGACAATATCGAAAAAGCCATTATGTTCGAAGCGATTCACTGGGCAAGAAGAGGCGTAATAAGCCAAAGCAATATTGCCCGGCACTTCGTGCCAATCGGGCGCATCGGCGGGCACATTTTCGCGTAAGATGTACCATTCGGCTTCCGTTAGTAGGCGAATGTTGCTTTGATTTTGCTCGGTTTTCCAATTGCAGAATGCCTTAGCTTCGAGTTGATTCACTTCTACAGGCCAGTTTAATGGCAGCGGGATTTCTTCCGCTAAGTTACGTTGCCACCATTTATCTTGTTTTAAGCGCCAAAAACGCGGCATTTGAGCTTGGGTGTAAGCGAGCCAAGCTTGGCCTTCTTCATTCCAAAATTGGGGTGAGCGATAGCCGCCAGCTTCGACGAATGCGAGGTACTCTTGATTCGAGACTAAGAATTTAGAGGCCTTAAAGTCGGCAACATTAAAGATTTTATGGCCGTATTCGTTATCCCAGCCGTAGGTTTCATCTTGTTCATTTTTACCTAAGGTCACTTTTTGCCCTTTTACTGCCAGTAAACTGTTGGCTTCGGCGGCGGTGAAGTCTTGGCACGCAGCCCAGAGGCTGTTGGGGGCGACATGTTCCAGCGGTAACTGACGGATGATCACCGATGAGGTTTCTAAGTGAATACGCTCGTGTTCAATGCCCATCAAAATGATCCAAGCTGGGCTGTCTTGAGTGATCGGCAGCGCCAGCGGCATTTCATCAATCACGCGATTGACGACAGCATTAACTTGTTGGCGATAGCTGCGCACTTCTGCCACGTTCGGCCATGGGTAATGTTGCTCGTTCAGATCGTCCCAAGACATCTCATCGACACCAATGGCAAACATGGATTCGAAGTTGTCGTTTACCCGTTCGTCGAGAAATTTGCCGAGTTTGAGTTTGTTGATATAGAAAGTGGCCGTATGGCCAAAATAAAAGATCAACGGATGGCGCAGCGGCTCGGCTTTGAGGTAATAGGCGCTATCTTCTTGGATCAAATCAAACAGTGACTCGTACAGAGCCCATGTCTGATTAAAGTACTGCTTAAGCTCTAGACGCTTTCCTGCCTCAGTTTCACCAATTAGCTTAAGGGTTTGAGGGGGTTGCGTTATGGGCTTATCCATAGGCAAGTAAACTCCGTATTTATCGGCCTGCGGTGCAAGCATGGGAACATGAATCTATCGTGTGTGACTCTATACGCGATTACTGGGCTCAGGATCACTTTGTCAGCTTAATGTGTGTTCTTATCTATCGACCCCTGACAAAGTAACAGAAAGGTGGCGTGAGTTTAAAGCTTCAAATGTGGGGGAATAGCCTTCTTTAGTAAACAACATTGAGGGGATGTTTAAGTAATAGTTCGAGTTACAAAGTGATTTATTATTAACTGCTTATTAATTCTTTTTCCGCACACAGATGTTTTTATTTTGTTGCTTAAATGTTATTTTTGGTGGGTTTTGGAGATGTATGATCTATGCTTCGCTAATTTTTTAGGTATTTGATATATGCAGCAGCTATTTTTTCGATTACTAGAAGACCGTTATTTTAATCGGTTGCCCGAGGAGTGGGTTCTCCGGTCAGCTCAGGAATCGGTTGATTTCGTTAGATTTTGGCTTGTACATTGCTTTCGCAGTGCCGCATCGGAACGAGAATTAGCAACTGTGCTGTTTTTTGCCGCTAAATGGAAGTTAGAAGAGCTAACTTTCTTAAGATCATTTTGGCAAAGTTATACGTCAGTTATGGATGAAGCTGCAGAGTTGACATTGAGGAATTTATATTTCCAGTTAATTCCTCAAATTAATCTTGATCACGACAATCATTTAGATGCGATAGCCATTTTTTCTTTTGTACTCAAGTTTGAACAACAAACGGTAGTATCTGAATTGCAACAGGGTGTAGCTTCTTCTTATCGAACTTGTCTACCCATTGCAATGCTTCAAGAGATACAAACTCTTTCATTACCTCATTTACATCCCATCACTGTTTATGGTCATCAAGGAAAAGGAAGTGTTGAATTTGATATTCGCAATAACACTCACTTCCCGACACAAATACCTAATGCTAGTTTAGTATTTGCGTGTGTTGAAAGAGTCGTTGCGCACTATTCTGGGACAAGCCTAGCATTTGCCGAGCCTATGGTCGTTTTACGCTATGAGGTTGGGCAATTTTACAAATGGCATTATGATGCAATACATGCACACACACCGGCAATTGAATCAGAGCTAAAGCAATTTGGTCAGCGTAATCGTACTGCGATACTGTACTTAAATGATGATTTCACTGGTGGGGAGACGGAGTTTAAAGCGCCATATATTCAAGTGCATCCTAAGGCTGGTTCTGTCTTAGTTTTTGATAATGTAGATTGTAATGGGCGAACTTTGATATCGTCTTTACATCGTGGCTGCGAAGTCAGTACTGGCCAAAAGTGGATATGTACACAATGGTTTAGAGATAAACCATTTTGGCTTAGGTCTGGGCTCTTAACTTAGTTTCAGCTAGATTAAATTTGAATCATTTTTTTACAAGCAAATTTTAGGCTAAATTATTTTATCCCTGAAGAGTATTTTCTCTGTCGGGGTTAAGGCAGAATGATCCCGTCCGTAAAATTATTATGCAGGATATAACGGGATGACAATTAACGCATTATGGATACCCGCCTGGTACGAACTTGATCAATCGATCGTAGTGGGTGTGACTGAGGAGTTTGTCTTTCATAAGACTGTCGCAAACGAGGCGTTAACGTTTTACAGCGGTGCAAAGGGGAGCGATGCCGTTAAGGCAACGGGAACCATTTCGGCCATTAAACATAATGTCTTGGGGGACATTGAAAGTGTCGATGCACAGGGGCTAGATTATACGCTGGTATTGCAAGATGGACGGCGTTTGTTAGTCAATGCTGAGGAAAATCCAGGATTGATTTATGAGTGGGTTGATGACAGCTGGCAGCCGTCAGATATGGTCATCACAGATTGGACGTTGGCAGTACAATTTGCGTCTTTATCACCATTGACGCTAATAAAGTAATTGAAGATTTATGGTTAAGTGACTAAATAGGTAAGTGCAGTTAGCGCTCGGGATCCGACGTTGACTGCACTTTTACTAGTTCTGCAAGTTGTTGGTATTCTTTTTTAATTTCTGCGACATATACATCGTGAAAATCCAGCTCATTAAACTCATCAAGGCAGAGCTGGTAACGGCGTAGCTGCTCAGCTTTTTCTTCAGCGGTATCCCATCTTTCCATTAGCATAGCGTTTGATTCCATTCATAAATTTAATTCCTTCTAAACTTCACATATTTTAACGACAGTTATTGCTATTTAACTGAGCTGGTTTGAAGTTTGTTGTATGCATTTATGCCGTTGGGCGTCATGCAATATAGAAATCTATTTAAAACAGGTGGTTGAAGTTAAGTTGTTGGTTTTACCGTATGGCTGCGCACTTTTTTCCTTATCTTATTCGGGACAAATGCTTACACAGGCGTCAGCCATAGCCCGATTGTGCTGCTATCTTGATGATTGGATTGTACCAGTGAGCATTTTGCTCTAGATTGATATTTCTTTGTTTTCATCTTCTGGCCTTGAGTCTTTTTTGCCCTTTGGAGCCGCGTAATGGCGAATATTCTGTTAGTTGCCAATCTCAACTGTGATCGGATTTTACTGCTTGATAAACCTTTAAAAACAGGTGGGCGTTTCCATTATCAAGATGGTGGACAAAGGCTCGGTGGTGGCGGGGCAAATACCGGATTAGGTTTAGTTTGGGCAGGGCATAGCGTCGCATTGGTGAGTCAAGTTGGCCGTGATGACATGGGAGATTGGCTCATTGCAGAAGCCAGCACTCAAGGACTCGATTGCCGTTTAGTGCAGCGCCGAGCCGGTAACACTTGCGAAATGTTACTCGTGATGACGCCAGACGGTGAACGGACGATTATTCGGCCACAAAGACCCATATTTGAGTTATCAGTGCCGCCGAAATGGCAGCCATGGGACGCGCTGTATTTTAATACTTCCGCAGAGGGTTCGGTCAGTTGGGCAAAGACAGCCTTGGAACATTGTCTTGTGGTCGCGCAATTAGCCAAAGATGACAGGCAGAGACCTTGTCATATTTTGCTGGCTTCCATGAGTGATATCCAAGGGCGTTGTGATGGCGCATCTTGGGAATATGGTAAAAGCATTGGCGGTGAGGGTTTGCGTTACTTTATTGTCACCGATGGCATCAATGGGGCTAAGGTTTACACGCACGATCAAGTGCAGCATGTTGCGGCTATCCCTGCCACTGTTGTTGATACCACAGGCGCTGGTGATGCCTATGCGGCGGGACTTATCCATAGTCTATGCACTGGTCAGTTGATTACAGAGGCGATGGCAGAAGGCGCTGTTTGGGCGGCATTTGCGGTGGCAACCGATAGTTCTATTCCGGGTGAGGCACTGAAACAGTATTTAGAGGCCAAGCAAATCGCATGATAAATCTGCGGGTGAGCTGTTTGAGTCATCTTTCTGTCATACGGCCTTGCTAGAGTATGTCTTGTGGCGCATAGCTCTTGTCCCTTAACGTTTTCTTGTGCTTTTCTCCCATTTATTGGTTAGTATGTTTTTTGCCTCGTTTCCCCAACGAGGCATTTTTTTGCTCAAAATAAAGTTATTCCATCGCAGAACATTCATAAAGCTGAAATGTTGTGCGGCCATGGCGCAATTGCGTTAACATTTATTCAACTAAAATAGTTATCGTGACATTAAAATAATAAGCCACTCAAGGTGTTCTGTAGTCTTTGGAGTTGAGAATGTGCAATAACGCTTGGAATGATAAGAATAAATTTATTTGGCTGCTCTCTGTTTTGCTTTTAGTCGCCTTTTTAGTGACCAGTGGTATCAGTTACAAGGTGGCCCATGACTCACTGAGTCAACAAATCGAGAGTAATACCCTCCCGCTCACAAGTGACAATATTTACTCAGAAATTCAACAGGATCTACTGCGACCTATCTTTATTTCATCCTTGATGGCGCAGGATACTTTTGTTCGCGATTGGACATTAAGCCACGAGCAAGAGCCAGAAAAACTGATCCGTTACCTTAAGGAAATCCAAGAGAAATACGATACAGTCACCAGTTTTTTTGTGTCAGAGAAAAGTCGGCATTATTACCACTCCACGGGCATTTTAAAACAAATCCAAGATGTTGAGCCCGATGATGCTTGGTACTTTCGAGTGCGATCTTTACCCGAATCTGAGCATTATGAGGTCAATATTGATGCTGATACGGCGGACCGCAGTAAAACAACCGTATTCGTGAACTATAAAGTTTTTGATTTTGAAGGCAAATTTATAGGCGTTACCGGTGTGGGTTTGGCGGTTGAAAAGGTTAAGTCTCTTATCGAGCTTTATCAAAAACGTTATAACCGTCGTGTGTTCTTTACCGATCGGCAGGGCAATGTCACTTTGCATGGCGATGAATATGATGGTGCGGATTCGCTGCAAACCAGTCCCGGGCTTGAAAATTTAGCGACGCGTATTTTAACCAGTCCAAGTGCGGCTTTTAGCTACCAGAGAAATGGTAAAACCGTTTATCTCAACAGCCGTCTAGTCCCTGAATTTAAGTGGTATTTGATTGTTGAGCAGGAAGATGCGCCACAGGAGCGGGAGTTACTGAATACTTTTTGGGGCAACTTGGGCTTAAGCCTAATGGTGACCCTAGGGATTTTATTCATCTCCAACATGACCTTAGGCCGTTATCAGCGCAAATTAGAAGTAATGGCGTCTACCGATAAGCTTACTGGCGCCGCTAACCGTCAGGTATTTGATGAGTACTTTAGGCAGGCGTTAGATAAGGCTAAGTTGGCACAATCGCCGATTTCAATACTCTTGCTGGATATTGACCACTTTAAAAAAGTGAACGACAGCTATGGGCATGGAATTGGTGACTTAGTATTAAAAACCATGACGAATATGCTGCGTGGTGCGCTGCAGCAAGAGGATATCTTATGTCGTTGGGGCGGCGAGGAGTTCTTGATTTTACTTCCGGGGATGGATTTAACCCGAGCGGCAGAACTGGCTGAACAGATCCGTGAGCTTATTTTCCAGCGGGAAATTAAAGTAAATGGATTACATATTTCTATTACTGCCAGTATTGGCGTCGCCGAGCATCAGGCGCAAGAGCCTGCAGAAGATCTGATCAAGCGTGCCGATCTGGCCTTGTATCAGGCTAAGGAAGCCGGTCGAAATCAAGTGGTGTTAAATCATTCATAGATTAAGCCCCGTACTTATTGGCGGCAAGATTGCGCCTACTTTTATCAGTTATTGAGCGAGTTGATTGTGTTACTGGCGTATCGCGGTTTACACTCTGGCGGTAATGCGAGATGGGTTATGGATTGGGGGTTACAGATGTCAAAGTTGTTGATCAATAAGTTATCTGGATTGTATTTAGGGACGCATCTGCAGGAAATGGCAGGTGTCGCCACAGGTATTGATGGCAAGCAAGCCGCTCAACAACTGCTAGTGCATGTCAGCCATGTTGAAGGGGATGCGCAGGCCGATCCTAAACACCATGGCGGACTCGATCGCGTGTTACATCATTTTCCCCGCGAGCATTACGGCCAATATCGTCGTTGGGATTTAATTACTCAACTCGAAGATGCGCCCAGCATGGGAGAGAATATCAGTACCGTTGGGCTGAACGAAACCCAAGTTAATATTGGTGATATCTTACAAATTGGCGCCGTAACAGTGCAGGTCACTCAGCCGCGTTCACCTTGCTTTAAGTTGAATCTGCAATTTGGGCACAAAGAATTTGCCCTCGCCATGCAGCAGAGCCAATTATGTGGTTGGTTTTATCGAGTATTAACGCCTGGGATTATTCAGTTAAACGATAAGATTGAACTGGTTGAGCGCCGTACGGACATTAGTGTCGCCGAGGCTATGGCACTCTATTTTTCGCCAACGTTTAATGCCAGCGGCTATGATCGTTTAGCTTGCTGCGAGGGATTAGCGCAGAGTTGGGTTAATAGCCTACAACGGCGCTTAGCATCACAGACAGTAGAAGATTGGCAAATGCGCTTATATGGGCCGAGCGGTAAATAAGTATTTATATCCATAATGATAAAACAGGGATGACCTTATGATTGAACCCGTTACAAAAAGTGATAAAGATTTTGGCTTACTGGTATACGCCTCCAGCTTTGTTGGATACTTAGTGCCGCTGGGCTCGATTCTTGGTCCTTTGATTATCTGGCTGATGAAGCGCGAAGAGTCCGCATTTGTCGATCAATGTGGCCGTAGTTGTTTGAACTTTAAATTGAGCCTGCTGATTTATGTAATCATCAGTGGCGTACTCGCTTTGGTCGGGATAGGTTTCATCTTTTTAGCCATACTCGGCATATTCGATTTGGTGTGCACTGTGCTGGCGATTATTAAAGCCAGTGAGGGTAAGGTATATAAATATCCATTAACGATTAAATTTATTGCTATGGATTGATCGCCTCTTTGAGTTAAAGAAAAACGCCTTAGATGTTTCCATCTAAGGCGTTTTTTTATGGTGTAAGTTTAAGGTGTTACACCTTAAACTTGGCCACTAAGGTATCTAAACGGTGTGACAGTTGATTCAAGGCTAAACTCGCCTGTGCTGCCGCTTGTGCAGTATTGGCTGTACGCTGAGTAATATCGTTGATTTCTGTGACGTTACGGTTGATGTCCTCCACAACTGTGGATTGTTCTTCCGTTGCCGCAGCAACTTGGATGTTCATGTCGCTGATAAGACCAATGCGATCGCTAATACCAATCAACGATTTGCTGGCTTCATCGACGGCCACAACACCTTCATGGGAGCGAGTACGACTCTGGGCCATCGCATTCACTGCGCGGCTGGCTTCAGATTGCAACTTGTCGATCATGCCTTGCACTTCATTGGTCGATGCTGCGGTACGTGATGCTAAGTTACGTACTTCATCGGCGACGACAGCAAAACCGCGTCCAGCTTCACCGGCACGTGCCGCTTCAATTGCCGCGTTGAGGGCTAACAAGTTTGTCTGTTCAGAGATAGCGCGGATCACATCTAAAATCCCACCAATGGATTTAGTGTGGGTCGCGAGGGACTCAATCACTTCACCCACTTGGCCAACGTCTTTTGACAATTGGTTAATGGTATCGCGGGCACGGGTTACCACAACTTGGCCACTGCTGGCTTCAGTGTCAGCATCTCTTGCTGCAACCGCTGCTTGGGCAGCATTACTGGCAATTTCGTTAACCGTCGCGCCCATTTCATTAATGGCTGTTACCACCATCATAGTGCGATCTTTCTGCTCGTGGCTGTCTTCTAAGGTTTGCTGTGCCTGATTCGACACATCCTTAGCGGAGAAACCGAGCTGTTCGCTGGTTTGCGCCACTTCGATGACCGAATCTTGGATCTTGCTGATAAAGCTGTTAAATCCCTTAGCGAGTTGCGCAATCTCATCTTCACCATTGACGGGTAAGCGTTGGCGTAAGTCACCTTCACCTTCGCCTATGTCGCGGAACATAGCGGCAACTTGGGCGATTGGACGACTCACAGAACCTGCAACAACGATAGCGAGGACGATAAAGCCCGCTGCAATCAACAGTGTCCAGATTAAGATCTGATAAGCCGCTTCGGTTAATAGCGCAAACACTTCGGCTTCTGGCACTTGTGCCACTAAGTACCAATCCATAGACGCGATATAACTACTGGCAACCAGCATGTTTTCGCCATTGATCTTGGTATTGATCAGATTGAAATCACCGCGGTTTAGTAAGGCGCTTGAGTTCGCATCTTTGTATAAGTTGCTTAGGTTGCTCTTACCAATTTGGCTCGTATCTGGGTGGAGTTTCACTTCACCCTTAGCATCCACTAAGTAAACAAATCCTGTTTCTTCAATCTTAAATGAACTCAGTAAACGCACCATATCATCGAGTGATTTAGCGAGCCCGACTAAGCCGCGACCATTGGGTTGCTGGTAGTTGATAAAGAGTTTGACTTCGCCGTTGGCCTCAGTGAATACGTTCAACATACGTTCTTGGCCACTGTCGCGGTAACCAAAAAACCAGCCATCCTGCGCAGGGGTGAGCACGCGCAAAAATCCATCCTGAGTGTAGTAGGCGGCAGTTTGTCTGTCGGCGTAGGAGGCTTGCGCTAGCTTATATTGACGCGTGATGTCTTTCAGTTGGGCAACCACTTGGGTTTCTTGGGCCTGTGGTCTGCCATCTTCGAGCCATTGGAGTAACATGCGGCTGCTGGCCAATTGCTCTGCGGCATTCATTAAAGTGGAGATGTCGAGATCGATCTTATTGCGGATCTGCATCATCAAACTCGGCAGTTCCGAACCTAACATCCGTTGTTGTACGACATTTTTAGCGCTACGTTGGCTTAATACGCCGACGAGTATGGTCGACAGCAGTACTGCAAATGTCACTGTTAGCAGAATTTTCTGCTTAATGGTGAGGTTATTAAATATCATAAATATCAATATGCCTGTGTTGGACTGTTTTGCGTTATCGGTCAACGCACTCTTGTACGGTAAAAGATTATCGGACAGGAATGTGAAAAGTTAACCTTAGTCTAGGCCGAATGCCGAATAAAACAGCGTATAAAACATTTATTTGGCCTAACCCGTTGGTCTTGCAGTAGATCTAATTATGTAGAGTATAGATGGAAATGGGATGGTGATTGAAAACGAACTCTTTTATGTGTGGCAATTGTGGCCAATGTCGTAGATAAGGGCAAGCAAAAAAAGTGAGATGTTTGTTTCAAAGGTGAAAAAAAGGCGCTCCACTGAGCGCCGCTATTAACATTTATTGGTGACAAGTTACATATTGGGGTAGTTAGGACCACCACCGCCTTCTGGCGTAACCCAAGTGATGTTTTGGCTAGGATCTTTAATATCGCAGGTTTTGCAGTGAATGCAGTTTTGCGCATTGATCACAAACTTAGGCTGACCCGCCTCTTCGACGATTTCATACACACCGGCAGGGCAATAGCGTTGTGCTGGCTCATTAAACTGAGTCAGGTTAATACCCAGTGGAATGCTGCTGTCTTTAAGGCGTAAATGGCAGGGCTGATCTTCTTCATGATAAGTGCTCGACAGATACACTGAGGACAGTTTATCGAAGCTGAGTTTGCCATCGGGTTTTGGGTAATCAATCTTGCTGTAAGCGCTCACTGAGGCCATTTGCGCATAGTCTGGGTGTTCATCACGCAGCGTGATAGGGAACTTTCCGCCAAACCAGTTTTGATCTATGTAATTGAAAGCGCCGCCTAAATAGGTACCAAACTTATGCATCGCAGGGCCAAAGTTGCGCGATTTATGCAGTTCTTCATGCAGCCAGCTTTCTTCGATATGAGTTTGGTAACAGTCGAGATCTTTACCGCCTTCAACACCCGCCATCATGGCTTTGCCTAAGGTTTCTGCCGCAACGATACCGCTCTTCATCGCAGTGTGAGTGCCTTTGATTTTCGCGAAATTCAAAGTCCCAGCGTTACAGCCGATGATCAGGCCGCCGGGGAAGCTCATCTTAGGAAGCGAGTTCAAGCCGCCTTTAGTAATCGCACGGGCGCCGTAACTGAGGCGTTCGCCGCCGGTTAAATACTTTGCAATCACAGGATGAGTTTTATAGCGCTGGAATTCGTCAAATGGACTCAGATGCGGATTCTTGTAGTTCAAATCAATAATGAGGCCGACAGCAATCTGGTTGTCTTCCATGTGATACAGAAAGCCGCCACCCGATGCGCCCTCGGTTAGTGGCCAGCCACCTGTGTGCACCACTTTGCCTTGCTCGTGTTGCTCGGCGGGGACTTTCCAAATTTCTTTGAAACCTAAGCCGTAATGCTGGGGTGTTTTACCATTATCTAAATGGTATTTTTCGATCAGTTGCTTGCCTAAGTGGCCGCGGCAACCCTCTGAGAAAACTGTATATTTAGCGTGTAGTTCCATGCCGGGCATAAAGCTATCTTTAGGCTCACCATCGGCGCCAACCCCCATATCGCCAATCAAAATACCTTTAACGCTGTTATCGACGTTAAATAGCAATTCACTGGCGGCAAAGCCGGGGAATATTTCGACACCGAGTTCTTCGGCGCGATTAGCAAGCCAGCGGGATAGATTACCGACACTAATAATAAAATTGCCGTCGTTGTGCATGGTTTTTGGCACAAAGGCATTGGGCATTACTCGGCCGTCGGTGGCGGAGCTGAGCAGGTAGATTTCGTCGTGGGTGACAGCGGTTTTCACTGGGGCGTCTTTCTCACGCCAGTCGCTGAACAGTTCGTCTAGCACTTTTGGCTCGAACACTGCGCCTGACAGAATATGCGCGCCCACTTCTGAGCCTTTTTCGACCACACAGACGGTAATTTCTTTACCTGAATCCTGTGATATTTGCATTAATCGACACGCGGTAGCCAAGCCTGCTGGACCTGCGCCGACGATAACAACATCGAATTCCATTGATTCGCGTTCCATCAGTTCACCTCTTCCTTCACGTTTCCCCGATTTTTATCGAGTGTTGTAGTTTGTAAACGGGCGTTTTATTGCTTGTACCACCTTACCTGAAAATAAGATAAGGTCACAGTGGATTAAGCGGCTGTTTAGTCTAATTCAGTACAAATCTTTAGGTGAGGACTGTCACAGATTATTCACGTTAGCGTGATCTAGGTTAATAAATACCTGTTTAGGGGTATAGCTAGTGAGTACTTTCGGCCTATAATCACCGGTGACGGTTCTCCGAGCTTGCCTACCTAAGTCGAAAGATATGGAGGTGTAGCCGTGGTAATAATGCCACCGGGGTGGGATAAGAAATGCTCTCACCTCCCACACTTGGAAAGGTGATCATGTCTCAATTACAAGACAGCTTTGGTCGAAAATTTCACTATTTGCGGATGTCAGTCACTGACGTTTGTAACTTCAAATGCAGTTATTGCCTCCCCGACGGTTATCATCCTGACGGCAAGCCAAAATTTTTATCTCTCAATGAGATAGCAAACTTAGTCTCTGCATTTAGCCAAGTCGGCACCCAAAAAATCCGCATAACAGGCGGCGAACCCACCCTGCGTAAAGACTTTACCGACATCATCCGTGTCGTCGCCGATAACCCGCGCATTCACACCATAGCTACAACCACTAATGGCTATAGACTTGAAAAGCATGCCAAAGAATGGTTCGATGCGGGATTGCGCCGTATCAATGTGTCTGTGGATAGCTTAGATCCTAAAATGTTTTACCAAATCACTGGCGAGAATAAATTCGACGAAGTGATGCGGGGGATCGATGCTGCCTTGTCGGCGGGTTTTGAGCGCGTAAAAATCAATGCCGTCCTGCTTAAAGGCCTAAACGATAAAGATTTACCGCGCTTTTTGCATTGGATTAAAAATACCCCAATCGATTTACGTTTTATCGAGTTGATGGAAACGGGTTTAGGTCGTGAGTATTTTGAAGCACATCATCTAGCGGGCGCCGACATTAAAGCGCAGTTACTGGCGGATGGTTGGCAATTAGATACGCCGAGCGCCGATGATGGTCCAGCGCAAAACTTTAGCCGCAGCGATTATCAAGGCCGTATTGGTTTGATCATGCCTTATGCGACTAACTTCTGTGCCAGCTGTAATCGTTTGCGCGTATCGGCGAAGGGTAAGTTGCACTTATGCCTCTTCACCGAATCCGGTGTCGATTTGCGTGATCTGCTGCAGCATTCTAGCCAACAAGCGGAATTAATTGAGCGTTTGCACGGTCAACTAGCCCAGAAGAAAGCCACCCATTACTTGCATCAAGGCATTACCGGTGTCACTCAGCATCTCGCCTCTATTGGTGGCTAGCCCGTATTTAGGTTCTTCATCACCGCCGATTTAGCGCGAGTATAAGCCCAATTTCCCTTGCACTTTGTTTGTCCTCAAACGAAAGGAACGCTCCAATGAGCAATGTATTTACCCATATTAATGCTGATGGCAACGCCCATATGGTCGATGTCACCGAAAAAGCCGTTACCGAGCGCGAAGCCCGTGCCGAAGCGTTTATCGAGATGGCGAGCACGACACTCGAGATGATCATGAGCGGCAGCCATCACAAGGGTGATGTGTTTGCCACTGCACGTATTGCGGGCATTCAAGCCGCGAAAAAAACCTCAGATCTTATCCCTTTATGCCATCCACTGATGCTGACTAAAGTTGAAGTCGATTTAGAGGCGCAGCCCGAACATAACCGCGTGCGTATCACTAGCCTGTGCAAATTATCCGGCAAAACAGGCGTAGAGATGGAAGCCTTAACTGCGGCCTCAGTGGCAGCATTGACTATCTACGATATGTGCAAAGCCGTGCAAAAAGATATGGTGATCTCCCAAGTCCGTTTGCTAGAAAAGCGTGGCGGCAAATCGGGACATTTTAAGGTTTAGCCTGACAGAATAGGCTTTAGCCACTCGGCAGATGACCAAGTGAGCGAAGCTGTCCAAGCTTAAGTCTGACGCGCTATTAGTGAGTCAGTTAAGCGGATCAAAAACGAGAGAAGATTATGATTAACGTGTTGTTTTTTGCTCAAGTTCGAGAATTATTGGGCACTGCTAAATTAAGCCTAGAGGCGAGCGAGCAGACACAAACCGCCGAAGCGTTACGTGCGACGCTGGCGGCTACCGACGATAAATGGGCTAAAGTATTAACCTCAGACAAATTGCTGGTGGCGGTAAATCAGACCATCAGTCAATGGGATACGCCTGTAAACGATGGTGATGAAGTCGCCTTTTTTCCACCCGTTACCGGAGGTTAATATGCGTTTATTACCCGCAGTTCGAGTGCAAGAAGTCGATTTTAGTGTGACCGACGAATATCGGCAGTTAGCTCAAGATGATAGCGATGGTGCCGTAGTCACTTTTGTCGGTAAAGTGCGTGATTTTAATGATGGCGCCGCGGTGACTGACTTAACCTTAGAGCACTATCCGGGCATGACAGAAGCCGTGCTAGAACAAATCGTTGTCGAGGCGCGCAGTCGCTGGCCACTGAACAAGGTCACTGTGATTCACCGCGTGGGCACTATGGCCTTAGGTGAGCAAATTGTATTTATCGGCGTAACCAGCGCTCATCGCAAAGCCGCCTTCGCGGCCTGTGAATTCTTAATCGACTTTTTAAAAACCAAAGCGCCATTCTGGAAATTAGAAGCGGGCGATCAAGGTAAAAGCTGGGTTGAGGCGAAAGATGCCGATGAACAAGCGGCCAAACTGTGGCAACACAAAGACTAGTTGCAGCAAAAATACGTGCAGTGGCAAGGCAGCTTGCTAGCTCAACATGAACGAAAAAACGGCACTGAGGTGACCCAATGCAGAGTTTGACCCGTTTCTTCCACTCCCTTGGTTTTGCATTGCTTTTTTGTTTGGCGGTAGTGCCATCGACCACCTGCGCAGCGGATAATGCGCCGATTCCAGCCATCGCCGCAGCGGCTAATATCAAGTTTGCCTTGGATGACATTGTTAAAAACTTCACCGCAGAAACGGGTCTTAAAGTACGGGTCTCTTATGGTTCATCGGGGAATTTTGTCGCGCAAATTCAACACGGTGCGCCGTTTGAAATGCTGCTGAGCGCTGATGAACGTTATATTGCCGAGCTGAACAAAGTCGGTTTTACCCAAGGCGAGGGCGTGCAGTATGCTGTTGGCCGTTTAGCCTTAGTCGCCCCTAAAAATTCACCGCTCGCACTAGATATCGAACTCAATGGCCTAAAAGCATTAATGGCCGCGGGTCAGCTAGAGCGTTTTGCGATTGCCAATCCTGACCATGCACCCTACGGTGAACGCGCCCGAGAGTTATTGAAAAAACTCGGTTTATGGGATGAGCTACAAACTAAGTTGATCTTAGGCGAAAACGCCTCCCAAGCGGCGCAGTTTGCGATCAGCGGCTCGACTCAAGGCGGAATTATTCCTTTATCACTGGCAATTGCGCCGCAGTTTCAGGCCCTCGGTCATTATTTAGCTCTGCCCTCAGATCTCCATGGTCCGCTGAATCAACGTATGGCGTTAATGCCTAAGGCGAGCTCAACCGCCGAGCGTTTCTACCAATATCTACAGTCAGACGCCGCCCGCGCCGTATTCACTCAATACGGTTTTGGTTTGCCAGTAAGTTAAGGATTCTAATGGATTGGCAGGCGCTGTGGTTATCGGTCAAACTCAGCAGCATCACAGTATTGGTGCTGATCCCCCTTGCGATACTCGCGGGGCGAGCTTTGGCCTATCGTCAATTTGTCGGCAAATCTTGGGTCGAGGCATTGATAATGGTGCCCTTAGTGCTACCGCCGACCGTGATTGGCTATTACTTGCTGGTGGGGCTCGGTAGTCAGAGTTGGCTCGGACAATGGATAGAGCAACTGACGGGCCAGCAGCTCGTGTTTCATTTTTCAGGCCTGGTTATCGCTTCTGTGTTCGTCAATATCCCTTTCGCGGTGCAGCCGATCCAGCGGGCCTTTGAAGCTGTGCCGAATGATGTCCGCGATGCCGCAGCTTGTTGCGGTATGAGCCGACTTAAAATATTACTTAAAATTGAATTGCCTATGGTGTGGCCGGGTGTGCTAACCGCGCTGGTGTTGTGTTTCTCCCATGTGCTCGGCGAGTTTGGCGTCGTGTTGATGATGGGCGGCAATATCGCTGGCGAGACTAAGACAATTTCGATTTCTATCTACGACAGCGTGCAAGCCTTCGATTTTAATGCCGCAGGCACTATGTCATTAGTGTTATTGCTATTTGCGGTAACGGCTTTGGCGCTGACTACCAGTTTGTCACGGCGTTTAGGAGGTCAGCGTGGCGCAAATCATCGCTGATCTGCAGTGTCAGATCCAAAATCATAAACACATTAAACTCAACGCTGATTTTCGTTGTAAAGCTGGCGAAGTGCTCGCTGTGGTGGGGCCGTCTGGCGGTGGCAAAACGACCTTGCTACGGATGATCGCTGGGTTGAATCATCCCGATACGGGCCAAATCCATTTTGGCGAACGGCCTTGGTTTGATGATAAAGCGCGCATCGCCTTGAGCCCACAGCAACGTCATATCGGCTATATGCCGCAGCATTTTGGTTTATTTCCGAATCTCACCGCCTTAGAGAATGTAGTCGCTGGGCTTGATCATATCCCCAAATCTGAGCGTATTGTGCGCGCTAAAGATTGGCTTGAGCGAGTCAATCTACAAGGTTTACCTGATAGATTGCCGGCGCATTTATCTGGCGGCCAGCGCCAGCGCGTCGCACTCGCTCGAGCACTTGCCCGTGAACCTTCAGTGTTATTGCTCGATGAGCCTTTCTCGGCAGTTGATCGGGAAACTCGCGAGCGTTTATACCTCGAATTGGCGCGCCTCAAAGAGCAGTTACTCTGCCCAGTGATCATGGTCACCCATGACTTAAATGAAGCTTTGTTGCTGGCTGATTCGATGATTTTGATCAGCCAAGGGAAGATGCTACAGCAGGGCGCACCTTTCGAAGTGTTGTCGCGGCCGCGCAATGAAGCTGTGGCGCGGCAGATGGGGCTAAGGAATATTTTCGACGGTGAAGTAGTATTTCAAGACAGCACAAAAGACCTCACTTGGCTTAAGTTTGGCGAGCAGTTGATCGCCTGCGACTATGGCAAAGACCGCGCCGTTGGCAGCAAAGTGCGTTGGGTGATCCCCAACCAAGGCATTCGCTTTAACTCGATATCTAACGGACGTTTATGCCGCAGTTTTAATAAGTTAGATGTCACTATCGACGCTATGCTGGTGATGGGGGAATCGGTCCGGCTTATCTGCTATGTCACAGGTACTCAGTTACAACTTAACACCGAAGTATCGCTGCATTTTGCCCAAAAGCTTGCACTCACGAAAGGAATGCAAACCACAGTGGCACTCAAATCCGAACAAATTCATATTTTGGAATAATGTGATATCGCACTCTAATGACGTTTTTTACCCAGCAATCCAATCCAAAATGTTCCTTCCAACAGCATGCCTAAGATGATAAATGCCATGGCGCTTGTGGCGCTGCCATAGGCGTAGGCGCTTAATGCTGCGACGAGTAGCAAGCTCAAGCTTATCCATCGTGTGACTGGTTTCATATTCGCTCCGATTAAAAATGCCATTACTTGACGCTTGGTTATCAGACTGACTTTAGTGGAGTGGGGTCTATTTGCCAATCGTACTTTTTGACGACACAAATGCACTAAAATACCCGCTAAAATTCAATCAGCTTGATTTTAAATGGCTAAATTATGGGTTTTTACTGGAAATGTCATGCTTCAAAATCCCCAGATGATCAAAGCTTAATCTTGTTTCCATCGGTGTTTCAGCTGGTATTAGGTAGACTTAACACATTAGCCACAAAATGATTGAGTGTTTGGCTTAAAAGCGAATAACTTAGGTGATGTTGAAAGGGCATTTTCGTTCCATTTATGACCAAACCTGAGTTAAGGCTAACGCAGTGTAATGCTATATCCAGTGGTAGATTATGGGAGATTGGTTTGGAATCGCGCACAGGGACAATTGGTCCCATCGACTTCTAACTGATTGTCTCATAAGATACAAACTTAGCTGATTTATATGGAGGAGCCATGAGACTATGGTTGCTAGGATTCATGTTGGTATCTGGCGTCGCAACAGCGGCTCAGAGCATGTATTCGATGTTTGCGGGTGGAGACTATGTACCGCCGCTGAGCGTTATGCAGCAGATCGAAAAAGGGTTCTCCGGCGTGATTGCCGAATTTAATATGGAAGAGCAAGAGGGTGAATTAGTCTACCAATTTGAGTTGATTAATCCCTTAGCCAATTCTATAACGCGGTTTGAATATCGTGCCCGCGATGGTAAGTTGCTGAAACAAAAGGCGAGTAAAGTGAGCGCGGATGATCTCGGTGAGGTTGAGGCGACACGTTTGATCGCAGCAAAAGATCAAACATTTTCAGGCTTAATCGCTAAGGCCACTAAAGATCATAAAGCCTTCTTAACCGAGGCAAAGTTAGATCACGACTTAGGTATCAGTTATTTAGAGCTTAAGCTGCTAGACGATACGGGTAAGTTTAAATTGGCTTTTGATGTCGAAAATTTACGCCCGTTACCTTTACTTAAGTGGGATTGAAACATCTAAGTCAATCGAGTGCTCAAACGAGTATTTAGGATTGAGGAGCGCAACATACAGATGAAAATACTTTTAGTTGAAGATGATGCGACGACGATTGATTACATCGTCAAAGGATTTCTTGAGCAGGGCCACAATATCGAAACGGCGAGCGATGGCCACCAAGGTTTGTCACTCGCCACGAGTAGTCAGTACGATCTCGTGATCCTCGACCGCATGTTACCCCAGCTCGATGGCTTGAAATTATTGGCCGCCTTAAGGGCAACGGGCAATCAAACCCCAGTGCTGATTTTATCTGCCTTAGCGCACGTTGATGAGCGAGTGAAAGGCTTGCGCGCGGGCGGCGACGACTATATGACTAAGCCGTTTGCCTTCTCTGAGTTATTAGTACGCGCCGAAAAACTCATGCACCGTGGCCAATCTGTGCCAGTCACCACAGATCTTGTTGTCGGTGGCTTGAAGATGGAATTACTGACCCGTAATGTGACGTTAGACGGGCAAGAGTTGATGCTGCAGCCGAAAGAGTTCCAGTTACTTAAGTATCTAATGGAACATGCGAATCAAGTGATTAGCCGCACCTTGTTGTTCGAAGCGGTGTGGGATTATCACTTCGACCCACGCACGAACGTTATCGATGTGCATATCGCAAAATTACGCCGTAAATTTGAAGAGTTAGGCCATGGCGAGCTTATCGAAACCGTGAGGGGAGCGGGTTATCGCTTACGACAAAGGCATTAAGCCTTACCAAAGCAGCGCCTGGCGCATCACAATCGTCTTTTCGACCTTAGTGACGGTGATCATTGGGGCGCTGCTATTTGCCCTCTATCGACAGTTGATCATCGAGCAACAGTATCAAGTGACTCAATATTTAGAGTCAGAGACCCAACGTTATCAGCAACTTGCCCTAACGGTTGACCGCCGTAGTTTTGCCGCTCAAATCCGCGCCGCCGATCCACAAACCGCCCTAATCGCTTGGCGTAATAGCTACGATATGGTCGGTGCCTTGAGCTTTATGCCCGAAGGCATGCCCATGCTGCCGCAAACGCGAGATTTCCCCATCTTAACCGGTGGCCCAGATAAGCTGCATATTCTTACCGGCGGGCTAGTGATGACCCGCTACGGCCCAGTGTTGATTGCGACCCGTACCGATAACTTAGCGACGTTAATCGATAAGTTTATCAGCGCTGCGGCGACAGCCGTGATGCTTACGATAGTGTTGACCTTAGCGCTCGGCTATCTATTTTCTAAGGCGATTTTACGTCGATTAGTGCAATACAACCGCTTGAGTGAACAGATTGAACGCGGCCACTATGACACCCGCTTGCCCTTGAGTTGGCGTCAGGATGAGTTTGATATGTTGGCGCTACAGTTTAATAATGTGCTGGATATCCTCGAAAATAACCTGATGGCAGTGCGCGGCGCGACCGATAATATCGCCCATGATTTACGTACTCCACTGTCACATATCCGCATTGGTCTTGAAGAGCTGGCGAGTAAACCCAGCGATGAGATTAGCGAAGGTTGCGCGATTTTGACCGAAGAGCTCGACCATTGTTTAGCGACATTTGATGCCATGTTGTCACTCACGCGTATCGAAGAAGGTCAGCAGACGCTCGATTTACAGGAGTTAAGCCTAGCGCAACTGTGTACCGATTTGTTGGACATGGCCGATGCCGTGGCAGAATCCAATGAGCAAACCTTGAGTTTGTCGCTGTTGGCTGACCATAAGGTATTTGGTGATAAATACTTATTATTCCAAGCTTTATATAATCTCGTCGATAACGCGATGAAGTATTCTGGCCAAGGTGCGCGGATTGAAATTATTCAGTCAGGCCCACAAATTCAGATCCGCGATAATGGCCCAGGTATTCCCGACGACAGTAAAGAAAGAGTGTTCGAACGCTTAGTGCGCCTCGACCCCAGCCGGCATTTACAGGGTACAGGTTTAGGATTATCTATGGTCAAAGCGATTCTCTCACGGCACAATGCCAAAATTAATCTCACGGATAATCATCCCGGCTTAGTGGTCACTATTAAGTTTTAAGTCGATGATCCGCATTACAGCGTCGAGAACTCCATGTATCGAATCATAGCCGATGAGGCTGACTTCCTTGTTATCTCCAAATCAGCCAAAGTGCATTTTCACAGTCAGGACGGAACAGCGGGCGTGGTGGCGCAGGCTGAGCAAGATTTAGGGATTAAGCTTTTTGCCGTGCATAGATTAGACACGCCCACGTCCGGTCTGTTGATTCTGGCCAAAAGCGCCGTCGCTGCCAAGCAATTTACAGAGCTATTTACCGCCCATAAAGTGCAGAAATATTACCTCGCCCTCGCGAAAGGTAAGCCGAAGAAGAAGCAAGGCTGGGTGATTGGTGACATGGCAAAGTCGCGTCGTAGTATGTTTAAACTGCTGCGCACTAAAGAAAATCCAGCGATTACGCAGTTTTTTTCATTAAGTGTCAGTGAGGGATTGCGTTTATATTTGCTTAAACCCCATTCGGGTAAAACCCACCAATTAAGAGTCGCCTTGGCAAGCTTAGGTGTGCCGATTTTAGGCGATGATTTATACGGTGGCGCTGTGGCGGATCGTTGCTATCTACATGCCTACTGTCTGCATTTTCGCTATGGCGATGAAGCGACTGGCTGGCGTGATTACGCGTATAGGGATGTGCCGACTCAAGGTGAACATTTTGCCGCTGAAGGTGTGATTGAGGCGCTGGTTGAATGGCGTGAACCCGACACTTTGGCTTGGCCGGCGAAAGGGGATTAATACTAGTCGCGTTATTTTTGCGCGAGGCTAGCAAGATTGACAAATACGATCTCGGCCTTCAGATTTTGCGCGGTACATGGCTTGGTCCGCTTGAATGAGTAGCTTGTCGGGCTGGCATCCTTGATAGATAGTGGCGTAACCTAAACTGGCATGGATTTTTATCGTAACACCTTGACAGACAAATGAATTGTCCTTAAGTACTTGATGCAATTTCTCGGCAACGGCTCTGGCGGCGGGTGCATCGGTCGTGGGCAAAATCACCAGAAACTCCTCACCACCGTGACGACATAGGGCATCATTTTCTCGCAATACTGATCCAAACAGTCTCGCCGCCGTCAGCAGGGCGAGATCACCCGCATCGTGACCGTATTGATCGTTAATCTCTTTAAAATGGTCAAGATCCAATAAAATCATACTGTAAGGCACATTATCCCTTAGCCAGCGTTGATGGATATTGTTGAGAAATTTATGCATAGCCCGGCGATTCCACAGGCCTGTGAGTTGATCCCGTTCCGCTAACATACGGATTTTGCTGACGAGTCGCGTCATGGCATTACCTATCATCACTATATTGATCAATAGGGTTAGAAATACATAAAACCACAGCACAGGTATCGCTTCGTGCGTGTGCATGGCGATAAACATCGGACTTTCTTCTGGGGAAAGCAGGGCGATAAAGATGCGAATGGTAAAAATACCCACCATAGCGACTAAAGGCATGACCATAGCGATGGCGACTCGATTCCCCGTATCGCGTTTAATGGCCTGATAGTTATCCCGCGTTAGCATGGTAAAAATCAGCGCGGCGCAGGCGGAGAAAACAACGGCTAAAGTACGTTCGGAGCTGAAGTTGGGGGGAACCGAGAGCATCACACTGGCCGTGATTGCCAAGAGAATAAGATCGGATTTAACGCTGGGACTCAGCCTGAAAAGTGCCTTAGTACCCCAGCGTAATAGGATGAATCCAAACAAAATCGCCATATCGGAGCAGAACCAAAAAAGGTAACTGGAGGCTTCTGAGCGTTGGCTGTTGAGTATGATGCCAAGGAGGACGAACAAGTTCGCCAGAGAAAAGCGCATACTGGCCCGAGTGGCAATCTGTAAGGGTTTAGCAATGAGTCCCCATGCCACGGCTGCAGTGCTGGCCAGTAGGCAAATAAATTTGATGAGTAATAGGGCTTGGGGATCAAATATCATTCTTTTACTTCATATTTTTCTTTAACTATAGATGCTTTTAGGCATTTTCGGTTGAAGTTTATAGGATATCTTAATCTGTATTTATTGGCAGGAGGCGGGCTGTATCCTCTTTTGAATTTATGGCGATATTAAGTGCCTTTATTTGGAGAATAACTTGGGTTATTTGCTGTCTTGTAAGCTAAAATGTTTGCAAATTCTGAGTCTTCCTGCGAAGTTAGACCCTGTTGATTCGCCTACAATCTCCGTTTTAAGGATGCCCTGTTATGGATACAAATAAATTACTTTTAGTCATTATTGCGATTCTATTGCCTCCTGTTGCCGTATTTTTGAAGGCTGGAGCGGGTAAAGATCTGCTGATAAACATAGTGTTGTGTTTACTGTTCTGGTTCCCCGGTTTATTACATGCTCTCTGGGTCGTGACCAAGGCATAAATTGCTTTCTTCCACACAATAAAAAGCGCAATTTAATTGCGCTTTTTATGCTTCAAAATGAGCCTTTCACTTAGAAAGGCAAGGTGTAGCCTATTGTCGCAGTACGTCCCATACCTTTGAAATAACGATCATTTGTACCCATGGTTTGTGAGTAATAATTGAAATAATCCTTATTAAACAGGTTTTGCAGACCTAAGCTCAGGGTGCCAGTGTAAAGCGGCATAGAGAAGGAAGCGTCGACTGTGGTATAGCCATCAAACTCAGCGTACTTGTTGCCCGCAGCATTTTTAAAGTCGCGGTCCATGTAGTAGTTGGCTTGAACGCGGGTCGACATGTCATTATCGAAGTTATGGGTCCAGAACAGGTTGATACGGTTGGGCGAAATATTTGCGCCATCGAGATCGGTATCGACTTTGTTATCACCATTTGAATCGTATTCGCCCTGTTGAATGGCGATGTTCATGCCAAGATCATCATTGCTGGTGAGGTAGGCGGTAACATTGGCTTCGATACCCTCGATGACACTTTTTTCCCGTTTCACGTCGTAGAAACCATCGCCATTTAAGGCTAAACGTGAGCCAAAATCAGTGGCCGAACGATAATACGCAAACTTAGCGCTTAAGTATTTACCTTGATAATCGGCACCGACTTCGACGTTATCTGTGACGATCGGCGTGAGCGCCAATGAGTCATCTATGCTGGGATTGTCGCCGGGGAAGCTTTTACCATCACGCAAAATACGGCCGATATCTGGCATACCAAAACCTTGGTTATAGCTGGTATAAACGCGAATGTCTGGGGTGATTTTGTAGGAGATACCGACGTTGAATAAGGTTTCATCGAAGTTGGCTGAACCGCCTGCAATCTGCTTGTTGCCCGCACCCCATAGGGTTTTGTAGTCATCAACGCTCAACTCGGCATGTTCGTAACGAACACCGGTCGATAAGGTTAGATTTTCAATTAAATCATAATCTAACTGCAAGTATGGCGCGACGTTGTCATAGGTGCTTTCCGGAACCCACGAAAAACCCGTTTTCACTAAATCTTGCTCTGTGGTATCGCGGAAAAGGTCGACACCGTAGGCCGCATCAATCCCTGAACCCGCAATATCCTTAGCGATCAAGGAGCTTTTCAACCCCCATTTAACTGAAGCGTTACGCGATTGCTCGTAGTACAAGCTTTCGCCTTTGCTGCCGACACCGCACTGGATCACTTGGTCGCTACCTTCGAAGGATGGATCGTAGAAACTGTCGAAACAGCCGCCACCATAAACCGCTTTAAAATCTTGATTAAAGAGTTGCAGGTTAAGTTGTTGGCCACCAATATTGGCGTGGGTATAAGTCAAACTGGTGGTGGTCACTTGGTTGTTAGCCGCTTCCCATGGCTGGGTTTCTTTAACTGCGCCCGTGGGCACGCCATTGGGTTTGTCGCCAGCAACGGGCATCCAATCGCCATTGTTGTCTGCGGTGTAATGGTTAACCATTAGTTCAAGGCGCGACTCGTCGAAGTTGTGGCCAAGCTTAATGAAGAAGTCGGTGCTTTGGCTGTCCATCGACTCGCCTTGAGTCGTATCCACACCTACGACGTTATGGTTGGCATCATAATAGACGCCATTATTGCGGTAGCTAACCGAACCTATCATGTCGATAAATTCTGACTCGCCAGAAAACGCATAGCTGGCACCAAAGCTTAAACCGTTAGATTTCATCGAATCAGGAACTGTCACATCGAAACTGGCAACCTGTTCGCTGTCGCCCGTCGGTTTTTTAGTGATGTAATTGATGATGCCGCCTTGGGCACCAAGACCGTGCATTGCATTGGCACCGTGAATGATTTCGATACGCTCAATCATGGCTGGATCTATGGTTTGACCCGAGCGACCCCCACTGCGAAGTGGGTTTGATTGCGGTACGCCGTCGATCATGATCAGCGGCGGGCGACCACGCAGGGTTTCGCCTGTGTTACTCATCTTCTGGCGGCTTGGCGAAAAGCTCGGGGCCAAGTTACCGATAATGGTCGACAAATCTTTAGTGGTGCGGAACTGTTCTTCTAACTGGGCGCGGTCGATGATAGTGACGGTATTGGGGATAGAGCTTGGGGATTTATCCATCCGACTCGCGGTGACAGTGATTTTTTCTATGTCTTTATTTTTGTTGTTTGAACTGCTATCGGGGGCATCTTGAGCCGCTGCGGATTGGCTTAGCGCTGCAAATACGGCTAAAGCAACAAACGAGGTTTTGAATGTCATATAACTTCCTGCTAAGGGCGAATGAGTTTGCTACACCGGCTCCTTTGGCATAAGTCCATATTCCATTAAACGGCATTACAAATGATAACGATTTTTATTCTGTCGCGATTTTAGGGGAGGAATAACGGCTTGTCTAGTCGTGCCATACAGGAAATTGCACGATTTGGCGGCAGTATTGATAAATTGCGACACAGCACTCGGTATAGAGATTTAACCCCAAGGGTTACCTAGTGCGAGTTCATGTTGTTAAGGCAGTGCCTTATTTAGCGTTTAGCATGGCTTTCGCGATCTGATGGTTGGATCGCAGGCTTGATGACTTGGCTTGATGACTTGGCTTGATAGCTTGGGGATTACTAACTGAGCTAGCATTAGTCGCGGGTTGGGCGATCTGACTTAGTGCTTTCGATTGTCTGGCGTGCAGGTGGAAGGCTATAATCGCGCCCCTTTATCCCGTATCCGCATTGGTGCCCTGATGAACCGTAAAAAGAAGATAAACCAAACCTTGAAGAGCAAGGCGAAAAAGGCCAATGCCAAACTGCAATCGAGCAATAAACCTAAATACATTGCGAAGGCGGATAGGGAATCATTAGCTGCAGAGGCCGATACCGAGTCGCATGCTGTGGCAGTTAACTCTGTGACTCTTGATAGTGAGTCTGCTGAATCATAAGTCTCCGTTCATTTCTTTGTATAATGTGACGGCCATGCTGGCATTCATTGAGTCTGCCGACATGGCGAGTTGCTGAATCCTAATTTGTAGTTGATATTGTCGCGAAGCCGCTATGTTACTCATCACCTAAGTCTTAGTGTGTTTCTAAGTAGCTGATGGGATTCTGATAAAAGGCCTTTTTATATCCTTGGGTTGAAGTATCTATCAGCCAAGGTTTAGGCAATAAGCTCAAAATGGAATAATGCAAATGTGGCGGTTTACCTTGAGCATTGCCCGTATCGCCGACAGTACCCAGCGTTTCACCTTGGTGGGCAATTAATCCCGTGGCTGATTCAATTTGTGCTAAATGGGCAAAGTAATGAATTTGCCATTTAGGACCTAGACCAACGACCACATTGCCTCCTTTAAAGAAGTCGCCTCGATAGAAGATCAGCATAGGCGTTGGCGCAATCACGGCGGTGTTTTTGGCGGCGAAAATATCTATCCCCTTGTGAACGCCCGATGGCCCCCAAGGTTCATACCAAAAGGTTTGGCTGTTCCAATCTTTACTGCTGGCGCCTTTGACTGGGATCACCGGTGACTCAGGGATCAGATAGCCGCCAAGTAGCAGTAACGATAGTGCTAATCCAACATATGTTAACTTCATTTTTTATCCTGCATTAAGCCTTAGTGCTTCTCTCAAGCTCTAATGAAAATCACTGAATTCCTGCCAATTTACGGGGTACTGCAAGCGCTCAGCTGCTGAAAGTGACAGCCAATAGGGGCGCCAAAAATAGTTGCACCAAAAATCGATATTCCCTTGAAGACTGCCGAGTGTATCGGGATCGACATGGGGAAAGCTTTCCCAAGGGTGAGGTAAAGTCGATTTAAACGCAATCAGTTGCTCTAATAGCGCTTTGTCGTTACTGATCAAGTTGACGCAGATCCCTGCAAAATCGGCATATTGGTTGGGCTGCAAGCTGGTTGCTCGGCTCCGATGGGTTATCAGGCCATTAAAGCTGGCGGCATGTTCGGGCAACACTGCAAAACCTGCATTGCCAATCAACGTATCTAAGGTTTGCTTGCCTTCTAAGGTCAGTAAGCCGTGAACTTGATAGGCATAAATATCGGCCTGCAATATTGGTGTCACTAGCGGATAGGCTTGGTTTGTCGGCAAGTCGATGTCGGGCATTTGCGCTGGATTAAAAATGGCGGCTAAGTCAGCTTGTCGCTCTAGTGTGCTGTTGCCATGAAAGGCGCTATGAAGATGTTTTTTTAGCCAAGTGAGCACAGACATGAGCGCGACCATATAAGGTGAATGTTAATTGATCATGGCGTTTCTAAAGATTCAAATCACCAATCTACTTCTGGCACTCGCGATTTTACTCATCCGCATACGGCCAGTCTGAAGTATTAATACCGTTAATTCAGTTGGATATTAAAAATGACAAGGCAAGCGATTTTATGCCCGTAACTAAGCTGTCGTCTAGGATTTAGCGTTAGCAGAAGGAAGCTTAATACTTAAGTTGTGAGCTTTTTAGCGCCGACAAATGGGAATCTGTCGAATGGTAAACCGCAGTGGCTCTCTTTTGTTGGAGAGCCACTGATTCAACGTTTTTAAGCGTACTGATAAAGGTTTAGATACACGTGCTGATAAGAGTTCAGCTTAAAACTTATCTGAACTCATGCGCGCCGGAATGATTTCAAACTCGGTTCTGTCTTTGGGTAAATAGGCTTTCCACCAAGCGTGATTCAAAGCTGGGCTATGGGCTAATTCACTCAGGTTGATCCCTTGACCAAATTCCGGCGTAAGCAGCGGCACGTCATAGGCCTTGGCGGCTACGGCTATGCGTTCCAATGGCTCGTACCAACTATGTAGCGCGAGATCGAAGCTGCTGTTATGTATCGGAATTAAGTGTCGTCCCCGCAGATCTAAATGCGCCCGCAGTGATTGCTCTGGCTGCATGTGGATATCTGCCCACATCTCATCGTAGGCACCTGTTTCGATTAAGGTTAAATCGAAAGGCCCAAGTCTGTCGCCAATCTTTTTAAAGCCGTCGAAGTAACCGGAATCACCGCTGAAAAACAGTTTTAGTTCAGGGGACTGAAGAACCCAAGATGCCCACAGGGTTTGGTTTTTGTCCGTGATACCACGGCCCGAAAAGTGCTGTGCCGGCGTCGCGGTAAAACTCAGACCCGCATGTTGGGTTTCTTGCCACCAATCGAGCGCGACTATCTTGCTGGCAGGCACGCCCCACGCGAGTAAATGTTTATCTACGCCCAGTGGCACGATAAATTTTTGCGTGCGCTCGGCGAGGGCTAAAATGCTGTATTTATCTAAATGATCATAATGATCGTGGGACAAAATAATCCCTGCGAGCACTGGAATATTATCGAGTTCGATCGGCAACTCGTGGAATCGTTTTGGCCCAAACATTTGGCTTGGCGAGGCGCGTTTCGAAAACACAGGATCGGTTAACCAATATTGACCGTTGAGTTGTAACAGCACTGCCGAATGGCTGATTTTATACAACATAGGTGTCACATCGGCCGTTGGGTTTAGCTGCATTTTTTGGATCGGTAATGGCTGGTTTGGGCTCTTATCTGGAATACTTTCAGTCATGTAACGCCATAAAATAGAAAGTAAATTACCTTCACCACTGTGGGTATTGGGAGCTTCATTGCTGAATTTGTCACCCTCAACATGCAGGGCCGGAGCATACTTTGTTAAATTGATTTAACATCTGCAAAAATCATCAATTCCCGATATTCATCACTGAGTAAAGAACGATGTAACTTACTCTTCATACTGCGTTTTTGTGTTATTTCGAGTTTTGCCAGATTCAGGGCCATTTGCCTCACTATCGCCAGTGATTCGGCAGTTCTTTGCTCGTGAACCCGACAAGCATCTTCTCGATAAACCACATCCAGGACCCAGTGCAGACTATTCTCAATTCGCCAGTGCCCTCGGACAGCAGTCGCAATATATTCGGGATCGATATCATGGGATGAGAGATAGAAACTGGTCTCTATGCTGGTCTTATTGCCTATTTTTCTGTGTCGCTCAACGGCTATTAATGTCTTAACTGAAGGCCACTTTTCTTGCAATTTAGGACTCAGCTTCGATGGGATTTGAAATGTAATCCGCTGCTCTGTTCTTCCATGCCCTTTTTCCGAACAGGCAAATTGGGCTAACTGACTGTCATCTTGGAAGGCCACATCATACTGCGCTTTAACTGCTTGAAATAAAGTCCGTTGATTACTTTTCACCTGCACCACATAATCTCCTTTACGAGCGACTATCGCTTCCAGCGTCGCGCGTTGGGCATGCAAAGCATCCATGGTTATCAATGCTTTATTTATGTTAAGCATGGTAATAAGTGATTGTACCGTTTCAATTTCCTTGCCTTTTCCAGAGGCCATTTCTTGATAAAGCGCCAAGCCATTATTAATGTCGAACGCACCCACGGAATGTAAGGTGTTGCACCCTAACTTAGAGGCACCTCTGAGTGTTTTACCATCGATAGCAATGATGGGTTTAGCTGATGCGGTGCGGATGTCGTTAATCCAACTGAATAGGCACAACTGCAAATTCTCCGGCCCTACGGCTTTTAGGATACGTGCTATGGTATGCCGACGGGGAATGCCATTAGCAAATGGACGAAACTTACGTAGCCACTCAAGTTTTAGCTCGCCGAACTGTTGAATTTCTGCCCAACCATCTTGCCCACTGGCTACTGCCGATAAGATTAAGAACAGTACGTCGACAAGGTTGTGGCGTTGATTAATGTGTGAGCGCGTGTCTTCGACTAAAGATAGGTGATCAAAAAGTGACATGGAGAGTGGCCAAGTAATTAATTTCTCATTCAGATCATGGTATTGCGCAATAGTTCATTTTTTAAAAAAGTGCGATCTCGCCCTGCCTCAACATGGGCCGAAGTCTTCGTGTTTACGTCAGTATTTGTTTGAGTGGCCATGATAAAGCTCCCGAGTCGTAGTAAGGTCAAGACAATCTTAAATCGCGTTTACAATATCAAAAAAAAGTAAACTACACAGTGTAGTTTACATGCGGCGGTGTAAAAGTAAACTGGATAGTGTAAAATGTCGACAAATGATTTTTATGGCGTAGGAAAACATGGCAGACAAACCCATTAAATTGACAAATTCTCAGCAGAAACGTGTGGCGATTCTGCGTGCCTCGCGGGAAGAGTTTATGGTCAATGGCTTTGTGGGTGCCAGTATGGACAGGATCGCCCTGCGGGCTAATGTCTCTAAACGTACTGTGTATAACCATTTTCCGAGCAAGGAAGTGCTGTTTGATACCACAACCACAGAGTTGTGGGCGCGGACGAAAGAAGCGGCGACCTTGGTTTACAATCCGGCGAAGAGTCTTGAAGATCAGTTGCAGCAAATTGCGCATCGATGCTGGGCTTTGTATCAGCAGCCTGAGTTTCTCGATGTGGCGCGGGTGGTGATGGCAGAGTTTATTCGCTCGCCTGTCCAAGCGACTGAAGCGATGGAGAAGCTCGCGAAGCAAGAAGGTGGACTCGAAGCTTGGCTTGCGGGAGCGGTAGAACACAAGGCGCTGAATATCGAAGATATTGTAATGGCATCGACTCAGTTTTGGGGCATGTTTAAGGCGTTTGCATTCTGGCCTAAGTTGTTCCATATGAAAAATAATGATGAGCACCAGCAAGCGATTATCGACGCCAATATCAAGATGTTTTTAGCCATGTATCGCGCTTAAACTTAGCGTTCCATGCTATTCAACATAAAGGCCGCATCTGCGGCCTTTATTATTTATGTAATACAGACTAGAGCAAGGCTGTCGCCAGTAGATAACCGACGGTCGATGCGGTACCCACGCCGATAAAACCTGGAATAATAAAGCTGTGGTTAATAATGAACTTACCTATTTTAGTTGTGCCTGAACGGTCAAAGCCGATACAGGCCAGATCGCTTGGATAGGTAGGCAGTACAAAATACCCGTAGCTTGCTGGAAGGAAAGCGATCAAGAGTTTTGGATCTACCCCTAAGGCGAGGCCCATAGGCGCAATGGCAGTCAGCGCCGCCGCTTGGCTATTAACAAGCTTAGAGATAAGGAACAGCACTAAGGCATAGGTCCAAGGCTGGCTTTGCACCACGTGGGACAGGTTTTCCTTAAGCACATCCATATGGCTGATGAAGAAAGTATCACTCATCCACGCCACACCAAATACTGAGAAAATCGCCACCATGCCCGCTTTAAAGACGGGGCCATTAGAGATGTTTGTCGGCTTGACTTTGCAGGTCATCAGGATAAATGCCCCCGCAATTAACATCATCATCTGGATGACTAAGTTCATCGATAATAAGGTCGTTTTCCCTTTAACTTCAAAGGCTGGGCGCAGTTCACTAAATGAACCAAGCAGCACCACAGCTGCGATAGCCGCAAAGAAAATCCCCGTTGCCCAGTAAGCTTCCTTAGGAAAGCGTTGGTTTAGCAGGGTTTCACTCTTGTCATAGATAAAAGCTCTTTGGACAGGATCTTGAATCCGTTCTTGGAACTCTTCGTCTTTATCTAAATCTTTACCGCGACGTAGGCTCCAAAGTGCTGCGGCCATGACGCCGCAAAGCGATGCCGGTACTGATACCATTAAAATTTCGAGCAGATTGTAGGCGCGGCCCACACCTTGCTGCGATGCGAGAATCGACACCATAGACACAACCGCTACCGACACGGGCGAAGCACAAATCGCCATTTGCGAAGCGACGGATGCCACCGCCATTGGGCGTTCTGGGCGAATATTTTTCTTCAGCGCGATGTCGGAAATAATCGGGAACATGGTGTAAACCACATGGCCTGTACCGCAAAGGAAGGTCAGCGTCCAAGTGGTCAGCGGTGCCAGAATGGTGATGTATTGTGGATGGCGGCGCAATAGGCGCTCGGCAAACTGCATCATCACATCCAAACCGCCTGCGGTTTGTAATACGGCCGCACAGCCGATCACCGCAAGAATCGTTAGCATAACTTGTACTGGCGGTTGTCCCGGCGCTAAACCAAAGACAAATATCAGTAAAAACAAACCTATGCCGCTGATGAGGCCGAGTCCCATACCGCCGTAGCGGGTACCGAGTAGTAGGCAACTAAGCACTACTATAAATTCCATTAAAATCATCCGATTTACCTCTGCGGAATCTACCGCTTAGCGATTTGTTGAGTGATGAGTGTTTTCAGGGACATTTTGCGGAGGAAAAGCGTTTGAAAAATTGCGCTGGCGCATGGTGTAAATAAAAAATGGCATGAGATTTTTCGGAAATTTGACCTAGCGCTAGAAACACGTGGCGATAAGTTGCATTCTGGTTAACGAGTTTTATGGATTGAGCTAAGACTTTGAGCTCGCTCCGTTTACAAGAACGATAACAATACTCAAGGATAAGCCCGTGAAACCAGCCACCTTTAATACCGAAGCCTTCGATGATTGGATCCGCAGTCGATTTGTCGAACTCAACTCAGAGCTCGAACAGCTTTACTACCAACAAGCGGATCGGGCCAATGTGCTCGATATTGGCGCAGAAGCTAAGTTGGCGCTGGAGAGCGAAGGGCGAGAGTTGATTAAGGCCTTGCTCGATGAGGGCAATACCGACGAAGGGTTCGACAGTGCTTTTGATTTACTCGGCAACGTGGGCCTGTATATGGCGGCCTGTCGGCGCCATGAAATTACCGAGCCTTCGCGGGAAACCACATCGCCATTAGCCGAAGCCTCCGCGCTTGCCATGCACATCGGTGCTTCGATAGGTGTCACGCCGCGCTTTGCTACCGCACATTTAACCACGCACAATCGGGCCCACAATGGCATTTATAAGCGCTTTACCGATTTGCCCGCTGAAAAGTTGTTTGTGGATTACAACACTAAAGGCATTTTGGCCTATAAACGCGCCGCAGATGCGTTATTGAAGATCCAGCCTTTGGGTATTTCCCATCCCATCAGTCACGATTTACTGCGAGTCGCTAAACAGGCATTGCAGGATGTGATTGAGTCGAATACTCAATTATTCAATCGATTAGATGCGGATCGTTTCTTTTACTGTGTTCGCCCTTACTACAAACCGTATCGGGTGGGCTCGGTCATTTATCGCGGCGCTAACGCCGGGGATTTTGCGGGGATTAATGTAATCGATTTAACGCTAGGTCTTTGTTTCGCCAATGAAGCTTCATATTCGCAAATGCTGGTGGACAAGTTTTTGTACATGATGCCAGAGGATCAGCAGATATTACGTGAGTGCATGCGTCGACCGAATTTGATGGATGATTTCCTGCAGGCAAAAGACAGTGCAAACCAAACTTGGTATCAAGAAAATTTGCGACTCTTTATCGAAGTCTGCGAATTACATGGCGAGACTGCGATTCAGCACCATAATGAGTTAGTGACTAAATATATTGCCGAGCCTTCGACAAGTATGGAGCAGCAGCATTTAGCTAAGGTTACCGCCAGTGGTCCGCCACTGCATGTGCTACTCGGTTCATTGGAGCGACTCAGAGATAGGCGCGCCGCCGCTCAGCGCAGCGATATTCGGACGCGTTATTATGAGATTAAAAAATTAAAAGAGAGTTTGAGATAACACTATGTTTGAAGACTTTAAAAAGGACTTTTACCTCGCGGGTCCAGGATATCTATTGAACCATTCCGTCGGTCGACCGTTAAAATCGACCGAACAGGTCTTCAAAGAAGCCTTTTTTGCGCCTTGGCAGGAATCGGGGCGCGAGCCTTGGGGTCAGTGGCTGAGTGTGATTGATGATTTTACCTTTGCCTTATCTAAACTGTTTAACGGCCACCAAAAGGACTTTTGTCCTCAGGTGAATCTCTCCAGTGCACTGACTAAAATCGTTATGTCACTCGACCGATTAAATCGCGAACACGCCGTTGTGTTGATGAGTGAGATTGATTTCCCCAGCATGGGTTTCGCCCTTAAAAAAGCGCTGCCAGCTAGCTGCGAAGTGCGTTTTATTCCTAAGGGACTTGATGTCACTGACCCTAATGTGTGGGACGCACATATCAGTCGCGATGTGGATCTGGTGTTTGTCAGCCATGCTTACTCGAATACGGGGCAGCAGGCGCCATTAGCCAATATTTTCACCATAGCCCGTGAGCGAGGCTGTTTGAGCTTAGTGGATGTAGCGCAATCGGCGGGGATTATTCCGCTAGATCTCGCGCGCCTACAACCTGATTTTTTAATCGGGTCGAGTGTGAAATGGCTCTGCGGCGGCCCTGGTGCCGCCTATCTTTGGGTTAATCCCGCCATATTGGATAGTTGTCAGCCAAAGGATGTGGGGTGGTTTAGCCACGAGAATCCGTTCGAGTTCGATATCCATGATTTTCGTTACCAGCATTCCGCAATGCGCTTCTGGGGTGGAACGCCTTCAATCGCCCCCTATGCCATTGCCGCCCACAGCATTCATTACCTTGCAGGTCTAGGCATCCCCTTGCTGCGTAAATGTAATCAGCAGTTAATTGATGCCGTGGCGCAAGAGCTCGAACATGAGTTTGTGTCGCCACGGGATGTTGAAAAGCGCAGCGGTACGCTGATTTTGCAGTTTGGTGAGCAACAACATCAAATCATGACGGCATTAGCGAACGCTAACATCAGCGTCGATGCTCGCAGCATGGGGATCCGGATTTCTCCGCATATTTATAACAATATGGCCGATATCAAGCTGTTACTGGACGTGATAAAAGCCAATCGGTAGCCAATTGATAATAGCTCTGCGGATAAATAGCTATGCTCAAACCTCAGTGTTTGGGCATTTTTTTAGAGTCGTTTACGATTTAGGCGCTCGATGATCTGACAGGCGTTCAAGTCGTGTACCACGTTAATGACCGTCGATGGCGCATCGGTAATCGCCCCTATCACCACTAAAATCGGGATGGTTTCCATTGGCAAGCCTAAGGTCGTGACGATAAAAATCTCTCCGAGGAAAGCGCCGCCCGGCACGCCGCCTATGATAAAGGCCGATAGAACCGAGATCAGTATGGTCACCATAAACACATCGACGGTGAAATCTAATCCGAGTAACGAGTAGATAAACACGATTTTTAGGGCTGTGATGATTGAGGCGCCGCCCTTATTTAAATTGACCAATAATGGCAGGCTGATGTCGGCGATTTCGGGTTTAATGCCCATCTTCAGCGCGGTTCTCAAGGTGACCGGTAGGGTGGCGAGTGATGATGTGGTGCCCAGTGCAGTCACTGCAGGTTCGAGGGCGAAGCGCCAGAATTGTTTAACCCCTTGAGTGCCACCGCCAAGCCAAGCGTAAAAGGTAGAACCCACGGTTAGATAAATAAAACAGGCGACCATAAACAGGCCAACTGCGCGGGCAAAGGTGATAACAAGCTGCGGATCTTGGCTTGCCATTGTAGAGGCGAAATAGGCACCGAGTCCCACGGGCGCTGCCACCATAATGATCGAGACTATCTTCATGATGACGGTATTAAAACTGTCGAGTAATGCCGAGACTTTTCTGCCATCTTCCCCCGATTGACCGATCGCAATCCCCGCAATAACCGACATGATGATCAGGGCTAAGATATTGGATTTTGATAATAAACCTGAAAAGTCATTGGTGGTCAGCAGGCCGACAAAATCCATCGTCCCCGCAGTATCGCTGATGGTTTGATTGAGATCTAAGGTAACACCTTGGGCGGGGTCGAAAAATAAGGCCAGCCCGATAATGCCCACGGCGGGGATAATCGCCATCACAATCGACACCGCCATGATTGACACTAACAGAGCGCCGAGTTTCTTCAAATCCGTCATGCGAGCAATGGAGGAGGTCACGCTAATGGCGACCAGTGGTACGATAATCATAAACAACAGGTTTAGAAAAATTTGCCCTAAAGGTTTGAACTTGAGCGCCAGTTCTGGCAAACCGACACCGAGCAGGCCGCCCAACAATAACGCGGTGAGCAGCACGATGGAATCTTTATAAGGTTTTATCTTTTGCCACATAGGTAAACGCCTATTTTTATGAATAAGAGCGTCCAAGTGATCAGGATTAATCGTGCTGCATGGACATTCTCAAGCTAGCGAGTCGTGTGCTATAGCGCAAGAACTGATTGGAAATGAATAGCTTTAATCTGCGAATAGGTTCAATTACGGGATTTGGGGCGAGTTGAAGGGCTCTGGCTAACGAAGCAATGCACTATCTTCTAAATGATCGCGAGGCTGTTAATAGAACAAGTGCAGCGTATCGATGACTGAGGAAATTTGTAAGGTGGTGGCTCAAGCTTAATCTGTCAAACGTCTGCTAAATTTCTGCAATTTATTGACAACACCTTAAATACCTACGGCATGCGACTTTTAGCACTCCGCTAAATGTGTTTAGTCATAGTGCGAAAAGTGTTGTAGGTTATCTTGTGTTTCATGGCTGGTTTGAAATCGTTAACGTTGGATCGTTATTTAGTGAAATGAGTCGTTAAATGGAGTATTTATTGTTTTAATTTAAACGGTTGAAAGTGAATCGATATATTTAACTAAATTGTATCAATGGTTTGAAAGTGAATGGGATGAAGTTGAACCTTTAGCAGCCACAAAGGATGGTCAAGTAATCCCAAATCCAATTGTTGCCTTGAAAGAGGGAGAGTTGGTTGGTGGTTAAGTCTTTACGCGTTTTTTGTCTCCAATGACTCAGAGGCAAGCTGTTTGGATTAATGCAGTTTTTGTTAATCTTAAAAATCGTAAACAGGGTATTAGTGCTCAATTGATCAATCATGCTGAAAAAACGTTAAAGAAATGTTTGAACCAGAATTATTGGTTTTTACTCATATACCGGTATTGTATTCAAATCTAGAATGGAAAGTCATTGCAACGCATGATGATCATTTCGTACTTAACAGTTCATTAGTGCAATCAGCATAAACACAGCAACGGCATCAAGATGATTTACTAAATTTGGCATTCTAGGTTTAGCTTTCTCTCGTGTGTAATACAGTAAATTTCAGCGTTTACTGCATACAGTAAAAGACAATGCCGATAGTTGACCTAAGGTTTTAGCTCCTTACCTTGTAAACGCTCAAGCACGTTTATGTTCAAAAGTGTATTCGTTTTTAACGTCTAAAAACCACTAAAGCTAAGTTGAAGGGAGGCTTAGTGAAGGGGCTGTGTGGCCTAAACTATTCGTGGTATGGGATTCTAAAGTCACGTAACGTTCACATCAGTCTGCTAAATTGACACGCTAGTCGTCCGCTCAGTTCATCGCCCATGTGCATAACTTTGACGGCAACACTAAATAACATTGTTTTATTTTTTAGTGGCTTAGCTAATTTTCTATCGACATGTTCTAGTTGCCAATATTAATGTACAGATAAAAATTAAAAATCAAGGATGGTGTCACAAGGTTTTACACTTGTGGCTAAGCGTGCATAAGGAGAATGCTCTGCACTCTAATAACAGCGTGTAATTGCGGCGTATCTCACCTGACTGTTTGTCGTAAGCATGTCAGTAAGTGATAGATGAATAGCGACAACCCTTCCAATAGATTGTTTTCATTTTTGCATTGTTATTGAAATACCACTTTTCGAATATTTTTTCGTTATAGGCTACGTATGAAAAAATCATTATTATCTTTGGCTATTATCGTTATTTTGTCAGTGGTGGGTATTCAGCTATTTATCATTCCGCTGTTTGAAAATAAAGTCAGGCATCCGACCGAGAAGTGGGCGCTGGAGTTGTTGACATCAGGTACGGCCAATATGCTTGCAGTGCAATTACAGCGGTTGCCGAAGCAAGACAGAGCTGCCTATTTACAGAATATGCAGGCTAATTTTGGCTATGAACTTGCAGTGCAAGACTATCAGCGGGATGACTTTTCAATTGTGCAGCAGCAATGGATTGATCAGAATAACGTCGTTGGCGAGCCGTTAACGGATTTAGTCTACCGAAGAATCGACGCCGATCAGCTGTTGGTGGTAATCAAACCTAATACCGTACCTAAATATTTAGTCAATGATGCTCAGCGTTGGAGTATGGGCGTATTTTATCTTATTACTCATGCGTTAAATCAGCAGCCAGAAAATAACTGGTACTCGATAATTGCTGTCATCAATGAGGATTTTGCCTACCCAGTGACATTGCAGTCACTTGGCGAGATACACGAAAATGATGAGTAGATGAGTGACTTATATGCAGGACGCATTATTGCGCTGTCTACTGCGGACTCTGATGCGATTGGCTACCCAGCAGACAGGGTGATGCAACGTATTGGCAGCAGCGACAAGGTGATTGTTCTAGGACCATTCAGCACTGAGTTAAAGCCTAAGGTGCAGTACATGTTAAGTATCTTTTATTTGATATTTGGCTTGTCGCTGTTAGTGCCGATTATCGTGTGGCTGATACCTGCGTGGCGCTCTATGTTAAGCCTGAATAAGGTCGCGATATTACTGGGGAAAGGCCATTTTGATACGCGTGCTAAATTTATTCATTTTAGCCATTTAAATCACTTGTCCAACACCATTAATATTATGGCGGAGAAAATTCAGCGATTAATTTCCTCACATAAAAATCTTGTAAATGCCGTCTCTCACGAATTACGCACACCTATCGCTCGGATAGAGTTTAATATTGAAATGCTGAGAAATAATACTCAGGATAATAATCAATTGACGCAACTAGATCGTATAGAGTTTTCATTAAATGAGTTAAATTCATTAGTGTCGGAAATGCTTGAACATGCGCGGTTTGATAGCGAAATACCGACACTGACATTTGAGTCAATTGAGCTCAACCACTGGCTAAGACAAGAGTTACTGCTGTGGCAAGAGGCTAACCCAACGATAGTCATCACTTTGCTTGGGCATGATGAATGCTTGGCAAGATTCGATAGGTTTTACATGAGCCGCGCCATCAGTAATTTAGTCCGAAATGCCATTGCCTATGGGCAAAGCCAGATCCACGTCGGCTACCAAAAAACAGCAAAAGGGTGGATGGTTTTCATTGAAGATGATGGTTATGGTGTGCCTTTGGCCTCCAGAGATAAAGTGTTTGAGCCGTTTTATCGAGACGATGAAAGCCGCAATCTGCAAAAGAGTGGGACAGGATTAGGCCTAGCCATTGTCAAGCAAATCATAGATTGGCATAGCGGTTCGGCGAGCGTTAACGCGTCAGCTTTAGGAGGCGCTCGTTTTGTCCTCAGTTGGCCAGACAAGAAGTTATGACGTATAGCAGGTTAAGGAGTAGGCGATAATGAAAACCAAAATTCTCATTGTAGAGGATGATTTGCCTTTAGCTGAGTCTATTCAGGAGTACCTTAACTATGAAGGTTACCAAGTAGAGATAATCTCAAACGGCGTGCTAGCAGTATCGAGGATTCTTGAATGGCAGCCCGCACTGGTGATACTCGACATCATGTTACCGCAAAAAGACGGCTTGAGTGTATGTCGAGAAGTAAGGTCGCAATATAACGGCTATATTTTGATGTTTACCGCGCGAGAAGAAGAAATAGATCAAATTGTTGGGCTTGAAATTGGCGCAGATGATTACTTACTTAAACCAGTCAAACCTCGTATGTTGCTGGCTAAAATCAAAGCGTTTTTAAGGCGAGATAGATTGGCCGAATCGATTGCTGAGAACCAGCAGGTAAGCGAATATATTAGTTTTGGCCAATTAGCGATCAATACCCAAAAGCGAAGCGTAACCTTAGCAGATGTAGCGATAAAGCTTACGGATGGAGAGTATGATCTGCTGATCACTTTAGCTAAAAATGCCGGAAATATATTGTCTCGTAACGATATAGTCGAACAAGTAAAAGGCTATTATTACGACGGTATAGAGCGCGGTGTTGATAATCATATTTGTCAGCTTAGAAAAAAACTCGGCGATGACGCGCGTAATCCATTTAAGATAAAAACGATTCGTTCAAAAGGGTATTTATTTGTCTCTAGCGGCTGGTGATATTGATTTTTTTGATATTAGTTACCGAGATGGATGACCGCTATTTATCTGGCTGGCAATAGAGTTTTCTATCTTAAACCAAATTTTCATTGTTTTAAAAATAAAATATATATCTAAAATATCTTATTCCCCGAGTAACATCTGTCATCAGTCATATATGTGTAGTGCAATACTAGTGAGTATTCATCTCTCATCATCCCCACTCTGCTTGAAGACGATATCTGATAGACCATGACAATTCCTGACAATATCCGACAACTGCTGAAGGCAGTTAATAAAAGCGTGTGTTAGCATTTTTCTATAAATTGAGAGAGCCAGTAATGTCATCATTAATTATTTTTTCTAACAAAATAATAAGGTAGAGAGCAATGGCGATAAAAATGTTAAACCGCAAATTAATGTTGGGACTTAGAGATATTATGAACAGAAGTAATAAAGGATTTATTATTGCGATAGCAGTCAGTTTTAGCACTATGTTGGCTGGCTGCGGCGAAAGTTCAGATGAGCTAGATGACAAGCTGACGCATACAGCAGACTTGTATTTCATCAATTCACTGCCCTATATGGCAGATTTTTATGTTGATAAACGAAATATAAGCACTGGTTACTCAGGATTGTTTGACAGTAATAATCTAGTTTCGGCGGATGTTAACACCAATGATATCGGCAGTGCCTACACCTATAGCTACAAAGCCATTAACAATATGATTAACTTAGGTGTGAAAGACAGCGTTAACGTTAATAAAGAAGAGCGTACTACGACAACCTTAAAAAATAGCGATGATCTTTGGGTCATCGCATGGGAAGCGTCAGGCGAAAGGCTACTGTCAGTTATAGACAAAAAGTCCAACAACACCGCCGATGTCTTCAATGTTCGTGTGTTTGCGAATGACAGCTACGATGTTTCTGTTGACGGTGTGAAAATATTGACCACAGAAAAAGGTAAAGTGACAGAGTATCTGAGTATAAATCATTGTGTCGATGGTCTTAAAGTTGCGGGTAGGGCGATAGATTTATGTGCTGCGAATATAGGAGGTACTTATTTATTGGTGGTCGATAACAATGGAAAACGTGTGATGGCAGAGGAATAGTTGGCAAGGATGCCATAGCTAATTGTTCACTACATAGCCTCCTACTGATTGCTTTATTTAAGCATTCGTCAGGAGGCCGTTATGTGTCCCCACTTAATTCACATTCATCTAATGGAACTGATATGGTTCTCTACCTTATATAGAACAAGTCTCTGTTGCTATTGGGGTCGCTGCGAGTGTAATGGCTAATGAGTGTGTGGGGATGGCTATTCAGTTAATTACATAAAATAATTCAATCGTCATCGATGCCCCGCTAGGCATAAAAAAGCCCCGACAATTCGGGGCTTAATCAATTTGCGAGTAAGGATTACAAGCTGTAGTACATTTCAAACTCAAGTGGGTGAGTTGTACGTGCTACACGCTCAGCTTCTGCTGTTTTCAGTACGATATAAGATTGGATGAAATCTTCACTGAACACGCCGCCTTTTGTCAGGAACTCATGGTCAGCTTGCAGATTTTCAAGGGCGTTTTCTAATGACGTTGCCACTTGTGGGATTTCAGCCGCTTCTTCTGGAGGCAGATCGTACAGATCTTTGTCCATCGCTTCACCTGGGTGGATCTTGTTTTGGATACCGTCAAGACCGGCCATCAGCAGCGCTGCGAAACCTAAGTATGGGTTGGCATGTGGATCTGGGAAACGCGCTTCGATACGACGGCCTTTAGGGCTTGGTACCACTGGAATACGGATTGATGCAGAGCGGTTACGTGCAGAGTAGGCCAACATAACAGGTGCTTCAAAGTGTGGCACTAAACGCTTGTAAGAGTTAGTGCTTGGGTTAGTGAAAGCGTTCAGCGCGCGAGCGTGCTTGATGATACCACCTATGTAGTACAGAGCCGTTTCACTTAAACCCGCGTATTTGTCGCCAGCGAACAAGTTAACACCGTCTTTCGCTAGAGATTGGTGAACGTGCATGCCGCTACCGTTGTCGCCAACGATGGGTTTAGGCATGAAAGTCGCCGTCTTACCGTAAGCGTGTGCCATGTTGTGCACTACGTACTTCAGAATTTGGATTTCATCCGCTTTTTTAGTTAACGTGTTGAAGCGAGTAGCGATTTCGTTTTGACCCGCAGTCGCCACTTCGTGGTGATGCGCTTCAACCACTTGACCCATTTCTTCAAGCACTAAACACATAGCGCTACGTAGGTCTTGTGATGAGTCAACCGGTGCAACTGGGAAGTAACCGCCTTTAGTGAATGGACGGTGACCTGTGTTGCCGCCTTCGTAGCTCGTGCCTGAGTTCCAAGCCGCTTCTTTAGCGTCGATCTTAACGAAACAACCTGACATGTCAGTACCGAAACGGACATCATCAAATAGGAAGAATTCTGGTTCTGGGCCAATCAAAACAGTATCAGCGATGCCAGTAGAGATTAGGTAAGCTTCTGCTTTTTTAGCAATTGAACGTGGGTCACGGTCATAGCCCGTCATAGTACCTGGTTCAAGAATGTCACAACGAATAAGCGCTGTGGTTTCTTCGGTGAAAGGATCCAGTACGAATGTCGTTGGATCTGGCATCAGTACCATGTCTGATTCGTTAATGCCCTTCCAACCTGCGATAGAAGAACCGTCAAACATTTTGCCGTCTTCGAAGAAATCAGCATCTACCTGATGAGAAGGAATAGATACGTGCTGCTCTTTACCTTTAGTGTCGGTAAAACGTAAATCAACGAACTTAACTTCTAATTCTTCAAGTTGCTTTAAAACTGATTCAACTGACATTCTCAAGCCTCCGGTAGGGTAAAGGGATGCCCCTTCTATAATCTTTTAACTCAAGTGATTTATCTTGAGCCAAACTTACGAGCGATAAAGCGATATCCATGCCAATAATTTAACTAGCTGATTAACATGGTTTAAAATCTTTTCTGCACTTAATTGGATCCGTGTTTGCACCACTATGGTGCTTTTGGCTGTGGCATTTTGGTGCATCAATCCATTTTGGTGCGCACTGAATTGATTCATGCAAACCTAGGCTTGGATCCAAAGTAATAACATGCACTGCTTATGTGGCAGTGAGATGGCAGTGACGCCTTGGCTCACATTAGTCGCATAAGGACAGCAGCACTGAATCTCGCACATCTTTTCAGCACAGAACTGGACTATTTCTCTGGGCTGTTTCGCCGCCGCATAATTGTCATAAAACCACGCTAGTCTGTCCAACTATGCCACGCATTCTCAATCCAATAGTGGTTTATTATCATATTATTAGGAAAGAGTTCGTTCTCATGTGCGAAATGTCTTTGCTGTTCACAGACCAAGTGAATCAATGATGCGTAATAAAGGTATAGCGTGACAATGAAGTGAGTCTTAAAGACATTCAGTTTTCGCGGTCATAAACGCCAATATGAATGCTCAGTTAAATGTACGCTAGAATTTATTGCTGTTTCTCGGCGAGATGATTTCATCGCGGTGACATCAATTTTAATTGATGTTTAAATCAGCAGAAATGGTTTGTATCTTGAGTATGAAAAATAGCAAATTCAGCTGCTTAACTCTAATTTTTCCTACGAATATGGCAATTGAACAGTGCTTTAACTGTGGCGGTAGAGGATTTCACTCAATGGAATATTGGCAGGAATTACCTAAGGATTGGCGGATTGTTTTATAAGTGTTCAAAAATAATCCTGTGCGCCAAGGCCGCTCTGGAGTAGAATGGCACGCTTTTTATTGTGGCGGATCGAGCTAGATCGCCATAACGAATTACTGAAATATCCCTATTTTTGTTTGATGGTAAGAGGTTTATCCGTGCTAGAGAATTTACGTAACATCGCCATTATTGCACACGTTGACCATGGCAAAACGACCCTGGTAGACAAGTTGCTGTCACAGTCAGGCACCCTTGCAACCCGAGGAGAAGCCACTGAGCGGGTGATGGACTCCAACGATCTTGAAAAGGAACGTGGGATCACGATTCTGGCAAAGAATACTGCCATCAAGTGGAACGACTACCGTATTAACATCGTTGATACCCCAGGCCACGCCGATTTCGGTGGTGAGGTTGAGCGTGTTCTGTCTATGGTTGACTCAGTATTATTGCTGGTTGATGCCGTTGACGGTCCAATGCCACAAACTCGCTTTGTAACGAAGAAAGCGTTTGCTCAAGGCTTAAAGCCAATCGTTGTTATCAACAAGATTGACCGTCCAGGTGCACGCCCAGATTGGGTTATCGACCAAGTATTCGACCTGTTCGACAACTTAGGCGCAACCGACGAGCAGTTAGATTTCCCAATCGTTTACGCTTCTGCGTTAAACGGTTTTGCAACGTTAGATCCGGATGTAGCGAGCGATGATATGACGCCGCTGTTCCAAACTATCGTTGAAAAAGTATCTTTCCCAGATGCTGACGCCGAAGGTTCGTTCCAGATGCAAATTTCGCAAATCGACTACAACTCATACGTGGGTGTTATCGGTATCGGCCGCATCAAGCGTGGTAGCGTTAAAACTAACCAACAAGTGACCATTATTGGCGCTGATGGCAAAACTCGTAACGGTAAAATGGGCCAAGTATTAGGTTACATGGGTCTAGACCGTACTGAAGTTGAAATTGCTAACGCGGGCGACATCGTTGCTATCACAGGTTTAGGCGAGCTGAAAATTTCTGACACTATCTGTGCCGTTGGCGCAGTAGAAGCAATGCCAGCTTTGACTGTTGATGAACCAACTCTGACTATGACTTTCCAAGTTAACACTTCACCTTTTGCTGGTAAAGAAGGTAAGTATGTGACTTCACGTAACATTCTGGAACGTCTGCAAACAGAATTAGTCCACAACGTGGCACTGCGTGTTGAAGAAACAGAAAGTCCAGATCGCTTCCGCGTATCTGGTCGTGGTGAATTGCATTTATCAATCCTGATTGAAAACATGCGTCGTGAAGGTTACGAGTTAGCCGTATCACGTCCAGAAGTAATTCTGAAAACCATCGACGGTGAACTGTGTGAACCATACGAGACCTTGACTGTTGACGTTGAAGAAGAAGATCAAGGCACAGTGATCGAGAAGTTAGGTACCCGTAAAGCTGAAATGAAAGACATGCAGCTTGATGGTAAAGGTCGTGTACGTATCGACTTCATCATCCCAAGTCGTGGTTTAATCGGTTTCCAAACTGAATTCTTAACTGCGACTTCTGGTACTGGTCTAATCTACCACTCGTTTGACCATTACGGTCCACACAAAGGTGGCGATATCGGTCAACGCGCTAACGGCGTATTGATCTCTAACGCAACGGGTAAGGCTCTGACCTTCGCACTGTTCGGTCTACAAGATCGCGGTCGTCTCTTTATCGGCCATGCCGCTGAAGTATACGAAGGCCAAGTAGTTGGTATCCACGCTCGCTCAAACGATCTGACAGTTAACTGTCTGAAAGGTAAGCAGCTGACCAACATGCGTGCTTCTGGTACTGACGAAGCTCAAGTACTGACTACGCCAATCACAATGACACTTGAGCAAGCGCTTGAGTTCATCGATGATGACGAATTAGTTGAAGTAACGCCTAAGAACATTCGTGTTCGTAAAAGACACTTGACTGAAAACGATCGTAAGCGCTTTAACCGCGCTTAATAATCGTCTGACTCGCCATTAAGGTGGGTCAATCGGTTTAAAAAGCCCTGCATAGGAAACTATGTGGGGCTTTTTGTTGGGAGTTATTTGGTCATTGGCATATCGATACTTTGCGCTATGGAGCTGATGTGAAGTTTTGTCGTTATACCAATCGTATTAAATATCTGTTCATTCAGCGGGAGTTCAAAGCGATTTAGACAAGGCGGAGGCTTGAAGGCATAGTGGTTCTCTGACCAAAGCCTCAAACGCAGTATAAAGCGCTTTGAAACCCGCCCTTCGGGAGTCTCACAGGCACCCCACTCCTGTGTTGCATTGACTTAAAAGGGAATAACCATTTCTGCGTCAATGCGCCTTGGATTGAGATGCCTGTGAGGCTCTGAACTGATTAGATATTTAATGTGACTGGTATTACATCGCTAAAAGCTTAGTGCTTGCTGCATATATTCTCTGAAGCACTGCGGGCTCTGCTCAAGGTTGTAACTCTGTTGTTCTGCAATGAGAAAATAGGGTATGCCGGCGATTAGTTAAAAATGCTGAATCTTAAGATCTTGTATTACTTCTCATTCATCACCCGTATGCAATTGCGGCCTGCTGATTTTGCGCGGTAGAGGGCGGCATCGGCTTGGCGAACGAGTTCTAAAGCATTTTGTTGGTTAGAGGGCTGCAGATAAGCGACTCCTGCGCTGACGGTCACAATACCAAGGTCGGTATTCGCATGTGGAATGGCAATGGTTTGGATCAACTGATGAATATGCTGCGCTGTGTCGAGTACCGTTTGTTTGTCGATATTGGGCAACAGAATGACAAATTCCTCACCACCGTAGCGAGCCACGAGTTCGCCGGCTCTGCGGCCCGACTGGGATAAAGTACTGGCCAGTTGTTTAAGGCAATCATCTCCAGCGAGGTGGCCATAGTGATCGTTAAATTGTTTGAAGTGGTCAATATCGAGCATGATGAGGGCAAGTGAGGAATTGCTCCTTTGTCCTCGGCTCCACTCCGTTTGTAACATCATGTCAAATAGGCGGCGGTTGCCGATATTGGTTAAACCATCGGTATTGCTGAGTATTTCGAGTTGCCTATTGGCTTCTGCCAGCTCACAGGTTCGTGCCTTGATCATGGATTCAAGTTCGACATTACGCTCTTCTAATCGGGCTATAGGGTAAATCTCCCCTTTGTGGGCTAAGCCGTAGGGGATGGATATGCCACTGTGGGCAATCATCCAGTCTTTGTGTTCTAAACGAAATATCAATACTAGCCGTGCCGTTTCGCGGGAAAACATAGGATTAGGGATGGGTAAGTGGATATGGAAAAAGGCAGTCACAACCACGAGGTTATCGGCAAGATCTTGTAAGGAGAGATCCAACATTTCGATATGGATCCGCTCAGGCACTTGTGCAAAGTCCTGCCGAGTGATCCGCACCCATTCTTCCCTGTCGGTCACAAGCTGGTCACTACTGCCAGCATAGCCGCTAAAGTTCTCACTAAAGCGGTGGGTTAGCCTGTCATCACGGGAGGAATACATCTCAATGTATTCGTCAAATAACGAACGGATCAGTTGCTGTCGGTTGGGTCGCATAGGCTAGCTCCATGTTAGGGAACATAAAACCACTTTTCTAGTATCCGTATAGGAATAGTATCTGCGATAAAATCGAGTGGTTGCCTGATTGTAGTCAATGTTATGACTTATATATTAAAAATACTGCAGATAGTGATGTAAGCCATTATCTGCAGTGTGCTTTTGGGGTATTAGCTAATGCCCAACTCTATCTATCACTTGGATCAGTTCCATGCGGATATTGTCGGGTGTTTGGCTGTGCCAGAAGCCAGCTAAGTAAGCGCCCAACGGCAGCATAAATAAGGCGATGAGCACTAATGCGAGTAATCCCTTATGGCCGAGTCTGTGGCCAGATTTAAGCCCTAGACCAAGGGCGGCTGATTTTGGACAAGCGGCGACGCAGCGCATACAGGCCTGGCATTCATCACTGCGCACCGTAGTCTTAGTGTGCACTATGATGTTGGCCGGGCAGGCGCGGGTGCACTTATCGCATTTCATCCCTTTTGCCTCGATAAGGCAATGCTGAGTATCACGGCGGATTTTTAATGGGCTCGCAAAGCTTAAGATCCCGAGCATGGCACCATAAGGGCACAAGTAACGGCAGAATCCCTGACGGCGCCATGCGGCTAAGGCAAGGATCAGCGCAAACACAAACAGGGTAACCAGGCTCGGCGTCAGAAAGAACAGTGCCATTTTTAAGTCGGCAATCTTGTGATAATTACCTTCGAGATAATAAGGAATCGACTCACTTGGCATGCCAATCACTATGTAGCAAAGGCCAAGCAGCAATAAATACTTGAGCATTCGCAGTGGCCAATCGAGCCAAGCGGGTGGTGTTAGTTCTGATTTAATAAAACGTTTTCTAACAGCATACAAATATTCCCCTGCCAAACCTAATGGGCAAGCCCAACCACAAAATGCCCGCTTACAAAGTAATCCTGTGAGCAATACCGCCGCTAACATCACCGCGGCTGCAGGATGGGTTTGATCCCACAAATTTAGACTGACAATGGCCTTCAGTTCAATGCCGCCGGCAATCGGCAGGAAAGCATCGACCACATCGGGGCGCATTAGCCAAGGTGTAATTCCTTGCTTAAGCAATACGGTATTGATTGTGTATTGCACGGCAACCAGTAAAAGCGATAGCGCTAAGACATGTTGAGTGCCTTCCCGCAGAGTATTGATCTTAATCTCACCTTTGATGGAGTCAGGTGCAAATTTATGTCCTACTAACGCTAAAGGTAGCGCAATAATCAGTCCGAGTGCTAAAGGCCAGTAAAGGCTGATCGCGACGGCGCTGACACTGAGTAACAGTGTTACGACAGCACCCCATTTCTGGCCGCTCCAATAGCTCAGTGAGGCGAGATACATGAGTGCCAACATCAGAGTAAGTGATTCGATAAAAGTCATAGTGATGAATACAGCAAATGAGTAACGAGTACAGTGTCTGAGTGATGAGCTGATGATAAGTTTTTGAGTACTAAATGAAATTGTGCGGCAGGTCTAAGATTGGTGCTATCAATCCACTAAAGCGCGCCATAGCTCACAAAGCCAAGTGTTTTGGGCTTGCACAAATAAAGTATGGCTTGCAGGGACAGCGGACGAGGTCACACAAAGCATAAAAAGCCCCAGCACTTTATTGTGCTAGGGCTTAGGAGTTAAGTAGCAGCTTTGGTTCAACTTTTTGATTTTTCGAGTGACTCGATAAATTTGTTCGACTCGTTAATCGATTTGTTCATCTCTTTAATCAGGCCCGAAATGTCGGTTTGCAGACTAGCAAATTCGCCTTTGATCGCGCCAATGGCTTGGGCGTTCAAGTTATGTTTGAGATACAGCATGTTGTCTTTCATGGCAGTGAGGATGGGCGGCATTTTTGATTCGGCGCGGTGCATGCTTTTGATCAATTGTTCGTAGGCGCGGCGGGTTTCTTTGAGCTTAGCTTCACTGTTACGGCGCAGGCTGGCTTTGCTGATTTCGCCAATTTCATCTTGCCATTCATCGAATAACGCTTCGGCCACATCTTCAACTTTCTTGATGCGGTTGCTGACGTTATCTGCGGCGCTTTGGGCCGATTCGTATTCATCCTTAGCCTTGTTATAAGCTTTTTCTAGGTTGCCGCCATCGTGGTTGAGCAGGGCTTGCATCTCTTCGAGGGCCGAGCTGAACTCTTTTTGGGCGTCCTCTTGGGATTCTTTTGCGTCTTTAACTCTGTCGACCATAATATCGCGTTTATGGTAACCGACCTTTTCCATCGCGCCATAATAGGCACTTTGGCAACCACTGAGCAGGAGGCTTGCGGCAATCAGGCCTGAAGTTATCAGTCTTTTCATTGGGTATTCCGTCACTGAGTGAATTGAAATTATTCGGCTTTAAATCGGGCCGCATCTATAATGCTCCACAGATGAAAGATAGCACCTATTACCGCGGGCACAATTAGCCACCAGAGTGCATAACCCACCACTGTGATAATGAAAAATAACAGTGCTGCCATAATGCGGCCTTGTAATAGTTGGCCTAACCCAGGGAAAAATAGGCTAGCAATAGCGGAAATAACGTTACCCGCAGATCCTTGTTGAGACATCAATTCACTCCTTAGACTTGTAAATTAAGTGTTTATTGTACGAAGATAACGAGTCTATACCAACAGAATCAAGAGAAACCCGTGACTAAGAAGATAGAACTGGCACAAATTCGCGTCTTATTCCTCGGGATCTGGCACTTCCTGCTGCATTTAAGGCGGCGTTTAGTGGAAGATCAGATCAATATTCGCGCCGGACACTTGGCCTATGTCACGCTATTGTCACTCGTGCCTATGGTGGCCGTGACTATGTCTATGTTGTCGGCTTTTCCGGTGTTTAAAGGCATACGTGGGCAGATAGAAGGCTTTGTTTACGAAAACTTTTTACCCGCTGCTGGCGACACAGTGCAAGTGTATATCAATGAATTTGTGGGCAATGCGTCTAAGGGCACAGCCGTGGGGATCGCGGCCTTAGTGGTGGTTGCCATTATGCTGATTTCCGCCATCGACAAATCCCTCAATAACATTTGGCGCACTAAAGAGAAGCGTTCCGTGGTGGTCGCATTTTCAATGTATTGGATGGTGCTGACGTTAGGGCCGGTATTGGTCGGGGCGAGTTTGGTCGCATCCTCCTATGTGATTTCACTGAAGGTGTTTGAAGCTGAAGCTTTATCGGGCATGTTGCCGATTTTTATTGCGCGCTTGCCCATGTTGTTTTCAGTTGCCGCATTCTTGCTGTTATACATGGTGGTGCCGAATCAGAAGGTGAAGTTTTTACATGCATTATTGGGGGCGATTGTGGCCGCGCTCTTGTTTGAGCTAGGTAAAAAAGGCTTTGCCTTGTACGTGACTCAGTTTCCAAGTTATGAAGCCATCTACGGTGCGCTCGCGACGATCCCCATAGTGTTTGTCTGGGTGTATTTATCGTGGATGATAGTGTTGCTCGGCGCGGAGATTACCGCGGCTATGCCAGAATATTTAGATTATGAATCGAGCTCAGATGACGAAACGGCCCTGAATGCCAAGCCATTAGCAGATGCCAGCCAAGGTGATTCGCCGTCAGTATTAACGTCGGCAGAGGTGACGGCACTCAAGGCCGTGGCGAAATCGGAATGACATCCCTAAACGGTTAAGTAATGGCGATAACACTTAATGCCTGAATCCTTTCCCCCTTTTATCCGCCGCGACTGGTTTAGCGTTGGCAATGGTCAGCAATTACATCTGGCGCAATACGGTAATCCTCAAGGTATCCCGCTGTTGTATTTGCACGGTGGTCCGGGAGCGGGTTGTGATATCGGTGATTTAGCTTTATTTAATGGCGAACAATATTGGATTTTGCTGTTAGATCAACGAGGTTCTGGACAATCCTTGCCCTTTGGCGATTTAGCCCATAATCATCTTAATGGTTTGATCTGCGATATGGAGGCGATTCGCCTTGCGTTAGGTATTGAGCGCTGGTGTTTAGCGGGTGGCTCCTTTGGGGCCACATTAGGCTTAATTTACAGCGGATTATTTCCCGAACGGGTGATAGCGCAAGTGCTGTGGGCGTTATTTATTCCGTCTAAAGAAGGCATAGATTGGCTCTATGGCGCTGATGGCGCGGCAAAATTACAACCTGCGGCTTATCGACTCTTCAGTGGCAATGCGCCTGTTGTGCCATCGCTGACTGAACTGCTGGATTTATATCATCTAGGTTTGGCATCGGCCGATGAAAACCTGCGACACCATTTTGCGCGGCGTTGGGTGCAGTGGGAGCTAAAGCTCGCGGGGGCGAGATTGGTGTTGCCAAGACAGTTACCCGACCCAGTATTAGCGTTAGCGGATATCGAATTGCACTTTGCTCGGCATGCTTACTTCAATGCCATGACTGTGTTGCAGCGCGTGACGCCTAGGGTGAAGGCGCGTAGTGTGTTATTGCAAGGAATGCAGGATGCGATTTGCCCAGCGGCGCTATTGCGCGATTTTGTGGCTGGGCTAGAGCATGCTATTACGATTCAATCTGTTGCCGACGGCGGACATATGCTCAATAGCGAGCCGTTGTTTTTGGCGGTCACAGCAGAAATCCATGCTATGTGGTCGTGGATACATCAAAGGGAAATACAATGAAAACATTGCTCATCAGTGCCAGTTTACTCGGATTAACTGCCTGTGCCGCCACGGCGCCCTCGCAAACCAGCGCGATTGACCCAGTGGCGACTCAGGAGACCGTGAGTGCACCCGTCGCGCCCAAAGTCGTTGAACTGGGTGGATTGACCAATGAAGCTGCCGCTGAGCGCTTGTATCAAGCGTTGATTAACGCTAACTACTTGGCCACCAAAGTTGCGCCAACTAAAGTCGCGGTGCAGTTTGGCGATAACCAGTTTTTGCTTGAGCCGAGTATTAATTCTGCGGGCATTGACCGTATTTTAATGAATCGTTTCTATGCCGTGCATCCGCAATTACAGGCTAGCCAAGAGCTGCCTGTGGTGATTGGCACTTTGAATCAGAAGCTTAATTTTGCCAAGTTCGTGTTGCTGGACCAAGGCGCTGTGATCCAAATACAAGGCACAGTCACCTTTGCCGATCGTGTTGAAATCGAAGAGTTACGTCGCTTTATGTTATGGACCAGTGGCGGTTTGGCGCAAGTCGCGCAATCCCTGCCAGATGGCATCGATCAATATATTCGTCCTATCCCGCTCATGCAGCAAATGGTTCCCGTTAAGCCTTAATCTAAAAAGGAAAAGTTGTGATTGCGTTAATTCAAAGAGTGAGCCGCGCCAGTGTGGTTGTCGATAATCAAACCATAGGTGCAATCGATAAGGGCCTGTTGGTTCTGCTGGGCGTCGAGCAGCAGGATACCCGCGAGAAGATGGAAAAACTGGCAACTAAAGTGATGAGTTATCGCGTGTTTTCCGATGAAAATGGCAAGATGAATCTCAACCTAGAGCAAGTCGGTGGCTCTTTGTTAGTGGTGTCGCAATTTACCTTGGCCGCCGATACGGGCCGTGGCTTGAGACCGAGTTTTTCTGGCGCCGGTACGCCGGATCAAGCCTTGGCACTCTATGAAGAGTTTGTCGCATTTTGCCGCGCTAAGGGCGTGACCACAGAAACCGGACAATTCGGCGCCGATATGCAAGTGTCCTTAGTCAATGATGGCCCTGTGACGTTTAATCTGCAGGTGTAGCTTCTTATCGCGCGCGAGATTGCGTTAGTTATTGTGTTAGCAATTACGTTAGCTATTAAATTAGTCATCGCGTGATTTTTCTGGGGGATAGAGTGTTGGATGTATTCGATGTTAAGTGTGAAGTGGGCTGTATCAAGGTAAGTGCACATGGATGAGCTACTTACTCAACTACTCATTCAATTACGCCAGACGTGGCACAGTACTATTCCTGTTAGTGAATTTATGCAGATTGCGCCACTCGATTACAGCGAGGGTGAATTTCGAGTCACTGCGCCGATAGCGCCCAATATTAATCTGCATCAGACCATGTTTGCCGGCAGCATTTATACCTTGATGACCTTAACTGGCTGGGGCTCTGTGTGGCTGAACCAGCAGTTAGCTGGCGTTGCGGGTGACATTGTGCTCGCCGATGCCCATATTCGTTATCTCGCTCCAGTGACTTGTGATCCTGTGGTAACAGTGCAATGGCCAACTGTGGATCTCAGTCCGTTGCAAAGAGGTCGCAGAGTGAAAGTGAAGTTAGCTGTGCAATTGTATTGTGATGCTAAGCTTTGTGCGGTATTTGAAGGTCTGTATGTCAGTGCGCCGAAAGCGAGTTAATTTATAGCGCGTCGCAATAACCTATAACCTTTCTAAAAAATCGAAGTGAATCTCAAATTTTATCCACTTTATTTCTTTTTTATGGGCGAATACACTAGCGTCATAGAGTTGAGATATTCCATCTGACTCTGAGTCATTTACCTATTTTCGGAGCAGATTATGTCTAAAGTCTTAATTTTAAAATCAAGCATTCTTGGCGGTTATTCACAATCTGCCGTGTTAATTGACCACTTAGCTAGCCACTGGGAAACCCAAGGCGCCGCTATCACAGTACGTGATTTAGGCGGTAAAGACGTGCTGCCTATGGTAGACGGTGAAATCGCTTCTGGTTTACGTGGTGGTGCAGAATTATCGGTACGTCAGCAAGAAATGCTCGCATTATCAGATACTTTAGTTGCTGAATTAAAAGCGAACGACACTATTGTTATCGCAGCGCCTATGTATAACTTCACTATTCCGGCTCAACTTAAAAACTGGATCGATTTCATTGCCCGCGCTGGTGTGACTTTTACTTACACTGAAACAGGTCCAAAAGGATTAGTGGAAGGTAAACGCGCTGTGTTAGTGACGACCCGTGGCGGCGCTCACAAAGATGGCCCAACGGATCACGTTGTACCTTACTTGAAAACGGTTTTAGGCTTTATTGGTATCACTAATGTTGAAGTGGTTTACGCCGAAGCGCTGAACATGGGTCCAGAAGCACACGACAAAGGCATGAGCGAAGCGAAACACAGTATCGATCAGCTGAAGGCTTAATCGTTCTGTTGCTTTAGTTGAGTCATTTGATTTAGCTGAATAAATAATGCCAGCCTTTGATAGGCTGGCATTTTTGTATGTCCATTTTAGCTCGGTTGGCAGCAGACTAAATCTTTAAGCGAACCGCCATTCACCACGCAGCCAAAACCATTGGCCTTGAGGATATCGCAGCCTTTTTGCGCTCGAATACCCGCGCCACAGTAGAGCACTAAGGGATGCTGTTTATTGTCGACTTGATGCAACCATTGGTCGAGCGTTGGCAATGGCACATTAATGGCTTGGGGCAAATGACCGGAGGCAAATTCCTCTGGTGAACGCACATCAATCACCCGTGCGCCTTGCTCAATCAATTGCCAACACTTTTCGCCCGAGTGACTGCCGGATAACTTAGCCAGTAAAGATTGAAACATAGCACTGCTCCTACCATTAGAAAATTTTCAATAAGTTTATTCTAATGAATCGGCGAGTGCAAGCGTTGCAGATTATCGGTAGTAATGGTTTGGTCTTTGGCCGTGCAAGAGCCCGAACTCTGCATTACTGGCGTTAGATAAGCTGCTACAAGCTAGATTTACAGCCTCGTTGATAGTGAATGATGTCGTGGAAGACCACGGCTTGGTCGTTGAGATTGCGATAGCCATGGGGTTTGTCGGCCGCAAATCGTACCGCTTCACCTTGCTTGAGCGTTTGCCACTGTCCATCGACTAACACTTCCATAGTGCCGCTTAATACAATGACATGTTCAGTGACGCCAGCTTCATGGGGCTCAGAAAGCCGCTCATAGTTTGGCAGCAGGGTGAGTTCAAACATCTCAAAACCAAAGCGGTCTTCAAAGGGAAACAGTGAGGCGACTAACATGCCGTCGGTGGCGGGTTGTTGGCGTAACTCGTCGGGCTTACGGAATACAGTGCCTTGGCTCTGGGGCGTGGGTTCGAGAAAGGTCGAAAGCGAGATATTGAATCCACTGGCAATTTTCCACAGCGTCGCAATGGTTGGGCTAGATTCACCGCGCTCGATTTGTCCTATCATGGCCTTGCTGACGCCCGTCTCTTGGGCGGCTTTATCTAAACTCCATCCCTTTTGGTTGCGAAGTGCTTTGAGTGTGGTGGCGAGATAGCTGTTTATCGTCTGCACAATGGCCCCATCATTTTTATAAAAATTTAGTTTGTACGTTATAACGCACAGGTGCTATCTTGTCCATCTCGTACGTTATAGCGCACAACATGGTGGCGACATCAATTGCAGAGAAAGGAAGTGGATATGTGGCGGAATGCAGGGAGTTTGTCAAAAATATCAGCGGGTTTTATTGCGGTATTAGTGGGTTATAGCAGCTCGGCTGTGATTATCTTTCAAGCGGCGGAAGCTGTGGGGGCGACACCTGCACAGATTAGTTCATGGCTGTGGGCTTTAGGTGTGGGCATGGGCGTGACCAGCATAGGATTATCGCTTTACTACAAGAATCCTATTCTAACGGCTTGGTCGACACCGGGCGCGGCGCTGATGGTGACCAGCCTAGCGGGATTGAGCGTCAACCAAGCCATAGGCGCATTTCTCGTGAGTTCGATGTTAATCACCCTTTGTGGGATGGCGGGCTGGATGGATAAACTTATTCGTATCATGCCGCAGTCAGTCGCTTCAGCCATGTTAGCGGGGATCCTGCTGCAATTTGGCCTAGGGCTATTTCAGGCGATGCAAACCCAGTTGAGCCTCATATTAATCATGCTGTTGGTGTTTGTCGTCGTTAAGCCTTTTGCGCCGCGCTACATCATCTTGCTGACCTTGCTGGTGGGCATAGGCATGAGCTTTCAATTGGATTTACTCAAGTTAGATGCCGTTCAGTTGCAACTGGCGATGCCGGTATGGACCATGCCTGAGTTTTCGCTGGCGAGTGTGATTGGCGTGGCGCTACCGCTGTTTGTGGTCACTATGGCCTCGCAAAATATGCCGGGCGTTGCTGCGCTACATGGAAATGGCTACAGGCCGCCAATTTCACCTTTGATCACCAGTACAGGGCTAATCGGGATCATCCTCGCGCCCTTTGGCGGTTTTGCTTTTAATTTATCCGCCATCACAGCGGCTATTTGCATGGGTAAAGAAGCTGATCCGAATCCCGCGACCCGTTACTGGGCGGCAGTGTGGGGCGGCGTATTTTACTTTATCACAGGGTTACTCGGCGCCACTGTGGTTGGCCTATTTACCGCTTTCCCGAAGGAATTAGTACTGGCCATCGCCGGATTAGCGCTGCTCGGCACTATCGCCAATAGCCTTACCGCCGCATTAGCTAAAACTGAAGATCGCGAGGTAGCGGTGATCACTTTCTTGGTCACGGCATCAGGTTTTAGTGTGCTGGGTATCGGCAGCGCATTTTGGGGACTGTTGCTTGGGTTTATTATCCATTGGTGGAACGGGCACAGAACTGCAGCTGTGGTTAAGCGAGCTAATACTTAACTGCATCAGAAGCGCCTTAGAGATGAATTTCAAAGACTAAGGCGCATGTTTTTTAGATTTTCTTTGTCATAAACACGCTATTGGGGTCGAGTTGGTAGGTGTCGAAGGGGCCACAGATTTCAAAACCAAATTTGCAGTAGAGCAGGCGAGCTGGGTTGAAGAAGTCCATCGAACCTGTCTCTAGACTCAGACGTTTGACGCCAGCGCTTTTGGCATCGTTAATCAAGTGCTGCAATATTTTAGACGCTATGCCTTGCTGTTTATAGGGCGCGGCGGTGCGCATGGATTTGATCTCGGCGTGCTCGGCATCTAACCATTTTAGTGCACCGCAGCCCGCTAAATTATTGCCATCCCACAGGGTCCAAAAGCGAATATTGGACTGACGCAGGCCATCAAGATCTAATGCGTGAACGCTTTCAGGCGGTGAAGTTGCTCGCATATCGTCGAGGTGTTCTTGCAGCAATGCCGCTATCTCAGGCCCTTTAAGGTCATCTAAGGTGATTTTCATGGCTCTTATGGCGGATTTCATGGCAGATCGGGTGCTCCCTATTATTTTGGCTTATCCATAATGGCGTCATCGCTTTGTTATCTGTGTGCAATTGTGCCGCAGTGGATAAGCAAATCTTAAGCCACAAAATGTTAATCAAAGCAAAAGTCGTAGGGCTTCACCCCCCTGCTTTTTGATCATGAGTCGACCAAGGAGGATTGCTCGTCCTATTCTTTTTGTTTTTATTTTTACAAGCTGCCATGACGCCCAACGGAGTTGAAAGCCTCTTGTTTTATATTTTTTCCAAAGGGTTGGCCTCACTCTGAACAATACCTATGTTTAACTGCCTCTTCGCCTTTCATAGCTCTCGTTCATAAACTTTGAAACTATTCACAGTGTCGGTGAGTGAATGCCATCGCAAGCCTGTAAAGGCGAATATCAGCAAACGCTAAGGCCAACAGTAAATTTTCTTTTAATTTAAAAACTGTCGAAGCGAATATAAAAATGGCCGAGCATAAGCTCGGCCATGATTTGTTCGGTTAAGCGTAAAACGGGTTGGTTTAATAGAATTTCTTCAGGTAGCTAGCCGCGTATTTTTGCAGTTTTCGGTTTGAACTTGTGGTCGCAATATTCTCAATCACGTCGCGGTAATCTGCGTTGCCTGAACTGGCTAGGGCTTTTGCCATGTTGGCATAGGTATCGATGGCGAGTTTGTCATCGGCCATTAAGCGTGGCGTTTTCAATTCGTTACTGAGTACGGCTAGGATAAAGTCATCGTACTGACGATCATCCATGATGCGTTTAGCCGCCAGGCGCATTAACTCTAAATCATCGCTGCGTAGGGCATTGGCAAAGGCATTATTTTGCTGACTTTGGTCGGCAGCATATTGGCTTTTATCCCCAAGAATTGTATTCCACTTTTTATATTGCGCTAGGTTTTCATTGGCTTGAGATGCGTATTTTTTGAGTTTTTTGTGATAGTTGCCATTTACAATGCTGTTGATGGTTTCGCTATATTTGTCGTTACCCGAGTAGGCTAAGCCTTTGACTAACCAAGCACTGTAGTCGATGGCATTTTTTTCAGTCGCTTGGGGTAAAGAGGCAAGCAGTTTAGCTTCGAGCACATCGTAAATTGCAGGATCAGATAAACCTGCAAGGCTTAACGATTCGATGGCTTGCTTCTGCTTAAACTCGTTGTCGCCCTTAAAGACCTCTTGGTAGGTTTCTACCGTGTATTCTTTGGCAAGCAGCGGCGCGGTTAATAAACTGATGCTCAGCAGCATCCCTGCAAACAGTGGTTTCATTGCTATGTCCTTATGTGGGTTGTAGGTGTTATCTGGTCCAGCGTTTAAAAATCAGCGAGGTATTAATGCCGCCAAATGCGAAATTATTACTCATCACATAATCCGTGTCGATTGGGCGCAGTTCGTTGCGGATATAGTCCAAATCGGCGCACTGTGGGTCGATATTCTCAAGATTGAGTGTTGGCGCAAACCAGCCCGCATTCATCATCTCAATACTCACCCAAGCTTCTAACGCGCCGCATGCGCCTAAGGTGTGGCCTGTGTAGCTTTTAAGGGATGATATTGGCGTCTCAGCGCCAAACACTGCTTGAGTCGCGTGACTCTCGGCAATGTCACCACGGTCGGTTGCTGTGCCGTGGGCGTTCACATAACCTATGGCGCTAGGCTCAAGCTGCGCATCTTTGAGGGCTAATCTGATGGCGATTTCCATGGTCTGCGCATTCGGTTGGGTGACGTGTTGACCGTCGGAATTTGTGCCAAAACCGACTAACTCAGCATAAATTTTTGCGCCACGCGCCTTGGCGTGTTCGAGCTCTTCGAGCACCAGTGTACAAGCCCCTTCGCCAATGACGAGGCCATCGCGATCTGCATCAAAGGGGCGTGGCGTGAGTTCGGGCGTCGAGTTTTTAGTGCTGGTGGCAAACAGGGTATCGAACACCACAGCTTCGGTGGGACAGAGTTCTTCGCCGCCGCCCGCTAGCATTAAGGTTTGCTGACCGTATTTGATGGCTTCATAGGCATAACCTATGCCTTGGCTCCCCGAAGTGCATGCGCTGCTGGTGGTGTGAATTCGGCCCTTTAAGCCGAAGAATACGCCAACGTTCACCGCTGTGGTGTGCGCCATCATGCGGATATAGCTAGTGGCGGTGACGCCCGACATATCACCGGTTTTCAGCATATCGCCAAAGGCGGTTATAGGATCGGTACTGCCCGTGGATGAACCGTAGGCGATGCCCATTTCACCCGAGGACACGATAGGGTCATCCAACAACCCCGCATCGAGTAAAGCAAGCTCACTGGCGCGAGTCGCCATGATGGAAACCCGACCCATAGAACGAATTTTCTTGCGTGAATAATGGCTTGGCACTTCAAAGTCAGTAATTGGCGCGGCTAAACGGGTGTTGAGGCCGTCGTATCTATCCCAGTCACTCATAGTGACGACGCAGTTTTTCTGCGCCTTAAGGCTCGCCGCTATCGTCGGCCAATCATGGCCTAGTGCGGTAATCCCGCCAATGCCTGTGATGACCACTCGCTTACCTAAACGTGTTTGATTTGTCGCCTCGCTCATTTAGATCATGCCTCCATTGACCGATATCACTTGGCGAGTGATATAGGCTGCATCGTCAGACATTAAAAATGCGGCTAAGGCGGCGATTTCATTGGGTTTACCCATACGGCGCATCGGCACTAATTGCTCGACCATGTCCTTAGGAATATCGGCCACCATGTCGGTTTCTATCAAGCCGGGGGCGATACAGTTGACGGTAATTTTGCGTTTTGCCAGCTCTAATGACAATGCCTTAGTCGCGCCGATAATACCCGCTTTGGAGGCGCTGTAATTCACTTGGCCACGGTTGCCCGCAATCCCAGATACCGAGGCTAAGGTGATGATACGTCCGCCTTTTCGGCCTTGTACCATAGGCATCACGCAGGGATGGATCACATTATAAAAGCCGTCTAAGTTAGTGTGGATCACGCTGTCCCACTCGCTTTCTGTCATGGCGGGAAAGGCGGTATCGCGGTTGATGCCGGCATTGAGGATCACGCCGTAATAGGCACCATTGGTCTCTATATCGGCTTCGATGGCAGCTTTGACTGTTGCGCGTTCGGCGACATCAAATTTGAGCAGGCTCACATTGACCCCAAGCGCGCGGATTTGCGTGGCAGTGTCATCGGCAGCCGTTTGGTTGCTGTGGTAATGCAGGGCGATATCGAAGCCTGCTTGCGCCAGTTTGAGGGCAATGGCTTTACCTATGCCGCGGCTCGAACCGGTAACTAATACTCTGTTATTCATCTATTTTCTCCACCCCATTTTATCTTTAGGAGTCGGTATGGGGTTTCATGTATTTATGGTAAAAAGCTGCTTGCTAAGGCGTTTAATCTAACGCTTTTATCGACAGGCTAATTCAGTTTGCTGCGATTGCATGGTCTTAGCATGCTCTCAGCATTGCCTCGATATTAGTGTCTAAGTCAGTGCCCAAGGCTTCTATTGCCCGCGTCTTGATTTCCATCAAGGTAGGCTTGCGTGTCTTGGGGTTGGAACACATTCACATTCGCTGTGGCGACTAAGATTTTCTCTTGATCGCTTCCGGCTTCGGGCAGCAGATAAATCTGGCAATCGAATACCGCTAATCCCGACTCCTCTTGATAGAGGCGGTTTACCTGGGTGCGATAGGTGCGCCCGAGTTCATATTGCTTGGCATGTAGCGCTAATTTGCGCGACCCCAACAGAAAACCCACGCGGATTTTATCATTGCGCAGCTTAGCTTCTACTCCGGCCAGTGCGGCAATACTTTGGGCCATATATTCGATACCCACATAATTGGGTACGGCTTGATGTTTATCGTCGAAATAGGGGCTTTTCGGCGTGATCGTGACTTCGGTAACTAAGGTATCGCGTTGATAAGAGATGATCTTATCGACCAAGATCATCGGCGCCCTGTGGGGGATGAAATCGGCAATATCCTGCTCGGCAAAGGGTAAATTGAATTGAATAAAGGGCATCATTTTAGGCCTCGTTACGGCAAAAAATCAGGCTGGCATTGCTGCCGCCAAAGGCGAAGGAATTACTCATCACATAATTAAGTGTGCCTTTTGCGGCGACTTGATGGCGTTCGACTAAGGCAATGGTCGGGTCCAGTGGATCGACTTGTCCGTCCCATTGATGGGGCGGCAGGGCTTGGCCGTGATTATGTGCCGAAAGCAATAAATAGCAGAAGGCGGCTTCAATCGCGCCAGCGGCACCTAAGGTATGGCCGACTAAGGGTTTGGTCGAGCTGCAGGGCGGGGTGTTGACGCCAAAGACTTCAACCACAGCACGGCTTTCCATCGCATCATTTTTGGGTGTCGCTGTGCCGTGTAAGTTAATGTAACCAATATCTTGTGCCGCAATATTTGCATCTTGCAAAGCCGCTTGCATGGCGGTGATTGCGCCAGCGCCTTCGGGATGAGGTGCGGAAATATGATGGGCATCGCTCGACTCGCCAATGCCAGCCAGCATGACATCGGACTCGCCCGCCGTCAGCACGAATAAGGCCGCGCCTTCGCCAATATTAATGCCATCACGGTTGATACTAAAGGGATTGCAGTGGCCTTTGGATACAGATTCCAAAGCATTAAAACCATTGACTGTCAGTTGGCACAAACTGTCGACACCGCCAACAATCACCATGTCGCAGAGGTTAGCGCTGAGTAAGCGTTTAGCACTGGCAAAGACTTTTGCGCTGGAGGAACAGGCGGTTGAAATCGTGTAGCAAGGGCCATCTAGCTCAAAATATTGGCGCAAAAAGTCGCTAGTGCTGCCGAGCTCCTGCTGGAAATAGTGGTAATCCGCCGGAAAGTGGCCGTGTTGATTGCGGTAGCTCAGGGCTGCTTCACCTTTAGAAATCCCCGAGGTGCTCGTGCCTAGCACAACGCCAATGCGGGTATGACCAAAATCCCGTTTCGCTTGTTCAATCGTGGCGGTAATTTGCTGCGCCGCGCATAGGAGTAACTGGTTATTGCGGCAATCGAACTGAATGAGCTTTGCAGGAATCGCCGGCAGCGCAGCCGTTACTGGCGCAACTAAGGTTGGGTGCTCGAACAGGAGTGTGTCACTGCGCTGCATCGCGCTGGTATCGCCCGCGATCAGGCGGTCTAAAACCTGCTGGGGATCTTGCCCTAGCGGGGTACACAATCCAATCTGTGTAATGGCTACTCTGTTCATCATAGCGTATCGTTTTTTACCATGGCACAGTGAAGTGCGGGTGTTAAAAGGTTAAATAGGTTCGATTTCTAGCTCAAACTTGGCCTGCGGCATAGTTAAGCTGATGTGGGCTTGCCATAGGGCATCGCCGACATTTTGTTGATAACGGATTTGCACCACATGGGTTTGTAGGGCGCTTTTACTGATCAAGGTATCTTGGCTATAGATCTGGCGACAGGGGATTGTATTCATTCGGGTATCGACAAGCGTGTCGCTCTGATTTCCCCCAACCGTATCGCAAAGGCCTGTCACTAAATGGCCACCTTCCAAATGGGCGCGAATACTCTGCTCAGGCCAATAGATCAGTTGCATCATCGCCATTAAATACTCGGCTTTAAAATTGTCGCCCAGCAGCACACTTTGTTCACTGCTGAGTGTATTCCCATCATAAACTAAGGTAAACAAAGCCTGTCCGAGTGGCGCTAAACCCACGAGCGTCATGCGCTCGCCCTCTAATTCCAATTGAGTGAGCAGTTCGTGGGACTCTTTGCCCACTTTAATACTGACTTTTTGGCTAAAGGACTGGGGTGAAGATACGCCAGTATTGCTGTCGGTTTGCTGACTCGCCATGATGGCGGGCTTAGTTGTAGACTCAGTCGCGGGCTGTATCCTAGGATGAGTCAACGGCTTATCGGTCGCTGGAGCAAAGTCATGGGGCAGTGGAGCCAAACAATAACGCATATCCTTAGTGAGCCCGACACAGGTCTGCCTGAACAGCAGTTCACTACAGCCCGTCAGCCCAAGGCTTGCGAGCAGCAGGGCGGCGATCACTGGCCATTTAGCGTTAAGGTTTAGCGGCATGCGGTTAGACCTCTACGAATGCTGGAGTGGCGGCTGGGTTTCTGCACAGTTCGACTATCATGTTCAGGCGGCGCTCCGAGTCTTTAACGAAGGGATTATTCGTATCCCAAGCATAGCCCGCCAGAATAGAACAAATCATCTGCTTGATACGCTCGTTCGGTTCTTCATAGAAGATCACGTCCTGAAAGCGGCCATCATACCAAGCTTGCACGTAGGTGCGAAAGGCATTCACGCCCTGCATCAAAGGCGTTGAGTAATCGGCTTGCCAGTCAACCACTTCGCCATTGAGTTGTTTCGTCAGGCATTTTGCAGCCATAGACGCCGATTGCATGGCGATAGTCACACCGGAGGAAAACACCGGATCGAGGAACTCGCCCGCATTACCCAATAGCGCGAACTTGTCGGTCGCAAGGCGTGACACATTGGCAGAATAACCTTTGAGCGTGGCGCATTCCTGCACCACTTTCGCATTGGCCAGCAGGACTTTTAGCCCTGGTTCTTCGTTGACTATGCTCATCAATTGTTGCTCAAGGGAACCTTGTAAACGCTCAAGTAATTGCGGCTCAGCCACCACGCCGAGTGAACAACGGCCATTGCTAAAGGGAATAAGCCAGTACCAAATGTCCTTATGTTCAGGGTGGACGCTGATCAAAATCTTATTACGATCAAAGTTAGCATCGCTGATGTTGTCTTCCACATGGGTAAAGATGGCGCTGCGCGGAGGCAGGCAAGACGGACTTTCTAAGTTGAGCAAGCGGGGTAACACGCGCCCAAACCCGCTGGCATCGAGCACATATTGGGCATTGATTTGATAAAGCTCGCCCTGTTCATTGCGCACTGTTAAGCGTGGGTTCTCGGTTAAATCTATCGCCTCGACTGTTTCGCCGTAGCGGATGTCGACCCCTTGGCTTTGGGCGGTATCGGCTAACAATTTATCGAAACTGGCGCGCTGCACTTGAAACGTGGTGCCCGGCCCTGGGGTAAATTTATCGGTAAAATCGAAGGTGGTATAAGTGCCATTGCGACGAAAGGCTGCGCCATTTTTATGCTGGAATCCGGCAGCATTGAGCGCATCTAACATGCCCGCTTCTTCAATAAACTGCATGCAGCAGGGCAGTAAACTTTCGCCGATGGAGAAGCGAGGAAAATGCTGTTTCTCTAACACAAGCACGCGCTTGCCTTGCTGGTGCAGCAGGCTAGCGGCGATGGCGCCTGAAGGCCCTGCGCCGATAATGGCCACATCGATATCAAGTGAATTTGGCATAGTTGATAAATCGTGAACGGGTGTAGACATGTAAAGTACGTTCCTTGTAGTCATCAGGACTTTTTAGTCGTTAGAAGCGGATTGTTGGTTAAAGCCTGTGTTGTAGTCAAAATCAACGGCGAAAGTAAAAAGGTAAACCCTATGCCCAGCGACAGGGTTAAGCCAAAGTAATGAATCGCTTGGGTCTGGCTAAAGGCAAGTAAACCAAAGGCCAATAGGGTCGAGCAGGCGGACATAAACACCGCCATCATCACTGCCTTGCCGTGGTTCTGGGCTGAGGCAAAAAACAAGCTGTAGTCTATGCCAATCCCGAAAACTAAAATGAGCGCCAGTGCATGGAACAGACTCAGTGGCGAGCCCGTTAAGCCTAAAGTTGCGAGCGTGAGCAAGGCCGCAAGGGCCGGCACGGCAACCACAACCGCCGCTTTTTTAAAGCCGAAATTCAGGCTAAACAACAAGAGTGCGATGCCAAGTGCAAGGGCTAACAGCTTTAGGGTTAACAGTCGGTAGTGGCCCATGACGGCGGAAATGTCCGCGACTTTATCGATAAGTTGTACGCTTTCATCATGGGCAAAACGCGCCTTAAGGGCATCGATTTGCTGAATGCCACCAAGGAGGACTATTGCACCCTGATCTGCTCCATGCTCAGCGCCATGTTCCGCGCTCGCCCTATCATCATTTAGCGCGCCTTTGCTGACTGGACTGTTAGCCTCATTCGGCGCTAGCCACAGCGGCGCGAGTTGCTTGCCAGCCTCTAGGGTAAAGAATGCTGCCGGCGTGATGTACTCTGTTTTCGCGGCCAAATAGGCGGCTTTCAGCTCTGGCTTTAGATTTTCATCCAACCCAAGGCTTGCGAGTACAGTATCTAGCTGAGTTTGATAGATTTCGCCCTGTAAGCGGTAAGCCGTGTCTTGCCTTTGATGGCTGGGCAGATACCGGCTGAGGCTCACATAGTTGCCTAACTCTTGGTTTGTTATGGCGGTATCAAGCATGGGTGACACTCGCTCAAGCTGTTGCAGCAACGCTTCTTCATTCGGTGCACGCACTAATAAGAATTGATTATCTGTGCCGCCACTGAGTAGCTGCCTGAGCTGATTTTCAGGTTCAGTCACGCTCGCGGGGCTTTGCTGCAAATGGCGAATATCGTCATCGACGGTTAACTTTGTTACGCCAACCAGACACCACACTACTATCACTAAAGCAAATAGGCCCATGCCTAACGGCGTGGTCAATTTGTTGGATAACTTAGTTAGATTAGCAAGGTAAGTCCCCGCGAGTGCTAATGGGCGGGAGCCTTCAGGCAAGCGACTGCCCGCGAGCAGCGGGTAGGCGAGAATGAGGGTGAGATAGGCCCCGAGCAGCCCCGCGGCGCAGAAGATGGCGACTTGCTGCATGCCGGGAAACGGCGCTAACCCTATGCCGACATAAGCCAAGGCGCTGGTGATAAAGGCGAGCGTCACCGTTGGGAAGATATACGCCACTGTGGCCTTTGCACTGCACTCGCTATCGCTTAAGCGCTCGCAGTAAAAATGAAAACTGTAGTCGATGGCAATGCCAATCAAGCTAGTGCCAAACACTAAGGTGAGTAAATGCAGCTCGCCAAACACGCTCAGGGTAAAAGTCACCGCCAGCAAGAGCCCGCTGGAGATAGTCACGATTGCCAGCAGTAGTGGCATTACGGAACGAAACGCCAGCCAGACTAAGGCTATTACTCCAAGTAAGGATGCAAGACCAAGGATCGAGATTTCACTCTTAGCCGTTTGTGTTGCCGCAATCGCGTGAAACAGAGCGCCAGCCTTAAGCACATTGATATCGGGATAGCTTTGTTTAACCGCATCCAATCCTTGGGTTAATGCCGTCATCTGCGCTAATTGCGCATTGGGATTAAAAGCGCTTTCTCGCCCCTTTGCCATAACAACCGCAGCGACATTATCGCCTTGATTCGCCAGCAGAATACCTTGGCTGGCTCTGACTTTTGAACTCGGCGCGAGGGCGAGTAAATTGGCCGGGAATAACAGTAGTGGATCTTGGGCGAGCAAGTTGCTATTGGCGTAACTGAAGGCGTTGTAAAGCTGCGCTGTGGCCGCCTCAAGCAGACTTTCAATATTTTGACTGGCTAACGCTTCGGCTTGCGGCGCTGTCAGTAATTTAAATCTGTGGGGAAAATAGAATTGTCCTAGCGCTTCGCCGAGCTGCATATCGGCGCTGCGGATATCGGTTAGCGCGCCTTGCATACCCCTTGCAACAAGGTCGTCTTCAAGCTTTTGCATCAAGAGCTTAGCCGCAGCGATTGCTGTGGTTTCATCCTTGGCGACAAGCGCAATATAGACTTGGTCGGCGAGCGTGGCTTCAACTTGGTTAAGGGCGCGTTCGGTGAGTTTGTCTTGCTGTAAATGCGGCAGCATGGCGAGGATATCGCTCTGCACCCGTGCGCCGTTTTGCCAAAGTTGCAGCGTCCACAAGCTTGCTGACAGCATCAATAGCAACCAAACGACGAGGCGCCACTTAGGGGAAGATTGCATCAAAGCTTGATTGGCGCGCGCGGCGAAGTTGGAGGCCATTGCTAAGGTTTACTCGCCTCGAGCGCCAACGCATACAGCGCTAATTCGGCTTCGCTGAGTGTGCCTTGGGAAAGCGCACTAAAGTCGATGCGGGTCATATCCTGCGGGCTCACACTCGGCGCTGCGCTCAGCAGCACTAAGGATTGCAAGGTATCGCCACCCTCTAACACCATATTGGCGATGGCTTTTTTCATTAAGGGATCTTTGGGCGTTAGGCCCAATTGCCATTGACCGTCTGGGCTCAAGCTTTCTATTTTTAACCTTTCTGTTTCTAAAAAGTGCAATTCGAAGTTTTCGCTTAAACCGGAAATATTCCCGCCCAGCATAGCGCGCACTAGGCTGGGTAATAAATCGCCCATAGCGGCGGCGCTCGCCGAGGCATCGGCCTTGCTGACTTGGACTCGACCTTGGCTATCGCGCTGGATCAGTTGTTTATCTTTTAAAATCAGCAGGGATTCGAAGGGCTTTATCTGTTGCCAGATCAGTCCTTGCTCTTTGTCGAAGATAAACTGGCCTTGGCTGATTAACGGCTTTTTGAGCACCTTTAACTGCCGGGATTGCACAAACTGACCTCGCACCGTTTCGCCCAAGTTAAGTTTTTGGCTTAAGGCGACAAGCTGCGCGGTATCCGCACTTTGGCTAAAGAGTGCTTGATAATTGCCGGGCGTGGCAAGCTTGTCACTTAAGGCTTCGGCCTTGAGTGGCTGGCTGCTTATCAAAGCAAAAGCTAAGCAGAGAAATAATGCACACTTTGTCAGCACAGGATTCTTTGCCAGCACAGAGATCTTGGCTAGTACTGAGTTCATTAGTCTTCCACCTTAGCGAGCAGGGTGGCTATTTTGTCACGGAACACCTCTGGAGTAACAAAGCACAATTCTTGGGTGCTCATATCGACCGCCGCCTGAATGGTATAACCCTTAGTGATGCGCGCACCGGTTTCGACATCGCGAATTTGATAATTGATCTTTAAGCGGTTTTCCCACTCGACTAACTCGGCGCGCACTGTGATTTTTTGCTCGAAGGTGCTGGCTTTGACGTACTTAATCTGCAAATCGATAATCGGCCACGCATAGTTGGATGCTTGCATTTGGCGGTAGTTGTAGCCCAGTTCATCGAGCAGTTTACAGCGGGCTATTTCGAAATAGCGCAGGTAATTGCCATGCCAAGTGATCCCCATAGAATCCACATCGTGGAAGGGAATTTGCATTTCCATATCGATAGTGAGCAATGATTTCATCGGCACACTTCCCATTCGCCCGCTTGGATTTTAGCTATGGTTTGGCGCAGAACGGCTTCGAGCGGCCTATCTTCAATTAAGGTTTCAAAATCGGCGCGCACTTGAGCGAGGGTTTTCGCCAATGAGGGTGTGAGCGAGGCTTCGCTCAGTTCATTCTGTGCGATACGCAGGCCAATACCTTGGGTCATGGCAAGTAATGCAGCCGCTGCGACTTGTTCGGTCAATTGCAGCACGCGCATACAGTCGCGGGCGGCGATAGTGCCCATGCTGACTTTGTCTTGATTGTGGCATTCGGTTGAGCGTGAGAACACGCTGGCGGGCATAGTGTGTTTGAGCGCTTCTGCCGTCCAAGCCGACACGCCAATTTGCACCGCTTTAAAACCATGATTAATCGCGCGGCGCGGACCGGTTGAGCCGGATAAGTTGGCAGGTAAGCCATTGTTAAACTTAGGGTCCATCACTAGCGCCATTTGGCGGTCGATAAGATCGGCTAAGTTGGCCACAGTATTCTTCAGTGAATCCATCGCAAAGGCGATATGGCCGCCGTAGAAATGGCCACCGTGGAGAATATGCTCGCCTTCGCCGTCGACAATCGGGTTGTCGTTGGCGCTGTTGACTTCGGTTTCGATAAACTGACGCATAAACGGCAGCGCATCCTGCAACACGCCGATAATGTGCGGTGCGCAGCGGATAGAATATCTGTCCTGCAGACGATCAGAATTACGTGGATGTTCGTGGTGATTCAAATCTTCACGGATCCAAGTGGCGATTTGGTTTTGCCCTAGATGCGGTTTGGCGGCAAATAAGATTTCATCAAAATGGTTCGAGTTGCCTTTTAGCGTTAACGAGGCCATGGCGGTAATGCGACTACTCAAGCGAGCAAGATATTGCGCGCGATCAAAGGCTAAGCAGGCTAAAGCTGTCATCACAGCCGTGCCGTTCATCAGGGCTAAGCCTTCTTTTGGACGCAGTACGTGCGGAGTGATGTTGAGCTTAGCGTACACATCTTGAGTCGCTTGGCGCTCACCTTGGTAAATCACTTCGCGCTCACCGACTAACACGGCGGCTAAATACGACAACGGCGTTAAATCCCCGCTGGCGCCGACCGAACCTTCCTCTGGGATGACTGGCACTATGTTGAGATTGAGCAAGGTTTCGATGCGCTTTAATAGCTCATAGGTCACGCCAGATTTGCCAATGGCTAAGGAGTTTAAACGGCAAGCCATCACAGCGCGGGCCTGCATTATGCTTAAGGTCTCGCCTAAACCGCAGCCGTGGAAGCGGGTTAAATGCAGTGGTAACTCGTGGACCAGATCTAAGCTCACATTGACAGTGCAAGAGTCGCCATAACCTGTGGTTACCCCATAGACCACGCCTTCTTCGTGCAGCAGGCTATCGATAAAGCGCGCACCTTTTTGGATATATTCTTGATAATCCGCATCGTCACAGAGCTTGACTGGTGCGCCCTTGGCGACGGCAACCACTTGTTCTAAGGTGAGATATTGACGGCCGAACTCAACAGTCTGTAGGTCTGATTGACTCTCGGAGTCGGCTGCTTTGGTGTGGCTCATGAATGCTGTGTCCTGTCTGTCTTATGATCCTGCTCTGGGATCGGCGATGATTCTGAAGTTGTCGGTAACGGATTAGGCTGTACTAAATGCGATGGGGCGCGTTGCAGTTCGCTGTCTTTGCGCCAAAAATCGAAGAAGTTAAACCATTGCAAAGGCTCGCATCTGGCAAAGTATTCTAAACGTTCGCTGTAGCGGTTCACGGCCTGCTGTAATCTGTCCATTCTGCCCGCTCTTGGGCCTTTCAGTGTTTCGGCGAAGCGTTCTAAATGCACCCGATAACGCCCTTGTTCCCGCAGGCAAAACATTAAAAACACTGGGCAATCGAGTAAGCCCGCCAAAATAAACGGCCCTTGTGGAAACGCGGCATCTTGGCCCATAAACGGCGCATACATGACGCGGCCTTGGGTATTCGATGAGGTTCTATCGCCCGCAATCACCACCAGTTCACCGTCCTCAATTTTTTGCTGTAACAGCATAGACGTTGCAGGATTCAGCTCGGTCACTTGAATAAGATTGAGCGTGCTATCTGGGTTTAACTGTTTGAGTACTTTATTAAAATTTTCCGCATGGCTAGTTAGCACCATGACGTTGACTTTGACTTTGCGCTGGTGGATGGAAATGGCGCGGCAAAGTTCTAAATTACCCAGGTGCGACACTAATAACACCGCGCCTTTGCCGCTTTCGAGCTGCTCGGCCAGCACTTGGCGGTCGGGGAAATCCACTTGGCTCAATTGGATGCGATCGCACCAGGCATCGATTCTATCGAGTGCGGCATTGCCAAAGGCTAAAAAGTGATTCAGGCTATCGCGCCAGCCTACGGGCTGATTGAGCTTAGGGTGTTGCGGCTCAAGTATTTGCACGCGGCGCAGAAAATCTAACGAGGCTTCACGGGTGACTTTGCCTGTGAGGAAAAAATAGCAAATCACCGGATACATAATGGCGCGGCATAACCAATGGCCGCCAAAGCGATAGCTCTCGGCTAACAGTTTGATGCCCCAGTAACTGCCGCGCTCCTTCATGCCCGACCAATGGGTGGCTGAAGTCGATTGTGTTGCTGACGAGCAATCGGAAGTTTGCACGGCTTGTTGCTGCGATTTACGCTTCAATAGCCAAGGCAGGCGTTTGAGCATGCCGAAAAATAACTTAGTGTGCAGCTTAGTGATGCGCACATTATCGGCCACAGCTTGAAAGTGACTGACGCCGCCTTCGGGATAAATCACCTTAGTCGGCTGGAATAGAATCTCTACCCCTTGCCAGTAGAGTTTAACCAGGATTTCGATATCAAAATCCATTCGTTCCGTTAAGGCTTGCTGGCAGAGTAATCGTTCGGTGGCGGCCAGCGGATAAACGCGAAAGCCACACATAGAATCTTGAATATCAAAGCTTAAGGTTTCGACCCATACCCAAAAATGGGTTAGATAACGGCCATAAAGCCTGCCCTTAGGTACAGATTCATCGTACTCAGGCTTACCCGAAATCAGCGCATCGGGTTTAGCTTGTGCCCTTGCTAACATAGCCGGAATATCAGCGAGATTATGTTGACCATCGGCATCGACCTGCAGCGCATGACTAAAGCCATCGCGGTAGGCACGGCGCAGCCCTGTCATCACAGCGGCGCCCTTACCACGATTGTACGGATGCAGCAGTAAAGTCACCCAAGGATAGGTTTGGGCTAAGGATTGCAGTAGATAACGGGTTTCGTCATTGCTGCCATCATCAATTAAATAACAGGGCAGATTAAATTGGGCCAGTTCAGCCAAGGTCTGGGCAATCGCGGCGCTGTGATTGTAATTGGGGATCACTAAGGCGAGTTTGATGGGCACCGACTCAACGAGGTCGCTGGGAGGCGTTAGCACTGACTTGCCTCATCGCCATTGTTGGTAGCAGATTTGCTGTTAACGGGCGTGCTATCAAAGGCGATACGGCCCGACGCGTAGCGGTTGTCACCATCGCTATAGGCGAAGGTGAGTTTTGCCTTAGCGGCATTATTGCTAATGCTTAAGGTGACTTCTTGGCCCGGCAGTATGAGTTGCTGAAACTTAAGCACTTCTAAGTTAGCGACCGCCTCTGAATAACCGAAATGTTGGCAGCCTAAACGCACCGCCCAATCTAATTGCGTCACGCCCGGCAACACGGCTTGTTCGGGGAAATGGCCGTTAAAATACTCAAGGTCGGCCGCCACTAACAGACGTAATTCAATGTTATCAATGCCGATATCGGAATGCAGGATCGGCGGTAAACTCGATTTAATCATGATCAAATAACTCGACTAACTGGGCAGTGACACGCTTACCCTGAGTGTTAAGCGGTAAAATATCGGGATAACGCCAGCGGCGTGGCAGGGTGACGCGCTCAAATTGGGTCAGTAAATGTGCTTTTAGGGCATTGTTAATACTGAGTTTGCCCTGTTCTTGCAGCACGTTTTTACCGAGTTCTGACAAGGTCACCACTGCGCCAAGTTGGGATTTAAACTGTGGCAGCACGACTAGTGCAGCTTGCTCAACGTAGGCGTGGCTGCACATGAGGGTTTCCATTTGCGCTAGCGACAGACGCTTCTCTTCAATCTTAACGATGCGATCGAGTCGGCCCTTGAGTCTAAACTGGCCATTTTCGGTTGGTTCGATTTTATCTTCGCAGCGCAGCCATTCATCGTCGGCCAAATAGGGCGATTTTAGCAGCAGGGCGCCGTCGGTCGGATCTTGATCTATTGAGATGCGGTCAAACACTTGCCAAGGTTCATCGGCCTCATGCTGACGGCGATAGGCGATGCCGCCGGTTTCGGTGCTACCAAAAATTTCGATGGGCAGGTGACCATAGCACTGGTTGACGCCTTTGGCCGCGACACAACTTAATGGACCGCCAGAGCTAAAAACTAAGCTCGGTGAGCGCAGTTGCCGCTCGTGCTCTAGCGCTTTAGGCAGGCGCGATAACTGGGCTGGGCTACTGATCAAACATAGGTTTGGCAGCAAGGCAGTGTAATAACTTAGGGTTTCTGGGTATTCAATCTGTTCGCTTAAAAACGGTCGACTGGCGGCGAGCGGCCACAAAATCTTGAATAATAGACCGTAAATATGCTGGTGGGAAACCGTGGATACCACACTGCAATGGGGCAAATGCTCGGCAAAAGTATGTTCGAGTACAGACACTTCGACATCTAACTGTTCAAGTGTTTTACGGATCGCCTTAGGTTCGCCGCTGCTGCCAGAGGTAAATAGCACTAACTCGCCGATAGCCTCGCTCGCGGGCCAAGGTTTGTTGGGCAGGCTCAGTTCCTTTTTCAGCAGCACAAAAGCTTGGCATTCGCACAAAGGCTTGTCTGACAAGATGCCATCAAACTGGTGCGTTAACTCGCTCAAGGTGCCGGTTTGGGTGTTGGGAGGCAGGATAATTTCTTTGCCCGCTAACAGCGCGGCACATAATCCAACGGCAAACAGATCGCTGGTATCACTCGCGAGCAGCCATTTTTTCTCGGGCGCGGCGAGCAGTTGCTCGTAAAAATACGCCACCTGATTGGTAAATAATGCACCTGTGACTATGTCGTGGTGATTAAAGCTGATCAGTTGTTGGGCAAATGGCCCTTGGGTTAACCAGTTTTTGAGTAGCTGTGTCATGTTTTCTTCAACCAAAAGCTGCGATATAACCATTCCCCACCGAGGAGCAGTCCCATGAGTACGTATGCAATCAAACCGTTATACAAGGTCCAGGTTGCTAAGCTAGTAAAGCCAGCGGTATAAGCCGCCATAGAGCCATTTAAGATAAATAAGCCGCACCAAAGTAGGGTGACTTTTTTAAGATACGGAATGGCTTCATCCGGTAAATCGGGTTCTTTCAAGCGGGCTAAACGCTCTATCATGCATGGGCCAGTGTGCAGTGAATAACTGAACAGAGCCAGCATAGTCAGATTTATTACCACAGGGTAATACAAAAGCCAGTCGCTGCGCTTGGCAAGATAACTGCCTGCCGTAAGCGCTATCCCCACAAACAGGGGCAATACTAACGTCTTAACCTTTTGCTTTTGTAGCATTAACCGCACAATCAGCAATAAACACAGCACGAGCGCTATGGTGCCAGCAGGCAAATAATTCAGCCCGAAATACACGGCAACCGGATAGGCCAGCAATGTGAGGGCGGTTAAGACTTGTAGCAAGGCGCGCATTAGTCTTTAACTAAACCTTCGATGGCATTCACCACATCGTTGACTGTGCGAACGGATTTAAATTCATCGGGTTGGATCTTTTTACCTGTCATTTGTTGCAGCTTTATCACTAAGTCGACGGCATCAATACTGTCGAGATCGAGCTCTTCATACAGGTTGGCTTCAGGCGTAATTGCCTCAGCCTCAATTTCGAATTCGTCCACTAAAATAGTGGTCAGCATGGCGAGGATTTGTTCACGATTTTGCATATTCCTCTCCTATGGGCGCTGGGATTCGATAAAGCTAGCTAAGCTGGCAACACTGTGGAAATGGGCCTTAGTGGCATCCGAATTAGCTTCGATTTTTACATCAAATTTTTTCTTGATGGCTAAGCCGAGTTCGAGGGCGTCGATTGAATCTAAGCCTAGACCTTCGCCAAAGAGCGGCGCATCGGTTTCAATATCATCAATACTGACGTCTTCAAGATCGAGACAGTCGATGATTAACTGCTTTATTTCATTGTTTAAGTTCATAATTATTTTCGAGTTGTTGTTTATAGTAGTGTTCAAGATCCCGCGTCAGCTGCCGGGCCATTTTTGCATGCTCACCTAAAGCCGCTTGGTACTTTTGGTAAGAAACTGTTGCGCCGATTTCAACGGCCATCCCCATGGTTTGCCTTGGGATCTCATACCAAGGTTGCTCCTTTGTCAAACCACGGACATTGGTACGCAGCACCACAGGTAAAATATCTGCTTCGGCCCTTAAGGCAATGTTGGCGGCGCCGCGGGCAAAGTCGTTAATGGTCGCGCCGACAACCGTGCGTGTGCCCTCGGGGAAGATAATTAAATTGGTGCCCCTCGCGAGCACTTCGCGGCAATCCTCTAGCATAAGTTCGGCGCCGCGATTGGGAATATAACCCGCGCAGGACACGACACCACGCAAAAACGGATTACGCCATAAACCTTGTTTGACTATGCAGCCCGCATTGGGCATGAGGCTTATCAACACGACCACATCCACTAAACTGGGATGATTGGCGATAACGATAACGCCTCTGGCGTCATGCAGTTGTTGTGCTCTATGGGTACTAACGTGGATCACGCCCGCCCAAGTGAGCATGGCGACAAAGCCTTTGAACATCAAATGCACGGCCTTTTGGACTCGAATAATCCGCGCCTCGGGTGTACCTGGCCAAAAACGTAGCAAAGGTAAAATGGTCAAGGAGCTTAACAAGCCGCCTAAGCCAAAGGCGATATAACAACTCACGCCGCCTAGCCAACGTGGCACATAATTCAGCCCAGCGCGGCGAGTTTCCGGCGGGCTATAAGCGGCATTGTGCATTGAATCTTCATAACCCTTGGCGGCGTCATGGCTGTGGGCTGTATTAAACATGCTCTAGCTTCCAATGCCATTGGGATAAGCAGCCTGAAATATTTTGCGCTGTCGCGAGGGCATGGAGCAAGGCGCCATAGGTCAAAGGATTTAATGCGCTTGACTCAGAGATGCGGCTCACTGTGAGCTTGACCGAAGCGGGCGCATCGAAGGGCGCGAGCTGTAGACCTAGGGCCAAAGGCAATTCAAATTCTTGGGTAAACTCGTCGTACACAGGTGGCACAGGATCATCACCGAAGACTAACAGCAAGGGCTCTGGCGACTGGTGTAACTGGGCGAAGGCTTCAACCATAGCTTGTGACAGGGTTTCGGTACCTGCCGCAACTGAAGTCGACGGCGCGGTATTGGCCGCCACTATGCCAAAAATGCCGCTGGCGGTGTTGTGCACCGACTGACTAAACCCTAAGGGCGATAAGGGCTGTTTGGCGATAATGTCTTCGAGCAGTCCGATAGTGCGCTGCAACTCTCCGTGGCGCGAGGCAAAAATAGAGCGGCAGTTCGCCGGCATATCGCACTGAAAACTCACTTCTAACATCATCTTAGTTAAGCGACTAAAACGGCGGCGTTGCATGGCGGGAACGTGTTTAAGTGCGGGAGCGGTCGTATCAGACTGTGGTGTGTCGAGATTTAATTCAGCCGAATTTTTGCACCAAGTTTGCCAACTTTGCGGGTGCTGATATTGGGGGGCCCAAGCGCCCCAAGAGAGAATGCTGAATGTTAGTTGCACTACTTTGCCTCGACACTAAATTTAAGCTGTCTTAGTTATCCCTAACTAAGTTAGAACATTGATGGATTGTCATACCATAGTGAGGTGTAAACGCGTTTGATGAATGACGATGACGCTAGGAATGCCCTCTACAGCGGGGCCATTCTACACTATGCCCCGCGTTTTTATGAAGCCATTTGCATTATGTGGGTTAACGTAATGGGATCAAGTGCTGTAATTTTCAGCGGTTAGATCGAATTTCTGTAACAAATATCGCAATAGCAGTCTCATTCGATAGGCTTTTTTATCCAAGCGATTTGCGCTCAAAATGAAAAAAGGCGCACTGAGCGCCTTTTGTCTTAACCACGGCAAGGTTTTACTGCGCCAGTTGGTTCTCGAGTTTGCGTAGGGCTACTTTTAGCTTGCGGGTATTTTCTGGACCCATACGGATCAGCAGTTTTTGCGCGCTAGGTAGTGCCTCTAAGTTAGGGTCAACAAATTGATAGTTAACGCTAATAGAGCTCAGCTCAATCGGATCTTCAATGATAGGCGCCGCGAGCAGTATTTTAATCGCTTGCAGCATTCTGTCGTTGAATTTTGCGTTGCTGTAGCCGAGTTCGGTAAAGGCTTCCTCTAGCATAGGCGTCATTTGCTTATAGGTGACCATTAACGTCTGTTCGTCCATTTTGGCAATCGAGTCAACATAGGCATCGTATCTGTGGAAACCCTCAGGATTTAAATACACTTTGTTGGTGATTTCAGAAACCGAGAAATGCTTCTCTGGGCCTTTGAGTGGACTCACTTTACGGGTCAATTCGCCCTGAGCTAAGTTATCGACAAACACCACAAATTGGCGTGCGAGATCTTGCTGCACTAATGCAGAACCCAGCACATTGTTATCGATAATGGCGAGGGCTTTTTGTTGTACGAATGCATCACTTTCAGCCAGTGCAGGTACAGGTTCAGCAACCACTTCCGGCTCAGTTACCGCAGGGGCTTCTGTGCTGTTGGTGTTTGTTTCTGTAACAGCAGGCGTCTCAGTGGTTTCTGGCTCTGGTGCACTTTCGAGCGTCATTGGCTCAGATGGCGGTGTTTCAGGCAATACAATTGGCGCGTTAGGGATGAGTTTAGGTGAATCTGAGTCACCGCTGTAGTAATAATATCCTCCGGCAGAAAGTAGCACGACGAGCACTATCGCAAAAAGCGCAAAGCGATTACTGCTAGAAGAAGTCTCTTGAGGTGTTATTCTATCATCTTGATTAACTTGCATTTTACATCCTTCGCTTGAGTGTCAAGGTAACTTGGTGAATACATGCAGCTAACATTAAAGTGCAAGAGCGGACTATTGGAATGCCGCTGGTTCATTTTGCAAGTTTCCATGCGCTTGTCTATAGCGCTTAGCTGAACATAAACAATTTATTTTGAGTATCGAGGCAGATGTCACTCCTCGGCCTTTACCCTTTACTGCCTAAGCCTGCACATAGTGCTGCTTGTGCCCGAGCCAACGTAAGATAATCGCATCGACTTTTTCGGGGGCCTGCGCCATCACATGATTTGCCAGTTTTTGAATGTGCGGGATCAAAGCTTGATCGCGCACCAGATCGGCAATTTTCAGCTCGGCCAGTCCCGTTTGCTTAGTGCCAAGCACTTCACCTGGGCCACGAATTTCTAAATCTTTCTGCGCTATGATGAAACCGTCATTACTTTGCCTGAGTACACTCAGTCTTTGGGTGGCGGTATGGCTCAATGGCGCCTTGTAGAGTAACACACAATGGCTTGCGATGGCGCCTCGACCGACTCGTCCGCGCAGTTGGTGTAGTTGTGCCAAACCTAAACGCTCAGGGTTTTCAATGATCATCAGGCTGGAATTAGGCACATCGACGCCGACTTCAATCACGGTTGTCGCCACCAGTAAATGAATTATGCCTGCCTTAAAATCGGCCATAATTTGCTGTTTCTCGGCGCTCTTTAATCTGCCATGCACGAGACCGATATTGAGCTCGGGTAGGGCGAGGCGCAGTTCTTCGGCCGTGTCTTCGGCGGCTTGGCATTCGAGCACTTCGGATTCTTCAATTAGAGTACACACCCAATAGGCCTGACGTTTATCTGTGATGACGGCGTTACGCACCCGTTCAAGCACTTCGTTGCGGCGTGAATCGGCAATCGCCACTGTGGTCACTGGGGTCCGTCCCGGCGGCAGTTCATCGATAATGGATGTGTCGAGATCCGCATAGGCCGTCATCGCTAGCGTGCGCGGGATCGGCGTTGCCGTCATGATCAATTGATGCGGATGGAACCCTTGGCTCACGCCCTTTTCCCGCAGACCTAAGCGTTGGTGTACGCCGAATCTGTGCTGCTCATCTATGATAATTAACGCGAGTTTATTAAAGACCACATGCTGCTGGAAGATGGCATGGGTGCCAATCACCATTTGGGCTGCGCCGGATTCAATATCCGCAAGGGATTGGGTTCTGGCCTTGCCTTTAAGTTTACCCGCTAGCCAACCGACTTTAAGCCCGAGTGGCTCAAACCAAGCGGCAAAGTTAGTTGCATGCTGTTCGGCCAATAGCTCGGTCGGCGCCATCATGGCAACTTGATACCCGTTTTCAATCGCTTGCAGCGCAGCCATCGCCGCGACTAAGGTTTTACCTGAACCAACATCCCCTTGCACTAAGCGCATCATAGGGTGCGGCTTTTCTAAATCTTTGCCTATATCGGCCACCACTCTTTGCTGGGCGCCCGTGGGCTTAAAAGGCAAAGCGGCGAGGAATGGATTGAGCAACTGCCCTGTGGCCTGCATGGTCACCGCTGCATCTAAATTACTGCGCTGTCTTAGGCGTAACATGCTGAGATTATGGGCGAGTAATTCTTCCTGCACGAGTCGCTGCTGTGCTGGGTGTTGGCCTAACTCTAAATCGAACTGAGAGATCCCCGCAGGAGGTCGATGCAGCGTCTGCAGCGCTTGTTTAAGGCTGATGTTGTTTGGTTGCAACTGCGGTGGCAGCAGCTCAGTTAAGCCGCCATCTTCCAGTAATACCAAAGCTTGCTCGGTCAGCTTGAGCCAACTCGCCTGTTTTAAGCCCTCAGTTGTGGGATAAATCGGCGTTAGGGTATCACTCAGATGAACATCTTCACCGGGATACACGACTTTATATTCTGGATGCACTATCTCAGCTTGATGGCCGCCACGGCGCACTTCACCGTAGGCGCGGATCATCAAACCGTTTTGCATGGCATTACGCTGGGCGACGGAAAAGTTAAAGAAGCGCAGGCTCAAGCTACCCGTATCGTCGCGCACATTACAGACCAGCATACGCTTGCGGCCTTGGATGATTTGCGTCGATTGGATTTCAGCCTCTATGGTGCCATAACTGCCGGGCATAAGGGCGGCAATCGGATAGATTTGGGTTCTGTCTTCGTAGCGCAGGGGTAAGTGAAACAGCAGATCTTGTACTGTTGTGATGCCGAGCTTTGCGAGTTTCTCCGCGACCTTTTTGGCGACGCCTTTGAGGTCGGTGATCGGAACAAGATCCAATTGCTGCAAGTCGTTAGCACTCAATCACTGTGAATTTATACATATATTAACAGAGAATAAGCGTAAGGCAATCTCAAGCAGAATGAGGGACTTATTTGCCAGCCTAGTTAACGTCAGTTTGCGGCCCTTAGGTTTCGGCTAAGGCCGCAAAAATTGTCGATATGAACGAAAGAGTAGTTAACCGTGAGCGTGATTACAGCCAGCCCTTTTGTTTGGCGATGCGTGCTGCATCGATTCGGTTGCTAGCATTGAGTTTAGCTATGGCTTCGGAGAGGTAGTTACGCACTGTGCCTTCGGCAATAAAGAGGGTTTCGGCGATGTCGGCGGTGGATTTGCCTTCACTGGCGAGGCGTAGGGCACGGCGCTCTTTATCGTTGAGTGGGTCGACGTCACCAATTGCCATCATGGCAAGTTCAGGATCGATCACTCGTTTTCCCGCCATAACTTGTTGAATCGCGTTGACTAAGGTTTCTGAGGGCGCATCTTTTAGCAGAAAACCGCCGACACCGGCTTCGATAGCACGTTTGATATAGCCCGCGCGGCCGAAGGTGGTGATGACCACTACCTTAGTTTGACTATGTTGATCCTTTAACCAAGCTGCCAGTTCTAGGCCTGTGCGGCCGGGCATTTCGATGTCGGTTAGCAAGAGATCAAAGCTTTGCTGTTTGAGTAGACTTAGGGCTTCATCGCCGTCACTCGCTTGGGTGATATTAAAGCCTCCCGCAAGCGTGAGGAGTGCGGCGAGCGCGCCTCGCACCATGGCTTGGTCTTCCGCCAATAAAATCTTCATTAATCTGTTGCTCCCTGCAAAGGTAAACTGACGGTAAATGCATATCCCTGTTCGAGATTATAGCTCAAGCTGCCGCCTAAGCTGTCAAGACGTTCGCGTATCCCCGTGAGGCCATTGCCTTCGGCGATAGGTTTACTTGGACCGTTATCTTTGACCTCAACGACTAGCCTATTTGGTTGCTGAATAAAGTCGATAATACATTGGCTTGCGCCGCTATGGCGTAAAATATTATTCACCAGTTCAGTGACAATTAATCCCAGCTGGCTCTCCATTCTGGCGGGCAGTTTAGGGATATTGCCTGTCAGCTCGACACTGACCCCTTTTTCCCTCAATAGTTTACACAGTTGTGTCACGCTATCCGCGAGACCCTTATGTTTGTAGTCAGATACGGTGTGGCGAATTTGGCTGAGGCTCTCACGGGCAATTTGGCCGAGTTCATTAAGCTGGATTGTGGCCAGTTGGTATTCTTGTTTTGCCAGCAGTTTTTCGGCCAGTTCCGCCTTAAGCGCAATCGAAGACAAGCTGTGGCCCATGATGTCGTGTAGATCGCGGGCAATGCGTTCTCGCTCAACCATAGCCGCTAAGGTACTAATTTCTTGCGTGCTTTGCTGTTCTTTCAATTTATGTTGATGACGCCTGCGTTCGGCTACGCCAAACATGCCTACACCGAGCACTAATCCTGAGCCGTAAAGCGGAAAGTAGTAACTGTTAAAGTCGTAAATCTCATTGAGGGCAAACAACAGGCCGATTAAGGCGGCAATAGCCAACAGACAGGTTCTGAGAGGGTAAAAAAAGCCAATGAAAAAAGCGGCAAAGGTGAATAACGAGATAGATCCTGGGTTGATTGGGGTGATTGCTGTGGCGATGGCCACCATCATCAGAATGGGGCGATAGGCGCTATTCTGATTGCACTTATAGGCCCAAAAATAACTGGCAATAAAAGGAACGAGCACGGCAAAGCTAAGGGCGATTTTCCATGCTGGATACGCATTGATGGCAAGCGGAATAAGATAAAAAACTAAGTTTATCAAATACACCCAAGCGAGTTTTCGTTCGAGCTGAAGATGCGTGCTTGTCATAATGCCTCTATGAAACTGTGTTGATTGTTAATCGTTAATCATTGAATGCCCGAAGGTGGCATCTGTGTTGATTTACTCTGTTTAGGAATTGGCGCTGGGGTCAGCGCCATCAGTCCCTGTTAACAACTTGGTCTAGTTTGCACTTAATTGCTGATTTTGTTGAAGCAAAAATTTTGCCCAACCGTAATCTGCTTGCACTTTCATCGCCTGTTGCCAATCTTCGATCGCGCCTTGGTTATCGCCTAATTCCTGTTTAGCGAGCCCGCGCCACGTATAAGCTTCGGCTTCACCCCAACAAATGTCGTCGCAAGGTTGGGCGAATCTTTCTATGGCGGTAGTGGCGAGCGTCTTAGCATTTTGCATGCTGCCGCCAAACATAGCAGGCGTGTTATAGGCGGCGATCGCTTTAACCAACGCGACCCGTGGATTTTGGGGTTCTAACTGCTCGGCGTTGGCAATGGCTTTGGCTGATTTCATGCCGAGCGTTGCGCCTTTGCTATTGTCTAAGGCGATTTGCATGCCATAAACCGACGATAACAGCGCTAGGGATTCCGCGTCAGCTTGGTTGGTTTCTAAGGCTTCTAATGCAGTTTGCGCATTAGTCAGTGCACTGCTGGCGAGGGCTTTCTGTCCCATGACATTGGCGCTAATGGCTAAGCGATAGTTCGCATAGGCTTTGTCGTAATCTTGGGTGCTGGCACTCAGTTGTTGCAACTGCGCTAGGTTCATCGAGTTTGAGGCCGCATCTATGCTGGTGATATTGGCATTAGAGGGCGACGCTGCAAACGCGTGGCTGCTGATAAGCGCTAAGCTTGAGACGATTAATAATGTTTTCATGGTATTTCCTTATTGAGTAAGTTTCCCCGTGTTTGGGTACGGACTCATTATCAGAAATTCGACAGGAGAAATGCAGACACAAAGGTCATGGCTTTAGCATGACAAATGTCATTAGTTGAAGGGTGTTAAACTTGTCTTTTTTAAAAAGTGCGAAACCCAGATAGCAAAACACCGGAGAGGCTCCGGTGTTTTAGGTGGATATTACCGCTGAGTGTTATACCAAAGCTTGAGCGCTCGCTTGGTTAATCAGGTAACCCATATAACCTAAATAGGTCAGTAGCAGCACAGAACCTTCGCGGCGGCCGATGCGTAATCCCGTCCATGCAAAAGGCAGAATCACTATGGCTAAAGCTAACATCACCATAAAGTCGAAGGGTTGGAAACCGAGTGACGATACTGGATGAACTATGGCAGTAACACCTAAAATGCCCAAAATATTGAACAGGTTAGAACCCACAACGTTACCGATAGCGATATCGCTTTGACCTTTTAGTGCCGCAATCACTGAGGTCACTAACTCTGGCATACTGGTACCAATCGCCACGATCGTTAAGCCGATAATGACTTCACTCACGCCGAAGGTTTTCGCTAAATCGACTGCGCCGTTTACAAACAGAATACCGCCGCCAACTAACATGCTAATGCCGACTAGGATGAACAGGCCAGAAAGCAGTGGGTTATTCGGCCCCGCTTCAATCTCTTCTTCATTCTTGCTGTTTTTCGAGCTGATATAGCTAAAGGTTAAATATCCGACCAGTAGAGACAGCAGTACCACACCGTCAATAAGGCTGAGTTCGCCGTCTAACAAGAGTCCCCAGAACAGTATAGACGCCAAGATCATCAAAGGGATATCGCGCTTCACCACTTGGGATTGCACTTGGATAGGGCGGATTAGCGCCGTGATCGCTAAGATCAAACCTATGTTGGCAATGTTAGAACCGATAACGTTACCTAAGGCGATACCACTGTTTCCGGCGAGGGCAGATTTGACACTCACGGCCAGTTCTGGTGCGCTAGTACCAAAGGCAACAATAGTTAAACCTATGATAAGTGGCGTAATACCTAAGCGTAGGGCGATAGCACTCGCGCCACGCACTAGGGCTTCGGCGCCTAAGGTTAAAATAATAAAACCGCCAATAATCGATAGTGTAATGAGCATCACGGGACTTTGTTAAACCTATATTGAATGGGAAAGTACAAGGGAATTATCCCTTAGCCGACAGGCGCACATGATACGTGGTTTTATCGGAGTTTGACAGAATATGTCATAAAAAAGCACGCCGAGGCGTGCTTGATTTGTGCCGATAACCATATTGGCCATCTCTCTGTTTAGCTGAGTTATTTTTGCGTTAACTCATCTTCCCAGCGAACTTGGGCACCACGAATGCCATCAACTCGTTCGCTAAAGGCTTTTTCAAGCACATGGCGTTTGATCTTAAGCGTCGGTGTCAGTACATCGTTCTCAATCGTCCAAGGATCGTTAACTACGATAATCGCATCGACGTGTTCGTGTGACTCTAAGTGCGGATTAACGCTATCAAGTGTCGCTTTGAGTGAAGCGCGGACTTCTTCGCGGGCCTGCAAACTAGCGCCTTCGGATAATTGCACTAATGCGACCGGGTGCGGCAGGCCTGAGCCAATCACGCAGATCAGTTCAACATGCGGGTCCTGCGCCAATTTACGTTCGATAGGCACGGGCGCGACATATTTACCCTTGGCGGTTTTGAAGTTGTCCTTCACTCGGCCAGTAATGGTCACGCAACCATCGGCATCGATAGAACATAAATCGCCAGTGTGGAAGAAACCATCGGCGTCGAAGGCTGCAGCAGTGGCTTCGGGCTGTAGGTAATAAGCCGTCATCAACCCTGGACTCTTAAGCAGTAGCTCACCATCTTCCCCTTGGCGGATCTGACAATCCTCAACCGGGCGACCTACTGTGCCAATTTTACTGGCATCGAAGGGGTAGTTGATAATCGAGTAGGCGCAGTTTTCTGTCATACCCCAAGCTTCACAGATATTCAGACCAATGCTGTGATACCAGTGGATCAGCGACGGCGGGATCGGCGCAGAGCCAGAGCCGAGCAAACGACAATGGTTCAAACCTAAGCCTTTATGGATTTTGTGCTTCACTAAGCTGCTGATTAACGGGATCTTCAGCAATAGATTCAGTTTACTAACACCAATTTTGTCTATGATGTTTTTCTGGAACAGGCTCCACAAACGCGGCACTGAGAAGAATACCGTCGGTTTTGCCCTTTGCACGTCGGCGACGAAGGAGTCGAGACTCTCGACGAAGGCGACCACGCTGCCGGAATAGAAGGATGAACCTTCAATCGCGACGCGCTCTGTGATGTGCGCTAAAGGCAAATAGGATAACAGTCTGTCATTGCCGTCGGTGCGTAAATCACGCACTACCGCTTGGCAAGTCCAGCCATAGCTAGCGAAGGTTTGAATCGCGCCCTTAGGTTGACCCGTTGAGCCAGAGGTATAGATAAGTGTCATCACTTGATCAGACGTCGGCAGCGGCGCATCAACTAGGGGATAACCTAGTTTAAGTAATTGTTCCCATTGATATTGGGCTGGCATTGTATCGTAGGGCATAGCAAGACGTAGCAGCTCGCCGCCGACACCGGCTTCTTGGTCGGCCCAATGGTCGAGCTTACCGAGGAAAATGGCTTTTGCACCGCTGTGTTGCAGCACATAGCGAATAGTATCTGTGTTGGCCGTTGGGTAAATCGGCACGCTGACATAGCCGCCGTGCATTAACGCAAGGTCGGTGATAAACCATTCGGCGCAGTTTTTAGAGAGTACGGCAATCTTATCACCGCGCTCTAGGCCTAGATGCTGTAAGGCACCGGCCAGTTGTTGCACTTTTTCTTGAACTTCGGCCCAAGTAAAATCAAGGAATTGACCCTTGATAGGTTGGCGTAAATAGACTTGATCCCCCTGAGTCTTAGCCCAGTGCGCCAGCATTTCAATTGGCGTCTTAATCGCTGTATCCATTGGCAATTCCCTGTTCTTTATTGTTATTTTGGCTGCACCCAGTATGGCGAGTTTTGTGAGCCCGCGCAAATAGTTTACAGGATTGTAAAATATGAGAATGGAGCCCAAAGCCTACAAGCTGAGTATGAAAATAAGATGAAGAGAATAGTAAGCGGAAACGAGGAGGGGGGGCGGTAACAAAAAAGGCGCCGCAGCGCCTTTTCGTTGGTTGAAGGGCAATGCTTGAGCTTACAGCTCCATCACGCCATCCATTTCAACTTGAGAATCCTTCGGCAATTGCTTCACACCGATAGCTGCACGGGCAGGGTATGGCTGGCTGAAGTAGCGGCTCATGATTTCATTCACTTTTGCAAAGTGGCTTAAATCAATCAGGAAGATGTTGAGCTTAACGATATCGTTGATTGAACCACCCGCAGCAGTACAAACTGCCGTTAGGTTTTCAAATACTTGCACCACTTGCGCTTCAAAATCATCGCTGACAATTTGCATTGTGCTTGGCACAAGCGGAATTTGGCCGGATAAATACACTGTGCTGCCGACTTTGACGGCCTGTGAATAAGTGCCGATGGCCGCAGGGGCGTTTTCGGTCGCTATAATGATTTTTTCTGCCATGACGTTCTCTCTAATGTTGAGTTAATTAACGATTACGGGACGTACGCAGCACTTCGGGCAGTACCCTAATTCGGCGCATCACGTTGGCTAAATGGATCCTGTCTTTCACCGAAATCCGTAGGTTTATCAAATACACGCGGCCATCTCGTTCCTCTGTGCTGAGGTTGTGAATGTTCGAGCCTTCGGCGGCAATAATCGAGGTGATCTTCGCCAGTGCGCCTTGGTGGTTAACGATTTCGACCCGCAGGTTGGCTTGGTAATCAACGCCTTCGACATTATCCCATTGCACAGGAATATACTTGTCAGGCTCGCCTTGATAACCACGAATGTTGGCGCAGTTTTCCATGTGCACCACTAAGCCCTTACCTTGGCTCACGTGGGCAATTACGGCATCACCAGGGATTGGGCGACAACAGTTGGCGAAGGTAACGAGCATGCCTTCGGCGCCACGAATTGGCATCATGTGGCCGTCGCGACTTTCTTGATTCTCAAGATTATCGCCAATCAAACGCTGGGCAATAACAATGCTCATGGCATTGCCAAGGCCGATGTCGGCGAGCAGTGAATCGAGCGTAGTATGTTTAGTATCGCGAATGACTTTTTCAATCTGCTCAGGCGGAATACTGTCGAGCTTGGTCTTGCCGAGCGCATGGTTGAGCAGGCGGCGACCAAGCGCAATCGCGTCATCCCCCTTAAGGCTCTTTAGCACTTGGCGGATTTTAGCGCGGGCCTTACCTGTTACCACAAAGTTCAACCAAGCGGCATTCGGACGCGCGCCTTTAGCGGTAATGATTTCAACAGTTTGGCCTGAAATTAACGGCTGGCTGAGCGGATACGCTTGACGGTTAACGCGGGCACCGACGCAAGTATTACCCACATCTGTATGCACTTCGTAGGCAAAGTCTACCGCGGTGGCATTCACGGGTAATTCCAGAATGCGACCTTCAGGGGTGAACACATAAATCTCTTCTGGGAAGAGTTCCGTCTTCACGTTTTCAACGAATTCGAATGAGGTGCTGGCGCTTTGTTGTAATTCCAACAAGCTTTGCATCCACTTACGGGCACGCACTTGAGTCGTCGTGCCTTGGCCTGTTTGCGCACCGCCTTTGTAGGCCCAGTGTGCGGCAACCCCTTTGTCGGCCATCTGGTCCATATCTTCGGTACGAATTTGGATCTCAACGGGCACGCCGTGTGGACCAAACAAAGAGGTATGGAGTGACTGATAACCGTTGGCTTTAGGGATAGCGATATAATCTTTGAAACGACCGGGGCGAGGTTTATACAGTCCGTGCATGGCGCCGAGTACGCGATAACAAGTGTCGATGGAATCGACTATTACCCTAAAGGCGTAGATATCCATCACTTCTTGGAATTGCAGCTCTTTACTCTGCATCTTGTTATAGATGGAGTAGAGATTTTTCTCGCGGCCTTTAACTTTCCCTGGAATACCAGCATCGTTTAAGCGCGTATAAACAGCGGCTTCAATGCCTTGGATCAATTCTTTGCGATTGCCACGGGCAGCTTTAACGACTTCTTTGAGCACCCGATATCGCATGGGGTAATACGCTTGGAAACCTAAGTCTTCTAACTCTGTCTTGATGTTATGGATACCGAGACGGTTAGCGATGGGCGCGTAGATTTCTAAGGTTTCGCGGGCGATACGGCGACGTTTGTCGGGACGCAGGGCGCCTAAGGTACGCATGTTGTGGGTTCTATCGGCAAGCTTGATGAGGATAACGCGGATATCCTGCGTCATCGCCATCATCATTTTACGGAAGTTTTCAGCCTGAGCTTCTTTCTTGTCGCGAAATTTAAGCTTATCGAGTTTTGACACGCCTTCGACGAGCTCGGCCACCGCGACACCGAATAGTTCGGCTAAATCGTCTTTGGTGACATGAGTATCTTCGATAGTGTCGTGGAGCAGTGCTGCCATCAGCGTCTCATGATCAAGACGCATGTCAGCAAGGATGCGGGCAACCGCAACCGGATGAGTAATATAAGGTTCGCCACTCGTGCGCATTTGCCCTTCGTGGGCATCACGCGCAATCTGGTACGCCTGCTTGAGCAATTCTACCTGTTCGGGTTCTAAGTAACCGGAAGCAGACTCCTTTAGACCTTCAAACAGATACAAGTGGCAGCTCTCCTTTTACCTAAACAGGTGTTTACTCAATAACGCTTATAATGAACGGCCTTCAGCAATGGCTGAAACTGCTGCAATTTCAGCTGCTTCACGTTCACGTACCGTTTGACGCTCATCGGCATCTAAGGTGTGAGCGTTAACTAAACCTAGTTCGATTTCACGCAGGGCGATAACCGTTGGTTTGTCGTTCATCTCTTCAACCATAGGGTCTTTACCCTGCACGGCGATTTGGCGAGCACGACGCGCCGCAACCAAGATCATATCAAAACGGTTGCCGATTTGTTCTACGGCGTCTTCTACAGTTACGCGAGCCATGTGTTGAAACTCCAGTTTATCTAGGGAAAAAATGACGCAAAATTGTACACTATGACAGTTGGCCTGCCAACAGATCGTTAAGCATATCATTTTGCGCGTGGATCTGACTAGCACAGGTTAAGCGCTGACTGCGAATGATGGCACGCAAATCAGCTAACGCAGTGTCAAAATCGTCGTTGACGATAATAAAATCATATTCTTTATAGTGGGACATTTCCGATACGGCTTGTGCCATACGGCTGGCGATCACATCGCTACTGTCTTGTCCACGTCCAGTGAGGCGGCGTTCTAGCTCGGCTTTCGAGGGTGGCAGAATAAACACACCAACGGCATCGGGCATCACAGCTTTCACTTGCTCGGCACCTTGCCAGTCGATATCGAGGAAAACATCGATACCTTGGGTGAGCGTATGCTCAATCACATGGCGCGAGGTACCATAGTAATTGCCGAACACTTCTGCCCATTCAAAAAAGGCATTTTCAGCAATTAACGCTTTGAATTGTTCAACATTAACGAAGTGATAGTGCTGACCATCGACTTCACCTGGGCGTGGCGCGCGGGTGGTATGTGATACCGAAACTTGCATGTCAGCGGGCTTGTCTTTAAGCAAGGCCGAGATTAGCGAGGATTTACCTGCGCCACTTGGCGCCGATACAATAAATAAATTTCCGCGTGCGGTCATGAGCTAAATATCCAATTGGTTAACGCAAGAAGGGCCCAGAAAGCCGAGCATTATACCGATTTTGACCATAGAAATACTAGGGCTTGTAGGCCAGCAATTTAAGCCTATTTTAGATTTACTTCGATATTATGCGCATTTTTATCCGACTAGGGTCGTAGGCACTGACAATTGCGGGTAAATTGGTTACGCTCGTGCGAATAAGGCAATGAGTGTCTTTTTGTATCTCGTTTTTACAACAATAAAGGAAGGGTCGTGCTGTTAAAATCTCTTTTCTGCCTTCACGGGCGTGATTCTCGTCCCCGCTTTGTCGCAATCAGTGTTGGCGTTTACGTCTGTATTGCACTTGCCGCCGCTGCTTTTGGGGCTAATTTCCTGATGTATTTACTGGCATTGTTGGGCTTACCTTTATTAGGGCTCGCCAGTTTGCGTCGTTTGGCCGATGCCGATAAGTCAAAAGGCTTGATTGGCGTATTCCTATTACCCTTATTTATTTTCATCGGCCTGTTAACGGCCGATGTGCATATTGCCTTAATCGGCTTGAGTTTAGTGTTAGCCGCTGGACTGACGTTTTGGGGCTGGCGTTTACCAGCGCCAACCATAGTCGATTATCGCTACGGTTATTTCGGCCCTGAGCTGGATGCGCCGACATCGCAAGCTATGCCTAGGCGCCGTGTGGAGCCGACGATCGTTCCAAAGGCGGCTAAAGAGATAGAAACTCAAGATGTTGCCGCAAGACAGGCAGCTAAATCGACCGCCCATGTTCAAGCGCAGCCTCAAGTTGAGCGACATGAGCCGCATGTTGAGCAGTCGCATGCACAGGCCATTAGCGCCGCAACGGCCTCTGAGGCAGTGGTGGATTTATCTGCCCATGTTGAACCCATTGCCTCTGAACCAATAGTCGCTGAGTCTAATGTCGCTGAACCTATTGTCGCCTCGACTATGAGCGCGGCGGATGCCGAGCTTGAGGCCCAGCGCCAAGATGAACTGCGTTTTGATGCCCTTACTCGGATGACGCAAAATCCTGCCGATTTTGTGGTGCCCGACAGTCATGCGGATTTTCGCCCCGCTAACACCAGTCGTGTCGAACAGACCAACATTGATTTAGTCGACGTTGATGTCGCCAATGTTGATTTAAAACATGTCGACTTTAACGCGGCCCAATTAGATCCCGCCGAACGCAGCACAGAACCGGGTTGGCGTTTTGCCGTCGAAGAAGATGACGAGCCGGACAGCTTTAACGATCGAGATCATGCCGAAGAAGTACGTCAACGCCGCGCCGAAGCGAAAGATTCAGGGTCGATGACCGAGCTGTTTCGCGGTTTGTTCGAGTTTTTAGCGCCGCTAAAGCGGTTCTTCGTACTACCTAAGATTAAGCTGCCTAAACTGGAACGCCGTTATTGGCGTCCTGCGGGGATAGGTTGTGGTGTTGTGGTCTTGCTCGCCTTGGTTTGGGGCCTTTGGCCAAGTGGCGACGCTGAGACGGGCGACGGCGCAGACGTGGTAACGGTTAACGCGATTGCGCCCTATGCGGGTGAGCGGGTGACGCTCGCGTTACCTGACGGCTTTTCTGTGGCGCTGGAGGATGACATCTTGATCATGCGTTGGCTCGGTGAGAAAGGTAAAGCGCAAAACCTGTGGTCCTTAGCGACGGCAAAGGGCGATAAAACCTGTAGCCTGTTGTCCTTTAACAATGGCACTGACTATCGCCCTGTGACTGTGGACTTGAAAGCTGACTCAGCAACCGAGGCACGATTTACGCCACTCGACACTCAAGCCATCATAGTGGACCTTGCGCGCCGTGGCAGTATCAGCCTGTGTGGTTATAAGTTCAGCCTTAAGGGCAGCCAAGCGATATTGGAACAGAATCGCACTTTTGGTGATTACATCGCGCGTTGATTTAGACTGCAAGTTAGGTCGCAGCTTAGGTCAGCAACATAAGCCGATAAAATTTCTGCTTAAATGCTAGTCACAATTAGAGGGGTAGATTACACTTTTGCTCCTCTTTTTCTCAAACCGCGTTTTAAATGCGTTATGCCTTGGTATTGGACGCGGCGGTTTGGGGAAACATCTTTAAATTTGAACTGAATATATCAATCCAGCATGAATACAGAATTGACCTTAGTCATTATGGCTGCAGGGCTAGGTAGTCGTTTCGGCGGCGATAAGCAATTGGCAGCGCTCGGTCCCGCTGGCGAACCCATGTTGGTGTTATCGATCATGTCGGCCATTCGCAGTGGCTTTCAGCGTGCTGTGCTGGTGATCCGCCCTGAGTTAGAAACTGAGTTAACTGAGATACTGACACGGTTTTTGCCCGCCGATTTTGCCTATCATTTTTGCTATCAAGCGCTAACGGATTTACCCGAAGCCGTGCAGCTGGCTGATCTTAGCCACAGGCTAAAACCTTGGGGCACGGCCCATGCTCTGTGGAGTGCGCGCCATCTTGTTAAAGGGCCAATGGCCGTTATCAATGCCGATGATTTTTATGGTGATAGCGCTTTTGCCTCGTTAGCCAAGGGCTTAAGCACTAGGCCAACTGAGTGGATGATGGTGGCCTATCCCATAGAACTCACTTTGTCGGAGCACGGCGGTGTTAACCGGGGTCTGTGTCAGGTAGAAAATGGTCAGCTTAAATCGGTTGCCGAATGGCTCAATATCGCCGGTCAAGGTGGGCAATTGGTGGGGGATGGCCCGCAGGGACATGCGAGTTTGCCGCCCGAGTCCTTGGTCTCTATGACTTGCTGGGGCTTCTCCCCAAGCATATTTGATGTTATTAAACGTGAATTAACCGAATTTATCGGGCAACAGGGTCAACAACCTAAGTCTGAATGTTACTTGCCCGCCATAGTACAAGCGGGCATTGAGCAAGGTTTGCCTGTCTATGTCGATGTTGCCTGTGAGCCTTGGCTCGGCGTGACTTATCCGCAGGACACTGTCTGGGTAAAACAAAAATTAACGGAGTTATTGAGTGATTAAACTCATCAGGCAGTCAGTGTTGCCTCATTTTGGTATTGAAGCCGCCGAGGCAAAAATTTCTGCCCTCGGTAATGGCCACATCAATGACACCCTTTTGGTGCGCTGGCCTGCGGGCGAATTAGTATTGCAGCGAATTAATACTGAGGTATTTAAAACGCCCAACGCTTTGGTTAGCAATGCCGATAAAATTAGCCATCATTTATGCGCTAAGTCGTTGCAGCAACAATACGGCCTCAAAGTCGTTAGCCCTTGTTTAACCCAAGAGGGTGAATTGGCTATCGATTTAGGCGAACAAGGATTTTGGCGCGCGATCAGTTATTTGCCCCACAGTCTCAGCATCGAAGTGGTTAAGTCAGAGCAAGAAGCAGAAATGGCGGCCAAGGCTTTTGGTCATTTTGCCTCAGCCTTGAGTGACTTTGATGCAACTCAGCTCGAAGATGTGATCCCCCAGTTTCACCATTTACCGGGACGCATGGCTTTGTTGCAACAGGCCGCCGAACTCGATAGCCAACACCGCTTAGATCTTTGCCGTCATTGGGTCGATTACGCGTTATCTCAGCAGGCGTTGCTGGATGAACTTGCAGAAATATCGCCAAAGCTGCCGCTGCGAATTTGTCATAACGACACTAAAATCAATAATATGCTTTTCGATAAACGCGACATGTCCAGTATGGCGATTATCGATTTAGATACCTGCATGAAAGGTCATCTGATGTATGACTTCGGCGACATGGTTCGCACTTTCTGCTCACCAGAGGAAGAAGACTCTACGGCCTTGGATAAAGTTCAAGTGCGCCAAGATATCTTCGCCGCGATTTGCCGTGGCTACTTGAGCGAGTTGGGTGATGTCTTAACGGAAGACGAAAAACGCAGTCTGTGGCTCGGTGCGCGGGTGATTTGTTTGATGATAGGCGTGCGTTTTTTAACGGATTACCTTAACGGTGATGTGTATTTCCATATTCACCGCGAAGGCCATAATCTGGACCGCGCCGCCAATCAGTTTACCCTGTACCAAAGCTTGCTCGACCAAGAAACGGTACTTAAAGCCCATTTCTAAGTCAGGATATGGATCTTGGTGATTGCGCCAGTTTCTAGATCTAATGCACTGATAAACAAAACGCCCAAGGCTTTGCTTTGGGCGTTTTGCTTTAATACGATTGCTTTAATACGATTGTTATAAGTTCTAGGCAGATGGCATTTCATCCGACATAATCGAGCAATATCCCTTAATATTATATGGATTTCTGCTTGGTATGGATTCACCTGTGGCGCAGCCGTTAGTCACTTGTTCACGGCGCAGCGAACGTAACTCTTGGTTAAGATGTACCCTGCGTCGCTCGCAGCAGGAGGCGGTTGCCTCGTCTATGACGACGGCGACCAGCGATAACTTCTTCAACGCCTACGCTATTTTTCTCGGTGCCAGCCTGGCGCAGATGGGCCTTGTCACAGGTTTACCTCAGCTTTTTGGCGCTATCTCCCAGCTTTTATCTGTCTGGCTCGCCAGCCATTTCTCCCGCCGAACTTTTATCGTCTTTTGCGCCGCCTTTCAGGCCTGTGTTGTGCTGGCGATGGGCGCGTTAGCCGCTTTCGGCCCTGAACATGCGGTGTGGATCTTTATCGCTTTAGCCGTTGGTTACCATGGCTTTATCAATCTAATTCAACCCCATTGGCGGGCGTGGATGGGTTCTATTGTTCCCGAACGGCGCCGAGGCACTTTCTTCGCCGCGCGCACTCGGCTCACTATGGGCGCATCTTTGAGTGTGTTCTTTATTGGCGGCGGCATACTCACCTTAACCGACTCGATGCAGATGGCGTGGCTGGGCTTTAGTCTGTTGTTTTCAATCGCGGCGATGGGGCGGTTTGTCTCGGCTTGGCTACTGCTGCAGATGCACGATCCCGATCCGCGCGAGCCCAAACAGCGCGGCGTATTTTTGCAGACGATTGGTCATTTTCGCGAGGCGTGGAAGGATAAAACCTTTCGTCAGTACAGTTTATTTGTCGCCAGTATGCAGGGCATGGTGGCGATCTCTGCGCCATTTTTTGCCGTTTATATGTTGGAAGGCTTGAAGTTTACTTATCTCGAATTTGTGTTCGCCAGCGTGGCTTCGATAGCGACTCAATTTGTCACCTTGCGATTTTGGGGTCGCTTTAGCGATCAATTCGGCAATCGTTTAGTGATGATCATCACCAGTTGTATGATCCCAAGCTTGCCGCTGATGTGGCTATTCTCGGATAACTATCTGTATATCTTAGCTATCCAAGCTTTCTCTGGCTTGGCGTGGAGCGGCTTCACCTTAAGCACGGCAAATTACCTTTATGATATCCGCCCCTTTCGCAGTGACTTTGCGACCTACGCCGCTTTGCAAGCAGCCTTGAGTGCGGCCTGTGTATTTGTTGGCGCGATATTGGGCGGCACTATTGCCTCCCATGCGGCGGACTTTTTAGTCTGGTCGGGTTGGGATAGCGTGTTATCTAGCCCTATCTTCGTGGTGTTTTTAGTCTCGACCGTGCTGCGAGCCTTAGTGACACTCTGGTTTATTCCGCGCTCGGTCGAACCCAAAGTGCGTCCAAGGCCGCAGTTACTGCAACTTATCTTCCGTATCCGCGGCTTTAATGCCATTTCGGGCGTGAGCCTAGATTGGCTCACTGTGGTGAAACGCCGCAAGCAATAGGGCGTGCAGCTAAAGCGTATAGTCGAAAAACTTACGGCTTTGCGGGCCAGCGCTATTACTGTTCACCTTATTGGGATGTTTATATTCATGCCAAGCCGCGACGTGTGTACCAGCGTTTAATTTAGGGAAATCCAGTCCATGCTCGCTGGTGTTGACTCTCTGTGGCTGAATGTTTCCCTTTGCCTGACTTGTTCCTTCCTGTGATATCTGGTTTGGTGCACTTTTTATTTGAGAGTCCGAGAGCGCCCTATCCGTCACTTGGCCTAAGGTTGGGCAATCCGTGGTTAACCCTAAAAATTGCAGCAAGCGACTCAAACTCCCCGCATGGCTTATATCGAGCGTGATAAAATCTTCTCGGCCATGAAAGTAAGCGAACACTTCGTTTTGATGCCTTTGATAACAATCAATAAGATGTGCCTCGTCTGCGGGATCCGCCACTTTATCGATTGCAAACGTGTGCCGAAAACTGCGTTTCATAATCGGGTGGAATCGCCCCGATTTTGCTTCTAAATGCGGCAGCATTTTACCCAGCAGCATTTGCATTGAAGGCACCCACTTCGCCATATCACGCTCGAGGTAGACAAATTTGGCCTGCGGGAATAGACCATCAAGCTGCCGATAATCACTAAAACAGGGCACATCAGAAATCGCATCGGCCAGCATAAACGCCTGTTTAGTAAAGGCCATGTGCGCCACTTTAAGCCCTTGCTCCAACAACGCCACACTCACACTCGTCGTGCCCGTACGCGGCAAACCTATGATAAAGACCTTCTGTTTTTCTACACTTGCCTGTTCACTACAGAGCGTATTCGAATTTTCAGGATTCATCTCATCTGGCGTGGCAATCTGTGAATGACCAGTGTGCTTTGCATTGCCAGTAAGTTGAACATGAGTAGTGATAGGTGAATTGTTGGCATTATCAGGCATAGATTGGGCTCAGAATGTGTGCAAGCGGTGCGGTGTAAGCAATGAGTTTTCACATCGGCGTTTCGCATTGGCGTGTCAAATCAGCGGCGATAGTATATCTCAGCACGAAGGCTTTGTCGGTGGCTATAGGCAATGCGCCACAGTATTGTATGGCCGCTGTACGATAAAAATGGATGACCACAAGGCTTAGAAACGAGATCCCAGATTACCAACAAGGCTCATTTCCCTGCATTGGATAGCATGTTGGTATTAATGAGGCGATTAGTAGCGCTATTCGACTCATAATGTGAGGCGAATATACTTGTTAATCGCCTCATTGTGTGAGTAGAATAGACTTGTTATTTGACTCGTGGGGTAAGATATGTGGATTTGGCAGCAAGCTGATTGGCCTAAGTTTCATTGGAATACCAGCAGCATTGATGCCTTGCTGCGTCAGGTCTATTTCAATCAGGGCCAGCTGCTTGGCAAGCAAATGCTAAGTCACGACGACAGCCTGTTAACGTCAGACGCTGCACTCGACAACTTACTCGACACTTTACTCGCTAATATTATCCATTCCAGTGCCATCGAAGGTGAAAAGCTCAATGCTGCTTCAGTGCGCTCATCCTTAGCTAAAAAACTCGGCGTGACTGAGGAAAAACCGTATCCGACCACAGCGCAAACCGATGGTTTAGCTGACATTATGCTCGATGCTTTAAAAAATTTAGAGACTGAATTAACCCTCGAACGGATATTGGGCTGGCATCAGCAGTTATTTCCGCAGGGTTATACATTGTTTAACCCCGTTATTGGTGGAACGCTACGTGGCGATACGCCTATGCAAGTTGTCTCAGGCCGCATTGATAAACCAACGGTGCATTTTGAGGCCCCCTCTCGAGCGACTTTGGACGCTGAACTCTCGCAGTTTATTGAATGGTTTAATGCTTCACGGCAAGCGCAGGATTTAGATCCGCTTATCCGTGCGGCGATAACACATCTTTGGTTTGTGACTCTGCATCCGCTTGATGATGGTAATGGGCGCATCACGCGTTTATTGACTGATTTAGCTTTGGCGCAAGCGGAGAAACGCTCGATTCGTTTTTACGCTATGTCGGTCAGTATTCTTGCCCGTCGCCAAGCCTACTATGACATTTTAGAGTCCACTCAAAGGGGCGATATAGATATTACTCCTTGGTTAGTCTGGTTTTTTGAAACCCTCAATGACTGCTTACTCAATGCGATGGCAGATGTGAGCAGAACGCTGTCGAAAACTCAGTATTGGCAAAGTGTCGACCAATCTTTATTGAGTCAGGAGCAAGCCAAAGTACTCAATCGGATGTTAGACGGCGATTTTGAACTAGGGATTAATAGTAGCCAGTACCAAAAAGTCGCGAAGGTGAGCCGCGCCACCGCCACCCGTCATCTTGCGCAAATGGTCGAACAGCGGTTTTTAGTTAAAGCCGACGCTGGCGGCCGAAGTACCAGATACTTATTGCCGAGTGGGAAATAGCGGCAAGTTTACCTTGGTGTTGGATGCTCAATTATCAATAGCCTAAAGCGCTTTTGAAAGTGTTGCTGCTGAGTCAGTCATAGGTAATTAGTTCCACTAAAGCCAAGCAGTATTAACACCCTATCGAAAAAGACAAAACCATTCTACAAATCGCAATTTGAATCAAATTGTTATGTGATATATTCGCTACAACTTTGATTGTTAGTCTTGGGGCATATCTGGCTATGGAACAGGTCGGGATTTACGAAAAGCTTATTACTCAACTCGTTGAGAAGCGCTTAGATAAGGACATATTTTTTGTTGGAGAAAGGAAGCTAGAGGTTGCTGAAGCAAGTATTTGGCTCTCACGTTTTCTGACTAAAGTAATTGAATATGTGATTAATTCTGTTCCAGCAGATGAAAACCAAGTTATCAATCAAATCAGTTTGGCTAATCGGTTAGTGATGTGGCTAAAGGAGCACATCAACGATGATGACTTTATCGATGAAAATCTCATCGATAGCCAAGGTCGGATACTCACGGCTCTTTTTAACAAACAAAATCCGATAGCGACTGATTTACCTAAATATACGGCCGCCATATTTCCATTAACGGGGCTCACTCAGAGTGAGCTTTTTTGCGGTAGTAATGCGGGATTATCACTTGAAAGTGAGCTTAAACGTGAGATCCGATCATCAGATAAAATCTATTGGCTGGTGTCATTTATCAAGTGGGCGGGGATTCGCATCTTCAAAAATGAACTTGAGGAATTTACACGCAGCGGTAAAGAGTTACGCATCATCACTACATCTTATATGGGCGCGACTGATGCTAAAGCCGTCGAGTTTCTAGCCAGTTTACCCAATACCCAAGTTAAATTAAGTTACAACACTAAGCGTGAAAGGCTGCATGCAAAGTCCTATTTATTTCTACGAAATACGGGTTTTCATACCGGTTATATCGGTTCTTCAAATCTATCGCATTCAGCATTGACCAGCGGGCTTGAGTGGAACCTTAAGATCACCACCCAAGAAATTCCCCACATCATTGAGAAATCACTCAGTACCTTTGAAACTTACTGGGAATCGAACGAATTTGAGCTTTTTAGCGGTGATGCGAGCAGTAAAGAAAAGTTAAACAATGCGCTCAAAGAAGCGAAAGGCAGTGGCACTGAATCGAGCTCATTTCATTTTGACATTAAGCCGTTTGCGCATCAGCGAGAAATCCTCGAACAGCTTGCAGTAGAGCGAAGCGTTCATGGCCGTTTTAAAAATCTCATCGTCGCGGCAACAGGCACGGGTAAGACGATTATTTCCGCGTTCGATTTCGCGCGGTTTTATAAACTGCATCCAGATGCCAATTTTCTATTTGTTGCCCACAGGCAAGAAATTTTACAGCAAGCCCTCAGTGCTTATCGCGGTGTGTTAAAGAATAATCAATTTGGCGAGCTATGGGTGGCAGAGCATAAACCTAATAGTTACAAACACTTATTTGCATCAATCCAAAGCTTGAATCTGCAACTTGATAACCTGCCATTAGCGGCTGATTTTTATGATTATATTGTGATTGATGAAGTCCATCATGTTGCTGCGAGCAGTTATCGTGGGCTATTAGCTCATTTTGAGCCGACTATTTTACTTGGGCTCACGGCAACCCCTGAACGGCATGATGGTGTCGATATTCTGGCTGACTTCTGCGGTGTCATTGCCGCTGAAATACGTTTACCCGAAGCTATTAATCAAAGGCATTTATGTCCATTTCAATATTTTGCGATCGACGATGAGACTGATCTTCGCAAGTTAAATTGGAGCCGTGGTCGTTATGACGTCGCTGAGCTGAGCAATTTATACACTCACAATGAACAACGCGTGACGCGCATTATTCGCAGCCTAGCTGAAACTGTAACTGATATTGGCAATATTAAGGCGTTAGCCTTTTGTGTAACCAAGGAACACGCCGAGTTTATGGCGAAGAAGTTCAATTTGGATGGGATCAGCGCCGATGTGCTGACCAGTGATAATAGCTCTGAACGCCAACAAAAGCGGCAGAGCTTGGTCAGTGGCAAATTATCCATTTTGTGTGTGGTGGATATTTTTAACGAAGGTGTCGACATTCCAGAAGTGGACACTTTGTTGTTTTTACGGCCAACGGAAAGCTTGACCATATTCCTGCAGCAACTTGGCCGTGGCTTGCGTCTCACCGATGATAAGCAGTGCTGCACAATATTAGATTTTGTCGGAAATTCACGTGATGAATATGATTTCTCGCAAAAATTTAGAGCATTAGTCGGTAAAACGAATCAGTCGATAAAAGATGAAATTCTCAATGACTTTCCGCATCTACCGCTAGGGTGTCGAATTGAACTTGAGGAAAAAGCGCAGTCAATGATTTTGCGTAATATTAGCCGCGCAACGCTCAATGCCAATCGATTAACCAGTCTAATAGCAAGCTATAAACATCAGACTAACTTGCCGCTCACGCTAGGCAATTTTTTACGCCTTAACCCGCAAGTGACCTTGGAAGATGTTTATCGAATAAAGATTAATGGCCAATGTGGATGGACTTTATTAGTCGAGGCTATTCAGGGTAATCAAATTGCTGAACCTACTGCGCAGTCCGATTTAGCCAAAGCCTATTATCGCGCGATTAACTTTCATTTACTCAGTTGTACTTCACTATCCTATTTAATCTTTATCAAACAGTTATGCCAAAATAATTTTGTATTTGACGGGCTAGATCCTATTCAAAATCAGTTTGCTTTGATGTGCCACTACAACTTTTGGGATAAATCAGGTACGGCTTTAAACGTTAAAAGTTTAACAGCTAGCTTGCTGATGCTACGCAACAAGCCATTACAGGAAGAGTTATTGGCTGTATTGGCGATACTGATCAACCGGATACACCATCAAGAAATGGATTTGGAATTACTCCAACCCACAGCGTTGAAAGTGCACTCTCGTTATACCCGTGAGCAAATTTTAGCGGCGATAGGAGCAAGCACATTTACAGCTAAATCGCCTTCTAGGGAGGGTGTGCTTGCGATTAATGAGCAAAACCTTGAGTGTTTATTTGTCACGTTAAACAAATCGGAGAAGCAGTTTTCACCGACAACCATGTATCACGACTATGCCATTAGCGAACATCTATTCCACTGGCAATCGCAAAATAGCGCACGGCCAGAACGAGGTAGGGGTCTTAGTTATATTGAGCAACAAAAGCTGGGGAAAACCGTGCTGTTGTTTGTTCGAGAGCAATCAAAAGATGAAAATGGCCGCACTATGGGATTCGTGAATTTTGGCAAAGTGCATTATGTCAGCCATAACGGTTCACAACCCATGAATGTCACATGGCGATTAGAACAGCCAATCCCTGATGTAATGTGGCATGACGCGGCTAAACTGGCAGTGGGCTAAGTACTACTTCCCCAGTCTTTCTGCCAACTTCTCCATCAAAAACTCAATCAATACACTCACCTTAGTCGGCAAATATTTCCGCTGTGGATAAACTGCATAAATGCCATGTTCGGGGAGGGGATGGTCTGTGAGCAAAGCTTGTAGGCGGCCTGTTTGTAAGTCGGCTTCGACCATGAAATGGGGTAGGTTGGCTATGCCTAAACCATCGAGTGTGGCTTGGTGAATGGCATCACTATTGTTGACTTGGTAGTTACCGTTCGGCTGGATACGAGTGGGACCCTGCGGGCTTAAGAAGGTCCATTCAACACCATCACGAAAATATGAGTAGAACAAACAGTTATGTTGGCTGAGTTGCTGCGGCGTTTGTGGTGCGCTGTGGCGAGCGAGATATTCGGGTGACGCGCAAATCACACTTAAACAAGGGGCAATTTTACGGGCAATCAGGCTTGAATCTGGCAGTTCGCCGATGCGAATCGCCAGATCGAATCCGCCAGCAATTAAATCGGTTGTCTTATCATCCATCTGCATTTGCAGCTGTATTTTTGGGTAACGGCGCAGAAACTCGGGGATCAAAGGGGCGATATACAAGCGGCCAAATACCATAGGAACCGCGATGCGCAGACTGCCCTGAGGCGCTTGTTGTAGCTCGGAAATAGCGTCCAAGCCGTCTGCTGCGAGCTTAACTGCAGGGCGAACGTAGTCGAAGTAGCGGGCGCCCGCCTCGGTAAGACTCAAGCTGCGAGTGGTACGGTGCAAGAGTTGAATACCCAGACTTTGTTCTAATAGGGTAATGCGTTTACTCACCGCCGATTTACTCAGCCCAAGCTTTTGACTTGCCAGCGAAAAGCTACCGCATTCCACTACAGTGACAAAGATAGGTAGTGCCGAAAAGTGCTCCATAGTGTCCTTAGATTATAACGTTCTCCTGCCATTATTGTTGATGATACCTCAACAGTCAGTTTTTATTATCGTGGTTATCATTGCCCTTGATGCAAGGTAGACTAATGCACTTGCTGATTGCGGGTGTCGACTGTGGAGCCTAGTTATTTTCGCGCTCATTGCTCATGTTTATCGCTTATCTTGATTACTGCTAGGCAGCTAGCCACAGCATTGCCGCAATCGCCGATGTACCATTCTCGATGCCACTGAACGGCATGGGGATGGGTCTTAGCTATTTTGTCAGAAAGAGAGTTCCTATGTCCTTGCCTTACCAATGGATTTTGTTCGATGCCGATGAAACCCTATTTTATTTCGATGCCTTAAAAGGGCTTAAGTTGATGTTTACTGAGTTTGGGGTCGATTTTACCCAAGCCGATTTTGACGAGTATCAGTTAGTTAACAAACCACTTTGGGTTGATTATCAAGATGGCAAGATAACTGCCGCCGAGTTGCAGACCATACGTTTCGAACCTTGGGCAGCCAAATTAGCCGTTACGGCCATGACGCTCAATAGTGCCTTTTTATCAGCAATGGCCGAAATTTGTTCGCCGCTACCCGGTGCCCGCGAGTTATTAGCCGCGCTGCAAGGTAAAGCTAAACTAGGCATCATTACCAACGGTTTCACTGAACTACAAACCGTGCGATTAGAGCGTACAGGCTTGCAGCATCATTTTGATATTTTAGTGATTTCAGAAAAAGTCGGCATAGCCAAACCCGATGTGGGTATCTTCGAGCACGCCTTCGAACTCATGGGTCATCCTGAGCGCGATACTGTGTTGATGGTCGGTGATAACCCGCATTCAGATATCCAAGGCGGCATCAATGCTGGTATTCATACATGCTGGTATAACGTCCACGGCCACGATGTACCTACCGGTATCACCCCGCACTATCAAGTGAGCTCGCACCAAGAGCTATATAGTTTATTGTTCGGGTAAGTCAGCAGGCACTCAGCTTATGCTGTGAGGTCACTTAGCGAGTCAGCACTTCACGGATCTTACTTGCTTTATTTTTACCTATGCCACCCACTTGCATCAATGCCTCTTCAGATGCTGTTACGACTGCCTCGACACTGGCGAAATGGCATAAAAGTTTATTGGCGAGTTTAGGGCCTACCTCGGGCAGGCTTTGCAAGATGAATAATTGCTGATTTTTCTTGCGTTTGGGCTTACGTCCCGTGAGGGTTAATTCATGTTCTCTACGGTTTAACTGAGTGGCGCAAAAATACAGAATTTTGGCTGTTTCAGTTTGCGATAAACTGCGCAGTATTGGGATGTGGAAATTGATTGAAATCGAGCACAGCGCCCCGATTAACGCTTCTCTGCGTATCTGGTAACATGCGATATCTTGTGTATAACCTTCGATAAGCAGGGCGGTATGATTTGGGCTCTGGGCTAATCTGGCCACCTGTGAGAATAGGCGCCCATCGCATAATGATTGCACGAGATCGGGCAACGTTTTACGTTCAATCAGCCAACCATTGAGTTGATAATCACCCAATTTTAGGTGTTTTTTGATGAGTGAGAGATCTTCGTGCAGTGACAGCTCATAGATGATTTTTTCTGCATGTTCTCTATCGTCATAAAAGAGAGGGATTTTAGCCGCTGCCAATTTTAGTGACCTCAACCGAGTGACATATTCAGGATTGTTTATGAGTGTAGTCAGGCTGAACAACAAGCACAAAAGTTGTCTTCAGCTTTCTGAGTCGCCTAGTTAAAATTTAGTTATACCTATATCTGTTCGTTTGGCCAGCTGCATTAAGACAGCGCTAGAAAATCAATGTGAGTTAGCTATGAGTGAATATCAGTATTATCGATTCGAATGTGTTAACAGTAGCTTGTCGGTCCCGCAGCGGAATAGATTAAGGAATATTTCTAGCCGAGCAGAGATCACCAGCCACTCCTTTTGTGTTCAATATCATTACGGTGATTTAAGTGCCGATCCAGAAGCCTTAATGATGGATTTTTTTGATATTGGCTTTTATTACACCAATTGGGGGCAAGTGAATGCGTATCTTAAATTGCCAGCTGGTACAATCCCCTGTGATTTCAAGGTAATAAATGCTGACTTTTATGTGTCTTTTGTCGAGAATAAGCAGAGCCAATTACTGATATTTACTTTAGAAGATGATTATCGCTACCGCGACGATGAGAGTGCCGAATCCTTTATGCTGCATTTGGTGGCACTAAGGACAGAACTATTAACTGGCGATTATCGTGCTTTGTATTTTCATTGGTTAAGCCAAGCCAACGATGATATGGCCTTAGAGCCCTTGCCGCTTATAGCCTTTGATTTTCACCAGTTAACCCAGGCGCAATTGGCATTTACCACACTCTTTGACATCCCTTTACCATCGATACGTGCTTTACATCTTTTGCTGCAGTCAATGCCTGCACATGTTCCTCAAACCACAGTATTAACGGCACAGCAGCATGTGGCTAAGCTAACGGATGCCGAGAAGGACGCGTTGCTCTGTCGCTTATTTGATGAAGGGAAAATATTTGCGGCGGATGCGTATGCGCTCCTCAGAACGCAAGAAACACCAATAGAGTACTCCGACTGGGTAACAGTTGCATCGTTGCATCCCTTCTGGCATGTAGCAGAAAAAGAAGTGATGCAAGAAGAGCGCATGGCAGAGGCCGAGCGTCTTGCCCGTGAGACGTTATTGCGTAATGAACATCTTGAAGCGGTTTATAAAGCGCATCAAAGCCATTGGGATAGTGCTTTTGAGGAGGCTGCTCGTGGGTGTGCCAGTGGCTATGATAATGCCGGCCGTACCTTGCAAGTGTTGTTCGATGCTTATCAACTAAAACAATCTTCCAGCGAGTTTAAACAAAATTTTGATGTTTTTATTAGCAAAAATAAACAGCGTAAAGCACTGATTAAGCGGTTAGAACCGTTACAGAAAATTATGCTATCGCTTTGATTTAGTTATTTTAATGCCAATAGCTATGGCGCTGTGATCGAGTGATGTGTTTTGCTTTATTAAAAGTGAATTAGATTGAGTCGCCAAGATAAATTCATGATAAATAAGGTTTTTTGTTTATTAGATTGTAGTTGAACACGACAACACACTATTCGACATTTGAAACTGAATGTCGGTTCTGCATTTCAATTAAATTCAGCGCTCCTTGGCAATGACTCCCCCCATTTCTTTTGTCTTTTATGGTCAGGCTACCCGCCGTAGCGGGTGAACTATGTTTAACGCCTATAGCGTGTGAATGTGTTTTTAACGGTTTAGGCGTGTAAACTTTGACTTACCTGCTTAGCGTGTACAGTAAGAGTTAAGAACAGTTTAAAGGTAAAGCTATCATGAAGGTGACCAACGCAAAGCAGTTGAGTACGTATCTAAAAGATGCGCGGCTGAACGCGCAGCTCTCGCAAAGCAAGGTGGCGAGCAAGGTAGGTATTCGCCAGGATACCGTTTCAAGCTTTGAGTTGAACCCGGATTCGACCAAATTAGAAACGCTGTTTAAGATCCTTTCTGCATTAAACCTAGAGCTAGATATAAAGGTTCGGAATCCACAGCTGGTGGATGAGCGCCGCCATGATAACGCCACAGTGCCTGAGCAAGAATGGAAGGAGGAGTGGTAATGGCAGTGCTTGATGTTTATATGAATGGCTACCTTGTGCGTGAATTGACCAAATCATCCACAGGCTCTCATCAGTTTGCGTATGCCCAGCAATGGCTAGCTATACCCGGTAGTCGCCCAATTTCTTTGTCTATGCTGCTACGCAAGCAAGCTTGTAAAGGTTTTTTTCCTGCGCCATAAAATACAAAAAGCCCTTACATTAAGGGCTTTTTGGTATCTAAAACGCGTTAACTATCATTACTCAACGTTTTGGATCTGCTCGCGCATTTGCTCGATGAGAACTTTAAGTTCCACTGCGGCCGAGGTGATCTCAGTGCTGATGGATTTAGAGGCGAGCGTGTTCGATTCGCGGTTAAATTCCTGCATCATAAAGTCTAAACGGCGACCTTCGCTGCCGCCTTTTTTGAGGATGCGGCGAGCTTCGGCCACATGGGCTTCGAGGCGATCCATTTCTTCGGCTACATCTTGTTTCTGTGCCAGTAGCACCATTTCTTGCTCGATGCGGGCTGGGTCGAGTTCACCTTTGATTTCTGCAAGGCGGTTGGTGAGCTTTTCGCGTTGATACAGCATCACAGTAGGCATGTGCTCGCGTACGACGGCGATTTGCTCGCTGACTCCGTCTAAGCGGCTTAGCAGCATGTCTTTGATGGCTTCGCCTTCACGGCCGCGGGCTTCGATGAATTGATCTATGGCTGAGTCGAAGGCTGTCATCAGATCGGCGCCGATGGCATCCATATCTTGTTCACCGCTTGCTAGCACGCCCGGCCAGCGCAGGATGTCGGTAAGATTGACATCGCCTTGTCCCGCTTCTTGCTTGAGCCAATTGGCAGCGCCTAATAGCTGTTTGGCCAGCGCTTGATTCAATTGCAGCTCGTTGTTGCTGTTGTCTGCCAGTTCGTAGCGCAGATTGACTTCGACTTTACCGCGGCTTAAGCGTTTACGTAGGCGGTCGCGCAGTACGGGTTCGAAACTGCGGAATTGCTCGGGTAAACGTAGGTAAGTTTCGAGATAACGTTGATTGACTGAGCGAATTTCCCAGGAGGCAGTGCCCCATTGTGCTTTGTGCTCGATGCGAGCGTAGGCTGTCATGCTTTGGATCATGGGTTGTCCTAATGGTTTAATCGTCATAATGAGAGGCGATTATAGAGTCAATGGCTTTAGGATTAAAGGCATACAGCAAATATACCCAAGCTGCCTGAAGATACGAGTTTCAGAGCGCCTAGAGTGCTTCAATTCAAGGCGCAGCAATGACAGAATGTCGGCTACCTTTTGAATTGATGCAACACGGAAGTGAAGTGCCCTAGGCGCTCCGTCCCGGCGGCTGATGTTAACCACGGGGTAACGCACTTTATGCTGCGTTGGTTATTTTTAAATTAGTCCACTAGGTCCGCAAATAACCGCAATCGCCATGGATGGCGTGAATGCCATTTATGCAGGAGCAATAAATGGCCTTGCCTAAAGTGCGTTAAACTCCCGCCGAATTCTGCATCTTCAGGTTGTTTGGGTATAAGGACACGGATTAGGCGTTTGAGTGTGATGAGTTTGCCAGAGAATCACCCTAAGCATGGCATCCTAAGGCTCAAGCCCTTATAATATGCAGCCAAAATCGGTCAATGACTCAGAATACAGGAATCTCGCATGCGTCCAAGTAACAGAACGCCAGCACAAACTCGTCCCATCACTATCACTCGCCAGTTTACGGCCCATGCCGAAGGTTCTGTGTTAGTGGAATTTGGCGAAACGAAAGTGCTTTGTACCGCCAGTTTTACTGAAGGTGTGCCACGTTTCCTTAAAGGTCAGGGTCAAGGCTGGGTGACGGCGGAATACGGCATGTTGCCACGTTCGACCCATAGCCGTATGGATCGTGAAGCCGCTCGCGGTAAGCAATCTGGCCGTACGCAAGAAATTCAACGTCTTATCGGCCGTGCTCTGCGCGCTTGTGTAGATATGAAAGCCCTAGGCGAAAACACTATCGTGATCGACTGTGATGTGATCCAAGCTGACGGTGGCACGCGCACTGCCTCTATCACTGGCGCCTGTGTGGCCTTAGTGGATGCGTTGAACTGGGCGCGTGGTAAAGGCATCATTAAATCTAACCCGCTGAAATTCCTTATCGCTGCTGTCAGCGTCGGTATTTACAAGGGTGAAGCGATCAGCGATCTAGAATATATCGAAGACAGCGCCGCTGAAACTGACATGAACGTGGTGATGACGGAAACGGGAAAGATCATCGAAATTCAAGGCACCGCCGAAGGCGAGCCATTCAGCCATGAAGAATTACTCGAACTGTTGGCATTGGCGAAGAACAGTATTCGTGAAATCGTCGATGTGCAGAAAGCCGCACTGAACTAATAGTGTGGTGATGAAAAAGCCCCGTCATGTTATAGAGTGCGGGGCTTTTTATTAGCTGCAACTTTTTATCTGCAGCAAGATGCTAGTGTTAGACAAGCCCTGTACCGAATTAAATGTTGCCTAATAAAACATTACCTAATAAAAACCAATAAAAGACAAAGCCGTAAGGAGATATTGTGAAAGCCTATCAACGTGAGTTTATTGAATTTGCCTTAGAACGTCAGGTGTTGCGATTTGGCGAATTTACCCTTAAATCGGGTCGTATCAGTCCTTATTTCTTCAATGCGGGTTTGTTCAATACTGGCCGTGACTTAGCGCGTTTAGGCCGTTTCTATGCCGCTGCGTTAGTGGATTCTGGCATTGATTACGATTTGCTGTTTGGCCCAGCTTATAAAGGCATTCCAATTGCGACCACTACGGCGGTTGCCCTGTGTGAACACCACGATATCGATATCCCTTACTGCTTTAACCGCAAAGAGAAAAAAGATCACGGCGAAGGTGGCAGCTTAGTCGGTAGCGAGCTGAAAGGCCGCGTTATGTTGGTGGATGATGTGATCACTGCCGGTACTGCGATTCGTGAGTCGATGGAAATCATCGAGGCCCATAAAGCTCAATTAGCGGGCGTGTTGATTGCCTTAGATAGACAAGAGAAAGGCAAGGGCGAGTTATCGGCTATTCAAGAGGTTGAGCGTGATTTTGGCTGCGGCATTGTGGCGATCATTAAACTTGCCGACCTCATTAGCTATCTGTCTGAAAAGCCTGGAATGGAAGTGCAACTTGCCGCCGTGAGTAAGTACCGCGAGCAATACGGTATTTAAATCGAGTGCTTAGACGCTGGTATTGATCAAGTGGCTATTATTGGCGTGGTGAAATCAGTTAATCAGCCAGCATTGATCTTAGCTAGGTTGAGAAAGGTTTGATTGAGTGGGTATGAAAACAAAAAACGCTGCGATTCTATAGAAGGAATCGCAGCGTTTTTTTGTTGATTTGTATTGATTGCTATTGATTTAGGCACGCTTTAAATCACATTACGCTTCAATACACTTATTTAGACTGATGCAAAAACTCGGCGCGGGTCGCAGGGTTAGATTTGAAAATACCGCCCAGCGCTGTGGTAGTGGTCGAACTGGTTGAGTCCATCACGCCACGGGATTTGACGCAGTAATGCACAGCATCCATTTTAACCGCAACGTCTTTGGTTTCGAGCAAGGTCTGCAGCGCGACTAACACTTGCTGAGTCAAACGCTCTTGCACTTGTGGACGTTGGGCAAAGAAACGCACGATGCGATTAATCTTTGATAAACCGATGATTTTTTTGCGTGGTAGATAAGCTATGGTCGCAGTACCATCGATCGTCACTAAATGGTGTTCGCAGGTGCTGGTGAGGCTAATGTCTTGCACTCGCACCATTTCATCGAAGCCCATCTTATTGTCGATAACCGTGATCTTAGGGAAGTTCGCATAATCTAAGCCGGAGAAGATTTCATCGACATACATTTTAGCAATGCGGCGCGGCGTATCGGCAAGGCTATCGTCTGACAGATCCAATGACATTAATGTCAGGATTTCTTTCATATGATGTTCAATCTTGTCTTTACGTTCTTCACTGCTGTAAACGCTTGGGAGCATTGGGGTTTCAAGTCCACGTTCTAATAACGCGGCTTGTACTTTTACTGCTGCTTCACTTAATGCCATCTTATCCTCCACTACGTCGTAGCGGCTATAACAGAAAATCTATGCTGAGCATCGAGTGGTACTTGGGTTGTTCAGCATCAGATACAAATTATTGTTATTCTCGCTAGGGCAAGAATAGGGGGAGGAGGATAACGCGAATTGCAGATAATTCATACCGAAAAACGCTCATGGGCCACATGAAAAAGCCGATAGTCCTTATCGGATATCGGCTTTAAATCAGCCACTGCCAATCGAGTTTAGACTGGCAGTGACGGCAAGTTTGAGTCAATCCTAAAGTTTTAGATTAGTTTTTAGGAAGCTGAGCATTTGTTCATAGTATTTCGCTCTATGCTCATCGTTATAAAAACCGTGCCCTTCATTGTCCATTACCAGTTTTTGGTAAGGATAATTGTGAGCTTTTAGGCCTTTTTCGAGGGATTCGAGTTGCTCAATTGGCGCTCGATCATCTTCACCACCATGCACCAATAGTATGTTTGCTTTGAGTTTATCGACATTTTCAGAAGGAGACATTGCTTTGAGTACCGCTTTATCTTGGCCAAGAACTTCTTTGAGGTAGCTAGTGCCTGAGCGTCTACTGGCAACATCGCCTTCATTAAACATCAATTCAAGATCATATACGCCAGCTACACCTATGGCGCATTTAAACATATCTGGAGCAAGTACGGCGCTCTGCAAGGCGCTGTAACCACCAAAACTGCCGCCAACGATACAGATACGCTCCTTATCGGCGAAACCTTGGTCAATGACATATTGGGTACCGTCGATGATATCGTGTTGTATATCTGAGCCCCATTTTTGATAACCTGCACGTTCAAATTGATCGCCATACCCGCCAGAACCACGGAAGTTGACTTGTAGAACTGCTATGCCTTGGCTCGCTAGATATTGGTTTTGCGGGTCAAAGCCCCACCAGTCACGGATACCGTGTGGACCACCGTGCGGATTGACAACTAAGGGCAGATTTTTAGCTTCTTTACCGAAAGGTAGCGTTAAATAACCGTTGATTGTTTTACCATCACGACTGGTGAAACTGATGGGTTTTACCTCCGCCATTTGCTCTGGGTCGAGCCATTTTTTAGCGGCTGCTAGATATTCAAGTTTCAGTTTTTTCGTATCAAATATGTAGTAATCACCAGGATTACGATCGTTAAATGCAACTACAACGAGTTTTTCAGCATCTCGGGTTTCGCTAACAATTCGGACTTGATGGCCTGGGAGGGCACCAAGCAGATCTTTTAGTAATTTAGCGTGCGAATCTTCATTATCGACGAAGGCATAACTTGGGTAGCCATTTTCAAACTCGACGGCGTAAAGTTGTTTATTGGTGCCGTTAATCCAAAAATTGCTGGGATCAACGTTTTTATCTTGAATAATCTCGCTCTTATCGCCCGTTTCTAGATTAATCTTGTAAACCCCTAAGGTTTCGCCTTGAGAGCGACCAGCGGCATAAATCGCATTTTTATCATCGGCGAAGGAAAGTGGCTGAAAGTCGGTTAGGCCTAGGTTTAGCTTGTCGGTATTTATCCAGTCACCGTCTTTGCGGTAGAACACTTTAGTGATGTTTTTACTGTCCTCACCTGCAACGAAGCGGACTTCACCCTCATGATCTGTCATAAAACGTGCTTGACCTATCGGGGCTCCTGTTATGCGTTTACGAACCCCAGTAAACAGGTCGACTCGATATACATCTTGTTTTAATTCAATATTCAGGCTGCTCTCGTTATTCCATGGAATAGCGTTGACCAGCATATAGCGCTCATCATCTGGCAGAGGATCGAGAATAAATGCTGTGGCATTTATTGCAGTATTTTTCTTAATATTTGAACCTGTTTGTTGTTCGCCACTTTCGAAGCCAAATAGATAAGCCTTTCGTGAACCATCGGCATTCACGGCCAATAATTCTCCGTAGTACTGCGGAACATCTCTCCAGCCTTTTAGGTATTCTTTCGCCAGTACCAGTCGCTCATTATTGGCCCATTCATAGTCGCCGACTTGAGCGTTACTGGGAAAGCGTATCGCATTAAGGAGTTTTTTGGTTTGAGCATCTAAGACTAAGAGAACATTTTTCCCCTCGACACTCGTGATTGCACTTAAGTATTTACCAGTAGGAGATATTTTAACATTTGAATATTCATCACCTTTACTGAACTGCTGTGCAAGCAATAAGGTATTGCTTGCAGTGGTATCGGCTGCAAAAGTATGTAGGGACAGCATAGGCAAGCAAAGCGTAGCTAAAGTCGCTAATCGGAAGAGTTTCATCTGACATCCTTGTAAATTTATTGTGTTTTTTGTTCTGAAAATGTTTTTTCGAGGATAAAACAAAAGTGCTAGTGAGACAAGAAGGGAAGGTTAAAAATAAAGGACGATAGTTACCGTCCTTTATGTTGAAAAATCATTAGCTTTGTAAAAGCTGATGTTGGATAAAACGCCACTGTTCATCGAAGTGTGTGGTAGGTTTGAGTTTGAATTCACTTCGTACAAATTGAGCGATTCGCCCTTCGGCAAAGGCTAACAGTAAATTGGCTAATATGGCTTCGTCTAAGTTGAAGCCTTTGCCTTCCCGCAGGGTTTTTTCCCGTAGTATTTGCTTCAACTGGGTTTCAATTTTGGCAAATAAATTACTAATGCGGCTGCGCAGACGCTCGTTTTCTCCGAGCAGCGCATCACCATTGAGGACGCGGGAAATACCTGGGTTACGCTCGGCAAAAATCAGCAACAGTTGCAGCACGAGTTGGCAGCGCTTCATGGTGTCTTTTTCGTCATCCATGATTAAATTGATGCGCGACAATAACGACTCTTCGATAAATTCGATTAAGCCTTCGAACATCCGTGCTTTACTCGGGAAGTGACGGTACAGGGCGGCTTCCGATACACCGACTTCTGAGGCCAGTTTCGCTGTGGTGATCCTTTGTCCTGGGTTGGTTTCTAGCATCTGTGCTAGACATTGCAAAATATGTTCACGACGATTAATTTTTGGGCTTACAGCCATTGCAGTATCCTTGGCTCAATAACGACTTAGGGGATTATTTAGTGCCTGAATGGCCAAATCCGCCTTCGCCACGATCTGAGCTGTCGAACTCATCAACGAGTTTGAATTGTGCTTGTACTACAGGAACAAACACCAGTTGTGCGAGACGATCGCCAATTTCTAAGGCAAAAGGACTGTCGCTACGATTCCAGCAAGACACCATTAAGGGTCCTTGGTAATCTGAATCTATGAGTCCGACCAGATTCCCCAGTACGATACCGTGTTTATGGCCAAGACCTGAGCGCGGCAAAATCACTGCAGCCAGTCCTGGATCGGCAACATGAATGGCAATCCCCGTTGGAATAAGCACGGTTTCACCCGGTGCTATAGTCAAAGTCGTGTCGATCATGGCGCGAAGATCCATGCCTGCACTGCCAGGTGTAGCGTAGGCAGGTAAAGGAAATTCTGAGCCGATACGGGAGTCGAGAATTTTTAATTCAATCGGTGTTTTCATGTATTTGTTTTTTCTTTCACTATCAACGAAAGTAATTGCCGTGCCAGCGTGAGTTTGTCAGTGGCAGGCAAATCTTGACTGCCGTGCGGCCAGAACACTCGCAGGGCATTGGCATCGGCATTAAAGCCTAGACCAGCAACGGAAACGTCGTTCGCGGCGATCATATTCAAGTTTTTACGTTTGAGTTTATCGCGCGCATAGGCTTCTACATCATGGGTTTCTGCGGCGAATCCCACCACAAAAGGACGGTTAGGTAAGCTCGCAACCGTGGCTAAGATATCAGGATTCCTCACCAGCGCAAGTTGCATTTCTTCTGCCGATTTTTTGATCTTACAGGTGGCGATGTCGCTAATGCGATAGTCAGCAACTGCGGCGCAGCCAATAAAAATATCTTTTTTATCAACGTTATCCATCACAGCGTCAAGCATATTTTGCGCCGATTCAACATCAATGCGGGTCACGCCTTCTGGGGTGCTTAGATTCACTGGACCAGAGACTAAGGTGACTGTTGCGCCCATATCTGCCGCGGCTTTGGCTAAGGCAAAGCCCATTTTTCCAGAACTGTGATTCGAAATGTATCTGACGGGATCAATTGCCTCACGGGTTGGCCCTGCGGTAATCAGCATTGACTGTCCTGCGAGTGGCTTAAGTTCAACAGCGTCATGAGTGGCAAAAAACTGCGTAGCGAGTGTGGCGATGTCTAATGGCTCTAGCATACGACCAGGGCCGACTTCACCACAGGCCTGACTGCCACTTGCGGGTCCCCAAAGGGTAAAGCCGCGGGATGCCAGACTGGTTAAATTCGCCTGCGTCGCCAGATTACGGTACATCTGCTGATTCATTGCTGGACACAGGGCAATTGGCGCCTCTGTGGCAAGACAAGTGGTGGTGATCAGCTCATCGGCCATGCCAGCATTAATCCGCGCCAATAGGTTTGCGGTTGCAGGGGCAATGATGACGAGATCTGCCCAACGAGCGAGTTCTATGTGGCCCATGGCTGCTTCTGCCGCAGGATCGAGTAGGCTTGAGGCAACGGGATGTCCAGATAACGCTTGCAGCGTCAAAGGCGTGATAAATTCCATCGCACTTTGGCTCATCACCACGCGCACATCAAAGCCACGCTCTTTTAAGCGACGGACTAAATCAGCACTTTTATAGGCCGCAATACCGCCGCCAATGCCCAGCAGGACGTTTTTATTTGTCAGACTCACAGACATCATCCGAGATAGAAATAATGCCGCCAACAATATCACAAAGCGGTGAATTTTAGTGACTCTCCCAGTTCAAAAAATCTCGTCCATACTCAGTAAAGTGAAAATATGTGAGATGACAGGGAGGTCGCATGGGCATTAAAGATTGGCCTGAGGGTGAAGGTCCCAGAGATAAACTGTTACAAAAAGGGGCGGGGCAACTGTCGGATGCGGAGTTATTGGCGGTATTACTGCGGAACGGTTTGGCGGGATTAAACGCGGTTGATCTTGCAAGGTCGCTGATTAGCGAGTTTGGCGGCCTCAGAAACCTGCTTTGTGCTCCTCGAAATCAGGTATGTCGACTTCCGGGCGTAGGACCTGTAAAATACGCCCAGCTTCAAGCTGCGGCCGAATTAGCGCGGCGTGTAGCGCAGGAAAATCTGCAAAGAGGTCAAGTTTTAACAAATCCCGATTTAACTCGGGACTATTTAATGAGACAATTGGCCGATCGCTCCTATGAAGTGTTCGCAGTTTTACTGCTGGATAGCCAGCATAGAGTGATTCAATTTGTCGAATTATTCCGCGGTACAATAGACTCAGCCTCAGTGTACCCACGTGAAGTGGTGAGCTTAGTGCTGGAGAAAAAGGCCGCAGCAGTTATAGTTTGTCATAATCATCCGTCTGGTAATGCCGAGCCCAGTCAGGCTGACCGACGAATAACTGAGCGATTAAAAAATGCGTTAGCAACCATAGACGTATCCTTATTGGACCATATGGTTGTAGGTGATCGAGAGATAGTTTCTTTTGCAGAACGAGGCTGGATTAATTAATCTAACACTTGATCTTCACTCTGTGATCGTGTATAAAATGCGCCCTCTTTGTGCCTCGGGCGACGGCCATACGAGGCACATTAATGCTCGAGCGTTAAAATTGTTTTTGGAGAAGATTGACATGTCAAGAGTATGCCAAGTTACTGGCAAGAAGCCTATGGTTGGTAACAACCGTTCGCACGCAAAAAACGCGACCCGTCGTCGTTTTTTACCTAACCTACAAAACCACCGTTTTTGGTTAGAAGAAGAAAAGCGTTTCGTACAGTTACGTGTATCTACTAAAGGTATTCGTCTGATCGACAAAAAAGGTATCGAAGTTGTTGTTGCTGAACTTCGTGCTCGTGGCGAGAAGGTATAATAGATCATGGCTAAAGCTAAAGGTAATCGTGAGAAGATCAAATTAGTTTCTACAGCTAAAACTGGTCACTTCTACACAACTGAAAAAAACAAGCGTAACATGCCTGAAAAAATGGAAATCAAAAAATTTGATCCAGTTATTCGTCAGCACGTTATCTACAAAGAAGCAAAAATCAAGTAATCCTTGGTTAGTGTTTCGAGAAAAGCCCCTTATTTAAGGGGCTTTTTTTTGCGCTTTTTTTAGTAGTTAGTCATCAAGTTGAGATAATTGCCTCGCAAACTTAAGCCGCTGAATCATCCCCTGTGAAGTTCAAAAAGTGTGAACACTTGCTGAAAATATAAAATAGTGCGGTTCTAAGCACTTTAAATCATTAAAACTGCACTTTAGTGAGAATAAATTCTTTATATGTGAATTAAAATGCAATACCATACGCGAAAGTTAGCGATTCATTCCTGGCATGTCATCGTGACGCTGCCAGCTCAAAAATGAGGTGTAAATAACGCGTGCGTACCACTGAACTGGTTGATGGATTTCGTCACTCTGCCCCCTATGTTAATGCTCACAGAGGTAAAACTTTTGTGGTCATGCTGGGTGGTGAAGCTTTGGCTCAGAATCACTTCCGCGCCATCTTAAATGATGTCGCCCTGCTGCACAGTTTAGGGATTAAAGTGGTGTTGGTGTATGGCGCTAGGCCACAGATTGATGCTGCATTAGCCGTCAATGGTATCGAACCTGCCTACCACGAAGGCGTGCGTATTACGGATGAAGACTCGCTCAAAGTGATCAAGCAAGTTGCGGGTGCACTGCAATTTGATATCACAGCGCGTTTATCCATGAGCTTAAGCAATACGCCGATGCAAGGTGCGCAAATTAACTTAGTGAGCGGCAACTTTGTGATTGCCCAGCCATTAGGCGTGGATAACGGCATCGATTTTTGCTTAAGCGGCAGAGTGCGTCGTATCGATGTGCAAGGCTTAAAACGTCAGTTAGATAATCACTGCATCGTGTTGATGGGGCCGATTGCAGCTTCTGTGACCGGTGAAAGCTTTAATTTAACCGCCGAGGAAATCGCGACCCAAGTGGCGATCAAGCTCAAAGCCGACAAGATGATCGGTTTTAGCTCACAGAACGGTATTCTCGATCGCAATGGCGACGTGATTGCCGAATTGATGCCAAACGATGCGCAAAACATCATGAGCAAGCTGGCCGAGCAAGGTTCTGCCTGCGTTGGCACTATGGCTTTTTTGAAAGCTAGTATCGATGCTTGCCGTAATGGCGTGCCGCGTTGTCACTTAGTCAGCTACCTCGAAGATGGCGCTTTGCTGCAGGAACTATTTTCTCGCGAAGGGATAGGCACGCAAATCGTGACTGAGAGTGCCGAGCGTTTACGCTGTGCGTCGATCGCTGATATTGGCGGTGTACTGAATCTTATTCGTCCATTGGAGGAACAAGGCATTCTGGTGCGTCGTAGCCGTGAGCAGCTCGAGATCGAAATCGAACAGTTCATGTTGATTGAACGCGATGGTTTAGTGATTGGTTGCGCCGCGCTCTATCCTTTTGAAGAGGACAATGCGGGCGAGTTTGCCTGTTTGGTGGTGCATCCCGATTATCGCGATGCCGACAGAGGCAGCTTGCTGCTGAAGAACATAATCAATCAGGCCAGAAACCGCGGCTATTCACGCTTGTTTGCGCTGACAACTCGCAGTATCCACTGGTTCCTTGAGCATGGTTTTGTGATTGAAGATGTTGAAGCGCTGCCACAGAAGAAAAAGCAGTTGTATAACTATCAACGTCGTTCAAAAATTCTCGCACTCGATCTCTAAGCTGTTCTAAATCAATCCAATCAATGCCAGCCTAGTACTCATATTAGCGCTGGCATTTTTGTTGACATAATATGTCTTTGTTGGCAGATTAAGGCTTCACTGCATGGGAGATGCTGCCAATGAGTCCTCAATACTTCGAACTCGCCCCTTTCACCCAGACCTCTATCTTCAGTTTTATCGTCTTATTGCTCGGTTTAGCGGGTATCTTGCTGATGTTATGGCTAAAAAATATGCCAAAGATAGCGAAGTCGGCGAGTTTAGGCATATTGCTGGTGATGATGGGCGGATTTTCATGGGTTTTTTATCAATCCAATAGTGCCGAGTTAGTGTTAACCGGTGCTGAACTCACGGTGGATGTGCCCTTCTATAAAGTGCAGTTATCCCGCGCGGATTTATTGCCCGCCGAGGCGCGGATAGTCGATTTGCAGCAAGAGGCTGATTTAACGCCGAGCTTTAAAACCAACGGCATAGGCATGCCAGGGTTTCAATTAGGCTGGTTTAATTTGCAGGGAAAAGGCAGGGCATTTCTGGCGATAACAGATAAATCGCAGTTAGTGTTAGTGCCAACGACTAAAGGCTACAGCTTATTACTGACAGTGCCGCAAGGCAGTGAATTTTTAGCACAGTTACAAAAGTAATCATCATATATCATCACTTCTGCAACTGTGTATCGGCGTGATAATGATCACGCCGTTTTGCGTTTATAGGCCATATCTAAGGTAAAAACTAACAGGGCACTCCAGATAAAACCGAAGGTCACGCCTTTTTCTGCATCGAAGGGCTCATTAAACAGACTCACCGCGAGGATGAACATGATACTTGGGCCAATATATTGGAAAAATCCAAGCATAGATAATGGAATCCGTACCGCCGCCCCTGCAAAACAGAGTAAAGGAATCGTGGTCACTATTCCCGCTGCCATTAACATCAGATTAAGTTGCCAATCGTTGGTCAGCATGCTCGCTGTCGCTGTGTCTAAGGTCGCGACTAGGTAAATTAGCGCGACCGGTAATAACACTGCGGTTTCGACCAATAAGCCAGCCTTAGCATCCACATTGACCTTTTTTCGCAGCAGCGCATAGAAACCAAAGGTGCCCGCGAGTGCCAGTGAGACAATTGGAATAGAGCCAAAAGAGATTAATTGGATCAACACACCAGCACTGGCGAGTGCGACCGCAAACCATTGCAGCTTACGTAACCTTTCGCCTAGAAATAACATACCAAGCAAGACGTTGAGCAGAGGGTTAATAAAGTAACCTAGGCTGGCATCGAGCATGTGATCGTTATTCACCGCCCAGATAAATAGCAGCCAGTTTCCCGCGATCAGTATGGAAGTAATGGTGAGAACCATCAGCTGTTTAGGTTGTTTAAACAGTAAGCGTAAGCGTGAGAACCCGCCGATAAACTGCATGATGATCAGCATAAACACGAATGACCAAATCACCCGATGGAGCAAAATTTCGGTGGCGGAGACATGATTGAGTAATTTGAAATATAAAGGCGCAAAACCCCACATGCAGTAAGCACTGACGGCGAGTAGGATCCCTTTGCGGTATTCAAGATCTTGCATGAGACATCTTCGAAGGACAACAAGACGGCGATTGTAGGTGTGTCCGAGCGCTGACTCAAGGGTATCGCTGCGTTCTTTTGGTTACAGAATATCTCGATAAGAGATTGAATGGCCGATCAACGCCAGTAAACATCTTACTAATCAAGATGTTTACTGGCTTTGAACTTTAAGACGGTAAATGAGTGTTCTGCGGTGGAGCGGCTAGCCCACCATATAAGTGCCTGTCCCAAAAGCGATATGTGTGCCTTGCTCATTATGCAATTCCATGCGGCACACCGACACCCGATTGCCCGCACGGATCACGCTGCCCGTCCCGGTAAAGGTGAGGCCGCGCCCCGGCCGCAGATAATCGACGCGCATGTCGATAGTGCCTAGGGTTTGCAGGCGTTGCTGCAGCTCTTCGACCGTCCAATCATCACGACTGGCGACCAGTCCTGCAAAGGCCGTGAGTCCGCCAACCACATCGAGTACCGTTGCTGTCACACCGCCGTGGAGGATTTGCTGGTGGATATTGCCAATCAGCTCAGGCTTCATGTTGATCACGACTTCGACGCCATCAATGTCATAACGCTTGATATCGAGTCCGAGTAGATTATGGAAAGGGACATGTTGATCAAATACTGCTGCGACTCTTCTCAAAGCTTCGGCTTGGATTGGGCTGGTCATTATTGGGTCCTTGTCACTGAGTGACTGCATTAGGCGTAATTGGATTTGTTATCAACACTGAGATGAACAAAGTGCGGCTTAGAGCAATTAATCTACAGTGAAGCGTGCGCAGAATGCAACTTAAGCCACATTGAAGATAAATTGGCTATGGGCATTATGGTTAGTTGAGTGAGGGCGATCTTAGTCGATGGCATTTAATCCTGAATGGGTTTACGACAAGATGGCGCAAGACAGGAGCGGTTCAGTCCTTTAGAATAGGCCCCCTCACTTTTGACATAGAAGATCCATGGAAAAGCTCGTACTCGACACGCATGATGATCCGCTCTCGCAACGCCTCGCACAGGTGTTTGGTTACCGTGACTTTAGGGACGGTCAGCGGGAGGTGATAGAACGTGTCTGCGGCGGGCAAGATTGCTTAGTGATCATGCCAACTGGCGGCGGTAAGAGTCTTTGCTATCAACTCCCCGCATTGCTGATGGATGGCATTACTATTGTCGTATCTCCATTGATTTCTTTGATGAAAGATCAAGTCGATAGCTTGTTACAAACCGGTGTTGCAGCGGCTTATCTCAATTCTTCATTGCCGCGAGAGCAGAGCGCCGAAGTATTAAGGCAGCTGCGCCACGGTGAGTTAAAACTACTTTATGTGTCACCAGAGCGCTTATTAAGCGCAGATTTTATTGAGCGAATGCAGTCGTTGCAGCTAGCGATGTTTGCCATCGATGAGGCGCATTGTATCAGTCAATGGGGTCATGATTTTCGCCCTGAATATGCGGCGCTCGGCCAGTTAAAGCAACTCTTCCCCTATGTGCCCATGATGGCGCTGACAGCAACAGCAGATCAGGCGACTCGACAGAGTATTTGCGAGCGACTTGGGATTGACCCTTTCCGTTTACTCTCGAGTTTCGATAGGCCTAATATTCGCTACACAGTCGCCGAAAAGCTCAATGCTGCGAATCAGTTACGGCATTTTCTAACCCAGCAAAATGGCACCAACGGGATTATTTACTGTAGCAGTCGACGCCGTGTCGACGAGGTTGCCGATCGTTTATGTCTACAAGGTTTTAATGCTAAGGCCTATCATGCGGGAATGACGCAGGAAGATCGTAGCGCAGTGCAGGACAGTTTCCTTAAGGATCAAATCGATATCGTGGTCGCCACTGTGGCTTTCGGTATGGGGATCAATAAGTCGAATGTGCGCTTTGTGGTGCATTACGATATTCCGAAAAGTATCGAGGCTTACTACCAGGAAACGGGCCGTGCCGGCCGCGATGGGCTTGATGCTGAGGCCTTTATGCTGTTTGATCCTGCCGATATTGGCCGGGTGCGACATTTGATTGAACAGTCTGAGCCCGGGCCGCAGCAGCAAGTCGAATTCCATAAACTCAATACCATGGCGGCGTTTGCTGAGGCGCAAACCTGCCGTCGCCAAGTGCTATTGCATTACTTTGATGAGAGCGCACTAGAGCCCTGTGGTAACTGCGATATTTGCCTCGATCCACCTAAGCGTTATAACGGTACCGAAGATGCACAAAAGGTGTTGTCATGTATTTTCCGTTTAGGGCAGCGTTTTGGTATTAATCACTTAATTGAAGTGTTGCGCGGCTCGAAAGGCGCCCCGATTGTTGACCGAGGCCATGACAAACTCACGACTTGGGGGATTGGTAAAGACAAGAGCCATGAGTATTGGTTGAGCGTGATCCGTCAATTGATCCACTTGGGACTGGCGAGTCAAGATGTGACGCGAGGTTCATCTATAACCTTAAACCCATCGGCACGACCTGTACTCAAAGGTGAGGTGGCATTAATGCTCGCCGAACCGCGTATTCAGTTAACGACCACAAAACGTAAGGTGAGCCAGTCCAAGGCGCCGCTGAACTACGATCGTAAGCTATTCGCGCGACTCAAGTTACTGCGAAGAACCATAGCTGAGCAGCAAGATGTGCCGCCGTATTTAGTGTTTAACGATGCGACGCTGGCGGAAATGGCGGCCATGATGCCGACCAGTGCAGGAGAAATGCTCGCCGTTAACGGAGTGGGCGAGCGTAAACTGAGTCGTTTCGGCGACGCTTTTCTCGATGAAATCGCGGCTTATTTAGCGGGAGAATAATGGTTTGGATTATTCTCACTGATTTTAACCTTACCCAATTGATTGATACTGGCGCGTTTAATTAAAGACTCTAACGTATCAAACTGTTGCATTGCCGCGATACCAATATTGACCTTGCTTGACCCCTGCCAAGGTTGAATACACGCCACTAATTGATTGACCACTTTCTTAGCACCAGCTGCAGAGGTGTGCGGTAGGATAATCACAATTTCATCCATCGAATATCGGATCAACTTATCTTGCTCTCGCAGTCTGTGCTGTAGCTCATCTTGCAGGGGCGGCAAGTCAACTTGCCGTATTTGACTCGCATTAAAAAGTAATAAAGACAGCGGATAATTACCTTGTTTGGCAATGTTTAAGGCCGATGTGACTTGTTGGAGCAGAGTTTCCGCAGGTTCCTCGCCTTCGATACGGGGCGCGCGATTATTTTGCTTATCAATCAAATACCAAATAGCCCCGCCGAGTAACGTCATCGCAATTAAGAATAGTGTGCTATAGGCCCAGTCTAGATTTAGCTCTTGGAGGATCGATGCCTTTTCTGGCGCAGGATTATTCTGATCCTGCTGCAGATCTTGCTGATTTAGACGGGCTTTAAATTGCAATATCCGTGCAGTGTTCATTCCTTGATTGGCGAGTTCGGCAGCTCGATAGCGTTTGAGTAAGAGAGAATAGGCCTGCTTAAAATCCCCTTGTTCTAAGTAGTGATCGCTCATGATCCCGTAAAATCCTTTGAGATCAGAATATTGTTTCAAATTTTCAGCTTGTTCAATGGCGTGATTCATTAGGATTAATGCAGCATCGTCTTGATGCTGGGCAAAGCGTATTTGTGCCAGCAGACGGGTCGAGCGCATCATAGCCAATTGCTGATTGTGCTTCTTGAAGGTATTGATGGCGATAGAAAGGTATTCTTCGGCTGTGTCGATGCGGCCCTTGTCGAGCGCGATCGATGCCAAAATGGCATAGCTGTAGGCAAGTTCTAGTGGCGAACCGAGTTCAGGCAGTAGTGCCTTTGCTTGTTTTTCGAGTTTATCACTGTAATCGAATTCTTGATTATCGAGCGCTATTCTGGCCGCATTTCGTAGAATGATATGTAATACCTTGCCCTTGGCATCTGAGCTCTGTATCCCTAAGTTGGTGTAATACATGGCCTGAGGGAAATCATGGGTTGCGTAGAGTAAATTACCCATGGCCGCATAAATATAAGCCTGTGGTGGCCAAACCCAATTCTGGGTCTGAGTATGAATGTCTGGATAAATATCGATGGCCAGACGTAAATCTTCGATGGCAGAGGAAAAGTTATCTGTGTCGGTATCGAGCTGACCACGTAAACGCAAGGCATCGACTAAGGCTTGGGGTTGTTGATAGCGTCGCGCTAAGGTAATGGCAGAATCCAGCAAGGGCAGCGCATTTTGAATATTGTTGTAGTTGAGATTGGCGTCGGCTTGGCAAATCAAAAAGTAGGGGCGAACTTGATCGAGCTTAAGCTGCTTAGCATTGGCCTCGCCTAGCTGAGCCACGTTGATCGCAGCTTCGTTCTCACCCAGTTGCAGCAGATTTTGGCATTTCAATACACTCAATCTGAGTCGATTAATGTCAGAAGTGGCACTGGTTGTAAATTGTTTTTCTAAGGCAGTGATTTGAGTCAGGGCTTTACTGGGATATTGGTAGACGAGTTCTGTGAGCTCATCCAACTGGTCTATAGCACTCGCCCCAAAGGGGATCAGCCATAACAACAGGCTCAGTACCAAGACGCGAAAATCCATTTTGTGCTCCATAAAATGCGTAAGCTCTAATATCCGTAATCACTAAGGCGAGATTATAACTTTTCTTAATCGTTTTTTGCATAAAAAACTATTATTTTCTAAGGGGAATAAGTTAACACATCCCCGTGCTTTCTGAATCCAAACAGCGGGTTAATTCTGTGGAATCGATCACTTATTTGGGGATAAAACTCACAGTTATTGGCCTTAAGACAGGTCGAATGAGTGGAACATGAAAAAATTAGGGTTGACACTCTCGGCTAGGCAAGTTAAATATTAACCGTCCTAGATTATTTGAACAGAATAAATATTTAAATAAAATGAATAGAATTAGCAGCAAACTCGACCTCCTACTCTCTCTCCTCGCGGTTTATACGCGGAGGTCTTAGTGTTGCACGCTCGATAAATAGGCAAAGCATTCACAAACCCCGCACAAATGTTGCGGGGTTTTTTGTATCTGAATACGGCAAAAGTTAAATAATGAACGATGACGTTCCATGGAGAAGCGAGATGTCTAACAGAGTAATTATATTTGATACGACCTTACGTGATGGTGAGCAGGCGTTAGCCGCAAGCCTTTCGGTCAAAGAAAAGTTACAGATCGCCATGGCGCTGGAACGTCTAGGTGTTGATGTGATGGAAGTGGGTTTTCCAGTCTCATCACCCGGTGATTTTGAGTCGGTGCAAACCATAGCGCGCACCATCAAAAATAGCCGGGTTTGTGCGTTATCCCGTGCACTTGAAAAGGACATCGATGCAGCGGCGCAGGCCTTGTCTGTTGCAGAGCAATTCCGTATCCATACGTTTATCTCGACATCGACGATTCATGTGGAAAGCAAGCTGAAGCGCTCGTTTGATCAAGTGTTAGAGATGGCGGTGGGCGCAGTGAAATACGCCCGTCGCTTTACCGACGATGTGGAGTTTTCCTGTGAGGATGCGGGACGCACGCCAATCGACAATCTGTGTCGCATGGTGGAGGCCGCCATTCTTGCTGGCGCGCGTACCATTAACATTCCCGATACGGTCGGTTACACAGTGCCAAGCGAGTTTGGCAATATTATCCAAACCCTGTTTAACCGCGTGCCGAACATCGACCAAGCGGTGATCTCAGTGCATTGCCACGATGACTTAGGTTTGTCAGTGGCGAACTCGATAACCGCAGTGCAACACGGTGCGCGTCAAATCGAATGTACGATCAATGGTATAGGTGAGCGTGCGGGTAACTGTTCATTGGAAGAGATCGCTATGATTTTAGCGACGCGTAAGGGCATGCTGGGTTTGGAAACAGGCATTAACGCCAAGGAAATCCACCGTACTTCTAATTTAGTCAGCCAGTTATGCAACATGCCTGTGCAGGCTAACAAAGCGATTGTCGGTGCCAATGCCTTTACCCATTCGTCGGGTATCCATCAGGATGGCATGCTGAAAGCGCAAAATACCTATGAAATCATGACGCCAGAAAGCATAGGTTTGAATCGCAACAATTTGAATATGACTTCGCGCTCTGGTCGCCATGTGATCAAGCATCGCATGGAAGAAATGGGTTATAGCGAGCACGATTACAATATGGATGCGCTGTACGAAGAGTTCCTAAAATTGGCGGATAAAAAAGGCCAAGTATTTGATTATGATTTAGAAGCCTTAGCCTTTATGGAAGCGCAGGCCGAAGAAGATAATCACTATCAATTGCAGCAATTAGTGGTGCAGTCTGACTCTACCGAAGGCGTTGCCACAGCGACCGTTCGTATCGAAGTGGGCGGTGAAATTAAAACCGAAGCCGCAACTGGTAATGGTCCCGTTGATGCCGCCTATAACGCCATTGCCCGTGCTACTGATCGTCGTATCGACATCATCAGCTACAAGTTAGGCGCTAAGGGCGTAGGCCAAAATGCCTTAGGTCAAGTGGACATTACCGCCGTTTACCACGAGCAAAACTTCCACGGTGTAGGTTTAGCGACCGATGTCGTTGAAGCCTCGGCGCGCGCTTTAGTGCATGTGATGAACTTAACGTGCCGCGCAGACAAGGTCGCCGATTACAAACAAAGCATGCAAAAGAATCGAGAGCTAGGCGGCGTTTAGTCGCTAAAAGCATCCAAAGAATAGCAAGGTCGAGCATAAAGCATTCATATTTAAAGGAGTTTGCTGGCGTATGAGTTATCAAGTAGCAGTATTAGCAGGGGACGGGATCGGCCCAGAAGTGATGGCAGAGGCGCGTAAAGTGCTGCGAGAAGTGGAAGCGCGATTCGATTTGAATATCGAATATACGCAATACGATGTCGGCGGTATTGCCATCGATAACCACGGTTGCCCACTACCCGATGCCACGCTAAAAGGTTGTGAAGCGGCCGATGCGATTCTATTTGGGTCAGTCGGTGGCCCTAAGTGGGAAAAGTTACCGCCAAATGAACAACCTGAGCGCGGCGCGCTATTGCCTCTGCGTGGTCACTTCGAACTGTTTTGCAATCTGCGCCCAGCCAAACTGCACGATGGCCTAGAGCATATGTCACCGCTGCGTAGCGATATCTCTGCCCGTGGCTTTGATGTTTTATGTGTGCGTGAGTTAACCGGCGGCATTTATTTTGGTAAACCAAAGGGCCGCCAAGGTGAAGGCGAGAATGAAGAAGCCTTCGATACCATGCGTTATAGCCGCCGCGAAATTAGCCGTATCGCCCGCATCGCCTTTGAAGCCGCTCGTGGTCGCCGTAAAAAAGTCACCTCAGTGGATAAAGCCAACGTACTGGCTTGTTCAGTGTTGTGGCGCCAAGTGGTTGAAGAAGTGGCGGTTGATTTTCCCGATGTCGAGCTGGAACATATCTATATCGATAACGCGACCATGCAACTGCTGCGTCGCCCCGATGAGTTCGATGTTATGTTATGTTCTAACCTGTTCGGTGACATTTTATCCGACGAAATCGCCATGTTAACGGGCTCTATGGGCTTGTTGTCCTCGGCGAGCATGAACAGCAGTGGTTTTGGTTTATTCGAACCAGCAGGTGGCAGTGCGCCGGATATCGCAGGTAAAGGGATAGCCAACCCAGTCGCGCAAATTCTGTCGGCAGCATTAATGTTGCGTCATAGCTTGAAACAAGAAGAAGCGGCGAGTGCGATTGAGCGCGCCGTGAGCAAAGCCTTGAACTCTGGCTATTTAACTGGCGAGCTGTTGAGCAGCGACAAGCGTAGCGAGGCTAAATCGACGGCAGAAATGGGTGACTTTATCGCCAATGCTATAAAGGAAGGTGTGTAATGACGAACGCAAAGACCTTGTACCAAAAAGTATGGGATGCACATATCGTTGCTGCTCCCGAGGGCGAAGCGCCGGTCATTTATGTCGACAGACATTTAGTCCATGAAGTGACGTCACCTCAAGCATTCAGCGGTTTGAAGGTCGCGGGCCGTAAGCTGCGCGCGCCGGAAAAAACCTTTGCGACTATGGATCATAATACTTCTACTCGCAGCGCCAGTTTAGATGCCCTAAGCCCGATGGCGCGCACTCAGGTGGAAACCCTAGCGCAAAACTGTAAAGACTTTGGCGTGCGCTTATATGATATTCACCATCCTAATCAAGGGATTGTGCATGTGATGGGCCCTGAGCTAGGCATTACCTTACCCGGCACTGTGATTGTGTGTGGCGATTCCCATACCGCCACCCACGGCGCCTTTGGCGCATTGGCGTTTGGGATTGGCACTTCGGAAGTTGAGCACGTGTTAGCAACGCAAACGCTGCGTCAGTTAAAAGCTAAAACCATGAAGATTGAAGTGCGTGGTCATGTCACCGATGGCGTGACCGCCAAAGATATCGTCCTCGCTATTATTGGCAAAATCGGCATGGATGGTGGCACGGGTTACGTGGTGGAGTTCTGCGGTGAGGCGATCGAAGCCCTGTCGATGGAAGGCCGCATGACAGTGTGTAACATGGCGATTGAGATGGGGGCTAAGGCGGGCATGGTCGCGCCGGATCAAACCACTTTCGATTATTTAGCAGGCCGCGAGTTTGCGCCAAAGGGCGAAGATTGGGCAGAGGCTGTCGCTTACTGGAAAGCCATTAAAACCGATGACGGCGCCGTGTTTGATGCTATCGTTGAGTTAGATGCTGCCGATATTGCGCCACAATTGACTTGGGGTACTAACCCAGGCCAAGTGGTGGCAATCGATGGCAAAGTACCTGACCCCATTAATGAAGCCAACCCAAGCACCCGAGCTAGTATGGAAAAGGCTTTAGAATACATTGGCCTGAGTGCGGGTACGCCGATGACAGATATCAGTATCAACAAGGTGTTTATCGGTTCATGTACTAACTCACGGATTGAAGATTTACGCAGCGCCGCTGTACATGCCAAGGGCCGTAAAGTCGCGAGTGGCGTCACCGCCATTGTCGTTCCAGGTTCTGGCCAAGTGAAAGCGCAAGCCGAAGCCGAAGGCTTAGATAAAATCTTTATCGAAGCGGGATTTGAGTGGCGTTTACCGGGTTGTTCTATGTGTTTAGCGATGAACGATGACAGATTAGAAGCGGGCGATCGCTGCGCGTCGACCAGTAATCGTAACTTCGAAGGTCGCCAAGGTCGTGGCAGTCGTACCCATTTGGTGAGTCCTGCAATGGCTGCAGCCGCTGCGGTGGCAGGGCATTTTGTCGATATTCGTAAACCTTACTAATTACCGTAAACCCAAGAGGAGAAGTTAATGCAACCTTTTACGACCCATACCGGGCTTGCGGTCATGATCGACAGCACCAATATCGATACGGATCAGATCATTCCGAAACAGTTTTTATCTAAGGTGACGCGCGATGGTTTTGGCGTGCACTTATTTCACGACTGGCGTTATTTAGATGATGCGGGCGATCAGCCTAATCCTGAGTTTTCATTGAATCAAAGCCGTTATAAAGGCGCGTCGATTCTTTTGGCACAGGAAAACTTTGGTTGTGGTTCAAGCCGCGAACATGCGCCTTGGGCGCTGGTAGACTTTGGTCTGCGTGCGATTATTGCGCCGAGTTTTGCTGACATTTTTTACGGTAACTCAATCAATAACGGCCTATTACCTGTGGCGTTAACCCACGCGCAGGTGCGCCAATTAATGGATGAAGTGGCGGCAGAAGCTGGTGCACAAATTACGGTTGATCTTACAAGCTGCAAAGTGATTTCACCGTCGGGCGCCGAGTTTAGTTTTACGCTGGCAGAATCGGCAAGGCATAAGCTGCTAAACGGTTTAGATGCAATTGGGTTGACCTTGTCCCACGCTGCGCAAATCAGTCAGTACGAAACCCAAATCCAAGGTTGGCGCCGTTAGTTTTTCAGCGCACATTTGTACTGGTGTTGAGTCCATATTTAAGCGCCTAGGTTAACACCTAGGCGCTTTTGTTTTAAATCATCATCTGCTCATGGTTTAACTAGTTGTATTTTAAGTGATAAATTTTTACTGCTGTTTGTGTGTCATAGATTTTTTGTATCGATAGTGATTTATGCTGAATTTGTTACAACTGAATGATTTACACGTGTTTCTACGATTTGTTATTTTATAGTGTTGTTTATTATAGTTATTTCTTGGTTTTACTATGTTGAGTTAATCAGCATTGCGAATAGTAACTGGGCGAATATAGGCTGAGTTTTGTTTAAATGGTGATATTGGTCACTAAATCTGTTTTAAAGTCATCGTTAGCTAGAGTTTTTGTCTGTTTTATCTCCTTTGGTTGCAAACATCTTCTGCATAAGTAAACTGCTTCGCATTCAATCTGGAGTGTTTGCTCTATTTTGCCTGAACGCCGCAGTACTCATTTAAGTAGTACAGATAACAATAATATTATTAGAGAATAACGATGCAAAAACGTCTTCTGACTTTGGCTGTCACTGCAGCCTTGTTAAGTTCAACGACTCAACTCCATGCCGCTGGTTTTCAATTAGCCGAATATTCTGCCACAGGCTTAGGCCGCGCCTTTGCCGGTGAAGCGGCTATGGCGGATAACGCTTCGGCTCAAGCACGTAATCCAGCCATGTTGACTTATCTCGAAGGTCGTCAGTTATCGACGGGTGGTATTTATGTCATGCCAAACGTCGATGTGACTGGCGATGTCAGCATTAGCTCGCCGTTGCTTGGCCCAGATCCTGTTGTTATGAAAGGTGGCGATGCACTGGATGTGGCCGATGACGCACTGGTGCCAAATTTTTACTACTCAAATCAGTTAAATGATCAATGGACTTGGGGCCTAGCGGTAAATTCTAACTATGGTCTAGCCACTGAATTACCTGCGACTCATGCCGCGGCTATTTTTGGTAACAAGACTTCGGTTACAACCGTTGAGTTTAATCCAAACATCGCTTATCGCATCAATGATGCAGTTAGCGTCGGTGCTGGTGTACGTATCGTTTATGGTGAAGGCGAAATTGGTGCGTCATTACCCGGTTGGGTCGATGGTATCAAACCGGCTCTGCCGGCTCCCGTGCAGGCAATGTTACCTCCTGGTGGCACTGAGCTGAAAAGCATGAAGGGTGATGATGTGGGTTACGGCTGGCAGTTAGGTGCAAGCTGGCAGATCAATCCGGCTCACCGTTTAGGCTTTGCGTATCACAGCGGCGTGCAATTGGACTTAGATGGTCATGCTTCTGGCGTCTTGTATACCGGTGGTCAAGACGTGTCTATTGAAGGTTATTTACCACTTGAATTACCCGCCTTTGCAGAAATTGCTTCCCACCACCAATTGACCGATAACTGGGCCATGCACGCGAGCGTGAACTGGACCGATTGGAGTGTATTTGATCAGCTGGTGGCTTATTTCCCCGGTGACGTTAAACCTAAGGGCGATTTAGAGTCTGATTTAGTTAAAGAAGAGCACTTTGAAGATAACTGGCGTTTCGCGCTGGGAACCACTTATCAAGTGAATAATGACTGGTTAGTGCGTGCCGGTGTTGCTTTAGACAAAACGGCAGCAAATGACGAGTGGCGCACAACTACAATTCCAGACTCTGACCGTCTGTGGTTCTCTGTCGGCGCCGGTTATCAGGCGACTAAGAATCTGAACGTTGATTTCGCTGTGACGTATATCAAAGCAACTGGCGATGCGCCAATCAACGAACAGCAAAACCTACTGGGCTTAGCGGCCGTTAACTTTAACGGTGAAGCGAGCGGCGATGTGTGGCTTGCGGGTATCCAGATGAGCTATAAGATGTAAGTTCTGCGTAGTTTAAAAAAGCCCTGAATCTTCAGGGCTTTTTTATGGCTATAGCATGGTGGCGTTATCTTGTTTTTCGGCTTGCTTGCTGGTCACGGCAGAAAATTGCGAATATATTCATTGCATAAGTAGTGGATCATGTTAACTCTCATCGGCATAAAAAGGCGGCGGAGAGTTCAGATAGGATAGTAGCGTGCAGAAAAAATACGTGGTGGCCTTAGATCAAGGCACGACCAGCTCAAGGGCGATAGTGTTCGATCACGACGCAAACATTGTCAGCGTGTCCCAGCGGGAATTCACCCAGTTGTATCCAAATCCGGGTTGGGTCGAGCATGATCCCATGGAGATCTGGGCAAGCCAAAGTTCAGTGTTAGTTGAAGTGCTGGCGCGCGCGGGGATCCACAGCGATGAAGTGGCCGCGATTGGCATTACCAATCAGCGGGAAACCACTGTCATTTGGGAGAAAGCCACAGGTAAACCTATCTATAACGCGATTGTGTGGCAGTGCCGTCGCAGCTCTGAAATTTGTGAGCAACTCAAAGCTCAAGGGCTAGAAGAGTATGTGCGTGAAAACACAGGTCTATTACTCGACCCTTATTTTTCAGGCACTAAGATCAAGTGGATTCTAGATAATGTGCCCAATGCCCGTGCGCAAGCTGAACGCGGTGAGTTGTTGTTCGGCACTATCGATACTTGGCTAGTGTGGAAGTTAACCGAGGGCAAAGTGCATGTCACCGATCCCACCAATGCCGCCCGTACTATGCTGTTTAATATCCATAGCCTGACTTGGGATAACAAGTTACTTGAAGCTTTAGACATTCCCTTGTCTTTGTTGCCCGAAGTGAAGCCTTCCTGCAGTGTGTATGGCACCACGCGGATTGCTGGTGAAGGCAGTGAAATTCAAGTCGCAGGGATGGCGGGTGACCAACAAGCGGCGCTTTTTGGTCAGCTGTGTGTCGAGCCCGGCATGGCGAAGAATACCTATGGCACTGGCTGTTTTTTATTGATGAATACCGGGACCAAAGCGGTGCGCTCTAACCATGGGTTACTGACCACAGTTGCCGTGGGACCGAAGGGCGAAGTGAACTATGCCCTCGAAGGTTCAGTCTTCATGGGTGGCGCGACTATTCAATGGCTGCGCGATGAACTTGGGCTTATTCGTGACGCCAGTGATACTGAGTATTTTGCCTCTAAGGTTGCTGATACCAATGGCGTGTATTTAGTGCCCGCTTTTGTTGGACTCGGGGCGCCCTATTGGGATCCCAATGCCCGTGGCGCACTCTTTGGTTTAACCCGTGGCGCCAACCGTAATCATATTATTCGGGCGGCGTTAGAATCCATTGCTTATCAGAGTAAAGATTTACTCGATGCCATGACCAAAGACAGTGGCGTTAGCCTTAAGCGTTTGAAGGTGGATGGCGGCGCGGTCGCCAATGATTTTTTGATGCAGTTTCAAGCCGATATCACCGATGTCGAAGTACTAAGGCCGAGTGTTTGTGAGACAACGGCTTTGGGCGCGGCATTTTTAGCTGGGTTGGCTGTGGGCTTTTGGGAGAGTGTCATAGAGCTTGAACATAAAGCTTGTATCGATAAGCATTTTATCCCCAATATTGATGCCGAGACTCGGGTGCGGTTATACGCAGGTTGGCAAGATGCCGTCGCCAGAACTCGCGCCTAAGCTATTCGACTTAAGCTATTCAACTCAAGTCGTTGGACTTGAGACATGCTTAAATTTGATATTGAGTTGGTTTGAAATGTGACTCGATTGTGGTGAATAAAAAGGCGCTAATCCAATAGGATTGCGCCTTTTCCGTTTTATATCAGTACGGATTTATACTTCAACATCAATAATATGTTGGCTCGGTGGCTTTTTGATTTCCGCGGTATCGTCGGTCACTTCAAGCACGACACCTTCTTTTAATTCTTCAGGCTCATCCGATGCACGGCGTTTCTTATTCTTGTTGTTATCAGCATCTTTGCGTCTCTCCCTGACTTTCGCGTCGCGCCCTTCTAACGTTGGGGGTAACTGCGACTGCGGTGTTTCATGGCTATCGGGGGCATTTGCGGTTGCACTACTCACTTGGTCGACGACCCGTTTAGGGTGCAACACGGCTTTCACTGGCTGCGCCAGCATGGCATAAATGCTATCCAATCCACTCATAACGCCTCCTTGTTATTACACCTCTTAACATGCTATCGGCAGGAGTCGGTCAATATTTAGACTATACGCGGTTTAAGTCGTTAAGGCGAATGAGGGGGGTTAAGATCTAAGCATGAGCAATCGATGCCAACCAAAGCAGCACTTATTGTGAAGCAACCACAGTGGTTGAGAAGGAACAATAGTAGCGAGTGTAAAATTGAGGTGAATTGCTGGATTCGCGTGGTGCTTTTGCGGGATTTGGAGTGGGAAAGTTGAGCTTTCTCGCAATCGTGACATTAATATTCAACCATCTGCAAAAAGATGATCGTCTTTATTTCGGATAATTATTTAGATTTTCGTGCTAAATCCAACTCTTTTTTGACTGTCTAATAAGGTTGAATTTAAGTTTATTTTGTTACTTTTTTGGGTTTAAGGTTATTCCTCGACCCGTGAAATCCCACAAATACAGAATTAATCCCCCATTTTTTCATCGCAAGCGCCTGAAAGCAAAAGGTTGCTGAATATTTCGGCGCAATTTATTCCTGTAGTTTCTTCTGTTTAGTCGATCTCTCGTGGTCTTTCATGTCAAAAGTTTGTCGCTAGGTTAAGTGCTCAACATGCTGCTAACCCGCTTGCCGACTGGATTTATAAAAGGATCCCATCCCTTGACATTGCTTGTCGACTGTCCCTAAACTTAGCAGTTGTGGGAAATAGTGGATATTTGTGGATCAAAAATCACAGGAAAGGATGAATTAGCGTGTTTCGTGGAGCCAGTGCTATCAACTTAGATACAAAGGGACGGATCGCTATTCCAGTGCGATACCGCGAGCCTTTGCAGCTTGAGCACCAAGGCCGCATCGTGATTACCGTCGACATCCAATCCGCTTGTTTACTCTTGTATCCAATCCATGAATGGGAATTGATCGAAGCTAAGTTGTTAAAGCTTTCCGATACCGATAAAACCCAGAGATCGTTAAAACGTCTGTTACTCGGTTACGCCCACGAAGTGGAGCTTGACGGTAATGGGCGCATATTACTGCCGCCACCGCTAAGGCAATATGCCAATTTAGATAAGCGCATCATGCTGGTGGGGCAATTGAACAAATTTGAGCTGTGGGATGAGCAATCTTGGCTGCAGCAAATCGATGAGTGTCAGGAAACGATTCGAAGTGAGGAGCTTGCCAGTAACGAGCGCCTCGCGGATTTTTCACTTTAACGAGTCGCGATAACTAAATAAGAAGAGAGTAATGAGTCAGGAATTTGCCCATTTATCCGTGCTGCTAGCAGAAACGGTTGGCGGTTTAAACATCAAAGACGATGGCATCTATATCGATGGCACGTTCGGCCGCGGTGGTCATTCAAGACAAGTGTTGCAGCAACTCGGTGAAAATGGCCGATTGATCGCAATCGACCGTGACCCTCAAGCAATTGAAGCGGCGAAACAATTTGCTGACGATCCACGCTTTCAAATCGTGCATGGTGGTTTTGGCCAATTAGCCGATTATGTCGAAGAGCTTGGGCTTGTGGGCAAAATTGATGGCGTATTACTCGATTTAGGTGTGTCTTCACCTCAGCTTGACGATGCCGAACGTGGTTTTAGTTTTATGCGTGATGGCCCGCTCGATATGCGCATGGATAACAGCCAAGGCCAAACAGCTGCTCAGTGGTTAGCTCGCGCAGAAATTGAGGATATGGCTTGGGTATTTAAAACCTACGGCGAAGAGAAAAACGCTCGTCATATTGCCCGTTGTATTGCTGCCGATCGTGATAAAACCCCGTTTTTACGAACCAAAGATTTAGCCGATTTGATCGCACGTATCACTAAGAGTAAAGAACGCAATAAGCACCCTGCTACTCGGGTGTTTCAGGCCATCCGGATTTATATCAACAGTGAATTAGATCAAATTGATCAAGCGCTTGAAGGTGCGGTCAATGTACTTGCTCCCCAAGGGCGTTTGTCGGTCATCAGTTTTCATTCTTTAGAAGACCGTATTGTGAAGCGCTTTATTCGCCGCCATAGCCAAGGCGAAAGTGTGCCGCACGGTTTTCCGGTCACTGAAGATCAAATTAACAAGTCGCGTAAGTTACGCGCCGTGGGCAAGGCGATTATGCCATCCGACGAAGAAATCGAACGCAATGCCAGAGCTCGCAGCTCAGTGTTACGCATTGCCGAGCGTTTAGATTACTAAGGGGCGCACTGTGAGTAAGCCCTCGATCAATCTGCCACGGATCGTCTTACACGATTTATGGCTGCACAAATGGATCTTGTTATTAGCTCTGCTCGTGCTTTGCAATGCGGTCGCGGTTGTTTATGGCAGCCATATTAGCCGCCAACTAACGACCCAGTGGGACCAGTTACTGCAAGAGCGAGACAGATTAGATATTGAGTGGCGCAATTTATTATTAGAAGAGCAATCGCAGACTGAACACAGTCGTATTACGCGCATTGCCTCTAAGGAACTCAACATGAGTCGTCCGTTACCCAGCGAAGAAGTTGTGGTAAAGGTGCCGTGAGTATAGGGAAGATATGACTAGGCAAGCGAAAATCAAACGGGTAAGCAAACCGCAGAAACCACAACTAATCCATTGGCGTTTATACGTAGTGGTGGGCTTTGTGTGTACGCTATTTTGCAGCCTAGTGGGACGCGCGGCCTACATTCAAATTATCGAACCTGATAAATTACGCCATGAAAGCGATATGCGTACTTTACGGACCACGAGCCGTGAGGTGCAGCGTGGTTTGATCACCGATCGCAATGGCGACATGTTGGCCGTCAGTGTGCCAGTGCGTGCCGTTTGGGCCGACCCTAAACAAGTTAACGATAACAATGGCTTTGCGGATATGCGCCGCTGGCAAGCTTTAGCTGATGTGTTGCATGAACCCGTAGAAGACATATTAGATCGGGTGCGCAGTAATCCCACCAAACGCTTTACCTACTTAAAACGCCAAATTACCCCAGCGGTCGCGGACTACATCACTCAGCTGAAATTACCCGGTGTTTTCCTTAAATCTGAATCCCGTCGCTATTACCCTGGTGGTGAAATCACTGCTCAGCTTATCGGTATCACTAACATTGATGATGTTGGGATTGAAGGCGTAGAAAATGCTTATAACAGCTGGCTAACGGGCACGCCTTCAAAACAGAAAGTGCGTAAATCCCGTGATGGTCATGTGGTTGAGCGCCTCGATATTATCCAAGAAGGCGAAAGCCCGAATGATCTTGTGCTCAGCATAGATCAACGGATTCAGCAGTTAGCGTACCGCGAGCTGAAACGCACCACAGAGATGAACCAAGCGACCTCAGGTTCCATCGTCGTACTCGATATTCACACTGGTGAAGTCTTGGCGATGGTAAATACACCTTCGTACAACCCTAATGCGCGCGACAATCTGCAAACCTTCAGAATGCGTAACCGCGCCATGACGGATACCTTCGAGCCAGGTTCGACCATTAAGCCCTTTGTGGTGGCTGCGGCGCTTGAAGCGGGCACAGTCAAATCGACTGACATTATTGCTACCTCACCGGGTTGGATGCGTTTAGGCGGCCGCCAAGTGCGCGACCCTGCCAACTACGGCGACATGAGTTTGGCGCGTATTCTGGCTAAATCCAGTAACGTCGGCATCAGTAAATTGGCCTTATCTATTCCGGTGCAGCAGTTACTCGGCACCTATCAATCTATGGGACTTGGGAGCTTCTCAGGGATCAACCTTACGGGTGAAAGCGCGGGCGTTATTCAAGACCGCCACCGTTGGTCTGACTTTGAGCGCGCCACCTTGTCCTTCGGTTACGGTTTAACGGCAACCACACTGCAACTTGCGCGCATGTATGCCACTTTAGGCAATGGCGGCACCTTATTCCCCGTTTCAATTATGAAACTGAAAAAGCCCCCCATCGGTGAGCGAGTTATTTCTGAAGATGTGGCCCACGATGTGATGCAAATGCTGGTCGGCGTGACAGAAAAAGGCGGCACCGGCACCTTGGCACATATCGATGGTTATCCGGTTGCGGGTAAGTCAGGCACAAGTCGTAAGGCGGTTGCTGGCGGTTATGGTGATGATTATGTTGCTTTATTTGCGGGTGTTGCCCCGGCGAATAATCCTAAGTTAGCGATAGTGGTGGTGGTGAACGAGCCCAAGGGCGATCTCTATTATGGTGGTTCAGTGGCGGGGCCGGCATTTGCCAAAGTCATGTCTGGCGCACTGCAAATGTTAAACGTTGAACCCATTACCGATAAAGAACAAGTGCAATTGGCGGGTATCGCCGCCGGGAGAGCAGAATGATGTTATTACGGGATCTGCTCGCGCCTTGGCTGCATTATGCCGGCGCGCAGTCCTTCAACGATTTGACCTTAGATAGCCGCGCAATACGTCGCGGCGATGTGTTTTTGGCGCTGCCAGGACATAAGGTTGATGGCAGACAGTTTATTGAAAAAGCCTTAGATTTAGGTGCCGCAGCCGTATTAGTCCACACGGATGATGCTGATCAGCATGGCAAAGTGTTGCACAGCGATAATGTCGAGCACGGCGTACAGATTTATGTCTTCCAGTTGAGTCGCCAAGTATCGGCCATCGCCGCCCAGCGTTATCCCTTAGCGCATCAGTCTGCGACAACATCCCTTGGGGTGATTGGTATTACTGGCACCAATGGTAAAACCTCGACAAGCCAACTGATTGCCCAGCTGATAACGTCGCTCGGCCGCAAAGCCGCTGTGATGGGCACCCTTGGCAATGGTCTCTGGGGCGAGCTTGTCGATAGCGGCAATACCACAACCGATGCTATTACCCTAATGCGTCAGTTGCATGACTTTGAAGCTCAAGGCGCGAGTGTCTGCGCCATGGAAGTGTCGAGCCACGGCTTAGTGCAAGGCCGCGTCGATGCAGTACCATTCGATGTTGCCGTATTTACCAATCTGACGCGCGATCACCTCGATTACCACGGCGACATGGAAAGCTATGCCGCCGCTAAGCAAAGCCTATTTCGTTTCAGCACCTTAGGTCACGGTTTATTGAATCTTGACGACGCGGTTGGTGCATCTTGGTTGACTGAACTTAAATCTGTGCCCGCGCAAATGTGGGGCTTTAGTATTGAAGGTCATCAAGCCGCGGCTTTTTATACTAAAAATGCCAAGTTTGACGATCAAGGTGTCAGTGCAACCTTAGTCTGGCCCGAGGGCGAGACTCAAATCCACTCACCACTTCTTGGTGCCTTTAATCTCTCTAATTTACTCGCGGCGCTATCGGCCTTGTATCTGCAAGGTTTTGATATGAAAGCGTTAGCCGAGCAAGTGCAATACTTAACGCCCGTCGCAGGTCGTATGGAGCGTTTCACAACTGCGAATAACATCACCTTAGTGGTCGATTATGCCCATACGCCAGATGCGATTGAACAAGCGTTAAACGCGCTACGTCGCCATTGTGCTGGTGATTTATGGTGCGTATTTGGCTGTGGTGGCGACCGTGATAAGGGCAAGCGTCCTTTGATGGGCAAAGCTGCAGAACAGTATGCTGACCGCATTATGGTGACGAGCGACAATGCGCGCAGTGAAGACCCCGCGCAAATTATTACCGATATTATCCAAGGGCTAACCCTCTCTCAGCAGGCATTGACTCAGGTTGACCGTATCGCAGCAATTAAAGAGGTTGTGGCATTGGCGAAACCTGGCGATGTGATCTTGCTGGCGGGCAAAGGCCACGAAACTTACCAAGAGGCGGCAGGTGTGCGTCATGACTATGATGAACGCGCCCTGGCACGCCAGTTATCGGAGCAATCAAGATGATCCCTCTTTCTCTCGAAACCCTAAGTCAACACTTAGGTACGTTTCGAGTCGGTGACGACGTCACCATCGAAGATTTAAGCAGTGATAGCCGCAAAATCGGCGCTGCAACCCTGTTTGTTGCCTTAAAGGGTGAGCGTTTTGATGGCCATGATTTTGCTGCAACCGCTATTGAGAACGGCGCCGCGGCGTTAATGGTCGAGCGCGAATTAGCCTTTGATATTCCGCAGCTGATTGTGCCCGATTGCCAAAAAGCCATGGGCGCGATAGGGGCTTATGTCCGTGACCAAATCAATCCGATTTGCGTGGCGTTAACAGGCTCAAACGGTAAGACCAGCGTGAAGGAAATGATCGCGACCATTCTGTCGGCAAAGCATCAAATCCTCTATACCGCTGGTAACTTCAACAATGAAATCGGTGTGCCGCTTACCTTGCTGCGTTTAACGCCAGCGGATGAGTTTGGAATATTCGAACTCGGCGCAAACCATAAAGGTGAAATTGATTACACCTCAGGACTCGTGCGTCCGAACGTGGCGCTGGTGAATAACGTCGGCAGCGCCCATTTAGAAGGATTTGGATCGCAAGCAGGTGTTGCGCAGGCTAAGTCGGAAATTTTTAATCATCTGCAGGCCGATGGCACCGCCATCATCAACGCCGATGATGCCTTCGCCGATTTTATGAGCGTTAAGGCAAGACCTTATAAGCAACTGAGTTTCTCCCAGCAAGATGGTGCTGCTAAACGTCATATCGATGTCATCGCTACAGGTCATAAGGCCAATGCCGATGGCTGCTATCGTTTTATGCTCAATTATTTAGGTGAATCTTGCCAAGTAGTCTTGCCACTGGCGGGCCGCCATCAGGTGAGTAATGCTCTCGCCGCTGCAAGTGTTTGTATTGCCTTGGGATTAAGTTTGACAGAGATCGCCGAAGGCTTAAGCCAATTAACACCAGTGAAAGGGCGCATGCAGCCGAGTCAATTGGGACGCGTACGTCTAATCGACGATAGCTATAACGCGAATCCTGTATCCGTTGGGGCGGCAATCGCTTGGTTAAAGGAAATTTCTGAAAATCGCTGTCTGGTACTGGGAGATTTAGGAGAATTAGGCGACAATGCGCCCCTTTTACACGCTGAACTAGGGCAACTGGCCAAGCAACAGGGGATAGATGCCTTGTTTTGCACCGGAACGTTAAGTCAGCACACGAGCCAAGCTTTCGGTGCGGAGCACTATGACAGTGTGGCGACGTTAGTAGAAAAACTCATAAAGCATATTAACCAGTTACCGGGACAGGTAACGGTTCTCGTTAAAGGTTCACGTAGCGCCGCCATGGAGCGGGTCGTGGATGGACTAACAGTAGCCTTCGGGCGTGGGGAGTTAGTGTAAATGCTGGTATATCTGGCCGAGTATTTAACCCGTTTTCATACCGGGTTTAACGTGTTTTCCTATGTGACATTTCGAGCCATTTTAGGCTTATTGACCGCATTGGTTTTTAGTTTATGGTTTGGCCCTAAACTGATCGAGCGTTTGCAATTACTGCAAATTGGTCAGGTCGTGCGTAACGACGGTCCAGAATCACATTTTAGTAAGCGTGGCACGCCGACTATGGGGGGCTTGCTCATATTAGCTGCTATCTTCATCAGCGTTTTGCTGTGGGGCGATCTCGGTAGCCGCTATGTGTGGGTGATGTTGTTTGTGCTCGGCAGTTTCGGTTTGATCGGCTTTATCGACGATTACCGCAAAGTGGTGCGTAAAGACACTAAAGGTTTGATCGCGCGCTGGAAGTACATTCTGCAATCTTTAGCTGCGCTGCTCATTGCTTTTTTCCTGTATGCCACAGCGGCAAATCCAGGTGAAACCCAGTTAGTGGTGCCTTTCTTTAAAGACGTGATGCCGCAACTTGGCGCCGTATTTATCGTATTGGCGTACTTCACTATTGTGGGGGCGAGTAATGCGGTGAACTTAACCGACGGCCTCGACGGTTTGGCCATTATGCCGACGGTAATGGTCGCGGCGGCGTTTGCCTTGATTGCTTATTTGTCCGGTCACGCGCAGTTTGCGAACTACTTACATATTCCGCATTTACCCGGTTCGGGTGAGCTTGTGATTGTCTGTACCGCCATTGTCGGTGCGGGTCTGGGCTTTTTGTGGTTCAACACTTACCCCGCGCAAGTCTTCATGGGTGATGTGGGTTCTCTGTCATTAGGTGCTGCACTGGGGACAATTGCAGTGCTCGTTCGCCAAGAGATTTTGTTAGTGATCATGGGCGGCGTGTTCGTGATGGAAACCCTGTCAGTCATCCTACAGGTGGGTTCGTACAAGTTACGCGGCCAGCGTATTTTCCGCATGGCGCCGATACACCATCACTATGAGTTAAAAGGCTGGCCTGAACCTCGAGTGATAGTGCGTTTTTGGATTATCTCGATATTCCTCGTCTTACTTGGCTTAGCCACGTTGAAGTTAAGGTAATTGGTTATGCAAAATCAGTATTCACACATAGTGTTAGGTTTAGGGGCAACGGGTTTGTCCGTCGTACGCTATTTGTGTGGAAAGGGCATTACGCCATTAGTGATGGACAGTCGTCGGCAGCCTCTGGGCGCCGAAACATTAGCGACGTCTTTTCCTGATGTGAAGCTGATTTCTGGCGGATTCGATTGCCGTTATCTCGTGCAAGCGACGCAGATCATTATCAGTCCCGGCATTGCCACTTATACCCCAGAGGTGCGTGCCGCTGTGGATATGGGCATCGAAGTGATTGGCGATGTGGAGCTATTTGCCCGTGAGATTGCTGACCGTAAACCTTGCGTTATCGGTATTACTGGCTCTAACGGTAAATCAACTGTCACTACGCTTGTAGGCGAAATGGCAAGGGAAGCGGGTATCGCCGTTGCCGTTGGTGGCAATATTGGTGTGCCTGCGCTGGATTTACTGCATGAAAATGCTGACTTATTTGTGCTTGAGTTGTCGAGCTTTCAACTGGAAACCACCCATAGCCTTAAGTGTGTGGTGGCGACCTGTTTGAATATCAGCGAAGACCACATGGACAGATACAGCGATATCGCTGCCTACCGCGAAGCTAAATTACGTTTATACAAACAAAGCCGCGCCGTTCTGTTTAATCGCGACGATAAACTGACCGTACCGCCAGAGCCAATGAACCAAAACAGTTTTGGTTTAGCTGCGCCAGAAGCGGATGAATGGGGTATCAGCGACAGCAAGATTTATCACGGCAGCAGCGAAATCATGCCGATCACAGAAGTCTCCCTGATTGGTAGCCATAACCATGCCAACCTACTCGCAGCAATGGCGCTTGTGGATGTTGTCGGTGTTGATAAACAGATTATGGCAAAAGTGGCGCGTAGCTTTACCGGCTTATCCCATCGCTGTGAAGTGGTTGGGATTAAGAATGGCGTGACCTATGTCAATGACTCGAAAGCGACGAACGTCGGTGCCACCGTTGCCGCGCTCGATGGTTTGAGTGATCACTTAGGCGATATTATTTTGATTGCTGGCGGTGACGGTAAAGGTGCTGATTTTAAACCTTTAGGCGTGGCGTTAGCGAAAGTGACACATCTCATTACCTTAGGTCGCGATGGCAAAAAGATTGCCGCCTTAAAAGACGGCGCGGTCAGAGTCGAGACTATGGCGGCAGCGGTAACGAAAGCCGCTGAATTGGCGACCTCTGGCGATATCGTTTTATTGTCGCCAGCCTGTGCCAGTTTAGATATGTACAGTAACTTTATGGCTCGAGGTGACGATTTCAGAAACCTTGTGGAGCAACTCAATGGCGAGTGATGTAAGACAACTCAGCCTATTTGGACGTTTGCGTTCAGCAATGCCCAATTGGCAACGTGATACCGAAGTGCCGGGCGTGCAATTGTACGATCGCGCTTTGCTCGCGGCTGTGTTGTCTTTGATTGGCTTTGGCTTTGTGATGGTGATGTCTGCCTCAATGCCAGAAGCGCAAACGCTCACGGGTAACCCGTTTTATTTTATGACTCGCCATGTGGGTTATTTAGTGGGTTGCTTAGCCATTGCGGCATTTGTATTAAGAGTTGAAATGCAAACTTGGCAGCGCTGGAGTCCCATGCTGCTATTGGTTGTGGGCTTGATGTTACTGGCGGTTTTAGTGGTCGGTACTACGGTAAACGGCGCGACGCGTTGGTTATCGATAGGCCCAATACGCATCCAAGTGGCGGAAGTCGCTAAGTTTGCCTTTGCCATCTACATGGCGGGCTATTTAGTGCGTCGTCATCAGGAAGTGCGCGAGAACGCTAAGGGTTTCTATAAACCGATTGCGGTATTTGCCATTTACGCTGTGCTGATTTTGATGCAGCCCGACTTAGGGACGGTTGTGGTGTTGTTCGTCGGTACCGTTGGCCTGCTGTTTTTAGCGGGTGCACGTTTGCTCGACTTCTTCGCCCTGATTTTTGCCGGCGTGTTGGCGTTTGTGGCTTTGGTGTTGTTAGAACCTTACCGTATGCGACGAGTGACATCGTTTATGGATCCTTGGCAAGATCCCTTTGGTAGCGGTTACCAGTTGACGCAATCTTTGATGGCCTATGGCCGTGGCGATTGGTTCGGCCAAGGGCTGGGCAACAGTATTCAGAAATTAGAATATCTGCCTGAAGCGCACACTGACTTCATCTTCGCCGTGATCGGGGAAGAATTAGGTTTTATCGGCATTATCGCTGTGTTATCTGTGCTGCTGTTTGTGGCGCTGAGATCGATTCGTTTGGGCAATTTGTGCTTAGCCATGGATAAGCCATTCGAAGGTTATTTAGGTTACGCCATTGGTATTTGGGTATGTTTCCAGACTGTGGTTAATGTTGGCGCGAGTATCGGCATGTTACCGACCAAAGGACTGACATTGCCTTTTATCAGCTACGGCGGCAGTAGTTTATGGGTCATGACGGCGGCAGCCATGATGTTGCTACGCATTGATTACGAACGGCGCATGACATTAGTTCAGGCCGTGCAAGGGAGATTGAAGTGATGACCCCAGCAGGAAAACGAATTTTAGTCATGGCGGGTGGTACGGGAGGACATGTGTTCCCGGCGCTGGCAGTGGCTAAGTATCTAGCTCAACAGGGTTGGCAAGTGCGTTGGTTAGGCACGGCCGATCGTATGGAGGCGCGTTTAGTGCCCCAGTATGGATTTGATATTGATTTCATCGATATCAAAGGTGTGCGCGGCAATGGTTTGATACGTAAACTCGCGGCGCCCTTTAAAGTGGTGCGCTCAATATTGCAGGCTAAGGCAGTGATTGCTGAGTTTAAACCCGATGTGGTACTTGGCATGGGCGGATTCGCCAGTGGCCCAGGTGGTGTGGCGGCAAGATTAGCCGGTATTCCATTGGTTCTGCATGAGCAAAATGCCATCCCAGGGATGACGAACAAGTTGTTGTCGCGGATTGCGACGCAAGTGTTATGTGCCTTTAAAAATACCTTTACCACAGTGAAGGCCAAGGTTGTTGGTAATCCGATTCGCCAAGAATTGATTGCCTTAGGTGCGCAGCCAAAGCCTGAGGCTGATAAAGCCTTAAAAGTATTAGTTGTCGGTGGCAGCCTTGGCGCCAAAGTCTTTAATGATCTGATGCCTGAAGCGGTTGCGATACTGAGTCAGCAGCAGTCCGTAACGGTTTGGCATCAAGTGGGTAAAGATAATCTTGCGGGCGTGAAGGCGGCTTATCAGCAACATGGGCAAGATGGCGGCGTGAATATCGCCGAGTTTATCGACGACATGGAAGCCGCCTATCGCTGGGCTGATGTAGTATTGTGCCGCGCCGGCGCGCTGACAGTGTCTGAGTTAGCGGCGGTGGGACTACCGAGTATTTTGGTGCCTTATCCCCATGCGGTGGATGATCACCAAACCCGTAACGGCCAAGTATTGGTTGAGGCTGGCGCTGCTTTCCTATTGCCACAGGCCATCTTGGACGTGAATAAACTGGCGGGCAAATTACAACTGCTCGCCAATGACAGAACAGAATTAGCCCGTATGGGCCAAAGAGCGAGAGACGTTGCCGTACTGGATGCGACTGAACAAGTTGCCGCAGTGTGCATCAGCCTTGCCGAGAAAGGATAAGCATGACTAAGACAGAAAGATACGCACAACTGAGAAGCATGATCCCAGAAATGCGCCGTATTAAGCGTATTCACTTCGTCGGCATTGGCGGCGCGGGCATGGGCGGGATCGCCGAAGTGTTAGTGAACGAAGGCTATCAGGTGAGTGGTTCGGATATCGCGCAAAATGCCGTGACCGACAGATTGTGTTTGCTCGGTGCCAAGATCCAAATTGGTCATGCCGCCGAAAACGTGCAGCAAGTGGATGTGGTCGTGGTCTCCACGGCGATTAATCTGGAAAACCCAGAAATCCTTGCCGCTAAAGAATTACGTATTCCTATTGTGCGCCGCGCCGAAATGCTAGCCGAGTTGATGCGTTATCGACATGGTGTGGCAATTGCGGGCACCCATGGTAAAACCACGACAACGAGCCTTATCGCCAGCGTGTACGGTCAAGCAGGGCGGGATCCAACCTTCGTGATTGGTGGCTTGCTCAATAGCGCCGGTACCAACGCCAGATTAGGCACCAGCCGTTACTTGATTGCCGAAGCCGATGAGAGTGATGCGAGCTTCCTGCATTTGCAGCCTATGGTCAGTGTGGTGACTAACATTGAAGCCGATCACATGGATACCTATGGCGGTGATTTCGAGAAATTGAAATCGACCTTCGTGGATTTCCTACATAACTTGCCGTTTTACGGTGTCGCAGTTGTGTGTATCGACGATGCTGTGGTGCGTGAAATCATGCCGCGGATTGGTCGTCAACTCGTGACCTATGGTTTTAGTGACGATGCGGACGTGCAAGCGCTGAATTTCAGTCAGCAAGGGCATCAGTGTCGTTTCACGGTGAGACGTAAAGGCAAAGCGGATCTCGATCTGGTGCTTAACTTGCCGGGTCAACATAACGTGCTTAATGCCTTAGCTGCGATAGCGGTCGCGACCGAAGATGAAATCGACGACAGCGCCATTACTCAGGCGTTAGTGGAGTTCCAAGGTATAGGTCGTCGTTTCCAGCATTTGGGCAAGTTTGCTACGCCCAAGGGCGAAGTTATGTTGGTCGATGATTATGGCCATCACCCCAGTGAAGTTGCCGCGACCATTAAAGCTGCCCGTGCAGGTTGGCCCGACAAGCGTCTGGTTATGGCGTATCAGCCACACAGATACACCCGTACCCGTGATTTGTATGAAGACTTTGTTGAAGTGTTATCCCAAGTGGATTGTTTACTGTTGCTCGATGTTTATAGTGCGGGTGAAGCGCCGATCACTGGTGCCGATGGCCGAGCCTTATGTCGCTCAATTCGCCTGCGTGGCCAGTTAGATCCGATCTTTATCGCCAGTCCTGATCAGCTCGCAGAGGTATTGCCCGATGTATTGCAAGAGGGGGATTTATTACTCACCCAAGGTGCGGGTAATATTGGCGCCCTGTCGCGATTGTTAGCGACGACAGAATTGGGCTTTGCCGTTGCGGAATTACCTGCGCAAGCAAGCTGATCTTTAGCGCTTGATCTGCGAGGAAAAGGAAATTTTGACCAAAGGATTTTGGTCTATATAATGGCCGGTTTTCTGTGGTTTTTAATGAGTGTCGTTAAAGGTGGAATGAAGCGTGTCTTGGAGTGATAAGAGGCGGCACTGGCGGGCAAGAATGTCACGGGTTAATTGGTATCTGTGGAGCGGGGCTGGCTTTTTATTCTTCATCGTGGCCAGTTTTGTATTTGGTGGATACCAGTTACATAAATTTTTAAACGATGCCAGCACCTTGCCGATTGAGGCCGTTGCCATCAAAGGTGAGCGGGCTTACACCTCAGATAAAGATATACAAGTGGCACTACAGGATTTAATGCAGCGGAGTTTTTTTAGTGCTGACATCACCTTAGTGCAACAGGCGTTAGAAGACTTACCTTGGGTGTATCGCGCGTCGGTAAGACGTGAGTGGCCAGCGAAACTCAGGGTGTATTTGCAAGAGCAGCAACCAGCAGCCCATTGGAATGGTGACGCTTGGTTGAATGTGCATGGTGAAGTGTTTGAAGCGCCATCGCACCCAGAGCTGGAGCATTTACCGCAGTTATCAGGGCCAGACGATATGGGTCTTGAGGTGTTGACTGTGTATGCACAGATCAACTCACTGTTAAAGATTAATGGCTTTACCTTAGCCAGTTTGAATTTGACGCCACGACATGCGTGGCACGCGACCTTAGGAAACGGCATCGTTTTAGATTTAGGTCGCGAAGATAAAATTGCACGGATACAGCGATTTATAACTGTTTATCCGGTTTTGGCGAAACAAGATAAATCCGTCGCCAGAGTAGATTTGCGCTACGACACAGGGTTGGCCGTAGGCTGGGGCGATGCACAAACAAGAGAGCCGATAATTAATGATCAAAAACCAAGATAGAAACCTTATCGTCGGATTGGATATAGGAACCTCTAAGGTCGCAGTGATCATAGGCGAAGTTCTGCCCGATGGCGAAATCAGCATAGTCGGCCTTGGCAACCATCCCTCAAGAGGCATGGATAAAGGTGGCGTTAACGATTTAGATTCGATCGTTCGCAGTGTGCAACGTGCGCTCGATCAAGCGGAATTAATGGCGGATTGCCAAGTTTCATCGGTTTACCTGAGCATCTCTGGTAAACACATCGCCTGCCAAAACGAAAACGGCATGGTATCGATCAACGATGAAGAAGTGACACAGGAAGACGTTGATAACGTGATCCACACCGCTCGCTCGGTGAAGATCCCGACCGAACGTCGCATCTTGCATGTGCTGCCACAGGAATATGCGATCGACGTGCAAGACGGCATTCGCAGCCCAATCGGTATGTCTGGCATGCGTATGGAAGCCAAGGTACACATAGTGACCTGCGCCAATGATATGGCGAAGAACATCACTAAGAGCGTTGAGCGTTGTGGCCTTAAGGTCGACGACTTAGTGTTCTCTGGGATTGCTTCGGCCGATGCCGTGCTGACCTTCGATGAGAAAGACTTAGGTGTGTGTATCGTCGATATCGGCGGTGGCACCACAGATATTGCGGTATATACCAACGGCGCCTTGCGTCATTGTGCGGTTGTGCCAGTTGCGGGCAACCAAGTGACCAACGACATTGCGAAGATTTTCCGCACGCCGTCGTCACATGCTGAACAAATTAAAGTGCAATTCGCCTGTGCGCGCAGCTCTATGGTGAGCCGCGAAGACAGTATCGAAGTGCCATCGGTAGGTGGACGTCCATCACGTAGCATGTCACGTCACACGCTCGCCGAAGTGGTAGAACCAAGATACCAAGAGCTGTTTGAGTTAGTGCTTAAAGAGTTGAAAGATAGTGGATTAGAAGATCAAATCGCCGCAGGTATTGTGCTCACAGGCGGCACAGCGTCGATTCAAGGTGTGGTCGATATTGCGGAAGCGACGTTCGGCATGCCAGTTCGTGTAGCGTCACCGCTACCGATCAAAGGTTTATATGAATACGTGGACCAGTCTATTTACTCCACTGGAGTCGGACTGCTTCATTACGGTGCACGACGGGTGATCGAACGTCAGTTCGAACGTCCTGAGCGCCAAGGGGTAACTAGCGCTTGGAATCGGGTCCAAAGCTGGTTTAAGGGCGAGTTTTAATTTTCATAACGCAGGCAACACGGAGATCAGACAATGTTTGAGATCATGGATACTCACTCAGACGACGCGGTGATTAAAGTCATCGGCGTTGGCGGCGGCGGCGGTAACGCTGTCGAACATATGGTCAAACACAACATTGAAGGTGTTGAGTTTATCGTTACCAATACAGACGCTCAGGCCCTGAGAAAATCAGGTGCAGGCTCAACGATTCAATTAGGACGCGATGTCACTAAAGGTCTCGGAGCAGGAGCAAACCCAGATGTGGGTCGTGCAGCAGCTGAAGAAGACCGCGAAAATATTCGCGCGGCGATCAAAGGCTCTGACATGATCTTTATCGCTGCCGGTATGGGCGGTGGTACAGGTACTGGTGCTGCACCAGTTGTCGCTCAAATCGCTCGCGAAGAAGGTATTCTGACCGTTGCGGTTGTGACTAAGCCTTTCCCATTCGAAGGGAAAAAACGTATGGCTTATGCTGAGCAAGGTATTGCTGAGCTGGCTAAGCACGTGGATTCGTTAATCACGATTCCAAACGAAAAATTATTAAAAGTATTGGGCCGTGGCACTTCTTTACTCGATGCGTTCGCCGCAGCGAATAACGTGTTACTTGGCGCGGTACAAGGTATTGCCGAGCTTATCACTCGTCCAGGTCTGATTAACGTCGACTTCGCCGACGTGAAGACTGTGATGTCTGAAATGGGTAATGCCATGATGGGTACAGGCGTCGCTCGCGGTGAAGACCGCGCTGAGGAAGCGGCTGAAGCTGCTGTTGCCAGCCCATTATTAGAAGACATCGACTTAGCCGGTGCGCGTGGTGTGTTAGTTAACATCACTGCGGGTATGGACATGAGTATCGAAGAATTCGAAACCGTGGGTAATCACGTTAAGGCTTATGCCTCTGACAACGCGACTGTGGTTGTTGGTGCTGTCATCGACCCAGAAATGAGTGATGAGCTGCGTGTAACCGTTGTTGCTACTGGTATTGGCGCTGAAAAGCGTCCAGATATTCAATTGGTGTCTAAGCCTGCGCCTCGTCCAGAGCCAGTGGTTGTGGAACCAAAAGTCGAAGCCTATGTCGAAGAAGCGGTACATGTGAACTACGCTGCGCCAAAAGGCAATGTATTGCCAGCTGCACCGCAGCCAGCTCCACAGCCTGCACCATCGACAAAGCATGAGTTGGATTATCTTGATATTCCAGCATTTTTGCGTAAGCAAGCCGACTAGCGATAGCGTTTGAGTCCAGCGTATATGTGAGGTGTTGTTCGTTTTGGGCAATACCACAGCATAGCTGCATTTGTGTGACATAATAAAGTTATGTTATTATGTTGAACCAGTGCGGCCAATTTGCCGACGCTGAGTGTACAAATATTAGGCTTTATAATTGGCATATAAACGGGTAACTGAATGATATTTCAAAGAACTGTTCAGAAAATGGTGAAAACTACTGGTGTCGGATTGCATTCTGGCAACAAGGTCACTTTGAGCATAATGCCCGCGCCCGTTAATTCAGGGATCGTACTCGTGCGCACTGATTTGAGTCCTGCCGTCGCCATTCCAGCGAAGGCGGAGCAAGTTCGTGAAACCACTATGTGTACGGCACTTGTCAATGACGAAGGTATTCGTATCTCGACCATTGAGCATTTATTTGCAGCACTTGCGGGTTTAGGCATAGATAACGCCGTAATCGAAGTGGACGCGCCTGAAATTCCTATTATGGATGGCAGTGCGAGCCCATTTGTCTTCTTATTGCAGTCTGCGGGTATTAAAGAGCAGGCCGCACCTAAGAAGTACTTAAAGATTAAGCGCCCTGTGCGTGTTGAAGATGGCGACAAATGGGCCGAGCTGAAACCCTTTAAAGGTTTTAGAGTCAATTTTAAAATTGACTTCGCGCACCCAGAAATCGCTCGTAGCCAACAGCATGTGGTGATGGACTTTTCAACGTCTGCTTTCGTGAAAGATATCAGCCGCGCCCGTACTTTTGGGTTTATGCGTGATATCGAATATCTGCGAGCCAACAACCTAGCGTTGGGCGGCAGCATGGAAAACGCAGTTGTACTTGATGAATATCGCGTCCTTAATCCCGACGGCCTGCGTTATGAGGACGAATTTGTTAAGCACAAAATTCTAGATGCGTTTGGTGATTTATATGTGGCAGGACACGCTATTTTAGGTGAGTTCACTGCGTATAAAACCGGTCATGCACTGAACAACCAATTGGTGCGTGCTTTACTGGCGCAACAGGATGCGTGGGAGTTGGTCAGCTTTGAAAAAGAAGCCGATGTTCCCGTTAGCTTTACCGTGCCAGGTGGCGCTGTATTTGCTTAAAAAGCGGTGAGATTATTTATTACGGCTGGCCAATGAGGCCAGCCGTTTTAGTTTTTCGCCCAGTGAACCGCCAATATTTTCCGCTAATGCCGCAATATGCTCCGCTGCCGCAGGGCTTAATTCTTTGTGTCCCTGTTTAGGCTTTGCGTCATACATTGCTAATCTCGGGTTGACTTTAACTTCGATTGCGGTAAGGATTGGGAGCGTTTCTGCTTGAAGCTGCGCTAGTAGCTTCGGCTTTTGGAAATTAATTCGTGCAGCCCAGGCCGCCGAAGTGGTTTCAACCACAAGAACACCCTGGCGGAAATTAGCGACTTTTAGCTGCTCCGCAACAGGACCTGCGATAATCTGCTTCACATGATGATTCAGATACATAAGTAGTTCCGCTTTCTCAGCAAGGTCGGGTAATTTGCCCGATTGATGCAGTAATTGGCTGAGATCTTGAGGGGGCTTTTTCATATGATAATAAGATGGCTGAATTAGGCTATGAGTGTAACAGTATTTATTCAAGGTCGGAATGGCGTCACACGCTGGCAGCCCAGTAAACGCTGGTTGCTACTTCCAATACTATTGTTGGCCACTGGGTCCGGTATTTATCAACACAACTCAGCTCGATTTCAAGATCAGCAAGCCAGTGTCGATAACGATCGTCTCGTGCGCGAACAACAAAAGAAACAAGTCTTAGAGCTAAAAAGTGCTACCGAGTCTCAACTGGCTATGCTAGTGACTCATGTTGCACGTATGCAAGCCAAGATCACCCGTCTCGAAGCCTTAGGTCAACAGGTCGCCCAGCAGAACAAATTGGATGAAGATTTTGATTTCTCATCCGAAGTTGGTGTGGGTGGTTTGAGCGAATTAGGCACTAACATCGAGCTCGGACAGCTTATCGACGATATGGATAAGTTGGCATCACGAATTGACAATAATAATGTTCAGCTTTCACTACTTGAAACAGTGTCATCTAACCACCATATAGATGATGAACGTTATATATCAGGGATCCCACTCGATAAAGGTTGGTTATCGTCGCCCTACGGATTACGTAATGACCCTTTCAACGGTCGCAGGACTGTGCATAAGGGGATTGATTTTACCGGCCGAGAAGGGGCCAAAGTTGTTGCGACTGCTGCAGGCGTAGTAACTTGGGCTGGGAACATGTTCGGTTATGGCGAGTTGGTCGAAATAGACCATGGTAACGGTTTACATACGCGTTATGGCCATAATAAAGCTTTGTCAGTGACTGTAGGTGATGTGGTCGCAAAAGGCGATGCAATCGCCAGTATGGGAAGCACTGGGCGCTCAACTGGGGCTCATGTGCATTATGAAGTGTTGCGCGGCGGACAGCAGATAGATCCACAAAAGTTTGTCTACCGCAAAGCAAGTTAATTGAAACATTGGCTTCTAGTTTCCATCATGGACTTGGCCTACAGAGAATAAGTGATTCAGATGTTTGGTAAATTACTGACAAAAGTATTTGGTAGTCGCAACGACCGCACCCTTAAAGGGCTTCAAAAGATCGTTATTAGCATTAACGCACTAGAAGCTGATTATGAAAAATTAACCGATGAAGCTTTAAAAGCGAAAACCGCTGAGTTCCGTGAGCGTTTAGCTGCCGGCGCTTCATTAGATAGCATCATGGCTGAAGCGTTTGCTACAGTACGTGAAGCGTCTAAGCGCGTATTTGACATGCGCCACTTCGACGTACAGTTACTCGGTGGTATGGTACTCGACAGTAACCGTATTGCTGAGATGCGTACGGGTGAAGGTAAAACCTTAACGGCAACCTTGCCCGCTTACTTAAACGCATTGACGGGCAAAGGCGTTCACGTGATTACAGTGAACGATTACTTAGCACGTCGTGATGCCGAGAATAACCGCCCACTGTTTGAGTTCCTCGGTTTAACCGTCGGCATCAACGTGGCAGGTCTCGGTCAGCATGAAAAGAAAGCTGCTTACAACGCCGATATCACTTACGGTACCAACAACGAGTTTGGTTTCGATTATCTGCGCGATAACATGGCGTTTTCACCGCAAGAACGAGTACAACGCCCGTTGCACTACGCCCTAATCGATGAAGTGGATTCAATCTTAATCGACGAAGCGCGTACGCCGCTGATCATCTCTGGCGCCGCTGAAGATAGCTCTGAGCTATACATCAAGATCAACACCTTGATCCCAAATCTTATCCGTCAAGATAAAGAAGATACGGAAGAGTATGTGGGTGAGGGTGACTACAGCATCGATGAAAAAGCCAAACAAGTGCATTTCACCGAGCGTGGTCAAGAAAAAGTAGAAAACCTGTTGATCGAGCGCGGCATGTTAGCCGAAGGCGACTCACTCTATTCTGCTGCAAATATTTCCCTATTACACCACGTCAACGCGGCGCTCCGTGCGCACACGCTGTTTGAGCGTGATGTGGATTACATAGTACAAGACAACGAAGTCATCATAGTCGATGAACACACAGGTCGTACTATGCCGGGTCGTCGTTGGTCAGAAGGCTTACATCAAGCTGTCGAAGCCAAAGAAGGCGTGCATATTCAAAACGAAAACCAAACATTGGCTTCAATTACCTTCCAGAACTACTTCCGTCAATATGAGAAGCTGGCAGGTATGACAGGTACAGCCGACACTGAAGCGTTCGAATTCCAACACATCTATGGATTAGACACAGTCGTTGTGCCGACTAACCGTCCAATGGTGCGTAAGGATATGGCGGATTTGGTTTACCTTACCGCCGATGAAAAATATCAAGCTATCATCAAAGACATTAAAGATTGCCGCGAACGTGGTCAGCCAGTATTGGTGGGTACAGTCTCAATCGAACAGTCTGAACTGTTAGCGCGCTTAATGGTGCAGGAAAAGATCCCGCACGAAGTCTTAAATGCTAAATTCCACGAACGAGAAGCTGAAATCGTCGCGCAAGCTGGCCGTACTGGTTCAGTGACCATTGCCACTAACATGGCAGGTCGTGGTACCGATATAGTGCTTGGCGGCAACTGGAACATGGAAATCGATGAACTCGATAATCCAACAGCTGAGCAAAAAGCTAAGATCAAAGCCGATTGGCAAATTCGTCATGATGAAGTCGTCGCCGCGGGTGGTTTGCACATCCTAGGTACCGAACGTCACGAATCACGCCGTATCGATAACCAGTTACGTGGTCGTGCGGGTCGTCAAGGTGACGCGGGTTCATCACGCTTCTACCTGTCGATGGAAGATAGCTTAATGCGTATCTTCGCCTCAGATCGTGTCTCTGGCATGATGAAAAAGCTGGGTATGGAAGAAGGCGAAGCTATCGAGCATCCATGGGTGTCTCGTGCAATCGAAAACGCTCAACGTAAAGTTGAAGCACGTAACTTCGATATCCGTAAGCAATTACTCGAATTTGATGACGTAGCTAACGATCAACGCCAAGTGGTTTACGCTCAGCGCAACGAGTTGATGGATGCGGAAAGTATTGAAGATACTATCCAAAACATTCAAGACGATGTGATTGGCGCGGTCATCGATCAGTATATTCCACCACAATCAGTAGAAGAGCTGTGGGATATCCCAGGTTTAGAGCAACGTCTACACCAAGAGTTTATGCTCAAACTACCTATCCAGGAATGGTTAGATAAAGAAGACGATCTGCATGAAGAGAGTCTGCGTGAGCGTATTATCACTGCTTGGGGCGATGCGTATAAGGCAAAAGAAGAAATGGTTGGCGCGCAAGTGCTACGCCAGTTCGAGAAAGCCGTTATGCTACAAACGCTCGATGGTCTGTGGAAAGAACACTTAGCGGCGATGGACCATCTGCGTCAAGGTATCCACTTACGTGGTTACGCACAGAAAAATCCAAAGCAAGAATATAAGCGTGAGTCGTTTGAGTTATTCCAACAACTATTGAATACCTTGAAACACGACGTCATCAGTGTGCTGTCGAAAGTACAAGTTCAAGCTCAGTCAGATGTGGAAGAAATGGAAGCGCGTCGTCGTGAAGAAGATGCCAAGATCCAGCGCGACTATCAACACGCAGCGGCAGAGTCGCTTGTTGGCGGTGGTGATGAGCATGAAGCTGTCACTGCGCAAGCACCGATGATCCGTGATGGTGAGAAAGTGGGTCGTAACGATCCTTGTCCTTGTGGCTCAGGTCGCAAGTACAAACAATGTCACGGCAAGTTAAGTTAATCTGCTGACAGATTAAGCCTACTGAATAAGTAGAATAGAACGCACACTAAAGGCAACTTATCTAAGTTGCCTTTTTATTTCTTCTTTATATAGCTAATCGTCGTTATTCCCCATTGAACATCATTTCACTCTCATCGATAGCCATTAAAGCAAAGCCATCGCTGTAGAAAATTTAACCGTTAAAACCCATTTTGTGGATAAGTCTGAGGATAAAGTGGGGGTATTATGTGACTAACTGAAAGTATTAAAAAAAGATCCATTTTTCTCAAAAAAGCCCTTGCGGACTTTCTCAATCTCCCTATAATGCGCATCCACTGACACGGCAGACAGCGTAACGCAAGTTAAGCAAAGAAAGCCAAGCAGTTCATCGGTTAGCGAGCTAAACGATGAGTGTTGAGAAAGTTGAAAAAACTACTTGACGCACTAACGGGAATGCGTAGAATACGCAGCCCTGACCCGCTGAAAACTCAGTGTGTGGTCAACGCTCTTTAACAATTTATCAAGCAAATCTGTGTGGACACTCACAGGTGTTGAGTTAATCGAAACTGCTTAACCTTCGGGTTGGCAGTCAAAAATTTAAATCAACCAATTCTTTCACGAGAGTGATGAGTGTTCATAGCAATATGTACAAAGACTTACTACTTATTTTCGAATAAATAGGAGTCGAACAGAATTCGTTGAGCCGTTTGACGTAAGTCAAACAACAAAACTTTAATTGAAGAGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGCAGCGGGAAGATAGTTTACTATCTTTGCCGGCGAGCGGCGGACGGGTGAGTAATGCCTAGGGATCTGCCCAGTCGAGGGGGATAACAGTTGGAAACGACTGCTAATACCGCATACGCCCTACGGGGGAAAGGAGGGGACCTTCGGGCCTTCCGCGATTGGATGAACCTAGGTGGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCCTAGCTGTTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGTAGGGAGGAAAGGTAATAGTTTAATACGCTATTACTGTGACGTTACCTACAGAAGAAGGACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTCCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGCGAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTCGAACTGGCGAACTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCTACTCGGAGTTTGGTGTCTTGAACACTGGGCTCTCAAGCTAACGCATTAAGTAGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCACGGAATTCGCTAGAGATAGCTTAGTGCCTTCGGGAACCGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCCTTATTTGCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGATAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCGAGTACAGAGGGTTGCAAAGCCGCGAGGTGGAGCTAATCTCACAAAGCTCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTGGATCAGAATGCCACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGCTGCAAAAGAAGTGGGTAGCTTAACCTTCGGGGGGGCGCTCACCACTTTGTGGTTCATGACTGGGGTGAAGTCGTAACAAGGTAGCCCTAGGGGAACCTGGGGCTGGATCACCTCCTTACCTATACGACTAACTTAATGTTTGTTGAGTGTTCACACAGATTTGCTTGGTAGAAGAAAGAGTAAAAGATGGGTCTGTAGCTCAGCTGGTTAGAGCGCACCCCTGATAAGGGTGAGGTCGGTGGTTCAAGTCCACTCTGACCCACCAATCCTTCCTTCTCAGCGTTGGAAAGTGACTCGTTTAGTGAACTAAACGTCGCCACTGTCCGCCTTGATAAGAAATGATTGGCAAACCAATCCTTTTAGAGGTTTGGAACATCTTACTTACTCGCACTGCATGTAAATGGGGCTATAGCTCAGCTGGGAGAGCGCCTGCCTTGCACGCAGGAGGTCTGCGGTTCGATCCCGCATAGCTCCACCATTTACACCTTATTCTGACTTTATTGCAAAGTAATAAGGCATATGCACTATGGATAGAGATGCCAAAGATAAACTTAAAAATTATCTTTGGCTTTTTTAAGCCCGCTCTTTAACAATTTGGAAAGCTGATAGTATTTAATTGCATGAGTCTGTCATGTAATTAATTACAAAACGTTCAATGCGAAAGCATTGAACATGAGTTCTCAAACACTTTATTAAGTGTCTTGAATATTCAAAAGTAAATTATTCTTTGCTGAATAGTTTACGTCTAAGGCGCGTCCACTTCTTTGGTCAGAAGTGAGACAAGTAAAACCAAGCTGGTCGCAATGCGACCCAGGTGAGTGAAACTCATTTGGGTTGTATGGTTAAGCGACTAAGCGTATACGGTGGATGCCTTGGCAGTCAGAGGCGATGAAGGACGTAGTAACTTGCGAAAAGCGTTGGCGAGCTAGTAACAAGCATTTGAGCTAACGATGTCCGAATGGGGGAACCCAGCAGCATAAGCTGTTATCGCAACATGAATACATAGTGTTGCGAGGCGAACCCGGGGAACTGAAACATCTAAGTACCCGGAGGAAAAGAAATCAACCGAGATTCCCCTAGTAGCGGCGAGCGAACGGGGATTAGCCCTTAAGTCAGTGGGGTGTTAGTGGAATGTGTTGGAAAGCACAGCGGCACAGGGTGATAGCCCCGTACATGAAAACTAACCATTGATGAAAACGAGTAAGGCGGGACACGTGACATCCTGTTTGAATATGGGGGGACCATCCTCCAAGGCTAAATACTCCTGACTGACCGATAGTGAACCAGTACCGTGAGGGAAAGGCGAAAAGAACCCCTGTGAGGGGAGTGAAATAGAACCTGAAACCGTATACGTACAAGCAGTGGGAGCGGTTCTTGAGACCGTGACTGCGTACCTTTTGTATAATGGGTCAGCGACTTACATTTTGTAGCGAGGTTAAGCGAATAGCGGAGCCGTAGGGAAACCGAGTGTTAACTGCGCGTTTAGTTGCAAGGTGTAGACCCGAAACCCGGTGATCTAGCCATGGGCAGGTTGAAGGTTGAGTAACATCAACTGGAGGACCGAACCGACTAATGTTGAAAAATTAGCGGATGACTTGTGGCTGGGGGTGAAAGGCCAATCAAACCGGGAGATATCTGGTTCTCCTCGAAAGCTATTTAGGTAGCGCCTCGAGCGAATACCATTGGGGGTAGAGCACTGTTAAGGCTAGGGGGTCATCCCGACTTACCAACCCTTTGCAAACTCCGAATACCAATGAGTACTACTCGGGAGACAGACGGCGGGTGCTAACGTCCGTCGTCAAAAGGGAAACAACCCAGACCGTCAGCTAAGGTCCCAAAGTACTAGCTAAGTGGGAAACGATGTGGGAAGGCTTAGACAGCTAGGATGTTGGCTTAGAAGCAGCCATCATTTAAAGAAAGCGTAATAGCTCACTAGTCGAGTCGGCCTGCGCGGAAGATGTAACGGGGCTAAGCTAGTCACCGAAGCTACGGGTGCACAGTTTACTGTGCGCGGTAGAGGAGCGTTCTGTAAGCCGTTGAAGGTGAAGGGGTAACCCACGCTGGAGGTATCAGAAGTGCGAATGCTGACATGAGTAACGATAAAGGGGGTGAAAAACCCCCTCGCCGAAAGACCAAGGGTTCCTGTCCAACGTTAATCGGGGCAGGGTGAGTCGACCCCTAAGGTGAGGCCGAAAGGCGTAATCGATGGGAAACAGATTAATATTTCTGTACTTCCGCTAACTGCGATGGAGAGACGGAGAAGGCTAGGCTAGCGCGGCGTTGGTAGTCCGCGTTTAAGGTGGTAGGTGGTGTTCTTAGGCAAATCCGGGGACACGTACTTTGATGTACACACTGAGAGCTGATGACGAGGTCCTAAGGGACTGAAGTAGTTGATGCCATGCTTCCAGGAAAATCTTCTAAGCTTCAGGTTAGTGGGAATCGTACCCCAAACCGACACAGGTGGTCGGGTAGAGAATACCAAGGCGCTTGAGAGAACTCGGCTGAAGGAACTAGGCAAAATGGTACCGTAACTTCGGGAGAAGGTACGCTCCTGTTGGTGATGAGACTTGCTCTCTAAGCTGACGGGAGTCGCAGATACCAGGTGGCTGCAACTGTTTATCAAAAACACAGCACTGTGCAAACTCGCAAGAGGAAGTATACGGTGTGACGCCTGCCCGGTGCCGGAAGGTTAATTGATTGGGTTATCGCAAGAGAAGCTCATGATCGAAGCCCCGGTAAACGGCGGCCGTAACTATAACGGTCCTAAGGTAGCGAAATTCCTTGTCGGGTAAGTTCCGACCTGCACGAATGGCGTAATGATGGCCACGCTGTCTCCAGCCGAGACTCAGTGAAGTTGAAATTGCGGTGAAGATGCCGTATACCCGCGGCTAGACGGAAAGACCCCGTGAACCTTTACTATAGCTTGGCACTGAACATTGAACCTACATGTGTAGGATAGGTGGGAGACTTTGAAGTTGTGACGCTAGTCATGATGGAGTCGTCCTTGAAATACCACCCTTGTAGTTTTGATGTTCTAACCTAGGTCCCTTATCGGGATTAGGGACAGTGCCTGGTGGGTAGTTTGACTGGGGCGGTCTCCTCCCAAAGAGTAACGGAGGAGCACGAAGGTTGGCTAAGTACGGTCGGACATCGTACGGTTAGTGCAATGGCATAAGCCAGCTTAACTGCGAGACATACACGTCGAGCAGGTACGAAAGTAGGTCATAGTGATCCGGTGGTTCTGAATGGAAGGGCCATCGCTCAACGGATAAAAGGTACTCCGGGGATAACAGGCTGATACCGCCCAAGAGTTCATATCGACGGCGGTGTTTGGCACCTCGATGTCGGCTCATCACATCCTGGGGCTGAAGTCGGTCCCAAGGGTATGGCTGTTCGCCATTTAAAGTGGTACGCGAGCTGGGTTCAGAACGTCGTGAGACAGTTCGGTCCCTATCTGCCGTGGGCGTTGGATGATTGAAGGGAGCTGCTCCTAGTACGAGAGGACCGGAGTGGACGAACCGCTGGTGTTCGGGTTGTCATGCCAATGGCATTGCCCGGTAGCTACGTTCGGAATCGATAACCGCTGAAAGCATCTAAGCGGGAAGCGAGCCCTAAGATGAGTCATCCCTAGGAATTTAATTCCTCTAAAGAGCCGTTCGAGACTAGGACGTTGATAGGCATGGTGTGTAAGCGTTGTGAGGCGTTGAGCTAACATGTACTAATGACTCGAGAGGCTTAACCATACAACCCAGATGGGTTTTACTGAAAATGTTCCAGACATTTTCTAGCACTTCCTCCATCCATGGAGGTCGTACGAAAAGCCTTAGACAGAATATCGACACTTGATGAAGTGCGAACTCAAACAAAGCAAATCAGCTTTCCGAATTATTATTAACGTGGTCCAGAGATGGACACGGTAATAAACAGAATTTGCTTGGTGACAATAGCCTTGTGGAACCACCTGATCCCATCCCGAACTCAGAAGTGAAACGCAAACGCGCCGATGGTAGTGTGGGGTCTCCCCATGTGAGAGTAGGTCATCGCCAAGCGCCTAATTTAAATAATCTTAATTGATTAGTATAGAATTGCTGATATGGCTCAGTCGGTAGAGCGCATCCTTGGTAAGGATGAGGTCCCCAGTTCGATTCTGGGTATCAGCACCATTACAATCTAGTGTCATTTCCTAGAATAAAAAATTTGCTTGGTGACAATAGCCTTGTGGAACCACCTGATCCCATCCCGAACTCAGAAGTGAAACGCAAACGCGCCGATGGTAGTGTGGGGTCTCCCCATGTGAGAGTAGGTCATCGCCAAGCGCCTAATTATCTCATTGAGATGTAACGAAGCCAGCTGAATAAGCTGGCTTTTTTGCGTTTGAAATTTGTAAAAATTCAGCCTGTTCTTCATTTCATTAGTAAGTCTTTCTCCCGTTATTCTCTCCATAACACCAAAGGTGCTGATTTAGTCCCCATAGCTATCAATGACCAGATACAAAAAAGCAGCCATAAGGCTGCTTTCTTCAATTGATACACAGCTATCGCGAGGTCTAAGCTTCTTTAAGCCACTGTGCGGCACGTTTAGCGAAGTAAGTTAAGATGCCGTCTGCGCCTGCGCGCTTAAAGCAAAGTAGAGATTCCATGACGATGGCTTTTTCGGCTAACCAACCATTTTGGATGGCGGCCATGTGCATGGCGTATTCACCACTGACTTGATAGGCGAATGTTGGCACGGCAAGTTCAGTTTTAACGCGGTGCACTATATCTAAATACGGCATGCCTGGCTTAACCATGACCATGTCAGCACCTTCTTGAATATCGAGTGCGACTTCATGCAGAGCTTCGTCACTGTTTGCAGGATCCATCTGGTAGCTGTGTTTATTGCCGCCTTTGAGATTGCCTGCTGAGCCTACTGCGTCACGAAACGGGCCGTAGTAGCTTGAAGAGTATTTTGCTGAGTAAGCCATGATTTGGGTGTTTACGTGGCCCGCAGCTTCTAAGGCTTGGCGAATAGCGCCTATGCGGCCATCCATCATGTCTGACGGTGCAACTATGTCTGCACCGGCTTCGGCGTGTGACAATGCTTGCTTCACTAAAATCTCGGTAGTGATGTCGTTTAGGATGTAGCCAGTCTCATCTATGATGCCGTCTTGACCATGAGTGGTGAAGGGGTCCAATGCTACGTCTGTCATGATGCCAAGCTGCGGAAAGGCTTTTTTAAGCTCACGTACAGCACGTTGCACTAAGGCATCGGCGTTATAAGCTTCTTCTGCCATTAAGGTCTTTTTCTCTGCAGGCGTCACAGGGAAGAGAGCAATTAATGGGATGCCTAACTCAACCAGTTCTTCGGCTTCTTTTAGTAGCAAATCGATTGAGTAACGTTCAATACCTGGCATAGAGGCGATCTTTTCACTGCGATTGGTGCCCTCTAGAACAAACATGGGATAAATCAAGTCATTGACCGTTAGTTGATTTTCGGCCATGAGACGACGGCTAAAATCATGTTTACGCATGCGGCGCATTCTACGCTGAGGGAAGGCACTGGTAATGATATTCACATTTGACTCCTAATTAGGTACTGGTCCTGCTTGAACTGATGAAAATAAGCTGACTTGATTACGTCCACCATTTTTCGCTTGGTAGAGGGCTTTATCCGCTTGTTCTAATAATTTGGTGCTGTGCTGATCGTTAGTGACGATATCGGCACTGACTCCGATACTGATAGTCAAAGGTATCGCTACACCTTCCCATTCGAAATTGATTGAGGCAACTGCGTCTCTTATCGTTTCAGCGACTTGGAGTGCACCATCTTCTGCTGTATTAGGCAGGATGATAGCAAACTCCTCGCCTCCATAACGTGATACGAGATCTGTTGGCCGCTTTAAATGTTGCTGTAATGTTTGGGCTATGGTTCGTAAGGCTTCATCGCCTGCAAGATGGCCGAATCTATCGTTGATCAGCTTGAAGCGATCGATATCGAGCATCAACAGGGCTATGGGTGTTTCTTGCCTGCGGCTGATCCGGCTTTCAGCGATCAAACGTTTATCGAAAGCACTGCGGTTTTTAACCCCAGTTAAACTGTCGATAGTGCTTTGTTCGGTGAGCTTTTGGTTTACCTCATGTAATTCTCTGAGGGTGATTTCAAGTTCTAAGGTGCGCTCTTGCACCATTTGCTCGAGTTTCTCATTGCTCTCGGCTTCCGCTTTTAGGGCTTCTTCCCGCGCACTGCGAATTTTCTGTGCTTGCTTCAAGGCCTCTTGCTGAATACGTAGCTTAGCTTTGCGTTCATCGTTATAGCGGATGGCAAGCACCAAAGACATAAAGATGATTTCAAACGTCAGGCCGAGCATGACAGGCGTTTGCGGTTTTAAATTGAGGTCAATCACGCTGAGATACAACAAGCTACTGATGCAAGCTCCTGTCAGCATACTCACCCAGCCAATAGTGTACATACGTGCCAGTTTATGGCCATTGATGGCCTGAGTAATAGCAAAGACCATAAGTACCAGGCTGAGCACTGCAACGGAAATGATCTCGATATAGAGCGCCGTTCCATATTGGATAAAAGGCGTGGCGAGTCCAATACATAGGATATACACAGCGCTATAGCGACAAGTCAGCAGCATGCGGCGGTTTAAATATTTGAGCTGTAATATTTTTTCAGTAAACATCAGCGCAAACGCCATAACGATTGGCAGTAAAAACGGCACCATAACTTGCTGTAATATTGGCCAATTTGGCCAGAGGAATCGAAAGGCAAAACCGTTGATGGTAGCGACGAGCAAGGTCATGCTCAGCACGTAGCCGGAATAATAGCTGTAACTGAATGAGCCCGATGCAATCGCAATAAAGAGGCTAAAGATACCAATCGCGGCCAGTACGCCAAGTTGGAAACCATGTTCGACGGCGGTTGATTCAGCAATCTTGGCTAAGTCGTTAGATGACCAGAGATTTAACGATACCGCAGCGCTGCCTTCGGTCTGAATATGCAAATAAAAAGTATGGGTATCATCCATGTTGAGTTTAAAAGGATACAGGAAGATATTGCTCAAAAATGGCCGTTCGTTATAGGGAAAGGTGTCGCCCATAAGCACAGTTTTAGCGAGCTTATTATTGACTAAATGATAGATAGTCACCTTGTCTAACAAGGGGTTATCTAACGCTAAAATACGTGAAAGTGCATCGTCTGGGCTGGTTAAGCTGAAACTCAGCCAAAAATCATGGCCGCTGAGTTTTTGAATGTCTTTATTGGTGAAGTTATGCCAATCCGTTTTTGGCAAGGCTTGGATCTCGGTAAATTCCTTCGCCGAAGTGGGGTGGCTCACCATCAACCAAGGCGTAAGGTTTAACGGGGTCGCCGTTGATTCATCAATGCTTAATAGGGTTGCGAATGAAGGTGTTGAACACATCCATAGCCACAGCAAGAGTATGAGTGACGAGTTTCGTTTAAGCATGGACTCGGTCCAAATTAAAAAAAGCTACGCTATTCTGGTAGCAACGCGCTGCAAACTCATCAGCATTTTCACCGCGCAAGTTTGCAATATAACTCGCGATGTAGGGGAGATATTCGGGCTTGTTCTTACTCGATTTAGGCTTAGGCCTCATGCTGCGAGGCAATAAGTACGGGCTATCGGTTTCGATTAAAATGCGGTTCTGCGGAATAAATGGCACGAGTTGCGCTAACTCTTGGCCACGTCTTTCATCGCAAACCCAGCCTGTAATGCCTAAATGCAAATCGAGGTCGATATAGGCTTCCATTTGGGCGTGAGTGCCAGTGAAACAATGTAAAAGCGCGCCACTAAGCTGTGGGCGATATTCCCGCAGGATAGCAATAAAGTCTTCGTGAGCTTCGCGCTCGTGCATCAGTACGGGCATTTGCAGTTCAACGGCTAAGGCGAGTTGGGCAATAAATGCTTGGCGCTGGATGGGGCGAGGAGAGAAATCACGATTGTAGTCTAAGCCACATTCGCCAACGGCAACCACTTGCGGTGCTTGGCAGAGTTCGGCTTGTTGCTTGCTTGAGTCAGCTTGCCAGCCACTGGCATGGTGAGGGTGTACGCCAGCAGTGCAATAAAGTTGATTGGGATATTGTTGGCAAATCGCGATGGCAGCTGCACTTTCAGTTAAATCGCTGCCAATAACAATTAACGGCGAAACCCCTTGGTCCGCCGCCGCTTGTATTATCGATGCTGTGTCTGGCTCGAGTGCGCTGCCAAGTAAATTGACAGCGATATCTATGTATGAGGGCATTAGTTTAGGCGTTTTACCCGTGCCTTGGCATTGCGCTCATCAGTGCCCATTAAGAAAGAGCCTAGGGCTGCTTTTTCAATGTAGACTTTTTCGCCTTGCTTTAAGTTGAACTTACGGCCGTCGGTTTGTTTCCATACCTGACCATTAGTGAATGTTACTTTGATAGCACCGTAGGGATCTTCAGTCATAGACTGTACTTCAAGATAAATCTTATCGACCGTATTTTCTTGAACGCGCTTAGCTTCCATCCCAAAGTCATCGACAATATTAGCGGCTGGTGCTTGGGGCGCGGTGGCCGCTACTGCTACAGCAGTCGGGGCTGCAACGGTTGCTACAGTCGCAGGCGCAACTTGAGTGCTCGCCAAAGCTGGTGTTACGCTTTTGACGTTTTCTGCTAGCTTGTCATAGCAAATCAAGCGGTCGAGTTTATCGGCTGTTGCTGCACATTGGGTTAACTGTTGTTCAATGCTGGCATTTGCTTGGACTGATACCAGCATAATTGCTGCTGCCATAAGAGTTAAGCGCATGAGAATGTTCCTTATTATATTATTCCCCTAACGCCATCGAGGACGTTAGTCGTTTGTTTGTTCGCTATCTTCGTCTGACTCGTCGTCACCTTGCTTGCTGTAGAAACGAGCCGCTAATAAACCGCCTTCGAACAATAACAGCATTGGTACGGCAAGCATAGTCTGGGAAATTACATCGGGTGGGGTTAACAGCATGCCCACAACGAAGGCGCCTACGACGATATAAGGACGTTTTTGCTTAAGTTCTTCAGGTGTTGTCACACCCGCCCAGCAAAGTAAAACAACGGCTATAGGGATTTCAAAGGATAGACCAAAGGCAAAGAACAGCTTCAGAATGAAGTTAAGGTAACTGGTAATGTCGGTCGCAACTTGAACGCCCTCTGGGGCATTGTTAGCAAAGAAGCCAAAAACGACGGGGAATACTATGTAATAAGCGAAGGCGATACCGAGATAAAACAATAAGGTACTGCTAAATAGCAGTGGCATCACTAAACGTTTTTCATGTTTGTATAAACCCGGTGCGACAAAGGACCAGATCTGGAACAACACATAGGGCACAGCCACAAAAAACGATAATACTAGGGTCAATTTAAAAGGCGCGAAGAAAGGGGCAGCGACATCGGTCGCAATCATGCTGCCACCAAGGGGTAAAGACTGCATTAAAGGTATTGCCATGTAATGGTAGATATCATTTGCCCAGTACACGCTGCAAATGAACACTAATAACACGCTGGCAATGGATTTTAGCAGCTTGGACCTAAGTTCAAGCAAATGGCTGATAAGTGGCTGCTGTTGCGACATGAATTATCCGTTGGCTTTCGGGTTAGAACGGGTATCCGCGCCCTGAGATGGCTCACTTGGAGTAGCGGGAGATGCCGACGTTGGACTCGCCTCTACAGTGGCAGTCGGATTAGCTTGGCTCGCTTGAGCTGGACTAGTGTGAACCTGGCTAGTTTGAACTGTGCTCGAAACTGGAGAATGTATTTGATTCTCTGGCGCAGGCGTATCTTTTACCGGATCCTGAACTTGATAAGGACGGTTCACCGACTGTGCCGCTTGCTTCAGTTGTTCAATAGACTCTTTGAGTTCAGGTGAGAGGTTAGATAAACCTTTGCTCTCGGCTTTTTTAAGATCTGCATGTAATTGCTCGATCTTCAGCTCCTGCTCAAGTTCTTCTTTTACCGAGTTAGCCATGCGTTTCATCGCACGGATCCAACTCGTTATCGAACGCACCGCAACCGGTAGACGTTCGGGGCCAAGAACCACTAGCCCCAAAACACCGATCAGCAGCAGCTCCATAAAGCCGATACCGTCAAACATAAAGAGTTACGCCTGTTCTTTGTTAGACTCAGGTTTCTTTTCAGTCGCCTGTTGAGTCGTTTGTGCTGTTTTAGCCGCTTCGGTGTCTTCTAATGCCTTTTTATCTTCTTCTGAAGACATGGCGTTTTTGAAGCCTTTCACCGCACCACCTAAATCACCGCCTAAAGAGCGTAATTTTTTAGTTCCAAACAACAAGACAACGATTAATGCAACGATAAGAAGTTGCCAAATACTAATACCACCCATGAAGGTATCCTCTTAATTGATATCGGTTCTATTATGAACCTATGATTTATTAGTGCTAGCTAAAATTTGCGATTCTTTGGCCTAGATCGCCATCCGACAAACCACAATATAATGCCTGAAACTAGACAAACATAAGGGGTCCATAGTGTAGCGTCTCTATTGATCAATAACGTGCCACAGATCAATAAAACTGCAGAAGTGATAAGCAAGTAGTTACTCTTGTGAGCCTGCTGCTGGTATTTCAAATACTTATCTAGCATTTGTTGCTGAGAACTCAGCAATTTCCTGCCTAACTTAAGGTTATCGTAAATCAGTTCAGGGAATTCTGGTAGTTTATCAGCCCAATATGGCAGTTTTGTGGATACCTTTTTAAACATAGCCTTAGGACCTACTTGGTCGGCCATCCACTGCTCTAAAAACGGTTTAGCTGTTTGCCATAAATCCAACTGCGGATACAGCTGACGACCTAAGCCTTCGATATAAAGTAAGGTCTTTTCCAGCAATACCAACTGCGGTTGTACCACGATATCGAAGTGGCGTGCAGTGCGGAATAATTCCAGTAACACATGACCAAAGGAAATCTCGTCGAGTGGCTTGTTGAACATAGGTTCGCACACTACTTTGATGGCCTGTTCAAAGGCCTGTAGATCGGTTTTTTCAGATACCCAACCTGATTCAATATAGAGCTGGGCAATTCGGTGATAATCGCGATTAAAGAAGGCGAGGAAATTCTCGGCGAGATAGCGTTTATCGACTTCGCTGAGTGTGCCCATAATGCCGCAATCGAGCCCAATGTAATAAGGGTTCTCTGGATGATCGCGGGAAATAAAGATATTGCCCGGATGCATGTCGGCATGGAAAAAGTTATCGCGGAACACTTGGGTGAAAAACAGCTCTACTCCGCGCTCAGCTAATAACTTAAAGTTGGTGCCTTGCGCTTTAAGGGCGGCAATATCCGAGACCGAAATACCGTAAATTCGTTCCATCACCATTAAGCGCGGATAGCAAAATTCTTCATACACATAAGGCACATAGAGCGCATCTGAGTCGAGGAAGTTGTTGCGTAACTTGACTGCGTTTAATGCTTCGAGCTTAAGGTTTAACTCACCTAAAATGGTCACTCGGTAATCTTCGATCACTTCGGCAGGGCGCAGGCGGTTGCCTTCACCGAGGAGGTATTCAATCAACTTGGCGGTTTGCGACATCAACTGCAGATCGGCCTGAATCTTGGTTTCTACATTCGGCCGTAGCACCTTAAGTACGACGTCTTTGCCGTTAGATTTTAAGGTCGCTGTATGCACTTGAGAAATAGAGGCTGATGCCAGCGGGGTTTCATTAAAGTCATCGAATAGGGATTCAATCGGCGCTTTTAGTTCAGCTTCAATCGCTTGGCGGGCGAGCGCACCATCAAAGGGCGGCACCTTGTCCTGTAGCATGGCTAATTCACTGGCCCATTCATCGCTGAGTAAATCGCGGCGGGTCGAGAGCATTTGCCCAAGCTTGATATAAACGGGGCCCAGTTCCTGCATCGCCAATTTTAAGCGTTCGCCACCGGGCTTATTTTTATGCTTATTGCGGATCCAGAACAGGCTATTACGGGCGAGTTTAAAGTACCAAGGTGTCATCTTTGGCGGCAATACGTCATCTAACCCGTACTGAAGCAAGGTCTTGATGACATGGTAACCGCGGCGGATACTGGCAAGGGTCATGGCTTAATTTGATCTCTTAATTGGGCAATCCGTTGTTCAATCGCGCGGGTATCGGCAACTAAATCATCCATTTGATCGCGAAAATGAAGTAACTCAATTGGGTGCGGTGCGAGGCGATACTCTTCAACGGCGAGTTGCCCTAAGTGGGAGCGCGTTTTACGCAACACTTCGAGTGCAAAACCCTTTGCCTGATGGCCTGTGCTGATGAGCTTGTGGGTGGGACCATCGCCTAAATAACGCGAAATTGGCTCGGCAAAATCAAACTCGATGCCGCGTAGATATTGGCTAAAGCTTTGTAATAAATTGAGATCGCCCTCAAGCTTGAGCTTATCTTGCTTGATAAGTTCAGTCAGGTTTGCGCCTTCGGACACGCGATAAAGCGTGGTCGCATCGGCATGCAGACTGACATCGACATCGCCTTCATAGCGGCTCAGTACTTGGATCTCTTTGGCAAACACCAAATAGAGTGGCCAGCTGAGCTGCGACAATTGGATGCGGAACACTTTGCCATGCAACTGGCGCTGGCGCGAATAGTAGTCACCCGCTTGAGCCTGAAGTTTTTTCAGGCCCGTTTCAATTGCGGCGCAGACAAGCAGCACAACTTCTTGCGGCATCATTAGAATTTATAACCGCGGTGCAATGCGACAATGCCGTCGGTCATGTTGGTGTAGTCGACTTGTTCAAAGCCTGCATCTACCATCATTTGCTTTAATGTGTCTTGATCTGGATGCATACGAATCGATTCAGCCAGATACTCGTAGCTGTCGGCATCTTTAGTTATGAGCTCGCCCATCTTAGGTAAGACTTTGAAGCTATATAAATCATACACTTTGCGCATAATTTCATGCTGAGGCTTAGAAAACTCTAGCACTAATAACTTGCCACCTGGTTTGAGCACGCGATTCATCGAACGCAGCGCCGCATCTTTATCGGTCACGTTACGCAGACCAAAGGCGATGGTAATGATGTCGAAGTGGTTGTCGGGAAACGGCAGTGCTTCGGCGTTGGCTTGCACATAACTGACATTGTTGACTATGCCCTTGTCGCGTAACTTAGTGCGACCGACTTTCAACATGGAATCGTTAATGTCAGCTAAAACCACTTCACCTTTATCGCCCACTAAGTGAGAGAATTTTGCGGTTAAGTCGCCGGTGCCGCCAGCCAAATCGAGCACTTTCATGCCAGGGCGTGCGCCAGATACTTCAATGGTGTAACGCTTCCAAAAACGGTGAATGCCGAAGGACATCACATCGTTCATTATGTCGTACTTAGCGGCAACAGAATGAAACACGCCGGCAACGAGTTCGGCCTTTTTATCGGCCTCTACGGTTTTGTAACCAAAGTGGGTACTTTTAGATTCGCCCTCAGACATCAATGTGTTCCTATTGTTTTGCAATGTATTAGTTGCGAGTGTAAGCCATCGCCTGCATTTGCTGAATCTTTGATCTATCACTCTGTGATCGAGCTAGTAAACTAACTGCAATGCCTTAGCTTGAGGCACACCCTAACAGTCGTAGGATTAAGTGCTTGTCGACCACGTAAAATGGACTGGCATTAGACCACAATATCAGGATTTGGTGAATAGCTTAGCGCCCAGTTGGCTATAACTGATGGCGCTCATTGCGCCAAAAACTGTATTCGAGCCTGCACATAAGCGTAGATAAGCTCGCTGAGGCTTTGGCCAACATCCCTAAATCCAGCTTCAATTCCCGCTTCGTGGCAACTTGGCGCGGCCTCGGCTAAATGCAAATATGCGCAAGGGCAGTGGCGGGCGATGTAACTGACATAATGCGCCGCATCGAGCAGCGGTACCCCCGCTGCCGTTGAGGCGCTACTGGGCATTTTAGCGATAGCATCGACGTCGAGCTCAAGTCCTACGGGCAAACCTGAGTCATTCAGTTTGGCGGCGATCTCAAGCAAGGCTTGGCTGAGGCGGATTTCGCGGCGAATCCAAATTTGTTGAAAACTATGCCAAGTGCCGCCAAATTCAGTAAGTTGGCTTAAGTTTACTTCGCTGTTTTTTAACTCGTGTAAACCCAATACATGGTAATAACCTAAGGCACCTCGATCGGCCGCATAGCTAAAACCATTGCCGCTGTGGCGGCCTTCGAGTAGGCGAAAATCCGAATGAGGGTCAAGATTCACCGCCGCAACTTGGCGCTGATAATGGCTGTAGGTTGCCATCAATAGACCATAGGCATTGTTGTGGCCGCCACCGATGAGTATGGGTTCTAAGCCCGCCTTTAAAATCGCACTGACGATGCGGATCACGCGCTCATCTAACTGCTCCACCGCATCGCGTAATTTATCTAAGGTGACGTCGGTATTATCTGTTGTATTGCTCGCGGTTTGTTGCTGCAGATCGGCAGTATTAACTTGGCCTAACACTAGGCATTCGGCACCAGAAAGAAAGCGATTAGATTGCAGATTAAGCCATTGGCGCATGCTGGTTGTGAACGCATCGGTCGCGCCGCCGCGGCCCAAATTGGCGCGTGGGCCGATATCTTCACCGACGCCGAAAATGGCGAAGCTTGCCCCGTGGGCTTTGGCAGTGGCGAGTATGGTCTCTAACGTATGTTCGTGATTAGCCAGATGGACGCATTGGCCTAATTTAGTTTCGCCGGTTCTTGGGCTGACTAATGAGGCAATATCTGTATGGGTAAAGGGAATAAATTCAGGCATAGATTGATATTCTTATTCGGCCGGAGTCGAGCTATTGTGGCCAGTGTGGCGCGAGGATTCAATGGGGAAACGCTGAAAATAGTGAATAAAAAAGCCAGCTTAGCTGGCTTTCTTGGCTGAACAGTGAGATTACTCAATGTCGAGTGGTTCAGGTGATAAAATCACACCTGTGTTATCAGCATAAATGTGATCGCCGGGTAAGAAAGTCACACCGCCAAAATTGACCGGAATTTCCACTTCGCCCACTGAATTGCCATCAGCACCCACTGGAATTGAGGCAAGGGCTTGAATACCAATGTCTAACTCTTCCAATGCATCCACATCGCGTACAGAACCGTAAACGATAATGCCTTCCCAGTTGTTGCTAACCGCAAGTTCAGCGATAGCACCATCGATCAGCGCACGGCGTAATGAGCCACCGCCATCAACGAGTAAGACTTTACCTTGACCATCCTCTTGTAGGACTTCGGTGATTAGGCCGTTGTCTTCAAAGCACTTGATGGTGCTAATAGAACCGCCAAAAGAGCTACAACCACCGTAGTTACTGAACATGGGCTCGACTACATCTACGACATCTAAGTACATGTCACATAATTCTGAGGTGTTGTATTCCATATTTACACTCCTATTGATCCGCGTTGCGTACAGTTTAGCGAGTTTACGCCAGTATAAAAGGGATTATTGAAAAGAAAAGTGTTATCAATCACGGTATTGTTGCCACTGGGCCGTTAACACATCTGTAGTAAAGTTTCGTCGAACTTTAGAGCTTATGCGGTAAAACTTGATATTAGTCATTAAATGACGGTTTTTTATTACAACTCTGTTGCATGCTTTGGTATTATATCCCAAGTTTAAATTAACTTACTTAACTTAATTTGCTTTAGGGGCATGGACCCCCCGTTACGCTTATGGACGGTAGAGGGCCTTCGAGAGGTGCCTATGGAAGCTGTAAATACGATTGAAATTATTGGTTATTTTGCCTCTGTAATGGTGGCGATCTCTCTAATGATGAAGAATATCATTTGGTTGCGATGGTTGAATTTTGTGGGATGTACCCTGTTTGTTGTTTATGGGGTGTTTATTTCTGCTTGGCCAGTGGCTGGCATGAATGCGTTTGTTGCTTGTATTAATATTTACCATTTGACTAAAATTTACCGCGCTAAGGCGAATACTCCCGCCGCAGCCTAAGTTTATTTGATTGTGTTGCTCTTGGGGTTCACATTTAGAGCAACACAATATCCTGTCTCAGTCCTATCGCAATTCCTGAAAAAATCCTTTAGCACCAGCATAACGTCCCTAGAATCCTTCTAGATCAAAGCTCCAGCCATGCTTCTTGTCATAAAACCTTCGAGCAGATGTCACTTAGCCGTCACAGGTCTTTTTTAGTCTCAAGTTATCTAATAGTGCTTTAGCTAATTCGATTCGGATTTGACCATTAGCAAACAGTAAATTAGCCGGCAAACAGTAAATAACAAATTCAATCGTGAGTAGGGCCTTGGTGAGTCAGTTGCGTGATACCAAGCACTCTAAGCGTCGTCGAGAGAATGCACAGATTAACATCGCAGTAAAACTCGCTGGCATGCCAGCAAGGAGCCTGTTTTATGTTGAGTTACGTTGCAGACTTAGATAAGCGAATGTTTTGCCATATCGTGAGTTTCACTCAACGTCATGGTTTATATGCCCATGCAAAGCGGGTGTCGACGAGTGGCGATGGGCATGTGTATTTATATTTGTCAGTGGGCTTGATGCTGACTCACGCTCAAGGGCAGGCCTTATTCAACTTGATGTTAGCGAGTTTTTTGGTGGAGTTGCCGCTGTATTTATTGTTGAAAAATAGCATTCGCCGTACACGACCCTGCCATGCGCTTGTGGGATTTGAGAGTGGCTTTGAGCCATCTGATCGCTTCAGTTTACCGTCGGGTCATACGGCCGCAGCTTTTGTAATGGCAACGAGTGTGGCGCAAGTTTATCCCGCAGCGGCTCCTATCGCGTTTTTATGGGCTTTTAGCATAGGGGGCTCACGTATTGCACTTGGCGTGCATTACCCCTTAGATATTTTGGCGGGAGCCTCATTGGGGAGCGGAGCGGTATTACTGGTTCATCCGGTGATTTAATTCAAGCAAGCGCACGTTAATCATTTCATCTCATTAGTTGGAATTCTCCTTCACTCATCAGTTTAAGGATACGTAATGCGAATACTCTACGGAGTTCAAGGCACAGGGAATGGCCACTTAAGTCGTGCACGCGTCATGGCTAAAGCACTGATGAAACAAGATATTGAAGTGGACTTTTTATTTTCGGGGCGTAAGCCTGATCAGTTTTTCGATATGGAATGTTTTGGCGATTATCGCGTACAAGCGGGCATGACGTTTGCGACTCACTCTGGGCGTGTGAATGTGCCACAAACGGTTCGGCAGAATCTGTCATTGTCGTTATTTAAAGATATTAAAGCCTTAGATCTCAGTTGCTACGATCTCGTGCTCAACGACTTTGAGCCAGTATCGGCTTGGGCGGCGCGGCGCCAAGGCGTGCCATCCATTGGCATTAGCCATCAGGCGGCGTTGACTCATCCGGTGCCGAAACTCGGTAGCACTTGGTTTAATGAAATGCTGCTCAATAACTTTGCCCCTGTGGATGTGGCGCTGGGTTGTCATTGGCACCACTTTGGTTTTCCTATCTTGCCGCCTTTTGTCGAAGTCGATGCCAGCCCATTCGAACACACGCATCAAATCTTAGTGTATTTGCCATTTGAGGATGCGGATATGATTGCTCGTTTCCTGTCGCATTTCAGTGACTATCAGTTTTTGGTGTACCACAGCCAACAGCCTAAGGGACAAGTTGCGGAACACATTAAGTGGCACGGCTTTAATCGTGATGGCTTTAAGCAACATCTTGCCAGCTGCGGCGGTGTGATAGGCAATGCGGGCTTTGAGCTTGCCAGCGAAGCATTAACCTTAGGTAAAAAGTTGTTAGTGAAACCACTGATTGGCCAGTTTGAGCAATTATCGAATGTTGCGGCGCTGCAACTCTTGGCGGCCGCAGACAGCATGATGAGTCTCGATGTGAACGTCGTGAAGCGCTGGCTGAAAACCGCATCACCTAATCCGATTGCCTATCCTCAGGTTGGGGATGCGCTGGTTAAATGGATTGATGGCGGCGATTGGCACGATAGTCAGCCGCTATGTAAAGAGCTGTGGAGCCAAGTTACGCTGCCTGATACTTGGCGTTAAATCAGCTCATAAGCTGTGGTGAAAAACTATTTAGGCACTAGCTGGGTCAATGGGGTTAGCGTATAATTTCGGCACTTTTGCTGTTAGCCCCTTTCAGCACGTCTATTTTGGCCTAGCTTGGCCGTTAATTATGTTGTTATAAATGAGCCTGAAAAAGTTTACCCAGTACAAGTTACTGTTTTTAATGTTGTTGTCGCTTTGTGTCTTACTCCCTTTTATTCCAGATCAGGCTTACCACATTCCCGGCCCAGATATCGCGAAGCAAAACTTCCATGTGATTGAGTTTAACGATCAAGGTCAGCTCCATGACGAAAGGCAATGGCAAGGTCTAGAGGCGCGCATATCCCAGCCAGAGCCTGCTCAAATCCCTGAACTATTGATTTTTGTCCACGGCTGGCACCACAGTGCAGAGCCTAAAGATGAAAACTTTATTGCCTTCGAGCAGTTTTATCAGCAGATGGCGGCGTCCGATACGCAGCGCAACTTATTGGGTTTGTATATTGGCTGGCGCGGCGATAAATACGATCCTTTCTGGCTCGATGGCTCAGATGATGCTAAAAGCTGGATTGAACCCTTAGATTTCCCTACCATTTTACAGCGTAAACGGGTTGCTAAACGCATAGGTCAGCTAGGTTTATCTCAACTGCTTGATAAGCTAGATGGCTTAGTGACAGAGCATAAACTGCTGCGTTATACCGTAATTGGTCATAGTCTTGGCGGGGCGGTTGTTTTGCATGCCAGTAAAGATCGCATCAAGGCTGCTATCGATCATCAGCAGGATAATCCCAATTTATTCTTACTGTTAAACCCTGCCGTGCCCGCGAAGGAGTACCAGCCACTCGATACCTTGGTGAGCTTAGATAGGCAAAAACCAAGTATGGTGGTATTGCAGTCGAAGGGCGATTTCGCGGTGAAAGAAGCTTTCAATTACATCAAGGATGGCGAGCGCGCCGTTGGTAATTCTTGGGCTATTACCCACGATATCGACAAATGCTCAGGTGGTAATTGCACTACGCCAATGAAGATGCCGTCGGCCTTGATGGCGCACGATCAAATCCCTGGCTGTATGATGACCTTGCCCCATTCTGGTTGGAAAATTCGTGCTCGTCTGCAGGCCCGTCGTACCGTGCAGACCTGTCCCGATGCCAATATGCAGGCAGTGTGGGTGCTGGCGGTGTCAGATGAAATCGTCTCAGGACACAATGGTATTCTCACACCGGATCATGCCAAAGCCTTGTCTGAAGTGATGAGTATGATTGATTTGTATCGAAATCAATTACCTAAACATGCGGTGGAAAGTCCCGCCAACCAAGATCTCGGCACCTTAGCGCCAGAGTCAGAAGCGGCAACATCCACACAAGGTGAAGCGCCTGTGCCACAAGAGCCGGTGATGATTGTCGAGCCTTTGCCTGAAGAACAAGGTTCACCGACGCCTGAGGTCAACGCACCATTGCAGGAACCAAACGTATCACAGGAACAAGATAAGTCTGCCGTTACTGAGGTACCGCACGCGCCAATCAAAGCGCCAGAACTAGAATTAAAACCAGAGTCCAAGCCAAATGACGGCGCAGAGCCTCAACCTAAGGTTGAGACTGAAGGCATAAAGTGATCGGTAGTGCTGAGAAGCATGTCGAATTCAGCTAGTGATGTTACTTGCTATGCACAGCCCTCAGGGCATTTTCTTGAAACCAAAATTGGCTGAGCTTAGAAAGCAATAGGCATATACATCAAGTGTTGTATGTCATTGGAAATAATAAGCTGGCCAATATACTTTGTGAGTCAATATGAGAGCTGAGTTAAGAGCAAAAATCATCACGGTTTGTCATAAAAAATTGAGACTAAAGGCGATAATGTCGGTGTCTCATTTTACGCCTTCTTTGCGAACAAGAATGATAATCCAGAGTTGCTTATGGAAGCGGCGACTTGGTGGATTCAAACTCATCGCTTAGATCATTTTGTTAATGCTCATAAGGTGATAGAAATAATCCAAAAGGAATCGGAAGAATAGATCCTAAATCGCTTACTGCGGATTTTTGGTTTGCCACCTGCGCCAAGTAAAAGTGATTAGCGCGGCGAGTATTAGGCTGACAATGGCCCATGTGGGGTATTTGTGCTGGTGGTAAAATGCCAGCATAGGAGCAATTTCATCGCGGATTAACACTGGGCCTAACCCAAAAAGTGCCACCCAAATGCTGGTGGCTAGGGCATTACCTATTACAAATTGCTTAAGTGGCATTTTAGCCAAGCCGCAGCCGATAAACATAAATTGTTTTAATCCCTCAACAAAACGACTAAGCACTAAAGCGGCAATGCCATATTGGCCAATAACGCCATGGATTTTGTCTTCCAATTGCGGTTTTATCCAGCCCTTCTTAAGCAATACATCACCAAAACGCCAACCGATAAAATATCCCAAGCTGTTACCGCAGAGTGCAGCGCAAGCTGCGACGATCAACACCAATGGCAGTGACATTTGCCCTGTAGCAGCTAATAAACTTGCGACAATCAATAAGGATTGCCCCGGCGCTGGAATGCCAAAACCCTCGACGGCAATCGCGACAAATAGCAGCACATAGCCATATTGCTGTAGCCAAGGGGTGAGTTCGGTGATGATATGTTGTAGCGCTTCTGGCATGGATGAAAGTGATCGAATCAGTGGGACAATTAGCCCATCATACTCGCCAACTTCTCTGGCTGGATAGATTTATGAGCAAAGTTTCATGCCAGTTTTAGTTAGCGGCGGTGAATAAGTGTAACGCAGGGGTTATTAGCTTTTACCTGAAATATAAAAAAGGCCGAACTCAGTTCGGCCTTAATGTCAGTACTGTCGCGACGATAAAACGACTATGCGAATGCCGCTTGTAGCTGTGGAACGACTTGTTTTTTACGGCTTAACACGCCGTCTAACCAGACTTTGCCATCGACGGTATCTTTACCGTAGGCGCGTTGGGTTAGGTCGCTATCGTCAGACACGATTAATAGTTCTGAGCCTTCTTTCATAATGTCAGTCAGCAGTAACAATACGCTGTGGCGATTGCCTTCCACTTTCAGTGCTTTGATATCGGCTTCTAACTCGGCCTTGATATCGTCAAATACTGACAGGTCGATCACTTCTAACTGGCCGATACCGACTAAGTTACCGTTCATGTTGAAGTCTTTAAAGTCACGCATAACGAGGTCGCGTGCTGGCGTGCCTTGTACAGCTGATTTCACTTTGAACATTTCCATGCCCAGTTCTTTGAAATCTTCAACGCCAGCAATCTCAGCTAAGGCTTCAACGCAACGAATGTCGGCAGTGGTACAAGTCGGCGATTTGAAAATCACGGTATCGCTCAGAATGGCGCACATCATGATGCCAGCGATATTTTTAGGGATAGCGACTTGGTAAAAGTCGAACATCATCTTGATCACTGTGTTGCTGCAACCTACTGGGCGGATCCAGCATTCTAACGGGGTATCTGTGGTCAGATCGCCAAGCTTGTGGTGATCGACAATCCCGACGATGGTCGCTTGAGCGATATCATCCGGTGCTTGAGTCAGTTCTGAATGGTCAACGATATACACTTGCTCGCCAGCGTAGCTGGTTTTGTATTCCGGTGCATCGAAGCCGAAACGTTCCAGAATAAAAGCGGTTTCGGGTGACAATTCACCTAAACGTGCGGCAATGGCTTCTTCACCGATTTGGTTTTTTAAATAGGCTAACGCGATAGCACCGCAGATTGAATCAGAATCGGGGATCTTGTGACCGACAACGTAAATAGGCATTTCAGGGACTCTCCTTTAAATTTGTGGCAATTTTAGCAATATTTGGCTCGGATCGGAAATAGCCAAAGCGCATATCACTGCGCGCTTAAGACGCTATAACGCTAAAAATCCCGAGCCTAGGCTAGGCGAAGATGGCTTAAGGGTTAGATCTTTACTTTTATCACGACTTTACGCACTGCTGTGGCGATATCGCCAGTGCCTGGCATATGCATGTCACCGGGGAAAAATAGCGCAAACATACCCGCTTTGAGTTGCACATAAGAGGCATTGGCTTTGTTTGTCTCGTAATCAAACTCGGCGTAATCGTGTTTGTCAAAATAGGGCTTTGAGGGTGTTTGGCCCGCTAGTGGCAAATAACCAAACTCTTCTTCACCGCTGAACACATATTGCACATCGATATATTTTTGATGTACTTCAAAGGGTTCTGTCGATTTATGCTTAGTGTGGTAGTCGTTGACTATCACAAAAATATTGTCGCCATCGAGGGGATAATTGCCCACGGGCAGCTGACTAAAGTCTGTGGTGGCAAGATGTTCTAATGCTGTGACAAGCCGAGGGCTTAGGGATTGATAAATATGGCGGTTTGCTAAGGTATCGACGATCATCGGTTACTCGACATGGCGGATGTGAATGAGTGAATTGTAAAGATTATAATCAGATAGTGGCCGTTAGGACATAAGCTTTAGACATAAAAAAACAGGCCATTGCATGGCCTGTTTTTATATTAAATGTCACTTTGTTCGCTAAGACTCGCTAAGACTCGCTAAGACTCGCTTAGTAAGTGTATTTAGCTTCTAAGAAGTAGTAGCCACCATTAAAACCAAACGGAGTGTTGGTTAATGGATAGACGAAAATGCCGTTGTTGCTGTTAATCGCTGGTGTTTTTTCGGGATACACATCAAACAGGTTTTGTACTCCAGCGGTGAAACTTAGCGCATCGGTTGCTGCGTATCTCACCGACAGATCCATCACCCATTGATCGCTGTAATTCACATCACCCGTGGCATAGCCAACAGTGTAATCACCAAAGTAGCTGAGGTGAATATTGGTCTTAAAGTCGCCTAGCTCATGGGTCAAGCCCAAGTTACCTGTGTTATGCGGCGTCGCCGAGGTCATGCGAGTTTGTTCTATGCTATCGAATAGCTTTTTGCCTAACCCATCTAAAATTTGTGGGAAATGAATATCTTGGATTTCAGTCTTATTGTAGGCATAGGCTAAGTTCGCCCGTAAATCACCCCAAGAGCCGATATCAAAATCTTGGCTCACTACAAGGTCAACGCCACGGGTACGGGTATCGACGGCGTTGATAAAGAAGCGCGCCGATTCTGCATTGGTGCCCGCCAGCGCTGCCGCTACGGCGGGCGAATCATTAGGCGTCACTGAGCTCGACAGAATAATGCGATCATCGATGGAGATTTGATATGCATCTAATGTCAGTGCTAAGCCTGTGTCATTGGTGTACACCAAGCCTAAACTGAATGACTGGGATAGTTCGGGATCTAACTCATTAATACCTAGTGCTTTGGTCACTGGCGATAGGGTGTTAAAAGTGCCTGACTCAATAGGCACCAGCTGACCAGTCACAGGATCGGGATTAAACAGCGTCGAGATATTGCTGAAGTACAGTTGTTGTACGCTCGGCGCTCTAAAGCCAGTGTTGATGGTACCGCGCAGGGCCAAGTTATCGGTGAAATCGTAACGGCCCGCTAATTTCCAACTGGTGTTGCCACCAAAGTCAGAATAGTTTTCGTAACGCAGCGCCGCCGCCCAGTAGAACTCATTGGTTAACTGGTTTTCGAGTTCCATATAAACGCCGGTGTTATCACGGGTTTCATCTACTTGCGACTCAGGGGTAAAACCAGTGAAACCTTGGCTGCCTGCAGCGCGGTTGTCGTAACCCCCATTGATGTATGAACCCGTTTCGCCGGCTTCAATTTGATAGCCGTTTTGGCGCCACGACACACCCGTCGCAAACAGGATTTCAGAGTCATTGGCAAAGGCAAAATAGCGCGAGGCATCGAGATTAAGATTGGTTTCACGAGTTGTGAGTGTGCCCGCGTCAAACTCGGTTGGGCTATCTGGGCCTAAAGAGGCGTTTAAGGTATTAACAATATTGTAGTTAAATGAGTTTTCGCCATGGCCAGCCGAAGTATCGATATTCCACTCGCCCAGGCTTAATTCATACCCCAGCACTAAGGACGCATCGATAATTTTAGGATTGATTTGCGGCAAGAAACCGTCTGGATAGATTTCGGTTAAGTTACGGGCGTCGAGTGCACGGCGGTAAAAGGCGCCTGACTTAGTTTCACGTTGGCTGATGCCACCAAACGCATAGAGCTTGCCATCTTTTGTGATGGGCTGGGCAGCGTTGATGAATAGACCTAAGTTGTCATATTCGGCATCGCCGACCTGGTGACTCTTACGGTTGAATGTGGCTTCGCGGGGATCGGCTGAGCCATCGGGTAATTTTTCATACTGTTGACGAGGGTCCAATCCGGCGCGGTTAGTGCTGTCTTTATGATGGGCCTCTAGGGAGACATTCACAAAGCCATCGTCACTTAAACTGATGCCGTGGTTTACACCCACACGCCATTGCTCGCCATCACCTATGTAGGTTTGTCCTGCTTGAGCCGAGATGCTACCGCCTTGGTCGCTGTCCTTAAGCACCACGTTAATGACGCCGGCAATCGCATCCGAGCCGTATTGTGCCGAGGCACCATCACGCAAAATTTCGATGCGCTTAATCGCCGTCATAGGGATAGCGTTTAAGTCGACGTTAGATGAACCTTTACCCACAGTACCACTTAAATGCACGAGCGCTGAGCCGTGACGACGTTTGCCGTTCACTAGTACTAAGGTGTGGTCGGGCGACAGGCCGCGCAAACTCGCGGGGCGCACCGCATCTGAACCGTCAGTAACTGAGGAGAATGGGAAGCTGTAGCTTGGAGCGGCAAATTGCAGTGCTTTAGCGGTTTCAGTCATGCCGGTCGATTCGAGCTGCTCGGCGGTGATAATGTCTACAGGGGCAACACTGTCAGTTGCCGTGCGCAGGGCTATGCGTGAACCTATGATCTCGATTTTCTCGAAGCCAGGCTGTTGTTCTTCGGCTTGAACAGCTGGAATGGCCAGTGTGGATGACACTGCCAATGCGATAATCCCTAGACGCATAAAAACCTCTCAATGATGTTTGTAACCCGATACTCGAATTTAGGAGTGCAGATTAAAGATAACGTAACAATTCAATGTGGAGAAGGTTATCACTGTAACAATTGTTACATCTAGACATAAATGTGCTAACTCAGAGCCGAGTGTTCTAGTTAATGCTTAAAATGCAGTTGAATATATGGATGAAACCTTGTTATCGGCAGATGCGCTTTGAGTGTTTTGAAGACTTAACTCGCTCGCTATATATTTATGGCGAGCGAGTTAAGTCAATGATATGAATTGTTTATTGACGTGCGCCATCGTGGTTTAATGCGTTATTTCGCTTTGATGGCATTGGTTTGAAGGAACACAGATTTGTCAGCGTTACGATTTCTCGCGGGAGCAACGGCATTTAATACCATGGCAGAACAAGGTCTTAAATCTGAGCATTTTACCCAGCTACTTGCTGCCTCTGGTGGACCTAAATGGCTTGGTATCGCTGGGTTAGATAAGTATCTGTTTTCTGAGTTTTTCAAAGCCAGACAAACGCCGCTTTATACATTAGGCGCGTCTTCAGGGGCGTGGCGTTTAGCGTGTTTGGCGCAGCAAGATCCCCTAGCAGCCTATGGCCGCTTAGAGAATTTTTATATTGGTCAGCGTTACGACAGCATGCCAACCCCGCAGGAAGTCAGTGCGCAAGTTAAAGGCGTAGTGCAGGGGATTTTAGGCGCCAGTAGTGGTAGTGAGATCGTTAACAACGCTCATATCCACAGTCATTTAGTGGTGTGCCGCGGCAAACATTTAAATCGTTTAACGGCTAAGTTACCTCTCGCTGCCGGACTGGCGATGACGGCGGCGACCAACTTATTGAGCCGCCGAAGTTTAGGCTGGCATTTTGAACGTGTGGTCTTTAGTCAACAAGCTGTAGCATCCCCCTTTTTGCAGCTCAATGATTTACCCAGTCAGCAGGCCGAATTAACCGAGGCCAATGTCGATGCTGTGATGTTAGCAACGGGGTCTATTCCGTTGTTGCTAGCCCCAGTGCCGAAAATTGAGGGCGTTGCCGATGGGCAATATTACGATGGCGGCATTACCGACTATCACTTCGACTTGCCTTTATCCCACGCAACTGGCTTGACGCTGTATCCGCATTTTTATCCAACGATCAGCCCAGGATGGTTCGATAAATCCTTGAAGTGGCGTCAAGGTCGAGCCAATTATCACAATGCACTCGTGTTGGCGCCAACGGCCGAATTTGTGGCTTCATTACCCTTTGGTAAAATCCCTGACCGCGAAGATTTTAAACAACTGGATACGGCGCAGCGGATGCAATATTGGCGAACAGCGGTTGCCTTGAGTGAACGTCTGGCCGAGGAGTTTGCCGAGGTTGTTGCTAAGGGAAATGTGCTGGAGCGACTAGAGCGCTTTTAGTTTTAAGTGAGGTGGCCCAGAAAACGGGGTTTAAAATGGCTAGTTTGAAATGACGGGTTTGAAATGCCCTGATTTAGCCTAGTTGGGTCGCCAATTTATTTTTTGCACTCAACGGCTTCAGGAAGGAAATTATTCCACCTGCTACACTCTAAGGTAATAGGGAATACGAATGCATTGATGTTCGTTTCCTTCTATTTGAGGGGCAGGAGGTTGCTATGCAAACACGAGAAATGGCGTTACTCATGCGCGATGCCTTGTTGCTGCCACAGGGACGGGTAGAGGTTCGTGTGGTCGACCCCGGTCATCTGCGCATGGTGGCCGATGTCTTTAAAGGAAAGTATGCGCTCGCCTTTGCCACCATCAGACCCCGCGGCAGTCCGCCCTGTTATCCCACCGCTACCCAGTGCGACATTATCGATTTTAATCAGCTAGAAGACGATTCACTCAGCATAGTGCTCGAAGGGCGCCAGCGCGTTAGCATCCTCTCGGCGGCGCAGGCCAAAGATAAGCTGTGGATGGCGCGCACTTTACCCTGCCGTAACTGGCAAGAAGAGCCGATTAAAGGTGAATTTGAGCTAATAAGCGCTGCATTAGAGCAATTTTATGAGGTGAATCCCGATCTGTTTGAGCTCTATTCTCAAGTGCATTTAGAAGATGCGGCTTGGGTAAGCCAGCGCTGGCTCGAGGTTTTACCTATGTATAATAAAGACAAATTAGTGTTAGTTAATCAGCCCGATTGCCACAAAACCTTGGACTTCGTGCTGCAACTTATCAAGTCCCATGTGGACTGAAAAACCACAAAAAGGCTAGAATAGCGGCTTTCTATAGCCGCTTTTTTTGTGATATGACTGCATCTGCCCGCGCCGCCCAACCTTCTTACATAGTGCTTCCTGTGAATGTCACAGATATACCCACAGTGTTTGCCTTTCTCGCGCAGCATTTTGCCCGCATAGGTGAAGATGTGTGGCGTCAGCGGATCCTCGATGGAAAAGTGCATTGGCGCGATGGCAGTTTGATCGCCTTGGACACCGAATATCGTCCTGCAGCACGGGTGTATTACTACCGTGAAGTGCCGGTCGAAGCGAAAATTCCCTTTGAAGAACAGATCCTATTCCAAGATGACAATGTCATTTTGGCCTATAAGCCACACTTTTTACCTGTGACCCCAAGTGGCAACTATGTGAACGAATGTCTGGTGCATAGACTGAGATTACGCACTGGTATAGACACTATCGCGCCCGCTCATAGGCTTGACCGTGAAACCGCAGGGGTGATCTTGATGACGGTCGCTCCCGAAACCCGCGCCCTTTATCATCAATTATTTATCGACGATGCTATTCGTAAAGACTATCAAGCGATTGCCAAGCTGACACCTGAAATTATTCAGCAATATCAACAGGGAAGCTTAACGTTACCCCTGCATTGGACGGTAAAAAACCGCATGCAGCCGAGCGACCCGAGTTTTACCATGAAAATCGTTGAAGGCGAGGCGAATACCCATTCCGAAATCAGTCTGGTGGCCATAAAAGGCGATCTTGGTTTGTTCCACCTGAGCCCAATTACCGGCAAAACTCACCAGTTGCGGGTGCATATGCAAAGCTTAGGTATGCCATTGTTAAACGACAGATTTTATCCAAGCTTATTACCCAAAGGGCCGGATAAGTTTGCGACGCCTCTTAAGTTGATGGCACAGCGTTTACGTTTCATCGATCCTGTGAGCGGGATCCATCACGACGTGCAATGTGAGGGTTTTGCGTTTTAGTCAGCGGAAAGCATTATTTCCAGTCCGCTTGAAGGAAATGATCGGGTGTAATTGTGCGCTTGGCCCTGTGATGGTATGAATACAAGCTGCAGTATCTGAGTCTCTGCCATAGGGAGCCATATTACGATTGTTTGAAGGAAGTCCATTATGACCAAATCTGCGATGATCTCTATCTTACTTTGCGCTGCGGTATTAGTGGCGGCCATGTTTTTTGGCGACTGGACGAAACAACCTTATTTAAAAGATATTGCCGCAGAGATCCGCAAGGGGACTGCGGTGATTGAACCGAAGAAACCGAGCTCGTCTGCGGCATCGACCATTGAGGCTGATGATGCAAATCAGCACTCTAAGGTGATTTGGGACCAAATGGTGCATGAGAAGCCGGCGAATAAGCCGCAGCCAGAGGCGTTGATTTTCAATCAAGCCCAGCCGAATAATGTCGACACCGATGAGGCTTTATCTAAAAATCTTCCGCCGCTGGATTCGGGTTCTGGTGCCACTCAACCTGAAACGGTTAAACATCATAAACCTGTTTAAGCTGTTATTCCTGTTTGGGTGGTTCGAACAAGGTTTCACTCATATCGCCATGTAAAACCGCAATTTTTGTCGGCGCACCATCATCCGCAAGTTCCAAAATCCCAATACCACTTTTATTCACTAAACGTTTGCTTGGTGCACCATTCGGTCTTCTGCCGCGCAGTGACATGCGTTTTCGTTTCGTCAGTAAGTTAAGCGGTGAAAAGTGGCCGTAAAGCCAGCCATTGAGTTTATCGAAAATCGGTAACAGCTTCTCGGGGAAGTGATTTTTAATGCCGCTGCAGGTGATTTGGTAGATGTTAGGGCTGCTGCGCCGAAAGCGAATACTGATGTCATAACTAAAGGAATAATGTACATCGCCGGAGAGGATCACAAATTCCTCTGGGGTTTTACGGTGCATAAAAATACTCAGTAGCGCATTGGCGGCACCGGGGTGGGCCATCCAGTTTTCGGCGTCCACCAACAAAGAAGCGCCCATCAGCGTGGCAGTGCGCTGTATCGCCTCAATCAATTTGACCCCAAACATAGGCGCAGGCGAGACGATAATGACCTTCGATTGGCCGAGCAACTCTTGCTGCAAATCCATTAACGCCTCCCAATCCATTAAGCCGGATGGTTTTGCGAGATTAGATTCGCTGCGCCAGCGGCGAGTGCGGGTATCGAGCACCACAAGTTTGGGTGAGGTCGCTAAGGTGTAATGCCACTGCTCAAATTTAAGTAATTCTTCGATGAGCAGATCCTGGCTTTCACTCTGTGGCTGTGCAAAAAAGGCATCGAGCAGCGGGCGGATCTCATCGGTAAATTTATCGGGTGCATTGCCAAGCCCTTGGAACAAGGTATAGCCAATCAGGGCATTACCAATAATGCGTTTCGAAAATGCATGCTCGTAGGCTGCGAGTTCCCATTTAGCGGTCAGGTTCCAGTCGTCGGTAACGTCGTGGTCGTCAAACATCATATAAACGGGAATGTGCGCCATAAAGCGCCTCACCTGCGTTAGCCCAGCTTTAAAGCCGAGTAAGTGTTGCCACTCATTTTGCCAGCGGGCGCTATTGGCGGGTGTTAAGCCATCGACATCCTTGGGTATATTGACTAATTCCCACAGTTCGGGCGACCAAGTGAGTAAATACAAGGCGATGATTTCATTCAGACTGACGAGATGATTCTCGGCGATGGAGGAAGTGAAAATCGGATGATTGCGATACCAACGCCATAGCGCGGTTTTGGCTGGATATTGAGTGTGCGGCAGCAGATTTTTATGGCGCTGATATAAATCGGCGGGCTGATAATGCAGCGCTTCACTACTGGCTATGTTCGCCCCTTCAAATTGCTCATGATGAAGGCCAAGTAAGTCGATGACTTGTCCTATGGCGTTTAACATCGGGCCAGCCACATCATCGATATACACCTGATCGCCACTCATCACGATAAGTGCGGGGCGATCGTCCGCTACACCCGTGTAGATTCTCTTGGCTAAATGGGTATCAGCGGCGACGAGCGCATCGTCACTGTGGTGATGCGGATTGCGGCAAGAGCCATGGAATACTTGGGTTAATTCTGGGGTAAAACGCACACTTGGCGATGAGGCATCGCCATAGTTCAGTTGGCAGATGCTGGCAAAAATATCCCCATGTTGGGCGGCGGACACTTGGTAGCTCATGACTCGATTCGGCGTGAGTAACTCTGGTCGCGACAGTATGATCAGATATTGATGGGCGTGCTGTCCGAGTGGCACATAGTGCACATCGGCATCCTGCAACGAAAACTCATGTTCACCTAGACGCAACGTAAGATCAGTTAATGGCTCACGACTCACAAACCACAAGGTAAAATGTGTGGCATCGCAGTGGCGCAGCATAGGGCCTGCCAATATCAGAGGAAGTGTCGGACTCAAATTCAGCCTATTGGTGCTCAGGTTAATCTCAAGTGAGTTAACCCTAAGTAAGTTTGGTTTAAAAGAGTAAACAAGTGCGTTGAATATGAAAAGCTTTTAGAATTGTCAGTCATACACATGAGTTGAAACTGAATGGGGCAATATGGCTAAGATCATGGTAACTGGCGCGACTGGGCTACTCGGCCGAGCTGTTGTAAAACAGCTTGAATTAACGGGACATGAGGTTGTGGCCACAGGTTTTAGCCGCGCAAGTGAGCGAGTGCATAAACTCGATCTGACAGCCCCTTTGGCTGTCGAGGCATTTATCGCCCGCGAACAACCGCAAGTCATAGTGCACTGCGCCGCCGAGCGTCGCCCCGATGTGTCGGAGCAAAATCCGCAGGCGGCATTGGCACTTAACTTAACCGCCAGCCAAGCCCTCGCTATGGCAGTTAAGGCCAATAATGCTTGGCTTATCTATATCTCCACCGACTATGTGTTCGATGGCACTCAGCCTAAGTACGCCGAAGATGCTGCCACTCATCCTGTTAATTTTTATGGTGAATCTAAGCTAAAGGGCGAAGAAATAGTGCTTAACACGAGTGCTGATTTTGCCGTGCTACGCTTGCCAATCCTTTACGGCCAAGTAGAGAAGCTGAGTGAATCTGCTGTGCTAGTGCTGGTTAACCAATTGATTGATCGTCGGGCTCAGGGTGTCGATCATTGGGCCGTGCGAAGTCCAACCTCGACCGCCGATATTGCCCAAGCCATAGACAAGATGATTGCACAGCATATTGAAGCGCCTATCGTGCAGGGGATTTATCACTTTAGCGCCGCAGAGACTATGACTAAGTATCAGATGCTGTTGACCTTAGGGGAGATTCTCCAACTCAGCACGGCGCACTTAACGCCTGAGTCGACACCGACAGATAACGCCAAACGGCCAAGGGACTGCACTTTAAGCTGTACGCGTTTAGCTGCGCTCGGGATTTGTTCTGAAATCAATTTTGCCACTGGCGTGCACCAAGCATTAACGCAATCGAGTGGTGCATTGGCCGCACTCGGACTCACTCTGTAGGAATCGCCATGGCTAAGATCTTAGGGACCAGTCTACAGGCTGCCGACCAAGTCGCATTACTGCGAACCTTAGGTTTGCTGCTGCAAATAGGCTTAACCACGTTTGCCGCCGATACTTTTGGTCTTAGCCTGCAAATGGAGCCGTTAGTGCATGTGTTAGTGCTGGAGGTGTTATACCTGTCACTCACATTGGCGCTACGTAAACCTTTGTTTGCCAAGGAGCGTGGCCTCTTTATTGCGCTCAGTTTAGACACGCTGTTTTGGATCTCTTGGCTGCACTTTTCCGGCGGCGCGACTAATGCGTTTATCTCGTTATTACTGTTGCCGATTGCACTTGCGGCTGTGACTTTGCCAATCTGGGCGCCTTGGGCGCTGACGGCTATTTCAACTCTTGCTTACAGCTTGATGATTTTTACTGTGCCCGAATCCCCCATGCAGCACCACGGTATGGACATGAGTTCCCACTATTTAGGCATGTGGTTTAACTTTGTGATTTCAGCTTTGGTGATGACAACCAGCGTGGCGCTGATTACTAAACGTATGCGCCGCCAAGACGCACTGTTAGCTTATATGCGCGAAGGACAATTACGTCAGGAACAACTCTTGGCCCTAGGCACGGCATCGGCGCAGATGGCGCATCAACTGGCTACGCCGCTCTCAAGCCTGAGGTTGTTACTCGATGAAGTCAAAGAAGATATGAGCGGCACTTCGATAACGGTTATCGAGATGGAGACTGCGTTAGGCCGATGTGAACATACGCTCACCGAGCTGCGATTGGCGACGGAGTCGATCCGCGATCGCCGCCAGCGGCCACAAAAGGTCGCTGAGCTGGTGGACGGTTTAAAGCAAAAAACCTTACTGCTCATGCCGCAGACCGAACTGAACTGGCTGTTTACCTGTGCGCCTGAGCAACTGGCAGAGCAAAGCATTCTGACCGATATGAGTCTAACGCCTGCCATCATGGCCTTGATTGAGAATGCCGCCCGCGCCAGTGTCGAAACCCTAGGTGCCCCGCAGGTGGATATCAGCTTAGATATCGCGCCAAGTGAAGCTCAAATCTATTTGCAGATCCGTGATTATGGCCCTGGTATCGCGCCCTCTTTATTGCCGCAATTGGGCACCTTATTAATTGAGAGTCCCAAGGGGTTAGGTGTTGCGCTGCTACTCAGTCATGCCAGCCTAGAGCGTTTGGGCGCAGAATTGATTCTGGCCAATCATCCCCAAGGCGGTACTGTGGCACAAATTCGCTTTACGATTCTAAAGGACAAAGCGGCATCGTTATCGCCCAAGACTAAGGCCGCTGACGGAGTCAGATCATGAAACGTTTGTTAATCGTCGAGGATGACCTTTCTCTGGCGAGTATTTTAGGCCGACGGCTCACGCGCCACGGCTTTGAGTGCCGCCTAACCCATGATGCCAGTGATGCTCTGCTGGTGGCGCGGGAGTTTCGTCCTAGCCACATTTTGTTGGACATGAAGCTGGCCGAAGCTAACGGCCTAGGGCTAATTGTGCCTCTACGTAACCTGCTGCCTAAGGTGACCATGGTGTTGCTCACCGGTTATGCCAGTATCGCCACTGCGGTGGAAGCGATTCGCCTCGGCGCCGATAATTATTTAGCTAAACCTGTGGATACTCAAACCTTACTCGCCGCGCTGGAAATGGAAGGGCATTCTCATACACTTCAAGAAGATGAAGTCGATGACTCGCCCCTTAATCCTAAGCGTTTGGAGTGGGAACACATTCAGCAAGTACTCAATGCTAACCAAGGCAATGTGTCCGCCACCGCCCGCCAACTCGGAATGCATCGCCGCACCTTACAACGTAAGTTGCTCAAGAAGCCTGTGGGTGAAAGTGGATATTTGAAGTAACGCTTTATTCTCGCATGCTGGCCAGTGAAGCTGACTCAAGCACATGACATAAGGATGCTGACGGGATTGAGGCTGGCTTAGTGCACCGCATTTGCGCATAAAAAATGCCACTCAAAAAAGGCTGAGTGGCATTAACAAGAAACGAGAAGTTTATCGATAAATTGGCACTAGGATTTTACGCTAGCGCCACGATACGATTATCTTATGCGATTAAAAGTTAACGGTTTCTTCGTTTGAGCAAGGGCCGTTAGTTTCACACACTTTATAGCTGAAGCTAGTGCCAGCTAGGTCGGTGAAACGGTCAATAAACGATGAACTGCTTGAAGTACCTACCGCAACGCCGTTACGGAAGATAGTGTACTCATCAGCGTTATGACCTTCCCATGCGAGTGCAACACGAATGCTGCCGCGACGAGATTTGTTAGCGTTTTTCACTGTCATTTCTAACGCCACTTCTGGTACTTCAACTGCAAATTCTTCAGAAGTGCTTGCTGTTGCGCCATCGTTGTCAGTCACAGTCAATGTCACTGTGTAGTTGCCTGACTGAGCATACTGATGATTTACACTTGCGCCTGAAGCAGTTGTGCCATCACCGAAGCTCCAGCTCCATGCAACGACATCACCGTCGCTGTCAGTACTGGTGCTAGTGAAGCTACCGTTACCGTTGGCAAAGCTTGCAGTAAAGCCCGCTGTTGGATCTTGGTTTGGTATCTCATCGCCACCGTCATAAGCACCCACTAGGCTCACACCTGTGTAGCTGCTATAGCCACTGACCATGACCCAATAAGTTCCTGTTTGGGCTGGATCGATAGGGCAGCTTTCGGCATTACCGGCTTTGTATGGACGGCAATCATAGCTACTAGAGGTAGGAGCTTGGCCGAATTTAACATACATGTCCGCATCACCCGTGCCGCCTGACATATTAAAGCTCAGGTTGGTTGCACCGCTCGCTACGTCGAGTTTGTAGTATTGTTTGCTACCAGATGCGCCCGCTAAACCCGTCACAGCGACACCGTTCGCGAGTTCAGTAGCTTCTGGTGGTGGCGGTGGTGGTGGCGTTTCACAAGGTGTGATACCCACAGCGGCAAACGCAGTAACCACATCGTCAGCACTTAAGCCTAAGTCAGTCGCTGCAGATTTAACGCCACAAGCGCCTTGCCAGAACAAGCTGTCAGCCGTCCAATATAATTGGTTAGCAACCACAAATGTTTGGAATGCAGTGCGTGTATCCCAGCCCGGTAAGTTTGCGAGTGTGTAGAAGGCTTTGTTGAAGACACCTGAACTGTGGTGTACGTCGATACCATCATAGTAATCGTCGATATGGCCGATTGAGATACCGTCTAAGGTTGGATCTGCCATGTAACGCAGTGCGCCATTGCCTTTAAAGATATCTGCACCGACTAACCAGTCGTTCGTGCCATGCATGTAAAATTCAGCAGCTTCACCCGCCATATCAGAGAAGGCTTCGTTCATGCCACCAGATTGAGCATCGTAAATCAGGCCTGAGTTTTGCTCAGTGAAACCATGGCTCACTTCGTGTGCCGAGACATCTAAGCTCACTAATGGATAGAAGGTCGTTGCGCCATCACCGAAGGTCATAGCACTGCCGTCCCAGAAGGCGTTTTCATAGTTGCTGCTGTAGTGAACCCGCATGGTTAACTGGAAAGTTAACGGCGCAGTGTTGTACCACTCGCTATACATGTTGTAGATCACGTTACCAAAATAGTGCGCATCGTTTAATGGCGAGTAAGCACCGTTGATCTCTTTAACGGTATTGCGTGGGCAAGTATAAGAGAAGGCTGTTGCGCCTGATGTGGCACCGTTTAAGTTAACGGTTTTCACGTTAGGTGAGTTCATGGTGCAGGTGTCACCACTCACTTCAACGTCTAAGTAAGAAAAATCCGTACCGTATTCGTATTGGCCAGTCTTGATGTTACCGCCAGGACCTGTACCGCTAGCGGCGTGGTTAATCCCTTCCCAACGCTTGAGCACTTCACCTGAGTGTGCGTCAATCATAGTGAATGGACGGCTTGGCTCATCGCCATATACCACAAAAGACGTCACATAAACGAGGCGAGTTTTAGCCGCATCATCCAGGTAAAGCCATAATTTAGCTTGTTTGTTTTCAGCTTTTAGGCCTGCAACAGCTTTCTCAGACTTGCCACGAATGGCAATGTCTAACGCTTTGTTAGCCGTTAAAGTGGGTTTGACAAAATCAGCCGACTTTTCAATATTTTTTAATACGCGGCCAGACATGTCGCTGTAGAAGCCCATGCTCGATTGTGACGTTGCAACAGAGAAACCATAAACAGGCACGTCATGGAAATATTGTTGTAAACGTTGCTTTTGCTTGCCTTGACCTAGGTCGAGTTTTTTCTCGACTTTGAGTTGGCTACCTGCGTCTAAGTTGAACTGAGACACTAAGTTATTGCTTGAAGACGCGCTTCTGAGTGTGCCGACATCGACAACATCAGCAGCAAATGCTGATGTGCCTAACGTGGCCGACACAGCAATGAAGAGTAGCGACAGTTTTTTGTTTAGCATCATGCTTAAAATCCTTTTTCCGTTTGTCATTAACGTTGGAATGCCATACTGCTTAACTGGCTCGGGCGAGCGTTTTGTTATTGTAATTTCTACAGGGTCGTCGGCATCCCTCTGCCATGGCCGAATAGAATGAGCTGTTGCTCAGGCGGAAATTCTCAGATTTTTATTCTGTGCAAAGCGTGAATAACCGCATGGCCTTAGATGTTATTTAAATGTAAAAGTGTTAACAATCGTACAAAGGAACTATCTTCATCATTCATTTAGGTGCTTTTAAATTTGAATTAGGACTTTTGGGTAATAGCGATTTAGCCTAGTCAAATGGCGGGTTTTGTGAGTTGTAAAAAGTGCTTTATTGATGGATTTTCATTGGATTAGGTTGGATTTTCTGGCGAGGTTAAGCGGGGTATTTCTAGGTGGGTGATTTTTAAGTGGGTAGATTTAAGGATTAGCGGTGCTTTTGGGCATAACCCTTTTTTAAGGGCAAAGATCCGTCGCTTTGCCGGATTCATTTTGAAAAATGCACTTTGCAAGAGCCATTTGAAAGATGCCCTTTGAAATATGTTCGTGGCAAGAGCCCGTCCGCATCGCGCGACTTGGCTCACTGTTCAGCTTATTTCACCAAGGTAACTCAGTGCCATCGTAGGCGAAAAAGCACCCTGTTTGTACCGGAGTGGCATTGGCAATGATCGGCAGTAAGCAGTTGGCTACATACTCAGGTGTAAACAGTTTGCCTTTAGGCACACTCTGTTGAAAGGGTTGAGACAATGGCGTATCAGTGGTGCCCGGATGCAGGGATAACACCACGCAGTGTTTCATGGTTCGTTGCCACTCAATCGACAGTGTTTTGAGAAACATATTCAGCGCAGCTTTTGAGGCGCGATAGCTGTACCAGCCACCTAATCTGTTATCGGTAATGCTGCCGACTTTTGCCGATATCACGGCAAATCTTGCCGAGTCACTTTGTTTTA sourmash-bio-sourmash-bc3c0f8/doc/genomes/shew_os223.fa000066400000000000000000017205301474157046300231060ustar00rootroot00000000000000>NC_011663.1 Shewanella baltica OS223, complete genome TTTTTTAAGGATAGCGATCCTTGAGATCTCGCTATAGAATACACCTCTTTTTTGATGATCTTTTGGGGATAACTAAGTGGCTGTTTCACTTTGGCAACAATGTATCGGACGATTGCAAGATGAGCTTTCTGCTCAGCAGTTCAGTATGTGGATCAGACCGTTACAAGCCGAAATGGATGGAGATACTCTGGTGCTTTATGCGCCCAACCGCTTCGTGCTTGATTGGGTCAGAGATAAGTACATCAATATCATTAATCAGTTCTTTACTGAGCAGATGGGCAGCGATGCACCTAAATTGCGTTTCGATATTGGTAGTCGTCCTTCGGCGAAAAAGCCAAGTGTGCCGGCACCTATAGCGCCAACTCGTGTAGCGAATACCCAAACTAAAGCGACGGTAGGCACGACGTTTAACGTCCAAGCCGAGCCGATGGCGAACGCGAATCACCGCAGTAACATCAATCCTAGCTACCAGTTTGATAACTTCGTAGAAGGTAAGTCGAACCAATTAGGTAAGGCTGCGGCGTTGCAAGTGGCTGAAAATCCAGGTGGTGCTTATAACCCATTATTCTTATATGGCGGAACAGGTCTAGGTAAGACTCACTTGTTGCACGCTGTCGGTAACGGGATCATTAAAAATAATCCGAATGCCAAAGTGGTTTACATGCATTCCGAGCGTTTTGTCCAAGACATGGTTAAAGCGCTGCAAAACAATGCGATCGAAGAATTTAAACGTTACTACCGTAGCGTCGATGCTTTGTTTATCGATGATATTCAATTTTTTGCCAATAAAGACAGATCCCAAGAAGAATTCTTCCATACCTTCAATGCCTTACTTGAGGGTAATCACCAAATAATTTTGACTTCAGATCGTTATCCAAAAGAGATCGACGGTGTTGAAGACAGATTGAAATCGCGTTTTGGTTGGGGTTTAACCGTGGCGATTGAGCCACCAGAGTTAGAAACCCGCGTGGCGATTTTGATGCGTAAAGCTCAAGAGAGTGGCATTAACTTGCCCGATGAAGTGGCCTTCTTTATTGCTAAACGTTTACGTTCAAACGTGCGTGAGCTAGAAGGGGCGTTAAACCGTGTTATCGCCAATGCTAACTTCACTGGCCGCCCAATTACCATTGATTTTGTTCGTGAAGCTTTACGTGACCTCTTGGCACTTCAAGAAAAATTAGTCACTATAGACAATATTCAGAAAACCGTAGCTGAATACTACAAGATCAAGATGGCTGATATGTTGTCTAAGCGCCGTTCACGCAGTGTCGCGCGGCCAAGACAAGTGGCAATGGCGTTATCCAAAGAATTAACTAACCAAAGCTTGCCGGAAATTGGTGATGCTTTTGGTGGTCGAGATCACACCACAGTGTTGCATGCCTGTCGTAAGATTGCTCAGCTACGGGAAGAGAGCCACGACATTAAAGAAGATTATGCTAATTTGATTAGAACCTTATCTTCTTAAAACAAGGAATTTGGACACTATGAAATTCTCAATTGATAGGGATGCCCTATTAAAGCCGTTGCAATTGGTGTGTGGTGCGGTAGAAAGACGCCATAATTTGCCCATTTTGTCTAACCTCCTTGTAGAAGTAAGTGGTCACTCACTTAAGCTGACCGGAACAGATTTAGAAGTCGAACTCGTGGGCCAAGTTGTGGTTCACGGTGATATCGAAGAAGGTCGTACTACGGTTCCGGCTAAAAAGCTGCTCGATATTGTTAAGTCTTTGCCAGAACAAAGCGAACTGAAAGTCGAACAACAAGATAATAAATGGTTGCTGCGTTCAGGCCGTAGCCGTTTTTCCTTAGCGACGTTGCCCGCAGAAGAATATCCCAACGTTGAAGCCTTCCAAGCTGAGATTGAATTTAGTTTGAAGCAGGGCGTATTGAAGTCGATTATCGACTCAACGCAATTTTCTATGGCTAACCAAGATGTGCGTTATTACCTCAACGGTTTATTGTTTGAAACTGAAGGTAATGTGTTGCGCGCTATTGCGACCGACGGCCACCGTTTAGCCTTAAGTCACCGTACGATTGAAGCACAATTACCTGAAAAACAAGTCATTGTTCCACGCAAAGGCGTAATGGAAATGGCGCGTTTATTGGAGTCGGACGATCTCGATATCGCGATTGCCATTGGTGATAATGCCATTCGTGCCACTACGGCTAACACAGTATTTACCAGTAAATTAGTCGATGGCCGCTTCCCTGATTATCGCCGTGTATTGCCAAAGGGCGGTGATAAAATAGTGATTGCGAGTCGTAATCACCTGAAGCAAGCGCTGACCCGCGCATCGATTTTATCTAACGAAAAATTCCGTGGTGTGCGTATTCAACTCGAAAATGGTCTGCTTAAGATCACCGCTAACAATCCTGAACAGGAAGAAGCGGAAGAGATCATCGATGTGGAATACAACAACCAAGCTTTAGAAATTGGTTTTAACGTCAGTTATTTACTCGACGTGCTCAACAACCTTAAGTCCGATGATGTGCGTATCACGCTCATTGATGGTAACTCCAGCGCCTTAATCGAAAACCATTTGGAAGAAGATTCCATGTATGTGGTTATGCCGATGCGCTTATAAACCATCGATTTTTACCTTACCCGTGTCTGTACCTCTGTCTTCGATATCTATGTCGCTTAACGGCGGCATAGTGTTTAATACGCAGCATTGGCCATTCCTTCACGCATTTACCGATAGCCTTAGCCTGTGGCAGCGATTGAGTTGCCTGTTAAGTGTTATGAATCCTTGCCTTCGCCGTTTACGCGGCTTAATGAGTAATGCATGAGTCTGACCCGTCTCAATATCGAAGCGTTTCGCAACATTCAGTCCGCACAGTTAATCCCTGCACCTGGGATTAACTTGATTTATGGCCAAAATGGCAGCGGCAAAACCAGTATTCTTGAGGCTATTTATTTTCTCGGTATGGGGCGATCGTTTCGCAGCCATTTATCCCAACGAGTGATTAATAACGACGACGATAAGTTAACGCTCTTTGCAACCTTAAATTTAGCGCGCGGTGACAGTAAAATCGGTTTGCGCCGATTTCGTAGTGGTGAGACTGAAGTGAGAATCGACGGCGAAAAAGTCAAACGTCTGTCGACATTAGCCGAAACGTTACCTATTCAAGTGATCACGCCTGAAAGCTTTTCTTTACTGTTTGAAGGCCCAAAATCCCGCCGACAATTTATCGATTGGGGCGCATTTCATGCTGACCCACAGTTTTATGGTGCTTGGACGAATGTTCGCCGCGTTCTTAAACAAAGAAATCAATTACTTAGAAATGGCTCGTCCTATAGTAATATTCAATTTTGGGACCAAGAGTTTGTGCGTTATGCCGAGCAGGTTACTGAAATACGAAACCATTATGTAGACTCGTTAAATGAGCTACTTAAGGGTATAATCGGGGAGTTTTTACCGAGTGTAGATGTGAAGGTTTCATTTACGCGCGGCTGGGATAGTAAAACGGATTTTGCAGAACTACTCGAAAACCAATATTCCAGAGATTTGGCCACAGGTCATACCGTCAGCGGTCCCCATAAAGCTGACCTTAGACTTCGTGTAGGCACTTTGCCGGCGCAGGATGCCCTGTCCCGTGGTCAATTAAAATTATTAGTCTGTGCACTGCGTATTGCACAGGGAAAGTTGCTCAAACAACAAATAGATAAACACAGTATTTATCTTGTGGATGATCTTCCGTCCGAGTTGGATGCACAGCATAGGCAGCTATTGCTTAAGCAGTTAACGGATACCGGAGCACAAGTTTTTGTGACCGCCATCGATCCTGCTGCAATAGTCGATTCGTTACACACGCCTCCCAGTAGGATGTTTCATGTGGAACAGGGACGTGTAACGGTAATTGAATAACCATTGAGAGAATAATATGTCAGAGAATAGTTACGATTCTTCGAGTATTAAGGTCCTTAAGGGCCTTGATGCAGTACGTAAGAGACCTGGGATGTATATTGGTGACACCGACGACGGTACGGGTCTACATCATATGGTGTTCGAAGTAGTCGATAACTCTATCGATGAAGCCTTAGCCGGCCACTGCAGCGATATTACCATTACTATCCATGTCGATGGTTCGGTTTCTGTTAAGGATGACGGCCGTGGTATTCCGGTCTCTATCCATGAGGAAGAAGGCGTATCGGCAGCAGAAGTTATCATGACGGTATTGCATGCCGGCGGTAAGTTCGACGATAACTCCTATAAAGTGTCTGGCGGTCTCCACGGTGTGGGTGTATCGGTTGTAAACGCTTTATCTAAAAAGCTGCAAATGACTATTCGTCGTGCAGGCAAAGTGTACGAGCAGTTTTATACTCATGGCGTGCCTGATGCACCAATCAAAGAGATTGGTGATGCGACTAAAACCGGTACCGAAATCCGTTTTTGGCCAAGTGAAGATACCTTTAGCGATGTCGAGTTTCACTTTGAAATTCTGGCAAAGCGCGTGCGCGAACTGTCATTCCTTAACTCAGGCGTAGGTATTCGTCTGGTTGATGAACGTGACAACAAGAATGAATTCTTTAAATACGAAGGCGGTATTAGCGCATTCGTCGATTATTTGAACCGTAATAAAACCCCCGTCAACAAAGACGTATTCCACTTTATGCAAGAGCGTGAAGATGGCATTACCGTTGAAGTGGCTATGCAGTGGAATGACGGTTATCAAGAAAATATTTTCTGTTTTACCAACAACATTCCACAGCGTGATGGTGGTACTCACTTAGTCGGTTTCCGTAGTGCGTTGACACGTAACCTCAACAGCTACATGGAAAACGAAGGCTATAACAAGAAAGGCAAAACTAGCGCGACTGGCGACGATGCCCGTGAAGGTTTGACTGCCGTTATCTCTGTTAAAGTGCCTGATCCTAAGTTCAGCTCGCAAACCAAAGATAAACTGGTTTCTAGTGAAGTGAAAACCGCGGTTGAACAAACTATGGGTGAGAAGCTGAACGATTATCTGCTGGAAAACCCAGCCGATGCTAAGTTGATTGTTGGTAAGATTGTCGATGCGGCCCGTGCCCGTGAAGCGGCGCGTAAAGCCCGTGAAATGACCCGTCGTAAAGGCGCACTCGATTTAGGTGGTTTGCCAGGTAAGCTGGCCGATTGCCAAGAAAAAGATCCTGGTCTTTCAGAAATTTACATAGTGGAAGGTGACTCTGCTGGCGGTAGCGCTAAGCAGGGACGTAACCGTAAGAACCAAGCGATTCTGCCACTGAAAGGTAAAATTCTAAACGTTGAGAAAGCACGCTTCGATAAGATGTTGTCTTCTCAAGAAGTGGCCACACTGATCACGGCGCTTGGCTGTGGTATTGGTCGCGACGAATATAACCCAGATAAAACTCGTTATCACAACATCATCATCATGACGGATGCTGACGTCGACGGCTCGCACATTCGTACCTTGCTGTTGACCTTCTTCTTCCGTCAAATGCCTGAATTGATAGAACGTGGTTATGTGTATATTGCTCAGCCACCACTATTTAAAGTGAAAAAGGGCAAGCAAGAACAGTATTTGAAAGACGAGCCTGCATTGACTCAATATCTTACGACTCAAGCATTGGATGGCACTATTGTGTACCCAACGCAAGGTGCGATTGGTATGTCAGGTGAGCCGTTAGAGCGTTTAGTGACTCAGTATCGTGAAGTGGAAGCCATTGTTGCCCGTTTAGAGCAACGCTACCCAACGCACATTACTAACCGTATGCTTTACCACCCAATGATCACCAACGAGATGTTGGCCGATGAAGGCAAGATGAAAGAGTGGATCGATGCCTTTATCCATGAGCTGGTGGAAATGGAAAGCAGTGGCGTACTGTATTCTGCTGAGCCGATTCTCGATCCTGAGCGTAAAGTGTATCTGCCTAAGATCACCATTCGTAAACACGGTATCGATACTAACTATCTGTTTACTTATGACTTCTTCCAATCAAGCGATTATCAACGTATCGGTAAGTTAGGTGCGGCGTTAGATGGCATGATTGAAGAAGGCGGCTATGTACAACGCGGCGAACGTGTTAAAGAAGTCGGCAGCTTCCTTGAAGCGCTAGATTGGACGATTTCTGAAGCCAAACGCGGCTTATACATTCAACGTTATAAAGGATTGGGTGAGATGAACCCTGAGCAATTGTGGGAAACTACAATGGATCCTGAATCACGCCGTATGTTGCAAGTCACGATTGATGATGCCGTGGGTGCCGATCAGTTGTTCACTTGTTTGATGGGTGATCAAGTTGAACCTCGTCGTGAGTTCATCGAAGCGAACGCATTGAACGTGGCTAACTTAGACGTATAGTGCTAGATGTATAATATTAGACGTGTAATGCGCGTTAAGTGACTCGCATTAGTACGTGAATGATTAAGGGAAAGCCAAGGCTTTCCCTTTTTTATCTCTATGATTTTATCTCTTTTATCGATCAATACCCGCTTCATCCGCAGCAACAATTTCAGTCTCAGCTTTAGCCGCACTCATCCACGCCTGCATTTCCGGACAGCTGATCACATGCTGCATATAGGCACTGCTTGCCTGCGAGACTTCAACACCGTAGGTGAGAAAGCGCATGACCACAGGCGCAAACATCATGTCGGCAATCGACCATTGGCCAAATAACCAAGTACCTTCCGCATGTGATGGGGCGAACTCGGCCATTTGTGCTGACCAAATCGCATCGATGCGCTCAATGTCTTTGCGGACCACATCTGTGAGCTCAACATAACGGCTCGCGCGTATGTTCATTGGTAGAGCGTTACGCACGGCATTAAAGCCAGAATGCATTTCACTGGCAATCGATCTGGCCTTGGCTTTTTGTTTAGGATCTTGCGGCCATGCACTACCGGCAAGATAGGTGTCGTTGAGGTATTCGCAAATCGAGAGTGAATCCCATACCGTGATATCGCCATCGACTAATGTCGGTACTTTTAACGTCGGCGAAATGGCTTTGAGTTGCTGATAAAAGCTGTCGGTATCGAGTTGCAGTAATACTTCGTTGAACTGCACGCCAGATTTCGCGGCCATTAACCAAGCCCGCAGTGACCAGCTAGAGTAGTTTTTATTGCCAATATAAAGATCCATTGTTTGTCCTTGAGTGAAATGCCGAGTGTTTCTGCAAAGCTACTATATGGGCTCATTTCACCTATGACTAATGAATATTTTACCCTTTCTACATCCGCTAATACGCGTCATTTCTCTTGAGCTTTCTAATCACTTTTATTGAATTTAAACGACTCACCGCATTGGGATAATTTAAGCATATCTTAATTACTTCGTTCGATGGAACTCGTCGGGATGAAGGCTAACTGATGAGAACAGTGGATGTGGCTGCGAGTGATCTATCCGGCGAGCGTGCTGATATTAATGTTACCAGCTGTGGTAAACGCCGATGAGGCTAATAAGTTATTGAACATGTGTAAGGCCTGCCATGGCGAGTCTGGCGTGAGTCGTTTTGAGCATATTCCTAACATTGGATGGCAAAACTCGGATTACCTGTTGAAGCAGCTTCGAGCCTTTAAGACGGGAAATCGCCAAGATCCCACTATGACTAAAGTCGCCCAATTGTTGAGCGAGGCTGATATGCAGCAGATGGCGGATTACTTTTACCTCGGAAAGGACAAATAACATGGATCTTAATCGTCGGGTTTTTATTAAAGGTGCGGTAGCGACGTCTGTTTTATCAGCTTTACCCGTTAAGTGGGTATTTGCCGATCTGCTTCCCGCAGCCGTCACTCCTCTGGATATATCAGCATTAACTTGGACTAAGGCAGAGGACTTGCCGAGTTATTTCAAAGTATTAAACACTCATCCACTGAATGCGTTTCCGCCGGAGCATAAACTTGCTCCTGCTGTGACGCCAGCAGATGCCTCATTTATCCGTTGGAATGGCATAGTGCCTGATTTTAAGGCGCTTGATGCTAAGACTTGGACTTTTAAAGTGGATGGCGAGTCCGTTGCTACCCCGAAAACCTACACAATCGATGAGCTCAAGCGCCGCTTTAAGCATTACACATTACAGTTACTGATTGAATGTGGTGGCAATAGCCGTTCAGGCTTCTTTCCTAGCACGAAAGGCAATCAATGGAATAATGCTGGGGTATTTTGCTCCGAGTGGACGGGTGTCTTGGTGAGCGACGTGCTTAAGGATTGCGGTATTAAAGCTGATGCTATTTACACGGGTCATCACGGTGTTGATCGGCATTTGAGTGGTAAAGGTGAGGCTATTTCACGCGGTGTGCCAATAAAGGCGGCGCTCGAAGAGCTTGGACTCATTGCATGGTCGATGAACGGTGAGGACATTCCTTATCTACATGGATATCCCTTACGTATTGTTTTTGGTGGCCGGCCGGGATCCGTTTCGCAAAAAGCGGCGACGGGGATCAGTGTGCGCAATAAAGTGCATGATGGACATAAAATGGAAGCGCCAGCGTATCAACTCCCTAAGTATCCGATAGCGCCAGGTGAGTCCGTTGACAATAAGGACTACCGAATCATTGAAGAAATGATAGTGAAGTCCTTGATTACTGAGCCTCAATCGGGAACAGAATTAGCTGTGGGCCAGAAGATAGCCATCGCAGGCCATGCTTGGGCGGGATTGCGGGAAGTGGTTAAAGTGGAAGTGAGTTATGACTATGGAACCCGCTGGTTTACCGCTGAGTTAGAAAAACCTAAAAACGTAGCGGCATGGCAGCAGTGGAAGATTGAATTGCAATTACCGTCGCAGGGATATTATGAAATCTGGGCCAAAGCGACCGATAGCGAAGGTGATAGTCAGCCTGTCGTACAACCGCAATGGAATCCTAAGGGATATATGTTTAATGGTTGCAATCGTATTGCCGTTAGAGTGGTGTAATGAGAGTACTGAACAAGCTATTTATTTTTCTTGGGCTAGTGTCATCCAGTATTTCGCTAGCCGCTGAGTATCCAATGGACCCTAAATCGGGTTTGATCATGGCTCCTGGCTGGGAGTTAGTGAATTATCAGTGTAATGCTTGCCATACCAGCATGATAGTCGTGCAAAATCATGGCGATAAAGCCTTTTGGAAAGAGACTCTGCAGTGGATGGTTGACACTCAAGGCCTGTGGGACTTGTCGGATACTTGGGAGCCAACCTTGAGTTATCTAAGCACTTATTATGGCCAAGTTGAAATGGATATGACGCTTTTTAGGCGACTTCCCTTGGAACCTAGCCTGCAGCCTGCGCTAGTTAAACCTTTCCCTAAGGAGCCAAAATGAAAGCAATTATCATAAGTTTGATTGCGCTCTGTGGCTTATCCACAGAGGTCTTTGGAGAGGTTTATCAAGCAGATATAGAGCAAGGTCAGGCATTGGCTACGGCACAGTGTGAGTCATGCCATGGCAATGCGGTGATCGCTATGGCCAAGCAATTCCCAAACTTAAAGGGACAGAAATTAGCTTATCTCAATAAACAGCTTGTCGATTTTAAAGCAGGTTTACGACTCGATCCTTTGATGCAAGCACAGGTCAGCGCGTTAAGCTCTGAGCAATTACAAGATGTGGCACTATTTTATAGCCAGCAAGTTACACCGAACCTAAGTGCGAATTAGCGTTATTGTGGGGACTATTTGAATGAAGTCGAATGTAGCTTGCTAGGAATGCGGGGTGATGTTTCTTGGACGCGATGATTAAGCGGCCTGTTAACCTAGACTGCGTTGTAAGCGATAGAAGTAGTCATGCCATTCTGCCTGCGCCGTTGCGGCTTGTTCATCGTCGACTAATTCGAATGTTACTGTTTGCCCTGGGCGAGTTTGGGCAAGCTTCCAAAGATCGGCTTCAATAATCACGGCTATTTGCGGATGACTGCTGGTGGTCTGCGCATCGGCAAGTAGCACGAGTGGTTGCCCTGAAGCGGAGACTTGAACCACTCCGGGGGTGACTGTGTGTGATTGCACAGGGAATGGCTTCGCCAATGTTAACGTATCACCTTTGAGTATTGCACTCGCACGATCACTTTCAGCTGAAAGTTGCCACTGTGTTTGCCAGAATGTTTGCTGAGATTTCAAAGAGAAGGCTTGAGTGTGCTTGGTCGCGAGTGCGCGAATATGCCCATCCCAACTTTTTTGAATTGCGCCAACTTGCCTTCTTAGTGGATGAGCTGAACCTAAGGTCAGGCGATCGCCCGCTTCTAATGCGCGGCCATGAAATCCACCGATACCTGAGGATAAATCGGTAGCGGCAGAATTCATCACTAGCGGTACTTTAACGCCGCCATCAACGGCAATATAAGCACGCATGCCTTTATTTCCCCCTCTAAGACTGAGTTTTTGCCCTGCCTTAATGGAGTAACGCCAGCCGCAAACTATGGGCTTATTGTCTATACGAGCATCGAAATCGGCACCGGTTAAGGCAATCCAAGCGTCGCGTTTAAACAAAAAAATCGATTGGCCGAAGGTCAATTCTAATACGGGTGTGTTATCTGGGTTTGATAATAGGCGGTTTGCTAAACACAGGGCAGGTCTATCTAAGGCGCCAGCTTGGGTTACACCTAAATGTCGGTATCCCATGCGTCCGAGATCTTGAACTGTGGTTAGTGCACCCGCTTTTATTACCTCGATCATCAAATATTCCGTCGTTAGCTGGCGAGTATGTCGTGAATGACTTAAAGGGAGACGAAATACGTATCACTTTATGAAGGGTGGACATATTGTCAGCTAAATGATGCCGGAATGTAAGTTTTTTGAGGTTTCAAGTTGTAACCAAGATTGTCCTAATAGAGGCTACAATCATGGGATTGCTGGCGTCAGAAATGAATTGAATATGTTACACTTAAGCAAGCCATTAGCATGTCTGCAACGTGTTTATATATAGCTTTAGCGGATCAAAATGGATTTATTATGGGCAATATTTAACTGACTTTTTAACAATTATTTATAATGACAGGTTGATGCTTCTTGATTAAATCACTTTTTAATAAGCTCTTTAATATTCGCGATACAGCGACGGTTGAGAGGCCCAAAAGTTTTTCAACCACTGACTTAGGTAAGTCGCTTGAACCTGCGCGAAAGCCAGCTAAACCTCTCGAAGACATTAGTGTGGTATCGGTGGATTTAGCCGCACTCTTTTATAGTTTGTTGTTTCCGACACGGGTTAAGGACACTGGCGGTGTAGCTAATAATCTTGAACGTCGAGTCATGGCGGAGATAGAGCAGGCCTTGGCATCGCCACAAGTGATCGCCGAAAAAGTGCTAAAGCTGCCGTCAAAGGTATTGGAGCTAGATCGTAAGCTGGCCGATCCTCAGTTTGATATTAAGGATCTATTAGCGCTTATTGAGCGAGATCCTTTACTGAGTATTGAGGTACTTAAGCTATGTAATTCCCCTGCGTTTAAACGTAGTGATAGGGATATCACCAGTTTGCAGCAGGCTTTGGTTCAGCTTGGTCGTGAACAGTTAAGAAAGTTTGTCACCACTTGTCTAGTGCGTGAAATGTTGGATATCAAGCCAATTTATTTTCGTCGTTTCGGCGCTGAAATTTGGCGCCACTCGATGCAGGTAGCCTTTCTCGCCAGTGAGTTAAGCTATGAAGATCAAGATACGGCTTTTTTATTGGGCTTATTGCATGATGTCGGCAAGCTGGCCATTTTCAAAATGCTACTCGATGCGTTTGTGCAGGCGGAGCCCGGAGAACAGCCTAATTCTGGGTTATTTAGACAAGTGATGACGACGAAATCGTTAACCTTAAGTTCATTGCTCGCTAAGCATTGGCAGTTACCCAATACCTTTGAAACAGAATTAGACAAGCTCGCCAGTGTGAACTCGCGCCCGCAAGCTGGGCTCGCCGCTGTGGTATGGCGGGCGAATGTGATCAGCGAGATTTCTATGCTGCATCAAGCAGGGCAATTGTCTCCTGAAATACTGGCGAATTTACTGCAGCAAGTTAACTTAGATCAAACTCAATTTGATGGCTTACATCAAAAACTGACGCAGTTCTGATTGGGCAGCTTATGAAATCAGGACAATAAAAAACGCGCTATTGAGCGCGTTTTTTATAAAGCGGTAATTTAAAAAACGTTAGTATCGTTATTGAAGTAGTGATATATCCGCGACGTGCAGGAACTGCTCGCGTAGATTATTCAACAATGCTAAACGATTGTTTTTCAACGCTTCATCATCTGCCATTACCATCACATCTTCGAAGAACTGATCCACGCTTTCACGTAGGCCAGCTAACAAGGTTAAGGCTTGTTGGTAATCGGCATTGGCAAACAATGGTGCCAGCAGTGGTTGTAGCTCGTTCAACTTGGCCGCTAATGCTTGTTCTGCGGCTTCAGTTAACAGACTCGCATTGATCGTGGTCGGTAAAGCACCTTCAACCTTAGCCAGAATGTTGGATACACGTTTGTTCGCTGCCGCAAGTGCGCTAGAAGCTTCTAGGCTTCTAAAGTGTGATACCGCATTGATACGGCTATCAAAATCAGCTGGGCGTGTTGGGCGACGTGCTAGCACGGCTAAGATCACATCAACACCAATGCCTTTATCTTGATACCAAGCGCGGAAGCGTGCCATTAAGAACTCTAACACTTCATCGCTTGCATTGGCGTTGCTTAAGTTAGTGCCATGCAGAGCTTGTGCTTTAGCAATAAGATCAACTAAATCAAGTGGTAGCTTGTTTTCTACTATGATACGTAGCACGCCAATTGCTGCGCGGCGTAGGGCAAAGGGATCGGCTGCGCCTTTAGGAGCTTGGCCAATACCAAAGATACCGACTAAAGTATCGAGCTTATCTGCTAATGCGACAGCGCAAGATACGCCAGCACTTGGCACTGTATCGCCAGAGAACTTAGGCTTGTATTGCTCTTCCATCGCAACTGCAACGGCTTCGGTTTCACCATCGAGGCGGGCGTAATGCATGCCCATAGTGCCTTGAGTGTCGGTAAATTCCATCACCATATTGGTCATCAAATCGGTTTTAGATAACAAACCTGCACGGGCTGCATCAACCGCATTCGCGCCAGTTTGCTCGGCAATAAAGGCGGCTAGGGCAGAGATACGATTTACTTTGTCTTTTAATGTACCCAGCTGCTGTTGGAACAATACTGTTTCTAAGCTAGGTAAACGTGATTCCAGCGTGTGTTTTTTATCAGTATTAAAGAAGAATTCGGCGTCGGCAAGACGTGGACGCACAACTTTCTCGTTACCAGAGATAATTTGCGCAGGATCTTTAGATTCTATGTTGGTCACAAAGATAAAGTTTGGCAGTAACTTGCCCGCGTCATCAAAGACTGGGAAGTATTTTTGATCGCCCTTCATGGTGTAGACCAAAGCTTCAGACGGTACGGCTAAGAATTTTTCTTCGAAGCTGGCGGTCAATACCACTGGCCATTCGACCAAAGAGGCGACTTCTTCAAGTAGGCTATCTTCGATATCGGCTGTACCACCAATCTTAGCGGCGGCTTTTTCGGCATCGGCTTTGATCAATGCTTTACGGCTTTCGTAATCAGCGATGACTTTGCCTTTTTCTTTCAGGTCGGCAAGGTAGTGATCAGCATGGGCTAATTCAAACTGCTTGAGTCCCATAAAGCGGTGACCGCGAACAGTGCGTGCCGATTTGATACCCAGCAATTCACCTTCAATCAACTCACTGCCTAACAACATAGTCGCGGTGTGCACAGGGCGAATAAATTGGGTTTTATTATTACCCCAACGCATTGGCTTAGGAATGGGCAATTTATCGAGTGCACGTTGAGCCATAGCAGCGATCAAACTTTTCGTTTCGACGCCTTCAACTTTAGCGTTATGGACTAACCATTCGCCTTTATCTGTCACTAAACGTTCGGCTTGTTCAACAGTAATACCATTCCCACGCGCCCAACCTTCGGCGGCTTTAGTCGGCTTACCTTCAGCATCAAAGGCTGAACTCACGGCGGGGCCACGTTTCTCAACTACTTTGTCGGCTTGTGCAAGCGCAAGTTCAGTGACGTTAATCGCCAGACGACGTGGCGCTGCGTACCAAACGGCAGAGCTAAATGCTAAATCGGCTTTGCTGAGTTCTTCGGTAAAGTTAGCTAAAAAGGATTCAGCCAGTTTACGCAGTGACTTAGGTGGTAACTCTTCTGTGCCTAACTCTATGAGTAAGTTTTCAAAATTCATGTTTTTACCTCTACTTACACATTGGGAAGCCAAGCGCTTCGCGTGCCTGATAATAAGATTCAGCGACCGCTTTCGCCATAGTACGAACGCGTAAGATGTAGCGTTGGCGCTCGGTCACTGAAATGGCATGGCGGGCATCGAGTAAGTTGAAGGCGTGCGAGGCTTTCATCACTTGCTCGTAGGCGGGCAGAGGTAAAGGTTTTTCCAGTGACAATAAATGCTGACACATTTTTTCGCACTGATCGAACAGGGTGAACATAAAGTCCACATCGGCGTGCTCGAAGTTATAGGTCGATTGCTCGACTTCGTTTTGATGGAACACATCGCCATAGGTGATGCGCCCAAGTGGACCATCGGTCCAAACGAGATCATAAACGCTATCAACACCTTGGATATACATAGCCAGACGCTCTAGGCCATAGGTAATTTCACCTGTGACTGGGCTGCATTCGATGCCGCCAACCTGTTGGAAGTAAGTAAACTGAGTTACTTCCATACCATTAAGCCAGATTTCCCAGCCTAAGCCCCAAGCACCTAGTGTTGGTGATTCCCAGTTATCTTCTACAAAGCGGATATCGTGGATTTGAGTATCGATACCCAGCGCTTGCAGTGAGCCTAAATACAGCTCTTGAATATTGTCAGGTGAAGGCTTCAACACGACTTGGAATTGGTAGTAGTGCTGCAGACGGTTAGGGTTTTCACCGTAACGACCGTCCGTAGGGCGGCGCGATGGCTGCACATAGGCACTGCTCATAGGTTCTGGCCCTAGAGAACGTAGGAAAGTTTGTGGGTGGAATGTTCCCGCGCCGACTTCCATATCTAGAGGTTGAACGATTGCACAGCCTTGCTGTGCCCAATATTCCTGCAGGGTGAGAATGAAACCCTGAAATGTTTTTACGTCGTGTTTTGTCGTCATGTCTACTTGTCTACTGCCGTCAGCCTGAATAGAAAATGGGTTCGATTATACCTTGTAGATATCTGCTTATGTAGGTTATTTTTTACCTTCTTAATTAAAACAATAAGGTTATATACCATGGAAGAGATCCGTTGTGGCTGGGTAAGTGATGATCCCTTGTATCGTGAGTATCATGACAAAGTGTGGGGGCGGCCGGTTTATGATCCTAAGGAGCTGTTTGCTAAGTTGTGTTTAGATGGTCAACAGGCTGGATTATCGTGGATTACGATCCTGAAGAAACAACAAAACTATGAGCAGGCTTTTGCCAATTTTGAACCAGAAGCGATTGCTGGCTTTGATGAGGCGAAAGTGGAGGAACTTATGTTGAATCCCGGCATAGTGCGCAATCGCTTGAAGGTGAACTCTATTATCAAGAATGCCAAAGGATATTTGGCCTATACTGCCGATGGGAAGGATTTCTCTGCATTCTTATGGAGCTTTGTTGGGGGTAAGCCCATAGTTAATCAGTTTACTGCTATGTCGCAGGTTCCAGCGCAAACGCCAGAATCAGAAGCCATGTCAAAAGCGTTAAAGAAGCTTGGATTTAATTTTGTCGGCCCAACAATTTGTTATGCCTTTATGCAGGCGGTGGGTATGGTGAATGACCACCTTGTAGAGTGTATTTCCTACCAGGCCTGTGTTGATTCCTGCGCCTGTCAGCATCACCGCGAACATCAGTAATCGCTGACCTTGCTGATGTGATGTAGGAGTACTCATAGCAATATTCGAGTTCTTGTCCATATACACAATAAACAATGCACAAAATGTAAAAAAGCATGTTCCACGTGGAACATGCTTTTATCTTCATTGGTGATATCTGGTTAAACCAAATAATTAAAACAGGAAAGCTACTCTCCCATTCTAGCCTTGAGGATAGAGTTTAGATTTTCTCAAAGAAAAATGGCTTACGTTTTTCCTTATCCCCATTAAGCTCATTCATCGTCTCAGCATCAAAAAGCTTACCGTTGACCATGGTATAAGTGACTTTATCTGTAACGCGAATATCGGCTAATGGATCACCATCAATAACGATTAAGTCTGCGAGCTTACCTGCTTTAATCGAGCCTAGCTGTTGTTCCATACCAAAGGTTGTCGCCGGATTAATGGTGGCCGTTTTCAATACCTCCATATTGCTCATGCCGCCTTGGGCGAACATCCACATTTCCCAATGTGCCGCTAAACCTTCACGCTGACCGTGGGCGCCAATATTAGGGTGGACCCCTAACTTATTGAGCTCGTTAGCGACTTTTGCAACATTGAAATGATTGTAGTGGCCATCTGGTGCATGCGGGCGTCGCATTGATCTCGCCTGCAAGATATCGGCGGGGACATACATGGACAGGCGTGGATGTGCCCATACATCGGTTTTGTCGTACCAGTAGTTTTCGCCTGAAATACCACCATAGGCGACCACTAAGGTTGGAGTGTAACCGACTTTAGTTTGGCTCCAGAATTGTTTGATGTCGTTATAAATCGCTGCCACTGGTAGAGAGTGCTCAACCGTTGTATGGCCATCGGCGACCATAGTGAGATTGTGTTGCAGCAAACTGCCACCCTCAGGCACGACCATCATTTCGAGTTCACGTGCGGCAGCGATGACTTGCTGCCTCTGGTTACGTCGCGGTTGGTTGTAGCTTTTTACGCTAAACGCCCCGACTTTTTTCAGACGTTCAAGATGAAACTTGGCATCATCGAGGGAGTCTATGTGCGATGTGTAGCCAGGCGCATTGGCACCATACAATATGGTGCCAGTAGAGAAAATACGTGGGCCAACTATGTGTCCTGCTTTTTGTTGTTCGGAAGCGGCAAAAATCTCTGTCGTGTCATTCGAGGGATCGTGAATAGTGGTGACACCCAAAGATAAACCGGAATACAGCGCCCAGTTCTGTTGTGGCACAATTTCATCATCTGCTTGCGGTCCATGGGCGTGGGCATCGAATAGCCCTGGCATGATCGATTTTCCTTTGATGTCGATGATCTGTGCATCTTTTGGAATCGCGATATTGGCATTACCTACGGCGACAATATGATTGTCTTTGACTATGACCACGCCATTATCTATGACTTGGTCATTTTCCATCGTAATCACTTTGCCACCGACGAAGGCGATTGTTCCACGTGGAACATCGGCCTTTTCGGTAAAGCCTAAATTGATAATACTTGGTTGAGCCTTTTCATCTTTATTGAGATATTGAGTATCAACCTTGGCTTGATAAAGTTCAGGGCCAAGCGTCCAATACAGTTGGTTGCTTTTACTGTTCCAACTAATACTTTCACCGGCGCGGATACTCAATTGGCTTACGGGTAGATTGTTGGCATTGGGTCCAATCTCTACAGTTTCGCCGTGTTTAGCAAAAGGCGTCACCCAAACCTTAAAGCGTTCGGCAAAGGCGAGTTGCCCACCATCGGGTGAAACTCTAAATTCAGTCGCATGCTTGCTGCTGTAATGAACCCGTTTATCGAAGCCATCTAAGTTGATAGATGATAACTGTGGCGTTTCATCTTCGCTGCTATTCATAAAAAAGACACGATCGGCATTGGCACCGAATTGAGCTTGGTAGCCATCGGCACTGATTTTCTTATTTTGTTTGCCTTTGATATCGACTTTATATAGTCCAGGCTCCTGCGACCAAGTTCTTGGTGTGAGGTTACCGCCTTGGGTTTTACGATACACCACAAGTTCACCATCGGGTGAAAAGGTGGGTTCTTCGTACTTTCCTGGTTCAGATGTTAGCTGCTTAGGTTTGCCACCTTTGGCGCTAATGACTTGTACTGTTCCTTGGTCTTGGTCATTCCAAGTGGTGAAAACAATATTTTTGCCATCGCGGGACCATTGGGGATACAGTTCTTCAATCCCATTATTGAGTTCAGTTAAGCGAGACATTTTTCCATCGGGTAATGATTTTAACCAAATCTTCCCTAGTGCTTCGAAGGCGACTTTACTGCCATCGGGAGAGACCTGCGCCATACGTAGCATTTTGACATCGAAAACATCTTTATCTATATCTTGCTTAAAGCGCACTGAGGGTTGTACTGCTAACTCAGTTTTTACGCTAAAGGGGATATCTGTGAGTGTTTTGCTGGCAACATTCAGGCGGTTAATCTTTCCCTTCGCCCAGAAGAAAATATCCTTGTTATCTAGAGTCCAGGCCATTGTCGGGTAGACGCCATGAATCGCCCAAGTCTCTTGCATATCACGGTCTAGTTCGCCAAACAGTTTAGTGGTTTCACCAGACTTGAGGTCGAGCAGGTAGAGTGATGAATTAAAATCATCGCGCTTTATATAGGCGAGCTTAGTGCCATCAGGACTTGGTGTTGGTCGAATTGCGCCACCGGTACCTTCAATCAAAATTTCAATGTCACCGGTTTGGGTATCATAACGTTTGATTTTGTAGATACCGTTAACTGAGTCCTTTGAATAGTGGAATGTTTTGCCTGGCGTATCATCTTGGCTAAAGTAGATATAACGGCCATCGGGCGAGTACGCGGGTTCGCCTAAATCTTTCTGATCATTTGGGCGTTCAGTTAGTTTGACGCCTTCGCCGCCCGCTACGTGGTATAGCCACACTTCACCCGCACCTAAACTGCGGCTGGCGGTAAAATGCTTACGTGCAACAAGATACTGAGAGTCAGGGCTCCAAGCGGGACTGTTTAGCAGCCTGAATGTTTCTTGGGTGACAGTTCGTGGATTGCTACCATCGGCATCCATGATCCAAATGTTATCACCACCATCGCCATCCGAGGTAAATGCGATGTGTTTGCCATCTGGGCTATAGACGGGCTGCATCTGCCAAGCAATACCTTGGGCTAATGGCTTAGCTTCACCGCCTGTTACTGGAATTTGATAAATATCGCCGAGTAAGTCGAAGACGATATTCTTGCCATCTGGACTGACACTTAGGTTCATCCAAGTGCCTTCACTAACATCAATTTTGACTTGCTCTAAGGGCGCATTTGCTGGAGCGTTAACATTCCATTCGGCCTTCTTAGTTTCTGCTGCGTTAACGGCGAGGGTATTCATCCCTAGAGCGAGGGCGATATAAAGCGGGGTAAGTTTAGATTTCAACATGTCACTTCCTGTTATCGTTCTTATGGACTGCTTGAGTGGTATTAATATTAAGATGAACTTTTGTTAATACTCAGTCTAGTTATCGCATAAATAGATAAGAAGGAAAACGTAAATACTGTTTCAATTGATGACCGAGCGAAGGATAAGTCATGGCGATATGGATAAGAGAGTTGGTATAGCAGAAATAGAACTAAGTCACTTGGTTCTATTTACTGAGGAGTATGGAGAATTAACTAAGAGTTGCCGAGTAGCTGGGAATAAAATTAGCTTGGATTAACCAGAGTGATCTCATCGCCTAAGTGGATCATGGCATCATTCAATACTCGGCAGGTTACACCGCCTCGCCAATCTGGCGTGAGCGCGGCTTCTAAACCGACATGTGCTATTTCCATTTTTTTACAGGGGTCGGTCTCACCCGTTATTTCAAGTCGCAACTCACCGATCTGTAAATGTTTACCAACATCGGCACTGCTAAATACGAGATCATCCACCAGCAGATTTGCTCTGCGAGTCGTCCAAGGTAAATCTGCCTCGATACTATGGCAGACCTGTTGCCACTGAAGTTTGGAAAGCACAGTGACTTGGCGTTTGCCTGGGCGACCAAACACATCTTTCTCTACACCACTGAGTTGGGTGACATTGGCACAGAGAACTTCATTCATTGGGCCGCGTTTTACAGTTTTATAGGCAATCGCCAGCAGTTTTGGCATAGTTTCTCACATCCTTTGTTTTATGAGTAAGTCGTTAACGATACGACTTAAAGTCCTTTTTTGATCAAATATTGATATGGCGTTTGCGTGGTTTCACTGGCAATTAAGGTGTGATCCATAAACTCACAAAAGCTCGGAATATCGCGCGTCGTTGCAGGGTCATCGGCAATGATCAGCAGTGTCTCGCCGGCAGCCATTTGTCTTACTGTTTTACGCACCATCATTACAGGCTCTGGGCAGCGTAAACCAAGAGCATCGAGTCTATGCTGTGCTGTTGAGAAAGCGTCGTTCATATTACCTCTACTGAACGGTTAAGCAGAATTAGCTAATGTTACTCCAGCCCCCATGATAAACCAAGTTGGCGGTTTCAATTTGTGATCTGGTTTTATTGATGTGTTCTATCAATACGAATGTTAGATATTGAATGTTCCACGTGGAACATGATGACGAATGGGTTTAAAAATTACTCGACGGCTTTATCAACGCTTTGAGATTGAGAGATTGAATGGACATCAGCGGTACTATTCTCAGGTGCTAAATCATTTGCTGCCGCCATAGTTTGTTGCTGCTGTTTGGACGCACGTTTGTAGCTATACATTCGCTGATCGGCTTTATGAATTAAGCTATTGGCTTCTTCGTACTCATAGGGTCCAGTATCAATAATGCCAAAGCTTAAGGAAATATTGTTTAAACTGAGTTTTATTTTTTGATTGAATCGAGTGATGAAATTATCCCGAGCTTGTTCTTCACGGCAGTTAGGCATAATGATACAGAACTCATCTCCACCATATCGAAAGCAATGATCCTCGGCACGGGTGATTTCTTTAATCGCTTTTGCTACAGCACGCAGTACTTCATCCCCTGATCTATGGCCTTGGGTGTCGTTGAATGACTTAAAATCATCTACGTCTAAGTAGATCACGCTGACGGGTTCGGCACGCCTTTGGGCAGCCCTGAGTATGCGCGTTGCCGTTTCTTGTAGATGCCTAACGTTGAGCAGGCCTGTTAAGGGATCGGTTTGCGACAGTTCTTCAAGCTGCTGCGTCCGCTCTTTGACTTTGCTTTCCATGCTTTGGGCATATTTTTCGGCTTTATCCTTAGCGTTCTCAATCTCGGATACTAAGCTGCGAATATAAGTATCAAATACCAGCGTGACATCAAACAGCACCAGTTTATCAATCGCGACACGGATACATTCGCCTTGATGTGTATCTTGCACTGAACAGTTGATCACGGCGTAGATAAGCTCTTTTAGTGTGTGTACCGCAGACAGGTAAAGCTTAGGCTCAACACCAATGCGTTTATGGACCAACCCAATCCGCAGGCGATTGTTTACGTACTCAAGATCATAAACACCATTAAATAAATCAAGCACATATCTTCTCTGTGCGGTACGTAAGCGGGCAAGGGTGTCTGAGTCTCCGATCAGCAGCGCAATTTCTGACACGCTGGTTTGTAAGCCATAAAAGTCATCGACGATTTTATCGATGTTTTCTTCAATGATGGGCTTAAAGGATTGCAGGGCCTTTACGTCCGCCCCAGTAAAAGAGAAGAGCGCCTTACGAGACTCGATCTCCAGCTCAGTAATCCGCATTTGTTCGAAAAGGGTCTGTTCCGTTTGTAGCATCAGGCATTCCTCTTGTAAAACAATGAATTGCATCCTTGCACTACACTGCCTAGATTTGGCTGATGATTGCTTGCCTGTTGTCGTCCACAATAGCCAGTGATGCCCTTGTAGTTAAGTTGATAACGGACTGATTTTCAAATGAACAGCAGAAGAAATGAGAAATATTAATATTTACGCTGGCAGAAAATTTGGCATAAAAAAGCCGGAACAAGTTCCGGCTTTGAAATTATTCGAGTAACGAATTAAACGCGTTCGAATACAGTGGCGATACCTTGGCCTAAACCAATACACATGGTCGCTAACCCTAAGGTCGCGTCTTTGTCTTCCATTAAGTTGATCAGCGTCGTAGAGATACGAGCGCCTGAACAACCCAGTGGGTGACCTAGTGCAATCGCGCCGCCGTTGAGGTTGATTTTGTCATCAACCACATCTGCTAAGCCTAAATCTTTCACACAAGGTAAAGACTGAGCAGCAAACGCTTCGTTAAGTTCAATCACATCTAAATCGGCAACTGTGATACCGGCACGAGCCAGTGCTTTTTGCGTGGCAGGCACTGGGCCGTAACCCATGATAGCCGCGTCACAACCTGCAACCGCCATAGAGCGAATACGTGCACGAATCGGCAAGCCAAGTGCGCGCGCTTTAGATTCTTCCATGATCAACATGGCTGAAGCGCCATCTGACAGTGCAGAAGAAGTACCAGCAGTTACAGTACCATTTACTGGATCAAATACGGGGCGCAGTGCAGCCAAAGATTCCATTGTGGTTTCTGGGCGGATCACTTCGTCTTGCAGGACTTTAATTAAGGCACCGTTAGCATCGTGACCTTCAATGCCATAGATTTCTTTGGCAAAGCGGCCTTCAACCGTCGCAGCATAAGCGCGTTGGTGTGAACGTACCGCAAAAGCATCTTGCTGTTCACGGGTAATACCGTGCAGTTTACCCAGCATTTCAGCCGTTAAACCCATCATGCCAGAGGCTTTTGCCACGTTGTTAGCAAGACCTGGGTGGAAGTCGACACCATGGCTCATTGGCACGTGGCCCATGTGTTCTACGCCACCAATAATAAAGGTGTCGCCCATGCCAGTCATAATCGCACGCGCAGCTTGATGGATAGCATCCATTGATGAACCACATAGGCGGTTAACCGTCACAGCGCCAGCAGTCTTTGGAATGCCCGCTAATAGCGAAGCGTTACGGGCGATGTTGAAGCCTTGCTCTAGGGTTTGTTGCACACAACCCCAAATCACGTCTTCAATCAGGTTTGGGTCGAGTTGCGGGTTACGTAGCAACAGACCTTTCATTAATTCGGCAGACAGAGTTTCTGCACGCACATTACGAAATACGCCAGCCTTTGAGCGGCCCATCGGAGTACGGATGCAGTCTACGATAACAGCTTGTTTCATGATTAAATTCCTTTCCCGCTAATTAGGCTTGGTAGTAGCTACCGTTATTGGCTGCTAATGTGCGCATCGCATCTGTGACTTGATACAGACCGCCCAAGTGAGCGTATTTGTCTGCTAAGGCCACAAAGTTTGCGACACCCATGGTATCCAAGTAACGGAACACGCCGCCTCTGAATGGCGGGAATCCTAAGCCATAAACTAGGCCCATATCCGCTTCGGCAGGCGATGCGACAATACCTTCTTCTAAACAGCGCACGGTTTCAATGATCATAGGGATCATAGTACGGGCAATAATGTCATCGGCTTCGAAGGCTTTTAACTCGCCAAAGGCATCTTTCAATAGACCGTAGCTTGTTGGATCGACATCTTTCTTCGGCTTGCCACGGCTATCGACGGAATAGACATAGAAACCTTTGCCATTTTTCTGGCCAAGACGTTTGTTCTCAAACATCACATCAATCGCGTCAGTACCAGATTTACCCATGCGATCAGGGAAACCTTCAGCCATTACAGCTTGGGCGTGATGACCTGTGTCTAAACCGACTACGTCGAGCAGGTAAGCTGGACCCATTGGCCAACCGAATTGTTTTTCCATCACTTTGTCTATTGCAGCAAAGTCGCCACCTTCGGCAAGTAAGCCGTTAAAGCCTGCAAAGTAAGGGAAGAGTACGCGGTTAACGAAGAAGCCTGGGCAATCATTAACAACGATTGGGGTCTTACCCATTTTGCTGGCATAAGCCACAACAGAGGCGATGGTTTCTTCAGAGCTGTGCTCGCCACGGATCACTTCGACCAAAGGCATTTTGTGCACTGGGTTGAAGAAATGCATACCGCAGAAACGCTCTGGTTTCTTCATGCTCTTAGCGAGCAAGTTGATTGAAATCGTTGAAGTGTTTGAGGCGATAATTGCATCTTCACTCACATAACCTTCAACTTCGGCTAAAACCTGTGCTTTGATTTTTGGATGTTCAACAACGGCTTCAACGACAACATCACTGTGTTTAATCGCGGCATAATCTAACGAAGGCGTAATGTTGTTCAGCACTTTGGCCATTTTTTCTGGCGTTGAACGGCCACGGGCGACTTGGGCAGACAGCAGTTTAGCGGCTTCATTGAGGCCTAAATCTAATGCTGGCTGGGCAATGTCTTTCATGACAATCGGTGTGCCTTTGCTGGCGCTTTGGTAAGCGATGCCGCCGCCCATAATGCCTGCACCTAATACGGCCGCGTTGTTGACTTCTTTCGCTAACTTGCCAGCTTTCTTCGCTTTACCTTTAACGAATTGATCGTTAAGGAAAATACCGATAAGTGCCTTAGCAACGTCAGTTTTAGCCAGCTTGATAAAGGCTTGATGTTCGATTTGTAATGCTTCAGCACGACCTTTAGTCGATGCTTGCTCAATCACGCTTACAGCAGCCATTGGTGCAGGGTAGTGTTTACCCGCAACCGCAAACACCATGCCTTTGGCTGTGGTGAATGACATCATAGCTTCAAGTTTTGGCAGTGTTAGTGCAGACAGTTTACGGTTACGACGTGCTTGCCAATCCAGCTTTTCAGCAACAGCATCTTTCAGCATTTGAATCGCAGCAGCTTCTAACGCTTGTGGTGCAACCACGGCATCAACAGCGCCGACTTTCAGTGCATCTTCAGCGCGTTGGTCTTTACCTGTGGTGATCCACTCAAGTGCGTTATCTGCACCAATCACACGTGGCAGACGAACTGTGCCGCCAAAACCTGGGATGATGCCGAGTTTAGTTTCAGGTAAACCGATTTTGGCTGTGGTGTCTGCGATACGGAAATCGGTTGTCAGAATGGTTTCACAGCCGCCACCTAAGGCAAAGCCTTTGATTGCTGATGCAGTTGGGAAAGGCAAGTCTTCGAGTTTGTTGAAGACTGCGTTGGCTTGTTCAACCCAAGAGAGCAATACGGCGTCATCTTGTGCGAACAGGCCTAAAAATTCGGTAATGTCGGCGCCCACAATAAAGGTATCTTTACTTGAGGTCAGCACTAACGCTTTGATATTGGAATCTTGCTTGATGCTGTCTAATGCCGCATCGAGCGAGGCGAGGGTTTCTCTGTCGAATTTGTTCACCGAACCGGGTGCGTTAAAGCACAGCTTGGCAATATTATCCTCGAGTAACTCAACCTGAATTGTAGGACTTTGGTAGATCATTGCTTGCTTCCTTTTGGCTTAAAAGTGATACCCACCATTATGTGTTGGTGCTAGTACGACTCTCTATCTCACAGCAGCTTCGGCCATCATTTGACCAGTTGTGTGCCAGTGTGCTTCGAAATTCGATAAATTACAACACCCAATTTAAACGACTGTTTAATTTCTTGTCGGCTAGAGTGATTTTTCACCTTTGTGATACACTGCACAGAGTGTAGAACACCTAGAATGCGTTAAATCACTTTGCGGATAAATAGGTCTTTGTGGCTGTTTACCTTAGACGTTTAAACGCTGCAGCCATGCTAGGGTTGAACATAACTCTTAAAGATAATCATGACGTTATCGCTTTAATGTTGTTTGTTCAGCGGCAATGTATTTTATATTGAGTGACAGTTTAGCGTTTTGATTGAATCTGTTGTTCATGATCAGATTAAGAATATGTGAGCAAATTCGGCTCATATTATTAACTTTTATTCATTGCAGGAGATGACAAATGGATCAGTTGGCTCATCACTATCGTGCCCATATTGCCGAGTTAAACCGTCGAGTCGCAGAGATTTTGTCTCGAGAAGCCTTGTCTGGTTTAGTGATCCATTCGGGTCAGCCGCATCGGATGTTTTTGGATGATATCAATTATCCCTTTAAGGCAAACCCGCACTTCAAGGCATGGTTGCCTGTGTTGGATAATCCGAATTGTTGGTTAGTGGTCAACGGCCGTGATAAGCCGCAGCTGATTTTTTATCGTCCTGTGGATTTTTGGCACAAAGTGTCTGATGTGCCTGATATGTTTTGGACTGAGTATTTCGATATTAAGCTGCTGACCAAGGCTGATAAGGTCGCTGAGTTTTTACCGACAGATATCGCCAATTGGGCCTATTTAGGTGAGCATTTAGATGTGGCCGAAGTGCTGGGTTTTACCAGTCGTAATCCCGATGCTGTGATGAGTTATTTGCATTACCACAGAACCACTAAAACCGAATATGAGCTGGAATGCATGCGCCGCGCGAATCAAATTGCGGTGCAGGGACATTTGGCGGCTAAAAATGCCTTTTATAATGGTGCGAGCGAGTTCGAAATCCAGCAGCACTATTTATCTGCCGTAGGCCAGAGCGAGAATGAGGTGCCCTATGGCAATATCATCGCCCTTAACCAAAATGCGGCGATTTTGCATTACACCGCACTTGAACACCAAAGCCCTGCGAAACGTTTGTCATTTCTTATCGATGCCGGCGCGAGTTACTTTGGCTATGCCTCTGATATCACCAGAACCTACGCATTTGAGAAGAATCGTTTCGATGAATTGATCACTGCGATGAACAAGGCGCAGCTAGAGCTTATCGACATGATGCGTCCGGGTGTGCGTTATCCCGATTTACACTTGGCCACCCATGCTAAAGTCGCGCAAATGCTATTGGATTTTGATTTAGCCACAGGTGATGTCCAAGGTTTGGTCGATCAAGGCATAACCAGTGCTTTCTTCCCCCATGGCTTAGGTCACATGTTAGGCCTACAAGTGCATGATGTTGGCGGCTTTTCCCACGATGAACGCGGTACTCATATCGCGGCGCCAGAGGCCCATCCATTCCTACGTTGCACCCGCATTTTAGCGCCAAACCAAGTGCTGACCATGGAACCTGGGTTATACATTATCGATACTCTGCTTAATGAGCTTAAACAAGATAGTCGTGGCCAACAGATCAACTGGCAAACGGTTGATGAGTTAAGGCCTTTTGGCGGTATTCGTATCGAAGATAACGTCATAGTGCATCAAGATAGAAACGAGAACATGACCCGTGAACTCGGTTTGACCGATTGATCATAGGGTAAAGATTAGGGCGGATGTGTGCTCGAAAGTTATAGCGTTCCCTGTGCAGATATCGTGATTGAAGAAGAGATAAAGCATAGCCGCTTTATCTCTTTTCTGTTTCATTGCGATAGCTTTGAAAAATTTAAGTTAGTACTCACTGACATAAAGCGTGACTATCCCGGCGCAAGCCATTATTGCTCTGCTTTTGTGGCTGGCGCACCGGATGATAGCGTCTTGATTGGTTCGAGTGATGACGGCGAACCTGCCGGCAGTGCTGGTCGCCCTATGTTAGCTGTGTTGCAAGGGGCGAATATCGGCGAAGTGGGTGCTGTTGTGGTGCGTTATTATGGCGGAACTAAGCTAGGCGTAGGTGGACTGGTTCGGGCTTATACGTCGGGCTTGCGGCAAGGTTTGGTTCAATTGCCGACGCGACTAAAACAGTTACGTTATCCCGCCAAGTTGCATTGTGATTATGCGCAGCTGCGCGATGTTGAGCATCTATTACAGCAAGTCGATGCCGTGATAATCGATAAGCAATTTGCCGAAGCGGTCGATATTGTGTTCGAGATAGGCAGGCAGCAACAGGCATTACTGTGTGAGTCGCTGGCGACGCTCAGTCAGGGAAGTTTGAGGCCTGAGTTTGAGCTGTGATGCCTGCGTAAATTATGCCAAAATAGCCCAAATGCTGAAAATACCTTTGCAGCCAATGGAATGATTGAACTCGCTTAATGCAATATAAAACCATAATACGAATCATAGGCCTGCTGATTGGTTTATTTTCAATCACTATGCTGCCACCAGCACTGATCGCAGTTTGGTATAACGATGGCGGCGGTACGGCGTTTATGCAGGCATTTTTTGTCAGCCTATTTATTGGTTTCTGGCTCTGGTATCCCAATCGCCACTGTAAAGAAGAATTGCGTACCCGAGAGGGTTTCTTGATTGTGGTCCTGTTCTGGACCGTACTGGGTTCTATCGGTGCTTTACCGTTTATCTTCTCGAGTCAACCCGACCTCAGCTGGACCGATAGTTTCTTCGAATCTTTCTCTGCGTTAACCACCACCGGAGCCACAGTGATTGTCGGGCTCGACTTCTTACCTAAAGCCATTCTGTTTTATCGCCATATGCTGCAATGGCTCGGCGGTATGGGGATTATTGTGCTCGCCGTGGCGATTTTACCTGTGCTTGGCGTAGGGGGAATGCAGCTCTATCGCGCTGAAATTCCGGGCCCAGTGAAAGACAGTAAGATGACCCCGCGGATCGCCGAAACCGCTAAGGCACTGTGGTATATCTATTTATTGCTAACTATTTCCTGTGCGACCGCTTATTGGCTGGCGGGCATGAGTGTGTTCGATGCGGTTTGCCATTCATTTTCAACTATCGCTATTGGCGGTTTTTCGACCCACGATGCTAGCATGGGGTATTTCGATAGTTCTGTGATTAACCTTATTTGTGTGTTCTTTTTAATCGTTTCTGCGGTTAACTTTAGCGTGCATTTTGCAGCATTTTCGCGGCGAGGGATTAACGTTAGAGTGTATTTTAAAGATGCTGAATTTAAGATGCTGGTGGCGATTCAACTCATTCTTACCGCGATTTGTTTCCTCACGCTTTATCATTCGGGCATTTATGATTCGCCGGAAGAAACCTTAGACCATGCCTTATTTCAGGCGGTTTCCGTGGCCACTACCGCAGGGTTTGGTACTGAAAGCTTCCACATGTGGCCGCTGTTTTTACCTATTCTATTGATATTTTCGAGCTTTATCGGTGGCTGTGGTGGCTCAACTGCGGGTGGGATTAAGGTGATTCGGGTCATTTTGCTGCTGTTACAGGGCTCGCGCGAATTAAAGCGACTGGTTCATCCTAAGGCGATGTTCTCTATCCGTATCGGTTCTAAAGCCTTGCCCGATCGCGTTGTCGATGCCGTGTGGGGATTCTTCTCTGCTTATGCGCTGGTGTTTGTGATCTGTATGCTTGCACTCATGGCGATGGGATTAGATGATATTACCGCCTTTAGTGCTACCGCGGCTTGCTTAAACAATCTAGGCCCAGGCTTAGGTGCGGTAGCCAGTAACTACGCCAGTATTGGCGATGGCGCCAAATGGGTATTGGTCGCGGCTATGCTGTTTGGTCGCCTCGAAGTATTTACCTTACTGATTTTGTTTACGCCAACTTTTTGGAAGAATTAATCATGCAGACGTTAATAATCTATTCGACGATTGATGGCCAAACATTGGCAATTTGCCAAAAAATGAAGACGATTGCAGAGCAGGCGGGAGAGCAGGTTGCTCTGGTCACATTAGAGCAGGCTGATGCGTTGAGCTTAGCTGATTTTGATAAAGTGCTGATCGGCGCCAGTATTCGTTATGGTAAGCATAGACCTGAACTTTACCAGTTTGTGAACCGCCACCATGCGGTGCTCGATACTAAGATTAATGGCTTTTTTACCGTTAATGTCGTGGCGCGCAAACCCTTAAAGAATACGCCTGAAACCAATCCTTATATGCAAAAGTTTCTGCAGCTTTCACTGTGGCAACCTCAGCAGTTAGAAGTGTTTGCGGGCAAGATTGATTATCCTAAATACGGTTTCTTCGATCGCACTATGATACGTTTCATCATGTGGATGACTAAAGGGCCAACCGATATCACAGGCACCTTTGAGTTTACCGATTGGGAAAAAGTCGACGCCTTCGGTAACCACTTTAGCCAGCACTAAAAACCATTAAGCCCTAATCACTTGCAAGCGATTAGGGCTTAAATTTAAGCTAGCTTTACCGTGTTACACTGCGCAGTAGCGGCTATGCAGAATCGAAATCACTTGGGTGACTTGTTCGTTCTTGAGCTTGTAGTAAACAATTTGCGAGCTCTTACGGGTATCCACCAGATCTTCAGCGCGTAATACGGCTAAATGCTGGGATAGTGCCGACTGGCTTAATGGCACAGTGGCATTGAGTTCAGTTACGCTCAACTCTTTGTCTAGTAATAGGCACAAAATCATTAGGCGATAAGGGTTCGCTATGGCCTTAAGCCATTTAGCCGCACTTTCCGCATTAGTTACCATTGCAGCTACATCTATGTCATTTTGCATAAGGTCACTCACTTCATCGCTTTTGATTAGATAATTCTAATTGAACACCTGTGTTTACGCAATCATACTGCTCAGCCATGCAGATCTCAATTTTTGCATCTCAGAATATGAGATACGGCTGATTTGTGATCCTATCAGAGGTTTATTTAACGTAATCTTGCCACAATGGTGACATTAACTCAGTGGATCGGTAATTCCCTATGACACGCGTTCTCGTACTTTATTTTACCCGTGGTGGCCACACGGCCAAGATTGCCAATGCAATTGCCGACCAATTAACCTTTCGTGGCGCTAAGGTCGATCTCGTTGATATCAATAGTGCTGCCGCGACTTGTATCAATTGGCCCGATTACGAGGTCGTCGCCCTAGGTGCCTGCGTACTCTATGGCACCTATGATAAGAGTGTGTTCAAGTTTATAGAGCAACATGCAGAGGCTTTGGGGGCATTGCCAAACAGTTTCTTCTGCGTCAACGTGGTAGCGAGAAATCCAGAAAAACGTATTCCTGAGAATAATAAATATCTGCAAAAGTTCATCGCTTTATCGCCGTGGACGCCAGCCGATCTTAAAATTATTGCGGGGAAAGTGGATTATCCTTCATGGCCTTGGTACGACAGGTTGATGATCCAGTTGATTATGAAGATGACCAAAGGCCCAACCGATCCTAAGTCGGTTATCGACTACACCGACTGGGCAGATGTAAAGGTCTATGCCGATCATCTGCTTGAATTAGTCGACGTTACCGCTATTTAGGACGGGTCAGGCTTAATAAAAAAGGCGCTTAACAAGTTAAGCGCCTTTTTAGTATCCAAGCTCTACAGAATTAATACTTCCAGAAACTCGGGTGGAATAACACTGCTACGGTTAGAATTTCTAAACGACCGAGTAACATGCCGAGTGACAACGCCCATTTCGCCACGTCGGGTAGGGTTGAGAAGTTACCCGCAGGGCCAATAATCGGCCCTAAGCCAGGACCTACGTTCGTGACCGCCGTGATAGCGCCAGTGAAACTGGTCATAGGGTCGAGCCCCGTGAGCACTAAAATCACCGACAAGCCTACAATCACCATTACAAACAACAGCACGAAAGTGATCAGCGAGCGCACGATATCTTCACTGATGATGCGATTGTTGTAGCGTTCCCTAAAGATACCGTTTGGATGACATTGCTGTTTCAATTGCTCGCGCATAATGGCGCCAGCGATTTGAAACCGGAAGATTTTAATCCCGCCTGAAGTTGAGCCAGAGCAGCTACCGACAAACATTAAGAACATAAAGGCGATGTTGGCAAAGGCGCCCCAAGCCCCGTAATCCGTTAAGCCATAACCTGTAGTGGTCACCACAGAAACCACGTTAAAGCTGGATAAACGCAGCGCATCGAGGAGTTCAATGTCGCGGGTTTGCCACAGCCAAAAGCCAATAGAACAAGACACGAAAGTCAGGAAAAACAAGAAGCCTTTGACCTGAGCATCGTTCCAAATGCGCAGATCCCTTTGCTGTATGGTGTGCACAAACATGAGTAGCGGTAAGCCACCCGCCGCCATAAAGAACACGCCAACCCAGTGAGCCGAGTGCGAAAACGCGGCCATTGAGCTATCAGATGTTGAGTAGCCGCCAGTCGATAGGGTGGTCATGGCATGGTTGATGGCTTGAAACCAAGTCATCCCCGCCAGATGATAGGCAACGCAGCAGGCAACAGTCAGCAAAATATAAATAAAGAATAAGTACTTAGCCATGCTCTGGGTGCGTGGTACGGCTTTATCGCTCCAATCGGACGATTCAGTGCGGAACAGCCGCATACCACCGACGTTCAAGAAGGGCAGAATGGCCACCGCCATCACGATAAAACCAATCCCGCCAAGCCACTGCAATAAAGAGCGCCAGATTAAGATACTGTGATCCATGGTATCCAACCCCGAGAGCACAGTAGAACCTGTGGTCGTGATGCCGGACATAGTCTCAAAGAAGGCGTCGGTATAGCCAATCCCGTGATACAGGGTAAAGGGCATGGCCGCAAACAGGCTCACGATCAACCACGTCAAGCTGGTCAGCAGGAACATATCGCGGATATTGAGGTTAATAGTCTTGCTCTGGCCATTGTGTAAACACAAGCTGGCACTAATGCCTGTCACTAACGCCGACAGCATGAAAGAGCCCACGGTTTCCTCGGCATTGAACACGGCGAGGAGCAGTGGGATCAACATGAAGGCTGTCAGCATGGACAAAAACAGCCCTAAAATAAACAATAGTGGTCTAAAGTTCAGCATAAGTTGGCTTACTTAGGTTCTTAGAAGAAGAAAGCACTTGGTTGGAAAAGCTTCTCGACTTCGCCGACAAACTTTTTATTCACTAGGAATAAGATCACGTGATCCCCTTGCTCTATCACGGTTTTGTCGTGGGCCATTAATACTTCGTCGTTGCGGACAATAGCACCAATGGTAGTTCCCGGTGGCAACTTAATATCGCCAATGGCTTTGCCAACCACTTTAGACGTGCTGGCATCTCCGTGGGCAATCGCTTCAATTGCCTCGGCGGCACCGCGACGTAGGGAGTAAACGTTACAGATATCACCCTGACGGATATGGGTTAACAGTGCCGAAATGGTGGCTTGCTGGGGTGAGATAGCAATGTCGATATTAGCTTCTTGGACGATATCCACATAGGCTTCGCGCTGGATCAGTACCATGACCTTTTTCGCGCCCATACGTTTTGCCAGTAATGCCGACATAATATTGGCTTCGTCGTCATTGGTCACCGCAATAAATACGTCGGTTTGATCTATGTGTTCTTCGAGCAGTAATTCTTGATCCGAAGCGTCACCGCAGAACACGGTAGTATTTTCTAGTTGCTCCGATAAGGCTTCAGCCCGCTCATACTTATGCTCAATCAGTTTAACTGAGTGGGTGCGCTCAAGGCGTTTAGCTAAACCTAAGCCTATGTTACCGCCACCGGCGATCATAATGTTGCGGTATGAGTTATCGAGCTTTTGCATTTCGCTCATAACAGCACGAATGTGGCGACTGTCGGCGACGAAGAAGACTTCATCATCGGCTTCGATAATCGTGGTACCGCGAGGCATGATAGGGCGGCCTTGGCGGAAAATCGCCGCAACCCGGGTATCAATATTCGGCATATGTTCGCGCAGTGCCGCTAACGCATTCCCCACCAGTGGACCGCCATAATAAGCGCGAACGGCCACTAGGCTTAGCCGTCCTTCGGCAAATTCGAGTACTTGTAGCGCGCCGGGATATTCAACGAGTCGCTGAATGTAAGCGGTCACTAACTGTTCAGGGGCGATTAATTCATCGATCACGAAGCCGCCGCGGGGGCGACCGTCGCTATTTTTGGTTTCACTGTCGATAAAGAGTTTATCGCGCATCGCCAGATAAGGTTCTGAACGAATACGGGCAATTTTTGTTGGCGTGCCGAAAAGGGTGTAAGCAATCTGACAGGCGGCCATGTTGCATTCATCGCTATTGGTTACCGCGATCAGCATGTCGGCATCTTCTGCGCCCGCTTCTCTGAGCACATCGGGGTGGGCTGCATGGCCCGCCACAACGCGAAGGTCATATTTATCTTGCAGGGCGCGTAGTCTGGATTTGTCATGATCGACTATGGTGATGTCATTATTCTCACCTACCAAATTCTCGGCCAAGGTTCCCCCGACTTGACCCGCACCTAATATGATAATTTTCATGGCCTTGCTTACTTCCTAATCGGTCGCTTTTAATAAACGGGCGTAATAGAACCCATCCATATTGTCTTGTCCTGGCGTGATTTGCCAACCTATATCCTGTGGCGAATTTTGCTGGGAGAGTTTGTCTAGCTTAGCATCTGCCGTTCTCTCTAAAAACGCACTGATCTGGTCGCGGTTTTCCTGCGGTAAAATCGAGCAAGTAGCATAAAGAAGCGTGCCACCGGGTTTGAGCCATTTCCAGCAATGATCGAGAATTTGCTGTTGTAGTTCAGCCAGTTCTTCGATGTCATGGTTTTTACGTAACCATTTAATATCGGGATGGCGGCGGATCACTCCTGTGGCCGAGCATGGCGCGTCCAGTAAGATACGATCAAATTGTCCACCTTGCCACCATGAGTCAATATTGGCCGCATCGCCATGGATAAGCTCCGCTTTTAATGACAAGCGGTCAAGGTTCTGTTGCACGCGTTCGAGGCGTTTGGCATCAAAGTCGACGGCAACCAGTTTAATGCTTGGTTCAAGTTCAAGTAGATGACAGCTTTTGCCACCGGGCGCCGCGCAGGCATCGAGGATTAACTCATTCGCTTGTGGCGCCAGTAATGTCGCCGCCCATTGCGCCGCGCCATCTTGCACTGAGGCTGCGCCTTCATGGAAGCGTGGCAGTGTGGCGACGTCTTTTGGATGCGCTAAAAGAATAGCATCGGCACTGCTGCCTTCGCTCGCTTCAATTTCGAGTTCGGCTAAGGCAGCAAGGTATTCGGTACGGCTCTGGGATAAGCGATTATTACGCAGCCACATAGGTGGGCGTTCATGGCTTTGTTGAATGATATCTTGCCACTGCTCGGGATAGGCTTCTTTCAAGCGCTTAATCAACCAAGCGGGCGTGTTGTAGCTTAAGGTATCTGACTCAGTACTCAGCGGCGAAAGCTGGCGCTGAATGTTGCGCAGTACGCCATTAACGACCTTAACCATGCCTTCAAATTTTAACTGGCGACAGGCTTCGGCGGTTTCTGAAATCGCGGCGTGGCTTGGAATGCGAGTGAAGTAAAGTTGATAGCAACCTATGATAAGCAGCTGGTGGATAATCCTTTGCTTACCTTTGAGTGGTTTCGCTAGGCATTCACTGATGCGTTTCTCTATTTGCGGCAAAGTGCGCATCACGCCGTAGCAGAGCTCGGCAAGTAGGGCTTTATCTTTGCCGCTGGCAAGGTGCTTTTGTTGATCGGGCAATGCCACTGAAAGTGACACGCCTTTTTCTAATACTTCGAAGATGGCTTTCGCAGCCAGCGCGCGCAAGTTCATACTATTGTGCCTCGTTTGCAAGGCGAGTATTCGGGCTAAACCATTCACCTCGAGCATTGAGAATATCGGCAACATTGAGCGGCTTTTTCCCCGGCAGCTGCATACTCAGCAGGGTTAACACGCCGTCGGCAGTAGCGACTTCTATGCCTTTTTTGCTGGCGCTGATAATAGTGCCCGGAGCGGCTGTGCTGGTGGTTTCACTAACTTGGGTCTGCCACACTTTAATAGTATTGCCTTGGTGCTCAAAGTAACTCACAGGCCAAGGATTAAAGGCACGGACTTCTTGCCAGAGTTGTTTGGCAGATTTATTCCAATCGAGTCTGGCTTCTTCTTTACTGAGTTTTTCTGCGTAGTTCGCTAAGGCTTCATCTTGTTTTTCAGCGGCTAAGGTGCCGTTTGCTAGACCTTCGAGGGCTTGCAATAGGGCAACAGGACCTTGTTCCGCCAGTTTCTCGTAAAGGCTGGCTGAGGTGTCGCTATCTTCAATCGGCAGATAGGTTTTCAGTAGCATATCGCCGGTATCTAAACCCACATCCATCTGCATAACGGTCACGCCGGTTTCTTTGTCGCCGGCCCAAAGTGCGCGTTGGATCGGTGCGGCACCGCGCCAGCGTGGCAAAATAGAACCGTGAACGTTAATACAACCTAAACGCGGCGTATCTAATACGACTTTTGGCAGGATAAGACCGTAGGCAACCACCACCATGATATCGGCATTCAGCGCGGCAAGTTCTTGTTGCGCGGGTTCTTTACGTAATGAACCAGGTTGATACACAGGAATATTGTTGGCGACGGCGAGTTCTTTTACTGGGCTGGCGGTCAGTTTCTTGCCGCGGCCAGCGGGTCTATCGGGCTGAGTGTAAACGCCAATCACATTATGGTGTGAGTTCAACAGTGCTTGTAAGTGGCGAGCGGCAAAATCCGGTGTTCCGGCAAAGATGATGTTGAGTGGTTTCAAATCTGATCCTATGCTTCTTTGGCGTCCAATCGGGCCGCTTTTTCAAGTTTTTGTTTGATCCGCTGACGTTTTAGCGGCGACAGATAATCGACAAACAATTTGCCTTTGAGGTGATCCATTTCGTGTTGAATACAGATAGCAAAAAGCTCATCGGCCTCAACAATAAATTCGTTGCCGTGCCTGTCTAAGGCTTTTACCGTAACAAATTCGGCGCGATCGACTTTGGCATAAATGCCCGGCACCGACAGACAACCTTCTTCATTGCAGAAGTCGCCGCTGCTGGCGATAATTTCGGGATTAATAAACACTTTAGGTCGTTCGACTTCATCTTGTAAGTCCATGACAATAAGTTGTTTATGGTAATCCACTTGGGTCGCGGCCAGACCGATACCTTTTTCTTGGTACATGGTTTCGAACATATCATCAATTTGAGTTTGCAGCTCGGCATTAAACTCTGTGATGGGTGTGGCTTGTGTCCGCAATCTTTCGTCAGGAAATTGTAATACTTTTAATTGTGCCATACATAAACTCTTAACAAGTCTGTCAAATCTCTGCCAGTTAGTTATACTGACTGATGCTAATGGGTTAATTTTAATCTTTAGTGCCTATCAATAACAGTAAAAGCTCAAACTAAGAGCCAAACAACATGGAAATACCCATGAAACGGTTAATTTTACTCGCGTTAATGACATTCAATTGCACGTTTGTTGCCGCCGATACCCTTACGCTGAAAGCGGGGCATCCTGAGTCATATGTGGTGAAAAAGGGCGATACTCTTTGGGATATCTCCGCAGCGTTTCTAAATGATCCATGGAAATGGCCACGTCTTTGGGATGTAAACCCACAGATTGCCAATCCCCATCTTATTTATCCTGGCGATCAACTCACACTCGTGTTTGTCGATGGTCAGCCTCGGCTTGTGCGCAACGGCGACATGGTCGACAGTGCTAAACCCCATGTAAAGAAGAGTCCTGAAGGACGGATCATTGCTAAGAGCAATGCGATTCCTGCCGTCGACTTGGCCTTAATTGAGCAGTATTTAGTGCAGAATCGCGTTGTCGCAACCGACTGGCTTGCCGCGCAACCTATGGTATTAGCAGGTGAAAGCCCATCGCGTCACCATATTGTGGGTGATGTGATTTATATCGATGCTGAACTGCCGATGAATCAAAAGTACGGTGTTTACGAACGTGGTCGTGAGTTTTTCAATAAAGCCTCAGGCGAAGCGATAGGACAAGAAGCCATTTTAGCATCAACTGGGCAAGTGGTTGAGTCTGGTAAGGTTTCAAAAATTAAGTTGTTAACCAGCTTTCGTGAAACCAAAGCGGGTTTCAGAGTCATGCCGATGGAAAGTGATTCCTTGATGTCAGCTTACTTCACCCCAAAACCGGCAGAGTTAAAGACGCCAGCCGTCGTGCTTGCAATCGAATCAAAGATGCGTGAAGCGGGTAAGTTAGATGTGGTGTACTTAGATAAAGGCACCCAAGATGGGGTTGAGCCCGGAGTCGTGTTTTCTATTTACCGTGATGGTGAAGTCATCGTCATCAACAATGATGGTATGCCAGTGCCTGCCACTGAGCGCACCGCCTACGATAACTTAATGGCATCTGTCTCATCGGATCGCGCTATTCAAATGCCAGATATCTATCATGGCAAACTGTTGGTGTTCAAAGTATTCGACAAAGCGAGCTTAGGTTTGATTGTCTCCACCGAGCGGCCTGTGCGTGTCGACGATAAGTTAATTGCCCCTGAGTCTTTAGCCTTAAGAGGTCAGTAATATCTGAACATTTGGTCGATTGGTTAGTTGTTTGCGCTGTATCTGGGCTGGGACCCGCTCGGATCCAACAATTATTAACGCACATGGAAGTGGACGACCTCAGGCAAAGGTTAGAGCACGAGAGAGAAGCGCTACCTTTATCTGACCATCTGCTCCATAGTCTTGCTATTGATTATCAAAAAGTCGATGTCGCCCTCGAATGGCAGCAACAAGCCGCTGACCATCATCTCGTTTGTTTCTCCGATCCGCTTTATCCGCCGTTATTAAAACAACTCTCAGATCCGCCCGCCGTCCTGTTCGTTAAAGGTTGTTTGGAGGCGTTAGCCCTTCCTTATCTTGCAATTGTTGGCAGTCGTAATGCTTCTCCAGGTGGATTGCAGGTCGCTTATCAGCTGGCTGCGGAGATGTCCTCAATGGGATTTGGTATCTGTAGCGGTATGGCGATGGGGATTGATGGCGCTGCCCATAAAGCCTGTGTTGAACATCAAGGGCGAACCATTGCTGTGCTCGGGACTGGCATTGAGACTGTTTATCCACGAAAACATCGGCAGCTTTATCATGATATTCAGCAACAAGGCTGTATACTTAGCGAGTTTTGGCCAGATGTCGGTCCCTTTGCGGGGAATTTCCCGAAACGAAATCGGATTATTAGCGGTCTTTCTTTAGGGACACTTGTGGTTGAAGCCTGTCGTAAAAGCGGTTCTTTAATCACAGCGAGGTTGGCGATGGAGCAAGGGCGTGAGGTGTTTGCCGTTCCGGGCTCAATCTTGGGTGGTTACCATCAAGGCTGTCATGATTTATTACGTGATGGGGCAAAACTTGTGGAAACAGCCGCCGATATAGTGGAGGAGTTGGCAAGTTTAACCGCTTACCACCTTGAAGAATTGAAGTTATGCCACCATATACAGCAAGGCGAGATTTGTAATTTGCCATTTTCGTCTCTGTTAGCTAGTGTAGGTTATGAAACTACATCCATTGATGCAGTAGTTGAACATAGTGGGAAAACGATAGATCTGGTGTTAGAACAAATGCTTGAACTTGAGTTGCAAGGTTGGGTGATTGCAGTACCCGGTGGTTACGTCAGAGTAAAGAGGAGCTAGCCATGTTTGATATCCTCATGTATCTATTTGAAAACTATGTTCACAGTGAAGTTGAACTACTAGTTGATGAAGACGAGTTAACCAAGGAACTCACTCGTGCCGGATTTCATCAATCCGAGATTTTAAAAGCGTTAACTTGGTTAGAGCGCTTAGCTGAATTACAAGAAGGGGATAAACCTTATCTTTGTAATCACGATCAGCATTCGTTTCGTATCTATACAAAGGACGAAATGGACAAGCTGGATGTGGAATCCCGCGGTTTCTTGCTGTTTTTAGAGCAAGTTAAAGTGCTGAATGTCGAAACCCGTGAAATGGTGATCGATAGAGTTATGGAGTTGGATGAGCCAACCCTGATTTTAGAAGATCTCAAATGGGTTATCTTAATGGTGTTGTTTAACGCTCCTGGTCATGAGTCCGCTTATGAGCAAATGGAAGATTTAATCTTCGAACAGCCCGAAGGCCGACTACATTCTTAATCGTTGTGAAAGATGAATTATGAGAGAAAGGAGGCTAAGACCTCCTTTTTTGATGCCATAAATTCCTTGATGGCACTGCGCTTGATATCCTAATGCGCTACGACAGGTGGTGAATGATTATGTCTAAGATTGATGAACAATTATTTAGTGCCCACGAACACGCACTGGAAAAAGAATACGAACTTTGCCCTGAATGTGGCAGCGAGTTATCGGTCAAACACAGTAAGCACGGCGGTTTTATTGGCTGCAATAATTACCCTAGCTGTAATTACACTCGTCCCTTAGTGCAGCACGAATCCATTGAAACTCAAGTGATTGAAGGTTCTGTATGTCCTGAATGCGGCCACGAACTGGCGGTAAAGTCTGGGCGATTTGGGATCTTTATCGGTTGCACTCAATACCCTAGCTGCACGCATATTGAAAAACAGGATCAAGCGAGTGACAAACCTGATATCCCTTGCCCAGAATGTAAAACGGGTAAACTGGAGCACAGAACGAGCCGCTTCGGAAAGAGTTTTTATGCCTGTAGCGCTTACCCTAAGTGCAAGTTTATTGTGAATTATCCTCCGGTTGCTGAAGCTTGCCCGAGCTGTGGTTTTGGGATCTTAGTCGAGCGTAAAGGTGCGGCGGGGATGCGTTTAGAATGTCCGCAAAAAACCTGCAAGTACAAGCGCCCCTTGTAGCGAGGCCATTGCATAATATTTGTTGCCTTAGTGCGGCTTGAGTATAATCCCCAACTGACCGTTAGATGGGGCGGCGAAGATTCGGGTAATGACAAAAACTGATAAGACGCATAAGGTGATTTGATAATGTTGCAGCTACACCCATCCGATATAAAAGACATCATTCTTCAAGGTGGCGTTATCGCTTATCCAACCGAGGCAGTTTATGGCCTAGGTTGCGACCCTGATAACGACACTGCTATCCAAAAGTTATTGGCGGTAAAACAGCGCCCATGGCAGAAAGGACTGATCTTAGTCGCCAGTGATTTTCAGCAGCTTTTAGCTTATGTTGATGAATCGCAATTGACGGCTGAGCAGCTTGAATTTGCATTTTCCAAGTGGCCCGGGCCTTTTACCTTTGTCATGCCAATCAAAGCGCATGTTTCAAAATACCTCTGTGGCGAATTTGACTCTATCGCAGTGCGAGTGTCGGCCCATGCTGGGGTACAAGCCCTGTGCCGCGCGCTTAATAAACCTTTAGTGTCGACCAGTGCTAACCTTGCAGGTGAAGATCCCGCATTAACTGCCGCTGAGATCCTCGCTGATTTTACCGGTAAAATCGATGCACTGGTATTAGGCGAACTCGGCGAACAACGTCAGCCCTCGACCATTATCGATGCACGAAGCGGCAAAATTTTACGAAATGGACAATGAGCAAACCAATAATAAGGATAACAAGATGAGTGTGCCTGATGCTGCAGTGGTAAAAGCGTTTTTACTCGATTTACAAAACCGAATTTGTGCCGGTTTACAGGCTCTGGATGGCCAAGCAAGGTTTGCCGCCGATTCATGGACCCGCGCCGAAGGCGGTGGTGGCACGAGCCGAGTATTAACCCAAGGTGCGGTATTCGAGCAAGCTGGAGTTAACTTTTCCCATGTGACTGGCGCCGCTATGCCGGCGTCGGCAACGGCGCATCGCCCAGAATTGGCGGGTCGCAGTTTTGAAGCTATGGGTGTGTCTTTAGTGATCCATCCTAACAATCCGTATATCCCGACGACTCACGCCAATGTGCGCTTCTTTATCGCGCAAAAAGAAGGTGCTGATCCTGTGTGGTGGTTTGGCGGTGGTTTTGACTTAACACCTTATTATCCATTTGAGGAAGATGTGCGCGAGTGGCATCAAACGTCTAAGGATATTTGTGCGCCTTTCGGGGATGAGGTTTATCCCAAGTATAAGAAATGGTGCGATGAGTATTTCTTCCTGCCGCATCGTAATGAAACCCGCGGTGTGGGCGGACTGTTTTTCGATGACTTGAATCAAGAAGGATTTGATCAAAGCTTTAGCTTTATGCAAGCCGTGGGCAATGGATTCTTAACCGCCTATGCGCCGATTGTTGAGCGTCGTAAGGATACTGAGTTCGGTGAGCGTGAGCGTCAGTTCCAACTCTATCGCCGTGGTCGTTATGTTGAGTTCAACTTAGTCTACGACAGAGGCACGCTGTTTGGTCTGCAGACAGGTGGACGTACCGAGTCTATCTTAATGTCGATGCCGCCATTAGTGCGTTGGCAATATGCTTATACGCCAGAGGCGGGAAGCCCAGAGGCAGATTTGTACGATAATTACCTCAAACCCCGCGACTGGGTTTAGCCAGTAATCTTATAAAGTGGATACAGCATCCACTTTATTGATTACGCATATGATCGGCCAGAGTTGAAATCGCCTCAAAGATGGCGGCTCGATGCTCTGGCTTTTTAATATTCTCTTCAATCGCAAACTTCATACAAAACAGCCATTGATCGCGCATTGCCTCATCAATATCGACATGCATGTGTCGTGCACGCAATGCGGGATGACCATATTTTTGCTGATAAAGCTGCGGCCCGCCGAGCCAACCACTTAAAAATTCAAACAACTTCTGCTCTGACTCCGCAATGGGCGCCCTATGAATGGCAAGCAGTGTTGTCGTCTCTGTTGAGCTTGCCATTTTCTGATAAAAACATTTAGTGATCGCGCGGATCACCTTTTCGCCGCCGATTAAGTCATAGGCGTTAGATTGACTCGGATCGCGATCATCCTGCGGCGGAGTGTGTTTACTAAAAATTTTTTTAAGCCAGTTCATTGGAATAGTCGTTCACTAAAGTAGAGTGTATTGAGCCGATGGCGCATTATAGCGTGAAACCTAGCGTGAAATTTAGTTGAGCATAATTCGTCTTGCGTGGGCTCAAAAGCGCGGTAGACTGCAAAGCCATTGAACCTAAAATTTCATATCGCTATGACAGACAGATACGCAGTGTTTGGCAATCCAATTAGCCACAGTAAATCGCCGTTCATCCATGGACAATTTGCCGCGCCCACCCAAGAGTCATTAACCTATGAAGCCATTCTCGCTCCGGTCGATGGTTTTGAAGCCTCATTGACGGCGTTTTTCAATGCGGGCGGCAAGGGCGCAAATGTGACCGTGCCTTTTAAGGAGCAAGCTTTTGCGCTGTGTGACAGTATCAGCCCCGAAGCCAAACTCGCTGGCGCAGTCAACACCTTAAGTTTATTGGCCGATGGCACTATTCGCGGTGATAACACCGACGGTTTAGGGCTGGTGGCCGATCTCATCGCTAACTTAGGCAGCTTGCAAGATCAGCGCGTGTTACTCATTGGCGCTGGTGGCGCGGCGCGCGGTTGTATTCTGCCGTTATTAAATGCGGAGATTGCACAACTGACCATCAGCAATCGCACCCACACAAAGGCGCAGCTTTTAGTCGATATCTTTACTTCTGTGGATAACGGCGCTTACGCGAGTAAAGTCACCGCCGTAGAAATGAGTGAGCTTGCGGGTGAGTTCGATATCATTATTAATTCAACCTCTGCTAGCCTTGCGGGCGAGTTGCCACCTTTGCCAGCACATATCATCACCACCCAAACCGTTTGTTATGACATGATGTATGGCGCCTCGATCACCGCGTTTAACCAATGGGCACTCTCCCAAGGTGCAGCGAAAGTCATTGATGGGCTAGGTATGTTAGTCGGACAAGCGGCCAAGAGTTTTACCTTATGGCGAGGTATTGAGCCCGACACCCAAGTGGTGTTGACGCTTTTGCGGGACAAACTAATGGCAGAGCCTAAGTAGGAAGCATGAATCAAAGTATTCTTTTCCCCGATCTGCAATATTGGGACGATGCCCAAGGGCAAATCTGTTTTATGGCGCAATCGCAGGGGATGAACATCAAGTGCTATATCAGTGCCGCCAAATTACGTGAGCTGAATGATTTCTCTGAACAGCCAAGTACCGATGAAGCGGCTACTATGCTGGCATTATTTGATGCGGTGAGATTTGACGCCGAAGAAATGGCAGAAGACTTAATCGAAGCCGAAGAGTTTGATGAGTTTGGCGCAGTACGCCTAGGCTAGCTTCATCGCGAGTTTCAGTGGATAGATTTTGAATCAATAAATAGCAAAAGGCGACAGAGACTGAATGAGTCTCTGCCGCCTTTCTTATATACTAATACTAAGCGACTAACGATAAGTGACTAGATGTTTTGTAACTCAACTAAGTATTCGTTTTTCAGTCTGACATAGTTGTCGGCAGATTCTGGCAAAAAGGCAATTTCGGCAGGTGTTAATGGACGAGCCTGCTTAGCTGGGCTGCCTACATATAAATAGCCGCTCTGTAATACCTTGCCCGGTGGCACTAAAGAGCCCGCACCTAAGATCACATCATCTTCTAAAATCGCACCGTCAAGAATAATCGCGCCCATGCCCACTAATACTCGGTTACCGACTTTGCAACCGTGAAGCATCGCCTTGTGACCAATAGTCACATCATCACCAATAATCAGTGGATGACCATCGGGACGAGCAGCTGACTTACGCGTCACATGCAATATGCTACCGTCTTGCACATTAGAGCGCTTACCAATACGAATATGGTTAACATCACCACGCGCCGCCACCATAGGCCAAATACTGGCATCAGTATCTAACGCAATATCGCCAACAAGCACCGAAGCTTCATCTACATACACATTATTGCCTAATTGAGGATGAATACCTTGGTAAGTTCTGAGTGGCCCTGACATAGAAAAGTCCCTAATTGAGTGTTTTTTTGCATTATAAAGGTTAAAAACTAGCTGGTCAGGTCAAATATCCAACGAACAGTGAGAAAATGACAATTATCTTAAAAATAGGGGTTGCGCACTTTCTCAATCTCCCTATAATGCGCATCCACTGACAAGGCAGACAGCGAAAGCAAGCTGACGAGTTAGATTGAATTGAATGGCTTACTCTCTGTTGAGGATGGCACTTAATTCAATCGCAGCAAGAAGTTGAAAAAACTCCTTGACGCGAAACGGGAAATGCGTAGAATACGCAGCCCTAGCCACCTGGAAGCGTTCGGCGCTCAGTGTGGTACTCAAGTCCTCTTACGGATATTGAGTTGCTCTTTAACAAGATAAAACAAGAAATCTGTGTGGACACTCACAGGTGTTGAGTTAATCGAAACTGCTTAACCTTCGGGTTGGCAGTCAAAAATTTAAATCAATGTAACAATGAGTGTTCATAGCAATATGTACAGTTTGTTTTAGCCCTTCTTTTATAGGGAGTGCGAGAAACAAAAAATCAGAATTCATTGAGCCGCTAAAGTCGTAAGACGGAAGCAACAAAACTTTAATTGAAGAGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGCAGCGGGAAGGTAGTTTACTATCTTTGCCGGCGAGCGGCGGACGGGTGAGTAATGCCTAGGGATCTGCCCAGTCGAGGGGGATAACAGTTGGAAACGACTGCTAATACCGCATACGCCCTAAGGGGGAAAGGAGGGGACCTTCGGGCCTTCCGCGATTGGATGAACCTAGGTGGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCCTAGCTGTTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGTAGGGAGGAAAGGTAGCAGCTTAATACGCTGTTGCTGTGACGTTACCTACAGAAGAAGGACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTCCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGCGAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTCGAACTGGCGAACTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCTACTCGGAGTTTGGTGTCTTGAACACTGGGCTCTCAAGCTAACGCATTAAGTAGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCACGGAATTCGCTAGAGATAGCTTAGTGCCTTCGGGAACCGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCCTTATTTGCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGATAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCGAGTACAGAGGGTTGCAAAGCCGCGAGGTGGAGCTAATCTCACAAAGCTCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTGGATCAGAATGCCACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGCTGCAAAAGAAGTGGGTAGCTTAACCTTCGGGGGGGCGCTCACCACTTTGTGGTTCATGACTGGGGTGAAGTCGTAACAAGGTAGCCCTAGGGGAACCTGGGGCTGGATCACCTCCTTACCTATACGACTAACTTAATTCTTGGTCGAAAAATGCTCCTGCATTTTCGACATTCGGTACATCCGTGTACCTTTTGAGTGTTCACACAGATTGCTTGTTTATCTTCTCTTAGGAGAAGTCAGAGCTGAATGCGCCGCTAGCCGGTACGCATTGTTCTTTAACAATTTGGAAAGCTGATAGTATGTAACGTGATGATGTCTGTCGTCATGTTACTTACAAACCGATGTAAACGTGGAAACGTTTGCATCATGAGTTCTCAAACACTTTATTAAGTGTCTTGAATATTCAAAAGTAAATTATTCTTTGCTGAATAGTTTACGTCTAAGGCGCGTCCACTTTCTTGGTCGGAAGTGAGACAAGTAAAACCAAGCTGGTCGCAATGCGACTCAGGTGAGTGAAACTCATTTGGGTTGTATGGTTAAGCGACTAAGCGTATACGGTGGATGCCTTGGCAGTCAGAGGCGATGAAGGACGTAGTAACTTGCGAAAAGCGTTGGCGAGCTAGTAACAAGCATTTGAGCTAACGATGTCCGAATGGGGGAACCCAGCAGCATAAGCTGTTATCGCAACATGAATACATAGTGTTGCGAGGCGAACGAGGGGAACTGAAACATCTAAGTACCCTTAGGAAAAGAAATCAACCGAGATTCCCCTAGTAGCGGCGAGCGAACGGGGATTAGCCCTTAAGTCAGTGGGGTGTTAGTGGAATGTGTTGGAAAGCACAGCGGCACAGGGTGATAGCCCCGTACATGAAAACTAACCATTGATGAAAACGAGTAAGGCGGGACACGTGACATCCTGTTTGAATATGGGGGGACCATCCTCCAAGGCTAAATACTCCTGACTGACCGATAGTGAACCAGTACCGTGAGGGAAAGGCGAAAAGAACCCCTGTGAGGGGAGTGAAATAGAACCTGAAACCGTATACGTACAAGCAGTGGGAGCGGTTCTTGAGACCGTGACTGCGTACCTTTTGTATAATGGGTCAGCGACTTACATTTTGTAGCGAGGTTAAGCGAATAGCGGAGCCGTAGGGAAACCGAGTGTTAACTGCGCGTTTAGTTGCAAGGTGTAGACCCGAAACCCGGTGATCTAGCCATGGGCAGGTTGAAGGTTGAGTAACATCAACTGGAGGACCGAACCGACTAATGTTGAAAAATTAGCGGATGACTTGTGGCTGGGGGTGAAAGGCCAATCAAACCGGGAGATATCTGGTTCTCCTCGAAAGCTATTTAGGTAGCGCCTCGAGCGAATACCATTGGGGGTAGAGCACTGTTAAGGCTAGGGGGTCATCCCGACTTACCAACCCTTTGCAAACTCCGAATACCAATGAGTACTACTCGGGAGACAGACGGCGGGTGCTAACGTCCGTCGTCAAAAGGGAAACAACCCAGACCGTCAGCTAAGGTCCCAAAGTACTAGCTAAGTGGGAAACGATGTGGGAAGGCTTAGACAGCTAGGATGTTGGCTTAGAAGCAGCCATCATTTAAAGAAAGCGTAATAGCTCACTAGTCGAGTCGGCCTGCGCGGAAGATGTAACGGGGCTAAGCTAGTCACCGAAGCTACGGGTGCACAGTTTACTGTGCGCGGTAGAGGAGCGTTCTGTAAGCCGTTGAAGGTGAAGGGGTAACCCACGCTGGAGGTATCAGAAGTGCGAATGCTGACATGAGTAACGATAAAGGGGGTGAAAAACCCCCTCGCCGAAAGACCAAGGGTTCCTGTCCAACGTTAATCGGGGCAGGGTGAGTCGACCCCTAAGGTGAGGCCGAAAGGCGTAATCGATGGGAAACAGATTAATATTTCTGTACTTCCGCTAACTGCGATGGAGAGACGGAGAAGGCTAGGCTAGCGCGGCGTTGGTAGTCCGCGTTTAAGGTGGTAGGTTGATTTCTTAGGCAAATCCGGGGAATCGCACTTTAGTGTGCAGACTGAGAGCTGATGACGAGGTCCTAAGGGACTGAAGTAGTTGATGCCATGCTTCCAGGAAAATCTTCTAAGCTTCAGGTTAGTGGGAATCGTACCCCAAACCGACACAGGTGGTCGGGTAGAGAATACCAAGGCGCTTGAGAGAACTCGGCTGAAGGAACTAGGCAAAATGGTACCGTAACTTCGGGAGAAGGTACGCTCCTGTTGGTGATGAGACTTGCTCTCTAAGCTGACGGGAGTCGCAGATACCAGGTGGCTGCAACTGTTTATCAAAAACACAGCACTGTGCAAACTCGCAAGAGGAAGTATACGGTGTGACGCCTGCCCGGTGCCGGAAGGTTAATTGATTGGGTTATCGCAAGAGAAGCTCATGATCGAAGCCCCGGTAAACGGCGGCCGTAACTATAACGGTCCTAAGGTAGCGAAATTCCTTGTCGGGTAAGTTCCGACCTGCACGAATGGCGTAATGATGGCCACGCTGTCTCCAGCCGAGACTCAGTGAAGTTGAAATTGCGGTGAAGATGCCGTATACCCGCGGCTAGACGGAAAGACCCCGTGAACCTTTACTATAGCTTGGCACTGAACATTGAACCTACATGTGTAGGATAGGTGGGAGACTTTGAAGTTGTGACGCTAGTCATGATGGAGTCGTCCTTGAAATACCACCCTTGTAGTTTTGATGTTCTAACCTAGGTCCCTTATCGGGATTAGGGACAGTGCCTGGTGGGTAGTTTGACTGGGGCGGTCTCCTCCCAAAGAGTAACGGAGGAGCACGAAGGTTGGCTAAGTACGGTCGGACATCGTACGGTTAGTGCAATGGCATAAGCCAGCTTAACTGCGAGACATACACGTCGAGCAGGTACGAAAGTAGGTCATAGTGATCCGGTGGTTCTGAATGGAAGGGCCATCGCTCAACGGATAAAAGGTACTCCGGGGATAACAGGCTGATACCGCCCAAGAGTTCATATCGACGGCGGTGTTTGGCACCTCGATGTCGGCTCATCACATCCTGGGGCTGAAGTCGGTCCCAAGGGTATGGCTGTTCGCCATTTAAAGTGGTACGCGAGCTGGGTTCAGAACGTCGTGAGACAGTTCGGTCCCTATCTGCCGTGGGCGTTGGATGATTGAAGGGAGCTGCTCCTAGTACGAGAGGACCGGAGTGGACGAACCGCTGGTGTTCGGGTTGTCATGCCAATGGCATTGCCCGGTAGCTACGTTCGGAATCGATAACCGCTGAAAGCATCTAAGCGGGAAGCGAGCCCTAAGATGAGTCATCCCTAGGAATTTAATTCCTCTAAAGAGCCGTTCGAGACTAGGACGTTGATAGGCATGGTGTGTAAGCGTTGTGAGGCGTTGAGCTAACATGTACTAATGACTCGAGAGGCTTAACCATACAACCCAGATGGGTTTTACTGAAAGCCTTAGACAGAATATCGACACTTGATGAAGTGCGAACTCAAACAAAGCAAATCAGCTTTCCGAATTATTATTAACGTGAGCCAGAGATGGACACGGTAATAAACAGAATTTGCTTGGTGACAATAGCCTTGTGGAACCACCTGATCCCATCCCGAACTCAGAAGTGAAACGCAAACGCGCCGATGGTAGTGTGGGGTCTCCCCATGTGAGAGTAGGTCATCGCCAAGCGCCTAATTTAGGTTAACGTGCAAACGTTAGACTAAAGGAGCGGTAGTTCAGTTGGTTAGAATACCGGCCTGTCACGCCGGGGGTCGCGGGTTCGAGTCCCGTCCGCTCCGCCAACATTTCGATAAGCCCTAACAGCAATGTTAGGGCTTTTTCGTATCTGGAATTTATCTATTTCAATCGTCGCGGCCGTCCCGTCCAACTGCATCCCTGCTTAACAGCCCAGTTCGGCATCGCTACCTCACATTCGTCGGCGTAAAATTTAAATCTCAGCTACTTTTGTCTAAGTTTGTCCCACTTATTTCATATTATGCCTTTCTCGCACAGAATGGCGCTTGTGCGCGACTTGGCAGTATTGACCTGTGACACTGATCTCGGACAAATTAAACGGTATAATGCCTTATGTCTTTCATGAGTAACGAAAATGAATTGTCGTCTTGGGTGTGGTGCTTGCTGTATTGCGCCTTCAATTAGCAGCTGTATTCCTGGTATGCCAAATGGTAAGGCTGCGGGTGAGCGTTGTGTGCAGCTGAGTGATGCCAATTTATGTTTGATCTTCGGCTCGCCCGATCGTCCCGCGGTTTGTAGTGATTTCGATGCATCGGAAGATGTATGTGGTCATTCGAACGAAGAGGCGCTTTGGTTGATTACCCAACTCGAAGCTCAAACCTCTCATTAAGTTTATCGCTGACAGGTGAAATATGCAGTTAACTCGTTATACCGATTTTGGCGTTCGAACCTTGATGTATTTGGCGGTTCAGCCAGACAGAACCACTTTATTTAGAATTGCTGAAATTACTCAGGTATTTGATCTGTCTCCAAATCATGTCTCTAAGATAGTGCATCATTTAGGCAAGATGGGTTACCTCGAAACCATTCGCGGCAAGATGGGCGGTTTCCGTTTGGGTAAGCCTGCGAGTGAGATTAACTTAGGTCAGTTAGTTCGGGTGTTAGAAAACTCGTTGGCGCCGATTGATTGCAGTAAACCTTACTGCCGATTCACGCCTGCCTGTAAGCTGAAAGGCGTGTTAGCACAAGCAGTTGAAGCGTATTTGAATGTGCTCGATCAATATACTCTGTTGGACATTGTGAGTAATCGTGATGAGTTACTTGCCTTGCTGCCTGACATGTCTATCTCAGTGTTACAGCTGGATTAGTTCCATTTTTGTGTGCAATGCCGATGGGGAGTAAGCTTGATGCTGGTACTAGATCAATCCCCTCGGTTTTTAGTCTTTGTAGATTGGCCTTTAAGAAGCGCATGGTTTCTGGATAAGGATGGGCAATCGCCACTAGGCTACCTTGAGTGTGTGCCTGACTTATCATTAAGTTAAATTGTCTTTCGAGTGCTTTGGTACTGGTATCATTGTCTAGAAACAGTTGGCGTTTTAATAACGGCACGCCCAACTGATCCGCTGTCTCTCCCGCCTTAGTGAACCTTGTTGTCATGCTATCGATAAAATACAGATGCTTTTGTTTGAGTGTTTCCATCACCCAAAGCATAGGCTCATCGAGTTGAGTCAGTAAGCTCCCCATATGGTTATTGGCACCTTTAGCAAACGGAACGCTGTTAATTGCATCGACAACACTGGCGCGTATTTGCGCCTCAGTCATAGTATTGGTCAATCCACCGAGCCCTAACGCCTTACCATTAAGCGCTTGCATAGGCAGATGCAGCATGATTTCGTGGCCCTTTTTATGGCCGGCATTTGCGAGTTCTTTGCCAAGAGGTGTATGAGGCAACACTGATAATGTGACTGTGCTGGGCAGTGCCAGCACGGCTTGATCGGTATGACGATAACCTATGTCATCAATAATGAGGGCAATTTGAGCAGCATGACTTTGAGAAATGCTTAGGGCCAATAGGGCCAAGGAATAAAGTAAGCGCACAGTTTCTTCTTATTAGTCATGATGCTGTATCCATGCAATGGCTGAGGTCACTTGATGATCTTGGGCCAGAGATGCATGGGCGGATAGGGCGTCGATTATAGCCAAATTACCCGCGCCGGAACCAGTTTCTGCGGCAATTTTTATGTCTGGGACGATTCCTATGTTGTGAATGTTTTCACCTCTGGGGGTATTGTACTGGGCAATGGTGAGTTTTATCGCGTTACCATCTTCTAAGATGGGGATCAGGCTTTGTACTGTGCCTTTGCCAAAACTGGTTTCACCTAACAGCTTAGCTCGACCATTTTCCTGCAGTGCGGCGGCGAGCACTTCGGATGCAGAGGCCGAGCCTTTGTTGATCAACACTAGCATGGGCACGTTAGTCAGCATGGTTTGTGGTGAGGCGTAGTAGTCGGAGTTGGCATCGAAGAAGCGGCCAGAGGTTGAGACTATTCGGCCTTTGGCGAGGAATAGGTCAGCAATTTTAACGGCTTGGTCGAGCAGGCCGCCGGGATTATTACGTAAATCTAAGATGAGCCCGGCGAGCGGTTTATTTTGCCATTGGCTAAGTGAACGCACCATATCTTCGGTCGAGTTTTCTTGGAAGCTACTTAATCTGACATACCCAATATTACCATCAAGCAATTGAGTGGTGACAGATTCGATAGTGATAGTGCTCGGCTTGAGTGACACCTCAAAGGTCGCATCGTCATTGCTGTGCTTAAGCACTAACTTAATGCTTTGATTGGTGAGGCTATGTTTTTTTATCTCGGCAAGGATATTAGCGAGGTTAGTCTCGGTCGCCAGTGTGTCGTTGAGCTTAACGATAATGTCGCCTGCGCGTATTCCCGCTTGTTCTGCTGGGGAATGGGCGAAGGGGGCGATGATGTTGATGTGGTCATTTTGGGTTGCGACTTCGAAGCCGAAACCAAAATATTCGCCGCGATTGGAGTCTTTGAGATCGAGTAATTCTTGGTGATTCAAAAAGCTGGAATAGGGATCTAAGTGCTCAAAAATACCTTTGATTGCCGCTTGCACTAACTCTTCTTTCGAAACGGTTTTTACGTAATAAGTTTCAACGGTTTCAAGTACATCTTGCAGTAACGGATAGTCAAAATCACTGCGATATGACTTAGCGTTCTCTTGACTAGAGAGGCTGATGGATAGGCCCAAAGTCAGGCCTAATCCAACGCAACTGATATAGCGGATCATGTGTTTCATAGCCGTCCCCTGTGATGAGGGGACCGTTATGATTAGCGACAGTATTTCGCTGGATCGACGGCTTGCCCCTTGTGCCTTATTTCAAAGTATAGGCCAGCCTCGCTCTGTCCACCCGAACGTCCGACTAAAGCTATCGCTTCGCCGCTTTTTACCATGTCGCCAGGGCTTTTTAATAGGGTTTGAGCATGGCCGTACAGGCTCATATAACCTTTGCCATGATCGATAACCATCACTAAACCAAAACCGCGTAACCAGTCGGCATAAATAACCTTACCACCTGAAACGGCTCGAATATTTTGCCCTTCTGGTGCGGATAACATAGTGCCTTTCCACACCACTTGGCCGGAACGTTGGCTGCCAAAGCTGGCACTGACGCGACCTTTAGTTGGCCATTTGAGTTTGCCACTTTGTTTACCAAAACCTTCCATCGAAGGGTTGTCTTTCATGGCCCGTAGCGCTTGTTCGACCACGCGCTTTAAGCTGGCCTCTTCAATTTGCAACTGTTCCAGCTCTGCGCCCTTAGAATTTAAGGTGCGTTGCAGCTCTGTGATGGTCCGTTGACGCTGATCTTGCTCTTGGCTCAAACGTTTGGCTTGGACCTGTTGTTCGGCCACGAGTTTGGTTAATTGTTTTTGCTTACTCGTTTGCGACACCTTGATCTCATCTAGCTCGGTCAAGGTTTGCTTTAATTCGTTGATCGACTTCATCCGCGCTTTATTGAGGTATTGGTAATAGGCCAACATACGCTCGATAGTGGCAGGACTTTGCTGGTTGAGCATCATCTTAGTGTAATCGTGATTACCGGCAAGATAGGCGCTCGACAGCTGTTTCGACAGTGTCTGTTGCTGACTCACTTTTAGCGTTTCTAACTCATCTTGACGTTGTTTCAGCTCGATCAGTTTCGTGTCTATTTGCGCTAAGCTATTTTTGGTGCTGTTGACTTTTTTGGCCGCTGACGCAATCGCTTCTTCATCGCTACGCAGCAATGACAGCAATTTTTCTCTTTGTTTACTGGTGGTTTTTAACGCGCTTTGTTGTTGATTTATTTGCGATTGGATTGATTTCAACTCGGACTGACGCTTCTCCAAATCAGAAGCGTTGGCCGAGAAAGATAACATCATAAAGCCAGCAAGAATGCTGGCTTTAACAAGGAGTCGAGTGCTCACTAGGTAACGGTTACTCTTTAATGTGGATTAAGGGCTTGCCCGTCATCTCTGTTGGAATGGCTTCGCCAATCAAGGTCAAGATAGTCGGTGCGACATCGCTGAGTTTACCACCTTCATCTATGGTCGCATCGCGGCCCACAAAGACGAAAGGTACCAGTTCACTGGTGTGGGCTGTGTGTGCCTGACCTGTGGTTTCGTCCGTCATTTGCTCGGCGTTGCCGTGGTCGGCGGTAATAATGCATTCACCTCCTACCTTCGCCAGCGCATCCACTACACGACCGATACAAGCGTCAACCGCTTCACAGGCTTTAACGGCGGCATCGAAATTACCCGTGTGGCCAACCATGTCGCCATTCGGATAATTACAGATGATCACGTCGTACTTGGTCGACTCAATCGCCGCCACTAACTTGTCCGTCAGCTCGGCTGAACTCATCTCAGGCTGTAGATCGTAAGTCGCCACTTTAGGTGAGTTGATTAGGATGCGATCTTCACCTTCGAATGGCTCTTCTTTACCGCCGTTGAAGAAGAAAGTCACATGGGCGTATTTCTCAGTTTCAGAGATTCGCAGTTGAGTGCGACCTTGTTTTTGCAGCACTTCACCTAAGGTATTTACCAAGTTTTCAGATGGATACGCGATAGGGGCTTTGATGTCGCCGGCGTACTCAGTCAGAGTGACAAAGTGCATTTTCGGCACCACAGCGCGTTCGAAACCATCGAAGTCTGGGTTGATAAAGCTACGGGTGATTTGGCGGGCACGGTCGGCACGGAAGTTCATAAATATCAGTGCATCGCCATCTTGCAAGGTCGCAACTTGGCCGTCGGCATCTGTAATTGCCGAAGAAGAGACGAACTCGTCGTTCTCGTTACGCTCGTAAGCCGCTTCCAATGCAGTAACAGCATTGTCGTATTGGAATTTTGACTTGCCTTGGGTGATTAAATCGTAGGCTTGCGACACGCGATCCCAACGATTATCCCTGTCCATTGCAAAGTAACGACCAATCATCGAGGCGATACGGCCATGGCCTAATGTAGTAAACAGATCATCAAAATGGCTCAAGCTGCCCTTAGCGCTGCGCGGCGGTGTATCGCGGCCATCGAGGAAAGCGTGCAGATAAACCTTAGTTGCGCCACGGGCAACGGCCATACGGCACATAGCTTCGATATGCTCTTCATGGCTGTGAACACCACCGGCAGAAAGCAGACCCATAATGTGCACTGCACCGCCGGCTTTAATGGCCGAGTCAACCGCATCACACAGGGCTGGATTTTGTTCAAACTCATGATCTGCAATGGCTTTACTGATGCGAGTGAGTTCTTGATATACCACACGGCCAGAGCCTAAGTTAATGTGGCCAACTTCAGAATTTCCCATCTGGCCATCGGGTAAGCCTACATCTAAGCCAGAACCTGAAATCAAACTATGGGGATATTGAGCGTTGAGACGGTCGAGTACCGGAGTATTGGCGTGGAAAATCGCATTGTTATGCGTGTTTTCACGGTAGCCCCAGCCATCGAGGATCAACAACGCGAGCGGACGTTTAGCTGTCGTCATAGTGATACCTTTAAGTTTTATAGAAGCGATAGAAATCTGAAATTGGTCAAATATTACTACGTTAGCGGGGTATGAAAAAGCGCTTTACCCCGATTAAGGTCATCGAATCACTAATTCGTCCGATTCTGTGGCTGGCTTGTAACAAGCACCTAGGCAATTGCGCAAAAAGTGCGATCTCGAACTGTGACTAAACTGCTGCAATCTGCCAAGGCAATCGGTATACTCTGTGCCCTTATCGAATTTTCGCCATTTATGCGGGCAATCATCATGCAAGAATATATCGAATTTTTTAAAGCTCACTCAATGTTAAGTCTGGCCTGGGTGGGATTATTTGTTGTGCTTGTTGCGAGTATCATCAAATCGAGCATGTCAAAAATCAAAAGTATTTCTCACCAAGAACTGACAATTATGGTGAACAGACAGGACGCTAAAGTCGTTGATGTGCGCTCTAATGACGAGTTCCGCAAGGGGCATATTGTTGATGCAATTAACGTTACGCTAGCAGATATTAAAAATAATCAGATATCTGCCCTTGAAAAGTTTAAAGCCAGTCCCATTATATTGGTATGCAATGCTGGCATGACCTCGTCTCAAGCCGCTCAGCTTTTAAGCCAACAAGGTTTCGAAAACCTGTATAACTTGAAAGGTGGTATGGGTGAATGGCAGGCAGCAAATATGCCTGTTTCAAAAAGCAAAAGATAAGTTGTCGGCGGCAGTTATGATTTAAGCTGCACCCTATCCTTTAGGCAGTGCAGCAATACAGTCATCATTGTTTGGCATACTGATCCTGCGGTCGCTAAGGGCTTAATGTAAGTTTTTAGACTGGTTCGATCGATACCCGAATATGGGTGCAAGTTGGGATGATACCGAGATCTGATACAGGCCTTGAGTAACATAAGCCCCGAGCCAATTTACGTTCAAAGATGAATTGGCTTTCAAATAATAATTGGCTTTCAACGAAAAGCCGATACAACATTGATAGGATAGGTAGGAAATTATGGCTGAAGTAGCAAACAACGAACAACAAGCCCCACAATTCAACATCCAACGTGTTTACACTAAAGATGTGTCTTTCGAAACGCCTAACAGCCCAGCTGTATTCCAAAAAGAATGGAACCCAGAAGTTAAGTTAGATTTAGACACGCGCAGCGCTAAATTAGCTGACGATGTGTATGAAGTTGTCCTGTCTTTAACGGTAACTGCACAAAACGGTGGCGATACTGCTTTCCTATGTGAAGTACAACAAGCCGGTATCTTCTCTATCACTGGTCTGACTGAGCCACAATTGGCCCATTCATTAGGTGCATACTGCCCTAACATCCTATTCCCATATGCTCGCGAAACCGTGGGCAGCTTAGTTGGTCGTGGTACTTTCCCACAACTGAACCTAGCACCAGTTAACTTCGACGCGCTGTTTGCCCAATACGTGCAACAACGTCAAGCTGCTGCGACTGCACCAGCCGCCGAAGAAGCTAACGCTTAATTACATGAAAAACTCTGCCGATATCACGGTATTAGGGGCGGGCTCTTATGGCACCGCCCTTGCCATTTCTTTAGCTAGCAACGGTCATAAGACCTTGTTATGGGGACACGATCCTGTCCACATGCAAACGCTCGCTCAAGACAAGTGCAACCAAGCGTTTTTGCCTGGTATTGCGTTTCCTGATTGTTTACAGATCGAAGCCGATTTAGCCAAAGCCTTAGCTGCCAGTAACAATGTGTTAGTGGTAGTGCCAAGCCATGTGTTTGGTAGTGTGCTTGAGCAAGCTAAACCGTTACTCCGTAGCGATGCGCGCATCGTATGGGCAACGAAAGGGCTCGAACCTGAAACCGGACGTTTGCTGCAAGATGTGGCCCGTGATGTGTTAGGTGAGCAATATCCGCTGGCGGTGTTATCTGGGCCGACCTTCGCGAAAGAATTAGCCATGGGTTTACCTACGGCCATTTCGGTTGCGGGGACTTGTCCAACGTTCACCAACGATCTCGTTGAGCTGTTACACAGCCCTAAACGTTTGCGGGTTTACGCCAACGACGACTTTACCGGACTGCAACTTGGCGGCGCGGTAAAGAACGTGATCGCGATTGGTGCGGGCATGTCTGACGGTATCGGCTTTGGTGCCAATGCCAGAACGGCCTTGATTACCCGTGGTTTAGTCGAGTTAACTCGTTTAGGCGAAGCCTTAGGTGCTAATGCGGCCACTTTTATGGGAATGGCGGGGTTAGGCGACTTAGTGTTGACCTGTACCGACAACCAATCCCGTAACCGTCGTTTCGGTTTAGCCTTAGGTAAAGGCTGTGATGTGATGACAGCACAGGCTGAAATCGGCCAAGTGGTTGAAGGTTATCGCAATACCAAGGAAGTGTTTACGCTTGCGAAACGCCTTGGGGTTGAAATGCCAATCACTGAGCAGATTTACCAAGTCTTGTACCAAGGTAAATCGCCCGTCGATGCTGCGAAAGAATTGCTCAGCAGAGAAAAGAAATCAGAAACGCCCGCGCAATAAACCGCGAGCCGTGATGATGCTGTAATGCCGAATAAAGGAAAGGGTGCTAAAATAGCGCCCTTTTTTGTGCCTAACGTTTGAGCTTATCTTTGCTCCGTTTAGCGGTTGCTGACTTGCTTGCTAAAGGTTCGCGAGTCAGCAAACTGCCCGTTGGCAGCCAATACCAATCAATTTATCTGTGTGGAGTAAGAGCGTGAAACATCATGATGTCATTATTATCGGAGCCGGCGCCGCGGGATTGATGTGTGCAGCGACCGCGGGTTACCGAGGCCGTGATGTACTCGTACTCGACAATGCCAAGCAAGCGGGGCGTAAAATCCTCATCAGCGGCGGCGGGCGTTGTAACTTCACCAACCTTAAAGTCGAGCCCGCTAACTTTCTCTGTGGTAATCCCCACTTTGTTAAGTCGGCACTCGCGCGTTATCCGTCGCAGCAGTTTATTGAACTGGTCGAGCGCCACGGCATTGAATACCACGAGCGCGATCACGGCCAACTGTTTTGCAATGACTCGGCTAAAGAAATAGTCACTATGCTACTGACCGAGTGTGAATGGGCGGGCGTGACGATTAAGCTGCGCACCGATATTCTGGCCGTTAAAAAGACTGAAGCAGGGCGCTTTGAGCTCAATACTTCTAACGGTGAACTGAGCTGCGACTCACTGGTTATCGCCACGGGCGGCTTATCCATGCCTAAACTTGGCGCTACGCCTTACGGTTATCAACTGGCCGAGCAGTTTGGTCTCAAAGTATTACCCACTCACGCAGGTCTGGTGCCTTTTACATGGCACAGTGAAGATAAAATTCGTTTCGAACCTTTGTCGGGCATAGCCGTGCCGAGCCGCATTACCGCCAAAGATGGCACTGCATTTAGCGAAGCGCTGCTATTCACTCACCGTGGCTTATCTGGCCCTGCTATTTTACAGATTTCTAACTATTGGAAAGCGGGCGAAACCATAGAAATCGATCTCTTGCCCAATATGGATTTGGCGCAATTGCTAGAGCAGCAGCTCACTGCCCATCCTAAGCAAAGCCTGCGCAACACCCTCTGCCAATGGTTGCCTAAACGTTTGGTCGAAGTGCTATTTGATGAAGCTCTGTTGAATAAAGCCTTAAATCAGCTAGTGCACGCCGAGCGCGCAAAACTGGTGGATGATATACACCGCTGGACACTATTGATGAACGGTACCGAAGGCTACCGCACCGCTGAAGTGACCTTAGGCGGTATTGATAGTAATGAGCTGTCATCCAAAACCATGGAAGCCAGCAAAGTCGCAGGACTCTACTTTATCGGTGAAGTCATGGACGTAAGTGGTTGGTTAGGCGGGTTTAATTTTCAATGGGCCTGGGCATCCGGCGTCGCCGCAGGGCAAGCGGTGTAGTTTAAGTAGGGAATTAGAACGAAGACAAAGTAGATTTACTTAGTTGTAATTCCGCCTATTGATGTTAGAGATATAAGCCTACTTATACTAGAAGTCGATTCCTAACGTAAAATTAGTCCGCCGTGGTATACCCCTGCTTTGGTGAATTAGGCTTATGAGGAAACGCCATTTTTAGCAACCCTTCTGAAATCATAGGCTTCAAGTAATTTTGTCGTAAGCTCTGAGGCTTACGATCTAATATCATTTCCAAAACAGAAACAGAGATGTAATGTTCTTGGCAGAGGCCAGAAATCAGTGACTTCATCGTAACTGTATCTAATCGACGCTGTTCTCTTGCAGGAGCAGCTAAAGCGTTAAGTTCTTGTCGAAATGATTCGCTTAACACATCAAGGCTATCGACAAATGGTTTGTCAATTAAAGAGCTGATAAACCTGCCTTTATCATCTCTAGTTTCGTTCGAGTCAATAGCATTACTGTTGTTATGCGTTAAGTTACCAATGCTATGCGTTAAGCTTCTGGCGTTATGCGTTAAGTTATTTATAGGAGTTAACGCATTAGAAAAAACTTCCTCTGGGGATGGCAACTCCATTCCTGGTAAGCTGTATGACTTATCTCTTTTCTTTCCACTTGCGACAAGAAAGCCCTTATCGACTAGTCGAGGTAAAGCCAGCGTAACATCACGAGAATGCTTGCTGGTTAGCTGACAAGCTCGTTCGTGGTTTACCCAGCCTTCAATTGCTGCGGTTACAACAATCATCTGCTCAAGTTCGTTGAGTGAATAAAAGGTGGTGTTACCAAACTTAACCGCTAACAACTGAGTTACTTCTTCTGGGATAAGGCTTGCGGTTGATAACTCTAAGAGGGTTTGCTCTGACGGTTCATATTTTTCGTACAACTTCGGGGTTCGCCACTTTGCCCATTCCCATCCACTATATATTTTGGGAATTCCTGAACCAGCACGCTCTCCCGCACCAATCATCAGAAACATTTGATGCATCCTGCGGTTTCTACAATCGCTTTCACCACCTTTTACTGCTATTTCTGGGGGGACACGCATCAACCCTGGGTTGCGAAAACCAAATAAATCAGGCCGCTTTACGATAAGGATTGAGCTTCGTCCTGTGTAATCTGCATGAACAAGCGTATTCACTAATGCTTCACGAATAGCTTCGTGGGCTTTTGAATCATCCAAGCGAATACCATTTTGAAGTTCAAATGGTACTTTTAAGTCCGTGACCAATTTCCGGTATGTACGACGATAGAAATCAAACACGTTTCCAGACCAAGATCCATCGGGGTAAAGGCGGTCCACCCAACGCTCAACACTGGCTTCATCTCGTTCACGATAGTCAACAAAATAATTCGGAACAGCGTCTTGAATGGCATCCCAAGTACCGAACATCAATACTCCAGCTAACGTTATACCTTCAACGCTAGTCTGACGATCTTTTCTCCATCCACCCAGCTTTTTAAACAGTTCAAAAAGGTCTAATTCCAGCCAAGGATGTTGCGGTTTAGCAACTGAAAATAAGTTCTTGTATGCTTTGAGACTGTCCATGTCGATATCAGAGAAGTCAAAACCAGTGAGAATTTTATCATCTCGGCTATCTTCTACCTGCTCTGCCATCATCCGCTTAACCTGTTCTTCACTACAGCGGTGATCACCTTCGTGTAGACGAGTGTAAGTCTCCTTCATAGGCTGATTTTTCAGATAAATAGGCTTTTGGTCACGACGAGCTGTTGGAATTTCTATAACTAGTAGTGATTTTCCTTCAATGTTCACCGTTTGAACACTATCATTTGTCAGTAAGTTTATGTTTACTTTGGCATTATTACCTGCGATATCAAACAATTGTTTGTGAACAATCTCTATATCTTCAATCCCAGCTAATGTCAGTTGTCCTTTTTTCTCACGCACACCGAGAACAACATAGCCACCACGAGAATTGGCCATTGCGCTGTAAGTTGGCCAAAAATCATCAGGTAATTTACCTTTACCATCTTGTCCTTGAGCCAATTTAAATTCCAACTCCGCTGACTCTGATAAGGCTTGAAGGTCGACTAAATCCTGTATTTGAAACATATCTATCGCTTACCACCCGACTTTTTAGTTAATGAACATACTTATTAGCTTCAGTTATTGATCAACTCAAAGTTCTTGCTTAATTAGCAACAGTCTTCATTCAAACCTTAGCTTTTTTGTTCTACTAATGGGTTTAACTGCTACAAATAAAAACAAAGCCACTGATAGTATGGAATATAACCTACAGTATCTTTCGTGCTATCACCAAGCGTATTTCTGCTAAGGTCTAAAAGTCGAACTTTGATAGATCGAAGGTGAGTTTTTTATTCATTGGCAATGAGTTAGCTGGAGTTTGGTCAATGGAATATTATGCAACATCCACTATAGCGGATAAAGGTTGAAGTGGATAATCAATGGATAGCTTATGCCATCAGATGTTTAGATATATGGCGGGAGTGCTTTCGTCGTTTTTCAAGGTGATCACAATGGGTGGCTTAGAAAGAATGACTCAGTTGGGGGAATTCTGAGATATAGAGTGCCACTGATATGGTTTTCGATGGCATGAAGCCATCGAATAGCCTATCGGAATAGTTCTTTTAAAAGAAACGGACGGGTGTTATAGCGGTGGACAGACTCGAAGGGCGGCACTGACTTGCAGTGCCTGCCGTCCAACCAATCTCTTTACCCAATCGCCAGTTTGGTTTCTGCGTAGCGCAGTTTTTTCACCCTTGGATTCGCGAGGAAATAGGCGAGCATGAGTGGGCCGAGGCGTCCCATGAACATCATAAAGATGATGATGGCTTGGCCTAAAGTGGTTAAGTTACCTGTTAAACCTCGGGACAAACCGACAGTGCCCAGTGCGGATATGGCCTCGAAGACGATATCGATTAACGGCGCTTTCTCGCTTAATACCAGTATGAAGATGGCCAGCCAAGATACGACCAGAGAGATGATGGTCAGCGCTAAGGCTTTGCTGACGGTATCTTTGGGGATTTCCCGTTTAAACACATAGATGGCTTCGTCGCGGCGTAAATAACCATAAGTGGCCAAAATCAGCACCATAAAGGTGACGACCTTAATGCCGCTGGCGGTACTTAATGAGCCGCCGCCGATAAACATCAGCACGAGTATGAGCAAGGTACTGGCGTCTTCGAGTTGGTCAATCGCTAGGGTATTAAAACCTGCGGTGCGCGGTGTAACGGCTTGAAACCATGAGGCTAACCATTTGCCGAGTTCGCTTAAGGGCGCCAAGGTGTTGGGGTTGTTGTACTCAATAAAGTAAATCGCAATCACAGCGACCGCGTTGATGACAATAGTGCCGGTGATCATCATGCGGCTGTAAACGCTGAGTTGTGACCAACGTTTGTTCCTACGCAGATCTATCCATACCGAAAATCCTAAACCGCCAATGATAAACAAACCTGTGATGGTCAGGTTGATCACGGGATCGGCCACATAGGGCATTAGGCTGTCGGCACTGAGGGCAAAACCTGCGTTGTTAAAGGCACTAATTGTATAGAAAAAACCGTGGAATAAGCTGGTTTGCCAGCCGAGTTCATGGCTCCAATGCACTGATAGAATCAGCATGCCTACGGCTTCAACCAACAGTGAAAACACAAATACGGCTTTGGCGGTCGAGACTAATGTCGATGTATCGGTTTGATTAAACGCCTCTTTTGCGACGGTTTGCTGTAGAAAGCCAATCTTACCGCCAAGGGCGATAAGGGTGACAATCGCAAAGGTCATCAGCCCAAGCCCGCCGCATTGGATTAGCAGCGCAATGACAATTTGGCCAAAGGGCGTAAAAACCGTGCCAGTATCGACAACAACCAACCCAGTTACAGTGACTGCTGACGTTGCGGTGAACAGACTTTGCAGCCAAGTAATAGAAGATTCGGTCGCTATCGGCAGTTTGAGTAAGCAAGTGCCTAGGATGATCAGCAGTGCAAAGCTGACGCTGAGGATAAAAGGCGGCGCACCAATAAGCTTTTTACCGGGATTCGCTTTGTGTTCAATTGCCGTGACTGAGGGATGCCATTGAACCACCTTACACCAACCTTGGTGCGAGATATTGTAACTCGGTGCGGCTGCCACATAGCAGCAAAGCATCGTTGGTTTGCAGCACAAAACTGCTATCCAGCTTACTGATCACCGTCAGATCCCGCTTCACCATAATGGCCGCGACTTTGCCTTTGGGATTACGCTCCACATTGGTTAAGGCAGCATCCTTGTCGTCGAGTGCCGAACCCAGCAACTTAGAAAGTACGGTTTGATCTAAATGCGCTTTAATATGGATTTCAACCACATACATACCATCGCCGATCGGTAAGAAATTACTCACCATAGGATAATTGAGCGACTGGGCGACGCGAATGCCCATTTCTTCCTCTGGATGTATGATGCGGGCAACGCCGAGTTTAGACACTATCGTGTGGTGCGCCTTAGTGCTGGCCTTGACCCAAATCACCTTCACGCCCAGATTCTTGAGTGCCAAGGTGCACAGCAAGCTAGATTGCATGTCTTCGCCGATGGCGACTAACACCACTTCACTGCTGGCAAGGTCTAACTCCCGCAGCGCCGTTTCGTCTGAGCAGTCGCAAATTACCGCTTCGGTGAGGCTTTCTACGTATTTCTCCACCATTTTGGGGTCGCTATCCACGCCTGTGACTGTGTGACCTAAGTGGATCAGCTCAAGGCTGGCGGCGATGCCAAATCGCCCTAAGCCAATAACGGTAAAATGCGCCATACAAACATCCTTTTACGATTTTTTTGAAAGTGTTGTTGATGATAAAAACGTTATTTTTGACTCATGTCAGTCAAGCGATAACCGAGTCCCGCTTCTGTCTTGATCAGCTGTTGCTCTGTGCTGTCGTTGAGCTTTTTGCGTAACTGACTGACCAAGATCCGTAGATAATGACTGTCATCGGTATGGGTTTCGCCCCAAATATCCCGCAGCAATTCAGTTTGTTTTACTAACTGGCCGGGGTGAGACATTAAGGTCTCAATCAGAGCGAATTCCTTTTTAGTTAAGGCGATTTCTTGCTGGTCGAGCCAAAGCTGGTGGGTACTTTTTTGCAGCTTAAGTGCGCCAAACTGCAGCACATCACTCAAGGCAGGTTCGTCCACTAAGTCGCGGACTAATACCTTAATGCGCGCGATTAACTCCCTGATCCCAAATGGCTTGCTGAGATAATCATTGGCGCCCGCTTCCAACAGGCGGATTTTCTCTTCCTCTTGATCGCGGGCGGTTAAGATAAGCACGGGCACTTTGTCATGCTGCCGAAGGTTTTGCAGCAAGCTGATGCCATCGCCGTCGGGTAAGCCAAGGTCGAGCACTAACACGTGTGGCCGTTGGGCTTGATATTGGGCGAGGGCAGAGGCAATCGAGGCGGCGCCATGATATTCAAAGCCTTCCGCTTCTAACGATATCCGCATAAAAGTGTGAATTTGCACTTCATCATCGACAACCAGCACCTTGTAAGCCATTTATTGCTCTCCCTTTTTAATCGGCAAAGCGATACGAATAAGGCTGCCCTTAGCCACAGGTTCGGCGCTGATCTGGCCGTCATGGGCGGTAACAATGCCCTTAGCAACGGCAAGCCCAAGGCCTGCGCCGCCATCGGTGGAGGGATGCTGGCGATAAAAGAGTTCAAAAATAGCCTGCGCCTCTGCGGCGGCGATGCCTACGCCCTCATCTTGAATATCTAACCGCAGCATATCGCCCTGTTGGTAAGCTTTGACTGTGACGGGTTTATCTAAGGGCGAATAGCGCAGCGCATTATCCAACACATTAAAAATGGCCTGTTCAATCAAAGAGCTGCTGATCATCAGTGGCGGCAGTTCGGCCTCGCTGTCGATAACAATTCGCGGCTGCGCGGCGGGAAAACGGCCGATAGCTTGGTGCAACACATTCATCATGGCGTCTTCACTCAGGCTGAACTTGAGGGCGCCGTGTTGTAACTTAGTGGCCTGTAACAGGTTTTCGATATATTGATGCAGACGATGACTCTCGGCGGCCGCGCTATCGATAAGCTCAGTCTTTTGCGACGCACTGAGCTTAGGCATGTATTCCTTCAGGGTCGTCAGGGTGCCTATTATGGTTGAGAGCGGGGTGCGTAAGTCATGGGAAACCGACAGTAAGATACTGTTACGTAGCTGAGCCTGTTCGAGTGCTGCTTGTTGGCGGCGATAAAGTTCGGCCAATTTGCTGGTGGTCAGAGCGACAAGTAGAAACACAGTTAGATTGAGGATGTCCTCAAGATTGAACATCTGTAGCGAATAGCGCGGCGTGGTAAACAGAAAATTAAAGCTAGTCGCCTCAAACACAGCTGCAAAATAGGCAAAGTTAGCGTTGCATTGCAGGGCCACAGCGACCACGGCCAATTGTAAGATCAGCAGCACGGCTATGGTGGAACCAGCAAAATAATCGATAAAAGCACTGCTAAAAATCGCGATAACTAGCACTGTGCCGGTAAAAAATGTTGGGTGCGAACGCCAGTATTGAGTCATGGGAGCCGCTATGGATGAAGTCATGGATAGAAAGACGCATATAAAGAAAGTGTCTGTACTCTAGCAAAGCCATTTTAAGATAGCGCGTAAAAAATCCGTAAAATTTATAGCCCTGTGAGTGGCTTAGCGTGCAGTTTGCTCGGGCTGTTCTCGCGCACTCATATCAAGCTGCGAGCTAATAATCAGGCTTACCCCATAGATTCATGGGGTAAGCCATTCTAGGCGTGGGCAGTTATCGGATTGACGCTAATGGGCTAGCTATCAAGTATTTACCCATCGATCAGTTTCCGAACATCTACTTATCAGCCCATTCTTTAGCTGAAGATCACCAAGGTATCGGCGGTGGTACTAAGCGCTTGCCGTCATAGCCTGGCACAGTATCAAAGCGTGTCGAGTGGGCTTCCCAATCCGCTTGAGCCTTAGCGATTTCGTCTTTGCTGTGGCCAACAAAGTTCCACCAAATACTGACAGGATCGGCCAGTGGTGCGCCGCCGATTAGCAATATGCGGCAACCCGCGTCGAGTTCGACGGTGACGCTTTCGCGTAACATCCCCAGATAGGCGAGATCGTTGTTGTTGAAGCTTTGGTCATCGATACGGAAAAAGCCTTCGAGTGGCATGAGGGCATATTCAAAACTCGGGTCGAGAGTCAGAGTGAGCTCAGTGTTTTCGGCGGCAAACAAATCCATCGCCACTATCGGCGAAAAGTGCAATGTGGGCGCCTGTTGATGCTGCCAACTGCCAATTAGTAGGGTGAAATCAACGCCCGCATCGTGCCACGTCGGGAGTTCGGGATAGTGATCGAATCTCGGCGCTGTATCTTTATGCTCAAGCGGCAGGGCAATCCACAGCTGCGCCGCGTGCATAGTGCTATGGCCTGCGACGGATTCTTCGGTGTGGGCGATACCGTGTCCGGCGGTCATCAAGTTAACTTGTTTAGGGCGGATCACTTGGGCGCTGCCGAGACTGTCCTTGTGCATGATTTCGCCTTCGAGCATCCAAGTAAAAGTTTGCAGCCCAATGTGCGGATGCTGGCCAACGTTTAGCTCAGGGCCGTCGGTGACTGGGCCAATATGGTCGAGGAAACACCAAGGGCCGATAAGACGACGCTCTTTTTGTGGGATCGCCCGTGCGACGGGAATGCCGCCGACATCACTGATTTTGGGGGAAATTCGTTGAACTTGAATGCGGCCATTTTCAATAGGGCATTCCTTAGGGCCACCATAAAACTGTGCTTGAACTTCACTCATGTTCGCTCCTTATTCCTTTGCGAGGACACACTTAGTTAGTGTTAGTCTGGCATGCTGAACTGGTTTCAGATCCCATCTTGCATTCGAGCCGAAGGTTTTAGCTTCGGGTATTCCCGTGGATTTTGCAAATCGACGCAGTCATATGACTGAGTAATTGAGCTGTTAAAATCGCCCTTCGATAACTCTGTTAACCCAATTCAAACGTGAATCTGTGACTGAGCCCGCGTGTATGGGAGTTTTTGCTGTCCTTTACTTTAGCAAGCCTGTATTTATGTGGGGCATTAGCGTTTGATGTTTTACTCGAGCCGTAATAGCGGACGAGTGTTGGGTTAGTCTCGGTGATTTTATCCTTCACGCGTTAAGTCAGATCTGCAAAATCGAGTCTATCTTTTATCGGTACAGGTGTTGGCGGCGACATTGCCAGCCTAAGCCGTAATCCATTCAGCATACTGCTTAACACTAACACAGATAGAATTCACATATAGAATTAGGCTGTCACTCAGCGTGATTGATTAAGGGGAAAGCGTGCTCGAAAATCAAATGATGTTACTGGCGGTATTTGAACGGGCGGCCTTGATGCTGATGACCTTGTTCTTTTTAACCCGCACTCGACCGTTCCAACGTTTATTCCAAAAGCGTAATCACACCCCCGCAGAGCTGGTCTCTGTGGCGGCGATATTTTGCTTATTTGCGGTGTTTAGTACCTATACCGGTATTGAGGTTGAAGGCGCGCTGGTTAACGTGCGGATTATTGCGATTATTTCTGGCGGGATTTTATTTGGCCCTTGGGTCGGCATTCCCGCGGGCGTGTTGTCGGGGCTGCATAGATATTTGATTGATATGGATGGTGATACTTCTATCCCGTGTTTAATCGCCAGCATAATTGCCGGTCTAATGGCTACCTGGATTTACTACAAATGCCCCAAATCTAAGTTATGGATTTATGGCATTGTGGCGGGCATGTTGTGCGAAACACTGACCATGTTGTTGATTTGGCTGCTGACAGAGCCGCACCAAGTGGGTGTTGAGATTGTGAGTCATATTGCCTATCCCATGATTGCGGGCACTGTGTGTATCGGACTGATTATCAAACTAGTGCAAGACCTCGACGATGAGAAAGAGCTTATTGCCGCCAAGCAGGCCAAGCTCGCGCTGGATATTGCCAACAAAACCTTACCTTTCTTCCGCAAAATTGATCGCCATGCCTTAGAGCAAGTGTGCGGTGTGATCCGTAATGAAATCCACGCCGATGCCGTGGCCATTACCGATACTCGCGATGTATTAGCCTATATGGGCGTTGGCAAAGATTACTACGAGGTCGATGGTCATCAAGCGATTAGCGAGATGACTCAAAGGGCGATAGAGCTAGATCAAAACATTATTAACAACGACTTACGCCAATATCACTTATCGGACTTTCATTCGGTCATCATAATCCCGCTGCGTGAAAACGGCACGGTAAGCGGCACGTTGAAGATTTATTATCGCAATAACTATCGGATTACCAGCTCGCTGCGGGAAATGGCGGTGGGCTTGTCGCAGTTGATTTCGACTCAGATGGAAGTGTCGCGTATCGAGCAACTGCAGGAGATGACCCGCAAGGCGGAGTTCACTGCGCTGCAGAGCAAGATTAACCCGCACTTTTTGTTTAATGCCCTCAATGCGATTTCCTCACTGATCCGTATTCGGCCGCAGCAGGCGCGGGAGTTGATTGCCAATCTGGCCGATTATCTGCGCTATAACCTAGCCAAGGGCGATGAGTTGATTGATATCCAAGAGGAAGTCAAACAAGTACGCGATTATGTGGCGATTGAGCAAGCGCGCTTTGGCGACAAGCTTGAAGTGGTGTTCGATGTCGACGATGTGCATTTTTGTGTACCTTGCCTCTTGTTACAGCCGCTGGTGGAAAACGCCATTTTGCATGGCATTCAGCCGCGCAGTGCGCCGGGTAGAGTGACCATAGAAGTGAAAAAACTCGATTCTGGCATTCGCGTGGCCGTGCGTGACACTGGTTATGGCATCAGCCAAGAGGTGATAGATGGCGTCGCCGCGGGTCGCATAGAAAGCAGCAGTATTGGGCTGATGAATGTGCACCAGAGGGTGAAGTTATTGTACGGCGAAGGGCTGCAGCTTAAACGGCTGGAGCCGGGGACTGAAGTCAGCTTTTATTTACCGGAAAATGAGGCGCAAGTATGTTAAAAGCCATCATAGTTGAAGATGAATATCTGGCCCGTGAGGAGCTAGAGTATTTAGTGAAGAGCCACAGTGAAATAGACATAGTGGCAAGCTTTGAGGATGGCTTGGAGGCCTTTAAATATCTGCAGGATCATGAGGTTGATGTGGTGTTTTTGGATATCCAAATTCCATCCATCGATGGCCTACTGTTGGCGAAGAATCTGCATAAATCGACCCATCCACCCCATGTGGTGTTTGTGACTGCCTATAAAGAATTTGCGGTAGAAGCGTTCGAGCTCGAAGCCTTCGATTATATTCTCAAGCCCTACAATGAGCCGCGGATCATCAGCTTATTGCAGAAGATTGAACAGGTGGGACGACAAGCGCCAAAGCCGCAACATGAAGCCGCGAGTAATGCGAGTCGTACCGTGAACTTAGTCAAAGGTGAGCGGATTATCGTTACGCCCTGCGAGCAAATTTATTACGCCGAGGCTGATGAAAAACTCACCTATGTCTATACCCGCACCGACCGTTATGTGATGCAGATGACCATTAGTGAGTTTGTCAGCCGCTTGCCCGCCGAGGGCTTTTTCCGGTGCCACCGTTCCTATTGCGTCAATATCAACAAGATCCGCGAAATCGTGCCTTGGTTCAATAGCACTTACCTGATCCGCTTGCATGATTTGTCGTTCGAGGTGCCCGTTAGCCGTAGTAATATTAAGGCTTTTCGACAGCTGATGAGGCTGTAATTGTCATTCATTCCTGCAGATCTGTATTTCATGCCTTAATTGATCGGCGCGAATGGCATCTCTCTTATAGTGGACTCAATTCCTGATCCCCTAAAGCGAGATGCATCATGACTAAAGAGATGAACCGTACTCGGTACCTGACCTTAGTTGGTACTATTATCACCCAGTTTGCACTCGGCTCAGTTTACACTTGGAGCCTGTTCAATGCGCAGTTGGCCGTTAAGCTCGACGAACCCGTCAGCCAAGTGGCGTTTGTGTTTGGTTTGCTGAGTTTGTCTTTGGCCGTGGCATCTTCCATGGCGGGCAAATTACAGGAACGTTTTGGCGTACGTAATGTCACCTTAGGCGCAGGCCTGTTACTCGGCGTTGGATTCTTACTCACTGCTCAAGCCAGTAACTTGATGATGCTTTACCTGTGCGCAGGGATTTTAGTGGGCTTTGCCGATGGCACTGGCTACTTGATGACTTTATCTAACTGCGTGAAGTGGTTCCCCGAACGCAAAGGGCTGATTTCGGCGTTAGCGATTGGCGCTTATGGTTTAGGTAGCCTCGGATTTAAGTACATCAACGTGCTGTTACTCGAAAATACTGGCCTTGAAACCACCTTCCAACTGTGGGGCTTAATCGCCATGGCGCTGGTGTTGTGTGGCGGCATGTTGATGAAAGATGCGCCAGCACAATCAGCCGCAAGTCAGCTGGCCGAAAGCCGCGACTTCACCCTCGCAGAAGCCATGCGTAAACCACAATACTGGATGTTGGCGCTGATGTTCCTGTCGGCCTGTATGAGTGGTTTGTATGTGATTGGTGTGGCTAAAGATATCGGCGAGAAGATGGTCGACTTGCCTGTACTGGTCGCCGCCAATGCTGTTGCTGTGATTGCTATGGCGAACCTCAGTGGTCGTTTAGTGCTGGGTATACTGTCGGACAAAATCCCTCGTATTCGCGTTATTTCACTGGCGCAGATCATCACCTTAGTCGGTATGGTGTTACTACTGTTTGTACCTTTGAATGCTAACCTGTTCTTCGTTGCCGTTGCCTGCGTTGCCTTTAGTTTTGGTGGCACTATTACTGTTTATCCCTCACTGGTGAGCGACTTCTTCGGCCTGAATAACCTAACTAAAAACTATGGGGTTATCTACTTAGGTTTTGGTGTCGGCAGCATTATCGGTTCGATTGTCGCCTCGTTATTCGGTGGTTTTATTGCGACCTTCAACGTGATTTTAGTGCTGCTGGTGGTGGCGTTAGTGATGTCATTAACCATACGTATGCCAGAGCCTAAAACCCCAGTGTCAGCCAAGAAAGCCACTAAGAGTACACCGCGCTTAGTGAACATGACGGCAGAATCGGCGTAAGCACAAACACTTAGTCCGGCTTAGCCAAAAGCTAAAACAAAAATCCGCAGCCAAATTGCTGCGGATTTTTTGCTTTTTTATCACCTCAACAACAGGTAACTTGATAGCCATAAAGCGTTATTCAGGATGTGTTGATGTTAACGGACCCTCTTTTTTGGTTGGTGGCGATACCCGCAGTCTTGATTACGGGGATTTCTAAATCCGGTTTTGCGGGTGGCGTGGGCGGCTTAACTGTCCCGCTATTGGCGCTGGCCATTAGTCCTACCACAGCGGCGGCGCTTATGTTACCGCTGCTGATTTATATGGATTTTTTAAGCGCGCGTTCTTGGTGGGGGCAACATAATCCGCGTCATTTATGGATATTACTGCCCGCGGCGATAGTGGGTATTTTTATCGCTTATCTGTTGTTTGATAGGTTGAATGAAGAATATCTACGGGCGATTTTAGGGTGTGTGTCCCTCGGTTTTGGTTTGTATGGTTTGATTCTGGGGGATAAAACTCAAGCATCACCTTCACCTCTGGTTGGACGCTTGTGTGGCTTAACTGCGGGATTTACTAGCTTTGTTGCCCATGCGGGCGGGCCGCCACTCAACGCTTATTTGTTACCGCTGCGTTTGGCTAAGGCGGAGTTTTTAGCCACGGCGGTGGTATTTTTTGCGGTGGTAAATCTGGTCAAGCTTATCCCCTACAGTTTGCTTGGGCAGATTAACCAAGAGAATATTTTAGTTTCCCTGTTACTGGTGCCGCTGGCATGGTTAGGGGTAAAGCTAGGTTTGTTTATTCAAGATAAAATTAACGACAGGCTATTTAAGCGCATCATCTTGATTTTAATGGTGCTGGTGGGCGTGCGTTTATTGTCGACGGCGTTATAGTTTTAAGCTTTGGGTTACATTAGTCTGCTAGAGTGCGCGGGTGTTGGGGCATTTTGATCCCCAGCCGCTCTGAATTTACCTGATAAAAACTCAAAATTACGTTACTGTGAAATCTGCCTGCAATGAGTTTGAAGCTATGAAACGATTACTGATAGTTATCTTAGCCTTAGGCCTTGGCGCTTGTGCTAGCGCGCCAGAACCTAAGCCTGTGGTAAAGCAAGTTGAACCCGCCACGGTTTGGAACGATAGCAATATCTCCGAACTGCATTCTGAGTGGCGCGGTGTGCCCTATCGTTTAGGCGGTGGCACTAAGCGGGGCATAGATTGCTCGGCCTTTGTGTCGGTCGCCTATCAGAAGATGTTGGGTATGACGCTGCCGCGCACGGTTGAAGAACAACAAGCCCTAGGCAAACCTGTGCCGAGAAATCAACTGCGCAAGGGCGACTTGGTATTTTTCAAGACAGGTTGGAGCACACACCATGTCGGCATTTATGTGGGAGACGATAATTTTCTCCATGTTTCCACTAGCCAAGGGGTGAAGATTTCGAGCCTGCATAACAGTTATTGGGCGTCTAAGTATTGGAATGCACGGCGAATTTAGCACATCTTCTTTATGCACTTTCTTTTATGCACTTGGGCTGATGAGTTAGCGGAGACAATATGTCCTTAGAGCCTGCGTTATCCCAGAGTGACCCTTTACCGACTTCTGTTTTTCTCGCCATTTTCTCGGCCGTATTCTTGCCCATGTTTTTGGCGGCGGTCGATCAAACCTTACTTGCCACGGCGACGCCAGCCATAGTTGAAGATCTTGGCGGCCTGCGTCAAGCCTCGTGGATCACTATCGGTTATATGCTGGCGATGGCCGCTTCTGTGCCGATTTATGGCTGGTTAGGGGATAACTTCGGTCGCGCGAAAATCCTGATGATCGCCATAGTGATTTTTGCACTGGGTTCCATCGTTTCGGCGAGTGCGGGCACTATGGATCATATGATCGCCGGACGTATCTTGCAGGGCATGGGCGGTGGCGGCTTGATGAGTTTGTCTCAATCTTTGATTGGCGAGTTAGTTCCCATTCGGCAGCGGGCGCGTTTCCAAGGCTATTTTGCTGCAATGTTTACCCTAGCGAGTGTTGGCGGCCCTGTCATTGGCGGTATTGTGGTGCATGCTTATTCGTGGCATTGGCTATTTTGGGCGAATATTCCACTAGCCATGCTGGCGGTATGGCGACTCAATGGGCTGCATAAGCGCAGCGTTAAACCCGTACGTCAAGGCAAGTTTGATTTAGTGGGCGTGGTCTTGTTTCCGACCATTATCACCGCACTGTTATATTGGTTATCTGTGGCGGGGCAAGAGTTTGCTTGGCTTTCTGCGACCAGCTTAGGCTTTGCCGTCTTTGTGGTGTTCGGCATTCTAGGTCTGCTGTTGTGGGAACGGCGATTGGCGAGCCCGTTTCTGCCGTTAGATTTACTGGCGAAGAAGGCCGTGTATATGCCACTACTAACGGCGGCGCTGTTTGCGGCTTGTTTGTTTGCGATGATCTTCTTCCTGCCGATTTATCTGCAAGTGGGTTTACACACCAATCCCGCCAAAACCGGTTTGCTGCTCTTGCCTATGACCTTTGGTATTGTCACTGGCTCGACGATAGCAGGCCGACTGTTAAGCAAAGATGTCGCGCCTAAGTGGTTGCCAACCTTTGGTATGGGCTTAGCTTTCATCGGCTTGTTATTGATTAGCTTTGTGCCGCCTAATGCCAATGTGATCGGCGGCTTAGGTGTGTTGGTCGGAATAGGACTAGGGACTGTGATGCCGAGCGTGCAGCTAGTGGTGCAGAGCGTGTCGGGCAAGTCGCGACTCAGCCAGATCACGGCCATGGTGTCACTGTGTCGTTCTATGGGCGCAGCTATCGGTACTGCGCTGTTTAGCGTGCTGCTTTATAGTCTGTTGCCACTCACGGGTTCAGAGCTTGGCATTGCCGCGATTAAAACATTGCCGACAGAGGTGGTGCATCACGCCTTCCAATATGGATTTATGGCCGCTGCGGGTGTGGCATTGTGTGCCGCGATAGTCGGTTTTCTATCCCCTGCGACGGCATTAAAAGATCACGATAGCGAAACTGCGCATTAAGCCTAGATTTGCAGGTGCACGACCCATTTAAGCCGTGTACTATTTGCGCTGAGCCGTATTTTTGTGGCCTTATTCGATTCCAGGACATAAGCATGACAACAACAAAACACTGGACCCTAAAGAGCATAGCCAACGAGTTGGGTGTCTCTAATGCCACTGTTTCTAACGCGTTTAATCGCCCCGACCAATTATCGGAAAAACGTCGTAACGACATTTTAGCTGCCTGCACTAAGTTGGGATATTTCGGGCCAAATAAAGCGGCTCAATCCCTACGTAAGGGCAAGTTTGATACTGTCGCCTTGGTACTGTCGGACAGCGTGGAATACATGGTGTCCGATCCCGTCGCCAGTAAATTTATGAAGGGCGTGGCCTCTGTCTTGGAGCAAGAAAAGCTCAATTTGCTGCTGTTTTCGGGCAGCTCAGACAGTGTGAATGCGGTGGCTGACTTTGTGGATGGTTTTATTTGTTATGGTCGCCCGCGCAATGCGCTGTTAGCCGAGCAGTTAAAGCAGGTTAAGAAAAAGGTCGTCACGGTCGATTTTGATATCCACCGCAATGCATCAGTGAGCATAGACAATAAGTTAGCTGCCTATGAAGTGGCGAAACTGGCATTGCAGTCACCCGCAGACAATGTGGCCATTTTAGGGTTACGTTTGCTCGATACCCACTTAACGTGCCGCGTTTATGATTTGAATTTGTTAGAAATCGACACCTCTGTTGCCCATCAAAGATTACAGGGTTACTTGCAAGCGATTGAAGAAGTGGGCGTTGTTCTGGGGCAGGACCGCATTTGGAATATCCCCGAGAGTAATGCCGACTATGCGGCGATAGCGGCAAAAGAAGCGTTGAATTCGACTCCACGGCCGAATGTATTTTTATGCATGAGTGACTTAATCGCCTTGAGTGTGCTCGGTGAGGCGCAGGCCATGGGGCTGAATGTTCCTGAAGATGTGCGAGTCGTGGGCTTTGATGGCATAGATGAAGGCACACGTTCAAATCCGCCGTTAACGACCGTCTATCAATACTCTGAGCAGAAGGGCCGGAAAGCGGCGCAAATGTTTGTTAGTGACGCCGTGCATGCGGAAGTCTTGGGCTATGAATTACGTCTCGGTAAGAGCTGCTAATCGCTACATCTTTTAACTAAAAAAGGCGCAATGAATGCGCCTTTTTGCTATCTCAAAACTATGCCGCTTAGCTTGAACTTAAACCTAAGCTCAAGTCAGAACAGGAACGCAAGCTTATGCTTGAGCCAGCAGTTTTGATAACGACACTTGTGTCTCTTTACCTTGCTTATCTAAAAGGATGATTTGGCCATCTTTATTGGCAAAGTCGAGGGTGAGGCTCGCCTGCGAATCGGCCGCTGTCCAAGATAAGGTTAACGTGCGTTCTGCTGATGCCATCGTAAAGCGGCCATCGAAGGCTGGGCTGCTATTACGCAGTTTAATCAGCTGAGTCAGCGCCTTAACGACAGGTTTCTGCATGGCTTGCTGCACTTTTTCACGGTTCAAGTAAGGGCGATTGATATCGCGACCGACATGGGTGTTGTTGAGCAGCTCCATATCGTTTGGAATCGCCAGCAGACCCGCGTAATACACCTGCGGAATACCCGGTGCGAAAAATTGAATCGCCCGTGCCATCAAATAATCTAGGTCATTCTGACCGAGGGCATCGTAATAGGTGCAGTTGATTTGATACAAATCGACGTTGCTGGCGGCCGCGCCTGTGGCTTGTAGACTCTCGCCCTTACTGTTGCCATGTATGGTTTCAACCAGATTATCGATTTGATTTTCGGTCAATAATCCGCGCAAGCCATCCATAGGGCCGACGTCGATAATGCCGATGCCGTCGTGGGTGTCGAGTACGGTAATGCAGTTGCGTGGAGCCATTTCCAGCCAATGGGTCAAGGCTTGGCAATCTTGGCTAAACAGCGTGTGCAAGATGAGCGGTGGCAGGGCGAAGTCGTACACCATGTCGACCCGCTTAGCGATTTCGACCTGAGTCATGTGGTGCGAATGTATTTCGGCCAGCGTTGTCATGCCTAACGCATTGGCTTGCTTAGCTAACTTATCCACGAACTCGAAGGACTCTTCAATCATAAAGCAGCTAGTGCCGGGCTTTTTAATTGCATAACCCGCCGCATCTAAGCGGATCAGATTCACTTTACTGTGATCGAAACGCGCCAGCACTCGGTTTAAATAGTCTTTACCCGCGGTGGAATGAACGTCGATATCGATTTGATTGCGGGTAAAGGTGGTCCAAAACTCTTCGGTCGAGCCATCGGCTAAGGTGTAAGTGCTAAAACAACTTCCCGGACGCGGACGATAGATAGCGGCTTGATCTTGCTCGCTTAACCCCTCAGGAAATACCTTGTCTTTGGTTAAAAACAAATCCCAATAAGCCGATTGTTTGCCATGCTGGAGCACGTCTTTAAATTCGGGTGACTCGGCGGACATATGGTTAACGATAAGGTCGGCCATGATGTCGTAGTCATCACCAATGCGTTTAACGTCATCCCAATTGCCTAAGCGTGAATCGACTTGCACATGGTCTATCGGATCGAAGCCCGCATCGCTGCCGTCAATGGGGAAATAGAAAGGCAAAAGATGTACGCCGCCGAATAGACCTTGCAACTCGTCATCGAGTAGCGCTTTGAGCTCCTTGAGCCCTGCATTGGTAATTCTGTCGACATAGGTGATCAGTTGCACTTGGTTTTTCATACTTTGCCCACGCTTGAGTTGTCTTTTTAGAGTAAAGGTTTATGGCAATCTTTAATTGATTGCACAACTAAACTTAATCGATTAAGTTGTATATTTAACCAATATGAAACATTATTCAAGTGGTTTGTTGGATTTTTGAGTTAGTCTCAAGTTTTGGGGGAGGCTGGCTCACTGCTGTACACAAAAGCCGAAACCCTTTCAGCATGCAGTTACTGAGCTGCTGAGAGCGAGTAGGTATAATAACGATAAGGTGAAATGGTATGGTAAGTGATTCAAAAGGTTGCGATAACCGCAATCACACCCGTATTCGGGTACTCACATATTTAATGTTTTTCATGTTCGCTATGACCTCAGATGCCGTGGGCGTCATTATTCCAGAGTTGATATCCCAGTTTGGTTTGTCCATGTCGCAGGCCAGTGCTTTCCATTATATGCCGATGATTTTTATCGCGATGAGTGGACTGTTTTTAGGCTTCTTGGCGGATAAGATTGGGCGTAAGTTAACCATTCTATTGGGGTTATTATTATTCGCGCTCGCCTGTTTCATGTTCGCGCTGGGGGAATCGTTTTACTATTTCTTGTTCCTACTCGCCTTTGTCGGCACGGCGATTGGCGTCTTTAAAACCGGCGCACTCGGGCTTATCGGCGATATCTCGACTTCCTCAAAACAGCATTCGAGCACTATGAATACGGTAGAGGGCTACTTTGGTGTCGGTGCCATGATAGGGCCGGCCATCGTTAGTTATTTACTGATCAGCGGCGTCTCTTGGAAATATTTGTATTTTGGCGCGGGTTGCTTCTGTTTAGTGCTGTGTTGGTTAGCTTACCGGGCGGATTATCCGCAAATCAAACGTTCTTCGACGGATGCCATTAACCTTGCCAGCACCTTTAAGATGATGAAAAACCCTTATGCATTAGGGTTCTCGCTCGCGATAGGTTTATACGTTGCCACCGAAGTGGCGATTTACGTGTGGATGCCGACCTTGCTGCAAAGCTATCAAGGTGATTACACCACATTGGCCGCTTATGCCCTGACGATCTTCTTTACCCTGCGTGCGGGTGGGCGCTTTTTAGGGGGGGGGGTGCTCGATCGCTTTCCGTGGCAGCAAGTGATGTTCTGGTTTAGCTTCGCGATAAGTGCTTGTTATCTTGGGTCTATGATCTACGGCATTGAGGCCGCTGTCATCTTGTTGCCCTTGTCCGGGCTGTTTATGTCGATGATGTATCCCACGCTTAACTCTAAGGGGATTAGCTGTTTCCCCGTGGATCAGCATGGCTCTGTGGCTGGGGTGATTTTGTTTTTTACTGCCGTGTCCGCCGCCGTTGGGCCCTTGTTGATGGGGTTCGTGGGCGATATTTTCGGCCATGTTAAATATGGCTTCTACCTCGCCACTGGTTTTGCCATTTTGTTATGCCTCCTGAGCGGATTTAATCTCGTTAAAGATCCTGCTCGTCATTTGTTAATGGCTGAAGCGCACTAATTTGGGCTGTGACAGAAAATATTTTTCTGTCACATTTCTTCAATTAATCAATACTTTGACCTAGTGCACCTAGAGTGCGAACCAAAACTGTGCAAAAAACGCACTTGCTTTTTGTTGCCAAGTTGCTAACTTGTTAACTTAATCGATTAAGTTGATTCAGGGTTAAGTTCAATATGACCTTGAGCAATCACTAAGATGGTTCTTCGATTAATCACAATTCGGTAGAGGCAGCCATGGGGAGATAGGGCTGCATCTCAAATATGGGTGGGGACATAAAAATGATAAAAAGCACATCACAGAAATTCTTGTTAAGTACATTGGCCGTGTTGGTGAGTTCTCACCTTTGGGCTGCCGAAGCACCACAACAGGAAGAAGCCAAGAAAGACGCGTTAGGTTTAGAAAGAATTACCGTGACTGGGGTTGCCCGCGGCACTCGTGTTATGGACTCGAGTGTGTCAGTCAGCAGCGTGTCACTGGCTGAGCTGGAAGTGAGCTCGCCACGTTCATCGGCCGAGGCATTTCGTATTATTCCGGGGATGAAGGTTGAGTCTACTGGTGGCGAAGGTAACGCTAACATTGCCGTGCGTGGTTTACCTGTGGCGTCTGGTGGTGCTAAGTTCCTACAAATTCAAGAAGATGGATTGCCAATCCTACAGTTTGGCGATATCGCCTTCGGTAACGCAGACATCTTTTTACGCTTAGATTCAACAGTGCAAACCATTGAATCTATCCGTGGTGGCTCGGCATCGACTGCGGCCAGTAATGCGCCCGGCGGCATTATCAACGTGATCAGTAAAACGGGCACCTCGGATTCGGGCAGTGTTGCTACCACTCTAGGGCTGGATTACGACACTTTCCGTACCGACTTTGAATATGGCACTAGCATCAACGACAGCCTGCGTTTCCATGTGGGCGGTTTTGTGCGCACGGGCGAAGGCCCACGCGACGCTGGCTATAACGCCAACAAAGGCGGCCAGATCAAAGCTAACTTAACCAAAGAATTCGATAAGGGTTATGTTCGTCTGTATTTCAAACATTTAGACGATAAAAGCATTGGTTACTTACCTATGCCTATGTACTCAGATGGCAGTTCAATCCCAGGTTTTGATGCTAAATCCGATGCGATTCAATCCGCTTATTTCCAATCAACCTTGAGCTTAGGTGCCGATGGCGAACGTCGCCGTGGCGATATGCGCGATGGCATGAATCCTGTAGTTAATTCTGTCGGCTTAGAAGCCTCCTTCGATTTAGGTAACGATTGGCAGGTAGAAAACCGTTTCCGCTTCTCCGATGTGAGCGGTAACTTCATTGCGCCATTCCCAGCCGAAGTGGCCAACGGTAGCGACATCGCGACCAGTATTGCAGGCACTGGCGCTAAGTTGATTTACGCCAATGGACCTAATTCTGGTAGTCAGATGGACGGTTTAGCCATGCGTATCAATACCTTTGATGTGCAGATGAATGACTTTGGCTCAATCGTAAACGACTTCAAATTGACTAAAACCTTCGACGATACCAGCGTGACCTTAGGTTATTACAATGCGGTGCAAAACATCAGCATGACCTGGATGTGGAACTCTTATCTGATGGAAGTAAAAGGCGATAATGCCGCGCTATTAGATGTAGTTGCCGCGGATGGTACTGCGTATTCTGACAATGGCCTCTATGGTTATGGCGTACCTTACTGGGGAAATTGTTGCCAACGTAATTACGATACCGACTACACCATTAAAGCGCCTTACCTCGCGTTAGCCTCGAGTTTTGGTGACTTATCGCTCGATGCCAGTGTGCGTTATGACAGTGGTGATGCGAGCGGTAACTACGCCGGTAGCGTCCAATCGCAAGTGGATATGAATCTAGATGGGGTGATTTCAATCCCTGAGCAGAGCGTGTCTTCCATCGATAATGCGAATCCTCAGCCAGTGAACTACGATTGGAGCTACACCTCTTACTCATTAGGTGCTAACTATCAGTTTGCCAGTGACTTAGCTGCTTTTGCACGCTTGAGCCACGGTGGCCGCGCCAATGCCGACCGTCTGTTATTTGGTAAGGTGCGTGCCGATGGTTCAGTGGCGAAGGAAGATGCGGTTGATATCGTTGACCAGTATGAGTTAGGTGTTAAGTACCGTTACGATGATCTGTCTGTGTTCGCGACGGCGTTCTACTCTGAAACCGAAGAGCAAAACTTTGAAGCCACCAGCCAACGTTTCTTCGACCGTAAGTACAAAGCCAAAGGTATCGAAATCGAGTCTGCCTATTTTATTGGTGACTTCGACTTTAGAGGCAACCTAACCTGGACCGATGCTGAGATTGCTAAAGATGCATTAACGCCAGAAGTGGTGGGTAACACCCCAAGAAGACAGGCTGACTTTATCTACTCTTTGATGGGCCGTTACAACTTTGACCAAGGTTCAGTGGGTGTTAATTTAATCGGTACCACGGATTCTTATGCTCAAGATAATAACGACTTAAGTTTCGACGGCTACAACCAAGTAAATGCCTTTGCAACTTACAATCTGACCGAAGCCATGAGTGTGTCTTTAAACGTCAACAACTTGTTTAATGTCACGGGTATTACTGAAGCGGAAGAAGGCTCAATCCCTGATAACAATATCATCCGTGCCCGTACCATTAATGGCCGCACCACGAGTGCGACGCTTAAATACGAGTTCTAAGCGGATCTTGCTACAGAAACTGTTTTTACAGAAAAAATGTTAAGCCCAAAACAGCCCATGAAAGTGGGCTTTTTGCCTGCTACAGAATGCAGAATATAGAATACAAAGTTGGAGTGAAAATGACGGTATTACTCAGTTTCGGTGAGGTGTTAGTAGACTTGCTGCCAACCGATGCCACGGGCAAGCATCATCAGCCGATTGCGGGTGGCGCGCCCGCCAATGTGGCCGTGGGTTACGCTAAACTCGGCGGTAAGAGTTACTTTGCCGGTGGCATCAGCGCCGACCATTATGGCGTTATGTTAAAGCAAGCCTTAGCGGATCAGGGCGTGGCGACCGATTATTTGGCCGAAGTGCCGGGCGCCGCAACCGCGACTGTGCTGGTGAATTTAGATGAACAGGGTGAACGTACCTTTGAGTTCAATCGTGATGGCACAGCCGATATGCGCTACAGCAATCAGCATTTTGACAATATTCCATGGCAGGGGATTGATATCTTTCATCTTTGCTCGAATACCTTTACTGAAACCGCCATCTTTAACACTAGCCTTTATGGTGCGCATTGCGCCAATGCGTTCGCGACTTTAGTGAGTTTTGATGTGAACCTGCGTTTATCCCTGTGGCAGGATACGTCTTTACTGGCCGAGCGGGTTGAACAGTGTTTTCGTTACACTCAAGTGCTGAAAATGAGCCGTGAAGAAGCGGACTATTTAGCGCTGGCGCGGGAAAAGAGTTTTGAAGATTATCTGCAGTTTTGCCTCGCCCAAGGGGTTGAGGTGATTTTGGTCACCGATGGTGCCAATCCAGTGCAATGCATCACAGCGCTGCAAAGGTTTAGTGTCCCAGTTCCCAAAATCTCTGCCGTCGATACTACGGCTGCGGGTGACAGTTTTATCTCAGGTTTCCTGTTCGCCTTAGGCTTAGAGCTGGAATACGATTTAGGCAATTTGACCTTGAGACAAAGGCTAGAAAGCAAAGCGCACTTAGAGGCTGCGGTGGTGTTTGCAACACGCTGCGGCGCGATTACCTGCGGGCAAAAAGGCGCATTCCCCTCTTTACCTACACTGGCGCAGGTTAGCTAAACCTGACTGCATTAACGATAAAATCCAAGGTGAGCCTGTTGAGTTGACCTTGGATTTTTTGCCTTTTAGATTTTCTGTTCGTCATTTTTCAATAGCATAGCGCGAACCTAATCACGTTTTAGTCGCCCCCTTTCCCGAGCATTTATGCTATTTTAGCTGCGACTTAAGGTCGAAGATGCAGTGGCTTCTTCCATGCTAGATGTCGACATTTTTTATTTCGGTAGGGTTTTATTTCAACATCATTGGGGATTTCCTATGAGCAAAGCAAAAATCGGTATTGTGACGGTAAGCGATCGCGCCAGCGCGGGGATTTATGAAGATATATCAGGCAAGGCGATTATCGACACGCTCAACGACTACCTCACCAGCGAGTGGGAGCCGATTTATCAAGTGATCCCCGATGAACAGGATGTGATTGAAGCGACGCTGATCAAAATGGCCGACGAGCAGGATTGCTGTTTAATTGTCACCACGGGTGGCACAGGACCTGCGAAACGCGATGTTACCCCTGAAGCCACAGAAGCCGTCTGTGATCGCATGATGCCAGGCTTCGGTGAGTTGATGCGTGCCGAGTCATTGAAATTTGTACCGACCGCCATTTTATCGCGCCAAACCGCAGGTTTGCGTGGCGACTCTTTGATTGTGAACTTGCCCGGCAAACCAAAATCGATTCGCGAATGTTTAGATGCCGTGTTCCCCGCAATTCCGTACTGCATCGACTTAATGGAAGGCCCGTATTTAGAGTGCAACGAAGCCGTGATCAAGCCATTCAGGCCGAAGGCTAAGTAAGCTTCTATCTAGCTTATAAAAACAATAAAGCCACTGAAATTCAATGGCTTTATTGTTTTGTTCGTTTAGGGCTCATAACTGATTAATCTGCTGCTTTAGCTCGTACTCTGGCTCTGTCACGATTTTTTCAAGTACTTGTACTCGCTCGATCAGTGCTTGGTTTTGTAAGCTGAGTTGAGTCACTTTATCCTTAAGATGGCTATCGTCTGCGGCGCCTACTCCAAATTGGCGCGCCTTTAGATAGTGCTTAAACATCTCTGTGGCACTGATTCCTATGATGAAGGTGACCATAATCACCATTAAGACATATTCGTTTTCCATAACAAACTCCTGTGATTATTGAAGTTTTCAACTTGGAAACGACAGGGTTATCGTTCCCAAGTTGATCAATCGATTCACCTTATAAACCGAAGATCACTTTTTCACTCTTTAGCGACCTTTCCATGCCAAAGGCGAGTAGTACCGCAATGGCGAGTGTGCCTAAGGTCGAAACGAATAACTGCAACATGGGCAAGTCTTTACCTTTGATAAAGTCCATTAATGCCTGTTGCTGACCCGAGACCGGTAGCCATTGCAGTATGTCTGGCGCTATGTTGTAGCTGGCGGCCATCGATAAGGCTAAGGGCACAAACAACACCATGGTTAGGTAGGATTGGGCTTCCTTAAAGGTTTTCGCCATAAAAGACACAAACAATTGCAGGCAAGCGGCCATTAACGCGACGGGAATACCGACCACTAACATCAGCGCCATAAATTCCGTGGTGATGTTGACGCTAAAGCCAAGCTCCTGCCAAGGTACAAAGGTGTAGGCGACTCTAGACACCAATAAAATCAGCACTAAGCCTAATAGGGCAAACAGACTCACAGCGATGATTTTCGACAGCACTAACTGGCGTGTGGTCAGTGGATGGCTGAGTAACAGAGCCAGCGAGTTACGTTCGCGCTCACCCGCACTGGTATCTATAGCGAGGTTCATGCCCGAGATAAATACCGAGTAAATCATAGTGAAAATCGCGATACCTAAGATCATCCCGCCCTTCGAATCTGTGGTCGCTTGGTCGTGGATACTGACTTTCAGTGGCTGCATCACGCGGGGATCGATACCGCGGGCAATCAGCCGTAGGCTGCCCATTTCTGTGCTGTAGGACTGGAGTTGCTTCTCGAGTCTGCGAATCGAGTTTTGCAGTTTTTCATCGGAATTATCGGCAACAATCGTGATCTCAGCGCCTTTGCCTTGATTCATCTGCGCGGCATAGTCGGGACTGATGATAAGCTCTATGGCTTTAAGCTCACTGTCCTTCACTTCACCGCTGCTAATGCCTTTATTCGACAAAAAACGCACTAAATCCGGCGCTTTATCGGGATTGGTGATAGTGATTTTAAGATCGTCTGGACTCGTCAGCTGACCAATTAACACCATAAACAGGCCGCACATGATCAGCGGCGTGCCGATGGCGTAGTAAAGCCCCGCCATCACAGAGCGCTTATCGCGGGCGGCGTCGATGAGTTCTTTACGAACCATAGCTATTATTTTATTCATGCTGTTATTCCTTTAACCATGTTCTTAAATGCCACGCCTTTACATATCAAGCTCTTAAAGGTTAAAACCTGTGGGCGTGATCTCTGCTGAGTCATCATGCTGCTATCCCTTCATCTGTGCCGATCAGTTGAATAAAGGCTTCTTCGAGCGAGTCTTTACCCGTTTGGGTACACAGTTCGGTGGGGCTGCCAATGGCGACGACTCTGCCCTGAGCCATAACAATCACTTGGTCACACAGCGCGGCGACTTCTTGCATCACATGGCTAGAGAACAGCACGCAATGTCCTTGATTCTTAAGGTCGATAAGAATGTCACGCAGCAGGCGAGTGCTCATCACATCGAGCCCTCGGGTTGGTTCGTCGAGGATAATATTGCTCGGGCTGTGGACTATGGCCTGTGCGAGGGCGGTTTTCATCCTTTGGCCTTGGGAGAAGCCTTTGCAGCGGCGATCGCTAATGTCTTCTAAACGTAATTTAGCAATCACTAGGCTGGTGGCCTCCTTAGCATCCTTGGATGATAAGCCGCTTAGCTCGGCAAAATAACGAATGTATTCCCGTGGCGTTAAGCGTTCGTACAGACCAAAAGGGTCGGGGAATAACCCGAGTTGCTGCTTAGCGCCGATAGGGTCGATGGCGACATCTATGCCTTCGATTTCTGCTTTGCCGTTATCGGGTTTCAGCAAGCCAAAAATCGTACGTAAACAGGTAGTTTTACCCGCGCCGTTGGGGCCGAGCAATCCTGTTATTTGGCCGTTTTGTGCGACGAAGCTTAAGTCATTGAGCGCCTGTACGTCACCGATGCGTTTGGAAAGATTGGATACTTTGATCATGGCTTACTCCTTGGCCCCAGTGCTGGCGTTATCATCTGCGGTGGTTTTTGTGCTGGTCGCGTTAAGCGGTTCTACCGAGCTGGCATTCAAATAAAAGCTGCGGCGCACATCTTTCTTTAAACATTCGCCGTCGAGATCTTTAACGGAACCCGAGCGAACGAGATCGGCGATAAGGTTATTGGCACAGGATTGATAGGCCACGCCGTGGGTCGCATAGGGGGCGACAAAGTGCTTGGCATTGGTGAGTTTTTCCATCGCTAGCTCGCCCCAACTCGGTGGTGTCGCGGGGTCGATTTCGCCGGATAGCAACAAGGTCGGAATATCGCTGCTGATGGGCTCGCTAAAGCTGTCATCGACCGCGGGGATTTTCCATACTTCACACGTGGCTTCTAAGCCTTCGAGCATAGTTTTGCCCATAAAGGAATGCTGAGCCTGCTCGCGCATGGCGGGCGTAATACGGTGCATATCTTCGCCACAGACCACGGATGCGTGCATACCCATCGCCATGCCAGCATTGTCGATAGTGAGGGAATACAAACCGAGTATGGGTTGGAAGTTGTGTTTCGCGGCTTGATGAATCGCATGGGGCACTAAGGCGCGCACATTGGCTTGATACAGCGCCATGCGGATGGAGCCGTAGAACTTACCGCGCGTCATAGTCAGCATAGTCTTTTCACCCGTGACTGGGTCGTAGACGTTTTCCATCACAGGCGCTTTGGCGAGTGAGGCCGCTACTTGGTCGAATTCGGCTTTAAGCTCAGGGAACTGGGCATGACAAGCGGTGGTTTCTTGGCAATCTTTAAAGAGTAAATCAAAGCCTCGATCAATCGACGCGCCGATTTCTAACACGCTTTGTTGCATCGGCACTATGCCGTCTAAGGTCACTGTGGCTAAGTGTGCCGGGTATAAACGCATGTAAAGCTGCGCCATACGCGTGCCGTAGGAGATGCCGTAAATATGCAGTTTTTTGTAGCCTAAATGCTGACGCACGGCTTCGAAGTCTTTGATCGCGTTGAGGCTACCGTATTGGGTGACGTCGGCATCGATTTTGGCTAAGCATTTTTGTGTTTCGGCTAAAGTGTCGGCATTGTCATCATCGAATGATAGTGGAGATTGCGCGCCTTCATCACAGGTCAGCAGATTCGAGCGGCCTGTGCCCCTTTGGTCGATTAGCAAGATATCCCGCTGTTGGCGGACTTTGCTCAGCATGGCATCGAAACCCGCCGCATTGTCTATGGCCGATTGTCCTGGGCCGCCAGCAATGGCGAGTAAGGCTTCTTCATGGTTGACGTTTTTCACCGCGGGTAAGACGACGTAATGCACTTGGATCTGTTTGCCATCGGGTTTGTTAGGGTTTTCTGGCACAGTCACAAAACCGCAGTTTAGGCGATCGGATACACCTTCCACATAACAAGTGTCGGTGTTATTAGCCATGTTCCTAGCGATATCAGCCGCCGCTTGTGTCTGTTGGCTTTTTACATCACTCGCCCACGTCGCCGTACTTGCCATCGCCAGCATAAGTGCGCTGACGCTTAACATGCGCTTGCACCATGTGGCATTAGTTGCGCCATGCCGTGGCCGAGTTGGCGAAGGGCGTTGAGCGGTTTTGCTTAACTTCCTGTGTGTCATGCTATCTATCATGCGCGCTTTCCTTTTTGCATTTCAGGACGAGTGTGTTAATTTAACTACCAGTGTATCGGTGTATCAATGTATTAATACAGTGTGTAGAGGCTAATGTTAGAACTTTTAAATGTCAACCCCAGTAGTGGTGAACCAATTTACAAGCAATTACACGAACAAGTCGTGCGTTTAATTGTGGGCGGTCAGCTACAAGTGGATGATGTCTTGCCCTCTGTGCGCCAGATTGCCGAGTATCTTGCGGTGAATCCTATGACTGTCTCCCGCGCGGTTCAGCAGCTTGTCGATCAGGGGTGGCTCGAACGCCGCCGCGGCCAAGCGACAAAAGTCGCGCTGCGCACTGAGACTATGGCATCGGGGGCGAGCATGCTCGAACCCCAACTCGATGCTTTGTTGTCGCAGGCTAAACAGTTAGGCGTCAGCCTGCCCGAGTTAATGCAATTGATAAATGAACGCTGGCAAGACTAGCGTTAACCTACTGTCACTTATCGATAAAATAGAATAAAAAAGGATGATATTCGATGGACCAAGCGTGCACACCAATACTTGAATTTAGCCAAGTGAATAAAGTCTTTCGCGGTAAAGGCGCAGGTGAAAAGTGGGCGCTTAAGGATTTATCGTTAACCTTGTCCGCGGGCATGGTGGTCGGGTTATTAGGGCAAAATGGGGCGGGTAAATCGACCCTAATGCGCTGCGCTTTGGGTGTGCTCATGCCGGATTCTGGCAGTATTGTTACCTTAGGCGAAACGCCCGAAAACTTAACTTCAAAGGCAAAGGAGCGCTTAGGTTATGTGCCGCAGCAACCTTTTGGTTATGAAGGTTTTACCATTGAACGGGCTCTCGATTTACACCGCAGTTTTTATCCCAATTGGGACATGCAATTAGAACAAGATTGGCTAGTACGGTTCGAGCTAGATGTGACTCAACAGGTGCAACGTTTATCTGTGGGGCAAAGGCAGTCGCTAGCCTTGATTATGGCGATGGCCTATCGCCCTGAGCTGTTGATCCTCGATGAGCCCGTCGCCAGTCTCGATCCGATTGTACGGCGTAAATTTATGGTCGATTTGTTCGATCTCGCCCTCGAATCGGGTTCAGCTGTATTGTTTTCATCCCATATCACCTCAGATATCGAGCGGGTTGCCAGTCATGTGGCGCTGATTAAACACGGCGAATTAGTGTTATTTAAAGAAATCGATGCCCTACGGGAACAGGTGCGCTTACTCACACTCGCACCCGAAACGGTTTTGCCTGAGTCGGCCACTGTGTTGAGTCGTGTCGGTGATTCAGTGCTGGTGGATTTAGGCGAGCACAGCGCGCCAATATCGGGCGTGTTAAAGAACGAAGCCCTTAATCTGGAACAGCTTTTTATGGAGCTACACAGATGAGTGCGCCAGAACTAAACATCACGCCGACGGCTAAGATGAAAGGCGCGCGGTTGATGAATCCATTTAATGGCATGCTGCGTCTTTGGTGTTTTGACGTCGGCAGTGTGAGCTTTTTAGGTACTGGCTTACTGGGAATGATCTTAGGTTGCATTACAGCGTTGCAGGGCAAAAGCGACAGTGCTGAATTGTTGTTTTCTATGGGAATCGTTTCAAGCTCGGCGGCGGTGGCATGGCAACTCATACGTTTGATGGCGAGCGAGTGCGCCCAGTTAATTCCCCATTATCGCCGCCATATTTATATCCAAAGTGCAGTGGTGTTAACCAGCGTATTTGGCATAAGCGTATTGTTTTGTTTAGCTTTGGGTTCAACGTCTAGCCTACCCACCTTAGTGTTGGCCTTGGTGATGAGTTTTGCCTTTATTTGCCTGTGTTTACTCAGCACTCAGTGGTTTTACGCCTCCTTTTTATTGTTTGTTTTAGTGCCGTTTATCTCTTTGATTACAGGTTATATTCCCCTATGGTTAAGCGCGATTGCACTGGTGGCGTTAGGTGCTTTCATTATCCGAATCTGCCGCGCTTTACCTTGGCGTGCTGAGGCGAGAACTGTGTACCTCAATGGCCTCGAAATGGGCTGGTTCTGGCTGCCCAATTTGCAATCGATGCGTATCTTGAGCCGCTTTGAGCGTTATCTGCATCCCACCAATTTTTTTATCGGCCCCATGCTGACGATTTTACTGCTGCTCATACCGGCAGTCTGCTTAGTGCTTGGGGTGTTATCGCACCAATTCCATCAGAATTTCCCCGTGTTACTGCTGTTGGCACAGTTCAGCGTGATTATGTGTTCGCTGGTGCATTGGAGCCGAGTGCAGCGTTCGCGCTCGACAGAAACCTTATTGCTGATGCCAGGATTTGATGGTCGAAAAGGACTCATAGCGGCTTTTTGCCGTGGGCAGCAACGACTGCTGAATGTGGTCGTTGGCATTATGCTGGCGTGTAGTCTGCTGCTGGGTTGGCTCAGTGGTGATCTGAATATGCAGTTAGTGGGTCATCTGGTGCTTAGCACTTACTGGGCCTGCGCTTTGACCTTAGGTTTTGGCTGCATGTGTCGCCGAGTGTTGCATGTCACGCTCACTATGATGGTTGTTGCGTGGCATTCCCTGTGGGTGTCCATCAGCCTTGCATCGTTACGCGATGGCGGCAATCTAGGCGATTGGTTTATCTGGGATATGCTCTTAATGATACTTGGGAGCATAGTACTGATTTGGGGCAAGAAAACCCTCTGGAAGGGAGATGTCATTAGCGGCTAAACGGATATAGATTGCAATGTTGGGACTCAGCTTGCACAAGATTAGAGCAAAAACTCTTAGTTTTTGACTGGCCTTCAGGTAGAATGGCGCAAAAGCGGTACACTTGTAAGCTAAAAGGTGAGTCTCTTATGTCTCTCGATCAATATCATGTTATTCGACTATTACAGCAGCAGAGCGAGTCTTTAAAAGACGCGATAGCGCTGGAAGGTTTTGAAATGGCCGCTCCTTGGTATCAGGTGAGCTGGCAAGCCTTTGACCAAATTAGCAGTAAAATTGCACAAGTGTTGATCGAACTTGGCGTACAAGTGCAAGACCGTTGCGTTATTTTGGCGCAAAACTGCCCACAGTGGACCTGTGCCGATGTCGGGACGCTTAAAAGTCGCGCCGTTGTGGTGCCGATTTACCCCACCAGTACCGTCGAGCAGGCGAGCTTTATCGTTAACGATGCGGCGGCGAAAGTCATTTTTGTCGACGATGCTAAACAATACGCTATGGCCTGCGAGCTTCAAAGCTTATGCCCAAGTCTTGAGCATGTGATTGTTTTTGATGCCAGCGTGGCACTTGCCGCTGACAAAGCTGAGCAGCATTGGCATTTAGATGCGCTGCTAGCGGGCGATGCAAAAGCGATTAAGCAAGAAGTTGAGCTTAACCAGCGTTTACAAGCGGCCAATCTTGATGATTTACTGACCCTTATTTATACCTCTGGCACCACGGGCGATCCTAAAGGCGTGATGCTGGATTACCGCAATATGGCCTCGACCATACGTCAACACGATCAAAAACTGGCTTTCCATTCTGGCGATGTGTCTTTGGCATTCTTGCCCTTGAGCCATGTCTTTGAACGCAGTTGGAGTTTTTACGTTTTGTGCCGTGGCGGTCATAACGTCTATTTGCAAAATACCCAAAGAGTGAAAGAGGCGATTAGCGCCGTGCGTCCGCACACCTTGTGTGTGGTGCCGCGTTTCCTCGAAAAAGTGTACAGCGCCGTACAAGATAAAGTCGCCAAGTCAGCCGATGGCCGTAAGAAGTTGTTTGCTTGGGCGATGCGTGTTGGTCAACGCCAGTTCGAAGTCGGCCAAGGTCGTGCTAAGGGCGGATTATGGTTGTCGCTGCAATGGCGTTTGGCCCACAAACTTGTGTATAGCAAGCTGCAAGCCGTGCTTGGCGGGCGTTTGAAGTTTATGCCCTGCGGTGGCGCGGCATTAGATGTGAATGTCGGTAGTTTCTTCCATGCGATTAATATTCCTGTGCTGTGTGGTTACGGCATGACGGAAACCAACGCAACTGTGACCTGCAATACCTTAGGTAATCGAGTGCCAGGTTCTAATGGTCAACCTCTGCTGGAAACCGAAATCAAGCTAGGCAAGGACGATGAGATTTTAGTCCGCGGCGCGACTGTGATGCGTGGCTATTACAATCGACCAGAAGATACCGCCGCCGCCTTCGAAGACGGTTGGTTAAAAACCGGTGATGCGGGACGTTTCGATGCGAACGGTAACTTATTTATTACCGACCGTATCAAAGAGTTAATGAAAACCTCTAACGGAAAATATATCGCGCCACAACGGGTTGAGGGTGCAGTGGGCTGTTGTCCTTTTATCGAGCAAGTGGCGATTATTGCCGATGCGCGTAACTATGTGACCGCCTTGATTGTGCCAGCGTTTGAGTCACTGGAAGCGTGGGCGAAGGACAAAGGGCTGAAGTATGAGTCGCCTTTAGAATTGCTACGCCATAGTCATGTGGTGGAACATTTTGAACAACGTCTCAAGCAGTTGCAGCATGAATTGGCGGGTTTTGAACAGATCAAAAAGTTCACACTGTTGCCAGAAGCCTTCTCGATGGAAGCGGGTTTAATCACCCCAACGCTCAAGTTGCGCCGCAAGATGATTTATCATAAATACGCCCACGAAATTAATGCTATGTACAACAGCTGATTGTTAAGTCGTTGTTACTAAAGGCCGCTGTTGCGGCCTTTTTTGTTTCTGCTACTTCCTGAAGCGCTTAACTTAGCTTAAAATTCGCTCCGTTTACTGGTGCGCACGAGGCGCAAGTTATCGGTATAAAGGGCGTAGTTTAAATGGAACTTTCGATTCGTAGTCTTTTCGCAGCCACTGCGGTGTTGTTATCTACTTTTTCTGTCACTCCTGTTGCTGTCGCTGCGGATTACGACAAAATCGTCCACGGACCTGTGGATTTAGCCCATGGCCAATGGCTTAATTTCTATCAAAAAGATCAGCAAGTGTGGGGCGAAATGCTCTACGGCGATGCGCCCGGCGTGCGTATTGACGATTATGGCCGAGCCGAAATTGTGGCGGTTTTTTACTATGACTTTGATAAAGGCGGCACTAAAGAAGTGATCGTCATGCTCAAGGATAATGGAATGGACCCATCCACTTTTAATCGGCCTCACAGTGGGCCACGATGTAGTCGCTAAAACTCATCAGAAAGAGGACGGGTCCACTTATGAAGATTACTACAATTGGTTTAGATATCGCAAAGTCTGTTTTTCATGCTGTTGGTGTCGATAAAGCAGGCAAGTTAATCAAAAAGAAAATGCTTAGACGCAAGGACTTACTTCCCTTTCTTGCTCAAATTGAGCCGTGTCTGATTGTGATGGAAGCCTGTAGTGGGGCAAATTATTGGGCCAGAGAATTTGAGCTGTTGGGCCATAACGTCAAGCTAATCGCACCTCAATTTGTGGTGCCTTTTCGGCAAGGTAATAAAAATGACTACAACGATGCGCTAGCCATCGCTGAAGCTGCGCAGCGACCCAACATGCGGTTTGTAAAACCCAAAAGCGTTGAGCAGCAAGATGTGCAACTGCTCCACCGCATGCGCGAACGATTAACTAAACAATCAACCGCCTTAATCAATCAAGTCAGAGGGATGTTAGCGGAATACGGCATTGTGATAACCAAAAGTAAATCGGCTTTTAAAGTACAGTTCCCAGATATTTTAGCGGATGAAACCAATGCGCTGACTACCAAAGGTCGGGCCATTTTTTATCAGTTATATGAAGAGTTTAGTGATATAGAAAAACGGCTTAAGGGTTGTGACGTTCAAGTGCTCACTGAAACTAAGAATAATGTGATTTGCCAGCGTTTAGAAACTATCCCTGGTATTGGCCCAGTCACCGCTACCGCCTTTTATGCGGCCGTCGGTGATGGTAAAGATTTCAGTAATGGTAGACACTTTTCAGCTTGGTGTGGCTTGGTTCCCAAGCAACACAGCAGTGGAGGTAAAGATAACCTCCTTGGAATAAGCAAGCGGGGAAATCCTTACTTACGTACCCTGTTCATCCACGGCGCCAGAGCTGTGTTACAGCACAGTTGTCATAAACAGGATAAATTTAGTTGCTGGGCAATCCAGTTAGCAGAACGCCGAGGTTTTAACCGAGCCTGTGTAGCCGTCGCCAATAAACTCGCGAGAATGGCATGGGTTATCGCGGCGCATGACGAAGAATATCGACTTCCAGTATAGTGCTGAATAGATTCAATAAACGTTAATAATTAACCAACCAGCAAGTTGCTAAGACACTTGAATTGATGATGAGACAGTCAGACTGATCTACTGAAAACCTTAGCTCGGCATAGGCTCATAATGAAGCCGCAAGGATGATAAGGACAGTAGAAGCAGATATCCATCAGGGCCAGAGGCATACCTCAATAACAGGCCGAATATATGGGTGCAATGAACTCTTCTCAAAATCGCTATCGAATGTCTTGCAAACCGGATGGGTCCATATATGCCGATGGTCAGCACCTGCGCGGCTATGGTTTTGATGGGGAAGAGCTGACTAAACTACCGCGCCTACAAGTTGTGCTCGATGAAGTGGCCCCAACCTTAACCACGTTTACCGTGGGCGGCGTCCGTAAAGTACTAAGCCAAATTCCGCCACAGCAATACCGTATGACCTATGATGTTGAAGCAATTGAAGATCCTGCCATTAAAGCGATTGTCGATGGCTCAACTAAGCTCAGGCCAACCTTAGTGGGTTATCGAAATTCCGAAGGTTCACCCGTCGATGTAAAAACCGCCGTCGAGTATAAATTACGTTATCCACTGACTCGCAAAGATAAAGATGCTCAAGGCATTGAGCGCCAATACAGCTTAGTGACCACCTTTGATCGCAGTGGTTATAGCGAAGAAGACGGTAGTTTTGTATTGGTGAGTGTGGCCTTTGAGGCCGATAACTTCGACTGGCTTAAAAGCGGCGGCGCTGAAATCCCCCGCACTGGCCCATTCTACAATTTTATGAGCATGGGCATGGGTAGCACTTGGGCAGGGCTTGCGAGTGAAAGTCATTATGCCCAAGGTCTGCTCGATGGTGCCTATGTTGCTTACGATGGCCGCAATGGCAATGTGATGTATTCGGGCGATTACAAGCAGGGTAAAAAAGTCGGTAAGTGGATGGAAGCCGATAGCCGAGAAACCTATTGGGAAGGCGAATACCTTGACGGTAAAAAACAAGGTAAATGGATTGCACAGTCAATATTCGATGAGGCCGATAACTTCGGTTTTGCTCACTATAATCAGGATGTGCTCGACGGCCCCTACGAAATTTATGCACCAGATTATGGCTCCAGCACCGAAAGCGCTAAGCAATTAATGGAAAAAGGCATTTATCGCAACGGAGTCAAAGACGGTGAATGGTTAGAGCAAGATGGGACGAAAGGTCACTATGAACAAGGCATAAAACAAGGGCCTTGGACTGAAAAGATATCGGTTGGGCATTACCGCGATAGTCTAGGTCAAGGGAGTTACCTTGAGGGGAAACGCACTGGCCCTTGGCTGTTTACTTTTGCTGGGGGCAGTAAAGCCGAGGTGAGTTATCTGAATGGCTTGCGCCAAGGCGAGGCTAAGTCATTCAATAAAGATAATGTGCTGCTTGTTACCAGCCATTATGAACAGGGCAGGCTCGATGGTCAGAGTATTTGGTACTCAAGCCCTAACGTAGTGAGTAGTATCTCTAATTATCGCCAAGGTGAGCTAGATGGCCCGCAAATGCATTTTGATACTAAGGGTGAACTGACAGGGTTAATGAGTTTTAAATTAAATGAAACCGTGACACTTAAGCCCTCGCAAGACGAATGCATTATGCGTAAAAATCCCTACGCAGCGGATTGTGCAGGAGTAATCAATCAAAAAAATACCGAGACCTCATCGATAAAGCACGGTGAACAGCGTGAGTATCATAGCAACGGCAGCTTGTACAAATTAGCTTACTACAGCAATGGCCTGTTAGATAAAACATACCAGTTTGATTCCAATGGCCGTTTGTTAGAGATGACAAGTTACCAAGCGGGTAAGGAATACGGTCCAAGTCTGCGTTATTCGACCACGGGCGGTTATGCTCTGCGTATTTATGCTCACAATGTCAATAATCGCCTCTCAGGCCCACGTTACAGTTTCTATGCTAACGGCCAATTAGATAAGCTGTGGAACTATTGCCAGCAAGAAGGGGAAACCTGGAATGGTGCGCCTTACTACTGGGATAACGCCATTGCGCGCTGTGGTATACAGCGGGAGTACTTTGATAATGGTGCTGTGAATTGCATCGAAGAGTTAGATAGCAATTATGCCGTAGATAAAGTGTGTTACGACATCAATGGCAAGTTATCGAGTGAAATGCTGCGTATCGATGAACAACATGTGGTCCATAAGCGCTATATCAATGGGGTGATTTATCAGGAAGAGCCGGGTTTTTCCGATTATTCGCATGTGACCAAAGGGCGCAAAATTTATCACTTAGAAAACCCTAAAAGCCATGGTGTGTTTAAGTCCTACCGCAGCGGCAAGTTAGAGTATGAAAAAATTTATGATATGGGTAAGGCTGGATGCTTTAAAAAGTATGATGCCAATGGCAAGCAAGCGGCAGAAACCGCCAGTTGCCAATTCTAAGGTCTAGGTTTGTTTAGCCTTTAAGGTCTGGTAGGCTTGGGATAAAGGATGAATTTGGTGCGGGTAGCTTAATCGCGATAATCATTAAATTCATCAGCTTATAGAATAACAATAAAAGGGAAGTCCGAATGTCAGTTACCGAATACAGCCTCGAAATCCACCCAAGATTCACCGAAACCGATGGCCTAGGTCATATCAACAACACAGTGATCCCCGTATGGTGCGAAGCAGCGCGCACGCCAATTTTCGAGATTTTTAATCCAGAGTTGGATTTGCAGCAATGGAACTTAATTGTCGCGGGCTTTACCGTGGCGTTTATCGCGCCAACCTATTACGGCAAAAGCGTGACAGTGAAAACCTTTGTCAGCCGCATCGGCAACAGCAGTTTCGAACTGACTCAAACCTGTTGGCAAGCCGGCAATAAAACCGCTGAAGTGAAAACTACACTGGTACATTACGACTACAGCTGCGAAAAAAGTCGCCCTATCCCTGAAGATATCCGCGAAATGCTCGCCAAGCTGAATGGTGAAAGCGCTTAATCTGCCCTGTCTCTCGGTCAGCGCAATTGCAATTGCGCTGCTGAAGCGTTAATTTGTCGACCCGCTAACTGTGATGCAGATCACTTACCTGTATCGCCTAATTTTTATCGGTTCGGATTAATCATGAGATTCTATTTTACCCGCATGCGCTCGAAGGATATTTGTCCACCAAGACAGCCACTTAAAAAGATTGTGTGGTCATGGATTGGGGCTTTCTGTGGCATTTACTTAGTCGCGAGTCTCGCAAATTATATGAGCGAAAACCTCGTCGGCACTATGTTTGTGATTGGCTCATTCGGCGCTTCTGCTGTGCTGGTGTACGGCGCACCGCTGGCGGAGTTTTCTCAACCGCGCAACTTGATTGGTGGTAGTGTGTTATCGGCCTTAATTGGGGTCGCCGTGTACCAAGTATGTGGCGATTATGTAGTGCTGGCCAGCGCCTTAGCGGTATCCCTGTCTATCGCATTTATGTACTTGACCCGCACTCTGCATCCTCCGGGCGGCGCCACGGCATTGATCGCCGTGATTGGCGGTGAAAACGTGCATCAACTCGGCTTTCTCTACGCGCTAATGCCAGTATTTCTCGGCTCAGTATTACTCTTACTGGTTGCGCTGGTGGTCAATAACCTCTCCACCGATCCCAAGCGACATTACCCTGTGTATTGGATTTAGCTAACTTGTTGTGAAATAGGCTATTTCAAAAACTTCTGCACATACCAGCAGCTGGCTTGGATAGCCAAATCTTGGTTTTTTGCCCATTTAAGTCCGTGGCGAACTAAGCGTTCGGCAAGTCCTTTACCGCGCAGTTCGTTGGGGACAAAGGTGCGGCTAAAATCGATATCTTTGCCTGATAGTTGATATTCCAACACGGCTTCATAGCCATCGACAGGGATAATAAAACGCTGTCTATCCTGTTGATGTTCAATGGCTATCTCAATATTTTCGTTATCTGACATACATTCTTCCCTCAAGGAGTGGCTTAAAGTGTGCTGGCAATTTAGCTATCGATTTGGTTAGCTTAGGCGACTTCGTGTTGTTTGAGAATCGCCAGCAGTTCTGCCGGTAAGTCGGCCTTTTTGCCGGTTTTAAAGTTAAACATCACCGCGACTGAAGTACCTAAGGTGGTTACTGCCTGCTGCTGTTTACTAAAAGCCTGATAATGCATGGTAAATCTATCGCTGTGAATATCGCTAATGCTGACACTCACTAATAGAGTATCGGGGAAGGTCACTGGGCGTTTATAGCGAGCCTGATTTTCGCTGATCACCGGGCCAATACCTGATTTATACAGGGTATCTAGCGGGAAGAGGCGATTGAAAAAGTCGATACGTGCTGTCTCAAAATAGCGGAAATAGACGACGTTGTTGACGTGTTGCAACGCATCCATTTCGCCCCAAGCCACAGGGATTTCGGTCACTATAGGGTGCTGTGCGAGAAACTTTTCCATTATGTTTTCCAGTGTGTTTGGCATTGAATTGCCGATCTAAAAATGCGAGTTGAAACGCTCGTTTAAGATGGTCAGACTAGCAGCATAAAGAACAAGCGACAAGCCATGGCGTTATAGGCATTTAGCGATAACATTAAGCGAAACAAGGAGCAGTGTATGATTAAACGAGTCGGAAACACCCATATTCAAGCTGAACACACAGCCTCTTGCCATTGCGGCGCTGTTGTTTTGCAATTATCGCTGCCTAATGGCATTGAAAATCCTCGCCGCTGCGATTGTTCTATCTGCCGCCGTAAGGGCGCGATTGTCGGCTCTGTACCGCTGGCGGGGATCAAGATCCTTAAGGGTGAAGATGTACTGAAATGCTATGAATTCAATACCAAAACTGCCAAGCATTACTTTTGCTCCAACTGCGGTATTTACACCCATCACCAGCGGCGTTCCAATCCCCATGAATATGGTTACAACATTGGTTGCCTCGAAGGGGTCAATCCATTCGATTTAGACAATGTGCCCACGAATGATGGCGTTAACCATCCTGCGGATCGTTAGATTATGAACATAGGTCAGTTTGCTGCACTCACTGGATTGTCGGCGCACACACTCAGATATTACGAGAAAATTGGCCTGCTGCAGTCTGTGGTGCGAAATGCGAGCGGGCACCGCGACTATGCGCAGAAAGAGGCGCAATGGATTGGGTTTATCAATCGCTTAAAAGAAACGGGTATGCCACTAAAGCAGATTTTGACCTATGCCGAACTGCGTGCTCAAGGCGATACAACCGCAGGCGAGCGACAACAATTACTTGAGCAACATGCCGCACAATTGGCTGAGCGTATTCGCCAAGAGCAGGCGCACCTGATCGCCCTCGAGCAAAAAATCCGCTATTACCAAACGCATTTCAGTGATTAAGCTTAGCGATTCGGTTCAGTCGCTCAGTTCCGCGTTTAAACCCTAATTGATTAATATTATTAGTTTTTATCTGTAGAACTTGTCTTTCTGCTTCAATCGCAGGGCTGCCTATGTTATTCATCAATCAGCCGTTGACTTAGAGTTAACTCTAAGGCGTAAGCTGACTTCTGTGGTCAAATCCTAAGGAGTCAAAATGAGTAATCAAAGGTATGTGCAGGGCTTAGCTAAGCTGACTGAAATCGACGGTGAAGCGGGCGAGAAAGTGATCAACAGTCTGGCAAATATTTGCCCCGACTTGGGCAAGTACATTATCGAATATCCTTTTGGGGATATTTATCAGCGTGAAGGACTCGATCTTAAGACGCGGGAGCTAGTCACGGTCGCGGCGCTCACGGCGTTGGGGCATTGTCAGCCGCAGCTTAACGTCCATATCAACGGCGCGTTGAATGTTGGTTGTGCTCCGCAGGAGATCGTCGAAGTGATACTGCAAATGTCTGTTTATGCGGGCTTCCCCGCAGCACTGAATGGCATGTTCGTCGCGAAAGCTGTCTTTGCCGAGCGGGAGTTAAATGTCGTTTGAGTCTATTTTGAGTTAGGTTTATAACACTTCATTGAGGCGATCTTAAGGTCGCTTTAATTTTACTCATAGGTATTACTTCCAAATTCATTTAAACGGTATTTCTGTTCCGCATCATCGACATAGAATCTCGTGGGAGATTCATTTCGCCGCTGAAGGACGATTCGGCAAGTTTTGTTAGTGGATTGCCAAAGTTAGTCGCTACACTGGCATAGGTGACGCGAGTGTCGCCATAGATTTTGCAGTTTACATCGGCAACTCAAGCGTGTCTTAATCGCAACAGGAATTTAAAATTATAATGAATTTAGGTCGTTACACTGAATCTGCCCGCCAGCACTTACGGCAAGTCATCAAGCCTTTGCCTAGCGAGCTCCAGCTAGGGTTGAGCAAGTTGTGGCGCTACCTAAGCCAAGGGTTTGCTAAGTTATCCTTTGCGCAAAAGCTGTATCTTATTGCCTTGATATTGGCCTTTAGTACTGACTTGATGGGTGTGGTCGCCGTGATTGCGGTTATTGCAATGGCCATCGAGTTTTGGCCAGTGTTCGAGCGGGTCTGGCACAGTTTGGCAGGCAAGGCTGTGTTGTTGCTGTTTTACGCTATTATTGCCAACTTTGCCTTAGGTTGGTCTGGCGCCATAGTTAACGAAGTGGTCGGGGTGTCGGCAAGTCATCTGGATTACACCCATAACTTAGCGATTTTACTCTATATGCCCGCTTGGTTTTTAATGGTGAGCGCAATTGTGCTGCTGGCATTGCAGTTGATTATACCCATCTATTTAGTATTAATTTTTTTGCTTAAACCTTTGGGTGTCAAGTCGTTACGGATCACCTCACATACATCATTTCGTAAAACGACCTTTACGATCCGTATCGTGTTGGCGGCTGTGGTCTTATATCACTTGAGCCTACTGATTGATTTAGAAACCCGTCTTCAAACCTCGTTCGCAGACTTTATTACAACAACTCAAGAAGCCTCTGAACATAATCCCGTCGAGCCAGCATTAGGGCTTATGCCGCATACGACCGATGTGCTGACGGCACCGATTGCTGAGTTAGCTAAGATTAAGCCCGAGCTTGAAGCCGACAAACCGGCTGATAAACAAACCGCCGATGAGCGCGAAGAAGCGGAACGCAAGCAAACCTATGCGTCGGTGCGGGCGCAATATCAGTATCTCGTGCGGCAATGGTTAGCGACGTTTGCCTTTGCGCTTGAAGCCAATAGCCGTTCGCGTTGTGAGAAGCCCGCCGAATCGAATGTGATCGAGTTAAATGACTATGAGATTTTGCAGATCACGCCGGATAAGAAAGCGGATTATGGTTATCGTTTTGAAGTGAAAAAGTGTATCTCGGCCGCGTTTCCACTGACGGAAAAGGGCAAATAGGCCGACCCATAAAAAAGCGACCATAAGGTCGCTTTCATTTTTGGCTAAATCAGCTTGGCTGAGTTAGTCGACTATTGCGTTCTTCAACAATCACGTTATTCAATCAGCAACTCATCGAAGCTGCTGATTTTACGGAACTCACCAGTGCGCTGTGTGCTATCGCGTACCATTTGGCAGGTCATCATGCCTGCAGCTTCGGCAGCCTTTAATTCTTCAATCACATCAGACACAAACAGCACTTGCTTAGGGCTTAAGCTGATAGTGTTGAGGATATTGCAGTAAGCCTGTTTATCTAATTTGTTGCCTGTACGGGTATCAAAATGACCGTTGAACATCTCAGTTAAATCACCGCCGTCACTGTGGCTGAATAGCAGTTTTTGCGCATCAACAGAGCCCGATGAAAAACTGTAAATACGAAGATTTTGCGCGCTAAAACGTTTCACCGCTTCAATAAAGTCAGGGAAGATATGGCCCTTAAATTCGCCATGGGCGTAGCCTTGCTTCCAGATCAAGCCTTGCAGAGTTTTGAGTGGTGTCGCTTTACGATCTTCGCTGACCCACTGCTGCAAAATTTCAGTCACGCGTGCTAAGTCCGCATCAGGCTCTAGGGCGATATCTTTAGTGTCACAAATACAGTTTTCAACCAATACATTGTGTTGGTTCTGTTCTAAAAAGTCTGGCAAGGCTTTGACAGAATAGGGGAATAGTACATCTTGGATAAAGTTTAGGTCTGTCGTGGTGCCTGCTGTGTCTACGACTATGGCTCTGATACCCATAATGTTTTGATACTCCACAATATGTTGGCTAAAAAATGCAACTCCGATCCTAAGCGTTATTGGCGAAATTGGCTAGTCTATTTCGCCCAAACACATGGGCAAATTGTGGTCGATATCACTAAGCTGATAGATTGAAATTTGATTAATCTAGTAATTTTAAAAGCTTATCCGCTGATTGTCGGAGAAAACACTAGCTTGATACTGGCTTTCATCTCGTTTATTGCGCTTAAGTTTGTGGCGGCCATAAGACTATGGTCTTAATAGCTAAGGGAAATTTAGCCTGCTATTGTAGCTAAAGTCCCTTTTCTATTCTGTGGCTTGCATGCTGTGGTTTTTCGGGAAATGAGACGGCATATTTTCAGCTGCAAATAGAGAGACCTTCAATGAAATATTTACCCGCGCTTGGCTTTGGTATCTTATTAGCTTTATTGAGTTTTATCAGTTTCTCATTAGTCGCGAGCGCGGGTTATATGCTGGATATGTTAAGTGCAGTGCCCAAGATTACGCCAAACAGTGTGGAGTACCTGTTGTTAGGGGCTCACGATGCGAGTTTGCTGATATTGCTAGCAGGTTTAGTGCTTTACGCTTATCACAGGATTTTTCCTAAACTTCCCTTCGATTGGTTCACCGCTGTGTTCATCCAAATGCCACTGGGTTTGGCTGTGCTGGCGTTAGATGGTGTTAGCTTAAACCTATTGTCATTCAAAGGTTTTGCGCTGACGCTGACCACCTTCGCCGCTTCCTTTGGGGTATTAGTCATCTTCTGGTTACTGCAGCGTCGAGCACGTCGTTTGTCTTTAGCCACGGTTAACGATTAGTTCAGCATATACCGCAGGATGCTTAACTGTTTTTCGGTAACTAAATTATGTAAAATCATTGGCCTAAATGGATTTGAATCGTAAGAGTAGGAGCCAATGAGTTCGATTGAATTTGATATTCCCGAAGATCACCGTATTGCCTTTCAGAAGTATTACAAACAAAAAGTCGCCCCTAAATGCGCACCGTTTGAAGCAAAACGTGAAGGCGTGAACCAAAGGCGGCGTGAAAATGCCAAGTTCACCACCCCAATCTACTACATAGGTATCACTATTATTTTGGGTGCATTCGCCGGGCTGCTCGGGGTCCAGTTTATGCTGGTGCCTATGGATTATTTTGTGCCTTTGATGATTGTCGCGTTCCTGCCCGCTCCCGTGATAGCGTTAGCGGTCATTGCCTCGGGTAAGGAAAGCAATGAACTCTATAATGCCGCCTTTGAAAAACTCTATCCCATTAGCCTTAAGTATTTTGGCGATGACTTTGCGCTAACGAGGGCGCCTAAGGTCGATTTTTCGACCTTTATTGAGTTGGGGCTACTCCCTAAAACGGATAAGCAGTACGCGCAAAATTGCCTTAAGGGAACCTATAACAAGGTGCCGTTCCAGTTTTACGAAGTCGATGCTTACAACACTAAAGCGAACGATAATGGCTCGGTTCAGCGAGAGGCCATCTTCAATGGTGTGCTGCTGGTTTGCAATATTCCTAAGCGATTCAATAGTACTACGCTGTTAGTCACGGATAAGGGCAGTTTAAAAAATCGCCTTGAGGGTTTTAAGACTAAGCTGTCGCGCGTCAAGCTTGAGGACGTAAGGTTCGAGCGGCGCTTTGAGGTTTATAGCAGCGACCAAGTCGAGGCGCGCTACTTACTCAATCCCTTGATGATGGAAAATTTGTTGGCTTTGAGCCGCGAGTTAAATGCTGAGGTTGAAGCCTGTTTCTATCAAAAACAGTTGCTGATTAAGATCCCGACAAAACACCATTTCTTTATCAATCAACTCGATTTAGATCGCCCCATCGATTTTAAGCGTAATATCGAGCAAATGTTTACCGAGTTAGGTTATACCTTCTCAATAATCGATACTTTAAAACTTGATAAGCATACTGGGCTCTAGAGTGTTAGGTTATTAACGAATAAGTGCATGGTATCTATTGGCCCTAGTGTTCTTCGGCTATTTAGCGTCAGTACAACTTTGCTGCGGTAGGTATATTCAGCGCCTACTGTGAGCGGTGAGTCAAACTGCATAGGCATGGAAGCTGGTACTCAAGTCATTGAACACGCTGTTGATCGCTAATGGGTTGGGTCTATGCTAGCCGAGCTTTTCCTTCGCTTGAATCTTATCAATCACAATCTTCAATATAGCCTGTCGAAGCTTTCTACAGGCTTGAGACACCATAACAATGAATCCCTAAACTTCCCTTCGATTGGTTCACCGCTGTGTTCATCCAAATGCTATTGGGCTGTGCTGGCGTTAGATGGTTTTAGCTTAAACCTATTGTCATTCAAAGGTTTTGCACTGACCACCTTCGCCGCTTCCTTTGGGGTATTAGTCATCTTCTGGTTACTGCAGCGAAGAGCTAAGCGCCTTGAGGCTATGCCCAGCTAATCGTTGCGGCATTTACAGTACATAACGTAGGCGCAGTGCCGTCTGCTCAACGGGTGACAGTGATAGCCATTGTTCTCGTATTTCCACCGCTTCCTGTGGATAGTCGGTGATCAGTACATCGGCTCCCAGTGCTAACATGCGTAGCATTTGCCGTGGATTGTTCACTGTCCACACATGAACTTGCTTGCCGCCCTTATGGGCTCTGGCGATAAACTTCTCATTAGTAATCGACTGATTTATCGCTAAGAATTGTGCATTCGACTTAATCGGGTCGCCGATAGATTGGGTGACAATCAGCCCTGTTTGGATTGAAGGGTCTAGTCGCTGCACTTCTTCAATGGCTCTTGCTTCGAGCGAGGTAATTACTACTTGTTGCGTCATTTTCTGTTGTTGCAGTAGGGCAACAACAGCCTGCGCTAAGGTGGGATCGGCTTGATTATATTTAAGCTCAATATTCAAACCTATCTTATTGCGCGCCTCATTGAGCATTTGTAGTAGCGTCGGCACGGATTCACCGGTAAAGGCGGCACTGTGCCAGCGACCCGCATCGAGTAGTTGTATTTCACTGGCGCTGAGATTGGCAACTCGCCGTGGATCGCCCGCGATACGCATTAAATCCGCATCGTGCAGCACCATTAAGGTGCCGTCAGCGGCGGTTTGCACATCTATTTCAGCATAATCTGCCTTTTCTGCAATTGCTAAACGCAAAGCCGCAAGGGTATTTTCTGGTGCCCTAGCTGCACCAGCTCGGTGGGCAGAAATGAGGATTTCATCCCTTTGTGGCTCACTGTTTAGGGCATACACTCCAAATCCCAGCATGATTAAAAAAGTCGTGACCAAGACGCCACTTAGAGTGTAAATCGACAGCTCTGAAGTCTTTTTTACTGTCTCTCGTCCAAGACGTTCTAACCCTGCAGGTTTCTCTCCCAGTTGGCGGTATAACAGCAAGATGATAAAGGCTTCGCCAAACATGCCGAGCAAGGACCAAGTTGCACTCAGCATAAAAATCAGTGCAGAGACGAGGGCCAACATGGCTAATTCTGCCGAAAGTGTCAGTGAAACCCGGTCGAGTAAGCTATCGGCCCCCCACGAGATCAACAGCATTAAGGGGTACAGTAAAGCCCCTATCCTTAACCACCAACCCGCCGCGTGCCACATAATCGCGCGTTCATTACCCGCACTTAACTTCCAACTGCGTTTGAGTGCCGTTAATGCATCGGTTGTTTCACTGACAATTATCGTCAGGGAAAAGGCCGCTTTGGCCCACAGATATAAGCTAATAAGCAGATAGATTAACGCTAAGAAGGCCATGCTCGATAAGGCTAAATACCATTCTACTGGCTTGAAATACAGGTAGTAATTGATGTCGTGTCCCTGTAGAAACACGCTAAAAATAAGATACCCTAATCCCATAAAAGGTACGCTGAGCAGCAGTAAGCAAGCCGCATATATGCTACCAAGGCGAATGAATACGGGTAAGCGCGCCGTGGCGAAAATCAGTGCCCGCACTGGGCCAATGGGAATATCGTATAACCCTGATGCGGCGAGAATCGTGACCCCTGCGCGCTGGGTAAAAAACACGGTAAAGCTGCTGAATAGGGTAATGATCATAAATAGCAAGCCATAGGGAGACATGGCGAAGGCGATCAAGTCGTAATTGGTTATGGTGACATTGCCCGTTTTGGCAATCAGATGGCGCAGTAGCCAGCCAAAAGACGGGACGATAATACCCGCCGACACTAAAGTAAATAGCAGTTCAAATTCAATGAGTTTTCTACCGCAACGACGAATGGCTTTAAGCGTGTCATGCCAGTAACTGTGTTCAGGATTGAGCATGCAGCTTCCTTATTCGTGCGAGTTAGGACTGTAATTTAGCAGTGTTTCAGGTTGGCGGTTAGTCATTGGTGCAATGTTTTAGATATGGGCAGGCTGTTAAAGTCTTCTTCTCAATGATTTTACAGAAGACCTATGGGTACATGATTACATTCGACAATGATTAACAGTGCTGCGCGACTGTGAGTGGCTAATCAAAGTATTGGCTTAGCTGCATGGCCTCAGCCATTACCTTTGCCAATATCTCGTTGATTCATATAGTGGGCTAATTGCGCTGCATCGGCATGGGGGCTGGCACTTAAGCCGCTGAACACACCCTGTTGATCGCTAATGGATCTATGCTGGCCGAGCTTTTCTTTTGCTTGAATCTCATCAATCACAATTTTAAAGCCGACTATAGCTTGGCGCAGTTTCTGCTGATACTCATCTGGCATAATTTGTTTATCTTGCAATAGCTTGGGTTCGTACTTTGTGACTAGCTTGTCCAACATGTCTTGAGTTTCGGTATCACTCAACAGGCTTGCGACACCATAACAATGCACCGCCGCATAATTCCATGTAGGCACCGCAGGTTTAGCATCATACCAAGTTGGCGAAATGTAAGCGTGTGCGCCGCTGAAAACCACTAATACCCGCTGCTTTTCAAGCTCTTGCCAATGTGAGTTCGCCCGTGAAACATGGCCATACAAGACGCCTTTCTCTCCCACAGTTGCATCGAGTACAAAGGGAATATGGGTCGCATTCAGTGAGTTCGAAACCAGCAAACTAAAGCTGTTTTGCGTGATAAAACCTGTGATGGCCTGCTGATCTGTCATCGCCATATTCTTAGGGGTATACATTGTCATCCTTGGGTTTCAACATGAGTGAAAGCATTATATTACAAGGAAAAAGTTGGGAGTCGATAGGGGATTTAGAATGTTAGTTTGTGGCTTATTAAAATCAAAATCGTAGGGGCTTGAACTCGGACTCGGAGATTAAACACTCGCATTACTGTGACTCGCAGCAAGTACATCCGTGTATGCTCGAACGTGGCATCCATGCCACGGACGGTCACATCCATGCGAGTGCTTAATCTAGTTAAGACCTGCGTATACATCTAGCCTCGTTAAAATCCGTGAATCAGACTTGCCTAGTTTGGTTCACGCGCAGAACTCAGCCATTTGGCATTCGTTGTTAACTAACGCGTTTTTGCCCCAAAGTGAGCTAAACCCATCTAGGATAGATCTTCCCCGTTGACCAAATTTTAGTCATGATACAAAGTGACCAATATTACTGGCTCAGAGCAATGCGCTGTTAGCCACCACAATGGACTTCAGATGAATACAGAAATGAAAGAAGCTCTAGTTAAAGTCCCACAGATTACGCTTGGCTTTTGGGTAATTAAAATTCTCGCAACTACCTTAGGTGAAACAGGCGGCGATGCCGTTTCCATGTCAATGGATCTAGGTTACTTGATTGGAACAGTCATTTTTATGGCTTTTTTCATTCTTGCGGTTGTGTTCCAAATCCGTGCGAAGAAATTCAATCCGTTTGTTTATTGGGTCGCAATCGTCGCATCAACTACTGTAGGTACGACGCTCGCTGATTTTGCAGACCGTTCCTTAGGTATTGGCTATCTGGGCGGCTCAACACTCCTCGCAGTGTTTTTAGCAAGTACGTTGTTTATTTGGTATAGAACACTAGGCAGTATTAGTGTTTCGTCAATTCACACACCTAAAGCAGAAGCGTTCTACTGGCTTACCATAATGTTTTCTCAGACGCTCGGTACAGCGCTAGGTGACTGGACCGCAGGTTCAGCTGATTTGGGTTATGTCGGCGCAGCTTACATTTTCAGCGGCGGGTTAGCATTAATCGTCGCAGCTTACTTCTTCACAAAAGTATCTCGAACAATTTTATTCTGGTCAGCGTTTATTCTTACCCGTCCACTTGGAGCCGTGGTTGGTGACTTTTTGGATAAACCTATCGCTCAGGGTGGTCTAGAATTCAGCCGCTATACGGCTACAGGTGTACTTTTGGTCGCCATAGTAATTCTGGTTATTGTACTGCCACAGCGCGCAGCCAGTAAATCACACTAATGTGTTGGTGGTGCTTAACGGGACGGGCTAAAGTCCATTCTCCTAAACCAAACGTTAATCATAAATTTCTCATGATCGTTTAGTTAGCAATCGTGCCAGTGAATCACTCTACACTAGACTGATTCAAGCTCACTTGATGACGGCACATTATCAACATAACGAGTTTGTGATGGTGGCGCAGTTTCCCGTAACGCAAATTGCATCAGGCTAATGGCTAAATTAGGTCATCAATATGCACGGGAGTTGATGAGATTAAGCACTGGTTTGTAAGTGACCGTTCGTGACAGGGATGTCACGGCCGAGCCCTCAAGGATGAGTTTATGGCGTGTCACTGGAAAGCCAGTGCTTAATCATTTTCTGATTATTTACACATTGCCGCAAACTCAGTACATGGTTGCTTAGTTGACACAATCAACCAATCTCGATATTGTTGATACAGTCAACTATATCGAGAGCCCTCTATGCCTTCATTGTCACTCAGTGAAACTTTGCACAGATTAGTGCATGCCTACAAAAAGCAATTGCGTAGCGATATTGCTGCACAAGAGATTGATTTACCTGTCACTCATATCCGTGCCTTGAAGGGGGTGTGTCGCAATCCTGAATCGACGGCCCAGTCTATTGCCCTGCGAATGCAGCGGGATAAAGCACAAATTACCCGTGTTCTTAATGAGTTACAGCAGGGTGGGTTGATCACTAAAATCGATAACCCAAAGGATGGCCGCAGCCAGCTACTGCGACCCACGGCAGAGGGGGAAAAGATCATGACCCAGATCAGTACCTCAGAACGTAAAACCGTGGCGCGTATGACGCAGGCGCTGAGCTCAGCCGAAGTCGACACCTTTATTAAACTTGCCAATCGGATCAGCAAAAGTGTGGATGAGGCGGCTGCCGCTAAAAGTGGTGCGGATTGCCCTGCTCAAAAAAGACCATCCAGTAAGCCAAAAGACCCGATTGGCAAAGGAGCCGTTGAGGCGCGCTTAATGCAAGAACACGCCATAGGAGAAAATCATAATGAATAAACCTGCACCACGTGAGCTGGCAGTGATCCGCTCGACTTATATCACCCCGCATATGTTACGCATGACCTTAGGCGGTGCGGGATTCGCTGGTTTTCCTATTGATCAAGAGAGTGCCTATATCAAGTTGTTATTCCCACAGGCGGGTGAACGTCCATTAATGCGAACCTACACGATTCGCCAGCAACGTGCTGATGAAATAGATGTAGATTTTGTACTGCACGATACCGACGGCCCCGCATCGAGCTGGGCGAAAACGGCGAAGGTTGGCGATGTCATTCAAATTGGCGGACCCGGGCTTAAAAAACTGATCAACTTTGAGGCAGATTGGTTTTTACTCGCAGGCGATATGACCGCCTTGCCCGCTATCAGCGTGAATTTGACACAATTACCAAACAATGCCGTGGGTTACGCTGTGATTGAAGTATTGAGCGAAGCCGACATTCAGTCTCTGGTGCATCCCGAAAATGTTGAGCTGCATTGGGTGATTAATCCTGAGGCCGATCCAGAAGGTCGCCCTTTAGTTGAGCGAATAGCCCAATTACCTTGGCTTACTGGTGAGCCCGCCGTGTGGATTGCTTGTGAATTTAACAGCATGCGCGCGCTAAGAAGATACTTTAAGCAAGCGCATTCACTGCCCAAGAGCCATTTTTACACTTCGAGCTATTGGAAAATTGGCTGTAACGAGGGCGAGCATAAGCTGGTAAAACAAGAAGATGAGCAGTTAGAAAATGCTTAGTTTGAGATAATGGCCTTAAAACAACTTGGTATCAATTGTTTAACATTTTAAGGGTTGGTATGATGAGCTAATGCATAAAGCATAGTAATCAATCATGGATGTGAGGTTCATTGTGTATGGGAAAACTATTTAATAAATATTTAGGTCTAGCTTTAATCTTTATTCCGTTCTTTGTAAGTTCTAATTTTGAGCCAGCAATAAAGGTTTTTGATACAAACGACGCCATTGTTTTGCCTAGTGTTGATACTTCTAGTTCGAAAGTGATATCAACAAAGGATGGTTTTTTATTTACTGGAATTAACCAGAATGGCTTTACTTCGTTATATTATTTTGACCAATCTGCCACAGCTCCCATCGAAGTAAATATTCCTGTCGCTAACAAGGAAAAATTCACAGTATTAGACCATAACTCCTTACAGAAAATAGGGGATGATTTATACATATTTTACTTAGATACGACTGACGATATTTTTTACTTTGATTTAGAGCATAAAAAGCTAGTTAATTGGGGGTTGTATCATGATTTAAAAAATAATAAAGAAATTTCACGGATTAGAGATGGTGTAGGCTCTCAATTATTTTCTGAGTTAAGTTGGATATCTTTTATAAATAATGAAAATTTGATATTTGCGAGAAATAATAAAGTTTACTCATATAATAGTAAGGATGATTTGCTTACCAAGTTAATGGATGGTGATGACTGGGGTAGTGAATTGATCCAGAATAATGGTAGTAATATATATTTTGAGTATGATGGCATACTTTATAAGACTGACGGCGTAAGATTGGATGATTTGTATAGTTATCCTAATGGTGAACAAGCTTTTGTATATAGAGATTCGGGTGATATTACATTAAGAGATAAGTACAACGCTCCGCCAGCAGATTTAACTAGTAAAGGTTTTTTTGCCATTTCATTCGGTTTTGAACAGTCGAATGATTATTTGTACTTAAATGGAGGTGGCTATTCTGGAGAGATAAATCTGGGGAATGGTTATGGATTTTCAAGTAACTCTGGCATTATCTCAAGTGAGAATACTATTACATTCTTGAAAAAGAGCCGTGCTACTAACTATGGTGATAGCTATCAGATACAAAGTTGTAGTCTAAATAAAGAACTTACCTGTAAAAATATAAATGTGCTACCTGAGATTACTCAAGGGCTAGATAATGTAACCCTATTTAATGTAGATGCCAGTAATGTTTTAGTGAATGCTAGATATGTTGAAGCTGGAATGATAGTTGAGACTAAGATGGTTTTAGTCAATCTTGTTACATCTGAGGTTAAAGAGGTTGGTGTTAATACAGGGCCGGAGCATACTTTGTTAAATTGATTTAACATCTGCAAAAATCATCAATTCCCGATATTCATCACTGAGTAAAGAACGATGTAACTTACTCTTCATACTGCGTTTTTGTGTTATTTCGAGTTTTGCCAGATTCAGGGCCATTCGCCTCACTATCGCCAGTGATTCGGCAGTTCTTTGCTCGTGAACCCGACAAGCATCTTCTCGATAAACCACATCCAGGACCCAGTGCAGACTATTCTCAATTCGCCAGTGCCCTCGGACAGCAGTCGCAATATATTCGGGATCGATATCATGGGATGAGAGATAGAAACTGGTCTCTATGCTGGTCTTATTGCCTATTTTTCTGTGTCGCTCAACGGCTATTAATGTCTTAACTGAAGGCCACTTTTCTTGCAATTTAGGACTCAGCTTCGATGGGATTTGAAATGTAATCCGCTGCTCTGTTCTTCCATGCCCTTTTTCCGAACAGGCAAATTGGGCTAACTGACTGTCATCTTGGAAGGCCACATCATACTGCGCTTTAACTGCTTGAAATAAAGTCCGTTGATTACTTTTCACCTGCACCACATAATCTCCTTTACGAGCGACTATCGCTTCCAGCGTCGCGCGTTGGGCATGCAAAGCATCCATGGTTATCAATGCTTTATTTATGTTAAGCATGGTAATAAGTGATTGTACCGTTTCAATTTCCTTGCCTTTTCCAGAGGCCATTTCTTGATAAAGCGCCAAGCCATTATTAATGTCGAACGCACCCACGGAATGTAAGGTGTTGCACCCTAACTTAGAGGCACCTCTGAGTGTTTTACCATCGATAGCAATGATGGGTTTAGCTGATGCGGTGCGGATGTCGTTAATCCAACTGAATAGGCACAACTGCAAATTCTCCGGCCCTACGGCTTTTAGGATACGTGCTATGGTATGCCGACGGGGAATGCCATTAGCAAATGGACGAAACTTACGTAGCCACTCAAGTTTTAGCTCGCCGAACTGTTGAATTTCTGCCCAACCATCTTGCCCACTGGCTACTGCCGATAAGATTAAGAACAGTACGTCGACAAGGTTGTGGCGTTGATTAATGTGTGAGCGCGTGTCTTCGACTAAAGATAGGTGATCAAAAAGTGACATGGAGAGTGGCCAAGTAATTAATTTCTCATTCAGATCATGGCATTGCGGAATAGTTCAATTTTTTTAACAAAGTGCGATCTCGCCCTGGGTGTTAATATAGATAAAAATGTTGTTGCTGATATATTGGCAAAGTCTGTAGATGAAATATTTATTCTCACTACAGCTGTTTTGGACGATAAAAAAACGTTATACAAGTACAAAATAAAAGAGGCTGCTTTAAGTAGTTTTCCTATTAAAAACTATCATGGTCAGTATCGTAGCAGCTTTATTGTTAGCGGGAGGTTGTTTTCTTTAAAGCGTGATGTTATTTCTGAAAATTTATTAAGTAATCAAAGATTTGAAGGGTTGTTTAATTTTGATGAAAATTATGATTTATTTAATCTGATATTTCTAAGCAATTTTGGACGGAATACTGGGCAGTTAAATTTCTCAGGTATCCATGGCATTAATAAAGTGCGCGATGGGATTGTTTCTTCTAGCGTTAAAACTCTCGAGAATAATTCTTATTATACTTTTATGGTTCATGATGATCTTACAAAGACTGATATTTTTAACGGTTTTGTTAATTATTGGCTTGACTCTAACGATGGATTTATATTTAGTTCATTAAATAACGTCTCCATTTTTAAAGATGGCGCGGTAAGAAGTTTGTATTCATATAATGATTCGTCAAAGATTAGGTTATTTAAACCAATTTCATACAATAAAGGTAATATTCTTTTTGATAATAATTATGTTTTAAATGTTAATGATTACAATGTATTTGATGTTTTTAAGCCAAATTTGAATGGTTACATGAGTTCAAATCCTGTTCAATGTGATGATAGTATATTTTACATTTGGAGTGATTCTAATGGTAATAGCCTTTGGAGGAGTAACAATAGCTCTTTATCAAGGATAGACCTGAATGGTAGGAACATTAGTTTAGAACTTGATTATTATAGTTTGTTGGACTCTTTGCTGTTATCTAGTTACAACAACTCTACATCTACTGATGATAGTTATTTTATAATTAACTGTAACGATTTTACAATTAAAGATATTTCACTTAAGCCAAATGATGCTACTAGTGTTAGTATTGTGAAAAGTGAACAAGGTGACTCATATGCTTATGTCAACTATTCTGATAAAATGGAGATTTATTATCTTAATGATTATGACTTTAAATTAAATAAAATATATACAACATATCTTTCTGATTATTTTTATCCAGAATATAATGTGATTCATCGAACTGCAGCAGGATTTTTTTATAATTATCAATATCAGTTCAGTAATGGAAACCTTGTTAAGATTGATAATGCTGATAATGAGTTGGTGGTTGCTAGCACTGGTGACTGCCACAGTGATGCTGATGACCCTCGAGGGTGGATTGTATGTGCTGTAACTATAAAAAATGGTAATTTAATGGAAGATGCCATACGTATTTATGATCTAAAGTTACAGAAACATGTTTACGTTAAGTTGAGCTATCTGGAGCCAGAGGCTAATATCAGTAACTTTACATATAGTAAAGGAAGATATTTTTTTAATGCGAATGTAGTTGGAATTGGTTATGAATTGTTAGCTCTCGATCAAAACTGCCTGATTGAATTAGATAATGGCGCAGATAACTGTACGTTACCACTTCCAAACTCAGCTCCAAGAATTTATAGCCATGAAATAGAAAGTTATTACAATGGACAATCCGTTTATTTCCCCATTAGAGCTGTTGATGAGGATTTAGACCTGTTGACGTATAAAATTATTAGTAAAGATGTAAATTGGTTATCTATTTCCAATAACGGTGTTTTAAATGGTGTGATTCCTAATAATGAAAAACCAAGAGTAATTAATTTAACTGTTGGAGTCTCAGATGGGAATGTTGAAACTACAAAGGAGATCAAATTAAAAATTTTAGAAAGCAATAACAATCATGATACCACGGAGAATCCGTCTACACCTCCAAGCAGTAACGGGGAGGGGGGGGGAAGCCTATCGCTATTTTTACTTTTTATCCTTTACCTAGTATCAACTTATCGCAGAAAATTCCAATTAGGGTTCTAGTAGTCAGTTTTGAATATTCAAAATATTTAGTTAGTTTTGCCATTGATGGCTTTGTTTGTGAAGCTATCAATGGCATTTTCTTTAACTGGTGTAGTTAGGTGTGTTGATGCACTAACTTGCCATTCTTCACCACATCTTTGCAGGGATTCACGCCGTAGGAATAGGCTAATTGCGCAGGAGTGGCGATATCCCACAGACAAAAATCGGCTTGTTTGCCAACGACTAAACTGCCGACGTTATCTTCAATCCCGAGTGCCTTGGCGGCATTTAAGGTGAGGCCTTTTAAGGCTTCCTCTGGTGTTAAGCGGAACAAGGTACAGCCCATATTCAGCATCAATAGGGTCGAGCAAATGGGGGATGAACCTGGGTTGAAATCGCTGGCGAGCACCATAGGTACGCCGTATTGACGCAGTAAATCGATAGGTGGTTTTTGGGTTTCCCGCAAAAAGTAAAATGCGCCCGGCAGCAGTACGGCGCAGGTGCCACTTTCACTTAAGGCTTTAACGCCTGCTTCATCTAAGTATTCAATATGATCCACAGACTTAGCGCCAAGACGTGCGGCCAGTGCGCTGCCGCCCATATTGGTTAGCTGCTCGGCGTGGAGTTTAATCTCTAAGCCCGCCGCTTTGGCTGCGCTGAGTACCCGTTCGGTTTGCTCAAGATTAAAGGCGATATTCTCGCAGAACACATCCACCGCATCGGCAAGGTTTTCGGCGATCACTGCGGGCAGCATCTTATTGATAATTAAGTCGACATAGCCGTCGCTGTTGTCTTTATATTCAGGCGGAATAGCATGGGCGCCAAGGAATGTGGTCTTCACATCTACGTGGTGATGCTTGCCGAGCTCGCGGGCAACACGCAGGATTTTCAATTCAGTTTCTGTATCTAAGCCGTAACCTGATTTGATCTCAACCGTAGTCACCCCTTCTTTGGCGAGCGCGTTTAAGCGCTGGCGGCCGAGTTCGAATAATTCTGCTTCATCCGCTTCGCGGCAGGCCTTAACGGTCGAAATAATGCCGCCACCTGAGCGGGCAATTTCTTCGTAACTCGCGCCTTGTAGGCGTAACTCAAATTCATTGGCGCGATTACCGGCAAAGATTAAGTGGGTGTGGGCATCAATCAATCCTGGAGTGATCCAGCCGCCTTTGCCTCTGTAAACGGGAATGGACAAAACATCGAAGGCAGGTAGTTCACTGCGGGGGCCAAGCCAAGCAATTTTGCCATCTTTAACTGCGATGGCCGCATTGGTGATGGCGCCATAAGGTGCTGATATGGAAGGGTCCATTGTGGCTAAGTTAACGTCAATCCAAACCTGATCCCAAGACATGTATTCCCCTCGCTGTGCTGGCAAATGTTAACCAAGTGATTAACGGTTTATTAAATTTATGTGAGCTAGCTTGTATTTACTTGTATATACAAGCTAGGATTGTAGCCGCTATTTTCACCGTTTGAAAGTGGCATGACAGACCCGTGTCTTTGTCGCCTTCGTCTGCGCCCCTTTAGCAAGCATGACTTTCGCAATGGCACAGGAACAAGAAGCTGAATTTAAGAGGATGTGTTTTGGCTATTCCTAAGTTTGCAGAAATTAAGCAATACATTATCTCCTCTATCGAATCTGGCGAGTGGGAAGAAAGCGCCCGCGTGCCTTCCGAGAATCAGCTTGCTGATTTGTTTACCTGCAGCCGAATGACGGCGCGACGTGCATTAACTGAGCTAACGGATAGCGGCGTACTCGAACGCTCCCAAGGGCTCGGCACCTTTGTGGCTGGGCGTAAATCCCAGTCATCTATGTTGGCGATTCGCAATATTGCCGATGAGATTAAAGACCGCGGCCACGGCTATAGCGTACAGCAATGGGTGTTAGAACAAACCGTCAGGGCGAGATCGCACTTTGTTAAAAAAATTGAACTATTCCGCAATGCCATGATCTGAATGAGAAATTAATTACTTGGCCACTCTCCATGTCACTTTTTGATCACCTATCTTTAGTCGAAGACACGCGCTCACACATTAATCAACGCCACAACCTTGTCGACGTACTGTTCTTAATCTTATCGGCAGTAGCCAGTGGGCAAGATGGTTGGGCAGAAATTCAACAGTTCGGCGAGCTAAAACTTGAGTGGCTACGTAAGTTTCGTCCATTTGCTAATGGCATTCCCCGTCGGCATACCATAGCACGTATCCTAAAAGCCGTAGGGCCGGAGAATTTGCAGTTGTGCCTATTCAGTTGGATTAACGACATCCGCACCGCATCAGCTAAACCCATCATTGCTATCGATGGTAAAACACTCAGAGGTGCCTCTAAGTTAGGGTGCAACACCTTACATTCCGTGGGTGCGTTCGACATTAATAATGGCTTGGCGCTTTATCAAGAAATGGCCTCTGGAAAAGGCAAGGAAATTGAAACGGTACAATCACTTATTACCATGCTTAACATAAATAAAGCATTGATAACCATGGATGCTTTGCATGCCCAACGCGCGACGCTGGAAGCGATAGTCGCTCGTAAAGGAGATTATGTGGTGCAGGTGAAAAGTAATCAACGGACTTTATTTCAAGCAGTTAAAGCGCAGTATGATGTGGCCTTCCAAGATGACAGTCAGTTAGCCCAATTTGCCTGTTCGGAAAAAGGGCATGGAAGAACAGAGCAGCGGATTACATTTCAAATCCCATCGAAGCTGAGTCCTAAATTGCAAGAAAAGTGGCCTTCAGTTAAGACATTAATAGCCGTTGAGCGACACAGAAAAATAGGCAATAAGACCAGCATAGAGACCAGTTTCTATCTCTCATCCCATGATATCGATCCCGAATATATTGCGACTGCTGTCCGAGGGCACTGGCGAATTGAGAATAGTCTGCACTGGGTCCTGGATGTGGTTTATCGAGAAGATGCTTGTCGGGTTCACGAGCAAAGAACTGCCGAATCACTGGCGATAGTGAGGCGAATGGCCCTGAATCTGGCAAAACTCGAAATAACACAAAAACGCAGTATGAAGAGTAAGTTACATCGTTCTTTACTCAGTGATGAATATCGGGAATTGATGATTTTTGCAGATGTTAAATCAATTTAACAAAGTATGCTCCGGCCCTGAACAAACCGTTGCCAGTACAACTATCGCAATTTCCCTTGGGCTTGAGCGCGGTGCAAAGGTATTCCATTCGATCTTAGTGCATTGCGAGCAAGGCTTACCGCTGCAAGTCGAAGAGCGTTATGTGAATCCCGAGTTGGTGCCCGACTATTTATTGCAGGATTTTACCTTGCTGACGCCCCACGAATATTTGTCCCAAGTGGCACCGTTGACCGAGGCGCATCACACGATTGAGGCGATTATCGCCAGCGCCGAATTACAGCAAAGGCTCGAGATCCCCGCGACTGAGCCCTGTTTACAGATTTTACGCCGCACTTGGTCACGCCAAGGCGTGGTGAGTTTTGCCAAGTTAGTTCACCCCGGCAGCCGCTTTAAACTCGGCGGCCACTTAACTTTTAGCAAGTAGCGCCAGTGGCGTTATTTACCGATGTAAATCAGTCACTCCAATAGCTGTACCCTTATAAGTTAAAAGATAAAAACACAATAAAAACAATAGTGCTAACCCGTATTAGCTACTCAGCGAATACGAGTTAGCACCACAGGTTGAAAGAGGAAAATTCAATGGACAAGCGACACGACCCAAGCCGCCGCATTATTGCACCGCATGGAACAAGATTAAGCTGCAAAAGCTGGTTGACCGAAGCGCCAATGCGCATGTTAATGAACAACTTACATCCCGATGTCGCCGAGCGCCCAGAAGACTTAGTCGTCTATGGTGGTATCGGCCGCGCCGCTCGCGACTGGGATTGCTATGACAAAATCATCGAAGTCTTACAACGCCTCGAAGATGACGAAACCTTATTAGTGCAATCGGGCAAACCTGTGGGCGTATTTCGCACCCATGCCGATGCACCGCGCGTGCTGATTGCTAACTCAAACCTAGTGCCACATTGGGCGAACTGGGAGCATTTCAACGAGTTAGATAAGCTAGGTTTGGCCATGTACGGCCAGATGACCGCAGGTTCTTGGATCTACATTGGTACACAAGGCATAGTTCAAGGTACCTACGAGACCTTTGTGTCTGTAGCGAAACAGCACTTTGAGGGTATCTCCAAAGGTAAATGGATCCTCACCGGCGGGTTAGGCGGCATGGGCGGCGCGCAAACGCTGGCGGGCACTATGGCTGGCTTCTCGGTGTTAGCCTGTGAAGTCGACGAGACTCGCATCGATTTCCGTTTGCGCACCCGCTATGTTGACAAAAAAGCCACTTCGCTCGATGAAGCATTGGCGATGATTGAAGAGGCAAACCAAGCTGGTAAGCCTGTATCTGTTGGCTTACTAGCAAATGCCGCCGATGTGTTTGCCGAACTGGTTAAGCGCGGCGTTACACCTGATGTCGTAACTGACCAAACCTCGGCCCACGATCCATTAAACGGTTATTTGCCGCAGGGTTGGACTATGGCAGAGGCCGCAGCCATGCGTAAAACCGACGAAGCGGGCGTAGTGAAAGCAGCAAAAGCCTCGATGGCGGTGCAAGTACAAGCCATGCTCGACCTGCAAACCGCGGGTGCAGCAACGCTCGATTACGGAAACAACATTCGCCAAATGGCGTTTGAAGTGGGCGTTGAAAACGCCTTTGATTTCCCAGGCTTTGTGCCTGCATACATTCGCCCGCTGTTCTGTGAGGGCATTGGCCCGTTCCGCTGGGTAGCACTGTCTGGCGATCCAGAAGATATCTATAAAACCGACGCCAAAGTGAAAGAACTTATTCCGGATAATCCACATCTGCACAATTGGTTAGACATGGCGCGTGAGCGTATCGCCTTCCAAGGTCTGCCTGCGCGTATCTGCTGGGTCGGCTTAAAAGATCGCGCTCGTTTAGCGTTAGCCTTTAACGAAATGGTCAAAAATGGTGAGTTGTCGGCGCCTGTGGTGATTGGCCGCGATCACTTAGATTCTGGCTCTGTTGCCAGCCCGAACCGCGAAACCGAATCTATGCTGGACGGCTCAGATGCGGTATCCGATTGGCCATTATTGAATGCACTACTCAACACCGCCAGCGGCGCGACTTGGGTATCTTTGCACCACGGCGGCGGCGTCGGCATGGGCTTTAGCCAACATTCGGGTGTGGTGATTGTGTGTGACGGTACCGATGCGGCGGCAAAACGGGTTGGCCGTGTGCTGTGGAATGACCCAGCGACAGGCGTGATGCGCCATGCCGATGCGGGCTACGAGATTGCGAAAAACTGCGCCAAAGAGCAGGGGCTCGACTTACCTATGCAAGAGTAGCCAATGCTAACTTCTTAAAATAACAATAATAACTAAAAGCTTGGAGCTAAAAATATGAAATCTGTGAATCACTTAGTGTTAACGCCGGGTAGCTTGAGCCTAGCGCAATTACGCGAAATCAGCCGCCATAAGCTGACACTCGAACTCGCGCCAGAGGCCATTACCGACATCAACACCAGCGCGCAAATCGTGCAAAAAGTGTTAGATGAAGGCCGCACCGTTTACGGCATCAACACGGGTTTTGGCTTGTTGGCCAATACCAAGATAGCGCCCGAAGATCTGCAATTGCTGCAACGCTCAATCGTGTTGTCGCACGCCGCAGGTACGGGCCAATACATGCAAGACGCCACAGTACGTTTGATGATGGTGTTAAAAATCAATTCATTAAGCCGTGGCTTCTCGGGCATTCGTTTAGAAGTGATTAACTTCTTAATCAGTTTAGTGAACGCCGAAGTCTATCCCTGCGTGCCAGAAAAAGGCTCAGTGGGCGCATCGGGCGATTTAGCGCCACTGGCGCACATGTGTCTGCCTATGCTGGGCGAAGGCGAGATGAGCTATCGAGGTGAGTTAATTTCCGCCGCCGAAGGTTTAGAAATCGCCGGCCTTAAGCCAATCGATTTAGCGGCCAAAGAAGGGCTCGCTTTGCTTAATGGTACACAAGCCTCGACTGCGCTGGCGTTAGAAGGGTTATTCCACGCCGAAGACTTGTTTGCTGCGAGTTCAGTGATCGGCGCCATGAGTGTTGAAGCGGCGATGGGCAGTCGTAGCCCGTTCGATCCTCGCATCCATGCAGCCCGTGGTCAGAAAGGTCAAATCGATTCGGCCATGATTTTCCGTCATCTATTGGGCGAAGAGTCTGAGATCAGCGTGAGCCATGCAAACTGTGAAAAGGTGCAAGATCCTTACTCACTGCGCTGTCAGCCACAGGTACTCGGTGCTTGCTTGACCCAAATTCGTCACGCGGCCGATGTGTTAGGCACTGAAGCTAATGGCGTAACCGATAACCCACTGGTGTTCCAAGACACGGGCGATATCATTTCGGGCGGTAACTTCCACGCTGAACCTGTGGCTATGGCGGCGGATAATCTGGCGATTGCAATTGCCGAACTGGGTTCTATTGCTGAGCGTCGTATCGCACTGTTAATCGATTCTAGCCTGTCGAAACTGCCGCCTTTCCTGGTGAAAAACGGCGGTGTGAACTCAGGCTTTATGATCGCTCAAGTGACAGCGGCAGCTTTGGCGTCTGAGAACAAAACCTACGCCCATCCAGCATCGGTAGATAGTTTACCAACATCGGCTAACCAAGAAGATCACGTCTCTATGGCGACTTTTGCGGCCCGTCGTTTACGCGATATGAGTGAAAACACTCGTGGTGTACTGGCAATTGAATTGTTAGCGGCGGCGCAGGGTTTAGATTTTCGCGCACCTTTAGCCCCAAGTACTGCGGTGGCTAAGGCCAAAACTGAATTGCGTGAAGTCGTCGCCTATTACGATAAAGACAGGTACTTCGCACCAGATATCGAAGCCGCGACAGACCTGTTATACACAGCAAGCTTTAACGCTTACCTGCCAGCAGGAACGCTAGCGAGCTTTTAAGCGCTTTAGTTCTTAGTGTGTAACGACTAAGCCGTTGTTGACTCGGTTGAGTTGGGTGTATTGGGGCGCTTTTGCTGAAATACTCAGGATTAACTCCCCAACTTGCTAAACATAACTGCTGAAATAAAACAAAATGCAGGCATAAGTGGATTTCTTATGCCTTTTTCTTGAGTGTTTCCCCTTTTTAGCTCGATCCTCATCTTTGCCCTTTTGGTGTGTTTAACACCGCAGACTCTGCACTTTTTGCGCTGATCTTTACATTGTCAATTTCAGTGGCTACTTACCCCTCAATAAACCGTCACTTTTGTGTTTTTATCCGCCATCGTTATACACATTTTAATATCTAAAACTATTTTTAATAACCATATGGTTAGGTTGTGGGACAATATGGACCTGCATTGCTAGACAAATTGTCCTTTTGGTTTTATTGGGCTTTAACTCTTTGAATAGGTGTTTTTTACATTATTTAAAGTTTTTAATTTCAATATTATTTATTTATTTAGATTTAAGTTTTGTGATTGGTTATTTGAGATCTCGATCTCATGTCATTATTTGTTATCTATTTCCTCTTTAAGGATAATCTTAAATTAAGTGTGGTCTTTAAGTGGGCCCACTCGATCGGTTGCAATCTTTACCACCGCAACCACTAGACTCATAACGACAAGAGTGTGAGAGGACGCAATGAACAGACGCCAGTTTTTTAGACTGTGTGCTGCCGGAGCTGCCACCTCAGCAATTTCTGCGTTGGGATTGATGTCCGAAAAGGCCTATGCGGCCGTTCGGGAATTCAAACTCATTAGCGCGAAAGAAACCCGCAATAACTGCTGCTATTGCTCAGTGGGTTGCGGATTGCTGATGTACAGCAAAGGAAGTAATGGCAAGAACGCCGAACAGAGTATTTTCCATATCGAAGGGGATGCAGACCATCCAGTCAACCGCGGGGCCTTGTGTCCTAAAGGCGCTGGTTTAGTGGATTATGTCAATAGTCCCAATCGTCTTAAATACCCAGAGTACCGTGCACCAGGCAGCAATGAATGGACACGTATCAGCTGGAGCGAGGCTTACCAACGCATCGCCCGCCTGATGAAAGATGATCGTGATGCTAACTTGATTGAAAAGAATTCTGCCGGGACGACGGTTAACCGTTGGCTTAGCACTGGGATGATGACCTCGTCGGCGATGCCGAACGAAGGCGGCTATATCACCCAGAAATTTGCACGGGCACTGGGCCTAGTTGCAATCGATACTATTGCGCGTAACTGACACTCTCCAACGGTAGCAAGTCTTGCTCCGACATTTGGACGTGGTGCCATGACCAACCACTGGATCGATATTAAAAATTCTAATTTGATCATTATCATGGGCGGTAATGCTGCAGAGGCTCACCCTGTCGGATTCGGCTGGGTTACAGAAGCGATGCAACATAACAACGCTAAACTTTTGGTGGTCGATCCCCGCTTCACCCGTAGCGCCGCTGTGGCCGATTATTATGCGCCGATCCGTTCGGGCACAGATATCGCCTTCCTGCTCGGGGTGATCCGCTATCTTATTGAAACCAAACAAGTGAACTACGACTATGTGAAGGCTTATACCAATGCCAGTTACATAGTGCGCGAAGACTTCTCTTTTAGTGAAGGTCTGTTCAGTGGTTTCGATGAAGAAACAGATAGCTACGATAAAGAGTCTTGGTACTACGAGTTAGACGAACAGGGTTACGCGAAAGTTGATCCTAGCTTCGAGCATCCACGCTGTGTGTGGAACTTAATGAAGCAACATGTAGATCGCTATGACTTTGAAACAGTAAGCAATATTACCGGTACGCCGATTCCGGACTATGAGATGGTTTGCCAGCAAATTGGCAGCACTCATACCCACGATAGAGCCGCCACTTTTATGTATGCCTTAGGTTGGACTCACCACACTAAAGGGGCGCAGAACATTCGCTCTATGGCTATGGTGCAGTTGCTGCTGGGTAACATTGGGGTGTTGGGCGGTGGTGTTAACGCACTACGTGGTCACTCTAACGTGCAAGGCGCGACCGATATGGGTTTATTGTGTCAAAGCTTGCCTGGTTACTTAAAGTTACCGAGTGAAAAAGACACTGATCTTAAGACCTATCTTGATCGTTACACGCCTGTCGCCTTGCGTCCGGGTCAAACCAACTATTGGCAGAATTACCCTAAATTCTTTATTTCGCAAATGAAGAGCTTCTGGGGCGATAACGCCACGGTTGAAAATGACTTTGGTTATGACTGGGTGCCTAAGTGGGATAAACAGTACGACTTTACTAAGCACTTAGATATGGCTTTCCACGGTGAAGTGAATGGCTACATTATCCAAGGTGTTAACGCCATTAATTCTATGCCGAACCGCAATAAGGTACTCAAAGCCTTGAGTAATCTTAAGTACATGGTGGTGCTGGATGCCTTGGCAACGGAAACCGCAACCTTCTGGCAAAACGCCGATGGCTTCAACGAGGTGAATCCCGCTGAAATCATGACGGAAGTGTTCCGCTTACCGACGACGTGTTTTGCTGAGGAAGAAGGTTCCATCGTCAACTCTGGACGTTGGATGCAGTGGCATTGGAAGGGCGCTAACCCGCCGGGTGAAGCCAAGCCAGATGCTGAGATCCTCTCCGGTGTCTTAATGGCAATGCGTGAACTGTACAAGCAAGAGGGCGGTAAGTTGCCTGAGCCAGTGCAAGCGATAAGTTGGGATTACCACAATCCTTACTCGCCGCATGCCGAAGAAGTTGCTCGCGAGCTTAACGGTAAGGATCTCGTGACTCAGCGTCAGCTCAGTAGCTTCTCTGAACTCAAAGCTGATGGTAGTACTGCTAGCGCCTGCTGGATTTATGCCGGTTCTTGGACCGAAGAGGGCAACCTGATGGCTCGCCGCGACAACCATGATCCTTCGGGTAAAGGTGTCACTCCGGGCTGGGCATTTGCTTGGCCAGCCAACCGTCGCGTGCTGTATAACCGCGCTTCTTGCGATATCAACGGCAAACCATGGGATCCCAAACGTACTATCGTCGAGTGGGTTGATGGTAAGTGGCATGGTATCGATGTGGGCGACTTCAATATGAAGTTAACCCCACAGGAATCCGCAGGTCCCTTCATCATGCAACCCGAAGGTGTGGGTCGCTTCTTTGCCCTTAAGTTGTTGGCCGAAGGTCCGTTCCCTGAGCACTACGAGCCGATGGAGTCGCCTATTGGGGTGAACCCGTTACACAAGGTCACCAGTAACCCTGCGGTACGTATGCTACCGGGTGTGAAGGAAACCTTAGGTTCCCATAAGGACTTCCCTTATGTGGCAACCACTTACTGTGTGACTGAACACTTTAATTTTTGGTCTACTCATGCCCGTTTGGCAGCGATTTCTATGCCGGAAACCTTCGTCGAAATCGATGAAATGTTGGCGGCAGAAAAAGGTATCGCCAATGGGGATTGGGTCACTGTCAGCTCTAAACGTGGCAGCATTGAAACCAAAGCCTTAGTGACTAAACGTCTGCAGCCGTTAAAGGTGAATGGTCAGTTAGTTCATACCGTCGGGTTACCGCGTCACGGCAGCCATAATGCGCTCACTCGTAAGAGTTACTCCTGTAACGTGCTCACCACGGAAGTGGGGGATGCAAATACACAGGTGCCTGAGTTCAAAGCATTTTTGGTTAACATCACTAAAGCGAAGGGGCTCTGATTATGGCATCACAAGACATTATCCGCCGCTCTGGTACTTCAGCGGTGACGCCTGCGGCAAAGGGGCGTAATTCGTCAGAAAAGGTCGCCAAGCTGTTCGACGCCACTAAGTGTAATGGCTGTAAAGCCTGCCAAGTGGCGTGTTCTGAGTGGAATGATTTACGGGAAGAAGTGGGCACGTTCCAAGGCAGTTATCAAAACCCTATGTCGCTATCGGGCCAATGTTGGACCCTGATGGAGTTCCATGAGGTGATGGAAAGCAACAAGCTGCGTTGGCAATTTACCCACTCGGCCTGTATGCACTGTGCCGACCCTGCCTGCTTAACTGCCTGTTCGACCTCGGGTGCGATTATCCAGCACGCCAATGGTGTGGTGGACTTCGATTCGGACAAGTGTATCGGCTGCGGTTATTGCGCCAGTGCTTGTCCCTTCGATGTGCCCAAAATCGATCCTAAGGACAACAAGGCCTACAAGTGCACTATGTGCTCTGACCGTTTGGCCGTGGGATTGGAACCTTCCTGTGTTAAGTCCTGTACCACAGGGGCGCTGCGTTTTGGCACCCGTGAGGACATGTTGTTTTACTCGCAAAAACGGGTTGAAGAACTGAAGGAAAGGGGCTTCGCTAAGGCAGGACTCTATAACCCTGAGGGCGTAGGTGGTACGGGTATGATGATGATATTGCACGATATCAATCAGCCCGAAAGCTACAACATGCCGGATAACCCGCAGATCCCGTTGTCACTCACTTTGTGGCAGGACTGGGTCAAACCTTTAGGCACTATGGGCATTCTTGCCACTGCCGCTGTCGCTTGTCTGCATAAGATTACCGTCGGTCGCAACATAGTGGAAGAGGACCAGCCGGGTTATCAACCGCCAGAGGAAGAGGCTAAGCAGGAAGAGGAGGGCGACAAATGAGCAAGCATGATAAGCAAGAAATGATAGTGCGTCACAAGCTGTTCGATCGCATCTGTCATTGGTTCATTGTGGCGGTTGGTCTAGTGACCTTTCTCACTGGCTTTTCCTTCTTTTATCCGTCGTTTCAATGGTTGGGGGCGATAGCGGGCACACCGCAATTGGCGAAGTTCATTCATCCCATTGCGGGCTTGATGATGTGCTTACCTTTGATGTTGATGTTGGTGCGTTACTACAAACACAACAAATGGGAAAAACACGACCTCGCTTGGATGCTCGCCATTAAGGATGTGATGTTCGAGAACGAGGAGAAAATCCCGCCTATCGGCCATTACAACCCTGGGCAAAAGGTATTGTTTCGCGCCTTCGTGCTGACATCTATCACTTTAACGGTGACGGGAATCATCATGTGGCAGCCTTACTTTGCGCCCTATTTCTCAGCCACTGTGGTGGGTTGGGCCATTCTGTTGCACGCCATTTGTGCGCTCATCATGTTGATTTTTGTGATGGTGCATTTCTGGATGGCGACATGGGTGGAAGGGTCGGTTACAGGCATGCTTTACGGCAAAGTCTCTAAGGCTTGGTGCCGCAAACATCACCCTGTGATGTTGGATGAACATTCGGACGTGGAGAAACATTAATGAGTCATACAGCCGAGATACCTCTGGTCCCCGGCAGCGAATCGCCGCTGGAACTAAAACCGTTAAAAGCGGCAGATCCCCACGTTGTGTATCACAGAAGGGCACATCGTTTGCTGTCATTGGCCAAGGACTCGCCGCTGGCTGACTACTTTGAGCTGTGCCGCCGTCTGGTCTCTATACAGGCCAAATTGGCGGAGGAGGCTGACTTCGGTCAGCTTCTCGCTTGGGGGAAAGATGAGGCGACTCCGCTTTCTCTGTTGGGCTCAGAGGCAGATAGCTATTGGCAGGGACTGCTACAGCAACTGTTAAGTGATTTGTTGCCGCAAGTGGATGAATCCATTGCTCGGGTAGTGCGTTTGCTGATGCAGCAATCTCCCGAGCAGCTGAGTTCGTGGGGGCGTTCACTGCGCCAAGGACATGTCAGTGAAGTACCTGCCCATTTTAGCTTATTCATCTGGGCAGCGATGGGCGTCTACTGGTCGCATTGGGCACCTATGGTCATCAAGCGGATGGATCAACGTAAGGTGGCACAACAGAGTATGTGCCCCGTATGTGGCTGTCATCCCGTGGCGAGCGTGATTGTCGATCAGCCCCGTGCTGGTTTGCGCTATCTGCATTGCAGTTTGTGTGAAAGCGAGTGGCATTACATTCGCGCCCATTGCACTAGTTGCGGTCAGGATAAAGAGATGACGATTTGGTCGCTGGATGATGCTCAGGCTCAAGTGCGGATCGAAAGCTGCGATGAATGTCATGGCTACACCAAGATGATGTTTGTTGAAATGTCACCTTCTATGGATGTGGTGGCTGACGATCTCGCAACCTTGATGTTAGACAGTGAACTAAATGCCAAAGGCTTTGGGGCGACAACATTGAACCCACTGCTAATGGCCCACGAAATCACGTAAGCCAAATAGCTTAAGTCAATGCAGGAGGGAGTCGGGTTGCTGATCCCTTCCTGCTTTTTATCCAACAGATGGCAATAACTTAAACAAGATGACACAGATGATGACCCAAGCACCCGAAACGCCACAAGCCTTGTACCGCGCTTTGCCCTCAATGGACAGCTTACTGGCGGAGCCTAGCTTTGTTGTATTGCAGCAGTCTTACGGCAAAGTGGCAGTAAAGGCGGCGCTGGATAGCCAGTTAAAGGCGGCGCGTAGCTTAATCGGCAGTGAGCGCCGGTTGCCGACATGGTGTGGCGAGCTTGCGCAATTGCACGGATGTTTGGTTGATCAACTGTCTAAAGATTACAGCCACAGCTTAATACCTGTGTGGAATCTTACTGGCACCATACTGCATACCAACCTCGGCCGAGCGCAGCAGTCAGAAGCGGCGATTCGAGCCGTGACTTCTGTTATGCGCTACCCCACACCGCTGGAGTTTGAGCTTGCGGCAGGTGAACGTGGTCACAGGGATAATGCGATCAGTGCTTTAATCCAGCGCTTAACGGGTGCCGAGGCTTGTTGTGTCGTGAATAACAATGCTGCTGCTGTGTTGTTGATGCTGTCAGCTGTGGCTGCGGGGAAAGAAGTCATAGTCTCCCGTGGTGAGTTGGTGGAAATTGGCGGCGCGTTTCGCATCCCTGACATTATGCGCCAAGCAGGCTGCACCTTAGTTGAGGTCGGCAGTACCAATCGGACACATTTTAAGGATTATGAGCAGGCGATTACTGAAAATACCGCTGCAATAATGAAGGTCCACACCAGTAATTATCATATCAGTGGCTTCACTGCCGCAGTGGAGGAGGCAAGACTGGGTCAGCTATGTTGTGAGCATGGCATATCACTCATTTCGGATCTTGGTAGTGGTTCACTGACGGACCTGCGCCGCTTTGGGCTAAAACAAGAACCTACGCCTCAAGCCATGCTGGCCGATGGTGTCGATTTAGTGAGTTTTTCCGGCGACAAACTATTGGGTGGCCCTCAGTCTGGTTTGATCGTTGGTAAACAGTCGCTGATTAAGCAGTTACAGAGTCATCCACTAAAGCGCGCCCTACGTTGCGATAAGCTGATTTTGGCGGCGTTAGAGGCAACATTGATCCACTATCTTAACCCTGAAACCTTAGACTCTGAGCTGCCCATTATGGCGAAATTTGCCCGTAGCCAAGAGACGTTATGGCAGATTGGTCGTCGCTTACAGCATGCTTTGGCAGCGCACTTTGAACCTTGTTATCGCATTGAATTAGTTGAGTGCCAAACACAGGTCGGCAGTGGCTCGCAGCCCGACACTTTTTTACCCTCAGTGGCGCTGTGTTTTACCGCACTCGATGGCGGTAGTTTAAGTTTGTTAGAACAAAAATTTAAGCAGGCGGCACGGCCTATCATAGGTCGCATGAGTAACGATCAACTCAGATTAGATTTACGCGGTCTTGATAGTGAAGCCGAGCTACTCGCCGAGCTGAGCACCTTAGGTATGCAGTTGTGATCATTGTAACTGCGGGTCATGTCGATCATGGCAAGAGTACGCTTATTCGCGCTTTGACTGGGATGAATACCGATCGTCTGCCGGAGGAGAAACGCCGCGGTATGACGATTGATCTCGGCTATGCGTTTATGCCGCTTAGGGATGGTTCGCGGCTTGCTTTTATTGATGTGCCTGGGCATGAAAAATTTATCAACAACATGCTGGTGGGCGTGAGTCATGCTCGGCATGCCTTACTCGTGCTGGCCTGTGATGATGGCGTTATGCCGCAAACCCGCGAACATCTGCAAATCCTGGCGCTTTTACCTTTGCACAGTTTGACCTTAGTACTGACTAAGCGGGATCTGGTTGATAGTGATATGGCTGAGCAGCTTGTCGCCGATGGCTTAACTTTGCTGGCCGAGTATGGCATTGAGGCCAGTGGTATTTTTCAGGTGTCGGCAAGTGAAACTGCCGACTTAGCATCCAATGGTGTGGCCGCGCTGAAGGCGCATATTCTTGATTTAGCTGAGCAGGAGTCTAAGCGGGCTGAGGATGCCAGCAGTTTTAGGATGACGCTCGATCGCGCCTTTAGTGTGAAGGGCGCAGGTTGTGTGGTTACGGGCACAGTGATCAGCGGCAATGTGTGCGTGGGTGATAGTCTTTTTAGCTCAGGACAAAAGGCCAAGTTGAGAGTACGAGGCATCCATTGTCAGGGACAAGAAGCCCAAAGTGCCGTCACGGGGACACGTGTAGCACTCAATTTAACCGGAGTTGATAACCATAGGTCGCCCGAGCGCGGCGATTGGCTGAGCAGCTTGCCTCAAGCTGAGCTCTGTAGCCGCCCTGTGGTGCATATTCGAGTCTTCGATTTATTAGGTATTGAGCCCTTGGTGCATTGGCAAAATGTTCACTGTCACCATGGCGCTGATCATACTTTAGGGCGAGTTTCGCTGTTAAATGAAGCTGATGCTGAAGGTTTTTATCTGGCGGAGTTGGTACTCGAAAAGCCGCTACTTTTATGTCAAGACGACCGTATCGTCCTGCGCCACATTGGCGGTAAACAGACTTTAGGTGCTGGGCGAGTATTGGCACTTAAAGTCCCCAATCGTAAGAAGCGAACACCTGAACGAGTTGCGAGGTTACAGCAAGTAGCGCGCTTGGATAATCCAGCCCATGTAGTGGCTTTACTCGGTCAAACTCAGGCACTAGAACTTGCTGAAATTGCGTGGCGTTGGCAATTAACCGACGAAGGGCTCGCTATGCTCTTAGCCCAAGCGGGTTTAGTCCGTCTTGGCGAGTTTGCCGTGGCCACACGTTTACTTGAGCAAGAAAAACAACAGTTTATTACTTTACTGAGCGAGTTCCATCAACAACATCCCGACCAACTTGGACTGGGACGTAATCGCCTGCAACGCATGAGCCACAGCGCTTTGCCGCCCGAATTAGCCAATAAAGTGCTCGATACGCTGTGCCAAGAGGGGCGCATGGTATTGCGTGGCGCCCTGTTGCAATTGGCCGAGCACAGAATAGTGCTCGATGCTGAGGCGCAGCATTGTTGGCAGGCACTTGCGCCTTGGCTTGCAGAGCAAACCGCCCCTGTGTGGGTCACCGAAATGGGCGAATACCTTGAGCTGGAGCCTGCCAAATTAAGGCTGCTGTGTTTCCAGCTGGTGCAACAGGGGTTTATTACTGCAGTGGTGAAAGACAGGTATTTACTCAGTGAACAACTTTGCTATTACGCCAGCTTAGTGCGTACTCACGTCGCTGAACACGGCAAGCTCGAAACCGCTGAATTTAAAGACTTAATAGGGCTTGGTCGCAAGGTCAGCATTCAGCTGCTTGAGTTTTTTGACAGAAGTGGTTTTACCCGTAGGAAATATCGCAGTAATAGCCGGGAAATTCGGGATGCGGAGCTTTTCTTCTCCCATGAGAATTGGCGGAAGAACATAGGAGTCGAACCTACCCAGGACTGCTGGCAGCCCTACCCGGATTTGAAATCCGGACGCCTCACCGGAGACGACGTTCTTCCAATAGAAGACCTAGGGATAGTAGAAAATGCTTAAAGATGATTCAAGGCGATTCGTATTTAGTTTATGGGTTTGTGATACAAGTCTTAAATTGTCATCAAACTAATTGTTTTTATTTGATTTTATAGTTTTATAGGGGCGCTATGGAATATCATTCACATGAAGTGGTTACTGAAAGAAACGTGGTCCTGTTAGAGCATGGACACGCATCTGAGCTGACTGATTACTTAGCCAATGAAGTACGGGTTGCGCTGGTCTATAACGGCATCGCCCATACCGTTATGTTGGCGAGCCCCCAAGATTTAGAAGCCTTTGCCATAGGTTTTACGCTGTCTGAACGTATCGTTTCCCATATCAATGACATCAAAGGGATTGAACTCGAATTCACCCGTGAAGGCGTGTTGATCCAAGTGGAAATCAACCAACGATGCTTTATGGAACTCAAACAACAAAGGCGCAATATGGCGGGGCGCACGGGTTGTGGACTGTGCGGTGTCGCCCAGCTCGAAGAGGCGGTCAAGCCTGTTATGCGGGTAGACTCAGATGCCAGATTCGACATCAGCCACTTACAGGTAGCACTTGGGCAAATTAAAGATAATCAACAGCATTTTAAGCTGACGGGTGCGACGCATGCGGCTATGGGGCTTAACCATCAAGGACAGATCATCGCCGCCTATGAAGATATAGGCAGGCATATCGCACTCGACAAGTTAATTGGCGCCTTTTCCGCTCACAATGTGGAACGTCCAGTGGCCGTATTGCTCACCAGTCGCGCGAGTTTTGAAATGGTGCAAAAGGCCGCCAGTGCCAATATCCAAATTCTGTTTGCTATGTCCGCCGTGACTTCACTGGCGTTGGAATTAGCAGAGAAAAGCAATATCACGCTTATCGGTTTTTGCCGCAATGGCAGGGCAACGCTTTACACCCACGGTTATCGACTGTTGGGACAGCACTGCGCTAGCTTAGCCAAAGCCATTTAAGCGTGAGGCCACTTAAGATTGGCGTTGTATATAACGACCTCAATCGCTGTGGCATAAAGGTTATAAGCGCATTGTGCTTATTTTTTTATAGTCTCGTTCATTAGGGTAATGACATGACATGGCAACAATTTAAACAAGAATACTTAGTTCGTTTTTGGGCTCCTATGCCAGCTGTGATCGCTGCGGGTATCCTTTCTACTTACTATTTTGGTCTGACTGGAACCTTCTGGGCTGTTACTGGGGAATTCACTCGTTGGGGCGGGCACTTGATGCAGCTTTTTGGCGCAGAGCCTGAAACCTGGGGATATTTCAAAGTGATAGGTCTGCAGGGGTCTCCTTTAGATCGTATCGACGGCATGATGATTATCGGTATGTTTGGTGGTTGTATTGCTGCTGCGCTCTGGGCCAATAACGTCAAACTGCGTATGCCACAGAGCCGTATCCGTATTGCTCAGGCGCTGATCGGCGGCATTATTGCTGGCTTTGGTGCCCGCTTGGCTATGGGCTGTAACTTAGCGGCCTTCTTCACGGGTATTCCGCAGTTCTCTTTGCATGCTTGGTTCTTTGCTCTGGCGACCGCTGCGGGCTCTTACTTTGGTGCAAGATTTACCTTACTGCCTATGTTCCGTATTCCGGTAAAGCTGAAGAAGGTGAATAAAGCGACTTCGATTAAGCAGGATGAAAATCAGGCTCGACGTCGTTTCCGCATCGGTATGTTAGTGTTTGTCGGCATCGTCGGTTGGGGCTTATTGACTATGTTCAATGCGCCTAAACTCGGCATAGCTATGCTTTGCGGAGTCGGATTTGGTCTGTTAATTGAACGGGCGCAAATCTGCTTTACCTCAGCGTTCCGCGATATGTGGATCACTGGTCGTACTCACATGGCGAAGGCCATTATCTTAGGTATGGCGGTCAGTGCGATAGGCATTTACAGCTATGTGCAACTGGGTGTGCCACCTAAAATTATGTGGGCGGGTCCTAATGCGGTGATCGGTGGCTTACTGTTTGGTTTTGGTATTGTGCTTGCTGGTGGTTGTGAAACGGGCTGGATGTACCGTGCAGTCGAAGGCCAAGTGCATTTCTGGTGGGTCGGTTTAGGCAACGTGTTAGGTTCGACTTTACTGGCGTATTACTGGGATGACTTAGCTCAACCCTTAGCGACCAACTGGGACAAAGTGAATTTATTGACCAGCTTTGGTGATAGGGGCGGCTTATTGCTGACCTATGTGCTGCTGGGACTTTCCTTTGGCGCTATGTTGCTATGGGAAAAACGTTTCTTTGCTCGCAAGGCGAAGCGTGATGAACAATTGCTAGCGGAGGCGGCGTAATGAATCAATATGTTCCAGATTATCAACTCGATATGGTCGGTGAACCTTGCCCTTATCCTGCAGTGGCCACCCTTGAAGCTATGCCTAAGTTAAAGCCCGGTGAAATCCTCGAGGTGATCAGTGACTGTCCGCAGTCCATCAACAATATTCCTCTCGACGCTAAAAATCATGGTTATAAAGTGTTGGAAATTGAGCAGAATGGACCAACTATCCGTTATCTGATCCAGCGTTAGCGATTGATTTTCGCTGAGGCGTAAAACTGAAAAAGGCACAAACTTATGTTTGTGCCTTTTTACTATCTCCTTTGGAAATACAATTAACTTCCTCAGGAAAGCTGCCTTCAGTCGATGTCTAATTGAGTTAACTCAGGCTTAGCCTTGGCAATCTAGCGGCAGATATTCGTCGGGAATGTCGCTACTGCTGCAAACCCAAGTGATATTGCTTTGCTCATCAACTTGTGGCGAAAGATAGACTTCGTAGCCTTGATAATCACCGAGGGCTTCGCTTCCTACAAAGCCGACAATGCCATCTGTGTACAAATTAAATGTCGCAATCTGCTCGTCCGTCAGCTTTAGGGCATCTAATTCAGTTGGCCATGCTTGGTTGGTTTGATAATAGCCAACTAAGATGTCGGTGATCGGCGTTAATGCTTGGTGCTGCTGATCGATACTGGCAATGGCGAGTGTCGCGTCAGGCACTGTCATGGCTGCTGGTGTCGCTTCGGTTGCGGCCGGGGCTTCATAGCTAGTGGCTGCACTCATCTTTTCTTGATATTGCTTGTATGCTGGCAACGCAAGCGCGGCGGCAACCCCAATAATCGCAGCAAAAATAATGAGTCCACCGCCTGCAGTGCCCATGCGCGGCATAAAGAGGCTAAATACAAATGCCCATGCACTGCGTGATGGCGGCGTGTATTCAGTTCCCTGCGCTTTGGCGTCGACTCTTGCCATACGCTTAGTCAGCCAAGGATAGCTGCCGTTAAGCTCGTGCAGTGACATCCAAAAACCACTCGATTCGCGAGCTTGGCGGATATATTGATCTACATTCATACGTTTCCATTGCTCTACGCCAGCGGCTAAAACGGCAACGGCATTGGTGGCAGAGCGTAAGCTGTTGCAGCAACGTAACCCGTGGAGATCACAGGTATATTCGCAGGCGCGAGAATAAGCCGCGCCCACTAAAGGCAACCAAGTGGCGAAGACTAAAAAGGGTGCTTTGCCAATATGATTGCGGCGAATATGGCCTAATTCGTGGCCGATATAGAAGTTAAGTGCATCTTTATCGGACTCAAGCGCATCCACAATCGATGAAAACAACACGATATAGTTACGGCCGAGGAAGCGGGTTGCCAGCGCGTTGAGCATGCCATCGGCGGCCAACAAGTAAGCCCGTGGTGGCTCTTTCATTTCTAAACGTTCGCAGCATGCTAAGTATTGTTTATGTAATTCAGGGAATTGCTCTGCATTGATTTCGACAGCCGTACCTTTTAGGTAGCTGATAAAAGCGGAGTGGCTGAAGAGATAAATAATGAAAAACATCAACACATAGAGCAGGGCCATGCCCAAAGTGCCTATGATCAATGCTGCCCAAACGATTCCAGAGATAATCGCTAACAGAGTGAATAGCGATTTTTCCTTCGAATAGACAATTTCTGTCATTTGAATCGGATTATCTTTCCCTGCGGTTGGGGTAATAACTTGTTCCATGTTGATCCTTTTCCTAGTGAAGTGACAAGCGGCGTGAAAATAGTCAATTTCGCTCGAAAATTCAACCACTATCACGCGTAATGCCAAAATTAACCGTTCTGTGATTCGGTTAAATCAGCATTATGCGCGACTGGCGTAGGAAGCTTGTGTCAGGATCTTGCGGCTTTTAGCTTGTTGTTCGTTGCCAAATGCGCTGGCAGTTTTCCGCAGGCAGTTTAAGGTGCCTTTATCGCCGTGCCCGCTTTAATGGCTGCTTGAGCGAGGCTGATAATTTGCTTCTCTGCGGTATTGGAAAGGCTTTCATGAACTTTGCTTAGCTCATCCATTTGTTGACCAAGCTTGTCCATTGATGTGCTCAGACTGTCGATCTCAATCTGTATCTTAGGATCTTCTTTATCGGTCGCAACATTGAGTAAAAGTTGCTGAGTTTGGCTGTTGATCGCATCGCCTTTTTGCTGCAGCTGTTCACCTATTGCCTGCATTTTTTCTCCCTGTTGTTGCATAGGCATGATGGCGGTTTTGATTTGCGCCACTATGGCGGGATCTGTGATCACGTAAGGCGTTCCTGCGGTTTTCACCCACAGATAATCGGCATGATAAGGATTGCCTTGGCGTTCAAGTTGCTCCCAATCGTCACTGTGACCAGCGCCAATAGTCATTTCGCCATCTTGCTGAACATAAATCCATGAGAAATCTGGCTGACTTTGGCTGAGCACTGATTTGCTGATGACTGATATGCCTCCATCGGCACTGTTGCTCGTTGTTTGGCCCTTTGTTTGGGTCGTCCAAATGGCGGTTGGGATCAGAGCGAGAATAAACAGACCGCCCATCAGTTTAGAGTGCGTATTCATAGTGCTTCCTTGTATGGCTATGTGGCTCGGCGGGTTATGCCTGAGCGCGTTAACTTGCTACCCTGCCACAGCAGAATCTGTGCCATAAAAATTTAATCTTCATTATCAATGTAATAATTGTTTAAGTGTCCGCGCACTGTTGCCGAGAAGGCGTAAGAGTGTCCGCTAATATGTCCGTTCAGGCTGGTGGACACGGGTGGCTAAGTCGACGTATTTCTTTCGCCGAGTTTGATTGTTAGCACTAGAATGAAGTGCTCGTGAGGTTGAGATAAAGGTGAATTTAATCACGATTTGTACACTATTTTGAGTGCAATTTAAGGCATGACTCTTTGAGTCAGGTTAGGTTAAGCCGATCTAGGGTTAGCTATGGCAAGCGTTGAAGTAAACCACAGAATATAAAGGAATTGAGATGGTAAAGTTAATCAATAAGATGTTGAATAATCGACTGGGCTTCACTAGGTATTGCGTCGCCGTTGTGATGGTGAGTTCGTTAAGTACAGGCGCCATGGCGGCCGATGCGCCTTCCTTTGACTGCGCAAAAGTCAATGCGGGCAGTATCGAAGCTATGGTTTGTAAGGATGCGGGACTGAGCCAACTCGATAATCAGTTAGCTGAGGTTTACAGCCAAGCGCTTGAAAAAGCAAAAAATGAACAGCCACCGACTTTAAAAGCGATGCAGCGTGGTTGGGTGAAAGGCCGTAATGAGTGTTGGAAGAGCGACGATAAACACGCGTGTATTGAAGCTAGCTATCAAACCCGTATTGCCGAACTGCAGGCACAATACCGTTTAGTCGACATTACCGGCCCTGTATCTTATGCCTGTGACGGCAATCCAGCCAATGAAGTCGTAGTAACTTATTTTAAAACCGAACCTGCTACCTTGATTGCTGAACACGGTGATAGTACTTCATTGATGTTTGTACAACCCAGCGGCAGTGGTGCTAAGTATCAAGGCCGCAATGAAAGTCTTTGGGAACATCAAGGCGAAGCAAAAGTGGTTTGGGGCTACGAAGCACCAGAAATGACCTGTAAACTGGTGACCAAATAGCGTTAATTGAAGGTCGCCATGAAACAAGCTCTTGTGCATATCGCCTTGGTCGTAAGAGATTACGACGAGGCAATTGATTTTTATGTGAATAAATTAAAATTCGAACTGGTTGAAGACACTTATCAAGCCGAGCAAGATAAGCGCTGGGTCGTCGTTGCCCCGCCGGGTTCTAAGGGCGCGTCTATTTTGTTAGCGCGGGCTTCTAAGCCAGAGCAGTTTGATTTTATTGGTAATCAGGCGGGTGGGCGGGTGTTCTTATTCTTGAATACCGATGACTTTTGGCGTGATTATCGCCGTATGGTCGCCGATGGCGTGGAGTTTGCGCGCGAGCCGCAAGAGCAAGACTACGGCACAGTCGCTGTGTTTAAAGATCTCTATGGTAATTTATGGGATTTACTGCAATTGAATGATGGGCATCCTATGGCGCACAGGTTAGCGTATCAGGATTCAGGATTAGAAGTTTACCCCTAATCCTAAGGTGTAACTGGTATCTCTGGTATTACCTATCACTGTTTGAGTGTCGTTGAAGTCATATTTAAATGACAAATAAATCCGCTGAGTGAGCAGATATTTAAGTCCAACGGTCGAGTTTGAGATGTAATCAATCGCTGGTATATCTGGGAAGGCAATTTGCAGATCGGCGTAGGACTCTAAAATGCCATCAAACCAATATTGTTTATAGTTAAGCGATGCTGCCCAAGCGTTAAGCTCTAGCTTAAAAGACTGTTGTCCAAGCCAAAAATGGTTGTAGGTTACGACGAAGTCTAATTTATCGCGCCCAGCACCCCAAAAACGATAACCAGGACCGAGCGCTGCATAATAGCTATGGCTATCTATGCCGAGCTTATCTTGTTGATAAAAATCTTCCTGACGTAACAGCCAATGCTCATTGATTAGATAGTCTAAGGAGTAGTGTCCTTCGAGGGTATTGCGCTTAGTTAAATCATCTTCGGTTTCGTAGCGTACTTCGGTCTTAATAGTGTGTCGCCATTGCAAGGTTTCCGCTGTGATATCCCCTTTGACATGCAGCTTCTGGCTATTTTTCTTGTTTTTAGTGACATCGAGTGAGGTTTCTACGCTGCCGTTATAATTCCAAGCTGAGTCCGTATAGTGTTGGGAAAATCTCAGCTCATCCACTGGGGTTTGTTGGTGATTAATCAATACCATGCCATGTTGTTCAGAAGCCTGAATACTGATGTCCTGTATTGAGCGGCCGACACTCCAAGGTTTAGGCGTTGAGACTTCAAAACTGAGAATAGCACTGCGCTGAATGGCAATCTTAGGGGCATAGCTGGTTTTGATGATTAAATCTTGATCGTTGAATTCTTTGATTTCACCGCTAATTTTATCGCCATTGAGCAAGAAAACCGTATCGGCTAAGCACAAGGGGCTGAGACTACAGAGCGTAGCAGCAAGGTACAATGCTGGATTGGATATCAGATTATGCATCTTGCTTGTGAGCTCAAAATAAGGGCGTTAGTGTATGGTTATTCTACCTGAACTCTAGCACTGCAAAGCAATAACATTCGTTAAAAGTACTGATAAGGGATTGAAAACTGGCTCGCAATCCCTTACTTATTCGCCGTGTTAGCGGGAATTAATTTCGGTGATCTTGAGGTTAATTAACTGCTTCTGCCATTGAGCTTTATCTATGCCTTCGACCCAAATCACGCTGCTTTTGCGCTGCTCGCCCTTACGCAGATAGGTTTCAGGCATGCGGAAAATGGCTTCCCATACTTCGAGTTTTGCGGTTTTGAGCAAAGTGCCATTATCGGCGAAGAAGCTGGCTTCCACTTCGACTAAGGTCACGTGTTCATTGCTGGTACTGGCAAGATCGAAACTGAGTCCGAGTTTATCGCCTTTCCATTGACCTTCGGCAAAGCTGACTTTGATCCCGTCTTTGGCGGTCGAGCCTAATAGCTCAGGCTGGGCAATCGCAGTGGCAGTCAAGGTGGTAATAGGCGCAGCCACAGTTTGAGTGGCAAGTGCAACACTTGGGACGCTGGCAATATTGCTTGAGGTGCTCACTGCCGTCGCTGCTAAGGCATTGGGCGCTGCTTTGGTTTCTGTGATCACATATTCCCAGGTAAAGTCGTCTTTAAGTCTAACTTGGGCGCCATTTTCTAGGGTGATTTGAGCTACGTCGGCGGCCATAACCGATGATGTTAGTAATAACAATGAGCTAAGTGCGCTAAGGCGTAATGAAGTTTGCATTAATTTTCTGGCTCCGATATCCGTCTGAGTCAATATTCTGCCCTAACATAAACAATTTGCCGCATATTAGTCACTGTCTAAGTCGTGTTTTGATGAATAAAGTGGATTTAGCTGGAAGATACTGACTCGAATACAGATTGAGCTCACTGAGTGTGGCACTATATTGATAGCGCGGTGACAGGACACAGTCAGATAGCGGCAATGATCAATGAATTAAGCGAGAGGAGATTAATATGCAGATTAAACATGGAATTTATGGCGTCGTTATTAGCTTGGCATTGAGCGCGGCAACATGGGCCGATGAAGCCCCTAAAGTGGGCTGCGCGGCTAAGTTAGAAGCCATCAGTGCCGAACTTGCCCAAGCCAAAGCGGCGGGTAATAAACATAAAGTCGATGGGCTCGAAAAGGCCTATTACGAAGTCTCAACCCATTGTGACGATGACAGTTTATATGCCGAGCGCGTCGCTAAAGTTGCGGCCTTAGAAGAAAAACTGACTGAGCGCCAAAACGAGTTGGCTAAAGCCATAGAAGAAGGCCGTTCTATGGATAAAATCAATAAGAAACGCAATAAGGTTGCCGAAGCGGAATTGGAGCTTGCTAAGGCGCGAGCCGAGCTAACCCAATAAATCGCATAAACCCAATCGTTCTGGCATGTATTAGGGCGTGTTGACGTTTTAGGTGTTTTTTTGCAGCGATTTGGCTGGCTTTTATGTAAGGCAAAGCCCGTGTGGTGTAGTTATTCTACATAAACGGGAACTTAGCCCGCGTAACCTATGCTCAAAACTGTGGATTCTGTACTGGAGTTATAGCCCGCGCTCAGGATGAAATGTTCGGTAATGCGGTATCTTAGCCCTGCTTCTGCGCCCCAACGGGTGGTTTTATCTTCTTCCCATGTGGGTTTGCCATCGACAATTTTGGGTGAAATATTATTCGTGTAAGACACAGTGCTGATATAGCCGCTGCCGCCCGCATAAAAGCTTAAGTCTGCAGTGAGGCTGTAGCTTAACCCTAAGCGAAAGAGGCTTTCTTGGCGATATGACAGCGCGGGTTCTGGCACTATTACCTCGGTTTCACGGCTGCGGGCATAGCCGAGGTAATAGCCCCATTGCTGGTAATTATTATCGAAATGATAGGGTGCTAATGTAATGCCGTAGAGATTGTCTGCGGCGGGAACGTAATCCAGCATGAGGGCGATATAAGAAGGATTGGCGGTTTTCTCTGATTTATCTGACATCACCAATGGCTTCGAAGTGCTATATTCGGCAGCTTGAAGTGGTTGGCTTAGGCTCAATAGTCCAAAGCTAAGACAGGCTAGCATAGATGAATAACGACTCATGGGATGATTTCCTTGGGAACAGGTTGAAGATTGTTCATTTTCCTTTGTGCTCGGTCAATTCAAAATGCTAAAATCTCTTTTAAAATAAGTGTTTGTAAATTTATTGTGTTTGGATATCAGTATTTTCTAGGGTGACATTAACCGAGGTCAACTCGGCGGTTACCTTTGTATGGGTTTGTTGCGCTAGCGCCTACGTTGGAGTGGAATTGTGGATCAGGAAATAAGGCTCGAAATTTCGGCATGGCTAGCGGGCTTTGGGATTGATAGCCAACCCTCGGACGGCATATCAACCAGCATCATGATCATTGCCTGCTTACTGCTCGCGGGCATTGCTTATTTCATTGTTCGACGCGTGGTCATACGTGCGGTCAACATGGTGATCCTGCGCTCCAAAGTTACCTGGGACGATGTGTTCATGCGTTACAAGGTGCTCGAAAAGCTGGCCATGCTAGTGCCTGCAATCGTGCTCAATCTATTAGTGCCCATCGCCTTAACCGAGCATCAAATCTTAAGTAATTTAGTCGATCGGCTACTGAGTATTTGGCTGGTGGTGCTGATGATCCGCGCTATTTATGCGGGTTTAGATGCGGTCGATGAAATTTCAGATGTGAATCTGGTGAGTCGGCGTTTACCCGTTAAAAGCTTTGTGCAGTTAACTAAGCTGTTTTTATTCTTTGTCGGTATTATCGTTTCTATCTCAATCTTGGCCGATCAGTCCCCTGTGTACTTCCTCAGCGGCTTAGGTGTGGCGACGGGTTTTGTGATGTTAGTGTTCCGCGACACTATCTTAGGTTTCGTGGCGGGCATTCAGTTAGCGGCTAACCGTATGGTGAGCAAGGGCGACTGGATCCAGATGGACAAATACGGCGCCGACGGTGCGGTAGAAGAAGTCTCGCTGACCACGGTCAAAGTGCGCAACTGGGACAAAACCATCACTATGATCCCCGCCTATGCCTTAGTGTCGGATGCGTTTCGTAACTGGCGGGGCATGTCTGAGTCGGGTGGTCGTAGGATCAAACGCGCCATCAATATCGATATCAACAGCATTAAGTTTTTGAGCGAGGAAGAGCGCGAGCGTCTGAGTAAGATCAATTGCTTAAAAGAATATTTCCCCGCGAAGATCAACGAAATCCGTGAGTCGAATGCCAGAGTGTCTGACCTCGACATGAAAGTGAACGGTCGCCATCTGACCAACGTCGGCACCTTCCGTGCTTACCTGCAGGAATACCTACAGCGTCACGATAAAATCCATAAGGAAATGACCTTAATGGTGCGCCAATTAGCGCCGACCACTGAGGGATTGCCGATCGAGGTTTATATCTTCACCAACGACACCCGCTGGGCATTTTATGAGGCGATACAAGCGGATATCTTCGACCATATTTTTGCGATATTGCCTGAGTTTGGTCTGCAGGCATTCCAAGCGCCAACGGGCAACGATATTCGCAGCTTAAAGTCGGTTAAGGTTGACTAGTGTTAATGACTGTCGACGATTATTGATTTAATTTCGAGAGATAAGAATCGCGGCTAAGGTAAGAAAGACACCGCCGCTGGTTCTGTCAAACCAATGTAGCTTGTTGCTCGCCTTTAGGCTGGGCGCAAGCACATTGGCCATGCTGGCATAAATCATCACAAAGCTAAAATCCACCACGGCCCAAGTGGCTGCTAGTATCATAAGTTGCGGTAATTGCGGCGCGGCAAGATCGATAAATTGCGGAAACAGCGCGGCAAAAAACAGTAAATCCTTTGGATTACTAATCCCCACCAGAAACGCCTGCTGATAAAGTTGCCTTGGCGTGCCTTTGCCCTTTAATTGGCTAACCTCAAGGGGTTGACCTTGGTTTTTGGTCAACAGCAGCTTGATCCCTAAAAATACTAAATAGGCCGCACCGCACCATTTTAGCAGGGTAAAACCGTATTCTGAGGCGCTGATAATGGCTCCTAATCCCGCTGCTGACGCCATCATCAAGACTAAAGCGGCACTCACACTGCCAAGGCCGGTGGCCATGCTGCGCATCTTGCCGAAATGGATCCCGTGGGACATAGACAGCATAGCGATGGGGCCGGGCGAGATCCCAATCAATACAATGGCGAAGAGATAGAGTAACCAAGTTTCGGGTTGCATGTGTGCCGCTCTAAATGGGGAAAATATTAATGCTTCGCACTCGCCACAAAATCAGGGATCACCTTAGGATGACGACCTTTAATGGTGCGATAAAAGCTTAAAATCTCGTGCATGTCGGTCGCGATATCACCACTGGGTTGCAGTGGCGTACGGATAACGACAGTGCGGCGGCTAAAGTCTAAGCCAACGGGAGTGATAGGCACGTGAGCCTTGCAGGCGATATGGTAGAAGCCGCACTTCCAGCGTGTTACTGGGCTGCGAGTGCCTTCGGGCGCCAACGCTAGCTTATAATCGGTTTTCGACTCAAACAGCTGCACTGCGGCATCGACCAAGTTGTTGTTTTTGCGTCTATCCACTGGGCTGCCGCCAATCGCGCGGAAGAACCAGCCCCAAGGCGGGATAAAGAGTTGGTGTTTACCGAGAAAGTGAATTCGAGTGCCAAGCGCCCCACGAGCTAAGATACCCACGATGAAGTCCCAGTTACTGGTATGCGGTGCGACTATGATGATGTATTTAGCGCAGTCGGGCAGTTGGCCTTCTATCTGCCAGCCTGAAAGCTTAAGTAGCCAACGGCAAAGTGGTGTAAACATAGTTCGGTGCCCAGAGTCGAAAAATTAATAATATCATCAGCTTTTTTAGATTTTAAATTGGCTTAAGCAAGATAGTCTGTGCTGAATTCGCTATAGTACAGTTTAACCTGATTGTGGGGGAACAATACTATGTTGAAAAATGGACTATTAAACAAGGTTAGCTGGCAGCGTGTTTTTGCGGCACTTTTACTCTTAGTGACTTTCCATGTGAGTGCGGTGCCGACGGATGAAATTACGCAGATGCTCAAAGGCCAAGAAGACGCCTGGAATCGCGGCGATCTCGATGCTTATATGCAAGGCTACTGGAAGAGCGAGCAATTGCGTTTTGTCTCTAACGGTAAGTTCCGTTTCGGCTGGGAGGATACGCTCGCTGCCTATAAGAAAAATTATCCCAATAAAGAAGCGCTTGGTGAGCTGAAGTTCACGATCAAAGAAATCAAAATGCTCAGCAATTACGCCGCTATGGTCGTTGGGCGCTGGGATTTACGGCGGATAAAAGATGCTCCAACGGGCGTGTTTACCCTGTTGATTGAGAAAATTGACGATCGCTGGGTGATCACTATGGATCACAGTTCGGACTAAGCTACTGCTTTAATTGAACAACAGCGCTGACTAAAGTGCGCCGTTTCAATACAAGGCTGCACAGTTGCTCAGTTTAGATTTGCGTTAGTAAACAGTCTGGCTTAGCGGGTGGCTCGGCGGGCAGGGTTAAATGGAAATCCTGCTGTCCGAGCTGTCTACCTTTGCCGTCGTATCTCACTTCGGTGAAATGCATGCCGCCCTCAGCATCATCAAGCAGAATGCTGGTGGAGCGCGTGCCATAGTCGGGATGGCGAATATAAATCGCGGCTAAGCGCCGTTCCCACTCGATCCCCACTCCCGTGTTAGGCAATTCATTATCTTGTGGTTGGGAGTCGTCTTTCATCAACTGTAACAGCGCTTGCACTTCTAGGTTGTCGGACTGATTGATCACGGCTTCTAGCGCTTGCTGTCCCTTCGCCATTTTCGGCCAAATATCATCGAGTGCACCATTGCTAATGGCATGAAACCCATCGGCGAGTTTGACTGTGTCTTTATTGATACTGTTAAAACAATATAAGTCAGTACCTTGGCCGAAGACGAGATTGAATGGCTGATAATTGTCACTGTGCTCAATGAGCCAATTCGGGCACACGAGCGACCCAGAGTTGAGCGTCATAGTGATGAGTTCGCCACGGCTACGCATGGCTTCTGGGTTCTTTTGCGGTACGCGCAGATTCGTCACTGCGGCGACTTGTCCCTGTTTATTCACCCCAAACCAAGTGCCGCCAGCCTGCAGATCTTTACCTGCGAGTATGTTTTCTTCCGGTGGCCAAAAGTGCGCTGGCGCGGTCGGTCTATGGTGAAACTCATCGCGGTTGGCACAGATGATTAAGGGATACTTAGGATGGGCATTAATGGCGACAAAGAGTATGCACATAGCAGGCTAAATTAATCCAAAGGTAGATTAACTTAGCCTGTCTGCTCACAAATGAACAGTGTTAGTGGTGCGAGTGCTTATCTGCTTTTGCTTTATCGTGTGTATGTTCATCGGAATGATCATGGGCACTAGGCGTTTCAGCAGTGCTCTGCACAGATGTGTGCGAAGTGCTAGGTTCGTGGGAATGGCCATGCGCGTGGCTGTGCGAATGACTATGCGCATGAGCTGCCGGTTTGCTCAGCATTAAGCGGCGTAGAAATTTACGTGGGCCTAAGCGCAGTAAGCTGGCGGCAAATAATCCCGACAGGATCAGTAGCGCGACTAAGTTCACCACTTCTGGCAAAGTGAATAGGGAGAACCACAGTGGCAAACGTAGTGCTGCGACTAAACTCAGCGCAATAATACTCAGTAATATTGCCCTTTGCGGCCATGTCATTAACTTAAGCTGCGCTATGTTAAGCACAGGTGCGGCAATCAGCGGCATCATAATCGCGATAGGGCTCCAGCCACTGTAGGCCAGTGCAAGAGCCAGCACCGCTGCGCCTAAGTTGCAGAAACGCATAGGTAAGAATACGAGTAACAGCACTATAACCTGCAGCAGCGGATTGCTTAATGGCACTGAAGGATGGCCGATTAAATTCACTAATACTAGGCTGAGTAAAATCCACGGTGCGCTGCGATCGACTAAATGTGCAAAGCCGAAACGTAGGGAATTACTGGGTAATTTAGTATGTGGATCGGTAATAGTGACGTTAGCGTGGCTTAAATAAGCGCTGAGAATAAAGACCACAAGTAACTGGAATAACGCTAACCAAGGACCGAGCAACAGGGCGGTGATCACTAAGGCTTCTGGCCCTGCTAAGCGTTGGAACCAGCGACGGGCTAAGCTGCTATCCTGCGGTGTCAGACCTAATTTAAAGCGTAGCGCTGCCGCACCATAGCTCAGCAGTAATATGGGCGCGAGTGTCAGTAGCCAAGTCATTAATTGTTCTGTGCTGTGATCATGGTGGGCATGTTCGTGGCCGCCAGAATCCATCAAGAGCAGCATCACGAGCAATCCTATGCCGAGCAAACTGCCGATACCCGCTTGGTATTCGTATTTGCCTTGCTTGTCGGTATCGTGCTGGCCGTGTGGTTGATGTAGCACCACATGCAAAATAGAACCGGTAACAAAGGCTTGTAAATAAACGGTATTTTCTAAACTGAGCTGAGTGATTAATTGCTCGCCCGCGAAGTAACCTACACCCGTAAGTAGCATCATCGCGACCAGCACTAAGCTAGCCCAGCGGGTGCCGACTTGAGGTTTGAGTAACCACCAAATGGCGAGGCCGACGGGGAGGCGATGCATAATGACCCCGAGGGCAAGTAAGATCGAACTGCCGTCTTGCTGCGCCAATACCATAGCGCCACCGTCGGTGATGGTGTGCAGTAGCAAGCCGCCAATCCCTAGGGATAAGGTGAGATTATGGGTGATTTCTGAATAACGGTGGAATAGGCGTTCGCTGGCGGTCGGTCCCCAGATGCCTAGGAATACAAAGACTACGGCTAACAAACCGCCATGCTGCAGTAGCTCTGGCAAGATGTGGATCAGCACTAATCCGCCTAATGAGACGAATATAAAGCCATCGAGCCCCTTCTGTAATCCGCTGCCCGATGAAAAGTAGCGATAAAACAGTGGCCCAATTAAGAGTGCGATACAGCTAGCAAGAAGATAGAGCATGGATTCCACGGAAAATTTGCTGAAAAAACGCCATAGTATACCAGCTGTAAGGCAAAGTGAGCAGTGACTTGCTGGCAATTTACCAAACTGACATTTTGCGCTGGGGTTTTCGGGGGCGAATTTTATTCACTTTGACCATTTTGCGGAGAAATTTTCAGATCTAAGCCGAGAAAAGGTTATACAATATGGGCTTATGTTGGCTTGGGTGTTGATACGTTAAATGGATATATTTTCTGCTGCGGTAATGTTGTTTTTGATCATGGACCCCCTGGGGAATTTGCCAATTTTTGCGTCGATTCTACGGCATATCGATCCCAAGAAACGTCGTAAGGTATTGATCCGCGAGTTACTCTTCGCCTTAGTGATCATGCTGTCTTTCCTTTATGCGGGTGAGGCTATCTTAAGTTTCTTAAATCTTCGCTCTGAATCTGTGAGTATCGCGGGCGGGATTATTCTGTTTCTTATCGCGATTAAAATGATTTTCCCCCAACCCGGTGGTGTCGTTGGATTAGCCGCGGGAGAAGAGCCATTTATCGTGCCTATGGCCATTCCCTTGATGGCAGGTCCATCGATTCTTGCCGCTTTGATTCTACTGGCGCATACCGATAGCTCACGCATGGGCGATTGGACCATAGCATTAGTGTCTGCTTGGGGGGCAAGCGCAGTGATCTTAATGTTTTATAAACTCTTCACCCGTGTGTTGGGTGAAAAAGGCCTCACTGCGGTTGAGCGTTTGATGGGCATGGTATTGGTCATGATCTCAGTACAAATGTTCCTCGACGGTATTTCAAATTATCTAAAAACCGCCGGCTAAACTTATTTCGCTCGCTTAAAACGAAACCGCATTAACGCTGTTAATGCGGTTTTTTTATGGCTGGGATTTGGCTGGAATGCACTCAGCATAGAGGTTTGCTGAATAAACGCTGACAAAAGTGTAAATCGTGCCAGCGAAGTAATGTATCAAGTTGTTACTTCAGCGCAGTGCTGATAATGTCAAAAACTCTGTCTCTCTATCTTGAGATAGATAAACCTTGAGTTGATGACTCATAAAGGCAAATTTGTCTTAATACAAAGAGACACTAGTCTTAATAAATAGAATGGCGTTGGCTGTTACAGTAAGGATGTTAAAAATGCGAAGTGCAAAAGGCGTGACAGCGTTTGGACTGATGTTGGCATTGATTAGCGGCGCGAGTATTGCGGCTCCGAATCATACGCCTGCTGATGCGGGTGTGATTAATAAAGATCGAATTTTATATTGGTTGATTAAACGTGGCGAAGTGGCCGCCGATGCCAGTGATAGCGTAAAGCAGGAAGCGATAGAAGCGTATATTCATCGTGCCACCTTGACTCAACCTAAGGCGCCGCGCATCGAAGTTGAAGCTGAACATGCGCGACTGCAAAGAGCTAAGTCATCGCAAATGATGCGATCACCTGCACAGCGTTTACTGGTTGATGCGGATGTAAAAAAAACCGTTAAAGTACTCTCTATATTAGTGGATTTTCCTGATTTAAAACATGATAACAATGGCTTAAGTGCGGGCGATACGGCCATGTATTACCCAAGTTATCCTGCGTCCCACTACAAGGCATTACTCTTTTCGACGACTGGCTTTAATGGTCCACAGGGGCAGACGCTAGATTCGGCTTATCAATATTATCAAGCGGTTTCAGGGCAAACCTTCTCCTTTACTGGGGATGTAAAGGGTTGGTATACCGCCTCACAAAATGCCGAATACTATGGTGCTAATGATCCAGATACTCGTAGTGATTCACAGGTCGAACTATTGGTAAAAGAAGCCGTATCTCAAGCCGTCGCGAATATGTCTCCTGCAGAGTTGGCTAGCTATGATGTTGAAGACCAAAATGATTTTAACAACAACGGTAATTACAACGAGCCCGATGGCATTATCGACCATGTGATGATTTTCCACTCTAGCATAGGTGAAGAAGCCGGCGGCGGAAAATTAGGTGCGAGTGCAATTTGGTCGCATCGATTTTTTGTCGACCAAGCAACTAATGGCTATGCGCTTCCTGGTACGGATAAAAAAGTATTCGGTTATACCATTCAGCCTATCGATGCGGCCACGGGCGTGTGTACCCACGAATTTGGCCATGATTTAGGTCTGCCAGATGAATACGATACCACTGAAAATGATAATAAAGACGGCTCGCCAGTAGGGTTATGGTCTTTGATGTCTGGTGGAAGTTGGGCTGGCGCTATCGCTGGTGCCCAGCCGAGTGGTTTTAGTCCCTATGCGCGCTCTTACCTACAGGAAAGATATAAAGGTAAGTGGTTGAATGAACGTGAAATCAGCTTAGATAGTATTCCAAAATCTGGCATGGAAGTAACGTTGAATGAAGCGGTTAATCATCAGGCGGTTAACCAGATCTCCATTCCACTTCCTCCAACGCCAATCCCCTTTAAAGCACCTTATCAGGGCAGTTATCAGTATTATTCTGGTCAAGGGGATATGCTGAATAACAGCATGAATTTCAATGTTACTTTGCCCGCAGTCACGACAGATAAACTGATCCTGAGTATGAAAGCCAGTTGGAGTATTGAACGTGACTATGACTACATGCAAGTGAAAGTGGATGGTGAGGCTATTGCGGGTAATCACACTAAGTTTAACAATGGGGTAAATAGCGCTCGTCATATTATTACTGGCGAGTCGTCTGCGATGGCTCAGGCGGAGGGCAATGATGCTTGGGTAACGTTAGAGTATGATTTATCTGCCTATTCAGGGAAAACGATTGCGGTCGAGATCAATTATGTCACCGATGACTATACCGGAGGGTCAGGTATCACTCTCGATAACATCAGCGTTAAACAAAATACCACTGAGTTCTATAGTGATAACGCCGAAGTTGCGGATAAAGTGACGTTCTATGGTTATAGTCGGATTAAGGACTCACGTCCTGGGCCAGCATCTCGCTATCTTATCCAACTGCGTAGCCATAACGGTGTCGATGCTGGCTTAAACGTGGCAAGTTTTGATCCTGGTGTATTAGTCTGGTTAGAAGATTTTGGTTACAGTGACAATAATGTCTCGGATCATGCGGGCCATGGCTTGATTGGCGTGATTGATGCTGACCAAAACCTGATTGGCACTCGCTCTACCGAGGTACAAATCCGCGATGCAGCCTTTAGCACAGTAAAACAAACCTTCTATTCTGGTGACAATAATCGCGATGCTGTCAGCCTGTTTGATGACAGTCTAGATTACAGTGCGCCGCTACAACCCCAGTCAGGTATGGTATTGCAGCAGTTAGGTTTGACGATGCAGGTACTATCGCAAGCCAGTAATAACAGCACAGCGGTTATTCGGTTAGCAAATGCAAATTCTGATGCTGTAGCGCCGTTAAAAGTCAGTTTTACTAATACAGCCAATCAATTAGTTGTAAACTTCACCAGTAATGTCAATGGTGGTGCGGGAGCATTAACCTACGCTTGGGATTTTGGTGATGGCACCACAAGTGCAGCGGCTTCGCCAACCCATACCTACGCAAGCGCAGGCTCTTATACTGTGACGCTTACAGTAACAGATGCGCAAAATACTAAAGATATTAGTAGTCTGACAGTGACCGTTACTGCACCAGTTACGCCGCCAGTCACACCTCCGGTGACTCCGCCGGCGAATGAGAGTTCTGGCGGTGGTAGTCTAGGTTGGTTGAGCCTTGCTTTCCTTGGCTTATTGACTTGGCGTCGCCAGTTAAATATTAGCTAAGTTAATCGCGTGATAGCAGATCAAAAATGCCTCTAATTTAGAGGCATTTTTATTTGTTATCAGTCATTAAAGCAAGCTTTGGCTAAAACTCGGATTCATAACTGACGGATAGCTTGCTCGGCCGCAGGCAGAAAGATTTCACTGACGATCATTTCGTGTTCACCGTGATGAAAATAGCGGCGGCGTCCCCAGAGTTCATGTTCGACACTCTGATCTAAACTTTCGGCTAAATGCGCGAGGCGACTACAGGTCTCAAAACTGGCGACTTCAATTCTGCCAGGGATAAAATGATCTTGGCTAAACAGCAGTTCACCTAAGGGGCGGGTGCCCAGACCAAGGAAATCCGCTTGGCGCTCGGCCATTAACTTTTCGGGGATCAAGGTTCTGGCAAACACCCAAGGGATTGAGTCGAGGCAAAGTAACACTTCGCGGATCCATACTGAGCCTTGCTGGGGATATTCGCCATCTAAGGGCGCATGCAGTCCTTCGCCAAGGACTTTGACTTCAAATTGAGTGCAGCAAGTTTTGAGTTTTTGAGTTAGGCTGCCAGTGGCAAGTAACCATTCTTTCAAGGGGGATTTGGGGAGTTTGTTGGCGCTTTCGGCACAAAACCACTGAATAGATTCACCATAGGGGAAGCTTAAGCTAGTCACATTCATTAAATACTCGTTACAATATAGGCAAACGTCGGCAGTCTAGCATGTCGCGACACTAGCATATATACCCAAGCGACCTCCTGATACTGATCTGGCGTTTTCAGGTTGTTTGGCTATATAGATTGGGAAGTTTCTTCCATTACCATTATTGTTTTTAGGGAGCCGTAAGCCGCTATGAAAAAATTACTCTCAGTCTGTCTGATCCTGTTTACCAGTGTTTTCAGTGTTTATGCGGCCGATGAAAAAGAAGCGGCACCTGCAGCGACGCCTACGGGTGAATACGCCTACTATGGTTTTGAGCCTGAAATCGTCACTAACTACATTTCTAACCGTAAGAAACTAGGGTTTGTGAAGATCAGCGTCGAGTTAATGGTTAAGAGCCCAGATGATTTGGTCACAGTTGAGCGTCACGATCCGCTATTGCGCGCCGCGATTGTTGAAGTGTTAGGTAATCAATCAGAAGAAAAGATTAAGTCACTCACTGGCCGTGAAGAAATCCGTCGTGAGTGCTTCGATATGGTCAATAATCTGCTCACCAAAGAAGCGGGCAAGGCCTTGATTGTAAACTTACTCTTTACCTCTTATCTGTACGATTAATTCATTGTCTTATTCTATGGGCTGACTTATTTCGGCCCATTGCGTTACAAGAGCTGCTATGTCAAAACCTGCCGTGAAATCCGTTCAATCAGCGACTGCGAAAACCGCAACCCGTGCAGTTAACATCCGCCAAAAAGTTAAAGCACCCAAGCAGGCTAAACCTGAAGGTAAAGGCAGCACCAAGCCAGTCAAAGACAGACCCAGAGTAGAAATCAAAAAAGCCCTGCATCCGCGTAACGCCCACTTGAATGGCTATGATTTTCCGGCGCTAATCAGTGCATTCCCTCGGCTGAAAACCTTTGTGCGGCCCACGCCCTATGGCGCGCTATCGATAGATTTTGCCGACCCGCTGGCAGTTAAAACCCTCAATGCGGCGCTACTCAAACATCACTATGGAATTGGAGCTTGGGATATTCCGCAGGGTGCACTTTGTCCTCCTATTCCCGGTCGCGTCGATTATGTGCATTACGTGGCAGATCTGTTAGCCGAAGGCGATAAGTCATGTGCCATAAGTAAGGCGAGGGTATTAGATATTGGCACTGGCGCGAACGGCATTTATCCCATATTGGGCAGCCAAGTGTACGGCTGGCAATTTGTTGCCTCGGATATCAGTGCCCATTCACTCACCAATGTGCAGAGTATTATCGAGCAAAACCCTGCGCTGCAAGGGCGGATAAGCTTACGATTACAGCCTGATGACAAGGCGGTCTTCAAGGGAATTGTTCAGCCCGAAGAACGTTTTGAGTTAACCCTGTGTAATCCTCCATTTCATGCCTCTATGGCCGAAGCGAGCGAAGGTACAAAGCGTAAGGTGAATAATCTACAGCTTAATCGGGGGAGCTCAGTAAAGGCTGCACCTAAGCTGAATTTCGGTGGTCAAGCGGCAGAGCTTTGGTGCCAAGGTGGAGAGCGACAGTTCTTGGCGACCATGATCCGTGAGAGTCAGATGTTTGCAGACCAATGCCTGTGGTTCACTAGCCTAGTGTCGAAGCAAGAAAACCTGAAACCTTGTTATCAAGCCTTAGCACAGTTGAAAGTCGATACAGTGAAGACGATTGAAATGCAGCAAGGCAATAAAATTACGCGGGTCTTAGCGTGGAGCTTCCAGTCGGCCGCTAAGCGCAAACTCTGGCGCGCTGAACACTTAGCCCGTTAAGATTTTCAGTTTACTTAATAACAAAACAGGCGCCATTATTGGCGCCTGTTTTTTTAAAGGTAAAAGAACCACTGTTACTGCGGCTTTTCTTCCTGATGATCACGGCGGAATAACGCCCACTCTTCCATGGCTTCACCGTTTGGCAAGGTACAGATGCCATGCTCACCTTCGGCGGTTTTCTCTATTGCCAAGCTACCACCGAGGGAGATGCAATACTCAGAGGCGGGATTGGCTATTTTAACTTGCGCAGCAGGCTTTTCAGCTTCGCCGCAACCCACGAGTGCCACAGTTGCCAGCAGTAAAGTTGCAGCAAGCGGTAATAGGCCTTTCATAGACACTCCTTGGTGACTTATTTACGCCCTAAAATTCGCAGAATGCGTAGGAATAAGTTGATGATATCCATATACAAAGCGGCTGCGCTATCGATGGCATTATCTATGGTTTTCGGGATTTGATTCGCCCGCCCCCAGTCGTAACCTATATACCCGCAGAAAATCAGCACGACGATCCAATCTAAAATCCCGTGGTGGGTATTAAAGATAAACACTTCAATCAGTTCGACGACGATGACGAGTACCAAAGCAATCGTGAGTGCGCCGGAAATTTTTTGGAAAAAGGCGGGGAAGAGTGTGCCTAAACACATCATGGCAATGGTCACTAGGCCTGTGATTCGAATGGCTTCCGTCACTAACTCAGGGTCGTATTGACTGACTACCATATTGATAATTAAGCCGAAGGGGACGACTACAAAGTTGTAGCCAATAAAGCTGACGACGGGATTGCTCGATTTTTGAAACAAATAAATCCCGAAGAAACAGGAGGCAAAATAGCCGATAAAGAAAATCCAAGGATTAACGCTGGCAATCGCTTCTGGGTCGATATTGGTGACCATCCAATAGTTAACCGCAAAACCCCAAATCAGCGTGAGGCCGATAACGAGGTTGTACATACCTGCGCCAACGATGGGATCGTCTGTGGTCATGCGATCAAATACACTGTTTTCCGAATCCATATGTTTTCCTTGTGATTTCCTTATGTTAACTGGTCGTTCAGCCTCACCAGCGTCGAGCTAAGTTAAGGCAAGTAAAGCCGTTTTTCAAGCGAGAATATCTTAAGCCTGAGTACCACAGACACTGCAGTTGCTGTGTTTCGGTAGTTTCATCTCACGAAACTCCATCGTCATCGCATCGATCATTAAAATGCGGCCCGCTAAGGTTTTACCCATGCCAGTGATGACCTTGATGGCTTCAACCGCCTGTAAACAACCTATCATGCCGACCACAGGGGCTAGGATGCCCGACTCGACACAGCTGAGTTGTTGCTCGCCAAATAGGGCGCTAAAGCAATGATAACAAGGTGTTTCGCTGTGATAATCGAACACAGTGACCATGCCTTCCATCCGAATTGCCGCAGCGGACACTAAGGGCACTTTATGCTTAAAACAGCTGAGATTGAGTTGTTCGCGCACACTGACATTATCGGTGCAGTCGAGCACTAGATTGTGATCGGCGACAAGGGCGTCAATCTCATGATCATCTAATACTGCATTGATAATGTTGATCTTAATAAAGGGATTGAGCTGACTCAGGCTCTGTTTGGCCGATTCGACTTTTGGCTGGCCGACATTGGCATCTTGATGCAGCACTTGGCGCTGCAGGTTAGAGAGTTCAACGTGATCAAAATCGACCAAGGTCAGCTCGCCAATCCCCGCAACTGTGAGGTATTGGCTGGCGGCGCAGCCTAATCCGCCTGCACCTATCATCAACACTTTCGCTTGTTTTAGGTGTTCTTGGCCGTCGATATCCATGGCTTTGATGGAGATCTGACGGCTGTATCTTAAGAGTTCGCTGTCGCTGAGAATATCTTCAGGCACATTCATGGAGATTTTCCTAGCAGAGCACGCTGTTAAAGGGCTCGACCGTCACTTGGGTGCCAGCGGGTGTATCCCCTTGGAATTGTTCCAATAAAACGAAACAGTTCGCTAGGCTCATTGAAGTCAACATGCCAGAACCTTGGCTACCAGTAATGGCTACCTCGAGTTTGCCCTGTGCATTACGGCTTAAAATACCCCGCTGATATTCGACACGGCCCGGTTGTTTACGCACCGGTGTGGTGAGTGTGGCGTCTAACATCAATGGCTCGACTGGGGTTAAACCTTGCATCTTGTTCAGGATAGGCCAAACCAGCTTATAAAACGTCACCATAGAAGACACGGGATTGCCCGGTAAACCACAGAATACCGCCTTGCCAATTTTTCCCATGGCGAAGGGCTTGCCTGGTTTGATGGCGAGTTTCCAGAAGGTGATTTTGCCTTCTTCATCGAGAATTTGTTTCGTGAAATCTGCTTCGCCGACCGACACGCCGCCCGAGGTCAGCACCATGTCGGCTTGGCTGGCGGCTTGGCGAAATGCCTGACGTATGGCTTCAGGATCATCGGCAATCACGCCTAAATCGAGCCATTCAACATTAGCGCGGCTGAGTAAACCTTGAATCGAGTAACGGTTTGAATCGTAAATTTGCCCTGGCGCGAGTTCGCTGCCAACAGGACGCAGTTCGTCACCCGTGGAGAAAAACGCCACCTTAAGTTGACGATAGACTCGCACTTGGCTGATACCAATAGTGGCCAATACGCCGAGTTCGGCGGCGCCAATTTGAATACCAGCAGTAAGTACTTTAGTGCCTTGGGTCAGTTCTTCACCACGGCAACGCACATTGGCACCTTGCGCCTTAGGATGGCGAATGTGAATAATCTCACCTTCAACTTCGGCTTCTTCCTGCATTTGTACTGTGTCATAACCCGCAGGCACGGGCGCGCCCGTCATGATCCGTATGCAGGTATTAGGTTTGGCTTCACCTTCAAAACCATGGCCGGCGAACGAAGTGCCGATGAGGCGTAATGAGGTGCGATTGGTTTGTGTATTCAAGTCGACAAAACGGAAGGCGTAACCGTCCATTGAGGAGTTGTCGAAGGGAGGCAGGTCGATGCAGGAAGCGAGATCTTCGGCAAGGACGCGGCCAAGGGCATGGGGAAGCAATACGACTTCGGTGTCTGACACTGGGCTAACTTGTTCGAGCAAAAGGGGAATAGCTTGATCTGGATGCATTAAGCTAGGTTGCGAACAAGGATCGGCTTTTACGGACATGAGGGTCTGCCTTATATGCAATGGGTCGTGATGACGCTGAATGAATTTCCACCTGACTATGATTGGCTATTATGCCACGGTGATCGGCGATGGCTATGATTTGCATGTGAGTTTCTGTGATAGGCCAGTCAGTTTTTCCTGCCACTTTGGATGATAACCTAGCAATAAGTAACGCAATTTGCGCAGCTAAGTCGAAGGCTGAACGCGAGTCGCTGGACAAATGAAAATCATTTTTATCAATGAATCATTAACTTAGCTAAAAATCTAAAGGTAAATTTTGTTGCTTTTGTCGACCCCCTTGATGACAATGTGCCCCATGATTCAGTTCGAATGCAAAAGAGGATTTACTATGCTGTCAGCCACTATGATCGAAAAGTTGAATGAACAAATTAATATGGAGTTCTTCTCCTCAAATTTATACCTGCAAATGAGCGCATGGTGTGAAGATCAGGGATTTGAAGGTGCAGCTAAGTTTATGCGTGAGCACGCGGACGAAGAGATGGGCCATATGCGTCGTCTGTTCACTTATGTGAGTGAAACCGGTGGCTTACCTTTGTTAGGGGCGATCGAAGCGCCGCAGTCACAGTTTAGTTCTTTACTGGCTCTGTTCGAACTTACCTATGAGCATGAGCAGTTGATCACCAGTCAGATCAATGCCTTGGCCCATGCCGCCTTTTCGAATCAAGACTACTCAACCTTTAATTTCCTGCAATGGTATGTGGCCGAGCAGCACGAAGAAGAAAAGCTGTTCAAGTCGATTGTCGATAAAATACGTTTAGTGGGTGAAGATGGTAAGGCGTTGTTCTTTATTGATAAAGATTTAGCTAAGTTAGCTTCTAAGGGCGGCGAAAGCATAATGAACGGTCAAGGCCAACCACAGGCTTAAGCTCGTTTTATTCGCCACAAAAAAGCAGCCTAGGCTGCTTTTTTGCTATTTATTCTTCAATTATTTTTGCTATTAGTTGCTAGCTTTCAGCATCTTAGATATTCACGCATCTATCTTGGTAATCGTGGTTGAGCAGTGCTGTGTGCTTGATGTGCAGCAAGGGGGTGATCTCGTCGAAGGGCAGTGGTTTAGAGATCAAATATCCTTGGGCGAAGTGGCAACCTAAATCACGCAGAAAGTGCCACTGGGCCTCGGTTTCTATGCCTTCGGCCACTAACTCCATTTCGAGACTTTGCCCCATCATGACGATAGCTTTGACTAAGTTAGCATTAGCGTTACTTTCGAGGGCATTTTCCACAAAGCTACGATCGATTTTAATCACTGAAATAGGGAATTTTTTCAAATAACTCAGGGAAGAATAACCTGTGCCAAAGTCATCGATATAGATTTGCGAGCCTAAATGGCGAATAGATTCGAGCGTGCTCAAGCAGTGGCTGGCATCGCCCATCAGCATGCTTTCTGTGATCTCAATATGCACATTGCGCGGGTTCACATGGTGGCGGTTAAAGCATTCCTGCAGCACTTGGTCGAATCCCATGCCGCGGGTGTTGATACATTGACGACCGGAAACATTAATCGCAACATTGATTTTTAAGCCACTTTTCTGCCACTGTCTCAAATCATGCATGGCTTGGTTGAGCACCCATTCGCCCATAGGTTCTATCAGTCCGGTTTCTTCGGCCAGCGGGATAAATTGATCCGGTGGGATCAGGCCAAAGTCAGGGTCTTGCCAGCGGATAAGGGCTTCGACGCTGCAGACGACTCCAGTTTGCATATCCACTATGGGTTGATAATGCAGGGCAAATTCATTTTGTGCCACGGCGCGGCGCAGGCGTTGCTCTAACTCCATCCGAGCGAACATGGCTTCATTCATGCCCGCAGTAAAGTATTTGAAATTGTTCCGTCCCGCATCTTTAGCTTGATACATGGCGGTTTCGGTATTGCGGGTGAGTGAGCTGACATCGAGGCCATCTTCGGGATAGACGCCGATACCAAAGCTGGCTGAAATCTGGATCTCGCGGTCATTTAAGTCAAACGGCATGCCTAAGGTTTCGATAATACGCGTTGCCATGTGTTCGATGGCGAGTTCGTTTGTCAGTCGCGGCAAAATAAGTACAAACTCATCGCCACCAATGCGGGCGATAAAGTCGGTTTTACCCGCAATGCCTTCTAAGCGTTTGGCCACATCTTTAAGCAATAGGTCGCCCGTGGCATGGCCTTGCACATCGTTGATGTATTTGAATTGGTCTAAGTCAATCAGCAGAATCGCCAGTTTGCGTGAATCATGCTGCGCCACATGGATTTCGTGCTGCAGGCGTTCATTAAACAACTTACGATTAGGCAGTCCCGTTAACGCATCGAAGTTGGCTTGATACCAAAGATCTTGCTCATATTGTTTACGTTTACTGATATCCATAAATAAACCAATGTGTTGGATTATTTGTCCTTGCTCATTTCGCACCACAGTAATGGCTAGATACTCGGGATACACTTGGCCATTTTTACGCTTATTCCAAACCTCGCCCTCCCATTTTCCTTCCGTTTGCAGCGACTCCCACATGGTGGTGTAAAAGTGCTGCGAATGACGGCTAGAACTGAGGATGGATGGACTGCGGCCTTTCACTTCTTCCAATGTATAGCCAGTAATATTGCAGAAAGCCGGATTGACCAGTTCGATATGGTTGTCGGCATCAGTCACCATTATGCCCTCGGCAGAATAGTCAAATACCGCAGCGGCGCGGGTGAGTGCATGGTTCGCCTTCTGCCGTTCGCTGATCATCCGGTTCATGGCGCGGGCCATGCGAGCGAGTTCATCAGTACCTGAGACGCGGATATTGATATTGAGCTTGGGATCTTCCGCCATTTGTTGCATGTCATTAGCAATCTTAGTGATTTTATCCATAAAGTTACGAATGATCTTCTGTCCGAGCCAGAGTGTAACGGTTGCGGTGAACAGGGCTGATAACAAGTACAGGCCGAGGAAGGTCAGGGCTTGGTTTTGTTGAAACTCGCTCTGCTGCGCCATGGTTTGGGTGATGTCTTTGGCGAGTTGATGCAGTTGTTCAATACGCTGGGTGGTGAGTGCCCACCATTTTTGGCTGTCAATCGGATGTGGCGGCATTAACAGCTGTGTCATCGCTTGGCTTAACTTGTCCTCACTGGCTGGGACTTGCGCGATGGCGTTGATCTCCGCCATCGGCAAGCCTTGCTGCTTCAGTTGCTGCAGCTTGGCCATGCTGAATTGGTATTGATCTATGCTGTTCTCAATGGTTTTCAGCGCTGGCTTAAATTTGGGAAGAAAGCGGGTCTCGTGATTGAGTTCATCTAAGTTGAGGTGAATCGTCGCCAGCGCGCTATTAAATTTTTGCAGTGAAACGTCATCGCCCTTCAGCAGATAGTCCTGAAAACTCCCCATTAAGCCACCGTAACCTAAGTTGGCATTAATATTCTGTGCCAGTGCGCTAATGCGGGTTTGCTGCTCAATTTGTTGGCTTATCTGCAGAATACGTTGATTCTCTGGCGAATCTTGCAGGTTTTGCAGTAACGATTTTCTATCGGAAATCGCTGCATTGCTCGCGTGCATCACGGCTCGGCCATATTCATACATGAAGGATCCTATGGTGCGGTAATCATTCAGATTGAGTGAGTCGCTGAGTAACAAATGATTGATTGCGCTACGTTCTTTTACGGCGAGTTCCTGCACCATTAGCAGGTTCAATAAATCGGTATAGGCGCGGGATTGGCTGATTTCGTTGGTCTGTAACTGCAATAGTTGAATGAGCTGCAACAGCTGAGTGTGCAGTTGACTATAGCGGTCAGTAAACGGGCTTTGTGTTTCGCTCGCAAGGGTTTGGCGTGCGTTGGCGAGTCTGTGGCCCGTGAGTGTGATGGCATCTAATAATGCGTTCATCTGACTGGCTTCAATACTCTTAGGATCGAGCGCCTCACAGAGGTGGGCTAAAGGTTCGCTGTTAAGTAACGTATTGAATGCGTTGGAGGTCGCCTGTTGTTGCGCGACTAGCGCGTTATCGGCGGGCAGTTGATTGGTGCTCAGGCCATATTCGTTTTGCAACTGATACAGTAATTCAGTGATTTGTACCGTCGTCTGCACCGCAAGATGGTTGTGCTTGGCCGAGTGTGATTCATTTATTAAGCCATGCATGCGAGTCAGAATAAAAAAACTCATCATCAACAAGGGGACAAGCGAAAACAGAATGAGCTTTTGCTTCAAACTGAGGTGGTGTAGCGCTGACGGCATTAAAGATCACTCCACTCGTTCGCCTAGCTAAAAAAGAAATTCAACGTAGTCACGTTGAACTTGAGTTGAAAATGGAACTAGGCACAGGGTGAGAATATTTAACATAGGCGGCCAGTCACGGCGACCTTTAGTTTATGGATATCTAATCTAGGTCACGAAATTAAGCAGATTGTTATATATCGAGCTAAGCAACTATTTTTATTAGTGTATTTTTTGCCTATCAAACAAACATTTCATTTGTCTGTGATGTGAGACAGTATTTCGTCATTCGAAAGAATGGCTTTATTTATTGCCCTTATGGGCAGTGATTCAGGGAATGGTATAACCGGTGGGATAAGCGATGTTGGACTGCAAATAAAGCCCAATAAAAAGGACGTTTGCCGCAGCGAAACGTCCCTTCGTCTTAAATTAATGACCTAAGTTTAGGTCATTGAAGTCTGACTTAACCGACCTTCGCCAATAGCGATTTACGGTAGACCACTATGTCTTCAATCGTCAGCACAGGCATATTGTGCAGTGCGCCGAAGGCGATAATTTCGGGTAAACGCGCCATAGTGCCGTCGGGATTGGTGACTTCACACAATACACCAGCAGGTTTTAAGCCAGCGAGTTGCACTAAATCGATCGTGCCTTCGGTGTGGCCGCGGCGAGTTAATACGCCGCCCGGTTGGGCGCGAAGTGGATACACATGGCCAGGGCGAGCGAGATCGCTTGGCTTAGCGTTGTCGGCAATCGCGGCTTTAATGGTGGTCACGCGGTCAGCGGCAGAAACCCCAGTCGTTACACCGACAGTTGCTTCAATGCTCACAGTAAATGCCGTGCCGTATTGGCTCGAGTTGTGCTCGACCATAGCAGGCAGTTCGAGGGCTTTGACTTTTTCATCGGGTAAACACAGGCAAACAATACCGCTACATTCACGGATGAGCATGGCCATTTGGGCGTTAGTCAGTGTCTCGGCAGCGAAAATTAAATCGCCTTCGTTTTCTCTATCTTCGTCATCGACAACTAACACACCTTGGCCTTGACGCAGGGCATTAAGACCCGCTTCAACGCGTTCGATAGCAGTACCAAAAGGAGCAAGTAAAGACTGATTCATGGTTAATATCCTTAAAAAATACGACATTGGAATATCCAGAATCAGGGCGTGCAGAAATACAACCTAATCAAACCTAATGGCTCGATTTTGCTGTAGCAATATCACTTCACTTTGGGGTGAAGTTATTGCAAAACATGAGGCTAACACTGCGATACATAAGCGCGTTGCACTTAAATACCACAAGGTGTTGTCTTACATTCTCTTCCATCCGGACTCAGTACTGTCGCCATTAAAGCAAACAGTCCATTACCGTCGGCTCTGGAATAGTCAGTTTTGCACTAGGCAAAATCTAAGGCACCAGATCTGCTGACCCCGAAAGTGATTTCGGGCGCTCGCGGGCTTTGGGGATGCTCAGTTAAATAATCACTTAGCGTTACCCATTTACCGCCGGTGGGGAGTTTCACCCCGCCCTGAGAATTTGCTTTTGAACCGTTTGCGCTCAAAAACGCGGTTATGTTGCTCTTATTTACCGCGAATGGCAACTTCTCGCGTTCAATGTTTGCGCTAGTGCACACCTGACCACTATCATATTGAACACATAAGCTTATTGACTTGCCGTTGCATTCTGCGACGGTCGTTATGGACGTCACAAGGACGTATAAATGCGATCTCTGCTGCTAACTTTAGGTATTGCCATGTTGTTATCCGGTTGTTCGACCTCAAAAGGGCAAGTACCCGCCCCAGTTGCTGAAAAAATTCCCCATGTGATGACCTTGCATGGCGTAACGCGCACCGATGATTACTATTGGCTGCGTGACGATGAACGCAAAGATCCTAAAGTGTTGGCCCATCTCGAAGCCGAGAATCGCTACACAGCGGCCTATTTCAAACCCTTAAAACCGCTGCAAGATGGTCTTTTTAAAGAGTTAACTGAGCGCTTAGTCGCCGATGAGTCGAGCGTGCCGTACCAATGGCACCAGCACAGTTATTACAGTCGTTATAAAGAAGGCGGCGAATATCCGTTAATCAGCCGTAAAGGCGCCGATGGCGTTGAGCAAGTGATGCTGGATGTTAACGAGCGAGCTAAGGGCCATGAGTTTTATGGCTTAGGCGGCGCCAGTGTTAGCCCTGATGAAACCATGTTGGCCTTTGGTGAAGACGTCCTTAGCCGCCGTGTATACCACATTTATTTTAAAGATTTGGCCTCGGGCGACATGATCGTCGACGTGCTTGAAAACACCGAAGGTCGGGTGGTGTGGGCGAACGACAACAAACATGTGTTCTATATCGCCAAAGATTTGCAGACCTTACTTGGCTACCAAGTGTATCGCCATGAGCTGGGCACTAAGCAATCGAGCGACGTGTTAGTGTATGAAGAACAAGACGATTCCTTTTATATCTCGCTCGGCAAAACCTTAGATGAGTCGCAAATTGTGTTGTTCCAAGAGAGCACTACGACGAGCGAAGTGTCAGTGCTCGATGCCAGCGAACCTCTGGGTTTATTCAAGCCAGTGTTAGCGCGGGAAGAAGGCCACGAATACAGCGTGTCTAAGCTGGGTGATACTTATTACATTCTCACCAACTGGCAGGCGACTAACTTCCGTTTGATGAAAGTTGCCATCAAAGATGCGGCGGATAAATCTAAGTGGCAAGAAGTGGTAGCGCATAACCCCAATGCCCGTATCGAAGACGAGTTAGTACTCAAGGATTATCTGATTATTCAGACCCGCGAAAACGGCCTGACGCGAATTAAAGTGATGCCGTTTAATGGTCAAAAGCCCTTCGAGCTAAGTTTTGATGAGCCCGCCTATGTGATGGGTTTAGATGTTAATGCCCAGCAAGACAGCAATAAGTTACGCATTTTCTACTCTAGCCTGACAACACCAGAAGCCGTATACGAATATTCGCTAGTGAATCCCGATAGACGGGATTTATTAAAACAAGATCAAGTCTTAGGCGGCTTCGATGCCAGCCAATATCGTGCCGAACGTGTGTTTATTACCGCCCGTGATGGTGTGAAAGTGCCCGTTTCTTTGGTGTATCGTAAAGATAAATTCAAGAAAGATGGCACTAACCCTCTGTATCAATACGGTTATGGATCTTACGGTTACACAGTTGAACCCGATTTTTCATCATCTGTTATCAGTCTGCTCGATCGTGGCTTTGTGTATGCCATCGCTCATGTGCGCGGTTCAGAAATGCTCGGCCGCCCTTGGTATGACGACGGTAAGCTGTTGAATAAACAAAACACCTTCAATGATTTTATCGATGTAACAACGGCATTAACGGCACAAGGTTATGGCGATAAAAACAAGGTCGTCGCCTCTGGCGGCAGCGCGGGAGGTTTACTAATGGGCGCAATTGCTAACCAAGCGCCGGATAAGTATTTTGCCATCGCCGCTCATGTGCCGTTTGTTGATGTGGTGACCACTATGCTGGACGAGTCGATTCCGCTGACGACCAATGAGTACGACGAGTGGGGCAACCCGAACGAGAAAACCTATTTTGATTACATGCTGAGTTACTCGCCCTATGACAATGTTAGCGAGCAAGAATACCCACATATGTTAGTCACGACGGGTTTGCATGACTCACAGGTGCAGTATTTTGAACCCGCCAAGTGGGTCGCTAAGCTGCGCGATGTTAAAAATCAATGGTACAAGAGCGACGATAAAGTCTTGTTACTTAACGTGGATATGGAAGCCGGCCACGGCGGTAAGAGTGGTCGCTATCGCCAGTATCAAGACACAGCCCAAGAATATGCGTTCTTCTTGAGTCTGTTAGGTATGGCAAAGTAGGTCATTCGCAAGTGACCGAATCAGCCGTCAGCCTAGGCAATACGGACATAGGCGCTTTACAGCAACATGAGTCACTGGGGGCAGACGCACTTGCCCCAGTGACTAGCGCTGTCGCTACCAAAGATATAGTTACAGAAGCTTCGACTATAGAAGAAACCCCAATAGCAGAATTAGTGAAAGAAGATGCGCTGAATCCCGTACCATTAAATTCTGCACCTCTGAAGCCTACACCCATAAAGTCATCATCCTTCTGGTATTTGTATCTAGTCCGCTGTGCCAACGGGCATTTATACACAGGCGTCACCACCGACGTAGCGCGACGCTTTAGCGAGCATCAGTCGGGTAGCATCAAATCTGCTAAGTATCTGCGCGGTAAAGGTCCGTTAACGCTGATGTATCAAGAGCAGGTTGGTAGTCACGGCGATGCACTACGGCGTGAGATTGCAGTTAAAAAGCTGAGTCGCAGTCAAAAGCTGGCATTGATTGAGTCTGTCGAACATAGTGCGATATGAAAGAGCAAAATCCTATTAAGTCATTGGTATTTGCATACAATGATTAAGCACTGTCATTACAGTGACTCGCAGCAAGTACATCCCTGTATGCTCGATCGTGACATCCATGTCACGGACGGTCACTTCTATGACAGTGCTTAATCCCGTTAACCACTGAGTATTCTGTTAGCCTGCATTCTGTCGTTACGTTGCAAAATTTAACAATGGGTGTCTCAGTATATACAGTGGTCTATCTTGAGGTGGAAAGCTTAAAAGCGAGTAGTGCCGAAGCTAAAAATGATGTAGATGATAGGGTTAACAAGCTTTTGTTAGGCCTTGGCAAATTTAATGTCGATGAGAAGAATGTTTCTGCGTCCTCAATCTCGACAGCGCCCGAGTATTCCTTCGGTGATAATGAAAAACGTGTGCTTGATGGTTACCGAGCTAGTCGCAATTTAAAAGTGACCCTCAATAACTTAGAACATCTAAACGCTTTTATGGATTTTGCCTTAAGTGTACAACTTGATGAAATCACACGCGTTGAGCTGAAATCCTCGAAAGAGGCTGAGCTTAAAAATGAGGCTAGAGCCTTGGCTGTTGCAGATGCTAAAGAAAGAGGAAGTCTATTGGCGAACGCTTTTGGGGCTACTATTGGCAAAATTTACAGCATTGGTTCTTCAACAGATGACAGCATTGGCCGTTATGGTGCCAACAATGGAATCGAGCGAATTGGTTCACGTATGGGGAAACCTCAAGTGACAGGGCAATATCTGCAGGAGAATATGGTTTTTTCAGCATCTATCAGTGTGGTATTTGATCTCGATGTTAAGTGATAGTTTTACTACAAAAGTTCGGATTGACACAAATAGTTATCGCGACATCAAAATTTCACAAGAGGTGTCTCGATAAACTTAGCTAAAAAGTGGAAAATGGAACTTCAAGATTGGATGTCTGTAGTGATCCTGTTTATGACCACTTTTCTTGTTACTGGATAATTACAAACGAAGTATATTTCGAACTTAAGTAATGTCTTTTAGTTCTGCATTCGAATAATAAAGGAATTATTGTGCATATTATTATTGGATTTTTAACGGCATTAGCAACAGTGCTTTTTGCATTGGATAGATTGGGGATTGACATAGGCTGGTTTAACCCGTGGGCTTGGCAGCGTCGCCGCGCATGGTTAAAGCAATCAACTGGACATCCTGCCTATGGTCTTAATAAGCCAATTGATGCAATTGCTTTGATAGCAACTGCAGCAGCGAAAATTGACGGTGATCTTTCAATTGAAGAAAAAGAAAAACTAAAAGTTATATTTCAAAAGACGTTCAATCAGTCTGAAAAAGATGCTGTACAACTGATTGGTGCAAGTGTTTATCTTTTAGGATCAGGAGAAGATGTTTTTCAAGCACCTGAGAAAGTATTAAAACTTTCACTAGAGAGCTTTTCTGAGGCACAAAGAACATCCTCAATAGAGTTAATTTCAGAAATAATAAATGTAGGCGGCCAACCATCTGAGGTTCAAACAGTTTTCTTCAAGAAAGTCAAAAAAATCATGACGTCAAATGCTAGTAGAAATGAGTGGTAGATAATTTAGCTTTGGCTTGAATAATAAATACGAGTATATCGAGACATCCATAGTCAAATTTTGCTCCGAAAAGACAAATAGGTAAAACTTTACCAACGACTGGCCTGAATTGAGGCAGAGAAAGTCTCAGGCGATGAAAAAAATGCCACTACAAGGGGTTAGTGGCATAAATGCATAAGTCGGGCACAAGCTTAAAAGGGTGGGTAGAAGACTGACATTTTGGCTGGGAACCGTTGCAACATCAGTCTCTACGCCGGGAAGTAGGTAAAACCTTTGGGTGTAGGGTGGTAAAACGGCGCGTTATTCCATGCTTACTGGGCCGTCGATTTTGTTAAATTTCACATCGCCAGAACCTGCCGCAAGAATCGTTAATCCCTTAGTGTTAAAGACATTAATGTCGCCTGAGCCATCTGTGATGGTCACTGGACTCTGCACATTGGCGACTTCGATGTTGCCTGAACCATCATCGATAGCGATATTGCCGCGAACATCAGTTACCTTGATTGCGCCAGAGCCATCGTCAATCGTGAGGTTGCCGCTGATTTTGCTGACTTCAATATCACCAGAGCCATCATCAATGCTGACATTGTTACCGCCATTGATAATGAGTTCACCTGAACCGTCTTTGACATTGATATCTGCTGTCATCTTGCTGATCAAAATGGCACCTGAGCCGTCATCGATATCCAGTGCTAAATTCGCAGGCACTTGCAGTTTTAGATCGATGTAGGGCGAGTAATTATTAAAACCGCTTTGTTCAAAATCGGCTTTGAGCTTGGCCTTGTTGGCCTTTTTCTCAAGGGTGAGAATGACTTTGCTGTCGTCGTTGCCGTCCTTATTGCTATAGATGTCGGCGACGAGTTTAATTTGGGTTAGGCCTTCGACACCGATAATTTCGAGGCTGCCTGCGCCAGTCTCGGCAATCAGGCCTTGCAAGTCTTGGCTATCGAGTGTGAGTTCGCGTTGCTGGTGGTCTAAATCGGGCATGCCAGCGGCGTTGACATTAATAATGCAGCCAGTGAGTGATACCCCTGCGGTAAACACCAATATAAAAGCGAGGGTGAATATAAATGGACGTAGTGACATGTTAGCTTCCTTGTTTATCTGGCACTCGTTTTTCGGGGGAGTGCTGAGTCGTCTTGCGATGGATAAGACATTGCACAGCCCATGCCAATTTTAAGTTTCATTTATAATCAATGGTTTAAACTGAAAGTGTCCGCGCTAGGCTGTGCGGACATGTCCGCAGAATGAGGGATTTACTCACAGATTAGTGAAATTGCTCATTTATTAAGTTAGCTTGAGCCTCATAGGTCTGGCCGGGCAAACATACAGACAATACAAAAAAACATAAGAAGGGATGCTGATGGATTATTTAACGACGAACAAGACGGCTTGGGATGCGCGCACTCGGGTACATTTAACTTCGGATTTTTACGATGTGGCGGGCTTCCTGCGTGGGAATACTTCATTACGGGAAATCGAGCTCAGCGAGCTGGATGTTGCTGGTAAAAGCTTGCTGCATCTGCAATGCCATTTTGGCTTAGATACCCTGTCGTGGGCGCGCATGGGCGCTAAAGTACTGGGCGTCGATTTGTCTGAGGTGGCGATTGATGAAGCACAAAAGCTGGCGCAGCAGACACAGTTGTCCGCCGAGTTTATTTGTAGCGATGTCTACAGTGTGGCGAGCAAGGTTGAGCCGCAAGATATTGTTTTTACGTCCTATGGTGCCATTGTTTGGCTACCGGATTTAACCCTGTGGGCGCAGACGGTTGTTGCTTGTTTAAAACCCGGCGGACAGTTTTACATGGCTGAGTTTCATCCGGCGCAACAGCTGTTCGATGGTTATAGCTATTTTAATCGGGGTGAGCCAGACATAGAGCAGGAAGGCACTTATACCGAAAATGCCGGTGATGATCAGCAAACCTTAATGTGTTGGTCACACAGTCTATCTGAAGTGATCAATGCGTTATTGCAGGCGGGCTTAGTGTTGGAGTTCTTCCACGAATTCGATTTCAGCCCGTACAACTGTTTCGAAGGGCTCGAAGCGCAAGCCGATGGTCGTTATGTGTTAACACACCAAGGGCAGAATGTGCCCTTGGTGTATAGCATAAGTGCTCGTAAGCCTGCGTAATGCTTGCTGTTTGTGATGGCGAGGATTAACGACTACGGTAAACCGGATACTTAAGGTAACGGTTATCGTAGTAAGGGCTGCGTTCATAGAACCAGCGCAGGCGTGCCTCGGGATCGGCGGCAAATTCGGCATTGGTCAGTGCTTTGTCGAACTCAGCTTGCAGCTTAGGATCATCTTTTAGCATCTTGGCCGCCAGCGGCTCAACCGCATAATCTTCAATGTATTCAGTGCGGGTGAAGATAGGATTAAAGAAACCCCATTGCAGCAAGGAATCTGGTGATTGCGGCTCCAGCAGTAAAATCGCCAGATCGCCCAGCGGTTGATCTGTGCTGATTTTAATGGTGCCAGCGGGCAGAGTGGTGCTGACTTTCGTCAACTCTGACTCGGCTTTGACCGTTTGATGGCCCTCAAAGTCCTTAGTGTTAAACACTGGATTACTGAGTTTGTATTGCTGTAATTTAAGCTCTGTCGGTTTGGTCAGGCGCGTCATGCGAATGCCGTGTATGTTCAGGCGATCAATCACTTGCGTCCATTGCGCCGGAATATAGTAAGCGCTCGGACGAGTGACTTTAATATCTGGCACTGTCTCGGCGATCACAGGCAGGTTGGGGTACAGCTTAGGTTGACCGTTCCAGCGCACCACCTCAGTGCCGCTAATCGGACTCTGCTCCAGCTTGTAATCTATGCCCTTAAAGTCCCAACCTTTTGCCAGCGGTGCCGATTTCCACGTGAGGGTGATCAGCGGTGAGGCGCGGTATTTATCTTCTTGAATCGCACTCTTCAGTTTTGTCGCCTGTTCGCCCACGGTTTTGAGGGTTTGCTCTAGCATCACATAGGTGCCGAGCACTCTTTGTTTAAAGGGTTTTAGGCTGTGGTTTTCAATCAAAATAGTCGGTAAATGGCGCGCATCACCATAACCGTTAGAGAAGCGTGGGCTAGGATTCCATAGCGACATGCCTTTGGTGATATCGGCATTATCCACGGCAAAAATTAGCGGGCCGGGAATATGGCCTTGCTCGGTTAACGCGGCTTCAACCGCGGGTCTGTAGCTGTTTTCGAGCCAGCGGTAACTGGCTGGGCTTAACCCTTGGGCGAGGTTATAACCAAAGGTGACATCGTATTGGTAATCTATGCCGTCGGTGACATGCACGTCGATATATAAATCTGGCTGCCACACGTTGATTGCGCGCAGCATGTGCTGCATTTCTAAGGTATCGGCCTTGGCATAATCGCGGTTAAGGTTGAGATTATTTGCGGTAGTGCGCCAGCCCATATTCACAGGACCACGTTGATTCACGCGATTAAATTCGCCGCTGCGCTCATGGGCATCGACACTAAACATAGGCACAAACAAGAGGTTGGCTTTATCCAGCAGGGCGTTTTTATCGCCCTTAATGATGTCGCGTAGCAGCATCATGCCGGCATCTTTACCGTCGATTTCTCCGGCATGAATCCCTGCTTGCACTAAAACCGTTGGCTTAGTGTTTTGCTTGAGGCGCGCCGACTCATTAATGCCTTCACTGCTGGCGATATACATCCAAATATCGCGCCCCTGTGGGCTCTTACCTAAGCTAACCTTTTGTAATTTATCGGTCTCGCCTGCAAGTTTGTCGAGCCAAGCGATAGTGGCCTCGTAGCTTGGGCTTTCAATACCACTACTTTGTTCAAACGGCGTCGCCCACTCGTTATCGACACTCAACATGAGCGATTCGCTGGCGCCATGCCAAGTGATACTCGGCGGTAAAATGGCATCGTTGACAACGGTGTTGGCTCGCTCAGGGATCGGCGCGGGCTCTGGCGCTACAGTGGTGGCATCGGTTGCGACAGTCGCAGCGGGTTCGGTTGCTTGGGTATTCGCAGCAGGGCTTACATCCTGTGGCGCTGTTGCTGGGGTTTCAGCTGGCGTTTTGTCGGTGACGGTTTGCGCCGCAGCGACTGGAGTCGAGGTCGGCAAATCTGTAGATGCTTGAGCTGTTTTGGCTAAGGTTTTTGCCTCGTTGGCAACAGCATCGACTGAGTAGAGACTACCCGCAGAAAGGCAGGCAATGGCGATAACGGAGAGGGAAAATAAGCGCATGAAAAGAGTCCATATATCGAATTTTTATTAGGACGGTATCTTTTCATTAAACGCCCCGTTAACGCAAGGGTGGTCAAGGGTTTATCTGGCATTCAGGCGGTGGGTTAATACGATTGGTATAAAACCCTGAATGACATAACAGGCTAATCCGCTTTGCCGGACGTTATCTGCGATGCGGGTTGCTTGTTATAGGCAAGTTCGATAATGGCGATGTCTTCGTAAAGCGCTTCGACGGCAATGGCGAGCTGATAAATAAGCCAGCTATATCCTGTGAGCGCAAATCCCTGCGGGACTTCGCCGTCGGTTGCCGCGGCATCTAAATCGGTTGATGATGCCGTGGTTGTTGATGCCACGATTGTCGATGTTATAGGGGCTGAGGCTAGCGCAAGCTGGAGTCTGTGTTGTAACTCTTGTTCTAAGCATTCTGGCAAGGTCGGTAATTGTTGAGCTTTACCCGCCACAAACTCCGCCAGTTGTTGTAAATAGAAGGCGAGCTCAGAGGCAACCGCATTGATCTGCAGATAGGCGTCTTTATCTTCTTCCTGCCAGCGAGTTAAAGGCAAGAGTTCGAGCAAGGTGATTAGACGCACTTGTTTATTGGCAAGTTGCGACAACAGTACTTTGTGTTTTTTGAGGGTTTTACTTTCCCATTCTAAGGAGAAAAACAGCTTCTTCTGGGTGAGTACGGCTTTCATTGCGGCCTCGAGTTTAGCGCGAAAATCTAGGTCGTGATTAGCTGGCGCCTTAAGATGCTGGGCGAGTACTTCGGCCATATTGTCGATGGCATGAGTGAGTTGAGAGCGCCAATCTTGTTTGGCTTTGATTGGGAAAATAAACAGCGAGACCAAAATAGCGATCACGCAGCCAGAAAGTATATTGGCCGTGCGCCACAGGGCCTCGGAGGTGTCGTGATTTGCATCGCCGACTACTATGATGATGGTAAATCCTGACACTAAATAGGCGTAACTGTAGCGTCCGGCGGAGATAAAACAAATCAAACTGACGGCCAAGATCAGCAGGCTCATGATCAGCCAGTAACTGTCAACTGTGGCAACCAGCATCACCCCATAAGCTGAGCCCAAACAGGTACCTATCGCTCTTTGCAGTGACTTTTCAATCGCGCCGCCGACCTGCGGCAGACTCATCATGATGACAACAATCGTCACCATGCTCCAAATAAAATGCGGCGGCGCCCAAATAGCATTAATAGTGACAGCAACGAGCAGGGCTAAACCGAGTTTTAAGGCGTGGACTGAACGGAAATGACGATAGATAAAGTCAGCAAAGGGTGTGGCTGGGGCAAATACAGACATAGGGCAGAATTCCTTTGGGCGTTAGCCAAGCGACATTACGTAGAGCCAGTATCGCAGAAAAACCGTGTCACTTGCGGCTATTGCGCCAGAAAGAAATACGAATTGCGAAATCTAGCAAATTGAGCTTAGTGGATAGCGCGGGAGAGATCACATTTAAATTCAGATATTTGAATTGGATTGGCTGAACAGTCGTGTGCAGAAGCGGGTTGTTATGCCGTGCAGTAGGTATAAAAATGGCTTGAATAAGGGTTGCCTATTCAAGCCGGTATTTGAAGCGTTGAGTGACTGTATTCGCTAGCTTAAGCTGCGCTCGCTATCCGCTTTAACGTCAACTTCTGTGTACTTATCCATGATCAAGGCACAAGCCGCATCGCCAGTGATATTGAGTGCGGTGCGGATCATGTCAAACACTCGGTCTAAGGCAAACAGCAAAGGTAAGCCATCGATAGGGATGCCAGCCGCTAACAGCACTGCCACCACTAAGAAAGACGGACCGGGCACACCCGCTTGACCAATTGCGCCTAAGGTTGAGGTGAAGATAATGGCCGCATAGGCCGTCATCGACAGGTCGACGTTATACATTTGAGCGAAGAACATGGCCACTAAACCATAATAAATCGCGTTGCCGCTCATATTGATCGTGGCGCCTAACGGCAGCACGAAGGCTGTGGTTGCCTTAGAGACTTTAAGATCTTCCTCGCAGGTTTCCATATTCACCGGCAGCGTCGCCATTGACGAGGCGGTCGATAACGCCATCACCTGTGGTTTCTTCATTGCCGAAATAAACTGGCGTGCCGACACTCTTGAGAAGAGCTGCACTACCAATGGAAAGAAGATAAAACCGTAGATCAGAATTGCGGCCACAAACACGGCGAATAACTTAAATACCACTTCTAACGCATCGAAACCGAAGGTGCCCACGGAATCGGCCATCAAACCAAATACACCAATAGGCGCAATAATCATCACGCAGTTGATCATCCACACAAAGGCGTCGACGACAGTGTTGAGTGCGGCCAAAATAGGCTTAGCACCATCGCCTTTAACCTTAGTCAGTGCGATACCGAAGAAGATACTAAAGACTAAGATTTGTAAGATGTTACCGCCGTTCAGCGATTCGAACACATTGGTTGGGATCATGCCGATAAAGGTATCCATCACCCCAGGCAATGCGCCGTGTTCCTTGGTTACTTCCATCAAATCTGAGCTGCTGTGGGCAGTGAAATCAACACCTGCGCCCGGTTGGAACAGATTACCCATCACCAACGCCAGCACCACGGCGATGCCAGATGTCACGATAAAGAAGCCGAAAGTGCCGATGCCTATCTTACCCGCCGATGGGCTATCGCCTAAACTCGCGGCACCGCTGATGATAGAAACTAACACTAGTGGGATCACTAGCATTTTGATTAAATGGATAAAAATGGTTCCGAGTGGTGCAAACACGCTTGCACTCTCGCCCATGATAAAACCGACTAGCGCGCCGATAATCATGGCAATAACCACTTGCACACCAATATTGCCTAATAAACTCTTGCTCTTCATTCTTGCGAAACTCCCCAGTAGCCGCAGCATTTTGTGTGATGTAGGCGATTTGAATCTAGACGCCGTAAACCTTAGTGATTAAGGCTTACTCTAGGCTAAAAGCCAATGCCCGACACTTTTTTTGTCCGGCTAAAGTACCACAATTGCATAGCAGGGGATTGTGAAACATTACCAACCTTGGTTTCAAAATAACCAAAGTCTAAATCACATAAGTGTTAAATGAGGAATACTTAAATTTATTGGCTTAAAAATGAATAAAGTTTATTTAATGGTGCGATTTTAGCTGCCGGATTGATGAATTGTGATTTTTGGGGCTGTTGCAACATCTGCTATATAAATTTAATTCGGATGCCCACCAATATAGTTGCCGTCGCAATGATGCCGTTGGGAGATATTAAGCGTCCATCCATCTTTAATGAGGGAGTCGCTGAGCTTTAAATGGTCGTGCAGCATTGACTCTGTGGGATCTTAATAGCATCAAGACTAAGTATTTGCCTTTGGCACGACAATAAAAAGCGCCGACTCATTGCTGGGTCGGCGCTTTTTTACAAGTAAGGCTATGGCTTAATGCTTAAGCGCTTATGCCTTTGGACCAGCGTTGCGGATAGCGTCAGACACTTGATACTTAGCGAAGTTTTTAGTGAAAGCTTCGGCCAGCTTCTGGGCGTATTCGGCGTATAGCGCTTTATCGCCCCAAGTGTTGATTGGGTTGAGTAAATTGCTGTCTACACCACTTACTGCCACAGGAACAGCGAGGTTCAATGTGTCTAAGTGGACGGTTTCAACATCTTTCAATTCACCGCTTACAATCGCATCGACGATGGCGCGAGTGGTGGGGATGTCAAAACGTTTACCCACACCGTGTGGACCGCCAGTCCAACCGGTGTTGACTAAGTACACTTGGCTACCGAAGGACTCAATGCGCTTCATTAACAGTTCAGCGTACACGCCAGCAGGGCGTGGGAAGAAAGGTGCACCGAAGCAGGTTGAGAAGGTCGATTGGATCGCTGAGGTTGAGCCGATTTCAGTCGAACCCACTTTAGCTGTGTAGCCAGACAAGAAGTGGTATGCCGCTTGTTCTTTGGTCAGGATAGACACAGGTGGTAATACGCCAGATACGTCGCAGGTTAAGAACACAACGGCATGTGGCTCGGCGCCGCAGTTGTCTTCTTTACGTTGGGCGATGTGCTCCAGCGGATACGCTGCGCGGGTGTTTTCCGTCAGGCTGCTGTCTTTGTAGTTGGGTACGCGGTGTTTGTCCATTACGACGTTTTCAAGCACAGTGCCAAAACGAATCGCATCCCAAATAACAGGTTCATTCTTCTGGCTTAAATCGATACATTTGGCGTAGCAACCGCCTTCAATATTGAATACGCCGCCAGGTGCCCAGCCGTGTTCATCGTCACCGATCAGGAAGCGTTTTGGATCGGCAGAGAGTGTGGTTTTACCTGTGCCAGATAAACCGAAAAATAGCGTAGTGTCGCCATCTTTACCTACGTTGGCCGAGCAGTGCATAGGTAGCACGCCTTGGGCTGGCAGTAGGAAGTTCTGCACTGAGAACATAGACTTTTTCATTTCGCCGGCGTATTTCAGGCCCGCGAGCAGCACTTTACGTTCGGCGAAGTTTAAAATCACGGTCGCATCTGAGTTAGTGCCATCACGCGCTGGATCGCAAACAAAGTCAGGGGCATTGATGATCTGCCACACAGGTTTGTCTTTACGATTAAATTCTTCAGGAATGATAAATAGATTGCGGGCGAACAGTTGGTGCCAAGCGTATTGCGTGGTCACACGTACTGGCTGGTAATGCTCTGTATCGGCACCGACTTCGAGATCAGAGACAAACAGTTCTTTATCGGCTAGGTAAGCTTCAACCCGTGCCCATAAACCTTCAAAAGCACCTGGGTCGAAAGCTTGATTGACAGGGCCCCATTCAATCTCTGCTTCTGAACTGGGTTCTCTTACGATAAAACGATCGCCGGGTGAGCGACCACTGCGAGCGCCTGTTTTAGCGACTAATGCGCCATTGGCTGTCAATTCACCTTCGCCGCGTAGCAGGGCAAATTCGACAAGTTGTGCTGTTGAGGGGTTAAAGTGAACGCGGTTTAATCCATCCGCCATAGTGAGGTCTCCATTTGATTTGTAAGGTTATTTTGTTATCGATCGTCTGCCGATTTTATTTTATTTATCACCTAAGCTTTGCTAGGTAACAAAGTGAGGCGATTGTAGCCTAAGCCGAGTGCAATGTGTGAGGGGATTGTGGGGATGAATAAAAAATGGGCGCCGAATCGGGCGCCCATATTGTTTAGCTTTGATGATCCGGTTAACCGATTATTGCTGTGTCGTTGACGTCTGACCGTTCGCAAATAATGCGGCTACGTCTATGGCATCAAAAATGTATTTTGCGTGGCAATACTCACAGCCCATTTCGATCTTGCCGTCTTCGGCGAGAATGGCTTCGATTTCCGCTTGATCTAAGGTCTTGATCGCGCCGGCACTGCGTTCATGGGAGCAAGTACACTTAAAGCTCACATCGATAGGATCGAATAGGCGCACTTCTTCCTGATGATATAGGCGATGTAACACGCTTTCAGCGTCGAGTGTGAACAGCTCTTCGGCCTTGATAGTCGACGTCAGTTGGCTCAAGTGTTCGAAGTCAGGGTTGTGATCTTCTTTGCTTGGCAGAATTTGTAGGAACATGCCTGCCGCTTGTTTGCCGTCGGCAAATAACCAAATGCCTGTTGGTAACTGTTCAGATTGATTGAAGTATTCTTCAACACAGGCCGCTAGCGTTGGCTTGTCGAGTGCGACCACACCTTGGTAGCGTTCACCTTCGTCGGGTGTTAAGGTGATGACCATATAGCCCTGACCGAAAAGATCGGTAAGGCTGGCATCATCGGCGAGTTCTGCATTCCAGCGAGCCACGCCGCGTAGCTCTTGTAGATTGTTACCGTTGATCACCGCTAAAGAAACAGGACCATTGCCCTGTAACTGTACGCTGATATCGCCGCTAAATTTCAATGTCGCCGTTAATAGTGAGGTGGCCGCCATTAATTCACCCAGCAGCACTTGTAGTACGGCTGGATATTCTTGGGCGCTGATCACTTGTTGATAGCTGTCTTGTAACTGCACCAGTTCACCACGCACATCGGCATTGTCGAACAAGTAGCGGTGTAAAGTATCTTGGTTCATCGTGTATCTCACTGTTAAAGGTCTTTAAAGCGCATGAGCTGGCGTCGTTGCTTTTTATCTGGTTTCTGATCGGGTATCGGGTTATTGAGCATGTTGAGACGTCGTTGCTCAGCATTTATGGCCCGTTTAGCCAGACTTGTCGCGGTTTCTTCGTACAATTCTTGTGCAATCGCACCACTTTGGCGTAATTGCGATAATTTTTTTATGACGATCTCTTTATCGTCATATCCTTGGCGTATCTTAAGGACGGCGCCTATCTCGGCATTCCTACTAGATTTGGCGCGCTGGCCGTTGTAATGTACTTTGCCGCCGTTAATCATTTCTTTGGCGATGGCACGGGTTTTATAGAAACGTGCAGCCCAAAGCCACTTATCGAGTCGAATGTCTGTTTTGTCGCTTTGACCTTGAGTCATAGTGTCTCCAGCTAAAAGATCGATGAGTTCACATGCCGAGCAAGAAAATGCGTGAAGTGCATTTCTATTATGTTAAAGGGTGGCGTGCTTCACACCCTTTTGTGGCGGGCAAAGTTAGCATAATTGGCCTATTTTCGCCAATTTGAATGCTGTTAAGGGCTTGTTGCCAAATGCAGCCTAGGTTAGCATGAGGCGGTTATTGCCTAATTATTATAATCAGAACAGAGACCTGTATCGAGGGTCCACATAAAATATGGATTTATTAGATAAAGTTATCGCAAAAGCCATTGGCATGCCCCATAAACCCCTAAGCCGGATCGTCTTCTGGTTGGGCTTTATTGTGATCATGTTGTTAGCTGCGCAAATTACATGGAAATTAGTGCCTACTAGCTCATCAGCCAGTGCTTGGTCCCCAACGCCCGTAAGCGTGAATGGCAAAGGTGCTGGTCAGGTTGATCTCGCAGGTCTGCAGCAGCTTGGACTCTTTGGTAAGGCGGATGCGACATCTGATAAACCTAAAGTCGAAGCGGTCGAGACTGTGACCGACGCACCTAAGACTACACTCTCTATTCAGCTGACTGGGGTTGTCGCATCGACAGCGGACCAGAAAGGCCTCGCAATTATTGAGTCAAACGGCAGCCAAGATACTTATAGCCTCGGCGATAAGATCAAGGGCACATCTGCATCACTCAAAGAAGTCTATGCCGACCGTATCATTATCACTAACGCTGGCCGTTACGAGACCTTAATGCTTGATGGCTTAGTGTACACCAGCCAAAGCCCAGCCAATCAGCAGTTACAACAAGCTAAGAGCAACAAGGCCGGCTCGGCAGTGAGCCGTGTTGATCAACGTAACAATGCCGATATTTCCCAAGAGCTCGCCGAATCCCGCACCGAATTGTTAGCCGACCCCAGTAAGATCACCGATTACATTGCCATTTCACCCGTGAGACAGGGTGATTCTGTGGCGGGTTATCGTCTGAATCCGGGTAAAGACGCCAATTTGTTCAAGCAGGCGGGTTTTAAAGCCAATGATTTAGCTAAATCGATTAATGGATACGACTTGACCGTAATGAGTCAGGCGTTAGAAATGATGAGCCAATTATCCGAGTTAACCGAAGTATCAATTATGGTTGAACGGGAAGGACAATTGGTTGAAATCATGTTTAGTTTGCCGCAATAACACGCGTTAGAGGAAAGAAAAAGAATGAATAACAAAAGGATTCGACGCAAGCTGATTGCAGGGATTGTTGCGGGTGCAGCCATGTTCTCCTCCCAATTCGCTTGGTCTGAACAGTATGCGGCCAACTTTAAGGGCACGGATATTCAAGAGTTTATCAACATAGTTGGTAAAAACTTGAACAAGACCATCATAGTCGACCCGACGATCCGCGGTAAAATCAACGTTCGCAGTTATGATCTGCTTAACGATGAACAGTATTACCAGTTTTTCCTCAACGTCTTACAAGTCTACGGTTATGCCATCGTCGAGATGGAAAATAACGTTATCAAAGTCATCAAAGACAAAGACGCCAAAACCGCAGCAATCCGAGTGGCTAATGATGCCGAACCTGGGATAGGCGATGAGATGGTCACGCGTATCGTGGCGCTGTATAACACTGAGGCTAAGCAATTAGCTCCGCTGTTACGCCAATTAAATGATAACGCGGGTGGCGGTAACGTAGTGAACTACGATCCCTCCAACGTCTTGATGTTGTCTGGTCGCGCTGCTGTGGTGAACAAGTTAGTTGAAATTGTTCGCCGCGTAGATAAACAGGGCGACACCTCAGTACAAGTGGTGCCGTTAGAGTTCGCCTCCGCCGGTGAAATGGTACGAATTATCGATACCCTTTATCGCGCCACGGCTAACCAATCTCAAATGCCTGGCCAAGCGCCGAAAGTGGTTGCTGACGAACGTATCAACGCTGTGGTTGTTAGCGGCGACGAAAAGAGCCGCCAACGTGTGGTCGAGCTCATTCATCGTCTCGATGCCGAGCAAGCTAGTACAGGCAACACTAAGGTGCGTTACTTACGTTATGCCAAAGCAGAAGATTTAGTTGAAGTGCTGACAGGTTTTGCCCAGAAATTAGAGGGCGAAAAAGATCCAAGTGCTCAAGCTGCAGGTGGTAAACGCCGCAATGAAATCAATATTATGGCCCATGCTGAGACTAACGCTTTAGTGATCAGTGCCGAACCCGATCAAATGCGGACTATCGAGAGCGTGATTAATCAGCTCGATATTCGCCGTGCTCAAGTGTTAGTTGAAGCGATTATCGTGGAAGTGGCTGAGGGCGATAACGTTGGCTTCGGGGTCCAATGGGCGGCTAAAGCGGGTGGTGGGACTCAGTTTAATAACTTAGGCCCAACCATAGGTGAGATTGGCGCTGGTGTATGGCAGGCACAGGGGGAAAAAGGCTCATCAATCACAACGCTTGACCCTGAAGGAAAGCCCGTAGTGACCACCAACCCAGATAAGCGCGGAGATGTAACGCTACTTGCCCAAGCATTAGGCAAAGTGAACGGTATGGCTTGGGGCGTGGCCATGGGTGACTTTGGCGCGCTGATCCAAGCCGTGTCGAGTGACACTAACTCAAACGTACTGGCGACACCGTCAATTACCACCTTAGATAACCAAGAAGCGTCTTTCATCGTCGGTGATGAAGTGCCTATTCTGACGGGCTCTACCGCCAGTTCGAGCAACAGCAACCCATTCCAAACCGTTGAACGTAAAGAGGTTGGGGTTAAGCTGAAAGTGGTGCCGCAGATCAACGAAGGTAATGCGGTTAAGCTGACGATTGAACAGGAAGTGTCAGGAGTTAACGGTAACACAGGTGTGGATATTTCCTTTGCTACTCGCCGTTTAACGACCACTGTTATGGCTGACTCAGGTCAAATCGTGGTTCTGGGTGGTTTGATCAACGAAGAAGTACAAGAAAGCATACAAAAAGTACCATTCTTGGGTGATATCCCCATCATTGGCCACTTATTTAAATCTTCTTCGAGCAAAAAGACCAAGAAAAACCTGATGATCTTCATCAAACCAACCATTATCCGTGATGGCATCACAATGGAAGGGATTGCTGGTCGTAAATATAACTATTTCAGAGCATTACAACTTGAGCAGCAAGAACGTGGCGTCAACTTAATGCCAAATACGAAAGTGCCTGTGTTGGAAGAATGGAATCAATCAGAGTATTTACCGCCAGAAGTTAACGCCATTTTAGAGCGTTATAAAGAAGGTAAAGGTTTAGATGCCCAGATGCGCCAAACGGATCCGACCTTAAAGTCACTGGAAGAAAACAAGAATAAAGATAAAACCAATGAGTGAAATACAATTATCCCAAGTCGATGACCTCAGCCAAGTCTCCAATGAGCTAGGTCTGGAAGCTGAAGGTGATGAGGTGTTTCGTTCGAGCAGTAAAGAGCGTTTACCCTTTGCCTTCGCCCATCGCCACGATGTGGTGCTTGCGCCGGGTGAAACGGGCGAGTTGAGCTTGTTTTATACCAGCAAAACACCGTTAACGGCCATGCTCGAAGCACGGCGTTATTCGGGTGTCGATTTGCCTTTGGTTAAGCTCGAAGTCACGAAGTTTGAGGCCAAACTTACTCAAGCGTATCAGGCTAACTCGTCTGAAGCGCAGCAGTTGATGGAAGACATCGGCAACGAGATGGATTTATTCACCCTTGCCGAAGAGCTGCCGCAGACTGAAGACTTGCTCGAAGGTGATGATGACGCCCCAATCATCAAACTGATTAATGCCTTGTTATCTGAAGCGATTAAAGAAGAAGCGTCGGATATCCACATCGAAACCTACGAGAAACAGCTGGTGGTGCGTTTCCGCATCGATGGTGTGCTCAAGGAAGTCCTCAAGCCAAACCGTAAGCTGTCGTCGCTGTTAGTGTCGCGGATTAAGGTGATGGCGCGCCTCGATATCGCCGAAAAACGTGTGCCACAGGATGGCCGTATTTCGCTGCGTATTGCTGGTCGTGCGGTGGATGTGCGGGTATCGACCATGCCATCGAGCCACGGCGAACGCGTCGTGCTGCGTCTGCTGGATAAAAATACCGGTAACCTCGATTTAGAACAACTGGGGATGACCGACGGTATTCGGGTTAAGTTTGAAGATCTTATTCGTCGCCCCCACGGCATCATTCTGGTCACTGGCCCAACGGGTTCGGGTAAAAGTACCACCTTGTATGCGGGTCTGACTGAAATCAACTCAAAAGACACCAACATTCTTACCGTGGAAGACCCTATCGAATACGAGCTAGACGGTATCGGTCAAACCCAAGTTAACACTAAGGCTGATATGACCTTCGCCCGAGGTCTGCGCGCGATTTTGCGTCAAGATCCCGATGTGGTGATGATCGGTGAAATCCGTGACTTGGAAACCGCGCAAATTGCGGTGCAAGCATCGCTCACTGGTCACATGGTAATTTCAACCCTGCATACCAACACAGCGTCTGGCGCGATAACGCGTTTGCAAGACATGGGCGTTGAGCCATTCCTCGTATCATCGAGTTTGCTCGGTGTGTTGGCGCAACGTTTGATCCGTACCCTTTGTCCAAAATGTAAAATCGAGCACGTGCCCGATGCGCGTGAGCGTGAGTTACTGGGCATAGCGGCCGATGACGACAGGCGTATTTTCCGTGCCAATGGCTGTAAATCCTGTGGTAACAATGGCTACCGTGGTCGAACGGGTATTCATGAGTTATTGCTGGTTGACGACAATGTCCGCGAATTGATCCATGGCGGCCGCGGTGAGTTAGCTATCGAAAAGTATATCCGTCAGTTTGTGCCGAGCATTCGTCACGATGGCATGAGCAAAGTGCTCGCGGGCATCACCACTCTCGAAGAAGTGCTACGTGTGACTCGCGAGGAGTAATTTATGCCAGCATTTGAATATAAGGCACTGGATGCCAAGGGAAAGCAGCTTAAGGGCGTAATCGAGGCGGATACCGCCAGACACGCCCGTAGCCAGTTGCGCGATCAGCGCATGATGCCCTTGGAAATTCTGCCTGTTTCCGAAAAAGAAGCCAAAGCTAAGAGTAGCAGCTTTTCGTTTTTCAAACGGGGTATCTCGGTCGCTGAGTTAGCCTTGATCACCCGTCAAATCGCGACCTTAGTGGCCGCGGGTTTACCGATTGAAGAATCCCTTAAAGCCGTTGGTCAGCAATGCGAGAAAGATCGCCTTGCGAGTATGATCATGGCGGTACGTTCGCGGGTGGTTGAAGGTTACAGTCTGGCCGATTCCTTGGCCGAATTTCCGCATATTTTCGATGATTTATACCGTGCCATGGTGGCCTCGGGTGAAAAATCCGGTCACTTAGAAGTAGTGCTTAATCGGTTGGCGGATTACACAGAGCGACGCCAACAGCTTAAATCTAAGTTGACTCAAGCCATGATTTATCCTGCGGTATTGACCACAGTGGCGATTGGGGTGATTTCCATCCTGCTGGCGGCGGTGGTGCCAAAAGTGGTTGGACAGTTTGAACACATGGGCGCCGAGTTACCGGCTTCTACACGCTTCTTGATTTCGGCCTCTGACTTTGTACAAAACTACGGTGTGTTTGTCGTTATCGCCCTAGTGATGCTATTCGCACTCTTTCGCCGCATGTTAAAGTCGCCTGCGTTTCGAATGAAGTACGACAATTTTTTATTGAGCATGCCGGTTGTGGGCCGCGTGAGTAAGGGCTTAAATACGGCACGTTTTGCACGGACCTTAAGTATCTTATCCGCAAGCTCTGTGCCTTTGCTTGACGGTATGCGTATCGCCAGTGAAGTATTGCAGAATGTGCGTGTGCGGGCTGCAGTGGATGATGCGACAGCGCGGGTGCGCGAAGGAACTAGCTTAGGTGCGGCATTGACTAATACTAAGTTGTTTCCGGCAATGATGTTATACATGATTGCCTCGGGCGAAAAGAGTGGTCAGCTTGAGCAAATGTTAGAACGCGCCGCCGATAACCAAGACAGGGAATTCGAGGGTAACGTCAACATAGCATTAGGGGTATTTGAACCTATGCTGGTGGTGAGTATGGCGTGTGTGGTGCTGTTTATCGTGATGGCGATTCTGCAGCCAATCTTAGCCTTGAATAACTTGATTAGCGGTTAATTTTTGAATTTATGGATTATGACCAAGTCGATTGGCATAGGATTTTTTAGGAGAAGTAGTCGATGCAAATGAACAAGAAACATCAAGGTTTTACCTTACTCGAAGTGATGGTGGTTATCGTTATTTTAGGGATTTTAGCCTCTATGGTGGTGCCTAACTTAATGGGTAACAAAGATAAAGCTGACCAACAAAAAGCGGTATCTGACATAGTCGCACTCGAAAATGCCTTAGATATGTACAAGTTAGATAACGGCATCTACCCAACGACCGAGCAAGGTTTAGAAGCCTTAGTGCAAAAGCCAACGATTTCGCCTGAGCCACGTAACTACCGTGAAGATGGTTATGTAAAACGTCTTCCAGAAGATCCATGGCGCAATAAGTACTTGCTGCTCAGCCCTGGCGAAAACGGCAAGCTAGATATATTCACTGCAGGCCCAGATGGTCAACCAGGTACCGAAGATGACATCGGTAACTGGAATTTACAGAATTTCCAATAAGATGTTAAAGCTGCGCCACGCTGGTTTCACCTTAATGGAGGTGATGCTAGTGATCTTGTTGATGGGACTCACGGCAGCGGCCGTGACTATGTCTATCGGCAATTCGGGCCCACAACAGGCCTTAGATAGAACGGCGCGGCAGTTTATCGCGGCCACTGAAATGGTGCTCGATGAGACCGTTTTGAGTGGCCAATTTATTGGTATTGTGATTGAGAAAACCAGTTATCAGTTTGTTTTTTATAAAGACGGTAAGTGGGAGCCTTTGGATAAAGACAGGCTCTTGTCTGAAAAGCAGATGGAGCCGGGTGTCGTGATGAACTTAGTGCTCGATGGTTTGCCTTTGGTACAAGACGATGAAGAAGACGATTCTTGGTTTGAAGAACCACTGATTGAGCCCTCTGCCGATGACAAGAAGAAACATCCTGAGCCACAGGTCATGCTATTCCCGAGCGGTGAAATGAGCGCTTTTGAGCTGACGTTTATCGCCAAAACTGACAAAGGTCAGCAGGCCGAAGCGTTAGTGGTCGGCGATGCGCTGGGCAGATTAACCATAGGGCGTCCCGATGAAACGCGCTAAGGGCATGACGTTACTCGAAGTGATTGTGGCCCTTGCAGTGTTTTCGATTGCGTCCGTGTCGATCACTAAAAGTTTGGGCGAACAGATGGCCAACATGCCAATTCTGGAATCGCGCACTTTGGCACAGTGGGTGGCCGATAATCAGATGGTTGATGCCAGACTCGAGCCGCAGTTTCCTGAAATCGGTAAAAAAGAAGGCCAAGTCGAACTTGCAGGAAAGGATTGGTATTGGCGTAAGGAAGTGGTGAAAACCACGGACGATAATTTTCGTATGATCCGCATTAGCGTGAGTGAAGACGAACGATATCAGCGTATTGCAGCACAAGTGAGCAGTTATGTCCTTAAAACTGACTAGTGTGCAGCGGGGATTTACCCTGCTAGAAATGCTGATCGCCATCGCGATTTTCGCCATGATAGGTTTGGCTTCTAATGCCGTGCTCAGCACTGTGCTGACCAACGATGAAGTGACGCGCGCTTTCTCTACTCGGCTTAAGGCCCTGCAGCAAGGCTTTGGTGCGATTGAGCGTGATCTCGCCCAAATGGTGGCGCGCACACCCAGATTACTCGAAGGGGGTCGAGGTTCGACCGTCTTCCAAACGGGCAATGATATCTTAGACTCGGAATCAGAAGCCTTAGTGTTTTACCGTTTAGGTTGGTTAAATCCCGACGGTTTATTACCGCGGGGCAGCTTACAGTCTGTGGCTTATGTGGTGCACGAAGGGCGACTGGAGCGTTGGTATTTTCCTTATCCTGAGCCTGAATTTGGTGCCGAGCCGATTAAGACAGTGGTCATAGATAAAGTATTGTCGGTGGAATATTCCTTCTTTATGGATGACAAATGGGAGCGCAAAGTCGAGGCGACTAAGTTGCCTAAAGCGATCGCCATGGAAATTGAGATTGAAGGCTTAGGCAAGATCCAACGTAAGTTCTTGCTGCCGCTAGGTGCTGCCGCACCTGACAAGTCCAAGGATGAAGACAATAAAGACGAAAATCCAGACGATGCCAACAAAGATAAAAACGGCTCAGGTAACGGCAGTGGAACCGGCAATGGTTCCGGTGACCCAGATTCTGGCGATTCCGGAGACGGTGATCCAAACGCAGAGGGCCGCGAATGAGAACCCAATCTCGGTCTAAACAGCGCGGCGTTGCACTGATCGTCGTGCTCTTGATTGTGGCTATGGTGGTGATCATCGCGACAAACATCACTGGCCGTAATCAGTTATCTATGCGCCGAACCTTAAACTTGGCCCAGTACGATCAAGCTTATTGGTATGCGATTTCCGCCGAAGAATTGGCGAAGAAGATTTTAAAACAAGATTTAGATGACTCTGAAGGTCGAGTCCACAGGCAACAATATTGGGCCATGGCCGATGTGGTGTTTCCTGCTGAATACGGTGAAATCGCGGGCAAGATCACCGATATGCGTGCCTGTTTTAACTTAAATGCCTTATCCGCGACCACGAAAGAGGTGGAAAACGGTCAACCGAAGCTGCCCTTGGCGGCGAAGCAATATAAGGCCTTGTTGGTGAGTTTAGGGATGGACGACTTTAGTGCGGATCGCCTGACTCAGACCTTAAAGGATTATATCGACGAAGACATGACGGCCAGCCCCTACGGCGCCGAAGATGCTGAATATGAATCGCGAAATGTGCCTTACCGTGCAGCCAATACGCTGATGAATCATCGCAGTGAGTTACGTGCGGTGATGGGATACACACAAGATATCTATTTGAAACTCTTGCCCTATGTTTGTGTGATCCCTGGCAATGACAGGCAATTGCTTAACGTCAATACTGTGGAAGTTGAGCAAGCGGCACTGTTAGCTGGCATGCTCGATAATCAGATTTCGGTCGGCGAAGCAGAAAGCATTATCAACCAACGCCCCGGCGATGGCTTTGCCAAAATCGAAGATTTTTATGAAAGCTCATCTATGGGCTCGATCAAGTGGGAAGGTGCCATGAAGTCGAGTTTTGTCATCGATAGCCAATATTTTCTGTTGGCCTCGGGGGCGAAAGTCGATAACGCTGTGTTTAGAATGGAAAGCGTGCTCAAAAAGGGCGGCGGCAACAAAATGGAAGTGCTCACCCGTCAGTTTGGTGGCCAAAAATAATGGCAGTGCTCACCCGTCAGGGTTTGATGTTGGTGGCCAAAAATAATAGCAACGAGCTCAGTCACGCTTTCTTGACTGGGACTGGTGGAGAAAAACTGTGAGTGAACGGCTATTTATTCGCTTAGGCAGAACAGCAGAGCAAGCATGCTCTTGGCTCGTTTGGTCTGAGCAAGAACAGGAAATTATCGCCTCTGGCGAGCTCGCCAATGCCCAAGGTTTGTCGACCTTAACTGAGCGTGCGGGTAATCGTCCCGTCGATGTGTTAGTACCCGCCGCGGCAATGACACTCACTAGCGTACATTTGCCCGAAAAAGGCCAACGCCAAGCCTTGAAAGCCTTGCCCTTTATGCTCGAAGAAGCTCTAGCTGATGATGTAGATGCCATGCATTTTACCGTCGGCCCGCGTGATGGTGATGCCTTAAGCGTGGTCGCGGTGGCCCATGAGCAAATGCAGACTTGGCTGAGTTGTTTGACCGAGGCGGGTTTAAAAGTGAAGCGCATCGTACCCGACTGCTTAGCCTTACCATTGCAAGAGTGCCAATGGGCGGCAATGAATTTTGGTAATGAACTATTACTGCGTACCGGATTAGCCACGGGCCAGAGTTTACCTTTGCCTTGGTTACCGATTGCGCTGGAACAACTTAGGCCCGCACAGGGCGAAGTGTCTGTCGCCAGTTATACCGACATGCAACTCGAAGGGGTTGAGCTTAAGCCACAATCACTCGAGCTGCCTATGTTAGTGTTAGCCCGCGGTATTCTGCATGCGCCAGTGAACTTACTGGGCGGCGTTTACACGCCTAAGCGTGAATACAGCAAACAGTTGATGATGTGGAAGAGTGCTGCCATCGTTATCGCCATCGCTTGTGTGCTGGCTTTAGTGAATAAAGGGCTGACCATACATCAAGTCAATGCGCAAACTGCGGAATTGACGGCACAGAGTGCAGATATTTATAAGCAAGTGATACCAGGCAGTACGCCGCAAAGTGGCATTTTACGACAGCAAATAGATATAGAACTGCGATCTTTGCAGGGACAGGGCAGTGGTGCCGAGTTTTTTGCCATGCTCGATGGTTTGCAGGATGCCTTTAAGCAAGTACCTGAACTTAAACCTAATAGCTTAAGATTCGAAAGCGCGCGTAATGAGATCCGCATGCAAGTAAGTGCGAAAAACTACGCGCAAATCGAGAAGTTTAAAGAGATCGTTGGTCGTCGTTTCCAACTCGACGGCGGCACTATGAATAGCGGTGAAGATCAAGTCACCAGTACCTTAACCTTGAGGAGCAAATAAGATGGATAATTTGCGGACTTGGTGGCAAGGGCTCGCATCGCGCGAGCAGCAGTTAGTCGGTTTTTGTTCGGTATTCTTAGTCATAGGTATTTTTTATTGGGGTGTTTGGACGCCAATCGCCACTGCCGAAGCCGATGCATTACGTAACTTCACTGTGCAGCAACAGACGTTGAACCATGTAAAACAAACGGCGAATAAAATTGCGAGTTTGAAGCAAAATGGCACTAAACCGACTATGTCGGGCAGTTTAAGCTCAGTGGTTAACCAAACTGCGGGCAATTATGGGTTAGTAATTACTCGCATGCAGCCCCAAGGCGATAAGATCCAAGTGTGGATGGACGATGTTCCTTTTGATGCCTTGTTAGGTTATTTGAGTGAGTTAGTGCAGAAAAAAGGACTTTCACTCGAAAGTGTTGATTTGGCAGAGGCCGATGCCCCTGGCTATGTCAAAGTTAGACGTATCCAACTGTCGCAATAAATTAAGGTGTAGCTGTGAGTTTGATAAAAAAAGTCATCATAGGCGTACTGATTTATTTAGTATTTTTAGTCGTTCTATTTCCCGCTAAGATTGCGATAGCGTTAGCGCCATTACCTGCAAATATCAGTATTTCGGGTGTGAGCGGCAGTATTTGGTCTGGCAGCATAGAAACCTTAAGTATGCCGCAACGCCAGTTAGAACAAGTGCGTTGGGATCTCAGCCCTTGGGCCTTGTTGTTAGGTAAAGTGAAAGTTGATTTTCAAGTGGGCAGCCGCGCCACACCTGTCAGCGCTAAGGGATTAGTCTCTTGGTCCATGGGCGGCTTAAGTGCGCAGGGTTTACGTTTCGAAGCGCCAGATAGCTTTTTACTCGGCAATGCAAAACTGCCCTTTAAAACCGAAATATCCGGTGAAGCTAGCCTATTAGTCGAGACGTTAGAGCAAGGTAAACCTTGGTGCGAGCAGTTATCTGGTAAGCTGTTTTTGAATCAAACCAATGTTAAAAACCAGTTTGGCAACTATCCACTAGGCAATATCGAACTTGGCCTGAGTTGCGTTGATGGCAAAGCGCAATTAGCCACAGATGAAGCTAAAAATCAGCTCGGCATCGTCGGCACTTTGCAGTTAGACGAAGGCAATATGGTCAAAGTAGCAGCTAAGATTAAAGAAACGGCTGAGCAACCAGAAGATATGCGTAAGTCACTGGGTATGCTAGGCAAACGCGGCAGCGACGGTTATTTCCCTGTGGTATATCAGGGTCGTATTCCTGGGTTATAGTGCTGTTTCAGCGCGCGATGATAGATCTGCATTATGATGAAAACGAAGGCACCTTAGGTGCCTTTTTTATTGTCTTTTTATCGTGTTAGATCATTGAGTAGCGAGTGATAGTCGGTAATCGCGGGGAAATCTTGAAAGACTTTGACTGGCTTTTTACTGTCTGGGTTAGCAATCCCCAATTGATGGCCTACGCCGGCAAGTTTAGCGGCCTTGAGTATGGGTTCGCTATCATCGATAAAGAGACAGCGACTTGGCTTAAGTTGAAACTTTTCAAATAAGGTTAGCCAAAATTGCGGATGCTCCTTGGGGTAACCTGTTTCGTGGCTGGAGATCATGGCATCGAGCCCTGTCTCCTTCTACCAACTGTACCGACCTCTAACTTAAATGTTGCAGGTGCAACTGCCGGTACAACTAATTTTACTCTTTCAATAACTGGTTGCACTGCGCCAATCGCTGACACACCCGTTAACACATTATTCATCGGTAACAACGTTCAGCCTGGTGGTATTCTTGGCAGTTCAGGTTCAGCTACCAATGTCGGGTTGCAACTGCTAAATGGCACACAATCTATTGATTTAACCGCCCCATCTCTACAGTCCGGTTTAGTCATTGGTGCTGCCGCAACTTCTGCTAGCCATGATTTTTCGGTTAGATACTATGCTACTGGCGCAGCAACAGCAGGTACTGTGGTGGGCTCAGTGCAGTACGAAGTTGCTTACGATTAAACTGCTATTGCTTTTCACCCTAGCCCTTAGCTCACTCTCACCAAGTGAGCTTTTTTAATGGTGCAACTATGAATTTTCCAATTTATTTTTTCTGCTTTTTTTCATTTATTAGCCTGTTCTTTCAAGGTTCTGCGAGCGCAAGCGTAGTGATGACTAATACCCGTATTATTTATCCAAATAACGTTCAAGAAACGACTGTTCAGTTAACTAATAATGATATGTCGCCAAATGTTGTTCAAGTATGGACAGATAATGGTGATGCTCAATCTACACCCGATACAGCTAACGGCCCATTTTTAGCCTTACCTGCAATATTTAAAATTGAACCCAAACGTGGGCAGTTAATCCGCTTAATTTATACCGGAGGAGAGTTACCGAAAGACCGGGAGAGTATTTTTTATTTTAATTTTCTACAGGTTCCATCTTTACCGAAATCGGATAGAGATGAAAATAAAATGGTACTTATGTTAAAAAATAGGATCAAAGTCTTTTATAGGCCGGTAGAAATGCCTTTTAGCTCAGATGATATTCATAAACACATAAGTTTTTCGGTGAAATATGCAGCTAATGGTTTAGTTGTCGAGGTCGATAATTCGTCTTCATTTTACGCTTCTTTAATTGATGCGAAAATAATTTCTGGTAAACAAGAGTTAATAGTTCCATTAGATATGGTAGAGCCAAAATCTAATATGTCAATTGAAGTAAGGAATGAAAATTTTGTTTTAACCTATCCGATGACAGTTGAGTTCATATTAGTAAATGACTTCGGTGGACATGTCAAAAATACATATATAATTGATAAGCCTGATAATTAGAGTGAAGATAACTTATGAAAGACTCAATATTATTAAAGTATAAATCATTTGGTGTGTTCCTTATTGCTCTCGTAACAATAGTAACGCCACTCGTAGTTGCAGATGAGTTGTCAGAGGGTAATGATGAGTCCCAAGAGTTTATATTTGATGATAGCCTTTTGTTCGGTGGTGGGTATGGTGAGAATTACCTTGCACGTTTTAACACAGTGGAAAAAATCCTCCCAGGTAGATATCAAATTGATATCTATCTCAATAATGAACGGCTAAATAGACAAATTATCAAATTTGTTGCTGAAGACTCTAAGAGGGTATTCCCTTGCTTAGATGTCAGCTTTTGGCGTGAAACGAATATTATTTCAACCTTTATTGATGAGACATTAATTGAAAATGGTCAGTGTGTATTACCCGAGAGCGTTGTGAGAGGGGTATCGACAAAATTTGATTCTGAACAGTTAAGATTAGATATAGCTATTCCGCAGGCGTATTTGCAGCAGACGCCGCGCGGATTTGTTGACCCAAACTCATGGGATGCAGGAGATACAGCTGGATTTATTAGTTATAACAGTAATTTTTATCAGACCACAAGTCGTGGTAATGGTAGTGATATGCGCACTTTTTACACTGGGCTAAATACAGGTGTTAATTGGGGTTTATGGCGACTAAAAAATCAATCGAGTTATCACTATAATGAGGTTAATCAAGACTCTCAAGACAGTTTTAATTCCATTCGGACATACGCCATACGTGCATTACCAGACACCCAAAGTGAATTATTGCTGGGTGAAGGTTACACACGAGGTAATGTCTTTGGAAGTTTAAGTTTTACAGGCATTCAGCTTCAGACTGATAATCGTATGTTGCCAGATTCACTACGAGGTTATGCTCCAGTAGTTAGAGGATTAGCGAATACTACAGCTAAGGTTGTAATTAAGCAGAATGGGGTGAGCATATATCAAACTACGGTGGCAGCTGGTCCTTTTGCTATAAGTGATTTATATCCTACAAGTTATGAGGGAGATCTCCTTGTTGAAATAACAGAAGCTGATGGCAGTATATCGAGTTTTAATGTGCCATTTAGTGCTGTCCCTGGTTCCTTACGTGAGGGGCAGTCCCAGTACGCATATTCGCTCGGTGAAGTATCCCACTATGGTTCTGGAGGTTATTTAGCTGATTTAACCTATGAGCATGGCTTGAGTAATGCATTAACGCTTAATACCAGTGGTCGGATTGGCAAGGATTATTTTGCCCTAAGTATAGGGACAGTTTTGGGAACTGAGTGGGGAGCATTTGGCTTTACTACAGTTCATTCAAGTTCGCGAATTGCAAAATACACAGAATATGATGAATGGAAGAATGGCTGGCGTGTTGGTGTTAATTATAGTCAATCATTCGATAGTGGAACTGCGGTTACTTTGGCCGGTTATCATTATTCAACAGAGTCTTATCTAGAGTTAAGCGATGTTCTAGGTTTAAGGGCTATGCTTTCAAACCAAGATAAATTTGTGTCAGCCACTTATCTGCAGCAGGCAGAGATGAGTATAAGCCTAAATCAGAGCTTAGATGATTTAGGTTACATTTATATCAGTGGGTCAAAGCGTCAATATCGAGATGGTCGTGCTGACGATGACCAAATTCAACTAGGATATTCAGTAAGTTTTGGCAGCATTAACTTAGGACTCAATTTTTCGCGCCAATATACCAGTGAGTTTGCAACACAAAATAGTTTTGCTGACTTACAAATAGATAAAGTTGATACTTATGATTCAAATAGGCAGAAAGAGGATTTATGGAGCCTTACATTTTCAATGCCTTTTGGTAGCACTCAATCTAACACGCTAAATACTGGTATTAGTCGTTCTTCTAGAGAAATGAACAATTATAATCTAGGCCTATCTGGTGCAGTTACAGATGATAAAACATGGGTTTATGGTTTAAGTGCTGCAAAGAGTAGTGCCAATAATAGTTTGAGTATCAATACGCAGAAACGATTTAATCAAGCAGCATTGACTGGGAGTCTCTCTATTGCAGATGAGTATCAACAGGTTAGTACAGGTATTAGTGGCGCAGTAGTGGCTCATTCTGGCGGCATTACATTAAGTCAAAACTTAAGTGAAACATTTGCGATTATTGAGGCTGAAGGAGCAAAAGGAGCCAAAGTGACTAATAGCTGGGGGACGGAGATAGATAGTGCAGGTTATGCAATTGTCCCCTCATTGACACCTTACCGTATCAATAAAGTTACGCTCGACACAGGTGATATGTTAAGTAGTACAGAGCTTTTAAATACACAACAACAAGTTGCGCCCTATGCAGGGGCCATTGTAAAGATGAAGTTCGATACACGGTATGGTATTGCCGTTTTATTTATGACGAGCTTAACTGATGCAAGCGTTATTCCAATCGGTGCCGAAGTGAAAGATCTCGATGGTCACATTATAGGTTTAGTCGGACAAGCGGGAATGGCTTATGTGCGGGCCCCTCAAGCGAACGGGCAACTTACTATAAATTGGGGAAATAAGGTTGGGCAACAGTGTCGTTTTGACTATGATTTGAGCATCAATGACAAGTCCCTGCAAAGATTTCCTGTTAAATGTATTCTGATAAATTAAGCGGTGAATTATGTGTAACTCTACAAAAAATATGTTAAAGCTAGTTTGCCTTTTTTTATTTTCCCAGTTCTTTATCCCTAGCGTAATGGCAAATTGTACTCGATATAATGACCCAAACTTCGGAGTATGGAGTGGTGCCTCAAATGAAAATTCTGGCATGCTTAACTTACCTAACGTAAATATAACAGATGAAAATTTTCAGCCAAATGGTACCCTTTTAGCCTCTAGTGTTGTGCCTTTTAGCACTTATGGAAAACAAGGTGGTTATGCACCTGAGCATATACTTTTTATCTGTGATGCAGTAGATGTTGGAAACATTTATGAGGCTTATGCAACTAATGGAGATAGTGCATATGGTGGTATGTATGAAGATGGTGCTGCGTATGGTATAAACAGTGCCTATGCGACCTACATGAAGAATCTCGCTATTCGAGTCAGAAATAATGAAATGGGAAATTATTTTACTCGCTACTGGCAGTATCGTCCTATGAAAAATCTAGACCGTGATTCAACAGGGCGAATTCTAATAAAAGCTAAAAATTTTACCGATATTACCGTCGAATTATTTCGAATTGGTGATACTCGTGGCTACATAATATCAGATCAATTACTTTATAATTATAGTCAACCAGCGGCTTATCTCGCCTTTGTAGGACCTAGCGTGACGTCCCCGACTGAAGGGAGAGATAGTGCAAGTCATTATCCTGGCTGGTACACTAACTGGCCTGGCAATATCAGTTTGTATAAACAATTATATATAAGGAGAAGCGGAGGCTGTCAGGGCCGGAGCATACTTTGTTAAATTGATTTAACATCTGCAAAAATCATCAATTCCCGATATTCATCACTGAGTAAAGAACGATGTAACTTACTCTTCATACTGCGTTTTTGTGTTATTTCGAGTTTTGCCAGATTCAGGGCCATTCGCCTCACTATCGCCAGTGATTCGGCAGTTCTTTGCTCGTGAACCCGACAAGCATCTTCTCGATAAACCACATCCAGGACCCAGTGCAGACTATTCTCAATTCGCCAGTGCCCTCGGACAGCAGTCGCAATATATTCGGGATCGATATCATGGGATGAGAGATAGAAACTGGTCTCTATGCTGGTCTTATTGCCTATTTTTCTGTGTCGCTCAACGGCTATTAATGTCTTAACTGAAGGCCACTTTTCTTGCAATTTAGGACTCAGCTTCGATGGGATTTGAAATGTAATCCGCTGCTCTGTTCTTCCATGCCCTTTTTCCGAACAGGCAAATTGGGCTAACTGACTGTCATCTTGGAAGGCCACATCATACTGCGCTTTAACTGCTTGAAATAAAGTCCGTTGATTACTTTTCACCTGCACCACATAATCTCCTTTACGAGCGACTATCGCTTCCAGCGTCGCGCGTTGGGCATGCAAAGCATCCATGGTTATCAATGCTTTATTTATGTTAAGCATGGTAATAAGTGATTGTACCGTTTCAATTTCCTTGCCTTTTCCAGAGGCCATTTCTTGATAAAGCGCCAAGCCATTATTAATGTCGAACGCACCCACGGAATGTAAGGTGTTGCACCCTAACTTAGAGGCACCTCTGAGTGTTTTACCATCGATAGCAATGATGGGTTTAGCTGATGCGGTGCGGATGTCGTTAATCCAACTGAATAGGCACAACTGCAAATTCTCCGGCCCTACGGCTTTTAGGATACGTGCTATGGTATGCCGACGGGGAATGCCATTAGCAAATGGACGAAACTTACGTAGCCACTCAAGTTTTAGCTCGCCGAACTGTTGAATTTCTGCCCAACCATCTTGCCCACTGGCTACTGCCGATAAGATTAAGAACAGTACGTCGACAAGGTTGTGGCGTTGATTAATGTGTGAGCGCGTGTCTTCGACTAAAGATAGGTGATCAAAAAGTGACATGGAGAGTGGCCAAGTAATTAATTTCTCATTCAGATCATGGCATTGCGGAATAGTTCAATTTTTTTAACAAAGTGCGATCTCGCCCTGCGGAGGCTGTGCGATTACAAATGTGACACCCTATGTGTTATTTCCAAAAATTACTCTTGCTGAGTTAAATAATAGTCAAACACGAGACGTAGATATTCAGATAGACTATCGTTGTCAAGCGAGTGCTACTATCAAAGGCCCAAATGTGGGGGCTAATGCCATTATGTTTAAAATAGATAGCGAAAACTATCATAAGGCGAAGGCGCTAGGACTAGTAAGTGGTGACGCGGTGTCGTTTTTAGTGACAAACTCTTATGATACCAACATGATGTCTAAAGGTGTTGGTGTTAAGTTGTCACGAAATGACCAGATGGAACAACCATTTTCCTACTCACCTAATCAGGTTAGCACAAGTGAGCAATATAGTGCAGGATGGAGACCATTGCTGGGTAATAAGGTCGGTAGTCTCGATGGTAATACGCTTTTTTCAGATACGTATAAAGCTACTTTAACAAAGTTACCAGGGATTTCACCCACTGCAGGGAATTTTTATGCAAAAGCTGAAGTTATTATTCGGGTTCAGTAGTTTTTTTCTATGTTTAGTTTTTCAACAAAGTGCTTTTGCTGGGGTTGTCGCGGATAGAAGTAGAATAGTTTTTAATGTTTCAGACAGGGAACAAGGGCTTTCATTCATTAATTTCAATAACTATCCCGTAATGGTTCAAATATGGGTAGATGATGGGGCGGTTAATTCAGGTCCTGATGTTGCAAATGCACCAATTCTACCGTATCCCGGTTTATTACAGTTTATGCCTAAAGAGCAGAAGCGCATAAATTTGCTCAATATCTCTGAGATAGCACCAAAAAAACAGGAACAATTATATTGGTTGAATATTTATGAAGTTCCTCCTAGGCCAACGGAGAGTATTGATGAAAGGGATGGGCAGCTATTAGTTGTCGCAGTGAGAACACAGATGAAGGTTTTTATTAGACCGAATGATCTCGTCATTAACGTTAATGAGATTCAAAAATTTCAAAAATTTTCGATAGAAGGGCGAACTATTAAAATCGAAAATAATACTCCTTATTTTGTCACTTATCAAGATGTTGTTGTGAGCCATAAAGACATTGAACATTCTATTTATCATGGTATGTTGTCACCGTACTCAGTTGAAACAGTATTGGCACCAAATAGCCAGGGCCGGAGCATACTTTGTTAAATTGATTTAACATCTGCAAAAATCATCAATTCCCGATATTCATCACTGAGTAAAGAACGATGTAACTTACTCTTCATACTGCGTTTTTGTGTTATTTCGAGTTTTGCCAGATTCAGGGCCATTCGCCTCACTATCGCCAGTGATTCGGCAGTTCTTTGCTCGTGAACCCGACAAGCATCTTCTCGATAAACCACATCCAGGACCCAGTGCAGACTATTCTCAATTCGCCAGTGCCCTCGGACAGCAGTCGCAATATATTCGGGATCGATATCATGGGATGAGAGATAGAAACTGGTCTCTATGCTGGTCTTATTGCCTATTTTTCTGTGTCGCTCAACGGCTATTAATGTCTTAACTGAAGGCCACTTTTCTTGCAATTTAGGACTCAGCTTCGATGGGATTTGAAATGTAATCCGCTGCTCTGTTCTTCCATGCCCTTTTTCCGAACAGGCAAATTGGGCTAACTGACTGTCATCTTGGAAGGCCACATCATACTGCGCTTTAACTGCTTGAAATAAAGTCCGTTGATTACTTTTCACCTGCACCACATAATCTCCTTTACGAGCGACTATCGCTTCCAGCGTCGCGCGTTGGGCATGCAAAGCATCCATGGTTATCAATGCTTTATTTATGTTAAGCATGGTAATAAGTGATTGTACCGTTTCAATTTCCTTGCCTTTTCCAGAGGCCATTTCTTGATAAAGCGCCAAGCCATTATTAATGTCGAACGCACCCACGGAATGTAAGGTGTTGCACCCTAACTTAGAGGCACCTCTGAGTGTTTTACCATCGATAGCAATGATGGGTTTAGCTGATGCGGTGCGGATGTCGTTAATCCAACTGAATAGGCACAACTGCAAATTCTCCGGCCCTACGGCTTTTAGGATACGTGCTATGGTATGCCGACGGGGAATGCCATTAGCAAATGGACGAAACTTACGTAGCCACTCAAGTTTTAGCTCGCCGAACTGTTGAATTTCTGCCCAACCATCTTGCCCACTGGCTACTGCCGATAAGATTAAGAACAGTACGTCGACAAGGTTGTGGCGTTGATTAATGTGTGAGCGCGTGTCTTCGACTAAAGATAGGTGATCAAAAAGTGACATGGAGAGTGGCCAAGTAATTAATTTCTCATTCAGATCATGGCATTGCGGAATAGTTCAATTTTTTTAACAAAGTGCGATCTCGCCCTGACCAAATAGCGACGTTATGAGCCCTAATACTGACATAATTAAGGCTTTCTATGTCGATGATGATGGTAATGTTCAGCAGTTTTTATCTCGTTTAAATTAGTTAGCCGATTATTGATGTTTGAGTTACTCCACTGCGGCTTTAATTTAGTGAGTATCTAAGTAAGCGATGATTTCTTTTTCTAATGAAAAGATAGCAGTGTCAAGCTGTAAAATATATTCTGGTAATTTTTTGTTATTATAATTTTTTTCTATATTTTCACATGTAAATGCTAATCTTTTTGCATTAATGATTCTTGCTGCACCTTTGATCTTGTGAGCAAGATCTTTCACTGCCTTTAGATCCTTGTTTTTAATTGCTTCATGTATAGATATTAAATCACTCTGATTACTTCTGAGTAATTCATTGAGCAGTTTACTCACTAGTTCAGTATTGTTTCCGGTTAACTTTTCAACAGCTTCTGCTTCAAAATGAGTCGCATTAATAATTTCTTGCTCAGGCGTAAAGCTCTTTAGCATTATATCAAGATCAGCAAGACTAATAGGCTTAAATAGGCAGTCATTCATGCCTGCTTTTAGGCATGCATCTTTAGCCTCTTTTTGTGCATTAGCAGTGTAACCTATAATGACTGGTGTATATAATAACTTGTCTTCTAACTGCCTAATGCTTCTACACAATTCATAACCATCCATTTCAGGCATATTACAATCAGTAATGACAATCTGGTATTCTTTACTCTTAAACATGGCTAATGCCATTGCACCATTTTCCGCCTCATCTACTTTATGACTAAGATAGTTTAATTGCTGTGCAAGGAGTAAACGATTAGCAGGGTGGTCATCAACAATGAGTATATCTAATGGTGTCATTGTGAATGATTCGGTTTGAGTCGTTGTGGTAACTCTAGCTGAAAAGTCATCTTCTAATTCGGGTAACGTCAGTATCATGCTAATTTTAGTCCCCTGATTTGGAGAGCTTTCAAGTGTTAATGTTCCACCCATCATTTCACAGAGGGAGTGACATATCATGAGTCCAAGTCCAGTTCCTCCATAGGTATTATTAGCACCATGTGCTTGGCTAAAAGGCGTAAACAATTTCTGTTGGTCCTCTTCTGAAATACCTATACCAGTGTCCGTTACTGTAAATAAAAGGGTGCGTTTTGCATGGACTAAGGTTTTAGATTGAATTTTTACTTTTACTGAGCCGTTATGAGTAAATTTAATTGCATTACCAATTAAGTTTGACAGTATTTGTTTTATTCTAATAGGGTCAACGAGAATATCCTTAGTGAGTTCTGGTGTGAAGTCTAACTGTAAAGAAATTCCTTTCTCTCTTGCAAGACCATCAAAGACTCTAACTACAGATGTCGTAATGTTCTTGAGATTACTTCTGACAGGTGAGAGATAGAGTTGACCCGCCTCAATACGTGCAATATCAAGAATATCACCGATCAGCTCAAGCAAGCCATTAGCGGAGTCATAGGCAACTTTAATTGAATTGATATCGAAATGTTGAGATTTTGTACGTTTCAAAGCTAGTTCAAGCATACCAATAATGGCATTCATAGGTGTACGAATTTCATGACTCATAGTCGCTAAGAAAGTACTCTTTGCTTGACTGGCACTATCTGCAATTTCTTTTGCGTACTCTAATTCTTCAACAAGCTTAACTCTGTCGCTGACATCTATCCAACCACCAATAATACCTTTAATATTACCAGCTTCATCACTGTATGATTTAAACCAATGATAGATGTTGTATTTTCTTTCATCTACGTGTAGTACTCGATCTCTAAATATTGGAATGCTGTCTTCTAATACCTGTCTATATTCACGATCGATATCAGGGGCTTCTCGTGAACGATTGGCACCTTCGACAGTTGTTTTATTAAGTACATCTTCGCGTGTAACATTGAAAACACTGAGGTAACTTTCATTACACATAGTGAGTTCATGTTGTTCATTTCTTACATAAATAGGATGGGGTATTGCATCAACAATGGATTGCATAAATTTTAGTTGTTGATCAAGCTCTTTTTTAGCTAATAACCGCTTACGATAATGGGTATGAGTATAAAGCGCCCAAATTATTGAAATTAAGATCATGATCGTTGAAGCAATTACAATAGTATAAATAGTGTACTCGTAGTCCTTCCAGCTTTGACGTGCAGGCAGAGCATTGCGACGCCAGCGATTCTCTGCCATCTGTAATTCATCAGGAGGGATGACCGTTAGAGTTTTATTGAGGATACTTTTAAGTAAATAGTTTGAACTTTCCATGGCAAATATTGCTGATGCACTGGGCACTTCATCTATTATCCTATTGACTTCTAAAACATCGCTGAAATAGCTGTCTACATAGAAATTAGCTAAAGCAAGTGGTGTAATAGTCGCATTTGCTGATCCTGTAGCTACATCATTGAATGCTTCTAGATAATTATCTGCGATTGTGATGTCTGCATTTGGATTGATTTTTCTAAGCTGGGAGTCACTAAATACTCCATCAGGAACTACAATTGTATACGCATCTATATCATCTTGTATCTTTTTTATTTTTTTTTGTGTTACAAGAACATAAGGTGAATTTGAAAACTCTTTAGTAAAACTAATGTTTTTATTAATATCCTCATTGGCTGAAAATATTGCTAGCATAGCTTTATGGTTAAGAATCGAGTTATGCAGTTCAGAGAATCTGCTGTTGATAATATAGTTAAATTTAAGTCCAGAATACATTTGTATTAAATCTAAAACTTCAACACCATATCCTTCAACATCTTTAGTGTCATCTAAGTAAGTTAGAGGGGCTGCGTATTTATTAATGGCTACTGTAATGATAGGGTTATTTTCTAACCAAGTAATTTCATCAGCAGTAAATGTGGGTAGTTCTGAGGTAAAAGGAATTGTACTCCCGCCGCCACTCCAACGTTTTTTAATTAATTTTCTTTGTTCAAGTGAAATATTACTAATGGCTTTATTAAAGATGCTTATGATAGCGCTATTATCTTCGGAGGCTGCAAATGCATTGCCATAGGAGTTAAGATGCAAGTGATTATCAAGCTTTAGCTTTCCTGAAAAGGTGTTGTTTATTAGATAATTGGCTGACACTTGATCTACTATGACGGCATCCACTTGCCCAAGAGCTGCAGCTGCAACAGCAGCTTGTCTCGATGAGAATTTTAGCAATTTTCTATTTGGGTAGAGTGCTGTTATTGCCTCATCAGGTAGATATTCATAGGCCATAGACATCGATTTTATGGGTTCGCTTATCCCTAAATCGGCATTTTTGAATATTGTGGGTTTATCAATGGTATAGGGGGCGCTTAATAAAAGCCCATGTAGTTGCTCAAATTTATTTGCTGTAGTGATGAGATCCACGTCTCCGTTAAGCACTGCAGTGATTAATTTCTCCCGAGACTTATACCGCTTAATATTAACATTAATATTGAGCATGCTGGTAATAATATGAAGATAGTCTGCACTCATTCCTTTATAATACTCTTGTTCACCATTAACTGTCATGTCAAATGGGGGGTAGTCGGGAGCAGAAATACCAACGATTAAGTCACTATAAAGGCTAAGTGTTTTTCTTTCTTCGGGAGACAGATTTATTTTTTCATTGATTTTGTCAGGATAACCTAATAAATGGTAGTGATGATAATTTATCTCATGTGCAAATGAATTGTGCACTACAATTATTGCCATTAGAATTATAAAAACTCTCATATTAAGCTTTGCCTTTTTGCAATTTCAATTATATCAACTAAGGTTCTTGCATTTAATTTATCTAGTAGCCTTGTCTTATATGTGCTAATTGTTTTGTTACTTAATAACATACGTTCAGCTATTTCTTTATTGGATAAACCTGTTGCTAACTGCTGTAGTACAACCATTTCGCGAGTAGAGAGCTTTTTTATTAATTCGGCCTCATCCTGACTTGATTTTGATATTATGTTGCTATTACTTTGAATGGGAAAATAGGAATGACCTGTAAGGATTGCTTTAAGTGCCCCTGTAACTTCTGTCATATCCTTTAGTTTAGAGACAAAACCTGATGCTCCTGCTTGAATGCAGCGCGAGACAAAGTGTTCAGAGGGTTGTGCTGTTAATACTAAAATAGAGGGACTATTTGGTAGTTTTCTTGACCTTTCGATCACCTCAAGTCCATCTAATTGAGGAATACCGATGTCGAGAATGACCGCATCTGGAGATAGATTTTTAATAAGCTTTAGTGCATCAACTCCATTGTTAGTTTCTGCGATAACTTCAAAGCCACTTTGTTCCAAAATGATTTTTAGTGCTAAAACGACAACCGGATGGTCATCGACTATCAATATTTTTTTTTTCATTCTATAGCTCCAAAAAATATTTTATTTAAAGATAGATTTTATCTCTCGTTGCCATCAGTGCGAAATAAAAATGTGCGCTTAGGATTTTTCCTACATCATGTTAGGTAAATTCTTTTGAATAATACTCGTATGATAATTGGTAGCTGTAATTTATTTGTTATATTTTTTTATTTTTATCACTCAAAAATGTTGTGCTTTGAATTGAGTTAAGAATTATCTGCAACATACAAAATTATAGTTGAATAGCTGACTATTTCTATCTAGGAATATCATTTTTTATAGCAAGGTCACAGATAGCGTATTTATGATTGTCATATTTTATTTTTTAAAGGATATTCAATGAGTAAGATTAGAGTGATGATCTTAGAAGATCACCCCTTCCAGCGGGCAGTGCTTGAGTATAATCTTGCGTCTCTTGCTGATGTTGAGGTGTTTGCTTTTGGAGCCGCTAAGGATGCGCTTGTTTGGCTTGATAAGCATCATAGTGCTGACATTGTAATATGCGATCTTATGATGGCAGATATTGATGGTCTATCTTTTCTACGTAAAGCGAAGGCCAGTTATGACATTGCTTCAGTGGTTTTGTTTAGTTGTATTGATCAGGAACTCAGACGTGCAGTATCACAGATGATTAAAATGCTTAATTTTGAGTATCTTGGTGATCTGAGTAAGTCTCCTTCAGTAGATAATTTACAGCGTATGCTAGATAGATTCGCCTGCAGTCGAACCAAGAAAACTGAGGCCGCTTCTGATTTATGCATGAGTAATAGTGTCGATAAATTTGTAGTTGAAGACTTTGAACGCGCTATATCTGAAAGGCAATTTGTAGGCTTTTATCAACCTAAGTTTGATGTTGCTGGTTTTAGTTTGAAAGGGGCTGAAATGCTTGCTCGCTGGCATCACCCTGATTTGGGATTGTTAAATCCAGGTAGCTTCATTGAACCGCTCATTTCACACGGCTTACTTGATGAGTTATTTTTACAACTTTTTGAGCAAGGTTGCAGCCTGCAGGAACGCCTGCAACAGCAAGGGTGTCAGATTTCCTTGGCCTATAACCTCGACATATCTCAACTCAATTCGACTAAGTTAGTGTATCGAATAGTTGATATGGTTAGGGAATTTAGCATTCTGCCGCAATTTATTACCCTTGAAATTACTGAGACAGGACTTCTCTCTGCACCAGCCGTAAATATGGAAAATCTAATCCGCTTACGAATGCAAGGATTTGAATTAGCAATTGATGATTTTGGCGCCGCATATTCTTCATTGGCTCGTCTATGTGAATTGCCTTTCTCGCAAATTAAATTGGATGGTTGTTTTGTGCATGCACTTAGTGCTGAGCCTAGATGCCAAGCTGCAATTTCCAGTGTCATTGCATTGTCTCACGCACTTAATATGGAATTAGTGATTGAAGGAATTGAAACAAATTCGCAATTGGTATTACTGCAACAGCTTGGGTGCACAACTGGTCAAGGGTATTGTTTTTCACGGCCAGTCGATGAGGAGACATTTATTCAGCGCTTTTTATCTGCAAGTTTATATTCTGATTCTTCAAAAGAAATCAGCCTCGAACTTTAGTGATAATGAACCTATTTACGGTCTCGGTTATCAAGGCTTTTCTGTTTACTTTCAGCCCTGCCTGCTCATGATTTTCTGGCCATACATAATATTGTCTTGGGCGTTTGAAACGGGCGATTTTGTCGGCGAGTAACAATTCGAGTTCACTTGCAATCGATTCTGTGTGATGAGTTAAACTGCCGCGAAGGATAGCGGCGGGGAGATTGCCAAATTGTGCATCGGGGATGGCGAAAACGATAGCATCATCTATCTGCGGGTGCTGTTTAAGCGCGGCTTCGATTTCTTCTGGTTGGATGTTTTCGCCGCCACAGATGAACATGTTATCGACGCGGCCAAGGATGTGTAGATTGCCATTTTCGTCCCATTCGCCCCTGTCTTTAGTGTAAAACCAGCCTTCTTGATCAACGCCAGCTTGTATTCCCTCAGGGGTGAGGTAGCCCATAAACAGGCATTCGCCGCGCACCCAAATCACATCGTCTTCGGTTTTAAGCTCACGCTTGGGGAGTAATTTGCCACTGCTGCCGTCGCTTAATGCGGGACCTGTGGTGATCTGCGAGCCCATTTCTGTCATGCCGTAACTCGTATAGCTGGCGATATTGCGTTGCTTAAGCTGCGCGAGTAAGTCTATCGAGACTGCGCCGCCGCCCAGCAATAGGGCTTTGATACTTTTAAGTTTACTACTTGTATCGGCTAATAACTTACTCAGTTGTGCTGGCACCAAAGAGAGGTGGGTTAGCTTATCTTGGTCAATTTGCTGACTAAGACTCAGCGCGTGATCTGGCATAACAACAGTGGCACCGACTAAGGCACAGCGGTTTAATATCGCTAGACCGCCAATGTGGAATAATGGCAAGGAGAGCAACCAAGCATCACCTTGTTCGAGCGCGATTAAGCTGCGAGCGCCTTCGGCATTAGCAATATGGTTGGCTAAGCAATGGACCGCCGCTTTTGGAAAACCGCTAGACCCCGACGTTAAAATCACATTGGCGGGTCGGGTAACATCGATAGGCTGGGCTAACTCGTTGCTAAGGCCATTAAAATCTAAGGCTAATTGGCTGCAATTCTGCAGATCAGAAATCTCAGCACGCCAAACATAGCGGATCTGATGTTCATCGATTAAACCTTGGATCTGCGCGAGAGGAAAACGGGGTGAGATAGGGAAAAAGATCGCCCCGATATCGATGCAGGCCCAATAGAGGCAAATCATCTCGAGGTTATTCACAGCAATACAGGCTAGTGGCTCACCCTCTGCAATGCCTGCTGCTCTTAGCTGCTGACCCAGAGCGATAACGCGCTGGCTAAGCTCAGAGTAACGAATGTCTTGTCCCGCCAACTTGCTGGCCGTCTGCTGTGGAAACGCCAGCGCGGCTTGGTGCACGGGGGACAGTGCTAGCGACTGCGATAGGCGGGTGTCAGACAAACTAGTTTTCCTCACGCTTGGCTGCACCCAAATCACTTTGCTGCGTTGAATCAATGGCGTGTTGTGCAATGAGTTCAAGTTGGGCGAGTGTGAGGCAGTGTTTTTTACCCGATGACACGATTAAATCTTGGCTAAAGGCGCTGAGGGTATCGAGTCCTGGGATTTCATCGGGCGTGAGAATCGCGGCTAAATGGGCCAGATCGCTAATGCCTAAACTGCTTTCAAGACTTGAACTGATAATGCAACGCACACCATAGCTTTGCGCCGTATCGATTAAGTTAGCTAGCTTTTCAATACTGCCGAGCAGCATAGGTTTGATGATGAGCGCAGTCAGGCCATTTTGCATCGCAAACTGATAATCGGGATCGTTGAGTGATTCATCGAGGGCGTAAGGCATACCAATTGCTTGATAAAATGCCAGATTATCCTGTGGATTCTGGCAAGGTTCCTCTATGTATTCGATGGTGTCCAGGGGTAGGCAAGCAGCAAATTCGATCGCTTGTTCCAGTGTAAACCCTCGGTTTGCGTCTAAACGTAGCTTGAGATCAGGGCGGGTGCGTAAAATGCCATAAATCAGGCTCAGTTCGTCTTCGACTGAGGTCTGCGCGACTTTAACTTTCACCGAATGAACATGCATGCCGAGCGCCGCAACTTTGTTATCGAGCAAGTCTTTGCCCGCATCCGTTGGATGATAAATCAAAGGGACTGTGGCGGTTTGTGGGCGGATAGCATCGAGCTTACCCATTAACTTGGCATGCAACAGACTTAAACCAAAGGCCAAGGAAGGGTAAGGACTTTGCTCGGCGTAATCCAGCAGGGTATCGATATGCAGACCGCAGAGTAGGGGTAATAACTCAGTTAACGCCGTTTGCACTTGAGCCAGTGTTTCGCGGCTAAATCCCACTAAGACTTGTTCATCGATATCTATGCCAGAGAGTGGGGCGATTTCAACTTCGACTTGTCTGTATTCGCCACCAACATTAGCGCTGGCGCAGGCTTGCAATACTAATCCAGCCCTGTGGTCGATGCGTTGCTTGCCTACGGGCAATAATACATCGAGCGGTAGTCGATATTGATACAGGTTAAGCGAAGTTAAGATCATATATTTCCTACATTTTAATTGTGTTGTTTACCCTAGCAGCTTAACTATTGTTGCGATTAATGCCTCGGGAGCTGCTTGGTGTATGTTGTGTCCGGCGCCGCTAATACTGTGGATGAGAATGGGCGCGTGTAATTGCCACTCTTGGGCGAGGGCGTGAAACTTGGCATCTTGGCTGCCCGCAAAGAAATGGCATTCACAGGCAAGCTGCGAAGGCACTTCCCACATCGATGCTTGGCGTGCAAGTGAAGTGGCTAAGAATATCTCTTTAAGCAGCTGTTTGGGATGCTGATCTAAGATGGCAGATCTCTGCTCGATTAAGGCTTTACGTTTGGCCGCAGTCATGTCGGCAAACACGGGCTGCTGATACCACAAGTTTAAAAAATCTTTGCTAGCCAGATGCAATAAACGCTCGGCCCACTGGGCATCATTTTTGGTTCTGGCCTTTTTTTCTTGCGGGTCTACCAGCCCTGGATGGCAGGATTCGAGGTTCAGGCTTAACAAGCGCTGCGGATAGGCTTTTGCAAGGTGCAAGGCAATACGCCCGCCTAAGGAATAACCGTAGAGATGAAACGGTTCGGGACTTGGATTAAGGCTATCGAGCCGCGCCATTATGCTTTCGACGCAAAAGTCGAAATCGAGCTTAGCCTCAGTCGCCCCTTCAGCTTGATTATCCCCATGGCCCGGCAGATCCAAACAAATACAATGGAAGTGCTGGCTTAGCGCAGGCATTAACGGCAACCAGTCGGCCTTAGTGCCTAAAAAGCCATGTAGTAGCACTAAGGTCGGTAGTGAAGGATCGCCGTAACGGGCCATGGTTGGCATTAGCTTTGTTTCACCCAAAGGTTGAGCGCGGCGATTTGGTCGCTGGCTTGGTTTTGGCTCACATTGACTTCAATGATCGAAGCCCCCTGAAAATCCAGCGCTTCATTATAACTATCTTGAAAATCGGCTAAATTATCCACTTGATTATAGGGCAAATTAAACATGGCTGCGGCGTAGCCAAATTCTAAGCCGTGGCTGAGTCGATAATAATCACTGCGGACTTGTTCATTGGGAACCGGCAATAAGTTAAAAATATTGCCGCCATCGTTATTGAGGATCACGATCACCAGCGGGCTGGTTAAGCCTTTGGCGATGGCCAGCGAGTTAAGATCGTGCAATTGTGATAAATCACCGATGATCAAGCTGGTAGGTTTGCCTTTGTGGGCCGCAATGCCGCAGGCGGTGGCGAGTAAGCCGTCAATACCCGAAGCGCCACGGTTGGTATAAGTGGTCGCTGTGCAGCAACTGACAGGGGCATACATGTCGTATAGTCTTACAGGGAGGCTGTTGCCGATAAAAAGCTGCTGTTCTAAGGGGCGAGTGTTAGCGATGGCGCGTATCACCTGCGCTTCACCAAATTCCCCTTGATCGATATTACGCACAAATAAGTTATGCAATTCATCGTTATAGGTGATTAAGGTATTCGCCCAGTTCGCCGAGGATGAACGATACCAGTTCAAGGCCGCAAACTGCTCTGCATTGGCATGCCAAATGTGCTTTGCATTGTGACTCGGATCTAGCCTGTCTTGCTCGGGTAACACCTGCCAGTAACTGTGCCAATTCTGCTCGGCGAGATAACCGATGACGCGTTTTGACAATAAACGCCCGCCGAATACGAGTACGCGATCCGCTTCCTGTAATAGATTGCGCGCCCTTGGATGTTGCAGCAGTTGGTCGATATTACCTATTGCCGCGGGATGTTGGCGCAGTTGCGATTGGGCATCGGTTAGCAAAGGCCAGCCAATTTTTTGCGATAACGCGATAAGTTGCTGTGGATCTTGTTCAGGCGTGAGTGTGCCGACAACGATGACGCCTTTACCGTGGACGAACCGCATAATGGCATCGTCACTGGGGCTGCTTAGTTGCTTGCACTTGCCGTATTGGGTATACGGGCGGGCGAGCTGCAGCCAAGTTTGCAGTGGTTTGAGGTAAGGCGATTCGGTATCGAGAATTACGCCACTGAGTTCACTCGGATATAAGGGCTCACGGTACATGCAGTTGATGTGCACTGGACGAGTTTGATTCGCCACCGCTTCATCCACTGTGGTGAGCAACGCCTGCGGTGCGATATGCGCATCCGGCGTTGGCAAATTCACTTGGGTGGCGTAATTCGCAAAAATCCCCGGTTGGACTATGGCTTGATTGGCGCCGCAGCCAAGTAATTCGGGTGGACGATCGCCGCTTAAGACGATCAAAGGTACGTGGGTTAGCCAAGCTTCAACAATCGCAGGATACAAGTTAGCGACTGCGGTGCCCGAGGTGGTAATGATAGCGACCGGTGCGCGGCTGGCTTTTGCCAGACCTAAGGCCATAAAGCCGAGTCCACGTTCGTCAAAATGCAGGTGACGCTGGAGTTTAGTTTGCTGGGCGGCGGCGAGGGTTAACGGCGTCGAGCGCGAGCCCGGTGCCATACACACATGTTGCACGCCAAGACGTGCTAACTCCTCAAGAATAAGGGCGCCCCAAATAAGATTAAGAGTGGCTGTATTTTCGGTCCGCATAGCTGAATCTATTAGGATAAAGATGGGCTTAGTGTAACGTGTTTATGCCCCAAGATGTATGCTTGGCTTAACAATTCTCGGCGGGTTTGTATGGGGGCTGAGGTGGAGCCTAAGCTAAGGCAAGGGATGCCTATCACCTCTGTACAGTGGCTAGTCTATGGGTTGTGTAAGCTGCTTTGGGCTAAATAATATGGTCAACAATCTTTTTAATCTGTCCACTTAGCCACACTAAATCATCATTCACTTTGCTTGGCTGATTTTTTTGCACTAAGAAAATGGTTGGCGCTTCGGCATGAGCATATAAACGTTCGGCTTCGAGTTGCGACAGTTGGCATGTGTGCAGTTCAGCCACATCACTTACCATCTCGTAAACCACGCGATAGGGCAATAACGACAGTGCCTGACTTTCACCTAGGTATTCAAACAAACTTGAAATACTGGTAATAGTAATTTCAGTATTGAGTTTAATGTTGCTCTTCAGACAAAACTCTTGGTAAGGACTGAGCTTAGAACTGGGATTACCGAGGTCGGTATTCACATAGGGATATTCTGCAATCGTCTCTAAAGTGATTTCCCGTTTGAAAATCGGATGATTACGGCTCGCAAGAAGGTAAACCTTATCCATGCTTTGCAGAGTGGTCGTCAGGAGTTTATCGCCAAAGGCGGCCACATCAGCCGTGCTTTTATTGCAGCAAATGCCCAGCGTAGTATCACCGTTAATAATGTCTTGGGTCGTATGCATAGACCAAGGGGTGATATTAATGTGCATCAGCTTCTGTTCATCTTTAATGGCCTGCATTAACGCTTTCGGAAAAGCGTAAGCAATCAAGCCGGGAGCAGCAATCTCGATATGCGTTTTCACTAAGTTAGCATTTGCGGCTCTGGCCTTATGAAACTTATCAGTACATTCAATGGCTTCTTTCGCGATTGGATATAAATGGGTCGCAAACTCATTGGGGAATAGTCCGTATTTACGACGAATGAAAAGTTCATCACCAAAAATTTCCCGTAGATTTTTAAGGCTACGACTAATTTTAGGAGCCGGAATATCAAGAGCCTTAGATACGTATGTTGCAGACTTGTTCTCATATAATGAAACGAGTATTTTCAAACTCAGAATATCTAGGCCATCCATGTTACTAATGTGCATTTTTTATATCCCTTTTATTGAGCAAATACTTTAAAGAACGATTAATTATGGCGTCATTAAATAAATTTAATGTGAAACATATCTCAACTTCAAAACTATAATATCGCAGTCTGCTAGTGTCGTTTTTAGATTTAATATTTTTTATATTTCAAAATGTGATAAAGATAAATTTGGAATTTTAAAAGGTGAAATTCATTTAATTGAATTTCACATTGGCGATTATCAATTATCTATTTTTACATTCAAGATTTGCGCTAAATGGTAATGAGATCGAATTCTTAACTTATTGTTTATTATTAAATTGCCATTGTTAAATGTGATCGTGTTCAAAAAAAATAAAACGCTTCACGTTAATACTAAAGTTGCGTTAAAAGAGATAACAAAAAATAAATAGGTTATTGGAGGCGTTATGAATACTTCAGTCATTCTCAGCTATGCGTTTATGTATTTCGTGCTGTTTGTAGCAGTGCCATTTGTTTTTGTCATGTTCGTCATGACCTGCATTAAGTTTTTTAAAGCAGGCCCCGATGAGGCAGAACATCATGAAATGAAACTTAAACCAGTACTGTTCGATAAACAAAGTTTTAACTTTTTTAAATCGTTAAAAACCGTAGTGGAAGGCAAATACGATGTTTGCTGTAATGTCCCGCTAGACACCGTATTTGAAATGGACCAGCATGACGCTGATTTAAATCACGAGTGCCGACTAGACTATGTGTTAATAGATCATGACTCATCTGAAGTGAAAATGGTGATCAGTGATTTTGGCAAAGAACACAACTCGCCAATGAAAAAGCTGTTTAGTAAGTTCAATATCAACTTTATTGAAATGAACCAACATAAACAGTGGGATACACAGAGCCTAAAGCAAGCGTTAGCCGTCTGATCCGACCGTCGGGTTCGGGTAACTCCGAACCCTGACACTCCTCTTCTCCCTTCCCAAAATAAATGTGAGGACTCCCTATGTCCAAAAGACAATGGCTGTGGTCGATTCGCTTGGGTGCAATTGCATTGCTTTTAAGCTTTGGTGCGACAAGTGCCGAGTCGGCCTTTCAAATTAACAGTGACACGACTTGTATGAAGTGCCACAAACGTAACGGTCAAATGTTAGGGCATCACGGCCAAGATGCGATGAAAATGACCTGTTCAACCTGTCATGGTGAAAAAGGCGACCACCCTAAAAAGCCGAATGATATCGTGGTGTTTGGTATGAAAGATGGCTCGGAGCTTGATACTCAGCTCGCCGTTTGTCACAAGTGCCATACACAGCGAAAACTGGGTGAAACCGAATGGACGCATAATGTGCACGCACAAAAAGTGCCCTGTGCCGCCTGTCATAAATTACATAGCCCAACCGAACCTATGGCAGACATGACGCCAAAAGTTCGCAGTGAGCTGTGCCGCAATTGCCATAGTGGCCAGTAGGGGTAGGTTATGGAAAATTCAAAAAGAAGATTTCTAAAAGGCACATGTGCCTTATTTGCTGGCGCTACGGGCGCATCACTGCTCGCCACAGTGAATGCCGCTGAAGGTGCTAAAGCAGAGTCAGATATTAAATATGCGATGATTCATGATGAAACTAAGTGTATCGGTTGCCGCGCATGTGAAGTGGCATGTCGTGAAACCAATCAAGTGCCAGAAGGGGTTTCTCGGGTCAGGATTGAACAAACTGGACCGTTCGGTGAGTATCCGAATGAGTATTATCACTTTAGCCGTATCTCTTGTCAGCATTGTGAAGATGCACCTTGTGTCAACGTCTGCCCAACGGGCGCAGCCTTTATTGATAAAGAAACCGGCATAGTGTCAGTCAATGCGTGGAAATGTGTCGGCTGCCAATATTGTATTGCGGCTTGCCCTTATAAAATTCGCTTTATTAACCCAGTCACCCACGCGGCGGATAAGTGTGATTTCTGCCGTGAGACGAATCTAGCTCAAGGAAAACAGCCTGCCTGCGTTGAGGCGTGTCCAACTAAGGCGTTAGTGTTTGGCAATTTAAAAGATCCGACGTCTGAGTTAGTACAAGTGCTGCGCGCTAATCCAACTCAACGCGCGAAAGTCGCCCTCGGAACGCGACCTAAGTTGTTCCGTATTCTGGCGAAATCCGGGGAGGTGATACTATGAGTGCCCTACATTTTGATACCTTAGTCTGGCATTGGCCGATTGCGATCTACCTGTTTTTGGCGGGTGTATCAGCAGGGGCTATGTTCTTCGCCGTACTGTTGAAACATTTTGTTTTGAAGCAAAACGCCTATCAGTCTGGCTTTGTGCGAGCCGCCTGTATTGTCGCCCCAGTCGCCGTGATGGCGGGCTTAGGCATATTGGTTGCAGACTTAACTAAACCCCTCGATTTCTGGAAGATTTTGGTGTTTTACAATCCTAAATCTGTGATGTCGATGGGTGTACTGATTCTGCTGATTTACCAAGTGTTTATGTTTATGTGGATAGGGTTAACCTTCCGCAAACCTGTCGATGCTTGGTGTGAATCGCGTTTCCCTATCGTCCTTAAGCTGACGGCTTTGCTAGCTCGGTTTGAGGCGACGATCACGGGGTTACTGGTGATTCTATCCTTAGCATTGGGTGCGTATACCGGCTTCTTGTTATCGGCGTTACCTGGTTATCCCATGCTGAAAAATCCTGTGCTGCCTTTACTGTTCTTGGCATCTGGGCTGTCTTCCGGCGCGGCATCTTCTTTACTCGGTGGCGTGTTGATGAATGGCAAACCGAATGACAAAGAGGTGCACTTTATCCACAAAGTGGAAATCCCGCTGATCTTAGTTGAGATTGTGCTGCTGTTTACCTTCTTTGTTGGCTTATTGCTCACCGGAGGACAGAGTAAAATTGCCGCCTTTAATGCCTTAGGTTATGGCTTCTGGGGCTGGATTTTCTGGGTAGGGATTATCGGACTCGGTTTATCAGGCCCGTTAGCGATGAACCTGTTTATGACTACGACGTCGAAACGTAAATGGGCTTATGTAGCAGGGACGGCTTGCTTGAGCTTAATCGGGGTTTTATTGCTACGTAACTTCATACTGTACACAGGACAAATGACGATCGCTTAAGGTGCGAACGCTTCATTCAAGACGAACAAGATGATAATAATCAAGGAAATCGACGGATCGCGGAAACAATGTCAAAGGAAAACATTGTAGATGAGGCCCTTGCGGCGCTGGCTGAGCTCTCAAGGGGTAAGTCTAGTCGCGGTTGGTTGTGTAACAGGGAGGTCGCTGGGCAAGGATGCCATATTTTACACTTCAATATTTTGCCCTGCCTCCAAATGGGCCTCGCCACCTCTATTCCTTAGGGTGGCGTTTCTTGCTTTAGGGGCCACTCGATTTTCCTTTACTCGCCTAAATTATTAATCCACTCACTCATCACTACATGGGTTTTGATCTTGATGATATCTGTGTGAGTGTCGATATATTCACGGATCTCATGCAGGCGGCGCATCGAGGGTGCATGCACGTACACGAGTAAATCCATATCACCGGAAATACCACGGCAAGTGATCACTTCGGGGATCGCTTGGAATACGTGTACTACGTCGGCGCAACGTGGACATTGATGCTGGATTTCGAAGTAAGCCGACACCCCTTCTTTTTGCGACTCACTCAAGAGCACTTGATAGCCACGGATCACCCCAGTCTGTTCGAGTTTTTTAATCCGCTCGGCTACCGCGCTGCGGGAAAGGTTCACTTGTTCGGCAATATTCGACACGCTCTGGCGTGCATCCTGACGCAAACTATGAATGATGGCGGTATCGAATTTATCCAAGGGTGACTCCAGTGAAAATGGCTATATTGGATCTGTGTGCAATTTTGCCATTGGTTTGAATGGCAACAGAATTTACTGGGTATTTGAGCTTATAATTTCACCACACCTTAAGCTGGCCATCATTTTGACGGAAATAACGCTATTTGCCGTGATTTTGCCGGTATGGGGCGTGGAGAACACTGAAATTAACGGCAGATTGTGGGGCAAAATCTCACTATACTGACAGCTCAAATACCACAGAATATTAACAGATGAATTCAAATAGCGGAACTATAGGTCGTTGGCAAGGTGCGGGTCTGATGGCGACCACTTTGCTCGGTACTGGGGTGTTTATTTTGCCTCAGATGACAATTGAAAAAGCGCAATCTGGCGCGCTAGTCGCATGGATATTGCTCACCTTAGCCATTATTCCTGTGGCGCTGGTCTTTGGTCGTTTAGCGAGTGTATTTCCCCATGCCGCTGGCCCCGCTTACTTTGTCGAAAAAGCCTTTGGTCGCACCGCTGGTCGAACAATTGGACTGATCTTTTTACTGGTTGTGCCCATGGGCGCCCCCGCCGCGATTTTGATGACCTTTCAGTTTGTGAATGCCTTAATGCCGATATCGGGTTGGTCCAAGGTCGGTGTTGAAGTATTAGTGATTTTTGGCCTGTTCCTGATTAATTTAAGGGGCATTCAAGTGTCGGCCAAGCTGCAATTTGGCTTAACGCTGTGCATAGTGGCCGTCGTTGTCCTCTTATTTGGCGCAAGCAGTTTTCAGCCCGGGCACCTGACAACACTTGCCAGCCATGGTATGCCTGAAATGCCAACCGTGATGATTGCCGCTGGCATCGCTTTTTGGAGTTTCTTGGGCATAGAAGCGATGACCCATCTGGCTGATGATTTTCGCCGTCCGCAGCAGGATATGATCCCCGCTATGATGATGGGCACTGTGCTGGTGGGCGTAATTTATGTGGCTTGTACACTATTGCTACTGTTAGTGCCGACGGATAAAAGCGTCGCCATGATCGGCGTGTTTGATCAGTTGCTCGGTGGTTACGGCGCGCAAGTGATTGGCGTTTTGGGGATCGCAAGTGGATTAGCTACCGTGAACGTGTATTCGGCCAGTGCGGCACGTTTAGTGTGGAGCTTTAGTTGCGAAGGGATTTTGCCGCGTTTCTTTGCGGTGAAGAACGCCCACGGCGTACCAATTCGCGCGCTGGCTGCACTGTTATCTGTGATGGCGAGCGTGATAGTGCTGACTTATCTTACTGGACAAGAGTTAGAACACCTTATTGCATGGAGCAACGGCGTATTTGTGGTGATCTATTTGATGGCAATGTTAGCGGCCGCTAAGTTATTACCGCGCCATAATTGGCCACTAGTAGCGCTGGGTTGTGCGTTTTGCTTGGCATTAGGTTTTGCCCTAGGTGCGAGCATGTCCTATGTGTTGGTGTTGATATTAGTGGTGGCGCCATTTCTCTGGTGGCAAAAGACCCACATCAGCCGCAAACAAGGTTTAGCGATTCCTGAGTAATATTCGGCTCCTCTGGTGCTAAATCAAAATGATGGGTTAATTAAGAAAGCCCCGAACTCTTTATGGTTATTAGAGTTCGGGGCTTTTTTGTGTGGCTGAGTGGATTTCGTAACTGGATATGATTGAGTGTGATTAAATTGAATGAAATAAAGAGGTAAACCTTAAGTTTGATGGCCGGTAATAGGTGTCGCAGAAGGATTGCACATTACTGATATTTAATTATATCTCGCGAGCGAGGCGCTAGTTTGTGTTCTATATTTTGATCATTCATACAGAAGGAGCATAAAAATGGATTCAGCTGCGTTACTCAATCGAGTGGATGAATTACCCAGGTTACCCAAGGCGATCAGTGAACTATTAGACGCAGTCAATGATGATAATGCGACAGTAAAAAGTATTGCCACTAAGGTTGCACACGATCCCTTAATCAGCGCGAGGGTATTACGCCTTGCTAACTGTGCCCACTATGGACGCAGTCGAGAGGTGGGTACGATCGACGAAGCCGTGGTGCGTCTTGGGATGCAAACCCTACGTACTTTAGTGCTTGCATCGGCCGTCATTGGCGCGGTTCCTAAGTGTGAAGGAATCGACTTAGCCCAGTTTTGGGGGCAAACCTTTGAGATTGCCCTCTATAGCCAAGAAATGGCGAAACGTTGTGGTGCTATGCCTGAAGAAGCATTTACCTGCGGTATCTTGCACCGTATTGGCGACTTGCTCATTGCGGCTGTGTCACCTAAAGATGCGGCAAAAATTGCTGAAGCTGTTGCGCAGGGTAAAGATAAGCATGAGTTTGAACGTGAGTTGCTTGGATATGATTCACCCGATATCGGCGCATTGCTGGCCAAAAATTGGAAATTCACCACAGCCTTGGTGGAAGGTATTTTATATCAAGATCATCCTAAGGATTCGGACCCATTCTCAAAACTTGCCGCATTATTATGCCTATCTTACAAAGTGCTGGATGAGTGGGACACTATTGAGGATGATGAGAAAACCAGTTGGTTATCCCAGTTAGCCACCAAGAAAGGATTAAGAATGGAGATGGGTGGATTGCGGACTAAGTTGACTGAGTTACGCGGTTCAGGGTTCGAAATCGGTAAAGCCTTAGCTTAGGATTTAATTGAGCATAGTGCTTTATTGTCTGGAGCTTTACTGAGTCGGTTTAACTAATGAGTGGCGTTCAACTGGCTAGTGTTTAACGAACTAAGGCTTAATTGACTAGGATTTAAGTGAGTTGGGTTTAAGTAAATCGCGCGGCAGGACAAATCATTTTTAGTCAAAGACGATATCAATAAGGTCTTAAGACTATAGCGTGAATGTGAAACGAGATAGCGTTTCGCATTCACGCTTTTTTATGCGACGGATTAATCGAATTAGTACTTTGATTAAACCGTTGAATCACTGGGGCGCCTGATCAATATGTCGATTGGCGGCGATGGCAAGATTGCGCATTTGAATTAACTCGCTGTGTTTGAGGTACAGCAAATCAGCGGTGCGACGAATGATTTGGTCTTCATATTGGGATAACTGCCCATCAGCATAGGCCAGTTGCCACATCGCGAGCAATAATTGCTGCTTTTGCGCTAGGCTAAATTCGGCGTTAATGGCTGAGGTAAATTCAAACAGTGAGGTCGCGCTACCTTGCACGCTTTTGGCCTCATCAATCAGTATCTTAGCGTCAGTTTCTGACATTGATAGCGTATCTGTCAGTAACTTAGGCAATAAGGCTTCTTCCTCTGCCGTTAAGGTATCATCGGCAAACACCACTTCAAGTAACATACTCGCCGCGGCGAGTTTAAGTTGATGCGCCTTGTCTTCTGGGGAGACTGCTTGCGTATGTGCTTCAAAGAATCGTCTTAACTTCGCAATCATATTTGGCGGTTCTCATGTTGGAGGGAATATTAAGGTAGAGTCGATTGGGGGGCGAAAGTTCACCCTAAGTACTGTATTGCTTAGGGTGAGATAAAACGGGATTAAAGTAGTGAGTTCAGACCTTTCATCAGTAAGTCTGAGGTGCCTTCAGTGCCATTGGTATTCAAGTAAGACTTAGCTATGTTGATCATCGGCATCGCCAGATCTTTAGAAATGCCCAATTTTTCAAAGGCGTCTAACACCATCGCACTGCCTTGCAGTGATGAACCTAAATCCCCCGCTTTGGATAACAAGCCAGAAACGCCTGATTTTGAGTCCAATTTAGGTGCTGCAGCTAACAAGCTATCGGCATTAGGAATGCTGGCAGCCAGTTTTGAATAGTCGGTTGTCCCTAGGCTAGATTGCGCTAAGCCTAATAAACTGCCTAAGCCACCTTCGGCTTGAGTTTGGTTTAAACCTAATTGTGACATGACGCTGCCAACCAAATCATTTGATTGAGTTGCTGTCGCCGTCGTGGCCGCTTTTTCAGTTTTGGCTTGAGTTCCGGCCAAGTTATCTAACCATCCTGCACTGGCTGGCGCCGCTAAAATACAAGTGCCGAGGAAGATAGAGAGGGTAAGAGTGTGTTTCATATGGAAGCGTCCTTTGATGGTTTGCGCATGTGTTCGCGGGACAGTTTAGCGGGTTTGCAACTTTTGTTGCAGTAGTTTTCTGTGATTTACGATTCACACTGGTGATTTCTGCTGTGCATTGCTACGGGATTTTCGGTATCCTTAGCGCAATTCTAAGTGTTAATTCATTTCAATTATTTGGAAAGTCGCATGTCACTGTCCGCAATTTTGTCAGAAGCCTATAACTTCTTCCGTAATCATCTGGCGCAACTCGCTATGCTGACGATTCCATTACTTTTGATCCAAGTAGGGATCCAGTTGTGGCTTGGCGTAGAGATGAGTAAAGTCGATCTTGAAAATCCACAGTTTGGCGCACCACACATGGTCGCCATGATGCTGTTACTGCTCACCTTTTCGGTATTAATCGCCGCCCTGACACTGTTTTTAGAACTGCGCTCTCAAGGGCATAGTCCTTCTGCGGGCATGGTGCTGAAGGCAAGTCTACCTTTTGTTCCACCACTATTATTAGCTGGGGTGTTTTCTGGTTTAGCGATTTTAGCGCCAGTGATGCTGTTTGCCGCCTTCGGTCCTTTGTGGCTGGTGGGGTTGGTTATTAGCTTCTATCTCTTCGCCCGTTTGGCCTATGTGAACTTTATGGTCGTGGTTGAGCGTTTAACACCGCTGGAAGCCATCAAGGGCAGTTTTAGCTTTAGTGGTCCTATCGTGCTGAGAACTATTGCACTATTAATGCTCTATCTGCCACTTTCAGTGGTGGGTAATCTGCTGTCAGGTGCTGCCGCTATGGGCGGATTACCGCTGCAAATGCTATCGGATACCTTTATGTCTTTCATTGGCTTATTCGTTAACGTGGCTTTGTTCCGCTTATATATGGTGTCGAAAAAACCAAGCGTCGAATAGCGCAAACTCAATTCAGCCTGCAGGGAATCACTCATGGGGACGAAAACGGTCATTAAGGATAGTGAGTTTATCCAAGATTTTTCGGCCTCCTTGAGTGATGACTACCTCAAGGCAAAAAGCTATGTGCAGGATGTGCCGACTCAGTCGCTACTGCACATTCGCAGTTTTACCCATAAGCTCACCGAGTTACTGGGGCAAGATAAGCAAATCGCGTTTTCGAGTCCGAATCTGTACGATAGGATCGAGCAACTAAATCAGCAGCGCGTTATCGATGTTAAGACGACTCGCGCGCTGCATCGATTGCGGGCCGACGGTAACCGCGGCGCTCATCCTGAAAAATATCACCTGACTCAAGCGCAATTGCTCGCGCTGGCGCAAAAATCTATTAAAGATGTTTTGGCGCTAGTCGAACACTTGTATCCCAAGGTTAAAGGCCAAGCCGCACCTGATTATCGTTATGAAGCGTCTGATGCCATGACCGCGAAGGATTTATGCTACCGCGCTGTGATGGAAGATGACGCCGAAGCCCAGTATCTAGTGGGAATATCCTTTAAGACCAAGGCCCTGATACTGAAAGAGCAAGAGCAGCAACAAGATCAAGAGTTTGCCACTTCAGCTGAAGCCACATCTAATCCAGAATCTTCTGGCGTTTCACCTCTTACTTCTCACATCAGCGCCGCTGACAGTTTTGCTCGTGCCGCCTATTGGTTTGCCCTTGCGGCGCCTCGGCATATGGAAGCGTTACATGAGCACGGCGTGGCCTTGATCCACGGTTATCAAGGTGTGCCAGAGGTGGCGAAAGGTGAGCAGCTGATTGCGACGGCGGCCGAGGCGGGTGTCGTTAATGCGATGGCGTTATTGGGATATTTTTATCTTGTCGGCAGTGAGTCGTTTCAACCCGATAGCAAGTTAGCGCAGCAATACTTACAGCGCGCCGCTGAGGGTGAGCAAACCGAAGCGATGGCAAATTTAGGTGTGCTGTATTATCAGCAAAAAAATCTAACTCAAGCCTATCATTTTATTAGCAAAGCCGCACAAGCGGGTTATCCCCACGCCCAGTATCATTTGGCATTAATGCTCGCCAATGGTGATGGCTGTACTCGAGATATGATTGCCAGCGAGTATTGGATGGCTGAAGCTGCCGAGCAAGGGCAACTCGATGCCATGCTGACGCGGGCGCAGCATATGCTCAATGATGATAATGCGTTTGGGAGTGACTTGACTCAGGCTGAGGATTATCTGCGCCAAGTGATCAAATACGGCCACAGTGTGCCTGCGATGATCGAGCTGAGTATGGCACTGGCTGACGGTATGTTAGGGCGAATTGATGTTGTGGGAGCCGCTGCTTTGCTGAAGTTGGCGCGACAACATGCCAATAAAAAAGAGGCCGAGATTATCGAGCCTCTTTGGCGTTCTCTTGCATTACAAATTAAAAATGTACTTAAAGTAACTCAAGATCCCGCAGAAATTCATAGCCTTAATCGCGCACAGACATTACTCGTCATTTGAGTAGAATAACGACACTTCACTCCTCGTTTCGCGATCACTAGCTTGCCAGAGCGAACAAAATTTAGTCTCGAAACGTCAACACGCACTAGTGTTCATCCTCTTTATGCTCAAAAGTCCGCCCTTGATGGCGCGTCGCATTGGATTCCCTAACAATCGTTTCTGAACCATAAACCTGGCTCTGTGGCTCATCTGCAGCATGGGCGTGTTGCTTGGCGTTTGCCTGTGCCTGATTAAATTTAGCCAACTGACGCGCCATGTAAAAACGGCTAAACAGGACTAAACAACCGAGCGCAATGGCGCCAAATAAAAGCACGAAGGGCAATAAAAATAGCGATAAACAGATAAGGCTAACGCTAATTGCCATAGCTAACCAAGCCCGTGGGCCAGAAATTCGGGTCTGCAGCGGGCTCTGTTGGAACTGTTGATAAATCATAAGAACTCCTCGATAGGCAGTGGACATTTAGTGTGCAATAGCGAGTGCGTCTAGGTCACGTTAATCTTGATTAATTTACTTTATTTTACCTCTGAGACCATTTGGCTGTTACGTATCATCTTGATATGTGATCTCGTAGTCGCCTATGTTGTGCCTCGAATAACAGTTGGTTAATTATATGTTTTTGCTCGAATCTTTTTGATGTAAATCTGAGCAATTTCTTTGCGAACTGTACAGAGTATGAGAAAATTAAGTTCAGATTAAGCTGCAGTTTTAACTCAGTTTGCCGCTTTATTAAATTAGTTTTAAATGTTCGTTCAACTCGCTCAGACACCCTCTTGTTTCACTGCTTTGTTAATAAATTAGACGCCAACATGAACTCATATTCGCTCAAGCAAAAAATTCTGTTCTCCGTCGTGGTAGCCCTTTCACTCGTGATTGGTTTGTTGTCATGGCAAAGTTATTCCAGCCAAAAGTCTCAGCTGTTAAAAAACAGCCTAGAACAAGTGCAGCGCTTAGGTGAGCAGCAGGCTGAGCGTATTCAAGAATGGTTGGCCGGCCGCCAAGATATTATGGGGGCACTGGCGAGCAAAGTTGAGGGTGATAGCCTAAATACGTTACAACAAGCACAAGCGTCTGGCCGTTTCCAGTTGACCTATTTTGGGAGCCAAGCGGGGCAAATGCTGGATTCTGATCCGAGTGTAGACCGTACTGGTTATGATCCTCGTAGCCGTCCTTGGTATCAACAAGCCATCAGTGCACGCGGTCCTATCTTGACTAAGCCTTACGTGGATGTGGCTTATAACATTCTGGTTGTGACTATGGCGCAGCCTACGGCACAAGGTGTTGTGGGTGGTGACTTATCTATCGCGAGCCTAGTTGACGGTGTTAATCGGATGAAGTTACCGGCCAACGGTTATGCCATCATGATGCACAAAGACGGCACGGTTATCGCTTATAAAGATCAAGCTAAGACGATGAAACCTATAGGTGAAATCGACAACGATCTTAATCGCAACTTGCCAGAACAGAGCCGTGCAGCGGGTACTCTGTTGCCTATGTATTTTGAAAATGAAGGACGCGACAAGTTAGTTTGGAGCGTTGATATCCCCAATACAGATTGGGAATTAGTGTTAGTGCTTGATAAGGAAACGCTAGAAGCGCCACTGTCGAGCCTGTTACTTACCCAACTGGGTTTATCGGCACTCGTACTGCTGTTGAGTGTGTTAGCGATTTCTTGGTTAGTTGGCATGTTACTGGGTCCATTGAGCCGAGTATCGCAGGCACTGGCGCGTATCGCCGATGGTAATGGTGATTTAACTCAGCGTATCTCAGTCGACACTCAAGATGAAGTGGGTGTACTTGCCGATAGCTTTAACCGTTTTGTCGGTAGTCAACATCAGCTGATCAGCCATATTCGCCAGTTAGCGAATGAGTTAGATGCCGATGCTGAGCGTAGTCTGGCCACCACACAAACCTCGGTGACTGAACTACAACGTCAGCAGCAAGAAGTGGCTATGGTCGCGACCGCAGTGACAGAAATGGCGAGTGCTACCAATGAGATTGCGGCCAATGCTGAAAACACGGCAACGGCGGCTCAACAATCTGCAGCGAGTAGTTTACAGGGTAAAGAGTTAGTCAATAAAACCCGTAACTCGATTAATTCGCTGGCCGATGAAGTGACTCAGGCGACCGATGTCATTGCTGATTTGAGCCGTCACGCTCAATCAATTTCAAGCATTCTAGCGACGATTCAAGGGATTGCAGAACAAACCAACTTGTTGGCGCTTAACGCTGCCATTGAAGCGGCACGTGCTGGTGAGCAAGGCCGTGGTTTTGCCGTGGTCGCGGATGAAGTGCGCGTGTTATCGCGCCGTACTCAAGATTCGACTCAAGAAGTGCATACCACGATTGAAACACTGCAACGTACAACCGCCAGAGCCGTCAGCTTGATGGAAAGCAGCCAAGCATTAGCGGGTCACAGTGTTGAAGATGCGAATGCGGCAGCAAAAGCCCTTGAAGAAATTACTCAAGCGGTCAATGTGATTTCAGATATGGCAGGCCAAATTGCTACCGCAGCGGAAGAGCAGACTCAAGTGACGGGTGAAATTACCCAAAACACAGTTGCGATTAAAGATGTAACCGATGAGATCACCGCATCGGCCATGGCAGATTTGGCACAGGCGCGTGGATTAAAAGCCCGTGCTAATGATCTTAATGCACAAGTCGCGACCTTTATTCTGTAACCACTATTTGTAGCGCTTCACTTTGATAAGAGCCCGATAACGCCTTTGCTTTATCGGGCTTTTTTATTGATGGTGATTTGGTGTGTTAAAGCATTTCTGCGGGAGTAAATAACTTAGCCACACTCTGACAAATCGTTTCTCTAATCCATTTATGCCCTTGATCTTCATCATTTCGTTGATGCCATAGCAGCACATAGGCCATGGGCATAAATTCGAAGGGTAAGGGCAGTAGTTTAAGCGGATAGAGCTGGCAGGCGTGCCTTGCAAAGCTCGAGGGCAGAGTGAAGATTAAATCGCTGTGGGCGCATACACTGGCGGCACCATAAAAATCGGGTACTGTCGTACTGATCCGGCGTCGATGGCCGAGATCGGCAAGAAAGTAATCCAGCGCCCACCAATCGCTGCCTTCACAGCGAACCTGCACATGGGCCATGTCGAGGTAGAGTGGGAGATTCCATTGGTCTGAAGTGAGCGCCGCCATCAGCGGGTGGTCTTCACGCACTAAACATACTTGTTGATCGGTAAACAGTGTTTGATAGGCAATCCCTTCGGGCAGGCGATCCACCTGAAAATCGGACAATGGATGCAAGTCCCGCCCTGAAATACCAAAGTCAATTTGCCCTTGTTGTAAATCCTGCATCGATTTTTCAGTCCAGACATAGGAGTCAAGTTTGACGTTGGGCGCCGTGGTCAGCATAGGGCCAATAAAATACGGGATTAAGGTTTCATAAGCGCTTTCCACCATGGCAAAGGAGAATTGTCGATTGCTACTGGCGGGATTGAAGGTCGGAGGCTGGGTGAGTTGATATAAGTCTTGCAAAATATTCGGCAGTTTTTGCCCAAGAATTAGGGCGTGCGCCGTCGGTTTTAAGCCGTGGGCCGTGCGCTGAAATAGGGGATCATCTAAGGTTTCTCTGAGCCGATTTAAGCTCTTACTGAGGGCTGACTGGCTGATATGTAATCGGCTGGCGGCGCGAGTCACGCTTTGCTCTTCTAGCAGAACTTTGAGTATGACCAGCAAGTTAAGATCGATTCTGGCTAAGTTGTCGATATTCACAGATATTCCTCAGAGGAAATTTACTTTGGAAATTATACCATTTCTGTTCATACCTTGAGTTAATTAAAATAGCGCCCACTGAAATACCTTTTCCTGATCAACTGAGATTATTTATGCGGCGCAATTTATTACCTATCCTGATGTCTATGGTGCTTCTCAGCCCATTGGCAATCGATATTTACTTACCTTCAATGCCAACTATGGCGGCAGAATTCGCCGTATCTGCCAGTGATGTGCAATCTACGATCGTATTATTTTTATTCGCCATGGGCTTAGGCCAAGTGTTGATCGGGCCATTAGCCGACCGTTATGGCCGCCGTCCTGTGGCAATCTTTGGAGTCTTACTCTACGGTGCGAGCAGCCTTTTAGCCGCTGCGGCTATCGAGTTCCATTGGTTACAGATTGCCCGTTTGCTGCAAGGTTTAGCTGCCTGTTCGACTTCTATCGTGGTTTTTAGCGCGGTGCGTGACTGCTACGATCAAAAAGAAGGCGCTAAGATTTACAGTTACTTAAATGGTGCCATTTGCGTTATTCCTGCATTAGCGCCGACATTAGGTGGTTTATTGGCAATGCAATTTGGTTGGCGCTCAACCTTCGTTTTCATGACGTTATATGCCATGTTGATGTTGCTCGTTGTCGGGTATCGCTTACCCGAGACTCGACCTGCTAATACGGTTACCACAGGGCCGCTTTACCGTTGGGGTCGATACAAGCCTGTACTGGGTAACACTCACTTTTTGTTTTATGCCTTTGCTTGTATGTCGGCGATGGCGGCGATTTTGTGTTACGTGTCGTATGCGCCTGTGTGGCTGATTGGGCATTTAGGGCTTTCTGAGCTCATGTTTAGTGGTCTATTCGGTTTTAATGCTGTGTTTAATATCGTGGCCTGTTTTACGGCGCCGCTGGTTATTCGCAAACTCGGCAATCGTCCGACAACGATTTTGGCGCTGTCCATACTCGTTCTGTCTGCCGCTATCACTGTGGCGGGTCAGCAATTCGGTCCGAGCACTGGGATGATGGCAGCGTTTGCCTTTATGTTGCCCATGTTGATGTTAGGCGTGGGATTTTCCTTCTTACTTGGTCCTGCGACTAGTATGGCACTGTCGGCCTTCGGTGAACGAGCTGGAACGGCCGCGGCTATGTTAGGTTTTATTCAGATGAGCGGCGCATCCGTGCTCGCTGGACTCGTACAGCAAACGAATTTAACCGCACCTTATGCAGTGGCCTTAGTGATGGGCGTATTTCCTATCGGTTTATTGTTGATGATGGCACTGAGTCGATTCGACCACTGGCATCAAGAGCAGTTAGCTGGCGAGCATTAGTAGTATTGAGTTCGGGAGAGATAAAGCGGGATTAGACTCATGATATGATCGATTGTGATTACGATGCTTTACGAGTAAACAGGGCTGTTTGTTAAATCAGTGCTGCTGAGTATATTCAGTAAACCCTTTGCCCGCAGACTTGCTAACCACGAGCTGCGGGCATTTTTTTATTTAAAAGCGACCGTTAAGACTAAGTGAAATATCAACGCCATCCCAATCGGGTTCAAGTGACTTAGCAATACCAGCTCGAGCAGATATTAAATCTGTGATACGCCAGTGGTAAGCAGTATTAATACCGTAAGGGTCTTCATCGTCTTGAATGCTGTAGTTTGCGCCGATAGACCAAGCGCGGGTAATGTACCAATCAGCAGCCAAGTTTACTGTTGTACTGTTATTGCTATTGTTATATTGGCCGTTAGGCCCGTTAGCTAGCGTATTATCAGACTGGCTATAGTAAACGTTTGCTTGCAAATCAATACCAGCTGTCGATTGCAAAGGAATAAAGCTGCGTATTCCTATGCCGAAGGCATCAGTATCAGAATTTCCTTTTGCGCTCAATCCAGCATCATAATCTCCGTCACTACTGGAGTAGCTAGCATAAACAGCCGAGGTAGCGTTAAAGTAATAACCTAAGTTAATGCCATAAGTATTAGCGTCATAAAAATCGCTATCTAGATTGAAGTAATTTGCACCAACAAACCATTTAGATGCGAAAACATATTCGCCATTGACACCATAAGAGTCTATATCGTCATTAAAACTATATTTACCACCAAAATTAGATGTTTGTGCTAGAAAGCCACTTAATGCATAGGGCGCAGAATCTTGACTAACTGGGGTAGAATAGTAGCGATAGTCAGCACTCCAGCCGCCATCACTAAATGCTTCACTTGAGGCGCCATAGTTAATATTGGCATCATGTTGGTAGGCTTTGTCTGTGGCTGCAAATGTATTTGTACTCAATAATCCGAATACAACTGCAATACTGATCAGTCTTTTGTGCATTTTATCGTCCTTGTTGTGGCTACTGAAAGCAAGAAATGTACTGAAAATACTAAGTGTTAGCAGTTTTAGCTTATTGACCTTAGCGTTCTTGCTGGCGTCACTATACGATGTTGTCACAAAAGTTCAACAAAAAAACAGCTTGTAAAATAGAGTTTTAAACAGGATTTCATTCTGAATATGGGTGGGTTGTGTGAGTTTTTAGGCTAAAGATAAGAGGACTGCCTTTGCCGCGTTTTTATTGTGCTATTTGTCGTTATCCCCAGAATGCTTGTCTGTGTGCGAGTGTTCAGCCTATCTCTCCAATGACTCAGTTGGTGGTTTTACAGCACCCGAGTGAAGTCGAACATAAAAAAAATAGTGTGCGGGTATTGAGTCTTGTTGTTGCTGAAGCGCAAGTTTATGTGGGCGAAAGCGAGGCCGACTTCAATGGTTTACGGGAACATTTAGCTGCGTGTAATAAGCCAATTTATCTCGTCTATCCCTCTGAGCAAAGTGTGAGTGTTGAACAGCAGCCTATCAATCCAGATTGTGTATTGCTCTTACTCGATGGTACTTGGCGCAAGGCGTTTAAGATGTTGCACCTGAATCCTTGGTTAACCCAGTATCCTGCGGTGCATTTGGCTGAAGGTTATGAGTCGCGCTACCGGATCCGTAAGTCGAGTCGTAGCGACAGTTTATCGACCTTAGAAGCTTGCGCTTATATGCTAAGCGCTCTTGATCCAGCACTGGATGTTCAGCCCTTAATGACGGCATTTGATGCTATGGTTGAGCTGCGGATTAAGGCGATGCCGAGCACTGTGCAGGCACGTTATCAGAACTCAAACAAACCTAACTGCGAGACTAAGTGAATGTTTTGACATTATTTAGCCTTAGAGGTTTTAGGTCTGATTTAATGCCTTTAAGTTATTGAGTTTTGATACTGCGTACGAATTAAATTAGTCAAAATCTTATTCAGATCACGCTAACTTAGTGTGAAATACGGTTTAATAATAAGGATTCATTTGCGCAGCAAATGATGAGATAGCTTTGCCAGCCAATTGCTTTGCAGGGGAGTGATTCCATTGTTAATCACAATTAGTTAATGGCGTGCTGGCAGGGCTATCGAGCTTTAGCTATATGTTTTTGTTAATAATAGTGAGAGTGCTTTGCCAGTCAATAATTGGCTTTGCAGGGGAGTGATTCCATTGTTAATCACAATTAGTTGATGGCGTGCTGGCAGGGCTATCGAGCTTTAAGCTATATGTTTTTCCGCAAGAAAGGTTTGGGTGAAGTTATCGACTGAACTCGGATAACCAAAAAGAAATCCTTGGGCAAAACCTTGGCCCATGTCTGTCATTATGTCCCTTTGTTGTTGAGTCTCTATGCCTTCGGCGACGAAGTGAATATCGAGTGCCTGTGCGAGTTGCACTATGGCATTGCAGAGCTTTTGTCCTGAAACTTCGTTTAAGCGCAAGGCAAACTCGGCATCAATTTTTACACTGTCTATGGGCAAAGTGCCTAAACGACTGAGTGAGGATAATCCAGTACCAAAATCATCGATAGCAATGCTGACACCTAGGGCTTTTATGCGGTTGAGAGTGAGTCGTGCTTGCTCAGGTTGGCGTAACAGTGCCGATTCGGTGATTTCTAAAATCAGTGCTTCAGCAGGTAAATTAGCCATGTCCAAGGCGTTAATTACTTGGTCGATAAAGTCTAAGTGTTCCAATTGCAGTGGTGACACATTGACTGAAATTCTAAAGTCTGCCTGATTATGTGCCCGCCATAGTGCCGCTTGCACACAAGCTTGTTCTAATACCCATTGACCAATACTGACAATTAACCCTGTTTCTTCCAGCAATGGTACAAAGTCGGCAGCGTCATAATACTCGGTAGGTGATTTTTGCCAGCGCAGTAATGCTTCGGCTGCAATCGTTTGATTTGTGTGTAAATCCACTATGGCCTGATAGGCGAGAGTGAACTGTTTTAAGCTGTGAGCATGTCTTAGACTATTGATTAACTGCAGCTTTAACTCTGCGGCCTGGCTCATAGCCTTATCGTAAAATACGAGGTGCGGTAAGCTCTTTTTAGCACAGTACATAGCCGTATCTGCAAGACTTATCAGTTGTCCCGCCTTATCACTGTGGGTCGGAAATAGTGCAATGCCGATACTTGTGCCTAAGAAAAACTTTTGTCCTTCAATTTCAAAGGGGCTCTCAAATAAGCTGAGTATCTGTTCTGCAAACAGTTGGATTTCATCCTTGCAGCTACAGTCTTGCAGCACTAAGGTAAATTCATCTCCACCCATCCTGGCTATGTCTGCTTTTTCGATACAACCGGCTTCGAGTCTGGCTGCGACGAGTTGTAATACTCTATCGCCCATGGAGTGGCCGAAGCTGTCATTGATATTTTTAAACCCATTGAGATCTAAAAACATCAGCGCACCTAATGCTAAGGGATCGGTTTTAAGTTTTTCGAGGGCACTATTGAGGATCTGCTTAAGGTGGAACCGGTTTGGCAATCCAGTCAAGGTATCGTACATAGCACGGGTCGTCAGCTGAGTTTCTAACAACTTTTTGGTACTAATGTCACTGAAAATATTCACATTATAGTGAGCGCCATCTTCGGTAATGATTTTTCGACTACTTTGCCAAGCAGGGAAATTACTACCATCTGAACGTCTTTTCCACGTTTCACCCTGCCAAAAGCCCCTTTGTTTTAGTGCAATTTGGCATTCGGTTTCTTCTCCTAAATGATGGCTTCTTAGCTCTAAAAATTCAGGGGTTTTACTAATAAGCTGCTCGGCGCTATAACCGCAAATACGGCACATAGCTTTATTAACGGCCAAAATCTGATCATCTGAGTCTGTGATATACACAGCTTCGGCACTTTCCTCGAGCGTCGTCGACAAAATCTCTTTAGGAATATGGGCAAAGCCATCGGTAATAATTTCGCGCATTTGATTACGGCGATTGAGCGCTTGAAATTCCACGAGCACAACGGGCTGTTGCTGCAGATAGCAGCGATAAAGGTTTACTCGAGTTGGCAGTTCATTGCCGAGTTGACTCAAGTGTAGCCAGTTGAATTCAACATTATTTCCTGCAATGGCTTCAAGGATCATTTCCCTCGCATATTCAACACTGTTGCGGCCGGATGATTGAATTCTCGGAGAAAAGTCATAGGGCGTCGCATTCACAAAGGCGTCATACTCAGTCTCAAAATAACCGAGTGTCGCTTGGTTGGCGCTAATAAAGCTGAAGCCTTGAATTAAGGCTAGGGGAGTTTTGTGGGGATGTGCTTGCCAGAGCGACAGCTTTTCTTGCAGTACTGGATCATCCGTACTGAGTTTCTTAAGTAAGTGTTGCCATCTGATCATTTAGGATACTCCCTTGTGACACATATAAGGTCACATCCTATTTTTTAGGTCTTCGTTGATACGAAGTTGCCCAATAGTACATCATTCCAATGGGGTTAAAAATAAACCTAATTGTTGTTTCACTCAAATAGAAACTCAAAAAGTGTAGTTTAAGACTTGTATTTAAGCATATTTTTTAAGAACACCTGCTGCATATTTCAAACTTTGACAGATTTTATGCTATGTTTTTCGCATCGTGTTTGAGTTGGCTGTTATCGTTCATCCATCTGATGACCAAATAAAGGAGGCCGGAAATGGCGCATTTATCTAAGGTGCTTATCTCAAAAATGTTGATGTCGTTCATACTGATTTCCAGTGTTCATTTTGCTGTAGCCGATGCCGCCGATGCCCCCAAGCCAAAATTAACCCTAGAGCAAAGAGCCGATAAAGCACGCATAGACTCCCAGCGATTAGAGCAGGCCCAATTAGAAAAGGCGCGCCAAGCGGCCAAAGAGAGGGCAGAGCGAGAAGATAAAATCAAACAGCAGTCCCAAACCAATGATTGGGAGGCTCAGCAAAAACTCAAGGCACAGAAACAGTTCGACGAAAGAGAAAGTCGCGAGCAAAAATATTTACGGGAAGCAAAAGAAGCCGCGGCAAAAGAGCGTAAAATCCCTAAGCCATAATCATGCCATCGACACAGTCGATTTCAGCCAGGCTGTGCGAGCATTATTTGTTGGCATTGCGACTATTCTCATTACTATGCACCTTACTATCCTAGGAATTGTTGTTGAATATCTGCCCCTTATGCCATGGCACTGAATTGCTCGCTTATCATCAAGATAAAAAGCGCGACTACCATCAATGCCAAACCTGCCAACTGGTGAGCGTGCCTCCTGAGTTTTATCTCTCGGCCGAGGCAGAAAAAGCCGAGTACGATAAGCACGAGAATCATGCCAATGACTTTGGCTATCAAACCTTTTTAAACCGCACACTCGAGCCACTACTTAGCCGTGTTAGCACTGCGGCACAAGGGCTCGATTTTGGCTGCGGCGAAGGCAAAGCCTTGAGCTTGATGGCAAAGGCGCGGGGTTATCAAATCGATAATTACGACCTTTATTACGCCAACAATCCTGAAGCCTTGGCTCGCCAATACGAGTTTATAACGTTAACTGAAGTCATAGAGCATGTGAGCGATGCCGCAGGTTTGCTTGCCCAACTTGATAGGTTATTAAAACCCAAGGGTATTTTGGCTGTGATGACCAAGCGCGTGCAAAATCAAACCGCCTTTGCCACCTGGCACTATAAAAATGACCCTACCCATATCAATTTTTACGCCGAAGCGACCTTCGAATGGCTCGCGCAGCACTATGGCTGGCGACTCGAAATCATCGATAAAGACGTAGTGTTTTTATATAAAGAGATGCATAAAGTAAATTAATGCAGTCGTAGTTGTTTAACTGAAATCATGTCCTTTGCAGCTAAGGCTATGTGTTGGCGTGATCCGCTAGTCGATAACGCCGACGTATTTCATCCACTAGACGATAGCGGGAACTTCGGGATTTTTCGTGCGGCGCAAATGCAGCGCTGACCTCATCTAGCAGCGTGACTGCCGTTGTCAGTTTGAGCAGCTCGTTATGACTCAGCTCGTCTTTGCTGGTTAAAGCGTTAATTGTGATTATGGCGAGTTCTTTGGCGGCAATATCTTCCGGTAATCCAGATTGAGTACATAAGCCATAGGTGCCCGCTAGGAAATGCTCCCATTCCTGCTGTATATGTTGCTCTAGATTGGTGTCTGGAATCCGCTCGAGGCCGTTGCCTTCTGGGGCGGTTAATTTTTCAATGGCTTCAAGATAGCGCCGCTGAAAGATGGGATAAATGTCTGTGTCTGTATCGAGTTGATGCAGCATAGTTAACCAAGCGCCATAGCCTTTGGCATAGTATTCAATTGCCAGCTGTTCATTATGTGTGCCAAAGACAGAATCGCAAACTATGCTGAGCTGCAGTCGATAGGAGCATGCAGGCATAACGCCTTGAGTTTGGTAGTTAATCAACCTAGCTTCTGTCTGCCCTGTCACTTCGAGCAGTTGTTGCCGAGTATAAAAATTGGCGTTCAGATACTCAAGTAGCAGCATATCAGTTGCTCCTTGGTGCCTGAGCACGGTCGAACATGCCATAGTCACGAACAACTTCGGCAACCCGCAGACGATAATCGGCGAATAATTGTTCGCGTCCTTGAGTTTGCGCCTCGCGGTGATATATCTGTTGTCGCCAAGCTTGTACCGCAGCCTCATTCTCCCAGAATGACAGCGATAATAAGCGTTCGGGATTGACCATGCTCTGAAACCGTTCGATTGAAATAAATCCTGCAATCTCACTCAGGCTCGATTTCAGGTCGGCGGCTATCTCTAAATACCTTTCTTGGCAGCCGGGATTCGGCACGACTTCAAAAATAACTACCATCATAGTTTTGTCTCCGCAGCTCTTTGATAGGTCTTTGCAATCGGCTTTAGGAAACTGCGTTTTTCACTCAAAATGAATTCTCCTTGCTTGGCCAACTGAAAATTTGCCTTGCCTGCGGCTTCTGTCTTCAGTTTGTCTCTATAGCGTTCATAGTGGGCCAGACTATCGAAGCTAATGAGACCAAATGCAGTGTCGTTGGTGCCTTCGTGTGGCAGAAAGTAGCCAAGGAGTTCGCCGCCGTAGGCTGGAATGATATCGCCCCAGTTTTCAGCATATTGGGCAAATTGTTCCAATTTGAACGGGTCTATTTTGTATTCGATAAAACAGGTAATTTTCATAGTGTTTGGCGCCATTGACTTGTGTGGTAAAGGTTTTAGCTGCATTGTAATTACCAGAAAAATTAATGCTTCGATGGTTATCGAACTGTTCCCGAATGAGATTACAAGCATGGAACCTAACATCGCTTTTATTGCCAACTTGATTGGTGACGCCGCCCGCTCACGTATGTTGATAGCTTTAATGGGCGGAGAGGCATTGACTGCGACAGAACTGGCTCTTGAAGCAGATATCACACCACAGACTGCCAGCAGCCATTTAACTAAGTTGGTAGAAGGCGAACTATTGCTCGTGCGTAAGCAGGGGAGGCATAAGTATTTTCAGCTGCAGAGCCGCCAAGTGGCCGAACTGTTGGAAAGTCTGCTCAACATAAGTGCTGCCATTGCGAACTCTAAGGTCATTCATGGACCTGCGGACCCTAGATTGAGACTGGCTCGTGTTTGTTATGACCATCTCGCGGGTGAATTGGGTGTTGCACTTTACGACAGCTTAAGTCGGCAGGATTTGATCGTCCATGAAGGGGGCGAAACAAAAATCACCGCAGCAGGGATGACCTTTTTTGCCAAACGTGGCGTAGAACACTCGTTGCTGGGTGCACCAGATGATGTATTCGATGTACCGAAAAGACGTCAATCTCGCCGTCCTTTATGTAAGTCATGCCTCGATTGGAGCGAGCGTCGCAGTCATCTTGCTGGTGTTCTAGGTCAATGGGTTCTTAAGGATATTTTGGATAAAGGCTGGGCAGAGAAAGCCCTCGACACCCGTGCCCTACAATTTTCAAGCCGAGGGTTAAAATCCTTTCGTGCTGACTATGGCATTGCGACAAAATAGACAATTTAATGTTGTCTATTTGAGGCTGAAAATCATATTGTTAATTATGGCTAGCCATTTTTACTTGTTATTCCAGAATTCAGATGGAAAGGAATAGAAGGCATTTGGATGGGGTGTTAATTTGAACTAAGTTAGGAGGCGTTGGATGATAAAAACATCTTTAATTTTATTCTTTCTTCATGTCTTCTCATTTTCTGTATTGGCAAATGAGGCGTTAAAATTTGAGGTCAATGGAAATAACTTTTATATAACAATATATAATAATTCTGAAAAAGAAGTTGAGCTTAATTATATTCTATGTTTTGAGCCATTGGGTGGTATCAGTTTTGATATAAAGGATGAAAATGGAGTCATCTATGGCATAAATGGCATGTTTGATTCTGGTTGTGCGTTAAACGAAAAAATATCAGTGCCTCGTTATGGCATTATTGGTCAGATAGTAGATAAAGACCTAGTTAAAATGATCTATGATCCCGTTCCTAAAAAATTACATGTAACAGCAATGTTTTGTGAGAATTATTTTACTAGGGAAAAATGCTTAACAAGCAATACACAAATTGTAGATTTTTCAGTAAAATAACTATTCTCATATTAATTCTTGAGTACAAAATGAGGCTACTCATAGTTAAGTGAATATGACACTTAAATTTTGCACCTATTTCGTAATTAACATAAATGTAGTCATAGAGCCTATAGAGACATGTTTACTGAGTGGCATTGTCACTCATGACGATATAGCTTTGCCATTTCGGTATAGAGGAAGGTATTAACAAGATGCAATCAGTCGATTTATCCCGTCCGCTACCCTTTCACCTAGAAAGGCTCAAGGGCCATGCATGGGTTTATCAGGAACGTTTGGAAAGTCTGGATTGGATCATGAATATGATCCGTACCGAATCCAGAACGATGGACTTATTGAGTATCAGTAATCGTCATCTCGCTCTTGCATCAGAGGCGCTGCATGGCGCCGCAGCTCCGGCTCGCGCCTTCGAGCATCTGCAGCAGATGCATTATCTGGGCATTAAGGGCTATAAACGTAAGCTGATGGCCGATGGCGATACCCTCAGTGTCAAGATCCCCGCAGACACCTTAGTGCTGAAACCACTGCCAGCAGATAACCGATTTTTATCCTATGCAGACTGGACTATCTATTTTTGGAGTGCGGTACTGCATGATCAGTACCAAGATATGACCTATTTGGTCAGCATCAAGGAGGCCGATAACTTTAATTGGGAGCTGGATCCGCCATCCCATATGATGAGTGATATTTTGGCCGGTTTCTTCCGTGTGAAAGCGGTCGATGTGGCAGCGTTGATCCAAGATTTCGTTAGCGCCACTGAGTTTGGTAAGATATCGACGGCATCTTACGATCATGTGGCGAATGTCACGCTGCCAACCATTGACGTCATGTTGAAAATCTTTGGCGGTGCCGAAGAAGTCCAATATCGGCAGGCCATGTACCTAGCGCTACAATCCCACGATACATTTTACAGTCTGCCACGTATGGCGGGCAAAGTAGTTGGGAATATTTCCTTGCCGCTTAGCGCGTTAGCAAAGTTAGCCTACCGTAAGTTTGGTTATACCTTAGGATTCTACAGCGATTATGTCCCAGAAGTTATTTATCAAATTGAGGATCAAGCTATTAGCTTCGAACAGTTAGAGCCGATAAGCTAAGTACTATTGGATTTGTCCCTTATACAAGGCCATTAAAATCGGGCTCATAATGGCTGTCACAGTTTTACTAAATGAGAAAACGGCAAACTTTATGGCAGTTGAGCCCTGTCTTAATGCTTCTTAGCCTCCAAGCGTGTCCGTGATTATTCTTACCTAGGTTAATTCACCACGCCACCTAAAATCCGCCTTTTTATGCCCAGTTAGCGGCTATTGTGGGGGCGGTTTCCCCTCTAAAATTAATTAAATCTCTTAAGAACTGATTCAACTACCGTAATTAAGTGTGCACGCATGGCCAGAAAATGCGGACGTTCAGACATTCTGAGTGCTGCTATGGAAGTGACCGTCCGCCCCATGGTCGCTTATTCACATCCATGCGATCGGTCCTATACAGCAGATGCCACGTTCGAGCACCTATGACTGGCTGTGCTTAACTTAATTTCATAATAAATCAATTGGTTAAGTATTGGTTTTACTTGCAAAAAATTTTTATACTCTCAAGATCGAACAGACTGATTTACCTCAAAGCTGGATTCCCGCCCACAAGCATTGCGGGAATGACGACTAGAGACGGTCTCCAATAAAGCGCAGTCTCTTTTCCTTCATCATTAAATCTATTAACAATTCCTCAATGTCCGTGTAGTTTAGGTAGCTTATAAAAACAACAATAGGTTATCGCTATGACTTTACCCTCTGTATCACCATCTGCTATCTCAACATCATCGATCCCAAGATCATTTGGGTTTAACTTTTCTGCAAACAAAGCCATTTTTTCCAAGACTTTACTCGCTTCGGCCTTAGCCTTGAGCTTTATGGGCGCGGGTTTGAGTTCGGCGCAAGCGGCGACCAGTGCTATGCCGGATGCGGCTAAGTTGGCGGCGGGTGTGGAGCAAAAGGTGATCGACTGGCGCCGCGATTTACATCAACACCCTGAGTTGTCAAATCGTGAATTTCGCACCAGCAAGATTATTGAAAAGCATCTGAAATCATTAGGGTTAGAAGTACAGACGGGTGTGGCGCATACAGGCGTGGTGGCCATTTTGAAAGGTGGCAAGTCTGGTCCGTTGATTGCGATTCGGGCGGATATGGATGCTCTACCTGTGACTGAGGTGGTCGATTTGCCCTTTGCCTCTAAAGCGACCGATACCTATCGAGATCAAAAAGTGGGGGTGATGCATGCCTGTGGGCACGATACCCATGTGGCCATGTTGATGGGCGTGGCTGAAAACCTCGTGAAAGTGAAAGATAGCCTCGCGGGTGATGTGATGTTTATCTTCCAACCAGCCGAAGAAGGCGCGCCAGACGGGGAAGAAGGCGGCGCGGAGTTAATGCTTAAAGAAGGGCTGTTTGCTAAGCGTAAACCTGACCAAGTGTTTGGTATGCATGTGACCTCCAGCATGCCGAGTGGCATGATTGGTGTGCGCAGTGGTCCGGCGATGGCGAGTGAAGATTCCTTTACGATCAAAGTGAAAGGCCGCCAAACCCACGGTTCGCGCCCTTGGAATGGGGTCGATCCTATTGTCGCTGCGGCGCAAATTATCACTAACGTGCAAACGATCGTCAGCCGTCAGGTGGATATCACTAAAGCCCCTGCCGTGGTGAGTTTTGGTGCGGTTAATGGTGGCATTCGCTCTAACATTATTCCCGATGAAGTCGAGCTGATTGGCACTATTCGCACCTTCGACCAGCCCATGCGCGCCGATATCAAACTGCGTTTAGCTGAAATGGCCGAGTTATCGGCGAAAACGCTGGGTGCGAGCGCGACCACAGAGATCCACCCAGGTTATCCTGTTGTGGTCAATAATCCCGAGTTAGTCGCTAGCATGCGTCCTGTGCTTGCCAGTGTGGTGGGCGATAAGATGCTGATTGAACCTGGATTAATCACAGGTGCCGAAGACTTTTCTTATTACGCCTTAGAAGCGCCGGGGATGTTTTTCTTCCTCGGGGTGACGCCAAAGGGAACTGACCCAGAAACCGCGGCCAGCAATCATTCTCCGGCATTCTATGTGGATGAAAGTGCGCTAAAAGTGGGTGTTGAGGCCATGACTAAAGTGGCGCTGACTGCGTTAAACGCTCAATAAGCCACGCAAGCCTTATTCCGTAAGCTCTTCGGTATAACTTGAGCTTACAAAAAATCGGCGCGGGATAAGCATCCAGAGCTTGATTTGTTAAACAATTTACCTCTATATGTGGATATTTACAGCAAGGAGCGAACAAGATGAAGCGCACGCTATCGGCTCTTGTGTTGGCCATGGGACTTTTTAACCCCCTAAGCCAAGCACAGGCCACCACGGCAGAAGACATTAAGAGTTTCACGCTAGATAACGGCATGAAAATCATGGTGCTAGAAGATGCCTCAATTCCCAATGCCAACATGTATTTGTTTTGGAAAGTCGGTTCACGTAACGAAGTGCCCGGCATTACCGGTATTTCGCACTTTTTCGAACACATGATGTTTAACGGTTCGAAAAAGTACGGTCCAAAAATGTTCGACCGCACTATGGAAGCCGCAGGTGGCGCGAATAATGCGTATACCACTGAAGACATGACGGTTTATACCGATTGGTTCCCCGCCAATGCGCTCGAAACTATGTTCGATCTCGAAGCCGACCGTATCGCAAACTTAGATATCAACCAAACCATGGTCGATAGCGAACGTGGTGTAGTGCAGTCGGAGCGCTCCACTGGCTTAGAAAACTCTAACTGGAATGCCCTTGAAGGCGAAATTAAAGGCGTGGCATTTTTAGCTTATCCTTATTCTTGGTCTGTGATCGGCCATGAGTCGGATATTGCCGCTTGGACGCTGGAAGATCTCGTGCAGTACCACAAAACCTATTACGCGCCGAACAATGCCGTGGTCGTGATTGCCGGTGATGTAAAGGTTGCCCAAGTTAAAGCCTTGGCCGACAAATACTTTGCGCCTATTCCGGCACAAACTCCGCCTAAAGCGATTCGCACCGTAGAACCTGAGCAAAAGGGCGAGCGTCGTACCTTTGTGCAAAAGGCATCGGTGAGTACACCTAATGTGATGCTGGCTTACCATATTCCTGCGGCGACTCACGCAGATTTTTATGCGCTGGATTTATTAAGCTCGATTTTAAGCCAAGGTAATAGCTCGCGTTTATATCAATCACTGGTTGATAAACAAGTGGCCTTAGAAGCGCAAACCTACATGCCGATGTCGGTCGATCCTAACCTCTTCTATGTGATGGGTGTTGCAACACCTGAGGTGAAAGCCTCGACGCTGGAGCAGGCGCTGATTGAACAAATCGATGCGATTGCGACAACGGGTGTTACTCAGCAAGAACTGGATAAAGTTAAAAATATCAAGTTGATGGATTTTTATCGTTCGATGGAAACCATTAATGGTAAGGCCAATACCATAGGTACCTATGAGATGTATTTTGGCAGTTACGACAAGCTATTTAATGCGCCAGAAGCCTATAACAAGGTGACGTCAGCGGATATTCAACGTGTTGCCCAAACCTATTTACGTAAATCGAATCGCACTGTAGCTGTGCTGGCGGCAAACGAGGAGATCTCTCAATGAAATCAACGGCAGCGAAATCGATGGCAATGAAATCGACAGCAATGAAATCACTTAAGATGGCATCACTCGGGAAACCTTCTAAGTTAATGGCCGCATTAGCCCTAGGCACAAGTTTAGCCTTAGCGGGTTGTGCTACGACATCGGCGCCAAAACGCGTTGAAACTGGCAGCTTTGTGATGCCAAGTTACGACAAGTTCGTGCTGGATAACGGCTTAACTGTGTATTTAATGCCGCAGCGCGAAGTGCCTTTAGTGACCTTAAATGCCGTGGTACGTGCGGGTGCTGTCAATGACACCACCGCAGGCGTGGCGCAAATGACGGCAGAAGGTTTGTTGTTAGGGGCGGCGGGTAAATCTAAGGCGGATATCGAGCAGCAAGTGGATTTTCTCGGTGCAAGTTTAGGCGCGGAAGCGGATAAAGAAGGCAGCTATCTCAGCGCCGATTTTATGGCGAAAGATACTGATGTCATGCTGGGCTTATTTAGCTCGGCGATGTTAACGCCGGACTTTGATGCTGCCGAGTTCGATAAACTCAAGCAACGCGCGATTGCGGGTTTGCAGCAAGATAAAGAAAGCCCACGGGCGGTGATTGGTCGTTACTTCGATAAGCTGGTATTTGGTGCCCATCCTTATGGTAATGCGGCTTCGGGTAACAGCGATTCGCTTGAACAAGTGACTGTGTCGCAACTGCGCGCTTTCCATAAGAGTTACTACCAACCGGCCAATACTGCGATTACCGTCGTGGGTGACTTTGATGTTGCTGCGATGAAGACCAAGCTGACGCAAACCTTTGGTCAGTGGAAAGGCAGCGAGAAACTGGTGCAGCCTGACTTAAATCAAGGTTTGCCTCAGTTGACTGCAGCGAAAGTTTTGCTGGTGGATAAACCCGATGCAATCGAAACCACCTTTGTGATCGGCGGTTTAGGCATTAGTCGTGACAATCCAGACTATGTCGGTTTAACGGTAGTGAACACTATTTTAGGTGGTCGTTTTACCTCTTGGCTTAACGACGAGCTGCGGGTAAACGCTGGCTTAACCTATGGCGCACGCTCAGGTTTTAGTCCTTATACCGACTCGGGTGTGTTTACTATTAGCACTTTCACTAAGACGGAAACCACTCAAGAAGCGATTGATCTCGCACTGAAAACCTATGCTCGTTTATGGGAGAAAGGCGTAGATCAAACCACCTTAGATTCGGCTAAAGCCTATGTGAAGGGCCAGTTCCCACCTAAGTTTGAAACCTCAGGCCAACTGGCGGGATTGTTGTCTGGCATGTATCTATACGGCTTCGATGATAAGTTTATCAATGAGTTCCAAGCTAAGGTGGATGGTTTGACCTTAGAAGAAACCCAAAGACTCGTGAAAACTTACTTCCCGCAAAAAGACCTGCAATTTGTACTGATTGGCAACGCGGCTAAGATTGCACCTACCGCCGCTAAGTATGGCAAAGTGCAAACCGTGGATATTAAAGCCACGGGCTTTGGCCAATAAGCTCAGTCATTTCAAGCTAAGTCATTTCAAACTAAGGCGTTAGCCTACGCTTTAGTTTAAGTAAACCCATTAAAAAGGTCTGCATTGCAGACCTTTTTTGTCTCTTATGGAACGCGTTTAACGCGCGCCAGGTTCGGCAAAGTGGGTCATCCACTTCTTCACTTCGTTATACCAGTAGACTGAGTTATTGGGTTTCATAATCCAGTGGTTTTCATCGGGGAAATAAATCATCCGCGATTCAACATGTCGACTTTGCAGGGTGTGAAACAGCTCGAAACCTTGGGTCGCCGGCACTAAGTAATCGAGTTGACCATGACTGACTAAGGTTGGAGTGTTGAAGTTAGCCGCAAAGTAATGGGGCGAAATGGCTTTATACAGTTCTGGATTATCCCAATAGTTGCCAAATCGGGTGCTGTGTACGGCAAAGTCCGACGCCATTTGTGCATACATGTCGTAAACCGCAGCATGGATCAATAGCGCTTTAAAGGGATGCGGCTGACCCAAAATAATCGATGTCAGGTAACCACCATAACTAGCACCACCCGCCACCATGCGATCACTGTCTATCCAGCTTTGTTGTTTAAACCAGTCTGCCGCCTTGAGCACATCCTCAAGGGGTTTTGTCTTCCAATCGGGATTGATGGAATCGGCAAATTCCTGACCAAAGCCGCTCGAACCGTGGAAGTTTGGCCACGCAGTGACATAACCCCAAGAAGCAAAGGTTTGCGCGTTCCAACGGAAATGGAAACCGTCGGTAATGGCATTGTGTGGCCCGCCGTGGACGAGCATAAACAATGGGTATTTTTTGCTGCGATCAAACCCTGCGGGATAGTGAACCCACATTTGAATATCTTTACCTTGATAGCCTTTGTAGGTCACTGATTCATAGGTACCTAGGTCGACATCTTTGAGAATATCGTCGTTAAAGTTTTCTAGGCGTTCAATCTTACCATTGCGAGGATTGATGCTGACCAAGCGAGCGGGATACAAGAAGCTCTGATTGGTGGCAATCAGTTCGCCGTCTTTGCCTATGGCGGGTTGGCTAAAATCGGTCGCCTCTGTGATGGCTTTTACTTTGCCGCTCTTGGCATCGATAGAGTAAATGCGGTTTGTGGCGGCGTCCTCCACAGTGGCATAAAAGCCCTTATTGTCCGGTGTCCAACTGAATTGAGAGACTGAACGGTCCCAGTCTGATGTGAGTGTTGTGAGTTTACGGCTGTCGATATCCAGCAGCATCAGCCTTGAGGTATCGGCGTAGAAACCGGGAATTTTCTGGCGGGTAAAGGCGAGCGTTTTACCGTTAGGGCTAAAGCTAGGATTCAGATCCGGCGCTTGATTATCTGCGGTGAGATTTTCGGCCTTGTTGTCTCCAATCGCAGCGAGAAATATATCAATTTTAGGATCGACTTTATTCTCTGAGCCATAGGCATTGAAGGCGATCCAGCGTTCATTGGGGGAAACATCATAGCTTGCACTGCTTTGGTCTGAGCGTGGTAATTCATGGCCCGCAGGCTGGGTGATATTTTCGACATTACCGCCTTTGGCCGGAATTCTAAATACGTGGGCTTGGCGACGTTCATCTAGCCAGTGATCAAATTGGGAATAGGGCAAGGTGTTCCATTGATGGGCGGAAACCTTATTGTCTGTATCTGTGGTGAGCTGGGTTTTCATTTCCTCCCAGCTTTGATTAGGAAAGATATTACTGATGAAATACAGATGCTTGCCAACCCACTTAATACTGTTAACGCCTGTGGGAATATCGGTTAAGCGCTGCGCTTCACCCGGACCGTCCATCGGCAGCAGGTAAATTTGGCTCGCCTCGTCTTCATTGCGCTGACTCACAAATGCCAAGGTTTTGCCGTCGGGGGAAAACACGGGCTCGCTCACCCTTAAACCTTTGGCTGTGATGGCTCGGTTATGCTTGCCATCGAAATGCCACAGCTGGATTGAGCCTTTATCGGCTTTTAGATCGTACTCTGTGACTGGCGCGATAATGTGTTCACCCGTGGGGGATACAATCGGGCTACCGATACGTTTGAGTTGCCAAAGCAATTCCGCTGAGAGCGGTTTGCCCTGTTCTGCAAATGCCGAGCTAAAGGGCATCAGTAAACAGAGTAATAAAATGCCTGTCTTCTTCACTATGAGTCTCCTTGTGATACAAACAGTTACTAGGATTATGATGTTGCGTATTTTTGCTAAGTGGCAAGTTAGCATGAAAACCCGCTATGCCTGAATAAGCTGTTATGTAAGCAACTGTGAATCTTTGTTGGGTATCTCAGCTCAAAAGCGGCGGATGTAGGCGGTAAACTGTCAGGCTAACTGCAATACAAAAGCGTTCATTCCGTCGTTAAAGGAATCGTTTGTAATGGGGGATTTGTCGATTGATGCTTTTAACGCTAATGTATTGATATGTTTCGGGTAGGTGCTAGAGATATGCCGCTTAAAATCGACCGTTTAAACCTTATGCTGGGTCTTGCCATTGTACTGAGCGTGTTTTCGTCCTTCTCAGTCATGGCCGCTAAGGTCAGTTTCAAGGAATTTTGTCCACAATTAGTGGGGCAATGGGCGGGTGATGCTGCCAAGGCGGGGGAGATTCCGAGAAAAGTGGCCGTCACGGGCTTTTGCTCCCACGATCAGCGCCAGTTGATTTTATCTGTGAGTATCGGCACGAGGGCGCCCTTTAGCGAAACTTGGTGGTTTAGGGAACAAGGCGATCAAGTATTACTGACCTATTACGACGGTGTGGCGGAAGATAAACAGCAACTCTTTAGTTTATATCGACAAAATGGGGATTATTCCCTGCTAGGGGAAGGCGTTGTTAATGCGAGACCCGCGTTGATCCAGTTACTCTTTGATGCTCAGACTCGGGCTCAAAATCAAGCTGGTGGATGGCAATGGACGCAGAATATCCAATATCTCGATGATGATATCGACCGCTATCAGCTGTTTCGCGGGATAGAGATGACGCCTGTCGCACCATCTCCTTAGTAGCCTTCTTAGCCGACCCTTAGCCACAAATCCGCGATTTAGCTTCGACAGCGTTTAAAGTGCGCCAGCAAAATCCAGTTGGCGCCACGCTTCATAGCTGATAATCGCCACCGCATTTGACAGATTCAAACTACGACTGGTGGCCGCCATTGGAATACGCAGTCTCTGCTCGGTCGGGATAGATTCGATAATCGCCATCGGCAAGCCGCGGGTTTCAGGGCCAAATAGCAGCACATCATCTTTCGCAAAACTCAGCTCAGTATGCGGACGACTGCCTTTAGTGGTGCAGGCCATAATGCGCTTACCTGCCATGGCTTCGAGGAAGGCATCGAAGTCTTTGTGGCGAGTTACATTGGTTAAGTCGGCATAATCTAAACCGGCGCGGCGCAACTTTTTCTCTTCTAGATCAAATCCTAATGGCTCAATCAAATGGAGCTGACAACCGTTGTTGGCGCATAAGCGAATGATATTACCTGTGTTAGGTGCGATTTCTGGCTCATAGAGTGCGATATGGAACATACTGGCCCGCCTATAGAAAAATGACGACCGATTATACGGCAAACGGCAGCTAAGCGCAGTGACAGAGTGATGCTTTATCACGTTCTCATTTGGATGAGAGGAATGAACGCCACAATCGTTGTCGTCTGCTAAGCTTGAGAAAGCCACCGAGGGACCACTCATGTTTAGTCACTTTATTACCTTGTGTATTTTTTCCGTGCTTTTCCTTCACAGCATTCTCACCCCAGCCTATGCCGCGGATGATGCTGTCATTAATGCTGGCGGCAAGCGGCAGATGACGGCTTATTTAGGGGAGATCCCTGGATTAATTAATGCCGATGGCACAGGGCCATTTGTGGAACTGGTTAAAGCAATTGATAGGGCAGATCCTGAGGTTGAAATTGATATTAAGGTTTTCCCGCTTTCACGGGCGATGCTAGGGGTGACGCTTGGTCGTGCTGATTTTGGTTTGCCCGCAATCCGTAATAATGCCGCGTTAGATGCCTTACCTTACCGTTTTAGTTCTGTTTCATTTGGGCAAGTCACCCACGTGCTTTATACCAATGTGGATAAGCCGATAACGAAGGCCATGTTATTTGATCAGCAGCAAACGGGGCGCGTATTTACCGTGGAGGCGATTCCAGATTACATGCCTATCCCTGCGGAGCCTTCAATAACCATTAAACGTTCTTTGCTAAAACTTTCCCATGGCCGTATCGATGCCTTCGTTTGGGCGCAGGAAGAGGCCGATATGATGCTGAAACAGTTGAAGTTGCATAATATCCACCGTGAAGACTTTGGCGATTTTGAAGATGTGTTTGTGATTGCCAAGGGCCCCGAAGGGGATGCCGTGGATGCATATTTGAGCCGCATGATTGAGCTACTGCGGCAGACTGGAGAGTTAAGCCAGATTTATCAAACTATCCATCTGTCCTACAATGACTGGCAACCTTAGTTTTTAAATCAAATAGTTAACTTGATTAACTCCCACCGATAACGAGATATTCATAAACTTCACTTGCTAGGAGTGAAGACTGAGAATAACATATCTTCACATCAAGATACTTTTTGAAGAGTTATTATGCCTTTTGTAATCGCCTTACTCGCCCCTGTATTTTGGGGGACGACCTATGCGTTAGTGAGTCTCTACTTACATGATATGTCGCCCTATTGGGTGGCTGTGTGGCGCGCGCTGCCTGCGGGGATACTGATGTTGATGCTGCGTCCTCGCTTGCCAACGCTGGTGTGGTCAAAGCTCGGTTTGCTGGCTTTTTGTAATATCGGTGCCTTCTTCACCTTGTTATTTATCGGCGCGTATCGTTTGCCGGGCGCAGTGGCGGGGACATTAGGGGCGACGCTGCCGTTAATTTTCTTAATCTTAGCTTGGGTGATTGATAAAAAACGCCCTGAGATTAAGTGGTTGCTATTAGGCTTGATGGGATTAGGAGGGGTGATTTTACTGCTTAACCCGTCGGCGGATCTCGATCCTATCGGCGTGTTGTGCATGTTGAGTGCCACGACGTTAATTGCTTTCTCTTCTCGCTGGATGCAAAAGTGGGATGTGGGTGACTTTTTAGTGCTCACAGCATGGCAGCTGTTTTTGGGAGGCTTGATGTTAATCCCGCTGGCGTGGTTTATGGCGGGCCCGCCGCAATTACCGAGCCTGACAGTGGTGCCATCGTTAATATGGTTAGTGATTGCCAACACCGCCATCGCTTATTGGGCTTGGTTGTGGTCGATGCGAAATTTAGGGCCAGAAATCATGGGCATGGTTGCTTTAGTCAATCCCGTGGTTGCCGTATCATTAGGGGTTATGATCGTGGGTGAGACATTAGACATGCGTCAGTGGACGGGGATAGGTGTAATATTACTGTCGCTGCTGTTGATGAAATTGCCACAGAATTTAAGGTTAAATCCATTTAAAAAAGTGCAGCCCTAGTCAGTATTTTGGGGTAAATGAGGGATAGATGCTAAAACGTTTGGATCTGAATTTACTACCCGTACTTGAAATCCTGCTTGAGGAACAAAGTGTGACGGCCGCTGCGGCGCGTTTGCATTTGAGCCAGTCGGCGGTGAGTAAACAACTGACACGCCTGAGGGAAGTGTTTGATGATCCTCTGTTTGAGCGCACCGCCTACGGCTTAAAGCCGACGCCTAAAGCTTTGTCGCTCGCGCCTGAATTGCGCCAATGTTTACAGCAATTGGCGCAATTTACCCGGCCCGACACCTTCGAGCCCGCCTTAAGTCAGCGGCAGTTTAGAATGCATTTGGTTGAAACCACATACTCGCTGACCTTCCCGCATTTTATGCCCTCACTGCTCGCCCAAGCGCCCGGTGTGAGTCTCAATTGCCAAACCTGGCGTCTCGATACTATGGACAGATTACTGCGCTGCGACATCGACTTAGCCATAGGTTGCCGCGAGTGGGACGAGCGTTCGCCCATGCATGTGAGTCATATTCCCGACGACTTGTGCCATGTCGAGTTAGTGCAGGATTATACTGTTTGTTTGATGCGCCGCGATCATCCGGCGTTAGCACAAGAGTGGAATTTAGACACCTTCTTGAGCTATCGCCATCTGCAAGTGGCTTTTGGTGGTATAGAGCATTGGTTGCTGGATGATGTGCTGCAACTTGAGGGGCGTCAGCGTGATATCGCCGTGAACATGACCGACTTCCAGAGCGCTCTGGCCCTGTGTGAACAGAGCGACTTGATCCTCTGCGCGCCGTCACGTTATGCGTTTGCTGTGATGAAATCTTTCGAGCTTCAGTCTTTACCATCGCCTATCAAGCTGATCCCAGGGGCGTATTTATTGATGTGGCATAAACACTTCGAGCACGACTTGAGCCATAAATGGCTGCGAGAACTGATCATCAGCCAAGTGCTGCAGTCGATTACGCCTCGGGTTTAGGCTCTTTTGGCGCAGCTAACCAAGCGGTTAGTGCTGGCAAGACTGTTTTCCCATCCGCTAGCCCTTGTTGATAGACGGCGGTCACTTTATCGATATTGCGATCGAGCCTGGATAAATTAAGTGGCTGCGCTGGGCGGATCACAAAGGCATTATCAGCTGCAACGGCTTGGTCTAACTCGGTTAATGCTTGATTATAAACTCGGTGCCGCTCACTTAACGCATGGGCAACCGCGGGGTAGCGTTTGTAGGCTTTGCGGGCTAACCAATTCATTTTCATTGCGGATTTTTGGTAATGCTGATCTTGAGTCAAGATCACCACTTGGCGCGCATAACCATCTTTGCGTGATTGCGCTAACGGTATCGGTTCGCCTATGCCGCCATCGAGGTAATGATGTTGGTTAATCACGCTGGGTTTAGACATAAAAGGCAGGCTAGAGGAGGCGATGAGCACGTCGAGTAATTTATCGTGGTCGTCAAAATCGGCCATACCGAAGTAATCGGTTTTCCCTGTATTGCAGTTAAAGGCGCCAACCTTAAATTCGGTATGACTATTTAAAAATGTATCAAAATCAAATGGCAGTAACTCATAGGCCATGCGGCGATAGGTAAAGTCAGTGTTGACATAGTTGCCTGTCGCCAGCCAATGGCGCAGGCCCATATAGCGTTTGTCGTTGAGATATTGCTGCTGAATTTGAAGATTACGACCGAATTGGCGGGACACATAGGACGCGGGATAAATGGCGCCCGCAGATACCCCAATCACATAGTTGAAGTTGAGCGACTGCTGTAAAAACGCGTCTAACACTCCCGCGGTATAAATGGCTCGAAGGCCGCCACCTTCCAACACTAAGGCCACATCTTTCATCGAGTTACATCTTTTTATCGGTTCATCCCAGCAGAATACCTCAGCTTAGCGCTGAAATGAATGCGCACAGGGCACACTTAAGAAGCGTGGTCGACGTCGGTGTGATGGGACGCTTGCTTGGGCTTTATGGGTAAGATATTGGTTACGCTACCCTTTGTGCCGTCTTTTTTAACACACTGTTTAAAGTGATAGGTAGGGTTAAAATTCGCCTGACTAAAGTATTGGCTAAGCGTATTGAGTGCTTCTTTCACTTTCGCGGGCTGCTTATCCCGCGCTGAAGTGACGCTATAAACGCTGTAGGTATTGAGTGACCACTCGGGTAAGAGGGGGACAAGCTGGCCGCTTTCGAGGTATTTGTGCACTTCAGGTTCGGGCAGGGCGGCAAATCCGAGACCATCGAGAGTGAGTTGGATTAATGCCTGCATATTGTTGACGGTAATGCGCTGACTTGTCAGTTCGAAGCACTCTTGGCTGCGGGCATGGGTCAAGGTTTCACTGTTAGCGCAGGGGTGATTAATGCGATTAAAGGCCGCCAATTCAGCTGGATGCTTAAAAATTTTATGCATTGGCAAATAATCGGGTGAGGCGCACAGCAACATGCGCCAATCGGCTAAGTGGCGGGCAATTAAGTTGGAGTCTGATAAAGGACCGATGCAAATTGCCAGATCTATGCCCTCGTTGACTATATCTATTGGGCCATCCTGCAGCAGTAAATTCAAACTTATCTGCGGATGCGCCGCCAACAGGTAGTTAAAGGGCTCGCTCAATAGGCCGCCGCCAAAACCTATGGGCGCGGCAATCTTTAATTCCCCCGAAGGAATCCCTTTCAAGTTATTGAGTTTCTGCTCTGTACTTTCGACTATCGCGATGACTTTTCGGCAATTCTCATAGAACAAGCTGCCCGCTTCTGTGAGGGTAAGATTGCGAGTATTGCGGTGGAATAGACTGATTCCCATATCCATTTCGAGCTTAGTCACTTGTTGACTCACCGCTGAGGAGCTGAGGTTTAACTGCTGTGCCGCGGCACTCATTGAGCCCTTTTCTGCGATTGCCGCAAAAACGATCATGCCTTTGATCATGTTTTTATTCATTTTTAAGTAACTCTTACAAGTGAATACCTAAAGTCTAACTTGAGCTGATTGTAACATAGGGATACATTGCCGTATTGAAAATTGTTCTCATTTACAATCTTTGTCCAATGGAGACTTGTCGGTTATGTTGTTAAGTCGGAAGAAGTACCTCGCTCAAGTGATCACCGCTTTATGTTTGTTGCCCGCAGTACCTGTGACTAATGCCCTTGCGGACACCGCGCCTGATAAAGAAAAGATGATGGAAAGAATCGTAGTGACCGCATCGGGATTTGAACAACAAATTCGCGACGCGCCCGCATCTATCAGTGTGATCACCCGTGAGGATCTGGATAACCGCTTTTACCGCGACTTAACCGATGCCATGTTAGAAGTCCCCGGTGTAGTGATTACCGGCGGCGCTGACCGTCAGGACATCAGCCTGCGGGGCATGGGCAGCCAATACACGTTAATTTTAGTGGATGGTAAACGCCAATCTTCGCGCGAGACACGTACTAACAGCGACGGTCCAGGTGTTGAAGGCGCTTGGACGCCACCATTGGCGGCCATCGACAGAATTGAAATTGTTCGCGGGCCTATGTCATCACTTTATGGTTCGGATGCCATTGGTGGCGTGATCAACATTATCACCCGTAAAGTGCCGAATGAGTGGCAGGGTGAAGTGCGACTCGACACGACATTGCAGGAAAAGTCGACCTCGGGAAATGTGTACCAGAGTAACTTTTTCGTCAACGGTGGTTTGATTAAAGATCTGCTGGGGATGCAACTGTACGGTCAATACACTCAGCGTGAAGAAGATAATATCTACGGCGGTTACCGTGGCCGTGATGCGACGAATCTGACCGCGCGTTTTGCGCTCACCCCCAATGAAAACCACGACATCATGCTCGAAGTTGGGGTGTCAAACCAAGAACTCGACAGCAGCTTAGGCAAAACCGTTACACCGCTGGCACCGGGCGAAGCCTGCGGTCGTCGCGGTTGTCCTCAATCTTCAACGACAGAATACGAAAACAGCACCATTTCGCTGTCACACACAGGGCGTTGGGACTTTGGTACCTCGGATTCTTATATTAAGCATGAGGTTTTCGATAACAAAACCCGCAAGATGAAGATCACTAACACGGATGTGCAATCTAGCCTGATCACCACCTTAGGCCAAAGCCATACGGCGACTTTTGGCGCGGCATTTAACCATCAAGATTTAACCGACGAAACCGGCAATCAGGTCAGCGACTTAACCGATATCAGCCGCAGACAATGGTCGGTATTTTCTGAGGATGAATGGCGCATAGTCGATAACTTTGCCCTGACCATGGGGCTGCGTTTGGATGATGATGAAAACTTTGGTCACCATGTCAGCCCGCGTGTTTACGGCGTTTGGGGATTAACCGACAGCACCACCTTAAAGGGTGGCGTGTCGACGGGCTTTAGGGCGCCGAGTTTGCGCCAGACAGTGCCCGATTGGGGACAAGTGAGCCGTGGCGGCAACATGTACGGTAATCCAGATTTACAACCCGAAACCTCAGTGAACTATGAGTTAGGTGTGTATTCGGATCTGACCGAGAGCATTACCGCCAGTGCGGGTGTGTTCTATAACGAGTTTAAAGATAAGATCACCCGCGTGGCCTGTCCTGCGACTCAGTGTACCGATGGCCCCAATCAATTTGGTTCCGACCCAACCACTTATGTGAATATTGACGATGCTGTGACTCAAGGGATTGAGCTGAGTATCGACTATCGGATTCGCTCAAACCTGAGCTTTACGGGTAACTACACTTACACGGATTCAGAGCAAAAGACGGGTGCTTACAAAGGTAGCCCATTGAATCAATTGCCTAAGCATTTGATCCAAGCGTCGTTCAACTACGAGCCTATTGATAACTTAAGCACTTGGTTGAGAGTGAACTACCGTGGCGAAGAGAGCCAACCGACGACGGGACCGTCGTCGAGCAGTTTTATCGCACCTTCTTACACTCTGCTGGATTTAGGGGCTAACTATCAGTTGAACGATAGCCTGAAATTTAGCGCGGGCATTTATAACGCCTTCGATAAGGACATTACCCAAGAGGAATACGGTTATCTTGAAGATGGTCGTCGTTATTGGTTAGGTATGACCTACAGTTTTTAAGCCGATACCTTATTGCTAACATTTAAACGAAAGCCAGAGTATTCACTCTGGCTTTTTTTTAGTTGTATTCATCAATAGGAATGACTATTCGTTTGCTAGCGGGTGGGCGCGGGCAAGTTCGATGGCCGCACCTATGGCGTTTCGCGCTTGGGGACTGTTTTTCCAGCACGTGGTGCGCAGCATGCTGGCGACTAAGTTCGCGATATCATCCACTTCGTGTTGTGCCAGTTGTTGTAGTGAACTGATACCAATTTCTTCGAAGCGCTTTATCACAGTGGGGCCAACGCCTTTGAGTGCCAATAAGGCATGTTTTTCTTGTTCATTAAAACCCATAACCCATACTCCTTTATGACGGGGATCGCGCTCAAGCTAGCATTAAGGCAAGACTACCTTTAAACCAAGTTAACAAAAAGGCCCTCGAAGGGCCTGTTTAGTCTTTATGAATCAACAATCATCCGTAAGTGTTACATATCACAGGGTTTGAGCGGTTTACGCAACTGGGCGCGCACGTTGTCCATGCCTGAATAAAACTCTTTCATCATCAAGAGACCCATCATCTTGCCGTTGTCGGTCACGATCAGATTGTCGGGATCCCTTGGGTCATTCTTGATCGCACCAATTAACTTCATCGACGTCATTTCTTTAAACAAGGCGGTATCTAAGTTGACGCCAAAGGTTTCGCGGAAATATTTACGTGATAAACGGCCGGAGAACATGCCCAGCAAGAAGCGGTATTGCATCACGTCTTTTTTGCTGTAGTTCTTTTGCTGCTCGACACCCATGTGGCCGGCGGCAATGCGTTCTTGGTACTTGCGCAACGAGAAAGTGTTTACATATAGGGTGTCATTCAAGAAGCTGAATGAGCCAGAACCTACGCCTAAATACTCGTCGTAATCGATAACGTATTCGTCAAACCCTTCGTCATCGGTTTTACCAAATGCCCATGCAGACAATTGATTATATTGGCCATTTAGGCTGTTTAAGATCTGGCGATATTGGTTAGCCATATCCGCCTGCGGCGCTGCTAACTTGCCTTTCACGCTCTTGCGAGTCTGGTGGGTGATCATCAGCGGATAAGTGGTAATTTGTCTTGGGTCGAGGCGCGATGCCATGTCGAGATCGTGTTGGATCACTTCATCGGTTTGGCCGCGGAAACCAAAAATCAGATCGACATTGATAATGGGGAACAGCTCTTTGGCGCCCATGATCTTGTCGAAGGTTTGTTGACCCGTACCGAACTTATCGAGTCGGTCAGTCATTTTTAAAATATCGTCATTAAAGCTTTGCACGCCGATGGACATGCGATCCACTAGGCCTTTGAGTTGCTTAAAGCCAGGGCTGTCTAAATGCTGCGGATCGGACTCGCAGGAGACTTCTTTAATGCTCGGGAATAGGGTTTTCGCGTGTTCGATAGTACGGGCTAATTCATCTTCTAATACTGTGGTGGTGCCGCCGCCAATGTACATAGATTCAAAGTCATAGCCGAGGGCTTTAACCATTTCCATCTCTTTACGCAATGAGATGAAATAGGCGCGTGCCTTGTCTTCCTTGAACATAAAACGGTGGAAGGTGCAGTAAGAACACAGGGTATGGCAGAAGGGGATATGCGCGTACAGCATATACTTTTTGCCTTCTACGGGCGCTGGCATCATTTCTGCGGAAAGGGTATCGAGGCGAAGATTCTTATCGACGTAGTACTGCATGACTCGTTCCATCGAGCCGAGCATCCAGTTAGGGACGGTAATATTCGCTTGATAAGGCGTTGCGATAGCGCCGCTGAGCGTTTGGATAACTGAAGACATAACGATTCCTATCTGTGAGTGCTAACCGTAAACACCGAAATTGCTTTAGGTAATTCACGACCACCTAGCGCAATAAGACTACATCGTATGTAATTGTTAAAACGTGAACTGGTTATAAGTTACGGTTTCCTAACAAGGAAATCACGGGCTTTTTGTTAAGCAGATCATGTTTTAGCGATCAAGAATGCATTTTTATCCTGTGCTTTGGGGCGGAATCTACTGTGAATTTGGGGAGTTAACTTAAGGTGTCAGTCGAAATGTGGGGATTAAAAAATAACCATTCTAGGCTTTATTGCTGGTGGATTGGCGCTTGTCTATCCGAGGCACGGCTGTCATCATCCGAGCGATAAAATAACCTCTTGATCTAAAGGGCTAGCTATGTTGGTAAACGTAAACCGCTTCTTCCCGCTGTTAGCACTTATAGGCGCTGTGACCGCTTATTTGATGCCGAGTTGGTTTACTGAGCTTAAATCCGCCATAGTGCCGCTGTTAGTGATCATCATGCTCTCTATGGGACTCACCTTAGATCTGCAGGATTTTGCTAATGCATTCAAACAAAAACGCGCCGTACTGACTGGGTTGATACTGCAATTTTCGGTCATGCCGCTCAGCGCATTATTGATCAGCCAGTTATTAGGATTAGATAGGGAACTCACCATCGGCATGGTGTTGGTGGGCAGTGTTGCAGGTGGCACTGCCTCAAACGTGGTCTGTTATTTGGCAAAAGGCGATGTGGCTTTATCGATAACCATGACGGCACTGTCGACCTTAGCGGGCGTAATATTAACGCCACTGATTATTAAGTTGTTGATTGGCGAAATGATCGATATCCCCTTCGCCGATATGCTACTGAGCTTAGTGAAAATCGTGCTGTTCCCTGTCAGCGCAGGCGTGATCATCAATCATTTCTTTAAATCGCTGGTGGCCAAAGTCGCTCCCGCATTGCCGTTGATTTCAATCTTGGCAATCGTGATGGCGATTACCATTATCGTGGCATTAAATGCCAATCAGTTTGATAAAGTTGGCCCCATTATTTTGCTGGCAGTGTTCTTACATAATGGTTTCGGCCTCGCTTTAGGCTACTTTTGCTGCCGTTTATTAGGCTTTAACCATACCGTCTGCAAAACCATTTCCATCGAAGTCGGCCTACAAAACTCTGGCCTCGCTACGGCTTTGTGCATCAAGTTTTTTAGCCCTATTTCTGCCATTCCCAGCGCGATATTCTCGATATGGCATAACCTATCAGGCGCCATCTTGGCGGGATATTGGGCGAGGGAAGCGCAAGATTAATAAGATGTTGCTATCGAAAATAGACAATACCTGTGACCCGAGCAAGCGCGGCCACAGGGTTAAATTGCTGTGACTAATTAACCGGTAATGGGGTCTCGACGGGCATGAGTGTTAGCTGCAAGATATTTTCTTGCCGGCTTAATGCCAGTGGCAGGGTGTAGTTGGGAAAGCGCAATCCGAATACGGCACCTTGTTGTAACAAGCGATACAGCTGATCTGTCATGGCAATCGTTAAGCTGGCACAACCATGCTCGTCGACTCTGCCTTCAAGTGGTAAAGCGATGTTGTCAGCTAAGGTTAGATACGCCGCCATACGCCAGTGCGAGTTTTCTCCAATATTGTATGTGCTGCCATGCAATTGGTTGTAGCCTAAACCACAGGCTTGCAAGCTTAATTGGGTTTTTGCCGCTGTTGGTAGCGAAAGAGTCTGTTGCCGCTGCTGTGCCAGAAACGTGCCCCACGCTAGTCGATTTATAACTGAGTCCAGTGAGATGGGAACATACTGATCTTGCTGTTTTAATGCATTAAAGCGGGCAATGCCTTGTTCCAATTTAGTGAGATCGTCTTCATATTCCAGTTGTGACTTAAGCAGTTCATAAGCGGGAGCCTTCGCTTCTTCCATGCCCGCTAACGCCGCTTTCTCGTAGTATGCCAGTGCTTGCACTTTATCGGCTTCTGCGCCCAAACCGCGCTCTTCCATACTGCCTAAATGATAGTTAGCGATGGCAACTTGACGCTTAGCACCTTGTAAAAATGCGGCTTTCGCACCGCTGTAGTCATTTTCGTTCAACAGCAGGGCGCCGTAGTTTGTCCACGCCGTACCGTTATCTGCTGCCATACCCAATTCCAGCCACTTTATGGCTTGCTGGCGTAACTGCGGGAGGTCGAGACTTTCTCTTTCGCTGCCGACACTTTGCATTGCTATTAAGTAAGCGCGCAGATACTCGCGTGAGGCATTATCCAGATAGAGATTATCGGGTTGGAGTTCAAAGGCTTGCCCAAAGTAACTCCCCGCTCGCTGCGCCGCTGCGATTGCTTTCGCTAGATAGAACCTTGAGTTGGGATCTGCCGGACTGTGTGCTCGTCCTAGGTTATATAAGGTCGGTAGTTTAAGACCCCGCGCCAGCGCTTCTTCTAACAAGGGGATGGCCTGCGTTTTTCGATCATTATCCAGCGCCATGCTGCCTAGATTATGGCTGGCACGGGCCGAACCTAAGGCTCTGGCTTGTTCATAGTATTGGCTGGCGGTGGCATTGTCTTGCGTGACGCCACGGCCGAGATGGTAGGCTTTACCCAGTAGAAATAACGCCTCAGCATTATTTTCTGCGGCTTGCTGCTGCGCTTGTTTTAAGAAAGCGTCGTATTGTTGTTGCTCAAATAGCTGTTCCAGCACGGATTTTTCCGCCGTTGCTGCAAAGCTTAGGGTCAACAGTAATAGGAGAGTCGCAATCGTGAATATCTTCATAACATCCTTGTAGTTAAGCTGGGTACTTGCAAGCGGTAGTGCTAGTGAAGTTGGCTAACATAGTGGCTTGTTATGCTACAAGCAAGCCCTGCGGTTTGCCACTGTGTTATGACTGGCGGATGAATTGCAGCGGTAGCACTGCCGTGACTATTGTGTGGCATTGCCAGTTTGAAGGGATCTACCTACTATTTTCTTATTACCTAGTTTACCTGCCCCAATAGCCGTTTTGCGGCTTTATGGCTCAATGTTAACGACGCTGATTTGTGTGTCACTATTGACTGCAAAAATTGGGTGTTGCTGCATAGATAAAAGCAGTTTTAGTAAGTGAACTCGTTGAGTTCCACGCACTCATTTGCGCCATAGCGTCGAAAGCCAGCTTGGCTCTTTGCACGACTTTGGCGGTGTAATTGCCATTGGGATCTTTATATTTAAAGGATACTTCGGCAATCACGGGTTGCTGCGAGGCACTTGGGGAGCTGGTGTACCACAGCGTGAGTTCGAGTGAGGCATCTATGCTACCTAAATCGATTTCACTGCCTTCAAACACATATTCGTTAATCGTAAGGCCACCGACTTTGGCGAGTGTGAGATTGTTACTTAAACCGTAAGCGTCGGCAAAGCCTGAAAAGTGACTATTTACATCGGCCATATCATTTAAGGTGCGAGTGTTTGGCGTGGTGGTTGAATGGCTATACACGATGCGGATGCCATTATTGATATCTGGACTGATATCGGCTTCGAGTTTAGTGTCGGCTTGATTATCGGTCGAGCTGAGATCTTCAAAGTTAGCAATAAATCGATCAGGGCTGCGAAATTTAAGGGTAACTTCACTTTTGCCATCGCTCGTTCGGTCACGAAAAATGTAACCTAACTGCTTTAGCTGACAACTGGTTGGGGTGTCATAGAAGGCTACTTGACGTGTTTTGGTTAATGCCCAAGAGCCTGTGATATTACGACTGATAGCGTTGCTAATGACTTGTTTAGCATCCGTTAGATAGTTATTGGCGGTTGTACTTTGAGTGGCGTAATTAAAGTGGCTTGGGTCGAGCAGTAACTTATATTCCCTTGATGTTTCAACAGGATTAGCCTGTGCTGCACATGAAGCGAGCAGGGCACTTGAGGCTAACGTGAATGACATCATCGCCTGTGAAGCTGTTTTCATCGGTTAATCCTTAAGTGATGTGCTGATGCAAGGTGGTTTACGCTTTCGTTATAACGACTTCTATTGCAACTTGAGTGTGTCATGAGCCACCACTATCGACAGTAATGACTCATGCGGCTGAATACTTAATGGTTATTTATCAGCCGATAAGGGTAAGTCGTCAACGATATACAAAGTGTTGCCGTCGATATCACTTTGCGCACTATCGAGATATTTCTGTTCTAAGGTGTTGTTATTACTCCAAGTGGCGAAGTCGTCGTCATTTAGCACACCCAAACGCTTATTATCAATGAGCCACAAACCTTCCATTTTGTCATGGGGATATTGTGTTTTGGCAGCCATATCTAGCACCAGTGATTTTGTCGCAGGTATGATGCCATTGGCTGCAAGTGCCTCCCAGCCATCGCTTAGCGCTACCTGTTCTAAGGTTTTACCTTTAAGGGTTAATCCTAGCGCTGCATCTTGGGCGATATCAGCTTGTGCAGTGACGGTCTCGAGATCCGTCGCGCCGCTTATATCTATCTTGTATACATGCTTCATCACATCAGTGTCGATATTAAAAAACTTGCCATCACGTTCTAACACTAAAAATTGGCTGTTACTTAAGGCAACGATCGCTGAGTTAGAGTTCTGGGCAATTTCTTGCTGATACAAATATTGGCCAATAGCGCCGGTCGCCAAGTTGATGGTCACGATTCGAGTCAGGGTATTATTGTTAACACTCTTATCGGGATTAGAAATGGTCGATTGCATTATGCCCACAAGTGTCGACTGATCGGGTGTGATAGTCAGCCCTTCCATCCCTCGGTTGGCGCGGCGATGAGCAAACTCTGCTGGTAGGCTATACAAGTTGCGTGCATCGTCTGCGAAAGGATTAATCCGCGCTATTTCTACGCCTTCAGCCGAGTAATGCACTATATGTGGACCATACTCATCGCTGACCCAAAAACTGCCATCATCCATAGCGGCAAGACCTTCGGAATCTAACCCATAATCATCGAGTTTGATGGGGTTGGTCTGGGCATTATAAGGCTGGCTCATGTCTTGACGAATGACTTCACCTGCTGCGTCATAGGGGATTTCTCCTGTGCCGCCAAGGCTGCTGCTATTGGGAAGTCCGGTGATCAAGCTGCCATCTGGGCGTTTAAATAAGATGTCTTTGACCTTAGTGATGGTGCCATTGGTTTGGAGTTCAAACAAACCGATACGTGGTGTGTAGTCCGGTGTGGGAAACATCTTACCTTTGCCTAAACTACCTGTGTAATCCGCATTCGGGCCGCGATCGGTGAGGGCATAAAATTGAATTGGATTATTGGGATTGGGCGCAGCATCAGAGCCATAGCCGCCGTTACGAATTTCAAATTTAGTACCAGGCATAGCGCCATTGTCTAAATCATTGCGCAGCACAGTGAAAGGTAGATGCACCGCTTTATCCGTAAACAGGTCGGCATTACATATATAGCTGCTATCAACCACTTCATTACCGTCGAGCTTGCCGTTATCGTTGCTATCAAGACCTGAATCCGTGCGAGTGCCGCCCATCCAGCAGTGGCTATCACCGCCAATTAAGGTGGTTTGTGTGGTCAGGCTACTGATGTCTTTATCATCCGAATTACAAGCACTCAGTAGCAAAACTGACAGGCACAAGCCTGAGGTGACGAGCACATTTTTCATCTTAATATATCCCGATTCTCAAAGTGATTAGCTATGTTGTTGGCAGGTAATATAAAGTTAGGCTGTGAACTAAATGTGACAAGTGTGCCGCTTTACGTATCGAGTACGAATACGGTGTTCACGAGTAATACGGTGTGTTGGTTGTGCATGAAAGCCCATGATGGCGTCGTTGATTGGCTGTGTCCGAAGTTTTTAGTGGTATTTAATATTGTGGAAGTGAGTGGTGGGCAAACTTCATTTCTGAACTATAGGCTTCACGCTCTTATTATTGGTTTAGTACTTTTGGCACTAAATCAATTTAATCATTTTTGGTAAATAATTTTTATAGAAATACAGTATGAATAATAGGTTAGGTTTATTGGCTGCGATTTATATTGCAGGTGCGTGACTCGCTTTATATCTGATTTAAAAATCTGCGGCCTAATAATTTTTGTTTTTAGGCCGCAGATTTAAAAGCTTTTTTCAGTGAACCTAAACTTAAAGCTTAAAATGCGATACCACGCCGACGAGTTGTTCCTTCGTCGCGGCTAAAGCTGTAGCGCTGTTAAGTGTGGCTTTACCGCTTTGATTCAGTTCATCGACCATTTCACGAATGGTGGTCAGATTACGGTTTATTTCTTCGGTCACGGCGCTTTGCTCTTCGGCGGCGGTGGCGATTTGAGTGCTAAGCCCATTAATATCGAACACATAAGCGGTCAGGGTATCGAGGTTTTCCGTTACCAGAGTGGTTGTGTCGGCGGCTTGTTGGCAACTGTGTTTGGTTTGCTCCATTGCGGTCACGGCTGCATCGGCACCGCGGCGTAAACGTTCAAGCATCTGATTAATTTCGGATGTACTGGCTTGAGTACGAGCGGCCAATGTACGCACTTCATCGGCGACAACGGCAAAGCCTCGACCTTGTTCACCTGCACGGGCGGCTTCGATGGCCGCATTAAGCGCGAGCAGATTGGTCTGATCGGCAATCTCGCCTATTACATTTAAAATAGCGGCGATATGACGAGTGTTTTCGTTCATATCATTAATCGCATCAGCAGCGACATCGACTTTAGTCACCAAGGCAATCACAGTGTTGGCGGCCTGTTCAACGGCTTTCTTCGATTTATCAGCTTGTTCATTGGCTTGCTGAGTAAACTGGGCTGCAGACGCGGCATCACCAGCAACATTATGGGCAGTCGCGCTCATTTCTGTCATGGCCGTTACGACTTGATCGGTTTCAACTGCGTGAGTTTGTAGCGCACCACTATTAGTATTGGATTGCACATTCAGTTGGCCAATTTGGTCAGACATGTGATCTGAGGCTTGGGTAATATCTGAGATGATATGGCGTAGATTATCGATAACGCGATCGAGATCGGTGGCGAGTGAGCAGAGTTCGTTGTCACCCTTTATGTTTGTCTTATGTGTCAGGTCGCCATTGGCTAGGCGTTCCATTACCAGCTTAATGTCATTGACGATTCGGACTAAGCGTTTGCTCATAAAGAGCGCGCTGAGAAGTGAGGCGACGATGGCAAATAAGATCAGGCCCAGTAACAAGCTTTCTGCGATAGCAATCTTACTTCCTGCCTGAGTTCCTAACTCATGGTTACGGATAGTGATTAGTGAAACGAGTTGATTAATTTGATCGCTGACTTTAGGACCAAGTTCACGACGTTCCTCAGTTAATTCCGCTTCTGTGGCGGTGCGCTCAGCAAGCGTAACTTCTTGTCGGTAAAGACGGACATATTGCTCTATCTGTGGCTGTAAAGTCTGAATGTTACGCGCATTTTGGCCCATGAAATTGGTATCAAACTCAGAGAGTTTAGCGACTAGCTGGCCAATGTTCTGTTGATGTTCGGTAATGCCTTTCTCATTATGGACTAGCGTCGCTAAACGAATTTCAAAGAATTCAGCACGTATATTGGTAAAGGTTTCTGCCAGTTGGGTATTGTTGACTAAGCGACGGCTAGTTTCACCGAGATCGGTAAAGCTACGATAGGATGAGATACCTATGATGATGAGTAGAATACTCGCGATCACCGAGGGCAAAGCCGTCAGAGTGCTGGTTTTAGCATTATTAAACATAGTGACCTTAAGGATGTAAAACGGATAGAAGAGGTGATGTAATAAAATTACACAATATGGTCATTTTACAGCCTGAAATAAGAGGGTGCAAATTGATGGTTGTTTAAATTGCGCCGTAAGACATTGTTTATTAATACTTGTTTTCATATTGTTGCATGTAATCAAACGCTGGGTTGATTATATATTGAGTTGCAAAATAACGTTGAATTCTAACTTATTGTTTTTAATTTATAATTTGTCAAATCTCCTAAATTTTGAAACATAGAAACAACACAGGTATTGCCGCTTGTGAGCGACATCACAGATTGAGTAAATGTTTTAAAATGTATCAATATTGGCATGAAGAAAAACACCTCGACGCTGCGAGGTGTTTTTAGTTAAACCGACGGTTTCTTTTATGCGTTGAGACGAGTCAATGGCAGTTAGTCTTTGTATTTCAAGGTGCCGTTAGCTTTGATTTCGTCAAACCACAGGTTGTGATGTTGGCTTGCCCAGTTTTCATCGCAGTAGCCAGATACCATACATTCCATACCACCTTCTGACATCACGGTTGCCATGAAGATGTGCACGATAGAAAAAGCACAGATCACGATAGCGCTCACTGAGTGCAGCACGAGTGCGATCAAGCTTAATGTACGGCTAGGTTCGAATAGGTTAGGGAAGAGTAACAACATGCCAGAGGCCGAAATCACTAATCCAAATAGGGCAAAGGCCCAGAACCACATTTTCTCGCCGGCGTTCGCAAAACCTGCGTCTGGGTGCTTACCTTTGAACGGGCCAAAGTTAATGTAACCGCCCACGACTAAGAACCACTTTACGTCGTACATTTTTGGCAGCTGGTTTTTCGCCCATAGGGTGGTCATTAATGCCCAACCTATCATGAAGGGAATCGCCATCACGTCATGGATTTGCTTCGCACCGTACACGATAGCTTCCCAGATGCCTTCGCCCAACCAAGGTTGGAAGAAGAAGCGACCCGCCAGCAAGGTTAAGCCCGTTAAAATCAACAGTAAGCACGGGATTGCGCCTAACCAGTGGATTGAAACATCAAACTTAGACCAGCGATGCACTAACTTGCCTGAAAAACCATGGTGCAGCTTGGAGATGCCGTTCACCTTGATAAATAAAACAAAGATAATGATCATACCAAATAGTGCTGCCATTAAGGCCGGCGCTAACCAGTTGCTACGTAGCTCTAATACCCGTAGATCATAGGTATTGATGGGTTGAGCATGAAACTCACTTTGGGAGGTAGTGTATCCCGTGGCACCATCCTTCAGTTGTGCCCAAATTTGGGCATCAGACTGTTGAACTTGATTCGCCAGTTCATCACCACCATTGGCTATACACACGGCACTAAATAGCAGTGCTATGGCTAAACCTATGTGTTTGAACCATTTGTTCATCATACATCCTCTCTCACTTAGTCGCGCCCGCCTGAGCAGGCGCTAAGCTAAGTCTGTGGACAGATCCTGTTGACCTGAATTAATTCCAAGCCGCGTTTTTAGCACCACGGTAAGCCACACGTTCACGGAAGATGTTTGAAACAATTTCAGCATCACCCGCTAACAGGGCCTTAGTGGCACACAGTTCTGCACACATAGGCAGTTTGCCTTCGGCAATACGGTTTGAACCGTATTTTTGGCGCTCTGCCTCTGAGTGGTTTTCTTCTGGGCCGCCAGCGCAGAAGGTACATTTATCCATCTTGCCACGGCTACCAAAGGCACCTTTTTTCGGGAACTGTGGTGCACCAAACGGACAGGCGTACAAGCAGTAACCGCAACCAATACAGGCATCTTTGTTGTGTAGCACTATGCCGTCGTCGGTTCTGTAGAAGCAATTTGCTGGGCAAACTGCCATACAAGGTGCATCGCTGCAATGCATACAGGCGACAGAGATAGAGGCTTCTCCACGTTGACCATCATTAATGGTCACTACGCGGCGGCGTTGGATCCCCCACTCGAGAGCGGAGTCGTTTTCGTTTTTACAGGCAGTGACACAACCGTTACATTCGATGCAGCGCTTGGTATCACACAAAAATTTCATTGTAGCCATAATGGCAATTCTCCTAGCTTAAGCTGCACTTACGCTTTAACGATCTGACACAGTGATGCCTTGGTTTCCTGCATTTGCGTCACAGGGTCATAACCATAGGTCAGCGCAGTGTTAGCGGATTCACCGATAACGTAAGGTACTGTGCCCTCTGGATAATTAGGCGCTAAACTTTCACCGTGCATCACACCCGCAAAGTGATATGGCATAAAGGTCACGCCAGGTTTAACGCGTGGCGTCACCATAGCTTGTACCTTAATACGGCCACCTTCGGCGCCTTCTAACCAGACGTCTTCACCGTTACGAATGCCGCGATCAGCAGCATCGGCAGGGCTTATCTCAACAAACATTTCCTGTTGAAGTTCAGCTAGCCAAGGGTTACAACGAGATTCTTCACCACCACCCTCGTATTCAACCAGACGGCCTGAGGTCAGTACCAGTGGGTACTTGCCGCTCATGTCTTTGTCTTGGATTGACTTATACAGCGTAGGCAGACGGTGCACTTGCATGTCGGCATAGGTGGGGTATTTGGCCACTAAGTCACGACGCGGCGTGTATAACGGTTCGCGGTGAACTGGCGCTTGGTCTGGGAAAGTCCAAACAATACAACGGGCTTTAGCGTTACCGAAAGGAATACAGCCATGCTTGATCGCTACGCGAACTATGCCGCCTGACAAATCAGTCTTCCAGTTTCTGCCTTCGGCTTCGGCTTTTTCTGCCGCCGTGAGATCATCCCACCAACCGAGTTGCTTGAGCAGCTTGTCGGAGAACTCTGGATAACCGTCTTGGATTTCAGCACCTTTAGAGAAAGAGCCTTCAGCCAATAGGTTTTTACCATTGTATTCAACGCCGTAACGGGCACGGAAGTTACCGCCACCGTCTTTAACGTGTTTGGATTGATCGTAAAGAATTTGGGTGCCTGGGTGTTTCGCTTCTGGTGTGCCCCAACATGGCCAAGGTAAACCGTAGGTTTCGCCTTTCGCTGGGCCGCCTGCGGCTTCCAGCGTCTTGTTGCTGAAGGTGCCCCAGTTTTGGGTGTGTAGTTTCAGGCGTTCAGGGCTTTGGCCCGTCATACCGATAGTCCACATGCCGCGGTTGATTTCGCGGGTGATGTCTTCAATCACAGGCAAATCATTTTCTTTGGCGATGTGTTTAGTGTACTGCTCAGCAAAACCGAGTTTTTGCGCTAAGCGATACATGATTTCGATATCGGTTTTAGATTCAAACAATGGCTCTATGACTTTTTCGCGCCATTGCTGACTGCGGCCAGAGTTTGAAACTGAGCCTTCAGTTTCAAATTGTGTCGCTGCTGGTAGCAAGTAAACACCATTCTTACGGCGATGCATCACACCTGCCATAGTTGGGAATGGATCGACTACCACCACAGTGTCCATCTTGTCTAAAGCATCACGCACATCACGTTGGCGGGTTTCGGTGTTCACCGATTGTCCCCAGAAAAACGCCATGCGAATGTTATCTTGCTGCGCGAGTTTGCCTTTTTCTTCTAGTACACCATCGTGCCAGCGAGAACAAGGGATACCCGGTGTAGTCATAGGTTCACGGCCAAGATAAGTGCCTTGATCGAAACGGCCTTTCATCCATTCCATGTCTAGATCCCACACGTGGGTCCAGTGAGTCCAAGCACCTGTTGTTAAGCCGTAGTAACCCGGTAAGTTATCGAACAACAAACCTAAGTCAGTCGCGCCTTGTACGTTATCGTGACCACGGAAAATGTTCGTTCCGCCGCCTGACACGCCCATGTTACCTAGCGCTAATTGCAGAATACAGTAAGCTCGGGTGTTGGCATTACCGACGTGGTGTTGAGTTCCACCCATACACCAGATCACAGTACCTGGGCGGTTATCAGCCATTAATTTTGCGGCTTGATAAACCTCGGCTTCGCTCACGCCTGTGATGTTTTCCACTTCTTTAGGTGGAAATTTTTTCGCCTCGGCGCGAATGGTGTCCATCTCGAAAACACGTTGTTGGATAAAGGTTTTATCCTCCCAACCGTTTTCAAAAATGTGCCATAACATACCGTAGATAAACGGAATATCAGTACCTGGACGCAATGCGCAGTGCAGATCTGAATGCGCCGCTGTGCGAGAGAAACGTGGATCAACTACGATCAACTTAGCGTTGTTTCTTTCTTTAGCGATCAGAATATGTTGCATCGCCACTGGGTGGGCTTCTGCAGGGTTAGCACCGATAAAGAAAATCGCATTCGCGTTTTGGATATCGTTAAAGGAGTTAGTTTGCGCACCGTAGCCCCAAGTGTTAGCAACACCGGCTACCGTGGTAGAGTGACAAATACGAGCTGAATGGTCGACGTTGTTTGTGCCCCACATGGCCGCCAGTTTGCGGTACATGTAGCAACCTTCGTTTGAGAATTTAGCGCTACCCATGAAGTACACTGAGTCTGGGCCGGATTCTTTACGAATGTTGAGCATTTGGTCGCCCACTTCGTTAATCGCATCTTCCCAAGAGATTTTTTTCCACTTGCCATCAACTAATTTCATTGGGTATTTCAGGCGTTTTTCACCGTGGCCATGCTCACGCAGTGCCGCACCTTTAGCACAGTGACCACCGGCATTGAAGGGGTGATCGAATGCAGGTTCTTGACCCGTCCACACGCCATTTTGTACTTCAGCATACAGACCACAACCGACGGCGCAGGCACTACAAATAGTACGTTTGACTTCGATGGGGGCATCATGTGGCACTTCTTTGGCTTCGACGCGACGCATCATGCCTGTGCCCATTAGTGATGCCGCCGCAATACCACCGGTGGCAATACCTGCGTGCTTCATAAATTGACGACGGCTAATGCCTAACGCCGGTTTGTCTGCCACTGGGGCGACACTAGACTTGCGAGTTAACTTCATCGCTGACTTCTCCTAGATTAGCTGCGCAAGCTATTGTAATAGCTGCTGATGTGCGCAGTTTCGTGGTAACCCTTTGACGCTTTGGTGGTTACCTTGCTGCTCTCGCTGGCCTGTGCCACGCTCATGCCGGTTGCGGCAATCGCGGCACCCGCTACGCTGCCGACGGTGAGCGCTTTAAGCAAAGATCGGCGAGATAAATCGCTGGGTTGCTGCTTCATGCTGACTCCTGAAATTAGGGTTTAGTTTTTTGAGATCGCACGGCTTTTTGTGGGATTCATCTTGTTATCAAAAAGTGAAGATTAGCGGTGGAAAAGATAATGAAATCATCTTGTTTACCATCAATCATCGCGCGTGACGGCAATATAGTAGGAAAGGGTTGAGTAGGAATTGATCTAGGACAGTTTGCAGTGTTGAAATCGGCTGCCGTACTCTTAAAGCGTTAACTCGCTCATAGTTTCGTAACTCCCTTGGGAGCAGGGCGCTGGCGGCTTTTTGTATCACAGCGTTTCAGGTGTTAAGTTTTTGTATTGTCGAATGAAGTACAGATAAAAGAACACCCCGCATTGCGGGGTGTTTTAAGGACCTAAAAACTGCGGTTTAACTTAGTCTTTATATTTTAAGTTGCCGTTGGCTTTGATTTCGTCATACCAGAGATTGTGGTGCTGGATTGCCCAGTTTTCATCACAATAGCCTGACTTCATACACTCCATACCGCCTTCGCTGAGTACGGTCGCCATCCAAATATGCACTATGGTAAAGGCAATCAGGATAATGGCGCTGATTGAGTGGATCAGTAGCGCGCCCATGGAGGCTTCGCGCGGTAAGTCTAAGCCTGGTAACACCAACATGATGCCAGAAACACTGATAAATAAACCAAACAACATCAGGGTCCAGAACCACATTTTTTCACCGGCGTTAGCAAAACCACTGTCGGGGTGTTTACCTTTGAATGGGCCAAAGTTGATATAACCACCTACCACTAAAAACCATTTCAAATCATACATCTTGAAGGTTTGTAGTGGCATCCATTTGACGACACAGACAGCCCAAGCCGCGATAAAGATGGGACCTGCCCAATCGTGGATGGTTTTGCTGCCGTAGATCAATGGCGCCCACAGATCAGGCCCTAACATTGGCTGAACAACATGCTTACCTAACATTATGGTTAAACCGGTAAACATCAGTGTCAGGCAGCTGCTCGCCATGATCCAGTGGATCCACAGATCCGCCTTAGACCAACGTGCCACTAACTTGCCTGAAAAGCCTTTGCTGAGTTTTGATGGACCATTCACCAGATAAAACAATAAGAAAGCCCCGAACACACCGACAACGGCCAACGCCATAATCGGGGTCAGATATTGGTTTCTCAGTTCTTTACCTTGGTTACCGGCAACGTTAATCAACACCCCAGTATCTATCCCTTTGGCAGTGGTATAGCCGGCATCGCCTGCTTTTACCGCCCGCCAAAGATCGGCATCGCTGGTTTGCGCTTTCGCCTGTTGTTGACTCGATTGCTGATCACTTGCGTGTACTGGGCTTGCGCTCATTACTGAACCTAATCCCAATCCCATGACGAGCACCAACAGAGCAAACAGACTGCGCAGTGATTTGTTTAACTGTTTGTTTAACATTTTCACTCCTTGCCAGAGGTTAGGGATGACTCCCTAACCCTCGCTCTGCTTAGTTAATCTCACCTGTCTTGGGGTTGTAACCCCAAATCACGTTAGGATTGCCACGAGAGGCCATACGTTGACGGTAGATATTGGATACCACTTCCGCATCACCCGCGAGCAGGGCTTTTGTCGAACAAAGCTCAGCACACATAGGTAGCTTGCCCTCAGCAATACGGTTGGCGCCGTATTTTTTACGCTCGGCTTCGGAGAAGGTTTCTTCTGGGCCACCGGCGCAGAAAGTACACTTGTCCATCTTGCCACGACTACCAAAGGCAGTCTTTTTCGGGAATTGCGGTGCACCAAATGGACAAGCATAGAAGCAATAACCACAACCGATACAGGTATCTTTGTTGTGTAGCACTATGCCGTCTACGGTACGGTAGAAACAATCTGCCGGACATACAGCCATACAGGGAGCGTCAGTACAGTGCATACATGCCACTGAAATTGAGGCTTCGCCAGGTAGACCATCGTTAATCGTCACCACGCGGCGACGTTGGATTCCCCACTCGAGAGCGGAGTCGTTTTCGTTCTTACATGCAGTGACACAACCGTTACACTCGATGCAGCGCTTGGTGTCACACAGAAATTTCATGACTGCCATAATGGCTTATCTCCTCATCAAGTAGGTTAGGCTTTGCTGATCTGGCACAAGCTGGACTTAGTTTCTTGCATCTGAGTCACAACGTCGTAGCCATAAGTTAAAATGGTGTTTGCCGATTCGCCTATCACGTAAGGCACTGTGCCTTCAGGATAGTTTTTCGCTAGGCTTTCGCCTTCAAACACACCAGCAAAGTGGTATGGCATAAAACATTCACCGGCAATAACGCGCGGTGTCACCATAGCCTTAACCGTGATCTTGGCGCCTTCTGGGCCATGCACAAAGACATTGTCACCGTCACGCACGCCGCGATCCGCAGCATCCGCAGGGTTCATCTCGATGAACATTTCTTGTTGCAGCTCAGCAAGCCAAGGGTTAGAACGGGATTCTTCACCGCCACCTTCGTATTCCACTAGACGACCAGAGGTCAGTGTCAGTGGGAAGCCTTGGGTAAAGTCCTTATCCTGGATTGACTTATACAAGGTCGGCAGACGCGCAACCATACGGTCTTCGTAGGTTGGGTACTTGGCGACTAAGTCACGACGAGGAGTGTAAAGTGGTTCGCGATGTTTCGGGATATCATCTGGGAAAGTCCAAACGATACAACGGGCTTTAGCATTACCATAGGGAATACAGCCATGCTTGATCGCAACACGTTGGATGCCACCAGAGATATCTGTCTTCCAGTTTTTGCCTTCGGCGCGTTTTTTCTCTGCATCTGTGAGCTCATCCCACCAACCCAATTGCTTGAGCATGTCGGCGGAGAATTCAGGATAACCATCTTGAATCTCACTGCCTTTAGAGAAAGAACCTTCGGCAAGAATGTTGACGCCATCATGCTCAACACCGTAACGGGCACGGAAGTTACCGCCACCGTCTTTCACGTGTTTTGATTGATCGTACAGGATCTGGGTACCAGGATGTTTCATCTCTGGTGTACCCCAACATGGCCAAGGTAAACCGTAGACTTCACCCTTAGCAGGGCCGCCCGGTGCTGTTAGATTATTGACATCGAAAGTGCCCCAGTTTTCTTGGTGCATTTTCAAACGCTCAGGGCTTTGACCTGTATAACCCACAGTCCACATACCGGTATTGAATTCGCGGGTTACATCTTCAATCAAAGGCTCTTCGCCATTCACTTGAATGTGCTTACAGAATTCTTTTTCAACGCCCCATTTTTTAGCGAACTTATACATGATGATGTGATCGGGCAGAGACTCAAACAGTGGCTCGATGACTTTAGTGCGCCATTGGAGCGAACGGTTTGAGGCCGATACTGAACCATAGGTTTCGAATTGCGTCGCCGCAGGCAATAGATACACACCATCTTTACGTTGGTGCATAACACCTGCCATGGTTGGGAAAGGATCGACCACAACCACAGTATCCATCTTGTTCAATGCTTCACGCACTTCACGACCACGGGTTTCAGTGTTAACCGATTGTCCCCAGAAGAACGCCAGACGGATATTGTCTTTCTGCGCAATTTTGGTTTTATCTTCTAACACCCCATCGTGCCAGCGAGAGCAAGGAATACCGGCTGAGGTTTGTGGTGTTTGTCCTAGGTATTCACCTTGGTCGAAACGGCTGGCAATCCATTTAGGATCGAGATCCCATACATTTGACCAGTGAGCCCACGCGCCAGTCGTTAAGCCGTAGTAACCGGGTAAGTTGTCGAATAACAAACCAAAGTCAGTCGCGCCTTGCACGTTATCGTGACCACGGAAGATGTTAGTCCCGCCACCAGAAACACCCATGTTGCCTAGGGCTAACTGTAAAATACAGTATGAACGGGTGTTGGCATTACCGACGTGATGCTGAGTACCGCCCATACACCAAACGATAGTGCCGGGTTTCGTTTCGGCCAGCATTTTAGCCACACGGTACATTTGCGCCTTAGGCACGCCTGCAACCAGTTCAACTTCTTCTGGGGTGTATTTTTTGGCTTCTTCACGGATACGCTCGATGCCGTATACGCGTTGTTTGATAAACTCTTTATCTTCCCAGCCGTTTTCAAAAATGTGCCATAACAGGCCATAAATGAAGGGGATATCTGTACCTGGGCGGATATGCACGTACTCGTCAGATTTCGCTGCAGTACGGGTGAAACGTGGATCAACAACAATCACTTTCGCGCCGCGTTCTTTGGCGACCAGAATATGTTGCATTGCCACTGGGTGCGCTTCACTTGGGTTTGAACCCACAAACATGATGCACTTAGTGTGGCGGATATCGTTGATAGAGTTAGTTTGCGCACCGTAGCCCCAAGTGTTAGCAACACCGGCTACCGTGGTAGAGTGACAAATACGGGCTGAATGGTCGACGTTGTTCGTGCCCCACAGTGCCGCTAATTTGCGATATAAATAAGCTTGTTCGTTCGAAAACTTGGCGCTACCCATAAAGTAAACTGAATCTGGACCTGACTCTTGACGAATCGCTGCCATCTTGTCGCCGACTTCATTAATCGCTTGATCCCAAGAGATCTTTTTCCACTTGCCGCCTTCTAACTTCATTGGGTATTTCAGGCGTTTTTCACCGTGGCCGTGCTCACGCAGTGCCGCACCTTTCGCGCAGTGTCCGCCTTGGTTAAAGGGATGGTCAAATGCCGGTTCTTGACCTGTCCACACGCCGTTTTGTACTTCTGCATATACACCACAGCCGACGGCGCAGTGAGAACAGATGGTACGTTTAACTTCGGTGGGAACATTGTGGCCTATGTCTTCTTGGGCTTGTGCTTTACGCATCATGCCTGTGCCCAGCATGGACGCCGCAGCGATGCCACCTGTGGCAAGACCTGCCGATTTTAAGAACTGACGACGATTGAGACCCAGTGCTGGCGCTTTAGATTCGACAACCTGGTCTGTTTTGCGGGTTAATCGCATCACACACTCTCCTCTGGTTAGTTGTTCAACGACGCATAATAGTTACGGATATGGTCGGTTTCGCGGTAGTTCTCACTCTTAGGCTCTGTGGTTTCTGCCGTTTTTGTCGAAGCTAACGCCTGACTGCTCACGGTTACCACTGCGCCTGCCGCACTGCCGAGTGCCAATGCTTTGAGCAATTGACGACGGCCCATGTCGGAAGCTGGCTTCTTCATAGTGTCTCCTTGTGTTGCGGGATAAGGTGGCACAAACGGCGACCACCTCGGTTATTGTGAAAGCCTAAGCTTTCGTTATTGTTTCTAAGGTGTGCGTGACTTCACCTTCGCTGTTTCAATTCATTTAGCATTGACATCTAGCGCGCACATGTCGCGCTATTATTAAGCTTAGTCTTAGTTCATTAATTCCACTGGTTTTGCTAAATCGCTGGTGGCCGGTTGTAGGTCGGCGCTGCCTGGGCAATTGACAGGAATATCTAAGCTCAATTGTTCAAAGGCACTGGCTTCCATTTCAAAGAAGGCTTTTGCCAGCTGGGCTACAGTCGCGTAAAACGCAGCGCTGGGCGCCTTAGCTAAGTTGTCGCAGAAGCGGCCAATCCAACTGCCGATATGGCGTTGGTAAAAAGCCAGTTGGCGATAGCTCGGCGCTTCTAAAATCAGGGTTCCCATCACTTCACACAGGGCGGCTACATGATCTTCGGGTTCTTTGACATTGTCTTCGCGTTCAAATCCGAGTTGCATCAGGTCTTGGCGCAATAATGCCAGCGGTTTGTCCATTAAGGAGCCTGTCATAAACCAGCTGCCATAGGGCAGGATTTCACCAGAGCCTACGCCGAGGAATAGCGCAAAATACTCGTCTTCAAGCTGTTCATCGCTGAACTGCTGGGCGGCAAGTTGGAGTGATAACCAAGCTTTGGTCATCTCGTTGTCTTCATTGGCATCGATTTCAAGATTGGCCAAAAATTGCAGCAGTTCAGGACTCGGCTGACGGCGCAATAGGGCGGCTAACAGTTGATAGATATCGGCTCTCAACTGATCGTTTTCTGAAACTTGTCTTACTGATTCGGTCATAGCGGATCTCTTATCGCAGTTGCTTTTCAGGATCTTGAAGAATGTCTTCAAACATGTCTTTTACCCGGCAGTCGCCACACATTTTCAAGCGCTCAATATTGGCAGAAAATGCGCTGTGCGCGCCGACCATATCCAGCATCCGATGCACCATAGATTGGGTCGCAAAAGGAGAGCCGCAGCGAATACATTCGAAGGGAGCTTCTTCTTTCAGGGTGTGTTGTTGCTGACGAGCAGCTTTATCAAAATTAATTTGTGGCGTTAGGCTTATCACTTTTTCAGGACAGGCCGCTTCACATAAGCCGCATTGCACGCAGTTTTGCTCAATAAAGTGTAGTGCAGGTTTGTCGCCGCCATCTTGCAATGCCACGGTTGGGCAAATTGCCACACAGGACATACACATAGTGCACTTTTCGACATTCACGCTGACTTTGCCAAAGGGAATGTTGTTGAGGCTAAGGCAAGTATCTATTTCACCCGCTTGGCTATTGAGGTGATCTATCGCTTCAAATAGCGTGTTGCGTTTAGTGGTCGAGGCAAATGCAGCGGGCACGATCACGGGCCAATCTAAGCTGATATCGAGCATCGCATCTAAATCGGCAAGCATGCTTGGGTCGATGAGGCTGATACGTTGCGGCTGACCCATCTCATCTAAAATACTGTTGGCTAACGAAAGTTCGCCTTTAAGCATTTGCGTAAGAGTTGGCGCCGTGGCGTCGGTATTGAGGATCAGCACTTGGCGCGCGCCCCATGCGAGTACCGACAGCCAGTGATCTAAGCTGGCAACAGTAATTTCTTCTAGGGCGACGGGGATAATGTCACCCGCTAAATCGTCGGTAATAAAGTCGGCGCCGACGGCATTATCGTGGAACAAAATTACCGGTGCGGTTTGGGCGTGCTCGCGAAAACGACTAACGACTTTGTTCAGATATGAATGTAAAGCCTGTGGCGTCGGTAAATCGTAACTGATGGCACCCGTTGGACAAGCGCTGGTACAACTGCCCGCGCCGTGGCAAAGGTATGGGTCGATTTCAATTTTCTTCGCCACGCTGCTAATTGCATCAGCTGGGCAAAAGTTAAGGCAACGATTACAACCGTTAATTCCATTACGGTCGTGTGCACAAATATCTGGGTTGATTTTGACGTAGCGAGGCTTGTCGAATTGGCCGACGAGATCGGGAATTTGCGCGAGGGCATCTTCGAGTTTCTCAGTGTCTTGACCGACATAGAAATAACCGGGTGGCAGCATTTCTAAGTTCAAACAAGCTGTACTGCTCAAATCGAGCACGATATCGAAATGGGGTTTACGAATCGACACTAAACTGAGTTCGGCCGCGCCATCTTGATGTTCAACACTCACTTGGAATTGGCCTAAAAAGCCTTTTACGCCAATGAGTTTGTTGTAGTAACTTTCGACATCGGTTGCCGCCGCCATCACACGCTCAAGGTGGGCTTCGTCTTGGCTGGTAATGGCTTCATTGGCTAAAATCACGCGATGGGCCATAGTGGACAATTTGTCCGCGGCAAGGCGAGCCAGATCTTCTGGGCCGATAATGAGGACATTGCCTTCGGTAGTGTAGCTGACGGTTGGCGGGATCAGATTCTGCAACATCTGAGTTTGCGCTAACACCTGTTGCCGCGCTTGTTTTAACGCTTCTTGGGTTTGATTGGTCATCAAATGAGTGCTCACATTGCGTTCCTGAACAGTACTTTGGCTGTTTCTTGTTTTACTGTATCTGGGCAGATTTAGGCGGCTGGTAATAAGTAAGTCATTGCCTTTTCTGCTTTTATCTAAGCTTTTTTATGTCTTAGTACAGCAATTCACATACCAAAGTTACGGACATTAAAATTAGCTAAACTGACGTTAAACAACTCGGGTTTGGGCCTGAGCCGAGGGCTCTGGTGCATTTTGGGGTAGGTCATCAGTCTCCCCATCCAAATTGTCCGCTATCGGCTCTGCTGCCAATTCGGTGTCGACTTCTTGAGTCGTGGTTTCGCTTACATCAAGACTAGCGGATGGATTAGTTTCTGCAGCTTGTTTGGCTAAGGCTGACTCTTCATCCGGCGTTTCGCTGTCTTTGGTGATGTAGCGGAAGACTTTTTGTGCTAATTCTGCTGAAACTTCGGCGCTGAGTTTGGGCTGATTACTGTAATCGAGCGCATATTCTAAAAGCCCATCTATCTCGTTGAAATGTGGTTGTTTCCACAGTGCTCGCAGCGCCGCAGATTTCGCCGCCGGATCGACATTCGCGCCCATAAAACTCGCAAAGCTGCCGCCAATTTCAATTTCGTCGGGATTGGGTAAATCCGCCGCCGTGAGCATGCGATTCGGATCGTCATGCTCGGCAGTATTGGACTCTACTGCGGGTTCTGTGGGATTAAGCGGCTCAACTGCCACAGTGGCTTCGGTCGCGATCGGCGCTTGAGGCGCGGCTTCTTCTTGAGTTTCTCGCGCTTGTTCAGCGGCAACTTGTTCGCGACGCTGGTTCCAGCGACTCAGTAACCCAGCCGTTTTTTGCGTGGATTCAGCCATTAGTCCCTCCGAAGTGGATCGTGACTCGGGCCTTGATTCTTACGGCCATCGACATGCTTACGGCGATGGGCGGCGATTTCGACTTCGCCATGCTGGGTGATAAAGGCTTCAATCCAACAAGCGATGGCTTCTGGCATCAATAGGTTGATAACAGGGGTATTGCCCTCAAGGCAACCTGCGGCGACGTTTTGGTCTGCCGACAGTGCTGCGGGTATCCAAGTGCCATCGGCCGTTTCATCGCACACTAAATACAGCATAGCGTTATCTACATCGAGATTGAGGCGATAGCTGGCGCGTTCGTCTTTGTGCAGTTCGAGGAGCACTAAGGTCGCGTCGTCAGGGGCTTGATGGGTGGCGGGAAGTAGGTGTTCAATTTCCCATTGTACTGAGGTCCAGCGGCCAACTTGTTTCTCTACTTTTTTAAGTGAAACGTACATGGGCCAAACATTCGTTGTATGTTGCATTGTCTCTCCGTCTCAGCCGTGTTGATGGACGAACCGTAGATCGAACGGATCGGTTTTACCAACAAATTGCGATAAAAGGTCACTGCAATATTGGTGCCAGTAAACATGGGGTATTAGTTTGAACTAGATCATCAAAAACCCCTTAGAAAGTGGACATTTTGTCCAGTACTGAGTTTTGTTTGGGACATAAAAGGGCTAATGAAAAATCAGCATTGCGGCAGATCACGCATTTAAACCCAAGTTTTATACCTAGGTATAAATATTGCTATGCTCAGGCCATGACTCGTTAAGCGGTAGCATTATGATTTCGGAAAACAGCATGGGCACAGATAAAGAAGTAGCGGCAGCTAACCCCACTCAGAAGCCTCATCATCAGTGTTCTTTTGTGAGAACCCAAGCGGAAGTACCGTTAACCATTGCCGTAAAAGCAGTGAATGAAGCTGGGGAAGTGCTGGATAAATTTGTCGCCTGCGAACGTCCACTTACCGTTTATTTAAACTGGCGTCCGATAGTGACGCTGATGACACTAGGGGCAAAACCTGAGTCTCTGGCGTTAGGTTATCTTAAGAACCAAGGCTTTATTTCGGATGTGAGCCTGCTGGACTCTGTCATCGTCGATTGGGATGTGAGTTCTGCGGCTGTGGTTACCCGTGAACAAACCGCCGATCTCGACGAGAAACTCTCCGAAAAAACCGTGACGTCAGGCTGTGGTCAAGGCACGGTTTATGGCAGTTTCATGCAGGATCTAGATAACATCAATTTACCTACACCGAGTTTGAAGCAAAGCACGCTGTATAGCTTATTGAAAAATATCAACGAATATAACGAAACCTATAAGAATGCTGGCGCTGTGCATGGCTGTGGTTTGTGCGAAGACGATAAGATTATGGCCTTCGTTGAAGATGTCGGCCGCCATAATGCCGTCGATACCTTGGCTGGGGATATGTGGCTGATGCAGGATCGTGGCGATAACAAAATATTCTATACCACAGGCCGCTTAACCTCTGAAATGGTGATTAAAGTCGCCAAGATGGGGATCCCCATTTTGCTATCACGTAGCGGCGTGACTCAGATGGGCTTAGCACTGGCGCAGCAGTTAGGCATTACTATTATCGCCCGCGCTAAAGGTCGACACTTTTTGGTGTATCACGGCAGTGAAAATCTGCAATTTGATGCCAATACGGCTCCTTAGCTATAAGTCAGCTAAGACAGTAATGCTTAGACTTGCATGCAGTAATAAAGAATAAGACAAGGAAACAAGATGACGTCTGCCAGTGAATTGGTTTACATGAGCGCGAAGCAAGTGGCCGAGTATTTAGATCTTAACGAGAAAAAAGTCTACGCCATGGCCAACGACAGAATTCTCCCCGCCACTAAAATCACCGGTAAATGGCTATTCCCGAAAGTGCTAATCGACCGTTGGGTGATGGATTCGTGTCACAGTGGCATGCTCACCGACCGTTTATTGATCACCGGCAGCGACGATCCACTCTTATCTATGCTGGTGGCGCGCTTGATGGCACAAGTCGGTAGCCGTGAGTTGATAAGCTACAGCGCGACAGGTTCACGCTTAGGATTAGAGTTACTCGCTAAAGGTTATGCCGATGTGTGTACCTTACACTGGGGCAGCATGGAGGATCGCAATATCCGTCATCCAGCCTTACTTAAAGGGTATAACAATCATCAACAATGGATCATGGTGCACGGTTACTCGCGTCAACAAGGGTTGATCATGCGTGCCGATATGCACCACAGATGCCAAGAGGAAGATAAAGTCGTGAACTTACCTTGGCGTTGGGTGAGTCGTCAGGGCGGCGCGGGTAGCCAGCAACATTTAGAACATTGGTTGTTAAAGCAAGGCGCTCGCTTAGATCAGCTAAATGTCGTGCTGACGGCCTATAGTGAACGGGAGCTGGCAGGTTATATCGCCCGTGGTGATGCCGATATAGGTTTTGGCTGTCAATCTGTGGCATTGGAGAGTGGTTTGAGTTTCGTGCCACTGATTAAAGAGTCCTTCGATTTCGTTATGCCGCAAAGCATTTACTTCCGTCGCCAGCTTCAACAACTCTTTACTATGTTGGCGAGCGGCCACTCGAGGCAAATGGCGGCGCTACTGGGTGGCTATGATCTTACCGACTGTGGACAGTTACTCTGGAGTGCGAGCTAATCCGCTCACTCGAAAATAGATGAAGGTGAATAGAGTCGAATGATCTCTGCATTACTTATGACAGTTACTTACGCTGCTACTTATATAGCTAGTTACCGAATAAGTATTAAAGAGATCTCTCCCTGTCCCTGATTAAGTCATTTGGGCTTTACTATGGCGATGGGATGAAGGCAGCGACTATGACTATATAGTTAAAGTATCTTTATCGCCGAATGTGTTAGCGCGTCATCTATATTGCTCTCCGTTATATTCCATTAACGGCTCTATATAAGAGTTGGGAGGAGATATTCCGGTTCGAGCAGTCGTTATCCTTATCTAAACCTAGTAGATTTTATACTTTGAATCTTCGCCTTGATGCAGTACATCCTCAAAATATGAGGTAGAACTGCAGAGAAAACGCTATTTTGTGGATAACTTTGGGGGTAAGTGGTGAGTATCTTGTGGCTAGCTTGAAGAATATGAATTAACTGCAAATTTCCTTAAAATAGGGGTTGCGCACTTTCTCAATCTCCCTATAATGCGCATCCACTGACACGGCAGACAGCGAAAGCGAGTTGACGAGTTAGGTTGAATTGAATGGCTTTTAACCACTTAATTCAATCGCAGCAAGAAGTTGAAAATACTCCTTGACGCGAAACGGGAAATGCGTAGAATACGCAGCCCTAGCCAACTGGAAGCGTTCGACGCTCAGTGTGGTACTCAAGTCCTCTTACGGATATTGAGTTGCTCTTTAACAAGATAAAACAAGAAATCTGTGTGGACACTCACAGGTGTTGAGTTAATCGAAACTGCTTAACCTTCGGGTTGGCAGTCAAAAATTTAAATCAATGTAACAATGAGTGTTCATAGCAATATGTACAGTTTGTTTCTTTTAGCGAGAAACAAAAAATCAGAATTCATTGAGCCGCTGAAGTCGTAAGACGGAAGCAAAAAACTTTAATTGAAGAGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGCAGCGGGAAGATAGCTTGCTATCTTTGCCGGCGAGCGGCGGACGGGTGAGTAATGCCTAGGGATCTGCCCAGTCGAGGGGGATAACAGTTGGAAACGACTGCTAATACCGCATACGCCCTACGGGGGAAAGGAGGGGACCTTCGGGCCTTCCGCGATTGGATGAACCTAGGTGGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCCTAGCTGTTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGTAGGGAGGAAAGGTAGCAGCTTAATACGCTGTTGCTGTGACGTTACCTACAGAAGAAGGACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTCCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGCGAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTCGAACTGGCGAACTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCTACTCGGAGTTTGGTGTCTTGAACACTGGGCTCTCAAGCTAACGCATTAAGTAGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCACGGAATTCGCTAGAGATAGCTTAGTGCCTTCGGGAACCGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCCTTATTTGCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGATAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCGAGTACAGAGGGTTGCAAAGCCGCGAGGTGGAGCTAATCCCACAAAGCTCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTGGATCAGAATGCCACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGCTGCAAAAGAAGTGGGTAGCTTAACCTTCGGGGGGGCGCTCACCACTTTGTGGTTCATGACTGGGGTGAAGTCGTAACAAGGTAGCCCTAGGGGAACCTGGGGCTGGATCACCTCCTTACCTATACGACTAACTTAATGTTTGTTGAGTGTTCACACAGATTGCTTGTTTATCTTCTCTTAGGAGAAGTCAGAGCTGAATGCGCCGCTAGCCGGTACGCATTGTTCTTTAACAATTTGGAAAGCTGATAGTATTTAATTGCATGAGTCTGTCATGTAATTAATTACAAAACGTTTAATGCGAAAGCATTGAACATGAGTTCTCAAACACTTTATTAAGTGTCTTGAATATTCAAAAAGTAAATTATTCTTTATTGAATAGTTTACGTCTAAGGCGCGTCCACTTTCTTGGTCGGAAGTGAGACAAGTAAAACCAAGCTGGTCGCAATGCGGCGCAGATGAGTGAAACTCATTTGGGTTGTATGGTTAAGCGACTAAGCGTATACGGTGGATGCCTTGGCAGTCAGAGGCGATGAAGGACGTAGTAACTTGCGAAAAGCGTTGGCGAGCTAGTAACAAGCATTTGAGCTAACGATGTCCGAATGGGGGAACCCAGCAGCATAAGCTGTTATCGCAACATGAATACATAGTGTTGCGAGGCGAACCCGGGGAACTGAAACATCTAAGTACCCGGAGGAAAAGAAATCAACCGAGATTCCCCTAGTAGCGGCGAGCGAACGGGGATTAGCCCTTAAGTCAGTGGGGTGTTAGTGGAATGTGTTGGAAAGCACAGCGGCACAGGGTGATAGCCCCGTACATGAAAACTAACCATTGATGAAAACGAGTAAGGCGGGACACGTGACATCCTGTTTGAATATGGGGGGACCATCCTCCAAGGCTAAATACTCCTGACTGACCGATAGTGAACCAGTACCGTGAGGGAAAGGCGAAAAGAACCCCTGTGAGGGGAGTGAAATAGAACCTGAAACCGTATACGTACAAGCAGTGGGAGCGGTTCTTGAGACCGTGACTGCGTACCTTTTGTATAATGGGTCAGCGACTTACATTTTGTAGCGAGGTTAAGCGAATAGCGGAGCCGTAGGGAAACCGAGTGTTAACTGCGCGTTTAGTTGCAAGGTGTAGACCCGAAACCCGGTGATCTAGCCATGGGCAGGTTGAAGGTTGAGTAACATCAACTGGAGGACCGAACCGACTAATGTTGAAAAATTAGCGGATGACTTGTGGCTGGGGGTGAAAGGCCAATCAAACCGGGAGATATCTGGTTCTCCTCGAAAGCTATTTAGGTAGCGCCTCGAGCGAATACCATTGGGGGTAGAGCACTGTTAAGGCTAGGGGGTCATCCCGACTTACCAACCCTTTGCAAACTCCGAATACCAATGAGTACTACTCGGGAGACAGACGGCGGGTGCTAACGTCCGTCGTCAAAAGGGAAACAACCCAGACCGTCAGCTAAGGTCCCAAAGTACTAGCTAAGTGGGAAACGATGTGGGAAGGCTTAGACAGCTAGGATGTTGGCTTAGAAGCAGCCATCATTTAAAGAAAGCGTAATAGCTCACTAGTCGAGTCGGCCTGCGCGGAAGATGTAACGGGGCTAAGCTAGTCACCGAAGCTACGGGTGCACAGTTTACTGTGCGCGGTAGAGGAGCGTTCTGTAAGCCGTTGAAGGTGAAGGGGTAACCCACGCTGGAGGTATCAGAAGTGCGAATGCTGACATGAGTAACGATAAAGGGGGTGAAAAACCCCCTCGCCGAAAGACCAAGGGTTCCTGTCCAACGTTAATCGGGGCAGGGTGAGTCGACCCCTAAGGTGAGGCCGAAAGGCGTAATCGATGGGAAACAGATTAATATTTCTGTACTTCCGCTAACTGCGATGGAGAGACGGAGAAGGCTAGGCTAGCGCGGCGTTGGTAGTCCGCGTTTAAGGTGGTAGGTTGATTTCTTAGGCAAATCCGGGGAATCGCACTTTAGTGTGCAGACTGAGAGCTGATGACGAGGTCCTAAGGGACTGAAGTAGTTGATGCCATGCTTCCAGGAAAATCTTCTAAGCTTCAGGTTAGTGGGAATCGTACCCCAAACCGACACAGGTGGTCGGGTAGAGAATACCAAGGCGCTTGAGAGAACTCGGCTGAAGGAACTAGGCAAAATGGTACCGTAACTTCGGGAGAAGGTACGCTCCTGTTGGTGATGAGACTTGCTCTCTAAGCTGACGGGAGTCGCAGATACCAGGTGGCTGCAACTGTTTATCAAAAACACAGCACTGTGCAAACTCGCAAGAGGAAGTATACGGTGTGACGCCTGCCCGGTGCCGGAAGGTTAATTGATTGGGTTATCGCAAGAGAAGCTCATGATCGAAGCCCCGGTAAACGGCGGCCGTAACTATAACGGTCCTAAGGTAGCGAAATTCCTTGTCGGGTAAGTTCCGACCTGCACGAATGGCGTAATGATGGCCACGCTGTCTCCAGCCGAGACTCAGTGAAGTTGAAATTGCGGTGAAGATGCCGTATACCCGCGGCTAGACGGAAAGACCCCGTGAACCTTTACTATAGCTTGGCACTGAACATTGAACCTACATGTGTAGGATAGGTGGGAGACTTTGAAGTTGTGACGCTAGTCATGATGGAGTCGTCCTTGAAATACCACCCTTGTAGTTTTGATGTTCTAACCTAGGTCCCTTATCGGGATTAGGGACAGTGCCTGGTGGGTAGTTTGACTGGGGCGGTCTCCTCCCAAAGAGTAACGGAGGAGCACGAAGGTTGGCTAAGTACGGTCGGACATCGTACGGTTAGTGCAATGGCATAAGCCAGCTTAACTGCGAGACATACACGTCGAGCAGGTACGAAAGTAGGTCATAGTGATCCGGTGGTTCTGAATGGAAGGGCCATCGCTCAACGGATAAAAGGTACTCCGGGGATAACAGGCTGATACCGCCCAAGAGTTCATATCGACGGCGGTGTTTGGCACCTCGATGTCGGCTCATCACATCCTGGGGCTGAAGTCGGTCCCAAGGGTATGGCTGTTCGCCATTTAAAGTGGTACGCGAGCTGGGTTCAGAACGTCGTGAGACAGTTCGGTCCCTATCTGCCGTGGGCGTTGGATGATTGAAGGGAGCTGCTCCTAGTACGAGAGGACCGGAGTGGACGAACCGCTGGTGTTCGGGTTGTCATGCCAATGGCATTGCCCGGTAGCTACGTTCGGAATCGATAACCGCTGAAAGCATCTAAGCGGGAAGCGAGCCCTAAGATGAGTCATCCCTAGGAATTTAATTCCTCTAAAGAGCCGTTCGAGACTAGGACGTTGATAGGCATGGTGTGTAAGCGTTGTGAGGCGTTGAGCTAACATGTACTAATGACTCGAGAGGCTTAACCATACAACCCAGATGGGTTTTACTGAAAGCCTTAGACAGAATATCGACACTTGATGAAGTGCGAACTCAAACAAAGCAAATCAGCTTTCCGAATTATTATTAACGTGAGCCAGAGATGGACACGGTAATAAACAGAATTTGCTTGGTGACAATAGCCTTGTGGAACCACCTGATCCCATCCCGAACTCAGAAGTGAAACGCAAACGCGCCGATGGTAGTGTGGGGTCTCCCCATGTGAGAGTAGGTCATCGCCAAGCGCCTAATTTAGGTTAACGTGCAAACGTTAGACTAAAGGAGCGGTAGTTCAGTTGGTTAGAATACCGGCCTGTCACGCCGGGGGTCGCGGGTTCGAGTCCCGTCCGCTCCGCCAACATTTCGATAAGCCCTAACAGCAATGTTAGGGCTTTTTCGTATCTGGAATTTATCTATTTCTATCGACGCGGCCATCCCGTCTAACAGCATCCCTGCTTGGTATCAATGCCTCTCGTTCATGAGCTCATCTTCACCCGCCAACATAAAGATGAAAGCCTCTACAGCAATGTAGAGGCTTTTTTCGTTGTGGAGAAATTAACATTATTGGTGATATCTCAAGCTGTTATTCGTTAAATTGTCCAAATTTTGTACCGTCTACTACTGTTTCTCTGATTGAATATTAACCTCTTTCGCTTAATAAATGCTTTTCCTCACAAGGTTCCACCTGTTTCTTGTCATTTTTAGCTGCTTAATAGGGCTTATTTAGGTGTGTTTATTGTTAAGCAAATACTTAAATTCCAAGAATGATTGAAATTGAGTTATTTGCCCCAAGATAGTCTTAAGTGCTGCAGTTTTGTAGCGTCCATCACTGAGGTCAATGCCATGCTTTATGATTTAACGGTTGTACAATTTAGCAAGATGCTGAAAAATTTAAGCGTTATTCTTGAAAAGGGCGAATGCTTCGCAAACCTCAAGAAAGTCGATATGGCGGTGCTATTAAACTCACGTTTAGCGCCGGATCAATTTAACCTTATTCGCCAAGTTCAAATTGCCTGTGATACTGCCAAAATTGGCGTGGCACGTTTGACCGATAACTTAGATACAGTGCCAAAGCACGATGATAGCGAAACGACTTTAGCTGATTTGCAAGCGCGTATTAGCTCAGTATTAGATTACTTAGCGACGTTCAAAGCGGATGATTTTGCTAATGCGGCCACGATCCATGTCTCACAACCGCGTTGGGAAGGAAAATATCTGACGGGTTATGAGTTTGCTATTGAGCATGCGATTCCGAACATTTACTTCCATGTAACAACAGCCTACGCGATTTTGCGCCACAACGGTGTTGAAATTGGCAAGAAAGATTACCTTGGCGCTATGCCATATAAGTCGTAAATCTAGGCTAGATGTAGTACTGTTGCGCAGCCTAATTGTTGCGCAGCAGCGCTTTGCCGCTTGAGCTTACCAACCTTTCTGCATTACCACCTGTTCTTTCTCTACCTATGTTATGCCGCTTTGTCGCTGGAAATTTAGGGCTCGTTAGGGTTCTGTTACGCCGATGAAGTCAATCAATACTCAGTTTTTCATTTCGACCTTACGTCTCGATTATTTTTATCCTGTTAGCATGTAATTTAATTTTGTTGGCTTAGGTAAATGATATGGGCCGTAAGTTTATGAGTCGCCAAATTATCTATTACTTAAATAGTGAATGGCGAATTGACTCATTATAAAGATGACCTGTTTGTTCGTTTTGAGCAGGGATTTACTGGAAGAGCCTATTTAAGGTTCTAATTAAAGGAGTTAGCTATGTCTATCGAATCTCAAGCCGTCGAGAGTCCAACGGATGCAACGACAACTAAGAGCAAGAAAGCAAAAAAAGCCGCCATTACTAGTGGTTTGCGCCTCAAAGACAAGTTGACCCAAGACACTCGCCGCCGAATTGAAATTCTGCACGAGCAACGCCAACTATCCAATCGTTTCGGCGTGGATATAGAAATTGACTAAAGCCATTGGCTTGTCGCTAAGTATTGGATATCACATCTAGATATTTAGCCAGCATGTACAGCTTCACGACGCCCGTCGTGGGGCTTGCTCACTCTCCTTAAACATTCACTTCTTAAATTCATATTCCATTATCGTGACCATCTCGCCCATATAATCTTCCCGTGAAATCTTTGACCAACCAAGCGTTTGATAAAATTCTGCTTTGTCTGCGGTAAACAAATAGATTTTTTGTAATCCGAGTGCTTTGGCATAATCAACGACGGCATTGACTAATAATTTCCCCAAGCCTTTATTTCGATAGTGTGCATTGACGTACACATTGGCGAGCCAAGGGCTGAGGTCGGTTCGGGTATCCATATCTGCTGCGGTTAATGAGGAGGAGCCCATGACTTGGTTCGCTTCTTCACAGATAAACATCTTAGGAATGGCGGCGTGGGTGAGGTACTCGCTCATCTGCTCAATGAGTGTGGTGACTGTGGCGTCCGCAGAACTTAAATGTGCCCATTCGTGATGATGCCATTGTGCCAGTTGTGGAATGTGCTGCGGGGCCGATTTTAGGTCGATAACTTTCATATAAATCTTTGGGCTTGTGTTATGCAAAGGGCTTCATGGTGCGACATTATGGCGCGAATACTTAAGTCCGTTCGCTGGTCTGAATGATTTGGCGACAGGATTAATTAAGCACGGTTTGGCTGAGTATACCGCTTAACTTGGGATCGGCAGAAATATGCTGGCTTCGAGTCCACCTTGTTGCGCGTGGCTAAAGCTGAGTTCGCCCTGATAACTGTCGACGATATCTTTACAGATTGATAATCCTAAGCCGTGGCCTTGAATGCTTTCATCTAGCCTAATGCCGCGCTCGATGATAATCGACAATGCGGCATCGGAGACGCCTTCGCCATCATCGCTGAGTATGATCCTATAACCTGCGGTGGGAGCCGTTTGATGCACTATACGGATCTCAATGTTCTTGCGGCTATGTTTGCAGGCGTTATCCAGTAAATTGCCTAAGAGTTCGAGTAAGTCATCGCGGTCAAATGGCATCACTAAGCCACTCGCGTAATGGCTATGAATAATGCGGTCTGGGTAGATGCGTTGCATCACTTGAATCAGCGGCGCGAGATCGTCATCGAGCACGGTATAACGCCCTGGAGTAGATAGGCCGACTATCTTGGTGCGCTTAAGCTCTCTCTCAATGATGTGGTGCAGGTTTGCGAGCACCTTGCTGCCCTCACTCCTCTGTTCCGGTGGAAGGCTTTCTAGGTAGGACTGCAGCCCTTGTAGCGGACGTTTCATTTCGTGGGCTAAGTTACCCAAGGCATTACGGGAGCGTTGTACTCGTTGGCCCAATTGATTGACTAAACGATCTATCTCCTCAATCAGCGGGATGATTTCGCTCGGCATTTGGTCTGGACTGATTTCTTGCCCTTGGGTGCGCATCTGCCGTATGGCTTCTGGCATCTGCTCTAAGGGTTTAAAGCCGCGTTTCAGTAGCTGATACTGCGCCATTAATAGAATAACAATCGTGAGCAACACCGCCGCGCTGGCAAAGGCTAAAAACTGTACTTGAGTTTGTTCGAGCTCGCTGATGTCTTCTGTCACCCACAGGGTAACTAATTGATCATCTTTGATTACGGCCTGACTGAACATCAGCCAATGTTCTTTGCCGACAAACTCAGTGGCTTGTTGGTGCTCACCCGCGGCCAGCTTCGGCATGTTCACGTTATGGTCGAATAGGGAGCGTGAGCGCAGGGTTTGCTCGCCGATTTGAATTGCAAAATAATGCCCAGAATTAACCCTGTGGTACACATTGGGCAAGCGGTCGGTGGTGAGCGCCCAGGTGCCGTCAGGATGGTTATTCAGGGCGGAGATCAGGCTGTCGGCATCGTGTTGCAATCGCGAGGCAACAAAATCTTGGGTCAATATTTGGATACCTTGGTACAGAAAATACAGTAACAGCACCATGGCAAAGGTGAGCGTGATCAGCAGATTGCGCGTCAGATAACCCTTGAGTGAATACACGCCTTTTACTCCTTGAGTCTATAACCTTGGCCGCGACGGGTTTCGATATAATCCTTGCCTAGGCGCTGGCGCAGGCGGTTGACGTACACTTCGATGACATTGCTGTCTTTGATTTGATCTTCTTCGTAAATGCGTTCACTGAGCTCTGACTTAGAGACGATTTTGCTGGGATTGACCATTAAATAATGCAGTAGGCGATATTCAATCTTAGTCAGCTCTATCACTTCACCGTCGACATTGGTGACGGCTTGTTTGTCGACATCGAGGGAGACGCCCGCATGTTGCAACACATTGTCGGCGCGGCCAAAGTGGCGGCGGATCAACACTTCGAGCCTTGCGAGTAGTTCTTCGATGTGAAAAGGCTTGCCGAGGTAATCATCGGCTCCCGCCTTGAGGCCATCGACCCGCTCGTGCCACGCATCGCGGGCGGTGAGGATCAGCACTGGCATGGCTAAGCCTTTTTGTCGCCATTGGCCGAGGACGTGTAAACCGGGTTTGCCGGGTAAACCGAGATCGAGGATCACGGCTTCGAAGTTTTCTTCTTCGCCTAAAAAGGCGCCATCAATACCGTTATCGGCGTGCTCGACGGTATAACCCGCCTTGTTAAGGGCGGGAATGAGGCGCGCGACCAGATCGGTATCATCTTCAATCAATAGCAAACGCATCAGTCGATCTTTTCCTTTAGCCACTCGCCGGTTTTGGCGTCAATCTTGATTTCATACACTATGCCATCGTCACGAAGGATTTCGAGCTCGTAGATGATCCTGCCATGTTTTTGCTCGACTTCGAGATCGAGCAACCGTCCCTTTAGCCGTGATTCATAGCGTTGCATTATGGTGTCGAAGGGCAAAACTTTGCCCTCCTTAACCCACTCGACCACCTTGTTTGAAAGATCATCGTCGTGGGCATTGCAGCTGGGGCTGCTAATCATCCCAACGGCCAAAGCCAGGAATATTAACGGTATCTTCATCGAATATGCCCTTTTCTGCGAGGTTGTGACAATTGCTGCATTGACTGAATGAGCCGACTTTGGGGTTATCTTGCACCATACGTCTTGGGATCTCATCGTGTTTGCGAATAAAAAATGCCTGTTCTGTGATCTTCTGCGGCCCAGCGCCTGCAAGCAGTGGCGTGCTGTTTTTCATCACCTTACCATTTTGCGCCGCATGTTGAACAAGATATTCCTCAATTCTAGCAGTGACTTGAGGATCTAGGCTGGCGTTGTCACCAAAGTGATTCTCAAGATTTGCGGTAATGGCGCGCCACTTGTCGGCGGGGAGTAAGTTCGCGGGGTAAGCCATGTGGCAGCTGCCACATTCTGCCGTATATTCCGCATTTTGTGGAATGGCGCGGCCATCGTCCGCATAGCCTGTGCCGGTTAAGGTTAGGCTGATACCGATAACACCAATGGCTAATGCCGCAGCTGTGAGTGATGAACCTAAGAACCAGCTAATAGGGTGTGCTTGTTTAGTCATGACATCTTCCTTTTTGTGCCGTTACCTTCTGTGGAAGGTATTGAAATTAATTGATCTAATTGAATGAGTTATAAGGTCGAAATTTAGTGGTTATTGCAGGCTTAACCAGAGTAAGGCATCGCCTTTTTCCTGTGGCGTGCAGTCGCGTTTAAAGGTCCAGTTACAGTTACGGGTAAACCATTTCTCGATTTTGGCTGAGTCGGTTAACCGATCTGCGGTAATCGACGGCGCCATAGGGTCAATGGGTTTACGGGTGTTTTGGTGCATCCCCATGTCGGTGACTTTGGCGGTATGGCAACTGGCGCAGCTACGACCGTCCATGTCCTGTAGCCAAAGCTTTTGTCCTGCGGCTGCGGTAAAGGGTCCAGCGCCTTGAGCTTGATAGCTTTGCAACTGCGTGCCGATGCGCTCAGCGCTAAGTGGCAGGCTTTGCACTTTACTGTTGGCGGCGCTAACGCTAAGGCTACTCAGCAGCAAGGTTGAAATTGCGACGATAGCAAGGCGACTTGTATTCGAGTGTCTGGTTAATCTGTTCATAGGGCATTCCTTTTGGGCTGGATGTCTTGTTGCTAAGCAAAATGGCTAATGCATTAATCTTCTTGTTCCACAGGGCGTTGTTTCTTGCCCGTGACCATGGCTCTGACTAAGTTTTCTTTGTGTAACAGGCTGCTGACGATGACGCCGCCAACGTGGATGACCACAAGGAACACGGTGAAGTTGGCAAAGAATTCATGGACTTCTTTTATGGCGCCTTCAGGCCAAGTGGCGAAAAAGCTCGTGGCTAATGGGCCGTGTCCGTCGGTGGCGTACAGTGCCATGCCTGAGAATCCCGTGATGGCGATGCTGGCAATCAAGGCCACTATCATCATTGCTCCAGCGGGGTTATGGCCTATGTAGTCTTTGGCTTTGCCTGTGATGAGCCCTTTGAGGTACACCAGCACTTCTTTAGGGCGAGTGATAAAGTTGCTAAAGCGCGCATTGTGGGTACCAATCACGCCCCACAGTAGACGAAACACCAGTAAAATTAAGATGCTGTAACCTGCGTAACTGTGGATGGTCATCCACTCGTCTTCGCCTTCAGTGAGGTAGGCTAAGGTGAAAAAGCCGACGAGAGACCAATGGAATATGCGGATCAACAGGTCCCAGACTTTGATGGTTTGCAGTGTGTTTGATGACATGGTGTGACGCTCCTAAGTGGGTTTAGGTGTAGGCTACTTAACCCAAACTTAAACCTCGCTTAAATTTTGAAGGGCGAACATTCAGTGAGTTGATGGGTTTGAGTGACACGCTTGCCTGGGTGACGAGCCTTAAATGTTAACGAAAGCTCACTTGTTAACCTCTTGGTATTACGGTAAATTCTGCCACCTCAATCGAGTGAAGTTAGGGACTAACTTATCTATGTCTACAGCCGTTATTCGCGCTTTCATCAGCCGCAATCCGAGTCGTAAATCTTTTTTTATTTATGCCGTTTTATTATTTTTGACTGAAGTCTTAATCGCTTTATATGCGCCTGCGGGTTTTATCCGTGGTTTTGTGGGCGATGTATTGGTGGTGATCTTACTGTTTTGCATGGCGCGCGTTGTCGTTCCTTTTACCCATCCAGTAGAAAATACCGCAGCAAAAAACACTGGCGATGGCATTAAGCGCTTATTTCAAACTCCTTGGCTGGCGTTTGCGGTATTGCTGTTTGCCTTTGCTATCGAGTTTGGCCAGTATTGGGGTTTAGTCGACAAGCTAGGCTTGGGCGGAAATCGCTTGGCGCGGATAGTGATAGGTAGCCATTTTGATCCACTCGATCTCGTGGCATATTTTGTCGGTTACTTGATCTTATTGGGCTGTTATTGGAAAAGCCGCCAGTCCTAGTGATTTGAATCCGCAAGAGCTGCGTGACATGTGTTTTCGCGGATAGGCGAGACAAGTGGGGTAAAGGATGCAACCGATAACGGCATTTTCACTGCTAACAGTACGTACTGATGGACTTGAGCTTAATCCACTGAAAATGCCTTTGTATTTCAATGGCCAACATACTCATACCTTTATTGCTGGCCTAGTGATTGAAGGCCAATATCGCTGCGTTTTACCCAATAAAACCTCGGGGTATCTGGTCATCACTTCTTTTGATTGTCCCTTCGAAGAATCAACCGAGTTTAGCCTGCTTGATGAAGCGTTTAAGCTCATTGCGACGACATCTTTGGCGCAAATGTACGATTCTTTTCTGCTTTATGCCCATTGGCCTATGACGGATAACCGACTGAGGTTGCACTATTACGGTCAGTTTGTGCTGGATTTAGTGATGACAACGGGCAGTTCTTGGTTAACTTTTCAGCCAAAACTTAAACTGGTTGAAGTTATCGATCCCCAAAGCGATCCACAAACAGCCTCCTCTCTTGCTGAATTAGCTCAGCGTTTAGCGCGGATTGATAATATAAACTGAGTGAGGTTTGAATTAAATTGAGTTAAAAAAGAAAGGGCTATTCGCCCTTTCATGTTCATCATTTATATCTATTACTCATGCGGAAAGTTTTATAAGCCTGCTTGCAAAGACGTTTAAAAACCTAAATAAAAGCTTCCTCACATCAGCTTGTTTGAGCCCCAAAAACCTTAGAAGAAACCTAAAGGATTCACATCGTAACTCACCAAGAGATTCTTAGTATTTTGATAGTGGTTGAGCATCATCTTATGGGTTTCGCGGCCAATACCGGATTTTTTGTATCCGCCAAAAGCGGCATGGGCAGGATAGGCATGGTAACAGTTAATCCACACGCGGCCCGCTTGAATGCCGCGTCCCATACGTTGAGCCGTGTTCATGTCTCGCGTCCACACACCAGCGCCTAAGCCGTACTCAGTATCGTTCGCGATCGCTAGGGCTTCGGCTTCATCTTTGAAAGTGGTGACTGAAATCACAGGGCCGAAGATCTCCTCTTGGAAGATACGCATCTTATTGTGGCCTTTCATAATGGTTGGGCTGATGTAGTAGCCTTCGCTTTGGCCGCCATCGAGTTGGCAAAGTGTGCCGCCCAGTAGCACTTCTGCGCCTTCAGCCTTACCTATGGCTAAGTAACTTAAGATTTTATCGAACTGTTCTTTGGAGACTTGAGCACCCACTTGAGTGTCGGTATCTAATGGATCGCCTTGCTTAATGGTTTGGGCGCGGGCGAGCACTTTTTCGATGAAGCGATCGAAAATCGACTCTTGGACTAGCACCCGTGACGGGCAAGTACAGACTTCACCTTGGTTAAAGAAGGCCAGCAACATACCTTCGACGGCTTTATTTAAATATTCGTCTTCATGGTCCATTACATCGGCAAAAAATACGTTCGGCGACTTACCGCCAAGCTCAACAGTTGATGGGATTAAGGATTCGGCGGCGCATTTTAAAATGTGATAACCAATTTCTGTAGAGCCCGTAAACGCAAGCTTAGCAATGCGTTTGCTGGTGGCCAGAGCTTGTCCCGCCTCCGCGCCAAAGCCGTTGACTACGTTAAGAATGCCCGCGGGTAATAGGTCTTCAATCAGCTCGAGTAATACCAGAATCGATACTGGTGTTTGCTCCGCAGGCTTGAGTACCACACAGTTACCCGCCGCCAGCGCAGGAGCGATTTTCCATGCCGCCATCAAGAGCGGGAAGTTCCAAGGAATGATCTGGCCGACTACTCCCAAAGGTTCTGGGAAGTGATAGCTGACTGTGTTGCCGTCGATATCGGCGGCGCTGCCTTCCTGTGCGCGGATACAACCGGCAAAGTAGCGGAAGTGATCGACAAATAAGGGCAAGTCGGCGTTTAAGGTTTCACGCACCGCTTTACCGTTTTCCCAGGTTTCGGCGACGGCTAAATATTCGAGATTTTGTTCGACCCGATCGGCAATGCGCAGTAACAGGTTTGAGCGTTCGGTGACTGAAGTTTTGCCCCATGCTTCTTTGGCTGCGTGGGCAGCATCTAAAGCTAATTCAATATCTTGTGCATCGGATCGCGGGATTTGGCAAAAGTTTTGGCCATTCACGGGTGAGCGATTGTCGAAGTATTTGCCATTGACTGGGGCGACCCATTTACCGCCGATGAAGTTGGCGTATTTTTCTTTGAAGTTAACGATTGCGCCTGCGGTTCCGGGTTGAGCATAAATCATAGAATCATTCCTTCTTGTGGATGGCATGCTGGGCGTTGGCGTTTTTTACGCTCTGAGCTTCAGCAAGGGAGTCTTGTTGATGCATCTCTAATGGATGCCTTTTTATATATTTTTCGTTTTGCTTTAAGCCAATCCTATTGATTGGCTTAGTGTTCTCTTATGCAACACCTAAGGCCTGCATACTAAGGGTGCATGGCAAGGAGAGTGCCAAACTGGCAAGAGTTGTACACTTGTTGCGCTAACAGTATGATGAAAAGCTAATTTTGCAGATTTAAAACAGTGTGGGCACTCAGGGAGAGAGGTGTGTCCTGTGGTCACTTTGTCATCGAACGGCACACTGCAACTGTGCCGAAACGGAACACAGCTCGGGGGAGCACGGCTATGGCAGTTAAACCGCATTTACCATCGACACAGGCGTGGTTGGCCGATTCGTGGCAGCGCAGCATTGGCGCTGGCTTGTCAGAATTTCAGTTACCCCAAGAGCTTAGGCTCGACGCCAGCGAACTAAAGCAGAAACATGAGCAATATCAAATGCTCATTGCATTAGTGCAATCCCATGCTTTGCCCTTGTTCAATCAGTTGATGGCGCATTCTAATAGCCGACTCTTGCTCTCCGATGCCGAGGGTTATGTGCTTAGGCACTGGGGCGTGAGCCGCTACTCCACAAAATTGGCGGACGTGGCTTTGGATATCGGGGTGAATTGGCTCGAACAGCACAAAGGCACCAATGCGATAGGGACGGCACTCACCGCCAAGCAAGCAGTATCTGTAGTGGGTGAGCAGCATTTTATTCGTCAGAATCGTTTTATGAGTTGCACCGCCTGCCCGATATTTTCTCCCCAAGGTGAGATGCTCGGGGTGATAGATATCACCAGTGAGCAGCAAAGACACACTCAGCAGACGCTGATGTTAGTGTCGAGCTTGGCGCAGCAGGTCGAGACCGCGCTCTTGTGTCACTTGCCCGATAGCCGTTATCGAATCGATTTAGCGGCGCAGCCAAACCTACTGAATTCGGGTTGGCAGGGCATAGTGATTGCCGATAGTGATGGTCGCATTGTGGGGTGCAATCCCATGGCGAAACAATTGCTTAGTCATGCCAAGGTTGGCGATTCCGTCGAGCAGTACTTAGGCGATAATTGGAGCCGTGCGGGTGGCTTTAATCAGCATTTAGACTTGCATCTACAGACTCAGCCCTTAAATATTCCCAGCATCAAGAGTCGGGTAGCGACTAGTAATAAATCGCTTAATCAACTGGGCGTGCGGTTTCGCGACCCTCAACTTGAGCGCGCATGGCAACATGCCAATAAGGTGATCACCAAGCAAATTCCGCTGTTAGTGCTCGGTGAAACCGGCGTCGGTAAAGAGCAGTTTGTTAAAAAACTCCACGCCCAGAGCGCGCGTCGCACCGAGCATTTAGTGGCGGTGAACTGCGCTGCATTGCCTGCCGAGTTAGTCGAATCTGAACTCTTTGGTTATCAGGCGGGCGCGTTTACTGGGGCAAATCGTACTGGATTTATCGGCAAAATTCGCCAAGCCCACGGCGGCTTCTTGTTTTTAGATGAAATTGGCGAAATGCCGCTGGCGGCCCAGAGTCGTTTACTTAGGGTGTTACAGGAGCGCGAAGTGGTACCCGTTGGCAGTAATCAAAGTTTTAAAGTTGATATTCAAATCATTGCCGCGACCCATATGGATTTGGAGCAACAAGTGGCGCAGGGGCTGTTTCGGCAGGATTTATTCTATCGATTGAATGGTTTGCAAGTACGTTTACCCGCGCTGCGTGAGCGGCAGGATATCGAGCGTATTATCCATAAATTGCATCGTAAACATCGCATTGCGCCGCAGGCGATCTGCCCTGAGTTGTTAGGGCTGCTGATGCAACACGACTGGCCCGGCAATTTGCGCGAGCTCGATAATCTTATGCAAGTGGCTTGCTTGATGGCAGAAGGTGATGACACCTTAACTTGGCAGCATTTACCCGATTATCTTGCACAAAAATTAGCGTGCGAGCCGTTAAAAGTGGATCCGTTAAATGCACAGCTGTTAAACACACAGCTGTTAAATGAGGAGCAGCCATTAGGTGAAGAGGTTAAAACCGGTCAAAACTCTGCATCACATCCGCTGGCTGGCAAAGTTGTGAGTGGCAAAGTTACAAGTGGCAACATTGCGGTGCAGCCAACTGCCAATGCGGTTCAATCGGATTCTCTCCACGAGGCGATTTATAGCAATGTGCTACAGGCATACCAAGCCTGTGGTGGCAATGTGAGCCAATGCGCCAAGCGTTTGGGGATCAGCCGTAATGCCTTGTATCGTCGATTAAAACAGATGGGACTTAAGGGTTAATATTAGCTTGCGACGCTTGTCTCTAAGCTCAATCAATCCCCCAACTCTCGCTTATCTCATCAATAACTTTCATTTTGCAGCAGGTTAGAACTTATAGGATTTAGCCTGCAGCTCTTGCATAGTGACAAGTTAACCAGAGTAACGCTTTGCAGCAGGCGAGTTTGAAGCCATAAAGACATCATTTTTTGCCATCTTTTGCGCTCGATCACTCTGGGTCATTCTCTTTAATCTGTTTAGATCTCAGCGTTTACTATTAGCCATAAGTCTGCTTGTTAATAGCGTCTATGCCTTTTATGGTAACTTTTTGAATATCGTTTAATACGATTTTCATGGCCTTCGGAGAGCAATTTATGTTTGATGCCTTACTACTGACGCAAGAAGATAAACGCACGTTTGCTCAAGTTTCACAGATCACAGAAGCTGATTTACCAGAAGGGGAAGTCTTAGTTGAGGTTGCCTATTCCTCGCTGAATTATAAAGATGGTTTAGCTGTGACGGGTGCTGGCAAAATCATTCGCCAGTTCCCTATGGTGCCAGGCATTGATTTTGCGGGCGTAGTGTGCGAATCGACGGATGCAAGATACCAAGTCGGCGATCAGGTTATTTTGACCGGCTGGGGCGTGGGCGAAAATCACTGGGGTGGTATGGCGCAAAAAGCGCGCGTTAAAGCCGACTGGCTGGTGCCTATGCCGCAAGGTTGTGATGCCGCTAAAGCCATGATGATAGGCACGGCTGGGCTTACGGCGATGTTGTGCGTGCAAGCGCTGCAGCAGGCTGGGATAACACCGCAAAGTGGGGATATTCTGGTCACAGGTGCCAGTGGTGGTGTGGGGTCGGTGGCGGTGACTTTGCTCGCAAATGCTGGCTATCGTGTGGTCGCCTGCAGTGGTCGGGTTGAACAAAATGCACCTTTACTGCGGGAGCTTGGTGCCGCAGAAGTGATTGATCGCAGTGAGCTTGAACAAGATGCTAAGCCGCTTGAGGCGCAGCGCTGGGCGGGTGTGGTCGATACCGTTGGCAACAAGATCCTCGCTAAAGCATTGGCGCAAATGCAATACGGTGGCGCCGCTGCTATTTGTGGTTTAGCGGGCGGTTTTGCATTACCCACAACCGTTATGCCATTTATTTTACGGGGTGTGAGTTTACTGGGAGTCGACTCTGTTTCTTGTCCATTTGAGAAACGAAAAGCCGCGTGGGAAGCAGTGATTAAAGCACTACCTGCACATTTCTATCAGCAAGCTTGCCAGCAGATATCATTGGCAGAGATTCCTGATTTCGCGAATAAAATTATCCAAGGCCAAGTCACAGGGCGTGTATTAGTTAAGCTGTAATCTAGCTTCTTATACTTTAAAATGATGGCAACACTGAGGTGTTGCCATCTACGACTATACCATCCGTTTTACCAAGTTTACTTGTCTTCATATTGGCTATTTCGGCAGTGTAATAGTCACTTCAAGACCGGTTGGTTCGCGGTTCTCGGCTTTAATCTTACCTTTGTGGGCGGTAATCGCCGCTTCAGTGATTGCCAGTCCAAGTCCCCAGCCGCCGCTCTCTCTCTGTCTTGCCGAGTCTGGGCGGTAGAAAGGTTTAAAGATGGCATCGAGTTCAGCGGTATCTATGCCTGGACCATCGTCTTTTATTGTAATTTGTACTTGATCAGCAGTCGCACTCGCCTGCAGGTGAATGTCGCTTGCGGCATAACGAATTGCATTACGCAGCAGATTTTCAATCGCGCGGGAAAGTGATTTAGGAAAGTGCGCTAGCTCAATTTCTTCATCGATATCGATAGTAATGCTTTTACCTTGCTGTTCGGCTTCAAATTCCGCATCGTCCAATACTTGGCTCAGGGATTCGGCCAAGCCTAAATGCACCTTGGTTTCGTTAGTGCTGAGCTTGACCCGCGAGAGTTCTAACAATTCACTGATAAGCTTTTCGAGTTGCTCTGCTTCATAACCTATGCGTTCAGTTTCGGCGGTTTGTTGACCTTTTTTACGGGCGAGGGCGAGGGATAATTGCAGTCGCGTCAATGGGGTACGCAGTTCATGTGAAATATCCCCCATGAGCCGCTGCTGATTATTCACCATAGCTTCGATGGAATCGGCCATGCTGTTAAAAGCCGTGGCGAGCTGACCAATTTCGTCGTTGCGTTGAGTGGTGGACTTATCGACACGGTTACTTAAGTCGCCTTCAGCAAGGGCATTGGCACTCTTTTTAAGTGAATTTAATGCCTTACCTAGATGCCAAGCGAGCAGGCCACATAATAGTCCCGACAGGAAAATCGCTAGGCTTAAAGTTAATAACTTATTTTCGGCGAAGAAGAAGAACCAAGGCCGCGGATGGTTATCCGGCAGACGGCCGTAGAGTGAATACGTCTCGGCGCCCAGATTGAATTGAAAGGGACCAAACACCAACTCATCTTTAAACTGATGGCTAATAGGTTGTTTTTCTTCATCTGCCATCAACATAAAGCTACGTACGCCACGACTCACTCTGTGGGTGTTGACGACTTGGCCTTGGCTGTTGACGAGATAAACCCGTAGCGGCTTGCCTTCCATGTCTCTGTGCCTTTCCCAGCGACGAAGAAAGTCGGATTCCAGTAAGGCAGGATTTTCTTGAATGCGCTGCGCTATGGTCGATAGAACCTTTTCAAGGTGAGGTGGCAGCGGCGCACGGTCGTGACTCTGCTGCAGCAATGGCAACAGACCGACCAAGGCAATGATCAGCGAGCTGCAGAGCCAGAATCCGAGCAATAATTTGATAAATAAACGATTAGGCACAGTCGATACCGCCGTTATGGAAGCCAAATATAACCTTTGCCACGGATAGTTTTCACCCTTGGCCGCCCATCGCTACGCTCGGGGAGTTTTTTACGTAGATTCGATAAATGCATGTCTAAGCTGCGATCGAAGGGCATGAGTTTTTTGCCGAGCACTTTCTCGTTTAACTCTTCCTTATTCATCAACTCTCCCGCGTGCAGCGCGAGGGTGTGCAGCAGAGTGAATTCTGTGCCTGTAAGTATGATTAACTGCTCGTTACAATAAGCCTCTTGGCGCGATGGGTCTAAGCGTAAATCGCCAAATTCTTGGGCGGGCGCGGCATGGATTTCTTGGGTTGTTAAGTTCGAGCGACGAATAATTGCGCGGATACGGGCGATTAATTCTCTGTCATTAAACGGCTTTGGCAGATAATCATCGGCGCCGATTTCAAGCCCAACTACGCGATCGATTTCATCGCCGCGGGCGGTCAGCATTAATACTGGCGTTTGCTTGTGCTGACGCAGTGCACGCAGGACTTCGAAGCCGTTCAATTTAGGCAGCATGACATCGAGTAAGATCAGATCGTAATCCGCACTCAGTGCCAGATCTAAACCTTGTTTACCGTCGTAGGCCAAGGTTAATTGAAACCCTTCTAACTCGAGCAGTTGCCCTAGTAGCTCCGATAAACCCAGATCGTCATCGATTAATAATATCCGACTCATACTGTTCCTTAATCTCTGTAATGTGGCCTTGCGTGCCCGTTTACACAGGCACTTTTAGCGCTTTGGCTGAGTATACCTGTTTTGATGTCAATTGCTGTGTAGCAATGTAAGCCAAAGTAAAGAATAGAGAATCTGCTATATCCAAGCCACCTCAAGATACGAGTTTCAGAGCGTCTAGGGTGATTCAATTCAAGGCGCATCAATGACAGAATGTCGGCCACCTTTTGAATTGATGCAACTTTACAGTGATTTACTCAGGCAACGTCCTCACTTACATATAGGGGTTTAGACTGGAAGGGTCGAAAGTGACTCGGCTTAAAAGGGTCGTCACTGTGTCTTATTACTTGCAATATTAAAGTTGAACCACTGAGGTTAACACTATGAAAACATTATCACCTTTGAAAGCGAGCCTGTTTGCAATTCTAGCGAGTTCTGCGGTTTTCGCGACGACAGTCAATGCTGAACCTATGGACGGTGGCTGTGATTTCTCCCGTTCTGAATTCCACAAAGGCGACCGCATGATGGGCGATCGTATGGAACATGGCGGCCGACATATGGATCAGGGCGGTATGCATCGTATGTTTGAAGGGCTGGATTTAACGGATGCCCAAAAGGCTGACATCAAAAAGTTATTTGCCGATCAGAAAGCTGCGCGAGCCGATAGCCGCCCAACGAAAGAACAGCGTTTGGCCCATCGCACTGAGATGCAGGCTTTGATGACTGCACCTAATTTTGATGAAGTGCAAGCCAAGGCACTGATGAGCGCGCAGCAAGAACAGCGTCAAGCTCAAGCCATTGAACGCATGAAGTTACATAATCAAATATACAATCTGTTGACGCTAGAGCAGCAAGCTAAGTTTAAAGCGCGATTTGAAGCACAAGCTGGTAAAGAACCTCGCGGTTAAGTGTCTTAGCTGATATACAATAGGGTCATAGCTTCCCAATATAGATCCCTTATGACTCAGACTTCCCAATACGATTTTTGGGTCAAACTGGCTAGCCGCGCTTCCGTGGCTACCGCTTTGATCCTTATTCTTATCAAGATGTTCGCTTGGCTTTATTCCGGCTCTGCCAGTATGTTGGCATCTTTGACGGATTCCTTTGCCGACGCACTCGCTTCCATCATCAACTTTATTGCAATTCGTTACGCGCTTGTGCCGCCGGATCATGACCACAGATATGGCCATGGTAAGGCAGAGCCGTTAGCGTCTTTAGCGCAATCCGCTTTTATTATGGGCTCCGCGTTCCTGTTACTTTTCTATGGTGGCGAGCGGTTATTAACGCCCGTTCCCGTCGAAAATGCCACCATAGGTGTGGTGGTTTCTGTTATCGCGATTATTATGACGTTAGCCTTAGTGACGTTGCAAAAACGAGCATTAGCAGCGACAAAAAGCACTGTGGTCGAAGCCGATTCACTGCATTACAAGTCAGACTTATTCCTCAATGGTGCGGTACTGTTAGCACTCGTCCTATCCCAATATGGTTGGTGGTGGGCTGACGGTTTGTTTGCAGTAATGATTGCATTCTATATTGGTCAGCAAGCCTTTGGCTTGGGCTATCGCTCAATTCAAGCCTTGCTAGATAGGGAGTTAGACGAAGAAACTCGCCAGCAAATCACGCAAATCGCAATGGAAGATCCACGGGTACAAGGTCTGCATGATTTGCGTACTCGTCAGGCTGGCAAAACCGTTTTTATCCAATTCCATTTAGAGCTTGATGGCAATCTGAGTTTGAATGAAGCCCACAGTATTGCCGATACCACAGGGATAAAGGTCAAGGGCGCCTTTGAAGATGCCGAGGTGATTATTCACCAAGATCCAGTCAAACCTGAAGTCAACGAAACATAAAGTAAGGGCAAGCGATGGCATGGATGCAGAAGCAAGGTCACAGATGGGATCATTCCACTCGATTGATATGTCGCTATCAGAGGCTATCGGCTCGCTTTGAGTTGATAGTCGAATCTGAGATTCGTGATGTTAGCGTTCCAGCAGATGCGGAAATTACGGTTTCAACTCAAGCGCAGTCCTCGAGAGTGAATCAAGCAGATAGTGAAGTCACTCCTTTGATAGTGGGGGCTTGGAGTCAGTTAGTCCTGCGTAAACATTACCGCAGAAAATTGATTTCGAAATATCTAACCAAACTTATCGAGTCCATATCTGGCCATTATTCCTTTGGCGCACCCGTATTATTTGTGTAGCGACTGGCCGAGTTGTCGTATCCGAATGTTTGGATGTCAGAAGGCGCCGCTACATCATCCGTTGGCTATATGCAAGTTTTATAGGATTGTTGCGGGTGCTAAGTTAACATCGTTAATTGTTTCAATTTAAAAAATAATACTTACAGTAAATATAGTTTTGTTACATAACGAATAACTATAACTTCCATTTTTCGACTCAAGCTGAATCAATGCTGTATGGTTTGTACTTTGATAATTTAAACAATGACACTTATTAATCACTTATTGTAATATCCGCGCCATCTAATACTTACGTTAAATCCTTATGGAAATATGATGAATAAATTATCAATTGTTGCTATCTCTTTACTATCTGCATTAGCGACAGCTCAAGCTTCTGCTGCAAATGACACCACAGGTTTCTATGTGGGCGGAGCCCTGAATAAAGTAACGCTTGATGGCGATGACGTTACTGACTCACGCTCTGGTACTGGCTTAGGTGCTTACGGTGGCTATAACTTCAACGAGTGGTTTGGTTTAGAAGCTAACCTGTTTGCCACAGGTGATTTAGGTAATAAAGATGTTGATGCTAGTGCTGGCGCATTAAGCTTCACGCCTAAGTTTACTGCACACATTAATGATATTTTCTCAGTTTATGCCAAAGCAGGCGTCGCAACTATGCTTGTAAAATTTGAGTCAAATGGTCAAAAGTTGGACTTTACTGGCTTTGGTTGGACTTACGGTGCAGGTGTTAATGCTGCCGTAACAGAGCATTTAAATATCCGCGTAAGCTACGATGTCACCACTGGCAAGTTAGATGCAGATAATAACTCTCTAGGGTTAAACGATATTGACACCGATATTAAGCAATTCGCTGTAGGTGTGCATTATCAATTCTAATTTTTTTATTTGGCATATTTAATTAATGCCAATGAAAATTAAATGAATTTCAAATAAAAAGGGATATGAAAATATCCCTTTTTACTTTTTTATTATTAATAACCTTGTGTCGGGATTACGAGTGATAACGTTATTTTATCTACTCAATCCATCGACAGTTCTTTTAGTTTTCGTGTTAAGGTATTTCGTCCCCAACCTAAACGTTTCGCCGCTTCCTGTTTATGGCCTTGGGTATGGCGCAGCGCAGTTTCTAGCAGTATGCGTTCGAACGCGGGCTGCACCTCAGTTAATAGATCGCTGTTTCCTTCCGAGAGTTTTTGATCTATCCACTCAGTCAGTGCCGATTGCCAATCTTGGCTGCCTTTCGCCGTGTGGGTGACACTCACAGGATCTTTCAGTAATTCTGGCGGTAAATCTTGGGGTAAAATTTCTTGCCCCGATGCCATAACGGTTAACCAGCGGCAGGTGTTTTCGAGTTGTCTGACATTACCGGGCCAAGGTAATTGTGAGAGTTTTACCGCGGTTTCCTTGGTCATTATTTTAGTTTCTACGCCAATTTCCTTCGCTGCCGAGGCGAGGAAATGGGTGGCGAGTTGGGGAATGTCTTCCCGTCTTTGTGACAGTGGCGGTAAGTGCACGCGGATCACGTTGAGGCGATGGAATAAGTCTTCCCTAAAGCCGCCTTTTTGCACTAATAATTCAAGATCTTGGTGGGTCGCCGCTATGATACGCACATCCACTTGCACTGCATTATGACCACCGACACGGTAGAACTGACCATCGGCAAGCACTCTGAGTAATCGTGTTTGTACATCTAGCGGCATGTCGCCGATTTCATCTAAAAAGAGCGTGCCACCGTTCGCTTGTTCGAAGCGACCTTGGCGCACATTGGCTGCGCCAGTGAAGGCACCTTTTTCGTGGCCGAATAGCTCTGATTCGATCAAATCCTTAGGGATCGCAGCCATGTTCAGCGCGATAAACGGCTTATCTTTACGTGGGCTGTGTTTGTGTAACGCGCCAGCCACTAATTCTTTACCCGTACCCGATTGGCCATTGATCAGCACGCTGATAGACGAGCGTGAAAGCCTGCCTATGGCGCGAAACACTTCCTGCATAGCTGGCGCTTCACCTATGATTTCGGGTGTTTTGACTTGGGCTTCCTGCGCGGGAGCGGGGCTCTGTTCAGTGGCATGGGTGAGTGCGCGCTCGACCAGAGAGATCGCCTCATCGATGTCGAAGGGTTTAGGCAGATACTCAAATGCGCCCGCTTGATAGGCACTTACGGCACTGTCTAAGTCTGAGTGCGCCGTCATAATGATCACGGGAATATGGGGGTAATGCACCTGCAATCGTTCGAGCAGGCTTAACCCGTCCGTGCCTGGCATACGGATGTCCGACACTATCACGTGCGGTTGCGATATTTCTAAGGCCTGCCACAGGGATTCGGCGGCGGCAAAGCTGGCGGTACTGAGCTTGGCACCTTGTAGTGCCTTTTCGAGTACCCAACGTATCGAGCTGTCATCGTCGAGGATCCACACTTGTTCACTAATTCGCATCTTGCTTCCTCATACGGTATGGGTAGGTACTGTCGAAATTATTTAGCGCCTAAAATCGGTAATGAAATGATGAATTCGGTGTGCCCAGCGCTGGAGAGGCAATCGATTCTTCCCGAGTGTAAACGGGCAATATTATGGGCAATTGAGAGGCCTAAACCTGAACCTTGCTCACGACCTGTCACCATTGGATAAAATAATGTGTCCATCAGCTCAGGCGGTATGCCGGGGCCGTTGTCGATAATAGACAGGGTTAACACTAGCTTGTGGCGCTGTGAACCTATGGTGACTTGGTGTTGAGTGCGAGTGCGGATCAAAATTTCGCCGCCCGAATGCTCAAGCGCCTGCACCGCATTTTGTAAAATGTTGAGCACGGCTTGCTGCATTTGGTCTGGGTCCATCTCGATATCGGGAATGGATGGATCATAATCCCGTTTGAGTTGGATGTTAGCGGGCAGCGCCATTTCAACTAATTTATAGACCTTTTGCACCACTTGATGGATGTTGTGCAGACTGTGCTGTGTTGGCCTTTGGGGGCCGAGCAGGCGATCCACTAAATTACGCAGCCGATCGGCTTGCTCTATGATGAGGGTAGTGAATTCCTTCAGCGCCGGATCATCCAGCTCCCGCGATAACAATTGCGCCGCACCGCGAAGACCACCTAAAGGATTTTTGATTTCATGGGCTAAGTTGCGCACCAAAAACTGCGCCGCTTGTTGCTGTGCATCTTGGCTCAGCTGCTGATGAATGCGTCTTTGTTGATCGACTTGGCGCAGTTCGAGCAGGCTGAGTTCAGCCTCGTCATCGAGGGGAATAAGGGTTAAATCAACGGTATGGTGCTGACCATCGAGCGTGACTAGGGCCGCCGTATTGACCGTTAAACCTTGGCCAGCCTTTACCGCATCCCTCAATAACTGGGCTTCGACACCGAGGATTTGATAATGCTCAGGCAAAGCTTGTTCAACGAGTCTATGGCTGCCAACACCTAATAATTGCTCTGCTGCTGCGTTGGCATAACAAGGCTTAAGATCCTTATCTATGACAAGTACAGCGGTAACTAAATGATTGAGCAGTGTCTCTTTATCCATCGACCGTTCCTAGAAATAGGGTTGCACCACCATGGTGCACCAATCTGGTGCAAGCTGCAACTTGTCTTCATCAAACGGTTTAGTCTGGGGTGGTTAATCTAACTCTATGTATTTAAAGTTCTTATACTAACTCTAACTATACAAATAGCGACATTAACGCTTAGCTGCTTGATGAAGAAATATCTTTCTTGGTGAACTTGATGCAAAGACTTTGCCGTTTTGAGTCAAAGCGTCAACGACTAAGGTATGTTCACCCCGGTCGATGTTTTTGAGTTTGAACGTACCGCCAATTTGCGGCTGCCCGACGGGGGTGCCATCGAGTTTGAGGGCCATTAAATACTGGCTTTTTAGCTCTGGGGTGATAGTCGCAACGACGTCGAAATCGCCATTATTATCTCTGACGGTTTCTTCTTCTGCTGGGGAAGTGATATTGACTTGATATTTAATGGATTCAATTGCTTTGTTTGCGTTATTTGAGTCTGTCTTAGGTAGAGGAAGGGCAATTTGATTTAAGGTCTTCTCTTTTAGCTCAACGATTTGGGCATTGGGCTGCGGCTCATCTGAGTAATGGACTTTACCATCTTTATCGACCCATTTATAAACCGTCGCTTGCGCTAACATGCTAAATAGCACTAAGCCGATGAGTGTGAGTGAACGCATGCTAAAGTCCCTGTAGCCTTGAGATAGAGTTAATCAAGATTAGTCTCTTTTACATCAAAAATCATGGTGTTTGTTATGCGGCGTCGTCACATGGAAGGCGTAAAGCGAGGCAGGAAAGGGGAAGCTAGAAACCGGACTGGGTTGAATGAGCTGAAGTTCGGGCACACTCATGTGCCCGAACTCGAGTTGTAAATTACACGCTTGCGTCGAGTTTTTTCTCGATCTCACTCGCTTTAACCCAAGCGGCATGTTGGGATAATTGAGTGGCATAGCGTTGAATGTGTTCAAATTTTTCGAGGTCGCCACTGTTAGCCAGTAGCTCCACCACGAAAGACATCATGATATCCGCACCTGTGAGCTTGTCTGCAACCAGGTAGGTTTTTCCGGCCAACATATCATTGAAATAGTGGCTTATCTTACTGACTTCTATCGCGGCATAACCTTCAAGGAAGTTGGTTTTACAGCCGTCCTTTTGCACGAACATACGCAGTAACAAAGGTAGCATGGCTGAGCTTTCGGCAAAGTGTAACCATTGCAGATAATCGACGTAGTCAGCGCTGTCTCTGGCGGGTGCTAAGGTGTCTGTCGCATATTTTTCAATCAGATACTCAGTGATCGCGCCTGACTCGGCAATCACTTGACCGTTCATTTCGATAACGGGTGATTTGCCTAACGGATGAATTTGCTTGAGTTCGGGTGGCGCGAGAAAAGTCTGGCTATCACGCTGATAACTCTTAATTTGATAAGGTTGACCGAGTTCTTCTAGCAACCAGATGATACGTTTAGAGCGTGATTTATTGAGATGGTGCAAGGTGATCATTGGCTTCCCTTTCGACTGTGAGAGTACATTTTTAGCTATCGCACCATTGTGGTGCGATAGCTATTTCTACCGCATTTAGCACACTCTAGCTTGTCATTATTCACTTCAAGCTGAGCGTGTCGCGTCATTTATCGCTTAGCCTCTAGCGTTCAGTTGCGCGACGATGGCCTGCGCAACGGCTTCGGATGATCCTGGGTTTTGACCTGTGATCAAATGGCCATCTGCGACAACAAAGCTCGTCCAATCATCACCTTTGACATAAGTGCCGCCGTTTTTCTGTAGCATGTCTTCGACTAAAAATGGCACGATATGAGTAAGTTGCACGGCTTCTTCTTCAGAATTACTGAAACCGGTAACGCGCTTGCCGCTGACCAATGGCTTACCATCACTGGCTTTAGGGTGCAGCAGTGCCGCAGGCGCATGACAGACAAGTCCTAGGGGTTTATTCGCTTGATAGAACTGTTCAATCAAGGTGATAGAGTGTTTATCTTCGGCAAGATCCCACAGTGGGCCGTGGCCGCCTGGATAGAAAACGGCATCATAATCAGCTGCTTTGATTTCACTTAAGCGAGTGGTATTTGCTAGCAGTGCTTGTGCCTCAGGATCTTTACTAAAGCGCGCAGTGGCTTCGGTTTGAAAGTCTGGCTCATCACTTTTTGGATCGAGCGGTGGTTGACCGCCCAATGGCGAGGCTAAGGTAATGTCAAAACCTTTATCTTTAAACTCATAAAAAGGTGAGGCGAATTCTTCGAGCCAAAAGCCGGTTTTTAAACCTGTGTCGCCAAGTTTATCGTGTGACGTAAGTACCATTAATATATTCATTTTCATACCTTTACCTAAGTTGCTCACATCAATACGTGTGAGCTTTGGGTTTAGTTCATTTGACGTTATAGGGCTAAGGTCAAAATGTCACGGCTCATTGCTGGGTTAATGTTGCCATGTTCGCCGAGTGCCACCATGCCGTGCAGTTCAAGCTGTTTCACTACGGTATCAACGTGGCTGGCATCGAGATCATAGGCAGATAAATGTGTCGGTATGCCCATGGCTTCGAAGAAAGCCTTAGTTTTTGCAATAGCGGCATCGATGCGCTCGTCATCGGTACCTGTGTTGATGTGCCACACTCGGTCTGCGTACTGGAGTAACTTTTCGCGCTTAGCCTCTTTGGTACATTGCAGCAGGGCAGGCAATACGATTGCCAATGTACGCGCATGGTCGATGTCGTAAAGCGCAGTCAGTTCATGGCCTATCATGTGGGTTGCCCAATCATGGGGAACGCCTACGCCAATCGTGCCGTTGAGGGCCATAGTGGCAACCCACATTAAGTTAGCGCGAATGTCGTAGTCTTCGGGTTGAGCCAGTACCTGTGGACCAAGCTCAACCAAGGTTTGTAACAGACCTTCGGCAAATCTGTCCTGTACGGCGGCGTTGACTGGGTAAGTGAGGTATTGCTCTGTGATATGCACAAAAGCATCGACGACACCGTTGGCCACTTGGCGTTCTGGCAGGGTGAAGGTTTTCGTCGGATCGAGTATTGAGAATTGTGGATAAACCAAGTCATTACGGAATGGCAGCTTAGCTTGCAGTGATTTACGGGTCACTACGCTGGCGTTGTTCATTTCTGAACCCGTCGCAGGCAAGGTTAATACAGAGCCAAAAGGCATAGCTTGAGTCACTTTTGCGCCCCAGCTAGTGAGGATGTCCCAAGGTTCACCTTCGAATACTGCTGCAGCGGCAACAAATTTAGTGCCATCGATAACAGAGCCACCACCCACTGCGAGGAGGAAGTCGATCTTCTGTTCGCGCACTTGGGCGACGGCTTTCATTAAGGTTTCATAGGTTGGGTTTGGCTCAATGCCATCGAACTCGACAACGAAACGATTGCCCAATGATTGTTTAACTTCATCTAGCGTGCCGGTTTTTCTGGCGCTGCTGCCGCCAAATAGGATCATGACTTTAGCATCGCTCGGTACTAAAGTATCAATTTCGGCGATAGTGTCTTTACCAAAACGGATACGGGTTGGGTTGTAATAGTTAAAATTTAGCATGGATGATTTATCCCTTATTACTTTAAGTGAGCTACTTTAAATTAGCAGTGTTAAATGCGCTGCGTTAATTAATCGCCCGTTAAATTAGTAGACCAGTCGTCTAATTCTAGGGCGTAAAAATGTATTCTTTAAAAATCTAGGCTGACTGATATCAGCAAATATTGGTCTTGAATCGGTATTAAGTTAAAGAGCCGTTTTAGGCGTTAAAATAGCTTTAGTCGTCACGATCGCACGTTCGAGTGCCGCGGGGCTGTGGCCTAACTTGTTCATTAAGCTGGCACCTAACCACATGTGATACAACATTTCAGCGGTCGATTGCGGATCTTGCTCGGCGATTGAACCGTCGTTTATGCCAACTTGAACACAAGTGGTTAACCTGTCTATGATGCCGGCGGAACCTTTAAGTAAGGCAACTCGCATGGCTTCGGATAAATCGGCCACTTCAGCACTGAGTTTCACGACGAGGCATTTTTGATCTACGCAGCCATCAGCTTGTACGTGCAACCATTGTTGCCAGTACTGCATCAAGCGTTGATAGCCCGTCAGTGTTGAGTTATTGAATAATGCGTCTAAATCTAACTGATATTTTTCGAAATAATCAGTTATGAGCGCTTCACCAAATTGTTCTTTGGATTTGAAGTAATGATAAAACGAGCCTTTAGGCACTTCGGCCGCTTGCAGCAGTAGTGATAAGCCGACGCTGGAGAAGCCTTTTCGTACGATCAGCTTATAGCCGATGTCGAGAATGTGCTGGCGAGTGGATTGAGTTTCAATTTTCATGTGGTCTAGGATAAATCAAAATAGACCGGTCGTCTAGTATTGTTTTTATCGGCGATATCAACACGCCCGATTTAGTCTTCTTTAAGTCGCGAGGTTTAAGCTGTCTTGGGTCTGCCTAATAAAGCTATCAAATTGATAAAAAGCCAGACGGATAAAGCATGCAGAGGGTGAAATTGTGGCAAGGAGAAATGGTTCGGTAGGACAAAATACCCACAGTGCTGAGATAAACCGTCACTGTGAGCATTAGATTCATAATTGATATTGAAACTTTTACTCAATCAAGATGGTTACAGGTAGCTGCAACAGTACTCTGCAATGGCTGTGTTACGAATTTTAAAGCTGACATTTGCAGCCAGTTCAAACTGAGTTCCTTCGTTAAATTCTTGCCAAGTGGTCGTTTCTGGTAGTAACACCTCAAAACGGCCGCTAATCACTTCCATGATTTCGCCTTGTGAAGTCGAGAACTCATACTCTCCAGGCTGCACTACGCCTAAGGTTTGCTTACTACCATCGGCAAAAAATACGCTACGACTGGCAACTTTACCATCAAAGTAAATATTGGCTTTTTTAGATACAGACACTTGCTCGAGTAAACTCATTTTTAATCCTCTATAAATTGGCTTGAGATGAATTTATATGCATATTTAATAAATTTCAATGCATTATTTGTCGCGTGGATGTGCGTATTGAATATAAGGCAATGCTAACGTTCGGTTTATAAAGTGTTTTCCACTATATCATTTTGTTTGAAATGGTCGGTATGGCTTGGCTGTCATTTAAAGTTATTAATCATTTTTCTATTTAAAAATAATTGGTTGGCAGCATTTAGGTTAACTTTTGATGAAAATAGTCTGCGTCGCTTTGTTTAGCTGAGATTCTTCTCCTATAGTTTTAGGGCAGATTTAATTTTGCAAAAAGTGAATGAATGGAATTAGAAGCGCGACAAGGAGTCTCACCATGGGTTTTACTCGTAAAATTCAGCTGATCAGTTTATTGATTGTCCTATTACCCCTCATATTTGCTACCGCGATAGTGACTTACATTGCTCGGGATGAGTTATTTGCCGAGGCTCAGTCTCGTTTAGTGGCGGTTCGCGAGATCAAGCAGCGACAGATTGTCGGTATGTTTCAGGACTTTTCAGATAACCTGCAGGCCGTCAGTGCGGTGATCGCCAGCCAAAAAAGCCTCGATACTATCTCCGACCTTGATGAGACCTTAAAGTCGTTAAATAAGTCTTTAGGTTTCTATGATCTGTTCATCATTCGGGATGACGGCACTGTGTTTTACACCGCCGCGAAGGAGTCGGATTTCGGCACAAATCTTCGCACTGGGCCCTACCATGATTCGGGATTAGCGCAATTATTCAACAAGGCTTTGGGGTCTACTGGTACTGTATTACTGCAAGACTTCTCCGCATACGCTCCTTCAAATGGTCAACCGGCAGCATTTATTGGTCAGTCGATTCAATTCAATCAGCAAAGATTGGTGGTGGCGGCTCAAGTATCAATCGACCGCATCAACCGAGTTATGCAGATCCGCGAAGGTATGGGGCAAAGTGGTGAGACCTATCTAATAGGTCCCAATAATCGCATGCGTTCCGATTCTTTTCTTGACCCGAACAATCGCACAGTGACCGCTTCCTTCGCTGGAACGGTTGCCCAGAATGGTGTCGACACACTTGCGGTGAAAGAGGCGCTGGCGGGTAAGACTGGTGTGATGCAAGTGAATGACTACAATCATAATCCGGTGTTATCTGCCTATTCTCCCGTTGTTGAACTTGGCTTACGTTGGGGCTTGATGGCTGAAATTGACGTGAGCGAAATCGCGGCGCCGGCTTATCGAATGTTGTTTATTGGCTTATTTGTTTGCGTGGCGGCCTTAGTGCTGGCATTCGCTGCTGCTAAGTTAGTCACAGGATTTGTGTTGAAGCCACTCGGCGGTGAGCCTGAGGACATGTGCAATTTGACCAGCATGATCGCCTCTGGCGATCTCACCCATAATCTACCCCATGTTCAAACGGATAATCATCTGATGAGCTGGTTGGCCCGGATGCAAACAAAGCTCAAAGAAATCATTAGTCAGCTGGTGGGCGTTGGACATGAACTCGAATCCGCCGCTGAACAAAATTCTGCCGCTATGACCCAAGCCGATTGCAGTATTCAGATGCAGGCGAAAGAGACCGATATGTTAGCAACCGCAGTCGAAGAAATGAGTTATGCCGCCGCTGAAATCAGCACTAATACGCTTAAATCCTCCGATGAAGTGTCAGCCTGTACCCATTCGAGTGAAATTCTTTCGCAAAATTTAGCCAGTACACGTAAAAGCTTGAAAGTGACCTTGAGCAGTTTTGCGACCATTCATCAACAGGTGGGCAGCCTTGAAGCCGACAGTCAGAAAATCGGTTCGGTATTGGCTGTGATTAATGCGATTGCTGAACAAACGAATCTGCTGTCACTTAATGCGGCGATTGAAGCGGCGAGAGCTGGTGAACATGGTCGTGGCTTTGCCGTAGTTGCCGATGAGGTGCGCCAGTTAGCGGTTAAGGTTCAGTTGGCGACTCAGGATATTGGCCAAGTGTTAAAAGGCATTCAACAGCAATCTGGCGTGTTGGCGCTGCATAGTGTGACTTGTACTACTGAAGCCTCTAAAACAGCAGAAGATGCCGATGGCATGCAAGCCGCAGTGGATGACATCGCTTTACGTTTGGAAACCTTAAAGGCATTAATGATCCAAACCGCCACCGCCGCGGAGGAGCAAACAACGGTTTCGGCAACGATTGCGCAGGGGATTGCGGGCTTGAGCGTGGCGGCGGAAGAAAATAGCGCGGCGATCAGCCAAGTGGCAGCGAGTACGCGTAGTTTGTTAGGGTTAGCGAATCAGCTTGGTTTAACAACGGCACAGTTTAAGGTGTAGGTATTTGCGCTGTATTTGCCGTGTTCAACCTTAGTTGATGCTGCAAAGATGACTGAGCTGGATGTTACACATCCGCCAGAAATGGATTTCTGGCGGATATTTTTATTGAGACTTCAGTTGTTTATTCCATGAGTGAGCATTAACGCTTACGCCAAATTGTCATTTCTGAGACAGAATGTTGGAATTTGCGTTTAGTCTCGCGGATGACGAAGGGGATTTCCTTCACTGCGACTAGCTCAAATTGGCCGATCAAGGTTTCGGTCAAGCCATCTAAGGTGGTGAAGTTCTCTCCTTTGACTTTAACGCCACCGAGCCATTTATCCTTGGGCGTATAATCCTCAAGCCAAGTGTATGGCGAAGCAATCACAAGGTATCCACCTTGATTAATCCGCTGGGCAATACTGGTTAAAAAGTGCTTAGGATCGTTCAGTCGGTCAATAAGGTTCGAGGCATAAACTAAGTCATAACCTGTATACATTGGCTTTAAATTACAGGCATCACCTTGCATAAAGTTGACTTTTTTCGCCTCAAGCTCATATCCAAGTTTAGCCAGACTCGCGCTCTTAAATTCCTGTAAGTCGCCCTCGGTACGTATGGTGTAACGTTTTTCACCTTGTTCGGTCAGCGCATAGGCCTGTTGAATAAAGCGCGCCGAAAAGTCGATACCATCAACATGATCAAACACTTTTGCCAGTTCAAAACTCGCTCGGCCTACGGAGCAGCCGATGTCGAGGGCTTTGGCCGTATGGGTGAGTTGAATTTCACTTAGCGCTTGCTGTACGCCATTGACGCAGAAGTTAGCAACCCCGAAATATTCGGCGCCATAGTGGAATTCAAGGTATTGGGAAATCAGTTCATCAGTTTCGTACACATTGACCTCGGCCATTTGGGCGGGATTTTGCGTGGATTCCACATAGCGGAAGCCAGCGTGTTGATAAAAATGTCGACGAAACGCATAGCGTGACGAAGCAATGGCTTCATTACCTGTGGAGATCCACGAACCACCTTTGATGAGATTATGTTTACCATCAAAGGTTGGGGTAGAAAAATCATCATAGAGTGGATGCACGGCAAAACCGTTAAAGCCGTCGATTGCGGTTTCTGTCCATTGCCAAACATTACCGACAATATCGAAAAAGCCATTATGTTCGAAGCGATTCACTGGGCAAGAAGAGGCGTAATAAGCCAAAGCAATATTGCCCGGCACTTCGTGCCAATCGGGCGCATCGGCGGGCACATTCTCGCGTAAGATGTACCATTCGGCTTCCGTCAGTAGGCGAATGTTGCTTTGCTTTTGCTCGGTTTTCCAATTGCAGAAGGCCTTAGCTTCGAGTTGATTCACTTCTACAGGCCAGTTTAATGGCAGCGGGATTTCTTCCGCTAAGTTACGTTGCCACCATTTATCTTGTTTTAAGCGCCAAAAACGCGGCATTTGAGCTTGGGTGTAAGCGAGCCAAGCTTGACCTTCTTCATTCCAAAATTGTGGTGAGCGATAGCCGCCAGCTTCGACGAATGCGAGATACTCTTGATTCGAGACTAAGAATTTAGAGGCCTTAAAGTCGGCAACATTAAAGATTTCATGGCCGTATTCGTTATCCCAGCCGTAGGTTTGATCTTGTTCAGTTTTACCTAAGGTCACTCTCTGCCCTTTTACTGCCAGTAAACGGTTGGTTTCGGCGGCGGCGAAGTCTTGGCACGCAGCCCAGAGGCTGTTGGGCGCGACATGTTCCAATGGCAGTTGGCGGATGATCACCGATGAGGTTTCTAAATGAATACGCTCATGTTCAATGCCCATCAAAATGATCCAAGCTGGGCTGTCTTGAGTGATCGGCAGTGTTAGAGGCATTTCATCAATCACGCGATTGACGACAGCATTAACTTGTTGGCGATAGCTGCGCACTTCTGCCACGTTCGGCCATGGGTAATGTTGCTCGTTCAGATCGTCCCAAGACATCTCATCGACACCAATGGCAAACATGGATTCGAAGTTGTCGTTTACCCGTTCGTCGAGAAATTTGCCGAGTTTGAGTTTGTTGATATAGAAAGTGGCCGTATGGCCAAAATAAAAGATTAACGGATGGCGCAGCGGCTCGGCTTTGAGGTAATAGGCGCTATCTTCTTGGATCAAATCAAACAGTGACTCGTACAGAGCCCATGTCTGATTAAAGTACTGCTTAAGCTCTAGACGCTTTCCTGCCTCAGTTTCACCAATTAGCTTAAGGGTTTGAGGGGGTTGCGTTATGGGCTTATCCATAGGCAAGTAAACTCCGTATTTATCGGCCTGCGGTGCAAGCATTGGAATATGAATCTATCGTGTGTGACTCTATACGCGATTACTGGGCTCAGGATCACTTTGTCAGCTTAATGTGTGTTCTTATCTATCGACCCCTGACAAAGTAACAGAAAGGTGGCGTGAGTTTAAAGCTTCAAATGTGGGGGAATAGCCTTCTTTAGTAAACAACATTGAGGGTATGTTTAAGTAATAGTTCGAGTTACAAAGTGATTTATTATTAACTGCTTATTAATTCTTTTTCCGCACACAGACGTGTTTGTTTTGTTGCTTAAATGTTGTTTTTGGTGGATTTTGGAGATGTATGATCTATGCTTCGCTAATTTTTTAGGTATTTGATATATGCAGCAGCTATTTTTTCGATTACTAGAAGACCGTTATTTTAATCGGTTGCCCGAGGAGTGGGTTCTCCGGTCAGCTCAGGAATCGGTTGATTTCGTTAGATTTTGGCTTGTACATTGCTTTCGCAGTGCCGCATCGGAACGAGAATTAGCAACTGTGCTGTTTTTTGCCGCTAAATGGAAGTTAGAAGAGCTAACTTTCTTAAGATCATTTTGGCAAAGTTATACGTCAGTTATGGATGAAGCTGCAGAGTTGACATTGAGGAATTTATATTTCCAGTTAATTCCTCAAATTAATCTTGATCACGACAATCATTTAGATGCGATAGCCATTTTTTCTTTTGTACTCAAGTTTGAACAACAAACGGTAGTATCTGAATTGCAACAGGGTGTAGCCTCTTCTTATCGAACTTGTCTACCCATTGAAATGCTGCAAGAGATACAAACTCTTTCATTACCTCATTTACATCCCATCACTGTTTATGGTCATCAAGGAAAAGGAAGTGTTGAATTTGATATTCGCAATAACACTCACTTCCCGACACAAATACCTAATGCTAGTTTAGTATTTGCGTGTGTTGAAAGAGTCGTTGCGCACTATTCTGGGACAAACCTAGCATTTGCTGAGCCTATGGTCGTTTTACGCTATGAGGTTGGGCAATTTTACAAATGGCATTATGATGCAATACATGCACACACACCGACAATTGAATCAGAACTAAAGCAATTTGGTCAGCGTAATCGTACTGCGATACTGTACCTAAATGATGATTTCACTGGTGGGGAGACGGAGTTTAAAGCGCCATATATTCAAGTGCATCCTAAGGCTGGTTCGGTCTTAGTTTTTGATAATGTAGATTGTAATGGGCGAACTTTGATATCGTCTTTACATCGTGGCTGCGAAGTCAGTACAGGCCAAAAGTGGATATGTACACAATGGTTTAGAGATAAGCCATTTTGGCTTAGATCTGGGCTCTTAACTTAGTTTCAACTAGATTAAATTTGAATCGTTTTCTTACAAGTAGATTTTAGGCTAAATTATTTTATCCCTGAAGAGTATTTTCTCTGTCGGGGTTAAGGCAGAATGATCCCGTCCGTAAAATTATTATGCAGGATATAACGGGATGACAATTAACGCATTATGGATACCCGCCTGGTACGAACTTGATCAATCGATCGTAGTGGGTGTGACTGAGGAGTTTGTCTTTCATAAGACTGTCGCAAACGAGGCGTTAACGTTTTACAGCGGTGCAAAGGGGAGTGATGCCGTTAAGGCAACGGGAACCATTTCGGCCATTAAACATAATGTCTTGGGGGACATTGAAAGTGTCGATGCACAGGGGCTCGATTATACGCTGGTATTGCAAGATGGACGGCGTTTGTTAGTCAATGCTGAGGAAAATCCAGGATTGATTTATGAGTGGGTTGATGACAGTTGGCAGCCGTCAGATATGGTCATCACAGATTGGACGTTGGCAGTACAATTTGCGTCTTTATCACCATTGACGCCAATAAAGTAATTGAAGATTTATCGTTAAGTGACTAAATAGGTAAGTGCAGTTAGCGCTCGGGATCCGACGTTGACTGCACTTTTACTAGTTCTGCAAGTTGTTGGTATTCTTTTTTAATTTCTGCGACATATACATCGTGAAAATCCAGCTCATTAAACTCATCAAGGCAGAGCTGATAACGGCGTAGCTGCTCAGCTTTTTCCTCAGCGGTATCCCATCTTTCCATTAGCATAGCGTTTGATTCCATTCATAAATTTAATTCCTTCTAAACTTCACATATTTTAACGACAGTTATTGCTATTTAACTGAGCTGGTTTGAAGTTTGTTGTATGCATTTATGCTGTTGGGTGTTATGAAATATAAAAATTTATTTTAAACAGAGGGTTGAAGTTAAGTTGTTGGTTTTACCGTATGGCTGCGCACTTTTTTCCTTATCTTATTCGGGACAAATGCTTACACAGGCGTCAGCCATAGCCCGATTGTGCTGCTATCTTGATGATTGGATTGTACCAGTGAGCATTTTGCTCTAGATTGATATTTCTTTGTTTTCATCTTCTGGCCTTGAGTCTTTTTTGCCCTTTGGAGCCGCGTAATGGCGAATATTCTGTTAGTTGCCAATCTCAACTGTGATCGGATTTTACTGCTTGATAAACCTTTAAAAACAGGTGGGCGTTTCCATTATCAAGATGGTGGACAAAGGCTCGGTGGTGGCGGGGCAAATACCGGATTAGGTTTAGTTTGGGCAGGGCATAGCGTCGCATTGGTGAGTCAAGTTGGCCGTGATGACATGGGAGATTGGCTCATTGCAGAAGCCAGCACTCAAGGACTCGATTGCCGTTTAGTACAGCGCCGAGCCGGTAACACTTGCGAAATGCTACTCGTGATGACGCCAGACGGTGAACGGACGATTATTCGGCCACAAAGACCCATATTTGAGTTATCTGTGCCGCCGAAATGGCAGCCATGGGACGCGCTGTATTTCAATACTTCCGCTGAGGGTTCGGTCAGTTGGGCAAAGACAGCCTTGGAACATTGTCTTGTGGTCGCGCAATTAGCCAAAGATGACAGGCAGAGACCTTGTCATATTTTACTGGCATCTATGAGTGATATCCAAGGGCGTTGTGATGGCGCCTCTTGGGAATATGGTAAAAGCATTGGCGGTGAGGGTTTGCGTTACTTTATTGTCACCGATGGCATCAATGGGGCTAAGGTTTACACGCACGATCAAGTGCAGCATGTTGCTGCTATCCCTGCCACTGTTGTTGATACCACAGGCGCTGGCGATGCCTATGCGGCGGGACTTATCCATAGTCTATGCACTGGTCAGTCGATTACCGAGGCGATGGCTGAAGGTGCTGTTTGGGCGGCATTTGCGGTGGCAACCGATAGTTCTATTCCGGGTGAGGCACTGAAACAGTATATAGAGGCGAAGCAAATTGCATGATGAATCTGCGGGCGAGCAGTTTGAGTCATCTTTCTGTCATACGGCCTTGCTAGAGTATGTCTTGTGGCGCATAGCTCTTGTCCCTTAACGTTTTCTTGTGCTTTTCTCCCATTTATTGGTTAGTATGTTTTTTGCCTCGTTTCCCCAACGAGGCATTTTTTTGCTCAAAATAAAGTTATTCCATCGCAGAATATTCATAAAGCTGAAATGTTGTGCGGTCATGGCGCAATTGCGTTAACATTTATTCAACTAAAATAGTTATCGTGACATTAAAATAATAAGCCACTCAAGGTGTTCTGTAGTCTTTGGAGTTGAGAATGGGCAATAACGCTTGGAATGATAAGAATAAATTTATTTGGCTGCTCTCTGTTTTGCTTTTAGTCGCCTTTTTAGTGACCAGTGGTATCAGTTACAAGGTGGCTCATGACTCACTGAGTCAACAAATCGAGAGTAATACCCTCCCGCTCACAAGTGACAATATTTACTCAGAAATTCAACAGGATCTACTGCGACCTATTTTTATTTCATCCTTGATGGCGCAGGATACTTTTGTTCGCGATTGGACATTAAGCCACGAGCAAGAGCCAGAAAAACTGATCCGTTATCTTAAGGAAATCCAAGAGAAATACGATACAGTCACCAGTTTTTTTGTGTCAGAGAAAAGTCGGCATTATTACCACTCCACGGGCATTTTAAAACAAATCCAAGATGTTGAGCCCGATGATGCTTGGTACTTTCGAGTGCGCTCTTTACCCGAATCTGAGCATTATGAGGTCAATATTGATGCTGATACGGCGGACCGCAGTAAAACAACCGTATTCGTAAACTATAAAGTTTTTGATTTTGAAGGCAAATTTATAGGCGTTACCGGTGTGGGTTTGGCGGTTGAAAAGGTTAAGTCCCTCATCGAGCTTTATCAAAAACGTTATAACCGTCGAGTGTTCTTTACCGATCGGCAGGGCAATGTCACTTTGCATGGCGATGAATATGATGGTGCGGATTCGCTGCAAACCAGTCCAGGGCTTGAAAATTTAGCGACGCGTATTTTAACCAGTCCAAGTGCGGCTTTTAGCTACCAGAGAAATGGTAAAACCGTTTATCTCAACAGCCGACTAGTCCCTGAGTTTAAGTGGTACTTGATTGTTGAGCAGGAAGATGCGCCTCAGGAGCGGGAGTTACTAAATACTTTTTGGGGCAACTTGGGCTTAAGCCTAATGGTGACCCTAGGGATTTTATTCATCTCCAACATGACCTTAGGTCGTTATCAGCGCAAATTAGAAGTAATGGCGTCTACCGATAAGCTTACTGGCGCCGCTAACCGTCAGGTATTTGATGAGTACTTTAGGCAGGCGTTAGATAAGGCTAAGTTGGCACAATCGCCGATTTCAATACTCTTGCTGGATATTGACCACTTTAAAAAAGTGAACGACAGCTATGGGCATGGGATTGGTGACTTAGTATTAAAAACCATGACGAATATGCTGCGTGGCGCGCTGCAGCAAGAGGATATCTTATGTCGTTGGGGCGGTGAGGAGTTCTTGATTTTACTTCCGGGGATGGATTTAACCCGAGCGGCAGAACTGGCTGAACAGATCCGTGAGCTTATTTTTCAGCGGGAAATTAAAGTGAATGGATTACATATTTCTATTACAGCCAGTATTGGCGTCGCCGAGCATCAGGCGCAAGAGCCTGCAGAAGATCTGATCAAGCGTGCCGATCTGGCCTTGTATCAGGCTAAGGAAGCCGGTCGAAATCAAGTAGTGTTAAATCATTCATAGATTCAGCCCCGTGCTTATTGGCGGCAAGATTGCGCCTACTTTTATGAGTTATTGAGCGAGTTGATTGTGTTACTGGTGTATCGCGGTTTACACTCTGGCGGTAATGCGAGATGGGTTATGGATTGGGGGTTACAGATGTCAAAGTTGTTGATCAATAAGTTATCTGGATTGTATTTAGGGACGCATCTGCAGGAAATGGCAGGTGTCGCCACAGGTATTGATGGCAAGCAAGCTGCTCAACAACTGCTAGTGCATGTCAGCCATGTTGAAGGGGATGCGCAGGCCGATCCTAAACACCATGGCGGACTCGATCGCGTGTTACATCATTTTCCCCGCGAGCATTACGGCCAATATCGTCGTTGGGATTTAATCACTCAACTCGAAGATGCGCCCAGCATGGGAGAGAACATCAGTACCGTTGGGCTAAACGAAACCCAAGTTAATATTGGTGATATCTTACAAATTGGCGCCGTAACAGTGCAGGTCACTCAGCCGCGTTCACCTTGCTTTAAGTTGAATCTGCAATTTGGGCACAAAGAATTTGCCCTCGCCATGCAGCAGAGCCAGTTATGTGGTTGGTTTTATCGAGTATTAACGCCTGGGATTATTCAGTTAAACGATAAGATTGAACTGGTTGAGCGCCGTACGGACATTAGTGTCGCCGAGGCTATGGCACTCTATTTTTCGCCAACGTTTAATGCCAGCGGCTATGATCGTTTAGCTTGCTGCGAGGGATTAGCGCAGAGTTGGGTTAATAGCCTACAACGCCGCTTAGCATCACAAACGGTGGAAGATTGGCAAATGCGCTTATATGGGCCGAGCGGTAAATAAGTATTTATACCCATAATGATAAAACAGGGATGACCTTATGATTGAACCCGTTACAAAAAGTGATAAAGATTTTGGCTTGTTGGTATACGCCTCAAGCTTTGTTGGATACCTAGTGCCGCTGGGCTCGATTCTTGGTCCTTTGATTATCTGGCTGATGAAGCGTGAAGAGTCCGCATTTGTCGATCAATGTGGCCGCAGTTGTTTGAACTTTAAATTGAGCCTGCTGATTTATGTAATCATCAGTGGCGTACTCGCTTTGGTCGGGATAGGTTTCATCTTTTTAGCCATACTCGGCATATTCGATTTGGTGTGCACTGTGCTGGCGATTATTAAGGCCAGTGAGGGTAAGGTATATAAATATCCATTAACGATTAAATTTATTGCTATGGATTAATCGCCTCTTTGAGTTAAAGAAAAACGCCTTAGATGTTTCCATCTAAGGCGTTTTTTTATGGTGTAAGTTTAAGGTTTTACACCTTAAACTTGGCCACTAAGGTATCTAAACGGTGTGACAGTTGATTCAAGGCTAAACTCGCCTGTGCTGCCGCTTGTGCAGTATTGGCTGTACGCTGAGTAATATCGTTGATTTCTGTGACGTTACGGTTGATGTCCTCCACAACTGTGGATTGTTCTTCCGTTGCCGCAGCAACTTGGATGTTCATGTCGCTGATAAGACCAATGCGATCGCTAATACCAATCAACGATTTGCTGGCTTCATCGACGGCCACAACACCTTCATGGGAGCGAGTACGACTCTGGGCCATCGCATTAACTGCGCGGCTGGCTTCCGATTGCAACTTGTCGATCATGCCTTGCACTTCATTGGTCGAAGCTGCGGTACGTGATGCTAAGTTACGTACTTCATCGGCGACGACAGCAAAACCGCGTCCAGCTTCACCGGCACGTGCCGCTTCAATTGCCGCGTTGAGGGCTAACAAGTTTGTCTGTTCAGAGATAGCGCGGATCACATCTAAAATCCCACCAATGGATTTAGTGTGGGTCGCGAGGGACTCAATCACTTCGCCCACTTGGCCAACGTCTTTTGACAATTGGTTAATGGTATCGCGGGCACGGGTTACCACAACTTGGCCGCTGCTGGCTTCAGTGTCAGCATCTCTTGCAGCAACCGCTGCTTGGGCAGCATTACTGGCAATTTCGTTAACCGTCGCGCCCATTTCATTAATGGCTGTTACCACCATCATAGTGCGATCTTTCTGCTCGTGGCTGTCTTCTAAGGTTTGCTGTGCCTGATTCGACACATCCTTAGCGGAGAAACCGAGCTGTTCGCTGGTTTGCGCCACTTCGATGACCGAATCTTGGATCTTGCTGATAAAGCTGTTAAATCCCTTAGCGAGTTGAGCAATCTCATCTTCACCATTGACGGGTAAGCGTTGGCGTAAGTCACCTTCACCTTCGCCTATGTCGCGGAACATAGCGGCAACTTGGGCAATGGGACGACTCACAGATCCTGCAACAACGATAGCTAGGACGATAAAGCCCGCTGCAATCAACAGTGTCCAGATTAAGATCTGATAAGCCGCTTCGGTTAATAGCGCAAACACTTCGGCTTCTGGCACTTGTGCCACTAAGTACCAATCCATAGATGCGATATAACTACTGGCAACCAGCATGTTTTCGCCATTGATCTTGGTATTGATCAGATTGAAATCACCGCGGTTTAGTAAGGCGCTTGAGTTCGCATCTTTGTATAAGTTGCTTAGGTTGCTCTTACCAATTTGGCTCGTATCTGGGTGGAGTTTCACTTCTCCCTTGGCATCCACTAAGTAAACAAATCCTGTTTCTTCAATCTTAAATGAACTCAGTAAACGCACCATATCATCGAGTGATTTAGCTAGCCCGACTAAGCCGCGACCATTGGGTTGCTGGTAGTTGATGAAGAGTTTGACTTCGCCGTTGGCCTCAGTGAATACGTTCAACATACGTTCTTGACCGCTGTCGCGGTAACCAAAAAACCAGCCATCCTGCGCTGGCGTGAGCACGCGCAAAAATCCATCCTGAGTGTAATAGGCGGCAGTTTGTCTGTCGGCGTAGGAGGCTTGCGCTAGCTTATATTGACGCGTGATGTCTTTCAGTTGGGCAACCACTTGGATTTCTTGGGCCTGTGGTCTGCCGTCTTCGAGCCATTGCAGTAACATGCGGCTGCTGGCCAATTGCTCTGCAGCATTCATTAAAGTGGAGATGTCGAGATCGATCTTATTGCGGATCTGCATCATCAAACTCGGCAGTTCCGAACCTAACATCCGTTGTTGTACGACATTTTTAGCGCTACGTTGGCTTAATACGCCGACGAGTATGGTCGACAGCAGTACTGCAAATGTCACTGTTAGCAGAATTTTCTGCTTAATGGTGAGGTTATTAAATATCATAAATATCAATATGCCTGTGTTGGACTGTTTTGCGTTATCGGTCAACGCACTCTTGTACGGTAAAAGATTATCGGACAGGAATGTGAAAAGTTAACCTTAGTCTAGGCCGAATGCCGAATAAAACAGCGTATAAAACATTTATTTGGCCTAACCCGTTGGTCTTGCAGTAGATCTAATTATGTAGAGTATAGATGGAAATGGGATGGTGATTGAAAACGAACTCTTTTATGTGTGGCAATTGTGGCCAATGTCGTAGCTAAGGGCAAGCAAAAAAAGTGAGATGTTTGTTTCAAAGGTGAAAAAAAAGGCGCTCCATTGAGCGCCGCTATTAACATTTATTGGTGACAAGTTACATATTGGGGTAGTTAGGACCACCACCGCCTTCTGGCGTAACCCAAGTGATATTTTGGCTAGGATCTTTAATGTCGCAGGTTTTGCAGTGAATGCAGTTTTGCGCATTGATCACAAATTTGGGCTGACCTGCCTCTTCGACGATTTCATACACACCGGCAGGGCAATAACGTTGTGCTGGCTCATTAAACTGAGTCAGGTTAATACCCAGTGGAATGCTGCTGTCTTTAAGACGTAAATGGCAGGGCTGATCTTCTTCATGATAAGTGCTCGACAGATACACTGAGGACAGTTTGTCGAAGCTGAGCTTGCCATCGGGTTTTGGGTAATCAATCTTGCTGTAAGCGCTCACTGAGGCCATTTGCGCATAGTCTGGGTGTTCATCACGCAGTGTGATAGGGAACTTTCCGCCAAACCAGTTTTGATCTATGTAATTGAAGGCGCCGCCTAAATAGGTACCAAACTTATGCATCGCAGGGCCAAAGTTGCGCGATTTATGCAGTTCTTCATGCAGCCAACTTTCTTCGATATGAGTTTGGTAACAGTCGAGATCTTTACCGCCTTCTACACCCGCCATCATGGCTTTGCCTAAGGTTTCTGCCGCAACGATACCGCTCTTCATCGCAGTGTGAGTGCCTTTGATTTTCGCGAAATTCAGAGTCCCAGCGTTACAGCCTATGATCAGGCCGCCGGGGAAGCTCATCTTAGGAAGCGAGTTCAAGCCGCCTTTTGTTATCGCACGGGCGCCGTAACTGAGACGTTCGCCACCGGTTAAATACTTTGCAATCACAGGATGAGTTTTATAGCGCTGGAATTCGTCAAATGGACTCAGATGCGGATTCTTGTAGTTCAAATCGATAATGAGGCCGACAGCAATCTGGTTGTCTTCCATGTGATAAAGGAAGCCGCCACCCGATGCGCCCTCAGTTAATGGCCAGCCACCTGTGTGCACCACTTTGCCTTGCTCGTGTTGCTCGGCGGGGACTTTCCAAATTTCTTTGAAACCTAAGCCGTAATGCTGGGGGGTTTTACCATTATCTAAATGGTATTTTTCGATCAGTTGCTTGCCTAAGTGGCCGCGGCAACCCTCTGAGAAAACAGTATATTTAGCGTGCAGTTCCATGCCGGGCATAAAGCTGTCTTTAGGCTCACCATCGGCGCCAACCCCCATATCGCCAATCAAAATACCTTTAACGCTGTTATCGGCATTAAATAGCAATTCACTGGCGGCAAAGCCGGGGAATATTTCGACACCCAGTTCTTCGGCGCGATTGGCAAGCCAGCGGGATAGATTACCGACACTGATAATAAAATTGCCGTCGTTGTGCATGGTTTTTGGCACAAAGGTATTAGGCATCACTCGGCCGTCGGTGGCGGAGCTGAGTAGGTAGATTTCGTCGTGGGTGACAGCGGTTTTCACTGGAGCGTCTTTCTCACGCCAGTCGCTGAACAGTTCGTCTAGCACTTTTGGCTCGAACACTGCGCCTGACAGAATATGCGCGCCCACTTCTGAGCCTTTTTCGACCACACAGACGGTAATTTCTTTACCTGAATCCTGTGATATTTGCATTAATCGACACGCGGTGGCCAAGCCTGCTGGACCTGCGCCGACGATAACAACATCGAATTCCATTGATTCGCGTTCCATCAGTTCACCTCTTCCTTCACGTTTCCCCGATTTTTATCGAGTGTTGTAGTTTGTAAACGGGCGTTTTATTGCTTGTACCACCTTACCTGAAAATAAGATAAGGTCACAGTGGATTAAGCGGCTGTTTAGTCTAATTCAGTACAAATCTTTAGGTGAGGACTGTCACAGATTATTCACGCTAGCGTGATCTAGGTTAATAAATACCTGTTTAGGGGTATAGCTAGTGAGTACTTTCGGCCTATAATCACCGGTGACGGTTCTCCGAGCTTGCCTACCTAAGTCGAAAGATATGGAGGTGTAGCCGTGGTAATAATGCCACCGGGGTGGGATAAGAAATGCTCTCACCTCCCACACTTGGAAAGGTGATCATGTCTCAATTACAAGACAGCTTTGGTCGAAAATTTCACTATTTGCGGATGTCAGTCACTGACGTTTGTAACTTCAAATGCAGTTATTGCCTCCCCGACGGTTATCATCCTGACGGTAAGCCAAAATTTCTATCTCTCAATGAGATAGCAAACTTAGTCTCTGCATTTAGCCAAGTCGGCACCCAAAAAATCCGCATAACAGGCGGCGAACCCACCCTGCGTAAAGACTTTACCGACATCATCCGTGTCGTCGCCGATAACCCGCGCATTCACACCATAGCGACAACCACTAATGGCTATAGACTCGAAAAGCATGCCAAAGAATGGTTCGATGCGGGCTTGCGCCGTATCAATGTGTCTGTGGATAGTTTAGATCCCAAAATGTTTTATCAAATCACCGGCGAGAATAAATTCGACGAAGTGATGCGGGGGATCGATGCTGCCTTGTCGGCGGGTTTTGAGCGCGTAAAAATCAATGCCGTCCTGCTTAAAGGGCTAAACGATAAAGATTTACCGCGCTTTTTGCATTGGATTAAAAATACGCCAATCGATTTACGTTTCATCGAGTTGATGGAAACGGGCCTAGGCCGTGAGTATTTTGAAGCACATCATTTAGCGGGCGCTGACATTAAAGCGCAGTTACTGGCGGATGGTTGGCAATTAGATACGCCGAGCGCCGATGATGGTCCAGCGCAAAACTTTAGCCGCAGTGATTATCAAGGTCGTATTGGTTTGATCATGCCCTATGCGACTAACTTCTGTGCCAGCTGTAATCGCCTGCGCGTATCGGCGAAGGGCAAGTTGCACTTGTGCCTCTTCACCGAATCCGGTGTCGATTTGCGTGATCTGCTGCAACATTCCAGCCAACAAGCGGAATTAATTGAGCGTTTGCACGGTCAACTAGCCCAGAAGAAAGCCACCCATTACTTGCATCAAGGCATTACCGGTGTCACTCAGCATCTCGCCTCTATTGGTGGCTAGCCCGTATTTAGGTTCTTCATCACCGCCGATTTAGCGCGAGTATAAGCCCAATTTCCCTTGCACTTTGTTTTGTCCTCAAACGAAAGGATCGCTCCAATGAGCAATGTATTTACCCATATTAATGCCGATGGCAACGCCCATATGGTCGATGTCACCGAAAAAGCCATCACCGAGCGCGAAGCCCGTGCCGAAGCGTTTATCGAGATGGCGAGCACGACACTCGAGATGATCATGAGTGGCAGCCACCATAAGGGTGATGTGTTTGCCACTGCACGTATTGCGGGTATTCAAGCCGCGAAAAAAACCTCAGATCTTATCCCTTTATGCCATCCACTGATGTTGACTAAGGTTGAAGTCGATTTAGAGGCGCAGCCCGAACATAACCGCGTGCGTATCACCAGTCTGTGTAAATTATCCGGCAAAACAGGCGTAGAGATGGAAGCCTTAACTGCGGCCTCAGTGGCAGCATTAACTATCTACGATATGTGCAAAGCCGTGCAAAAAGATATGGTGATCTCCCAAGTTCGTTTGCTAGAAAAACGCGGCGGCAAGTCGGGACATTTTAAGGTTTAGCCTGACAGAATAGGCTTTAGCCACTCGGCAGATTAGCAAGTGAGCGAAGCTGTCCAAGCTTAAGTCTGACGCGTTATTGGTGAGTCAGTTTAGCGGATCAAAAACGAGAGAAGATTATGATTAACGTGTTGTTTTTTGCTCAAGTTCGAGAATTGTTGGGCACAGCTAAATTAAGCCTAGAGGCGAGCGAGCAGACACAAACCGCCGAAGCGTTACGTGCGACGCTGGCGGCTACCGACGATAAATGGGCCAAAGTATTAACCTCAGACAAATTGCTGGTGGCGGTAAATCAGACCATCAGTCAATGGGATACGCCTGTAAAGGATGGTGATGAAGTCGCCTTTTTCCCACCCGTTACCGGAGGTTAATATGCGTTTATTACCCGCAGTTCGAGTGCAAGAAGTCGATTTTAGTGTGACCGACGAATATCGTCAGTTGGCTCAAGATGATAGCGATGGTGCCGTAGTCACTTTCGTCGGTAAAGTGCGTGATTTTAATGATGGCGCCGCGGTGACTGACCTAACCTTAGAGCACTATCCTGGCATGACCGAAGCCGTGCTAGAGCAAATCGTTGTCGAGGCGCGTAGTCGTTGGCCACTGAACAAGGTCACTGTGATTCACCGCGTGGGCACTATGGCCTTAGGTGAGCAAATTGTGTTTATCGGGGTAACCAGCGCCCATCGCAAAGCTGCTTTTGCTGCCTGTGAATTTTTAATCGACTTTTTAAAAACCAAAGCGCCATTCTGGAAATTAGAAGCGGGTGAGCAAGGTAAAAGCTGGGTTGAGGCGAAAGATGCCGATGAACAAGCTGCCAAATTGTGGCAACACAAAGACTAGTTGCAGCAAAAATACGCGCAGTGGCAACGCAGCTTGCTAGCTCAAGATAAACGCGAACACGGCACTGAGGTGACCCAATGCAGAGTTTGACCCGTTTCTTCCACTCCCTAGGTTTTGCATTACTGTTTTGCTTAGCCGTCGTACCATCGACCAGTCGCGCAGCGGATAATGCGCAGATTCCTGCCATCGCCGCGGCGGCTAATATCAAGTTTGCCTTGGATGACATTGTTAAAAACTTCACCGCAGAAACGGGTCTCAAAGTACGGGTTTCCTATGGTTCATCGGGAAATTTTGTCGCGCAAATTCAACACGGTGCGCCGTTTGAAATGCTGCTGAGCGCCGATGAGCGTTATATTGCCGAGCTGAACAAAGTCGGTTTTACCCAAGGCGAGGGCGTGCAGTATGCCGTTGGCCGTTTAGCCTTAGTCGCCCCTAAAAATTCACCGCTCGCACTAGATATCGAACTCAATGGCCTAAAAGCATTAATGGCCGCGGGTCAGCTAGAGCGCTTTGCGATCGCCAATCCTGACCATGCACCCTACGGCGAACGCGCCCGTGAGTTATTGAAAAAGCTCGGGTTATGGGATGAACTACAAAGTAAGTTGATCTTAGGCGAAAACGCCTCCCAAGCGGCGCAGTTTGCGATCAGCGGCTCGACTCAAGGCGGAATTATTCCTTTATCACTGGCGATTGCGCCGCAGTTTCAGGCCCTCGGTCATTATTTAGCTCTGCCCTCAGATCTCCACGCACCGTTGAATCAACGTATGGCGTTAATGCCTAAGGCGAGCTCAACCGCCGAGCGTTTCTACCAATACCTACAATCAGACGCCGCCCGCTCCGTATTTATTCAATACGGTTTTGGTTTGCCGGTAAGTTAAGGATTCCAATGGATTGGCAGGCGCTGTGGTTATCGGTCAAACTCAGTAGCATCACAGTCTTGGTGCTGATCCCCCTTGCGATACTCGCGGGGCGAGCTTTGGCCTACCGCCAATTTGTCGGTAAATCTTGGGTCGAAGCGCTGATCATGGTGCCCTTAGTGTTACCGCCGACCGTGATTGGCTATTACTTGCTGGTGGGGCTCGGTAGTCAGAGTTGGCTCGGACAATGGATAGAGCAACTAACTGGCCAGCAGCTCGTGTTTCATTTTTCAGGTCTGGTTATCGCTTCCGTGTTCGTCAATATCCCCTTTGCGGTGCAGCCGATCCAGCGGGCCTTTGAAGCTGTGCCGAATGATGTCCGCGATGCCGCAGCTTGTTGTGGCATGAGCCGACTTAAAATATTGCTTAAGATTGAATTGCCTATGGTGTGGCCGGGTGTGCTAACCGCGCTGGTGTTGTGTTTCTCCCATGTGCTCGGCGAGTTTGGCGTCGTGTTGATGATGGGCGGCAATATCGCTGGCGAGACTAAGACAATTTCGATTTCTATCTACGACAGTGTGCAAGCCTTCGATTTTAATGCCGCAGGCACTATGTCATTAGTGTTATTACTGTTTGCGGTAACGGCTTTGGCGCTCACTACCAGTTTGTCACGGCGTTTAGGAGGTCAGCGTGGCGCAAATCATCGCTGATCTGCAGTGTCAGATCCAAAACCATAAACACATTAAACTCAACGCTAATTTTCGTTGTAAAGCTGGCGAAGTGCTCGCTGTCGTGGGACCGTCTGGCGGTGGCAAAACGACCTTGCTACGGATGATCGCTGGATTAAATCATCCCGATACGGGCCAAATCCATTTTGGCGAACGACCTTGGTTTGATGGCAACGCGCGTATCGCCTTGAGCCCACAGCAACGTCATATCGGTTATATGCCGCAGCATTTTGGTTTATTTCCGAATCTCACCGCCTTAGAAAATGTAGTCGCTGGACTTGACCATATCCCTAAATCGGAGCGCATTGTACGCGCGAAAGATTGGCTTGAGAGAGTCAACCTACAAGGCCTACCCGATAGATTGCCGGCGCATTTATCTGGCGGTCAGCGCCAGCGTGTTGCGCTTGCCCGAGCACTTGCCCGCGAACCTTCAGTGTTATTACTCGATGAGCCTTTTTCGGCAGTTGATCGGGAAACCCGCGAGCGTTTATATCTCGAACTGGCGCGCCTCAAAGAGCAGTTACTCTGCCCAGTGATCATGGTTACCCATGACTTAAATGAAGCTTTGTTGCTGGCTGATTCGATGATTTTGATCAGCCAAGGGAAGATGCTACAGCAGGGCGCACCTTTCGAAGTGTTGTCGCGGCCGCGCAATGAAGCTGTGGCGCGGCAGATGGGGCTAAGGAATATTTTCGACGGTGAAGTAGTGTTTCAAGACAGCACAAAAGACCTCACTTGGCTTAAGTTTGGCGAGCAGTTAATTGCCTGCGACTATGGCAAAGACCGCGCCGTTGGCAGCAAAGTGCGTTGGGTGATCCCCAACCAAGGCATTCGCTTTAACTCGATATCTAACGGACGTTTATGCCGCAGTTTTAATAAGTTAGATGTCACTATCGACGCGATGCTGGTGATGGGGGAATCGGTCCGGCTTATCTGCTATGTGACAGGTACTCAGTTACAGCTTAACACCGAAGTCTCGCTGCATTTTGCCCAAAAACTTGCACTCACGAAAGGAATGCAAACCACAGTGGCACTCAAATCCGAACAAATTCATATTTTGGAATAATGAATCGTCTTAGCTGTCATCTTGTATTGTGGCGTTTAGCTCGCTGATCAATTGGACTATTGTTGTTTGAAACTCGGTTAGTTCGACTTCACAATTGTTATTGCCTTCTTGTCTGATTTTTTCTTCGAGTAACCAAGCTTGCTCTGCAATTGATGTGAGTCCTAACATGCTAATCGTTCCGGCACTTTTATGGATATTGTCAGCAAGTTGAGGGGCTGTCAGTTGAATATCCTGAAGTGAGCTTTCGAGTTCAACTTTAAACATTAATATCATTTTGCTTAATGTCTCTCCGCGACCAAATGCATTTTCTAATATTGTCATATCAATAAGTGCGGGTTTTGAGAGTGGGAGCCATTTCAATAGCATTTCATGTAGTTCATCTAGATGCAGCGGTTTTGCCAGTAAGTCATCCATTCCCACCGCGACACATCTTTGTTTTTGCTCATTTAATACGTTGGCTGTTAGTGCAATGATGGGGACGTGTTGGCCATCGATTTCCTGTGCTCTCACTTGTTGGGCAAAAGTGTAGCCATCCATTACTGGCATGCGGCAATCACATAATATTAAGTCGTAATGAGTCTGTTGCATGAGTGAGAGCGCTTGGGCGCCGTTATCTGCAACATCGCATTCTAAACTCAACTGTTTTAGCTGTCGTTGCACCAATATCTGATTCAGCTGATGATCTTCAACCAATAAAATTTTACATTCAGGCCAATTAACCTTAAGTGCAATTTCCCTCTGCGGTGTCGAGACTATGGTATCAATCCAACCCATATTGATATAACAAACATCCTTCACAGCTTGCGATGTTAAGGGATTTGCATTCAATAAACATTGCTCATTTTGGTATTTATAACCGAGCGCTTCATTAAGCGGAGTGCACAATATATGTTGTCGATCATCCGCAGCATCATCGGTAAAAAATAAGTCTGCTTGGTTTTTATTCTCTGTTAATACAATGCCTAATGAAGCCAAATGCCAAGACAAAGTGTGGCGTAATTTCATATCTTTGATTGCGATATGTGCGTTGAGTCCCTGCAACTTTTCATCAAATACATCATGGGCAATAACGGGTAAACGAATACGTAAACCGATAAAGGTGCCTAAATTAGGCGCGCTATTCAAAACAATTTCACCGCCCATCATTTCGGCCAAAGTGTGGCTAATAGCAAGGCCTAAACCAGCTCCACCATAACTGCGGGAGGTTGAGATGTCAGCTTGCTCAAAAATATTAAATATTCGTGCTTGCGCCTCAATGCTCATACCTATGCCAGTATCTTGTACGCCAATGAGCAGTAATTGCTGACCGTCCTCTTCGCTTTCAAGCTTGACCGAGACTTCGATAAACCCGCGTTCAGTGAATTTAATGGCGTTATTGACCCAATTGAACAGGATTTGCTTTATGCGGAGACTATCTCCGGTGACTCGTGCAGCTACGCCTCGGTCAACGTGTAAATAGAAGCTTAAGTTCTTTGTCCGTAAATCATTCATCACCATGTTAAGCGTACTATCGAGAACTTCACGCAATAGGATAGGGGTCGATTCTAAGCGCATTTTTCCCATGTCTATTTTAGAAAAATCGAGGATATTATTGATAATTTGCAGGATTTCTTCCGCGCCATTATTAATGATCTGCACCATTTGTTTTTGTTCTATGCCGAGCGTGGTAAAGCTGAGTTGTTCTGCCATACCCACAAGTCCGTTCATCGGGGTGCGGATCTCATGGCTCATGGCGGCAAGAAATTCCGCTTTTCTTTTAGTGGTTTGCTCAGCATTTTCTTTGGCGATAGCGAGGGCATTTTCGACAATACGGCGTTTGTTAATTTCTTGTCTGAGTTTTAAATAGGCTATGGTTAAGCCGCCAATAAAAAGCAGTAAACCTATCGCTATTGGCAGCAATGTCGTGAGCAAGGTGTGCCAAGAGATGCCTTCGTTGTAATTTACCGATAGCCAAGTATTGCGGATCTGCTGTTTTTCTTTCTCACTCATACTGGAGAAAACGCGATTAATTAATGGGATAAGTGGCCGATAGGGCTCACGAACTGCGACAGCAAGTTCGTCTCGATAAGGTGTGGGAGCCGCTACTCGTAATTCATCGTCGAATTTATCGTCGATAATCATGGCGACAACCGCTAGATTACCTATGTAAGCCGCTGCTTTTCCCTTTGCGACTAAGGCTAAGCCGTCATCTGGAGTGGAGGAAACCACTACATCAATATTTGGGTAAGCCTTCTTTATGCTAGGCAGTAGTAGATTATCTGTGATAACAACGCGTTGAAAGTTGAGTTCAGATAGACCTGCGATTCGTGTGCTACTGCGTGCTGTTGCAATGACAATTGGGAAGGAAGTAAAAGCTTCGCTCATGTGCCAGTCTGGATATTGTGCACTGAGTCGAGTCGGTAAAATGGCGAGGTCTATTTCGCTCCGAATCAGCGCGCTTTGTATTTCGTTCCAAGTTCGTGAGTCTTGAGCCGCATAATCTAGCCCTAGCTTCTCTCTGAATTGTTCAAGGTAATCTGCAATTAAACCAATTAAGACGCCTTTGTCGTTGACCGAAGAGAATGGTGTCCAATGATCTACAAATCCGACTCTGAGTGATGGTAGGCCTGCTAACCATTCGCGCTCATCTGGTCTAAGTAGAAAATTACTATGGCCTAAAAAATGAAGATCGCCCATACCAAGCCAAGTCACTTCGAGTGCATTTCTCTCTTCAACACTTAGCGCCATTAAGGCGTTATCGAGTTGTGTTACAAGCAGGCTATTGTTTTTAGTGATCCCTAAATGCAAGTTTTCCATCATCAGTGGTGCTTGGGCAACGATCAGGAGTTCAGGATGTGCACTAATGTACTGGTTAGCAACGTGGAGATTGCCTATATAGGCATCAGTTGTTTTATCTATGACAGAGGAAAGTGCTTGCTCTGTATTCGGATAGGTCACTTCGGTCACAGTTGGATAATGCAATTTGACTAGCTTTTCAGTTGAGAACCCTTTTTCAACGGCGATCCGAGCTTGGCCAAATTCACTCACATCGCGAAAAAATCGGCTGTCGTAACGAATAACAACAACAGTAGGGGAGGTGTAGTATGAGCGGCTAAACATAAGGCATTGAGTGCGTTCAACAGTACGAAAAGCATCAAGCAGGACATCAACTTCCCCATTACAGCCAGCAGAATAGAGGCTCATCCAATCGGGATAAATCTTCCAGTCAATTTGGTAATGAAGATTTTTGGCGAGCAGTTGTATCAACTCAACACTAAACCCCGTTGTTATCTGGTCATTAATCTGTTGAAAAGGTCCCCAGCCGTTGGCAAGTACTCCAACTCGAATCGTGGTTGTGACTGATTGCTCGGCATGAGACATAAAAGTCATGCACAGTAGAGATAAGCCACCGAAAATACAAAGAAGTTGATAACGCAATTTTTGAGTAGTACTCATCTCTATGGCCAAAGTGTATTAAGTCTCGAAAAAATCATCGATGCCAAGTAGTATAGATAAGGCATCCATAAGTTCACATTATAAGGAGTTGTTTATGGCTTTTAAGATCATCCTTGCCGACGATCATCCTATTATACTCACTGGCGTGCGTAGTTTGATGGCTGGAATACAGCCAAGTTGTGAAATTGTCGCTGAAGCAAATAATGTTTCCGAATTGTGGCATACATTGGCGCAACATGAATGCGACTTATTGATCACAGACTTTAGCATGCCTAACGATGACAATGTTGATGGTATGGCCATGATTAAACAGCTGAGACGAAAGTATCCCAACTTACCTATAATAGTGCTAACTCAAGTGCATAATTTAGGTGTGCTGCAAGCCCTCATGCAAATCGGAATTAATGGTTTATTATTAAAAAAATCAGTCATAGCTGAATTGGCCAATACTGTTAAAAAAGTATTATCGGGTTCAGTGTATATTGGTGATGCTGTTCGTGAATTGTTAAATGAACAAGGAATTAATCAATATAGCACCCCAATTGAACTTTCATTGAAAGAAATTGAGGTGGTACGCTTGCTGGCAAATGGCATGTCGGTTACGCAAGTTGCCAGTTATTTAAATCGTAGTGTGAAAACCATTAGTACCCAAAAAACCAATGCAATGCAAAAACTCGGTTTAAAAAGTGACAGTGAATTGTTTCATTATGCACAGCAGCAGGGCTTATTTTAATTTCAAAGTGGAGGGATTTCAGCCTCGAACATAATAGTTACATCCCCTTGGTAAGTACTTCCTGGATATTTCATCATTTCAGTTAAGTTGGTTTTGTTAATTTCAAATAAAATTTTTCTTGCAACATTGATATATTTTCCATCTAAATTTATAAACTCGAACTTTGATGTCGATAGCGGCACTGGTATTCCTGCACCATTGATATAAATGATTTCAATATTTGTTTTGTGACTGTTTTTAATGTTTTTCAGCTGGCACTGGTCATTGTTTTGATATTGACAATAACTCAATGATACATAAAATCTTGCGCTACTCCAGAGTCTGAATGGTAATTCCGCCTTTAAGTTAGGTGGGGTTTTACCGCTGTTTATCCATGGTGCCCAACCTTCGGGTGGTTGCAAGATCACTTTATTGCTGCCAGCAGGGAACTCAACCTTAAGTTGATGCGTGACTCTTAAGGTAAATTCTATGGTTACGTTTGAGTCTGAATAGGTCGCATCACCAAAATCGAGATCTTTATTGTGTCCAAGGCTTAAGGTTAAGCTGCCTTTATAAGTGCCATTTTCCATTTTTAATGGATCTGGCGGTTGCAGTTTATAACCAATATAAATCCTTTGAAGGGTGACATTCTTTGATGATGTGAATTCTGGCTTAAATTCATTATTGTAACAAACGCCACCAGATTGTTGGTTAGCGGTTCTTATGTCATAAAAATAATCTAAAATGTTACTGCTTGCCCATACAGTTAATCTGCGATCGCAATCGCCTTGAACGTTATTTAACCAATATGTTCCTGTGTTTCCTGCTTCTTTAACGACATTCGGAGTGTGAGATGCCCCAATTTGTGTTAGCTTAAACTTCATTTTATGTGTTGTGCTGCTGCCTTTTTTTGTTAGTTCTACGGTTTTTTCACCAGTAAACTTTTGATAGGTAATCGCATGTTCTAATTTTTGATCATTCTTTATTAATTTTTCTAATATTTTAGTCTCTACTTTTATTGCTTGTTCCTTGTCTAAAGATGCTGTGCCATTGCACCAAAAATCTGGGCCAGAGCTGTTTGGTACTGAGCAAGGGGTAGTATTAATAAACTTACTGCCATTAATATCATAGGGCGCAGGTTTATATTCAGCAGTAACCTGAATGGTAGCAGCCTCTGTTAATGAGCTGAGTAGTAGATATCCCATCAAAATATATAAAGCGTAACGATAGTTCACTTGGCTAGGCTCTCTGTTAATGTTTGCTGTGCCCATGCTTGGGCGTTGCAAGTGATATTGCCTAATAGCAACAGGTTCTGTTCACGTTTCACTCGGTGTAAGTCGAGTTCAAGGTTGCACTCTTGTATACCTTGATACTCCACTTGCAGGCTTGGGTGGCGTTCACTCATTTCTACGGCAAAAAAGCCATCGGCCTCACTCAAGCTGCGCCCCGCATGGTTGGCGATGAGCGCGCCTTTGAGTGGTAATCCCTGCTGATTCACTAAGCGGCCAATCACAGTAACAGTCTTCATGACTTTGATTTGCTGGTAACTGACGCCTCCCTTATTGAGGTGGTAGTGCAATACGCTCGGTTGGATGACTGAAGGTGTGTCATCAAAGTCCTGTATGGCGAGTTGTACGCTACCACTGCGATAGGCGGCCACTGGCACTAAGTTGCGCCCGGGTTTTAAGGTAACACTGCCACCGTGCTCATCTTCTGCTTGTAAAATGATGTCTGGGGTATCAGCCATGACATCGATTATCATGCCCGCATCGTGGTTTTGGCTTTGGCCTGAGATGCTGAGTTCACCCTGTTGTCCTACCGCAAGCATACTATCGAGGTTGATGCCGCCTGTGATGCCACTGTTATAGGAGGATGACTGTGCATAGGCATCGCCGCTGATTATGTCGTTATGAAAGCGGCTATTGACGGTTGCACCAACGCCATAGCTATCCGTGTTTAGTCCCATCCCTATGGTTTCAATACCACGCCAGTCTAGTTGTTGTTGGTAATCAACATAGGCATGCTGTTCTTGCCCGCCATAACGTGAAGTTCTGCTGCCTAAGCCGCCACCGATTCGGCGTGTTTGATCGCCTAAGTTCATACTGAAATAAAGTGCTCCGCCTTGGTCGCGCTGACCTAAGGTGTTAATACTGCCGGGACGATCGAACGCATTTAAACTCCAACTCATTTGGCGTCCCCAGAGTTTGCCGCTGTATTGCCAGCCTAAATCAATCCCTTGCCCTTGCTCAGACTGATGTGACAAATAGGCTGAAACAGAGTGACCTCCGCCTAAGTTATGTTGCAGCGATACACTCGACTTTTGAGGATTAGAGCTTGTGCCATCTCTGCTCTGTGATTGACTGAGTTGTTGTTGATGGCTGATCACAAAACTGCCGCGGTTATAACTGTAAACCGCTTGTAAATCAAACTCACTGCCCTGTTGTTCACTCATAATCCAATTGCCAAAAAAACGCAGTTGTTGGTTCCAGTCCCAATCAGCCGAAACGCTGAAGTGCCAGCTCTGATCCATAAATTGGGCGGCAGCGCCGAGAATGACCGCAGGAGTGAGTAAGTAGTTTGCCAATACGCCTGTGCTGAGTCCGTCTCTACTCTCACCATCCCAGCGAGTAAGATCTTGTAACTGTTCGCCAGCAAACAGGTTCACCCGAAAGGGTTCTTCGGTGTTTTGCCATTGAATGGGTTTATTAATAATTTCTCGACGACGCTCAGTGATTTGGCCATCTTCCAAAATACGAATTTCTACTTCATATATGCCAGATGGTAATACTTTAGTGTCTATGGCTTGTAAGCCAGCTGCAATTTGTTGTGAGTTGATCAGCTGCCCATTTCGATAAATCTCTACCATACCGGAACGATTCGGGGTGACGTAAATAGGGGTGGCACTGGCGTAGGCTTGGTCAATCAACAATTGATTGGAGTGACCCAACATAACGCCCATCACAGTTTGTGAGCTCCCACTATAGATGATGGGCTGACGAACTAAACCTTGGGCATAGGGCGAGAAATAGCCAAATCGATAGAAATATCCTTCGTTTTGTTGCTCTGCATATAACTGTTGAATACTCGGGTTGAGTGTCTCTTCATTATTTTGAGTAACTTCACCTTCTAATAACGTGGTCCATTGGCCTAAACTCGCTTGGGCCTGCAAATTATAACGGCCTGCGTGAGCGCTCTCATTGGATTGACTCCAATTGAGCTGTTGTCGAATTAGCGCACCGTAGCTGCCTTGTTCAGGCAAGGCATAGTAGTGTAAGTTTGACTCTGGCTTCTCGATTTTGGCCGTTAAAATCGCTAACTGAGAGTTTTCTAAATTGTACTCAATAGCTAATATGTCACCTTGACAAGTTTTTTGACACAAACCCAACTTATGCTTTTGGTTTAATTGTGTTATCCAAAATTGTTGCTCACGTTCGTTTATATCGCTCTCACTTATATCAATGAGTTTGAGTAGCTGCACACTGCTATCAAGTCCTAACACTATTTCACCTTCGCCAAGCAATTTATTATCAATGGTGATTCTTACTGTTAATCAGGGCGGGATTAAGCTTTGTTAAATCTCTTAACCAAAGAAAATCTCGCTCCTAAAATCGCCATTCCATGCAGCTTTTCTTATCTTGGTTGGTTTGCTGGCTTTTAGCGGATGCTGCTCTAAAAGGTTCAGCACGATACGACGAAATAGCGCCATCTTCTTCGCGTTTTCACGGTCACCAATCCTTGAACAGTCTTCCCTAAAAGTGACGTCAAGCACCCAGTGTTGCAGGTTCTCGATGTGCCAATGCTGACGCACCGCTTTGGCTGCAAGTTCGACGTCTAACGGTAAGGAACTTAAGTAATAATCCGTATTTATGGTTGCAATGCCTTTACATGTTCGTTGACGTTCAACTGCTATGAGTGACTTGGCTGATGGCCACTTTTTCGCGAGCGCCTGTGGCAGCTTTGCAGGAATTTGAAACGTTGTGCGACGCTCCTTACGGCCATGCCCCTTGTCATCCTCTTCGATACTTAATATGTCGACGCCGTCGTTATCCCAGTGAGGTTGAAATTGCATCTCAATGGCCTGCCTCAGTAGCGGTTGGTTGCCTTTTACCTGAACAACATAGTCGCCTTTAGCTTTGATGATTTGTTGAAGCGTTTCCCGCTGGCAGTGAAGTGCATCAAACGTTACAACAGCATCTTTAATGTCTAACATGTGCAACAGCGCTTGAACCGTTGCGATTTCGTTACCTTTGGTCTGTGTATCTTGCTGATACAGCACAATTCCTTGTTCGGTATCAAAGGCCGAAACGAGGTGTAGCGCATTTTTAGTACCATGTTGATTAACAGCACCCCGCAAGGTTTTGCCGTCAATAGCGATTAATGGCTTGGATGAGGTTTCTCTTAAACTATTTGCCCAACTAAAGAGTGTCATAACCAGTATTTCGGTATCGATCACTTTAATGATCCGAGCAATGGAATGCCGAGTTGGGATCCCTTTATCAAAATTTCGATGTTGACGCAGCCAAGATAGATTCTCTTGGCCGAATTCTTCAATAGCTTGCCAGCCATCACAACCTGAAGCAATTGCGGCTAAAACAAGAAAAATAATATCAACTAAATCATGATCTAGGTTGATGTGAGAGCGAGGGTCTTCGATGAGTTTCAAGTGTTCAAATAAGGACATGGATTAGGCTCAGTAGAATAACATGCCCGATCAGATCATGCAGATAGGAAGAAGTTCCCTACTGTTAACAAAGTATGATCCCGCCCTGTACTGTTAATGGGGCATTAAAAAAATGCTCAGAAAATCCATCCGGCAAGTCTTGATACTGTTTTATTAGCCTGAGTGAGTCTGATGGCGCGTTATTAGCCAACGAGCAAGAGCTAAAAATTAGCGTTGCTATCAGAGTAGTAATGTTTAGAGATGACAAGTGTTGTGCCTCATATCGTAAAAAGGTCATGCATGAGTCGTTAACTTGAACGTTAATAATAAAGCTCCCGTTGCGGGAGCTTTATTAAAATTTAAACTAATAATTTTATGGTTTCTTAGGTGCTTCAGATTCGAAAATCATATTTACCATGCCTCGATAGTTACCTGCGGCATAGCCTTTTTCGGGAACTGTTGGAATGATCTCTAATCCAAGCATTTTACCCGTTTTTGCATTGTCGACATCGACTAATTCTGTCGGAGTTGTCGCTAGTGCAATACCGCCAACTTTAACTGTCAGCGCTATGTTTTCGTTGCCACTTGCCATAATGGCAGGGTTTGATAAATAACCCGTAATAGCACCAATAGTACTTTTAACGTCTAATTGTTTGTTTAACGAGTCTAACTTTTGTGTATGAGGGTTGTATGCTAACTTTTGCTCACTGTTGATCCAGTTATCATTACCCGATGGTTGCACATAAAAGTTTTCCGATGGAATTTGTGCTATAACGGTGACTTGATGTTCAATTTTTTCTGCCGCAAATGAATTCATGGCCACTAATAGGGTTATTGGGGCCGCCTTCCATATCTTCATTACTGTCTCCTTTTGGGATGCATTAGTTACCAATAACTATGCTTTGGGTTTTATCGCCTTCAATTAATTGAAAGCGATAGGTTTCGTCACTATTTTTTACCAAGCGTTTTTTTGATCCCGGCAAAATATGATATTTGGTTGCTGTGGAACACTCTTTTGCTCTTTTGTTGCAAGCTTCAAAAAAATCTAAAATGATAGTCGTATTCCCGTTATTTTTTATTTCTGCACCATTTGAGTCTTCAGTAATCTCAGTGGAATACTCTGGATTTTCTGGTGTAATAAATAGAAATGCACCATAGCCAGTTAATAGCTGTATTCCAGCTGAGATTTCTTTGCTATTCTGTTCAATCTGTTTCTCATCAAAACCATCATTATGTTTGGGTTGCACTGGCACAAAACGAACTCTAAAATAGCGCTCAGCATCCTTTTTTTCTAAAAAAAGTAATCGGGTAGCCTGCACACCCTTTGCCGGAATGATTAAGCGCGATGGGCTTGCTATTAAAGTACGATTTTCTGCCTCTTGTTCTGGCACTTCATAGGGCTGGCCATTTTCATCATATTTTATTTCGTTAATCGATATTTTAATAAACGCCGTCGAATCACCCATGTTCATTATTTTTTTTAAATAAGTCGTTTTATTGCTTGGCATGTAATCAAATAAGGTTCCTATACTTATTTCTGCCGCCGCTTGTGTTTGGTTGCTGAAAAAAACAATAAATAATAAAGTTAAATAAAATCGCATTTTATCAATAGGCCTTGATCGAGTTTCATGGGTTAGTTTTTACTGTGATTCAACGTTTGGAGATATTTTATACGGATGTTTTTTATGCATTAATAGGAATGTTCCTATAATAGGCAATTATAAATATGTGAGTTTTAGGATTTGTCTGATAGAAGCTAGTGCTGACTTAGTTAAAATAAATCAGATTATTAATGCTCTGTAGGTATTTATGCTGTCAGTTAAACGTAAAAAAGTGAGTGAAGATATTGCAAACTGCATTATGTCTGATGTGGTCATGCCTTTTTATCAACCCATTATCGGGGTTGATAAAACCGTTATTGGGTATGAGGCTCTTGCTCGTCGTTGGGATGCCAAGCGTCATGCATATATGGGTATTGATTTTTTGTTGTTAGATAAAAATACTTCTCTGCACATTGATATATGGATGTTGAGGTCGATTATCAAAGATTTACCTCGGTTAGCCGAGAAAGGGGCTGGCATACTCTCCATTAACTTAAATCCCATGCCATACAGTGTCACTTACCAAAATCTATTGCAGATGTTGTTGTTACAGGCGAAAGTACTGGAGATCAATATTTGGTTTGAAGTGCTTGAACATACGTTGTTAGATGATAAGCAGCATGAATTGATTGAAATGTTACGGGCACGAGGGGCTAGCATCGTGCTGGATGATTTTGGTACCCAAGAATGTAACTTCTTACGTATGATGGTACTGCCCTATGATGTGATTAAATTAGACCGTTCACTTTTGCTGCAAGCTTGTCGTTCATCACATTCAATGCGCATGCTGGCAAGCTTGGTAGAGTATTTACAGCGACTCGGAATGCAGGTTGTTTGTGAAGGTGTTGAGACTTTAACGCACATCGATGTGGCAACCTTATTGGGCTGTGATTACCAGCAAGGGTATGCCCATGCTATGCCTGCGCCTATCTCACACTTGAGTGAGTTGCGAGGTCATATAAGCTAACGATTCACTGCGCTTTTACCTCAACTCACGTTGCATAGCTGCATTATTATTGTGAGATATTTTTCGGTTTCCGCTTACCCAGCAATCCAATCCAAAATGTTCCTTCCAACAGCATGCCTAAGATGATAAATGCCACTGCGCTTGTGGCGCTGCCATAGGCGTAGGCGCTTAATGCTGCGACGAGTAGCAAGCTCAAGCTTATCCATCGTGTGACTGGTTTCATATTCGCTCCGATTAAAAATGCCATTACTTGACGCTTGGTTATCAGACTGACTTTAGTTGAGTGCGGTCTATTTGCCAATCGTACTTTTTGACGACACAGATGCACTAAAATACCCGCTAAAATTCAATCAGCTTGATTTTAAATGGCTAAATTATGGTTTTTCACTGGAAATGTCATTCTTCAAAATCCCCAGATGATCAAAGCTTAATCTTGTTTCCATCGGTGTTTCAGCTGGTATTAGGTAGACTTAACACATTAGCCACAAAATGATTGAGTGTTTGGCTTAAAAGCGAATAACTTAGGTGATGTTGAAAGGGGATTTTCGTTCCATTTATGACCAAACCTGAGTTAAGGCTGACGCAGTGTGGTGCTATATCCAGTGGTAGATTATGGGAGATTGGTTTGGAATCGCGCTCAGGGACAATTGGTCCCATCGACTTCTAACTGATTGTCTCATAAGATACAAACTTAGCTGATTTATATGGAGGAGCCATGAGACTATGGTTGCTAGGATTCATGTTGGTATCTGGCGTCGCAACTGCGGCTCAGAGCATGTATTCGATGTTTGCGGGTGGAGACTATGTACCGCCGCTGAGCGTTATGCAGCAGATCGAAAAAGGGTTCTCCGGCGTGATTGCCGAATTTAATATGGAAGAGCAAGAGGGTGAATTAGTCTACCAATTTGAGTTGATTAATCCCTTGGCCAATTCTATAACGCGGTTTGAATATCGTGCCCGCGATGGTAAGTTGCTGAAACAAAAGGCGAGTAAAGTGAGCGCGGATGATCTCGGTGAGGTTGAGGCGACACGTTTGATCGCGGCAAAAGATCAAACATTTTCAGGCTTAATCGCTAAGGCCACTAAAGATCACAAAGCCTTCTTAACCGAGGCAAAGTTAGATCACGACTTAGGTATCAGTTATTTAGAGCTTAAGCTGCTGGACGATACGGGTAAGTTTAAATTGGCTTTTGATGTCGAAAATTTACGCCCGTTACCTTTACTCAAGTGGGATTGAAACATCTAAGTCAATCGAGTGCTCAAACGAGTATTTAGGATTGAGGAGCGCAACATACAGATGAAAATACTTTTAGTTGAAGATGATGCGACGACGATTGATTACATCGTCAAAGGATTTCTTGAGCAGGGCCACAATATCGAAACGGCGAGCGATGGCCACCAAGGACTGTCACTCGCCACAAGTAGTCAGTACGATCTCGTGATCCTCGACCGCATGTTACCCCAGCTCGATGGCTTAAAATTATTGGCCGCCTTAAGGGCAACGGGCAATCAAACCCCAGTGCTGATTTTATCTGCCTTAGCGCACGTTGATGAGCGAGTGAAAGGCTTGCGCGCGGGCGGCGACGACTATATGACTAAGCCGTTTGCCTTCTCTGAGTTATTAGTACGCGCCGAAAAACTCATGCACCGTGGCCAATCTGTGCCTGTCACCACAGATCTCGTTGTCGGTGGCTTGAAGATGGAATTACTGACCCGTAATGTGACCTTAGACGGGCAAGAGTTGATGCTGCAGCCAAAAGAGTTCCAGTTACTTAAGTATCTGATGGAACATGCGAATCAAGTGATTAGCCGCACCTTATTGTTCGAAGCAGTGTGGGATTATCACTTCGACCCGCGCACGAACGTTATCGATGTGCATATCGCAAAATTACGCCGTAAATTTGAAGAGTTAGGCCATGGCGAGCTTATCGAAACCGTGAGGGGAGCGGGTTATCGCTTACGACAAAGGCATTAAGCCTTACCAAAGCAGCGCCTGGCGCATCACAATCGTCTTTTCGACCTTAGTGACGGTGATCATTGGGGCGCTGCTATTTGCCCTCTATCGACAGTTGATCATCGAGCAACAGTATCAAGTGACTCAATATTTAGAGTCAGAGACCCAACGTTATCAGCAACTTGCCCTAACGGTTGACCGCCGTAGTTTTGCCGCTCAAATCCGCGCCGCCGATCCACAAACCGCCCTAATCGCTTGGCGTAATAGCTACGATATGGTCGGTGCCTTGAGCTTTATGCCCGAAGGCATGCCCATGCTGCCGCAAACGCGAGATTTCCCCATCTTAACCGGTGGCCCAGATAAGCTGCATATTCTTACCGGCGGGCTAGTGATGACCCGCTACGGCCCAGTGTTGATTGCGACCCGTACCGATAACTTAGCGACGTTAATCGATAAGTTTATCAGCGCTGCGGCGACAGCCGTGATGCTTACGATAGTGTTGACCTTAGCACTCGGCTATCTATTTTCTAAGGCGATTTTACGTCGATTAGTGCAATACAACCGCTTGAGTGAACAGATTGAACGCGGCCACTATGATACCCGCTTGCCCTTGAGTTGGCGTCAGGATGAGTTTGATATGTTGGCGCTGCAGTTTAATAATGTGCTGGATATCCTCGAAAATAACCTGATGGCAGTGCGCGGCGCGACCGATAATATCGCCCATGATTTACGTACTCCACTGTCACATATTCGCATTGGTCTTGAAGAGCTGGCGAGTAAGCCGAGTGATGAGATTAGCGAAGGTTGCGCGATTTTGACCGAAGAGCTCGATCATTGTTTAGCGACATTTGATGCCATGTTGTCGCTCACGCGTATCGAAGAAGGTCAGCAGACGCTCGATTTACAGGAGTTAAGCCTAGCGCAACTGTGTACCGATTTGTTGGACATGGCCGATGCCGTGGCAGAATCCAATGAGCAAACCTTGAGTTTGTCGCTGTTGGCTGACCATAAGGTATTTGGTGATAAATACTTATTATTCCAAGCTTTATATAATCTCGTCGATAACGCGATGAAGTATTCTGGCCAAGGTGCGCGGATTGAAATTATCCAGTCAGGCCCGCAAATTCAGATCCGCGATAATGGCCCCGGTATTCCCGACGACAGTAAAGAAAGAGTGTTCGAACGCTTAGTGCGCCTCGACCCTAGCCGGCATTTACAGGGTACAGGTTTAGGATTATCTATGGTCAAAGCGATTCTCTCACGGCACAATGCCAAAATTAATCTCACGGATAATCATCCCGGCTTAGTGGTCACTATTAAGTTTTAAGTCGATGATCCGCATTACAACGTCGAGAACTCCATGTATCGAATCATAGCCAATGAGGCTGATTTTATTGTTATTTCCAAATCAGCCAAAGTGCATTTCCACAGTCAGGACGGAACAGCGGGCGTGGTGGCGCAGGCTGAGCAAGATCTGGGGATTAAGCTTTTTGCCGTGCATAGATTAGACTCGCCCACGTCCGGTCTGTTGATTTTGGCCAAGAGCGCCGTCGCCGCTAAGCAATTTACTGAGCTATTTACCGCCCATAAAGTGCAGAAGTATTATCTCGCCCTCGCTAAAGGCAAGCCGAAGAAGAAGCAAGGCTGGGTGATTGGTGACATGGCAAAGTCGCGGCGTAGCATGTTTAAACTGCTGCGCACTAAAGAGAATCCAGCGATTACGCAGTTTTTTTCATTGAGTGTCAGTGAGGGATTGCGTTTATATTTGCTTAAACCCCATTCGGGTAAAACCCATCAATTAAGAGTCGCCTTAGCAAGCTTAGGTGTGCCGATTTTAGGCGATGATTTATACGGCGGCATGGCGGCGGATCGTTGTTATCTGCACGCCTACTGTCTGCATTTTCGCTATGGCGATGAAGCGACTGGCTGGCGTGATTATGCGTATAGGGATGTGCCGACTCAAGGTGAACATTTTGCCGCTGAAGGTGTGATTGAGGCGCTGGTTGAATGGTTTGAACCCAATACTTTGGCTTGGCCGGCGAAAGGGGATTAATACGGGTCGCGTTATTTTTGCGTGAGGCTAGCAAGATTTTGCGAAAGCCTAGCAAGCCTGGCAAATACGGTCTCGGCCTTCAGATTTTGCGCGGTACATGGCTTGGTCCGCTTGAATGAGTAGCTTGTCGGGCTGGCATCCTTGATAGATAGTGGCGTAACCTAAGCTAGCATGGATTTTTATCGTAACACCTTGCCAGATAAATGAATTGTCCTTGAGTACTTGATGTAATTTCTCGGCAACGGCTCTGGCGGCGGGTGCATCGGTCGCGGGCAAAATCACCAGAAACTCCTCACCACCGTGACGACATAGGGCATCATTTTCTCGCAATACCGATCCGAACAGTCTCGCCGCCGTCAGCAGGGCGAGATCACCCGCATCGTGACCGTATTGATCGTTAATCTCTTTAAAATGGTCAAGATCCAATAAAATCATACTGTAAGGCACATTATCCCTTAGCCAGCGTTGATGGATATTGTTGAGAAACTTATGCATAGCCCGCCGATTCCACAGGCCTGTGAGTTGATCCCGTTCCGCTAACATACGGATTTTGCTGACGAGTCGCGTCATGGCATTACCTATCATCACGATATTGATCAATAGGGTTAGAAATACATAAAACCACAGCACAGGTATCGCTTCGTGCGTGTGCATGGCGATAAACATCGGACTTTCTTCTGGGGAAAGTAGGGCGATAAAGATGCGAATGATAAAAATACCCACCATAGCGACTAAAGGCATAACCATAGCGATGGCGACTCGATTCCCCGTATCGCGTTTAATGGCCTGATAGTTATCCCGCGTTAGCATGGTAAAAATCAGCGCGGCGCAGGCGGAGAAAACAACGGCTAAAGTACGTTCGGAGCTGAAGTTGGGGGGAACCGAGAGCATCACACTGGCCGTGATTGCCAAGAGAATAAGATCGGATTTAACGCTGGGACTCAGCCTGAAAAGTGCCTTAGTACCCCAGCGTAATAGGATGAATCCAAACAAAATCGCCATATCGGAGCAGAACCAAAAAAGGTAGCTGGAGGCTTCTGAGCGTTGGCTGTTGAGTATGATGCCAAGGAGGACGAACAAGTTCGCCAGAGAAAAGCGCATACTGGCCCGAGTGGCAATCTGTAAGGGTTTAGCAATGAGTCCCCATGCCACGGCTGCAGTGCTGGCCAGTAGGCAAATAAATTTGATGAGTAATAGGGCTTGGGGATCAAATATCATTCTTTTACTTCATATTTTTCTTTAACTATAGATGCTTTTAGGCATTTTCGGTTGAAGTTTATAGGATATCTTAACCTGTATTTATCGGCAGGAGGTGGGCTGTATCCTCTTTTGAATTTATGGCGATATTAAGTGCCTTTATTTAGAGAATAACTTGGGTTATTTGCTGTCTTGTAAGCTAAAATGTTTGCAAATTCTGAGTCTTCCTGCGAAGTTAGACCCTGTTGATTCGCCTACAATCTCCGTTTTAAGGATGCCCTGTTATGGATACAAATAAATTACTCTTAGTCATTATTGCGATTCTATTGCCTCCTGTTGCCGTATTTTTGAAGGCAGGAGCGGGTAAAGATCTGCTGATAAACATAGTGTTGTGTTTACTGTTTTGGTTCCCCGGTTTATTACATGCTCTCTGGGTCGTGACTAAGGCATAATTTGCTTTCTTCCACACAATAAAAAAGCGCAATGTAATTGCGCTTTTTTATGCTTCAAAATGAGCCTTTCACTTAGAAAGGCAAGGTGTAGCCTATTGTCGCAGTACGTCCCATACCTTTGAAATAACGATCATTTGTACCCATGGTTTGTGAGTAATAATTGAAATAATCCTTATTAAACAGGTTTTGCAGACCTAAGCTCAGGGTGCCAGTGTAAAGCGGCATAGAGAAGGAAGCATCGACTGTGGTATAGCCATCAAATTCCGCATACTTGTTGCCCGCGGCATTTTTAAAGTCGCGGTCCATGTAGTAGTTGGCTTGAACGCGGGTCGACATGTCATTATCGAAGTTATGGGTCCAGAACAGGTTGATACGGTTGGGCGAAATATTTGCGCCATCGAGATCGGTATCGACTTTGTTATCACCATTTGAATCGTATTCGCCCTGTTGAATGGCGATGTTCATGCCAAGATCATCATTGCTGGTGAGGTAGGCGGTAACATTGGCTTCGATACCCTCGATGACACTTTTTTCCCGCTTCACGTCGTAGAAACCATCGCCATTTAAGGCTAAACGTGAGCCAAAATCAGTGGCCGAACGATAATATGCAAACTTAGCGCTTAAGTATTTACCTTGATAATCGGCACCAACTTCGACGTTATCTGTGACGATCGGCGTGAGTGCCAATGAGTCATCTATGCTGGGATTGTCGCCTGGGAAGCTTTTACCATCACGCAGAATACGGCCGATATCCGGCATACCAAAACCTTGGTTATAGCTGGTATAAACGCGAATGTCTGGGGTGATTTTGTAGGAGATACCGACGTTGAATAAGGTTTCATCGAAGGTGGCCGAACCGCCTGCAATTTGCTTGTTGCCCGCACCCCATAGGGTTTTGTAGTCATCGACACTCAACTCGGCATGTTCGTAACGAACACCAGTCGATAAGGTTAGATTTTCAATTAAATCATAATCTAACTGCAAGTATGGCGCGACGTTGTCATAGGTGCTTTCCGGAACCCACGAAAAACCCGTTTTCACTAAATCTTGCTCTGTGGTATCGCGGAAAAGGTCGACACCGTAGGCCGCATCAATCCCTGAACCCGCAATATCTTTAGCAATCAATGAGCTTTTTAAACCCCATTTAACTGAAGCATTACGCGATTGCTCGTAGTATAAGCTTTCGCCTTTACTGCCGACACCGCACTGGATCACTTGGTCGCTACCTTCGAAGGATGGATCGTAGAAACTGTCGAAACAGCCGCCACCATAAACCGCTTTAAAATCTTGATTAAAGAGTTGTAGGTTAAGTTGTTGGCCACCAATGTTGGCGTGGGTGTAAGTCAAACTGGTGGTGGTCACTTGATTGTTGGCCGCTTCCCATGGCTGAGTTTCTTTAACTGCGCCCGTGGGCACGCCATTGGGCTTGTCGCCAGCAACGGGCATCCAATCGCCATTGTTGTCTGCGGTGTAATGGTTAACCATTAGTTCAAGGCGCGACTCGTCGAAGTTGTGGCCAAGCTTAATGAAGAAGTCGGTGCTTTGGCTGTCCATCGACTCGCCTTGAGTCGTATCCACACCTACGACGTTATGGTTGGCATCATAATAGACGCCATTATTGCGGTAGCTAACCGAACCTATCATGTCGATAAATTCTGACTCGCCAGAAAACGCATAGCTGGCACCAAAGCTTAAACCGTTAGATTTCATCGAATCAGGAACTGTCACATCGAAACTGGCAACCTGTTCGCTGTCGCCCGTCGGTTTTTTAGTGATGTAATTGATGATGCCGCCTTGGGCACCAAGACCGTGCATTGCATTGGCACCGTGAATGATTTCGATACGCTCAATCATGGCTGGATCTATGGTTTGACCCGAGCGACCCCCACTGCGAAGTGGGTTTGATTGCGGTACGCCGTCGATCATGATCAGCGGCGGGCGACCACGCAGGGTTTCGCCTGTGTTACTCATCTTCTGGCGGCTTGGCGAAAAGCTCGGGGCCAAGTTACCGATAATGGTCGACAAATCTTTAGTGGTGCGGAACTGTTCTTCTAACTGGGCGCGGTCGATGATAGTGACGGTATTGGGGATAGAGCTTGGGGATTTATCCATCCGACTCGCGGTGACAGTGATTTTTTCTATGTCTTTATTTTTGTTGTTTGAACTGCTATCGGGGGCATCTTGAGCCGCTGCGGATTGGCTTAGCGCTGCAAATACGGCTAAAGCAACAAACGAGGTTTTGAATGTCATATAACTTCCTGCTAAGGGCGAATGAGTTTGCTACACCGGCTCCTTTGGCATAAGTCCATATTCCATTAAACGGCATTACAAATGATAACGATTTTTATTCTGTCGCGATTTTAGGGGAGGAATAACGGCTTGTCTAGTCGTGTCATACAGGAAATTGCACGATTTGGCAGCAGTATTGATAAATTGCGACACAGCACTCGGTATAGAGATTTAACCCCAAGGGTTACCTAGTGCGAGTTCATGTTGTTAAGGCTGTGCCTTATTTAGCGTTTGGCATGGCTTGCGCGATCTGATGGTTTGGTCGCAGGTTTGATGACTTGGCTTGATGACTTGGCTTGATGACTTGGCTTGATGACTTGGCTTGATGACTTGGGGATTACTAACTGAGCTAACATGAGTCGCGGGTTGGGCGATCTGACTTAGCGCTTTAGATTGTCTGGCGTGCAGGTGGAAGGCTATAATCGCGGCCCTTTATCCCGTATCCGCATTGGTGCCCTGATGAACCGTAAAAAGAAGATAAACCAAACCTTGAAGAGCAAGGCGAAAAAGGCCAATGCCAAACTGCAATCGAGCAATAAACCTAAATACATTGCGAAGGCGGATAGGGAATCATTAGCTGCAGAGGCCGATAGCGAGTCGCATGCTGCGGCAGTTAACTCTGTGACTCTTGATAGTAAGTCTGCTGAATCATAAGTCTCCGTTCATTTCTTCGTATAATGTGTCGGCCAGCTGGCATTCAGTGAGTTTGCCGACATGGCGAGTTGCTGAACCCTCATTTGTAGTTGATATTGTCGCGAAGCCGCTATGTTACTCATCACCTAAGTCTTAGTGTGTCTCTAAGTAGGTGATGGGGTTTTGATAAAAGGCCTTTTTATATCCTTGGGTTGAAGTATCTATCAGCCAAGGTTTAGGCAATAAGCTCAAAATGGAATAATGCAAATGTGGCGGTTTACCTTGAGCATTACCCGTATCGCCGACAGTGCCCAGCGTTTCACCTTGGTGGGCAATTAATCCCGTGGCTGATTCAATTTGTGCTAAATGGGCAAAGTAATGAATTTGCCATTTAGGACCTAGACCAACGACCACATTGCCTCCTTTAAAGAAGTCGCCTCGATAGAAGATCAGCATAGGCGTTGGCGCAATCACGGCAGTGTTTTTGGCGGCGAAAATATCTATCCCCTTATGTACGCCCGATGGGCCCCAAGGTTCATACCAAAAGGTTTGGCTGTTCCAATCTTTACTGCTGGCGCCTTTGACTGGGATCACCGGTGACTCAGGGATCAGATAGCCGCCAAGTAGCAGTAACGCTAGTGCTAATCCAACATATGTTAACTTCATTTTTTATCCTGCATTAAGCCTTAGTGCTTCTCTCAAGCTTTAATGAAAATCACTGAATTCCTGCCAATTTACGGGGTACTGTAAGCGTTCAGCCGCTGAAAGTGATAACCAATAGGGGCGCCAAAAATAGTTGCACCAAAAATCGATATTCCCTTGAAGACTGCCGAGTATATCGGGATCGACATCGGGAAAGCTTTCCCAAGGGTGAGGTAAAGTCGATTTGAACGCAATCAGTTGTTCTAATAGTGCTTTGTCGTTGCTGATTAAACTGACGCAGATCCCCGCAAAATCGGCATATTGGTGGAGCTGCAAGCTGGTCGCTTGGCTCGGATGGGTTATCAGGCCATTAAAGCTGGCGGCATGTTCGGGCAATACCGCAAAACCTGCATTGTCGATTAACCTATCTAAGGTTTGCTTGCCTTCTAAGGTCAGTAAGCCGTGAACTTGATAGGCATAAATATCGGCCTGCACTATTGATGTTACTTGCGGATAGGCTTGGTTTGTCGGCAAGTCGATGTCGGGCATTTGCGCGGGATGAAAAATGGCGGCTAAGTCAGCTTGTCGCTCTAGTATGCTGTTGCCATGAAAGGCGCTATGAAGATGTTTTTTTAGCCAAGTGAGCACAGACATGAGCGCGACCATATAAGGTGAATGTTAATTGATCATGGCGTATCTAAAGATTCAAATCACCAATCTACTTCTTGTACCCGCGATTTTACTCATCCGTATACGGCCAGCTTGAAGGATTAATACCGTTAATTCAGTTGGATATTTAAAATGACAAGGCAAGTGATTTTATGCCTGTAACTAAGCTGTCGTCTAGGGTTTAGCGTTAGCAGGAAGAAGCTTAATACTTAAGTTGTGAGCTTTTTAGCGCCGACAAATGGGAATCTGTCGAATGGTAAAGAGCTGTGACTCAGCAGCCTGAGTTTCTCGATGTGGCACGTTTGCATTCTGGCCTAAGCTGTTCCATATGAAAAATAATGATGAGCACCAGCAAGCGATTATCGACGCCAATATCAAGATGTTTTTGGCCATGTATAGCGCTTAAACATAGTGTTCCATGCTATTCAACATAAAGGCCGCATCTGCGGCCTTTGTTATTTATGTAATACAGACTAGAGCAAGGCTGTCGCCAGTAGATAACCGACGGTCGATGCGGTACCCACGCCGATAAAACCTGGAATAATAAAGCTGTGGTTAATAATGAACTTACCTATTTTAGTTGTACCTGAACGGTCAAAGCCGATACAGGCCAGATCGCTTGGATAAGTAGGTAGCACAAAATACCCGTAGCTTGCAGGGAGGAAAGCGATCAAGAGTTTTGGATCGACTCCTAAGGCGAGACCCATAGGCGCAATGGCAGTCAGCGCCGCTGCTTGGCTATTAACAAGCTTAGAGATGAGGAACAGCACTAAGGCATAGGTCCAAGGCTGGCTTTGCACCACGTGGGACAGGTTTTCCTTAAGCACATCCATATGGCTGATGAAGAAAGTATCACTCATCCACGCCACCCCAAATACTGAGAAAATCGCCACCATGCCCGCTTTAAAGACCGGGCCATTAGAGATGTTTGTCGGTTTGACTTTGCAGGTCATTAGAATAAATGCCCCCGCAATTAACATCATCATCTGGATGACTAAGTTCATCGATAATAAGGTCGTTTTCCCTTTAACATCAAAGGCAGGGCGCAGTTCACTAAAGGAACCTAACAGCACCACAGCTGCAATAGCCGCAAAGAAAATCCCCGTTGCCCAGTAAGCTTCCTTAGGAAAACGTTGGTTTAGCAGGGTTTCGCTCTTGTCATAGATAAAAGCTCTTTGGACAGGATCTTGAATCCGTTCTTGGAACTCTTCGTCTTTATCTAAATCTTTACCGCGACGTAGACTCCAAAGTGCGGCGGCCATCACACCGCAAAGTGATGCAGGTACGGATACCATTAAAATTTCGAGCAGATTGTAGGCGCGGCCCACACCTTGCTGCGATGCGAGAATCGACACCATAGAGACAACCGCTACCGACACGGGCGAAGCACAAATCGCCATTTGCGAGGCGACGGATGCCACAGCCATTGGGCGTTCTGGGCGAATATTTTTCTTCAGCGCGATATCGGAAATAATCGGGAACATGGTGTAAACCACATGGCCCGTACCGCAAAGAAAGGTCAGCGTCCAAGTGGTCAGCGGCGCCAGAATGGTGATGTATTGTGGATGGCGGCGCAGTAGGCGCTCAGCAAACTGCATCATCACATCCAAACCGCCTGCGGTTTGTAATACGGCCGCACAGCCGATCACCGCAAGAATCGTTAGCATAACTTGTACTGGCGGTTGTCCCGGCGCTAAACCGAAGACAAATATCAGTAAAAACAAACCTATACCGCTGATAAGGCCGAGTCCCATACCGCCGTAGCGGGTACCGAGTAGTAGGCAACTAAGCACTACTATAAATTCCATTAAAATCATCCGATTTACCTCTGCGGAATCTACCGCTTAGCGATTTGTTGAGTTATGAGTGTTTTCAGGGACATTTTGCGGAGGAAAAGCGTTTGAAAAATTGCGCTGGCGCATGGTGTAAATAAAAAATGGCATGAGATTTTTCGGAAATTTGACCTAGCGCTAGAAACACGTGGCGATAAGTTGCATTCTGGTTAACGAGTTTTATGGATTGAGCTAAGACTTTGAGCTCGCTCCGTTTACAAGAACGATAACAATACTCAAGGATAAGCCCGTGAAACCAGCCACCTTTAACACCGAAGCCTTCGATGATTGGATCCGCAGTCGCTTTGTCGAACTCAACTCAGAGCTAGAACAGCTTTACTACCAACAAACAGATCGGGCCAATGTGCTCGATATTGGCGCAGAAGCTAAGCTGGCGCTGGAGAGCGAAGGGCGAGAGTTGATTAAGGCCTTGCTCGATGAGGGCAATACCGACGAAGGGTTCGACAGTGCTTTTGATTTACTCGGCAACGTGGGCCTGTATATGGCGGCTTGTCGACGCCATGAAATTACCGAGCCTTCGCGGGAAACCACATCGCCATTAGCCGAAGCCTCTGCGCTTGCCATGCACATCGGCGCTTCAATAGGCGTCACGCCGCGCTTTGCCACCGCACATTTAACTACGCACAATCGTGCCCACAATGGCATTTATAAGCGCTTTACCGATCTTCCCGCTGAAAAGTTGTTTGTGGATTACAACACTAAGGGCATTTTGGCCTATAAACGCGCCGCCGATGCGCTATTGAAGATCCAGCCTTTAGGTATTACCCATCCCATCAGTCACGATTTACTGCGAGTCGCTAAACAGGCATTGCAGGATGTGATTGAGTCGAATACTCAATTATTCAATCGATTAGATACGGATCGTTTCTTTTACTGTGTTCGCCCTTACTACAAACCTTATCGGGTGGGCTCGGTCATTTATCGCGGTGCCAACGCCGGGGATTTTGCGGGGATTAATGTGATCGATTTAACACTAGGTCTTTGTTTCGCCAATGAAGCTTCCTACTCGCAAATGCTGGTGGACAAGTTTTTGTACATGATGCCAGAGGATCAGCAGATATTACGTGAGTGCATGCGTCGACCGAATTTGATGGATGATTTCCTGCAGGCAAAAGACAGTGCAAACCAAACTTGGTATCAAGAAAATTTGCGACTCTTTATTGAAGTCTGCGAATTACATGGCGAGACTGCGATTCAGCACCATAATGAGTTAGTGACTAAATATATTGCCGAGCCTTCGACAAGTATGGAGCAGCAGCATTTAGCTAAGGTTACCGCCAGTGGTCCGCCACTTCATGTGCTACTCGGTTCATTGGAGCGACTCAGAGATAGGCGCGCCGCCGTTCAGCGCAGCGATATTCGGACGCGTTATTATGAGATTAAAAAATTAAAAGAGAGTTTGAGATAACACTATGTTTGAAGACTTTAAAAAGGACTTTTACCTCGCGGGTCCAGGATATCTATTGAACCATTCCGTCGGTCGACCGTTAAAATCGACCGAACAGGTCTTCAAAGAAGCCTTTTTTGCGCCTTGGCAGGAATCGGGGCGCGAGCCTTGGGGTCAGTGGTTGAGTGTGATAGATGATTTTACCTTTGCCTTATCCAAACTGTTTAACGGCCACCAAAAGGACTTTTGCCCTCAGGTTAATCTCTCCAGTGCACTGACTAAAATCGTTATGTCACTCGACCGATTAAATCGCGAACACGCCGTAGTGTTGATGAGTGAGATTGATTTCCCCAGCATGGGTTTCGCCCTTAAAAAAGCGCTGCCAGCTAGCTGCGAAGTGCGTTTTATTCCTAAGGGACTCGATGTCACTGACCCTAATGTGTGGGACGCGCATATCAGCCGCGATGTGGATCTGGTGTTTGTCAGCCATGCTTACTCGAATACGGGGCAGCAGGCGCCATTAGCCAATATTTTCACCATAGCCCGTGAGCGAGGCTGTTTGAGCTTAGTGGATGTAGCGCAATCGGCGGGGATTATTCCGCTGGATCTTGCACGCCTACAACCTGACTTTTTAATCGGTTCGAGTGTGAAATGGCTCTGCGGCGGCCCTGGTGCCGCCTATCTTTGGGTTAATCCCGCCATATTGGAGAGTTGCCAGCCAAAGGATGTGGGGTGGTTTAGCCACGAGAATCCGTTCGAGTTCGATATCCATGATTTTCGTTACCAGCATTCCGCAATGCGCTTCTGGGGTGGAACGCCTTCAATCGCCCCCTATGCCATTGCTGCCCACAGCATTCATTATCTTGCGGGTTTAGGCATCCCCTTGCTGCGTAAATGTAATCAGCAGTTAATTGATGCCGTGGCGCAAGAGCTCGAACATGAGTTTGTGTCGCCACGGGATGTTGAAAAACGCAGCGGTACGCTGATTTTGCAGTTTGGTGAGCAACAACATCAAATCATGACGGCATTAGCGAACGCGAATATCAGCGTCGATGCTCGCAGCATGGGGATCCGTATTTCGCCGCATATCTACAACAATATGGCCGATATCAAGTTGTTACTGGACGTGATAAAAGCCAATCGGTAGCCAATTGATAAGAGCTCTGTGGATAAATAGCTATGCCCAAACCTCAGTGTTTGGGCATTTTTTTAGAGTCGTTTACGATTTAGCCGCTCGATGATCTGGCAGGCGTTCAAGTCGTGTACCACGTTAATGACCGTCGATGGTGCATCGGTAATTGCCCCTATCACCACTAAAATTGGTATGGTTTCCATTGGCAAGCCTAAGGTCGTGACGATAAAAATCTCTCCGAGGAAAGCGCCGCCCGGCACGCCGCCTATGATAAAGGCCGATAGAACCGAAATCAGTATTGTCACCATAAACACATCGACGGTGAAATCTAATCCGAGTAACGAGTAGATAAACACGATTTTTAGGGCTGTGATGATTGAGGCGCCGCCCTTATTTAAATTGACCAATAATGGCAGGCTGATGTCGGCGATTTCGGGTTTAATGCCCATCTTCAGCGCGGTTCTCAAGGTGACGGGTAGGGTGGCGAGTGATGATGTGGTGCCCAGTGCCGTCACTGCAGGTTCGAGGGCGAAGCGCCAGAATTGTTTAATCCCTTGAGTGCCACCGCCAAGCCAAGCGTAAAAGGTAGAACCCACGGTTAGATAAATAAAACAGGCGACCATAAACAGGCCAACGGCGCGGGCAAAGGTGATAACAAGCTGCGGATCTTGGCTTGCCATTGTCGAGGCGAAATAGGCACCGAGTCCCACGGGCGCTGCCACCATAATGATCGAGACTATCTTCATGATGACGGTATTAAAACTGTCGAGTAATGCCGAGACTTTTCTGCCATCTTCCCCCGATTGACCTATCGCAATCCCCGCAATAACCGACATGATGATCAGGGCTAAGATATTGGATTTTGATAATAAACCTGAAAAGTCATTGGTGGTCAGCAGGCCGACAAAATCCATCGTCCCCGCAGTATCGCTGATGGTTTGATTGAGATCTAAGGTAACACCTTGGGCGGGGTCGAAAAATAAGGCCAGCCCGATAATGCCCACGGCGGGGATAATCGCCATCACAATCGACACCGCCATGATTGACACTAACAGAGCGCCGAGTTTCTTCAAATCCGTCATGCGAGCAATGGAGGAGGTCACGCTAATGGCGACCAGCGGTACGATAATCATAAACAACAAGTTTAGAAAAATTTGCCCTAAAGGTTTGAGCTTGAGCGCCAGTTCTGGCAACCCGACACCGAGCACGCCGCCCAGTAACAACGCGGTGAGCAGCACGATGGAATCTTTATAAGGTTTTATCTTTTGCCACATAGGTAAACGCCTATTGTTATTTTTATGAATAAGATGCGCCCAAGTGATCAGGATTAATCGTGCTGCATGGACATTATCAAGCTAGCGATTGCATTGAGTGAGTGCAAGGTGTGATTGATTTTAAATCGAAAAGGTTTGAAATCTAGACGTGATGGATTTTGATTTTTTATGATTAATGGATTTGGCTTTATAGCTAGTCAAAGCTTCATCTATAAAAAAGCCCCGACAAGTCGGGGCTTTAAACATCTTAAGCCAGTGGCTTACAGGCTGTAGTACATTTCAAACTCAAGTGGGTGAGTTGTACGTGCTACACGCTCAGCTTCAGCTGTTTTCAGTACGATATAAGATTGGATGAAATCTTCACTGAACACGCCGCCTTTAGTCAGGAACTCATGGTCAGCTTGCAGGTTTTCAAGGGCGTTTTCTAATGACGTTGCCACTTGTGGGATTTCAGCCGCTTCTTCTGGAGGCAGATCGTACAGATCTTTGTCCATCGCTTCACCTGGGTGGATCTTGTTTTGGATACCGTCAAGACCCGCCATCAGCAGCGCTGCGAAACCTAAGTATGGGTTTGCATGTGGATCTGGGAAACGCGCTTCGATACGACGGCCTTTAGGGCTTGGTACCACAGGAATGCGGATTGATGCAGAGCGGTTACGGGCAGAGTAGGCCAACATAACAGGTGCTTCAAAGTGTGGCACTAAACGCTTGTAAGAGTTAGTGCTTGGGTTAGTGAAAGCGTTCAGCGCGCGAGCGTGCTTGATGATACCACCTATGTAGTACAGAGCCGTTTCACTTAAACCCGCGTATTTGTCGCCAGCGAACAAGTTAACACCGTCTTTCGCCAGAGATTGGTGAACGTGCATGCCGCTACCGTTGTCGCCAACGATGGGTTTAGGCATGAAAGTCGCCGTCTTACCGTAAGCGTGTGCCATGTTGTGCACTACGTACTTCAGAATTTGGATTTCATCCGCTTTTTTAGTTAACGTGTTGAAGCGAGTTGCGATTTCGTTTTGACCCGCAGTCGCCACTTCGTGGTGATGCGCTTCAACCACTTGACCCATTTCTTCAAGCACTAAACACATAGCGCTACGTAGGTCTTGTGATGAGTCAACCGGTGCAACTGGGAAGTAACCGCCTTTAGTGAATGGACGGTGACCTGTGTTGCCGCCTTCGTAGCTAGTGCCTGAGTTCCAAGCCGCTTCTTTAGCGTCGATCTTAACGAAACAACCTGACATGTCAGTACCGAAACGGACATCATCAAATAGGAAGAATTCTGGTTCTGGGCCAATCAAAACAGTATCAGCGATACCAGTAGAGATTAGGTAAGCTTCTGCTTTTTTAGCAATTGAACGTGGATCACGGTCATAACCCGTCATAGTACCTGGTTCAAGAATGTCACAACGGATCAGTGCTGTGGTTTCTTCGGTGAAAGGATCCAGTACGAATGTCGTTGGATCTGGCATCAGTACCATGTCTGATTCGTTAATGCCCTTCCAACCTGCGATAGAGGAACCGTCAAACATTTTGCCGTCTTCGAAGAAATCAGCATCTACCTGATGAGAAGGAATAGATACGTGCTGCTCTTTACCTTTAGTGTCGGTAAAACGTAAATCAACGAACTTAACTTCTAATTCTTCAAGTTGCTTTAAAACTGATTCAACTGACATTCTCAAGCCTCCGGTAGGGTAAAGGGATGCCCCTTCTATAATCTTTTAACTCAAGTGATTTATCTTGAGCCAAACTTACGAGCGATAAAGCGATATCCATGCCAATAATTTAACTAGCTGATTAACATGGTTTAAAATCTTTGTTGCACTTAATTGGATTCGTGTTTGCACCACTATGGTGCTTTTGGCTGTGGCATTTTGGTGCATCAATCCATTTTGGTGCGCACTGAATTGATTCATGCAAATCTAGGCTTGGATCCAAACTAATAACATGCACTGCTTATGTGGCAGTGAGATGGCAGTGACGCCTTGGCTCACATTAGTCGCATAAGGACAGCAGCAGTGAATCTAGCACATCTTTTCAGCACAGAACGGGACTATTTCTCTGGGCTGTTTCGCCGCCGCAAAATTGTCATAAAACCACGCTAGTCTGTCCAACTATGCCACGCATTCTAAATCCAATAGTGGTTTATTATCATATTATTAGGAAAGAGTTCGTTCTCATGTGCGAAATGTCTTTGCTGTTCACAGACCAAGTGAATCAATGATGCGTAATAAAGGTATAGCGTGACAATGAAGTGAGTCTTAAAGACATTCAGTTTTCGCGGTCATAAACGCCAATATGAATGCTCAGTTAAATGTACGCTAGAATTTATTGCTGTTTCTCGGCGAGATGATTTCATCGCGGTGACATCAATTTTAATTGATGTTTAAATCAGCAGAAATGGTTTGCGTCTAGAGTATGAAAAATAGCAAATTCAGCTGCTTAACTCGGATTTTTCCTACGAATATGGCAATTGAACAGTGCTTTAACTGTGGCGGTAGAGGATTTCACTCAATGGAATATTGGCAGGAATTACCCAAGGATCGGCGGATTGTTTTATAAGTGTTCAAAAATAATCCTGTGCGCCAAGGCCGCTCTGGAGTAGAATGGCACGCTTTTTATTGTGGCGGATCGAGCTAGATCGCCATAACGAATTACTGAAATATCCCTATTTTTGTTTGATGGTAAGAGGTTTATCCGTGCTAGAGAATTTACGTAACATCGCCATTATTGCACACGTTGACCATGGCAAAACGACCCTGGTAGACAAGTTGCTGTCACAGTCAGGCACCCTTGCAACCCGAGGAGAAGCCACTGAGCGGGTGATGGACTCCAACGATCTTGAAAAGGAACGTGGGATCACGATTCTGGCAAAGAATACTGCCATCAAGTGGAACGACTACCGTATTAACATCGTTGATACCCCAGGCCACGCCGATTTCGGTGGTGAGGTTGAGCGTGTTCTGTCTATGGTTGACTCAGTATTATTGCTGGTTGATGCCGTTGACGGTCCAATGCCACAAACTCGCTTCGTAACGAAGAAAGCGTTTGCTCAAGGCTTAAAGCCAATCGTTGTTATCAACAAGATTGACCGTCCAGGTGCACGCCCAGATTGGGTTATCGACCAAGTATTCGACCTGTTCGACAACTTAGGCGCAACTGACGAGCAGTTAGACTTCCCAATCGTTTACGCTTCTGCGTTAAACGGTTTTGCAACTTTAGATCCGGATGTAGCGAGCGATGATATGACGCCGCTGTTCCAAACTATCGTTGAAAAAGTATCTTTCCCAGATGCTGACGCCGAAGGTTCGTTCCAGATGCAAATTTCGCAAATCGACTACAACTCATACGTGGGTGTTATCGGTATCGGCCGCATCAAGCGTGGTAGCGTTAAAACTAACCAACAAGTGACCATTATTGGCGCTGATGGCAAAACTCGTAACGGTAAAATGGGCCAAGTATTAGGTTACATGGGTCTAGACCGTACTGAAGTTGAAATTGCTAACGCGGGCGACATCGTTGCTATCACAGGTTTAGGCGAGCTGAAAATTTCTGACACTATCTGTGCCGTTGGCGCAGTAGAAGCAATGCCAGCTTTGACTGTTGATGAACCCACTCTGACCATGACTTTCCAAGTTAATACTTCACCTTTTGCTGGTAAAGAAGGTAAGTATGTGACTTCACGTAACATTCTGGAACGTCTGCAAACAGAATTAGTCCACAACGTGGCACTGCGTGTTGAAGAAACAGAAAGTCCAGATCGCTTCCGCGTATCTGGTCGTGGTGAATTGCATTTATCAATCCTGATTGAAAACATGCGTCGTGAAGGTTACGAGTTAGCCGTATCACGTCCAGAAGTAATTCTGAAAACCATCGACGGTGAACTGTGTGAACCATACGAGACGTTGACTGTTGACGTTGAAGAAGAAGATCAAGGTACAGTGATCGAGAAGTTAGGTACCCGTAAAGCTGAAATGAAAGACATGCAGCTTGATGGTAAAGGTCGTGTACGTATCGACTTCATCATCCCAAGCCGTGGTTTAATCGGTTTCCAAACTGAATTCTTAACTGCGACTTCTGGTACTGGTCTAATCTACCACTCGTTCGACCATTACGGTCCACACAAAGGTGGCGATATCGGTCAACGCGCTAACGGCGTATTGATCTCTAACGCTACTGGTAAGGCTCTGACCTTCGCACTGTTTGGTCTACAGGATCGCGGTCGTCTCTTTATCGGCCATGCCGCTGAAGTATACGAAGGCCAAGTAGTTGGTATCCACGCTCGCTCAAACGATCTGACAGTTAACTGTCTGAAAGGTAAGCAGCTGACTAACATGCGTGCTTCTGGTACTGACGAAGCTCAAGTACTGACTACGCCAATCACAATGACACTTGAGCAAGCGCTTGAGTTCATCGATGATGACGAATTAGTTGAAGTAACACCTAAGAACATTCGTGTTCGTAAGAGACACTTGACTGAAAACGATCGTAAGCGCTTTAACCGCGCTTAATGATCGTCTGACTCGCCATTAAGGTGGGTCAATCGGTTTAAAAAGCCCTGCATAGGAAACTATGTGGGGCTTTTTGTTGGGAGTTATTTGGTCATGGGCATGTCGATAGTTTGCGCTATGGAGCTGATGTGGCGTTTTATTCGTTACATCGCTAAAAGCGTAGTGCTTGCAGCATATATTCTCTGTGGCACGTTGGGCTCTGCTCAACGTTGTAACTCTGTTGTTCTGTGATGAGAAGATAGAGTATACCGGAGATTAGTTAAAAATGCTGAATCTTAAGATCTTGTATTACTTCTCATTCATCACCCGTATGCAATTGCGGCCTGCTGATTTTGCACGGTAAAGCGCGGCATCGGCCTGGCGAACTAACTCTAAAGGGCTTTGTTGGTTTGAGGGTTGCAGATAAGCGACTCCTGCGCTGACGGTCACAATACCAAGGTCGGTATTCGCATGTGGAATGGCAATGGCTTGGATTAACTGATGAATATGCTGCGCTGTGTCAAGTACCGTTTGTTTGTCGATATTGGGCAACAGAATGACAAATTCCTCACCACCGTAGCGAGCCACGAGTTCGCCGGCCCTGCGGCCCGACTGGGATAAAGTACTGGCCAGTTGTTTAAGGCAATCATCTCCCGCGAGGTGGCCATAGTGATCGTTAAATTGTTTGAAGTGGTCAATATCGAGCATGATGAGAGCCAGTGAGGAGTTGCTCCTTTGTCCTCGGCTCCACTCCGTTTGTAACATCATGTCAAATAGGCGGCGGTTGCCGATATTGGTTAAACCATCGGTATTGCTGAGTATTTCGAGTTGCCTATTAGCTTCTGCAAGCTCACAGGTTCGTGCCTTGATCATGGATTCAAGTTCGACATTACGCTCTTCTAATCGGGCTATAGGGTAAATCTCCCCTTTGTGGGCTAAGCCGTAGGGGATGGATATGCCACTGTGGGCAATCATCCAGTCTTTGTGTTCTAAACGAAATATCAATACTAGCCGTGCCGTTTCGCGGGAAAACATAGGATTAGGGATGGGTAAGTGGATATGGAAAAAGGCAGTCACAACCACGAGGTTATCAGCAAGATCTTGTAAGGAGAGATCTAACATTTCGATATGGATCCGCTCAGGCACTTGTGCAAAGTCCTGCCGAGTGATCCGCACCCATTCTTCCCTGTCGGTGACAAGCTGGTCACTACTGCCAGCATAGCCGCTAAAGTTCTCACTAAAGCGGTGGGTTAGCCTGTCATCACGGGAGGAATACATCTCAATATATTCGTCAAATAACGAACGGATCAGTTGCTGTCGGTTGGGTCGCATAGGCTAGCTCCGTGATGGAACATAAAACCACTTATCTGGTATTCGCATAGGAATAGTATCAGCGATAAAATCGAGTGGTTGCCTGATTGTAGTCAATGTTATGACTTATATATTAAAAATACTGCAGATAGTGATGTAAGCCATTATCTGCAGTGTGCTTTTTGGGGGATTAGCTAATGCCCAACTCTATCTATCACTTGGATCAGTTCCATGCGGATATTGTCGGGTGTTTGGCTATGCCAGAAACCCGCTAAGTAAGCGCCCAACGGCAGAATAAATAATGCGATTAGCACTAATGCCAGCAATCCCTTGTGGCCGAGTCTGTGGCCGGATTTAAGTCCTAGGCCAAGGGCGGCAGATTTTGGACAAGCGGCGACGCAGCGCATACAGGCTTGGCACTCGTCACTGCGAACCGTGGTTTTGGTGTGTACTATGATGTTTGCTGGGCAGGCGCGGGTGCACTTGTCGCATTTCATTCCTTTTGCTTCGATAAGACAGTGCTGGCTATCGCGGCGGATTTTTAAGGGGCTCGCAAAACTTAAGATCCCGAGTATGGCACCATAGGGGCACAAGTAACGGCAGAATCCCTGACGGCGCCATGCGGCTAAGGCAAGGATCAACGCAAACACTAATAAGGTAATTAGGCTAGGTGTTAGAAAGAACAGTGCCATCTTCAAGTCGGCAATCTTATGGTAATTGCCATCGAGATAATTAGGAATCGATTGCGCTGGCATACCAATCACTATGTAACAGAGGCCAAGTAGCAATAGATACTTGAGCATACGCAGTGGCCAATCGAGCCAAGCGGGTGGTGTTAGTTCTGATTTAATAAAGCGTTTTCTAAAGGCATACAGATATTCCCCTGCCAAACCTAATGGGCAAGCCCAGCCACAAAATGCTCGCTTGCAGAGTAATCCTGTGAGTAATACCGCAGCTAACATCACTGCCGCTGCAGGATGAGTTTGATCCCATAAATTCAGACTGACAATGGCCTTAAGCTCAATGCCGCCAGCAATTGGCAGGAAAGCATCGACCACATCTGGGCGCATGAGCCAAGGTGTAATCCCTTGCTTAAGCAGTATGGTATTGATTGTGTATTGCACGGCAACCAGTAAAAGCGATAGCGCCAAGACATGCTGAGTGCCTTGCCGCAGGGTGTTGATTTTAATCTCACCCTTGAAGGAATCAGGTGCAAATTTATGCCCCAATAATGCCAACGGCAGCGCAATAATCAGTCCGAGTGCCAATGGCCAGTAAAGGCTAATCGCGACGGCGCCGACGCTGAGTAACAGTGTGGCGACAGCGCCCCATTTCTGGCCGCTCCAATAGCTGAGCGAGGCGAGATACATGAGTGCCAACATCAGGGTGAGTGATTCGATAAAAGTCATAGTGATGAATACAGCGAATGAGTAACGAGTACAGTGTCTGAGTGATGAACTGATGATAAGTTTTTGAGTACTAAGTGAAATTGTGCGACAGGTCTAAGATTGGTGCTATCAATCCACTAAAGCGCGCCATAGCTCACAAAGCCAAGTGTTTTGGGCTTGCACAAAAAAAGAAGGGATTGCAGGGAGGCGAGATGAGTATCGTTACGACTCATCGACAAGTGAAAGGGAAGTATGGAAGCCTAGATGTCTATATATCCAAACGATCTAATATTCTTGTTTGATATATCAAATAGTTATTAATTCGTTATCCATTACCTTATAAAGGCATAGAATCCGACTCAAATCATGCGTTGAATTGGAGTCATTGATGGAACAATTACCGATATTGAATTTAAGCGATTGGCATGCGGGTGGGCAAAGTCGTCACGCCTTCATTACTAAACTTGCCGATGCAGCTAGAAGGATAGGTTTTTTCTATTTAACTGGCCATGGCATTGAGCTCGCGAGACAGCAGCAAGTACTTCAGTTAGCTAGGGATTTCTTTGCGCTCCCTCTCGCCGATAAGCTCGCGGTGAAAATGACGAACACGCCTCATTTTCGTGGTTATACCCGTTTACAAGGTGAATTGACCTTAGGGAAGCCGGATTTGCGTGAGCAATTTGACATAATGCAAGAGGAAATCGCTAATCCTCCGCGTTCGGATGTGCCAAAATGGTCGCAATTACAAGGACCTAATCAATGGCCGTCCCAATTACCGCAGATGAAAACGATTTTATTGGAATGGCAACAGGACTTAGCCAATACCAGTGTTACGCTATTGAAGGCGCTCGCTGTTGCATTAGAGCAATCAGAGACCGCATTCGATGCCACGATTGATGCTGGCCCATATCAACACATGAAGCTTATCCGTTATCCAAGTGCGAATGGAGAGACTTCTGGTCAAGGGGTGGGAGCGCACAAAGATCCTGGCTACCTCACCTTAGTGCTGCAAGATAAGCAATCGGGTTTAGAAGTTCAGACTGACGAAGGTTGGTTAAGCATACCGCCCTTGGAGGGCGCCTTTGTTGTTAATATAGGTGAGTTACTTGAGTTAGCCTCTAACGGGTATTTAAAAGCCACTTATCACAGGGTCACTAGCCCACCTGCTGGAGTTGAGCGCTACTCTTGCGCCTTTTTTATGGCGGCACAGTTGAACGCTACGGTTCCCTTGTTACCCTTGCCTGAGTATTTGGCACGCCAAGCCCAAGGGCCTGCAAGCGATCCATTAAATCCACTCTTTCACCAAGTTGGCCAAAACGTATTAAAAGGGCGTTTACGCTCACATCCTGATGTTGCGAAAGCGCATTACGCCTCATTTGCCAATGCTATTTAATTGTTATTAGGATCTATTATGCAGTTTAAAACCAAATTACTTCACACCCATCATACCGCTGAGAACTCAACGGGTGCGCTCACGACACCTATCTATCAAAGTAGCACTTTTGCCTATGGCAGTGCCGAAGATGGTCGAGCGCGTTTTGCCGGTGAACAGCCGGGTTTTATCTACAGTCGTATGGCGAACCCCACAGTACAGGCTCTTGAACACCAATTGGCCCAGCTAGAGGGGGCTGATGACGCGCTCGTGGTTGCGAGTGGTATGGCGGCAATCAGCAGTATTTTCTATGCCTTGGCCAGTCAAGGTGATGAAATCGCATTTATCGATCCTGTATATGGTGGTACTGCGGCATTTCTAATCCAAACGCTGACCCGCGCGGGCATTACTGTGCGCCGTTATCAAGATGACGACCATTTATTGTCTGAGATAAGCCCCGAGACAAAAATTATCTTATTTGAGCCTATTACCAATCCGACCTTAAAAGTCAGTGATTATCGTAAGGTCAAGCTCGCCGCTGAGCGGGTTGGGGCAATCACCATTTGTGATAATACGTTTTTAACGCCGTATTTATTCAAACCGCTGGAACATGGCATAGATATCGTGATGCACAGTGCCACTAAGTATTTAAGTGGCCATGGCGATTTAATCGCAGGTGTTGTCGCTGGACCTAAGGCTTTAATGCAGCCAATTCGCACCATAGCACTTAAGCATATTGGTGCTGCTATTGGGCCACAGGAAGCCTATTTACTGCAGCGCGGTTTACGTACTTTAGCGTTGCGAATGGATGCCCATCTTGCGGGAGCCGCTAAAGTCGCTGCTTTTTTAGCATCACATCCGAGCGTCGCAAAAGTCTATTATCCTGGGCTTTCACAGGACCCAGGGCATCAAGTTTTGAGTGAAACGATAAATGCCTTTGGCGGTATGGTGAGCATTGAACTTGAAGGCGGGTTTAGCCGAGCGGCGCAGTTTTTAGATAATTTGAAGCTATTTACTCAAGCGGTCAGTTTAGGCGATCTAGAAAGCTTAGCTTGCCATCCTGCAAGTACCACACATGCGGCAATGGATGTCGCGAGCAGGCTAGCGTCTGGGGTGAACGATAACTTGATCAGACTGAGTATTGGCGTTGAAGATCCTCAAGATTTAATTGCCGATCTTGAACAAGCGTTAGCGATATAAGACGGATAATTTGAATCCGGCCTGTAGCTAAGATCGTGTGACAACTCGCGGTAGATTTTTCCTGCGGGATTAATTTGATGAATGGTCAATCACTTAGGGTGATGTGCAGTGTCAGCAACTCACTAACTCACTAACTCACTAGCAATGACTCATATTAAGCGGCGATGAGTTAGCCCAAGCTGTTAGTTCTATTTTCTTTAATTGAAACTAACAGCTTGCGCCTCGAATGCGTCAAAGGTATTCAAGCTAACTTTTTGATTTTTCAAGTGACTCGATAAATTTGTTCGACTCATTAATCGATTTGTTCATCTCTTTAATCAGGCCCGAAATATCGGTTTGCAGACTGGCAAATTCGCCTTTGATTGCACCAATGGCTTGGGCGTTCAAGTTATGCTTGAGATACAGCATGTTGTCTTTCATGGCGGTGAGGATGGGTGGCATTTTTGATTCGGCGCGGTGCATGCTTTTGATCAATTGTTCGTAGGCGCGGCGCGTTTCTTTGAGCTTAGCTTCACTGTTACGGCGCAGGCTGGCTTTGCTGATCTCGCCAATTTCATCTTGCCACTCATCGAATAACGCTTCGGCTACATCTTCAACTTTCTTGATGCGGTTGCTGACGTTATCTGCGGCGCTTTGGGCCGATTCGTATTCATCCTTGGCCTTGTTATAGGCTTTTTCTAGGTTGCCGCCATCGTGATTGAGTAGGGCTTGCATCTCTTCGAGGGCCGAGCTGAACTCTTTTTGGGCGTCCTCTTGGGATTCTTTCGCGTCTTTAACTCTGTCGACCATAATATCGCGTTTATGGTAACCGACCTTTTCCATCGCGCCATAATAGGCACTTTGGCAACCACTGAGCAGGAGGCTTGCGGCAATCAGGCCTGAAGTTATCAGTCTTTTCATTGGGTATTCCGTCACTGAGTGAATTGAAATTAATCGGCCTTAAATCGGGCCGCATCGATAATGCTCCACAGATGAAAGATAGCACCTATTACCGCGGGCACAATTAGCCACCAGAGTGCATAACCCACCACTGTGATAATGAAAAATAACAGTGCGGCCATAATGCGGCCTTGTAATAGTTGGCCTAACCCAGGGAAAAATAGGCTAGCAATAGCGGAAATAACGTTACCCGCAGATCCTTGTTGAGACATCAATTCACTCCTTAGACTTGTAAATTAAGTGTTTATTGTACGAAGATAACGAGTCTATACCAACAGAATCAAGAGAAACCCGTGACTAAGAAGATAGAACTGGCACAAATTCGCGTCTTATTCCTCGGGATCTGGCACTTCCTGCTGCATTTAAGGCGGCGTTTAGTGGAAGATCAGATCAATATCCGCGCCGGACATTTGGCCTATGTCACGCTATTGTCACTCGTGCCTATGGTGGCCGTGACTATGTCTATGTTGTCGGCGTTTCCGGTGTTTAAGGGCATACGTGGGCAGATAGAAGGCTTTGTTTACGAAAACTTTTTACCCGCTGCTGGCGACACAGTGCAAGTGTATATCAATGAATTTGTTGGCAATGCGTCTAAGGGCACTGCAGTGGGGATCTCGGCCTTAGTAGTGGTTGCCATTATGCTGATATCCGCTATCGACAAATCACTCAATAACATTTGGCGCACTAAAGAGAAGCGTTCCGTGGTTGTCGCATTTTCTATGTATTGGATGGTGCTAACGTTAGGGCCGGTATTGGTCGGGGCGAGTTTGGTCGCATCTTCCTATGTGATTTCACTGAAGGTGTTTGAAGCTGAAGCTTTATCGGGCATGTTGCCGATTTTTATTGCGCGTTTGCCCATGTTGTTTTCAGTTGCCGCATTCCTGCTGTTATACATGGTGGTGCCGAATCAGAAGGTGAAGTTTTTACATGCGTTATTGGGGGCGATTGTGGCCGCGCTCTTGTTTGAGCTAGGGAAGAAAGGCTTTGCCTTGTACGTGACTCAGTTTCCAAGTTATGAAGCCATCTACGGCGCGCTCGCGACGATCCCCATAGTGTTTGTCTGGGTGTATTTATCGTGGATGATAGTGTTGCTCGGCGCGGAAATTACCGCGGCTATGCCAGAATATTTAGATTATGAATCGAGCTCAGATGACGAAACGGCCCTGAATGCCAAGCCATTAGCAGATGCCAGCCAAGGTGATTCGTCGTCAGTATTAACGTCGGCAGAGGTGACGGCACTCAAGGCCGTGGCGAAATCGGAATGACATCCCTAAACGGTTAAGTAATGGCGATAACACTTAATGCCTGAATCAGTGCCCGAATCCTTTCCCCCTTTTATCCGCCGCGACTGGTTTAGCGTTGGCAATGGTCAGCAATTACATCTGGCGCAATACGGTAATCCTCAAGGTATCCCGCTGTTATATTTGCACGGTGGTCCGGGTGCGGGTTGTGATATCGGTGATTTAGCCTTATTTAATGGCGAACAATATTGGATTTTGCTGTTAGATCAACGAGGTTCTGGACAATCCTTGCCCTTTGGCGATTTAGCCCATAATCATCTTAATGGTTTGATCTGCGATATGGAGGCGATTCGCCTCGCGTTAGGTATTGAGCGCTGGTGTTTAGCGGGTGGCTCCTTTGGGGCCACCTTAGGCTTAATTTACAGCGGATTATTCCCCGAGCGGGTGATAGCGCAAGTGCTGTGGGCGTTATTTATTCCGTCTAAAGAAGGCATAGATTGGCTCTATGGCGCTGACGGCGCGGCAAAATTGCAACCTGCGGCTTATCGACTCTTCAGTGGCAATGCGCCTGTTGTGCCATCGCTGACTGAACTGCTGGATTTATATCATCTAGGTTTGGCATCGGCCGATGAAAACCTGCGACACCATTTTGCGCGGCGTTGGGTTCAGTGGGAGCTAAAGCTCGCGGGGGCGAGATTGGTGTTGCCAAGACAGTTACCAGACCCAGTATTAGCGTTAGCGGATATCGAATTGCACTTTGCTCGGCATGCTTACTTCAATGCCATGACTGTGTTGCAGCGCGTGACGCCTAGGGTGAAGGCGCGTAGTGTGTTATTGCAAGGAATGCAGGATGCGATTTGCCCAGCGGCGCTATTGCGCGATTTTTTGGCTGGGTTAGAGCATGCTATTACGATTCAATCTGTTGCCGACGGCGGACATATGCTCAATAGCGAGCCGTTGTTTTTGGCGGTCACAGCAGAAATCCATGCTATGTGGTCGTGGATACATCAAAGGGAAATACAATGAAAACATTGCTCATCAGTGCCAGTTTACTCGGATTAACTGCCTGTGCCGCCACGGCGCCCTCGCAAACCAGCGCGATTGACCCAGTGGTGGCTCAGGAGAACGTGAGTGCACCCGTCGCGCCCAAAGTCGTTGAACTGGGTGGATTGACCAATGAAGCTGCCGCTGAGCGCTTGTATCAAGCGTTGATTAACGCTAACTACTTGGCCACCAAAGTTGCGCCAACTAAAGTCGCGGTGCAGTTTGGCGATAACCAGTTTTTGCTTGAGCCGAGTATTAATTCTGCGGGCATTGACCGTATTTTAATGAATCGTTTCTATGCCGTGCATCCGCAATTACAGGCTAGCCAAGAGCTGCCTGTGGTGATTGGCACTTTGAATCAAAAGCTTAATTTTGCCAAGTTCGTGTTGCTGGACCAAGGCGCAGTGATCCAAATACAAGGCACAGTCACCTTTGCCGACCGTGTTGAAATCGAAGAGTTACGTCGTTTTATGCTGTGGACCAATGGCGGTTTGGCACAAGTCGCACAATCCCTGCCAGATGGCATCGATCAATATATTCGTCCTATCCCGCTCATGCAGCAAATGGTTCCCGTTAAGCCTTAATCTAAAAAGGAAAAGTTGTGATTGCGTTAATTCAAAGAGTGAGCCGCGCTAGTGTGGTTGTCGATAATCAAACCATAGGTGCAATCGATAAGGGCCTGTTGGTTCTGCTGGGCGTCGAGCAGCAGGATACCCGCGAGAAGATGGAAAAACTGGCAACTAAAGTGATGAGTTATCGCGTGTTTTCCGATGAGAATGGCAAGATGAATCTCAACCTAGAGCAAGTCGGTGGCTCTTTATTAGTGGTGTCGCAATTTACCTTGGCTGCCGATACGGGCCGCGGTCTGAGACCGAGTTTTTCGGGCGCAGGTACGCCAGATCAAGCCTTGGCACTCTATGAAGAGTTTGTCGCATTTTGCCGCGCTAAGGGCGTGACCACAGAAACCGGACAATTCGGCGCTGATATGCAAGTGTCCTTAGTCAATGATGGCCCTGTGACTTTTAATCTGCAGGTGTAGCTTCTTATCGCGCGAGTGATTGCGTTAGTTATTGTGTTAGCAATTACGTTAGCTATTACATTAGCTATTAAATTAGTCATCGCGTGGTTTTTCTGGGGGATAGAGTGTTGAACTTATTCGGTGTTAGCTGTGAAGAGGGTTGTATCGAGGTAAATGCACATGGATGAATTACTTACTCAACTACTCATTCAATTACGCCAGACGTGGCACAGTACTATTCCTGTTAGTGAATTTATGCAGATTGCGCCACTCGATTACAGCGAGGGTGAGTTTCGAGTCACAGCGCCGATAGCGCCTAATATTAATCTGCATCAGACCATGTTTGCCGGCAGCATTTATACCTTGATGACCTTAACTGGCTGGGGCGCTGTGTGGCTGAACCAGCAGTTAGCTGGCGTTGCGGGTGACATTGTGCTCGCCGATGCCCATATTCGTTATCTCGCCCCAGTGACTTGTGATCCTGTGGTAACAGTGCAATGGCCAGATGTGGATCTCAGCCCGTTGCAAAGAGGTCGCAGAGTGAAAGTGAAGTTAGCTGTGCAATTGTATTGTGATGCTAAGCTTTGTGCGGTATTCGAAGGCCTGTATGTCAGTGCGCCGAAAGCGAGTTAATTTATAGCGCGTCGCAACAACCTATAACCTTTCTAAAAAATCGAAATGAATCTCAAATTTTATCCACTTTATTTCTTTTTTATGGGCGAATACACTAGCGTCATAGAGTTGAGATATTCCATCTGACTCTCAGTCATTTACCTATTTTCGGAGCAGATTATGTCTAAAGTCTTAATTTTAAAATCAAGCATTCTTGGCGGTTATTCACAATCTGCCGTGTTAATTGACCACTTAGCTAGCCACTGGGAAACCCAAGGCGCCGCTATTACAGTACGTGATTTAGGCGGTAAAGACGTGCTGCCTATGGTAGACGGTGAAATCGCTTCTGGTTTACGTGGTGGTGCAGAATTATCGGCACGTCAGCAAGAAATGCTCGCATTATCAGATACCTTAGTTTCTGAATTAAAAGCGAACGACACTATTGTTATCGCAGCACCTATGTATAACTTCACTATTCCGGCTCAACTTAAAAACTGGATCGATTTCATTGCCCGCGCTGGTGTGACTTTTACTTACACTGAAACAGGTCCAAAAGGATTAGTGGAAGGTAAACGCGCTGTGTTAGTGACGACCCGTGGCGGCGCTCACAAAGATGGCCCAACGGATCACGTTGTACCTTACTTGAAAACGGTTTTAGGCTTTATTGGTATCACTAATGTTGAAGTGGTTTACGCCGAAGCGCTGAACATGGGTCCTGAAGCACACGACAAAGGCATGAGCGAAGCGAAACACAGTATCGATCAGCTGAAGGCTTAATTGTCCCATTGATTAGCTGAGTCATTTGATTTAGCTGAATAAATAATGCCAGCCTTAATAGGCTGGCATTTTTGTATGTGCATTTAAGCTAAGCACGCTTGGTTTAAGTGGTTAGGTTTGAGCTACTAGGTTTAAGTCGCAGGCTAAGCTGTTTATTCGCAAAGGCTGCAACCTTTAGCAAGGGCGCTAGTCTCAACATAACGCACCTTAGTGCTGGCTTGGTCGCGATTACGACTGATGCTAAAGCGTGCAGGGCGTTCAGTTTCATGCGGCTTTTCAGCAGGGTTAACCTGTGCGGCTTCAAACGGTAACGCTTTGGCATGATTCGTTTTATGCATACATCACCTCGCAGATATCTAAACTATCACTACATGCAGATTGTTGCCGAACAACCAAGGTTTAAATTAGCTCGGTTGGCAGCAGACTAAATCTTTGAGCGACCCCCCATTCACCACGCAGCCAAAACCATTGGCCTTGAGGATATCGCAGCCTTTTTGCGCTCGAATACCCGCGCCACAGTAGAGCACGAAGGGATGCTGTTTATTGTCGACTTGATGCAACCATTGGTCGAGCGTTGGCAATGGCACATTAATGGCTTGGGGCAAATGACCGGAGGCAAATTCCTCTGGTGAACGCACATCAATCACCCGTGCGCCTTGCTCAATCAATTGCCAACACTTTTCGCCCGAGTGGCTGCCGGATAACTTAGCCAGTAAAGATTGAAACATAGCATTGCTCCTACCATTAGAAAATTTTCAATAAGTTTATTCTAATGAATTGGCGAGTGCAAGCGTTGCAAATTATCATAGTGAAGGGTTTGGTCTTTGGCCGTGCAAGAGCCCGAACTCGGCATTACTGGCATTAGATAAGCTGCTACAAGCTAGATTCACAGCCTCGTTGATAGTGAATGATGTCGTGGAATACCACGGCTTGCTCGTTTAGATTGCGATAGCCATGGGGTTTGTCGGCCGCAAATCGTACCGCTTCACCTTGCTTGAGCGTTTGCCACTGTCCATCGACTAACACTTCCATCGTGCCGCTTAATACAATGACATGTTCAGTGACGCCAGCTTCATGGGGCTCAGAAAGCCGCTCATAGTTTGGCAGCAGGGTGAGTTCAAACATCTCAAAACCAAATCGGTCTTCAAAGGGAAACAGTGAGGCAACTAACATGCCGTCGGTGGCGGGTTGTTGGCGTAACTCGTCGGGCTTACGGAATACAGCGCCTTGGCTCTGGGGCGTGGGTTCGAGAAAGGTCGAAAGCGAGATATTGAATCCACTGGCAATTTTCCACAGCGTCGCAATGGTTGGGCTCGATTCGCCGCGCTCGATTTGTCCTATCATGGCCTTGCTGACGCCCGTCTCTTGGGCAGCTTTATCTAAACTCCATCCCTTTTGGTTGCGAAGTGCTTTAAGCGTGGTGGCGAGATAGCTGTTTATCGTCTGCACAATGGCCCCATCATTTTTATAAAAATTTAGTTTGTACGTTATAACGCACAGATGTTATCTTGTCCATCTCGTACGTTATAGCGCACAACGTGGTGGCGACAGCAATTGCAGAGGAAGGGAGTGGATATGTGGCGAGATGCAGGGAGTTTGTCAAAAATATCAGCGGGTTTTATTGCAGTATTAGTGGGTTATAGCAGCTCGGCTGTGATTATCTTTCAAGCGGCGGAGGCCGTGGGTGCGACACCTGCACAGATTAGTTCATGGCTGTGGGCTTTAGGTGTGGGCATGGGCGTGACCAGCATAGGATTATCGCTTTACTACAAGAATCCTATTCTAACGGCTTGGTCGACACCGGGCGCGGCGCTGATGGTGACCAGCCTAGCGGGATTGAGCGTCAACCAAGCCATAGGCGCGTTTCTGGTGAGTTCGATTTTAATCACCCTTTGCGGGGTGGCGGGCTGGATGGATAAACTTATTCGTATCATGCCGCAGTCAGTCGCTTCAGCCATGTTAGCGGGGATCCTGCTGCAATTTGGGCTAGGGCTATTTCAGGCTATGCAAACCCAGCTGAGCCTCATATTAATCATGCTGTTGGTGTTTGTCGTGGTTAAGCCTTTTGCGCCGCGCTACATCATCTTGCTGACCTTGCTGGTGGGCATTGGCATGAGCTTTCAATTGGATTTACTCAAGTTAGATGCCGTTCAGTTGCAACTGGCGATGCCAGTGTGGACCACGCCTGAGTTTTCGCTGGCGAGTGTAATTGGCGTGGCGCTACCGCTGTTTGTAGTGACTATGGCCTCGCAAAATATGCCAGGAGTTGCTGCGCTGCACGGTAATGGCTACAGGCCGCCTATTTCGCCTTTGATCACGAGCACAGGGCTAATCGGGGTAATTCTGGCGCCCTTTGGCGGTTTTGCTTTTAACTTATCCGCCATCACAGCGGCTATCTGCATGGGGAAAGAGGCGGATCCGAATCCGGCGACCCGCTACTGGGCGGCGGTATGGGGCGGGGTGTTTTACTTTATCACTGGGTTACTCGGCGCCACTGTGGTTGGTCTGTTTTCCGCTTTCCCGAAGGAATTAGTACTAGCCATCGCCGGATTAGCGCTGCTTGGCACTATCGCCAATAGCCTGACTGCCGCATTAGCTAAAACCGAAGATCGCGAAGTCGCGGTGATCACTTTCTTGGTCACGGCATCAGGTTTTAGTGTGCTGGGTATCGGCAGCGCATTTTGGGGACTGTTGCTTGGGTTTATTATCCATTGGTGGAACGGGCACAGAACTGCAGCTGTGGTGAAGCGAGCGAATAATTAGCTGCAGCACAGCGCCTTAGTAGATGAATTTAAAAGACTAAGGCGCATGTGTTTTAGATTTTCTTTGTCATAAACACGCTATTGGGGTCGAGTTGGTAGCCGTCGAAGGGGCCACAGATTTCAAAACCAAATTTGCAGTAGAGCAGGCGAGCTGGGTTGAAGAAGTCCATCGAACCTGTCTCTAGACTCAGACGTTTGACGCCAGCGCTTTTGGCATCGTTAATCAAGTGCTGCAATATTTTAGACGCTATGCCTTGCTGTTTATAGGGCGCGGCGGTGCGCATGGATTTGATCTCGGCGTGCTCAGCATCTAACCATTTTAGTGCACCGCAGCCCGCTAAATTGTTGCCATCCCATAGGGTCCAAAAACGGATATTGGGCTGACGCAGGCCATCAAGATCTAATGCGTGAACGCTTTCAGGCGGTGAAGTTGCCCGCATATCGTCGAGGTGTTCTTGCAGCAATGCCGCTATCTCAGGCCCTTTAAGGTCATCTAAGGTGATTTTCATAGTAGCTTTCATAGTGAGTCTCATGGCAGTTCGGGTGCTCCCTATTATTTTGGCTTATCCATAATGGCGTCATCGCGTTGTTATCTGTTTGCCAATGCGCCGCTGTGGATAAGCAAATGCTAGGCCACAAACTTTTTATCAAAGCAAAAATCAAAGTCGTGGGGCTTCACCCCACACCCGACCAAGGAGGACTGCTCGTCCTATCCTCCTTGGATGCTCCAAGACGCCCCTAACGGAGTTGAAGGCCCCTTGTGCTCATTGCCAAATTTGCTATCGCTTCCGAAGGATGCGTCCCTGCACCTGCGAAAGCTAGTCGGTCATCCATGACCGAACTCACGCAATTTGTCTATTCGCCCTGCGGCAACTCCGAGGGGATGGTGTATTCTTTTGACTTTAGAATTGTCTGATTGTCCTTAAATCATAAAAGAGTAGCAGTCCTGTTTCTGCTTGTGGTACGAGTTTTAGCCTGAATTAAGGGTAGAAATGCAGTAAATCAACGTCATGAATTCACCCAAATTTAGCGCGGAAAAATAGTAAAGCTAAAACAATATCAAGTGACTACTATCTCAGTTGTATAAAAAATAGCTGCTAGAAAAAGCAAACTCCTATAACATTATTTATGCAAATTAACATAAGTGTGACAGCGCCAAACTCCAACAAGCGATTAAACAGCGCGGCGAATAACTACTAAAACGTTCGCAATAATCATAATTAATAGAGGTGGATATGGGCTGGAAAGGTACTGTTAGAGCAATCAATGCAGCAGCCAAAAAAGCTGAAAGAAATGCCAAAAAGTCTCAAAGAGAGCTAGCACTTAGACATAAAGAACATGCAAAAATGCACGAGTTAGAACAGGCCGCTTATGATGTAGAATTTTTTGAAAATTATATAGAAATTGTTCAGTCGATGCACAAAGAGTGTGGTGATAGTATTAATTGGGAAAAAGTTGCAGTTATAACCGAACCGAGTAAACCTATTTTTATCAAAGAATTAGAGTTGGAGGCGATTAGTCAAAAAGAACAATATCAACCGAGTTTTTTTGACCGTTTATTTAAACGTATTGAAAACAAGCTGAGAGCTCTAGACGCCGCAATTGAGTCTGCTAAAAAGCAAGATCAAATTAATTTTGAGTTGTCACTTGAAAAGTGGCAGCAGAATTATAAAGAGTGGGCGGAAAGTGTTGAGCAAGCTAATTTGCTTATAGCTGGTGATCCAGAAGCAAGAATTAACACGATCAAAGAGCTTAATCCTTTTAGCGAGTTAGACACTTTAGGTTCTAGTTTACATTTTTCAGTATTGGAGAATTATCTTCTAACCATAAATATAAATATTCGTGGCGCAGATATTATTCCAAATGAGCAAAAATCGTTGCTTAAAAGTGGTAAATTGTCAGTTAAGAAAATGCCTGTAAGCAAGTTCAACGAATTGTATCAGGACTATGTATGTAGTTCAGTTCTGCGCGTTGCTAGAGAAACTTTTGCAATCTTGCCTGACGAGTTTGTGTTGATTAATGCTACCGATAGGCTGTTAAACAAGGTTACTGGATATTTTGAAGAGCAAACTATATTGTCGGTGTATGTATCTAGGAGCACTTTGAATAGTTTGAATATGGATTTAATTGATCCTTCTGACAGCATGCGAAATTTTATCCATAACATGTCCTTTAAGCCGACAAAAGGTTTTGAGCCTGTTGTGCCAGTAGACGTTATGACACTTGCGTAAATTGGATGACTAAAGGGAACTGTTAATGTGTGGTATTGGCATTTGTTATTTTTCTAAAATTGCCATCGCTAAATGTTAGCTCTAGTCTTTGAACCTAAGGTTACGCAGATGCTCGAAAGGCAAACCATGGTGATTGGTGAGACGACCGTCCGCCCCATGGACGGGGCGGTCGAGCATCCAAGGATGGACTTGCTGCGTGTCGTCGAGTCAGTGCCATGGTAGGCCTCGCTCCGTGGTGTGTCGTCGGGCCTGTATCATAGTTGGTCTCACTTCATCGCTTCACTAGCTTTTCAACTGGCCTTACTTTCAGTTTTGATATTCGCAAATAGATCTGCACGCTGAAAGGGGAAGTAACTATTTCAGATGATTTTGGGGTAGTTGTGGTTGACCTAATAGGGCAACGATGAGGATGAAGTCATCTTCTTTTGTGAAGTAGGCGGTGTGTTTACCAATAGGAAAGTCGAAACCGTTGGGGTAAATATCGTCGCAGCTTCTGCCTAATCAGGGGTTATCGGCTAACATTTGCAAGCCGGATATTAGGCTTTGTTTGTATTAGTTCTCTAAGCCACCCACTATAAATTTTTCCTTCCCATACTCAACATTATCCAAACGGATATAAAAATGGCCGAGGATAAGCTCGGCCATAATTTTCTCGATTAAGCGTAAAACGGGCTGGTTTAATAGAATTTCTTCAGGTAGCTAGCCGCGTATTTTTGCAGTTTTCGGTTCGAACTTGTGGTCGCAATATTCTCAATCACGTCGCGGTAATCGGCATTGCCTGAGCTTGCTAGGGCTTTTGCCATGTTGGCATAGGTATCGATGGCGAGTTTGTCATCGGCCATTAAGCGTGGCGTTTTCAATTCGTTACTGAGTACGGCTAGGATAAAGTCATCGTACTGACGATCATCCATGATGCGTTTAGCCGCTAGTCGCATTAACTCTAAATCATCGCTGCGAAGGGCATTGGCAAAGGCGTTATTTTGCTGACTTTGGTCGGCAGTATATTGGCTTTTATCCCCAAGAATGGCATTCCACTTTTTATATTGCGTTAGGTTTTCATTGGCTTGAGATGCGTATTTTTTGAGTTTTTTGTGATAGTTGCCATTCATAATGCTGTTGATGGTTTCGCTGTACTTGTCGTTACCCGAATAGGCTAAGCCTTTGACTAACCAAGCACTATAGTCGATGGCATTTTTTTCAGTCGCTTGGGGTAAAGAGGCAAGCAGTTTAGCTTCGAGCACGTCGTATATCGCAGGATCGGATAAACCTGCAAGGCTTAAGGATTCGATGGCTTGCTTCTGCTTAAATTCGTTGTCGCCCTTAAAGACTTCTTGATAGGTTTCTACCGTGTATTCTTTGGCAAGCAGCGGCGCGGTTAATAAACTGATGCTCAGCAGCATCCCTGCAAACAGTAGTTTCATTGCTATGTCCTTATGTGGGTTTTAGGTGTTATCTGGTCCAGCGTTTAAAAATCAGCGAGGTATTAATGCCGCCAAAGGCGAAATTATTACTCATCACATAATCCGTGTCGATTGGGCGCAGTTCGTTGCGGATATAGTCCAAATCAGCGCACTGTGGGTCGATATTCTCAAGATTGAGTGTTGGCGCAAACCAGCCCGCATTCATCATCTCAATACTCACCCAAGCTTCTAACGCACCGCATGCGCCTAAGGTGTGGCCCGTGTAGCTTTTAAGGGATGAAATTGGCGTCTCAGCGCCAAACACCGCCTGAGTCGCGTGACTCTCGGCAATATCACCACGGTCGGTTGCTGTGCCGTGGGCGTTCACATAACCTATGGCGCTAGGCTCAAGCTGCGCATCTTTGAGGGCTAATCTGATGGCGATTTCCATGGTCTGCGCATTCGGTTGGGTGACGTGTTGGCCGTCGGAATTTGTGCCAAAACCGACTAACTCAGCATAAATTTTTGCACCACGCGCCTTGGCGTGTTCGAGCTCTTCGAGCACCAGTGTACAAGCCCCTTCGCCAATGACGAGGCCATCGCGATCCGCATCAAAGGGGCGTGGCGTGAGTTCGGGCGTCGAGTTTTTAGTGCTGGTGGCAAACAGGGTATCGAACACAACAGCTTCGGTGGGGCAGAGTTCTTCGCCGCCGCCCGCCAACATTAAGGTTTGCTGACCATATTTGATGGCTTCATAGGCATAACCTATGCCTTGGCTACCCGAAGTGCACGCGCTGCTGGTGGTGTGAATTCGGCCCTTTAAGCCGAAGAAGACGCCAACGTTCACCGCTGTGGTGTGCGCCATCATGCGGATATAACTCGTTGCGGTGATGCCCGACATATCGCCCGTTTTTAGCATATCGCCAAAGGCGGTAATAGGATCGGTACTGCCCGTGGATGAACCGTAGGCGATGCCCATTTCACCTGAGGATACGATAGGGTCATCCAACAACCCCGCATCGAGTAAAGCCAGTTCACTGGCGCAGGTCGCCATGATAGAAACCCGACCCATAGAACGAATTTTCTTGCGTGAATAATGGCTTGGCACTTCAAAGTCAGTAATTGGCGCGGCTAAACGGGTGTTAAGTCCGTCGTATCTATCCCAGTCACTCATAGTGACGACGCAGTTTTTCTGCGCCTTAAGGCTCGCCGCTATCGTCGGCCAATCATGGCCTAGGGCGGTAATCCCGCCAACACCTGTGATGACCACTCGTTTACCCAAGCGTGTTTGATTTGTCGCCTCGCTCATTTAGATCATGCCTCCATTGACCGATATCACTTGGCGAGTGATATAGGCGGCATCGTCAGACATTAAAAATGCGGCTAAGGCTGCGATTTCATTGGGTTTACCCATGCGGCGCATTGGCACTAATTGCTCGACCATGTCCTTAGGAATATCGGCCACCATGTCGGTTTCTATCAAGCCGGGGGCGATACAGTTGACGGTGATTTTGCGTTTTGCCAGTTCTAATGACAGTGCCTTAGTCGCGCCGATAATGCCCGCTTTGGAGGCGCTGTAATTCACTTGGCCACGGTTGCCCGCAATTCCAGATACCGAAGCTAAGGTGATGATACGTCCACCTTTGCGGCCTTGTACCATAGGCATCACACAGGGGTGGATGACATTATAAAAACCGTCTAAGTTAGTGTGAATGACGCTGTCCCACTCGCTTTCTGTCATGGCGGGAAAGGCAGTATCGCGGTTGATGCCAGCATTGAGGATCACGCCGTAATAGGCACCATTGGCCTCGATATCGGCTTCGATGGCGGCTTTGACTGTCGCGCGGTCGGCAACATCAAATTTCAGCAGACTCACACCGACCCCAAGCGCGCGGATTTGCGTGGCAGTGTCATCGGCAGCCGTTTGATTGCTGTGGTAATGCAGGGCGATATCGAAGCCTGCTGCGGCCAGTTTGAGGGCAATGGCTTTACCTATGCCGCGGCTCGAACCTGTGACTAATACTCTGTTATTCATTTGTTTTCTCCACCCCATTTTATCTTTAGGAGTCGGTATGGGGTTTCATGTATTTATGGTAAAAGGCTGCTTGCTAAGGCGTTTAATCTAACGCTTTTATCGACAGGCTAATTCAGTTTGCTGCGATTGCATGGTCTCAGCATTGCCTCGATATTAGTGTCTAAGTCAGTGTCCAAGACTTCTATTGCCCGCGTCTTGATTTCCATCAAGGTAGGCTTGCGTGTCTTGAGGTTGGAACACATTCACATTCGCTGTGGCGACTAAGATTTTCTCTTGATCGCTTCCGGCTTCGGGCAGCAGATAAATCTTGCAATCGAATACCGCTAATCCCGACTCCTCTTGATAGAGGCGGTTTACATGGGTGCGATAGGTGCGCCCGAGTTCATATTGCTTGGCATGTAGCGCTAATTTGCGCGACCCCAACAGAAAACCCACGCGGATTTTATCATTGCGCAGCTTAGCTTCTACTCCGGCCAGTGCGGCAATACTTTGGGCCATATATTCGATACCCACATAATTGGGTACGGCTTTATGTTTATCGTCGAAATAGGGGCTTTGGGTTGTGATCGTGACTTCGGTAACTAAGGTATCGCGTTGATAAGAGATGATCTTATCGACCAAGATCATCGGCGCCCTGTGGGGGATGAAATCGGCAATATCCTGCTCGGCAAAGGGTAAATTGAATTGAATAAAGGGCATCATTTTAGGCCTCGTTACGGCAAAAAATCAGGCTGGCATTGCTGCCGCCAAAGGCGAAGGAATTACTCATCACATAATTAAGTGTGCCTTTTGCGGCGACTTGATGGCGTTCGACTAAGGCAATGGACGGGTCCTGTGGATCGACTTGTCCGTCCCATTGATGGGGCGGCAGGGCTTGGCCGTGATTATGTGCCGAAAGTAATAAGTAGCAGAAGGCGGCTTCAATCGCGCCAGCGGCACCTAAGGTATGGCCGACTAAGGGTTTGGTCGAGCTGCAGGGCGGGGTGTTGGCGCCAAATACTTCAACCACAGCACGGCTTTCCATCGCATCATTTTTGGGTGTCGCTGTGCCGTGCAAGTTAATGTAACCAATGTCTTGTGCCGCAATATTCGCATCTTGCAAAGCCGCTTGCATGGCGGTGATTGCGCCAGCGCCTTCGGGGTGTGGCGCCGAAATATGATGGGCATCGCTCGACTCACCAATGCCAGCCAGCATCACATCGGACTCGCCCCTCGTCAGCACGAATAAGGCTGCGCCTTCGCCGATATTGATGCCATCACGGTTGATACTAAAGGGATTGCAGTGGCCTTTGGACACAGATTCCAAAGCATTAAAACCATTGACTGTCAGTTGGCACAAACTGTCGACACCGCCAACAATCACCATGTCACAGAGGTTAGCGCTGAGTAAGCGTTTAGCACTGGCAAAGACTTTTGCGCTGGAGGAACAGGCGGTTGAAATCGTATAGCAAGGGCCATCTAGCTCAAAATATTGGCGTAAAAAGTCGCTAGTGCTGCCGAGCTCCTGCTGGAAATAGTGGTAATCCGCCGGAAAGTGGCCGTGTTGATTGCGATAGCTCAGGGCCGCTTCACCTTTAGAAATCCCCGAGGTGCTCGTGCCTAGCACAACGCCAATGCGGGCATGACCCAAATCCCGTTTCGCTTGTTCAATCGTGGCGGTAATTTGCTGCGCCGCGCATAGGAGTAACTGGTTATTGCGGCAATCGAACTGAATGAGCTTTGCAGGAATTGCCGGTAGCGCAGCTGTCACTGGCGCAACTAAGGTTGGGTGCTCGAACAGGAGTGTGTCACTGCGCTGCATCGCGCTGGTATCGCCCGCGATCAGGCGGTCTAAAACCTGCTGGGGATCTTGCCCTAGCGGGGTACACAATCCAATCTGTGTAATGGCTACTCTGTTCATCATAGCGTATCGTTTTTTTACCATGGCACAGCGAAGTGCGGGTGTTAAAAGGTTAAATAGGTTCGATTTCTAGCTCAAACTTGGCCTGCGGCATAGTTAAGCTGATGTGGGCTTGCCATAGGGCATCGCCGACATTTTGTTGATAACGAATTTGCACCACATGGGTTTGCAGGGCGCTTTTACTGATCAAGGTATCTTGGCTATAGATCTGGCGACAGGGGATTGTAATTACTCGGGTATCGACAAGCGTGTCGCTCTGATTTCCCCCAACCGTATCGCAAAGACCAGTCACTAAATGGCCACCTTCCAAATGGGCGCGAATACTCTGCTCAGGCCAATAGATCAGTTGCATCATCGCCATTAAATACTCGGCTTTAAAATTGTCGCCCAGCAGCACACTTTGTTCACTGCTGAGTGTATTCCCATCATAAACTAAGGTAAACAAAGCCTGTCCGAGTGGCGCTAAACCCACGAGCGTCATGCGCTCGCCCTCTAATTCCAATTGAGTGAGCAGTTCGTGGGACTCTTTGCCCACTTTAATACTGACTTTTTGGCTAAAGGACTGGGGTGAAGATACGCCAGTATTGCTGTCGGTTTGCTGACTCGCCATGATGGCGGGCTTTGCCGTTGGCTTAGTCAATGGCTTATCGGTCGCGGGAGCAAAGTCATGGGGCAGTGGAGCCAAACAATAACGCATATCCTTAGCGAGCCCGACACAGGTCTGCCTGAACAGCAGTTCACTACAGCCCGTCAGCCCAAGGCTTGCGAGCAGCAGGGCGGGGATCACTAACCATCTAGCGTTAAGGTTTAGCGGCATGCGGTTAGACCTCTGCGAATGCTGGAGTGGCAGCTGGGTTTCGGCACAGTTCGACTATCATGTTCAGGCGGCGCTCCGAGTCTTTAACGAAGGGATTATTTGTATCCCAAGCATAGCCCGCCAGAATAGAACAAATCATCTGCTTGATACGCTCGTTCGGTTCTTCATAGAAGATCACGTCCTGAAAGCGGCCATCATACCAAGCTTGCACATAGGTACGAAAGGCATTCACGCCCTGCATCAAAGGTGTTGAGTAATCGGCTTGCCAGTCAACCGTTTCGCCATTGAGTTGTTTCGTCAGGCATTTTGCTGCCATAGACGCCGATTGCATGGCGATAGTCACACCGGAGGAAAATACCGGGTCGAGGAACTCGCCCGCATTACCCAATAGCGCGAACTTGTCGGTCGCAAGGCGTGACACATTGGCGGAATAACCTTTGAGCGTGGCGCATTCCTGTACTACTTTAGCGTTTGCCAGCAGGGCTTTAAGCCCAGGTTCTTCATTGACTATGCTCATCAATTGTTGCTCAAGCGAACCTTGTAAACGCTCAAGTAACTGCGGCTCAGCCACCACGCCGAGTGAGCAACGGCCATTGCTAAAGGGAATGAGCCAATACCAAATGTCTTTATGTTCAGGGTGGACGCTGATCAAAATCTTATTACGATCGAAGCTAGCATCGCTGATGTTGTCTTCCACATGGGTAAAGATGGCGCTGCGCGGTGGCAGGCAAGATGGACTTTCTAAGTTGAGCAAGCGGGGTAATACGCGACCAAAACCGCTGGCGTCGAGCACATATTGGGCATTGATTTGATAAAGCTCGCCCTGTTCATTACGCACCGTTAAGCGTGGGTTCTCGGTTAAATCTATCGCCTCGACCGTTTCGCCGTAGCGAATGTCGACCCCTTGGCTTTGGGCCGTATCGGCTAACAGTTTATCGAAACTGGCGCGCTGCACTTGAAACGTGGTGCCCGGCCCAGGAGTAAATTTATCGGTAAAATCGAAGGTGGTGTAAGTTCCATTGCGACGAAAGGCGGCGCCATTTTTATGTTGGAATCCAGCGGCATTGAGCGCATCTAACATACCTGCTTCTTCAATAAACTGCATGCAGCAGGGCAGTAAACTTTCGCCGATGGAGAAGCGAGGAAAATGCTGTTTTTCTAACACAAGCACGCGCTTGCCTTGCTGGTGTAGCAGGCTGGCAGCGATGGCGCCCGAAGGCCCTGCGCCGATAATGGCCACATCGACATCAAGTGAATTTGGTATAGCTGATAAATCGTGAACCTGTGTAGACATGTAAAATACGTTCCTTGTAGTCATCAGGACTTTTTTGTCGTTAGAAGCTGATTGGTGGTTAAAGCCAGTGTTGTGGTCAAAATCAACGGCGAAAGTAAAAAGGTAAACCCTATGCCCAGTGATAGGGTTAAGCCAAAGTAGTGAATCGCTTGGGTCTGGCTAAAGGCAAGTAAACCAAAGGCCAATAGGGTCGAGCAGGCGGACATAAACACCGCCATCATCACTGCCTTGCCGTGGTTCTGGGCTGAAGCAAAAAACAAGCTGTAGTCTATCCCAATCCCGAAAACTAAAATGAGCGCGAGTGCATGGAACAGACTCAGTGGTGAGCCCGTTAAGCCTAAGGTTGCGAGCGTGAGCAAGGCCGCAAGGGCTGGCACGGCAACCACAACCGCCGCTTTTTTAAAGCCGAAATTCAGGCTAAACAACAAGAGTGCGATGCCAAGTGCAAGGGCTAACAGTTTTAGGGTTAACAGTCGGTAATGGCCCATGACGGCGGAGATATCCGCGACTTTATCGATAAGTTGTACGCTTTCATCATGGGTAAAACGCGCCTTAAGGGCATCGATTTGCTGAATGCCGCCAAGGAGGACTATTGCACCATAATCAGCTCCATAATCAGCTCCATAATCGGCACCCTGATCTGCTCCATGCTCCGCGCCATGTTCTGCGCTTGCCCCATCATCATTTAGCGCGCCATTGCTGACTGGACTGTTAGCCTCATTTGGCGCGAGCCACAGCGGCGCGAGTTGCTTGCCGGCCCCTAGCGTAAAGAATGCAGCAGGCGTGATGTACTCTGTTTTCGCGGCCAAATAGGCGGCTTGCAGTTCTGGCTTTAGATTTTCATCCAAACCAAGGCTTGTGAGCACAGTATCTATCTGAGTTTGATAGATTTCGCCCTGTAAGTGGTAAGCCGTGTCTTGTTTTCGATGGCTGGGCAGATAACGACTGAGGCTGACATAGTTGCCTAACTCTTGGTTAGCGATAGCGGCATTGAGCATGGGTGACACTCGCTCAAGCTGCTGCAGCAACGCTTCTTCGCTCGGTGCGCGCACCAATAAGAATTGATTGTCTGTGCCGCCACTGAGTAACTGGCGTAGCTGGTTTTCTGGCTCAGTTACGCTTGCAGGGCTTTGCTGTAAATGACGAATATCGTCATCGACGGTTAACTTTGTTACGCCAACCAGACACCAGACTAGTATCACCAAAGCAAACATGCCCATGCCTAACGGCGTGGTAAATCGTTTCGATAACTGAGTGAGACTGGAAAGATAAGTCCCCGCGAGCGCTAACGGGCGGGAGCCTGCGGGTAAGCGACTGCCGGCTAACAGTGGATAGGCGAGAATGAGGGTGAGATAGGCCCCAAGTAAACCTGCGGCGCAGAAAATCGCGACTTGCTGCATGCCGGGAAACGGCGCTAAACCTATGCCGACATAAGCTAAAGCGCTGGTGATAAAGGCGAGCGTCACCGTTGGGAAGATATACGCCACTGTGGCCTTTGCGCTCCGCTCTGTATCGCTTAAGCGCTCGCAGTAAAAATGAAAACTGTAGTCGATGGCAATGCCAATCAAGCTAGTGCCAAACACTAAGGTGAGTAAATGCAGCTCGCCAAACACACTTAAGGTAAAAGTCACGGCGAGCAACAGCCCGCTGGAAATAGTGACAATCGCCAACAGTAGCGGCATTACAGAACGAAACGCCAACCAAACTAAGGCTATCACCCCAAGTAAGGATGCTAGGCCGAGGATCGATATTTCACTCTTAGCCGTTTGCGTTGCCGCAAGGGCATGAAACAGGGCGCCAGCTTTGAGCACGGTAATATCTGGGTAGCTTTGTTTAACGGCATCCAATCCTTGGGTTAATGCCGTCATCTGCGCTAATTGCGCATTGGGATTAAAAGCGCTTTCTCGCCCCTTTGCCATGACGACAGCGGCAACATTATCGCCTTGATTCGCGAGCAGTATCCCTTGGCTGGCCCTGACTTTTGAACTCGGTGCGAGGGTGAGTAAATTGGCGGGGAATAACAGTAACGGATCTTGGGCGAGCAAGTTGCTATTGGCGTAACTAAAGGCGTTGTAAAGCTGAGCTGTGGCGGCCTCAATCAGACTTTCAATATTTTGGCTGGCTAACGCTTCGGCTTGCGGCGCTGTCAGTAATTCAAATCTGTGGGGGAAATAGAATTGCCCTAGCGCTTCACCGAGCTGTATATCGGCGCTGCGGATATCCGTTAGCGCGCCTTGCTTACCCCTTGCCACAAGGTCAGCTTCAAGCTTTTGCATCAACAGCTTAGCCGCAGAGATTGCCGTAGTTTCATCCTTGGCGACAAGCGCAATATAGACTTGGTCGGCGAGCGTGGCTTCAACTTGGTTAAGGGCGCGCTCTGTGAGTTTGTCTTGCTGTAAATGCGGCAGCATGGCGAGGATATCGCTCTGCACCCGTGCGCCGTTTTGCCAAAGTTGCAGCGTCCACAAACTTGCTGACAGCATCAATAGCAACCAAATGGCGAGGCGCCACTTAGGGGAAGATTGCATCAAAGCTTGATTGGCGCGCGCGGCGAAGTTGGAGGCCATGGCTAAGGTTTACTCGCCTCGAGTGCCAACGCATACTGCGCTAATTCGGCTTCGCTGAGTGTGCCTTGGGAAAGCGCACTAAAGTCGATGCGGGTCATATCCTGTGGTGTCACATTCGGCGCGGCGCTCAGCAGCACTAAGGATTGCAACACATCGCTACCTTCTAACACCATATTGGCGATAGCTTTTTTCATTAAGGGATCTTTGGGCGTTAGGCCTAATTGCCATTGGCCGTCTGGGCTCAAGCTTTCTGGGCTTAACTTTTCTGTGTTTAACTTGTCTGTAATGAGAAAGTGCAGTTCGAAGTTTTCGCTTAAACCGGAAATATCCCCGCCCAGCATAGCGCGCACTAGGTTGGGTAATAAATCGCCCATAGCGGCAGCGCTCGCCGAGGCATCGGCCTTGCTGACCTGCACTCGACCTTGGCTATCACGCTGGATCAGTTGCTTATCTTTTAAAATCAGCAGGGATTCGAAGGGCTTTATCTGCTGCCAGATCAGCCCTTGGGTTTGATCGAAGATAAACTGACCTTGGCTAATTAACGGCTTTTTGAGCACCTTTAACTGCCGAGATTGCACAAACTGACCTCGCACCGTTTCGCCCAAGTTAAGTTTTTGGCTTAAGGCGACAAGCTGCGCGGTATCTGCACTTTGGCTAAAGAGTGTTTGATAAGCGCCGGGCGCGGCATGCTTGTCACTTAAGGTTTCAGCCTTGAGTGGCAGGCTTATCAAAGCAAAAGCTAAGCAGAGAAATAATGCACACTTCGCCAGCACAGGATTCTTCGCCAGCGCAGAGCTCTTTGCGGGTACAGAGTTCATTAGTCTTCCACCTTAGCGAGCAGGGTGGCGATTTTGTCACGGAACACCTCTGGGGTAACAAAGCACAATTCTTGGGTGCTCATATCGACCGCCGCCTGAATGGTATAACCCTTAGTGATGCGCGCACCGGTTTCGACATCGCGAATTTGATAATTGATCTTTAAGCGGTTTTCCCACTCAACTAACTCGGCGCGCACTGTGATTTTTTGCTCGAAGGTGCTGGCTTTGACGTACTTAATCTGCAAATCGATAATCGGCCACGCATAGTTGGATGCTTGCATTTGGCGGTAGTTGTAGCCCAACTCATCGAGCAGTTTGCAGCGGGCTATTTCGAAATAACGCAGGTAATTGCCATGCCAAGTAATCCCCATAGAATCCACATCGTGGAAGGGGATTTGCATTTCCATATCGATAGTGAGCAATGATTTCATCGGCACACTTCCCATTCGCCCGCTTGGATTTTAGCTATGGTTTGGCGCAGCACAGTTTCGAGCGGCCTATCTTCAATTAAGGTTTCAAAATCGGCGCGCACTTGAGCGAGGGTTTTCGCCAATGAGGGTGTGAGCGAGGCTTCGCTCAGTTCATTTTGAGCGATACGCAGACCAATACCTTGGGTCATGGCAAGTAATGCAGCCGCTGCGACTTGTTCGGTCAATTGCAGCACGCGCATACAATCGCGGGCGGCGATAGTACCCATGCTCACCTTGTCTTGGTTGTGGCATTCGGTCGAGCGAGAGAACACGCTGGCCGGCATAGTGTGCTTGAGCGCTTCTGCCGTCCAAGCCGACACGCCAATTTGTACCGCTTTAAAGCCATGATTAATCGCGCGGCGTGGACCGGTTGAGCCGGATAGGTTCGCAGGTAAGCCATTGTTAAACTTAGGGTCCATTACTAGCGCCATTTGGCGGTCGATAAGATCGGCTAAGTTGGCCACAGTATTTTTCAGTGAATCCATCGCAAAGGCGATATGGCCGCCGTAGAAATGGCCACCGTGGAGAATATGCTCGCCTTCGCCGTCGACAATCGGGTTGTCGTTGGCGCTGTTGATTTCGGTTTCGATAAACTGACGCATAAACGGCAGGGCATCCTGCAACACGCCGATAATGTGTGGTGCGCAGCGGATGGAATATCTGTCCTGCAGACGATCAGAATTACGTGGATGTTCGTGGTGATTCAAATCTTCCCGTATCCAAGTGGCGATTTGGTTTTGCCCTGGATGCGGTTTGGCGGCAAATAAGATTTCATCAAAATGGTTCGAGTTGCCTTTTAGCGTTAACGAAGCCATGGCGGTAATGCGACTACTCAAGCGAGCAAGATATTGCGCGCGATCAAAGGCTAAGCAGGCTAAAGCTGTCATCACGGCGGTGCCGTTCATCAGGGCTAAGCCTTCTTTTGGACGCAGGACATGCGGGATGATGTTGAGCTTAGCGTACACATCTTGAGTCGCTTGGCGCTCACCTTGGTAAATCACTTCGCGCTCACCGACTAACACGGCGGCTAAATACGACAACGGCGTTAAATCCCCGCTGGCGCCGACCGAACCTTCCTCTGGGATCACTGGCACTATGTTGAGATTGAGCAAGGTTTCGATACGCTTTAATAGCTCATAGGTCACGCCAGATTTGCCAATGGCTAAGGAGTTTAAACGGCAAGCCATCACAGCGCGGGCCTGCATTATGCTTAAGGTCTCGCCTAAACCGCAGCCGTGGAAGCGGGTTAAATGCAGTGGCAATTCGTGGACCAGATCTAAGCTCACATTGACAGTGCAAGAGTCGCCATAACCTGTGGTTACGCCATAGACCACGCCTTCTTCGTGCAGCAGGCTATCGATAAAGCGCGCACCTTTTTGGATATATTCTTGATAATCCGCATCGTCACAGAGCTTGACTGGCGCGCCCTTGGCGACGGCAACCACTTGTTCTAAGGTGAGATATTGACGGCCGAACTCAACAGTCTGTAGGTCTGATTGACTCTCGGAGTCGGCTGCTTTGGTGTGGCTCATGAATGCTGTGTCCTGTCTGTCTTATGATCCTGCTCTGGGATCGGCGATGATGCTGAAGTTGTCGGTAACGGATTAGGCGGTACTAAATGCGATGGGGTGCGTTGTAGCTCGTTGTCTTTGCGCCAAAAATCGAAGAAGTTAAACCATTGCAAAGGCTCGCGTCTGGCAAAGTATTCTAAACGTTCGCTGTAGCGGTTCACGGCCTGCTGTAATCTGTCCATTCTGCCCGCTCTTGGGCCTTTCAGTGTTTCGGCGAAGCGTTCTAAATGCACCCGATAACGGCCTTGTTCCCGCAGGCAAAACATTAAAAACACAGGGCAATCGAGTAAGCCCGCCAAAATAAACGGCCCTTGTGGAAACGCGGCATCTTGGCCCATAAACGGCGCATACATGACGCGGCCTTGGGTATTCGATGAGGTTCTATCACCCGCAATCACCACCAGTTCACCGTCCTCAATTTTTTGCTGTAACAGCATAGACGTTGCAGGATTCAGCTCGGTCACTTGAATAAGATTGAGCGTGCTATCTGGGTTTAACTGTTTGAGTACCTTATTGAAGTTTTCCGCATGGCTAGTTAGCACCATGACGTTCACTTTGACTTTGCGCTGGTGGATTGAAATGGCGCGGCAAAGTTCTAAATTACCAAGGTGCGACACTAATAACACCGCGCCTTTGCCGCTTTCGAGCTGATCGGCCAGTACTTGGCGGTCGGGGAAATCCACTTGGCTCAATTGGATGCGATCGCACCAGGCATCGATTCTATCGAGTGCGGCATTGCCAAAGGCTAAAAAGTGATTCAGGCTATCGCGCCAGCTTACAGGCTGATTGAGCTTAGGATGTTGCGGCTCAAGTATTTGCACACGGCGCAGAAAATCTAATGAGGCTTCACGGGTGACTTTGCCTGTGAGGAAAAAATAGCAAATCACCGGATACATAATGGCGCGGCACAACCAATGGCCGCCAAAGCGATAGCTTTCGGCTAACAGTTTGATGCCCCAGTAACTGCCGCGCTCCTTCATGCCCGACCAATGGGTGGCTGAAGTTGATGATGTTGCTGACGAGCAATGGGGAGTTTGTGCGGTTTGTTGCTGCGATTTACGCTGTGATTTACGCTTCAATAGCCAAGGCAGGCGTTTGAGCATGCCGAAAAACAACTTAGTGTGCAGCTTAGTGATGCGCACATTATCTGCCACAGCTTGAAAGTGACTGACGCCGCCTTCGGGATAAATCACCTTAGTCGGTTGGAATAAAATCTCTACCCCTTGCCAGTAAAGTTTAACCAGGATTTCGATATCAAAATCCATTCGCTCCGTTAAGGCTTGCTGGCACAGTAATCGCTCGGTGGCGGCCAGCGGATAAACGCGAAAGCCACACATAGAATCTTGAATATCAAAGCTTAAGGTTTCGATCCATACCCAAAAATGGGTTAAGTAGCGGCCATAAAGCCTGCCCTTAGGCACGGATTCATCGTACTCAGGCTTACCCGAAATCAGCGCATCGGGTTTAGCTTGTGCCCTTGCTAACATAGCCGGAATATCAGCGAGATTATGTTGACCATCGGCATCGACCTGCAGTGCATGGCTAAAGCCATCGCGGTAGGCACGGCGCAGCCCTGTCATCACAGCGGCGCCCTTACCACGATTGTACGGATGCAGCAGTAAAGTTAGCCAAGGATAGGTTTGGGCTAAGGATTGCAGTAGATAACGGGTTTCATCATTGCTGCCATCATCAATTAAATAACAGGGCAGATTAAATTGGGCCAGTTCGGCCAAGGTCTGGGCAATCGCGGCGCTGTGATTGTAATTGGGGATCACTAAGGCGAGTTTGATGGGCGCCGACTCAACAAGGTCGCTGGGAGGCGTTAGCACTGACTTGCCTCATCGCTAATGTTGGTAGCAGATTTGCTGTTAACGGGCGTGCTATCAAAGGCGATACGGCCTGAAGCGTAACGGTTTTCACCATCGCTATAGGCGAAGGTGAGTTTTGCCTTAGCGGCATTATTGCTAATGTTTAAGGTGACTTCTTGGCCAGGCAGTATGAGTTGCTGAAACTTAAGCACTTCTAAGTTAGCGACCGCCTCTGAATAACCGAAATGTTGGCAGCCTAAACGCACCGCCCAATCTAATTGCGTCACGCCCGGCAACACGGCTTGTTCGGGGAAATGGCCGTTAAAATACTCAAGGTCGGCCGCCACTAACAGACGTAATTCAATGTTATCAATGCCGATATCGGAATGCAGGATCGGCGGTAAACTCGATTTAATCATGATCAAATAACTCGACTAACTGGGCAGTGACACGCTTACCCTGAGCATTAAGCGGTAAAATATCCGGATAACGCCAGCGGCGTGGCAGCGTGACGCGCTCAAATTGGGTCAGTAAATGTGCTTTTAGGGCATTGTTAATACTGAGTTTGCCCTGTTCTTGCAGCACGTTTTTACCGAGTTCTGACAAGGTCACCACTGCGCCAAGTTGGGATTTAAACTGTGGCAGCACGACTAGTGCAGCTTGCTCAACGTAGGCGTGGCTACACATGAGGGTTTCCATTTGCGCTAGCGACAGACGCTTCTCTTCAATCTTAACGATGCGATCGAGTCGGCCCTTGAGTCTAAACTGGCCATTTTCGGTTGGTTCGATTTTATCTTCGCAGCGCAGCCATTCATCGTCAGCCAAATAGGGCGATTTTAGCAGCAGGGCGCCGTCGGTCGGATCTTGATCTATTGAGATGCGGTCAAACACTTGCCAAGGTTCATCGGCCTCATGCTGACGGCGATAGGCGATGCCGCCCGTTTCTGTGCTACCAAAAATTTCGATGGGCAGGTGACCATAGCACTGGTTGACGCCCTTGGCCGCGGCAAAACTCAATGGCCCGCCAGAGCTAAATACTAAGCTCGGTGAGCGCAGTTGCCGCTCGTGCTCTAGCGCTTTAGGCAGGCGCGATAACTGGGCTGGGCTACTAATCAAACACAGGTTTGGCAGCAAGGCAGTGTAATAACTTAGGGTTTCTGGGTATTCAATCTGCTCGCTTAAAAACGGTCGACTGGCGGCGAGCGGCCACAAAATCTTGAATAACAGACCGTAAATATGCTGGTGGGAAACCGTGGATACCACGCTGCAATGGGGCAAATGCTCGGCAAAAGTATGTTCGAGCACAGACACTTCGACATCTAACTGTTCAAGTGTTTTACGGATCGCCTTAGGTTCGCCGCTGCTGCCAGAGGTAAAGAGCACTAACTCGCCGATAGCCTCGCTCGCGGGCCAAGGTTTGTTGGGCAGGCTCAGTTCCTTTTTCAGCAGCACAAAAGCTTGGCATTCGCACAAAGGCTTGTCTGACAAGATGCCATCAAACTGGTGTGTTAACTCGCTCAAGGTGCCGGTTTGGGTGTTCGGTGGCAGGATAATTTCTTTGCCCGCTAACAGCGCGGCACATAATCCGACGGCAAACAGATCGCTGGTATCACTCGCGAGCAGCCATTTTTTCTCGGGCGCGGCGAGCAGTTGCTCGTAAAAATACGCCACCTGATTGGTAAATAATGCACCTGTGACTATGTCGTGGTGATTAAAGCTGATCAGTTGTTGGGCAAAGGGCCCTTGGGTTAACCAGTTTTTGAGTAGCTGTGTCATGTTTTCTTCAACCAAAAGCTGCGATATAACCATTCCCCACCGAGGAGCAGTCCCATGAGTACGTATGCAATCAAACCGTTATACAAGGTCCAGGTTGCTAAGCTAGTAAAGCCAGCGGTATAAGCCGCCATTGAGCCATTTAAGATAAATAAGCCGCACCAAAGTAGGGTGACTTTTTTAAGATACGGAATGGCTTCATCCGGTAAATCGGGTTCTTTCAAGCGGGCTAAACGCTCTATCATGCATGGGCCAGTGTGCAGTGAATAACTGAACAGAGCCAGCATAGTCAGATTTATTACCACTGGGTAATACAAAAGCCAGTCGCTGCGCTTGGCAAGATAACTGCCTGCCGTAAGCGCAATCCCCACCAACAGGGGCAATACTAGCGTCTTAACCTTTTGCTTTTGTAGCATTAACCGCACAATTAGCAATACACACAGCACGAGCGCTATGGTGCCAGCAGGCAAATAATTCAGCCCGAAATACACGGCAAGCGGATAGGCCAGCAATGTGAGGGCGGTTAAGACTTGTAGCAAGGCGCGCATTAGTCTTTAACTAAACCTTCGATGGCATTCACCACATCGTTGACTGTGCGAACGGATTTAAATTCATCGGGTTGGATCTTTTTACCCGTCAGTTGCTGCAGCTTGATCACTAAGTCGACGGCATCAATACTGTCGAGATCGAGCTCTTCATACAGGTTGGCCTCAGGCGTAATCGCCTCGGCCTCAATTTCGAATTCGTCCACTAAAATAGTGGTCAGCATAGCGAGGATTTGTTCACGATTTTGCATATTCCTCTCCTATGGACGCTGTGATTCGATAAAGCTAGCTAAGCTGGCAACACTGTGAAAATGGGCCTTAGTGGCATCCGAATTAGCCTCGATTTTGACATCAAATTTTTTCTTGATGGCTAAGCCGAGTTCGAGGGCGTCGATTGAATCTAAGCCTAGACCTTCGCCAAAGAGCGGCGCATCGGTTTCAATATCATCAATACTGACGTCTTCAAGATCGAGACAGTCGATGATTAACTGCTTTATTTCATTGTTTAAGTTCATAATTATTTTCGAGTTGTTGTTTATAGTAGTGTTCAAGATCCCGCGTCAGCTGCCGGGCCATTTTTGCATGCTCACCTAAAGCCGCTTGGTACTTTTGGTAAGAAACTGTTGCGCCGATTTCAACGGCCATCCCCATGGTTTGCCTTGGGATCTCATACCAAGGTTGCTCCTTTGTCAAACCACGGACATTGGTACGCAGCACCACAGGTAAAATATCTGCTTCGGCCCTTAAGGCAATGTTGGCGGCGCCGCGGGCAAAGCCGTTAATGGTCGCGCCGACAACCGTGCGTGTGCCCTCGGGGAAGATAATTAAATTGGTGCCCCTCGCGAGCACTTCGCGGCAATCCTCTAGCATAAGTTCGGCGCCGCGATTGGGAATATAACCCGCGCAGGACACGACACCACGCAAAAATGGATTACGCCATAAACCTTGTTTGACTATGCAGCCCGCATTGGGCATGAGGCTTATCAACACTACCACATCCACTAAACTGGGATGGTTGGCGATCACGATAACGCCTCTGGCGTCATGCAGTTGTTGTGCTCTGTGCGTGCTAACGTGGATCACGCCCGCCCAAGTGAGCATGGCGACAAAGCCTTTGAACATCAGATGCACGGCCTTTTGGACTCGAATAATCCGCGCCTCGGGTGTGCCTGGCCAAAAACGCAGCAAAGGCAAAATGGTCAAGGAGCTTAACAAGCCGCCTAAGCCAAAGGCGATATAACAACTCACGCCGCCTAACCAGCGTGGCACATAATTAAGCCCAGCGCGGCGAGTCTCCGGCGGGGTATAAGCGGCATTGTGCATTGAATCTTCATAACCCTTGGCGGCGTCATGGCTGTGGGCTGTATTAAACATGCTCTAGCTTCCAATGCCATTGGGATAAGCAGCCTGAAATATTTTGCGCTGTCGCTAGGGCGTGGAGCAAGGCGCCATAGGTCAAAGGATTTAATGCGCTTGACTCTGAGATGCGGCTGACTGTGAGCTTGACCGAAGCGGGCGCATCGAAGGGCGCGAGCTGTAGACCTAGGGCCAAAGGCAATTCAAATTCTTGGGTAAACTCGTCGTACACAGGTGGCACAGGATCATCACCGAAGACTAACAGCAAGGGCTCTGGCGACTGGTGTAACTGGGCGAAGGCTTCAACCATAGCTTGTGATAGGGTTTCGGTACCTGCGGCAACTGAGGTCGACGGCGCCGTATTGGCCGCCACTATGCCAAAAATGCCGCTGGCGGTGTTGTGCACCGACTGACTAAACCCTAAGGGCGATAAGGGCTGTTTGGCGATAATGTCTTCGAGCAGTCCGATAGTGCGCTGCAACTCTCCGTGGCGCGAGGCAAAAATAGAGCGGCAGTTCGCCGGCATATCACACTGAAAACTCACCTCTAACATCATCTTAGTTAAGCGACTAAAACGGCGGCGTTGCATGGCGGGAACGTGTTTGAGTGCGGGAGCGGTCGTATCAGACTGTGGTGTGTCGAGATTTAATTCAGCCGAGTTTTTGCACCAAGTTTGCCAACTTTGCGGGTGCTGATATTGGGGGGCCCAAGCGCCCCAAGAGAGAATGCTGAATGTTAGTTGCACTACTTTGCCTCGACACTAAATTTAAGCTGTCTTAGTTATCCCTAACTAAGTTAGAACATTGATGGATTGTCATACCATAGTGAGGTGTAAACGCGTTTGATGAATGACGATGACGCTAGGAATGCCCTCTACAGCGGGGCCATTCTACACTATGCCCCGCGTTTTTATGAAGCCATTTGCATTGTATGGGTTAACGTAATGGGATCAAGTGCTGTGATTTTCAGCAGTTAGATCGAATTTCTGTAACAAATATCGCAATGGCAGTCTCATTCGACAGGCTTTTTTATCCTAGCGATTTGCGCTCAAAATGAAAAAAGGCGCACTGAGCGCCTTTTGTCTTCACCACGGCAAGGTTTTACTGTGCCAGCTGGTTCTCGAGTTTACGTAGAGCCACTTTTAGCTTGCGGGTATTTTCTGGACCCATACGGATCAGCAGTTTTTGCGCGCTAGGTAGTGCCTCTAAGTTAGGGTCAACAAATTGATAGTTAACGCTAATAGAGCTCAGCTCAATCGGATCTTCAATGATAGGCGCCGCGAGCAGTATTTTAATCGCTTGCAGCATTCTGTCGTTGAATTTAGCGTTGCTGTAGCCGAGTTCGGTAAAGGCTTCCTCTAGCATAGGCGTCATTTGCTTATAGGTGACCATTAACGTCTGTTCGTCCATTTTGGCAATCGAGTCAACATAGGCATCGTATCTGTGGAAACCCTCAGGGTTTAAATACACTTTGTTGGTGATTTCAGAAACCGAGAAATGCTTCTCTGGGCCTTTGAGTGGACTCACTTTACGGGTCAATTCGCCCTGAGCTAAGTTATCGACAAACACCACAAATTGGCGCGCGAGATCTTGCTGCACTAATGCAGAACCCAGCACATTGTTATCGATAATAGCGAGGGCTTTTTGTTGTACGAATGCATCACTTTCAGCCAGTGCAGGTACAGGTTCAGCAACCACTTCCGGCTCAGTTACCGCAGGGGCTTCTGTGCTGTTGGTGTTCGTTTCTGTAACAGCAGGCGTCGTTTCAGTGGTTTCTGGCTCTGGCGCACTTTCGAGCGTCATAGGCTCAGATGGCGGTGTTTCAGGTAATACAATTGGCGCGTTAGGGATGAGTTTAGGTGAATCTGAGTCACCGCTGTAGTAATAATATCCTCCGGCAGAAAGTAGCACGACGAGCACTATCGCAAAAAGCGCAAAGCGATTACTGCTAGAAGAAGTCTCTTGAGGTGTTATTCTATCATCTTGATTAACTTGCATTTTACATCCTTCGCTTGAGTGTCAAGGTAACTTGGTGAATACATGCAGCTAACATTAAAGTGCAAGAGCGGACTATTGGAATACCGCTGGTTCATTTTGCAAGTTTCCATGCGCTTGTCTATAGCGCTTAGCTGAACATAAACAATTTATTTTGAGTATCGAGGCAGATGTCACTCCTCGGCCTTTCCCCTTTACTACCTAAGCCTGCACATAGTGCTGCTTGTGCCCGAGCCAACGTAAGATAATCGCATCGACTTTTTCGGGGGCCTGCGCCATCACATGATTTGCCAGTTTTTGAATATGCGGGATCAAAGCTTGATCGCGCACCAGATCGGCAATTTTCAGCTCGGCCAGTCCCGTTTGCTTAGTGCCAAGCACTTCACCTGGGCCACGAATTTCTAAATCTTTCTGCGCTATGATGAAACCGTCATTACTTTGCCTGAGTACACTCAGTCTTTGGGTGGCGGTATTGCTCAATGGCGCCTTGTAGAGTAACACACAATGGCTTGCGATGGCGCCTCGACCGACTCGTCCGCGCAGTTGGTGTAGCTGTGCTAAACCTAAACGTTCAGGGTTTTCAATGATCATCAGGCTGGAATTAGGCACGTCGACGCCGACTTCAATCACTGTTGTCGCCACCAGTAAATGAATTATGCCCGCCTTAAAATCAGCCATGATTTGCTGTTTCTCGGCGCTCTTTAATCTGCCATGCACGAGACCGATATTGAGCTCGGGCAGGGCGAGACGCAGTTCTTCGGCGGTGTCTTCGGCGGCTTGGCATTCGAGCACTTCGGATTCTTCAATCAGAGTGCACACCCAATAGGCCTGACGTTTATCTGTGATGACGGCGTTACGCACCCGTTCAAGCACTTCGTTGCGGCGAGAATCGGCAATCGCCACTGTGGTCACGGGTGTCCGTCCCGGCGGCAGTTCATCGATAATGGATGTGTCGAGATCCGCATAGGCTGTCATCGCTAGCGTGCGCGGGATCGGCGTTGCCGTCATGATCAATTGATGCGGGTGGAAACCTTGGCTCACGCCCTTTTCCCGTAGGCCTAAGCGTTGGTGTACGCCGAATCTGTGCTGCTCATCTATGATAATTAACGCGAGTTTATTAAAGACCACATGCTGCTGGAAGATGGCATGGGTGCCAATCACCATCTGGGCTGCGCCGGATTCAATATCCGCAAGGGATTGCGCTCTTGCCTTGCCTTTGAGTTTACCCGCTAGCCAACCGACTTTAAGTCCGAGTGGCTCAAACCAAGCGGCAAAGTTAGTTGCATGTTGCTCGGCCAATAGCTCGGTTGGCGCCATCATGGCAACTTGATAGCCGTTTTCAATCGCTTGAAGAGCCGCCATCGCCGCGACTAAGGTTTTACCTGAACCTACATCCCCTTGCACTAAGCGCATCATAGGGTGCGGTTGTTCTAAATCTTTGCCTATATCGGCCACCACTCTTTGCTGGGCGCCTGTGGGCTTAAAAGGCAAAGCGGCAAGGAAGGGATTGAGCAACTGCCCCGTGGCCTGCATGGTGACGGCAGCGTCTAAATTACTGCGCTGTCTTAGGCGTAACATGCTGAGATTATGGGCGAGTAATTCTTCCTGCACGAGTCGCTGCTGGGCTGGGTGTTGGCCTAACTCTAAATCGAACTGTGAGATCCCCGCAGGAGGTCGATGCAGCGTTTGCAACGCTTGTTTAAGGCTGATGTTGTTTGGTTGCAACTGCGGTGGCAGCAGCTCAGTTAAGCCGCCATCTTCCAGTAATACCAAAGCTTGCTCGGTCAGCTTGAGCCAACTTGCCTGTTTTAATCCCTCAGTTGTGGGATAAATCGGCGTTAGGGTATCACTCAGATGAACATCTTCACCGGGATACACGACTTTATATTCTGGATGCACTATCTCAGCTTGATGGCCGCCACGGCGCACTTCACCGTAGGCGCGGATCATCAAACCGTTTTGCATGGCATTACGCTGGGCGATAGAGAAGTTAAAAAAGCGCAGGCTCAAGCTGCCCGTATCGTCGCGCACATTGCAGACCAGCATACGCTTGCGGCCTTGAATGATTTGCGTCGATTGGATTTCAGCTTCTATGGTGCCGTAACTGCCGGGCATGAGGGCGGCAATCGGATAGATTTGGGTTCTGTCTTCGTAGCGCAGGGGTAAGTGAAACAGCAGATCTTGTACTGTTGTGATGCCGAGCTTTGCGAGTTTCTCCGCGACCTTTTTGGCGACGCCTTTGAGGTCGGTGATCGGAACAAGATCCAATTGCTGCAAGTCGTTAGCACTCAATCACTGTGAATTTATACATATATTAACAGAGAATAAGCGTAAGGCAATCTCAAGCAGAATGAGGGACATATTTGCCTGCATAACTAACGTCATTTTGCGGCCCTTAGGTTTTAGCTAAGGGCCGCAAAAATTGTCGATATGAACGAATGAGCGGTTAACCGTTAATCTGATTACAACCAGCCCTTTTGTTTGGCGATACGGGCGGCGTCGATTCGGTTGCTGGCATTGAGTTTAGCTATGGCTTCGGAGAGGTAGTTACGCACTGTGCCTTCGGCAATAAAGAGGGTTTCGGCGATGTCGGCGGTGGATTTGCCTTCACTGGCGAGGCGTAGGGCGCGGCGTTCTTTATCGTTGAGTGGGTCAACGTCACCAATTGCCATCATGGCCAGTTCAGGATCGATAACCCGTTTTCCCGCCATAACTTGTTGAATCGCGTTGACTAAGGTTTCTGAGGGCGCATCTTTTAGCAGAAAACCGCCGACACCGGCTTCGATGGCGCGTTTGATATAGCCCGCGCGGCCGAAGGTGGTAATCACCATGACCTTAGTTTGACTATGTTGATCCTTTAACCAAGCTGCCAGCTCTAGGCCTGTGCGGCCGGGCATTTCGATGTCGGTTAGCAAGAGATCAAAGCTTTGCTGTTTGAGTAGGCCTAGGGCCTCGTCGCCGTCACTCGCTTGGGTGATATTAAAGCCTCCCGCAAGCGTGAGGAGTGCGGCGAGCGCGCCTCGCACCATGGCTTGGTCTTCTGCCAATAAAATCTTCATTAATCTGTTGCTCCCTGCAAAGGTAAACTGACGGTAAATGCATATCCCTGTTCGAGATTATAGCTCAAGCTGCCGCCTAAGCTGTCGAGACGTTCGCGTATCCCCGTGAGGCCATTGCCTTCGGCGATAGATTTACTTGGACCGTTATCTTTGACCTCAACGACTAGCTTATCTGGTTGCTGAATAAAGTCGATAATACATTGGCTTGCGCCGCTATGGCGTAAAATATTATTTACCAGTTCAGTGACAATTAATCCCAGCTGGCTCTCCATTCTGGCGGGCAGTTTAGGGATATTGCCTGTCAGCTCAACACTGACTCCTTTTTCCCTCAATAGTTTACACAGTTGTGTCACGCTATCCGCGAGACCTTTATGTTTGTAGTCAGATACGGTGTGGCGAATTTGGCTGAGGCTCTCGCGGGCAATTTGACCGAGTTCATTAAGCTGGATTGTGGCCAGTTGGTATTGCTGTTTTGCCAGCAGTTTTTCTGCCAGTTCCGCCTTAAGCGCAATCGAAGACAAGCTGTGGCCCATGATGTCGTGTAGATCGCGGGCAATGCGTTCTCGCTCAACCATAGCCGCTAAGGTGCTAATTTCTTGCGTGCTTTGCTGTTCTTTCAATTTATGTTGATGACGCCTGCGTTCGGCTACGCCAAACATGCCTACACCGAGCACTAATCCTGAGCCGTAAAGCGGAAAGTAGTAACTGTTAAAGTCGTAAATTTCATTGAGGGCAAACAACAGGCCGATTAAGGCGGCAATAGCCAACAGACAGGTTCTGAGAGGGTAAAAAAAGCCAATGAAAAAAGCGGCAAAGGTGAATAACGAGATAGATCCCGGGTTGATTGGGGTGATTGCTGTGGCGATGGCCACCATCATCAGAATGGGGCGATAGGCGCTATTCTGATTGCACTTATAGGCCCAAAAATAACTGGCAATAAAAGGAACGAGCACGGCAAAGCTAAGGGCGATTTTCCATGCTGGATACGCATTGATGGCAAGCGGAATAAGATAAAACACTAAGTTTATCAAATACACCCAAGCGAGTTTTCGTTCGAGCTGAAGATGCGTGCTTATCATAATGCCTCTGTGAAACTGTGTTGATTGTTAATCGTTAATCATTGAATGCCCGAAGGTGGCATCTGTGTTGATTTATTCTGTTTAGGGATTGGCGCTGGGGTCAGCGCCATCAGTCCCTGTTAACAACTTGGTCTAGTTTGCACTTAATTGCTGATTTTGTTGAAGCAAAAATTTTGCCCAACCGTAATCTGCTTGCACTTTCATCGCCTGTTGCCAATCTTCACTCGCGCCTTGGCTATCGCCTAATTCCTGTTTAGCGAGCCCGCGCCACGTGTAAGCTTCGGCTTCACCCCAACAAATGCCGTCGCAAGGTTGGGCGAATCTTTCTATGGCCGTGGTGGCGAGCGTCTTAGCATTTTGCATGCTGCCGCCAAACATAGCTGGTGTGTTATAGGCGGCGATCGCTTTAACCAAGGCGACCCGTGGATTTTGGGGTTCTAACTGCTCGGCGTTGGCAATGGCTTTGGCTGATTTCATCCCGAGAGCTGCGCCTTTGCTATTGTCTAAAGCGATTTGCATGCCATAAACCGACGATAACAGCGCGAGGGATTCCGCGTCAGCTTGGCTGGTGTCTAAGGCTTCTAATGCAGTTTGCGCATTAGTCAGTGCACTGCTGGCGAGGACTTTTTGTCCCATGACATTGGCGCTAATGGCTAAGCGATAGTTCGCATAGGCTTTGTCGTAATCTTGGGTGCTGGCACTGAGTTGTTGCAGCTGCGCTAGGTTCATCGAGTTTGAGGCCGCATCTATGCTGGTGATATTGGCGTTAGAGGGCGACGCTGCAAACGCGTGGCTGCTGATAAGCGCTAAGCTTGAGACGATTAATAATGTTTTCATGGTATTTCCTTATTGAGTAAGTTTCCCCGTGTTTGGGTACGGACTCATTATCAGAAATTCGACAGGAAAAATGCAGACACAAAGGTCATGGCTTTAGCATGACAAATGTCATCAGTTTAAGGGTGTTAAATTTGTCTTTTTTTAAAAGTGCGAAACCCAGATAGCAAAACACCGGAGAGGCTCCGGTGTTTCAAGTGGATATTACCGCTGGCTGTTATACCAAAGCTTGAGCGCTCGCTTGGTTAATCAGGTAACCCATATAACCTAAATAGGTCAGTAGCAGCACAGAACCTTCGCGGCGGCCGATACGTAATCCCGTCCATGCAAAAGGCAGAATCACTATGGCTAAGGCTAACATCACCATAAAGTCGAAGGGTTGGAAACCGAGTGACGATACAGGATGCACTATGGCGGTAACGCCTAAAATACCCAAAATATTGAAAAGGTTAGAGCCAACAACGTTACCGATAGCGATATCGCTTTGACCTTTTAGTGCCGCAATCACTGAGGTCACTAACTCTGGCATACTGGTGCCAATCGCTACGATCGTTAAGCCGATAATGACTTCACTCACGCCGAAGGTTTTCGCTAAATCTACCGCGCCGTTTACAAACAGAATACCGCCGCCAACTAACATGCTAATGCCGACTAGGATGAACAGGCCAGAAAGCAGTGGGTTATTGGGGCCCGCTTCAATTTCTTCCTCATTATTGCTGTTTTTCGAGCTGATATAGCTAAAGGTTAAATATCCAACCAGCAGTGAAAGCAGTACCACACCGTCAATCAGGCTGAGTTCGCCGTCTAACAAGAGTCCCCAGAACAGCATAGACGCCAAGATCATCAAAGGGATATCGCGCTTCACTACTTGGGATTGCACTTGGATAGGGCGGATTAAAGCCGTGATCGCTAAGATCAAACCTATGTTGGCAATGTTGGAGCCGATAACGTTACCTAAGGCGATACCACTGTTTCCGGCGAGGGCAGATTTAACACTCACGGCCAGTTCTGGTGCGCTAGTACCAAAGGCAACAATAGTTAAGCCTATGATAAGTGGCGTAATACCTAAGCGTAGGGCGATAGCACTCGCGCCACGCACTAGGGCTTCGGCGCCTAAGGTTAAAATAATAAAACCGCCAATAATCGAGAGTGTAATGAGCATCACGGGACTTTGTTAAACCTATATTGAATGAGAAAGTACAAGGGAATCATCCCTTAGCCGACAGGCGCACATGATACGTGGTTTTATCGAAATTTGACAGAATATGTCATAAAAAAGCACGCCGAGGCGTGCTTGATTTGTGCCGATAACCATATTGGCCATCTCTCTGTTTAGTCGAGTTATTTTTGCGTTAACTCATCTTCCCAGCGAACTTGGGCGCCACGAATGCCATCAACTCTTTCGCTAAAGGCTTTTTCAAGCACATGGCGTTTGATCTTAAGCGTCGGTGTCAGTACATCGTTCTCAATCGTCCAAGGATCGCTAACCACGATAATGGCATCCACATGTTCGTGTGACTCTAAGTGCGGATTCACGCTATCGAGTGTCGCCTTGAGTGAAGTGCGGACTTCTTCGCGGGCCTGCAGATTAGCGCCTTCGGATAACTGCACTAATGCGACCGGATGCGGCAGGCCTGAGCCAATCACGCAGATCAGTTCAACATGCGGGTCCTGCGCCAATTTACGTTCGATAGGCACGGGCGCGACATATTTACCCTTGGCGGTTTTGAAGTTGTCCTTCACTCGGCCGGTAATGGTCACGCAACCATCGGCATCAATAGAGCATAAATCGCCAGTGTGGAAGAAACCATCGGCGTCGAATGCTGCCGCAGTGGCTTCGGGCTGTAGATAATAAGCCGTCATCAACCCTGGACTCTTAAGCAGTAGCTCACCATCTTCCCCTTGGCGGATCTGACAATCCTCAACCGGGCGACCTACTGTGCCAATTTTACGGGCATCGAAGGGGTAGTTGATAATCGAGTAGGCGCAGTTTTCTGTCATACCCCAAGCTTCACAGATATTCAGACCAATGCTGTGATACCAGTGGATCAGCGACGGCGGGATCGGCGCAGAGCCAGAGCCGAGCAAACGACAATGGTTCAAACCTAAGCCTTTATGGATTTTGTGTTTGACTAAGCTGCTGATTAACGGGATCTTCAGCAATAGATTCAGTTTACTAACACCAATTTTGTCTATGATGTTTTTCTGGAACAGGCTCCACAAACGCGGCACTGAGAAAAATACCGTCGGTTTCGCCCTTTGCACGTCGGCGACGAAGGAGTCGAGACTCTCGACGAAGGCGACCACGCTGCCGGAATAGAAGGATGAACCTTCAATCGCGACGCGCTCTGTGATGTGCGCTAAAGGCAAATAGGATAACAATCTGTCGTTGCCGTCGGTGCGTAAATCACGCACTACCGCTTGGCAAGTCCAGCCATAGCTAGCGAAGGTTTGAATCGCGCCCTTAGGTTGACCCGTTGAGCCAGAGGTATAGATAAGTGTCATCACTTGATCAGGCGTCGGTAGCGGGGCATCAACTAGGGGATAACCTAGTTTAAGTAATTGTTCCCATTGATATTGGGCTGGCATTGTATCGTAGGGCATAGCAAGACGTAGCAGCTCGCCGCCGACACCGGCTTCTTGGTCGGCCCAATGGTCGAGCTTGCCGAGGAAAATGGCTTTTGCACCGCTGTGTTGCAGCACATAGCGAATGGTATCTGTGTTGGCCGTTGGGTAAATCGGCACGCTGACATAGCCGCCGTGCATTAACGCAAGGTCGGTGATAAACCATTCGGCGCAGTTTTTAGAGAGTACGGCAATCTTATCACCGCGCTCTAGGCCTAGATGCTGTAAGGCACCGGCCAGTTGCTGAACTTTTTCTTGAACTTCGGCCCAAGTAAAATCAAGGAATTGACCCTTGATAGGTTGGCGTAAATAGACTTGATCCCCCTGAGTCTTAGCCCAGTGCGCCAGCATTTCAATTGGCGTCTTAATCGCTGTATCCATTGGCAATTCCCTGTTCTTTATTGTTATTTTGGCTGCACCCAGTATGGCGAGTTTTGTGAGCCCGCGCAAATAGTTTACAGGATTGTAAAATATGAGAATGGAGCCCAAAGCCTACAAGCTGAGTATGAAAATAAGATGAAGAGAATGGCAAGCGTCAGAGAGGCTGGTGACGGCAACAAAAAAGGCGCCGCAGCGCCTTTTTGTTGGTTGAAGGGGAATGCCAGAGCTTACAGCTCCATCACGCCATCCATTTCAACTTGAGAATCCTTCGGCAATTGCTTCACGCCGATAGCTGCACGGGCAGGGTATGGCTGGCTGAAGTAACGGCTCATGATTTCATTCACTTTTGCAAAGTGGCTTAAATCAATCAGGAAGATGTTGAGCTTAACGATATCGTTGATTGAACCGCCCGCAGCAGTACAAACCGCCGTCAGATTTTCAAATACTTGCACCACTTGCGCTTCAAAATCATCGCTGACAATTTGCATTGTGCTTGGCACAAGCGGAATTTGGCCGGATAAATATACTGTGCTGCCGACTTTGACGGCCTGTGAATAAGTGCCGATGGCCGCAGGGGCGTTTTCGGTCGCTATAATGATTTTTTCTGCCATGACGCTCTCTCTAATGTTGAGTTAATTAACGATTACGGGACGTACGCAGCACTTCGGGCAGTACCCTAATTCGGCGCATCACGTTGGCTAAATGGATCCTGTCTTTCACCGAAATCCGCAGGTTGATCAAATACACGCGGCCATCTCGTTCCTCTGTACTGAGGTTGTGAATGTTCGAGCCTTCGGCGGCAATGATCGAGGTGATCTTCGCCAGTGCGCCTTGGTGGTTAACGATTTCCACCCTCAGGTTAGCTTGGTAATCAACGCCTTCAACATTATCCCATTGCACAGGAATATACTTGTCAGGCTCGCCTTGATAACCACGAATGTTGGCGCAGTTTTCCATGTGCACCACTAAGCCCTTACCTTGGCTCACGTGGGCAATCACGGCATCACCAGGAATTGGGCGGCAACAGTTGGCGAAGGTAACGAGCATGCCTTCGGCGCCACGGATTGGCATCATGTGGCCGTCGCGGCTTTCTTGATTCTCAAGATTATCGCCAATCAAACGCTGGGCGATAACGATGCTCATGGCATTGCCAAGGCCGATGTCGGCGAGCAGTGAATCGAGCGTGGTATGTTTAGTATCGCGAATGACTTTTTCAATCTGCTCAGGCGGAATACTGTCGAGCTTAGTCTTGCCGAGCGCATGGTTGAGCAGGCGGCGACCGAGCGCAATCGCGTCATCACCCTTAAGGCTTTTCAGCACTTGGCGGATTTTGGCGCGGGCCTTACCTGTTACCACAAAGTTCAGCCAAGCGGCATTCGGACGCGCGCCTTTGGCGGTAATGATTTCAACAGTTTGGCCTGAAATTAACGGCTGGCTGAGCGGATACGCTTGGCGGTTAACGCGGGCACCGACGCAGGTATTACCCACATCTGTATGCACTTCGTAGGCAAAGTCTACCGCGGTGGCATTCACGGGTAATTCCAGAATGCGACCTTCAGGGGTGAACACATAAATCTCTTCTGGGAAGAGTTCCGTCTTCACGTTTTCAACGAATTCGAATGAGGTGCTGGCGCTTTGTTGTAATTCCAACAAGCTTTGCATCCACTTACGGGCACGCACTTGAGTCGTCGTGCCTTGGCCTGTTTGCGCACCGCCTTTGTAGGCCCAGTGTGCAGCGACCCCTTTGTCAGCCATCTGATCCATATCTTCGGTACGAATTTGGATCTCAACGGGCACGCCGTGTGGACCAAATAAAGAGGTATGGAGTGACTGATAACCGTTGGCTTTAGGGATAGCGATATAATCTTTGAAACGGCCGGGACGAGGTTTGTACAGTCCGTGCATGGCGCCGAGTACGCGATAACAAGTGTCGATGGAATCGACTATCACCCTAAAGGCGTAGATATCCATCACTTCTTGGAATTGCAGCTCTTTACTCTGCATCTTGTTATAGATGGAGTAGAGATTTTTCTCGCGGCCTTTAACTTTCCCTGGAATACCTGCATCGTTTAAGCGCGTATAAACAGCGGCTTCAATGCCTTGGATCAATTCTTTGCGATTGCCACGGGCAGCTTTAACGACTTCTTTGAGCACCCGATATCGCATGGGGTAATACGCTTGGAAACCTAAGTCTTCTAACTCTGTCTTGATGTTATGGATACCGAGACGGTTAGCGATGGGCGCGTAGATTTCTAAGGTTTCGCGGGCGATACGGCGACGTTTGTCGGGACGCAGGGCGCCTAAGGTACGCATATTGTGGGTTCTATCGGCAAGCTTGATGAGGATAACGCGGATATCCTGCGTCATCGCCATCATCATTTTACGGAAGTTTTCAGCCTGAGCTTCTTTCTTGTCGCGAAATTTTAGCTTATCGAGTTTTGACACGCCTTCGACGAGCTCGGCCACCGCGACACCGAATAGTTCGGCTAAATCGTCTTTGGTGACATGAGTATCTTCGATAGTGTCGTGGAGCAGTGCTGCCATCAGCGTCTCATGATCAAGACGCATGTCAGCAAGGATGCGGGCAACCGCAACCGGATGAGTAATATAAGGTTCGCCACTCGTGCGCATTTGCCCTTCGTGGGCATCACGCGCAATCTGGTACGCCTGCTTGAGCAATTCTACCTGTTCGGGTTCTAAGTAACCGGAAGCAGACTCCTTTAGACCTTCAAACAGATACAAGTGGCAGCTCTCCTTTTACCTAAACAGGTGTTTACTCAACAACTCTTATAATGAACGGCCTTCAGCAATGGCTGAAACTGCTGCAATTTCAGCTGCTTCACGTTCACGTACCGTTTGACGCTCATCGGCATCTAAGGTGTGAGCGTTAACTAAACCTAGTTCGATTTCACGCAGGGCGATAACCGTTGGTTTGTCGTTCATCTCTTCAACCATAGGGTCTTTACCCTGCACGGCGATTTGGCGAGCACGACGCGCCGCAACCAAGATCATATCAAAACGGTTGCCGATTTGTTCTACGGCGTCTTCTACAGTTACGCGAGCCATGTGTTGAAACTCCAGTTTATCTAGGGAAAAAATGACGCAAAATTGTACACTATGACAGTTGGCCTGCCAACAGATCGTTAAGCATATCATTTTGCGCGTGGATCTGACTAGCACAGGTTAAGCGCTGACTGCGAATGATGGCACGCAAATCAGCTAACGCAGTGTCAAAATCGTCGTTGACGATAATAAAATCATATTCTTTATAGTGGGACATTTCCGATACGGCTTGTGCCATACGGCTGGCGATCACATCGCTACTGTCTTGTCCACGTCCAGTGAGGCGGCGTTCTAGCTCGGCTTTCGAGGGTGGCAGAATAAACACACCAACGGCATCGGGCATCACAGCTTTCACTTGCTCGGCACCTTGCCAGTCGATATCGAGGAAAACATCGATACCTTGGGTGAGCGTATGCTCAATCACATGGCGCGAGGTACCATAGTAATTGCCGAACACTTCTGCCCATTCAAAAAAGGCATTTTCAGCAATTAACGCTTTGAATTGTTGAACATTAACGAAGTGATAGTGCTGACCATCGACTTCACCTGGGCGTGGCGCGCGGGTGGTATGTGATACCGAAACTTGCATGTCAGCGGGCTTGTCTTTAAGCAAGGCCGAGATCAGCGAGGATTTACCTGCGCCACTTGGCGCCGACACAATAAATAAATTTCCGCGTGCGGTCATGAGCTAAATATCCAATTGGTTAACGCAAGAAGGGCCCAGAAAGCCGAACATTATACCGATTTTGACCATAGAAATACTAGGGCTTGTAGGCCAGCAATTTAAGCCTATTTTAGATTTACTTCGATATTATGCGCATTTTTATCCGACTAGGGTCGTAGGCACTGACAATTGCGGGTAAATTGGTTACGCTCGTGCGAATAAGGCAATGAGTGTCTTTTTGTATCTCGTTTTTACAACAATAAAGGAAGGGTCGTGCTGTTAAAATCTCTTTTCTGCCTTCACGGGCGTGATTCTCGTCCCCGCTTTGTCGCAATCAGTGTTGGCGTTTACGTCTGTATTGCACTTGCCGCCGCTGCTTTTGGGGCTAATTTCTTGATGTATTTACTGGCATTGTTGGGCTTACCTTTATTAGGGCTCGCCAGTTTGCGTCGTTTGGCCGATGCCGCTAAGTCAAAAGGCTTGATTGGCGTATTCCTATTACCCTTATTTATTTTCATCGGGCTGTTAACGGCCGATGTGCATATTGCCTTAATCGGCTTGAGTTTAGTGTTAGCCGCTGGACTGACGTTTTGGGGCTGGCGTTTACCGGCGCCAACCATAGTCGATTATCGATACGGTTACTTCGGCCCTGAGCTGGATGCGCCCACATCACAAGCTATGCCTAGGCGCCGTGTGGAGCCGACGATCGTTCCAAAGGCGGCTAAAGAGATAGCAACTCAAGACGTTGCCGCAAGACAGGCAGCTAAATCGACAGCCCATGTTCAAGCGCAGCCTCAAGTTGAGCGACATGAGCCGCATGTTGAGCTGTCGCATGCACAGGCCATTAGTGCCGCAACGGCCTCTGAGCCTGAGGTGGATTTATCTGCCCATGTTGAACCCATTGCCTCTGAACCAATAGTCGCTGAATCTAATGTCGCTGAACCTATTGTCGCCTCGACTATGAGCGCGGCGGATGCCGAGCTTGAGGCCCAGCGCCAAGATGAACTGCGTTTTGATGCCCTTACTCGGATGACGCAAAATCCTGCCGATTTTGTGGTGCCCGACAGTCATGCGGATTTTCGCCCCGCTAACGCCAGTCGTGTCGAACAGACCAATATTGATTTAGTCGACGTTGATGTCGCTAATGTTGATTTAAAACATGTCGACTTTAACGCAGCCCAATTAGATCCCGCCGAACGCAGTACAGAACCGGGTTGGCGTTTTGCCGCCGCCGAAGAAGATGACGAGCCGGACAGCTTTAACGATCGAGATCATGCCGAAGAAGTACGTCAACGCCGCGCCGAAGCGAAAGATTCTGGCTCGATGACCGAGCTGTTTCGCGGTTTGTTCGAGTTTTTAGCGCCGCTAAAGCGGTTCTTCGTACTACCTAAGATTAAGCTGCCTAAACTGGAACGCCGTTATTGGCGTCCTGCGGGGATAGGTTGTGGTGTTGTGGTCTTGCTCGCCTTAGTCTGGGGCCTTTGGCCAAATGGCGATGCTGAAACGGGCGACGGCGCAGACGTGGTAACGGTTAACGCGATTGCGCCCTATGCGGGTGAGCGGGTGACGCTCGCGCTACCGGACGGGTTTTCTGTGGCGCTGGAGGATGACATCTTGATCATGCGTTGGCTCGGTGAGAAAGGTAAAGCGCAAAACCTGTGGTCCTTAGCGACGGCAAAGGGCGATAAAACCTGTAGCCTTTTGTCTTTTAACAATGGCACTGACTATCGCCCTGTGACTGTGGACTTGAAAGCCGACTCAGCAACCGAGGCGCGATTCACGCCACTCGACACTCAAGCCATCATAGTGGACCTTGCGCGCCGTGGCAGTATCAGCCTGTGTGGTTATAAGTTCAGCCTTAAGGGCAGCCAAGCGATATTGGAACAGAATCGCACTTTTGGTGATTACATCGCGCGTTGATTTAGACTGCAAGTTAGGTCGCAGCTTAGGTCAGCAACATAAGTCGATAAAATTTCTGCTTAAATGCTAGTCACAATTAGAGGGGTAGATTACACTTTTGCTCCTCTTTTTCTCAAACCGCGTTTTAAATGCGTTATGCCTTGGTATTGGACGCGGCGGTTTGGGGAAACATCTTTAAATTTGAACTGAATATATCAATCCAGCATGAATACAGAATTGACCTTAGTCATTATGGCTGCAGGGCTAGGTAGTCGTTTCGGCGGCGATAAGCAATTGGCAGCGCTCGGTCCCGCTGGCGAACCCATGTTGGTGTTATCGATCATGTCGGCTATTCGCAGTGGCTTTCAGCGCGCTGTGCTGGTGATCCGCCCAGAGTTAGAAACTGAGTTAACTGAGATACTGACACGGTTTTTACCCGCCGATTTCGCCTATCATTTTTGCTATCAAGCCTTAACGGATTTACCCGAAGCGGCGCAGCTGGCGGATCTCAGTCACAGGCTAAAACCTTGGGGCACGGCCCATGCTCTTTGGAGTGCGCGCCATCTTGTTAAAGGACCAATGGCCGTTATCAATGCCGATGATTTTTATGGTGATAGCGCTTTTGCCTCGTTAGCCAAAGGCTTAAGCAGGAGACCGACAGAGTGGATGATGGTGGCTTATCCCATAGAACTCACATTGTCGGAGCACGGCGGTGTTAACCGGGGTCTGTGTCTGGTGGAAAATGGTCAGCTTAAATCGGTTGCCGAGTGGCTTAATATCGCCGATCAAGCAGGTCAATTGGTGGGTGATGGCCCACAGGGGCATGCGAGTTTGCCGCCAGCGTCCTTGGTATCTATGACATGCTGGGGCTTCTCCCCAAGCATATTTGATGTTATTAAACGTGAATTAACCGAATTTATTGGGCAACAGGGTCAACAACCTAAGTCTGAATGTTACTTGCCCGCGATAGTACAAGCGGGCATTGAGCAAGGTTTGCCTGTCTATGTCGATGTTGCCTGTGAGCCTTGGCTCGGCGTGACTTATCCGCAGGACACTGTCTGGGTAAAACAAAAATTAACGGAGTTATTGAGTGATTAAACTCATCAGGCAGTCAGTGTTGCCTCATTTTGGTATTGAAGCCGCCGAGGCAAAAATTTCTGCCCTCGGTAATGGCCACATCAATGACACCCTGTTGGTGCGCTGGCCAGCGGGCGAATTAGTATTGCAGCGAATTAATACTGAGGTATTTAAAACGCCCAACGCTTTGGTTAGCAATGCCGATAAAATTAGCCATCATTTATGCGCTAAATCGTTGCAGCAACAATACGGCCTGAAAGTCGTTAGCCCTTGTTTAACCCAAGAGGGTGAATTGGCTATCGATTTAGGCGAACAAGGATTTTGGCGCGCGATCAGTTACTTGCCCCATAGTCTCAGTATTGAAGTGGTTAAGTCGGAACAAGAAGCAGAGATGGCGGCTAAGGCCTTTGGTCACTTTGCTTCAGCCCTGAGTGACTTTGATGCAACTCAGCTCGAAGATGTGATCCCTCAATTTCACCATTTACCGGGACGCATGGCTTTGTTGCAACAGGCCGCCGAACTCGATAGCCAACACCGTTTAGATCTTTGCCGTCATTGGGTCGATTACGCGTTATCTCAGCAGGCTTTGCTGGATGAACTCGCGGAAATATCGCCAAAGCTGCCGCTGCGAATTTGTCATAACGACACTAAAATCAATAATATGCTTTTCGATAAACGCGACATGTCCAGTATGGCGATTATCGATTTAGATACCTGCATGAAAGGTCATCTGATGTATGACTTCGGCGACATGGTTCGCACTTTCTGCTCACCAGAGGAAGAAGACTCTACGGCCTTGGATAAAGTTCAAGTGCGCCAAGATATCTTCGCCGCGATTTGCCGTGGCTACTTGAGCGAGTTGGGTGATGTCTTAACGGAAGACGAAAAACGCAGTCTGTGGCTCGGTGCGCGGGTGATTTGTTTGATGATAGGCGTGCGTTTTTTAACGGATTACCTTAACGGTGATGTGTATTTCCATATTCACCGCGAAGGCCATAATCTGGACCGCGCCGCCAATCAGTTTACCCTGTACCAAAGCTTGCTCGACCAAGAAACGGTACTTAAAGCCCATTTCTAAGTCAGGATATGGATCTTGGTGATTGCGCCAGTTTCTAGATTTAATGCACTGATAAACAAAACGCCCAAGGCTTTGCTTTGGGCGTTTTGCTTTAATACGATTGGTATAAGCCCTAGGCAGATGGCATTTCATCCGACATAATCGAGCAATATCCCTTAATATTATATGGATTTGTGCTTGGTATGGATTCACCTGTGGCGCAGCCGTTAGTCACTTGTTCACGGCGCAGCGAACGTAACTCTTGGTTAAGATGTACCCTGCGTCGCTCGCAGCAGGAGGCGGTTGCCTCGTCTATGATGACGGCGACCAGCGATAATTTCTTCAATGCCTACGCTATTTTTCTCGGTGCCAGCCTAGCGCAGATGGGCTTTGTCACGGGGTTACCTCAGCTTTTTGGCGCTATCTCCCAGCTTTTATCTGTCTGGCTCGCCAGCCATTTCTCCCGTCGAACTTTTATCGTCTTTTGCGCCGCCTTTCAGGCCTGTGTTGTGCTGGCGATGGGCGCGTTAGCCGCTTTCGGCCCTGAACATGCGGTGTGGATCTTTATCGCTTTAGCCGTTGGTTACCATGGCTTTATCAATCTAATTCAACCCCATTGGCGGGCGTGGATGGGTTCTATTGTTCCCGAACGGCGCCGTGGTACTTTCTTCGCCGCGCGCACTCGGCTCACTATGGGCGCATCTTTGAGTGTGTTCTTTATTGGCGGCGGCATACTCACCTTAACCGATTCGATGCAGATGGCGTGGCTGGGCTTTAGTCTGCTGTTTTCTATCGCGGCGATGGGGCGGTTTGTGTCGGCTTGGTTACTGTTGCAGATGCACGATCCCGATCCGCGTGAGCCCAAACAGCGCGGCGTATTTTTGCAGACGATTGGTCATTTTCGTGAGGCGTGGAAGGATAAAACTTTTCGTCAGTACAGTTTGTTTGTCGCCAGTATGCAGGGCATGGTGGCGATCTCAGCGCCATTTTTTGCCGTTTATATGTTGGAAGGCTTGAAGTTTACTTATCTCGAATTTGTGTTCGCCAGCGTGGCTTCGATAGCGACTCAATTTGTCACCCTGCGATTTTGGGGTCGCTTTAGCGATCAATTCGGCAATCGTTTAGTGATGATCATTACCAGTTGTATGATCCCAAGCTTGCCGCTGATGTGGCTATTCTCGGATAACTATCTGTATATCTTAGCAATCCAAGCTTTCTCTGGCTTGGCGTGGAGCGGCTTCACCTTAAGCACGGCAAATTACCTTTATGATATCCGACCCTTTCGCAGTGACTTTGCGACCTACGCCGCTTTGCAAGCAGCCTTGAGTGCGGCCTGTGTATTTGTTGGCGCGATATTGGGCGGCACGATTGCCTCCCATGCGGCGGATTTTTTAGTTTGGTCGGGTTGGAATAGCGTGTTATCGAGCCCTATCTTCGTGGTGTTCTTTGTTTCGACCGTGCTGCGAGCCTTAGTGACACTCTGGTTTATTCCGCGCTCGGTCGAACCTAAAGTGCGTCCAAGGCCGCAGCTACTGCAACTTATCTTTCGTATCCGCGGCTTTAATGCCATTTCGGGCGTGAGCCTCGATTGGCTCACTGTGGTGAAACGCCGCAAGCAATAAAGACGTGCGACTAAAGCGTATAGTCGAAAAACTTACGGCTTTGCGGGCCAGCGCTATTACTGTTCACCTTATTGGGATGTTTATATTCATGCCAAGCCGCGACGTGTGTACCAGCGTTTAATTTAGGGAAATCCAGTCCATGCTCGCTGGTGTTGACTGGCAGTGATTGAACGGCTGCCTTTTCCTGACTCGTTCCTTCCTGAGATATCTGGTTCGGTGCACTTTTTATTTGAGAGTCCGAGAATGCCCTATCTGTCACTTGACCTAAGGTTGGGCAATCCGTGGTTAACCCCAAAAATTGCAACAAGCGACTCAAGCTCCCCGCATGGCTTATGTCGAGTGAGATAAAATCTTCTCGGCCATGAAAGTAAGCGAGCACTTCGTTTTGATGCCTTTGATAACAAGCAATAAGATGTGCCTCGTCTGCGGGATCCGCCACTTTATCGATAGCAAACGTGTGCCGAAAACTGCGTTTCATAATCGGGTGGAATCGCCCCGATTTTGCTTCTAAATGCGGCAGCATTTTACCCAGCAGCATTTGCATTGAAGGCACCCACTTCGCCATATCACGCTCGAGGTAGACAAATTTGGCCTGCGGGAATAGACCATCAAGCTGCCGATAATCACTAAAACAGGGTACATCAGAAATCGCATCGGCCAGCATAAACGCCTGTTTAGTAAAGGCCATGTGCGCCACTTTTAGCCCTTGCTCCAACAACGCCACACTCACACTCGTCGTACCCGTACGCGGTAGGCCTGTGATAAAGACCTTCTGTTTTTCTACACTTGCCTGTTCACTACTGAGCGTACTGGACTGTTCGGGATGCATCTCATCTGGCGTGGTAATCTGTGAATGACCAGTGTGCTGCGTATTGGCGGCATGATCTGCATTGCCAATATGTTGTGCATCAACAGTACGCTGTGCATTAGCAGTACATTGTACGTGAGTAGAGGCTGGTGAATTGTTGGCATTATCAGGCATAGATTGGGCTCAGAATGTGTGTGCAAGCGGTGCGGTGTAAGCAATGAGTTTTCACATCGGCGTTTTGCATTGGCGTGTCAAATCAGCGGCGACAGTATATCTCAGCACGAAGGCTTTGTCGGTAGTATACCCAAACAACCTGAAGATGCAGGATTCAGCGGGAGTTTAACGCACTTTAGGCAAGGCGGTTATTTGCAGACCTAGCGGGCTAAGTTAAAAATAACCAACACAGCATAAAGTGCGTTACCCCGTGGTTAACATCAGCCGCCGGGACGGAGCGCCTAGGGCACTCCACTTCCGTGTTGCATCAATTCAAAAGGTGGCCGACATTCTGTCATTGATGCGCCTTGAATTGAATCACCCTAGACGCTCTGAAACTCGTATATTCAGGTAGCTTGGGTATATACATAAAAGTCACGAACAACGGCAATGCGCCACAGTATTGTATGGCCGCTGTACGATAAAAATGGATGACCACAAGGCTTAGAAACTGAGCTCCCGGATTACCAACAAGGCTCATTTCCCTGCATTGGATAGCCATGTTGGTATTAATGAGGCGATTATTAGCGCTATTCGACTCATAATGTGAGGTGAATATATTTGTTAATCGCCTCATTATGTGAGTAGAATAGACTTGTTATTTGACTCGTGGGGTAAGATATGTGGATCTGGCAGCAAGCTGATTGGCCTAAGTTTCATTGGAATACCAGCAGTATTAATGCCTTGCTGCGTCAGGTCTATTTCAATCAGGGCCAACTGCTTGGCAAGCAAATGCTAAGTCAGGACGACAGCCTGTTAACGTCAGACGCTGCACTCGACACTTTACTCGCTAACATTATCCATTCCAGTGCGATCGAAGGTGAAAAGCTCAATGCCGCTTCAGTGCGCTCATCCTTAGCTAAAAAACTCGGCGTGACTGAGGAAAAACCGTATCCGACCACAGCGCAAACCGATGGTTTAGCTGACATTATGCTCGATGCTTTAAAAAATTTAGAGACTGAATTAACCCTCGAACGGATATTGGGCTGGCATCAGCAGTTATTTCCGCAGGGTTATACATTGTTTAACCCCGTTATTGGTGGAACGCTACGTGGCGATACGCCTATGCAAGTTGTCTCAGGCCGCATTGATAAACCAACGGTGCATTTTGAGGCCCCATCTCGAGCGACTTTGGACGCAGAACTCTCGCAGTTTATTGAATGGTTTAATGCTTCACGGCAAGATCTGGGTTTAGATCCGCTTATCCGCGCGGCGATAACTCACCTTTGGTTTGTGACTCTGCATCCGCTTGATGATGGTAATGGGCGCATCACGCGTTTATTGACTGATTTAGCTTTGGCGCAAGCGGAGAAACGCTCGATTCGTTTTTACGCTATGTCGGTCAGTATTCTTGCCCGTCGCCAAGCCTACTATGACATTTTAGAGTCCACTCAAAGGGGCGATGTAGATATTACTCCTTGGTTAGTCTGGTTTTTTGAAACCCTCAATGACTGCTTACTCAATGCGATGGTAGATGTGAGCAGAACGCTGTCGAAAACTCAGTATTGGCAAAGTGTCGACCAATCTTTATTGAGTCAAGAGCAAGCCAAAGTACTCAATCGCATGTTAGACGGCGATTTTGAACTAGGGATTAATAGTAGCCAGTACCAAAAAGTCGCGAAGGTGAGCCGCGCCACCGCCACCCGTCATCTTGCGCAAATGGTCGAACAGCGCTTTTTAGTAAAAGCCGACGCCGGCGGCCGAAGTACGAGGTACTTATTGCCGAGTGGGAAATAGCGGCAAGTTTACCTTGGTGTTGGATGCTCAATTATCAATAGCCTAAAGCGCTTTTGAAAGTGTTCCTACTGAGTCAGTCATAGGTAATATGTTCCACTAAAGCCAAGCAGTATTAACTCCCTATCGAAAAAGACAAAACCATTCTACAAATCGCAATTTGAATCAAATTGTTATGTGATATATTCGCTGCAACTTTGATTGTTAGTCTTGGGGCATATCTGGCTATGGAACAGGTCGGGATTTACGAAAAGCTTATTACTCAACTCGTTGAGCAGCGCTTAGATAAGGACATATTTTTTGTTGGAGAAAGGAAGCTAGAGGTTGCTGAAGCAAGTATTTGGCTCTCACGTTTTCTGACTAAAGTAATTGAATATGTGATTAATTCTGTTCCAGCAGATGAAAACCAAGTTATCAATCAAATCAGTTTGGCTAATCGGTTAGTGATGTGGCTAAAAGAGCACATCAACGATGATGACTTTATTGATGAAAATCTCATCGATAGCCAAGGTCGGATACTCACGGCTCTTTTTAACAAACAGAATCCGATAGCGACTGATTTACCTAAATATACGGCCGCCATATTTCCATTAACGGGGCTAACTCAGAGTGAGCTTTTTTGCGGTAGTAATGCGGGATTATCACTTGAAAGTGAGCTTAAACGTGAGATCCGATCATCAGATAAAATCTATTGGTTGGTGTCATTTATCAAGTGGGCGGGGATTCGCATCTTCAAAAATGAACTTGAGGAATTTACACGCAGTGGTAAAGAGTTACGTATCATCACTACATCTTATATGGGCGCGACTGATGCTAAAGCCGTCGAGTTTCTAGCCAGTTTACCCAATACCCAAGTCAAATTAAGTTACAACACTAAACGCGAAAGGCTGCATGCAAAGTCTTATTTATTTCTACGAAATACGGGTTTTAATACCGGTTATATCGGTTCTTCAAATTTATCGCATTCTGCATTGACCAGCGGGCTTGAGTGGAACCTTAAGATCACCACCCAAGAAATTCCCCACATCATTGAGAAATCACTCAGTACCTTTGAAACTTACTGGGAATCGAACGAATTTGAGCTTTTTAGCGGTGATGCGAGCAGTAAAGAAAAGTTAAACAATGCGTTGAAAGAAGCGAAAGGCAGTGGCACTGAATCGAGCTCATTTCATTTTGACATTAAGCCGTTTGCGCATCAGCGAGAAATCCTCGAACAGCTTGCAGTAGAGCGAAGCGTTCATGGCCGTTTTAAAAATCTCATCGTCGCGGCAACAGGCACGGGTAAGACGATTATTTCCGCGTTCGATTTCGCGCGGTTTTATAAACTGCATCCAGATGCCAATTTTCTATTTGTTGCCCACAGGCAAGAAATTTTACAGCAAGCCCTCAGTGCTTATCGCGGTGTGTTAAAGAATAATCAATTTGGCGAGCTATGGGTGGCAGAGCATAAACCTAATAGTTACAAACACTTATTTGCATCAATCCAAAGCTTGAATCTGCAACTTGATAACCTGCCATTAGCGGCTGATTTTTATGATTATATTGTGATTGATGAAGTCCATCATGTTGCTGCGAGCAGTTATCGTGGGCTATTAGCTCATTTTGAGCCGACTATTTTACTTGGGCTCACGGCAACCCCTGAACGGCATGATGGTGTCGATATTCTGGCTGACTTCTGCGGTGTTATTGCCGCTGAAATACGTTTACCCGAAGCTATTAATCAACGGCATTTATGTCCATTTCAATATTTTGCGATCGACGATAACACCGACCTTAGCAAGTTAAATTGGAGCCGTGGTCGTTATGACGTCGCTGAGCTGAGCAATTTATACACTCACAATGAACAACGCGTGACGCGCATTATTCGCAGCCTAGCTGAAACTGTAACTGATATTGGCAATATTAAGGCGTTAGCCTTTTGTGTAACCAAGGAACACGCCGAGTTTATGGCGAAGAAGTTCAATTTGGCTGGGATCAGCGCCGATGTGCTGACCAGTGATAATAGCTCTGAACGGCAACAAAAACGGCAGAGCTTGGTCAGTGGCAAATTATCCATTTTGTGTGTGGTGGATATTTTTAACGAAGGTGTCGACATTCCAGAAGTGGACACCTTGTTGTTTTTACGGCCAACGGAAAGCTTGACCATATTCCTGCAGCAACTTGGCCGTGGCTTGCGTCTCACCGATGATAAGCAGTGCTGCACAATATTAGATTTTGTCGGAAATTCACGTGATGAATATGATTTCTCGCAAAAATTTAGAGCATTAGTCGGTAAAACGAATCAGTCGATAAAAGATGAAATTCTCAATGACTTTCCGCATCTACCGCTAGGGTGTCGAATTGAACTTGAGGAAAAAGCGCAGTCAATGATTTTGCGTAATATTAGCCGCGCAACGCTCAATGCCAATCGATTAACCAGTCTAATAGCAAGCTATAAACATCAGACTAACTTGCCGCTCACGCTAGGCAATTTTTTACGCCTTAACCCGCAAGTGACCTTGGAAGATGTTTATCGAATAAAGATTAATGGCCAATGTGGATGGACTTTATTAGTCGAGGCTGTTCAGGGTAATCAAATTGCTGAACCTACTGCGCAGTCCGATTTAGCCAAAGCCTATTATCGCGCGATTAACTTCCACTTACTCAGTTGTACTTCATTATCCTATTTAATCTTTATCAAACAGTTATGCCAAAATGATTTTGTCTTTAATGGGTTGGATCCTATTCAAAATCAGTTTGCTTTGATGTGTCATTACAACTTTTGGGATAAGTCAGGTACGGCTTTAAACGTTAACAGTTTAGCGGCTAGCTTGCTGATGCTACGCAACAAGCCATTACAGGAAGAGTTATTGGCTGTATTGGCGATACTGATCAACCGGATACACCATCAAGAAATGGATTTGGAATTGGCGCTGCCCAGCGCATTGAAAGTGCACTCTCGTTATACCCGTGAGCAAATTTTAGCGGCGATAGGAGCAAGCACATTTACAGCTAAATCGCCTTCTAGGGAGGGTGTGCTTGCGATTAATGAGCAAAATCTTGAATGTTTATTTGTGACGTTAAACAAATCAGAGAAGCAGTTTTCACCGACAACCATGTATCACGACTATGCCATTAGCGAACATCTATTCCACTGGCAATCGCAAAATAGCGCACGGCCAGAACGAGGTAGGGGTTTAAGTTATATTGAGCAACAAAAGCTGGGGAAAGCCGTGCTGTTGTTTGTTCGAGAGCAATCAAAAGATGAAAATGGCCGCACTATGGGATTTGTTAATTTTGGAAAAGTACATTATGTCAGTCATAACGGTTCACAACCCATGAATGTCACATGGCGATTAGAGCAGCCAATCCCTGATGTAATGTGGCATGACGCGGCTAAACTGGCAGTGGGCTAAGTACTACTTTCCCAGTCTTTCTGCTAACTTCTCCATCAAAAACTCGATCAATACACTCACCTTAGTCGGCAAATATTTTCGCTGCGGATAAACCGTATAAATGCCATGTTCTGGGAGGGGATGGTCTGCGAGCAAGGCTTGTAGGCGGCCTGTTTGTAAGTCGGTGTCGACCATGAAACGGGGCAGGTTGGCTATGCCTAAACCATCGAGTGTGGCTTGGTGGATGGCATCACTATTGTTGACTTGATAGTTACCGTTCGGCTGAATTCGAGTTGGGCCCTGCGGGCTTAAGAAGGTCCATTCAACACCATCACGAAAATATGAGTAGAACAAACAGTTATGTTGGCTGAGTTGCTGCGGCGTTTGTGGCGCAGCGTGGCGAGCGAGATATTCGGGTGAGGCGCAAATCACACTTAAACAAGGGGCAATTTTACGGGCAATAAGACTTGAATCTGGCAGTTCGCCGATGCGAATCGCCAGATCGAATCCGCCAGCAATTAAATCGGTTGTCTTATCATCCATCTGCATTTGCAGCTGTATTTTTGGGTAACGGCGCAGAAACTCGGGGATCAAAGGGGCGATATACAAGCGGCCAAATACCATAGGGACAGCGAAGCGCAAATTGCCCTGAGGTGCTTGTTGTAGCTCGGAAATAGCGTCCAATCCTTCCTCTGCGAGTTTAACAGCAGGGCGAACGTAGTCGAAGTAGCGGGCGCCCGCTTCGGTAAGACTTAAGCTGCGAGTGGTACGGCGCAAAAGTTGAATACCCAGACTTTGTTCTAATAGGGTAATGCGTTTACTCACCGCCGATTTACTCAGCCCAAGCTTTTGACTTGCCAGTGAAAAGCTACCGCATTCCACTACAGTGACAAAGATAGGTAGTGCCGAAAAGTGCTCCATGGTGTCCTTAGATTATCGCGTTCCCCAGCCATTATTGTTGATGATACCTCAACAGTCAGTTTTTATTATCGTGGTTATCATTGCCCTTGATGCAAGGTAGACTAATGCACTTGCTGATTGCGGGTGTCGACTGTGGAGCCTAGTTATTTGCGCTCATCGTTCATGTTTATTGCTTATCTTGATTACTGCTAGGCAGCTAGCCACAGCATTGCCGCAATCGCTGATGTACCATTCTCGATGCCACTGAACGGCATGGGGATGGGTTTTAGCTATTTTGTCAGAAAGAGAGTTCCTATGTCCTTGCCTTACCAATGGATTTTGTTCGATGCCGATGAAACCCTATTTTATTTTGATGCCTTAAAAGGGCTTAAGTTGATGTTTAGTGAGTTTGGGGTCGATTTTACCCAAGCCGATTTTGATGAGTATCAGTTGGTTAACAAACCGCTTTGGGTCGATTATCAAGATGGCAAAATAACTGCCGCCGAGTTGCAGACCATACGTTTCGAACCTTGGGCAGCCAAATTATCAGTCACGGCCATGACGCTCAATAGTGCATTTTTATCAGCAATGGCCGAAATTTGTTCGCCGCTACCTGGTGCTCGCGAGTTATTAGCCGCGCTGCAAGGTAAAGCTAAACTAGGCATCATTACCAACGGTTTCACTGAACTACAAACCGTGCGATTAGAGCGTACAGGCTTGCAGCATCATTTTGATATTTTAGTGATTTCAGAAAAAGTCGGTATCGCTAAACCCGATGTGGGTATCTTCGATCACGCCTTCGAACTCATGGGCCATCCTGAGCGCGATACTGTGTTGATGGTCGGCGATAACCCGCATTCAGATATCCAAGGCGGCATCAATGCTGGTATTCATACATGCTGGTATAACGTCCACGGCCACGACGTACCTGCCGGTATCACCCCGCACTATCAAGTGGGCTCGCACCAAGAGCTGCAAAAAATCCTGTTACCGTAAGCGGCTTATCACTGAGCCTAGTTCAGCACTTCGCACTCGATATTTTTGCTAAGAGAGAGGGCTTTAACTGATATTCCCTCAGTGCCATAAAATACAAAAAGCCCTTACATTAAGGGCTTTTTAGTATCTAAAACGTGTTAACTATCATTACTCTACGTTTTGGATCTGAATGTTTTCTTTATAGATAAAACTATTATTTAACAATGACTAATGTATTTTTTTTGCAGTAATCCGCATTCATGCCCACCAATATACCCACCAGTTACATGTGGCTTAGCGAAGTTAGCTTGTGCTTGGGTGTTGTTAATTAATTTTCCGCGAAACGCAATAAACCCAATCCTATCCGTACTAAAGCTGCATTGGCTGTTTGTACATTGCAGTGAGTTTTTAGGTCTCTTTATGCAGGAAAGATAAATTATCTATGTCCTGAGATTGGTTTAACTGCTTGTAGTAGGGCTAATGTTTGCTCTGTCAGTGGAATTATGTGATCACGATACATGAGAATATGCACACGATGCATTTTCATTCTTGTCGCAGGGAAATGCCAAATACGCTCTTCAAAATTGATTTCATCCCAACGAGCAGCAGCCGCTTCGTGCAGCGGCATCTGAATTGGTAGTTTAATTTTCTGATATTATTATGTTTTTAGTAATTTGATTTTAGTGGGTGTTACTAAAGTCGAGTGCTAGGACATTTATTCAACTAGTAATGATTATGCTCGATGTAACTGTAATGACGGTTATTTACTTTGTTCATTTTTTTTGTTCGTCAATTCGTGAAGCTTCGAGATCCCGTCTGCAAGTTCTTTGTTAAGATTCAATTGTTTTGCAAGTTCTTCACATGGGGACAGAGGACTACATGCACGAGAAGGTGTCATTATGCGCTTAAATTTATGGTTTTCTTTTTGCATCATTAAACGCTCTTTTTGTTCTGCCTTTTGCTTTGCTAATTCACGCTTTTCGGCTAAGTAACTTTGGTAGTTGTCATTTATCAGCTTCGATATAATCTCAGCCTTTTTTTCTCCTTTGCACATCTGCGTTAACTGCTCTGATACTGACCGTTCGATGCTAATCGAAAGCGATACTGTTTCGTTATTTTTACGGCAATTTTTGACGCGCCATGCGTTTCTCATTTTTCGAGCAAGTTCTTTTGCTTTTGAAAGGCTATAGCCTTGGCGTACTAATTTTTGCCCTAACGATATTATAAAATTGTTTACGTTTACTGTAGTTTCGGTCGAATTAAACCTAATCTCATTTCTTTTTAAATATGCAATTTCCCAAACGAGGTTGAATGTGCTCTTAAGTGCAATAAATTCAAATAGTTCTGTTGCCGCTCTGGCATTTAGGTCACTCATTGGTCATACCTCATCAGTAATTTTTGATTTTTAATGTAACATGTAATGTAACATGTGTGCAATGATGTTATATGTACCGCCGTTATAGTACTTTTAAAGGCTTTAAAGCTGATTTTTAGGATACTTAATTGAACTTACTGCACTCAATGAAAAATGCTACTTCCGATTCAAAGTTACTACTTCTCCGGTGGGCTGCATCAATCGAATTTTTGAAAAAGGCGTATAACATTGACCCTTTACTCGCTCCAATGGAACCTAAACAATTGGCTCAGACTTTCGGGTTAACTGAAAGGCGGGTAAAGAGTGCACTGACATATCTAGTTCAGGAAGGCTATGTGGATAAGTATCCTAATCGTTATCTGAAGCGAGACGGTGTGTTAGGTCCTAGATTTGAGTATTTTTTAACTGATGCAACTTGGCGCGGATGGAATGAATTACTTGGTGAGTGTCTATTCAAAAATGAATTTATTCATGCACTTCTTGATAAGAACACTACGTTTCCACAACAAGAGTTTAGATTGCTTTGGGCTGCTTTAGTCTTATTAGCCGATAGGTTTGGGTATGTCGTCGGCTACAGCGAGACAGAACTAAAAAAAATGTTAGGTATGTCGGATAAAGAATTTAAGCTAAGCATCCAAGCGATAAAACGTACGGATGGTGTTTGTATGCTTGCTAGGGAGGTTAGTCGTACTGCTTTGTTCACTCGCTTACCCGCAATTTTTCAGATACGTACTAAAAGTTATACTGGGCAACCAGTGCGATTAGGAGTGCCGTTACCATTTGACTTTTTTAGACAGTTTGATTTCTTAAATAAGTTTTCTATCTATCAACGCCGCTCTAGGAAGCACCGAGCCTTACGAAAGTTTGCTCCTCAAAATTCGGAGCTTACTGATGAGCAGTATTTTAATTTATCGAAAATTTTTACTAATAAGAAGCTCTATGCGTATACACACCACTTTTGTATGTTAATAGTCTGTTCGCTATCACAAGAGTTAACTACTGCCGATTTATGTAACTATAAATGTCTGGAACAACGAGTAAGATCTGAATTAGACAAGGGCTTTAATCAGGCTATGTTTGCTGAAACTACTTCTGTCAGCGATGAAAGGAGTAAGTCTAATAGTGATCTTTCACTACTAAGAGAGTTCACATTTGAAATAATGAGCCGTGAAATCGCTGATAAAATTACTCTGCTTGGTAAGCAACTTAATAGATTTCTTAGCATTTCTGGAGGGTCTATCAATCTAATGTTTTTTTTACCAAAACAGCACATGTTTTTAACTTTTTTGGAGTCTGAAGAACAATCTAAGATTGAAGATGAGGATAGATCTCAGTTTGAACAAGGAGATTACAATCTCCGCAAAAAGCAGGATCTTAAACTCACAGCATTGATGCTATATGTAACGTCTAGTAAACAACTTAAAGACTGTACTGTTTATGGTGAAAAAATACTCACTACTGTAGTGGTAAAGTAAAATTGGGCACGGTTTTTGGTGTCATAGAAAGAAACATTAACTCCAGAGCCGATGTTGTTCATCTATCGCTAATAATGTTTGGGACGTTCAAGACATTTTTTAGCTGAACTCCGAAGATAGTTAGGGTGACTAATTCGGTATTGGTAGTATATAGAGCCTGTTAGTAACTCTTTTCATACACTTTATTTGACCTGTAATGTATAAAAATACTGTGGGTACTAACTCTATGAATTAATCATAGGAGATATACTCATGGAACACATCCTTCGTCAATTAGTAACCCTTAATCAATACCATTACAACGAGCTTTACTTGTTAAAGCTCAAACAAATTTTATCCCAACAGTTTCATGCCAATAGACGAATTACCTGTATTCGTTTTGATTTACGATTTCCGCAAAACTCTACTTATCGTGATCCTCAATGCATATCGCGTTTCTTTGACTCATTTAAAGCCAAGTTGAAGGTATGGGATAAAAATCGTAAAGGAGTCCATCCTTTAAATATGAGCTATGTTTGGGTAAGAGAACAAGATAGCTCCCATAACTGGCATTACCACGTGGCGGTATTTATGAATAAGGATGCTTGCATCCATATTGGGTGTTTGGATTTAAATCGGGATAACATGTTTAGCCGTATAGTTGGAGCTTGGGCGAGTGCAATAGGCCGGATAGATATTGAATGCCACTCATTAGTACAAGTTCCGCAGCAAGCTGTAAAGCATTTAAATATGAATGACGCTCAATTTACTTTTTGCTTGCAAGATTGGTGGTTTAGATATAACTACTTGGCTAAGGTTGCGACTAAGGATATCTATGATGGTAACCGTAACATTGGTTATAGTCAGAAGGTGTTCAATTAAAAAAATAAAGAGGGCATTGAATGCCCTCTTTTAACTTAGTTAGCAAGTTTCATGAGAAAGTTCATATTTCTAGCTCCACAAAGTCCTTATCAATATCGTCCTGCTCAATACCGATATAACGCAGGGTGGTTTCCTCTGCGCTGTGCCTGAGCATCTTCATTACTCGGCCAATATCTTTGGTTTGTTGATACAGCAGATAGCCTCGCGTCTTACGCATTGAGTGGGTGCCTAAGGCGATTTTGAGCTCTTCCCCAATCATCGCTAGGGCGCGGCAAATATAGCGGCGTGACAAAGGCGCGGGTTTAATGTGCTTCGCTCTTTGATTACGGTAGGACTGAAACAGGTAGATATGGTTAGGATGCTCGCGCTGAATACGCTGTATCCGTTCCAGCGTCTTTGAGTTGAGCTTGATATTGGCGATTTTGCCAGTTTTGGATTCCTTAATGATAAGTCGATCGCCCTGGATATCGCTGAACTTAATCGAGAGTAAGTCGGATATCCGCAGGGCGAGATTAAGGCCGATATTCCACACATCGGCCAGTTGTTGGCTAAACTGTCTTTCCAGTAGGTAGCTGATAAGCTTAACCGTATCTTGGTCTTTAACCGCGTGAACCTCAGGCATGGTCATGTTCCTCTTTTCTGTTATAGCGTTTTTTTGGGCACTTTCGTTGTTGAAAGCCGCATGGGGAAAGGGTTTGATTGTTCCCGTTTTAGGATTCTGGGAACTTGATTTTGTTTAGTGCTAATTCAAACTGACCTTCACCTTGTAAACCCCCATATCGAAATAAGTAGCAAAGTTATGAGTAAATAAATTGATTAATGATCTGCTTTCTCGCTCGCCAGTCCAGCCACTGAATAAGTTATAGACCTGCTTACTGTTAAAGCAGATATCGATGTCTTTGACGAGTTCTGGATAGGGATGACCTCGATAAGCAAAATCTGTGACGTATACTAAACGCCAATGTTCCTGCTGTTTCTCTAGCCGAATCTCTACGGGATGAAAGCCACCATCTTCGGCGCTGTAGTCGGGATCTCTAAAATTGAGCGTGATCGCATTGGCAACTTTGGGATCGACTTCAGCCAGTTCCTTTTCAATGACTTGGTAAAAACGATTAGGCATTAATGGCAACTGGTTACGGTTAATGATTAACTTCATGGAATATTCCTTTTTCAAAAATTGCGCATATGTAAGAGGCTAGGAGCCATGGCTCCTAGCCTCTTGACGTGTTTGCTGGTGGGATAAGTAATTGCGTTAAATGAGCCTGATGAATCGTAATGGAGTGCATGGATACGCTTTGGCGTTACAAGGTTATCGAAGGGCTAAGTGTTAGTATGGGTTGATAGTTATCGAGCTATAGCATTGACTGAGAAAGCGGACATACCGGTAACGTTTGCAAAACTAGGTATGGACGCTCCAATTGGGCTGTTCGTTGTTGCCGTGGTTCAAGTACACGCCGTTAATCACCCGTCGCCATTCACCCGTAGGCACACCATAAGTGTCACAGGGGCAAACATAGCGGCAAAATGCAATTTCCTTGGGTAGGTGTTCGATAACGTCTCGAGGAAAGACAAACAGTACTGTTTTATGTAACTGGCTGTGGGGAGCAACAATCGGCTTACCAATCACTTGACCCATGGTATTAAGTGGGCGTGCGAACATCGGCAAAATCAGTTCAATCGCCAAACGGTTACGTAGTTCAGGAAAAGCGGCGCAGGCCTCCGCGACAAAGGCCTGAGAAAAGCTGTCGGTAAATTCAATCATGTTGGACTCCTTGATTGGGAATGATGAAAGGCTGCTGAATGCGCGGCTAGAGTAAGCCTCGTTCAGCAAATGACACTGGTGTGCGACCAACCACAATATGGTCAAGTACACGAACGTCGATAAGCCCCAATGCATCGGTTAACCGTTTAGTGATGTTCTTGTCTGCAGTTGAAGGTTCAGACTCGCCGGACGGATGGTTATGGGCAAAGATGACCGCCGCCGCATTGACGGCTAATACTGCCTTAACCACTTCTCGCGGATAGACACTGGCGGCATCTAAGGTTCCAAAAAACAACTCTTTAAACTCAAGTAATTGATGCTGGTTATCTAGCAGCATCACCGCAAACACTTCCCGCTCATATCCTCCCAGTTTGTAAGTCAGAAAATCCTTAGTGGCTTGTGGGTTGGTATAAGCGTCTTTTTTCACATAGCGCTCGGCGATGATATTGGCGGCAGAGGCTAGGATCGCCTCATCAGATTGGGGCCACAGGCTTAGATGTTGAGACATTGATTTGGTATGCATTGTTTTTGTGGACATAGTCAGGCTCCTTAAAAGGGCAAATGGGTTTATGCACAGGAGTGATATATATCTAAAAAAATGTTGAATTGGATGCTTGGTAAATGAGTTTGGCCTAGTGGCATAGTTAATCGTTGGGATCAAGCTTAGGTAAGGGCGCTGCAAGTTTAAAAATACCGTCACTGGTTATTGAATGAGATTAACGACCAGGACCTCATCATGAAACTAATTAAACTAAAAGACGTTATCGCATCTACAGGCTTAGGGCGTTCGACCATTTACAAATATATTGAAAACGGTATATTTCCAAAAGCAGTATCACTTGGATGCCGTTCAGTTGCTTGGGTAGAGAGTGAAATACAAGATTGGATATTAGCGAGGATTGAAGAGCGGGACTTACAAAATGAAAAAGCTTGCTAAGCTCTAATGACGTTATAAATGGGGAGCTATTAGCTCCCCATTGTGTATACACTAGCTTTTACTATCGTTCTCAACTGCCCATTTGTAACCTGCAATAACACCAAGCAAAAACAGCTCAGCTATCGGTTTGTGCATCACAAAGTTAAACATAGAAAGACTCCTTTAAAGTACGGTAGATACCAACGCCAACCGCAAAGCCAGCAATAAATGCAATAGGCATAATGCCTCCTTAATTGAATACTAAAAGTAAAAAACAGCTGGTGGCATTGAGTCCACCAGCTGCTTTGTTGTGCCGTAAATTGGTTACAGCAAAGATATGATATATATGTGAAAAAAATTGGAGTAATTTTAAATACTGGTTAACAATCTCCATCATTCCTCATGGTTTTAAATAAACCTAAGGCGAAATTAAATGAAATTAATCAGACAAAAAGAAGTGATGGATTGTACTGGGCTAGGGCGTTCGACTATTTATAACTACATCGCAGATAAAACATTCCCAAAACCGGTAAGCCTTGGTTTAAGAGCGGTAGCCTGGGTCGAAAGCGAAGTGCAAGATTGGATTTTAGAAAGGATAGAAGCAAGGGATGAAGATCAGGTAGCTATGTGAGTCTGATAAAAATATTGGATTCAAAGTTGATTAGTGATGTAAAAGGCTGGAGTAAACAGCCATTAGAATTCTAATGGCAGTTTACTCTAAAGGGATTAACACTAAGTAGTCACAATAAAATCATATTCACGTTTTGCGTTTACCATCGTGCAACCTAAAAGCCCACCTGCGAATGCAAACCAAAGTGGATGCTTCGAATTTGAAGCCTTAGCTAGCAACATTGCAGCTAAAGTGCCCATCACAGCTCCTTTTACTTGAGGACTCGAAGCTTCACCATTTATTATCTCTGAAACTAATTGTTCACAGTTAAAACTGAACACGTTGTAACGGTTTGATTGATCGAGTTTGCTCTTTGCTCTTTCTTGAAATTGTTGAGTTTGAAGAGCGGTAAGGCCACTGGTTACCACCTTGATATTTTTCCCATCAGCAAATTGCTCTACTGTACATATCTCAACATCACCATCAGGAGTGTTGTGTATGACTCGATTATCATCTAAGTAAGCACCAGCATGCTCGACTAGGTTTTTAGAGCGATAAAGAATATCGCCAATTTTAAGTTTTTGCTTCATAACGAAGGCTCCATAAGTTAAAGTATGCATATACATAATCACCATCAAAATACGTTTAACGTATTCGATTGCGATTAATCTATACTTATGCACTTTATTGATCAAGTGTTATTTTGCCAATGGTGATAAAAATGCAAGATAAATTAGTTTCATACTCTTCCGTTTTAGGCGTTGTTGTTGCTAATAAGCGCAAAGAATTTGGCATAGAACAAAGTGTAATGGCTGACTGTATGGGGTTGTCACAAGCTAGCTATAGCCGTTTAGAATCAGGTAAGTCGACATTTTCAGTCGATCAACTGTTTGAGTGTGCACAGGCATTGAACATACCAGCAGCAGAGCTTTTTAATTCTGTCGTAAATACAGTAGGCAACTTGCAGTCAAGCAATGACGTAGTCGTACAAGGCCAGCCTCGAGGCAATACAACTAAAGCTAAAGCTGCAGAAGGTAGTGGCGTTGGGACCTTTATTGCTGGTGCAGCATTGGGTGCGCTTATTATTGGATTAGCGGGTAAGTCGAAGTGATAGTTAAACTAGTTTTAAGGGTTAGAGCAAACAGCTGGACTAAAAACACCACAGTAAAGTTATCTTTTTCTAGGAATACTCTTACTTTCACCACAATATGACAGATGTTATGATCTGCTGTATTCATATTTAGCCTTTAATGGACTTTTTTTGGTGCAGCATAATGCCAGTTAAATCAATATTTTTTGCAATTCCAGTGCAAAATGTTCGCCAGCAATTAGGCCTTCCACAAGAAAATTTAGTAAAAGCACAGGGAATATCCTTTGTTACAGTGAATCGTTGGGGAAATGGTCAAACGGCGCCTTCAAAGATAGCTCAAATATCGTTCGGTAATTACAGTGCAGAAATGATTGAACAAGGAAAGTTGAAGAATGACCGCGTCTGAATCTCATACCCGCATAGAACTAATTGACAAGCAACTTGCTCATGCTGGTTGGACTGAAAACCGGCGATCGCTTGTTGAGGCGTTTTCGTTAAAGGTTTTAGAACCTGAAAAAAACTATACGAGTGAACAATTTGCTGACTATGTTCTTTTAGGCAGTGATGGGAAACCGATTGCTGTTGTAGAAGCTAAGCTCTCATCTAGAGATGAACTCGCGGGAAAACGCCAGGCAGCCGACTACGCTGATGCTATAAAATCTCAATTCGGCCAAGAGCCCTATATATTTCTTACCAATGGTAAGGAAATTCAATTTTGGGATAGAGAACACTATTCTCCCAGAAAAGTGGCTGGATTTTATACGAGAGATGATTTAGAGCGCTTACAGCATCAGCGAAAATATGCACAACCTCTCCACCAAATAACAATTAGTGCTGACATTGCAGGACGAGATTATCAAAATGAAGCTATTCGTCGTGTAACCGAAGATGTAGACGCCGCGAAACGAGAATTCCTTCTCGTAATGGCCACTGGTTCAGGTAAAACTCGCACCACTATTGCACTGGTTGATATATTACTGCGTTCCAAACGCGTACAACGTGTACTATTTCTTGCAGATCGTCGCGAACTAGTGCGTCAGGCCATGTCAGAATTTAAATCTCATTTACCTAACGAGAGCTTAGCTCGAATTGAATCAGGCGAAACTTCTGGAGCTCGTATTCAATTTTCAACATATCCTAGCATGATGCAGGTATATGGAAAGTTATCAGTAGGGTACTACGACCTAGTCGTTGCGGACGAAAGTCACCGTTCAATCTACCAACGTTATAAATCAATATTCGACCATTTTGATGCTATTCGACTGGGTTTAACAGCCACACCTACAGACTATATTGATCACAACACTTTCAAGATGTTCGGTTGCTGTGATGGTATTCCCAGCTATTACTACGGTTATGAACAAGCCATTGCAGACAAAAATTTGGTGAATTATCGAGTATTGGATTCGCAAACAAACTTTCAACTTAAAGGTATCCAAGGTGATGAACTTCCTGAATCATTAAAACAAATGGTCTGTGATCAAGGCGTAGAGCTGGATGAACTCAACTTTAATGGCAGCAACCTCGAAAAGGGCATAATAAATCAAGGCACTAACGACAGCATGATTCGTGAATTCATGGATAAGTGTCGCAAAGACATACGTGGTCTGCCACATAAAACTATCATATTTGCAATGAGTCACGCTCACGCCAAACGGTTGAATGAAAGTTTCAATCGGCTATACCCAGAGCTCCAGCGGCAAGGTATGGCCGAGATTATTGATAGTCACATGGATCGTGCGGATTCAACACTAGATGATTTCAAATATAAAACCATGCCACGCGTTGCCATCTCTGTCGATATGCTCGACACTGGGTTGGATGTTCCTGCTATTCAGAATTTGGTGTTCGCCAAACCTGTTTTCAGCAACGTAAAGTTCTGGCAGATGATTGGGCGTGGTACGCGGCTTCACGCTGACAAAGCCACCGGCGAAATCAAGAAAGATTTTTTGATCATCGACTATTGGAAAAACTTTGCTTATTTCAAACTCAAACCAGATGGCGAGCTTGCTCACTCAAGCGAACCACTTCCAGTACGTTTGTTTCGCTTACAACTGGAAAAGTGGCAATTTCTCGATTCGCAGAAGCAAGATACAAGTGCAAGTGTCGACGAATTACAGGCAATGCTACAAACATTGCCTTGGAAAAGTATCAATGTGCGTCCTTATTTGGATGAGCTCGATGCGCTAATGCATCATTGGCCCGAGCCCAGCCTAAACATACTCGAACACCTGTCGAAGGTTATTGCTCCGTTATTGCGCCTTGCACCTATAACAGGGCTCGAAGAACTCCAATTTCGAATTTGGTGCGAGCGTCTCACAATTGCCTGGCTCAAAGGGGATGTAAAAGAGCAAGCCACGCAAAGGCAGCGTATTCAAGACGCTATTGGGAGCCTAGCCGACAATATTCCCGAGGTTCAGAGTGTACAAGCTGAGCGAGAATGGGTGAGTACGGATGGTTTTTGGCAACATTTAGATGTATCAAGATTGGCTACATTACGAATAATTTTTGCTTCATTAATGCGTTACCGTATAAGTAAACTAGAGAACACTGTCGAGATTAATTTACCCGATAAAATTACCCAACGGAGTTGGATTATCTATGGCCCAACGGGAGAAGGCGCATTTGCTGAAAGCTATCGGGAACAAGTCGAGGCGCTAGTACGTCGCTTGGCCGAACAGTTGCCGGAATTAGCCAAGCTCAAACAGGGAGGAACCCTGAATGACGACGAATTGGAAAATATCGGTCAAGCACTCAATCAAACAGACCTATTCGTCACAGAAGGCAATTTATGCAAAATATTCGAAGCGCCTACAGCATCATTAACGGATTGTCTGCGTCATATTTTGTGTGATGCTGTTAATTTACCCAACCGTGAAGAACGAATTAGTACTGCTTTTGACGATTTCATTGCCGCACACGGCTACCTGCACGCTAATCAACTTAACTATCTGCGTGCAATTAAAGTAGCTGTGTTACGCCATGGTAGTATTACGCGAGCAATACTCAGCGAGAAACCATTTTCGCGTGTAGGTAAAGTCGAAGTATTATTTCAGCAACAAGATATAGAAGAACTTATTAGTCTCGCCAACCAGCTGTTGGTCGAGGTAGCTTAATCCCCAAGGATACATTGAATGCTTGCTCCACACGTAAAGAAAAATGTCGACGAACTTTGGAACCGCTTTTGGGCCGCAGGATTGACTAACCCTTTGGTTGCAGTTGAACAAATTACATACTTACTGTTTCTTAAGCGGCTAGAAGATATCGATATAAAACGCCAGCAACGTGATCTGCCGTCTATTTATGAAGGTTTCGAAACCTGTAAATGGAGCTATATCCGTCAGGAAAAAACAAATCCTGGTCATTTGATTGATGTGGTTTTTCCTTGGCTTCGAGAGCTAGATAAACACTTTAAGGCAGCATCCGAAGAACATAGCGAGCTTGCAAGTCTTAATAACCGTATGGCAGATGCTTATTTTCAGCTTGATCCTAATAAAGGAAAAGTCCTCTCTGATGCTATCGATAAAATAGACGAGTTATTTGCCCGTGCTGGAGATGGATCCGCCACCCAAGATATTATGGGTGATACCTTTGAGTATCTACTTAGCGAAATGGCCACCGCCGGTAAAAATGGTCAGTTTCGAACACCACGTCATCTGATCCGTTTTATGGTGGAGCTGATGGATCCTGAGCCTGGCCAACGAGTCATCGATCCTGCAGCCGGTACAGGAGGTTTTTTATTCAGCACCCAACAATATTTAATGCGCAAATATTCAGCTACAGAAAATTTGGTATTGGAATGGGACGGTACACCGCATCGTACCGATGGTGCAGCGGCTACCCCAGATCAATATTCGGCCATTCACAGTGGAGCCAACTTTGTGGGTTTGGACAACGACCGTACAATGGCGCGTATTGGTTGGATGAATCTGATATTGCACGATATTACCGACCCGCATTTGCTACAAGGTGACTCTCTCAGCAAGAGGGAAGGCAAGCCAAAACAACTCTCTGATTTGTTAGCGTCGGAAGTGTATGATTTTGTGCTAGCCAACCCTCCTTTTACTGGCATAATTGATAGTGACGATCTGGAACCAGATTCTATTTTATTCCCGCGAGTTGGTGGTAAAGGCAAAAAGAAAGACGATTCGATCACTAACAAAAGTGAGTTGCTATTTTTGTGGTTGATGCTCGACTTATTGCGTGTGGGTGGCCGCTGTGCGGTGATTATTCCAGAGGGAGTATTTTTTGGCAACACAGATGCTCACATGCGCTTGCGCCGCGAATTATTGACCGAACATGTCGTCGAGGGTGTGATCTCATTACCAGGAGGCGTATTTCAACCTTATACAGGCGTTAAAACGTCAATACTTATCTTCCGTAAGGAAACCCGCCGCGACGACAAACAAGCTTTCACTGGTAGCAACTCACCATGCACTGAACATGTTTGGTTTTATGAAGTTGAAGAAGACGGTTTTTCGATGGATGCCAAACGTAATGACCGTCCTGACAAAAATAACGATTTATGGGATGCACTGGGAAAATTCAAAGTCTGGCTTAGCCATGGTCGTGACGGAGCATTGCGAAATAAGACATCACTATTGCAGCCCAGCTATTACACAGAGCGCTGGCGCCATTCCATGCTACGCGACACCGCCAATAAACTTACCCCTGCGGGCGAAGCGTTTTCCGGATTATCCGATACCAGCATGTGGGATGGAAAAATTTGGAGTATCCACGAGCTGTTTCCTGAACTTCCAGCAGATCCAAAAGATGCAGAAAACTTGGTACGTACCGCCGTTGGAAACACAGTACAGGAACTCGTTATACAGAATTTTCATCCTGTAGCTTTAAAACTTTGGCATGATTTACTTAGCGTGGGCATTTCAGAAAAAATAACAGATGAAAAATTAGTTTTAAAATGGAAAAAAGCTGCCAAAAAAGCAGAGGCTGAATTTATCAAAGAAGCCCGCACCTTCGAGAAGTTTTTTGAAAAAGAGAATTCTTTTGCACTGCTCATTTGGAAAGAACTAATAAAAGAAGCTCTTACAAACGCACAAAAAGACGAATTTGTACTTGGAGTTCTCAAAGTAAACCAACCACCAGAATTGCTAACACAACATGACTTACCCTCTAAGCTCGAAGGGCTCGCTCGCGAAGTTGCAAAGCTGGATGGCTTTGATGTGAACTTGCGAAGTCTGCCAGTTGATCAGGTCTCCGAACTCAAGTCCGGGAAGCACTGGGTTGTGCCAGTACGCTCCTGGGCGCGTAACGACGAATGGCAATCGGAAGATGGTCATCAGTTCGGCTCCCACGATGCAGATGGATTAGTTCGCTCCACTTATGTACAAGCTATGCACGAAGCCGGGCTTTACGATAACAAAGGCAAGCTAAAAGACGACTTGCTCGACCCGGATTGTATTGAAGCGCGGGACTGGAACCTTTCTGCAGGCCAGTACAAACCCTTTGATTTTACCCAACTGAAAAGTGATAAGAGTGTTACCGAAATAATTGGTGAGCTTAAGGATACTGAGCATCAGATCATTAGTGGACTAGATAAGTTGCTGGCAATAGTGGAGGGCCGGGAATGAAATTGCCAGAAGGTTGGGTAGAAACAACCATTGGAAATATTATTGATGACATGCAACCAGGATTCTCACAAAAACCTGGTAAGGAAGATGGAGATACTACTCCACAAATTCGAACGCACAATATCAGTCCTGATGGGAAATTAACCCTTGAAGGCATTAAACATGTAACGGCTAGCAATAAGGAATCAGAGCGTTATTCTCTTACAAAAGGAGACGTTGTTTTCAATAATACAAACAGCGAGGAATGGGTTGGTAAAACTGCAGTGTTCGATCAAGAGGGTGAATTTGTTTTTTCGAACCATATAACGCGATTACGTGCTAATAGTAAATTGATTACCCCGGATTTTTTGGCGGCTTATTTGCAATTTTTATGGTCTATGGGTTTTTCAAAAACGCGAGCAAAGCGCTGGGTTAGTCAGGCTGGTATCGAGGGTTCAACGTTAGCATTATTTAGGATTCCTCTCCCATCTCTCCCTGAACAAGAACGCATCGTAGATGTGCTGCAACAAGTAGGGATCGTCGCCAAAGCGAAGCAATCTATTGATGACCATATTGACAACTTAGTCCGCACTGCGTACTGGGAGCACTTTAGTGAATGGTACACGGCTGATGGATTGCGTGACCCTGTGCGTATATCTGATATTGTAGCAGACTCACAGTACGGCGTATCTGAAGCAATGAGTGAAACTGGCAAGCAAGCAATATTGCGGATGAACAGCATTACCACCAGTGGCTGGTTGAACTTAGCTGATCTGAAATATGCAACGTTGTCCGAAAAAGATATTAAGGCCACCACACTGCTCAACGGTGATTTGCTCTTCAATCGGACTAATTCAAAGGAGTTAGTGGGGAAATGTGCTATCTGGCGCGGTGCAAAAGAGCCATTCAGTTATGCCTCCTATATCGTTCGCTTTCGAATGAAAGAGGGCATATTGCCTGAGTATATATGGGCAACACTTAACAGCTCTTACGGTAAGTACCGGTTGATGAATTCTGCGAAACAAGCGGTTAGTATGGCCAACGTAAGCCCGACTGATTTAGGTCGCATAACAGTTCCGTTACCGCCGTTAGCGTTGCAAGAAAAATTTGCGAAACTGATCAATCATATTGAAACTTTACGTCAGGAGATGCTTAACAAACAGGATCAATATTCAGAACTTCAAACACTAGTAACACAGCAGGCATTGCTTGGTGAACACACCGCTCAATGGCGTGATGAAAACAGAGAGAAGGTTTTGGAGGCTGCAAAAGCTCGCGATATTTTACTACGTGAACAGGGAGTAAAAATCACCAAGTTTGCACTAGAAAAAAAACACCCCAAAAAACTAGTAAAAGAAAGAGAACGACCCGCAAGACAATGGGTTGTCGATGAGCTCAGTGAGTTTCAGCGTCAAGTATTGGGCGCATTTCTTGCCTACCACGATCAGCCACTACTTGCGGAAGATGCCGAGGTGTTCGCTCGTTTTTGTGAGGACGACGAAGTGTTAGAGCATCTTAGCGCTTTTGGTTCAGCCTTAAACAACCGAATCCGCCGTACCTTAAGTCAGCTAGCCTCACTAGGTTTGATCGCCAAAGTCACCTTGCCCAAGGACAATTTGGAAACGGGTGAACGAGAATACCTTAAAGCCTTTCGACCTTTGCGCGAAGAGGAGTTCAGAAGGCTGGAGGATGTTTCGGAATTACGTAAATTGCTTGCAGTGAATGAGGCATCATATTACTTCACCGTAATGCAGGATTTTGAATCATCCGAACATGCCGGTGCAGGTGGCATGTTCCAAGTGATATCAATAATTGACGATAGCGAAAAAGATGTCTCCTACCTTATTGATAGTGGTAAGCACTATGAGTCGCTTTGTGAACTGGCTGTAGATATTGCCGTCCATTTAGGTGTACGTACTGAGCAGATAGAACTGGAGACGGATTAAGACTATGCGCTTACGTTATCTACACCTACAGAATTACCCACCAATCAAAGATATTAGCGTTTGCTTTGCTAGTGGCTCACCTTTGAATCGAGAATGTGCCATCCGCTTTGTGGTGGGTGTGAATGGCAGCGGTAAGTCAAACCTATTGCGTGCCGTGACCGAAGTGTTTCTAGCGTTGGCAGACCAGCGCTTACCACCCTTTCCGGTTACATTGGTCTACGAACTTGGCCAGCGTGGAAAATCAGGCCACTTCACTTTCGTGGTAAGCTGCCCTGGCAACAAAGCGAAGGCTTGTGTGTGGATAGCCGAAGGCTGGCATTGGCCGGACACCACCGAGAAGCACATCTTTGCAGAAAATCTTGCACAGTTGCACGAAACTGGGCTATTTGAAAAATTCCGTGAACATATCGCTCGCGGCACTTGGCCACAACAACCCGGCGTTGCTATGCCAAAGGCCGTATTGGCTTATTCAACGGGTGATCAGCGTCCCTGGCAATCTATTTGGAACCGAAACCAGGATGGCCAAGGTCTACTTGATAACAGTGAAGAAGCCGATTTCACCAATGAGCGCCCCGCAGGCTGGTCATCAGCACAAGAGGCTGCACTACAAGCAGCCAAGCAAACCGATGGGGATCTTGCTATCAGCCGTATGGCGATTGATTCCGACTTATACCGCCGTCCCATCTTGCTGAATGCCATGCTACTTAAATGTGCCTTGTTAGCTGTCGCCTTGCCTCAAGCATTAGCAAATGAATCAGAGTACAAGATTGAATTACAAACGCTGCTTGAAAGTTGGGGATGGCACCAGCTAGTATCGGCAAGTTTTCGAAGCCACTTGCAAACCCAAAATTGGGGTCGACAACTCCGCGAAACTGCTCATGATTGGTTATCTTGCGCGGGTGAAGTCATTGCCGAGTCACATCCACAGGAAACACGGCGTTCTTTGTTTTTTGATTTAAAGGGCGTGAGCAATGGCCGTGACTTGCTAACTATTGATACCAGTGACACATGGTCCAATCAAGGTGAAGCTCTTTTAGATTTACTTGGTGGCAAGAATACCAGCTCATTTGAGCTATTCACTCGTTTATTGGCGCTCAATCAGGCGGGCCTATTTGATGATGTGTTATTGCGATTACGCCGCCAGCCACTGCCAGAGCCAAATACCGAGGCCGCCGAACACGACATTGGTATTTTACGTTACGAAGAACTTTCCGACGGTGAGCAAATGGTATTGGGACGTATGGCGCTCTTCCATTTACTAAAGGGACAGCAGGACGCACTATTATTATTAGATGAGCCGGAAACGCATTTCAACGACAAATGGAAACGAGAAATCGTTGATATCATCGACGATGCCATTGGTGATACTGCTAACGATGTTTTGATTTCTAGCCACTCAGCGATTGTTCTATCTGATGTATTTAATAATGAAATCATAATGTTAGAGAAAGCGGGTAACGGTTCAGATGTACGTAGCGTCGATGCCCCTACCTTTGCTGCTGATCCAAGTTCTTTGATGATGACAATCTTTGATGCTGATGACAGTATCGGTAAGCGGGCACAAGAGTTTATCGAAAACAAACTGGAACAAGCAACGGGTACTCCAGACGAGATTGTCGACCTTGAGCGTTTGATAGCGCGTATGGGGTCTGGTTTCTACCGTAGCGAATTGCGCACTTTGTTGAATTCATGGCGGAGTGAAAATGCTAAAGCCAATTAACCAAGCTATATGTTCAGGTGCTTTGGCAGCTGGCCAAAATTTGATGCGCTCGTTCGCGCTTTGGCTTTGTGATACAAGAATAGATGCTACACATATAACAGAGGCTAACTGCCTGAAAAGACTACCTAGCCATATCGAAGGTAATTGGCTTTGGGATTTTCTACAAAAAAAAACTAAAAAAACGCCTTTACTAGAGCACGCATGCACTGTAGCCGAGTTATCGACCCCGCATAAGAATGCATTACGTACTTGGGTTATGGCCGTGTCCAATGTGCCTCAGCATCTTCTTCATCCGCCGCAAGCAAACTTACCAACAACCCCGCCGTTGCCTAAGGCTGAATGGGAACACTTCAAAATACTAATGCTTGCCTTCTACTCTAAGGGGCTACATGAAATTGGATTGCCTTATTTAGATAATGGAACTCCTACAAATAACAAAATAATGTGGCTCACTTACCACCGATTTCGCGATGCATTTTTAAATGCCAATCGAATTGGTAACAACCCTGATAATGGTAATATTTGCGTGATATGCGGCGGTGCCATGGAGCAAATTGATGTGGATCATTGGATTTGTGAGGCCGCTTATCCGTTACTATCAGTATGTGCGTATAACTTGATACCTATATGCAGCGTATGCAATAAAAGACCTAATAAGGGAACGAAAAATGTTCATACTGATGGCAGTTTTAATGATTGGTATCATCCCTATTTACGCCAGGTAAAAGGCGGTTTACAACTGAACTATTTATTACAACCCAAGCCTTCAGTAAAGGTCAGCTCAGTCTTTATAGTGGATCAACCTAAGACAGTAAATTTGGATAGCTTACTAAATTTAAGTGATCGCTGGACTAAGCAATTTAAAAGTGAGTATTTAAATAAACAAGATGAAATCCGACGATTAAAAGCTAGAAGGGAACAAAGAAATGGTCCGCCACTGACCCAAGCAGATCTTGAAAACAAAATTGATGAAGATAAAGAGATAATGTCACCTCAAAGTGCAGATTTTGAGGTACGGCAACTAGTTTGTGCTGCTATTCTCGAAAGGGCACGGCTTGATGCATGGCAACAAGAACTGGAGTTGGAAGATTGAGAGTAGTAATTGTATGCTAACTTTTTGCAGATATTTTTAGCGATGAATCATCGCTAGTTTATTTTTTGTTTTGATTAGAAGTGTACTGGGCTTACTTAGAGAGTAATTATTTTGTGCTGAGCAGACCTTGCATTAAAAAAGGTGCTTTCCTGTTCTTTCTTTGAGGCTAGTTATAATGATTTCCCATTGTTAGTAGGTTTGAAATAATTAAATTATAAGCTGGTAACTATACCAGGGAGGCCTCACTTAGTTAGGCTTCCTTATGTTTTCTGTCACATAATTTAAAGCATGCAGGCTGCATGGAAAATGGCCTTATTACTTAGTTGATAATTGACTGCAACTTCTCTGATAACTTCGTCAAAGCTTCCTTCCGCTCATCAAAATAATCATGCTGGTTATAGATCCCTTCTACACCTTTAAGCTTATGATTCAAGCAACGCTCTGCTACATGCCCAAGAGTACCTTGCTTGGCTAGTAGGGTGCGGCAAGTGCGGCGCAAGTCGTGGACGGTGAAGTGAGCCATATCACCCATTAAGTTAGGTGGCTGTTTCTTCTTGCCGGCTTCATGACCAAAAAACTTAGTGATCGCTCTGTTTAATGTGTCTGAGCCCATGTGTGGGTTCTTGCTTGAACGTCTGCTAGGAAACACATATTCAGAGCCACAAGCCCTTACCTTTAATTCTTGTAACCATGTCATAACCACTTCAGGCAATGGGATTGCTATGCCAACACCTGATTTACTTCGCTCTTTCGATAAGTTCCAAACCTTCTGCTCTAGATCAAACTCTTTCCATGGCGCTTCTGCCAATTCAGACTTCCTAATACCCAGAGTAACCAGTAACGCACAAGCTAGATAATTGTCCCGACTAAAGCTGTCGTTGTTGTCACGTGCTGCTTTAAAAAACTGAGTGAGCTCTTCGATGGTTAAGGCTCTGTCTTTACTTTTTTCTACTCCACCAGCATCAGAAACAGAAAATGCACTCGCTGGATTTGCGCTAAGCAGATCCAGTTTTATGCCGTGATTAAAAAGTTGTTTGCTGTACATCAGAGCGTCGTTAGCGATTGCTGGGCGGTTAGTGGCTGCAATGGCCTGTAATACTGCTCTTATGTCTCTGGCTGTGACGCTGTCGACGTTGAAGCTACCTATATGGGGAGCAATGTCTTTACGGTAGATACGTTCGGGGATTTCGTGATGTTTGAGTCTTTTGATATTACCCAAATGCCAATCAGCAAAGAGATCATCAACAGTTTTAATTTCGGCTTGTTCGGCGCGTTTCTTGGCAACAAGAGGGTCTAAGCCATCACGAAACTGCTTCATTTTAATTGCGGCTTCAGCGCGTGCATCTGCCAATGAAAGATCACTGTATTTAGCCAGTGTCATCTCTTTGCGTTTCTTGTTGGCGGTGTAGCGTAGCATCCAAAATGGTTTGCCGGATTTGGGTACAACAAAATATAGGCCTTCACCATCGGCGAATCGACCAGGCTCTCCCTTGGATTTTGCTGCTACTTCCTTTGCGGTTAACTTTCCCATCAGCCCTTAGTCTCCAGTTAAATACAAGGGTGGGTGTTTTTACCCACCACTATGCATTGACTATATACTAAGTTACTCACCATATCACCCACCATTTATCGTTGGTAAGTGGTGGGTATTAATGGACCTCTTTGGATTGATATTTAATTAAACCCTTAAAAATCAGTAAATAAATGTTTTATTTGGACTCTGTTGGATGTTATTCCACGTTTTGGATCTGCTCGCGCATTTGCTCGATAAGAACTTTAAGTTCCACTGCGGCCGAGGTGATCTCAGTGCTGATGGATTTAGAGGCGAGCGTGTTCGATTCGCGGTTGAATTCCTGCATCATAAAATCTAAACGACGACCTTCGCTGCCGCCTTTTTTGAGGATGCGGCGGGCTTCGGCCACATGGGCTTCGAGGCGATCCATTTCTTCGGCCACATCTTGTTTCTGTGCCAGTAGCACCATTTCTTGCTCGATGCGGGCTGGGTCGAGTTCACCTTTGATTTCTGCAAGGCGGTTGGTGAGCTTTTCGCGTTGATACAGCATCACAGTAGGCATGTGCTCACGTACGACGGCGATTTGCTCGCTCACGCCATCTAAGCGGCTTAGCAGCATGTCTTTGATGGCTTCGCCTTCACGGCCACGGGCTTCGATGAATTGATCTATGGCTGAATCGAAGGCTGTCATTAGATCGGCGCCGATGGCATCCATATCTTGTTCACCGCTTGCTAGCACGCCCGGCCAGCGAAGGATGTCGGTCAGATTGACATCGCCTTGTCCCGCTTCTTGCTTGAGCCAAGTGGCGGCGCCTAATAGCTGTTTTGCTAGCGCTTGATTCAACTGCAGCTCGTTATTGCTGTTGTCTGCCAGTTCGTAGCGCAGATTGACTTCGACTTTACCGCGGCTTAAGCGTTTACGTAGGCGGTCGCGCAGTACGGGTTCGAAGCTGCGGAATTGCTCGGGTAAGCGTAGGTAAGTTTCGAGATAGCGTTGATTGACTGAGCGAATTTCCCAGGAGGCTGTGCCCCATTGTGCTTTGTGCTCGATGCGAGCGTAGGCTGTCATGCTTTGGATCATGGGTTGTCCTAATGGTTTAATCGTCATAATGAGAGGCGATTATAGAGTCAATGGCTTTAGGATTAAAGGCATACAGTAAATATACCCAAGCTGCCTGAAGATACGAGTTTCAGAGCGCCTAGGGTGCTTCAATTCAAGGCGCAGCAATGACAGAATGTCGGCTACCTTTTGAAATGATGCAGAACGGAAGTGAAGTGCCCTAGGCGCTCCGTCCCGGCGGCTGATGTTAACCACGGGGTAACGCACTTTATGCTGCGTTGGTTATTTTTAACTTAGTCCACTAGGTCCGCAAATAACCGCAATCGCCATGGATGGCGTGAATGCCATTTATGCAGGAGCAATAAATGGCCTTGCCTAAAGTGCGTTAAACTTCCGCTGAATTCTGCATCTTCAGGTTGTTTGGGTATAAGGACACGGATTAGGCGTTTGAGTGTGATGAGTTTGCCAGATAATCACCCTAAGCATGGCATCCTAAGGCTCAAGCCCTTATAATATGCAGCCAAAATCGGTCAATGACTCAGAATACAGGAATCTCGCATGCGTCCAAGTAACAGAACGCCAGCACAAACTCGTCCCATCACTATCACTCGCCAGTTTACGGCCCATGCCGAAGGTTCTGTGTTAGTGGAATTTGGCGAAACGAAAGTGCTTTGTACCGCCAGTTTTACTGAAGGTGTGCCACGTTTCCTTAAAGGTCAGGGTCAAGGCTGGGTGACGGCGGAATACGGCATGTTGCCACGTTCGACCCATAGCCGTATGGATCGTGAAGCCGCTCGCGGTAAGCAATCTGGCCGTACGCAAGAAATTCAACGTCTTATCGGTCGTGCTCTGCGCGCTTGTGTGGATATGAAAGCTCTAGGCGAAAACACTATCGTGATCGATTGTGATGTGATCCAAGCTGACGGTGGCACGCGCACTGCTTCTATCACTGGCGCTTGTGTGGCCTTAGTGGATGCGCTGAACTGGGCACGCGGTAAAGGCATCATTAAATCTAACCCGCTTAAATTCCTTATCGCTGCAGTCAGCGTCGGTATTTACAAGGGTGAAGCGATAAGCGATCTAGAATATATCGAAGACAGCGCCGCTGAAACTGACATGAACGTGGTGATGACGGAAACGGGCAAGATCATCGAAATTCAAGGCACCGCCGAAGGCGAGCCATTCAGCCACGAAGAATTACTCGAACTGTTGGCATTGGCGAAGAACAGTATTCGTGAAATCGTCGATGTGCAGAAAGCCGCACTGAACTAATAGTGTGGTGATGAAAAAGCCCCGTCATGTTATAGAGTGCGGGGCTTTTTATTAGCTGCAACTTTTTATCTGCAGCAAGATGCTAGTGTTAGACAAGCCCTGTACCGAATTAAATGTTGCCCAATAAAACATTACCTAATAAAAACCAATAAAAGACAAAGCCGTAAGGAGATATTGTGAAAGCCTATCAACGTGAGTTTATTGAATTTGCCTTAGAACGTCAGGTGTTGCGATTTGGCGAATTTACCCTTAAATCGGGTCGTATCAGTCCTTATTTCTTCAATGCGGGTTTGTTCAATACTGGCCGCGATCTAGCACGTTTAGGCCGTTTCTATGCCGCTGCATTAGTGGATTCAGGCATTGATTACGATTTGCTGTTTGGCCCAGCTTATAAAGGCATTCCAATTGCGACCACTACGGCGGTTGCCCTGTGTGAACACCACGATATCGATATCCCTTATTGCTTTAACCGCAAAGAGAAAAAAGATCACGGCGAAGGTGGCAGCTTAGTCGGTAGCGAGCTGAAAGGCCGCGTTATGTTGGTGGATGATGTGATCACTGCCGGTACTGCGATTCGTGAGTCGATGGAAATCATCGAGGCCCATAAAGCTCAATTAGCGGGCGTATTGATTGCCTTAGACAGGCAAGAGAAAGGCAAGGGCGAGTTATCTGCTATTCAAGAGGTTGAGCGTGATTTTGGCTGCGGCATTGTTGCGATCATTAAACTTGCCGACCTCATTAGCTATTTATCAGAAAAGCCTGGAATGGAAGTGCAACTTGCCGCCGTGAGCCAGTACCGCGAGCAATACGGTATTTAAGTCTAGCGTTTGACGGCTGGTATTGATCAAGTGGCTATTATTGGCGTGGTGAAATCAGTTAATCAGCCGGCATTCATCTTAGCTAGGCCGAGAAAGGTTTGATTGAGTGGGTATGAAAACAAAAAACGCTGCGATTCTATAGAAGGAATCGCAGCGTTTTTTTGTTGAGTAGTATTGATTGCTATTGATTTAGGCACGTTTTAAATAGCGTTAAGTTTCAATACATTTATTTAGACTGGTGCAAAAACTCGGCGCGGGTCGCAGGGTTAGACTTGAAAATACCGCCCAGCGCTGTGGTGGTGGTTGAACTGGTTGAGTCCATCACGCCACGGGATTTTACGCAGTAATGCACTGCATCCATTTTAACCGCAACGTCTTTGGTTTCAAGCAAGGTTTGTAGCGCGACTAACACTTGCTGAGTCAAACGCTCTTGCACCTGTGGACGTTGGGCAAAGAAACGCACGATGCGATTAATCTTTGATAAACCAATGATTTTTTTGCGTGGTAGATAAGCTATGGTCGCAGTGCCATCGATCGTCACTAAATGGTGTTCGCAGGTACTGGTGAGGCTAATGTCTTGCACTCGCACCATTTCATCGAAGCCCATCTTATTGTCGATAACTGTGATCTTAGGGAAGTTCGCATAATCTAAGCCAGAGAAGATTTCATCGACGTACATTTTAGCAATGCGGCGCGGCGTATCGGCAAGGCTATCGTCTGACAGATCCAATGACATTAATGTCAGGATTTCTTTCATATGATGTTCAATCTTGTCTTTACGTTCTTCACTGCTGTAAACGCTTGGGAGCATTGGGGTTTCAAGCCCACGTTCTAATAACGCGGCTTGTACTTTTACTGCTGCTTCACTTAATGCCATCTTATCCTCCACTACGTCGTAGCGGCTATAACAGAAAACCTATGCTGAGCATCGAGTGGTACTTGGGCTGTTCAGCATCAGATACAAATTATTGTTATTCTCGCTAGGGCAAGAATAGGGGGAGGAGGATAGCGCGAATTGCAGATAATTCATACCGAAAAACGCTCATGGGTCACATGAAAAAGCCGATAGTCCTTATCGGATATCGGCTTTAAATCAGCCACTGCCAATCTAGTTTAGACTGGCAGTGACGGCAAGTTTGAGTAAATCCTAAAGTTTTAGATTGGTTTTTAGGAAGCTGAGCATTTGTTCATAGTATTTCGCTCTATGCTCATCGTTATAAAAACCGTGCCCTTCATTGTCCATTACCAGTTTTTGGTAAGGATAATTGTGAGCTTTTAGGCCCTTCTCGAGGGATTCGAGTTGCTCAATTGGCGCTCGCTCATCTTCACCACCATGCACCAATAGTATGTTTGCTTTGAGTTTATCGACGTTTTCAGAAGGTGACATTGCTTTGAGTACCGCTTTATCTTGGCCAAGAACTTCTTTGAGGTAGCTAGTGCCTGAGCGTCTACTGGCAACATCGCCTTCATTAAACATCAATTCAAGATCATATACGCCAGCTACACCTATGGCGCATTTAAACATATCTGGAGCAAGTACGGCGCTCTGCAAGGCGCTGTAACCACCAAAACTGCCGCCAACGATACAGATACGCTCCTTATCGGCGAAACCTTGGTCAATGACATATTGGGTACCGTCGATGATATCGTGTTGTATATCTGAGCCCCATTTTTGATAACCTGCACGTTCAAATTCATCGCCATATCCACCCGAGCCACGGAAGTTGACCTGAAGAACGGCTATACTTTGGCTCGCTAGATATTGGTTTTGCGGGTCAAAGCCCCACCAGTCACGAATACCGTGTGGACCACCGTGTGGATTGACGACTAAGGGCAGATTTTTAGCTTCTTTACCGAAAGGTAGCGTTAAATAACCGTTGATTGTCTTACCATCACGACTGGTGAAACTGATGGGCTTTACCTCAGCCATTTGTTCTGGGTCGAGCCATTTCTTTGCGGCGGCTAAGTATTCAAGTTTCAGTTTTTTCGTATCAAATATGTAGTAATCACCAGGATTGCGATCGTTAAATGCAACTACAACGAGTTTTTCAGCATCTCGGGTTTCGCTAACAATTCGTACTTGATGGCCTGGGAGGGCACCAAGCAGATCTTTTAGTAATTTAGCGTGCGAATCTTCATTATCGACGAAGGCATAACTTGGGTAGCCATTTTCAAACTCGACGGCGTAAAGTTGTTTATTGGTGCCGTTAATCCAAAAATTGCTGGGATCAACGTTTTTATCTTGAATAATCTCGCTCTTATCGCCCGTTTCTAGATTAATCTTGTAAACCCCTAAGGTTTCGCCTTGAGAGCGACCAGCGGCATAAATCGCATTTTTATCATCGGCGAAGGAAAGTGGCTGAAAGTCGGTTAGGCCTAGGTTTAGCTTGTCGGTATTTATCCAGTCACCGTCTTTGCGGTAGAACACTTTAGTGATGTTTTTACTGTCCTCACCTGCAACGAAGCGGACTTCACCCTCATGATCTGTCATAAAACGTGCTTGACCTATCGGGGCTCCTGTTATGCGTTTACGAACCCCAGTAAACAGGTCGACTCGATATACATCTTGTTTTAATTCAATATTCAGGCTGCTCTCGTTATTCCATGGAATAGCGTTGACCAGCATATAGCGCTCATCATCTGGCAGAGGATCGAGAATAAATGCTGTGGCATTTATTGCAGTATTTTTCTTAATATTTGAACCTGTTTGTTGTTCGCCACTTTCGAAGCCAAATAGATAAGCCTTTCGTGAACCATCGGCATTCACGGCCAATAATTCTCCGTAGTACTGCGGAACATCTCTCCAGCCTTTTAGGTATTCTTTCGCCAGTACCAGTCGCTCATTATTGGCCCATTCATAGTCGCCGACTTGAGCGTTACTGGGAAAGCGTATCGCATTAAGGAGTTTTTTGGTTTGAGCATCTAAGACTAAGAGAACATTTTTCCCCTCGACACTTGTGATTGCACTTAAGTATTTACCAGTAGGAGATATTTTAACATTTGAATATTCATCACCTTTACTGAACTGCTGTGCAAGCAATAAGGTATTGCTTGCAGTGGTATCGGCTGCAAAAGTGTGTAGGGACAGCATAGGCAAGCAAAGCGTAGCCAAAGTCGCTAATCGGAAGAGTTTCATCTGACATCCTTGTAAATTTATTGTGTTTTTTGTTCTGAAAATGTTTTTCGAGGATAAAACAAAAGTGCTAGTGAGACAAGAAGGGAAGGTTAAAAATAAAGGACGATAGTTACCGTCCTTTATGTTGAAAAATCATTAGCTTTGTAAAAGCTGATGTTGGATAAAACGCCACTGTTCATCGAAGTGTGTGGTAGGTTTGAGTTTGAATTCACTTCGTACAAATTGAGCGATTCGCCCTTCGGCAAAGGCTAACAGTAAATTGGCTAATATGGCTTCGTCTAAGTTGAAGCCTTTGCCTTCCCGCAGGGTTTTTTCCCGTAGGATTTGCTTCAACTGGGTTTCAATCTTGGCAAATAAATTACTAATGCGGCTGCGCAGGCGTTCGTTTTCTCCGAGCAGAGCATCACCATTGAGGACGCGGGAAATACCTGGGTTACGCTCGGCAAAAATCAGTAACAGTTGCAGCACGAGTTGGCAGCGTTTCATGGTGTCTTTTTCGTCATCCATGATTAAATTGATGCGCGACAATAAAGACTCTTCGATAAATTCGATTAAGCCTTCGAACATCCGCGCTTTACTCGGGAAGTGACGGTACAGGGCGGCTTCCGATACACCGACTTCTGAGGCCAGTTTCGCTGTGGTGATCCTTTGTCCTGGGTTGGTTTCTAGCATCTGTGCTAGACATTGCAAAATATGTTCACGACGATTAATTTTTGGGCTTACAGCCATTGCAGTATCCTTGGCTCAATAACGACTTAGGGGATTATTTAGTACCTGAATGGCCAAATCCGCCTTCACCACGATCTGAGCTGTCGAACTCATCAACGAGTTTGAATTGTGCTTGTACTACAGGAACAAACACCAGTTGTGCGAGACGATCGCCAATTTCTAAGGCGAATGGACTGTCGCTACGATTCCAGCAAGACACCATTAAGGGTCCTTGGTAATCGGAATCGATGAGTCCGACCAGATTCCCCAGTACGATACCGTGTTTATGGCCAAGACCTGAGCGCGGCAAAATCACTGCAGCCAGTCCTGGATCGGCAACATGAATGGCAATCCCCGTTGGAATAAGCACGGTTTCACCCGGTGCTATGGTCAAAGTCGTGTCGATCATGGCGCGAAGATCCATGCCTGCACTGCCAGGTGTAGCGTAGGCAGGTAAAGGAAATTCTGAGCCGATACGGGAGTCGAGAATTTTTAATTCAATCGGTGTTTTCATGTATTTGTTTTTTCTTTCACTATCAACGAAAGTAATTGCCGTGCCAGCGTGAGTTTGTCAGTGGCAGGCAAATCTTGACTGCCGTGCGGCCAGAACACTCGCAGGGCATTGGCATCGGCATTAAAGCCTAGACCAGCAACGGAAACGTCGTTCGCGGCGATCATATTCAAGTTTTTACGTTTGAGTTTATCGCGCGCATAGGCTTCTACATCATGGGTTTCTGCGGCGAATCCCACCACAAAAGGACGGTTAGGTAAGCTCGCAACCGTGGCTAAGATATCAGGATTCCTCACCAGCGCAAGTTGCATTTCTTCTGCCGATTTTTTGATCTTACAGGTGGCGATGTCGCTAATGCGATAGTCAGCAACTGCGGCGCAGCCAATAAAAATATCTTGTTTATCAACGTTATCCATCACAGCGTCGAGCATATTTTGCGCCGATTCAACATCAATGCGGGTCACGCCTTCAGGGGTACTTAGATTTACTGGGCCAGAGACTAAGGTGACTGTTGCGCCCATATCTGCCGCGGCTTTGGCTAAGGCAAAGCCCATTTTTCCAGAACTGTGATTCGAAATGTATCTGACGGGATCAATCGCCTCACGGGTGGGCCCAGCGGTAATCAGCAGTGACTGTCCTGCGAGCGGTTTAAGTTCAGCGGTATCATGAGTGGCAAAAAACTGCGTAGCGAGTGTTGCGATGTCTAATGGCTCTAACATACGGCCTGGGCCGACTTCACCACAGGCCTGACTGCCACTTGCGGGTCCCCAAAGGGTAAAGCCGCGGGATGCCAGACTGGTTAAATTCGCCTGCGTCGCCAGATTACGGTACATCTGCTGATTCATTGCCGGACACAGGGCGATAGGCGCCTCTGTGGCAAGACAAGTGGTGGTGATCAGCTCATCGGCCATGCCGGCATTAATCCGCGCCAATAGGTTTGCGGTTGCAGGGGCGATGATGACGAGATCTGCCCAACGAGCGAGTTCTATGTGGCCCATGGCTGCTTCTGCCGCAGGATCGAGTAGGCTTGAGGCAACGGGATGTCCAGATAACGCTTGCAGCGTCAAAGGCGTGATAAATTCCATCGCACTTTGGCTCATCACCACGCGCACATCAAAGCCACGCTCTTTTAAGCGACGGACTAAATCAGCACTTTTATAGGCCGCAATACCGCCGCCAATGCCCAGCAGGACGTTTTTATTTGTCAGACTCACAGACATCATCCGAGATAGAAATAATGCCGCCAACAATATCACAAAGCGGTGAATTTTAGTGACTCTCCCAGTTCAAAAAATCTCGTCCATACTCAGTAAAGTGAAAATATGTGAGATGACAGGGAGGTCGCATGGGCATTAAAGATTGGCCTGAGGGGGAAGGTCCCAGAGATAAACTGTTACAAAAAGGGGCGGGACAACTGTCGGATGCGGAGTTATTGGCGGTATTACTGCGGAACGGTTTGGCGGGATTAAACGCGGTTGATCTTGCAAGGTCGCTTATTAGCGAGTTTGGCGGCCTCAGAAACCTGCTTTGTGCTCCTCGAAATCAGGTGTGTCGACTTCCGGGCGTAGGACCTGTAAAATACGCCCAGCTTCAAGCTGCGGCCGAATTAGCGCGGCGTGTAGCGCAGGAAAATCTGCAAAGAGGTCAAGTTTTAACAAATCCCGATTTAACTCGGGACTATTTAATGAGACAATTGGCCGATCGCTCCTATGAAGTGTTCGCAGTTTTACTGCTGGATAGCCAGCATAGAGTGATTCAATTTGTCGAATTATTCCGCGGTACAATAGACTCAGCCTCAGTGTACCCACGTGAAGTGGTGAGCTTAGTGCTGGAGAAAAAGGCCGCAGCAGTTATAGTTTGTCATAATCATCCGTCTGGTATTGCCGAGCCCAGTCAGGCTGACCGACGAATAACTGAGCGATTAAAAAATGCGTTAGCAACCATAGACGTATCCTTATTGGACCATATGGTTGTAGGTGATCGAGAGATAGTTTCTTTTGCAGAACGAGGCTGGATTAATTAATCTAACACTTGATCTTCACTCTGTGATCGTGTATAAAATGCGCCCTCTTTGTGCCTCGGGCGACGGCCATACGAGGCACATTAATGCTCGAGCGTTAAAATTGTTTTTGGAGAAGATTGACATGTCAAGAGTATGCCAAGTTACTGGCAAGAAGCCGATGGTTGGTAACAACCGTTCGCACGCAAAAAACGCGACCCGTCGTCGTTTTTTACCTAACCTACAAAACCACCGTTTTTGGTTAGAAGAAGAAAAGCGTTTCGTACAGTTACGTGTATCTACTAAAGGTATTCGTCTGATCGACAAAAAAGGTATCGAAGTTGTTGTTGCTGAACTTCGTGCTCGTGGCGAGAAGGTATAATAGATCATGGCTAAAGCTAAAGGTAATCGTGAGAAGATCAAATTAGTTTCTACAGCTAAAACTGGTCACTTCTACACAACTGAAAAAAACAAGCGTAACATGCCTGAAAAAATGGAAATCAAAAAATTTGATCCAGTTATTCGTCAGCACGTTATCTACAAAGAAGCAAAAATCAAGTAATCCTTGGTTAGTGTTTCGAGAAAAGCCCCTTATTTAAGGGGCTTTTTTTTGCGCTTTTTTTAGTAGTTAGTCATCAAGTTGAGATAATTGCCTCGCAAACTTAAGCCGCTGAATCATCCCCTGTGAAGTTAAAAAAGTGTGAACACTTGCTGAAAATATAAAATAGTGCGGTTCTAAGCACTTTAAATCATTAAAACTGCACTTTAATGAGAATAAATTCTTTATATGTGAATTAAAATGCAATACCATACGCGAAAGTTAGCGATTCATTCTTGGCATGTCATCGTGACGCTGCCAGCTCAAAAATGAGGTGTAAATAACGCGTGCGTACCACTGAACTGGTTGATGGATTTCGTCACTCTGCCCCCTATGTTAATGCTCACAGAGGTAAAACTTTTGTGGTCATGCTGGGTGGTGAAGCTTTGGCTCAGAATCACTTCCGCGCCATCTTAAATGATGTCGCCCTGCTGCACAGTTTAGGGATTAAAGTGGTGTTGGTGTATGGCGCTAGGCCGCAGATTGATGCTGCATTAGCCGTCAATGGTATCGAACCTGCCTACCACGAAGGCGTGCGTATTACGGATGAAGACTCGCTCAAAGTGATCAAGCAAGTTGCGGGTGCACTGCAATTTGATATCACAGCGCGTTTATCCATGAGCTTAAGCAATACGCCGATGCAAGGTGCGCAAATTAACTTAGTGAGCGGCAACTTTGTGATTGCCCAGCCATTAGGCGTGGATAACGGTATCGATTTTTGTTTGAGCGGTAGAGTGCGTCGTATCGATGTGCAAGGCTTAAAACGTCAGTTAGATAATCACTGCATAGTGTTGATGGGGCCGATTGCGGCTTCTGTGACCGGTGAAAGCTTTAATTTAACCGCCGAGGAAATCGCAACTCAAGTGGCGATCAAGCTCAAAGCCGACAAGATGATCGGTTTTAGCTCACAGAACGGTATTCTCGATCGCAATGGCGACGTGATTGCCGAATTGATGCCAAACGATGCGCAAAACATCATGAGCAAGCTGGCCGAGCAAGGTTCTGCCTGCGTAGGCACTATGGCTTTTTTGAAAGCCAGTATCGATGCTTGCCGTAATGGCGTACCGCGTTGTCACTTAGTCAGCTACCTCGAAGATGGCGCTTTGTTGCAGGAACTGTTTTCTCGCGAAGGGATAGGCACGCAAATCGTGACTGAGAGTGCCGAGCGTTTACGCTGTGCGTCGATCGCTGATATTGGCGGTGTACTGAACCTTATTCGTCCATTGGAGGAACAAGGCATTCTGGTGCGTCGTAGCCGTGAACAGCTCGAGATCGAAATCGAACAGTTCATGTTGATTGAACGCGATGGTTTAGTGATTGGTTGCGCCGCGCTCTATCCTTTTGAAGAGGACAATGCGGGCGAGTTTGCCTGTTTGGTGGTGCATCCCGATTATCGCGATGCCGACAGAGGCAGTTTGCTGCTAAAGAACATAATCAATCAGGCCAGAAACCGCGGTTATTCACGCTTGTTTGCGCTGACAACTCGGAGCATTCACTGGTTCCTTGAGCATGGTTTTGTGATTGAAGATGTTGAAGCGCTGCCACAGAAGAAAAAGCAGTTGTATAACTATCAACGTCGTTCAAAAATTCTCGCGCTCGATCTCTAAGCCGTTCTAAATCAATCTACTCAATGCCAGCCTAGTACTCATATTAGCGCTGGCATTTTTGTTGACATAATATGTCTTTATTGGCAGATTAAGGCTTCACTGCACGGGAGATGCTGCCAATGAGTCCTCAATACTTCGAACTCGCCCCTTTCACCCAGACCTCTATCTTCAGTTTTATCGTCTTATTGCTCGGTTTAGCGGGTATCTTGCTGATGTTATGGCTAAAAAATATGCCTAAGATAGCGAAGTCGGCGAGTTTAGGCATATTGCTGGTGATGATGGGCGGATTTTCATGGGTTTTTTATCAATCCAATAGTGCCGAATTAGTGTTAACCGATACTGAGCTCACAGTGGATGTGCCCTTCTATAAAGTGCAGTTATCCCGCGTAGATTTATTGCCCGCCGAGGCGCGGATAGTCGATTTGCAGCAAGAGGCTGATTTAACGCCGAGCTTTAAAACCAACGGCATAGGCATGCCAGGGTTTCAATTAGGCTGGTTTAATTTGCAGGGGAAAGGTAGAGCATTTCTGGCGATAACAGATAAATCGCAGTTAGTGTTAGTGCCAACGACTAAAGGCTACAGTTTATTACTGACAGTGCCGCAAGGGAGTGAATTTTTAGCACAGTTACAAAAGTAATCATCATATATCATCACTTCTGCAACTGTGTATCGGCGTGATAATGATCACGCCGTTTTGCGTTTATAGGCCATATCTAAGGTAAAGACTAACAGGGCACTCCAGATAAAACCGAAGGTCACGCATTTTTCTGCATCGAAGGGCTCATTAAACAGACTCACCGCGAGGATGAACATGATACTTGGGCCGATATATTGGAAAAATCCAAGCATAGATAATGGGATCCGTACCGCCGCCCCTGCAAAACAGAGTAAAGGGATCGTGGTCACTATTCCCGCTGCCATTAACATCAGATTAAGTTGCCAATCGTTTGTCAGCATGCTTGCTGTCGCTGTGTCTAAGGTCGCGACTAGGTAAATTAGCGCGACCGGTAATAACACTGCGGTTTCGACCAATAAGCCAGCCTTAGCATCCACATTTACCTTTTTTCGCAGCAGCGCATAGAAACCAAAGGTGCCCGCGAGTGCCAGTGAGACAATCGGAATAGAGCCAAAAGAGATTAATTGGATTAACACGCCAGCGCTGGCGAGTGCGACCGCAAACCATTGCAATTTACGTAACCTTTCGCCTAGAAATAACATGCCAAGCAAGACGTTGAGTAGAGGGTTGATAAAGTAACCTAAACTGGCATCGAGCATGTGATCGTTATTCACCGCCCAGATAAATAGCAGCCAGTTTCCAGCAATCAGTATGGAAGTAATGGTGAGAACCATCAGCTGTTTAGGTTGTTTAAACAGTAAGCGTAAGCGTGAGAACCCGCCGATAAACTGCATAATGATCAGCATAAACACGAACGACCAAATTACGCGATGGAGCAAAATTTCGGTGGCAGAGACATGGTTGAGTAATTTGAAATATAAAGGCGCAAAACCCCACATACAGTAAGCACTGACGGCGAGTAGGATCCCTTTGCGGTATTCAAGATCTTGCATGAGACATCTTCGAAGGACAACAAGACGGCGATTGTAGGTGTGTCTGACCGCTGACTCAAGGGTATCGCCGCGTTCTTTTGGTTACAGAATATCTCGATAAGAAATTAAATAGCCGATTAACGCCAGTAAACATCTTGCTAATCATGATGTTTACTGGCTTTGATCTTTAATGCCGTAAATGAGTTATCTGCGGTGAAACGGCTAGCCCACCATATAAGTGCCTGTCCCAAAAGCGATATGTGTGCCTTGTTCATTATGCAATTCCATGCGGCATACCGACACCCGATTGCCCGCACGGATCACGCTGCCCGTCCCGGTAAAGGTGAGGCCGCGCCCCGGCCGCAGATAATCAACGCGCATATCGATAGTGCCTAGGGTTTGCAGGCGTTGCTGCAGCTCTTCGACCGCCCAATCATCACGACTGGCAATCAGTCCTGCAAAGGCGGTGAGTCCGCCAACCACATCGAGTACCGTTGCTGTCACACCGCCGTGGAGGATTTGCTGGTGGATATTGCCAATTAGCTCAGGCTTCATGTTGATCACGACTTCAACGCCATCAATGTCATAACGCTTGATATCGAGTCCGAGTAGATTATGGAAAGGGACATGTTGATCAAATACTGCTGCGACTTTTCTCAAAGCTTCGGCTTGGATTGGGCTGGTCATTATTGGGTCCTTGTCACTGAGTGACTGCATTAGGCGTAATTGGATTTGTTATCAACACTGAGATGAACAAAGTGCGGCTTAGAGCAATTAATCTACAGTGAAGCTCACCGCGAATGCAACTCTGTCGCTGAAATGAAGGTACTGTAACGAAATAACCTAGACGGATGATCTCATATTGACGCAAGACAGAGCGGTTTAGGCCTCTATACCCAAGTTAGCGCAACCTGTGAGTTTCAGAATGCCTTCGGCGCTTCGTCTCGGCGGGCTTTAAAGTACTTTACCCCGCAGTTCGTTATTTTTGGCTTAGTCCATTAGGGGTTAAAATAGCTACAATCGCGATGAATGGCCTCGTCTAAAGTACTTTAAATTACCGCTGAATCCTGCATTTTGAGCTAGTTTGGGTATAGAATAGGCCCCCTCACTTTTGACATAGAAGATCCATGGAAAAGCTCGTACTCGACACGCATGATGATCCGCTCTCGCAACGCCTCGCACAGGTATTTGGTTACCGTGACTTTAGGGACGGTCAGCGGGAGGTGATAGAACGTGTCTGCGGCGGGCAAGATTGCTTAGTGATCATGCCAACGGGCGGCGGTAAGAGTCTTTGCTATCAACTCCCCGCGTTGCTGATGGATGGCATTACAATTGTCGTATCTCCATTGATTTCTTTGATGAAAGATCAAGTCGATAGCTTGTTGCAAACTGGTGTTGCAGCGGCTTATCTCAATTCTTCATTGCCGCGAGAGCAGAGCGCCGAAGTATTAAGGCAGCTGCGCCACGGTGAGTTAAAACTGCTTTATGTGTCACCGGAGCGTTTATTAAGCGCAGATTTTATTGAGCGAATGCAGTCGTTGCAGCTAGCGATGTTTGCCATCGACGAGGCGCACTGTATCAGTCAATGGGGTCATGATTTTCGCCCTGAATATGCGGCGCTCGGCCAGTTAAAGCAACTCTTCCCCTATGTGCCTATGATGGCGCTGACAGCAACGGCGGATCAGGCGACTCGGCAGAGTATTTGCGAGCGACTTGGGATTGACCCGTTCCGTTTACTCTCTAGTTTCGACAGGCCTAATATTCGCTACACAGTGGCGGAAAAGCTCAATGCTGCGAATCAGTTACGGCATTTTTTAACCCAGCAAAATGGCACCAACGGGATTATTTATTGCAGCAGCCGACGCCGTGTCGACGAGGTTGCCGATCGTTTATGTCTGCAAGGTTTTAATGCTAAGGCCTATCATGCGGGAATGACGCAGGAAGATCGTGGCGCCGTGCAGGATAGTTTCCTTAAAGATCAAATCGATATCGTGGTCGCCACTGTGGCTTTCGGTATGGGGATCAATAAATCGAATGTGCGCTTTGTGGTGCATTACGATATTCCTAAAAGTATCGAAGCCTATTATCAGGAAACGGGCCGAGCGGGCCGTGATGGGCTTGATGCCGAGGCCTTTATGTTGTTTGATCCTGCCGATATTGGCCGGGTGCGACATTTGATTGAACAGTCTGAGCCCGGGCCGCAGCAGCAAGTCGAATTCCATAAACTCAATACCATGGCGGCGTTTGCCGAGGCGCAAACCTGCCGTCGTCAAGTGCTATTGCATTACTTTGATGAGAGCGCACTAGAGCCCTGTGGCAACTGCGATATTTGCCTCGATCCGCCTAAGCGTTATAACGGCACCGAAGATGCGCAAAAGGTGTTGTCATGTATTTTCCGTTTAGGGCAGCGTTTTGGTATTAATCACTTAATTGAAGTGTTGCGCGGCTCGAAAGGCGCCCCGATAGTCGACAGGGGTCATGATAAACTCACGACTTGGGGAATTGGCAAAGATAAGAGTCATGAGTATTGGTTGAGCGTGATCCGTCAGTTGATCCACTTGGGACTGGCGAGTCAGGATGTGACGCGAGGTTCATCTATAACCTTAAACCCATCGGCACGACCTGTACTCAAAGGTGAGGTGGCGTTAATGCTCGCCGAACCTCGTATTCAGTTAACGACCACAAAACGCAAGGTGAGCCAGTCTAAGGCGCCGCTGAACTACGACCGTAAGCTGTTCGCGCGACTCAAGTTACTGCGAAGAACCATAGCAGAGCAGCAAGATGTGCCGCCGTATTTAGTGTTTAACGATGCGACGCTGGCGGAAATGGCGGCCATGATGCCAACCAGCGCGGGGGAAATGCTCGCCGTCAACGGGGTTGGTGAGCGTAAACTGAGTCGTTTCGGCGACGCTTTTCTCGATGAAATCGCGGCTTATTTAGCGGGAGAATAATGGTTTGGATTATTCTCACTGATTTTAACCTTGCCCAATTGATTGATACTGGCGCGCTTAATTAAAGACTCTAACGTATCAAACTGTTGCATTGCCGCGATACCAATATTGACCTTGCTTGACCCCTGCCAAGGTTGAATACACGCCACTAATTGATTGACCACTTTCTTAGCACCAGCTGCAGAGGTGTGCGGTAGGATAATCACAATTTCATCCATCGAATATCGGATCAACTTATCTTGCTCTCGCAGTCTGTGCTGTAGCTCATCTTGCAGAGGCGGCAAGTCAACTTGCCGTATTTGACTCGCATTAAAGAGTAATAAAGACAGCGGATAATTACCTTGTTTGGCACTGTTCAAGGATGATGTGGCTTGTTGGAGCAGAGTTTCCGCAGGCTCCTCGCCTTCGATACGGGGCGCGCGATTATTTTGCTTATCAATCAAATACCAAATAGCCCCACCGAGTAACGTCATCGCAATTAAGAACAAAGTGCTATAGGCCCAATCTAGATTTAACTCTTGGAGGATCGATGCCTTTTCTGGCGCAGGATTATTCTGATCCTGCTGCAGATCTTGCTGATTTAGACGGGCTTTAAATTGCAATATCCGTGCAGTGTTCATCCCTTGATTGGCGAGTTCGGCAGCTCGATAGCGTTTGAGTAAGAAAGAATAGGCCTGCTTAAAATCCCCTTGTTCTAAGTAGTGATCGCTCATGATCCCGTAAAATCCTTTGAGATCAGAATATTGTTTCAGATTTTCAGCTTGTTCAATGGCGTGATTCATTAGGATTAATGCAGCATCGTCTTGATGCTGGGCAAAGCGTATTTGTGCCAGCAGGCGGGTCGATAACATTACCGCTATCTGTTGATTCTGTTTTTTGAAGGTATTGATGGCGATAGAAATATATTCTTCAGCATTATCAATGCGGCCCTTGTCGAGTGCGATTGAAGCTAAAATGGCGTAGCTGTAGGCGAGCTCTAGTGGCGAACCGAGTTCAGGTAGTAGTGCCTTTGCTTGTTTCTCGAGTTTATCACTGTAATCGAACTCTTGATTATCGAGCGCTATCCTAGCTGCATTACATAAAATGAAATGTAATACCTTGCCCTTAGCATCCGAGCTCTGTAATGCAAGGTTGGTGTAATACATGGCTTGGGGCAAGTCATGGGTTGCGTAGAGTAAGTTGCCCATGGCCGCATAAACATAAGCTTGTGGTGGCCAAATCCAATTTTGGGTCTGAGTATGAATGTCAGGATAAATATCGATGGCCAGACGTAAATCTTCGATGGCAGAGGAGAAATTATCCGTATTGGTATCGAGCTGACCACGTAAACGTAAGGCATCGACTAATGCTTGGGGCTGTTGATAGCGTCGCGCTAAGGTAATGGCAGAATCCAGCAAGGGCAGCGCATTTTGAATATTGTTGTAGTTGAGGTTAGCGTCTGCTTGGCAAATCAAAAAGTAGGGGCGAACTTGATCGAGCTTAAGTTGTTTAGCATTGGCCTCGCCTAGCTGAGCCACGTTGATCGCAGCTTCGTTCTCACCCAGTTGCAGCAGATTTTGGCATTTCAATACACTCAATCTGAGTCGATTAATGTCAGAAGTGGCGCGGGTCGTAAATTGTTTTTCTAAGGCAGTGATTTGAGTCAGGGCTTTACTGGGATATTGGTAGACGAGTTCTGTGAGCTCATCCAACTGGTCTATAGCACTCGCCCCAAAGGGGATCAGCCATAACAACAGGCTCAGTACCAAGACGCGAAAATCCATTTTGTGCTCCATAAAATGCGTAAGCTCTAATATCCGTAATCACTAAGGCGAGATTATAACTTTTCTTAATCGTTTTTTGCATAAAAAACTATTATTTTCTAAGGGGAATAAGTTAACACATCCCAGTGCTTTCTGAATCCAAACAGCGAGTTAATTCTGTGGAATCGATCACTTATTTGGGGATAAAACTCACAGTTATTGGCCTTAAGACAGGTCGAATGAGTGGAACATGAAAAAATTAGGGTTGACACTCTCGGCTAGGCAAGTTAAATATTAACCGTCCTAGATTATTTGAACAGAATAAATATTTAAATAAAATGAATAGAATTAGCAGCAAACTCGACCTCCTCCTCTCTCTCCTCGCGGTTTATACGCGGAGGTCTTAGTGTTGCACGCTCGATAAATAGGCAAAGCATTCACAAACCCCGCACCAATGTTGCGGGGTTTTTTGTATCTGAATACGGCAAAAGTTAAATAATGAACGATGACGTTCCATGGAGAAGCGAGATGTCTAACAGAGTAATTATATTTGATACGACCTTACGTGATGGTGAGCAGGCGTTAGCCGCAAGCCTTTCGGTCAAAGAAAAGTTACAGATCGCCATGGCGCTGGAACGTCTAGGTGTTGATGTGATGGAAGTGGGTTTTCCAGTCTCATCACCCGGTGATTTTGAGTCGGTGCAAACCATAGCGCGCACCATCAAAAATAGCCGCGTTTGTGCGTTATCCCGCGCACTTGAAAAGGACATCGATGCTGCGGCGCAGGCCTTGTCTGTTGCAGATCAATTCCGTATCCATACGTTTATCTCGACATCGACGATTCATGTGGAAAGCAAGCTGAAGCGCTCGTTTGATCAAGTGTTAGAGATGGCGGTGGGCGCAGTGAAATACGCCCGTCGCTTTACCGACGATGTGGAGTTTTCCTGTGAGGATGCGGGACGTACGCCAATCGACAATCTGTGTCGTATGGTAGAGGCCGCCATTCTTGTTGGCGCGCGTACCATTAACATTCCTGATACGGTCGGTTACACAGTGCCAAGCGAGTTTGGTACTATTATCCAAACCCTGTTTAACCGCGTGCCGAACATCGACCAAGCGGTGATCTCAGTGCATTGCCACGATGACTTAGGTTTATCTGTGGCTAACTCGATAACCGCAGTGCAACACGGTGCACGTCAAATCGAATGTACGATCAATGGCATAGGTGAGCGTGCGGGTAACTGTTCATTGGAAGAGATCGCTATGATTTTAGCGACGCGTAAGGGCATGTTGGGTTTGGAAACAGGCATTAACGCCAAGGAAATCCACCGTACTTCTAATTTAGTCAGCCAGTTATGCAACATGCCAGTGCAGGCGAACAAGGCGATTGTCGGTGCCAATGCCTTTACCCATTCGTCGGGTATCCATCAGGATGGCATGTTAAAAGCGCAAAATACCTATGAAATCATGACCCCAGAAAGCATAGGTTTGAATCGCAACAATTTGAATATGACTTCGCGCTCAGGTCGCCATGTGATCAAGCATCGCATGGAGGAAATGGGTTATAGCGAGCACGATTACAATATGGATGCGCTGTACGAAGAGTTCCTAAAATTGGCGGATAAAAAAGGCCAAGTATTTGATTATGATTTAGAAGCCTTAGCCTTTATGGAAGCACAGGCCGAAGAAGACAATCACTATCAATTGCAGCAATTAGTGGTGCAGTCTGACTCTACCGAAGGCGTTGCCACAGCTACCGTTCGTATTGAAGTGGGCGGTGAAATTAAAACCGAAGCCGCAACTGGCAATGGTCCCGTTGATGCCGCCTATAACGCCATTGCCCGTGCTACTGATCGTCGTATCGACATCATCAGCTACAAGTTAGGCGCTAAGGGCGTAGGCCAAAATGCCTTAGGTCAAGTGGACATTACCGCGGTTTACCACGAGCAAAACTTCCACGGTGTAGGTTTAGCGACCGATGTCGTTGAAGCCTCGGCGCGCGCTTTAGTGCATGTGATGAACTTAACGTGCCGTGCAGACAAGGTCGCCGATTACAAACAAAGCATGCAAAAGAATCGAGAGCTAGGCGGCGTCTAGTCGCTAAAAGCTTCCAAAGAATAGCAAGGTCGAGCATAAAGCATTCACATTTAAAGGAGTTGGCTGGCGTATGAGTTATCAAGTAGCAGTATTAGCAGGGGACGGGATCGGCCCAGAAGTGATGGCAGAGGCGCGTAAAGTGCTACGTGAAGTGGAAGCGCGATTCGATTTGAATATCGAATATACGCAATACGATGTCGGCGGTATTGCCATCGATAACCACGGTTGCCCACTACCCGATGCCACGCTAAAAGGTTGTGAAGCGGCCGATGCGATTCTATTTGGTTCAGTCGGCGGCCCTAAGTGGGAAAAGTTACCGCCAAATGAACAACCTGAGCGCGGCGCGCTATTGCCTCTGCGTGGTCACTTCGAACTGTTTTGCAATCTGCGCCCAGCCAAACTGCACGATGGCCTAGAGCATATGTCACCGCTGCGCAGCGATATTTCTGCCCGTGGCTTTGATGTTTTATGTGTGCGTGAGTTAACCGGTGGCATTTATTTTGGTAAGCCAAAGGGCCGCCAAGGTGAAGGCGAGAATGAAGAAGCCTTCGATACCATGCGTTATAGCCGCCGCGAAATTAGCCGTATCGCCCGCATCGCCTTTGAAGCCGCCCGTGGTCGCCGTAAAAAAGTCACCTCAGTGGATAAAGCCAACGTACTGGCTTGTTCAGTGTTGTGGCGCCAAGTGGTTGAAGAAGTGGCAGTTGATTTCCCCGATGTTGAGCTGGAACATATCTATATCGATAACGCGACCATGCAGCTGCTGCGTCGCCCCGATGAGTTCGATGTGATGTTATGTTCTAACCTGTTCGGTGACATTTTATCCGACGAAATCGCCATGTTAACGGGCTCTATGGGCTTGTTGTCCTCGGCGAGCATGAACAGCAGTGGTTTTGGTTTGTTCGAACCAGCAGGTGGCAGTGCGCCGGATATCGCAGGTAAAGGGATAGCCAACCCAGTCGCGCAAATTCTGTCAGCAGCATTAATGTTACGTCATAGCTTGAAACAAGAAGAAGCGGCGAGTGCGATTGAGCGCGCCGTGAGTAAAGCCTTGAACTCTGGCTATTTAACCGGTGAGTTATTGAGCAGCGACAAGCGTAGTGAAGCGAAATCGACGGCAGAAATGGGTGACTTTATCGCCAATGCTATAAAGGAAGGTGTGTAATGACGAACGCAAAGACCTTGTACCAAAAAGTATGGGATGCACATATCGTTACTGCTCCCGAGGGCGAAGCGCCGGTCATTTATGTCGACAGACATTTAGTCCATGAAGTGACGTCACCACAAGCATTCAGCGGTTTGAAAGTCGCGGGCCGTAAGCTGCGCGCGCCGGAAAAAACCTTTGCCACTATGGATCATAATACTTCTACTCGCAGCGCCAGCTTAGATGCCCTAAGCCCGATGGCGCGCACTCAGGTGGAAACCCTAGCGCAAAACTGTAAAGACTTTGGCGTGCGTTTATATGATATTCACCATCCTAATCAAGGGATTGTGCATGTGATGGGCCCAGAGTTAGGCATTACCTTACCTGGCACTGTGATTGTGTGTGGCGATTCCCATACCGCGACCCATGGCGCCTTTGGGGCATTGGCGTTTGGGATTGGTACATCGGAAGTTGAGCACGTTCTAGCAACGCAAACGCTGCGTCAGTTAAAAGCTAAGACCATGAAGATTGAAGTGCGTGGCCATGTCACCGATGGCGTGACCGCCAAAGATATAGTTCTGGCGATCATTGGCAAAATCGGCATGGATGGTGGCACGGGCTATGTGGTGGAGTTCTGCGGTGAGGCGATTGAAGCCCTGTCGATGGAAGGCCGCATGACAGTGTGTAACATGGCGATTGAGATGGGGGCTAAGGCGGGCATGGTCGCGCCGGATCAAACCACTTTCGACTATTTAGCAGGCCGCGAGTTTGCGCCAAAGGGCGAAGATTGGACTGAGGCTGTCGCTTACTGGAAAGCCATTAAAACCGATGACGGCGCGGTGTTTGATGCTGTCGTTGAGTTAGATGCCGCCGATATTGCACCGCAATTGACTTGGGGTACTAACCCCGGCCAAGTGGTGGCAATCGATGGCAAAGTACCTGACCCCATTAATGAAGCCAACCCAAGCACCCGAGCTAGTATGGAAAAGGCTTTAGAATACATTGGCCTGAGTGCGGGTACGCCGATGACAGACATCAGTATCAACAAGGTGTTTATCGGTTCTTGTACTAACTCACGGATTGAAGATTTACGCAGCGCCGCCGTACATGCCAAAGGCCGTAAAGTCGCGAGTGGCGTCACCGCCATTGTCGTTCCAGGTTCTGGCCAAGTGAAAGCGCAAGCTGAAGCCGAAGGCTTAGATAAAATCTTTATCGAAGCGGGGTTTGAGTGGCGTTTACCGGGTTGCTCTATGTGTTTAGCGATGAACGATGACAGATTAGAAGCGGGCGATCGCTGCGCGTCGACCAGTAATCGTAACTTCGAAGGTCGCCAAGGTCGCGGCAGTCGTACTCATTTGGTGAGTCCTGCAATGGCTGCAGCCGCTGCGGTGGCAGGGCATTTTGTCGATATTCGTAAACCTTACTAATTACCGTAAACCCAAGAGGAGAAGTTAATGCAACCTTTTACGACCCATACCGGGCTTGCGGTCATGATCGACAGCACCAATATCGATACGGATCAGATCATTCCGAAACAGTTTTTATCTAAGGTGACGCGCGATGGTTTTGGCGTGCACTTATTTCACGACTGGCGTTATTTAGATGATGCGGGCGATCAGCCTAATCCTGAGTTTTCATTGAATCAAAGCCGTTATAAAGGCGCGTCGATTCTTTTGGCACAGGAAAACTTTGGTTGTGGTTCAAGCCGCGAACATGCGCCTTGGGCGCTGGTAGACTTTGGTCTGCGTGCGATTATTGCGCCGAGTTTTGCTGACATTTTTTACGGTAACTCAATCAATAACGGCCTATTACCTGTGGCGTTAACCCACGCGCAGGTGCGCCAATTAATGGATGAAGTGGCGGCAGAAGCTGGTGCACAAATTACGGTTGATCTTACAAGCTGCAAAGTGATTTCACCGTCGGGCGCCGAGTTTAGTTTTACGCTGGCAGAATCGGCAAGGCATAAGTTGCTAAACGGTTTAGATGCGATTGGACTGACCTTGTCCCATGCTGCGCAAATCAGTCAGTACGAAACCCAAATCCAAGGCTGGCGCCGTTAGTTTTTCAGCGCACATTTGTACTGGTGTTGAGTCCATATTTAAGCGCCTAGGTTAACACCTAGGCGCTTTTGTTTTAAATCATCATCTGCTCATGGTTTAACTAATTGTATTTGAAGTGATAAATTTTTATTGCTGTTTGTGTGTCATAGAATTTTTGTATCGATAGTGATTTATGCTGAATTTGTTACATCTGAATGATTTACACGTGTTTCTGAGATTTTTTATTTTATTTTGTTGTTTATTATGGTTATTTTTTTATTTAACCATGTTGAGTTAATCAGCGTTGCGAATAGTAACTGGGCGAATATAGGCTGAGTTTTGTTTAAATGGTGATATTGGTCACTAAATCTGTTTTAAAGTCATCGTTAGCTAGAGTTTTTGTCTGTTTTATCTCCTTTGGTTGCAAACATCTTCTGCATAAGTAAACTGCTTCGCATTCAATCTGGAGTGTTTGCTCTATTTTGCCTGAACGCCGCAGTACTCATTTAAGTAGTACAGATAACAATAATATTATTAGAGAATAACGATGCAAAAACGTCTTCTGACTTTGGCTGTCACTGCAGCCTTGTTAAGTTCAACGACTCAACTCCATGCCGCTGGTTTTCAATTAGCCGAATATTCTGCCACAGGCTTAGGCCGCGCCTTTGCCGGTGAAGCGGCTATGGCGGATAACGCTTCGGCTCAAGCGCGTAATCCAGCCATGTTGACTTATCTCGAAGGTCGTCAGTTATCGACGGGTGGTATTTATGTCATGCCAAACGTCGATGTGACTGGCGATGTCAGCATTAGCTCGCCGTTGCTTGGCCCAGATCCTGTTGTTATGAAAGGTGGCGATGCACTGGATGTGGCCGATGACGCACTGGTGCCAAACTTTTATTACTCAAATCAGTTAAATGATCAATGGACTTGGGGCCTAGCGGTAAATTCTAACTATGGTCTAGCCACTGAATTACCTGCGACTCACGCCGCGGCTATTTTTGGTAACAAGACTTCGGTTACAACCGTTGAGTTTAATCCAAACATCGCTTATCGCATCAATGATGCTGTTAGCGTCGGTGCTGGTGTACGTATCGTTTATGGTGAAGGCGAAATTGGTGCGTCATTACCCGGTTGGGTCGATGGCATCAAACCTGCTCTGCCGGCTCCTGTGCAGGCTGTGTTACCTCCAGGTGGCACTGAGCTGAAAAGCATGAAGGGGGATGATGTGGGTTACGGCTGGCAGTTAGGTGCAAGCTGGCAGATCAATCCGGCTCACCGTTTAGGTTTTGCGTATCATAGCGGCGTGCAATTGGACTTAGATGGTCATGCTTCTGGCGTCTTGTATACCGGCGGCCAAGATGTGTCTATCGAAGGGTATTTACCCCTTGAATTGCCCGCCTTTGCAGAGATAGCTTCCCACCACCAATTGACCGATAACTGGGCCATGCACGCGAGCGTGAACTGGACCGATTGGAGTGTATTTGATCAGCTGGTGGCTTATTTCCCCGGTGACGTTAAACCTAAGGGCGATTTAGAGTCTGATTTAGTTAAAGAAGAACACTTTGAAGATAACTGGCGTTTCGCGCTGGGAACCACTTATCAAGTGAATAATGACTGGTTAGTGCGTGCCGGTGTTGCTTTAGACAAAACGGCAGCAAATGACGAGTGGCGCACAACTACAATTCCAGACTCTGACCGTCTGTGGTTCTCTGTCGGCGCCGGTTATCAGGCGACTAAGAATCTGAACGTTGATTTTGCTGTGACTTATATTAAAGCGACTGGCGATGCGCCAATCAACGAACAGCAAAACCTACTGGGCTTAGCGGCCGTTAACTTTAACGGTGAAGCGAGCGGCGATGTGTGGCTTGCGGGTATCCAGATGAGCTATAAGATGTAAGTTCTGCGTAGTTTAAAAAAGCCCTGAATCTTCAGGGCTTTTTTATGGCTATAGCATGGTGGCGTTATCTTGTTTTACGGCTTGCTTGCTGGTCATGGCAGAAAATTGCGAATATATTCATTGCTAAGTAGTGGTTCATGTTAATTCTCGCCGGCATAAAAAGGCGGCGGAGAGTTCAGATAGGATAGTAGCGTGCAGAAAAAATACGTGGTGGCCTTAGATCAAGGCACGACCAGCTCAAGGGCGATAGTGTTCGATCACGACGCAAACATTGTCAGCGTGTCCCAGCGGGAATTCACCCAGTTGTATCCAAATCCGGGTTGGGTCGAGCATGATCCCATGGAGATCTGGGCAAGCCAAAGTTCAGTGTTAGTTGAAGTGCTGGCGCGCGCGGGGATCCACAGCGATGAAGTGGCCGCGATTGGCATTACTAATCAGCGGGAAACCACTGTCATTTGGGAGAAAGCCACAGGTAAACCTATCTATAACGCGATTGTGTGGCAGTGCCGTCGTAGCTCTGAAATTTGTGAGCAACTCAAAGCTCAAGGGCTAGAAGAGTATGTGCGTGAAAACACAGGTCTATTACTCGACCCTTATTTTTCAGGCACTAAGATCAAGTGGATTCTAGATAATGTGCCCAATGCCCGTGCGCAAGCTGAACGCGGTGAGTTGTTGTTCGGCACTATCGATACTTGGCTAGTGTGGAAGTTAACCGAGGGCAAAGTGCATGTCACCGATCCCACCAATGCCGCCCGTACTATGCTGTTTAATATCCATAGCCTGACTTGGGATAACAAGTTACTTGAAGCTTTAGACATTCCCTTGTCTTTGTTGCCCGAAGTGAAGCCTTCCTGCAGTGTGTATGGCACCACGCGGATTGCTGGTGAAGGCAGTGAAATTCAAGTCGCAGGGATGGCGGGTGACCAACAAGCGGCGCTTTTTGGTCAGCTGTGTGTCGAGCCCGGCATGGCGAAGAATACCTATGGCACTGGCTGTTTTTTATTGATGAATACCGGGACCAAAGCGGTGCGCTCTAACCATGGGTTACTGACCACAGTTGCCGTGGGACCGAAGGGCGAAGTGAACTATGCCCTCGAAGGTTCAGTCTTCATGGGTGGCGCGACCATTCAATGGCTGCGGGATGAACTTGGGCTTATTCGTGACGCCAGTGATACTGAGTATTTTGCCTCTAAGGTTGCTGATACCAATGGCGTGTATTTAGTGCCCGCTTTTGTGGGACTCGGGGCACCTTATTGGGATCCCAATGCCCGTGGCGCACTCTTTGGTTTAACCCGTGGCGCCAACCGTAATCATATTATTCGGGCGGCGTTAGAATCCATTGCTTATCAGAGTAAAGATTTACTCGATGCCATGACCAAAGACAGTGGTGTCAGCCTTAAGCGTTTAAAGGTGGATGGCGGCGCGGTCGCCAATGATTTTTTGATGCAGTTTCAAGCCGATATCACCGATGTCGAAGTACTAAGGCCGAGTGTTTGTGAGACAACGGCTTTGGGCGCGGCATTTTTAGCTGGGTTGGCTGTGGGCTTTTGGGAGAGTGTCATTGAGCTTGAACATAAGGCTTGTATCGATAAGCATTTTATCCCCAATATTGATGCCGAGAATCGGGGGCGGTTATACGCAGGTTGGCAGGATGCCGTCGCCAGAACTCGCGCCTAAGCTATTCGACTTAAGCTATTCAACTCAAGTCGTTGGACTTGAGGCATGCTTAGATTTGATATTGAGTTTGAGATTGAGTTGGTTTGAAATATGACTCGATTGTGGTGAATAAAAAGGCGCTAATCCCATGGGATTAGCGCCTTTTCCGTTTTATATCAGTACGGATTTATACTTCAACATCAATAATATGTTGGCTTGGTGGCTTTTTGATTTCCGCGGTATCGTCGGTCACTTCAAGCACGACACCTTCTTTTAATTCTTCAGGCTCATCCGATGCACGGCGTTTCTTATTCTTGTTGTTATCAGCATCTTTGCGCCTCTCCCTGACTTTCGCGTCGCGCCCTTCTAACGTTGGGGGTAACTGCGACTGCGGTGTTTCATGGCTATCGGGGGCATTTGCGGTTGCACTACTCACTTGGTCGACGACCCGTTTAGGGTGCAACACGGCTTTCACTGGCTGCGCCAGCATGGCATAAATGCTATCCAATCCACTCATAACGCCTCCTTGTTATTACACCTCTTAACATGTTATCGGCAGGAGTCGGTCAATATTTAGACTATACGCGGTTTAAGTCGTTAAGGCGAATGAGGGGGGTTAAGATCTAAGCATGAGCAATCGATGCTAACCAAAGCGGCACTCATTGTGAAGGAACCACAGTGGTTGAGAAGGAACAATAGTAGCGAATGCAAAATTGAGGTGAATTGTTGGATTCGCGTGGTGCTTTGCGGGATTTGGAGTGGGAACGTTGAGCTTTCTCGCAATCGTGACATTAATATTCAACCATCTGCAAAAAGATGATCGTCTTTATTTCGGATAATTATTTAGATTTTCGTGCTAAATCCAACTCTTTTTTGACTGTCTAATAAGGTTGAATTTAAGTTTATTTTGTTACTTTTTTGGGTTTAAGGTTATTCCTCGACCCGTAAAATTCCACAAATACAGAATTAATCCTCCATTTTTTTCATCGCAAGCGCCTGAAAGCAAAAGGTTGCTGAATATTTCGGCGCAATTTATTCCTGTAGTTTCTTCTGTTTAGTCGATCTCTCGTGGTCTTTCATGTCAAAAGTTTGTCGCTAGGTTAAGTGCTCAACATGCTGCTAACCCGCTTGCCGACTGGATTTATAAAAGGATCCCATCCCTTGACATTGCTTGTCGACTGTCCCTAAACTTAGCAGTTGTGGGAAATAGTGGATATTTGTGGATCAAAAATCACAGGAAAGGATGAATTAGCGTGTTTCGTGGAGCCAGTGCTATCAACTTAGATACAAAGGGACGGATCGCTATTCCAGTGCGATACCGCGAACCTTTGCAGCTTGAGCACCAAGGCCGCATCGTGATTACCGTCGACATCCAATCCGCTTGTTTACTCTTGTATCCAATCCATGAATGGGAATTGATCGAAGCTAAGTTGTTAAAGCTTTCCGATACCGATAAAACCCAGAGATCGTTAAAACGTCTGTTACTCGGTTACGCCCACGAAGTGGAGCTTGACGGTAATGGGCGCATATTACTGCCGCCACCGCTAAGGCAATATGCTAATTTAGATAAGCGCATCATGCTGGTGGGGCAATTGAACAAATTTGAGCTGTGGGATGAGCAATCTTGGCTGCAGCAAATCGATGAGTGTCAGGAAACGATTCGAAGTGAGGAGCTTGCTAGTAACGAGCGTCTCGCGGATTTTTCACTTTAACGAGTCGCGATAACTAAATAAGAAGAGAGTAATGAGTCAGGAATTTGCCCATTTATCCGTGCTGCTAGCAGAAACGGTTGGCGGTTTAAACATCAAAGACGATGGCATCTATATCGATGGCACGTTCGGCCGCGGTGGTCATTCAAGACAAGTGTTGCAGCAACTCGGTGAAAATGGCCGATTGATCGCAATCGACCGTGACCCTCAAGCGATTGAAGCGGCAAAACAATTTGCTGACGATCCACGCTTTCAAATCGTGCATGGTGGTTTTGGCCAATTAGCCGATTACGTTGAAGAGCTCGGGCTTGTGGGCAAAATAGATGGCGTATTACTCGATTTAGGTGTGTCTTCACCTCAGCTCGACGATGCCGAACGTGGTTTTAGTTTTATGCGTGATGGCCCGCTCGATATGCGCATGGATAACAGTCAAGGTCAAACGGCTGCTCAGTGGTTAGCACGCGCAGAAATTGAGGATATGGCTTGGGTATTTAAAACCTACGGCGAAGAGAAAAACGCTCGCCATATTGCCCGTTGTATCGCTGCCGATCGTGATAAAACCCCGTTTTTACGAACTAAAGATTTAGCCGATTTGATCGCGCGTATCACTAAAAGTAAAGAACGCAATAAGCACCCTGCAACCCGAGTGTTTCAGGCAATCCGGATTTATATCAATAGTGAATTAGATCAAATTGATCAAGCGCTTGAAGGTGCCGTCAATGTACTGGCTCCCCAAGGGCGCTTGTCGGTCATCAGTTTCCACTCTTTAGAAGATCGTATCGTGAAGCGCTTTATTCGCCGCCATAGCCAAGGCGAAAGTGTGCCGCACGGTTTTCCGGTCACTGAAGATCAAATTAACAAGTCGCGTAAGTTACGCGCCGTGGGCAAGGCGATTATGCCATCCGACGAAGAAATCGAACGCAATGCCAGAGCTCGCAGCTCAGTGTTACGCATTGCCGAGCGTTTAGATTACTAAGGGGCGCACTGTGAGTAAGCCCTCGATCAATCTGCCACGGATCGTCTTACACGATTTATGGCTGCACAAATGGATCTTGTTATTAGCTCTGCTCGTGCTCTGCAATGCGGTCGCTGTTGTTTATGGCAGCCATATTAGCCGCCAACTAACGACCCAGTGGGACCAGTTACTGCAAGAGCGAGACAGATTAGATATTGAGTGGCGCAATTTATTATTAGAAGAGCAATCGCAGACTGAACACAGTCGTATTACGCGCATTGCCTCTAAGGAACTCAACATGAGTCGTCCGTTACCCAGCGAAGAAGTTGTGGTAAAGGTGCCGTGAGTATAGGGAAGATATGACTAGGCAAGCGAAAATCAAACGGGTAAGCAAACCGCAGAAACCACAACTAATCCATTGGCGTTTATACGTAGTGGTGGGGTTTGTGTGTACGCTATTTTGCAGCCTAGTGGGACGCGCGGCCTACATTCAAATTATCGAGCCTGACAAATTACGCCATGAAAGCGATATGCGTACCTTACGGACCACGAGCCGTGAGGTGCAGCGTGGTTTGATCACCGATCGCAATGGCGACATGTTGGCCGTCAGTGTGCCAGTGCGTGCCGTTTGGGCCGATCCTAAACAAGTTAACGATAACAATGGTTTTGCGGATATGCGCCGCTGGCAAGCGTTAGCTGATGTGTTGCATGAACCCGTAGAAGACATATTAGATCGGGTGCGCAGTAATCCCACCAAACGCTTTACCTACTTAAAACGCCAAATTACTCCCGCGGTCGCGGACTACATCACTCAGCTGAAATTACCCGGTGTTTTCCTTAAATCTGAATCCCGCCGCTATTACCCTGGTGGTGAAATCACTGCTCAGCTTATCGGTATCACAAACATTGATGATGTTGGGATTGAAGGCGTAGAAAATGCTTATAACAGCTGGCTAACGGGCACGCCTTCAAAACAGAAAGTGCGTAAATCCCGCGATGGTCATGTGGTTGAGCGCCTCGATATTATCCAAGAAGGCGAAAGCCCGAATGATCTTGTGCTCAGCATAGATCAACGGATTCAGCAGTTAGCGTACCGCGAGCTGAAACGCACCACAGAAATGAACCAAGCGACCTCAGGTTCCATCGTCGTACTCGATATTCACACTGGTGAAGTCTTGGCGATGGTGAATACGCCATCGTACAACCCCAATGCGCGCGACAATCTGCAAACCTTCAGAATGCGTAACCGCGCCATGACAGATACCTTCGAGCCAGGTTCGACCATTAAGCCCTTTGTAGTGGCTGCGGCGCTTGAAGCGGGCACAGTTAAATCGACCGACATTATTGCCACTTCACCGGGTTGGATGCGTTTAGGCGGCCGCCAAGTGCGCGATCCTGCCAACTACGGCGACATGAGTTTGGCGCGTATTCTGGCGAAATCCAGTAACGTCGGCATCAGTAAATTGGCCTTGTCTATTCCGGTGCAGCAATTACTCGGCACCTATCAATCTATGGGGCTCGGTAGTTTCTCCGGGATCAACCTTACGGGTGAAAGCGCGGGGGTTATTCAAGACCGCCACCGTTGGTCTGACTTTGAGCGCGCCACCTTGTCCTTCGGTTACGGTTTAACGGCAACCACACTGCAACTTGCACGCATGTATGCCACTTTAGGCAATGGAGGCACCTTATTCCCCGTCTCAATTATGAAACTGAAAAAGCCCCCCGTCGGTGAGCGAGTTATTTCTGAAGATGTGGCCCACGATGTGATGCAAATGCTGGTCGGCGTGACAGAAAAAGGCGGCACCGGCACATTGGCTCATATTGACGGTTATCCGGTTGCAGGCAAGTCAGGCACTAGTCGTAAGGCGGTTGCTGGCGGTTATGGTGATGATTATGTTGCATTATTTGCAGGTGTTGCCCCGGCGAATAATCCTAAGTTAGCGATAGTGGTCGTCGTGAACGAGCCTAAGGGCGATCTCTATTATGGTGGTTCAGTGGCGGGGCCGGCATTTGCCAAAGTCATGTCTGGCGCACTGCAAATGTTAAACGTTGAACCCATTACCGATAAAGAACAAGTGCAATTGGCGGGTATCGCCGCCGGGAGAGCAGAATGATGTTATTACGGGATCTGCTCGCGCCTTGGCTGCATTATGCCGGCGCGCAGTCCTTCAACGATTTGACCTTAGATAGCCGCGCAATACGTCGCGGCGATGTGTTTTTGGCGCTGCCAGGACATAAGGTTGATGGCAGACAGTTTATTGAAAAAGCCTTAGATTTAGGTGCCGCAGCCGTATTAGTCCATACGGATGATGCTGATCAGCATGGCAAAGTGTTGCACAGAGATAATGCCGAGCACGGCGTGCAGATTTATGTCTTCCAGTTGAGTCGCCAAGTATCAGCCATCGCCGCCCAGCGTTATCCCTTAGCGCATCAGTCTGCGACAACATCCCTTGGGGTGATTGGTATCACTGGAACCAATGGTAAAACGTCGACAAGTCAATTGATTGCCCAGCTGATAACGTCGCTCGGCCGCAAAGCCGCCGTGATGGGCACCCTTGGCAATGGTCTCTGGGGCGAGCTTGTCGATAGCGGCAATACCACAGCCGATGCGATTACCCTAATGCGTCAGTTGCATGACTTTGAAGCCCAAGGCGCGAGTGTCTGCGCCATGGAAGTGTCGAGCCACGGCTTAGTGCAAGGCCGCGTCGATGCAGTACCATTCGATGTTGCCGTGTTTACCAATCTGACGCGCGATCACCTCGATTACCACGGTGATATGGAAAGCTATGCCGCCGCTAAGCAAAGCCTATTTCGCTTCAGCACTTTAGGTCACGGTTTATTGAATCTCGACGACACGGTTGGCGAGTCTTGGTTGACTGAACTTAAATCTGTGCCTGCGCAAATGTGGGGCTTTAGTATTGAAGGTCATCAAGCCGCGGCTTTTTATACTAAAGATGCCAAGTTTGACGATCAAGGTGTCAGTGCAACCTTAGTGTGGCCCGAGGGCGAAACTGAAATTCACTCACCACTACTTGGTGCCTTTAATCTCTCTAATTTACTCGCGGCGCTCTCAGCCTTGTATCTGCAAGGTTTTGATATGAAAGCGTTAGCCGAGCAAGTGCAATACCTAACGCCCGTCGCAGGTCGTATGGAGCGTTTCACAACTGCGAAGAACATCACCTTAGTGGTCGATTATGCCCATACGCCAGATGCGATTGAACAAGCGTTAAATGCGCTACGTCGACATTGTGTCGGTGATTTATGGTGCGTATTTGGTTGTGGTGGCGACCGCGATAAGGGCAAGCGTCCTTTGATGGGCCAAGCTGCAGAGCAATATGCTGACCGCATTATGGTGACGAGCGACAATGCGCGCAGTGAAGACCCCGCGCAAATTATTACCGATATTATCCAAGGGCTAACCCATCCTCAGCAGGCATTGACTCAGGTTGACCGTATCGCAGCAATTAAAGAGGTTGTGGCATTGGCGAAACCTGGCGATGTGATCTTGCTTGCGGGCAAAGGCCACGAAACTTACCAAGAGGCGGCAGGTGTGCGTCATGACTATGATGAACGCGCCCTGGCACGCCAGTTATCGGAGCAATCAAGATGATCCCTCTTTCTCTCGAGACCCTAAGTCAACACTTAGGTACGTTTCGAGTCGGTGACGACGTCACCATCGAAGATTTAAGCAGTGATAGCCGCAAAATCGGCGCTGCAACCCTGTTTGTAGCCTTAAAGGGTGAGCGTTTTGATGGCCATGATTTTGCTGCTACCGCTATTGAGAACGGCGCCGCGGCGTTAATGGTCGAGCGCGAGTTAGCCTTTGATATTCCGCAGCTGATTGTGCCCGATTGCCAAAAAGCCATGGGCGCGATAGGGGCTTATGTCCGTGATCAAATCAATCCGATTTGCGTGGCTTTGACTGGCTCAAACGGCAAGACCAGCGTGAAGGAAATGATCGCGACCATCCTGTCGGCAAAGCATCAAATCCTCTATACCGCTGGTAACTTCAATAATGAAATCGGTGTGCCGCTTACCTTGCTGCGTTTAACGTCAGCGGATGAGTTTGGGATATTCGAGCTCGGGGCTAACCATAAAGGTGAAATTGATTACACCTCGGGACTCGTGCGTCCGAACGTGGCGCTGGTAAATAACGTCGGCAGCGCCCATTTAGAAGGATTTGGATCGCAGGCGGGTGTCGCGCAGGCTAAGTCGGAAATTTTTAATCATTTGCAGGCCGATGGCACCGCCATCATCAACGCCGATGATGCCTTCGCCGATTTTATGAGCGTTAAGGCAAGACCTTATAAGCAACTGAGCTTCTCCCAGCAAGATGGCGCCGCTAAACGTCATATCGATGTCATCGCGACAGGTCATAAGACCAATGCCGATGGTTGCTATCGTTTTATGCTCAGTTATTTAGGTGAATCTTGCCAAGTAGTTTTGCCGCTGGCGGGTCGCCATCAGGTGAGTAATGCACTAGCCGCTGCAAGTGTTTGTATTGCCTTGGGATTAAGTTTGACAGAGATCGCCGAAGGCTTAAGCCAATTAACGCCAGTGAAAGGGCGTATGCAGCCGAGTCAATTGGGACGCGTACGTCTAATCGACGATAGCTATAACGCGAATCCTGTGTCCGTTGGGGCGGCAATCGCTTGGTTAAAGGAAATTTCTGAAAATCGCTGTCTGGTACTGGGAGATTTAGGAGAATTAGGCGACAATGCGCCCCTTTTACACGCTGAACTAGGGCAACTGGCCAAGCAACAGGGGATAGATGCCTTGTTTTGCACCGGAACGTTAAGTCAGCACACGAGCCAAGCTTTCGGTGCGGAGCACTATGACAGTGTGGCGACGTTAGTAGAAAAACTCATAAAGCATATTAACCAGTTACCGGGACAGGTAACGGTTCTCGTTAAAGGTTCACGTAGCGCCGCCATGGAGCGGGTCGTGGATGGACTAACAGTAGCCTTCGGGCGTGGGGAGTTAGTGTAAATGCTGGTATATCTGGCCGAGTATTTAACCCGTTTTCATACCGGGTTTAACGTGTTTTCCTATGTAACGTTTCGAGCCATTTTAGGCTTATTAACCGCATTGGTTTTTAGCTTATGGTTTGGCCCTAAACTGATTGAACGTTTGCAATTACTGCAAATTGGTCAGGTCGTGCGTAACGACGGTCCAGAATCACATTTTAGTAAGCGTGGCACGCCGACTATGGGTGGCTTGCTCATATTAGCCGCCATCTTCATCAGCGTATTGCTGTGGGGCGATCTCGGTAGCCGCTATGTGTGGGTGATGTTGTTTGTGCTCGGCAGTTTCGGTTTGATCGGCTTTATCGACGATTACCGCAAAGTGGTGCGTAAAGACACTAAAGGTTTGATCGCGCGCTGGAAGTACATTCTGCAATCTTTAGCTGCGCTGCTCATTGCTTTTTTCCTGTATGCCACAGCGGCAAATCCAGGTGAAACCCAGTTAGTGGTGCCTTTCTTTAAAGACGTGATGCCGCAACTTGGCGCCGTATTTATCGTATTGGCGTACTTCACTATTGTGGGTTCGAGCAATGCGGTGAACTTAACCGACGGCCTAGACGGTTTGGCCATTATGCCGACGGTAATGGTCGCGGCGGCGTTTGCCTTGATAGCTTATTTATCGGGTCACGCGCAGTTTGCGAACTACTTACATATTCCGCATTTACCCGGTTCGGGTGAGCTTGTGATTGTCTGTACCGCCATTGTCGGTGCGGGTCTGGGCTTTTTGTGGTTCAACACTTACCCTGCGCAAGTCTTCATGGGCGATGTGGGTTCTCTGTCATTAGGTGCTGCACTGGGCACAATTGCAGTACTCGTTCGCCAAGAGATTTTATTAGTGATCATGGGCGGCGTATTCGTGATGGAAACCCTGTCAGTCATTCTGCAGGTGGGTTCGTACAAGTTACGCGGCCAGCGTATTTTCCGCATGGCGCCGATACACCATCACTATGAGTTAAAAGGTTGGCCCGAACCTCGAGTTATAGTGCGTTTTTGGATTATCTCGATATTCCTCGTCTTACTTGGCTTAGCCACGTTGAAGTTAAGGTAATTGGTTATGCAAAATCAGTATTCACACATAGTGTTAGGTTTAGGGGCAACGGGTTTGTCCGTCGTACGCTATTTGTGTGGAAAGGGCATTACGCCATTGGTGATGGACAGTCGTCGGCAGCCTCCGGGCGCCGAAACATTAGCGACGTCTTTTCCTGATGTGAAGCTGATCTCTGGCGGATTCGATTGCCGTTATCTCGTGCAAGCGACGCAGATCATTATCAGTCCCGGCATTGCCACTTATACCCCAGAGGTGCGTGCCGCTGTGGATATGGGCATCGAAGTGATTGGCGATGTGGAGCTATTTGCCCGTGAGATTGCTGACCGTAAACCTTGCGTTATCGGTATTACTGGCTCTAACGGTAAATCAACTGTCACTACGCTTGTGGGCGAAATGGCAAGGGAAGCGGGTATTGCCGTTGCCGTTGGTGGCAATATCGGTGTGCCTGCGCTGGATTTACTGCATGAAAATGCCGACTTATTTGTGCTTGAGTTGTCGAGCTTTCAATTGGAAACCACCCATAGCCTTAAGTGTGTGGTGGCGACCTGTTTGAATATCAGCGAAGACCACATGGACAGATACAGCGATATCGCTGCCTACCGCGAAGCTAAATTACGTTTATACAAACAAAGCCGCGCCGTGCTGTTTAATCGCGACGATAAACTGACCGTACCGCCAGAGCCAATGAACCAAAACAGTTTCGGTTTAGCTGCGCCAGAAGCGGATGAATGGGGTATCAGCGACAGCAAGATTTATCACGGCAGCAGCGAAATCATGCCGATCACAGAAGTCTCCCTGATTGGCAGCCATAACCATGCCAACCTACTCGCAGCAATGGCGCTTGTGGATGTTGTCGGTGTTGATAAACAGATTATGGCAAAAGTGGCGCGTAGCTTTACCGGCTTATCCCATCGCTGTGAAGTGGTTGGGATTAAGAATGGCGTGACCTATGTCAATGACTCAAAAGCGACGAACGTCGGCGCCACCGTTGCCGCGCTAGATGGTTTGAGTGATCACTTAGGCGATATTATTTTGATTGCTGGCGGCGACGGCAAAGGTGCTGATTTTAAACCTTTAGGCGTGGCATTAGCGAAAGTAACACACCTCATTACCTTAGGTCGCGATGGCAAAAAGATTGCCGCCTTAAAAGACGGCGCGGTCAGAGTCGAGACTATGGCGGCAGCGGTAACGAAAGCCGCTGAATTGGCGACCTCTGGCGATATCGTTTTATTGTCGCCAGCCTGTGCCAGTTTAGATATGTACAGTAACTTTATGGCTCGAGGTGACGATTTTAGAAACCTTGTGGAGCAACTCAATGGCGAGTGATGTAAGACAACTCAGCCTATTTGGGCGTTTGCGTTCAGCAATGCCCAATTGGCAACGTGATACCGAAGTGCCGGGCGTGCAATTGTACGACCGCGCTTTGCTCGCGGCTGTGTTGTCTTTGATTGGCTTTGGCTTTGTGATGGTGATGTCGGCCTCAATGCCAGAAGCGCAAACGCTCACGGGTAACCCGTTTTATTTTATGACTCGCCATGTGGGTTATTTAGTGGGTTGCTTAGTCATTGCGGCATTTGTATTAAGAGTTGAAATGCAAACTTGGCAGCGCTGGAGCCCCATGTTGCTATTGGTTGTGGGCTTGATGTTACTGGCGGTTTTAGTGGTCGGTACTACGGTAAACGGCGCGACGCGTTGGTTATCGATAGGCCCAATACGCATCCAAGTGGCGGAAGTCGCTAAGTTTGCCTTTGCCATCTACATGGCGGGCTATTTAGTGCGTCGTCATCAGGAAGTGCGCGAGAACGCTAAGGGTTTCTATAAACCGATTGCGGTATTTGCCATTTACGCTGTGCTGATTTTGATGCAGCCCGACTTAGGGACGGTTGTGGTGTTGTTCGTCGGTACCGTTGGCCTGCTGTTTTTAGCGGGTGCACGTTTGCTCGACTTCTTCGCCCTGATTTTTGCCGGTGTGTTGGCGTTTGTGGCTTTGGTGTTGTTAGAACCTTACCGTATGCGACGAGTGACATCGTTTATGGATCCTTGGCAAGATCCCTTCGGTAGCGGTTACCAGTTGACGCAATCTTTGATGGCCTATGGCCGTGGCGATTGGTTTGGCCAAGGGCTGGGCAACAGTATTCAAAAATTAGAATATCTGCCCGAAGCGCACACTGACTTTATCTTTGCCGTGATCGGCGAAGAATTAGGTTTTATCGGCATTATTGCCGTGTTATCCGTGCTGCTGTTTGTGGCGCTGAGATCGATTCGTTTGGGCAATTTGTGCTTAGCCATGGATAAGCCCTTCGAAGGTTATTTAGGTTACGCCATTGGTATTTGGGTATGTTTCCAGACTGTGGTTAATGTCGGCGCGAGTATCGGCATGTTACCGACCAAAGGTCTGACATTGCCTTTTATCAGCTACGGCGGCAGTAGTTTATGGGTCATGACAGCAGCAGCCATGATGTTGCTACGCATTGATTACGAGCGGCGCATGACTTTAGTTCAGGCCGTGCAAGGGAGATTGAAGTGATGACCCCAGCAGGAAAACGTATTTTAGTCATGGCGGGTGGTACGGGAGGACATGTGTTCCCGGCGCTGGCAGTGGCTAAGTATCTAGCTCAACAGGGTTGGCAAGTGCGTTGGTTAGGCACGGCCGATCGTATGGAGGCGCGTTTAGTGCCCCAGTATGGATTTGATATTGATTTCATCGATATCAAAGGTGTGCGCGGCAATGGTTTGATACGTAAACTCGCGGCGCCCTTTAAAGTGGTGCGCTCAATATTGCAGGCTAAGGCCGTGATAGCTGAGTTTAAACCCGATGTCGTGCTTGGCATGGGCGGATTCGCCAGTGGCCCAGGTGGTGTGGCGGCAAGATTAGCCGGTATTCCATTGGTACTACATGAGCAAAATGCCATCCCAGGGATGACGAACAAGTTGTTGTCGCGGATTGCGACGCAAGTGTTATGTGCCTTTAAAAATACCTTTACCACAGTGAAGGCCAAGGTTGTTGGTAATCCGATTCGCCAAGAATTGATTGCCTTAGGCGCAGAACCAAAGCCAGAGGCTGATGAAGCCTTAAAAGTATTAGTTGTCGGTGGCAGCCTTGGCGCCAAAGTCTTTAACGATCTGATGCCTGAAGCGGTTGCGATTCTGAGTCAGCAACAGTCCGTTACGGTTTGGCATCAAGTGGGTAAAGATAATCTTGCGGGCGTGAAGGCGGCTTATCAGCAACATGGGCAAGATGGCGGCGTGAATATCGCCGAGTTTATAGACGACATGGAAGCCGCTTATCGCTGGGCTGATGTGGTATTGTGCCGCGCCGGTGCGCTGACGGTGTCTGAGTTAGCGGCGGTGGGGCTACCGAGTATTCTGGTGCCTTATCCCCATGCGGTGGATGATCACCAAACCCGTAACGGTCAAGTATTGGTTGAGGCTGGCGCAGCTTTCCTATTGCCACAGGCCATCTTGGACGTGAATAAACTGGCGGGCAAATTACAACTGCTCGCCAATGACAGAACAGAATTAGCCCGTATGGGCCAAAGAGCGAGAGATGTTGCCGTACTGGATGCGACTGAACAAGTTGCCGCAGTGTGCATCAGCCTTGCTGAGAAAGGATAAGCATGACTAAGACAGAAAGATATGCACAACTGAGAAGCATGATCCCAGAAATGCGCCGTATTAAGCGCATTCACTTCGTCGGCATTGGCGGCGCGGGCATGGGCGGGATCGCCGAAGTGTTAGTGAACGAAGGCTATCAGGTGAGTGGTTCGGATATCGCGCAAAATGCCGTGACCGACAGATTGTGTTTGCTCGGTGCCAAGATCCAAATTGGTCATGCTGCCGAAAACGTGCAGCAAGTAGATGTGGTCGTGGTCTCCACGGCGATTAATCTGGAAAACCCAGAAATCCTTGCTGCCAAAGAATTACGTATTCCTATCGTGCGTCGCGCCGAAATGCTAGCCGAGTTGATGCGTTACCGTCATGGTGTGGCGATTGCCGGCACCCATGGTAAAACGACGACAACCAGCCTTATCGCCAGCGTGTACGGTCAAGCGGGACGGGATCCTACCTTCGTGATTGGTGGCTTGCTCAATAGTGCTGGTACCAACGCCAGATTAGGCACCAGCCGTTACTTGATTGCCGAAGCCGATGAGAGCGATGCGAGCTTCCTGCACCTGCAGCCTATGGTCAGTGTGGTGACTAACATTGAAGCAGATCACATGGATACCTATGGTGGTGATTTCGAAAAATTGAAATCGACCTTCGTGGATTTCCTACATAACTTGCCGTTCTACGGTGTCGCAGTTGTGTGTATCGACGATGCTGTAGTGCGTGAAATCATGCCGCGGATTGGTCGTCAACTCGTGACCTACGGTTTTAGTGACGATGCGGACGTGCAAGCGCTGAATTTCAGTCAGCAAGGCCATCAGTGTCGTTTCACTGTAAGACGTAAAGGCAAAGCTGATCTCGATCTGGTGCTTAACTTGCCGGGTCAACATAACGTGCTTAATGCCTTGGCTGCGATTGCGGTCGCGACCGAAGATGAAATCGACGACAGCGCCATTACTCAGGCGTTAGTGGAGTTCCAAGGTATAGGTCGTCGTTTCCAGCATTTGGGCAAGTTTGCTACGCCTAAGGGCGAAGTTATGTTAGTCGATGATTATGGCCATCACCCCAGTGAAGTCGCCGCGACCATTAAAGCCGCCCGTGCAGGTTGGCCCGACAAACGTCTGGTTATGGCGTATCAGCCACACAGATACACACGTACCCGTGATTTGTATGAAGACTTTGTTGAAGTGTTATCCCAAGTGGATTGTTTACTGTTGCTCGATGTTTATAGTGCGGGTGAAGCGCCGATCACTGGTGCCGATGGCCGAGCCTTATGTCGCTCAATTCGCCTGCGTGGCCAGTTAGATCCGATCTTTATCGCCAGTCCTGATCAGCTCGCAGAGGTATTGCCCGATGTATTGCAAGAGGGGGATTTATTACTCACCCAAGGTGCAGGTAATATTGGCGCCCTGTCGCGATTGTTAGCGACGACAGAATTGGGCTTTGCCGTTGCGGAATTACCTGCGCAAGCAAGCTGATCTTTAGCGCTTGATCTGCGAGGAAAAGGAAATTTTGACCAAAGGATTTTGGTCTATATAATGGCCGGTTTTCTGTGGTTTTTAATGAGTGTCGTTAAAGGTGGAATGAAGCGTGTCTTGGAGTGATAAGAGGCGGCACTGGCGGGCAAGAATGTCACGGGTTAATTGGTATCTGTGGAGCGGGGCTGGCTTTTTATTCTTCATCGTGGCCAGTTTTGTATTTGGTGGATACCAGTTACATAAATTTTTAAACGATGCCAGCACCTTGCCGATTGAGGCCGTTGCCATCAAAGGTGAGCGGGCTTATACCACAGATAAAGATATACAAGTGGCACTACAGGATTTAATGCAGCGGAGTTTTTTTAGTGCTGACATCACCTTAGTGCAACAGGCGTTAGAAGACTTACCTTGGGTGTATCGCGCGTCGGTAAGACGTGAGTGGCCAGCGAAACTCAGGGTGTATTTGCAAGAGCAGCAGCCGGCGGCCCATTGGAATGGTGACTCCTGGTTGAATGTGCATGGTGAAGTGTTTGAAGCGCCATCGCATCCAGAGCTGGAGCATTTACCGCAGTTATCTGGGCCAGACGATATGGGCCTCGAGGTGTTGACTGTCTATGCACAGATCAACTCCCTGTTAAAGATTAATGGCTTTACCTTAGCCAGTTTGTATTTGACGCCACGACATGCGTGGCACGCGACCTTAGGAAACGGCATCGTTTTAGATTTAGGTCGAGAAGATAAAATTGCACGGATACAGCGATTTATTACTGTTTATCCGGTTTTGGCGAAACAAGAGAAATCCGTTGCCAGAGTAGATTTGCGCTACGACACAGGGTTGGCCGTAGGCTGGGGCGATGCACAAACAAGAGAGCCGATAATTAATGATCAAAAACCAAGATAGAAACCTTATCGTCGGATTGGACATAGGAACCTCTAAGGTCGCAGTGATCATAGGCGAAGTTCTGCCCGATGGCGAAATCAGCATAGTCGGCCTTGGCAACCATCCCTCAAGGGGCATGGATAAAGGTGGTGTTAACGATTTAGATTCGATCGTTCGCAGTGTACAACGTGCGCTCGATCAAGCGGAATTAATGGCCGATTGCCAAGTGTCATCGGTTTACCTGAGCATCTCTGGTAAACACATCGCCTGCCAAAACGAAAACGGCATGGTTTCGATCAACGATGAAGAAGTGACACAGGAAGACGTTGATAACGTGATCCACACGGCTCGCTCGGTGAAGATCCCGACCGAACGTCGCATCTTGCATGTGCTGCCGCAGGAATATGCGATCGACGTGCAAGACGGCATTCGCAGCCCAATCGGTATGTCTGGCATGCGTATGGAAGCCAAGGTACACATAGTGACCTGCGCCAATGATATGGCGAAGAACATCACTAAGAGCGTTGAGCGTTGTGGCCTTAAGGTCGACGACTTAGTGTTCTCTGGGATTGCTTCGGCCGATGCCGTGCTGACCTTCGATGAGAAAGACTTAGGTGTGTGTATCGTCGATATCGGCGGTGGCACCACAGATATTGCTGTATATACCAACGGCGCTTTGCGTCATTGTGCGGTTGTGCCAGTTGCGGGCAACCAAGTGACCAACGACATTGCGAAGATTTTCCGCACGCCGTCGTCACATGCTGAACAAATTAAAGTGCAATTCGCCTGTGCGCGCAGCTCTATGGTGAGCCGCGAAGACAGTATCGAAGTGCCATCGGTAGGTGGACGTCCATCACGTAGCATGTCACGTCACACGCTCGCCGAAGTAGTAGAACCAAGATACCAAGAGCTGTTTGAGTTAGTACTTAAAGAGTTGAAAGATAGTGGATTAGAAGATCAAATCGCCGCAGGTATTGTGCTCACAGGTGGCACAGCGTCGATTCAAGGTGTGGTCGATATCGCGGAAGCGACGTTCGGCATGCCAGTTCGTGTAGCGTCACCGCTACCGATCAAAGGTTTATATGAATACGTGGACCAGTCTATTTACTCCACTGGAGTCGGACTGCTTCATTACGGTGCACGACGGGTGATCGAACGTCAGTTCGAACGTCCTGAGCGCCAAGGGGTAACTAGCGCTTGGAATCGGGTCCAAAGCTGGTTTAAGGGCGAATTTTAATTTTCATAACGCAGGCAACACGGAGATCAGACAATGTTTGAGATCATGGATACTCACTCAGACGACGCGGTGATTAAAGTCATCGGCGTTGGCGGCGGCGGCGGTAACGCTGTCGAACATATGGTCAAACACAACATTGAAGGTGTTGAGTTTATCGTTACCAATACAGACGCTCAGGCCCTGAGAAAATCAGGTGCAGGCTCAACGATTCAATTAGGACGCGATGTCACTAAAGGTCTCGGAGCAGGAGCAAACCCAGATGTGGGTCGTGCAGCAGCTGAAGAAGACCGCGAAAACATTCGCGCGGCGATTAAAGGCTCAGACATGATCTTTATCGCTGCCGGTATGGGCGGTGGTACAGGTACTGGTGCTGCACCAGTTGTCGCTCAAATCGCTCGCGAAGAAGGTATTCTGACCGTTGCGGTTGTGACTAAGCCTTTCCCATTCGAAGGGAAAAAGCGTATGGCTTATGCCGAGCAAGGTATTGCTGAGCTGGCTAAGCACGTGGATTCGTTAATCACGATTCCAAATGAAAAATTATTAAAAGTATTGGGCCGTGGCACTTCTTTACTCGATGCGTTCGCCGCAGCGAATAACGTGTTACTTGGCGCGGTACAAGGTATTGCCGAGCTTATCACTCGTCCAGGTCTGATTAACGTCGACTTCGCCGACGTGAAGACTGTGATGTCTGAAATGGGTAATGCTATGATGGGTACAGGCGTCGCTCGCGGTGAAGACCGTGCTGAAGAAGCGGCTGAAGCTGCTGTTGCCAGCCCATTATTAGAAGACATCGACTTAGCCGGTGCGCGTGGTGTGTTAGTTAACATCACTGCGGGTATGGACATGAGTATCGAAGAATTCGAAACCGTGGGTAATCACGTTAAGGCTTATGCCTCTGACAACGCGACTGTGGTTGTTGGTGCTGTCATCGACCCAGAAATGAGTGATGAGCTGCGTGTAACGGTTGTTGCTACAGGTATCGGTGCTGAAAAGCGTCCAGATATTCAATTGGTGTCTAAGCCTGCGCCTCGTCCAGAGCCAGTGGTTGTGGAACCAAAAGTCGAAGCCTATGTCGAAGAAGCGGTACATGTGAACTACGCTGCGCCAAAAGGCAACGTATTGCCAGCGGCACCACAGCCAGCTCCACAGCCTGCACCATCGACAAAGCATGAGTTGGATTATCTTGATATTCCAGCATTTTTGCGTAAGCAAGCCGACTAGCGATAGCGTTTGAGTCCAGCGTATATGTGAGGTGTTGTTCGTTTTGGGCAATACCACAGCATAGCTGCATTTGTGTGACATAATAAAGTTATGTTATTATGTTGAACCAGTGCGGCCAATTTGCCGACGCTGAGTGTACAAATATTAGGCTTTATAATTGGCATATAAACGGGTAACTGAATGATATTTCAAAGAACTGTTCAGAAAATGGTGAAAACTACTGGTGTCGGATTGCATTCTGGCAACAAGGTCACTTTGAGCATAATGCCCGCGCCCGTTAATTCAGGGATCGTACTCGTGCGCACCGACTTGAGTCCTGCCGTCGCCATTCCAGCGAAGGCGGAGCAAGTTCGTGAAACCACTATGTGTACGGCACTTGTCAATGACGAAGGTATTCGTATCTCGACCATTGAGCATTTATTTGCAGCACTTGCGGGTTTAGGCATAGATAACGCCGTAATCGAAGTGGACGCGCCTGAAATTCCTATTATGGATGGCAGTGCGAGCCCGTTCGTCTTCTTATTGCAGTCTGCGGGTATTAAAGAGCAGGCCGCACCTAAGAAGTACTTAAAGATTAAGCGCCCTGTGCGGGTTGAAGATGGCGACAAATGGGCCGAGCTAAAACCCTTTAAAGGTTTTAGAGTCAATTTTAAAATTGACTTCGCGCACCCAGAGATCGCTCGTAGCCAACAGCATGTGGTGATGGACTTTTCAACTTCTGCTTTCGTGAAAGATATCAGCCGCGCCCGTACTTTTGGGTTTATGCGTGATATCGAATATCTGCGAGCCAACAACCTAGCGTTGGGCGGCAGCATGGAAAACGCAGTTGTACTTGATGAATATCGCGTCCTTAATCCCGACGGCCTGCGTTATGAGGACGAATTTGTTAAGCACAAAATTCTAGATGCGTTTGGTGATTTATATGTGGCTGGACACGCTATTTTAGGTGAGTTCACTGCGTATAAAACCGGTCATGCACTGAACAACCAATTGGTGCGTGCTTTACTGGCGCAACAGGATGCGTGGGAGTTGGTCAGCTTTGAAAAAGAAGCCGATGTTCCCGTTAGCTTTACCGTGCCAGGTGGCGCTGTATTTGCTTAAAAAGCGGTGAGATTATTTATTACGGCTGGCCAATGAGGCCAGCCGTTTTAGTTTTTCGCCCAGTGAACCGCCAATATTTTCCGCTAATGCCGCAATATGCTCCGCTGCCGCAGGGCTTAATTCTTTGTGTCCCTGTTTAGGCTTTGCGTCATACATTGCTAATCTCGGGTTGACTTTAACTTCAATTGCGGTAAGGATTGGGAGCGTTTCTGCTTGAAGCTGCGCTAGTAGCTTCGGCTTTTGGAAATTAATTCGTGCAGCCCAGGCCGCCGAAGTGGTTTCAACCACAAGAACACCCTGGCGGAAATTAGCGACTTTTAGCTGCTCCGCAACAGGACCTGCGATAATCTGCTTCACATGATGATTCAGATACATAAGTAGTTCCGCTTTCTCAGCAAGGTCGGGTAATTTGCCCGATTGATGCAGTAATTGGCTGAGATCTTGAGGGGGCTTTTTCATATGATAATAAGATGGCTGAATTAGGCTATGAGTGTAACAGTATTTATTCAAGGTCGGAATGGCGTCACACGCTGGCAGCCCAGTAAACGCTGGTTGCTACTTCCAATACTATTGTTGGCCACTGGGTCCGGTATTTATCAACACAACTCAGCTCGATTTCAAGATCAGCAAGCCAGTGTCGATAACGATCGTCTCGTGCGCGAACAACAAAAGAAACAAGTCTTAGAGCTAAAAAGTGCTACCGAGTCTCAACTGGCTATGCTAGTGACTCATGTTGCACGTATGCAAGCCAAGATCACCCGTCTCGAAGCCTTAGGTCAACAGGTCGCCCAGCAGAACAAATTGGATGAAGATTTTGATTTCTCATCCGAAGTTGGTGTGGGTGGTTTGAGCGAATTAGGCACTAACATCGAGCTCGGACAGCTTATCGACGATATGGATAAGTTGGCATCACGAATTGACAATAATAATGTTCAGCTTTCACTACTTGAAACAGTGTCATCTAACCACCATATAGATGATGAACGTTATATATCAGGGATCCCACTCGATAAAGGTTGGTTATCGTCGCCCTACGGATTACGTAATGACCCTTTCAACGGTCGCAGGACTGTGCATAAGGGGATTGATTTTACCGGCCGAGAAGGGGCCAAAGTTGTTGCGACTGCTGCAGGCGTAGTAACTTGGGCTGGGAACATGTTCGGTTATGGCGAGTTGGTCGAAATAGACCATGGTAACGGTTTACATACGCGTTATGGCCATAATAAAGCTTTGTCAGTGACTGTAGGTGATGTGGTCGCAAAAGGCGATGCAATCGCCAGTATGGGAAGCACTGGGCGCTCAACTGGGGCTCATGTGCATTATGAAGTGTTGCGCGGCGGACAGCAGATAGATCCACAAAAGTTTGTCTACCGCAAAGCAAGTTAATTGAAACATTGGCTTCTAGTTTCCATCATGGACTTGGCCTACAGAGAATAAGTGATTCAGATGTTTGGTAAATTACTGACAAAAGTATTTGGTAGTCGCAACGACCGCACCCTAAAAGGGCTTCAAAAGATCGTTATTAGCATTAACGCACTAGAAGCTGATTATGAAAAATTAACCGATGAAGCATTAAAAGCGAAAACTGCTGAGTTCCGTGAGCGTTTAGCTGCCGGCGCTTCATTAGATAGCATCATGGCTGAAGCGTTTGCGACAGTGCGTGAAGCGTCTAAGCGCGTATTTGACATGCGCCACTTCGACGTACAGTTACTCGGTGGTATGGTACTCGACAGTAACCGTATTGCTGAGATGCGTACGGGTGAAGGTAAAACCTTAACTGCAACCTTACCCGCTTACTTGAACGCATTGACGGGCAAAGGCGTTCACGTGATTACCGTGAACGATTACTTAGCACGTCGTGATGCCGAGAATAACCGCCCACTGTTTGAGTTCCTCGGTTTAACCGTCGGCATCAATGTGGCAGGTCTCGGTCAGCATGAAAAGAAAGCTGCTTACAACGCCGATATCACTTACGGTACCAACAACGAGTTTGGTTTCGATTATCTGCGCGATAACATGGCGTTTTCACCGCAAGAACGAGTACAACGCCCGTTGCACTACGCCCTAATCGATGAAGTGGATTCAATCTTAATCGACGAAGCGCGTACGCCGCTGATCATCTCTGGCGCCGCTGAAGATAGCTCTGAGCTATACATCAAGATCAACACCTTGATCCCTAATCTTATCCGTCAAGATAAAGAAGATACGGAAGAGTATGTGGGTGAGGGCGACTACAGTATCGATGAAAAAGCCAAACAAGTGCATTTCACCGAGCGTGGTCAAGAAAAAGTAGAAAACCTGTTGATCGAGCGCGGCATGTTAGCCGAAGGCGACTCACTCTATTCTGCTGCAAATATTTCCCTATTACACCACGTCAACGCGGCGCTCCGTGCGCACACGCTGTTTGAGCGTGATGTGGATTACATAGTACAAGACAACGAAGTCATCATAGTCGATGAACACACGGGTCGTACTATGCCGGGTCGTCGTTGGTCAGAAGGCTTACATCAAGCTGTCGAAGCCAAAGAAGGCGTGCATATTCAAAACGAAAACCAAACATTGGCTTCGATCACTTTCCAGAATTACTTCCGTCAATACGAGAAGCTGGCAGGTATGACAGGTACAGCCGACACTGAAGCGTTCGAATTCCAACACATCTATGGATTAGACACAGTCGTTGTGCCGACTAACCGTCCAATGGTGCGTAAGGATATGGCGGATTTGGTTTACCTGACCGCCGATGAAAAATATCAAGCTATCATCAAAGACATTAAAGATTGCCGCGAACGTGGTCAGCCAGTATTGGTGGGTACAGTCTCAATCGAACAGTCTGAACTGTTAGCGCGCTTAATGGTGCAGGAAAAGATCCCGCACGAAGTCTTAAATGCTAAATTCCACGAACGAGAAGCTGAAATCGTCGCGCAAGCTGGCCGTACTGGTTCAGTGACTATTGCGACGAACATGGCTGGTCGTGGTACCGACATCGTGCTTGGCGGCAACTGGAACATGGAAATCGATGAACTCGATAATCCAACCGCTGAGCAAAAAGCTAAGATCAAAGCCGATTGGCAAATTCGTCATGATGAAGTCGTTGCTGCGGGTGGTTTACACATCCTAGGTACTGAACGTCACGAATCACGCCGTATCGACAACCAGCTACGTGGTCGTGCGGGTCGTCAAGGTGACGCGGGTTCATCACGTTTCTACCTGTCGATGGAAGATAGCTTAATGCGCATCTTCGCCTCGGATCGTGTCTCTGGCATGATGAAAAAGCTGGGTATGGAAGAAGGCGAAGCGATCGAGCATCCATGGGTGTCTCGTGCAATCGAAAACGCTCAACGTAAAGTTGAAGCACGTAACTTTGATATTCGTAAGCAATTACTTGAATTTGATGACGTCGCTAACGATCAGCGCCAAGTGGTTTATGCTCAGCGCAACGAGTTGATGGATGCGGAAAGTATTGAAGATACTATCCAAAACATTCAAGACGATGTGATTGGCGCGGTCATCGATCAGTATATTCCACCACAATCAGTAGAAGAGCTGTGGGATATCCCAGGTTTAGAGCAACGTCTACACCAAGAGTTTATGCTCAAACTACCTATCCAGGAATGGTTAGATAAAGAAGACGATCTGCATGAAGAGAGTCTGCGTGAGCGTATTATCACTGCTTGGGGCGATGCGTATAAGGCAAAAGAAGAAATGGTTGGCGCGCAAGTGCTACGCCAGTTCGAGAAAGCCGTTATGCTACAAACGCTCGATGGTCTGTGGAAAGAACACTTAGCGGCGATGGACCATCTGCGTCAAGGTATCCACTTACGTGGTTACGCACAGAAAAATCCAAAGCAAGAATATAAGCGTGAGTCGTTTGAGTTATTCCAACAACTATTGAATACCTTGAAACATGACGTCATCAGTGTGCTGTCGAAAGTACAAGTTCAAGCTCAGTCAGATGTGGAAGAAATGGAAGCGCGTCGTCGTGAAGAAGATGCCAAGATCCAGCGCGACTATCAACACGCAGCGGCAGAGTCGCTTGTTGGCGGTGGTGATGAGCATGAAGCTGTCACTGCGCAAGCGCCGATGATCCGTGATGGTGAGAAAGTGGGTCGTAACGATCCTTGTCCTTGTGGCTCAGGTCGCAAGTACAAACAATGTCACGGCAAGTTAAGTTAATCTGCTGACAGATTAAGCCTACTGAATAAGTAGAATAAAACGCACACTAAAGGCAACTTAACTAAGTTGCCTTTTTATTTCCTCTTTTATAAGCCTCGCGCTGACTCTCGTGTCATCCGGGTTACATTCTTACTCTTCTTTATATCGCTAATCGTCGTTATTCCCCATTGAACATCATTTCATTCTCATCGATAGCCATTAAAGCAAACCCACCGCTGTAGCAAATTTAACCGTAAAAACCCATTTTGTGGATAAGTCTGAGGATAAAGTGGGGGTATTGTGTGACTAACTGAAAGTATTAAAAAAAGATCCATTTTTCTCAAAAAAGCCCTTGCGGACTTTCTCAATCTCCCTATAATGCGCATCCACTGACACGGCAGACAGCGTAACGCAAGTTAAGCAAAGAAAGCCAAGCAGTTCATCGGTTAGCGAGCTAAACGATGAGTGTTGAGAAAGTTGAAAAAACTACTTGACGCACTAACGGGAATGCGTAGAATACGCAGCCCTGACCCGCTGAAAACTCAGTGTGTGGTCAACGCTCTTTAACAATTTATCAAGCAAATCTGTGTGGACACTCACAGGTGTTGAGTTAATCGAAACTGCTTAACCTTCGGGTTGGCAGTCAAAAATTTAAATCAACCAATTCTTTCACGAGAGTGATGAGTGTTCATAGCAATATGTACAAAGACTTACTACTTATTTTCGAATAAATAGAAGTCGAACAGAATTCGTTGAGCCGTTTGACGTAAGTCAAACAACAAAACTTTAATTGAAGAGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGCAGCGGAAAGATAGTTTACTATCTTTGCCGGCGAGCGGCGGACGGGTGAGTAATGCCTAGGGATCTGCCCAGTCGAGGGGGATAACAGTTGGAAACGACTGCTAATACCGCATACGCCCTAAGGGGGAAAGGAGGGGACCTTCGGGCCTTCCGCGATTGGATGAACCTAGGTGGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCCTAGCTGTTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGTAGGGAGGAAAGGTAGCAGCTTAATACGCTGTTGCTGTGACGTTACCTACAGAAGAAGGACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTCCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGCGAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTCGAACTGGCGAACTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCTACTCGGAGTTTGGTGTCTTGAACACTGGGCTCTCAAGCTAACGCATTAAGTAGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCACGGAATTCGCTAGAGATAGCTTAGTGCCTTCGGGAACCGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCCTTATTTGCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGATAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCGAGTACAGAGGGTTGCAAAGCCGCGAGGTGGAGCTAATCTCACAAAGCTCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTGGATCAGAATGCCACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGCTGCAAAAGAAGTGGGTAGCTTAACCTTCGGGGGGGCGCTCACCACTTTGTGGTTCATGACTGGGGTGAAGTCGTAACAAGGTAGCCCTAGGGGAACCTGGGGCTGGATCACCTCCTTACCTATACGACTAACTTAATGTTTGCTGAGTGTTCACACAGATTTGCTTGATAGAAGAAAGAGTAAAAGATGGACCTAGTTAAATAGGTTTATCAAAGATGGGTCTGTAGCTCAGCTGGTTAGAGCGCACCCCTGATAAGGGTGAGGTCGGTGGTTCAAGTCCACTCTGACCCACCAAATCTTCGTTCTCCTTTGTTAAATTAACACTCGTTTAGTGAACTAAACGTCACGTTAATTTGCCGCGGATAACTTGATTTGGCAAACCAATCCTTTAGAGGTTTGGAACATCTTACTTACTCGCACTGCATGTAAATGGGGCTATAGCTCAGCTGGGAGAGCGCCTGCCTTGCACGCAGGAGGTCTGCGGTTCGATCCCGCATAGCTCCACCATTTACACCTTATTCTGACTTTATTGCAAAGTAATAAGGCATATGCACTATGGATAGAGATGCCAAAGATAAACTTAAAAATTATCTTTGGCTTTTTTAAGCCCGCTCTTTAACAATTTGGAAAGCTGATAGTATGTAACGTGATGATGTCTGTCGTCATGTTACTTACAAACCGATGTAAACGTGGAAACGTTTGCATCATGAGTTCTCAAACACTTTATTAAGTGTCTTGAATATTCAAAAGTAAATTATTCTTAGTTGAATAGTTTACGTCTAAGGCGCGTCCACTTCTTTGGTCAGAAGTGAGACAAGTAAAACCAAGCTGGTCGCAATGCGGCGCAGATGAGTGAAACTCATTTGGGTTGTATGGTTAAGCGACTAAGCGTATACGGTGGATGCCTTGGCAGTCAGAGGCGATGAAGGACGTAGTAACTTGCGAAAAGCGTTGGCGAGCTAGTAACAAGCATTTGAGCTAACGATGTCCGAATGGGGGAACCCAGCAGCATAAGCTGTTATCGCAACATGAATACATAGTGTTGCGAGGCGAACCCGGGGAACTGAAACATCTAAGTACCCGGAGGAAAAGAAATCAACCGAGATTCCCCTAGTAGCGGCGAGCGAACGGGGATTAGCCCTTAAGTCAGTGGGGTGTTAGTGGAATGTGTTGGAAAGCACAGCGGCACAGGGTGATAGCCCCGTACATGAAAACTAACCATTGATGAAAACGAGTAAGGCGGGACACGTGACATCCTGTTTGAATATGGGGGGACCATCCTCCAAGGCTAAATACTCCTGACTGACCGATAGTGAACCAGTACCGTGAGGGAAAGGCGAAAAGAACCCCTGTGAGGGGAGTGAAATAGAACCTGAAACCGTATACGTACAAGCAGTGGGAGCGGTTCTTGAGACCGTGACTGCGTACCTTTTGTATAATGGGTCAGCGACTTACATTTTGTAGCGAGGTTAAGCGAATAGCGGAGCCGTAGGGAAACCGAGTGTTAACTGCGCGTTTAGTTGCAAGGTGTAGACCCGAAACCCGGTGATCTAGCCATGGGCAGGTTGAAGGTTGAGTAACATCAACTGGAGGACCGAACCGACTAATGTTGAAAAATTAGCGGATGACTTGTGGCTGGGGGTGAAAGGCCAATCAAACCGGGAGATATCTGGTTCTCCTCGAAAGCTATTTAGGTAGCGCCTCGAGCGAATACCATTGGGGGTAGAGCACTGTTAAGGCTAGGGGGTCATCCCGACTTACCAACCCTTTGCAAACTCCGAATACCAATGAGTACTACTCGGGAGACAGACGGCGGGTGCTAACGTCCGTCGTCAAAAGGGAAACAACCCAGACCGTCAGCTAAGGTCCCAAAGTACTAGCTAAGTGGGAAACGATGTGGGAAGGCTTAGACAGCTAGGATGTTGGCTTAGAAGCAGCCATCATTTAAAGAAAGCGTAATAGCTCACTAGTCGAGTCGGCCTGCGCGGAAGATGTAACGGGGCTAAGCTAGTCACCGAAGCTACGGGTGCACAGTTTACTGTGCGCGGTAGAGGAGCGTTCTGTAAGCCGTTGAAGGTGAAGGGGTAACCCACGCTGGAGGTATCAGAAGTGCGAATGCTGACATGAGTAACGATAAAGGGGGTGAAAAACCCCCTCGCCGAAAGACCAAGGGTTCCTGTCCAACGTTAATCGGGGCAGGGTGAGTCGACCCCTAAGGTGAGGCCGAAAGGCGTAATCGATGGGAAACAGATTAATATTTCTGTACTTCCGCTAACTGCGATGGAGAGACGGAGAAGGCTAGGCTAGCGCGGCGTTGGTAGTCCGCGTTTAAGGTGGTAGGTTGATTTCTTAGGCAAATCCGGGGAATCGCACTTTAGTGTGCAGACTGAGAGCTGATGACGAGGTCCTAAGGGACTGAAGTAGTTGATGCCATGCTTCCAGGAAAATCTTCTAAGCTTCAGGTTAGTGGGAATCGTACCCCAAACCGACACAGGTGGTCGGGTAGAGAATACCAAGGCGCTTGAGAGAACTCGGCTGAAGGAACTAGGCAAAATGGTACCGTAACTTCGGGAGAAGGTACGCTCCTGTTGGTGATGAGACTTGCTCTCTAAGCTGACGGGAGTCGCAGATACCAGGTGGCTGCAACTGTTTATCAAAAACACAGCACTGTGCAAACTCGCAAGAGGAAGTATACGGTGTGACGCCTGCCCGGTGCCGGAAGGTTAATTGATTGGGTTATCGCAAGAGAAGCTCATGATCGAAGCCCCGGTAAACGGCGGCCGTAACTATAACGGTCCTAAGGTAGCGAAATTCCTTGTCGGGTAAGTTCCGACCTGCACGAATGGCGTAATGATGGCCACGCTGTCTCCAGCCGAGACTCAGTGAAGTTGAAATTGCGGTGAAGATGCCGTATACCCGCGGCTAGACGGAAAGACCCCGTGAACCTTTACTATAGCTTGGCACTGAACATTGAACCTACATGTGTAGGATAGGTGGGAGACTTTGAAGTTGTGACGCTAGTCATGATGGAGTCGTCCTTGAAATACCACCCTTGTAGTTTTGATGTTCTAACCTAGGTCCCTTATCGGGATTAGAGACAGTGCCTGGTGGGTAGTTTGACTGGGGCGGTCTCCTCCCAAAGAGTAACGGAGGAGCACGAAGGTTGGCTAAGTACGGTCGGACATCGTACGGTTAGTGCAATGGCATAAGCCAGCTTAACTGCGAGACATACACGTCGAGCAGGTACGAAAGTAGGTCATAGTGATCCGGTGGTTCTGAATGGAAGGGCCATCGCTCAACGGATAAAAGGTACTCCGGGGATAACAGGCTGATACCGCCCAAGAGTTCATATCGACGGCGGTGTTTGGCACCTCGATGTCGGCTCATCACATCCTGGGGCTGAAGTCGGTCCCAAGGGTATGGCTGTTCGCCATTTAAAGTGGTACGCGAGCTGGGTTCAGAACGTCGTGAGACAGTTCGGTCCCTATCTGCCGTGGGCGTTGGATGATTGAAGGGAGCTGCTCCTAGTACGAGAGGACCGGAGTGGACGAACCGCTGGTGTTCGGGTTGTCATGCCAATGGCATTGCCCGGTAGCTACGTTCGGAATCGATAACCGCTGAAAGCATCTAAGCGGGAAGCGAGCCCTAAGATGAGTCATCCCTAGGAATTTAATTCCTCTAAAGAGCCGTTCGAGACTAGGACGTTGATAGGCATGGTGTGTAAGCGTTGTGAGGCGTTGAGCTAACATGTACTAATGACTCGAGAGGCTTAACCATACAACCCAGATGGGTTTTACTGAAAATGTTCCAGACATTTTCTAGCACTTCCTCCATCCATGGAGGTCGTACGAAAAGCCTTAGACAGAATATCGACACTTGATGAAGTGTGAACTCAAACAAAGCAAATCAGCTTTCCGAATTATTATTAACGTGGTCCAGAGATGGACACGGTAATAAACAGAATTTGCTTGGTGACAATAGCCTTGTGGAACCACCTGATCCCATCCCGAACTCAGAAGTGAAACGCAAACGCGCCGATGGTAGTGTGGGGTCTCCCCATGTGAGAGTAGGTCATCGCCAAGCGCTTAATTATAATCTTAATTGATTAGTATAGAATTGCTGATATGGCTCAGTCGGTAGAGCGCATCCTTGGTAAGGATGAGGTCCCCAGTTCGATTCTGGGTATCAGCACCATTACAATCTAGTGTCATTTCCTAGAATACAAAATTTGCTTGGTGACAATAGCTTTGTGGAACCACCTGATCCCATACCGAACTCAGAAGTGAAACGCAAACGCGCCGATGGTAGTGTGGGGTCTCCCCATGTGAGAGTAGGTCATCGCCAAGCGCCTAATTATCTCGTAGAGATGTAACGAAGCCAGCTGAATAAGCTGGCTTTTTTGCATCTGTATTTATGAATTTCAGCCTAGGTTGCCCTTAATACCATTTACATTAATTATGTGGTCTAATTAATTGTCACTTCCTAATCATACAAAATATGAATAATCCCAACATCGCAGCCTTAGTGAAATCGGAGAAAAGCGCACGCAAACGTATGCGTTATCTTACTTCATTTCACTGAAGGTCATTCTCGTACCGCCATCGCAAGCATGCTTAAAATCAGTAGAGCCAGTGTGAATCGATGGATTGTTTGAGCGATCTAGACGATACGCCTAATCCGGGCCGTCCTGCGACATTATCCACAGAACAACAAGCCAAGCTCAAAAATGTTCGTGCAACACCAAAGCCTATTTGAACAAGGTGGCAGGTTGGTGGCCAAGGACGTCGGCCATTTTATCCAGAATGAATTCGGCATGACGTTTAAGCAAGCGAATATCTACCGTTTGCTACATCAACTCGGCTTCTCATGGATTACTACTCGCTCTCGCCACCCGAAACAATCTGAGGCCACACAAGGGGCTTTTAAAAAACTTCCGGATTGCAACGGCATTCGGACGCCACGCACTGGTCATCATGGACGGAGCTGGTTGGCATCAACAGGATTTTACCGATGACTTTGATAATCTCACCCTCCTCAAGTTGCCTCCTTAATCACCGGAGCTGAATCCGATAGATCAGGTATTGCAATGGCCGCGTCAAAATATGTTGGCTAATCGTTGTTTTAGTGGCTATGACGATATTGTCGAGCAGTGTTCCATCGCATTGAATACCTTCATTGAGAAGAAAGTAAGAGTGATAGAATTGTGTACTAGACAGTGGACCGTTCTGACCAGTCAATTAATAGAATTGGTATCATAGGCTTTGTTCGATCTAGTTTACTAAGCTTGGACTATCTCCTCAAAAGGGCTATGACGCTTTTAGCTTGTTACAAACCTTAGTTGCTGAACTTAAAGTGAGTATAGGATTTACTATCTTGACAGAAACTTGAAACTCTAGGAGCTGTATATTACAGAGCTTCTGCAATGCTCTTCTAGCCGTTATACATCCTTCATACACAAAGCTATCAATCGCTAGACTGTCATTAATGGGCAAAGATTTATAGAGCTTATGCTGAGCTGGCTGTTTCTGTCTCGTTTTGACTAGTGCATATATGGGTTCAATTTAGAGTGTGATAAGTAAAACCTAGGGTAAAAGAGCTGAAACAGAGCTATCCAATCAGAAGCTGATCCTGGTATTTGCTAACTATTTGCTATATTGATGACAATGAGCCGCTTTTCTAACAAGCGGCTGCTATTTTCTGTGGTGATAGCTATTATTTGGGAAATCTACTTCATTCTCTGCATCATGAAAGCGATTCGGACGGGGCTATTACTCTTATTGTTCCATACATTACCTTTTGAGATGGCTTATTAATTAATGTCGACATTTATAATCTGAATAATCTAGCTGATTAACTTACTGGAATTGTCATCTACAGGATCAGTTTTTCACTGGGTTTAGATGCGTTTGTGGATAACTTTGGGGGTAAGTGGTGAGTATCTTGTGGCTAGCTTGAAGAATATGAATTAACTGCAAATTTCCTTAAAATAGGGGTTGCGCACTTTCTCAATCTCCCTATAATGCGCATCCACTGACACGGCAGACAGCGAAAGCGAGCTGACGAGTTAGGTTGAATTGAATGGCTTTTAACCACTTAATTCAATCGCAGCAAGAAGTTGAAAATACTCCTTGACGCGAAACGGGAAATGCGTAGAATACGCAGCCCTAGCCAACTGGAAGCGTTCGACGCTCAGTGTGGTACTCAAGTCCTCTTACGGATATTGAGTTGCTCTTTAACAAGATAAAACAAGAAATCTGTGTGGACACTCACAGGTGTTGAGTTAATCGAAACTGCTTAACCTTCGGGTTGGCAGTCAAAAATTTAAATCAATGTAACAATGAGTGTTCATAGCAATATGTACAGTTTGTTTCTTTTAGCGAGAAACAAAAAATCAGAATTCATTGAGCCGCTGAAGTCGTAAGACGGAAGCAAAAAACTTTAATTGAAGAGTTTGATCATGGCTCAGATTGAACGCTGGCGGCAGGCCTAACACATGCAAGTCGAGCGGCAGCGGGAAGATAGCTTGCTATCTTTGCCGGCGAGCGGCGGACGGGTGAGTAATGCCTAGGGATCTGCCCAGTCGAGGGGGATAACAGTTGGAAACGACTGCTAATACCGCATACGCCCTACGGGGGAAAGGAGGGGACCTTCGGGCCTTCCGCGATTGGATGAACCTAGGTGGGATTAGCTAGTTGGTGAGGTAATGGCTCACCAAGGCGACGATCCCTAGCTGTTCTGAGAGGATGATCAGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGCACAATGGGGGAAACCCTGATGCAGCCATGCCGCGTGTGTGAAGAAGGCCTTCGGGTTGTAAAGCACTTTCAGTAGGGAGGAAAGGTAGCAGCTTAATACGCTGTTGCTGTGACGTTACCTACAGAAGAAGGACCGGCTAACTCCGTGCCAGCAGCCGCGGTAATACGGAGGGTCCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTTGTTAAGCGAGATGTGAAAGCCCCGGGCTCAACCTGGGAATTGCATTTCGAACTGGCGAACTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGCACGAAAGCGTGGGGAGCAAACAGGATTAGATACCCTGGTAGTCCACGCCGTAAACGATGTCTACTCGGAGTTTGGTGTCTTGAACACTGGGCTCTCAAGCTAACGCATTAAGTAGACCGCCTGGGGAGTACGGCCGCAAGGTTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTACTCTTGACATCCACGGAATTCGCTAGAGATAGCTTAGTGCCTTCGGGAACCGTGAGACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTTGTGAAATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCCTATCCTTATTTGCCAGCACGTAATGGTGGGAACTCTAGGGAGACTGCCGGTGATAAACCGGAGGAAGGTGGGGACGACGTCAAGTCATCATGGCCCTTACGAGTAGGGCTACACACGTGCTACAATGGCGAGTACAGAGGGTTGCAAAGCCGCGAGGTGGAGCTAATCTCACAAAGCTCGTCGTAGTCCGGATTGGAGTCTGCAACTCGACTCCATGAAGTCGGAATCGCTAGTAATCGTGGATCAGAATGCCACGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTGGGCTGCAAAAGAAGTGGGTAGCTTAACCTTCGGGGGGGCGCTCACCACTTTGTGGTTCATGACTGGGGTGAAGTCGTAACAAGGTAGCCCTAGGGGAACCTGGGGCTGGATCACCTCCTTACCTATACGACTAACTTAATGTTTGTTGAGTGTTCACACAGATTGCTTGTTTATCTTCTCTTAGGAGAAGTCAGAGCTGAATGCGCCGCTAGCCGGTACGCATTGTTCTTTAACAATTTGGAAAGCTGATAGTATTTAATTGCATGAGTCTGTCATGTAATTAATTACAAAACGTTTAATGCGAAAGCATTGAACATGAGTTCTCAAACACTTTATTAAGTGTCTTGAATATTCAAAAAGTAAATTATTCTTTATTGAATAGTTTACGTCTAAGGCGCGTCCACTTTCTTGGTCGGAAGTGAGACAAGTAAAACCAAGCTGGTCGCAATGCGACTCAGATGAGTGAAACTCATTTGGGTTGTATGGTTAAGCGACTAAGCGTATACGGTGGATGCCTTGGCAGTCAGAGGCGATGAAGGACGTAGTAACTTGCGAAAAGCGTTGGCGAGCTAGTAACAAGCATTTGAGCTAACGATGTCCGAATGGGGGAACCCAGCAGCATAAGCTGTTATCGCAACATGAATACATAGTGTTGCGAGGCGAACCCGGGGAACTGAAACATCTAAGTACCCGGAGGAAAAGAAATCAACCGAGATTCCCCTAGTAGCGGCGAGCGAACGGGGATTAGCCCTTAAGTCAGTGGGGTGTTAGTGGAATGTGTTGGAAAGCACAGCGGCACAGGGTGATAGCCCCGTACATGAAAACTAACCATTGATGAAAACGAGTAAGGCGGGACACGTGACATCCTGTTTGAATATGGGGGGACCATCCTCCAAGGCTAAATACTCCTGACTGACCGATAGTGAACCAGTACCGTGAGGGAAAGGCGAAAAGAACCCCTGTGAGGGGAGTGAAATAGAACCTGAAACCGTATACGTACAAGCAGTGGGAGCGGTTCTTGAGACCGTGACTGCGTACCTTTTGTATAATGGGTCAGCG sourmash-bio-sourmash-bc3c0f8/doc/index.md000066400000000000000000000140511474157046300206650ustar00rootroot00000000000000# Welcome to sourmash! sourmash is a command-line tool and Python/Rust library for **metagenome analysis** and **genome comparison** using k-mers. It supports the compositional analysis of metagenomes, rapid search of large sequence databases, and flexible taxonomic profiling with both NCBI and GTDB taxonomies ([see our prepared databases for more information](databases.md)). sourmash works well with sequences 30kb or larger, including bacterial and viral genomes. You might try sourmash if you want to - * identify which reference genomes to use for metagenomic read mapping; * search all Genbank microbial genomes with a sequence query; * cluster hundreds or thousands of genomes by similarity; * taxonomically classify genomes or metagenomes against NCBI and/or GTDB; * search thousands of metagenomes with a query genome or sequence; New! The sourmash project also supports [querying all 1 million publicly available metagenomes in the Sequence Read Archive](https://branchwater.sourmash.bio/). Give it a try! Our **vision**: sourmash strives to support biologists in analyzing modern sequencing data at high resolution and with full context, including all public reference genomes and metagenomes. ## Mission statement This project's mission is to provide practical tools and approaches for analyzing extremely large sequencing data sets, with an emphasis on high resolution results. Our designs follow these guiding principles: * genomic and metagenomic analyses should be able to make use of all available reference genomes. * metagenomic analyses should support assembly independent approaches, to avoid biases stemming from low coverage or high strain variability. * private and public databases should be equally well supported. * a variety of data structures and algorithms are necessary to support a wide set of use cases, including efficient command-line analysis, real-time queries, and massive-scale batch analyses. * our tools should be well behaved members of the bioinformatics analysis tool ecosystem, and use common installation approaches, standard formats, and semantic versioning. * our tools should be robustly tested, well documented, and supported. * we discuss scientific and computational tradeoffs and make specific recommendations where possible, admitting uncertainty as needed. ## How does sourmash work? Underneath, sourmash uses [FracMinHash sketches](https://www.biorxiv.org/content/10.1101/2022.01.11.475838) for fast and lightweight sequence comparison; FracMinHash builds on [MinHash sketching](https://en.wikipedia.org/wiki/MinHash) to support both Jaccard similarity _and_ containment analyses with k-mers. This significantly expands the range of operations that can be done quickly and in low memory. sourmash also implements a number of new and powerful techniques for analysis, including minimum metagenome covers and alignment-free ANI estimation. sourmash is inspired by [mash](https://mash.readthedocs.io), and supports most mash analyses. sourmash also implements an expanded set of functionality for metagenome and taxonomic analysis. While sourmash is currently single-threaded, the [branchwater plugin for sourmash](https://github.com/sourmash-bio/sourmash_plugin_branchwater) provides faster and lower-memory multithreaded implementations of several important sourmash features - sketching, searching, and gather (metagenome decomposition). It does so by implementing higher-level functions in Rust on top of the core Rust library of sourmash. As a result it provides some of the same functionality as sourmash, but 10-100x faster and in 10x lower memory. Note that this code is functional and tested, but does not have all of the features of sourmash. Code and features will be integrated back into sourmash as they mature. sourmash development was initiated with a grant from the Moore Foundation under the Data Driven Discovery program, and has been supported by further funding from the NIH and NSF. Please see [funding acknowledgements](funding.md) for details! ## Using sourmash ### Tutorials and examples These tutorials are command line tutorials that should work on Mac OS X and Linux. They require about 5 GB of disk space and 5 GB of RAM. * [Installing sourmash with conda](tutorial-install.md) * [The first sourmash tutorial - making signatures, comparing, and searching](tutorial-basic.md) * [Using sourmash LCA to do taxonomic classification](tutorials-lca.md) * [Analyzing the genomic and taxonomic composition of an environmental genome using GTDB and sample-specific MAGs with sourmash](tutorial-lemonade.md) * [Some sourmash command line examples!](sourmash-examples.ipynb) ### How-To Guides * [Classifying genome and metagenome sketches](classifying-signatures.md) * [Working with private collections of genome sketches](sourmash-collections.ipynb) * [Using the `LCA_Database` API](using-LCA-database-API.ipynb) * [Building plots from `sourmash compare` output](plotting-compare.ipynb). * [A short guide to using sourmash output with R](other-languages.md). ## Frequently Asked Questions * [Frequently asked questions](faq.md) ### How sourmash works under the hood * [An introduction to k-mers for genome comparison and analysis](kmers-and-minhash.ipynb) * [Support, versioning, and migration between versions](support.md) ### Reference material * [Full table of contents for all docs](toc.md) * [UNIX command-line documentation](command-line.md) * [Genbank and GTDB databases and taxonomy files](databases.md) * [Python examples using the API](api-example.md) * [Publications about sourmash](publications.md) * [A guide to the internal design and structure of sourmash](sourmash-internals.md) * [Funding acknowledgements](funding.md) ## Developing and extending sourmash * [Releasing a new version of sourmash](release.md) ```{toctree} --- maxdepth: 1 hidden: true --- sidebar command-line databases api-example ``` sourmash-bio-sourmash-bc3c0f8/doc/kmers-and-minhash.ipynb000066400000000000000000002360701474157046300236140ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# An introduction to k-mers for genome comparison and analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "k-mers provide sensitive and specific methods for comparing and analyzing genomes.\n", "\n", "This notebook provides pure Python implementations of some of the basic k-mer comparison techniques implemented in sourmash, including hash-based subsampling techniques.\n", "\n", "### Running this notebook.\n", "\n", "You can run this notebook interactively via mybinder; click on this button:\n", "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dib-lab/sourmash/latest?labpath=doc%2Fkmers-and-minhash.ipynb)\n", "\n", "A rendered version of this notebook is available at [sourmash.readthedocs.io](https://sourmash.readthedocs.io) under \"Tutorials and notebooks\".\n", "\n", "You can also get this notebook from the [doc/ subdirectory of the sourmash github repository](https://github.com/dib-lab/sourmash/tree/latest/doc). See [binder/environment.yaml](https://github.com/dib-lab/sourmash/blob/latest/binder/environment.yml) for installation dependencies.\n", "\n", "### What is this?\n", "\n", "This is a Jupyter Notebook using Python 3. If you are running this via [binder](https://mybinder.org), you can use Shift-ENTER to run cells, and double click on code cells to edit them.\n", "\n", "Contact: C. Titus Brown, ctbrown@ucdavis.edu. Please [file issues on GitHub](https://github.com/dib-lab/sourmash/issues/) if you have any questions or comments!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating Jaccard similarity and containment\n", "\n", "Given any two collections of k-mers, we can calculate similarity and containment using the union and intersection functionality in Python." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "def jaccard_similarity(a, b):\n", " a = set(a)\n", " b = set(b)\n", "\n", " intersection = len(a.intersection(b))\n", " union = len(a.union(b))\n", "\n", " return intersection / union" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def jaccard_containment(a, b):\n", " a = set(a)\n", " b = set(b)\n", "\n", " intersection = len(a.intersection(b))\n", "\n", " return intersection / len(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's try these functions out on some simple examples!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "a = [\"ATGG\", \"AACC\"]\n", "b = [\"ATGG\", \"CACA\"]\n", "c = [\"ATGC\", \"CACA\"]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jaccard_similarity(a, a)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jaccard_containment(a, a)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.3333333333333333" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jaccard_similarity(b, a)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jaccard_similarity(a, c)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.5" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jaccard_containment(b, a)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib_venn import venn2, venn3\n", "\n", "venn2([set(a), set(b)])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "venn3([set(a), set(b), set(c)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating k-mers from DNA sequences\n", "\n", "To extract k-mers from DNA sequences, we walk over the sequence with a sliding window:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def build_kmers(sequence, ksize):\n", " kmers = []\n", " n_kmers = len(sequence) - ksize + 1\n", "\n", " for i in range(n_kmers):\n", " kmer = sequence[i : i + ksize]\n", " kmers.append(kmer)\n", "\n", " return kmers" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['ATGGACCAGATATAGGGAGAG',\n", " 'TGGACCAGATATAGGGAGAGC',\n", " 'GGACCAGATATAGGGAGAGCC',\n", " 'GACCAGATATAGGGAGAGCCA',\n", " 'ACCAGATATAGGGAGAGCCAG',\n", " 'CCAGATATAGGGAGAGCCAGG',\n", " 'CAGATATAGGGAGAGCCAGGT',\n", " 'AGATATAGGGAGAGCCAGGTA',\n", " 'GATATAGGGAGAGCCAGGTAG',\n", " 'ATATAGGGAGAGCCAGGTAGG',\n", " 'TATAGGGAGAGCCAGGTAGGA',\n", " 'ATAGGGAGAGCCAGGTAGGAC',\n", " 'TAGGGAGAGCCAGGTAGGACA']" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "build_kmers(\"ATGGACCAGATATAGGGAGAGCCAGGTAGGACA\", 21)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the k-mers that are output, you can see how the sequence shifts to the right - look at the pattern in the middle.\n", "\n", "So, now, you can compare two sequences!" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "seq1 = \"ATGGACCAGATATAGGGAGAGCCAGGTAGGACA\"\n", "seq2 = \"ATGGACCAGATATTGGGAGAGCCGGGTAGGACA\"\n", "# differences: ^ ^" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 0.09090909090909091\n" ] } ], "source": [ "K = 10\n", "kmers1 = build_kmers(seq1, K)\n", "kmers2 = build_kmers(seq2, K)\n", "\n", "print(K, jaccard_similarity(kmers1, kmers2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading k-mers in from a file\n", "\n", "In practice, we often need to work with 100s of thousands of k-mers, and this means loading them in from sequences in files.\n", "\n", "There are three cut-down genome files in the `genomes/` directory that we will use below:\n", "\n", "```\n", "akkermansia.fa\n", "shew_os185.fa\n", "shew_os223.fa\n", "```\n", "The latter two are two strains of *Shewanella baltica*, and the first one is an unrelated genome *Akkermansia muciniphila*." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import screed # a library for reading in FASTA/FASTQ\n", "\n", "\n", "def read_kmers_from_file(filename, ksize):\n", " all_kmers = []\n", " for record in screed.open(filename):\n", " sequence = record.sequence\n", "\n", " kmers = build_kmers(sequence, ksize)\n", " all_kmers += kmers\n", "\n", " return all_kmers" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "akker_kmers = read_kmers_from_file(\"genomes/akkermansia.fa\", 31)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['AAATCTTATAAAATAACCACATAACTTAAAA',\n", " 'AATCTTATAAAATAACCACATAACTTAAAAA',\n", " 'ATCTTATAAAATAACCACATAACTTAAAAAG',\n", " 'TCTTATAAAATAACCACATAACTTAAAAAGA',\n", " 'CTTATAAAATAACCACATAACTTAAAAAGAA']" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "akker_kmers[:5]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "499970\n" ] } ], "source": [ "print(len(akker_kmers))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "shew1_kmers = read_kmers_from_file(\"genomes/shew_os185.fa\", 31)\n", "shew2_kmers = read_kmers_from_file(\"genomes/shew_os223.fa\", 31)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see the relationship between these three like so:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "akker vs shew1 0.0\n", "akker vs shew2 0.0\n", "shew1 vs shew2 0.23675152210020398\n" ] } ], "source": [ "print(\"akker vs shew1\", jaccard_similarity(akker_kmers, shew1_kmers))\n", "print(\"akker vs shew2\", jaccard_similarity(akker_kmers, shew2_kmers))\n", "print(\"shew1 vs shew2\", jaccard_similarity(shew1_kmers, shew2_kmers))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "akker vs shew1 0.0\n", "akker vs shew2 0.0\n", "shew1 vs shew2 0.38397187523995907\n" ] } ], "source": [ "print(\"akker vs shew1\", jaccard_containment(akker_kmers, shew1_kmers))\n", "print(\"akker vs shew2\", jaccard_containment(akker_kmers, shew2_kmers))\n", "print(\"shew1 vs shew2\", jaccard_containment(shew1_kmers, shew2_kmers))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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KblYMZv8NrtwMFVlw9G3wzGr40Qz4r2fgxnXwrRNh1nR49UW4bor+Xu2dsLQHnP09+P49kKngki9BUQPsvh3aBdbEvc3yhfDEO/r9T46Cmy+Ci34D51fCFXdDbgwWFMK022H2J/rfCdNZ/3XNDs9XwOmlUNUCOe0gP4Urn4IHzoOas+G862HyIzB/FpwzHLasgf95Hkp/AJddCr8EuBguOQFWPgoP1kOwBkIAs+HCa+Gl2bCyDI68Gy78Ntx/Haz5KSwLAk/BgOvh2hugzMY/Q6IEEMlCqbRV1bMivJB4Qv1A6pnAZgCKaCOPKnZSRAt9OYF1ABzFaio5BoD3GEMPtjKerQD0pYkMFDEEEJoJEQPaySafOoC9HjJAOyG0C5QaVDBxQRp1wTpyi5u+8Hm4Sd/M4cYcMvPqDnqMz14bRqjHDvodU0soL0rx6EVsems8AMNP30Lp0Tu/+CVRRNuyUTForcsimNlERlbiIhRI9s81qV4LLsCQNuhbBZ8Wwa6+8H19TZmxGj7S15QN/WDSGv1+QgPkNMOj8aIy80+Cx/6l32cqGKe9d4btt7NF437hgNLwPoGt76qD0dk94oqFIu9C0SIYdzm8B7AK8oIQOQ9qAM6CVW+j76et0O9MWAPwVaiug96LocdnkLMBRj4UP0YhRA+Hlo426uIPoN2QU4S+zwZBW3Df77NcFpNK67W1wtPtmrBvoDcNDGIcG1lEFW9wNGfyMQs5ljZ6AVBLCQC/4gbayWcQi7iUuWQRZSJP8SRlBGkjhxqu5Om9x36CC9jKCQRpYSb37/28nuHcy+1kUc/pPMORVCVndEb3+taEq/7Kh7+8gQ1zv4ZCmHr7z/f+rrl2OM9dfjuZufWMn/EMg6dU0VhdRFaPXXv/T06v3dRtHH7QNo699k1ev/k7PHPxvcQi2Yz52kMEMhJX0kCsGw/q13tD5SD4+kb4QxXcejT87GP47bHQoK8pR2yFd8ZD8yJ4rydUDYF1PWHddv37S8+HtSOheAc88Rc4tkF/fvmp8I8zIJoBf3lgX5t/HAa3zoQ9veAHjybp5ULnN6ArVh5+Gy65HZ7dFa/5eyQ0xiD4MAz5FlQ8C8fWoXfKGAJbn4djfgDrH4She6D3x9AzE2K50DARvrkVBg6Gihfhr/0h/Av46zfhhkfgawrkRdjbZ2+Cox+GrzZDwf3wW1v+AF0jrc8IKzpO8m3Uk8WzzOI45tCTVr7E46xkKr/gVsJkI+jUrBgB6hjBZTzCf/ILtjGBdxlFG0FWMZXL+Cm3cBMFbGNOPL4LMIMX+Ak3M5gFvM40AMawme9xCzdxF0cxj5e5Lmm7VbB7XvOaF6Yy4pw5XPTMzYyYPocFv54JwKCTNvOV/72F/3jqLoaeOo+Fv0/etg7WvjSWvD5buWjOTZxy612sffEyGrcnXoRbVBfPcUsWXD4LZs3RnunvHoc5U6HkVmjKhkA83e4P86F3HQy9Fa6/BAZ9BhkKWoPQ2BMmfgbb74YxG+Dqi/Yd/6m3oO42+OZzcPuX9n0+ayPsnA3/dw88eQ7UJutodHYDOt55uw3GFULD1cRHj+gnyH/DQ+VwcT+4JRdaA/HR3kPwSiPk9IXb/wdOK4EtmRALQ3A7DL4W3q6Bn2ZD+Eo4G+ABmPptmNMAN18Dc66GmR1t3Qsf74Kye+AP98H5lv8Buo7rRTc5z6KNIH9iFkNYwHSWAjCaar7Pr/kRdzORheSwA4ACdtOTTymhkXzClLKCSgazIl7V7HB2EADG8hE7OOwLbU1mIdvioYqetFIYnzCZxgpiBNlOflK2S7Lxzs+xa91kjv6mPucJVy+mZddQAPJLWskt1raNu3wFKhakblM++aV1tHV4iEDLrp5kFe4+aBvbFpzIwBOXIAHoP3EHofxaqpcmWjsWlHThHBuCMHUWnLIAfqHPj69Ww4ZfawGdtRB66mtKbgzemQM1d8HaP0BLLhy7HcY0QkYYfhb//vWLYcsBCk8/sAhWHf3Fzy+shqw2eCHZ4kudPWQcn6nyIYxYDuML4J7ZcM0mOGI8XPUd2LAFflEF/30KrOsD2wGGQesSeLwG7loMjzZB/hSoHQ+782H3LNgIcCEs3hAv+v0RTP45+j69DxZvO8AODD+EdbugeDlJ3k/2kdZr6yzRjQF/YgYFVHExr+/9vCqeARFFeJMvM5K3AZjIKhoYQCMh2gmwg5H0oYq+1NFMP6rjF3k9oymIhwrW7rdP20eMJ49qALZRsNfSjxgKCH1oTO5UI927WBnZ9ax5YSQAq58dRSi/BoCd6wpQcePWvzIUlFA4uJHhZ2wi3NCXqqW9CTcFqV09kaFTlx20jaweu6heqnc/3bW+B20NJRSPqk3YxlggyeF5FJgyAwZUwTP7rilL41kt7QJ3fBm+rK8p1SGojBcduXs0BKI6EyEIjPoEHtB/H/48Ckor9fuX9ys+PXsc9NR/N+b2huZ4H5/XC3aUwqQDxLkPeQIHwvH5yq/D843w4z3wk9nw0FBYuwweXRrPKNoNGQ/DWTPQ99M6yKmPh1O+BScPh3XDoHUi7CmE3S/orYmYC6MHou+nfKh/AEYC3AujesZjxf+APh1/uD/B4ChkjCHZ+8k20iq6VsR0E1/BtJARVHMCuWzjXm4HYCLPU0tfPouHAfqxhHPRKU7FNDOG1/k9PwEUJazgNJYDMIaXeZwfIUTJZieX8hgAr/EfvEAJgiKbnVwQz1z4kGNYz6kIUQK0czoPJf1IkmjiF+uf3/0WjdUjiYbzeeainzN4yoscedmTrHrmElY/G0CC7Rw140kA1v79GKqWnIpIFAm2M37mQ0gAMrJiHHHeX5j/8++DEvqMnc/gKfrhMv/e06hcdBaRtgLm/tcdFA5azlm/fJJjrvkH79/3TZ69/A5QwrDTntubTpYIsWQd3d+NgGUnQPE26KuvKf/5PKztC6/qa8oxS+DBeNraih5w8Q06XaygDh5/dN+xfv0szLwK7rsE8hrgscf15z+bBjNHQzAKOU3wmz/pz58fAZedoz+XGFz/9L7Jt8TPuJPPXbv68Icw/RM4SoGcCW//GNYCzIV+t8GVAqoEKl+AJzq+czf8ZRZcfQ1k9IYdf4fHAe6AJ++BS+6FQCa03wNPAjwMx8yAyUGIZkL4bnjIFTOPmrReW1FdDdEl2kC5XAj0TmsjTmHLnc00T3BN/maXqOnVwhsnuiZdKgU8qxRf9I5FpgCjrTfHkGaiKPVIOhuwIrzgliFF98nc7vg4X7dpyvX+Of47DZ183mypFQarSPt1tUJ091jQhjMIVTp+RrvbNPhKdNuUorMtiozoehNPiG5nnoL3CG31gejmuyg0120O1neN6HoTT4iufzzdzErvC9KePFcsCkgRB+u7RnS9iRFdVxGqzoSYt4ffDXne3RnjixzM0/XPCM5fpP26mvBCKpGoEGzwbpHvcEaUqAe3l++czh0GpVrx0ySxf0g8Z72LpP0GUmUqip+GYhk7XZu/eUhasr17bgfmUA5D2m9Qg+W4X3Tj1FjUjv3krPGup7uzyE+iqyC+3LxzDvV7g7uoR6nOslVShlWiu9Widuwnb6F3J9O2lfgptFCr1AGLl++PEV1vYcn1tOom2mZRO/aT+0kIPLhNeQxFdZ/Otq7xIon0WRNe8BaWXE9LRFeVqXr8MukQaA+QtTHtQxTL2dOjjYivJtEOPTrTk2kHr+pmcBOVVjRi5U3koxDDUu/Fdav6eO+cOicC8epzh6YinYYYLKMJpbzj6cbxT4ghf6H3clm3lXg3Vv1FqpRKuHTjpnQaYrAMyx6eRnTTQfanIaTVO55hJBCltqeJ5x4IpWrYb78wg2vxnuiqMtVK4kM2dyNKyFuatt1ELaemdxsq4P26EvvYfOj/8m+YEIO7iWBRPBes31xvtcXt2Uev570TYlh9mJ8m0CqViu8cnTib0mCHwTo2o5RlI1Orb6YNsN8W6F4mZ3WIzEr3e7uNOWFqihPfuNL9rOrCd7bgl+wcb7LGysYsFd34kmBLT9BWer7s/rjuuqF+WoXWTFe8Vr39in9Gcd6iHqUszayyY9i4GhfspJoSCudmI23uFd5oIMb6IX7yclcnkbXwedbggs0qDV+gKyObbmG56Koy1YAejnmfQFuAHu+7N8SwpbTVRwsiYnTHW1WqhfgW5QbXECG+KaeV2HVDrbSpXevp9ax7J9RWH+Ze25Nnk1Ldrobnn37tDdZbUeDm89giuqpMbcEvyyezKkJkr3JfHmdtUQt1hSG7zbCQFd0+glLVWJh6ZOgWMeBjOxq2c+i40Ma2raXkj0FX7SihUCwa56cVaBVKpSyH3D/92t2sQSlbdrWxTXRVmaoAquxq31KyN4bIX+geb3dbiZ+83BiwIGVH0yvUNqXseIZ0EAGW2NW43ZMkH9rcvnX0fTAE7c6f3Y5KjMVjM+02w0LWdGExxKFYiF8ydNzJCpSybTcbW0VXlakd+CVvN7M2g15/d763++mwVppz/SK6rcBHKT+qUnXApyk/riEVtGFTLLcDuz1d0EM7f6xSK/5zLsGdzq2125wV5pMj/JSXu1CptPW9hfilX7uLBXZkLOyP7aKrylQbfpl8kKhQ+nvnLpZYdFSUWND2PmER25VK4yhL5+2+n7bjG7rCVpSyfWTtiBtMlak1+KVSU/6iHAped97uyBsGNlFZkmO3GRYRAd5JeytKrcdMqjmFMFZc8wRwhOjGeROwJYXDckp/l0Noi3NWqtXnt7LwqFy7zbCQd5SyLE/8XUyYwQl8iFKOKErkGNFVZSoMvA44d/idKiQqDCwLOKLQeXswylvHB31UL3elUqy3rDUdZphvWXuGA7HFCWGFDhwjugCqTNXilw6auSOTfg/YP6n2wYSwj7IVaoAPLG9Vqc+wobCKAYAG9CjaMThKdGFvfNfyIhS20OODHAr/ZV98d+3QJraV+iWO2wq81o0qYt3lfcwSYauJAK/Gd212DI4T3TjvATvtNsISSh7MIWuD9Z1iZ2ErS8b6JY6rgDeUosk+C1QMHT5rsM0G//EmSu2y24jP40jRjRc7fxU/TKxJVBh0SyaZ26ybWKvPb2Xe5BCIX+K47yvlgI1Rtcf1KtButyk+YDFKObLUpiNFF0CVqUbgJfwgvMHmIEP+K8OS7X3q89t47aRMH9XJna+Ug0ouas/rDUzB83SyHqUW221EZzj6xlNlqgnfCG9jkCE3ZpBZnT7h3ZPXxmsnZdCe6ZcKYu85SnA7UGozMA9TnyEdbATestuIg+Fo0QUfCu/gGzPI2J76rIaG3Dbmnuw3wXVuxoBSG9DiYIQ3dVQAb8Tj545F9J56zkfKJQ84Fyi025a0EymKUHF/jEjf1JRX7BDccMgvgvuuUi7ZKFJkOHAaLnCAHM4GYJ7TBRdcJLoAUi65wDlAb7ttSTuRgihb72yn7bDuFaCpLWrh7eNDPhHcGNrDdUwifEKIDEULr5+2R0olnwJv4xIxc5XoAki5BIGTgFF225J2VFCx/bpm6qfnJf9dFGuHNbN0TK5PshSagddTuAOEtYgUA9OBfLtNcREKWIhSy+w2JBlcJ7odSLmMBE7GD95B/bRmtn83GxVKbAgaCUZ5f0LYRwsftqHzcB2VBJ80IjnAmUCp3aa4gDA6nLDZbkOSxbWiCyDl0gvdSb0f520bFGbrnRApPnicd09eG29OCvhkaa9Cb7uyRCmPTEiJBNDOhPdHcl2nHr3SrM5uQ7qCq0UXQMolE5gKDLfblrQTy46x7bY2mscf2IPdXNrMBxOyfVITtxWYpxRb7TYkLYiMAiYDfnh4JsNnwHso5ZwqfUnietHtQMrlcOAEwPtD6vppzdRcGyKWr0MrLVntLDwq4qN6uJvQE2bOq0ucSkTy0Q7FALtNcQAtwLsotcluQ7qLZ0QXQMolBBwHjAW8PXkUzY2y/TvNLJoZYNmoHJ94t3vQK8y22G2IpYiMRjsUfvV6PwPmO61wTVfxlOh2EI/1TgIG2W1LGlkPLGS2CqGHoV72hsLozQSXK+WDessHQnu9JwJDbbbESvagi49vstuQVOJJ0e1AyqU/2kMottuWFFIJLIjvpLwXEQYDxwO9bLEqPcSAlcBS12cmpAqREvR17me3KWmkBT1ButoNix2SxdOi20FcfMegvQQ3DsMj6ATwVars4KXqROgPHAkMwb0hlhZ0TeVVSuGILVYch4gXH7LtwDJgOUp5thKbL0S3AymXHHQqzmjckYRej/b0Po1vZ5QwIuSjHzSjALdsq16J3mFhk43Fxt2DiKAdibFAf3uN6RZNwGpglVfitgfDV6LbgZSLoOO9Y4CBOMv7bQe2AqtVmep2OpQIGcBh6AdN3+4eLw20ob341UpRZ7Mt7kWkCC2+hwOpqdmRfrahnYoKtyzhTQW+FN39ief59kNPRA3A+uGaAmrRQrsFqFFl6YljiZCNfsh0vOzYOUIBO9Be7Tag2reTY+lAJAMYgc5b74+zHAqAOnTK31qUqrfXFHvwveh+nngIoj9agEuBHkAqi8WE0bOyHUK7TZXZk+gtQm+0+A5CP2zSFYbYxT6RrVIK+zfk9AMimehrOwQYDGTZYIUCqtFlFzehlPdLtB4CI7oJEK9u1uMArxDakwigJ60Uegv5jlcDWmD3vuwS2EQQIYReUl0IFMRfhehzzUA/fPZ/AKn4K4w+18b4z4b9/62U2Z7GdnT8tw86k6c4/r4nqfeEG9EORS16RFPj5tVj6cCIriFpRBDP1DrwMyJB9AinEB1q+vwrg31OhUKn8EXRS7CbP/dqBHaiVIu1J+E+jOgaDAaDhTgtyG4wGAyexoiuwWAwWIgRXYPBYLAQI7oGg8FgIUZ0DQaDwUKM6HYDEdkkIi0i0igiu0XkHyLi5XKSiMjXReSj+DlXici/RORku+0yGNyCEd3u8xWlVD56KfF24Lc225M2ROSHwK+Ae4AS9CqnPwDn22iWIc2IyFtxp8KOFW2ew4huilC6OtLf0EV0PIeIFAJ3At9RSj2nlGpSSrUrpV5SSv3IbvsM6UFEhgJT0IsjzrPXGm9gRDdFiEgucAnwod22pInJ6NoMz9ttiMFSZqD79GPATHtN8QYZdhvgAV4QkQiQh15rfpbN9qSL3kCtUipityEGS5kBPAAsAD4UkRKl1HabbXI1xtPtPhcopYrQXuB3gbdFpNRek9LCTqBYdOlAgw+IT5AOAeYopRajN4j8ur1WuR8juilCKRVVSj2HLgjixdn8D9AFxy+w2Q6DdcwE5iqlauP/fhoTYug2xmtJEaJL552HLpe32mZzUo5Sql5E7gB+Hw+nzEXvcnEGME0pdZOtBhpSiojkABcDQRGpjn+cBRSJyHil1DL7rHM3RnS7z0siEkXP7lYAM5VSK222KS0ope6P34C3AU+ha+YuBu621TBDOrgAPWobB/9WdH4OOs57ow02eQJT2tFgMHwBEXkFWKmUuvFzn18M/AYYaCZVu4YRXYPBYLAQM5FmMBgMFmJE12AwGCzEiK7BYDBYiBFdg8FgsBAjugaDwWAhRnQNBoPBQozoGgwGg4UY0TUYDAYLMaJrMBgMFvL/DCDfFismjj4AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "venn3([set(akker_kmers), set(shew1_kmers), set(shew2_kmers)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's hash!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Choose a hash function!\n", "\n", "We need to pick a hash function that takes DNA k-mers and converts them into numbers.\n", "\n", "Both the [mash](https://mash.readthedocs.io/en/latest/) software for MinHash, and the [sourmash](https://sourmash.readthedocs.io) software for modulo and MinHash, use MurmurHash:\n", "\n", "https://en.wikipedia.org/wiki/MurmurHash\n", "\n", "this is implemented in the 'mmh3' library in Python.\n", "\n", "The other thing we need to do here is take into account the fact that DNA is double stranded, and so\n", "\n", "```\n", "hash_kmer('ATGG')\n", "```\n", "should be equivalent to\n", "```\n", "hash_kmer('CCAT')\n", "```\n", "Following mash's lead, for every input k-mer we will choose a *canonical* k-mer that is the lesser of the k-mer and its reverse complement." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "import mmh3\n", "\n", "\n", "def hash_kmer(kmer):\n", " # calculate the reverse complement\n", " rc_kmer = screed.rc(kmer)\n", "\n", " # determine whether original k-mer or reverse complement is lesser\n", " if kmer < rc_kmer:\n", " canonical_kmer = kmer\n", " else:\n", " canonical_kmer = rc_kmer\n", "\n", " # calculate murmurhash using a hash seed of 42\n", " hash = mmh3.hash64(canonical_kmer, 42)[0]\n", " if hash < 0:\n", " hash += 2**64\n", "\n", " # done\n", " return hash" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is now a function that we can use to turn any DNA \"word\" into a number:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13663093258475204077" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hash_kmer(\"ATGGC\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same input word always returns the same number:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13663093258475204077" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hash_kmer(\"ATGGC\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "as does its reverse complement:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13663093258475204077" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hash_kmer(\"GCCAT\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and nearby words return very different numbers:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1777382721305265773" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hash_kmer(\"GCCAA\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Note that hashing collections of k-mers doesn't change Jaccard calculations:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "def hash_kmers(kmers):\n", " hashes = []\n", " for kmer in kmers:\n", " hashes.append(hash_kmer(kmer))\n", " return hashes" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "shew1_hashes = hash_kmers(shew1_kmers)\n", "shew2_hashes = hash_kmers(shew2_kmers)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.23675152210020398\n" ] } ], "source": [ "print(jaccard_similarity(shew1_kmers, shew2_kmers))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.2371520123045373\n" ] } ], "source": [ "print(jaccard_similarity(shew1_hashes, shew2_hashes))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(ok, it changes it a little, because of the canonical k-mer calculation!)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Implementing subsampling with modulo hashing\n", "\n", "We are now ready to implement k-mer subsampling with modulo hash.\n", "\n", "We need to pick a sampling rate, and know the maximum possible hash value.\n", "\n", "For a sampling rate, let's start with 1000.\n", "\n", "The MurmurHash function turns k-mers into numbers between 0 and `2**64 - 1` (the maximum 64-bit number).\n", "\n", "Let's define these as variables:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "scaled = 1000\n", "MAX_HASH = 2**64" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, choose the range of hash values that we'll keep." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.844674407370955e+16\n" ] } ], "source": [ "keep_below = MAX_HASH / scaled\n", "print(keep_below)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and write a filter function:" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "def subsample_modulo(kmers):\n", " keep = []\n", " for kmer in kmers:\n", " if hash_kmer(kmer) < keep_below:\n", " keep.append(kmer)\n", " # otherwise, discard\n", "\n", " return keep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now let's apply this to our big collections of k-mers!" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "akker_sub = subsample_modulo(akker_kmers)\n", "shew1_sub = subsample_modulo(shew1_kmers)\n", "shew2_sub = subsample_modulo(shew2_kmers)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "499970 502\n", "499970 513\n", "499970 503\n" ] } ], "source": [ "print(len(akker_kmers), len(akker_sub))\n", "print(len(shew1_kmers), len(shew1_sub))\n", "print(len(shew2_kmers), len(shew2_sub))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So we go from ~500,000 k-mers to ~500 hashes! Do the Jaccard calculations change??" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "akker vs akker, total 1.0\n", "akker vs akker, sub 1.0\n" ] } ], "source": [ "print(\"akker vs akker, total\", jaccard_similarity(akker_kmers, akker_kmers))\n", "print(\"akker vs akker, sub\", jaccard_similarity(akker_sub, akker_sub))" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "akker vs shew1, total 0.0\n", "akker vs shew1, sub 0.0\n" ] } ], "source": [ "print(\"akker vs shew1, total\", jaccard_similarity(akker_kmers, shew1_kmers))\n", "print(\"akker vs shew1, sub\", jaccard_similarity(akker_sub, shew1_sub))" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "shew1 vs shew2, total 0.23675152210020398\n", "shew1 vs shew2, sub 0.2281795511221945\n" ] } ], "source": [ "print(\"shew1 vs shew2, total\", jaccard_similarity(shew1_kmers, shew2_kmers))\n", "print(\"shew1 vs shew2, sub\", jaccard_similarity(shew1_sub, shew2_sub))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And you can see that the numbers are different, but not very much - the Jaccard similarity is being *estimated*, so it is not exact but it is close." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Let's visualize --" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "venn3([set(akker_kmers), set(shew1_kmers), set(shew2_kmers)])" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "venn3([set(akker_sub), set(shew1_sub), set(shew2_sub)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other pointers\n", "\n", "[Sourmash: a practical guide](https://sourmash.readthedocs.io/en/latest/using-sourmash-a-guide.html)\n", "\n", "[Classifying signatures taxonomically](https://sourmash.readthedocs.io/en/latest/classifying-signatures.html)\n", "\n", "[Pre-built search databases](https://sourmash.readthedocs.io/en/latest/databases.html)\n", "\n", "## A full list of notebooks\n", "\n", "[An introduction to k-mers for genome comparison and analysis](kmers-and-minhash.ipynb)\n", "\n", "[Some sourmash command line examples!](sourmash-examples.ipynb)\n", "\n", "[Working with private collections of signatures.](sourmash-collections.ipynb)\n", "\n", "[Using the LCA_Database API.](using-LCA-database-API.ipynb)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "py38arm", "language": "python", "name": "py38arm" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 2 } sourmash-bio-sourmash-bc3c0f8/doc/legacy-databases.md000066400000000000000000000105331474157046300227500ustar00rootroot00000000000000# Legacy Databases Sourmash databases have evolved over time. We have changed how the database is stored (uncompressed `.zip`) and how we name each signature. All SBT databases below are in `.sbt.zip` format. Note that the SBT and LCA databases can be used with sourmash v3.5 and later, while Zipfile collections can only be used with sourmash v4.1.0 and up. We detail these changes below, and include links to legacy databases. See [github.com/sourmash-bio/databases](https://github.com/sourmash-bio/databases) for a Snakemake workflow that builds current and legacy databases. ## Sourmash signature names Earlier versions of sourmash databases were built using individual signatures that were calculated as follows: ``` sourmash compute -k 4,5 \ -n 2000 \ --track-abundance \ --name-from-first \ -o {output} \ {input} sourmash compute -k 21,31,51 \ --scaled 2000 \ --track-abundance \ --name-from-first \ -o {output} \ {input} ``` We moved away from this strategy because `--name-from-first` named each signature from the name of the first sequence in the FASTA file. While the species name of the organism was present in this name, the accession number corresponded to the accession of the first sequence fragment in the file, not the genome assembly. As such, we revised our strategy so that signatures are named by genome assembly accession and species name. This requires the `assembly_summary.txt` file to be parsed. ## Sourmash database compression ## Legacy databases ### RefSeq microbial genomes - SBT These database are formatted for use with `sourmash search` and `sourmash gather`. They are calculated with a scaled value of 2000. Approximately 91,000 microbial genomes (including viral and fungal) from NCBI RefSeq. * [RefSeq k=21, 2018.03.29][0] - 3.3 GB - [manifest](https://osf.io/wamfk/download) * [RefSeq k=31, 2018.03.29][1] - 3.3 GB - [manifest](https://osf.io/x3aut/download) * [RefSeq k=51, 2018.03.29][2] - 3.4 GB - [manifest](https://osf.io/zpkau/download) ### Genbank microbial genomes - SBT These database are formatted for use with `sourmash search` and `sourmash gather`. Approximately 98,000 microbial genomes (including viral and fungal) from NCBI Genbank. * [Genbank k=21, 2018.03.29][3] - 3.9 GB - [manifest](https://osf.io/vm5kb/download) * [Genbank k=31, 2018.03.29][4] - 3.9 GB - [manifest](https://osf.io/p87ec/download) * [Genbank k=51, 2018.03.29][5] - 3.9 GB - [manifest](https://osf.io/cbxg9/download) [0]: https://sourmash-databases.s3-us-west-2.amazonaws.com/zip/refseq-k21.sbt.zip [1]: https://sourmash-databases.s3-us-west-2.amazonaws.com/zip/refseq-k31.sbt.zip [2]: https://sourmash-databases.s3-us-west-2.amazonaws.com/zip/refseq-k51.sbt.zip [3]: https://sourmash-databases.s3-us-west-2.amazonaws.com/zip/genbank-k21.sbt.zip [4]: https://sourmash-databases.s3-us-west-2.amazonaws.com/zip/genbank-k31.sbt.zip [5]: https://sourmash-databases.s3-us-west-2.amazonaws.com/zip/genbank-k51.sbt.zip ### Genbank microbial genomes - LCA These databases are formatted for use with `sourmash lca`; they are v2 LCA databases and will work with sourmash v2.0a11 and later. They are calculated with a scaled value of 10000 (1e5). Approximately 87,000 microbial genomes (including viral and fungal) from NCBI Genbank. * [Genbank k=21, 2017.11.07](https://osf.io/d7rv8/download), 109 MB * [Genbank k=31, 2017.11.07](https://osf.io/4f8n3/download), 120 MB * [Genbank k=51, 2017.11.07](https://osf.io/nemkw/download), 125 MB The above LCA databases were calculated as follows: ``` sourmash lca index genbank-genomes-taxonomy.2017.05.29.csv \ genbank-k21.lca.json.gz -k 21 --scaled=10000 \ -f --traverse-directory .sbt.genbank-k21 --split-identifiers ``` See [github.com/dib-lab/2018-ncbi-lineages](https://github.com/dib-lab/2018-ncbi-lineages) for information on preparing the genbank-genomes-taxonomy when signatures are generated using `--name-from-first`. ### GTDB databases - SBT All files below are available [here](https://osf.io/wxf9z/). Release 89 * [GTDB k=31, release 89](https://osf.io/5mb9k/download) Release 95 * [GTDB k=21, scaled=1000](https://osf.io/4yhe2/download) * [GTDB k=31, scaled=1000](https://osf.io/4n3m5/download) * [GTDB k=51, scaled=1000](https://osf.io/c8wj7/download) sourmash-bio-sourmash-bc3c0f8/doc/more-info.md000066400000000000000000000133171474157046300214550ustar00rootroot00000000000000# Additional information on sourmash ## Computational requirements Read more about the [computational requirements, here.](requirements.md) ## Prepared search databases We offer a number of [prepared search databases.](databases.md) You can read about the supported database formats [here.](databases-advanced.md) ## Other MinHash implementations for DNA In addition to [mash][0], also see: * [RKMH][1]: Read Classification by Kmers * [mashtree][2]: For building trees using Mash * [Finch][3]: "Fast sketches, count histograms, better filtering." * [BBMap and SendSketch][13]: part of Brian Bushnell's tool collection. * [PATRIC](https://patricbrc.org/) uses MinHash for genome search. If you are interested in exactly how these MinHash approaches calculate the hashes of DNA sequences, please see some simple Python code in sourmash, [utils/compute-dna-mh-another-way.py][4] ## Papers and references [On the resemblance and containment of documents][20], Broder, 1997. The original MinHash paper! [Mash: fast genome and metagenome distance estimation using MinHash.][21], Ondov et al. 2016. [sourmash: a library for MinHash sketching of DNA.][22], Brown and Irber, 2017. [Improving MinHash via the Containment Index with Applications to Metagenomic Analysis][23], Koslicki and Zabeti, 2017. [Ultra-fast search of all deposited bacterial and viral genomic data](http://dx.doi.org/10.1038/s41587-018-0010-1), Bradley et al., 2019. [Streaming histogram sketching for rapid microbiome analytics](https://www.biorxiv.org/content/10.1101/408070v1), Rowe et al., 2019. [Dashing: Fast and Accurate Genomic Distances with HyperLogLog](https://www.biorxiv.org/content/10.1101/501726v2), Baker and Langmead, 2019. ## Presentations and posters [Taxonomic classification of microbial metagenomes using MinHash signatures][12], Brooks et al., 2017. Presented at ASM. ## Blog posts We (and others) have a number of blog posts on sourmash and MinHash more generally: * [Some background on k-mers, and their use in taxonomy][15] * From the Phillippy lab: [mash screen: what's in my sequencing run?][14] * [Applying MinHash to cluster RNAseq samples][5] * [MinHash signatures as ways to find samples, and collaborators?][6] * [Efficiently searching MinHash Sketch collections][7]: indexing and search 42,000 bacterial genomes with Sequence Bloom Trees. * [Quickly searching all the microbial genomes, mark 2 - now with archaea, phage, fungi, and protists!][8]: indexing and searching 50,000 microbial genomes, round 2. * [What metadata should we put in MinHash Sketch signatures?][9]: crowdsourcing ideas for what metadata belongs in a signature file. * [Minhashing all the things (part 1): microbial genomes][10]: on approaches to computing MinHashes for large collections of public data. * [Comparing genome sets extracted from metagenomes][16] * [Taxonomic examinations of genome bins from Tara Oceans][17] * [Classifying genome bins using a custom reference database, part I][18] * [Classifying genome bins using a custom reference database, part II][19] ## JSON format for the signature The JSON format is not necessarily final; this is a TODO item for future releases. In particular, we'd like to update it to store more metadata for samples. ## Interoperability with mash The hashing functions used by sourmash and mash are the same, but we are still [working on ways to convert the file formats][11]. Please keep an eye on `sourmash signature import` and `sourmash signature export`! ## Developing sourmash Please see: * [Developer information][24] * [Release workflow][25] ## Known issues For at least some versions of matplotlib, users may encounter an error "Failed to connect to server socket:" or "RuntimeError: Invalid DISPLAY variable". This is because by default matplotlib tries to connect to X11 to use the Tkinter backend. The solution is to force the use of the 'Agg' backend in matplotlib; see [this stackoverflow answer](https://stackoverflow.com/a/34294056) or [this sourmash issue comment](https://github.com/sourmash-bio/sourmash/issues/254#issuecomment-304274590). Newer versions of matplotlib do not seem to have this problem. ```{toctree} --- hidden: --- README.md legacy-databases.md databases-advanced.md plotting-compare.ipynb sourmash-sketch.md ``` [0]:https://github.com/marbl/Mash [1]:https://github.com/edawson/rkmh [2]:https://github.com/lskatz/mashtree/blob/master/README.md [3]:https://github.com/onecodex/finch-rs [4]:https://github.com/sourmash-bio/sourmash/blob/latest/utils/compute-dna-mh-another-way.py [5]:http://ivory.idyll.org/blog/2016-sourmash.html [6]:http://ivory.idyll.org/blog/2016-sourmash-signatures.html [7]:http://ivory.idyll.org/blog/2016-sourmash-sbt.html [8]:http://ivory.idyll.org/blog/2016-sourmash-sbt-more.html [9]:http://ivory.idyll.org/blog/2016-sourmash-signatures-metadata.html [10]:http://blog.luizirber.org/2016/12/28/soursigs-arch-1/ [11]:https://github.com/marbl/Mash/issues/27 [12]:https://osf.io/mu4gk/ [13]:http://seqanswers.com/forums/showthread.php?t=74019 [14]:https://genomeinformatics.github.io/mash-screen/ [15]:http://ivory.idyll.org/blog/2017-something-about-kmers.html [16]:http://ivory.idyll.org/blog/2017-comparing-genomes-from-metagenomes.html [17]:http://ivory.idyll.org/blog/2017-taxonomy-of-tara-ocean-genomes.html [18]:http://ivory.idyll.org/blog/2017-classify-genome-bins-with-custom-db-part-1.html [19]:http://ivory.idyll.org/blog/2017-classify-genome-bins-with-custom-db-part-2.html [20]:http://ieeexplore.ieee.org/document/666900/?reload=true [21]:https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0997-x [22]:http://joss.theoj.org/papers/3d793c6e7db683bee7c03377a4a7f3c9 [23]:https://www.biorxiv.org/content/early/2017/09/04/184150 [24]:developer.md [25]:release.md sourmash-bio-sourmash-bc3c0f8/doc/other-languages.md000066400000000000000000000025751474157046300226530ustar00rootroot00000000000000# Using `sourmash` output with R and other languages Most of the sourmash shell commands output CSV files upon request; these files have headers and are straightforward to load into R. Below are some code snippets and links that might be useful. ## R code for working with compare output (by Taylor Reiter) `sourmash compare` can output matrices in a CSV format, which can easily be read into R. For example, if you download the Eschericia signature collection as in [the sourmash tutorial](tutorial-basic.md#make-and-search-a-database-quickly), then the shell command ```shell sourmash compare ecoli_many_sigs/*.sig --csv ecoli.cmp.csv ``` will output a file `ecoli.cmp.csv` that can be loaded into R like so: ```r sourmash_comp_matrix <- read.csv("ecoli.cmp.csv") # Label the rows rownames(sourmash_comp_matrix) <- colnames(sourmash_comp_matrix) # Transform for plotting sourmash_comp_matrix <- as.matrix(sourmash_comp_matrix) ``` See the output of plotting and clustering this matrix by downloading and opening [this html](https://raw.githubusercontent.com/sourmash-bio/sourmash/latest/doc/_static/ecoli-cmp.html), produced by [this RMarkdown file](https://raw.githubusercontent.com/sourmash-bio/sourmash/latest/doc/_static/ecoli-cmp.Rmd). You can download the `ecoli.cmp.csv` file itself [here](https://raw.githubusercontent.com/sourmash-bio/sourmash/latest/doc/_static/ecoli.cmp.csv). sourmash-bio-sourmash-bc3c0f8/doc/plotting-compare.ipynb000066400000000000000000003226761474157046300236020ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building plots from `sourmash compare` output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Running this notebook.\n", "\n", "You can run this notebook interactively via mybinder; click on this button:\n", "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dib-lab/sourmash/latest?labpath=doc%2Fplotting-compare.ipynb)\n", "\n", "A rendered version of this notebook is available at [sourmash.readthedocs.io](https://sourmash.readthedocs.io) under \"Tutorials and notebooks\".\n", "\n", "You can also get this notebook from the [doc/ subdirectory of the sourmash github repository](https://github.com/dib-lab/sourmash/tree/latest/doc). See [binder/environment.yaml](https://github.com/dib-lab/sourmash/blob/latest/binder/environment.yml) for installation dependencies.\n", "\n", "### What is this?\n", "\n", "This is a Jupyter Notebook using Python 3. If you are running this via [binder](https://mybinder.org), you can use Shift-ENTER to run cells, and double click on code cells to edit them.\n", "\n", "Contact: C. Titus Brown, ctbrown@ucdavis.edu. Please [file issues on GitHub](https://github.com/dib-lab/sourmash/issues/) if you have any questions or comments!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running `sourmash compare` and generating figures in Python\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need to generate a similarity matrix with compare. (If you want to generate this programmatically, it's just a `numpy` matrix.)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.8.5.dev0. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kloaded 7 signatures total. \n", "\u001b[K\n", "0-SRR2060939_1.fa...\t[1. 0.356 0.078 0.086 0. 0. 0. ]\n", "1-SRR2060939_2.fa...\t[0.356 1. 0.072 0.078 0. 0. 0. ]\n", "2-SRR2241509_1.fa...\t[0.078 0.072 1. 0.074 0. 0. 0. ]\n", "3-SRR2255622_1.fa...\t[0.086 0.078 0.074 1. 0. 0. 0. ]\n", "4-SRR453566_1.fas...\t[0. 0. 0. 0. 1. 0.382 0.364]\n", "5-SRR453569_1.fas...\t[0. 0. 0. 0. 0.382 1. 0.386]\n", "6-SRR453570_1.fas...\t[0. 0. 0. 0. 0.364 0.386 1. ]\n", "min similarity in matrix: 0.000\n", "\u001b[Ksaving labels to: compare-demo.labels.txt\n", "\u001b[Ksaving comparison matrix to: compare-demo\n" ] } ], "source": [ "!sourmash compare ../tests/test-data/demo/*.sig -o compare-demo" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "# import the `fig` module from sourmash:\n", "from sourmash import fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `sourmash.fig` module contains code to load the similarity matrix and associated labels:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "matrix, labels = fig.load_matrix_and_labels(\"compare-demo\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, `matrix` is a numpy matrix and `labels` is a list of labels (by default, filenames)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "matrix:\n", " [[1. 0.356 0.078 0.086 0. 0. 0. ]\n", " [0.356 1. 0.072 0.078 0. 0. 0. ]\n", " [0.078 0.072 1. 0.074 0. 0. 0. ]\n", " [0.086 0.078 0.074 1. 0. 0. 0. ]\n", " [0. 0. 0. 0. 1. 0.382 0.364]\n", " [0. 0. 0. 0. 0.382 1. 0.386]\n", " [0. 0. 0. 0. 0.364 0.386 1. ]]\n", "labels: ['SRR2060939_1.fastq.gz', 'SRR2060939_2.fastq.gz', 'SRR2241509_1.fastq.gz', 'SRR2255622_1.fastq.gz', 'SRR453566_1.fastq.gz', 'SRR453569_1.fastq.gz', 'SRR453570_1.fastq.gz']\n" ] } ], "source": [ "print(\"matrix:\\n\", matrix)\n", "print(\"labels:\", labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `plot_composite_matrix` function returns a generated plot, along with the labels and matrix as re-ordered by the clustering:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, reordered_labels, reordered_matrix = fig.plot_composite_matrix(matrix, labels)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "reordered matrix:\n", " [[1. 0.382 0.364 0. 0. 0. 0. ]\n", " [0.382 1. 0.386 0. 0. 0. 0. ]\n", " [0.364 0.386 1. 0. 0. 0. 0. ]\n", " [0. 0. 0. 1. 0.356 0.078 0.086]\n", " [0. 0. 0. 0.356 1. 0.072 0.078]\n", " [0. 0. 0. 0.078 0.072 1. 0.074]\n", " [0. 0. 0. 0.086 0.078 0.074 1. ]]\n", "reordered labels: ['SRR453566_1.fastq.gz', 'SRR453569_1.fastq.gz', 'SRR453570_1.fastq.gz', 'SRR2060939_1.fastq.gz', 'SRR2060939_2.fastq.gz', 'SRR2241509_1.fastq.gz', 'SRR2255622_1.fastq.gz']\n" ] } ], "source": [ "print(\"reordered matrix:\\n\", reordered_matrix)\n", "print(\"reordered labels:\", reordered_labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Customizing plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to customize the plots, please see the code for `plot_composite_matrix` in [sourmash/fig.py](https://github.com/sourmash-bio/sourmash/blob/latest/src/sourmash/fig.py), which is reproduced below; you can modify the code in place to (for example) [use custom dendrogram colors](https://stackoverflow.com/questions/38153829/custom-cluster-colors-of-scipy-dendrogram-in-python-link-color-func)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import scipy.cluster.hierarchy as sch\n", "\n", "\n", "def plot_composite_matrix(\n", " D, labeltext, show_labels=True, vmax=1.0, vmin=0.0, force=False\n", "):\n", " \"\"\"Build a composite plot showing dendrogram + distance matrix/heatmap.\n", "\n", " Returns a matplotlib figure.\n", "\n", " If show_labels is True, display labels. Otherwise, no labels are\n", " shown on the plot.\n", " \"\"\"\n", " if D.max() > 1.0 or D.min() < 0.0:\n", " error(\n", " \"This matrix doesn't look like a distance matrix - min value {}, max value {}\",\n", " D.min(),\n", " D.max(),\n", " )\n", " if not force:\n", " raise ValueError(\"not a distance matrix\")\n", " else:\n", " notify(\"force is set; scaling to [0, 1]\")\n", " D -= D.min()\n", " D /= D.max()\n", "\n", " if show_labels:\n", " pass\n", "\n", " fig = pylab.figure(figsize=(11, 8))\n", " ax1 = fig.add_axes([0.09, 0.1, 0.2, 0.6])\n", "\n", " # plot dendrogram\n", " Y = sch.linkage(D, method=\"single\") # centroid\n", "\n", " Z1 = sch.dendrogram(\n", " Y,\n", " orientation=\"left\",\n", " labels=labeltext,\n", " no_labels=not show_labels,\n", " get_leaves=True,\n", " )\n", " ax1.set_xticks([])\n", "\n", " xstart = 0.45\n", " width = 0.45\n", " if not show_labels:\n", " xstart = 0.315\n", " scale_xstart = xstart + width + 0.01\n", "\n", " # re-order labels along rows, top to bottom\n", " idx1 = Z1[\"leaves\"]\n", " reordered_labels = [labeltext[i] for i in idx1]\n", "\n", " # reorder D by the clustering in the dendrogram\n", " D = D[idx1, :]\n", " D = D[:, idx1]\n", "\n", " # show matrix\n", " axmatrix = fig.add_axes([xstart, 0.1, width, 0.6])\n", "\n", " im = axmatrix.matshow(\n", " D, aspect=\"auto\", origin=\"lower\", cmap=pylab.cm.YlGnBu, vmin=vmin, vmax=vmax\n", " )\n", " axmatrix.set_xticks([])\n", " axmatrix.set_yticks([])\n", "\n", " # Plot colorbar.\n", " axcolor = fig.add_axes([scale_xstart, 0.1, 0.02, 0.6])\n", " pylab.colorbar(im, cax=axcolor)\n", "\n", " return fig, reordered_labels, D" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = plot_composite_matrix(matrix, labels)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "smash-notebooks", "language": "python", "name": "smash-notebooks" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 2 } sourmash-bio-sourmash-bc3c0f8/doc/podar-lineage.csv000066400000000000000000000227531474157046300224700ustar00rootroot00000000000000accession,taxid,superkingdom,phylum,class,order,family,genus,species,strain AE000782,224325,Archaea,Euryarchaeota,Archaeoglobi,Archaeoglobales,Archaeoglobaceae,Archaeoglobus,Archaeoglobus fulgidus,Archaeoglobus fulgidus DSM 4304 NC_000909,243232,Archaea,Euryarchaeota,Methanococci,Methanococcales,Methanocaldococcaceae,Methanocaldococcus,Methanocaldococcus jannaschii,Methanocaldococcus jannaschii DSM 2661 NC_003272,103690,Bacteria,Cyanobacteria,,Nostocales,Nostocaceae,Nostoc,Nostoc sp. PCC 7120, AE009441,178306,Archaea,Crenarchaeota,Thermoprotei,Thermoproteales,Thermoproteaceae,Pyrobaculum,Pyrobaculum aerophilum,Pyrobaculum aerophilum str. IM2 AE009950,186497,Archaea,Euryarchaeota,Thermococci,Thermococcales,Thermococcaceae,Pyrococcus,Pyrococcus furiosus,Pyrococcus furiosus DSM 3638 AE009951,190304,Bacteria,Fusobacteria,Fusobacteriia,Fusobacteriales,Fusobacteriaceae,Fusobacterium,Fusobacterium nucleatum, AE010299,188937,Archaea,Euryarchaeota,Methanomicrobia,Methanosarcinales,Methanosarcinaceae,Methanosarcina,Methanosarcina acetivorans,Methanosarcina acetivorans C2A AE009439,190192,Archaea,Euryarchaeota,Methanopyri,Methanopyrales,Methanopyraceae,Methanopyrus,Methanopyrus kandleri,Methanopyrus kandleri AV19 NC_003911,246200,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Ruegeria,Ruegeria pomeroyi,Ruegeria pomeroyi DSS-3 AE006470,194439,Bacteria,Chlorobi,Chlorobia,Chlorobiales,Chlorobiaceae,Chlorobaculum,Chlorobaculum tepidum,Chlorobaculum tepidum TLS AE015928,226186,Bacteria,Bacteroidetes,Bacteroidia,Bacteroidales,Bacteroidaceae,Bacteroides,Bacteroides thetaiotaomicron,Bacteroides thetaiotaomicron VPI-5482 AL954747,228410,Bacteria,Proteobacteria,Betaproteobacteria,Nitrosomonadales,Nitrosomonadaceae,Nitrosomonas,Nitrosomonas europaea,Nitrosomonas europaea ATCC 19718 BX119912,243090,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Rhodopirellula,Rhodopirellula baltica,Rhodopirellula baltica SH 1 BX571656,273121,Bacteria,Proteobacteria,Epsilonproteobacteria,Campylobacterales,Helicobacteraceae,Wolinella,Wolinella succinogenes,Wolinella succinogenes DSM 1740 AE017180,243231,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfuromonadales,Geobacteraceae,Geobacter,Geobacter sulfurreducens,Geobacter sulfurreducens PCA AE017226,243275,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Spirochaetaceae,Treponema,Treponema denticola,Treponema denticola ATCC 35405 BX950229,267377,Archaea,Euryarchaeota,Methanococci,Methanococcales,Methanococcaceae,Methanococcus,Methanococcus maripaludis,Methanococcus maripaludis S2 AE017221,262724,Bacteria,Deinococcus-Thermus,Deinococci,Thermales,Thermaceae,Thermus,Thermus thermophilus,Thermus thermophilus HB27 BA000001,70601,Archaea,Euryarchaeota,Thermococci,Thermococcales,Thermococcaceae,Pyrococcus,Pyrococcus horikoshii,Pyrococcus horikoshii OT3 BA000023,273063,Archaea,Crenarchaeota,Thermoprotei,Sulfolobales,Sulfolobaceae,Sulfolobus,Sulfolobus tokodaii,Sulfolobus tokodaii str. 7 NC_007951,266265,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Burkholderiaceae,Paraburkholderia,Paraburkholderia xenovorans,Paraburkholderia xenovorans LB400 CP000492,290317,Bacteria,Chlorobi,Chlorobia,Chlorobiales,Chlorobiaceae,Chlorobium,Chlorobium phaeobacteroides,Chlorobium phaeobacteroides DSM 266 NC_008751,391774,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfovibrionales,Desulfovibrionaceae,Desulfovibrio,Desulfovibrio vulgaris,Desulfovibrio vulgaris DP4 CP000568,203119,Bacteria,Firmicutes,Clostridia,Clostridiales,Ruminococcaceae,Ruminiclostridium,Ruminiclostridium thermocellum,Ruminiclostridium thermocellum ATCC 27405 CP000561,410359,Archaea,Crenarchaeota,Thermoprotei,Thermoproteales,Thermoproteaceae,Pyrobaculum,Pyrobaculum calidifontis,Pyrobaculum calidifontis JCM 11548 CP000609,402880,Archaea,Euryarchaeota,Methanococci,Methanococcales,Methanococcaceae,Methanococcus,Methanococcus maripaludis,Methanococcus maripaludis C5 CP000607,290318,Bacteria,Chlorobi,Chlorobia,Chlorobiales,Chlorobiaceae,Chlorobium,Chlorobium phaeovibrioides,Chlorobium phaeovibrioides DSM 265 CP000660,340102,Archaea,Crenarchaeota,Thermoprotei,Thermoproteales,Thermoproteaceae,Pyrobaculum,Pyrobaculum arsenaticum,Pyrobaculum arsenaticum DSM 13514 CP000667,369723,Bacteria,Actinobacteria,Actinobacteria,Micromonosporales,Micromonosporaceae,Salinispora,Salinispora tropica,Salinispora tropica CNB-440 CP000679,351627,Bacteria,Firmicutes,Clostridia,Thermoanaerobacterales,Thermoanaerobacterales Family III. Incertae Sedis,Caldicellulosiruptor,Caldicellulosiruptor saccharolyticus,Caldicellulosiruptor saccharolyticus DSM 8903 CP000702,390874,Bacteria,Thermotogae,Thermotogae,Thermotogales,Thermotogaceae,Thermotoga,Thermotoga petrophila,Thermotoga petrophila RKU-1 CP000139,435590,Bacteria,Bacteroidetes,Bacteroidia,Bacteroidales,Bacteroidaceae,Bacteroides,Bacteroides vulgatus,Bacteroides vulgatus ATCC 8482 NC_009665,402882,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Shewanellaceae,Shewanella,Shewanella baltica,Shewanella baltica OS185 CP000816,453591,Archaea,Crenarchaeota,Thermoprotei,Desulfurococcales,Desulfurococcaceae,Ignicoccus,Ignicoccus hospitalis,Ignicoccus hospitalis KIN4/I CP000850,391037,Bacteria,Actinobacteria,Actinobacteria,Micromonosporales,Micromonosporaceae,Salinispora,Salinispora arenicola,Salinispora arenicola CNS-205 CP000909,324602,Bacteria,Chloroflexi,Chloroflexia,Chloroflexales,Chloroflexaceae,Chloroflexus,Chloroflexus aurantiacus,Chloroflexus aurantiacus J-10-fl CP000924,340099,Bacteria,Firmicutes,Clostridia,Thermoanaerobacterales,Thermoanaerobacteraceae,Thermoanaerobacter,Thermoanaerobacter pseudethanolicus,Thermoanaerobacter pseudethanolicus ATCC 33223 CP000969,126740,Bacteria,Thermotogae,Thermotogae,Thermotogales,Thermotogaceae,Thermotoga,Thermotoga sp. RQ2, CP001013,395495,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,,Leptothrix,Leptothrix cholodnii,Leptothrix cholodnii SP-6 CP001071,349741,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Akkermansiaceae,Akkermansia,Akkermansia muciniphila,Akkermansia muciniphila ATCC BAA-835 AP009380,431947,Bacteria,Bacteroidetes,Bacteroidia,Bacteroidales,Porphyromonadaceae,Porphyromonas,Porphyromonas gingivalis,Porphyromonas gingivalis ATCC 33277 NC_010730,436114,Bacteria,Aquificae,Aquificae,Aquificales,Hydrogenothermaceae,Sulfurihydrogenibium,Sulfurihydrogenibium sp. YO3AOP1, CP001097,290315,Bacteria,Chlorobi,Chlorobia,Chlorobiales,Chlorobiaceae,Chlorobium,Chlorobium limicola,Chlorobium limicola DSM 245 CP001110,324925,Bacteria,Chlorobi,Chlorobia,Chlorobiales,Chlorobiaceae,Pelodictyon,Pelodictyon phaeoclathratiforme,Pelodictyon phaeoclathratiforme BU-1 CP001130,380749,Bacteria,Aquificae,Aquificae,Aquificales,Aquificaceae,Hydrogenobaculum,Hydrogenobaculum sp. Y04AAS1, NZ_CH959311,52598,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Sulfitobacter,Sulfitobacter sp. EE-36, NZ_CH959317,314267,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Sulfitobacter,Sulfitobacter sp. NAS-14.1, CP001251,515635,Bacteria,Dictyoglomi,Dictyoglomia,Dictyoglomales,Dictyoglomaceae,Dictyoglomus,Dictyoglomus turgidum,Dictyoglomus turgidum DSM 6724 NC_011663,407976,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Shewanellaceae,Shewanella,Shewanella baltica,Shewanella baltica OS223 CP000916,309803,Bacteria,Thermotogae,Thermotogae,Thermotogales,Thermotogaceae,Thermotoga,Thermotoga neapolitana,Thermotoga neapolitana DSM 4359 NZ_DS996397,411464,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfovibrionales,Desulfovibrionaceae,Desulfovibrio,Desulfovibrio piger,Desulfovibrio piger ATCC 29098 CP001230,123214,Bacteria,Aquificae,Aquificae,Aquificales,Hydrogenothermaceae,Persephonella,Persephonella marina,Persephonella marina EX-H1 CP001472,240015,Bacteria,Acidobacteria,Acidobacteriia,Acidobacteriales,Acidobacteriaceae,Acidobacterium,Acidobacterium capsulatum,Acidobacterium capsulatum ATCC 51196 AP009153,379066,Bacteria,Gemmatimonadetes,Gemmatimonadetes,Gemmatimonadales,Gemmatimonadaceae,Gemmatimonas,Gemmatimonas aurantiaca,Gemmatimonas aurantiaca T-27 CP001941,439481,Archaea,Euryarchaeota,,,,Aciduliprofundum,Aciduliprofundum boonei,Aciduliprofundum boonei T469 NC_013968,309800,Archaea,Euryarchaeota,Halobacteria,Haloferacales,Haloferacaceae,Haloferax,Haloferax volcanii,Haloferax volcanii DS2 NZ_KE136524,226185,Bacteria,Firmicutes,Bacilli,Lactobacillales,Enterococcaceae,Enterococcus,Enterococcus faecalis,Enterococcus faecalis V583 NZ_KQ961402,542,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Zymomonas,Zymomonas mobilis, NZ_CP015081,243230,Bacteria,Deinococcus-Thermus,Deinococci,Deinococcales,Deinococcaceae,Deinococcus,Deinococcus radiodurans,Deinococcus radiodurans R1 NZ_ABZS01000228,432331,Bacteria,Aquificae,Aquificae,Aquificales,Hydrogenothermaceae,Sulfurihydrogenibium,Sulfurihydrogenibium yellowstonense,Sulfurihydrogenibium yellowstonense SS-5 NZ_JGWU01000001,1458259,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Alcaligenaceae,Bordetella,Bordetella bronchiseptica,Bordetella bronchiseptica D989 NZ_FWDH01000003,31899,Bacteria,Firmicutes,Clostridia,Thermoanaerobacterales,Thermoanaerobacterales Family III. Incertae Sedis,Caldicellulosiruptor,Caldicellulosiruptor bescii, NC_009972,316274,Bacteria,Chloroflexi,Chloroflexia,Herpetosiphonales,Herpetosiphonaceae,Herpetosiphon,Herpetosiphon aurantiacus,Herpetosiphon aurantiacus DSM 785 NC_005213,228908,Archaea,Nanoarchaeota,,Nanoarchaeales,Nanoarchaeaceae,Nanoarchaeum,Nanoarchaeum equitans,Nanoarchaeum equitans Kin4-M sourmash-bio-sourmash-bc3c0f8/doc/publications.md000066400000000000000000000054031474157046300222530ustar00rootroot00000000000000# Publications Below are publications from the sourmash team. ## sourmash fundamentals [Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2), Irber et al., 2022. This is the core technical paper describing both FracMinHash and `sourmash gather`. [Large-scale sequence comparisons with sourmash](https://f1000research.com/articles/8-1006), Pierce et al., 2019. This is the original sourmash use case paper. ## Evaluation and benchmarking [Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-022-05103-0), Portik et al., 2022. This paper shows that sourmash is extremely sensitive and very specific for taxonomic profiling. ## Petabase-scale search [Biogeographic Distribution of Five Antarctic Cyanobacteria Using Large-Scale k-mer Searching with sourmash branchwater](https://www.biorxiv.org/content/10.1101/2022.10.27.514113v1), Lumian et al., 2022. This paper uses sourmash and branchwater to search ~500,000 public metagenomes for 5 query genomes, validates the results using mapping, and discusses the biogeography of the query species. [Sourmash Branchwater Enables Lightweight Petabyte-Scale Sequence Search](https://www.biorxiv.org/content/10.1101/2022.11.02.514947v1), Irber et al., 2022. This paper describes the technical underpinnings of the first version of sourmash branchwater, for petabase-scale search. ## Advanced uses of sourmash [Single-cell transcriptomics for the 99.9% of species without reference genomes](https://www.biorxiv.org/content/10.1101/2021.07.09.450799v1.abstract), Botvinnik et al., 2021. This paper uses sourmash (and many other techniques!) to analyze single cell data from the Chinese horseshoe bat. [Meta-analysis of metagenomes via machine learning and assembly graphs reveals strain switches in Crohn’s disease](https://www.biorxiv.org/content/10.1101/2022.06.30.498290v1.abstract), Reiter et al., 2022. This paper uses sourmash and [spacegraphcats](https://spacegraphcats.github.io/spacegraphcats/) to detect and analyze strain-specific signals in fecal microbiomes from the iHMP. [Protein k-mers enable assembly-free microbial metapangenomics](https://www.biorxiv.org/content/10.1101/2022.06.27.497795v1), Reiter et al., 2022. This paper develops a technique to use protein k-mers to analyze metapangenome graphs from metagenomes. ## Additional works Dr. Luiz Irber's PhD thesis, [Decentralizing Indices for Genomic Data](https://github.com/luizirber/phd/releases), describes several additional features of the sourmash ecosystem, including wort, which monitors the SRA for new data sets and sketches them automatically. sourmash-bio-sourmash-bc3c0f8/doc/release-notes/000077500000000000000000000000001474157046300220015ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/doc/release-notes/releases.md000066400000000000000000000003471474157046300241320ustar00rootroot00000000000000# sourmash release notes Please see the [github releases page](https://github.com/sourmash-bio/sourmash/releases) for detailed release notes for each version! ```{toctree} :maxdepth: 2 sourmash-4.0 sourmash-3.0 sourmash-2.0 ``` sourmash-bio-sourmash-bc3c0f8/doc/release-notes/sourmash-2.0.md000066400000000000000000000054661474157046300244740ustar00rootroot00000000000000# sourmash v2.0 release notes We are pleased to announce release 2.0 of sourmash! This release is the first full release of sourmash since 1.0, and provides much expanded core functionality as well as many more utility functions. The full documentation for this version is available at https://sourmash.readthedocs.io/en/v2.0/. Documentation for the latest released version is at http://sourmash.readthedocs.io/en/stable/. **This release breaks compatibility with sourmash 1.0: the sourmash Python API, command-line, and signature file formats have all changed substantially.** Please post questions about migrating to sourmash 2.0 in the [sourmash issue tracker](https://github.com/sourmash-bio/sourmash/issues/new). ## Major new features since 1.0 This is a list of substantial new features and functionality in sourmash 2.0. * Added Sequence Bloom Tree search to enable similarity and containment queries on very large collections of signatures in low memory; see `sourmash index`, `sourmash search`, and `sourmash gather` in [the command line documentation](../command-line.md). * Added "LCA databases" for fast searching of large databases in not-so-low memory; see [`sourmash lca index` in command-line docs](../command-line.md#sourmash-lca-subcommands-for-in-memory-taxonomy-integration). * Created [precomputed databases](../databases.md) for most of GenBank genomes. * Added taxonomic reporting functionality in the `sourmash lca` submodule - [see command-line docs](../command-line.md#sourmash-lca-subcommands-for-in-memory-taxonomy-integration). * Added signature manipulation utilities in the `sourmash signature` submodule - [see command-line docs](../command-line.md#sourmash-signature-subcommands-for-signature-manipulation) * Introduced new modulo hash or "scaled" signatures for containment analysis; see [Using sourmash: a practical guide](../using-sourmash-a-guide.md#what-resolution-should-my-signatures-be-and-how-should-i-create-them) and [more details in the Python API examples](../api-example.md#advanced-features-of-sourmash-minhash-objects---scaled-and-num). * Switched to using JSON instead of YAML for signatures. * Many performance optimizations! * Many more tests! * A much cleaner and more robust [Python API](../api-example.md). * Installation via bioconda is now recommended (and actively maintained :) * Support for building signatures from BAM files. ## Other features of note * Renamed Python library to `sourmash` from `sourmash_lib`. * Signatures now default to a license of CC0. * Implemented `sourmash watch` for classifying streams of data (see [blog post](http://ivory.idyll.org/blog/2017-sourmash-sra-microbial-wgs.html)). * Implemented `sourmash categorize` for classifying many signatures (see [blog post](http://ivory.idyll.org/blog/2017-sourmash-sra-microbial-wgs.html)). * CSV output enabled from most commands. sourmash-bio-sourmash-bc3c0f8/doc/release-notes/sourmash-3.0.md000066400000000000000000000044071474157046300244670ustar00rootroot00000000000000# sourmash v3.0 release notes We are pleased to announce release 3.0 of sourmash! This release includes internal changes like the new Rust backend (replacing C++) and lays the groundwork for future improvements. The full documentation for this version is available at https://sourmash.readthedocs.io/en/v3.0.0/. Documentation for the latest released version is at http://sourmash.readthedocs.io/en/stable/. **This release is compatible with sourmash 2.0: the sourmash Python API, command-line, signature and databases file formats are all the same.** We are releasing 3.0 to indicate the build system and internal implementation changed. Please post questions about migrating to sourmash 3.0 in the [sourmash issue tracker](https://github.com/sourmash-bio/sourmash/issues/new). ## Highlighted changes since 2.0 This is a list of substantial new features and functionality since sourmash 2.0. For more details check the [releases page on GitHub](https://github.com/sourmash-bio/sourmash/releases). Features: - Replacing C++ with Rust ([#424](https://github.com/sourmash-bio/sourmash/pull/424)) - Create an `Index` abstract base class ([#556](https://github.com/sourmash-bio/sourmash/pull/556)) - Dayhoff and HP encoding for proteins ([#689](https://github.com/sourmash-bio/sourmash/pull/689)) ([#758](https://github.com/sourmash-bio/sourmash/pull/758)) - Add `sourmash signature filter` to do abundance filtering. ([#748](https://github.com/sourmash-bio/sourmash/pull/748)) - Parallelized compare function with multiprocessing ([#709](https://github.com/sourmash-bio/sourmash/pull/709)) - add compute signatures for 10x bam file ([#713](https://github.com/sourmash-bio/sourmash/pull/713)) Improvements: - improve error handling in `sourmash lca index`. ([#798](https://github.com/sourmash-bio/sourmash/pull/798)) - Include more base deps: numpy, scipy and matplotlib ([#770](https://github.com/sourmash-bio/sourmash/pull/770)) - use `bam2fasta` package to simplify `sourmash compute` ([#768](https://github.com/sourmash-bio/sourmash/pull/768)) - add a `--abundances-from` flag to sourmash signature intersect, to preserve abundances ([#747](https://github.com/sourmash-bio/sourmash/pull/747)) - Compare outputs can be saved to an output dir ([#715](https://github.com/sourmash-bio/sourmash/pull/715)) sourmash-bio-sourmash-bc3c0f8/doc/release-notes/sourmash-4.0.md000066400000000000000000000072711474157046300244720ustar00rootroot00000000000000# sourmash v4.0 release notes ```{contents} :depth: 2 ``` We are pleased to announce release 4.0 of sourmash! This release contains many feature improvements and new functionality, as well as many breaking changes with sourmash 2.x and 3.x. Please see [our migration guide](../support.md#migrating-from-sourmash-v3x-to-sourmash-v4x) for guidance on updating to sourmash v4, and post questions about migrating to sourmash 4.0 in the [sourmash issue tracker](https://github.com/sourmash-bio/sourmash/issues/new). ## Major changes for 4.0 ### Numerical output and search results are unchanged There are no changes to numerical output or search results in this release; you should get the same results with v4 as you get with v3, except where command-line parameters need to be adjusted as noted below (see: protein ksize #1277, lca summarize changes #1175, sourmash gather on signatures without abundance #1328). Please [file an issue](https://github.com/sourmash-bio/sourmash/issues) if your results change! ### New or changed behavior * default SBT storage is now .sbt.zip (#1174, #1170) * add `sourmash sketch` command for creating signatures (#1159) * protein ksizes in MinHash are now divided by 3, except in `sourmash compute` (#1277) * refactor MinHash API and implementation: add, iadd, merge, hashes, and max_hash (#1282, #1154, #1139, #1301) * add HyperLogLog implementation (#1223) * `SourmashSignature.name` is now a property (not a method): use `str(sig)` instead of `name()` (#1179, #1232) * `lca summarize` no longer merges all signatures, and uses hash abundance by default (#1175) * `index `and `lca index` (#1186, #1222) now support `--from-file` and no longer require signature files on command line * `--traverse-directory` is now on by default for signature loading behavior (#1178) * `sourmash sketch` and `sourmash compute` no longer create empty signatures from empty files and stdin (#1347) * `sourmash sketch` and `sourmash compute` set `sig.filename` to empty string when filename is `-` (#1347) ### Feature removal * remove Python 2.7 support (& end Python 2 compatibility) (#1145, #1144) * remove `lca gather` (#1307) * remove 10x support from `sourmash compute` (#1229) * remove `dump` command (#1157) ### Feature/function deprecations * deprecate `sourmash compute` (#1159) * deprecate `load_signatures`, `sourmash.load_one_signature`, `create_sbt_index`, and `load_sbt_index` (#1279, #1304) * deprecate `import_csv` in favor of new `sourmash sig import --csv` (#1281) ## Refactoring, improvements, and minor bug fixes: * accept file list in `sourmash sig cat` (#1236) * add unique_intersect_bp and gather_result_rank to gather CSV output (#1219) * remove deprecated minhash functions (#1149) * fix Rust panic error in signature creation (#1172) * cache nodes in SBT during search (#1161) * fix two bugs in gather `--output-unassigned` (#1156) ## Documentation updates * major update and cleanup of docs given new functionality; add sourmash sketch documentation (#1283) * add information about versioning, migrations, etc to the docs (#1153, #1283) ## Infrastructure and CI changes: * update finch requirement from 0.3.0 to 0.4.1 (#1290) * update rand for test, and activate "js" feature for getrandom (#1275) * dev updates (configs and doc) (#1298) * move wheel building from Travis to GitHub Actions (#1295) * fix new clippy warnings from Rust 1.49 (#1267) * use tox for running tests locally (#696) * CI: small build fixes (#1252) * CI: Fix releases in GitHub Actions (#1250) * update build_wheel action paths * CI: moving python tests from travis to GH actions (#1249) * CI: move wheel building to GitHub actions (#1244) * remove last .rst file from docs (#1185) * update CI for latest branch name change (#1150) sourmash-bio-sourmash-bc3c0f8/doc/release.md000066400000000000000000000226341474157046300212040ustar00rootroot00000000000000# Releasing a new version of sourmash These are adapted from the khmer release docs, originally written by Michael Crusoe. ## Checklist Here's a checklist to copy/paste into an issue: ``` Release candidate testing: - [ ] Command line tests pass for a release candidate - [ ] All eight release candidate wheels are built Releasing to PyPI: - [ ] RC tag(s)s deleted on github - [ ] Release tag cut - [ ] Release notes written - [ ] All eight release wheels built - [ ] Release wheels uploaded to pypi - [ ] tar.gz distribution uploaded to pypi After release to PyPI and conda-forge/bioconda packages built: - [ ] [PyPI page](https://pypi.org/project/sourmash/) updated - [ ] Zenodo DOI successfully minted upon new github release - [see search results](https://zenodo.org/search?page=1&size=20&q=sourmash&sort=mostrecent) - [ ] `pip install sourmash` installs the correct version - [ ] [conda-forge sourmash-minimal-feedstock](https://github.com/conda-forge/sourmash-minimal-feedstock) has updated `sourmash-minimal` to the correct version - [ ] `mamba create -n smash-release -y sourmash` installs the correct version Optional but recommended: - [ ] PR submitted to update pyodide version - [ ] PR submitted to update spack version ``` ## Creating the build environment with conda You can most easily set up your build environment with conda. Your conda version will need to be at least `v4.9.0`. You can check your conda version with `conda --version` and update with `conda update conda`. Create the basic build environment: ``` mamba create -y -n sourmash-rc python=3.10 pip \ cxx-compiler make twine tox tox-conda rust ``` Then activate it with `conda activate sourmash-rc`. ## Writing release notes Draft release notes can be created with `git log --oneline v4.6.1..latest`, but should then be edited manually. We suggest putting PRs in the following categories: ``` Major new features: Minor new features: Bug fixes: Cleanup and documentation updates: Developer updates: Dependabot updates: ``` A convenient way to edit release notes is to put them in a [hackmd.io](https://hackmd.io) document and edit/display them there; then, create a "draft release notes for v..." issue and paste the markdown into the release PR. ## Testing a release 0\. First things first: check if Read the Docs is building properly for `latest`. The build for the `latest` branch on [Read the Docs] should be passing, and also the [rendered docs] should be up to date. [Read the Docs]: https://readthedocs.org/projects/sourmash/builds/ [rendered docs]: https://sourmash.readthedocs.io/en/latest/ 1\. The below should be done in a clean checkout: ``` cd $(mktemp -d) git clone https://github.com/sourmash-bio/sourmash cd sourmash ``` 2\. Set your new version number and release candidate. You might want to check [the releases page] for next version number, or you can run `make last-tag` and check the output. ``` new_version=4.X.X rc=rc1 ``` Next create a new branch to work on release candidates and the version bump: ``` git switch -c release/v${new_version} ``` and update the version number in `pyproject.toml` and `flake.nix`: ``` sed -i -e "s|version = .*$|version = \"${new_version}\"|g" pyproject.toml sed -i -e "s|version = .*$|version = \"${new_version}\";|g" flake.nix ``` Commit the changes and push the branch: ``` git add pyproject.toml flake.nix git commit -m "${new_version} release branch" git push -u origin release/v${new_version} ``` and then open a PR for the new branch by following the link printed by ``` echo "https://github.com/sourmash-bio/sourmash/pull/new/release/v${new_version}" ``` [the releases page]: https://github.com/sourmash-bio/sourmash/releases Once the checks for the PR work, let's trigger the automatic wheel building by creating a tag: ``` git tag -a v${new_version}${rc} -m "${new_version} release candidate ${rc}" git push origin refs/tags/v${new_version}${rc} ``` 3\. Test the release candidate. Bonus: repeat on macOS: ``` python -m pip install -U pip cd .. python -m venv testenv1 python -m venv testenv2 python -m venv testenv3 # First we test the tag cd testenv1 source bin/activate git clone --depth 1 --branch release/v${new_version} https://github.com/sourmash-bio/sourmash.git cd sourmash python -m pip install -r requirements.txt pytest && cargo test # Secondly we test via pip cd ../../testenv2 deactivate source bin/activate python -m pip install build python -m pip install -e git+https://github.com/sourmash-bio/sourmash.git@release/v${new_version}#egg=sourmash[test] cd src/sourmash pytest && cargo test make dist cp dist/sourmash*tar.gz ../../../testenv3/ # Is the distribution in testenv2 complete enough to build another # functional distribution? cd ../../../testenv3/ deactivate source bin/activate python -m pip install sourmash*tar.gz tar xzf sourmash-${new_version}.tar.gz cd sourmash-${new_version} python -m pip install -r requirements.txt pytest && cargo test ``` 4\. Do any final testing: * check that the binder demo notebook is up to date 5\. Wait for GitHub Actions to finish running on the release candidate tag. Wait for the [various cibuildwheel actions](https://github.com/sourmash-bio/sourmash/actions) to finish and upload; the [latest release](https://github.com/sourmash-bio/sourmash/releases) should have nine wheel files attached to it. 6\. Remove release candidate tags NOTE: If you delete the rc tag before the rc wheels are done building, they may get added to the wrong release. ``` cd ../../sourmash git tag -d v${new_version}${rc} git push --delete origin v${new_version}${rc} ``` ## How to make a final release When you've got a thoroughly tested release candidate, cut a release like so: 1\. Merge the pull request bumping the version. Once the PR is merged, change back to the `latest` branch and pull the new commit: ``` git checkout latest git pull --rebase ``` 2\. Create the final tag and push to GitHub: ``` git tag -a v${new_version} -m "${new_version} release tag" git push --tags origin ``` (make sure to be in the `latest` branch when creating the final tag!) 3\. Upload wheels from GitHub Releases to PyPI [GitHub Actions will automatically build wheels and upload them to GitHub Releases](https://github.com/sourmash-bio/sourmash/actions?query=workflow%3Acibuildwheel). This will take about 45 minutes, or more. After they're built, they must be copied over to PyPI manually. You can do this in two ways: you can manually download all the files from [the releases page], or, if you have the [`GitHub CLI`](https://cli.github.com/), you can use that to download the packages. Download the wheels with the `GitHub CLI`: ``` mkdir -p wheel && cd wheel gh release download v${new_version} ``` or download them manually. Once you have them downloaded, upload them to PyPI like so: ``` twine upload *.whl ``` twine will correctly determine the version from the filenames. 4\. Once the wheels are uploaded, publish the new release on PyPI (requires an authorized account). ``` cd .. make dist twine upload dist/sourmash-${new_version}.tar.gz ``` (This must be done *after* the wheels are available, because some of the conda package build steps require the source dist and are automatically triggered when a new version shows up on PyPI.) 5\. Edit the release on GitHub; there will already be one associated with the tag you pushed. Copy and paste in the release notes. Note that there will also be releases associated with the Rust `core` package, which is versioned differently than `sourmash`. These will be of the form `rXX.YY.ZZ`, e.g. `r0.9.0`. Please just ignore them :) ## Conda-forge The [sourmash-minimal feedstock](https://github.com/conda-forge/sourmash-minimal-feedstock/) in [conda-forge](https://conda-forge.org/) picks up new versions from PyPI (need the sdist to be published) and opens a new PR. Check if there are any dependency changes, with special attention to the minimum supported Rust version. After tests pass, merge it and wait for the `sourmash-minimal` package to show up in conda-forge: ``` conda search sourmash-minimal={new_version} ``` [An example conda-forge PR for `4.6.0`](https://github.com/conda-forge/sourmash-minimal-feedstock/pull/37). [An example bioconda PR for `4.6.0`](https://github.com/bioconda/bioconda-recipes/pull/38205). ## Bioconda The BiocondaBot has an `autobump` feature that should pick up new releases from PyPI, and open a PR in Bioconda. Review any changes (especially dependency versions, since these don't get picked up). Note that you need to wait for the `sourmash-minimal` package prepared in the previous section to be available for installation, and tests are going to fail in Bioconda before that. An example PR for [`3.4.0`](https://github.com/bioconda/bioconda-recipes/pull/23171). ## Pyodide You will need to open a PR on pyodide manually, e.g. [pyodide#4075 for 4.8.3](https://github.com/pyodide/pyodide/pull/4075). ## Spack You will need to open a PR on spack manually, e.g. [spack#40062 for 4.8.4](https://github.com/spack/spack/pull/40062). ## Announce it! If a bioinformatics software is released and no one tweets, is it really released? Examples: - [3.4.1](https://twitter.com/ctitusbrown/status/1286652952828993537) - [3.4.0](https://twitter.com/luizirber/status/1283157954598858752) - [3.0.0](https://twitter.com/luizirber/status/1213588144458649600) ## After release Update version to next minor version + `-dev`, e.g. [4.8.3-dev as in this PR](https://github.com/sourmash-bio/sourmash/pull/2584), per [sourmash#2517](https://github.com/sourmash-bio/sourmash/issues/2517). sourmash-bio-sourmash-bc3c0f8/doc/requirements.md000066400000000000000000000013711474157046300223020ustar00rootroot00000000000000# Computational requirements sourmash has no particular memory requirements; it will need to hold the largest single sequence you have in memory, but the individual signatures are quite small and we do no special buffer allocation. sourmash's intensive computation is almost entirely computing k-mers and hashes of k-mers. It will create a sketch of several megabases in a second or so on a rather slow 2016 Mac laptop. MinHash sketches and signatures are quite small on disk. sourmash should run with no modification on Linux and Mac OS X, under Python 3.10 and later. Please see [the development repository README][0] for information on source code, tests, and continuous integration. [0]:https://github.com/sourmash-bio/sourmash/blob/latest/README.md sourmash-bio-sourmash-bc3c0f8/doc/runtime.txt000066400000000000000000000000051474157046300214520ustar00rootroot000000000000003.10 sourmash-bio-sourmash-bc3c0f8/doc/sidebar.md000066400000000000000000000034541474157046300211740ustar00rootroot00000000000000## Tutorials and examples These tutorials are command line tutorials that should work on Mac OS X and Linux. They require about 5 GB of disk space and 5 GB of RAM. * [Installing sourmash with conda](tutorial-install.md) * [The first sourmash tutorial - making signatures, comparing, and searching](tutorial-basic.md) * [Using sourmash LCA to do taxonomic classification](tutorials-lca.md) * [Analyzing the genomic and taxonomic composition of an environmental genome using GTDB and sample-specific MAGs with sourmash](tutorial-lemonade.md) * [Some sourmash command line examples!](sourmash-examples.ipynb) ## How-To Guides * [Classifying genome and metagenome sketches](classifying-signatures.md) * [Working with private collections of genome sketches](sourmash-collections.ipynb) * [Using the `LCA_Database` API](using-LCA-database-API.ipynb) * [Building plots from `sourmash compare` output](plotting-compare.ipynb). * [A short guide to using sourmash output with R](other-languages.md). ## Frequently Asked Questions * [Frequently asked questions](faq.md) ## How sourmash works under the hood * [An introduction to k-mers for genome comparison and analysis](kmers-and-minhash.ipynb) * [Support, versioning, and migration between versions](support.md) ## Reference material * [UNIX command-line documentation](command-line.md) * [Genbank and GTDB databases and taxonomy files](databases.md) * [Python examples using the API](api-example.md) * [Publications about sourmash](publications.md) * [A guide to the internal design and structure of sourmash](sourmash-internals.md) * [Funding acknowledgements](funding.md) ## Developing and extending sourmash * [Getting started with sourmash development](developer.md) * [Releasing a new version of sourmash](release.md) ## [Full table of contents for all docs](toc.md) sourmash-bio-sourmash-bc3c0f8/doc/sourmash-collections.ipynb000066400000000000000000002577321474157046300244730ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# sourmash: working with private collections of signatures\n", "\n", "### Running this notebook.\n", "\n", "You can run this notebook interactively via mybinder; click on this button:\n", "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dib-lab/sourmash/latest?labpath=doc%2Fsourmash-collections.ipynb)\n", "\n", "A rendered version of this notebook is available at [sourmash.readthedocs.io](https://sourmash.readthedocs.io) under \"Tutorials and notebooks\".\n", "\n", "You can also get this notebook from the [doc/ subdirectory of the sourmash github repository](https://github.com/dib-lab/sourmash/tree/latest/doc). See [binder/environment.yaml](https://github.com/dib-lab/sourmash/blob/latest/binder/environment.yml) for installation dependencies.\n", "\n", "### What is this?\n", "\n", "This is a Jupyter Notebook using Python 3. If you are running this via [binder](https://mybinder.org), you can use Shift-ENTER to run cells, and double click on code cells to edit them.\n", "\n", "Contact: C. Titus Brown, ctbrown@ucdavis.edu. Please [file issues on GitHub](https://github.com/dib-lab/sourmash/issues/) if you have any questions or comments!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## download a bunch of genomes" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/t/dev/sourmash/doc/big_genomes\n", " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "100 459 100 459 0 0 1017 0 --:--:-- --:--:-- --:--:-- 1017\n", "100 61.1M 100 61.1M 0 0 2932k 0 0:00:21 0:00:21 --:--:-- 3468k\n" ] } ], "source": [ "!mkdir -p big_genomes\n", "!curl -L https://osf.io/8uxj9/?action=download | (cd big_genomes && tar xzf -)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## compute signatures for each file" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/t/dev/sourmash/doc/big_genomes\n", "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kcomputing signatures for files: 0.fa, 1.fa, 10.fa, 11.fa, 12.fa, 13.fa, 14.fa, 15.fa, 16.fa, 17.fa, 18.fa, 19.fa, 2.fa, 20.fa, 21.fa, 22.fa, 23.fa, 24.fa, 25.fa, 26.fa, 27.fa, 28.fa, 29.fa, 3.fa, 30.fa, 31.fa, 32.fa, 33.fa, 34.fa, 35.fa, 36.fa, 37.fa, 38.fa, 39.fa, 4.fa, 40.fa, 41.fa, 42.fa, 43.fa, 44.fa, 45.fa, 46.fa, 47.fa, 48.fa, 49.fa, 5.fa, 50.fa, 51.fa, 52.fa, 53.fa, 54.fa, 55.fa, 56.fa, 57.fa, 58.fa, 59.fa, 6.fa, 60.fa, 61.fa, 62.fa, 63.fa, 7.fa, 8.fa, 9.fa\n", "\u001b[KComputing a total of 1 signature(s).\n", "\u001b[K... reading sequences from 0.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 0.fa\n", "\u001b[Ksaved signature(s) to 0.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 1.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 1.fa\n", "\u001b[Ksaved signature(s) to 1.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 10.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 10.fa\n", "\u001b[Ksaved signature(s) to 10.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 11.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 11.fa\n", "\u001b[Ksaved signature(s) to 11.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 12.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 12.fa\n", "\u001b[Ksaved signature(s) to 12.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 13.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 13.fa\n", "\u001b[Ksaved signature(s) to 13.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 14.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 14.fa\n", "\u001b[Ksaved signature(s) to 14.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 15.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 15.fa\n", "\u001b[Ksaved signature(s) to 15.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 16.fa\n", "\u001b[Kcalculated 1 signatures for 4 sequences in 16.fa\n", "\u001b[Ksaved signature(s) to 16.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 17.fa\n", "\u001b[Kcalculated 1 signatures for 2 sequences in 17.fa\n", "\u001b[Ksaved signature(s) to 17.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 18.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 18.fa\n", "\u001b[Ksaved signature(s) to 18.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 19.fa\n", "\u001b[Kcalculated 1 signatures for 9 sequences in 19.fa\n", "\u001b[Ksaved signature(s) to 19.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 2.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 2.fa\n", "\u001b[Ksaved signature(s) to 2.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 20.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 20.fa\n", "\u001b[Ksaved signature(s) to 20.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 21.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 21.fa\n", "\u001b[Ksaved signature(s) to 21.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 22.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 22.fa\n", "\u001b[Ksaved signature(s) to 22.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 23.fa\n", "\u001b[Kcalculated 1 signatures for 5 sequences in 23.fa\n", "\u001b[Ksaved signature(s) to 23.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 24.fa\n", "\u001b[Kcalculated 1 signatures for 3 sequences in 24.fa\n", "\u001b[Ksaved signature(s) to 24.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 25.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 25.fa\n", "\u001b[Ksaved signature(s) to 25.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 26.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 26.fa\n", "\u001b[Ksaved signature(s) to 26.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 27.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 27.fa\n", "\u001b[Ksaved signature(s) to 27.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 28.fa\n", "\u001b[Kcalculated 1 signatures for 3 sequences in 28.fa\n", "\u001b[Ksaved signature(s) to 28.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 29.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 29.fa\n", "\u001b[Ksaved signature(s) to 29.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 3.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 3.fa\n", "\u001b[Ksaved signature(s) to 3.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 30.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 30.fa\n", "\u001b[Ksaved signature(s) to 30.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 31.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 31.fa\n", "\u001b[Ksaved signature(s) to 31.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 32.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 32.fa\n", "\u001b[Ksaved signature(s) to 32.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 33.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 33.fa\n", "\u001b[Ksaved signature(s) to 33.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 34.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 34.fa\n", "\u001b[Ksaved signature(s) to 34.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 35.fa\n", "\u001b[Kcalculated 1 signatures for 7 sequences in 35.fa\n", "\u001b[Ksaved signature(s) to 35.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 36.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 36.fa\n", "\u001b[Ksaved signature(s) to 36.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 37.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 37.fa\n", "\u001b[Ksaved signature(s) to 37.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 38.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 38.fa\n", "\u001b[Ksaved signature(s) to 38.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 39.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 39.fa\n", "\u001b[Ksaved signature(s) to 39.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 4.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 4.fa\n", "\u001b[Ksaved signature(s) to 4.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 40.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 40.fa\n", "\u001b[Ksaved signature(s) to 40.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 41.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 41.fa\n", "\u001b[Ksaved signature(s) to 41.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 42.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 42.fa\n", "\u001b[Ksaved signature(s) to 42.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 43.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 43.fa\n", "\u001b[Ksaved signature(s) to 43.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 44.fa\n", "\u001b[Kcalculated 1 signatures for 2 sequences in 44.fa\n", "\u001b[Ksaved signature(s) to 44.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 45.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 45.fa\n", "\u001b[Ksaved signature(s) to 45.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 46.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 46.fa\n", "\u001b[Ksaved signature(s) to 46.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 47.fa\n", "\u001b[Kcalculated 1 signatures for 2 sequences in 47.fa\n", "\u001b[Ksaved signature(s) to 47.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 48.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 48.fa\n", "\u001b[Ksaved signature(s) to 48.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 49.fa\n", "\u001b[Kcalculated 1 signatures for 228 sequences in 49.fa\n", "\u001b[Ksaved signature(s) to 49.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 5.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 5.fa\n", "\u001b[Ksaved signature(s) to 5.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 50.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 50.fa\n", "\u001b[Ksaved signature(s) to 50.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 51.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 51.fa\n", "\u001b[Ksaved signature(s) to 51.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 52.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 52.fa\n", "\u001b[Ksaved signature(s) to 52.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 53.fa\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[Kcalculated 1 signatures for 1 sequences in 53.fa\n", "\u001b[Ksaved signature(s) to 53.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 54.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 54.fa\n", "\u001b[Ksaved signature(s) to 54.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 55.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 55.fa\n", "\u001b[Ksaved signature(s) to 55.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 56.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 56.fa\n", "\u001b[Ksaved signature(s) to 56.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 57.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 57.fa\n", "\u001b[Ksaved signature(s) to 57.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 58.fa\n", "\u001b[Kcalculated 1 signatures for 30 sequences in 58.fa\n", "\u001b[Ksaved signature(s) to 58.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 59.fa\n", "\u001b[Kcalculated 1 signatures for 5 sequences in 59.fa\n", "\u001b[Ksaved signature(s) to 59.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 6.fa\n", "\u001b[Kcalculated 1 signatures for 76 sequences in 6.fa\n", "\u001b[Ksaved signature(s) to 6.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 60.fa\n", "\u001b[Kcalculated 1 signatures for 11 sequences in 60.fa\n", "\u001b[Ksaved signature(s) to 60.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 61.fa\n", "\u001b[Kcalculated 1 signatures for 47 sequences in 61.fa\n", "\u001b[Ksaved signature(s) to 61.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 62.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 62.fa\n", "\u001b[Ksaved signature(s) to 62.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 63.fa\n", "\u001b[Kcalculated 1 signatures for 4 sequences in 63.fa\n", "\u001b[Ksaved signature(s) to 63.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 7.fa\n", "\u001b[Kcalculated 1 signatures for 3 sequences in 7.fa\n", "\u001b[Ksaved signature(s) to 7.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 8.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in 8.fa\n", "\u001b[Ksaved signature(s) to 8.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from 9.fa\n", "\u001b[Kcalculated 1 signatures for 3 sequences in 9.fa\n", "\u001b[Ksaved signature(s) to 9.fa.sig. Note: signature license is CC0.\n" ] } ], "source": [ "!cd big_genomes/ && sourmash sketch dna -p k=31,scaled=1000 --name-from-first *.fa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare them all" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kloaded 1 sigs from 'big_genomes/0.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/1.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/10.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/11.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/12.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/13.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/14.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/15.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/16.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/17.fa.sig'10 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/18.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/19.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/2.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/20.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/21.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/22.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/23.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/24.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/25.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/26.fa.sig'20 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/27.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/28.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/29.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/3.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/30.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/31.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/32.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/33.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/34.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/35.fa.sig'30 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/36.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/37.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/38.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/39.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/4.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/40.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/41.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/42.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/43.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/44.fa.sig'40 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/45.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/46.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/47.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/48.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/49.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/5.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/50.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/51.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/52.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/53.fa.sig'50 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/54.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/55.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/56.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/57.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/58.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/59.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/6.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/60.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/61.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/62.fa.sig'60 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/63.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/7.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/8.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/9.fa.sig'g'\n", "\u001b[Kloaded 64 signatures total. \n", "\u001b[K\n", "min similarity in matrix: 0.000\n", "\u001b[Ksaving labels to: compare_all.mat.labels.txt\n", "\u001b[Ksaving comparison matrix to: compare_all.mat\n", "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kloading comparison matrix from compare_all.mat...\n", "\u001b[K...got 64 x 64 matrix.\n", "\u001b[Kloading labels from compare_all.mat.labels.txt\n", "\u001b[Ksaving histogram of matrix values => compare_all.mat.hist.png\n", "\u001b[Kwrote dendrogram to: compare_all.mat.dendro.png\n", "\u001b[Kwrote numpy distance matrix to: compare_all.mat.matrix.png\n" ] } ], "source": [ "!sourmash compare big_genomes/*.sig -o compare_all.mat\n", "!sourmash plot compare_all.mat" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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AxLzqirIr+vxvv/1WzZo1q/k4EAhcaSQAAAAAMYbCxAaSXAlWRwAalauuuko+n6/m44QErkEAABo79jAB7IfCxAbKK3/46/bOyXerRbLHwjRA7PP5fErLu/zP79Spkw4cOCC/3689e/bo5ptvjlg2AAAAALGBwsRmktwJ8rr5tgKhVF3GNWIeKzx+/Hjl5OQoMTFRK1asiEJKAAAQTwxFeIUJe5gAluOVNQDUw+bNmy947KGHHrIgCQAAAICGQGECAHUoLS1V79699emnn+rDDz9Uhw4d9POf/1xnzpxRQkKCli1bpvbt21sdEwAA20lqOy3k8+UlMxooST0YRkRXmAQNI2LnAnB5KExshrHCQN3CvUaSkpK0adMmTZo0SdLZMcPLli1Tenq6tm7dqnnz5mnhwoXRiAoAAADAIhQmNsNYYaBu4Y4VdjqdSk1NrfnY4/EoPT1dkuRyueR08qMUAIDGzvj+P5E8HwBr8Vu+DTBWGLBGZWWlXnjhBb322mtWRwEAAAAQYRQmNsBYYSA8VzpW+JwnnnhCo0ePVseOHa/8ZAAAIK4ZRmSn5ER24g6Ay0FhYjOMFQbqdjljhc1mzpypDh06aNiwYRFIBAAAACDW8MoaAOphwIAB2r9/vz777DMNGjRIM2bMUFZWlt5//3316NFDs2fPtjoiAACwECtMAPuhMAGAOpSWlurrr7+Wz+fTH/7wB3Xv3l2PPPKIxowZI5/Pp8TERKsjAgAQl+JqbHAdKEwA+6EwAYA6mMcKS9KkSZO0ePHimmk5AAAAAOyFwsRmyv3VKvNXWR0DiGnhXiPmscKVlZU6cuSInn76aZ08eVIzZ85Uz549Ix0TAADEFcf3t0ieD4CVKExsJnvudqsjADGvuqLsij7/q6++0scff6y1a9fK6XRq8ODB+uijjyKUDgAAAEAsoDCxgWAwaHUEoFFp3ry5OnfurNatW0s6uwKlqqpKTic/UgEAaKzYwwSwH367twHDMGru75x8t1okeyxMA8Q+n8+ntLzL//ykpCQ1b95c3333nZxOp/x+P2UJAAAAYDP8hm8zSe4Eed18W4FQqi7jGjl/rPCoUaM0a9YsDRw4UJWVlcrNzY1CSgAAEE9YYQLYD6+sAaAeNm/efMFjO3futCAJAAAAgIZAYQIAdSgtLVXv3r316aef6sMPP1T37t01f/58vfXWW0pOTtby5ct13XXXWR0TAICYlNR22iWfKy+Z0YBJosuQQ0YEJ9tE8lwALg+Fic0wVhioW7jXSFJSkjZt2qRJkyZJkk6cOKG//OUvKioq0p49e5Sbm6vFixdHIyoAAAAAi1CY2AxjhYG6hTtW2Ol0KjU1tebjo0ePKiMjQ4Zh6Ec/+pEeffTRSEcEAABxxjCMCO9hYtR9EICoojCxAcYKAw2rY8eO2rNnjyoqKrR9+3Z98803VkcCAAAAEGEUJjbAWGEgPFc6Vviaa67R6NGj1bdvX91yyy3q0qVLxLIBAID4dHaFSeRWhbDCBLAehYnNMFYYqNvljBU2GzlypEaOHKmCggJde+21EUgFAAAAIJbwyhoA6mHAgAHav3+/PvvsM40aNUrvvvuuTp06pXbt2mnhwoVWxwMAABYzDEeE9zBhSg5gNQoTAAhh3759Gj9+vBwOh+6880698cYb+slPfiKHwyG/368JEyYoKSnJ6pgAAFgm1NhgyV6jg0NhrDBgP1yFABBCenq6tmzZoh07dqhTp07asGGD3nvvPRUUFGj27NlasGCB1REBAAAARAErTGym3F+tMn+V1TGAmBbONdKqVaua+y6XS06nUy6XS9LZzWNvuummiOcDAADxh7fkAPZDYWIz2XO3Wx0BiHnVFWVhf05JSYm2bdumqVOn6tSpU/rZz36mkpISvfPOO1FICAAAAMBq1JY2EAwGrY4A2JrP59OIESO0bNkyuVwupaamqqioSG+99ZamTJlidTwAABADzq0wieQNgLVYYWID589o3zn5brVI9liYBoh9Pp9PaXn1OzYQCOjhhx/W888/r86dO6uqqkoOh0MOh0MpKSlq0qRJVLMCAAAAsAaFic0kuRPkdfNtBUKpCuMaWbNmjXbt2qXS0lLl5ubqV7/6lZYuXVpTmjBSGAAASEzJAeyIV9YAEMLw4cM1fPjwWo/98pe/tCgNAAAAgIZCYQIAIezbt0/jx4+Xw+FQy5Yt9cYbb+jLL7/UmDFj5PP5dM8992jatGlWxwQAIGqS2ob+37nykhkNlCTGGY6zt0ieD4ClKExshrHCQN3CuUbS09O1ZcsWeb1eTZkyRRs2bNDbb7+txYsXKz09PYopAQAAAFiJwsRmGCsM1C2cscKtWrWque9yuSRJR44c0dNPP62TJ09q5syZ6tmzZ8QzAgCA+BLpyTZMyQGsR2FiA4wVBqKvpKRE27Zt02OPPaaPP/5Ya9euldPp1ODBg/XRRx9ZHQ8AAABAhFGY2ABjhYHwhDNW+NzxI0aM0LJly3TNNdeoc+fOat26tSTJ6XSqqqpKTic/TgEAaMwMw6j1e3kkzgfAWvyGbzOMFQbqFs5Y4UAgoIcffljPP/+8OnfuLElq3ry5vvvuOzmdTvn9fsoSAAAAwIb4LR8AQlizZo127dql0tJS5ebm6sknn9SsWbM0cOBAVVZWKjc31+qIAAAgBhgyZCiCe5iIFSaA1ShMACCEzp07q1u3bgoGg2rZsqV+9rOf6d5771VCQoL8fr+mTJmi/v37Wx0TAIDLxtjgCInwpq+MFQasR2ECACGYxwpv3LhRBQUFkqSVK1fq8OHD1gYEAAAAEBUUJjZT7q9Wmb/K6hhATAvnGjGPFT5/v5K1a9dq9uzZEc0GAADilGGcvUXyfAAsRWFiM9lzt1sdAYh51RVlYX/OubHCU6dOlSSVlpbq2LFj6tatW6TjAQAAAIgBFCY2EAwGrY4A2Nr5Y4VdLpckaePGjRo8eLDFyQAAQMwwpAju+SpVR/BcAC4LhYkNnD+jfefku9Ui2WNhGiD2+Xw+peXV79iLjRWWzr4dZ9asWdEJCAAAAMByFCY2k+ROkNfNtxUIpSqMa+RiY4UHDBigkpISZWRkRDElAACIK+xhAtgOr6wBIIThw4dr+PDhFzz+t7/9zYI0AAAAABoKhQkAhPDJJ59o1KhRcjqdSk5O1urVq7V8+XKtWLFChmFo6tSpGjRokNUxAQAIKanttEs+V14yowGT2BgrTADboTCxGcYKA3UL5xrp0qWLioqKJEkzZszQ+vXrtWjRIn388ccqKyvTvffeS2ECAAAA2BCFic0wVhioWzhjhc9NxZGksrIy3XjjjerUqZPKy8tVWlqqFi1aRCMiAACINw5FdkpOJM8F4LJwGdoAY4WB6MrPz9ett96q7du3q2PHjurXr5+6deum22+/XePGjbM6HgAAAIAoYIWJDTBWGAhPOGOFJalPnz4qLi7W3Llz9fLLL+vtt9/WwYMH5ff71atXL/Xp06fWdQgAABohw1CQPUwAW6EwsRnGCgN1C2escEVFhTyesyVkSkqKkpKSlJiYKI/HI6fTqYqKCgWDQQoTAAAAwGZ4ZQ0AIeTn52vevHlyOBxKTU3V8uXLVVFRoR49eigQCGjs2LFyOHh3IwAAjZ7x/S2S5wNgKQoTAAhh4MCBGjhwoFatWqVx48bJ6/XqoYceUmFhoXw+n7788kurIwIAEHJssMTo4AbhMM7eInk+AJaiMAGAOlRXV2vdunVq06aNJGnSpElavHix0tPTLU4GAAAAIFooTGym3F+tMn+V1TGAmBbuNfLmm29q6NChmj9/viorK3XkyBE9/fTTOnnypGbOnKmePXtGKSkAAIgbhhHZjVrZHw2wHIWJzWTP3W51BCDmVVeU1fvYQCCgNWvWaMOGDZo/f76++uorffzxx1q7dq2cTqcGDx6sjz76KIppAQAAAFiBwsQGgsGg1REA21q5cqUefPDBmo1dmzdvrs6dO6t169aSJKfTqaqqKjmd/DgFAKBRY9NXwHb4Dd8Gzh9nunPy3WqR7LEwDRD7fD6f0vLqd+yBAwdUXFyslStX6uDBg5oyZYqaN2+u7777Tk6nU36/n7IEAAAAsCF+y7eZJHeCvG6+rUAoVWFcI3PmzKm5n5mZqQULFmjXrl0aOHCgKisrlZubG42IAAAg3jAlB7AdXlkDQD3t3btXktSzZ0/t3LnT4jQAAAAAoonCBABCOHLkiG6//XZlZGRIktauXashQ4bI4XDI7/dryZIl6t69u8UpAQB2l9R2Wsjny0tmNFASXBJTcgDboTCxGcYKA3UL9xrJycnRunXraj5+77335HK5tGPHDi1YsECvv/56pCMCAAAAsBiFic0wVhioWzhjhSWpqKhI2dnZys7O1qxZs+RyuSSd3Tz2pptuikZEAAAQb5iSA9iOw+oAuHKMFQaiJy0tTYcOHVJhYaFOnjyp9evX69SpU8rKytKYMWN01113WR0RAAAAQBSwwsQGGCsMhCecscIej0cez9lrasiQIdq9e7fuv/9+FRUV6aOPPtKUKVP07rvvRi8sAACID0zJAWyHwsRmGCsM1C2cscKlpaVq2rSpJKmwsFBdu3ZVdXW1HA6HUlJS1KRJk2jFBAAAAGAhXlkDQAgffPCBpk6dKq/Xqw4dOmj06NG655575HA45HA4tHDhQqsjAgCAWMAeJoDtUJgAQAhdu3ZVSUmJMjIyVFJSIq/Xq8GDB+utt95ScnKymjVrZnVEAIANMDY4/gVlKBjBUcBBGhPAcmz6CgB1yMnJUUFBgQoKChQIBPSXv/xFRUVFmjlzpnJzc62OBwAAACAKWGFiM+X+apX5q6yOAcS0cK+R88cK33fffcrIyJBhGPrRj36kRx99NEopAQBAXImBTV8XLVqkefPm6fjx48rIyFBeXp6ys7Mvefwbb7yhuXPn6uDBg0pJSVG/fv30u9/9Ti1atLiS5IBtUJjYTPbc7VZHAGJedUVZvY89N1bY6/Xq8ccf19GjR7Vnzx5VVFRo+/bt+uabb6KYFAAAoH5Wr16t8ePHa9GiRcrKytKrr76q/v3768CBA2rbtu0Fx3/wwQd65JFHtGDBAg0aNEhffPGFRo8erccee0zr16+34F8AxB7ekmMDSa4EqyMAtuXxeNSkSRMZhqEhQ4bok08+0ejRo9W3b1/99a9/VZcuXayOCAAAYoERhVsYXnrpJT366KN67LHH1LVrV+Xl5alNmzZavHjxRY//8MMP1b59e40bN04dOnTQnXfeqVGjRmnv3r1h/sMB+2KFiQ0Y520utXfqTxgrDNTB5/MpLa9+x15srPAjjzyikSNHqqCgQNdee230ggIAgEbP5/PV+tjj8cjj8dR6zO/3a9++fXrmmWdqPd63b1/t2rXrouft2bOnnnvuOW3evFn9+/fXyZMntW7dOv30pz+N7D8AiGO8srYZr9tJYQLUoSqMa8Q8Vjg3N1cPPfSQTp06pXbt2jFWGAAAnGUYZ2+RPJ+kNm3a1Hp42rRpmj59eq3HvvrqKwUCAbVs2bLW4y1bttSJEycuevqePXvqjTfe0LBhw3TmzBlVVVVp8ODB+j//5/9E7t8AxDleWQNACP3791f//v1rPfanP/3JojQAAKCxOXbsmJo1a1bzsXl1yfkMU2ETDAYveOycAwcOaNy4cXr++ed177336vjx45o0aZJGjx6t119/PTLhgThHYQIAIRw5ckS33367MjIyJElr165VRUWFxowZI5/Pp3vuuUfTpk2zOCUAIB4ktb30/16Ul8xowCSIiihNyWnWrFmtwuRirrnmGiUkJFywmuTkyZMXrDo5Z/bs2crKytKkSZMkSTfffLOaNGmi7OxszZw5U2lpaRH4RwDxjcLEZhgpDNQt3OskJydH69atq/l4+PDhWrx4sdLT0yMdDQAAIGxut1u33Xab8vPz9fOf/7zm8fz8fN13330X/ZyysjI5nbVfDiYknB0mEQwGoxcWiCMUJjZw/g+0zJnvWZgEiA/hjBWWpKKiImVnZys7O1szZszQkSNH9PTTT+vkyZOaOXOmevbsGaWkAAAgblzGZJs6zxeGCRMmaMSIEcrMzFSPHj20ZMkSlZSUaPTo0ZKkZ599Vl988YVWrFghSRo0aJAef/xxLV68uOYtOePHj9cdd9yh6667LoL/ECB+UZjYQHllwOoIgG2lpaXp0KFD8nq9evzxx7VmzRp9/PHHWrt2rZxOpwYPHqyPPvrI6pgAAKCRGzZsmL7++mu98MILOn78uLp3767NmzerXbt2kqTjx4+rpKSk5viRI0eqtLRUL7/8sp5++mk1b95cvXr10pw5c6z6JwAxh8LEZnZOvkctkt1WxwBiWjhjhc8f3TdkyBC9//776ty5s1q3bi1JcjqdqqqqumBJKwAAaGSiNCUnHGPGjNGYMWMu+tzy5csveOxf/uVf9C//8i9hfx2gseA3fJtJcjsYKwzUIZyxwqWlpWratKkkqbCwUDfddJP27t2r7777Tk6nU36/n7IEAAAAsCF+yweAED744ANNnTpVXq9XHTp0UG5urjp16qSBAweqsrJSubm5VkcEAACxIAZWmACILAoTGyj3By56H8CVu/baa+X1euVwOHTmzBkFg0EdO3ZMVVVV8nq96t69u9URAQAxItTYYInRwbbn+P4WyfMBsBSXIQCEkJ6eri1btmjHjh3q1KmTNmzYoJdeekk7duxQbm4uK0wAAAAAm2KFic2U+6tV5q+yOgYQ08K5Rlq1alVz3+Vy6T/+4z+UkZEht9utrKwsTZw4MRoRAQBAvDEU4bfkRO5UAC4PhYnN9M0rtDoCEPOqK8rC/pySkhJt27ZNL774ok6dOlXzeCDA2+AAAAAAO6IwsYFEF++sAqLJ5/NpxIgRWrZsmQKBgHw+X81zCQkJFiYDAAAxw1BkV4WwwgSwHIWJDRjnLf3bOflutUj2WJgGiH0+n09pefU7NhAI6OGHH9bzzz+vzp07q7KyUgcOHJDf79eePXt08803RzUrAAAAAGtQmNiAeTKO1823FQilKoxrZM2aNdq1a5dKS0uVm5urJ598UuPHj1dOTo4SExO1YsWKKCYFAADxIugwFHREbllIJM8F4PLwyhoAQhg+fLiGDx9+weMPPfSQBWkAAAAANBQKExs4f4WJebUJgCtXUFCg3NxcVVVVacKECdqwYYM2bdqkadOm6Te/+Y3V8QAADSSp7bSQz5eXzGigJIhJhhHhKTmsMAGsRmFiM+WVjBUG6hLONXLmzBnNnz9ff/3rX+V2uyVJd9xxh3JycnT69OloRQQAAABgMQoTm7lvYZHVEYCYF85Y4V27dikpKUmDBg2S1+vV4sWLlZaWFsV0AAAgLjElB7Ad5tHaAGOFgej58ssv9fnnn+vPf/6znnjiCU2fPt3qSAAAAAAaACtMbOBMZXXN/XfG9tQNLZtamAaIfeGMFW7evLnuvPNOud1u9erVS7Nnz45qNgAAEKcMQ4rkZBv2MAEsR2FiM0muBMYKA3UIZ6zwHXfcoby8PElScXGxrr/++iilAgAAABBLeGUNACG0aNFCgwcP1l133SWHw6GlS5fq2Wef1caNGxUIBHT48GEtWLDA6pgAAMBqTMkBbIfCBABCqK6u1kcffaRgMKjq6moFg0HNnj2bt+YAgA0xNhhXhE1fAdtht1AACGH//v2qqKjQzp079T//5//Uyy+/bHUkAAAAAA2AFSY2U15ZrTJ/ldUxgJgWzjXSunVrSVIwGNS3336r1NTUaMUCAADxzBHhTV8jeS4Al4XCxGbuW1hkdQQg5lVXlNX72GuuuUYOh0Ndu3ZVRUWFioq4xgAAAIDGgLfk2EAwGLQ6AmBbW7ZsUVJSkv7v//2/euuttzRhwgSrIwEAgFh0boVJJG8ALMUKExswzttB+52xPXVDy6YWpgFin8/nU1pe/Y+/6qqrJEnNmzfXt99+G5VMAAAAAGILhYnNJLkS5HXzbQVCqQrjGunbt6/++Mc/KicnRxUVFXrppZeimAwAAMSroHH2FsnzAbAWr6wBIISEhAS9+eabVscAAAAA0MAoTAAghE8++USjRo2S0+lUcnKyVq9erVdffVVvvfWWkpOTtXz5cl133XVWxwQA1FNS22mXfK68ZEYDJoHtMCUHsB0KE5thrDBQt3CukS5dutRMxpkxY4bWr1+vv/zlLyoqKtKePXuUm5urxYsXRysqAAAAAItQmNgMY4WBuoUzVtjlctXcLysrU+fOnZWRkSHDMPSjH/1Ijz76aDQiAgCAeGMYZ2+RPB8ASzFW2AYYKwxEV35+vm699VZt375dHTp00J49e1RRUaFt27bpm2++sToeAAAAgChghYkNMFYYCE+4Y4X79Omj4uJizZ07V0uXLtXo0aPVt29f3XLLLerSpUvUcgIAgDjCHiaA7VCY2AxjhYG6hTNWuKKiQh6PR5KUkpIiv9+vkSNHauTIkSooKNC1114brZgAAAAALMQrawAIIT8/X/PmzZPD4VBqaqqWL1+uhx56SKdOnVK7du20cOFCqyMCAIBY4FBkNzxg8wTAchQmNlBeGbjofQBXbuDAgUpOTlZubq6+/PJL5efn609/+pPVsQAAlxBqbLDE6GBEEZu+ArZDYQIAIZw5c0bz58/XX//6V7ndbqvjAAAAAGggFCY2c6ayWmX+KqtjADEtnGtk165dSkpK0qBBg+T1erV48WK1atUqiukAAEBcYtNXwHYoTGxm2JIPrY4AxLzqirJ6H/vll1/q888/V1FRkd577z1Nnz5dr7zyShTTAQAAAIgFbCVkA1d5XVZHAGyrefPmuvPOO+V2u9WrVy8dOHDA6kgAACAGBWUoaETwJlaYAFZjhYkNGOdtCLV1fLZaX+21MA0Q+3w+n9Ly6nfsHXfcoby8swcXFxfr+uuvj1ouAAAAALGDwsRmktwJ8rr5tgKhVIVxjbRo0UKDBw/WXXfdJYfDoaVLl0YxGQAAiFuMFQZsh1fWAFCHsWPHauzYsVbHAAAAANCAKEwAoA6lpaXq3bu3Pv30U3344Yfq3Lmz+vbtK0kqLy+X3+9XcXGxxSkBoHFIajst5PPlJTMaKAlgwpQcwHYoTGym3M9YYaAu4V4jSUlJ2rRpkyZNmiRJcrvdKigokCStXLlShw8fjnREAAAAABajMLGZvnmFVkcAYl44Y4Ulyel0KjU19aLPrV27VrNnz45ELAAAEM8M4+wtkucDYCm2ErKBRBffRsAKpaWlOnbsmLp162Z1FAAAAAARxgoTGzh/rPDOyXerRbLHwjRA7AtnrHAoGzdu1ODBg6/8RAAAIP6xhwlgOxQmNsNYYaBu4YwVDmXt2rWaNWtWRM4FAAAAILbwyhoA6mHAgAHav3+/PvvsM40aNUpDhgxRSUmJMjIyrI4GAABigfH9LZLnA2ApChMAqENpaam+/vpr+Xw+/eEPf1D37t0lSRs2bJDH49G+fftqHgMAXLlQo4MZG4xYFXQYCkbwbTSRPBeAy8NuoQBQh3NjhYcOHVrr8Tlz5igrK8uiVAAAAACiiRUmNlPuD6jMX2V1DCCmhXuNXGys8Oeffy7DMNS2bdtIRgMAAPGKTV8B26EwsYFgMFhzP3tugXVBgDhRXVF2xeeYM2eOnnnmGU2fPv3KAwEAAACIORQmNnCmstrqCECjcvjwYUlS+/btrQ0CAABih2GcvUXyfAAsRWFiM1vH36XWVydZHQOIaT6fT2l5l//5//7v/65PP/1U/fr109///ncdOnRI27dvl8vlilhGAAAAANaiMLGZJLdDXjffViCUqsu4RsxjhXfu3ClJGjlypCZOnEhZAgBAY+dQZEdqMJ4DsByvrAGgHjZv3nzRx5cvX96wQQAAAAA0CAoTG0hyJ1z0PoDImDhxov7t3/5Nbdu21bJly+R2u62OBABxLanttJDPl5fMaKAkQAQZivAeJpE7FYDLQ2FiM+X+asYKA3UI5xopLi7WiRMntHPnTs2aNUvr1q3TL37xiyimAwAAABALKExsJnvudqsjADEvnLHCu3fvVt++fSVJ/fr107JlyyhMAADAhQxDcjAlB7ATthKygSQXb8MBouXbb79Vs2bNJEkpKSn67//+b4sTAQAAAGgIrDCxAeO89nnv1J8wJQeoQzhjha+66ir5fD5JZ8uTq6++OnrBAABA/HJEeIVJJM8F4LLwytpmvG4nhQlQh3DGCv/4xz/W/Pnz9cgjj2jLli3KysqKYjIAAAAAsYK35ABACLfeeqtatWql7OxsHThwQEOGDLE6EgAAiEFBw4j4DYC1WIoAACF88skn2r17t5xOp7799lv5/X7GCgNoFILB6sv+XG+70GOBGRsMW3Iosn+O5k/bgOW4DAEghC5duqioqEg7duzQHXfcofXr11sdCQAAAEADYIWJzZT5q6yOAMS8cK4Tl8v1w+eVlenGG2+MRiQAABDvDCOyo4B5Sw5gOQoTm8mc+Z7VEYCYV11RFtbx+fn5mjx5slwul377299GKRUAAACAWMJbcgCgDn369FFxcbGGDh2qJUuWWB0HAADEonNjhSN5A2ApVpjY0N6pveV1J1gdA4hZPp9PaXn1O7aiokIej0eSlJKSIr/fH71gAAAAAGIGhYkNed0J8rr51gKXUhXG9ZGfn6958+bJ4XAoNTVVy5cvj14wAAAQvyK9KoQVJoDleFUNACEMHDhQAwcOtDoGAAAAgAZGYWJDZf4qVpgAEbJv3z6NHz9eDodDLVu21BtvvFFrcg4A2JVhXHqru6S200J+btnR0M8DtmR8f4vk+QBYilfVNlTur2a8MBBCONdHenq6tmzZIq/XqylTpmjDhg164IEHopgOAAAAQCygMLGh7LnbrY4AxLRwxgq3atWq5r7L5ZLTyY9NAABwoaDDUDCC+45E8lwALg9jhQGgHkpKSrRt2zb2MwEAAAAaCf5UagPBYLDWx3un/oQ9TIAQwhkrfO74ESNGaNmyZexfAgAALs4wzt4ieT4AluJVtQ2UVwZqfex1OylMgBDCGSscCAT08MMP6/nnn1fnzp2jmAoAAABALOEtOQAQwpo1a7Rr1y7l5ubq7rvv1urVq62OBAAAYpHDiPwNgKVYhgAAIdx000268cYbFQwGlZycrJ/+9KdavXq18vLylJSUpH/9139VmzZtrI4JABEXanRwecmMkJ8bDFZHOg4Q+xgrDNgOK0wAIIQuXbqoqKhIO3bs0B133KH169frpZde0o4dO5Sbm6vc3FyrIwIAAACIAlaY2EC5v/YeJmWmjwHUVuavqvex52/yWlZWprZt2yojI0Nut1tZWVmaOHFiNCICAIA44zAkRyT/HM0KE8ByFCY2lDlzm9URgJhWXVEW1vH5+fmaPHmyXC6X7rvvPjVr1qzmuUCAghIAAACwI96SAwB16NOnj4qLizV06FDt2LFDPp+v5rmEhAQLkwEAgFhxbqpwJG8ArMUKExs4/y0574ztqRtaNrUwDRD7fD6f0vLqd2xFRYU8Ho8kKSUlRX6/XwcOHJDf79eePXt08803Ry8oAAAAAMtQmNhMkitBXjffViCUqjCukfz8fM2bN08Oh0Opqalavny5UlNTlZOTo8TERK1YsSKKSQEAQLyI9KoQVpgA1uMtOQAQwsCBA7Vjxw5t375da9askdfr1UMPPaTdu3dr+/btjBQGAAAxY9GiRerQoYMSExN12223aefOnSGPr6io0HPPPad27drJ4/GoY8eOWrp0aQOlBWIfSxEAoB5WrVqlcePG6dSpU1ZHAYCISGo7LeTz5SUzLvvchsHf5ND4GIYhI4LLQsI91+rVqzV+/HgtWrRIWVlZevXVV9W/f38dOHBAbdu2vejnPPjgg/ryyy/1+uuvq1OnTjp58qSqquo/TRCwOwoTmymvrA5rZCrQGIV7jVRXV2vdunWsJgEAADHrpZde0qOPPqrHHntMkpSXl6ctW7Zo8eLFmj179gXHv/vuu9qxY4f+8z//U1dffbUkqX379g0ZGYh5FCY2c9/CIqsjADEv3LHCb775poYOHar58+dHKREAAIh3Vu5h4vf7tW/fPj3zzDO1Hu/bt6927dp10c/ZuHGjMjMzNXfuXP3xj39UkyZNNHjwYOXm5iopKelKogO2QWFiA4kulr0C0RIIBLRmzRpt2LCBwgQAADQ4n89X62OPx1Mzwe+cr776SoFAQC1btqz1eMuWLXXixImLnvc///M/9cEHHygxMVHr16/XV199pTFjxui///u/2ccE+B6FiQ2cqayuuc9YYaBu4YwVXrlypR588EE5HBSTAADg0qK1wsT8luBp06Zp+vTpl/ic2gGCweAl90Kprq6WYRh64403lJKSIuns23qGDh2qhQsXssoEEIWJ7TBWGKhbOGOFDxw4oOLiYq1cuVIHDx7UU089pQULFkQxHQAAwA+OHTumZs2a1XxsXl0iSddcc40SEhIuWE1y8uTJC1adnJOWlqb09PSaskSSunbtqmAwqP/3//6fbrjhhgj9C4D4xStrAAhhzpw5NfczMzMpSwAAwMU5pIgOiPr+XM2aNatVmFyM2+3Wbbfdpvz8fP385z+veTw/P1/33XffRT8nKytLa9eu1enTp5WcnCxJ+o//+A85HA61bt06Mv8GIM5RmABACEeOHNHtt9+ujIwMJScn69SpU0pNTbU6FgDUKZpjgwFcyMpNXyVpwoQJGjFihDIzM9WjRw8tWbJEJSUlGj16tCTp2Wef1RdffKEVK1ZIkn7xi18oNzdXv/rVrzRjxgx99dVXmjRpkn7961/zdhzgexQmAFCHnJwcrVu3zuoYAAAAlzRs2DB9/fXXeuGFF3T8+HF1795dmzdvVrt27SRJx48fV0lJSc3xycnJys/P17/8y78oMzNTLVq00IMPPqiZM2da9U8AYg6Fic2UV1arzF9ldQwgpoV7jRQVFSk7O1vZ2dmaNWvWJTdPAwAAjZfDOHuLlOBlnGvMmDEaM2bMRZ9bvnz5BY/deOONys/PD/8LAY0EhYnN3LewyOoIQMyrriir97FpaWk6dOiQvF6vHn/8ca1fv173339/FNMBAAAAiAXMybSBYDBodQTAtjwej5o0aSLDMDRkyBDt37/f6kgAACAGndvDJJI3ANZihYkNnP/2gHfG9tQNLZtamAaIfT6fT2l59Tu2tLRUTZuevaYKCwvVtWvX6AUDAAAAEDMoTGwmyZUgr5tvKxBKVRjXyAcffKCpU6fK6/WqQ4cOys3NjWIyAAAQr6yekgMg8nhlDQAh9O/fX/3797c6BgAAAIAGRmECACEcOXJEt99+uzIyMiRJa9euVWpqqkpKSnTDDTdo37596t69u8UpATRGSW2nhXy+vGRGAyUBIJ19m3wkJ+kxlQ+wHoWJzTBWGKhbuNdITk6O1q1bV+uxOXPmKCsrK5KxAAAAAMQQChObYawwULdwxgpLUlFRkbKzs5Wdna1Zs2bpyJEjMgxDbdu2jVJCAAAQbwzH2VskzwfAWlyGNsBYYSB60tLSdOjQIRUWFurkyZNav3695syZo4kTJ1odDQAAAEAUscLEBhgrDIQnnLHCHo9HHo9HkjRkyBC9+eabatKkidq3bx+1fAAAIP4wJQewHwoTm2GsMFC3cMYKl5aWqmnTsyVkYWGh+vTpoz/84Q/q16+f/v73v+vQoUPavn27XC5XtOICAAAAsABvyQGAED744APddtttys7O1hdffKFf/OIX2rlzp95991316dNHr7zyCmUJAACoWWESyRsAa7EUAQBC6N+/v+6880717t1bb7/9tiZPnqzu3bvrrrvuksPh0BNPPKElS5YwWhhAVIQaHczYYCC2GIrwW3IidyoAl4kVJgBQh6SkJG3atElDhw6teey9995TQUGBZs+erQULFliYDgAAAEA0sMLEZsorq1Xmr7I6BhDTwr1GnE6nUlNTaz127m04Pp9PN910U8SyAQCA+OQwzt4iJcgSE8ByFCY2c9/CIqsjADGvuqLsis9x6tQp/exnP1NJSYneeeedCKQCAAAAEEt4S44NBINBqyMAjU5qaqqKior01ltvacqUKVbHAQAAFmPTV8B+WGFiA8Z5P03fGdtTN7RsamEaIPb5fD6l5V3+51dVVcnhcMjhcCglJUVNmjSJWDYAAAAAsYHCxGaSXAnyuvm2AqFUXcY1MmDAAO3fv1+fffaZBg4cqK1bt9aUJgsXLoxCSgAAEE8ivSqEFSaA9XhlDQD1sHnz5lofP/fccxYlAQAAANAQKEwAIIR9+/Zp/Pjxcjgcatmypd544w29+uqrWrFihQzD0NSpUzVo0CCrYwKIU0ltp4V8vrxkRgMlAXClDIchI4JjciJ5LgCXh8LEZhgrDNQtnGskPT1dW7Zskdfr1ZQpU7RhwwYtWrRIH3/8scrKynTvvfdSmAAAAAA2RGFiM4wVBuoWzljhVq1a1dx3uVxyOp3q1KmTysvLVVpaqhYtWkQjIgAAiDPsYQLYD4WJDTBWGIi+kpISbdu2TVOnTtXx48fVrVs3BQIBLV++3OpoAAAAAKKAwsQGGCsMhCfcscI+n08jRozQsmXLVF5eriVLlujgwYPy+/3q1auX+vTpU+s6BAAAjQ8rTAD7oTCxgfLKQK2PGSsMhBbOWOFAIKCHH35Yzz//vDp37qzTp08rMTFRHo9HTqdTFRUVCgaDFCYAAACAzfDKGgBCWLNmjXbt2qXS0lLl5ubqySef1NChQ9WjRw8FAgGNHTtWDofD6pgAAMBqEV5hIv4WA1iOwgQAQhg+fLgGDhyo3r1769NPP1VGRoaGDRumTz/9VJs2bVJVFVOpAFwaY4OBxsNhnL1F8nwArEVhAgB1SEpK0qZNmzRp0qSax1588UXl5OTo9OnTFiYDAAAAEC0UJjZzprJaZX7+4g2EEu414nQ6lZqaWuuxtLS0SEYCAABxjk1fAfuhMLGZYUs+tDoCEPOqK8qsjgAAAAAgxlGY2MBVXpfVEQAAAIBGzXCcvUXyfACsRWFiA+ePM906Plutr/ZamAaIfT6fT2l5VqcAAAAAEMsoTGwmyZ0gr5tvKxBK1WVcIwMGDND+/fv12WefadSoUfrss8+0ceNGBQIBHT58WAsWLIhCUgAAEC/YwwSwH15ZA0A9bN68+YLHZs+ebUESAAAAAA2BwgQAQti3b5/Gjx8vh8Ohli1b6o033tDvf/97vfXWW0pOTtby5ct13XXXWR0TgEWS2k4L+Xx5yYwGSgLAaoZh1HqrfCTOB8BaFCY2U+5nrDBQl3CukfT0dG3ZskVer1dTpkzRn/70J/3lL39RUVGR9uzZo9zcXC1evDiKaQEAAABYgcLEZvrmFVodAYh54YwVbtWqVc19l8ul//qv/1JGRoYMw9CPfvQjPfroo9GICAAA4gx7mAD2w7AqG0h08W0Eoq2kpETbtm3Tr3/9a+3Zs0cVFRXatm2bvvnmG6ujAQAAAIgCVpjYwPnvb9w5+W61SPZYmAaIfeGOFfb5fBoxYoSWLVum1NRUjR49Wn379tUtt9yiLl26RC0nAACIH6wwAeyHwsRmGCsM1C2cscKBQEAPP/ywnn/+eXXu3FmSNHLkSI0cOVIFBQW69tproxUTAAAAgIV4ZQ0AIaxZs0a7du1SaWmpcnNz9eSTT2r9+vU6deqU2rVrp4ULF1odEQAAxABWmAD2Q2ECACH06NFDDscP+wT9+Mc/1uuvv67Kykp9/vnn+vLLL9W+fXvrAgKIulCjgxkbDOAch3H2FsnzAbAWhQkA1CEnJ0fr1q2TJFVUVGjZsmVKT0/X1q1bNW/ePFaZAAAAADZEYWIz5f6AyvxVVscAYlq410hRUZGys7OVnZ2tWbNmKT09XdLZMcNOJz9GAQDA2fGjEV1hErlTAbhM/KZvA8FgsOZ+9twC64IAcaK6oqzex6alpenQoUPyer16/PHHtX79et1///2qrKzUCy+8oNdeey2KSQEAAABYheLSBs5UVlsdAbAtj8ejJk2ayDAMDRkyRPv375ckPfHEExo9erQ6duxobUAAABATHEYw4jcA1mKFic1sHX+XWl+dZHUMIKb5fD6l5dXv2NLSUjVt2lSSVFhYqK5du2rmzJnq0KGDhg0bFr2QAAAAACxFYWIzSW6HvG6+rUAoVWFcIx988IGmTp0qr9erDh066Mknn1THjh2VlZWl999/Xz169NDs2bOjmBYAAMQDpuQA9sMrawAIoX///urfv3+txyorKy1KAwAAAKChUJjYQLk/cNH7AK7ckSNHdPvttysjI0OStHbtWk2ePFmbNm3StGnT9Jvf/MbihACuVFLbaSGfLy+Z0UBJAMQzQ5HdIJIFJoD1KExspryymrHCQB3CvUZycnK0bt26mo9ffPFF5eTk6PTp05GOBgAAACBGUJjYzH0Li6yOAMS8cMYKS1JRUZGys7OVnZ2tWbNmKS0tLUrJAABAvIr0ZBum5ADWY6ywDSS6+DYC0ZKWlqZDhw6psLBQJ0+e1Pr1662OBAAAAKABsMLEBgzjh3c4bh2frdZXey1MA8S+cMYKezweeTweSdKQIUO0e/du3X///dELBwAA4hJTcgD7oTCxmSR3AmOFgTqEM1a4tLRUTZs2lSQVFhaqa9eu0YoFAAAAIIbwyhoAQvjggw80depUeb1edejQQbm5uXr22We1ceNGBQIBHT58WAsWLLA6JgAAsJhDkd3vgDfdA9ajMLEBxgoD0dO1a1eVlJQoIyNDJSUl+uabb5Senq4mTZrIMAz16tXL6ogA6sDYYAANgbfkAPZDYQIAdTCPFV60aJE+/vhjlZWV6d5779WgQYMsTAcAAAAgGihMbKa8MqAyf5XVMYCYFu41Yh4r3KlTJ5WXl6u0tFQtWrSIUkoAABBPDCMoI4KjgCN5LgCXh8LEBoLBH36Y3rdwl4VJgPhQXVFW72PPjRX2er16/PHHtX79evXr10/dunVTIBDQ8uXLoxcUAAAAgGUoTGzgTFW11REA2zKPFc7Pz9f777+vgwcPyu/3q1evXurTp0+t8d4AAKDxYQ8TwH4oTGxm9RM/1k2tU6yOAcQ0n8+ntLz6HWseK3zLLbdo165d8ng8cjqdqqioUDAYpDABAAAAbIbCxGYSXQ553XxbgVCqwrhGLjZW+OTJk+rRo4cCgYDGjh0rh4PBfwAANHaMFQbsh1fWABBC//791b9//1qPTZw4URMnTrQoEQAAAICGQGFiA0muhIveB3DlAoGAHnnkEX3xxRdq3769/vCHP8jlclkdC4BJUttpl3yuvGRGAyYB0Fg5jKAcEZxsE8lzAbg8FCY2U15ZzVhhoA7hXCNvv/22rr/+er3xxhuaO3eu3n77bQ0bNiyK6QAAAADEAgoTm7lvYZHVEYCYF85Y4f/8z//ULbfcIkn60Y9+pA0bNlCYAACACzAlB7Af9hKygUQX30YgWrp27ar3339fkrRt2zZ9++231gYCAAAA0CBYYWID548z3To+W62v9lqYBoh94YwVHjhwoLZv36577rlH3bt3V6tWraKaDQAAxCdDkf1rNAtMAOtRmNhMkjuBscJAHcIZK+xwOLRgwQJJ0vTp09W7d+9oxQIAAAAQQ3gvBwCEcOLECd19993q3bu3EhMTdeedd1odCQAAxKBze5hE8gbAWixFAIAQWrVqpT//+c/q3bu3Zs6cqYEDB6p79+664YYblJ6eLkl67rnn1KdPH4uTAvYWamywxOhgANZjrDBgPxQmAFCHpKQkbdq0SZMmTap5LCUlRQUFBdaFAgAAABBVFCY2U+6vVpm/yuoYQEwL9xpxOp1KTU2t9djp06eVk5Oj9PR0vfzyy7r66qsjGREAAMQZxgoD9kNhYjN98wqtjgDEvOqKsis+R1FRkVq0aKEVK1Zo+vTp+v3vfx+BZAAAAABiBZu+2kCii28j0NBatGghSXrggQe0f/9+a8MAAADLOaJwA2AtVpjYgGH8sF5v5+S71SLZY2EaIPb5fD6l5V3+5/v9fgWDQXk8HhUWFqpTp04RywYAAAAgNlCY2EySO0FeN99WIJSqy7hGBgwYoP379+uzzz7Tz372M61Zs0ZNmjSRx+PR0qVLo5ASAADEE6bkAPbDK2sAqIfNmzfX+vi3v/2tRUkAAAAANAQKEwAI4ZNPPtGoUaPkdDqVnJys1atXa8CAAXI4HPL7/VqyZIm6d+9udUwg7iW1nRby+fKSGQ2UBAAuD1NyAPuhMLGZcn+AscJAHcK5Rrp06aKioiJJ0owZM7R+/Xq99957crlc2rFjhxYsWKDXX389WlEBAAAAWITCxAaCwR/e35g9t8C6IECcCGessMvlqrlfVlamG2+8seYxn8+nm266KeL5AABA/GGFCWA/TKuygTOV1VZHAGwtPz9ft956q7Zv366OHTvq1KlTysrK0pgxY3TXXXdZHQ8AAABAFLDCxGa2jr9Lra9OsjoGENPCHSvcp08fFRcXa+7cuVqyZImeeeYZFRUV6aOPPtKUKVP07rvvRi0rAACIDw5F9q/R/GUbsB6Fic0kuR2MFQbqEM5Y4YqKCnk8HklSSkqK/H6/qqur5XA4lJKSoiZNmkQrJgAAAAALUVwCQAj5+fnKycnRPffco/fee0/333+/7rnnHt1zzz0aM2aMZs2aZXVEAAAQAwwjKEcEb4YRrPuLmixatEgdOnRQYmKibrvtNu3cubNen1dUVCSn06lbbrkl7K8J2BlLEWyg3B+46H0AV659+/aqqqqS0+nUP//5TyUnJ8vj8ejMmTOSpMTERIsTAvGBscEA7M7qTV9Xr16t8ePHa9GiRcrKytKrr76q/v3768CBA2rbtu0lP++7777TI488op/85Cf68ssvrzA1YC+sMAGAEM6NFd6xY4fuuOMObdiwQcuWLVNhYaGeffZZzZs3z+qIAAAAeumll/Too4/qscceU9euXZWXl6c2bdpo8eLFIT9v1KhR+sUvfqEePXo0UFIgfrDCxGbKK6tV5q+yOgYQ08K5Rsxjhbt166b09PSa55xOfowCAIDobfrq8/lqPe7xeGr2VzvH7/dr3759euaZZ2o93rdvX+3ateuSX2PZsmU6fPiwVq5cqZkzZ0YkN2An/KZvM/ctLLI6AhDzqivKwjo+Pz9fkydPlsvl0m9/+1tJUmVlpV544QW99tpr0YgIAAAgSWrTpk2tj6dNm6bp06fXeuyrr75SIBBQy5Ytaz3esmVLnThx4qLnPXjwoJ555hnt3LmTPwABl8CVYQOJLt5ZBUTTxcYKP/HEExo9erQ6duxodTwAABADHIrwHibf/99jx46pWbNmNY+bV5eczzBqBwgGgxc8JkmBQEC/+MUvNGPGDHXu3DkieQE7ojCxgfN/CG4dn63WV3stTAPEPp/Pp7S8+h17sbHCM2fOVIcOHTRs2LDohQQAAJDUrFmzWoXJxVxzzTVKSEi4YDXJyZMnL1h1IkmlpaXau3eviouL9Zvf/EaSVF1drWAwKKfTqa1bt6pXr16R+0cAcYrCxAbMk3G8br6tQChVYVwj+fn5mjdvnhwOh1JTU5WXl6d27dopKytL77//vnr06KHZs2dHMS0AAIgHxmWOAg51vvpyu9267bbblJ+fr5///Oc1j+fn5+u+++674PhmzZrp73//e63HFi1apPfff1/r1q1Thw4dLj84YCO8sgaAEAYOHKiBAwfWeqyystKiNAAAABc3YcIEjRgxQpmZmerRo4eWLFmikpISjR49WpL07LPP6osvvtCKFSvkcDjUvXv3Wp9/7bXXKjEx8YLHgcaMwsQGyisDF70P4Mp98sknGjVqlJxOp5KTk7V69WrdeuutNZNynnvuOfXp08filEBsSGo77ZLPlZfMaMAkANDwHEaE9zAJ81zDhg3T119/rRdeeEHHjx9X9+7dtXnzZrVr106SdPz4cZWUlEQuINAIUJjYzBnGCgN1Cuca6dKli4qKzk6fmjFjhtavX6+UlBQVFBREKR0AAMDlGTNmjMaMGXPR55YvXx7yc6dPn37B9B2gsaMwsZlhSz60OgIQ88IZK+xyuWrul5WV6cYbb9Tp06eVk5Oj9PR0vfzyy7r66qujERMAAMQRh36YbBOp8wGwFtehDSQ6+TYC0ZSfn69bb71V27dvV8eOHVVUVKQdO3aoX79+/CUGAAAAsClWmNjAmarqmvurn/j/dFPr5taFAeJAOGOFJalPnz4qLi7W3LlztWTJEj3zzDOSpAceeECvvfZadEICAIC44jCCckRwSk4kzwXg8lCY2EyiK4GxwkAdwhkrXFFRIY/HI0lKSUnRmTNnah4rLCxUp06dohUTAAAAgIV4ZQ0AIeTn52vevHlyOBxKTU3VvHnz1LNnTzVp0kQej0dLly61OiIAAIgBVk/JARB5FCYAEEJaWpqqq39429t1112nm2++WZs2bdK0adPUpk0bC9MBDSvU2GCJ0cEAGjcjwoWJQWECWI7CBABCSE9P15YtW+T1ejVlyhRt2LBBL774onJycnT69Gmr4wEAAACIEgoTmzlTWa0yf5XVMYCYFs410qpVq5r7LpdLTqdTaWlp0YgFAADiWML3t0ieD4C1KExsZtiSD62OAMS86oqysD+npKRE27Zt09SpU6OQCAAAAECsoTCxgau8LqsjALbm8/k0YsQILVu2TC4X1xsAALgQY4UB+6EwsQHjvB2hto7PVuurvRamAWKfz+dTWl79jg0EAnr44Yf1/PPPq3PnzlHNBQAAACB2UJjYTJI7QV4331YglKowrpE1a9Zo165dKi0tVW5urp588knt379fGzduVCAQ0OHDh7VgwYIopgUAAPGAscKA/fDKGgBCGD58uIYPH17rsWHDhmn27NkWJQIAAADQEChMACCEffv2afz48XI4HGrZsqXeeOMNdevWTenp6ZKk5557Tn369LE4JRAZSW2nhXy+vGRGAyUBgPjDChPAfihMbKbcz1hhoC7hXCPp6enasmWLvF6vpkyZog0bNiglJUUFBQXRCwgAAADAchQmNtM3r9DqCEDMC2escKtWrWruu1wuOZ1OnT59Wjk5OUpPT9fLL7+sq6++OhoxAQBAHEkwzt4ieT4A1nJYHQBXLtHFtxGItpKSEm3btk0DBw5UUVGRduzYoX79+mn69OlWRwMAAAAQBawwsYHzxwrvnHy3WiR7LEwDxL5wxgqfO37EiBFatmyZXC6XWrRoIUl64IEH9Nprr0UnJAAAiCvsYQLYD4WJzTBWGKhbOGOFA4GAHn74YT3//PPq3Lmz/H6/gsGgPB6PCgsL1alTpygmBQAAAGAVXlkDQAhr1qzRrl27VFpaqtzcXD355JOaO3eumjRpIo/Ho6VLl1odEQAAxACHEZTDCEb0fACsRWECACH06NFDDscP+wT16tVLkpSXlyenkx+hiC+MDQaA6DEi/JYcg7fkAJbjt30AqENOTo7WrVsnSaqsrNRLL72knTt3as+ePcrNzdWSJUssTggAAAAg0ihMbKbcH1CZv8rqGEBMC/caKSoqUnZ2trKzs/Xwww8rIyNDbrdbWVlZmjhxYpRSAgCAeJLw/S2S5wNgLQoTGyir+OHFX/bcAuuCAHGiuqKs3sempaXp0KFD8nq9evzxx/XOO++oWbNmNc8HAoFoRAQAAABgMUfdhyDWnamstjoCYFsej0dNmjSRYRgaMmSIiouL5fP5ap5PSODvPwAA4IexwpG8AbAWK0xs5p2xWbqhZbLVMYCY5vP5lJZXv2NLS0vVtGlTSVJhYaEGDhyoxYsXy+/3a8+ePbr55pujFxQAAACAZShMbCbJ5ZDXzbcVCKUqjGvkgw8+0NSpU+X1etWhQwfl5ubK4/EoJydHiYmJWrFiRRSTAgCAeMFYYcB+eGUNACH0799f/fv3r/XYQw89pIceesiiRAAAAAAaAoWJDSS5Ey56H0BklJaWqnfv3vr000/14Ycfqnv37nr55Ze1YsUKGYahqVOnatCgQVbHBCRJSW2nXfK58pIZDZgEABqXBOPsLZLnA2AtChObKfdXM1YYqEO410hSUpI2bdqkSZMm1Ty2aNEiffzxxyorK9O9995LYQIAAADYDIWJzfTNK7Q6AhDzwhkrLElOp1Opqam1HuvUqZPKy8tVWlqqFi1aRDIeAACIQ5GebMOUHMB6FCY2kOhiOjTQ0Pr166du3bopEAho+fLlVscBAAAAEGEUJjZgGD/Uzzsn360WyR4L0wCxL5yxwpf6/CVLlujgwYPy+/3q1auX+vTpU+taBAAAjQsrTAD7oTCxmSR3AmOFgTqEM1b4YhwOhxITE+XxeOR0OlVRUaFgMEhhAgAAANgIr6wBoB4GDBig/fv367PPPtOoUaM0dOhQ9ejRQ4FAQGPHjpXDwVvjAABozByK8AqTyJ0KwGWiMAGAehgxYoT27Nmj3bt364YbblB6eroSExP13HPPqU+fPlbHg40Eg9Uhn/e2Cz0amNHBAGANhxFUghGM6PkAWIvCBADqUF1drXXr1qlNmzaSpJSUFBUUFFgbCgAAAEBUUZjYTLk/oDJ/ldUxgJgW7jXy5ptvaujQoZo/f74k6fTp08rJyVF6erpefvllXX311dGICQAA4ohDkX0bDW/JAaxHYWIDweAPy/Wy5xZYFwSIE9UVZfU+NhAIaM2aNdqwYUNNYVJUVKQWLVpoxYoVmj59un7/+99HKyoAAAAAi1Bc2sCZytDvdwdw+VauXKkHH3yw1qauLVq0kCQ98MAD2r9/v0XJAABALDk3VjiSNwDWYoWJzWwdf5daX51kdQwgpvl8PqXl1e/YAwcOqLi4WCtXrtTBgwf11FNP6X/9r/8lj8ejwsJCderUKapZAQAAAFiDwsRmktwOed18W4FQqsK4RubMmVNzPzMzU88884x69uypJk2ayOPxaOnSpdGICAAA4kykV4WwwgSwHq+sAaCe9u7dK0nat2+fxUkAAAAARBuFiQ2U+wMXvQ/gyn3yyScaNWqUnE6nkpOTtXr1aiUnJ6ukpEQ33HCD9u3bp+7du1sdEzbibTcj5PNlR6c1UBIAQDgSDCnBCNZ9YBjnA2AtChMbKK/8oST5pqySscJAHcK5Rrp06aKioiJJ0owZM7R+/XqNGDFCc+bMUVZWVrQiAgAAALAYhYnNDFvyodURgJgXzlhhl8tVc7+srEw33nijPv/8cxmGobZt20YjHgAAiEPsYQLYD2OFbSDRybcRiKb8/Hzdeuut2r59uzp27Kg5c+Zo4sSJVscCAAAAEEWsMLEBw/ihfn5nbE/d0LKphWmA2BfOWGFJ6tOnj4qLizV37lwtXrxYktS+ffuoZAMAAPGJFSaA/VCY2ECSO6Hm/tVN3IwVBuoQzljhiooKeTweSVJKSopOnDihTz/9VP369dPf//53HTp0SNu3b6/11h0AAAAA8Y9X1gAQQn5+vubNmyeHw6HU1FQtX75c06adnVIycuRITZw4kbIEAACwwgSwIQoTAAihffv2qqqqktPp1D//+U9VV1frV7/6lTZt2qRp06YxUhhhS2obeixweUnoscIAgNjkMCI7CpjCBLAehQkAhHCxscIvvviicnJydPr0aYvTAQAAAIgWChObKfdXq8xfZXUMIKaFc41cbKxwWlpaNGIBAIA45jCCchjBiJ4PgLUoTGymb16h1RGAmFddURbW8fn5+Zo8ebJcLpd++9vfRikVAAAAgFjisDoArlyii28jEE3nxgoPHTpUS5YssToOAACIQY4o3ABYixUmNmAYP+wItXPy3WqR7LEwDRD7fD6f0vLqd6x5rLDf749eMAAAAAAxg8LEZpLcCfK6+bYCoVSFcY1cbKzws88+q40bNyoQCOjw4cNasGBBFNMCAIB4wFhhwH54ZQ0AIQwcOFADBw6s9djs2bM1e/ZsixIBAAAAaAgUJgAQwieffKJRo0bJ6XQqOTlZq1at0uDBgyVJ5eXl8vv9Ki4utjglYk1S22mXfK68ZEYDJgEANJQE4+wtkucDYC0KE5sp9wcYKwzUIZxrpEuXLioqKpIkzZgxQ++8844KCgokSStXrtThw4ejEREAAACAxShMbCAY/GFGe/bcAuuCAHEinLHCLper5n5ZWZluvPHGmo/Xrl3LW3MAAIAkyWEE5TCCdR8YxvkAWItpVTZwprLa6giAreXn5+vWW2/V9u3b1bFjR0lSaWmpjh07pm7dulmcDgAAAEA0sMLEBsr9gZr774zN0g0tky1MA8S+cMYKS1KfPn1UXFysuXPnasmSJXrmmWe0cePGmr1MAAAAmJID2A+Fic0kuRyMFQbqEM5Y4YqKCnk8HklSSkqK/H6/pLNvx5k1a1ZU8gEAAACwHq+sASCE/Px8zZs3Tw6HQ6mpqVq+fLlKS0tVUlKijIwMq+MBAIAYwQoTwH4oTGwgyZ1w0fsArlz79u1VVVUlp9Opf/7zn6qurtZ3332n1q1b6+6779Y999yjadMuPUIW9hRqbLDE6GAAaIwciuwGkWw2CViPwgQAQjCPFV6/fr02b96sxYsXKz093eJ0AAAAAKKFwsRmyv3VKvNXWR0DiGnhXCMXGyu8aNEiPf300zp58qRmzpypnj17RiMmAACIJ4ZkRPJtNLwlB7AchYnN9M0rtDoCEPOqK8rCOj4/P1+TJ0+Wy+XSuHHj9PHHH2vt2rVyOp0aPHiwPvrooyglBQAAAGAV3hpnA4kuvo1ANJ0bKzx06FCtWLFCnTt3VuvWrdWqVSs5nU5VVbGqCwCAxs6Iwg2AtVhhYgPGeWv/dk6+Wy2SPRamAWKfz+dTWl79jr3YWOHmzZvru+++k9PplN/vl9PJj1IAAADAbvgt32aS3Anyuvm2AqFUhXGNXGys8G233aaBAweqsrJSubm5UUwKAADihRHhPUwiuh8KgMvCK2sACGHgwIEaOHBgrcd69uypnTt3WpQIAAAAQEOgMAGAeli1apXGjRunU6dO6Ve/+pU2bdqkadOm6Te/+Y3V0XCZqqrPXPK5pu1nh/zc8pIZkY4DAIhzDkV2g0h2KQSsR2FiY8FgUOWVAatjADEn3NHb1dXVWrdundq0aSNJevHFF5WTk6PTp09HIx4AAACAGEBhYlPBYFBDX9mtfUe/sToKEHPCHSv85ptvaujQoZo/f74kKS0tLRqxAABAHDOMoAwjGNHzAbAWK71sqrwyQFkCREAgENCaNWs0bNgwq6MAAAAAaECsMGkE9k7tLa87weoYQMwIZ6zwypUr9eCDD8rhoF8GAACXZnx/i+T5AFiLwsRmyv3VKvNX1dqjwcuoYaCWcMYKHzhwQMXFxVq5cqUOHjyop556SgsWLIhiOgAAAACxgFfRNlDu/2Fj1+y52y1MAtjPnDlzau5nZmZqwYIFevbZZ7Vx40YFAgEdPnyYAgUAAJxdYRLBZSGsMAGsR2FiA4ku3ioARNuqVat09OhRSdItt9yigoICJSUlacKECRYnw+UKNTq49MizDZgEAGAHvCUHsB8KExswzquyd06+Wy2SPSrzVylz5nsWpgLs4/yxwpWVlXrppZe0c+dO7dmzR7m5uVqyZInVEQEAAABEGIWJzSRdZL+SsvPesgNAtfb4qY/zxwofPHhQGRkZcrvdysrK0sSJE6OUEgAAxBOHcfYWyfMBsBaFSSOQOXOb1RGAmFJdUVbvY8+NFd6wYYPmz5+vb7/9Vs2aNav1PAAAAAD7YfMLm0pyJSiz3VVWxwDinnms8FVXXSWfz1fzfEICI7sBAMAPe5hE8gbAWqwwsSnDMLR2dA+VV/LXb8DM5/MpLa9+x5rHCi9ZskQHDhyQ3+/Xnj17dPPNN0c1KwAAQH0tWrRI8+bN0/Hjx5WRkaG8vDxlZ2df9Ni3335bixcv1v79+1VRUaGMjAxNnz5d9957bwOnBmIXhYmNGYZxwX4mAKSqMK6Li40V/tOf/qScnBwlJiZqxYoV0YgIAADijGFEeKxwmOdavXq1xo8fr0WLFikrK0uvvvqq+vfvrwMHDqht27YXHF9YWKg+ffroxRdfVPPmzbVs2TINGjRI//Zv/6Zbb701Qv8KIL7xahoA6mnv3r2SpIceekgPPfSQxWkAAAB+8NJLL+nRRx/VY489JknKy8vTli1btHjxYs2ePfuC4/Py8mp9/OKLL+qdd97Rn//8ZwoT4HsUJgBQh4KCAuXm5qqqqkoTJkzQ/Pnz5XA45Pf7tWTJEnXv3t3qiI1SVfWZkM83bX/hL4fnKy+ZEck4AIBGLtL7jpw71/l7p0mSx+ORx+Op9Zjf79e+ffv0zDPP1Hq8b9++2rVrV72+XnV1tUpLS3X11VdfdmbAbihMbCwYDLKHCXAR4YwVPnPmjObPn6+//vWvcrvdkqQBAwbI5XJpx44dWrBggV5//fVoRQUAAI1cmzZtan08bdo0TZ8+vdZjX331lQKBgFq2bFnr8ZYtW+rEiRP1+jrz58/XP//5Tz344INXlBewEwoTmwoGgxr6ym7tO/qN1VGAmBPOWOFdu3YpKSlJgwYNktfr1eLFi9WqVStJZ//ic9NNN0UrJgAAiCPRWmFy7NgxNWvWrOZx8+qSWp9j2vgkGAxe8NjFrFq1StOnT9c777yja6+99rLyAnZEYWJT5ZUByhIgAr788kt9/vnnKioq0nvvvafp06crNzdXP/vZz1RSUqJ33nnH6ogAAMDGmjVrVqswuZhrrrlGCQkJF6wmOXny5AWrTsxWr16tRx99VGvXrlXv3r2vOC9gJxQmjcDeqb3ldSdYHQOIGeGMFW7evLnuvPNOud1u9erVS7Nnz1ZqaqqKior00UcfacqUKXr33XejmhcAAMQ+h3H2Fsnz1Zfb7dZtt92m/Px8/fznP695PD8/X/fdd98lP2/VqlX69a9/rVWrVumnP/3plcQFbInCxGbK/dUq81fV2qPB605gvDBwnnDGCt9xxx01u8gXFxerbdu2qq6ulsPhUEpKipo0aRKllAAAAPU3YcIEjRgxQpmZmerRo4eWLFmikpISjR49WpL07LPP6osvvtCKFSsknS1LHnnkEf3v//2/9eMf/7hmdUpSUpJSUlIs+3cAsYRX0TZQ7v9hY9fsudstTALYT4sWLTR48GDdddddcjgcmjVrlu655x45HA45HA4tXLjQ6ogAACAGRGsPk/oaNmyYvv76a73wwgs6fvy4unfvrs2bN6tdu3aSpOPHj6ukpKTm+FdffVVVVVUaO3asxo4dW/P4L3/5Sy1fvjwC/wIg/lGY2ECiy2F1BMDWMjIy5HK5VFVVpa+++koej0dnzpwdaZuYmGhxusarrrHBpUeebaAkAABIMoIyjGBEzxeuMWPGaMyYMRd9zlyCFBQUXEYooHGhMLGB83e+3jn5brVI9qjMX6XMme9ZmAqwB/NY4YqKCmVmZio9PV1bt27VvHnzWGUCAAAA2BCFic0kXWS/krLz3rIDQLX2+KnLxcYKp6enS5JcLpecTn6MAgAA69+SAyDy+E2/Ecicuc3qCEBMqa4oq/exFxsr/Morr6iyslIvvPCCXnvttSgmBQAAAGAVNr+wqSRXgjLbXWV1DCDumccKHzhwQJL0xBNPaPTo0erYsaPFCQEAQCwwjMjfAFiLFSY2ZRiG1o7uofJK3o4DmPl8PqXl1e9Y81jh66+/XjNnzlSHDh00bNiwqGUEAAAAYC0KExszDOOC/UwASFVhXBfmscIvvviicnJylJWVpffff189evTQ7Nmhp7UAAAD7cyiyy/d5KwBgPV5NA0Adxo4dq7Fjx9Z8XFlZaWEaAAAAAA2BwgQAQti3b5/Gjx8vh8Ohli1b6o033pDL5VJJSYluuOEG7du3T927d7c6ZtzyV5++5HMp7eeF/NzykhmRjgMAwGWL9L4j7GECWI/CpBEIBoPsZQKcJ5yxwunp6dqyZYu8Xq+mTJmiDRs26IEHHtCcOXOUlZUVxZQAAAAArERhYnPBYFBDX9mtfUe/sToKEDPCGSvcqlWrmvsul0tOp1Off/65DMNQ27ZtoxEPAADEIeP7WyTPB8Ba7CVkc+WVAcoSIAJKSkq0bds2DRw4UHPmzNHEiROtjgQAAAAgilhh0ojsndpbXneC1TEAy4UzVvjc8SNGjNCyZctUUlIiSWrfvn1UsgEAgPjEHiaA/VCY2Ey5v7rW/gzn3/e6ExgzDCi8scKBQEAPP/ywnn/+eXXu3Flvv/22Pv30U/Xr109///vfdejQIW3fvl0ulyuKiQEAAAA0NF4920z23O1WRwBsZc2aNdq1a5dKS0uVm5urJ598Ujt37pQkjRw5UhMnTqQsAQAA7GEC2BCFiQ0kOtmKBoiWzp07q1u3bgoGg2rZsqX69eunvn376syZM0pISFBycrLVEeNaqNHB3x2Z1IBJAAC4Mg7j7C2S5wNgLQqTOBcMBjXi9T01H++d+pNab7sp81cpc+Z7VkQDbME8Vvjdd9/VsmXLlJ6erq1bt2revHlauHCh1TEBAAAARBiFSZwrrwyo+Ni3NR973c5L7lNS5g80UCogtp2/t09dzGOF3W630tPTaz52OvkxCgAAeEsOYEf8pt+IZM7cZnUEICZUV5SF/TnnxgpPnTpVklRZWakXXnhBr732WqTjAQAAAIgBbH5hc0muBGW2u8rqGEBcO3+s8LkNXp944gmNHj1aHTt2tDgdAACIBYYRjPgNgLVYYWJzhmFo7egeKq/k7TjAOT6fT2l59TvWPFZYkmbOnKkOHTpo2LBh0QsJAAAAwFIUJo2AYRiX3NcEaIyqwrgezGOFf/WrX2nGjBnKysrS+++/rx49emj27NlRTAsAAOIBe5gA9sOraAAIYfjw4Ro+fHitx375y19alAYAAABAQ6EwAYB6WLVqlcaNG6dTp05p/vz5euutt5ScnKzly5fruuuuszpe1FQEfCGff6H425DPv3T/6yGfLy+ZEW4kAABikmGcvUXyfACsRWGCRi0YDKq8qtzqGGhgZZXhTcmprq7WunXr1KZNG504cUJ/+ctfVFRUpD179ig3N1eLFy+OUlIAAAAAVqEwQaMVDAb1yF8f0f5T+62OggYWKA9vE+Q333xTQ4cO1fz583X06FFlZGTIMAz96Ec/0qOPPhqllAAAIJ6whwlgP4wVRqNVXlVOWYI6BQIBrVmzpmYiTseOHbVnzx5VVFRo27Zt+uabbyxOCAAAACAaWGECSCp4sEBJziSrY6CB+Hw+pT2ZVq9jV65cqQcffFAOx9l++ZprrtHo0aPVt29f3XLLLerSpUs0owIAgDjhUGT/Gs1ftgHrUZjYUDAYVHlleG85aIzKKgMKVrtkOCqV5EyS1+W1OhIaSJWrqt7HHjhwQMXFxVq5cqUOHjyop556SgsWLNDIkSNVUFCga6+9NopJAQAAAFiFwsRmgsGghr6yW/uO8jaB+slV067PWB0CMWzOnDk19zMzM7VgwQI99NBDOnXqlNq1a6eFCxdamA4AAMSMCE/JYRMTwHoUJjZTXhmgLAGiYNWqVTp69Kgk6c4779SKFSv06aefatu2bRo0aJDF6aLHYSSEfL6uscET3mZTXABAY8G2r4DdUJjY2N6pveV1h36x05iVVZbr7jU5VsdAHDh/rLAkLVq0SB9//LHKysp077332rowAQAAABorChMb87oT5HXzLb4kI0GGo1LS2Yk5aDzKKsvCOv78scKS1KlTJ5WXl6u0tFQtWrSIRkQAABBnjO//E8nzAbAWr6bj1LmNXcv89d+8Epd295q7rY6ABhQor/+myOfGCm/YsKGmMOnXr5+6deumQCCg5cuXRyklAAAAACtRmMShUBu7lvuZjlNfSc4k3XrtrSo+WWx1FMQw81hhn8+nJUuW6ODBg/L7/erVq5f69OkjI6K7vAEAgHhjGA4ZRuSGAUfyXAAuD4VJHAq1sWv23IKGDRPHDMPQv/b7V96O0wj5fD6lPZlWr2PNY4WnTZumxMREeTweOZ1OVVRUKBgMUpgAAAAANkNhEud2Tr6bkuQKGIYhr8trdQw0sCpX/d/KdrGxwr/73e/Uo0cPBQIBjR07tmb1CQAAaMyYkgPYDYVJnEs6bwrO3qk/kSRlznzPqjiAre3du1eSNHHiRE2cONHiNAAAAACiicLERpiIA0TPqlWrNG7cOH3xxRfq27evJKm8vFx+v1/FxfbdB6dZ+7khn/cdmRzy+eog+yoBABqHs+tLIjklB4DVeIVtY2VsAAtcVLjTpaqrq7Vu3Tq1adNGbrdbBQUFks5uCHv48OEoJAQAAABgNQoTG8ucuc3qCEBMqq4oC+v4N998U0OHDq0ZK3zO2rVrNXv27EhGAwAAcYs9TAC7YadCm0lyJSiz3VVWxwBsIxAIaM2aNRo2bFitx0tLS3Xs2DF169bNomQAAAAAookVJjZjGIbWju6h8krejgNcis/nU1pe/Y5duXKlHnzwwQsm4WzcuFGDBw+OfDgAABCXDMMhw4jc36MjeS4Al4fCxIYMw2ADWCCEqjCujwMHDqi4uFgrV67UwYMH9dRTT2nBggVau3atZs2aFcWUAAAAAKzEq2oACGHOnDk19zMzM7VgwQKVlpaqpKREGRkZFiYDAACxhT1MALuhMAGAeli1apWOHj0qSWratKk2bNggj8ejffv2qXv37hanC+3L8v8b8vn2XVZd8rnykhmRjgMAgC0Z3/8nkucDYC3eGAcAdTh/rPA5c+bMUVZWloWpAAAAAEQTK0xw2YLBIJvLIi6V+avCOt48Vvjzzz+XYRhq27ZtNOIBAIA4xAoTwH4oTHBZgsGghr6yW/uOfmN1FCBs1RVl9T723FjhDRs21BQmc+bM0TPPPKPp06dHKSEAAAAAq1GYxJlgMBj2X8ejobwyQFmCRsE8Vvjw4cOSpPbt21uYCgAAxB6HIrvjAbsnAFajMIkjsbqqY+/U3vK6E6yOAdSbz+dTWl79jjWPFe7du7dat26tfv366e9//7sOHTqk7du3y+VyRTUzAAAAgIZFYRJH6lrVUeZvuP1Ezl/l4nUnyOvmv0qIH1Vh/PfVPFZ47969NR+PHDlSEydOpCwBAAAyDEOGEcE9TCJ4LgCXh1e5NpI5c5vVEQBbO78skaTly5dbEwQAAABA1FGYxLkkV4Iy212lvTH2Nh3AblatWqVx48bp1KlTuuuuu+RwOOT3+7VkyRJ1797d6nghte+yKuTzRz4b3kBJAACwM+P7WyTPB8BKFCZxzjAMrR3do8HH+5b5q5Q5873v7zNaGPEl3I2Tq6urtW7dOrVp00aS9N5778nlcmnHjh1asGCBXn/99WjEBAAAAGAhChMbMAzD0j1EeCsQ4k04Y4Ul6c0339TQoUNrxgqf27PE5/Pppptuing+AAAQf4zv/xPJ8wGwFrOqcFnOvRUIsLtAIKA1a9Zo2LBhNY+dOnVKWVlZGjNmjO666y4L0wEAAACIFlaY4LJY9VYgIBLCGSu8cuVKPfjgg3I4fuiXU1NTVVRUpI8++khTpkzRu+++G52gAAAgjjgU2b9H87dtwGoUJrhsVr8VCLhc4YwVPnDggIqLi7Vy5UodPHhQTz31lObPny+Hw6GUlBQ1adIkikkBAAAAWIVXuwAQwpw5c2ruZ2ZmauLEibrnnnvkcDjkcDi0cOFCC9MBAIBYwR4mgP1QmABACEeOHNHtt9+ujIwMJScny+l0yjAMBYNBnT59WsOHD1dxcXFUM2z74lDI5wf1+GPI58tLZkQyDgAAuAjDMGQYESxMInguAJeHwgQA6pCTk6N169bVfFxQUCDp7P4mhw8ftigVAAAAgGiiMEHMCwaDbC6LiCrzV4V1fFFRkbKzs5Wdna1Zs2bV/MVn7dq1mj17djQiAgCAuGN8f4vk+QBYicIEMS0YDGroK7u17+g3VkeBjVRXlNX72LS0NB06dEher1ePP/641q9fr/vvv1+lpaU6duyYunXrFsWkAAAAAKzCrCrEtPLKAGUJLOXxeNSkSRMZhqEhQ4Zo//79kqSNGzdq8ODB1oYDAAAx4+yWr44I3lhhAliNFSaIG3un9pbXnWB1DNiAz+dTWl79ji0tLVXTpk0lSYWFheratauks2/HmTVrVpQSAgAAALAahUmcK/Pbe2+P8/ea8LoT5HXzX1lcuaow/nv0wQcfaOrUqfJ6verQoYNyc3NVWlqqkpISZWRkRDElAACIL+xhAtgNrz7jXObMbVZHAGytf//+6t+/f63HmjZtqr/97W8WJQIAAADQEChM4tStbZqr+Ni3VscAbO/IkSO6/fbba1aTrF27Vj179lR6erok6bnnnlOfPn2immFQjz+GfP7Pu0dE9esDAIC6GYZRM0kvUucDYC0Kkzi18rE7GsUP0TJ/lTJnvvf9fXu//QgNJ9yxwjk5OVq3bl3NxykpKSooKIhwKgAAAACxhMIkThmG0ej28+DtR4iUcMYKS1JRUZGys7OVnZ2tWbNm6fTp08rJyVF6erpefvllXX311VFKCgAA4gd7mAB2w1hhxLQkV4Iy211ldQw0YmlpaTp06JAKCwt18uRJrV+/XkVFRdqxY4f69eun6dOnWx0RAAAAQBQ0riUKiDuGYWjt6B4qr+TtOIiccMYKezweeTweSdKQIUO0e/du3X///ZKkBx54QK+99lqUUgIAgHhiyCEjgn+PjuS5AFweChPEvMb49iNEVzhjhUtLS9W0aVNJUmFhobp27aqKigp5PB4VFhaqU6dO0YoJAAAAwEK8CgWAED744ANNnTpVXq9XHTp00Pjx49WzZ081adJEHo9HS5cutToiAACICexhAtgNhQkAhNC/f38lJSUpNzdXR48e1YcffqjJkycrLy9PTmd4P0L/ePCIkpKbXPS5sX2WX/LzyktmhPV1AABAwzO+/08kzwfAWhQmABDCmTNnNH/+fP31r3+V2+1WZWWl7rzzTu3cuVN79uxRbm6ulixZYnVMAAAAABFGYQL7CAalyvDGxaKR8v+z3ofu2rVLSUlJGjRokLxeryZNmqSMjAy53W5lZWVp4sSJUQwKAADihWEYMowIrjCJ4LkAXB4KE9hDMCgtvVc69m9WJ0E8qAjW+9Avv/xSn3/+uYqKivTee+9p+vTp6tatW83zgQATnAAAAAA7YlYV7KGyjLIEUdG8eXPdeeedcrvd6tWrl4qLi+Xz+WqeT0hIsDAdAACIHY4o3ABYiRUmsJ+JhyS31+oUiGU+n/S/rqvXoXfccYfy8vIkScXFxerbt68OHDggv9+vPXv26Oabb45iUAAAAABWoTCJU2V+3gZQiz8gBT3yGhVnyxL3xSeRAJIkd/2vnxYtWmjw4MG666675HA4tHTpUn300UfKyclRYmKiVqxYEcWgAAAgXjAlB7AfCpM4lTlzm9URYtAyHUn8hdUhYENjx47V2LFjaz6+/vrr9dBDD1mYCAAAAEC0UZjEkSRXgjLbXaW9R7+xOgrQ6KxatUrjxo3TkSNH9POf/1xnzpxRQkKCli1bpvbt29frHE/f96YMh+uizy3MHxm5sAAAwALG97dIng+AlShM4ohhGFo7uofKK3k7zgX8ZdK8Tj/cB0IJY6ywJFVXV2vdunVq06aNnE6nli1bpvT0dG3dulXz5s3TwoULoxQUAAAAgFUoTOKMYRjyuvm2XShBMirO3v1dJ2ujIPaFMVZYkt58800NHTpU8+fPl8fjUXp6uiTJ5XLJ6eR6BAAAZ39PN4wI7mESwXMBuDzMqoI9uLxSmx9bnQI2FAgEtGbNGg0bNqzW45WVlXrhhRc0btw4i5IBAADUtmjRInXo0EGJiYm67bbbtHPnzpDH79ixQ7fddpsSExN1/fXX65VXXmmgpEB84E+jsAfDkH79rlTJ23FQD2GMFV65cqUefPBBORy1++UnnnhCo0ePVseOHaOREAAAxB2HIvv36PDOtXr1ao0fP16LFi1SVlaWXn31VfXv318HDhxQ27ZtLzj+888/14ABA/T4449r5cqVKioq0pgxY5SamqohQ4ZE6h8BxDUKE9iHYTBOGPUTxljhAwcOqLi4WCtXrtTBgwf11FNPqUWLFurQocMFq04AAACs8tJLL+nRRx/VY489JknKy8vTli1btHjxYs2ePfuC41955RW1bdtWeXl5kqSuXbtq7969+t3vfkdhAnyPwiRGBINn91Tw+XwWJwHs79x1du66C2XOnDk19zMzMzVp0iS1a9dOWVlZev/999WjR4+L/hJyvnNfJ1hdecljyk9feiNan+90nTkBAEDknPvf3vr8rnBOqe+fMiI42abU98/vs9R+feDxeOTxeGo95vf7tW/fPj3zzDO1Hu/bt6927dp10fPv3r1bffv2rfXYvffeq9dff12VlZVyuS4+2Q9oTChMYkRpaakkqU2bNhYnARqP0tJSpaSk1Pv4vXv3Sjq7f0m4X0eS/Mc3XvKY32S+dennwvpqAAAgUurzu4Lb7VarVq3Upk1OxL9+cnLyBa8Ppk2bpunTp9d67KuvvlIgEFDLli1rPd6yZUudOHHiouc+ceLERY+vqqrSV199pbS0tCv/BwBxjsIkRlx33XU6duyYmjZtyo7YQJQFg0GVlpbquuvqt4/JleL6BgAgvoTzu0JiYqI+//xz+f3+qOQw/+5gXl1yPvOxF/v8uo6/2ONAY0VhEiMcDodat25tdQyg0QhnZcmV4voGACD+hPO7QmJiohITE6OYJrRrrrlGCQkJF6wmOXny5AWrSM5p1arVRY93Op1q0aJF1LIC8YSxwgAAAAAQx9xut2677Tbl5+fXejw/P189e/a86Of06NHjguO3bt2qzMxM9i8BvkdhAgAAAABxbsKECXrttde0dOlS/eMf/9BTTz2lkpISjR49WpL07LPP6pFHHqk5fvTo0Tp69KgmTJigf/zjH1q6dKlef/11TZw40ap/AhBzeEsOAAAAAMS5YcOG6euvv9YLL7yg48ePq3v37tq8ebPatWsnSTp+/LhKSkpqju/QoYM2b96sp556SgsXLtR1112n3//+94wUBs5jBMOZlQUAAAAAANAI8JYcAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAA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"text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "\n", "Image(filename=\"compare_all.mat.matrix.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## make a fast(er) search database for all of them" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kloading 64 files into SBT\n", "\u001b[Kloaded 1 sigs from 'big_genomes/0.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/1.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/10.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/11.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/12.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/13.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/14.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/15.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/16.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/17.fa.sig'10 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/18.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/19.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/2.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/20.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/21.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/22.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/23.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/24.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/25.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/26.fa.sig'20 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/27.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/28.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/29.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/3.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/30.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/31.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/32.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/33.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/34.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/35.fa.sig'30 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/36.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/37.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/38.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/39.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/4.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/40.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/41.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/42.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/43.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/44.fa.sig'40 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/45.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/46.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/47.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/48.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/49.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/5.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/50.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/51.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/52.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/53.fa.sig'50 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/54.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/55.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/56.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/57.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/58.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/59.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/6.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/60.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/61.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/62.fa.sig'60 sigs total\n", "\u001b[Kloaded 1 sigs from 'big_genomes/63.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/7.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/8.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'big_genomes/9.fa.sig'g'\n", "\u001b[K\n", "\u001b[Kloaded 64 sigs; saving SBT under \"all-genomes\"\n", "\u001b[KFinished saving nodes, now saving SBT index file.\n", "\u001b[KFinished saving SBT index, available at /Users/t/dev/sourmash/doc/all-genomes.sbt.zip\n", "\n" ] } ], "source": [ "!sourmash index -k 31 all-genomes big_genomes/*.sig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can now use this to search, and gather." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[K\r\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\r\n", "\r", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\r\n", "\r\n", "\r", "\u001b[KCannot open file 'shew_os185.fa.sig'\r\n" ] } ], "source": [ "!sourmash search shew_os185.fa.sig all-genomes --threshold=0.001" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kcomputing signatures for files: fake-metagenome.fa\n", "\u001b[KComputing a total of 1 signature(s).\n", "\u001b[K... reading sequences from fake-metagenome.fa\n", "\u001b[Kcalculated 1 signatures for 3 sequences in fake-metagenome.fa\n", "\u001b[Ksaved signature(s) to fake-metagenome.fa.sig. Note: signature license is CC0.\n" ] } ], "source": [ "# (make fake metagenome again, just in case)\n", "!cat genomes/*.fa > fake-metagenome.fa\n", "!rm -f fake-metagenome.fa.sig\n", "!sourmash sketch dna -p k=31,scaled=1000 fake-metagenome.fa" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kselect query k=31 automatically.\n", "\u001b[Kloaded query: fake-metagenome.fa... (k=31, DNA)\n", "\u001b[Kloaded 1 databases. \n", "\n", "\n", "overlap p_query p_match\n", "--------- ------- -------\n", "0.5 Mbp 42.2% 10.5% NC_011663.1 Shewanella baltica OS223,...\n", "499.0 kbp 38.4% 18.5% CP001071.1 Akkermansia muciniphila AT...\n", "0.5 Mbp 19.4% 4.9% NC_009665.1 Shewanella baltica OS185,...\n", "\n", "found 3 matches total;\n", "the recovered matches hit 100.0% of the query\n", "\n" ] } ], "source": [ "!sourmash gather fake-metagenome.fa.sig all-genomes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## build a database with taxonomic information --\n", "\n", "for this, we need to provide a metadata file that contains accession => tax information." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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accessiontaxidsuperkingdomphylumclassorderfamilygenusspeciesstrain
0AE000782224325ArchaeaEuryarchaeotaArchaeoglobiArchaeoglobalesArchaeoglobaceaeArchaeoglobusArchaeoglobus fulgidusArchaeoglobus fulgidus DSM 4304
1NC_000909243232ArchaeaEuryarchaeotaMethanococciMethanococcalesMethanocaldococcaceaeMethanocaldococcusMethanocaldococcus jannaschiiMethanocaldococcus jannaschii DSM 2661
2NC_003272103690BacteriaCyanobacteriaNaNNostocalesNostocaceaeNostocNostoc sp. PCC 7120NaN
3AE009441178306ArchaeaCrenarchaeotaThermoproteiThermoprotealesThermoproteaceaePyrobaculumPyrobaculum aerophilumPyrobaculum aerophilum str. IM2
4AE009950186497ArchaeaEuryarchaeotaThermococciThermococcalesThermococcaceaePyrococcusPyrococcus furiosusPyrococcus furiosus DSM 3638
.................................
59NZ_ABZS01000228432331BacteriaAquificaeAquificaeAquificalesHydrogenothermaceaeSulfurihydrogenibiumSulfurihydrogenibium yellowstonenseSulfurihydrogenibium yellowstonense SS-5
60NZ_JGWU010000011458259BacteriaProteobacteriaBetaproteobacteriaBurkholderialesAlcaligenaceaeBordetellaBordetella bronchisepticaBordetella bronchiseptica D989
61NZ_FWDH0100000331899BacteriaFirmicutesClostridiaThermoanaerobacteralesThermoanaerobacterales Family III. Incertae SedisCaldicellulosiruptorCaldicellulosiruptor besciiNaN
62NC_009972316274BacteriaChloroflexiChloroflexiaHerpetosiphonalesHerpetosiphonaceaeHerpetosiphonHerpetosiphon aurantiacusHerpetosiphon aurantiacus DSM 785
63NC_005213228908ArchaeaNanoarchaeotaNaNNanoarchaealesNanoarchaeaceaeNanoarchaeumNanoarchaeum equitansNanoarchaeum equitans Kin4-M
\n", "

64 rows × 10 columns

\n", "
" ], "text/plain": [ " accession taxid superkingdom phylum class \\\n", "0 AE000782 224325 Archaea Euryarchaeota Archaeoglobi \n", "1 NC_000909 243232 Archaea Euryarchaeota Methanococci \n", "2 NC_003272 103690 Bacteria Cyanobacteria NaN \n", "3 AE009441 178306 Archaea Crenarchaeota Thermoprotei \n", "4 AE009950 186497 Archaea Euryarchaeota Thermococci \n", ".. ... ... ... ... ... \n", "59 NZ_ABZS01000228 432331 Bacteria Aquificae Aquificae \n", "60 NZ_JGWU01000001 1458259 Bacteria Proteobacteria Betaproteobacteria \n", "61 NZ_FWDH01000003 31899 Bacteria Firmicutes Clostridia \n", "62 NC_009972 316274 Bacteria Chloroflexi Chloroflexia \n", "63 NC_005213 228908 Archaea Nanoarchaeota NaN \n", "\n", " order family \\\n", "0 Archaeoglobales Archaeoglobaceae \n", "1 Methanococcales Methanocaldococcaceae \n", "2 Nostocales Nostocaceae \n", "3 Thermoproteales Thermoproteaceae \n", "4 Thermococcales Thermococcaceae \n", ".. ... ... \n", "59 Aquificales Hydrogenothermaceae \n", "60 Burkholderiales Alcaligenaceae \n", "61 Thermoanaerobacterales Thermoanaerobacterales Family III. Incertae Sedis \n", "62 Herpetosiphonales Herpetosiphonaceae \n", "63 Nanoarchaeales Nanoarchaeaceae \n", "\n", " genus species \\\n", "0 Archaeoglobus Archaeoglobus fulgidus \n", "1 Methanocaldococcus Methanocaldococcus jannaschii \n", "2 Nostoc Nostoc sp. PCC 7120 \n", "3 Pyrobaculum Pyrobaculum aerophilum \n", "4 Pyrococcus Pyrococcus furiosus \n", ".. ... ... \n", "59 Sulfurihydrogenibium Sulfurihydrogenibium yellowstonense \n", "60 Bordetella Bordetella bronchiseptica \n", "61 Caldicellulosiruptor Caldicellulosiruptor bescii \n", "62 Herpetosiphon Herpetosiphon aurantiacus \n", "63 Nanoarchaeum Nanoarchaeum equitans \n", "\n", " strain \n", "0 Archaeoglobus fulgidus DSM 4304 \n", "1 Methanocaldococcus jannaschii DSM 2661 \n", "2 NaN \n", "3 Pyrobaculum aerophilum str. IM2 \n", "4 Pyrococcus furiosus DSM 3638 \n", ".. ... \n", "59 Sulfurihydrogenibium yellowstonense SS-5 \n", "60 Bordetella bronchiseptica D989 \n", "61 NaN \n", "62 Herpetosiphon aurantiacus DSM 785 \n", "63 Nanoarchaeum equitans Kin4-M \n", "\n", "[64 rows x 10 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas\n", "\n", "df = pandas.read_csv(\"podar-lineage.csv\")\n", "df" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[KBuilding LCA database with ksize=31 scaled=10000 moltype=DNA.\n", "\u001b[Kexamining spreadsheet headers...\n", "\u001b[K** assuming column 'accession' is identifiers in spreadsheet\n", "\u001b[K64 distinct identities in spreadsheet out of 64 rows.\n", "\u001b[K64 distinct lineages in spreadsheet out of 64 rows.\n", "\u001b[K... loaded 64 signatures.H01000003.1 Caldicellulo (64 of 64); skipped 0 so far\n", "\u001b[Kloaded 19993 hashes at ksize=31 scaled=10000\n", "\u001b[K64 assigned lineages out of 64 distinct lineages in spreadsheet.\n", "\u001b[K64 identifiers used out of 64 distinct identifiers in spreadsheet.\n", "\u001b[Ksaving to LCA DB: taxdb.lca.json\n" ] } ], "source": [ "!sourmash lca index podar-lineage.csv taxdb big_genomes/*.sig -C 3 --split-identifiers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This database 'taxdb.lca.json' can be used for search and gather as above:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[K\r\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\r\n", "\r", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\r\n", "\r\n", "\r", "\u001b[Kselect query k=31 automatically.\r\n", "\r", "\u001b[Kloaded query: fake-metagenome.fa... (k=31, DNA)\r\n", "\r", "\u001b[Kloading from taxdb.lca.json...\r", "\r", "\u001b[Kloaded LCA taxdb.lca.json\r", "\r", "\u001b[K \r", "\r", "\u001b[Kloaded 1 databases.\r\n", "\r\n", "\r\n", "overlap p_query p_match\r\n", "--------- ------- -------\r\n", "0.6 Mbp 46.7% 11.6% NC_011663.1 Shewanella baltica OS223,...\r\n", "0.5 Mbp 38.7% 19.3% CP001071.1 Akkermansia muciniphila AT...\r\n", "0.5 Mbp 14.6% 3.9% NC_009665.1 Shewanella baltica OS185,...\r\n", "\r\n", "found 3 matches total;\r\n", "the recovered matches hit 100.0% of the query\r\n", "\r\n" ] } ], "source": [ "!sourmash gather fake-metagenome.fa.sig taxdb.lca.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...but can also be used for taxonomic summarization:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[K\r\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\r\n", "\r", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\r\n", "\r\n", "\r", "\u001b[K\r", "\u001b[K\r", "\u001b[K... loading database taxdb.lca.json\r", "\r", "\u001b[K\r", "\u001b[K\r", "\u001b[Kloaded 1 LCA databases. ksize=31, scaled=10000 moltype=DNA\r\n", "\r", "\u001b[Kfinding query signatures...\r\n", "\r", "\u001b[K\r", "\u001b[K\r", "\u001b[K... loading fake-metagenome.fa (file 1 of 1)\r", "38.7% 53 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Akkermansiaceae;Akkermansia;Akkermansia muciniphila;Akkermansia muciniphila ATCC BAA-835 fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "38.7% 53 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Akkermansiaceae;Akkermansia;Akkermansia muciniphila fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "38.7% 53 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Akkermansiaceae;Akkermansia fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "38.7% 53 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Akkermansiaceae fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "38.7% 53 Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "38.7% 53 Bacteria;Verrucomicrobia;Verrucomicrobiae fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "38.7% 53 Bacteria;Verrucomicrobia fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "100.0% 137 Bacteria fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "61.3% 84 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "61.3% 84 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "61.3% 84 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "61.3% 84 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "61.3% 84 Bacteria;Proteobacteria;Gammaproteobacteria fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "61.3% 84 Bacteria;Proteobacteria fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "22.6% 31 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica;Shewanella baltica OS223 fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "14.6% 20 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica;Shewanella baltica OS185 fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n", "\r", "\u001b[K\r", "\u001b[K\r", "\u001b[Kloaded 1 signatures from 1 files total.\r\n" ] } ], "source": [ "!sourmash lca summarize --query fake-metagenome.fa.sig --db taxdb.lca.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other pointers\n", "\n", "[Sourmash: a practical guide](https://sourmash.readthedocs.io/en/latest/using-sourmash-a-guide.html)\n", "\n", "[Classifying signatures taxonomically](https://sourmash.readthedocs.io/en/latest/classifying-signatures.html)\n", "\n", "[Pre-built search databases](https://sourmash.readthedocs.io/en/latest/databases.html)\n", "\n", "## A full list of notebooks\n", "\n", "[An introduction to k-mers for genome comparison and analysis](kmers-and-minhash.ipynb)\n", "\n", "[Some sourmash command line examples!](sourmash-examples.ipynb)\n", "\n", "[Working with private collections of signatures.](sourmash-collections.ipynb)\n", "\n", "[Using the LCA_Database API.](using-LCA-database-API.ipynb)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python (myenv)", "language": "python", "name": "myenv" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 } sourmash-bio-sourmash-bc3c0f8/doc/sourmash-examples.ipynb000066400000000000000000001012301474157046300237500ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Some sourmash command line examples!\n", "\n", "[sourmash](https://sourmash.readthedocs.io/en/latest/) is research software from the Lab for Data Intensive Biology at UC Davis. It implements MinHash and modulo hash.\n", "\n", "Below are some examples of using sourmash. They are computed in a Jupyter Notebook so you can run them yourself if you like!\n", "\n", "Sourmash works on *signature files*, which are just saved collections of hashes.\n", "\n", "Let's try it out!\n", "\n", "### Running this notebook.\n", "\n", "You can run this notebook interactively via mybinder; click on this button:\n", "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dib-lab/sourmash/latest?labpath=doc%2Fsourmash-examples.ipynb)\n", "\n", "A rendered version of this notebook is available at [sourmash.readthedocs.io](https://sourmash.readthedocs.io) under \"Tutorials and notebooks\".\n", "\n", "You can also get this notebook from the [doc/ subdirectory of the sourmash github repository](https://github.com/dib-lab/sourmash/tree/latest/doc). See [binder/environment.yaml](https://github.com/dib-lab/sourmash/blob/latest/binder/environment.yml) for installation dependencies.\n", "\n", "### What is this?\n", "\n", "This is a Jupyter Notebook using Python 3. If you are running this via [binder](https://mybinder.org), you can use Shift-ENTER to run cells, and double click on code cells to edit them.\n", "\n", "Contact: C. Titus Brown, ctbrown@ucdavis.edu. Please [file issues on GitHub](https://github.com/dib-lab/sourmash/issues/) if you have any questions or comments!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute scaled signatures" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kcomputing signatures for files: genomes/akkermansia.fa, genomes/shew_os185.fa, genomes/shew_os223.fa\n", "\u001b[KComputing a total of 1 signature(s).\n", "\u001b[K... reading sequences from genomes/akkermansia.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in genomes/akkermansia.fa\n", "\u001b[Ksaved signature(s) to akkermansia.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from genomes/shew_os185.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in genomes/shew_os185.fa\n", "\u001b[Ksaved signature(s) to shew_os185.fa.sig. Note: signature license is CC0.\n", "\u001b[K... reading sequences from genomes/shew_os223.fa\n", "\u001b[Kcalculated 1 signatures for 1 sequences in genomes/shew_os223.fa\n", "\u001b[Ksaved signature(s) to shew_os223.fa.sig. Note: signature license is CC0.\n" ] } ], "source": [ "!rm -f *.sig\n", "!sourmash sketch dna -p k=21,k=31,k=51,scaled=1000 genomes/*.fa --name-from-first -f" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This outputs three signature files, each containing three signatures (one calculated at k=21, one at k=31, and one at k=51)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "akkermansia.fa.sig shew_os185.fa.sig shew_os223.fa.sig\r\n" ] } ], "source": [ "ls *.sig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now use these signature files for various comparisons." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Search multiple signatures with a query\n", "\n", "The below command queries all of the signature files in the directory with the `shew_os223` signature and finds the best Jaccard similarity:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[K\r\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\r\n", "\r", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\r\n", "\r\n", "\r", "\u001b[Kselecting specified query k=31\r\n", "\r", "\u001b[Kloaded query: NC_011663.1 Shewanella baltica... (k=31, DNA)\r\n", "\r", "\u001b[Kloading from akkermansia.fa.sig...\r", "\r", "\u001b[Kloaded 1 signatures from akkermansia.fa.sig\r", "\r", "\u001b[Kloading from shew_os185.fa.sig...\r", "\r", "\u001b[Kloaded 1 signatures from shew_os185.fa.sig\r", "\r", "\u001b[Kloading from shew_os223.fa.sig...\r", "\r", "\u001b[Kloaded 1 signatures from shew_os223.fa.sig\r", "\r", "\u001b[K \r", "\r", "\u001b[Kloaded 3 signatures.\r\n", "\r\n", "2 matches:\r\n", "similarity match\r\n", "---------- -----\r\n", "100.0% NC_011663.1 Shewanella baltica OS223, complete genome\r\n", " 22.8% NC_009665.1 Shewanella baltica OS185, complete genome\r\n" ] } ], "source": [ "!sourmash search -k 31 shew_os223.fa.sig *.sig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The below command uses Jaccard containment instead of Jaccard similarity:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kselecting specified query k=31\n", "\u001b[Kloaded query: NC_011663.1 Shewanella baltica... (k=31, DNA)\n", "\u001b[Kloaded 3 signatures. \n", "\n", "2 matches:\n", "similarity match\n", "---------- -----\n", "100.0% NC_011663.1 Shewanella baltica OS223, complete genome\n", " 37.3% NC_009665.1 Shewanella baltica OS185, complete genome\n" ] } ], "source": [ "!sourmash search -k 31 shew_os223.fa.sig *.sig --containment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Performing all-by-all queries\n", "\n", "We can also compare all three signatures:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kloaded 1 sigs from 'akkermansia.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'shew_os185.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'shew_os223.fa.sig'g'\n", "\u001b[Kloaded 3 signatures total. \n", "\u001b[K\n", "0-CP001071.1 Akke...\t[1. 0. 0.]\n", "1-NC_009665.1 She...\t[0. 1. 0.228]\n", "2-NC_011663.1 She...\t[0. 0.228 1. ]\n", "min similarity in matrix: 0.000\n" ] } ], "source": [ "!sourmash compare -k 31 *.sig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...and produce a similarity matrix that we can use for plotting:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kloaded 1 sigs from 'akkermansia.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'shew_os185.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'shew_os223.fa.sig'g'\n", "\u001b[Kloaded 3 signatures total. \n", "\u001b[K\n", "0-CP001071.1 Akke...\t[1. 0. 0.]\n", "1-NC_009665.1 She...\t[0. 1. 0.228]\n", "2-NC_011663.1 She...\t[0. 0.228 1. ]\n", "min similarity in matrix: 0.000\n", "\u001b[Ksaving labels to: genome_compare.mat.labels.txt\n", "\u001b[Ksaving comparison matrix to: genome_compare.mat\n" ] } ], "source": [ "!sourmash compare -k 31 *.sig -o genome_compare.mat" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kloading comparison matrix from genome_compare.mat...\n", "\u001b[K...got 3 x 3 matrix.\n", "\u001b[Kloading labels from genome_compare.mat.labels.txt\n", "\u001b[Ksaving histogram of matrix values => genome_compare.mat.hist.png\n", "\u001b[Kwrote dendrogram to: genome_compare.mat.dendro.png\n", "\u001b[Kwrote numpy distance matrix to: genome_compare.mat.matrix.png\n", "0\tCP001071.1 Akkermansia muciniphila ATCC BAA-835, complete genome\n", "1\tNC_009665.1 Shewanella baltica OS185, complete genome\n", "2\tNC_011663.1 Shewanella baltica OS223, complete genome\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "!sourmash plot genome_compare.mat\n", "\n", "from IPython.display import Image\n", "Image(filename='genome_compare.mat.matrix.png') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and for the R aficionados, you can output a CSV version of the matrix:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kloaded 1 sigs from 'akkermansia.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'shew_os185.fa.sig'g'\n", "\u001b[Kloaded 1 sigs from 'shew_os223.fa.sig'g'\n", "\u001b[Kloaded 3 signatures total. \n", "\u001b[K\n", "0-CP001071.1 Akke...\t[1. 0. 0.]\n", "1-NC_009665.1 She...\t[0. 1. 0.228]\n", "2-NC_011663.1 She...\t[0. 0.228 1. ]\n", "min similarity in matrix: 0.000\n" ] } ], "source": [ "!sourmash compare -k 31 *.sig --csv genome_compare.csv" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"CP001071.1 Akkermansia muciniphila ATCC BAA-835, complete genome\",\"NC_009665.1 Shewanella baltica OS185, complete genome\",\"NC_011663.1 Shewanella baltica OS223, complete genome\"\r", "\r\n", "1.0,0.0,0.0\r", "\r\n", "0.0,1.0,0.22846441947565543\r", "\r\n", "0.0,0.22846441947565543,1.0\r", "\r\n" ] } ], "source": [ "!cat genome_compare.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is now a file that you can load into R and examine - see [our documentation](https://sourmash.readthedocs.io/en/latest/other-languages.html) on that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## working with metagenomes\n", "\n", "Let's make a fake metagenome:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[K\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n", "\n", "\u001b[Kcomputing signatures for files: fake-metagenome.fa\n", "\u001b[KComputing a total of 1 signature(s).\n", "\u001b[K... reading sequences from fake-metagenome.fa\n", "\u001b[Kcalculated 1 signatures for 3 sequences in fake-metagenome.fa\n", "\u001b[Ksaved signature(s) to fake-metagenome.fa.sig. Note: signature license is CC0.\n" ] } ], "source": [ "!rm -f fake-metagenome.fa*\n", "!cat genomes/*.fa > fake-metagenome.fa\n", "!sourmash sketch dna -p k=31,scaled=1000 fake-metagenome.fa" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the `sourmash gather` command to see what's in it:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[K\r\n", "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\r\n", "\r", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\r\n", "\r\n", "\r", "\u001b[Kselect query k=31 automatically.\r\n", "\r", "\u001b[Kloaded query: fake-metagenome.fa... (k=31, DNA)\r\n", "\r", "\u001b[Kloading from shew_os185.fa.sig...\r", "\r", "\u001b[Kloaded 1 signatures from shew_os185.fa.sig\r", "\r", "\u001b[Kloading from shew_os223.fa.sig...\r", "\r", "\u001b[Kloaded 1 signatures from shew_os223.fa.sig\r", "\r", "\u001b[Kloading from akkermansia.fa.sig...\r", "\r", "\u001b[Kloaded 1 signatures from akkermansia.fa.sig\r", "\r", "\u001b[K \r", "\r", "\u001b[Kloaded 3 signatures.\r\n", "\r\n", "\r\n", "overlap p_query p_match\r\n", "--------- ------- -------\r\n", "499.0 kbp 38.4% 100.0% CP001071.1 Akkermansia muciniphila AT...\r\n", "494.0 kbp 38.0% 100.0% NC_009665.1 Shewanella baltica OS185,...\r\n", "490.0 kbp 23.6% 62.7% NC_011663.1 Shewanella baltica OS223,...\r\n", "\r\n", "found 3 matches total;\r\n", "the recovered matches hit 100.0% of the query\r\n", "\r\n" ] } ], "source": [ "!sourmash gather fake-metagenome.fa.sig shew*.sig akker*.sig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other pointers\n", "\n", "[Sourmash: a practical guide](https://sourmash.readthedocs.io/en/latest/using-sourmash-a-guide.html)\n", "\n", "[Classifying signatures taxonomically](https://sourmash.readthedocs.io/en/latest/classifying-signatures.html)\n", "\n", "[Pre-built search databases](https://sourmash.readthedocs.io/en/latest/databases.html)\n", "\n", "## A full list of notebooks\n", "\n", "[An introduction to k-mers for genome comparison and analysis](kmers-and-minhash.ipynb)\n", "\n", "[Some sourmash command line examples!](sourmash-examples.ipynb)\n", "\n", "[Working with private collections of signatures.](sourmash-collections.ipynb)\n", "\n", "[Using the LCA_Database API.](using-LCA-database-API.ipynb)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "smash-notebooks", "language": "python", "name": "smash-notebooks" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 } sourmash-bio-sourmash-bc3c0f8/doc/sourmash-internals.md000066400000000000000000001116271474157046300234230ustar00rootroot00000000000000# A guide to the internal design and structure of sourmash ```{contents} Contents :depth: 3 ``` sourmash was created in 2015, and has been repeatedly reorganized, refactored, and optimized to support ever larger databases, faster queries, and new use cases. We've also regularly added new functionality and features. So sourmash can be pretty complicated internally, and our user-facing documentation only covers a fraction of its potential! This document is a brain dump intended for expert users and sourmash developers who want to understand how, why, and when to use various sourmash features. It is unlikely ever to be comprehensive, so the information you are interested in may not yet exist in this document, but we are always happy to add to it - [just ask in an issue!](https://github.com/sourmash-bio/sourmash/issues) ## Signatures and sketches sourmash operates on sketches. Each sketch is a collection of hashes, which are in turn built from k-mers by applying a hash function (currently always murmurhash) and a filtering function. Each sketch is contained in a signature wrapper that contains some metadata. Internally, sketches (class `MinHash`) contain the following information: * a set of hashes; * an optional abundance for each hash (when `track_abund` is True); * a seed; * a k-mer size; * a molecule type; * either a `num` (for MinHash) or a `scaled` value (for FracMinHash); Signature objects (class `SourmashSignature`) contain a sketch (property `.minhash`) as well as additional information: * an optional `name` * an optional `filename` * a license (currently must be CC0); * an `md5sum(...)` method that returns a hash of the sketch. For now, we tend to refer to signatures and sketches interchangeably, because they are almost entirely 1:1 in the code base (but see [sourmash#616](https://github.com/sourmash-bio/sourmash/issues/616)). The default signature interchange/serialization format is JSON, optionally gzipped. This format is written and read by Rust code. In general, a lot of effort in sourmash is spent managing collections of signatures _before_ actually doing comparisons with them; see manifests, and `Index` objects, below. ### Making sketches Sketches are produced by hashing k-mers with murmurhash and then keeping either the lowest `num` hashes (for MinHashes sketches) or keeping all hashes below `2**64 / scaled` (for FracMinHash sketches). This has the effect of selecting approximately one hash for every `scaled` k-mers - so, when sketching a set of 100,000 distinct k-mers, a scaled value of 1,000 would yield approximately 100 hashes to be retained in the sketch. The default MinHash sketches use parameters so that they are compatible with mash sketches. See [utils/compute-dna-mh-another-way.py](https://github.com/sourmash-bio/sourmash/blob/latest/utils/compute-dna-mh-another-way.py) for details on how k-mers are hashed. Note that if hashes are produced some other way (with a different hash function) or from some source other than DNA, sourmash can still work with them; only `sourmash sketch` actually cares about DNA sequences, everything else works with hashes. ### Compatibility checking The point of the signatures and sketches is to enable certain kinds of rapid comparisons - Jaccard similarity and number of overlapping k-mers, specifically. However, these comparisons can only be done between compatible sketches. Here, "compatible" means - * the same MurmurHash seed (default 42); * the same k-mer size/ksize (see k-mer sizes, below); * the same molecule type (see molecule types, below); * the same `num` or `scaled` (although see [this downsampling discussion](api-example.md#downsampling-signatures), and the next two sections); sourmash uses selectors (`Index.select(...)`) to select sketches with compatible ksizes, molecule types, and sketch types. ### Scaled (FracMinHash) sketches support similarity and containment Per our discussion in [Irber et al., 2022](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2), FracMinHash sketches can always be compared by downsampling to the max of the two scaled values. (This is not always indexed collections of sketches, e.g. SBTs; see [sourmash#1799](https://github.com/sourmash-bio/sourmash/issues/1799).) In practice, sourmash does all necessary downsampling dynamically, but returns the original sketches. This means that (for example) you can do a low-resolution/high-scaled search quickly by specifying a high `scaled` value, and then use a higher resolution comparison with the resulting matches for a more refined and accurate analysis (see below, [Speeding up `gather` and `search`](#speeding-up-gather-and-search).) ### Num (MinHash) sketches support Jaccard similarity "Regular" MinHash (or "num MinHash") sketches are implemented the same way as in mash. However, they are less well supported in sourmash, because they don't offer the same opportunities for metagenome analysis. (See also [sourmash#1354](https://github.com/sourmash-bio/sourmash/issues/1354).) Num MinHash sketches can always be compared by downsampling to a common `num` value. This may need to be done manually using `sourmash sig downsample`, however. ### Conversion between Scaled (FracMinHash) and Num (MinHash) signatures with `downsample` As discussed in the previous sections, it is possible to adjust the `scaled` and `num` values to compare two FracMinHash signatures or two Num MinHash signatures. However, it is also possible to covert between the `scaled` and `num` signatures with the `sourmash sig downsample` command. For more details, review the [command line docs for `sig downsample`](https://sourmash.readthedocs.io/en/latest/command-line.html#sourmash-signature-downsample-decrease-the-size-of-a-signature). ### Operations you can do safely with FracMinHash sketches As described in [Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2), FracMinHash sketches support a wide range of operations that mirror actions taken on the underlying data set _without_ revisiting the underlying data. This allows users to build sketches once (requiring the original data) and then do all sorts of manipulations on the sketches, and know that the results of the sketch manipulations represent what would happen if they did the same thing on the original data. For example, * set unions, intersections, and subtractions all perform the same when done on the sketches as when applied to the underling data. So, for example, you can sketch two files separately and merge the sketches (with `sig merge`), and get the same result as if you'd concatenated the files first and then sketched them. * if you filter hashes on abundance with `sig filter`, you get the same result as if you filtered the data set on k-mer abundance and then sketched it. * downsampling: you can sketch the original data set at a high resolution (e.g. scaled=100) and then downsample it later (to e.g. scaled=1000), and get the same result as if you'd sketched the data set at scaled=1000. ## K-mer sizes There is no explicit restriction on k-mer sizes built into sourmash. For highly specific genome and metagenome comparisons, we typically use k=21, k=31, or k=51. For a longer discussion, see [Assembly Free Analysis with K-mers](https://github.com/mblstamps/stamps2022/blob/main/kmers_and_sourmash/2022-stamps-assembly-free%20class.pdf) from STAMPS 2022 and a more general overview at [Using sourmash:a practical guide](using-sourmash-a-guide.md). ## Molecule types - DNA, protein, Dayhoff, and hydrophobic-polar sourmash supports four different sequence encodings, which we refer to as "molecule": DNA (`--dna`), protein (`--protein`), [Dayhoff encoding](https://en.wikipedia.org/wiki/Margaret_Oakley_Dayhoff#Table_of_Dayhoff_encoding_of_amino_acids), (`--dayhoff`), and [hydrophobic-polar](sourmash-sketch.md#protein-encodings) (`--hp`). All FracMinHash sketches have exactly one molecule type, and can only be compared to the same molecule type (and ksize). DNA moltype sketches can be constructed from DNA input sequences using `sourmash sketch dna`. Protein, Dayhoff, and HP moltype sketches can be constructed from protein input sequences using `sourmash sketch protein`, or from DNA input sequences using `sourmash sketch translate`; `translate` will translate in all six reading frames (see also [orpheum](https://github.com/czbiohub/orpheum) from [Botvinnik et al., 2021](https://www.biorxiv.org/content/10.1101/2021.07.09.450799v1)). By default protein sketches will be created; dayhoff sketches can be created by including `dayhoff` in the param string, e.g. `sourmash sketch protein -p dayhoff`, and hydrophobic-polar sketches can be built with `hp` in the param string, e.g. `sourmash sketch protein -p hp`. ## Manifests Manifests are catalogs of sketches: they include most of the information about a sketch, except for the actual hashes. The idea of manifests is that you can use them to identify *which* sketches you are interested in before actually working with them (loading them, for example). sourmash makes extensive use of signature manifests to support rapid selection and lazy loading of signatures based on signature metadata (name, ksize, moltype, etc.) See [Blog post: Scaling sourmash to millions of samples](http://ivory.idyll.org/blog/2021-sourmash-scaling-to-millions.html) for some of the motivation. Manifests are an internal format that is not meant to be particularly human readable, but the CSV format can be loaded into a spreadsheet program if you're curious :). If a collection of signatures is in a zipfile (`.zip`) or SBT zipfile (`.sbt.zip`), manifests must be named `SOURMASH-MANIFEST.csv`. They can also be stored directly on disk in CSV/gzipped CSV, or in a sqlite database; see `sourmash sig manifest`, `sourmash sig check`, and `sourmash sig collect` for manifest creation, management, and export utilities. Where signatures are stored individually in `Index` collections, e.g. as separate files in a zipfile, manifests may be stored alongside them; for other subclasses of `Index` such as the inverted indices, manifests are generated dynamically by the class itself. Currently (sourmash 4.x) manifests do not contain information about the hash seed or sketch license. This will be fixed in the future - see [sourmash#1849](https://github.com/sourmash-bio/sourmash/issues/1849). Manifests are very flexible and, especially when stored in a sqlite database, can be extremely performant for organizing hundreds of thousands to millions of sketches. Please see `StandaloneManifestIndex` for a lazy-loading `Index` class that supports such massive-scale organization. ## Index implementations The `Index` class and its various subclasses (in `sourmash.index`) are containers that provide an API for organizing, selecting, and searching (potentially) large numbers of signatures. `sourmash sig summarize` is a good way to determine what type of `Index` class is used to handle a collection. Loading and saving of `Index` objects is handled separately from the class: loading can be done in Python via the `sourmash.load_file_as_index(...)` method, while creation and/or updating of `Index` objects is done via `sourmash.sourmash_args.SaveSignaturesToLocation(...)`. These are the same APIs used by the command-line functionality. There are quite a few different `Index` subclasses and they all have distinct features. We have a high-level guide to which collection type to use [here](command-line.md#loading-many-signatures). Conceptually, `Index` classes are either organized around storing individual signatures often with metadata that permits loading, selecting, and/or searching them more efficiently (e.g. `ZipFileLinearIndex` and `SBTs`); or they store signatures as inverted indices (`LCA_Database` and `SqliteIndex`) that permit certain kinds of fast queries. Unless otherwise noted, the `Index` classes below can be loaded concurrently in "read only" mode - that is, you should build the collection _once_, and then use it from multiple processes. We currently do not test for or support concurrent read/write. Note also that (generally speaking) memory footprints will be additive, so loading the same `LCA_Database` twice will consume twice the memory. (If you're interested in concurrency, we suggest using the sqlite containers - see `SqliteIndex`.) ### In-memory storage and search. The simplest way to handle collections of signatures is to load them into memory, but it is also the least performant and most memory intensive mechanism! `LinearIndex` and `MultiIndex` both support sketches loaded from JSON files; both will load the sketches once and then keep them in memory. `LinearIndex` does not use manifests while `MultiIndex` builds a manifest as it loads the sketches. Note that `MultiIndex` is the class used to load signatures from pathlists, directory hierarchies, and so on; because it stores sketches in memory, this can incur a significant memory penalty (see [sourmash#1899](https://github.com/sourmash-bio/sourmash/issues/1899)). Therefore where possible we suggest building a standalone manifest (`StandaloneManifestIndex`) to do lazy loading from the disk instead; you can use `sourmash sig collect` to do this. ### Zipfile collections `ZipFileLinearIndex` stores signature files in a zip file with an accompanying manifest. This is the most versatile and compressed option for working with large collections of sketches - it supports rapid selection and loading of specific sketches from disk, and can store and search any mixture of sketch types (ksize, molecule type, scaled values, etc.) By default, `ZipFileLinearIndex` stores one signature (equiv. one sketch) in each member file in the zip archive. Each signature is stored uncompressed. The accompanying manifest stores the full member file path in `internal_location`, so that sketches can be retrieved directly. Searching a `ZipFileLinearIndex` is done linearly, as implied by the name. This is fine for `gather` but if you are doing repeated queries with `search` you may want to use an SBT or LCA database instead; see below. In the future we expect to parallelize searching `ZipFileLinearIndex` files in Rust; see [sourmash#1752](https://github.com/sourmash-bio/sourmash/issues/1752). `ZipFileLinearIndex` does support zip files without manifests as well as multiple signatures in a single file; this was originally intended to support simply zipping entire directory hierarchies into a zipfile. However, this slows down performance and is not recommended. If you have an existing zipfile (or really any collection of signatures) and you want to turn them into a proper `ZipFileLinearIndex`, you can use `sig cat -o combined.zip` to create a `ZipFileLinearIndex` file named `combined.zip` that will have a manifest and signatures broken out into individual files. ### Sequence Bloom Trees (SBTs) Sequence Bloom Trees (SBTs; see [the Kingsford Lab page for details](http://www.cs.cmu.edu/~ckingsf/software/bloomtree/)) provide a faster (but more memory intensive) on-disk storage and search mechanism. In brief, SBTs implement a binary tree organization of an arbitrary number of signatures; each internal node is a Bloom filter containing all of the hashes for the nodes below them. This permits potentially rapid elimination of irrelevant nodes on search. SBTs are restricted to storing and searching sketches with the same/single k-mer size and molecule type, as well as either a single num value or a single scaled value. We suggest using SBTs when you are doing multiple Jaccard search or containment searches with genomes via `sourmash search`. ### Lowest common ancestor (LCA) databases The `LCA_Database` index class stores signatures in an inverted index, where a Python dictionary is used to link individual hashes back to signatures and/or taxonomic lineages. This supports the individualized hash analyses used in the `lca` submodule. LCA databases only support a single ksize, moltype, and scaled. They can only be used with FracMinHash (scaled) sketches. The default `LCA_Database` class is serialized via JSON, and loads everything into memory when requested. The load time incurs a significant latency penalty when used from the command line, as well as having a potentially large memory footprint; this makes it difficult to use the default `LCA_Database` for very large databases, e.g. genbank bacteria. The newer `LCA_SqliteDatabase` (based on `SqliteIndex`, described below) also supports LCA-style queries, and is stored on disk, is fast to load, and uses very little memory. The tradeoff is that the underlying sqlite database can be quite large. `LCA_SqliteDatabase` should also support rapid concurrent access (see [sourmash#909](https://github.com/sourmash-bio/sourmash/issues/909)). Both types of LCA database can be constructed with `sourmash lca index`. ### SqliteIndex The `SqliteIndex` storage class uses sqlite3 to store hashes and sketch information for search and retrieval; see [this blog post](http://ivory.idyll.org/blog/2022-storing-ulong-in-sqlite-sourmash.html) for background information and details. These are fast, low-memory, on-disk databases, with the tradeoff that they can be quite large. This is probably currently the best solution for concurrent access to sketches via e.g. a Web server (see also [sourmash#909](https://github.com/sourmash-bio/sourmash/issues/909)). `SqliteIndex` can only contain FracMinHash sketches and can only store sketches with the same scaled parameter. However, it can store multiple ksizes and moltypes as long as the same scaled is used. `SqliteIndex` objects can be constructed using `sourmash sig cat ... -o filename.sqldb`. ### Standalone manifests The `StandaloneManifestIndex` class loads standalone manifests generated by `sourmash sig collect`. They support rapid selection and lazy loading on potentially extremely large collections of signatures. The underlying mechanism uses the `internal_location` field of manifests to point to the container file. When particular sketches are requested, the container file is loaded into an `Index` object with `sourmash.load_file_as_index` and the `md5` values of the requested sketches are used as a picklist to retrieve the desired signatures. Thus, while standalone manifests can point at any kind of container, including JSON files or LCA databases, they are most efficient when `internal_location` points at a file with either a single sketch in it, or a manifest that supports direct loading of sketches. Therefore, we suggest using standalone manifest indices. Note that sourmash interprets paths to locations in standalone manifests relative to the manifest filename; see the `--relpath` behavior in `sig check` and `sig collect` to output manifests that deal with relative filenames properly. Note that searching a standalone manifest is currently done through a linear iteration, and does not use any features of indexed containers such as SBTs or LCAs. This is fine for `gather` with the default approach, but is probably suboptimal for a `search`. ### Pathlists and `--from-file` All (or most) sourmash commands natively support taking in lists of signature collections via pathlists, `--from-file`, or paths to directories. This is useful for situations where you have thousands of signature files and don't want to provide them explicitly on the command line; you can simply put a list of the files in a text file, and pass it in directly (or use `--from-file` to pass it in). Both pathlists and files passed to `--from-file` contain a list of paths to be loaded; relatives paths will be interpreted relative to the current working directory of sourmash. Pathlists should be universally available on sourmash commands. When `--from-file` is available for a command, sourmash will behave as if the file paths in the file were provided on the command line. We suggest avoiding pathlists. Instead, we suggest using `--from-file` or a standalone manifest index (generated with `sourmash sig collect`). This is because the signatures from pathlists are loaded into memory (see `MultiIndex`, above) it is generally a bad idea to use them - they may be slow to load and may consume a lot of memory. They also do not support good loading error messages; see [sourmash#1414](https://github.com/sourmash-bio/sourmash/issues/1414). ### Extensions for outputting index classes Most commands that support saving signatures will save them in a variety of formats, based on the extension provided (see [sourmash#1890](https://github.com/sourmash-bio/sourmash/issues/1890) for exceptions). The supported extensions are - * `.zip` for `ZipFileLinearIndex` * `.sqldb` for `SqliteIndex` * `.sig` or `.sig.gz` for JSON/gzipped JSON * `dirname/` to save in a directory hierarchy The default signature save format is JSON, if the extension is not recognized. ## Speeding up `gather` and `search` There are two primary search commands in sourmash: `gather` and `search`. `gather` calculates a minimum metagenome cover as discussed in [Irber et al., 2022](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2). It is mostly intended for querying a database with a metagenome, although it can be used with genome queries, as well. This approach relies on overlaps between genomes and metagenomes and can only be used with FracMinHash sketches. `search` does a straight Jaccard similarity search on MinHash and FracMinHash sketches (or, with `--containment`, a containment search on FracMinHash sketches). It is typically used to find matches to a query genome sketch in a large database of sketches. The `prefetch` command does a containment search and is intended for power users; it is a standalone implementation of the prefetch algorithm discussed below for `gather`. It only works with FracMinHash sketches. Note that all of these commands work with any and all `Index` collection/container types, and will return the same results however the collections are organized - see the "online behavior" section, below. In practice this means that you can provide additional collections of signatures via the command line without building a combined index of all your signatures. It also means that the only reason to choose different collections/containers is for optimization - you should select the containers that help you achieve the desired performance characteristics for your search (i.e. the right memory/time/disk space tradeoffs). ### Running `search` many times on the same database `search` typically is used to search a large database of sketches for all similarity or containment matches above a threshold. Depending on the query and the database, certain kinds of database indices may make search much faster, especially when only a few matches are expected. If you are doing many searches against the same database, indexing the database as an SBT (with `sourmash index`) or as a `SqliteIndex`/sqldb database is likely to provide a significant speed increase, albeit with increased memory usage (SBT) or increased disk space (sqldb). Conversely, `ZipFileLinearIndex` and the default `LCA_Database` are likely to be poor choices for many searches - the former only supports linear searches, and the latter needs to be loaded from disk and deserialized each time. ### Running `gather` once `gather` is typically used to search a metagenome against a large database of sketches, as part of finding a minimum set cover. This can be quite slow! Our current implementation (as of sourmash 4.1.0, [pull request sourmash#1370](https://github.com/sourmash-bio/sourmash/pull/1370)) does a single pass across the database to find all matches with a Jaccard similarity or containment above the provided threshold, and then organizes the matches for rapid min-set-cov analysis. This single pass across the database is called a "prefetch", and it is also implemented in the `prefetch` subcommand. With this single pass approach, benchmarks - [sourmash#2014](https://github.com/sourmash-bio/sourmash/issues/2014) - show that a linearly searchable database is performant enough to be used with `gather`. We therefore suggest using a `ZipFileLinearIndex` container with gather, or in cases where low-memory concurrency is desired, a `SqliteIndex` container. ### Using `prefetch` and `gather` together If you want to use `prefetch` independently of `gather`, you can use the prefetch output as a picklist passed into gather - see [picklists](#picklists), below. This can be useful when you want to experiment with different threshold parameters for `gather` - first, do a very sensitive/low-threshold search with `prefetch` and save the results to a CSV file with `-o`, Repeated gathers and searches. CTB Using prefetch explicitly. CTB ### Using a higher scaled value With FracMinHash sketches, you can downsample the query to make both `search` and `gather` _much_ faster. A good rule of thumb is to use a scaled value that is about 5x smaller than the minimum overlap to detect; so, if you want to be able to detect 50kb of similarity, you can use a scaled value of 10,000. Conversely, the default scaled value of 1,000 (for DNA sketches) should robustly detect overlaps of 5kb. You can supply `--scaled` to `gather` and `prefetch` to dynamically downsample the query FracMinHash. For `search` you will need to use `sourmash sig downsample` to generate a downsampled sketch. ### Running `gather` many times - `multigather` In situations where loading the search database is slow (e.g. `LCA_Database` or zipfiles with very large manifests), the `sourmash multigather` command supports many queries against many databases. (We don't particularly suggest using `multigather`; we would prefer to make search databases faster. But it's there! :) ### Much faster search and gather with branchwater We also have a reasonably stable plugin, [pyo3_branchwater](https://github.com/sourmash-bio/pyo3_branchwater), that implements multithreaded operations using Rust. It is 100-1000 times faster than sourmash, and 5-50 times lower memory. In exchange, it's not quite as flexible as the full sourmash package. But if you're running into speed or memory problems, you should give it a try! ## Taxonomy and assigning lineages All sourmash taxonomy handling is done within the `lca` and `tax` subcommands (CLI) and submodules (Python). In the case of the `lca` subcommands, the taxonomic information is incorporated into the LCA database construction (see the `lca index` command), while the `tax` subcommands load taxonomic information on demand from taxonomy databases (CSVs or databases). sourmash anchors all taxonomy to identifiers, and uses the signature name to do so - this is the name as set by the `--name` parameter to `sourmash sketch`, and output by `sourmash sig describe` as the `signature:` field. ### Identifier handling sourmash prefers identifiers to be the first space-separated token in the signature name. This token can contain any alphanumeric letters other than space, and should contain at most one period. The version of the identifier will be the component after the period. So, for example, for a signature name of ``` CP001941.1 Aciduliprofundum boonei T469, complete genome ``` the identifier would be `CP001941.1` and the version would be 1. There are no other constraints placed on the identifier, and versions are not handled in any special way other than as below. The `lca index` and `tax` commands both support some modified identifier handling in sourmash 3.x and 4.x, but in the future, we plan to deprecate these as they mostly cause confusion and internal complexity. The two modifiers are: * `--keep-full-identifiers` will use the entire signature name instead of just the first space-separated token. It is by default off (set to False). * `--keep-identifier-versions` turns on keeping the full identifier, including what is after the first period. It is by default off (set to False), stripping identifiers of their version on load. When it is on (True), identifiers are not stripped of their version on load. ### Taxonomies, or lineage spreadsheets sourmash supports arbitrary (free) taxonomies, and new taxonomic lineages can be created and used internally as long as they are provided in the appropriate spreadsheet format. You can also mix and match taxonomies as you need; for example, it is entirely legitimate in sourmash-land to combine the GTDB taxonomy for bacterial and archaeal sequence classification, with the NCBI taxonomy for eukaryotic and viral sequence classification. (You probably don't want to mix and match within superkingdoms, though!) As of sourmash v4, lineage spreadsheets should contain columns for superkingdom, phylum, class, order, family, genus, and species. Some commands may also support a 'strain' column, although this is inconsistently handled within sourmash internally at the moment. For spreadsheet organization, `lca index` expects the columns to be present in order from superkingdom on down, while the `tax` subcommands use CSV column headers instead. We are planning to consolidate around the `tax` subcommand handling in the future (see [sourmash#2198](https://github.com/sourmash-bio/sourmash/issues/2198)). An example spreadsheet is [here, bacteria_refseq_lineage.csv](https://github.com/sourmash-bio/sourmash/blob/latest/tests/test-data/tax/bacteria_refseq_lineage.csv). (The `taxid` column is not used by most sourmash functions and is mostly ignored, but it is needed for the `kreport` and `bioboxes` report formats.) ### `LCA_SqliteDatabase` - a special case The `LCA_SqliteDatabase` index class can serve multiple purposes: as an index of sketches (for regular search and gather); as a taxonomy database for use with the `tax` subcommands; and as an LCA database for use with the `lca` subcommands. When used as a taxonomy database, an `LCA_SqliteDatabase` file contains the same SQL tables as a sqlite taxonomy database. When used as an LCA database, an `LCA_SqliteDatabase` dynamically loads the taxonomic lineages from the sqlite database and applies them to the individual hashes, permitting the same kind of hash-to-lineage query capability as the `LCA_Database`. ## Picklists Picklists are a generic mechanism used to select a (potentially small) subset of signatures for search/display. The general idea of picklists is that you create a list of signatures you're interested in - by name, or identifier, or md5sum - and then supply that list in a csvfile on the command line via `--picklist`. For example, `--picklist list.csv:colname:ident` would load the values in the column named `colname` in the file `list.csv` as identifiers to be used to restrict the search. The support picklist column types are `name`, `ident` (space-delimited identifier), `identprefix` (identifier with version removed), `md5`, `md5prefix8`, and `md5short`. Generally the `md5` and derived values are used to reference signatures found some other way with sourmash, while the identifiers are more broadly useful. There are also four special column types that can be used without a column name: `gather`, `prefetch`, `search`, and `manifest`. These take the CSV output of the respective sourmash commands as inputs for picklists, so that you can use prefetch to generate a picklist and then use that picklist with `--picklist prefetch_out.csv.gz::prefetch`. ### Differing internal behavior Picklists behave differently with different `Index` classes. For indexed databases like SBT, LCA, and `SqliteIndex`, the search is done _first_, and then only those results that match the picklist are selected. For linear search databases like `ZipFileLinearIndex` or standalone manifests, picklists are _first_ used to subselect the desired signatures, and only those signatures are searched. This means that picklists can dramatically speed up searches on some `Index` types, but won't affect performance on others. But the results will be the same. ### Taxonomy / lineage spreadsheets as picklists Note that lineage CSV spreadsheets, as consumed by `sourmash tax` commands and as output by `sourmash tax grep`, can be used as `ident` picklists. ## Online and streaming; and adding to collections of sketches. One of the big challenges with Big Data is looking at it all at once - loading all your data into memory, for example, will fail with really large data sets. The ability to look at subsets of data without looking at _all_ of it is called "streaming" (much like when you watch a streaming movie online - you can start watching the movie without downloading the whole video, and you can also usually jump to a particular location in the video without downloading the intervening bits.) Another related challenge is analyzing data against a database that is constantly growing, either because you're adding to it or because it's being updated by others. For example, in genomics, often you want to repeat the same analysis you did last time but with more reference genomes. With many software packages, this requires rebuilding your indexed database, which can be challenging for large genomes. In computer science parlance, the ability to add new data at the end _without_ performing an expensive reindexing operation is referred to as "online". sourmash tackles these challenges in a few different ways, and does its best to support streaming and online behavior. First, all sourmash commands can take multiple databases and will return the same results with multiple databases as they would with a single database containing the same sketches, unless otherwise noted. This allows you to incrementally expand your sketch collections over time without building new databases. _Performance_ may vary (i.e. if you're using an SBT to do search, and you add an unindexed collection of sketches to the search, the search may take longer than if you'd add the new sketches to the SBT) but the _results_ will be the same. In this sense, many of the sourmash algorithms are online. Second, several sourmash algorithms use _streaming_ when searching databases - in particular, `prefetch` will load and unload sketches as it goes, as long as the underlying collection data structure supports it (`.sig.gz` and LCA JSON databases do _not_, but zip files, SBTs, and SQLite databases _do_). This lets you do containment searches against really large collections without consuming large amounts of memory. Another example is the `manysearch` command in the [pyo3_branchwater](https://github.com/sourmash-bio/pyo3_branchwater) plugin, which loads and searches a limited number of metagenomes from a large collection, rather than loading the entire collection into memory - which would be impossible. Last but not least, one of the interesting guarantees that FracMinHash sketches provide is that no hash is ever _removed_ when sketching. This supports various types of input streaming, which we haven't spent too much time exploring, but (for example) means that "watching" sequencing runs and/or downloads of sequencing data, and reporting interim results with certainty, is possible. If you're interested in making use of this, please reach out! ### Gather on multiple collections, and order of search and reporting Since `sourmash gather` will pick only one "best match" if there are several (and will ignore the others), the order of searching can matter for large collections. How does this work? In brief, sourmash doesn't guarantee a particular load order for sketches in a single collection, but it _does_ guarantee that collections are loaded and searched in their entirety in the order that you provide them. So, for example, if you have a large zipfile database of sketches that contains duplicates, you can't predict which of the duplicates will be chosen as a match; but you _can_ build your own collection of prioritized matches as a separate database, and put it first on the command line. A practical application of this might be to list the GTDB "representatives" database first on the command line, with the full GTDB database second, in order to prioritize choosing representative genomes as matches over the rest. This also plays a role in the order of reporting for `prefetch` output - `prefetch` will report matching sketches in the order it encounters them, which will match the order in which collections are given to `sourmash prefetch` on the command line. ## Formats natively understood by sourmash sourmash should always autodetect the format of a collection or database, in most cases based on its content (and not its filename). Please file a bug report if this doesn't work for you! `sourmash sig summarize` is a good way to examine the properties of a signature collection. ### Reading and writing gzipped CSV files (As of sourmash v4.5) When a CSV filename is specified (e.g. `sourmash gather ... -o mygather.csv`), you can always provide a name that ends with `.gz` to produce a gzip-compressed file instead. This can save quite a bit of space for prefetch results and manifests in particular! All sourmash commands that take in a CSV (via manifest, or picklist, or taxonomy) will autodetect a gzipped CSV based on content (the file does not need to end with `.gz`). The one exception is manifests, where the CSV needs to end with `.gz` to be loaded as a gzipped CSV; see [sourmash#2214](https://github.com/sourmash-bio/sourmash/issues/2214) for an issue to fix this. sourmash-bio-sourmash-bc3c0f8/doc/sourmash-sketch.md000066400000000000000000000423631474157046300227050ustar00rootroot00000000000000# `sourmash sketch` documentation ```{contents} Contents :depth: 3 ``` Most of the commands in sourmash work with **signatures**, which contain information about genomic or proteomic sequences. Each signature contains one or more **sketches**, which are compressed versions of these sequences. Using sourmash, you can search, compare, and analyze these sequences in various ways. To create a signature with one or more sketches, you use the `sourmash sketch` command. There are three main commands: ``` sourmash sketch dna sourmash sketch protein sourmash sketch translate sourmash sketch fromfile ``` The `sketch dna` command reads in **DNA sequences** and outputs **DNA sketches**. The `sketch protein` command reads in **protein sequences** and outputs **protein sketches**. The `sketch translate` command reads in **DNA sequences**, translates them in all six frames, and outputs **protein sketches**. The `sketch fromfile` command takes in a CSV file containing the locations of genomes and proteomes, and outputs all of the requested sketches. It is primarily intended for large-scale database construction. (`fromfile` is a new command as of sourmash v4.4.0.) All `sourmash sketch` commands take FASTA or FASTQ sequences as input; input data can be uncompressed, compressed with gzip, or compressed with bzip2. The output will be one or more signature files that can be used by other sourmash commands. ## Quickstart ### DNA sketches for genomes and reads To create a DNA sketch for a genome, run: ``` sourmash sketch dna genome.fna ``` This will create an output file `genome.fna.sig` in the current directory, containing a single DNA signature for the entire genome, calculated using the default parameters. Sourmash can work with unassembled reads; run ``` sourmash sketch dna -p k=21,k=31,k=51,abund metagenome.fq.gz ``` to create three abundance-weighted sketches at k=21, 31, and 51, for the given FASTQ file. By default, `sketch dna` ignores bad k-mers (e.g. non-ACGT characters in DNA). If `--check-sequence` is provided, `sketch dna` will error exit on the first bad k-mer. ### Building a combined sketch from two or more files If you have multiple files, sourmash will by default create one sketch for _each_ file. For situations such as paired-end read files from Illumina sequencing, you may instead want to build a combined sketch. You can build a combined sketch in two ways. First, you can use `--name/--merge` to build a single (named) sketch out of multiple input files: ``` sourmash sketch dna -p k=31 sample_R1.fq.gz sample_R2.fq.gz \ --name "sample" -o sample.zip ``` Here you need to specify a name because sourmash does not pick a default name when given multiple files; you also need to provide an output file name because sourmash doesn't pick a default output name in this situation. Second, you can stream the input files into `sourmash sketch` via stdin: ``` gunzip -c sample_R?.fq.gz | sourmash sketch dna -p k=31 - \ -o sample.zip ``` As above, you need to specify an output filename because sourmash can't guess a good default for streaming input. The `--name` option can still be specified if you want to name the output sketch something other than `-`. Note that the order of sequences or sequence files does not affect the output of `sourmash sketch` at all: you do not need to interleave reads or provide the input files in a consistent order. ### Protein sketches for genomes and proteomes The command: ``` sourmash sketch translate genome.fna ``` will output a protein sketch in `./genome.fna.sig`, calculated by translating the genome sequence in all six frames and then using the default protein sketch parameters. K-mers may include stop codons and stop codons are considered valid protein-coding sequence. And ``` sourmash sketch protein -p k=25,scaled=500 -p k=27,scaled=250 genome.faa ``` outputs two protein sketches to `./genome.faa.sig`, one calculated with k=25 and scaled=500, the other calculated with k=27 and scaled=250. If you want to use different encodings, you can specify them in a few ways; here is a parameter string that specifies a dayhoff encoding for the k-mers: ``` sourmash sketch protein -p k=25,scaled=500,dayhoff genome.faa ``` ### Translated DNA sketches for metagenomes The command ``` sourmash sketch translate metagenome.fq ``` will take each read in the FASTQ file and translate the read into amino acid sequence in all six possible coding frames. No attempt is made to determine the right frame (but we are working on ways to determine this; see [orpheum](https://github.com/czbiohub/orpheum)). We suggest using this primarily on unassembled metagenome data. For most microbial genomes, it is both higher quality and more efficient to first predict the coding sequences (using e.g. prodigal) and then use `sketch protein` to build signatures. ### Bulk sketch construction from many files (This was added as of sourmash v4.4.0.) The `sourmash sketch fromfile` command is intended for use when building many signatures as part of a larger workflow. It supports a variety of options to build new signatures, parallelize signature construction, and otherwise aid in tracking and managing database construction. The command ``` sourmash sketch fromfile datasets.csv -p dna -p protein -o database.zip ``` will ingest a CSV spreadsheet containing (at a minimum) the three columns `name`, `genome_filename`, and `protein_filename`, and build all of the signatures requested by the parameter strings. Other columns in this file will be ignored. If no protein, hp, or dayhoff sketches are requested, `protein_filename` can be empty for a given row; likewise, if no DNA sketches are requested, `genome_filename` can be empty for a given row. Some of the key command-line options supported by `fromfile` are: * `-o/--output-signatures` will save generated signatures to any of the [standard supported output formats](command-line.md#choosing-signature-output-formats). * `-o/--output-csv-info` will save a CSV file of input filenames and parameter strings for use with the `sourmash sketch` command line; this can be used to construct signatures in parallel. * `--already-done` will take a list of existing signatures/databases to check against; signatures with matching names and parameter strings will not be rebuilt. * `--output-manifest-matching` will output a manifest of already-existing signatures, which can then be used with `sourmash sig cat` to collate signatures across databases; see [using manifests](command-line.md#using-standalone-manifests-to-explicitly-refer-to-collections-of-files). (This provides [`sourmash sig check` functionality](command-line.md#sourmash-signature-check---compare-picklists-and-manifests) in `sketch fromfile`.) If you would like help and advice on constructing large databases, or pointers to code for generating the `fromfile` CSV format, please ask [on the sourmash issue tracker](https://github.com/sourmash-bio/sourmash/issues) or [gitter support channel](https://gitter.im/sourmash-bio/community). ## More detailed documentation ### Input formats `sourmash sketch` auto-detects and reads FASTQ or FASTA files, either uncompressed or compressed with gzip or bzip2. The filename doesn't matter; `sourmash sketch` will figure out the format from the file contents. You can also stream any of these formats into `sourmash sketch` via stdin by using `-` as the input filename. For example, ``` gunzip -c data/GCF*.fna.gz | sourmash sketch dna - -o out.sig ``` will make a single DNA signature from all of the FASTA sequences in `data/GCF*.fna.gz`. Note, for signatures calculated from stdin, the signature filename attribute will be left empty, and `sourmash sig describe` will output `** no name **`. ### Input contents and output signatures By default, `sourmash sketch` will produce signatures for each input *file*. If the file contains multiple FASTA/FASTQ records, these records will be merged into the output signature. You can provide a *list of FASTA files* in a text file to `sourmash sketch` by passing the text file path in via `--from-file`. If you specify `--singleton`, `sourmash sketch` will produce signatures for each *record*. If you specify `--merge `, sourmash sketch will produce signatures for all input files and combine them into one signature. The output signature(s) will be saved in locations that depend on your input parameters. By default, `sourmash sketch` will put the signatures in the current directory, in a file named for the input file with a `.sig` suffix. If you specify `-o`, all of the signatures will be placed in that file. ### Protein encodings `sourmash sketch protein` and `sourmash sketch translate` output protein sketches by default, but can also use the `dayhoff` and `hp` encodings. The [Dayhoff encoding](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-367/tables/1) collapses multiple amino acids into a smaller alphabet so that amino acids that share biochemical properties map to the same character. The hp encoding divides amino acids into hydrophobic and polar (hydrophilic) amino acids, collapsing amino acids with hydrophobic side chains together and doing the same for polar amino acids. We are still in the process of benchmarking these encodings; ask [on the issue tracker](https://github.com/sourmash-bio/sourmash/issues) if you are interested in updates. Note that stop characters (`*`) are considered valid in all three encodings, and are not truncated. For example, amino acid sequences that contain stop characters at the end will produce a k-mer containing the stop character, and that k-mer will be hashed and potentially included in the sketch. ### Parameter strings The `-p` argument to `sourmash sketch` provides parameter strings to sourmash, and these control what signatures and sketches are calculated and output. Zero or more parameter strings can be given to sourmash. Each parameter string produces at least one sketch. A parameter string is a space-delimited collection that can contain one or more fields, comma-separated. * `k=` - create a sketch at this k-mer size; can provide more than one time in a parameter string. Typically `ksize` is between 4 and 100. * `scaled=` - create a scaled MinHash with k-mers sampled deterministically at 1 per `` value. This controls sketch compression rates and resolution; for example, a 5 Mbp genome sketched with a scaled of 1000 would yield approximately 5,000 k-mers. `scaled` is incompatible with `num`. See [our guide to signature resolution](using-sourmash-a-guide.md#what-resolution-should-my-signatures-be-and-how-should-i-create-them) for more information. * `num=` - create a standard MinHash with no more than `` k-mers kept. This will produce sketches identical to [mash sketches](https://mash.readthedocs.io/en/latest/). `num` is incompatible with `scaled`. See [our guide to signature resolution](using-sourmash-a-guide.md#what-resolution-should-my-signatures-be-and-how-should-i-create-them) for more information. * `abund` / `noabund` - create abundance-weighted (or not) sketches. See [Classify signatures: Abundance Weighting](classifying-signatures.md#abundance-weighting) for details of how this works. * `dna`, `protein`, `dayhoff`, `hp` - create this kind of sketch. Note that `sourmash sketch dna -p protein` and `sourmash sketch protein -p dna` are invalid; please use `sourmash sketch translate` for the former. * `seed=` - set the random number seed used for k-mer hashing. This is for advanced users who want to choose a completely different set of k-mers for sketches! The default is 42. For all field names but `k`, if multiple fields in a parameter string are provided, the last one encountered overrides the previous values. For `k`, if multiple ksizes are specified in a single parameter string, sketches for all ksizes specified are created. If a field isn't specified, then the default value for that sketch type is used; so, for example, `sourmash sketch dna -p abund` would calculate a sketch with `k=31,scaled=1000,abund`. See below for the defaults. ### Default parameters The default parameters for sketches are as follows: * dna: `k=31,scaled=1000,noabund` * protein: `k=10,scaled=200,noabund` * dayhoff: `k=16,scaled=200,noabund` * hp: `k=42,scaled=200,noabund` These were chosen by a committee of PhDs as being good defaults for an initial analysis, so, beware :). More seriously, the DNA parameters were chosen based on the analyses done by Koslicki and Falush in [MetaPalette: a k-mer Painting Approach for Metagenomic Taxonomic Profiling and Quantification of Novel Strain Variation](https://msystems.asm.org/content/1/3/e00020-16). The protein, dayhoff, and hp parameters were selected based on unpublished research results and/or magic formulas. We are working on publishing the results! Please ask on the [issue tracker](https://github.com/sourmash-bio/sourmash/issues) if you are curious. ### More complex parameter string examples Below are some more complicated `sourmash sketch` command lines: * `sourmash sketch dna -p k=51` - default to a scaled=1000 and noabund for a k-mer size of 51 (based on moltype/command) * `sourmash sketch dna -p k=31,k=51,k=21` - create one signature with multiple ksizes, using the defaults otherwise * `sourmash sketch translate -p k=20,num=500,protein -p k=19,num=400,dayhoff,abund -p k=30,scaled=200,hp` - create three signatures with different ksizes, moltypes, and scaled/num. ### Signature naming Signature names are displayed in the output for search, gather, and compare, and can be specified in a few different ways. With default arguments, `sourmash sketch` does not set a name, and the filename is used in display output. You can set a name using `--name`, but this has the side effect of merging the sequence records before signature creation. So, for example, `sourmash sketch dna genome1.fa genome2.fa --name genome1 -o genome.sig` would produce one signature after combining `genome1.fa` and `genome2.fa`. The option `--name-from-first` will set the signature name from the first record header encountered in each file. When used with `--singleton`, this will name each signature based on the record that it is created from. You can examine the signature name using `sourmash sig describe`. Individual signature renaming can be done from the command line using `sourmash sig split` to create individual files for each signature, and then `sourmash sig rename`. ### Locations for output files Signature files can contain multiple signatures and sketches. Use `sourmash sig fileinfo` to summarize the contents of a signature file, and `sourmash sig describe` to get details on the contents of a file. You can use `-o ` to specify a file output location for all the output signatures; `-o -` means stdout. This does not merge signatures unless `--merge` is provided. Specify `--outdir` to put all the signatures in a specific directory. ### Output file formats Sourmash can read and write signatures in many different formats, and `sourmash sketch ... -o ` supports all of the standard output formats. Our recommendation is to output to zip files - e.g. `filename.zip` - as this is the smallest and most flexible signature storage format. Please see [Choosing signature output formats](command-line.md#choosing-signature-output-formats) for more details. ### Downsampling and flattening signatures Creating signatures is probably the most time consuming part of using sourmash, and it is the only part that requires access to the raw data. Moreover, the output signatures are generally much smaller than the input data. So, we generally suggest creating a large set of signatures once. To support this, sourmash can do two kinds of signature conversion without going back to the raw data. First, you can downsample `num` and `scaled` signatures using `sourmash sig downsample`. For any sketch created with `num` parameter, you can decrease that `num`. And, for any `scaled` parameter, you can increase the `scaled`. This will decrease the size of the sketch accordingly; for example, going from a `num` of 5000 to a `num` of 1000 will decrease the sketch size by a factor of 5, and going from a `scaled` of 1000 to a `scaled` of 10000 will decrease the sketch size by a factor of 10. (Note that decreasing `num` or increasing `scaled` will increase calculation speed and lower the accuracy of your results.) Second, you can flatten abundances using `sourmash sig flatten`. For any sketch created with `abund`, you can convert it to a `noabund` sketch. This will decrease the sketch size, although not necessarily by a lot. Unfortunately, changing the k-mer size or using different DNA/protein encodings cannot be done on a sketch, and you need to create new signatures from the raw data for that. ### Examining the output of `sourmash sketch` You can use `sourmash sig describe` to get detailed information about the contents of a signature file, and `sourmash sig fileinfo` to get a human-readable summary of the contents. This can help if you want to see exactly what a particular `sourmash sketch` command does! ### Filing issues and asking for help We try to provide good documentation and error messages, but may not succeed in answer all your questions! So we're happy to help out! Please post questions [on the sourmash issue tracker](https://github.com/sourmash-bio/sourmash/issues). If you find something confusing or buggy about the documentation or about sourmash, we'd love to fix it -- for you *and* for everyone else! sourmash-bio-sourmash-bc3c0f8/doc/storage.md000066400000000000000000000035471474157046300212320ustar00rootroot00000000000000# Storing SBTs We support different storage options for the internal SBT data. ## Available storages ### FSStorage The initial storage schema. Saves internal SBT data in a hidden directory near the SBT JSON description. - Pros: easy to create - Cons: annoying to distribute (thousands of files). We used to create a tar file of JSON + hidden directory, which requires extracting and using more disk space. ### ZipStorage Similar to FSStorage, but saves the internal SBT data in a `zip` file. - Pros * easy to distribute (one file) - Cons * still need to distribute and download everything (you need the full zip file available locally to be able to use the SBT). ### IPFSStorage Uses IPFS to store internal SBT data, allowing partial database download. - Pros * easy to distribute (one file, the SBT JSON description) * only data needed for analysis is downloaded * benefits from more people storing the data in their computers and sharing bandwidth - Cons: * needs IPFS daemon running in the computer * takes longer to run if data is not prefetched ### RedisStorage Meant to be a fast in-memory storage. There won't be a public Redis server to provide the internal SBT data, but this storage is a good option for loading data from others sources and sharing with other processes or servers in your private network. - Pros * Shareable between processes or servers in a network * Faster access time than reading from disk (probably?) - Cons * No public server for the data (need to convert from other sources) ## Converting an existing tree to use a new storage You can convert SBTs to another storage using the `sourmash storage convert` command: ``` bash $ sourmash storage convert -b new_storage_type database.sbt.json ``` For example: to convert a tree to IPFSStorage, do ``` bash $ sourmash storage convert -b ipfs database.sbt.json ``` sourmash-bio-sourmash-bc3c0f8/doc/support.md000066400000000000000000000260531474157046300212770ustar00rootroot00000000000000# Support, Versioning, and Migration ```{contents} :depth: 3 ``` ## Asking questions and filing bugs We do our best to support sourmash users! Users have found important bugs, and some of our best features have come from user requests. Please help us improve sourmash for everyone by asking questions as you have them! Please ask questions and file bug descriptions [on the GitHub issue tracker for sourmash, sourmash-bio/sourmash/issues][0]. You can also ask questions of Titus on Twitter at [@ctitusbrown][1]. [0]:https://github.com/sourmash-bio/sourmash/issues [1]:https://twitter.com/ctitusbrown/ ## Versioning and stability of features and APIs We do our best to guarantee stability of features and APIs within major versions - because of this, upgrading from (e.g.) sourmash v3.4 to sourmash v3.5 should be a simple matter of installing the new version. We also recommend using _version pinning_ for software and workflows that depend on sourmash, e.g. specifying `sourmash >=3,<4` for software that is tested with sourmash 3.x. Read on for details! Upgrading major versions (to sourmash 4.0, for example) will often involve more work; see the [next section](#upgrading-major-versions) for more our suggested process. ### Semantic versioning Our goal is to support the use of sourmash in pipelines and applications by communicating clearly about bug fixes, feature additions, and feature changes in sourmash. Versions are tagged in a `vMAJOR.MINOR.PATCH` format, following the [Semantic Versioning] convention. From their definition: "Given a version number MAJOR.MINOR.PATCH, increment the: * MAJOR version when you make incompatible API changes, * MINOR version when you add functionality in a backwards compatible manner, and * PATCH version when you make backwards compatible bug fixes." So, for example, * Major releases, like v4.0.0, may break backwards compatibility at the command line as well as top-level Python/Rust APIs. * Minor releases, like v4.1.0, will remain backwards compatible but may introduce significant new features. * Patch releases, like v4.1.1, are for minor bug fixes; full backwards compatibility is retained. We do sometimes (rarely!) alter behavior in minor versions by fixing bugs; this will be documented in release notes. ### Version pinning For software and workflows that depend on sourmash, we recommend pinning versions to the current _major_ release of sourmash. For example, with Python toolchains such as pip, you should be able to use: ``` sourmash>=3,<4 ``` to pin the version requirement to any sourmash v3.x release. For conda, the same syntax should work. ### Command line stability We intend that all command-line commands, command-line options, input formats, and output formats will be fully backwards compatible within major versions. That is, you should never see old behavior change when you upgrade within a major sourmash release (barring bug fixes!). Moreover, if you rely on a feature introduced in v3.3.0, that feature will not break in v3.4.0, but will also not be backported to version 3.2.0. ### Output file formats In particular, the CSV output file formats are guaranteed to be stable within major versions, with one caveat: we may add or rearrange columns between releases. You should use column headers/column names to parse CSV files, and not depend on column order. ### Python API We intend to guarantee the Python API at the top level, i.e. functions and classes available from the `sourmash` top-level module will be stable within major versions. The latest minor release (e.g. v3.5) before a new major release (v4.0) will contain deprecations for all top-level API changes at the time of the first major release. See below for our suggested migration procedure. ### Python version support sourmash v3.x supports Python 2.7 as well as Python 3.x, through Python 3.8. sourmash v4.0 dropped support for versions of Python before Python 3.7, and our intent is that it will support as-yet unreleased versions of Python 3.x (e.g. 3.10) moving forward. For future versions of sourmash, we plan to follow the [Scientific Python SPEC 0](https://scientific-python.org/specs/spec-0000/) proposal for Python version support. For example, this means that we dropped support for Python 3.9 on October 10, 2023. ### Rust API The Rust API is not yet at 1.0 and should not be regarded as stable. ## Upgrading major versions If you depend on sourmash, we recommend using the following process: * pin sourmash to the major version you developed against, e.g. `sourmash >=3,<4`. * when ready to upgrade sourmash, upgrade to the latest minor release within that major version (e.g. sourmash 3.5.x). * scan for deprecations that affect you, check [the release notes](https://github.com/sourmash-bio/sourmash/releases), and fix any major issues noted. * upgrade to the next major version (e.g. sourmash 4.0) and run your integration tests or workflow. * fix outstanding issues. In particular, we recommend upgrading major versions of sourmash in isolation, without adding any new features to your software. ### Migrating from sourmash v3.x to sourmash v4.x. If you want to upgrade workflows and scripts from prior releases of sourmash to sourmash v4.0, we suggest doing this in two stages. First, upgrade to the latest version of sourmash 3.5.x (currently [v3.5.1](https://github.com/sourmash-bio/sourmash/releases/tag/v3.5.1)), which is compatible with all files and command lines used in previous versions of sourmash (v2.x and v3.x). After upgrading to 3.5.x, scan the sourmash output for deprecation warnings and fix those. Next, upgrade to the latest version of 4.x, which will introduce some backwards incompatibilities based upon the deprecation warnings. The major changes are detailed below; please see the [full release notes for 4.0](release-notes/sourmash-4.0.md) for all the details and links to the code changes. ### Sourmash command line If you use sourmash from the command line, there are a few major changes in 4.0 that you should know about. First, **`sourmash compute` is deprecated in favor of [`sourmash sketch`](sourmash-sketch.md)**, which provides quite a bit more flexibility in creating signatures. Second, **`sourmash index` will now save databases in the Zip format (`.sbt.zip`) instead of the old JSON+subdirectory format** (see [updated docs](command-line.md#sourmash-index---build-an-sbt-index-of-signatures)). You can revert to the old behavior by explicitly specifying the `.sbt.json` filename for output when running `sourmash index`. Third, all sourmash commands that operate on signatures should now be able to directly read from lists of signatures in signature files, SBT databases, LCA databases, directories, and files containing lists of filenames (see [updated docs](command-line.md#advanced-command-line-usage)). Fourth, if you use `sourmash lca` commands, **`sourmash lca gather` has been removed**. In addition, there are some **changes in how `summarize` works**: it now uses abundances by default, and no longer combines all signatures before summarizing. Specify `--ignore-abundance` and combine your signatures using `sourmash sig merge` to recover the old behavior. Note also that `lca summarize` now includes a new column, `filename`, in the CSV output. Finally, **k-mer sizes have changed for amino acid sequences** in v4. If you use protein, Dayhoff, or HP signatures, we now interpret k-mer sizes differently on the command line. Briefly, k-mer sizes for protein/dayhoff/hp signatures are now the size of the k-mer in amino acid space, *not* the space of the k-mer in DNA space (as previously used). In practice this means that you need to divide all your old k-mer sizes by 3 when working with k-mers in amino acid space! Note also that while `sourmash compute` still behaves the same way in v4.x as it did in sourmash 3.5.x, `sourmash sketch translate` and `sourmash sketch protein` both use the *new* approach to amino acid k-mer sizes, as do all of the the command line options for searching, manipulation, and display. Again, in practice this means that you need to divide all your old k-mer sizes by 3 if they apply to amino acid k-mers. There are several minor changes where error messages should occur appropriately: * `--traverse-directory` is no longer needed on the command line for `sourmash index` or other functions; directory traversal happens automatically. * the command lines for `sourmash index` and `sourmash lca index` no longer require signature files to be specified, which can break existing command lines. To fix this, reorder arguments so that any signatures are specified at the end of the command line. ### Python API First, all k-mer sizes for `protein`, `dayhoff`, and `hp` signatures have changed in the Python layer to be "correct", i.e., to be the size of the protein k-mer. Previously they were 3\*k, i.e. based on the size of the DNA k-mer from which the protein sequence would have been created. Second, the `MinHash` class API has changed significantly! * `get_mins()` has been deprecated in favor of `.hashes`, which is a dictionary that contains abundances. * `merge` now just modifies `MinHash` objects in-place, and no longer returns the merged object; use `__iadd__` (`+=`) for the old behavior, or `__add__` (`+`) to create a new merged object. * `max_hash` has been deprecated in favor of `scaled`. * instead of `downsample_scaled(s)` use `downsample(scaled=s)` * instead of `downsample_n(m)` use `downsample(num=m)` * `is_molecule_type` has been replaced with a property, `moltype` -- instead of `is_molecule_type(t)` use `moltype == t`. Third, `SourmashSignature` objects no longer have a `name()` method but instead a `name` property, which can be assigned to. This property is now `None` when no name has been assigned. Note that `str(sig)` should now be used to retrieve a display name, and should replace all previous uses of `sig.name()`. Fourth, a few top-level functions have been deprecated: `load_signatures(...)`, `load_one_signature(...)`, `create_sbt_index(...)`, and `load_sbt_index(...)`. * `load_signatures(...)`, `load_one_signature(...)` should be replaced with `load_file_as_signatures(...)`. Note there is currently no top-level way to load signatures from strings. For now, if you need that functionality, you can use `sourmash.signature.load_signatures(...)` and `sourmash.signature.load_one_signature(...)`, but please be aware that these are not considered part of the public API that is under semantic versioning, so they may change in the next minor point release; this is tracked in https://github.com/sourmash-bio/sourmash/issues/1312. * `load_sbt_index(...)` have been deprecated. Please use `load_file_as_index(...)` instead. * `create_sbt_index(...)` has been deprecated. There is currently no replacement, although you can use it directly from `sourmash.sbtmh` if necessary. Fifth, directory traversal now happens by default when loading signatures, so remove `traverse=True` arguments to several functions in `sourmash_args` - `load_dbs_and_sigs`, `load_file_as_index`, `and load_file_as_signatures`. Please post questions and concerns to the [sourmash issue tracker](https://github.com/sourmash-bio/sourmash/issues) and we'll be happy to help! sourmash-bio-sourmash-bc3c0f8/doc/toc.md000066400000000000000000000012241474157046300203410ustar00rootroot00000000000000# Full table of contents ```{toctree} --- maxdepth: 2 --- api-example.md api.md classifying-signatures.md command-line.md databases-advanced.md databases.md dev_plugins.md developer.md faq.md funding.md index.md kmers-and-minhash.ipynb legacy-databases.md more-info.md other-languages.md plotting-compare.ipynb publications.md release.md requirements.md sourmash-collections.ipynb sourmash-examples.ipynb sourmash-internals.md sourmash-sketch.md storage.md support.md tutorial-install.md tutorial-basic.md tutorial-lemonade.md tutorial-lin-taxonomy.md tutorial-long.md tutorials-lca.md tutorials.md using-LCA-database-API.ipynb using-sourmash-a-guide.md ``` sourmash-bio-sourmash-bc3c0f8/doc/tutorial-basic.md000066400000000000000000000216271474157046300225070ustar00rootroot00000000000000# The first sourmash tutorial - making signatures, comparing, and searching You'll need about 5 GB of free disk space, and about 5 GB of RAM to search GenBank. The tutorial should take about 20 minutes total to run. In fact, we have successfully tested it on [binder.pangeo.io](https://binder.pangeo.io/v2/gh/binder-examples/r-conda/master?urlpath=urlpath%3Drstudio) if you want to give it a try! ## Install sourmash You'll need to [install sourmash](tutorial-install.md) first! ## Generate a signature for Illumina reads Download some reads and a reference genome: ``` mkdir ~/data cd ~/data curl -L https://osf.io/ruanf/download -o ecoliMG1655.fa.gz curl -L https://osf.io/q472x/download -o ecoli_ref-5m.fastq.gz ``` Compute a scaled signature from our reads: ``` mkdir ~/sourmash cd ~/sourmash sourmash sketch dna -p scaled=10000,k=31 ~/data/ecoli_ref*.fastq.gz -o ecoli-reads.sig ``` ## Compare reads to assemblies Use case: how much of the read content is contained in the reference genome? Build a signature for an E. coli genome: ``` sourmash sketch dna -p scaled=1000,k=31 ~/data/ecoliMG1655.fa.gz -o ecoli-genome.sig ``` and now evaluate *containment*, that is, what fraction of the read content is contained in the genome: ``` sourmash search ecoli-reads.sig ecoli-genome.sig --containment ``` and you should see: ``` select query k=31 automatically. loaded query: /home/jovyan/data/ecoli_ref-5m... (k=31, DNA) loaded 1 signatures. 1 matches: similarity match ---------- ----- 31.0% /home/jovyan/data/ecoliMG1655.fa.gz ``` Try the reverse, too! ``` sourmash search ecoli-genome.sig ecoli-reads.sig --containment ``` ## Make and search a database quickly. Suppose that we have a collection of signatures (made with `sourmash compute` as above) and we want to search it with our newly assembled genome (or the reads, even!). How would we do that? Let's grab a sample collection of 50 E. coli genomes and unpack it -- ``` mkdir ecoli_many_sigs cd ecoli_many_sigs curl -O -L https://github.com/sourmash-bio/sourmash/raw/latest/data/eschericia-sigs.tar.gz tar xzf eschericia-sigs.tar.gz rm eschericia-sigs.tar.gz cd ../ ``` This will produce 50 files named `ecoli-N.sig` in the directory `ecoli_many_sigs/` -- ``` ls ecoli_many_sigs ``` Let's turn this into an easily-searchable database with `sourmash index` -- ``` sourmash index ecolidb ecoli_many_sigs/*.sig ``` and now we can search! ``` sourmash search ecoli-genome.sig ecolidb.sbt.zip -n 20 ``` You should see output like this: ``` select query k=31 automatically. loaded query: /home/ubuntu/data/ecoliMG1655.... (k=31, DNA) loaded 0 signatures and 1 databases total. 49 matches; showing first 20: similarity match ---------- ----- 75.9% NZ_JMGW01000001.1 Escherichia coli 1-176-05_S4_C2 e117605... 73.0% NZ_JHRU01000001.1 Escherichia coli strain 100854 100854_1... 71.9% NZ_GG774190.1 Escherichia coli MS 196-1 Scfld2538, whole ... 70.5% NZ_JMGU01000001.1 Escherichia coli 2-011-08_S3_C2 e201108... 69.8% NZ_JH659569.1 Escherichia coli M919 supercont2.1, whole g... 59.9% NZ_JNLZ01000001.1 Escherichia coli 3-105-05_S1_C1 e310505... 58.3% NZ_JHDG01000001.1 Escherichia coli 1-176-05_S3_C1 e117605... 56.5% NZ_MIWF01000001.1 Escherichia coli strain AF7759-1 contig... 56.1% NZ_MOJK01000001.1 Escherichia coli strain 469 Cleandata-B... 56.1% NZ_MOGK01000001.1 Escherichia coli strain 676 BN4_676_1_(... 50.5% NZ_KE700241.1 Escherichia coli HVH 147 (4-5893887) acYxy-... 50.3% NZ_APWY01000001.1 Escherichia coli 178200 gec178200.conti... 48.8% NZ_LVOV01000001.1 Escherichia coli strain swine72 swine72... 48.8% NZ_MIWP01000001.1 Escherichia coli strain K6412 contig_00... 48.7% NZ_AIGC01000068.1 Escherichia coli DEC7C gecDEC7C.contig.... 48.2% NZ_LQWB01000001.1 Escherichia coli strain GN03624 GCID_EC... 48.0% NZ_CCQJ01000001.1 Escherichia coli strain E. coli, whole ... 47.3% NZ_JHMG01000001.1 Escherichia coli O121:H19 str. 2010EL10... 47.2% NZ_JHGJ01000001.1 Escherichia coli O45:H2 str. 2009C-4780... 46.5% NZ_JHHE01000001.1 Escherichia coli O103:H2 str. 2009C-327... ``` ## Compare many signatures and build a tree. Compare all the things: ``` sourmash compare ecoli_many_sigs/* -o ecoli_cmp ``` Optionally, parallelize to 8 threads using `-p 8`: ``` sourmash compare -p 8 ecoli_many_sigs/* -o ecoli_cmp ``` and then plot: ``` sourmash plot --pdf --labels ecoli_cmp ``` which will produce files named `ecoli_cmp.matrix.pdf` and `ecoli_cmp.dendro.pdf`. Here's a PNG version: ![E. coli comparison plot](_static/ecoli_cmp.matrix.png) ## What's in my metagenome? Download a database containing all of the GenBank microbial genomes: ``` curl -L -o genbank-k31.lca.json.gz https://osf.io/4f8n3/download ``` Next, run the 'gather' command to see what's in your ecoli genome -- ``` sourmash gather ecoli-genome.sig genbank-k31.lca.json.gz ``` and you should get: ``` loaded query: /home/diblions/data/ecoliMG165... (k=31, DNA) loading from genbank-k31.lca.json.gz... loaded 1 databases. overlap p_query p_match --------- ------- ------- 4.9 Mbp 100.0% 100.0% LRDF01000001.1 Escherichia coli strai... found 1 matches total; the recovered matches hit 100.0% of the query ``` In this case, the output is kind of boring because this is a single genome. But! You can use this on metagenomes (assembled and unassembled) as well; you've just got to make the signature files. To see this in action, here is gather running on a signature generated from some sequences that assemble (but don't align to known genomes) from the [Shakya et al. 2013 mock metagenome paper.][2] ``` wget https://github.com/sourmash-bio/sourmash/raw/latest/doc/_static/shakya-unaligned-contigs.sig sourmash gather -k 31 shakya-unaligned-contigs.sig genbank-k31.lca.json.gz ``` This should yield: ``` loaded query: mqc500.QC.AMBIGUOUS.99.unalign... (k=31, DNA) loaded 1 databases. overlap p_query p_match --------- ------- ------- 1.4 Mbp 11.0% 58.0% JANA01000001.1 Fusobacterium sp. OBRC... 1.0 Mbp 7.7% 25.9% CP001957.1 Haloferax volcanii DS2 pla... 0.9 Mbp 7.4% 11.8% BA000019.2 Nostoc sp. PCC 7120 DNA, c... 0.7 Mbp 5.9% 23.0% FOVK01000036.1 Proteiniclasticum rumi... 0.7 Mbp 5.3% 17.6% AE017285.1 Desulfovibrio vulgaris sub... 0.6 Mbp 4.9% 11.1% CP001252.1 Shewanella baltica OS223, ... 0.6 Mbp 4.8% 27.3% AP008226.1 Thermus thermophilus HB8 g... 0.6 Mbp 4.4% 11.2% CP000031.2 Ruegeria pomeroyi DSS-3, c... 480.0 kbp 3.8% 7.6% CP000875.1 Herpetosiphon aurantiacus ... 410.0 kbp 3.3% 10.5% CH959317.1 Sulfitobacter sp. NAS-14.1... 1.4 Mbp 2.2% 11.8% LN831027.1 Fusobacterium nucleatum su... 0.5 Mbp 2.1% 5.3% CP000753.1 Shewanella baltica OS185, ... 420.0 kbp 1.9% 7.7% FNDZ01000023.1 Proteiniclasticum rumi... 150.0 kbp 1.2% 4.6% AE000513.1 Deinococcus radiodurans R1... 150.0 kbp 1.2% 8.2% CP000969.1 Thermotoga sp. RQ2, comple... 290.0 kbp 1.1% 4.1% CH959311.1 Sulfitobacter sp. EE-36 sc... 1.2 Mbp 1.0% 5.0% CP013328.1 Fusobacterium nucleatum su... 110.0 kbp 0.9% 3.7% FRDZ01000215.1 Enterococcus faecalis ... 0.6 Mbp 0.8% 2.8% CP000527.1 Desulfovibrio vulgaris DP4... 70.0 kbp 0.6% 1.2% CP000850.1 Salinispora arenicola CNS-... 340.0 kbp 0.6% 3.3% KQ235732.1 Fusobacterium nucleatum su... 60.0 kbp 0.5% 0.7% CP000270.1 Burkholderia xenovorans LB... 50.0 kbp 0.4% 2.6% CP001080.1 Sulfurihydrogenibium sp. Y... 50.0 kbp 0.4% 3.2% L77117.1 Methanocaldococcus jannaschi... found less than 40.0 kbp in common. => exiting found 24 matches total; the recovered matches hit 73.1% of the query ``` If you use the `-o` flag, gather will write out a csv that contains additional information. The column headers and their meanings are: + `intersect_bp`: the approximate number of base pairs in common between the query and the match + `f_orig_query`: fraction of original query; the fraction of the original query that is contained within the match + `f_match`: fraction of match; the fraction of the match that is contained within the query + `f_unique_to_query`: fraction unique to query; the fraction of the query that uniquely overlaps with the match + `f_unique_weighted`: fraction unique to query weighted by abundance; fraction unique to query, weighted by abundance in the query It is straightforward to build your own databases for use with `search` and `gather`; see `sourmash index`, above, [the LCA tutorial][4], or [our notebook on working with private collections of signatures][5]. [Return to index][3] [0]:http://ivory.idyll.org/blog/2016-sourmash-sbt-more.html [1]:databases.md [2]:https://pubmed.ncbi.nlm.nih.gov/23387867/ [3]:index.md [4]:tutorials-lca.md [5]:sourmash-collections.ipynb sourmash-bio-sourmash-bc3c0f8/doc/tutorial-install.md000066400000000000000000000020021474157046300230560ustar00rootroot00000000000000# Installing sourmash This tutorial should run without modification on Linux or Mac OS X, under [Miniforge](https://github.com/conda-forge/miniforge). You'll need about 5 GB of free disk space. ## Install miniforge If you don't have the `mamba` command installed, you'll need to install [miniforge](https://github.com/conda-forge/miniforge#install). On Linux, this should work: ``` curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" bash Miniforge3-$(uname)-$(uname -m).sh -b ~/miniforge3/bin/mamba init echo 'source ~/.bashrc' > ~/.bash_profile source ~/.bash_profile ``` otherwise, follow the instructions [here](https://github.com/conda-forge/miniforge#install). ## Install sourmash To install sourmash, create a new environment named `smash` and install sourmash: ``` mamba create -y -n smash sourmash-minimal ``` and then activate: ``` conda activate smash ``` You should now be able to use the `sourmash` command: ``` sourmash info ``` Voila! sourmash-bio-sourmash-bc3c0f8/doc/tutorial-lemonade.md000066400000000000000000000512321474157046300232050ustar00rootroot00000000000000# Analyzing the genomic and taxonomic composition of an environmental genome using GTDB and sample-specific MAGs with sourmash C. Titus Brown, Taylor Reiter, and Tessa Pierce Ward July 2022 Based on a tutorial developed for MBL STAMPS 2022. You'll need 5 GB of disk space and 5 GB of RAM in order to run this tutorial. It will take about 30 minutes of compute time to execute all the commands. --- ```{contents} :depth: 2 ``` In this tutorial, we'll use sourmash to analyze the composition of a metagenome, both genomically and taxonomically. We'll also use sourmash to classify some MAGs and integrate them into our analysis. ## Install sourmash First, we need to install the software! We'll use conda/mamba to do this. The below command installs [sourmash](http://sourmash.readthedocs.io/) and [GNU parallel](https://www.gnu.org/software/parallel/). Run: ``` # create a new environment mamba create -n smash -y -c conda-forge -c bioconda sourmash parallel ``` to install the software, and then run ``` conda activate smash ``` to activate the conda environment so you can run the software. ```{note} Victory conditions: your prompt should start with `(smash) ` and you should now be able to run `sourmash` and have it output usage information!! ``` ## Create a working subdirectory Make a directory named `kmers` and change into it. ``` mkdir ~/kmers cd ~/kmers ``` ## Download a database and a taxonomy spreadsheet. We're going to start by doing a reference-based _compositional analysis_ of the lemonade metagenome from [Taylor Reiter's STAMPS 2022 tutorial on assembly and binning](https://github.com/mblstamps/stamps2022/blob/main/assembly_and_binning/tutorial_assembly_and_binning.md). For this purpose, we're going to need a database of known genomes. We'll use the GTDB genomic representatives database, containing ~65,000 genomes - that's because it's smaller than the full GTDB database (~320,000) or Genbank (~1.3m), and hence faster. But you can download and use those on your own, if you like! You can find the link to a prepared GTDB RS207 database for k=31 on the [the sourmash prepared databases page](https://sourmash.readthedocs.io/en/latest/databases.html). Let's download it to the current directory: ``` curl -JLO https://osf.io/3a6gn/download ``` This will create a 1.7 GB file: ``` ls -lh gtdb-rs207.genomic-reps.dna.k31.zip ``` and you can examine the contents with sourmash `sig summarize`: ``` sourmash sig summarize gtdb-rs207.genomic-reps.dna.k31.zip ``` which will show you: ``` >path filetype: ZipFileLinearIndex >location: /home/stamps2022/kmers/gtdb-rs207.genomic-reps.dna.k31.zip >is database? yes >has manifest? yes >num signatures: 65703 >** examining manifest... >total hashes: 212454591 >summary of sketches: > 65703 sketches with DNA, k=31, scaled=1000, abund 212454591 total hashes ``` There's a lot of things to digest in this output but the two main ones are: * there are 65,703 genome sketches in this database, for a k-mer size of 31 * this database represents 212 *billion* k-mers (multiply number of hashes by the scaled number) If you want to read more about what, exactly, sourmash is doing, please see [Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2), Irber et al., 2022. We also want to download the accompanying taxonomy spreadsheet: ``` curl -JLO https://osf.io/v3zmg/download ``` and uncompress it: ``` gunzip gtdb-rs207.taxonomy.csv.gz ``` This spreadsheet contains information connecting Genbank genome identifiers to the GTDB taxonomy - take a look: ``` head -2 gtdb-rs207.taxonomy.csv ``` will show you: ``` >ident,superkingdom,phylum,class,order,family,genus,species >GCF_000566285.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Enterobacteriaceae,g__Escherichia,s__Escherichia coli ``` Let's index the taxonomy database using SQLite, for faster access later on: ``` sourmash tax prepare -t gtdb-rs207.taxonomy.csv \ -o gtdb-rs207.taxonomy.sqldb -F sql ``` This creates a file `gtdb-rs207.taxonomy.sqldb` that contains all the information in the CSV file, but which is faster to load than the CSV file. ## Download and prepare sample reads Next, let's download one of the metagenomes from [the assembly and binning tutorial](https://github.com/mblstamps/stamps2022/blob/main/assembly_and_binning/tutorial_assembly_and_binning.md#retrieving-the-data). We'll use sample SRR8859675 for today, and you can view sample info [here](https://www.ebi.ac.uk/ena/browser/view/SRR8859675?show=reads) on the ENA. To download the metagenome from the ENA, run: ``` wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR885/005/SRR8859675/SRR8859675_1.fastq.gz wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR885/005/SRR8859675/SRR8859675_2.fastq.gz ``` Now we're going to prepare the metagenome for use with sourmash by converting it into a _signature file_ containing _sketches_ of the k-mers in the metagenome. This is the step that "shreds" all of the reads into k-mers of size 31, and then does further data reduction by [sketching](https://en.wikipedia.org/wiki/Streaming_algorithm) the resulting k-mers. To build a signature file, we run `sourmash sketch dna` like so: ``` sourmash sketch dna -p k=31,abund SRR8859675*.gz \ -o SRR8859675.sig.gz --name SRR8859675 ``` Here we're telling sourmash to sketch at k=31, and to track k-mer multiplicity (with 'abund'). We sketch _both_ metagenome files together into a single signature named `SRR8859675` and stored in the file `SRR8859675.sig.gz`. When we run this, we should see: >`calculated 1 signature for 3452142 sequences taken from 2 files` which tells you how many reads there are in these two files! If you look at the resulting files, ``` ls -lh SRR8859675* ``` you'll see that the signature file is _much_ smaller (2.5mb) than the metagenome files (~600mb). This is because of the way sourmash uses a reduced representation of the data, and it's what makes sourmash fast. Please see the paper above for more info! Also note that the GTDB prepared database we downloaded above was built using the same `sourmash sketch dna` command, but applied to 65,000 genomes and stored in a zip file. ## Find matching genomes with `sourmash gather` At last, we have the ingredients we need to analyze the metagenome against GTDB! * the software is installed * the GTDB database is downloaded * the metagenome is downloaded and sketched Now, we'll run the [sourmash gather](https://sourmash.readthedocs.io/en/latest/command-line.html#sourmash-gather-find-metagenome-members) command to find matching genomes. Run gather - this will take ~6 minutes: ``` sourmash gather SRR8859675.sig.gz gtdb-rs207.genomic-reps.dna.k31.zip --save-matches matches.zip ``` Here we are saving the matching genome sketches to `matches.zip` so we can rerun the analysis if we like. The results will look like this: ``` overlap p_query p_match avg_abund --------- ------- ------- --------- 2.0 Mbp 0.4% 31.8% 1.3 GCF_004138165.1 Candidatus Chloroploc... 1.9 Mbp 0.5% 66.9% 2.1 GCF_900101955.1 Desulfuromonas thioph... 0.6 Mbp 0.3% 23.3% 3.2 GCA_016938795.1 Chromatiaceae bacteri... 0.6 Mbp 0.5% 27.3% 6.6 GCA_016931495.1 Chlorobiaceae bacteri... ... found 22 matches total; the recovered matches hit 5.3% of the abundance-weighted query ``` In this output: * the last column is the name of the matching GTDB genome * the first column is the estimated overlap between the metagenome and that genome, in base pairs (estimated from shared k-mers) * the second column, `p_query` is the percentage of metagenome k-mers (weighted by multiplicity) that match to the genome; this will approximate the percentage of _metagenome reads_ that will map to this genome, if you map. * the third column, `p_match`, is the percentage of the genome k-mers that are matched by the metagenome; this will approximate the percentage of _genome bases_ that will be covered by mapped reads; * the fourth column is the estimated mean abundance of this genome in the metagenome. The other interesting number is here: >`the recovered matches hit 5.3% of the abundance-weighted query` which tells you that you should expect about 5.3% of the metagenome reads to map to these 22 reference genomes. ```{note} You can try running gather without abundance weighting: `sourmash gather SRR8859675.sig.gz matches.zip --ignore-abundance` How does the output differ? The main number that changes bigly is: >`the recovered matches hit 2.4% of the query (unweighted)` which represents the proportion of _unique_ kmers in the metagenome that are not found in any genome. This is (approximately) the following number: * suppose you assembled the entire metagenome perfectly into perfect contigs (**note, this is impossible, although you can get close with "unitigs"**); * and then matched all the genomes to the contigs; * approximately 2.4% of the bases in the contigs would have genomes that match to them. Interestingly, this is the _only_ number in this entire tutorial that is essentially impossible to estimate any way other than with k-mers. This number is also a big underestimate of the "true" number for the metagenome - we'll explain more later :) ``` ## Build a taxonomic summary of the metagenome We can use these matching genomes to build a taxonomic summary of the metagenome using [sourmash tax metagenome](https://sourmash.readthedocs.io/en/latest/command-line.html#sourmash-tax-subcommands-for-integrating-taxonomic-information-into-gather-results) like so: ``` # rerun gather, save the results to a CSV sourmash gather SRR8859675.sig.gz matches.zip -o SRR8859675.x.gtdb.csv # use tax metagenome to classify the metagenome sourmash tax metagenome -g SRR8859675.x.gtdb.csv \ -t gtdb-rs207.taxonomy.sqldb -F human -r order ``` this shows you the rank, taxonomic lineage, and weighted fraction of the metagenome at the 'order' rank. At the bottom, we have a script to plot the resulting taxonomy using [metacoder](https://grunwaldlab.github.io/metacoder_documentation/) - here's what it looks like: ![metacoder output](https://raw.githubusercontent.com/mblstamps/stamps2022/main/kmers_and_sourmash/metacoder_gather.png) ## Interlude: why reference-based analyses are problematic for environmental metagenomes Reference-based metagenome classification is highly dependent on the organisms present in our reference databases. For well-studied environments, such as human-associated microbiomes, your classification percentage is likely to be quite high. In contrast, this is an environmental metagenome, and you can see that we're estimating only 5.3% of it will map to GTDB reference genomes! Wow, that's **terrible**! Our taxonomic and/or functional analysis will be based on only 1/20th of the data! What could we do to improve that?? There are two basic options - (1) Use a more complete reference database, like the entire GTDB, or Genbank. This will only get you so far, unfortunately. (See exercises at end.) (2) Assemble and bin the metagenome to produce new reference genomes! There are other things you could think about doing here, too, but these are probably the "easiest" options. And what's super cool is that we did the second one as part of [Taylor Reiter's STAMPS 2022 tutorial on assembly and binning](https://github.com/mblstamps/stamps2022/blob/main/assembly_and_binning/tutorial_assembly_and_binning.md). So can we include that in the analysis?? Yes, yes we can! We can integrate the three MAGs that Taylor generated during her tutorial into the sourmash analysis. We'll need to: * download the three genomes; * sketch them with k=31; * re-run sourmash gather with both GTDB _and_ the MAGs. Let's do it!! ## Update gather with information from MAGs First, download the MAGs: ``` # Download 3 MAGs generated by ATLAS curl -JLO https://osf.io/fejps/download curl -JLO https://osf.io/jf65t/download curl -JLO https://osf.io/2a4nk/download ``` This will produce three files, `MAG*.fasta`. Now sketch them: ``` sourmash sketch dna MAG*.fasta --name-from-first ``` here, `--name-from-first` is a convenient way to give them distinguishing names based on the name of the first contig in the FASTA file; you can see the names of the signatures by doing: ``` sourmash sig describe MAG1.fasta.sig ``` Now, let's re-do the metagenome classification with the MAGs: ``` sourmash gather SRR8859675.sig.gz MAG*.sig matches.zip -o SRR8859675.x.gtdb+MAGS.csv ``` and look, we classify a lot more! ``` overlap p_query p_match avg_abund --------- ------- ------- --------- 2.3 Mbp 12.1% 99.9% 39.4 MAG2_1 2.2 Mbp 26.5% 99.9% 92.4 MAG3_1 2.0 Mbp 0.4% 31.8% 1.3 GCF_004138165.1 Candidatus Chloroploc... 1.9 Mbp 0.5% 66.9% 2.1 GCF_900101955.1 Desulfuromonas thioph... 1.0 Mbp 2.7% 100.0% 20.3 MAG1_1 0.6 Mbp 0.3% 23.2% 3.1 GCA_016938795.1 Chromatiaceae bacteri... 0.6 Mbp 0.1% 24.5% 2.1 GCA_016931495.1 Chlorobiaceae bacteri... ... found 24 matches total; the recovered matches hit 43.5% of the abundance-weighted query ``` Here we see a few interesting things - (1) The three MAG matches are all ~100% present in the metagenome. (2) They are all at high abundance in the metagenome, because assembly needs genomes to be ~5x or more in abundance in order to work! (3) Because they're at high abundance and 100% present, they account for _a lot_ of the metagenome! What's the remaining 50%? There are several answers - (1) most of the constitutent genomes aren't in the reference database; (2) not everything in the metagenome is high enough coverage to bin into MAGs; (3) not everything in the metagenome is bacterial or archaeal, and we didn't do viral or eukaryotic binning; (4) some of what's in the metagenome k-mers may simply be erroneous (although with abundance weighting, this is likely to be a small chunk of things) ## Classify the taxonomy of the MAGs; update metagenome classification Now we can also classify the genomes and update the taxonomic summary of the metagenome! First, classify the genomes using GTDB; this will use trace overlaps between contigs in the MAGs and GTDB genomes to tentatively identify the _entire_ bin. ``` for i in MAG*.fasta.sig do # get 'MAG' prefix. => NAME NAME=$(basename $i .fasta.sig) # search against GTDB echo sourmash gather $i gtdb-rs207.genomic-reps.dna.k31.zip \ --threshold-bp=5000 \ -o ${NAME}.x.gtdb.csv done | parallel ``` (This will take about a minute.) Here, we're using a for loop and [GNU parallel](https://www.gnu.org/software/parallel/) to classify the three genomes in parallel. If you scan the results quickly, you'll see that one MAG has matches in genus Prosthecochloris, another MAG has matches to Chlorobaculum, and one has matches to Candidatus Moranbacteria. Let's classify them "officially" using sourmash and an average nucleotide identity threshold of 0.8 - ``` sourmash tax genome -g MAG*.x.gtdb.csv \ -t gtdb-rs207.taxonomy.sqldb -F human \ --ani 0.8 ``` This is an extremely liberal ANI threshold, incidentally; in reality you'd probably want to do something more stringent, as at least one of these is probably a new species. You should see: ``` >sample name proportion lineage >----------- ---------- ------- >MAG3_1 5.3% d__Bacteria;p__Bacteroidota;c__Chlorobia;o__Chlorobiales;f__Chlorobiaceae;g__Prosthecochloris;s__Prosthecochloris vibrioformis >MAG2_1 5.0% d__Bacteria;p__Bacteroidota;c__Chlorobia;o__Chlorobiales;f__Chlorobiaceae;g__Chlorobaculum;s__Chlorobaculum parvum_B >MAG1_1 1.1% d__Bacteria;p__Patescibacteria;c__Paceibacteria;o__Moranbacterales;f__UBA1568;g__JAAXTX01;s__JAAXTX01 sp013334245 ``` The proportion here is the fraction of k-mers in the MAG that are annotated. Now let's turn this into a lineage spreadsheet: ``` sourmash tax genome -g MAG*.x.gtdb.csv \ -t gtdb-rs207.taxonomy.sqldb -F lineage_csv \ --ani 0.8 -o MAGs ``` This will produce a file `MAGs.lineage.csv`; let's take a look: ``` cat MAGs.lineage.csv ``` You should see: ``` >ident,superkingdom,phylum,class,order,family,genus,species >MAG1_1,d__Bacteria,p__Patescibacteria,c__Paceibacteria,o__Moranbacterales,f__UBA1 568,g__JAAXTX01,s__JAAXTX01 sp013334245 >MAG2_1,d__Bacteria,p__Bacteroidota,c__Chlorobia,o__Chlorobiales,f__Chlorobiaceae, g__Chlorobaculum,s__Chlorobaculum parvum_B >MAG3_1,d__Bacteria,p__Bacteroidota,c__Chlorobia,o__Chlorobiales,f__Chlorobiaceae, g__Prosthecochloris,s__Prosthecochloris vibrioformis ``` And if we re-classify the metagenome using the combined information, we see: ``` sourmash tax metagenome -g SRR8859675.x.gtdb+MAGS.csv \ -t gtdb-rs207.taxonomy.sqldb MAGs.lineage.csv \ -F human -r order ``` Now only 56.5% remains unclassified, which is much better than before! ## Interlude: where we are and what we've done so far To recap, we've done the following: * analyzed a metagenome's composition against 65,000 GTDB genomes, using 31-mers; * found that a disappointingly small fraction of the metagenome can be identified this way. * incorporated MAGs built from the metagenome into this analysis, bumping up the classification rate to ~45%; * added taxonomic output to both sets of analyses. ATLAS only bins bacterial and archaeal genomes, so we wouldn't expect much in the way of viral or eukaryotic genomes to be binned. But... how much even _assembles_? Let's pick a few of the matching genomes out from GTDB and evaluate how many of the k-mers from that genome match to the unassembled metagenome, and then how many of them match to the assembled contigs. First, download the contigs: ``` curl -JLO https://osf.io/jfuhy/download ``` this produces a file `SRR8859675_contigs.fasta`. Sketch the contigs into a sourmash signature - ``` sourmash sketch dna SRR8859675_contigs.fasta --name-from-first ``` Now, extract one of the top gather matches to use as a query; this is "Chromatiaceae bacterium": ``` sourmash sig cat matches.zip --include GCA_016938795.1 -o GCA_016938795.sig ``` ### Evaluate containment of known genomes in reads vs assembly ```{note} If you want to just start here, you can download the files needed for the below sourmash searches from [this link](https://github.com/mblstamps/stamps2022/raw/main/kmers_and_sourmash/assembly-loss-files.zip). ``` Now do a containment search of this genome against both the unassembled metagenome and the assembled (but unbinned) contigs - ``` sourmash search --containment GCA_016938795.sig \ SRR8859675*.sig* --threshold=0 --ignore-abund ``` We see: ``` similarity match ---------- ----- 23.3% SRR8859675 4.7% SRR8859675_0 ``` where the first match (at 23.3% containment) is to the metagenome. (You'll note this matches the % in the gather output, too.) The second match is to the assembled contigs, and it's 4.7%. That means ~19% of the k-mers that match to this GTDB genome are present in the unassembled metagenome, but are lost during the assembly process. Why? Some thoughts and answers It _could_ be that the GTDB genome is full of errors, and those errors are shared with the metagenome, and assembly is squashing those errors. Yay! But this is extremely unlikely... This GTDB genome was built and validated entirely independently from this sample... It's much more likely (IMO) that one of two things is happening: (1) this sample is at low abundance in the metagenome, and assembly can only recover parts of it. (2) this sample contains _several_ strain variants of this genome, and assembly is squashing the strain variation, because that's what assembly does. Note, you can try the above with another one of the top gather matches and you'll see it's *entirely* lost in the process of assembly - ``` sourmash sig cat matches.zip --include GCF_004138165.1 -o GCF_004138165.sig sourmash search --containment GCF_004138165.sig \ SRR8859675*.sig* \ --ignore-abund --threshold=0 ``` ## Summary and concluding thoughts Above, we demonstrated a _reference-based_ analysis of shotgun metagenome data using sourmash. We then _updated our references_ using the MAGs produced from assembly and binning tutorial, which increased our classification rate substantially. Last but not least, we looked at the loss of k-mer information due to metagenome assembly. All of these results were based on 31-mer overlap and containment - k-mers FTW! A few points: * We would have gotten slightly different results using k=21 or k=51; more of the metagenome would have been classified with k=21, while the classification results would have been more closely specific to genomes with k=51; * sourmash is a nice one-stop-shop tool for doing this, but you could have gotten similar results by using other tools. * Next steps here could include mapping reads to the genomes we found, and/or doing functional analysis on the matching genes and genomes. sourmash-bio-sourmash-bc3c0f8/doc/tutorial-lin-taxonomy.md000066400000000000000000000573051474157046300240660ustar00rootroot00000000000000# Analyzing Metagenome Composition using the LIN taxonomic framework Tessa Pierce Ward March 2023 requires sourmash v4.8+ --- ```{contents} :depth: 2 ``` This tutorial uses the `sourmash taxonomy` module, which was introduced via [blog post](https://bluegenes.github.io/sourmash-tax/) and was recently shown to perfom well for taxonomic profiling of long (and short) reads in [Evaluation of taxonomic classification and profiling methods for long-read shotgun metagenomic sequencing datasets](https://link.springer.com/article/10.1186/s12859-022-05103-0), Portik et al., 2022. In this tutorial, we'll use sourmash gather to analyze metagenomes using the [LIN taxonomic framework](https://dl.acm.org/doi/pdf/10.1145/3535508.3545546). Specifically, we will analyze plant metagenomes with a low-level pathogen spike-in. The goal is to see if we can correctly assign the pathogen sequence to its LINgroup, which includes all known pathogenic strains. - `barcode1` - highest spike-in (75 picogram/microliter pathogen DNA) - `barcode3` - lower spike-in (7.5 picogram/microliter pathogen DNA) - `barcode5` - no spike-in The pathogen is `Ralstonia solanacearum` in the `Phylum IIB sequevar 1` group. This data is courtesy of [The Laboratory of Plant & Atmospheric Microbiology & (Meta)Genomics](https://sites.google.com/vt.edu/lab-vinatzer/home) in collaboration with USDA APHIS. ## Install sourmash First, we need to install the software! We'll use conda/mamba to do this. The below command installs [sourmash](http://sourmash.readthedocs.io/). Install the software: ``` # create a new environment mamba create -n smash -y -c conda-forge -c bioconda sourmash ``` then activate the conda environment: ``` conda activate smash ``` > Victory conditions: your prompt should start with > `(smash) ` > and you should now be able to run `sourmash` and have it output usage information!! ## Create a working subdirectory Make a directory named `smash_lin`, change into it: ``` mkdir -p ~/smash_lin cd ~/smash_lin ``` Now make a couple useful folders: ``` mkdir -p inputs mkdir -p databases ``` ## Download relevant data ### First, download a database and taxonomic information Here, we know the spike-in is a pathogenic seqevar of Ralstonia. We will download a database containing signatures of 27 Ralstonia genomes (pathogenic and not) and the corresponding taxonomic and lingroup information. ``` # database curl -JLO https://osf.io/vxsta/download mv ralstonia*.zip ./databases/ralstonia.zip # taxonomy csv curl -JLO https://raw.githubusercontent.com/bluegenes/2023-demo-sourmash-LIN/main/databases/ralstonia-lin.taxonomy.GCA-GCF.csv mv ralstonia-lin.taxonomy.GCA-GCF.csv ./databases # lingroup csv curl -JLO https://raw.githubusercontent.com/bluegenes/2023-demo-sourmash-LIN/main/inputs/ralstonia.lingroups.csv mv ralstonia.lingroups.csv ./databases ls databases # look at the database files ``` ### Next, download pre-made sourmash signatures made from the input metagenomes ``` # download barcode 1 sig curl -JLO https://osf.io/ujntr/download mv barcode1_22142.sig.zip ./inputs/ # download barcode 3 signature curl -JLO https://osf.io/2h9wx/download mv barcode3_31543.sig.zip ./inputs # download barcode 5 signature curl -JLO https://osf.io/k8nw5/download mv barcode5_36481.sig.zip ./inputs # look at available input files ls inputs ``` ## Look at the signatures Let's start with the `barcode1` (highest spike-in) sample ### First, let's look at the metagenome signature. By running `sourmash sig fileinfo`, we can see information on the signatures available within the zip file. Here, you can see I've generated the metagenome signature with `scaled=1000` and built two ksizes, `k=31` and `k=51` Run: ``` sourmash sig fileinfo ./inputs/barcode1_22142.sig.zip ``` In the output, you should see: ``` ** loading from './inputs/barcode1_22142.sig.zip' path filetype: ZipFileLinearIndex location: /home/jovyan/smash_lin/inputs/barcode1_22142.sig.zip is database? yes has manifest? yes num signatures: 2 total hashes: 914328 summary of sketches: 1 sketches with DNA, k=31, scaled=1000, abund 426673 total hashes 1 sketches with DNA, k=51, scaled=1000, abund 487655 total hashes ``` ### We can also look at the database Here, you can see I've generated the database with `scaled=1000` and built three ksizes, `k=21`, `k=31` and `k=51` Run: ``` sourmash sig fileinfo ./databases/ralstonia.zip ``` In the output, you should see: ``` ** loading from './databases/ralstonia.zip' path filetype: ZipFileLinearIndex location: /home/jovyan/databases/ralstonia.zip is database? yes has manifest? yes num signatures: 81 ** examining manifest... total hashes: 445041 summary of sketches: 27 sketches with DNA, k=21, scaled=1000, abund 148324 total hashes 27 sketches with DNA, k=31, scaled=1000, abund 148111 total hashes 27 sketches with DNA, k=51, scaled=1000, abund 148606 total hashes ``` There's a lot of things to digest in this output but the two main ones are: * there are 27 genomes represented in this database, each of which are sketched at k=21,k=31,k=51 * this database represents ~445 *million* k-mers (multiply number of hashes by the scaled number) ## Run sourmash gather using ksize 51 Now let's run `sourmash gather` to find the closest reference genome(s) in the database. If you want to read more about what sourmash is doing, please see [Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers](https://www.biorxiv.org/content/10.1101/2022.01.11.475838v2), Irber et al., 2022. Run: ``` query="inputs/barcode1_22142.sig.zip" database="databases/ralstonia.zip" gather_csv_output="barcode1_22141.k51.gather.csv" sourmash gather $query $database -k 51 -o $gather_csv_output ``` You should see the following output: ``` selecting specified query k=51 loaded query: barcode1_22142... (k=51, DNA) --ading from 'databases/ralstonia.zip'... loaded 81 total signatures from 1 locations. after selecting signatures compatible with search, 27 remain. Starting prefetch sweep across databases. Found 7 signatures via prefetch; now doing gather. overlap p_query p_match avg_abund --------- ------- ------- --------- 105.0 kbp 0.0% 2.0% 1.0 GCA_002251655.1 Ralstonia solanacear... found less than 50.0 kbp in common. => exiting found 1 matches total; the recovered matches hit 0.0% of the abundance-weighted query. the recovered matches hit 0.0% of the query k-mers (unweighted). ``` The first step of gather found all potential matches (7), and the greedy algorithm narrowed this to a single best match, `GCA_002251655.1` which shared an estimated 105 kbp with the metagenome (a very small percentage of the total dataset.) This is expected, though, since the dataset is a plant metagenome with a small `Ralstonia` spike-in. ## Add taxonomic information and summarize up lingroups `sourmash gather` finds the smallest set of reference genomes that contains all the known information (k-mers) in the metagenome. In most cases, `gather` will find many metagenome matches. Here, we're only looking for `Ralstonia` matches and we only have a single gather result. Regardless, let's use `sourmash tax metagenome` to add taxonomic information and see if we've correctly assigned the pathogenic sequence. ### First, let's look at the relevant taxonomy files. These commands will show the first few lines of each file. If you prefer, you can look at a more human-friendly view by opening the files in a spreadsheet program. - **taxonomy_csv:** `databases/ralstonia-lin.taxonomy.GCA-GCF.csv` - the essential columns are `lin` (`14;1;0;...`) and `ident` (`GCF_00`...) - **lingroups information:** `databases/ralstonia.lingroups.csv` - both columns are essential (`name`, `lin`) Look at the taxonomy file: ``` head -n 5 databases/ralstonia-lin.taxonomy.GCA-GCF.csv ``` You should see: ``` lin,species,strain,filename,accession,ident 14;1;0;0;0;0;0;0;0;0;6;0;1;0;1;0;0;0;0;0,Ralstonia solanacearum,OE1_1,GCF_001879565.1_ASM187956v1_genomic.fna,GCF_001879565.1,GCF_001879565.1 14;1;0;0;0;0;0;0;0;0;6;0;1;0;0;0;0;0;0;0,Ralstonia solanacearum,PSS1308,GCF_001870805.1_ASM187080v1_genomic.fna,GCF_001870805.1,GCF_001870805.1 14;1;0;0;0;0;0;0;0;0;2;1;0;0;0;0;0;0;0;0,Ralstonia solanacearum,FJAT_1458,GCF_001887535.1_ASM188753v1_genomic.fna,GCF_001887535.1,GCF_001887535.1 14;1;0;0;0;0;0;0;0;0;2;0;0;4;4;0;0;0;0;0,Ralstonia solanacearum,Pe_13,GCF_012062595.1_ASM1206259v1_genomic.fna,GCF_012062595.1,GCF_012062595.1 ``` > The key columns are: > - `ident`, containing identifiers matching the database sketches > - `lin`, containing the species information. Now, let's look at the lingroups file ``` head -n5 databases/ralstonia.lingroups.csv ``` You should see: ``` name,lin Phyl II,14;1;0;0;0;3;0 Phyl IIA,14;1;0;0;0;3;0;1;0;0 Phyl IIB,14;1;0;0;0;3;0;0 Phyl IIB seq1 and seq2,14;1;0;0;0;3;0;0;0;0;1;0;0;0;0 ``` > Here, we have two columns: > - `name` - the name for each lingroup. > - `lin` - the LIN prefix corresponding to each group. ### Now, run `sourmash tax metagenome` to integrate taxonomic information into `gather` results Using the `gather` output we generated above, we can integrate taxonomic information and summarize up "ranks" (lin positions). We can produce several different types of outputs, including a `lingroup` report. `lingroup` format summarizes the taxonomic information at each `lingroup`, and produces a report with 4 columns: - `name` (from lingroups file) - `lin` (from lingroups file) - `percent_containment` - total % of the file matched to this lingroup - `num_bp_contained` - estimated number of bp matched to this lingroup > Since sourmash assigns all k-mers to individual genomes, no reads/base pairs are "assigned" to higher taxonomic ranks or lingroups (as with Kraken-style LCA). Here, "percent_containment" and "num_bp_contained" is calculated by summarizing the assignments made to all genomes in a lingroup. This is akin to the "contained" information in Kraken-style reports. Run `tax metagenome`: ``` gather_csv_output="barcode1_22141.k51.gather.csv" taxonomy_csv="databases/ralstonia-lin.taxonomy.GCA-GCF.csv" lingroups_csv="databases/ralstonia.lingroups.csv" sourmash tax metagenome -g $gather_csv_output -t $taxonomy_csv \ --lins --lingroup $lingroups_csv ``` You should see: ``` loaded 1 gather results from 'barcode1_22141.k51.gather.csv'. loaded results for 1 queries from 1 gather CSVs Starting summarization up rank(s): 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 Read 11 lingroup rows and found 11 distinct lingroup prefixes. ``` and the results: ``` name lin percent_containment num_bp_contained Phyl II 14;1;0;0;0;3;0 0.02 108000 Phyl IIB 14;1;0;0;0;3;0;0 0.02 108000 Phyl IIB seq1 and seq2 14;1;0;0;0;3;0;0;0;0;1;0;0;0;0 0.02 108000 IIB seq1 14;1;0;0;0;3;0;0;0;0;1;0;0;0;0;0;0 0.02 108000 ``` Here, the most specific lingroup we assign to is `Phyl IIB seq1`, which is the pathogenic lingroup that was spiked in, yay! Note that the other groups in the output all contain this group. #### Now output the lingroup report to a file (instead of to the terminal) use `-o` to provide an output basename for taxonomic output. ``` gather_csv_output="barcode1_22141.k51.gather.csv" taxonomy_csv="databases/ralstonia-lin.taxonomy.GCA-GCF.csv" lingroups_csv="databases/ralstonia.lingroups.csv" sourmash tax metagenome -g $gather_csv_output -t $taxonomy_csv \ --lins --lingroup $lingroups_csv \ -o "barcode1" ``` > You should see `saving 'lingroup' output to 'barcode1.lingroup.tsv'` in the output. #### Optionally, write multiple output formats You can use `-F` to specify additional output formats. Here, I've added `csv_summary`. Note that while the `lingroup` format will be generated automatically if you specify the `--lingroup` file, you can also specify it with `-F lingroup` if you want, as I've done here. Run: ``` gather_csv_output="barcode1_22141.k51.gather.csv" taxonomy_csv="databases/ralstonia-lin.taxonomy.GCA-GCF.csv" lingroups_csv="databases/ralstonia.lingroups.csv" sourmash tax metagenome -g $gather_csv_output -t $taxonomy_csv \ --lins --lingroup $lingroups_csv \ -F lingroup csv_summary -o "barcode1" ``` You should see the following in the output: ``` saving 'csv_summary' output to 'barcode1.summarized.csv'. saving 'lingroup' output to 'barcode1.lingroup.txt'. ``` The `csv_summary` format is the **full** summary of this sample, e.g. the summary at each taxonomic rank (LIN position). It also includes an entry with the `unclassified` portion at each rank. > Note: Multiple output formats require the `-o` `--output-base` to be specified, as each must be written to a file. Here's an abbreviated version of the `gather` results for `barcode1`, with lingroup information added: | | **ksize** | **scaled** | **best overlap** | **gather match(es)** | **lingroup** | **lin** | | ------- | --------- | ---------- | ---------------- | -------------------- | ------------ | ---------------------------------- | | **bc1** | 51 | 1000 | 105 kb | GCA_002251655.1 | IIB seq1 | 14;1;0;0;0;3;0;0;0;0;1;0;0;0;0;0;0 | | **bc1** | 31 | 1000 | 173 kb | GCA_002251655.1 | IIB seq1 | 14;1;0;0;0;3;0;0;0;0;1;0;0;0;0;0;0 | ### Now run with `barcode3` sample #### sourmash gather Run: ``` query="inputs/barcode3_31543.sig.zip" database="databases/ralstonia.zip" gather_csv_output="barcode3_31543.dna.k51.gather.csv" sourmash gather $query $database -k 51 -o $gather_csv_output ``` You should see: ``` selecting specified query k=51 loaded query: barcode3_31543... (k=51, DNA) loading from 'databases/ralstonia.zip'... loaded 81 total signatures from 1 locations. after selecting signatures compatible with search, 27 remain. Starting prefetch sweep across databases. Found 0 signatures via prefetch; now doing gather. found less than 50.0 kbp in common. => exiting found 0 matches total; the recovered matches hit 0.0% of the query k-mers (unweighted). ``` #### gather found no sequence matches! But, we can lower the detection threshold: ``` query="inputs/barcode3_31543.sig.zip" database="databases/ralstonia.zip" gather_csv_output="barcode3_31543.k51.gather.csv" # use a 10kb detection threshold sourmash gather $query $database -k 51 --threshold-bp 10000 -o $gather_csv_output ``` This time, you should see: ``` selecting specified query k=51 loaded query: barcode3_31543... (k=51, DNA) loading from 'databases/ralstonia.zip'... loaded 81 total signatures from 1 locations. after selecting signatures compatible with search, 27 remain. Starting prefetch sweep across databases. Found 6 signatures via prefetch; now doing gather. overlap p_query p_match avg_abund --------- ------- ------- --------- 12.0 kbp 0.0% 0.2% 1.0 GCA_000750575.1 Ralstonia solanacear... found 1 matches total; the recovered matches hit 0.0% of the abundance-weighted query. the recovered matches hit 0.0% of the query k-mers (unweighted). ``` You'll notice that while we have an estimated ~12kbp overlap, the matched genome (`GCA_000750575.1`) is different from the one matched above for `barcode5`. If you run `sourmash tax metagenome` on this output, you'll see that this genome belongs to `Phyl IIB seq 2` group, which is a sister group to the correct `Phyl IIB seq 1` group that we expected. So we have a match but it's not the right one -- why not? ### What happened? Use `prefetch` to investigate `sourmash gather` has two steps: first, it runs a `prefetch` to find ALL genome matches, and then uses a greedy approach to select the smallest set of genomes that contain ('cover') all known sequence content. Let's run `prefetch` independently so we can look at the results of the first step. Here, let's use `--threshold-bp 0` to get all possible matches. Run: ``` query="inputs/barcode3_31543.sig.zip" prefetch_csv_output="barcode3_31543.k51.prefetch.csv" database="databases/ralstonia.zip" sourmash prefetch $query $database -k 51 --threshold-bp 0 -o $prefetch_csv_output ``` You should see: ``` selecting specified query k=51 loaded query: barcode3_31543... (k=51, DNA) query sketch has scaled=1000; will be dynamically downsampled as needed. --tal of 10 matching signatures so far.tonia.zip' loaded 81 total signatures from 1 locations. after selecting signatures compatible with search, 27 remain. -- total of 15 matching signatures. saved 15 matches to CSV file 'barcode3_31543.k51.prefetch.csv' of 487043 distinct query hashes, 12 were found in matches above threshold. a total of 487031 query hashes remain unmatched. final scaled value (max across query and all matches) is 1000 ``` Here, the output is telling us we found matches to 15 of the 27 Ralstonia genomes. But only **12 k-mers** were shared between the metagenome sample and the genomes. Remember that sourmash uses a representative subsample of all k-mers, so here these 12 k-mers represent ~ 12kb of sequence (12 * scaled). We've found that this is sufficient to detect presence of an organism, but at this low level, it can be hard to distinguish between closely-related genomes. Let's open the prefetch output to see how those 12 k-mers matched between different genomes. #### Look at the `barcode3_31543.k51.prefetch.csv` file > Use a spreadsheet program on your computer or use `less -S barcode3_31543.k51.prefetch.csv` to see the file on the terminal. If using `less`, hit `q` when you want to exit and return to your terminal prompt. The first column contains the estimated number of base pairs matched between our query and each matching reference genome. You'll notice there are four genomes that match 12kb of sequence, one of which is the "correct" genome (`GCA_002251655.1`, which is in the `IIB seq1` lingroup). **What is happening here?** When faced with equally good matches, `sourmash gather` makes a random choice about which genome to assign these k-mers to. This happens primarily with highly similar genomes and/or very small sequence matches. If this happens and you need to distinguish between these genomes, we recommend trying a lower scaled value (higher resolution). "scaled" refers to the systematic downsampling: we keep rougly 1/scaled k-mers (`scaled=1000` keeps ~1 of every 1000 unique k-mers). `scaled=1` keeps all k-mers, but our signature storage is not optimized for this use case. To see if we could robustly assign the correct sequevar for `barcode3` using a higher resolution sketch, I also ran `gather` using `scaled=100`. Here's an abbreviated version of the `gather` results for `barcode3`, with lingroup information added: | | **ksize** | **scaled** | **best overlap** | **gather match(es)** | **lingroup** | **lin** | | ------- | --------- | ---------- | ---------------- | -------------------- | ------------ | ---------------------------------- | | **bc3** | 51 | 1000 | 12kb | GCA_000750575.1 | IIB seq2 | 14;1;0;0;0;3;0;0;0;0;1;0;0;0;0;1;0 | | **bc3** | 31 | 1000 | 28 kb | GCA_002251655.1 | IIB seq1 | 14;1;0;0;0;3;0;0;0;0;1;0;0;0;0;0;0 | | **bc3** | 51 | 100 | 14.8 kb | GCA_002251655.1 | IIB seq1 | 14;1;0;0;0;3;0;0;0;0;1;0;0;0;0;0;0 | | **bc3** | 31 | 100 | 21.1 kb | GCA_002251655.1 | IIB seq1 | 14;1;0;0;0;3;0;0;0;0;1;0;0;0;0;0;0 | We typically use k=51 for strain-level matching and k=31 for species-level matching. Notice that running at k=31 with scaled 1000 found the right match. However, if you run prefetch for `k=31`, you see there are three matches with `28kb` overlap, so we just got lucky that `gather` selected the right one for this test case. In contrast, by sketching the `Ralstonia` genomes and metagenome at higher resolution (`scaled=100`), we had sufficient information to correctly assign the sequence to the `IIB seq1` lingroup at either ksize. ### Now try the `barcode5` sample You can also run the `barcode5` file using the same commands as above: ``` query="inputs/barcode5_36481.sig.zip" database="databases/ralstonia.zip" gather_csv_output="barcode5_36481.dna.k51.gather.csv" sourmash gather $query $database -k 51 -o $gather_csv_output ``` You should see: ``` selecting specified query k=51 loaded query: barcode5_36481... (k=51, DNA) -- loaded 81 total signatures from 1 locations. after selecting signatures compatible with search, 27 remain. Starting prefetch sweep across databases. Found 0 signatures via prefetch; now doing gather. found less than 50.0 kbp in common. => exiting found 0 matches total; the recovered matches hit 0.0% of the query k-mers (unweighted). ``` No matches are found. If you drop the threshold-bp to 0 (`--threshold-bp 0`), you can find ~1kbp overlap (a single k-mer match!). **Note, we do not recommend trusting/using results with fewer than 3 k-mer matches (3kbp at scaled=1000)**. Especially in larger databases (e.g. NCBI/GTDB), a single k-mer match might actually be from contamination in the reference genome rather than true genome content, so you may end up assigning the wrong lineage. Requiring 3 k-mers (representing ~3kb of matching sequence) makes it more likely your matches represent true genome content. I then ran this file at higher resolution to see how the results changed. In each case, very few k-mers matched and we could not robustly identify a specific `Ralstonia` genome or lingroup. As it turns out, `barcode5` does not have a `Ralstonia` spike-in, so this is a good thing! Here's an abbreviated version of the `gather` results for `barcode5`, with lingroup information added in cases with a single gather match: | | **ksize** | **scaled** | **best overlap** | **gather match(es)** | **lingroup** | **lin** | | ------- | --------- | ---------- | ---------------- | -------------------- | ------------ | ---------------------------------- | | **bc5** | 51 | 1000 | 1 kbp | GCA_000750575.1 | IIB seq2 | 14;1;0;0;0;3;0;0;0;0;1;0;0;0;0;1;0 | | **bc5** | 31 | 1000 | 0 | N/A | | | | **bc5** | 51 | 100 | 300bp | all | | | | **bc5** | 31 | 100 | 1.2 kb | all | | | | **bc5** | 51 | 10 | 120 bp | all | | | | **bc5** | 31 | 10 | 670 bp | all | | | | **bc5** | 51 | 5 | 150 bp | all | | | | **bc5** | 31 | 5 | 500 bp | all | | | **Again, while I've used a threshold-bp of 0 to get the gather match at scaled=1000, we do not typically trust gather matches with less than `3*scaled` overlap (< 3 k-mers matched).** Even at very high resolution (scaled=5), we matched nearly all Ralstonia genomes and could not distinguish a single lingroup. We typically recommend running at `scaled=1000` (our default), as this works for most microbial use cases. You can run at higher resolution (lower scaled) if you need to, but higher resolution signatures are larger and can take significantly longer to build and search - use at your own risk :). ## Summary and concluding thoughts The LIN taxonomic framework may be useful distinguishing groups below the species level. We can now use LINs and lingroups with `sourmash tax metagenome`. For low level matches, the gather greedy approach can struggle. We are working on ways to better warn users about this behavior and welcome feedback and suggestions on our [issue tracker](https://github.com/sourmash-bio/sourmash/issues/new).sourmash-bio-sourmash-bc3c0f8/doc/tutorial-long.md000066400000000000000000000553741474157046300223730ustar00rootroot00000000000000# Quick Insights from Sequencing Data with sourmash Note: this tutorial was developed for and first presented at [ANGUS 2019](https://angus.readthedocs.io/en/2019/sourmash.html). ## Objectives 1. Discuss k-mers and their utility 2. Compare RNA-seq samples quickly 3. Detect eukaryotic contamination in raw RNA-seq reads 4. Compare reads to an assembly 5. Build your own database for searching 6. Other sourmash databases ## Introduction to k-mers A "k-mer" is a word of DNA that is k long: ``` ATTG - a 4-mer ATGGAC - a 6-mer ``` Typically we extract k-mers from genomic assemblies or read data sets by running a k-length window across all of the reads and sequences -- e.g. given a sequence of length 16, you could extract 11 k-mers of length six from it like so: ``` AGGATGAGACAGATAG ``` becomes the following set of 6-mers: ``` AGGATG GGATGA GATGAG ATGAGA TGAGAC GAGACA AGACAG GACAGA ACAGAT CAGATA AGATAG ``` Today we will be using a tool called [sourmash](https://f1000research.com/articles/8-1006) to explore k-mers! ## Why k-mers, though? Why not just work with the full read sequences? Computers *love* k-mers because there's no ambiguity in matching them. You either have an exact match, or you don't. And computers love that sort of thing! Basically, it's really easy for a computer to tell if two reads share a k-mer, and it's pretty easy for a computer to store all the k-mers that it sees in a pile of reads or in a genome. ## Long k-mers are species specific k-mers are most useful when they're *long*, because then they're *specific*. That is, if you have a 31-mer taken from a human genome, it's pretty unlikely that another genome has that exact 31-mer in it. (You can calculate the probability if you assume genomes are random: there are 431 possible 31-mers, and 431 = 4,611,686,018,427,387,904\. So, you know, a lot.) Essentially, *long k-mers are species specific*. Check out this figure from the [MetaPalette paper](http://msystems.asm.org/content/1/3/e00020-16): ![](_static/kmers-metapalette.png) Here, Koslicki and Falush show that k-mer similarity works to group microbes by genus, at k=40\. If you go longer (say k=50) then you get only very little similarity between different species. ## Using k-mers to compare samples So, one thing you can do is use k-mers to compare read data sets to read data sets, or genomes to genomes: data sets that have a lot of similarity probably are similar or even the same genome. One metric you can use for this comparisons is the Jaccard distance, which is calculated by asking how many k-mers are *shared* between two samples vs how many k-mers in total are in the combined samples. ``` only k-mers in both samples ---------------------------- all k-mers in either or both samples ``` A Jaccard distance of 1 means the samples are identical; a Jaccard distance of 0 means the samples are completely different. Jaccard distance works really well when we don't care how many times we see a k-mer. When we keep track of the abundance of a k-mer, say for example in RNA-seq samples where the number of read counts matters, we use cosine distance instead. These two measures can be used to search databases, compare RNA-seq samples, and all sorts of other things! The only real problem with it is that there are a *lot* of k-mers in a genome -- a 5 Mbp genome (like E. coli) has 5 m k-mers! About two years ago, [Ondov et al. (2016)](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0997-x) showed that [MinHash approaches](https://en.wikipedia.org/wiki/MinHash) could be used to estimate Jaccard distance using only a small fraction (1 in 10,000 or so) of all the k-mers. The basic idea behind MinHash is that you pick a small subset of k-mers to look at, and you use those as a proxy for *all* the k-mers. The trick is that you pick the k-mers randomly but consistently: so if a chosen k-mer is present in two data sets of interest, it will be picked in both. This is done using a clever trick that we can try to explain to you in class - but either way, trust us, it works! We have implemented a MinHash approach in our [sourmash software](https://github.com/sourmash-bio/sourmash/), which can do some nice things with samples. We'll show you some of these things next! ## Installing sourmash To install sourmash, run: ``` conda install -y -c conda-forge -c bioconda sourmash ``` ## Creating signatures A signature is a compressed representation of the k-mers in the sequence. Depending on your application, we recommend different ways of preparing sequencing data to create a signature. In a genome or transcriptome, we expect that the k-mers we see are accurate. We can create signatures from these type of sequencing data sets without any preparation. We demonstrate how to create a signature from high-quality sequences below. First, download a genome assembly: ``` cd ~ mkdir sourmash_data cd sourmash_data curl -L https://osf.io/963dg/download -o ecoliMG1655.fa.gz gunzip -c ecoliMG1655.fa.gz | head ``` Compute a scaled MinHash from the assembly: ``` sourmash compute -k 21,31,51 --scaled 2000 --track-abundance -o ecoliMG1655.sig ecoliMG1655.fa.gz ``` For raw sequencing reads, we expect that many of the unique k-mers we observe will be due to errors in sequencing. Unlike with high-quality sequences like transcriptomes and genomes, we need to think carefully about how we want to create each signature, as it will depend on the downstream application. + **Comparing reads against high quality sequences**: Because our references that we are comparing or searching against only contain k-mers that are likely real, we don't want to trim potentially erroneous k-mers. Although most of the k-mers would be errors that we would trim, there is a chance we could accidentally remove **real** biological variation that is present at low abundance. Instead, we only want to trim adapters. + **Comparing reads against other reads**: Because both datasets likely have many erroneous k-mers, we want to remove the majority of these so as not to falsely deflate similarity between samples. Therefore, we want to trim what are likely erroneous k-mers from sequencing errors, as well as adapters. Let's download some raw sequencing reads and demonstrate what k-mer trimming looks like. First, download a read file: ``` curl -L https://osf.io/pfxth/download -o ERR458584.fq.gz gunzip -c ERR458584.fq.gz | head ``` Next, perform k-mer trimming using a library called khmer. K-mer trimming removes low-abundant k-mers from the sample. ``` trim-low-abund.py ERR458584.fq.gz -V -Z 10 -C 3 --gzip -M 3e9 -o ERR458584.khmer.fq.gz ``` Finally, calculate a signature from the trimmed reads. ``` sourmash compute -k 21,31,51 --scaled 2000 --track-abundance -o ERR458584.khmer.sig ERR458584.khmer.fq.gz ``` ![qc](_static/Sourmash_flow_diagrams_QC.png) ![compute](_static/Sourmash_flow_diagrams_compute.png) We can prepare signatures like this for any sequencing data file! For the rest of the tutorial, we have prepared signatures for each sequencing data set we will be working with. ## Compare many RNA-seq samples quickly Use case: how similar are my samples to one another? Traditionally in RNA-seq workflows, we use MDS plots to determine how similar our samples are. Samples that are closer together on the MDS plot are more similar. However, to get to this point, we have to trim our reads, download or build a reference transcriptome, quantify our reads using a tool like Salmon, and then read the counts into R and make an MDS plot. This is a lot of steps to go through just to figure out how similar your samples are! Luckily, we can use sourmash to quickly compare how similar our samples are. We [generated signatures](https://github.com/taylorreiter/yeast-rna-sigs/blob/master/Snakefile) for the majority of the rest of the [Schurch et al. experiment](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4878611/) we have been working with this week. Below we download and compare the 647 signatures, and then produce a plot that shows how similar they are to one another. First, download and uncompress the signatures. ``` curl -o schurch_sigs.tar.gz -L https://osf.io/p3ryg/download tar xf schurch_sigs.tar.gz ``` Next, compare the signatures using sourmash. ``` sourmash compare -k 31 -o schurch_compare_matrix schurch_sigs/*sig ``` This outputs a comparison matrix and a set of labels. The matrix is symmetrical, and contains numbers 0-1 that captures similarity between samples. 0 means there are no k-mers in common between two samples, while 1 means all k-mers are shared. Lastly, we plot the comparison matrix. ``` sourmash plot --labels schurch_compare_matrix ``` ![](_static/schurch_comp.matrix.png) We see there are two major blocks of similar samples, which makes sense given that we have WT and SNF2 knockout samples. However, we also see that some of our samples are outliers! If this were our experiment, we would want to investigate the outliers further to see what caused them to be so dissimilar. ## Detect Eukaryotic Contamination in Raw RNA Sequencing data Use case: Search for the presence of unexpected organisms in raw RNA-seq reads For most analysis pipelines, there are many steps that need to be executed before we get to the analysis and interpretation of what is in our sample. This often means we are 10-15 steps into our analysis before we find any problems. However, if our reads contain contamination, we want to know that as quickly as possible so we can remove the contamination and solve any issues that led to the contamination. Using sourmash, we can quickly check if we have any unexpected organisms in our sequencing samples. We do this by comparing a signature from our reads against a database of known signatures from publicly available reference sequences. We have generated sourmash databases for all publicly available Eukaryotic RNA samples (we used the `*rna_from_genomic*` files from RefSeq and Genbank...however keep in mind that not all sequenced genomes have these files!). This database includes fungi, plants, vertebrates, invertebrates, and protazoa. It does not include human, so we incorporate that separately. We also built another database of the ~700 recently [re-assembled](https://academic.oup.com/gigascience/article/8/4/giy158/5241890) marine transcriptomes from the [MMETSP project](https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001889). These databases allow us to detect common organisms that might be unexpectedly present in our sequencing data. First, let's download and uncompress our three databases: human, MMETSP, and everything else! ``` wget -O sourmash_euk_rna_db.tar.gz https://osf.io/vpk8s/download tar xf sourmash_euk_rna_db.tar.gz ``` Next, let's download a signature from some sequencing reads. We'll work with some sequencing reads from a wine fermentation. ``` wget -O wine.sig https://osf.io/5vsjq/download ``` We expected fungus and grape to be metabolically active in these samples. Let's check which organisms we detect. ``` sourmash gather -k 31 --scaled 2000 -o wine.csv wine.sig sourmash_euk_rna_db/*sbt.json sourmash_euk_rna_db/*sig ``` If we take a look at the output, we see: ``` == This is sourmash version 2.0.1. == == Please cite Brown and Irber (2016), doi:10.21105/joss.00027. == loaded query: wine_fermentation... (k=31, DNA) downsampling query from scaled=2000 to 2000 loaded 1 signatures and 2 databases total. overlap p_query p_match avg_abund --------- ------- ------- --------- 2.2 Mbp 79.0% 28.5% 122.2 Sc_YJM1477_v1 Saccharomyces cerevis... 0.8 Mbp 1.4% 2.2% 6.2 12X Vitis vinifera (wine grape) 2.1 Mbp 0.4% 1.6% 9.3 GLBRCY22-3 Saccharomyces cerevisiae... 124.0 kbp 0.1% 0.7% 3.1 Aureobasidium pullulans var. pullula... 72.0 kbp 0.0% 0.1% 1.9 Mm_Celera Mus musculus (house mouse) 1.9 Mbp 0.0% 0.5% 3.7 Sc_YJM1460_v1 Saccharomyces cerevis... 1.8 Mbp 0.1% 0.5% 14.1 ASM18217v1 Saccharomyces cerevisiae... 2.1 Mbp 0.1% 0.3% 17.7 R008 Saccharomyces cerevisiae R008 ... 1.9 Mbp 0.0% 0.1% 3.1 ASM32610v1 Saccharomyces cerevisiae... found less than 18.0 kbp in common. => exiting found 9 matches total; the recovered matches hit 81.2% of the query ``` ...which is almost exactly what we expect, except we see some house mouse! And I promise Ratatouille was not making this wine. Using this method, we have now identified contamination in our reads. We could align to the mouse genome to remove these reads, however the best strategy to remove these reads may vary on a case by case basis. ## Compare reads to assemblies Use case: how much of the read content is contained in the reference genome? First we’ll download some reads from an E. coli genome, then we will generate a signature from them ``` curl -L https://osf.io/frdz5/download -o ecoli_ref-5m.fastq.gz sourmash compute -k 31 --scaled 2000 ~/sourmash_data/ecoli_ref-5m.fastq.gz -o ecoli-reads.sig ``` ![search](_static/Sourmash_flow_diagrams_search.png) Build a signature for an E. coli genome: ``` sourmash compute --scaled 2000 -k 31 ~/sourmash_data/ecoliMG1655.fa.gz -o ecoli-genome.sig ``` and now evaluate *containment*, that is, what fraction of the read content is contained in the genome: ``` sourmash search -k 31 ecoli-reads.sig ecoli-genome.sig --containment ``` and you should see: ``` loaded query: /home/diblions/data/ecoli_ref-... (k=31, DNA) loaded 1 signatures. 1 matches: similarity match ---------- ----- 9.7% /home/diblions/data/ecoliMG1655.fa.gz ``` Why are only 10% or so of our k-mers from the reads in the genome!? Any ideas? Try the reverse - why is it bigger? ``` sourmash search -k 31 ecoli-genome.sig ecoli-reads.sig --containment ``` (...but 100% of our k-mers from the genome are in the reads!?) ## Make and search a database quickly. Suppose that we have a collection of signatures (made with `sourmash compute` as above) and we want to search it with our newly assembled genome (or the reads, even!). How would we do that? Let's grab a sample collection of 50 E. coli genomes and unpack it -- ``` mkdir ecoli_many_sigs cd ecoli_many_sigs curl -O -L https://github.com/sourmash-bio/sourmash/raw/master/data/eschericia-sigs.tar.gz tar xzf eschericia-sigs.tar.gz rm eschericia-sigs.tar.gz cd ../ ``` This will produce 50 files named `ecoli-N.sig` in the `ecoli_many_sigs` -- ``` ls ecoli_many_sigs ``` Let's turn this into an easily-searchable database with `sourmash index` -- ``` sourmash index -k 31 ecolidb ecoli_many_sigs/*.sig ``` One point to make with this is that the search can quickly narrow down which signatures match your query, without losing any matches. It's a clever example of how computer scientists can actually make life better :). ---- And now we can search! ``` sourmash search ecoli-genome.sig ecolidb.sbt.json -n 20 ``` You should see output like this: ``` # running sourmash subcommand: search select query k=31 automatically. loaded query: /home/tx160085/data/ecoliMG165... (k=31, DNA) loaded SBT ecolidb.sbt.json Searching SBT ecolidb.sbt.json 49 matches; showing first 20: similarity match ---------- ----- 75.9% NZ_JMGW01000001.1 Escherichia coli 1-176-05_S4_C2 e117605... 73.0% NZ_JHRU01000001.1 Escherichia coli strain 100854 100854_1... 71.9% NZ_GG774190.1 Escherichia coli MS 196-1 Scfld2538, whole ... 70.5% NZ_JMGU01000001.1 Escherichia coli 2-011-08_S3_C2 e201108... 69.8% NZ_JH659569.1 Escherichia coli M919 supercont2.1, whole g... 59.9% NZ_JNLZ01000001.1 Escherichia coli 3-105-05_S1_C1 e310505... 58.3% NZ_JHDG01000001.1 Escherichia coli 1-176-05_S3_C1 e117605... 56.5% NZ_MIWF01000001.1 Escherichia coli strain AF7759-1 contig... 56.1% NZ_MOJK01000001.1 Escherichia coli strain 469 Cleandata-B... 56.1% NZ_MOGK01000001.1 Escherichia coli strain 676 BN4_676_1_(... 50.5% NZ_KE700241.1 Escherichia coli HVH 147 (4-5893887) acYxy-... 50.3% NZ_APWY01000001.1 Escherichia coli 178200 gec178200.conti... 48.8% NZ_LVOV01000001.1 Escherichia coli strain swine72 swine72... 48.8% NZ_MIWP01000001.1 Escherichia coli strain K6412 contig_00... 48.7% NZ_AIGC01000068.1 Escherichia coli DEC7C gecDEC7C.contig.... 48.2% NZ_LQWB01000001.1 Escherichia coli strain GN03624 GCID_EC... 48.0% NZ_CCQJ01000001.1 Escherichia coli strain E. coli, whole ... 47.3% NZ_JHMG01000001.1 Escherichia coli O121:H19 str. 2010EL10... 47.2% NZ_JHGJ01000001.1 Escherichia coli O45:H2 str. 2009C-4780... 46.5% NZ_JHHE01000001.1 Escherichia coli O103:H2 str. 2009C-327... ``` identifying what genome is in the signature. Some pretty good matches but nothing above %75. Why? What are some things we should think about when we're doing taxonomic classification? ## What's in my metagenome? First, let's download and upack the database we'll use for classification ``` cd ~/sourmash_data curl -L https://osf.io/4f8n3/download -o genbank-k31.lca.json.gz gunzip genbank-k31.lca.json.gz ``` This database is a GenBank index of all the microbial genomes -- this one contains sketches of all 87,000 microbial genomes (including viral and fungal). See [available sourmash databases](http://sourmash.rtfd.io/en/latest/databases.html) for more information. After this database is unpacked, it produces a file `genbank-k31.lca.json`. Next, run the 'lca gather' command to see what's in your ecoli genome -- ``` sourmash lca gather ecoli-genome.sig genbank-k31.lca.json ``` and you should get: ``` loaded 1 LCA databases. ksize=31, scaled=10000 loaded query: /home/diblions/data/ecoliMG165... (k=31) overlap p_query p_match --------- ------- -------- 4.9 Mbp 100.0% 2.3% Escherichia coli Query is completely assigned. ``` In this case, the output is kind of boring because this is a single genome. But! You can use this on metagenomes (assembled and unassembled) as well; you've just got to make the signature files. To see this in action, here is gather running on a signature generated from some sequences that assemble (but don't align to known genomes) from the [Shakya et al. 2013 mock metagenome paper](https://www.ncbi.nlm.nih.gov/pubmed/23387867). ``` wget https://github.com/sourmash-bio/sourmash/raw/master/doc/_static/shakya-unaligned-contigs.sig sourmash lca gather shakya-unaligned-contigs.sig genbank-k31.lca.json ``` This should yield: ``` loaded 1 LCA databases. ksize=31, scaled=10000 loaded query: mqc500.QC.AMBIGUOUS.99.unalign... (k=31) overlap p_query p_match --------- ------- -------- 1.8 Mbp 14.6% 9.1% Fusobacterium nucleatum 1.0 Mbp 7.8% 16.3% Proteiniclasticum ruminis 1.0 Mbp 7.7% 25.9% Haloferax volcanii 0.9 Mbp 7.4% 11.8% Nostoc sp. PCC 7120 0.9 Mbp 7.0% 5.8% Shewanella baltica 0.8 Mbp 6.0% 8.6% Desulfovibrio vulgaris 0.6 Mbp 4.9% 12.6% Thermus thermophilus 0.6 Mbp 4.4% 11.2% Ruegeria pomeroyi 480.0 kbp 3.8% 7.6% Herpetosiphon aurantiacus 410.0 kbp 3.3% 10.5% Sulfitobacter sp. NAS-14.1 150.0 kbp 1.2% 4.5% Deinococcus radiodurans (** 1 equal matches) 150.0 kbp 1.2% 8.2% Thermotoga sp. RQ2 140.0 kbp 1.1% 4.1% Sulfitobacter sp. EE-36 130.0 kbp 1.0% 0.7% Streptococcus agalactiae (** 1 equal matches) 100.0 kbp 0.8% 0.3% Salinispora arenicola (** 1 equal matches) 100.0 kbp 0.8% 4.2% Fusobacterium sp. OBRC1 60.0 kbp 0.5% 0.7% Paraburkholderia xenovorans 50.0 kbp 0.4% 3.2% Methanocaldococcus jannaschii (** 2 equal matches) 50.0 kbp 0.4% 0.3% Bacteroides vulgatus (** 1 equal matches) 50.0 kbp 0.4% 2.6% Sulfurihydrogenibium sp. YO3AOP1 30.0 kbp 0.2% 0.7% Fusobacterium hwasookii (** 3 equal matches) 30.0 kbp 0.2% 0.0% Pseudomonas aeruginosa (** 2 equal matches) 30.0 kbp 0.2% 1.6% Persephonella marina (** 1 equal matches) 30.0 kbp 0.2% 0.4% Zymomonas mobilis 20.0 kbp 0.2% 1.1% Sulfurihydrogenibium yellowstonense (** 6 equal matches) 20.0 kbp 0.2% 0.5% Ruminiclostridium thermocellum (** 5 equal matches) 20.0 kbp 0.2% 0.1% Streptococcus parasanguinis (** 4 equal matches) 20.0 kbp 0.2% 0.8% Fusobacterium sp. HMSC064B11 (** 2 equal matches) 20.0 kbp 0.2% 0.4% Chlorobium phaeobacteroides (** 1 equal matches) 20.0 kbp 0.2% 0.7% Caldicellulosiruptor bescii 10.0 kbp 0.1% 0.0% Achromobacter xylosoxidans (** 53 equal matches) 10.0 kbp 0.1% 0.2% Geobacter sulfurreducens (** 17 equal matches) 10.0 kbp 0.1% 0.5% Fusobacterium sp. HMSC065F01 (** 15 equal matches) 10.0 kbp 0.1% 0.3% Nitrosomonas europaea (** 14 equal matches) 10.0 kbp 0.1% 0.5% Wolinella succinogenes (** 13 equal matches) 10.0 kbp 0.1% 0.5% Thermotoga neapolitana (** 12 equal matches) 10.0 kbp 0.1% 0.5% Thermus amyloliquefaciens (** 10 equal matches) 10.0 kbp 0.1% 0.1% Desulfovibrio desulfuricans (** 9 equal matches) 10.0 kbp 0.1% 0.4% Fusobacterium sp. CM22 (** 8 equal matches) 10.0 kbp 0.1% 0.2% Desulfovibrio piger (** 7 equal matches) 10.0 kbp 0.1% 0.5% Thermus kawarayensis (** 6 equal matches) 10.0 kbp 0.1% 0.5% Pyrococcus furiosus (** 5 equal matches) 10.0 kbp 0.1% 0.5% Aciduliprofundum boonei (** 4 equal matches) 10.0 kbp 0.1% 0.2% Desulfovibrio sp. A2 (** 3 equal matches) 10.0 kbp 0.1% 0.3% Desulfocurvus vexinensis (** 2 equal matches) 10.0 kbp 0.1% 0.0% Enterococcus faecalis 22.1% (2.8 Mbp) of hashes have no assignment. ``` What do the columns here mean? Why might some of things in a metagenome be unassigned? ---- It is straightforward to build your own databases for use with `search` and `lca gather`; this is of interest if you have dozens or hundreds of sequencing data sets in your group. Ping us if you want us to write that up. ## Final thoughts on sourmash There are many tools like Kraken and Kaiju that can do taxonomic classification of individual reads from metagenomes; these seem to perform well (albeit with high false positive rates) in situations where you don't necessarily have the genome sequences that are in the metagenome. Sourmash, by contrast, can estimate which known genomes are actually present, so that you can extract them and map/align to them. It seems to have a very low false positive rate and is quite sensitive to strains. Above, we've shown you a few things that you can use sourmash for. Here is a (non-exclusive) list of other uses that we've been thinking about -- * detect contamination in sequencing data; * index and search private sequencing collections; * search all of SRA for overlaps in metagenomes sourmash-bio-sourmash-bc3c0f8/doc/tutorials-lca.md000066400000000000000000000147431474157046300223510ustar00rootroot00000000000000# Using sourmash LCA to do taxonomic classification The `sourmash lca` sub-commands do k-mer classification using an "lowest common ancestor" approach. See "Some discussion" below for links and details. This tutorial should run without modification on Linux or Mac OS X, under [Miniconda](https://docs.conda.io/en/latest/miniconda.html). You'll need about 5 GB of free disk space to download the database, and about 5 GB of RAM to search it. The tutorial should take about 20 minutes total to run. Note, we have successfully tested it on [binder.pangeo.io](https://binder.pangeo.io/v2/gh/binder-examples/r-conda/master?urlpath=urlpath%3Drstudio) if you want to give it a try! ## Install miniconda If you don't have the `conda` command installed, you'll need to install miniconda for Python 3.x. On Linux, this should work: ``` wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh bash Miniconda3-latest-Linux-x86_64.sh -b echo export PATH="$HOME/miniconda3/bin:$PATH" >> ~/.bash_profile source ~/.bash_profile ``` otherwise, follow [the miniconda install](https://docs.conda.io/en/latest/miniconda.html). ## Enable [bioconda](https://bioconda.github.io/) ``` conda config --add channels defaults conda config --add channels bioconda conda config --add channels conda-forge ``` ## Install sourmash To install sourmash, create a new environment named `smash` and install sourmash: ``` conda create -y -n smash sourmash ``` and then activate: ``` conda activate smash ``` You should now be able to use the `sourmash` command: ``` sourmash info ``` ## Download some files Next, download a genbank LCA database for k=31: ``` curl -L -o genbank-k31.lca.json.gz https://osf.io/4f8n3/download ``` Download a random genome from genbank: ``` curl -L -o some-genome.fa.gz https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/178/875/GCF_000178875.2_ASM17887v2/GCF_000178875.2_ASM17887v2_genomic.fna.gz ``` Create a signature for this genome: ``` sourmash sketch dna -p scaled=1000,k=31 --name-from-first some-genome.fa.gz ``` Now, classify the signature with sourmash `lca classify`, ``` sourmash lca classify --db genbank-k31.lca.json.gz \ --query some-genome.fa.gz.sig ``` and this will give you a taxonomic identification of your genome bin, classified using all of the genbank microbial genomes: ``` loaded 1 LCA databases. ksize=31, scaled=10000 finding query signatures... outputting classifications to stdout ID,status,superkingdom,phylum,class,order,family,genus,species,strain ... classifying NC_016901.1 Shewanella baltica OS678, complete genome (file 1 of"NC_016901.1 Shewanella baltica OS678, complete genome",found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Shewanellaceae,Shewanella,Shewanella baltica, classified 1 signatures total ``` You can also summarize the taxonomic distribution of the content with `lca summarize`: ``` sourmash lca summarize --db genbank-k31.lca.json.gz \ --query some-genome.fa.gz.sig ``` which will show you: ``` loaded 1 LCA databases. ksize=31, scaled=10000 finding query signatures... loaded 1 signatures from 1 files total. 97.9% 520 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella 97.9% 520 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae 97.9% 520 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales 99.6% 529 Bacteria;Proteobacteria;Gammaproteobacteria 99.6% 529 Bacteria;Proteobacteria 99.6% 529 Bacteria 45.4% 241 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica ``` To apply this to your own genome(s), replace `some-genome.fa.gz` above with your own filename(s). You can also specify multiple databases and multiple query signatures on the command line; separate them with `--db` or `--query`. ### Building your own LCA database (This is an abbreviated version of [this blog post](http://ivory.idyll.org/blog/2017-classify-genome-bins-with-custom-db-try-again.html), updated to use the `sourmash lca` commands.) Download some pre-calculated signatures: ``` curl -L https://osf.io/bw8d7/download -o delmont-subsample-sigs.tar.gz tar xzf delmont-subsample-sigs.tar.gz ``` Next, grab the associated taxonomy spreadsheet ``` curl -O -L https://github.com/ctb/2017-sourmash-lca/raw/master/tara-delmont-SuppTable3.csv ``` Build a sourmash LCA database named `delmont.lca.json`: ``` sourmash lca index -f tara-delmont-SuppTable3.csv delmont.lca.json delmont-subsample-sigs/*.sig ``` ### Using the LCA database to classify signatures We can now use `delmont.lca.json` to classify signatures with k-mers according to the database we just created. (Note, the database is completely self-contained at this point.) Let's classify a single signature: ``` sourmash lca classify --db delmont.lca.json \ --query delmont-subsample-sigs/TARA_RED_MAG_00003.fa.gz.sig ``` and you should see: ``` loaded 1 databases for LCA use. ksize=31 scaled=10000 outputting classifications to stdout ID,status,superkingdom,phylum,class,order,family,genus,species TARA_RED_MAG_00003,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, classified 1 signatures total ``` You can classify a bunch of signatures and also specify an output location for the CSV: ``` sourmash lca classify --db delmont.lca.json \ --query delmont-subsample-sigs/*.sig \ -o out.csv ``` The `lca classify` command supports multiple databases as well as multiple queries; e.g. `sourmash lca classify --db delmont.lca.json other.lca.json` will classify based on the combination of taxonomies in the two databases. ## Some discussion Sourmash LCA is using k-mers to do taxonomic classification, using the "lowest common ancestor" approach (pioneered by [Kraken](http://ccb.jhu.edu/software/kraken/MANUAL.html), and described [here](http://ivory.idyll.org/blog/2017-something-about-kmers.html)), to identify each k-mer. From this it can either find a consensus taxonomy between all the k-mers (`sourmash classify`) or it can summarize the mixture of k-mers present in one or more signatures (`sourmash summarize`). The `sourmash lca index` command can be used to prepare custom taxonomy databases; sourmash will happily ingest any taxonomy, whether or not it matches NCBI. See [the spreadsheet from Delmont et al., 2017](https://github.com/ctb/2017-sourmash-lca/blob/master/tara-delmont-SuppTable3.csv) for an example format. [Return to index][3] [0]:http://ivory.idyll.org/blog/2016-sourmash-sbt-more.html [1]:databases.md [2]:https://www.ncbi.nlm.nih.gov/pubmed/233877 [3]:index.md sourmash-bio-sourmash-bc3c0f8/doc/tutorials.md000066400000000000000000000037621474157046300216130ustar00rootroot00000000000000# sourmash tutorials and notebooks ## The first three tutorials! These tutorials are command line tutorials that should work on Mac OS X and Linux. They require about 5 GB of disk space and 5 GB of RAM. * [The first sourmash tutorial - making signatures, comparing, and searching](tutorial-basic.md) * [Using sourmash LCA to do taxonomic classification](tutorials-lca.md) * [Analyzing the genomic and taxonomic composition of an environmental genome using GTDB and sample-specific MAGs with sourmash](tutorial-lemonade.md) ## Background and details These next three tutorials are all notebooks that you can view, run yourself, or run interactively online via the [binder](https://mybinder.org) service. * [An introduction to k-mers for genome comparison and analysis.](kmers-and-minhash.ipynb) * [Some sourmash command line examples!](sourmash-examples.ipynb) * [Working with private collections of signatures.](sourmash-collections.ipynb) ## Advanced tutorials and more information For more information on analyzing sequencing data with sourmash, check out our [longer tutorial](tutorial-long.md). Read [using `sourmash taxonomy` with the Life Identification Number (LIN) taxonomic framework](tutorial-lin-taxonomy.md) for some of our newer taxonomic features. If you are a Python programmer, you might also be interested in our [API examples](api-example.md) as well as a short guide to [Using the `LCA_Database` API.](using-LCA-database-API.ipynb) If you prefer R, we have [a short guide to using sourmash output with R](other-languages.md). ## Customizing matrix and dendrogram plots in Python If you're interested in customizing the output of `sourmash plot`, which produces comparison matrices and dendrograms, please see [Building plots from `sourmash compare` output](plotting-compare.ipynb). ## Contents: ```{toctree} :maxdepth: 2 tutorial-basic tutorials-lca kmers-and-minhash sourmash-examples sourmash-collections tutorial-long tutorial-lemonade api-example using-LCA-database-API other-languages ``` sourmash-bio-sourmash-bc3c0f8/doc/using-LCA-database-API.ipynb000066400000000000000000000565121474157046300242020ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using the `LCA_Database` API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`LCA_Database` objects combine a fast in-memory storage of signatures\n", "indexed by their hash values, with taxonomic lineage storage. They are\n", "limited to storing scaled DNA signatures with a single ksize; the scaled\n", "and ksize values are specified at creation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Running this notebook.\n", "\n", "You can run this notebook interactively via mybinder; click on this button:\n", "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dib-lab/sourmash/latest?labpath=doc%2Fusing-LCA-database-API.ipynb)\n", "\n", "A rendered version of this notebook is available at [sourmash.readthedocs.io](https://sourmash.readthedocs.io) under \"Tutorials and notebooks\".\n", "\n", "You can also get this notebook from the [doc/ subdirectory of the sourmash github repository](https://github.com/dib-lab/sourmash/tree/latest/doc). See [binder/environment.yaml](https://github.com/dib-lab/sourmash/blob/latest/binder/environment.yml) for installation dependencies.\n", "\n", "### What is this?\n", "\n", "This is a Jupyter Notebook using Python 3. If you are running this via [binder](https://mybinder.org), you can use Shift-ENTER to run cells, and double click on code cells to edit them.\n", "\n", "Contact: C. Titus Brown, ctbrown@ucdavis.edu. Please [file issues on GitHub](https://github.com/dib-lab/sourmash/issues/) if you have any questions or comments!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating an `LCA_Database` object\n", "\n", "Create an `LCA_Database` like so:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sourmash\n", "\n", "db = sourmash.lca.LCA_Database(ksize=31, scaled=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create signatures for some genomes, load them, and add them:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "\u001b[K\r\n", "== This is sourmash version 4.8.2. ==\r\n", "\r", "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\r\n", "\r\n", "\r", "\u001b[Kcomputing signatures for files: genomes/akkermansia.fa, genomes/shew_os185.fa, genomes/shew_os223.fa\r\n", "\r", "\u001b[KComputing a total of 1 signature(s) for each input.\r\n", "\r", "\u001b[Kskipping genomes/akkermansia.fa - already done\r\n", "\r", "\u001b[Kskipping genomes/shew_os185.fa - already done\r\n", "\r", "\u001b[Kskipping genomes/shew_os223.fa - already done\r\n" ] } ], "source": [ "!sourmash sketch dna -p k=31,scaled=1000 genomes/*" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "sig1 = sourmash.load_one_signature(\"akkermansia.fa.sig\", ksize=31)\n", "sig2 = sourmash.load_one_signature(\"shew_os185.fa.sig\", ksize=31)\n", "sig3 = sourmash.load_one_signature(\"shew_os223.fa.sig\", ksize=31)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "490" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.insert(sig1, ident=\"akkermansia\")\n", "db.insert(sig2, ident=\"shew_os185\")\n", "db.insert(sig3, ident=\"shew_os223\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run `search` and `gather` via the `Index` API" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Result(score=1.0, signature=SourmashSignature('CP001071.1 Akkermansia muciniphila ATCC BAA-835, complete genome', 6822e0b7), location=None)]\n" ] } ], "source": [ "from pprint import pprint\n", "\n", "pprint(db.search(sig1, threshold=0.1))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Result(score=1.0, signature=SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', b47b13ef), location=None),\n", " Result(score=0.22846441947565543, signature=SourmashSignature('NC_011663.1 Shewanella baltica OS223, complete genome', ae6659f6), location=None)]\n" ] } ], "source": [ "pprint(db.search(sig2, threshold=0.1))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Result(score=1.0, signature=SourmashSignature('NC_011663.1 Shewanella baltica OS223, complete genome', ae6659f6), location=None)\n" ] } ], "source": [ "pprint(db.best_containment(sig3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Retrieve all signatures with `signatures()`" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CP001071.1 Akkermansia muciniphila ATCC BAA-835, complete genome\n", "NC_009665.1 Shewanella baltica OS185, complete genome\n", "NC_011663.1 Shewanella baltica OS223, complete genome\n" ] } ], "source": [ "for i in db.signatures():\n", " print(i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Access identifiers and names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The list of (unique) identifiers in the database can be accessed via the attribute `ident_to_idx`, which maps to integer identifiers; identifiers can also retrieve full names, which are taken from `sig.name()` upon insertion." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['akkermansia', 'shew_os185', 'shew_os223'])\n" ] } ], "source": [ "pprint(db._ident_to_idx.keys())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'akkermansia': 'CP001071.1 Akkermansia muciniphila ATCC BAA-835, complete '\n", " 'genome',\n", " 'shew_os185': 'NC_009665.1 Shewanella baltica OS185, complete genome',\n", " 'shew_os223': 'NC_011663.1 Shewanella baltica OS223, complete genome'}\n" ] } ], "source": [ "pprint(db._ident_to_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Access hash values directly" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The attribute `_hashval_to_idx` contains a mapping from individual hash values to sets of `idx` indices.\n", "\n", "See the method `_find_signatures()` for an example of how this is used in `search` and `gather`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1300 hash values total in this database\n" ] } ], "source": [ "print(f\"{len(db._hashval_to_idx)} hash values total in this database\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "belonging to signatures with idx {0, 1, 2}\n" ] } ], "source": [ "all_idx = set()\n", "for idx_set in db._hashval_to_idx.values():\n", " all_idx.update(idx_set)\n", "print(f\"belonging to signatures with idx {all_idx}\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "first_three_hashvals = list(db._hashval_to_idx)[:3]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hashval 17302105753387 belongs to idxs {0}\n", "hashval 95741036335406 belongs to idxs {0}\n", "hashval 165640715598232 belongs to idxs {0}\n" ] } ], "source": [ "for hashval in first_three_hashvals:\n", " print(f\"hashval {hashval} belongs to idxs {db._hashval_to_idx[hashval]}\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "490 hashvals belong to query idx 2\n", "query idx 2 matches to ident shew_os223\n", "query idx 2 matches to name NC_011663.1 Shewanella baltica OS223, complete genome\n" ] } ], "source": [ "query_idx = 2\n", "hashval_set = set()\n", "for hashval, idx_set in db._hashval_to_idx.items():\n", " if query_idx in idx_set:\n", " hashval_set.add(hashval)\n", "\n", "print(f\"{len(hashval_set)} hashvals belong to query idx {query_idx}\")\n", "\n", "ident = db._idx_to_ident[query_idx]\n", "print(f\"query idx {query_idx} matches to ident {ident}\")\n", "\n", "name = db._ident_to_name[ident]\n", "print(f\"query idx {query_idx} matches to name {name}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lineage storage and retrieval" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from sourmash.lca.lca_utils import LineagePair" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "superkingdom = LineagePair(\"superkingdom\", \"Bacteria\")\n", "phylum = LineagePair(\"phylum\", \"Verrucomicrobia\")\n", "klass = LineagePair(\"class\", \"Verrucomicrobiae\")\n", "\n", "lineage = (superkingdom, phylum, klass)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "499" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db = sourmash.lca.LCA_Database(ksize=31, scaled=1000)\n", "db.insert(sig1, lineage=lineage)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ident 'CP001071.1 Akkermansia muciniphila ATCC BAA-835, complete genome' has idx 0\n", "lid for idx 0 is 0\n", "lineage for lid 0 is Bacteria;Verrucomicrobia;Verrucomicrobiae\n" ] } ], "source": [ "# by default, the identifier is the signature name --\n", "ident = sig1.name\n", "idx = db._ident_to_idx[ident]\n", "print(f\"ident '{ident}' has idx {idx}\")\n", "\n", "lid = db._idx_to_lid[idx]\n", "print(f\"lid for idx {idx} is {lid}\")\n", "\n", "lineage = db._lid_to_lineage[lid]\n", "display = sourmash.lca.display_lineage(lineage)\n", "print(f\"lineage for lid {lid} is {display}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lineage manipulation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Default taxonomy ranks for lineages\n", "\n", "While sourmash lineage functions can work with any taxonomy ranks and any taxonomy names, both the NCBI and GTDB taxonomies use superkingdom/phylum/etc, so there is a hard coded list availalbe via `sourmash.lca.taxlist()`." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['superkingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species', 'strain']\n" ] } ], "source": [ "print(list(sourmash.lca.taxlist()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given a taxonomy as a list, you can then construct a lineage like so:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[LineagePair(rank='superkingdom', name='Bacteria'),\n", " LineagePair(rank='phylum', name='Verrucomicrobia'),\n", " LineagePair(rank='class', name='Verrucomicrobiae'),\n", " LineagePair(rank='order', name='Verrucomicrobiales'),\n", " LineagePair(rank='family', name='Akkermansiaceae'),\n", " LineagePair(rank='genus', name='Akkermansia'),\n", " LineagePair(rank='species', name='Akkermansia muciniphila'),\n", " LineagePair(rank='strain', name='Akkermansia muciniphila ATCC BAA-835')]\n" ] } ], "source": [ "linstr1 = [\n", " \"Bacteria\",\n", " \"Verrucomicrobia\",\n", " \"Verrucomicrobiae\",\n", " \"Verrucomicrobiales\",\n", " \"Akkermansiaceae\",\n", " \"Akkermansia\",\n", " \"Akkermansia muciniphila\",\n", " \"Akkermansia muciniphila ATCC BAA-835\",\n", "]\n", "\n", "lineage1 = [LineagePair(*pair) for pair in zip(sourmash.lca.taxlist(), linstr1)]\n", "pprint(lineage1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Akkermansiaceae;Akkermansia;Akkermansia muciniphila;Akkermansia muciniphila ATCC BAA-835'" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# display lineages as strings with 'sourmash.lca.display_lineage()'\n", "sourmash.lca.display_lineage(lineage1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## sourmash lowest-common-ancestor functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The LCA functionality available in sourmash is built around some simple lineage manipulation functions -- `build_tree` and `find_lca`.\n", "\n", "First, let's define some more lineages --" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "lineage2 is Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica;Shewanella baltica OS185\n", "lineage3 is Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica;Shewanella baltica OS223\n" ] } ], "source": [ "linstr2 = [\n", " \"Bacteria\",\n", " \"Proteobacteria\",\n", " \"Gammaproteobacteria\",\n", " \"Alteromonadales\",\n", " \"Shewanellaceae\",\n", " \"Shewanella\",\n", " \"Shewanella baltica\",\n", " \"Shewanella baltica OS185\",\n", "]\n", "lineage2 = [LineagePair(*pair) for pair in zip(sourmash.lca.taxlist(), linstr2)]\n", "\n", "linstr3 = [\n", " \"Bacteria\",\n", " \"Proteobacteria\",\n", " \"Gammaproteobacteria\",\n", " \"Alteromonadales\",\n", " \"Shewanellaceae\",\n", " \"Shewanella\",\n", " \"Shewanella baltica\",\n", " \"Shewanella baltica OS223\",\n", "]\n", "lineage3 = [LineagePair(*pair) for pair in zip(sourmash.lca.taxlist(), linstr3)]\n", "\n", "print(\"lineage2 is\", sourmash.lca.display_lineage(lineage2))\n", "print(\"lineage3 is\", sourmash.lca.display_lineage(lineage3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, build a tree structure that collapses these lineages where it can, and run some LCA analyses. Lineages 1 and 2 collapse to superkingdom:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((LineagePair(rank='superkingdom', name='Bacteria'),), 2)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tree = sourmash.lca.build_tree([lineage1, lineage2])\n", "sourmash.lca.find_lca(tree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "while lineages 2 and 3 collapse to species:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((LineagePair(rank='superkingdom', name='Bacteria'),\n", " LineagePair(rank='phylum', name='Proteobacteria'),\n", " LineagePair(rank='class', name='Gammaproteobacteria'),\n", " LineagePair(rank='order', name='Alteromonadales'),\n", " LineagePair(rank='family', name='Shewanellaceae'),\n", " LineagePair(rank='genus', name='Shewanella'),\n", " LineagePair(rank='species', name='Shewanella baltica')),\n", " 2)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tree = sourmash.lca.build_tree([lineage2, lineage3])\n", "sourmash.lca.find_lca(tree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Convenience functions let you make use of LCA_Database stored lineages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's create a database from 3 signatures, and this time we'll store lineages in there:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "490" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db = sourmash.lca.LCA_Database(ksize=31, scaled=1000)\n", "db.insert(sig1, lineage=lineage1)\n", "db.insert(sig2, lineage=lineage2)\n", "db.insert(sig3, lineage=lineage3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, for any collection of hashes, you can retrieve all the lineage assignments into a dictionary:\n", "`{ hashval: set of lineages }`" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "num hashvals: 494\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/6s/_f373w1d6hdfjc2kjstq97s80000gp/T/ipykernel_3384/490137846.py:1: DeprecatedWarning: get_mins is deprecated as of 3.5 and will be removed in 5.0. Use .hashes property instead.\n", " assignments = sourmash.lca.gather_assignments(sig2.minhash.get_mins(), [db])\n" ] } ], "source": [ "assignments = sourmash.lca.gather_assignments(sig2.minhash.get_mins(), [db])\n", "print(\"num hashvals:\", len(assignments))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, this particular hashvalue belongs to two different lineages:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{(LineagePair(rank='superkingdom', name='Bacteria'),\n", " LineagePair(rank='phylum', name='Proteobacteria'),\n", " LineagePair(rank='class', name='Gammaproteobacteria'),\n", " LineagePair(rank='order', name='Alteromonadales'),\n", " LineagePair(rank='family', name='Shewanellaceae'),\n", " LineagePair(rank='genus', name='Shewanella'),\n", " LineagePair(rank='species', name='Shewanella baltica'),\n", " LineagePair(rank='strain', name='Shewanella baltica OS185')),\n", " (LineagePair(rank='superkingdom', name='Bacteria'),\n", " LineagePair(rank='phylum', name='Proteobacteria'),\n", " LineagePair(rank='class', name='Gammaproteobacteria'),\n", " LineagePair(rank='order', name='Alteromonadales'),\n", " LineagePair(rank='family', name='Shewanellaceae'),\n", " LineagePair(rank='genus', name='Shewanella'),\n", " LineagePair(rank='species', name='Shewanella baltica'),\n", " LineagePair(rank='strain', name='Shewanella baltica OS223'))}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "assignments[196037984804395]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "sourmash also includes functions to summarize assignments by counting the number of\n", "times a particular lineage occurs; this is\n", "used by `sourmash lca classify` and `sourmash lca summarize`." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "311 hashes have LCA: Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica;Shewanella baltica OS185\n", "183 hashes have LCA: Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica\n" ] } ], "source": [ "counter = sourmash.lca.count_lca_for_assignments(assignments)\n", "\n", "# count_lca_for_assignments returns a collections.Counter object\n", "for lineage, count in counter.most_common():\n", " print(f\"{count} hashes have LCA: {sourmash.lca.display_lineage(lineage)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Other pointers\n", "\n", "[Sourmash: a practical guide](https://sourmash.readthedocs.io/en/latest/using-sourmash-a-guide.html)\n", "\n", "[Classifying signatures taxonomically](https://sourmash.readthedocs.io/en/latest/classifying-signatures.html)\n", "\n", "[Pre-built search databases](https://sourmash.readthedocs.io/en/latest/databases.html)\n", "\n", "## A full list of notebooks\n", "\n", "[An introduction to k-mers for genome comparison and analysis](kmers-and-minhash.ipynb)\n", "\n", "[Some sourmash command line examples!](sourmash-examples.ipynb)\n", "\n", "[Working with private collections of signatures.](sourmash-collections.ipynb)\n", "\n", "[Using the LCA_Database API.](using-LCA-database-API.ipynb)" ] } ], "metadata": { "kernelspec": { "display_name": "smash-notebooks", "language": "python", "name": "smash-notebooks" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.3" } }, "nbformat": 4, "nbformat_minor": 4 } sourmash-bio-sourmash-bc3c0f8/doc/using-sourmash-a-guide.md000066400000000000000000000224521474157046300240570ustar00rootroot00000000000000# Using sourmash: a practical guide ```{contents} :depth: 2 ``` So! You've installed sourmash, run a few of the tutorials and commands, and now you actually want to *use* it. This guide is here to answer some of your questions, and explain why we can't answer others. (If you have additional questions, please [file an issue!](https://github.com/sourmash-bio/sourmash/issues)) ## What k-mer size(s) should I use? You can build signatures at a variety of k-mer sizes all at once, and (unless you are working with very large metagenomes) the resulting signature files will still be quite small. We suggest including k=31 and k=51. k=51 gives you the most stringent matches, and has very few false positives. k=31 may be more sensitive at the genus level. Why 31 and 51, specifically? To a large extent these numbers were picked out of a hat, based on our reading of papers like the [Metapalette paper (Koslicki and Falush, 2016](http://msystems.asm.org/content/1/3/e00020-16). You could go with k=49 or k=53 and probably get very similar results to k=51. The general rule is that longer k-mer sizes are less prone to false positives. But you can pick your own parameters. One additional wrinkle is that we provide a number of [precalculated databases](databases.md) at k=21, k=31, and k=51. It is often convenient to calculate signatures at these sizes so that you can use these databases. You'll notice that all of the above numbers are odd. That is to avoid occasional minor complications from palindromes in numerical calculations, where the forward and reverse complements of a k-mer are identical. This cannot happen if k is odd. It is not enforced by sourmash, however, and it probably doesn't really matter. (When we have blog posts or publications providing more formal guidance, we'll link to them here!) ## What resolution should my signatures be and how should I create them? sourmash supports two ways of choosing the resolution or size of your signatures: using `num` to specify the maximum number of hashes, or `scaled` to specify the compression ratio. Which should you use? We suggest calculating all your signatures using `-p scaled=1000`. This will give you a compression ratio of 1000-to-1 while making it possible to detect regions of similarity in the 10kb range. For comparison with more traditional MinHash approaches like `mash`, if you have a 5 Mbp genome and use `-p scaled=1000`, you will extract approximately 5000 hashes. So a scaled of 1000 is equivalent to using `-p num=5000` with mash on a 5 Mbp genome. The difference between using `num` and `scaled` is in metagenome analysis: fixing the number of hashes with `num` limits your ability to detect rare organisms, or alternatively results in very large signatures (e.g. if you use n larger than 10000). `scaled` will scale your resolution with the diversity of the metagenome. You can read more about this in this blog post from the mash folk, [Mash Screen: What's in my sequencing run?](https://genomeinformatics.github.io/mash-screen/) What we do with sourmash and `scaled` is similar to the 'modulo hash' mentioned in that blog post. (Again, when we have formal guidance on this based on benchmarks, we'll link to it here.) ## What kind of input data does sourmash work on? sourmash has been used most extensively with Illumina read data sets and assembled genomes, transcriptomes, and metagenomes. The high error rate of PacBio and Nanopore sequencing is problematic for k-mer based approaches and we have not yet explored how to tune parameters for this kind of sequencing. On a more practical note, `sourmash sketch` will autodetect FASTA and FASTQ formats, whether they are uncompressed, gzipped, or bzip2-ed. Nothing special needs to be done. ## How should I prepare my data? Raw Illumina read data sets should be k-mer abundance trimmed to get rid of the bulk of erroneous kmers. We suggest a command like the following, using [trim-low-abund from the khmer project](https://peerj.com/preprints/890/) -- ``` trim-low-abund.py -C 3 -Z 18 -V -M 2e9 ``` This is safe to use on genomes, metagenomes, and transcriptomes. If you are working with large genomes or diverse metagenomes, you may need to increase the `-M` parameter to use more memory. See [the khmer docs for trim-low-abund.py](https://khmer.readthedocs.io/en/v2.1.2/user/scripts.html#trim-low-abund-py) and [the semi-streaming preprint](https://peerj.com/preprints/890/) for more information. For high coverage genomic data, you can do very stringent trimming with an absolute cutoff, e.g. ``` trim-low-abund.py -C 10 -M 2e9 ``` will eliminate all k-mers that appear fewer than 10 times in your data set. This kind of trimming will dramatically reduce your sensitivity when working with metagenomes and transcriptomes, however, where there are always real low-abundance k-mers present. ## Could you just give us the !#%#!$ command line? Sorry, yes! See below. ### Calculating signatures for read files: ``` trim-low-abund -C 3 -Z 18 -V -M 2e9 input-reads-1.fq input-reads-2.fq ... sourmash sketch dna -p scaled=1000,k=21,k=31,k=51 input-reads*.fq.abundtrim \ --merge SOMENAME -o SOMENAME-reads.sig ``` The first command trims off low-abundance k-mers from high-coverage reads; the second takes all the trimmed read files, subsamples k-mers from them at 1000:1, and outputs a single merged signature named 'SOMENAME' into the file `SOMENAME-reads.sig`. ### Calculating a combined signature for multiple read files ``` sourmash sketch dna -p scaled=1000,k=21,k=31,k=51 sample_*.fq.gz \ --name "combined sketch for sample" -o sample.zip ``` This will build combined sketches of all `*.fq.gz` files in the directory for three ksizes, k=21, k=31, and k=51. The three sketches will be named `combined sketch for sample` and be saved to `sample.zip`. ### Creating signatures for individual genome files: ``` sourmash sketch dna -p scaled=1000,k=21,k=31,k=51 *.fna.gz --name-from-first ``` This command creates signatures for all `*.fna.gz` files, and names each signature based on the first FASTA header in each file (that's what the option `--name-from-first` does). The signatures will be placed in `*.fna.gz.sig`. ### Creating signatures from a collection of genomes in a single file: ``` sourmash sketch dna -p scaled=1000,k=21,k=31,k=51 file.fa --singleton ``` This creates signatures for all individual FASTA sequences in `file.fa`, names them based on their FASTA headers, and places them all in a single `.sig` file, `file.fa.sig`. (This behavior is triggered by the option `--singleton`, which tells sourmash to treat each individual sequence in the file as an independent sequence.) ## How do I store and search collections of signatures? sourmash supports a variety of signature loading and storage options for flexibility. If you have only a few hundred signatures, here are some options - * you can put all your signature files in a directory and search them all using the path to the directory. * you can use `sourmash sig cat` to concatenate multiple signatures into a single file. * you can compress any signature file using `gzip` and sourmash will load them. If you have more than a few hundred genome signatures that you regularly search, it might be worth creating an indexed database of them that will support faster searches. sourmash supports two types of indexed databases: Sequence Bloom Trees, or SBTs; and reverse indices, or LCAs. (You can read more detail about their implementation and design considerations [in Chapter 2 of Dr. Luiz Irber's thesis, "Efficient indexing of collections of signatures"](https://github.com/luizirber/phd/releases/download/2020.09.28/thesis.pdf).) ### Sequence Bloom Tree (SBT) indexed databases Sequence Bloom Trees (SBTs) (see [Solomon and Kingsford, 2016](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4804353/)) are on disk databases that support low-memory query of 10s-100s of thousands of signatures. They can be created using `sourmash index`. SBTs are the lowest-memory way to run search or gather on a collection of signatures. The tradeoff is that they may be quite large on disk, because SBTs also contain intermediate nodes in the tree. The default way to store SBTs is in a Zip file, named `.sbt.zip`, that can be built and searched directly from the command line. ### Reverse indexed (LCA) databases Reverse indexed or LCA databases are *in-memory* databases that, once loaded from disk, support fast search and gather across 10s of thousands of signatures. They can be created using `sourmash lca index` ([docs](command-line.md#sourmash-lca-index---build-an-lca-database)) LCA databases are currently stored in JSON files (that can be gzipped). As these files get larger, the time required to load them from disk can be substantial. LCA databases are also currently (sourmash 2.0-4.0) the only databases that support the inclusion of taxonomic information in the database, and there is an associated collection of commands [under `sourmash lca`](command-line.md#sourmash-lca-subcommands-for-in-memory-taxonomy-integration). However, they can also be used as regular indexed databases for search and gather as above. (These are called "LCA databases" because they originally were created to support "lowest common ancestor" taxonomic analyses, e.g. like Kraken; their functionality has evolved a lot since, but their name hasn't changed to match!) sourmash-bio-sourmash-bc3c0f8/flake.lock000066400000000000000000000037461474157046300204340ustar00rootroot00000000000000{ "nodes": { "nixpkgs": { "locked": { "lastModified": 1736241350, "narHash": "sha256-CHd7yhaDigUuJyDeX0SADbTM9FXfiWaeNyY34FL1wQU=", "owner": "NixOS", "repo": "nixpkgs", "rev": "8c9fd3e564728e90829ee7dbac6edc972971cd0f", "type": "github" }, "original": { "owner": "NixOS", "ref": "nixpkgs-unstable", "repo": "nixpkgs", "type": "github" } }, "root": { "inputs": { "nixpkgs": "nixpkgs", "rust-overlay": "rust-overlay", "utils": "utils" } }, "rust-overlay": { "inputs": { "nixpkgs": [ "nixpkgs" ] }, "locked": { "lastModified": 1736303309, "narHash": "sha256-IKrk7RL+Q/2NC6+Ql6dwwCNZI6T6JH2grTdJaVWHF0A=", "owner": "oxalica", "repo": "rust-overlay", "rev": "a0b81d4fa349d9af1765b0f0b4a899c13776f706", "type": "github" }, "original": { "owner": "oxalica", "repo": "rust-overlay", "type": "github" } }, "systems": { "locked": { "lastModified": 1681028828, "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=", "owner": "nix-systems", "repo": "default", "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e", "type": "github" }, "original": { "owner": "nix-systems", "repo": "default", "type": "github" } }, "utils": { "inputs": { "systems": "systems" }, "locked": { "lastModified": 1731533236, "narHash": "sha256-l0KFg5HjrsfsO/JpG+r7fRrqm12kzFHyUHqHCVpMMbI=", "owner": "numtide", "repo": "flake-utils", "rev": "11707dc2f618dd54ca8739b309ec4fc024de578b", "type": "github" }, "original": { "owner": "numtide", "repo": "flake-utils", "type": "github" } } }, "root": "root", "version": 7 } sourmash-bio-sourmash-bc3c0f8/flake.nix000066400000000000000000000103641474157046300202740ustar00rootroot00000000000000{ inputs = { nixpkgs.url = "github:NixOS/nixpkgs/nixpkgs-unstable"; utils.url = "github:numtide/flake-utils"; rust-overlay = { url = "github:oxalica/rust-overlay"; inputs = { nixpkgs.follows = "nixpkgs"; }; }; }; outputs = { self, nixpkgs, rust-overlay, utils }: utils.lib.eachDefaultSystem (system: let overlays = [ (import rust-overlay) ]; pkgs = import nixpkgs { inherit system overlays; }; rustVersion = pkgs.rust-bin.stable.latest.default.override { #extensions = [ "rust-src" ]; extensions = [ "llvm-tools-preview" ]; #targets = [ "x86_64-unknown-linux-musl" ]; targets = [ "wasm32-unknown-unknown" "wasm32-unknown-emscripten" ]; }; rustPlatform = pkgs.makeRustPlatform { cargo = rustVersion; rustc = rustVersion; }; inherit (pkgs) lib; python = pkgs.python311Packages; stdenv = if pkgs.stdenv.isDarwin then pkgs.overrideSDK pkgs.stdenv "11.0" else pkgs.stdenv; commonArgs = { src = ./.; stdenv = stdenv; preConfigure = lib.optionalString stdenv.isDarwin '' export MACOSX_DEPLOYMENT_TARGET=10.14 ''; buildInputs = lib.optionals stdenv.isDarwin [ pkgs.libiconv pkgs.darwin.apple_sdk.frameworks.Security ]; nativeBuildInputs = with rustPlatform; [ cargoSetupHook maturinBuildHook bindgenHook ]; }; in with pkgs; { packages = { lib = rustPlatform.buildRustPackage ( commonArgs // { name = "libsourmash"; copyLibs = true; cargoLock.lockFile = ./Cargo.lock; nativeBuildInputs = with rustPlatform; [ bindgenHook ]; }); sourmash = python.buildPythonPackage ( commonArgs // rec { pname = "sourmash"; version = "4.8.14"; format = "pyproject"; cargoDeps = rustPlatform.importCargoLock { lockFile = ./Cargo.lock; }; propagatedBuildInputs = with python; [ cffi deprecation cachetools bitstring numpy scipy matplotlib screed ]; DYLD_LIBRARY_PATH = "${self.packages.${system}.lib}/lib"; }); docker = let bin = self.defaultPackage.${system}; in pkgs.dockerTools.buildLayeredImage { name = bin.pname; tag = bin.version; contents = [ bin ]; config = { Cmd = [ "/bin/sourmash" ]; WorkingDir = "/"; }; }; }; defaultPackage = self.packages.${system}.sourmash; devShells.default = pkgs.mkShell.override { stdenv = stdenv; } (commonArgs // { nativeBuildInputs = with rustPlatform; [ bindgenHook ]; buildInputs = [ rustVersion openssl pkg-config git stdenv.cc.cc.lib (python312.withPackages (ps: with ps; [ virtualenv ])) (python311.withPackages (ps: with ps; [ virtualenv tox cffi ])) (python310.withPackages (ps: with ps; [ virtualenv ])) #rust-cbindgen maturin wasmtime wasm-pack nodejs_20 #emscripten #py-spy #heaptrack cargo-all-features cargo-watch cargo-limit cargo-outdated cargo-udeps cargo-deny cargo-nextest #cargo-llvm-cov cargo-component cargo-codspeed #cargo-semver-checks nixpkgs-fmt ]; shellHook = '' export MACOSX_DEPLOYMENT_TARGET=10.14 ''; # Needed for matplotlib LD_LIBRARY_PATH = lib.makeLibraryPath [ pkgs.stdenv.cc.cc.lib ]; # workaround for https://github.com/NixOS/nixpkgs/blob/48dfc9fa97d762bce28cc8372a2dd3805d14c633/doc/languages-frameworks/python.section.md#python-setuppy-bdist_wheel-cannot-create-whl SOURCE_DATE_EPOCH = 315532800; # 1980 # exporting to fix doc building errors in sphinx LC_ALL="C.utf8"; }); }); } sourmash-bio-sourmash-bc3c0f8/include/000077500000000000000000000000001474157046300201115ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/include/sourmash.h000066400000000000000000000436601474157046300221340ustar00rootroot00000000000000/* c bindings to the sourmash library */ #ifndef SOURMASH_H_INCLUDED #define SOURMASH_H_INCLUDED #include #include #include #include enum HashFunctions { HASH_FUNCTIONS_MURMUR64_DNA = 1, HASH_FUNCTIONS_MURMUR64_PROTEIN = 2, HASH_FUNCTIONS_MURMUR64_DAYHOFF = 3, HASH_FUNCTIONS_MURMUR64_HP = 4, HASH_FUNCTIONS_MURMUR64_SKIPM1N3 = 5, HASH_FUNCTIONS_MURMUR64_SKIPM2N3 = 6, }; typedef uint32_t HashFunctions; enum SourmashErrorCode { SOURMASH_ERROR_CODE_NO_ERROR = 0, SOURMASH_ERROR_CODE_PANIC = 1, SOURMASH_ERROR_CODE_INTERNAL = 2, SOURMASH_ERROR_CODE_MSG = 3, SOURMASH_ERROR_CODE_UNKNOWN = 4, SOURMASH_ERROR_CODE_MISMATCH_K_SIZES = 101, SOURMASH_ERROR_CODE_MISMATCH_DNA_PROT = 102, SOURMASH_ERROR_CODE_MISMATCH_SCALED = 103, SOURMASH_ERROR_CODE_MISMATCH_SEED = 104, SOURMASH_ERROR_CODE_MISMATCH_SIGNATURE_TYPE = 105, SOURMASH_ERROR_CODE_NON_EMPTY_MIN_HASH = 106, SOURMASH_ERROR_CODE_MISMATCH_NUM = 107, SOURMASH_ERROR_CODE_NEEDS_ABUNDANCE_TRACKING = 108, SOURMASH_ERROR_CODE_CANNOT_UPSAMPLE_SCALED = 109, SOURMASH_ERROR_CODE_NO_MIN_HASH_FOUND = 110, SOURMASH_ERROR_CODE_EMPTY_SIGNATURE = 111, SOURMASH_ERROR_CODE_MULTIPLE_SKETCHES_FOUND = 112, SOURMASH_ERROR_CODE_INVALID_DNA = 1101, SOURMASH_ERROR_CODE_INVALID_PROT = 1102, SOURMASH_ERROR_CODE_INVALID_CODON_LENGTH = 1103, SOURMASH_ERROR_CODE_INVALID_HASH_FUNCTION = 1104, SOURMASH_ERROR_CODE_INVALID_SKIPMER_FRAME = 1105, SOURMASH_ERROR_CODE_INVALID_SKIPMER_SIZE = 1106, SOURMASH_ERROR_CODE_INVALID_TRANSLATE_FRAME = 1107, SOURMASH_ERROR_CODE_READ_DATA = 1201, SOURMASH_ERROR_CODE_STORAGE = 1202, SOURMASH_ERROR_CODE_HLL_PRECISION_BOUNDS = 1301, SOURMASH_ERROR_CODE_ANI_ESTIMATION_ERROR = 1401, SOURMASH_ERROR_CODE_IO = 100001, SOURMASH_ERROR_CODE_UTF8_ERROR = 100002, SOURMASH_ERROR_CODE_PARSE_INT = 100003, SOURMASH_ERROR_CODE_SERDE_ERROR = 100004, SOURMASH_ERROR_CODE_NIFFLER_ERROR = 100005, SOURMASH_ERROR_CODE_CSV_ERROR = 100006, SOURMASH_ERROR_CODE_ROCKS_DB_ERROR = 100007, SOURMASH_ERROR_CODE_ZIP_ERROR = 100008, }; typedef uint32_t SourmashErrorCode; typedef struct SourmashComputeParameters SourmashComputeParameters; typedef struct SourmashHyperLogLog SourmashHyperLogLog; typedef struct SourmashKmerMinHash SourmashKmerMinHash; typedef struct SourmashNodegraph SourmashNodegraph; typedef struct SourmashRevIndex SourmashRevIndex; typedef struct SourmashSearchResult SourmashSearchResult; typedef struct SourmashSignature SourmashSignature; typedef struct SourmashZipStorage SourmashZipStorage; typedef uint32_t ScaledType; /** * Represents a string. */ typedef struct { /** * Pointer to the UTF-8 encoded string data. */ char *data; /** * The length of the string pointed to by `data`. */ uintptr_t len; /** * Indicates that the string is owned and must be freed. */ bool owned; } SourmashStr; bool computeparams_dayhoff(const SourmashComputeParameters *ptr); bool computeparams_dna(const SourmashComputeParameters *ptr); void computeparams_free(SourmashComputeParameters *ptr); bool computeparams_hp(const SourmashComputeParameters *ptr); const uint32_t *computeparams_ksizes(const SourmashComputeParameters *ptr, uintptr_t *size); void computeparams_ksizes_free(uint32_t *ptr, uintptr_t insize); SourmashComputeParameters *computeparams_new(void); uint32_t computeparams_num_hashes(const SourmashComputeParameters *ptr); bool computeparams_protein(const SourmashComputeParameters *ptr); ScaledType computeparams_scaled(const SourmashComputeParameters *ptr); uint64_t computeparams_seed(const SourmashComputeParameters *ptr); void computeparams_set_dayhoff(SourmashComputeParameters *ptr, bool v); void computeparams_set_dna(SourmashComputeParameters *ptr, bool v); void computeparams_set_hp(SourmashComputeParameters *ptr, bool v); void computeparams_set_ksizes(SourmashComputeParameters *ptr, const uint32_t *ksizes_ptr, uintptr_t insize); void computeparams_set_num_hashes(SourmashComputeParameters *ptr, uint32_t num); void computeparams_set_protein(SourmashComputeParameters *ptr, bool v); void computeparams_set_scaled(SourmashComputeParameters *ptr, uint32_t scaled); void computeparams_set_seed(SourmashComputeParameters *ptr, uint64_t new_seed); void computeparams_set_track_abundance(SourmashComputeParameters *ptr, bool v); bool computeparams_track_abundance(const SourmashComputeParameters *ptr); uint64_t hash_murmur(const char *kmer, uint64_t seed); void hll_add_hash(SourmashHyperLogLog *ptr, uint64_t hash); void hll_add_sequence(SourmashHyperLogLog *ptr, const char *sequence, uintptr_t insize, bool force); uintptr_t hll_cardinality(const SourmashHyperLogLog *ptr); double hll_containment(const SourmashHyperLogLog *ptr, const SourmashHyperLogLog *optr); void hll_free(SourmashHyperLogLog *ptr); SourmashHyperLogLog *hll_from_buffer(const char *ptr, uintptr_t insize); SourmashHyperLogLog *hll_from_path(const char *filename); uintptr_t hll_intersection_size(const SourmashHyperLogLog *ptr, const SourmashHyperLogLog *optr); uintptr_t hll_ksize(const SourmashHyperLogLog *ptr); uintptr_t hll_matches(const SourmashHyperLogLog *ptr, const SourmashKmerMinHash *mh_ptr); void hll_merge(SourmashHyperLogLog *ptr, const SourmashHyperLogLog *optr); SourmashHyperLogLog *hll_new(void); void hll_save(const SourmashHyperLogLog *ptr, const char *filename); double hll_similarity(const SourmashHyperLogLog *ptr, const SourmashHyperLogLog *optr); const uint8_t *hll_to_buffer(const SourmashHyperLogLog *ptr, uintptr_t *size); void hll_update_mh(SourmashHyperLogLog *ptr, const SourmashKmerMinHash *optr); SourmashHyperLogLog *hll_with_error_rate(double error_rate, uintptr_t ksize); void kmerminhash_add_from(SourmashKmerMinHash *ptr, const SourmashKmerMinHash *other); void kmerminhash_add_hash(SourmashKmerMinHash *ptr, uint64_t h); void kmerminhash_add_hash_with_abundance(SourmashKmerMinHash *ptr, uint64_t h, uint64_t abundance); void kmerminhash_add_many(SourmashKmerMinHash *ptr, const uint64_t *hashes_ptr, uintptr_t insize); void kmerminhash_add_protein(SourmashKmerMinHash *ptr, const char *sequence); void kmerminhash_add_sequence(SourmashKmerMinHash *ptr, const char *sequence, bool force); void kmerminhash_add_word(SourmashKmerMinHash *ptr, const char *word); double kmerminhash_angular_similarity(const SourmashKmerMinHash *ptr, const SourmashKmerMinHash *other); void kmerminhash_clear(SourmashKmerMinHash *ptr); uint64_t kmerminhash_count_common(const SourmashKmerMinHash *ptr, const SourmashKmerMinHash *other, bool downsample); bool kmerminhash_dayhoff(const SourmashKmerMinHash *ptr); void kmerminhash_disable_abundance(SourmashKmerMinHash *ptr); void kmerminhash_enable_abundance(SourmashKmerMinHash *ptr); void kmerminhash_free(SourmashKmerMinHash *ptr); const uint64_t *kmerminhash_get_abunds(SourmashKmerMinHash *ptr, uintptr_t *size); const uint64_t *kmerminhash_get_mins(const SourmashKmerMinHash *ptr, uintptr_t *size); uintptr_t kmerminhash_get_mins_size(const SourmashKmerMinHash *ptr); HashFunctions kmerminhash_hash_function(const SourmashKmerMinHash *ptr); void kmerminhash_hash_function_set(SourmashKmerMinHash *ptr, HashFunctions hash_function); bool kmerminhash_hp(const SourmashKmerMinHash *ptr); SourmashKmerMinHash *kmerminhash_intersection(const SourmashKmerMinHash *ptr, const SourmashKmerMinHash *other); uint64_t kmerminhash_intersection_union_size(const SourmashKmerMinHash *ptr, const SourmashKmerMinHash *other, uint64_t *union_size); bool kmerminhash_is_compatible(const SourmashKmerMinHash *ptr, const SourmashKmerMinHash *other); bool kmerminhash_is_protein(const SourmashKmerMinHash *ptr); double kmerminhash_jaccard(const SourmashKmerMinHash *ptr, const SourmashKmerMinHash *other); uint32_t kmerminhash_ksize(const SourmashKmerMinHash *ptr); uint64_t kmerminhash_max_hash(const SourmashKmerMinHash *ptr); SourmashStr kmerminhash_md5sum(const SourmashKmerMinHash *ptr); void kmerminhash_merge(SourmashKmerMinHash *ptr, const SourmashKmerMinHash *other); SourmashKmerMinHash *kmerminhash_new(uint32_t scaled, uint32_t k, HashFunctions hash_function, uint64_t seed, bool track_abundance, uint32_t n); uint32_t kmerminhash_num(const SourmashKmerMinHash *ptr); void kmerminhash_remove_from(SourmashKmerMinHash *ptr, const SourmashKmerMinHash *other); void kmerminhash_remove_hash(SourmashKmerMinHash *ptr, uint64_t h); void kmerminhash_remove_many(SourmashKmerMinHash *ptr, const uint64_t *hashes_ptr, uintptr_t insize); uint64_t kmerminhash_seed(const SourmashKmerMinHash *ptr); const uint64_t *kmerminhash_seq_to_hashes(SourmashKmerMinHash *ptr, const char *sequence, uintptr_t insize, bool force, bool bad_kmers_as_zeroes, bool is_protein, uintptr_t *size); void kmerminhash_set_abundances(SourmashKmerMinHash *ptr, const uint64_t *hashes_ptr, const uint64_t *abunds_ptr, uintptr_t insize, bool clear); double kmerminhash_similarity(const SourmashKmerMinHash *ptr, const SourmashKmerMinHash *other, bool ignore_abundance, bool downsample); bool kmerminhash_skipm1n3(const SourmashKmerMinHash *ptr); bool kmerminhash_skipm2n3(const SourmashKmerMinHash *ptr); void kmerminhash_slice_free(uint64_t *ptr, uintptr_t insize); bool kmerminhash_track_abundance(const SourmashKmerMinHash *ptr); void nodegraph_buffer_free(uint8_t *ptr, uintptr_t insize); bool nodegraph_count(SourmashNodegraph *ptr, uint64_t h); bool nodegraph_count_kmer(SourmashNodegraph *ptr, const char *kmer); double nodegraph_expected_collisions(const SourmashNodegraph *ptr); void nodegraph_free(SourmashNodegraph *ptr); SourmashNodegraph *nodegraph_from_buffer(const char *ptr, uintptr_t insize); SourmashNodegraph *nodegraph_from_path(const char *filename); uintptr_t nodegraph_get(const SourmashNodegraph *ptr, uint64_t h); uintptr_t nodegraph_get_kmer(const SourmashNodegraph *ptr, const char *kmer); const uint64_t *nodegraph_hashsizes(const SourmashNodegraph *ptr, uintptr_t *size); uintptr_t nodegraph_ksize(const SourmashNodegraph *ptr); uintptr_t nodegraph_matches(const SourmashNodegraph *ptr, const SourmashKmerMinHash *mh_ptr); SourmashNodegraph *nodegraph_new(void); uintptr_t nodegraph_noccupied(const SourmashNodegraph *ptr); uintptr_t nodegraph_ntables(const SourmashNodegraph *ptr); void nodegraph_save(const SourmashNodegraph *ptr, const char *filename); const uint8_t *nodegraph_to_buffer(const SourmashNodegraph *ptr, uint8_t compression, uintptr_t *size); void nodegraph_update(SourmashNodegraph *ptr, const SourmashNodegraph *optr); void nodegraph_update_mh(SourmashNodegraph *ptr, const SourmashKmerMinHash *optr); SourmashNodegraph *nodegraph_with_tables(uintptr_t ksize, uintptr_t starting_size, uintptr_t n_tables); void revindex_free(SourmashRevIndex *ptr); const SourmashSearchResult *const *revindex_gather(const SourmashRevIndex *ptr, const SourmashSignature *sig_ptr, double threshold, bool _do_containment, bool _ignore_abundance, uintptr_t *size); uint64_t revindex_len(const SourmashRevIndex *ptr); SourmashRevIndex *revindex_new_with_paths(const SourmashStr *const *search_sigs_ptr, uintptr_t insigs, const SourmashKmerMinHash *template_ptr, uintptr_t threshold, const SourmashKmerMinHash *const *queries_ptr, uintptr_t inqueries, bool keep_sigs); SourmashRevIndex *revindex_new_with_sigs(const SourmashSignature *const *search_sigs_ptr, uintptr_t insigs, const SourmashKmerMinHash *template_ptr, uintptr_t threshold, const SourmashKmerMinHash *const *queries_ptr, uintptr_t inqueries); ScaledType revindex_scaled(const SourmashRevIndex *ptr); const SourmashSearchResult *const *revindex_search(const SourmashRevIndex *ptr, const SourmashSignature *sig_ptr, double threshold, bool do_containment, bool _ignore_abundance, uintptr_t *size); SourmashSignature **revindex_signatures(const SourmashRevIndex *ptr, uintptr_t *size); SourmashStr searchresult_filename(const SourmashSearchResult *ptr); void searchresult_free(SourmashSearchResult *ptr); double searchresult_score(const SourmashSearchResult *ptr); SourmashSignature *searchresult_signature(const SourmashSearchResult *ptr); void signature_add_protein(SourmashSignature *ptr, const char *sequence); void signature_add_sequence(SourmashSignature *ptr, const char *sequence, bool force); bool signature_eq(const SourmashSignature *ptr, const SourmashSignature *other); SourmashKmerMinHash *signature_first_mh(const SourmashSignature *ptr); void signature_free(SourmashSignature *ptr); SourmashSignature *signature_from_params(const SourmashComputeParameters *ptr); SourmashStr signature_get_filename(const SourmashSignature *ptr); SourmashStr signature_get_license(const SourmashSignature *ptr); SourmashKmerMinHash **signature_get_mhs(const SourmashSignature *ptr, uintptr_t *size); SourmashStr signature_get_name(const SourmashSignature *ptr); uintptr_t signature_len(const SourmashSignature *ptr); SourmashSignature *signature_new(void); void signature_push_mh(SourmashSignature *ptr, const SourmashKmerMinHash *other); SourmashStr signature_save_json(const SourmashSignature *ptr); void signature_set_filename(SourmashSignature *ptr, const char *name); void signature_set_mh(SourmashSignature *ptr, const SourmashKmerMinHash *other); void signature_set_name(SourmashSignature *ptr, const char *name); SourmashSignature **signatures_load_buffer(const char *ptr, uintptr_t insize, bool _ignore_md5sum, uintptr_t ksize, const char *select_moltype, uintptr_t *size); SourmashSignature **signatures_load_path(const char *ptr, bool _ignore_md5sum, uintptr_t ksize, const char *select_moltype, uintptr_t *size); const uint8_t *signatures_save_buffer(const SourmashSignature *const *ptr, uintptr_t size, uint8_t compression, uintptr_t *osize); char sourmash_aa_to_dayhoff(char aa); char sourmash_aa_to_hp(char aa); /** * Clears the last error. */ void sourmash_err_clear(void); /** * Returns the panic information as string. */ SourmashStr sourmash_err_get_backtrace(void); /** * Returns the last error code. * * If there is no error, 0 is returned. */ SourmashErrorCode sourmash_err_get_last_code(void); /** * Returns the last error message. * * If there is no error an empty string is returned. This allocates new memory * that needs to be freed with `sourmash_str_free`. */ SourmashStr sourmash_err_get_last_message(void); /** * Initializes the library */ void sourmash_init(void); /** * Frees a sourmash str. * * If the string is marked as not owned then this function does not * do anything. */ void sourmash_str_free(SourmashStr *s); /** * Creates a sourmash str from a c string. * * This sets the string to owned. In case it's not owned you either have * to make sure you are not freeing the memory or you need to set the * owned flag to false. */ SourmashStr sourmash_str_from_cstr(const char *s); char sourmash_translate_codon(const char *codon); SourmashStr **zipstorage_filenames(const SourmashZipStorage *ptr, uintptr_t *size); void zipstorage_free(SourmashZipStorage *ptr); SourmashStr **zipstorage_list_sbts(const SourmashZipStorage *ptr, uintptr_t *size); const uint8_t *zipstorage_load(const SourmashZipStorage *ptr, const char *path_ptr, uintptr_t insize, uintptr_t *size); SourmashZipStorage *zipstorage_new(const char *ptr, uintptr_t insize); SourmashStr zipstorage_path(const SourmashZipStorage *ptr); void zipstorage_set_subdir(SourmashZipStorage *ptr, const char *path_ptr, uintptr_t insize); SourmashStr zipstorage_subdir(const SourmashZipStorage *ptr); #endif /* SOURMASH_H_INCLUDED */ sourmash-bio-sourmash-bc3c0f8/matplotlibrc000066400000000000000000000000161474157046300211020ustar00rootroot00000000000000backend : Agg sourmash-bio-sourmash-bc3c0f8/paper.bib000066400000000000000000000067541474157046300202670ustar00rootroot00000000000000@article{Ondov:2015, title={Fast genome and metagenome distance estimation using MinHash}, author={Ondov, Brian D and Treangen, Todd J and Mallonee, Adam B and Bergman, Nicholas H and Koren, Sergey and Phillippy, Adam M}, journal={bioRxiv}, pages={029827}, year={2015}, publisher={Cold Spring Harbor Labs Journals}, doi={10.1101/029827}, url={https://doi.org/10.1101/029827} } @article{Brown:2016, doi = {10.21105/joss.00027}, url = {https://doi.org/10.21105/joss.00027}, year = {2016}, publisher = {The Open Journal}, volume = {1}, number = {5}, pages = {27}, author = {C. Titus Brown and Luiz Irber}, title = {sourmash: a library for MinHash sketching of DNA}, journal = {Journal of Open Source Software} } @article{Pierce:2019, doi = {10.12688/f1000research.19675.1}, url = {https://doi.org/10.12688/f1000research.19675.1}, year = {2019}, month = jul, publisher = {F1000 Research Ltd}, volume = {8}, pages = {1006}, author = {N. Tessa Pierce and Luiz Irber and Taylor Reiter and Phillip Brooks and C. Titus Brown}, title = {Large-scale sequence comparisons with sourmash}, journal = {F1000Research} } @article{gather, doi = {10.1101/2022.01.11.475838}, url = {https://doi.org/10.1101/2022.01.11.475838}, title={Lightweight compositional analysis of metagenomes with FracMinHash and minimum metagenome covers}, author={Irber, Luiz Carlos and Brooks, Phillip T and Reiter, Taylor E and Pierce-Ward, N Tessa and Hera, Mahmudur Rahman and Koslicki, David and Brown, C Titus}, journal={bioRxiv}, year={2022}, publisher={Cold Spring Harbor Laboratory} } @article{branchwater, doi = {10.1101/2022.11.02.514947}, url={https://doi.org/10.1101/2022.11.02.514947}, title={Sourmash Branchwater Enables Lightweight Petabyte-Scale Sequence Search}, author={Irber, Luiz Carlos and Pierce-Ward, N Tessa and Brown, C Titus}, journal={bioRxiv}, year={2022}, publisher={Cold Spring Harbor Laboratory} } @article{koslicki2019improving, doi={10.1016/j.amc.2019.02.018}, url={https://doi.org/10.1016/j.amc.2019.02.018}, title={Improving minhash via the containment index with applications to metagenomic analysis}, author={Koslicki, David and Zabeti, Hooman}, journal={Applied Mathematics and Computation}, volume={354}, pages={206--215}, year={2019}, publisher={Elsevier} } @article{hera2023deriving, doi={10.1101/gr.277651.123}, url={https://doi.org/10.1101/gr.277651.123}, title={Deriving confidence intervals for mutation rates across a wide range of evolutionary distances using FracMinHash}, author={Rahman Hera, Mahmudur and Pierce-Ward, N Tessa and Koslicki, David}, journal={Genome Research}, pages={gr--277651}, year={2023}, publisher={Cold Spring Harbor Lab} } @article{hera2023fast, doi = {10.1101/2023.11.06.565843}, url = {https://doi.org/10.1101/2023.11.06.565843}, title={Fast, lightweight, and accurate metagenomic functional profiling using FracMinHash sketches}, author={Rahman Hera, Mahmudur and Liu, Shaopeng and Wei, Wei and Rodriguez, Judith S and Ma, Chunyu and Koslicki, David}, journal={bioRxiv}, pages={2023--11}, year={2023}, publisher={Cold Spring Harbor Laboratory} } @article{portik2022evaluation, doi={10.1186/s12859-022-05103-0}, url={https://doi.org/10.1186/s12859-022-05103-0}, title={Evaluation of taxonomic profiling methods for long-read shotgun metagenomic sequencing datasets}, author={Portik, Daniel M and Brown, C Titus and Pierce-Ward, N Tessa}, journal={Bioinformatics}, year={2022} } sourmash-bio-sourmash-bc3c0f8/paper.md000066400000000000000000000145641474157046300201310ustar00rootroot00000000000000--- title: 'sourmash v4: A multitool to quickly search, compare, and analyze genomic and metagenomic data sets' tags: - FracMinHash - MinHash - k-mers - Python - Rust authors: - name: Luiz Irber orcid: 0000-0003-4371-9659 equal-contrib: true affiliation: 1 - name: N. Tessa Pierce-Ward orcid: 0000-0002-2942-5331 equal-contrib: true affiliation: 1 - name: Mohamed Abuelanin orcid: 0000-0002-3419-4785 affiliation: 1 - name: Harriet Alexander orcid: 0000-0003-1308-8008 affiliation: 2 - name: Abhishek Anant orcid: 0000-0002-5751-2010 affiliation: 9 - name: Keya Barve orcid: 0000-0003-3241-2117 affiliation: 1 - name: Colton Baumler orcid: 0000-0002-5926-7792 affiliation: 1 - name: Olga Botvinnik orcid: 0000-0003-4412-7970 affiliation: 3 - name: Phillip Brooks orcid: 0000-0003-3987-244X affiliation: 1 - name: Daniel Dsouza orcid: 0000-0001-7843-8596 affiliation: 9 - name: Laurent Gautier orcid: 0000-0003-0638-3391 affiliation: 9 - name: Mahmudur Rahman Hera orcid: 0000-0002-5992-9012 affiliation: 4 - name: Hannah Eve Houts orcid: 0000-0002-7954-4793 affiliation: 1 - name: Lisa K. Johnson orcid: 0000-0002-3600-7218 affiliation: 1 - name: Fabian Klötzl orcid: 0000-0002-6930-0592 affiliation: 5 - name: David Koslicki orcid: 0000-0002-0640-954X affiliation: 4 - name: Marisa Lim orcid: 0000-0003-2097-8818 affiliation: 1 - name: Ricky Lim orcid: 0000-0003-1313-7076 affiliation: 9 - name: Bradley Nelson orcid: 0009-0001-1553-932X affiliation: 9 - name: Ivan Ogasawara orcid: 0000-0001-5049-4289 affiliation: 9 - name: Taylor Reiter orcid: 0000-0002-7388-421X affiliation: 1 - name: Camille Scott orcid: 0000-0001-8822-8779 affiliation: 1 - name: Andreas Sjödin orcid: 0000-0001-5350-4219 affiliation: 6 - name: Daniel Standage orcid: 0000-0003-0342-8531 affiliation: 7 - name: S. Joshua Swamidass orcid: 0000-0003-2191-0778 affiliation: 8 - name: Connor Tiffany orcid: 0000-0001-8188-7720 affiliation: 9 - name: Pranathi Vemuri orcid: 0000-0002-5748-9594 affiliation: 3 - name: Erik Young orcid: 0000-0002-9195-9801 affiliation: 1 - name: C. Titus Brown orcid: 0000-0001-6001-2677 corresponding: true affiliation: 1 affiliations: - name: University of California Davis, Davis, CA, United States of America index: 1 - name: Woods Hole Oceanic Institution, Woods Hole, MA, Unites States of America index: 2 - name: Chan-Zuckerberg Biohub, San Francisco, CA, United States of America index: 3 - name: Pennsylvania State University, University Park, PA, United States of America index: 4 - name: Max Planck Institute for Evolutionary Biology, Plön, Germany index: 5 - name: Swedish Defence Research Agency (FOI), Stockholm, Sweden index: 6 - name: National Bioforensic Analysis Center, Fort Detrick, MD, United States of America index: 7 - name: Washington University in St Louis, St Louis, MO, United States of America index: 8 - name: No affiliation index: 9 date: 31 Jan 2024 bibliography: paper.bib --- # Summary sourmash is a command line tool and Python library for sketching collections of DNA, RNA, and amino acid k-mers for biological sequence search, comparison, and analysis [@Pierce:2019]. sourmash's FracMinHash sketching supports fast and accurate sequence comparisons between datasets of different sizes [@gather], including taxonomic profiling [@portik2022evaluation], functional profiling [@hera2023fast], and petabase-scale sequence search [@branchwater]. From release 4.x, sourmash is built on top of Rust and provides an experimental Rust interface. FracMinHash sketching is a lossy compression approach that represents data sets using a "fractional" sketch containing $1/S$ of the original k-mers. Like other sequence sketching techniques (e.g. MinHash, [@Ondov:2015]), FracMinHash provides a lightweight way to store representations of large DNA or RNA sequence collections for comparison and search. Sketches can be used to identify samples, find similar samples, identify data sets with shared sequences, and build phylogenetic trees. FracMinHash sketching supports estimation of overlap, bidirectional containment, and Jaccard similarity between data sets and is accurate even for data sets of very different sizes. Since sourmash v1 was released in 2016 [@Brown:2016], sourmash has expanded to support new database types and many more command line functions. In particular, sourmash now has robust support for both Jaccard similarity and Containment calculations, which enables analysis and comparison of data sets of different sizes, including large metagenomic samples. As of v4.4, sourmash can convert these to estimated Average Nucleotide Identity (ANI) values, which can provide improved biological context to sketch comparisons [@hera2023deriving]. # Statement of Need Large collections of genomes, transcriptomes, and raw sequencing data sets are readily available in biology, and the field needs lightweight computational methods for searching and summarizing the content of both public and private collections. sourmash provides a flexible set of programmatic tools for this purpose, together with a robust and well-tested command-line interface. It has been used in over 350 publications (based on citations of @Brown:2016 and @Pierce:2019) and it continues to expand in functionality. # Acknowledgements This work was funded in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative [GBMF4551 to CTB]. It is also funded in part by the National Science Foundation [#2018522 to CTB] and PIG-PARADIGM (Preventing Infection in the Gut of developing Piglets–and thus Antimicrobial Resistance – by disentAngling the interface of DIet, the host and the Gastrointestinal Microbiome) from the Novo Nordisk Foundation to CTB. Notice: This manuscript has been authored by BNBI under Contract No. HSHQDC-15-C-00064 with the DHS. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the USG retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for USG purposes. Views and conclusions contained herein are those of the authors and should not be interpreted to represent policies, expressed or implied, of the DHS. # References sourmash-bio-sourmash-bc3c0f8/pixi.lock000066400000000000000000022204111474157046300203130ustar00rootroot00000000000000version: 5 environments: default: channels: - url: https://conda.anaconda.org/conda-forge/ indexes: - https://pypi.org/simple packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/_sysroot_linux-64_curr_repodata_hack-3-h69a702a_16.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.8-h166bdaf_0.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.40-h4852527_7.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.40-ha1999f0_7.conda - 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kind: conda name: zstd version: 1.5.6 build: hb46c0d2_0 subdir: osx-arm64 url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda sha256: 2d4fd1ff7ee79cd954ca8e81abf11d9d49954dd1fef80f27289e2402ae9c2e09 md5: d96942c06c3e84bfcc5efb038724a7fd depends: - __osx >=11.0 - libzlib >=1.2.13,<2.0.0a0 license: BSD-3-Clause license_family: BSD purls: [] size: 405089 timestamp: 1714723101397 sourmash-bio-sourmash-bc3c0f8/pyproject.toml000066400000000000000000000222341474157046300214050ustar00rootroot00000000000000[build-system] requires = [ "maturin>=1,<1.9.0", "cffi", ] build-backend = 'maturin' [project] name = "sourmash" description = "tools for comparing biological sequences with k-mer sketches" readme = "README.md" version = "4.8.14" authors = [ { name="Luiz Irber" }, { name="N. Tessa Pierce-Ward" }, { name="Mohamed Abuelanin" }, { name="Harriet Alexander" }, { name="Abhishek Anant" }, { name="Keya Barve" }, { name="Colton Baumler" }, { name="Olga Botvinnik" }, { name="Phillip Brooks" }, { name="Luca Cappelletti" }, { name="Peter Cock" }, { name="Daniel Dsouza" }, { name="Jade Gardner" }, { name="Laurent Gautier" }, { name="Tim Head" }, { name="Mahmudur Rahman Hera" }, { name="Hannah Eve Houts" }, { name="Lisa K. Johnson" }, { name="Fabian Klötzl" }, { name="David Koslicki" }, { name="Katrin Leinweber" }, { name="Marisa Lim" }, { name="Ricky Lim" }, { name="Bradley Nelson" }, { name="Ivan Ogasawara" }, { name="Taylor Reiter" }, { name="Camille Scott" }, { name="Andreas Sjödin" }, { name="Connor T. Skennerton" }, { name="Jason Stajich" }, { name="Daniel Standage" }, { name="S. Joshua Swamidass" }, { name="Connor Tiffany" }, { name="Pranathi Vemuri" }, { name="Erik Young" }, { name="Nick H" }, { name="C. Titus Brown" }, ] maintainers = [ { name="Luiz C. Irber, Jr", email="luiz@sourmash.bio" }, { name="C. Titus Brown", email="titus@idyll.org" }, { name="N. Tessa Pierce-Ward", email="ntpierce@ucdavis.edu" }, ] classifiers = [ "Development Status :: 5 - Production/Stable", "Environment :: Console", "Environment :: MacOS X", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Natural Language :: English", "Operating System :: POSIX :: Linux", "Operating System :: MacOS :: MacOS X", "Programming Language :: Rust", "Programming Language :: Python :: 3.12", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.10", "Topic :: Scientific/Engineering :: Bio-Informatics", ] dependencies = [ "screed>=1.1.3,<2", "cffi>=1.14.0", "numpy", "matplotlib", "scipy", "deprecation>=2.0.6", "cachetools>=4,<6", "bitstring>=3.1.9,<5", ] requires-python = ">=3.10" [metadata] license = { text = "BSD 3-Clause License" } authors = [ { name="Luiz Irber", orcid="0000-0003-4371-9659" }, { name="N. Tessa Pierce-Ward", orcid="0000-0002-2942-5331" }, { name="Mohamed Abuelanin", orcid="0000-0002-3419-4785" }, { name="Harriet Alexander", orcid="0000-0003-1308-8008" }, { name="Abhishek Anant", orcid="0000-0002-5751-2010" }, { name="Keya Barve", orcid="0000-0003-3241-2117" }, { name="Colton Baumler", orcid="0000-0002-5926-7792" }, { name="Olga Botvinnik", orcid="0000-0003-4412-7970" }, { name="Phillip Brooks", orcid="0000-0003-3987-244X" }, { name="Luca Cappelletti", orcid="0000-0002-1269-2038" }, { name="Peter Cock", orcid="0000-0001-9513-9993" }, { name="Daniel Dsouza", orcid="0000-0001-7843-8596" }, { name="Jade Gardner", orcid="0009-0005-0787-5752" }, { name="Laurent Gautier", orcid="0000-0003-0638-3391" }, { name="Tim Head", orcid="0000-0003-0931-3698" }, { name="Mahmudur Rahman Hera", orcid="0000-0002-5992-9012" }, { name="Hannah Eve Houts", orcid="0000-0002-7954-4793" }, { name="Lisa K. Johnson", orcid="0000-0002-3600-7218" }, { name="Fabian Klötzl", orcid="0000-0002-6930-0592" }, { name="David Koslicki", orcid="0000-0002-0640-954X" }, { name="Katrin Leinweber", orcid="0000-0001-5135-5758" }, { name="Marisa Lim", orcid="0000-0003-2097-8818" }, { name="Ricky Lim", orcid="0000-0003-1313-7076" }, { name="Bradley Nelson", orcid="0009-0001-1553-932X" }, { name="Ivan Ogasawara", orcid="0000-0001-5049-4289" }, { name="Taylor Reiter", orcid="0000-0002-7388-421X" }, { name="Camille Scott", orcid="0000-0001-8822-8779" }, { name="Andreas Sjödin", orcid="0000-0001-5350-4219" }, { name="Connor T. Skennerton", orcid="0000-0003-1320-4873" }, { name="Jason Stajich", orcid="0000-0002-7591-0020" }, { name="Daniel Standage", orcid="0000-0003-0342-8531" }, { name="S. Joshua Swamidass", orcid="0000-0003-2191-0778" }, { name="Connor Tiffany", orcid="0000-0001-8188-7720" }, { name="Pranathi Vemuri", orcid="0000-0002-5748-9594"}, { name="Erik Young", orcid="0000-0002-9195-9801" }, { name="Nick H", orcid="0000-0002-1685-302X" }, { name="C. Titus Brown", orcid="0000-0001-6001-2677" }, ] [project.urls] "Homepage" = "https://sourmash.bio/" "Documentation" = "https://sourmash.readthedocs.io" "CI" = "https://github.com/sourmash-bio/sourmash/actions" "Source" = "https://github.com/sourmash-bio/sourmash" "Tracker" = "https://github.com/sourmash-bio/sourmash/issues" [project.scripts] "sourmash" = "sourmash.__main__:main" [project.optional-dependencies] test = [ "pytest>=6.2.4,<8.4.0", "pytest-cov>=4,<7.0", "pytest-xdist>=3.1", "pyyaml>=6,<7", "diff-cover>=7.3", "covdefaults>=2.2.2", "recommonmark", "hypothesis", "build", ] demo = [ "jupyter", "jupyter_client", "ipython", ] doc = [ "sphinx>=6,<9", "myst-parser", "Jinja2", "alabaster", "sphinxcontrib-napoleon", "nbsphinx", "sphinx-design==0.6.1", "ipython", "docutils>=0.17.1,<0.22", ] storage = [ "ipfshttpclient>=0.4.13", "redis", ] # hmm, I think this is a hack, but it works... # https://github.com/pypa/pip/issues/10393#issuecomment-941885429 all = ["sourmash[test,demo,doc,storage]"] [tool.maturin] python-source = "src" manifest-path = "src/core/Cargo.toml" bindings = "cffi" include = [ { path = "include/sourmash.h", format = ["sdist","wheel"] }, { path = "requirements.txt", format = ["sdist"] }, { path = "Cargo.*", format = ["sdist"] }, { path = "tests/**/*", format = ["sdist"] }, { path = "CITATION.cff", format = ["sdist", "wheel"] }, { path = "LICENSE", format = ["sdist"] }, ] exclude = [ { path = "**/__pycache__/*", format = ["sdist", "wheel"] }, ] features = ["maturin", "branchwater"] locked = true module-name = "sourmash._lowlevel" # macOS deployment target SDK version [tool.maturin.target.x86_64-apple-darwin] macos-deployment-target = "10.14" [tool.maturin.target.aarch64-apple-darwin] macos-deployment-target = "11.0" [tool.ruff] extend-exclude = ["*.ipynb"] [tool.ruff.lint] extend-select = [ "UP", # pyupgrade ] ignore = ["F401", "E712", "E402", "F821", "E722", "E741", "F811", "F403", "F822"] [tool.isort] known_third_party = ["deprecation", "hypothesis", "mmh3", "numpy", "pytest", "screed", "sourmash_tst_utils"] multi_line_output = 3 include_trailing_comma = true force_grid_wrap = 0 line_length = 88 known_first_party = ["sourmash"] [tool.cibuildwheel] build = "cp310-*" skip = "*-win32 *-manylinux_i686 *-musllinux_* *-ppc64le *-s390x" before-all = [ "curl https://sh.rustup.rs -sSf | sh -s -- -y --default-toolchain=stable", "cargo update --dry-run", ] macos.before-build = [ "rustup target add aarch64-apple-darwin", "rustup target add x86_64-apple-darwin", ] build-verbosity = 3 [tool.cibuildwheel.environment] CARGO_REGISTRIES_CRATES_IO_PROTOCOL="sparse" PATH="$HOME/.cargo/bin:$PATH" [tool.cibuildwheel.linux] before-all = [ "curl https://sh.rustup.rs -sSf | sh -s -- -y --default-toolchain=stable", "cargo update --dry-run", "if [ -f /etc/system-release ]; then yum -y install llvm-toolset-7.0; fi", ] before-build = [ "if [ -f /etc/system-release ]; then source scl_source enable llvm-toolset-7.0; fi", "if [ -f /etc/system-release ]; then source scl_source enable devtoolset-10; fi", ] [tool.cibuildwheel.pyodide.environment] # avoid needing a nightly compiler RUSTC_BOOTSTRAP=1 [tool.cibuildwheel.pyodide] before-build = [ "rustup target add wasm32-unknown-emscripten" ] [tool.cibuildwheel.linux.environment] CARGO_REGISTRIES_CRATES_IO_PROTOCOL="sparse" PATH="$HOME/.cargo/bin:$PATH" LIBCLANG_PATH="/opt/rh/llvm-toolset-7.0/root/usr/lib64" LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/opt/rh/llvm-toolset-7.0/root/usr/lib64" C_INCLUDE_PATH="/opt/rh/devtoolset-10/root/usr/lib/gcc/aarch64-redhat-linux/10/include:/opt/rh/devtoolset-10/root/usr/lib/gcc/x86_64-redhat-linux/10/include" [tool.pytest.ini_options] addopts = "--doctest-glob='doc/*.md' -n4" norecursedirs = [ "utils", "build", "buildenv", ".tox", ".asv", ".eggs", ] testpaths = [ "tests", "doc", ] [tool.coverage] html.show_contexts = true html.skip_covered = false paths.source = [ "src", ".tox*/*/lib/python*/site-packages", ".tox*/pypy*/site-packages", ".tox*\\*\\Lib\\site-packages", "*/src", "*\\src", "*/tests", "*\tests", ] report.fail_under = 88 run.parallel = true run.plugins = ["covdefaults"] [tool.pixi.project] channels = ["conda-forge"] platforms = ["linux-64", "linux-aarch64", "osx-64", "osx-arm64", "win-64"] [tool.pixi.feature.dev.dependencies] clangdev = ">=16.0.6,<16.1" cxx-compiler = ">=1.7.0,<1.8" git = "2.41.0.*" libclang = ">=16.0.6,<16.1" pandoc = "3.1.3.*" python = "3.10.*" rust = ">=1.80.0,<1.81" tox = ">=4.11.0,<5" # prefer conda deps over PyPI screed = ">=1.1.2,<2" cffi = ">=1.14.0" numpy = "*" matplotlib = "*" scipy = "*" deprecation = ">=2.0.6" cachetools = ">=4,<6" bitstring = ">=3.1.9,<5" [tool.pixi.environments] default = { features = ["dev", "test", "demo", "doc", "storage", "all"], solve-group = "default" } [tool.pixi.system-requirements] macos = "11.0" sourmash-bio-sourmash-bc3c0f8/requirements.txt000066400000000000000000000000121474157046300217430ustar00rootroot00000000000000-e .[all] sourmash-bio-sourmash-bc3c0f8/shell.nix000066400000000000000000000004011474157046300203100ustar00rootroot00000000000000(import ( fetchTarball { url = "https://github.com/edolstra/flake-compat/archive/99f1c2157fba4bfe6211a321fd0ee43199025dbf.tar.gz"; sha256 = "0x2jn3vrawwv9xp15674wjz9pixwjyj3j771izayl962zziivbx2"; } ) { src = ./.; }).shellNix sourmash-bio-sourmash-bc3c0f8/src/000077500000000000000000000000001474157046300172555ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/000077500000000000000000000000001474157046300202055ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/CHANGELOG.md000066400000000000000000000411201474157046300220140ustar00rootroot00000000000000# Changelog All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). ## [unreleased] ## [0.18.0] - 2024-12-20 MSRV: 1.66 Changes/additions: * add skipmer capacity to sourmash python layer via ffi (#3446) * add skipmers; switch to reading frame approach for translation, skipmers (#3395) * Refactor: Use to_writer/from_reader across the codebase (#3443) * adjust `Signature::name()` to return `Option` instead of `filename()` and `md5sum()` (#3434) * propagate zipfile errors (#3431) Updates: * Bump proptest from 1.5.0 to 1.6.0 (#3437) * Bump roaring from 0.10.8 to 0.10.9 (#3438) * Bump serde from 1.0.215 to 1.0.216 (#3436) * Bump statrs from 0.17.1 to 0.18.0 (#3426) * Bump roaring from 0.10.7 to 0.10.8 (#3423) * Bump needletail from 0.6.0 to 0.6.1 (#3427) * Bump web-sys from 0.3.72 to 0.3.74 (#3411) * Bump js-sys from 0.3.72 to 0.3.74 (#3412) * Bump roaring from 0.10.6 to 0.10.7 (#3413) * Bump serde_json from 1.0.132 to 1.0.133 (#3402) * Bump serde from 1.0.214 to 1.0.215 (#3403) ## [0.17.2] - 2024-11-15 MSRV: 1.66 Changes/additions: * enforce a single scaled on a `CollectionSet` (#3397) * change `sig_from_record` to use scaled from `Record` to downsample (#3387) Updates: * Upgrade rocksdb to 0.22.0, bump MSRV to 1.66 (#3383) * Bump thiserror from 1.0.68 to 2.0.3 (#3389) * Bump csv from 1.3.0 to 1.3.1 (#3390) * Bump tempfile from 3.13.0 to 3.14.0 (#3391) ## [0.17.1] - 2024-11-11 Changes/additions: * fix: Avoid re-calculating md5sum on clone and conversion to KmerMinHashBTree (#3385) * build: simplify Rust release (#3392) ## [0.17.0] - 2024-11-05 Changes/additions: * standardize on u32 for scaled, and introduce `ScaledType` (#3364) * panic when `FSStorage::load_sig` encounters more than one `Signature` in a JSON record (#3333) Updates: * Bump needletail from 0.5.1 to 0.6.0 (#3376) * Bump histogram from 0.11.0 to 0.11.1 (#3377) * Bump serde from 1.0.210 to 1.0.214 (#3368) * Bump serde_json from 1.0.128 to 1.0.132 (#3358) * Fix clippy lints from 1.83 beta (#3357) ## [0.16.0] - 2024-10-15 MSRV: 1.65 Changes/additions: * refactor `calculate_gather_stats` to disallow repeated downsampling (#3352) * improve downsampling behavior on `KmerMinHash`; fix `RevIndex::gather` bug around `scaled`. (#3342) * derive Hash for `HashFunctions` (#3344) Updates: * Bump web-sys from 0.3.70 to 0.3.72 (#3354) * Bump tempfile from 3.12.0 to 3.13.0 (#3340) ## [0.15.2] - 2024-09-25 MSRV: 1.65 Changes/additions: * add `Manifest::intersect_manifest` to Rust core (#3305) * propagate error from `RocksDB::open` on bad directory (#3306, #3307) Updates: * Bump getset from 0.1.2 to 0.1.3 (#3328) * Bump memmap2 from 0.9.4 to 0.9.5 (#3326) * Bump codspeed-criterion-compat from 2.6.0 to 2.7.2 (#3324) * Bump serde_json from 1.0.127 to 1.0.128 (#3316) * Bump serde from 1.0.209 to 1.0.210 (#3318) * Bump serde from 1.0.208 to 1.0.209 (#3310) * Bump serde_json from 1.0.125 to 1.0.127 (#3309) ## [0.15.1] - 2024-08-20 MSRV: 1.65 Changes/additions: * Misc Rust updates to core (#3297) * Implement resumability for revindex (#3275) * Resolve issue for high precision MLE estimation (#3296) * Added union method to HLL (#3293) Updates: * Bump camino from 1.1.7 to 1.1.9 (#3301) * Bump web-sys from 0.3.69 to 0.3.70 (#3299) * Bump serde_json from 1.0.120 to 1.0.125 (#3288) (#3280) (#3267) (#3302) * Bump serde from 1.0.204 to 1.0.208 (#3289) (#3298) * Bump tempfile from 3.10.1 to 3.12.0 (#3279) (#3287) ## [0.15.0] - 2024-07-27 MSRV: 1.65 Changes/additions: * RocksDB storage and self-contained RevIndex with internal storage #3250 * Enable codspeed for Rust perf tracking (#3231) Updates * Bump roaring from 0.10.5 to 0.10.6 (#3245) * Bump serde from 1.0.203 to 1.0.204 (#3244) * Bump counter from 0.5.7 to 0.6.0 (#3235) * Bump log from 0.4.21 to 0.4.22 (#3236) * Bump serde_json from 1.0.117 to 1.0.120 (#3234) * Bump proptest from 1.4.0 to 1.5.0 (#3222) ## [0.14.1] - 2024-06-19 MSRV: 1.65 Changes/additions: * adjust how ANI is calculated in the revindex code. (#3218) Updates: * Bump histogram from 0.10.2 to 0.11.0 (#3216) * Bump histogram from 0.10.1 to 0.10.2 (#3207) * Bump statrs from 0.16.1 to 0.17.1 (#3205) * Bump roaring from 0.10.4 to 0.10.5 (#3206) * Bump primal-check from 0.3.3 to 0.3.4 (#3208) * Bump niffler from 2.5.0 to 2.6.0 (#3204) ## [0.14.0] - 2024-06-10 MSRV: 1.65 Changes/additions: * fix cargo fmt for updated `disk_revindex.rs` code (#3197) * fix RocksDB-based gather & other rust-based infelicities revealed by plugins (#3193) * use correct denominator in f_unique_to_query (#3138) * fix clippy warnings about max_value (#3146) * allow get/set record.filename (#3121) Updates: * Bump statrs from 0.16.0 to 0.16.1 (#3186) * Bump serde from 1.0.202 to 1.0.203 (#3175) * Bump ouroboros from 0.18.3 to 0.18.4 (#3176) * Bump itertools from 0.12.1 to 0.13.0 (#3166) * Bump camino from 1.1.6 to 1.1.7 (#3169) * Bump serde from 1.0.201 to 1.0.202 (#3168) * Bump serde_json from 1.0.116 to 1.0.117 (#3159) * Bump serde from 1.0.200 to 1.0.201 (#3160) * Bump roaring from 0.10.3 to 0.10.4 (#3142) * Bump histogram from 0.10.0 to 0.10.1 (#3141) * Bump num-iter from 0.1.44 to 0.1.45 (#3140) * Bump serde from 1.0.199 to 1.0.200 (#3144) * Bump serde from 1.0.198 to 1.0.199 (#3130) * Bump serde_json from 1.0.115 to 1.0.116 (#3124) * Bump serde from 1.0.197 to 1.0.198 (#3122) * Bump histogram from 0.9.1 to 0.10.0 (#3109) * Bump enum_dispatch from 0.3.12 to 0.3.13 (#3102) * Bump serde_json from 1.0.114 to 1.0.115 (#3101) * Bump rayon from 1.9.0 to 1.10.0 (#3098) ## [0.13.1] - 2024-03-23 MSRV: 1.65 Changes/additions: * Implement file parsing for webassembly (#3047) * fix `calculate_gather_stats` `threshold=0` bug (#3052) * fix clippy beta issues (#3088) Updates: * Bump wasm-bindgen-test from 0.3.41 to 0.3.42 (#3063) * Bump web-sys from 0.3.68 to 0.3.69 (#3061) * Bump log from 0.4.20 to 0.4.21 (#3062) * Bump rayon from 1.8.1 to 1.9.0 (#3058) * Bump tempfile from 3.10.0 to 3.10.1 (#3059) * Bump serde_json from 1.0.113 to 1.0.114 (#3044) * Bump serde from 1.0.196 to 1.0.197 (#3045) * Bump itertools from 0.12.0 to 0.12.1 (#3043) ## [0.13.0] - 2024-02-23 MSRV: 1.65 Changes/additions: * Calculate all gather stats in rust; use for rocksdb gather (#2943) * adjust protein ksize for record/manifest (#3019) * Allow changing storage location for a collection in RevIndex (#3015) * make core Manifest booleans python compatible (core) (#3007) Updates: * Bump roaring from 0.10.2 to 0.10.3 (#3014) * Bump histogram from 0.9.0 to 0.9.1 (#3002) * Bump chrono from 0.4.33 to 0.4.34 (#3000) * Bump web-sys from 0.3.67 to 0.3.68 (#2998) * Bump num-iter from 0.1.43 to 0.1.44 (#2997) * Bump wasm-bindgen-test from 0.3.40 to 0.3.41 (#2996) ## [0.12.1] - 2024-02-10 MSRV: 1.65 Changes/additions: * bump rust core version to r0.12.1 (#2988) * Clean up and refactor `KmerMinHash::merge` in core (#2973) * core: add scaled selection to manifest; add helper functions for collection and sig/sketch usage (#2948) * core: enable downsample within select (#2931) Updates: * Deps: update typed-builder and histogram, bump MSRV to 1.65 (#2858) * Bump tempfile from 3.9.0 to 3.10.0 (#2979) * Bump rkyv from 0.7.43 to 0.7.44 (#2978) * Bump memmap2 from 0.9.3 to 0.9.4 (#2958) * Bump chrono from 0.4.31 to 0.4.33 (#2957) * Bump serde from 1.0.195 to 1.0.196 (#2956) * Bump serde_json from 1.0.111 to 1.0.113 (#2955) * Bump web-sys from 0.3.66 to 0.3.67 (#2939) * Bump wasm-bindgen-test from 0.3.39 to 0.3.40 (#2938) * Bump rayon from 1.8.0 to 1.8.1 (#2937) * Bump ouroboros from 0.18.2 to 0.18.3 (#2936) * Bump histogram from 0.8.4 to 0.9.0 (#2935) * Bump wasm-bindgen from 0.2.89 to 0.2.90 (#2925) * Bump histogram from 0.8.3 to 0.8.4 (#2923) * Bump serde_json from 1.0.110 to 1.0.111 (#2902) * Bump serde from 1.0.194 to 1.0.195 (#2901) * Bump serde_json from 1.0.108 to 1.0.110 (#2896) * Bump ouroboros from 0.18.1 to 0.18.2 (#2894) * Bump tempfile from 3.8.1 to 3.9.0 (#2893) * Bump memmap2 from 0.9.2 to 0.9.3 (#2889) * Bump memmap2 from 0.9.0 to 0.9.2 (#2882) * Bump rkyv from 0.7.42 to 0.7.43 (#2880) * Bump ouroboros from 0.18.0 to 0.18.1 (#2875) * Bump once_cell from 1.18.0 to 1.19.0 (#2874) * Bump rkyv from 0.7.40 to 0.7.42 (#2863) * Bump csv from 1.2.0 to 1.3.0 (#2862) * Bump roaring from 0.10.1 to 0.10.2 (#2865) * Bump web-sys from 0.3.65 to 0.3.66 (#2864) * Bump byteorder from 1.4.3 to 1.5.0 (#2866) * Bump proptest from 1.3.1 to 1.4.0 (#2837) ## [0.12.0] - 2023-11-26 MSRV: 1.64 Added: - Initial implementation for `Manifest`, `Selection`, and `Picklist` following the Python API. (#2230) - `Collection` is a new abstraction for working with a set of signatures. A collection needs a `Storage` for holding the signatures (on-disk, in-memory, or remotely), and a `Manifest` to describe the metadata for each signature. (#2230) - Expose CSV parsing and RocksDB errors. (#2230) - New module `sourmash::index::revindex::disk_revindex` with the on-disk RevIndex implementation based on RocksDB. (#2230) - Add `iter` and `iter_mut` methods for `Signature`. (#2230) - Add `load_sig` and `save_sig` methods to `Storage` trait for higher-level data manipulation and caching. (#2230) - Add `spec` method to `Storage` to allow constructing a concrete `Storage` from a string description. (#2230) - Add `InnerStorage` for synchronizing parallel access to `Storage` implementations. (#2230) - Add `MemStorage` for keeping signatures in-memory (mostly for debugging and testing). (#2230) - Add new `branchwater` feature (enabled by default), which can be disabled by downstream projects to limit bringing heavy dependencies like rocksdb. (#2230) - Add new `rkyv` feature (disabled by default), making `MinHash` serializable with the `rkyv` crate. (#2230) - Add semver checks for CI (so we bump versions accordingly, or avoid breaking changes). (#2230) - Add cargo deny config. (#2724) - Benchmarks for seq_to_hashes in protein mode. (#1944) - Oxidize ZipStorage. (#1909) - Move greyhound-core into sourmash. (#1238) - add `MinHash.kmers_and_hashes(...)` and `sourmash sig kmers`. (#1695) - Produce list of hashes from a sequence. (#1653) Changed: - Rename `HashFunctions` variants to follow camel-case, so `Murmur64Protein` instead of `murmur64_protein`. (#2230) - `LinearIndex` is now implemented as a thin layer on top of `Collection`. (#2230) - Move `GatherResult` to `sourmash::index` module. (#2230) - Move `sourmash::index::revindex` to `sourmash::index::mem_revindex` (this is the Greyhound version of revindex, in-memory only). It was also refactored internally to build a version of a `LinearIndex` that will be merged in the future with `sourmash::index::LinearIndex`. (#2230) - Move `select` method from `Index` trait into a separate `Select` trait, and implement it for `Signature` based on the new `Selection` API. (#2230) - Move `SigStore` into `sourmash::storage` module, and remove the generic. Now it always stores `Signature`. Also implement `Select` for it. (#2230) - Disable `musllinux` wheels (need to figure out how to build rocksdb for it). (#2230) - Reorganize traits for easier wasm and native compilation. (#1836) - Adjust dayhoff and hp encodings to tolerate stop codons in the protein sequence. (#1673) Fixed: - Reduce features combinations on Rust checks (takes much less time to run). (#2230) - Build: MSRV check for 1.64. (#2680) - maturin: move deprecated definition from Cargo.toml to pyproject.toml. (#2597) - Fix broken crates.io badge. (#2556) - Fix unnecessary typecasts in Rust. (#2366) - Fix `Signature.minhash` API during `sourmash sketch`. (#2329) - Return Err for angular_similarity when abundance tracking is off. (#2327) - Update various descriptions to talk about k-mers, not just DNA. (#2137) - Fix downsample_scaled in `core`. (#2108) - speed up `SeqToHashes` `translate`. (#1946) - Speed-up `SeqToHashes()`. (#1938) - Fix containment calculation for nodegraphs. (#1862) - Fix panic bug in `sourmash sketch` dna with bad input and `--check-sequence`. (#1702) - Fix Rust panic in `MinHash.seq_to_hashes`. (#1701) - Beta lints. (#2841 #2630 #2596 #2298 #1791 #1786 #1760) Removed: - Remove BIGSI and SBT code. (#2732) ## [0.11.0] - 2021-07-07 Added: - Add HyperLogLog implementation (#1223) Changed: - Update `MinHash.set_abundances` to remove hash if 0 abund; handle negative abundances. (#1575) - Improving `MinHash.remove_many(...)` performance (#1571) - Improved intersection and union calculations (#1475) - Bump MSRV to 1.42 (and other dep fixes) (#1461) - Rework the `find` functionality for `Index` classes (#1392) - Rationalize `SourmashSignature.name` and `str(sig)` (#1179) Fixed: - Fix needless borrows as suggested by clippy (#1636) - Fix Rust 1.59 lints (#1600) - Clean up clippy lints from 1.52 (#1505) - Fix clippy lints introduced in 1.51 (#1407) - CI/Rust: update and fix cbindgen config (#1473) - pin needletail version to keep MSRV at 1.37 (#1393) - Update proptest requirement from 0.9.6 to 1.0.0 (#1344) - Fix clippy lints introduced in 1.50 and update nix configs (#1332) - Update finch requirement from 0.3.0 to 0.4.1 (#1290) - update rand for test, and activate "js" feature for getrandom (#1275) - Fix new clippy warnings from Rust 1.49 (#1267) - CI: small build fixes (#1252) Removed: - Remove 10x support in compute (#1229) ## [0.10.0] - 2020-10-08 Added: - Add `clear` option to set_abundances(...) method (#1046) Changed: - Replace mx by scaled (#1139) Fixed: - Fix Rust panic error in signature creation (#1172) - Update typed-builder requirement from 0.6.0 to 0.7.0 (#1121) - update CI for latest branch name change (#1150) - Update typed-builder requirement from 0.6.0 to 0.7.0 (#1121) ## [0.9.0] - 2020-07-13 Added: - Cache md5sum calculation (#1058) - Expose more of the API for wasm (signature and ComputeParameters) (#1058) - Getters and setters for ComputeParameters (#1058) Changed: - Migrate from failure to thiserror (#1058) - Bump MSRV to 1.37 (#1058) Fixed: - Use the derive feature in serde instead of serde_derive (#1058) - Use nohash-hasher crate instead of previous NoHashHasher from finch. - Update typed-builder to 0.6.0 (#1058) - stricter niffler versions and add new gz feature to it (#1070) ## [0.8.0] - 2020-06-26 Added: - compute-optimized MinHash (for small scaled or large cardinalities) (#1045) ## [0.7.0] - 2020-05-12 Changed: - Hide internal representation in core (#986) Fixed: - update FFI and cbindgen (#986) ## [0.6.0] - 2020-04-28 Added: - Nodegraph implementation based on khmer.Nodegraph (#799) ## [0.5.0] - 2020-02-08 Added: - add_hash_with_abundance method in core library (#892) Changed: - More refactoring of MinHash comparison code (#882) - Replace mins_push and abunds_push with set_abundances (#887) Fixed: - add_hash with num doesn't set abundances properly (#891) ## [0.4.0] - 2020-01-26 Added: - Compute improvements: Parameter sets for defining signatures, add_protein implemented (#845) - add_many for faster insertion of multiple hashes (#826) Changed: - Compare/similarity now have a downsample argument (#856) Fixed: - Improve sketching performance with lookup tables for complement and DNA validation (#861) (#865) - Use tarpaulin instead of grcov (#862) - set up publishing workflow for NPM and crates.io (#824) ## [0.3.0] - 2020-01-05 Added: - Similarity with abundance method for MinHash (#808) - Experimental support for indices in Rust (#773) - Experimental SBT with MQF internal nodes in Rust (#772) Changed: - Make the sourmash crate library-only (#812) Fixed: - Use once_cell instead of lazy_static and lazy-init (#815) - Fix mem leak in get_mins (#807) - Fixes for WASI and WASM compilation (#771) (#723) [unreleased]: https://github.com/sourmash-bio/sourmash/compare/r0.13.1...HEAD [0.13.1]: https://github.com/sourmash-bio/sourmash/compare/r0.13.0...r0.13.1 [0.13.0]: https://github.com/sourmash-bio/sourmash/compare/r0.12.1...r0.13.0 [0.12.1]: https://github.com/sourmash-bio/sourmash/compare/r0.12.0...r0.12.1 [0.12.0]: https://github.com/sourmash-bio/sourmash/compare/r0.11.0...r0.12.0 [0.11.0]: https://github.com/sourmash-bio/sourmash/compare/r0.10.0...r0.11.0 [0.10.0]: https://github.com/sourmash-bio/sourmash/compare/r0.9.0...r0.10.0 [0.9.0]: https://github.com/sourmash-bio/sourmash/compare/r0.9.0...r0.10.0 [0.8.0]: https://github.com/sourmash-bio/sourmash/compare/r0.8.0...r0.9.0 [0.7.0]: https://github.com/sourmash-bio/sourmash/compare/r0.7.0...r0.8.0 [0.6.0]: https://github.com/sourmash-bio/sourmash/compare/r0.6.0...r0.7.0 [0.5.0]: https://github.com/sourmash-bio/sourmash/compare/r0.5.0...r0.6.0 [0.4.0]: https://github.com/sourmash-bio/sourmash/compare/r0.4.0...r0.5.0 [0.3.0]: https://github.com/sourmash-bio/sourmash/compare/r0.3.0...r0.4.0 sourmash-bio-sourmash-bc3c0f8/src/core/Cargo.toml000066400000000000000000000060741474157046300221440ustar00rootroot00000000000000[package] name = "sourmash" version = "0.18.0" authors = ["Luiz Irber ", "N. Tessa Pierce-Ward ", "C. Titus Brown "] description = "tools for comparing biological sequences with k-mer sketches" repository = "https://github.com/sourmash-bio/sourmash" keywords = ["minhash", "bioinformatics"] categories = ["science", "algorithms", "data-structures"] license = "BSD-3-Clause" edition = "2021" readme = "README.md" autoexamples = false autobins = false rust-version = "1.71.1" [lib] name = "sourmash" crate-type = ["lib", "staticlib", "cdylib"] bench = false [features] from-finch = ["dep:finch"] parallel = ["dep:rayon"] maturin = [] branchwater = ["dep:rocksdb", "parallel"] rkyv = ["dep:rkyv"] default = [] [dependencies] az = "1.0.0" byteorder = "1.4.3" camino = { version = "1.1.9", features = ["serde1"] } cfg-if = "1.0" counter = "0.6.0" csv = "1.3.1" enum_dispatch = "0.3.13" finch = { version = "0.6.0", optional = true } fixedbitset = "0.4.0" getset = "0.1.3" histogram = "0.11.1" itertools = "0.14.0" log = "0.4.22" md5 = "0.7.0" memmap2 = "0.9.5" murmurhash3 = "0.0.5" needletail = { version = "0.6.1", default-features = false } niffler = { version = "2.4.0", default-features = false, features = [ "gz" ] } nohash-hasher = "0.2.0" num-iter = "0.1.45" once_cell = "1.18.0" ouroboros = "0.18.5" piz = "0.5.0" primal-check = "0.3.4" rayon = { version = "1.10.0", optional = true } rkyv = { version = "0.7.44", optional = true } roaring = "0.10.10" roots = "0.0.8" serde = { version = "1.0.217", features = ["derive"] } serde_json = "1.0.135" statrs = "0.18.0" streaming-stats = "0.2.3" thiserror = "2.0" twox-hash = "1.6.0" typed-builder = "0.18.0" vec-collections = "0.4.3" [dev-dependencies] codspeed-criterion-compat = "2.7.2" proptest = { version = "1.6.0", default-features = false, features = ["std"]} rand = "0.8.2" tempfile = "3.15.0" [[bench]] name = "compute" harness = false [[bench]] name = "nodegraph" harness = false [[bench]] name = "minhash" harness = false [[bench]] name = "gather" harness = false [package.metadata.cargo-all-features] skip_optional_dependencies = true denylist = ["maturin"] skip_feature_sets = [ ["branchwater", "parallel"], # branchwater implies parallel ] ## Wasm section. Crates only used for WASM, as well as specific configurations [target.'cfg(all(target_arch = "wasm32", target_os="unknown"))'.dependencies] js-sys = "0.3.72" web-sys = { version = "0.3.72", features = ["console", "File", "FileReaderSync"] } wasm-bindgen = "0.2.89" getrandom = { version = "0.2", features = ["js"] } [target.'cfg(all(target_arch = "wasm32"))'.dependencies] chrono = { version = "0.4.32", features = ["wasmbind"] } [target.'cfg(all(target_arch = "wasm32", target_os="unknown"))'.dev-dependencies] wasm-bindgen-test = "0.3.42" ### These crates don't compile on wasm [target.'cfg(not(target_arch = "wasm32"))'.dependencies.rocksdb] version = "0.23.0" optional = true default-features = false features = [ "bindgen-runtime", "snappy", "zstd" ] [target.'cfg(not(target_arch = "wasm32"))'.dev-dependencies] criterion = "0.5.1" sourmash-bio-sourmash-bc3c0f8/src/core/README.md000066400000000000000000000030231474157046300214620ustar00rootroot00000000000000 # sourmash 🦀 [![](https://img.shields.io/crates/v/sourmash.svg)](https://crates.io/crates/sourmash) [![Rust API Documentation on docs.rs](https://docs.rs/sourmash/badge.svg)](https://docs.rs/sourmash) [![build-status]][github-actions] [![codecov](https://codecov.io/gh/sourmash-bio/sourmash/branch/latest/graph/badge.svg)](https://codecov.io/gh/sourmash-bio/sourmash) License: 3-Clause BSD [build-status]: https://github.com/sourmash-bio/sourmash/workflows/Rust%20checks/badge.svg [github-actions]: https://github.com/sourmash-bio/sourmash/actions?query=workflow%3A%22Rust+checks%22 ---- Compute MinHash signatures for nucleotide (DNA/RNA) and protein sequences. This is the core library used by sourmash. It exposes a C API that can be called from FFI in other languages, and it is how we use it in Python for building the sourmash application (CLI and Python API). ---- sourmash is a product of the [Lab for Data-Intensive Biology](http://ivory.idyll.org/lab/) at the [UC Davis School of Veterinary Medicine](http://www.vetmed.ucdavis.edu). ## Support Please ask questions and files issues [on Github](https://github.com/sourmash-bio/sourmash/issues). ## Development Development happens on github at [sourmash-bio/sourmash](https://github.com/sourmash-bio/sourmash). ## Minimum supported Rust version Currently the minimum supported Rust version is 1.71.1. sourmash-bio-sourmash-bc3c0f8/src/core/benches/000077500000000000000000000000001474157046300216145ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/benches/compute.rs000066400000000000000000000115561474157046300236460ustar00rootroot00000000000000use codspeed_criterion_compat::{black_box, criterion_group, criterion_main, Criterion}; use std::fs::File; use std::io::{Cursor, Read}; use needletail::parse_fastx_reader; use sourmash::cmd::ComputeParameters; use sourmash::signature::Signature; fn add_sequence(c: &mut Criterion) { let cp = ComputeParameters::default(); let template_sig = Signature::from_params(&cp); let mut data: Vec = vec![]; let mut f = File::open("../../tests/test-data/ecoli.genes.fna").unwrap(); let _ = f.read_to_end(&mut data); let data = data.repeat(10); let data_upper = data.to_ascii_uppercase(); let data_lower = data.to_ascii_lowercase(); let data_errors: Vec = data .iter() .enumerate() .map(|(i, x)| if i % 89 == 1 { b'N' } else { *x }) .collect(); let mut group = c.benchmark_group("add_sequence"); group.sample_size(10); group.bench_function("valid", |b| { b.iter(|| { let fasta_data = Cursor::new(data_upper.clone()); let mut sig = template_sig.clone(); let mut parser = parse_fastx_reader(fasta_data).unwrap(); while let Some(rec) = parser.next() { sig.add_sequence(&rec.unwrap().seq(), false).unwrap(); } }); }); group.bench_function("lowercase", |b| { b.iter(|| { let fasta_data = Cursor::new(data_lower.clone()); let mut sig = template_sig.clone(); let mut parser = parse_fastx_reader(fasta_data).unwrap(); while let Some(rec) = parser.next() { sig.add_sequence(&rec.unwrap().seq(), false).unwrap(); } }); }); group.bench_function("invalid kmers", |b| { b.iter(|| { let fasta_data = Cursor::new(data_errors.clone()); let mut sig = template_sig.clone(); let mut parser = parse_fastx_reader(fasta_data).unwrap(); while let Some(rec) = parser.next() { sig.add_sequence(&rec.unwrap().seq(), true).unwrap(); } }); }); group.bench_function("force with valid kmers", |b| { b.iter(|| { let fasta_data = Cursor::new(data_upper.clone()); let mut sig = template_sig.clone(); let mut parser = parse_fastx_reader(fasta_data).unwrap(); while let Some(rec) = parser.next() { sig.add_sequence(&rec.unwrap().seq(), true).unwrap(); } }); }); } fn add_sequence_protein(c: &mut Criterion) { let mut cp = ComputeParameters::default(); cp.set_protein(true); cp.set_dna(false); cp.set_scaled(200); cp.set_ksizes(vec![30]); let template_sig = Signature::from_params(&cp); let mut data: Vec = vec![]; let (mut f, _) = niffler::from_path("../../tests/test-data/genome-s10.fa.gz").unwrap(); let _ = f.read_to_end(&mut data); let data = data.repeat(10); let data_upper = data.to_ascii_uppercase(); let data_lower = data.to_ascii_lowercase(); let data_errors: Vec = data .iter() .enumerate() .map(|(i, x)| if i % 89 == 1 { b'N' } else { *x }) .collect(); let mut group = c.benchmark_group("add_sequence_protein"); group.sample_size(10); group.bench_function("valid", |b| { b.iter(|| { let fasta_data = Cursor::new(data_upper.clone()); let mut sig = template_sig.clone(); let mut parser = parse_fastx_reader(fasta_data).unwrap(); while let Some(rec) = parser.next() { sig.add_protein(&rec.unwrap().seq()).unwrap(); } }); }); group.bench_function("lowercase", |b| { b.iter(|| { let fasta_data = Cursor::new(data_lower.clone()); let mut sig = template_sig.clone(); let mut parser = parse_fastx_reader(fasta_data).unwrap(); while let Some(rec) = parser.next() { sig.add_protein(&rec.unwrap().seq()).unwrap(); } }); }); group.bench_function("invalid kmers", |b| { b.iter(|| { let fasta_data = Cursor::new(data_errors.clone()); let mut sig = template_sig.clone(); let mut parser = parse_fastx_reader(fasta_data).unwrap(); while let Some(rec) = parser.next() { sig.add_protein(&rec.unwrap().seq()).unwrap(); } }); }); group.bench_function("force with valid kmers", |b| { b.iter(|| { let fasta_data = Cursor::new(data_upper.clone()); let mut sig = template_sig.clone(); let mut parser = parse_fastx_reader(fasta_data).unwrap(); while let Some(rec) = parser.next() { sig.add_protein(&rec.unwrap().seq()).unwrap(); } }); }); } criterion_group!(compute, add_sequence, add_sequence_protein); criterion_main!(compute); sourmash-bio-sourmash-bc3c0f8/src/core/benches/gather.rs000066400000000000000000000046301474157046300234370ustar00rootroot00000000000000use std::fs::File; use std::io::BufReader; use std::path::PathBuf; use sourmash::collection::Collection; use sourmash::signature::Signature; use sourmash::sketch::Sketch; use sourmash::{index::calculate_gather_stats, storage::SigStore}; use codspeed_criterion_compat::{black_box, criterion_group, criterion_main, Criterion}; fn gather_stats_benchmarks(c: &mut Criterion) { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/track_abund/47.fa.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sig = Signature::from_reader(reader) .expect("Loading error") .swap_remove(0); let orig_query = sig.minhash().unwrap(); let query = orig_query.clone(); let total_weighted_hashes = orig_query.sum_abunds(); let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/track_abund/63.fa.sig"); // load collection to get sig in sigstore let signatures = Signature::from_path(filename).expect("cant find file"); let collection = Collection::from_sigs(signatures).expect("cant make collection"); let match_sig: SigStore = collection.sig_for_dataset(0).expect("cant load sig"); let test_cases = vec![(false, false), (true, false), (false, true), (true, true)]; let mut group = c.benchmark_group("gather_stats"); for (calc_abund_stats, calc_ani_ci) in test_cases { let test_name = format!( "abund{}_ani_ci{}", calc_abund_stats as u8, calc_ani_ci as u8 ); group.bench_function(&test_name, |b| { b.iter(|| { calculate_gather_stats( black_box(&orig_query), black_box(query.clone()), black_box(match_sig.clone()), black_box(42), // Example match_size black_box(1), // Example gather_result_rank black_box(200), black_box(total_weighted_hashes.try_into().unwrap()), black_box(calc_abund_stats), black_box(calc_ani_ci), black_box(None), // don't set custom confidence intervals ) .expect("error calculating gather stats"); }); }); } group.finish(); } criterion_group!(gather, gather_stats_benchmarks); criterion_main!(gather); sourmash-bio-sourmash-bc3c0f8/src/core/benches/minhash.rs000066400000000000000000000057111474157046300236150ustar00rootroot00000000000000use std::fs::File; use std::io::BufReader; use std::path::PathBuf; use sourmash::signature::{Signature, SigsTrait}; use sourmash::sketch::minhash::{KmerMinHash, KmerMinHashBTree}; use sourmash::sketch::Sketch; use codspeed_criterion_compat::{black_box, criterion_group, criterion_main, Criterion}; fn intersection(c: &mut Criterion) { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/gather-abund/genome-s10.fa.gz.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let mut sigs = Signature::from_reader(reader).expect("Loading error"); let mh = if let Sketch::MinHash(mh) = &sigs.swap_remove(0).sketches()[0] { mh.clone() } else { unimplemented!() }; let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/gather-abund/genome-s11.fa.gz.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let mut sigs = Signature::from_reader(reader).expect("Loading error"); let mh2 = if let Sketch::MinHash(mh) = &sigs.swap_remove(0).sketches()[0] { mh.clone() } else { unimplemented!() }; let mut group = c.benchmark_group("minhash"); group.sample_size(10); group.bench_function("intersection", |b| { b.iter(|| { mh.intersection(&mh2).unwrap(); }); }); group.bench_function("intersection_size", |b| { b.iter(|| { mh.intersection_size(&mh2).unwrap(); }); }); let mut mh1 = KmerMinHash::builder() .num(0) .max_hash(1_000_000) .ksize(21) .build(); let mut mh2 = KmerMinHash::builder() .num(0) .max_hash(1_000_000) .ksize(21) .build(); let mut mh1_btree = KmerMinHashBTree::builder() .num(0) .max_hash(1_000_000) .ksize(21) .build(); let mut mh2_btree = KmerMinHashBTree::builder() .num(0) .max_hash(1_000_000) .ksize(21) .build(); for i in 0..=1_000_000 { if i % 2 == 0 { mh1.add_hash(i); mh1_btree.add_hash(i); } if i % 45 == 0 { mh2.add_hash(i); mh2_btree.add_hash(i); } } group.bench_function("large intersection", |b| { b.iter(|| { mh1.intersection(&mh2).unwrap(); }); }); group.bench_function("large intersection_size", |b| { b.iter(|| { mh1.intersection_size(&mh2).unwrap(); }); }); group.bench_function("large intersection btree", |b| { b.iter(|| { mh1_btree.intersection(&mh2_btree).unwrap(); }); }); group.bench_function("large intersection_size btree", |b| { b.iter(|| { mh1_btree.intersection_size(&mh2_btree).unwrap(); }); }); } criterion_group!(minhash, intersection); criterion_main!(minhash); sourmash-bio-sourmash-bc3c0f8/src/core/benches/nodegraph.rs000066400000000000000000000027461474157046300241420ustar00rootroot00000000000000use std::fs::File; use std::io::{BufWriter, Cursor, Read}; use sourmash::sketch::nodegraph::Nodegraph; use codspeed_criterion_compat::{black_box, criterion_group, criterion_main, Criterion}; fn save_load(c: &mut Criterion) { let mut data: Vec = vec![]; let mut f = File::open("../../tests/test-data/.sbt.v3/internal.0").unwrap(); let _ = f.read_to_end(&mut data); let mut group = c.benchmark_group("nodegraph"); group.sample_size(10); let mut reader = Cursor::new(data.clone()); let ng = Nodegraph::from_reader(&mut reader).unwrap(); group.bench_function("load nodegraph", |b| { b.iter(|| { let mut reader = Cursor::new(data.clone()); let _ng = Nodegraph::from_reader(&mut reader).unwrap(); }); }); group.bench_function("save nodegraph", |b| { b.iter(|| { let mut buf = Vec::new(); let mut writer = BufWriter::new(&mut buf); ng.save_to_writer(&mut writer).unwrap(); }); }); group.bench_function("save compressed nodegraph", |b| { b.iter(|| { let mut buf = Vec::new(); let mut writer = niffler::get_writer( Box::new(&mut buf), niffler::compression::Format::Gzip, niffler::compression::Level::One, ) .unwrap(); ng.save_to_writer(&mut writer).unwrap(); }); }); } criterion_group!(nodegraph, save_load); criterion_main!(nodegraph); sourmash-bio-sourmash-bc3c0f8/src/core/build.rs000066400000000000000000000041751474157046300216610ustar00rootroot00000000000000use std::env; fn main() { let crate_dir = env::var("CARGO_MANIFEST_DIR").unwrap(); copy_c_bindings(&crate_dir); } #[cfg(not(feature = "maturin"))] fn copy_c_bindings(_crate_dir: &str) {} #[cfg(feature = "maturin")] fn copy_c_bindings(crate_dir: &str) { use std::path::{Path, PathBuf}; fn find_root_dir(crate_dir: &str) -> &Path { let root_dir = Path::new(crate_dir); if root_dir.join("pyproject.toml").is_file() { return root_dir; } let root_dir = Path::new(crate_dir).parent().unwrap().parent().unwrap(); if root_dir.join("pyproject.toml").is_file() { return root_dir; } panic!("Couldn't find pyproject.toml to determine root dir"); } fn find_target_dir(out_dir: &str) -> PathBuf { use std::ffi::OsStr; let mut components = Path::new(out_dir).iter(); while let Some(dir) = components.next_back() { if dir == OsStr::new("target") { break; } } let mut dir: PathBuf = components.collect(); if dir.as_os_str().is_empty() { panic!("Couldn't find target dir based on OUT_DIR"); } else { dir.push("target"); dir } } let root_dir = find_root_dir(crate_dir); let header_path = root_dir.join("include").join("sourmash.h"); let header = std::fs::read_to_string(header_path).expect("error reading header"); // strip directives, not supported by the cffi C parser let new_header: String = header .lines() .filter_map(|s| { if s.starts_with('#') { None } else { Some({ let mut s = s.to_owned(); s.push('\n'); s }) } }) .collect(); let out_dir = env::var("OUT_DIR").unwrap(); let target_dir = find_target_dir(&out_dir); std::fs::create_dir_all(&target_dir).expect("error creating target dir"); let out_path = target_dir.join("header.h"); std::fs::write(out_path, new_header).expect("error writing header"); } sourmash-bio-sourmash-bc3c0f8/src/core/cbindgen.toml000066400000000000000000000005361474157046300226570ustar00rootroot00000000000000header = "/* c bindings to the sourmash library */" include_guard = "SOURMASH_H_INCLUDED" language = "C" style = "type" [parse] clean = true [parse.expand] crates = ["sourmash"] features = ["branchwater"] [enum] rename_variants = "QualifiedScreamingSnakeCase" [export] exclude = ["HLL", "NoHashHasher", "HashIntersection"] [fn] sort_by = "Name" sourmash-bio-sourmash-bc3c0f8/src/core/examples/000077500000000000000000000000001474157046300220235ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/examples/generate_mqfs.rs000066400000000000000000000051301474157046300252100ustar00rootroot00000000000000use std::collections::HashMap; use std::fs::File; use std::io::BufReader; use std::path::PathBuf; use std::result::Result; use mqf::MQF; use sourmash::signature::Signature; use sourmash::sketch::Sketch; fn main() { let mh_paths: HashMap = { [ (6, "6d6e87e1154e95b279e5e7db414bc37b".into()), (7, "60f7e23c24a8d94791cc7a8680c493f9".into()), (8, "0107d767a345eff67ecdaed2ee5cd7ba".into()), (9, "f71e78178af9e45e6f1d87a0c53c465c".into()), (10, "f0c834bc306651d2b9321fb21d3e8d8f".into()), (11, "4e94e60265e04f0763142e20b52c0da1".into()), (12, "b59473c94ff2889eca5d7165936e64b3".into()), ] .into_iter() .cloned() .collect() }; let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("tests/test-data/.sbt.v5_mhmt/"); let mh_sigs: HashMap = mh_paths .into_iter() .map(|(k, v)| { let mut mhpath = filename.clone(); mhpath.push(v); let file = File::open(mhpath).unwrap(); let reader = BufReader::new(file); let sig: Result, _> = serde_json::from_reader(reader); (k, sig.unwrap()[0].clone()) }) .collect(); let mut mqfs: HashMap = HashMap::default(); for i in (0..=5).rev() { println!("Creating MQF {}", i); let mut mqf = MQF::new(1, 18); let left = i * 2 + 1; for child in left..=left + 1 { if mh_sigs.contains_key(&child) { println!("Loading values from MH {}", child); if let Sketch::MinHash(sig) = &mh_sigs[&child].signatures[0] { sig.mins() .iter() .map(|h| { dbg!(*h % u64::pow(2, 26)); mqf.insert(*h % u64::pow(2, 26), 1) //mqf.insert(*h, 1) }) .count(); }; } else if mqfs.contains_key(&child) { let mut cmqf = mqfs.get_mut(&child).unwrap(); mqf.merge(&mut cmqf).expect("Error merging"); } else { // TODO: shouldn't happen... unimplemented!() } } println!("Save MQF {} for later", i); mqfs.insert(i, mqf); println!("Saving MQFs to disk"); let mut internal = filename.clone(); internal.push(format!("internal.{}", i)); mqfs[&i].serialize(internal).unwrap(); } } sourmash-bio-sourmash-bc3c0f8/src/core/src/000077500000000000000000000000001474157046300207745ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/src/ani_utils.rs000066400000000000000000000263661474157046300233460ustar00rootroot00000000000000// Streamlined set of utils for containment --> ANI estimation // Equations based off of: https://github.com/KoslickiLab/mutation-rate-ci-calculator // Reference: https://doi.org/10.1101/2022.01.11.475870 use roots::{find_root_brent, SimpleConvergency}; use statrs::distribution::{ContinuousCDF, Normal}; use crate::{Error, ScaledType}; fn exp_n_mutated(l: f64, k: f64, r1: f64) -> f64 { let q = r1_to_q(k, r1); l * q } fn var_n_mutated(l: f64, k: f64, r1: f64, q: Option) -> Result { if r1 == 0.0 { return Ok(0.0); } let q = q.unwrap_or_else(|| r1_to_q(k, r1)); let var_n = l * (1.0 - q) * (q * (2.0 * k + (2.0 / r1) - 1.0) - 2.0 * k) + k * (k - 1.0) * (1.0 - q).powi(2) + (2.0 * (1.0 - q) / (r1.powi(2))) * ((1.0 + (k - 1.0) * (1.0 - q)) * r1 - q); if var_n < 0.0 { Err(Error::ANIEstimationError { message: "varN is less than 0.0".into(), }) } else { Ok(var_n) } } fn exp_n_mutated_squared(l: f64, k: f64, p: f64) -> Result { let var_n = var_n_mutated(l, k, p, None)?; let exp_n_squared = exp_n_mutated(l, k, p).powi(2); Ok(var_n + exp_n_squared) } fn probit(p: f64) -> f64 { Normal::new(0.0, 1.0).unwrap().inverse_cdf(p) } fn r1_to_q(k: f64, r1: f64) -> f64 { 1.0 - (1.0 - r1).powi(k as i32) } // prior versions of ani estimation also returned this value by default. // BUT, it's not really something we need to calculate for prefetch/gather, // since there will always be something in common (or comparison would not be happening) // I'm not sure where the right place to put this back in is.. perhaps add minhash.contained_ani() // and return this there. // Usage: // let prob_nothing_in_common = // get_exp_probability_nothing_common(n_unique_kmers, ksize, point_estimate, f_scaled)?; // let prob_threshold = prob_threshold.unwrap_or(1e-3); #[allow(dead_code)] fn get_exp_probability_nothing_common( ani_estimate: f64, ksize: f64, f_scaled: f64, n_unique_kmers: f64, ) -> Result { if ani_estimate == 0.0 || ani_estimate == 1.0 { Ok(1.0 - ani_estimate) } else { // Calculate the expected log probability. let exp_nmut = exp_n_mutated(n_unique_kmers, ksize, 1.0 - ani_estimate); let mut expected_log_probability = (n_unique_kmers - exp_nmut) * (1.0 - f_scaled).ln(); if expected_log_probability.is_infinite() { expected_log_probability = f64::NEG_INFINITY; } // Return the exponential of the expected log probability. Ok(expected_log_probability.exp()) } } /// Streamlined function for ANI from containment. /// todo: report ANI as % in 5.0? pub fn ani_from_containment(containment: f64, ksize: f64) -> f64 { if containment == 0.0 { 0.0 } else if containment == 1.0 { 1.0 } else { 1.0 - (1.0 - containment.powf(1.0 / ksize)) } } // Calculate containment to ANI with confidence intervals pub fn ani_ci_from_containment( containment: f64, ksize: f64, scaled: ScaledType, n_unique_kmers: u64, confidence: Option, ) -> Result<(f64, f64), Error> { if containment == 0.0 { return Ok((0.0, 0.0)); } else if containment == 1.0 { return Ok((1.0, 1.0)); } let confidence = confidence.unwrap_or(0.95); // conversions needed throughout let scaled_f64 = scaled as f64; let f_scaled = 1.0 / scaled_f64; let n_unique_kmers = n_unique_kmers as f64; let alpha = 1.0 - confidence; let z_alpha = probit(1.0 - alpha / 2.0); let bias_factor = 1.0 - (1.0 - f_scaled).powi(n_unique_kmers as i32); let term_1 = (1.0 - f_scaled) / (f_scaled * (n_unique_kmers).powi(3) * bias_factor.powi(2)); let term_2 = |pest: f64| { n_unique_kmers * exp_n_mutated(n_unique_kmers, ksize, pest) - exp_n_mutated_squared(n_unique_kmers, ksize, pest).unwrap_or(0.0) }; let term_3 = |pest: f64| { var_n_mutated(n_unique_kmers, ksize, pest, None).unwrap_or(0.0) / (n_unique_kmers).powi(2) }; let var_direct = |pest: f64| term_1 * term_2(pest) + term_3(pest); let f1 = |pest: f64| { (1.0 - pest).powi(ksize as i32) + z_alpha * var_direct(pest).sqrt() - containment }; let f2 = |pest: f64| { (1.0 - pest).powi(ksize as i32) - z_alpha * var_direct(pest).sqrt() - containment }; let mut convergency = SimpleConvergency { eps: 1e-15, max_iter: 1000, }; let dist_sol1 = find_root_brent(0.0000001, 0.9999999, &f1, &mut convergency).unwrap_or_default(); let dist_sol2 = find_root_brent(0.0000001, 0.9999999, &f2, &mut convergency).unwrap_or_default(); Ok((1.0 - dist_sol1, 1.0 - dist_sol2)) } #[cfg(test)] mod tests { use super::*; use std::f64::EPSILON; #[test] fn test_containment_to_ani_zero() { let contain = 0.0; let ksize = 21; let scaled = 10; let n_unique_kmers = 100; let confidence = Some(0.95); let res = ani_from_containment(contain, ksize as f64); assert_eq!(res, 0.0); let (ci_low, ci_high) = ani_ci_from_containment(contain, ksize as f64, scaled, n_unique_kmers, confidence) .unwrap(); eprintln!("{}", ci_low); eprintln!("{}", ci_high); assert_eq!(ci_low, 0.0); assert_eq!(ci_high, 0.0); } #[test] fn test_containment_to_ani_one() { let contain = 1.0; let ksize = 21; let scaled = 10; let n_unique_kmers = 100; let confidence = None; let res = ani_from_containment(contain, ksize as f64); assert_eq!(res, 1.0); let (ci_low, ci_high) = ani_ci_from_containment(contain, ksize as f64, scaled, n_unique_kmers, confidence) .unwrap(); assert_eq!(ci_low, 1.0); assert_eq!(ci_high, 1.0); } #[test] fn test_containment_to_ani_scaled1() { let contain = 0.5; let ksize = 21; let scaled = 1; let n_unique_kmers = 10000; let confidence = None; let ani = ani_from_containment(contain, ksize as f64); assert!((ani - 0.9675317785238916) < EPSILON); let (ci_low, ci_high) = ani_ci_from_containment(contain, ksize as f64, scaled, n_unique_kmers, confidence) .unwrap(); assert!((ci_low - 0.9635213980271021) < EPSILON); assert!((ci_high - 0.9712900870335944) < EPSILON); } #[test] fn test_containment_to_ani_scaled100() { let contain = 0.1; let ksize = 31; let scaled = 100; let n_unique_kmers = 10000; let confidence = None; let ani = ani_from_containment(contain, ksize as f64); assert!((ani - 0.9284145445194744) < EPSILON); let (ci_low, ci_high) = ani_ci_from_containment(contain, ksize as f64, scaled, n_unique_kmers, confidence) .unwrap(); assert!((ci_low - 0.9094445232754665) < EPSILON); assert!((ci_high - 0.9467922076143345) < EPSILON); } #[test] fn test_containment_to_ani_scaled100_2() { let contain = 0.5; let ksize = 21; let scaled = 100; let n_unique_kmers = 10000; let confidence = None; let ani = ani_from_containment(contain, ksize as f64); assert!((ani - 0.9675317785238916) < EPSILON); let (ci_low, ci_high) = ani_ci_from_containment(contain, ksize as f64, scaled, n_unique_kmers, confidence) .unwrap(); assert!((ci_low - 0.9569003945603415) < EPSILON); assert!((ci_high - 0.9762879360833708) < EPSILON); } #[test] fn test_var_n_mutated_zero() { let r = 0.0; let ksize = 31; let nkmers = 200; let var_n_mut = var_n_mutated(nkmers as f64, ksize as f64, r, None).unwrap(); // Assuming the function returns a Result assert_eq!(var_n_mut, 0.0, "Expected variance to be 0 for r=0"); } #[test] fn test_var_n_mutated_value_error() { let r = 10.0; let ksize = 31; let nkmers = 200; match var_n_mutated(nkmers as f64, ksize as f64, r, None) { Err(e) => assert_eq!( e.to_string(), "error while calculating ANI confidence intervals: varN is less than 0.0", "Unexpected error message" ), Ok(_) => panic!("Expected an error, but got Ok"), } } #[test] fn test_var_n_mutated_success() { let r = 0.4; let ksize = 31; let nkmers = 200_000; let var_n_mut = var_n_mutated(nkmers as f64, ksize as f64, r, None).unwrap(); // Assuming the function returns a Result let expected = 0.10611425440741508; assert!( (var_n_mut - expected).abs() < f64::EPSILON, "Variance did not match expected value" ); } #[test] fn test_r1_to_q() { let k = 2.0; let r1 = 0.5; let result = r1_to_q(k, r1); let expected = 0.75; assert!( (result - expected).abs() < EPSILON, "The result of r1_to_q({}, {}) was {}, but {} was expected", k, r1, result, expected ); } #[test] fn test_exp_n_mutated() { let l = 100.0; let k = 2.0; let r1 = 0.5; // Calculate the expected result based on the inputs let expected_q = r1_to_q(k, r1); let expected_result = l * expected_q; let result = exp_n_mutated(l, k, r1); assert!( (result - expected_result).abs() < EPSILON, "The result of exp_n_mutated({}, {}, {}) was {}, but {} was expected", l, k, r1, result, expected_result ); } #[test] fn test_get_exp_probability_nothing_common_ani_zero() { let ani_estimate = 0.0; let ksize = 31.0; let f_scaled = 0.1; let n_unique_kmers = 1000.0; let result = get_exp_probability_nothing_common(ani_estimate, ksize, f_scaled, n_unique_kmers) .unwrap(); assert_eq!( result, 1.0, "Expected probability for ani_estimate of 0 to be 1.0" ); } #[test] fn test_get_exp_probability_nothing_common_ani_one() { let ani_estimate = 1.0; let ksize = 31.0; let f_scaled = 0.1; let n_unique_kmers = 1000.0; let result = get_exp_probability_nothing_common(ani_estimate, ksize, f_scaled, n_unique_kmers) .unwrap(); assert_eq!( result, 0.0, "Expected probability for ani_estimate of 1 to be 0.0" ); } #[test] fn test_get_exp_probability_nothing_common() { let contain = 0.1; let ksize = 31 as f64; let scaled = 10; let f_scaled = 1.0 / scaled as f64; let n_unique_kmers = 1000; let ani = ani_from_containment(contain, ksize); let result = get_exp_probability_nothing_common(ani, ksize, f_scaled, n_unique_kmers as f64) .unwrap(); assert!( result >= 0.0 && result <= 1.0, "The result should be a valid probability" ); assert!((result - 0.000026561398887587855) < EPSILON); } } sourmash-bio-sourmash-bc3c0f8/src/core/src/cmd.rs000066400000000000000000000155551474157046300221200ustar00rootroot00000000000000use getset::{CopyGetters, Getters, Setters}; use typed_builder::TypedBuilder; use crate::encodings::HashFunctions; use crate::signature::Signature; use crate::sketch::minhash::{max_hash_for_scaled, KmerMinHashBTree}; use crate::sketch::Sketch; impl Signature { pub fn from_params(params: &ComputeParameters) -> Signature { let template = build_template(params); Signature::builder() .hash_function("0.murmur64") .name(params.merge.clone()) .filename(None) .signatures(template) .build() } } #[allow(dead_code)] #[derive(TypedBuilder, CopyGetters, Getters, Setters)] pub struct ComputeParameters { #[getset(get = "pub", set = "pub")] #[builder(default = vec![21, 31, 51])] ksizes: Vec, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] check_sequence: bool, #[getset(get_copy = "pub", set = "pub")] #[builder(default = true)] dna: bool, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] dayhoff: bool, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] hp: bool, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] skipm1n3: bool, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] skipm2n3: bool, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] singleton: bool, #[getset(get_copy = "pub", set = "pub")] #[builder(default = 0u32)] scaled: u32, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] force: bool, #[getset(get = "pub", set = "pub")] #[builder(default = None)] output: Option, // TODO: check #[getset(get_copy = "pub", set = "pub")] #[builder(default = 500u32)] num_hashes: u32, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] protein: bool, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] name_from_first: bool, #[getset(get_copy = "pub", set = "pub")] #[builder(default = 42u64)] seed: u64, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] input_is_protein: bool, #[getset(get = "pub", set = "pub")] #[builder(default = None)] merge: Option, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] track_abundance: bool, #[getset(get_copy = "pub", set = "pub")] #[builder(default = false)] randomize: bool, #[getset(get = "pub", set = "pub")] #[builder(default = "CC0".into())] license: String, #[getset(get_copy = "pub", set = "pub")] #[builder(default = 2usize)] processes: usize, } impl Default for ComputeParameters { fn default() -> Self { Self::builder().build() } } pub fn build_template(params: &ComputeParameters) -> Vec { let max_hash = max_hash_for_scaled(params.scaled); params .ksizes .iter() .flat_map(|k| { let mut ksigs = vec![]; if params.protein { ksigs.push(Sketch::LargeMinHash( KmerMinHashBTree::builder() .num(params.num_hashes) .ksize(*k) .hash_function(HashFunctions::Murmur64Protein) .max_hash(max_hash) .seed(params.seed) .abunds(if params.track_abundance { Some(Default::default()) } else { None }) .build(), )); } if params.dayhoff { ksigs.push(Sketch::LargeMinHash( KmerMinHashBTree::builder() .num(params.num_hashes) .ksize(*k) .hash_function(HashFunctions::Murmur64Dayhoff) .max_hash(max_hash) .seed(params.seed) .abunds(if params.track_abundance { Some(Default::default()) } else { None }) .build(), )); } if params.hp { ksigs.push(Sketch::LargeMinHash( KmerMinHashBTree::builder() .num(params.num_hashes) .ksize(*k) .hash_function(HashFunctions::Murmur64Hp) .max_hash(max_hash) .seed(params.seed) .abunds(if params.track_abundance { Some(Default::default()) } else { None }) .build(), )); } if params.skipm1n3 { ksigs.push(Sketch::LargeMinHash( KmerMinHashBTree::builder() .num(params.num_hashes) .ksize(*k) .hash_function(HashFunctions::Murmur64Skipm1n3) .max_hash(max_hash) .seed(params.seed) .abunds(if params.track_abundance { Some(Default::default()) } else { None }) .build(), )); } if params.skipm2n3 { ksigs.push(Sketch::LargeMinHash( KmerMinHashBTree::builder() .num(params.num_hashes) .ksize(*k) .hash_function(HashFunctions::Murmur64Skipm2n3) .max_hash(max_hash) .seed(params.seed) .abunds(if params.track_abundance { Some(Default::default()) } else { None }) .build(), )); } if params.dna { ksigs.push(Sketch::LargeMinHash( KmerMinHashBTree::builder() .num(params.num_hashes) .ksize(*k) .hash_function(HashFunctions::Murmur64Dna) .max_hash(max_hash) .seed(params.seed) .abunds(if params.track_abundance { Some(Default::default()) } else { None }) .build(), )); } ksigs }) .collect() } sourmash-bio-sourmash-bc3c0f8/src/core/src/collection.rs000066400000000000000000000473741474157046300235140ustar00rootroot00000000000000use std::ops::{Deref, DerefMut}; use camino::Utf8Path as Path; use camino::Utf8PathBuf as PathBuf; use crate::encodings::Idx; use crate::manifest::{Manifest, Record}; use crate::prelude::*; use crate::storage::{FSStorage, InnerStorage, MemStorage, SigStore, ZipStorage}; use crate::{Error, Result, ScaledType}; #[cfg(feature = "parallel")] use rayon::prelude::*; /// a Manifest and Storage, combined. Can contain any collection of signatures. #[derive(Clone)] pub struct Collection { manifest: Manifest, storage: InnerStorage, } /// A consistent collection of signatures. Can be created using `select`. #[derive(Clone)] pub struct CollectionSet { collection: Collection, } impl Deref for CollectionSet { type Target = Collection; fn deref(&self) -> &Self::Target { &self.collection } } impl DerefMut for CollectionSet { fn deref_mut(&mut self) -> &mut Self::Target { &mut self.collection } } impl TryFrom for CollectionSet { type Error = crate::Error; fn try_from(collection: Collection) -> Result { let first = if let Some(first) = collection.manifest.first() { first } else { // empty collection is consistent ¯\_(ツ)_/¯ return Ok(Self { collection }); }; let (min_scaled, max_scaled) = collection.min_max_scaled().expect("empty collection!?"); if min_scaled != max_scaled { return Err(Error::MismatchScaled); } collection .manifest .iter() .skip(1) .try_for_each(|c| first.check_compatible(c))?; Ok(Self { collection }) } } impl CollectionSet { pub fn into_inner(self) -> Collection { self.collection } pub fn selection(&self) -> Selection { todo!("Extract selection from first sig") } /// Replace the storage with a new one. /// /// # Safety /// /// This method doesn't check if the manifest matches what is in the /// storage (which can be expensive). It is up to the caller to /// guarantee the manifest and storage are in sync. pub unsafe fn set_storage_unchecked(&mut self, storage: InnerStorage) { self.storage = storage; } } impl Collection { pub fn new(manifest: Manifest, storage: InnerStorage) -> Self { Self { manifest, storage } } pub fn iter(&self) -> impl Iterator { self.manifest.iter().enumerate().map(|(i, r)| (i as Idx, r)) } #[cfg(feature = "parallel")] pub fn par_iter(&self) -> impl IndexedParallelIterator { self.manifest .par_iter() .enumerate() .map(|(i, r)| (i as Idx, r)) } pub fn len(&self) -> usize { self.manifest.len() } pub fn is_empty(&self) -> bool { self.manifest.len() == 0 } pub fn manifest(&self) -> &Manifest { &self.manifest } pub fn storage(&self) -> &InnerStorage { &self.storage } pub fn check_superset(&self, other: &Collection) -> Result { self.iter() .zip(other.iter()) .all(|((id1, rec1), (id2, rec2))| id1 == id2 && rec1 == rec2) .then(|| self.len()) // TODO: right error here .ok_or(Error::MismatchKSizes) } pub fn from_zipfile>(zipfile: P) -> Result { let storage = ZipStorage::from_file(zipfile)?; // Load manifest from standard location in zipstorage let manifest = Manifest::from_reader(storage.load("SOURMASH-MANIFEST.csv")?.as_slice())?; Ok(Self { manifest, storage: InnerStorage::new(storage), }) } #[cfg(all(feature = "branchwater", not(target_arch = "wasm32")))] pub fn from_rocksdb>(dirname: P) -> Result { use crate::index::revindex::{RevIndex, RevIndexOps}; let path = dirname.as_ref().as_str().to_string(); let index = RevIndex::open(path, true, None)?; let collection: Collection = index.collection().clone().into_inner(); Ok(collection) } pub fn from_sigs(sigs: Vec) -> Result { let storage = MemStorage::new(); #[cfg(feature = "parallel")] let iter = sigs.into_par_iter(); #[cfg(not(feature = "parallel"))] let iter = sigs.into_iter(); let records: Vec<_> = iter .enumerate() .flat_map(|(i, sig)| { let path = format!("{}", i); let mut record = Record::from_sig(&sig, &path); let path = storage.save_sig(&path, sig).expect("Error saving sig"); record.iter_mut().for_each(|rec| { rec.set_internal_location(path.clone().into()); }); record }) .collect(); Ok(Self { manifest: records.into(), storage: InnerStorage::new(storage), }) } pub fn from_paths(paths: &[PathBuf]) -> Result { // TODO: // - figure out if there is a common path between sigs for FSStorage? Ok(Self { manifest: paths.into(), storage: InnerStorage::new( FSStorage::builder() .fullpath("".into()) .subdir("".into()) .build(), ), }) } pub fn record_for_dataset(&self, dataset_id: Idx) -> Result<&Record> { Ok(&self.manifest[dataset_id as usize]) } pub fn sig_for_dataset(&self, dataset_id: Idx) -> Result { let match_path = if self.manifest.is_empty() { "" } else { self.manifest[dataset_id as usize] .internal_location() .as_str() }; let selection = Selection::from_record(&self.manifest[dataset_id as usize])?; let sig = self.storage.load_sig(match_path)?.select(&selection)?; assert_eq!(sig.signatures.len(), 1); Ok(sig) } pub fn sig_from_record(&self, record: &Record) -> Result { let match_path = record.internal_location().as_str(); let selection = Selection::from_record(record)?; let sig = self.storage.load_sig(match_path)?.select(&selection)?; assert_eq!(sig.signatures.len(), 1); Ok(sig) } pub fn intersect_manifest(&mut self, mf: &Manifest) { self.manifest = self.manifest.intersect_manifest(mf); } // CTB: question, should we do something about num here? pub fn min_max_scaled(&self) -> Option<(&ScaledType, &ScaledType)> { self.manifest.first().map(|first| { self.manifest .iter() .fold((first.scaled(), first.scaled()), |f, r| { (f.0.min(r.scaled()), f.1.max(r.scaled())) }) }) } } impl Select for Collection { fn select(mut self, selection: &Selection) -> Result { self.manifest = self.manifest.select(selection)?; Ok(self) } } #[cfg(test)] mod test { use camino::Utf8PathBuf as PathBuf; use std::fs::File; use std::io::BufReader; use super::Collection; use crate::encodings::HashFunctions; use crate::manifest::Manifest; use crate::prelude::Select; use crate::selection::Selection; use crate::signature::Signature; #[cfg(all(feature = "branchwater", not(target_arch = "wasm32")))] use crate::Result; #[test] fn sigstore_selection_with_downsample() { // load test sigs let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/47+63-multisig.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); // create Selection object let mut selection = Selection::default(); selection.set_scaled(2000); // load sigs into collection + select compatible signatures let cl = Collection::from_sigs(sigs) .unwrap() .select(&selection) .unwrap(); // count collection length assert_eq!(cl.len(), 6); for (idx, _rec) in cl.iter() { // need to pass select again here so we actually downsample let this_sig = cl.sig_for_dataset(idx).unwrap().select(&selection).unwrap(); let this_mh = this_sig.minhash().unwrap(); assert_eq!(this_mh.scaled(), 2000); } } #[test] fn sigstore_selection_with_downsample_too_low() { // load test sigs let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/47+63-multisig.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); // create Selection object let mut selection = Selection::default(); selection.set_scaled(500); // load sigs into collection + select compatible signatures let cl = Collection::from_sigs(sigs) .unwrap() .select(&selection) .unwrap(); // no sigs should remain assert_eq!(cl.len(), 0); } #[test] fn sigstore_selection_scaled_handle_num_sig() { // load test sigs let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); // four num=500 sigs filename.push("../../tests/test-data/genome-s11.fa.gz.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); assert_eq!(sigs.len(), 4); // create Selection object let mut selection = Selection::default(); selection.set_scaled(1000); // load sigs into collection + select compatible signatures let cl = Collection::from_sigs(sigs) .unwrap() .select(&selection) .unwrap(); // no sigs should remain assert_eq!(cl.len(), 0); } #[test] fn sigstore_selection_num() { // load test sigs let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); // four num=500 sigs filename.push("../../tests/test-data/genome-s11.fa.gz.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); let sigs_copy = sigs.clone(); assert_eq!(sigs.len(), 4); // create Selection object let mut selection = Selection::default(); selection.set_num(500); // load sigs into collection + select compatible signatures let cl = Collection::from_sigs(sigs) .unwrap() .select(&selection) .unwrap(); // all sigs should remain assert_eq!(cl.len(), 4); //now select diff num and none should remain selection.set_num(100); let cl2 = Collection::from_sigs(sigs_copy) .unwrap() .select(&selection) .unwrap(); assert_eq!(cl2.len(), 0); } #[test] fn sigstore_selection_num_handle_scaled_sig() { // load test sigs let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); // four num=500 sigs filename.push("../../tests/test-data/47+63-multisig.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); assert_eq!(sigs.len(), 6); // create Selection object let mut selection = Selection::default(); selection.set_num(500); // load sigs into collection + select compatible signatures let cl = Collection::from_sigs(sigs) .unwrap() .select(&selection) .unwrap(); // no sigs should remain assert_eq!(cl.len(), 0); } #[test] fn collection_intersect_manifest() { // load test sigs let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); // four num=500 sigs filename.push("../../tests/test-data/genome-s11.fa.gz.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); assert_eq!(sigs.len(), 4); // load sigs into collection + select compatible signatures let mut cl = Collection::from_sigs(sigs).unwrap(); // all sigs should remain assert_eq!(cl.len(), 4); // grab first record let manifest = cl.manifest(); let record = manifest.iter().next().unwrap().clone(); let vr = vec![record]; // now intersect: let manifest2 = Manifest::from(vr); cl.intersect_manifest(&manifest2); assert_eq!(cl.len(), 1); } #[test] fn sigstore_sig_from_record() { // load test sigs let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/47+63-multisig.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); // create Selection object let mut selection = Selection::default(); selection.set_scaled(2000); // load sigs into collection + select compatible signatures let cl = Collection::from_sigs(sigs) .unwrap() .select(&selection) .unwrap(); // no sigs should remain assert_eq!(cl.len(), 6); for (_idx, rec) in cl.iter() { dbg!("record scaled is: {}", rec.scaled()); let this_sig = cl.sig_from_record(rec).unwrap(); let this_mh = this_sig.minhash().unwrap(); assert_eq!(this_mh.scaled(), 2000); } } #[test] #[should_panic] // for now... fn sigstore_sig_from_record_2() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/short.sig.gz"); let v = [filename]; let collection = Collection::from_paths(&v).expect("no sigs!?"); // pull off first record let v: Vec<_> = collection.iter().collect(); let (_idx, rec) = v.first().expect("no records in collection?!"); // this will panic with "unimplemented" because there are two // sketches and that is not supported. let _first_sig = collection.sig_from_record(rec).expect("no sig!?"); } #[test] fn sigstore_selection_moltype_zip() { // load test sigs let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/prot/hp.zip"); // create Selection object let mut selection = Selection::default(); selection.set_scaled(200); selection.set_moltype(HashFunctions::Murmur64Hp); // load sigs into collection + select compatible signatures let cl = Collection::from_zipfile(&filename) .unwrap() .select(&selection) .unwrap(); // count collection length assert_eq!(cl.len(), 2); for (idx, _rec) in cl.iter() { let this_sig = cl.sig_for_dataset(idx).unwrap(); let this_mh = this_sig.minhash().unwrap(); assert_eq!(this_mh.scaled(), 200); } } #[test] fn sigstore_selection_moltype_sig() { // load test sigs let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename .push("../../tests/test-data/prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); // create Selection object let mut selection = Selection::default(); selection.set_moltype(HashFunctions::Murmur64Hp); // load sigs into collection + select compatible signatures let cl = Collection::from_sigs(sigs) .unwrap() .select(&selection) .unwrap(); // count collection length assert_eq!(cl.len(), 1); for (idx, _rec) in cl.iter() { // need to pass select again here so we actually downsample let this_sig = cl.sig_for_dataset(idx).unwrap().select(&selection).unwrap(); let this_mh = this_sig.minhash().unwrap(); assert_eq!(this_mh.scaled(), 100); } } #[test] fn collection_from_collectionset() -> () { use crate::collection::CollectionSet; let base_path = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let test_sigs = vec![PathBuf::from("../../tests/test-data/prot/all.zip")]; let full_paths: Vec = test_sigs .into_iter() .map(|sig| base_path.join(sig)) .collect(); let collection = Collection::from_zipfile(&full_paths[0]).unwrap(); let mut selection = Selection::default(); selection.set_moltype(HashFunctions::Murmur64Protein); selection.set_scaled(200); let collection = collection.select(&selection).expect("should pass"); let (min_scaled, max_scaled) = collection.min_max_scaled().expect("not empty"); assert_eq!(*min_scaled, *max_scaled); assert_eq!(*min_scaled, 200); let _cs: CollectionSet = collection.try_into().expect("should pass"); } #[test] #[should_panic] fn collection_from_collectionset_fail() -> () { use crate::collection::CollectionSet; let base_path = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let test_sigs = vec![PathBuf::from("../../tests/test-data/prot/all.zip")]; let full_paths: Vec = test_sigs .into_iter() .map(|sig| base_path.join(sig)) .collect(); let collection = Collection::from_zipfile(&full_paths[0]).unwrap(); let _cs: CollectionSet = collection.try_into().expect("should fail"); } #[test] #[cfg(all(feature = "branchwater", not(target_arch = "wasm32")))] fn collection_from_rocksdb_storage() -> Result<()> { use crate::index::revindex::{RevIndex, RevIndexOps}; use camino::Utf8PathBuf as PathBuf; use tempfile::TempDir; let basedir = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let mut zip_collection = basedir.clone(); zip_collection.push("../../tests/test-data/track_abund/track_abund.zip"); let outdir = TempDir::new()?; let zip_copy = PathBuf::from( outdir .path() .join("sigs.zip") .into_os_string() .into_string() .unwrap(), ); std::fs::copy(zip_collection, zip_copy.as_path())?; let selection = Selection::builder().ksize(31).scaled(10000).build(); let collection = Collection::from_zipfile(zip_copy.as_path())?.select(&selection)?; let output: PathBuf = outdir.path().join("index").try_into().unwrap(); // Step 1: create an index let index = RevIndex::create(output.as_path(), collection.clone().try_into()?, false)?; // Step 2: internalize the storage for the index { let mut index = index; index .internalize_storage() .expect("Error internalizing storage"); } // Step 3: Create a new collection from rocksdb let new_collection = Collection::from_rocksdb(output.as_path())?; // Step 4: assert all content is the same for (a, b) in collection.iter().zip(new_collection.iter()) { assert_eq!(a, b); } Ok(()) } } sourmash-bio-sourmash-bc3c0f8/src/core/src/encodings.rs000066400000000000000000000443501474157046300233210ustar00rootroot00000000000000use serde::{Deserialize, Serialize}; use std::collections::HashMap; use std::hash::{BuildHasher, BuildHasherDefault, Hash}; use std::str; use nohash_hasher::BuildNoHashHasher; use once_cell::sync::Lazy; use vec_collections::AbstractVecSet; use crate::Error; // To consider there: use a slab allocator for IdxTracker // https://twitter.com/tomaka17/status/1391052081272967170 // Pro-tip: you might be able to save a lot of hashmap lookups // if you replace a `HashMap` with a `HashMap` // and a `Slab`. This might be very useful if K is something // heavy such as a `String`. pub type Color = u64; pub type Idx = u32; type IdxTracker = (vec_collections::VecSet<[Idx; 8]>, u64); type ColorToIdx = HashMap>; #[derive(Debug, Clone, PartialEq, Eq, Hash)] #[cfg_attr( feature = "rkyv", derive(rkyv::Serialize, rkyv::Deserialize, rkyv::Archive) )] #[non_exhaustive] pub enum HashFunctions { Murmur64Dna, Murmur64Protein, Murmur64Dayhoff, Murmur64Hp, Murmur64Skipm1n3, Murmur64Skipm2n3, Custom(String), } impl HashFunctions { pub fn dna(&self) -> bool { *self == HashFunctions::Murmur64Dna } pub fn protein(&self) -> bool { *self == HashFunctions::Murmur64Protein } pub fn dayhoff(&self) -> bool { *self == HashFunctions::Murmur64Dayhoff } pub fn hp(&self) -> bool { *self == HashFunctions::Murmur64Hp } pub fn skipm1n3(&self) -> bool { *self == HashFunctions::Murmur64Skipm1n3 } pub fn skipm2n3(&self) -> bool { *self == HashFunctions::Murmur64Skipm2n3 } } impl std::fmt::Display for HashFunctions { fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { write!( f, "{}", match self { HashFunctions::Murmur64Dna => "DNA", HashFunctions::Murmur64Protein => "protein", HashFunctions::Murmur64Dayhoff => "dayhoff", HashFunctions::Murmur64Hp => "hp", HashFunctions::Murmur64Skipm1n3 => "skipm1n3", HashFunctions::Murmur64Skipm2n3 => "skipm2n3", HashFunctions::Custom(v) => v, } ) } } impl TryFrom<&str> for HashFunctions { type Error = Error; fn try_from(moltype: &str) -> Result { match moltype.to_lowercase().as_ref() { "dna" => Ok(HashFunctions::Murmur64Dna), "dayhoff" => Ok(HashFunctions::Murmur64Dayhoff), "hp" => Ok(HashFunctions::Murmur64Hp), "protein" => Ok(HashFunctions::Murmur64Protein), "skipm1n3" => Ok(HashFunctions::Murmur64Skipm1n3), "skipm2n3" => Ok(HashFunctions::Murmur64Skipm2n3), v => unimplemented!("{v}"), } } } const COMPLEMENT: [u8; 256] = { let mut lookup = [0; 256]; lookup[b'A' as usize] = b'T'; lookup[b'C' as usize] = b'G'; lookup[b'G' as usize] = b'C'; lookup[b'T' as usize] = b'A'; lookup[b'N' as usize] = b'N'; lookup }; #[inline] pub fn revcomp(seq: &[u8]) -> Vec { seq.iter() .rev() .map(|nt| COMPLEMENT[*nt as usize]) .collect() } static CODONTABLE: Lazy> = Lazy::new(|| { [ // F ("TTT", b'F'), ("TTC", b'F'), // L ("TTA", b'L'), ("TTG", b'L'), // S ("TCT", b'S'), ("TCC", b'S'), ("TCA", b'S'), ("TCG", b'S'), ("TCN", b'S'), // Y ("TAT", b'Y'), ("TAC", b'Y'), // * ("TAA", b'*'), ("TAG", b'*'), // * ("TGA", b'*'), // C ("TGT", b'C'), ("TGC", b'C'), // W ("TGG", b'W'), // L ("CTT", b'L'), ("CTC", b'L'), ("CTA", b'L'), ("CTG", b'L'), ("CTN", b'L'), // P ("CCT", b'P'), ("CCC", b'P'), ("CCA", b'P'), ("CCG", b'P'), ("CCN", b'P'), // H ("CAT", b'H'), ("CAC", b'H'), // Q ("CAA", b'Q'), ("CAG", b'Q'), // R ("CGT", b'R'), ("CGC", b'R'), ("CGA", b'R'), ("CGG", b'R'), ("CGN", b'R'), // I ("ATT", b'I'), ("ATC", b'I'), ("ATA", b'I'), // M ("ATG", b'M'), // T ("ACT", b'T'), ("ACC", b'T'), ("ACA", b'T'), ("ACG", b'T'), ("ACN", b'T'), // N ("AAT", b'N'), ("AAC", b'N'), // K ("AAA", b'K'), ("AAG", b'K'), // S ("AGT", b'S'), ("AGC", b'S'), // R ("AGA", b'R'), ("AGG", b'R'), // V ("GTT", b'V'), ("GTC", b'V'), ("GTA", b'V'), ("GTG", b'V'), ("GTN", b'V'), // A ("GCT", b'A'), ("GCC", b'A'), ("GCA", b'A'), ("GCG", b'A'), ("GCN", b'A'), // D ("GAT", b'D'), ("GAC", b'D'), // E ("GAA", b'E'), ("GAG", b'E'), // G ("GGT", b'G'), ("GGC", b'G'), ("GGA", b'G'), ("GGG", b'G'), ("GGN", b'G'), ] .iter() .cloned() .collect() }); // Dayhoff table from // Peris, P., López, D., & Campos, M. (2008). // IgTM: An algorithm to predict transmembrane domains and topology in // proteins. BMC Bioinformatics, 9(1), 1029–11. // http://doi.org/10.1186/1471-2105-9-367 // // Original source: // Dayhoff M. O., Schwartz R. M., Orcutt B. C. (1978). // A model of evolutionary change in proteins, // in Atlas of Protein Sequence and Structure, // ed Dayhoff M. O., editor. // (Washington, DC: National Biomedical Research Foundation; ), 345–352. // // | Amino acid | Property | Dayhoff | // |---------------|-----------------------|---------| // | C | Sulfur polymerization | a | // | A, G, P, S, T | Small | b | // | D, E, N, Q | Acid and amide | c | // | H, K, R | Basic | d | // | I, L, M, V | Hydrophobic | e | // | F, W, Y | Aromatic | f | static DAYHOFFTABLE: Lazy> = Lazy::new(|| { [ // a (b'C', b'a'), // b (b'A', b'b'), (b'G', b'b'), (b'P', b'b'), (b'S', b'b'), (b'T', b'b'), // c (b'D', b'c'), (b'E', b'c'), (b'N', b'c'), (b'Q', b'c'), // d (b'H', b'd'), (b'K', b'd'), (b'R', b'd'), // e (b'I', b'e'), (b'L', b'e'), (b'M', b'e'), (b'V', b'e'), // e (b'F', b'f'), (b'W', b'f'), (b'Y', b'f'), // stop aa (b'*', b'*'), ] .iter() .cloned() .collect() }); // HP Hydrophobic/hydrophilic mapping // From: Phillips, R., Kondev, J., Theriot, J. (2008). // Physical Biology of the Cell. New York: Garland Science, Taylor & Francis Group. ISBN: 978-0815341635 // // | Amino acid | HP // |---------------------------------------|---------| // | A, F, G, I, L, M, P, V, W, Y | h | // | N, C, S, T, D, E, R, H, K, Q | p | static HPTABLE: Lazy> = Lazy::new(|| { [ // h (b'A', b'h'), (b'F', b'h'), (b'G', b'h'), (b'I', b'h'), (b'L', b'h'), (b'M', b'h'), (b'P', b'h'), (b'V', b'h'), (b'W', b'h'), (b'Y', b'h'), // p (b'N', b'p'), (b'C', b'p'), (b'S', b'p'), (b'T', b'p'), (b'D', b'p'), (b'E', b'p'), (b'R', b'p'), (b'H', b'p'), (b'K', b'p'), (b'Q', b'p'), // stop aa (b'*', b'*'), ] .iter() .cloned() .collect() }); #[inline] pub fn translate_codon(codon: &[u8]) -> Result { if codon.len() == 1 { return Ok(b'X'); } if codon.len() == 2 { let mut v = codon.to_vec(); v.push(b'N'); match CODONTABLE.get(str::from_utf8(v.as_slice()).unwrap()) { Some(aa) => return Ok(*aa), None => return Ok(b'X'), } } if codon.len() == 3 { match CODONTABLE.get(str::from_utf8(codon).unwrap()) { Some(aa) => return Ok(*aa), None => return Ok(b'X'), } } Err(Error::InvalidCodonLength { message: format!("{}", codon.len()), }) } #[inline] pub fn aa_to_dayhoff(aa: u8) -> u8 { match DAYHOFFTABLE.get(&aa) { Some(letter) => *letter, None => b'X', } } pub fn aa_to_hp(aa: u8) -> u8 { match HPTABLE.get(&aa) { Some(letter) => *letter, None => b'X', } } #[inline] pub fn to_aa(seq: &[u8], dayhoff: bool, hp: bool) -> Result, Error> { let mut converted: Vec = Vec::with_capacity(seq.len() / 3); for chunk in seq.chunks(3) { if chunk.len() < 3 { break; } let residue = translate_codon(chunk)?; if dayhoff { converted.push(aa_to_dayhoff(residue)); } else if hp { converted.push(aa_to_hp(residue)); } else { converted.push(residue); } } Ok(converted) } pub const VALID: [bool; 256] = { let mut lookup = [false; 256]; lookup[b'A' as usize] = true; lookup[b'C' as usize] = true; lookup[b'G' as usize] = true; lookup[b'T' as usize] = true; lookup }; #[derive(Serialize, Deserialize, Default)] pub struct Colors { colors: ColorToIdx, } impl Colors { pub fn new() -> Colors { Default::default() } /// Given a color and a new idx, return an updated color /// /// This might create a new one, or find an already existing color /// that contains the new_idx /// /// Future optimization: store a count for each color, so we can track /// if there are extra colors that can be removed at the end. /// (the count is decreased whenever a new color has to be created) pub fn update<'a, I: IntoIterator>( &mut self, current_color: Option, new_idxs: I, ) -> Result { if let Some(color) = current_color { if let Some(idxs) = self.colors.get_mut(&color) { let idx_to_add: Vec<_> = new_idxs .into_iter() .filter(|new_idx| !idxs.0.contains(new_idx)) .collect(); if idx_to_add.is_empty() { // Easy case, it already has all the new_idxs, so just return this color idxs.1 += 1; Ok(color) } else { // We need to either create a new color, // or find an existing color that have the same idxs let mut idxs = idxs.clone(); idxs.0.extend(idx_to_add.into_iter().cloned()); let new_color = Colors::compute_color(&idxs); if new_color != color { self.colors.get_mut(&color).unwrap().1 -= 1; if self.colors[&color].1 == 0 { self.colors.remove(&color); }; }; self.colors .entry(new_color) .and_modify(|old_idxs| { assert_eq!(old_idxs.0, idxs.0); old_idxs.1 += 1; }) .or_insert_with(|| (idxs.0, 1)); Ok(new_color) } } else { unimplemented!("throw error, current_color must exist in order to be updated. current_color: {:?}, colors: {:#?}", current_color, &self.colors); } } else { let mut idxs = IdxTracker::default(); idxs.0.extend(new_idxs.into_iter().cloned()); idxs.1 = 1; let new_color = Colors::compute_color(&idxs); self.colors .entry(new_color) .and_modify(|old_idxs| { assert_eq!(old_idxs.0, idxs.0); old_idxs.1 += 1; }) .or_insert_with(|| (idxs.0, 1)); Ok(new_color) } } fn compute_color(idxs: &IdxTracker) -> Color { let s = BuildHasherDefault::::default(); s.hash_one(&idxs.0) } pub fn len(&self) -> usize { self.colors.len() } pub fn is_empty(&self) -> bool { self.colors.is_empty() } pub fn contains(&self, color: Color, idx: Idx) -> bool { if let Some(idxs) = self.colors.get(&color) { idxs.0.contains(&idx) } else { false } } pub fn indices(&self, color: &Color) -> Indices { // TODO: what if color is not present? Indices { iter: self.colors.get(color).unwrap().0.iter(), } } pub fn retain(&mut self, f: F) where F: FnMut(&Color, &mut IdxTracker) -> bool, { self.colors.retain(f) } } pub struct Indices<'a> { iter: vec_collections::VecSetIter>, } impl<'a> Iterator for Indices<'a> { type Item = &'a Idx; fn next(&mut self) -> Option { self.iter.next() } } #[cfg(test)] mod test { use super::*; use std::convert::TryFrom; #[test] fn colors_update() { let mut colors = Colors::new(); let color = colors.update(None, &[1_u32]).unwrap(); assert_eq!(colors.len(), 1); dbg!("update"); let new_color = colors.update(Some(color), &[1_u32]).unwrap(); assert_eq!(colors.len(), 1); assert_eq!(color, new_color); dbg!("upgrade"); let new_color = colors.update(Some(color), &[2_u32]).unwrap(); assert_eq!(colors.len(), 2); assert_ne!(color, new_color); } #[test] fn colors_retain() { let mut colors = Colors::new(); let color1 = colors.update(None, &[1_u32]).unwrap(); assert_eq!(colors.len(), 1); // used_colors: // color1: 1 dbg!("update"); let same_color = colors.update(Some(color1), &[1_u32]).unwrap(); assert_eq!(colors.len(), 1); assert_eq!(color1, same_color); // used_colors: // color1: 2 dbg!("upgrade"); let color2 = colors.update(Some(color1), &[2_u32]).unwrap(); assert_eq!(colors.len(), 2); assert_ne!(color1, color2); // used_colors: // color1: 1 // color2: 1 dbg!("update"); let same_color = colors.update(Some(color2), &[2_u32]).unwrap(); assert_eq!(colors.len(), 2); assert_eq!(color2, same_color); // used_colors: // color1: 1 // color1: 2 dbg!("upgrade"); let color3 = colors.update(Some(color1), &[3_u32]).unwrap(); assert_ne!(color1, color3); assert_ne!(color2, color3); // used_colors: // color1: 0 // color2: 2 // color3: 1 // This is the pre color-count tracker, where it is needed // to call retain to maintain colors //assert_eq!(colors.len(), 3); //colors.retain(|c, _| [color2, color3].contains(c)); assert_eq!(colors.len(), 2); } #[test] fn test_dna_method() { assert!(HashFunctions::Murmur64Dna.dna()); assert!(!HashFunctions::Murmur64Protein.dna()); assert!(!HashFunctions::Murmur64Dayhoff.dna()); } #[test] fn test_protein_method() { assert!(HashFunctions::Murmur64Protein.protein()); assert!(!HashFunctions::Murmur64Dna.protein()); assert!(!HashFunctions::Murmur64Dayhoff.protein()); } #[test] fn test_dayhoff_method() { assert!(HashFunctions::Murmur64Dayhoff.dayhoff()); assert!(!HashFunctions::Murmur64Dna.dayhoff()); assert!(!HashFunctions::Murmur64Protein.dayhoff()); } #[test] fn test_hp_method() { assert!(HashFunctions::Murmur64Hp.hp()); assert!(!HashFunctions::Murmur64Dna.hp()); assert!(!HashFunctions::Murmur64Protein.hp()); } #[test] fn test_skipm1n3_method() { assert!(HashFunctions::Murmur64Skipm1n3.skipm1n3()); assert!(!HashFunctions::Murmur64Dna.skipm1n3()); assert!(!HashFunctions::Murmur64Protein.skipm1n3()); } #[test] fn test_skipm2n3_method() { assert!(HashFunctions::Murmur64Skipm2n3.skipm2n3()); assert!(!HashFunctions::Murmur64Dna.skipm2n3()); assert!(!HashFunctions::Murmur64Protein.skipm2n3()); } #[test] fn test_display_hashfunctions() { assert_eq!(HashFunctions::Murmur64Dna.to_string(), "DNA"); assert_eq!(HashFunctions::Murmur64Protein.to_string(), "protein"); assert_eq!(HashFunctions::Murmur64Dayhoff.to_string(), "dayhoff"); assert_eq!(HashFunctions::Murmur64Hp.to_string(), "hp"); assert_eq!(HashFunctions::Murmur64Skipm1n3.to_string(), "skipm1n3"); assert_eq!(HashFunctions::Murmur64Skipm2n3.to_string(), "skipm2n3"); assert_eq!( HashFunctions::Custom("custom_string".into()).to_string(), "custom_string" ); } #[test] fn test_try_from_str_valid() { assert_eq!( HashFunctions::try_from("dna").unwrap(), HashFunctions::Murmur64Dna ); assert_eq!( HashFunctions::try_from("protein").unwrap(), HashFunctions::Murmur64Protein ); assert_eq!( HashFunctions::try_from("dayhoff").unwrap(), HashFunctions::Murmur64Dayhoff ); assert_eq!( HashFunctions::try_from("hp").unwrap(), HashFunctions::Murmur64Hp ); assert_eq!( HashFunctions::try_from("skipm1n3").unwrap(), HashFunctions::Murmur64Skipm1n3 ); assert_eq!( HashFunctions::try_from("skipm2n3").unwrap(), HashFunctions::Murmur64Skipm2n3 ); } #[test] #[should_panic(expected = "not implemented: unknown")] fn test_try_from_str_invalid() { HashFunctions::try_from("unknown").unwrap(); } } sourmash-bio-sourmash-bc3c0f8/src/core/src/errors.rs000066400000000000000000000163431474157046300226650ustar00rootroot00000000000000use thiserror::Error; #[derive(Debug, Error)] #[non_exhaustive] pub enum SourmashError { /// Raised for internal errors in the libraries. Should not happen. #[error("internal error: {message:?}")] Internal { message: String }, #[error("new scaled smaller than previous; cannot upsample")] CannotUpsampleScaled, #[error("must have same num: {n1} != {n2}")] MismatchNum { n1: u32, n2: u32 }, #[error("different ksizes cannot be compared")] MismatchKSizes, #[error("DNA/prot minhashes cannot be compared")] MismatchDNAProt, #[error("mismatch in scaled; comparison fail")] MismatchScaled, #[error("mismatch in seed; comparison fail")] MismatchSeed, #[error("different signatures cannot be compared")] MismatchSignatureType, #[error("sketch needs abundance for this operation")] NeedsAbundanceTracking, #[error("Expected a MinHash sketch in this signature")] NoMinHashFound, #[error("Empty signature")] EmptySignature, #[error("Multiple sketches found, expected one")] MultipleSketchesFound, #[error("Invalid hash function: {function:?}")] InvalidHashFunction { function: String }, #[error("Can only set {message:?} if the MinHash is empty")] NonEmptyMinHash { message: String }, #[error("invalid DNA character in input k-mer: {message}")] InvalidDNA { message: String }, #[error("invalid protein character in input: {message}")] InvalidProt { message: String }, #[error("Codon is invalid length: {message}")] InvalidCodonLength { message: String }, #[error("Skipmer ksize must be >= n ({n}), but got ksize: {ksize}")] InvalidSkipmerSize { ksize: usize, n: usize }, #[error("Skipmer frame number must be < n ({n}), but got start: {start}")] InvalidSkipmerFrame { start: usize, n: usize }, #[error("Frame number must be 0, 1, or 2, but got {frame_number}")] InvalidTranslateFrame { frame_number: usize }, #[error("Set error rate to a value smaller than 0.367696 and larger than 0.00203125")] HLLPrecisionBounds, #[error("error while calculating ANI confidence intervals: {message}")] ANIEstimationError { message: String }, #[error(transparent)] ReadDataError(#[from] ReadDataError), #[error(transparent)] StorageError(#[from] crate::storage::StorageError), #[error(transparent)] SerdeError(#[from] serde_json::error::Error), #[error(transparent)] NifflerError(#[from] niffler::Error), #[error(transparent)] Utf8Error(#[from] std::str::Utf8Error), #[error(transparent)] IOError(#[from] std::io::Error), #[error(transparent)] CsvError(#[from] csv::Error), #[cfg(not(all(target_arch = "wasm32", target_os = "unknown")))] #[error(transparent)] Panic(#[from] crate::ffi::utils::Panic), #[cfg(not(target_arch = "wasm32"))] #[cfg(feature = "branchwater")] #[error(transparent)] RocksDBError(#[from] rocksdb::Error), #[error(transparent)] ZipError(#[from] piz::result::ZipError), } #[derive(Debug, Error)] pub enum ReadDataError { #[error("Could not load data")] LoadError, } #[repr(u32)] pub enum SourmashErrorCode { // no error NoError = 0, // panics and internals Panic = 1, Internal = 2, Msg = 3, Unknown = 4, // Compatibility errors MismatchKSizes = 1_01, MismatchDNAProt = 1_02, MismatchScaled = 1_03, MismatchSeed = 1_04, MismatchSignatureType = 1_05, NonEmptyMinHash = 1_06, MismatchNum = 1_07, NeedsAbundanceTracking = 1_08, CannotUpsampleScaled = 1_09, NoMinHashFound = 1_10, EmptySignature = 1_11, MultipleSketchesFound = 1_12, // Input sequence errors InvalidDNA = 11_01, InvalidProt = 11_02, InvalidCodonLength = 11_03, InvalidHashFunction = 11_04, InvalidSkipmerFrame = 11_05, InvalidSkipmerSize = 11_06, InvalidTranslateFrame = 11_07, // index-related errors ReadData = 12_01, Storage = 12_02, // HLL errors HLLPrecisionBounds = 13_01, // ANI errors ANIEstimationError = 14_01, // external errors Io = 100_001, Utf8Error = 100_002, ParseInt = 100_003, SerdeError = 100_004, NifflerError = 100_005, CsvError = 100_006, RocksDBError = 100_007, ZipError = 100_008, } #[cfg(not(all(target_arch = "wasm32", target_os = "unknown")))] impl SourmashErrorCode { pub fn from_error(error: &SourmashError) -> SourmashErrorCode { match error { SourmashError::Internal { .. } => SourmashErrorCode::Internal, SourmashError::Panic { .. } => SourmashErrorCode::Panic, SourmashError::CannotUpsampleScaled { .. } => SourmashErrorCode::CannotUpsampleScaled, SourmashError::MismatchNum { .. } => SourmashErrorCode::MismatchNum, SourmashError::NeedsAbundanceTracking { .. } => { SourmashErrorCode::NeedsAbundanceTracking } SourmashError::MismatchKSizes => SourmashErrorCode::MismatchKSizes, SourmashError::MismatchDNAProt => SourmashErrorCode::MismatchDNAProt, SourmashError::MismatchScaled => SourmashErrorCode::MismatchScaled, SourmashError::MismatchSeed => SourmashErrorCode::MismatchSeed, SourmashError::MismatchSignatureType => SourmashErrorCode::MismatchSignatureType, SourmashError::NonEmptyMinHash { .. } => SourmashErrorCode::NonEmptyMinHash, SourmashError::NoMinHashFound => SourmashErrorCode::NoMinHashFound, SourmashError::EmptySignature => SourmashErrorCode::EmptySignature, SourmashError::MultipleSketchesFound => SourmashErrorCode::MultipleSketchesFound, SourmashError::InvalidDNA { .. } => SourmashErrorCode::InvalidDNA, SourmashError::InvalidProt { .. } => SourmashErrorCode::InvalidProt, SourmashError::InvalidCodonLength { .. } => SourmashErrorCode::InvalidCodonLength, SourmashError::InvalidHashFunction { .. } => SourmashErrorCode::InvalidHashFunction, SourmashError::InvalidSkipmerFrame { .. } => SourmashErrorCode::InvalidSkipmerFrame, SourmashError::InvalidSkipmerSize { .. } => SourmashErrorCode::InvalidSkipmerSize, SourmashError::InvalidTranslateFrame { .. } => SourmashErrorCode::InvalidTranslateFrame, SourmashError::ReadDataError { .. } => SourmashErrorCode::ReadData, SourmashError::StorageError { .. } => SourmashErrorCode::Storage, SourmashError::HLLPrecisionBounds { .. } => SourmashErrorCode::HLLPrecisionBounds, SourmashError::ANIEstimationError { .. } => SourmashErrorCode::ANIEstimationError, SourmashError::SerdeError { .. } => SourmashErrorCode::SerdeError, SourmashError::IOError { .. } => SourmashErrorCode::Io, SourmashError::NifflerError { .. } => SourmashErrorCode::NifflerError, SourmashError::Utf8Error { .. } => SourmashErrorCode::Utf8Error, SourmashError::CsvError { .. } => SourmashErrorCode::CsvError, #[cfg(not(target_arch = "wasm32"))] #[cfg(feature = "branchwater")] SourmashError::RocksDBError { .. } => SourmashErrorCode::RocksDBError, SourmashError::ZipError { .. } => SourmashErrorCode::ZipError, } } } sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/000077500000000000000000000000001474157046300215405ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/cmd/000077500000000000000000000000001474157046300223035ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/cmd/compute.rs000066400000000000000000000110111474157046300243170ustar00rootroot00000000000000use std::slice; use crate::cmd::ComputeParameters; use crate::ScaledType; use crate::ffi::utils::ForeignObject; pub struct SourmashComputeParameters; impl ForeignObject for SourmashComputeParameters { type RustObject = ComputeParameters; } #[no_mangle] pub unsafe extern "C" fn computeparams_new() -> *mut SourmashComputeParameters { SourmashComputeParameters::from_rust(ComputeParameters::default()) } #[no_mangle] pub unsafe extern "C" fn computeparams_free(ptr: *mut SourmashComputeParameters) { SourmashComputeParameters::drop(ptr); } #[no_mangle] pub unsafe extern "C" fn computeparams_seed(ptr: *const SourmashComputeParameters) -> u64 { let cp = SourmashComputeParameters::as_rust(ptr); cp.seed() } #[no_mangle] pub unsafe extern "C" fn computeparams_set_seed( ptr: *mut SourmashComputeParameters, new_seed: u64, ) { let cp = SourmashComputeParameters::as_rust_mut(ptr); cp.set_seed(new_seed); } ffi_fn! { unsafe fn computeparams_ksizes(ptr: *const SourmashComputeParameters, size: *mut usize) -> Result<*const u32> { let cp = SourmashComputeParameters::as_rust(ptr); let output = cp.ksizes().clone(); *size = output.len(); // FIXME use a SourmashSlice_u32? Ok(Box::into_raw(output.into_boxed_slice()) as *const u32) } } #[no_mangle] pub unsafe extern "C" fn computeparams_ksizes_free(ptr: *mut u32, insize: usize) { // FIXME use a SourmashSlice_u32? if ptr.is_null() { return; } Vec::from_raw_parts(ptr, insize, insize); } ffi_fn! { unsafe fn computeparams_set_ksizes( ptr: *mut SourmashComputeParameters, ksizes_ptr: *const u32, insize: usize, ) -> Result<()> { let cp = SourmashComputeParameters::as_rust_mut(ptr); let ksizes = { assert!(!ksizes_ptr.is_null()); slice::from_raw_parts(ksizes_ptr, insize) }; cp.set_ksizes(ksizes.into()); Ok(()) } } #[no_mangle] pub unsafe extern "C" fn computeparams_protein(ptr: *const SourmashComputeParameters) -> bool { let cp = SourmashComputeParameters::as_rust(ptr); cp.protein() } #[no_mangle] pub unsafe extern "C" fn computeparams_set_protein(ptr: *mut SourmashComputeParameters, v: bool) { let cp = SourmashComputeParameters::as_rust_mut(ptr); cp.set_protein(v); } #[no_mangle] pub unsafe extern "C" fn computeparams_dayhoff(ptr: *const SourmashComputeParameters) -> bool { let cp = SourmashComputeParameters::as_rust(ptr); cp.dayhoff() } #[no_mangle] pub unsafe extern "C" fn computeparams_set_dayhoff(ptr: *mut SourmashComputeParameters, v: bool) { let cp = SourmashComputeParameters::as_rust_mut(ptr); cp.set_dayhoff(v); } #[no_mangle] pub unsafe extern "C" fn computeparams_hp(ptr: *const SourmashComputeParameters) -> bool { let cp = SourmashComputeParameters::as_rust(ptr); cp.hp() } #[no_mangle] pub unsafe extern "C" fn computeparams_set_hp(ptr: *mut SourmashComputeParameters, v: bool) { let cp = SourmashComputeParameters::as_rust_mut(ptr); cp.set_hp(v); } #[no_mangle] pub unsafe extern "C" fn computeparams_dna(ptr: *const SourmashComputeParameters) -> bool { let cp = SourmashComputeParameters::as_rust(ptr); cp.dna() } #[no_mangle] pub unsafe extern "C" fn computeparams_set_dna(ptr: *mut SourmashComputeParameters, v: bool) { let cp = SourmashComputeParameters::as_rust_mut(ptr); cp.set_dna(v); } #[no_mangle] pub unsafe extern "C" fn computeparams_track_abundance( ptr: *const SourmashComputeParameters, ) -> bool { let cp = SourmashComputeParameters::as_rust(ptr); cp.track_abundance() } #[no_mangle] pub unsafe extern "C" fn computeparams_set_track_abundance( ptr: *mut SourmashComputeParameters, v: bool, ) { let cp = SourmashComputeParameters::as_rust_mut(ptr); cp.set_track_abundance(v); } #[no_mangle] pub unsafe extern "C" fn computeparams_num_hashes(ptr: *const SourmashComputeParameters) -> u32 { let cp = SourmashComputeParameters::as_rust(ptr); cp.num_hashes() } #[no_mangle] pub unsafe extern "C" fn computeparams_set_num_hashes( ptr: *mut SourmashComputeParameters, num: u32, ) { let cp = SourmashComputeParameters::as_rust_mut(ptr); cp.set_num_hashes(num); } #[no_mangle] pub unsafe extern "C" fn computeparams_scaled(ptr: *const SourmashComputeParameters) -> ScaledType { let cp = SourmashComputeParameters::as_rust(ptr); cp.scaled() } #[no_mangle] pub unsafe extern "C" fn computeparams_set_scaled( ptr: *mut SourmashComputeParameters, scaled: u32, ) { let cp = SourmashComputeParameters::as_rust_mut(ptr); cp.set_scaled(scaled); } sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/cmd/mod.rs000066400000000000000000000000211474157046300234210ustar00rootroot00000000000000pub mod compute; sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/hyperloglog.rs000066400000000000000000000126011474157046300244410ustar00rootroot00000000000000use std::ffi::CStr; use std::os::raw::c_char; use std::slice; use crate::prelude::*; use crate::signature::SigsTrait; use crate::sketch::hyperloglog::HyperLogLog; use crate::ffi::minhash::SourmashKmerMinHash; use crate::ffi::utils::ForeignObject; pub struct SourmashHyperLogLog; impl ForeignObject for SourmashHyperLogLog { type RustObject = HyperLogLog; } #[no_mangle] pub unsafe extern "C" fn hll_new() -> *mut SourmashHyperLogLog { SourmashHyperLogLog::from_rust(HyperLogLog::default()) } #[no_mangle] pub unsafe extern "C" fn hll_free(ptr: *mut SourmashHyperLogLog) { SourmashHyperLogLog::drop(ptr); } ffi_fn! { unsafe fn hll_with_error_rate( error_rate: f64, ksize: usize, ) -> Result<*mut SourmashHyperLogLog> { let hll = HyperLogLog::with_error_rate(error_rate, ksize)?; Ok(SourmashHyperLogLog::from_rust(hll)) } } #[no_mangle] pub unsafe extern "C" fn hll_ksize(ptr: *const SourmashHyperLogLog) -> usize { SourmashHyperLogLog::as_rust(ptr).ksize() } #[no_mangle] pub unsafe extern "C" fn hll_cardinality(ptr: *const SourmashHyperLogLog) -> usize { SourmashHyperLogLog::as_rust(ptr).cardinality() } #[no_mangle] pub unsafe extern "C" fn hll_similarity( ptr: *const SourmashHyperLogLog, optr: *const SourmashHyperLogLog, ) -> f64 { SourmashHyperLogLog::as_rust(ptr).similarity(SourmashHyperLogLog::as_rust(optr)) } #[no_mangle] pub unsafe extern "C" fn hll_containment( ptr: *const SourmashHyperLogLog, optr: *const SourmashHyperLogLog, ) -> f64 { SourmashHyperLogLog::as_rust(ptr).containment(SourmashHyperLogLog::as_rust(optr)) } #[no_mangle] pub unsafe extern "C" fn hll_intersection_size( ptr: *const SourmashHyperLogLog, optr: *const SourmashHyperLogLog, ) -> usize { SourmashHyperLogLog::as_rust(ptr).intersection(SourmashHyperLogLog::as_rust(optr)) } ffi_fn! { unsafe fn hll_add_sequence( ptr: *mut SourmashHyperLogLog, sequence: *const c_char, insize: usize, force: bool ) -> Result<()> { let hll = SourmashHyperLogLog::as_rust_mut(ptr); let buf = { assert!(!ptr.is_null()); slice::from_raw_parts(sequence as *mut u8, insize) }; hll.add_sequence(buf, force) } } #[no_mangle] pub unsafe extern "C" fn hll_add_hash(ptr: *mut SourmashHyperLogLog, hash: u64) { let hll = SourmashHyperLogLog::as_rust_mut(ptr); hll.add_hash(hash); } ffi_fn! { unsafe fn hll_merge( ptr: *mut SourmashHyperLogLog, optr: *const SourmashHyperLogLog, ) { let hll = SourmashHyperLogLog::as_rust_mut(ptr); let ohll = SourmashHyperLogLog::as_rust(optr); // FIXME raise an exception properly hll.merge(ohll)?; } } ffi_fn! { unsafe fn hll_update_mh( ptr: *mut SourmashHyperLogLog, optr: *const SourmashKmerMinHash, ) { let hll = SourmashHyperLogLog::as_rust_mut(ptr); let mh = SourmashKmerMinHash::as_rust(optr); mh.update(hll)? } } #[no_mangle] pub unsafe extern "C" fn hll_matches( ptr: *const SourmashHyperLogLog, mh_ptr: *const SourmashKmerMinHash, ) -> usize { let hll = SourmashHyperLogLog::as_rust(ptr); let mh_hll = SourmashKmerMinHash::as_rust(mh_ptr).as_hll(); hll.intersection(&mh_hll) } ffi_fn! { unsafe fn hll_from_path(filename: *const c_char) -> Result<*mut SourmashHyperLogLog> { // FIXME use buffer + len instead of c_str let c_str = { assert!(!filename.is_null()); CStr::from_ptr(filename) }; let (mut input, _) = niffler::from_path(c_str.to_str()?)?; let hll = HyperLogLog::from_reader(&mut input)?; Ok(SourmashHyperLogLog::from_rust(hll)) } } ffi_fn! { unsafe fn hll_from_buffer(ptr: *const c_char, insize: usize) -> Result<*mut SourmashHyperLogLog> { // FIXME use SourmashSlice_u8? let buf = { assert!(!ptr.is_null()); slice::from_raw_parts(ptr as *mut u8, insize) }; let hll = HyperLogLog::from_reader(buf)?; Ok(SourmashHyperLogLog::from_rust(hll)) } } ffi_fn! { unsafe fn hll_save(ptr: *const SourmashHyperLogLog, filename: *const c_char) -> Result<()> { let hll = SourmashHyperLogLog::as_rust(ptr); // FIXME use buffer + len instead of c_str let c_str = { assert!(!filename.is_null()); CStr::from_ptr(filename) }; hll.save(c_str.to_str()?)?; Ok(()) } } ffi_fn! { unsafe fn hll_to_buffer(ptr: *const SourmashHyperLogLog, size: *mut usize) -> Result<*const u8> { let hll = SourmashHyperLogLog::as_rust(ptr); // TODO: remove this let compression = 1; let mut buffer = vec![]; { let mut writer = if compression > 0 { let level = match compression { 1 => niffler::compression::Level::One, 2 => niffler::compression::Level::Two, 3 => niffler::compression::Level::Three, 4 => niffler::compression::Level::Four, 5 => niffler::compression::Level::Five, 6 => niffler::compression::Level::Six, 7 => niffler::compression::Level::Seven, 8 => niffler::compression::Level::Eight, _ => niffler::compression::Level::Nine, }; niffler::get_writer(Box::new(&mut buffer), niffler::compression::Format::Gzip, level)? } else { Box::new(&mut buffer) }; hll.save_to_writer(&mut writer)?; } let b = buffer.into_boxed_slice(); *size = b.len(); // FIXME use SourmashSlice_u8? Ok(Box::into_raw(b) as *const u8) } } sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/index/000077500000000000000000000000001474157046300226475ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/index/mod.rs000066400000000000000000000020761474157046300240010ustar00rootroot00000000000000#[cfg(not(target_arch = "wasm32"))] #[cfg(feature = "branchwater")] pub mod revindex; use crate::signature::Signature; use crate::ffi::signature::SourmashSignature; use crate::ffi::utils::{ForeignObject, SourmashStr}; pub struct SourmashSearchResult; impl ForeignObject for SourmashSearchResult { type RustObject = (f64, Signature, String); } #[no_mangle] pub unsafe extern "C" fn searchresult_free(ptr: *mut SourmashSearchResult) { SourmashSearchResult::drop(ptr); } #[no_mangle] pub unsafe extern "C" fn searchresult_score(ptr: *const SourmashSearchResult) -> f64 { let result = SourmashSearchResult::as_rust(ptr); result.0 } #[no_mangle] pub unsafe extern "C" fn searchresult_filename(ptr: *const SourmashSearchResult) -> SourmashStr { let result = SourmashSearchResult::as_rust(ptr); (result.2).clone().into() } #[no_mangle] pub unsafe extern "C" fn searchresult_signature( ptr: *const SourmashSearchResult, ) -> *mut SourmashSignature { let result = SourmashSearchResult::as_rust(ptr); SourmashSignature::from_rust((result.1).clone()) } sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/index/revindex.rs000066400000000000000000000172171474157046300250510ustar00rootroot00000000000000use std::slice; use camino::Utf8PathBuf as PathBuf; use crate::ffi::index::SourmashSearchResult; use crate::ffi::minhash::SourmashKmerMinHash; use crate::ffi::signature::SourmashSignature; use crate::ffi::utils::{ForeignObject, SourmashStr}; use crate::index::revindex::mem_revindex::RevIndex; use crate::index::Index; use crate::prelude::*; use crate::signature::{Signature, SigsTrait}; use crate::sketch::minhash::KmerMinHash; use crate::sketch::Sketch; use crate::ScaledType; pub struct SourmashRevIndex; impl ForeignObject for SourmashRevIndex { type RustObject = RevIndex; } // TODO: remove this when it is possible to pass Selection thru the FFI fn from_template(template: &Sketch) -> Selection { let (num, scaled) = match template { Sketch::MinHash(mh) => (mh.num(), mh.scaled()), Sketch::LargeMinHash(mh) => (mh.num(), mh.scaled()), _ => unimplemented!(), }; Selection::builder() .ksize(template.ksize() as u32) .num(num) .scaled(scaled) .build() } ffi_fn! { unsafe fn revindex_new_with_paths( search_sigs_ptr: *const *const SourmashStr, insigs: usize, template_ptr: *const SourmashKmerMinHash, threshold: usize, queries_ptr: *const *const SourmashKmerMinHash, inqueries: usize, keep_sigs: bool, ) -> Result<*mut SourmashRevIndex> { let search_sigs: Vec = { assert!(!search_sigs_ptr.is_null()); slice::from_raw_parts(search_sigs_ptr, insigs) .iter() .map(|path| { let mut new_path = PathBuf::new(); new_path.push(SourmashStr::as_rust(*path).as_str()); new_path }) .collect() }; let template = { assert!(!template_ptr.is_null()); //TODO: avoid clone here Sketch::MinHash(SourmashKmerMinHash::as_rust(template_ptr).clone()) }; let queries_vec: Vec; let queries: Option<&[KmerMinHash]> = if queries_ptr.is_null() { None } else { queries_vec = slice::from_raw_parts(queries_ptr, inqueries) .iter() .map(|mh_ptr| // TODO: avoid this clone SourmashKmerMinHash::as_rust(*mh_ptr).clone()) .collect(); Some(queries_vec.as_ref()) }; let selection = from_template(&template); let revindex = RevIndex::new( search_sigs.as_ref(), &selection, threshold, queries, keep_sigs, )?; Ok(SourmashRevIndex::from_rust(revindex)) } } ffi_fn! { unsafe fn revindex_new_with_sigs( search_sigs_ptr: *const *const SourmashSignature, insigs: usize, template_ptr: *const SourmashKmerMinHash, threshold: usize, queries_ptr: *const *const SourmashKmerMinHash, inqueries: usize, ) -> Result<*mut SourmashRevIndex> { let search_sigs: Vec = { assert!(!search_sigs_ptr.is_null()); slice::from_raw_parts(search_sigs_ptr, insigs) .iter() .map(|sig| SourmashSignature::as_rust(*sig)) .cloned() .collect() }; let template = { assert!(!template_ptr.is_null()); //TODO: avoid clone here Sketch::MinHash(SourmashKmerMinHash::as_rust(template_ptr).clone()) }; let queries_vec: Vec; let queries: Option<&[KmerMinHash]> = if queries_ptr.is_null() { None } else { queries_vec = slice::from_raw_parts(queries_ptr, inqueries) .iter() .map(|mh_ptr| // TODO: avoid this clone SourmashKmerMinHash::as_rust(*mh_ptr).clone()) .collect(); Some(queries_vec.as_ref()) }; let selection = from_template(&template); let revindex = RevIndex::new_with_sigs(search_sigs, &selection, threshold, queries)?; Ok(SourmashRevIndex::from_rust(revindex)) } } #[no_mangle] pub unsafe extern "C" fn revindex_free(ptr: *mut SourmashRevIndex) { SourmashRevIndex::drop(ptr); } ffi_fn! { unsafe fn revindex_search( ptr: *const SourmashRevIndex, sig_ptr: *const SourmashSignature, threshold: f64, do_containment: bool, _ignore_abundance: bool, size: *mut usize, ) -> Result<*const *const SourmashSearchResult> { let revindex = SourmashRevIndex::as_rust(ptr); let sig = SourmashSignature::as_rust(sig_ptr); if sig.signatures.is_empty() { *size = 0; return Ok(std::ptr::null::<*const SourmashSearchResult>()); } let mh = if let Sketch::MinHash(mh) = &sig.signatures[0] { mh } else { // TODO: what if it is not a mh? unimplemented!() }; let results: Vec<(f64, Signature, String)> = revindex .find_signatures(mh, threshold, do_containment, true)? .into_iter() .collect(); // FIXME: use the ForeignObject trait, maybe define new method there... let ptr_sigs: Vec<*const SourmashSearchResult> = results .into_iter() .map(|x| Box::into_raw(Box::new(x)) as *const SourmashSearchResult) .collect(); let b = ptr_sigs.into_boxed_slice(); *size = b.len(); Ok(Box::into_raw(b) as *const *const SourmashSearchResult) } } ffi_fn! { unsafe fn revindex_gather( ptr: *const SourmashRevIndex, sig_ptr: *const SourmashSignature, threshold: f64, _do_containment: bool, _ignore_abundance: bool, size: *mut usize, ) -> Result<*const *const SourmashSearchResult> { let revindex = SourmashRevIndex::as_rust(ptr); let sig = SourmashSignature::as_rust(sig_ptr); if sig.signatures.is_empty() { *size = 0; return Ok(std::ptr::null::<*const SourmashSearchResult>()); } let mh = if let Sketch::MinHash(mh) = &sig.signatures[0] { mh } else { // TODO: what if it is not a mh? unimplemented!() }; // TODO: proper threshold calculation let threshold: usize = (threshold * (mh.size() as f64)) as _; let counter = revindex.counter_for_query(mh); dbg!(&counter); let results: Vec<(f64, Signature, String)> = revindex .gather(counter, threshold, mh) .unwrap() // TODO: proper error handling .into_iter() .map(|r| { let filename = r.filename().to_owned(); let sig = r.get_match(); (r.f_match(), sig, filename) }) .collect(); // FIXME: use the ForeignObject trait, maybe define new method there... let ptr_sigs: Vec<*const SourmashSearchResult> = results .into_iter() .map(|x| Box::into_raw(Box::new(x)) as *const SourmashSearchResult) .collect(); let b = ptr_sigs.into_boxed_slice(); *size = b.len(); Ok(Box::into_raw(b) as *const *const SourmashSearchResult) } } #[no_mangle] pub unsafe extern "C" fn revindex_scaled(ptr: *const SourmashRevIndex) -> ScaledType { let revindex = SourmashRevIndex::as_rust(ptr); if let Sketch::MinHash(mh) = revindex.template() { mh.scaled() } else { unimplemented!() } } #[no_mangle] pub unsafe extern "C" fn revindex_len(ptr: *const SourmashRevIndex) -> u64 { let revindex = SourmashRevIndex::as_rust(ptr); revindex.len() as u64 } ffi_fn! { unsafe fn revindex_signatures( ptr: *const SourmashRevIndex, size: *mut usize, ) -> Result<*mut *mut SourmashSignature> { let revindex = SourmashRevIndex::as_rust(ptr); let sigs = revindex.signatures(); // FIXME: use the ForeignObject trait, maybe define new method there... let ptr_sigs: Vec<*mut SourmashSignature> = sigs .into_iter() .map(|x| Box::into_raw(Box::new(x)) as *mut SourmashSignature) .collect(); let b = ptr_sigs.into_boxed_slice(); *size = b.len(); Ok(Box::into_raw(b) as *mut *mut SourmashSignature) } } sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/minhash.rs000066400000000000000000000320301474157046300235330ustar00rootroot00000000000000use std::ffi::CStr; use std::os::raw::c_char; use std::slice; use crate::encodings::{aa_to_dayhoff, aa_to_hp, translate_codon}; use crate::ffi::utils::{ForeignObject, SourmashStr}; use crate::ffi::HashFunctions; use crate::signature::SeqToHashes; use crate::signature::SigsTrait; use crate::sketch::minhash::KmerMinHash; pub struct SourmashKmerMinHash; impl ForeignObject for SourmashKmerMinHash { type RustObject = KmerMinHash; } #[no_mangle] pub unsafe extern "C" fn kmerminhash_new( scaled: u32, k: u32, hash_function: HashFunctions, seed: u64, track_abundance: bool, n: u32, ) -> *mut SourmashKmerMinHash { let mh = KmerMinHash::new(scaled, k, hash_function.into(), seed, track_abundance, n); SourmashKmerMinHash::from_rust(mh) } #[no_mangle] pub unsafe extern "C" fn kmerminhash_free(ptr: *mut SourmashKmerMinHash) { SourmashKmerMinHash::drop(ptr); } #[no_mangle] pub unsafe extern "C" fn kmerminhash_slice_free(ptr: *mut u64, insize: usize) { // FIXME if ptr.is_null() { return; } Vec::from_raw_parts(ptr, insize, insize); } ffi_fn! { unsafe fn kmerminhash_add_sequence(ptr: *mut SourmashKmerMinHash, sequence: *const c_char, force: bool) -> Result<()> { let mh = SourmashKmerMinHash::as_rust_mut(ptr); // FIXME: take buffer and len instead of c_char let c_str = { assert!(!sequence.is_null()); CStr::from_ptr(sequence) }; mh.add_sequence(c_str.to_bytes(), force) } } ffi_fn! { unsafe fn kmerminhash_seq_to_hashes(ptr: *mut SourmashKmerMinHash, sequence: *const c_char, insize: usize, force: bool, bad_kmers_as_zeroes: bool, is_protein: bool, size: *mut usize) -> Result<*const u64> { let mh = SourmashKmerMinHash::as_rust_mut(ptr); let buf = { assert!(!ptr.is_null()); slice::from_raw_parts(sequence as *const u8, insize) }; let mut output: Vec = Vec::with_capacity(insize); // Call SeqToHashes::new and handle errors let ready_hashes = SeqToHashes::new( buf, mh.ksize(), force, is_protein, mh.hash_function(), mh.seed(), )?; if force && bad_kmers_as_zeroes{ for hash_value in ready_hashes{ match hash_value{ Ok(x) => output.push(x), Err(err) => return Err(err), } } }else{ for hash_value in ready_hashes { match hash_value{ Ok(0) => continue, Ok(x) => output.push(x), Err(err) => return Err(err), } } } *size = output.len(); // FIXME: make a SourmashSlice_u64 type? Ok(Box::into_raw(output.into_boxed_slice()) as *const u64) } } ffi_fn! { unsafe fn kmerminhash_add_protein(ptr: *mut SourmashKmerMinHash, sequence: *const c_char) -> Result<()> { let mh = SourmashKmerMinHash::as_rust_mut(ptr); // FIXME: take buffer and len instead of c_char let c_str = { assert!(!sequence.is_null()); CStr::from_ptr(sequence) }; mh.add_protein(c_str.to_bytes()) } } #[no_mangle] pub unsafe extern "C" fn kmerminhash_clear(ptr: *mut SourmashKmerMinHash) { let mh = SourmashKmerMinHash::as_rust_mut(ptr); mh.clear(); } #[no_mangle] pub unsafe extern "C" fn kmerminhash_add_hash(ptr: *mut SourmashKmerMinHash, h: u64) { let mh = SourmashKmerMinHash::as_rust_mut(ptr); mh.add_hash(h); } #[no_mangle] pub unsafe extern "C" fn kmerminhash_add_hash_with_abundance( ptr: *mut SourmashKmerMinHash, h: u64, abundance: u64, ) { let mh = SourmashKmerMinHash::as_rust_mut(ptr); mh.add_hash_with_abundance(h, abundance); } #[no_mangle] pub unsafe extern "C" fn kmerminhash_add_word(ptr: *mut SourmashKmerMinHash, word: *const c_char) { let mh = SourmashKmerMinHash::as_rust_mut(ptr); // FIXME: take buffer and len instead of c_char let c_str = { assert!(!word.is_null()); CStr::from_ptr(word) }; mh.add_word(c_str.to_bytes()); } ffi_fn! { unsafe fn sourmash_translate_codon(codon: *const c_char) -> Result { // FIXME: take buffer and len instead of c_char let c_str = { assert!(!codon.is_null()); CStr::from_ptr(codon) }; Ok(translate_codon(c_str.to_bytes())? as c_char) } } #[no_mangle] pub unsafe extern "C" fn sourmash_aa_to_dayhoff(aa: c_char) -> c_char { aa_to_dayhoff(aa as u8) as c_char } #[no_mangle] pub unsafe extern "C" fn sourmash_aa_to_hp(aa: c_char) -> c_char { aa_to_hp(aa as u8) as c_char } #[no_mangle] pub unsafe extern "C" fn kmerminhash_remove_hash(ptr: *mut SourmashKmerMinHash, h: u64) { let mh = SourmashKmerMinHash::as_rust_mut(ptr); mh.remove_hash(h); } #[no_mangle] pub unsafe extern "C" fn kmerminhash_remove_many( ptr: *mut SourmashKmerMinHash, hashes_ptr: *const u64, insize: usize, ) { let mh = SourmashKmerMinHash::as_rust_mut(ptr); // FIXME: make a SourmashSlice_u64 type? let hashes = { assert!(!hashes_ptr.is_null()); slice::from_raw_parts(hashes_ptr as *mut u64, insize) }; // FIXME: proper exception here mh.remove_many(hashes.iter().copied()) .expect("Hash removal error"); } ffi_fn! { unsafe fn kmerminhash_get_mins(ptr: *const SourmashKmerMinHash, size: *mut usize) -> Result<*const u64> { let mh = SourmashKmerMinHash::as_rust(ptr); let output = mh.mins(); *size = output.len(); // FIXME: make a SourmashSlice_u64 type? Ok(Box::into_raw(output.into_boxed_slice()) as *const u64) } } ffi_fn! { unsafe fn kmerminhash_md5sum(ptr: *const SourmashKmerMinHash) -> Result { let mh = SourmashKmerMinHash::as_rust(ptr); let output = mh.md5sum(); Ok(output.into()) } } ffi_fn! { unsafe fn kmerminhash_add_many( ptr: *mut SourmashKmerMinHash, hashes_ptr: *const u64, insize: usize, ) -> Result<()> { let mh = SourmashKmerMinHash::as_rust_mut(ptr); // FIXME: make a SourmashSlice_u64 type? let hashes = { assert!(!hashes_ptr.is_null()); slice::from_raw_parts(hashes_ptr, insize) }; for hash in hashes { mh.add_hash(*hash); } Ok(()) } } ffi_fn! { unsafe fn kmerminhash_get_abunds(ptr: *mut SourmashKmerMinHash, size: *mut usize) -> Result<*const u64> { let mh = SourmashKmerMinHash::as_rust(ptr); if let Some(abunds) = mh.abunds() { *size = abunds.len(); Ok(Box::into_raw(abunds.into_boxed_slice()) as *const u64) } else { //throw error, can't get abund unimplemented!() } } } #[no_mangle] pub unsafe extern "C" fn kmerminhash_get_mins_size(ptr: *const SourmashKmerMinHash) -> usize { let mh = SourmashKmerMinHash::as_rust(ptr); mh.size() } ffi_fn! { unsafe fn kmerminhash_set_abundances( ptr: *mut SourmashKmerMinHash, hashes_ptr: *const u64, abunds_ptr: *const u64, insize: usize, clear: bool, ) -> Result<()> { let mh = SourmashKmerMinHash::as_rust_mut(ptr); // FIXME: make a SourmashSlice_u64 type? let hashes = { assert!(!hashes_ptr.is_null()); slice::from_raw_parts(hashes_ptr, insize) }; // FIXME: make a SourmashSlice_u64 type? let abunds = { assert!(!abunds_ptr.is_null()); slice::from_raw_parts(abunds_ptr, insize) }; let mut pairs: Vec<_> = hashes.iter().cloned().zip(abunds.iter().cloned()).collect(); pairs.sort_unstable(); // Reset the minhash if clear { mh.clear(); } mh.add_many_with_abund(&pairs)?; Ok(()) } } #[no_mangle] pub unsafe extern "C" fn kmerminhash_is_protein(ptr: *const SourmashKmerMinHash) -> bool { let mh = SourmashKmerMinHash::as_rust(ptr); mh.is_protein() } #[no_mangle] pub unsafe extern "C" fn kmerminhash_dayhoff(ptr: *const SourmashKmerMinHash) -> bool { let mh = SourmashKmerMinHash::as_rust(ptr); mh.dayhoff() } #[no_mangle] pub unsafe extern "C" fn kmerminhash_hp(ptr: *const SourmashKmerMinHash) -> bool { let mh = SourmashKmerMinHash::as_rust(ptr); mh.hp() } #[no_mangle] pub unsafe extern "C" fn kmerminhash_skipm1n3(ptr: *const SourmashKmerMinHash) -> bool { let mh = SourmashKmerMinHash::as_rust(ptr); mh.skipm1n3() } #[no_mangle] pub unsafe extern "C" fn kmerminhash_skipm2n3(ptr: *const SourmashKmerMinHash) -> bool { let mh = SourmashKmerMinHash::as_rust(ptr); mh.skipm2n3() } #[no_mangle] pub unsafe extern "C" fn kmerminhash_seed(ptr: *const SourmashKmerMinHash) -> u64 { let mh = SourmashKmerMinHash::as_rust(ptr); mh.seed() } #[no_mangle] pub unsafe extern "C" fn kmerminhash_track_abundance(ptr: *const SourmashKmerMinHash) -> bool { let mh = SourmashKmerMinHash::as_rust(ptr); mh.track_abundance() } #[no_mangle] pub unsafe extern "C" fn kmerminhash_disable_abundance(ptr: *mut SourmashKmerMinHash) { let mh = SourmashKmerMinHash::as_rust_mut(ptr); mh.disable_abundance(); } ffi_fn! { unsafe fn kmerminhash_enable_abundance(ptr: *mut SourmashKmerMinHash) -> Result<()> { let mh = SourmashKmerMinHash::as_rust_mut(ptr); mh.enable_abundance()?; Ok(()) } } #[no_mangle] pub unsafe extern "C" fn kmerminhash_num(ptr: *const SourmashKmerMinHash) -> u32 { let mh = SourmashKmerMinHash::as_rust(ptr); mh.num() } #[no_mangle] pub unsafe extern "C" fn kmerminhash_ksize(ptr: *const SourmashKmerMinHash) -> u32 { let mh = SourmashKmerMinHash::as_rust(ptr); mh.ksize() as u32 } #[no_mangle] pub unsafe extern "C" fn kmerminhash_max_hash(ptr: *const SourmashKmerMinHash) -> u64 { let mh = SourmashKmerMinHash::as_rust(ptr); mh.max_hash() } #[no_mangle] pub unsafe extern "C" fn kmerminhash_hash_function( ptr: *const SourmashKmerMinHash, ) -> HashFunctions { let mh = SourmashKmerMinHash::as_rust(ptr); mh.hash_function().into() } ffi_fn! { unsafe fn kmerminhash_hash_function_set(ptr: *mut SourmashKmerMinHash, hash_function: HashFunctions) -> Result<()> { let mh = SourmashKmerMinHash::as_rust_mut(ptr); mh.set_hash_function(hash_function.into()) } } ffi_fn! { unsafe fn kmerminhash_merge(ptr: *mut SourmashKmerMinHash, other: *const SourmashKmerMinHash) -> Result<()> { let mh = SourmashKmerMinHash::as_rust_mut(ptr); let other_mh = SourmashKmerMinHash::as_rust(other); mh.merge(other_mh)?; Ok(()) } } #[no_mangle] pub unsafe extern "C" fn kmerminhash_is_compatible( ptr: *const SourmashKmerMinHash, other: *const SourmashKmerMinHash, ) -> bool { let mh = SourmashKmerMinHash::as_rust(ptr); let other_mh = SourmashKmerMinHash::as_rust(other); mh.check_compatible(other_mh).is_ok() } ffi_fn! { unsafe fn kmerminhash_add_from(ptr: *mut SourmashKmerMinHash, other: *const SourmashKmerMinHash) -> Result<()> { let mh = SourmashKmerMinHash::as_rust_mut(ptr); let other_mh = SourmashKmerMinHash::as_rust(other); mh.add_from(other_mh) } } ffi_fn! { unsafe fn kmerminhash_remove_from(ptr: *mut SourmashKmerMinHash, other: *const SourmashKmerMinHash) -> Result<()> { let mh = SourmashKmerMinHash::as_rust_mut(ptr); let other_mh = SourmashKmerMinHash::as_rust(other); mh.remove_from(other_mh) } } ffi_fn! { unsafe fn kmerminhash_count_common(ptr: *const SourmashKmerMinHash, other: *const SourmashKmerMinHash, downsample: bool) -> Result { let mh = SourmashKmerMinHash::as_rust(ptr); let other_mh = SourmashKmerMinHash::as_rust(other); mh.count_common(other_mh, downsample) } } ffi_fn! { unsafe fn kmerminhash_intersection(ptr: *const SourmashKmerMinHash, other: *const SourmashKmerMinHash) -> Result<*mut SourmashKmerMinHash> { let mh = SourmashKmerMinHash::as_rust(ptr); let other_mh = SourmashKmerMinHash::as_rust(other); let isect = mh.intersection(other_mh)?; let mut new_mh = mh.clone(); new_mh.clear(); new_mh.add_many(&isect.0)?; Ok(SourmashKmerMinHash::from_rust(new_mh)) } } ffi_fn! { unsafe fn kmerminhash_intersection_union_size(ptr: *const SourmashKmerMinHash, other: *const SourmashKmerMinHash, union_size: *mut u64) -> Result { let mh = SourmashKmerMinHash::as_rust(ptr); let other_mh = SourmashKmerMinHash::as_rust(other); if let Ok((common, union_s)) = mh.intersection_size(other_mh) { *union_size = union_s; return Ok(common); } *union_size = 0; Ok(0) } } ffi_fn! { unsafe fn kmerminhash_jaccard(ptr: *const SourmashKmerMinHash, other: *const SourmashKmerMinHash) -> Result { let mh = SourmashKmerMinHash::as_rust(ptr); let other_mh = SourmashKmerMinHash::as_rust(other); mh.jaccard(other_mh) } } ffi_fn! { unsafe fn kmerminhash_similarity(ptr: *const SourmashKmerMinHash, other: *const SourmashKmerMinHash, ignore_abundance: bool, downsample: bool) -> Result { let mh = SourmashKmerMinHash::as_rust(ptr); let other_mh = SourmashKmerMinHash::as_rust(other); mh.similarity(other_mh, ignore_abundance, downsample) } } ffi_fn! { unsafe fn kmerminhash_angular_similarity(ptr: *const SourmashKmerMinHash, other: *const SourmashKmerMinHash) -> Result { let mh = SourmashKmerMinHash::as_rust(ptr); let other_mh = SourmashKmerMinHash::as_rust(other); mh.angular_similarity(other_mh) } } sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/mod.rs000066400000000000000000000043621474157046300226720ustar00rootroot00000000000000//! # Foreign Function Interface for calling sourmash from a C API //! //! Primary client for now is the Python version, using CFFI and maturin. #![allow(clippy::missing_safety_doc)] #[macro_use] pub mod utils; pub mod cmd; pub mod hyperloglog; pub mod index; pub mod minhash; pub mod nodegraph; pub mod signature; pub mod storage; use std::ffi::CStr; use std::os::raw::c_char; use crate::_hash_murmur; #[no_mangle] pub unsafe extern "C" fn hash_murmur(kmer: *const c_char, seed: u64) -> u64 { let c_str = { assert!(!kmer.is_null()); CStr::from_ptr(kmer) }; _hash_murmur(c_str.to_bytes(), seed) } #[repr(u32)] pub enum HashFunctions { Murmur64Dna = 1, Murmur64Protein = 2, Murmur64Dayhoff = 3, Murmur64Hp = 4, Murmur64Skipm1n3 = 5, Murmur64Skipm2n3 = 6, } impl From for crate::encodings::HashFunctions { fn from(v: HashFunctions) -> crate::encodings::HashFunctions { use crate::encodings::HashFunctions::{ Murmur64Dayhoff, Murmur64Dna, Murmur64Hp, Murmur64Protein, Murmur64Skipm1n3, Murmur64Skipm2n3, }; match v { HashFunctions::Murmur64Dna => Murmur64Dna, HashFunctions::Murmur64Protein => Murmur64Protein, HashFunctions::Murmur64Dayhoff => Murmur64Dayhoff, HashFunctions::Murmur64Hp => Murmur64Hp, HashFunctions::Murmur64Skipm1n3 => Murmur64Skipm1n3, HashFunctions::Murmur64Skipm2n3 => Murmur64Skipm2n3, } } } impl From for HashFunctions { fn from(v: crate::encodings::HashFunctions) -> HashFunctions { use crate::encodings::HashFunctions::{ Murmur64Dayhoff, Murmur64Dna, Murmur64Hp, Murmur64Protein, Murmur64Skipm1n3, Murmur64Skipm2n3, }; match v { Murmur64Dna => HashFunctions::Murmur64Dna, Murmur64Protein => HashFunctions::Murmur64Protein, Murmur64Dayhoff => HashFunctions::Murmur64Dayhoff, Murmur64Hp => HashFunctions::Murmur64Hp, Murmur64Skipm1n3 => HashFunctions::Murmur64Skipm1n3, Murmur64Skipm2n3 => HashFunctions::Murmur64Skipm2n3, _ => todo!("Not supported, probably custom"), } } } sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/nodegraph.rs000066400000000000000000000140751474157046300240640ustar00rootroot00000000000000use std::ffi::CStr; use std::os::raw::c_char; use std::slice; use crate::prelude::*; use crate::sketch::nodegraph::Nodegraph; use crate::ffi::minhash::SourmashKmerMinHash; use crate::ffi::utils::ForeignObject; pub struct SourmashNodegraph; impl ForeignObject for SourmashNodegraph { type RustObject = Nodegraph; } #[no_mangle] pub unsafe extern "C" fn nodegraph_new() -> *mut SourmashNodegraph { SourmashNodegraph::from_rust(Nodegraph::default()) } #[no_mangle] pub unsafe extern "C" fn nodegraph_free(ptr: *mut SourmashNodegraph) { SourmashNodegraph::drop(ptr); } #[no_mangle] pub unsafe extern "C" fn nodegraph_buffer_free(ptr: *mut u8, insize: usize) { if ptr.is_null() { return; } Vec::from_raw_parts(ptr, insize, insize); } #[no_mangle] pub unsafe extern "C" fn nodegraph_with_tables( ksize: usize, starting_size: usize, n_tables: usize, ) -> *mut SourmashNodegraph { let ng = Nodegraph::with_tables(starting_size, n_tables, ksize); SourmashNodegraph::from_rust(ng) } #[no_mangle] pub unsafe extern "C" fn nodegraph_count(ptr: *mut SourmashNodegraph, h: u64) -> bool { let ng = SourmashNodegraph::as_rust_mut(ptr); ng.count(h) } #[no_mangle] pub unsafe extern "C" fn nodegraph_count_kmer( ptr: *mut SourmashNodegraph, kmer: *const c_char, ) -> bool { let ng = SourmashNodegraph::as_rust_mut(ptr); // FIXME use buffer + len instead of cstr let c_str = { assert!(!kmer.is_null()); CStr::from_ptr(kmer) }; ng.count_kmer(c_str.to_bytes()) } #[no_mangle] pub unsafe extern "C" fn nodegraph_get(ptr: *const SourmashNodegraph, h: u64) -> usize { let ng = SourmashNodegraph::as_rust(ptr); ng.get(h) } #[no_mangle] pub unsafe extern "C" fn nodegraph_get_kmer( ptr: *const SourmashNodegraph, kmer: *const c_char, ) -> usize { let ng = SourmashNodegraph::as_rust(ptr); // FIXME use buffer + len instead of cstr let c_str = { assert!(!kmer.is_null()); CStr::from_ptr(kmer) }; ng.get_kmer(c_str.to_bytes()) } #[no_mangle] pub unsafe extern "C" fn nodegraph_expected_collisions(ptr: *const SourmashNodegraph) -> f64 { let ng = SourmashNodegraph::as_rust(ptr); ng.expected_collisions() } #[no_mangle] pub unsafe extern "C" fn nodegraph_ksize(ptr: *const SourmashNodegraph) -> usize { let ng = SourmashNodegraph::as_rust(ptr); ng.ksize() } #[no_mangle] pub unsafe extern "C" fn nodegraph_hashsizes( ptr: *const SourmashNodegraph, size: *mut usize, ) -> *const u64 { let ng = SourmashNodegraph::as_rust(ptr); let st = ng.tablesizes(); let b = st.into_boxed_slice(); *size = b.len(); // FIXME: Use SourmashSlice_u64? Box::into_raw(b) as *const u64 } #[no_mangle] pub unsafe extern "C" fn nodegraph_ntables(ptr: *const SourmashNodegraph) -> usize { let ng = SourmashNodegraph::as_rust(ptr); ng.ntables() } #[no_mangle] pub unsafe extern "C" fn nodegraph_noccupied(ptr: *const SourmashNodegraph) -> usize { let ng = SourmashNodegraph::as_rust(ptr); ng.noccupied() } #[no_mangle] pub unsafe extern "C" fn nodegraph_matches( ptr: *const SourmashNodegraph, mh_ptr: *const SourmashKmerMinHash, ) -> usize { let ng = SourmashNodegraph::as_rust(ptr); let mh = SourmashKmerMinHash::as_rust(mh_ptr); ng.matches(mh) } #[no_mangle] pub unsafe extern "C" fn nodegraph_update( ptr: *mut SourmashNodegraph, optr: *const SourmashNodegraph, ) { let ng = SourmashNodegraph::as_rust_mut(ptr); let ong = SourmashNodegraph::as_rust(optr); // FIXME raise an exception properly ong.update(ng).unwrap(); } #[no_mangle] pub unsafe extern "C" fn nodegraph_update_mh( ptr: *mut SourmashNodegraph, optr: *const SourmashKmerMinHash, ) { let ng = SourmashNodegraph::as_rust_mut(ptr); let mh = SourmashKmerMinHash::as_rust(optr); mh.update(ng).unwrap(); } ffi_fn! { unsafe fn nodegraph_from_path(filename: *const c_char) -> Result<*mut SourmashNodegraph> { // FIXME use buffer + len instead of c_str let c_str = { assert!(!filename.is_null()); CStr::from_ptr(filename) }; let (mut input, _) = niffler::from_path(c_str.to_str()?)?; let ng = Nodegraph::from_reader(&mut input)?; Ok(SourmashNodegraph::from_rust(ng)) } } ffi_fn! { unsafe fn nodegraph_from_buffer(ptr: *const c_char, insize: usize) -> Result<*mut SourmashNodegraph> { // FIXME use SourmashSlice_u8? let buf = { assert!(!ptr.is_null()); slice::from_raw_parts(ptr as *mut u8, insize) }; let ng = Nodegraph::from_reader(buf)?; Ok(SourmashNodegraph::from_rust(ng)) } } ffi_fn! { unsafe fn nodegraph_save(ptr: *const SourmashNodegraph, filename: *const c_char) -> Result<()> { let ng = SourmashNodegraph::as_rust(ptr); // FIXME use buffer + len instead of c_str let c_str = { assert!(!filename.is_null()); CStr::from_ptr(filename) }; ng.save(c_str.to_str()?)?; Ok(()) } } ffi_fn! { unsafe fn nodegraph_to_buffer(ptr: *const SourmashNodegraph, compression: u8, size: *mut usize) -> Result<*const u8> { let ng = SourmashNodegraph::as_rust(ptr); let mut buffer = vec![]; { let mut writer = if compression > 0 { let level = match compression { 1 => niffler::compression::Level::One, 2 => niffler::compression::Level::Two, 3 => niffler::compression::Level::Three, 4 => niffler::compression::Level::Four, 5 => niffler::compression::Level::Five, 6 => niffler::compression::Level::Six, 7 => niffler::compression::Level::Seven, 8 => niffler::compression::Level::Eight, _ => niffler::compression::Level::Nine, }; niffler::get_writer(Box::new(&mut buffer), niffler::compression::Format::Gzip, level)? } else { Box::new(&mut buffer) }; ng.save_to_writer(&mut writer)?; } let b = buffer.into_boxed_slice(); *size = b.len(); // FIXME use SourmashSlice_u8? Ok(Box::into_raw(b) as *const u8) } } sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/signature.rs000066400000000000000000000225521474157046300241150ustar00rootroot00000000000000use std::convert::TryInto; use std::ffi::CStr; use std::io; use std::os::raw::c_char; use std::slice; use crate::errors::SourmashError; use crate::encodings::HashFunctions; use crate::signature::Signature; use crate::sketch::Sketch; use crate::ffi::cmd::compute::SourmashComputeParameters; use crate::ffi::minhash::SourmashKmerMinHash; use crate::ffi::utils::{ForeignObject, SourmashStr}; use crate::prelude::ToWriter; pub struct SourmashSignature; impl ForeignObject for SourmashSignature { type RustObject = Signature; } // Signature methods #[no_mangle] pub unsafe extern "C" fn signature_new() -> *mut SourmashSignature { SourmashSignature::from_rust(Signature::default()) } #[no_mangle] pub unsafe extern "C" fn signature_from_params( ptr: *const SourmashComputeParameters, ) -> *mut SourmashSignature { let params = SourmashComputeParameters::as_rust(ptr); SourmashSignature::from_rust(Signature::from_params(params)) } #[no_mangle] pub unsafe extern "C" fn signature_free(ptr: *mut SourmashSignature) { SourmashSignature::drop(ptr); } #[no_mangle] pub unsafe extern "C" fn signature_len(ptr: *const SourmashSignature) -> usize { let sig = SourmashSignature::as_rust(ptr); sig.size() } ffi_fn! { unsafe fn signature_add_sequence(ptr: *mut SourmashSignature, sequence: *const c_char, force: bool) -> Result<()> { let sig = SourmashSignature::as_rust_mut(ptr); // FIXME replace with buffer + len let c_str = { assert!(!sequence.is_null()); CStr::from_ptr(sequence) }; sig.add_sequence(c_str.to_bytes(), force) } } ffi_fn! { unsafe fn signature_add_protein(ptr: *mut SourmashSignature, sequence: *const c_char) -> Result<()> { let sig = SourmashSignature::as_rust_mut(ptr); // FIXME replace with buffer + len let c_str = { assert!(!sequence.is_null()); CStr::from_ptr(sequence) }; sig.add_protein(c_str.to_bytes()) } } ffi_fn! { unsafe fn signature_set_name(ptr: *mut SourmashSignature, name: *const c_char) -> Result<()> { let sig = SourmashSignature::as_rust_mut(ptr); // FIXME replace with buffer + len let c_str = { assert!(!name.is_null()); CStr::from_ptr(name) }; if let Ok(name) = c_str.to_str() { sig.set_name(name) } Ok(()) } } ffi_fn! { unsafe fn signature_set_filename(ptr: *mut SourmashSignature, name: *const c_char) -> Result<()> { let sig = SourmashSignature::as_rust_mut(ptr); // FIXME replace with buffer + len let c_str = { assert!(!name.is_null()); CStr::from_ptr(name) }; if let Ok(name) = c_str.to_str() { sig.set_filename(name) } Ok(()) } } ffi_fn! { unsafe fn signature_push_mh(ptr: *mut SourmashSignature, other: *const SourmashKmerMinHash) -> Result<()> { let sig = SourmashSignature::as_rust_mut(ptr); let mh = SourmashKmerMinHash::as_rust(other); sig.push(Sketch::MinHash(mh.clone())); Ok(()) } } ffi_fn! { unsafe fn signature_set_mh(ptr: *mut SourmashSignature, other: *const SourmashKmerMinHash) -> Result<()> { let sig = SourmashSignature::as_rust_mut(ptr); let mh = SourmashKmerMinHash::as_rust(other); sig.reset_sketches(); sig.push(Sketch::MinHash(mh.clone())); Ok(()) } } ffi_fn! { unsafe fn signature_get_name(ptr: *const SourmashSignature) -> Result { let sig = SourmashSignature::as_rust(ptr); if let Some(ref name) = sig.name { Ok(name.clone().into()) } else { Ok("".into()) } } } ffi_fn! { unsafe fn signature_get_filename(ptr: *const SourmashSignature) -> Result { let sig = SourmashSignature::as_rust(ptr); Ok(sig.filename().into()) } } ffi_fn! { unsafe fn signature_get_license(ptr: *const SourmashSignature) -> Result { let sig = SourmashSignature::as_rust(ptr); Ok(sig.license().into()) } } ffi_fn! { unsafe fn signature_first_mh(ptr: *const SourmashSignature) -> Result<*mut SourmashKmerMinHash> { let sig = SourmashSignature::as_rust(ptr); match sig.signatures.first() { Some(Sketch::MinHash(mh)) => { Ok(SourmashKmerMinHash::from_rust(mh.clone())) }, Some(Sketch::LargeMinHash(mh_btree)) => { Ok(SourmashKmerMinHash::from_rust(mh_btree.into())) }, _ => Err(SourmashError::Internal { message: "found unsupported sketch type".to_string() }), } } } ffi_fn! { unsafe fn signature_eq(ptr: *const SourmashSignature, other: *const SourmashSignature) -> Result { let sig = SourmashSignature::as_rust(ptr); let other_sig = SourmashSignature::as_rust(other); Ok(sig == other_sig) } } ffi_fn! { unsafe fn signature_save_json(ptr: *const SourmashSignature) -> Result { let sig = SourmashSignature::as_rust(ptr); let mut st: Vec = vec![]; sig.to_writer(&mut st)?; Ok(SourmashStr::from_string(String::from_utf8_unchecked(st))) } } ffi_fn! { unsafe fn signature_get_mhs(ptr: *const SourmashSignature, size: *mut usize) -> Result<*mut *mut SourmashKmerMinHash> { let sig = SourmashSignature::as_rust(ptr); let output = sig.sketches(); // FIXME: how to fit this into the ForeignObject trait? let ptr_sigs: Vec<*mut Signature> = output.into_iter().map(|x| { Box::into_raw(Box::new(x)) as *mut Signature }).collect(); let b = ptr_sigs.into_boxed_slice(); *size = b.len(); Ok(Box::into_raw(b) as *mut *mut SourmashKmerMinHash) } } ffi_fn! { unsafe fn signatures_save_buffer(ptr: *const *const SourmashSignature, size: usize, compression: u8, osize: *mut usize) -> Result<*const u8> { // FIXME: review this for ForeignObject let sigs = { assert!(!ptr.is_null()); slice::from_raw_parts(ptr, size) }; let rsigs: Vec<&Signature> = sigs.iter().map(|x| SourmashSignature::as_rust(*x)).collect(); let mut buffer = vec![]; { let mut writer = if compression > 0 { let level = match compression { 1 => niffler::compression::Level::One, 2 => niffler::compression::Level::Two, 3 => niffler::compression::Level::Three, 4 => niffler::compression::Level::Four, 5 => niffler::compression::Level::Five, 6 => niffler::compression::Level::Six, 7 => niffler::compression::Level::Seven, 8 => niffler::compression::Level::Eight, _ => niffler::compression::Level::Nine, }; niffler::get_writer(Box::new(&mut buffer), niffler::compression::Format::Gzip, level)? } else { Box::new(&mut buffer) }; rsigs.to_writer(&mut writer)?; } let b = buffer.into_boxed_slice(); *osize = b.len(); Ok(Box::into_raw(b) as *const u8) } } ffi_fn! { unsafe fn signatures_load_path(ptr: *const c_char, _ignore_md5sum: bool, ksize: usize, select_moltype: *const c_char, size: *mut usize) -> Result<*mut *mut SourmashSignature> { // FIXME use buffer + len instead of cstr let buf = { assert!(!ptr.is_null()); CStr::from_ptr(ptr) }; // FIXME take select_moltype as enum let moltype: Option = if select_moltype.is_null() { None } else { let mol = CStr::from_ptr(select_moltype).to_str()?; Some(mol.try_into()?) }; // TODO: implement ignore_md5sum let k = match ksize { 0 => None, x => Some(x) }; let (mut input, _) = niffler::from_path(buf.to_str()?)?; let filtered_sigs = Signature::load_signatures(&mut input, k, moltype, None)?; // FIXME: use the ForeignObject trait, maybe define new method there... let ptr_sigs: Vec<*mut SourmashSignature> = filtered_sigs.into_iter().map(|x| { Box::into_raw(Box::new(x)) as *mut SourmashSignature }).collect(); let b = ptr_sigs.into_boxed_slice(); *size = b.len(); Ok(Box::into_raw(b) as *mut *mut SourmashSignature) } } ffi_fn! { unsafe fn signatures_load_buffer(ptr: *const c_char, insize: usize, _ignore_md5sum: bool, ksize: usize, select_moltype: *const c_char, size: *mut usize) -> Result<*mut *mut SourmashSignature> { // FIXME use buffer + len instead of cstr let buf = { assert!(!ptr.is_null()); slice::from_raw_parts(ptr as *mut u8, insize) }; // FIXME take select_moltype as enum let moltype: Option = if select_moltype.is_null() { None } else { let mol = CStr::from_ptr(select_moltype).to_str()?; Some(mol.try_into()?) }; let k = match ksize { 0 => None, x => Some(x) }; // TODO: implement ignore_md5sum let mut reader = io::BufReader::new(buf); let filtered_sigs = Signature::load_signatures(&mut reader, k, moltype, None)?; // FIXME: use the ForeignObject trait, maybe define new method there... let ptr_sigs: Vec<*mut SourmashSignature> = filtered_sigs.into_iter().map(|x| { Box::into_raw(Box::new(x)) as *mut SourmashSignature }).collect(); let b = ptr_sigs.into_boxed_slice(); *size = b.len(); Ok(Box::into_raw(b) as *mut *mut SourmashSignature) } } sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/storage.rs000066400000000000000000000066511474157046300235620ustar00rootroot00000000000000use std::os::raw::c_char; use std::slice; use std::sync::Arc; use crate::ffi::utils::{ForeignObject, SourmashStr}; use crate::prelude::*; use crate::storage::ZipStorage; pub struct SourmashZipStorage; impl ForeignObject for SourmashZipStorage { type RustObject = Arc; } ffi_fn! { unsafe fn zipstorage_new(ptr: *const c_char, insize: usize) -> Result<*mut SourmashZipStorage> { let path = { assert!(!ptr.is_null()); let path = slice::from_raw_parts(ptr as *mut u8, insize); std::str::from_utf8(path)? }; let zipstorage = ZipStorage::from_file(path)?; Ok(SourmashZipStorage::from_rust(Arc::new(zipstorage))) } } #[no_mangle] pub unsafe extern "C" fn zipstorage_free(ptr: *mut SourmashZipStorage) { SourmashZipStorage::drop(ptr); } ffi_fn! { unsafe fn zipstorage_load(ptr: *const SourmashZipStorage, path_ptr: *const c_char, insize: usize, size: *mut usize) -> Result<*const u8> { let storage = SourmashZipStorage::as_rust(ptr); let path = { assert!(!path_ptr.is_null()); let path = slice::from_raw_parts(path_ptr as *mut u8, insize); std::str::from_utf8(path)? }; let buffer = storage.load(path)?; let b = buffer.into_boxed_slice(); *size = b.len(); Ok(Box::into_raw(b) as *const u8) } } ffi_fn! { unsafe fn zipstorage_list_sbts( ptr: *const SourmashZipStorage, size: *mut usize, ) -> Result<*mut *mut SourmashStr> { let storage = SourmashZipStorage::as_rust(ptr); let sbts = storage.list_sbts()?; // FIXME: use the ForeignObject trait, maybe define new method there... let ptr_sigs: Vec<*mut SourmashStr> = sbts .into_iter() .map(|x| Box::into_raw(Box::new(SourmashStr::from_string(x)))) .collect(); let b = ptr_sigs.into_boxed_slice(); *size = b.len(); Ok(Box::into_raw(b) as *mut *mut SourmashStr) } } ffi_fn! { unsafe fn zipstorage_filenames( ptr: *const SourmashZipStorage, size: *mut usize, ) -> Result<*mut *mut SourmashStr> { let storage = SourmashZipStorage::as_rust(ptr); let files = storage.filenames()?; // FIXME: use the ForeignObject trait, maybe define new method there... let ptr_sigs: Vec<*mut SourmashStr> = files .into_iter() .map(|x| Box::into_raw(Box::new(SourmashStr::from_string(x)))) .collect(); let b = ptr_sigs.into_boxed_slice(); *size = b.len(); Ok(Box::into_raw(b) as *mut *mut SourmashStr) } } ffi_fn! { unsafe fn zipstorage_set_subdir( ptr: *mut SourmashZipStorage, path_ptr: *const c_char, insize: usize, ) -> Result<()> { let storage = SourmashZipStorage::as_rust_mut(ptr); let path = { assert!(!path_ptr.is_null()); let path = slice::from_raw_parts(path_ptr as *mut u8, insize); std::str::from_utf8(path)? }; (*Arc::get_mut(storage).unwrap()).set_subdir(path.to_string()); Ok(()) } } ffi_fn! { unsafe fn zipstorage_path(ptr: *const SourmashZipStorage) -> Result { let storage = SourmashZipStorage::as_rust(ptr); if let Some(ref path) = storage.path() { Ok(path.clone().into_string().into()) } else { Ok("".into()) } } } ffi_fn! { unsafe fn zipstorage_subdir(ptr: *const SourmashZipStorage) -> Result { let storage = SourmashZipStorage::as_rust(ptr); if let Some(ref path) = storage.subdir() { Ok(path.clone().into()) } else { Ok("".into()) } } } sourmash-bio-sourmash-bc3c0f8/src/core/src/ffi/utils.rs000066400000000000000000000203271474157046300232520ustar00rootroot00000000000000use std::borrow::Cow; use std::cell::RefCell; use std::ffi::CStr; use std::mem; use std::os::raw::c_char; use std::panic; use std::ptr; use std::slice; use std::str; use std::thread; use thiserror::Error; use crate::errors::SourmashErrorCode; use crate::Error; thread_local! { pub static LAST_ERROR: RefCell> = const { RefCell::new(None) }; } #[allow(clippy::wrong_self_convention)] pub trait ForeignObject: Sized { type RustObject; #[inline] unsafe fn from_rust(object: Self::RustObject) -> *mut Self { Box::into_raw(Box::new(object)) as *mut Self } #[inline] unsafe fn from_ref(object: &Self::RustObject) -> *const Self { object as *const Self::RustObject as *const Self } #[inline] unsafe fn as_rust<'a>(pointer: *const Self) -> &'a Self::RustObject { &*(pointer as *const Self::RustObject) } #[inline] unsafe fn as_rust_mut<'a>(pointer: *mut Self) -> &'a mut Self::RustObject { &mut *(pointer as *mut Self::RustObject) } #[inline] unsafe fn into_rust(pointer: *mut Self) -> Box { Box::from_raw(pointer as *mut Self::RustObject) } #[inline] unsafe fn drop(pointer: *mut Self) { if !pointer.is_null() { drop(Self::into_rust(pointer)); } } } macro_rules! ffi_fn { // a function that catches panics and returns a result (err goes to tls) ( $(#[$attr:meta])* unsafe fn $name:ident($($aname:ident: $aty:ty),* $(,)*) -> Result<$rv:ty> $body:block ) => { #[no_mangle] $(#[$attr])* pub unsafe extern "C" fn $name($($aname: $aty,)*) -> $rv { $crate::ffi::utils::landingpad(|| $body) } }; // a function that catches panics and returns nothing (err goes to tls) ( $(#[$attr:meta])* unsafe fn $name:ident($($aname:ident: $aty:ty),* $(,)*) $body:block ) => { #[no_mangle] $(#[$attr])* pub unsafe extern "C" fn $name($($aname: $aty,)*) { // this silences panics and stuff $crate::ffi::utils::landingpad(|| { $body; Ok(0 as std::os::raw::c_int) }); } }; } /// An error thrown by `landingpad` in place of panics. #[derive(Error, Debug)] #[error("sourmash panicked: {0}")] pub struct Panic(String); /// Returns the last error message. /// /// If there is no error an empty string is returned. This allocates new memory /// that needs to be freed with `sourmash_str_free`. #[no_mangle] pub unsafe extern "C" fn sourmash_err_get_last_message() -> SourmashStr { LAST_ERROR.with(|e| { if let Some(ref err) = *e.borrow() { let msg = err.to_string(); /* TODO: iter_causes is a failure method for cause in err.iter_causes() { write!(&mut msg, "\n caused by: {}", cause).ok(); } */ SourmashStr::from_string(msg) } else { Default::default() } }) } /// Returns the panic information as string. #[no_mangle] pub unsafe extern "C" fn sourmash_err_get_backtrace() -> SourmashStr { /* TODO: bring back when backtrace is available in std::error LAST_ERROR.with(|e| { if let Some(ref error) = *e.borrow() { if let Some(backtrace) = error.backtrace() { use std::fmt::Write; let mut out = String::new(); write!(&mut out, "stacktrace: {}", backtrace.to_string()).ok(); SourmashStr::from_string(out) } else { Default::default() } } else { Default::default() } }) */ SourmashStr::default() } /// Clears the last error. #[no_mangle] pub unsafe extern "C" fn sourmash_err_clear() { LAST_ERROR.with(|e| { *e.borrow_mut() = None; }); } /// Initializes the library #[no_mangle] pub unsafe extern "C" fn sourmash_init() { set_panic_hook(); } /// Returns the last error code. /// /// If there is no error, 0 is returned. #[no_mangle] pub unsafe extern "C" fn sourmash_err_get_last_code() -> SourmashErrorCode { LAST_ERROR.with(|e| { if let Some(ref err) = *e.borrow() { SourmashErrorCode::from_error(err) } else { SourmashErrorCode::NoError } }) } fn set_last_error(err: Error) { LAST_ERROR.with(|e| { *e.borrow_mut() = Some(err); }); } pub unsafe fn set_panic_hook() { panic::set_hook(Box::new(|info| { let thread = thread::current(); let thread = thread.name().unwrap_or("unnamed"); let message = match info.payload().downcast_ref::<&str>() { Some(s) => *s, None => match info.payload().downcast_ref::() { Some(s) => &**s, None => "Box", }, }; let description = match info.location() { Some(location) => format!( "thread '{}' panicked with '{}' at {}:{}", thread, message, location.file(), location.line() ), None => format!("thread '{}' panicked with '{}'", thread, message), }; set_last_error(Panic(description).into()) })); } pub unsafe fn landingpad(f: F) -> T where F: FnOnce() -> Result + panic::UnwindSafe, { match panic::catch_unwind(f) { Ok(Ok(result)) => result, Ok(Err(err)) => { set_last_error(err); mem::zeroed() } Err(_) => mem::zeroed(), } } /// Represents a string. #[repr(C)] pub struct SourmashStr { /// Pointer to the UTF-8 encoded string data. pub data: *mut c_char, /// The length of the string pointed to by `data`. pub len: usize, /// Indicates that the string is owned and must be freed. pub owned: bool, } impl Default for SourmashStr { fn default() -> SourmashStr { SourmashStr { data: ptr::null_mut(), len: 0, owned: false, } } } impl SourmashStr { pub fn new(s: &str) -> SourmashStr { SourmashStr { data: s.as_ptr() as *mut c_char, len: s.len(), owned: false, } } pub fn from_string(mut s: String) -> SourmashStr { s.shrink_to_fit(); let rv = SourmashStr { data: s.as_ptr() as *mut c_char, len: s.len(), owned: true, }; mem::forget(s); rv } pub unsafe fn free(&mut self) { if self.owned { String::from_raw_parts(self.data as *mut _, self.len, self.len); self.data = ptr::null_mut(); self.len = 0; self.owned = false; } } pub fn as_str(&self) -> &str { unsafe { str::from_utf8_unchecked(slice::from_raw_parts(self.data as *const _, self.len)) } } } impl Drop for SourmashStr { fn drop(&mut self) { unsafe { self.free() } } } impl From for SourmashStr { fn from(string: String) -> SourmashStr { SourmashStr::from_string(string) } } impl From<&str> for SourmashStr { fn from(string: &str) -> SourmashStr { SourmashStr::new(string) } } impl<'a> From> for SourmashStr { fn from(cow: Cow<'a, str>) -> SourmashStr { match cow { Cow::Borrowed(string) => SourmashStr::new(string), Cow::Owned(string) => SourmashStr::from_string(string), } } } ffi_fn! { /// Creates a sourmash str from a c string. /// /// This sets the string to owned. In case it's not owned you either have /// to make sure you are not freeing the memory or you need to set the /// owned flag to false. unsafe fn sourmash_str_from_cstr(s: *const c_char) -> Result { let s = CStr::from_ptr(s).to_str()?; Ok(SourmashStr { data: s.as_ptr() as *mut _, len: s.len(), owned: true, }) } } /// Frees a sourmash str. /// /// If the string is marked as not owned then this function does not /// do anything. #[no_mangle] pub unsafe extern "C" fn sourmash_str_free(s: *mut SourmashStr) { if !s.is_null() { (*s).free() } } impl ForeignObject for SourmashStr { type RustObject = SourmashStr; } sourmash-bio-sourmash-bc3c0f8/src/core/src/from.rs000066400000000000000000000073071474157046300223140ustar00rootroot00000000000000use finch::sketch_schemes::mash::MashSketcher; use finch::sketch_schemes::SketchScheme; use crate::encodings::HashFunctions; use crate::sketch::minhash::KmerMinHash; /* TODO: - also convert scaled sketches - sourmash Signature equivalent is the finch Sketch, write conversions for that too */ impl From for KmerMinHash { fn from(other: MashSketcher) -> KmerMinHash { let values = other.to_vec(); let mut new_mh = KmerMinHash::new( 0, values.first().unwrap().kmer.len() as u32, HashFunctions::Murmur64Dna, 42, true, values.len() as u32, ); let hash_with_abunds: Vec<(u64, u64)> = values.iter().map(|x| (x.hash, x.count as u64)).collect(); new_mh .add_many_with_abund(&hash_with_abunds) .expect("Error adding hashes with abund"); new_mh } } #[cfg(test)] mod test { use std::collections::HashMap; use std::collections::HashSet; use crate::encodings::HashFunctions; use crate::signature::SigsTrait; use crate::sketch::minhash::KmerMinHash; use finch::sketch_schemes::mash::MashSketcher; use needletail::kmer::CanonicalKmers; use needletail::Sequence; use super::*; #[test] fn finch_behavior() { let mut a = KmerMinHash::new(0, 10, HashFunctions::Murmur64Dna, 42, true, 20); let mut b = MashSketcher::new(20, 10, 42); let seq = b"TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA"; let rc = seq.reverse_complement(); a.add_sequence(seq, false).unwrap(); for (_, kmer, _) in CanonicalKmers::new(seq, &rc, 10) { b.push(kmer, 0); } let b_hashes = b.to_vec(); let s1: HashSet<_> = a.mins().into_iter().collect(); let s2: HashSet<_> = b_hashes.iter().map(|x| x.hash).collect(); let i1 = &s1 & &s2; assert!(i1.len() == a.size()); assert!(i1.len() == b_hashes.len()); if let Some(abunds) = a.abunds() { let mins = a.mins(); let smap: HashMap<_, _> = mins.iter().zip(abunds.iter()).collect(); println!("{:?}", smap); for item in b_hashes.iter() { assert!(smap.contains_key(&{ item.hash })); assert!( **smap.get(&{ item.hash }).unwrap() == ((item.count + item.extra_count) as u64) ); } } } #[test] fn from_finch() { let mut a = KmerMinHash::new(0, 10, HashFunctions::Murmur64Dna, 42, true, 20); let mut b = MashSketcher::new(20, 10, 42); let seq = b"TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA"; let rc = seq.reverse_complement(); a.add_sequence(seq, false).unwrap(); for (_, kmer, _) in CanonicalKmers::new(seq, &rc, 10) { b.push(kmer, 0); } let c = KmerMinHash::from(b); let s1: HashSet<_> = a.mins().into_iter().collect(); let s2: HashSet<_> = c.mins().into_iter().collect(); let i1 = &s1 & &s2; assert!(i1.len() == a.mins().len()); assert!(i1.len() == c.mins().len()); if let Some(a_abunds) = a.abunds() { if let Some(c_abunds) = c.abunds() { let a_mins = a.mins(); let a_smap: HashMap<_, _> = a_mins.iter().zip(a_abunds.iter()).collect(); let c_mins = c.mins(); let c_smap: HashMap<_, _> = c_mins.iter().zip(c_abunds.iter()).collect(); for item in a_smap.iter() { assert!(c_smap.contains_key(*item.0)); assert!(c_smap.get(*item.0).unwrap() == item.1); } } } } } sourmash-bio-sourmash-bc3c0f8/src/core/src/index/000077500000000000000000000000001474157046300221035ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/src/index/linear.rs000066400000000000000000000245621474157046300237340ustar00rootroot00000000000000use std::collections::HashSet; use std::sync::atomic::{AtomicUsize, Ordering}; use camino::Utf8PathBuf as PathBuf; use log::info; #[cfg(feature = "parallel")] use rayon::prelude::*; use crate::collection::CollectionSet; use crate::encodings::Idx; use crate::index::{GatherResult, Index, Selection, SigCounter}; use crate::selection::Select; use crate::signature::SigsTrait; use crate::sketch::minhash::KmerMinHash; use crate::sketch::Sketch; use crate::storage::SigStore; use crate::Result; /// Supports parallel search without a particular index. pub struct LinearIndex { collection: CollectionSet, template: Sketch, } impl LinearIndex { pub fn from_collection(collection: CollectionSet) -> Self { let sig = collection.sig_for_dataset(0).unwrap(); let template = sig.sketches().swap_remove(0); Self { collection, template, } } pub fn sig_for_dataset(&self, dataset_id: Idx) -> Result { self.collection.sig_for_dataset(dataset_id) } pub fn collection(&self) -> &CollectionSet { &self.collection } pub fn template(&self) -> &Sketch { &self.template } pub fn location(&self) -> Option { unimplemented!() } pub fn counter_for_query(&self, query: &KmerMinHash) -> SigCounter { let processed_sigs = AtomicUsize::new(0); let template = self.template(); #[cfg(feature = "parallel")] let sig_iter = self.collection.par_iter(); #[cfg(not(feature = "parallel"))] let sig_iter = self.collection.iter(); let counters = sig_iter.filter_map(|(dataset_id, record)| { let filename = record.internal_location(); let i = processed_sigs.fetch_add(1, Ordering::SeqCst); if i % 1000 == 0 { info!("Processed {} reference sigs", i); } let search_sig = self .collection .sig_for_dataset(dataset_id) .unwrap_or_else(|_| panic!("error loading {:?}", filename)); let mut search_mh = None; if let Some(Sketch::MinHash(mh)) = search_sig.select_sketch(template) { search_mh = Some(mh); }; let search_mh = search_mh.expect("Couldn't find a compatible MinHash"); let (large_mh, small_mh) = if query.size() > search_mh.size() { (query, search_mh) } else { (search_mh, query) }; let (size, _) = small_mh .intersection_size(large_mh) .unwrap_or_else(|_| panic!("error computing intersection for {:?}", filename)); if size == 0 { None } else { let mut counter: SigCounter = Default::default(); counter[&(dataset_id as Idx)] += size as usize; Some(counter) } }); let reduce_counters = |mut a: SigCounter, b: SigCounter| { a.extend(&b); a }; #[cfg(feature = "parallel")] let counter = counters.reduce(SigCounter::new, reduce_counters); #[cfg(not(feature = "parallel"))] let counter = counters.fold(SigCounter::new(), reduce_counters); counter } pub fn search( &self, counter: SigCounter, similarity: bool, threshold: usize, ) -> Result> { let mut matches = vec![]; if similarity { unimplemented!("TODO: threshold correction") } for (dataset_id, size) in counter.most_common() { if size >= threshold { matches.push( self.collection .record_for_dataset(dataset_id)? .internal_location() .to_string(), ); } else { break; }; } Ok(matches) } pub fn gather_round( &self, dataset_id: Idx, match_size: usize, query: &KmerMinHash, round: usize, ) -> Result { let match_path = self .collection .record_for_dataset(dataset_id)? .internal_location() .into(); let match_sig = self.collection.sig_for_dataset(dataset_id)?; let result = self.stats_for_match(match_sig, query, match_size, match_path, round as u32)?; Ok(result) } fn stats_for_match( &self, match_sig: SigStore, query: &KmerMinHash, match_size: usize, match_path: PathBuf, gather_result_rank: u32, ) -> Result { let template = self.template(); let mut match_mh = None; if let Some(Sketch::MinHash(mh)) = match_sig.select_sketch(template) { match_mh = Some(mh); } let match_mh = match_mh.expect("Couldn't find a compatible MinHash"); // Calculate stats let f_orig_query = match_size as f64 / query.size() as f64; let f_match = match_size as f64 / match_mh.size() as f64; let filename = match_path.into_string(); let name = match_sig.name(); let unique_intersect_bp = (match_mh.scaled() as usize * match_size) as u64; let (intersect_orig, _) = match_mh.intersection_size(query)?; let intersect_bp: u64 = match_mh.scaled() as u64 * intersect_orig; let f_unique_to_query = intersect_orig as f64 / query.size() as f64; let match_ = match_sig; // TODO: all of these let f_unique_weighted = 0.; let average_abund = 0.; let median_abund = 0.; let std_abund = 0.; let md5 = "".into(); let f_match_orig = 0.; let remaining_bp = 0; let total_weighted_hashes = 0; let n_unique_weighted_found = 0; let query_containment_ani = 0.0; let match_containment_ani = 0.0; let max_containment_ani = 0.0; let average_containment_ani = 0.0; let query_containment_ani_ci_low = None; let query_containment_ani_ci_high = None; let match_containment_ani_ci_low = None; let match_containment_ani_ci_high = None; let sum_weighted_found = 0; Ok(GatherResult { intersect_bp, f_orig_query, f_match, f_unique_to_query, f_unique_weighted, average_abund, median_abund, std_abund, filename, name, md5, match_, f_match_orig, unique_intersect_bp, gather_result_rank, remaining_bp, sum_weighted_found, total_weighted_hashes, n_unique_weighted_found, query_containment_ani, query_containment_ani_ci_low, query_containment_ani_ci_high, match_containment_ani, match_containment_ani_ci_low, match_containment_ani_ci_high, max_containment_ani, average_containment_ani, }) } pub fn gather( &self, mut counter: SigCounter, threshold: usize, query: &KmerMinHash, ) -> std::result::Result, Box> { let mut match_size = usize::MAX; let mut matches = vec![]; let template = self.template(); while match_size > threshold && !counter.is_empty() { let (dataset_id, size) = counter.most_common()[0]; if threshold == 0 && size == 0 { break; } match_size = if size >= threshold { size } else { break; }; let result = self.gather_round(dataset_id, match_size, query, matches.len())?; // Prepare counter for finding the next match by decrementing // all hashes found in the current match in other datasets // TODO: maybe par_iter? let mut to_remove: HashSet = Default::default(); to_remove.insert(dataset_id); for (dataset, value) in counter.iter_mut() { let dataset_sig = self.collection.sig_for_dataset(*dataset)?; let mut match_mh = None; if let Some(Sketch::MinHash(mh)) = dataset_sig.select_sketch(template) { match_mh = Some(mh); } let match_mh = match_mh.expect("Couldn't find a compatible MinHash"); let (intersection, _) = query.intersection_size(match_mh)?; if intersection as usize > *value { to_remove.insert(*dataset); } else { *value -= intersection as usize; }; } to_remove.iter().for_each(|dataset_id| { counter.remove(dataset_id); }); matches.push(result); } Ok(matches) } pub fn signatures_iter(&self) -> impl Iterator + '_ { (0..self.collection.len()).map(move |dataset_id| { self.collection .sig_for_dataset(dataset_id as Idx) .expect("error loading sig") }) } } impl Select for LinearIndex { fn select(self, selection: &Selection) -> Result { let Self { collection, template, } = self; let collection = collection.into_inner().select(selection)?.try_into()?; Ok(Self { collection, template, }) } } impl Index<'_> for LinearIndex { type Item = SigStore; fn insert(&mut self, _node: Self::Item) -> Result<()> { unimplemented!() } fn save>(&self, _path: P) -> Result<()> { unimplemented!() } fn load>(_path: P) -> Result<()> { unimplemented!() } fn len(&self) -> usize { self.collection.len() } fn signatures(&self) -> Vec { self.collection() .iter() .map(|(i, p)| { self.collection() .sig_for_dataset(i as Idx) .unwrap_or_else(|_| panic!("Error processing {}", p.internal_location())) }) .collect() } fn signature_refs(&self) -> Vec<&Self::Item> { unimplemented!() } } sourmash-bio-sourmash-bc3c0f8/src/core/src/index/mod.rs000066400000000000000000000344011474157046300232320ustar00rootroot00000000000000//! # Indexing structures for fast similarity search //! //! An index organizes signatures to allow for fast similarity search. //! Some indices also support containment searches. pub mod linear; #[cfg(not(target_arch = "wasm32"))] #[cfg(feature = "branchwater")] pub mod revindex; pub mod search; use std::path::Path; use getset::{CopyGetters, Getters, Setters}; use log::trace; use serde::{Deserialize, Serialize}; use stats::{median, stddev}; use typed_builder::TypedBuilder; use crate::ani_utils::{ani_ci_from_containment, ani_from_containment}; use crate::encodings::Idx; use crate::index::search::{search_minhashes, search_minhashes_containment}; use crate::prelude::*; use crate::selection::Selection; use crate::signature::SigsTrait; use crate::sketch::minhash::KmerMinHash; use crate::storage::SigStore; use crate::Error::CannotUpsampleScaled; use crate::Result; #[derive(TypedBuilder, CopyGetters, Getters, Setters, Serialize, Deserialize, Debug, PartialEq)] pub struct GatherResult { #[getset(get_copy = "pub")] intersect_bp: u64, #[getset(get_copy = "pub")] f_orig_query: f64, #[getset(get_copy = "pub")] f_match: f64, #[getset(get_copy = "pub")] f_unique_to_query: f64, #[getset(get_copy = "pub")] f_unique_weighted: f64, #[getset(get_copy = "pub")] average_abund: f64, #[getset(get_copy = "pub")] median_abund: f64, #[getset(get_copy = "pub")] std_abund: f64, #[getset(get = "pub")] filename: String, #[getset(get = "pub")] name: String, #[getset(get = "pub")] md5: String, #[serde(skip)] match_: SigStore, #[getset(get_copy = "pub")] f_match_orig: f64, #[getset(get_copy = "pub")] unique_intersect_bp: u64, #[getset(get_copy = "pub")] gather_result_rank: u32, #[getset(get_copy = "pub")] remaining_bp: u64, #[getset(get_copy = "pub")] n_unique_weighted_found: u64, #[getset(get_copy = "pub")] total_weighted_hashes: u64, #[getset(get_copy = "pub")] sum_weighted_found: u64, #[getset(get_copy = "pub")] query_containment_ani: f64, #[getset(get_copy = "pub")] #[serde(skip_serializing_if = "Option::is_none")] query_containment_ani_ci_low: Option, #[getset(get_copy = "pub")] #[serde(skip_serializing_if = "Option::is_none")] query_containment_ani_ci_high: Option, #[getset(get_copy = "pub")] match_containment_ani: f64, #[getset(get_copy = "pub")] #[serde(skip_serializing_if = "Option::is_none")] match_containment_ani_ci_low: Option, #[getset(get_copy = "pub")] #[serde(skip_serializing_if = "Option::is_none")] match_containment_ani_ci_high: Option, #[getset(get_copy = "pub")] average_containment_ani: f64, #[getset(get_copy = "pub")] max_containment_ani: f64, } impl GatherResult { pub fn get_match(&self) -> Signature { self.match_.clone().into() } } type SigCounter = counter::Counter; pub trait Index<'a> { type Item: Comparable; //type SignatureIterator: Iterator; fn find(&self, search_fn: F, sig: &Self::Item, threshold: f64) -> Result> where F: Fn(&dyn Comparable, &Self::Item, f64) -> bool, { Ok(self .signature_refs() .into_iter() .flat_map(|node| { if search_fn(&node, sig, threshold) { Some(node) } else { None } }) .collect()) } fn search( &self, sig: &Self::Item, threshold: f64, containment: bool, ) -> Result> { if containment { self.find(search_minhashes_containment, sig, threshold) } else { self.find(search_minhashes, sig, threshold) } } //fn gather(&self, sig: &Self::Item, threshold: f64) -> Result>; fn insert(&mut self, node: Self::Item) -> Result<()>; fn batch_insert(&mut self, nodes: Vec) -> Result<()> { for node in nodes { self.insert(node)?; } Ok(()) } fn save>(&self, path: P) -> Result<()>; fn load>(path: P) -> Result<()>; fn signatures(&self) -> Vec; fn signature_refs(&self) -> Vec<&Self::Item>; fn len(&self) -> usize { self.signature_refs().len() } fn is_empty(&self) -> bool { self.len() == 0 } /* fn iter_signatures(&self) -> Self::SignatureIterator; */ } impl Comparable for &N where N: Comparable, { fn similarity(&self, other: &L) -> f64 { (*self).similarity(other) } fn containment(&self, other: &L) -> f64 { (*self).containment(other) } } #[allow(clippy::too_many_arguments)] pub fn calculate_gather_stats( orig_query: &KmerMinHash, remaining_query: KmerMinHash, match_sig: SigStore, match_size: usize, gather_result_rank: u32, sum_weighted_found: u64, total_weighted_hashes: u64, calc_abund_stats: bool, calc_ani_ci: bool, confidence: Option, ) -> Result<(GatherResult, (Vec, u64))> { // get match_mh let match_mh = match_sig.minhash().expect("cannot retrieve sketch"); // it's ok to downsample match, but query is often big and repeated, // so we do not allow downsampling of query in this function. if match_mh.scaled() > remaining_query.scaled() { return Err(CannotUpsampleScaled); } let match_mh = match_mh .clone() .downsample_scaled(remaining_query.scaled()) .expect("cannot downsample match"); // calculate intersection let isect = match_mh .intersection(&remaining_query) .expect("could not do intersection"); let isect_size = isect.0.len(); trace!("isect_size: {}", isect_size); trace!("query.size: {}", remaining_query.size()); //bp remaining in subtracted query let remaining_bp = (remaining_query.size() - isect_size) as u64 * remaining_query.scaled() as u64; // stats for this match vs original query let (intersect_orig, _) = match_mh.intersection_size(orig_query).unwrap(); let intersect_bp = match_mh.scaled() as u64 * intersect_orig; let f_orig_query = intersect_orig as f64 / orig_query.size() as f64; let f_match_orig = intersect_orig as f64 / match_mh.size() as f64; // stats for this match vs current (subtracted) query let f_match = match_size as f64 / match_mh.size() as f64; let unique_intersect_bp = match_mh.scaled() as u64 * isect_size as u64; let f_unique_to_query = isect_size as f64 / orig_query.size() as f64; // // get ANI values let ksize = match_mh.ksize() as f64; let query_containment_ani = ani_from_containment(f_orig_query, ksize); let match_containment_ani = ani_from_containment(f_match_orig, ksize); let mut query_containment_ani_ci_low = None; let mut query_containment_ani_ci_high = None; let mut match_containment_ani_ci_low = None; let mut match_containment_ani_ci_high = None; if calc_ani_ci { let n_unique_kmers = match_mh.n_unique_kmers(); let (qani_low, qani_high) = ani_ci_from_containment( f_unique_to_query, ksize, match_mh.scaled(), n_unique_kmers, confidence, )?; query_containment_ani_ci_low = Some(qani_low); query_containment_ani_ci_high = Some(qani_high); let (mani_low, mani_high) = ani_ci_from_containment( f_match, ksize, match_mh.scaled(), n_unique_kmers, confidence, )?; match_containment_ani_ci_low = Some(mani_low); match_containment_ani_ci_high = Some(mani_high); } let average_containment_ani = (query_containment_ani + match_containment_ani) / 2.0; let max_containment_ani = f64::max(query_containment_ani, match_containment_ani); // set up non-abundance weighted values let mut f_unique_weighted = f_unique_to_query; let mut average_abund = 1.0; let mut median_abund = 1.0; let mut std_abund = 0.0; // should these default to the unweighted numbers? let mut n_unique_weighted_found = 0; let mut sum_total_weighted_found = 0; // If abundance, calculate abund-related metrics (vs current query) if calc_abund_stats { // take abunds from subtracted query let (abunds, unique_weighted_found) = match match_mh.inflated_abundances(&remaining_query) { Ok((abunds, unique_weighted_found)) => (abunds, unique_weighted_found), Err(e) => { return Err(e); } }; n_unique_weighted_found = unique_weighted_found; sum_total_weighted_found = sum_weighted_found + n_unique_weighted_found; f_unique_weighted = n_unique_weighted_found as f64 / total_weighted_hashes as f64; average_abund = n_unique_weighted_found as f64 / abunds.len() as f64; // todo: try to avoid clone for these? median_abund = median(abunds.iter().cloned()).unwrap(); std_abund = stddev(abunds.iter().cloned()); } let result = GatherResult::builder() .intersect_bp(intersect_bp) .f_orig_query(f_orig_query) .f_match(f_match) .f_unique_to_query(f_unique_to_query) .f_unique_weighted(f_unique_weighted) .average_abund(average_abund) .median_abund(median_abund) .std_abund(std_abund) .filename(match_sig.filename()) .name(match_sig.name()) .md5(match_sig.md5sum()) .match_(match_sig) .f_match_orig(f_match_orig) .unique_intersect_bp(unique_intersect_bp) .gather_result_rank(gather_result_rank) .remaining_bp(remaining_bp) .n_unique_weighted_found(n_unique_weighted_found) .query_containment_ani(query_containment_ani) .query_containment_ani_ci_low(query_containment_ani_ci_low) .query_containment_ani_ci_high(query_containment_ani_ci_high) .match_containment_ani_ci_low(match_containment_ani_ci_low) .match_containment_ani_ci_high(match_containment_ani_ci_high) .match_containment_ani(match_containment_ani) .average_containment_ani(average_containment_ani) .max_containment_ani(max_containment_ani) .sum_weighted_found(sum_total_weighted_found) .total_weighted_hashes(total_weighted_hashes) .build(); Ok((result, isect)) } #[cfg(test)] mod test_calculate_gather_stats { use super::*; use crate::cmd::ComputeParameters; use crate::encodings::HashFunctions; use crate::signature::Signature; use crate::sketch::minhash::KmerMinHash; use crate::sketch::Sketch; // use std::f64::EPSILON; // TODO: use f64::EPSILON when we bump MSRV const EPSILON: f64 = 0.01; #[test] fn test_calculate_gather_stats() { let scaled = 10; let params = ComputeParameters::builder() .ksizes(vec![31]) .scaled(scaled) .build(); let mut match_sig = Signature::from_params(¶ms); // create two minhash let mut match_mh = KmerMinHash::new(scaled, 31, HashFunctions::Murmur64Dna, 42, true, 0); match_mh.add_hash_with_abundance(1, 5); match_mh.add_hash_with_abundance(3, 3); match_mh.add_hash_with_abundance(5, 2); match_mh.add_hash_with_abundance(8, 2); match_mh.add_hash_with_abundance(11, 2); // Non-matching hash match_sig.reset_sketches(); match_sig.push(Sketch::MinHash(match_mh.clone())); match_sig.set_filename("match-filename"); match_sig.set_name("match-name"); eprintln!("num_sketches: {:?}", match_sig.size()); eprintln!("match_md5: {:?}", match_sig.md5sum()); // Setup orig_query minhash with abundances and non-matching hash let mut orig_query = KmerMinHash::new(scaled, 31, HashFunctions::Murmur64Dna, 42, true, 0); orig_query.add_hash_with_abundance(1, 3); orig_query.add_hash_with_abundance(3, 2); orig_query.add_hash_with_abundance(5, 1); orig_query.add_hash_with_abundance(6, 1); // Non-matching hash orig_query.add_hash_with_abundance(8, 1); orig_query.add_hash_with_abundance(10, 1); // Non-matching hash let query = orig_query.clone(); let total_weighted_hashes = orig_query.sum_abunds(); let match_size = 4; let gather_result_rank = 0; let calc_abund_stats = true; let calc_ani_ci = false; let (result, _isect) = calculate_gather_stats( &orig_query, query, match_sig.into(), match_size, gather_result_rank, 0, total_weighted_hashes.try_into().unwrap(), calc_abund_stats, calc_ani_ci, None, ) .unwrap(); // first, print all results assert_eq!(result.filename(), "match-filename"); assert_eq!(result.name(), "match-name"); assert_eq!(result.md5(), "f54b271a62fb7e2856e7b8a33e741b6e"); assert_eq!(result.gather_result_rank, 0); assert_eq!(result.remaining_bp, 20); // results from match vs current query assert_eq!(result.f_match, 0.8); assert_eq!(result.unique_intersect_bp, 40); assert_eq!(result.f_unique_to_query, 4.0 / 6.0); eprintln!("{}", result.f_unique_weighted); assert_eq!(result.f_unique_weighted, 7. / 9.); assert_eq!(result.average_abund, 1.75); assert_eq!(result.median_abund, 1.5); assert_eq!(result.std_abund, 0.82915619758885); // results from match vs orig_query assert_eq!(result.intersect_bp, 40); assert_eq!(result.f_orig_query, 4.0 / 6.0); assert_eq!(result.f_match_orig, 4.0 / 5.0); assert!((result.average_containment_ani - 0.98991665567826).abs() < EPSILON); assert!((result.match_containment_ani - 0.9928276657672302).abs() < EPSILON); assert!((result.query_containment_ani - 0.9870056455892898).abs() < EPSILON); assert!((result.max_containment_ani - 0.9928276657672302).abs() < EPSILON); assert_eq!(result.total_weighted_hashes, 9); assert_eq!(result.n_unique_weighted_found, 7); assert_eq!(result.sum_weighted_found, 7); } } sourmash-bio-sourmash-bc3c0f8/src/core/src/index/revindex/000077500000000000000000000000001474157046300237275ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/src/index/revindex/disk_revindex.rs000066400000000000000000000506051474157046300271410ustar00rootroot00000000000000use std::cmp::max; use std::hash::{BuildHasher, BuildHasherDefault}; use std::path::Path; use std::sync::atomic::{AtomicUsize, Ordering}; use std::sync::{Arc, RwLock}; use byteorder::{LittleEndian, WriteBytesExt}; use log::{info, trace}; use rayon::prelude::*; use rocksdb::MergeOperands; use crate::collection::{Collection, CollectionSet}; use crate::encodings::{Color, Idx}; use crate::index::revindex::{ self as module, stats_for_cf, Datasets, DbStats, HashToColor, QueryColors, RevIndexOps, MANIFEST, PROCESSED, STORAGE_SPEC, VERSION, }; use crate::index::{calculate_gather_stats, GatherResult, SigCounter}; use crate::manifest::Manifest; use crate::prelude::*; use crate::sketch::minhash::{KmerMinHash, KmerMinHashBTree}; use crate::sketch::Sketch; use crate::storage::{ rocksdb::{cf_descriptors, db_options, ALL_CFS, DB, HASHES, METADATA}, InnerStorage, RocksDBStorage, Storage, }; use crate::Result; const DB_VERSION: u8 = 1; fn compute_color(idxs: &Datasets) -> Color { let s = BuildHasherDefault::::default(); s.hash_one(idxs) } #[derive(Clone)] pub struct RevIndex { db: Arc, collection: Arc, processed: Arc>, } pub(crate) fn merge_datasets( _: &[u8], existing_val: Option<&[u8]>, operands: &MergeOperands, ) -> Option> { let mut datasets = existing_val .and_then(Datasets::from_slice) .unwrap_or_default(); for op in operands { let new_vals = Datasets::from_slice(op).unwrap(); datasets.union(new_vals); } // TODO: optimization! if nothing changed, skip as_bytes() datasets.as_bytes() } /* TODO: need the repair_cf variant, not available in rocksdb-rust yet pub fn repair(path: &Path) { let opts = db_options(); DB::repair(&opts, path).unwrap() } */ impl RevIndex { pub fn create(path: &Path, collection: CollectionSet) -> Result { let mut opts = db_options(); opts.create_if_missing(true); opts.create_missing_column_families(true); // prepare column family descriptors let cfs = cf_descriptors(); let db = Arc::new(DB::open_cf_descriptors(&opts, path, cfs).unwrap()); let processed_sigs = AtomicUsize::new(0); let collection = Arc::new(collection); let processed = Arc::new(RwLock::new(Self::load_processed( db.clone(), collection.clone(), true, )?)); let index = Self { db, collection, processed: processed.clone(), }; index.collection.par_iter().for_each(|(dataset_id, _)| { // check if this dataset_id was processed already // call map_hashes_colors only if not already processed if !processed.read().unwrap().contains(&dataset_id) { let i = processed_sigs.fetch_add(1, Ordering::SeqCst); if i % 1000 == 0 { info!("Processed {} reference sigs", i); } index.map_hashes_colors(dataset_id as Idx); // if cached in a new field in the RevIndex, // then update the cache too processed.write().unwrap().extend([dataset_id]); } }); index.save_collection().expect("Error saving collection"); info!("Compact SSTs"); index.compact(); info!( "Done! Processed {} reference sigs", processed_sigs.into_inner() ); Ok(module::RevIndex::Plain(index)) } pub fn open>( path: P, read_only: bool, storage_spec: Option<&str>, ) -> Result { let mut opts = db_options(); opts.create_if_missing(true); opts.create_missing_column_families(true); // prepare column family descriptors let cfs = cf_descriptors(); let db = if read_only { Arc::new(DB::open_cf_descriptors_read_only( &opts, path.as_ref(), cfs, false, )?) } else { Arc::new(DB::open_cf_descriptors(&opts, path.as_ref(), cfs)?) }; let collection = Arc::new(Self::load_collection_from_rocksdb( db.clone(), storage_spec, )?); let processed = Arc::new(RwLock::new(Self::load_processed( db.clone(), collection.clone(), false, )?)); Ok(module::RevIndex::Plain(Self { db, collection, processed, })) } fn load_processed( db: Arc, collection: Arc, assume_empty: bool, ) -> Result { let cf_metadata = db.cf_handle(METADATA).unwrap(); if let Some(rdr) = db.get_pinned_cf(&cf_metadata, PROCESSED)? { // convert rdr to Datasets Datasets::from_slice(&rdr) .ok_or_else(|| todo!("throw error from deserializing Datasets")) } else if assume_empty { Ok(Datasets::default()) } else { let all_datasets: Vec<_> = (0..collection.manifest().len()).map(|v| v as Idx).collect(); Ok(Datasets::new(&all_datasets)) } } fn load_collection_from_rocksdb( db: Arc, storage_spec: Option<&str>, ) -> Result { let cf_metadata = db.cf_handle(METADATA).unwrap(); let rdr = db.get_cf(&cf_metadata, VERSION)?.unwrap(); assert_eq!(rdr[0], DB_VERSION); let rdr = db.get_cf(&cf_metadata, MANIFEST)?.unwrap(); let manifest = Manifest::from_reader(&rdr[..])?; let spec = match storage_spec { Some(spec) => spec.into(), None => { let db_spec = db.get_cf(&cf_metadata, STORAGE_SPEC)?; String::from_utf8(db_spec.unwrap()).map_err(|e| e.utf8_error())? } }; let storage = if spec == "rocksdb://" { InnerStorage::new(RocksDBStorage::from_db(db.clone())) } else { InnerStorage::from_spec(spec)? }; Collection::new(manifest, storage).try_into() } fn save_collection(&self) -> Result<()> { let cf_metadata = self.db.cf_handle(METADATA).unwrap(); // save DB version // TODO: probably should go together with a more general // saving procedure used in create/update self.db.put_cf(&cf_metadata, VERSION, [DB_VERSION])?; // write manifest let mut wtr = vec![]; { self.collection.manifest().to_writer(&mut wtr)?; } self.db.put_cf(&cf_metadata, MANIFEST, &wtr[..])?; // write storage spec let spec = self.collection.storage().spec(); // TODO: check if spec if memstorage, would probably have to // save into rocksdb in that case! self.db.put_cf(&cf_metadata, STORAGE_SPEC, spec)?; Ok(()) } fn map_hashes_colors(&self, dataset_id: Idx) { let search_sig = self .collection .sig_for_dataset(dataset_id) .expect("Couldn't find a compatible Signature"); let search_mh = &search_sig.sketches()[0]; let colors = Datasets::new(&[dataset_id]).as_bytes().unwrap(); let cf_hashes = self.db.cf_handle(HASHES).unwrap(); let hashes = match search_mh { Sketch::MinHash(mh) => mh.mins(), Sketch::LargeMinHash(mh) => mh.mins(), _ => unimplemented!(), }; let mut hash_bytes = [0u8; 8]; for hash in hashes { (&mut hash_bytes[..]) .write_u64::(hash) .expect("error writing bytes"); self.db .merge_cf(&cf_hashes, &hash_bytes[..], colors.as_slice()) .expect("error merging"); } // finished processing this dataset, // do a merge_cf in the PROCESSED key in metadata // to account for that. let cf_metadata = self.db.cf_handle(METADATA).unwrap(); self.db .merge_cf(&cf_metadata, PROCESSED, colors.as_slice()) .expect("error merging"); } } impl RevIndexOps for RevIndex { fn counter_for_query(&self, query: &KmerMinHash) -> SigCounter { info!("Collecting hashes"); let cf_hashes = self.db.cf_handle(HASHES).unwrap(); let hashes_iter = query.iter_mins().map(|hash| { let mut v = vec![0_u8; 8]; (&mut v[..]) .write_u64::(*hash) .expect("error writing bytes"); (&cf_hashes, v) }); info!("Multi get"); self.db .multi_get_cf(hashes_iter) .into_iter() .filter_map(|r| r.ok().unwrap_or(None)) .flat_map(|raw_datasets| { let new_vals = Datasets::from_slice(&raw_datasets).unwrap(); new_vals.into_iter() }) .collect() } fn prepare_gather_counters( &self, query: &KmerMinHash, ) -> (SigCounter, QueryColors, HashToColor) { let cf_hashes = self.db.cf_handle(HASHES).unwrap(); let hashes_iter = query.iter_mins().map(|hash| { let mut v = vec![0_u8; 8]; (&mut v[..]) .write_u64::(*hash) .expect("error writing bytes"); (&cf_hashes, v) }); /* build a HashToColors for query, and a QueryColors (Color -> Datasets) mapping. Loading Datasets from rocksdb for every hash takes too long. */ let mut query_colors: QueryColors = Default::default(); let mut counter: SigCounter = Default::default(); info!("Building hash_to_colors and query_colors"); let hash_to_colors = query .iter_mins() .zip(self.db.multi_get_cf(hashes_iter)) .filter_map(|(k, r)| { let raw = r.ok().unwrap_or(None); raw.map(|raw| { let new_vals = Datasets::from_slice(&raw).unwrap(); let color = compute_color(&new_vals); query_colors .entry(color) .or_insert_with(|| new_vals.clone()); counter.update(new_vals); (*k, color) }) }) .collect(); (counter, query_colors, hash_to_colors) } fn matches_from_counter(&self, counter: SigCounter, threshold: usize) -> Vec<(String, usize)> { info!("get matches from counter"); counter .most_common() .into_iter() .filter_map(|(dataset_id, size)| { if size >= threshold { let row = &self .collection .record_for_dataset(dataset_id) .expect("dataset not found"); let name = [row.name(), row.filename(), row.md5()] .into_iter() .find(|v| !v.is_empty()) .unwrap(); // guaranteed to succeed because `md5` always exists Some((name.into(), size)) } else { None } }) .collect() } fn gather( &self, mut counter: SigCounter, query_colors: QueryColors, hash_to_color: HashToColor, threshold: usize, orig_query: &KmerMinHash, selection: Option, ) -> Result> { let mut match_size = usize::MAX; let mut matches = vec![]; let mut query = KmerMinHashBTree::from(orig_query.clone()); let mut sum_weighted_found = 0; let _selection = selection.unwrap_or_else(|| self.collection.selection()); let total_weighted_hashes = orig_query.sum_abunds(); // or set this with user --track-abundance? let calc_abund_stats = orig_query.track_abundance(); // todo: let user pass these options in let calc_ani_ci = false; let ani_confidence_interval_fraction = None; while match_size > threshold && !counter.is_empty() { trace!("counter len: {}", counter.len()); trace!("match size: {}", match_size); let (dataset_id, size) = counter.k_most_common_ordered(1)[0]; match_size = if size >= threshold { size } else { break }; // handle special case where threshold was set to 0 if match_size == 0 { break; } let match_sig = self.collection.sig_for_dataset(dataset_id)?; let match_mh = match_sig.minhash().unwrap().clone(); // make downsampled minhashes let max_scaled = max(match_mh.scaled(), query.scaled()); let match_mh = match_mh .downsample_scaled(max_scaled) .expect("cannot downsample match"); // repeatedly downsample query, then extract to KmerMinHash // => calculate_gather_stats query = query .downsample_scaled(max_scaled) .expect("cannot downsample query"); let query_mh = KmerMinHash::from(query.clone()); // just calculate essentials here let gather_result_rank = matches.len() as u32; // grab the specific intersection: // Calculate stats let (gather_result, isect) = calculate_gather_stats( orig_query, query_mh, match_sig, match_size, gather_result_rank, sum_weighted_found, total_weighted_hashes, calc_abund_stats, calc_ani_ci, ani_confidence_interval_fraction, ) .expect("could not calculate gather stats"); // use intersection from calc_gather_stats to make a KmerMinHash. let mut isect_mh = match_mh.clone(); isect_mh.clear(); isect_mh.add_many(&isect.0)?; // keep track of the sum weighted found sum_weighted_found = gather_result.sum_weighted_found(); matches.push(gather_result); trace!("Preparing counter for next round"); // Prepare counter for finding the next match by decrementing // all hashes found in the current match in other datasets // TODO: not used at the moment, so just skip. query.remove_many(match_mh.iter_mins().copied())?; // is there a better way? // TODO: Use HashesToColors here instead. If not initialized, // build it. isect .0 .iter() .filter_map(|hash| hash_to_color.get(hash)) .flat_map(|color| { // TODO: remove this clone query_colors.get(color).unwrap().clone().into_iter() }) .for_each(|dataset| { // TODO: collect the flat_map into a Counter, and remove more // than one at a time... counter.entry(dataset).and_modify(|e| *e -= 1); }); counter.remove(&dataset_id); } Ok(matches) } fn update(mut self, collection: CollectionSet) -> Result { // TODO: verify new collection manifest is a superset of current one, // and the initial chunk is the same self.collection.check_superset(&collection)?; info!("sigs in the original index: {}", self.collection.len()); self.collection = Arc::new(collection); info!( "sigs in the new index once finished: {}", self.collection.len() ); let processed = self.processed.clone(); info!( "sigs left to process: {}", self.collection.len() - processed.read().unwrap().len() ); // process the remainder let processed_sigs = AtomicUsize::new(0); self.collection.par_iter().for_each(|(dataset_id, _)| { // check if this dataset_id was processed already // call map_hashes_colors only if not already processed if !processed.read().unwrap().contains(&dataset_id) { let i = processed_sigs.fetch_add(1, Ordering::SeqCst); if i % 1000 == 0 { info!("Processed {} reference sigs", i); } self.map_hashes_colors(dataset_id as Idx); // if cached in a new field in the RevIndex, // then update the cache too processed.write().unwrap().extend([dataset_id]); } }); self.save_collection().expect("Error saving collection"); info!("Compact SSTs"); self.compact(); info!( "Processed additional {} reference sigs", processed_sigs.into_inner() ); Ok(module::RevIndex::Plain(self)) } fn check(&self, quick: bool) -> DbStats { stats_for_cf(self.db.clone(), HASHES, true, quick) } fn compact(&self) { for cf_name in ALL_CFS { let cf = self.db.cf_handle(cf_name).unwrap(); self.db.compact_range_cf(&cf, None::<&[u8]>, None::<&[u8]>) } } fn flush(&self) -> Result<()> { self.db.flush_wal(true)?; for cf_name in [HASHES, METADATA] { let cf = self.db.cf_handle(cf_name).unwrap(); self.db.flush_cf(&cf)?; } Ok(()) } fn collection(&self) -> &CollectionSet { &self.collection } fn internalize_storage(&mut self) -> Result<()> { // check if collection is already internal, if so return if self.collection.storage().spec() == "rocksdb://" { return Ok(()); } // build new rocksdb storage from db let new_storage = RocksDBStorage::from_db(self.db.clone()); // use manifest to copy from current storage to new one self.collection() .par_iter() .try_for_each(|(_, record)| -> Result<()> { let path = record.internal_location().as_str(); let sig_data = self.collection.storage().load(path).unwrap(); new_storage.save(path, &sig_data)?; Ok(()) })?; // Replace storage for collection. // Using unchecked version because we just used the manifest // above to make sure the storage is still consistent unsafe { if let Some(v) = Arc::get_mut(&mut self.collection) { v.set_storage_unchecked(InnerStorage::new(new_storage)) } } // write storage spec let cf_metadata = self.db.cf_handle(METADATA).unwrap(); let spec = "rocksdb://"; self.db.put_cf(&cf_metadata, STORAGE_SPEC, spec)?; Ok(()) } fn convert(&self, _output_db: module::RevIndex) -> Result<()> { todo!() /* if let RevIndex::Color(db) = output_db { let other_db = db.db; let cf_hashes = self.db.cf_handle(HASHES).unwrap(); info!("start converting colors"); let mut color_bytes = [0u8; 8]; let iter = self .db .iterator_cf(&cf_hashes, rocksdb::IteratorMode::Start); for (key, value) in iter { let datasets = Datasets::from_slice(&value).unwrap(); let new_idx: Vec<_> = datasets.into_iter().collect(); let new_color = Colors::update(other_db.clone(), None, new_idx.as_slice()).unwrap(); (&mut color_bytes[..]) .write_u64::(new_color) .expect("error writing bytes"); other_db .put_cf(&cf_hashes, &key[..], &color_bytes[..]) .unwrap(); } info!("finished converting colors"); info!("copying sigs to output"); let cf_sigs = self.db.cf_handle(SIGS).unwrap(); let iter = self.db.iterator_cf(&cf_sigs, rocksdb::IteratorMode::Start); for (key, value) in iter { other_db.put_cf(&cf_sigs, &key[..], &value[..]).unwrap(); } info!("finished copying sigs to output"); Ok(()) } else { todo!() } */ } } sourmash-bio-sourmash-bc3c0f8/src/core/src/index/revindex/mem_revindex.rs000066400000000000000000000346231474157046300267670ustar00rootroot00000000000000use std::sync::atomic::{AtomicUsize, Ordering}; use camino::Utf8Path as Path; use camino::Utf8PathBuf as PathBuf; use log::{debug, info}; #[cfg(feature = "parallel")] use rayon::prelude::*; use crate::collection::Collection; use crate::encodings::{Colors, Idx}; use crate::index::linear::LinearIndex; use crate::index::revindex::HashToColor; use crate::index::{GatherResult, Index, SigCounter}; use crate::prelude::*; use crate::signature::{Signature, SigsTrait}; use crate::sketch::minhash::KmerMinHash; use crate::sketch::Sketch; use crate::Result; pub struct RevIndex { linear: LinearIndex, hash_to_color: HashToColor, colors: Colors, } impl LinearIndex { fn index( self, threshold: usize, merged_query: Option, queries: Option<&[KmerMinHash]>, ) -> RevIndex { let processed_sigs = AtomicUsize::new(0); #[cfg(feature = "parallel")] let sig_iter = self.collection().par_iter(); #[cfg(not(feature = "parallel"))] let sig_iter = self.collection().iter(); let filtered_sigs = sig_iter.enumerate().filter_map(|(dataset_id, _)| { let i = processed_sigs.fetch_add(1, Ordering::SeqCst); if i % 1000 == 0 { info!("Processed {} reference sigs", i); } let search_sig = self .collection() .sig_for_dataset(dataset_id as Idx) .expect("Error loading sig") .into(); RevIndex::map_hashes_colors( dataset_id as Idx, &search_sig, queries, &merged_query, threshold, self.template(), ) }); #[cfg(feature = "parallel")] let (hash_to_color, colors) = filtered_sigs.reduce( || (HashToColor::new(), Colors::default()), HashToColor::reduce_hashes_colors, ); #[cfg(not(feature = "parallel"))] let (hash_to_color, colors) = filtered_sigs.fold( (HashToColor::new(), Colors::default()), HashToColor::reduce_hashes_colors, ); RevIndex { hash_to_color, colors, linear: self, } } } impl RevIndex { pub fn new( search_sigs: &[PathBuf], selection: &Selection, threshold: usize, queries: Option<&[KmerMinHash]>, _keep_sigs: bool, ) -> Result { // If threshold is zero, let's merge all queries and save time later let merged_query = queries.and_then(|qs| Self::merge_queries(qs, threshold)); let collection = Collection::from_paths(search_sigs)?.select(selection)?; let linear = LinearIndex::from_collection(collection.try_into()?); Ok(linear.index(threshold, merged_query, queries)) } pub fn from_zipfile>( zipfile: P, selection: &Selection, threshold: usize, queries: Option<&[KmerMinHash]>, _keep_sigs: bool, ) -> Result { // If threshold is zero, let's merge all queries and save time later let merged_query = queries.and_then(|qs| Self::merge_queries(qs, threshold)); let collection = Collection::from_zipfile(zipfile)?.select(selection)?; let linear = LinearIndex::from_collection(collection.try_into()?); Ok(linear.index(threshold, merged_query, queries)) } fn merge_queries(qs: &[KmerMinHash], threshold: usize) -> Option { if threshold == 0 { let mut merged = qs[0].clone(); for query in &qs[1..] { merged.merge(query).unwrap(); } Some(merged) } else { None } } pub fn new_with_sigs( search_sigs: Vec, selection: &Selection, threshold: usize, queries: Option<&[KmerMinHash]>, ) -> Result { // If threshold is zero, let's merge all queries and save time later let merged_query = queries.and_then(|qs| Self::merge_queries(qs, threshold)); let collection = Collection::from_sigs(search_sigs)?.select(selection)?; let linear = LinearIndex::from_collection(collection.try_into()?); let idx = linear.index(threshold, merged_query, queries); Ok(idx) } fn map_hashes_colors( dataset_id: Idx, search_sig: &Signature, queries: Option<&[KmerMinHash]>, merged_query: &Option, threshold: usize, template: &Sketch, ) -> Option<(HashToColor, Colors)> { let mut search_mh = None; if let Some(Sketch::MinHash(mh)) = search_sig.select_sketch(template) { search_mh = Some(mh); } let search_mh = search_mh.expect("Couldn't find a compatible MinHash"); let mut hash_to_color = HashToColor::new(); let mut colors = Colors::default(); if let Some(qs) = queries { if let Some(ref merged) = merged_query { let (matched_hashes, intersection) = merged.intersection(search_mh).unwrap(); if !matched_hashes.is_empty() || intersection > threshold as u64 { hash_to_color.add_to(&mut colors, dataset_id, matched_hashes); } } else { for query in qs { let (matched_hashes, intersection) = query.intersection(search_mh).unwrap(); if !matched_hashes.is_empty() || intersection > threshold as u64 { hash_to_color.add_to(&mut colors, dataset_id, matched_hashes); } } } } else { let matched = search_mh.mins(); let size = matched.len() as u64; if !matched.is_empty() || size > threshold as u64 { hash_to_color.add_to(&mut colors, dataset_id, matched); } }; if hash_to_color.is_empty() { None } else { Some((hash_to_color, colors)) } } pub fn search( &self, counter: SigCounter, similarity: bool, threshold: usize, ) -> Result> { self.linear.search(counter, similarity, threshold) } pub fn gather( &self, mut counter: SigCounter, threshold: usize, query: &KmerMinHash, ) -> Result> { let mut match_size = usize::MAX; let mut matches = vec![]; while match_size > threshold && !counter.is_empty() { let (dataset_id, size) = counter.most_common()[0]; match_size = if size >= threshold { size } else { break }; let result = self .linear .gather_round(dataset_id, match_size, query, matches.len())?; if let Some(Sketch::MinHash(match_mh)) = result.match_.select_sketch(self.linear.template()) { // Prepare counter for finding the next match by decrementing // all hashes found in the current match in other datasets for hash in match_mh.iter_mins() { if let Some(color) = self.hash_to_color.get(hash) { counter.subtract(self.colors.indices(color).cloned()); } } counter.remove(&dataset_id); matches.push(result); } else { unimplemented!() } } Ok(matches) } pub fn template(&self) -> Sketch { self.linear.template().clone() } // TODO: mh should be a sketch, or even a sig... pub(crate) fn find_signatures( &self, mh: &KmerMinHash, threshold: f64, containment: bool, _ignore_scaled: bool, ) -> Result> { // TODO: proper threshold calculation let threshold: usize = (threshold * (mh.size() as f64)) as _; let counter = self.counter_for_query(mh); debug!( "number of matching signatures for hashes: {}", counter.len() ); let mut results = vec![]; for (dataset_id, size) in counter.most_common() { let match_size = if size >= threshold { size } else { break }; let match_sig = self.linear.sig_for_dataset(dataset_id)?; let match_path = self .linear .collection() .record_for_dataset(dataset_id)? .internal_location(); let mut match_mh = None; if let Some(Sketch::MinHash(mh)) = match_sig.select_sketch(self.linear.template()) { match_mh = Some(mh); } let match_mh = match_mh.unwrap(); if size >= threshold { let score = if containment { size as f64 / mh.size() as f64 } else { size as f64 / (mh.size() + match_size - size) as f64 }; let filename = match_path.to_string(); let mut sig: Signature = match_sig.clone().into(); sig.reset_sketches(); sig.push(Sketch::MinHash(match_mh.clone())); results.push((score, sig, filename)); } else { break; }; } Ok(results) } pub fn counter_for_query(&self, query: &KmerMinHash) -> SigCounter { query .iter_mins() .filter_map(|hash| self.hash_to_color.get(hash)) .flat_map(|color| self.colors.indices(color)) .cloned() .collect() } } impl Index<'_> for RevIndex { type Item = Signature; fn insert(&mut self, _node: Self::Item) -> Result<()> { unimplemented!() } fn save>(&self, _path: P) -> Result<()> { unimplemented!() } fn load>(_path: P) -> Result<()> { unimplemented!() } fn len(&self) -> usize { self.linear.len() } fn signatures(&self) -> Vec { self.linear .signatures() .into_iter() .map(|sig| sig.into()) .collect() } fn signature_refs(&self) -> Vec<&Self::Item> { unimplemented!() } } #[cfg(test)] mod test { use super::*; use crate::index::revindex::prepare_query; use crate::Result; #[test] fn revindex_new() -> Result<()> { let selection = Selection::builder().ksize(31).scaled(10000).build(); let search_sigs = [ "../../tests/test-data/gather/GCF_000006945.2_ASM694v2_genomic.fna.gz.sig".into(), "../../tests/test-data/gather/GCF_000007545.1_ASM754v1_genomic.fna.gz.sig".into(), ]; let index = RevIndex::new(&search_sigs, &selection, 0, None, false)?; assert_eq!(index.colors.len(), 3); Ok(()) } #[test] fn revindex_many() -> Result<()> { let selection = Selection::builder().ksize(31).scaled(10000).build(); let search_sigs = [ "../../tests/test-data/gather/GCF_000006945.2_ASM694v2_genomic.fna.gz.sig".into(), "../../tests/test-data/gather/GCF_000007545.1_ASM754v1_genomic.fna.gz.sig".into(), "../../tests/test-data/gather/GCF_000008105.1_ASM810v1_genomic.fna.gz.sig".into(), ]; let index = RevIndex::new(&search_sigs, &selection, 0, None, false)?; //dbg!(&index.linear.collection().manifest); /* dbg!(&index.colors.colors); 0: 86 1: 132 2: 91 (0, 1): 53 (0, 2): 90 (1, 2): 26 (0, 1, 2): 261 union: 739 */ //assert_eq!(index.colors.len(), 3); assert_eq!(index.colors.len(), 7); Ok(()) } #[test] fn revindex_from_sigs() -> Result<()> { let selection = Selection::builder().ksize(31).scaled(10000).build(); let search_sigs: Vec = [ "../../tests/test-data/gather/GCF_000006945.2_ASM694v2_genomic.fna.gz.sig", "../../tests/test-data/gather/GCF_000007545.1_ASM754v1_genomic.fna.gz.sig", "../../tests/test-data/gather/GCF_000008105.1_ASM810v1_genomic.fna.gz.sig", ] .into_iter() .map(|path| Signature::from_path(path).unwrap().swap_remove(0)) .collect(); let index = RevIndex::new_with_sigs(search_sigs, &selection, 0, None)?; /* dbg!(&index.colors.colors); 0: 86 1: 132 2: 91 (0, 1): 53 (0, 2): 90 (1, 2): 26 (0, 1, 2): 261 union: 739 */ //assert_eq!(index.colors.len(), 3); assert_eq!(index.colors.len(), 7); Ok(()) } #[test] fn revindex_from_zipstorage() -> Result<()> { let selection = Selection::builder() .ksize(19) .scaled(100) .moltype(crate::encodings::HashFunctions::Murmur64Protein) .build(); let index = RevIndex::from_zipfile( "../../tests/test-data/prot/protein.zip", &selection, 0, None, false, ) .expect("error building from ziptorage"); assert_eq!(index.colors.len(), 3); let query_sig = Signature::from_path( "../../tests/test-data/prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", ) .expect("Error processing query") .swap_remove(0) .select(&selection)?; let mut query_mh = None; if let Some(q) = prepare_query(query_sig, &selection) { query_mh = Some(q); } let query_mh = query_mh.expect("Couldn't find a compatible MinHash"); let counter_rev = index.counter_for_query(&query_mh); let counter_lin = index.linear.counter_for_query(&query_mh); let results_rev = index.search(counter_rev, false, 0).unwrap(); let results_linear = index.linear.search(counter_lin, false, 0).unwrap(); assert_eq!(results_rev, results_linear); let counter_rev = index.counter_for_query(&query_mh); let counter_lin = index.linear.counter_for_query(&query_mh); let results_rev = index.gather(counter_rev, 0, &query_mh).unwrap(); let results_linear = index.linear.gather(counter_lin, 0, &query_mh).unwrap(); assert_eq!(results_rev.len(), 1); assert_eq!(results_rev, results_linear); Ok(()) } } sourmash-bio-sourmash-bc3c0f8/src/core/src/index/revindex/mod.rs000066400000000000000000001007471474157046300250650ustar00rootroot00000000000000pub mod disk_revindex; pub mod mem_revindex; use std::collections::HashMap; use std::hash::{Hash, Hasher}; use std::path::Path; use std::sync::Arc; use byteorder::{LittleEndian, WriteBytesExt}; use enum_dispatch::enum_dispatch; use getset::{Getters, Setters}; use nohash_hasher::BuildNoHashHasher; use roaring::RoaringBitmap; use serde::{Deserialize, Serialize}; use crate::collection::CollectionSet; use crate::encodings::{Color, Colors, Idx}; use crate::index::{GatherResult, SigCounter}; use crate::prelude::*; use crate::signature::Signature; use crate::sketch::minhash::KmerMinHash; use crate::sketch::Sketch; use crate::storage::rocksdb::{db_options, COLORS, DB}; use crate::HashIntoType; use crate::Result; // DB metadata saved in the METADATA column family const MANIFEST: &str = "manifest"; const STORAGE_SPEC: &str = "storage_spec"; const VERSION: &str = "version"; const PROCESSED: &str = "processed"; type QueryColors = HashMap; type HashToColorT = HashMap>; #[derive(Serialize, Deserialize)] pub struct HashToColor(HashToColorT); #[enum_dispatch(RevIndexOps)] pub enum RevIndex { //Color(color_revindex::ColorRevIndex), Plain(disk_revindex::RevIndex), //Mem(mem_revindex::RevIndex), } #[enum_dispatch] pub trait RevIndexOps { /* TODO: need the repair_cf variant, not available in rocksdb-rust yet pub fn repair(index: &Path, colors: bool); */ fn counter_for_query(&self, query: &KmerMinHash) -> SigCounter; fn matches_from_counter(&self, counter: SigCounter, threshold: usize) -> Vec<(String, usize)>; fn prepare_gather_counters( &self, query: &KmerMinHash, ) -> (SigCounter, QueryColors, HashToColor); fn update(self, collection: CollectionSet) -> Result where Self: Sized; fn compact(&self); fn flush(&self) -> Result<()>; fn convert(&self, output_db: RevIndex) -> Result<()>; fn check(&self, quick: bool) -> DbStats; fn gather( &self, counter: SigCounter, query_colors: QueryColors, hash_to_color: HashToColor, threshold: usize, query: &KmerMinHash, selection: Option, ) -> Result>; fn collection(&self) -> &CollectionSet; fn internalize_storage(&mut self) -> Result<()>; } impl HashToColor { fn new() -> Self { HashToColor(HashMap::< HashIntoType, Color, BuildNoHashHasher, >::with_hasher(BuildNoHashHasher::default())) } fn get(&self, hash: &HashIntoType) -> Option<&Color> { self.0.get(hash) } fn len(&self) -> usize { self.0.len() } fn is_empty(&self) -> bool { self.0.is_empty() } fn add_to(&mut self, colors: &mut Colors, dataset_id: Idx, matched_hashes: Vec) { let mut color = None; matched_hashes.into_iter().for_each(|hash| { color = Some(colors.update(color, &[dataset_id]).unwrap()); self.0.insert(hash, color.unwrap()); }); } fn reduce_hashes_colors( a: (HashToColor, Colors), b: (HashToColor, Colors), ) -> (HashToColor, Colors) { let ((small_hashes, small_colors), (mut large_hashes, mut large_colors)) = if a.0.len() > b.0.len() { (b, a) } else { (a, b) }; small_hashes.0.into_iter().for_each(|(hash, color)| { large_hashes .0 .entry(hash) .and_modify(|entry| { // Hash is already present. // Update the current color by adding the indices from // small_colors. let ids = small_colors.indices(&color); let new_color = large_colors.update(Some(*entry), ids).unwrap(); *entry = new_color; }) .or_insert_with(|| { // In this case, the hash was not present yet. // we need to create the same color from small_colors // into large_colors. let ids = small_colors.indices(&color); let new_color = large_colors.update(None, ids).unwrap(); assert_eq!(new_color, color); new_color }); }); (large_hashes, large_colors) } } impl FromIterator<(HashIntoType, Color)> for HashToColor { fn from_iter(iter: T) -> Self where T: IntoIterator, { HashToColor(HashToColorT::from_iter(iter)) } } impl RevIndex { /* TODO: need the repair_cf variant, not available in rocksdb-rust yet pub fn repair(index: &Path, colors: bool) { if colors { color_revindex::repair(index); } else { disk_revindex::repair(index); } } */ pub fn create>( index: P, collection: CollectionSet, colors: bool, ) -> Result { if colors { todo!() //color_revindex::ColorRevIndex::create(index) } else { disk_revindex::RevIndex::create(index.as_ref(), collection) } } pub fn open>(index: P, read_only: bool, spec: Option<&str>) -> Result { let opts = db_options(); let cfs = DB::list_cf(&opts, index.as_ref())?; if cfs.into_iter().any(|c| c == COLORS) { // TODO: ColorRevIndex can't be read-only for now, // due to pending unmerged colors todo!() //color_revindex::ColorRevIndex::open(index, false) } else { disk_revindex::RevIndex::open(index, read_only, spec) } } } pub fn prepare_query(search_sig: Signature, selection: &Selection) -> Option { let sig = search_sig.select(selection).ok(); sig.and_then(|sig| { if let Sketch::MinHash(mh) = sig.sketches().swap_remove(0) { Some(mh) } else { None } }) } #[derive(Debug, Default, Clone)] pub enum Datasets { #[default] Empty, Unique(Idx), Many(RoaringBitmap), } impl Hash for Datasets { fn hash(&self, state: &mut H) where H: Hasher, { match self { Self::Empty => todo!(), Self::Unique(v) => v.hash(state), Self::Many(v) => { for value in v.iter() { value.hash(state); } } } } } impl IntoIterator for Datasets { type Item = Idx; type IntoIter = Box>; fn into_iter(self) -> Self::IntoIter { match self { Self::Empty => Box::new(std::iter::empty()), Self::Unique(v) => Box::new(std::iter::once(v)), Self::Many(v) => Box::new(v.into_iter()), } } } impl Extend for Datasets { fn extend(&mut self, iter: T) where T: IntoIterator, { if let Self::Many(v) = self { v.extend(iter); return; } let mut it = iter.into_iter(); while let Some(value) = it.next() { match self { Self::Empty => *self = Datasets::Unique(value), Self::Unique(v) => { if *v != value { *self = Self::Many([*v, value].iter().copied().collect()); } } Self::Many(v) => { v.extend(it); return; } } } } } impl Datasets { fn new(vals: &[Idx]) -> Self { if vals.is_empty() { Self::Empty } else if vals.len() == 1 { Self::Unique(vals[0]) } else { Self::Many(RoaringBitmap::from_sorted_iter(vals.iter().copied()).unwrap()) } } fn from_slice(slice: &[u8]) -> Option { use byteorder::ReadBytesExt; if slice.len() == 8 { // Unique Some(Self::Unique( (&slice[..]).read_u32::().unwrap(), )) } else if slice.len() == 1 { // Empty Some(Self::Empty) } else { // Many Some(Self::Many(RoaringBitmap::deserialize_from(slice).unwrap())) } } fn as_bytes(&self) -> Option> { match self { Self::Empty => Some(vec![42_u8]), Self::Unique(v) => { let mut buf = vec![0u8; 8]; (&mut buf[..]) .write_u32::(*v) .expect("error writing bytes"); Some(buf) } Self::Many(v) => { let mut buf = vec![]; v.serialize_into(&mut buf).unwrap(); Some(buf) } } } fn union(&mut self, other: Datasets) { match self { Datasets::Empty => match other { Datasets::Empty => (), Datasets::Unique(_) | Datasets::Many(_) => *self = other, }, Datasets::Unique(v) => match other { Datasets::Empty => (), Datasets::Unique(o) => { if *v != o { *self = Datasets::Many([*v, o].iter().copied().collect()) } } Datasets::Many(mut o) => { o.extend([*v]); *self = Datasets::Many(o); } }, Datasets::Many(ref mut v) => v.extend(other), } } fn len(&self) -> usize { match self { Self::Empty => 0, Self::Unique(_) => 1, Self::Many(ref v) => v.len() as usize, } } fn contains(&self, value: &Idx) -> bool { match self { Self::Empty => false, Self::Unique(v) => v == value, Self::Many(ref v) => v.contains(*value), } } } #[derive(Getters, Setters, Debug)] pub struct DbStats { #[getset(get = "pub")] total_datasets: usize, #[getset(get = "pub")] total_keys: usize, #[getset(get = "pub")] kcount: usize, #[getset(get = "pub")] vcount: usize, #[getset(get = "pub")] vcounts: histogram::Histogram, } fn stats_for_cf(db: Arc, cf_name: &str, deep_check: bool, quick: bool) -> DbStats { use byteorder::ReadBytesExt; use histogram::Histogram; let cf = db.cf_handle(cf_name).unwrap(); let iter = db.iterator_cf(&cf, rocksdb::IteratorMode::Start); let mut kcount = 0; let mut vcount = 0; // Using power values from https://docs.rs/histogram/0.8.3/histogram/struct.Config.html#resulting-size let mut vcounts = Histogram::new(12, 64).expect("Error initializing histogram"); let mut datasets: Datasets = Default::default(); for result in iter { let (key, value) = result.unwrap(); let _k = (&key[..]).read_u64::().unwrap(); kcount += key.len(); //println!("Saw {} {:?}", k, Datasets::from_slice(&value)); vcount += value.len(); if !quick && deep_check { let v = Datasets::from_slice(&value).expect("Error with value"); vcounts.increment(v.len() as u64).unwrap(); datasets.union(v); } //println!("Saw {} {:?}", k, value); } DbStats { total_datasets: datasets.len(), total_keys: kcount / 8, kcount, vcount, vcounts, } } #[cfg(test)] mod test { use camino::Utf8PathBuf as PathBuf; use tempfile::TempDir; use crate::collection::Collection; use crate::prelude::*; use crate::selection::Selection; use crate::storage::{InnerStorage, RocksDBStorage}; use crate::Result; use super::{prepare_query, RevIndex, RevIndexOps}; #[test] fn revindex_index() -> Result<()> { let mut basedir = PathBuf::from(env!("CARGO_MANIFEST_DIR")); basedir.push("../../tests/test-data/scaled/"); let siglist: Vec<_> = (10..=12) .map(|i| { let mut filename = basedir.clone(); filename.push(format!("genome-s{}.fa.gz.sig", i)); filename }) .collect(); let selection = Selection::builder().ksize(31).scaled(10000).build(); let output = TempDir::new()?; let mut query = None; let query_sig = Signature::from_path(&siglist[0])? .swap_remove(0) .select(&selection)?; if let Some(q) = prepare_query(query_sig, &selection) { query = Some(q); } let query = query.unwrap(); let collection = Collection::from_paths(&siglist)?.select(&selection)?; let index = RevIndex::create(output.path(), collection.try_into()?, false)?; let counter = index.counter_for_query(&query); let matches = index.matches_from_counter(counter, 0); assert_eq!(matches, [("../genome-s10.fa.gz".into(), 48)]); Ok(()) } #[test] fn revindex_update() -> Result<()> { let mut basedir = PathBuf::from(env!("CARGO_MANIFEST_DIR")); basedir.push("../../tests/test-data/scaled/"); let siglist: Vec<_> = (10..=11) .map(|i| { let mut filename = basedir.clone(); filename.push(format!("genome-s{}.fa.gz.sig", i)); filename }) .collect(); let selection = Selection::builder().ksize(31).scaled(10000).build(); let output = TempDir::new()?; let mut new_siglist = siglist.clone(); { let collection = Collection::from_paths(&siglist)?.select(&selection)?; RevIndex::create(output.path(), collection.try_into()?, false)?; } let mut filename = basedir.clone(); filename.push("genome-s12.fa.gz.sig"); new_siglist.push(filename); let mut query = None; let query_sig = Signature::from_path(&new_siglist[2])? .swap_remove(0) .select(&selection)?; if let Some(q) = prepare_query(query_sig, &selection) { query = Some(q); } let query = query.unwrap(); let new_collection = Collection::from_paths(&new_siglist)?.select(&selection)?; let index = RevIndex::open(output.path(), false, None)?.update(new_collection.try_into()?)?; let counter = index.counter_for_query(&query); let matches = index.matches_from_counter(counter, 0); assert!(matches[0].0.ends_with("/genome-s12.fa.gz")); assert_eq!(matches[0].1, 45); Ok(()) } #[test] fn revindex_load_and_gather() -> Result<()> { let mut basedir = PathBuf::from(env!("CARGO_MANIFEST_DIR")); basedir.push("../../tests/test-data/scaled/"); let siglist: Vec<_> = (10..=12) .map(|i| { let mut filename = basedir.clone(); filename.push(format!("genome-s{}.fa.gz.sig", i)); filename }) .collect(); let selection = Selection::builder().ksize(31).scaled(10000).build(); let output = TempDir::new()?; let mut query = None; let query_sig = Signature::from_path(&siglist[0])? .swap_remove(0) .select(&selection)?; if let Some(q) = prepare_query(query_sig, &selection) { query = Some(q); } let query = query.unwrap(); { let collection = Collection::from_paths(&siglist)?.select(&selection)?; let _index = RevIndex::create(output.path(), collection.try_into()?, false); } let index = RevIndex::open(output.path(), true, None)?; let (counter, query_colors, hash_to_color) = index.prepare_gather_counters(&query); let matches = index.gather( counter, query_colors, hash_to_color, 0, &query, Some(selection), )?; assert_eq!(matches.len(), 1); assert_eq!(matches[0].name(), ""); // signature name is empty assert_eq!(matches[0].f_match(), 1.0); Ok(()) } #[test] fn revindex_load_and_gather_2() -> Result<()> { let mut basedir = PathBuf::from(env!("CARGO_MANIFEST_DIR")); basedir.push("../../tests/test-data/gather/"); let against = vec![ "GCF_000006945.2_ASM694v2_genomic.fna.gz.sig", "GCF_000007545.1_ASM754v1_genomic.fna.gz.sig", "GCF_000008105.1_ASM810v1_genomic.fna.gz.sig", "GCF_000008545.1_ASM854v1_genomic.fna.gz.sig", "GCF_000009085.1_ASM908v1_genomic.fna.gz.sig", "GCF_000009505.1_ASM950v1_genomic.fna.gz.sig", "GCF_000009525.1_ASM952v1_genomic.fna.gz.sig", "GCF_000011885.1_ASM1188v1_genomic.fna.gz.sig", "GCF_000016045.1_ASM1604v1_genomic.fna.gz.sig", "GCF_000016785.1_ASM1678v1_genomic.fna.gz.sig", "GCF_000018945.1_ASM1894v1_genomic.fna.gz.sig", "GCF_000195995.1_ASM19599v1_genomic.fna.gz.sig", ]; let against: Vec<_> = against .iter() .map(|sig| { let mut filename = basedir.clone(); filename.push(sig); filename }) .collect(); // build 'against' sketches into a revindex let selection = Selection::builder().ksize(21).scaled(10000).build(); let output = TempDir::new()?; let collection = Collection::from_paths(&against)?.select(&selection)?; let _index = RevIndex::create(output.path(), collection.try_into()?, false); let index = RevIndex::open(output.path(), true, None)?; let mut query = None; let mut query_filename = basedir.clone(); query_filename.push("combined.sig"); let query_sig = Signature::from_path(query_filename)? .swap_remove(0) .select(&selection)?; if let Some(q) = prepare_query(query_sig, &selection) { query = Some(q); } let query = query.unwrap(); let (counter, query_colors, hash_to_color) = index.prepare_gather_counters(&query); let matches = index.gather( counter, query_colors, hash_to_color, 5, // 50kb threshold &query, Some(selection), )?; // should be 11, based on test_gather_metagenome_num_results assert_eq!(matches.len(), 11); fn round5(a: f64) -> f64 { (a * 1e5).round() / 1e5 } let match_ = &matches[0]; let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_003198.1"); assert_eq!(match_.f_match(), 1.0); assert_eq!(round5(match_.f_unique_to_query()), round5(0.33219645)); let match_ = &matches[1]; let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_000853.1"); assert_eq!(match_.f_match(), 1.0); assert_eq!(round5(match_.f_unique_to_query()), round5(0.13096862)); let match_ = &matches[2]; let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_011978.1"); assert_eq!(match_.f_match(), 0.898936170212766); assert_eq!(round5(match_.f_unique_to_query()), round5(0.115279)); let match_ = &matches[3]; let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_002163.1"); assert_eq!(match_.f_match(), 1.0); assert_eq!(round5(match_.f_unique_to_query()), round5(0.10709413)); let match_ = &matches[4]; let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_003197.2"); assert_eq!(round5(match_.f_match()), round5(0.31340206)); assert_eq!(round5(match_.f_unique_to_query()), round5(0.103683)); let match_ = &matches[5]; dbg!(match_); let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_009486.1"); assert_eq!(round5(match_.f_match()), round5(0.4842105)); assert_eq!(round5(match_.f_unique_to_query()), round5(0.0627557)); let match_ = &matches[6]; dbg!(match_); let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_006905.1"); assert_eq!(round5(match_.f_match()), round5(0.161016949152542)); assert_eq!( round5(match_.f_unique_to_query()), round5(0.0518417462482947) ); let match_ = &matches[7]; dbg!(match_); let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_011080.1"); assert_eq!(round5(match_.f_match()), round5(0.125799573560768)); assert_eq!( round5(match_.f_unique_to_query()), round5(0.04024556616643930) ); let match_ = &matches[8]; dbg!(match_); let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_011274.1"); assert_eq!(round5(match_.f_match()), round5(0.0919037199124727)); assert_eq!( round5(match_.f_unique_to_query()), round5(0.0286493860845839) ); let match_ = &matches[9]; dbg!(match_); let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_006511.1"); assert_eq!(round5(match_.f_match()), round5(0.0725995316159251)); assert_eq!( round5(match_.f_unique_to_query()), round5(0.021145975443383400) ); let match_ = &matches[10]; dbg!(match_); let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_011294.1"); assert_eq!(round5(match_.f_match()), round5(0.0148619957537155)); assert_eq!( round5(match_.f_unique_to_query()), round5(0.0047748976807639800) ); Ok(()) } #[test] // a more detailed/focused version of revindex_load_and_gather_2, // added in sourmash#3193 for debugging purposes. fn revindex_load_and_gather_3() -> Result<()> { let mut basedir = PathBuf::from(env!("CARGO_MANIFEST_DIR")); basedir.push("../../tests/test-data/gather/"); let against = vec![ "GCF_000016785.1_ASM1678v1_genomic.fna.gz.sig", "GCF_000018945.1_ASM1894v1_genomic.fna.gz.sig", "GCF_000008545.1_ASM854v1_genomic.fna.gz.sig", ]; let against: Vec<_> = against .iter() .map(|sig| { let mut filename = basedir.clone(); filename.push(sig); filename }) .collect(); // build 'against' sketches into a revindex let selection = Selection::builder().ksize(21).scaled(10000).build(); let output = TempDir::new()?; let collection = Collection::from_paths(&against)?.select(&selection)?; let _index = RevIndex::create(output.path(), collection.try_into()?, false); let index = RevIndex::open(output.path(), true, None)?; let mut query = None; let mut query_filename = basedir.clone(); query_filename.push("combined.sig"); let query_sig = Signature::from_path(query_filename)? .swap_remove(0) .select(&selection)?; if let Some(q) = prepare_query(query_sig, &selection) { query = Some(q); } let query = query.unwrap(); let (counter, query_colors, hash_to_color) = index.prepare_gather_counters(&query); let matches = index.gather( counter, query_colors, hash_to_color, 0, &query, Some(selection), )?; // should be 3. // see sourmash#3193. assert_eq!(matches.len(), 3); fn round5(a: f64) -> f64 { (a * 1e5).round() / 1e5 } let match_ = &matches[0]; let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_000853.1"); assert_eq!(match_.f_match(), 1.0); assert_eq!(round5(match_.f_unique_to_query()), round5(0.13096862)); assert_eq!(match_.unique_intersect_bp, 1920000); assert_eq!(match_.remaining_bp, 12740000); assert_eq!(round5(match_.query_containment_ani()), round5(0.90773763)); let match_ = &matches[1]; let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_011978.1"); assert_eq!(match_.f_match(), 0.898936170212766); assert_eq!(round5(match_.f_unique_to_query()), round5(0.115279)); assert_eq!(match_.unique_intersect_bp, 1690000); assert_eq!(match_.remaining_bp, 11050000); assert_eq!(round5(match_.query_containment_ani()), round5(0.9068280)); let match_ = &matches[2]; dbg!(match_); let names: Vec<&str> = match_.name().split(' ').take(1).collect(); assert_eq!(names[0], "NC_009486.1"); assert_eq!(round5(match_.f_match()), round5(0.4842105)); assert_eq!(round5(match_.f_unique_to_query()), round5(0.0627557)); assert_eq!(match_.unique_intersect_bp, 920000); assert_eq!(match_.remaining_bp, 10130000); assert_eq!(round5(match_.query_containment_ani()), round5(0.90728512)); Ok(()) } #[test] fn revindex_move() -> Result<()> { let basedir = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let mut zip_collection = basedir.clone(); zip_collection.push("../../tests/test-data/track_abund/track_abund.zip"); let outdir = TempDir::new()?; let zip_copy = PathBuf::from( outdir .path() .join("sigs.zip") .into_os_string() .into_string() .unwrap(), ); std::fs::copy(zip_collection, zip_copy.as_path())?; let selection = Selection::builder().ksize(31).scaled(10000).build(); let collection = Collection::from_zipfile(zip_copy.as_path())?.select(&selection)?; let output = outdir.path().join("index"); let query = prepare_query(collection.sig_for_dataset(0)?.into(), &selection).unwrap(); { RevIndex::create(output.as_path(), collection.try_into()?, false)?; } { let index = RevIndex::open(output.as_path(), false, None)?; let counter = index.counter_for_query(&query); let matches = index.matches_from_counter(counter, 0); assert!(matches[0].0.starts_with("NC_009665.1")); assert_eq!(matches[0].1, 514); } let new_zip = outdir .path() .join("new_sigs.zip") .into_os_string() .into_string() .unwrap(); std::fs::rename(zip_copy, &new_zip)?; // RevIndex can't know where the new sigs are assert!(RevIndex::open(output.as_path(), false, None).is_err()); let index = RevIndex::open(output.as_path(), false, Some(&format!("zip://{}", new_zip)))?; let counter = index.counter_for_query(&query); let matches = index.matches_from_counter(counter, 0); assert!(matches[0].0.starts_with("NC_009665.1")); assert_eq!(matches[0].1, 514); Ok(()) } #[test] fn revindex_internalize_storage() -> Result<()> { let basedir = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let mut zip_collection = basedir.clone(); zip_collection.push("../../tests/test-data/track_abund/track_abund.zip"); let outdir = TempDir::new()?; let zip_copy = PathBuf::from( outdir .path() .join("sigs.zip") .into_os_string() .into_string() .unwrap(), ); std::fs::copy(zip_collection, zip_copy.as_path())?; let selection = Selection::builder().ksize(31).scaled(10000).build(); let collection = Collection::from_zipfile(zip_copy.as_path())?.select(&selection)?; let output = outdir.path().join("index"); let query = prepare_query(collection.sig_for_dataset(0)?.into(), &selection).unwrap(); let index = RevIndex::create(output.as_path(), collection.try_into()?, false)?; let (counter, query_colors, hash_to_color) = index.prepare_gather_counters(&query); let matches_external = index .gather( counter, query_colors, hash_to_color, 0, &query, Some(selection.clone()), ) .expect("failed to gather!"); { let mut index = index; index .internalize_storage() .expect("Error internalizing storage"); let (counter, query_colors, hash_to_color) = index.prepare_gather_counters(&query); let matches_internal = index.gather( counter, query_colors, hash_to_color, 0, &query, Some(selection.clone()), )?; assert_eq!(matches_external, matches_internal); } let new_path = outdir.path().join("new_index_path"); std::fs::rename(output.as_path(), new_path.as_path())?; let index = RevIndex::open(new_path, false, None)?; let (counter, query_colors, hash_to_color) = index.prepare_gather_counters(&query); let matches_moved = index.gather( counter, query_colors, hash_to_color, 0, &query, Some(selection.clone()), )?; assert_eq!(matches_external, matches_moved); Ok(()) } #[test] fn rocksdb_storage_from_path() -> Result<()> { let basedir = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let mut zip_collection = basedir.clone(); zip_collection.push("../../tests/test-data/track_abund/track_abund.zip"); let outdir = TempDir::new()?; let zip_copy = PathBuf::from( outdir .path() .join("sigs.zip") .into_os_string() .into_string() .unwrap(), ); std::fs::copy(zip_collection, zip_copy.as_path())?; let selection = Selection::builder().ksize(31).scaled(10000).build(); let collection = Collection::from_zipfile(zip_copy.as_path())?.select(&selection)?; let output = outdir.path().join("index"); // Step 1: create an index let index = RevIndex::create(output.as_path(), collection.try_into()?, false)?; // Step 2: internalize the storage for the index { let mut index = index; index .internalize_storage() .expect("Error internalizing storage"); } // Step 3: load rocksdb storage from path // should have the same content as zipfile // Iter thru collection, make sure all records are present let collection = Collection::from_zipfile(zip_copy.as_path())?.select(&selection)?; assert_eq!(collection.len(), 2); let col_storage = collection.storage(); let spec; { let rdb_storage = RocksDBStorage::from_path(output.as_os_str().to_str().unwrap()); spec = rdb_storage.spec(); collection.iter().for_each(|(_, r)| { assert_eq!( rdb_storage.load(r.internal_location().as_str()).unwrap(), col_storage.load(r.internal_location().as_str()).unwrap() ); }); } // Step 4: verify rocksdb storage spec assert_eq!( spec, format!("rocksdb://{}", output.as_os_str().to_str().unwrap()) ); let storage = InnerStorage::from_spec(spec)?; collection.iter().for_each(|(_, r)| { assert_eq!( storage.load(r.internal_location().as_str()).unwrap(), col_storage.load(r.internal_location().as_str()).unwrap() ); }); Ok(()) } #[test] fn rocksdb_storage_fail_bad_directory() -> Result<()> { let testdir = TempDir::new()?; match RevIndex::open(testdir, true, None) { Err(_) => Ok(()), Ok(_) => panic!("test should not reach here"), } } } sourmash-bio-sourmash-bc3c0f8/src/core/src/index/search.rs000066400000000000000000000015301474157046300237150ustar00rootroot00000000000000use crate::index::Comparable; pub fn search_minhashes(node: &dyn Comparable, query: &L, threshold: f64) -> bool { node.similarity(query) > threshold } pub fn search_minhashes_containment( node: &dyn Comparable, query: &L, threshold: f64, ) -> bool { node.containment(query) > threshold } pub fn search_minhashes_find_best() -> fn(&dyn Comparable, &L, f64) -> bool { /* TODO: implement the proper function, as a closure that modifies `best_so_far` let mut _best_so_far = 0.; move |node, query, threshold| { let sim = node.similarity(query); if sim > best_so_far { best_so_far = sim; true } else { if sim > threshold { true } else { false } } } */ unimplemented!(); } sourmash-bio-sourmash-bc3c0f8/src/core/src/lib.rs000066400000000000000000000033321474157046300221110ustar00rootroot00000000000000//! # Compute, compare and search signatures for nucleotide (DNA/RNA) and protein sequences. //! //! sourmash is a command-line tool and Python library for computing //! [MinHash sketches][0] from DNA sequences, comparing them to each other, //! and plotting the results. //! This allows you to estimate sequence similarity between even very //! large data sets quickly and accurately. //! //! [0]: https://en.wikipedia.org/wiki/MinHash //! //! sourmash can be used to quickly search large databases of genomes //! for matches to query genomes and metagenomes. //! //! sourmash also includes k-mer based taxonomic exploration and //! classification routines for genome and metagenome analysis. These //! routines can use the NCBI taxonomy but do not depend on it in any way. //! Documentation and further examples for each module can be found in the module descriptions below. // TODO: remove this line and update all the appropriate type names for 1.0 #![allow(clippy::upper_case_acronyms)] pub mod errors; pub use errors::SourmashError as Error; pub type Result = std::result::Result; pub mod prelude; pub mod cmd; pub mod ani_utils; pub mod collection; pub mod encodings; pub mod index; pub mod manifest; pub mod selection; pub mod signature; pub mod sketch; pub mod storage; #[cfg(feature = "from-finch")] pub mod from; use cfg_if::cfg_if; use murmurhash3::murmurhash3_x64_128; cfg_if! { if #[cfg(all(target_arch = "wasm32", target_os = "unknown"))] { // Explicitly keeping emscripten and wasi out of this pub mod wasm; } else { pub mod ffi; } } type HashIntoType = u64; pub type ScaledType = u32; pub fn _hash_murmur(kmer: &[u8], seed: u64) -> u64 { murmurhash3_x64_128(kmer, seed).0 } sourmash-bio-sourmash-bc3c0f8/src/core/src/manifest.rs000066400000000000000000000451311474157046300231540ustar00rootroot00000000000000use std::collections::HashSet; use std::fs::File; use std::hash::{Hash, Hasher}; use std::io::{BufRead, BufReader, Read, Write}; use std::ops::Deref; use camino::Utf8PathBuf as PathBuf; use getset::{CopyGetters, Getters, Setters}; #[cfg(feature = "parallel")] use rayon::prelude::*; use serde::de; use serde::{Deserialize, Serialize}; use crate::encodings::HashFunctions; use crate::prelude::*; use crate::signature::SigsTrait; use crate::sketch::Sketch; use crate::{Result, ScaledType}; /// Individual manifest record, containing information about sketches. #[derive(Debug, Serialize, Deserialize, Clone, CopyGetters, Getters, Setters)] pub struct Record { #[getset(get = "pub", set = "pub")] internal_location: PathBuf, #[getset(get = "pub", set = "pub")] md5: String, md5short: String, #[getset(get_copy = "pub", set = "pub")] ksize: u32, moltype: String, #[getset(get = "pub")] num: u32, #[getset(get = "pub")] scaled: ScaledType, #[getset(get = "pub")] n_hashes: usize, #[getset(get_copy = "pub", set = "pub")] #[serde(serialize_with = "intbool", deserialize_with = "to_bool")] with_abundance: bool, #[getset(get = "pub", set = "pub")] name: String, #[getset(get = "pub", set = "pub")] filename: String, } fn intbool(x: &bool, s: S) -> std::result::Result where S: serde::Serializer, { if *x { s.serialize_i32(1) } else { s.serialize_i32(0) } } fn to_bool<'de, D>(deserializer: D) -> std::result::Result where D: de::Deserializer<'de>, { match String::deserialize(deserializer)? .to_ascii_lowercase() .as_ref() { "0" | "false" | "False" => Ok(false), "1" | "true" | "True" => Ok(true), other => Err(de::Error::invalid_value( de::Unexpected::Str(other), &"0/1, true/false, True/False are the only supported values", )), } } /// A description of a collection of sketches. #[derive(Debug, Default, Serialize, Deserialize, Clone)] pub struct Manifest { records: Vec, } impl Record { /// Build a Record from a Signature pub fn from_sig(sig: &Signature, path: &str) -> Vec { sig.iter() .map(|sketch| { let (mut ksize, md5, with_abundance, moltype, n_hashes, num, scaled, hash_function) = match sketch { Sketch::MinHash(mh) => ( mh.ksize() as u32, mh.md5sum(), mh.track_abundance(), mh.hash_function(), mh.size(), mh.num(), mh.scaled(), mh.hash_function(), ), Sketch::LargeMinHash(mh) => ( mh.ksize() as u32, mh.md5sum(), mh.track_abundance(), mh.hash_function(), mh.size(), mh.num(), mh.scaled(), mh.hash_function(), ), _ => unimplemented!(), }; let md5short = md5[0..8].into(); ksize = match hash_function { HashFunctions::Murmur64Protein | HashFunctions::Murmur64Dayhoff | HashFunctions::Murmur64Hp => ksize / 3, _ => ksize, }; Self { internal_location: path.into(), moltype: moltype.to_string(), name: sig.name_str(), ksize, md5, md5short, with_abundance, filename: sig.filename(), n_hashes, num, scaled, } }) .collect() } pub fn moltype(&self) -> HashFunctions { self.moltype.as_str().try_into().unwrap() } pub fn check_compatible(&self, other: &Record) -> Result<()> { /* if self.num != other.num { return Err(Error::MismatchNum { n1: self.num, n2: other.num, } .into()); } */ use crate::Error; if self.ksize() != other.ksize() { return Err(Error::MismatchKSizes); } if self.moltype() != other.moltype() { // TODO: fix this error return Err(Error::MismatchDNAProt); } /* if self.scaled() < other.scaled() { return Err(Error::MismatchScaled); } if self.seed() != other.seed() { return Err(Error::MismatchSeed); } */ Ok(()) } } impl PartialEq for Record { // match everything but internal_location fn eq(&self, other: &Self) -> bool { self.md5 == other.md5 && self.ksize == other.ksize && self.moltype == other.moltype && self.scaled == other.scaled && self.num == other.num && self.n_hashes == other.n_hashes && self.with_abundance == other.with_abundance && self.name == other.name && self.filename == other.filename } } impl Eq for Record {} impl Hash for Record { fn hash(&self, state: &mut H) { self.md5.hash(state); self.ksize.hash(state); self.moltype.hash(state); self.scaled.hash(state); self.num.hash(state); self.n_hashes.hash(state); self.with_abundance.hash(state); self.name.hash(state); self.filename.hash(state); } } impl Manifest { pub fn from_reader(rdr: R) -> Result { let mut records = vec![]; let mut rdr = csv::ReaderBuilder::new() .comment(Some(b'#')) .from_reader(rdr); for result in rdr.deserialize() { let record: Record = result?; records.push(record); } Ok(Manifest { records }) } pub fn to_writer(&self, mut wtr: W) -> Result<()> { wtr.write_all(b"# SOURMASH-MANIFEST-VERSION: 1.0\n")?; let mut wtr = csv::Writer::from_writer(wtr); for record in &self.records { wtr.serialize(record)?; } Ok(()) } pub fn internal_locations(&self) -> impl Iterator { self.records.iter().map(|r| r.internal_location.as_str()) } pub fn iter(&self) -> impl Iterator { self.records.iter() } pub fn intersect_manifest(&self, other: &Manifest) -> Self { // extract tuples from other mf: let pairs: HashSet<_> = other.iter().collect(); let records = self .records .iter() .filter(|row| pairs.contains(row)) .cloned() .collect(); Self { records } } } impl Select for Manifest { // select only records that satisfy selection conditions; also update // scaled value to match. fn select(self, selection: &Selection) -> Result { let Manifest { mut records } = self; // TODO: with num as well? records.retain_mut(|row| { let mut valid = true; valid = if let Some(ksize) = selection.ksize() { row.ksize == ksize } else { valid }; valid = if let Some(abund) = selection.abund() { valid && row.with_abundance() == abund } else { valid }; valid = if let Some(moltype) = selection.moltype() { valid && row.moltype() == moltype } else { valid }; valid = if let Some(scaled) = selection.scaled() { // num sigs have row.scaled = 0, don't include them let v = valid && row.scaled != 0 && row.scaled <= scaled; // if scaled is set, update! if v { row.scaled = scaled }; v } else { valid }; valid = if let Some(num) = selection.num() { valid && row.num == num } else { valid }; valid }); Ok(Manifest { records }) } } impl From> for Manifest { fn from(records: Vec) -> Self { Manifest { records } } } impl From<&[PathBuf]> for Manifest { fn from(paths: &[PathBuf]) -> Self { #[cfg(feature = "parallel")] let iter = paths.par_iter(); #[cfg(not(feature = "parallel"))] let iter = paths.iter(); let records: Vec = iter .flat_map(|p| { let recs: Vec = Signature::from_path(p) .unwrap_or_else(|_| panic!("Error processing {:?}", p)) .into_iter() .flat_map(|v| Record::from_sig(&v, p.as_str())) .collect(); recs }) .collect(); Manifest { records } } } impl From<&PathBuf> for Manifest { fn from(pathlist: &PathBuf) -> Self { let file = File::open(pathlist).unwrap_or_else(|_| panic!("Failed to open {:?}", pathlist)); let reader = BufReader::new(file); let paths: Vec = reader .lines() .map(|line| line.unwrap_or_else(|_| panic!("Failed to read line from {:?}", pathlist))) .map(PathBuf::from) .collect(); paths.as_slice().into() } } impl Deref for Manifest { type Target = Vec; fn deref(&self) -> &Self::Target { &self.records } } #[cfg(test)] mod test { use camino::Utf8PathBuf as PathBuf; use std::fs::File; use std::io::Write; use tempfile::TempDir; use super::Manifest; use crate::collection::Collection; use crate::encodings::HashFunctions; use crate::selection::{Select, Selection}; #[test] fn manifest_from_pathlist() { let temp_dir = TempDir::new().unwrap(); let utf8_output = PathBuf::from_path_buf(temp_dir.path().to_path_buf()) .expect("Path should be valid UTF-8"); let mut filename = utf8_output.join("sig-pathlist.txt"); //convert to camino utf8pathbuf filename = PathBuf::from(filename); // build sig filenames let base_path = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let test_sigs = vec![ "../../tests/test-data/47.fa.sig", "../../tests/test-data/63.fa.sig", ]; let full_paths: Vec<_> = test_sigs .into_iter() .map(|sig| base_path.join(sig)) .collect(); // write a file in test directory with a filename on each line let mut pathfile = File::create(&filename).unwrap(); for sigfile in &full_paths { writeln!(pathfile, "{}", sigfile).unwrap(); } // load into manifest let manifest = Manifest::from(&filename); assert_eq!(manifest.len(), 2); } #[test] #[should_panic(expected = "Failed to open \"no-exist\"")] fn manifest_from_pathlist_nonexistent_file() { let filename = PathBuf::from("no-exist"); let _manifest = Manifest::from(&filename); } #[test] #[should_panic] fn manifest_from_pathlist_badfile() { let temp_dir = TempDir::new().unwrap(); let utf8_output = PathBuf::from_path_buf(temp_dir.path().to_path_buf()) .expect("Path should be valid UTF-8"); let mut filename = utf8_output.join("sig-pathlist.txt"); //convert to camino utf8pathbuf filename = PathBuf::from(filename); let mut pathfile = File::create(&filename).unwrap(); writeln!(pathfile, "Valid line").unwrap(); pathfile.write_all(&[0xED, 0xA0, 0x80]).unwrap(); // invalid UTF-8 // load into manifest let _manifest = Manifest::from(&filename); } #[test] #[should_panic] fn manifest_from_paths_badpath() { let base_path = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let test_sigs = vec![ PathBuf::from("no-exist"), PathBuf::from("../../tests/test-data/63.fa.sig"), ]; let full_paths: Vec = test_sigs .into_iter() .map(|sig| base_path.join(sig)) .collect(); // load into manifest let _manifest = Manifest::from(&full_paths[..]); // pass full_paths as a slice } #[test] fn manifest_to_writer_bools() { let base_path = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let test_sigs = vec![ PathBuf::from("../../tests/test-data/47.fa.sig"), PathBuf::from("../../tests/test-data/track_abund/63.fa.sig"), ]; let full_paths: Vec = test_sigs .into_iter() .map(|sig| base_path.join(sig)) .collect(); let manifest = Manifest::from(&full_paths[..]); // pass full_paths as a slice let temp_dir = TempDir::new().unwrap(); let utf8_output = PathBuf::from_path_buf(temp_dir.path().to_path_buf()) .expect("Path should be valid UTF-8"); let filename = utf8_output.join("sigs.manifest.csv"); let mut wtr = File::create(&filename).expect("Failed to create file"); manifest.to_writer(&mut wtr).unwrap(); // check that we can reopen the file as a manifest + properly check abund let infile = File::open(&filename).expect("Failed to open file"); let m2 = Manifest::from_reader(&infile).unwrap(); for record in m2.iter() { eprintln!("{:?}", record.name()); if record.name().contains("OS185") { assert_eq!(record.with_abundance(), false) } else { assert_eq!(record.with_abundance(), true) } } } #[test] fn manifest_to_writer_moltype_dna() { let base_path = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let test_sigs = vec![PathBuf::from("../../tests/test-data/47.fa.sig")]; let full_paths: Vec = test_sigs .into_iter() .map(|sig| base_path.join(sig)) .collect(); let manifest = Manifest::from(&full_paths[..]); // pass full_paths as a slice let temp_dir = TempDir::new().unwrap(); let utf8_output = PathBuf::from_path_buf(temp_dir.path().to_path_buf()) .expect("Path should be valid UTF-8"); let filename = utf8_output.join("sigs.manifest.csv"); let mut wtr = File::create(&filename).expect("Failed to create file"); manifest.to_writer(&mut wtr).unwrap(); // check that we can reopen the file as a manifest + properly check abund let infile = File::open(&filename).expect("Failed to open file"); let m2 = Manifest::from_reader(&infile).unwrap(); for record in m2.iter() { eprintln!("{:?} {}", record.name(), record.moltype()); assert_eq!(record.moltype().to_string(), "DNA"); } } #[test] fn manifest_selection() { let base_path = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let test_sigs = vec![PathBuf::from("../../tests/test-data/prot/all.zip")]; let full_paths: Vec = test_sigs .into_iter() .map(|sig| base_path.join(sig)) .collect(); let collection = Collection::from_zipfile(&full_paths[0]).unwrap(); let manifest = collection.manifest().clone(); // check selection on manifest works let mut selection = Selection::default(); selection.set_ksize(19); let prot_collect = manifest.select(&selection).unwrap(); // eprintln!("{}", &prot_collect); assert_eq!(prot_collect.len(), 6); selection.set_moltype(HashFunctions::Murmur64Protein); let manifest = collection.manifest().clone(); let protein_only = manifest.select(&selection).unwrap(); assert_eq!(protein_only.len(), 2); let manifest = collection.manifest().clone(); selection = Selection::default(); selection.set_scaled(100); let scaled100 = manifest.select(&selection).unwrap(); assert_eq!(scaled100.len(), 6); // check that 'scaled' is updated let manifest = collection.manifest().clone(); selection = Selection::default(); selection.set_scaled(400); let scaled400 = manifest.select(&selection).unwrap(); assert_eq!(scaled400.len(), 6); let max_scaled = scaled400 .iter() .map(|r| r.scaled()) .max() .expect("no records?!"); assert_eq!(*max_scaled, 400); } #[test] fn manifest_intersect() { let temp_dir = TempDir::new().unwrap(); let utf8_output = PathBuf::from_path_buf(temp_dir.path().to_path_buf()) .expect("Path should be valid UTF-8"); let filename = utf8_output.join("sig-pathlist.txt"); // build sig filenames let base_path = PathBuf::from(env!("CARGO_MANIFEST_DIR")); let test_sigs = vec![ "../../tests/test-data/47.fa.sig", "../../tests/test-data/63.fa.sig", ]; let full_paths: Vec<_> = test_sigs .into_iter() .map(|sig| base_path.join(sig)) .collect(); // write a file in test directory with a filename on each line let mut pathfile = File::create(&filename).unwrap(); for sigfile in &full_paths { writeln!(pathfile, "{}", sigfile).unwrap(); } // load into manifest let manifest = Manifest::from(&filename); assert_eq!(manifest.len(), 2); // now do just one sketch - let test_sigs2 = vec!["../../tests/test-data/63.fa.sig"]; let filename2 = utf8_output.join("sig-pathlist-single.txt"); let full_paths: Vec<_> = test_sigs2 .into_iter() .map(|sig| base_path.join(sig)) .collect(); let mut pathfile2 = File::create(&filename2).unwrap(); for sigfile in &full_paths { writeln!(pathfile2, "{}", sigfile).unwrap(); } // load into another manifest let manifest2 = Manifest::from(&filename2); assert_eq!(manifest2.len(), 1); // intersect with itself => same. let new_mf = manifest2.intersect_manifest(&manifest); assert_eq!(new_mf.len(), 1); // intersect with other => single. let new_mf = manifest.intersect_manifest(&manifest2); assert_eq!(new_mf.len(), 1); } } sourmash-bio-sourmash-bc3c0f8/src/core/src/prelude.rs000066400000000000000000000013411474157046300230010ustar00rootroot00000000000000use std::io::Write; use crate::Result; pub use crate::selection::{Select, Selection}; pub use crate::signature::Signature; pub use crate::storage::Storage; pub trait ToWriter { fn to_writer(&self, writer: &mut W) -> Result<()> where W: Write; } pub trait Update { fn update(&self, other: &mut O) -> Result<()>; } pub trait FromFactory { fn factory(&self, name: &str) -> Result; } /// Implemented by anything that wants to read specific data from a storage. pub trait ReadData { fn data(&self) -> Result<&D>; } // TODO: split into two traits, Similarity and Containment? pub trait Comparable { fn similarity(&self, other: &O) -> f64; fn containment(&self, other: &O) -> f64; } sourmash-bio-sourmash-bc3c0f8/src/core/src/selection.rs000066400000000000000000000057471474157046300233440ustar00rootroot00000000000000use getset::{CopyGetters, Getters, Setters}; use typed_builder::TypedBuilder; use crate::encodings::HashFunctions; use crate::manifest::Record; use crate::{Result, ScaledType}; #[derive(Default, Debug, TypedBuilder, Clone)] pub struct Selection { #[builder(default, setter(strip_option))] ksize: Option, #[builder(default, setter(strip_option))] abund: Option, #[builder(default, setter(strip_option))] num: Option, #[builder(default, setter(strip_option))] scaled: Option, #[builder(default, setter(strip_option))] containment: Option, #[builder(default, setter(strip_option))] moltype: Option, #[builder(default, setter(strip_option))] picklist: Option, } #[derive(Default, TypedBuilder, CopyGetters, Getters, Setters, Clone, Debug)] pub struct Picklist { #[getset(get = "pub", set = "pub")] #[builder(default = "".into())] coltype: String, #[getset(get = "pub", set = "pub")] #[builder(default = "".into())] pickfile: String, #[getset(get = "pub", set = "pub")] #[builder(default = "".into())] column_name: String, #[getset(get = "pub", set = "pub")] #[builder] pickstyle: PickStyle, } #[derive(Clone, Default, Debug)] #[repr(u32)] pub enum PickStyle { #[default] Include = 1, Exclude = 2, } pub trait Select { fn select(self, selection: &Selection) -> Result where Self: Sized; } impl Selection { pub fn ksize(&self) -> Option { self.ksize } pub fn set_ksize(&mut self, ksize: u32) { self.ksize = Some(ksize); } pub fn abund(&self) -> Option { self.abund } pub fn set_abund(&mut self, value: bool) { self.abund = Some(value); } pub fn num(&self) -> Option { self.num } pub fn set_num(&mut self, num: u32) { self.num = Some(num); } pub fn scaled(&self) -> Option { self.scaled } pub fn set_scaled(&mut self, scaled: ScaledType) { self.scaled = Some(scaled); } pub fn containment(&self) -> Option { self.containment } pub fn set_containment(&mut self, containment: bool) { self.containment = Some(containment); } pub fn moltype(&self) -> Option { self.moltype.clone() } pub fn set_moltype(&mut self, value: HashFunctions) { self.moltype = Some(value); } pub fn picklist(&self) -> Option { self.picklist.clone() } pub fn set_picklist(&mut self, value: Picklist) { self.picklist = Some(value); } pub fn from_record(row: &Record) -> Result { Ok(Self { ksize: Some(row.ksize()), abund: Some(row.with_abundance()), moltype: Some(row.moltype()), num: None, scaled: Some(*row.scaled()), containment: None, picklist: None, }) } } sourmash-bio-sourmash-bc3c0f8/src/core/src/signature.rs000066400000000000000000002001731474157046300233460ustar00rootroot00000000000000//! # Compressed representations of genomic data //! //! A signature is a collection of sketches for a genomic dataset. use core::iter::FusedIterator; use std::fs::File; use std::io; use std::path::Path; use std::str; use cfg_if::cfg_if; use itertools::Itertools; #[cfg(feature = "parallel")] use rayon::prelude::*; use serde::{Deserialize, Serialize}; use typed_builder::TypedBuilder; use crate::encodings::{aa_to_dayhoff, aa_to_hp, revcomp, to_aa, HashFunctions, VALID}; use crate::errors::SourmashError; use crate::prelude::*; use crate::sketch::minhash::KmerMinHash; use crate::sketch::Sketch; use crate::Error; use crate::HashIntoType; // TODO: this is the behavior expected from Sketch, but that name is already // used. Sketchable? pub trait SigsTrait { fn size(&self) -> usize; fn to_vec(&self) -> Vec; fn ksize(&self) -> usize; fn check_compatible(&self, other: &Self) -> Result<(), Error>; fn seed(&self) -> u64; fn hash_function(&self) -> HashFunctions; fn add_hash(&mut self, hash: HashIntoType); fn add_sequence(&mut self, seq: &[u8], force: bool) -> Result<(), Error> { let ready_hashes = SeqToHashes::new( seq, self.ksize(), force, false, self.hash_function(), self.seed(), )?; for hash_value in ready_hashes { match hash_value { Ok(0) => continue, Ok(x) => self.add_hash(x), Err(err) => return Err(err), } } // Should be always ok Ok(()) } fn add_protein(&mut self, seq: &[u8]) -> Result<(), Error> { let ready_hashes = SeqToHashes::new( seq, self.ksize(), false, true, self.hash_function(), self.seed(), )?; for hash_value in ready_hashes { match hash_value { Ok(0) => continue, Ok(x) => self.add_hash(x), Err(err) => return Err(err), } } // Should be always ok Ok(()) } } impl SigsTrait for Sketch { fn size(&self) -> usize { match *self { Sketch::MinHash(ref mh) => mh.size(), Sketch::LargeMinHash(ref mh) => mh.size(), Sketch::HyperLogLog(ref hll) => hll.size(), } } fn to_vec(&self) -> Vec { match *self { Sketch::MinHash(ref mh) => mh.to_vec(), Sketch::LargeMinHash(ref mh) => mh.to_vec(), Sketch::HyperLogLog(ref hll) => hll.to_vec(), } } fn ksize(&self) -> usize { match *self { Sketch::MinHash(ref mh) => mh.ksize(), Sketch::LargeMinHash(ref mh) => mh.ksize(), Sketch::HyperLogLog(ref hll) => hll.ksize(), } } fn seed(&self) -> u64 { match *self { Sketch::MinHash(ref mh) => mh.seed(), Sketch::LargeMinHash(ref mh) => mh.seed(), Sketch::HyperLogLog(ref hll) => hll.seed(), } } fn hash_function(&self) -> HashFunctions { match *self { Sketch::MinHash(ref mh) => mh.hash_function(), Sketch::LargeMinHash(ref mh) => mh.hash_function(), Sketch::HyperLogLog(ref hll) => hll.hash_function(), } } fn add_hash(&mut self, hash: HashIntoType) { match *self { Sketch::MinHash(ref mut mh) => mh.add_hash(hash), Sketch::LargeMinHash(ref mut mh) => mh.add_hash(hash), Sketch::HyperLogLog(ref mut hll) => hll.add_hash(hash), } } fn check_compatible(&self, other: &Self) -> Result<(), Error> { match *self { Sketch::MinHash(ref mh) => match other { Sketch::MinHash(ref ot) => mh.check_compatible(ot), _ => Err(Error::MismatchSignatureType), }, Sketch::LargeMinHash(ref mh) => match other { Sketch::LargeMinHash(ref ot) => mh.check_compatible(ot), _ => Err(Error::MismatchSignatureType), }, Sketch::HyperLogLog(ref hll) => match other { Sketch::HyperLogLog(ref ot) => hll.check_compatible(ot), _ => Err(Error::MismatchSignatureType), }, } } fn add_sequence(&mut self, seq: &[u8], force: bool) -> Result<(), Error> { match *self { Sketch::MinHash(ref mut mh) => mh.add_sequence(seq, force), Sketch::LargeMinHash(ref mut mh) => mh.add_sequence(seq, force), Sketch::HyperLogLog(_) => unimplemented!(), } } fn add_protein(&mut self, seq: &[u8]) -> Result<(), Error> { match *self { Sketch::MinHash(ref mut mh) => mh.add_protein(seq), Sketch::LargeMinHash(ref mut mh) => mh.add_protein(seq), Sketch::HyperLogLog(_) => unimplemented!(), } } } #[derive(Debug, Clone)] pub enum ReadingFrame { DNA { fw: Vec, rc: Vec, len: usize, // len gives max_index for kmer iterator }, Protein { fw: Vec, len: usize, }, } impl std::fmt::Display for ReadingFrame { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { match self { ReadingFrame::DNA { fw, rc, len } => { let fw_str = String::from_utf8_lossy(fw).to_string(); let rc_str = String::from_utf8_lossy(rc).to_string(); write!( f, "Type: DNA ({}bp), Forward: {}, Reverse Complement: {}", len, fw_str, rc_str ) } ReadingFrame::Protein { fw, len } => { let fw_str = String::from_utf8_lossy(fw).to_string(); write!(f, "Type: Protein ({}aa), Forward: {}", len, fw_str) } } } } impl ReadingFrame { pub fn new_dna(sequence: &[u8]) -> Self { let fw = sequence.to_ascii_uppercase(); let rc = revcomp(&fw); let len = sequence.len(); ReadingFrame::DNA { fw, rc, len } } pub fn new_protein(sequence: &[u8], dayhoff: bool, hp: bool) -> Self { let seq = sequence.to_ascii_uppercase(); let fw: Vec = if dayhoff { seq.iter().map(|&aa| aa_to_dayhoff(aa)).collect() } else if hp { seq.iter().map(|&aa| aa_to_hp(aa)).collect() } else { seq }; let len = fw.len(); ReadingFrame::Protein { fw, len } } pub fn new_skipmer( sequence: &[u8], start: usize, m: usize, n: usize, ) -> Result { let seq = sequence.to_ascii_uppercase(); if start >= n { return Err(SourmashError::InvalidSkipmerFrame { start, n }); } // do we need to round up? (+1) let mut fw = Vec::with_capacity(((seq.len() * m) + 1) / n); seq.iter().skip(start).enumerate().for_each(|(i, &base)| { if i % n < m { fw.push(base.to_ascii_uppercase()); } }); let len = fw.len(); let rc = revcomp(&fw); Ok(ReadingFrame::DNA { fw, rc, len }) } // this is the only one that doesn't uppercase in here b/c more efficient to uppercase externally :/ pub fn new_translated( sequence: &[u8], frame_number: usize, dayhoff: bool, hp: bool, ) -> Result { if frame_number > 2 { return Err(SourmashError::InvalidTranslateFrame { frame_number }); } // Translate sequence into amino acids let mut fw = Vec::with_capacity(sequence.len() / 3); // NOTE: b/c of chunks(3), we only process full codons and ignore leftover bases (e.g. 1 or 2 at end of frame) sequence .iter() .skip(frame_number) // Skip the initial bases for the frame .take(sequence.len() - frame_number) // Adjust length based on skipped bases .chunks(3) // Group into codons (triplets) using itertools .into_iter() .filter_map(|chunk| { let codon: Vec = chunk.cloned().collect(); // Collect the chunk into a Vec to_aa(&codon, dayhoff, hp).ok() // Translate the codon }) .for_each(|aa| fw.extend(aa)); // Extend `fw` with amino acids let len = fw.len(); // return protein reading frame Ok(ReadingFrame::Protein { fw, len }) } /// Get the forward sequence. #[inline] pub fn fw(&self) -> &[u8] { match self { ReadingFrame::DNA { fw, .. } => fw, ReadingFrame::Protein { fw, .. } => fw, } } /// Get the reverse complement sequence (if DNA). #[inline] pub fn rc(&self) -> &[u8] { match self { ReadingFrame::DNA { rc, .. } => rc, _ => panic!("Reverse complement is only available for DNA frames"), } } #[inline] pub fn length(&self) -> usize { match self { ReadingFrame::DNA { len, .. } => *len, ReadingFrame::Protein { len, .. } => *len, } } /// Get the type of the frame as a string. pub fn frame_type(&self) -> &'static str { match self { ReadingFrame::DNA { .. } => "DNA", ReadingFrame::Protein { .. } => "Protein", } } } pub struct SeqToHashes { k_size: usize, force: bool, seed: u64, frames: Vec, frame_index: usize, // Index of the current frame kmer_index: usize, // Current k-mer index within the frame last_position_check: usize, // Index of last base we validated } impl SeqToHashes { pub fn new( seq: &[u8], k_size: usize, force: bool, is_protein: bool, hash_function: HashFunctions, seed: u64, ) -> Result { let mut ksize: usize = k_size; // Adjust kmer size for protein-based hash functions if is_protein || hash_function.protein() || hash_function.dayhoff() || hash_function.hp() { ksize = k_size / 3; } // Generate frames based on sequence type and hash function let frames = if hash_function.dna() { Self::dna_frames(seq) } else if is_protein { Self::protein_frames(seq, &hash_function) } else if hash_function.protein() || hash_function.dayhoff() || hash_function.hp() { Self::translated_frames(seq, &hash_function)? } else if hash_function.skipm1n3() || hash_function.skipm2n3() { Self::skipmer_frames(seq, &hash_function, ksize)? } else { return Err(SourmashError::InvalidHashFunction { function: format!("{:?}", hash_function), }); }; Ok(SeqToHashes { k_size: ksize, force, seed, frames, frame_index: 0, kmer_index: 0, last_position_check: 0, }) } /// generate frames from DNA: 1 DNA frame (fw+rc) fn dna_frames(seq: &[u8]) -> Vec { vec![ReadingFrame::new_dna(seq)] } /// generate frames from protein: 1 protein frame fn protein_frames(seq: &[u8], hash_function: &HashFunctions) -> Vec { vec![ReadingFrame::new_protein( seq, hash_function.dayhoff(), hash_function.hp(), )] } /// generate translated frames: 6 protein frames fn translated_frames( seq: &[u8], hash_function: &HashFunctions, ) -> Result, SourmashError> { // since we need to revcomp BEFORE making ReadingFrames, uppercase the sequence here let sequence = seq.to_ascii_uppercase(); let revcomp_sequence = revcomp(&sequence); let frames = (0..3) .flat_map(|frame_number| { vec![ ReadingFrame::new_translated( &sequence, frame_number, hash_function.dayhoff(), hash_function.hp(), ), ReadingFrame::new_translated( &revcomp_sequence, frame_number, hash_function.dayhoff(), hash_function.hp(), ), ] }) .collect::, _>>()?; Ok(frames) } /// generate skipmer frames: 3 DNA frames (each with fw+rc) fn skipmer_frames( seq: &[u8], hash_function: &HashFunctions, ksize: usize, ) -> Result, SourmashError> { let (m, n) = if hash_function.skipm1n3() { (1, 3) } else { (2, 3) }; if ksize < n { return Err(SourmashError::InvalidSkipmerSize { ksize, n }); } let frames = (0..3) .flat_map(|frame_number| vec![ReadingFrame::new_skipmer(seq, frame_number, m, n)]) .collect::, _>>()?; Ok(frames) } fn out_of_bounds(&self, frame: &ReadingFrame) -> bool { self.kmer_index + self.k_size > frame.length() } } impl Iterator for SeqToHashes { type Item = Result; fn next(&mut self) -> Option { while self.frame_index < self.frames.len() { let frame = &self.frames[self.frame_index]; // Do we need to move to the next frame? if self.out_of_bounds(frame) { self.frame_index += 1; self.kmer_index = 0; // Reset for the next frame self.last_position_check = 0; continue; } let result = match frame { ReadingFrame::DNA { .. } => { let kmer = &frame.fw()[self.kmer_index..self.kmer_index + self.k_size]; let rc = frame.rc(); // Validate k-mer bases for j in std::cmp::max(self.kmer_index, self.last_position_check) ..self.kmer_index + self.k_size { if !VALID[frame.fw()[j] as usize] { if !self.force { // Return an error if force is false return Some(Err(Error::InvalidDNA { message: String::from_utf8(kmer.to_vec()).unwrap(), })); } else { // Skip the invalid k-mer self.kmer_index += 1; return Some(Ok(0)); } } self.last_position_check += 1; } // Compute canonical hash // For a ksize = 3, and a sequence AGTCGT (len = 6): // +-+---------+---------------+-------+ // seq RC |i|i + ksize|len - ksize - i|len - i| // AGTCGT ACGACT +-+---------+---------------+-------+ // +-> +-> |0| 2 | 3 | 6 | // +-> +-> |1| 3 | 2 | 5 | // +-> +-> |2| 4 | 1 | 4 | // +-> +-> |3| 5 | 0 | 3 | // +-+---------+---------------+-------+ // (leaving this table here because I had to draw to // get the indices correctly) let krc = &rc[frame.length() - self.k_size - self.kmer_index ..frame.length() - self.kmer_index]; let hash = crate::_hash_murmur(std::cmp::min(kmer, krc), self.seed); Ok(hash) } ReadingFrame::Protein { .. } => { let kmer = &frame.fw()[self.kmer_index..self.kmer_index + self.k_size]; Ok(crate::_hash_murmur(kmer, self.seed)) } }; self.kmer_index += 1; // Advance k-mer index for valid k-mers return Some(result); } None // No more frames or k-mers } } #[derive(Serialize, Deserialize, Debug, Clone, TypedBuilder)] #[cfg_attr( feature = "rkyv", derive(rkyv::Serialize, rkyv::Deserialize, rkyv::Archive) )] pub struct Signature { #[serde(default = "default_class")] #[builder(default = default_class())] class: String, #[serde(default)] #[builder(default)] email: String, #[builder(setter(into))] hash_function: String, #[builder(default)] filename: Option, #[serde(skip_serializing_if = "Option::is_none")] pub(crate) name: Option, #[serde(default = "default_license")] #[builder(default = default_license())] license: String, pub(crate) signatures: Vec, #[serde(default = "default_version")] #[builder(default = default_version())] version: f64, } fn default_license() -> String { "CC0".to_string() } fn default_class() -> String { "sourmash_signature".to_string() } fn default_version() -> f64 { 0.4 } impl Signature { pub fn name(&self) -> Option { self.name.clone() } /// return name, if not None; or "" if None. pub fn name_str(&self) -> String { self.name().unwrap_or("".into()) } pub fn set_name(&mut self, name: &str) { self.name = Some(name.into()) } pub fn filename(&self) -> String { if let Some(filename) = &self.filename { filename.clone() } else { "".into() } } pub fn set_filename(&mut self, name: &str) { self.filename = Some(name.into()) } pub fn size(&self) -> usize { self.signatures.len() } pub fn sketches(&self) -> Vec { self.signatures.clone() } pub fn reset_sketches(&mut self) { self.signatures = vec![]; } pub fn push(&mut self, sketch: Sketch) { self.signatures.push(sketch); } pub fn license(&self) -> String { self.license.clone() } pub fn class(&self) -> String { self.class.clone() } pub fn hash_function(&self) -> String { self.hash_function.clone() } pub fn email(&self) -> String { self.email.clone() } pub fn md5sum(&self) -> String { if self.signatures.len() == 1 { match &self.signatures[0] { Sketch::MinHash(mh) => mh.md5sum(), Sketch::LargeMinHash(mh) => mh.md5sum(), Sketch::HyperLogLog(_) => unimplemented!(), } } else { // TODO: select the correct signature unimplemented!() } } pub fn select_sketch(&self, sketch: &Sketch) -> Option<&Sketch> { if let Sketch::MinHash(template) = sketch { for sk in &self.signatures { if let Sketch::MinHash(mh) = sk { if mh.check_compatible(template).is_ok() { return Some(sk); } } else { unimplemented!() } } } else { unimplemented!() } None } // return single corresponding sketch pub fn get_sketch(&self) -> Option<&Sketch> { if self.signatures.len() != 1 { if self.signatures.len() > 1 { todo!("Multiple sketches found! Please run select first."); } return None; } self.signatures.iter().find(|sk| { matches!( sk, Sketch::MinHash(_) | Sketch::LargeMinHash(_) | Sketch::HyperLogLog(_) ) }) } // return minhash directly pub fn minhash(&self) -> Option<&KmerMinHash> { if self.signatures.len() != 1 { if self.signatures.len() > 1 { todo!("Multiple sketches found! Please run select first."); } return None; } self.signatures.iter().find_map(|sk| { if let Sketch::MinHash(mh) = sk { Some(mh) } else { None } }) } pub fn from_path>(path: P) -> Result, Error> { let mut reader = io::BufReader::new(File::open(path)?); Signature::from_reader(&mut reader) } pub fn from_reader(rdr: R) -> Result, Error> where R: io::Read, { let (rdr, _format) = niffler::get_reader(Box::new(rdr))?; let sigs: Vec = serde_json::from_reader(rdr)?; Ok(sigs) } pub fn load_signatures( buf: R, ksize: Option, moltype: Option, _scaled: Option, ) -> Result, Error> where R: io::Read, { let orig_sigs = Signature::from_reader(buf)?; let flat_sigs = orig_sigs.into_iter().flat_map(|s| { s.signatures .iter() .map(|mh| { let mut new_s = s.clone(); new_s.signatures = vec![mh.clone()]; new_s }) .collect::>() }); let filtered_sigs = flat_sigs.filter_map(|mut sig| { let good_mhs: Vec = sig .signatures .into_iter() .filter(|sig| { match sig { Sketch::MinHash(mh) => { if let Some(k) = ksize { if k != mh.ksize() { return false; } }; match &moltype { Some(x) => { if mh.hash_function() == *x { return true; } } None => return true, // TODO: match previous behavior }; } Sketch::LargeMinHash(mh) => { if let Some(k) = ksize { if k != mh.ksize() { return false; } }; match &moltype { Some(x) => { if mh.hash_function() == *x { return true; } } None => return true, // TODO: match previous behavior }; } Sketch::HyperLogLog(_) => unimplemented!(), }; false }) .collect(); if good_mhs.is_empty() { return None; }; sig.signatures = good_mhs; Some(sig) }); Ok(filtered_sigs.collect()) } pub fn add_sequence(&mut self, seq: &[u8], force: bool) -> Result<(), Error> { cfg_if! { if #[cfg(feature = "parallel")] { self.signatures .par_iter_mut() .try_for_each(|sketch| { sketch.add_sequence(seq, force) } )?; } else { for sketch in self.signatures.iter_mut(){ sketch.add_sequence(seq, force)?; } } } Ok(()) } pub fn add_protein(&mut self, seq: &[u8]) -> Result<(), Error> { cfg_if! { if #[cfg(feature = "parallel")] { self.signatures .par_iter_mut() .try_for_each(|sketch| { sketch.add_protein(seq) } )?; } else { self.signatures .iter_mut() .try_for_each(|sketch| { sketch.add_protein(seq) } )?; } } Ok(()) } pub fn iter_mut(&mut self) -> IterMut<'_> { let length = self.signatures.len(); IterMut { iter: self.signatures.iter_mut(), length, } } pub fn iter(&self) -> Iter<'_> { let length = self.signatures.len(); Iter { iter: self.signatures.iter(), length, } } } pub struct IterMut<'a> { iter: std::slice::IterMut<'a, Sketch>, length: usize, } impl<'a> IntoIterator for &'a mut Signature { type Item = &'a mut Sketch; type IntoIter = IterMut<'a>; fn into_iter(self) -> IterMut<'a> { self.iter_mut() } } impl<'a> Iterator for IterMut<'a> { type Item = &'a mut Sketch; fn next(&mut self) -> Option<&'a mut Sketch> { if self.length == 0 { None } else { self.length -= 1; self.iter.next() } } fn size_hint(&self) -> (usize, Option) { (self.length, Some(self.length)) } } pub struct Iter<'a> { iter: std::slice::Iter<'a, Sketch>, length: usize, } impl<'a> Iterator for Iter<'a> { type Item = &'a Sketch; fn next(&mut self) -> Option<&'a Sketch> { if self.length == 0 { None } else { self.length -= 1; self.iter.next() } } fn size_hint(&self) -> (usize, Option) { (self.length, Some(self.length)) } } impl FusedIterator for Iter<'_> {} impl ExactSizeIterator for Iter<'_> { fn len(&self) -> usize { self.length } } impl Clone for Iter<'_> { fn clone(&self) -> Self { Iter { iter: self.iter.clone(), length: self.length, } } } impl ToWriter for Signature { fn to_writer(&self, writer: &mut W) -> Result<(), Error> where W: io::Write, { serde_json::to_writer(writer, &vec![&self])?; Ok(()) } } impl ToWriter for Vec<&Signature> { fn to_writer(&self, writer: &mut W) -> Result<(), Error> where W: io::Write, { serde_json::to_writer(writer, &self)?; Ok(()) } } impl Select for Signature { fn select(mut self, selection: &Selection) -> Result { self.signatures.retain(|s| { let mut valid = true; valid = if let Some(ksize) = selection.ksize() { let k = s.ksize() as u32; let adjusted_ksize = match s.hash_function() { HashFunctions::Murmur64Protein | HashFunctions::Murmur64Dayhoff | HashFunctions::Murmur64Hp => ksize * 3, _ => ksize, }; k == adjusted_ksize } else { valid }; // keep compatible scaled if applicable valid = if let Some(sel_scaled) = selection.scaled() { match s { Sketch::MinHash(mh) => valid && mh.scaled() <= sel_scaled, // TODO: test LargeMinHash // Sketch::LargeMinHash(lmh) => valid && lmh.scaled() <= sel_scaled as u64, _ => valid, // other sketch types or invalid cases } } else { valid // if selection.scaled() is None, keep prior valid }; /* valid = if let Some(abund) = selection.abund() { valid && *s.with_abundance() == abund } else { valid }; valid = if let Some(moltype) = selection.moltype() { valid && s.moltype() == moltype } else { valid }; */ valid }); // downsample the retained sketches if needed. if let Some(sel_scaled) = selection.scaled() { for sketch in self.signatures.iter_mut() { // TODO: also account for LargeMinHash if let Sketch::MinHash(mh) = sketch { if mh.scaled() < sel_scaled { *sketch = Sketch::MinHash(mh.clone().downsample_scaled(sel_scaled)?); } } } } Ok(self) } } impl Default for Signature { fn default() -> Signature { Signature { class: default_class(), email: "".to_string(), hash_function: "0.murmur64".to_string(), license: default_license(), filename: None, name: None, signatures: Vec::::new(), version: default_version(), } } } impl PartialEq for Signature { fn eq(&self, other: &Signature) -> bool { let metadata = self.class == other.class && self.email == other.email && self.hash_function == other.hash_function && self.filename == other.filename && self.name == other.name; // TODO: find the right signature // as long as we have a matching if let Sketch::MinHash(mh) = &self.signatures[0] { if let Sketch::MinHash(other_mh) = &other.signatures[0] { return metadata && (mh == other_mh); } } else { unimplemented!() } metadata } } impl TryInto for Signature { type Error = Error; fn try_into(self) -> Result { match self.signatures.len() { 1 => self .signatures .into_iter() .find_map(|sk| { if let Sketch::MinHash(mh) = sk { Some(mh) } else { None } }) .ok_or(Error::NoMinHashFound), 0 => Err(Error::EmptySignature), _ => Err(Error::MultipleSketchesFound), } } } #[cfg(test)] mod test { use std::fs::File; use std::io::{BufReader, Read}; use std::path::PathBuf; use needletail::parse_fastx_reader; use crate::cmd::ComputeParameters; use crate::encodings::HashFunctions; use crate::signature::{ReadingFrame, SeqToHashes, SigsTrait}; use super::Signature; use crate::prelude::Select; use crate::selection::Selection; use crate::sketch::Sketch; #[test] fn load_sig() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/.sbt.v3/60f7e23c24a8d94791cc7a8680c493f9"); let mut reader = BufReader::new(File::open(filename).unwrap()); let sigs = Signature::load_signatures( &mut reader, Some(31), Some("DNA".try_into().unwrap()), None, ) .unwrap(); let _sig_data = sigs[0].clone(); // TODO: check sig_data } #[test] fn load_signature() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/genome-s10+s11.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); assert_eq!(sigs.len(), 4); let sig = sigs.get(0).unwrap(); assert_eq!(sig.class, "sourmash_signature"); assert_eq!(sig.email, ""); if let Some(ref filename) = sig.filename { assert_eq!(filename, "-"); } assert_eq!(sig.hash_function, "0.murmur64"); if let Some(ref name) = sig.name { assert_eq!(name, "genome-s10+s11"); } assert_eq!(sig.signatures.len(), 1); } #[test] fn signature_from_computeparams() { let params = ComputeParameters::builder() .ksizes(vec![2, 3, 4]) .num_hashes(3u32) .build(); let mut sig = Signature::from_params(¶ms); sig.add_sequence(b"ATGC", false).unwrap(); assert_eq!(sig.signatures.len(), 3); dbg!(&sig.signatures); assert_eq!(sig.signatures[0].size(), 3); assert_eq!(sig.signatures[1].size(), 2); assert_eq!(sig.signatures[2].size(), 1); assert_eq!(sig.name_str(), ""); } #[test] fn signature_slow_path() { let params = ComputeParameters::builder() .ksizes(vec![2, 3, 4, 5]) .num_hashes(3u32) .build(); let mut sig = Signature::from_params(¶ms); sig.add_sequence(b"ATGCTN", true).unwrap(); assert_eq!(sig.signatures.len(), 4); dbg!(&sig.signatures); assert_eq!(sig.signatures[0].size(), 3); assert_eq!(sig.signatures[1].size(), 3); assert_eq!(sig.signatures[2].size(), 2); assert_eq!(sig.signatures[3].size(), 1); } #[test] fn signature_add_sequence_protein() { let params = ComputeParameters::builder() .ksizes(vec![3, 6]) .num_hashes(3u32) .protein(true) .dna(false) .build(); let mut sig = Signature::from_params(¶ms); sig.add_sequence(b"ATGCAT", false).unwrap(); assert_eq!(sig.signatures.len(), 2); dbg!(&sig.signatures); assert_eq!(sig.signatures[0].size(), 3); assert_eq!(sig.signatures[1].size(), 1); } #[test] fn signature_add_protein() { let params = ComputeParameters::builder() .ksizes(vec![3, 6]) .num_hashes(3u32) .protein(true) .dna(false) .build(); let mut sig = Signature::from_params(¶ms); sig.add_protein(b"AGY").unwrap(); assert_eq!(sig.signatures.len(), 2); dbg!(&sig.signatures); assert_eq!(sig.signatures[0].size(), 3); assert_eq!(sig.signatures[1].size(), 2); } #[test] fn signature_skipm2n3_add_sequence() { let params = ComputeParameters::builder() .ksizes(vec![3, 4, 5, 6]) .num_hashes(3u32) .dna(false) .skipm2n3(true) .build(); let mut sig = Signature::from_params(¶ms); sig.add_sequence(b"ATGCATGA", false).unwrap(); assert_eq!(sig.signatures.len(), 4); dbg!(&sig.signatures); assert_eq!(sig.signatures[0].size(), 3); assert_eq!(sig.signatures[1].size(), 3); eprintln!("{:?}", sig.signatures[2]); assert_eq!(sig.signatures[2].size(), 3); assert_eq!(sig.signatures[3].size(), 1); } #[test] fn signature_skipm1n3_add_sequence() { let params = ComputeParameters::builder() .ksizes(vec![3, 4, 5, 6]) .num_hashes(10u32) .dna(false) .skipm1n3(true) .build(); let mut sig = Signature::from_params(¶ms); sig.add_sequence(b"ATGCATGAATGAC", false).unwrap(); assert_eq!(sig.signatures.len(), 4); dbg!(&sig.signatures); assert_eq!(sig.signatures[0].size(), 5); assert_eq!(sig.signatures[1].size(), 4); assert_eq!(sig.signatures[2].size(), 1); assert_eq!(sig.signatures[3].size(), 0); } #[test] fn signature_skipm2n3_add_sequence_too_small() { let ksize = 2; let params = ComputeParameters::builder() .ksizes(vec![ksize]) .num_hashes(10u32) .dna(false) .skipm2n3(true) .build(); let mut sig = Signature::from_params(¶ms); let result = sig.add_sequence(b"ATGCATGA", false); match result { Err(error) => { // Convert the error to a string and check the message let error_message = format!("{}", error); assert_eq!( error_message, "Skipmer ksize must be >= n (3), but got ksize: 2" ); } _ => panic!("Expected SourmashError::InvalidSkipmerSize"), } } #[test] fn signature_skipm1n3_add_sequence_too_small() { let params = ComputeParameters::builder() .ksizes(vec![2]) .num_hashes(10u32) .dna(false) .skipm1n3(true) .build(); let mut sig = Signature::from_params(¶ms); let result = sig.add_sequence(b"ATGCATGA", false); match result { Err(error) => { // Convert the error to a string and check the message let error_message = format!("{}", error); assert_eq!( error_message, "Skipmer ksize must be >= n (3), but got ksize: 2" ); } _ => panic!("Expected SourmashError::InvalidSkipmerSize"), } } #[test] fn signature_add_sequence_cp() { let mut cp = ComputeParameters::default(); cp.set_dayhoff(true); cp.set_protein(true); cp.set_hp(true); cp.set_dna(true); let mut sig = Signature::from_params(&cp); let mut data: Vec = vec![]; let mut f = File::open("../../tests/test-data/ecoli.genes.fna").unwrap(); let _ = f.read_to_end(&mut data); let mut parser = parse_fastx_reader(&data[..]).unwrap(); while let Some(record) = parser.next() { let record = record.unwrap(); sig.add_sequence(&record.seq(), false).unwrap(); } assert_eq!(sig.size(), 12); for sk in sig.iter() { assert_eq!(sk.size(), 500); } } #[test] fn load_minhash_from_signature() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/47.fa.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); assert_eq!(sigs.len(), 1); let sig = sigs.get(0).unwrap(); let mh = sig.minhash().unwrap(); assert_eq!(mh.scaled(), 1000); } #[test] fn load_single_sketch_from_signature() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/47.fa.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); assert_eq!(sigs.len(), 1); let sig = sigs.get(0).unwrap(); let mhdirect = sig.minhash().unwrap(); let sketch = sig.get_sketch().unwrap(); if let Sketch::MinHash(mh) = sketch { assert_eq!(mh.scaled(), 1000); assert_eq!(mhdirect, mh); // should be the same } else { // error assert!(false); } } #[test] #[should_panic] fn get_sketch_multisketch_panic() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/47.fa.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); assert_eq!(sigs.len(), 1); let sig = sigs.get(0).unwrap(); let mut mhdirect = sig.minhash().unwrap().clone(); // change slightly and push into new_sig mhdirect.add_sequence(b"ATGGA", false).unwrap(); let new_sketch = Sketch::MinHash(mhdirect.clone()); let mut new_sig = sig.clone(); new_sig.push(new_sketch); // check there are now two sketches in new_sig assert_eq!(new_sig.signatures.len(), 2); let _ = new_sig.get_sketch(); } #[test] #[should_panic] fn load_minhash_multisketch_panic() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/47.fa.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); assert_eq!(sigs.len(), 1); let sig = sigs.get(0).unwrap(); let mut mhdirect = sig.minhash().unwrap().clone(); // change slightly and push into new_sig mhdirect.add_sequence(b"ATGGA", false).unwrap(); let new_sketch = Sketch::MinHash(mhdirect.clone()); let mut new_sig = sig.clone(); new_sig.push(new_sketch); // check there are now two sketches in new_sig assert_eq!(new_sig.signatures.len(), 2); let _ = new_sig.minhash(); } #[test] fn selection_with_downsample() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/47+63-multisig.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); // create Selection object let mut selection = Selection::default(); selection.set_scaled(2000); // iterate and check scaled for sig in &sigs { let modified_sig = sig.clone().select(&selection).unwrap(); for sketch in modified_sig.iter() { if let Sketch::MinHash(mh) = sketch { dbg!("scaled: {:?}", mh.scaled()); assert_eq!(mh.scaled(), 2000); } } } } #[test] fn selection_protein() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push( "../../tests/test-data/prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", ); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); // create Selection object let mut selection = Selection::default(); let prot_ksize = 19; selection.set_ksize(prot_ksize); let selected_sig = sigs[0].clone().select(&selection).unwrap(); let mh = selected_sig.minhash().unwrap(); assert_eq!(mh.ksize(), prot_ksize as usize * 3); } #[test] fn selection_dayhoff() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push( "../../tests/test-data/prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", ); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); // create Selection object let mut selection = Selection::default(); let prot_ksize = 19; selection.set_ksize(prot_ksize); selection.set_moltype(crate::encodings::HashFunctions::Murmur64Dayhoff); let selected_sig = sigs[0].clone().select(&selection).unwrap(); let mh = selected_sig.minhash().unwrap(); assert_eq!(mh.ksize(), prot_ksize as usize * 3); } #[test] fn selection_hp() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename .push("../../tests/test-data/prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); // create Selection object let mut selection = Selection::default(); let prot_ksize = 19; selection.set_ksize(prot_ksize); selection.set_moltype(crate::encodings::HashFunctions::Murmur64Hp); let selected_sig = sigs[0].clone().select(&selection).unwrap(); let mh = selected_sig.minhash().unwrap(); assert_eq!(mh.ksize(), prot_ksize as usize * 3); } #[test] fn selection_protein2() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push( "../../tests/test-data/prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", ); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); // create Selection object let mut selection = Selection::default(); let prot_ksize = 19; selection.set_ksize(prot_ksize * 3); let selected_sig = sigs[0].clone().select(&selection).unwrap(); let mh = selected_sig.minhash(); assert!(mh.is_none()); } #[test] fn selection_scaled_too_low() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/47+63-multisig.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); // create Selection object let mut selection = Selection::default(); selection.set_scaled(100); // iterate and check no sigs are returned (original scaled is 1000) for sig in &sigs { let modified_sig = sig.clone().select(&selection).unwrap(); assert_eq!(modified_sig.size(), 0); } } #[test] fn test_readingframe_dna() { let sequence = b"AGTCGT"; let frame = ReadingFrame::new_dna(sequence); assert_eq!(frame.fw(), sequence.as_slice()); assert_eq!(frame.rc(), b"ACGACT".as_slice()); } #[test] fn test_fw_dna() { let dna_frame = ReadingFrame::DNA { fw: b"ATCG".to_vec(), rc: b"CGAT".to_vec(), len: 4, }; assert_eq!(dna_frame.fw(), b"ATCG"); } #[test] fn test_rc_dna() { let dna_frame = ReadingFrame::DNA { fw: b"ATCG".to_vec(), rc: b"CGAT".to_vec(), len: 4, }; assert_eq!(dna_frame.rc(), b"CGAT"); } #[test] fn test_length_dna() { let dna_frame = ReadingFrame::DNA { fw: b"ATCG".to_vec(), rc: b"CGAT".to_vec(), len: 4, }; assert_eq!(dna_frame.length(), 4); } #[test] fn test_frame_type_dna() { let dna_frame = ReadingFrame::DNA { fw: b"ATCG".to_vec(), rc: b"CGAT".to_vec(), len: 4, }; assert_eq!(dna_frame.frame_type(), "DNA"); } #[test] fn test_fw_protein() { let protein_frame = ReadingFrame::Protein { fw: b"MVHL".to_vec(), len: 4, }; assert_eq!(protein_frame.fw(), b"MVHL"); } #[test] #[should_panic(expected = "Reverse complement is only available for DNA frames")] fn test_rc_protein_panics() { let protein_frame = ReadingFrame::Protein { fw: b"MVHL".to_vec(), len: 4, }; protein_frame.rc(); } #[test] fn test_length_protein() { let protein_frame = ReadingFrame::Protein { fw: b"MVHL".to_vec(), len: 4, }; assert_eq!(protein_frame.length(), 4); } #[test] fn test_frame_type_protein() { let protein_frame = ReadingFrame::Protein { fw: b"MVHL".to_vec(), len: 4, }; assert_eq!(protein_frame.frame_type(), "Protein"); } #[test] fn test_readingframe_display_protein() { // Create a Protein ReadingFrame let protein_frame = ReadingFrame::Protein { fw: b"MVHLK".to_vec(), len: 5, }; let output = format!("{}", protein_frame); // Assert the output matches the expected format assert_eq!(output, "Type: Protein (5aa), Forward: MVHLK"); } #[test] fn test_seqtohashes_frames_dna() { let sequence = b"AGTCGT"; let hash_function = HashFunctions::Murmur64Dna; let k_size = 3; let seed = 42; let force = false; let is_protein = false; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); let frames = sth.frames.clone(); assert_eq!(frames.len(), 1); assert_eq!(frames[0].fw(), sequence.as_slice()); assert_eq!(frames[0].rc(), b"ACGACT".as_slice()); } #[test] fn test_seqtohashes_frames_is_protein() { let sequence = b"MVLSPADKTNVKAAW"; let hash_function = HashFunctions::Murmur64Protein; let k_size = 3; let seed = 42; let force = false; let is_protein = true; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); let frames = sth.frames.clone(); assert_eq!(frames.len(), 1); assert_eq!(frames[0].fw(), sequence.as_slice()); } #[test] fn test_readingframe_protein() { let sequence = b"MVLSPADKTNVKAAW"; let hash_function = HashFunctions::Murmur64Protein; let frame = ReadingFrame::new_protein(sequence, hash_function.dayhoff(), hash_function.hp()); assert_eq!(frame.fw(), sequence.as_slice()); } #[test] #[should_panic] fn test_seqtohashes_frames_is_protein_try_access_rc() { // test panic if trying to access rc let sequence = b"MVLSPADKTNVKAAW"; let hash_function = HashFunctions::Murmur64Protein; let k_size = 3; let seed = 42; let force = false; let is_protein = true; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); let frames = sth.frames.clone(); // protein frame doesn't have rc; this should panic eprintln!("{:?}", frames[0].rc()); } #[test] fn test_seqtohashes_frames_is_protein_dayhoff() { let sequence = b"MVLSPADKTNVKAAW"; let dayhoff_seq = b"eeebbbcdbcedbbf"; let hash_function = HashFunctions::Murmur64Dayhoff; let k_size = 3; let seed = 42; let force = false; let is_protein = true; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); let frames = sth.frames.clone(); assert_eq!(frames.len(), 1); assert_eq!(frames[0].fw(), dayhoff_seq.as_slice()); } #[test] fn test_seqtohashes_frames_is_protein_hp() { let sequence = b"MVLSPADKTNVKAAW"; let hp_seq = b"hhhphhpppphphhh"; let hash_function = HashFunctions::Murmur64Hp; let k_size = 3; let seed = 42; let force = false; let is_protein = true; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); let frames = sth.frames.clone(); assert_eq!(frames.len(), 1); assert_eq!(frames[0].fw(), hp_seq.as_slice()); } #[test] fn test_seqtohashes_frames_translate_protein() { let sequence = b"AGTCGTCGAGCT"; let hash_function = HashFunctions::Murmur64Protein; let k_size = 3; let seed = 42; let force = false; let is_protein = false; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); let frames = sth.frames.clone(); assert_eq!(frames[0].fw(), b"SRRA".as_slice()); assert_eq!(frames[1].fw(), b"SSTT".as_slice()); assert_eq!(frames[2].fw(), b"VVE".as_slice()); assert_eq!(frames[3].fw(), b"ARR".as_slice()); assert_eq!(frames[4].fw(), b"SSS".as_slice()); assert_eq!(frames[5].fw(), b"LDD".as_slice()); } #[test] fn test_readingframe_translate() { let sequence = b"AGTCGT"; let frame_start = 3; // four frames but translate can only let result = ReadingFrame::new_translated(sequence, frame_start, false, false); match result { Err(error) => { // Convert the error to a string and check the message let error_message = format!("{}", error); assert_eq!(error_message, "Frame number must be 0, 1, or 2, but got 3"); } _ => panic!("Expected SourmashError::InvalidTranslateFrame"), } } #[test] fn test_readingframe_skipmer() { let sequence = b"AGTCGT"; let m = 2; let n = 3; let num_frames = 4; // four frames but n is only 3 let result = ReadingFrame::new_skipmer(sequence, num_frames, m, n); match result { Err(error) => { // Convert the error to a string and check the message let error_message = format!("{}", error); assert_eq!( error_message, "Skipmer frame number must be < n (3), but got start: 4" ); } _ => panic!("Expected SourmashError::InvalidSkipmerFrame"), } } #[test] fn test_seqtohashes_frames_skipmer_m1n3() { let sequence = b"AGTCGTCGAGCT"; let hash_function = HashFunctions::Murmur64Skipm1n3; // Represents m=1, n=3 let k_size = 3; // K-mer size is not directly relevant for skipmer frame validation let seed = 42; // Seed is also irrelevant for frame structure let force = false; let is_protein = false; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); let frames = sth.frames.clone(); eprintln!("Frames: {:?}", frames); assert_eq!(frames.len(), 3); // Three skipmer frames // Expected skipmer sequences for m=1, n=3 (keep-1, skip-2) assert_eq!(frames[0].fw(), b"ACCG".as_slice()); assert_eq!(frames[0].rc(), b"CGGT".as_slice()); assert_eq!(frames[1].fw(), b"GGGC".as_slice()); assert_eq!(frames[1].rc(), b"GCCC".as_slice()); assert_eq!(frames[2].fw(), b"TTAT".as_slice()); assert_eq!(frames[2].rc(), b"ATAA".as_slice()); } #[test] fn test_seqtohashes_frames_skipmer_m2n3() { let sequence = b"AGTCGTCGAGCT"; let hash_function = HashFunctions::Murmur64Skipm2n3; let k_size = 3; let seed = 42; let force = false; let is_protein = false; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); let frames = sth.frames; eprintln!("Frames: {:?}", frames); assert_eq!(frames.len(), 3); // Three skipmer frames // Expected skipmer sequences for m=1, n=3 (keep-1, skip-2) assert_eq!(frames[0].fw(), b"AGCGCGGC".as_slice()); assert_eq!(frames[0].rc(), b"GCCGCGCT".as_slice()); assert_eq!(frames[1].fw(), b"GTGTGACT".as_slice()); assert_eq!(frames[1].rc(), b"AGTCACAC".as_slice()); assert_eq!(frames[2].fw(), b"TCTCAGT".as_slice()); assert_eq!(frames[2].rc(), b"ACTGAGA".as_slice()); } #[test] fn test_seqtohashes_dna() { let sequence = b"AGTCGT"; let hash_function = HashFunctions::Murmur64Dna; let k_size = 3; let seed = 42; let force = false; let is_protein = false; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); // Expected k-mers from the forward and reverse complement sequence let expected_kmers = vec![ (b"AGT".to_vec(), b"ACT".to_vec()), (b"GTC".to_vec(), b"GAC".to_vec()), (b"TCG".to_vec(), b"CGA".to_vec()), (b"CGT".to_vec(), b"ACG".to_vec()), ]; // Compute expected hashes from expected kmers let expected_hashes: Vec = expected_kmers .iter() .map(|(fw_kmer, rc_kmer)| crate::_hash_murmur(std::cmp::min(fw_kmer, rc_kmer), seed)) .collect(); // Collect hashes from SeqToHashes let sth_hashes: Vec = sth.map(|result| result.unwrap()).collect(); eprintln!("SeqToHashes hashes: {:?}", sth_hashes); // Check that SeqToHashes matches expected hashes in order assert_eq!( sth_hashes, expected_hashes, "Hashes do not match in order for SeqToHashes" ); } #[test] fn test_seqtohashes_dna_2() { let sequence = b"AGTCGTCA"; let k_size = 7; let seed = 42; let force = true; // Force skip over invalid bases if needed let is_protein = false; // Initialize SeqToHashes iterator using the new constructor let mut seq_to_hashes = SeqToHashes::new( sequence, k_size, force, is_protein, HashFunctions::Murmur64Dna, seed, ) .unwrap(); // Define expected hashes for the kmer configuration. let expected_kmers = ["AGTCGTC", "GTCGTCA"]; let expected_krc = ["GACGACT", "TGACGAC"]; // Compute expected hashes by hashing each k-mer with its reverse complement let expected_hashes: Vec = expected_kmers .iter() .zip(expected_krc.iter()) .map(|(kmer, krc)| { // Convert both kmer and krc to byte slices and pass to _hash_murmur crate::_hash_murmur(std::cmp::min(kmer.as_bytes(), krc.as_bytes()), seed) }) .collect(); // Compare each produced hash from the iterator with the expected hash for expected_hash in expected_hashes { let hash = seq_to_hashes.next().unwrap().ok().unwrap(); assert_eq!(hash, expected_hash, "Mismatch in DNA hash"); } } #[test] fn test_seqtohashes_is_protein() { let sequence = b"MVLSPADKTNVKAAW"; let hash_function = HashFunctions::Murmur64Protein; let k_size = 3; let seed = 42; let force = false; let is_protein = true; let sth = SeqToHashes::new(sequence, k_size * 3, force, is_protein, hash_function, seed).unwrap(); // Expected k-mers for protein sequence let expected_kmers = vec![ b"MVL".to_vec(), b"VLS".to_vec(), b"LSP".to_vec(), b"SPA".to_vec(), b"PAD".to_vec(), b"ADK".to_vec(), b"DKT".to_vec(), b"KTN".to_vec(), b"TNV".to_vec(), b"NVK".to_vec(), b"VKA".to_vec(), b"KAA".to_vec(), b"AAW".to_vec(), ]; // Compute hashes for expected k-mers let expected_hashes: Vec = expected_kmers .iter() .map(|fw_kmer| crate::_hash_murmur(fw_kmer, 42)) .collect(); // Collect hashes from SeqToHashes let sth_hashes: Vec = sth.map(|result| result.unwrap()).collect(); eprintln!("SeqToHashes hashes: {:?}", sth_hashes); // Check that SeqToHashes matches expected hashes in order assert_eq!(sth_hashes, expected_hashes, "Hashes do not match in order"); } #[test] fn test_seqtohashes_translate() { let sequence = b"AGTCGTCGAGCT"; let hash_function = HashFunctions::Murmur64Protein; let k_size = 9; // needs to be *3 for protein let seed = 42; let force = false; let is_protein = false; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); let expected_kmers = vec![ b"SRR".as_slice(), b"RRA".as_slice(), b"SST".as_slice(), b"STT".as_slice(), b"VVE".as_slice(), b"ARR".as_slice(), b"SSS".as_slice(), b"LDD".as_slice(), ]; // Compute expected hashes let expected_hashes: Vec = expected_kmers .iter() .map(|fw_kmer| crate::_hash_murmur(fw_kmer, seed)) .collect(); // Collect hashes from SeqToHashes let sth_hashes: Vec = sth.map(|result| result.unwrap()).collect(); eprintln!("SeqToHashes hashes: {:?}", sth_hashes); // Check that SeqToHashes matches expected hashes in order assert_eq!( sth_hashes, expected_hashes, "Hashes do not match in order for SeqToHashes" ); } #[test] fn test_seqtohashes_skipm1n3() { let sequence = b"AGTCGTCGAGCT"; let hash_function = HashFunctions::Murmur64Skipm1n3; let k_size = 3; let is_protein = false; let seed = 42; let force = false; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); // Expected k-mers for skipmer (m=1, n=3) across all frames let expected_kmers = vec![ (b"ACC".as_slice(), b"GGT".as_slice()), (b"CCG".as_slice(), b"CGG".as_slice()), (b"GGG".as_slice(), b"CCC".as_slice()), (b"GGC".as_slice(), b"GCC".as_slice()), (b"TTA".as_slice(), b"TAA".as_slice()), (b"TAT".as_slice(), b"ATA".as_slice()), ]; // Compute expected hashes let expected_hashes: Vec = expected_kmers .iter() .map(|(fw_kmer, rc_kmer)| crate::_hash_murmur(std::cmp::min(fw_kmer, rc_kmer), seed)) .collect(); // Collect hashes from SeqToHashes let sth_hashes: Vec = sth.map(|result| result.unwrap()).collect(); eprintln!("SeqToHashes hashes: {:?}", sth_hashes); // Check that SeqToHashes matches expected hashes in order assert_eq!( sth_hashes, expected_hashes, "Hashes do not match in order for SeqToHashes" ); } #[test] fn test_seq2hashes_skipm2n3() { let sequence = b"AGTCGTCGAGCT"; let hash_function = HashFunctions::Murmur64Skipm2n3; let k_size = 7; let is_protein = false; let seed = 42; let force = false; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); // Expected k-mers for skipmer (m=2, n=3) let expected_kmers = vec![ (b"AGCGCGG".as_slice(), b"CCGCGCT".as_slice()), (b"GCGCGGC".as_slice(), b"GCCGCGC".as_slice()), (b"GTGTGAC".as_slice(), b"GTCACAC".as_slice()), (b"TGTGACT".as_slice(), b"AGTCACA".as_slice()), (b"TCTCAGT".as_slice(), b"ACTGAGA".as_slice()), ]; // Compute expected hashes let expected_hashes: Vec = expected_kmers .iter() .map(|(fw_kmer, rc_kmer)| crate::_hash_murmur(std::cmp::min(fw_kmer, rc_kmer), seed)) .collect(); // Collect hashes from SeqToHashes let sth_hashes: Vec = sth.map(|result| result.unwrap()).collect(); eprintln!("SeqToHashes hashes: {:?}", sth_hashes); // Check that SeqToHashes matches expected hashes in order assert_eq!( sth_hashes, expected_hashes, "Hashes do not match in order for SeqToHashes" ); } #[test] fn test_seqtohashes_skipm2n3_2() { let sequence = b"AGTCGTCA"; let hash_function = HashFunctions::Murmur64Skipm2n3; let k_size = 5; let seed = 42; let force = true; let is_protein = false; let sth = SeqToHashes::new(sequence, k_size, force, is_protein, hash_function, seed).unwrap(); let frames = sth.frames.clone(); for fr in frames { eprintln!("{}", fr); } let expected_kmers = vec![ (b"AGCGC".as_slice(), b"GCGCT".as_slice()), (b"GCGCA".as_slice(), b"TGCGC".as_slice()), (b"GTGTA".as_slice(), b"TACAC".as_slice()), ]; // Compute expected hashes let expected_hashes: Vec = expected_kmers .iter() .map(|(fw_kmer, rc_kmer)| crate::_hash_murmur(std::cmp::min(fw_kmer, rc_kmer), seed)) .collect(); // Collect hashes from SeqToHashes let sth_hashes: Vec = sth.map(|result| result.unwrap()).collect(); eprintln!("SeqToHashes hashes: {:?}", sth_hashes); // Check that SeqToHashes matches expected hashes in order assert_eq!( sth_hashes, expected_hashes, "Hashes do not match in order for SeqToHashes" ); } } sourmash-bio-sourmash-bc3c0f8/src/core/src/sketch/000077500000000000000000000000001474157046300222555ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/src/sketch/hyperloglog/000077500000000000000000000000001474157046300246105ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/src/sketch/hyperloglog/estimators.rs000066400000000000000000000157211474157046300273560ustar00rootroot00000000000000use core::{ cmp, ops::{Add, AddAssign, Shl, Sub, SubAssign}, }; pub type CounterType = u8; /// Trait for types that can be used as multiplicity integers. pub trait MultiplicityInteger: Shl + Copy + AddAssign + SubAssign + Eq + Sub + Add + TryFrom + Ord { /// The zero value. const ZERO: Self; /// The one value. const ONE: Self; /// Convert the value to a `f64`. fn to_f64(self) -> f64; } macro_rules! impl_multiplicity_integer { ($($t:ty),*) => { $( impl MultiplicityInteger for $t { const ONE: Self = 1; const ZERO: Self = 0; fn to_f64(self) -> f64 { self as f64 } } )* }; } impl_multiplicity_integer!(u8, u16, u32); pub fn counts(registers: &[CounterType], q: usize) -> Vec { let mut counts = vec![M::ZERO; q + 2]; for k in registers { counts[*k as usize] += M::ONE; } counts } #[allow(clippy::many_single_char_names)] pub fn mle(counts: &[M], p: usize, q: usize, relerr: f64) -> f64 { let m: M = M::ONE << p; // If all of the registers are equal to zero, then we return zero. if counts[0] == m { return 0.0; } // If all of the registers are equal to the maximal possible value // that a register may have, then we return infinity. if counts[q + 1] == m { return f64::INFINITY; } let (k_min, _) = counts .iter() .enumerate() .find(|(_, v)| **v != M::ZERO) .unwrap(); let k_min_prime = cmp::max(1, k_min); let (k_max, _) = counts .iter() .enumerate() .rev() .find(|(_, v)| **v != M::ZERO) .unwrap(); let k_max_prime = cmp::min(q, k_max); let mut z = 0.; for i in num_iter::range_step_inclusive(k_max_prime as i32, k_min_prime as i32, -1) { z = 0.5 * z + counts[i as usize].to_f64(); } // ldexp(x, i) = x * (2 ** i) z *= 2f64.powi(-(k_min_prime as i32)); let mut c_prime = counts[q + 1]; if q >= 1 { c_prime += counts[k_max_prime]; } let mut g_prev = 0.; let a = z + (counts[0].to_f64()); let b = z + (counts[q + 1].to_f64()) * 2f64.powi(-(q as i32)); let m_prime = (m - counts[0]).to_f64(); let mut x = if b <= 1.5 * a { // weak lower bound (47) m_prime / (0.5 * b + a) } else { // strong lower bound (46) m_prime / (b * (1. + b / a).ln()) }; let mut delta_x = x; let del = relerr / m.to_f64().sqrt(); while delta_x > x * del { // secant method iteration let kappa: usize = az::saturating_cast(2. + x.log2().floor()); // x_prime in [0, 0.25] let mut x_prime = x * 2f64.powi(-(cmp::max(k_max_prime, kappa) as i32) - 1); let x_pp = x_prime * x_prime; // Taylor approximation (58) let mut h = x_prime - (x_pp / 3.) + (x_pp * x_pp) * (1. / 45. - x_pp / 472.5); // Calculate h(x/2^k), see (56), at this point x_prime = x / (2^(k+2)) for _k in num_iter::range_step_inclusive(kappa as i32 - 1, k_max_prime as i32, -1) { let h_prime = 1. - h; h = (x_prime + h * h_prime) / (x_prime + h_prime); x_prime += x_prime; } // compare (53) let mut g = c_prime.to_f64() * h; for k in num_iter::range_step_inclusive(k_max_prime as i32 - 1, k_min_prime as i32, -1) { let h_prime = 1. - h; // Calculate h(x/2^k), see (56), at this point x_prime = x / (2^(k+2)) h = (x_prime + h * h_prime) / (x_prime + h_prime); g += counts[k as usize].to_f64() * h; x_prime += x_prime; } g += x * a; delta_x = if (g > g_prev) | (m_prime >= g) { // see (54) delta_x * (m_prime - g) / (g - g_prev) } else { 0. }; x += delta_x; g_prev = g } m.to_f64() * x } /// Calculate the joint maximum likelihood of A and B. /// /// Returns a tuple (only in A, only in B, intersection) pub fn joint_mle( k1: &[CounterType], k2: &[CounterType], p: usize, q: usize, ) -> (usize, usize, usize) { if p < 8 { joint_mle_dispatch::(k1, k2, p, q) } else if p < 16 { joint_mle_dispatch::(k1, k2, p, q) } else { assert!(p == 16 || p == 17 || p == 18); joint_mle_dispatch::(k1, k2, p, q) } } /// Calculate the joint maximum likelihood of A and B. /// /// Returns a tuple (only in A, only in B, intersection) fn joint_mle_dispatch( k1: &[CounterType], k2: &[CounterType], p: usize, q: usize, ) -> (usize, usize, usize) where >::Error: std::fmt::Debug, { let mut c1 = vec![M::ZERO; q + 2]; let mut c2 = vec![M::ZERO; q + 2]; let mut cu = vec![M::ZERO; q + 2]; let mut cg1 = vec![M::ZERO; q + 2]; let mut cg2 = vec![M::ZERO; q + 2]; let mut ceq = vec![M::ZERO; q + 2]; for (k1_, k2_) in k1.iter().zip(k2.iter()) { match k1_.cmp(k2_) { cmp::Ordering::Less => { c1[*k1_ as usize] += M::ONE; cg2[*k2_ as usize] += M::ONE; } cmp::Ordering::Greater => { cg1[*k1_ as usize] += M::ONE; c2[*k2_ as usize] += M::ONE; } cmp::Ordering::Equal => { ceq[*k1_ as usize] += M::ONE; } } cu[*cmp::max(k1_, k2_) as usize] += M::ONE; } for (i, (&v, &u)) in cg1.iter().zip(ceq.iter()).enumerate() { c1[i] += v + u; } for (i, (&v, &u)) in cg2.iter().zip(ceq.iter()).enumerate() { c2[i] += v + u; } let c_ax = mle(&c1, p, q, 0.01); let c_bx = mle(&c2, p, q, 0.01); let c_abx = mle(&cu, p, q, 0.01); let mut counts_axb_half = vec![M::ZERO; q + 2]; let mut counts_bxa_half = vec![M::ZERO; q + 2]; counts_axb_half[q] = M::try_from(k1.len()).unwrap(); counts_bxa_half[q] = M::try_from(k2.len()).unwrap(); for _q in 0..q { counts_axb_half[_q] = cg1[_q] + ceq[_q] + cg2[_q + 1]; debug_assert!(counts_axb_half[q] >= counts_axb_half[_q]); let multiplicity_q = counts_axb_half[_q]; counts_axb_half[q] -= multiplicity_q; counts_bxa_half[_q] = cg2[_q] + ceq[_q] + cg1[_q + 1]; debug_assert!(counts_bxa_half[q] >= counts_bxa_half[_q]); let multiplicity_q = counts_bxa_half[_q]; counts_bxa_half[q] -= multiplicity_q; } let c_axb_half = mle(&counts_axb_half, p, q - 1, 0.01); let c_bxa_half = mle(&counts_bxa_half, p, q - 1, 0.01); let cx1 = 1.5 * c_bx + 1.5 * c_ax - c_bxa_half - c_axb_half; let cx2 = 2. * (c_bxa_half + c_axb_half) - 3. * c_abx; ( (c_abx - c_bx) as usize, (c_abx - c_ax) as usize, cmp::max(0, (0.5 * (cx1 + cx2)) as usize), ) } sourmash-bio-sourmash-bc3c0f8/src/core/src/sketch/hyperloglog/mod.rs000066400000000000000000000346471474157046300257530ustar00rootroot00000000000000/* Based on the HyperLogLog implementations in khmer https://github.com/dib-lab/khmer/blob/fb65d21eaedf0d397d49ae3debc578897f9d6eb4/src/oxli/hllcounter.cc using the maximum likelihood estimators from https://oertl.github.io/hyperloglog-sketch-estimation-paper/paper/paper.pdf first implemented for genomics in dashing https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1875-0 */ use std::cmp; use std::fs::File; use std::io; use std::path::Path; use byteorder::{BigEndian, ReadBytesExt, WriteBytesExt}; use serde::{Deserialize, Serialize}; use crate::encodings::HashFunctions; use crate::prelude::*; use crate::signature::SigsTrait; use crate::sketch::KmerMinHash; use crate::Error; use crate::HashIntoType; pub mod estimators; use estimators::CounterType; #[derive(Debug, Default, Clone, PartialEq, Eq, Serialize, Deserialize)] #[cfg_attr( feature = "rkyv", derive(rkyv::Serialize, rkyv::Deserialize, rkyv::Archive) )] pub struct HyperLogLog { registers: Vec, p: usize, q: usize, ksize: usize, } impl HyperLogLog { pub fn with_error_rate(error_rate: f64, ksize: usize) -> Result { let p = f64::ceil(f64::log2(f64::powi(1.04 / error_rate, 2))); HyperLogLog::new(p as usize, ksize) } pub fn new(p: usize, ksize: usize) -> Result { if !(4..=18).contains(&p) { return Err(Error::HLLPrecisionBounds); } let size = (1_usize) << p; let registers = vec![0; size]; Ok(HyperLogLog { registers, ksize, p, q: 64 - p, // FIXME: allow setting q explicitly }) } pub fn merge(&mut self, other: &HyperLogLog) -> Result<(), Error> { self.check_compatible(other)?; self.registers .iter_mut() .zip(other.registers.iter()) .for_each(|(a, b)| *a = cmp::max(*a, *b)); Ok(()) } pub fn add_word(&mut self, word: &[u8]) { let hash = crate::_hash_murmur(word, 42); // TODO: decide on seed self.add_hash(hash); } pub fn add_many(&mut self, hashes: &[HashIntoType]) -> Result<(), Error> { for min in hashes { self.add_hash(*min); } Ok(()) } pub fn cardinality(&self) -> usize { if self.p < 8 { estimators::mle( &estimators::counts::(&self.registers, self.q), self.p, self.q, 0.01, ) as usize } else if self.p < 16 { estimators::mle( &estimators::counts::(&self.registers, self.q), self.p, self.q, 0.05, ) as usize } else { assert!(self.p == 16 || self.p == 17 || self.p == 18); estimators::mle( &estimators::counts::(&self.registers, self.q), self.p, self.q, 0.1, ) as usize } } pub fn union(&self, other: &HyperLogLog) -> usize { let (only_a, only_b, intersection) = estimators::joint_mle(&self.registers, &other.registers, self.p, self.q); only_a + only_b + intersection } pub fn similarity(&self, other: &HyperLogLog) -> f64 { let (only_a, only_b, intersection) = estimators::joint_mle(&self.registers, &other.registers, self.p, self.q); intersection as f64 / (only_a + only_b + intersection) as f64 } pub fn containment(&self, other: &HyperLogLog) -> f64 { let (only_a, _, intersection) = estimators::joint_mle(&self.registers, &other.registers, self.p, self.q); intersection as f64 / (only_a + intersection) as f64 } pub fn intersection(&self, other: &HyperLogLog) -> usize { let (_, _, intersection) = estimators::joint_mle(&self.registers, &other.registers, self.p, self.q); intersection } // save pub fn save>(&self, path: P) -> Result<(), Error> { // TODO: if it ends with gz, open a compressed file // might use get_output here? self.save_to_writer(&mut File::create(path)?)?; Ok(()) } pub fn save_to_writer(&self, wtr: &mut W) -> Result<(), Error> where W: io::Write, { wtr.write_all(b"HLL")?; wtr.write_u8(1)?; // version wtr.write_u8(self.p as u8)?; // number of bits used for indexing wtr.write_u8(self.q as u8)?; // number of bits used for counting leading zeroes wtr.write_u8(self.ksize as u8)?; // ksize wtr.write_all(self.registers.as_slice())?; Ok(()) } pub fn from_reader(rdr: R) -> Result where R: io::Read, { let (mut rdr, _format) = niffler::get_reader(Box::new(rdr))?; let signature = rdr.read_u24::()?; assert_eq!(signature, 0x484c4c); let version = rdr.read_u8()?; assert_eq!(version, 1); let p = rdr.read_u8()? as usize; let q = rdr.read_u8()? as usize; let ksize = rdr.read_u8()? as usize; let n_registers = 1 << p; let mut registers = vec![0u8; n_registers]; rdr.read_exact(&mut registers)?; Ok(HyperLogLog { registers, p, q, ksize, }) } pub fn from_path>(path: P) -> Result { let mut reader = io::BufReader::new(File::open(path)?); HyperLogLog::from_reader(&mut reader) } } impl SigsTrait for HyperLogLog { fn size(&self) -> usize { self.registers.len() } fn to_vec(&self) -> Vec { self.registers.iter().map(|x| *x as u64).collect() } fn ksize(&self) -> usize { self.ksize } fn seed(&self) -> u64 { // TODO: support other seeds 42 } fn hash_function(&self) -> HashFunctions { //TODO support other hash functions HashFunctions::Murmur64Dna } fn add_hash(&mut self, hash: HashIntoType) { let value = hash >> self.p; let index = (hash - (value << self.p)) as usize; let leftmost = value.leading_zeros() + 1 - (self.p as u32); let old_value = self.registers[index]; self.registers[index] = cmp::max(old_value, leftmost as CounterType); } fn check_compatible(&self, other: &HyperLogLog) -> Result<(), Error> { if self.ksize() != other.ksize() { Err(Error::MismatchKSizes) } else if self.size() != other.size() { // TODO: create new error Err(Error::MismatchNum { n1: self.size() as u32, n2: other.size() as u32, }) } else { Ok(()) } } } impl Update for KmerMinHash { fn update(&self, other: &mut HyperLogLog) -> Result<(), Error> { for h in self.mins() { other.add_hash(h); } Ok(()) } } #[cfg(test)] mod test { use std::collections::HashSet; use std::hash::Hasher; use std::hash::{DefaultHasher, Hash}; use std::io::{BufReader, BufWriter, Read}; use std::path::PathBuf; use crate::signature::SigsTrait; use needletail::{parse_fastx_file, parse_fastx_reader, Sequence}; use super::HyperLogLog; // TODO: pull more tests from khmer HLL #[test] fn hll_add() { const ERR_RATE: f64 = 0.01; const N_UNIQUE: usize = 3356; const KSIZE: u8 = 21; let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/ecoli.genes.fna"); let mut hll = HyperLogLog::with_error_rate(ERR_RATE, KSIZE as usize).unwrap(); let mut counter: HashSet> = HashSet::new(); let mut parser = parse_fastx_file(filename).unwrap(); while let Some(record) = parser.next() { let record = record.unwrap(); let norm_seq = record.normalize(false); let rc = norm_seq.reverse_complement(); hll.add_sequence(&norm_seq, false).unwrap(); for (_, kmer, _) in norm_seq.canonical_kmers(KSIZE, &rc) { counter.insert(kmer.into()); } } assert_eq!(counter.len(), N_UNIQUE); let abs_error = (1. - (hll.cardinality() as f64 / N_UNIQUE as f64)).abs(); assert!(abs_error < ERR_RATE, "{}", abs_error); } #[test] fn hll_joint_mle() { const ERR_RATE: f64 = 0.01; const KSIZE: u8 = 21; const N_UNIQUE_H1: usize = 500741; const N_UNIQUE_H2: usize = 995845; const N_UNIQUE_U: usize = 995845; const INTERSECTION: usize = 500838; const SIMILARITY: f64 = 0.502783; const CONTAINMENT_H1: f64 = 1.; const CONTAINMENT_H2: f64 = 0.502783; let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/genome-s10.fa.gz"); let mut hll1 = HyperLogLog::with_error_rate(ERR_RATE, KSIZE as usize).unwrap(); let mut hll2 = HyperLogLog::with_error_rate(ERR_RATE, KSIZE as usize).unwrap(); let mut hllu = HyperLogLog::with_error_rate(ERR_RATE, KSIZE as usize).unwrap(); let mut buf = vec![]; let (mut reader, _) = niffler::from_path(filename).unwrap(); reader.read_to_end(&mut buf).unwrap(); let mut parser = parse_fastx_reader(&buf[..]).unwrap(); while let Some(record) = parser.next() { let record = record.unwrap(); let norm_seq = record.normalize(false); hll1.add_sequence(&norm_seq, false).unwrap(); hllu.add_sequence(&norm_seq, false).unwrap(); } let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/genome-s10+s11.fa.gz"); let mut buf = vec![]; let (mut reader, _) = niffler::from_path(filename).unwrap(); reader.read_to_end(&mut buf).unwrap(); let mut parser = parse_fastx_reader(&buf[..]).unwrap(); while let Some(record) = parser.next() { let record = record.unwrap(); let norm_seq = record.normalize(false); hll2.add_sequence(&norm_seq, false).unwrap(); hllu.add_sequence(&norm_seq, false).unwrap(); } let abs_error = (1. - (hll1.cardinality() as f64 / N_UNIQUE_H1 as f64)).abs(); assert!(abs_error < ERR_RATE, "{}", abs_error); let abs_error = (1. - (hll2.cardinality() as f64 / N_UNIQUE_H2 as f64)).abs(); assert!(abs_error < ERR_RATE, "{}", abs_error); let abs_error = (1. - (hll1.union(&hll2) as f64 / N_UNIQUE_U as f64)).abs(); assert!(abs_error < ERR_RATE, "{}", abs_error); let similarity = hll1.similarity(&hll2); let abs_error = (1. - (similarity / SIMILARITY)).abs(); assert!(abs_error < ERR_RATE, "{} {}", similarity, SIMILARITY); let containment = hll1.containment(&hll2); let abs_error = (1. - (containment / CONTAINMENT_H1)).abs(); assert!(abs_error < ERR_RATE, "{} {}", containment, CONTAINMENT_H1); let containment = hll2.containment(&hll1); let abs_error = (1. - (containment / CONTAINMENT_H2)).abs(); assert!(abs_error < ERR_RATE, "{} {}", containment, CONTAINMENT_H2); let intersection = hll1.intersection(&hll2) as f64; let abs_error = (1. - (intersection / INTERSECTION as f64)).abs(); assert!(abs_error < ERR_RATE, "{} {}", intersection, INTERSECTION); hll1.merge(&hll2).unwrap(); let abs_error = (1. - (hllu.similarity(&hll1) / 1.)).abs(); assert!(abs_error < ERR_RATE, "{}", abs_error); let abs_error = (1. - (hllu.containment(&hll1) / 1.)).abs(); assert!(abs_error < ERR_RATE, "{}", abs_error); let abs_error = (1. - (hll1.containment(&hllu) / 1.)).abs(); assert!(abs_error < ERR_RATE, "{}", abs_error); let intersection = hll1.intersection(&hllu) as f64; let abs_error = (1. - (intersection / N_UNIQUE_U as f64)).abs(); assert!(abs_error < ERR_RATE, "{} {}", intersection, N_UNIQUE_U); } #[test] fn save_load_hll() { let mut hll = HyperLogLog::with_error_rate(0.01, 1).expect("error building HLL"); for i in 1..5000 { hll.add_hash(i) } let mut buf = Vec::new(); { let mut writer = BufWriter::new(&mut buf); hll.save_to_writer(&mut writer).unwrap(); } let mut reader = BufReader::new(&buf[..]); let hll_new: HyperLogLog = HyperLogLog::from_reader(&mut reader).expect("Loading error"); assert_eq!(hll_new.p, hll.p); assert_eq!(hll_new.q, hll.q); assert_eq!(hll_new.registers, hll.registers); assert_eq!(hll_new.ksize, hll.ksize); } #[test] /// Test to cover corner cases in the MLE calculation /// that may happen at resolutions 16, 17 or 18, i.e. /// cases with 2^16 == 65536, 2^17 == 131072, 2^18 == 262144. /// /// In such cases, the MLE multiplicities which were earlier /// implemented always using a u16 type, may overflow. fn test_mle_corner_cases() { for precision in [16, 17, 18] { let mut hll = HyperLogLog::new(precision, 21).unwrap(); for i in 1..5000 { let mut hasher = DefaultHasher::new(); i.hash(&mut hasher); let hash = hasher.finish(); hll.add_hash(hash) } let cardinality = hll.cardinality(); assert!(cardinality > 4500 && cardinality < 5500); // We build a second hll to check whether the union of the two // hlls is consistent with the cardinality of the union. let mut hll2 = HyperLogLog::new(precision, 21).unwrap(); for i in 5000..10000 { let mut hasher = DefaultHasher::new(); i.hash(&mut hasher); let hash = hasher.finish(); hll2.add_hash(hash) } let mut hll_union = hll.clone(); hll_union.merge(&hll2).unwrap(); let cardinality_union = hll_union.cardinality(); assert!( cardinality_union > 9500 && cardinality_union < 10500, "precision: {}, cardinality_union: {}", precision, cardinality_union ); let intersection = hll.intersection(&hll2); assert!(intersection < 500); } } } sourmash-bio-sourmash-bc3c0f8/src/core/src/sketch/minhash.rs000066400000000000000000001545501474157046300242640ustar00rootroot00000000000000use std::cmp::Ordering; use std::collections::{BTreeMap, BTreeSet}; use std::f64::consts::PI; use std::fmt::Write; use std::io; use std::iter::Peekable; use std::str; use std::sync::Mutex; use itertools::Itertools; use serde::de::Deserializer; use serde::ser::{SerializeStruct, Serializer}; use serde::{Deserialize, Serialize}; use typed_builder::TypedBuilder; use crate::encodings::HashFunctions; use crate::prelude::ToWriter; use crate::signature::SigsTrait; use crate::sketch::hyperloglog::HyperLogLog; use crate::Error; use crate::{ScaledType, _hash_murmur}; pub fn max_hash_for_scaled(scaled: ScaledType) -> u64 { match scaled { 0 => 0, // scaled == 0 indicates this is a num minhash 1 => u64::MAX, _ => (u64::MAX as f64 / scaled as f64) as u64, } } pub fn scaled_for_max_hash(max_hash: u64) -> ScaledType { match max_hash { 0 => 0, // scaled == 0 indicates this is a num minhash _ => (u64::MAX as f64 / max_hash as f64) as ScaledType, } } #[derive(Debug, TypedBuilder)] #[cfg_attr( feature = "rkyv", derive(rkyv::Serialize, rkyv::Deserialize, rkyv::Archive) )] pub struct KmerMinHash { num: u32, ksize: u32, #[builder(setter(into), default = HashFunctions::Murmur64Dna)] hash_function: HashFunctions, #[builder(default = 42u64)] seed: u64, #[builder(default = u64::MAX)] max_hash: u64, #[builder(default)] mins: Vec, #[builder(default)] abunds: Option>, #[builder(default)] //#[cfg_attr(feature = "rkyv", with(rkyv::with::Lock))] #[cfg_attr(feature = "rkyv", with(rkyv::with::Skip))] md5sum: Mutex>, } impl PartialEq for KmerMinHash { fn eq(&self, other: &KmerMinHash) -> bool { // TODO: check all other fields? self.md5sum() == other.md5sum() } } impl Clone for KmerMinHash { fn clone(&self) -> Self { KmerMinHash { num: self.num, ksize: self.ksize, hash_function: self.hash_function.clone(), seed: self.seed, max_hash: self.max_hash, mins: self.mins.clone(), abunds: self.abunds.clone(), md5sum: Mutex::new(Some(self.md5sum())), } } } impl Default for KmerMinHash { fn default() -> KmerMinHash { KmerMinHash { num: 1000, ksize: 21, hash_function: HashFunctions::Murmur64Dna, seed: 42, max_hash: 0, mins: Vec::with_capacity(1000), abunds: None, md5sum: Mutex::new(None), } } } impl Serialize for KmerMinHash { fn serialize(&self, serializer: S) -> Result where S: Serializer, { let n_fields = match &self.abunds { Some(_) => 8, _ => 7, }; let mut partial = serializer.serialize_struct("KmerMinHash", n_fields)?; partial.serialize_field("num", &self.num)?; partial.serialize_field("ksize", &self.ksize)?; partial.serialize_field("seed", &self.seed)?; partial.serialize_field("max_hash", &self.max_hash)?; partial.serialize_field("mins", &self.mins)?; partial.serialize_field("md5sum", &self.md5sum())?; if let Some(abunds) = &self.abunds { partial.serialize_field("abundances", abunds)?; } partial.serialize_field("molecule", &self.hash_function.to_string())?; partial.end() } } impl<'de> Deserialize<'de> for KmerMinHash { fn deserialize(deserializer: D) -> Result where D: Deserializer<'de>, { #[derive(Deserialize)] struct TempSig { num: u32, ksize: u32, seed: u64, max_hash: u64, md5sum: String, mins: Vec, abundances: Option>, molecule: String, } let tmpsig = TempSig::deserialize(deserializer)?; let num = if tmpsig.max_hash != 0 { 0 } else { tmpsig.num }; let hash_function = match tmpsig.molecule.to_lowercase().as_ref() { "protein" => HashFunctions::Murmur64Protein, "dayhoff" => HashFunctions::Murmur64Dayhoff, "hp" => HashFunctions::Murmur64Hp, "dna" => HashFunctions::Murmur64Dna, "skipm1n3" => HashFunctions::Murmur64Skipm1n3, "skipm2n3" => HashFunctions::Murmur64Skipm2n3, _ => unimplemented!(), // TODO: throw error here }; // This shouldn't be necessary, but at some point we // created signatures with unordered mins =( let (mins, abunds) = if let Some(abunds) = tmpsig.abundances { let mut values: Vec<(_, _)> = tmpsig.mins.iter().zip(abunds.iter()).collect(); values.sort(); let mins = values.iter().map(|(v, _)| **v).collect(); let abunds = values.iter().map(|(_, v)| **v).collect(); (mins, Some(abunds)) } else { let mut values: Vec<_> = tmpsig.mins.into_iter().collect(); values.sort_unstable(); (values, None) }; Ok(KmerMinHash { num, ksize: tmpsig.ksize, seed: tmpsig.seed, max_hash: tmpsig.max_hash, md5sum: Mutex::new(Some(tmpsig.md5sum)), mins, abunds, hash_function, }) } } impl ToWriter for KmerMinHash { fn to_writer(&self, writer: &mut W) -> Result<(), Error> where W: io::Write, { serde_json::to_writer(writer, &self)?; Ok(()) } } impl KmerMinHash { pub fn new( scaled: ScaledType, ksize: u32, hash_function: HashFunctions, seed: u64, track_abundance: bool, num: u32, ) -> KmerMinHash { let mins = if num > 0 { Vec::with_capacity(num as usize) } else { Vec::with_capacity(1000) }; let abunds = if track_abundance { Some(Vec::with_capacity(mins.capacity())) } else { None }; let max_hash = max_hash_for_scaled(scaled); KmerMinHash { num, ksize, hash_function, seed, max_hash, mins, abunds, md5sum: Mutex::new(None), } } pub fn num(&self) -> u32 { self.num } pub fn is_protein(&self) -> bool { self.hash_function == HashFunctions::Murmur64Protein } pub fn max_hash(&self) -> u64 { self.max_hash } pub fn scaled(&self) -> ScaledType { scaled_for_max_hash(self.max_hash) } pub fn clear(&mut self) { self.mins.clear(); if let Some(ref mut abunds) = self.abunds { abunds.clear(); } } pub fn is_empty(&self) -> bool { self.mins.is_empty() } pub fn set_hash_function(&mut self, h: HashFunctions) -> Result<(), Error> { if self.hash_function == h { return Ok(()); } if !self.is_empty() { return Err(Error::NonEmptyMinHash { message: "hash_function".into(), }); } self.hash_function = h; Ok(()) } pub fn track_abundance(&self) -> bool { self.abunds.is_some() } pub fn enable_abundance(&mut self) -> Result<(), Error> { if !self.mins.is_empty() { return Err(Error::NonEmptyMinHash { message: "track_abundance=True".into(), }); } self.abunds = Some(vec![]); Ok(()) } pub fn disable_abundance(&mut self) { self.abunds = None; } fn reset_md5sum(&self) { let mut data = self.md5sum.lock().unwrap(); if data.is_some() { *data = None; } } pub fn md5sum(&self) -> String { let mut data = self.md5sum.lock().unwrap(); if data.is_none() { let mut buffer = String::with_capacity(20); let mut md5_ctx = md5::Context::new(); write!(&mut buffer, "{}", self.ksize()).unwrap(); md5_ctx.consume(&buffer); buffer.clear(); for x in &self.mins { write!(&mut buffer, "{}", x).unwrap(); md5_ctx.consume(&buffer); buffer.clear(); } *data = Some(format!("{:x}", md5_ctx.compute())); } data.clone().unwrap() } pub fn add_hash(&mut self, hash: u64) { self.add_hash_with_abundance(hash, 1); } pub fn add_hash_with_abundance(&mut self, hash: u64, abundance: u64) { let current_max = match self.mins.last() { Some(&x) => x, None => u64::MAX, }; if hash > self.max_hash && self.max_hash != 0 { // This is a scaled minhash, and we don't need to add the new hash return; } if self.num == 0 && self.max_hash == 0 { // why did you create this minhash? it will always be empty... return; } if abundance == 0 { self.remove_hash(hash); return; } // From this point on, hash is within scaled (or no scaled specified). // empty mins? add it. if self.mins.is_empty() { self.mins.push(hash); if let Some(ref mut abunds) = self.abunds { abunds.push(abundance); self.reset_md5sum(); } return; } if hash <= self.max_hash || hash <= current_max || (self.mins.len() as u32) < self.num { // "good" hash - within range, smaller than current entry, or // still have space available let pos = match self.mins.binary_search(&hash) { Ok(p) => p, Err(p) => p, }; if pos == self.mins.len() { // at end - must still be growing, we know the list won't // get too long self.mins.push(hash); self.reset_md5sum(); if let Some(ref mut abunds) = self.abunds { abunds.push(abundance); } } else if self.mins[pos] != hash { // didn't find hash in mins, so inserting somewhere // in the middle; shrink list if needed. self.mins.insert(pos, hash); if let Some(ref mut abunds) = self.abunds { abunds.insert(pos, abundance); } // is it too big now? if self.num != 0 && self.mins.len() > (self.num as usize) { self.mins.pop(); if let Some(ref mut abunds) = self.abunds { abunds.pop(); } } self.reset_md5sum(); } else if let Some(ref mut abunds) = self.abunds { // pos == hash: hash value already in mins, inc count by abundance abunds[pos] += abundance; } } } pub fn set_hash_with_abundance(&mut self, hash: u64, abundance: u64) { let mut found = false; if let Ok(pos) = self.mins.binary_search(&hash) { if self.mins[pos] == hash { found = true; if let Some(ref mut abunds) = self.abunds { abunds[pos] = abundance; } } } if !found { self.add_hash_with_abundance(hash, abundance); } } pub fn add_word(&mut self, word: &[u8]) { let hash = _hash_murmur(word, self.seed); self.add_hash(hash); } pub fn remove_hash(&mut self, hash: u64) { if let Ok(pos) = self.mins.binary_search(&hash) { if self.mins[pos] == hash { self.mins.remove(pos); self.reset_md5sum(); if let Some(ref mut abunds) = self.abunds { abunds.remove(pos); } } }; } pub fn remove_from(&mut self, other: &KmerMinHash) -> Result<(), Error> { for min in &other.mins { self.remove_hash(*min); } Ok(()) } pub fn remove_many>(&mut self, hashes: T) -> Result<(), Error> { for min in hashes { self.remove_hash(min); } Ok(()) } pub fn merge(&mut self, other: &KmerMinHash) -> Result<(), Error> { self.check_compatible(other)?; let max_size = self.mins.len() + other.mins.len(); let mut merged: Vec = Vec::with_capacity(max_size); let mut merged_abunds: Option> = if self.abunds.is_some() && other.abunds.is_some() { Some(Vec::with_capacity(max_size)) } else { None }; let mut self_iter = self.mins.iter(); let mut other_iter = other.mins.iter(); let mut self_abunds_iter = self.abunds.iter().flatten(); let mut other_abunds_iter = other.abunds.iter().flatten(); let mut self_value = self_iter.next(); let mut other_value = other_iter.next(); while self_value.is_some() { let value = self_value.unwrap(); match other_value { None => { merged.push(*value); merged.extend(self_iter); if let Some(v) = merged_abunds.as_mut() { v.extend(self_abunds_iter) } break; } Some(x) if x < value => { merged.push(*x); other_value = other_iter.next(); if let Some(v) = other_abunds_iter.next() { if let Some(n) = merged_abunds.as_mut() { n.push(*v) } } } Some(x) if x == value => { merged.push(*x); other_value = other_iter.next(); self_value = self_iter.next(); if let (Some(v), Some(s)) = (other_abunds_iter.next(), self_abunds_iter.next()) { if let Some(n) = merged_abunds.as_mut() { n.push(*v + *s) } } } Some(x) if x > value => { merged.push(*value); self_value = self_iter.next(); if let Some(v) = self_abunds_iter.next() { if let Some(n) = merged_abunds.as_mut() { n.push(*v) } } } Some(_) => {} } } if let Some(value) = other_value { merged.push(*value); } merged.extend(other_iter); if let Some(n) = merged_abunds.as_mut() { n.extend(other_abunds_iter) } if merged.len() > (self.num as usize) && (self.num as usize) != 0 { merged.truncate(self.num as usize); if let Some(v) = merged_abunds.as_mut() { v.truncate(self.num as usize) } } self.mins = merged; self.abunds = merged_abunds; self.reset_md5sum(); Ok(()) } pub fn add_from(&mut self, other: &KmerMinHash) -> Result<(), Error> { for min in &other.mins { self.add_hash(*min); } Ok(()) } pub fn add_many(&mut self, hashes: &[u64]) -> Result<(), Error> { for min in hashes { self.add_hash(*min); } Ok(()) } pub fn add_many_with_abund(&mut self, hashes: &[(u64, u64)]) -> Result<(), Error> { for item in hashes { self.add_hash_with_abundance(item.0, item.1); } Ok(()) } pub fn count_common(&self, other: &KmerMinHash, downsample: bool) -> Result { if downsample && self.scaled() != other.scaled() { let (first, second) = if self.scaled() > other.scaled() { (self, other) } else { (other, self) }; let downsampled_mh = second.clone().downsample_scaled(first.scaled())?; first.count_common(&downsampled_mh, false) } else { self.check_compatible(other)?; let iter = if self.size() < other.size() { Intersection::new(self.mins.iter(), other.mins.iter()) } else { Intersection::new(other.mins.iter(), self.mins.iter()) }; Ok(iter.count() as u64) } } pub fn intersection(&self, other: &KmerMinHash) -> Result<(Vec, u64), Error> { self.check_compatible(other)?; if self.num != 0 { // Intersection for regular MinHash sketches let mut combined_mh = KmerMinHash::new( self.scaled(), self.ksize, self.hash_function.clone(), self.seed, self.abunds.is_some(), self.num, ); combined_mh.merge(self)?; combined_mh.merge(other)?; let it1 = Intersection::new(self.mins.iter(), other.mins.iter()); // TODO: there is probably a way to avoid this Vec here, // and pass the it1 as left in it2. let i1: Vec = it1.cloned().collect(); let it2 = Intersection::new(i1.iter(), combined_mh.mins.iter()); let common: Vec = it2.cloned().collect(); Ok((common, combined_mh.mins.len() as u64)) } else { Ok(intersection(self.mins.iter(), other.mins.iter())) } } // FIXME: intersection_size and count_common should be the same? // (for scaled minhashes) pub fn intersection_size(&self, other: &KmerMinHash) -> Result<(u64, u64), Error> { self.check_compatible(other)?; if self.num != 0 { // Intersection for regular MinHash sketches let mut combined_mh = KmerMinHash::new( self.scaled(), self.ksize, self.hash_function.clone(), self.seed, self.abunds.is_some(), self.num, ); combined_mh.merge(self)?; combined_mh.merge(other)?; let it1 = Intersection::new(self.mins.iter(), other.mins.iter()); // TODO: there is probably a way to avoid this Vec here, // and pass the it1 as left in it2. let i1: Vec = it1.cloned().collect(); let it2 = Intersection::new(i1.iter(), combined_mh.mins.iter()); Ok((it2.count() as u64, combined_mh.mins.len() as u64)) } else { Ok(intersection_size(self.mins.iter(), other.mins.iter())) } } // calculate Jaccard similarity, ignoring abundance. pub fn jaccard(&self, other: &KmerMinHash) -> Result { self.check_compatible(other)?; if let Ok((common, size)) = self.intersection_size(other) { Ok(common as f64 / u64::max(1, size) as f64) } else { Ok(0.0) } } // compare two minhashes, with abundance; // calculate their angular similarity. pub fn angular_similarity(&self, other: &KmerMinHash) -> Result { self.check_compatible(other)?; if self.abunds.is_none() || other.abunds.is_none() { return Err(Error::NeedsAbundanceTracking); } // TODO: check which one is smaller, swap around if needed let abunds = self.abunds.as_ref().unwrap(); let other_abunds = other.abunds.as_ref().unwrap(); let mut prod = 0; let mut other_iter = other.mins.iter().enumerate(); let mut next_hash = other_iter.next(); let a_sq: u64 = abunds.iter().map(|a| (a * a)).sum(); let b_sq: u64 = other_abunds.iter().map(|a| (a * a)).sum(); for (i, hash) in self.mins.iter().enumerate() { while let Some((j, k)) = next_hash { match k.cmp(hash) { Ordering::Less => next_hash = other_iter.next(), Ordering::Equal => { // Calling `get_unchecked` here is safe since // both `i` and `j` are valid indices // (`i` and `j` came from valid iterator calls) unsafe { prod += abunds.get_unchecked(i) * other_abunds.get_unchecked(j); } break; } Ordering::Greater => break, } } } let norm_a = (a_sq as f64).sqrt(); let norm_b = (b_sq as f64).sqrt(); if norm_a == 0. || norm_b == 0. { return Ok(0.0); } let prod = f64::min(prod as f64 / (norm_a * norm_b), 1.); let distance = 2. * prod.acos() / PI; Ok(1. - distance) } pub fn similarity( &self, other: &KmerMinHash, ignore_abundance: bool, downsample: bool, ) -> Result { if downsample && self.scaled() != other.scaled() { // downsample to larger of two scaled let (first, second) = if self.scaled() > other.scaled() { (self, other) } else { (other, self) }; let downsampled_mh = second.clone().downsample_scaled(first.scaled())?; first.similarity(&downsampled_mh, ignore_abundance, false) } else if ignore_abundance || self.abunds.is_none() || other.abunds.is_none() { self.jaccard(other) } else { self.angular_similarity(other) } } pub fn dayhoff(&self) -> bool { self.hash_function == HashFunctions::Murmur64Dayhoff } pub fn hp(&self) -> bool { self.hash_function == HashFunctions::Murmur64Hp } pub fn skipm1n3(&self) -> bool { self.hash_function == HashFunctions::Murmur64Skipm1n3 } pub fn skipm2n3(&self) -> bool { self.hash_function == HashFunctions::Murmur64Skipm2n3 } pub fn mins(&self) -> Vec { self.mins.clone() } pub fn iter_mins(&self) -> impl Iterator { self.mins.iter() } pub fn abunds(&self) -> Option> { self.abunds.clone() } // create a downsampled copy of self pub fn downsample_max_hash(self, max_hash: u64) -> Result { if self.max_hash == 0 { // CTB: this is a num minhash. Should we just blithely return? Ok(self) } else { let scaled = scaled_for_max_hash(max_hash); self.downsample_scaled(scaled) } } pub fn sum_abunds(&self) -> u64 { if let Some(abunds) = &self.abunds { abunds.iter().sum() } else { self.size() as u64 } } pub fn to_vec_abunds(&self) -> Vec<(u64, u64)> { if let Some(abunds) = &self.abunds { self.mins .iter() .cloned() .zip(abunds.iter().cloned()) .collect() } else { self.mins .iter() .cloned() .zip(std::iter::repeat(1)) .collect() } } pub fn as_hll(&self) -> HyperLogLog { let mut hll = HyperLogLog::with_error_rate(0.01, self.ksize()).unwrap(); for h in &self.mins { hll.add_hash(*h) } hll } // Approximate total number of kmers // this could be improved by generating an HLL estimate while sketching instead // (for scaled minhashes) pub fn n_unique_kmers(&self) -> u64 { self.size() as u64 * self.scaled() as u64 // + (self.ksize - 1) for bp estimation } // create a downsampled copy of self pub fn downsample_scaled(self, scaled: ScaledType) -> Result { if self.scaled() == scaled || self.scaled() == 0 { Ok(self) } else if self.scaled() > scaled { Err(Error::CannotUpsampleScaled) } else { let mut new_mh = KmerMinHash::new( scaled, self.ksize, self.hash_function.clone(), self.seed, self.abunds.is_some(), self.num, ); if self.abunds.is_some() { new_mh.add_many_with_abund(&self.to_vec_abunds())?; } else { new_mh.add_many(&self.mins)?; } Ok(new_mh) } } pub fn inflate(&mut self, abunds_from: &KmerMinHash) -> Result<(), Error> { self.check_compatible(abunds_from)?; // check that abunds_from has abundances if abunds_from.abunds.is_none() { return Err(Error::NeedsAbundanceTracking); } let self_iter = self.mins.iter(); let abunds_iter = abunds_from.abunds.as_ref().unwrap().iter(); let abunds_from_iter = abunds_from.mins.iter().zip(abunds_iter); let (mins, abunds): (Vec, Vec) = self_iter .merge_join_by(abunds_from_iter, |&self_val, &(other_val, _)| { self_val.cmp(other_val) }) .filter_map(|either| match either { itertools::EitherOrBoth::Both(self_val, (_other_val, &other_abund)) => { Some((self_val, other_abund)) } _ => None, }) .unzip(); self.mins = mins; self.abunds = Some(abunds); self.reset_md5sum(); Ok(()) } pub fn inflated_abundances(&self, abunds_from: &KmerMinHash) -> Result<(Vec, u64), Error> { self.check_compatible(abunds_from)?; // check that abunds_from has abundances if abunds_from.abunds.is_none() { return Err(Error::NeedsAbundanceTracking); } let self_iter = self.mins.iter(); let abunds_iter = abunds_from.abunds.as_ref().unwrap().iter(); let abunds_from_iter = abunds_from.mins.iter().zip(abunds_iter); let (abundances, total_abundance): (Vec, u64) = self_iter .merge_join_by(abunds_from_iter, |&self_val, &(other_val, _)| { self_val.cmp(other_val) }) .filter_map(|either| match either { itertools::EitherOrBoth::Both(_self_val, (_other_val, other_abund)) => { Some(*other_abund) } _ => None, }) .fold((Vec::new(), 0u64), |(mut acc_vec, acc_sum), abund| { acc_vec.push(abund); (acc_vec, acc_sum + abund) }); Ok((abundances, total_abundance)) } pub fn from_reader(rdr: R) -> Result where R: std::io::Read, { let (rdr, _format) = niffler::get_reader(Box::new(rdr))?; let mh: KmerMinHash = serde_json::from_reader(rdr)?; Ok(mh) } } impl SigsTrait for KmerMinHash { fn size(&self) -> usize { self.mins.len() } fn to_vec(&self) -> Vec { self.mins.clone() } fn ksize(&self) -> usize { self.ksize as usize } fn seed(&self) -> u64 { self.seed } fn hash_function(&self) -> HashFunctions { self.hash_function.clone() } fn add_hash(&mut self, hash: u64) { self.add_hash_with_abundance(hash, 1); } fn check_compatible(&self, other: &KmerMinHash) -> Result<(), Error> { /* if self.num != other.num { return Err(Error::MismatchNum { n1: self.num, n2: other.num, } .into()); } */ if self.ksize != other.ksize { return Err(Error::MismatchKSizes); } if self.hash_function != other.hash_function { // TODO: fix this error return Err(Error::MismatchDNAProt); } // TODO: if supporting downsampled to be compatible //if self.max_hash < other.max_hash { if self.max_hash != other.max_hash { return Err(Error::MismatchScaled); } if self.seed != other.seed { return Err(Error::MismatchSeed); } Ok(()) } } struct Intersection> { iter: Peekable, other: Peekable, } impl> Intersection { pub fn new(left: I, right: I) -> Self { Intersection { iter: left.peekable(), other: right.peekable(), } } } impl> Iterator for Intersection { type Item = T; fn next(&mut self) -> Option { loop { let res = match (self.iter.peek(), self.other.peek()) { (Some(ref left_key), Some(ref right_key)) => left_key.cmp(right_key), _ => return None, }; match res { Ordering::Less => { self.iter.next(); } Ordering::Greater => { self.other.next(); } Ordering::Equal => { self.other.next(); return self.iter.next(); } } } } } //############# // A MinHash implementation for low scaled or large cardinalities #[derive(Debug, TypedBuilder)] #[cfg_attr( feature = "rkyv", derive(rkyv::Serialize, rkyv::Deserialize, rkyv::Archive) )] pub struct KmerMinHashBTree { num: u32, ksize: u32, #[builder(setter(into), default = HashFunctions::Murmur64Dna)] hash_function: HashFunctions, #[builder(default = 42u64)] seed: u64, #[builder(default = u64::MAX)] max_hash: u64, #[builder(default)] mins: BTreeSet, #[builder(default)] abunds: Option>, #[builder(default = 0u64)] current_max: u64, #[builder(default)] //#[cfg_attr(feature = "rkyv", with(rkyv::with::Lock))] #[cfg_attr(feature = "rkyv", with(rkyv::with::Skip))] md5sum: Mutex>, } impl PartialEq for KmerMinHashBTree { fn eq(&self, other: &KmerMinHashBTree) -> bool { // TODO: check all other fields? self.md5sum() == other.md5sum() } } impl Clone for KmerMinHashBTree { fn clone(&self) -> Self { KmerMinHashBTree { num: self.num, ksize: self.ksize, hash_function: self.hash_function.clone(), seed: self.seed, max_hash: self.max_hash, mins: self.mins.clone(), abunds: self.abunds.clone(), current_max: self.current_max, md5sum: Mutex::new(self.md5sum.lock().unwrap().clone()), } } } impl Default for KmerMinHashBTree { fn default() -> KmerMinHashBTree { KmerMinHashBTree { num: 1000, ksize: 21, hash_function: HashFunctions::Murmur64Dna, seed: 42, max_hash: 0, mins: Default::default(), abunds: None, current_max: 0, md5sum: Mutex::new(None), } } } impl Serialize for KmerMinHashBTree { fn serialize(&self, serializer: S) -> Result where S: Serializer, { let n_fields = match &self.abunds { Some(_) => 8, _ => 7, }; let mut partial = serializer.serialize_struct("KmerMinHashBTree", n_fields)?; partial.serialize_field("num", &self.num)?; partial.serialize_field("ksize", &self.ksize)?; partial.serialize_field("seed", &self.seed)?; partial.serialize_field("max_hash", &self.max_hash)?; partial.serialize_field("mins", &self.mins)?; partial.serialize_field("md5sum", &self.md5sum())?; if let Some(abunds) = &self.abunds { let abs: Vec = abunds.values().cloned().collect(); partial.serialize_field("abundances", &abs)?; } partial.serialize_field("molecule", &self.hash_function.to_string())?; partial.end() } } impl<'de> Deserialize<'de> for KmerMinHashBTree { fn deserialize(deserializer: D) -> Result where D: Deserializer<'de>, { #[derive(Deserialize)] struct TempSig { num: u32, ksize: u32, seed: u64, max_hash: u64, md5sum: String, mins: Vec, abundances: Option>, molecule: String, } let tmpsig = TempSig::deserialize(deserializer)?; let num = if tmpsig.max_hash != 0 { 0 } else { tmpsig.num }; let hash_function = match tmpsig.molecule.to_lowercase().as_ref() { "protein" => HashFunctions::Murmur64Protein, "dayhoff" => HashFunctions::Murmur64Dayhoff, "hp" => HashFunctions::Murmur64Hp, "dna" => HashFunctions::Murmur64Dna, _ => unimplemented!(), // TODO: throw error here }; let current_max; // This shouldn't be necessary, but at some point we // created signatures with unordered mins =( let (mins, abunds) = if let Some(abunds) = tmpsig.abundances { let mut values: Vec<(_, _)> = tmpsig.mins.iter().zip(abunds.iter()).collect(); values.sort(); let mins: BTreeSet<_> = values.iter().map(|(v, _)| **v).collect(); let abunds = values.into_iter().map(|(v, x)| (*v, *x)).collect(); current_max = *mins.iter().next_back().unwrap_or(&0); (mins, Some(abunds)) } else { current_max = 0; (tmpsig.mins.into_iter().collect(), None) }; Ok(KmerMinHashBTree { num, ksize: tmpsig.ksize, seed: tmpsig.seed, max_hash: tmpsig.max_hash, md5sum: Mutex::new(Some(tmpsig.md5sum)), mins, abunds, hash_function, current_max, }) } } impl ToWriter for KmerMinHashBTree { fn to_writer(&self, writer: &mut W) -> Result<(), Error> where W: io::Write, { serde_json::to_writer(writer, &self)?; Ok(()) } } impl KmerMinHashBTree { pub fn new( scaled: ScaledType, ksize: u32, hash_function: HashFunctions, seed: u64, track_abundance: bool, num: u32, ) -> KmerMinHashBTree { let mins = Default::default(); let abunds = if track_abundance { Some(Default::default()) } else { None }; let max_hash = max_hash_for_scaled(scaled); KmerMinHashBTree { num, ksize, hash_function, seed, max_hash, mins, abunds, current_max: 0, md5sum: Mutex::new(None), } } pub fn num(&self) -> u32 { self.num } pub fn is_protein(&self) -> bool { self.hash_function == HashFunctions::Murmur64Protein } pub fn max_hash(&self) -> u64 { self.max_hash } pub fn scaled(&self) -> ScaledType { scaled_for_max_hash(self.max_hash) } pub fn clear(&mut self) { self.mins.clear(); if let Some(ref mut abunds) = self.abunds { abunds.clear(); } self.current_max = 0; } pub fn is_empty(&self) -> bool { self.mins.is_empty() } pub fn set_hash_function(&mut self, h: HashFunctions) -> Result<(), Error> { if self.hash_function == h { return Ok(()); } if !self.is_empty() { return Err(Error::NonEmptyMinHash { message: "hash_function".into(), }); } self.hash_function = h; Ok(()) } pub fn track_abundance(&self) -> bool { self.abunds.is_some() } pub fn enable_abundance(&mut self) -> Result<(), Error> { if !self.mins.is_empty() { return Err(Error::NonEmptyMinHash { message: "track_abundance=True".into(), }); } self.abunds = Some(Default::default()); Ok(()) } pub fn disable_abundance(&mut self) { self.abunds = None; } fn reset_md5sum(&self) { let mut data = self.md5sum.lock().unwrap(); if data.is_some() { *data = None; } } pub fn md5sum(&self) -> String { let mut data = self.md5sum.lock().unwrap(); if data.is_none() { let mut buffer = String::with_capacity(20); let mut md5_ctx = md5::Context::new(); write!(&mut buffer, "{}", self.ksize()).unwrap(); md5_ctx.consume(&buffer); buffer.clear(); for x in &self.mins { write!(&mut buffer, "{}", x).unwrap(); md5_ctx.consume(&buffer); buffer.clear(); } *data = Some(format!("{:x}", md5_ctx.compute())); } data.clone().unwrap() } pub fn add_hash_with_abundance(&mut self, hash: u64, abundance: u64) { if hash > self.max_hash && self.max_hash != 0 { // This is a scaled minhash, and we don't need to add the new hash return; } if self.num == 0 && self.max_hash == 0 { // why did you create this minhash? it will always be empty... return; } if abundance == 0 { // well, don't add it. return; } // From this point on, hash is within scaled (or no scaled specified). // empty mins? add it. if self.mins.is_empty() { self.mins.insert(hash); self.reset_md5sum(); if let Some(ref mut abunds) = self.abunds { abunds.insert(hash, abundance); } self.current_max = hash; return; } if hash <= self.max_hash || hash <= self.current_max || (self.mins.len() as u32) < self.num { // "good" hash - within range, smaller than current entry, or // still have space available if self.mins.insert(hash) { self.reset_md5sum(); if hash > self.current_max { self.current_max = hash; } } if let Some(ref mut abunds) = self.abunds { *abunds.entry(hash).or_insert(0) += abundance; } // is it too big now? if self.num != 0 && self.mins.len() > (self.num as usize) { let last = *self.mins.iter().next_back().unwrap(); self.mins.remove(&last); self.reset_md5sum(); if let Some(ref mut abunds) = self.abunds { abunds.remove(&last); } self.current_max = *self.mins.iter().next_back().unwrap(); } } } pub fn add_word(&mut self, word: &[u8]) { let hash = _hash_murmur(word, self.seed); self.add_hash(hash); } pub fn remove_hash(&mut self, hash: u64) { if self.mins.remove(&hash) { self.reset_md5sum(); if let Some(ref mut abunds) = self.abunds { abunds.remove(&hash); } } if hash == self.current_max { self.current_max = *self.mins.iter().next_back().unwrap_or(&0); } } pub fn remove_many>(&mut self, hashes: T) -> Result<(), Error> { for min in hashes { self.remove_hash(min); } Ok(()) } pub fn merge(&mut self, other: &KmerMinHashBTree) -> Result<(), Error> { self.check_compatible(other)?; let union = self.mins.union(&other.mins); let to_take = if self.num == 0 { usize::MAX } else { self.num as usize }; self.mins = union.take(to_take).cloned().collect(); if let Some(abunds) = &self.abunds { if let Some(oabunds) = &other.abunds { let mut new_abunds = BTreeMap::new(); for hash in &self.mins { *new_abunds.entry(*hash).or_insert(0) += abunds.get(hash).unwrap_or(&0) + oabunds.get(hash).unwrap_or(&0); } self.abunds = Some(new_abunds) } } // Better safe than sorry, but could check in other places to avoid // always resetting self.reset_md5sum(); Ok(()) } pub fn add_from(&mut self, other: &KmerMinHashBTree) -> Result<(), Error> { for min in &other.mins { self.add_hash(*min); } Ok(()) } pub fn add_many(&mut self, hashes: &[u64]) -> Result<(), Error> { for min in hashes { self.add_hash(*min); } Ok(()) } pub fn add_many_with_abund(&mut self, hashes: &[(u64, u64)]) -> Result<(), Error> { for item in hashes { self.add_hash_with_abundance(item.0, item.1); } Ok(()) } pub fn count_common(&self, other: &KmerMinHashBTree, downsample: bool) -> Result { if downsample && self.scaled() != other.scaled() { // downsample to the larger of the two scaled values let (first, second) = if self.scaled() > other.scaled() { (self, other) } else { (other, self) }; let downsampled_mh = second.clone().downsample_scaled(first.scaled())?; first.count_common(&downsampled_mh, false) } else { self.check_compatible(other)?; let iter = if self.size() < other.size() { Intersection::new(self.mins.iter(), other.mins.iter()) } else { Intersection::new(other.mins.iter(), self.mins.iter()) }; Ok(iter.count() as u64) } } pub fn intersection(&self, other: &KmerMinHashBTree) -> Result<(Vec, u64), Error> { self.check_compatible(other)?; if self.num != 0 { let mut combined_mh = KmerMinHashBTree::new( self.scaled(), self.ksize, self.hash_function.clone(), self.seed, self.abunds.is_some(), self.num, ); combined_mh.merge(self)?; combined_mh.merge(other)?; let it1 = Intersection::new(self.mins.iter(), other.mins.iter()); // TODO: there is probably a way to avoid this Vec here, // and pass the it1 as left in it2. let i1: Vec = it1.cloned().collect(); let i2: Vec = combined_mh.mins.iter().cloned().collect(); let it2 = Intersection::new(i1.iter(), i2.iter()); let common: Vec = it2.cloned().collect(); Ok((common, combined_mh.mins.len() as u64)) } else { // Intersection for scaled MinHash sketches Ok(intersection(self.mins.iter(), other.mins.iter())) } } pub fn intersection_size(&self, other: &KmerMinHashBTree) -> Result<(u64, u64), Error> { self.check_compatible(other)?; if self.num != 0 { let mut combined_mh = KmerMinHashBTree::new( self.scaled(), self.ksize, self.hash_function.clone(), self.seed, self.abunds.is_some(), self.num, ); combined_mh.merge(self)?; combined_mh.merge(other)?; let it1 = Intersection::new(self.mins.iter(), other.mins.iter()); // TODO: there is probably a way to avoid this Vec here, // and pass the it1 as left in it2. let i1: Vec = it1.cloned().collect(); let i2: Vec = combined_mh.mins.iter().cloned().collect(); let it2 = Intersection::new(i1.iter(), i2.iter()); Ok((it2.count() as u64, combined_mh.mins.len() as u64)) } else { Ok(intersection_size(self.mins.iter(), other.mins.iter())) } } // calculate Jaccard similarity, ignoring abundance. pub fn jaccard(&self, other: &KmerMinHashBTree) -> Result { self.check_compatible(other)?; if let Ok((common, size)) = self.intersection_size(other) { Ok(common as f64 / u64::max(1, size) as f64) } else { Ok(0.0) } } // compare two minhashes, with abundance; // calculate their angular similarity. pub fn angular_similarity(&self, other: &KmerMinHashBTree) -> Result { self.check_compatible(other)?; if self.abunds.is_none() || other.abunds.is_none() { return Err(Error::NeedsAbundanceTracking); } let abunds = self.abunds.as_ref().unwrap(); let other_abunds = other.abunds.as_ref().unwrap(); let mut prod = 0; let a_sq: u64 = abunds.values().map(|a| (a * a)).sum(); let b_sq: u64 = other_abunds.values().map(|a| (a * a)).sum(); for (hash, value) in abunds.iter() { if let Some(oa) = other_abunds.get(hash) { prod += value * oa } } let norm_a = (a_sq as f64).sqrt(); let norm_b = (b_sq as f64).sqrt(); if norm_a == 0. || norm_b == 0. { return Ok(0.0); } let prod = f64::min(prod as f64 / (norm_a * norm_b), 1.); let distance = 2. * prod.acos() / PI; Ok(1. - distance) } pub fn similarity( &self, other: &KmerMinHashBTree, ignore_abundance: bool, downsample: bool, ) -> Result { if downsample && self.scaled() != other.scaled() { // downsample to larger of two scaled let (first, second) = if self.scaled() > other.scaled() { (self, other) } else { (other, self) }; let downsampled_mh = second.clone().downsample_scaled(first.scaled())?; first.similarity(&downsampled_mh, ignore_abundance, false) } else if ignore_abundance || self.abunds.is_none() || other.abunds.is_none() { self.jaccard(other) } else { self.angular_similarity(other) } } pub fn dayhoff(&self) -> bool { self.hash_function == HashFunctions::Murmur64Dayhoff } pub fn hp(&self) -> bool { self.hash_function == HashFunctions::Murmur64Hp } pub fn hash_function(&self) -> HashFunctions { self.hash_function.clone() } pub fn mins(&self) -> Vec { self.mins.iter().cloned().collect() } pub fn iter_mins(&self) -> impl Iterator { self.mins.iter() } pub fn abunds(&self) -> Option> { self.abunds .as_ref() .map(|abunds| abunds.values().cloned().collect()) } // create a downsampled copy of self pub fn downsample_max_hash(self, max_hash: u64) -> Result { if self.max_hash == 0 { // CTB: this is a num minhash. Just blithely return. Ok(self) } else { let scaled = scaled_for_max_hash(max_hash); self.downsample_scaled(scaled) } } // create a downsampled copy of self pub fn downsample_scaled(self, scaled: ScaledType) -> Result { if self.scaled() == scaled || self.scaled() == 0 { Ok(self) } else if self.scaled() > scaled { Err(Error::CannotUpsampleScaled) } else { let mut new_mh = KmerMinHashBTree::new( scaled, self.ksize, self.hash_function.clone(), self.seed, self.abunds.is_some(), self.num, ); if self.abunds.is_some() { new_mh.add_many_with_abund(&self.to_vec_abunds())?; } else { new_mh.add_many(&self.mins())?; } Ok(new_mh) } } pub fn to_vec_abunds(&self) -> Vec<(u64, u64)> { if let Some(abunds) = &self.abunds { abunds.iter().map(|(a, b)| (*a, *b)).collect() } else { self.mins .iter() .cloned() .zip(std::iter::repeat(1)) .collect() } } pub fn sum_abunds(&self) -> u64 { if let Some(abunds) = &self.abunds { abunds.values().sum() } else { self.size() as u64 } } pub fn from_reader(rdr: R) -> Result where R: std::io::Read, { let (rdr, _format) = niffler::get_reader(Box::new(rdr))?; let mh: KmerMinHashBTree = serde_json::from_reader(rdr)?; Ok(mh) } } impl SigsTrait for KmerMinHashBTree { fn size(&self) -> usize { self.mins.len() } fn to_vec(&self) -> Vec { self.mins() } fn ksize(&self) -> usize { self.ksize as usize } fn seed(&self) -> u64 { self.seed } fn hash_function(&self) -> HashFunctions { self.hash_function.clone() } fn add_hash(&mut self, hash: u64) { self.add_hash_with_abundance(hash, 1); } fn check_compatible(&self, other: &KmerMinHashBTree) -> Result<(), Error> { /* if self.num != other.num { return Err(Error::MismatchNum { n1: self.num, n2: other.num, } .into()); } */ if self.ksize != other.ksize { return Err(Error::MismatchKSizes); } if self.hash_function != other.hash_function { // TODO: fix this error return Err(Error::MismatchDNAProt); } if self.max_hash != other.max_hash { return Err(Error::MismatchScaled); } if self.seed != other.seed { return Err(Error::MismatchSeed); } Ok(()) } } impl From for KmerMinHash { fn from(other: KmerMinHashBTree) -> KmerMinHash { let mut new_mh = KmerMinHash::new( other.scaled(), other.ksize() as u32, other.hash_function(), other.seed(), other.track_abundance(), other.num(), ); let mins = other.mins.into_iter().collect(); let abunds = other .abunds .map(|abunds| abunds.values().cloned().collect()); new_mh.mins = mins; new_mh.abunds = abunds; new_mh.md5sum = other.md5sum; new_mh } } impl From<&KmerMinHashBTree> for KmerMinHash { fn from(other: &KmerMinHashBTree) -> KmerMinHash { let mut new_mh = KmerMinHash::new( other.scaled(), other.ksize() as u32, other.hash_function(), other.seed(), other.track_abundance(), other.num(), ); let mins = other.mins.iter().copied().collect(); let abunds = other .abunds .as_ref() .map(|abunds| abunds.values().cloned().collect()); new_mh.mins = mins; new_mh.abunds = abunds; new_mh.md5sum = Mutex::new(other.md5sum.lock().unwrap().clone()); new_mh } } impl From for KmerMinHashBTree { fn from(other: KmerMinHash) -> KmerMinHashBTree { let mut new_mh = KmerMinHashBTree::new( other.scaled(), other.ksize() as u32, other.hash_function(), other.seed(), other.track_abundance(), other.num(), ); let mins: BTreeSet = other.mins.into_iter().collect(); let abunds = other .abunds .map(|abunds| mins.iter().cloned().zip(abunds).collect()); new_mh.mins = mins; new_mh.abunds = abunds; new_mh.md5sum = other.md5sum; new_mh } } fn intersection<'a>( me_iter: impl Iterator, other_iter: impl Iterator, ) -> (Vec, u64) { let mut me = me_iter.peekable(); let mut other = other_iter.peekable(); let mut common: Vec = vec![]; let mut union_size = 0; loop { match (me.peek(), other.peek()) { (Some(ref left_key), Some(ref right_key)) => { let res = left_key.cmp(right_key); match res { Ordering::Less => { me.next(); union_size += 1; } Ordering::Greater => { other.next(); union_size += 1; } Ordering::Equal => { other.next(); common.push(***left_key); me.next(); union_size += 1; } }; } (None, Some(_)) => { other.next(); union_size += 1; } (Some(_), None) => { me.next(); union_size += 1; } _ => break, }; } (common, union_size as u64) } fn intersection_size<'a>( me_iter: impl Iterator, other_iter: impl Iterator, ) -> (u64, u64) { let mut me = me_iter.peekable(); let mut other = other_iter.peekable(); let mut common = 0; let mut union_size = 0; loop { match (me.peek(), other.peek()) { (Some(ref left_key), Some(ref right_key)) => { let res = left_key.cmp(right_key); match res { Ordering::Less => { me.next(); union_size += 1; } Ordering::Greater => { other.next(); union_size += 1; } Ordering::Equal => { other.next(); me.next(); common += 1; union_size += 1; } }; } (None, Some(_)) => { other.next(); union_size += 1; } (Some(_), None) => { me.next(); union_size += 1; } _ => break, }; } (common as u64, union_size as u64) } sourmash-bio-sourmash-bc3c0f8/src/core/src/sketch/mod.rs000066400000000000000000000007421474157046300234050ustar00rootroot00000000000000pub mod hyperloglog; pub mod minhash; pub mod nodegraph; use serde::{Deserialize, Serialize}; use crate::sketch::hyperloglog::HyperLogLog; use crate::sketch::minhash::{KmerMinHash, KmerMinHashBTree}; #[derive(Debug, Clone, Serialize, Deserialize)] #[serde(untagged)] #[cfg_attr( feature = "rkyv", derive(rkyv::Serialize, rkyv::Deserialize, rkyv::Archive) )] pub enum Sketch { MinHash(KmerMinHash), LargeMinHash(KmerMinHashBTree), HyperLogLog(HyperLogLog), } sourmash-bio-sourmash-bc3c0f8/src/core/src/sketch/nodegraph.rs000066400000000000000000001003111474157046300245660ustar00rootroot00000000000000use std::fs::File; use std::io; use std::path::Path; use std::slice; use byteorder::{BigEndian, ByteOrder, LittleEndian, ReadBytesExt, WriteBytesExt}; use fixedbitset::FixedBitSet; use crate::prelude::*; use crate::sketch::minhash::{KmerMinHash, KmerMinHashBTree}; use crate::Error; use crate::HashIntoType; #[derive(Debug, Default, Clone)] pub struct Nodegraph { bs: Vec, ksize: usize, occupied_bins: usize, unique_kmers: usize, } // TODO: not checking for unique_kmers, // since it is not saved in a khmer nodegraph impl PartialEq for Nodegraph { fn eq(&self, other: &Nodegraph) -> bool { self.bs == other.bs && self.occupied_bins == other.occupied_bins && self.ksize == other.ksize } } impl Update for Nodegraph { fn update(&self, other: &mut Nodegraph) -> Result<(), Error> { other.occupied_bins = other .bs .iter_mut() .zip(&self.bs) .enumerate() .map(|(i, (bs, bs_me))| { bs.union_with(bs_me); if i == 0 { bs.count_ones(..) } else { 0 } }) .sum(); Ok(()) } } impl Update for KmerMinHash { fn update(&self, other: &mut Nodegraph) -> Result<(), Error> { for h in self.mins() { other.count(h); } Ok(()) } } impl Update for KmerMinHashBTree { fn update(&self, other: &mut Nodegraph) -> Result<(), Error> { for h in self.mins() { other.count(h); } Ok(()) } } impl Nodegraph { pub fn new(tablesizes: &[usize], ksize: usize) -> Nodegraph { let mut bs = Vec::with_capacity(tablesizes.len()); for size in tablesizes.iter() { bs.push(FixedBitSet::with_capacity(*size)); } Nodegraph { bs, ksize, occupied_bins: 0, unique_kmers: 0, } } pub fn with_tables(tablesize: usize, n_tables: usize, ksize: usize) -> Nodegraph { let mut tablesizes = Vec::with_capacity(n_tables); let mut i = u64::max((tablesize - 1) as u64, 2); if i % 2 == 0 { i -= 1 } while tablesizes.len() != n_tables { if primal_check::miller_rabin(i) { tablesizes.push(i as usize); } if i == 1 { break; } i -= 2; } Nodegraph::new(tablesizes.as_slice(), ksize) } pub(crate) fn count_kmer(&mut self, kmer: &[u8]) -> bool { let h = _hash(kmer); self.count(h) } pub fn count(&mut self, hash: HashIntoType) -> bool { let mut is_new_kmer = false; for (i, bitset) in self.bs.iter_mut().enumerate() { let bin = hash % bitset.len() as u64; if !bitset.put(bin as usize) { if i == 0 { self.occupied_bins += 1; } is_new_kmer = true; } } if is_new_kmer { self.unique_kmers += 1 } is_new_kmer } pub fn get(&self, hash: HashIntoType) -> usize { for bitset in &self.bs { let bin = hash % bitset.len() as u64; if !bitset.contains(bin as usize) { return 0; } } 1 } pub(crate) fn get_kmer(&self, kmer: &[u8]) -> usize { let h = _hash(kmer); self.get(h) } pub fn expected_collisions(&self) -> f64 { let min_size = self.bs.iter().map(|x| x.len()).min().unwrap(); let n_ht = self.bs.len(); let occupancy = self.occupied_bins; let fp_one = occupancy as f64 / min_size as f64; f64::powf(fp_one, n_ht as f64) } pub fn tablesize(&self) -> usize { self.bs.iter().map(|x| x.len()).sum() } pub fn noccupied(&self) -> usize { self.occupied_bins } pub fn matches(&self, mh: &KmerMinHash) -> usize { mh.iter_mins().filter(|x| self.get(**x) == 1).count() } pub fn ntables(&self) -> usize { self.bs.len() } pub fn ksize(&self) -> usize { self.ksize } pub fn into_bitsets(self) -> Vec { self.bs } // save pub fn save>(&self, path: P) -> Result<(), Error> { // TODO: if it ends with gz, open a compressed file // might use get_output here? self.save_to_writer(&mut File::create(path)?)?; Ok(()) } pub fn save_to_writer(&self, wtr: &mut W) -> Result<(), Error> where W: io::Write, { wtr.write_all(b"OXLI")?; wtr.write_u8(4)?; // version wtr.write_u8(2)?; // ht_type wtr.write_u32::(self.ksize as u32)?; // ksize wtr.write_u8(self.bs.len() as u8)?; // n_tables wtr.write_u64::(self.occupied_bins as u64)?; // n_occupied for count in &self.bs { let tablesize = count.len(); wtr.write_u64::(tablesize as u64)?; let byte_size = tablesize / 8 + 1; let (div, rem) = (byte_size / 4, byte_size % 4); // Once this issue and PR are solved, this is a one liner: // https://github.com/BurntSushi/byteorder/issues/155 // https://github.com/BurntSushi/byteorder/pull/166 //wtr.write_u32_from::(&count.as_slice()[..div])?; let slice = &count.as_slice()[..div]; let buf = unsafe { let len = std::mem::size_of_val(slice); slice::from_raw_parts(slice.as_ptr() as *const u8, len) }; wtr.write_all(buf)?; // Replace when byteorder PR is released if rem != 0 { let mut cursor = [0u8; 4]; LittleEndian::write_u32(&mut cursor, count.as_slice()[div]); for item in cursor.iter().take(rem) { wtr.write_u8(*item)?; } } } Ok(()) } pub fn from_reader(rdr: R) -> Result where R: io::Read, { let (mut rdr, _format) = niffler::get_reader(Box::new(rdr))?; let signature = rdr.read_u32::()?; assert_eq!(signature, 0x4f58_4c49); let version = rdr.read_u8()?; assert_eq!(version, 0x04); let ht_type = rdr.read_u8()?; assert_eq!(ht_type, 0x02); let ksize = rdr.read_u32::()?; let n_tables = rdr.read_u8()?; let occupied_bins = rdr.read_u64::()? as usize; let mut bs = Vec::with_capacity(n_tables as usize); for _i in 0..n_tables { let tablesize: usize = rdr.read_u64::()? as usize; let byte_size = tablesize / 8 + 1; let rem = byte_size % 4; let blocks: Vec = { let mut blocks = vec![0; byte_size / 4]; rdr.read_u32_into::(&mut blocks)?; if rem != 0 { let mut values = [0u8; 4]; for item in values.iter_mut().take(rem) { let byte = rdr.read_u8().expect("error reading bins"); *item = byte; } let mut block = vec![0u32; 1]; LittleEndian::read_u32_into(&values, &mut block); blocks.push(block[0]); } blocks }; let counts = FixedBitSet::with_capacity_and_blocks(tablesize, blocks); bs.push(counts); } Ok(Nodegraph { bs, ksize: ksize as usize, occupied_bins, unique_kmers: 0, // This is a khmer issue, it doesn't save unique_kmers }) } pub fn from_path>(path: P) -> Result { let mut reader = io::BufReader::new(File::open(path)?); Nodegraph::from_reader(&mut reader) } pub fn tablesizes(&self) -> Vec { self.bs.iter().map(|x| x.len() as u64).collect() } pub fn n_occupied_bins(&self) -> usize { self.occupied_bins } pub fn unique_kmers(&self) -> usize { self.unique_kmers } pub fn similarity(&self, other: &Nodegraph) -> f64 { let result: usize = self .bs .iter() .zip(&other.bs) .map(|(bs, bs_other)| bs.intersection(bs_other).count()) .sum(); let size: usize = self .bs .iter() .zip(&other.bs) .map(|(bs, bs_other)| bs.union(bs_other).count()) .sum(); result as f64 / size as f64 } pub fn containment(&self, other: &Nodegraph) -> f64 { let result: usize = self .bs .iter() .zip(&other.bs) .map(|(bs, bs_other)| bs.intersection(bs_other).count()) .sum(); let size: usize = self.bs.iter().map(|bs| bs.count_ones(..)).sum(); result as f64 / size as f64 } } fn twobit_repr(a: u8) -> HashIntoType { match a as char { 'A' => 0, 'C' => 2, 'G' => 3, 'T' => 1, _ => unimplemented!(), } } fn twobit_comp(a: u8) -> HashIntoType { match a as char { 'A' => 1, 'C' => 3, 'G' => 2, 'T' => 0, _ => unimplemented!(), } } fn uniqify_rc(f: HashIntoType, r: HashIntoType) -> HashIntoType { if f < r { f } else { r } } fn _hash(kmer: &[u8]) -> HashIntoType { let ksize = kmer.len(); let mut hash = 0; let mut rev = 0; hash |= twobit_repr(kmer[0]); rev |= twobit_comp(kmer[ksize - 1]); let mut i = 1; let mut j: isize = (ksize - 2) as isize; while i < ksize { hash <<= 2; rev <<= 2; hash |= twobit_repr(kmer[i]); rev |= twobit_comp(kmer[j as usize]); i += 1; j -= 1; } uniqify_rc(hash, rev) } #[cfg(test)] mod test { use super::*; use std::io::{BufReader, BufWriter}; use std::path::PathBuf; use proptest::collection::vec; use proptest::num::u64; use proptest::proptest; // Generate with khmer: // >>> a = khmer.Nodegraph(3, 23, 6) // >>> a.count("ACG") // >>> a.count("TTA") // >>> a.count("CGA") // >>> a.save("test.ng") // and dumping test.ng with xxd: // $ xxd -i test.ng static RAW_DATA: &[u8] = &[ 0x4f, 0x58, 0x4c, 0x49, 0x04, 0x02, 0x03, 0x00, 0x00, 0x00, 0x06, 0x03, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x13, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x01, 0x11, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0c, 0x01, 0x0d, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0a, 0x08, 0x0b, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x21, 0x00, 0x07, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x54, 0x05, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, ]; static COMPRESSED_RAW_DATA: &[u8] = &[ 0x1f, 0x8b, 0x08, 0x08, 0x73, 0x88, 0x9f, 0x5e, 0x00, 0x03, 0x74, 0x65, 0x73, 0x74, 0x2e, 0x6e, 0x67, 0x00, 0xf3, 0x8f, 0xf0, 0xf1, 0x64, 0x61, 0x62, 0x66, 0x60, 0x60, 0x60, 0x03, 0x11, 0x20, 0x20, 0x0c, 0xa5, 0x19, 0x38, 0x19, 0x05, 0x61, 0x4c, 0x1e, 0x46, 0x5e, 0x28, 0x8b, 0x8b, 0x83, 0x1b, 0xca, 0x52, 0x64, 0x60, 0x87, 0xb2, 0x42, 0x58, 0xa1, 0x0c, 0x36, 0x00, 0x8d, 0xf0, 0xa9, 0x8b, 0x4f, 0x00, 0x00, 0x00, ]; proptest! { #[test] fn count_and_get(hashes in vec(u64::ANY, 1..500)) { let mut ng: Nodegraph = Nodegraph::new(&[1000], 3); for hash in hashes { ng.count(hash); assert_eq!(ng.get(hash), 1); } } } #[test] fn load_compressed() { let mut reader = BufReader::new(COMPRESSED_RAW_DATA); let ng: Nodegraph = Nodegraph::from_reader(&mut reader).expect("Loading error"); assert_eq!(ng.tablesizes(), &[19, 17, 13, 11, 7, 5]); assert_eq!(ng.ksize(), 3); assert_eq!(ng.get_kmer(b"ACG"), 1); assert_eq!(ng.get_kmer(b"TTA"), 1); assert_eq!(ng.get_kmer(b"CGA"), 1); } #[test] fn count_and_get_nodegraph() { let mut ng: Nodegraph = Nodegraph::new(&[10], 3); ng.count(801084876663808); assert_eq!(ng.get(801084876663808), 1); assert_eq!(ng.unique_kmers(), 1); } #[test] fn containment() { let mut ng1: Nodegraph = Nodegraph::new(&[31], 3); let mut ng2: Nodegraph = Nodegraph::new(&[31], 3); (0..20).for_each(|i| { if i % 2 == 0 { ng1.count(i); }; ng2.count(i); }); assert_eq!(ng1.containment(&ng2), 1.0); assert_eq!(ng1.similarity(&ng2), 0.5); assert_eq!(ng1.unique_kmers(), 10); assert_eq!(ng2.unique_kmers(), 20); } #[test] fn load_save_nodegraph() { let mut datadir = PathBuf::from(env!("CARGO_MANIFEST_DIR")); datadir.push("../../tests/test-data/.sbt.v3/"); for i in 0..=5 { let mut filename = datadir.clone(); filename.push(format!("internal.{}", i)); let data = std::fs::read(filename).unwrap(); let mut reader = BufReader::new(&data[..]); let ng: Nodegraph = Nodegraph::from_reader(&mut reader).expect("Loading error"); let mut buf = Vec::new(); { let mut writer = BufWriter::new(&mut buf); ng.save_to_writer(&mut writer).unwrap(); } let chunk_size = 8; for (c1, c2) in data.to_vec().chunks(chunk_size).zip(buf.chunks(chunk_size)) { assert_eq!(c1, c2); } assert_eq!(data.len(), buf.len()); } } #[test] fn binary_repr_load() { let mut reader = BufReader::new(RAW_DATA); let khmer_ng: Nodegraph = Nodegraph::from_reader(&mut reader).expect("Loading error"); assert_eq!(khmer_ng.tablesizes(), &[19, 17, 13, 11, 7, 5]); assert_eq!(khmer_ng.ksize(), 3); assert_eq!(khmer_ng.get_kmer(b"ACG"), 1); assert_eq!(khmer_ng.get_kmer(b"TTA"), 1); assert_eq!(khmer_ng.get_kmer(b"CGA"), 1); let mut ng = Nodegraph::with_tables(23, 6, 3); ng.count_kmer(b"ACG"); ng.count_kmer(b"TTA"); ng.count_kmer(b"CGA"); let mut buf = Vec::new(); { let mut writer = BufWriter::new(&mut buf); ng.save_to_writer(&mut writer).unwrap(); } assert_eq!(buf.len(), 79); assert_eq!(&RAW_DATA, &buf.as_slice()); } #[test] fn binary_repr_save() { let mut ng = Nodegraph::with_tables(23, 6, 3); ng.count_kmer(b"ACG"); ng.count_kmer(b"TTA"); ng.count_kmer(b"CGA"); let mut buf = Vec::new(); { let mut writer = BufWriter::new(&mut buf); ng.save_to_writer(&mut writer).unwrap(); } let mut reader = BufReader::new(&buf[..]); let new_ng: Nodegraph = Nodegraph::from_reader(&mut reader).expect("Loading error"); assert_eq!(new_ng.tablesizes(), &[19, 17, 13, 11, 7, 5]); assert_eq!(new_ng.ksize(), 3); assert_eq!(new_ng.get_kmer(b"ACG"), 1); assert_eq!(new_ng.get_kmer(b"TTA"), 1); assert_eq!(new_ng.get_kmer(b"CGA"), 1); assert_eq!(buf.len(), 79); assert_eq!(&RAW_DATA, &buf.as_slice()); } #[test] fn update_nodegraph() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/.sbt.v3/internal.0"); let ng_parent: Nodegraph = Nodegraph::from_path(filename).expect("Loading error"); filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/.sbt.v3/internal.1"); let ng_1: Nodegraph = Nodegraph::from_path(filename).expect("Loading error"); filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/.sbt.v3/internal.2"); let ng_2: Nodegraph = Nodegraph::from_path(filename).expect("Loading error"); let mut ng_0: Nodegraph = Nodegraph::new(&[99991, 99989, 99971, 99961], 1); ng_1.update(&mut ng_0).expect("Error in update"); ng_2.update(&mut ng_0).expect("Error in update"); assert_eq!(ng_0.bs, ng_parent.bs); //assert_eq!(ng_0.occupied_bins, ng_parent.occupied_bins); } #[test] fn update_nodegraph_many() -> Result<(), Box> { let mut leaf1 = Nodegraph::with_tables(100, 3, 5); for kmer in &["AAAAA", "AAAAT", "AAAAC"] { leaf1.count(_hash(kmer.as_bytes())); } let mut leaf2 = Nodegraph::with_tables(100, 3, 5); for kmer in &["AAAAA", "AAAAT", "AAAAG"] { leaf2.count(_hash(kmer.as_bytes())); } let mut leaf3 = Nodegraph::with_tables(100, 3, 5); for kmer in &["AAAAA", "AAAAT", "CAAAA"] { leaf3.count(_hash(kmer.as_bytes())); } let mut leaf4 = Nodegraph::with_tables(100, 3, 5); for kmer in &["AAAAA", "CAAAA", "GAAAA"] { leaf4.count(_hash(kmer.as_bytes())); } let mut leaf5 = Nodegraph::with_tables(100, 3, 5); for kmer in &["AAAAA", "AAAAT", "GAAAA"] { leaf5.count(_hash(kmer.as_bytes())); } let h = _hash(b"AAAAT"); for leaf in &[leaf1, leaf2, leaf3, leaf5] { assert_eq!(leaf.get(h), 1); } assert_eq!(leaf4.get(h), 0); Ok(()) } #[test] fn load_nodegraph() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/.sbt.v3/internal.0"); //let data = include_bytes!("data/internal.0"); let ng: Nodegraph = Nodegraph::from_path(filename).expect("Loading error"); assert_eq!(ng.tablesizes(), [99991, 99989, 99971, 99961]); assert_eq!(ng.n_occupied_bins(), 2416); assert_eq!(ng.get(1877811740), 0); for h in [ 1877811749, 1339603207230, 5641354835174, 10502027926594, 11550845136154, 12183113567732, 14062071191653, 14580861632266, 18722876140337, 20714320729467, 22732389403804, 24134363957219, 30606147678309, 30841792132441, 31130970675642, 32760645340554, 33190965408032, 33960067474598, 35413666412010, 37166860055638, 38008340488610, 38631948370393, 38946626358857, 39177463395973, 39396232170068, 40000457533067, 41548684950793, 42975853122398, 43119393989323, 43377695911881, 49367718187361, 49468277378328, 50266038601832, 51636068122286, 56622962479482, 58428533496606, 58971444597606, 59372670276820, 59452528403612, 61074441390615, 62130354354877, 62702978264830, 64430859773984, 65419869837915, 65663647257358, 67872638217057, 68827108109263, 69134145403133, 70436552236751, 70880519905358, 78004711377952, 81502993782978, 84636365982041, 85239629151685, 94266407193778, 98142256300701, 98837920540443, 99930975216128, 100653760748845, 102082282949673, 102530908835648, 103010972337870, 103329805967682, 103652023867250, 104130252812879, 112760650992638, 114779375695317, 115796389594898, 117864921668170, 119763283100790, 120285237540732, 121866736124647, 122140892054804, 122995254140976, 123065069359489, 123405856681590, 128261346941417, 130618284885748, 131310062444107, 133580282506938, 139762252968300, 148434659896290, 150472163116319, 151610888790844, 151736593364935, 152145317861349, 154119208822262, 154803963303860, 164146490870545, 166146331478050, 166719940886532, 173367021064967, 173503876669758, 173949973069402, 175345218226732, 175559849681044, 177057739236298, 182134979074863, 185526639726849, 186188120396587, 191078441509481, 191784713609488, 196150349451960, 196584209022550, 196853921592387, 197752504251580, 198597053692927, 200567230796156, 201179164742411, 202960515626517, 203378213499023, 210822710165852, 211915017282095, 213613291536686, 215418355892998, 216444054660744, 216772483699428, 218586803538885, 219619606513837, 221322641419906, 221692515333150, 222646058515199, 223103766020907, 223436957406949, 225216425962890, 225962923363564, 227026140769845, 227790244540446, 228251083676258, 231710804058239, 233288106176435, 235385609463388, 235438505061770, 238869764444344, 239420157045937, 241121021240187, 241671335688938, 242838856557679, 244786468497109, 247140303430449, 248336783901894, 250357693564448, 253975323975963, 256375919657769, 259301238714261, 265736169322750, 265781739304017, 266725362494513, 267345873524094, 271342665825792, 274876788032658, 275360996806051, 275711441656065, 276221877341287, 277115529175674, 277862338800417, 280967669495427, 281817613252845, 281897628539431, 282200323162036, 284620358398045, 284881057128884, 285925400570356, 289038917997203, 289724862541255, 290309864993733, 294086384353867, 295503963521838, 296966685834878, 299005107402724, 300199234365396, 300617258525997, 301443933468348, 302667628736144, 305781540735975, 308107503975413, 308473366560206, 311148974624393, 311393227334671, 312856558437716, 314634385460120, 315140251773348, 316147818305256, 317314266550052, 318043998368340, 319121931997971, 324333149672473, 324779561826125, 326855577904572, 327646715321140, 332098363218169, 333944737799563, 334160175766170, 335584394916553, 335971123608722, 336472954791992, 338443948117005, 338762957149102, 341091055062112, 341724341043975, 343240684449173, 344010897833199, 345196014534640, 347580313704916, 348815216366639, 348987115477673, 350399163507829, 357535517122796, 358595265377108, 358821394913517, 359452645935849, 362124977362793, 366354200059782, 366535672236781, 369474755519844, 370249620342175, 372037414685096, 373949557068914, 374319819178480, 374609596539290, 374615513078797, 375780195152331, 379102542404949, 379241504134406, 379468459802010, 379661395441316, 382035531157070, 383008100523152, 383135333541903, 383850900061929, 384049466048679, 386263487549463, 389141313731258, 390332660259608, 393516543506060, 400967959890432, 401487977714282, 403579902131163, 406955472999822, 408962716867059, 409903018669983, 410861197839878, 414355853800959, 416580890530128, 418934773149726, 419642123579295, 421963163293847, 423404494960378, 424303224424616, 424596150389604, 427230335237565, 429952924284227, 430664272577516, 432630098291297, 434623968464695, 435267549331128, 435277763415865, 435874505125675, 437654980371254, 438061138128325, 438738288109196, 439177016005977, 445344075816835, 445802335759252, 446710003143163, 447467518423055, 449641727299803, 450058424424520, 450112320572118, 450125274173050, 452241247094714, 452829154656306, 454813132622585, 456174765596578, 456493632715805, 456717723773303, 461156956524045, 462211497323948, 463604028403361, 465228093393002, 466250095735125, 469687793491358, 471922058927200, 472039595540269, 472566025949945, 472595419353109, 472977022618999, 473018780652067, 473772140307174, 474570287539184, 474912397870603, 476325119891604, 476526896773980, 476855560317170, 480232815782455, 484291524803718, 485278877010947, 487732314724511, 491715999174683, 494276065129917, 495846359323641, 506531113930798, 507871334392190, 508031302306958, 508934816424512, 509939413858428, 510737910464301, 512514768813167, 513350289212553, 517460246914282, 523321188654478, 524296526109332, 525762219690878, 526111205078257, 527062179866457, 527591752682839, 527920198105606, 530316966667021, 532977797373940, 533221992957154, 533383900955463, 537527309474265, 538136383284668, 538939534540869, 539777176029418, 539873986742508, 543935720187395, 545273268128445, 549484636278027, 551381720133873, 553977959695484, 555321949850378, 555828795847874, 557285930201258, 558008777268240, 558433475619762, 558892016080993, 559199414492426, 560748186311107, 561604684739024, 562789967643507, 563343385252253, 563775395645616, 564616206473372, 565020390122451, 568901431510366, 572526115602502, 573767900523468, 573851852316852, 576624529060777, 576874504697497, 578856083248351, 579395263040626, 579656586099131, 584217116139474, 587458649504773, 591009756408904, 592792708776319, 592997432856726, 594482884410814, 596004492939074, 596726606390901, 597875929908982, 600179982751750, 601000534535072, 601440269988372, 601603906866038, 602082770371066, 604883041984487, 605545396594434, 606419362199228, 607833403537880, 609555580824872, 609609500753196, 611579272742038, 612206643585093, 612640334623643, 612821302220884, 617021904160724, 617244669177560, 617309228629787, 618709483466270, 620059729516362, 620849299055244, 621083126852990, 622843084945666, 623088556560813, 627738708322473, 628002002108775, 628967244202734, 630034340392901, 632757066611488, 634340585739407, 634691502028135, 635939425862264, 637603178700210, 637880811482435, 644557275230225, 644935615624623, 645793929303122, 646731502743275, 646973138978211, 647900742708077, 649351154360370, 653652775436966, 655230244020599, 668170744538822, 670595660720839, 671785773373187, 672641554971634, 672821857332020, 673587502056476, 676044446355190, 677295740685782, 679716691783353, 682874745971459, 682963108550465, 683897063771844, 685246440558482, 686035384279530, 687129162879229, 687440351836027, 688990372747831, 690608944213791, 691680901171966, 694851976547107, 694869046270466, 700054088308311, 701010566680671, 701156706346414, 702431887238370, 702728791577749, 703127461004015, 703460523248065, 705302678110381, 707793984897058, 707799855432305, 707962189637436, 707993631271976, 708854130532070, 710403353214581, 710927468728191, 711091480855740, 712661928452840, 715334925158742, 715763419567022, 715896323316677, 717568681000032, 717790011003345, 719139881875323, 722537026567926, 722774506110892, 723332805980528, 724621545164802, 724746920000049, 727030394121071, 727262050490847, 728279662753580, 730854175545196, 731361512976697, 734622692371860, 736290151677476, 737921635760471, 738115824615020, 739389456325310, 742704052187442, 746469097917429, 748064810280445, 749144352424687, 753113822684627, 753423569783277, 755196264392026, 758186007844395, 758543555642030, 759083903793759, 761260029175908, 767230586289375, 770167973924874, 770328708409334, 772165475523258, 772947318346532, 774312511311396, 774365323868051, 774964429534347, 775558532281404, 779330069525835, 781344931111517, 787747218685488, 788027556261557, 790211243959626, 790890494413778, 792003960897692, 792629819473398, 797511060014001, 797622366845781, 799257433888961, 800060479182618, 801084876663808, 802340523858506, 803596407436267, ] .iter() { assert_eq!(ng.get(*h), 1); } } } sourmash-bio-sourmash-bc3c0f8/src/core/src/storage/000077500000000000000000000000001474157046300224405ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/src/storage/mod.rs000066400000000000000000000457051474157046300236000ustar00rootroot00000000000000use std::collections::{BTreeMap, HashMap}; use std::ffi::OsStr; use std::fs::{DirBuilder, File}; use std::io::{BufReader, BufWriter, Read, Write}; use std::ops::Deref; use std::sync::{Arc, RwLock}; use camino::Utf8Path as Path; use camino::Utf8PathBuf as PathBuf; use cfg_if::cfg_if; use once_cell::sync::OnceCell; use serde::{Deserialize, Serialize}; use thiserror::Error; use typed_builder::TypedBuilder; use crate::errors::ReadDataError; use crate::prelude::*; use crate::signature::SigsTrait; use crate::sketch::minhash::KmerMinHash; use crate::sketch::Sketch; use crate::{Error, Result}; /// An abstraction for any place where we can store data. pub trait Storage { /// Save bytes into path fn save(&self, path: &str, content: &[u8]) -> Result; /// Load bytes from path fn load(&self, path: &str) -> Result>; /// Args for initializing a new Storage fn args(&self) -> StorageArgs; /// Load signature from internal path fn load_sig(&self, path: &str) -> Result { let raw = self.load(path)?; let mut vs = Signature::from_reader(&mut &raw[..])?; if vs.len() > 1 { unimplemented!("only one Signature currently allowed"); } let sig = vs.swap_remove(0); Ok(sig.into()) } /// Return a spec for creating/opening a storage fn spec(&self) -> String; /// Save signature to internal path fn save_sig(&self, path: &str, sig: Signature) -> Result { let mut buffer = vec![]; { sig.to_writer(&mut buffer).unwrap(); } self.save(path, &buffer) } } #[non_exhaustive] #[derive(Debug, Error)] pub enum StorageError { #[error("Path can't be empty")] EmptyPathError, #[error("Path not found: {0}")] PathNotFoundError(String), #[error("Error reading data from {0}")] DataReadError(String), #[error("Storage for path {1} requires the '{0}' feature to be enabled")] MissingFeature(String, String), } /// InnerStorage: a catch-all type that allows using any Storage in /// parallel contexts. /// /// Arc allows ref counting to share it between threads; /// RwLock makes sure there is only one writer possible (and a lot of readers); /// dyn Storage so we can init with anything that implements the Storage trait. // Send + Sync + 'static is kind of a cheat to avoid lifetimes issues: we // should get rid of that 'static if possible... -- Luiz. #[derive(Clone)] pub struct InnerStorage(Arc>); #[derive(TypedBuilder, Default, Clone)] pub struct SigStore { #[builder(setter(into))] filename: String, #[builder(setter(into))] name: String, #[builder(setter(into))] metadata: String, storage: Option, #[builder(setter(into), default)] data: OnceCell, } impl PartialEq for SigStore { fn eq(&self, other: &Self) -> bool { self.filename == other.filename && self.name == other.name && self.metadata == other.metadata && self.data == other.data } } #[derive(Debug, Clone, Serialize, Deserialize)] #[serde(untagged)] pub enum StorageArgs { FSStorage { path: String }, } /// Store files locally into a directory #[derive(TypedBuilder, Debug, Clone, Default)] pub struct FSStorage { /// absolute path for the directory where data is saved. fullpath: PathBuf, subdir: String, } /// Store files in a zip file. #[ouroboros::self_referencing] pub struct ZipStorage { mapping: Option, #[borrows(mapping)] #[covariant] archive: piz::ZipArchive<'this>, subdir: Option, path: Option, #[borrows(archive)] #[covariant] metadata: Metadata<'this>, } /// Store data in memory (no permanent storage) #[derive(TypedBuilder, Debug, Clone, Default)] pub struct MemStorage { //store: HashMap>, sigs: Arc>>, } #[cfg(all(feature = "branchwater", not(target_arch = "wasm32")))] pub mod rocksdb; #[cfg(all(feature = "branchwater", not(target_arch = "wasm32")))] pub use self::rocksdb::RocksDBStorage; pub type Metadata<'a> = BTreeMap<&'a OsStr, &'a piz::read::FileMetadata<'a>>; // ========================================= impl InnerStorage { pub fn new(inner: impl Storage + Send + Sync + 'static) -> InnerStorage { InnerStorage(Arc::new(RwLock::new(inner))) } pub fn from_spec(spec: String) -> Result { Ok(match spec { x if x.starts_with("fs") => { let path = x.split("://").last().expect("not a valid path"); InnerStorage::new(FSStorage::new("", path)) } x if x.starts_with("memory") => InnerStorage::new(MemStorage::new()), x if x.starts_with("rocksdb") => { let path = x.split("://").last().expect("not a valid path"); cfg_if! { if #[cfg(all( feature = "branchwater", not(target_arch = "wasm32")))] { InnerStorage::new(RocksDBStorage::from_path(path)) } else { return Err(StorageError::MissingFeature("branchwater".into(), path.into()).into()) } } } x if x.starts_with("zip") => { let path = x.split("://").last().expect("not a valid path"); InnerStorage::new(ZipStorage::from_file(path)?) } _ => todo!("storage not supported, throw error"), }) } } impl Storage for InnerStorage { fn save(&self, path: &str, content: &[u8]) -> Result { self.0.save(path, content) } fn load(&self, path: &str) -> Result> { self.0.load(path) } fn args(&self) -> StorageArgs { self.0.args() } fn load_sig(&self, path: &str) -> Result { let mut store = self.0.load_sig(path)?; store.storage = Some(self.clone()); Ok(store) } fn spec(&self) -> String { self.0.spec() } } impl From<&StorageArgs> for FSStorage { fn from(other: &StorageArgs) -> FSStorage { match other { StorageArgs::FSStorage { path } => { let mut fullpath = PathBuf::new(); fullpath.push("."); fullpath.push(path); FSStorage { fullpath, subdir: path.clone(), } } } } } impl Storage for RwLock where L: ?Sized + Storage, { fn save(&self, path: &str, content: &[u8]) -> Result { self.read().unwrap().save(path, content) } fn load(&self, path: &str) -> Result> { self.read().unwrap().load(path) } fn args(&self) -> StorageArgs { self.read().unwrap().args() } fn load_sig(&self, path: &str) -> Result { self.read().unwrap().load_sig(path) } fn spec(&self) -> String { self.read().unwrap().spec() } } impl FSStorage { pub fn new(location: &str, subdir: &str) -> FSStorage { let mut fullpath = PathBuf::new(); fullpath.push(location); fullpath.push(subdir); FSStorage { fullpath, subdir: subdir.into(), } } pub fn set_base(&mut self, location: &str) { let mut fullpath = PathBuf::new(); fullpath.push(location); fullpath.push(&self.subdir); self.fullpath = fullpath; } } impl Storage for FSStorage { fn save(&self, path: &str, content: &[u8]) -> Result { if path.is_empty() { return Err(StorageError::EmptyPathError.into()); } let fpath = self.fullpath.join(path); DirBuilder::new() .recursive(true) .create(fpath.parent().unwrap())?; let file = File::create(&fpath)?; let mut buf_writer = BufWriter::new(file); buf_writer.write_all(content)?; Ok(path.into()) } fn load(&self, path: &str) -> Result> { let path = self.fullpath.join(path); let file = File::open(path)?; let mut buf_reader = BufReader::new(file); let mut contents = Vec::new(); buf_reader.read_to_end(&mut contents)?; Ok(contents) } fn args(&self) -> StorageArgs { StorageArgs::FSStorage { path: self.subdir.clone(), } } fn load_sig(&self, path: &str) -> Result { let raw = self.load(path)?; let mut vs = Signature::from_reader(&mut &raw[..])?; if vs.len() > 1 { unimplemented!("only one Signature currently allowed when using 'load_sig'"); } let sig = vs.swap_remove(0); Ok(sig.into()) } fn spec(&self) -> String { format!("fs://{}", self.subdir) } } fn lookup<'a, P: AsRef>( metadata: &'a Metadata, path: P, ) -> Result<&'a piz::read::FileMetadata<'a>> { let path = path.as_ref(); metadata .get(&path.as_os_str()) .ok_or_else(|| StorageError::PathNotFoundError(path.to_string()).into()) .copied() } fn find_subdirs<'a>(archive: &'a piz::ZipArchive<'a>) -> Result> { let subdirs: Vec<_> = archive .entries() .iter() .filter(|entry| entry.is_dir()) .collect(); if subdirs.len() == 1 { Ok(Some(subdirs[0].path.as_str().into())) } else { Ok(None) } } impl Storage for ZipStorage { fn save(&self, _path: &str, _content: &[u8]) -> Result { unimplemented!(); } fn load(&self, path: &str) -> Result> { let metadata = self.borrow_metadata(); let entry = lookup(metadata, path).or_else(|_| { if let Some(subdir) = self.borrow_subdir() { lookup(metadata, subdir.to_owned() + path) .map_err(|_| StorageError::PathNotFoundError(path.into())) } else { Err(StorageError::PathNotFoundError(path.into())) } })?; let mut reader = BufReader::new( self.borrow_archive() .read(entry) .map_err(|_| StorageError::DataReadError(path.into()))?, ); let mut contents = Vec::new(); reader.read_to_end(&mut contents)?; Ok(contents) } fn args(&self) -> StorageArgs { unimplemented!(); } fn load_sig(&self, path: &str) -> Result { let raw = self.load(path)?; let mut vs = Signature::from_reader(&mut &raw[..])?; if vs.len() > 1 { unimplemented!("only one Signature currently allowed"); } let sig = vs.swap_remove(0); Ok(sig.into()) } fn spec(&self) -> String { format!("zip://{}", self.path().unwrap_or_else(|| "".into())) } } impl ZipStorage { pub fn from_file>(location: P) -> Result { let zip_file = File::open(location.as_ref())?; let mapping = unsafe { memmap2::Mmap::map(&zip_file)? }; let mut storage = ZipStorageTryBuilder { mapping: Some(mapping), archive_builder: |mapping: &Option| { piz::ZipArchive::new(mapping.as_ref().unwrap()) }, metadata_builder: |archive: &piz::ZipArchive| { Ok(archive .entries() .iter() .map(|entry| (entry.path.as_os_str(), entry)) .collect()) }, subdir: None, path: Some(location.as_ref().into()), } .try_build()?; let subdir = find_subdirs(storage.borrow_archive())?; storage.with_mut(|fields| *fields.subdir = subdir); Ok(storage) } pub fn path(&self) -> Option { self.borrow_path().clone() } pub fn subdir(&self) -> Option { self.borrow_subdir().clone() } pub fn set_subdir(&mut self, path: String) { self.with_mut(|fields| *fields.subdir = Some(path)) } pub fn list_sbts(&self) -> Result> { Ok(self .borrow_archive() .entries() .iter() .filter_map(|entry| { let path = entry.path.as_str(); if path.ends_with(".sbt.json") { Some(path.into()) } else { None } }) .collect()) } pub fn filenames(&self) -> Result> { Ok(self .borrow_archive() .entries() .iter() .map(|entry| entry.path.as_str().into()) .collect()) } } impl SigStore { pub fn new_with_storage(sig: Signature, storage: InnerStorage) -> Self { let name = sig.name_str(); let filename = sig.filename(); SigStore::builder() .name(name) .filename(filename) .data(sig) .metadata("") .storage(Some(storage)) .build() } pub fn name(&self) -> String { self.name.clone() } } impl Select for SigStore { fn select(mut self, selection: &Selection) -> Result { // TODO: find better error let sig = self.data.take().ok_or(Error::MismatchKSizes)?; self.data = OnceCell::with_value(sig.select(selection)?); Ok(self) } } impl std::fmt::Debug for SigStore { fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result { write!( f, "SigStore [filename: {}, name: {}, metadata: {}]", self.filename, self.name, self.metadata ) } } impl ReadData for SigStore { fn data(&self) -> Result<&Signature> { if let Some(sig) = self.data.get() { Ok(sig) } else if let Some(storage) = &self.storage { let sig = self.data.get_or_init(|| { let raw = storage.load(&self.filename).unwrap(); Signature::from_reader(&mut &raw[..]) .unwrap() // TODO: select the right sig? .swap_remove(0) }); Ok(sig) } else { Err(ReadDataError::LoadError.into()) } } } impl SigStore { pub fn save(&self, path: &str) -> Result { if let Some(storage) = &self.storage { if let Some(data) = self.data.get() { let mut buffer = Vec::new(); data.to_writer(&mut buffer)?; Ok(storage.save(path, &buffer)?) } else { unimplemented!() } } else { unimplemented!() } } } impl From for Signature { fn from(other: SigStore) -> Signature { other.data.get().unwrap().to_owned() } } impl Deref for SigStore { type Target = Signature; fn deref(&self) -> &Signature { self.data.get().unwrap() } } impl From for SigStore { fn from(other: Signature) -> SigStore { let name = other.name_str(); let filename = other.filename(); SigStore::builder() .name(name) .filename(filename) .data(other) .metadata("") .storage(None) .build() } } impl TryInto for SigStore { type Error = crate::Error; fn try_into(self) -> std::result::Result { let sig: Signature = self.into(); sig.try_into() } } impl Comparable for SigStore { fn similarity(&self, other: &SigStore) -> f64 { let ng: &Signature = self.data().unwrap(); let ong: &Signature = other.data().unwrap(); // TODO: select the right signatures... // TODO: better matching here, what if it is not a mh? if let Sketch::MinHash(mh) = &ng.signatures[0] { if let Sketch::MinHash(omh) = &ong.signatures[0] { return mh.similarity(omh, true, false).unwrap(); } } unimplemented!() } fn containment(&self, other: &SigStore) -> f64 { let ng: &Signature = self.data().unwrap(); let ong: &Signature = other.data().unwrap(); // TODO: select the right signatures... // TODO: better matching here, what if it is not a mh? if let Sketch::MinHash(mh) = &ng.signatures[0] { if let Sketch::MinHash(omh) = &ong.signatures[0] { let common = mh.count_common(omh, false).unwrap(); let size = mh.size(); return common as f64 / size as f64; } } unimplemented!() } } #[derive(Serialize, Deserialize, Debug)] pub struct DatasetInfo { pub filename: String, pub name: String, pub metadata: String, } impl From for SigStore { fn from(other: DatasetInfo) -> SigStore { SigStore { filename: other.filename, name: other.name, metadata: other.metadata, storage: None, data: OnceCell::new(), } } } impl Comparable for Signature { fn similarity(&self, other: &Signature) -> f64 { // TODO: select the right signatures... // TODO: better matching here, what if it is not a mh? if let Sketch::MinHash(mh) = &self.signatures[0] { if let Sketch::MinHash(omh) = &other.signatures[0] { return mh.similarity(omh, true, false).unwrap(); } } unimplemented!() } fn containment(&self, other: &Signature) -> f64 { // TODO: select the right signatures... // TODO: better matching here, what if it is not a mh? if let Sketch::MinHash(mh) = &self.signatures[0] { if let Sketch::MinHash(omh) = &other.signatures[0] { let common = mh.count_common(omh, false).unwrap(); let size = mh.size(); return common as f64 / size as f64; } } unimplemented!() } } impl MemStorage { pub fn new() -> Self { Self { sigs: Arc::new(RwLock::new(HashMap::default())), } } } impl Storage for MemStorage { fn save(&self, _path: &str, _content: &[u8]) -> Result { unimplemented!() } fn load(&self, path: &str) -> Result> { let store = self.sigs.read().unwrap(); let sig = store.get(path).unwrap(); let mut buffer = vec![]; { sig.to_writer(&mut buffer).unwrap(); } Ok(buffer) } fn args(&self) -> StorageArgs { unimplemented!() } fn load_sig(&self, path: &str) -> Result { Ok(self.sigs.read().unwrap().get(path).unwrap().clone()) } fn save_sig(&self, path: &str, sig: Signature) -> Result { // side-step saving to store let sig_store: SigStore = sig.into(); self.sigs.write().unwrap().insert(path.into(), sig_store); Ok(path.into()) } fn spec(&self) -> String { "memory://".into() } } sourmash-bio-sourmash-bc3c0f8/src/core/src/storage/rocksdb.rs000066400000000000000000000101031474157046300244300ustar00rootroot00000000000000use std::sync::Arc; use rocksdb::{ColumnFamilyDescriptor, Options}; use crate::storage::{Storage, StorageArgs, StorageError}; use crate::Result; // Column families pub(crate) const HASHES: &str = "hashes"; pub(crate) const COLORS: &str = "colors"; pub(crate) const METADATA: &str = "metadata"; // Column family for using rocksdb as a Storage pub(crate) const STORAGE: &str = "storage"; pub(crate) const ALL_CFS: [&str; 3] = [HASHES, METADATA, STORAGE]; pub type DB = rocksdb::DBWithThreadMode; /// Store data in RocksDB #[derive(Debug, Clone)] pub struct RocksDBStorage { db: Arc, } impl RocksDBStorage { pub fn from_path(path: &str) -> Self { let mut opts = db_options(); opts.create_if_missing(true); opts.create_missing_column_families(true); opts.prepare_for_bulk_load(); // prepare column family descriptors let cfs = cf_descriptors(); let db = Arc::new(DB::open_cf_descriptors(&opts, path, cfs).unwrap()); Self { db } } pub fn from_db(db: Arc) -> Self { Self { db: db.clone() } } } impl Storage for RocksDBStorage { fn save(&self, path: &str, content: &[u8]) -> Result { let cf_storage = self.db.cf_handle(STORAGE).unwrap(); // TODO(lirber): deal with conflict for path? self.db.put_cf(&cf_storage, path.as_bytes(), content)?; Ok(path.into()) } fn load(&self, path: &str) -> Result> { let cf_storage = self.db.cf_handle(STORAGE).unwrap(); let data = self.db.get_cf(&cf_storage, path.as_bytes())?; data.ok_or_else(|| StorageError::DataReadError(path.into()).into()) } fn args(&self) -> StorageArgs { unimplemented!() } fn spec(&self) -> String { format!("rocksdb://{}", self.db.path().display()) } } pub(crate) fn cf_descriptors() -> Vec { let mut cfopts = Options::default(); cfopts.set_max_write_buffer_number(16); cfopts.set_merge_operator_associative( "datasets operator", crate::index::revindex::disk_revindex::merge_datasets, ); cfopts.set_min_write_buffer_number_to_merge(10); // Updated default from // https://github.com/facebook/rocksdb/wiki/Setup-Options-and-Basic-Tuning#other-general-options cfopts.set_level_compaction_dynamic_level_bytes(true); let cf_hashes = ColumnFamilyDescriptor::new(HASHES, cfopts); let mut cfopts = Options::default(); cfopts.set_max_write_buffer_number(16); cfopts.set_merge_operator_associative( "datasets operator", crate::index::revindex::disk_revindex::merge_datasets, ); // Updated default cfopts.set_level_compaction_dynamic_level_bytes(true); let cf_metadata = ColumnFamilyDescriptor::new(METADATA, cfopts); let mut cfopts = Options::default(); cfopts.set_max_write_buffer_number(16); // Updated default cfopts.set_level_compaction_dynamic_level_bytes(true); let cf_storage = ColumnFamilyDescriptor::new(STORAGE, cfopts); let mut cfopts = Options::default(); cfopts.set_max_write_buffer_number(16); // Updated default cfopts.set_level_compaction_dynamic_level_bytes(true); vec![cf_hashes, cf_metadata, cf_storage] } pub(crate) fn db_options() -> rocksdb::Options { let mut opts = rocksdb::Options::default(); opts.set_max_open_files(500); // Updated defaults from // https://github.com/facebook/rocksdb/wiki/Setup-Options-and-Basic-Tuning#other-general-options opts.set_bytes_per_sync(1048576); let mut block_opts = rocksdb::BlockBasedOptions::default(); block_opts.set_block_size(16 * 1024); block_opts.set_cache_index_and_filter_blocks(true); block_opts.set_pin_l0_filter_and_index_blocks_in_cache(true); block_opts.set_format_version(6); opts.set_block_based_table_factory(&block_opts); // End of updated defaults opts.increase_parallelism(rayon::current_num_threads() as i32); //opts.max_background_jobs = 6; // opts.optimize_level_style_compaction(); // opts.optimize_universal_style_compaction(); opts } sourmash-bio-sourmash-bc3c0f8/src/core/src/wasm.rs000066400000000000000000000176451474157046300223260ustar00rootroot00000000000000// When the `wee_alloc` feature is enabled, use `wee_alloc` as the global // allocator. #[cfg(feature = "wee_alloc")] #[global_allocator] static ALLOC: wee_alloc::WeeAlloc = wee_alloc::WeeAlloc::INIT; use needletail::parse_fastx_reader; use wasm_bindgen::prelude::*; use crate::cmd::ComputeParameters as _ComputeParameters; use crate::encodings::HashFunctions; use crate::prelude::ToWriter; use crate::signature::Signature as _Signature; use crate::signature::SigsTrait; use crate::sketch::minhash::KmerMinHash as _KmerMinHash; use crate::ScaledType; #[wasm_bindgen] pub struct KmerMinHash(_KmerMinHash); #[wasm_bindgen] pub struct Signature(_Signature); #[wasm_bindgen] pub struct ComputeParameters(_ComputeParameters); #[wasm_bindgen] impl KmerMinHash { #[wasm_bindgen(constructor)] pub fn new_with_scaled( num: u32, ksize: u32, is_protein: bool, dayhoff: bool, hp: bool, seed: u32, scaled: ScaledType, track_abundance: bool, ) -> KmerMinHash { // TODO: at most one of (prot, dayhoff, hp) should be true let hash_function = if dayhoff { HashFunctions::Murmur64Dayhoff } else if hp { HashFunctions::Murmur64Hp } else if is_protein { HashFunctions::Murmur64Protein } else { HashFunctions::Murmur64Dna }; KmerMinHash(_KmerMinHash::new( scaled, ksize, hash_function, seed as u64, track_abundance, num, )) } #[wasm_bindgen] pub fn add_sequence_js(&mut self, buf: &str) -> Result<(), JsErrors> { self.0.add_sequence(buf.as_bytes(), true)?; Ok(()) } #[wasm_bindgen] pub fn to_json(&mut self) -> Result { let mut st: Vec = vec![]; self.0.to_writer(&mut st)?; Ok(unsafe { String::from_utf8_unchecked(st) }) } } #[wasm_bindgen] impl ComputeParameters { #[wasm_bindgen(constructor)] pub fn new_with_params() -> ComputeParameters { let params = _ComputeParameters::default(); ComputeParameters(params) } #[wasm_bindgen] pub fn set_ksizes(&mut self, ksizes: Vec) { self.0.set_ksizes(ksizes); } #[wasm_bindgen] pub fn set_scaled(&mut self, scaled: ScaledType) { self.0.set_scaled(scaled); } #[wasm_bindgen] pub fn set_num(&mut self, num: u32) { self.0.set_num_hashes(num); } #[wasm_bindgen] pub fn set_protein(&mut self, is_protein: bool) { self.0.set_protein(is_protein); } #[wasm_bindgen] pub fn set_dayhoff(&mut self, dayhoff: bool) { self.0.set_dayhoff(dayhoff); } #[wasm_bindgen] pub fn set_hp(&mut self, hp: bool) { self.0.set_hp(hp); } #[wasm_bindgen] pub fn set_track_abundance(&mut self, track: bool) { self.0.set_track_abundance(track); } #[wasm_bindgen] pub fn set_seed(&mut self, seed: u32) { self.0.set_seed(seed.into()); } } #[wasm_bindgen] impl Signature { #[wasm_bindgen(constructor)] pub fn new_from_params(params: &ComputeParameters) -> Signature { //let params = ComputeParameters::default(); Signature(_Signature::from_params(¶ms.0)) } #[wasm_bindgen] pub fn add_sequence_js(&mut self, buf: &str) -> Result<(), JsErrors> { self.0.add_sequence(buf.as_bytes(), true)?; Ok(()) } #[wasm_bindgen] pub fn add_from_file( &mut self, fp: web_sys::File, callback: Option, ) -> Result<(), JsErrors> { let wf = SyncFile::new(fp, callback); let (rdr, _format) = niffler::send::get_reader(Box::new(wf))?; let mut parser = parse_fastx_reader(std::io::BufReader::with_capacity( 1024 << 14, // 16 MiB rdr, ))?; while let Some(record) = parser.next() { let record = record?; self.0.add_sequence(&record.seq(), true)?; } Ok(()) } #[wasm_bindgen] pub fn to_json(&mut self) -> Result { let mut st: Vec = vec![]; self.0.to_writer(&mut st)?; Ok(unsafe { String::from_utf8_unchecked(st) }) } pub fn size(&self) -> usize { self.0.size() } } #[derive(thiserror::Error, Debug)] pub enum JsErrors { #[error(transparent)] SourmashError(#[from] crate::Error), #[error(transparent)] SerdeError(#[from] serde_json::error::Error), #[error(transparent)] NifflerError(#[from] niffler::Error), #[error(transparent)] NeedletailError(#[from] needletail::errors::ParseError), } impl Into for JsErrors { fn into(self) -> JsValue { let error = js_sys::Error::new(&self.to_string()); error.into() } } #[cfg(test)] mod test { use super::*; use wasm_bindgen_test::*; #[wasm_bindgen_test] fn wasm_test() { let mut params = ComputeParameters::new_with_params(); params.set_ksizes(vec![19, 29, 49]); let sig = Signature::new_from_params(¶ms); assert_eq!(sig.size(), 3); } } // ============================== use js_sys::Number; use js_sys::Uint8Array; use once_cell::sync::Lazy; use web_sys::FileReaderSync; thread_local! { static FILE_READER_SYNC: Lazy = Lazy::new(|| { FileReaderSync::new().expect("Failed to create FileReaderSync. Is it running in a web worker context?") }); } /// Wrapper around a `web_sys::File` that implements `Read` and `Seek`. pub struct SyncFile { file: web_sys::File, pos: u64, cb: Option, } /// Because this needs to be initialized in a Web Worker, it is safe to make it Send. /// (hopefully. I don't think they can be sent across Web Workers, nor accessed from other WW) unsafe impl Send for SyncFile {} impl SyncFile { pub fn new(file: web_sys::File, cb: Option) -> Self { Self { file, pos: 0, cb } } /// File size in bytes. pub fn size(&self) -> u64 { let size = self.file.size(); if size <= Number::MAX_SAFE_INTEGER { return size as u64; } else { panic!("size is not safe to convert to integer from float") } } fn set_pos(&mut self, pos: u64) { self.pos = pos; self.cb.as_ref().map(|f| { let arr = js_sys::Array::new_with_length(1); arr.set(0, self.progress().into()); f.apply(&JsValue::null(), &arr) .expect("Error calling progress callback"); }); } /// Current progress on the file pub fn progress(&self) -> f64 { self.pos as f64 / self.file.size() } } impl std::io::Read for SyncFile { fn read(&mut self, buf: &mut [u8]) -> Result { let current_offset = self.pos; let new_offset_f64 = current_offset as f64; let new_offset_end_f64 = current_offset.saturating_add( u64::try_from(buf.len()).map_err(|_| std::io::Error::other("Can't convert to u64"))?, ) as f64; let blob = self .file .slice_with_f64_and_f64(new_offset_f64, new_offset_end_f64) .map_err(|_| std::io::Error::other("failed to slice file"))?; let array_buffer = FILE_READER_SYNC .with(|frs| frs.read_as_array_buffer(&blob)) .map_err(|_| std::io::Error::other("failed to read as array buffer"))?; let array = Uint8Array::new(&array_buffer); let read_bytes = usize::try_from(array.byte_length()) .map_err(|_| std::io::Error::other("read too many bytes at once"))?; // Copy to output buffer array.copy_to(&mut buf[..read_bytes]); // Update position self.set_pos( current_offset .checked_add(read_bytes as u64) .ok_or_else(|| std::io::Error::other("new position too large"))?, ); Ok(read_bytes) } } sourmash-bio-sourmash-bc3c0f8/src/core/tests/000077500000000000000000000000001474157046300213475ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/core/tests/dedicated_worker.rs000066400000000000000000000002511474157046300252120ustar00rootroot00000000000000#![cfg(all(target_arch = "wasm32", target_os = "unknown"))] use wasm_bindgen_test::wasm_bindgen_test_configure; wasm_bindgen_test_configure!(run_in_dedicated_worker); sourmash-bio-sourmash-bc3c0f8/src/core/tests/finch.rs000066400000000000000000000000011474157046300227730ustar00rootroot00000000000000 sourmash-bio-sourmash-bc3c0f8/src/core/tests/minhash.rs000066400000000000000000000747671474157046300233710ustar00rootroot00000000000000use std::fs::File; use std::io::BufReader; use std::path::PathBuf; use proptest::collection::vec; use proptest::num::u64; use proptest::proptest; use sourmash::encodings::HashFunctions; use sourmash::prelude::ToWriter; use sourmash::signature::SeqToHashes; use sourmash::signature::{Signature, SigsTrait}; use sourmash::sketch::minhash::{ max_hash_for_scaled, scaled_for_max_hash, KmerMinHash, KmerMinHashBTree, }; use sourmash::sketch::Sketch; use sourmash::ScaledType; // TODO: use f64::EPSILON when we bump MSRV const EPSILON: f64 = 0.01; #[test] fn throws_error() { let mut mh = KmerMinHash::new(0, 4, HashFunctions::Murmur64Dna, 42, false, 1); assert!( mh.add_sequence(b"ATGR", false).is_err(), "R is not a valid DNA character" ); } #[test] fn merge() { let mut a = KmerMinHash::new(0, 10, HashFunctions::Murmur64Dna, 42, false, 20); let mut b = KmerMinHash::new(0, 10, HashFunctions::Murmur64Dna, 42, false, 20); a.add_sequence(b"TGCCGCCCAGCA", false).unwrap(); b.add_sequence(b"TGCCGCCCAGCA", false).unwrap(); a.add_sequence(b"GTCCGCCCAGTGA", false).unwrap(); b.add_sequence(b"GTCCGCCCAGTGG", false).unwrap(); a.merge(&b).unwrap(); assert_eq!( a.to_vec(), vec![ 2996412506971915891, 4448613756639084635, 8373222269469409550, 9390240264282449587, 11085758717695534616, 11668188995231815419, 11760449009842383350, 14682565545778736889, ] ); } #[test] fn invalid_dna() { let mut a = KmerMinHash::new(0, 3, HashFunctions::Murmur64Dna, 42, false, 20); a.add_sequence(b"AAANNCCCTN", true).unwrap(); assert_eq!(a.mins().len(), 3); let mut b = KmerMinHash::new(0, 3, HashFunctions::Murmur64Dna, 42, false, 20); b.add_sequence(b"NAAA", true).unwrap(); assert_eq!(b.mins().len(), 1); } #[test] fn similarity() -> Result<(), Box> { let mut a = KmerMinHash::new(0, 20, HashFunctions::Murmur64Hp, 42, true, 5); let mut b = KmerMinHash::new(0, 20, HashFunctions::Murmur64Hp, 42, true, 5); a.add_hash(1); b.add_hash(1); b.add_hash(2); assert!((a.similarity(&a, false, false)? - 1.0).abs() < EPSILON); assert!((a.similarity(&b, false, false)? - 0.5).abs() < EPSILON); Ok(()) } #[test] fn similarity_2() -> Result<(), Box> { let mut a = KmerMinHash::new(0, 5, HashFunctions::Murmur64Dna, 42, true, 5); let mut b = KmerMinHash::new(0, 5, HashFunctions::Murmur64Dna, 42, true, 5); a.add_sequence(b"ATGGA", false)?; a.add_sequence(b"GGACA", false)?; a.add_sequence(b"ATGGA", false)?; b.add_sequence(b"ATGGA", false)?; assert!( (a.similarity(&b, false, false)? - 0.705).abs() < EPSILON, "{}", a.similarity(&b, false, false)? ); Ok(()) } #[test] fn similarity_3() -> Result<(), Box> { let mut a = KmerMinHash::new(0, 20, HashFunctions::Murmur64Dayhoff, 42, true, 5); let mut b = KmerMinHash::new(0, 20, HashFunctions::Murmur64Dayhoff, 42, true, 5); a.add_hash(1); a.add_hash(1); a.add_hash(5); a.add_hash(5); b.add_hash(1); b.add_hash(2); b.add_hash(3); b.add_hash(4); assert!((a.similarity(&a, false, false)? - 1.0).abs() < EPSILON); assert!((a.similarity(&b, false, false)? - 0.23).abs() < EPSILON); assert!((a.similarity(&a, true, false)? - 1.0).abs() < EPSILON); assert!((a.similarity(&b, true, false)? - 0.2).abs() < EPSILON); Ok(()) } #[test] fn angular_similarity_requires_abundance() -> Result<(), Box> { let mut a = KmerMinHash::new(0, 20, HashFunctions::Murmur64Dayhoff, 42, false, 5); let mut b = KmerMinHash::new(0, 20, HashFunctions::Murmur64Dayhoff, 42, false, 5); a.add_hash(1); b.add_hash(1); assert!(a.angular_similarity(&b).is_err()); Ok(()) } #[test] fn angular_similarity_btree_requires_abundance() -> Result<(), Box> { let mut a = KmerMinHashBTree::new(0, 20, HashFunctions::Murmur64Dayhoff, 42, false, 5); let mut b = KmerMinHashBTree::new(0, 20, HashFunctions::Murmur64Dayhoff, 42, false, 5); a.add_hash(1); b.add_hash(1); assert!(a.angular_similarity(&b).is_err()); Ok(()) } #[test] fn dayhoff() { let mut a = KmerMinHash::new(0, 6, HashFunctions::Murmur64Dayhoff, 42, false, 10); let mut b = KmerMinHash::new(0, 6, HashFunctions::Murmur64Protein, 42, false, 10); a.add_sequence(b"ACTGAC", false).unwrap(); b.add_sequence(b"ACTGAC", false).unwrap(); assert_eq!(a.size(), 2); assert_eq!(b.size(), 2); } #[test] fn hp() { let mut a = KmerMinHash::new(0, 6, HashFunctions::Murmur64Hp, 42, false, 10); let mut b = KmerMinHash::new(0, 6, HashFunctions::Murmur64Protein, 42, false, 10); a.add_sequence(b"ACTGAC", false).unwrap(); b.add_sequence(b"ACTGAC", false).unwrap(); assert_eq!(a.size(), 2); assert_eq!(b.size(), 2); } #[test] fn max_for_scaled() { assert_eq!(max_hash_for_scaled(100), 184467440737095520); } proptest! { #[test] fn oracle_mins(hashes in vec(u64::ANY, 1..10000)) { let mut a = KmerMinHash::new(0, 21, HashFunctions::Murmur64Protein, 42, true, 1000); let mut b = KmerMinHashBTree::new(0, 21, HashFunctions::Murmur64Protein, 42, true, 1000); let mut c: KmerMinHash = Default::default(); c.set_hash_function(HashFunctions::Murmur64Protein).unwrap(); c.enable_abundance().unwrap(); let mut d: KmerMinHashBTree = Default::default(); d.set_hash_function(HashFunctions::Murmur64Protein).unwrap(); d.enable_abundance().unwrap(); let mut to_remove = vec![]; for hash in &hashes { a.add_hash(*hash); b.add_hash(*hash); if hash % 2 == 0 { to_remove.push(*hash); } } c.add_from(&a).unwrap(); c.remove_many(to_remove.iter().copied()).unwrap(); d.add_from(&b).unwrap(); d.remove_many(to_remove.iter().copied()).unwrap(); assert_eq!(a.mins(), b.mins()); assert_eq!(c.mins(), d.mins()); assert_eq!(a.count_common(&c, false).unwrap(), b.count_common(&d, false).unwrap()); assert_eq!(a.count_common(&c, true).unwrap(), b.count_common(&d, true).unwrap()); assert_eq!(a.abunds(), b.abunds()); assert_eq!(c.abunds(), d.abunds()); assert!((a.similarity(&c, false, false).unwrap() - b.similarity(&d, false, false).unwrap()).abs() < EPSILON); } } proptest! { #[test] fn oracle_mins_scaled(hashes in vec(u64::ANY, 1..10000)) { let scaled = 100; let mut a = KmerMinHash::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); let mut b = KmerMinHashBTree::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); let mut c = KmerMinHash::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); let mut d = KmerMinHashBTree::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); let mut to_remove = vec![]; for hash in &hashes { a.add_hash(*hash); b.add_hash(*hash); if hash % 2 == 0 { to_remove.push(*hash); } } c.add_many(&hashes).unwrap(); d.add_many(&hashes).unwrap(); c.remove_many(to_remove.iter().copied()).unwrap(); d.remove_many(to_remove.iter().copied()).unwrap(); a.remove_hash(hashes[0]); b.remove_hash(hashes[0]); assert_eq!(a.mins(), b.mins()); assert_eq!(c.mins(), d.mins()); assert_eq!(a.md5sum(), b.md5sum()); assert_eq!(c.md5sum(), d.md5sum()); assert_eq!(a.is_protein(), b.is_protein()); assert_eq!(a.num(), b.num()); assert_eq!(a.seed(), b.seed()); assert_eq!(a.ksize(), b.ksize()); assert_eq!(a.scaled(), b.scaled()); assert_eq!(a.track_abundance(), b.track_abundance()); assert_eq!(a.hash_function(), b.hash_function()); assert_eq!(a.abunds(), b.abunds()); assert_eq!(c.abunds(), d.abunds()); assert!((a.similarity(&c, false, false).unwrap() - b.similarity(&d, false, false).unwrap()).abs() < EPSILON); assert!((c.similarity(&a, false, false).unwrap() - d.similarity(&b, false, false).unwrap()).abs() < EPSILON); assert!((a.similarity(&c, true, false).unwrap() - b.similarity(&d, true, false).unwrap()).abs() < EPSILON); assert!((c.similarity(&a, true, false).unwrap() - d.similarity(&b, true, false).unwrap()).abs() < EPSILON); assert_eq!(a.count_common(&c, false).unwrap(), b.count_common(&d, false).unwrap()); assert_eq!(c.count_common(&a, false).unwrap(), d.count_common(&b, false).unwrap()); assert_eq!(a.count_common(&c, true).unwrap(), b.count_common(&d, true).unwrap()); assert_eq!(c.count_common(&a, true).unwrap(), d.count_common(&b, true).unwrap()); let mut e = a.downsample_max_hash(100).unwrap(); let scaled = scaled_for_max_hash(100); let mut f = b.downsample_scaled(scaled).unwrap(); assert_eq!(f.scaled(), scaled); // Can't compare different scaled without explicit downsample assert!(c.similarity(&e, false, false).is_err()); assert!(d.similarity(&f, false, false).is_err()); assert!(c.similarity(&e, true, false).is_err()); assert!(d.similarity(&f, true, false).is_err()); assert!((c.similarity(&e, true, true).unwrap() - d.similarity(&f, true, true).unwrap()).abs() < EPSILON); assert!((e.similarity(&c, true, true).unwrap() - f.similarity(&d, true, true).unwrap()).abs() < EPSILON); assert!((c.similarity(&e, false, true).unwrap() - d.similarity(&f, false, true).unwrap()).abs() < EPSILON); assert!((e.similarity(&c, false, true).unwrap() - f.similarity(&d, false, true).unwrap()).abs() < EPSILON); // Can't compare different scaled without explicit downsample assert!(e.count_common(&c, false).is_err()); assert!(f.count_common(&d, false).is_err()); assert_eq!(e.count_common(&c, true).unwrap(), f.count_common(&d, true).unwrap()); assert_eq!(c.count_common(&e, true).unwrap(), d.count_common(&f, true).unwrap()); // disable abundances e.disable_abundance(); f.disable_abundance(); // Can't compare different scaled without explicit downsample assert!(c.similarity(&e, false, false).is_err()); assert!(d.similarity(&f, false, false).is_err()); assert!(c.similarity(&e, true, false).is_err()); assert!(d.similarity(&f, true, false).is_err()); assert!((c.similarity(&e, true, true).unwrap() - d.similarity(&f, true, true).unwrap()).abs() < EPSILON); assert!((e.similarity(&c, true, true).unwrap() - f.similarity(&d, true, true).unwrap()).abs() < EPSILON); assert!((c.similarity(&e, false, true).unwrap() - d.similarity(&f, false, true).unwrap()).abs() < EPSILON); assert!((e.similarity(&c, false, true).unwrap() - f.similarity(&d, false, true).unwrap()).abs() < EPSILON); // Can't compare different scaled without explicit downsample assert!(e.count_common(&c, false).is_err()); assert!(f.count_common(&d, false).is_err()); assert_eq!(e.count_common(&c, true).unwrap(), f.count_common(&d, true).unwrap()); assert_eq!(c.count_common(&e, true).unwrap(), d.count_common(&f, true).unwrap()); } } proptest! { #[test] fn prop_merge(seq1 in "[ACGT]{6,100}", seq2 in "[ACGT]{6,200}") { let scaled: ScaledType = 10; let mut a = KmerMinHash::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); let mut b = KmerMinHashBTree::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); let mut c = KmerMinHash::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); let mut d = KmerMinHashBTree::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); a.add_sequence(seq1.as_bytes(), false).unwrap(); b.add_sequence(seq1.as_bytes(), false).unwrap(); c.add_sequence(seq2.as_bytes(), false).unwrap(); d.add_sequence(seq2.as_bytes(), false).unwrap(); a.merge(&c).unwrap(); b.merge(&d).unwrap(); assert_eq!(a.mins(), b.mins()); assert_eq!(c.mins(), d.mins()); assert_eq!(a.abunds(), b.abunds()); assert_eq!(c.abunds(), d.abunds()); assert_eq!(a.intersection_size(&c).unwrap(), b.intersection_size(&d).unwrap()); assert_eq!(c.intersection(&a).unwrap(), d.intersection(&b).unwrap()); assert!((a.similarity(&c, false, false).unwrap() - b.similarity(&d, false, false).unwrap()).abs() < EPSILON); assert!((a.similarity(&c, true, false).unwrap() - b.similarity(&d, true, false).unwrap()).abs() < EPSILON); let mut e = a.downsample_max_hash(100).unwrap(); let scaled = scaled_for_max_hash(100); let mut f = b.downsample_scaled(scaled).unwrap(); assert!((e.similarity(&c, false, true).unwrap() - f.similarity(&d, false, true).unwrap()).abs() < EPSILON); assert!((e.similarity(&c, true, true).unwrap() - f.similarity(&d, true, true).unwrap()).abs() < EPSILON); e.disable_abundance(); f.disable_abundance(); assert!((e.similarity(&c, false, true).unwrap() - f.similarity(&d, false, true).unwrap()).abs() < EPSILON); assert!((e.similarity(&c, true, true).unwrap() - f.similarity(&d, true, true).unwrap()).abs() < EPSILON); e.clear(); f.clear(); assert!(e.is_empty()); assert!(f.is_empty()); } } #[test] fn load_save_minhash_sketches() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/genome-s10+s11.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); let sig = sigs.get(0).unwrap(); let sketches = sig.sketches(); let mut buffer = vec![]; if let Sketch::MinHash(mh) = &sketches[0] { let bmh: KmerMinHashBTree = mh.clone().into(); { bmh.to_writer(&mut buffer).unwrap(); } let new_mh = KmerMinHash::from_reader(&buffer[..]).unwrap(); let new_bmh = KmerMinHashBTree::from_reader(&buffer[..]).unwrap(); assert_eq!(mh.md5sum(), new_mh.md5sum()); assert_eq!(bmh.md5sum(), new_bmh.md5sum()); assert_eq!(bmh.md5sum(), new_mh.md5sum()); assert_eq!(mh.md5sum(), new_bmh.md5sum()); assert_eq!(mh.mins(), new_mh.mins()); assert_eq!(bmh.mins(), new_bmh.mins()); assert_eq!(bmh.mins(), new_mh.mins()); assert_eq!(mh.mins(), new_bmh.mins()); assert_eq!(mh.abunds(), new_mh.abunds()); assert_eq!(bmh.abunds(), new_bmh.abunds()); assert_eq!(bmh.abunds(), new_mh.abunds()); assert_eq!(mh.abunds(), new_bmh.abunds()); assert!( (mh.similarity(&new_mh, false, false).unwrap() - bmh.similarity(&new_bmh, false, false).unwrap()) .abs() < EPSILON ); assert!( (mh.similarity(&new_mh, true, false).unwrap() - bmh.similarity(&new_bmh, true, false).unwrap()) .abs() < EPSILON ); buffer.clear(); let imh: KmerMinHash = bmh.clone().into(); { imh.to_writer(&mut buffer).unwrap(); } let new_mh = KmerMinHash::from_reader(&buffer[..]).unwrap(); let new_bmh = KmerMinHashBTree::from_reader(&buffer[..]).unwrap(); assert_eq!(mh.md5sum(), new_mh.md5sum()); assert_eq!(bmh.md5sum(), new_bmh.md5sum()); assert_eq!(bmh.md5sum(), new_mh.md5sum()); assert_eq!(mh.md5sum(), new_bmh.md5sum()); assert_eq!(mh.mins(), new_mh.mins()); assert_eq!(bmh.mins(), new_bmh.mins()); assert_eq!(bmh.mins(), new_mh.mins()); assert_eq!(mh.mins(), new_bmh.mins()); assert_eq!(mh.abunds(), new_mh.abunds()); assert_eq!(bmh.abunds(), new_bmh.abunds()); assert_eq!(bmh.abunds(), new_mh.abunds()); assert_eq!(mh.abunds(), new_bmh.abunds()); assert_eq!(mh.to_vec(), new_mh.to_vec()); assert_eq!(bmh.to_vec(), new_bmh.to_vec()); assert_eq!(bmh.to_vec(), new_mh.to_vec()); assert_eq!(mh.to_vec(), new_bmh.to_vec()); assert_eq!(mh.to_vec_abunds(), new_mh.to_vec_abunds()); assert_eq!(bmh.to_vec_abunds(), new_bmh.to_vec_abunds()); assert_eq!(bmh.to_vec_abunds(), new_mh.to_vec_abunds()); assert_eq!(mh.to_vec_abunds(), new_bmh.to_vec_abunds()); assert!( (mh.similarity(&new_mh, false, false).unwrap() - bmh.similarity(&new_bmh, false, false).unwrap()) .abs() < EPSILON ); assert!( (mh.similarity(&new_mh, true, false).unwrap() - bmh.similarity(&new_bmh, true, false).unwrap()) .abs() < EPSILON ); } } #[test] fn load_save_minhash_sketches_abund() { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/gather-abund/reads-s10-s11.sig"); let file = File::open(filename).unwrap(); let reader = BufReader::new(file); let sigs = Signature::from_reader(reader).expect("Loading error"); let sig = sigs.get(0).unwrap(); let sketches = sig.sketches(); let mut buffer = vec![]; if let Sketch::MinHash(mh) = &sketches[0] { let bmh: KmerMinHashBTree = mh.clone().into(); { bmh.to_writer(&mut buffer).unwrap(); } let new_mh = KmerMinHash::from_reader(&buffer[..]).unwrap(); let new_bmh = KmerMinHashBTree::from_reader(&buffer[..]).unwrap(); assert_eq!(mh.md5sum(), new_mh.md5sum()); assert_eq!(bmh.md5sum(), new_bmh.md5sum()); assert_eq!(bmh.md5sum(), new_mh.md5sum()); assert_eq!(mh.md5sum(), new_bmh.md5sum()); assert_eq!(mh.mins(), new_mh.mins()); assert_eq!(bmh.mins(), new_bmh.mins()); assert_eq!(bmh.mins(), new_mh.mins()); assert_eq!(mh.mins(), new_bmh.mins()); assert_eq!(mh.abunds(), new_mh.abunds()); assert_eq!(bmh.abunds(), new_bmh.abunds()); assert_eq!(bmh.abunds(), new_mh.abunds()); assert_eq!(mh.abunds(), new_bmh.abunds()); assert_eq!(mh.to_vec(), new_mh.to_vec()); assert_eq!(bmh.to_vec(), new_bmh.to_vec()); assert_eq!(bmh.to_vec(), new_mh.to_vec()); assert_eq!(mh.to_vec(), new_bmh.to_vec()); assert_eq!(mh.to_vec_abunds(), new_mh.to_vec_abunds()); assert_eq!(bmh.to_vec_abunds(), new_bmh.to_vec_abunds()); assert_eq!(bmh.to_vec_abunds(), new_mh.to_vec_abunds()); assert_eq!(mh.to_vec_abunds(), new_bmh.to_vec_abunds()); assert!( (mh.similarity(&new_mh, false, false).unwrap() - bmh.similarity(&new_bmh, false, false).unwrap()) .abs() < EPSILON ); assert!( (mh.similarity(&new_mh, true, false).unwrap() - bmh.similarity(&new_bmh, true, false).unwrap()) .abs() < EPSILON ); buffer.clear(); let imh: KmerMinHash = bmh.clone().into(); { imh.to_writer(&mut buffer).unwrap(); } let new_mh = KmerMinHash::from_reader(&buffer[..]).unwrap(); let new_bmh = KmerMinHashBTree::from_reader(&buffer[..]).unwrap(); assert_eq!(mh.md5sum(), new_mh.md5sum()); assert_eq!(bmh.md5sum(), new_bmh.md5sum()); assert_eq!(bmh.md5sum(), new_mh.md5sum()); assert_eq!(mh.md5sum(), new_bmh.md5sum()); assert_eq!(mh.mins(), new_mh.mins()); assert_eq!(bmh.mins(), new_bmh.mins()); assert_eq!(bmh.mins(), new_mh.mins()); assert_eq!(mh.mins(), new_bmh.mins()); assert_eq!(mh.abunds(), new_mh.abunds()); assert_eq!(bmh.abunds(), new_bmh.abunds()); assert_eq!(bmh.abunds(), new_mh.abunds()); assert_eq!(mh.abunds(), new_bmh.abunds()); assert!( (mh.similarity(&new_mh, false, false).unwrap() - bmh.similarity(&new_bmh, false, false).unwrap()) .abs() < EPSILON ); assert!( (mh.similarity(&new_mh, true, false).unwrap() - bmh.similarity(&new_bmh, true, false).unwrap()) .abs() < EPSILON ); } } #[test] fn merge_empty_scaled() { let scaled = 10; let mut a = KmerMinHash::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); let mut b = KmerMinHashBTree::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); let c = KmerMinHash::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); let d = KmerMinHashBTree::new(scaled, 6, HashFunctions::Murmur64Dna, 42, true, 0); a.merge(&c).unwrap(); b.merge(&d).unwrap(); assert!(a.is_empty()); assert!(b.is_empty()); a.add_hash_with_abundance(0, 0); assert!(a.is_empty()); b.add_hash_with_abundance(0, 0); assert!(b.is_empty()); a.clear(); assert!(a.is_empty()); b.clear(); assert!(b.is_empty()); } #[test] fn check_errors() { let scaled = 10; let mut a = KmerMinHash::new(scaled, 6, HashFunctions::Murmur64Dna, 42, false, 0); let mut b = KmerMinHashBTree::new(scaled, 6, HashFunctions::Murmur64Dna, 42, false, 0); // sequence too short: OK assert!(a.add_sequence(b"AC", false).is_ok()); assert!(b.add_sequence(b"AC", false).is_ok()); // invalid base, throw error assert!(a.add_sequence(b"ACTGNN", false).is_err()); assert!(b.add_sequence(b"ACTGNN", false).is_err()); a.add_hash(1); b.add_hash(1); // Can't set abundance after something was inserted assert!(a.enable_abundance().is_err()); assert!(b.enable_abundance().is_err()); // Can't change hash function after insertion assert!(a.set_hash_function(HashFunctions::Murmur64Hp).is_err()); assert!(b.set_hash_function(HashFunctions::Murmur64Hp).is_err()); // setting to the same hash function is fine assert!(a.set_hash_function(HashFunctions::Murmur64Dna).is_ok()); assert!(b.set_hash_function(HashFunctions::Murmur64Dna).is_ok()); let c = KmerMinHash::new(scaled, 7, HashFunctions::Murmur64Dna, 42, true, 0); let d = KmerMinHashBTree::new(scaled, 7, HashFunctions::Murmur64Dna, 42, true, 0); // different ksize assert!(a.check_compatible(&c).is_err()); assert!(b.check_compatible(&d).is_err()); let c = KmerMinHash::new(scaled, 6, HashFunctions::Murmur64Protein, 42, true, 0); let d = KmerMinHashBTree::new(scaled, 6, HashFunctions::Murmur64Protein, 42, true, 0); // different hash_function assert!(a.check_compatible(&c).is_err()); assert!(b.check_compatible(&d).is_err()); let c = KmerMinHash::new(scaled, 6, HashFunctions::Murmur64Dna, 31, true, 0); let d = KmerMinHashBTree::new(scaled, 6, HashFunctions::Murmur64Dna, 31, true, 0); // different seed assert!(a.check_compatible(&c).is_err()); assert!(b.check_compatible(&d).is_err()); } //fn prop_merge(seq1 in "[ACGT]{6,100}", seq2 in "[ACGT]{6,200}") { proptest! { #[test] fn load_save_minhash_dayhoff(seq in "FLYS*CWLPGQRMTHINKVADER{0,1000}") { let scaled = 10; let mut a = KmerMinHash::new(scaled, 3, HashFunctions::Murmur64Dayhoff, 42, true, 0); let mut b = KmerMinHashBTree::new(scaled, 3, HashFunctions::Murmur64Dayhoff, 42, true, 0); a.add_protein(seq.as_bytes()).unwrap(); b.add_protein(seq.as_bytes()).unwrap(); let mut buffer_a = vec![]; let mut buffer_b = vec![]; { a.to_writer(&mut buffer_a).unwrap(); b.to_writer(&mut buffer_b).unwrap(); } assert_eq!(buffer_a, buffer_b); let c = KmerMinHash::from_reader(&buffer_b[..]).unwrap(); let d = KmerMinHashBTree::from_reader(&buffer_a[..]).unwrap(); assert!((a.similarity(&c, false, false).unwrap() - b.similarity(&d, false, false).unwrap()).abs() < EPSILON); assert!((a.similarity(&c, true, false).unwrap() - b.similarity(&d, true, false).unwrap()).abs() < EPSILON); } } proptest! { #[test] fn load_save_minhash_hp(seq in "FLYS*CWLPGQRMTHINKVADER{0,1000}") { let scaled = 10; let mut a = KmerMinHash::new(scaled, 3, HashFunctions::Murmur64Hp, 42, true, 0); let mut b = KmerMinHashBTree::new(scaled, 3, HashFunctions::Murmur64Hp, 42, true, 0); a.add_protein(seq.as_bytes()).unwrap(); b.add_protein(seq.as_bytes()).unwrap(); let mut buffer_a = vec![]; let mut buffer_b = vec![]; { a.to_writer(&mut buffer_a).unwrap(); b.to_writer(&mut buffer_b).unwrap(); } assert_eq!(buffer_a, buffer_b); let c = KmerMinHash::from_reader(&buffer_b[..]).unwrap(); let d = KmerMinHashBTree::from_reader(&buffer_a[..]).unwrap(); assert!((a.similarity(&c, false, false).unwrap() - b.similarity(&d, false, false).unwrap()).abs() < EPSILON); assert!((a.similarity(&c, true, false).unwrap() - b.similarity(&d, true, false).unwrap()).abs() < EPSILON); } } proptest! { #[test] fn load_save_minhash_dna(seq in "ACGTN{0,1000}") { let scaled = 10; let mut a = KmerMinHash::new(scaled, 21, HashFunctions::Murmur64Dna, 42, true, 0); let mut b = KmerMinHashBTree::new(scaled, 21, HashFunctions::Murmur64Dna, 42, true, 0); a.add_sequence(seq.as_bytes(), true).unwrap(); b.add_sequence(seq.as_bytes(), true).unwrap(); let mut buffer_a = vec![]; let mut buffer_b = vec![]; { a.to_writer(&mut buffer_a).unwrap(); b.to_writer(&mut buffer_b).unwrap(); } assert_eq!(buffer_a, buffer_b); let c = KmerMinHash::from_reader(&buffer_b[..]).unwrap(); let d = KmerMinHashBTree::from_reader(&buffer_a[..]).unwrap(); assert!((a.similarity(&c, false, false).unwrap() - b.similarity(&d, false, false).unwrap()).abs() < EPSILON); assert!((a.similarity(&c, true, false).unwrap() - b.similarity(&d, true, false).unwrap()).abs() < EPSILON); } } proptest! { #[test] fn seq_to_hashes(seq in "ACGTGTAGCTAGACACTGACTGACTGAC") { let scaled = 1; let mut mh = KmerMinHash::new(scaled, 21, HashFunctions::Murmur64Dna, 42, true, 0); mh.add_sequence(seq.as_bytes(), false)?; // .unwrap(); let mut hashes: Vec = Vec::new(); let ready_hashes = SeqToHashes::new(seq.as_bytes(), mh.ksize(), false, false, mh.hash_function(), mh.seed())?; for hash_value in ready_hashes{ match hash_value{ Ok(0) => continue, Ok(x) => hashes.push(x), Err(_) => (), } } mh.mins().sort_unstable(); hashes.sort_unstable(); assert_eq!(mh.mins(), hashes); } #[test] fn seq_to_hashes_2(seq in "QRMTHINK") { let scaled = 1; let mut mh = KmerMinHash::new(scaled, 3, HashFunctions::Murmur64Protein, 42, true, 0); mh.add_protein(seq.as_bytes())?; // .unwrap(); let mut hashes: Vec = Vec::new(); let ready_hashes = SeqToHashes::new(seq.as_bytes(), mh.ksize(), false, true, mh.hash_function(), mh.seed())?; for hash_value in ready_hashes { match hash_value{ Ok(0) => continue, Ok(x) => hashes.push(x), Err(_) => (), } } mh.mins().sort_unstable(); hashes.sort_unstable(); assert_eq!(mh.mins(), hashes); } } #[test] fn test_inflate() { // Setup minhash_a with some mins but no abundances let mut a = KmerMinHash::new(5, 3, HashFunctions::Murmur64Hp, 42, true, 0); a.add_hash(10); a.add_hash(20); a.add_hash(30); // Setup minhash_b with mins, some of which match a, and with abundances let mut b = KmerMinHash::new(5, 3, HashFunctions::Murmur64Hp, 42, true, 0); b.add_hash_with_abundance(10, 2); b.add_hash_with_abundance(20, 4); b.add_hash_with_abundance(40, 6); // Non-matching hash // Attempt to inflate minhash_a using minhash_b's abundances assert!(a.inflate(&b).is_ok()); a.inflate(&b).unwrap(); eprintln!("{:?}", a.to_vec_abunds()); assert_eq!(a.to_vec_abunds(), vec![(10, 2), (20, 4)]); } #[test] fn test_inflated_abundances() { // Setup minhash_a with some mins but no abundances let mut a = KmerMinHash::new(5, 3, HashFunctions::Murmur64Hp, 42, false, 0); a.add_hash(10); a.add_hash(20); a.add_hash(30); // Setup minhash_b with mins, some of which match a, and with abundances let mut b = KmerMinHash::new(5, 3, HashFunctions::Murmur64Hp, 42, true, 0); b.add_hash_with_abundance(10, 2); b.add_hash_with_abundance(20, 4); b.add_hash_with_abundance(40, 9); // Non-matching hash // Attempt to inflate minhash_a using minhash_b's abundances assert!(a.inflate(&b).is_ok()); let (abunds, total_abund) = a.inflated_abundances(&b).unwrap(); assert_eq!(abunds, vec![2, 4]); assert_eq!(total_abund, 6); } #[test] fn test_inflate_noabund() { // Setup minhash a with some mins but no abundances let mut a = KmerMinHash::new(5, 3, HashFunctions::Murmur64Dna, 42, false, 0); a.add_hash(10); a.add_hash(20); a.add_hash(30); let b = a.clone(); let result = a.inflate(&b); assert!(matches!( result, Err(sourmash::Error::NeedsAbundanceTracking) )); } #[test] fn test_inflated_abunds_noabund() { // Setup minhash a with some mins but no abundances let mut a = KmerMinHash::new(5, 3, HashFunctions::Murmur64Dna, 42, false, 0); a.add_hash(10); a.add_hash(20); a.add_hash(30); let result = a.inflated_abundances(&a); assert!(matches!( result, Err(sourmash::Error::NeedsAbundanceTracking) )); } #[test] fn test_sum_abunds() { let mut a = KmerMinHash::new(5, 3, HashFunctions::Murmur64Dna, 42, true, 0); a.add_hash_with_abundance(10, 2); a.add_hash_with_abundance(20, 4); a.add_hash_with_abundance(40, 9); assert_eq!(a.sum_abunds(), 15); } #[test] fn test_sum_abunds_noabund() { let mut a = KmerMinHash::new(5, 3, HashFunctions::Murmur64Dna, 42, false, 0); a.add_hash(10); a.add_hash(20); a.add_hash(30); assert_eq!(a.sum_abunds(), 3); } #[test] fn test_n_unique_kmers() { let mut mh = KmerMinHash::new(10, 21, HashFunctions::Murmur64Dna, 42, true, 0); mh.add_hash(10); mh.add_hash(20); mh.add_hash(30); assert_eq!(mh.n_unique_kmers(), 30) } #[test] fn test_scaled_downsampling_kmerminhash() { let mh = KmerMinHash::new(10, 21, HashFunctions::Murmur64Dna, 42, true, 0); // downsampling to same scaled is OK: let new_mh = mh.clone().downsample_scaled(10).unwrap(); assert_eq!(new_mh.scaled(), 10); // downsampling is OK: let new_mh = mh.clone().downsample_scaled(100).unwrap(); assert_eq!(new_mh.scaled(), 100); // upsampling not ok let e = mh.clone().downsample_scaled(1).unwrap_err(); assert!(matches!(e, sourmash::Error::CannotUpsampleScaled)); } #[test] fn test_scaled_downsampling_kmerminhashbtree() { let mh = KmerMinHashBTree::new(10, 21, HashFunctions::Murmur64Dna, 42, true, 0); // downsampling to same scaled is OK: let new_mh = mh.clone().downsample_scaled(10).unwrap(); assert_eq!(new_mh.scaled(), 10); // downsampling is OK: let new_mh = mh.clone().downsample_scaled(100).unwrap(); assert_eq!(new_mh.scaled(), 100); // upsampling not ok let e = mh.clone().downsample_scaled(1).unwrap_err(); assert!(matches!(e, sourmash::Error::CannotUpsampleScaled)); } sourmash-bio-sourmash-bc3c0f8/src/core/tests/node.rs000066400000000000000000000002211474157046300226350ustar00rootroot00000000000000#![cfg(all(target_arch = "wasm32", target_os = "unknown"))] use wasm_bindgen_test::*; #[wasm_bindgen_test] fn pass() { assert_eq!(1, 1); } sourmash-bio-sourmash-bc3c0f8/src/core/tests/service_worker.rs000066400000000000000000000002471474157046300247510ustar00rootroot00000000000000#![cfg(all(target_arch = "wasm32", target_os = "unknown"))] use wasm_bindgen_test::wasm_bindgen_test_configure; wasm_bindgen_test_configure!(run_in_service_worker); sourmash-bio-sourmash-bc3c0f8/src/core/tests/shared_worker.rs000066400000000000000000000002461474157046300245560ustar00rootroot00000000000000#![cfg(all(target_arch = "wasm32", target_os = "unknown"))] use wasm_bindgen_test::wasm_bindgen_test_configure; wasm_bindgen_test_configure!(run_in_shared_worker); sourmash-bio-sourmash-bc3c0f8/src/core/tests/storage.rs000066400000000000000000000116001474157046300233570ustar00rootroot00000000000000use std::path::PathBuf; use tempfile::TempDir; use sourmash::signature::Signature; use sourmash::storage::{FSStorage, InnerStorage, Storage, StorageArgs, ZipStorage}; #[test] fn zipstorage_load_file() -> Result<(), Box> { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/v6.sbt.zip"); let zs = ZipStorage::from_file(filename.to_str().unwrap())?; let data = zs.load("v6.sbt.json")?; let description: serde_json::Value = serde_json::from_slice(&data[..])?; assert_eq!(description["version"], 6); Ok(()) } #[test] fn zipstorage_load_manifest() -> Result<(), Box> { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/prot/protein.sbt.zip"); let zs = ZipStorage::from_file(filename.to_str().unwrap())?; let _data = zs.load("protein.manifest.csv").expect("error loading file"); Ok(()) } #[test] fn zipstorage_list_sbts() -> Result<(), Box> { let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/v6.sbt.zip"); let zs = ZipStorage::from_file(filename.to_str().unwrap())?; let sbts = zs.list_sbts()?; assert_eq!(sbts.len(), 1); Ok(()) } #[cfg(feature = "parallel")] #[test] fn zipstorage_parallel_access() -> Result<(), Box> { use rayon::prelude::*; use sourmash::signature::SigsTrait; let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/v6.sbt.zip"); let zs = ZipStorage::from_file(filename.to_str().unwrap())?; let total_hashes: usize = [ ".sbt.v3/f71e78178af9e45e6f1d87a0c53c465c", ".sbt.v3/f0c834bc306651d2b9321fb21d3e8d8f", ".sbt.v3/4e94e60265e04f0763142e20b52c0da1", ".sbt.v3/6d6e87e1154e95b279e5e7db414bc37b", ".sbt.v3/0107d767a345eff67ecdaed2ee5cd7ba", ".sbt.v3/b59473c94ff2889eca5d7165936e64b3", ".sbt.v3/60f7e23c24a8d94791cc7a8680c493f9", ] .par_iter() .map(|path| { let data = zs.load(path).unwrap(); let sigs = Signature::from_reader(&data[..]).expect("Loading error"); sigs.iter() .map(|v| v.sketches().iter().map(|mh| mh.size()).sum::()) .sum::() }) .sum(); assert_eq!(total_hashes, 3500); Ok(()) } #[test] fn innerstorage_save_sig() -> Result<(), Box> { let output = TempDir::new()?; let fst = FSStorage::new("".into(), output.path().as_os_str().to_str().unwrap()); let instorage = InnerStorage::new(fst); let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/genome-s10.fa.gz.sig"); let sig = Signature::from_path(filename)?.swap_remove(0); let new_path = instorage.save_sig("test", sig.clone())?; dbg!(new_path); let loaded_sig = instorage.load_sig("test")?; assert_eq!(sig.name_str(), loaded_sig.name()); assert_eq!(sig.md5sum(), loaded_sig.md5sum()); Ok(()) } #[test] fn innerstorage_load() -> Result<(), Box> { let output = TempDir::new()?; let fst = FSStorage::new("".into(), output.path().as_os_str().to_str().unwrap()); let instorage = InnerStorage::new(fst); let mut filename = PathBuf::from(env!("CARGO_MANIFEST_DIR")); filename.push("../../tests/test-data/genome-s10.fa.gz.sig"); let sig = Signature::from_path(filename)?.swap_remove(0); let new_path = instorage.save_sig("test", sig.clone())?; dbg!(new_path); let raw_data = instorage.load("test")?; let loaded_sig = Signature::from_reader(raw_data.as_slice())?.swap_remove(0); assert_eq!(sig.name(), loaded_sig.name()); assert_eq!(sig.md5sum(), loaded_sig.md5sum()); Ok(()) } #[test] fn innerstorage_args() -> Result<(), Box> { let output = TempDir::new()?; let path = output.path().as_os_str().to_str().unwrap(); let fst = FSStorage::new("".into(), path); let instorage = InnerStorage::new(fst); let args = instorage.args(); assert!(matches!(args, StorageArgs::FSStorage { .. })); let StorageArgs::FSStorage { path: p } = args; assert_eq!(p, path); Ok(()) } #[test] fn innerstorage_from_args() -> Result<(), Box> { let output = TempDir::new()?; let path = output.path().as_os_str().to_str().unwrap(); let fst = FSStorage::new("".into(), path); let args = fst.args(); let instorage = InnerStorage::new(FSStorage::from(&args)); let inargs = instorage.args(); assert!(matches!(inargs, StorageArgs::FSStorage { .. })); let StorageArgs::FSStorage { path: p1 } = inargs; assert_eq!(p1, path); assert!(matches!(args, StorageArgs::FSStorage { .. })); let StorageArgs::FSStorage { path: p2 } = args; assert_eq!(p2, path); Ok(()) } sourmash-bio-sourmash-bc3c0f8/src/core/tests/test.rs000066400000000000000000000001451474157046300226740ustar00rootroot00000000000000#[test] fn test_murmur() { assert_eq!(sourmash::_hash_murmur(b"ACG", 42), 1731421407650554201) } sourmash-bio-sourmash-bc3c0f8/src/core/tests/web.rs000066400000000000000000000002401474157046300224660ustar00rootroot00000000000000#![cfg(all(target_arch = "wasm32", target_os = "unknown"))] use wasm_bindgen_test::wasm_bindgen_test_configure; wasm_bindgen_test_configure!(run_in_browser); sourmash-bio-sourmash-bc3c0f8/src/sourmash/000077500000000000000000000000001474157046300211165ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/__init__.py000066400000000000000000000102331474157046300232260ustar00rootroot00000000000000"""A library for creating k-mer sketches from biological sequences, comparing them to each other, and working with the results. Public API: load_file_as_signatures(...) - load `[SourmashSignature, ]` from filename load_file_as_index(...) - load collections of `SourmashSignature`s save_signatures(...) - save `[SourmashSignature, ]` class SourmashSignature - one or more hash sketches class MinHash - hash sketch class Please see https://sourmash.readthedocs.io/en/latest/api.html for API docs. The sourmash code is available at github.com/sourmash-bio/sourmash/ under the BSD 3-Clause license. """ from deprecation import deprecated from importlib.metadata import version __all__ = [ "MinHash", "SourmashSignature", "load_one_signature", "SourmashSignature", "load_file_as_index", "load_file_as_signatures", "save_signatures", "create_sbt_index", "load_signatures", # deprecated - remove in 5.0 "load_sbt_index", # deprecated - remove in 5.0 "search_sbt_index", # deprecated - remove in 5.0 ] from ._lowlevel import ffi, lib ffi.init_once(lib.sourmash_init, "init") VERSION = version(__name__) from .minhash import MinHash, get_minhash_default_seed, get_minhash_max_hash DEFAULT_SEED = get_minhash_default_seed() MAX_HASH = get_minhash_max_hash() from .signature import ( load_signatures_from_json, load_one_signature_from_json, SourmashSignature, save_signatures_to_json, ) @deprecated( deprecated_in="3.5.1", removed_in="5.0", current_version=VERSION, details="Use load_file_as_signatures instead.", ) def load_signatures(*args, **kwargs): """Load a JSON string with signatures into classes. Returns list of SourmashSignature objects. Note, the order is not necessarily the same as what is in the source file. This function has been deprecated as of 3.5.1; please use 'load_file_as_signatures' instead. Note that in 4.0, the 'quiet' argument has been removed and the function no longer outputs to stderr. Moreover, do_raise is now True by default. """ return load_signatures_from_json(*args, **kwargs) @deprecated( deprecated_in="4.8.9", removed_in="5.0", current_version=VERSION, details="Use load_file_as_signatures instead.", ) def load_one_signature(*args, **kwargs): """Load a JSON string with signatures into classes. Returns list of SourmashSignature objects. Note, the order is not necessarily the same as what is in the source file. """ return load_one_signature_from_json(*args, **kwargs) @deprecated( deprecated_in="4.8.9", removed_in="5.0", current_version=VERSION, details="use sourmash_args.SaveSignaturesToLocation instead.", ) def save_signatures(*args, **kwargs): return save_signatures_to_json(*args, **kwargs) from .sbtmh import load_sbt_index as load_sbt_index_private from .sbtmh import search_sbt_index as search_sbt_index_private @deprecated( deprecated_in="3.5.1", removed_in="5.0", current_version=VERSION, details="Use load_file_as_index instead.", ) def load_sbt_index(*args, **kwargs): """Load and return an SBT index. This function has been deprecated as of 3.5.1; please use 'load_file_as_index' instead. """ return load_sbt_index_private(*args, **kwargs) @deprecated( deprecated_in="3.5.1", removed_in="5.0", current_version=VERSION, details="Use the new Index API instead.", ) def search_sbt_index(*args, **kwargs): """\ Search an SBT index `tree` with signature `query` for matches above `threshold`. Usage: for match_sig, similarity in search_sbt_index(tree, query, threshold): ... This function has been deprecated as of 3.5.1; please use 'idx = load_file_as_index(...); idx.search(query, threshold=...)' instead. """ return search_sbt_index_private(*args, **kwargs) from .sbtmh import create_sbt_index from . import lca from . import tax from . import sbt from . import sbtmh from . import sbt_storage from . import signature from . import sig from . import cli from . import commands from .sourmash_args import load_file_as_index from .sourmash_args import load_file_as_signatures sourmash-bio-sourmash-bc3c0f8/src/sourmash/__main__.py000066400000000000000000000010741474157046300232120ustar00rootroot00000000000000""" The main entry point for sourmash, defined in pyproject.toml. Can also be executed as 'python -m sourmash'. """ def main(arglist=None): import sourmash args = sourmash.cli.parse_args(arglist) if hasattr(args, "subcmd"): mod = getattr(sourmash.cli, args.cmd) submod = getattr(mod, args.subcmd) mainmethod = getattr(submod, "main") else: mod = getattr(sourmash.cli, args.cmd) mainmethod = getattr(mod, "main") retval = mainmethod(args) raise SystemExit(retval) if __name__ == "__main__": main() sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/000077500000000000000000000000001474157046300216655ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/.gitignore000066400000000000000000000000151474157046300236510ustar00rootroot00000000000000__pycache__/ sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/__init__.py000066400000000000000000000127721474157046300240070ustar00rootroot00000000000000"""Define the top-level command line interface for sourmash This module handles user input when sourmash is invoked from the command line. A top-level parser is defined for the `sourmash` command, and subparsers are defined for each subcommand. Some sourmash operations are grouped together using the `sourmash ` pattern, and these are organized in their own CLI submodules, each with a dedicated directory. """ from argparse import ArgumentParser, RawDescriptionHelpFormatter, SUPPRESS import os import sys import sourmash from . import utils # Commands from . import categorize from . import compare from . import compute from . import gather from . import import_csv from . import info from . import index from . import migrate from . import multigather from . import plot from . import prefetch from . import sbt_combine from . import search from . import watch # Subcommand groups from . import lca from . import sig from . import sig as signature from . import sketch from . import storage from . import tax from . import scripts class SourmashParser(ArgumentParser): _citation_printed = False def __init__(self, citation=True, **kwargs): super().__init__(**kwargs) self.citation = citation @classmethod def print_citation(cls): if cls._citation_printed: return from sourmash.logging import notify notify(f"\n== This is sourmash version {sourmash.VERSION}. ==") notify("== Please cite Irber et. al (2024), doi:10.21105/joss.06830. ==\n") cls._citation_printed = True def _subparser_from_name(self, name): """Given a name, get the subparser instance registered with this parser.""" container = self._actions if name is None: return None for action in container: if action.choices is None: continue elif name in action.choices: return action.choices[name] def print_help(self): self.print_citation() super().print_help() def parse_args(self, args=None, namespace=None): if (args is None and len(sys.argv) == 1) or ( args is not None and len(args) == 0 ): self.print_help() raise SystemExit(1) args = super().parse_args(args=args, namespace=namespace) if ("quiet" not in args or not args.quiet) and self.citation: self.print_citation() if "subcmd" in args and args.subcmd is None: self._subparser_from_name(args.cmd).print_help() raise SystemExit(1) # BEGIN: dirty hacks to simultaneously support new and previous interface if hasattr(args, "subcmd") and args.subcmd == "import": args.subcmd = "ingest" # END: dirty hacks to simultaneously support new and previous interface return args def get_parser(): module_descs = { "tax": 'Integrate taxonomy information based on "gather" results', "lca": "Taxonomic operations", "sketch": "Create signatures", "sig": "Manipulate signature files", "storage": "Operations on storage", "scripts": "Plug-ins", } alias = { "sig": "signature", "ext": "scripts", } expert = set( ["categorize", "import_csv", "migrate", "multigather", "sbt_combine", "watch"] ) clidir = os.path.dirname(__file__) basic_ops = utils.command_list(clidir) # provide a list of the basic operations - not expert, not submodules. user_ops = [op for op in basic_ops if op not in expert and op not in module_descs] usage = " Basic operations\n" for op in user_ops: docstring = getattr(sys.modules[__name__], op).__doc__ helpstring = f"sourmash {op:s} --help" usage += f" {helpstring:25s} {docstring:s}\n" # next, all the subcommand ones - dive into subdirectories. cmd_group_dirs = next(os.walk(clidir))[1] cmd_group_dirs = filter(utils.opfilter, cmd_group_dirs) cmd_group_dirs = sorted(cmd_group_dirs) cmd_group_usage = [cmd for cmd in cmd_group_dirs if cmd not in alias.values()] for dirpath in cmd_group_usage: usage += "\n " + module_descs[dirpath] + "\n" usage += f" sourmash {dirpath:s} --help\n" if dirpath in alias: usage += f" sourmash {alias[dirpath]:s} --help\n" desc = ( "Create, compare, and manipulate k-mer sketches of biological sequences.\n\nUsage instructions:\n" + usage ) parser = SourmashParser( prog="sourmash", description=desc, formatter_class=RawDescriptionHelpFormatter, usage=SUPPRESS, ) parser._optionals.title = "Options" parser.add_argument( "-v", "--version", action="version", version="sourmash " + sourmash.VERSION ) parser.add_argument( "-q", "--quiet", action="store_true", help="don't print citation information" ) sub = parser.add_subparsers( title="Instructions", dest="cmd", metavar="cmd", help=SUPPRESS, ) for op in basic_ops + cmd_group_dirs: getattr(sys.modules[__name__], op).subparser(sub) parser._action_groups.reverse() return parser def parse_args(arglist=None): """ Return an argparse 'args' object from parsing arglist. By default pulls arguments from sys.argv. Example usage: ``` args = parse_args(['sig', 'filter', '-m', '10']) sourmash.sig.filter.__main__.filter(args) ``` """ return get_parser().parse_args(arglist) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/categorize.py000066400000000000000000000022401474157046300243710ustar00rootroot00000000000000"'sourmash categorize' - query an SBT for bes match, with many signatures." from sourmash.cli.utils import add_ksize_arg, add_moltype_args def subparser(subparsers): subparser = subparsers.add_parser("categorize") subparser.add_argument( "database", help="location of signature collection/database to load" ) subparser.add_argument( "queries", nargs="+", help="locations of signatures to categorize" ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) add_ksize_arg(subparser) subparser.add_argument( "--threshold", default=0.08, type=float, help="minimum threshold for reporting matches; default=0.08", ) subparser.add_argument( "--ignore-abundance", action="store_true", help="do NOT use k-mer abundances if present", ) add_moltype_args(subparser) # TODO: help messages in these subparser.add_argument("--csv", help="output summary CSV to this file") subparser.add_argument("--load-csv", default=None) def main(args): import sourmash return sourmash.commands.categorize(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/compare.py000066400000000000000000000100241474157046300236620ustar00rootroot00000000000000"""create a similarity matrix comparing many samples""" usage = """ The `compare` subcommand compares one or more signatures (created with `sketch`) using estimated Jaccard index [1] or (if signatures are created with `-p abund`) the angular similarity [2]). The default output is a text display of a similarity matrix where each entry `[i, j]` contains the estimated Jaccard index between input signature `i` and input signature `j`. The output matrix can be saved to a file with `--output ` and used with the `sourmash plot` subcommand (or loaded with `numpy.load(...)`. Using `--csv ` will output a CSV file that can be loaded into other languages than Python, such as R. Command line usage: ``` sourmash compare file1.sig [ file2.sig ... ] ``` **Note:** compare by default produces a symmetric similarity matrix that can be used as an input to clustering. With `--containment`, however, this matrix is no longer symmetric and cannot formally be used for clustering. [1] https://en.wikipedia.org/wiki/Jaccard_index [2] https://en.wikipedia.org/wiki/Cosine_similarity#Angular_distance_and_similarity --- """ from sourmash.cli.utils import ( add_ksize_arg, add_moltype_args, add_picklist_args, add_pattern_args, add_scaled_arg, ) def subparser(subparsers): subparser = subparsers.add_parser("compare", description=__doc__, usage=usage) subparser.add_argument( "signatures", nargs="*", help="list of signatures to compare", default=[] ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="F", help="file to which output will be written; default is terminal " "(standard output)", ) subparser.add_argument( "--ignore-abundance", action="store_true", help="do NOT use k-mer abundances even if present", ) subparser.add_argument( "--containment", action="store_true", help="calculate containment instead of similarity", ) subparser.add_argument( "--max-containment", action="store_true", help="calculate max containment instead of similarity", ) subparser.add_argument( "--avg-containment", "--average-containment", action="store_true", help="calculate average containment instead of similarity", ) subparser.add_argument( "--estimate-ani", "--ANI", "--ani", action="store_true", help="return ANI estimated from jaccard, containment, average containment, or max containment; see https://doi.org/10.1101/2022.01.11.475870", ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) subparser.add_argument( "-f", "--force", action="store_true", help="continue past errors in file loading", ) subparser.add_argument( "--csv", metavar="F", help="write matrix to specified file in CSV format (with column headers)", ) subparser.add_argument( "--labels-to", "--labels-save", help="a CSV file containing label information", ) subparser.add_argument( "-p", "--processes", metavar="N", type=int, default=None, help="Number of processes to use to calculate similarity", ) subparser.add_argument( "--distance-matrix", action="store_true", help="output a distance matrix, instead of a similarity matrix", ) subparser.add_argument( "--similarity-matrix", action="store_false", dest="distance_matrix", help="output a similarity matrix; this is the default", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) add_pattern_args(subparser) add_scaled_arg(subparser) def main(args): import sourmash return sourmash.commands.compare(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/compute.py000066400000000000000000000114121474157046300237120ustar00rootroot00000000000000"""compute sequence signatures for inputs""" usage = """ ** WARNING: the sourmash compute command is DEPRECATED as of 4.0 and ** will be removed in 5.0. Please see the 'sourmash sketch' command instead. sourmash compute -k 21,31,51 *.fa *.fq Create MinHash sketches at k-mer sizes of 21, 31 and 51, for all FASTA and FASTQ files in the current directory, and save them in signature files ending in '.sig'. You can rapidly compare these files with `compare` and query them with `search`, among other operations; see the full documentation at http://sourmash.rtfd.io/. The key options for compute are: * `-k/--ksize [, ]: k-mer size(s) to use, e.g. -k 21,31,51 * `-n/--num ` or `--scaled `: set size or resolution of sketches * `--track-abundance`: track abundances of hashes (default False) * `--dna or --protein`: nucleotide and/or protein signatures (default `--dna`) * `--merge `: compute a merged signature across all inputs. * `--singleton`: compute individual signatures for each sequence. * `--name-from-first`: set name of signature from first sequence in file. * `-o/--output`: save all computed signatures to this file. Please see -h for all of the options as well as more detailed help. --- """ from sourmash.minhash import get_minhash_default_seed from sourmash.cli.utils import add_construct_moltype_args, add_num_arg def ksize_parser(ksizes): # get list of k-mer sizes for which to compute sketches if "," in ksizes: ksizes = ksizes.split(",") ksizes = list(map(int, ksizes)) else: ksizes = [int(ksizes)] return ksizes def subparser(subparsers): subparser = subparsers.add_parser("compute", description=__doc__, usage=usage) sketch_args = subparser.add_argument_group("Sketching options") sketch_args.add_argument( "-k", "--ksizes", default="21,31,51", type=ksize_parser, help="comma-separated list of k-mer sizes; default=%(default)s", ) sketch_args.add_argument( "--track-abundance", action="store_true", help="track k-mer abundances in the generated signature", ) sketch_args.add_argument( "--scaled", type=float, default=0, help="choose number of hashes as 1 in FRACTION of input k-mers", ) add_construct_moltype_args(sketch_args) sketch_args.add_argument( "--input-is-protein", action="store_true", help="Consume protein sequences - no translation needed.", ) sketch_args.add_argument( "--seed", type=int, default=get_minhash_default_seed(), help="seed used by MurmurHash; default=%(default)i", ) file_args = subparser.add_argument_group("File handling options") file_args.add_argument( "-f", "--force", action="store_true", help="recompute signatures even if the file exists", ) file_args.add_argument( "-o", "--output", help="output computed signatures to this file" ) file_args.add_argument( "--output-dir", "--outdir", help="output computed signatures to this directory", ) file_args.add_argument( "--singleton", action="store_true", help="compute a signature for each sequence record individually", ) file_args.add_argument( "--merge", "--name", type=str, default="", metavar="FILE", help="merge all input files into one signature file with the specified name", ) file_args.add_argument( "--name-from-first", action="store_true", help="name the signature generated from each file after the first " "record in the file", ) file_args.add_argument( "--randomize", action="store_true", help="shuffle the list of input filenames randomly", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "--check-sequence", action="store_true", help="complain if input sequence is invalid", ) subparser.add_argument( "--license", default="CC0", type=str, help="signature license. Currently only CC0 is supported.", ) subparser.add_argument("filenames", nargs="+", help="file(s) of sequences") subparser._positionals.title = "Required arguments" subparser._optionals.title = "Miscellaneous options" add_num_arg(sketch_args, 500) def main(args): from sourmash.command_compute import compute from sourmash.logging import notify notify( """\ ** WARNING: the sourmash compute command is DEPRECATED as of 4.0 and ** will be removed in 5.0. Please see the 'sourmash sketch' command instead. """ ) return compute(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/gather.py000066400000000000000000000146521474157046300235210ustar00rootroot00000000000000"""search a metagenome signature against dbs""" usage = """ The `gather` subcommand selects the best reference genomes to use for a metagenome analysis, by finding the smallest set of non-overlapping matches to the query in a database. This is specifically meant for metagenome and genome bin analysis. (See "Classifying Signatures" [1] in the command line documentation for more information on the different approaches that can be used here.) If the input signature was created with `-p abund`, output will be abundance weighted (unless `--ignore-abundances` is specified). `-o/--output` will create a CSV file containing the matches. `gather`, like `search`, will load all of provided signatures into memory. You can use `sourmash index` to create a Sequence Bloom Tree (SBT) that can be quickly searched on disk; this is the same format in which we provide GenBank and other databases. Command line usage: ``` sourmash gather query.sig [ list of signatures or SBTs ] ``` Example output for an unweighted/noabund query: ``` overlap p_query p_match --------- ------- -------- 1.4 Mbp 11.0%% 58.0%% JANA01000001.1 Fusobacterium sp. OBRC... 1.0 Mbp 7.7%% 25.9%% CP001957.1 Haloferax volcanii DS2 pla... 0.9 Mbp 7.4%% 11.8%% BA000019.2 Nostoc sp. PCC 7120 DNA, c... 0.7 Mbp 5.9%% 23.0%% FOVK01000036.1 Proteiniclasticum rumi... 0.7 Mbp 5.3%% 17.6%% AE017285.1 Desulfovibrio vulgaris sub... ``` Example output for a weighted query: ``` overlap p_query p_match avg_abund --------- ------- ------- --------- 9.3 Mbp 0.8%% 97.5%% 6.7 NC_007951.1 Burkholderia xenovorans ... 7.3 Mbp 2.3%% 99.9%% 23.9 NC_003272.1 Nostoc sp. PCC 7120 DNA,... 7.0 Mbp 8.9%% 100.0%% 94.5 BX119912.1 Rhodopirellula baltica SH... 6.6 Mbp 1.4%% 100.0%% 16.3 NC_009972.1 Herpetosiphon aurantiacu... ... ``` The command line option `--threshold-bp` sets the threshold below which matches are no longer reported; by default, this is set to 50kb. see the Appendix in Classifying Signatures [1] for details. Note: Use `sourmash gather` to classify a metagenome against a collection of genomes with no (or incomplete) taxonomic information. Use `sourmash lca summarize` to classify a metagenome using a collection of genomes with taxonomic information. [1] https://sourmash.readthedocs.io/en/latest/classifying-signatures.html --- """ from sourmash.cli.utils import ( add_ksize_arg, add_moltype_args, add_picklist_args, add_scaled_arg, add_pattern_args, ) def subparser(subparsers): subparser = subparsers.add_parser("gather", description=__doc__, usage=usage) subparser.add_argument("query", help="query signature") subparser.add_argument( "databases", nargs="+", help="signatures/SBTs to search", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument("-d", "--debug", action="store_true") subparser.add_argument( "-n", "--num-results", default=None, type=int, metavar="N", help="number of results to report (default: terminate at --threshold-bp)", ) subparser.add_argument( "-o", "--output", metavar="FILE", help="output CSV containing matches to this file", ) subparser.add_argument( "--save-matches", metavar="FILE", help="save gather matched signatures from the database to the specified file", ) subparser.add_argument( "--save-prefetch", metavar="FILE", help="save all prefetch-matched signatures from the databases to the " "specified file or directory", ) subparser.add_argument( "--save-prefetch-csv", metavar="FILE", help="save a csv with information from all prefetch-matched signatures " "to the specified file", ) subparser.add_argument( "--threshold-bp", metavar="REAL", type=float, default=5e4, help="reporting threshold (in bp) for estimated overlap with remaining query (default=50kb)", ) subparser.add_argument( "--output-unassigned", metavar="FILE", help="output unassigned portions of the query as a signature to the " "specified file", ) subparser.add_argument( "--ignore-abundance", action="store_true", help="do NOT use k-mer abundances if present", ) subparser.add_argument( "--md5", default=None, help="select the signature with this md5 as query" ) subparser.add_argument( "--cache-size", default=0, type=int, metavar="N", help="number of internal SBT nodes to cache in memory (default: 0, cache all nodes)", ) # advanced parameters subparser.add_argument( "--linear", dest="linear", action="store_true", help="force a low-memory but maybe slower database search", ) subparser.add_argument( "--no-linear", dest="linear", action="store_false", ) subparser.add_argument( "--no-prefetch", dest="prefetch", action="store_false", help="do not use prefetch before gather; see documentation", ) subparser.add_argument( "--prefetch", dest="prefetch", action="store_true", help="use prefetch before gather; see documentation", ) subparser.add_argument( "--estimate-ani-ci", action="store_true", help="also output confidence intervals for ANI estimates", ) subparser.add_argument( "--fail-on-empty-database", action="store_true", help="stop at databases that contain no compatible signatures", ) subparser.add_argument( "--no-fail-on-empty-database", action="store_false", dest="fail_on_empty_database", help="continue past databases that contain no compatible signatures", ) subparser.set_defaults(fail_on_empty_database=True) subparser.add_argument( "--create-empty-results", action="store_true", help="create an empty results file even if no matches.", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) add_pattern_args(subparser) add_scaled_arg(subparser, 0) def main(args): import sourmash return sourmash.commands.gather(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/import_csv.py000066400000000000000000000012331474157046300244230ustar00rootroot00000000000000"""'sourmash import_csv' description goes here""" from sourmash.logging import notify def subparser(subparsers): subparser = subparsers.add_parser("import_csv") subparser.add_argument("mash_csvfile", help="CSV file with mash sketches") subparser.add_argument( "-o", "--output", help="save signature generated from data to this file (default stdout)", ) def main(args): import sourmash notify("** WARNING: 'import_csv' is deprecated as of sourmash 4.0, and will") notify("** be removed in sourmash 5.0; use 'sourmash sig import --csv' instead.") notify("") return sourmash.commands.import_csv(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/index.py000066400000000000000000000054131474157046300233510ustar00rootroot00000000000000"""index signatures for rapid search""" usage = """ sourmash index -k 31 dbname *.sig Create an on-disk database of signatures that can be searched in low memory with 'search' and 'gather'. All signatures must be the same k-mer size, molecule type, and num/scaled; the standard signature selectors (-k/--ksize, --scaled, --dna/--protein) choose which signatures to be added. The key options for index are: * `-k/--ksize `: k-mer size to select * `--dna` or --protein`: nucleotide or protein signatures (default `--dna`) If `dbname` ends with `.sbt.json`, index will create the database as a collection of multiple files, with an index `dbname.sbt.json` and a subdirectory `.sbt.dbname`. If `dbname` ends with `.sbt.zip`, index will create a zip archive containing the multiple files. For sourmash v2 and v3, `sbt.json` will be added automatically; this behavior will change in sourmash v4 to default to `.sbt.zip`. --- """ from sourmash.cli.utils import ( add_ksize_arg, add_moltype_args, add_picklist_args, add_scaled_arg, ) def subparser(subparsers): subparser = subparsers.add_parser("index", description=__doc__, usage=usage) subparser.add_argument( "sbt_name", help="name to save index into; .sbt.zip or .sbt.json file" ) subparser.add_argument("signatures", nargs="*", help="signatures to load into SBT") subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--n_children", metavar="D", type=int, default=2, help="number of children for internal nodes; default=2", ) subparser.add_argument( "--append", action="store_true", default=False, help="add signatures to an existing SBT", ) subparser.add_argument( "-x", "--bf-size", metavar="S", type=float, default=1e5, help="Bloom filter size used for internal nodes", ) subparser.add_argument( "-f", "--force", action="store_true", help='try loading *all* files in provided subdirectories, not just .sig files"', ) subparser.add_argument( "-s", "--sparseness", metavar="FLOAT", type=float, default=0.0, help="What percentage of internal nodes will not be saved; ranges " "from 0.0 (save all nodes) to 1.0 (no nodes saved)", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) add_scaled_arg(subparser, 0) def main(args): import sourmash return sourmash.commands.index(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/info.py000066400000000000000000000016361474157046300232000ustar00rootroot00000000000000"""display sourmash version and other information""" import os import screed import sourmash from sourmash.logging import notify from sourmash.plugins import list_all_plugins def subparser(subparsers): subparser = subparsers.add_parser("info") subparser.add_argument( "-v", "--verbose", action="store_true", help="report versions of khmer and screed", ) def info(verbose=False): "Report sourmash version + version of installed dependencies." notify(f"sourmash version {sourmash.VERSION}") notify(f"- loaded from path: {os.path.dirname(__file__)}") notify("") if verbose: notify("khmer version: None (internal Nodegraph)") notify("") notify(f"screed version {screed.__version__}") notify(f"- loaded from path: {os.path.dirname(screed.__file__)}") list_all_plugins() def main(args): info(verbose=args.verbose) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/lca/000077500000000000000000000000001474157046300224245ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/lca/__init__.py000066400000000000000000000022421474157046300245350ustar00rootroot00000000000000"""Define the command line interface for sourmash lca The top level CLI is defined in ../__init__.py. This module defines the CLI for `sourmash lca` operations. """ from . import classify from . import compare_csv from . import index from . import rankinfo from . import summarize from ..utils import command_list from argparse import SUPPRESS, RawDescriptionHelpFormatter import os import sys def subparser(subparsers): subparser = subparsers.add_parser( "lca", formatter_class=RawDescriptionHelpFormatter, usage=SUPPRESS ) desc = "Operations\n" clidir = os.path.dirname(__file__) ops = command_list(clidir) for subcmd in ops: docstring = getattr(sys.modules[__name__], subcmd).__doc__ helpstring = f"sourmash lca {subcmd:s} --help" desc += f" {helpstring:33s} {docstring:s}\n" s = subparser.add_subparsers( title="Taxonomic utilities", dest="subcmd", metavar="subcmd", help=SUPPRESS, description=desc, ) for subcmd in ops: getattr(sys.modules[__name__], subcmd).subparser(s) subparser._action_groups.reverse() subparser._optionals.title = "Options" sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/lca/classify.py000066400000000000000000000026061474157046300246170ustar00rootroot00000000000000"""classify genomes""" def subparser(subparsers): subparser = subparsers.add_parser("classify") subparser.add_argument( "--db", nargs="+", action="append", help="databases to use to classify" ) subparser.add_argument( "--query", nargs="*", default=[], action="append", help="query signatures to classify", ) subparser.add_argument( "--query-from-file", help="file containing list of signature files to query" ) subparser.add_argument( "--threshold", metavar="T", type=int, default=5, help="minimum number of hashes needed for a taxonomic classification (default: 5)", ) subparser.add_argument( "--majority", action="store_true", help="use majority vote classification instead of lca", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="output debugging output" ) subparser.add_argument( "-o", "--output", metavar="FILE", default="-", help="output CSV to the specified file; by default output to stdout", ) subparser.add_argument("--scaled", type=float) def main(args): import sourmash return sourmash.lca.command_classify.classify(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/lca/compare_csv.py000066400000000000000000000026421474157046300253030ustar00rootroot00000000000000"""compare spreadsheets""" def subparser(subparsers): # Dirty hack to simultaneously support new and previous interface # If desired, this function can be removed with a major version bump. for cmd in ("compare", "compare_csv"): subparser = subparsers.add_parser(cmd) subparser.add_argument("csv1", help="taxonomy spreadsheet output by classify") subparser.add_argument("csv2", help="custom taxonomy spreadsheet") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="output debugging output" ) subparser.add_argument( "-C", "--start-column", metavar="C", default=2, type=int, help="column at which taxonomic assignments start; default=2", ) subparser.add_argument( "--tabs", action="store_true", help="input spreadsheet is tab-delimited; default is commas", ) subparser.add_argument( "--no-headers", action="store_true", help="no headers present in taxonomy spreadsheet", ) subparser.add_argument("-f", "--force", action="store_true") def main(args): import sourmash return sourmash.lca.command_compare_csv.compare_csv(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/lca/index.py000066400000000000000000000050521474157046300241070ustar00rootroot00000000000000"""create LCA database""" from sourmash.cli.utils import add_ksize_arg, add_moltype_args, add_picklist_args def subparser(subparsers): subparser = subparsers.add_parser("index") subparser.add_argument("csv", help="taxonomy spreadsheet") subparser.add_argument("lca_db_out", help="output database name") subparser.add_argument( "signatures", nargs="*", help="signatures or directory of signatures to index (optional if provided via --from-file)", ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) subparser.add_argument("--scaled", metavar="S", default=10000, type=float) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="output debugging output" ) subparser.add_argument( "-C", "--start-column", metavar="C", default=2, type=int, help="column at which taxonomic assignments start; default=2", ) subparser.add_argument( "--tabs", action="store_true", help="input spreadsheet is tab-delimited; default is commas", ) subparser.add_argument( "--no-headers", action="store_true", help="no headers present in taxonomy spreadsheet", ) subparser.add_argument( "--split-identifiers", action="store_true", help="split names in signatures on whitespace", ) subparser.add_argument( "--keep-identifier-versions", action="store_true", help="do not remove accession versions", ) subparser.add_argument("-f", "--force", action="store_true") subparser.add_argument("--report", help="output a report on anomalies, if any") subparser.add_argument( "--require-taxonomy", action="store_true", help="ignore signatures with no taxonomy entry", ) subparser.add_argument( "--fail-on-missing-taxonomy", action="store_true", help="fail quickly if taxonomy is not available for an identifier", ) subparser.add_argument( "-F", "--database-format", help="format of output database; default is 'json')", default="json", choices=["json", "sql"], ) add_ksize_arg(subparser, default=31) add_moltype_args(subparser) add_picklist_args(subparser) def main(args): import sourmash return sourmash.lca.command_index.index(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/lca/rankinfo.py000066400000000000000000000013221474157046300246030ustar00rootroot00000000000000"""database rank info""" def subparser(subparsers): subparser = subparsers.add_parser("rankinfo") subparser.add_argument("db", nargs="+") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="output debugging output" ) subparser.add_argument("--scaled", metavar="FLOAT", type=float) subparser.add_argument( "--minimum-num", type=int, default=0, help="Minimum number of different lineages a k-mer must be in to be counted", ) def main(args): import sourmash return sourmash.lca.command_rankinfo.rankinfo_main(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/lca/summarize.py000066400000000000000000000026761474157046300250250ustar00rootroot00000000000000"""summarize mixture""" def subparser(subparsers): subparser = subparsers.add_parser("summarize") subparser.add_argument( "--db", nargs="+", action="append", help="one or more LCA databases to use" ) subparser.add_argument( "--query", nargs="*", default=[], action="append", help="one or more signature files to use as queries", ) subparser.add_argument( "--query-from-file", help="file containing list of signature files to query" ) subparser.add_argument( "--threshold", metavar="T", type=int, default=5, help="minimum number of hashes to require for a match", ) subparser.add_argument( "-o", "--output", metavar="FILE", help="file to which CSV output will be written", ) subparser.add_argument( "--scaled", metavar="FLOAT", type=float, help="scaled value to downsample to" ) subparser.add_argument( "--ignore-abundance", action="store_true", help="ignore hash abundances in query signatures do not weight results", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="output debugging output" ) def main(args): import sourmash return sourmash.lca.command_summarize.summarize_main(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/migrate.py000066400000000000000000000004531474157046300236710ustar00rootroot00000000000000"'sourmash migrate' - migrate an SBT database to the latest version." def subparser(subparsers): subparser = subparsers.add_parser("migrate") subparser.add_argument("sbt_name", help="name to save SBT into") def main(args): import sourmash return sourmash.commands.migrate(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/multigather.py000066400000000000000000000071711474157046300245720ustar00rootroot00000000000000"'sourmash multigather' - gather many signatures against multiple databases." usage = """ The `multigather` subcommand runs 'gather' for multiple query sequences against the same collection of sequences. The main use for multigather is to amortize the cost of loading databases over many gather queries, so it is most useful when searching against databases that are slow to load. Usage: ``` sourmash multigather --query [ ...] --db ``` For each query signature, the following output files are created in the current working directory: * .csv - 'gather' CSV output, same as 'gather -o' * .matches.sig - 'gather' matching sigs, same as 'gather --save-matches' * .unassigned.sig - 'gather' unassigned hashes, same as 'gather --output-unassigned' where 'base' is the basename of the 'source file' from the query, or, if empty, the md5sum from the signature - use `sourmash sig describe` to retrieve these. The following commands: ``` sourmash gather query1.sig db1 sourmash gather query2.sig db1 ``` can be turned into a multigather command like so: ``` sourmash multigather --query query1.sig query2.sig --db db1 ``` """ from sourmash.cli.utils import add_ksize_arg, add_moltype_args, add_scaled_arg def subparser(subparsers): subparser = subparsers.add_parser("multigather") subparser.add_argument( "--query", nargs="*", default=[], action="append", help="query signature" ) subparser.add_argument( "--query-from-file", help="file containing list of signature files to query" ) subparser.add_argument( "--db", nargs="+", action="append", help="signatures/SBTs to search", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument("-d", "--debug", action="store_true") subparser.add_argument( "--threshold-bp", metavar="REAL", type=float, default=5e4, help="threshold (in bp) for reporting results (default=50,000)", ) subparser.add_argument( "--ignore-abundance", action="store_true", help="do NOT use k-mer abundances if present", ) subparser.add_argument( "--estimate-ani-ci", action="store_true", help="also output confidence intervals for ANI estimates", ) subparser.add_argument( "--fail-on-empty-database", action="store_true", help="stop at databases that contain no compatible signatures", ) subparser.add_argument( "--force-allow-overwrite-output", action="store_true", help="allow output files to be overwritten", ) subparser.add_argument( "--no-fail-on-empty-database", action="store_false", dest="fail_on_empty_database", help="continue past databases that contain no compatible signatures", ) subparser.set_defaults(fail_on_empty_database=True) subparser.add_argument( "--output-dir", "--outdir", help="output CSV results to this directory", ) subparser.add_argument( "-U", "--output-add-query-md5sum", action="store_true", help="add md5sum of each query to ensure unique output file names", ) subparser.add_argument( "-E", "--extension", type=str, default=".sig", help="write signature files with this extension ('.sig' by default)", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_scaled_arg(subparser, 0) def main(args): import sourmash return sourmash.commands.multigather(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/plot.py000066400000000000000000000045731474157046300232260ustar00rootroot00000000000000"""plot distance matrix made by 'compare'""" def subparser(subparsers): subparser = subparsers.add_parser("plot") subparser.add_argument("distances", help='output from "sourmash compare"') subparser.add_argument( "--pdf", action="store_true", help="output PDF; default is PNG" ) subparser.add_argument( "--labels", action="store_true", default=None, help="show sample labels on dendrogram/matrix", ) subparser.add_argument( "--no-labels", action="store_false", dest="labels", help="do not show sample labels", ) subparser.add_argument( "--labeltext", help="filename containing list of labels (overrides signature names); implies --labels", ) subparser.add_argument( "--indices", action="store_true", default=None, help="show sample indices but not labels; overridden by --labels", ) subparser.add_argument( "--no-indices", action="store_false", dest="indices", help="do not show sample indices", ) subparser.add_argument( "--vmin", default=0.0, type=float, help="lower limit of heatmap scale; default=%(default)f", ) subparser.add_argument( "--vmax", default=1.0, type=float, help="upper limit of heatmap scale; default=%(default)f", ) subparser.add_argument( "--subsample", type=int, metavar="N", help="randomly downsample to this many samples, max", ) subparser.add_argument( "--subsample-seed", type=int, default=1, metavar="S", help="random seed for --subsample; default=1", ) subparser.add_argument( "-f", "--force", action="store_true", help="forcibly plot non-distance matrices" ) subparser.add_argument( "--output-dir", metavar="DIR", help="directory for output plots" ) subparser.add_argument( "--csv", metavar="F", help="write clustered matrix and labels out in CSV format (with column" " headers) to this file", ) subparser.add_argument( "--labels-from", "--labels-load", help="a CSV file containing label information to use on plot; implies --labels", ) def main(args): import sourmash return sourmash.commands.plot(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/prefetch.py000066400000000000000000000047161474157046300240470ustar00rootroot00000000000000"""search a signature against dbs, find all overlaps""" from sourmash.cli.utils import ( add_ksize_arg, add_moltype_args, add_picklist_args, add_scaled_arg, add_pattern_args, ) def subparser(subparsers): subparser = subparsers.add_parser("prefetch") subparser.add_argument("query", help="query signature") subparser.add_argument( "databases", nargs="*", help="one or more databases to search", ) subparser.add_argument( "--db-from-file", default=None, help="list of paths containing signatures to search", ) subparser.add_argument( "--linear", action="store_true", help="force linear traversal of indexes to minimize loading time and memory use", ) subparser.add_argument( "--no-linear", dest="linear", action="store_false", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument("-d", "--debug", action="store_true") subparser.add_argument( "-o", "--output", metavar="FILE", help="output CSV containing matches to this file", ) subparser.add_argument( "--save-matches", metavar="FILE", help="save all matching signatures from the databases to the " "specified file or directory", ) subparser.add_argument( "--threshold-bp", metavar="REAL", type=float, default=5e4, help="reporting threshold (in bp) for estimated overlap with remaining query hashes (default=50kb)", ) subparser.add_argument( "--save-unmatched-hashes", metavar="FILE", help="output unmatched query hashes as a signature to the specified file", ) subparser.add_argument( "--save-matching-hashes", metavar="FILE", help="output matching query hashes as a signature to the specified file", ) subparser.add_argument( "--md5", default=None, help="select the signature with this md5 as query" ) subparser.add_argument( "--estimate-ani-ci", action="store_true", help="also output confidence intervals for ANI estimates", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) add_pattern_args(subparser) add_scaled_arg(subparser, 0) def main(args): import sourmash return sourmash.commands.prefetch(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sbt_combine.py000066400000000000000000000007051474157046300245250ustar00rootroot00000000000000"""combine multiple Sequence Bloom Trees""" def subparser(subparsers): subparser = subparsers.add_parser("sbt_combine") subparser.add_argument("sbt_name", help="name to save SBT into") subparser.add_argument("sbts", nargs="+", help="SBTs to combine to form a new SBT") subparser.add_argument("-x", "--bf-size", metavar="S", type=float, default=1e5) def main(args): import sourmash return sourmash.commands.sbt_combine(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/scripts/000077500000000000000000000000001474157046300233545ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/scripts/__init__.py000066400000000000000000000032521474157046300254670ustar00rootroot00000000000000"""Provide a mechanism to add CLI plugins to sourmash. See https://sourmash.readthedocs.io/en/latest/dev_plugins.html for docs, src/sourmash/plugins.py for core sourmash implementation code, and https://github.com/sourmash-bio/sourmash_plugin_template for a template repo for making new plugins. """ # CTB TODO: # * provide suggestions for documentation & metadata for authors: # * provide guidance on how to test your CLI plugin at the CLI # (minimal testing regime: sourmash scripts, look for description etc.) import argparse import sourmash # Here, we decorate this module with the various extension objects # e.g. 'sourmash scripts foo' will look up attribute 'scripts.foo' # and we will return the extension class object, which will then # be run by sourmash.__main__. This dictionary is loaded below # by sourmash.plugins.add_cli_scripts. _extension_dict = {} def __getattr__(name): if name in _extension_dict: return _extension_dict[name] raise AttributeError(name) def subparser(subparsers): subparser = subparsers.add_parser( "scripts", usage=argparse.SUPPRESS, formatter_class=argparse.RawDescriptionHelpFormatter, aliases=["ext"], ) # get individual help strings: descrs = list(sourmash.plugins.get_cli_scripts_descriptions()) if descrs: description = "\n".join(descrs) else: description = "(No script plugins detected!)" s = subparser.add_subparsers( title="available plugin/extension commands", dest="subcmd", metavar="subcmd", help=argparse.SUPPRESS, description=description, ) _extension_dict.update(sourmash.plugins.add_cli_scripts(s)) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/search.py000066400000000000000000000106031474157046300235040ustar00rootroot00000000000000"""search a signature against other signatures""" usage = """ The `search` subcommand searches a collection of signatures or SBTs for matches to the query signature. It can search for matches with either high Jaccard similarity [1] or containment; the default is to use Jaccard similarity, unless `--containment` is specified. `-o/--output` will create a CSV file containing the matches. `search` will load all of provided signatures into memory, which can be slow and somewhat memory intensive for large collections. You can use `sourmash index` to create a Sequence Bloom Tree (SBT) that can be quickly searched on disk; this is the same format in which we provide GenBank and other databases. Command line usage: ``` sourmash search query.sig [ list of signatures or SBTs ] ``` Example output: ``` 49 matches; showing first 20: similarity match ---------- ----- 75.4%% NZ_JMGW01000001.1 Escherichia coli 1-176-05_S4_C2 e117605... 72.2%% NZ_GG774190.1 Escherichia coli MS 196-1 Scfld2538, whole ... 71.4%% NZ_JMGU01000001.1 Escherichia coli 2-011-08_S3_C2 e201108... 70.1%% NZ_JHRU01000001.1 Escherichia coli strain 100854 100854_1... 69.0%% NZ_JH659569.1 Escherichia coli M919 supercont2.1, whole g... ... ``` [1] https://en.wikipedia.org/wiki/Jaccard_index When `--containment` is provided, the containment of the query in each of the search signatures or databases is reported. --- """ from sourmash.cli.utils import ( add_ksize_arg, add_moltype_args, add_picklist_args, add_scaled_arg, add_pattern_args, ) def subparser(subparsers): subparser = subparsers.add_parser("search", description=__doc__, usage=usage) subparser.add_argument("query", help="query signature") subparser.add_argument( "databases", nargs="+", help="signatures/SBTs to search", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="output debug information" ) subparser.add_argument( "-t", "--threshold", metavar="T", default=0.08, type=float, help="minimum threshold for reporting matches; default=0.08", ) subparser.add_argument( "--save-matches", metavar="FILE", help="output matching signatures to the specified file", ) subparser.add_argument( "--best-only", action="store_true", help="report only the best match (with greater speed)", ) subparser.add_argument( "-n", "--num-results", default=3, type=int, metavar="N", help="number of results to display to user; 0 to report all", ) subparser.add_argument( "--containment", action="store_true", help="score based on containment rather than similarity", ) subparser.add_argument( "--max-containment", action="store_true", help="score based on max containment rather than similarity", ) subparser.add_argument( "--estimate-ani-ci", action="store_true", help="for containment searches, also output confidence intervals for ANI estimates", ) subparser.add_argument( "--ignore-abundance", action="store_true", help="do NOT use k-mer abundances if present; note: has no effect if " "--containment or --max-containment is specified", ) subparser.add_argument( "-o", "--output", metavar="FILE", help="output CSV containing matches to this file", ) subparser.add_argument( "--md5", default=None, help="select the signature with this md5 as query" ) subparser.add_argument( "--fail-on-empty-database", action="store_true", help="stop at databases that contain no compatible signatures", ) subparser.add_argument( "--no-fail-on-empty-database", action="store_false", dest="fail_on_empty_database", help="continue past databases that contain no compatible signatures", ) subparser.set_defaults(fail_on_empty_database=True) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) add_pattern_args(subparser) add_scaled_arg(subparser, 0) def main(args): import sourmash return sourmash.commands.search(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/000077500000000000000000000000001474157046300224475ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/__init__.py000066400000000000000000000031151474157046300245600ustar00rootroot00000000000000"""Define the command line interface for sourmash sig The top level CLI is defined in ../__init__.py. This module defines the CLI for `sourmash sig` operations. """ from . import cat from . import split from . import describe from . import downsample from . import extract from . import filter from . import flatten from . import fileinfo from . import fileinfo as summarize from . import grep from . import kmers from . import check from . import collect from . import intersect from . import inflate from . import manifest from . import merge from . import rename from . import subtract from . import ingest from . import export from . import overlap from ..utils import command_list from argparse import SUPPRESS, RawDescriptionHelpFormatter import os import sys def subparser(subparsers): subparser = subparsers.add_parser( "sig", formatter_class=RawDescriptionHelpFormatter, usage=SUPPRESS, aliases=["signature"], ) desc = "Operations\n" clidir = os.path.dirname(__file__) ops = command_list(clidir) for subcmd in ops: docstring = getattr(sys.modules[__name__], subcmd).__doc__ helpstring = f"sourmash sig {subcmd:s} --help" desc += f" {helpstring:33s} {docstring:s}\n" s = subparser.add_subparsers( title="Manipulate signature files", dest="subcmd", metavar="subcmd", help=SUPPRESS, description=desc, ) for subcmd in ops: getattr(sys.modules[__name__], subcmd).subparser(s) subparser._action_groups.reverse() subparser._optionals.title = "Options" sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/cat.py000066400000000000000000000032121474157046300235660ustar00rootroot00000000000000"""concatenate signature files""" usage = """ ### `sourmash signature cat` - concatenate multiple signatures together Concatenate signature files. For example, sourmash signature cat file1.sig file2.sig -o all.sig will combine all signatures in `file1.sig` and `file2.sig` and put them in the file `all.sig`. """ from sourmash.cli.utils import ( add_moltype_args, add_ksize_arg, add_picklist_args, add_pattern_args, ) def subparser(subparsers): # working on this subparser = subparsers.add_parser("cat", description=__doc__, usage=usage) subparser.add_argument("signatures", nargs="*") subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="provide debugging output" ) subparser.add_argument( "-o", "--output", metavar="FILE", default="-", help="output signature to this file (default stdout)", ) subparser.add_argument( "-u", "--unique", action="store_true", help="keep only distinct signatures, removing duplicates (based on md5sum)", ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) add_ksize_arg(subparser) add_moltype_args(subparser) add_pattern_args(subparser) add_picklist_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.cat(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/check.py000066400000000000000000000061051474157046300241000ustar00rootroot00000000000000"""check signature collections against a picklist""" usage = """ sourmash sig check --picklist ... -o miss.csv -m manifest.csv This will check the signature contents of against the given picklist, optionally outputting the unmatched picklist rows to 'miss.csv' and optionally outputting a manifest of the matched signatures to 'manifest.csv'. By default, 'sig check' requires a pre-existing manifest for collections; this prevents potentially slow manifest rebuilding. You can turn this check off with '--no-require-manifest'. """ from sourmash.cli.utils import ( add_moltype_args, add_ksize_arg, add_picklist_args, add_pattern_args, ) def subparser(subparsers): subparser = subparsers.add_parser("check", usage=usage) subparser.add_argument("signatures", nargs="*") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="provide debugging output" ) subparser.add_argument( "-o", "--output-missing", metavar="FILE", help="output picklist with remaining unmatched entries to this file", ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) subparser.add_argument( "-m", "--save-manifest-matching", help="save a manifest of the matching entries to this file.", ) subparser.add_argument( "--fail-if-missing", action="store_true", help="exit with an error code (-1) if there are any missing picklist values.", ) subparser.add_argument( "--no-require-manifest", help="do not require a manifest; generate dynamically if needed", action="store_true", ) subparser.add_argument( "-F", "--manifest-format", help="format of manifest output file; default is 'csv')", default="csv", choices=["csv", "sql"], ) subparser.add_argument( "--abspath", "--use-absolute-paths", help="convert all locations to absolute paths", action="store_true", ) subparser.add_argument( "--no-abspath", help="do not convert all locations to absolute paths", action="store_false", dest="abspath", ) subparser.add_argument( "--relpath", "--use-relative-paths", help="convert all locations to paths relative to the output manifest", action="store_true", ) subparser.add_argument( "--no-relpath", help="do not convert all locations to paths relative to the output manifest", action="store_false", dest="relpath", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_pattern_args(subparser) add_picklist_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.check(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/collect.py000066400000000000000000000050151474157046300244470ustar00rootroot00000000000000"""collect manifest information across many files""" usage = """ sourmash sig collect -o all.sqlmf This will collect manifests from across many files and save the information into a standalone manifest database. By default, 'sig collect' requires a pre-existing manifest for collections; this prevents potentially slow manifest rebuilding. You can turn this check off with '--no-require-manifest'. """ from sourmash.cli.utils import ( add_moltype_args, add_ksize_arg, add_picklist_args, add_pattern_args, ) def subparser(subparsers): subparser = subparsers.add_parser("collect", usage=usage) subparser.add_argument("locations", nargs="*", help="locations of input signatures") subparser.add_argument("-o", "--output", help="manifest output file", required=True) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="provide debugging output" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) subparser.add_argument( "--no-require-manifest", help="do not require a manifest; generate dynamically if needed", action="store_true", ) subparser.add_argument( "-F", "--manifest-format", help="format of manifest output file; default is 'csv')", default="sql", choices=["csv", "sql"], ) subparser.add_argument( "--merge-previous", action="store_true", help="merge new manifests into existing", ) subparser.add_argument( "--abspath", "--use-absolute-paths", help="convert all locations to absolute paths", action="store_true", ) subparser.add_argument( "--no-abspath", help="do not convert all locations to absolute paths", action="store_false", dest="abspath", ) subparser.add_argument( "--relpath", "--use-relative-paths", help="convert all locations to paths relative to the output manifest", action="store_true", ) subparser.add_argument( "--no-relpath", help="do not convert all locations to paths relative to the output manifest", action="store_false", dest="relpath", ) add_ksize_arg(subparser) add_moltype_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.collect(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/describe.py000066400000000000000000000030731474157046300246040ustar00rootroot00000000000000"""show details of signature""" usage = """ ### `sourmash signature describe` - display detailed information about signatures Display signature details. For example, sourmash sig describe tests/test-data/47.fa.sig will display: signature filename: tests/test-data/47.fa.sig signature: NC_009665.1 Shewanella baltica OS185, complete genome source file: 47.fa md5: 09a08691ce52952152f0e866a59f6261 k=31 molecule=DNA num=0 scaled=1000 seed=42 track_abundance=0 size: 5177 signature license: CC0 """ from sourmash.cli.utils import ( add_moltype_args, add_ksize_arg, add_picklist_args, add_pattern_args, ) def subparser(subparsers): subparser = subparsers.add_parser("describe", description=__doc__, usage=usage) subparser.add_argument("signatures", nargs="*") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="provide debugging output" ) subparser.add_argument( "--csv", metavar="FILE", help="output information to a CSV file" ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) add_pattern_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.describe(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/downsample.py000066400000000000000000000037001474157046300251720ustar00rootroot00000000000000"""downsample one or more signatures""" usage = """ ### `sourmash signature downsample` - decrease the size of a signature Downsample one or more signatures. With `downsample`, you can -- * increase the `scaled` value for a signature created with `-p scaled=SCALED`, shrinking it in size; * decrease the `num` value for a traditional num MinHash, shrinking it in size; * try to convert a `scaled` signature to a `num` signature; * try to convert a `num` signature to a `scaled` signature. For example, sourmash signature downsample file1.sig file2.sig --scaled 100000 -o downsampled.sig will output each signature, downsampled to a scaled value of 100000, to `downsampled.sig`; and sourmash signature downsample --num 500 scaled_file.sig -o downsampled.sig will try to convert a scaled MinHash to a num MinHash. """ from sourmash.cli.utils import ( add_moltype_args, add_ksize_arg, add_picklist_args, add_num_arg, ) def subparser(subparsers): subparser = subparsers.add_parser("downsample", description=__doc__, usage=usage) subparser.add_argument("signatures", nargs="*") subparser.add_argument( "--scaled", type=int, default=0, help="scaled value to downsample to" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="FILE", help="output signature to this file (default stdout)", default="-", ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) add_num_arg(subparser, 0) def main(args): import sourmash return sourmash.sig.__main__.downsample(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/export.py000066400000000000000000000020011474157046300243330ustar00rootroot00000000000000"""export a signature, e.g. to mash""" usage = """ ### `sourmash signature export` - export signatures to mash. Export signatures from sourmash format. Currently only supports mash dump format. For example, sourmash signature export filename.sig -o filename.sig.msh.json """ from sourmash.cli.utils import add_ksize_arg, add_moltype_args def subparser(subparsers): subparser = subparsers.add_parser("export", description=__doc__, usage=usage) subparser.add_argument("filename") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="FILE", help="output signature to this file (default stdout)", ) subparser.add_argument( "--md5", default=None, help="select the signature with this md5 as query" ) add_ksize_arg(subparser) add_moltype_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.export(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/extract.py000066400000000000000000000047551474157046300245060ustar00rootroot00000000000000"""extract one or more signatures""" usage = """ ### `sourmash signature extract` - extract signatures from a collection Extract the specified signature(s) from a collection of signatures. For example, sourmash signature extract *.sig -k 21 --dna -o extracted.sig will extract all nucleotide signatures calculated at k=21 from all .sig files in the current directory. There are currently two other useful selectors for `extract`: you can specify (part of) an md5sum, as output in the CSVs produced by `search` and `gather`; and you can specify (part of) a name. For example, sourmash signature extract tests/test-data/*.fa.sig --md5 09a0869 will extract the signature from `47.fa.sig` which has an md5sum of `09a08691ce52952152f0e866a59f6261`; and sourmash signature extract tests/test-data/*.fa.sig --name NC_009665 will extract the same signature, which has an accession number of `NC_009665.1`. #### Using picklists with `sourmash sig extract` As of sourmash 4.2.0, `extract` also supports picklists, a feature by which you can select signatures based on values in a CSV file. See [the command line docs](https://sourmash.readthedocs.io/en/latest/command-line.html) for more information. """ from sourmash.cli.utils import ( add_moltype_args, add_ksize_arg, add_picklist_args, add_pattern_args, ) def subparser(subparsers): subparser = subparsers.add_parser("extract", description=__doc__, usage=usage) subparser.add_argument("signatures", nargs="*") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="FILE", help="output signature to this file (default stdout)", default="-", ) subparser.add_argument( "--md5", default=None, help="select signatures whose md5 contains this substring", ) subparser.add_argument( "--name", default=None, help="select signatures whose name contains this substring", ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_pattern_args(subparser) add_picklist_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.extract(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/fileinfo.py000066400000000000000000000022251474157046300246150ustar00rootroot00000000000000"""provide summary information on the given file""" usage = """ sourmash sig fileinfo This will provide a summary of the sketch contents in the given file. JSON output can be generated in place of the normal human-readable output with '--json-out'. 'sig summarize' and 'sig fileinfo' are aliases for the same command. """ def subparser(subparsers): subparser = subparsers.add_parser("fileinfo", aliases=["summarize"], usage=usage) subparser.add_argument("path") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="output debug information" ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--rebuild-manifest", help="forcibly rebuild the manifest", action="store_true" ) subparser.add_argument( "--json-out", help="output information in JSON format only", action="store_true" ) def main(args): import sourmash return sourmash.sig.__main__.fileinfo(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/filter.py000066400000000000000000000035671474157046300243210ustar00rootroot00000000000000"""filter k-mers on abundance""" usage = """ ### `sourmash signature filter` - remove hashes based on abundance Filter the hashes in the specified signature(s) by abundance, by either `-m/--min-abundance` or `-M/--max-abundance` or both. Abundance selection is inclusive, so `-m 2 -M 5` will select hashes with abundance greater than or equal to 2, and less than or equal to 5. For example, sourmash signature -m 2 *.sig will output new signatures containing only hashes that occur two or more times in each signature. The `filter` command accepts the same selectors as `extract`. """ from sourmash.cli.utils import add_moltype_args, add_ksize_arg def subparser(subparsers): subparser = subparsers.add_parser("filter", description=__doc__, usage=usage) subparser.add_argument("signatures", nargs="+") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="FILE", help="output signature to this file (default stdout)", default="-", ) subparser.add_argument( "--md5", type=str, default=None, help="select signatures whose md5 contains this substring", ) subparser.add_argument( "--name", type=str, default=None, help="select signatures whose name contains this substring", ) subparser.add_argument( "-m", "--min-abundance", type=int, default=1, help="keep hashes >= this minimum abundance", ) subparser.add_argument( "-M", "--max-abundance", type=int, default=None, help="keep hashes <= this maximum abundance", ) add_ksize_arg(subparser) add_moltype_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.filter(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/flatten.py000066400000000000000000000032271474157046300244620ustar00rootroot00000000000000"""remove abundances""" usage = """ ### `sourmash signature flatten` - remove abundance information from signatures Flatten the specified signature(s), removing abundances and setting track_abundance to False. For example, sourmash signature flatten *.sig -o flattened.sig will remove all abundances from all of the .sig files in the current directory. The `flatten` command accepts the same selectors as `extract`. """ from sourmash.cli.utils import add_moltype_args, add_ksize_arg, add_picklist_args def subparser(subparsers): subparser = subparsers.add_parser("flatten", description=__doc__, usage=usage) subparser.add_argument("signatures", nargs="*") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="FILE", help="output signature to this file (default stdout)", default="-", ) subparser.add_argument( "--md5", default=None, help="select signatures whose md5 contains this substring", ) subparser.add_argument( "--name", default=None, help="select signatures whose name contains this substring", ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.flatten(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/grep.py000066400000000000000000000060661474157046300237660ustar00rootroot00000000000000"""extract one or more signatures by substr/regex match""" usage = """ sourmash sig grep [... ] This will search for the provided pattern in the files or databases, using the signature metadata, and output matching signatures. Currently 'grep' searches the 'name', 'filename', and 'md5' fields as displayed by `sig describe`. 'pattern' can be a string or a regular expression. 'sig grep' uses the built-in Python regexp module, 're', to implement regexp searching. See https://docs.python.org/3/howto/regex.html and https://docs.python.org/3/library/re.html for details. The '-v' (exclude), '-i' (case-insensitive), and `-c` (count) options of 'grep' are supported. '-o/--output' can be used to output matching signatures to a specific location. By default, 'sig grep' requires a pre-existing manifest for collections; this prevents potentially slow manifest rebuilding. You can turn this check off with '--no-require-manifest'. """ from sourmash.cli.utils import add_moltype_args, add_ksize_arg, add_picklist_args def subparser(subparsers): subparser = subparsers.add_parser("grep", usage=usage) subparser.add_argument("pattern", help="search pattern (string/regex)") subparser.add_argument("signatures", nargs="*") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="output debug information" ) subparser.add_argument( "-o", "--output", metavar="FILE", help="output matching signatures to this file (default stdout)", default="-", ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures, independent of filename", ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) subparser.add_argument( "-v", "--invert-match", help="select non-matching signatures", action="store_true", ) subparser.add_argument( "-i", "--ignore-case", help="ignore case distinctions (search lower and upper case both)", action="store_true", ) subparser.add_argument( "--no-require-manifest", help="do not require a manifest; generate dynamically if needed", action="store_true", ) subparser.add_argument( "--csv", help="save CSV file containing signature data in manifest format" ) subparser.add_argument( "--silent", "--no-signatures-output", help="do not output signatures", action="store_true", ) subparser.add_argument( "-c", "--count", help="only output a count of discovered signatures; implies --silent", action="store_true", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) def main(args): import sourmash.sig.grep return sourmash.sig.grep.main(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/inflate.py000066400000000000000000000016351474157046300244500ustar00rootroot00000000000000"""borrow abundances from one signature => one or more other signatures""" from sourmash.cli.utils import add_moltype_args, add_ksize_arg, add_picklist_args def subparser(subparsers): subparser = subparsers.add_parser("inflate") subparser.add_argument("signature_from") subparser.add_argument("other_sigs", nargs="+") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="FILE", default="-", help="output signature to this file (default stdout)", ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.inflate(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/ingest.py000066400000000000000000000024501474157046300243130ustar00rootroot00000000000000"""ingest/import a mash or other signature""" usage = """ sourmash sig ingest --csv [ ] -o Ingest num sketches from a simple CSV format, or alternatively a JSON formatproduced by 'mash info -d'. The CSV file should contain one line per sketch, with the first column containing 'murmur64', the second being '42', the third and fourth being the k-mer size and the name, and the remaining columns being the hashes. """ def subparser(subparsers): # Dirty hack to simultaneously support new and previous interface # If desired, this function can be removed with a major version bump. for cmd in ("ingest", "import"): subparser = subparsers.add_parser(cmd, usage=usage) subparser.add_argument( "--csv", action="store_true", help="import in Mash CSV format" ) subparser.add_argument("filenames", nargs="+") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="FILE", default="-", help="output signature to this file (default stdout)", ) def main(args): import sourmash return sourmash.sig.__main__.ingest(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/intersect.py000066400000000000000000000035631474157046300250300ustar00rootroot00000000000000"""intersect two or more signatures""" usage = """ ### `sourmash signature intersect` - intersect two (or more) signatures Output the intersection of the hash values in multiple signature files. For example, sourmash signature intersect file1.sig file2.sig file3.sig -o intersect.sig will output the intersection of all the hashes in those three files to `intersect.sig`. The `intersect` command flattens all signatures, i.e. the abundances in any signatures will be ignored and the output signature will have `track_abundance` turned off. See `sourmash signature flatten` for more details. Note: `intersect` only creates one output file, with one signature in it. """ from sourmash.cli.utils import add_moltype_args, add_ksize_arg, add_picklist_args def subparser(subparsers): subparser = subparsers.add_parser("intersect", description=__doc__, usage=usage) subparser.add_argument("signatures", nargs="*") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="FILE", default="-", help="output signature to this file (default stdout)", ) subparser.add_argument( "-A", "--abundances-from", metavar="FILE", help="intersect with & take abundances from this signature", ) subparser.add_argument("--set-name", help="set name for output signature") subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.intersect(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/kmers.py000066400000000000000000000073131474157046300241460ustar00rootroot00000000000000"""show k-mers/sequences matching the signature hashes""" usage = """ ### `sourmash signature kmers` - extract k-mers and/or sequences that match to signatures Given one or more compatible sketches and some sequence files, extract the k-mers and/or sequences corresponding to the hash values in the sketch. Because the sourmash hash function is one-way, this requires FASTA or FASTQ sequence files in addition to the sketch. For example, sourmash sig kmers --signatures sig1.sig --sequences seqfile.fasta \ --save-sequences matches.fasta --save-kmers kmer-matches.csv will search `seqfile.fasta` for matching sequences and k-mers, and produce two files. The file `matches.fasta` will contain FASTA sequences that match the hashes in the input signature, while the file `kmer-matches.csv` provides the matching k-mers and hash values, together with their originating filename and sequence name. If the sketch is a protein sketch (protein, dayhoff, or hp), then the input sequences are assumed to be protein. To search DNA sequences for translated protein hashes, provide the `--translate` flag to `sig kmers`. `--save-sequences` and `--save-kmers` are both optional. If neither are given, basic statistics on k-mer matching are given. Please note that `--save-kmers` can be very slow on large files! The input sketches are the source of the input hashes. So, for example, If `--scaled=1` sketches are provided, `sig kmers` can be used to yield all the k-mers and their matching hashes. Likewise, if the sketch is built from the intersection of two other sketches, only the k-mers and hash values present in both sketches will be used. Likewise, the input sequences are used for matching; they do not need to be the same sequences that were used to create the sketches. Input sequences can be in FASTA or FASTQ format, and either flat text or compressed with gzip or bzip2; formats are auto-detected. By default, `sig kmers` ignores bad k-mers (e.g. non-ACGT characters in DNA). If `--check-sequence` is provided, `sig kmers` will error exit on the first bad k-mer. If `--check-sequence --force` is provided, `sig kmers` will provide error messages (and skip bad sequences), but will continue processing input sequences. """ from sourmash.cli.utils import add_moltype_args, add_ksize_arg, add_picklist_args def subparser(subparsers): subparser = subparsers.add_parser("kmers", description=__doc__, usage=usage) subparser.add_argument("--signatures", nargs="*", default=[]) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) subparser.add_argument( "--sequences", nargs="+", required=True, help="FASTA/FASTQ/bz2/gz files with sequences", ) subparser.add_argument( "--save-kmers", help="save k-mers and hash values to a CSV file" ) subparser.add_argument( "--save-sequences", help="save sequences with matching hashes to a FASTA file" ) subparser.add_argument( "--translate", action="store_true", help="translate DNA k-mers into amino acids (for protein, dayhoff, and hp sketches)", ) subparser.add_argument( "--check-sequence", action="store_true", help="complain if input sequence is invalid (NOTE: only checks DNA)", ) def main(args): import sourmash return sourmash.sig.__main__.kmers(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/manifest.py000066400000000000000000000031121474157046300246240ustar00rootroot00000000000000"""create a manifest for a collection of signatures""" usage = """ sourmash sig manifest -o manifest.csv This will output a sourmash manifest in CSV format. This manifest can be used as a picklist with --picklist manifest.csv::manifest. The manifest will be rebuilt by iterating over the signatures in the file unless --no-rebuild-manifest is specified; for large collections, rebuilding the manifest can take a long time! See also the 'describe' and 'fileinfo' commands under 'sourmash sig'. """ def subparser(subparsers): subparser = subparsers.add_parser("manifest", usage=usage) subparser.add_argument("location") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="output debug information" ) subparser.add_argument( "-o", "--output", "--csv", metavar="FILE", help="output information to a CSV file", required=True, ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--no-rebuild-manifest", help="use existing manifest if available", action="store_true", ) subparser.add_argument( "-F", "--manifest-format", help="format of manifest output file; default is 'csv')", default="csv", choices=["csv", "sql"], ) def main(args): import sourmash return sourmash.sig.__main__.manifest(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/merge.py000066400000000000000000000036101474157046300241200ustar00rootroot00000000000000"""merge one or more signatures""" usage = """ ### `sourmash signature merge` - merge two or more signatures into one Merge two (or more) signatures. For example, sourmash signature merge file1.sig file2.sig -o merged.sig will output the union of all the hashes in `file1.sig` and `file2.sig` to `merged.sig`. All of the signatures passed to merge must either have been created with `-p abund`, or not. If they have `track_abundance` on, then the merged signature will have the sum of all abundances across the individual signatures. The `--flatten` flag will override this behavior and allow merging of mixtures by removing all abundances. Note: `merge` only creates one output file, with one signature in it, in the JSON `.sig` format. """ from sourmash.cli.utils import add_moltype_args, add_ksize_arg, add_picklist_args def subparser(subparsers): subparser = subparsers.add_parser("merge", description=__doc__, usage=usage) subparser.add_argument("signatures", nargs="*") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="FILE", default="-", help="output signature to this file (default stdout)", ) subparser.add_argument( "--flatten", action="store_true", help="remove abundances from all signatures" ) subparser.add_argument("--set-name", "--name", help="rename merged signature") subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.merge(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/overlap.py000066400000000000000000000026231474157046300244740ustar00rootroot00000000000000"""see detailed comparison of signatures""" usage = """ ### `sourmash signature overlap` - detailed comparison of two signatures' overlap Display a detailed comparison of two signatures. This calculates the Jaccard similarity (as in `sourmash compare` or `sourmash search`) and the Jaccard containment in both directions (as with `--containment`). It also displays the number of hash values in the union and intersection of the two signatures, as well as the number of disjoint hash values in each signature. This command has two uses - first, it is helpful for understanding how similarity and containment are calculated, and second, it is useful for analyzing signatures with very small overlaps, where the similarity and/or containment might be very close to zero. For example, sourmash signature overlap file1.sig file2.sig will display the detailed comparison of `file1.sig` and `file2.sig`. """ from sourmash.cli.utils import add_moltype_args, add_ksize_arg def subparser(subparsers): subparser = subparsers.add_parser("overlap", description=__doc__, usage=usage) subparser.add_argument("signature1") subparser.add_argument("signature2") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) add_ksize_arg(subparser) add_moltype_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.overlap(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/rename.py000066400000000000000000000031671474157046300242770ustar00rootroot00000000000000"""rename signature""" usage = """ ### `sourmash signature rename` - rename a signature Rename the display name for one or more signatures - this is the name output for matches in `compare`, `search`, `gather`, etc. For example, sourmash signature rename file1.sig "new name" -o renamed.sig will place a renamed copy of the hashes in `file1.sig` in the file `renamed.sig`. If you provide multiple signatures, all will be renamed to the same name. """ from sourmash.cli.utils import ( add_moltype_args, add_ksize_arg, add_picklist_args, add_pattern_args, ) def subparser(subparsers): subparser = subparsers.add_parser("rename", description=__doc__, usage=usage) subparser.add_argument("signatures", nargs="*") subparser.add_argument("name") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-d", "--debug", action="store_true", help="print debugging output" ) subparser.add_argument( "-o", "--output", metavar="FILE", help="output renamed signature to this file (default stdout)", default="-", ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_pattern_args(subparser) add_picklist_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.rename(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/split.py000066400000000000000000000045151474157046300241610ustar00rootroot00000000000000"""split signature files""" usage = """ ### `sourmash signature split` - split signatures into individual files Split each signature in the input file(s) into individual files, with standardized names. For example, sourmash signature split tests/test-data/2.fa.sig will create 3 files, `f372e478.k=21.scaled=1000.DNA.dup=0.2.fa.sig`, `f3a90d4e.k=31.scaled=1000.DNA.dup=0.2.fa.sig`, and `43f3b48e.k=51.scaled=1000.DNA.dup=0.2.fa.sig`, representing the three different DNA signatures at different ksizes created from the input file `2.fa`. The format of the names of the output files is standardized and stable for major versions of sourmash: currently, they are period-separated with fields: * `md5sum` - a unique hash value based on the contents of the signature. * `k=` - k-mer size. * `scaled=` or `num=` - scaled or num value for MinHash. * `` - the molecule type (DNA, protein, dayhoff, or hp) * `dup=` - a non-negative integer that prevents duplicate signatures from colliding. * `basename` - basename of first input file used to create signature; if none provided, or stdin, this is `none`. If `--outdir` is specified, all of the signatures are placed in outdir. Note: `split` only saves files in the JSON `.sig` format. """ from sourmash.cli.utils import add_moltype_args, add_ksize_arg, add_picklist_args def subparser(subparsers): subparser = subparsers.add_parser("split", description=__doc__, usage=usage) subparser.add_argument("signatures", nargs="*") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "--output-dir", "--outdir", help="output signatures to this directory", ) subparser.add_argument( "-f", "--force", action="store_true", help="try to load all files as signatures" ) subparser.add_argument( "--from-file", help="a text file containing a list of files to load signatures from", ) subparser.add_argument( "-E", "--extension", type=str, default=".sig", help="write files with this extension ('.sig' by default)", ) add_ksize_arg(subparser) add_moltype_args(subparser) add_picklist_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.split(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sig/subtract.py000066400000000000000000000033041474157046300246500ustar00rootroot00000000000000"""subtract one or more signatures""" usage = """ ### `sourmash signature subtract` - subtract other signatures from a signature Subtract all of the hash values from one signature that are in one or more of the others. For example, sourmash signature subtract file1.sig file2.sig file3.sig -o subtracted.sig will subtract all of the hashes in `file2.sig` and `file3.sig` from `file1.sig`, and save the new signature to `subtracted.sig`. To use `subtract` on signatures calculated with `-p abund`, you must specify `--flatten`. Note: `subtract` only creates one output file, with one signature in it. """ from sourmash.cli.utils import add_moltype_args, add_ksize_arg def subparser(subparsers): subparser = subparsers.add_parser("subtract", description=__doc__, usage=usage) subparser.add_argument("signature_from") subparser.add_argument("subtraction_sigs", nargs="+") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", metavar="FILE", default="-", help="output signature to this file (default stdout)", ) subparser.add_argument( "--flatten", action="store_true", help="remove abundance from signatures before subtracting", ) subparser.add_argument( "-A", "--abundances-from", metavar="FILE", help="intersect with & take abundances from this signature", ) subparser.add_argument("--set-name", help="set name for output signature") add_ksize_arg(subparser) add_moltype_args(subparser) def main(args): import sourmash return sourmash.sig.__main__.subtract(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sketch/000077500000000000000000000000001474157046300231465ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sketch/__init__.py000066400000000000000000000023421474157046300252600ustar00rootroot00000000000000"""Define the command line interface for sourmash sketch The top level CLI is defined in ../__init__.py. This module defines the CLI for `sourmash sketch` operations. """ from . import dna from . import dna as rna from . import protein from . import protein as aa from . import protein as prot from . import translate from . import fromfile from ..utils import command_list from argparse import SUPPRESS, RawDescriptionHelpFormatter import os import sys def subparser(subparsers): subparser = subparsers.add_parser( "sketch", formatter_class=RawDescriptionHelpFormatter, usage=SUPPRESS ) desc = "Operations\n" clidir = os.path.dirname(__file__) ops = command_list(clidir) for subcmd in ops: docstring = getattr(sys.modules[__name__], subcmd).__doc__ helpstring = f"sourmash sketch {subcmd:s} --help" desc += f" {helpstring:33s} {docstring:s}\n" s = subparser.add_subparsers( title="Create signatures", dest="subcmd", metavar="subcmd", help=SUPPRESS, description=desc, ) for subcmd in ops: getattr(sys.modules[__name__], subcmd).subparser(s) subparser._action_groups.reverse() subparser._optionals.title = "Options" sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sketch/dna.py000066400000000000000000000062201474157046300242620ustar00rootroot00000000000000"""create DNA signatures""" usage = """ sourmash sketch dna data/*.fna.gz The 'sketch dna' command reads in DNA sequences and outputs DNA sketches. By default, 'sketch dna' uses the parameter string 'k=31,scaled=1000,noabund'. This creates sketches with a k-mer size of 31, a scaled factor of 1000, and no abundance tracking of k-mers. You can specify one or more parameter strings of your own with -p, e.g. 'sourmash sketch dna -p k=31,noabund -p k=21,scaled=100,abund'. Note that a single `-p` parameter string can contain multiple ksize values, but only a single scaled value or abundance value, e.g. -p k=21,k=31,abund 'sourmash sketch' takes input sequences in FASTA and FASTQ, uncompressed or gz/bz2 compressed. Please see the 'sketch' documentation for more details: https://sourmash.readthedocs.io/en/latest/sourmash-sketch.html """ import sourmash from sourmash.logging import notify, print_results, error from sourmash import command_sketch assert command_sketch.DEFAULTS["dna"] == "k=31,scaled=1000,noabund" def subparser(subparsers): subparser = subparsers.add_parser( "dna", aliases=["rna", "nucleotide", "nt"], usage=usage ) subparser.add_argument( "--license", default="CC0", type=str, help="signature license. Currently only CC0 is supported.", ) subparser.add_argument( "--check-sequence", action="store_true", help="complain if input sequence is invalid DNA", ) subparser.add_argument( "-p", "--param-string", default=[], help="signature parameters to use.", action="append", ) subparser.add_argument("filenames", nargs="*", help="file(s) of sequences") file_args = subparser.add_argument_group("File handling options") file_args.add_argument( "-f", "--force", action="store_true", help="recompute signatures even if the file exists", ) subparser.add_argument( "--from-file", help="a text file containing a list of sequence files to load" ) file_args.add_argument( "-o", "--output", help="output computed signatures to this file" ) file_args.add_argument( "--set-name", "--name", "--merge", dest="merge", type=str, default="", metavar="FILE", help="name the output sketch as specified; note, merges all input " "files while sketching", ) file_args.add_argument( "--output-dir", "--outdir", help="output computed signatures to this directory", ) file_args.add_argument( "--singleton", action="store_true", help="compute a signature for each sequence record individually", ) file_args.add_argument( "--name-from-first", action="store_true", help="name the signature generated from each file after the first " "record in the file", ) file_args.add_argument( "--randomize", action="store_true", help="shuffle the list of input filenames randomly", ) def main(args): import sourmash.command_sketch return sourmash.command_sketch.dna(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sketch/fromfile.py000066400000000000000000000055321474157046300253300ustar00rootroot00000000000000"""create signatures from a CSV file""" usage = """ sourmash sketch fromfile --output-signatures -p <...> The 'sketch fromfile' command takes in a CSV file with list of names and filenames to be used for building signatures. It is intended for batch use, when building large collections of signatures. One or more parameter strings must be specified with '-p'. One or more existing collections of signatures can be provided via '--already-done' and already-existing signatures (based on name and sketch type) will not be recalculated or output. If a location is provided via '--output-signatures', signatures will be saved to that location. Please see the 'sketch' documentation for more details: https://sourmash.readthedocs.io/en/latest/sourmash-sketch.html """ import sourmash from sourmash.logging import notify, print_results, error from sourmash import command_sketch def subparser(subparsers): subparser = subparsers.add_parser("fromfile", usage=usage) subparser.add_argument( "csvs", nargs="+", help="input CSVs providing 'name', 'genome_filename', and 'protein_filename'", ) subparser.add_argument( "-p", "--param-string", default=[], help="signature parameters to use.", action="append", ) subparser.add_argument( "--already-done", nargs="+", default=[], help="one or more collections of existing signatures to avoid recalculating", ) subparser.add_argument( "--license", default="CC0", type=str, help="signature license. Currently only CC0 is supported.", ) subparser.add_argument( "--check-sequence", action="store_true", help="complain if input sequence is invalid (NOTE: only checks DNA)", ) file_args = subparser.add_argument_group("File handling options") file_args.add_argument( "-o", "--output-signatures", help="output computed signatures to this file", ) file_args.add_argument( "--force-output-already-exists", action="store_true", help="overwrite/append to --output-signatures location", ) file_args.add_argument( "--ignore-missing", action="store_true", help="proceed with building possible signatures, even if some input files are missing", ) file_args.add_argument( "--output-csv-info", help="output information about what signatures need to be generated", ) file_args.add_argument( "--output-manifest-matching", help="output a manifest file of already-existing signatures", ) file_args.add_argument( "--report-duplicated", action="store_true", help="report duplicated names" ) def main(args): import sourmash.command_sketch return sourmash.command_sketch.fromfile(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sketch/protein.py000066400000000000000000000064761474157046300252150ustar00rootroot00000000000000"""create protein signatures""" usage = """ sourmash sketch protein data/*.fna.gz The 'sketch protein' command reads in protein sequences and outputs protein sketches. By default, 'sketch protein' uses the parameter string 'k=10,scaled=200,noabund'. This corresponds to an amino-acid k-mer size of 10, a scaled factor of 200, and no abundance tracking of k-mers. You can specify one or more parameter strings of your own with -p, e.g. 'sourmash sketch protein -p k=11,noabund -p k=12,scaled=100,abund'. Note that a single `-p` parameter string can contain multiple ksize values, but only a single scaled value or abundance value e.g. -p k=11,k=12,scaled=100,abund. 'sourmash sketch' takes input sequences in FASTA and FASTQ, uncompressed or gz/bz2 compressed. Please see the 'sketch' documentation for more details: https://sourmash.readthedocs.io/en/latest/sourmash-sketch.html """ import sourmash from sourmash.logging import notify, print_results, error from sourmash import command_sketch assert command_sketch.DEFAULTS["protein"] == "k=10,scaled=200,noabund" def subparser(subparsers): subparser = subparsers.add_parser("protein", aliases=["aa", "prot"], usage=usage) subparser.add_argument( "--license", default="CC0", type=str, help="signature license. Currently only CC0 is supported.", ) subparser.add_argument( "-p", "--param-string", default=[], help="signature parameters to use.", action="append", ) subparser.add_argument("filenames", nargs="*", help="file(s) of sequences") file_args = subparser.add_argument_group("File handling options") file_args.add_argument( "-f", "--force", action="store_true", help="recompute signatures even if the file exists", ) file_args.add_argument( "-o", "--output", help="output computed signatures to this file" ) subparser.add_argument( "--from-file", help="a text file containing a list of sequence files to load" ) file_args.add_argument( "--set-name", "--name", "--merge", dest="merge", type=str, default="", metavar="FILE", help="name the output sketch as specified; note, merges all input " "files while sketching", ) file_args.add_argument( "--output-dir", "--outdir", help="output computed signatures to this directory", ) file_args.add_argument( "--singleton", action="store_true", help="compute a signature for each sequence record individually", ) file_args.add_argument( "--name-from-first", action="store_true", help="name the signature generated from each file after the first " "record in the file", ) file_args.add_argument( "--randomize", action="store_true", help="shuffle the list of input filenames randomly", ) file_args.add_argument( "--dayhoff", action="store_true", help="compute sketches using the dayhoff alphabet instead", ) file_args.add_argument( "--hp", action="store_true", help="compute sketches using the dayhoff alphabet instead", ) def main(args): import sourmash.command_sketch return sourmash.command_sketch.protein(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/sketch/translate.py000066400000000000000000000065231474157046300255230ustar00rootroot00000000000000"""create protein signature from DNA/RNA sequence""" usage = """ sourmash sketch translate data/*.fna.gz The 'sketch translate' command reads in DNA sequences and outputs protein sketches. By default, 'sketch translate' uses the parameter string 'k=10,scaled=200,noabund'. This corresponds to a DNA k-mer size of 30 (and an amino-acid k-mer size of 10), a scaled factor of 200, and no abundance tracking of k-mers. You can specify one or more parameter strings of your own with -p, e.g. 'sourmash sketch translate -p k=11,noabund -p k=12,scaled=100,abund'. 'sourmash sketch' takes input sequences in FASTA and FASTQ, uncompressed or gz/bz2 compressed. Please see the 'sketch' documentation for more details: https://sourmash.readthedocs.io/en/latest/sourmash-sketch.html """ from sourmash import command_sketch assert command_sketch.DEFAULTS["protein"] == "k=10,scaled=200,noabund" import sourmash from sourmash.logging import notify, print_results, error def subparser(subparsers): subparser = subparsers.add_parser("translate", usage=usage) subparser.add_argument( "--license", default="CC0", type=str, help="signature license. Currently only CC0 is supported.", ) subparser.add_argument( "--check-sequence", action="store_true", help="complain if input sequence is invalid DNA", ) subparser.add_argument( "-p", "--param-string", default=[], help="signature parameters to use.", action="append", ) subparser.add_argument("filenames", nargs="*", help="file(s) of sequences") file_args = subparser.add_argument_group("File handling options") file_args.add_argument( "-f", "--force", action="store_true", help="recompute signatures even if the file exists", ) file_args.add_argument( "-o", "--output", help="output computed signatures to this file" ) subparser.add_argument( "--from-file", help="a text file containing a list of sequence files to load" ) file_args.add_argument( "--set-name", "--name", "--merge", dest="merge", type=str, default="", metavar="FILE", help="name the output sketch as specified; note, merges all input " "files while sketching", ) file_args.add_argument( "--output-dir", "--outdir", help="output computed signatures to this directory", ) file_args.add_argument( "--singleton", action="store_true", help="compute a signature for each sequence record individually", ) file_args.add_argument( "--name-from-first", action="store_true", help="name the signature generated from each file after the first " "record in the file", ) file_args.add_argument( "--randomize", action="store_true", help="shuffle the list of input filenames randomly", ) file_args.add_argument( "--dayhoff", action="store_true", help="compute sketches using the dayhoff alphabet instead", ) file_args.add_argument( "--hp", action="store_true", help="compute sketches using the dayhoff alphabet instead", ) def main(args): import sourmash.command_sketch return sourmash.command_sketch.translate(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/storage/000077500000000000000000000000001474157046300233315ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/storage/__init__.py000066400000000000000000000021221474157046300254370ustar00rootroot00000000000000"""Define the command line interface for sourmash storage The top level CLI is defined in ../__init__.py. This module defines the CLI for `sourmash storage` operations. """ from . import convert from ..utils import command_list from argparse import SUPPRESS, RawDescriptionHelpFormatter import os import sys def subparser(subparsers): subparser = subparsers.add_parser( "storage", formatter_class=RawDescriptionHelpFormatter, usage=SUPPRESS ) desc = "Operations\n" clidir = os.path.dirname(__file__) ops = command_list(clidir) for subcmd in ops: docstring = getattr(sys.modules[__name__], subcmd).__doc__ helpstring = f"sourmash storage {subcmd:s} --help" desc += f" {helpstring:33s} {docstring:s}\n" s = subparser.add_subparsers( title="Storage utilities", dest="subcmd", metavar="subcmd", help=SUPPRESS, description=desc, ) for subcmd in ops: getattr(sys.modules[__name__], subcmd).subparser(s) subparser._action_groups.reverse() subparser._optionals.title = "Options" sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/storage/convert.py000066400000000000000000000006221474157046300253630ustar00rootroot00000000000000"'sourmash storage convert' - convert an SBT to use a different back end." def subparser(subparsers): subparser = subparsers.add_parser("convert") subparser.add_argument("sbt", help="name to save SBT into") subparser.add_argument("-b", "--backend", type=str, help="Backend to convert to") def main(args): import sourmash return sourmash.sbt.convert_cmd(args.sbt, args.backend) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/tax/000077500000000000000000000000001474157046300224615ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/tax/__init__.py000066400000000000000000000024111474157046300245700ustar00rootroot00000000000000"""Define the command line interface for sourmash tax The top level CLI is defined in ../__init__.py. This module defines the CLI for `sourmash tax` operations. """ from . import metagenome from . import genome from . import annotate from . import prepare from . import grep from . import summarize from ..utils import command_list from argparse import SUPPRESS, RawDescriptionHelpFormatter import os import sys def subparser(subparsers): subparser = subparsers.add_parser( "tax", formatter_class=RawDescriptionHelpFormatter, usage=SUPPRESS, aliases=["taxonomy"], ) desc = "Operations\n" clidir = os.path.dirname(__file__) ops = command_list(clidir) for subcmd in ops: docstring = getattr(sys.modules[__name__], subcmd).__doc__ helpstring = f"sourmash tax {subcmd:s} --help" desc += f" {helpstring:33s} {docstring:s}\n" s = subparser.add_subparsers( title="Integrate taxonomy information based on 'gather' results", dest="subcmd", metavar="subcmd", help=SUPPRESS, description=desc, ) for subcmd in ops: getattr(sys.modules[__name__], subcmd).subparser(s) subparser._action_groups.reverse() subparser._optionals.title = "Options" sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/tax/annotate.py000066400000000000000000000057531474157046300246560ustar00rootroot00000000000000"""annotate gather results with taxonomy information""" usage = """ sourmash tax annotate --gather-csv [ ... ] --taxonomy-csv [ ... ] The 'tax annotate' command reads in gather results CSVs and annotates them with taxonomic information. By default, 'tax annotate' produces a gather CSV with an additional 'lineage' column containing the taxonomic information for each database match. Please see the 'tax annotate' documentation for more details: https://sourmash.readthedocs.io/en/latest/command-line.html#sourmash-tax-annotate-annotates-gather-output-with-taxonomy """ import sourmash from sourmash.logging import notify, print_results, error def subparser(subparsers): subparser = subparsers.add_parser("annotate", aliases=["annotate"], usage=usage) subparser.add_argument( "-g", "--gather-csv", nargs="*", default=[], action="extend", help="CSV output files from sourmash gather", ) subparser.add_argument( "--from-file", metavar="FILE", default=None, help="input many gather results as a text file, with one gather CSV per line", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-t", "--taxonomy-csv", "--taxonomy", metavar="FILE", nargs="*", required=True, action="extend", help="database lineages CSV", ) subparser.add_argument( "-o", "--output-dir", default="", help="directory for output files" ) subparser.add_argument( "--keep-full-identifiers", action="store_true", help="do not split identifiers on whitespace", ) subparser.add_argument( "--keep-identifier-versions", action="store_true", help="after splitting identifiers, do not remove accession versions", ) subparser.add_argument( "--fail-on-missing-taxonomy", action="store_true", help="fail quickly if taxonomy is not available for an identifier", ) subparser.add_argument( "-f", "--force", action="store_true", help="continue past errors in file and taxonomy loading", ) subparser.add_argument( "--lins", "--lin-taxonomy", action="store_true", default=False, help="use LIN taxonomy in place of standard taxonomic ranks. Note that the taxonomy CSV must contain LIN lineage information.", ) subparser.add_argument( "--ictv", "--ictv-taxonomy", action="store_true", default=False, help="use ICTV taxonomy in place of standard taxonomic ranks. Note that the taxonomy CSV must contain ICTV ranks.", ) def main(args): if not args.gather_csv and not args.from_file: raise ValueError( "No gather CSVs found! Please input via '-g' or '--from-file'." ) return sourmash.tax.__main__.annotate(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/tax/genome.py000066400000000000000000000117551474157046300243160ustar00rootroot00000000000000"""classify genomes from gather results""" usage = """ sourmash tax genome --gather-csv [ ... ] --taxonomy-csv [ ... ] The 'tax genome' command reads in genome gather result CSVs and reports likely classification for each query genome. By default, classification uses a containment threshold of 0.1, meaning at least 10 percent of the query was covered by matches with the reported taxonomic rank and lineage. You can specify an alternate classification threshold or force classification by taxonomic rank instead, e.g. at species or genus-level. The default output format consists of five columns, 'query_name,status,rank,fraction,lineage', where 'fraction' is the fraction of the query matched to the reported rank and lineage. The 'status' column provides additional information on the classification, and can be: - 'match' - this query was classified - 'nomatch'- this query could not be classified - 'below_threshold' - this query was classified at the specified rank, but the query fraction matched was below the containment threshold Use '-F human' to display human-readable output instead. Optionally, you can report classifications in 'krona' format, but note that this forces classification by rank, rather than containment threshold. Please see the 'tax genome' documentation for more details: https://sourmash.readthedocs.io/en/latest/command-line.html#sourmash-tax-genome-classify-a-genome-using-gather-results """ import argparse import sourmash from sourmash.logging import notify, print_results, error from sourmash.cli.utils import ( add_tax_threshold_arg, check_rank, check_tax_outputs, add_rank_arg, ) def subparser(subparsers): subparser = subparsers.add_parser("genome", aliases=["classify"], usage=usage) subparser.add_argument( "-g", "--gather-csv", action="extend", nargs="*", default=[], help="CSVs output by sourmash gather for this sample", ) subparser.add_argument( "--from-file", metavar="FILE", default=None, help="input many gather results as a text file, with one gather CSV per line", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-t", "--taxonomy-csv", "--taxonomy", metavar="FILE", nargs="*", required=True, action="extend", help="database lineages CSV", ) subparser.add_argument( "-o", "--output-base", default="-", help="base filepath for output file(s) (default stdout)", ) subparser.add_argument( "--output-dir", default="", help="directory for output files" ) subparser.add_argument( "--keep-full-identifiers", action="store_true", help="do not split identifiers on whitespace", ) subparser.add_argument( "--keep-identifier-versions", action="store_true", help="after splitting identifiers, do not remove accession versions", ) subparser.add_argument( "--fail-on-missing-taxonomy", action="store_true", help="fail quickly if taxonomy is not available for an identifier", ) subparser.add_argument( "-F", "--output-format", default=[], nargs="*", action="extend", choices=["csv_summary", "krona", "human", "lineage_csv"], help="choose output format(s)", ) subparser.add_argument( "-f", "--force", action="store_true", help="continue past survivable errors in loading taxonomy database or gather results", ) subparser.add_argument( "--lins", "--lin-taxonomy", action="store_true", default=False, help="use LIN taxonomy in place of standard taxonomic ranks. Note that the taxonomy CSV must contain 'lin' lineage information.", ) subparser.add_argument( "--lingroup", "--lingroups", metavar="FILE", default=None, help="CSV containing 'name', 'lin' columns, where 'lin' is the lingroup prefix. Will restrict classification to these groups.", ) subparser.add_argument( "--ictv", "--ictv-taxonomy", action="store_true", default=False, help="use ICTV taxonomy in place of standard taxonomic ranks. Note that the taxonomy CSV must contain ICTV ranks.", ) add_tax_threshold_arg(subparser, 0.1) add_rank_arg(subparser) def main(args): try: if not args.gather_csv and not args.from_file: raise ValueError( "No gather CSVs found! Please input via '-g' or '--from-file'." ) if args.rank: args.rank = check_rank(args) args.output_format = check_tax_outputs(args, rank_required=["krona"]) except ValueError as exc: error(f"ERROR: {str(exc)}") import sys sys.exit(-1) return sourmash.tax.__main__.genome(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/tax/grep.py000066400000000000000000000047771474157046300240070ustar00rootroot00000000000000"""search taxonomies and output picklists.""" usage = """ sourmash tax grep --taxonomy-csv [ ... ] `sourmash tax grep` searches taxonomies for matching strings, optionally restricting the string search to a specific taxonomic rank. It creates new files containing matching taxonomic entries; these new files can serve as taxonomies and can also be used as picklists. `tax grep` only searches taxonomic ranks, not identifier strings. Use `sig grep` to search for identifiers in sketch collections. Please see the 'tax grep' documentation for more details: https://sourmash.readthedocs.io/en/latest/command-line.html#sourmash-tax-grep-subset-taxonomies-and-create-picklists-based-on-taxonomy-string-matches """ import sourmash from sourmash.logging import notify, print_results, error def subparser(subparsers): subparser = subparsers.add_parser("grep", usage=usage) subparser.add_argument("pattern") subparser.add_argument( "-r", "--rank", help="search only this rank", choices=[ "superkingdom", "phylum", "class", "order", "family", "genus", "species", ], ) subparser.add_argument( "-v", "--invert-match", help="select non-matching lineages", action="store_true" ) subparser.add_argument( "-i", "--ignore-case", help="ignore case distinctions (search lower and upper case both)", action="store_true", ) subparser.add_argument( "--silent", "--no-picklist-output", help="do not output picklist", action="store_true", ) subparser.add_argument( "-c", "--count", help="only output a count of discovered lineages; implies --silent", action="store_true", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-t", "--taxonomy-csv", "--taxonomy", metavar="FILE", nargs="+", required=True, action="extend", help="database lineages", ) subparser.add_argument( "-o", "--output", default="-", help="output file (defaults to stdout)", ) subparser.add_argument( "-f", "--force", action="store_true", help="continue past errors in file and taxonomy loading", ) def main(args): return sourmash.tax.__main__.grep(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/tax/metagenome.py000066400000000000000000000113231474157046300251540ustar00rootroot00000000000000"""summarize metagenome gather results""" usage = """ sourmash tax metagenome --gather-csv [ ... ] --taxonomy-csv [ ... ] The 'tax metagenome' command reads in metagenome gather result CSVs and summarizes by taxonomic lineage. The default output format consists of four columns, 'query_name,rank,fraction,lineage', where 'fraction' is the fraction of the query matched to that reported rank and lineage. The summarization is reported for each taxonomic rank. Alternatively, you can output results at a specific rank (e.g. species) in 'krona', 'lineage_summary', and 'human' formats. Use '-F human' to display human-readable output. Please see the 'tax metagenome' documentation for more details: https://sourmash.readthedocs.io/en/latest/command-line.html#sourmash-tax-metagenome-summarize-metagenome-content-from-gather-results """ import sourmash from sourmash.logging import notify, print_results, error from sourmash.cli.utils import add_rank_arg, check_rank, check_tax_outputs def subparser(subparsers): subparser = subparsers.add_parser("metagenome", usage=usage) subparser.add_argument( "-g", "--gather-csv", action="extend", nargs="*", default=[], help="CSVs from sourmash gather", ) subparser.add_argument( "--from-file", metavar="FILE", default=None, help="input many gather results as a text file, with one gather CSV per line", ) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output-base", default="-", help="base filepath for output file(s) (default stdout)", ) subparser.add_argument( "--output-dir", default="", help="directory for output files" ) subparser.add_argument( "-t", "--taxonomy-csv", "--taxonomy", metavar="FILE", action="extend", nargs="+", required=True, help="database lineages CSV", ) subparser.add_argument( "--keep-full-identifiers", action="store_true", help="do not split identifiers on whitespace", ) subparser.add_argument( "--keep-identifier-versions", action="store_true", help="after splitting identifiers, do not remove accession versions", ) subparser.add_argument( "--fail-on-missing-taxonomy", action="store_true", help="fail quickly if taxonomy is not available for an identifier", ) subparser.add_argument( "-F", "--output-format", default=[], nargs="*", action="extend", choices=[ "human", "csv_summary", "krona", "lineage_summary", "kreport", "lingroup", "bioboxes", ], help="choose output format(s)", ) subparser.add_argument( "-f", "--force", action="store_true", help="continue past errors in taxonomy database loading", ) subparser.add_argument( "--lins", "--lin-taxonomy", action="store_true", default=False, help="use LIN taxonomy in place of standard taxonomic ranks. Note that the taxonomy CSV must contain 'lin' lineage information.", ) subparser.add_argument( "--lingroup", "--lingroups", metavar="FILE", default=None, help="CSV containing 'name', 'lin' columns, where 'lin' is the lingroup prefix. For 'tax metagenome' runs with a single 'gather' file (single query), providing this file will allow us to output a 'lingroup' report containing taxonomic summarization for each group. For multiple queries, we recommend the 'csv_summary' output format.", ) subparser.add_argument( "--ictv", "--ictv-taxonomy", action="store_true", default=False, help="use ICTV taxonomy in place of standard taxonomic ranks. Note that the taxonomy CSV must contain ICTV ranks.", ) add_rank_arg(subparser) def main(args): try: if not args.gather_csv and not args.from_file: raise ValueError( "No gather CSVs found! Please input via '-g' or '--from-file'." ) if args.rank: args.rank = check_rank(args) args.output_format = check_tax_outputs( args, rank_required=["krona", "lineage_summary"], incompatible_with_lins=["bioboxes", "kreport"], use_lingroup_format=True, ) except ValueError as exc: error(f"ERROR: {str(exc)}") import sys sys.exit(-1) return sourmash.tax.__main__.metagenome(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/tax/prepare.py000066400000000000000000000040401474157046300244670ustar00rootroot00000000000000"""combine multiple taxonomy databases into one.""" usage = """ sourmash tax prepare --taxonomy-csv [ ... ] -o The 'tax prepare' command reads in one or more taxonomy databases and saves them into a new database. It can be used to combine databases in the desired order, as well as output different database formats. Please see the 'tax prepare' documentation for more details: https://sourmash.readthedocs.io/en/latest/command-line.html#sourmash-tax-prepare-prepare-and-or-combine-taxonomy-files """ import sourmash from sourmash.logging import notify, print_results, error def subparser(subparsers): subparser = subparsers.add_parser("prepare", usage=usage) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-t", "--taxonomy-csv", "--taxonomy", metavar="FILE", nargs="+", required=True, action="extend", help="database lineages", ) subparser.add_argument( "-o", "--output", required=True, help="output file", ) subparser.add_argument( "-F", "--database-format", help="format of output file; default is 'sql')", default="sql", choices=["csv", "sql"], ) subparser.add_argument( "--keep-full-identifiers", action="store_true", help="do not split identifiers on whitespace", ) subparser.add_argument( "--keep-identifier-versions", action="store_true", help="after splitting identifiers, do not remove accession versions", ) subparser.add_argument( "--fail-on-missing-taxonomy", action="store_true", help="fail quickly if taxonomy is not available for an identifier", ) subparser.add_argument( "-f", "--force", action="store_true", help="continue past errors in file and taxonomy loading", ) def main(args): return sourmash.tax.__main__.prepare(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/tax/summarize.py000066400000000000000000000041561474157046300250550ustar00rootroot00000000000000"""summarize taxonomy/lineage information""" usage = """ sourmash tax summarize [ ... ] The 'tax summarize' command reads in one or more taxonomy databases or lineage files (produced by 'tax annotate'), combines them, and produces a human readable summary. Please see the 'tax summarize' documentation for more details: https://sourmash.readthedocs.io/en/latest/command-line.html#command-line.html#sourmash-tax-summarize-print-summary-information-for-lineage-spreadsheets-or-taxonomy-databases """ import sourmash from sourmash.logging import notify, print_results, error def subparser(subparsers): subparser = subparsers.add_parser("summarize", usage=usage) subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "taxonomy_files", metavar="FILE", nargs="+", action="extend", help="database lineages", ) subparser.add_argument( "-o", "--output-lineage-information", help="output a CSV file containing individual lineage counts", ) subparser.add_argument( "--keep-full-identifiers", action="store_true", help="do not split identifiers on whitespace", ) subparser.add_argument( "--keep-identifier-versions", action="store_true", help="after splitting identifiers, do not remove accession versions", ) subparser.add_argument( "-f", "--force", action="store_true", help="continue past errors in file and taxonomy loading", ) subparser.add_argument( "--lins", "--lin-taxonomy", action="store_true", default=False, help="use LIN taxonomy in place of standard taxonomic ranks.", ) subparser.add_argument( "--ictv", "--ictv-taxonomy", action="store_true", default=False, help="use ICTV taxonomy in place of standard taxonomic ranks. Note that the taxonomy CSV must contain ICTV ranks.", ) def main(args): return sourmash.tax.__main__.summarize(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/utils.py000066400000000000000000000221151474157046300234000ustar00rootroot00000000000000from glob import glob import os import argparse from sourmash.logging import notify from sourmash.sourmash_args import check_scaled_bounds, check_num_bounds def add_moltype_args(parser): parser.add_argument( "--protein", dest="protein", action="store_true", help="choose a protein signature; by default, a nucleotide signature is used", ) parser.add_argument( "--no-protein", dest="protein", action="store_false", help="do not choose a protein signature", ) parser.set_defaults(protein=False) parser.add_argument( "--dayhoff", dest="dayhoff", action="store_true", help="choose Dayhoff-encoded amino acid signatures", ) parser.add_argument( "--no-dayhoff", dest="dayhoff", action="store_false", help="do not choose Dayhoff-encoded amino acid signatures", ) parser.set_defaults(dayhoff=False) parser.add_argument( "--hp", "--hydrophobic-polar", dest="hp", action="store_true", help="choose hydrophobic-polar-encoded amino acid signatures", ) parser.add_argument( "--no-hp", "--no-hydrophobic-polar", dest="hp", action="store_false", help="do not choose hydrophobic-polar-encoded amino acid signatures", ) parser.set_defaults(hp=False) parser.add_argument( "--skipm1n3", "--skipmer-m1n3", dest="skipm1n3", action="store_true", help="choose skipmer (m1n3) signatures", ) parser.add_argument( "--no-skipm1n3", "--no-skipmer-m1n3", dest="skipm1n3", action="store_false", help="do not choose skipmer (m1n3) signatures", ) parser.set_defaults(skipm1n3=False) parser.add_argument( "--skipm2n3", "--skipmer-m2n3", dest="skipm2n3", action="store_true", help="choose skipmer (m2n3) signatures", ) parser.add_argument( "--no-skipm2n3", "--no-skipmer-m2n3", dest="skipm2n3", action="store_false", help="do not choose skipmer (m2n3) signatures", ) parser.set_defaults(skipm2n3=False) parser.add_argument( "--dna", "--rna", "--nucleotide", dest="dna", default=None, action="store_true", help="choose a nucleotide signature (default: True)", ) parser.add_argument( "--no-dna", "--no-rna", "--no-nucleotide", dest="dna", action="store_false", help="do not choose a nucleotide signature", ) parser.set_defaults(dna=None) def add_construct_moltype_args(parser): add_moltype_args(parser) parser.set_defaults(dna=True) def add_ksize_arg(parser, *, default=None): "Add -k/--ksize to argparse parsers, with specified default." if default: message = f"k-mer size to select; default={default}" else: message = "k-mer size to select; no default." parser.add_argument( "-k", "--ksize", metavar="K", default=default, type=int, help=message, ) # https://stackoverflow.com/questions/55324449/how-to-specify-a-minimum-or-maximum-float-value-with-argparse#55410582 def range_limited_float_type(arg): """Type function for argparse - a float within some predefined bounds""" min_val = 0 max_val = 1 try: f = float(arg) except ValueError: raise argparse.ArgumentTypeError("\n\tERROR: Must be a floating point number.") if f < min_val or f > max_val: raise argparse.ArgumentTypeError( f"\n\tERROR: Argument must be >{str(min_val)} and <{str(max_val)}." ) return f def add_tax_threshold_arg(parser, containment_default=0.1, ani_default=None): parser.add_argument( "--containment-threshold", default=containment_default, type=range_limited_float_type, help=f"minimum containment threshold for classification; default={containment_default}", ) parser.add_argument( "--ani-threshold", "--aai-threshold", default=ani_default, type=range_limited_float_type, help=f"minimum ANI threshold (nucleotide gather) or AAI threshold (protein gather) for classification; default={ani_default}", ) def add_picklist_args(parser): parser.add_argument( "--picklist", default=None, help="select signatures based on a picklist, i.e. 'file.csv:colname:coltype'", ) parser.add_argument( "--picklist-require-all", default=False, action="store_true", help="require that all picklist values be found or else fail", ) def add_pattern_args(parser): parser.add_argument( "--include-db-pattern", default=None, help="search only signatures that match this pattern in name, filename, or md5", ) parser.add_argument( "--exclude-db-pattern", default=None, help="search only signatures that do not match this pattern in name, filename, or md5", ) def opfilter(path): return not path.startswith("__") and path not in ["utils"] def command_list(dirpath): paths = glob(os.path.join(dirpath, "*.py")) filenames = [os.path.basename(path) for path in paths] basenames = [ os.path.splitext(path)[0] for path in filenames if not path.startswith("__") ] basenames = filter(opfilter, basenames) return sorted(basenames) def add_scaled_arg(parser, default=None): parser.add_argument( "--scaled", metavar="FLOAT", type=check_scaled_bounds, help="downsample to this scaled; value should be between 100 and 1e6", ) def add_num_arg(parser, default=0): parser.add_argument( "-n", "--num-hashes", "--num", metavar="N", type=check_num_bounds, default=default, help="num value should be between 50 and 50000", ) def check_rank(args): """Check '--rank'/'--position'/'--lin-position' argument matches selected taxonomy.""" standard_ranks = [ "strain", "species", "genus", "family", "order", "class", "phylum", "superkingdom", ] if args.lins: if args.rank.isdigit(): return str(args.rank) raise argparse.ArgumentTypeError( f"Invalid '--rank'/'--position' input: '{args.rank}'. '--lins' is specified. Rank must be an integer corresponding to a LIN position." ) elif args.rank in standard_ranks: return args.rank else: raise argparse.ArgumentTypeError( f"Invalid '--rank'/'--position' input: '{args.rank}'. Please choose: 'strain', 'species', 'genus', 'family', 'order', 'class', 'phylum', 'superkingdom'" ) def add_rank_arg(parser): parser.add_argument( "-r", "--rank", "--position", "--lin-position", help="For non-default output formats. Classify to this rank (tax genome) or summarize taxonomy at this rank and above (tax metagenome). \ Note that the taxonomy CSV must contain lineage information at this rank, and that LIN positions start at 0. \ Choices: 'strain', 'species', 'genus', 'family', 'order', 'class', 'phylum', 'superkingdom' or an integer LIN position", ) def check_tax_outputs( args, rank_required=["krona"], incompatible_with_lins=None, use_lingroup_format=False, ): "Handle ouput format combinations" # check that rank is passed for formats requiring rank. if not args.rank: if any(x in rank_required for x in args.output_format): raise ValueError( f"Rank (--rank) is required for {', '.join(rank_required)} output formats." ) if args.lins: # check for outputs incompatible with lins if incompatible_with_lins: if any(x in args.output_format for x in incompatible_with_lins): raise ValueError( f"The following outputs are incompatible with '--lins': : {', '.join(incompatible_with_lins)}" ) # check that lingroup file exists if needed if args.lingroup: if use_lingroup_format and "lingroup" not in args.output_format: args.output_format.append("lingroup") elif "lingroup" in args.output_format: raise ValueError( "Must provide lingroup csv via '--lingroup' in order to output a lingroup report." ) elif args.lingroup or "lingroup" in args.output_format: raise ValueError( "Must enable LIN taxonomy via '--lins' in order to use lingroups." ) # check that only one output format is specified if writing to stdout if len(args.output_format) > 1: if args.output_base == "-": raise ValueError( f"Writing to stdout is incompatible with multiple output formats {args.output_format}" ) elif not args.output_format: # change to "human" for 5.0 args.output_format = ["csv_summary"] return args.output_format sourmash-bio-sourmash-bc3c0f8/src/sourmash/cli/watch.py000066400000000000000000000022421474157046300233450ustar00rootroot00000000000000"""classify a stream of sequences""" from sourmash.cli.utils import add_ksize_arg, add_moltype_args, add_num_arg def subparser(subparsers): subparser = subparsers.add_parser("watch") subparser.add_argument("sbt_name", help="name of SBT to search") subparser.add_argument("inp_file", nargs="?", default="/dev/stdin") subparser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) subparser.add_argument( "-o", "--output", help="save signature generated from data here" ) subparser.add_argument( "--threshold", metavar="T", default=0.05, type=float, help="minimum threshold for matches (default=0.05)", ) subparser.add_argument( "--input-is-protein", action="store_true", help="Consume protein sequences - no translation needed", ) add_moltype_args(subparser) subparser.add_argument( "--name", type=str, default="stdin", help="name to use for generated signature" ) add_ksize_arg(subparser) add_num_arg(subparser, 500) def main(args): import sourmash return sourmash.commands.watch(args) sourmash-bio-sourmash-bc3c0f8/src/sourmash/command_compute.py000066400000000000000000000435531474157046300246540ustar00rootroot00000000000000""" Functions implementing the 'compute' command and related functions. """ import os import os.path import sys import random import screed from . import sourmash_args from .signature import SourmashSignature from .logging import notify, error, set_quiet from .utils import RustObject from ._lowlevel import ffi, lib from .command_sketch import ( _compute_individual, _compute_merged, ComputeParameters, add_seq, set_sig_name, DEFAULT_MMHASH_SEED, ) def compute(args): """Compute the signature for one or more files. Use cases: sourmash compute multiseq.fa => multiseq.fa.sig, etc. sourmash compute genome.fa --singleton => genome.fa.sig sourmash compute file1.fa file2.fa -o file.sig => creates one output file file.sig, with one signature for each input file. sourmash compute file1.fa file2.fa --merge merged -o file.sig => creates one output file file.sig, with all sequences from file1.fa and file2.fa combined into one signature. """ set_quiet(args.quiet) if args.license != "CC0": error("error: sourmash only supports CC0-licensed signatures. sorry!") sys.exit(-1) if args.input_is_protein and args.dna: notify("WARNING: input is protein, turning off nucleotide hashing") args.dna = False args.protein = True if args.scaled: if args.scaled < 1: error("ERROR: --scaled value must be >= 1") sys.exit(-1) if args.scaled != round(args.scaled, 0): error("ERROR: --scaled value must be integer value") sys.exit(-1) if args.scaled >= 1e9: notify("WARNING: scaled value is nonsensical!? Continuing anyway.") if args.num_hashes != 0: notify("setting num_hashes to 0 because --scaled is set") args.num_hashes = 0 notify("computing signatures for files: {}", ", ".join(args.filenames)) if args.randomize: notify("randomizing file list because of --randomize") random.shuffle(args.filenames) # get list of k-mer sizes for which to compute sketches ksizes = args.ksizes notify("Computing signature for ksizes: {}", str(ksizes)) num_sigs = 0 if args.dna and args.protein: notify("Computing both nucleotide and protein signatures.") num_sigs = 2 * len(ksizes) elif args.dna and args.dayhoff: notify("Computing both nucleotide and Dayhoff-encoded protein signatures.") num_sigs = 2 * len(ksizes) elif args.dna and args.hp: notify("Computing both nucleotide and hp-encoded protein signatures.") num_sigs = 2 * len(ksizes) elif args.dna: notify("Computing only nucleotide (and not protein) signatures.") num_sigs = len(ksizes) elif args.protein: notify("Computing only protein (and not nucleotide) signatures.") num_sigs = len(ksizes) elif args.dayhoff: notify( "Computing only Dayhoff-encoded protein (and not nucleotide) signatures." ) num_sigs = len(ksizes) elif args.hp: notify("Computing only hp-encoded protein (and not nucleotide) signatures.") num_sigs = len(ksizes) if args.protein or args.dayhoff or args.hp: notify("") notify( "WARNING: you are using 'compute' to make a protein/dayhoff/hp signature," ) notify("WARNING: but the meaning of ksize has changed in 4.0. Please see the") notify("WARNING: migration guide to sourmash v4.0 at http://sourmash.rtfd.io/") notify("") bad_ksizes = [str(k) for k in ksizes if k % 3 != 0] if bad_ksizes: error("protein ksizes must be divisible by 3, sorry!") error("bad ksizes: {}", ", ".join(bad_ksizes)) sys.exit(-1) notify("Computing a total of {} signature(s) for each input.", num_sigs) if num_sigs == 0: error("...nothing to calculate!? Exiting!") sys.exit(-1) if args.merge and not args.output: error("ERROR: must specify -o with --merge") sys.exit(-1) if args.output and args.output_dir: error("ERROR: --output-dir doesn't make sense with -o/--output") sys.exit(-1) if args.track_abundance: notify("Tracking abundance of input k-mers.") signatures_factory = _signatures_for_compute_factory(args) if args.merge: # single name specified - combine all _compute_merged(args, signatures_factory) else: # compute individual signatures _compute_individual(args, signatures_factory) class _signatures_for_compute_factory: "Build signatures on demand, based on args input to 'compute'." def __init__(self, args): self.args = args def __call__(self): args = self.args params = ComputeParameters( ksizes=args.ksizes, seed=args.seed, protein=args.protein, dayhoff=args.dayhoff, hp=args.hp, dna=args.dna, num_hashes=args.num_hashes, track_abundance=args.track_abundance, scaled=args.scaled, ) sig = SourmashSignature.from_params(params) return [sig] def _compute_individual(args, signatures_factory): # this is where output signatures will go. save_sigs = None # track: is this the first file? in cases where we have empty inputs, # we don't want to open any outputs. first_file_for_output = True # if args.output is set, we are aggregating all output to a single file. # do not open a new output file for each input. open_output_each_time = True if args.output: open_output_each_time = False for filename in args.filenames: if open_output_each_time: # for each input file, construct output filename sigfile = os.path.basename(filename) + ".sig" if args.output_dir: sigfile = os.path.join(args.output_dir, sigfile) # does it already exist? skip if so. if os.path.exists(sigfile) and not args.force: notify("skipping {} - already done", filename) continue # go on to next file. # nope? ok, let's save to it. assert not save_sigs save_sigs = sourmash_args.SaveSignaturesToLocation(sigfile) # # calculate signatures! # # now, set up to iterate over sequences. with screed.open(filename) as screed_iter: if not screed_iter: notify(f"no sequences found in '{filename}'?!") continue # open output for signatures if open_output_each_time: save_sigs.open() # or... is this the first time to write something to args.output? elif first_file_for_output: save_sigs = sourmash_args.SaveSignaturesToLocation(args.output) save_sigs.open() first_file_for_output = False # make a new signature for each sequence? if args.singleton: n_calculated = 0 for n, record in enumerate(screed_iter): sigs = signatures_factory() try: add_seq( sigs, record.sequence, args.input_is_protein, args.check_sequence, ) except ValueError as exc: error(f"ERROR when reading from '{filename}' - ") error(str(exc)) sys.exit(-1) n_calculated += len(sigs) set_sig_name(sigs, filename, name=record.name) save_sigs_to_location(sigs, save_sigs) notify( "calculated {} signatures for {} sequences in {}", n_calculated, n + 1, filename, ) # nope; make a single sig for the whole file else: sigs = signatures_factory() # consume & calculate signatures notify(f"... reading sequences from {filename}") name = None for n, record in enumerate(screed_iter): if n % 10000 == 0: if n: notify("\r...{} {}", filename, n, end="") elif args.name_from_first: name = record.name try: add_seq( sigs, record.sequence, args.input_is_protein, args.check_sequence, ) except ValueError as exc: error(f"ERROR when reading from '{filename}' - ") error(str(exc)) sys.exit(-1) notify("...{} {} sequences", filename, n, end="") set_sig_name(sigs, filename, name) save_sigs_to_location(sigs, save_sigs) notify( f"calculated {len(sigs)} signatures for {n + 1} sequences in {filename}" ) # if not args.output, close output for every input filename. if open_output_each_time: save_sigs.close() notify( f"saved {len(save_sigs)} signature(s) to '{save_sigs.location}'. Note: signature license is CC0." ) save_sigs = None # if --output-dir specified, all collected signatures => args.output, # and we need to close here. if args.output and save_sigs is not None: save_sigs.close() notify( f"saved {len(save_sigs)} signature(s) to '{save_sigs.location}'. Note: signature license is CC0." ) def _compute_merged(args, signatures_factory): # make a signature for the whole file sigs = signatures_factory() total_seq = 0 for filename in args.filenames: # consume & calculate signatures notify("... reading sequences from {}", filename) n = None with screed.open(filename) as f: for n, record in enumerate(f): if n % 10000 == 0 and n: notify("\r... {} {}", filename, n, end="") add_seq( sigs, record.sequence, args.input_is_protein, args.check_sequence ) if n is not None: notify("... {} {} sequences", filename, n + 1) total_seq += n + 1 else: notify(f"no sequences found in '{filename}'?!") if total_seq: set_sig_name(sigs, filename, name=args.merge) notify( "calculated 1 signature for {} sequences taken from {} files", total_seq, len(args.filenames), ) # at end, save! save_siglist(sigs, args.output) def add_seq(sigs, seq, input_is_protein, check_sequence): for sig in sigs: if input_is_protein: sig.add_protein(seq) else: sig.add_sequence(seq, not check_sequence) def set_sig_name(sigs, filename, name=None): if filename == "-": # if stdin, set filename to empty. filename = "" for sig in sigs: if name is not None: sig._name = name sig.filename = filename def save_siglist(siglist, sigfile_name): "Save multiple signatures to a filename." # save! with sourmash_args.SaveSignaturesToLocation(sigfile_name) as save_sig: for ss in siglist: save_sig.add(ss) notify(f"saved {len(save_sig)} signature(s) to '{save_sig.location}'") def save_sigs_to_location(siglist, save_sig): "Save multiple signatures to an already-open location." import sourmash for ss in siglist: save_sig.add(ss) class ComputeParameters(RustObject): __dealloc_func__ = lib.computeparams_free def __init__( self, *, ksizes=(21, 31, 51), seed=42, protein=False, dayhoff=False, hp=False, dna=True, num_hashes=500, track_abundance=False, scaled=0, ): self._objptr = lib.computeparams_new() self.seed = seed self.ksizes = ksizes self.protein = protein self.dayhoff = dayhoff self.hp = hp self.dna = dna self.num_hashes = num_hashes self.track_abundance = track_abundance self.scaled = scaled @classmethod def from_manifest_row(cls, row): "convert a CollectionManifest row into a ComputeParameters object" is_dna = is_protein = is_dayhoff = is_hp = False if row["moltype"] == "DNA": is_dna = True elif row["moltype"] == "protein": is_protein = True elif row["moltype"] == "hp": is_hp = True elif row["moltype"] == "dayhoff": is_dayhoff = True else: assert 0 if is_dna: ksize = row["ksize"] else: ksize = row["ksize"] * 3 p = cls( ksizes=[ksize], seed=DEFAULT_MMHASH_SEED, protein=is_protein, dayhoff=is_dayhoff, hp=is_hp, dna=is_dna, num_hashes=row["num"], track_abundance=row["with_abundance"], scaled=row["scaled"], ) return p def to_param_str(self): "Convert object to equivalent params str." pi = [] if self.dna: pi.append("dna") elif self.protein: pi.append("protein") elif self.hp: pi.append("hp") elif self.dayhoff: pi.append("dayhoff") else: assert 0 # must be one of the previous if self.dna: kstr = [f"k={k}" for k in self.ksizes] else: # for protein, divide ksize by three. kstr = [f"k={k // 3}" for k in self.ksizes] assert kstr pi.extend(kstr) if self.num_hashes != 0: pi.append(f"num={self.num_hashes}") elif self.scaled != 0: pi.append(f"scaled={self.scaled}") else: assert 0 if self.track_abundance: pi.append("abund") # noabund is default if self.seed != DEFAULT_MMHASH_SEED: pi.append(f"seed={self.seed}") # self.seed return ",".join(pi) def __repr__(self): return f"ComputeParameters(ksizes={self.ksizes}, seed={self.seed}, protein={self.protein}, dayhoff={self.dayhoff}, hp={self.hp}, dna={self.dna}, num_hashes={self.num_hashes}, track_abundance={self.track_abundance}, scaled={self.scaled})" def __eq__(self, other): return ( self.ksizes == other.ksizes and self.seed == other.seed and self.protein == other.protein and self.dayhoff == other.dayhoff and self.hp == other.hp and self.dna == other.dna and self.num_hashes == other.num_hashes and self.track_abundance == other.track_abundance and self.scaled == other.scaled ) @staticmethod def from_args(args): ptr = lib.computeparams_new() ret = ComputeParameters._from_objptr(ptr) for arg, value in vars(args).items(): try: getattr(type(ret), arg).fset(ret, value) except AttributeError: pass return ret @property def seed(self): return self._methodcall(lib.computeparams_seed) @seed.setter def seed(self, v): return self._methodcall(lib.computeparams_set_seed, v) @property def ksizes(self): size = ffi.new("uintptr_t *") ksizes_ptr = self._methodcall(lib.computeparams_ksizes, size) size = size[0] ksizes = ffi.unpack(ksizes_ptr, size) lib.computeparams_ksizes_free(ksizes_ptr, size) return ksizes @ksizes.setter def ksizes(self, v): return self._methodcall(lib.computeparams_set_ksizes, list(v), len(v)) @property def protein(self): return self._methodcall(lib.computeparams_protein) @protein.setter def protein(self, v): return self._methodcall(lib.computeparams_set_protein, v) @property def dayhoff(self): return self._methodcall(lib.computeparams_dayhoff) @dayhoff.setter def dayhoff(self, v): return self._methodcall(lib.computeparams_set_dayhoff, v) @property def hp(self): return self._methodcall(lib.computeparams_hp) @hp.setter def hp(self, v): return self._methodcall(lib.computeparams_set_hp, v) @property def dna(self): return self._methodcall(lib.computeparams_dna) @dna.setter def dna(self, v): return self._methodcall(lib.computeparams_set_dna, v) @property def moltype(self): if self.dna: moltype = "DNA" elif self.protein: moltype = "protein" elif self.hp: moltype = "hp" elif self.dayhoff: moltype = "dayhoff" else: assert 0 return moltype @property def num_hashes(self): return self._methodcall(lib.computeparams_num_hashes) @num_hashes.setter def num_hashes(self, v): return self._methodcall(lib.computeparams_set_num_hashes, v) @property def track_abundance(self): return self._methodcall(lib.computeparams_track_abundance) @track_abundance.setter def track_abundance(self, v): return self._methodcall(lib.computeparams_set_track_abundance, v) @property def scaled(self): return self._methodcall(lib.computeparams_scaled) @scaled.setter def scaled(self, v): return self._methodcall(lib.computeparams_set_scaled, int(v)) sourmash-bio-sourmash-bc3c0f8/src/sourmash/command_sketch.py000066400000000000000000001053111474157046300244500ustar00rootroot00000000000000""" Functions implementing the 'sketch' subcommands and related functions. """ import sys import os from collections import defaultdict, Counter import csv import shlex import screed import sourmash from .signature import SourmashSignature from .logging import notify, error, set_quiet, print_results from sourmash import sourmash_args from sourmash.sourmash_args import check_scaled_bounds, check_num_bounds from sourmash.sig.__main__ import _summarize_manifest, _SketchInfo from sourmash.manifest import CollectionManifest from .utils import RustObject from ._lowlevel import ffi, lib DEFAULT_MMHASH_SEED = 42 DEFAULTS = dict( dna="k=31,scaled=1000,noabund", protein="k=10,scaled=200,noabund", dayhoff="k=16,scaled=200,noabund", hp="k=42,scaled=200,noabund", ) def _parse_params_str(params_str): "Parse a parameter string of the form 'k=ks,num=num,scaled=scaled,abund'." moltype = None params = {} params["ksize"] = [] items = params_str.split(",") for item in items: if item == "abund": params["track_abundance"] = True elif item == "noabund": params["track_abundance"] = False elif item.startswith("k"): if len(item) < 3 or item[1] != "=": raise ValueError("k takes a parameter, e.g. 'k=31'") params["ksize"].append(int(item[2:])) elif item.startswith("num"): if len(item) < 5 or item[3] != "=": raise ValueError("num takes a parameter, e.g. 'num=500'") if params.get("scaled"): raise ValueError("cannot set both num and scaled in a single minhash") try: num = item[4:] num = int(num) except ValueError: raise ValueError(f"cannot parse num='{num}' as a number") num = check_num_bounds(num) params["num"] = int(item[4:]) params["scaled"] = 0 elif item.startswith("scaled"): if len(item) < 8 or item[6] != "=": raise ValueError("scaled takes a parameter, e.g. 'scaled=1000'") if params.get("num"): raise ValueError("cannot set both num and scaled in a single minhash") try: scaled = item[7:] scaled = int(scaled) except ValueError: raise ValueError(f"cannot parse scaled='{scaled}' as an integer") scaled = check_scaled_bounds(scaled) params["scaled"] = scaled params["num"] = 0 elif item.startswith("seed"): if len(item) < 6 or item[4] != "=": raise ValueError("seed takes a parameter, e.g. 'seed=42'") params["seed"] = int(item[5:]) elif item in ("protein", "dayhoff", "hp", "dna"): moltype = item else: raise ValueError(f"unknown component '{item}' in params string") return moltype, params class _signatures_for_sketch_factory: "Build sigs on demand, based on args input to 'sketch'." def __init__(self, params_str_list, default_moltype): # first, set up defaults per-moltype defaults = {} for moltype, pstr in DEFAULTS.items(): mt, d = _parse_params_str(pstr) assert mt is None # defaults cannot have moltype set! defaults[moltype] = d self.defaults = defaults # next, fill out params_list self.params_list = [] self.mult_ksize_by_3 = True if params_str_list: # parse each params_str passed in, using default_moltype if none # provided. for params_str in params_str_list: moltype, params = _parse_params_str(params_str) if moltype and moltype != "dna" and default_moltype == "dna": raise ValueError( f"Incompatible sketch type ({default_moltype}) and parameter override ({moltype}) in '{params_str}'; maybe use 'sketch translate'?" ) elif moltype == "dna" and default_moltype and default_moltype != "dna": raise ValueError( f"Incompatible sketch type ({default_moltype}) and parameter override ({moltype}) in '{params_str}'" ) elif moltype is None: if default_moltype is None: raise ValueError( "No default moltype and none specified in param string" ) moltype = default_moltype self.params_list.append((moltype, params)) else: if default_moltype is None: raise ValueError( "No default moltype and none specified in param string" ) # no params str? default to a single sig, using default_moltype. self.params_list.append((default_moltype, {})) def get_compute_params(self, *, split_ksizes=False): for moltype, params_d in self.params_list: # get defaults for this moltype from self.defaults: default_params = self.defaults[moltype] def_seed = default_params.get("seed", DEFAULT_MMHASH_SEED) def_num = default_params.get("num", 0) def_abund = default_params["track_abundance"] def_scaled = default_params.get("scaled", 0) def_dna = default_params.get("is_dna", moltype == "dna") def_protein = default_params.get("is_protein", moltype == "protein") def_dayhoff = default_params.get("is_dayhoff", moltype == "dayhoff") def_hp = default_params.get("is_hp", moltype == "hp") # handle ksize specially, for now - multiply by three? def_ksizes = default_params["ksize"] ksizes = params_d.get("ksize") if not ksizes: ksizes = def_ksizes # 'command sketch' adjusts k-mer sizes by 3 if non-DNA sketch. if self.mult_ksize_by_3 and not def_dna: ksizes = [k * 3 for k in ksizes] def make_param(ksizes): return ComputeParameters( ksizes=ksizes, seed=params_d.get("seed", def_seed), protein=def_protein, dayhoff=def_dayhoff, hp=def_hp, dna=def_dna, num_hashes=params_d.get("num", def_num), track_abundance=params_d.get("track_abundance", def_abund), scaled=params_d.get("scaled", def_scaled), ) if split_ksizes: for ksize in ksizes: params_obj = make_param([ksize]) yield params_obj else: params_obj = make_param(ksizes) yield params_obj def __call__(self, *, split_ksizes=False): "Produce a new set of signatures built to match the param strings." sigs = [] for params in self.get_compute_params(split_ksizes=split_ksizes): sig = SourmashSignature.from_params(params) sigs.append(sig) return sigs def _add_from_file_to_filenames(args): "Add filenames from --from-file to args.filenames" from .sourmash_args import load_pathlist_from_file if args.from_file: file_list = load_pathlist_from_file(args.from_file) args.filenames.extend(file_list) def _execute_sketch(args, signatures_factory): "Once configured, run 'sketch' the same way underneath." set_quiet(args.quiet) if not args.filenames: error("error: no input filenames provided! nothing to do - exiting.") sys.exit(-1) if args.license != "CC0": error("error: sourmash only supports CC0-licensed signatures. sorry!") sys.exit(-1) notify(f"computing signatures for files: {', '.join(args.filenames)}") if args.merge and not args.output: error("ERROR: must specify -o with --merge") sys.exit(-1) if args.output and args.output_dir: error("ERROR: --output-dir doesn't make sense with -o/--output") sys.exit(-1) # get number of output sigs: num_sigs = len(signatures_factory.params_list) notify(f"Computing a total of {num_sigs} signature(s) for each input.") if num_sigs == 0: error("...nothing to calculate!? Exiting!") sys.exit(-1) if args.merge: # single name specified - combine all _compute_merged(args, signatures_factory) else: # compute individual signatures _compute_individual(args, signatures_factory) def dna(args): """Compute a DNA signature for one or more files. CTB: make usable via Python? """ # for dna: args.input_is_protein = False try: signatures_factory = _signatures_for_sketch_factory(args.param_string, "dna") except ValueError as e: error(f"Error creating signatures: {str(e)}") sys.exit(-1) _add_from_file_to_filenames(args) _execute_sketch(args, signatures_factory) def protein(args): """Compute a protein signature for one or more files. CTB: make usable via Python? """ # for protein: args.input_is_protein = True args.check_sequence = False # provide good defaults for dayhoff/hp/protein! if args.dayhoff and args.hp: raise ValueError("cannot set both --dayhoff and --hp") if args.dayhoff: moltype = "dayhoff" elif args.hp: moltype = "hp" else: moltype = "protein" try: signatures_factory = _signatures_for_sketch_factory(args.param_string, moltype) except ValueError as e: error(f"Error creating signatures: {str(e)}") sys.exit(-1) _add_from_file_to_filenames(args) _execute_sketch(args, signatures_factory) def translate(args): """Compute protein signatures from DNA/RNA, for one or more files. CTB: make usable via Python? """ # for translate: args.input_is_protein = False # provide good defaults for dayhoff/hp/protein! if args.dayhoff and args.hp: raise ValueError("cannot set both --dayhoff and --hp") if args.dayhoff: moltype = "dayhoff" elif args.hp: moltype = "hp" else: moltype = "protein" try: signatures_factory = _signatures_for_sketch_factory(args.param_string, moltype) except ValueError as e: error(f"Error creating signatures: {str(e)}") sys.exit(-1) _add_from_file_to_filenames(args) _execute_sketch(args, signatures_factory) def _compute_sigs(to_build, output, *, check_sequence=False): "actually build the signatures in 'to_build' and output them to 'output'" save_sigs = sourmash_args.SaveSignaturesToLocation(output) save_sigs.open() for (name, filename), param_objs in to_build.items(): assert param_objs # now, set up to iterate over sequences. with screed.open(filename) as screed_iter: if not screed_iter: error(f"ERROR: no sequences found in '{filename}'?!") sys.exit(-1) # build the set of empty sigs sigs = [] is_dna = param_objs[0].dna for p in param_objs: if p.dna: assert is_dna sig = SourmashSignature.from_params(p) sigs.append(sig) input_is_protein = not is_dna # read sequence records & sketch notify(f"... reading sequences from {filename}") for n, record in enumerate(screed_iter): if n % 10000 == 0: if n: notify("\r...{} {}", filename, n, end="") try: add_seq(sigs, record.sequence, input_is_protein, check_sequence) except ValueError as exc: error(f"ERROR when reading from '{filename}' - ") error(str(exc)) sys.exit(-1) notify("...{} {} sequences", filename, n, end="") set_sig_name(sigs, filename, name) for sig in sigs: save_sigs.add(sig) notify( f"calculated {len(sigs)} signatures for {n + 1} sequences in {filename}" ) save_sigs.close() notify( f"saved {len(save_sigs)} signature(s) to '{save_sigs.location}'. Note: signature license is CC0." ) def _output_csv_info(filename, sigs_to_build): "output information about what signatures to build, in CSV format" output_n = 0 with sourmash_args.FileOutputCSV(filename) as csv_fp: w = csv.DictWriter( csv_fp, fieldnames=["filename", "sketchtype", "output_index", "name", "param_strs"], ) w.writeheader() output_n = 0 for (name, filename), param_objs in sigs_to_build.items(): param_strs = [] # should all be the same! if param_objs[0].dna: assert all(p.dna for p in param_objs) sketchtype = "dna" else: assert not any(p.dna for p in param_objs) sketchtype = "protein" for p in param_objs: param_strs.append(p.to_param_str()) row = dict( filename=filename, sketchtype=sketchtype, param_strs="-p " + " -p ".join(param_strs), name=name, output_index=output_n, ) w.writerow(row) output_n += 1 def fromfile(args): if args.license != "CC0": error("error: sourmash only supports CC0-licensed signatures. sorry!") sys.exit(-1) if args.output_signatures and os.path.exists(args.output_signatures): if not args.force_output_already_exists: error( f"** ERROR: output location '{args.output_signatures}' already exists!" ) error("** Not overwriting/appending.") error( "** Use --force-output-already-exists if you want to overwrite/append." ) sys.exit(-1) # now, create the set of desired sketch specs. try: # omit a default moltype - must be provided in param string. sig_factory = _signatures_for_sketch_factory(args.param_string, None) except ValueError as e: error(f"Error creating signatures: {str(e)}") sys.exit(-1) # take the signatures factory => convert into a bunch of ComputeParameters # objects. build_params = list(sig_factory.get_compute_params(split_ksizes=True)) # confirm that they do not adjust seed, which is not supported in # 'fromfile' b/c we don't store that info in manifests. (see #1849) for p in build_params: if p.seed != DEFAULT_MMHASH_SEED: error("** ERROR: cannot set 'seed' in 'sketch fromfile'") sys.exit(-1) # cross-product all of the names in the input CSV file # with the sketch spec(s) provided on the command line. to_build = defaultdict(list) all_names = {} total_rows = 0 skipped_sigs = 0 n_missing_name = 0 n_duplicate_name = 0 duplicate_names = set() for csvfile in args.csvs: with sourmash_args.FileInputCSV(csvfile) as r: for row in r: name = row["name"] if not name: n_missing_name += 1 continue genome = row["genome_filename"] proteome = row["protein_filename"] total_rows += 1 if name in all_names: n_duplicate_name += 1 duplicate_names.add(name) else: all_names[name] = (genome, proteome) fail_exit = False if n_duplicate_name: if args.report_duplicated: notify("duplicated:\n" + "\n".join(sorted(duplicate_names))) error( f"** ERROR: {n_duplicate_name} entries have duplicate 'name' records. Exiting!" ) fail_exit = True if n_missing_name: error(f"** ERROR: {n_missing_name} entries have blank 'name's? Exiting!") fail_exit = True if fail_exit: sys.exit(-1) # load manifests from '--already-done' databases => turn into # ComputeParameters objects, indexed by name. already_done = defaultdict(list) already_done_rows = [] for filename in args.already_done: idx = sourmash.load_file_as_index(filename) manifest = idx.manifest assert manifest # for each manifest row, for row in manifest.rows: name = row["name"] if name: # build a ComputeParameters object for later comparison p = ComputeParameters.from_manifest_row(row) # add to list for this name already_done[name].append(p) # matching name? check if we already have sig. if so, store! if name in all_names: if p in build_params: already_done_rows.append(row) already_done_manifest = CollectionManifest(already_done_rows) if args.already_done: notify(f"Loaded {len(already_done)} pre-existing names from manifest(s)") notify(f"collected {len(already_done_rows)} rows for already-done signatures.") ## now check which are already done and track only those that are ## need to be done. total_sigs = 0 missing = defaultdict(list) missing_count = 0 for name, (genome, proteome) in all_names.items(): plist = already_done.get(name, []) # check list of already done against build parameters for p in build_params: total_sigs += 1 # does this signature already exist? if p not in plist: # nope - figure out genome/proteome needed filename = genome if p.dna else proteome filetype = "genome" if p.dna else "proteome" if filename: # add to build list to_build[(name, filename)].append(p) else: notify(f"WARNING: fromfile entry '{name}' is missing a {filetype}") missing[name].append(p) missing_count += 1 else: skipped_sigs += 1 ## we now have 'to_build' which contains the things we can build, ## and 'missing', which contains anything we cannot build. Report! notify(f"Read {total_rows} rows, requesting that {total_sigs} signatures be built.") if already_done_manifest: info_d = _summarize_manifest(already_done_manifest) print_results("---") print_results("summary of already-done sketches:") for ski in info_d["sketch_info"]: mh_type = f"num={ski['num']}" if ski["num"] else f"scaled={ski['scaled']}" mh_abund = ", abund" if ski["abund"] else "" sketch_str = f"{ski['count']} sketches with {ski['moltype']}, k={ski['ksize']}, {mh_type}{mh_abund}" print_results(f" {sketch_str: <50} {ski['n_hashes']} total hashes") print_results("---") if args.output_manifest_matching: already_done_manifest.write_to_filename(args.output_manifest_matching) notify( f"output {len(already_done_manifest)} already-done signatures to '{args.output_manifest_matching}' in manifest format." ) if missing: error("** ERROR: we cannot build some of the requested signatures.") error( f"** {missing_count} total signatures (for {len(missing)} names) cannot be built." ) if args.ignore_missing: error("** (continuing past this error because --ignore-missing was set)") else: sys.exit(-1) notify( f"** {total_sigs - skipped_sigs} new signatures to build from {len(to_build)} files;" ) if not to_build: notify("** Nothing to build. Exiting!") sys.exit(0) if skipped_sigs: notify(f"** {skipped_sigs} already exist, so skipping those.") else: notify("** we found no pre-existing signatures that match.") ## first, print out a summary of to_build: print_results("---") print_results("summary of sketches to build:") counter = Counter() for filename, param_objs in to_build.items(): for p in param_objs: assert len(p.ksizes) == 1 ksize = p.ksizes[0] if not p.dna: ksize //= 3 ski = _SketchInfo( ksize=ksize, moltype=p.moltype, scaled=p.scaled, num=p.num_hashes, abund=p.track_abundance, ) counter[ski] += 1 for ski, count in counter.items(): mh_type = f"num={ski.num}" if ski.num else f"scaled={ski.scaled}" mh_abund = ", abund" if ski.abund else "" sketch_str = ( f"{count} sketches with {ski.moltype}, k={ski.ksize}, {mh_type}{mh_abund}" ) print_results(f" {sketch_str: <50}") print_results("---") ## now, onward ho - do we build anything, or output stuff, or just exit? if args.output_signatures: # actually compute _compute_sigs( to_build, args.output_signatures, check_sequence=args.check_sequence ) if args.output_csv_info: # output info necessary to construct _output_csv_info(args.output_csv_info, to_build) notify( f"** {total_sigs} total requested; output {total_sigs - skipped_sigs}, skipped {skipped_sigs}" ) class _signatures_for_compute_factory: "Build signatures on demand, based on args input to 'compute'." def __init__(self, args): self.args = args def __call__(self): args = self.args params = ComputeParameters( ksizes=args.ksizes, seed=args.seed, protein=args.protein, dayhoff=args.dayhoff, hp=args.hp, dna=args.dna, num_hashes=args.num_hashes, track_abundance=args.track_abundance, scaled=args.scaled, ) sig = SourmashSignature.from_params(params) return [sig] def _compute_individual(args, signatures_factory): # this is where output signatures will go. save_sigs = None # track: is this the first file? in cases where we have empty inputs, # we don't want to open any outputs. first_file_for_output = True # if args.output is set, we are aggregating all output to a single file. # do not open a new output file for each input. open_output_each_time = True if args.output: open_output_each_time = False for filename in args.filenames: if open_output_each_time: # for each input file, construct output filename sigfile = os.path.basename(filename) + ".sig" if args.output_dir: sigfile = os.path.join(args.output_dir, sigfile) # does it already exist? skip if so. if os.path.exists(sigfile) and not args.force: notify("skipping {} - already done", filename) continue # go on to next file. # nope? ok, let's save to it. assert not save_sigs save_sigs = sourmash_args.SaveSignaturesToLocation(sigfile) # # calculate signatures! # # now, set up to iterate over sequences. with screed.open(filename) as screed_iter: if not screed_iter: notify(f"no sequences found in '{filename}'?!") continue # open output for signatures if open_output_each_time: save_sigs.open() # or... is this the first time to write something to args.output? elif first_file_for_output: save_sigs = sourmash_args.SaveSignaturesToLocation(args.output) save_sigs.open() first_file_for_output = False # make a new signature for each sequence? if args.singleton: n_calculated = 0 for n, record in enumerate(screed_iter): sigs = signatures_factory() try: add_seq( sigs, record.sequence, args.input_is_protein, args.check_sequence, ) except ValueError as exc: error(f"ERROR when reading from '{filename}' - ") error(str(exc)) sys.exit(-1) n_calculated += len(sigs) set_sig_name(sigs, filename, name=record.name) save_sigs_to_location(sigs, save_sigs) notify( "calculated {} signatures for {} sequences in {}", n_calculated, n + 1, filename, ) # nope; make a single sig for the whole file else: sigs = signatures_factory() # consume & calculate signatures notify(f"... reading sequences from {filename}") name = None for n, record in enumerate(screed_iter): if n % 10000 == 0: if n: notify("\r...{} {}", filename, n, end="") elif args.name_from_first: name = record.name try: add_seq( sigs, record.sequence, args.input_is_protein, args.check_sequence, ) except ValueError as exc: error(f"ERROR when reading from '{filename}' - ") error(str(exc)) sys.exit(-1) notify("...{} {} sequences", filename, n, end="") set_sig_name(sigs, filename, name) save_sigs_to_location(sigs, save_sigs) notify( f"calculated {len(sigs)} signatures for {n + 1} sequences in {filename}" ) # if not args.output, close output for every input filename. if open_output_each_time: save_sigs.close() notify( f"saved {len(save_sigs)} signature(s) to '{save_sigs.location}'. Note: signature license is CC0." ) save_sigs = None # if --output-dir specified, all collected signatures => args.output, # and we need to close here. if args.output and save_sigs is not None: save_sigs.close() notify( f"saved {len(save_sigs)} signature(s) to '{save_sigs.location}'. Note: signature license is CC0." ) def _compute_merged(args, signatures_factory): # make a signature for the whole file sigs = signatures_factory() total_seq = 0 for filename in args.filenames: # consume & calculate signatures notify("... reading sequences from {}", filename) n = None with screed.open(filename) as f: for n, record in enumerate(f): if n % 10000 == 0 and n: notify("\r... {} {}", filename, n, end="") add_seq( sigs, record.sequence, args.input_is_protein, args.check_sequence ) if n is not None: notify("... {} {} sequences", filename, n + 1) total_seq += n + 1 else: notify(f"no sequences found in '{filename}'?!") if total_seq: set_sig_name(sigs, filename, name=args.merge) notify( "calculated 1 signature for {} sequences taken from {} files", total_seq, len(args.filenames), ) # at end, save! save_siglist(sigs, args.output) def add_seq(sigs, seq, input_is_protein, check_sequence): for sig in sigs: if input_is_protein: sig.add_protein(seq) else: sig.add_sequence(seq, not check_sequence) def set_sig_name(sigs, filename, name=None): if filename == "-": # if stdin, set filename to empty. filename = "" for sig in sigs: if name is not None: sig._name = name sig.filename = filename def save_siglist(siglist, sigfile_name): "Save multiple signatures to a filename." # save! with sourmash_args.SaveSignaturesToLocation(sigfile_name) as save_sig: for ss in siglist: save_sig.add(ss) notify(f"saved {len(save_sig)} signature(s) to '{save_sig.location}'") def save_sigs_to_location(siglist, save_sig): "Save multiple signatures to an already-open location." import sourmash for ss in siglist: save_sig.add(ss) class ComputeParameters(RustObject): __dealloc_func__ = lib.computeparams_free def __init__( self, *, ksizes=(21, 31, 51), seed=42, protein=False, dayhoff=False, hp=False, dna=True, num_hashes=500, track_abundance=False, scaled=0, ): self._objptr = lib.computeparams_new() self.seed = seed self.ksizes = ksizes self.protein = protein self.dayhoff = dayhoff self.hp = hp self.dna = dna self.num_hashes = num_hashes self.track_abundance = track_abundance self.scaled = scaled @classmethod def from_manifest_row(cls, row): "convert a CollectionManifest row into a ComputeParameters object" is_dna = is_protein = is_dayhoff = is_hp = False if row["moltype"] == "DNA": is_dna = True elif row["moltype"] == "protein": is_protein = True elif row["moltype"] == "hp": is_hp = True elif row["moltype"] == "dayhoff": is_dayhoff = True else: assert 0 if is_dna: ksize = row["ksize"] else: ksize = row["ksize"] * 3 p = cls( ksizes=[ksize], seed=DEFAULT_MMHASH_SEED, protein=is_protein, dayhoff=is_dayhoff, hp=is_hp, dna=is_dna, num_hashes=row["num"], track_abundance=row["with_abundance"], scaled=row["scaled"], ) return p def to_param_str(self): "Convert object to equivalent params str." pi = [] if self.dna: pi.append("dna") elif self.protein: pi.append("protein") elif self.hp: pi.append("hp") elif self.dayhoff: pi.append("dayhoff") else: assert 0 # must be one of the previous if self.dna: kstr = [f"k={k}" for k in self.ksizes] else: # for protein, divide ksize by three. kstr = [f"k={k // 3}" for k in self.ksizes] assert kstr pi.extend(kstr) if self.num_hashes != 0: pi.append(f"num={self.num_hashes}") elif self.scaled != 0: pi.append(f"scaled={self.scaled}") else: assert 0 if self.track_abundance: pi.append("abund") # noabund is default if self.seed != DEFAULT_MMHASH_SEED: pi.append(f"seed={self.seed}") # self.seed return ",".join(pi) def __repr__(self): return f"ComputeParameters(ksizes={self.ksizes}, seed={self.seed}, protein={self.protein}, dayhoff={self.dayhoff}, hp={self.hp}, dna={self.dna}, num_hashes={self.num_hashes}, track_abundance={self.track_abundance}, scaled={self.scaled})" def __eq__(self, other): return ( self.ksizes == other.ksizes and self.seed == other.seed and self.protein == other.protein and self.dayhoff == other.dayhoff and self.hp == other.hp and self.dna == other.dna and self.num_hashes == other.num_hashes and self.track_abundance == other.track_abundance and self.scaled == other.scaled ) @staticmethod def from_args(args): ptr = lib.computeparams_new() ret = ComputeParameters._from_objptr(ptr) for arg, value in vars(args).items(): try: getattr(type(ret), arg).fset(ret, value) except AttributeError: pass return ret @property def seed(self): return self._methodcall(lib.computeparams_seed) @seed.setter def seed(self, v): return self._methodcall(lib.computeparams_set_seed, v) @property def ksizes(self): size = ffi.new("uintptr_t *") ksizes_ptr = self._methodcall(lib.computeparams_ksizes, size) size = size[0] ksizes = ffi.unpack(ksizes_ptr, size) lib.computeparams_ksizes_free(ksizes_ptr, size) return ksizes @ksizes.setter def ksizes(self, v): return self._methodcall(lib.computeparams_set_ksizes, list(v), len(v)) @property def protein(self): return self._methodcall(lib.computeparams_protein) @protein.setter def protein(self, v): return self._methodcall(lib.computeparams_set_protein, v) @property def dayhoff(self): return self._methodcall(lib.computeparams_dayhoff) @dayhoff.setter def dayhoff(self, v): return self._methodcall(lib.computeparams_set_dayhoff, v) @property def hp(self): return self._methodcall(lib.computeparams_hp) @hp.setter def hp(self, v): return self._methodcall(lib.computeparams_set_hp, v) @property def dna(self): return self._methodcall(lib.computeparams_dna) @dna.setter def dna(self, v): return self._methodcall(lib.computeparams_set_dna, v) @property def moltype(self): if self.dna: moltype = "DNA" elif self.protein: moltype = "protein" elif self.hp: moltype = "hp" elif self.dayhoff: moltype = "dayhoff" else: assert 0 return moltype @property def num_hashes(self): return self._methodcall(lib.computeparams_num_hashes) @num_hashes.setter def num_hashes(self, v): return self._methodcall(lib.computeparams_set_num_hashes, v) @property def track_abundance(self): return self._methodcall(lib.computeparams_track_abundance) @track_abundance.setter def track_abundance(self, v): return self._methodcall(lib.computeparams_set_track_abundance, v) @property def scaled(self): return self._methodcall(lib.computeparams_scaled) @scaled.setter def scaled(self, v): return self._methodcall(lib.computeparams_set_scaled, int(v)) sourmash-bio-sourmash-bc3c0f8/src/sourmash/commands.py000066400000000000000000001637161474157046300233070ustar00rootroot00000000000000""" Functions implementing the main command-line subcommands. """ import csv import os import os.path import sys import shutil import io import screed from .compare import ( compare_all_pairs, compare_serial_containment, compare_serial_max_containment, compare_serial_avg_containment, ) from . import MinHash from .sbtmh import load_sbt_index, create_sbt_index from . import signature as sig from . import sourmash_args from .logging import notify, error, print_results, set_quiet from .sourmash_args import FileOutput, FileOutputCSV, SaveSignaturesToLocation from .search import prefetch_database, PrefetchResult from .index import LazyLinearIndex WATERMARK_SIZE = 10000 def _get_screen_width(): # default fallback is 80x24 (col, rows) = shutil.get_terminal_size() return col def compare(args): "Compare multiple signature files and create a distance matrix." import numpy set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) pattern_search = sourmash_args.load_include_exclude_db_patterns(args) inp_files = list(args.signatures) if args.from_file: more_files = sourmash_args.load_pathlist_from_file(args.from_file) inp_files.extend(more_files) progress = sourmash_args.SignatureLoadingProgress() # load in the various signatures siglist = [] ksizes = set() moltypes = set() size_may_be_inaccurate = False for filename in inp_files: notify(f"loading '{filename}'", end="\r") loaded = sourmash_args.load_file_as_signatures( filename, ksize=args.ksize, select_moltype=moltype, picklist=picklist, yield_all_files=args.force, progress=progress, pattern=pattern_search, ) loaded = list(loaded) if not loaded: notify( f"\nwarning: no signatures loaded at given ksize/molecule type/picklist from {filename}" ) # add to siglist; track ksizes/moltypes s = None for s in loaded: siglist.append((s, filename)) ksizes.add(s.minhash.ksize) moltypes.add(sourmash_args.get_moltype(s)) if s is None: notify( f"\nwarning: no signatures loaded at given ksize/molecule type/picklist from {filename}" ) # error out while loading if we have more than one ksize/moltype if len(ksizes) > 1 or len(moltypes) > 1: break if not siglist: error("no signatures found! exiting.") sys.exit(-1) # check ksizes and type if len(ksizes) > 1: error("multiple k-mer sizes loaded; please specify one with -k.") ksizes = sorted(ksizes) error("(saw k-mer sizes {})".format(", ".join(map(str, ksizes)))) sys.exit(-1) if len(moltypes) > 1: error("multiple molecule types loaded; please specify --dna, --protein") sys.exit(-1) notify(" " * 79, end="\r") notify(f"loaded {format(len(siglist))} signatures total.") if picklist: sourmash_args.report_picklist(args, picklist) # check to make sure they're potentially compatible - either using # scaled, or not. scaled_sigs = [s.minhash.scaled for (s, _) in siglist] is_scaled = all(scaled_sigs) is_scaled_2 = any(scaled_sigs) # complain if it's not all one or the other if is_scaled != is_scaled_2: error("ERROR: cannot mix scaled signatures with num signatures") sys.exit(-1) is_containment = False if args.containment or args.max_containment or args.avg_containment: is_containment = True containment_args = [ args.containment, args.max_containment, args.avg_containment, ] if sum(containment_args) > 1: notify("ERROR: cannot specify more than one containment argument!") sys.exit(-1) # complain if --containment and not is_scaled if is_containment and not is_scaled: error( "must use scaled signatures with --containment, --max-containment, and --avg-containment" ) sys.exit(-1) # complain if --ani and not is_scaled return_ani = False if args.estimate_ani: return_ani = True if return_ani and not is_scaled: error("must use scaled signatures with --estimate-ani") sys.exit(-1) # notify about implicit --ignore-abundance: if is_containment or return_ani: track_abundances = any(s.minhash.track_abundance for s, _ in siglist) if track_abundances: notify( "NOTE: --containment, --max-containment, --avg-containment, and --estimate-ani ignore signature abundances." ) # CTB: note, up to this point, we could do everything with manifests # w/o actually loading any signatures. I'm not sure the manifest # API allows it tho. # if using scaled sketches or --scaled, downsample to common max scaled. printed_scaled_msg = False if is_scaled: max_scaled = max(s.minhash.scaled for s, _ in siglist) if args.scaled: args.scaled = int(args.scaled) max_scaled = max(max_scaled, args.scaled) if max_scaled > args.scaled: notify( f"WARNING: --scaled specified {args.scaled}, but max scaled of sketches is {max_scaled}" ) notify(f"WARNING: continuing with scaled value of {max_scaled}.") new_siglist = [] for s, filename in siglist: if not size_may_be_inaccurate and not s.minhash.size_is_accurate(): size_may_be_inaccurate = True if s.minhash.scaled != max_scaled: if not printed_scaled_msg: notify( f"NOTE: downsampling to scaled value of {format(max_scaled)}" ) printed_scaled_msg = True with s.update() as s: s.minhash = s.minhash.downsample(scaled=max_scaled) new_siglist.append((s, filename)) else: new_siglist.append((s, filename)) siglist = new_siglist elif args.scaled is not None: error("ERROR: cannot specify --scaled with non-scaled signatures.") sys.exit(-1) if len(siglist) == 0: error("no signatures!") sys.exit(-1) notify("") # build the distance matrix numpy.set_printoptions(precision=3, suppress=True) # do all-by-all calculation labeltext = [str(ss) for ss, _ in siglist] sigsonly = [ss for ss, _ in siglist] if args.containment: similarity = compare_serial_containment(sigsonly, return_ani=return_ani) elif args.max_containment: similarity = compare_serial_max_containment(sigsonly, return_ani=return_ani) elif args.avg_containment: similarity = compare_serial_avg_containment(sigsonly, return_ani=return_ani) else: similarity = compare_all_pairs( sigsonly, args.ignore_abundance, n_jobs=args.processes, return_ani=return_ani, ) # if distance matrix desired, switch to 1-similarity if args.distance_matrix: matrix = 1 - similarity else: matrix = similarity if len(siglist) < 30: for i, (ss, filename) in enumerate(siglist): # for small matrices, pretty-print some output name_num = f"{i}-{str(ss)}" if len(name_num) > 20: name_num = name_num[:17] + "..." print_results( "{:20s}\t{}".format( name_num, matrix[ i, :, ], ) ) if args.distance_matrix: print_results("max distance in matrix: {:.3f}", numpy.max(matrix)) else: print_results("min similarity in matrix: {:.3f}", numpy.min(matrix)) # shall we output a matrix to stdout? if args.output: labeloutname = args.output + ".labels.txt" notify(f"saving labels to: {labeloutname}") with open(labeloutname, "w") as fp: fp.write("\n".join(labeltext)) notify(f"saving comparison matrix to: {args.output}") with open(args.output, "wb") as fp: numpy.save(fp, matrix) # output labels information via --labels-to? if args.labels_to: labeloutname = args.labels_to notify(f"saving labels to: {labeloutname}") with sourmash_args.FileOutputCSV(labeloutname) as fp: w = csv.writer(fp) w.writerow( ["sort_order", "md5", "label", "name", "filename", "signature_file"] ) for n, (ss, location) in enumerate(siglist): md5 = ss.md5sum() sigfile = location label = str(ss) name = ss.name filename = ss.filename w.writerow([str(n + 1), md5, label, name, filename, sigfile]) # output CSV? if args.csv: with FileOutputCSV(args.csv) as csv_fp: w = csv.writer(csv_fp) w.writerow(labeltext) for i in range(len(labeltext)): y = [] for j in range(len(labeltext)): y.append(str(matrix[i][j])) w.writerow(y) if size_may_be_inaccurate: if args.distance_matrix: notify( "WARNING: size estimation for at least one of these sketches may be inaccurate. ANI distances will be set to 1 for these comparisons." ) else: notify( "WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will be set to 1 for these comparisons." ) def plot(args): "Produce a clustering matrix and plot." import matplotlib as mpl mpl.use("Agg") import numpy import pylab import scipy.cluster.hierarchy as sch from . import fig as sourmash_fig # load files D_filename = args.distances notify(f"loading comparison matrix from {D_filename}...") with open(D_filename, "rb") as f: D = numpy.load(f) # not sure how to change this to use f-strings notify("...got {} x {} matrix.", *D.shape) # see sourmash#2790 for details :) if args.labeltext or args.labels or args.labels_from: if args.labeltext and args.labels_from: notify("ERROR: cannot supply both --labeltext and --labels-from") sys.exit(-1) display_labels = True args.labels = True # override => labels always true elif args.labels is None and not args.indices: # default to labels args.labels = True display_labels = True elif args.indices or (not args.labels and args.indices is None): # turn on indices only, not label names args.indices = True display_labels = True else: display_labels = False if args.labels_from: labelfilename = args.labels_from notify(f"loading labels from CSV file '{labelfilename}'") labeltext = [] with sourmash_args.FileInputCSV(labelfilename) as r: for row in r: order, label = row["sort_order"], row["label"] labeltext.append((int(order), label)) labeltext.sort() labeltext = [t[1] for t in labeltext] elif args.labels: if args.labeltext: labelfilename = args.labeltext else: labelfilename = D_filename + ".labels.txt" notify(f"loading labels from text file '{labelfilename}'") with open(labelfilename) as f: labeltext = [x.strip() for x in f] if len(labeltext) != D.shape[0]: error("{} labels != matrix size, exiting", len(labeltext)) sys.exit(-1) elif args.indices: # construct integer labels labeltext = [str(i + 1) for i in range(D.shape[0])] else: assert not display_labels labeltext = [""] * D.shape[0] if args.pdf: ext = ".pdf" else: ext = ".png" # build filenames, decide on PDF/PNG output dendrogram_out = os.path.basename(D_filename) + ".dendro" + ext matrix_out = os.path.basename(D_filename) + ".matrix" + ext hist_out = os.path.basename(D_filename) + ".hist" + ext # output to a different directory? if args.output_dir: if not os.path.isdir(args.output_dir): os.mkdir(args.output_dir) dendrogram_out = os.path.join(args.output_dir, dendrogram_out) matrix_out = os.path.join(args.output_dir, matrix_out) hist_out = os.path.join(args.output_dir, hist_out) # make the histogram notify(f"saving histogram of matrix values => {hist_out}") fig = pylab.figure(figsize=(8, 5)) pylab.hist(numpy.array(D.flat), bins=100) fig.savefig(hist_out) ### make the dendrogram: fig = pylab.figure(figsize=(8, 5)) ax1 = fig.add_axes([0.1, 0.1, 0.7, 0.8]) ax1.set_xticks([]) ax1.set_yticks([]) # subsample? if args.subsample: numpy.random.seed(args.subsample_seed) sample_idx = list(range(len(labeltext))) numpy.random.shuffle(sample_idx) sample_idx = sample_idx[: args.subsample] np_idx = numpy.array(sample_idx) D = D[numpy.ix_(np_idx, np_idx)] labeltext = [labeltext[idx] for idx in sample_idx] ### do clustering Y = sch.linkage(D, method="single") sch.dendrogram( Y, orientation="right", labels=labeltext, no_labels=not display_labels ) fig.savefig(dendrogram_out) notify(f"wrote dendrogram to: {dendrogram_out}") ### make the dendrogram+matrix: (fig, rlabels, rmat) = sourmash_fig.plot_composite_matrix( D, labeltext, show_labels=display_labels, vmin=args.vmin, vmax=args.vmax, force=args.force, ) fig.savefig(matrix_out) notify(f"wrote numpy distance matrix to: {matrix_out}") if len(labeltext) < 30: # for small matrices, print out sample numbering for FYI. for i, name in enumerate(labeltext): print_results("{}\t{}", i, name) # write out re-ordered matrix and labels if args.csv: with FileOutputCSV(args.csv) as csv_fp: w = csv.writer(csv_fp) w.writerow(rlabels) for i in range(len(rlabels)): y = [] for j in range(len(rlabels)): y.append(f"{rmat[i][j]}") w.writerow(y) notify(f"Wrote clustered matrix and labels out to {args.csv}") def import_csv(args): "Import a CSV file full of signatures/hashes." with open(args.mash_csvfile, newline="") as fp: reader = csv.reader(fp) siglist = [] for row in reader: hashfn = row[0] hashseed = int(row[1]) # only support a limited import type, for now ;) assert hashfn == "murmur64" assert hashseed == 42 _, _, ksize, name, hashes = row ksize = int(ksize) hashes = hashes.strip() hashes = list(map(int, hashes.split(" "))) e = MinHash(len(hashes), ksize) e.add_many(hashes) s = sig.SourmashSignature(e, filename=name) siglist.append(s) notify(f"loaded signature: {name} {s.md5sum()[:8]}") notify(f"saving {len(siglist)} signatures to JSON") with SaveSignaturesToLocation(args.output) as save_sig: save_sig.add_many(siglist) def sbt_combine(args): inp_files = list(args.sbts) notify(f"combining {len(inp_files)} SBTs") tree = load_sbt_index(inp_files.pop(0)) for f in inp_files: new_tree = load_sbt_index(f) # TODO: check if parameters are the same for both trees! tree.combine(new_tree) notify(f'saving SBT under "{args.sbt_name}".') tree.save(args.sbt_name) def index(args): """ Build a Sequence Bloom Tree index of the given signatures. """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) if args.append: tree = load_sbt_index(args.sbt_name) else: tree = create_sbt_index(args.bf_size, n_children=args.n_children) if args.sparseness < 0 or args.sparseness > 1.0: error("sparseness must be in range [0.0, 1.0].") if args.scaled: args.scaled = int(args.scaled) notify(f"downsampling signatures to scaled={args.scaled}") inp_files = list(args.signatures) if args.from_file: more_files = sourmash_args.load_pathlist_from_file(args.from_file) inp_files.extend(more_files) if not inp_files: error("ERROR: no files to index!? Supply on command line or use --from-file") sys.exit(-1) notify(f"loading {len(inp_files)} files into SBT") progress = sourmash_args.SignatureLoadingProgress() n = 0 ksizes = set() moltypes = set() nums = set() scaleds = set() for f in inp_files: siglist = sourmash_args.load_file_as_signatures( f, ksize=args.ksize, select_moltype=moltype, yield_all_files=args.force, picklist=picklist, progress=progress, ) # load all matching signatures in this file ss = None for ss in siglist: ksizes.add(ss.minhash.ksize) moltypes.add(sourmash_args.get_moltype(ss)) nums.add(ss.minhash.num) with ss.update() as ss: if args.scaled: ss.minhash = ss.minhash.downsample(scaled=args.scaled) if ss.minhash.track_abundance: ss.minhash = ss.minhash.flatten() scaleds.add(ss.minhash.scaled) tree.insert(ss) n += 1 if not ss: continue # check to make sure we aren't loading incompatible signatures if len(ksizes) > 1 or len(moltypes) > 1: error("multiple k-mer sizes or molecule types present; fail.") error("specify --dna/--protein and --ksize as necessary") error( "ksizes: {}; moltypes: {}", ", ".join(map(str, ksizes)), ", ".join(moltypes), ) sys.exit(-1) if nums == {0} and len(scaleds) == 1: pass # good elif scaleds == {0} and len(nums) == 1: pass # also good else: error("trying to build an SBT with incompatible signatures.") error("nums = {}; scaleds = {}", repr(nums), repr(scaleds)) sys.exit(-1) notify("") # did we load any!? if n == 0: error("no signatures found to load into tree!? failing.") sys.exit(-1) if picklist: sourmash_args.report_picklist(args, picklist) notify(f'loaded {n} sigs; saving SBT under "{args.sbt_name}"') tree.save(args.sbt_name, sparseness=args.sparseness) if tree.storage: tree.storage.close() def search(args): from .search import ( search_databases_with_flat_query, search_databases_with_abund_query, ) set_quiet(args.quiet, args.debug) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) pattern_search = sourmash_args.load_include_exclude_db_patterns(args) # set up the query. query = sourmash_args.load_query_signature( args.query, ksize=args.ksize, select_moltype=moltype, select_md5=args.md5 ) notify( f"loaded query: {str(query)[:30]}... (k={query.minhash.ksize}, {sourmash_args.get_moltype(query)})" ) if args.scaled: if not query.minhash.scaled: error("cannot downsample a signature not created with --scaled") sys.exit(-1) if args.scaled != query.minhash.scaled: notify( f"downsampling query from scaled={query.minhash.scaled} to {int(args.scaled)}" ) with query.update() as query: query.minhash = query.minhash.downsample(scaled=args.scaled) # set up the search databases is_containment = args.containment or args.max_containment if is_containment: if args.containment and args.max_containment: notify("ERROR: cannot specify both --containment and --max-containment!") sys.exit(-1) databases = sourmash_args.load_dbs_and_sigs( args.databases, query, not is_containment, picklist=picklist, pattern=pattern_search, fail_on_empty_database=args.fail_on_empty_database, ) # handle signatures with abundance if query.minhash.track_abundance: if args.ignore_abundance: if query.minhash.track_abundance: # abund sketch + ignore abundance => flatten sketch. with query.update() as query: query.minhash = query.minhash.flatten() elif args.containment or args.max_containment: # abund sketch + keep abundance => no containment searches notify( "ERROR: cannot do containment searches on an abund signature; maybe specify --ignore-abundance?" ) sys.exit(-1) else: # forcibly ignore abundances if query has no abundances args.ignore_abundance = True # do the actual search if query.minhash.track_abundance: try: results = search_databases_with_abund_query( query, databases, threshold=args.threshold, do_containment=args.containment, do_max_containment=args.max_containment, best_only=args.best_only, unload_data=True, ) except TypeError as exc: error(f"ERROR: {str(exc)}") sys.exit(-1) else: results = search_databases_with_flat_query( query, databases, threshold=args.threshold, do_containment=args.containment, do_max_containment=args.max_containment, best_only=args.best_only, unload_data=True, estimate_ani_ci=args.estimate_ani_ci, ) n_matches = len(results) if args.best_only: args.num_results = 1 if not args.num_results or n_matches <= args.num_results: print_results(f"{len(results)} matches above threshold {args.threshold:0.3f}:") else: print_results( f"{len(results)} matches above threshold {args.threshold:0.3f}; showing first {args.num_results}:" ) n_matches = args.num_results size_may_be_inaccurate = False jaccard_ani_untrustworthy = False # output! print_results("similarity match") print_results("---------- -----") for sr in results[:n_matches]: pct = f"{sr.similarity * 100:.1f}%" name = sr.match._display_name(60) print_results("{:>6} {}", pct, name) if sr.cmp_scaled is not None: if not size_may_be_inaccurate and sr.size_may_be_inaccurate: size_may_be_inaccurate = True if not is_containment and sr.cmp.jaccard_ani_untrustworthy: jaccard_ani_untrustworthy = True if args.best_only: notify("** reporting only one match because --best-only was set") writer = None if args.output: with FileOutputCSV(args.output) as fp: for sr in results: # if this is the first result we're writing, initialize the csv, return writer if writer is None: writer = sr.init_dictwriter(fp) sr.write(writer) # save matching signatures upon request if args.save_matches: notify(f'saving all matched signatures to "{args.save_matches}"') with SaveSignaturesToLocation(args.save_matches) as save_sig: for sr in results: save_sig.add(sr.match) if picklist: sourmash_args.report_picklist(args, picklist) if size_may_be_inaccurate: notify( "WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will not be reported for these comparisons." ) if jaccard_ani_untrustworthy: notify( "WARNING: Jaccard estimation for at least one of these comparisons is likely inaccurate. Could not estimate ANI for these comparisons." ) def categorize(args): "Use a database to find the best match to many signatures." from .index import MultiIndex from .search import make_jaccard_search_query set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) # eliminate names we've already categorized already_names = set() if args.load_csv: with open(args.load_csv, newline="") as fp: r = csv.reader(fp) for row in r: already_names.add(row[0]) # load search database db = sourmash_args.load_file_as_index(args.database) if args.ksize or moltype: db = db.select(ksize=args.ksize, moltype=moltype) # utility function to load & select relevant signatures. def _yield_all_sigs(queries, ksize, moltype): for filename in queries: mi = MultiIndex.load_from_path(filename, False) mi = mi.select(ksize=ksize, moltype=moltype) yield from mi.signatures_with_location() csv_w = None csv_fp = None if args.csv: csv_fp = open(args.csv, "w", newline="") csv_w = csv.writer(csv_fp) search_obj = make_jaccard_search_query(threshold=args.threshold) for orig_query, loc in _yield_all_sigs(args.queries, args.ksize, moltype): # skip if we've already done signatures from this file. if loc in already_names: continue notify( f"loaded query: {str(orig_query)[:30]}... (k={orig_query.minhash.ksize}, {orig_query.minhash.moltype})" ) if args.ignore_abundance and orig_query.minhash.track_abundance: query = orig_query.copy() with query.update() as query: query.minhash = query.minhash.flatten() else: if orig_query.minhash.track_abundance: notify( "ERROR: this search cannot be done on signatures calculated with abundance." ) notify("ERROR: please specify --ignore-abundance.") sys.exit(-1) query = orig_query.copy() results = [] for sr in db.find(search_obj, query): match = sr.signature if match.md5sum() != query.md5sum(): # ignore self. results.append((orig_query.similarity(match), match)) if results: results.sort(key=lambda x: -x[0]) # reverse sort on similarity best_hit_sim, best_hit_query = results[0] notify(f"for {query}, found: {best_hit_sim:.2f} {best_hit_query}") best_hit_query_name = best_hit_query.name if csv_w: csv_w.writerow([loc, query, best_hit_query_name, best_hit_sim]) else: notify(f"for {query}, no match found") if csv_fp: csv_fp.close() def gather(args): from .search import GatherDatabases, format_bp set_quiet(args.quiet, args.debug) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) pattern_search = sourmash_args.load_include_exclude_db_patterns(args) # load the query signature & figure out all the things query = sourmash_args.load_query_signature( args.query, ksize=args.ksize, select_moltype=moltype, select_md5=args.md5 ) notify( f"loaded query: {str(query)[:30]}... (k={query.minhash.ksize}, {sourmash_args.get_moltype(query)})" ) # verify signature was computed right. if not query.minhash.scaled: error("query signature needs to be created with --scaled") sys.exit(-1) if args.scaled and args.scaled != query.minhash.scaled: notify( f"downsampling query from scaled={query.minhash.scaled} to {int(args.scaled)}" ) with query.update() as query: query.minhash = query.minhash.downsample(scaled=args.scaled) # empty? if not len(query.minhash): error("no query hashes!? exiting.") sys.exit(-1) # set up the search databases cache_size = args.cache_size if args.cache_size == 0: cache_size = None databases = sourmash_args.load_dbs_and_sigs( args.databases, query, False, cache_size=cache_size, picklist=picklist, pattern=pattern_search, fail_on_empty_database=args.fail_on_empty_database, ) if args.linear: # force linear traversal? databases = [LazyLinearIndex(db) for db in databases] size_may_be_inaccurate = False if args.prefetch: # note: on by default! notify("Starting prefetch sweep across databases.") prefetch_query = query.copy() if prefetch_query.minhash.track_abundance: with prefetch_query.update() as prefetch_query: prefetch_query.minhash = prefetch_query.minhash.flatten() noident_mh = prefetch_query.minhash.to_mutable() save_prefetch = SaveSignaturesToLocation(args.save_prefetch) save_prefetch.open() # set up prefetch CSV output prefetch_csvout_fp = None prefetch_csvout_w = None if args.save_prefetch_csv: prefetch_csvout_fp = FileOutputCSV(args.save_prefetch_csv).open() query_mh = prefetch_query.minhash scaled = query_mh.scaled counters = [] ident_mh = noident_mh.copy_and_clear() for db in databases: counter = None try: counter = db.counter_gather(prefetch_query, args.threshold_bp) except ValueError: # catch "no signatures to search" ValueError if empty db. continue save_prefetch.add_many(counter.signatures()) # update found/not found hashes from the union/intersection of # found. union_found = counter.union_found ident_mh.add_many(union_found) noident_mh.remove_many(union_found) # optionally calculate and output prefetch info to csv if prefetch_csvout_fp: for found_sig in counter.signatures(): # calculate intersection stats and info prefetch_result = PrefetchResult( prefetch_query, found_sig, cmp_scaled=scaled, threshold_bp=args.threshold_bp, estimate_ani_ci=args.estimate_ani_ci, ) if prefetch_csvout_w is None: prefetch_csvout_w = prefetch_result.init_dictwriter( prefetch_csvout_fp ) prefetch_result.write(prefetch_csvout_w) counters.append(counter) # flush csvout so that things get saved progressively if prefetch_csvout_fp: prefetch_csvout_fp.flush() display_bp = format_bp(args.threshold_bp) notify( f"Prefetch found {len(save_prefetch)} signatures with overlap >= {display_bp}." ) save_prefetch.close() if prefetch_csvout_fp: prefetch_csvout_fp.close() else: counters = databases # we can't track unidentified hashes w/o prefetch noident_mh = None ident_mh = None ## ok! now do gather - notify("Doing gather to generate minimum metagenome cover.") found = 0 is_abundance = query.minhash.track_abundance and not args.ignore_abundance orig_query_mh = query.minhash if not orig_query_mh.size_is_accurate(): size_may_be_inaccurate = True gather_iter = GatherDatabases( query, counters, threshold_bp=args.threshold_bp, ignore_abundance=args.ignore_abundance, noident_mh=noident_mh, ident_mh=ident_mh, estimate_ani_ci=args.estimate_ani_ci, ) screen_width = _get_screen_width() sum_f_uniq_found = 0.0 result = None ### open output handles as needed for (1) saving CSV (2) saving matches # save matching signatures? if args.save_matches: notify(f"saving all matches to '{args.save_matches}'") save_sig_obj = SaveSignaturesToLocation(args.save_matches) save_sig = save_sig_obj.__enter__() else: save_sig_obj = None save_sig = None # save CSV? csv_outfp = io.StringIO() csv_writer = None try: for result in gather_iter: found += 1 sum_f_uniq_found += result.f_unique_to_query if found == 1: # first result? print header. if is_abundance: print_results("") print_results("overlap p_query p_match avg_abund") print_results("--------- ------- ------- ---------") else: print_results("") print_results("overlap p_query p_match") print_results("--------- ------- -------") # print interim result & save in `found` list for later use pct_query = f"{result.f_unique_weighted * 100:.1f}%" pct_genome = f"{result.f_match * 100:.1f}%" if is_abundance: name = result.match._display_name(screen_width - 41) average_abund = f"{result.average_abund:.1f}" print_results( "{:9} {:>7} {:>7} {:>9} {}", format_bp(result.intersect_bp), pct_query, pct_genome, average_abund, name, ) else: name = result.match._display_name(screen_width - 31) print_results( "{:9} {:>7} {:>7} {}", format_bp(result.intersect_bp), pct_query, pct_genome, name, ) # write out CSV if args.output: if csv_writer is None: csv_writer = result.init_dictwriter(csv_outfp) result.write(csv_writer) # save matches? if save_sig is not None: save_sig.add(result.match) if args.num_results and found >= args.num_results: break finally: if save_sig_obj: save_sig_obj.close() save_sig_obj = None save_sig = None # report on thresholding - if gather_iter.query: # if still a query, then we failed the threshold. notify(f"found less than {format_bp(args.threshold_bp)} in common. => exiting") # basic reporting: if found: print_results(f"\nfound {found} matches total;") if found == args.num_results: print_results( f"(truncated gather because --num-results={args.num_results})" ) else: display_bp = format_bp(args.threshold_bp) notify(f"\nNo matches found for --threshold-bp at {display_bp}.") if found: if is_abundance and result: p_covered = result.sum_weighted_found / result.total_weighted_hashes p_covered *= 100 print_results( f"the recovered matches hit {p_covered:.1f}% of the abundance-weighted query." ) print_results( f"the recovered matches hit {sum_f_uniq_found * 100:.1f}% of the query k-mers (unweighted)." ) print_results("") if gather_iter.scaled != query.minhash.scaled: print_results( f"WARNING: final scaled was {gather_iter.scaled}, vs query scaled of {query.minhash.scaled}" ) # save CSV? if (found and args.output) or args.create_empty_results: with FileOutputCSV(args.output) as fp: fp.write(csv_outfp.getvalue()) # save unassigned hashes? if args.output_unassigned: remaining_query = gather_iter.query if not (remaining_query.minhash or noident_mh): notify("no unassigned hashes to save with --output-unassigned!") else: notify(f"saving unassigned hashes to '{args.output_unassigned}'") if noident_mh: remaining_mh = remaining_query.minhash.to_mutable() remaining_mh += noident_mh remaining_query.minhash = remaining_mh if is_abundance: abund_query_mh = remaining_query.minhash.inflate(orig_query_mh) remaining_query.minhash = abund_query_mh with SaveSignaturesToLocation(args.output_unassigned) as save_sig: save_sig.add(remaining_query) if picklist: sourmash_args.report_picklist(args, picklist) if size_may_be_inaccurate: notify( "WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will not be reported for these comparisons." ) # DONE w/gather function. def multigather(args): "Gather many signatures against multiple databases." from .search import GatherDatabases, format_bp set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) if not args.db: error("Error! must specify at least one database with --db") sys.exit(-1) if not args.query and not args.query_from_file: error("Error! must specify at least one query signature with --query") sys.exit(-1) # flatten --db and --query args.db = [item for sublist in args.db for item in sublist] inp_files = [item for sublist in args.query for item in sublist] if args.query_from_file: more_files = sourmash_args.load_pathlist_from_file(args.query_from_file) inp_files.extend(more_files) # need a query to get ksize, moltype for db loading query = next( iter( sourmash_args.load_file_as_signatures( inp_files[0], ksize=args.ksize, select_moltype=moltype ) ) ) notify( f"loaded first query: {str(query)[:30]}... (k={query.minhash.ksize}, {sourmash_args.get_moltype(query)})" ) databases = sourmash_args.load_dbs_and_sigs( args.db, query, False, fail_on_empty_database=args.fail_on_empty_database ) # run gather on all the queries. n = 0 size_may_be_inaccurate = False output_base_tracking = set() # make sure we are not reusing 'output_base' for queryfile in inp_files: # load the query signature(s) & figure out all the things for query in sourmash_args.load_file_as_signatures( queryfile, ksize=args.ksize, select_moltype=moltype ): notify( f"loaded query: {str(query)[:30]}... (k={query.minhash.ksize}, {sourmash_args.get_moltype(query)})" ) # verify signature was computed right. if not query.minhash.scaled: error("query signature needs to be created with --scaled; skipping") continue if args.scaled and args.scaled != query.minhash.scaled: notify( f"downsampling query from scaled={query.minhash.scaled} to {int(args.scaled)}" ) with query.update() as query: query.minhash = query.minhash.downsample(scaled=args.scaled) # empty? if not len(query.minhash): error("no query hashes!? skipping to next..") continue counters = [] prefetch_query = query.copy() if prefetch_query.minhash.track_abundance: with prefetch_query.update() as prefetch_query: prefetch_query.minhash = prefetch_query.minhash.flatten() ident_mh = prefetch_query.minhash.copy_and_clear() noident_mh = prefetch_query.minhash.to_mutable() counters = [] for db in databases: try: counter = db.counter_gather(prefetch_query, args.threshold_bp) except ValueError: # catch "no signatures to search" ValueError if empty db. continue counters.append(counter) # track found/not found hashes union_found = counter.union_found noident_mh.remove_many(union_found) ident_mh.add_many(union_found) found = 0 is_abundance = query.minhash.track_abundance and not args.ignore_abundance orig_query_mh = query.minhash gather_iter = GatherDatabases( query, counters, threshold_bp=args.threshold_bp, ignore_abundance=args.ignore_abundance, noident_mh=noident_mh, ident_mh=ident_mh, ) screen_width = _get_screen_width() sum_f_uniq_found = 0.0 result = None query_filename = query.filename if not query_filename or query_filename == "-": # use md5sum if query.filename not properly set output_base = query.md5sum() elif args.output_add_query_md5sum: # Uniquify the output file if all signatures were made from the same file (e.g. with --singleton) assert query_filename and query_filename != "-" # first branch output_base = os.path.basename(query_filename) + "." + query.md5sum() else: output_base = os.path.basename(query_filename) if args.output_dir: output_base = os.path.join(args.output_dir, output_base) # track overwrites of output files! if output_base in output_base_tracking: error( f"ERROR: detected overwritten outputs! '{output_base}' has already been used. Failing." ) if args.force_allow_overwrite_output: error("continuing because --force-allow-overwrite was specified") else: error( "Consider using '-U/--output-add-query-md5sum' to build unique outputs" ) error("and/or '--force-allow-overwrite-output'") sys.exit(-1) output_base_tracking.add(output_base) output_matches = output_base + ".matches.sig" save_sig_obj = SaveSignaturesToLocation(output_matches) save_sig = save_sig_obj.__enter__() notify(f"saving all matching signatures to '{output_matches}'") # write out basic CSV file output_csv = output_base + ".csv" notify(f'saving all CSV matches to "{output_csv}"') csv_out_obj = FileOutputCSV(output_csv) csv_outfp = csv_out_obj.__enter__() csv_writer = None for result in gather_iter: found += 1 sum_f_uniq_found += result.f_unique_to_query if found == 1: # first result? print header. if is_abundance: print_results("") print_results("overlap p_query p_match avg_abund") print_results("--------- ------- ------- ---------") else: print_results("") print_results("overlap p_query p_match") print_results("--------- ------- -------") # print interim result & save in a list for later use pct_query = f"{result.f_unique_weighted * 100:.1f}%" pct_genome = f"{result.f_match * 100:.1f}%" if is_abundance: name = result.match._display_name(screen_width - 41) average_abund = f"{result.average_abund:.1f}" print_results( "{:9} {:>7} {:>7} {:>9} {}", format_bp(result.intersect_bp), pct_query, pct_genome, average_abund, name, ) else: name = result.match._display_name(screen_width - 31) print_results( "{:9} {:>7} {:>7} {}", format_bp(result.intersect_bp), pct_query, pct_genome, name, ) ## @CTB if csv_writer is None: csv_writer = result.init_dictwriter(csv_outfp) result.write(csv_writer) save_sig.add(result.match) # check for size estimation accuracy, which impacts ANI estimation if not size_may_be_inaccurate and result.size_may_be_inaccurate: size_may_be_inaccurate = True # report on thresholding - if gather_iter.query.minhash: # if still a query, then we failed the threshold. notify( f"found less than {format_bp(args.threshold_bp)} in common. => exiting" ) # basic reporting print_results("\nfound {} matches total;", found) # close saving etc. save_sig_obj.close() save_sig_obj = save_sig = None csv_out_obj.close() csv_out_obj = csv_outfp = csv_writer = None if is_abundance and result: p_covered = result.sum_weighted_found / result.total_weighted_hashes p_covered *= 100 print_results( f"the recovered matches hit {p_covered:.1f}% of the abundance-weighted query." ) print_results( f"the recovered matches hit {sum_f_uniq_found * 100:.1f}% of the query k-mers (unweighted)." ) print_results("") if found == 0: notify("nothing found... skipping.") continue output_unassigned = output_base + f".unassigned{args.extension}" remaining_query = gather_iter.query if noident_mh: remaining_mh = remaining_query.minhash.to_mutable() remaining_mh += noident_mh.downsample(scaled=remaining_mh.scaled) remaining_query.minhash = remaining_mh if is_abundance: abund_query_mh = remaining_query.minhash.inflate(orig_query_mh) remaining_query.minhash = abund_query_mh if found == 0: notify("nothing found - entire query signature unassigned.") elif not remaining_query: notify("no unassigned hashes! not saving.") else: notify(f'saving unassigned hashes to "{output_unassigned}"') with SaveSignaturesToLocation(output_unassigned) as save_sig: save_sig.add(remaining_query) n += 1 # fini, next query! # done! report at end. notify(f"\nconducted gather searches on {n} signatures") if size_may_be_inaccurate: notify( "WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will not be reported for these comparisons." ) def watch(args): "Build a signature from raw FASTA/FASTQ coming in on stdin, search." set_quiet(args.quiet) if args.input_is_protein and args.dna: notify("WARNING: input is protein, turning off nucleotide hashing.") args.dna = False args.protein = True if args.dna and args.protein: notify('ERROR: cannot use "watch" with both nucleotide and protein.') if args.dna: moltype = "DNA" is_protein = False dayhoff = False hp = False elif args.protein: moltype = "protein" is_protein = True dayhoff = False hp = False elif args.dayhoff: moltype = "dayhoff" is_protein = True dayhoff = True hp = False else: moltype = "hp" is_protein = True dayhoff = False hp = True tree = load_sbt_index(args.sbt_name) # check ksize from the SBT we are loading ksize = args.ksize if ksize is None: leaf = next(iter(tree.leaves())) tree_mh = leaf.data.minhash ksize = tree_mh.ksize E = MinHash( ksize=ksize, n=args.num_hashes, is_protein=is_protein, dayhoff=dayhoff, hp=hp ) notify(f"Computing signature for k={ksize}, {moltype} from stdin") def do_search(): results = [] streamsig = sig.SourmashSignature(E, filename="stdin", name=args.name) for similarity, match, _ in tree.search( streamsig, threshold=args.threshold, best_only=True, ignore_abundance=True, do_containment=False, ): results.append((similarity, match)) return results notify("reading sequences from stdin") watermark = WATERMARK_SIZE # iterate over input records n = 0 with screed.open(args.inp_file) as screed_iter: for n, record in enumerate(screed_iter): # at each watermark, print status & check cardinality if n >= watermark: notify(f"\r... read {n} sequences", end="") watermark += WATERMARK_SIZE if do_search(): break if args.input_is_protein: E.add_protein(record.sequence) else: E.add_sequence(record.sequence, False) results = do_search() if not results: notify(f"... read {n} sequences, no matches found.") else: results.sort(key=lambda x: -x[0]) # take best similarity, found_sig = results[0] print_results("FOUND: {}, at {:.3f}", found_sig, similarity) if args.output: notify(f"saving signature to '{args.output}'") streamsig = sig.SourmashSignature(E, filename="stdin", name=args.name) with SaveSignaturesToLocation(args.output) as save_sig: save_sig.add(streamsig) def migrate(args): "Migrate an SBT database to the latest version." tree = load_sbt_index(args.sbt_name, print_version_warning=False) notify(f'saving SBT under "{args.sbt_name}".') tree.save(args.sbt_name, structure_only=True) def prefetch(args): "Output the 'raw' results of a containment/overlap search." # load databases from files, too. if args.db_from_file: more_db = sourmash_args.load_pathlist_from_file(args.db_from_file) args.databases.extend(more_db) if not args.databases: notify("ERROR: no databases or signatures to search!?") sys.exit(-1) if not ( args.save_unmatched_hashes or args.save_matching_hashes or args.save_matches or args.output ): notify( "WARNING: no output(s) specified! Nothing will be saved from this prefetch!" ) # figure out what k-mer size and molecule type we're looking for here ksize = args.ksize moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) pattern_search = sourmash_args.load_include_exclude_db_patterns(args) # load the query signature & figure out all the things query = sourmash_args.load_query_signature( args.query, ksize=args.ksize, select_moltype=moltype, select_md5=args.md5 ) notify( f"loaded query: {str(query)[:30]}... (k={query.minhash.ksize}, {sourmash_args.get_moltype(query)})" ) # verify signature was computed with scaled. if not query.minhash.scaled: error("query signature needs to be created with --scaled") sys.exit(-1) # if with track_abund, flatten me query_mh = query.minhash orig_query_mh = query_mh if query_mh.track_abundance: query_mh = query_mh.flatten() if args.scaled and args.scaled != query_mh.scaled: notify( f"downsampling query from scaled={query_mh.scaled} to {int(args.scaled)}" ) query_mh = query_mh.downsample(scaled=args.scaled) notify( f"query sketch has scaled={query_mh.scaled}; will be dynamically downsampled as needed." ) common_scaled = query_mh.scaled # empty? if not len(query_mh): error("no query hashes!? exiting.") sys.exit(-1) with query.update() as query: query.minhash = query_mh ksize = query_mh.ksize # set up CSV output, write headers, etc. csvout_fp = None csvout_w = None if args.output: csvout_fp = FileOutputCSV(args.output).open() # track & maybe save matches progressively matches_out = SaveSignaturesToLocation(args.save_matches) matches_out.open() if args.save_matches: notify(f"saving all matching database signatures to '{args.save_matches}'") # iterate over signatures in db one at a time, for each db; # find those with sufficient overlap ident_mh = query_mh.copy_and_clear() noident_mh = query_mh.to_mutable() did_a_search = False # track whether we did _any_ search at all! size_may_be_inaccurate = False total_signatures_loaded = 0 sum_signatures_after_select = 0 for dbfilename in args.databases: notify(f"loading signatures from '{dbfilename}'", end="\r") db = sourmash_args.load_file_as_index(dbfilename) total_signatures_loaded += len(db) # force linear traversal? if args.linear: db = LazyLinearIndex(db) db = db.select(ksize=ksize, moltype=moltype, containment=True) sum_signatures_after_select += len(db) db = sourmash_args.apply_picklist_and_pattern(db, picklist, pattern_search) if not db: notify(f"...no compatible signatures in '{dbfilename}'; skipping") continue for result in prefetch_database( query, db, args.threshold_bp, estimate_ani_ci=args.estimate_ani_ci ): match = result.match # ensure we're all on the same page wrt scaled resolution: common_scaled = max( match.minhash.scaled, query.minhash.scaled, common_scaled ) query_mh = query.minhash.downsample(scaled=common_scaled) match_mh = match.minhash.downsample(scaled=common_scaled) if ident_mh.scaled != common_scaled: ident_mh = ident_mh.downsample(scaled=common_scaled) if noident_mh.scaled != common_scaled: noident_mh = noident_mh.downsample(scaled=common_scaled) # track found & "untouched" hashes. ident_mh += query_mh & match_mh.flatten() noident_mh.remove_many(match_mh) # output match info as we go if csvout_fp: if csvout_w is None: csvout_w = result.init_dictwriter(csvout_fp) result.write(csvout_w) # output match signatures as we go (maybe) matches_out.add(match) if matches_out.count % 10 == 0: notify( f"total of {matches_out.count} matching signatures so far.", end="\r", ) # keep track of inaccurate size estimation if not size_may_be_inaccurate and result.size_may_be_inaccurate: size_may_be_inaccurate = True did_a_search = True # flush csvout so that things get saved progressively if csvout_fp: csvout_fp.flush() # delete db explicitly ('cause why not) del db notify("--") notify( f"loaded {total_signatures_loaded} total signatures from {len(args.databases)} locations." ) notify( f"after selecting signatures compatible with search, {sum_signatures_after_select} remain." ) if not did_a_search: notify( "ERROR in prefetch: after picklists and patterns, no signatures to search!?" ) sys.exit(-1) notify("--") notify(f"total of {matches_out.count} matching signatures.") matches_out.close() if csvout_fp: notify(f"saved {matches_out.count} matches to CSV file '{args.output}'") csvout_fp.close() assert len(query_mh) == len(ident_mh) + len(noident_mh) notify( f"of {len(query_mh)} distinct query hashes, {len(ident_mh)} were found in matches above threshold." ) notify(f"a total of {len(noident_mh)} query hashes remain unmatched.") notify(f"final scaled value (max across query and all matches) is {common_scaled}") if args.save_matching_hashes: filename = args.save_matching_hashes notify(f"saving {len(ident_mh)} matched hashes to '{filename}'") sig_name = "" if query.name: sig_name = f"{query.name}-known" # restore abundances, if present in original query if orig_query_mh.track_abundance: ident_mh = ident_mh.inflate(orig_query_mh) ss = sig.SourmashSignature(ident_mh, name=sig_name) with SaveSignaturesToLocation(filename) as save_sig: save_sig.add(ss) if args.save_unmatched_hashes: filename = args.save_unmatched_hashes sig_name = "" if query.name: sig_name = f"{query.name}-unknown" notify(f"saving {len(noident_mh)} unmatched hashes to '{filename}'") # restore abundances, if present in original query if orig_query_mh.track_abundance: noident_mh = noident_mh.inflate(orig_query_mh) ss = sig.SourmashSignature(noident_mh, name=sig_name) with SaveSignaturesToLocation(filename) as save_sig: save_sig.add(ss) if picklist: sourmash_args.report_picklist(args, picklist) if size_may_be_inaccurate: notify( "WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will not be reported for these comparisons." ) return 0 sourmash-bio-sourmash-bc3c0f8/src/sourmash/compare.py000066400000000000000000000321561474157046300231250ustar00rootroot00000000000000"""Functionality for comparing many signatures, used in sourmash compare.""" import itertools from functools import partial import time import multiprocessing from sourmash.sketchcomparison import FracMinHashComparison from .logging import notify from sourmash.np_utils import to_memmap def compare_serial(siglist, ignore_abundance, *, downsample=False, return_ani=False): """Compare all combinations of signatures and return a matrix of similarities. Processes combinations serially on a single process. Best to use when there is few signatures. :param list siglist: list of signatures to compare :param boolean ignore_abundance If the sketches are not abundance weighted, or ignore_abundance=True, compute Jaccard similarity. If the sketches are abundance weighted, calculate the angular similarity. :param boolean downsample by scaled if True :return: np.array similarity matrix """ import numpy as np n = len(siglist) jaccard_ani_untrustworthy = False potential_false_negatives = False # Combinations makes all unique sets of pairs, e.g. (A, B) but not (B, A) iterator = itertools.combinations(range(n), 2) similarities = np.ones((n, n)) for i, j in iterator: if return_ani: ani_result = siglist[i].jaccard_ani(siglist[j], downsample=downsample) if not potential_false_negatives and ani_result.p_exceeds_threshold: potential_false_negatives = True if not jaccard_ani_untrustworthy and ani_result.je_exceeds_threshold: jaccard_ani_untrustworthy = True ani = ani_result.ani if ani is None: ani = 0.0 similarities[i][j] = similarities[j][i] = ani else: similarities[i][j] = similarities[j][i] = siglist[i].similarity( siglist[j], ignore_abundance=ignore_abundance, downsample=downsample ) if jaccard_ani_untrustworthy: notify( "WARNING: Jaccard estimation for at least one of these comparisons is likely inaccurate. Could not estimate ANI for these comparisons." ) if potential_false_negatives: notify( "WARNING: Some of these sketches may have no hashes in common based on chance alone (false negatives). Consider decreasing your scaled value to prevent this." ) return similarities def compare_serial_containment(siglist, *, downsample=False, return_ani=False): """Compare all combinations of signatures and return a matrix of containments. Processes combinations serially on a single process. Best to only use when there are few signatures. :param list siglist: list of signatures to compare :param boolean downsample by scaled if True :return: np.array similarity matrix """ import numpy as np n = len(siglist) potential_false_negatives = False containments = np.ones((n, n)) for i in range(n): for j in range(n): if i == j: containments[i][j] = 1 elif return_ani: ani_result = siglist[j].containment_ani( siglist[i], downsample=downsample ) ani = ani_result.ani if not potential_false_negatives and ani_result.p_exceeds_threshold: potential_false_negatives = True if ani is None: ani = 0.0 containments[i][j] = ani else: containments[i][j] = siglist[j].contained_by( siglist[i], downsample=downsample ) if potential_false_negatives: notify( "WARNING: Some of these sketches may have no hashes in common based on chance alone (false negatives). Consider decreasing your scaled value to prevent this." ) return containments def compare_serial_max_containment(siglist, *, downsample=False, return_ani=False): """Compare all combinations of signatures and return a matrix of max_containments. Processes combinations serially on a single process. Best to only use when there are few signatures. :param list siglist: list of signatures to compare :param boolean downsample by scaled if True :return: np.array similarity matrix """ import numpy as np n = len(siglist) potential_false_negatives = False # Combinations makes all unique sets of pairs, e.g. (A, B) but not (B, A) iterator = itertools.combinations(range(n), 2) containments = np.ones((n, n)) for i, j in iterator: if return_ani: ani_result = siglist[j].max_containment_ani( siglist[i], downsample=downsample ) ani = ani_result.ani if not potential_false_negatives and ani_result.p_exceeds_threshold: potential_false_negatives = True if ani is None: ani = 0.0 containments[i][j] = containments[j][i] = ani else: containments[i][j] = containments[j][i] = siglist[j].max_containment( siglist[i], downsample=downsample ) if potential_false_negatives: notify( "WARNING: Some of these sketches may have no hashes in common based on chance alone (false negatives). Consider decreasing your scaled value to prevent this." ) return containments def compare_serial_avg_containment(siglist, *, downsample=False, return_ani=False): """Compare all combinations of signatures and return a matrix of avg_containments. Processes combinations serially on a single process. Best to only use when there are few signatures. :param list siglist: list of signatures to compare :param boolean downsample by scaled if True :return: np.array similarity matrix """ import numpy as np n = len(siglist) potential_false_negatives = False # Combinations makes all unique sets of pairs, e.g. (A, B) but not (B, A) iterator = itertools.combinations(range(n), 2) containments = np.ones((n, n)) for i, j in iterator: if return_ani: cmp = FracMinHashComparison(siglist[j].minhash, siglist[i].minhash) ani = cmp.avg_containment_ani if ani is None: ani = 0.0 if not potential_false_negatives and cmp.potential_false_negative: potential_false_negatives = True containments[i][j] = containments[j][i] = ani else: containments[i][j] = containments[j][i] = siglist[j].avg_containment( siglist[i], downsample=downsample ) if potential_false_negatives: notify( "WARNING: Some of these sketches may have no hashes in common based on chance alone (false negatives). Consider decreasing your scaled value to prevent this." ) return containments def similarity_args_unpack(args, ignore_abundance, *, downsample, return_ani=False): """Helper function to unpack the arguments. Written to use in pool.imap as it can only be given one argument.""" sig1, sig2 = args if return_ani: ani = sig1.jaccard_ani(sig2, downsample=downsample).ani if ani is None: ani = 0.0 return ani else: return sig1.similarity( sig2, ignore_abundance=ignore_abundance, downsample=downsample ) def get_similarities_at_index( index, ignore_abundance, downsample, siglist, *, return_ani=False ): """Returns similarities of all the combinations of signature at index in the siglist with the rest of the indices starting at index + 1. Doesn't redundantly calculate signatures with all the other indices prior to index - 1 :param int index: generate masks from this image :param boolean ignore_abundance If the sketches are not abundance weighted, or ignore_abundance=True, compute Jaccard similarity. If the sketches are abundance weighted, calculate the angular similarity. :param boolean downsample by scaled if True :param siglist list of signatures :return: list of similarities for the combinations of signature at index with rest of the signatures from index+1 """ startt = time.time() sig_iterator = itertools.product([siglist[index]], siglist[index + 1 :]) func = partial( similarity_args_unpack, ignore_abundance=ignore_abundance, downsample=downsample, return_ani=return_ani, ) similarity_list = list(map(func, sig_iterator)) notify( f"comparison for index {index} done in {time.time() - startt:.5f} seconds", end="\r", ) return similarity_list def compare_parallel( siglist, ignore_abundance, downsample, n_jobs, *, return_ani=False ): """Compare all combinations of signatures and return a matrix of similarities. Processes combinations parallely on number of processes given by n_jobs :param list siglist: list of signatures to compare :param boolean ignore_abundance If the sketches are not abundance weighted, or ignore_abundance=True, compute Jaccard similarity. If the sketches are abundance weighted, calculate the angular similarity. :param boolean downsample by scaled if True :param int n_jobs number of processes to run the similarity calculations on :return: np.array similarity matrix """ import numpy as np # Starting time - calculate time to keep track in case of lengthy siglist start_initial = time.time() # Create a memory map of the siglist using numpy to avoid memory burden # while accessing small parts in it siglist, _ = to_memmap(np.array(siglist)) notify("Created memmapped siglist") # Check that length of combinations can result in a square similarity matrix length_siglist = len(siglist) # Initialize with ones in the diagonal as the similarity of a signature with # itself is one similarities = np.eye(length_siglist, dtype=np.float64) memmap_similarities, filename = to_memmap(similarities) notify("Initialized memmapped similarities matrix") # Initialize the function using func.partial with the common arguments like # siglist, ignore_abundance, downsample, for computing all the signatures # The only changing parameter that will be mapped from the pool is the index func = partial( get_similarities_at_index, siglist=siglist, ignore_abundance=ignore_abundance, downsample=downsample, return_ani=return_ani, ) notify("Created similarity func") # Initialize multiprocess.pool pool = multiprocessing.Pool(processes=n_jobs) # Calculate chunk size, by default pool.imap chunk size is 1 chunksize, extra = divmod(length_siglist, n_jobs) if extra: chunksize += 1 notify("Calculated chunk size for multiprocessing") # This will not generate the results yet, since pool.imap returns a generator result = pool.imap(func, range(length_siglist), chunksize=chunksize) notify("Initialized multiprocessing pool.imap") # Enumerate and calculate similarities at each of the indices # and set the results at the appropriate combination coordinate # locations inside the similarity matrix for index, l in enumerate(result): startt = time.time() col_idx = index + 1 for idx_condensed, item in enumerate(l): memmap_similarities[index, col_idx + idx_condensed] = memmap_similarities[ idx_condensed + col_idx, index ] = item notify( f"Setting similarities matrix for index {index} done in {time.time() - startt:.5f} seconds", end="\r", ) notify("Setting similarities completed") pool.close() pool.join() notify( f"Time taken to compare all pairs parallely is {time.time() - start_initial:.5f} seconds " ) return np.memmap(filename, dtype=np.float64, shape=(length_siglist, length_siglist)) def compare_all_pairs( siglist, ignore_abundance, downsample=False, n_jobs=None, return_ani=False ): """Compare all combinations of signatures and return a matrix of similarities. Processes combinations either serially or based on parallely on number of processes given by n_jobs :param list siglist: list of signatures to compare :param boolean ignore_abundance If the sketches are not abundance weighted, or ignore_abundance=True, compute Jaccard similarity. If the sketches are abundance weighted, calculate the angular similarity. :param boolean downsample by scaled if True :param int n_jobs number of processes to run the similarity calculations on, if number of jobs is None or 1, compare serially, otherwise parallely. :return: np.array similarity matrix """ if n_jobs is None or n_jobs == 1: similarities = compare_serial( siglist, ignore_abundance=ignore_abundance, downsample=downsample, return_ani=return_ani, ) else: similarities = compare_parallel( siglist, ignore_abundance, downsample, n_jobs, return_ani=return_ani ) return similarities sourmash-bio-sourmash-bc3c0f8/src/sourmash/distance_utils.py000066400000000000000000000330761474157046300245130ustar00rootroot00000000000000""" Utilities for jaccard/containment --> distance estimation Equations from: https://github.com/KoslickiLab/mutation-rate-ci-calculator Reference: https://doi.org/10.1101/2022.01.11.475870 """ from dataclasses import dataclass, field from scipy.optimize import brentq from scipy.stats import norm as scipy_norm from scipy.stats import binom import numpy as np from math import log, exp from .logging import notify def check_distance(dist): if not 0 <= dist <= 1: raise ValueError(f"Error: distance value {dist:.4f} is not between 0 and 1!") else: return dist def check_prob_threshold(val, threshold=1e-3): """ Check likelihood of no shared hashes based on chance alone (false neg). If too many exceed threshold, recommend user lower their scaled value. # !! when using this, keep count and recommend user lower scaled val """ exceeds_threshold = False if threshold is not None and val > threshold: exceeds_threshold = True return val, exceeds_threshold def check_jaccard_error(val, threshold=1e-4): exceeds_threshold = False if threshold is not None and val > threshold: exceeds_threshold = True return val, exceeds_threshold @dataclass class ANIResult: """Base class for distance/ANI from k-mer containment.""" dist: float p_nothing_in_common: float p_threshold: float = 1e-3 size_is_inaccurate: bool = False p_exceeds_threshold: bool = field(init=False) def check_dist_and_p_threshold(self): # check values self.dist = check_distance(self.dist) self.p_nothing_in_common, self.p_exceeds_threshold = check_prob_threshold( self.p_nothing_in_common, self.p_threshold ) def __post_init__(self): self.check_dist_and_p_threshold() @property def ani(self): if self.size_is_inaccurate: return None return 1 - self.dist @dataclass class jaccardANIResult(ANIResult): """Class for distance/ANI from jaccard (includes jaccard_error).""" jaccard_error: float = None je_threshold: float = 1e-4 def __post_init__(self): # check values self.check_dist_and_p_threshold() # check jaccard error if self.jaccard_error is not None: self.jaccard_error, self.je_exceeds_threshold = check_jaccard_error( self.jaccard_error, self.je_threshold ) else: raise ValueError("Error: jaccard_error cannot be None.") @property def ani(self): # if jaccard error is too high (exceeds threshold), do not trust ANI estimate if self.je_exceeds_threshold or self.size_is_inaccurate: return None return 1 - self.dist @dataclass class ciANIResult(ANIResult): """ Class for distance/ANI from containment: with confidence intervals. Set CI defaults to None, just in case CI can't be estimated for given sample. """ dist_low: float = None dist_high: float = None def __post_init__(self): # check values self.check_dist_and_p_threshold() if self.dist_low is not None and self.dist_high is not None: self.dist_low = check_distance(self.dist_low) self.dist_high = check_distance(self.dist_high) @property def ani_low(self): if self.dist_high is None or self.size_is_inaccurate: return None return 1 - self.dist_high @property def ani_high(self): if self.dist_low is None or self.size_is_inaccurate: return None return 1 - self.dist_low def r1_to_q(k, r1): r1 = float(r1) q = 1 - (1 - r1) ** k return float(q) def var_n_mutated(L, k, r1, *, q=None): # there are computational issues in the variance formula that we solve here # by the use of higher-precision arithmetic; the problem occurs when r is # very small; for example, with L=10,k=2,r1=1e-6 standard precision # gives varN<0 which is nonsense; by using the mpf type, we get the correct # answer which is about 0.000038. if r1 == 0: return 0.0 r1 = float(r1) if q is None: # we assume that if q is provided, it is correct for r1 q = r1_to_q(k, r1) varN = ( L * (1 - q) * (q * (2 * k + (2 / r1) - 1) - 2 * k) + k * (k - 1) * (1 - q) ** 2 + (2 * (1 - q) / (r1**2)) * ((1 + (k - 1) * (1 - q)) * r1 - q) ) if varN < 0.0: # this seems to happen only with super tiny test data raise ValueError("Error: varN <0.0!") return float(varN) def exp_n_mutated(L, k, r1): q = r1_to_q(k, r1) return L * q def exp_n_mutated_squared(L, k, p): return var_n_mutated(L, k, p) + exp_n_mutated(L, k, p) ** 2 def probit(p): return scipy_norm.ppf(p) def handle_seqlen_nkmers(ksize, *, sequence_len_bp=None, n_unique_kmers=None): if n_unique_kmers is not None: return n_unique_kmers elif sequence_len_bp is None: # both are None, raise ValueError raise ValueError( "Error: distance estimation requires input of either 'sequence_len_bp' or 'n_unique_kmers'" ) else: n_unique_kmers = sequence_len_bp - (ksize - 1) return n_unique_kmers def set_size_chernoff(set_size, scaled, *, relative_error=0.05): """ Computes the probability that the estimate: sketch_size * scaled deviates from the true set_size by more than relative_error. This relies on the fact that the sketch_size is binomially distributed with parameters sketch_size and 1/scale. The two-sided Chernoff bounds are used. This is depreciated in favor of set_size_exact_prob due to the later being accurate even for very small set sizes @param set_size: The number of distinct k-mers in the given set @param relative_error: the desired relative error (defaults to 5%) @return: float (the upper bound probability) """ upper_bound = 1 - 2 * np.exp(-(relative_error**2) * set_size / (scaled * 3)) return upper_bound def set_size_exact_prob(set_size, scaled, *, relative_error=0.05): """ Computes the exact probability that the estimate: sketch_size * scaled deviates from the true set_size by more than relative_error. This relies on the fact that the sketch_size is binomially distributed with parameters sketch_size and 1/scale. The CDF of the binomial distribution is used. @param set_size: The number of distinct k-mers in the given set @param relative_error: the desired relative error (defaults to 5%) @return: float (the upper bound probability) """ # Need to check if the edge case is an integer or not. If not, don't include it in the equation pmf_arg = -set_size / scaled * (relative_error - 1) if pmf_arg == int(pmf_arg): prob = ( binom.cdf(set_size / scaled * (relative_error + 1), set_size, 1 / scaled) - binom.cdf(-set_size / scaled * (relative_error - 1), set_size, 1 / scaled) + binom.pmf(-set_size / scaled * (relative_error - 1), set_size, 1 / scaled) ) else: prob = binom.cdf( set_size / scaled * (relative_error + 1), set_size, 1 / scaled ) - binom.cdf(-set_size / scaled * (relative_error - 1), set_size, 1 / scaled) return prob def get_expected_log_probability(n_unique_kmers, ksize, mutation_rate, scaled_fraction): """helper function Note that scaled here needs to be between 0 and 1 (e.g. scaled 1000 --> scaled_fraction 0.001) """ exp_nmut = exp_n_mutated(n_unique_kmers, ksize, mutation_rate) try: return (n_unique_kmers - exp_nmut) * log(1.0 - scaled_fraction) except: return float("-inf") def get_exp_probability_nothing_common( mutation_rate, ksize, scaled, *, n_unique_kmers=None, sequence_len_bp=None ): """ Given parameters, calculate the expected probability that nothing will be common between a fracminhash sketch of a original sequence and a fracminhash sketch of a mutated sequence. If this is above a threshold, we should suspect that the two sketches may have nothing in common. The threshold needs to be set with proper insights. Arguments: n_unique_kmers, ksize, mutation_rate, scaled Returns: float - expected likelihood that nothing is common between sketches """ n_unique_kmers = handle_seqlen_nkmers( ksize, sequence_len_bp=sequence_len_bp, n_unique_kmers=n_unique_kmers ) f_scaled = 1.0 / float(scaled) if mutation_rate == 1.0: return 1.0 elif mutation_rate == 0.0: return 0.0 return exp( get_expected_log_probability(n_unique_kmers, ksize, mutation_rate, f_scaled) ) def containment_to_distance( containment, ksize, scaled, *, n_unique_kmers=None, sequence_len_bp=None, confidence=0.95, estimate_ci=False, prob_threshold=1e-3, ): """ Containment --> distance CI (one step) """ sol1, sol2, point_estimate = None, None, None n_unique_kmers = handle_seqlen_nkmers( ksize, sequence_len_bp=sequence_len_bp, n_unique_kmers=n_unique_kmers ) if containment == 0: # point_estimate = 1.0 point_estimate = sol1 = sol2 = 1.0 elif containment == 1: # point_estimate = 0.0 point_estimate = sol1 = sol2 = 0.0 else: point_estimate = 1.0 - containment ** (1.0 / ksize) if estimate_ci: try: alpha = 1 - confidence z_alpha = probit(1 - alpha / 2) f_scaled = ( 1.0 / scaled ) # these use scaled as a fraction between 0 and 1 bias_factor = 1 - (1 - f_scaled) ** n_unique_kmers term_1 = (1.0 - f_scaled) / ( f_scaled * n_unique_kmers**3 * bias_factor**2 ) def term_2(pest): return n_unique_kmers * exp_n_mutated( n_unique_kmers, ksize, pest ) - exp_n_mutated_squared(n_unique_kmers, ksize, pest) def term_3(pest): return ( var_n_mutated(n_unique_kmers, ksize, pest) / n_unique_kmers**2 ) def var_direct(pest): return term_1 * term_2(pest) + term_3(pest) def f1(pest): return ( (1 - pest) ** ksize + z_alpha * np.sqrt(var_direct(pest)) - containment ) def f2(pest): return ( (1 - pest) ** ksize - z_alpha * np.sqrt(var_direct(pest)) - containment ) sol1 = brentq(f1, 0.0000001, 0.9999999) sol2 = brentq(f2, 0.0000001, 0.9999999) except ValueError as exc: # afaict, this only happens with extremely small test data notify( "WARNING: Cannot estimate ANI confidence intervals from containment. Do your sketches contain enough hashes?" ) notify(str(exc)) sol1 = sol2 = None # Do this here, so that we don't need to reconvert distance <--> identity later. prob_nothing_in_common = get_exp_probability_nothing_common( point_estimate, ksize, scaled, n_unique_kmers=n_unique_kmers ) return ciANIResult( point_estimate, prob_nothing_in_common, dist_low=sol2, dist_high=sol1, p_threshold=prob_threshold, ) def jaccard_to_distance( jaccard, ksize, scaled, *, n_unique_kmers=None, sequence_len_bp=None, prob_threshold=1e-3, err_threshold=1e-4, ): """ Given parameters, calculate point estimate for mutation rate from jaccard index. Uses formulas derived mathematically to compute the point estimate. The formula uses approximations, therefore a tiny error is associated with it. A lower bound of that error is also returned. A high error indicates that the point estimate cannot be trusted. Threshold of the error is open to interpretation, but suggested that > 10^-4 should be handled with caution. Note that the error is NOT a mutation rate, and therefore cannot be considered in something like mut.rate +/- error. Arguments: jaccard, ksize, scaled, n_unique_kmers # Returns: tuple (point_estimate_of_mutation_rate, lower_bound_of_error) # Returns: JaccardANIResult Note: point estimate does not consider impact of scaled, but p_nothing_in_common can be useful for determining whether scaled is sufficient for these comparisons. """ error_lower_bound = None n_unique_kmers = handle_seqlen_nkmers( ksize, sequence_len_bp=sequence_len_bp, n_unique_kmers=n_unique_kmers ) if jaccard == 0: point_estimate = 1.0 error_lower_bound = 0.0 elif jaccard == 1: point_estimate = 0.0 error_lower_bound = 0.0 else: point_estimate = 1.0 - (2.0 * jaccard / float(1 + jaccard)) ** ( 1.0 / float(ksize) ) exp_n_mut = exp_n_mutated(n_unique_kmers, ksize, point_estimate) var_n_mut = var_n_mutated(n_unique_kmers, ksize, point_estimate) error_lower_bound = ( 1.0 * n_unique_kmers * var_n_mut / (n_unique_kmers + exp_n_mut) ** 3 ) prob_nothing_in_common = get_exp_probability_nothing_common( point_estimate, ksize, scaled, n_unique_kmers=n_unique_kmers ) return jaccardANIResult( point_estimate, prob_nothing_in_common, jaccard_error=error_lower_bound, p_threshold=prob_threshold, je_threshold=err_threshold, ) sourmash-bio-sourmash-bc3c0f8/src/sourmash/exceptions.py000066400000000000000000000036761474157046300236650ustar00rootroot00000000000000from ._lowlevel import lib __all__ = ["SourmashError"] exceptions_by_code = {} class SourmashError(Exception): code = None def __init__(self, msg): Exception.__init__(self) self.message = msg self.rust_info = None def __str__(self): rv = self.message if self.rust_info is not None: return f"{rv}\n\n{self.rust_info}" return rv class IndexNotSupported(SourmashError): def __init__(self): SourmashError.__init__( self, "This index format is not supported in this version of sourmash" ) class IndexNotLoaded(SourmashError): def __init__(self, msg): SourmashError.__init__(self, f"Cannot load sourmash index: {str(msg)}") def _make_error(error_name, base=SourmashError, code=None): class Exc(base): pass Exc.__name__ = Exc.__qualname__ = error_name if code is not None: Exc.code = code globals()[Exc.__name__] = Exc __all__.append(Exc.__name__) return Exc def _get_error_base(error_name): pieces = error_name.split("Error", 1) if len(pieces) == 2 and pieces[0] and pieces[1]: base_error_name = pieces[0] + "Error" base_class = globals().get(base_error_name) if base_class is None: base_class = _make_error(base_error_name) return base_class return SourmashError def _make_exceptions(): for attr in dir(lib): if not attr.startswith("SOURMASH_ERROR_CODE_"): continue code = getattr(lib, attr) if code == 1104: exceptions_by_code[code] = ValueError elif code < 100 or code > 10000: error_name = attr[20:].title().replace("_", "") base = _get_error_base(error_name) exc = _make_error(error_name, base=base, code=getattr(lib, attr)) exceptions_by_code[exc.code] = exc else: exceptions_by_code[code] = ValueError _make_exceptions() sourmash-bio-sourmash-bc3c0f8/src/sourmash/fig.py000066400000000000000000000045171474157046300222440ustar00rootroot00000000000000#! /usr/bin/env python """ Make plots using the distance matrix+labels output by `sourmash compare`. """ from .logging import error, notify try: import numpy import pylab import scipy.cluster.hierarchy as sch except (RuntimeError, ImportError): pass def load_matrix_and_labels(basefile): """Load the comparison matrix and associated labels. Returns a square numpy matrix & list of labels. """ D = numpy.load(open(basefile, "rb")) labeltext = [x.strip() for x in open(basefile + ".labels.txt")] return (D, labeltext) def plot_composite_matrix( D, labeltext, show_labels=True, vmax=1.0, vmin=0.0, force=False ): """Build a composite plot showing dendrogram + distance matrix/heatmap. Returns a matplotlib figure. If show_labels is True, display labels. Otherwise, no labels are shown on the plot. """ if D.max() > 1.0 or D.min() < 0.0: error( "This matrix doesn't look like a distance matrix - min value {}, max value {}", D.min(), D.max(), ) if not force: raise ValueError("not a distance matrix") else: notify("force is set; scaling to [0, 1]") D -= D.min() D /= D.max() if show_labels: pass fig = pylab.figure(figsize=(11, 8)) ax1 = fig.add_axes([0.09, 0.1, 0.2, 0.6]) # plot dendrogram Y = sch.linkage(D, method="single") # centroid Z1 = sch.dendrogram( Y, orientation="left", labels=labeltext, no_labels=not show_labels, get_leaves=True, ) ax1.set_xticks([]) xstart = 0.45 width = 0.45 if not show_labels: xstart = 0.315 scale_xstart = xstart + width + 0.01 # re-order labels along rows, top to bottom idx1 = Z1["leaves"] reordered_labels = [labeltext[i] for i in idx1] # reorder D by the clustering in the dendrogram D = D[idx1, :] D = D[:, idx1] # show matrix axmatrix = fig.add_axes([xstart, 0.1, width, 0.6]) im = axmatrix.matshow( D, aspect="auto", origin="lower", cmap=pylab.cm.YlGnBu, vmin=vmin, vmax=vmax ) axmatrix.set_xticks([]) axmatrix.set_yticks([]) # Plot colorbar. axcolor = fig.add_axes([scale_xstart, 0.1, 0.02, 0.6]) pylab.colorbar(im, cax=axcolor) return fig, reordered_labels, D sourmash-bio-sourmash-bc3c0f8/src/sourmash/hll.py000066400000000000000000000072551474157046300222600ustar00rootroot00000000000000# -*- coding: UTF-8 -*- import sys from tempfile import NamedTemporaryFile from ._lowlevel import ffi, lib from .utils import RustObject, rustcall, decode_str from .exceptions import SourmashError from .minhash import to_bytes, MinHash class HLL(RustObject): __dealloc_func__ = lib.hll_free def __init__(self, error_rate, ksize): self._objptr = lib.hll_with_error_rate(error_rate, ksize) def __len__(self): return self.cardinality() def cardinality(self): return self._methodcall(lib.hll_cardinality) @property def ksize(self): return self._methodcall(lib.hll_ksize) def add_sequence(self, sequence, force=False): "Add a sequence into the sketch." self._methodcall(lib.hll_add_sequence, to_bytes(sequence), len(sequence), force) def add_kmer(self, kmer): "Add a kmer into the sketch." if len(kmer) != self.ksize: raise ValueError(f"kmer to add is not {self.ksize} in length") self.add_sequence(kmer) def add(self, h): if isinstance(h, str): return self.add_kmer(h) return self._methodcall(lib.hll_add_hash, h) def update(self, other): if isinstance(other, HLL): return self._methodcall(lib.hll_merge, other._objptr) elif isinstance(other, MinHash): return self._methodcall(lib.hll_update_mh, other._objptr) else: # FIXME: we could take sets here too (or anything that can be # converted to a list of ints...) raise TypeError("Must be a HyperLogLog or MinHash") def similarity(self, other): if isinstance(other, HLL): return self._methodcall(lib.hll_similarity, other._objptr) else: # FIXME: we could take sets here too (or anything that can be # converted to a list of ints...) raise TypeError("other must be a HyperLogLog") def containment(self, other): if isinstance(other, HLL): return self._methodcall(lib.hll_containment, other._objptr) else: # FIXME: we could take sets here too (or anything that can be # converted to a list of ints...) raise TypeError("other must be a HyperLogLog") def intersection(self, other): if isinstance(other, HLL): return self._methodcall(lib.hll_intersection_size, other._objptr) else: # FIXME: we could take sets here too (or anything that can be # converted to a list of ints...) raise TypeError("other must be a HyperLogLog") @staticmethod def load(filename): hll_ptr = rustcall(lib.hll_from_path, to_bytes(filename)) return HLL._from_objptr(hll_ptr) @staticmethod def from_buffer(buf): hll_ptr = rustcall(lib.hll_from_buffer, buf, len(buf)) return HLL._from_objptr(hll_ptr) def save(self, filename): self._methodcall(lib.hll_save, to_bytes(filename)) def to_bytes(self, compression=1): size = ffi.new("uintptr_t *") rawbuf = self._methodcall(lib.hll_to_buffer, size) size = size[0] rawbuf = ffi.gc(rawbuf, lambda o: lib.nodegraph_buffer_free(o, size), size) buf = ffi.buffer(rawbuf, size) return buf def count(self, h): self.add(h) def get(self, h): raise NotImplementedError("HLL doesn't support membership query") def matches(self, mh): if not isinstance(mh, MinHash): # FIXME: we could take sets here too (or anything that can be # converted to a list of ints...) raise ValueError("mh must be a MinHash") return self._methodcall(lib.hll_matches, mh._objptr) sourmash-bio-sourmash-bc3c0f8/src/sourmash/index/000077500000000000000000000000001474157046300222255ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/index/__init__.py000066400000000000000000001245721474157046300243510ustar00rootroot00000000000000"""An Abstract Base Class for collections of signatures, plus implementations. APIs and functionality ---------------------- Index classes support three sets of API functionality - 'select(...)', which selects subsets of signatures based on ksize, moltype, and other criteria, including picklists. 'find(...)', and the 'search', 'gather', and 'counter_gather' implementations built on top of 'find', which search for signatures that match a query. 'signatures()', which yields all signatures in the Index subject to the selection criteria. Classes defined in this file ---------------------------- Index - abstract base class for all Index objects. LinearIndex - simple in-memory storage of signatures. LazyLinearIndex - lazy selection and linear search of signatures. ZipFileLinearIndex - simple on-disk storage of signatures. MultiIndex - in-memory storage and selection of signatures from multiple index objects, using manifests. All signatures are kept in memory. StandaloneManifestIndex - load manifests directly, and do lazy loading of signatures on demand. No signatures are kept in memory. CounterGather - an ancillary class returned by the 'counter_gather()' method. """ import os import sourmash from abc import abstractmethod, ABC from collections import namedtuple, Counter from sourmash.search import ( make_jaccard_search_query, make_containment_query, calc_threshold_from_bp, ) from sourmash.manifest import CollectionManifest from sourmash.logging import debug_literal from sourmash.signature import load_signatures_from_json, save_signatures_to_json from sourmash.minhash import ( flatten_and_downsample_scaled, flatten_and_downsample_num, flatten_and_intersect_scaled, ) # generic return tuple for Index.search and Index.gather IndexSearchResult = namedtuple("Result", "score, signature, location") class Index(ABC): # this will be removed soon; see sourmash#1894. is_database = False # 'manifest', when set, implies efficient selection and direct # access to signatures. Signatures may be stored in the manifest # or loaded on demand from disk depending on the class, however. manifest = None @abstractmethod def __len__(self): "Return the number of signatures in this Index object." @property def location(self): "Return a resolvable location for this index, if possible." return None @abstractmethod def signatures(self): "Return an iterator over all signatures in the Index object." def signatures_with_location(self): "Return an iterator over tuples (signature, location) in the Index." for ss in self.signatures(): yield ss, self.location def _signatures_with_internal(self): """Return an iterator of tuples (ss, internal_location). Unlike 'signatures_with_location()', this iterator should return _all_ signatures in the object, not just those that remain after selection/filtering. This is an internal API for use in generating manifests, and may change without warning. This method should be implemented separately for each Index object. """ raise NotImplementedError @abstractmethod def insert(self, signature): """ """ @abstractmethod def save(self, path, storage=None, sparseness=0.0, structure_only=False): """ """ @classmethod @abstractmethod def load(cls, location, leaf_loader=None, storage=None, print_version_warning=True): """ """ def find(self, search_fn, query, **kwargs): """Use search_fn to find matching signatures in the index. search_fn follows the protocol in JaccardSearch objects. Generator. Returns 0 or more IndexSearchResult objects. """ # first: is this query compatible with this search? search_fn.check_is_compatible(query) # ok! continue! # this set of signatures may be heterogenous in scaled/num values; # define some processing functions to downsample appropriately. query_mh = query.minhash assert not query_mh.track_abundance if query_mh.scaled: # make query and subject compatible w/scaled. query_scaled = query_mh.scaled def prepare_subject(subj_mh): return flatten_and_downsample_scaled(subj_mh, query_scaled) def prepare_query(query_mh, subj_mh): return flatten_and_downsample_scaled(query_mh, subj_mh.scaled) else: # num query_num = query_mh.num def prepare_subject(subj_mh): return flatten_and_downsample_num(subj_mh, query_num) def prepare_query(query_mh, subj_mh): return flatten_and_downsample_num(query_mh, subj_mh.num) # now, do the search! for subj, location in self.signatures_with_location(): subj_mh = prepare_subject(subj.minhash) # note: we run prepare_query here on the original query minhash. query_mh = prepare_query(query.minhash, subj_mh) assert not query_mh.track_abundance assert not subj_mh.track_abundance shared_size, total_size = query_mh.intersection_and_union_size(subj_mh) query_size = len(query_mh) subj_size = len(subj_mh) score = search_fn.score_fn(query_size, shared_size, subj_size, total_size) if search_fn.passes(score): # note: here we yield the original signature, not the # downsampled minhash. if search_fn.collect(score, subj): yield IndexSearchResult(score, subj, location) def search_abund(self, query, *, threshold=None, **kwargs): """Return list of IndexSearchResult with angular similarity above 'threshold'. Results will be sorted by similarity, highest to lowest. """ if not query.minhash.track_abundance: raise TypeError( "'search_abund' requires query signature with abundance information" ) # check arguments if threshold is None: raise TypeError("'search_abund' requires 'threshold'") threshold = float(threshold) # do the actual search: matches = [] for subj, loc in self.signatures_with_location(): if not subj.minhash.track_abundance: raise TypeError( "'search_abund' requires subject signatures with abundance information" ) score = query.similarity(subj, downsample=True) if score >= threshold: matches.append(IndexSearchResult(score, subj, loc)) # sort! matches.sort(key=lambda x: -x.score) return matches def search( self, query, *, threshold=None, do_containment=False, do_max_containment=False, best_only=False, **kwargs, ): """Return list of IndexSearchResult with similarity above 'threshold'. Results will be sorted by similarity, highest to lowest. Optional arguments accepted by all Index subclasses: * do_containment: default False. If True, use Jaccard containment. * best_only: default False. If True, allow optimizations that may. May discard matches better than threshold, but first match is guaranteed to be best. """ # check arguments if threshold is None: raise TypeError("'search' requires 'threshold'") threshold = float(threshold) search_obj = make_jaccard_search_query( do_containment=do_containment, do_max_containment=do_max_containment, best_only=best_only, threshold=threshold, ) # do the actual search: matches = list(self.find(search_obj, query, **kwargs)) # sort! matches.sort(key=lambda x: -x.score) return matches def prefetch(self, query, threshold_bp, **kwargs): """Return all matches with minimum overlap. Generator. Returns 0 or more IndexSearchResult namedtuples. """ if not self: # empty database? quit. raise ValueError("no signatures to search") # default best_only to False best_only = kwargs.get("best_only", False) search_fn = make_containment_query( query.minhash, threshold_bp, best_only=best_only ) yield from self.find(search_fn, query, **kwargs) def best_containment(self, query, threshold_bp=None, **kwargs): """Return the match with the best Jaccard containment in the Index. Returns an IndexSearchResult namedtuple or None. """ results = self.prefetch(query, threshold_bp, best_only=True, **kwargs) results = sorted(results, key=lambda x: (-x.score, x.signature.md5sum())) try: return next(iter(results)) except StopIteration: return None def peek(self, query_mh, *, threshold_bp=0): """Mimic CounterGather.peek() on top of Index. This is implemented for situations where we don't want to use 'prefetch' functionality. It is a light wrapper around the 'best_containment(...)' method. """ from sourmash import SourmashSignature # build a signature to use with self.gather... query_ss = SourmashSignature(query_mh) # run query! try: result = self.best_containment(query_ss, threshold_bp=threshold_bp) except ValueError: result = None if not result: return [] # if matches, calculate intersection & return. intersect_mh = flatten_and_intersect_scaled(result.signature.minhash, query_mh) return [result, intersect_mh] def consume(self, intersect_mh): "Mimic CounterGather.consume on top of Index. Yes, this is backwards." pass def counter_gather(self, query, threshold_bp, **kwargs): """Returns an object that permits 'gather' on top of the current contents of this Index. The default implementation uses `prefetch` underneath, and returns the results in a `CounterGather` object. However, alternate implementations need only return an object that meets the public `CounterGather` interface, of course. """ with query.update() as prefetch_query: prefetch_query.minhash = prefetch_query.minhash.flatten() # find all matches and construct a CounterGather object. counter = CounterGather(prefetch_query) for result in self.prefetch(prefetch_query, threshold_bp, **kwargs): counter.add(result.signature, location=result.location) # tada! return counter @abstractmethod def select( self, ksize=None, moltype=None, scaled=None, num=None, abund=None, containment=None, ): """Return Index containing only signatures that match requirements. Current arguments can be any or all of: * ksize * moltype * scaled * num * containment 'select' will raise ValueError if the requirements are incompatible with the Index subclass. 'select' may return an empty object or None if no matches can be found. """ def select_signature( ss, *, ksize=None, moltype=None, scaled=0, num=0, containment=False, abund=None, picklist=None, ): "Check that the given signature matches the specified requirements." # ksize match? if ksize and ksize != ss.minhash.ksize: return False # moltype match? if moltype and moltype != ss.minhash.moltype: return False # containment requires scaled; similarity does not. if containment: if not scaled: raise ValueError("'containment' requires 'scaled' in Index.select'") if not ss.minhash.scaled: return False # 'scaled' and 'num' are incompatible if scaled: if ss.minhash.num: return False if num: # note, here we check if 'num' is identical; this can be # changed later. if ss.minhash.scaled or num != ss.minhash.num: return False if abund: # note: minhash w/abund can always be flattened if not ss.minhash.track_abundance: return False if picklist is not None and ss not in picklist: return False return True class LinearIndex(Index): """An Index for a collection of signatures. Can load from a .sig file. Note: See MultiIndex for an in-memory class that uses manifests. Concrete class; signatures held in memory; does not use manifests. """ def __init__(self, _signatures=None, filename=None): self._signatures = [] if _signatures: self._signatures = list(_signatures) self.filename = filename @property def location(self): return self.filename def signatures(self): return iter(self._signatures) def __bool__(self): return bool(self._signatures) def __len__(self): return len(self._signatures) def insert(self, node): self._signatures.append(node) def save(self, path): with open(path, "w") as fp: save_signatures_to_json(self.signatures(), fp) @classmethod def load(cls, location, filename=None): "Load signatures from a JSON signature file." si = load_signatures_from_json(location, do_raise=True) if filename is None: filename = location lidx = LinearIndex(si, filename=filename) return lidx def select(self, **kwargs): """Return new LinearIndex containing only signatures that match req's. Does not raise ValueError, but may return an empty Index. """ _check_select_parameters(**kwargs) siglist = [] for ss in self._signatures: if select_signature(ss, **kwargs): siglist.append(ss) return LinearIndex(siglist, self.location) class LazyLinearIndex(Index): """An Index for lazy linear search of another database. Wrapper class; does not use manifests. One of the main purposes of this class is to _force_ linear 'find' on index objects. So if this class wraps an SBT, for example, the SBT find method will be overriden with the linear 'find' from the base class. There are very few situations where this is an improvement, so use this class wisely! A few notes: * selection criteria defined by 'select' are only executed when signatures are actually requested (hence, 'lazy'). * this class stores the provided index 'db' in memory. If you need a class that does lazy loading of signatures from disk and does not store signatures in memory, see StandaloneManifestIndex. * if you want efficient manifest-based selection, consider MultiIndex (signatures in memory). """ def __init__(self, db, selection_dict={}): self.db = db self.selection_dict = dict(selection_dict) def signatures(self): "Return the selected signatures." db = self.db.select(**self.selection_dict) yield from db.signatures() def signatures_with_location(self): "Return the selected signatures, with a location." db = self.db.select(**self.selection_dict) yield from db.signatures_with_location() def __bool__(self): try: next(iter(self.signatures())) return True except StopIteration: return False def __len__(self): db = self.db.select(**self.selection_dict) return len(db) def insert(self, node): raise NotImplementedError def save(self, path): raise NotImplementedError @classmethod def load(cls, path): raise NotImplementedError def select(self, **kwargs): """Return new object yielding only signatures that match req's. Does not raise ValueError, but may return an empty Index. """ _check_select_parameters(**kwargs) selection_dict = dict(self.selection_dict) for k, v in kwargs.items(): if k in selection_dict: if selection_dict[k] != v: raise ValueError(f"cannot select on two different values for {k}") selection_dict[k] = v return LazyLinearIndex(self.db, selection_dict) class ZipFileLinearIndex(Index): """\ A read-only collection of signatures in a zip file. Does not support `insert` or `save`. Concrete class; signatures dynamically loaded from disk; uses manifests. """ is_database = True def __init__( self, storage, *, selection_dict=None, traverse_yield_all=False, manifest=None, use_manifest=True, ): self.storage = storage self.selection_dict = selection_dict self.traverse_yield_all = traverse_yield_all self.use_manifest = use_manifest # do we have a manifest already? if not, try loading. if use_manifest: if manifest is not None: debug_literal("ZipFileLinearIndex using passed-in manifest") self.manifest = manifest else: self._load_manifest() else: self.manifest = None if self.manifest is not None: assert not self.selection_dict, self.selection_dict if self.selection_dict: assert self.manifest is None def _load_manifest(self): "Load a manifest if one exists" try: manifest_data = self.storage.load("SOURMASH-MANIFEST.csv") except (KeyError, FileNotFoundError): self.manifest = None else: debug_literal(f"found manifest on load for {self.storage.path}") # load manifest! from io import StringIO manifest_data = manifest_data.decode("utf-8") manifest_fp = StringIO(manifest_data) self.manifest = CollectionManifest.load_from_csv(manifest_fp) def __bool__(self): "Are there any matching signatures in this zipfile? Avoid calling len." try: next(iter(self.signatures())) except StopIteration: return False return True def __len__(self): "calculate number of signatures." # use manifest, if available. m = self.manifest if self.manifest is not None: return len(m) # otherwise, iterate across all signatures. n = 0 for _ in self.signatures(): n += 1 return n @property def location(self): return self.storage.path def insert(self, signature): raise NotImplementedError def save(self, path): raise NotImplementedError @classmethod def load(cls, location, traverse_yield_all=False, use_manifest=True): "Class method to load a zipfile." from ..sbt_storage import ZipStorage # we can only load from existing zipfiles in this method. if not os.path.exists(location): raise FileNotFoundError(location) storage = ZipStorage(location) return cls( storage, traverse_yield_all=traverse_yield_all, use_manifest=use_manifest ) def _signatures_with_internal(self): """Return an iterator of tuples (ss, internal_location). Note: does not limit signatures to subsets. """ # list all the files, without using the Storage interface; currently, # 'Storage' does not provide a way to list all the files, so :shrug:. for filename in self.storage._filenames(): # should we load this file? if it ends in .sig OR we are forcing: if ( filename.endswith(".sig") or filename.endswith(".sig.gz") or self.traverse_yield_all ): sig_data = self.storage.load(filename) for ss in load_signatures_from_json(sig_data): yield ss, filename def signatures(self): "Load all signatures in the zip file." selection_dict = self.selection_dict manifest = None if self.manifest is not None: manifest = self.manifest assert not selection_dict # yield all signatures found in manifest for filename in manifest.locations(): data = self.storage.load(filename) for ss in load_signatures_from_json(data): # in case multiple signatures are in the file, check # to make sure we want to return each one. if ss in manifest: yield ss # no manifest! iterate. else: storage = self.storage # if no manifest here, break Storage class encapsulation # and go for all the files. (This is necessary to support # ad-hoc zipfiles that have no manifests.) for filename in storage._filenames(): # should we load this file? if it ends in .sig OR force: if ( filename.endswith(".sig") or filename.endswith(".sig.gz") or self.traverse_yield_all ): if selection_dict: def select(x): return select_signature(x, **selection_dict) else: def select(x): return True data = self.storage.load(filename) for ss in load_signatures_from_json(data): if select(ss): yield ss def select(self, **kwargs): "Select signatures in zip file based on ksize/moltype/etc." _check_select_parameters(**kwargs) # if we have a manifest, run 'select' on the manifest. manifest = self.manifest traverse_yield_all = self.traverse_yield_all if manifest is not None: manifest = manifest.select_to_manifest(**kwargs) return ZipFileLinearIndex( self.storage, selection_dict=None, traverse_yield_all=traverse_yield_all, manifest=manifest, use_manifest=True, ) else: # no manifest? just pass along all the selection kwargs to # the new ZipFileLinearIndex. assert manifest is None if self.selection_dict: # combine selects... d = dict(self.selection_dict) for k, v in kwargs.items(): if k in d: if d[k] is not None and d[k] != v: raise ValueError(f"incompatible select on '{k}'") d[k] = v kwargs = d return ZipFileLinearIndex( self.storage, selection_dict=kwargs, traverse_yield_all=traverse_yield_all, manifest=None, use_manifest=False, ) class CounterGather: """This is an ancillary class that is used to implement "fast gather", post-prefetch. It tracks and summarize matches for efficient min-set-cov/'gather'. The class constructor takes a query MinHash that must be scaled, and then takes signatures that have overlaps with the query (via 'add'). After all overlapping signatures have been loaded, the 'peek' method is then used at each stage of the 'gather' procedure to find the best match, and the 'consume' method is used to remove a match from this counter. This particular implementation maintains a collections.Counter that is used to quickly find the best match when 'peek' is called, but other implementations are possible ;). Note that redundant matches (SourmashSignature objects) with duplicate md5s are collapsed inside the class, because we use the md5sum as a key into the dictionary used to store matches. """ def __init__(self, query): "Constructor - takes a query SourmashSignature." query_mh = query.minhash if not query_mh.scaled: raise ValueError("gather requires scaled signatures") # track query self.orig_query_mh = query_mh.copy().flatten() self.scaled = query_mh.scaled # use these to track loaded matches & their locations self.siglist = {} self.locations = {} # ...and also track overlaps with the progressive query self.counter = Counter() # fence to make sure we do add matches once query has started. self.query_started = 0 def add(self, ss, *, location=None, require_overlap=True): "Add this signature in as a potential match." if self.query_started: raise ValueError("cannot add more signatures to counter after peek/consume") # upon insertion, count & track overlap with the specific query. overlap = self.orig_query_mh.count_common(ss.minhash, True) if overlap: md5 = ss.md5sum() self.counter[md5] = overlap self.siglist[md5] = ss self.locations[md5] = location # note: scaled will be max of all matches. self.downsample(ss.minhash.scaled) elif require_overlap: raise ValueError("no overlap between query and signature!?") def downsample(self, scaled): "Track highest scaled across all possible matches." if scaled > self.scaled: self.scaled = scaled return self.scaled def signatures(self): "Return all signatures." yield from self.siglist.values() @property def union_found(self): """Return a MinHash containing all found hashes in the query. This calculates the union of the found matches, intersected with the original query. """ orig_query_mh = self.orig_query_mh # create empty MinHash from orig query found_mh = orig_query_mh.copy_and_clear() # for each match, intersect match with query & then add to found_mh. for ss in self.siglist.values(): intersect_mh = flatten_and_intersect_scaled(ss.minhash, orig_query_mh) found_mh.add_many(intersect_mh) return found_mh def peek(self, cur_query_mh, *, threshold_bp=0): "Get next 'gather' result for this database, w/o changing counters." self.query_started = 1 # empty? nothing to search. counter = self.counter if not counter: return [] siglist = self.siglist assert siglist scaled = self.downsample(cur_query_mh.scaled) cur_query_mh = cur_query_mh.downsample(scaled=scaled) if not cur_query_mh: # empty query? quit. return [] # CTB: could probably remove this check unless debug requested. if cur_query_mh.contained_by(self.orig_query_mh, downsample=True) < 1: raise ValueError("current query not a subset of original query") # are we setting a threshold? try: x = calc_threshold_from_bp(threshold_bp, scaled, len(cur_query_mh)) threshold, n_threshold_hashes = x except ValueError: # too high to ever match => exit return [] # Find the best match using the internal Counter. most_common = counter.most_common() dataset_id, match_size = most_common[0] # below threshold? no match! if match_size < n_threshold_hashes: return [] ## at this point, we have a legitimate match above threshold! # pull match and location. match = siglist[dataset_id] # calculate containment # CTB: this check is probably redundant with intersect_mh calc, below. cont = cur_query_mh.contained_by(match.minhash, downsample=True) assert cont assert cont >= threshold # calculate intersection of this "best match" with query. match_mh = match.minhash.downsample(scaled=scaled).flatten() intersect_mh = cur_query_mh & match_mh location = self.locations[dataset_id] # build result & return intersection return (IndexSearchResult(cont, match, location), intersect_mh) def consume(self, intersect_mh): "Maintain the internal counter by removing the given hashes." self.query_started = 1 if not intersect_mh: return siglist = self.siglist counter = self.counter most_common = counter.most_common() # Prepare counter for finding the next match by decrementing # all hashes found in the current match in other datasets; # remove empty datasets from counter, too. for dataset_id, _ in most_common: # CTB: note, remaining_mh may not be at correct scaled here. # this means that counters that _should_ be empty might not # _be_ empty in some situations. This does not # lead to incorrect results, merely potentially overfull # 'counter' objects. The tradeoffs to fixing this would # need to be examined! (This could be fixed in self.downsample().) remaining_mh = siglist[dataset_id].minhash intersect_count = intersect_mh.count_common(remaining_mh, downsample=True) if intersect_count: counter[dataset_id] -= intersect_count if counter[dataset_id] == 0: del counter[dataset_id] class MultiIndex(Index): """ Load a collection of signatures, and retain their original locations. One specific use for this is when loading signatures from a directory; MultiIndex will record which specific files provided which signatures. Creates a manifest on load. Note: this is an in-memory collection, and does not do lazy loading: all signatures are loaded upon instantiation and kept in memory. There are a variety of loading functions: * `load` takes a list of already-loaded Index objects, together with a list of their locations. * `load_from_directory` traverses a directory to load files within. * `load_from_path` takes an arbitrary pathname and tries to load it as a directory, or as a .sig file. * `load_from_pathlist` takes a text file full of pathnames and tries to load them all. Concrete class; signatures held in memory; builds and uses manifests. """ def __init__(self, manifest, parent, *, prepend_location=False): """Constructor; takes manifest containing signatures, together with the top-level location. """ self.manifest = manifest self.parent = parent self.prepend_location = prepend_location if prepend_location and self.parent is None: raise ValueError("must set 'parent' if 'prepend_location' is set") @property def location(self): return self.parent def signatures(self): for row in self.manifest.rows: yield row["signature"] def signatures_with_location(self): for row in self.manifest.rows: loc = row["internal_location"] # here, 'parent' may have been removed from internal_location # for directories; if so, add it back in. if self.prepend_location: loc = os.path.join(self.parent, loc) yield row["signature"], loc def _signatures_with_internal(self): """Return an iterator of tuples (ss, location) CTB note: here, 'internal_location' is the source file for the index. This is a special feature of this (in memory) class. """ for row in self.manifest.rows: yield row["signature"], row["internal_location"] def __len__(self): if self.manifest is None: return 0 return len(self.manifest) def insert(self, *args): raise NotImplementedError @classmethod def load(cls, index_list, source_list, parent, *, prepend_location=False): """Create a MultiIndex from already-loaded indices. Takes two arguments: a list of Index objects, and a matching list of source strings (filenames, etc.) If the source is not None, then it will be used to override the location provided by the matching Index object. """ assert len(index_list) == len(source_list) # yield all signatures + locations def sigloc_iter(): for idx, iloc in zip(index_list, source_list): # override internal location if location is explicitly provided if iloc is None: iloc = idx.location for ss in idx.signatures(): yield ss, iloc # build manifest; note, ALL signatures are stored in memory. # CTB: could do this on demand? # CTB: should we use get_manifest functionality? # CTB: note here that the manifest is created by iteration # *even if it already exists.* This could be changed to be more # efficient... but for now, use StandaloneManifestIndex if you # want to avoid this when loading from multiple files. manifest = CollectionManifest.create_manifest(sigloc_iter()) # create! return cls(manifest, parent, prepend_location=prepend_location) @classmethod def load_from_directory(cls, pathname, *, force=False): """Create a MultiIndex from a directory. Takes directory path plus optional boolean 'force'. Attempts to load all files ending in .sig or .sig.gz, by default; if 'force' is True, will attempt to load _all_ files, ignoring errors. Will not load anything other than JSON signature files. """ from ..sourmash_args import traverse_find_sigs if not os.path.isdir(pathname): raise ValueError(f"'{pathname}' must be a directory.") index_list = [] source_list = [] traversal = traverse_find_sigs([pathname], yield_all_files=force) for thisfile in traversal: try: idx = LinearIndex.load(thisfile) index_list.append(idx) rel = os.path.relpath(thisfile, pathname) source_list.append(rel) except (OSError, sourmash.exceptions.SourmashError) as exc: if force: continue # ignore error else: raise ValueError(exc) # stop loading! # did we load anything? if not, error if not index_list: raise ValueError(f"no signatures to load under directory '{pathname}'") return cls.load(index_list, source_list, pathname, prepend_location=True) @classmethod def load_from_path(cls, pathname, force=False): """ Create a MultiIndex from a path (filename or directory). Note: this only uses LinearIndex.load(...), so will only load signature JSON files. """ if not os.path.exists(pathname): raise ValueError(f"'{pathname}' must exist.") if os.path.isdir(pathname): # traverse return cls.load_from_directory(pathname, force=force) # load as a .sig/JSON file index_list = [] source_list = [] try: idx = LinearIndex.load(pathname) index_list = [idx] source_list = [pathname] except (OSError, sourmash.exceptions.SourmashError): if not force: raise ValueError(f"no signatures to load from '{pathname}'") return None return cls.load(index_list, source_list, pathname) @classmethod def load_from_pathlist(cls, filename): """Create a MultiIndex from all files listed in a text file. Note: this will attempt to load signatures from each file, including zip collections, etc; it uses 'load_file_as_index' underneath. """ from ..sourmash_args import load_pathlist_from_file, load_file_as_index idx_list = [] src_list = [] file_list = load_pathlist_from_file(filename) for fname in file_list: idx = load_file_as_index(fname) src = fname idx_list.append(idx) src_list.append(src) return cls.load(idx_list, src_list, filename) def save(self, *args): raise NotImplementedError def select(self, **kwargs): "Run 'select' on the manifest." _check_select_parameters(**kwargs) new_manifest = self.manifest.select_to_manifest(**kwargs) return MultiIndex( new_manifest, self.parent, prepend_location=self.prepend_location ) class StandaloneManifestIndex(Index): """Load a standalone manifest as an Index. This class is useful for the situation where you have a directory with many signature collections underneath it, and you don't want to load every collection each time you run sourmash. Instead, you can run 'sourmash sig collect -o ' to output a manifest and then use this class to load directly. Sketch type selection, picklists, and pattern matching will all work directly on the manifest and will load signatures only upon demand. One feature of this class is that external paths to sketches in the 'internal_location' field of the manifests will be loaded properly. This permits manifests to be constructed for various collections of signatures that reside elsewhere, and not just below a single directory prefix. By default paths are interpreted relative to the location of the manifest, unless an absolute path is provided in the 'internal_location' field. StandaloneManifestIndex does _not_ store signatures in memory. This class also overlaps in concept with MultiIndex when MultiIndex.load_from_pathlist is used to load other Index objects. However, this class does not store any signatures in memory, unlike MultiIndex. """ is_database = True def __init__(self, manifest, location, *, prefix=None): """Create object. 'location' is path of manifest file, 'prefix' is prepended to signature paths when loading non-abspaths.""" assert manifest is not None self.manifest = manifest self._location = location self.prefix = prefix @classmethod def load(cls, location, *, prefix=None): """Load manifest file from given location. If prefix is None (default), it is automatically set from dirname. Set prefix='' to avoid this, or provide an explicit prefix. """ if not os.path.isfile(location): raise ValueError(f"provided manifest location '{location}' is not a file") m = CollectionManifest.load_from_filename(location) if prefix is None: # by default, calculate paths relative to manifest location. prefix = os.path.dirname(location) return cls(m, location, prefix=prefix) @property def location(self): "Return the path to this manifest." return self._location def signatures_with_location(self): "Return an iterator over all signatures and their locations." yield from self._signatures_with_internal() def signatures(self): "Return an iterator over all signatures." for ss, loc in self._signatures_with_internal(): yield ss def _signatures_with_internal(self): """Return an iterator over all sigs of (sig, internal_location) Note that this is implemented differently from most Index objects in that it only lists subselected parts of the manifest, and not the original manifest. This was done out of convenience: we don't currently have access to the original manifest in this class. """ # collect all internal locations picklist = self.manifest.to_picklist() for iloc in self.manifest.locations(): # prepend location with prefix? if not iloc.startswith("/") and self.prefix: iloc = os.path.join(self.prefix, iloc) idx = sourmash.load_file_as_index(iloc) idx = idx.select(picklist=picklist) for ss in idx.signatures(): yield ss, iloc def __len__(self): "Number of signatures in this manifest (after any select)." return len(self.manifest) def __bool__(self): "Is this manifest empty?" return bool(self.manifest) def save(self, *args): raise NotImplementedError def insert(self, *args): raise NotImplementedError def select(self, **kwargs): "Run 'select' on the manifest." _check_select_parameters(**kwargs) new_manifest = self.manifest.select_to_manifest(**kwargs) return StandaloneManifestIndex(new_manifest, self._location, prefix=self.prefix) def _check_select_parameters(**kw): "Check 'select' parameters for types/conversion." params = set(kw) params -= {"ksize", "num", "moltype", "scaled", "abund", "picklist", "containment"} if params: raise ValueError(f"unknown 'select' parameters: {params}") ksize = kw.get("ksize") if ksize is not None: if not isinstance(ksize, int): raise ValueError( f"ksize value '{ksize}' must be an integer, is: {type(ksize)}" ) moltype = kw.get("moltype") if moltype is not None: if moltype not in ["DNA", "protein", "dayhoff", "hp", "skipm1n3", "skipm2n3"]: raise ValueError(f"unknown moltype: {moltype}") scaled = kw.get("scaled") if scaled is not None: if not isinstance(scaled, int): raise ValueError( f"scaled value '{scaled}' must be an integer, is: {type(scaled)}" ) containment = kw.get("containment") if containment is not None: if not isinstance(containment, bool): raise ValueError( f"containment value '{containment}' must be a bool, is: {type(containment)}" ) abund = kw.get("abund") if abund is not None: if not isinstance(abund, bool): raise ValueError(f"abund value '{abund}' must be a bool, is: {type(abund)}") num = kw.get("num") if num is not None: if not isinstance(num, int): raise ValueError(f"num value '{num}' must be an integer, is: {type(num)}") sourmash-bio-sourmash-bc3c0f8/src/sourmash/index/revindex.py000066400000000000000000000176721474157046300244400ustar00rootroot00000000000000""" RevIndex - a rust-based reverse index by hashes. """ import weakref from sourmash.index import Index, IndexSearchResult from sourmash.minhash import MinHash from sourmash.signature import SourmashSignature from sourmash._lowlevel import ffi, lib from sourmash.utils import RustObject, rustcall, decode_str, encode_str class RevIndex(RustObject, Index): __dealloc_func__ = lib.revindex_free def __init__( self, *, signatures=None, signature_paths=None, template=None, threshold=0, queries=None, keep_sigs=False, ): self.template = template self.threshold = threshold self.queries = queries self.keep_sigs = keep_sigs self.signature_paths = signature_paths self._signatures = signatures if signature_paths is None or signatures is None: # delay initialization self._objptr = ffi.NULL else: self._init_inner() def _init_inner(self): if self._objptr != ffi.NULL: # Already initialized return if ( self.signature_paths is None and not self._signatures and self._objptr == ffi.NULL ): raise ValueError("No signatures provided") elif (self.signature_paths or self._signatures) and self._objptr != ffi.NULL: raise NotImplementedError("Need to update RevIndex") attached_refs = weakref.WeakKeyDictionary() queries_ptr = ffi.NULL queries_size = 0 if self.queries: # get list of rust objects collected = [] for obj in queries: rv = obj._get_objptr() attached_refs[rv] = obj collected.append(rv) queries_ptr = ffi.new("SourmashSignature*[]", collected) queries_size = len(queries) template_ptr = ffi.NULL if self.template: if isinstance(self.template, MinHash): template_ptr = self.template._get_objptr() else: raise ValueError("Template must be a MinHash") search_sigs_ptr = ffi.NULL sigs_size = 0 collected = [] if self.signature_paths: for path in self.signature_paths: collected.append(encode_str(path)) search_sigs_ptr = ffi.new("SourmashStr*[]", collected) sigs_size = len(signature_paths) self._objptr = rustcall( lib.revindex_new_with_paths, search_sigs_ptr, sigs_size, template_ptr, self.threshold, queries_ptr, queries_size, self.keep_sigs, ) elif self._signatures: # force keep_sigs=True, and pass SourmashSignature directly to RevIndex. for sig in self._signatures: collected.append(sig._get_objptr()) search_sigs_ptr = ffi.new("SourmashSignature*[]", collected) sigs_size = len(self._signatures) self._objptr = rustcall( lib.revindex_new_with_sigs, search_sigs_ptr, sigs_size, template_ptr, self.threshold, queries_ptr, queries_size, ) def signatures(self): self._init_inner() size = ffi.new("uintptr_t *") sigs_ptr = self._methodcall(lib.revindex_signatures, size) size = size[0] sigs = [] for i in range(size): sig = SourmashSignature._from_objptr(sigs_ptr[i]) sigs.append(sig) for sig in sigs: yield sig # if self._signatures: # yield from self._signatures # else: # raise NotImplementedError("Call into Rust and retrieve sigs") def __len__(self): if self._objptr: return self._methodcall(lib.revindex_len) else: return len(self._signatures) def insert(self, node): if self._signatures is None: self._signatures = [] self._signatures.append(node) def save(self, path): pass @classmethod def load(cls, location): pass def select(self, ksize=None, moltype=None, **kwargs): if self.template: if ksize: self.template.ksize = ksize if moltype: self.template.moltype = moltype else: # TODO: deal with None/default values self.template = MinHash(ksize=ksize, moltype=moltype) # def search(self, query, *args, **kwargs): # """Return set of matches with similarity above 'threshold'. # # Results will be sorted by similarity, highest to lowest. # # Optional arguments: # * do_containment: default False. If True, use Jaccard containment. # * ignore_abundance: default False. If True, and query signature # and database support k-mer abundances, ignore those abundances. # # Note, the "best only" hint is ignored by LCA_Database # """ # if not query.minhash: # return [] # # # check arguments # if "threshold" not in kwargs: # raise TypeError("'search' requires 'threshold'") # threshold = kwargs["threshold"] # do_containment = kwargs.get("do_containment", False) # ignore_abundance = kwargs.get("ignore_abundance", False) # # self._init_inner() # # size = ffi.new("uintptr_t *") # results_ptr = self._methodcall( # lib.revindex_search, # query._get_objptr(), # threshold, # do_containment, # ignore_abundance, # size, # ) # # size = size[0] # if size == 0: # return [] # # results = [] # for i in range(size): # match = SearchResult._from_objptr(results_ptr[i]) # if match.score >= threshold: # results.append(IndexSearchResult(match.score, match.signature, match.filename)) # # return results # # def gather(self, query, *args, **kwargs): # "Return the match with the best Jaccard containment in the database." # if not query.minhash: # return [] # # self._init_inner() # # threshold_bp = kwargs.get("threshold_bp", 0.0) # threshold = threshold_bp / (len(query.minhash) * self.scaled) # # results = [] # size = ffi.new("uintptr_t *") # results_ptr = self._methodcall( # lib.revindex_gather, query._get_objptr(), threshold, True, True, size # ) # size = size[0] # if size == 0: # return [] # # results = [] # for i in range(size): # match = SearchResult._from_objptr(results_ptr[i]) # if match.score >= threshold: # results.append(IndexSearchResult(match.score, match.signature, match.filename)) # # results.sort(reverse=True, # key=lambda x: (x.score, x.signature.md5sum())) # # return results[:1] @property def scaled(self): return self._methodcall(lib.revindex_scaled) class SearchResult(RustObject): __dealloc_func__ = lib.searchresult_free @property def score(self): return self._methodcall(lib.searchresult_score) @property def signature(self): sig_ptr = self._methodcall(lib.searchresult_signature) return SourmashSignature._from_objptr(sig_ptr) @property def filename(self): result = decode_str(self._methodcall(lib.searchresult_filename)) if result == "": return None return result sourmash-bio-sourmash-bc3c0f8/src/sourmash/index/sqlite_index.py000066400000000000000000001164671474157046300253060ustar00rootroot00000000000000"""sqlite3 based Index, CollectionManifest, and LCA_Database implementations. These classes support a variety of flexible and fast on-disk storage, search, and retrieval functions. SqliteIndex stores full scaled signatures; sketches are stored as reverse-indexed collections of hashes. Search is optimized via the reverse index. Num and abund sketches are not supported. All scaled values must be the same upon insertion. Multiple moltypes _are_ supported. SqliteCollectionManifest provides a full implementation of the manifest API. It can store details for all signature types. When used as part of a SqliteIndex database, it does not support independent insertion. LCA_SqliteDatabase builds on top of SqliteIndex and LineageDB_Sqlite (in the tax submodule) to provide a full on-disk implementation of LCA_Database. Using these classes ------------------- These classes are fully integrated into sourmash loading. Internally, use `sqlite_index.load_sqlite_index(...)` to load a specific file; this will return the appropriate SqliteIndex, StandaloneManifestIndex, or LCA_Database object. Use `CollectionManifest.load_from_filename(...)` to load the manifest directly as a manifest object. Implementation Details ---------------------- SqliteIndex: * Hashes with values above MAX_SQLITE_INT=2**63-1 are transformed into signed long longs upon insertion, and then back into ulong longs upon retrieval. * Hash overlap is calculated via a SELECT. * SqliteIndex relies on SqliteCollectionManifest for manifest functionality, including signature selection and picklists. SqliteCollectionManifest: * each object maintains info about whether it is being "managed" by a SqliteIndex class or not. If it is, `_insert_row(...)` cannot be called directly. * `select(...)` operates directly with SQL queries, except for picklist selection, which involves inspect each manifest row in Python. In addition to being (much) simpler, this ends up being faster in some important real world situations, even for millions of rows! * filter_on_rows and filter_on_columns also both operate in Python, not SQL. * for this reason, the `locations()` method returns a superset of locations. This is potentially very significant if you do a select with a picklist that ignores most sketches - the `locations()` method will ignore the picklist. Limitations: * all of these classes share a single connection object, and it could get confusing quickly if you simultaneously insert and query. We suggest separating creation and insertion. That having been said, these databases should work fine for many simultaneous queries; just don't write :). """ import time import os import sqlite3 from collections import defaultdict import itertools from bitstring import BitArray from sourmash.index import Index, _check_select_parameters from sourmash.exceptions import IndexNotSupported from sourmash import MinHash, SourmashSignature from sourmash.index import IndexSearchResult, StandaloneManifestIndex from sourmash.picklist import SignaturePicklist from sourmash.logging import debug_literal from sourmash import sqlite_utils from sourmash.lca.lca_db import cached_property from sourmash.manifest import BaseCollectionManifest # converters for unsigned 64-bit ints: if over MAX_SQLITE_INT, # convert to signed int. MAX_SQLITE_INT = 2**63 - 1 def convert_hash_to(x): return BitArray(uint=x, length=64).int if x > MAX_SQLITE_INT else x def convert_hash_from(x): return BitArray(int=x, length=64).uint if x < 0 else x def load_sqlite_index(filename, *, request_manifest=False): """Load a SqliteIndex, SqliteCollectionManifest, or LCA_SqliteDatabase. This is the main top-level API for loading an Index-like object. The logic is roughly: * does this database have both index and lineage tables? If so, return an LCA_SqliteDatabase. * if it only has an index, return a SqliteIndex. * if it only has a manifest, return a StandaloneManifestIndex. If you would like only a manifest, specify 'request_manifest=True'. """ conn = sqlite_utils.open_sqlite_db(filename) if conn is None: debug_literal("load_sqlite_index: conn is None.") return c = conn.cursor() internal_d = sqlite_utils.get_sourmash_internal(c) is_index = False is_manifest = False is_lca_db = False if "SqliteIndex" in internal_d: v = internal_d["SqliteIndex"] if v != "1.0": raise IndexNotSupported is_index = True debug_literal("load_sqlite_index: it's an index!") if is_index and "SqliteLineage" in internal_d: v = internal_d["SqliteLineage"] if v != "1.0": raise IndexNotSupported is_lca_db = True debug_literal("load_sqlite_index: it's got a lineage table!") if "SqliteManifest" in internal_d: v = internal_d["SqliteManifest"] if v != "1.0": raise IndexNotSupported is_manifest = True debug_literal( f"load_sqlite_index: it's a manifest! request_manifest: {request_manifest}" ) # every Index is a Manifest! if is_index or is_lca_db: assert is_manifest idx = None if is_index and not request_manifest: conn.close() if is_lca_db: debug_literal("load_sqlite_index: returning LCA_SqliteDatabase") idx = LCA_SqliteDatabase.load(filename) else: debug_literal("load_sqlite_index: returning SqliteIndex") idx = SqliteIndex(filename) elif is_manifest: managed_by_index = False if is_index: assert request_manifest managed_by_index = True prefix = os.path.dirname(filename) mf = SqliteCollectionManifest(conn, managed_by_index=managed_by_index) idx = StandaloneManifestIndex(mf, filename, prefix=prefix) debug_literal("load_sqlite_index: returning StandaloneManifestIndex") return idx class SqliteIndex(Index): is_database = True # NOTE: we do not need _signatures_with_internal for this class # because it supplies a manifest directly :tada:. def __init__(self, dbfile, *, sqlite_manifest=None, conn=None): "Constructor. 'dbfile' should be valid filename or ':memory:'." self.dbfile = dbfile # no connection? connect and/or create! if conn is None: conn = self._open(dbfile) # build me a SQLite manifest class to use for selection. if sqlite_manifest is None: sqlite_manifest = SqliteCollectionManifest(conn, managed_by_index=True) self.manifest = sqlite_manifest self.conn = conn # set 'scaled'. c = self.conn.cursor() c.execute("SELECT DISTINCT scaled FROM sourmash_sketches") scaled_vals = c.fetchall() if len(scaled_vals) > 1: raise ValueError( "this database has multiple scaled values, which is not currently allowed" ) if scaled_vals: self.scaled = scaled_vals[0][0] else: self.scaled = None @classmethod def _open(cls, dbfile, *, empty_ok=True): "Connect to existing SQLite database or create new." try: conn = sqlite3.connect(dbfile) c = conn.cursor() c.execute("PRAGMA cache_size=10000000") c.execute("PRAGMA synchronous = OFF") c.execute("PRAGMA journal_mode = MEMORY") c.execute("PRAGMA temp_store = MEMORY") if not empty_ok: c.execute("SELECT * FROM sourmash_hashes LIMIT 1") c.fetchone() except (sqlite3.OperationalError, sqlite3.DatabaseError): raise ValueError(f"cannot open '{dbfile}' as SqliteIndex database") return conn @classmethod def load(self, dbfile): "Load an existing SqliteIndex from dbfile." return SqliteIndex(dbfile) @classmethod def create(cls, dbfile, *, append=False): "Create a new SqliteIndex in dbfile." conn = cls._open(dbfile, empty_ok=True) cls._create_tables(conn.cursor(), ignore_exists=append) conn.commit() return cls(dbfile, conn=conn) @classmethod def _create_tables(cls, c, *, ignore_exists=False): "Create sqlite tables for SqliteIndex" try: sqlite_utils.add_sourmash_internal(c, "SqliteIndex", "1.0") SqliteCollectionManifest._create_tables(c) c.execute( """ CREATE TABLE IF NOT EXISTS sourmash_hashes ( hashval INTEGER NOT NULL, sketch_id INTEGER NOT NULL, FOREIGN KEY (sketch_id) REFERENCES sourmash_sketches (id) ) """ ) c.execute( """ CREATE INDEX IF NOT EXISTS sourmash_hashval_idx ON sourmash_hashes ( hashval, sketch_id ) """ ) c.execute( """ CREATE INDEX IF NOT EXISTS sourmash_hashval_idx2 ON sourmash_hashes ( hashval ) """ ) c.execute( """ CREATE INDEX IF NOT EXISTS sourmash_sketch_idx ON sourmash_hashes ( sketch_id ) """ ) except (sqlite3.OperationalError, sqlite3.DatabaseError): if not ignore_exists: raise ValueError("cannot create SqliteIndex tables") return c def cursor(self): return self.conn.cursor() def close(self): self.conn.close() def commit(self): self.conn.commit() def __len__(self): return len(self.manifest) def insert(self, ss, *, cursor=None, commit=True): """ Insert a signature into the sqlite database. If a cursor object is supplied, use that cursor instead of generating a new one. If 'commit' is True, commit after add; otherwise, do not. """ if cursor: c = cursor else: c = self.conn.cursor() if ss.minhash.num: raise ValueError("cannot store 'num' signatures in SqliteIndex") if ss.minhash.track_abundance: raise ValueError("cannot store signatures with abundance in SqliteIndex") if self.scaled is not None and self.scaled != ss.minhash.scaled: raise ValueError( f"this database can only store scaled values={self.scaled}" ) elif self.scaled is None: self.scaled = ss.minhash.scaled # ok, first create and insert a manifest row row = BaseCollectionManifest.make_manifest_row( ss, None, include_signature=False ) self.manifest._insert_row(c, row, call_is_from_index=True) # retrieve ID of row for retrieving hashes: c.execute("SELECT last_insert_rowid()") (sketch_id,) = c.fetchone() # insert all the hashes hashes_to_sketch = [] for h in ss.minhash.hashes: hh = convert_hash_to(h) hashes_to_sketch.append((hh, sketch_id)) c.executemany( "INSERT INTO sourmash_hashes (hashval, sketch_id) VALUES (?, ?)", hashes_to_sketch, ) if commit: self.conn.commit() @property def location(self): return self.dbfile def signatures(self): "Return an iterator over all signatures in the Index object." for ss, loc in self.signatures_with_location(): yield ss def signatures_with_location(self): "Return an iterator over tuples (signature, location) in the Index." c = self.conn.cursor() for ss, loc, iloc in self._load_sketches(c): yield ss, loc def save(self, *args, **kwargs): raise NotImplementedError def find(self, search_fn, query, **kwargs): search_fn.check_is_compatible(query) # check compatibility, etc. query_mh = query.minhash if self.scaled > query_mh.scaled: query_mh = query_mh.downsample(scaled=self.scaled) picklist = None if self.manifest.selection_dict: picklist = self.manifest.selection_dict.get("picklist") c1 = self.conn.cursor() c2 = self.conn.cursor() debug_literal("running _get_matching_sketches...") t0 = time.time() xx = self._get_matching_sketches(c1, query_mh.hashes, query_mh._max_hash) for sketch_id, n_matching_hashes in xx: debug_literal( f"...got sketch {sketch_id}, with {n_matching_hashes} matching hashes in {time.time() - t0:.2f}" ) # # first, estimate sketch size using sql results. # query_size = len(query_mh) subj_size = self._load_sketch_size(c2, sketch_id, query_mh._max_hash) total_size = query_size + subj_size - n_matching_hashes shared_size = n_matching_hashes score = search_fn.score_fn(query_size, shared_size, subj_size, total_size) debug_literal( f"APPROX RESULT: score={score} qsize={query_size}, ssize={subj_size} total={total_size} overlap={shared_size}" ) # do we pass? if not search_fn.passes(score): debug_literal(f"FAIL score={score}") # CTB if we are doing containment only, we could break loop here. # but for Jaccard, we must continue. # see 'test_sqlite_jaccard_ordering' if search_fn.passes(score): subj = self._load_sketch(c2, sketch_id) if search_fn.collect(score, subj): if picklist is None or subj in picklist: yield IndexSearchResult(score, subj, self.location) def _select(self, *, num=0, track_abundance=False, **kwargs): _check_select_parameters(**kwargs) "Run a select! This just modifies the manifest." # check SqliteIndex specific conditions on the 'select' if num: raise ValueError("cannot select on 'num' in SqliteIndex") if track_abundance: raise ValueError("cannot store or search signatures with abundance") # create manifest if needed manifest = self.manifest if manifest is None: manifest = SqliteCollectionManifest(self.conn, managed_by_index=True) # modify manifest manifest = manifest.select_to_manifest(**kwargs) return manifest def select(self, *args, **kwargs): sqlite_manifest = self._select(*args, **kwargs) # return a new SqliteIndex with a new manifest, but same old conn. return SqliteIndex(self.dbfile, sqlite_manifest=sqlite_manifest, conn=self.conn) # # Actual SQL queries, etc. # def _load_sketch_size(self, c1, sketch_id, max_hash): "Get sketch size for given sketch, downsampled by max_hash." if max_hash <= MAX_SQLITE_INT: c1.execute( """ SELECT COUNT(hashval) FROM sourmash_hashes WHERE sketch_id=? AND hashval >= 0 AND hashval <= ?""", (sketch_id, max_hash), ) else: c1.execute( "SELECT COUNT(hashval) FROM sourmash_hashes WHERE sketch_id=?", (sketch_id,), ) (n_hashes,) = c1.fetchone() return n_hashes def _load_sketch(self, c, sketch_id, *, match_scaled=None): "Load an individual sketch. If match_scaled is set, downsample." start = time.time() c.execute( """ SELECT id, name, scaled, ksize, filename, moltype, seed FROM sourmash_sketches WHERE id=?""", (sketch_id,), ) debug_literal( f"load sketch {sketch_id}: got sketch info in {time.time() - start:.2f}" ) sketch_id, name, scaled, ksize, filename, moltype, seed = c.fetchone() if match_scaled is not None: scaled = max(scaled, match_scaled) is_protein = 1 if moltype == "protein" else 0 is_dayhoff = 1 if moltype == "dayhoff" else 0 is_hp = 1 if moltype == "hp" else 0 mh = MinHash( n=0, ksize=ksize, scaled=scaled, seed=seed, is_protein=is_protein, dayhoff=is_dayhoff, hp=is_hp, ) template_values = [sketch_id] hash_constraint_str = "" max_hash = mh._max_hash if max_hash <= MAX_SQLITE_INT: hash_constraint_str = ( "sourmash_hashes.hashval >= 0 AND sourmash_hashes.hashval <= ? AND" ) template_values.insert(0, max_hash) else: debug_literal("NOT EMPLOYING hash_constraint_str") debug_literal( f"finding hashes for sketch {sketch_id} in {time.time() - start:.2f}" ) c.execute( f"SELECT hashval FROM sourmash_hashes WHERE {hash_constraint_str} sourmash_hashes.sketch_id=?", template_values, ) debug_literal( f"loading hashes for sketch {sketch_id} in {time.time() - start:.2f}" ) for (hashval,) in c: hh = convert_hash_from(hashval) mh.add_hash(hh) debug_literal(f"done loading sketch {sketch_id} {time.time() - start:.2f})") return SourmashSignature(mh, name=name, filename=filename) def _load_sketches(self, c): "Load sketches based on manifest _id column." for row in self.manifest.rows: sketch_id = row["_id"] assert row["num"] == 0 moltype = row["moltype"] is_protein = 1 if moltype == "protein" else 0 is_dayhoff = 1 if moltype == "dayhoff" else 0 is_hp = 1 if moltype == "hp" else 0 ksize = row["ksize"] scaled = row["scaled"] seed = row["seed"] mh = MinHash( n=0, ksize=ksize, scaled=scaled, seed=seed, is_protein=is_protein, dayhoff=is_dayhoff, hp=is_hp, ) c.execute( "SELECT hashval FROM sourmash_hashes WHERE sketch_id=?", (sketch_id,) ) for (hashval,) in c: mh.add_hash(convert_hash_from(hashval)) ss = SourmashSignature(mh, name=row["name"], filename=row["filename"]) yield ss, self.dbfile, sketch_id def _get_matching_sketches(self, c, hashes, max_hash): """ For hashvals in 'hashes', retrieve all matching sketches, together with the number of overlapping hashes for each sketch. CTB: we do not use sqlite manifest conditions on this select, because it slows things down in practice. """ c.execute("DROP TABLE IF EXISTS sourmash_hash_query") c.execute( "CREATE TEMPORARY TABLE sourmash_hash_query (hashval INTEGER PRIMARY KEY)" ) hashvals = [(convert_hash_to(h),) for h in hashes] c.executemany( "INSERT OR IGNORE INTO sourmash_hash_query (hashval) VALUES (?)", hashvals ) # # set up SELECT conditions # conditions = [] template_values = [] # downsample? => add to conditions max_hash = min(max_hash, max(hashes)) if max_hash <= MAX_SQLITE_INT: select_str = "sourmash_hashes.hashval >= 0 AND sourmash_hashes.hashval <= ?" conditions.append(select_str) template_values.append(max_hash) # format conditions conditions.append("sourmash_hashes.hashval=sourmash_hash_query.hashval") conditions = " AND ".join(conditions) c.execute( f""" SELECT DISTINCT sourmash_hashes.sketch_id,COUNT(sourmash_hashes.hashval) as CNT FROM sourmash_hashes, sourmash_hash_query WHERE {conditions} GROUP BY sourmash_hashes.sketch_id ORDER BY CNT DESC """, template_values, ) return c class SqliteCollectionManifest(BaseCollectionManifest): """ A SQLite-based manifest, used both for SqliteIndex and as a standalone manifest class. This class serves two purposes: * first, it is a fast, on-disk manifest that can be used in place of CollectionManifest. * second, it can be included within a SqliteIndex (which stores hashes too). In this case, however, new entries must be inserted by SqliteIndex rather than directly in this class. In the latter case, the SqliteCollectionManifest is created with managed_by_index set to True. """ def __init__(self, conn, *, selection_dict=None, managed_by_index=False): """ Here, 'conn' should already be connected and configured. Use 'create(filename)' to create a new database. Use 'create_from_manifest(filename, manifest) to create a new db from an existing manifest object. Use 'load_from_filename' to load from file. """ assert conn is not None self.conn = conn self.selection_dict = selection_dict self.managed_by_index = managed_by_index self._num_rows = None @classmethod def create(cls, filename): "Connect to 'filename' and create the tables as a standalone manifest." conn = sqlite3.connect(filename) cursor = conn.cursor() cls._create_tables(cursor) return cls(conn) @classmethod def create_or_open(cls, filename): "Connect to 'filename' and create tables if not exist." conn = sqlite3.connect(filename) cursor = conn.cursor() try: cls._create_tables(cursor) except sqlite3.OperationalError: pass return cls(conn) @classmethod def load_from_manifest(cls, manifest, *, dbfile=":memory:", append=False): "Create a new sqlite manifest from an existing manifest object." return cls._create_manifest_from_rows( manifest.rows, location=dbfile, append=append ) @classmethod def create_manifest(cls, locations_iter, *, include_signature=False): """Create a manifest from an iterator that yields (ss, location) Stores signatures in manifest rows by default. Note: do NOT catch exceptions here, so this passes through load excs. Note: this method ignores 'include_signature'. """ def rows_iter(): for ss, location in locations_iter: row = cls.make_manifest_row(ss, location, include_signature=False) yield row return cls._create_manifest_from_rows(rows_iter()) @classmethod def _create_tables(cls, cursor): "Create the manifest table." # this is a class method so that it can be used by SqliteIndex to # create manifest-compatible tables. sqlite_utils.add_sourmash_internal(cursor, "SqliteManifest", "1.0") cursor.execute( """ CREATE TABLE sourmash_sketches (id INTEGER PRIMARY KEY, name TEXT, num INTEGER NOT NULL, scaled INTEGER NOT NULL, ksize INTEGER NOT NULL, filename TEXT, moltype TEXT NOT NULL, with_abundance BOOLEAN NOT NULL, md5sum TEXT NOT NULL, seed INTEGER NOT NULL, n_hashes INTEGER NOT NULL, internal_location TEXT, UNIQUE(internal_location, md5sum) ) """ ) def add_row(self, row): c = self.conn.cursor() self._insert_row(c, row) def _insert_row(self, cursor, row, *, call_is_from_index=False): "Insert a new manifest row." # check - is this manifest managed by SqliteIndex? If so, prevent # insertions unless SqliteIndex is the one calling it. if self.managed_by_index and not call_is_from_index: raise Exception("must use SqliteIndex.insert to add to this manifest") row = dict(row) if "seed" not in row: row["seed"] = 42 cursor.execute( """ INSERT OR IGNORE INTO sourmash_sketches (name, num, scaled, ksize, filename, md5sum, moltype, seed, n_hashes, with_abundance, internal_location) VALUES (:name, :num, :scaled, :ksize, :filename, :md5, :moltype, :seed, :n_hashes, :with_abundance, :internal_location)""", row, ) self._num_rows = None # reset cache def __bool__(self): "Is this manifest empty?" if self._num_rows is not None: return bool(self._num_rows) try: next(iter(self.rows)) return True except StopIteration: return False def __eq__(self, other): "Check equality on a row-by-row basis. May fail on out-of-order rows." for a, b in itertools.zip_longest(self.rows, other.rows): # ignore non-required keys. for k in self.required_keys: if a[k] != b[k]: return False return True def __len__(self): "Number of rows." # can we use cached value? if self._num_rows is not None: return self._num_rows # self.rows is a generator, so can't use 'len' self._num_rows = sum(1 for _ in self.rows) return self._num_rows def __iadd__(self, other): c = self.conn.cursor() for row in other.rows: self._insert_row(c, row) return self def __add__(self, other): new_mf = self.create(":memory:") new_mf += self new_mf += other return new_mf def close(self): self.conn.commit() def _make_select(self): """Build a set of SQL SELECT conditions and matching value tuple that can be used to select the right sketches from the database. Returns a triple 'conditions', 'values', and 'picklist'. 'conditions' is a list that should be joined with 'AND'. The picklist is simply retrieved from the selection dictionary. """ conditions = [] values = [] picklist = None if self.selection_dict: select_d = self.selection_dict if "ksize" in select_d and select_d["ksize"]: conditions.append("sourmash_sketches.ksize = ?") values.append(select_d["ksize"]) if "num" in select_d and select_d["num"] > 0: conditions.append("sourmash_sketches.num > 0") if "scaled" in select_d and select_d["scaled"] > 0: conditions.append("sourmash_sketches.scaled > 0") if "containment" in select_d and select_d["containment"]: conditions.append("sourmash_sketches.scaled > 0") if "moltype" in select_d and select_d["moltype"] is not None: moltype = select_d["moltype"] assert moltype in ("DNA", "protein", "dayhoff", "hp"), moltype conditions.append(f"sourmash_sketches.moltype = '{moltype}'") picklist = select_d.get("picklist") return conditions, values, picklist def select_to_manifest(self, **kwargs): "Create a new SqliteCollectionManifest with the given select args." # Pass along all the selection kwargs to a new instance if self.selection_dict: debug_literal("sqlite manifest: merging selection dicts") # combine selects... d = dict(self.selection_dict) for k, v in kwargs.items(): if k in d: if d[k] is not None and d[k] != v: raise ValueError(f"incompatible select on '{k}'") d[k] = v kwargs = d new_mf = SqliteCollectionManifest(self.conn, selection_dict=kwargs) # if picklist, make sure we fill in 'found'. picklist = kwargs.get("picklist") if picklist is not None: debug_literal("sqlite manifest: iterating through picklist") _ = len(self) # this forces iteration through rows. return new_mf @property def rows(self): "Return rows that match the selection." c1 = self.conn.cursor() conditions, values, picklist = self._make_select() if conditions: conditions = conditions = "WHERE " + " AND ".join(conditions) else: conditions = "" debug_literal(f"sqlite manifest rows: executing select with '{conditions}'") c1.execute( f""" SELECT id, name, md5sum, num, scaled, ksize, filename, moltype, seed, n_hashes, internal_location FROM sourmash_sketches {conditions} """, values, ) debug_literal("sqlite manifest: entering row yield loop") for ( _id, name, md5sum, num, scaled, ksize, filename, moltype, seed, n_hashes, iloc, ) in c1: row = dict( num=num, scaled=scaled, name=name, filename=filename, n_hashes=n_hashes, with_abundance=False, ksize=ksize, md5=md5sum, internal_location=iloc, moltype=moltype, md5short=md5sum[:8], seed=seed, _id=_id, ) if picklist is None or picklist.matches_manifest_row(row): yield row def filter_rows(self, row_filter_fn): """Create a new manifest filtered through row_filter_fn. This is done in memory, inserting each row one at a time. """ def rows_iter(): for row in self.rows: if row_filter_fn(row): yield row return self._create_manifest_from_rows(rows_iter()) def filter_on_columns(self, col_filter_fn, col_names): "Create a new manifest based on column matches." def row_filter_fn(row): x = [row[col] for col in col_names if row[col] is not None] return col_filter_fn(x) return self.filter_rows(row_filter_fn) def locations(self): """Return all possible locations for signatures. CTB: this may be a (big) superset of locations, if picklists are used. See test_sqlite_manifest_locations. Use set(row['internal_locations'] for row in self.rows) if you want an exact set of locations; will be slow for big manifests tho. """ c1 = self.conn.cursor() conditions, values, picklist = self._make_select() if conditions: conditions = conditions = "WHERE " + " AND ".join(conditions) else: conditions = "" c1.execute( f""" SELECT DISTINCT internal_location FROM sourmash_sketches {conditions} """, values, ) return (iloc for (iloc,) in c1) def __contains__(self, ss): "Check to see if signature 'ss' is in this manifest." md5 = ss.md5sum() c = self.conn.cursor() c.execute("SELECT COUNT(*) FROM sourmash_sketches WHERE md5sum=?", (md5,)) (val,) = c.fetchone() if bool(val): picklist = self.picklist return picklist is None or ss in self.picklist return False @property def picklist(self): "Return the picklist, if any." if self.selection_dict: return self.selection_dict.get("picklist") return None def to_picklist(self): "Convert this manifest to a picklist." pl = SignaturePicklist("manifest") pl.pickset = {pl._get_value_for_manifest_row(row) for row in self.rows} return pl @classmethod def _create_manifest_from_rows( cls, rows_iter, *, location=":memory:", append=False ): """Create a SqliteCollectionManifest from a rows iterator. Internal utility function. CTB: should enable converting in-memory sqlite db to on-disk, probably with sqlite3 'conn.backup(...)' function. """ try: mf = cls.create(location) except (sqlite3.OperationalError, sqlite3.DatabaseError) as exc: if not append: raise Exception( f"cannot create sqlite3 db at '{location}'; exception: {str(exc)}" ) db = load_sqlite_index(location, request_manifest=True) mf = db.manifest cursor = mf.conn.cursor() for row in rows_iter: mf._insert_row(cursor, row) mf.conn.commit() return mf class LCA_SqliteDatabase(SqliteIndex): """ A wrapper class for SqliteIndex + lineage db => LCA_Database functionality. """ is_database = True def __init__(self, dbfile, *, lineage_db=None, sqlite_manifest=None): # CTB note: we need to let SqliteIndex open dbfile here, so can't # just pass in a conn. super().__init__(dbfile, sqlite_manifest=sqlite_manifest) c = self.conn.cursor() c.execute("SELECT DISTINCT ksize, moltype FROM sourmash_sketches") res = list(c) if len(res) > 1: raise TypeError( "can only have one ksize & moltype in an LCA_SqliteDatabase" ) if len(res) == 0: raise ValueError("cannot load an LCA_SqliteDatabase") self.ksize, self.moltype = res[0] debug_literal(f"setting ksize and moltype to {self.ksize}, {self.moltype}") if lineage_db is not None: self.lineage_db = lineage_db ## the below is done once, but could be implemented as something ## ~dynamic. self._build_index() @classmethod def load(cls, filename): "Load LCA_SqliteDatabase from a single file." from sourmash.tax.tax_utils import LineageDB_Sqlite # first, load the SqliteIndex: try: debug_literal("sqlite_index: loading LCA_SqliteDatabase as SqliteIndex.") obj = cls(filename) except sqlite3.OperationalError: raise ValueError(f"cannot open '{filename}' as a SQLite index.") # now, toss in the lineage DB. lineage_db = LineageDB_Sqlite(obj.conn) obj.lineage_db = lineage_db obj._build_index() return obj @classmethod def create(cls, filename, idx, lineage_db): "Create a LCA_SqliteDatabase in a single file from existing idx/ldb." from sourmash.tax.tax_utils import MultiLineageDB # first, save/create signatures... sqlidx = SqliteIndex.create(filename) for ss in idx.signatures(): sqlidx.insert(ss) # now, save the lineage_db into the same database out_lineage_db = MultiLineageDB() out_lineage_db.add(lineage_db) out_lineage_db._save_sqlite(None, conn=sqlidx.conn) # and voila! return, I guess? return cls.load(filename) def _build_index(self): "Rebuild the mappings that support identifier <-> lineage." mf = self.manifest lineage_db = self.lineage_db ident_to_idx = {} next_lid = 0 idx_to_lid = {} lineage_to_lid = {} lid_to_lineage = {} for row in mf.rows: name = row["name"] if name: # this is a bit of a hack. we try identifiers _with_ and # _without_ versions, and take whichever works. There is # definitely a better way to do this, but I can't think # of one right now. ident = name.split(" ")[0] lineage = lineage_db.get(ident) # try with identifier version if lineage is None: # nope - remove version.x ident = name.split(".")[0] lineage = lineage_db.get(ident) idx = row["_id"] # this is only present in sqlite manifests. ident_to_idx[ident] = idx if lineage: lid = lineage_to_lid.get(lineage) # manufacture new lid? if lid is None: lid = next_lid next_lid += 1 lineage_to_lid[lineage] = lid lid_to_lineage[lid] = lineage # assign idx <-> lid idx_to_lid[idx] = lid self.ident_to_idx = ident_to_idx self.idx_to_lid = idx_to_lid self.lid_to_lineage = lid_to_lineage # prevent insertions def insert(self, *args, **kwargs): raise NotImplementedError # return correct type on select def select(self, *args, **kwargs): sqlite_manifest = self._select(*args, **kwargs) return LCA_SqliteDatabase( self.dbfile, sqlite_manifest=sqlite_manifest, lineage_db=self.lineage_db ) ### LCA_Database API/protocol. def downsample_scaled(self, scaled): "Downsample the scaled for querying." if scaled < self.scaled: raise ValueError(f"cannot decrease scaled from {self.scaled} to {scaled}") # CTB: maybe return a new LCA_Database? Right now this isn't how # the lca_db protocol works tho. self.scaled = scaled def get_lineage_assignments(self, hashval, *, min_num=None): """ Get a list of lineages for this hashval. """ x = [] idx_list = self.hashval_to_idx.get(hashval, []) if min_num is None or len(idx_list) >= min_num: for idx in idx_list: lid = self.idx_to_lid.get(idx, None) if lid is not None: lineage = self.lid_to_lineage[lid] x.append(lineage) return x @cached_property def idx_to_ident(self): "Map individual idx to ident." d = defaultdict(set) for ident, idx in self.ident_to_idx.items(): assert idx not in d d[idx] = ident return d @property def hashval_to_idx(self): "Dynamically interpret the SQL 'hashes' table like it's a dict." return _SqliteIndexHashvalToIndex(self) @property def hashvals(self): "Return all hashvals" return iter(_SqliteIndexHashvalToIndex(self)) def get_identifiers_for_hashval(self, hashval): "Return identifiers associated with this hashval" idxlist = self.hashval_to_idx[hashval] for idx in idxlist: yield self.idx_to_ident[idx] class _SqliteIndexHashvalToIndex: """ Internal wrapper class to retrieve keys and key/value pairs for hashval -> [ list of idx ]. """ def __init__(self, sqlidx): self.sqlidx = sqlidx def __iter__(self): "Get all hashvals." c = self.sqlidx.conn.cursor() c.execute("SELECT DISTINCT hashval FROM sourmash_hashes") for (hashval,) in c: yield hashval def get(self, key, dv=None): "Retrieve idxlist for a given hash." sqlidx = self.sqlidx c = sqlidx.cursor() hh = convert_hash_to(key) c.execute("SELECT sketch_id FROM sourmash_hashes WHERE hashval=?", (hh,)) x = [convert_hash_from(h) for (h,) in c] return x or dv def __getitem__(self, key): "Retrieve idxlist for a given hash; raise KeyError if not present." v = self.get(key) if v is None: raise KeyError(key) return v sourmash-bio-sourmash-bc3c0f8/src/sourmash/lca/000077500000000000000000000000001474157046300216555ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/lca/__init__.py000066400000000000000000000006461474157046300237740ustar00rootroot00000000000000"LCA and reverse index utilities." from .lca_db import LCA_Database from .lca_utils import ( taxlist, zip_lineage, build_tree, find_lca, gather_assignments, display_lineage, count_lca_for_assignments, ) from .command_index import index from .command_classify import classify from .command_summarize import summarize_main from .command_rankinfo import rankinfo_main from .__main__ import main sourmash-bio-sourmash-bc3c0f8/src/sourmash/lca/__main__.py000066400000000000000000000020421474157046300237450ustar00rootroot00000000000000""" Command-line entry point for 'python -m sourmash.lca' """ import sys import argparse from . import classify, index, summarize_main, rankinfo_main from .command_compare_csv import compare_csv from ..logging import set_quiet, error usage = """ sourmash lca [] - work with taxonomic information. ** Commands can be: index - create LCA database classify --db --query - classify genomes summarize --db --query - summarize mixture rankinfo - database rank info compare_csv - compare spreadsheets ** Use '-h' to get subcommand-specific help, e.g. sourmash lca index -h """ def main(arglist=None): args = sourmash.cli.get_parser().parse_args(arglist) submod = getattr(sourmash.cli.sig, args.subcmd) mainmethod = getattr(submod, "main") return mainmethod(args) if __name__ == "__main__": main(sys.argv) sourmash-bio-sourmash-bc3c0f8/src/sourmash/lca/command_classify.py000066400000000000000000000123341474157046300255450ustar00rootroot00000000000000#! /usr/bin/env python """ Classify individual signature files down to deepest possible node. """ import sys import csv from .. import sourmash_args from ..sourmash_args import load_file_as_signatures from ..logging import notify, error, debug, set_quiet from . import lca_utils from .lca_utils import check_files_exist DEFAULT_THRESHOLD = 5 # how many counts of a taxid at min def classify_signature(query_sig, dblist, threshold, majority): """ Classify 'query_sig' using the given list of databases. Insist on at least 'threshold' counts of a given lineage before taking it seriously. Return (lineage, status) where 'lineage' is a tuple of LineagePairs and 'status' is either 'nomatch', 'found', or 'disagree'. This function proceeds in two stages: * first, build a list of assignments for all the lineages for each hashval. (For e.g. kraken, this is done in the database preparation step; here, we do it dynamically each time. * then, across all the hashvals, count the number of times each linage shows up, and filter out low-abundance ones (under threshold). Then, determine the LCA of all of those. """ # gather assignments from across all the databases assignments = lca_utils.gather_assignments(query_sig.minhash.hashes, dblist) # now convert to trees -> do LCA & counts counts = lca_utils.count_lca_for_assignments(assignments) debug(counts.most_common()) # ok, we now have the LCAs for each hashval, and their number of # counts. Now build a tree across "significant" LCAs - those above # threshold. tree = {} if counts and majority: majority_vote, count = counts.most_common()[0] if count > threshold: lca_utils.build_tree([majority_vote], tree) else: for lca, count in counts.most_common(): if count < threshold: break # update tree with this set of assignments lca_utils.build_tree([lca], tree) status = "nomatch" if not tree: return [], status # now find lowest-common-ancestor of the resulting tree. lca, reason = lca_utils.find_lca(tree) if reason == 0: # leaf node debug("END", lca) status = "found" else: # internal node => disagreement debug("MULTI", lca) status = "disagree" debug("lineage is:", lca) return lca, status def classify(args): """ main single-genome classification function. """ if not args.db: error("Error! must specify at least one LCA database with --db") sys.exit(-1) set_quiet(args.quiet, args.debug) # flatten --db and --query args.db = [item for sublist in args.db for item in sublist] args.query = [item for sublist in args.query for item in sublist] if not check_files_exist(*args.db): sys.exit(-1) # load all the databases dblist, ksize, scaled = lca_utils.load_databases(args.db, args.scaled) # find all the queries notify("finding query signatures...") inp_files = list(args.query) if args.query_from_file: more_files = sourmash_args.load_pathlist_from_file(args.query_from_file) inp_files.extend(more_files) if not check_files_exist(*inp_files): sys.exit(-1) if not inp_files: error( "Error! must specify at least one query signature with --query or --query-from-file" ) sys.exit(-1) # set up output csvfp = csv.writer(sys.stdout) notify(f"outputting classifications to {args.output}") with sourmash_args.FileOutputCSV(args.output) as outfp: csvfp = csv.writer(outfp) csvfp.writerow(["ID", "status"] + list(lca_utils.taxlist())) # for each query, gather all the matches across databases total_count = 0 n = 0 total_n = len(inp_files) for query_filename in inp_files: n += 1 for query_sig in load_file_as_signatures(query_filename, ksize=ksize): notify("\r\033[K", end="") notify(f"... classifying {query_sig} (file {n} of {total_n})", end="\r") debug("classifying", query_sig) total_count += 1 # make sure we're looking at the same scaled value as database if query_sig.minhash.scaled != scaled: with query_sig.update() as query_sig: downsample_mh = query_sig.minhash.downsample(scaled=scaled) query_sig.minhash = downsample_mh # do the classification lineage, status = classify_signature( query_sig, dblist, args.threshold, args.majority ) debug(lineage) # output each classification to the spreadsheet row = [str(query_sig), status] row += lca_utils.zip_lineage(lineage) # when outputting to stdout, make output intelligible if not args.output: notify("\r\033[K", end="") csvfp.writerow(row) notify("\r\033[K", end="") notify(f"classified {total_count} signatures total") if __name__ == "__main__": sys.exit(classify(sys.argv[1:])) sourmash-bio-sourmash-bc3c0f8/src/sourmash/lca/command_compare_csv.py000066400000000000000000000063101474157046300262260ustar00rootroot00000000000000#! /usr/bin/env python """ Compare two taxonomy spreadsheets. """ import sys from collections import defaultdict from ..logging import notify, error, print_results, set_quiet from . import lca_utils from .lca_utils import zip_lineage from .command_index import load_taxonomy_assignments def compare_csv(args): if args.start_column < 2: error("error, --start-column cannot be less than 2") sys.exit(-1) set_quiet(args.quiet, args.debug) # first, load classify-style spreadsheet notify(f"loading classify output from: {args.csv1}") assignments0, num_rows0 = load_taxonomy_assignments( args.csv1, start_column=3, force=args.force ) notify( f"loaded {len(set(assignments0.values()))} distinct lineages, {num_rows0} rows" ) notify("----") # next, load custom taxonomy spreadsheet delimiter = "," if args.tabs: delimiter = "\t" notify(f"loading custom spreadsheet from: {args.csv2}") assignments, num_rows = load_taxonomy_assignments( args.csv2, delimiter=delimiter, start_column=args.start_column, use_headers=not args.no_headers, force=args.force, ) notify( f"loaded {len(set(assignments.values()))} distinct lineages, {num_rows} rows" ) # now, compute basic differences: missing_1 = set(assignments0.keys()) - set(assignments.keys()) missing_2 = set(assignments.keys()) - set(assignments0.keys()) if missing_2: notify(f"missing {len(missing_2)} assignments in classify spreadsheet.") if missing_1: notify(f"missing {len(missing_1)} assignments in custom spreadsheet.") if missing_1 or missing_2: notify("(these will not be evaluated any further)") else: notify("note: all IDs are in both spreadsheets!") # next, look at differences in lineages common = set(assignments0.keys()) common.intersection_update(assignments.keys()) n_total = 0 n_different = 0 n_compat = 0 n_incompat = 0 incompat_rank = defaultdict(int) for k in common: n_total += 1 v0 = assignments0[k] v1 = assignments[k] if v0 != v1: n_different += 1 tree = lca_utils.build_tree([v0]) lca_utils.build_tree([v1], tree) lca, reason = lca_utils.find_lca(tree) if reason == 0: # compatible lineages n_compat += 1 print_results("{},compatible,{}", k, ";".join(zip_lineage(lca))) else: n_incompat += 1 print_results("{},incompatible,{}", k, ";".join(zip_lineage(lca))) rank = next(iter(lca_utils.taxlist())) if lca: rank = lca[-1].rank incompat_rank[rank] += 1 notify(f"{n_total} total assignments, {n_different} differ between spreadsheets.") notify(f"{n_compat} are compatible (one lineage is ancestor of another.") notify(f"{n_incompat} are incompatible (there is a disagreement in the trees).") if n_incompat: for rank in lca_utils.taxlist(): notify(f"{incompat_rank[rank]} incompatible at rank {rank}") if __name__ == "__main__": sys.exit(compare_csv(sys.argv[1:])) sourmash-bio-sourmash-bc3c0f8/src/sourmash/lca/command_index.py000066400000000000000000000320011474157046300250300ustar00rootroot00000000000000#! /usr/bin/env python """ Build a lowest-common-ancestor database with given taxonomy and genome sigs. """ import sys import csv import os from collections import defaultdict from sourmash import sourmash_args from sourmash.sourmash_args import load_file_as_signatures from sourmash.logging import notify, error, debug, set_quiet from . import lca_utils from .lca_db import LCA_Database from sourmash.sourmash_args import DEFAULT_LOAD_K def load_taxonomy_assignments( filename, *, delimiter=",", start_column=2, use_headers=True, force=False, split_identifiers=False, keep_identifier_versions=False, ): """ Load a taxonomy assignment spreadsheet into a dictionary. The 'assignments' dictionary that's returned maps identifiers to lineage tuples. """ from sourmash.tax.tax_utils import LineagePair # parse spreadsheet! # CTB note: can't easily switch to FileInputCSV, because of # janky way we do/don't handle headers here. See issue #2198. fp = open(filename, newline="") r = csv.reader(fp, delimiter=delimiter) row_headers = ["identifiers"] row_headers += ["_skip_"] * (start_column - 2) row_headers += list(lca_utils.taxlist()) # first check that headers are interpretable. if use_headers: notify("examining spreadsheet headers...") first_row = next(iter(r)) n_disagree = 0 for column, value in zip(row_headers, first_row): if column == "_skip_": continue if column.lower() != value.lower(): notify(f"** assuming column '{value}' is {column} in spreadsheet") n_disagree += 1 if n_disagree > 2: error("whoa, too many assumptions. are the headers right?") error("expecting {}", ",".join(row_headers)) if not force: sys.exit(-1) notify("...continue, because --force was specified.") # convert into a lineage pair assignments = {} num_rows = 0 n_species = 0 n_strains = 0 for row in r: if row and row[0].strip(): # want non-empty row num_rows += 1 lineage = list(zip(row_headers, row)) lineage = [x for x in lineage if x[0] != "_skip_"] ident = lineage[0][1] lineage = lineage[1:] # fold, spindle, and mutilate ident? if split_identifiers: ident = ident.split(" ")[0] if not keep_identifier_versions: ident = ident.split(".")[0] # clean lineage of null names, replace with 'unassigned' lineage = [(a, lca_utils.filter_null(b)) for (a, b) in lineage] lineage = [LineagePair(a, b) for (a, b) in lineage] # remove end nulls while lineage and lineage[-1].name == "unassigned": lineage = lineage[:-1] # store lineage tuple if lineage: # check duplicates if ident in assignments: if assignments[ident] != tuple(lineage): if not force: raise Exception(f"multiple lineages for identifier {ident}") else: assignments[ident] = tuple(lineage) if lineage[-1].rank == "species": n_species += 1 elif lineage[-1].rank == "strain": n_species += 1 n_strains += 1 fp.close() # this is to guard against a bug that happened once and I can't find # any more, when building a large GTDB-based database :) --CTB if len(assignments) * 0.2 > n_species and len(assignments) > 50: if not force: error("") error("ERROR: fewer than 20% of lineages have species-level resolution!?") error( "({} species assignments found, of {} assignments total)", n_species, len(assignments), ) error("** If this is intentional, re-run the command with -f.") sys.exit(-1) return assignments, num_rows def generate_report( record_duplicates, record_no_lineage, record_remnants, unused_lineages, unused_identifiers, filename, ): """ Output a report of anomalies from building the index. """ with open(filename, "w") as fp: print(f"Duplicate signatures: {len(record_duplicates)}", file=fp) fp.write("\n".join(record_duplicates)) fp.write("\n") print(f"----\nUnused identifiers: {len(unused_identifiers)}", file=fp) fp.write("\n".join(unused_identifiers)) fp.write("\n") print( f"----\nNo lineage provided for these identifiers: {len(record_no_lineage)}", file=fp, ) fp.write("\n".join(record_no_lineage)) fp.write("\n") print( f"----\nNo signatures found for these identifiers: {len(record_remnants)}", file=fp, ) fp.write("\n".join(record_remnants)) fp.write("\n") print(f"----\nUnused lineages: {len(unused_lineages)}", file=fp) for lineage in unused_lineages: fp.write(";".join(lca_utils.zip_lineage(lineage))) fp.write("\n") def index(args): """ main function for building an LCA database. """ if args.start_column < 2: error("error, --start-column cannot be less than 2") sys.exit(-1) set_quiet(args.quiet, args.debug) args.scaled = int(args.scaled) if args.ksize is None: args.ksize = DEFAULT_LOAD_K moltype = sourmash_args.calculate_moltype(args, default="DNA") picklist = sourmash_args.load_picklist(args) db_outfile = args.lca_db_out if args.database_format == "json": if not ( db_outfile.endswith(".lca.json") or db_outfile.endswith(".lca.json.gz") ): # logic -> db.save db_outfile += ".lca.json" else: assert args.database_format == "sql" if not db_outfile.endswith(".lca.sql"): db_outfile += ".lca.sql" if os.path.exists(db_outfile): error(f"ERROR: output file {db_outfile} already exists. Not overwriting.") sys.exit(-1) notify(f"saving to LCA DB: {format(db_outfile)}") notify( f"Building LCA database with ksize={args.ksize} scaled={args.scaled} moltype={moltype}." ) # first, load taxonomy spreadsheet delimiter = "," if args.tabs: delimiter = "\t" assignments, num_rows = load_taxonomy_assignments( args.csv, delimiter=delimiter, start_column=args.start_column, use_headers=not args.no_headers, force=args.force, split_identifiers=args.split_identifiers, keep_identifier_versions=args.keep_identifier_versions, ) notify( f"{len(assignments)} distinct identities in spreadsheet out of {num_rows} rows." ) notify( f"{len(set(assignments.values()))} distinct lineages in spreadsheet out of {num_rows} rows." ) db = LCA_Database(args.ksize, args.scaled, moltype) inp_files = list(args.signatures) if args.from_file: more_files = sourmash_args.load_pathlist_from_file(args.from_file) inp_files.extend(more_files) # track duplicates md5_to_name = {} # # main loop, connecting lineage ID to signature. # n = 0 total_n = len(inp_files) record_duplicates = set() record_no_lineage = [] record_remnants = set(assignments) record_used_lineages = set() record_used_idents = set() n_skipped = 0 for filename in inp_files: n += 1 it = load_file_as_signatures( filename, ksize=args.ksize, select_moltype=moltype, picklist=picklist, yield_all_files=args.force, ) for sig in it: notify("\r\033[K", end="") notify( f"\r... loading signature {str(sig)[:30]} ({n} of {total_n}); skipped {n_skipped} so far", end="", ) debug(filename, sig) # block off duplicates. if sig.md5sum() in md5_to_name: debug( "WARNING: in file {}, duplicate md5sum: {}; skipping", filename, sig.md5sum(), ) record_duplicates.add(sig.name) continue md5_to_name[sig.md5sum()] = str(sig) # parse identifier, potentially with splitting if sig.name: ident = sig.name else: ident = sig.filename orig_ident = ident if args.split_identifiers: # hack for NCBI-style names, etc. # split on space... ident = ident.split(" ")[0] if not args.keep_identifier_versions: # ...and on period. ident = ident.split(".")[0] lineage = assignments.get(ident) # punt if no lineage and --require-taxonomy if lineage is None and args.require_taxonomy: if args.fail_on_missing_taxonomy: notify(f"ERROR: no taxonomy found for identifier '{ident}'") if args.split_identifiers: notify(f"(Identifier extracted from name: '{orig_ident})')") sys.exit(-1) debug("(skipping, because --require-taxonomy was specified)") n_skipped += 1 continue # add the signature into the database. try: db.insert(sig, ident=ident, lineage=lineage) except ValueError as e: error( "ERROR: cannot insert signature '{}' (md5 {}, loaded from '{}') into database.", sig, sig.md5sum()[:8], filename, ) error("ERROR: {}", str(e)) sys.exit(-1) if lineage: # remove from our list of remaining ident -> lineage record_remnants.remove(ident) # track ident as used record_used_idents.add(ident) record_used_lineages.add(lineage) # track lineage info - either no lineage, or this lineage used. else: debug("WARNING: no lineage assignment for {}.", ident) record_no_lineage.append(ident) # end main add signatures loop if n_skipped: notify( f"... loaded {total_n} signatures; skipped {n_skipped} because of --require-taxonomy." ) else: notify(f"... loaded {total_n} signatures.") # check -- did we find any signatures? if n == 0: error("ERROR: no signatures found. ??") sys.exit(1) # check -- did the signatures we found have any hashes? if not db.hashvals: error("ERROR: no hash values found - are there any signatures?") sys.exit(1) notify( f"loaded {len(db.hashvals)} hashes at ksize={args.ksize} scaled={args.scaled}" ) if picklist: sourmash_args.report_picklist(args, picklist) # summarize: notify( f"{len(record_used_lineages)} assigned lineages out of {len(set(assignments.values()))} distinct lineages in spreadsheet." ) unused_lineages = set(assignments.values()) - record_used_lineages notify( f"{len(record_used_idents)} identifiers used out of {len(set(assignments))} distinct identifiers in spreadsheet." ) assert record_used_idents.issubset(set(assignments)) unused_identifiers = set(assignments) - record_used_idents # now, save! db.save(db_outfile, format=args.database_format) ## done! # output a record of stuff if requested/available: if record_duplicates or record_no_lineage or record_remnants or unused_lineages: if record_duplicates: notify(f"WARNING: {len(record_duplicates)} duplicate signatures.") if record_no_lineage: notify( f"WARNING: no lineage provided for {len(record_no_lineage)} signatures." ) if record_remnants: notify( f"WARNING: no signatures for {len(record_remnants)} spreadsheet rows." ) if unused_lineages: notify(f"WARNING: {len(unused_lineages)} unused lineages.") if unused_identifiers: notify(f"WARNING: {len(unused_identifiers)} unused identifiers.") if args.report: notify(f"generating a report and saving in '{args.report}'") generate_report( record_duplicates, record_no_lineage, record_remnants, unused_lineages, unused_identifiers, args.report, ) else: notify("(You can use --report to generate a detailed report.)") if __name__ == "__main__": sys.exit(index(sys.argv[1:])) sourmash-bio-sourmash-bc3c0f8/src/sourmash/lca/command_rankinfo.py000066400000000000000000000045021474157046300255350ustar00rootroot00000000000000#! /usr/bin/env python """ Summarize rank-specific information from LCAs in one or more databases. """ import sys from collections import defaultdict from ..logging import error, debug, set_quiet, notify from . import lca_utils def make_lca_counts(dblist, min_num=0): """ Collect counts of all the LCAs in the list of databases. CTB this could usefully be converted to a generator function. """ # gather all hashvalue assignments from across all the databases assignments = defaultdict(set) for lca_db in dblist: for hashval in lca_db.hashvals: lineages = lca_db.get_lineage_assignments(hashval, min_num=min_num) if lineages: assignments[hashval].update(lineages) # now convert to trees -> do LCA & counts counts = defaultdict(int) for hashval, lineages in assignments.items(): # for each list of tuple_info [(rank, name), ...] build # a tree that lets us discover lowest-common-ancestor. debug(lineages) tree = lca_utils.build_tree(lineages) # now find either a leaf or the first node with multiple # children; that's our lowest-common-ancestor node. lca, reason = lca_utils.find_lca(tree) counts[lca] += 1 return counts def rankinfo_main(args): """ rankinfo! """ if not args.db: error("Error! must specify at least one LCA database with --db") sys.exit(-1) set_quiet(args.quiet, args.debug) if args.scaled: args.scaled = int(args.scaled) # load all the databases dblist, ksize, scaled = lca_utils.load_databases(args.db, args.scaled) # count all the LCAs across these databases counts = make_lca_counts(dblist, args.minimum_num) # collect counts across all ranks counts_by_rank = defaultdict(int) for lineage, count in counts.items(): if lineage: lineage_tup = lineage[-1] counts_by_rank[lineage_tup.rank] += count # output! total = float(sum(counts_by_rank.values())) if total == 0: notify("(no hashvals with lineages found)") else: for rank in lca_utils.taxlist(): count = counts_by_rank.get(rank, 0) print(f"{rank}: {count} ({count / total * 100.0:.1f}%)") if __name__ == "__main__": sys.exit(rankinfo_main(sys.argv[1:])) sourmash-bio-sourmash-bc3c0f8/src/sourmash/lca/command_summarize.py000066400000000000000000000147201474157046300257450ustar00rootroot00000000000000#! /usr/bin/env python """ Summarize the taxonomic content of the given signatures, combined. """ import sys import csv from collections import defaultdict from .. import sourmash_args from ..logging import notify, error, print_results, set_quiet, debug from . import lca_utils from .lca_utils import check_files_exist from sourmash.index import MultiIndex DEFAULT_THRESHOLD = 5 def summarize(hashvals, dblist, threshold, ignore_abundance): """ Classify 'hashvals' using the given list of databases. Insist on at least 'threshold' counts of a given lineage before taking it seriously. Return (lineage, counts) where 'lineage' is a tuple of LineagePairs. """ # gather assignments from across all the databases assignments = lca_utils.gather_assignments(hashvals, dblist) # now convert to trees -> do LCA & counts if not ignore_abundance: counts = lca_utils.count_lca_for_assignments(assignments, hashvals) else: # flatten counts = lca_utils.count_lca_for_assignments(assignments, None) debug(counts.most_common()) # ok, we now have the LCAs for each hashval, and their number # of counts. Now aggregate counts across the tree, going up from # the leaves. aggregated_counts = defaultdict(int) for lca, count in counts.most_common(): if count < threshold: break if not lca: aggregated_counts[lca] += count # climb from the lca to the root. while lca: aggregated_counts[lca] += count lca = lca[:-1] debug(aggregated_counts) return aggregated_counts def load_singletons_and_count(filenames, ksize, scaled, ignore_abundance): "Load individual signatures and count them individually." total_count = 0 n = 0 total_n = len(filenames) for filename in filenames: n += 1 idx = sourmash_args.load_file_as_index(filename) idx = idx.select(ksize=ksize) for query_sig, query_filename in idx.signatures_with_location(): notify("\r\033[K", end="") notify( f"... loading {query_sig} (file {n} of {total_n})", total_n, end="\r" ) total_count += 1 if ignore_abundance and query_sig.minhash.track_abundance: notify("NOTE: discarding abundances in query, since --ignore-abundance") # rebuild hashvals individually hashvals = defaultdict(int) count_signature(query_sig, scaled, hashvals) yield query_filename, query_sig, hashvals notify("\r\033[K", end="") notify(f"loaded {total_count} signatures from {n} files total.") def count_signature(sig, scaled, hashvals): "Downsample sig to given scaled, count hashvalues." mh = sig.minhash.downsample(scaled=scaled) if mh.track_abundance: abunds = mh.hashes for hashval, count in abunds.items(): hashvals[hashval] += count else: for hashval in mh.hashes: hashvals[hashval] += 1 def output_results(lineage_counts, total_counts, filename=None, sig=None): """\ Output results in ~human-readable format. """ for lineage, count in lineage_counts.items(): if lineage: lineage = lca_utils.zip_lineage(lineage, truncate_empty=True) lineage = ";".join(lineage) else: lineage = "(root)" p = count / total_counts * 100.0 p = f"{p:.1f}%" print_results( f"{p:5} {count:>5} {lineage} {filename}:{sig.md5sum()[:8]} {sig}" ) def output_csv(lineage_counts, total_counts, csv_fp, filename, sig, write_header=True): """\ Output results in CSV. """ w = csv.writer(csv_fp) if write_header: headers = ["count"] + list(lca_utils.taxlist()) headers += ["filename", "sig_name", "sig_md5", "total_counts"] w.writerow(headers) for lineage, count in lineage_counts.items(): debug("lineage:", lineage) row = [count] + lca_utils.zip_lineage(lineage, truncate_empty=False) row += [filename, sig.name, sig.md5sum(), total_counts] w.writerow(row) def summarize_main(args): """ main summarization function. """ if not args.db: error("Error! must specify at least one LCA database with --db") sys.exit(-1) set_quiet(args.quiet, args.debug) if args.scaled: args.scaled = int(args.scaled) ignore_abundance = args.ignore_abundance # flatten --db and --query lists args.db = [item for sublist in args.db for item in sublist] args.query = [item for sublist in args.query for item in sublist] if not check_files_exist(*args.db): sys.exit(-1) # load all the databases dblist, ksize, scaled = lca_utils.load_databases(args.db, args.scaled) if ignore_abundance: notify( "Ignoring any k-mer abundances in query, since --ignore-abundance given." ) # find all the queries notify("finding query signatures...") inp_files = args.query if args.query_from_file: more_files = sourmash_args.load_pathlist_from_file(args.query_from_file) inp_files.extend(more_files) if not inp_files: error("Error! must specify at least one query signature with --query") sys.exit(-1) if not check_files_exist(*inp_files): sys.exit(-1) # summarize each signature individually csv_fp = None write_header = True if args.output: csv_fp = open(args.output, "w", newline="") try: for filename, sig, hashvals in load_singletons_and_count( inp_files, ksize, scaled, ignore_abundance ): # get the full counted list of lineage counts in this signature lineage_counts = summarize( hashvals, dblist, args.threshold, ignore_abundance ) if not ignore_abundance: total = float(sum(hashvals.values())) else: total = float(len(hashvals)) output_results(lineage_counts, total, filename=filename, sig=sig) if csv_fp: output_csv( lineage_counts, total, csv_fp, filename, sig, write_header=write_header, ) write_header = False finally: if csv_fp: csv_fp.close() if __name__ == "__main__": sys.exit(summarize_main(sys.argv[1:])) sourmash-bio-sourmash-bc3c0f8/src/sourmash/lca/lca_db.py000066400000000000000000000577471474157046300234570ustar00rootroot00000000000000"LCA database class and utilities." import os import json import gzip from collections import OrderedDict, defaultdict, Counter import functools import sourmash from sourmash.minhash import _get_max_hash_for_scaled from sourmash.logging import notify, error, debug from sourmash.index import Index, IndexSearchResult, _check_select_parameters from sourmash.picklist import passes_all_picklists def cached_property(fun): """A memoize decorator for class properties.""" @functools.wraps(fun) def get(self): try: return self._cache[fun] except AttributeError: self._cache = {} except KeyError: pass ret = self._cache[fun] = fun(self) return ret return property(get) class LCA_Database(Index): """ An in-memory database that indexes signatures by hash, and provides optional taxonomic lineage classification. Follows the `Index` API for `insert`, `search`, `gather`, and `signatures`. Identifiers `ident` must be unique, and are taken by default as the entire signature name upon insertion. This can be overridden with the `ident` keyword argument in `insert`. Integer `idx` indices can be used as keys in dictionary attributes: * `_idx_to_lid`, to get an (optional) lineage index. * `_idx_to_ident`, to retrieve the unique string identifier for that `idx`. Integer `lid` indices can be used as keys in dictionary attributes: * `_lid_to_idx`, to get a set of `idx` with that lineage. * `_lid_to_lineage`, to get a lineage for that `lid`. `_lineage_to_lid` is a dictionary with tuples of LineagePair as keys, `lid` as values. `_ident_to_name` is a dictionary from unique str identifer to a name. `_ident_to_idx` is a dictionary from unique str identifer to integer `idx`. `_hashval_to_idx` is a dictionary from individual hash values to sets of `idx`. """ is_database = True # we set manifest to None to avoid implication of fast on-disk access to # sketches. This may be revisited later. manifest = None def __init__(self, ksize, scaled, moltype="DNA"): self.ksize = int(ksize) self.scaled = int(scaled) self.filename = None self.moltype = moltype self._next_index = 0 self._next_lid = 0 self._ident_to_name = {} self._ident_to_idx = {} self._idx_to_lid = {} self._lineage_to_lid = {} self._lid_to_lineage = {} self._hashval_to_idx = defaultdict(set) self.picklists = [] @property def location(self): """Return source filename. Part of the Index protocol. """ return self.filename def __len__(self): """Return number of sketches. Part of the Index protocol. """ return self._next_index def _invalidate_cache(self): """Force rebuild of signatures after an 'insert'. Internal method. """ if hasattr(self, "_cache"): del self._cache def _get_ident_index(self, ident, fail_on_duplicate=False): """Get (create if necessary) a unique int idx, for each identifier. Internal method. """ idx = self._ident_to_idx.get(ident) if fail_on_duplicate: assert idx is None # should be no duplicate identities if idx is None: idx = self._next_index self._next_index += 1 self._ident_to_idx[ident] = idx return idx def _get_lineage_id(self, lineage): """Get (create if necessary) a unique lineage ID for each LineagePair tuples." Internal method of this class. """ # does one exist already? lid = self._lineage_to_lid.get(lineage) # nope - create one. Increment next_lid. if lid is None: lid = self._next_lid self._next_lid += 1 # build mappings self._lineage_to_lid[lineage] = lid self._lid_to_lineage[lid] = lineage return lid def insert(self, sig, ident=None, lineage=None): """Add a new signature into the LCA database. Takes optional arguments 'ident' and 'lineage'. 'ident' must be a unique string identifer across this database; if not specified, the signature name (sig.name) is used. 'lineage', if specified, must contain a tuple of LineagePair objects. Method unique to this class. """ minhash = sig.minhash if minhash.ksize != self.ksize: raise ValueError( f"cannot insert signature with ksize {minhash.ksize} into DB (ksize {self.ksize})" ) if minhash.moltype != self.moltype: raise ValueError( f"cannot insert signature with moltype {minhash.moltype} into DB (moltype {self.moltype})" ) # downsample to specified scaled; this has the side effect of # making sure they're all at the same scaled value! try: minhash = minhash.downsample(scaled=self.scaled) except ValueError: raise ValueError("cannot downsample signature; is it a scaled signature?") if not ident: ident = str(sig) if ident in self._ident_to_name: raise ValueError(f"signature '{ident}' is already in this LCA db.") # before adding, invalide any caching from @cached_property self._invalidate_cache() # store full name self._ident_to_name[ident] = sig.name # identifier -> integer index (idx) idx = self._get_ident_index(ident, fail_on_duplicate=True) if lineage: try: lineage = tuple(lineage) # (LineagePairs*) -> integer lineage ids (lids) lid = self._get_lineage_id(lineage) # map idx to lid as well. self._idx_to_lid[idx] = lid except TypeError: raise ValueError("lineage cannot be used as a key?!") for hashval in minhash.hashes: self._hashval_to_idx[hashval].add(idx) return len(minhash) def __repr__(self): return f"LCA_Database('{self.filename}')" def signatures(self): """Return all of the signatures in this LCA database. Part of the Index protocol. """ from sourmash import SourmashSignature if self.picklists: pl = self.picklists for v in self._signatures.values(): if passes_all_picklists(v, pl): yield v else: for v in self._signatures.values(): yield v def _signatures_with_internal(self): """Return all of the signatures in this LCA database. Part of the Index protocol; used for buulding manifests. """ for idx, ss in self._signatures.items(): yield ss, idx def select( self, ksize=None, moltype=None, num=0, scaled=0, abund=None, containment=False, picklist=None, **kwargs, ): """Select a subset of signatures to search. As with SBTs, queries with higher scaled values than the database can still be used for containment search, but not for similarity search. See SBT.select(...) for details, and _find_signatures for implementation. Will always raise ValueError if a requirement cannot be met. """ _check_select_parameters( ksize=ksize, num=num, moltype=moltype, scaled=scaled, containment=containment, abund=abund, picklist=picklist, **kwargs, ) if num: raise ValueError("cannot use 'num' MinHashes to search LCA database") if scaled > self.scaled and not containment: raise ValueError( f"cannot use scaled={scaled} on this database (scaled={self.scaled})" ) if ksize is not None and self.ksize != ksize: raise ValueError( f"ksize on this database is {self.ksize}; this is different from requested ksize of {ksize}" ) if moltype is not None and moltype != self.moltype: raise ValueError( f"moltype on this database is {self.moltype}; this is different from requested moltype of {moltype}" ) if abund: raise ValueError("LCA databases do not support sketches with abund=True") if picklist is not None: self.picklists.append(picklist) if len(self.picklists) > 1: raise ValueError( "we do not (yet) support multiple picklists for LCA databases" ) return self @classmethod def load(cls, db_name): """Load LCA_Database from a JSON file. Method specific to this class. """ from .lca_utils import taxlist from sourmash.tax.tax_utils import LineagePair if not os.path.isfile(db_name): raise ValueError( f"'{db_name}' is not a file and cannot be loaded as an LCA database" ) try: from sourmash.index.sqlite_index import LCA_SqliteDatabase return LCA_SqliteDatabase.load(db_name) except ValueError: pass xopen = open if db_name.endswith(".gz"): xopen = gzip.open with xopen(db_name, "rt") as fp: try: first_ch = fp.read(1) except ValueError: first_ch = "X" if not first_ch or first_ch[0] != "{": raise ValueError(f"'{db_name}' is not an LCA database file.") fp.seek(0) load_d = {} try: load_d = json.load(fp) except json.decoder.JSONDecodeError: pass if not load_d: raise ValueError( "cannot parse database file '{}' as JSON; invalid format." ) version = None db_type = None try: version = load_d.get("version") db_type = load_d.get("type") except AttributeError: pass if db_type != "sourmash_lca": raise ValueError(f"database file '{db_name}' is not an LCA db.") version = float(version) if version < 2.0 or "lid_to_lineage" not in load_d: raise ValueError( "Error! This is an old-style LCA DB. You'll need to rebuild or download a newer one." ) ksize = int(load_d["ksize"]) scaled = int(load_d["scaled"]) moltype = load_d.get("moltype", "DNA") if moltype != "DNA": assert ksize % 3 == 0 ksize = int(ksize / 3) db = cls(ksize, scaled, moltype) # convert lineage_dict to proper lineages (tuples of LineagePairs) lid_to_lineage_2 = load_d["lid_to_lineage"] lid_to_lineage = {} lineage_to_lid = {} for k, v in lid_to_lineage_2.items(): v = dict((x[0], x[1]) for x in v) vv = [] for rank in taxlist(): name = v.get(rank, "") vv.append(LineagePair(rank, name)) vv = tuple(vv) lid_to_lineage[int(k)] = vv lineage_to_lid[vv] = int(k) db._lid_to_lineage = lid_to_lineage db._lineage_to_lid = lineage_to_lid # convert hashval -> lineage index keys to integers (looks like # JSON doesn't have a 64 bit type so stores them as strings) hashval_to_idx_2 = load_d["hashval_to_idx"] hashval_to_idx = {} for k, v in hashval_to_idx_2.items(): hashval_to_idx[int(k)] = v db._hashval_to_idx = hashval_to_idx db._ident_to_name = load_d["ident_to_name"] db._ident_to_idx = load_d["ident_to_idx"] db._idx_to_lid = {} for k, v in load_d["idx_to_lid"].items(): db._idx_to_lid[int(k)] = v if db._ident_to_idx: db._next_index = max(db._ident_to_idx.values()) + 1 else: db._next_index = 0 if db._idx_to_lid: db._next_lid = max(db._idx_to_lid.values()) + 1 else: db._next_lid = 0 db.filename = db_name return db def save(self, db_name, *, format="json"): if format == "sql": self.save_to_sql(db_name) else: assert format == "json" self.save_to_json(db_name) def save_to_json(self, db_name): """Save LCA_Database to a JSON file. Method specific to this class. """ if os.path.exists(db_name): raise ValueError( f"LCA database {db_name} already exists; not overwriting or appending" ) xopen = open if db_name.endswith(".gz"): xopen = gzip.open with xopen(db_name, "wt") as fp: # use an OrderedDict to preserve output order save_d = OrderedDict() save_d["version"] = "2.1" save_d["type"] = "sourmash_lca" save_d["license"] = "CC0" if self.moltype != "DNA": ksize = self.ksize * 3 else: ksize = self.ksize save_d["ksize"] = ksize save_d["scaled"] = self.scaled save_d["moltype"] = self.moltype # convert lineage internals from tuples to dictionaries d = OrderedDict() for k, v in self._lid_to_lineage.items(): d[k] = dict([(vv.rank, vv.name) for vv in v]) save_d["lid_to_lineage"] = d # convert values from sets to lists, so that JSON knows how to save save_d["hashval_to_idx"] = dict( (k, list(v)) for (k, v) in self._hashval_to_idx.items() ) save_d["ident_to_name"] = self._ident_to_name save_d["ident_to_idx"] = self._ident_to_idx save_d["idx_to_lid"] = self._idx_to_lid save_d["lid_to_lineage"] = self._lid_to_lineage json.dump(save_d, fp) def save_to_sql(self, dbname): "Save this LCA_Database into an LCA_SqliteDatabase" from sourmash.index.sqlite_index import LCA_SqliteDatabase from sourmash.tax.tax_utils import LineageDB if os.path.exists(dbname): raise ValueError( f"LCA database {dbname} already exists; not overwriting or appending" ) # create a new in-memory lineage db... assignments = {} available_ranks = set() # track ranks, too for ident, idx in self._ident_to_idx.items(): lid = self._idx_to_lid.get(idx) if lid is not None: lineage = self._lid_to_lineage[lid] assignments[ident] = lineage for pair in lineage: available_ranks.add(pair.rank) ldb = LineageDB(assignments, available_ranks) # ...and pass over to create, using 'self' as index. LCA_SqliteDatabase.create(dbname, self, ldb) def downsample_scaled(self, scaled): """ Downsample to the provided scaled value, i.e. eliminate all hashes that don't fall in the required range. This applies to this database in place. Method specific to LCA databases. """ if scaled == self.scaled: return elif scaled < self.scaled: raise ValueError(f"cannot decrease scaled from {self.scaled} to {scaled}") self._invalidate_cache() max_hash = _get_max_hash_for_scaled(scaled) # filter out all hashes over max_hash in value. new_hashvals = defaultdict(set) for k, v in self._hashval_to_idx.items(): if k < max_hash: new_hashvals[k] = v self._hashval_to_idx = new_hashvals self.scaled = scaled @property def hashvals(self): "Return all hashvals stored in this database." return self._hashval_to_idx.keys() def get_lineage_assignments(self, hashval, min_num=None): """Get a list of lineages for this hashval. Method specific to LCA Databases. """ x = [] idx_list = self._hashval_to_idx.get(hashval, []) if min_num and len(idx_list) < min_num: return [] for idx in idx_list: lid = self._idx_to_lid.get(idx, None) if lid is not None: lineage = self._lid_to_lineage[lid] x.append(lineage) return x def get_identifiers_for_hashval(self, hashval): """ Get a list of identifiers for signatures containing this hashval """ idx_list = self._hashval_to_idx.get(hashval, []) for idx in idx_list: yield self._idx_to_ident[idx] @cached_property def _signatures(self): """Create a _signatures member dictionary that contains {idx: sigobj}. Internal method of this class. """ from sourmash import MinHash, SourmashSignature is_protein = False is_hp = False is_dayhoff = False if self.moltype == "protein": is_protein = True elif self.moltype == "hp": is_hp = True elif self.moltype == "dayhoff": is_dayhoff = True minhash = MinHash( n=0, ksize=self.ksize, scaled=self.scaled, is_protein=is_protein, hp=is_hp, dayhoff=is_dayhoff, ) debug("creating signatures for LCA DB...") mhd = defaultdict(minhash.copy_and_clear) temp_vals = defaultdict(list) # invert the hashval_to_idx dictionary for hashval, idlist in self._hashval_to_idx.items(): for idx in idlist: temp_hashes = temp_vals[idx] temp_hashes.append(hashval) # 50 is an arbitrary number. If you really want # to micro-optimize, list is resized and grow in this pattern: # 0, 4, 8, 16, 25, 35, 46, 58, 72, 88, ... # (from https://github.com/python/cpython/blob/b2b4a51f7463a0392456f7772f33223e57fa4ccc/Objects/listobject.c#L57) if len(temp_hashes) > 50: mhd[idx].add_many(temp_hashes) # Sigh, python 2... when it goes away, # we can do `temp_hashes.clear()` instead. del temp_vals[idx] # We loop temp_vals again to add any remainder hashes # (each list of hashes is smaller than 50 items) for sig, vals in temp_vals.items(): mhd[sig].add_many(vals) sigd = {} for idx, mh in mhd.items(): ident = self._idx_to_ident[idx] name = self._ident_to_name[ident] ss = SourmashSignature(mh, name=name) ss.into_frozen() if passes_all_picklists(ss, self.picklists): sigd[idx] = ss debug("=> {} signatures!", len(sigd)) return sigd def find(self, search_fn, query, **kwargs): """ Do a Jaccard similarity or containment search, yield results. Here 'search_fn' should be an instance of 'JaccardSearch'. As with SBTs, queries with higher scaled values than the database can still be used for containment search, but not for similarity search. See SBT.select(...) for details. Part of the Index protocol. """ search_fn.check_is_compatible(query) # make sure we're looking at the same scaled value as database query_mh = query.minhash query_scaled = query_mh.scaled if self.scaled > query_scaled: query_mh = query_mh.downsample(scaled=self.scaled) query_scaled = query_mh.scaled def prepare_subject(x): return x # identity else: def prepare_subject(subj): return subj.downsample(scaled=query_scaled) # collect matching hashes for the query: c = Counter() query_hashes = set(query_mh.hashes) for hashval in query_hashes: idx_list = self._hashval_to_idx.get(hashval, []) for idx in idx_list: c[idx] += 1 debug("number of matching signatures for hashes: {}", len(c)) # for each match, in order of largest overlap, for idx, count in c.most_common(): # pull in the hashes. This reconstructs & caches all input # minhashes, which is kinda memory intensive...! # NOTE: one future low-mem optimization could be to support doing # this piecemeal by iterating across all the hashes, instead. subj = self._signatures.get(idx) if subj is None: # must be because of a picklist exclusion assert self.picklists continue subj_mh = prepare_subject(subj.minhash) # all numbers calculated after downsampling -- query_size = len(query_mh) subj_size = len(subj_mh) shared_size = query_mh.count_common(subj_mh) total_size = len(query_mh + subj_mh) score = search_fn.score_fn(query_size, shared_size, subj_size, total_size) # CTB note to self: even with JaccardSearchBestOnly, this will # still iterate over & score all signatures. We should come # up with a protocol by which the JaccardSearch object can # signal that it is done, or something. # For example, see test_lca_jaccard_ordering, where # for containment we could be done early, but for Jaccard we # cannot. if search_fn.passes(score): if search_fn.collect(score, subj): if passes_all_picklists(subj, self.picklists): yield IndexSearchResult(score, subj, self.location) @cached_property def _lid_to_idx(self): """Connect lineage id lid (int) to idx set (set of ints)."" Method specific to LCA databases. """ d = defaultdict(set) for idx, lid in self._idx_to_lid.items(): d[lid].add(idx) return d @cached_property def _idx_to_ident(self): """Connect idx (int) to ident (str). Method specific to LCA databases. """ d = defaultdict(set) for ident, idx in self._ident_to_idx.items(): assert idx not in d d[idx] = ident return d def load_single_database(filename, verbose=False): "Load a single LCA database; return (db, ksize, scaled)" dblist, ksize, scaled = load_databases([filename], verbose=verbose) return dblist[0], ksize, scaled def load_databases(filenames, scaled=None, verbose=True): "Load multiple LCA databases; return (dblist, ksize, scaled)" ksize_vals = set() scaled_vals = set() moltype_vals = set() dblist = [] # load all the databases for db_name in filenames: if verbose: notify("\r\033[K", end="") notify(f"... loading database {format(db_name)}", end="\r") lca_db = LCA_Database.load(db_name) ksize_vals.add(lca_db.ksize) if len(ksize_vals) > 1: raise Exception("multiple ksizes, quitting") if scaled and scaled > lca_db.scaled: lca_db.downsample_scaled(scaled) scaled_vals.add(lca_db.scaled) moltype_vals.add(lca_db.moltype) if len(moltype_vals) > 1: raise Exception("multiple moltypes, quitting") dblist.append(lca_db) ksize = ksize_vals.pop() scaled = scaled_vals.pop() moltype = moltype_vals.pop() if verbose: notify("\r\033[K", end="") notify( f"loaded {len(dblist)} LCA databases. ksize={ksize}, scaled={scaled} moltype={moltype}" ) return dblist, ksize, scaled sourmash-bio-sourmash-bc3c0f8/src/sourmash/lca/lca_utils.py000066400000000000000000000161671474157046300242210ustar00rootroot00000000000000""" Utility functions for lowest-common-ancestor analysis tools. """ from os.path import exists from collections import namedtuple, defaultdict, Counter from .lca_db import LCA_Database, load_single_database, load_databases __all__ = [ "taxlist", "zip_lineage", "build_tree", "find_lca", "load_single_database", "load_databases", "gather_assignments", "count_lca_for_assignments", "LineagePair", "display_lineage", "make_lineage", "pop_to_rank", "is_lineage_match", ] try: # py2/py3 compat from itertools import zip_longest except ImportError: from itertools import izip_longest as zip_longest from sourmash.logging import notify, error, debug # type to store an element in a taxonomic lineage LineagePair = namedtuple("LineagePair", ["rank", "name"]) def check_files_exist(*files): ret = True not_found = [] for f in files: if not exists(f): not_found.append(f) ret = False if len(not_found): error( "Error! Could not find the following files." " Make sure the file paths are specified correctly.\n{}".format( "\n".join(not_found) ) ) return ret # ordered list of taxonomic ranks def taxlist(include_strain=True): """ Provide an ordered list of taxonomic ranks. """ yield from [ "superkingdom", "phylum", "class", "order", "family", "genus", "species", ] if include_strain: yield "strain" # produce an ordered list of tax names from lineage def zip_lineage(lineage, include_strain=True, truncate_empty=False): """ Given an iterable of LineagePair objects, return list of lineage names. This utility function handles species/strain and empty lineage entries gracefully. >>> x = [ LineagePair('superkingdom', 'a'), LineagePair('phylum', 'b') ] >>> zip_lineage(x) ['a', 'b', '', '', '', '', '', ''] >>> x = [ LineagePair('superkingdom', 'a'), LineagePair(None, ''), LineagePair('class', 'c') ] >>> zip_lineage(x) ['a', '', 'c', '', '', '', '', ''] """ empty = LineagePair(None, "") pairs = zip_longest( taxlist(include_strain=include_strain), lineage, fillvalue=empty ) pairs = list(pairs) # eliminate empty if so requested if truncate_empty: last_lineage_tup = pairs[-1][1] while pairs and last_lineage_tup == empty: pairs.pop(-1) if pairs: last_lineage_tup = pairs[-1][1] row = [] for taxrank, lineage_tup in pairs: # validate non-empty tax, e.g. superkingdom/phylum/class in order. if lineage_tup != empty and lineage_tup.rank != taxrank: raise ValueError( f"incomplete lineage at {taxrank} - is {lineage_tup.rank} instead" ) row.append(lineage_tup.name) return row def display_lineage(lineage, include_strain=True, truncate_empty=True): return ";".join( zip_lineage( lineage, include_strain=include_strain, truncate_empty=truncate_empty ) ) # filter function toreplace blank/na/null with 'unassigned' def filter_null(x): return ( "unassigned" if x is None or x.strip() in ("[Blank]", "na", "null", "") else x ) null_names = set(["[Blank]", "na", "null"]) def build_tree(assignments, initial=None): """ Builds a tree of dictionaries from lists of LineagePair objects in 'assignments'. This tree can then be used to find lowest common ancestor agreements/confusion. """ if initial is None: tree = {} else: tree = initial if not assignments: raise ValueError("empty assignment passed to build_tree") for assignment in assignments: node = tree for lineage_tup in assignment: if lineage_tup.name: child = node.get(lineage_tup, {}) node[lineage_tup] = child # shift -> down in tree node = child return tree def find_lca(tree): """ Given a tree produced by 'find_tree', find the first node with multiple children, OR the only leaf in the tree. Return (lineage_tup, reason), where 'reason' is the number of children of the returned node, i.e. 0 if it's a leaf and > 1 if it's an internal node. """ node = tree lineage = [] while 1: if len(node) == 1: # descend to only child; track path lineage_tup = next(iter(node.keys())) lineage.append(lineage_tup) node = node[lineage_tup] elif len(node) == 0: # at leaf; end return tuple(lineage), 0 else: # len(node) > 1 => confusion!! return tuple(lineage), len(node) def gather_assignments(hashvals, dblist): """ Gather assignments from across all the databases for all the hashvals. Ignores counts of the hashvals. """ assignments = defaultdict(set) for hashval in hashvals: for lca_db in dblist: lineages = lca_db.get_lineage_assignments(hashval) if lineages: assignments[hashval].update(lineages) return assignments def count_lca_for_assignments(assignments, hashval_counts=None): """ For each hashval, count the LCA across its assignments. If hashval_counts is not None, it must be a dictionary that maps { hashval: hashval_count }; this is then used to weight the counts. """ counts = Counter() for hashval in assignments: # for each list of tuple_info [(rank, name), ...] build # a tree that lets us discover lowest-common-ancestor. lineages = assignments[hashval] tree = build_tree(lineages) # now find either a leaf or the first node with multiple # children; that's our lowest-common-ancestor node. lca, reason = find_lca(tree) if hashval_counts: counts[lca] += hashval_counts[hashval] else: counts[lca] += 1 return counts def is_lineage_match(lin_a, lin_b, rank): """ check to see if two lineages are a match down to given rank. """ for a, b in zip(lin_a, lin_b): assert a.rank == b.rank if a.rank == rank: if a == b: return 1 if a != b: return 0 return 0 def pop_to_rank(lin, rank): "Remove lineage tuples from given lineage `lin` until `rank` is reached." lin = list(lin) txl = taxlist() before_rank = [] for txl_rank in txl: if txl_rank != rank: before_rank.append(txl_rank) else: break # are we already above rank? if lin and lin[-1].rank in before_rank: return tuple(lin) while lin and lin[-1].rank != rank: lin.pop() return tuple(lin) def make_lineage(lineage): "Turn a ; or ,-separated set of lineages into a tuple of LineagePair objs." from sourmash.tax.tax_utils import LineagePair lin = lineage.split(";") if len(lin) == 1: lin = lineage.split(",") lin = [LineagePair(rank, n) for (rank, n) in zip(taxlist(), lin)] lin = tuple(lin) return lin sourmash-bio-sourmash-bc3c0f8/src/sourmash/logging.py000066400000000000000000000074731474157046300231310ustar00rootroot00000000000000import sys from io import StringIO _quiet = False _debug = False def set_quiet(val, print_debug=False): global _quiet, _debug _quiet = bool(val) _debug = bool(print_debug) def print_results(s, *args, **kwargs): if _quiet: return print(s.format(*args, **kwargs), file=sys.stdout) sys.stdout.flush() def notify(s, *args, **kwargs): "A simple logging function => stderr." if _quiet: return print("\r\033[K", end="", file=sys.stderr) print(s.format(*args, **kwargs), file=sys.stderr, end=kwargs.get("end", "\n")) if kwargs.get("flush"): sys.stderr.flush() def debug(s, *args, **kwargs): "A debug logging function => stderr." if _quiet or not _debug: return print("\r\033[K", end="", file=sys.stderr) print(s.format(*args, **kwargs), file=sys.stderr, end=kwargs.get("end", "\n")) if kwargs.get("flush"): sys.stderr.flush() def debug_literal(s, *args, **kwargs): "A debug logging function => stderr." if _quiet or not _debug: return print("\r\033[K", end="", file=sys.stderr) print(s, file=sys.stderr, end=kwargs.get("end", "\n")) if kwargs.get("flush"): sys.stderr.flush() def error(s, *args, **kwargs): "A simple error logging function => stderr." print("\r\033[K", end="", file=sys.stderr) print(s.format(*args, **kwargs), file=sys.stderr) if kwargs.get("flush"): sys.stderr.flush() def test_notify(): global _quiet qsave = _quiet saveerr, sys.stderr = sys.stderr, StringIO() try: _quiet = False notify("hello, world") finally: _quiet = qsave saveerr, sys.stderr = sys.stderr, saveerr print(type(saveerr)) assert "hello, world\n" in saveerr.getvalue() def test_notify_flush(): global _quiet qsave = _quiet saveerr, sys.stderr = sys.stderr, StringIO() try: _quiet = False notify("hello, world", flush=True) finally: _quiet = qsave saveerr, sys.stderr = sys.stderr, saveerr print(type(saveerr)) assert "hello, world" in saveerr.getvalue() def test_notify_end(): global _quiet qsave = _quiet saveerr, sys.stderr = sys.stderr, StringIO() try: _quiet = False notify("hello, world", end="FOO") finally: _quiet = qsave saveerr, sys.stderr = sys.stderr, saveerr print(type(saveerr)) assert "hello, worldFOO" in saveerr.getvalue() def test_notify_quiet(): global _quiet qsave = _quiet saveerr, sys.stderr = sys.stderr, StringIO() try: _quiet = True notify("hello, world") finally: _quiet = qsave saveerr, sys.stderr = sys.stderr, saveerr print(type(saveerr)) assert "hello, world" not in saveerr.getvalue() def test_error(): global _quiet qsave = _quiet saveerr, sys.stderr = sys.stderr, StringIO() try: _quiet = False error("hello, world") finally: _quiet = qsave saveerr, sys.stderr = sys.stderr, saveerr print(type(saveerr)) assert "hello, world\n" in saveerr.getvalue() def test_error_flush(): global _quiet qsave = _quiet saveerr, sys.stderr = sys.stderr, StringIO() try: _quiet = False error("hello, world", flush=True) finally: _quiet = qsave saveerr, sys.stderr = sys.stderr, saveerr print(type(saveerr)) assert "hello, world" in saveerr.getvalue() def test_error_quiet(): # error should still output even if _quiet is True global _quiet qsave = _quiet saveerr, sys.stderr = sys.stderr, StringIO() try: _quiet = True error("hello, world") finally: _quiet = qsave saveerr, sys.stderr = sys.stderr, saveerr print(type(saveerr)) assert "hello, world" in saveerr.getvalue() sourmash-bio-sourmash-bc3c0f8/src/sourmash/manifest.py000066400000000000000000000265651474157046300233140ustar00rootroot00000000000000""" Manifests for collections of signatures. """ import csv import ast import gzip import os.path from abc import abstractmethod import itertools from sourmash import picklist, index class BaseCollectionManifest: """ Signature metadata for a collection of signatures. Manifests support selection and rapid lookup of signatures. * 'select_to_manifest(...)' matches the Index selector protocol * 'rows' is a public iterable that can be used to iterate over the manifest contents. * 'locations()' returns all distinct locations for e.g. lazy loading * supports container protocol for signatures, e.g. 'if ss in manifest: ...' """ # each manifest row must have the following, although they may be empty. required_keys = ( "internal_location", "md5", "md5short", "ksize", "moltype", "num", "scaled", "n_hashes", "with_abundance", "name", "filename", ) @classmethod @abstractmethod def load_from_manifest(cls, manifest, **kwargs): "Load this manifest from another manifest object." @classmethod def load_from_filename(cls, filename): # SQLite db? db = cls.load_from_sql(filename) if db is not None: return db # not a SQLite db? CTB: fix this to actually try loading this as .gz... if filename.endswith(".gz"): xopen = gzip.open else: xopen = open with xopen(filename, "rt", newline="") as fp: return cls.load_from_csv(fp) @classmethod def load_from_csv(cls, fp): "load a manifest from a CSV file." manifest_list = [] firstline = fp.readline().rstrip() if not firstline.startswith("# SOURMASH-MANIFEST-VERSION: "): raise ValueError("manifest is missing version header") version = firstline[len("# SOURMASH-MANIFEST-VERSION: ") :] if float(version) != 1.0: raise ValueError(f"unknown manifest version number {version}") r = csv.DictReader(fp) if not r.fieldnames: raise ValueError("missing column headers in manifest") for k in cls.required_keys: if k not in r.fieldnames: raise ValueError(f"missing column '{k}' in manifest.") row = None # do row type conversion introws = ("num", "scaled", "ksize", "n_hashes") boolrows = ("with_abundance",) for row in r: for k in introws: row[k] = int(row[k]) for k in boolrows: row[k] = bool(ast.literal_eval(str(row[k]))) row["signature"] = None manifest_list.append(row) return CollectionManifest(manifest_list) @classmethod def load_from_sql(cls, filename): from sourmash.index.sqlite_index import load_sqlite_index db = load_sqlite_index(filename, request_manifest=True) if db is not None: return db.manifest return None def write_to_filename(self, filename, *, database_format="csv", ok_if_exists=False): if database_format == "csv": from .sourmash_args import FileOutputCSV if ok_if_exists or not os.path.exists(filename): with FileOutputCSV(filename) as fp: return self.write_to_csv(fp, write_header=True) elif os.path.exists(filename) and not ok_if_exists: raise Exception("output manifest already exists") elif database_format == "sql": from sourmash.index.sqlite_index import SqliteCollectionManifest SqliteCollectionManifest.load_from_manifest( self, dbfile=filename, append=ok_if_exists ) @classmethod def write_csv_header(cls, fp): "write header for manifest CSV format" fp.write("# SOURMASH-MANIFEST-VERSION: 1.0\n") w = csv.DictWriter(fp, fieldnames=cls.required_keys) w.writeheader() def write_to_csv(self, fp, write_header=False): "write manifest CSV to specified file handle" w = csv.DictWriter(fp, fieldnames=self.required_keys, extrasaction="ignore") if write_header: self.write_csv_header(fp) for row in self.rows: # don't write signature! if "signature" in row: del row["signature"] w.writerow(row) @classmethod def make_manifest_row(cls, ss, location, *, include_signature=True): "make a manifest row dictionary." mh = ss.minhash row = {} row["md5"] = ss.md5sum() row["md5short"] = row["md5"][:8] row["ksize"] = int(mh.ksize) row["moltype"] = mh.moltype row["num"] = int(mh.num) row["scaled"] = int(mh.scaled) row["n_hashes"] = len(mh) row["with_abundance"] = mh.track_abundance row["name"] = ss.name row["filename"] = ss.filename row["internal_location"] = location assert set(row.keys()) == set(cls.required_keys) # if requested, include the signature in the manifest. if include_signature: row["signature"] = ss return row @classmethod def create_manifest(cls, locations_iter, *, include_signature=True): """Create a manifest from an iterator that yields (ss, location) Stores signatures in manifest rows by default. Note: do NOT catch exceptions here, so this passes through load excs. """ manifest_list = [] for ss, location in locations_iter: row = cls.make_manifest_row( ss, location, include_signature=include_signature ) manifest_list.append(row) return cls(manifest_list) ## implement me @abstractmethod def __add__(self, other): "Add two manifests" @abstractmethod def __bool__(self): "Test if manifest is empty" @abstractmethod def __len__(self): "Get number of entries in manifest" @abstractmethod def __eq__(self, other): "Check for equality of manifest based on rows" @abstractmethod def select_to_manifest(self, **kwargs): "Select compatible signatures" @abstractmethod def filter_rows(self, row_filter_fn): "Filter rows based on a pattern matching function." @abstractmethod def filter_on_columns(self, col_filter_fn, col_names): "Filter on column values." @abstractmethod def locations(self): "Return a list of distinct locations" @abstractmethod def __contains__(self, ss): "Determine if a particular SourmashSignature is in this manifest." @abstractmethod def to_picklist(self): "Convert manifest to a picklist." def _check_row_values(self): "check that manifest rows have legit types/values." for row in self.rows: index._check_select_parameters( num=row["num"], ksize=row["ksize"], moltype=row["moltype"], scaled=row["scaled"], abund=row["with_abundance"], ) class CollectionManifest(BaseCollectionManifest): """ An in-memory manifest that simply stores the rows in a list. """ def __init__(self, rows=[]): "Initialize from an iterable of metadata dictionaries." self.rows = [] self._md5_set = set() self._add_rows(rows) @classmethod def load_from_manifest(cls, manifest, **kwargs): "Load this manifest from another manifest object." return cls(manifest.rows) def add_row(self, row): self._add_rows([row]) def _add_rows(self, rows): md5set = self._md5_set # only iterate once, in case it's a generator for row in rows: self.rows.append(row) md5set.add(row["md5"]) def __iadd__(self, other): if self is other: raise Exception("cannot directly add manifest to itself") self._add_rows(other.rows) return self def __add__(self, other): mf = CollectionManifest(self.rows) mf._add_rows(other.rows) return mf def __bool__(self): return bool(self.rows) def __len__(self): return len(self.rows) def __eq__(self, other): "Check equality on a row-by-row basis. May fail on out-of-order rows." for a, b in itertools.zip_longest(self.rows, other.rows): if a is None or b is None: return False # ignore non-required keys. for k in self.required_keys: if a[k] != b[k]: return False return True def _select( self, *, ksize=None, moltype=None, scaled=0, num=0, containment=False, abund=None, picklist=None, ): """Yield manifest rows for sigs that match the specified requirements. Internal method; call `select_to_manifest` instead. """ index._check_select_parameters( ksize=ksize, num=num, abund=abund, moltype=moltype, scaled=scaled ) matching_rows = self.rows if ksize: matching_rows = (row for row in matching_rows if row["ksize"] == ksize) if moltype: matching_rows = (row for row in matching_rows if row["moltype"] == moltype) if scaled or containment: matching_rows = ( row for row in matching_rows if row["scaled"] and not row["num"] ) if num: matching_rows = ( row for row in matching_rows if row["num"] and not row["scaled"] ) if abund: # only need to concern ourselves if abundance is _required_ matching_rows = (row for row in matching_rows if row["with_abundance"]) if picklist: matching_rows = ( row for row in matching_rows if picklist.matches_manifest_row(row) ) # return only the internal filenames! yield from matching_rows def select_to_manifest(self, **kwargs): "Do a 'select' and return a new CollectionManifest object." new_rows = self._select(**kwargs) return CollectionManifest(new_rows) def filter_rows(self, row_filter_fn): "Create a new manifest filtered through row_filter_fn." new_rows = [row for row in self.rows if row_filter_fn(row)] return CollectionManifest(new_rows) def filter_on_columns(self, col_filter_fn, col_names): "Create a new manifest based on column matches." def row_filter_fn(row): x = [row[col] for col in col_names if row[col] is not None] return col_filter_fn(x) return self.filter_rows(row_filter_fn) def locations(self): "Return all distinct locations." seen = set() for row in self.rows: loc = row["internal_location"] # track/remove duplicates if loc not in seen: seen.add(loc) yield loc def __contains__(self, ss): "Does this manifest contain this signature?" md5 = ss.md5sum() return md5 in self._md5_set def to_picklist(self): "Convert this manifest to a picklist." pl = picklist.SignaturePicklist("manifest") pl.pickset = {pl._get_value_for_manifest_row(row) for row in self.rows} return pl sourmash-bio-sourmash-bc3c0f8/src/sourmash/minhash.py000066400000000000000000001222401474157046300231200ustar00rootroot00000000000000""" sourmash submodule that provides MinHash class and utility functions. class MinHash - core MinHash class. class FrozenMinHash - read-only MinHash class. """ from .distance_utils import ( jaccard_to_distance, containment_to_distance, set_size_exact_prob, ) from .logging import notify import numpy as np __all__ = [ "get_minhash_default_seed", "get_minhash_max_hash", "hash_murmur", "MinHash", "FrozenMinHash", ] from collections.abc import Mapping from . import VERSION from ._lowlevel import ffi, lib from .utils import RustObject, rustcall from .exceptions import SourmashError from deprecation import deprecated # default MurmurHash seed MINHASH_DEFAULT_SEED = 42 def get_minhash_default_seed(): "Return the default seed value used for the MurmurHash hashing function." return MINHASH_DEFAULT_SEED # we use the 64-bit hash space of MurmurHash only # this is 2 ** 64 - 1 in hexadecimal MINHASH_MAX_HASH = 0xFFFFFFFFFFFFFFFF def get_minhash_max_hash(): "Return the maximum hash value." return MINHASH_MAX_HASH def _get_max_hash_for_scaled(scaled): "Convert a 'scaled' value into a 'max_hash' value." if scaled == 0: return 0 elif scaled == 1: return get_minhash_max_hash() return min(int(round(get_minhash_max_hash() / scaled, 0)), MINHASH_MAX_HASH) def _get_scaled_for_max_hash(max_hash): "Convert a 'max_hash' value into a 'scaled' value." if max_hash == 0: return 0 return min(int(round(get_minhash_max_hash() / max_hash, 0)), MINHASH_MAX_HASH) def to_bytes(s): # Allow for strings, bytes or int # Single item of byte string = int if isinstance(s, bytes): return s if not isinstance(s, str | bytes | int): raise TypeError("Requires a string-like sequence") if isinstance(s, str): s = s.encode("utf-8") elif isinstance(s, int): s = bytes([s]) return s def hash_murmur(kmer, seed=MINHASH_DEFAULT_SEED): "hash_murmur(string, [,seed])\n\n" "Compute a hash for a string, optionally using a seed (an integer). " "The current default seed is returned by hash_seed()." return lib.hash_murmur(to_bytes(kmer), seed) def translate_codon(codon): "Translate a codon into an amino acid." try: return rustcall(lib.sourmash_translate_codon, to_bytes(codon)).decode("utf-8") except SourmashError as e: raise ValueError(e.message) def flatten_and_downsample_scaled(mh, *scaled_vals): "Flatten MinHash object and downsample to max of scaled values." assert mh.scaled assert all(x > 0 for x in scaled_vals) mh = mh.flatten() scaled = max(scaled_vals) if scaled > mh.scaled: return mh.downsample(scaled=scaled) return mh def flatten_and_downsample_num(mh, *num_vals): "Flatten MinHash object and downsample to min of num values." assert mh.num assert all(x > 0 for x in num_vals) mh = mh.flatten() num = min(num_vals) if num < mh.num: return mh.downsample(num=num) return mh def flatten_and_intersect_scaled(mh1, mh2): "Flatten and downsample two scaled MinHash objs, then return intersection." scaled = max(mh1.scaled, mh2.scaled) mh1 = mh1.flatten().downsample(scaled=scaled) mh2 = mh2.flatten().downsample(scaled=scaled) return mh1 & mh2 class _HashesWrapper(Mapping): "A read-only view of the hashes contained by a MinHash object." def __init__(self, h): self._data = h def __getitem__(self, key): return self._data[key] def __repr__(self): return repr(self._data) def __len__(self): return len(self._data) def __iter__(self): return iter(self._data) def __eq__(self, other): return list(self.items()) == list(other.items()) def __setitem__(self, k, v): raise RuntimeError("cannot modify hashes directly; use 'add' methods") class MinHash(RustObject): """\ The core sketch object for sourmash. MinHash objects store and provide functionality for subsampled hash values from DNA, RNA, and amino acid sequences. MinHash also supports both the standard MinHash behavior (bounded size or ``num``) and a non-standard MinHash, called "modulo hash" behavior, or ``scaled``. Please see the API examples at https://sourmash.readthedocs.io/en/latest/api-example.html#sourmash-minhash-objects-and-manipulations for more information. Basic usage: >>> from sourmash import MinHash >>> mh1 = MinHash(n=20, ksize=3) >>> mh1.add_sequence('ATGAGAGACGATAGACAGATGAC') >>> mh2 = MinHash(n=20, ksize=3) >>> mh2.add_sequence('ATGAGActCGATAGaCAGATGAC') >>> round(mh1.similarity(mh2), 2) 0.85 """ __dealloc_func__ = lib.kmerminhash_free def __init__( self, n, ksize, *, is_protein=False, dayhoff=False, hp=False, skipm1n3=False, skipm2n3=False, track_abundance=False, seed=MINHASH_DEFAULT_SEED, max_hash=0, mins=None, scaled=0, ): """\ Create a sourmash.MinHash object. To create a standard (``num``) MinHash, use: ``MinHash(, , ...)`` To create a ``scaled`` MinHash, use ``MinHash(0, , scaled=, ...)`` Optional arguments: * is_protein (default False) - aa k-mers * dayhoff (default False) - dayhoff encoding * hp (default False) - hydrophilic/hydrophobic aa * skipm1n3 (default False) - skipmer (m1n3) * skipm2n3 (default False) - skipmer (m2n3) * track_abundance (default False) - track hash multiplicity * mins (default None) - list of hashvals, or (hashval, abund) pairs * seed (default 42) - murmurhash seed """ # support max_hash in constructor, for now. if max_hash: if scaled: raise ValueError("cannot set both max_hash and scaled") scaled = _get_scaled_for_max_hash(max_hash) if scaled and n: raise ValueError("cannot set both n and max_hash") if not n and not scaled: raise ValueError("cannot omit both n and scaled") if dayhoff or hp: is_protein = False if dayhoff: hash_function = lib.HASH_FUNCTIONS_MURMUR64_DAYHOFF ksize = ksize * 3 elif hp: hash_function = lib.HASH_FUNCTIONS_MURMUR64_HP ksize = ksize * 3 elif is_protein: hash_function = lib.HASH_FUNCTIONS_MURMUR64_PROTEIN ksize = ksize * 3 elif skipm1n3: hash_function = lib.HASH_FUNCTIONS_MURMUR64_SKIPM1N3 elif skipm2n3: hash_function = lib.HASH_FUNCTIONS_MURMUR64_SKIPM2N3 else: hash_function = lib.HASH_FUNCTIONS_MURMUR64_DNA self._objptr = lib.kmerminhash_new( scaled, ksize, hash_function, seed, track_abundance, n ) if mins: if track_abundance: self.set_abundances(mins) else: self.add_many(mins) def __copy__(self): "Create a new copy of this MinHash." a = MinHash( self.num, self.ksize, is_protein=self.is_protein, dayhoff=self.dayhoff, hp=self.hp, skipm1n3=self.skipm1n3, skipm2n3=self.skipm2n3, track_abundance=self.track_abundance, seed=self.seed, max_hash=self._max_hash, ) a.merge(self) return a copy = __copy__ def __getstate__(self): "support pickling via __getstate__/__setstate__" # note: we multiple ksize by 3 here so that # pickle protocols that bypass __setstate__ # get a ksize that makes sense to the Rust layer. See #2262. # CTB/NTP note: if you add things below, you might want to put # them at the end, because we use internal indexes in a few places. # see especially `_set_num_scaled()` in sig/__main__.my. # My apologies. return ( self.num, self.ksize if self.is_dna or self.skipm1n3 or self.skipm2n3 else self.ksize * 3, self.is_protein, self.dayhoff, self.hp, self.skipm1n3, self.skipm2n3, self.hashes, None, self.track_abundance, self._max_hash, self.seed, ) def __setstate__(self, tup): "support pickling via __getstate__/__setstate__" ( n, ksize, is_protein, dayhoff, hp, skipm1n3, skipm2n3, mins, _, track_abundance, max_hash, seed, ) = tup self.__del__() hash_function = ( lib.HASH_FUNCTIONS_MURMUR64_DAYHOFF if dayhoff else lib.HASH_FUNCTIONS_MURMUR64_HP if hp else lib.HASH_FUNCTIONS_MURMUR64_PROTEIN if is_protein else lib.HASH_FUNCTIONS_MURMUR64_SKIPM1N3 if skipm1n3 else lib.HASH_FUNCTIONS_MURMUR64_SKIPM2N3 if skipm2n3 else lib.HASH_FUNCTIONS_MURMUR64_DNA ) scaled = _get_scaled_for_max_hash(max_hash) self._objptr = lib.kmerminhash_new( scaled, ksize, hash_function, seed, track_abundance, n ) if track_abundance: self.set_abundances(mins) else: self.add_many(mins) def __eq__(self, other): "equality testing via ==" return self.__getstate__() == other.__getstate__() def copy_and_clear(self): "Create an empty copy of this MinHash." a = MinHash( self.num, self.ksize, is_protein=self.is_protein, dayhoff=self.dayhoff, hp=self.hp, skipm1n3=self.skipm1n3, skipm2n3=self.skipm2n3, track_abundance=self.track_abundance, seed=self.seed, max_hash=self._max_hash, ) return a def add_sequence(self, sequence, force=False): "Add a sequence into the sketch." self._methodcall(lib.kmerminhash_add_sequence, to_bytes(sequence), force) def seq_to_hashes( self, sequence, *, force=False, bad_kmers_as_zeroes=False, is_protein=False ): """Convert sequence to hashes without adding to the sketch. If input sequence is DNA and this is a protein, dayhoff, or hp MinHash, translate the DNA appropriately before hashing. If input sequence is protein, set is_protein=True. If `force = True` and `bad_kmers_as_zeroes = True`, invalid kmers hashes will be represented as `0`. """ if is_protein and self.moltype not in ("protein", "dayhoff", "hp"): raise ValueError("cannot add protein sequence to DNA MinHash") if bad_kmers_as_zeroes and not force: raise ValueError( "cannot represent invalid kmers as 0 while force is not set to True" ) size = ffi.new("uintptr_t *") hashes_ptr = self._methodcall( lib.kmerminhash_seq_to_hashes, to_bytes(sequence), len(sequence), force, bad_kmers_as_zeroes, is_protein, size, ) size = size[0] try: return ffi.unpack(hashes_ptr, size) finally: lib.kmerminhash_slice_free(hashes_ptr, size) def kmers_and_hashes(self, sequence, *, force=False, is_protein=False): """Convert sequence into (k-mer, hashval) tuples without adding it to the sketch. If input sequence is DNA and this is a protein, dayhoff, or hp MinHash, translate the DNA appropriately before hashing. If input sequence is protein, set is_protein=True. If 'force' is True, invalid k-mers will be represented with 'None'. """ import screed bad_kmers_as_zeroes = False if force: bad_kmers_as_zeroes = True sequence = sequence.upper() hashvals = self.seq_to_hashes( sequence, force=force, is_protein=is_protein, bad_kmers_as_zeroes=bad_kmers_as_zeroes, ) if bad_kmers_as_zeroes: hashvals = [None if h == 0 else h for h in hashvals] ksize = self.ksize translate = False if self.moltype == "DNA": pass elif is_protein: pass else: # translate input DNA sequence => aa assert self.moltype in ("protein", "dayhoff", "hp") translate = True ksize = self.ksize * 3 # special code for translation - if translate: # forward AND reverse complement => twice the k-mers n_kmers = (len(sequence) - ksize + 1) * 2 assert n_kmers == len(hashvals) # generate reverse complement of sequence seqrc = screed.rc(sequence) hash_i = 0 for frame in (0, 1, 2): # get forward k-mers for start in range(0, len(sequence) - ksize + 1 - frame, 3): kmer = sequence[start + frame : start + frame + ksize] yield kmer, hashvals[hash_i] hash_i += 1 # get rc k-mers for start in range(0, len(seqrc) - ksize + 1 - frame, 3): kmer = seqrc[start + frame : start + frame + ksize] yield kmer, hashvals[hash_i] hash_i += 1 else: # otherwise, all very straightforward :) n_kmers = len(sequence) - ksize + 1 assert n_kmers == len(hashvals) for i, hashval in zip(range(0, n_kmers), hashvals): kmer = sequence[i : i + ksize] yield kmer, hashval def add_kmer(self, kmer): "Add a kmer into the sketch." if self.is_dna or self.skipm1n3 or self.skipm2n3: if len(kmer) != self.ksize: raise ValueError(f"kmer to add is not {self.ksize} in length") else: if len(kmer) != self.ksize * 3: raise ValueError(f"kmer to add is not {self.ksize * 3} in length") self.add_sequence(kmer) def add_many(self, hashes): """Add many hashes to the sketch at once. ``hashes`` can be either an iterable (list, set, etc.), or another ``MinHash`` object. """ if isinstance(hashes, MinHash): self._methodcall(lib.kmerminhash_add_from, hashes._objptr) else: self._methodcall(lib.kmerminhash_add_many, list(hashes), len(hashes)) def remove_many(self, hashes): """Remove many hashes from a sketch at once. ``hashes`` can be either an iterable (list, set, etc.), or another ``MinHash`` object. """ if isinstance(hashes, MinHash): self._methodcall(lib.kmerminhash_remove_from, hashes._objptr) else: self._methodcall(lib.kmerminhash_remove_many, list(hashes), len(hashes)) def __len__(self): "Number of hashes." return self._methodcall(lib.kmerminhash_get_mins_size) @deprecated( deprecated_in="3.5", removed_in="5.0", current_version=VERSION, details="Use .hashes property instead.", ) def get_mins(self, with_abundance=False): """Return list of hashes or if ``with_abundance`` a list of (hash, abund). """ mins = self.hashes if not with_abundance: return mins.keys() return mins @deprecated( deprecated_in="3.5", removed_in="5.0", current_version=VERSION, details="Use .hashes property instead.", ) def get_hashes(self): "Return the list of hashes." return self.hashes.keys() @property def hashes(self): size = ffi.new("uintptr_t *") mins_ptr = self._methodcall(lib.kmerminhash_get_mins, size) size = size[0] try: if self.track_abundance: size_abunds = ffi.new("uintptr_t *") abunds_ptr = self._methodcall(lib.kmerminhash_get_abunds, size_abunds) size_abunds = size_abunds[0] assert size == size_abunds result = dict( zip(ffi.unpack(mins_ptr, size), ffi.unpack(abunds_ptr, size)) ) lib.kmerminhash_slice_free(abunds_ptr, size) return _HashesWrapper(result) else: d = ffi.unpack(mins_ptr, size) return _HashesWrapper({k: 1 for k in d}) finally: lib.kmerminhash_slice_free(mins_ptr, size) @property def seed(self): return self._methodcall(lib.kmerminhash_seed) @property def num(self): return self._methodcall(lib.kmerminhash_num) @property def scaled(self): mx = self._methodcall(lib.kmerminhash_max_hash) if mx: return _get_scaled_for_max_hash(mx) return 0 @property def is_dna(self): return not ( self.is_protein or self.dayhoff or self.hp or self.skipm1n3 or self.skipm2n3 ) @property def is_protein(self): return self._methodcall(lib.kmerminhash_is_protein) @property def dayhoff(self): return self._methodcall(lib.kmerminhash_dayhoff) @property def hp(self): return self._methodcall(lib.kmerminhash_hp) @property def skipm1n3(self): return self._methodcall(lib.kmerminhash_skipm1n3) @property def skipm2n3(self): return self._methodcall(lib.kmerminhash_skipm2n3) @property def ksize(self): k = self._methodcall(lib.kmerminhash_ksize) if not self.is_dna and not self.skipm1n3 and not self.skipm2n3: assert k % 3 == 0 k = int(k / 3) return k @property @deprecated( deprecated_in="3.5", removed_in="5.0", current_version=VERSION, details="Use scaled instead.", ) def max_hash(self): return self._methodcall(lib.kmerminhash_max_hash) # a non-deprecated `max_hash` property for internal testing purposes only @property def _max_hash(self): return self._methodcall(lib.kmerminhash_max_hash) @property def track_abundance(self): return self._methodcall(lib.kmerminhash_track_abundance) @track_abundance.setter def track_abundance(self, b): if self.track_abundance == b: return if b is False: self._methodcall(lib.kmerminhash_disable_abundance) elif len(self) > 0: raise RuntimeError( "Can only set track_abundance=True if the MinHash is empty" ) else: self._methodcall(lib.kmerminhash_enable_abundance) def add_hash(self, h): "Add a single hash value." return self._methodcall(lib.kmerminhash_add_hash, h) def add_hash_with_abundance(self, h, a): "Add a single hash value with an abundance." if self.track_abundance: return self._methodcall(lib.kmerminhash_add_hash_with_abundance, h, a) else: raise RuntimeError( "Use track_abundance=True when constructing " "the MinHash to use add_hash_with_abundance." ) def clear(self): "Clears all hashes and abundances." return self._methodcall(lib.kmerminhash_clear) def count_common(self, other, downsample=False): """\ Return the number of hashes in common between ``self`` and ``other``. Optionally downsample ``scaled`` objects to highest ``scaled`` value. """ if not isinstance(other, MinHash): raise TypeError("Must be a MinHash!") return self._methodcall( lib.kmerminhash_count_common, other._get_objptr(), downsample ) def intersection_and_union_size(self, other): "Calculate intersection and union sizes between `self` and `other`." if not isinstance(other, MinHash): raise TypeError("Must be a MinHash!") if not self.is_compatible(other): raise TypeError("incompatible MinHash objects") usize = ffi.new("uint64_t *") common = self._methodcall( lib.kmerminhash_intersection_union_size, other._get_objptr(), usize ) usize = ffi.unpack(usize, 1)[0] return common, usize def downsample(self, *, num=None, scaled=None): """Copy this object and downsample new object to either `num` or `scaled`. """ # first, evaluate provided parameters -- # at least one must be specified! if num is None and scaled is None: raise ValueError("must specify either num or scaled to downsample") # both cannot be specified if num is not None and scaled is not None: raise ValueError("cannot specify both num and scaled") if num is not None: # cannot downsample a scaled MinHash with num: if self.scaled: raise ValueError("cannot downsample a scaled MinHash using num") # cannot upsample if self.num < num: raise ValueError("new sample num is higher than current sample num") # acceptable num value? make sure to set max_hash to 0. max_hash = 0 elif scaled is not None: # cannot downsample a num MinHash with scaled if self.num: raise ValueError("cannot downsample a num MinHash using scaled") if self.scaled > scaled: raise ValueError( f"new scaled {scaled} is lower than current sample scaled {self.scaled}" ) # acceptable scaled value? reconfigure max_hash, keep num 0. max_hash = _get_max_hash_for_scaled(scaled) num = 0 # end checks! create new object: a = MinHash( num, self.ksize, is_protein=self.is_protein, dayhoff=self.dayhoff, hp=self.hp, skipm1n3=self.skipm1n3, skipm2n3=self.skipm2n3, track_abundance=self.track_abundance, seed=self.seed, max_hash=max_hash, ) # copy over hashes: if self.track_abundance: a.set_abundances(self.hashes) else: a.add_many(self) return a def flatten(self): """If track_abundance=True, return a new flattened MinHash.""" if self.track_abundance: # create new object: a = MinHash( self.num, self.ksize, is_protein=self.is_protein, dayhoff=self.dayhoff, hp=self.hp, skipm1n3=self.skipm1n3, skipm2n3=self.skipm2n3, track_abundance=False, seed=self.seed, max_hash=self._max_hash, ) a.add_many(self) return a return self def jaccard(self, other, downsample=False): "Calculate Jaccard similarity of two MinHash objects." if self.num != other.num: err = f"must have same num: {self.num} != {other.num}" raise TypeError(err) return self._methodcall( lib.kmerminhash_similarity, other._get_objptr(), True, downsample ) def jaccard_ani( self, other, *, downsample=False, jaccard=None, prob_threshold=1e-3, err_threshold=1e-4, ): "Use jaccard to estimate ANI between two MinHash objects." if not (self.scaled and other.scaled): raise TypeError("Error: can only calculate ANI for scaled MinHashes") self_mh = self other_mh = other scaled = self.scaled if downsample: scaled = max(self_mh.scaled, other_mh.scaled) self_mh = self.downsample(scaled=scaled) other_mh = other.downsample(scaled=scaled) if jaccard is None: jaccard = self_mh.similarity(other_mh, ignore_abundance=True) avg_sketch_kmers = (len(self_mh) + len(other_mh)) / 2 avg_n_kmers = round( avg_sketch_kmers * scaled ) # would be better if hll estimate - see #1798 j_aniresult = jaccard_to_distance( jaccard, self_mh.ksize, scaled, n_unique_kmers=avg_n_kmers, prob_threshold=prob_threshold, err_threshold=err_threshold, ) # null out ANI if either mh size estimation is inaccurate if not self.size_is_accurate() or not other.size_is_accurate(): j_aniresult.size_is_inaccurate = True return j_aniresult def similarity(self, other, ignore_abundance=False, downsample=False): """Calculate similarity of two sketches. If the sketches are not abundance weighted, or ignore_abundance=True, compute Jaccard similarity. If the sketches are abundance weighted, calculate the angular similarity, a distance metric based on the cosine similarity. Note, because the term frequencies (tf-idf weights) cannot be negative, the angle will never be < 0deg or > 90deg. See https://en.wikipedia.org/wiki/Cosine_similarity """ return self._methodcall( lib.kmerminhash_similarity, other._get_objptr(), ignore_abundance, downsample, ) def angular_similarity(self, other): "Calculate the angular similarity." if not (self.track_abundance and other.track_abundance): raise TypeError( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." ) return self._methodcall(lib.kmerminhash_angular_similarity, other._get_objptr()) def is_compatible(self, other): return self._methodcall(lib.kmerminhash_is_compatible, other._get_objptr()) def contained_by(self, other, downsample=False): """ Calculate how much of self is contained by other. """ if not (self.scaled and other.scaled): raise TypeError( "Error: can only calculate containment for scaled MinHashes" ) denom = len(self) if not denom: return 0.0 total_denom = float( denom * self.scaled ) # would be better if hll estimate - see #1798 bias_factor = 1.0 - (1.0 - 1.0 / self.scaled) ** total_denom containment = self.count_common(other, downsample) / (denom * bias_factor) # debiasing containment can lead to vals outside of 0-1 range. constrain. if containment >= 1: return 1.0 elif containment <= 0: return 0.0 else: return containment def containment_ani( self, other, *, downsample=False, containment=None, confidence=0.95, estimate_ci=False, prob_threshold=1e-3, ): "Use self contained by other to estimate ANI between two MinHash objects." if not (self.scaled and other.scaled): raise TypeError("Error: can only calculate ANI for scaled MinHashes") self_mh = self other_mh = other scaled = self.scaled if downsample: scaled = max(self_mh.scaled, other_mh.scaled) self_mh = self.downsample(scaled=scaled) other_mh = other.downsample(scaled=scaled) if containment is None: containment = self_mh.contained_by(other_mh) n_kmers = len(self_mh) * scaled # would be better if hll estimate - see #1798 c_aniresult = containment_to_distance( containment, self_mh.ksize, self_mh.scaled, n_unique_kmers=n_kmers, confidence=confidence, estimate_ci=estimate_ci, prob_threshold=prob_threshold, ) # null out ANI if either mh size estimation is inaccurate if not self.size_is_accurate() or not other.size_is_accurate(): c_aniresult.size_is_inaccurate = True return c_aniresult def max_containment(self, other, downsample=False): """ Calculate maximum containment. """ if not (self.scaled and other.scaled): raise TypeError( "Error: can only calculate containment for scaled MinHashes" ) min_denom = min((len(self), len(other))) if not min_denom: return 0.0 total_denom = float( min_denom * self.scaled ) # would be better if hll estimate - see #1798 bias_factor = 1.0 - (1.0 - 1.0 / self.scaled) ** total_denom max_containment = self.count_common(other, downsample) / ( min_denom * bias_factor ) # debiasing containment can lead to vals outside of 0-1 range. constrain. if max_containment >= 1: return 1.0 elif max_containment <= 0: return 0.0 else: return max_containment def max_containment_ani( self, other, *, downsample=False, max_containment=None, confidence=0.95, estimate_ci=False, prob_threshold=1e-3, ): "Use max_containment to estimate ANI between two MinHash objects." if not (self.scaled and other.scaled): raise TypeError("Error: can only calculate ANI for scaled MinHashes") self_mh = self other_mh = other scaled = self.scaled if downsample: scaled = max(self_mh.scaled, other_mh.scaled) self_mh = self.downsample(scaled=scaled) other_mh = other.downsample(scaled=scaled) if max_containment is None: max_containment = self_mh.max_containment(other_mh) min_n_kmers = min(len(self_mh), len(other_mh)) n_kmers = min_n_kmers * scaled # would be better if hll estimate - see #1798 c_aniresult = containment_to_distance( max_containment, self_mh.ksize, scaled, n_unique_kmers=n_kmers, confidence=confidence, estimate_ci=estimate_ci, prob_threshold=prob_threshold, ) # null out ANI if either mh size estimation is inaccurate if not self.size_is_accurate() or not other.size_is_accurate(): c_aniresult.size_is_inaccurate = True return c_aniresult def avg_containment(self, other, *, downsample=False): """ Calculate average containment. Note: this is average of the containments, *not* count_common/ avg_denom """ if not (self.scaled and other.scaled): raise TypeError( "Error: can only calculate containment for scaled MinHashes" ) c1 = self.contained_by(other, downsample) c2 = other.contained_by(self, downsample) return (c1 + c2) / 2 def avg_containment_ani(self, other, *, downsample=False, prob_threshold=1e-3): """ Calculate average containment ANI. Note: this is average of the containment ANI's, *not* ANI using count_common/ avg_denom """ if not (self.scaled and other.scaled): raise TypeError("Error: can only calculate ANI for scaled MinHashes") a1 = self.containment_ani( other, downsample=downsample, prob_threshold=prob_threshold ).ani a2 = other.containment_ani( self, downsample=downsample, prob_threshold=prob_threshold ).ani if any([a1 is None, a2 is None]): return None return (a1 + a2) / 2 def __add__(self, other): if not isinstance(other, MinHash): raise TypeError("can only add MinHash objects to MinHash objects!") if self.num and other.num: if self.num != other.num: raise TypeError( f"incompatible num values: self={self.num} other={other.num}" ) new_obj = self.to_mutable() new_obj += other return new_obj __or__ = __add__ def __iadd__(self, other): if not isinstance(other, MinHash): raise TypeError("can only add MinHash objects to MinHash objects!") self._methodcall(lib.kmerminhash_merge, other._get_objptr()) return self def merge(self, other): if not isinstance(other, MinHash): raise TypeError("can only add MinHash objects to MinHash objects!") self._methodcall(lib.kmerminhash_merge, other._get_objptr()) def intersection(self, other): if not isinstance(other, MinHash): raise TypeError("can only intersect MinHash objects") if self.track_abundance or other.track_abundance: raise TypeError("can only intersect flat MinHash objects") ptr = self._methodcall(lib.kmerminhash_intersection, other._get_objptr()) return MinHash._from_objptr(ptr) __and__ = intersection def set_abundances(self, values, clear=True): """Set abundances for hashes from ``values``, where ``values[hash] = abund`` If ``abund`` value is set to zero, the ``hash`` will be removed from the sketch. ``abund`` cannot be set to a negative value. """ if self.track_abundance: hashes = [] abunds = [] for h, v in values.items(): hashes.append(h) if v < 0: raise ValueError("Abundance cannot be set to a negative value.") abunds.append(v) self._methodcall( lib.kmerminhash_set_abundances, hashes, abunds, len(hashes), clear ) else: raise RuntimeError( "Use track_abundance=True when constructing " "the MinHash to use set_abundances." ) def add_protein(self, sequence): "Add a protein sequence." self._methodcall(lib.kmerminhash_add_protein, to_bytes(sequence)) @property def moltype(self): # TODO: test in minhash tests if self.is_protein: return "protein" elif self.dayhoff: return "dayhoff" elif self.hp: return "hp" elif self.skipm1n3: return "skipm1n3" elif self.skipm2n3: return "skipm2n3" else: return "DNA" def to_mutable(self): "Return a copy of this MinHash that can be changed." return self.__copy__() def to_frozen(self): "Return a frozen copy of this MinHash that cannot be changed." new_mh = self.__copy__() new_mh.into_frozen() return new_mh def into_frozen(self): "Freeze this MinHash, preventing any changes." self.__class__ = FrozenMinHash def inflate(self, from_mh): """return a new MinHash object with abundances taken from 'from_mh' note that this implicitly does an intersection: hashes that have no abundance in 'from_mh' are set to abundance 0 and removed from 'self'. """ if not self.track_abundance and from_mh.track_abundance: orig_abunds = from_mh.hashes abunds = {h: orig_abunds.get(h, 0) for h in self.hashes} abund_mh = from_mh.copy_and_clear() abund_mh.downsample(scaled=self.scaled) abund_mh.set_abundances(abunds) return abund_mh else: raise ValueError( "inflate operates on a flat MinHash and takes a MinHash object with track_abundance=True" ) @property def sum_abundances(self): if self.track_abundance: return sum(v for v in self.hashes.values()) return None @property def mean_abundance(self): if self.track_abundance: return np.mean(list(self.hashes.values())) return None @property def median_abundance(self): if self.track_abundance: return np.median(list(self.hashes.values())) return None @property def std_abundance(self): if self.track_abundance: return np.std(list(self.hashes.values())) return None @property def unique_dataset_hashes(self): """ Approximate total number of hashes (num_hashes *scaled). """ if not self.scaled: raise TypeError( "can only approximate unique_dataset_hashes for scaled MinHashes" ) # TODO: replace set_size with HLL estimate when that gets implemented return len(self) * self.scaled # + (self.ksize - 1) for bp estimation def size_is_accurate(self, relative_error=0.20, confidence=0.95): """ Computes the probability that the estimate: sketch_size * scaled deviates from the true set_size by more than relative_error. This relies on the fact that the sketch_size is binomially distributed with parameters sketch_size and 1/scaled. The two-sided Chernoff bounds are used. Returns True if probability is greater than or equal to the desired confidence. """ if not self.scaled: raise TypeError( "Error: can only estimate dataset size for scaled MinHashes" ) if any([not (0 <= relative_error <= 1), not (0 <= confidence <= 1)]): raise ValueError( "Error: relative error and confidence values must be between 0 and 1." ) # to do: replace unique_dataset_hashes with HLL estimation when it gets implemented probability = set_size_exact_prob( self.unique_dataset_hashes, self.scaled, relative_error=relative_error ) return probability >= confidence class FrozenMinHash(MinHash): def add_sequence(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def add_kmer(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def add_many(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def remove_many(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def add_hash(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def add_hash_with_abundance(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def clear(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def set_abundances(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def add_protein(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def downsample(self, *, num=None, scaled=None): if scaled and self.scaled == scaled: return self if num and self.num == num: return self down_mh = MinHash.downsample(self, num=num, scaled=scaled) down_mh.into_frozen() return down_mh def flatten(self): if not self.track_abundance: return self flat_mh = MinHash.flatten(self) flat_mh.into_frozen() return flat_mh def __iadd__(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def merge(self, *args, **kwargs): raise TypeError("FrozenMinHash does not support modification") def to_mutable(self): "Return a copy of this MinHash that can be changed." mut = MinHash.__new__(MinHash) state_tup = self.__getstate__() mut.__setstate__(state_tup) return mut def to_frozen(self): "Return a frozen copy of this MinHash that cannot be changed." return self def into_frozen(self): "Freeze this MinHash, preventing any changes." pass def __setstate__(self, tup): "support pickling via __getstate__/__setstate__" ( n, ksize, is_protein, dayhoff, hp, skipm1n3, skipm2n3, mins, _, track_abundance, max_hash, seed, ) = tup self.__del__() hash_function = ( lib.HASH_FUNCTIONS_MURMUR64_DAYHOFF if dayhoff else lib.HASH_FUNCTIONS_MURMUR64_HP if hp else lib.HASH_FUNCTIONS_MURMUR64_PROTEIN if is_protein else lib.HASH_FUNCTIONS_MURMUR64_SKIPM1N3 if skipm1n3 else lib.HASH_FUNCTIONS_MURMUR64_SKIPM2N3 if skipm2n3 else lib.HASH_FUNCTIONS_MURMUR64_DNA ) scaled = _get_scaled_for_max_hash(max_hash) self._objptr = lib.kmerminhash_new( scaled, ksize, hash_function, seed, track_abundance, n ) if track_abundance: MinHash.set_abundances(self, mins) else: MinHash.add_many(self, mins) def __copy__(self): return self copy = __copy__ sourmash-bio-sourmash-bc3c0f8/src/sourmash/nodegraph.py000066400000000000000000000125221474157046300234410ustar00rootroot00000000000000# -*- coding: UTF-8 -*- from struct import pack, unpack import sys from tempfile import NamedTemporaryFile from ._lowlevel import ffi, lib from .minhash import to_bytes, MinHash from .utils import RustObject, rustcall, decode_str from .exceptions import SourmashError class Nodegraph(RustObject): __dealloc_func__ = lib.nodegraph_free def __init__(self, ksize, starting_size, n_tables): self._objptr = lib.nodegraph_with_tables(ksize, int(starting_size), n_tables) @staticmethod def load(filename): ng_ptr = rustcall(lib.nodegraph_from_path, to_bytes(filename)) return Nodegraph._from_objptr(ng_ptr) @staticmethod def from_buffer(buf): ng_ptr = rustcall(lib.nodegraph_from_buffer, buf, len(buf)) return Nodegraph._from_objptr(ng_ptr) def save(self, filename): self._methodcall(lib.nodegraph_save, to_bytes(filename)) def to_bytes(self, compression=1): size = ffi.new("uintptr_t *") rawbuf = self._methodcall(lib.nodegraph_to_buffer, compression, size) size = size[0] rawbuf = ffi.gc(rawbuf, lambda o: lib.nodegraph_buffer_free(o, size), size) buf = ffi.buffer(rawbuf, size) return buf def update(self, other): if isinstance(other, Nodegraph): return self._methodcall(lib.nodegraph_update, other._objptr) elif isinstance(other, MinHash): return self._methodcall(lib.nodegraph_update_mh, other._objptr) else: # FIXME: we could take sets here too (or anything that can be # converted to a list of ints...) raise TypeError("Must be a Nodegraph or MinHash") def count(self, h): if isinstance(h, str): return self._methodcall(lib.nodegraph_count_kmer, to_bytes(h)) return self._methodcall(lib.nodegraph_count, h) def get(self, h): if isinstance(h, str): return self._methodcall(lib.nodegraph_get_kmer, to_bytes(h)) return self._methodcall(lib.nodegraph_get, h) def n_occupied(self): return self._methodcall(lib.nodegraph_noccupied) def ksize(self): return self._methodcall(lib.nodegraph_ksize) def hashsizes(self): size = ffi.new("uintptr_t *") ptr = self._methodcall(lib.nodegraph_hashsizes, size) size = size[0] hashsizes = ffi.unpack(ptr, size) lib.kmerminhash_slice_free(ptr, size) return hashsizes @property def expected_collisions(self): return self._methodcall(lib.nodegraph_expected_collisions) def matches(self, mh): if not isinstance(mh, MinHash): # FIXME: we could take sets here too (or anything that can be # converted to a list of ints...) raise ValueError("mh must be a MinHash") return self._methodcall(lib.nodegraph_matches, mh._objptr) def to_khmer_nodegraph(self): import khmer try: load_nodegraph = khmer.load_nodegraph except AttributeError: load_nodegraph = khmer.Nodegraph.load with NamedTemporaryFile() as f: self.save(f.name) f.file.flush() f.file.seek(0) return load_nodegraph(f.name) def extract_nodegraph_info(filename): """Open the given nodegraph file and return a tuple of information. Returns: the k-mer size, the table size, the number of tables, the version of the table format, and the type of table flag. Keyword argument: filename -- the name of the nodegraph file to inspect """ ksize = None n_tables = None table_size = None signature = None version = None ht_type = None occupied = None uint_size = len(pack("I", 0)) uchar_size = len(pack("B", 0)) ulonglong_size = len(pack("Q", 0)) try: with open(filename, "rb") as nodegraph: (signature,) = unpack("4s", nodegraph.read(4)) (version,) = unpack("B", nodegraph.read(1)) (ht_type,) = unpack("B", nodegraph.read(1)) (ksize,) = unpack("I", nodegraph.read(uint_size)) (n_tables,) = unpack("B", nodegraph.read(uchar_size)) (occupied,) = unpack("Q", nodegraph.read(ulonglong_size)) (table_size,) = unpack("Q", nodegraph.read(ulonglong_size)) if signature != b"OXLI": raise ValueError( f"Node graph '{filename}' is missing file type " "signature" + str(signature) ) except: raise ValueError(f"Node graph '{filename}' is corrupt ") return ksize, round(table_size, -2), n_tables, version, ht_type, occupied def calc_expected_collisions(graph, force=False, max_false_pos=0.2): fp_all = graph.expected_collisions if fp_all > max_false_pos: print("**", file=sys.stderr) print("** ERROR: the graph structure is too small for ", file=sys.stderr) print("** this data set. Increase data structure size.", file=sys.stderr) print("** Do not use these results!!", file=sys.stderr) print("**", file=sys.stderr) print( f"** (estimated false positive rate of {fp_all:.3f};", file=sys.stderr, end=" ", ) print(f"max recommended {max_false_pos:.3f})", file=sys.stderr) print("**", file=sys.stderr) if not force: raise SystemExit(1) return fp_all sourmash-bio-sourmash-bc3c0f8/src/sourmash/np_utils.py000066400000000000000000000014721474157046300233310ustar00rootroot00000000000000import tempfile def to_memmap(array): """Write a memory mapped array Create a memory-map to an array stored in a binary file on disk. Memory-mapped files are used for accessing small segments of large files on disk, without reading the entire file into memory. :param np.array array to memory map :return: np.array large_memmap memory mapped array :return: str filename name of the file that memory mapped array is written to """ import numpy as np filename = tempfile.NamedTemporaryFile( prefix="array", suffix=".mmap", delete=False ).name shape = array.shape f = np.memmap(filename, mode="w+", shape=shape, dtype=array.dtype) f[:] = array[:] del f large_memmap = np.memmap(filename, dtype=array.dtype, shape=shape) return large_memmap, filename sourmash-bio-sourmash-bc3c0f8/src/sourmash/picklist.py000066400000000000000000000252161474157046300233200ustar00rootroot00000000000000"""Picklist code for extracting subsets of signatures. Picklists serve as a central inclusion/exclusion mechanism for sketches. Each picklist object operates on a particular type of value, and uses values specified by the user (if using an external picklist) or works with the output of sourmash (manifests or search/prefetch/gather output). Two key features of picklists is that they can be passed into Index.select and operate efficiently on manifests, so when used with e.g. zipfiles, only the selected sketches are loaded. """ import csv import os from enum import Enum # set up preprocessing functions for column stuff preprocess = {} # exact matches preprocess["name"] = lambda x: x preprocess["md5"] = lambda x: x # identifier matches/prefix foo - space delimited identifiers preprocess["identprefix"] = lambda x: x.split(" ")[0].split(".")[0] preprocess["ident"] = lambda x: x.split(" ")[0] # match 8 characters preprocess["md5prefix8"] = lambda x: x[:8] preprocess["md5short"] = lambda x: x[:8] # all meta-coltypes use the same preprocessing of tuple => (ident, md5short) def combine_ident_md5(x): "preprocess (name, md5) tup into (ident, md5short) tup" name, md5 = x ident = name.split(" ")[0] md5 = md5[:8] return (ident, md5) preprocess["manifest"] = combine_ident_md5 preprocess["prefetch"] = combine_ident_md5 preprocess["gather"] = combine_ident_md5 preprocess["search"] = combine_ident_md5 class PickStyle(Enum): INCLUDE = 1 EXCLUDE = 2 class SignaturePicklist: """Picklist class for subsetting collections of signatures. Initialize using ``SignaturePicklist.from_picklist_args(argstr)``, which takes an argument str like so: 'pickfile:column:coltype'. Here, 'pickfile' is the path to a CSV file; 'column' is the name of the column to select from the CSV file; and 'coltype' is the type of matching to do on that column. 'coltype's that are currently supported: * 'name' - exact match to signature's name * 'md5' - exact match to signature's md5sum * 'md5prefix8' - match to 8-character prefix of signature's md5sum * 'md5short' - same as md5prefix8 * 'ident' - exact match to signature's identifier * 'identprefix' - match to signature's identifier, before '.' Identifiers are constructed by using the first space delimited word in the signature name. You can also use 'gather', 'prefetch', 'search', and 'manifest' as column types; these take the CSV output of 'gather', 'prefetch', 'search', and 'manifest' as picklists. 'column' must be left blank in this case: e.g. use 'pickfile.csv::gather'. These "meta-coltypes" use composite selection on (ident, md5short) tuples. """ meta_coltypes = ("manifest", "gather", "prefetch", "search") supported_coltypes = ( "md5", "md5prefix8", "md5short", "name", "ident", "identprefix", ) def __init__( self, coltype, *, pickfile=None, column_name=None, pickstyle=PickStyle.INCLUDE ): "create a picklist of column type 'coltype'." # first, check coltype... valid_coltypes = set(self.meta_coltypes) valid_coltypes.update(self.supported_coltypes) if coltype not in valid_coltypes: raise ValueError(f"invalid picklist column type '{coltype}'") self.orig_coltype = coltype self.orig_colname = column_name # if we're using gather, prefetch, manifest, or search, set column_name # automatically (after checks). if coltype in self.meta_coltypes: if column_name: raise ValueError(f"no column name allowed for coltype '{coltype}'") if coltype == "prefetch": column_name = "(match_name, match_md5)" else: column_name = "(name, md5)" self.coltype = coltype self.pickfile = pickfile self.column_name = column_name self.pickstyle = pickstyle self.preprocess_fn = preprocess[coltype] self.pickset = None self.found = set() self.n_queries = 0 @classmethod def from_picklist_args(cls, argstr): "load a picklist from an argument string 'pickfile:col:coltype:style'" picklist = argstr.split(":") pickstyle = PickStyle.INCLUDE # pickstyle specified? if len(picklist) == 4: pickstyle_str = picklist.pop() if pickstyle_str == "include": pickstyle = PickStyle.INCLUDE elif pickstyle_str == "exclude": pickstyle = PickStyle.EXCLUDE else: raise ValueError( f"invalid picklist 'pickstyle' argument 4: '{pickstyle_str}' must be 'include' or 'exclude'" ) if len(picklist) != 3: raise ValueError(f"invalid picklist argument '{argstr}'") assert len(picklist) == 3 pickfile, column, coltype = picklist return cls(coltype, pickfile=pickfile, column_name=column, pickstyle=pickstyle) def _get_sig_attribute(self, ss): "for a given SourmashSignature, return relevant picklist value." coltype = self.coltype if coltype in self.meta_coltypes: # gather, prefetch, search, manifest q = (ss.name, ss.md5sum()) elif coltype in ("md5", "md5prefix8", "md5short"): q = ss.md5sum() elif coltype in ("name", "ident", "identprefix"): q = ss.name else: raise ValueError( f"picklist get_sig_attribute {coltype} has unhandled branch" ) return q def _get_value_for_manifest_row(self, row): "return the picklist value from a manifest row" if self.coltype in self.meta_coltypes: # gather, prefetch, search, manifest q = (row["name"], row["md5"]) else: if self.coltype == "md5": colkey = "md5" elif self.coltype in ("md5prefix8", "md5short"): colkey = "md5short" elif self.coltype in ("name", "ident", "identprefix"): colkey = "name" else: raise ValueError( f"picklist get_value_for_row {colkey} has unhandled branch" ) q = row.get(colkey) assert q q = self.preprocess_fn(q) return q def _get_value_for_csv_row(self, row): "return the picklist value from a CSV pickfile row - supplied by user, typically" # customize for each type of meta_coltypes if self.coltype == "manifest": q = (row["name"], row["md5"]) elif self.coltype == "prefetch": q = (row["match_name"], row["match_md5"]) elif self.coltype in ("gather", "search"): q = (row["name"], row["md5"]) else: q = row[self.column_name] if q: q = self.preprocess_fn(q) return q def init(self, values=[]): "initialize a Picklist object with given values." if self.pickset is not None: raise ValueError("already initialized?") self.pickset = set(values) return self.pickset def load(self, *, allow_empty=False): "load pickset, return num empty vals, and set of duplicate vals." from . import sourmash_args pickset = self.init() pickfile = self.pickfile coltype = self.coltype column_name = self.column_name if not os.path.exists(pickfile) or not os.path.isfile(pickfile): raise ValueError(f"pickfile '{pickfile}' must exist and be a regular file") n_empty_val = 0 dup_vals = set() # CTB note: for zipfiles, not clear to me what a good "default" name would be for a # picklist CSV inside a zip (default_csv_name for FileInputCSV). with sourmash_args.FileInputCSV(pickfile) as r: self.pickfile = pickfile if not r.fieldnames: if not allow_empty: raise ValueError( f"empty or improperly formatted pickfile '{pickfile}'" ) else: return 0, 0 if not (column_name in r.fieldnames or coltype in self.meta_coltypes): raise ValueError(f"column '{column_name}' not in pickfile '{pickfile}'") for row in r: col = self._get_value_for_csv_row(row) if not col: n_empty_val += 1 continue # look for duplicate values or empty values if col in pickset: dup_vals.add(col) else: self.add(col) return n_empty_val, dup_vals def add(self, value): "Add a value to this picklist." self.pickset.add(value) def __contains__(self, ss): "does this signature match anything in the picklist?" # pull out the relevant signature attribute q = self._get_sig_attribute(ss) # mangle into the kinds of values we support here q = self.preprocess_fn(q) # add to the number of queries performed, self.n_queries += 1 # determine if ok or not. if self.pickstyle == PickStyle.INCLUDE: if q in self.pickset: self.found.add(q) return True elif self.pickstyle == PickStyle.EXCLUDE: if q not in self.pickset: self.found.add(q) return True return False def matches_manifest_row(self, row): "does the given manifest row match this picklist?" q = self._get_value_for_manifest_row(row) self.n_queries += 1 if self.pickstyle == PickStyle.INCLUDE: if q in self.pickset: self.found.add(q) return True elif self.pickstyle == PickStyle.EXCLUDE: if q not in self.pickset: self.found.add(q) return True return False def matched_csv_row(self, row): """did the given CSV row object match this picklist? This is used for examining matches/nomatches to original picklist file. """ q = self._get_value_for_csv_row(row) self.n_queries += 1 if q in self.found: return True return False def filter(self, it): "yield all signatures in the given iterator that are in the picklist" for ss in it: if self.__contains__(ss): yield ss def passes_all_picklists(ss, picklists): "does the signature 'ss' pass all of the picklists?" for picklist in picklists: if ss not in picklist: return False return True sourmash-bio-sourmash-bc3c0f8/src/sourmash/plugins.py000066400000000000000000000142511474157046300231540ustar00rootroot00000000000000""" Support for plugins to sourmash via importlib.metadata entrypoints. Plugin entry point names: * 'sourmash.load_from' - Index class loading. * 'sourmash.save_to' - Signature saving. * 'sourmash.cli_script' - command-line extension. CTB TODO: * consider using something other than 'name' for loader fn name. Maybe __doc__? * try implement picklist plugin? """ DEFAULT_LOAD_FROM_PRIORITY = 99 DEFAULT_SAVE_TO_PRIORITY = 99 import itertools import argparse from .logging import debug_literal, error, notify, set_quiet # cover for older versions of Python that don't support selection on load # (the 'group=' below). from importlib.metadata import entry_points # load 'load_from' entry points. NOTE: this executes on import of this module. try: _plugin_load_from = entry_points(group="sourmash.load_from") except TypeError: from importlib_metadata import entry_points _plugin_load_from = entry_points(group="sourmash.load_from") # load 'save_to' entry points as well. _plugin_save_to = entry_points(group="sourmash.save_to") # aaaaand CLI entry points: _plugin_cli = entry_points(group="sourmash.cli_script") _plugin_cli_once = False ### def get_load_from_functions(): "Load the 'load_from' plugins and yield tuples (priority, name, fn)." debug_literal(f"load_from plugins: {_plugin_load_from}") # Load each plugin, for plugin in _plugin_load_from: try: loader_fn = plugin.load() except (ModuleNotFoundError, AttributeError) as e: debug_literal( f"plugins.load_from_functions: got error loading {plugin.name}: {str(e)}" ) continue # get 'priority' if it is available priority = getattr(loader_fn, "priority", DEFAULT_LOAD_FROM_PRIORITY) # retrieve name (which is specified by plugin?) name = plugin.name debug_literal(f"plugins.load_from_functions: got '{name}', priority={priority}") yield priority, name, loader_fn def get_save_to_functions(): "Load the 'save_to' plugins and yield tuples (priority, fn)." debug_literal(f"save_to plugins: {_plugin_save_to}") # Load each plugin, for plugin in _plugin_save_to: try: save_cls = plugin.load() except (ModuleNotFoundError, AttributeError) as e: debug_literal( f"plugins.load_from_functions: got error loading {plugin.name}: {str(e)}" ) continue # get 'priority' if it is available priority = getattr(save_cls, "priority", DEFAULT_SAVE_TO_PRIORITY) # retrieve name (which is specified by plugin?) name = plugin.name debug_literal(f"plugins.save_to_functions: got '{name}', priority={priority}") yield priority, save_cls class CommandLinePlugin: """ Provide some minimal common CLI functionality - -q and -d. Subclasses should call super().__init__(parser) and super().main(args). """ command = None description = None def __init__(self, parser): parser.add_argument( "-q", "--quiet", action="store_true", help="suppress non-error output" ) parser.add_argument( "-d", "--debug", action="store_true", help="provide debugging output" ) def main(self, args): set_quiet(args.quiet, args.debug) def get_cli_script_plugins(): global _plugin_cli_once x = [] for plugin in _plugin_cli: name = plugin.name mod = plugin.module try: script_cls = plugin.load() except (ModuleNotFoundError, AttributeError): if _plugin_cli_once is False: error( f"ERROR: cannot find or load module for cli_script plugin '{name}'" ) continue command = getattr(script_cls, "command", None) if command is None: # print error message only once... if _plugin_cli_once is False: error( f"ERROR: no command provided by cli_script plugin '{name}' from {mod}; skipping" ) else: x.append(plugin) _plugin_cli_once = True return x def get_cli_scripts_descriptions(): "Build the descriptions for command-line plugins." for plugin in get_cli_script_plugins(): name = plugin.name script_cls = plugin.load() command = getattr(script_cls, "command") description = getattr(script_cls, "description", "") if description: description = description.splitlines()[0] if not description: description = f"(no description provided by plugin '{name}')" yield f"sourmash scripts {command:16s} - {description}" def add_cli_scripts(parser): "Configure parsing for command-line plugins." d = {} for plugin in get_cli_script_plugins(): name = plugin.name script_cls = plugin.load() usage = getattr(script_cls, "usage", None) description = getattr(script_cls, "description", None) epilog = getattr(script_cls, "epilog", None) formatter_class = getattr(script_cls, "formatter_class", argparse.HelpFormatter) subparser = parser.add_parser( script_cls.command, usage=usage, description=description, epilog=epilog, formatter_class=formatter_class, ) debug_literal( f"cls_script plugin '{name}' adding command '{script_cls.command}'" ) obj = script_cls(subparser) d[script_cls.command] = obj return d def list_all_plugins(): plugins = itertools.chain(_plugin_load_from, _plugin_save_to, _plugin_cli) plugins = list(plugins) if not plugins: notify("\n(no plugins detected)\n") notify("") notify("the following plugins are installed:") notify("") notify( f"{'plugin type':<20s} {'from python module':<30s} {'v':<5s} {'entry point name':<20s}" ) notify(f"{'-' * 20} {'-' * 30} {'-' * 5} {'-' * 20}") for plugin in plugins: name = plugin.name mod = plugin.module version = plugin.dist.version group = plugin.group notify(f"{group:<20s} {mod:<30s} {version:<5s} {name:<20s}") sourmash-bio-sourmash-bc3c0f8/src/sourmash/save_load.py000066400000000000000000000365631474157046300234420ustar00rootroot00000000000000""" Index object/sigfile loading and signature saving code. This is the middleware code responsible for loading and saving signatures in a variety of ways. --- Command-line functionality goes in sourmash_args.py. Low-level JSON reading/writing is in signature.py. Index objects are implemented in the index submodule. Public API: * load_file_as_index(filename, ...) -- load a sourmash.Index class * SaveSignaturesToLocation(filename) - bulk signature output APIs for plugins to use: * class Base_SaveSignaturesToLocation - to implement a new output method. CTB TODO: * consider replacing ValueError with IndexNotLoaded in the future. """ import sys import os import gzip from io import StringIO import zipfile import itertools import traceback import screed import sourmash from . import plugins as sourmash_plugins from .logging import notify, debug_literal from .exceptions import IndexNotLoaded from .index.sqlite_index import load_sqlite_index, SqliteIndex from .sbtmh import load_sbt_index from .lca.lca_db import load_single_database from . import signature as sigmod from .index import LinearIndex, ZipFileLinearIndex, MultiIndex from .manifest import CollectionManifest def load_file_as_index(filename, *, yield_all_files=False): """Load 'filename' as a database; generic database loader. If 'filename' contains an SBT or LCA indexed database, or a regular Zip file, will return the appropriate objects. If a Zip file and yield_all_files=True, will try to load all files within zip, not just .sig files. If 'filename' is a JSON file containing one or more signatures, will return an Index object containing those signatures. If 'filename' is a directory, will load *.sig underneath this directory into an Index object. If yield_all_files=True, will attempt to load all files. """ return _load_database(filename, yield_all_files) def SaveSignaturesToLocation(location): """ Provides a context manager that saves signatures in various output formats. Usage: with SaveSignaturesToLocation(filename_or_location) as save_sigs: save_sigs.add(sig_obj) """ save_list = itertools.chain(_save_classes, sourmash_plugins.get_save_to_functions()) for priority, cls in sorted(save_list, key=lambda x: x[0]): debug_literal(f"trying to match save function {cls}, priority={priority}") if cls.matches(location): debug_literal(f"{cls} is a match!") return cls(location) raise Exception( f"cannot determine how to open location {location} for saving; this should never happen!?" ) ### Implementation machinery for _load_databases def _load_database(filename, traverse_yield_all, *, cache_size=None): """Load file as a database - list of signatures, LCA, SBT, etc. Return Index object. This is an internal function used by other functions in sourmash_args. """ loaded = False # load plugins plugin_fns = sourmash_plugins.get_load_from_functions() # aggregate with default load_from functions & sort by priority load_from_functions = sorted(itertools.chain(_loader_functions, plugin_fns)) # iterate through loader functions, sorted by priority; try them all. # Catch ValueError & IndexNotLoaded but nothing else. for priority, desc, load_fn in load_from_functions: db = None try: debug_literal( f"_load_databases: trying loader fn - priority {priority} - '{desc}'" ) db = load_fn( filename, traverse_yield_all=traverse_yield_all, cache_size=cache_size ) except (ValueError, IndexNotLoaded): debug_literal(f"_load_databases: FAIL with ValueError: on fn {desc}.") debug_literal(traceback.format_exc()) debug_literal("(continuing past exception)") if db is not None: loaded = True debug_literal("_load_databases: success!") break if loaded: assert db is not None return db raise ValueError(f"Error while reading signatures from '{filename}'.") _loader_functions = [] def add_loader(name, priority): "decorator to add name/priority to _loader_functions" def dec_priority(func): _loader_functions.append((priority, name, func)) return func return dec_priority @add_loader("load from stdin", 10) def _load_stdin(filename, **kwargs): "Load collection from .sig file streamed in via stdin" db = None if filename == "-": # load as LinearIndex, then pass into MultiIndex to generate a # manifest. lidx = LinearIndex.load(sys.stdin, filename="-") db = MultiIndex.load((lidx,), (None,), parent="-") return db @add_loader("load from standalone manifest", 30) def _load_standalone_manifest(filename, **kwargs): from sourmash.index import StandaloneManifestIndex try: idx = StandaloneManifestIndex.load(filename) except gzip.BadGzipFile as exc: raise IndexNotLoaded(exc) return idx @add_loader("load from list of paths", 50) def _multiindex_load_from_pathlist(filename, **kwargs): "Load collection from a list of signature/database files" db = MultiIndex.load_from_pathlist(filename) return db @add_loader("load from path (file or directory)", 40) def _multiindex_load_from_path(filename, **kwargs): "Load collection from a directory." traverse_yield_all = kwargs["traverse_yield_all"] db = MultiIndex.load_from_path(filename, traverse_yield_all) return db @add_loader("load SBT", 60) def _load_sbt(filename, **kwargs): "Load collection from an SBT." cache_size = kwargs.get("cache_size") try: db = load_sbt_index(filename, cache_size=cache_size) except (FileNotFoundError, TypeError) as exc: raise IndexNotLoaded(exc) return db @add_loader("load revindex", 70) def _load_revindex(filename, **kwargs): "Load collection from an LCA database/reverse index." db, _, _ = load_single_database(filename) return db @add_loader("load collection from sqlitedb", 20) def _load_sqlite_db(filename, **kwargs): return load_sqlite_index(filename) @add_loader("load collection from zipfile", 80) def _load_zipfile(filename, **kwargs): "Load collection from a .zip file." db = None if filename.endswith(".zip"): traverse_yield_all = kwargs["traverse_yield_all"] try: db = ZipFileLinearIndex.load( filename, traverse_yield_all=traverse_yield_all ) except FileNotFoundError as exc: # turn this into an IndexNotLoaded => proper exception handling by # _load_database. raise IndexNotLoaded(exc) return db @add_loader("catch FASTA/FASTQ files and error", 1000) def _error_on_fastaq(filename, **kwargs): "This is a tail-end loader that checks for FASTA/FASTQ sequences => err." success = False try: with screed.open(filename) as it: _ = next(it) success = True except: pass if success: raise Exception( f"Error while reading signatures from '{filename}' - got sequences instead! Is this a FASTA/FASTQ file?" ) ### Implementation machinery for SaveSignaturesToLocation class Base_SaveSignaturesToLocation: "Base signature saving class. Track location (if any) and count." def __init__(self, location): self.location = location self.count = 0 @classmethod def matches(cls, location): "returns True when this class should handle a specific location" raise NotImplementedError def __repr__(self): raise NotImplementedError def __len__(self): return self.count def open(self): pass def close(self): pass def __enter__(self): "provide context manager functionality" self.open() return self def __exit__(self, type, value, traceback): "provide context manager functionality" self.close() def add(self, ss): self.count += 1 def add_many(self, sslist): for ss in sslist: self.add(ss) def _get_signatures_from_rust(siglist): # this function deals with a disconnect between the way Rust # and Python handle signatures; Python expects one # minhash (and hence one md5sum) per signature, while # Rust supports multiple. For now, go through serializing # and deserializing the signature! See issue #1167 for more. json_str = sigmod.save_signatures_to_json(siglist) yield from sigmod.load_signatures_from_json(json_str) class SaveSignatures_NoOutput(Base_SaveSignaturesToLocation): "Do not save signatures." def __repr__(self): return "SaveSignatures_NoOutput()" @classmethod def matches(cls, location): return location is None def open(self): pass def close(self): pass class SaveSignatures_Directory(Base_SaveSignaturesToLocation): "Save signatures within a directory, using md5sum names." def __init__(self, location): super().__init__(location) def __repr__(self): return f"SaveSignatures_Directory('{self.location}')" @classmethod def matches(cls, location): "anything ending in /" if location: return location.endswith("/") def close(self): pass def open(self): try: os.mkdir(self.location) except FileExistsError: pass except: notify(f"ERROR: cannot create signature output directory '{self.location}'") sys.exit(-1) def add(self, ss): super().add(ss) md5 = ss.md5sum() # don't overwrite even if duplicate md5sum outname = os.path.join(self.location, f"{md5}.sig.gz") if os.path.exists(outname): i = 0 while 1: outname = os.path.join(self.location, f"{md5}_{i}.sig.gz") if not os.path.exists(outname): break i += 1 with open(outname, "wb") as fp: sigmod.save_signatures_to_json([ss], fp, compression=1) class SaveSignatures_SqliteIndex(Base_SaveSignaturesToLocation): "Save signatures within a directory, using md5sum names." def __init__(self, location): super().__init__(location) self.location = location self.idx = None self.cursor = None @classmethod def matches(cls, location): "anything ending in .sqldb" if location: return location.endswith(".sqldb") def __repr__(self): return f"SaveSignatures_SqliteIndex('{self.location}')" def close(self): self.idx.commit() self.cursor.execute("VACUUM") self.idx.close() def open(self): self.idx = SqliteIndex.create(self.location, append=True) self.cursor = self.idx.cursor() def add(self, add_sig): for ss in _get_signatures_from_rust([add_sig]): super().add(ss) self.idx.insert(ss, cursor=self.cursor, commit=False) # commit every 1000 signatures. if self.count % 1000 == 0: self.idx.commit() class SaveSignatures_SigFile(Base_SaveSignaturesToLocation): "Save signatures to a .sig JSON file." def __init__(self, location): super().__init__(location) self.keep = [] self.compress = 0 if self.location.endswith(".gz"): self.compress = 1 @classmethod def matches(cls, location): # match anything that is not None or "" return bool(location) def __repr__(self): return f"SaveSignatures_SigFile('{self.location}')" def open(self): pass def close(self): if self.location == "-": sigmod.save_signatures_to_json(self.keep, sys.stdout) else: # text mode? encode in utf-8 mode = "w" encoding = "utf-8" # compressed? bytes & binary. if self.compress: encoding = None mode = "wb" with open(self.location, mode, encoding=encoding) as fp: sigmod.save_signatures_to_json(self.keep, fp, compression=self.compress) def add(self, ss): super().add(ss) self.keep.append(ss) class SaveSignatures_ZipFile(Base_SaveSignaturesToLocation): "Save compressed signatures in an uncompressed Zip file." def __init__(self, location): super().__init__(location) self.storage = None @classmethod def matches(cls, location): "anything ending in .zip" if location: return location.endswith(".zip") def __repr__(self): return f"SaveSignatures_ZipFile('{self.location}')" def close(self): # finish constructing manifest object & save manifest = CollectionManifest(self.manifest_rows) manifest_name = "SOURMASH-MANIFEST.csv" manifest_fp = StringIO() manifest.write_to_csv(manifest_fp, write_header=True) manifest_data = manifest_fp.getvalue().encode("utf-8") self.storage.save(manifest_name, manifest_data, overwrite=True, compress=True) self.storage.flush() self.storage.close() def open(self): from .sbt_storage import ZipStorage do_create = True if os.path.exists(self.location): do_create = False storage = None try: storage = ZipStorage(self.location, mode="w") except zipfile.BadZipFile: pass if storage is None: raise ValueError(f"File '{self.location}' cannot be opened as a zip file.") if not storage.subdir: storage.subdir = "signatures" # now, try to load manifest try: manifest_data = storage.load("SOURMASH-MANIFEST.csv") except (FileNotFoundError, KeyError): # if file already exists must have manifest... if not do_create: raise ValueError( f"Cannot add to existing zipfile '{self.location}' without a manifest" ) self.manifest_rows = [] else: # success! decode manifest_data, create manifest rows => append. manifest_data = manifest_data.decode("utf-8") manifest_fp = StringIO(manifest_data) manifest = CollectionManifest.load_from_csv(manifest_fp) self.manifest_rows = list(manifest._select()) self.storage = storage def _exists(self, name): try: self.storage.load(name) return True except KeyError: return False def add(self, add_sig): if not self.storage: raise ValueError("this output is not open") for ss in _get_signatures_from_rust([add_sig]): buf = sigmod.save_signatures_to_json([ss], compression=1) md5 = ss.md5sum() storage = self.storage path = f"{storage.subdir}/{md5}.sig.gz" location = storage.save(path, buf) # update manifest row = CollectionManifest.make_manifest_row( ss, location, include_signature=False ) self.manifest_rows.append(row) super().add(ss) _save_classes = [ (10, SaveSignatures_NoOutput), (20, SaveSignatures_Directory), (30, SaveSignatures_ZipFile), (40, SaveSignatures_SqliteIndex), (1000, SaveSignatures_SigFile), ] sourmash-bio-sourmash-bc3c0f8/src/sourmash/sbt.py000066400000000000000000001437131474157046300222710ustar00rootroot00000000000000#!/usr/bin/env python """ An implementation of sequence bloom trees, Solomon & Kingsford, 2015. """ from collections import namedtuple, Counter from collections.abc import Mapping from copy import copy import json import math import os from random import randint, random import sys from tempfile import NamedTemporaryFile from cachetools import Cache from io import StringIO from .exceptions import IndexNotSupported from .sbt_storage import FSStorage, IPFSStorage, RedisStorage, ZipStorage from .logging import error, notify, debug from .index import ( Index, IndexSearchResult, CollectionManifest, _check_select_parameters, ) from .picklist import passes_all_picklists from .nodegraph import Nodegraph, extract_nodegraph_info, calc_expected_collisions STORAGES = { "FSStorage": FSStorage, "IPFSStorage": IPFSStorage, "RedisStorage": RedisStorage, "ZipStorage": ZipStorage, } NodePos = namedtuple("NodePos", ["pos", "node"]) class GraphFactory: """Build new nodegraphs (Bloom filters) of a specific (fixed) size. Parameters ---------- ksize: int k-mer size. starting_size: int size (in bytes) for each nodegraph table. n_tables: int number of nodegraph tables to be used. """ def __init__(self, ksize, starting_size, n_tables): self.ksize = ksize self.starting_size = starting_size self.n_tables = n_tables def __call__(self): return Nodegraph(self.ksize, self.starting_size, self.n_tables) def init_args(self): return (self.ksize, self.starting_size, self.n_tables) class _NodesCache(Cache): """A cache for SBT nodes that calls .unload() when the node is removed from cache. This is adapted from the LFU cache in https://github.com/tkem/cachetools, but removing the largest node ids first (those near the bottom/leaves of the SBT). """ def __init__(self, maxsize, getsizeof=None): Cache.__init__(self, maxsize, getsizeof) self.__counter = Counter() def __getitem__(self, key, cache_getitem=Cache.__getitem__): value = cache_getitem(self, key) self.__counter[key] -= 1 return value def __setitem__(self, key, value, cache_setitem=Cache.__setitem__): cache_setitem(self, key, value) self.__counter[key] -= 1 def __delitem__(self, key, cache_delitem=Cache.__delitem__): cache_delitem(self, key) del self.__counter[key] def popitem(self): """Remove and return the `(key, value)` pair least recently used.""" try: # Select least frequently used keys, # limit to 50 items to avoid dealing with huge lists common = self.__counter.most_common()[:50] # common might include different values, so let's use # only keys that have the same value as the first one # (all those with the same count are least frequently used items) count = common[0][1] # we want to remove the item closest to the leaves, # and since node ids increase as they get farther from the root # we just need to select the maximum key/node id (key, _) = max(c for c in common if c[1] == count) except IndexError: msg = f"{self.__class__.__name__} is empty" raise KeyError(msg) from None else: value = self.pop(key) value.unload() return (key, value) class SBT(Index): """A Sequence Bloom Tree implementation allowing generic internal nodes and leaves. The default node and leaf format is a Bloom Filter (like the original implementation), but we also provide a MinHash leaf class (in the sourmash.sbtmh.SigLeaf class) Parameters ---------- factory: Factory Callable for generating new datastores for internal nodes. d: int Number of children for each internal node. Defaults to 2 (a binary tree) storage: Storage, default: None A Storage is any place where we can save and load data for the nodes. If set to None, will use a FSStorage. cache_size: int, default None Number of internal nodes to cache in memory. If set to None, will not remove any nodes from memory (cache grows without bounds). Notes ----- We use two dicts to store the tree structure: One for the internal nodes, and another for the leaves (datasets). """ is_database = True def __init__(self, factory, *, d=2, storage=None, cache_size=None): self.factory = factory self._nodes = {} self._missing_nodes = set() self._leaves = {} self.d = d self.next_node = 0 self.storage = storage if cache_size is None: cache_size = sys.maxsize self._nodescache = _NodesCache(maxsize=cache_size) self._location = None self.picklists = [] self.manifest = None @property def location(self): return self._location def signatures(self): if self.manifest: # if manifest, use it & load using direct path to storage. # this will be faster when using picklists. from .signature import load_one_signature_from_json manifest = self.manifest # iteratively select picklists; no other selection criteria # apply to SBTs, since ksize etc are fixed as part of indexing. for picklist in self.picklists: manifest = manifest.select_to_manifest(picklist=picklist) for loc in manifest.locations(): buf = self.storage.load(loc) # if more than one signature can be in a file, we need # to recheck picklists here. ss = load_one_signature_from_json(buf) yield ss else: # no manifest? iterate over all leaves. for k in self.leaves(): ss = k.data if passes_all_picklists(ss, self.picklists): yield ss def _signatures_with_internal(self): """Return an iterator of tuples (ss, storage_path, internal_location). Note: does not limit signatures to subsets. """ for k in self.leaves(): ss = k.data yield ss, k._path def select( self, ksize=None, moltype=None, num=0, scaled=0, containment=False, abund=None, picklist=None, **kwargs, ): """Make sure this database matches the requested requirements. Will always raise ValueError if a requirement cannot be met. The only tricky bit here is around downsampling: if the scaled value being requested is higher than the signatures in the SBT, we can use the SBT for containment but not for similarity. This is because: * if we are doing containment searches, the intermediate nodes can still be used for calculating containment of signatures with higher scaled values. This is because only hashes that match in the higher range are used for containment scores. * however, for similarity, _all_ hashes are used, and we cannot implicitly downsample or necessarily estimate similarity if the scaled values differ. """ _check_select_parameters( ksize=ksize, num=num, moltype=moltype, scaled=scaled, containment=containment, abund=abund, picklist=picklist, **kwargs, ) # pull out a signature from this collection - first_sig = next(iter(self.signatures())) db_mh = first_sig.minhash # check ksize. if ksize is not None and db_mh.ksize != ksize: raise ValueError( f"search ksize {ksize} is different from database ksize {db_mh.ksize}" ) # check moltype. if moltype is not None and db_mh.moltype != moltype: raise ValueError( f"search moltype {moltype} is different from database moltype {db_mh.moltype}" ) # containment requires 'scaled'. if containment: if not db_mh.scaled: raise ValueError( "cannot search this SBT for containment; signatures are not calculated with scaled" ) # 'num' and 'scaled' do not mix. if num: if not db_mh.num: raise ValueError( "this database was created with 'scaled' MinHash sketches, not 'num'" ) if num != db_mh.num: raise ValueError(f"num mismatch for SBT: num={num}, {db_mh.num}") if scaled: if not db_mh.scaled: raise ValueError( "this database was created with 'num' MinHash sketches, not 'scaled'" ) # we can downsample SBTs for containment operations. if scaled > db_mh.scaled and not containment: raise ValueError( f"search scaled value {scaled} is less than database scaled value of {db_mh.scaled}" ) if abund: raise ValueError("SBT indices do not support sketches with abund=True") if picklist is not None: self.picklists.append(picklist) if len(self.picklists) > 1: raise ValueError("we do not (yet) support multiple picklists for SBTs") return self def new_node_pos(self, node): # note: node is not actually used in this function! CTB if not self._nodes: self.next_node = 1 return 0 if not self._leaves: self.next_node = 2 return 1 min_leaf = min(self._leaves.keys()) next_internal_node = None if self.next_node <= min_leaf: for i in range(min_leaf): if all( ( i not in self._nodes, i not in self._leaves, i not in self._missing_nodes, ) ): next_internal_node = i break if next_internal_node is None: self.next_node = max(self._leaves.keys()) + 1 else: self.next_node = next_internal_node return self.next_node def insert(self, signature): "Add a new SourmashSignature in to the SBT." from .sbtmh import SigLeaf leaf = SigLeaf(signature.md5sum(), signature) self.add_node(leaf) def add_node(self, node): pos = self.new_node_pos(node) if pos == 0: # empty tree; initialize w/node. n = Node(self.factory, name="internal." + str(pos)) self._nodes[0] = n pos = self.new_node_pos(node) # Cases: # 1) parent is a Leaf (already covered) # 2) parent is a Node (with empty position available) # - add Leaf, update parent # 3) parent is a Node (no position available) # - this is covered by case 1 # 4) parent is None # this can happen with d != 2, in this case create the parent node p = self.parent(pos) if isinstance(p.node, Leaf): # Create a new internal node # node and parent are children of new internal node n = Node(self.factory, name="internal." + str(p.pos)) self._nodes[p.pos] = n c1, c2 = self.children(p.pos)[:2] self._leaves[c1.pos] = p.node self._leaves[c2.pos] = node del self._leaves[p.pos] for child in (p.node, node): child.update(n) elif isinstance(p.node, Node): self._leaves[pos] = node node.update(p.node) elif p.node is None: n = Node(self.factory, name="internal." + str(p.pos)) self._nodes[p.pos] = n c1 = self.children(p.pos)[0] self._leaves[c1.pos] = node node.update(n) else: # this branch should never be reached; put guard in to make sure! assert 0 # update all parents! p = self.parent(p.pos) while p: self._rebuild_node(p.pos) node.update(self._nodes[p.pos]) p = self.parent(p.pos) def _find_nodes(self, search_fn, *args, **kwargs): "Search the tree using `search_fn`." unload_data = kwargs.get("unload_data", True) # initialize search queue with top node of tree matches = [] visited, queue = set(), [0] # while the queue is not empty, load each node and apply search # function. while queue: node_p = queue.pop(0) # repair while searching. node_g = self._leaves.get(node_p, None) if node_g is None: if node_p in self._nodescache: node_g = self._nodescache[node_p] else: node_g = self._nodes.get(node_p, None) if node_g is None: if node_p in self._missing_nodes: self._rebuild_node(node_p) node_g = self._nodes[node_p] else: continue self._nodescache[node_p] = node_g # if we have not visited this node before, if node_p not in visited: visited.add(node_p) # apply search fn. If return false, truncate search. if search_fn(node_g, *args): # leaf node? it's a match! if isinstance(node_g, Leaf): matches.append(node_g) # internal node? descend. elif isinstance(node_g, Node): if kwargs.get("dfs", True): # defaults search to dfs for c in self.children(node_p): queue.insert(0, c.pos) else: # bfs queue.extend(c.pos for c in self.children(node_p)) if unload_data: node_g.unload() return matches def find(self, search_fn, query, **kwargs): """ Do a Jaccard similarity or containment search, yield results. Here 'search_fn' should be an instance of 'JaccardSearch'. Queries with higher scaled values than the database can still be used for containment search, but not for similarity search. See SBT.select(...) for details. """ from .sbtmh import SigLeaf search_fn.check_is_compatible(query) query_mh = query.minhash # figure out downsampling using the first leaf in the tree -- a_leaf = next(iter(self.leaves())) tree_scaled = a_leaf.data.minhash.scaled # scaled? if tree_scaled: assert query_mh.scaled # pick the larger scaled of the query & node scaled = max(query_mh.scaled, tree_scaled) if query_mh.scaled < tree_scaled: query_mh = query_mh.downsample(scaled=tree_scaled) # provide function to downsample leaf_node as well if scaled == tree_scaled: def downsample_node(x): return x else: def downsample_node(node_mh): return node_mh.downsample(scaled=scaled) else: assert query_mh.num # pick the smaller num of the query & node min_num = min(query_mh.num, a_leaf.data.minhash.num) # downsample query once: if query_mh.num > min_num: query_mh = query_mh.downsample(num=min_num) # provide function to downsample leaf nodes. if min_num == a_leaf.data.minhash.num: def downsample_node(x): return x else: def downsample_node(node_mh): return node_mh.downsample(num=min_num) query_size = len(query_mh) # store scores here so we don't need to recalculate results = {} # construct a function to pass into ._find_nodes; this function # will be used to prune tree searches based on internal node scores, # in addition to finding leaf nodes. def node_search(node, *args, **kwargs): is_leaf = False # leaf node? downsample so we can do signature comparison. if isinstance(node, SigLeaf): is_leaf = True subj_mh = downsample_node(node.data.minhash) subj_size = len(subj_mh) subj_mh = subj_mh.flatten() assert not subj_mh.track_abundance shared_size, total_size = query_mh.intersection_and_union_size(subj_mh) else: # Node / Nodegraph by minhash comparison # no downsampling needed -- shared_size = node.data.matches(query_mh) subj_size = node.metadata.get("min_n_below", -1) if subj_size == -1: raise ValueError( "ERROR: no min_n_below on this tree, cannot search." ) total_size = subj_size # approximate; do not collect # calculate score (exact, if leaf; approximate, if not) score = search_fn.score_fn(query_size, shared_size, subj_size, total_size) if search_fn.passes(score): if is_leaf: # terminal node? keep. if search_fn.collect(score, node.data): results[node.data] = score return True else: # it's a good internal node, keep. return True return False # & execute! for n in self._find_nodes(node_search, **kwargs): ss = n.data # filter on picklists if passes_all_picklists(ss, self.picklists): yield IndexSearchResult(results[ss], ss, self.location) def _rebuild_node(self, pos=0): """Recursively rebuilds an internal node (if it is not present). Parameters ---------- pos: int node to be rebuild. Any internal node under it will be rebuild too. If you want to rebuild all missing internal nodes you can use pos=0 (the default). """ node = self._nodes.get(pos, None) if node is not None: # this node was already build, skip return node = Node(self.factory, name=f"internal.{pos}") self._nodes[pos] = node for c in self.children(pos): if c.pos in self._missing_nodes or isinstance(c.node, Leaf): cnode = c.node if cnode is None: self._rebuild_node(c.pos) cnode = self._nodes[c.pos] cnode.update(node) def parent(self, pos): """Return the parent of the node at position ``pos``. If it is the root node (position 0), returns None. Parameters ---------- pos: int Position of the node in the tree. Returns ------- NodePos : A NodePos namedtuple with the position and content of the parent node. """ if pos == 0: return None p = int(math.floor((pos - 1) / self.d)) if p in self._leaves: return NodePos(p, self._leaves[p]) node = self._nodes.get(p, None) return NodePos(p, node) def children(self, pos): """Return all children nodes for node at position ``pos``. Parameters ---------- pos: int Position of the node in the tree. Returns ------- list of NodePos A list of NodePos namedtuples with the position and content of all children nodes. """ return [self.child(pos, c) for c in range(self.d)] def child(self, parent, pos): """Return a child node at position ``pos`` under the ``parent`` node. Parameters ---------- parent: int Parent node position in the tree. pos: int Position of the child one under the parent. Ranges from [0, arity - 1], where arity is the arity of the SBT (usually it is 2, a binary tree). Returns ------- NodePos A NodePos namedtuple with the position and content of the child node. """ cd = self.d * parent + pos + 1 if cd in self._leaves: return NodePos(cd, self._leaves[cd]) node = self._nodes.get(cd, None) return NodePos(cd, node) def save(self, path, storage=None, sparseness=0.0, structure_only=False): """Saves an SBT description locally and node data to a storage. Parameters ---------- path : str path to where the SBT description should be saved. storage : Storage, optional Storage to be used for saving node data. Defaults to FSStorage (a hidden directory at the same level of path) sparseness : float How much of the internal nodes should be saved. Defaults to 0.0 (save all internal nodes data), can go up to 1.0 (don't save any internal nodes data) structure_only: boolean Write only the index schema and metadata, but not the data. Defaults to False (save data too) Returns ------- str full path to the new SBT description """ info = {} info["d"] = self.d info["version"] = 6 info["index_type"] = self.__class__.__name__ # TODO: check # choose between ZipStorage and FS (file system/directory) storage. # default to ZipStorage, unless .sbt.json is specified in filename. kind = None if not path.endswith(".sbt.json"): kind = "Zip" if not path.endswith(".sbt.zip"): path += ".sbt.zip" storage = ZipStorage(path, mode="w") backend = "FSStorage" assert path[-8:] == ".sbt.zip" name = os.path.basename(path[:-8]) # align the storage prefix with what we do for FSStorage, below. subdir = f".sbt.{name}" storage_args = FSStorage("", subdir, make_dirs=False).init_args() storage.save(subdir + "/", b"") storage.subdir = subdir index_filename = os.path.abspath(path) else: # path.endswith('.sbt.json') assert path.endswith(".sbt.json") name = os.path.basename(path) name = name[:-9] index_filename = os.path.abspath(path) if storage is None: kind = "FS" # default storage location = os.path.dirname(index_filename) # align subdir names with what we do above for ZipStorage subdir = f".sbt.{name}" # when we go to default of FSStorage, use full location for # storage, e.g. location/.sbt.{name}/ storage = FSStorage(location, subdir) index_filename = os.path.join(location, index_filename) backend = [k for (k, v) in STORAGES.items() if v is type(storage)][0] storage_args = storage.init_args() info["storage"] = {"backend": backend, "args": storage_args} info["factory"] = { "class": GraphFactory.__name__, "args": self.factory.init_args(), } nodes = {} leaves = {} internal_nodes = set(self._nodes).union(self._missing_nodes) total_nodes = len(self) + len(internal_nodes) manifest_rows = [] for n, (i, node) in enumerate(self): if node is None: continue if isinstance(node, Node): if random() - sparseness <= 0: continue data = { # TODO: start using md5sum instead? "filename": os.path.basename(node.name), "name": node.name, } try: node.metadata.pop("max_n_below") except (AttributeError, KeyError): pass data["metadata"] = node.metadata if structure_only is False: # trigger data loading before saving to the new place node.data node.storage = storage if kind == "Zip": new_name = node.save(os.path.join(subdir, data["filename"])) assert new_name.startswith(subdir + "/") # strip off prefix new_name = new_name[len(subdir) + 1 :] data["filename"] = new_name else: data["filename"] = node.save(data["filename"]) if isinstance(node, Node): nodes[i] = data else: leaves[i] = data row = node.make_manifest_row(data["filename"]) if row: manifest_rows.append(row) if n % 100 == 0: notify( f"{format(n + 1)} of {format(total_nodes)} nodes saved", end="\r" ) # now, save the index file and manifests. # # for zipfiles, they get saved in the zip file. # for FSStorage, we use the storage.save function. # # for everything else (Redis, IPFS), the index gets saved locally. # the nodes/leaves are saved/loaded from the datatabase, and # the index is used to get their names for loading. # (CTB: manifests are not yet supported for Redis and IPFS) # notify("Finished saving nodes, now saving SBT index file.") info["nodes"] = nodes info["signatures"] = leaves # finish constructing manifest object & save manifest = CollectionManifest(manifest_rows) manifest_name = f"{name}.manifest.csv" manifest_fp = StringIO() manifest.write_to_csv(manifest_fp, write_header=True) manifest_data = manifest_fp.getvalue().encode("utf-8") if kind == "Zip": manifest_name = os.path.join(storage.subdir, manifest_name) manifest_path = storage.save( manifest_name, manifest_data, overwrite=True, compress=True ) elif kind == "FS": manifest_name = manifest_name manifest_path = storage.save(manifest_name, manifest_data, overwrite=True) else: manifest_path = None if manifest_path: info["manifest_path"] = manifest_path # now, save index. tree_data = json.dumps(info).encode("utf-8") if kind == "Zip": save_path = f"{name}.sbt.json" storage.save(save_path, tree_data, overwrite=True) storage.flush() elif kind == "FS": storage.save(index_filename, tree_data, overwrite=True) else: # save tree locally. with open(index_filename, "wb") as tree_fp: tree_fp.write(tree_data) notify(f"Finished saving SBT index, available at {format(index_filename)}\n") return path @classmethod def load( cls, location, *, leaf_loader=None, storage=None, print_version_warning=True, cache_size=None, ): """Load an SBT description from a file. Parameters ---------- location : str path to the SBT description. leaf_loader : function, optional function to load leaf nodes. Defaults to ``Leaf.load``. storage : Storage, optional Storage to be used for saving node data. Defaults to FSStorage (a hidden directory at the same level of path) Returns ------- SBT the SBT tree built from the description. """ tempfile = None sbt_name = None tree_data = None if storage is None: if ZipStorage.can_open(location): storage = ZipStorage(location) else: if not location.endswith(".sbt.zip"): location2 = location + ".sbt.zip" if ZipStorage.can_open(location2): storage = ZipStorage(location2) if storage: sbts = storage.list_sbts() if len(sbts) == 1: tree_data = storage.load(sbts[0]) tempfile = NamedTemporaryFile() tempfile.write(tree_data) tempfile.flush() dirname = os.path.dirname(tempfile.name) sbt_name = os.path.basename(tempfile.name) if sbt_name is None: dirname = os.path.dirname(os.path.abspath(location)) sbt_name = os.path.basename(location) if sbt_name.endswith(".sbt.json"): sbt_name = sbt_name[:-9] sbt_fn = os.path.join(dirname, sbt_name) if not sbt_fn.endswith(".sbt.json") and tempfile is None: sbt_fn += ".sbt.json" try: with open(sbt_fn) as fp: jnodes = json.load(fp) except NotADirectoryError as exc: raise ValueError(str(exc)) if tempfile is not None: tempfile.close() version = 1 if isinstance(jnodes, Mapping): version = jnodes["version"] if leaf_loader is None: leaf_loader = Leaf.load loaders = { 1: cls._load_v1, 2: cls._load_v2, 3: cls._load_v3, 4: cls._load_v4, 5: cls._load_v5, 6: cls._load_v6, } try: loader = loaders[version] except KeyError: raise IndexNotSupported() # if version >= 6: # if jnodes.get("index_type", "SBT") == "LocalizedSBT": # loaders[6] = LocalizedSBT._load_v6 if version < 3 and storage is None: storage = FSStorage(dirname, f".sbt.{sbt_name}") elif storage is None: klass = STORAGES[jnodes["storage"]["backend"]] if jnodes["storage"]["backend"] == "FSStorage": storage = FSStorage(dirname, jnodes["storage"]["args"]["path"]) elif storage is None: storage = klass(**jnodes["storage"]["args"]) obj = loader( jnodes, leaf_loader, dirname, storage, print_version_warning=print_version_warning, cache_size=cache_size, ) obj._location = location if "manifest_path" in jnodes: manifest_path = jnodes["manifest_path"] manifest_data = storage.load(manifest_path) manifest_data = manifest_data.decode("utf-8") manifest_fp = StringIO(manifest_data) obj.manifest = CollectionManifest.load_from_csv(manifest_fp) else: obj.manifest = None return obj @staticmethod def _load_v1( jnodes, leaf_loader, dirname, storage, *, print_version_warning=True, cache_size=None, ): if jnodes[0] is None: raise ValueError("Empty tree!") sbt_nodes = {} sbt_leaves = {} sample_bf = os.path.join(dirname, jnodes[0]["filename"]) ksize, tablesize, ntables = extract_nodegraph_info(sample_bf)[:3] factory = GraphFactory(ksize, tablesize, ntables) for i, jnode in enumerate(jnodes): if jnode is None: continue jnode["filename"] = os.path.join(dirname, jnode["filename"]) if "internal" in jnode["name"]: jnode["factory"] = factory sbt_node = Node.load(jnode, storage) sbt_nodes[i] = sbt_node else: sbt_node = leaf_loader(jnode, storage) sbt_leaves[i] = sbt_node tree = SBT(factory, cache_size=cache_size) tree._nodes = sbt_nodes tree._leaves = sbt_leaves return tree @classmethod def _load_v2( cls, info, leaf_loader, dirname, storage, *, print_version_warning=True, cache_size=None, ): nodes = {int(k): v for (k, v) in info["nodes"].items()} if nodes[0] is None: raise ValueError("Empty tree!") sbt_nodes = {} sbt_leaves = {} sample_bf = os.path.join(dirname, nodes[0]["filename"]) k, size, ntables = extract_nodegraph_info(sample_bf)[:3] factory = GraphFactory(k, size, ntables) for k, node in nodes.items(): if node is None: continue node["filename"] = os.path.join(dirname, node["filename"]) if "internal" in node["name"]: node["factory"] = factory sbt_node = Node.load(node, storage) sbt_nodes[k] = sbt_node else: sbt_node = leaf_loader(node, storage) sbt_leaves[k] = sbt_node tree = cls(factory, d=info["d"], cache_size=cache_size) tree._nodes = sbt_nodes tree._leaves = sbt_leaves return tree @classmethod def _load_v3( cls, info, leaf_loader, dirname, storage, *, print_version_warning=True, cache_size=None, ): nodes = {int(k): v for (k, v) in info["nodes"].items()} if not nodes: raise ValueError("Empty tree!") sbt_nodes = {} sbt_leaves = {} factory = GraphFactory(*info["factory"]["args"]) max_node = 0 for k, node in nodes.items(): if node is None: continue if "internal" in node["name"]: node["factory"] = factory sbt_node = Node.load(node, storage) sbt_nodes[k] = sbt_node else: sbt_node = leaf_loader(node, storage) sbt_leaves[k] = sbt_node max_node = max(max_node, k) tree = cls(factory, d=info["d"], storage=storage, cache_size=cache_size) tree._nodes = sbt_nodes tree._leaves = sbt_leaves tree._missing_nodes = { i for i in range(max_node) if i not in sbt_nodes and i not in sbt_leaves } if print_version_warning: error( "WARNING: this is an old index version, please run `sourmash migrate` to update it." ) error( "WARNING: proceeding with execution, but it will take longer to finish!" ) tree._fill_min_n_below() return tree @classmethod def _load_v4( cls, info, leaf_loader, dirname, storage, *, print_version_warning=True, cache_size=None, ): nodes = {int(k): v for (k, v) in info["nodes"].items()} if not nodes: raise ValueError("Empty tree!") sbt_nodes = {} sbt_leaves = {} factory = GraphFactory(*info["factory"]["args"]) max_node = 0 for k, node in nodes.items(): if "internal" in node["name"]: node["factory"] = factory sbt_node = Node.load(node, storage) sbt_nodes[k] = sbt_node else: sbt_node = leaf_loader(node, storage) sbt_leaves[k] = sbt_node max_node = max(max_node, k) tree = cls(factory, d=info["d"], storage=storage, cache_size=cache_size) tree._nodes = sbt_nodes tree._leaves = sbt_leaves tree._missing_nodes = { i for i in range(max_node) if i not in sbt_nodes and i not in sbt_leaves } tree.next_node = max_node return tree @classmethod def _load_v5( cls, info, leaf_loader, dirname, storage, *, print_version_warning=True, cache_size=None, ): nodes = {int(k): v for (k, v) in info["nodes"].items()} leaves = {int(k): v for (k, v) in info["leaves"].items()} if not leaves: raise ValueError("Empty tree!") sbt_nodes = {} sbt_leaves = {} if storage is None: klass = STORAGES[info["storage"]["backend"]] if info["storage"]["backend"] == "FSStorage": storage = FSStorage(dirname, info["storage"]["args"]["path"]) elif storage is None: storage = klass(**info["storage"]["args"]) factory = GraphFactory(*info["factory"]["args"]) max_node = 0 for k, node in nodes.items(): node["factory"] = factory sbt_node = Node.load(node, storage) sbt_nodes[k] = sbt_node max_node = max(max_node, k) for k, node in leaves.items(): sbt_leaf = leaf_loader(node, storage) sbt_leaves[k] = sbt_leaf max_node = max(max_node, k) tree = cls(factory, d=info["d"], storage=storage, cache_size=cache_size) tree._nodes = sbt_nodes tree._leaves = sbt_leaves tree._missing_nodes = { i for i in range(max_node) if i not in sbt_nodes and i not in sbt_leaves } return tree @classmethod def _load_v6( cls, info, leaf_loader, dirname, storage, *, print_version_warning=True, cache_size=None, ): nodes = {int(k): v for (k, v) in info["nodes"].items()} leaves = {int(k): v for (k, v) in info["signatures"].items()} if not leaves: raise ValueError("Empty tree!") sbt_nodes = {} sbt_leaves = {} if storage is None: klass = STORAGES[info["storage"]["backend"]] if info["storage"]["backend"] == "FSStorage": storage = FSStorage(dirname, info["storage"]["args"]["path"]) elif storage is None: storage = klass(**info["storage"]["args"]) factory = GraphFactory(*info["factory"]["args"]) max_node = 0 for k, node in nodes.items(): node["factory"] = factory sbt_node = Node.load(node, storage) sbt_nodes[k] = sbt_node max_node = max(max_node, k) for k, node in leaves.items(): sbt_leaf = leaf_loader(node, storage) sbt_leaves[k] = sbt_leaf max_node = max(max_node, k) tree = cls(factory, d=info["d"], storage=storage, cache_size=cache_size) tree._nodes = sbt_nodes tree._leaves = sbt_leaves tree._missing_nodes = { i for i in range(max_node) if i not in sbt_nodes and i not in sbt_leaves } return tree def _fill_min_n_below(self): """\ Propagate the smallest hash size below each node up the tree from the leaves. """ def fill_min_n_below(node, *args, **kwargs): original_min_n_below = node.metadata.get("min_n_below", sys.maxsize) min_n_below = original_min_n_below children = kwargs["children"] for child in children: if child.node is not None: if isinstance(child.node, Leaf): min_n_below = min(len(child.node.data.minhash), min_n_below) else: child_n = child.node.metadata.get("min_n_below", sys.maxsize) min_n_below = min(child_n, min_n_below) if min_n_below == 0: min_n_below = 1 node.metadata["min_n_below"] = min_n_below return original_min_n_below != min_n_below self._fill_up(fill_min_n_below) def _fill_internal(self): def fill_nodegraphs(node, *args, **kwargs): children = kwargs["children"] for child in children: if child.node is not None: child.node.update(node) return True self._fill_up(fill_nodegraphs) def _fill_up(self, search_fn, *args, **kwargs): visited, queue = set(), list(reversed(sorted(self._leaves.keys()))) debug("started filling up") processed = 0 while queue: node_p = queue.pop(0) parent = self.parent(node_p) if parent is None: # we are in the root, no more nodes available to search assert len(queue) == 0 return was_missing = False if parent.node is None: if parent.pos in self._missing_nodes: self._rebuild_node(parent.pos) parent = self.parent(node_p) was_missing = True else: continue siblings = self.children(parent.pos) if node_p not in visited: visited.add(node_p) for sibling in siblings: visited.add(sibling.pos) try: queue.remove(sibling.pos) except ValueError: pass if search_fn(parent.node, children=siblings, *args) or was_missing: queue.append(parent.pos) processed += 1 if processed % 100 == 0: debug("processed {}, in queue {}", processed, len(queue), sep="\r") def __len__(self): return len(self._leaves) def print_dot(self): print( """ digraph G { nodesep=0.3; ranksep=0.2; margin=0.1; node [shape=ellipse]; edge [arrowsize=0.8]; """ ) for i, node in self._nodes.items(): if isinstance(node, Node): print(f'"{node.name}" [shape=box fillcolor=gray style=filled]') for j, child in self.children(i): if child is not None: print(f'"{node.name}" -> "{child.name}"') print("}") def print(self): visited, stack = set(), [0] while stack: node_p = stack.pop() node_g = self._nodes.get(node_p, None) if node_p not in visited and node_g is not None: visited.add(node_p) depth = int(math.floor(math.log(node_p + 1, self.d))) print(" " * 4 * depth, node_g) if isinstance(node_g, Node): stack.extend( c.pos for c in self.children(node_p) if c.pos not in visited ) def __iter__(self): for i, node in self._nodes.items(): yield (i, node) for i, node in self._leaves.items(): yield (i, node) def _parents(self, pos=0): if pos == 0: yield None else: p = self.parent(pos) while p is not None: yield p.pos p = self.parent(p.pos) def leaves(self, with_pos=False, unload_data=True): for pos, data in self._leaves.items(): if with_pos: yield (pos, data) else: yield data if unload_data: data.unload() def combine(self, other): larger, smaller = self, other if len(other) > len(self): larger, smaller = other, self n = Node(self.factory, name="internal.0", storage=self.storage) larger._nodes[0].update(n) smaller._nodes[0].update(n) new_nodes = {} new_nodes[0] = n new_leaves = {} levels = int(math.ceil(math.log(len(larger), self.d))) + 1 current_pos = 1 n_previous = 0 n_next = 1 for level in range(1, levels + 1): for tree in (larger, smaller): for pos in range(n_previous, n_next): if tree._nodes.get(pos, None) is not None: new_node = copy(tree._nodes[pos]) new_node.name = f"internal.{current_pos}" new_nodes[current_pos] = new_node elif tree._leaves.get(pos, None) is not None: new_node = copy(tree._leaves[pos]) new_leaves[current_pos] = new_node current_pos += 1 n_previous = n_next n_next = n_previous + int(self.d**level) current_pos = n_next # TODO: do we want to return a new tree, or merge into this one? self._nodes = new_nodes self._leaves = new_leaves return self class Node: "Internal node of SBT." def __init__(self, factory, name=None, path=None, storage=None): self.name = name self.storage = storage self._factory = factory self._data = None self._path = path self.metadata = {} def __str__(self): return f"*Node:{self.name} [occupied: {self.data.n_occupied()}, fpr: {calc_expected_collisions(self.data, True, 1.1):.2}]" def save(self, path): buf = self.data.to_bytes(compression=1) return self.storage.save(path, buf, overwrite=True) @property def data(self): if self._data is None: if self._path is None: self._data = self._factory() else: data = self.storage.load(self._path) self._data = Nodegraph.from_buffer(data) return self._data @data.setter def data(self, new_data): self._data = new_data def unload(self): if self.storage: # Don't unload data if there is no Storage # TODO: Check that data is actually in the storage? self._data = None @staticmethod def load(info, storage=None): new_node = Node( info["factory"], name=info["name"], path=info["filename"], storage=storage ) new_node.metadata = info.get("metadata", {}) return new_node def update(self, parent): parent.data.update(self.data) if "min_n_below" in self.metadata: min_n_below = min( parent.metadata.get("min_n_below", sys.maxsize), self.metadata.get("min_n_below"), ) if min_n_below == 0: min_n_below = 1 parent.metadata["min_n_below"] = min_n_below class Leaf: def __init__(self, metadata, data=None, name=None, storage=None, path=None): self.metadata = metadata if name is None: name = metadata self.name = name self.storage = storage self._data = data self._path = path def __str__(self): return f"**Leaf:{self.name} [occupied: {self.data.n_occupied()}, fpr: {calc_expected_collisions(self.data, True, 1.1):.2}] -> {self.metadata}" def make_manifest_row(self, location): return None @property def data(self): if self._data is None: data = self.storage.load(self._path) self._data = Nodegraph.from_buffer(data) return self._data @data.setter def data(self, new_data): self._data = new_data def unload(self): if self.storage: # Don't unload data if there is no Storage # TODO: Check that data is actually in the storage? self._data = None def save(self, path): buf = self.data.to_bytes(compression=1) return self.storage.save(path, buf) def update(self, parent): parent.data.update(self.data) @classmethod def load(cls, info, storage=None): return cls( info["metadata"], name=info["name"], path=info["filename"], storage=storage ) def filter_distance(filter_a, filter_b, n=1000): """ Compute a heuristic distance per bit between two Bloom filters. Parameters ---------- filter_a : Nodegraph filter_b : Nodegraph n : int Number of positions to compare (in groups of 8) Returns ------- float The distance between both filters (from 0.0 to 1.0) """ from numpy import array A = filter_a.graph.get_raw_tables() B = filter_b.graph.get_raw_tables() distance = 0 for q, p in zip(A, B): a = array(q, copy=False) b = array(p, copy=False) for i in map(lambda x: randint(0, len(a)), range(n)): distance += sum( map( int, [ not bool((a[i] >> j) & 1) ^ bool((b[i] >> j) & 1) for j in range(8) ], ) ) return distance / (8.0 * len(A) * n) def convert_cmd(name, backend): "Convert an SBT to use a different back end." from .sbtmh import SigLeaf options = backend.split("(") backend = options.pop(0) backend = backend.lower().strip("'") kwargs = {} if options: print(options) options = options[0].split(")") options = [options.pop(0)] # options = {} else: options = [] if backend.lower() in ("ipfs", "ipfsstorage"): backend = IPFSStorage elif backend.lower() in ("redis", "redisstorage"): backend = RedisStorage elif backend.lower() in ("zip", "zipstorage"): backend = ZipStorage kwargs["mode"] = "w" elif backend.lower() in ("fs", "fsstorage"): backend = FSStorage if options: options = [os.path.dirname(options[0]), os.path.basename(options[0])] else: # this is the default for SBT v2 tag = ".sbt." + os.path.basename(name) if tag.endswith(".sbt.json"): tag = tag[:-9] path = os.path.dirname(name) options = [path, tag] else: error(f"backend not recognized: {backend}") with backend(*options, **kwargs) as storage: sbt = SBT.load(name, leaf_loader=SigLeaf.load) sbt.save(name, storage=storage) sourmash-bio-sourmash-bc3c0f8/src/sourmash/sbt_storage.py000066400000000000000000000367521474157046300240210ustar00rootroot00000000000000import abc from io import BytesIO import os import shutil import sys import tarfile from tempfile import NamedTemporaryFile import zipfile from abc import ABC from pathlib import Path import warnings from ._lowlevel import ffi, lib from .utils import RustObject, rustcall, decode_str from .minhash import to_bytes class Storage(ABC): @abc.abstractmethod def save(self, path, content, *, overwrite=False): pass @abc.abstractmethod def load(self, path): pass def list_sbts(self): return [] def init_args(self): return {} def __enter__(self): return self def __exit__(self, type, value, traceback): self.close() def close(self): pass def can_open(self, location): return False class FSStorage(Storage): def __init__(self, location, subdir, make_dirs=True): self.location = location self.subdir = subdir if make_dirs: fullpath = os.path.join(location, subdir) if not os.path.exists(fullpath): os.makedirs(fullpath) def init_args(self): return {"path": self.subdir} def save(self, path, content, overwrite=False): "Save a node/leaf." newpath = path fullpath = os.path.join(self.location, self.subdir, path) if os.path.exists(fullpath): # check for content, if same return path, with open(fullpath, "rb") as f: old_content = f.read() if old_content == content: return path if overwrite: pass # fine to overwrite file! else: # different content, need to find new path to save newpath = None n = 0 while newpath is None: testpath = f"{fullpath}_{n}" if os.path.exists(testpath): n += 1 else: # testpath is available, use it as newpath newpath = f"{path}_{n}" fullpath = os.path.join(self.location, self.subdir, newpath) with open(fullpath, "wb") as f: f.write(content) return newpath def load(self, path): path = Path(self.location) / self.subdir / path return path.read_bytes() class ZipStorage(RustObject, Storage): __dealloc_func__ = lib.zipstorage_free def __init__(self, path, *, mode="r"): if mode == "w": self.__inner = _RwZipStorage(path) else: self.__inner = None path = os.path.abspath(path) self._objptr = rustcall(lib.zipstorage_new, to_bytes(path), len(path)) @staticmethod def can_open(location): return zipfile.is_zipfile(location) @property def path(self): if self.__inner: return self.__inner.path return decode_str(self._methodcall(lib.zipstorage_path)) @property def subdir(self): if self.__inner: return self.__inner.subdir return decode_str(self._methodcall(lib.zipstorage_subdir)) @subdir.setter def subdir(self, value): if self.__inner: self.__inner.subdir = value else: self._methodcall(lib.zipstorage_set_subdir, to_bytes(value), len(value)) def _filenames(self): if not self._objptr: return self.__inner._filenames() size = ffi.new("uintptr_t *") paths_ptr = self._methodcall(lib.zipstorage_filenames, size) size = size[0] paths = [] for i in range(size): path = decode_str(paths_ptr[i][0]) paths.append(path) return paths def save(self, path, content, *, overwrite=False, compress=False): if self.__inner: return self.__inner.save( path, content, overwrite=overwrite, compress=compress ) raise NotImplementedError() def load(self, path): if not self._objptr: return self.__inner.load(path) try: size = ffi.new("uintptr_t *") rawbuf = self._methodcall( lib.zipstorage_load, to_bytes(path), len(path), size ) size = size[0] rawbuf = ffi.gc(rawbuf, lambda o: lib.nodegraph_buffer_free(o, size), size) buf = ffi.buffer(rawbuf, size) # TODO: maybe avoid the [:] here, it triggers a copy... return buf[:] except ValueError: raise FileNotFoundError(path) def list_sbts(self): if self.__inner: return self.__inner.list_sbts() size = ffi.new("uintptr_t *") paths_ptr = self._methodcall(lib.zipstorage_list_sbts, size) size = size[0] paths = [] for i in range(size): path = decode_str(paths_ptr[i][0]) paths.append(path) return paths def init_args(self): return {"path": self.path} def flush(self): if self.__inner: self.__inner.flush() def close(self): if self.__inner: self.__inner.close() @staticmethod def can_open(location): return zipfile.is_zipfile(location) class _RwZipStorage(Storage): def __init__(self, path): self.path = os.path.abspath(path) dirname = os.path.dirname(self.path) if not os.path.exists(dirname): os.makedirs(dirname) self.bufferzip = None # Turns out we can't delete/modify an entry in a zipfile easily, # so we need to check some things: if not os.path.exists(self.path): # If the file doesn't exist open it in write mode. self.zipfile = zipfile.ZipFile( path, mode="w", compression=zipfile.ZIP_STORED ) else: # If it exists, open it in read mode and prepare a buffer for # new/duplicated items. During close() there are checks to see # how the original file needs to be updated (append new items, # deal with duplicates, and so on) self.zipfile = zipfile.ZipFile(path, "r") self.bufferzip = zipfile.ZipFile(BytesIO(), mode="w") self.subdir = "" subdirs = [f for f in self.zipfile.namelist() if f.endswith("/")] if len(subdirs) == 1: self.subdir = subdirs[0] def _filenames(self): return [info.filename for info in self.zipfile.infolist()] def _content_matches(self, zf, path, content): info = zf.getinfo(path) entry_content = zf.read(info) if entry_content == content: return True return False def _generate_filename(self, zf, path, content): try: matches = self._content_matches(zf, path, content) if matches: return path, False except KeyError: # entry not there yet, use that path return path, True # content does not match - generate new path based on path newpath = None n = 0 while newpath is None: testpath = f"{path}_{n}" try: matches = self._content_matches(zf, testpath, content) if matches: return testpath, False else: n += 1 except KeyError: return testpath, True assert 0 # should never get here! def _write_to_zf(self, zf, path, content, *, compress=False): compress_type = zipfile.ZIP_STORED if compress: compress_type = zipfile.ZIP_DEFLATED # save to zipfile zf.writestr(path, content, compress_type=compress_type) # set permissions zi = zf.getinfo(path) perms = 0o444 << 16 # give a+r access if path.endswith("/"): perms = 0o755 << 16 # directories get u+rwx, a+rx zi.external_attr = perms def save(self, path, content, *, overwrite=False, compress=False): # ignore UserWarnings for duplicate filenames. with warnings.catch_warnings(): warnings.simplefilter("ignore") # First try to save to self.zipfile, if it is not writable # or would introduce duplicates then try to save it in the buffer if overwrite: newpath = path do_write = True else: newpath, do_write = self._generate_filename(self.zipfile, path, content) if do_write: try: self._write_to_zf(self.zipfile, newpath, content, compress=compress) except (ValueError, RuntimeError): # Can't write in the zipfile, write in buffer instead # CTB: do we need to generate a new filename wrt to the # bufferzip, too? Not sure this code is working as intended... if self.bufferzip: self._write_to_zf( self.bufferzip, newpath, content, compress=compress ) else: # Throw error, can't write the data raise ValueError("can't write data") return newpath def _load_from_zf(self, zf, path): # we repeat these steps for self.zipfile and self.bufferzip, # so better to have an auxiliary method try: return zf.read(path) except KeyError: path = os.path.join(self.subdir, path) return zf.read(path) def load(self, path): try: return self._load_from_zf(self.zipfile, path) except KeyError: if self.bufferzip: return self._load_from_zf(self.bufferzip, path) else: raise FileNotFoundError(path) def close(self): # TODO: this is not ideal; checking for zipfile.fp is looking at # internal implementation details from CPython... # might not have self.zipfile if was invalid zipfile and __init__ # failed. if hasattr(self, "zipfile"): if self.zipfile is not None or self.bufferzip is not None: self.flush(keep_closed=True) self.zipfile.close() self.zipfile = None def flush(self, *, keep_closed=False): # This is a bit complicated, but we have to deal with new data # (if the original zipfile is read-only) and possible duplicates. if self.bufferzip is None: # The easy case: close (to force flushing) and reopen the zipfile if self.zipfile is not None: self.zipfile.close() if not keep_closed: self.zipfile = zipfile.ZipFile( self.path, mode="a", compression=zipfile.ZIP_STORED ) else: # The complicated one. Need to consider: # - Is there data in the buffer? # - If there is, is any of it # * duplicated? # * new data? buffer_names = set(self.bufferzip.namelist()) zf_names = set(self.zipfile.namelist()) if buffer_names: new_data = buffer_names - zf_names duplicated = buffer_names & zf_names if duplicated: # bad news, need to create new file... # create a temporary file to write the final version, # which will be copied to the right place later. tempfile = NamedTemporaryFile(delete=False) final_file = zipfile.ZipFile(tempfile, mode="w") all_data = buffer_names.union(zf_names) for item in all_data: if item in duplicated or item in buffer_names: # we prioritize writing data from the buffer to the # final file self._write_to_zf( final_file, item, self.bufferzip.read(item) ) else: # it is only in the zipfile, so write from it self._write_to_zf(final_file, item, self.zipfile.read(item)) # close the files, remove the old one and copy the final # file to the right place. self.zipfile.close() final_file.close() os.unlink(self.path) shutil.move(tempfile.name, self.path) if not keep_closed: self.zipfile = zipfile.ZipFile( self.path, mode="a", compression=zipfile.ZIP_STORED ) elif new_data: # Since there is no duplicated data, we can # reopen self.zipfile in append mode and write the new data self.zipfile.close() if keep_closed: raise Exception("unexpected error") else: zf = zipfile.ZipFile( self.path, mode="a", compression=zipfile.ZIP_STORED ) for item in new_data: self._write_to_zf(zf, item, self.bufferzip.read(item)) self.zipfile = zf # finally, close the buffer and release memory self.bufferzip.close() self.bufferzip = None def list_sbts(self): return [f for f in self.zipfile.namelist() if f.endswith(".sbt.json")] def __del__(self): self.close() class IPFSStorage(Storage): def __init__(self, pin_on_add=True, **kwargs): import ipfshttpclient self.ipfs_args = kwargs self.pin_on_add = pin_on_add self.api = ipfshttpclient.connect(**self.ipfs_args) def save(self, path, content, *, overwrite=False): new_obj = self.api.add_bytes(content) if self.pin_on_add: self.api.pin.add(new_obj) return new_obj # TODO: the above solution is quick and dirty. # we actually want something more organized, # like putting all the generated objects inside the same dir. # Check this call using the files API for an example. # api.files_write("/test/file", io.BytesIO(b"hi"), create=True) # # This is also required to bring the IPFSStorage closer to what the # ZipStorage is doing now. def load(self, path): return self.api.cat(path) def init_args(self): return self.ipfs_args def __exit__(self, type, value, traceback): # TODO: do nothing for now, # but we actually want something more organized, # like putting all the generated objects inside the same dir. # Use the files API, # add files without flush(), # and then flush it here? pass class RedisStorage(Storage): def __init__(self, **kwargs): import redis self.redis_args = kwargs self.conn = redis.Redis(**self.redis_args) def save(self, path, content, *, overwrite=False): if not isinstance(content, bytes): content = bytes(content) self.conn.set(path, content) return path def load(self, path): return self.conn.get(path) def init_args(self): # TODO: do we want to remove stuff like password from here? return self.redis_args def __exit__(self, type, value, traceback): pass sourmash-bio-sourmash-bc3c0f8/src/sourmash/sbtmh.py000066400000000000000000000042041474157046300226050ustar00rootroot00000000000000from io import BytesIO import sys from .sbt import Leaf, SBT, GraphFactory from . import signature def load_sbt_index(filename, *, print_version_warning=True, cache_size=None): "Load and return an SBT index." return SBT.load( filename, leaf_loader=SigLeaf.load, print_version_warning=print_version_warning, cache_size=cache_size, ) def create_sbt_index(bloom_filter_size=1e5, n_children=2): "Create an empty SBT index." factory = GraphFactory(1, bloom_filter_size, 4) tree = SBT(factory, d=n_children) return tree def search_sbt_index(tree, query, threshold): """\ Search an SBT index `tree` with signature `query` for matches above `threshold`. Usage: for match_sig, similarity in search_sbt_index(tree, query, threshold): ... """ for score, match, _ in tree.search(query, threshold=threshold, unload_data=True): yield match, score class SigLeaf(Leaf): def __str__(self): return f"**Leaf:{self.name} -> {self.metadata}" def make_manifest_row(self, loc): from .index import CollectionManifest row = CollectionManifest.make_manifest_row(self.data, loc, include_signature=0) return row def save(self, path): # this is here only for triggering the property load # before we reopen the file (and overwrite the previous # content...) self.data buf = signature.save_signatures_to_json([self.data], compression=1) return self.storage.save(path, buf) def update(self, parent): mh = self.data.minhash parent.data.update(mh) min_n_below = parent.metadata.get("min_n_below", sys.maxsize) min_n_below = min(len(mh), min_n_below) if min_n_below == 0: min_n_below = 1 parent.metadata["min_n_below"] = min_n_below @property def data(self): if self._data is None: buf = BytesIO(self.storage.load(self._path)) self._data = signature.load_one_signature_from_json(buf) return self._data @data.setter def data(self, new_data): self._data = new_data sourmash-bio-sourmash-bc3c0f8/src/sourmash/search.py000066400000000000000000001023551474157046300227430ustar00rootroot00000000000000""" Code for searching collections of signatures. """ import csv import numpy as np from enum import Enum from dataclasses import dataclass from .minhash import MinHash from .signature import SourmashSignature from .sketchcomparison import FracMinHashComparison, NumMinHashComparison def calc_threshold_from_bp(threshold_bp, scaled, query_size): """ Convert threshold_bp (threshold in estimated bp) to fraction of query & minimum number of hashes needed. """ threshold = 0.0 n_threshold_hashes = 0 if threshold_bp: if threshold_bp < 0: raise TypeError("threshold_bp must be non-negative") # if we have a threshold_bp of N, then that amounts to N/scaled # hashes: n_threshold_hashes = float(threshold_bp) / scaled # that then requires the following containment: threshold = n_threshold_hashes / query_size # is it too high to ever match? if threshold > 1.0: raise ValueError("requested threshold_bp is unattainable with this query") return threshold, n_threshold_hashes class SearchType(Enum): JACCARD = 1 CONTAINMENT = 2 MAX_CONTAINMENT = 3 def make_jaccard_search_query( *, do_containment=False, do_max_containment=False, best_only=False, threshold=None ): """\ Make a "flat" search object for Jaccard search & containment. """ if do_containment and do_max_containment: raise TypeError("'do_containment' and 'do_max_containment' cannot both be True") # configure search - containment? ignore abundance? best only? search_cls = JaccardSearch if best_only: search_cls = JaccardSearchBestOnly if do_containment: search_obj = search_cls(SearchType.CONTAINMENT, threshold) elif do_max_containment: search_obj = search_cls(SearchType.MAX_CONTAINMENT, threshold) else: search_obj = search_cls(SearchType.JACCARD, threshold) return search_obj def make_containment_query(query_mh, threshold_bp, *, best_only=True): "Make a search object for containment, with threshold_bp." if not query_mh: raise ValueError("query is empty!?") scaled = query_mh.scaled if not scaled: raise TypeError("query signature must be calculated with scaled") # are we setting a threshold? threshold, _ = calc_threshold_from_bp(threshold_bp, scaled, len(query_mh)) if best_only: search_obj = JaccardSearchBestOnly(SearchType.CONTAINMENT, threshold=threshold) else: search_obj = JaccardSearch(SearchType.CONTAINMENT, threshold=threshold) return search_obj class JaccardSearch: """ A class used by Index classes for searching/gathering. """ def __init__(self, search_type, threshold=None): "Constructor. Takes type of search, and optional threshold." score_fn = None require_scaled = False if search_type == SearchType.JACCARD: score_fn = self.score_jaccard elif search_type == SearchType.CONTAINMENT: score_fn = self.score_containment require_scaled = True elif search_type == SearchType.MAX_CONTAINMENT: score_fn = self.score_max_containment require_scaled = True self.score_fn = score_fn self.require_scaled = require_scaled if threshold is None: threshold = 0 self.threshold = float(threshold) def check_is_compatible(self, sig): """ Is this query compatible with this type of search? Raise TypeError if not. """ if self.require_scaled: if not sig.minhash.scaled: raise TypeError("this search requires a scaled signature") if sig.minhash.track_abundance: raise TypeError("this search cannot be done with an abund signature") def passes(self, score): """Return True if this score meets or exceeds the threshold. Note: this can be used whenever a score or estimate is available (e.g. internal nodes on an SBT). `collect(...)`, below, decides whether a particular signature should be collected, and/or can update the threshold (used for BestOnly behavior). """ if score and score >= self.threshold: return True return False def collect(self, score, match_sig): "Return True if this match should be collected." return True def score_jaccard(self, query_size, shared_size, subject_size, total_size): "Calculate Jaccard similarity." if total_size == 0: return 0 return shared_size / total_size def score_containment(self, query_size, shared_size, subject_size, total_size): "Calculate Jaccard containment." if query_size == 0: return 0 return shared_size / query_size def score_max_containment(self, query_size, shared_size, subject_size, total_size): "Calculate Jaccard max containment." min_denom = min(query_size, subject_size) if min_denom == 0: return 0 return shared_size / min_denom class JaccardSearchBestOnly(JaccardSearch): "A subclass of JaccardSearch that implements best-only." def collect(self, score, match): "Raise the threshold to the best match found so far." self.threshold = max(self.threshold, score) return True @dataclass class BaseResult: """ Base class for sourmash search results. Since we need some additional info (scaled vs num minhashes) to properly initialize a SketchComparison, this class doesn't actually do anything other than define some functions needed by *Result classes. """ query: SourmashSignature match: SourmashSignature filename: str = None ignore_abundance: bool = False # optionally ignore abundances # need these for scaled result comparisons estimate_ani_ci: bool = False ani_confidence: float = 0.95 threshold_bp: int = None cmp_scaled: int = None write_cols: list = None potential_false_negative: bool = False def init_result(self): self.mh1 = self.query.minhash self.mh2 = self.match.minhash def build_fracminhashcomparison(self): self.cmp = FracMinHashComparison( self.mh1, self.mh2, cmp_scaled=self.cmp_scaled, threshold_bp=self.threshold_bp, ignore_abundance=self.ignore_abundance, estimate_ani_ci=self.estimate_ani_ci, ani_confidence=self.ani_confidence, ) self.cmp_scaled = self.cmp.cmp_scaled self.query_scaled = self.mh1.scaled self.match_scaled = self.mh2.scaled self.size_may_be_inaccurate = self.cmp.size_may_be_inaccurate def build_numminhashcomparison(self, cmp_num=None): self.cmp = NumMinHashComparison( self.mh1, self.mh2, cmp_num=cmp_num, ignore_abundance=self.ignore_abundance ) self.cmp_num = self.cmp.cmp_num self.query_num = self.mh1.num self.match_num = self.mh2.num self.size_may_be_inaccurate = self.cmp.size_may_be_inaccurate def get_cmpinfo(self): # grab signature /minhash metadata # note, with so few columns written for current SearchResult, we could move these to PrefetchResult initialization # I've left here for now because I think at some point we want to output more info from search.. self.ksize = self.mh1.ksize self.moltype = self.mh1.moltype self.query_name = self.query.name self.query_filename = self.query.filename self.query_md5 = self.query.md5sum() self.match_name = self.match.name self.match_filename = self.match.filename # sometimes filename is not set in sig (match_filename is None), # and `search` is able to pass in the filename. if self.filename is None and self.match_filename is not None: self.filename = self.match_filename self.match_md5 = self.match.md5sum() # set these from self.match_* self.md5 = self.match_md5 self.name = self.match_name # could define in PrefetchResult instead, same reasoning as above self.query_abundance = self.mh1.track_abundance self.match_abundance = self.mh2.track_abundance self.query_n_hashes = len(self.mh1) self.match_n_hashes = len(self.mh2) @property def pass_threshold(self): return self.cmp.pass_threshold def shorten_md5(self, md5): return md5[:8] def to_write(self, columns=[]): # convert comparison attrs into a dictionary # that can be used by csv dictwriter info = { k: v for k, v in self.__dict__.items() if k in columns and v is not None } return info def init_dictwriter(self, csv_handle): # initialize the csv, return writer (do once) w = csv.DictWriter(csv_handle, fieldnames=self.write_cols) w.writeheader() return w def prep_result(self): # shorten or modify any columns that need modifying self.query_md5 = self.shorten_md5(self.query_md5) def write(self, w): # write result dictionary using csv dictwriter self.prep_result() w.writerow(self.to_write(columns=w.fieldnames)) @property def resultdict(self): # instead of writing, just return dictionary of what we want to write self.prep_result() return self.to_write(columns=self.write_cols) @dataclass class SearchResult(BaseResult): """ SearchResult class supports 'sourmash search' operations. """ similarity: float = None cmp_num: int = None searchtype: SearchType = None # columns for standard SearchResult output search_write_cols = [ "similarity", "md5", "filename", "name", # here we use 'filename' "query_filename", "query_name", "query_md5", "ani", ] ci_cols = ["ani_low", "ani_high"] search_write_cols_ci = search_write_cols + ci_cols def init_sigcomparison(self): self.init_result() if any([self.mh1.scaled, self.mh2.scaled]): self.build_fracminhashcomparison() elif any([self.mh1.num, self.mh2.num]): self.build_numminhashcomparison(cmp_num=self.cmp_num) self.get_cmpinfo() # grab comparison metadata def __post_init__(self): self.init_sigcomparison() # build sketch comparison self.check_similarity() if self.cmp_scaled is not None and self.searchtype is not None: self.estimate_search_ani() # define columns we want to write self.write_cols = self.search_write_cols if self.estimate_ani_ci: self.write_cols = self.search_write_cols_ci def check_similarity(self): # for now, require similarity for SearchResult # future: consider returning SearchResult *during* search, and passing SearchType in. # then allow similarity to be calculated here according to SearchType. if self.similarity is None: raise ValueError("Error: Must provide 'similarity' for SearchResult.") def estimate_search_ani(self): # future: could estimate ANI from abund searches if we want (use query containment?) if self.cmp_scaled is None: raise TypeError("Error: ANI can only be estimated from scaled signatures.") if self.searchtype == SearchType.CONTAINMENT: self.cmp.estimate_ani_from_mh1_containment_in_mh2( containment=self.similarity ) self.ani = self.cmp.ani_from_mh1_containment_in_mh2 if self.estimate_ani_ci: self.ani_low = self.cmp.ani_from_mh1_containment_in_mh2_low self.ani_high = self.cmp.ani_from_mh1_containment_in_mh2_high elif self.searchtype == SearchType.MAX_CONTAINMENT: self.cmp.estimate_max_containment_ani() self.ani = self.cmp.max_containment_ani if self.estimate_ani_ci: self.ani_low = self.cmp.max_containment_ani_low self.ani_high = self.cmp.max_containment_ani_high elif self.searchtype == SearchType.JACCARD: self.cmp.estimate_jaccard_ani(jaccard=self.similarity) self.jaccard_ani_untrustworthy = self.cmp.jaccard_ani_untrustworthy self.ani = self.cmp.jaccard_ani # this can be set from any of the above self.potential_false_negative = self.cmp.potential_false_negative @dataclass class PrefetchResult(BaseResult): """ PrefetchResult class supports 'sourmash prefetch' operations. """ # current prefetch columns prefetch_write_cols = [ "intersect_bp", "jaccard", "max_containment", "f_query_match", "f_match_query", "match_filename", "match_name", # here we use 'match_filename' "match_md5", "match_bp", "query_filename", "query_name", "query_md5", "query_bp", "ksize", "moltype", "scaled", "query_n_hashes", "query_abundance", "query_containment_ani", "match_containment_ani", "average_containment_ani", "max_containment_ani", "potential_false_negative", ] #'match_abundance' ci_cols = [ "query_containment_ani_low", "query_containment_ani_high", "match_containment_ani_low", "match_containment_ani_high", ] prefetch_write_cols_ci = prefetch_write_cols + ci_cols def init_sigcomparison(self): # shared prefetch/gather initialization self.init_result() if all([self.mh1.scaled, self.mh2.scaled]): self.build_fracminhashcomparison() else: raise TypeError( "Error: prefetch and gather results must be between scaled signatures." ) self.get_cmpinfo() # grab comparison metadata self.intersect_bp = self.cmp.total_unique_intersect_hashes self.max_containment = self.cmp.max_containment self.query_bp = self.mh1.unique_dataset_hashes self.match_bp = self.mh2.unique_dataset_hashes self.threshold = self.threshold_bp self.estimate_containment_ani() def estimate_containment_ani(self): self.cmp.estimate_all_containment_ani() self.query_containment_ani = self.cmp.ani_from_mh1_containment_in_mh2 self.match_containment_ani = self.cmp.ani_from_mh2_containment_in_mh1 self.average_containment_ani = self.cmp.avg_containment_ani self.max_containment_ani = self.cmp.max_containment_ani self.potential_false_negative = self.cmp.potential_false_negative if self.estimate_ani_ci: self.handle_ani_ci() def handle_ani_ci(self): self.query_containment_ani_low = self.cmp.ani_from_mh1_containment_in_mh2_low self.query_containment_ani_high = self.cmp.ani_from_mh1_containment_in_mh2_high self.match_containment_ani_low = self.cmp.ani_from_mh2_containment_in_mh1_low self.match_containment_ani_high = self.cmp.ani_from_mh2_containment_in_mh1_high def build_prefetch_result(self): # unique prefetch values self.jaccard = self.cmp.jaccard self.f_query_match = ( self.cmp.mh2_containment_in_mh1 ) # db_mh.contained_by(query_mh) self.f_match_query = ( self.cmp.mh1_containment_in_mh2 ) # query_mh.contained_by(db_mh) # set write columns for prefetch result self.write_cols = self.prefetch_write_cols if self.estimate_ani_ci: self.write_cols = self.prefetch_write_cols_ci def __post_init__(self): self.init_sigcomparison() self.build_prefetch_result() def prep_prefetch_result(self): # explicitly name so we can use this within GatherResult too self.scaled = self.cmp_scaled # in prefetch, we shorten all md5's self.query_md5 = self.shorten_md5(self.query_md5) self.md5 = self.shorten_md5(self.md5) self.match_md5 = self.shorten_md5(self.match_md5) def prep_result(self): # overwrite base prep_result self.prep_prefetch_result() @property def prefetchresultdict(self): # just return dictionary of what we want to write self.prep_prefetch_result() return self.to_write(columns=self.write_cols) @dataclass class GatherResult(PrefetchResult): gather_querymh: MinHash = None gather_result_rank: int = None orig_query_len: int = None orig_query_abunds: list = None sum_weighted_found: int = None total_weighted_hashes: int = None noident_len: int = 0 gather_write_cols = [ "intersect_bp", "f_orig_query", "f_match", "f_unique_to_query", "f_unique_weighted", "average_abund", "median_abund", "std_abund", "filename", "name", "md5", "f_match_orig", "unique_intersect_bp", "gather_result_rank", "remaining_bp", "query_filename", "query_name", "query_md5", "query_bp", "ksize", "moltype", "scaled", "query_n_hashes", "query_abundance", "query_containment_ani", "match_containment_ani", "average_containment_ani", "max_containment_ani", "potential_false_negative", "n_unique_weighted_found", "sum_weighted_found", "total_weighted_hashes", ] ci_cols = [ "query_containment_ani_low", "query_containment_ani_high", "match_containment_ani_low", "match_containment_ani_high", ] gather_write_cols_ci = gather_write_cols + ci_cols def init_gathersketchcomparison(self): # compare remaining gather hashes with match. Force at cmp_scaled. Force match flatten(), bc we don't need abunds. self.gather_comparison = FracMinHashComparison( self.gather_querymh, self.match.minhash.flatten() ) def check_gatherresult_input(self): # check we have what we need: if self.cmp_scaled is None: raise ValueError( "Error: must provide comparison scaled value ('cmp_scaled') for GatherResult" ) if self.gather_querymh is None: raise ValueError( "Error: must provide current gather sketch (remaining hashes) for GatherResult" ) if self.gather_result_rank is None: raise ValueError("Error: must provide 'gather_result_rank' to GatherResult") if not self.total_weighted_hashes: # catch total_weighted_hashes = 0 as well raise ValueError( "Error: must provide sum of all abundances ('total_weighted_hashes') to GatherResult" ) if not self.orig_query_abunds: raise ValueError( "Error: must provide original query abundances ('orig_query_abunds') to GatherResult" ) def build_gather_result(self): # build gather-specific attributes # the 'query' that is passed into gather is all _matched_ hashes, after subtracting noident_mh # this affects estimation of original query information, and requires us to pass in orig_query_len and orig_query_abunds. # we also need to overwrite self.query_bp, self.query_n_hashes, and self.query_abundance # todo: find a better solution? self.query_bp = self.orig_query_len * self.query.minhash.scaled self.query_n_hashes = self.orig_query_len # calculate intersection with query hashes: self.unique_intersect_bp = self.gather_comparison.total_unique_intersect_hashes # calculate fraction of subject match with orig query self.f_match_orig = self.cmp.mh2_containment_in_mh1 # calculate fractions wrt first denominator - genome size self.f_match = ( self.gather_comparison.mh2_containment_in_mh1 ) # unique match containment self.f_orig_query = len(self.cmp.intersect_mh) / self.orig_query_len assert ( self.gather_comparison.intersect_mh.contained_by( self.gather_comparison.mh1_cmp ) == 1.0 ) # calculate fractions wrt second denominator - metagenome size assert ( self.gather_comparison.intersect_mh.contained_by( self.gather_comparison.mh2_cmp ) == 1.0 ) self.f_unique_to_query = ( len(self.gather_comparison.intersect_mh) / self.orig_query_len ) # here, need to make sure to use the mh1_cmp (bc was downsampled to cmp_scaled) self.remaining_bp = ( self.noident_len + self.gather_comparison.mh1_cmp.unique_dataset_hashes - self.gather_comparison.total_unique_intersect_hashes ) # calculate stats on abundances, if desired. self.average_abund, self.median_abund, self.std_abund = None, None, None if not self.ignore_abundance: self.query_weighted_unique_intersection = ( self.gather_comparison.weighted_intersection( from_abundD=self.orig_query_abunds ) ) self.average_abund = self.query_weighted_unique_intersection.mean_abundance self.median_abund = self.query_weighted_unique_intersection.median_abundance self.std_abund = self.query_weighted_unique_intersection.std_abundance # 'query' will be flattened by default. reset track abundance if we have abunds self.query_abundance = ( self.query_weighted_unique_intersection.track_abundance ) # calculate scores weighted by abundances self.n_unique_weighted_found = ( self.query_weighted_unique_intersection.sum_abundances ) self.f_unique_weighted = ( self.n_unique_weighted_found / self.total_weighted_hashes ) else: self.f_unique_weighted = self.f_unique_to_query self.query_abundance = False def __post_init__(self): self.check_gatherresult_input() self.init_sigcomparison() # initialize original sketch vs match sketch comparison (inherited from PrefetchResult) self.init_gathersketchcomparison() # initialize remaining gather sketch vs match sketch comparison self.build_gather_result() # build gather-specific attributes # set write columns for prefetch result self.write_cols = self.gather_write_cols if self.estimate_ani_ci: self.write_cols = self.gather_write_cols_ci def prep_gather_result(self): # for gather, we only shorten the query_md5 self.scaled = self.cmp_scaled self.query_md5 = self.shorten_md5(self.query_md5) def prep_result(self): # overwrite base prep_result self.prep_gather_result() @property def gatherresultdict(self): # just return dictionary of what we want to write self.prep_gather_result() return self.to_write(columns=self.write_cols) @property def prefetchresultdict(self): # get all prefetch cols from gatherresult prefetch_cols = self.prefetch_write_cols if self.estimate_ani_ci: prefetch_cols = self.prefetch_write_cols_ci self.jaccard = self.cmp.jaccard self.f_query_match = ( self.cmp.mh2_containment_in_mh1 ) # db_mh.contained_by(query_mh) self.f_match_query = ( self.cmp.mh1_containment_in_mh2 ) # query_mh.contained_by(db_mh) self.prep_prefetch_result() return self.to_write(columns=prefetch_cols) def format_bp(bp): "Pretty-print bp information." bp = float(bp) if bp < 500: return f"{bp:.0f} bp" elif bp <= 500e3: return f"{round(bp / 1e3, 1):.1f} kbp" elif bp < 500e6: return f"{round(bp / 1e6, 1):.1f} Mbp" elif bp < 500e9: return f"{round(bp / 1e9, 1):.1f} Gbp" return "???" def search_databases_with_flat_query(query, databases, **kwargs): results = [] found_md5 = set() for db in databases: search_iter = db.search(query, **kwargs) for score, match, filename in search_iter: md5 = match.md5sum() if md5 not in found_md5: results.append((score, match, filename)) found_md5.add(md5) # sort results on similarity (reverse) results.sort(key=lambda x: -x[0]) # redefine searchtype and pass in here # repetitive/not optimal - would it be better to produce SearchResult from db.search? estimate_ani_ci = False search_type = SearchType.JACCARD if kwargs.get("do_containment"): search_type = SearchType.CONTAINMENT if kwargs.get("estimate_ani_ci"): estimate_ani_ci = True elif kwargs.get("do_max_containment"): search_type = SearchType.MAX_CONTAINMENT if kwargs.get("estimate_ani_ci"): estimate_ani_ci = True x = [] for score, match, filename in results: x.append( SearchResult( query, match, similarity=score, filename=filename, searchtype=search_type, estimate_ani_ci=estimate_ani_ci, ) ) return x def search_databases_with_abund_query(query, databases, **kwargs): results = [] found_md5 = set() if kwargs.get("do_containment") or kwargs.get("do_max_containment"): raise TypeError("containment searches cannot be done with abund sketches") for db in databases: search_iter = db.search_abund( query, **kwargs ) # could return SearchResult here instead of tuple? for score, match, filename in search_iter: md5 = match.md5sum() if md5 not in found_md5: results.append((score, match, filename)) found_md5.add(md5) # sort results on similarity (reverse) results.sort(key=lambda x: -x[0]) x = [] for score, match, filename in results: x.append(SearchResult(query, match, similarity=score, filename=filename)) return x ### ### gather code ### def _find_best(counters, query, threshold_bp): """ Search for the best containment, return precisely one match. """ best_result = None best_intersect_mh = None # find the best score across multiple counters, without consuming for counter in counters: result = counter.peek(query.minhash, threshold_bp=threshold_bp) if result: (sr, intersect_mh) = result if best_result is None or sr.score > best_result.score: best_result = sr best_intersect_mh = intersect_mh if best_result: # remove the best result from each counter for counter in counters: counter.consume(best_intersect_mh) # and done! return best_result, best_intersect_mh return None, None class GatherDatabases: "Iterator object for doing gather/min-set-cov." def __init__( self, query, counters, *, threshold_bp=0, ignore_abundance=False, noident_mh=None, ident_mh=None, estimate_ani_ci=False, ): # track original query information for later usage? track_abundance = query.minhash.track_abundance and not ignore_abundance self.orig_query = query self.orig_query_bp = query.minhash.unique_dataset_hashes self.orig_query_filename = query.filename self.orig_query_name = query.name self.orig_query_md5 = query.md5sum()[:8] # do we pay attention to abundances? query_mh = query.minhash query_hashes = query_mh.hashes if track_abundance: orig_query_abunds = query_hashes else: orig_query_abunds = {k: 1 for k in query_hashes} # adjust for not found... if noident_mh is None: # create empty noident_mh = query_mh.copy_and_clear() self.noident_mh = noident_mh.to_frozen() if ident_mh is None: query_mh = query_mh.to_mutable() query_mh.remove_many(noident_mh) else: query_mh = ident_mh.to_mutable() orig_query_mh = query_mh.flatten() # query.minhash will be assigned to repeatedly in gather; make mutable. query = query.to_mutable() query.minhash = orig_query_mh cmp_scaled = query.minhash.scaled # initialize with resolution of query self.result_n = 0 self.query = query self.counters = counters self.threshold_bp = threshold_bp self.track_abundance = track_abundance self.orig_query_mh = orig_query_mh self.orig_query_abunds = orig_query_abunds self.cmp_scaled = 0 # initialize with something very low! self._update_scaled(cmp_scaled) self.estimate_ani_ci = ( estimate_ani_ci # by default, do not report ANI confidence intervals ) def _update_scaled(self, scaled): max_scaled = max(self.cmp_scaled, scaled) if self.cmp_scaled != max_scaled: self.cmp_scaled = max_scaled # CTB note: this can be expensive self.orig_query_mh = self.orig_query_mh.downsample(scaled=scaled) self.noident_mh = self.noident_mh.downsample(scaled=scaled) # NOTE: orig_query_abunds can be used w/o downsampling orig_query_abunds = self.orig_query_abunds self.noident_query_sum_abunds = sum( orig_query_abunds[k] for k in self.noident_mh.hashes ) self.total_weighted_hashes = sum( orig_query_abunds[k] for k in self.orig_query_mh.hashes ) self.total_weighted_hashes += self.noident_query_sum_abunds if max_scaled != scaled: return max_scaled return max_scaled @property def scaled(self): return self.cmp_scaled def __iter__(self): return self def __next__(self): query = self.query if not self.query.minhash: raise StopIteration # may be changed: counters = self.counters # will not be changed:: threshold_bp = self.threshold_bp orig_query_abunds = self.orig_query_abunds # find the best match! best_result, intersect_mh = _find_best(counters, query, threshold_bp) if not best_result: # no matches at all for this cutoff! raise StopIteration best_match = best_result.signature filename = best_result.location # Is the best match computed with scaled? Die if not. match_scaled = best_match.minhash.scaled assert match_scaled # pick the highest scaled / lowest resolution. scaled = self._update_scaled(match_scaled) # CTB note: this means that if a high scaled/low res signature is # found early on, resolution will be low from then on. # retrieve various saved things, after potential downsampling orig_query_mh = self.orig_query_mh total_weighted_hashes = self.total_weighted_hashes noident_mh = self.noident_mh orig_query_len = len(orig_query_mh) + len(noident_mh) # eliminate hashes under this new resolution. query_mh = query.minhash.downsample(scaled=scaled) found_mh = best_match.minhash.downsample(scaled=scaled).flatten() # construct a new query, subtracting hashes found in previous one. new_query_mh = query_mh.to_mutable() new_query_mh.remove_many(found_mh) new_query = SourmashSignature(new_query_mh) # compute weighted information for remaining query hashes query_hashes = set(new_query_mh.hashes) n_weighted_missed = sum(orig_query_abunds[k] for k in query_hashes) n_weighted_missed += self.noident_query_sum_abunds sum_weighted_found = total_weighted_hashes - n_weighted_missed # build a GatherResult result = GatherResult( self.orig_query, best_match, cmp_scaled=scaled, filename=filename, gather_result_rank=self.result_n, gather_querymh=query.minhash, ignore_abundance=not self.track_abundance, threshold_bp=threshold_bp, orig_query_len=orig_query_len, orig_query_abunds=self.orig_query_abunds, estimate_ani_ci=self.estimate_ani_ci, sum_weighted_found=sum_weighted_found, total_weighted_hashes=total_weighted_hashes, noident_len=len(self.noident_mh) * self.noident_mh.scaled, ) self.result_n += 1 self.query = new_query return result ### ### prefetch code ### def prefetch_database(query, database, threshold_bp, *, estimate_ani_ci=False): """ Find all matches to `query_mh` >= `threshold_bp` in `database`. """ scaled = query.minhash.scaled assert scaled # iterate over all signatures in database, find matches for result in database.prefetch( query, threshold_bp ): # future: could return PrefetchResult directly here result = PrefetchResult( query, result.signature, threshold_bp=threshold_bp, estimate_ani_ci=estimate_ani_ci, ) assert result.pass_threshold yield result sourmash-bio-sourmash-bc3c0f8/src/sourmash/sig/000077500000000000000000000000001474157046300217005ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/sig/__init__.py000066400000000000000000000001211474157046300240030ustar00rootroot00000000000000from .__main__ import * # bring all functions into top-level from . import grep sourmash-bio-sourmash-bc3c0f8/src/sourmash/sig/__main__.py000066400000000000000000001500001474157046300237660ustar00rootroot00000000000000""" Command-line entry point for 'python -m sourmash.sig' """ __all__ = [ "cat", "split", "describe", "manifest", "overlap", "merge", "intersect", "inflate", "subtract", "rename", "extract", "filter", "flatten", "downsample", "ingest", "export", "kmers", "fileinfo", "check", "collect", ] import sys import csv import json import os from collections import defaultdict, namedtuple, Counter import re import screed import sourmash from sourmash.sourmash_args import FileOutput from sourmash.logging import ( set_quiet, error, notify, print_results, debug, debug_literal, _debug, ) from sourmash import sourmash_args from sourmash.minhash import _get_max_hash_for_scaled from sourmash.manifest import CollectionManifest usage = """ sourmash signature [] - manipulate/work with signature files. ** Commands can be: cat [ ... ] - concatenate all signatures describe [ ... ] - show details of signature downsample [ ... ] - downsample one or more signatures extract [ ... ] - extract one or more signatures filter [ ... ] - filter k-mers on abundance flatten [ ... ] - remove abundances intersect [ ...] - intersect one or more signatures manifest - build a manifest merge [ ...] - merge one or more signatures rename - rename signature split [ ...] - split signatures into single files subtract [...] - subtract one or more signatures import [ ... ] - import a mash or other signature export - export a signature, e.g. to mash overlap - see detailed comparison of sigs check --picklist ... - check picklist against (many) sigs collect -o manifest.sqlmf - collect sigs metadata into manifest ** Use '-h' to get subcommand-specific help, e.g. sourmash signature merge -h """ def _check_abundance_compatibility(sig1, sig2): if sig1.minhash.track_abundance != sig2.minhash.track_abundance: raise ValueError( f"incompatible signatures: track_abundance is {sig1.minhash.track_abundance} in first sig, {sig2.minhash.track_abundance} in second" ) def _extend_signatures_with_from_file(args, *, target_attr="signatures"): # extend input signatures with --from-file if args.from_file: more_files = sourmash_args.load_pathlist_from_file(args.from_file) sigs = list(getattr(args, target_attr)) sigs.extend(more_files) setattr(args, target_attr, sigs) def _set_num_scaled(mh, num, scaled): "set num and scaled values on a MinHash object" mh_params = list(mh.__getstate__()) # Number of hashes is 0th parameter mh_params[0] = num # Scale is 10th parameter mh_params[10] = _get_max_hash_for_scaled(scaled) mh.__setstate__(mh_params) assert mh.num == num assert mh.scaled == scaled ##### actual command line functions def cat(args): """ concatenate all signatures into one file. """ set_quiet(args.quiet, args.debug) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) pattern_search = sourmash_args.load_include_exclude_db_patterns(args) encountered_md5sums = defaultdict(int) # used by --unique # open output for saving sigs save_sigs = sourmash_args.SaveSignaturesToLocation(args.output) save_sigs.open() _extend_signatures_with_from_file(args) # start loading! progress = sourmash_args.SignatureLoadingProgress() loader = sourmash_args.load_many_signatures( args.signatures, ksize=args.ksize, moltype=moltype, picklist=picklist, progress=progress, yield_all_files=args.force, force=args.force, pattern=pattern_search, ) for ss, sigloc in loader: md5 = ss.md5sum() encountered_md5sums[md5] += 1 if args.unique and encountered_md5sums[md5] > 1: continue save_sigs.add(ss) notify(f"loaded {len(save_sigs)} signatures total.") if picklist: sourmash_args.report_picklist(args, picklist) save_sigs.close() notify(f"output {len(save_sigs)} signatures") multiple_md5 = [1 for cnt in encountered_md5sums.values() if cnt > 1] if multiple_md5: notify(f"encountered {sum(multiple_md5)} MinHashes multiple times") if args.unique: notify("...and removed the duplicates, because --unique was specified.") def split(args): """ split all signatures into individual files """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) _extend_signatures_with_from_file(args) output_names = set() output_scaled_template = ( "{md5sum}.k={ksize}.scaled={scaled}.{moltype}.dup={dup}.{basename}" + args.extension ) output_num_template = ( "{md5sum}.k={ksize}.num={num}.{moltype}.dup={dup}.{basename}" + args.extension ) if args.output_dir: if not os.path.exists(args.output_dir): notify(f"Creating --output-dir {args.output_dir}") os.mkdir(args.output_dir) progress = sourmash_args.SignatureLoadingProgress() loader = sourmash_args.load_many_signatures( args.signatures, ksize=args.ksize, moltype=moltype, picklist=picklist, progress=progress, yield_all_files=args.force, force=args.force, ) for sig, sigloc in loader: # save each file individually -- md5sum = sig.md5sum()[:8] minhash = sig.minhash basename = os.path.basename(sig.filename) if not basename or basename == "-": basename = "none" params = dict( basename=basename, md5sum=md5sum, scaled=minhash.scaled, ksize=minhash.ksize, num=minhash.num, moltype=minhash.moltype, ) if minhash.scaled: output_template = output_scaled_template else: # num assert minhash.num output_template = output_num_template # figure out if this is duplicate, build unique filename n = 0 params["dup"] = n output_name = output_template.format(**params) while output_name in output_names: params["dup"] = n output_name = output_template.format(**params) n += 1 output_names.add(output_name) if args.output_dir: output_name = os.path.join(args.output_dir, output_name) if os.path.exists(output_name): notify(f"** overwriting existing file {format(output_name)}") # save! with sourmash_args.SaveSignaturesToLocation(output_name) as save_sigs: save_sigs.add(sig) notify(f"writing sig to {output_name}") notify(f"loaded and split {len(progress)} signatures total.") if picklist: sourmash_args.report_picklist(args, picklist) def describe(args): """ provide basic info on signatures """ set_quiet(args.quiet, args.debug) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) pattern_search = sourmash_args.load_include_exclude_db_patterns(args) _extend_signatures_with_from_file(args) # write CSV? w = None csv_obj = None if args.csv: csv_obj = sourmash_args.FileOutputCSV(args.csv) csv_fp = csv_obj.open() w = csv.DictWriter( csv_fp, [ "signature_file", "md5", "ksize", "moltype", "num", "scaled", "n_hashes", "seed", "with_abundance", "name", "filename", "license", "sum_hashes", ], extrasaction="ignore", ) w.writeheader() # start loading! progress = sourmash_args.SignatureLoadingProgress() loader = sourmash_args.load_many_signatures( args.signatures, ksize=args.ksize, moltype=moltype, picklist=picklist, progress=progress, yield_all_files=args.force, force=args.force, pattern=pattern_search, ) for sig, location in loader: # extract info, write as appropriate. signature_file = location mh = sig.minhash ksize = mh.ksize moltype = mh.moltype scaled = mh.scaled num = mh.num seed = mh.seed n_hashes = len(mh) sum_hashes = sum(mh.hashes.values()) with_abundance = 0 if mh.track_abundance: with_abundance = 1 md5 = sig.md5sum() name = sig.name p_name = name or "** no name **" filename = sig.filename p_filename = filename or "** no name **" license = sig.license if w: w.writerow(locals()) print_results( """\ --- signature filename: {location} signature: {p_name} source file: {p_filename} md5: {md5} k={ksize} molecule={moltype} num={num} scaled={scaled} seed={seed} track_abundance={with_abundance} size: {n_hashes} sum hashes: {sum_hashes} signature license: {license} """, **locals(), ) if csv_obj: csv_obj.close() if picklist: sourmash_args.report_picklist(args, picklist) def manifest(args): """ build a signature manifest """ set_quiet(args.quiet, args.debug) try: loader = sourmash_args.load_file_as_index( args.location, yield_all_files=args.force ) except ValueError: error(f"Cannot open '{args.location}' as a sourmash signature collection.") error("Use -d/--debug for details.") sys.exit(-1) rebuild = True if args.no_rebuild_manifest: debug("sig manifest: not forcing rebuild.") rebuild = False else: debug("sig manifest: forcing rebuild.") manifest = sourmash_args.get_manifest(loader, require=True, rebuild=rebuild) manifest._check_row_values() manifest.write_to_filename( args.output, database_format=args.manifest_format, ok_if_exists=args.force ) notify(f"manifest contains {len(manifest)} signatures total.") notify(f"wrote manifest to '{args.output}' ({args.manifest_format})") def overlap(args): """ provide detailed comparison of two signatures """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) sig1 = sourmash_args.load_one_signature( args.signature1, ksize=args.ksize, select_moltype=moltype ) sig2 = sourmash_args.load_one_signature( args.signature2, ksize=args.ksize, select_moltype=moltype ) notify(f"loaded one signature each from {args.signature1} and {args.signature2}") try: similarity = sig1.similarity(sig2) except ValueError: raise cont1 = sig1.contained_by(sig2) cont2 = sig2.contained_by(sig1) sig1_file = args.signature1 sig2_file = args.signature2 name1 = sig1.name name2 = sig2.name md5_1 = sig1.md5sum() md5_2 = sig2.md5sum() ksize = sig1.minhash.ksize moltype = sig1.minhash.moltype num = sig1.minhash.num size1 = len(sig1.minhash) size2 = len(sig2.minhash) scaled = sig1.minhash.scaled hashes_1 = set(sig1.minhash.hashes) hashes_2 = set(sig2.minhash.hashes) num_common = len(hashes_1 & hashes_2) disjoint_1 = len(hashes_1 - hashes_2) disjoint_2 = len(hashes_2 - hashes_1) num_union = len(hashes_1.union(hashes_2)) print( """\ first signature: signature filename: {sig1_file} signature: {name1} md5: {md5_1} k={ksize} molecule={moltype} num={num} scaled={scaled} second signature: signature filename: {sig2_file} signature: {name2} md5: {md5_2} k={ksize} molecule={moltype} num={num} scaled={scaled} similarity: {similarity:.5f} first contained in second: {cont1:.5f} second contained in first: {cont2:.5f} number of hashes in first: {size1} number of hashes in second: {size2} number of hashes in common: {num_common} only in first: {disjoint_1} only in second: {disjoint_2} total (union): {num_union} """.format(**locals()) ) def merge(args): """ merge one or more signatures. """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) _extend_signatures_with_from_file(args) first_sig = None mh = None # start loading! progress = sourmash_args.SignatureLoadingProgress() loader = sourmash_args.load_many_signatures( args.signatures, ksize=args.ksize, moltype=moltype, picklist=picklist, progress=progress, yield_all_files=args.force, force=args.force, ) for sigobj, sigloc in loader: # first signature? initialize a bunch of stuff if first_sig is None: first_sig = sigobj mh = first_sig.minhash.copy_and_clear() # forcibly remove abundance? if args.flatten: mh.track_abundance = False try: sigobj_mh = sigobj.minhash if not args.flatten: _check_abundance_compatibility(first_sig, sigobj) else: sigobj_mh.track_abundance = False mh.merge(sigobj_mh) except (TypeError, ValueError) as exc: error( "ERROR when merging signature '{}' ({}) from file {}", sigobj, sigobj.md5sum()[:8], sigloc, ) error(str(exc)) sys.exit(-1) if not len(progress): error("no signatures to merge!?") sys.exit(-1) merged_sigobj = sourmash.SourmashSignature(mh, name=args.set_name) with sourmash_args.SaveSignaturesToLocation(args.output) as save_sigs: save_sigs.add(merged_sigobj) notify(f"loaded and merged {len(progress)} signatures") if picklist: sourmash_args.report_picklist(args, picklist) def intersect(args): """ intersect one or more signatures by taking the intersection of hashes. This function always removes abundances unless -A specified. """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) _extend_signatures_with_from_file(args) first_sig = None mins = None # start loading! progress = sourmash_args.SignatureLoadingProgress() loader = sourmash_args.load_many_signatures( args.signatures, ksize=args.ksize, moltype=moltype, picklist=picklist, progress=progress, yield_all_files=args.force, force=args.force, ) for sigobj, sigloc in loader: if first_sig is None: first_sig = sigobj mins = set(sigobj.minhash.hashes) else: # check signature compatibility -- if no ksize/moltype specified # 'first_sig' may be incompatible with later sigs. if not sigobj.minhash.is_compatible(first_sig.minhash): error("incompatible minhashes; specify -k and/or molecule type.") sys.exit(-1) mins.intersection_update(sigobj.minhash.hashes) if first_sig is None: notify("no signatures provided to intersect!?") sys.exit(-1) # forcibly turn off track_abundance, unless --abundances-from set. intersect_mh = first_sig.minhash.copy_and_clear().flatten() intersect_mh.add_many(mins) # borrow abundances from a signature? if args.abundances_from: notify(f"loading signature from {args.abundances_from}, keeping abundances") abund_sig = sourmash_args.load_one_signature( args.abundances_from, ksize=args.ksize, select_moltype=moltype ) if not abund_sig.minhash.track_abundance: error("--track-abundance not set on loaded signature?! exiting.") sys.exit(-1) intersect_mh = intersect_mh.inflate(abund_sig.minhash) intersect_sigobj = sourmash.SourmashSignature(intersect_mh, name=args.set_name) with sourmash_args.SaveSignaturesToLocation(args.output) as save_sigs: save_sigs.add(intersect_sigobj) notify(f"loaded and intersected {len(progress)} signatures") if picklist: sourmash_args.report_picklist(args, picklist) def inflate(args): """ inflate one or more other signatures from the first. """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) inflate_sig = sourmash_args.load_query_signature( args.signature_from, ksize=args.ksize, select_moltype=moltype ) inflate_from_mh = inflate_sig.minhash ksize = inflate_from_mh.ksize moltype = inflate_from_mh.moltype if not inflate_from_mh.track_abundance: error(f"ERROR: signature '{inflate_sig.name}' from ") error(f"file '{args.signature_from}' has no abundances.") sys.exit(-1) # start loading! progress = sourmash_args.SignatureLoadingProgress() loader = sourmash_args.load_many_signatures( args.other_sigs, ksize=ksize, moltype=moltype, picklist=picklist, progress=progress, yield_all_files=args.force, force=args.force, ) with sourmash_args.SaveSignaturesToLocation(args.output) as save_sigs: for sigobj, sigloc in loader: inflated_mh = sigobj.minhash.inflate(inflate_from_mh) inflated_sigobj = sourmash.SourmashSignature(inflated_mh, name=sigobj.name) save_sigs.add(inflated_sigobj) if len(progress) == 0: error("no signatures to inflate!?") sys.exit(-1) notify(f"loaded and intersected {len(save_sigs)} signatures") if picklist: sourmash_args.report_picklist(args, picklist) def subtract(args): """ subtract one or more signatures from another """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) from_sigobj = sourmash_args.load_one_signature( args.signature_from, ksize=args.ksize, select_moltype=moltype ) if args.abundances_from: # it's ok to work with abund signatures if -A. args.flatten = True from_mh = from_sigobj.minhash if from_mh.track_abundance and not args.flatten: error("Cannot use subtract on signatures with abundance tracking, sorry!") sys.exit(1) subtract_mins = set(from_mh.hashes) notify(f"loaded signature from {args.signature_from}...", end="\r") progress = sourmash_args.SignatureLoadingProgress() for sigfile in args.subtraction_sigs: for sigobj in sourmash_args.load_file_as_signatures( sigfile, ksize=args.ksize, select_moltype=moltype, progress=progress ): if not sigobj.minhash.is_compatible(from_mh): error("incompatible minhashes; specify -k and/or molecule type.") sys.exit(-1) if sigobj.minhash.track_abundance and not args.flatten: error( "Cannot use subtract on signatures with abundance tracking, sorry!" ) sys.exit(1) subtract_mins -= set(sigobj.minhash.hashes) notify(f"loaded and subtracted signatures from {sigfile}...", end="\r") if not len(progress): error("no signatures to subtract!?") sys.exit(-1) # build new minhash with new mins subtract_mh = from_sigobj.minhash.copy_and_clear().flatten() subtract_mh.add_many(subtract_mins) # borrow abundances from somewhere? if args.abundances_from: notify(f"loading signature from {args.abundances_from}, keeping abundances") abund_sig = sourmash_args.load_one_signature( args.abundances_from, ksize=args.ksize, select_moltype=moltype ) if not abund_sig.minhash.track_abundance: error("--track-abundance not set on loaded signature?! exiting.") sys.exit(-1) subtract_mh = subtract_mh.inflate(abund_sig.minhash) subtract_sigobj = sourmash.SourmashSignature(subtract_mh, name=args.set_name) with sourmash_args.SaveSignaturesToLocation(args.output) as save_sigs: save_sigs.add(subtract_sigobj) notify(f"loaded and subtracted {len(progress)} signatures") def rename(args): """ rename one or more signatures. """ set_quiet(args.quiet, args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) pattern_search = sourmash_args.load_include_exclude_db_patterns(args) _extend_signatures_with_from_file(args) save_sigs = sourmash_args.SaveSignaturesToLocation(args.output) save_sigs.open() # start loading! progress = sourmash_args.SignatureLoadingProgress() loader = sourmash_args.load_many_signatures( args.signatures, ksize=args.ksize, moltype=moltype, picklist=picklist, progress=progress, yield_all_files=args.force, force=args.force, pattern=pattern_search, ) for sigobj, sigloc in loader: sigobj = sigobj.to_mutable() sigobj._name = args.name save_sigs.add(sigobj) save_sigs.close() notify(f"set name to '{args.name}' on {len(save_sigs)} signatures") if picklist: sourmash_args.report_picklist(args, picklist) def extract(args): """ extract signatures. """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) pattern_search = sourmash_args.load_include_exclude_db_patterns(args) _extend_signatures_with_from_file(args) # further filtering on md5 or name? filter_fn = None if args.md5 is not None or args.name is not None: def filter_fn(row): # match? keep = False if args.name: name = row["name"] or row["filename"] if args.name in name: keep = True if args.md5 and args.md5 in row["md5"]: keep = True return keep # ok! filtering defined, let's go forward save_sigs = sourmash_args.SaveSignaturesToLocation(args.output) save_sigs.open() # start loading! total_rows_examined = 0 for filename in args.signatures: idx = sourmash_args.load_file_as_index(filename, yield_all_files=args.force) idx = idx.select(ksize=args.ksize, moltype=moltype) idx = sourmash_args.apply_picklist_and_pattern(idx, picklist, pattern_search) manifest = sourmash_args.get_manifest(idx) total_rows_examined += len(manifest) # do the extra pattern matching on name/md5 that is part of 'extract'. # CTB: This should be deprecated and removed at some point, since # --include/--exclude now do the same thing. if filter_fn and not pattern_search: sub_manifest = manifest.filter_rows(filter_fn) sub_picklist = sub_manifest.to_picklist() try: idx = idx.select(picklist=sub_picklist) except ValueError: error( "** This input collection doesn't support 'extract' with picklists or patterns." ) error("** EXITING.") error("**") error("** You can use 'sourmash sig cat' with a picklist or pattern,") error("** and then pipe the output to 'sourmash sig extract") sys.exit(-1) for ss in idx.signatures(): save_sigs.add(ss) notify(f"loaded {total_rows_examined} total that matched ksize & molecule type") save_sigs.close() if not save_sigs: error("no matching signatures to save!") sys.exit(-1) notify(f"extracted {len(save_sigs)} signatures from {len(args.signatures)} file(s)") if picklist: sourmash_args.report_picklist(args, picklist) def filter(args): """ filter hashes by abundance in all of the signatures """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) progress = sourmash_args.SignatureLoadingProgress() save_sigs = sourmash_args.SaveSignaturesToLocation(args.output) save_sigs.open() for filename in args.signatures: siglist = sourmash_args.load_file_as_signatures( filename, ksize=args.ksize, select_moltype=moltype, progress=progress ) siglist = list(siglist) # select! if args.md5 is not None: siglist = [ss for ss in siglist if args.md5 in ss.md5sum()] if args.name is not None: siglist = [ss for ss in siglist if args.name in str(ss)] for ss in siglist: mh = ss.minhash if not mh.track_abundance: notify(f"ignoring signature {ss} - track_abundance not set.") continue abunds = mh.hashes abunds2 = {} for k, v in abunds.items(): if v >= args.min_abundance: if args.max_abundance is None or v <= args.max_abundance: abunds2[k] = v filtered_mh = mh.copy_and_clear() filtered_mh.set_abundances(abunds2) ss = ss.to_mutable() ss.minhash = filtered_mh save_sigs.add(ss) save_sigs.close() notify(f"loaded {len(progress)} total that matched ksize & molecule type") notify(f"extracted {len(save_sigs)} signatures from {len(args.signatures)} file(s)") def flatten(args): """ flatten one or more signatures, removing abundances. """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) _extend_signatures_with_from_file(args) save_sigs = sourmash_args.SaveSignaturesToLocation(args.output) save_sigs.open() # start loading! progress = sourmash_args.SignatureLoadingProgress() loader = sourmash_args.load_many_signatures( args.signatures, ksize=args.ksize, moltype=moltype, picklist=picklist, progress=progress, yield_all_files=args.force, force=args.force, ) for ss, sigloc in loader: # select! if args.md5 is not None: if args.md5 not in ss.md5sum(): continue # skip if args.name is not None: if args.name not in ss.name: continue # skip ss = ss.to_mutable() ss.minhash = ss.minhash.flatten() save_sigs.add(ss) save_sigs.close() notify(f"loaded {len(progress)} total that matched ksize & molecule type") notify(f"extracted {len(save_sigs)} signatures from {len(args.signatures)} file(s)") if picklist: sourmash_args.report_picklist(args, picklist) def downsample(args): """ downsample num and scaled signatures, and also interconvert. """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) _extend_signatures_with_from_file(args) if not args.num_hashes and not args.scaled: error("ERROR: must specify either --num or --scaled value") sys.exit(-1) if args.num_hashes and args.scaled: error("ERROR: cannot specify both --num and --scaled") sys.exit(-1) # open output for saving sigs save_sigs = sourmash_args.SaveSignaturesToLocation(args.output) save_sigs.open() # start loading! progress = sourmash_args.SignatureLoadingProgress() loader = sourmash_args.load_many_signatures( args.signatures, ksize=args.ksize, moltype=moltype, picklist=picklist, progress=progress, yield_all_files=args.force, force=args.force, ) for ss, sigloc in loader: sigobj = ss.to_mutable() mh = sigobj.minhash if args.scaled: # downsample scaled to scaled? straightforward. if mh.scaled: mh_new = mh.downsample(scaled=args.scaled) # try to turn a num into a scaled - trickier. else: # first check: can we? max_hash = _get_max_hash_for_scaled(args.scaled) mins = mh.hashes if max(mins) < max_hash: raise ValueError( "this num MinHash does not have enough hashes to convert it into a scaled MinHash." ) mh_new = mh.copy() _set_num_scaled(mh_new, 0, args.scaled) elif args.num_hashes: # downsample num to num? straightforward. if mh.num: mh_new = mh.downsample(num=args.num_hashes) # try to turn a scaled into a num - trickier. else: # first check: can we? if len(mh) < args.num_hashes: raise ValueError(f"this scaled MinHash has only {len(mh)} hashes") mh_new = mh.copy() _set_num_scaled(mh_new, args.num_hashes, 0) sigobj.minhash = mh_new save_sigs.add(sigobj) save_sigs.close() notify(f"loaded {len(progress)} signatures") notify(f"output {len(save_sigs)} downsampled signatures", len(save_sigs)) if picklist: sourmash_args.report_picklist(args, picklist) def ingest(args): """ import a signature into sourmash format. """ set_quiet(args.quiet) siglist = [] if args.csv: for filename in args.filenames: with open(filename, newline="") as csv_fp: reader = csv.reader(csv_fp) siglist = [] for row in reader: hashfn = row[0] hashseed = int(row[1]) # only support a limited import type, for now ;) assert hashfn == "murmur64" assert hashseed == 42 _, _, ksize, name, hashes = row ksize = int(ksize) hashes = hashes.strip() hashes = list(map(int, hashes.split(" "))) e = sourmash.MinHash(len(hashes), ksize) e.add_many(hashes) s = sourmash.SourmashSignature(e, filename=name) siglist.append(s) notify(f"loaded signature: {name} {s.md5sum()[:8]}") else: for filename in args.filenames: with open(filename) as fp: x = json.loads(fp.read()) ksize = x["kmer"] num = x["sketchSize"] assert x["hashType"] == "MurmurHash3_x64_128" assert x["hashBits"] == 64 assert x["hashSeed"] == 42 xx = x["sketches"][0] hashes = xx["hashes"] mh = sourmash.MinHash(ksize=ksize, n=num, is_protein=False) mh.add_many(hashes) s = sourmash.SourmashSignature(mh, filename=filename) siglist.append(s) notify(f"saving {len(siglist)} signatures to JSON") with sourmash_args.SaveSignaturesToLocation(args.output) as save_sigs: save_sigs.add_many(siglist) def export(args): """ export a signature to mash format """ set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) query = sourmash_args.load_query_signature( args.filename, ksize=args.ksize, select_moltype=moltype, select_md5=args.md5 ) mh = query.minhash x = {} x["kmer"] = mh.ksize x["sketchSize"] = len(mh) x["hashType"] = "MurmurHash3_x64_128" x["hashBits"] = 64 x["hashSeed"] = mh.seed ll = list(mh.hashes) x["sketches"] = [{"hashes": ll}] with FileOutput(args.output, "wt") as fp: print(json.dumps(x), file=fp) notify(f"exported signature {query} ({query.md5sum()[:8]})") def kmers(args): """ retrieve k-mers and/or sequences contained by the minhashes """ from sourmash.search import format_bp set_quiet(args.quiet) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) _extend_signatures_with_from_file(args) first_sig = None query_mh = None # start loading! progress = sourmash_args.SignatureLoadingProgress() loader = sourmash_args.load_many_signatures( args.signatures, ksize=args.ksize, moltype=moltype, picklist=picklist, progress=progress, yield_all_files=args.force, force=args.force, ) for sigobj, sigloc in loader: # first signature? initialize a bunch of stuff if first_sig is None: first_sig = sigobj query_mh = first_sig.minhash.copy_and_clear() # remove abundance as it has no purpose here -- query_mh.track_abundance = False try: sigobj_mh = sigobj.minhash sigobj_mh.track_abundance = False query_mh.merge(sigobj_mh) except (TypeError, ValueError) as exc: error( "ERROR when merging signature '{}' ({}) from file {}", sigobj, sigobj.md5sum()[:8], sigloc, ) error(str(exc)) sys.exit(-1) if not len(progress): error("no signatures in query!?") sys.exit(-1) notify(f"loaded and merged {len(progress)} signatures") if picklist: sourmash_args.report_picklist(args, picklist) is_protein = False if query_mh.moltype == "DNA": if args.translate: error("ERROR: cannot use --translate with DNA sketches.") sys.exit(-1) else: is_protein = True if args.translate: # input sequence is DNA is_protein = False if not query_mh: notify("ERROR: no hashes in query signature!?") sys.exit(-1) notify("") notify("merged signature has the following properties:") notify( f"k={query_mh.ksize} molecule={query_mh.moltype} num={query_mh.num} scaled={query_mh.scaled} seed={query_mh.seed}" ) notify(f"total hashes in merged signature: {len(query_mh)}") notify("") notify("now processing sequence files for matches!") found_mh = query_mh.copy_and_clear() # open outputs... save_kmers = None kmer_w = None if args.save_kmers: save_kmers = sourmash_args.FileOutputCSV(args.save_kmers) save_kmers.open() kmer_w = csv.DictWriter( save_kmers.fp, fieldnames=["sequence_file", "sequence_name", "kmer", "hashval"], ) kmer_w.writeheader() save_seqs = None if args.save_sequences: save_seqs = sourmash_args.FileOutput(args.save_sequences) save_seqs.open() # figure out protein vs dna is_protein = False if query_mh.moltype != "DNA": if not args.translate: is_protein = True n_files_searched = 0 n_sequences_searched = 0 n_bp_searched = 0 n_kmers_found = 0 n_sequences_found = 0 n_bp_saved = 0 progress_threshold = 1e6 progress_interval = 1e6 for filename in args.sequences: notify(f"opening sequence file '{filename}'") n_files_searched += 1 with screed.open(filename) as f: for record in f: seq_mh = query_mh.copy_and_clear() # protein? dna? if is_protein: seq_mh.add_protein(record.sequence) else: try: seq_mh.add_sequence(record.sequence, not args.check_sequence) except ValueError as exc: seqname = record.name if len(seqname) > 40: seqname = seqname[:37] + "..." notify(f"ERROR in sequence '{seqname}', file '{filename}'") notify(str(exc)) if args.force: notify("(continuing)") continue else: sys.exit(-1) if seq_mh.intersection(query_mh): # match! # output matching sequences: if save_seqs: save_seqs.fp.write(f">{record.name}\n{record.sequence}\n") n_sequences_found += 1 n_bp_saved += len(record.sequence) # output matching k-mers: if kmer_w: seq = record.sequence kh_iter = seq_mh.kmers_and_hashes( seq, force=False, is_protein=is_protein ) for kmer, hashval in kh_iter: if hashval in query_mh.hashes: found_mh.add_hash(hashval) n_kmers_found += 1 d = dict( sequence_file=filename, sequence_name=record.name, kmer=kmer, hashval=hashval, ) kmer_w.writerow(d) # add seq_mh to found_mh found_mh += seq_mh.intersection(query_mh) # provide progress indicator based on bp... n_sequences_searched += 1 n_bp_searched += len(record.sequence) if n_bp_searched >= progress_threshold: notify( f"... searched {n_bp_searched} from {n_files_searched} files so far" ) while n_bp_searched >= progress_threshold: progress_threshold += progress_interval # END major for loop. Now, clean up! if save_kmers: save_kmers.close() if save_seqs: save_seqs.close() if not n_sequences_searched: notify("ERROR: no sequences searched!?") sys.exit(-1) # ...and report! notify("DONE.") notify( f"searched {n_sequences_searched} sequences from {n_files_searched} files, containing a total of {format_bp(n_bp_searched)}." ) if save_seqs: notify( f"matched and saved a total of {n_sequences_found} sequences with {format_bp(n_bp_saved)}." ) if kmer_w: notify(f"matched and saved a total of {n_kmers_found} k-mers.") # calculate overlap, even for num minhashes which ordinarily don't # permit it, because here we are interested in knowing how many # of the expected hashes we found. query_hashes = set(query_mh.hashes) found_hashes = set(found_mh.hashes) cont = len(query_hashes.intersection(found_hashes)) / len(query_hashes) notify(f"found {len(found_mh)} distinct matching hashes ({cont * 100:.1f}%)") if not kmer_w and not save_seqs: notify("NOTE: see --save-kmers or --save-sequences for output options.") _SketchInfo = namedtuple("_SketchInfo", "ksize, moltype, scaled, num, abund") def _summarize_manifest(manifest): info_d = {} # use a namedtuple to track counts of distinct sketch types and n hashes total_size = 0 counter = Counter() hashcounts = Counter() for row in manifest.rows: ski = _SketchInfo( ksize=row["ksize"], moltype=row["moltype"], scaled=row["scaled"], num=row["num"], abund=row["with_abundance"], ) counter[ski] += 1 hashcounts[ski] += row["n_hashes"] total_size += row["n_hashes"] # store in info_d info_d["total_hashes"] = total_size sketch_info = [] for ski, count in counter.items(): sketch_d = dict(ski._asdict()) sketch_d["count"] = count sketch_d["n_hashes"] = hashcounts[ski] sketch_info.append(sketch_d) info_d["sketch_info"] = sketch_info return info_d # NOTE: also aliased as 'summarize' def fileinfo(args): """ provide summary information on the given path (collection, index, etc.) """ set_quiet(args.quiet, args.debug) text_out = False if not args.json_out: text_out = True # load as index! try: notify(f"** loading from '{args.path}'") idx = sourmash_args.load_file_as_index(args.path, yield_all_files=args.force) except ValueError: error(f"Cannot open '{args.path}' as a sourmash signature collection.") error("Use -d/--debug for details.") sys.exit(-1) def print_bool(x): return "yes" if x else "no" info_d = {} info_d["path_filetype"] = type(idx).__name__ info_d["location"] = "" if not idx.location else idx.location info_d["is_database"] = bool(idx.is_database) info_d["has_manifest"] = bool(idx.manifest) info_d["num_sketches"] = len(idx) if text_out: print_results(f"path filetype: {info_d['path_filetype']}") print_results(f"location: {info_d['location']}") print_results(f"is database? {print_bool(info_d['is_database'])}") print_results(f"has manifest? {print_bool(info_d['has_manifest'])}") print_results(f"num signatures: {info_d['num_sketches']}") # also have arg to fileinfo to force recalculation notify("** examining manifest...") manifest = sourmash_args.get_manifest( idx, rebuild=args.rebuild_manifest, require=False ) manifest._check_row_values() if manifest is None: # actually can't find any file type to trigger this, but leaving it # in for future eventualities, I guess? notify("** no manifest and cannot be generated; exiting.") sys.exit(0) info_d.update(_summarize_manifest(manifest)) if text_out: print_results(f"total hashes: {info_d['total_hashes']}") print_results("summary of sketches:") for ski in info_d["sketch_info"]: mh_type = f"num={ski['num']}" if ski["num"] else f"scaled={ski['scaled']}" mh_abund = ", abund" if ski["abund"] else "" sketch_str = f"{ski['count']} sketches with {ski['moltype']}, k={ski['ksize']}, {mh_type}{mh_abund}" print_results(f" {sketch_str: <50} {ski['n_hashes']} total hashes") else: assert args.json_out print(json.dumps(info_d)) def check(args): """ check signature db(s) against a picklist. """ from sourmash.picklist import PickStyle set_quiet(args.quiet, args.debug) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) sourmash_args.load_include_exclude_db_patterns(args) _extend_signatures_with_from_file(args) if not picklist: error("** No picklist provided?! Exiting.") sys.exit(-1) if picklist.pickstyle == PickStyle.EXCLUDE and args.output_missing: error("** ERROR: Cannot use an 'exclude' picklist with '-o/--output-missing'") sys.exit(-1) # require manifests? require_manifest = True if args.no_require_manifest: require_manifest = False debug("sig check: manifest will not be required") else: debug("sig check: manifest required") # abspath/relpath checks if args.abspath and args.relpath: error("** Cannot specify both --abspath and --relpath; pick one!") sys.exit(-1) if args.relpath or args.abspath and not args.save_manifest_matching: notify( "** WARNING: --abspath and --relpath only have effects when saving a manifest" ) relpath = "." if args.relpath and args.save_manifest_matching: output_manifest_dir = os.path.dirname(args.save_manifest_matching) relpath = os.path.relpath(os.curdir, output_manifest_dir) total_manifest_rows = CollectionManifest([]) # start loading! total_rows_examined = 0 for filename in args.signatures: # if saving a manifest, think about how to rewrite locations. if args.abspath: # convert to abspath new_iloc = os.path.abspath(filename) elif args.relpath: # interpret paths relative to manifest directory. if filename.startswith("/"): notify( f"** WARNING: cannot convert abspath {filename} into relative path." ) new_iloc = os.path.join(relpath, filename) else: # default: paths are relative to cwd. This breaks when sketches # are in subdirectories; will be deprecated for v5. new_iloc = filename idx = sourmash_args.load_file_as_index(filename, yield_all_files=args.force) idx = idx.select(ksize=args.ksize, moltype=moltype) if idx.manifest is None and require_manifest: error(f"ERROR on filename '{filename}'.") error("sig check requires a manifest by default, but no manifest present.") error("specify --no-require-manifest to dynamically generate one.") sys.exit(-1) # has manifest, or ok to build (require_manifest=False) - continue! new_manifest = sourmash_args.get_manifest(idx, require=True) sub_manifest = new_manifest.select_to_manifest(picklist=picklist) total_rows_examined += len(new_manifest) # rewrite locations so that each signature can be found by filename # of its container; this follows `sig collect` logic. for row in sub_manifest.rows: row["internal_location"] = new_iloc total_manifest_rows.add_row(row) # the len(sub_manifest) here should only be run when needed :) if _debug: debug_literal( f"examined {len(new_manifest)} new rows, found {len(sub_manifest)} matching rows" ) notify(f"loaded {total_rows_examined} signatures.") sourmash_args.report_picklist(args, picklist) # output picklist of non-matching in same format as input picklist n_missing = len(picklist.pickset - picklist.found) if args.output_missing and n_missing: pickfile = picklist.pickfile # go through the input file and pick out missing rows. n_input = 0 n_output = 0 with sourmash_args.FileInputCSV(pickfile) as r: with open(args.output_missing, "w", newline="") as outfp: w = csv.DictWriter(outfp, fieldnames=r.fieldnames) w.writeheader() for row in r: n_input += 1 if not picklist.matched_csv_row(row): n_output += 1 w.writerow(row) notify( f"saved {n_output} non-matching rows of {n_input} picklist rows to '{args.output_missing}'" ) elif args.output_missing: notify( f"(no remaining picklist entries; not saving to '{args.output_missing}')" ) # save manifest of matching! if args.save_manifest_matching and total_manifest_rows: mf = total_manifest_rows mf.write_to_filename( args.save_manifest_matching, database_format=args.manifest_format ) notify( f"wrote {len(mf)} matching manifest rows to '{args.save_manifest_matching}'" ) elif args.save_manifest_matching: notify( f"(not saving matching manifest to '{args.save_manifest_matching}' because no matches)" ) if args.fail_if_missing and n_missing: error("** ERROR: missing values, and --fail-if-missing requested. Exiting.") sys.exit(-1) def collect(args): "Collect signature metadata across many locations, save to manifest" set_quiet(False, args.debug) if os.path.exists(args.output): if args.merge_previous: pass else: error(f"ERROR: '{args.output}' already exists!") error("ERROR: please remove it, or use --merge-previous to merge") sys.exit(-1) elif args.merge_previous: notify( f"WARNING: --merge-previous specified, but output file '{args.output}' does not already exist?" ) # abspath/relpath checks if args.abspath and args.relpath: error("** Cannot specify both --abspath and --relpath; pick one!") sys.exit(-1) # load previous manifest for --merge-previous. This gets tricky with # mismatched manifest types, which we forbid. try: if args.manifest_format == "sql": # create on-disk manifest from sourmash.index.sqlite_index import SqliteCollectionManifest if args.merge_previous: collected_mf = SqliteCollectionManifest.create_or_open(args.output) else: collected_mf = SqliteCollectionManifest.create(args.output) else: # create in-memory manifest that will be saved as CSV assert args.manifest_format == "csv" if args.merge_previous and os.path.exists(args.output): collected_mf = CollectionManifest.load_from_filename(args.output) else: collected_mf = CollectionManifest() if not isinstance(collected_mf, CollectionManifest): raise Exception except: error( f"ERROR loading '{args.output}' with --merge-previous. Is it of type {args.manifest_format}?" ) sys.exit(-1) if args.merge_previous: notify(f"merging new locations with {len(collected_mf)} previous rows.") # require manifests? yes by default, since generating can be slow. require_manifest = True if args.no_require_manifest: require_manifest = False debug("sig check: manifest will not be required") else: debug("sig check: manifest required") n_files = 0 # load from_file _extend_signatures_with_from_file(args, target_attr="locations") relpath = None if args.relpath: output_manifest_dir = os.path.dirname(args.output) relpath = os.path.relpath(os.curdir, output_manifest_dir) # iterate through, loading all the manifests from all the locations. for n_files, loc in enumerate(args.locations): notify(f"Loading signature information from {loc}.") if n_files and n_files % 100 == 0: notify(f"... loaded {len(collected_mf)} sigs from {n_files} files") idx = sourmash.load_file_as_index(loc) if idx.manifest is None and require_manifest: error(f"ERROR on location '{loc}'") error( "sig collect requires a manifest by default, but no manifest present." ) error("specify --no-require-manifest to dynamically generate one.") sys.exit(-1) mf = sourmash_args.get_manifest(idx) # decide how to rewrite locations to container: if args.abspath: # convert to abspath new_iloc = os.path.abspath(loc) elif args.relpath: # interpret paths relative to manifest directory if loc.startswith("/"): notify(f"** WARNING: cannot convert abspath {loc} into relative path.") new_iloc = os.path.join(relpath, loc) else: # default: paths are relative to cwd. This breaks when sketches # are in subdirectories; will be deprecated for v5. new_iloc = loc for row in mf.rows: row["internal_location"] = new_iloc collected_mf.add_row(row) if args.manifest_format == "csv": collected_mf.write_to_filename( args.output, database_format="csv", ok_if_exists=args.merge_previous ) else: collected_mf.close() notify(f"saved {len(collected_mf)} manifest rows to '{args.output}'") return 0 def main(arglist=None): args = sourmash.cli.get_parser().parse_args(arglist) submod = getattr(sourmash.cli.sig, args.subcmd) mainmethod = getattr(submod, "main") return mainmethod(args) if __name__ == "__main__": main(sys.argv) sourmash-bio-sourmash-bc3c0f8/src/sourmash/sig/grep.py000066400000000000000000000107241474157046300232130ustar00rootroot00000000000000""" Command-line entry point for 'python -m sourmash.sig grep' """ import sys import re from sourmash import logging, sourmash_args from sourmash.logging import notify, error, debug, print_results from sourmash.manifest import CollectionManifest from .__main__ import _extend_signatures_with_from_file def main(args): """ extract signatures by pattern match. """ # basic argument parsing logging.set_quiet(args.quiet, args.debug) moltype = sourmash_args.calculate_moltype(args) picklist = sourmash_args.load_picklist(args) _extend_signatures_with_from_file(args) # build the search pattern pattern = args.pattern if args.ignore_case: pattern = re.compile(pattern, re.IGNORECASE) else: pattern = re.compile(pattern) if args.invert_match: def search_pattern(vals): return all(not pattern.search(val) for val in vals) else: def search_pattern(vals): return any(pattern.search(val) for val in vals) # require manifests? require_manifest = True if args.no_require_manifest: require_manifest = False debug("sig grep: manifest will not be required") else: debug("sig grep: manifest required") # are we doing --count? if so, enforce --silent so no sigs are printed. if args.count: args.silent = True # define output type: signatures, or no? if args.silent: notify("(no signatures will be saved because of --silent/--count).") save_sigs = sourmash_args.SaveSignaturesToLocation(None) else: notify(f"saving matching signatures to '{args.output}'") save_sigs = sourmash_args.SaveSignaturesToLocation(args.output) save_sigs.open() # are we outputting a CSV? if so, initialize that, too. csv_obj = None if args.csv: csv_obj = sourmash_args.FileOutputCSV(args.csv) csv_fp = csv_obj.open() CollectionManifest.write_csv_header(csv_fp) # start loading! total_rows_examined = 0 for filename in args.signatures: idx = sourmash_args.load_file_as_index(filename, yield_all_files=args.force) idx = idx.select(ksize=args.ksize, moltype=moltype, picklist=picklist) # get (and maybe generate) the manifest. manifest = idx.manifest if manifest is None: if require_manifest: error(f"ERROR on filename '{filename}'.") error( "sig grep requires a manifest by default, but no manifest present." ) error("specify --no-require-manifest to dynamically generate one.") sys.exit(-1) else: manifest = sourmash_args.get_manifest(idx, require=False) # find all matching rows. sub_manifest = manifest.filter_on_columns( search_pattern, ["name", "filename", "md5"] ) total_rows_examined += len(manifest) # write out to CSV, if desired. if args.csv: sub_manifest.write_to_csv(csv_fp) # just print out number of matches? if args.count: print_results(f"{len(sub_manifest)} matches: {filename}") elif not args.silent: # nope - do output signatures. convert manifest to picklist, apply. sub_picklist = sub_manifest.to_picklist() try: idx = idx.select(picklist=sub_picklist) except ValueError: error("** This input collection doesn't support 'grep' with picklists.") error("** EXITING.") error("**") error("** You can use 'sourmash sig cat' with a picklist,") error("** and then pipe the output to 'sourmash sig grep -") sys.exit(-1) # save! for ss in idx.signatures(): save_sigs.add(ss) # done with the big loop over all indexes! if args.silent: pass else: notify(f"loaded {total_rows_examined} total that matched ksize & molecule type") if save_sigs: notify( f"extracted {len(save_sigs)} signatures from {len(args.signatures)} file(s)" ) save_sigs.close() else: error("no matching signatures found!") sys.exit(-1) if args.csv: notify(f"wrote manifest containing all matches to CSV file '{args.csv}'") csv_obj.close() if picklist: sourmash_args.report_picklist(args, picklist) sourmash-bio-sourmash-bc3c0f8/src/sourmash/signature.py000066400000000000000000000352471474157046300235040ustar00rootroot00000000000000#! /usr/bin/env python """ Save and load MinHash sketches in a JSON format, along with some metadata. """ import sys import os import weakref from enum import Enum import contextlib from .logging import error from . import MinHash from .minhash import to_bytes, FrozenMinHash from ._lowlevel import ffi, lib from .utils import RustObject, rustcall, decode_str SIGNATURE_VERSION = 0.4 class SigInput(Enum): FILE_LIKE = 1 PATH = 2 BUFFER = 3 UNKNOWN = 4 class SourmashSignature(RustObject): "Main class for signature information." __dealloc_func__ = lib.signature_free def __init__(self, minhash, name="", filename=""): self._objptr = lib.signature_new() if name: self.name = name if filename: self.filename = filename self.minhash = minhash @property def minhash(self): return FrozenMinHash._from_objptr(self._methodcall(lib.signature_first_mh)) @minhash.setter def minhash(self, value): # TODO: validate value is a MinHash self._methodcall(lib.signature_set_mh, value._objptr) def __hash__(self): return hash(self.md5sum()) def __str__(self): return self._display_name() def __repr__(self): name = self.name md5pref = self.md5sum()[:8] if name == md5pref: return f"SourmashSignature({md5pref})" else: # name != md5pref: return f"SourmashSignature('{name}', {md5pref})" # def minhashes(self): # size = ffi.new("uintptr_t *") # mhs_ptr = self._methodcall(lib.signature_get_mhs, size) # size = ffi.unpack(size, 1)[0] # # mhs = [] # for i in range(size): # mh = MinHash._from_objptr(mhs_ptr[i]) # mhs.append(mh) # # return mhs def md5sum(self): "Calculate md5 hash of the bottom sketch, specifically." return decode_str(self.minhash._methodcall(lib.kmerminhash_md5sum)) def __eq__(self, other): return self._methodcall(lib.signature_eq, other._objptr) @property def _name(self): return decode_str(self._methodcall(lib.signature_get_name)) @_name.setter def _name(self, value): self._methodcall(lib.signature_set_name, to_bytes(value)) @property def name(self): return decode_str(self._methodcall(lib.signature_get_name)) @name.setter def name(self, value): self._methodcall(lib.signature_set_name, to_bytes(value)) def __ne__(self, other): return not self == other @property def filename(self): return decode_str(self._methodcall(lib.signature_get_filename)) @filename.setter def filename(self, value): self._methodcall(lib.signature_set_filename, to_bytes(value)) @property def license(self): return decode_str(self._methodcall(lib.signature_get_license)) def _display_name(self, max_length=0): name = self._name filename = self.filename if name: if max_length and len(name) > max_length: name = name[: max_length - 3] + "..." elif filename: name = filename if max_length and len(name) > max_length: name = "..." + name[-max_length + 3 :] else: name = self.md5sum()[:8] assert not max_length or len(name) <= max_length return name def similarity(self, other, ignore_abundance=False, downsample=False): "Compute similarity with the other signature." return self.minhash.similarity( other.minhash, ignore_abundance=ignore_abundance, downsample=downsample ) def jaccard(self, other): "Compute Jaccard similarity with the other MinHash signature." return self.minhash.similarity( other.minhash, ignore_abundance=True, downsample=False ) def jaccard_ani( self, other, *, downsample=False, jaccard=None, prob_threshold=1e-3, err_threshold=1e-4, ): "Use jaccard to estimate ANI between two FracMinHash signatures." return self.minhash.jaccard_ani( other.minhash, downsample=downsample, jaccard=jaccard, prob_threshold=prob_threshold, err_threshold=err_threshold, ) def contained_by(self, other, downsample=False): "Compute containment by the other signature. Note: ignores abundance." return self.minhash.contained_by(other.minhash, downsample=downsample) def containment_ani( self, other, *, downsample=False, containment=None, confidence=0.95, estimate_ci=False, ): "Use containment to estimate ANI between two FracMinHash signatures." return self.minhash.containment_ani( other.minhash, downsample=downsample, containment=containment, confidence=confidence, estimate_ci=estimate_ci, ) def max_containment(self, other, downsample=False): "Compute max containment w/other signature. Note: ignores abundance." return self.minhash.max_containment(other.minhash, downsample=downsample) def max_containment_ani( self, other, *, downsample=False, max_containment=None, confidence=0.95, estimate_ci=False, ): "Use max containment to estimate ANI between two FracMinHash signatures." return self.minhash.max_containment_ani( other.minhash, downsample=downsample, max_containment=max_containment, confidence=confidence, estimate_ci=estimate_ci, ) def avg_containment(self, other, downsample=False): """ Calculate average containment. Note: this is average of the containments, *not* count_common/ avg_denom """ return self.minhash.avg_containment(other.minhash, downsample=downsample) def avg_containment_ani(self, other, *, downsample=False): """ Calculate average containment ANI. Note: this is average of the containment ANI's, *not* ANI using count_common/ avg_denom """ return self.minhash.avg_containment_ani(other.minhash, downsample=downsample) def add_sequence(self, sequence, force=False): self._methodcall(lib.signature_add_sequence, to_bytes(sequence), force) def add_protein(self, sequence): self._methodcall(lib.signature_add_protein, to_bytes(sequence)) @staticmethod def from_params(params): ptr = rustcall(lib.signature_from_params, params._get_objptr()) return SourmashSignature._from_objptr(ptr) def __len__(self): return self._methodcall(lib.signature_len) def __getstate__(self): # enable pickling return ( self.minhash, self.name, self.filename, ) def __setstate__(self, tup): (mh, name, filename) = tup self.__del__() self._objptr = lib.signature_new() if name: self.name = name if filename: self.filename = filename self.minhash = mh def __reduce__(self): return ( SourmashSignature, (self.minhash, self.name, self.filename), ) def __copy__(self): a = SourmashSignature( self.minhash, name=self.name, filename=self.filename, ) return a copy = __copy__ def to_frozen(self): "Return a frozen copy of this signature." new_ss = self.copy() new_ss.__class__ = FrozenSourmashSignature return new_ss def to_mutable(self): "Return a mutable copy of this signature." return self.copy() def into_frozen(self): "Freeze this signature, preventing attribute changes." # this will always be the case b/c minhash property returns FrozenMH: # assert isinstance(self.minhash, FrozenMinHash) self.__class__ = FrozenSourmashSignature class FrozenSourmashSignature(SourmashSignature): "Frozen (immutable) signature class." @SourmashSignature.minhash.setter def minhash(self, value): raise ValueError("cannot set .minhash on FrozenSourmashSignature") @SourmashSignature._name.setter def _name(self, value): raise ValueError("cannot set ._name on FrozenSourmashSignature") @SourmashSignature.name.setter def name(self, value): raise ValueError("cannot set .name on FrozenSourmashSignature") @SourmashSignature.filename.setter def filename(self, value): raise ValueError("cannot set .filename on FrozenSourmashSignature") def add_sequence(self, sequence, force=False): raise ValueError("cannot add sequence data to FrozenSourmashSignature") def add_protein(self, sequence): raise ValueError("cannot add protein sequence to FrozenSourmashSignature") def __copy__(self): return self copy = __copy__ def to_frozen(self): "Return a frozen copy of this signature." return self def to_mutable(self): "Turn this object into a mutable object." mut = SourmashSignature.__new__(SourmashSignature) state_tup = self.__getstate__() mut.__setstate__(state_tup) return mut def into_frozen(self): "Freeze this signature, preventing attribute changes." self.__class__ = FrozenSourmashSignature @contextlib.contextmanager def update(self): """Make a mutable copy of this signature for modification, then freeze. This is a context manager that implements: new_sig = this_sig.copy() new_sig.to_mutable() # modify new_sig new_sig.into_frozen() This could be made more efficient by _not_ copying the signature, but that is non-intuitive and leads to hard-to-find bugs. """ new_copy = self.to_mutable() yield new_copy new_copy.into_frozen() def _detect_input_type(data): """\ Determine how to load input from `data`. Returns SigInput enum. Checks for: - Python file-like objects - JSON text (uncompressed sigs) - Compressed memory buffers - filename """ if ( hasattr(data, "read") or hasattr(data, "fileno") or hasattr(data, "mode") ): # file-like object return SigInput.FILE_LIKE elif hasattr(data, "find"): # check if it is uncompressed sig try: if data.find("sourmash_signature") > 0: return SigInput.BUFFER except TypeError: if data.find(b"sourmash_signature") > 0: return SigInput.BUFFER elif data.startswith(b"\x1f\x8b"): # gzip compressed return SigInput.BUFFER try: if os.path.exists(data): # filename return SigInput.PATH except (ValueError, TypeError): # No idea... return SigInput.UNKNOWN return SigInput.UNKNOWN def load_signatures_from_json( data, ksize=None, select_moltype=None, ignore_md5sum=False, do_raise=False, ): """Load a JSON string with signatures into classes. Returns iterator over SourmashSignature objects. Note, the order is not necessarily the same as what is in the source file. """ if ksize is not None: ksize = int(ksize) else: ksize = 0 if not data: return if select_moltype is None: select_moltype = ffi.NULL else: try: select_moltype = select_moltype.encode("utf-8") except AttributeError: pass input_type = _detect_input_type(data) if input_type == SigInput.UNKNOWN: if do_raise: raise ValueError( "Error in parsing signature; quitting. Cannot open file or invalid signature" ) return size = ffi.new("uintptr_t *") try: if input_type == SigInput.FILE_LIKE: if ( hasattr(data, "mode") and "t" in data.mode ): # need to reopen handler as binary data = data.buffer buf = data.read() data.close() data = buf input_type = SigInput.BUFFER elif input_type == SigInput.PATH: sigs_ptr = rustcall( lib.signatures_load_path, data.encode("utf-8"), ignore_md5sum, ksize, select_moltype, size, ) if input_type == SigInput.BUFFER: if hasattr(data, "encode"): data = data.encode("utf-8") sigs_ptr = rustcall( lib.signatures_load_buffer, data, len(data), ignore_md5sum, ksize, select_moltype, size, ) size = size[0] sigs = [] for i in range(size): sig = SourmashSignature._from_objptr(sigs_ptr[i]) sigs.append(sig) for sig in sigs: yield sig.to_frozen() except Exception: if do_raise: raise def load_one_signature_from_json( data, ksize=None, select_moltype=None, ignore_md5sum=False ): sigiter = load_signatures_from_json( data, ksize=ksize, select_moltype=select_moltype, ignore_md5sum=ignore_md5sum ) try: first_sig = next(sigiter) except StopIteration: raise ValueError("no signatures to load") try: next(sigiter) except StopIteration: return first_sig raise ValueError("expected to load exactly one signature") def save_signatures_to_json(siglist, fp=None, compression=0): "Save multiple signatures into a JSON string (or into file handle 'fp')" attached_refs = weakref.WeakKeyDictionary() # get list of rust objects collected = [] for obj in siglist: rv = obj._get_objptr() attached_refs[rv] = obj collected.append(rv) siglist_c = ffi.new("SourmashSignature*[]", collected) size = ffi.new("uintptr_t *") # save signature into a string (potentially compressed) rawbuf = rustcall( lib.signatures_save_buffer, siglist_c, len(collected), compression, size ) size = size[0] # associate a finalizer with rawbuf so that it gets freed buf = ffi.gc(rawbuf, lambda o: lib.nodegraph_buffer_free(o, size), size) if compression: result = ffi.buffer(buf, size)[:] else: result = ffi.string(buf, size) if fp is None: # return string return result else: try: # write to file fp.write(result) except TypeError: fp.write(result.decode("utf-8")) return None sourmash-bio-sourmash-bc3c0f8/src/sourmash/sketchcomparison.py000066400000000000000000000227501474157046300250520ustar00rootroot00000000000000""" Sketch Comparison Classes """ import numpy as np from dataclasses import dataclass from .minhash import MinHash @dataclass class BaseMinHashComparison: """Class for standard comparison between two MinHashes""" mh1: MinHash mh2: MinHash ignore_abundance: bool = False # optionally ignore abundances jaccard_ani_untrustworthy: bool = False def downsample_and_handle_ignore_abundance(self, cmp_num=None, cmp_scaled=None): """ Downsample and/or flatten minhashes for comparison """ if self.ignore_abundance: self.mh1_cmp = self.mh1.flatten() self.mh2_cmp = self.mh2.flatten() else: self.mh1_cmp = self.mh1 self.mh2_cmp = self.mh2 if cmp_scaled is not None: self.mh1_cmp = self.mh1_cmp.downsample(scaled=cmp_scaled) self.mh2_cmp = self.mh2_cmp.downsample(scaled=cmp_scaled) elif cmp_num is not None: self.mh1_cmp = self.mh1_cmp.downsample(num=cmp_num) self.mh2_cmp = self.mh2_cmp.downsample(num=cmp_num) else: raise ValueError("Error: must pass in a comparison scaled or num value.") def check_compatibility_and_downsample(self, cmp_num=None, cmp_scaled=None): if not any( [(self.mh1.num and self.mh2.num), (self.mh1.scaled and self.mh2.scaled)] ): raise TypeError("Error: Both sketches must be 'num' or 'scaled'.") # need to downsample first because is_compatible checks scaled (though does not check num) self.downsample_and_handle_ignore_abundance( cmp_num=cmp_num, cmp_scaled=cmp_scaled ) if not self.mh1_cmp.is_compatible(self.mh2_cmp): raise TypeError("Error: Cannot compare incompatible sketches.") self.ksize = self.mh1.ksize self.moltype = self.mh1.moltype @property def intersect_mh(self): # flatten and intersect return self.mh1_cmp.flatten().intersection(self.mh2_cmp.flatten()) @property def jaccard(self): return self.mh1_cmp.jaccard(self.mh2_cmp) def estimate_jaccard_ani(self, jaccard=None): jinfo = self.mh1_cmp.jaccard_ani(self.mh2_cmp, jaccard=jaccard) # propagate params self.jaccard_ani = jinfo.ani if jinfo.p_exceeds_threshold: self.potential_false_negative = True self.jaccard_ani_untrustworthy = jinfo.je_exceeds_threshold @property def angular_similarity(self): # Note: this currently throws TypeError if self.ignore_abundance. return self.mh1_cmp.angular_similarity(self.mh2_cmp) @property def cosine_similarity(self): return self.angular_similarity @dataclass class NumMinHashComparison(BaseMinHashComparison): """Class for standard comparison between two num minhashes""" cmp_num: int = None def __post_init__(self): "Initialize NumMinHashComparison using values from provided MinHashes" if self.cmp_num is None: # record the num we're doing this comparison on self.cmp_num = min(self.mh1.num, self.mh2.num) self.check_compatibility_and_downsample(cmp_num=self.cmp_num) @property def size_may_be_inaccurate(self): return False # not using size estimation, can ignore @dataclass class FracMinHashComparison(BaseMinHashComparison): """Class for standard comparison between two scaled minhashes""" cmp_scaled: int = None # optionally force scaled value for this comparison threshold_bp: int = 0 estimate_ani_ci: bool = False ani_confidence: float = 0.95 # pfn_threshold: float = 1e-3 def __post_init__(self): "Initialize ScaledComparison using values from provided FracMinHashes" if self.cmp_scaled is None: # comparison scaled defaults to maximum scaled between the two sigs self.cmp_scaled = max(self.mh1.scaled, self.mh2.scaled) self.check_compatibility_and_downsample(cmp_scaled=self.cmp_scaled) self.potential_false_negative = False @property def pass_threshold(self): return self.total_unique_intersect_hashes >= self.threshold_bp @property def size_may_be_inaccurate(self): # if either size estimation may be inaccurate # NOTE: do we want to do this at original scaled instead? if not self.mh1_cmp.size_is_accurate() or not self.mh2_cmp.size_is_accurate(): return True return False @property def total_unique_intersect_hashes(self): """ approx equal to intersect_bp To get true bp estimates, we would need to add `(k-1)`. However, this complicates the iterative gather algorithm, so let's stick with hashes. """ return ( len(self.intersect_mh) * self.cmp_scaled ) # + (ksize-1) #for bp estimation @property def mh1_containment_in_mh2(self): return self.mh1_cmp.contained_by(self.mh2_cmp) def estimate_ani_from_mh1_containment_in_mh2(self, containment=None): # build result once m1_cani = self.mh1_cmp.containment_ani( self.mh2_cmp, containment=containment, confidence=self.ani_confidence, estimate_ci=self.estimate_ani_ci, ) # prob_threshold=self.pfn_threshold) # propagate params self.ani_from_mh1_containment_in_mh2 = m1_cani.ani if m1_cani.p_exceeds_threshold: # only update if True self.potential_false_negative = True if self.estimate_ani_ci: self.ani_from_mh1_containment_in_mh2_low = m1_cani.ani_low self.ani_from_mh1_containment_in_mh2_high = m1_cani.ani_high @property def mh2_containment_in_mh1(self): return self.mh2_cmp.contained_by(self.mh1_cmp) def estimate_ani_from_mh2_containment_in_mh1(self, containment=None): m2_cani = self.mh2_cmp.containment_ani( self.mh1_cmp, containment=containment, confidence=self.ani_confidence, estimate_ci=self.estimate_ani_ci, ) # prob_threshold=self.pfn_threshold) self.ani_from_mh2_containment_in_mh1 = m2_cani.ani if m2_cani.p_exceeds_threshold: self.potential_false_negative = True if self.estimate_ani_ci: self.ani_from_mh2_containment_in_mh1_low = m2_cani.ani_low self.ani_from_mh2_containment_in_mh1_high = m2_cani.ani_high @property def max_containment(self): return self.mh1_cmp.max_containment(self.mh2_cmp) def estimate_max_containment_ani(self, max_containment=None): mc_ani_info = self.mh1_cmp.max_containment_ani( self.mh2_cmp, max_containment=max_containment, confidence=self.ani_confidence, estimate_ci=self.estimate_ani_ci, ) # prob_threshold=self.pfn_threshold) # propagate params self.max_containment_ani = mc_ani_info.ani if mc_ani_info.p_exceeds_threshold: self.potential_false_negative = True if self.estimate_ani_ci: self.max_containment_ani_low = mc_ani_info.ani_low self.max_containment_ani_high = mc_ani_info.ani_high @property def avg_containment(self): return self.mh1_cmp.avg_containment(self.mh2_cmp) @property def avg_containment_ani(self): "Returns single average_containment_ani value. Sets self.potential_false_negative internally." self.estimate_ani_from_mh1_containment_in_mh2() self.estimate_ani_from_mh2_containment_in_mh1() if any( [ self.ani_from_mh1_containment_in_mh2 is None, self.ani_from_mh2_containment_in_mh1 is None, ] ): return None else: return ( self.ani_from_mh1_containment_in_mh2 + self.ani_from_mh2_containment_in_mh1 ) / 2 def estimate_all_containment_ani(self): "Estimate all containment ANI values." self.estimate_ani_from_mh1_containment_in_mh2() self.estimate_ani_from_mh2_containment_in_mh1() if any( [ self.ani_from_mh1_containment_in_mh2 is None, self.ani_from_mh2_containment_in_mh1 is None, ] ): # self.estimate_max_containment_ani() self.max_containment_ani = None else: self.max_containment_ani = max( [ self.ani_from_mh1_containment_in_mh2, self.ani_from_mh2_containment_in_mh1, ] ) def weighted_intersection(self, from_mh=None, from_abundD={}): # map abundances to all intersection hashes. abund_mh = self.intersect_mh.copy_and_clear() abund_mh.track_abundance = True # if from_mh is provided, it takes precedence over from_abund dict if from_mh is not None and from_mh.track_abundance: from_abundD = from_mh.hashes if from_abundD: # this sets any hash not present in abundD to 1. Is that desired? Or should we return 0? abunds = {k: from_abundD.get(k, 1) for k in self.intersect_mh.hashes} abund_mh.set_abundances(abunds) return abund_mh # if no abundances are passed in, return intersect_mh # future note: do we want to return 1 as abundance instead? return self.intersect_mh sourmash-bio-sourmash-bc3c0f8/src/sourmash/sourmash_args.py000066400000000000000000000637601474157046300243610ustar00rootroot00000000000000""" Utility functions for sourmash CLI commands. The sourmash_args submodule contains functions that help with various command-line functions. Library functions in this module often directly send output to stdout/stderr in support of the CLI, and/or call sys.exit to exit. argparse functionality: * check_scaled_bounds(args) -- check that --scaled is reasonable * check_num_bounds(args) -- check that --num is reasonable * get_moltype(args) -- verify that moltype selected is legit * calculate_moltype(args) -- confirm that only one moltype was selected * load_picklist(args) -- create a SignaturePicklist from --picklist args * report_picklist(args, picklist) -- report on picklist value usage/matches * load_include_exclude_db_patterns(args) -- load --include-db-pattern / --exclude-db-pattern * apply_picklist_and_pattern(db, ...) -- subselect db on picklist and pattern signature/database loading functionality: * load_query_signature(filename, ...) -- load a single signature for query * traverse_find_sigs(filenames, ...) -- find all .sig and .sig.gz files * load_dbs_and_sigs(filenames, query, ...) -- load databases & signatures * load_pathlist_from_file(filename) -- load a list of paths from a file * load_many_signatures(locations) -- load many signatures from many files * get_manifest(idx) -- retrieve or build a manifest from an Index * class SignatureLoadingProgress - signature loading progress bar * load_file_as_signatures(filename, ...) -- load a list of signatures signature and file output functionality: * class FileOutput - file output context manager that deals w/stdout well * class FileOutputCSV - file output context manager for CSV files misc support: * FileInputCSV - context manager for reading CSVs """ import sys import os import csv import gzip from io import TextIOWrapper import re import zipfile import contextlib import argparse from .logging import notify, error, debug_literal from .index import LinearIndex from .picklist import SignaturePicklist, PickStyle from .manifest import CollectionManifest from .save_load import SaveSignaturesToLocation, load_file_as_index, _load_database DEFAULT_LOAD_K = 31 def check_scaled_bounds(arg): f = float(arg) if f < 0: raise argparse.ArgumentTypeError("ERROR: scaled value must be positive") if f < 100: notify("WARNING: scaled value should be >= 100. Continuing anyway.") if f > 1e6: notify("WARNING: scaled value should be <= 1e6. Continuing anyway.") return f def check_num_bounds(arg): f = int(arg) if f < 0: raise argparse.ArgumentTypeError("ERROR: num value must be positive") if f < 50: notify("WARNING: num value should be >= 50. Continuing anyway.") if f > 50000: notify("WARNING: num value should be <= 50000. Continuing anyway.") return f def get_moltype(sig, require=False): mh = sig.minhash if mh.moltype in ("DNA", "dayhoff", "hp", "protein", "skipm1n3", "skipm2n3"): moltype = mh.moltype else: raise ValueError(f"unknown molecule type for sig {sig}") return moltype def calculate_moltype(args, default=None): moltype = default n = 0 if args.dna: moltype = "DNA" n += 1 if args.dayhoff: moltype = "dayhoff" n += 1 if args.hp: moltype = "hp" n += 1 if args.protein: moltype = "protein" n += 1 if args.skipm1n3: moltype = "skipm1n3" if args.skipm2n3: moltype = "skipm2n3" if n > 1: error( "cannot specify more than one of --dna/--rna/--nucleotide/--protein/--hp/--dayhoff/--skipm1n3/--skipm2n3" ) sys.exit(-1) return moltype def load_picklist(args): "Load a SignaturePicklist from --picklist arguments." picklist = None if args.picklist: try: picklist = SignaturePicklist.from_picklist_args(args.picklist) notify( f"picking column '{picklist.column_name}' of type '{picklist.coltype}' from '{picklist.pickfile}'" ) n_empty_val, dup_vals = picklist.load() except ValueError as exc: error("ERROR: could not load picklist.") error(str(exc)) sys.exit(-1) notify(f"loaded {len(picklist.pickset)} distinct values into picklist.") if n_empty_val: notify( f"WARNING: {n_empty_val} empty values in column '{picklist.column_name}' in picklist file" ) if dup_vals: notify( f"WARNING: {len(dup_vals)} values in picklist column '{picklist.column_name}' were not distinct" ) return picklist def report_picklist(args, picklist): if picklist.pickstyle == PickStyle.INCLUDE: notify( f"for given picklist, found {len(picklist.found)} matches to {len(picklist.pickset)} distinct values" ) n_missing = len(picklist.pickset - picklist.found) elif picklist.pickstyle == PickStyle.EXCLUDE: notify( f"for given picklist, found {len(picklist.found)} matches by excluding {len(picklist.pickset)} distinct values" ) n_missing = 0 if n_missing: notify(f"WARNING: {n_missing} missing picklist values.") # Note - picklist_require_all is currently only relevant for PickStyle.INCLUDE if args.picklist_require_all: error("ERROR: failing because --picklist-require-all was set") sys.exit(-1) def load_include_exclude_db_patterns(args): if args.picklist and (args.include_db_pattern or args.exclude_db_pattern): error( "ERROR: --picklist and --include-db-pattern/--exclude cannot be used together." ) sys.exit(-1) if args.include_db_pattern and args.exclude_db_pattern: error( "ERROR: --include-db-pattern and --exclude-db-pattern cannot be used together." ) sys.exit(-1) if args.include_db_pattern: pattern = re.compile(args.include_db_pattern, re.IGNORECASE) def search_pattern(vals): return any(pattern.search(val) for val in vals) elif args.exclude_db_pattern: pattern = re.compile(args.exclude_db_pattern, re.IGNORECASE) def search_pattern(vals): return all(not pattern.search(val) for val in vals) else: search_pattern = None return search_pattern def apply_picklist_and_pattern(db, picklist, pattern): assert not (picklist and pattern) if picklist: db = db.select(picklist=picklist) elif pattern: manifest = db.manifest if manifest is None: error(f"ERROR on filename '{db.location}'.") error("--include-db-pattern/--exclude-db-pattern require a manifest.") sys.exit(-1) manifest = manifest.filter_on_columns(pattern, ["name", "filename", "md5"]) pattern_picklist = manifest.to_picklist() db = db.select(picklist=pattern_picklist) return db def load_query_signature(filename, ksize, select_moltype, select_md5=None): """Load a single signature to use as a query. Uses load_file_as_signatures underneath, so can load from collections and indexed databases. """ try: sl = load_file_as_signatures( filename, ksize=ksize, select_moltype=select_moltype ) sl = list(sl) except (OSError, ValueError): error(f"Cannot open query file '{filename}'") sys.exit(-1) if len(sl) and select_md5: found_sig = None for sig in sl: sig_md5 = sig.md5sum() if sig_md5.startswith(select_md5.lower()): # make sure we pick only one -- if found_sig is not None: error(f"Error! Multiple signatures start with md5 '{select_md5}'") error("Please use a longer --md5 selector.") sys.exit(-1) else: found_sig = sig sl = [found_sig] if len(sl) and ksize is None: ksizes = set([ss.minhash.ksize for ss in sl]) if len(ksizes) == 1: ksize = ksizes.pop() sl = [ss for ss in sl if ss.minhash.ksize == ksize] notify(f"select query k={ksize} automatically.") elif DEFAULT_LOAD_K in ksizes: sl = [ss for ss in sl if ss.minhash.ksize == DEFAULT_LOAD_K] notify(f"selecting default query k={DEFAULT_LOAD_K}.") elif ksize: notify(f"selecting specified query k={ksize}") if len(sl) != 1: error(f"When loading query from '{filename}'", filename) error(f"{len(sl)} signatures matching ksize and molecule type;") error("need exactly one. Specify --ksize or --dna, --rna, or --protein.") sys.exit(-1) return sl[0] def _check_suffix(filename, endings): for ending in endings: if filename.endswith(ending): return True return False def traverse_find_sigs(filenames, yield_all_files=False): """Find all .sig and .sig.gz files in & beneath 'filenames'. By default, this function returns files with .sig and .sig.gz extensions. If 'yield_all_files' is True, this will return _all_ files (but not directories). """ endings = (".sig", ".sig.gz") for filename in filenames: # check for files in filenames: if os.path.isfile(filename): if yield_all_files or _check_suffix(filename, endings): yield filename # filename is a directory -- traverse beneath! elif os.path.isdir(filename): for root, dirs, files in os.walk(filename): for name in sorted(files): fullname = os.path.join(root, name) if yield_all_files or _check_suffix(fullname, endings): yield fullname def load_dbs_and_sigs( filenames, query, is_similarity_query, *, cache_size=None, picklist=None, pattern=None, fail_on_empty_database=False, ): """ Load one or more Index objects to search - databases, etc. 'select' on compatibility with query, and apply picklists & patterns. """ query_mh = query.minhash # set selection parameter for containment containment = True if is_similarity_query: containment = False databases = [] total_signatures_loaded = 0 sum_signatures_after_select = 0 for filename in filenames: notify(f"loading from '{filename}'...", end="\r") try: db = _load_database(filename, False, cache_size=cache_size) except ValueError as e: # cannot load database! notify(f"ERROR on loading from '{filename}':") notify(str(e)) sys.exit(-1) total_signatures_loaded += len(db) # get compatible signatures - moltype/ksize/num/scaled try: db = db.select( moltype=query_mh.moltype, ksize=query_mh.ksize, num=query_mh.num, scaled=query_mh.scaled, containment=containment, ) except ValueError as exc: # incompatible collection specified! notify(f"ERROR: cannot use '{filename}' for this query.") notify(str(exc)) if fail_on_empty_database: sys.exit(-1) else: db = LinearIndex([]) # 'select' returns nothing => all signatures filtered out. fail! if not db: notify(f"no compatible signatures found in '{filename}'") if fail_on_empty_database: sys.exit(-1) sum_signatures_after_select += len(db) # last but not least, apply picklist! db = apply_picklist_and_pattern(db, picklist, pattern) databases.append(db) # display num loaded/num selected notify("--") notify( f"loaded {total_signatures_loaded} total signatures from {len(databases)} locations." ) notify( f"after selecting signatures compatible with search, {sum_signatures_after_select} remain." ) print("") return databases def load_pathlist_from_file(filename): "Load a list-of-files text file." try: with open(filename) as fp: file_list = [x.rstrip("\r\n") for x in fp] file_list = set(file_list) if not file_list: raise ValueError("pathlist is empty") for checkfile in file_list: if not os.path.exists(checkfile): raise ValueError( f"file '{checkfile}' inside the pathlist does not exist" ) except OSError: raise ValueError(f"pathlist file '{filename}' does not exist") except OSError: raise ValueError(f"cannot open file '{filename}'") except UnicodeDecodeError: raise ValueError(f"cannot parse file '{filename}' as list of filenames") return file_list class FileOutput: """A context manager for file outputs that handles sys.stdout gracefully. Usage: with FileOutput(filename, mode) as fp: ... does what you'd expect, but it handles the situation where 'filename' is '-' or None. This makes it nicely compatible with argparse usage, e.g. p = argparse.ArgumentParser() p.add_argument('--output') args = p.parse_args() ... with FileOutput(args.output, 'wt') as fp: ... will properly handle no argument or '-' as sys.stdout. """ def __init__(self, filename, mode="wt", *, newline=None, encoding="utf-8"): self.filename = filename self.mode = mode self.fp = None self.newline = newline self.encoding = encoding def open(self): if self.filename == "-" or self.filename is None: return sys.stdout self.fp = open( self.filename, self.mode, newline=self.newline, encoding=self.encoding ) return self.fp def close(self): if self.fp is not None: # in case of stdout self.fp.close() def __enter__(self): return self.open() def __exit__(self, type, value, traceback): # do we need to handle exceptions here? if self.fp: self.fp.close() return False class FileOutputCSV(FileOutput): """A context manager for CSV file outputs. Usage: with FileOutputCSV(filename) as fp: ... does what you'd expect, but it handles the situation where 'filename' is '-' or None. This makes it nicely compatible with argparse usage, e.g. p = argparse.ArgumentParser() p.add_argument('--output') args = p.parse_args() ... with FileOutputCSV(args.output) as w: ... will properly handle no argument or '-' as sys.stdout. """ def __init__(self, filename): self.filename = filename self.fp = None def open(self): if self.filename == "-" or self.filename is None: return sys.stdout if self.filename.endswith(".gz"): self.fp = gzip.open(self.filename, "wt", newline="") else: self.fp = open(self.filename, "w", newline="") return self.fp class _DictReader_with_version: """A version of csv.DictReader that allows a comment line with a version, e.g. # SOURMASH-MANIFEST-VERSION: 1.0 The version is stored as a 2-tuple in the 'version_info' attribute. """ def __init__(self, textfp, *, delimiter=","): self.version_info = [] # is there a '#' in the raw buffer pos 0? ch = textfp.buffer.peek(1) try: ch = ch.decode("utf-8") except UnicodeDecodeError: raise csv.Error("unable to read CSV file") # yes - read a line from the text buffer => parse if ch.startswith("#"): line = textfp.readline() assert line.startswith("# "), line # note, this can set version_info to lots of different things. # revisit later, I guess. CTB. self.version_info = line[2:].strip().split(": ", 2) # build a DictReader from the remaining stream self.reader = csv.DictReader(textfp, delimiter=delimiter) self.fieldnames = self.reader.fieldnames def __iter__(self): yield from self.reader @contextlib.contextmanager def FileInputCSV( filename, *, encoding="utf-8", default_csv_name=None, zipfile_obj=None, delimiter=",", ): """A context manager for reading in CSV files in gzip, zip or text format. Assumes comma delimiter, and uses csv.DictReader. Note: does not support stdin. Note: it seems surprisingly hard to write code that generically handles any file handle being passed in; the manifest loading code, in particular, uses ZipStorage.load => StringIO obj, which doesn't support peek etc. So for now, this context manager is focused on situations where it owns the file handle (opens/closes the file). """ fp = None if zipfile_obj and not default_csv_name: raise ValueError("must provide default_csv_name with a zipfile_obj") # first, try to load 'default_csv_name' from a zipfile: if default_csv_name: # were we given a zipfile obj? if zipfile_obj: try: zi = zipfile_obj.getinfo(default_csv_name) with zipfile_obj.open(zi) as fp: textfp = TextIOWrapper(fp, encoding=encoding, newline="") r = _DictReader_with_version(textfp, delimiter=delimiter) yield r except (zipfile.BadZipFile, KeyError): pass # uh oh, we were given a zipfile_obj and it FAILED. # no matter what, if given zipfile_obj don't try .gz or regular csv return else: try: with zipfile.ZipFile(filename, "r") as zip_fp: zi = zip_fp.getinfo(default_csv_name) with zip_fp.open(zi) as fp: textfp = TextIOWrapper(fp, encoding=encoding, newline="") r = _DictReader_with_version(textfp, delimiter=delimiter) yield r # if we got this far with no exceptions, we found # the CSV in the zip file. exit generator! return except (zipfile.BadZipFile, KeyError): # no zipfile_obj => it's ok to continue onwards to .gz # and regular CSV. pass # ok, not a zip file - try .gz: try: with gzip.open(filename, "rt", newline="", encoding=encoding) as fp: fp.buffer.peek(1) # force exception if not a gzip file r = _DictReader_with_version(fp, delimiter=delimiter) yield r return except gzip.BadGzipFile: pass # neither zip nor gz; regular file! with open(filename, newline="", encoding=encoding) as fp: r = _DictReader_with_version(fp, delimiter=delimiter) yield r class SignatureLoadingProgress: """A wrapper for signature loading progress reporting. Instantiate this class once, and then pass it to load_file_as_signatures with progress=. Alternatively, call obj.start_file(location, iter) each time you start loading signatures from a new file via iter. You can optionally notify of reading a file with `.notify(location)`. """ def __init__(self, reporting_interval=10): self.n_sig = 0 self.interval = reporting_interval self.screen_width = 79 def __len__(self): return self.n_sig def short_notify(self, msg_template, *args, **kwargs): """Shorten the notification message so that it fits on one line. Good for repeating notifications with end='\r' especially... """ msg = msg_template.format(*args, **kwargs) end = kwargs.get("end", "\n") w = self.screen_width if len(msg) > w: truncate_len = len(msg) - w + 3 msg = "<<<" + msg[truncate_len:] notify(msg, end=end) def notify(self, location): self.short_notify( f"...{self.n_sig} sigs so far. Now reading from file '{location}'", end="\r" ) def start_file(self, location, loader): n_this = 0 n_before = self.n_sig try: for result in loader: # track n from this file, as well as total n n_this += 1 n_total = n_before + n_this if n_this and n_total % self.interval == 0: self.short_notify( "...loading from '{}' / {} sigs total", location, n_total, end="\r", ) yield result except KeyboardInterrupt: # might as well nicely handle CTRL-C while we're at it! notify("\n(CTRL-C received! quitting.)") sys.exit(-1) finally: self.n_sig += n_this self.short_notify(f"Loaded {n_this} sigs from '{location}'", end="\r") def load_many_signatures( locations, progress, *, yield_all_files=False, ksize=None, moltype=None, picklist=None, force=False, pattern=None, ): """ Load many signatures from multiple files, with progress indicators. Takes ksize, moltype, and picklist selectors. If 'yield_all_files=True' then tries to load all files in specified directories. If 'force=True' then continues past survivable errors. Yields (sig, location) tuples. """ for loc in locations: try: # open index, idx = load_file_as_index(loc, yield_all_files=yield_all_files) idx = idx.select(ksize=ksize, moltype=moltype) idx = apply_picklist_and_pattern(idx, picklist, pattern) # start up iterator, loader = idx.signatures_with_location() # go! n = 0 # count signatures loaded for sig, sigloc in progress.start_file(loc, loader): yield sig, sigloc n += 1 notify(f"loaded {n} signatures from '{loc}'", end="\r") except ValueError as exc: # trap expected errors, and either power through or display + exit. if force: notify(f"ERROR: {str(exc)}") notify("(continuing)") continue else: notify(f"ERROR: {str(exc)}") sys.exit(-1) except KeyboardInterrupt: notify("Received CTRL-C - exiting.") sys.exit(-1) n_files = len(locations) notify(f"loaded {len(progress)} signatures total, from {n_files} files") def get_manifest(idx, *, require=True, rebuild=False): """ Retrieve a manifest for this idx, loaded with `load_file_as_index`. Even if a manifest exists and `rebuild` is True, rebuild the manifest. If a manifest does not exist or `rebuild` is True, try to build one. If a manifest cannot be built and `require` is True, error exit. In the case where `require=False` and a manifest cannot be built, may return None. Otherwise always returns a manifest. """ m = idx.manifest # has one, and don't want to rebuild? easy! return! if m is not None and not rebuild: debug_literal("get_manifest: found manifest") return m debug_literal(f"get_manifest: no manifest found / rebuild={rebuild}") # need to build one... try: notify("Generating a manifest...") m = CollectionManifest.create_manifest( idx._signatures_with_internal(), include_signature=False ) debug_literal("get_manifest: rebuilt manifest.") except NotImplementedError: if require: error(f"ERROR: manifests cannot be generated for {idx.location}") sys.exit(-1) else: debug_literal("get_manifest: cannot build manifest, not req'd") return None return m def load_file_as_signatures( filename, *, select_moltype=None, ksize=None, picklist=None, yield_all_files=False, progress=None, pattern=None, _use_manifest=True, ): """Load 'filename' as a collection of signatures. Return an iterable. If 'filename' contains an SBT or LCA indexed database, or a regular Zip file, will return a signatures() generator. If a Zip file and yield_all_files=True, will try to load all files within zip, not just .sig files. If 'filename' is a JSON file containing one or more signatures, will return a list of those signatures. If 'filename' is a directory, will load *.sig underneath this directory into a list of signatures. If yield_all_files=True, will attempt to load all files. Applies selector function if select_moltype, ksize or picklist are given. 'pattern' is a function that returns True on matching values. """ if progress: progress.notify(filename) db = _load_database(filename, yield_all_files) # test fixture ;) if not _use_manifest and db.manifest: db.manifest = None db = db.select(moltype=select_moltype, ksize=ksize) # apply pattern search & picklist db = apply_picklist_and_pattern(db, picklist, pattern) loader = db.signatures() if progress is not None: return progress.start_file(filename, loader) else: return loader def load_one_signature( filename, *, select_moltype=None, ksize=None, picklist=None, yield_all_files=False, pattern=None, ): db = _load_database(filename, yield_all_files) db = db.select(moltype=select_moltype, ksize=ksize) # apply pattern search & picklist db = apply_picklist_and_pattern(db, picklist, pattern) loader = db.signatures() # load exactly one! try: ss = next(iter(loader)) except StopIteration: raise ValueError(f"no signatures in '{filename}'? expected exactly one.") # make sure there's not a second one... try: _ = next(iter(loader)) raise ValueError( f"more than one signature in '{filename}'; expected exactly one" ) except StopIteration: pass return ss sourmash-bio-sourmash-bc3c0f8/src/sourmash/sqlite_utils.py000066400000000000000000000045441474157046300242200ustar00rootroot00000000000000""" Common utility functions for handling sqlite3 databases. """ import os import sqlite3 from .logging import debug_literal def open_sqlite_db(filename): """ Is this a pre-existing sqlite3 database? Return connection object if so. Otherwise, return None. """ debug_literal("open_sqlite_db: started") # does it already exist/is it non-zero size? # note: sqlite3.connect creates the file if it doesn't exist, which # we don't want in this function. if not os.path.exists(filename) or os.path.getsize(filename) == 0: debug_literal("open_sqlite_db: no file/zero sized file") return None # can we connect to it? try: conn = sqlite3.connect(filename) except (sqlite3.OperationalError, sqlite3.DatabaseError): debug_literal("open_sqlite_db: cannot connect.") return None # check for the 'sourmash_internal' table. cursor = conn.cursor() try: cursor.execute("SELECT DISTINCT key, value FROM sourmash_internal") except (sqlite3.OperationalError, sqlite3.DatabaseError): debug_literal("open_sqlite_db: cannot read sourmash_internal.") # is this a taxonomy DB? try: cursor.execute("SELECT * FROM taxonomy LIMIT 1") except (sqlite3.OperationalError, sqlite3.DatabaseError): debug_literal("open_sqlite_db: cannot read 'taxonomy', either.") return None return conn def add_sourmash_internal(cursor, use_type, version): """ Add use_type/version to sourmash_internal table. """ cursor.execute( """ CREATE TABLE IF NOT EXISTS sourmash_internal ( key TEXT UNIQUE, value TEXT ) """ ) d = get_sourmash_internal(cursor) val = d.get(use_type) if val is not None: # do version compatibility foo here? if version != val: raise Exception( f"sqlite problem: for {use_type}, want version {version}, got version {val}" ) else: cursor.execute( """ INSERT INTO sourmash_internal (key, value) VALUES (?, ?) """, (use_type, version), ) def get_sourmash_internal(cursor): """ Retrieve a key/value dictionary from sourmash_internal. """ cursor.execute("SELECT DISTINCT key, value FROM sourmash_internal") d = dict(cursor) return d sourmash-bio-sourmash-bc3c0f8/src/sourmash/tax/000077500000000000000000000000001474157046300217125ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/src/sourmash/tax/__init__.py000066400000000000000000000000331474157046300240170ustar00rootroot00000000000000from .__main__ import main sourmash-bio-sourmash-bc3c0f8/src/sourmash/tax/__main__.py000066400000000000000000000562431474157046300240160ustar00rootroot00000000000000""" Command-line entry point for 'python -m sourmash.tax' """ import sys import csv import os from collections import defaultdict, Counter from dataclasses import asdict, fields import re import sourmash from ..sourmash_args import FileOutputCSV, FileInputCSV, FileOutput from sourmash.logging import set_quiet, error, notify, print_results from . import tax_utils from .tax_utils import ( MultiLineageDB, RankLineageInfo, LINLineageInfo, AnnotateTaxResult, ICTVRankLineageInfo, ) usage = """ sourmash taxonomy [] - manipulate/work with taxonomy information. or sourmash tax [] ** Commands can be: annotate -g [ ... ] -t [ ...] - annotate gather CSVs with taxonomic lineages genome -g [ ... ] -t [ ...] - taxonomic classification of genomes from gather results metagenome -g [ ... ] -t [ ...] - summarize taxonomic information for metagenome gather results ** Use '-h' to get subcommand-specific help, e.g. sourmash taxonomy metagenome -h """ # outfile utils _output_type_to_ext = { "csv_summary": ".summarized.csv", "classification": ".classifications.csv", "krona": ".krona.tsv", "lineage_summary": ".lineage_summary.tsv", "annotate": ".with-lineages.csv", "human": ".human.txt", "lineage_csv": ".lineage.csv", "kreport": ".kreport.txt", "lingroup": ".lingroup.tsv", "bioboxes": ".bioboxes.profile", } def make_outfile(base, output_type, *, output_dir=""): limit_float_decimals = False if base == "-": limit_float_decimals = True return base, limit_float_decimals ext = _output_type_to_ext[output_type] fname = base + ext if output_dir: fname = os.path.join(output_dir, fname) notify(f"saving '{output_type}' output to '{fname}'.") return fname, limit_float_decimals ##### taxonomy command line functions def metagenome(args): """ summarize taxonomic information for metagenome gather results """ set_quiet(args.quiet) # first, load taxonomic_assignments try: tax_assign = MultiLineageDB.load( args.taxonomy_csv, keep_full_identifiers=args.keep_full_identifiers, keep_identifier_versions=args.keep_identifier_versions, force=args.force, lins=args.lins, ictv=args.ictv, ) available_ranks = tax_assign.available_ranks except ValueError as exc: error(f"ERROR: {str(exc)}") sys.exit(-1) if not tax_assign: error( f"ERROR: No taxonomic assignments loaded from {','.join(args.taxonomy_csv)}. Exiting." ) sys.exit(-1) if args.rank and args.rank not in available_ranks: error( f"ERROR: No taxonomic information provided for rank {args.rank}: cannot summarize at this rank" ) sys.exit(-1) # next, collect and load gather results gather_csvs = tax_utils.collect_gather_csvs( args.gather_csv, from_file=args.from_file ) try: query_gather_results = tax_utils.check_and_load_gather_csvs( gather_csvs, tax_assign, force=args.force, fail_on_missing_taxonomy=args.fail_on_missing_taxonomy, keep_full_identifiers=args.keep_full_identifiers, keep_identifier_versions=args.keep_identifier_versions, lins=args.lins, ictv=args.ictv, ) except ValueError as exc: error(f"ERROR: {str(exc)}") sys.exit(-1) if not query_gather_results: notify("No gather results loaded. Exiting.") sys.exit(-1) single_query_output_formats = ["kreport", "lingroup", "bioboxes"] desired_single_outputs = [] if len(query_gather_results) > 1: # working with multiple queries desired_single_outputs = [ x for x in args.output_format if x in single_query_output_formats ] if desired_single_outputs: notify( f"WARNING: found results for multiple gather queries. Can only output multi-query result formats: skipping {', '.join(desired_single_outputs)}" ) # remove single query outputs from output format args.output_format = [ x for x in args.output_format if x not in single_query_output_formats ] if ( not args.output_format ): # or do we want to insert `human` here so we always report something? error("ERROR: No output formats remaining.") sys.exit(-1) # for each queryResult, actually summarize at rank, reporting any errors that occur. for queryResult in query_gather_results: try: queryResult.build_summarized_result() except ValueError as exc: error(f"ERROR: {str(exc)}") sys.exit(-1) # if lingroup file is passed in, read it lingroups = None if args.lingroup is not None: try: lingroups = tax_utils.read_lingroups(args.lingroup) except ValueError as exc: error(f"ERROR: {str(exc)}") sys.exit(-1) # write summarized output in human-readable format if "lineage_summary" in args.output_format: lineage_outfile, limit_float = make_outfile( args.output_base, "lineage_summary", output_dir=args.output_dir ) ## aggregate by lineage by query lineageD, query_names = tax_utils.aggregate_by_lineage_at_rank( query_gather_results=query_gather_results, rank=args.rank, by_query=True ) with FileOutputCSV(lineage_outfile) as out_fp: tax_utils.write_lineage_sample_frac(query_names, lineageD, out_fp, sep="\t") # write summarized --> krona output tsv if "krona" in args.output_format: krona_results, header = tax_utils.format_for_krona( query_gather_results, rank=args.rank ) krona_outfile, limit_float = make_outfile( args.output_base, "krona", output_dir=args.output_dir ) with FileOutputCSV(krona_outfile) as out_fp: tax_utils.write_krona(header, krona_results, out_fp) if "human" in args.output_format: summary_outfile, limit_float = make_outfile( args.output_base, "human", output_dir=args.output_dir ) with FileOutput(summary_outfile) as out_fp: human_display_rank = args.rank or "species" if args.lins and not args.rank: human_display_rank = query_gather_results[0].ranks[-1] # lowest rank tax_utils.write_human_summary( query_gather_results, out_fp, human_display_rank ) # write summarized output csv single_query_results = query_gather_results[0] if "csv_summary" in args.output_format: summary_outfile, limit_float = make_outfile( args.output_base, "csv_summary", output_dir=args.output_dir ) with FileOutputCSV(summary_outfile) as out_fp: tax_utils.write_summary( query_gather_results, out_fp, limit_float_decimals=limit_float, lingroups=lingroups, ) # write summarized --> kreport output tsv if "kreport" in args.output_format: kreport_outfile, limit_float = make_outfile( args.output_base, "kreport", output_dir=args.output_dir ) with FileOutputCSV(kreport_outfile) as out_fp: header, kreport_results = single_query_results.make_kreport_results() tax_utils.write_output( header, kreport_results, out_fp, sep="\t", write_header=False ) # write summarized --> LINgroup output tsv if "lingroup" in args.output_format and lingroups is not None: lingroupfile, limit_float = make_outfile( args.output_base, "lingroup", output_dir=args.output_dir ) with FileOutputCSV(lingroupfile) as out_fp: header, lgreport_results = single_query_results.make_lingroup_results( LINgroupsD=lingroups ) tax_utils.write_output( header, lgreport_results, out_fp, sep="\t", write_header=True ) # write cami bioboxes format if "bioboxes" in args.output_format: bbfile, limit_float = make_outfile( args.output_base, "bioboxes", output_dir=args.output_dir ) with FileOutputCSV(bbfile) as out_fp: header_lines, bb_results = single_query_results.make_cami_bioboxes() tax_utils.write_bioboxes(header_lines, bb_results, out_fp, sep="\t") def genome(args): """ taxonomic classification of genomes from gather results """ set_quiet(args.quiet) # first, load taxonomic_assignments try: tax_assign = MultiLineageDB.load( args.taxonomy_csv, keep_full_identifiers=args.keep_full_identifiers, keep_identifier_versions=args.keep_identifier_versions, force=args.force, lins=args.lins, ictv=args.ictv, ) available_ranks = tax_assign.available_ranks lg_ranks = None all_lgs = None if args.lingroup: lingroups = tax_utils.read_lingroups(args.lingroup) lg_ranks, all_lgs = tax_utils.parse_lingroups(lingroups) except ValueError as exc: error(f"ERROR: {str(exc)}") sys.exit(-1) if not tax_assign: error( f"ERROR: No taxonomic assignments loaded from {','.join(args.taxonomy_csv)}. Exiting." ) sys.exit(-1) if args.rank and args.rank not in available_ranks: error( f"ERROR: No taxonomic information provided for rank {args.rank}: cannot classify at this rank" ) sys.exit(-1) # get gather_csvs from args gather_csvs = tax_utils.collect_gather_csvs( args.gather_csv, from_file=args.from_file ) try: query_gather_results = tax_utils.check_and_load_gather_csvs( gather_csvs, tax_assign, force=args.force, fail_on_missing_taxonomy=args.fail_on_missing_taxonomy, keep_full_identifiers=args.keep_full_identifiers, keep_identifier_versions=args.keep_identifier_versions, lins=args.lins, ictv=args.ictv, ) except ValueError as exc: error(f"ERROR: {str(exc)}") sys.exit(-1) if not query_gather_results: notify("No results for classification. Exiting.") sys.exit(-1) # for each queryResult, summarize at rank and classify according to thresholds, reporting any errors that occur. n_total = len(query_gather_results) classified_results = [] found_error = False for queryResult in query_gather_results: try: queryResult.build_classification_result( rank=args.rank, ani_threshold=args.ani_threshold, containment_threshold=args.containment_threshold, lingroup_ranks=lg_ranks, lingroups=all_lgs, ) classified_results.append(queryResult) except ValueError as exc: found_error = True notify(f"ERROR: {str(exc)}") n_classified = len(classified_results) if n_classified == 0: notify("No queries could be classified. Exiting.") sys.exit(-1) else: classif_perc = (float(n_classified) / float(n_total)) * 100 notify( f"classified {n_classified}/{n_total} queries ({classif_perc:.2f}%). Writing results" ) # write outputs if "csv_summary" in args.output_format: summary_outfile, limit_float = make_outfile( args.output_base, "classification", output_dir=args.output_dir ) with FileOutputCSV(summary_outfile) as out_fp: tax_utils.write_summary( classified_results, out_fp, limit_float_decimals=limit_float, classification=True, ) # write summarized output in human-readable format if "human" in args.output_format: summary_outfile, limit_float = make_outfile( args.output_base, "human", output_dir=args.output_dir ) with FileOutput(summary_outfile) as out_fp: tax_utils.write_human_summary( query_gather_results, out_fp, args.rank or "species", classification=True, ) # The following require a single rank: # note: interactive krona can handle mult ranks, do we want to enable? if "krona" in args.output_format: krona_results, header = tax_utils.format_for_krona( query_gather_results=query_gather_results, rank=args.rank, classification=True, ) krona_outfile, limit_float = make_outfile( args.output_base, "krona", output_dir=args.output_dir ) with FileOutputCSV(krona_outfile) as out_fp: tax_utils.write_krona(header, krona_results, out_fp) if "lineage_csv" in args.output_format: lineage_outfile, _ = make_outfile( args.output_base, "lineage_csv", output_dir=args.output_dir ) lineage_results = [] header = None for q_res in query_gather_results: if not header: ranks = list(q_res.ranks) if ( "strain" in ranks ): # maintains prior functionality.. but we could keep strain now, i think? ranks.remove("strain") header = ["ident", *ranks] lineageD = q_res.classification_result.as_lineage_dict( q_res.query_info, ranks ) lineage_results.append(lineageD) with FileOutputCSV(lineage_outfile) as out_fp: tax_utils.write_output(header, lineage_results, out_fp) # if there was a classification error, exit with err code if found_error: if not args.force: sys.exit(-1) def annotate(args): """ Annotate gather results with taxonomic lineage for each match. Produces gather csv with lineage information as the final column. """ set_quiet(args.quiet) try: # first, load taxonomic_assignments tax_assign = MultiLineageDB.load( args.taxonomy_csv, keep_full_identifiers=args.keep_full_identifiers, keep_identifier_versions=args.keep_identifier_versions, force=args.force, lins=args.lins, ictv=args.ictv, ) except ValueError as exc: error(f"ERROR: {str(exc)}") sys.exit(-1) if not tax_assign: error( f"ERROR: No taxonomic assignments loaded from {','.join(args.taxonomy_csv)}. Exiting." ) sys.exit(-1) # get csv from args input_csvs = tax_utils.collect_gather_csvs( args.gather_csv, from_file=args.from_file ) # handle each gather csv separately for n, in_csv in enumerate(input_csvs): try: # Check for a column we can use to find lineage information: with FileInputCSV(in_csv) as r: header = r.fieldnames # check for empty file if not header: raise ValueError(f"Cannot read from '{in_csv}'. Is file empty?") # look for the column to match with taxonomic identifier id_col = None col_options = ["name", "match_name", "ident", "accession"] for colname in col_options: if colname in header: id_col = colname break if not id_col: raise ValueError( f"Cannot find taxonomic identifier column in '{in_csv}'. Tried: {', '.join(col_options)}" ) notify( f"Starting annotation on '{in_csv}'. Using ID column: '{id_col}'" ) # make output file for this input out_base = os.path.basename(in_csv.rsplit(".csv")[0]) this_outfile, _ = make_outfile( out_base, "annotate", output_dir=args.output_dir ) out_header = header + ["lineage"] with FileOutputCSV(this_outfile) as out_fp: w = csv.DictWriter(out_fp, out_header) w.writeheader() n = 0 n_missed = 0 for n, row in enumerate(r): # find lineage and write annotated row taxres = AnnotateTaxResult( raw=row, id_col=id_col, lins=args.lins, ictv=args.ictv, keep_full_identifiers=args.keep_full_identifiers, keep_identifier_versions=args.keep_identifier_versions, ) taxres.get_match_lineage( tax_assignments=tax_assign, fail_on_missing_taxonomy=args.fail_on_missing_taxonomy, ) if taxres.missed_ident: # could not assign taxonomy n_missed += 1 w.writerow(taxres.row_with_lineages()) rows_annotated = (n + 1) - n_missed if not rows_annotated: raise ValueError( f"Could not annotate any rows from '{in_csv}'." ) else: notify( f"Annotated {rows_annotated} of {n + 1} total rows from '{in_csv}'." ) except ValueError as exc: if args.force: notify(str(exc)) notify("--force is set. Attempting to continue to next file.") else: error(f"ERROR: {str(exc)}") sys.exit(-1) def prepare(args): "Combine multiple taxonomy databases into one and/or translate formats." notify("loading taxonomies...") try: tax_assign = MultiLineageDB.load( args.taxonomy_csv, force=args.force, keep_full_identifiers=args.keep_full_identifiers, keep_identifier_versions=args.keep_identifier_versions, ) except ValueError as exc: error("ERROR while loading taxonomies!") error(str(exc)) sys.exit(-1) notify(f"...loaded {len(tax_assign)} entries.") notify(f"saving to '{args.output}', format {args.database_format}...") try: tax_assign.save(args.output, args.database_format) except ValueError as exc: error("ERROR while saving!") error(str(exc)) sys.exit(-1) notify("done!") def grep(args): term = args.pattern tax_assign = MultiLineageDB.load(args.taxonomy_csv, force=args.force) silent = args.silent or args.count notify(f"searching {len(args.taxonomy_csv)} taxonomy files for '{term}'") if args.invert_match: notify( "-v/--invert-match specified; returning only lineages that do not match." ) if args.rank: notify(f"limiting matches to {args.rank} level") # build the search pattern pattern = args.pattern if args.ignore_case: pattern = re.compile(pattern, re.IGNORECASE) else: pattern = re.compile(pattern) # determine if lineage matches. def find_pattern(lineage, select_rank): for lp in lineage: if select_rank is None or lp.rank == select_rank: if pattern.search(lp.name): return True return False if args.invert_match: def search_pattern(l, r): return not find_pattern(l, r) else: search_pattern = find_pattern match_ident = [] for ident, lineage in tax_assign.items(): if search_pattern(lineage, args.rank): match_ident.append((ident, lineage)) if silent: notify(f"found {len(match_ident)} matches.") notify("(no matches will be saved because of --silent/--count") else: with FileOutputCSV(args.output) as fp: w = csv.writer(fp) w.writerow(["ident"] + list(RankLineageInfo().taxlist[:-1])) for ident, lineage in sorted(match_ident): w.writerow([ident] + [x.name for x in lineage]) notify( f"found {len(match_ident)} matches; saved identifiers to picklist file '{args.output}'" ) def summarize(args): "Summarize multiple taxonomy databases." notify("loading taxonomies...") try: tax_assign = MultiLineageDB.load( args.taxonomy_files, force=args.force, keep_full_identifiers=args.keep_full_identifiers, keep_identifier_versions=args.keep_identifier_versions, lins=args.lins, ictv=args.ictv, ) except ValueError as exc: error("ERROR while loading taxonomies!") error(str(exc)) sys.exit(-1) notify(f"...loaded {len(tax_assign)} entries.") print_results(f"number of distinct taxonomic lineages: {len(tax_assign)}") # count the number of distinct lineage names seen rank_counts = defaultdict(int) name_seen = set() for v in tax_assign.values(): for vv in v: name = vv.name rank = vv.rank if name not in name_seen: rank_counts[rank] += 1 name_seen.add(name) rank_count_items = list(rank_counts.items()) rank_count_items.sort(key=lambda x: x[1]) for rank, count in rank_count_items: rank_name_str = f"{rank}:" print_results(f"rank {rank_name_str:<20s} {count} distinct taxonomic lineages") if args.output_lineage_information: notify("now calculating detailed lineage counts...") lineage_counts = Counter() for v in tax_assign.values(): tup = v while tup: lineage_counts[tup] += 1 tup = tup[:-1] notify("...done!") with FileOutputCSV(args.output_lineage_information) as fp: w = csv.writer(fp) w.writerow(["rank", "lineage_count", "lineage"]) # output in order of most common for lineage, count in lineage_counts.most_common(): rank = lineage[-1].rank if args.lins: inf = LINLineageInfo(lineage=lineage) elif args.ictv: inf = ICTVRankLineageInfo(lineage=lineage) else: inf = RankLineageInfo(lineage=lineage) lin = inf.display_lineage() w.writerow([rank, str(count), lin]) n = len(lineage_counts) notify(f"saved {n} lineage counts to '{args.output_lineage_information}'") def main(arglist=None): args = sourmash.cli.get_parser().parse_args(arglist) submod = getattr(sourmash.cli.sig, args.subcmd) mainmethod = getattr(submod, "main") return mainmethod(args) if __name__ == "__main__": main(sys.argv) sourmash-bio-sourmash-bc3c0f8/src/sourmash/tax/tax_utils.py000066400000000000000000003220411474157046300243020ustar00rootroot00000000000000""" Utility functions for taxonomy analysis tools. """ import os import csv from collections import abc, defaultdict from itertools import zip_longest from typing import NamedTuple from dataclasses import dataclass, field, replace, asdict, fields import gzip from sourmash import sqlite_utils, sourmash_args from sourmash.exceptions import IndexNotSupported from sourmash.distance_utils import containment_to_distance import sqlite3 __all__ = [ "get_ident", "ascending_taxlist", "collect_gather_csvs", "load_gather_results", "check_and_load_gather_csvsreport_missing_and_skipped_identities", "aggregate_by_lineage_at_rankformat_for_krona", "write_output", "write_bioboxes", "parse_lingroups", "combine_sumgather_csvs_by_lineage", "write_lineage_sample_frac", "MultiLineageDB", "RankLineageInfo", "LINLineageInfo", ] from sourmash.logging import notify from sourmash.sourmash_args import load_pathlist_from_file RANKCODE = { "superkingdom": "D", "kingdom": "K", "phylum": "P", "class": "C", "order": "O", "family": "F", "genus": "G", "species": "S", "unclassified": "U", } ICTV_RANKS = ( "realm", "subrealm", "kingdom", "subkingdom", "phylum", "subphylum", "class", "subclass", "order", "suborder", "family", "subfamily", "genus", "subgenus", "species", "name", ) NCBI_RANKS = ( "superkingdom", "phylum", "class", "order", "family", "genus", "species", "strain", ) class LineagePair(NamedTuple): rank: str name: str = None taxid: int = None @dataclass(frozen=True, order=True) class BaseLineageInfo: """ This BaseLineageInfo class defines a set of methods that can be used to handle summarization and manipulation of taxonomic lineages with hierarchical taxonomic ranks. Inputs: required: ranks: tuple or list of hierarchical ranks optional: lineage: tuple or list of LineagePair lineage_str: `;`- or `,`-separated string of names If no lineage information is provided, result will be a BaseLineageInfo with provided ranks and no lineage names. Input lineage information is only used for initialization of the final `lineage` and will not be used or compared in any other class methods. """ # need to set compare=False for any mutable type to keep this class hashable ranks: () # require ranks lineage: tuple = None # tuple of LineagePairs lineage_str: str = field( default=None, compare=False ) # ';'- or ','-separated str of lineage names def __post_init__(self): "Initialize according to passed values" # ranks must be tuple for hashability if isinstance(self.ranks, list): object.__setattr__(self, "ranks", tuple(self.ranks)) if self.lineage is not None: self._init_from_lineage_tuples() elif self.lineage_str is not None: self._init_from_lineage_str() else: self._init_empty() def __eq__(self, other): if ( other == () ): # just handy: if comparing to a null tuple, don't try to find its lineage before returning False return False return all([self.ranks == other.ranks and self.lineage == other.lineage]) @property def taxlist(self): return self.ranks @property def ascending_taxlist(self): return self.ranks[::-1] @property def lowest_rank(self): if not self.filled_ranks: return None return self.filled_ranks[-1] def rank_index(self, rank): self.check_rank_availability(rank) return self.ranks.index(rank) def name_at_rank(self, rank): "Return the lineage name at this rank" self.check_rank_availability(rank) if not self.filled_ranks or rank not in self.filled_ranks: return None rank_idx = self.rank_index(rank) return self.filled_lineage[rank_idx].name @property def filled_lineage(self): """Return lineage down to lowest non-empty rank. Preserves missing ranks above.""" # Would we prefer this to be the default returned by lineage?? if not self.filled_ranks: return () lowest_filled_rank_idx = self.rank_index(self.filled_ranks[-1]) return self.lineage[: lowest_filled_rank_idx + 1] @property def lowest_lineage_name(self): "Return the name of the lowest filled lineage" if not self.filled_ranks: return None return self.filled_lineage[-1].name @property def lowest_lineage_taxid(self): "Return the taxid of the lowest filled lineage" if not self.filled_ranks: return None return self.filled_lineage[-1].taxid def _init_empty(self): "initialize empty genome lineage" new_lineage = [] for rank in self.ranks: new_lineage.append(LineagePair(rank=rank)) # set lineage and filled_ranks (because frozen, need to do it this way) object.__setattr__(self, "lineage", tuple(new_lineage)) object.__setattr__(self, "filled_ranks", ()) def _init_from_lineage_tuples(self): "initialize from tuple/list of LineagePairs, allowing empty ranks and reordering if necessary" new_lineage = [] # check this is a list or tuple of lineage tuples: for rank in self.ranks: new_lineage.append(LineagePair(rank=rank)) for lin_tup in self.lineage: # now add input tuples in correct spots. This corrects for order and allows empty values. if not isinstance(lin_tup, LineagePair): raise ValueError(f"{lin_tup} is not tax_utils LineagePair.") if lin_tup.rank: # skip this tuple if rank is None or "" (empty lineage tuple. is this needed?) try: # find index for this rank rank_idx = self.rank_index(lin_tup.rank) except ValueError as e: raise ValueError( f"Rank '{lin_tup.rank}' not present in {', '.join(self.ranks)}" ) from e new_lineage[rank_idx] = lin_tup # build list of filled ranks filled_ranks = [a.rank for a in new_lineage if a.name is not None] # set lineage and filled_ranks object.__setattr__(self, "lineage", tuple(new_lineage)) object.__setattr__(self, "filled_ranks", tuple(filled_ranks)) def _init_from_lineage_str(self): """ Turn a ; or ,-separated set of lineages into a list of LineagePair objs. """ new_lineage = self.lineage_str.split(";") if len(new_lineage) == 1: new_lineage = self.lineage_str.split(",") new_lineage = [ LineagePair(rank=rank, name=n) for (rank, n) in zip_longest(self.ranks, new_lineage) ] # build list of filled ranks filled_ranks = [a.rank for a in new_lineage if a.name is not None] object.__setattr__(self, "lineage", tuple(new_lineage)) object.__setattr__(self, "filled_ranks", tuple(filled_ranks)) def zip_lineage(self, truncate_empty=False): """ Return lineage names as a list """ if truncate_empty: zipped = [a.name for a in self.filled_lineage] else: zipped = [a.name for a in self.lineage] # replace None with empty string ("") if None in zipped: zipped = ["" if x is None else x for x in zipped] return zipped def zip_taxid(self, truncate_empty=False): """ Return taxids as a list """ if truncate_empty: zipped = [a.taxid for a in self.filled_lineage] else: zipped = [a.taxid for a in self.lineage] # replace None with empty string (""); cast taxids to str zipped = ["" if x is None else str(x) for x in zipped] return zipped def display_lineage(self, truncate_empty=True, null_as_unclassified=False, sep=";"): "Return lineage names as ';'-separated list" lin = sep.join(self.zip_lineage(truncate_empty=truncate_empty)) if null_as_unclassified and lin == "" or lin is None: return "unclassified" else: return lin def display_taxid(self, truncate_empty=True, sep=";"): "Return lineage taxids as ';'-separated list" return sep.join(self.zip_taxid(truncate_empty=truncate_empty)) def check_rank_availability(self, rank): if rank in self.ranks: # rank is available return True raise ValueError(f"Desired Rank '{rank}' not available for this lineage.") def rank_is_filled(self, rank, other=None): self.check_rank_availability(rank) if other is not None: if rank in self.filled_ranks and rank in other.filled_ranks: return True elif rank in self.filled_ranks: return True return False def is_compatible(self, other): if self.ranks == other.ranks: return True return False def is_lineage_match(self, other, rank): """ check to see if two lineages are a match down to given rank. """ self.check_rank_availability(rank) if not self.is_compatible(other): raise ValueError( "Cannot compare lineages from taxonomies with different ranks." ) # always return false if rank is not filled in either of the two lineages if self.rank_is_filled(rank, other=other): rank_idx = self.rank_index(rank) a_lin = self.lineage[: rank_idx + 1] b_lin = other.lineage[: rank_idx + 1] if a_lin == b_lin: return 1 return 0 def pop_to_rank(self, rank): "Return new LineageInfo with ranks only filled to desired rank" # are we already above rank? self.check_rank_availability(rank) if not self.rank_is_filled(rank): return replace(self) # if not, make filled_lineage at this rank + use to generate new LineageInfo new_lineage = self.lineage_at_rank(rank) new = replace(self, lineage=new_lineage) # replace doesn't run the __post_init__ properly. reinitialize. new._init_from_lineage_tuples() return new def lineage_at_rank(self, rank): "Return tuple of LineagePairs at specified rank." # are we already above rank? self.check_rank_availability(rank) if not self.rank_is_filled(rank): return self.filled_lineage # if not, return lineage tuples down to desired rank rank_idx = self.rank_index(rank) return self.filled_lineage[: rank_idx + 1] def find_lca(self, other): """ If an LCA match exists between self and other, find and report LCA lineage. If not, return None. """ for rank in self.ascending_taxlist: if self.is_lineage_match(other, rank): return self.pop_to_rank(rank) return None @dataclass(frozen=True, order=True) class RankLineageInfo(BaseLineageInfo): """ This RankLineageInfo class uses the BaseLineageInfo methods for a standard set of taxonomic ranks. Inputs: optional: ranks: tuple or list of hierarchical ranks default: ('superkingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species', 'strain') lineage: tuple or list of LineagePair lineage_str: `;`- or `,`-separated string of names lineage_dict: dictionary of {rank: name} If no inputs are provided, result will be RankLineageInfo with default ranks and no lineage names. Input lineage information is only used for initialization of the final `lineage` and will not be used or compared in any other class methods. """ ranks: tuple = NCBI_RANKS lineage_dict: dict = field(default=None, compare=False) # dict of rank: name def __post_init__(self): "Initialize according to passed values" # ranks must be tuple for hashability if isinstance(self.ranks, list): object.__setattr__(self, "ranks", tuple(self.ranks)) if self.lineage is not None: self._init_from_lineage_tuples() elif self.lineage_str is not None: self._init_from_lineage_str() elif self.lineage_dict is not None: self._init_from_lineage_dict() elif self.ranks: self._init_empty() def _init_from_lineage_dict(self): """ Initialize from lineage dict, e.g. from lineages csv. Use NCBI taxids if available as '|'-separated 'taxpath' column. Allows empty ranks/extra columns and reordering if necessary """ null_names = set(["[Blank]", "na", "null", "NA", ""]) if not isinstance(self.lineage_dict, (dict)): raise ValueError(f"{self.lineage_dict} is not dictionary") new_lineage = [] taxpath = [] # build empty lineage and taxpath for rank in self.ranks: new_lineage.append(LineagePair(rank=rank)) # check for NCBI taxpath information taxpath_str = self.lineage_dict.get("taxpath", []) if taxpath_str: taxpath = taxpath_str.split("|") if len(taxpath) > len(self.ranks): raise ValueError( f"Number of NCBI taxids ({len(taxpath)}) exceeds number of ranks ({len(self.ranks)})" ) # now add rank information in correct spots. This corrects for order and allows empty ranks and extra dict keys for key, val in self.lineage_dict.items(): name, taxid = None, None try: rank, name = key, val rank_idx = self.rank_index(rank) except ValueError: continue # ignore dictionary entries (columns) that don't match a rank if taxpath: try: taxid = taxpath[rank_idx] except IndexError: taxid = None # filter null if name is not None and name.strip() in null_names: name = None new_lineage[rank_idx] = LineagePair(rank=rank, name=name, taxid=taxid) # build list of filled ranks filled_ranks = [a.rank for a in new_lineage if a.name] # set lineage and filled_ranks object.__setattr__(self, "lineage", tuple(new_lineage)) object.__setattr__(self, "filled_ranks", tuple(filled_ranks)) @dataclass(frozen=True, order=True) class ICTVRankLineageInfo(RankLineageInfo): """ This ICTV RankLineageInfo class uses the RankLineageInfo methods but uses the 15-rank ICTV taxonomy. It also allows for a 'name' column in the taxonomy, which reflects that virus name is sometimes used as a sub-species rank. Inputs: optional: ranks: tuple or list of hierarchical ranks default: ('realm','subrealm','kingdom','subkingdom','phylum','subphylum', 'class','subclass','order','suborder','family','subfamily', 'genus','subgenus','species','name') lineage: tuple or list of LineagePair lineage_str: `;`- or `,`-separated string of names lineage_dict: dictionary of {rank: name} If no inputs are provided, result will be ICTVRankLineageInfo with default ranks and no lineage names. Input lineage information is only used for initialization of the final `lineage` and will not be used or compared in any other class methods. """ ranks: tuple = ICTV_RANKS lineage_dict: dict = field(default=None, compare=False) # dict of rank: name def __post_init__(self): "Initialize according to passed values" object.__setattr__(self, "ranks", ICTV_RANKS) if self.lineage is not None: self._init_from_lineage_tuples() elif self.lineage_str is not None: self._init_from_lineage_str() elif self.lineage_dict is not None: self._init_from_lineage_dict() elif self.ranks: self._init_empty() def _init_from_lineage_dict(self): """ Initialize from lineage dict, e.g. from lineages csv. Allows empty ranks/extra columns and reordering if necessary """ null_names = set(["[Blank]", "na", "null", "NA", ""]) if not isinstance(self.lineage_dict, (dict)): raise ValueError(f"{self.lineage_dict} is not dictionary") new_lineage = [] # build empty lineage and taxpath for rank in self.ranks: new_lineage.append(LineagePair(rank=rank)) # now add rank information in correct spots. This corrects for order and allows empty ranks and extra dict keys for key, val in self.lineage_dict.items(): name = None try: rank, name = key, val rank_idx = self.rank_index(rank) except ValueError: continue # ignore dictionary entries (columns) that don't match a rank # filter null if name is not None and name.strip() in null_names: name = None new_lineage[rank_idx] = LineagePair(rank=rank, name=name) # build list of filled ranks filled_ranks = [a.rank for a in new_lineage if a.name] # set lineage and filled_ranks object.__setattr__(self, "lineage", tuple(new_lineage)) object.__setattr__(self, "filled_ranks", tuple(filled_ranks)) @dataclass(frozen=True, order=True) class LINLineageInfo(BaseLineageInfo): """ This LINLineageInfo class uses the BaseLineageInfo methods for hierarchical LIN taxonomic 'ranks'. Inputs (at least one required): n_lin_positions: the number of lineage positions lineage_str: `;`- or `,`-separated LINS string If both `n_lin_positions` and `lineage_str` are provided, we will initialize a `LINLineageInfo` with the provided n_lin_positions, and fill positions with `lineage_str` values. If the number of positions is less than provided lineages, initialization will fail. Otherwise, we will insert blanks beyond provided data in `lineage_str`. If no information is passed, an empty LINLineageInfo will be initialized (n_lin_positions=0). Input lineage information is only used for initialization of the final `lineage` and will not be used or compared in any other class methods. """ ranks: tuple = field( default=None, init=False, compare=False ) # we will set this within class instead lineage: tuple = None # init with n_positions if you want to set a specific number of positions n_lin_positions: int = field(default=None, compare=False) def __post_init__(self): "Initialize according to passed values" # ranks must be tuple for hashability if self.lineage is not None: self._init_from_lineage_tuples() elif self.lineage_str is not None: self._init_from_lineage_str() else: self._init_empty() def __eq__(self, other): """ Check if two LINLineageInfo match. Since we sometimes want to match LINprefixes, which have fewer total ranks, with full LINs, we only check for the filled_lineage to match and don't check that the number of lin_positions match. """ if ( other == () ): # if comparing to a null tuple, don't try to find its lineage before returning False return False return self.filled_lineage == other.filled_lineage def _init_ranks_from_n_lin_positions(self): new_ranks = [str(x) for x in range(0, self.n_lin_positions)] object.__setattr__(self, "ranks", new_ranks) def _init_empty(self): "initialize empty genome lineage" # first, set ranks from n_positions if self.n_lin_positions is None: # set n_lin_positions to 0 for completely empty LINLineageInfo object.__setattr__(self, "n_lin_positions", 0) self._init_ranks_from_n_lin_positions() new_lineage = [] for rank in self.ranks: new_lineage.append(LineagePair(rank=rank)) # set lineage and filled_ranks (because frozen, need to do it this way) object.__setattr__(self, "lineage", tuple(new_lineage)) object.__setattr__(self, "filled_ranks", ()) object.__setattr__(self, "n_filled_pos", 0) def _init_from_lineage_str(self): """ Turn a ; or ,-separated set of lineages into a list of LineagePair objs. """ new_lineage = self.lineage_str.split(";") if len(new_lineage) == 1: new_lineage = self.lineage_str.split(",") if self.n_lin_positions is not None: if self.n_lin_positions < len(new_lineage): raise ( ValueError( "Provided 'n_lin_positions' has fewer positions than provided 'lineage_str'." ) ) self._init_ranks_from_n_lin_positions() else: n_lin_positions = len(new_lineage) object.__setattr__(self, "n_lin_positions", n_lin_positions) self._init_ranks_from_n_lin_positions() # build lineage and n_filled_pos, filled_ranks new_lineage = [ LineagePair(rank=rank, name=n) for (rank, n) in zip_longest(self.ranks, new_lineage) ] filled_ranks = [a.rank for a in new_lineage if a.name is not None] object.__setattr__(self, "lineage", tuple(new_lineage)) object.__setattr__(self, "filled_ranks", tuple(filled_ranks)) object.__setattr__(self, "n_filled_pos", len(filled_ranks)) def _init_from_lineage_tuples(self): "initialize from tuple/list of LineagePairs, building ranks as you go" new_lineage = [] ranks = [] # check this is a list or tuple of lineage tuples: for lin_tup in self.lineage: # make sure we're adding tax_utils.LineagePairs if not isinstance(lin_tup, LineagePair): raise ValueError(f"{lin_tup} is not tax_utils LineagePair.") new_lineage.append(lin_tup) ranks.append(lin_tup.rank) # build list of filled ranks filled_ranks = [a.rank for a in new_lineage if a.name is not None] # set lineage and filled_ranks object.__setattr__(self, "lineage", tuple(new_lineage)) object.__setattr__(self, "n_lin_positions", len(new_lineage)) object.__setattr__(self, "ranks", tuple(ranks)) object.__setattr__(self, "filled_ranks", tuple(filled_ranks)) object.__setattr__(self, "n_filled_pos", len(filled_ranks)) def is_compatible(self, other): """ Since we sometimes want to match LINprefixes with full LINs, we don't want to enforce identical ranks. Here we just look to make sure self and other share any ranks (LIN positions). Since ranks are positions, this should be true for LINLineageInfo unless one is empty. However, it should prevent comparison between other LineageInfo instances and LINLineageInfo. """ # do self and other share any ranks? if any(x in self.ranks for x in other.ranks): return True return False @dataclass class LineageTree: """ Builds a tree of dictionaries from lists of LineagePair or LineageInfo objects in 'assignments'. This tree can then be used to find lowest common ancestor agreements/confusion. """ assignments: list = field(compare=False) def __post_init__(self): self.tree = {} self.add_lineages(self.assignments) def add_lineage(self, lineage): if isinstance( lineage, BaseLineageInfo | RankLineageInfo | LINLineageInfo | ICTVRankLineageInfo, ): lineage = lineage.filled_lineage node = self.tree for lineage_tup in lineage: if lineage_tup.name: child = node.get(lineage_tup, {}) node[lineage_tup] = child # shift -> down in tree node = child def add_lineages(self, lineages): if not lineages: raise ValueError("empty assignment passed to build_tree") if not isinstance(lineages, abc.Iterable): raise ValueError( "Must pass in an iterable containing LineagePair or LineageInfo objects." ) for lineageInf in lineages: self.add_lineage(lineageInf) def find_lca(self): """ Given a LineageTree tree, find the first node with multiple children, OR the only leaf in the tree. Return (lineage_tup, reason), where 'reason' is the number of children of the returned node, i.e. 0 if it's a leaf and > 1 if it's an internal node. """ node = self.tree lca = [] while 1: if len(node) == 1: # descend to only child; track path lineage_tup = next(iter(node.keys())) lca.append(lineage_tup) node = node[lineage_tup] elif len(node) == 0: # at leaf; end return tuple(lca), 0 else: # len(node) > 1 => confusion!! return tuple(lca), len(node) def ordered_paths(self, include_internal=False): """ Find all paths in the nested dict in a depth-first manner. Each path is a tuple of lineage tuples that lead from the root to a leaf node. Optionally include internal nodes by building them up from leaf nodes (for ordering). """ paths = [] stack = [((), self.tree)] while stack: path, node = stack.pop() for key, val in node.items(): if len(val) == 0: # leaf node # if want internal paths, build up from leaf if include_internal: internal_path = path while internal_path: if internal_path not in paths: paths.append(internal_path) if isinstance(internal_path, abc.Iterable): internal_path = internal_path[:-1] # now add leaf path paths.append(path + (key,)) else: # not leaf, add to stack stack.append((path + (key,), val)) return paths def get_ident(ident, *, keep_full_identifiers=False, keep_identifier_versions=False): # split identifiers = split on whitespace # keep identifiers = don't split .[12] from assembly accessions "Hack and slash identifiers." if not keep_full_identifiers: ident = ident.split(" ")[0] if not keep_identifier_versions: ident = ident.split(".")[0] return ident def ascending_taxlist(include_strain=True): """ Provide an ordered list of taxonomic ranks: strain --> superkingdom """ ascending_taxlist = [ "species", "genus", "family", "order", "class", "phylum", "superkingdom", ] if include_strain: ascending_taxlist = ["strain"] + ascending_taxlist yield from ascending_taxlist def collect_gather_csvs(cmdline_gather_input, *, from_file=None): """ collect gather files from cmdline; --from-file input """ gather_csvs = [] # ignore command line duplicates for gf in cmdline_gather_input: if gf not in gather_csvs: gather_csvs.append(gf) else: notify(f"ignoring duplicated reference to file: {gf}") # ignore pathlist duplicates if from_file: more_files = load_pathlist_from_file(from_file) for gf in more_files: if gf not in gather_csvs: gather_csvs.append(gf) else: notify(f"ignoring duplicated reference to file: {gf}") return gather_csvs def read_lingroups(lingroup_csv): lingroupD = {} n = None with sourmash_args.FileInputCSV(lingroup_csv) as r: header = r.fieldnames # check for empty file if not header: raise ValueError( f"Cannot read lingroups from '{lingroup_csv}'. Is file empty?" ) if "lin" not in header or "name" not in header: raise ValueError( f"'{lingroup_csv}' must contain the following columns: 'name', 'lin'." ) for n, row in enumerate(r): lingroupD[row["lin"]] = row["name"] if n is None: raise ValueError(f"No lingroups loaded from {lingroup_csv}.") n_lg = len(lingroupD.keys()) notify(f"Read {n + 1} lingroup rows and found {n_lg} distinct lingroup prefixes.") return lingroupD def parse_lingroups(lingroupD): # find the ranks we need to consider all_lgs = set() lg_ranks = set() for lg_prefix in lingroupD.keys(): # store lineage info for LCA pathfinding lg_info = LINLineageInfo(lineage_str=lg_prefix) all_lgs.add(lg_info) # store rank so we only go through summarized results at these ranks lg_rank = str(lg_info.lowest_rank) lg_ranks.add(lg_rank) return lg_ranks, all_lgs def load_gather_results( gather_csv, tax_assignments, *, seen_queries=None, force=False, skip_idents=None, fail_on_missing_taxonomy=False, keep_full_identifiers=False, keep_identifier_versions=False, lins=False, ictv=False, ): "Load a single gather csv" if not seen_queries: seen_queries = set() header = [] gather_results = {} with sourmash_args.FileInputCSV(gather_csv) as r: header = r.fieldnames # check for empty file if not header: raise ValueError( f"Cannot read gather results from '{gather_csv}'. Is file empty?" ) this_querytaxres = None for n, row in enumerate(r): # try reading each gather row into a TaxResult try: filt_row = filter_row( row, GatherRow ) # filter row first to allow extra (unused) columns in csv gatherRow = GatherRow(**filt_row) except TypeError as exc: raise ValueError( f"'{gather_csv}' is missing columns needed for taxonomic summarization. Please run gather with sourmash >= 4.4." ) from exc # check if we've seen this query already in a different gather CSV if gatherRow.query_name in seen_queries: # do not allow loading of same query from a second CSV. raise ValueError( f"Gather query {gatherRow.query_name} was found in more than one CSV. Cannot load from '{gather_csv}'." ) taxres = TaxResult( raw=gatherRow, keep_full_identifiers=keep_full_identifiers, keep_identifier_versions=keep_identifier_versions, lins=lins, ictv=ictv, ) taxres.get_match_lineage( tax_assignments=tax_assignments, skip_idents=skip_idents, fail_on_missing_taxonomy=fail_on_missing_taxonomy, ) # add to matching QueryTaxResult or create new one if not this_querytaxres or not this_querytaxres.is_compatible(taxres): # get existing or initialize new this_querytaxres = gather_results.get( gatherRow.query_name, QueryTaxResult(taxres.query_info, lins=lins, ictv=ictv), ) this_querytaxres.add_taxresult(taxres) gather_results[gatherRow.query_name] = this_querytaxres if not gather_results: raise ValueError(f"No gather results loaded from {gather_csv}.") else: notify(f"loaded {len(gather_results)} gather results from '{gather_csv}'.") return ( gather_results, header, ) # , gather_queries # can use the gather_results keys instead def check_and_load_gather_csvs( gather_csvs, tax_assign, *, fail_on_missing_taxonomy=False, force=False, keep_full_identifiers=False, keep_identifier_versions=False, lins=False, ictv=False, ): """ Load gather csvs, checking for empties and ids missing from taxonomic assignments. """ if not isinstance(gather_csvs, list): gather_csvs = [gather_csvs] gather_results = {} header = [] n_ignored = 0 for n, gather_csv in enumerate(gather_csvs): these_results = {} try: these_results, header = load_gather_results( gather_csv, tax_assign, seen_queries=gather_results.keys(), force=force, keep_full_identifiers=keep_full_identifiers, keep_identifier_versions=keep_identifier_versions, fail_on_missing_taxonomy=fail_on_missing_taxonomy, lins=lins, ictv=ictv, ) except ValueError as exc: if force: if "found in more than one CSV" in str(exc): notify("Cannot force past duplicated gather query. Exiting.") raise if "Failing, as requested via --fail-on-missing-taxonomy" in str(exc): raise notify(str(exc)) notify( "--force is set. Attempting to continue to next set of gather results." ) n_ignored += 1 continue else: notify("Exiting.") raise # add these results to gather_results gather_results.update(these_results) # some reporting num_gather_csvs_loaded = n + 1 - n_ignored notify( f"loaded results for {len(gather_results)} queries from {str(num_gather_csvs_loaded)} gather CSVs" ) # count and report missing and skipped idents report_missing_and_skipped_identities(gather_results) # just return the list of QueryTaxResults query_results_list = list(gather_results.values()) return query_results_list def report_missing_and_skipped_identities(gather_results): """ Report match ids/accessions from gather results that are not present in taxonomic assignments, either by accident (missed) or request (skipped). """ ident_missed = set() ident_skipped = set() total_n_missed = 0 total_n_skipped = 0 total_taxresults = 0 for querytaxres in gather_results.values(): ident_missed.update(querytaxres.missed_idents) ident_skipped.update(querytaxres.skipped_idents) # totals are total rows in gather that were missed - do we want to report these at all? total_n_missed += querytaxres.n_missed total_n_skipped += querytaxres.n_skipped total_taxresults += len(querytaxres.raw_taxresults) if ident_missed: notify( f"of {total_taxresults} gather results, lineage assignments for {total_n_missed} results were missed." ) notify( f"The following are missing from the taxonomy information: {', '.join(ident_missed)}" ) def aggregate_by_lineage_at_rank(query_gather_results, rank, *, by_query=False): """ Aggregate list of summarized_lineage_results at rank, keeping query names or not (but this aggregates across queries if multiple). """ lineage_summary = defaultdict(float) if by_query: lineage_summary = defaultdict(dict) all_queries = [] for queryResult in query_gather_results: query_name = queryResult.query_name all_queries.append(query_name) if rank not in queryResult.summarized_ranks: raise ValueError(f"Error: rank '{rank}' not available for aggregation.") for res in queryResult.summarized_lineage_results[rank]: lineage = res.lineage.display_lineage(null_as_unclassified=True) if by_query: lineage_summary[lineage][query_name] = ( res.fraction ) # v5?: res.f_weighted_at_rank else: lineage_summary[lineage] += res.fraction # if aggregating across queries divide fraction by the total number of queries if not by_query: n_queries = len(all_queries) for lin, fraction in lineage_summary.items(): lineage_summary[lin] = fraction / n_queries return lineage_summary, all_queries def format_for_krona(query_gather_results, rank, *, classification=False): """ Aggregate and format for krona output. Single query recommended, but we don't want query headers. """ # make header header = query_gather_results[0].make_krona_header(min_rank=rank) krona_results = [] # do we want to block more than one query for summarization? if len(query_gather_results) > 1: notify( "WARNING: results from more than one query found. Krona summarization not recommended.\n" "Percentage assignment will be normalized by the number of queries to maintain range 0-100%." ) if classification: # for classification, just write the results for q_res in query_gather_results: if q_res.classified_ranks != [rank]: q_res.build_classification_result(rank=rank) header = q_res.make_krona_header(min_rank=rank) # unclassified is 'correct' in that it is the part not classified to this match, # but also misleading, since we're using best_only and there may # be more matches that are not included here, making % unclassified seem higher than it would # be with summarization. We previously excluded it -- is that the behavior we want to keep? krona_results.extend( [q_res.krona_classified] ) # , q_res.krona_unclassified]) else: lineage_summary, _ = aggregate_by_lineage_at_rank( query_gather_results, rank, by_query=False ) # sort by fraction lin_items = list(lineage_summary.items()) lin_items.sort(key=lambda x: -x[1]) # reformat lineage for krona_results printing unclassified_fraction = 0 for lin, fraction in lin_items: # save unclassified fraction for the end if lin == "unclassified": unclassified_fraction = fraction continue else: lin_list = lin.split(";") krona_results.append((fraction, *lin_list)) # handle unclassified if unclassified_fraction: len_unclassified_lin = len(header) - 1 unclassifed_lin = ["unclassified"] * len_unclassified_lin krona_results.append((unclassified_fraction, *unclassifed_lin)) return krona_results, header def write_krona(header, krona_results, out_fp, *, sep="\t"): "write krona output" # CTB: do we want to optionally allow restriction to a specific rank # & above? NTP: think we originally kept krona to a specific rank, but # that may have been how we were plotting, since krona plots can be # hierarchical? Probably worth changing/extending to multilevel to # take advantage of full krona plot features tsv_output = csv.writer(out_fp, delimiter=sep) tsv_output.writerow(header) for res in krona_results: tsv_output.writerow(res) def write_output(header, results, out_fp, *, sep=",", write_header=True): """ write pre-generated results list of rows, with each row being a dictionary """ output = csv.DictWriter(out_fp, header, delimiter=sep) if write_header: output.writeheader() for res in results: output.writerow(res) def write_bioboxes(header_lines, results, out_fp, *, sep="\t"): """ write pre-generated results list of rows, with each row being list. """ for inf in header_lines: out_fp.write(inf + "\n") for res in results: res = sep.join(res) + "\n" out_fp.write(res) def write_summary( query_gather_results, csv_fp, *, sep=",", limit_float_decimals=False, classification=False, lingroups=None, ): """ Write taxonomy-summarized gather results for each rank. """ w = None for q_res in query_gather_results: header, summary = q_res.make_full_summary( limit_float=limit_float_decimals, classification=classification, lingroups=lingroups, ) if w is None: w = csv.DictWriter(csv_fp, header, delimiter=sep) w.writeheader() for res in summary: w.writerow(res) def write_human_summary( query_gather_results, out_fp, display_rank, classification=False ): """ Write human-readable taxonomy-summarized gather results for a specific rank. """ for queryResult in query_gather_results: results = queryResult.make_human_summary( display_rank=display_rank, classification=classification ) if classification: out_fp.write("sample name status proportion cANI lineage\n") out_fp.write("----------- ------ ---------- ---- -------\n") for rD in results: out_fp.write( "{query_name:<15s} {status} {f_weighted_at_rank} {query_ani_at_rank} {lineage}\n".format( **rD ) ) else: out_fp.write("sample name proportion cANI lineage\n") out_fp.write("----------- ---------- ---- -------\n") for rD in results: out_fp.write( "{query_name:<15s} {f_weighted_at_rank} {query_ani_at_rank} {lineage}\n".format( **rD ) ) def write_lineage_sample_frac(sample_names, lineage_dict, out_fp, *, sep="\t"): """ takes in a lineage dictionary with sample counts (output of aggregate_by_lineage_at_rank) and produces a tab-separated file with fractions for each sample. input: {lin_a: {sample1: 0.4, sample2: 0.17, sample3: 0.6} lin_b: {sample1: 0.0, sample2: 0.0, sample3: 0.1} lin_c: {sample1: 0.3, sample2: 0.4, sample3: 0.2}} output: lineage sample1 sample2 sample3 lin_a 0.4 0.17 0.6 lin_b 0.0 0.0 0.1 lin_c 0.3 0.4 0.2 """ header = ["lineage"] + sample_names w = csv.DictWriter(out_fp, header, delimiter=sep) w.writeheader() blank_row = {query_name: 0 for query_name in sample_names} unclassified_row = None for lin, sampleinfo in sorted(lineage_dict.items()): # add lineage and 0 placeholders row = {"lineage": lin} row.update(blank_row) # add info for query_names that exist for this lineage row.update(sampleinfo) # if unclassified, save this row for the end if lin == "unclassified": row.update({"lineage": "unclassified"}) unclassified_row = row continue # write row w.writerow(row) if unclassified_row: w.writerow(unclassified_row) class LineageDB(abc.Mapping): "Base LineageDB class built around an assignments dictionary." def __init__(self, assign_d, avail_ranks): self.assignments = assign_d self.available_ranks = set(avail_ranks) def __getitem__(self, ident): "Retrieve the lineage tuple for identifer (or raise KeyError)" return self.assignments[ident] def __iter__(self): "Return all identifiers for this db." return iter(self.assignments) def __len__(self): "Return number of lineages" return len(self.assignments) def __bool__(self): "Are there any lineages at all in this database?" return bool(self.assignments) @classmethod def load( cls, filename, *, delimiter=",", force=False, keep_full_identifiers=False, keep_identifier_versions=True, lins=False, ictv=False, ): """ Load a taxonomy assignment CSV file into a LineageDB. 'keep_full_identifiers=False' will split identifiers from strings using whitespace, e.g. 'IDENT other name stuff' => 'IDENT' 'keep_identifier_versions=False' will remove trailing versions, e.g. 'IDENT.1' => 'IDENT'. """ include_strain = False if not keep_identifier_versions and keep_full_identifiers: raise ValueError( "keep_identifer_versions=False doesn't make sense with keep_full_identifiers=True" ) if not os.path.exists(filename): raise ValueError(f"'{filename}' does not exist") if os.path.isdir(filename): raise ValueError(f"'{filename}' is a directory") with sourmash_args.FileInputCSV(filename, delimiter=",") as r: header = r.fieldnames if not header: raise ValueError(f"cannot read taxonomy assignments from {filename}") identifier = "ident" # check for ident/identifier, handle some common alternatives if "ident" not in header: # check for ident/identifier, handle some common alternatives if "identifiers" in header: identifier = "identifiers" header = ["ident" if "identifiers" == x else x for x in header] elif "accession" in header: identifier = "accession" header = ["ident" if "accession" == x else x for x in header] elif "lineage" in header and any( ["name" in header, "match_name" in header] ): return cls.load_from_gather_with_lineages( filename, force=force, lins=lins, ictv=ictv ) else: header_str = ",".join([repr(x) for x in header]) raise ValueError( f"No taxonomic identifiers found; headers are {header_str}" ) if lins: notify("Trying to read LIN taxonomy assignments.") if "lin" not in header: raise ValueError( f"'lin' column not found: cannot read LIN taxonomy assignments from {filename}." ) if ictv: notify("Trying to read ICTV taxonomy assignments.") # check that all ranks are in header ranks = list(ICTVRankLineageInfo().taxlist) if not set(ranks).issubset(header): raise ValueError("Not all taxonomy ranks present") if not lins and not ictv: # is "strain" an available rank? if "strain" in header: include_strain = True # check that all ranks are in header ranks = list(RankLineageInfo().taxlist) if not include_strain: ranks.remove("strain") if not set(ranks).issubset(header): # for now, just raise err if not all ranks are present. # in future, we can define `ranks` differently if desired # return them from this function so we can check the `available` ranks raise ValueError("Not all taxonomy ranks present") assignments = {} num_rows = 0 n_species = 0 n_strains = 0 n_pos = None # now parse and load lineages for n, row in enumerate(r): num_rows += 1 if lins: lineageInfo = LINLineageInfo(lineage_str=row["lin"]) if n_pos is not None: if lineageInfo.n_lin_positions != n_pos: raise ValueError( "For taxonomic summarization, all LIN assignments must use the same number of LIN positions." ) else: # set n_pos with first entry n_pos = lineageInfo.n_lin_positions ranks = lineageInfo.ranks elif ictv: # read lineage from row dictionary lineageInfo = ICTVRankLineageInfo(lineage_dict=row) else: # read lineage from row dictionary lineageInfo = RankLineageInfo(lineage_dict=row) # get identifier ident = row[identifier] # fold, spindle, and mutilate ident? ident = get_ident( ident, keep_full_identifiers=keep_full_identifiers, keep_identifier_versions=keep_identifier_versions, ) # store lineage tuple lineage = lineageInfo.filled_lineage if lineage: # check duplicates if ident in assignments: if assignments[ident] != lineage: if not force: raise ValueError( f"multiple lineages for identifier {ident}" ) else: assignments[ident] = lineage if not lins and not ictv: if lineage[-1].rank == "species": n_species += 1 elif lineage[-1].rank == "strain": n_species += 1 n_strains += 1 return LineageDB(assignments, ranks) @classmethod def load_from_gather_with_lineages( cls, filename, *, force=False, lins=False, ictv=False ): """ Load an annotated gather-with-lineages CSV file produced by 'tax annotate' into a LineageDB. """ if not os.path.exists(filename): raise ValueError(f"'{filename}' does not exist") if os.path.isdir(filename): raise ValueError(f"'{filename}' is a directory") with sourmash_args.FileInputCSV(filename) as r: header = r.fieldnames if not header: raise ValueError(f"cannot read taxonomy assignments from {filename}") ident_col = None if "name" in header: ident_col = "name" elif "match_name" in header: ident_col = "match_name" if "lineage" not in header or ident_col is None: raise ValueError( "Expected headers 'name'/'match_name' and 'lineage' not found. Is this a with-lineages file?" ) ranks = None assignments = {} num_rows = 0 n_species = 0 n_strains = 0 # now parse and load lineages for n, row in enumerate(r): num_rows += 1 name = row[ident_col] ident = get_ident(name) if lins: lineageInfo = LINLineageInfo(lineage_str=row["lineage"]) elif ictv: lineageInfo = ICTVRankLineageInfo(lineage_str=row["lineage"]) else: lineageInfo = RankLineageInfo(lineage_str=row["lineage"]) if ranks is None: ranks = lineageInfo.taxlist lineage = lineageInfo.filled_lineage # check duplicates if ident in assignments: if assignments[ident] != lineage: # this should not happen with valid # sourmash tax annotate output, but check anyway. if not force: raise ValueError( f"multiple lineages for identifier {ident}" ) else: assignments[ident] = lineage if isinstance(lineageInfo, RankLineageInfo): if lineage[-1].rank == "species": n_species += 1 elif lineage[-1].rank == "strain": n_species += 1 n_strains += 1 return LineageDB(assignments, ranks) class LineageDB_Sqlite(abc.Mapping): """ A LineageDB based on a sqlite3 database with a 'sourmash_taxonomy' table. """ # NOTE: 'order' is a reserved name in sql, so we have to use 'order_'. columns = ( "superkingdom", "phylum", "order_", "class", "family", "genus", "species", "strain", ) table_name = "sourmash_taxonomy" def __init__(self, conn, *, table_name=None): self.conn = conn # provide for legacy support for pre-sourmash_internal days... if table_name is not None: self.table_name = table_name # check that the right table is there. c = conn.cursor() try: c.execute(f"SELECT * FROM {self.table_name} LIMIT 1") except (sqlite3.DatabaseError, sqlite3.OperationalError): raise ValueError("not a taxonomy database") # check: can we do a 'select' on the right table? self.__len__() c = conn.cursor() # get available ranks... ranks = set() for column, rank in zip(self.columns, RankLineageInfo().taxlist): query = f'SELECT COUNT({column}) FROM {self.table_name} WHERE {column} IS NOT NULL AND {column} != ""' c.execute(query) (cnt,) = c.fetchone() if cnt: ranks.add(rank) self.available_ranks = ranks self.cursor = c @classmethod def load(cls, location): "load taxonomy information from an existing sqlite3 database" conn = sqlite_utils.open_sqlite_db(location) if not conn: raise ValueError("not a sqlite taxonomy database") table_name = None c = conn.cursor() try: info = sqlite_utils.get_sourmash_internal(c) except sqlite3.OperationalError: info = {} if "SqliteLineage" in info: if info["SqliteLineage"] != "1.0": raise IndexNotSupported table_name = "sourmash_taxonomy" else: # legacy support for old taxonomy DB, pre sourmash_internal. try: c.execute("SELECT * FROM taxonomy LIMIT 1") table_name = "taxonomy" except sqlite3.OperationalError: pass if table_name is None: raise ValueError("not a sqlite taxonomy database") return cls(conn, table_name=table_name) def _make_tup(self, row): "build a tuple of LineagePairs for this sqlite row" tup = [LineagePair(n, r) for (n, r) in zip(RankLineageInfo().taxlist, row)] return tuple(tup) def __getitem__(self, ident): "Retrieve lineage for identifer" c = self.cursor c.execute( f"SELECT superkingdom, phylum, class, order_, family, genus, species, strain FROM {self.table_name} WHERE ident=?", (ident,), ) # retrieve names list... names = c.fetchone() if names: # ...and construct lineage tuple tup = self._make_tup(names) while tup and not tup[-1].name: tup = tup[:-1] return tup raise KeyError(ident) def __bool__(self): "Do we have any info?" return bool(len(self)) def __len__(self): "Return number of rows" c = self.conn.cursor() c.execute(f"SELECT COUNT(DISTINCT ident) FROM {self.table_name}") (nrows,) = c.fetchone() return nrows def __iter__(self): "Return all identifiers" # create new cursor so as to allow other operations c = self.conn.cursor() c.execute(f"SELECT DISTINCT ident FROM {self.table_name}") for (ident,) in c: yield ident def items(self): "return all items in the sqlite database" c = self.conn.cursor() c.execute( f"SELECT DISTINCT ident, superkingdom, phylum, class, order_, family, genus, species, strain FROM {self.table_name}" ) for ident, *names in c: yield ident, self._make_tup(names) class MultiLineageDB(abc.Mapping): "A wrapper for (dynamically) combining multiple lineage databases." # NTP: currently, later lineage databases will override earlier ones. # Do we want to report/summarize shadowed identifiers? def __init__(self): self.lineage_dbs = [] @property def available_ranks(self): "build the union of available ranks across all databases" # CTB: do we need to worry about lineages of shadowed identifiers? x = set() for db in self.lineage_dbs: x.update(db.available_ranks) return x def add(self, db): "Add a new lineage database" self.lineage_dbs.insert(0, db) def __iter__(self): "Return all identifiers (once)" seen = set() for db in self.lineage_dbs: for k in db: if k not in seen: seen.add(k) yield k def items(self): "Return all (identifiers, lineage_tup), masking duplicate idents" seen = set() for db in self.lineage_dbs: for k, v in db.items(): if k not in seen: seen.add(k) yield k, v def shadowed_identifiers(self): seen = set() dups = set() for db in self.lineage_dbs: for k, v in db.items(): if k in seen: dups.add(k) else: seen.add(k) return seen def __getitem__(self, ident): "Return lineage tuple for first match to identifier." for db in self.lineage_dbs: if ident in db: return db[ident] # not found? KeyError! raise KeyError(ident) def __len__(self): "Return number of distinct identifiers. Currently iterates over all." # CTB: maybe we can make this unnecessary? x = set(self) return len(x) def __bool__(self): "True if any contained database has content." return any(bool(db) for db in self.lineage_dbs) def save(self, filename_or_fp, file_format): assert file_format in ("sql", "csv") is_filename = False try: filename_or_fp.write except AttributeError: is_filename = True if file_format == "sql": if not is_filename: raise ValueError( "file format '{file_format}' requires a filename, not a file handle" ) self._save_sqlite(filename_or_fp) elif file_format == "csv": # we need a file handle; open file. fp = filename_or_fp if is_filename: if filename_or_fp.endswith(".gz"): fp = gzip.open(filename_or_fp, "wt", newline="") else: fp = open(filename_or_fp, "w", newline="") try: self._save_csv(fp) finally: # close the file we opened! if is_filename: fp.close() def _save_sqlite(self, filename, *, conn=None): from sourmash import sqlite_utils if conn is None: db = sqlite3.connect(filename) else: assert not filename db = conn cursor = db.cursor() try: sqlite_utils.add_sourmash_internal(cursor, "SqliteLineage", "1.0") except sqlite3.OperationalError: raise ValueError("attempt to write a readonly database") try: # CTB: could add 'IF NOT EXIST' here; would need tests, too. cursor.execute( """ CREATE TABLE sourmash_taxonomy ( ident TEXT NOT NULL, superkingdom TEXT, phylum TEXT, class TEXT, order_ TEXT, family TEXT, genus TEXT, species TEXT, strain TEXT ) """ ) except sqlite3.OperationalError: # already exists? raise ValueError(f"taxonomy table already exists in '{filename}'") # follow up and create index cursor.execute( "CREATE UNIQUE INDEX sourmash_taxonomy_ident ON sourmash_taxonomy(ident);" ) for ident, tax in self.items(): x = [ident, *[t.name for t in tax]] # fill the taxonomy tuple with empty values until it's the # right length for the SQL statement - while len(x) < 9: x.append("") cursor.execute( "INSERT INTO sourmash_taxonomy (ident, superkingdom, phylum, class, order_, family, genus, species, strain) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)", x, ) db.commit() def _save_csv(self, fp): headers = ["identifiers"] + list(RankLineageInfo().taxlist) w = csv.DictWriter(fp, fieldnames=headers) w.writeheader() for n, (ident, tax) in enumerate(self.items()): row = {} row["identifiers"] = ident # convert tax LineagePairs into dictionary for t in tax: row[t.rank] = t.name # add strain if needed if "strain" not in row: row["strain"] = "" w.writerow(row) @classmethod def load(cls, locations, **kwargs): "Load one or more taxonomies from the given location(s)" force = kwargs.get("force", False) if isinstance(locations, str): raise TypeError("'locations' should be a list, not a string") tax_assign = cls() for location in locations: # try faster formats first loaded = False # sqlite db? try: this_tax_assign = LineageDB_Sqlite.load(location) loaded = True except ValueError: pass # CSV file? if not loaded: try: this_tax_assign = LineageDB.load(location, **kwargs) loaded = True except (ValueError, csv.Error) as exc: # for the last loader, just pass along ValueError... if not force: raise ValueError( f"cannot read taxonomy assignments from '{location}': {str(exc)}" ) # nothing loaded, goodbye! if not loaded and not force: raise ValueError(f"cannot read taxonomy assignments from '{location}'") if loaded: tax_assign.add(this_tax_assign) return tax_assign def filter_row(row, dataclass_type): """ Filter the row to only include keys that exist in the dataclass fields. This allows extra columns to be passed in with the gather csv while still taking advantage of the checks for required columns that come with dataclass initialization. """ valid_keys = {field.name for field in fields(dataclass_type)} # 'match_name' and 'name' should be interchangeable (sourmash 4.x) if "match_name" in row.keys() and "name" not in row.keys(): row["name"] = row.pop("match_name") return {k: v for k, v in row.items() if k in valid_keys} @dataclass class GatherRow: """ Class to facilitate safely reading in Gather CSVs. The fields here should be match those in "gather_write_cols" in `search.py` To ensure all columns required for taxonomic summarization are present, this class contains no defaults for these columns and thus will throw a TypeError if any of these columns are missing in the passed gather input. All other fields have default None. Usage: with sourmash_args.FileInputCSV(gather_csv) as r: for row in enumerate(r): filt_row = filter_row(row, GatherRow) # filter first to allow extra columns gatherRow = GatherRow(**filt_row) """ # essential columns query_name: str name: str # match_name f_unique_weighted: float f_unique_to_query: float unique_intersect_bp: int remaining_bp: int query_md5: str query_filename: str # new essential cols: requires 4.4x query_bp: int ksize: int scaled: int # non-essential, but used if available query_n_hashes: int = None sum_weighted_found: int = None total_weighted_hashes: int = None @dataclass class QueryInfo: "Class for storing query information" query_name: str query_md5: str query_filename: str query_bp: int ksize: int scaled: int query_n_hashes: int = None total_weighted_hashes: int = 0 def __post_init__(self): "Initialize and cast types" self.query_bp = int(self.query_bp) self.ksize = int(self.ksize) self.scaled = int(self.scaled) self.query_n_hashes = int(self.query_n_hashes) if self.query_n_hashes else 0 self.total_weighted_hashes = ( int(self.total_weighted_hashes) if self.total_weighted_hashes else 0 ) @property def total_weighted_bp(self): return self.total_weighted_hashes * self.scaled @dataclass class BaseTaxResult: """ Base class for sourmash taxonomic annotation. """ raw: dict # csv row keep_full_identifiers: bool = False keep_identifier_versions: bool = False match_ident: str = field(init=False) skipped_ident: bool = False missed_ident: bool = False match_lineage_attempted: bool = False lins: bool = False ictv: bool = False def get_ident(self, id_col=None): # split identifiers = split on whitespace # keep identifiers = don't split .[12] from assembly accessions "Hack and slash identifiers." if id_col: self.match_ident = self.raw[id_col] else: self.match_ident = self.raw.name if not self.keep_full_identifiers: self.match_ident = self.match_ident.split(" ")[0] else: # overrides version bc can't keep full without keeping version self.keep_identifier_versions = True if not self.keep_identifier_versions: self.match_ident = self.match_ident.split(".")[0] def get_match_lineage( self, tax_assignments, skip_idents=None, fail_on_missing_taxonomy=False ): if skip_idents and self.match_ident in skip_idents: self.skipped_ident = True else: lin = tax_assignments.get(self.match_ident) if lin: if self.lins: self.lineageInfo = LINLineageInfo(lineage=lin) elif self.ictv: self.lineageInfo = ICTVRankLineageInfo(lineage=lin) else: self.lineageInfo = RankLineageInfo(lineage=lin) else: self.missed_ident = True self.match_lineage_attempted = True if self.missed_ident and fail_on_missing_taxonomy: raise ValueError( f"Error: ident '{self.match_ident}' is not in the taxonomy database. Failing, as requested via --fail-on-missing-taxonomy" ) @dataclass class AnnotateTaxResult(BaseTaxResult): """ Class to enable taxonomic annotation of any sourmash CSV. """ id_col: str = "name" def __post_init__(self): if self.id_col not in self.raw.keys(): raise ValueError(f"ID column '{self.id_col}' not found.") self.get_ident(id_col=self.id_col) if self.lins: self.lineageInfo = LINLineageInfo() else: self.lineageInfo = RankLineageInfo() def row_with_lineages(self): lineage = self.lineageInfo.display_lineage(truncate_empty=True) rl = {"lineage": lineage} rl.update(self.raw) return rl @dataclass class TaxResult(BaseTaxResult): """ Class to store taxonomic result of a single row from a gather CSV, including accessible query information (QueryInfo) and matched taxonomic lineage. TaxResult tracks whether lineage matching has been attempted and whether the lineage matching failed due to missing or skipped lineage identifiers. Initialize TaxResult using GatherRow, which ensures all required fields are present. The QueryInfo in TaxResult is used to ensure only compatible gather results generated from the same query are summarized during taxonomic summarization. Usage: with sourmash_args.FileInputCSV(gather_csv) as r: for row in enumerate(r): filt_row = filter_row(row, GatherRow) # this filters any extra columns gatherRow = GatherRow(**filt_row) # this checks for required columns and raises TypeError for any missing # initialize TaxResult tax_res = TaxResult(raw=gatherRow) # get match lineage tax_res.get_match_lineage(taxD=taxonomic_assignments) Use RankLineageInfo, ICTVLineageInfo, or LINLineageInfo to store lineage information. """ raw: GatherRow query_name: str = field(init=False) query_info: QueryInfo = field(init=False) def __post_init__(self): self.get_ident() self.query_name = self.raw.query_name # convenience self.query_info = QueryInfo( query_name=self.raw.query_name, query_md5=self.raw.query_md5, query_filename=self.raw.query_filename, query_bp=self.raw.query_bp, query_n_hashes=self.raw.query_n_hashes, total_weighted_hashes=self.raw.total_weighted_hashes, ksize=self.raw.ksize, scaled=self.raw.scaled, ) # cast and store the imp bits self.f_unique_to_query = float(self.raw.f_unique_to_query) self.f_unique_weighted = float(self.raw.f_unique_weighted) self.unique_intersect_bp = int(self.raw.unique_intersect_bp) if self.lins: self.lineageInfo = LINLineageInfo() elif self.ictv: self.lineageInfo = ICTVRankLineageInfo() else: self.lineageInfo = RankLineageInfo() @dataclass class SummarizedGatherResult: """ Class for storing summarized lineage information. Automatically checks for out-of-range values and estimates ANI. Methods included for returning formatted results for different outputs. """ rank: str fraction: float lineage: RankLineageInfo f_weighted_at_rank: float bp_match_at_rank: int query_ani_at_rank: float = None def __post_init__(self): self.check_values() def check_values(self): if any([self.fraction > 1, self.f_weighted_at_rank > 1]): raise ValueError( "Summarized fraction is > 100% of the query! This should not be possible. Please check that your input files come directly from a single gather run per query." ) # is this true for weighted too, or is that set to 0 when --ignore-abundance is used? if any( [self.fraction <= 0, self.f_weighted_at_rank <= 0] ): # this shouldn't actually happen, but it breaks ANI estimation, so let's check for it. raise ValueError( "Summarized fraction is <=0% of the query! This should not occur." ) def set_query_ani(self, query_info): self.query_ani_at_rank = containment_to_distance( self.fraction, query_info.ksize, query_info.scaled, n_unique_kmers=query_info.query_n_hashes, sequence_len_bp=query_info.query_bp, ).ani def as_lineage_dict(self, query_info, ranks): """ Format to dict for writing lineage-CSV file suitable for use with sourmash tax ... -t. """ lD = {} lD["ident"] = query_info.query_name for rank in ranks: lin_name = self.lineage.name_at_rank(rank) if lin_name is None: lin_name = "" lD[rank] = lin_name return lD def as_summary_dict(self, query_info, limit_float=False, lingroups=None): sD = asdict(self) sD["lineage"] = self.lineage.display_lineage(null_as_unclassified=True) # if lingroups, convert lingroup number to lingroup name if lingroups is not None and sD["lineage"] in lingroups.keys(): sD["lineage"] = lingroups[sD["lineage"]] elif ( lingroups and sD["lineage"] != "unclassified" and sD["lineage"] not in lingroups.keys() ): return None sD["query_name"] = query_info.query_name sD["query_md5"] = query_info.query_md5 sD["query_filename"] = query_info.query_filename sD["total_weighted_hashes"] = str(query_info.total_weighted_hashes) sD["bp_match_at_rank"] = str(self.bp_match_at_rank) if limit_float: sD["fraction"] = f"{self.fraction:.3f}" sD["f_weighted_at_rank"] = f"{self.f_weighted_at_rank:.3f}" if self.query_ani_at_rank: sD["query_ani_at_rank"] = f"{self.query_ani_at_rank:.3f}" else: sD["fraction"] = str(self.fraction) sD["f_weighted_at_rank"] = str(self.f_weighted_at_rank) return sD def as_human_friendly_dict(self, query_info): sD = self.as_summary_dict(query_info=query_info, limit_float=True) sD["f_weighted_at_rank"] = f"{self.f_weighted_at_rank * 100:>4.1f}%" if self.query_ani_at_rank is not None: sD["query_ani_at_rank"] = f"{self.query_ani_at_rank * 100:>3.1f}%" else: sD["query_ani_at_rank"] = "- " return sD def as_kreport_dict(self, query_info): """ Produce kreport dict for named taxonomic groups. """ lowest_assignment_rank = "species" sD = {} sD["num_bp_assigned"] = str(0) sD["ncbi_taxid"] = None # total percent containment, weighted to include abundance info sD["percent_containment"] = f"{self.f_weighted_at_rank * 100:.2f}" sD["num_bp_contained"] = str( int(self.f_weighted_at_rank * query_info.total_weighted_bp) ) if isinstance(self.lineage, LINLineageInfo): raise ValueError("Cannot produce 'kreport' with LIN taxonomy.") if self.lineage != RankLineageInfo(): this_rank = self.lineage.lowest_rank sD["rank_code"] = RANKCODE[this_rank] sD["sci_name"] = self.lineage.lowest_lineage_name taxid = self.lineage.lowest_lineage_taxid if taxid: sD["ncbi_taxid"] = str(taxid) # the number of bp actually 'assigned' at this rank. Sourmash assigns everything # at genome level, but since kreport traditionally doesn't include 'strain' or genome, # it is reasonable to state that sourmash assigns at 'species' level for this. # can be modified later. if this_rank == lowest_assignment_rank: sD["num_bp_assigned"] = sD["num_bp_contained"] else: sD["sci_name"] = "unclassified" sD["rank_code"] = RANKCODE["unclassified"] sD["num_bp_assigned"] = sD["num_bp_contained"] return sD def as_lingroup_dict(self, query_info, lg_name): """ Produce lingroup report dict for lingroups. """ sD = {} # total percent containment, weighted to include abundance info sD["percent_containment"] = f"{self.f_weighted_at_rank * 100:.2f}" sD["num_bp_contained"] = str( int(self.f_weighted_at_rank * query_info.total_weighted_bp) ) sD["lin"] = self.lineage.display_lineage() sD["name"] = lg_name return sD def as_cami_bioboxes(self): """ Format taxonomy-summarized gather results as CAMI profiling Bioboxes format. Columns are: TAXID RANK TAXPATH TAXPATHSN PERCENTAGE """ if isinstance(self.lineage, LINLineageInfo): raise ValueError("Cannot produce 'bioboxes' with LIN taxonomy.") if self.lineage != RankLineageInfo(): # if not unassigned taxid = self.lineage.lowest_lineage_taxid if taxid: taxpath = self.lineage.display_taxid(sep="|") taxid = str(taxid) else: taxpath = None taxpathsn = self.lineage.display_lineage(sep="|") percentage = ( f"{(self.f_weighted_at_rank * 100):.2f}" # fix at 2 decimal points ) return [taxid, self.rank, taxpath, taxpathsn, percentage] return [] @dataclass class ClassificationResult(SummarizedGatherResult): """ Inherits from SummarizedGatherResult Class for storing query classification information. Automatically checks for out-of-range values and estimates ANI. Checks classification status according to provided containment and ANI thresholds. Methods included for returning formatted results for different outputs. """ "Class for storing query classification information" status: str = field(init=False) def __post_init__(self): # check for out of bounds values, default "nomatch" if no match at all self.check_values() self.status = "nomatch" # None? def set_status(self, query_info, containment_threshold=None, ani_threshold=None): # if any matches, use 'below_threshold' as default; set 'match' if meets threshold if any([containment_threshold is not None, ani_threshold is not None]): self.status = "below_threshold" self.set_query_ani(query_info=query_info) if ( ani_threshold is not None ): # if provided, just use ani thresh, don't use containment threshold if self.query_ani_at_rank >= ani_threshold: self.status = "match" # v5?: switch to using self.f_weighted_at_rank here elif ( containment_threshold is not None and self.fraction >= containment_threshold ): self.status = "match" def build_krona_result(self, rank=None): krona_classified, krona_unclassified = None, None if rank is not None and rank == self.rank: lin_as_list = self.lineage.display_lineage().split(";") krona_classification = ( self.fraction, *lin_as_list, ) # v5?: f_weighted_at_rank krona_classified = krona_classification # handle unclassified - do we want/need this? unclassified_fraction = 1.0 - self.fraction # v5?: f_weighted_at_rank len_unclassified_lin = len(lin_as_list) unclassifed_lin = ["unclassified"] * (len_unclassified_lin) krona_unclassified = (unclassified_fraction, *unclassifed_lin) return krona_classified, krona_unclassified @dataclass class QueryTaxResult: """ Class for storing all TaxResults (gather results rows) for a query. Checks query compatibility prior to adding a TaxResult. Stores raw TaxResults and provides methods for summarizing up ranks and reporting these summarized results as metagenome summaries or genome classifications. Contains methods for formatting results for different outputs. """ query_info: QueryInfo # initialize with QueryInfo dataclass lins: bool = False ictv: bool = False def __post_init__(self): self.query_name = self.query_info.query_name # for convenience self._init_taxresult_vars() self._init_summarization_vars() self._init_classification_results() def _init_taxresult_vars(self): self.ranks = [] self.raw_taxresults = [] self.skipped_idents = set() self.missed_idents = set() self.n_missed = 0 self.n_skipped = 0 self.perfect_match = set() def _init_summarization_vars(self): self.sum_uniq_weighted = defaultdict(lambda: defaultdict(float)) self.sum_uniq_to_query = defaultdict(lambda: defaultdict(float)) self.sum_uniq_bp = defaultdict(lambda: defaultdict(int)) self.summarized_ranks = [] self._init_summarization_results() def _init_summarization_results(self): self.total_f_weighted = defaultdict(float) # 0.0 self.total_f_classified = defaultdict(float) # 0.0 self.total_bp_classified = defaultdict(int) # 0 self.summarized_lineage_results = defaultdict(list) def _init_classification_results(self): self.status = "nomatch" self.classified_ranks = [] self.classification_result = None self.krona_classified = None self.krona_unclassified = None self.krona_header = [] def is_compatible(self, taxresult): return ( taxresult.query_info == self.query_info and taxresult.lins == self.lins and taxresult.ictv == self.ictv ) @property def ascending_ranks(self): if not self.ranks: return [] else: return self.ranks[::-1] def add_taxresult(self, taxresult): # check that all query parameters match if self.is_compatible(taxresult=taxresult): if not taxresult.match_lineage_attempted: raise ValueError( "Error: Cannot add TaxResult. Please use get_match_lineage() to add taxonomic lineage information first." ) if not self.ranks: self.ranks = taxresult.lineageInfo.ranks if taxresult.skipped_ident: self.n_skipped += 1 self.skipped_idents.add(taxresult.match_ident) elif taxresult.missed_ident: self.n_missed += 1 self.missed_idents.add(taxresult.match_ident) self.raw_taxresults.append(taxresult) else: raise ValueError( "Error: Cannot add TaxResult: query information does not match." ) def summarize_up_ranks(self, single_rank=None, force_resummarize=False): if self.summarized_ranks: # has already been summarized if force_resummarize: self._init_summarization_vars() else: raise ValueError( "Error: already summarized using rank(s): '{', '.join(self.summarized_ranks)}'. Use 'force_resummarize=True' to reset and resummarize" ) # set ranks levels to summarize self.summarized_ranks = self.ascending_ranks if single_rank: if single_rank not in self.summarized_ranks: raise ValueError( f"Error: rank '{single_rank}' not in available ranks ({', '.join(self.summarized_ranks)})" ) self.summarized_ranks = [single_rank] for taxres in self.raw_taxresults: lininfo = taxres.lineageInfo if ( lininfo and lininfo.filled_lineage ): # won't always have lineage to summarize (skipped idents, missed idents) # notify + track perfect matches if taxres.f_unique_to_query >= 1.0: if taxres.match_ident not in self.perfect_match: notify( f"WARNING: 100% match! Is query '{self.query_name}' identical to its database match, '{taxres.match_ident}'?" ) self.perfect_match.add(taxres.match_ident) # add this taxresult to summary for rank in self.summarized_ranks: if ( rank in lininfo.filled_ranks ): # only store if this rank is filled. lin_at_rank = lininfo.pop_to_rank(rank) self.sum_uniq_weighted[rank][lin_at_rank] += ( taxres.f_unique_weighted ) self.sum_uniq_to_query[rank][lin_at_rank] += ( taxres.f_unique_to_query ) self.sum_uniq_bp[rank][lin_at_rank] += ( taxres.unique_intersect_bp ) # reset ranks levels to the ones that were actually summarized + that we can access for summarized result self.summarized_ranks = [ x for x in self.summarized_ranks if x in self.sum_uniq_bp.keys() ] if single_rank and single_rank not in self.summarized_ranks: raise ValueError( f"Error: rank '{single_rank}' was not available for any matching lineages." ) def build_summarized_result(self, single_rank=None, force_resummarize=False): # just reset if we've already built summarized result (avoid adding to existing)? Or write in an error/force option? self._init_summarization_results() # if taxresults haven't been summarized, do that first if not self.summarized_ranks or force_resummarize: self.summarize_up_ranks( single_rank=single_rank, force_resummarize=force_resummarize ) # catch potential error from running summarize_up_ranks separately and passing in different single_rank if single_rank and single_rank not in self.summarized_ranks: raise ValueError( f"Error: rank '{single_rank}' not in summarized rank(s), {','.join(self.summarized_ranks)}" ) # rank loop is currently done in __main__ for rank in self.summarized_ranks[ ::-1 ]: # reverse so that results are in descending order sum_uniq_to_query = self.sum_uniq_to_query[rank] # should be lineage: value # first, sort sorted_sum_uniq_to_query = list(sum_uniq_to_query.items()) sorted_sum_uniq_to_query.sort(key=lambda x: -x[1]) for lineage, f_unique in sorted_sum_uniq_to_query: # does this ever happen? do we need it? if ( f_unique == 0 ): # no annotated results for this query. do we need to handle this differently now? continue f_weighted_at_rank = self.sum_uniq_weighted[rank][lineage] bp_intersect_at_rank = self.sum_uniq_bp[rank][lineage] sres = SummarizedGatherResult( lineage=lineage, rank=rank, f_weighted_at_rank=f_weighted_at_rank, fraction=f_unique, bp_match_at_rank=bp_intersect_at_rank, ) sres.set_query_ani(query_info=self.query_info) self.summarized_lineage_results[rank].append(sres) # NTP Note: These change by rank ONLY when doing best_only (selecting top hit at that particular rank) # now that I pulled best_only into separate fn, these don't need to be dicts... self.total_f_classified[rank] += f_unique self.total_f_weighted[rank] += f_weighted_at_rank self.total_bp_classified[rank] += bp_intersect_at_rank # record unclassified if self.lins: lineage = LINLineageInfo() elif self.ictv: lineage = ICTVRankLineageInfo() else: lineage = RankLineageInfo() query_ani = None f_unique = 1.0 - self.total_f_classified[rank] if f_unique > 0: f_weighted_at_rank = 1.0 - self.total_f_weighted[rank] bp_intersect_at_rank = ( self.query_info.query_bp - self.total_bp_classified[rank] ) sres = SummarizedGatherResult( lineage=lineage, rank=rank, f_weighted_at_rank=f_weighted_at_rank, fraction=f_unique, bp_match_at_rank=bp_intersect_at_rank, query_ani_at_rank=query_ani, ) self.summarized_lineage_results[rank].append(sres) def build_classification_result( self, rank=None, ani_threshold=None, containment_threshold=0.1, force_resummarize=False, lingroup_ranks=None, lingroups=None, ): if containment_threshold is not None and not 0 <= containment_threshold <= 1: raise ValueError( f"Containment threshold must be between 0 and 1 (input value: {containment_threshold})." ) if ani_threshold is not None and not 0 <= ani_threshold <= 1: raise ValueError( f"ANI threshold must be between 0 and 1 (input value: {ani_threshold})." ) self._init_classification_results() # init some fields if not self.summarized_ranks or force_resummarize: self.summarize_up_ranks( single_rank=rank, force_resummarize=force_resummarize ) # catch potential error from running summarize_up_ranks separately and passing in different single_rank self.classified_ranks = self.summarized_ranks # if a rank is provided, we need to classify ONLY using that rank if rank: if rank not in self.summarized_ranks: raise ValueError( f"Error: rank '{rank}' not in summarized rank(s), {','.join(self.summarized_ranks)}" ) else: self.classified_ranks = [rank] if lingroup_ranks: notify("Restricting classification to lingroups.") self.classified_ranks = [ x for x in self.classified_ranks if x in lingroup_ranks ] if not self.classified_ranks: raise ValueError("Error: no ranks remain for classification.") # CLASSIFY using summarization--> best only result. Best way = use ANI or containment threshold classif = None for this_rank in self.classified_ranks: # ascending order or just single rank # reset for this rank f_weighted = 0.0 f_unique_at_rank = 0.0 bp_intersect_at_rank = 0 sum_uniq_to_query = self.sum_uniq_to_query[this_rank] # sort the results and grab best sorted_sum_uniq_to_query = list(sum_uniq_to_query.items()) sorted_sum_uniq_to_query.sort(key=lambda x: -x[1]) # select best-at-rank only this_lineage, f_unique_at_rank = sorted_sum_uniq_to_query[0] # if in desired lineage groups, continue (or??) if lingroups and this_lineage not in lingroups: # ignore this lineage and continue up continue bp_intersect_at_rank = self.sum_uniq_bp[this_rank][this_lineage] f_weighted = self.sum_uniq_weighted[this_rank][this_lineage] classif = ClassificationResult( rank=this_rank, fraction=f_unique_at_rank, lineage=this_lineage, f_weighted_at_rank=f_weighted, bp_match_at_rank=bp_intersect_at_rank, ) classif.set_status( self.query_info, containment_threshold=containment_threshold, ani_threshold=ani_threshold, ) # determine whether to move on to a higher tax rank (if avail) if ( classif.status == "match" or classif.status == "nomatch" ): # not sure we want/need the `nomatch` part... break # store the final classification result self.classification_result = classif # could do this later, in __main__.py, for example ( self.krona_classified, self.krona_unclassified, ) = self.classification_result.build_krona_result(rank=rank) self.krona_header = self.make_krona_header(min_rank=rank) def make_krona_header(self, min_rank): "make header for krona output" if min_rank is None: return [] if min_rank not in self.summarized_ranks: raise ValueError(f"Rank '{min_rank}' not present in summarized ranks.") else: rank_index = self.ranks.index(min_rank) return ["fraction"] + list(self.ranks[: rank_index + 1]) def check_classification(self): if not self.classification_result: raise ValueError("query not classified yet.") def check_summarization(self): if not self.summarized_lineage_results: raise ValueError("lineages not summarized yet.") def make_human_summary(self, display_rank, classification=False): results = [] if classification: self.check_classification() display_rank_results = [self.classification_result] else: self.check_summarization() display_rank_results = self.summarized_lineage_results[display_rank] display_rank_results.sort(key=lambda res: -res.f_weighted_at_rank) for res in display_rank_results: results.append(res.as_human_friendly_dict(query_info=self.query_info)) return results def make_full_summary( self, classification=False, limit_float=False, lingroups=None ): results = [] rD = {} if classification: self.check_classification() header = [ "query_name", "status", "rank", "fraction", "lineage", "query_md5", "query_filename", "f_weighted_at_rank", "bp_match_at_rank", "query_ani_at_rank", ] rD = self.classification_result.as_summary_dict( query_info=self.query_info, limit_float=limit_float ) del rD["total_weighted_hashes"] results.append(rD) else: self.check_summarization() header = [ "query_name", "rank", "fraction", "lineage", "query_md5", "query_filename", "f_weighted_at_rank", "bp_match_at_rank", "query_ani_at_rank", "total_weighted_hashes", ] lingroup_ranks = set() if lingroups is not None: for lin in lingroups.keys(): # e.g. "14;1;0;0;0;0;0;0;0;0" => 9 lin_rank = len(lin.split(";")) - 1 lingroup_ranks.add(lin_rank) for rank in self.summarized_ranks[::-1]: # descending # if lingroups are provided, only report summary for specified lingroups if lingroup_ranks: if int(rank) not in lingroup_ranks: continue unclassified = [] rank_results = self.summarized_lineage_results[rank] rank_results.sort( key=lambda res: -res.fraction ) # v5?: f_weighted_at_rank) for res in rank_results: rD = res.as_summary_dict( query_info=self.query_info, limit_float=limit_float, lingroups=lingroups, ) if rD is None: continue # save unclassified for the end if rD["lineage"] == "unclassified": unclassified.append(rD) else: results.append(rD) results += unclassified return header, results def make_kreport_results(self): """ Format taxonomy-summarized gather results as kraken-style kreport. STANDARD KREPORT FORMAT: - `Percent Reads Contained in Taxon`: The cumulative percentage of reads for this taxon and all descendants. - `Number of Reads Contained in Taxon`: The cumulative number of reads for this taxon and all descendants. - `Number of Reads Assigned to Taxon`: The number of reads assigned directly to this taxon (not a cumulative count of all descendants). - `Rank Code`: (U)nclassified, (R)oot, (D)omain, (K)ingdom, (P)hylum, (C)lass, (O)rder, (F)amily, (G)enus, or (S)pecies. - `NCBI Taxon ID`: Numerical ID from the NCBI taxonomy database. - `Scientific Name`: The scientific name of the taxon. Example reads-based `kreport` with all columns: ``` 88.41 2138742 193618 K 2 Bacteria 0.16 3852 818 P 201174 Actinobacteria 0.13 3034 0 C 1760 Actinomycetia 0.13 3034 45 O 85009 Propionibacteriales 0.12 2989 1847 F 31957 Propionibacteriaceae 0.05 1142 352 G 1912216 Cutibacterium 0.03 790 790 S 1747 Cutibacterium acnes ``` SOURMASH KREPORT FORMAT: To best represent the sequence dataset, please build sourmash signatures with abundance tracking to enable utilization of sequence abundance information during sourmash gather and taxonomic summarization. While this format typically records the percent of number of reads assigned to taxa, we can create comparable output by reporting the percent of base pairs (percent containment) the total number of base pairs matched. Using sourmash default scaled values, these numbers will be estimates from FracMinHash k-mer comparisons. If using sourmash scaled=1 (not recommended for most use cases), these results will be based on all k-mers. `sourmash gather` assigns k-mers to individual genoems. Since the lowest kreport rank is "species," we use the "Assigned to Taxon" column to report assignments summarized to species level. - `Percent Contained in Taxon`: Percent of all base pairs contained by this taxon (weighted by abundance if tracked) - `Estimated base pairs Contained in Taxon`: Number of base pairs contained by this taxon (weighted by abundance if tracked) - `Estimated base pairs Assigned to Taxon`: Number of base pairs at species-level (weighted by abundance if tracked) - `Rank Code`: (U)nclassified, (R)oot, (D)omain, (K)ingdom, (P)hylum, (C)lass, (O)rder, (F)amily, (G)enus, or (S)pecies. - `NCBI Taxon ID` will not be reported (blank entries). - `Scientific Name`: The scientific name of the taxon. In the future, we may wish to report the NCBI taxid when we can (NCBI taxonomy only). """ self.check_summarization() header = [ "percent_containment", "num_bp_contained", "num_bp_assigned", "rank_code", "ncbi_taxid", "sci_name", ] if self.query_info.total_weighted_hashes == 0: raise ValueError( "ERROR: cannot produce 'kreport' format from gather results before sourmash v4.5.0" ) required_ranks = set(RANKCODE.keys()) acceptable_ranks = list(self.ranks) + ["unclassified", "kingdom"] if not required_ranks.issubset(set(acceptable_ranks)): raise ValueError( "ERROR: cannot produce 'kreport' format from ranks {', '.join(self.ranks)}" ) kreport_results = [] unclassified_recorded = False # want to order results descending by rank for rank in self.ranks: if ( rank == "strain" ): # no code for strain, can't include in this output afaik continue rank_results = self.summarized_lineage_results[rank] for res in rank_results: kresD = res.as_kreport_dict(self.query_info) if kresD["sci_name"] == "unclassified": # SummarizedGatherResults have an unclassified lineage at every rank, to facilitate reporting at a specific rank. # Here, we only need to report it once, since it will be the same fraction for all ranks if unclassified_recorded: continue else: unclassified_recorded = True kreport_results.append(kresD) return header, kreport_results def make_lingroup_results( self, LINgroupsD ): # LingroupsD is dictionary {lg_prefix: lg_name} """ Report results for the specified LINGroups. Keep LCA paths in order as much as possible. """ self.check_summarization() header = ["name", "lin", "percent_containment", "num_bp_contained"] if self.query_info.total_weighted_hashes == 0: raise ValueError( "ERROR: cannot produce 'lingroup' format from gather results before sourmash v4.5.0" ) # find the ranks we need to consider lg_ranks, all_lgs = parse_lingroups(LINgroupsD) # grab summarized results matching LINgroup prefixes lg_results = {} for rank in lg_ranks: rank_results = self.summarized_lineage_results[rank] for res in rank_results: if res.lineage in all_lgs: # is this lineage in the list of LINgroups? this_lingroup_name = LINgroupsD[ res.lineage.display_lineage(truncate_empty=True) ] lg_resD = res.as_lingroup_dict(self.query_info, this_lingroup_name) lg_results[res.lineage] = lg_resD # We want to return in ~ depth order: descending each specific path in order # use LineageTree to find ordered paths lg_tree = LineageTree(all_lgs) ordered_paths = lg_tree.ordered_paths(include_internal=True) # store results in order: lingroup_results = [] for lg in ordered_paths: # get LINInfo object lg_LINInfo = LINLineageInfo(lineage=lg) # get result, if we have it lg_res = lg_results.get(lg_LINInfo) if lg_res: lingroup_results.append(lg_res) return header, lingroup_results def make_cami_bioboxes(self): """ info: https://github.com/CAMI-challenge/contest_information/blob/master/file_formats/CAMI_TP_specification.mkd columns: TAXID - specifies a unique alphanumeric ID for a node in a reference tree such as the NCBI taxonomy RANK - superkingdom --> strain TAXPATH - the path from the root of the reference taxonomy to the respective taxon TAXPATHSN - scientific names of taxpath PERCENTAGE (0-100) - field specifies what percentage of the sample was assigned to the respective TAXID example: #CAMI Submission for Taxonomic Profiling @Version:0.9.1 @SampleID:SAMPLEID @Ranks:superkingdom|phylum|class|order|family|genus|species|strain @@TAXID RANK TAXPATH TAXPATHSN PERCENTAGE 2 superkingdom 2 Bacteria 98.81211 2157 superkingdom 2157 Archaea 1.18789 1239 phylum 2|1239 Bacteria|Firmicutes 59.75801 1224 phylum 2|1224 Bacteria|Proteobacteria 18.94674 28890 phylum 2157|28890 Archaea|Euryarchaeotes 1.18789 91061 class 2|1239|91061 Bacteria|Firmicutes|Bacilli 59.75801 28211 class 2|1224|28211 Bacteria|Proteobacteria|Alphaproteobacteria 18.94674 183925 class 2157|28890|183925 Archaea|Euryarchaeotes|Methanobacteria 1.18789 1385 order 2|1239|91061|1385 Bacteria|Firmicutes|Bacilli|Bacillales 59.75801 356 order 2|1224|28211|356 Bacteria|Proteobacteria|Alphaproteobacteria|Rhizobacteria 10.52311 204455 order 2|1224|28211|204455 Bacteria|Proteobacteria|Alphaproteobacteria|Rhodobacterales 8.42263 2158 order 2157|28890|183925|2158 Archaea|Euryarchaeotes|Methanobacteria|Methanobacteriales 1.18789 """ # build CAMI header info header_title = "# Taxonomic Profiling Output" version_info = "@Version:0.10.0" program = "@__program__:sourmash" sample_info = f"@SampleID:{self.query_info.query_name}" # taxonomy_id = "@TaxonomyID:2021-10-01" # store this with LineageDB, maybe? ranks = list(self.ranks) # if 'strain' in ranks: # ranks.remove('strain') rank_info = f"@Ranks:{'|'.join(ranks)}" header_lines = [header_title, sample_info, version_info, rank_info, program] colnames = ["@@TAXID", "RANK", "TAXPATH", "TAXPATHSN", "PERCENTAGE"] header_lines.append("\t".join(colnames)) # now build results in CAMI format bioboxes_results = [] # order results by rank (descending), then percentage for rank in ranks: rank_results = self.summarized_lineage_results[rank] for res in rank_results: bb_info = res.as_cami_bioboxes() if bb_info: bioboxes_results.append(bb_info) return header_lines, bioboxes_results sourmash-bio-sourmash-bc3c0f8/src/sourmash/utils.py000066400000000000000000000040251474157046300226310ustar00rootroot00000000000000import weakref from ._lowlevel import ffi, lib from .exceptions import exceptions_by_code, SourmashError attached_refs = weakref.WeakKeyDictionary() class RustObject: __dealloc_func__ = None _objptr = None _shared = False def __init__(self): raise TypeError(f"Cannot instanciate {self.__class__.__name__!r} objects") @classmethod def _from_objptr(cls, ptr, shared=False): rv = object.__new__(cls) rv._objptr = ptr rv._shared = shared return rv def _methodcall(self, func, *args): return rustcall(func, self._get_objptr(), *args) def _get_objptr(self): if not self._objptr: raise RuntimeError("Object is closed") return self._objptr def __del__(self): if self._objptr is None or self._shared: return f = self.__class__.__dealloc_func__ if f is not None: rustcall(f, self._objptr) self._objptr = None def decode_str(s): """Decodes a SourmashStr""" try: if s.len == 0: return "" return ffi.unpack(s.data, s.len).decode("utf-8", "replace") finally: if s.owned: lib.sourmash_str_free(ffi.addressof(s)) def encode_str(s): """Encodes a SourmashStr""" rv = ffi.new("SourmashStr *") if isinstance(s, str): s = s.encode("utf-8") rv.data = ffi.from_buffer(s) rv.len = len(s) # we have to hold a weak reference here to ensure our string does not # get collected before the string is used. attached_refs[rv] = s return rv def rustcall(func, *args): """Calls rust method and does some error handling.""" lib.sourmash_err_clear() rv = func(*args) err = lib.sourmash_err_get_last_code() if not err: return rv msg = lib.sourmash_err_get_last_message() cls = exceptions_by_code.get(err, SourmashError) exc = cls(decode_str(msg)) backtrace = decode_str(lib.sourmash_err_get_backtrace()) if backtrace: exc.rust_info = backtrace raise exc sourmash-bio-sourmash-bc3c0f8/tests/000077500000000000000000000000001474157046300176305ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/tests/conftest.py000066400000000000000000000072021474157046300220300ustar00rootroot00000000000000import os import sys from hypothesis import settings, Verbosity import pytest import matplotlib.pyplot as plt plt.rcParams.update({"figure.max_open_warning": 0}) from sourmash_tst_utils import TempDirectory, RunnerContext sys.stdout = sys.stderr @pytest.fixture def runtmp(): with TempDirectory() as location: yield RunnerContext(location) @pytest.fixture def run(): yield RunnerContext(os.getcwd()) @pytest.fixture(params=[True, False]) def track_abundance(request): return request.param @pytest.fixture(params=[True, False]) def dayhoff(request): return request.param @pytest.fixture(params=[True, False]) def hp(request): return request.param @pytest.fixture(params=[True, False]) def keep_identifiers(request): return request.param @pytest.fixture(params=[True, False]) def keep_versions(request): return request.param @pytest.fixture(params=[2, 5, 10]) def n_children(request): return request.param @pytest.fixture(params=["--linear", "--no-linear"]) def linear_gather(request): return request.param @pytest.fixture(params=["--prefetch", "--no-prefetch"]) def prefetch_gather(request): return request.param @pytest.fixture(params=[True, False]) def use_manifest(request): return request.param @pytest.fixture(params=["json", "sql"]) def lca_db_format(request): return request.param @pytest.fixture(params=["csv", "sql"]) def manifest_db_format(request): return request.param @pytest.fixture(params=["sig", "sig.gz", "zip", ".d/", ".sqldb"]) def sig_save_extension(request): return request.param @pytest.fixture(params=["sig", "sig.gz", "zip", ".d/"]) def sig_save_extension_abund(request): return request.param # these should both always succeed for 'sig check' and 'sig collect' output # manifests. @pytest.fixture(params=["--abspath", "--relpath"]) def abspath_or_relpath(request): return request.param # this will fail if subdirs used; see #3008. but this ensures v4 behavior of # sig collect/sig check works, where manifest paths are interpreted relative # to cwd. @pytest.fixture(params=["--no-abspath", "--abspath", "--relpath"]) def abspath_relpath_v4(request): return request.param # --- BEGIN - Only run tests using a particular fixture --- # # Cribbed from: http://pythontesting.net/framework/pytest/pytest-run-tests-using-particular-fixture/ def pytest_collection_modifyitems(items, config): fixture_name = config.option.usesfixture if fixture_name is not None: selected_items = [] deselected_items = [] for item in items: if fixture_name in getattr(item, "fixturenames", ()): selected_items.append(item) else: deselected_items.append(item) config.hook.pytest_deselected(items=deselected_items) items[:] = selected_items # --- END - Only run tests using a particular fixture --- # def pytest_addoption(parser): parser.addoption( "--usesfixture", action="store", default=None, help="just run tests that use a particular fixture", ) parser.addoption( "--run-hypothesis", action="store_true", help="run hypothesis tests" ) def pytest_runtest_setup(item): if item.config.getoption("--run-hypothesis"): if not any(mark for mark in item.iter_markers(name="hypothesis")): pytest.skip("--run-hypothesis option set, running only hypothesis tests") settings.register_profile("ci", max_examples=1000) settings.register_profile("dev", max_examples=10) settings.register_profile("debug", max_examples=10, verbosity=Verbosity.verbose) settings.load_profile(os.getenv("HYPOTHESIS_PROFILE", "default")) sourmash-bio-sourmash-bc3c0f8/tests/sourmash_tst_utils.py000066400000000000000000000156231474157046300241640ustar00rootroot00000000000000"Various utilities used by sourmash tests." import sys import os import tempfile import shutil import subprocess import collections import pprint import traceback from io import StringIO from pathlib import Path from importlib import resources from importlib.metadata import entry_points SIG_FILES = [ os.path.join("demo", f) for f in ( "SRR2060939_1.sig", "SRR2060939_2.sig", "SRR2241509_1.sig", "SRR2255622_1.sig", "SRR453566_1.sig", "SRR453569_1.sig", "SRR453570_1.sig", ) ] def scriptpath(scriptname="sourmash"): """Return the path to the scripts, in both dev and install situations.""" # note - it doesn't matter what the scriptname is here, as long as # it's some script present in this version of sourmash. path = os.path.join(os.path.dirname(__file__), "../") if os.path.exists(os.path.join(path, scriptname)): return path path = os.path.join(os.path.dirname(__file__), "../../EGG-INFO/") if os.path.exists(os.path.join(path, scriptname)): return path for path in os.environ["PATH"].split(":"): if os.path.exists(os.path.join(path, scriptname)): return path def _runscript(scriptname): """Find & run a script with exec (i.e. not via os.system or subprocess).""" namespace = {"__name__": "__main__"} namespace["sys"] = globals()["sys"] try: (script,) = entry_points(name=scriptname, group="console_scripts") script.load()() return 0 except ValueError: pass path = scriptpath() scriptfile = os.path.join(path, scriptname) if os.path.isfile(scriptfile): if os.path.isfile(scriptfile): exec( # pylint: disable=exec-used compile(Path(scriptfile).read_text(), scriptfile, "exec"), namespace ) return 0 return -1 ScriptResults = collections.namedtuple("ScriptResults", ["status", "out", "err"]) def runscript(scriptname, args, **kwargs): """Run a Python script using exec(). Run the given Python script, with the given args, in the given directory, using 'exec'. Mimic proper shell functionality with argv, and capture stdout and stderr. When using :attr:`fail_ok`=False in tests, specify the expected error. """ __tracebackhide__ = True sysargs = [scriptname] sysargs.extend(args) cwd = os.getcwd() in_directory = kwargs.get("in_directory", cwd) fail_ok = kwargs.get("fail_ok", False) try: status = -1 oldargs = sys.argv sys.argv = sysargs oldin = None if "stdin_data" in kwargs: oldin, sys.stdin = sys.stdin, StringIO(kwargs["stdin_data"]) oldout, olderr = sys.stdout, sys.stderr sys.stdout = StringIO() sys.stdout.name = "StringIO" sys.stderr = StringIO() os.chdir(in_directory) try: print("running:", scriptname, "in:", in_directory, file=oldout) print("arguments", sysargs, file=oldout) status = _runscript(scriptname) except SystemExit as err: status = err.code if status is None: status = 0 except: # pylint: disable=bare-except traceback.print_exc(file=sys.stderr) status = -1 finally: sys.argv = oldargs out, err = sys.stdout.getvalue(), sys.stderr.getvalue() sys.stdout, sys.stderr = oldout, olderr if oldin: sys.stdin = oldin os.chdir(cwd) if status != 0 and not fail_ok: print(out) print(err) assert False, (status, out, err) return ScriptResults(status, out, err) def get_test_data(filename): filepath = resources.files("sourmash") / "tests" / "test-data" / filename if not filepath.exists() or not os.path.isfile(filepath): filepath = os.path.join(os.path.dirname(__file__), "test-data", filename) return filepath class TempDirectory: def __init__(self): self.tempdir = tempfile.mkdtemp(prefix="sourmashtest_") def __enter__(self): return self.tempdir def __exit__(self, exc_type, exc_value, traceback): try: shutil.rmtree(self.tempdir, ignore_errors=True) except OSError: pass if exc_type: return False class SourmashCommandFailed(Exception): def __init__(self, msg): Exception.__init__(self, msg) self.message = msg class RunnerContext: """ I am a RunnerContext object from sourmash_tst_utils. I have methods 'run_sourmash' and 'run', which run Python scripts. Take a look at my 'location', 'last_command' and 'last_result' attributes! You can use the 'output' method to build filenames in my temp directory. """ def __init__(self, location): self.location = location self.last_command = None self.last_result = None def run_sourmash(self, *args, **kwargs): "Run the sourmash script with the given arguments." kwargs["fail_ok"] = True if "in_directory" not in kwargs: kwargs["in_directory"] = self.location cmdlist = ["sourmash"] cmdlist.extend(str(x) for x in args) self.last_command = " ".join(cmdlist) self.last_result = runscript("sourmash", args, **kwargs) if self.last_result.status: raise SourmashCommandFailed(self.last_result.err) return self.last_result sourmash = run_sourmash def run(self, scriptname, *args, **kwargs): "Run a script with the given arguments." if "in_directory" not in kwargs: kwargs["in_directory"] = self.location self.last_command = " ".join(args) self.last_result = runscript(scriptname, args, **kwargs) return self.last_result def output(self, path): return os.path.join(self.location, path) def __str__(self): s = "" if self.last_command: s += f"Last command run:\n{repr(self.last_command)}\n" if self.last_result: s += "\nLAST RESULT:\n" s += f"- exit code: {self.last_result.status}\n\n" if self.last_result.out: s += f"- stdout:\n---\n{self.last_result.out}---\n" else: s += "(no stdout)\n\n" if self.last_result.err: s += f"- stderr:\n---\n{self.last_result.err}---\n" else: s += "(no stderr)\n" return s def in_tempdir(fn): def wrapper(*args, **kwargs): with TempDirectory() as location: ctxt = RunnerContext(location) newargs = [ctxt] + list(args) return fn(*newargs, **kwargs) return wrapper def in_thisdir(fn): def wrapper(*args, **kwargs): ctxt = RunnerContext(os.getcwd()) newargs = [ctxt] + list(args) return fn(*newargs, **kwargs) return wrapper 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Mycobacterium tuberculosis strain 2926STDY5723801 genome assembly, contig: ERS519109SCcontig000084, whole genome shotgun 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Mycobacterium tuberculosis strain 2926STDY5723547 genome assembly, contig: ERS518857SCcontig000077, whole genome shotgun 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Salmonella enterica subsp. enterica serovar Typhi genome assembly 10209_5#45, scaffold ERS239666SCcontig000001, whole genome shotgun 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Mycobacterium abscessus subsp. abscessus strain 855 genome assembly, contig: ERS373388SCcontig000013, whole genome shotgun 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Herbaspirillum sp. CF444 PMI16_contig_1.1, whole genome shotgun 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Escherichia coli strain upec-248 upec-248_ctg_102, whole genome shotgun 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Candidatus Uhrbacteria bacterium GW2011_GWC2_40_450 UU04_C0001, whole genome shotgun 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Escherichia coli KTE139 acwoz-supercont1.1, whole genome shotgun 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Mycobacterium tuberculosis strain 2926STDY5723476 genome assembly, contig: ERS518787SCcontig000060, whole genome shotgun 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Prevotella sp. P5-60 contig1, whole genome shotgun 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Neisseria sp. HMSC70E02 genomic scaffold Scaffold6, whole genome shotgun 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Enterobacter cloacae strain MNCRE30 CRE30_contig_1, whole genome shotgun 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Salmonella enterica subsp. enterica serovar Typhi genome assembly 10607_2#11, scaffold ERS327362SCcontig000001, whole genome shotgun 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Stachybotrys chartarum IBT 40293 unplaced genomic scaffold scaffold1, whole genome shotgun 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4157046300277750ustar00rootroot00000000000000[{"class":"sourmash_signature","email":"","filename":"genbank/bacteria/GCA_001478155.1/GCA_001478155.1_Salmonella_enterica_CVM_N31414-SQ_v1.0_genomic.fna.gz","hash_function":"0.murmur64","license":"CC0","name":"JYVN01000001.1 Salmonella enterica subsp. enterica serovar Saintpaul strain CVM N31414 N31414_contig_1, whole genome shotgun 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Burkholderia pseudomallei MSHR2451 genomic scaffold scaffold1, whole genome shotgun 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Salmonella enterica subsp. enterica serovar Enteritidis strain CFSAN057735 CFSAN057735_contig_1, whole genome shotgun 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Mycobacterium tuberculosis strain 2926STDY5723447 genome assembly, contig: ERS518758SCcontig000065, whole genome shotgun 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Burkholderia pseudomallei genome assembly 7909_4#19, scaffold ERS008829SCcontig000001, whole genome shotgun 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Sebaldella termitidis ATCC 33386, complete 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Saccharomyces cerevisiae R008 chromosome I, whole genome shotgun 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se":"CC0","name":"MUWD01000001.1 Burkholderia cenocepacia strain VC5305 VC5305_contig-0, whole genome shotgun sequence","signatures":[{"abundances":[1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,13,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,6,2,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,13,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,2,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1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Staphylococcus aureus strain 3688STDY6124881 genome assembly, contig: ERS746454SCcontig000030, whole genome shotgun 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Brucella melitensis bv. 1 str. M28-12 M28-12_contig029, whole genome shotgun 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Bacterium SIT6 genome assembly, contig: contig00001, whole genome shotgun 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Escherichia coli genome assembly FHI43, scaffold scaffold-1_contig-16.0_1_1401_[organism:Escherichia","signatures":[{"abundances":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,9,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,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Pseudomonas aeruginosa genome assembly P7_London_17_VIM_2_06_09, contig P7_London_17_VIM_2_06_09_CONTIG1, whole genome shotgun 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/.sbt.subset/8963a3ad29f88d2b9869c51ac102ddf6000066400000000000000000000144151474157046300302170ustar00rootroot00000000000000[{"class":"sourmash_signature","email":"","filename":"genbank/bacteria/GCA_001781095.1/GCA_001781095.1_ASM178109v1_genomic.fna.gz","hash_function":"0.murmur64","license":"CC0","name":"MFKU01000001.1 Candidatus Kaiserbacteria bacterium RIFCSPHIGHO2_01_FULL_53_31 rifcsphigho2_01_scaffold_12122, whole genome shotgun 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Burkholderia pseudomallei strain NAU2B-5 NAU2B-5_10, whole genome shotgun 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Pseudomonas aeruginosa PAO1-CipR contig00001, whole genome shotgun 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Streptococcus sp. HMSC036H09 genomic scaffold Scaffold0, whole genome shotgun 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Pseudomonas sp. ICMP 10191 scaffold1, whole genome shotgun 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Staphylococcus sp. HMSC58E11 genomic scaffold Scaffold1, whole genome shotgun 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Candidatus Uhrbacteria bacterium GW2011_GWE2_46_68 UX57_C0001, whole genome shotgun 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Escherichia coli KTE156 genomic scaffold acEuf-supercont1.1, whole genome shotgun 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Flexibacter litoralis DSM 6794, complete 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on":"0.murmur64","license":"CC0","name":"LLUJ01000001.1 Pseudomonas aeruginosa strain WH-SGI-V-07283 WH_SGI_V_07283_1, whole genome shotgun 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Streptococcus sanguinis strain 1112_SSAN 0_6903_113542, whole genome shotgun 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Mycobacterium sp. H092 H092_contig000001, whole genome shotgun 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Salmonella enterica subsp. enterica serovar Typhimurium str. DT104 genome assembly 8490_2#85, scaffold ERS157242SCcontig000001, whole genome shotgun 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Mycobacterium tuberculosis BTB03-012 genomic scaffold adOXf-supercont1.1, whole genome shotgun 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Neorhizobium galegae bv. officinalis genome assembly NgalHAMBI1189Draft, contig contig1, whole genome shotgun 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Enterobacter kobei strain GN02266 contig1, whole genome shotgun 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Escherichia coli O121:H19 str. 2010C-4966 contig1, whole genome shotgun 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bacteria/GCA_001902355.1/GCA_001902355.1_ASM190235v1_genomic.fna.gz","hash_function":"0.murmur64","license":"CC0","name":"CP018364.1 Klebsiella pneumoniae strain Kp_Goe_62629, complete 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Methylobacterium sp. Leaf117 contig_1, whole genome shotgun 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Serratia marcescens strain 2880STDY5682866 genome assembly, contig ERS381334SCcontig000001, whole genome shotgun 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ense":"CC0","name":"CP004441.1 Saccharomyces cerevisiae YJM990 chromosome I genomic sequence","signatures":[{"abundances":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,92,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,92,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,3,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,3,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,92,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,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Pseudomonas aeruginosa PAO579 Contig01, whole genome shotgun 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Burkholderia ubonensis strain MSMB1176WGS MSMB1176WGS_10, whole genome shotgun sequence","signatures":[{"abundances":[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,7,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1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Bacillus cereus strain MOD1_Bc97 Bc97_MED_contig_1, whole genome shotgun 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Lactobacillus plantarum subsp. plantarum ATCC 14917 = JCM 1149 = CGMCC 1.2437 Scaffold1, whole genome shotgun 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Photobacterium leiognathi strain CUB1 NODE_1, whole genome shotgun 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Pseudomonas moraviensis strain UCD-KL30 scaffold_0, whole genome shotgun 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K-12 substr. 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K-12 substr. MG1655, complete genome", "signatures": [ { "ksize": 21, "max_hash": 0, "md5sum": "3b3f65dba0dad5429f45e7023e269d7c", "mins": [ 4519324738618364, 5243256295096602, 15176520465972589, 15680769339693652, 18497857373523252, 18755330737671003, 21624573369793591, 23062839395285483, 25725997040217348, 34666218892018070, 55529519525316327, 63656212937639342, 75298225208683694, 80926437301475488, 82402909138949714, 84251831438776403, 89592827748722822, 96489177501377939, 107016255164726803, 110175257989954062, 117055601844984303, 119550996164252532, 120076047914471482, 124833414081263892, 132504286561547362, 139936877543357875, 142399575232998988, 156589708717876073, 157600036558613226, 170884286104686349, 180176045311510443, 183173484525153304, 183191513011246829, 190851827918094774, 196816659105826119, 200502895665513902, 206617899443568343, 208648556119029728, 214757880775076502, 221439947106598531, 222377104477853357, 229634467206794208, 229858927555422443, 230403662973256315, 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Salmonella enterica subsp. enterica serovar Typhimurium str. LT2 chromosome, complete 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Sphingobacteriaceae bacterium DW12 contig100_scaffold73, whole genome shotgun 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directory contains multiple signatures with different metadata but the same contents (and md5sum). fb2c4c88.k=31.scaled=1000.DNA.dup=0.63.sig000066400000000000000000000746251474157046300324370ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/tests/test-data/duplicate-sigs[{"class":"sourmash_signature","email":"","hash_function":"0.murmur64","filename":"/dev/fd/63","name":"GCA_009816935.1 Francisella tularensis strain=06-2412, 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Bifunctional aspartokinase/homoserine dehydrogenase 1 [Escherichia coli str. K-12 substr. MG1655] MRVLKFGGTSVANAERFLRVADILESNARQGQVATVLSAPAKITNHLVAMIEKTISGQDALPNISDAERIFAELLTGLAA AQPGFPLAQLKTFVDQEFAQIKHVLHGISLLGQCPDSINAALICRGEKMSIAIMAGVLEARGHNVTVIDPVEKLLAVGHY LESTVDIAESTRRIAASRIPADHMVLMAGFTAGNEKGELVVLGRNGSDYSAAVLAACLRADCCEIWTDVDGVYTCDPRQV PDARLLKSMSYQEAMELSYFGAKVLHPRTITPIAQFQIPCLIKNTGNPQAPGTLIGASRDEDELPVKGISNLNNMAMFSV SGPGMKGMVGMAARVFAAMSRARISVVLITQSSSEYSISFCVPQSDCVRAERAMQEEFYLELKEGLLEPLAVTERLAIIS VVGDGMRTLRGISAKFFAALARANINIVAIAQGSSERSISVVVNNDDATTGVRVTHQMLFNTDQVIEVFVIGVGGVGGAL LEQLKRQQSWLKNKHIDLRVCGVANSKALLTNVHGLNLENWQEELAQAKEPFNLGRLIRLVKEYHLLNPVIVDCTSSQAV ADQYADFLREGFHVVTPNKKANTSSMDYYHQLRYAAEKSRRKFLYDTNVGAGLPVIENLQNLLNAGDELMKFSGILSGSL SYIFGKLDEGMSFSEATTLAREMGYTEPDPRDDLSGMDVARKLLILARETGRELELADIEIEPVLPAEFNAEGDVAAFMA NLSQLDDLFAARVAKARDEGKVLRYVGNIDEDGVCRVKIAEVDGNDPLFKVKNGENALAFYSHYYQPLPLVLRGYGAGND VTAAGVFADLLRTLSWKLGV >NP_414544.1 homoserine kinase [Escherichia coli str. K-12 substr. MG1655] MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGRFADKLPSEPRENIVYQCWERFCQELGKQI PVAMTLEKNMPIGSGLGSSACSVVAALMAMNEHCGKPLNDTRLLALMGELEGRISGSIHYDNVAPCFLGGMQLMIEENDI ISQQVPGFDEWLWVLAYPGIKVSTAEARAILPAQYRRQDCIAHGRHLAGFIHACYSRQPELAAKLMKDVIAEPYRERLLP GFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVADWLGKNYLQNQEGFVHICRLDTAGARVLEN sourmash-bio-sourmash-bc3c0f8/tests/test-data/ecoli.genes.fna000066400000000000000000000070331474157046300244020ustar00rootroot00000000000000>gi|556503834:337-2799 Escherichia coli str. K-12 substr. MG1655, complete genome ATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGTGTTGCCGATATTC TGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCT GGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATT TTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCG TCGATCAGGAATTTGCCCAAATAAAACATGTCCTGCATGGCATTAGTTTGTTGGGGCAGTGCCCGGATAG CATCAACGCTGCGCTGATTTGCCGTGGCGAGAAAATGTCGATCGCCATTATGGCCGGCGTATTAGAAGCG CGCGGTCACAACGTTACTGTTATCGATCCGGTCGAAAAACTGCTGGCAGTGGGGCATTACCTCGAATCTA CCGTCGATATTGCTGAGTCCACCCGCCGTATTGCGGCAAGCCGCATTCCGGCTGATCACATGGTGCTGAT GGCAGGTTTCACCGCCGGTAATGAAAAAGGCGAACTGGTGGTGCTTGGACGCAACGGTTCCGACTACTCT GCTGCGGTGCTGGCTGCCTGTTTACGCGCCGATTGTTGCGAGATTTGGACGGACGTTGACGGGGTCTATA CCTGCGACCCGCGTCAGGTGCCCGATGCGAGGTTGTTGAAGTCGATGTCCTACCAGGAAGCGATGGAGCT TTCCTACTTCGGCGCTAAAGTTCTTCACCCCCGCACCATTACCCCCATCGCCCAGTTCCAGATCCCTTGC CTGATTAAAAATACCGGAAATCCTCAAGCACCAGGTACGCTCATTGGTGCCAGCCGTGATGAAGACGAAT TACCGGTCAAGGGCATTTCCAATCTGAATAACATGGCAATGTTCAGCGTTTCTGGTCCGGGGATGAAAGG GATGGTCGGCATGGCGGCGCGCGTCTTTGCAGCGATGTCACGCGCCCGTATTTCCGTGGTGCTGATTACG CAATCATCTTCCGAATACAGCATCAGTTTCTGCGTTCCACAAAGCGACTGTGTGCGAGCTGAACGGGCAA TGCAGGAAGAGTTCTACCTGGAACTGAAAGAAGGCTTACTGGAGCCGCTGGCAGTGACGGAACGGCTGGC CATTATCTCGGTGGTAGGTGATGGTATGCGCACCTTGCGTGGGATCTCGGCGAAATTCTTTGCCGCACTG GCCCGCGCCAATATCAACATTGTCGCCATTGCTCAGGGATCTTCTGAACGCTCAATCTCTGTCGTGGTAA ATAACGATGATGCGACCACTGGCGTGCGCGTTACTCATCAGATGCTGTTCAATACCGATCAGGTTATCGA AGTGTTTGTGATTGGCGTCGGTGGCGTTGGCGGTGCGCTGCTGGAGCAACTGAAGCGTCAGCAAAGCTGG CTGAAGAATAAACATATCGACTTACGTGTCTGCGGTGTTGCCAACTCGAAGGCTCTGCTCACCAATGTAC ATGGCCTTAATCTGGAAAACTGGCAGGAAGAACTGGCGCAAGCCAAAGAGCCGTTTAATCTCGGGCGCTT AATTCGCCTCGTGAAAGAATATCATCTGCTGAACCCGGTCATTGTTGACTGCACTTCCAGCCAGGCAGTG GCGGATCAATATGCCGACTTCCTGCGCGAAGGTTTCCACGTTGTCACGCCGAACAAAAAGGCCAACACCT CGTCGATGGATTACTACCATCAGTTGCGTTATGCGGCGGAAAAATCGCGGCGTAAATTCCTCTATGACAC CAACGTTGGGGCTGGATTACCGGTTATTGAGAACCTGCAAAATCTGCTCAATGCAGGTGATGAATTGATG AAGTTCTCCGGCATTCTTTCTGGTTCGCTTTCTTATATCTTCGGCAAGTTAGACGAAGGCATGAGTTTCT CCGAGGCGACCACGCTGGCGCGGGAAATGGGTTATACCGAACCGGACCCGCGAGATGATCTTTCTGGTAT GGATGTGGCGCGTAAACTATTGATTCTCGCTCGTGAAACGGGACGTGAACTGGAGCTGGCGGATATTGAA ATTGAACCTGTGCTGCCCGCAGAGTTTAACGCCGAGGGTGATGTTGCCGCTTTTATGGCGAATCTGTCAC AACTCGACGATCTCTTTGCCGCGCGCGTGGCGAAGGCCCGTGATGAAGGAAAAGTTTTGCGCTATGTTGG CAATATTGATGAAGATGGCGTCTGCCGCGTGAAGATTGCCGAAGTGGATGGTAATGATCCGCTGTTCAAA GTGAAAAATGGCGAAAACGCCCTGGCCTTCTATAGCCACTATTATCAGCCGCTGCCGTTGGTACTGCGCG GATATGGTGCGGGCAATGACGTTACAGCTGCCGGTGTCTTTGCTGATCTGCTACGTACCCTCTCATGGAA GTTAGGAGTCTGA >gi|556503834:2801-3733 Escherichia coli str. K-12 substr. MG1655, complete genome ATGGTTAAAGTTTATGCCCCGGCTTCCAGTGCCAATATGAGCGTCGGGTTTGATGTGCTCGGGGCGGCGG TGACACCTGTTGATGGTGCATTGCTCGGAGATGTAGTCACGGTTGAGGCGGCAGAGACATTCAGTCTCAA CAACCTCGGACGCTTTGCCGATAAGCTGCCGTCAGAACCACGGGAAAATATCGTTTATCAGTGCTGGGAG CGTTTTTGCCAGGAACTGGGTAAGCAAATTCCAGTGGCGATGACCCTGGAAAAGAATATGCCGATCGGTT CGGGCTTAGGCTCCAGTGCCTGTTCGGTGGTCGCGGCGCTGATGGCGATGAATGAACACTGCGGCAAGCC GCTTAATGACACTCGTTTGCTGGCTTTGATGGGCGAGCTGGAAGGCCGTATCTCCGGCAGCATTCATTAC GACAACGTGGCACCGTGTTTTCTCGGTGGTATGCAGTTGATGATCGAAGAAAACGACATCATCAGCCAGC AAGTGCCAGGGTTTGATGAGTGGCTGTGGGTGCTGGCGTATCCGGGGATTAAAGTCTCGACGGCAGAAGC CAGGGCTATTTTACCGGCGCAGTATCGCCGCCAGGATTGCATTGCGCACGGGCGACATCTGGCAGGCTTC ATTCACGCCTGCTATTCCCGTCAGCCTGAGCTTGCCGCGAAGCTGATGAAAGATGTTATCGCTGAACCCT ACCGTGAACGGTTACTGCCAGGCTTCCGGCAGGCGCGGCAGGCGGTCGCGGAAATCGGCGCGGTAGCGAG CGGTATCTCCGGCTCCGGCCCGACCTTGTTCGCTCTGTGTGACAAGCCGGAAACCGCCCAGCGCGTTGCC GACTGGTTGGGTAAGAACTACCTGCAAAATCAGGAAGGTTTTGTTCATATTTGCCGGCTGGATACGGCGG GCGCACGAGTACTGGAAAACTAA sourmash-bio-sourmash-bc3c0f8/tests/test-data/fake-abund/000077500000000000000000000000001474157046300235135ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/tests/test-data/fake-abund/README.md000066400000000000000000000005331474157046300247730ustar00rootroot00000000000000signature: ``` sourmash compute -k 31 --track-abundance ../podar-ref/1.fa ../podar-ref/1.fa ../podar-ref/1.fa ../podar-ref/1.fa ../podar-ref/1.fa ../podar-ref/63.fa --merge "query" -o query.sig --scaled=1000 ``` database: ``` sourmash lca index ../podar-lineage.csv matches.lca.json ../podar-ref/{1,63}.fa.sig -k 31 -C 3 --split-identifiers ``` sourmash-bio-sourmash-bc3c0f8/tests/test-data/fake-abund/matches.lca.json.gz000066400000000000000000000153211474157046300272110ustar00rootroot00000000000000\^matches.lca.json}ۮqF_a;E rp@&%<$X1YFRDX=OwuW_U/ӛ_ˋo^Ӈ>{7o_=(Og믓|zf)'݇?Moi|7u/_~ǏyW_}/Ƿ?LJ3૷>9{ͷY_û7oop՛?}k}?}>O>|o^WׇW?~~<{z|xN_hz񧇷߼z"~ċC)%0>8޼156%P{^je2D=r&#łJQQkNG:nX{3=ܛGhz sL~:*M s| Jua-t}wn(Z.vS˜ε/NEƲs}&gfُ7s̨ca%c} I{TU\RCY+avL4RDm\Iw{B5 $xߊ0&q=0Ϝ,/_+'άg␣Gl-%g:[ њL v릯LP [P '[zͿƶ r*Z%h7}'# N.X mI%9@Y%G<\䫨͢k Hq矪a%|l,eaKo(Sz F%u8PDؑKiM_S^-0.:j "10 zB-Oe&IX{@iqHK3v,K.qT=V/hL0NqOirzV|[h'= <~ O68 Dy'͔w@]_k+dAf3'A ;FY I!,O=N}åӍv.J&n,sFY92:&9llY8  `xM;,((vk) P 4āqUV%BY ;"8r){RoĞ9s,iz6σ H 0?A4j0 sk,Ł=ѣ4)/%ʎx8"_ Ғ!f^u_ɊK L A,.uc_xxƏFx0NoD VA4C_*xF%<AON""XbX8J6- D ,f< M2%O`wzt+Gw,k@VN *K Aa@ѝ 1#R-GRKX'l,hA3$qJs!B]~VObB0 *"zC,[Rx-T`D۪l Sr%ܦ6ԓڟ1N0B]"LYzԫKSR@XAyK*(.AO0tmC-j,Ǿvɦdۗb3NePliL@P>{d;&?mD~)1kkrB^0RiC{TBd@q'VO )mx89/X'{ө M ذM>p-[4@q)b͹Gx#fM{6ґ0w08`;1h(!ق裗\pfIC[ y1SrAd |!7Lxs%K4-HGE4y ڍuJ) fNyɝ\,$;ϫeN{aZ1@vbMR~.>{Hxo @5%XBHQ5D,pIgYntbgIl7c %%/ȏp7V*piYP@B薰D#<8.,]ruhC"z>,Eي x֎#mv1g[ؔdʜ@2C2~wpH6UInjՓh:^n 䠌0ӬK@aa~Oɹ`OJHOʛ,m 1y B Z9!)  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P125109 complete genome,../fasta/GCF_000009505.1_ASM950v1_genomic.fna.gz,CC0 GCF_000009505.1_ASM950v1_genomic.fna.gz.sig,405ae3300f28ca5fe5c223cbf7e28734,21,DNA,0,10000,471,42,0,NC_011294.1 Salmonella enterica subsp. enterica serovar Enteritidis str. P125109 complete genome,../fasta/GCF_000009505.1_ASM950v1_genomic.fna.gz,CC0 GCF_000009505.1_ASM950v1_genomic.fna.gz.sig,0842f7edb426fc4fa2701c107e678279,31,DNA,0,10000,461,42,0,NC_011294.1 Salmonella enterica subsp. enterica serovar Enteritidis str. P125109 complete genome,../fasta/GCF_000009505.1_ASM950v1_genomic.fna.gz,CC0 GCF_000009525.1_ASM952v1_genomic.fna.gz.sig,d883538a0c983a863fa4b6e5fcd19612,11,DNA,0,10000,148,42,0,NC_011274.1 Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91 complete genome,../fasta/GCF_000009525.1_ASM952v1_genomic.fna.gz,CC0 GCF_000009525.1_ASM952v1_genomic.fna.gz.sig,9133bd71b86628b38c665ab7e5eb8712,21,DNA,0,10000,457,42,0,NC_011274.1 Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91 complete genome,../fasta/GCF_000009525.1_ASM952v1_genomic.fna.gz,CC0 GCF_000009525.1_ASM952v1_genomic.fna.gz.sig,afadabf39aec247929e84a29fd797117,31,DNA,0,10000,461,42,0,NC_011274.1 Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91 complete genome,../fasta/GCF_000009525.1_ASM952v1_genomic.fna.gz,CC0 GCF_000011885.1_ASM1188v1_genomic.fna.gz.sig,feef9e4d39fecd3d9292b76c0cc72b81,11,DNA,0,10000,155,42,0,"NC_006511.1 Salmonella enterica subsp. enterica serovar Paratyphi A str. 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CT18, complete genome",../fasta/GCF_000195995.1_ASM19599v1_genomic.fna.gz,CC0 sourmash-bio-sourmash-bc3c0f8/tests/test-data/gather/campy-picklist.csv000066400000000000000000000015361474157046300264430ustar00rootroot00000000000000signature_file,md5,ksize,moltype,num,scaled,n_hashes,seed,with_abundance,name,filename,license GCF_000009085.1_ASM908v1_genomic.fna.gz.sig,752280e9969ce750e2c80477c1b7b0e7,11,DNA,0,10000,61,42,0,"NC_002163.1 Campylobacter jejuni subsp. jejuni NCTC 11168 = ATCC 700819 chromosome, complete genome",../fasta/GCF_000009085.1_ASM908v1_genomic.fna.gz,CC0 GCF_000009085.1_ASM908v1_genomic.fna.gz.sig,eba0eb3ce984cc53c36f134a752c52c5,21,DNA,0,10000,157,42,0,"NC_002163.1 Campylobacter jejuni subsp. jejuni NCTC 11168 = ATCC 700819 chromosome, complete genome",../fasta/GCF_000009085.1_ASM908v1_genomic.fna.gz,CC0 GCF_000009085.1_ASM908v1_genomic.fna.gz.sig,953156e9f4da8cf22e7e0b4b88261fae,31,DNA,0,10000,167,42,0,"NC_002163.1 Campylobacter jejuni subsp. jejuni NCTC 11168 = ATCC 700819 chromosome, complete genome",../fasta/GCF_000009085.1_ASM908v1_genomic.fna.gz,CC0 sourmash-bio-sourmash-bc3c0f8/tests/test-data/gather/combined.sig000066400000000000000000001540741474157046300252670ustar00rootroot00000000000000[ { "class": "sourmash_signature", "email": "", "filename": "-", "hash_function": "0.murmur64", "signatures": [ { "ksize": 21, "max_hash": 1844674407370955, "md5sum": "b92dbf45dd57867cbec2321ccfa55af8", "mins": [ 452975526679, 2041852347834, 2198162296894, 4688264423161, 6578133384180, 7407228233150, 9256413976137, 10112959329386, 10488238202736, 12583140977701, 13661764940876, 14854646762163, 17974881956302, 21097252720982, 21433679014216, 21624830622604, 22436748869063, 23620876127490, 23947937042923, 24544394093088, 24788755838072, 25043460490743, 26181501887498, 26287827857059, 26528877136305, 26552154020911, 26691099330778, 26717466207597, 26996850963968, 28611954941059, 29007262853675, 30236028046364, 32720583388614, 33325482419549, 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P125109 complete genome NC_011294.1 Salmonella enterica subsp. enterica serovar Enteritidis str. P125109 complete genome NC_011274.1 Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91 complete genome NC_011274.1 Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91 complete genome NC_011274.1 Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91 complete genome "NC_006511.1 Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150, complete genome" "NC_006511.1 Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150, complete genome" "NC_006511.1 Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150, complete genome" "NC_011080.1 Salmonella enterica subsp. enterica serovar Newport str. SL254, complete genome" "NC_011080.1 Salmonella enterica subsp. enterica serovar Newport str. SL254, complete genome" "NC_011080.1 Salmonella enterica subsp. enterica serovar Newport str. SL254, complete genome" "NC_003198.1 Salmonella enterica subsp. enterica serovar Typhi str. CT18, complete genome" "NC_003198.1 Salmonella enterica subsp. enterica serovar Typhi str. CT18, complete genome" "NC_003198.1 Salmonella enterica subsp. enterica serovar Typhi str. CT18, complete genome" sourmash-bio-sourmash-bc3c0f8/tests/test-data/gather/salmonella-picklist.csv000066400000000000000000000137171474157046300274650ustar00rootroot00000000000000signature_file,md5,ksize,moltype,num,scaled,n_hashes,seed,with_abundance,name,filename,license GCF_000006945.2_ASM694v2_genomic.fna.gz.sig,323c1a1712b0949268dd6fb93be63ae2,11,DNA,0,10000,150,42,0,"NC_003197.2 Salmonella enterica subsp. enterica serovar Typhimurium str. LT2, complete genome",../fasta/GCF_000006945.2_ASM694v2_genomic.fna.gz,CC0 GCF_000006945.2_ASM694v2_genomic.fna.gz.sig,263c2de20b597d6e33b81ec91d8672b5,21,DNA,0,10000,485,42,0,"NC_003197.2 Salmonella enterica subsp. enterica serovar Typhimurium str. LT2, complete genome",../fasta/GCF_000006945.2_ASM694v2_genomic.fna.gz,CC0 GCF_000006945.2_ASM694v2_genomic.fna.gz.sig,dc12a6d8fd63122aa68f78facf9bed94,31,DNA,0,10000,490,42,0,"NC_003197.2 Salmonella enterica subsp. enterica serovar Typhimurium str. LT2, complete genome",../fasta/GCF_000006945.2_ASM694v2_genomic.fna.gz,CC0 GCF_000007545.1_ASM754v1_genomic.fna.gz.sig,df24140b1c6cad16b30abeaf03019eb5,11,DNA,0,10000,158,42,0,"NC_004631.1 Salmonella enterica subsp. enterica serovar Typhi Ty2, complete genome",../fasta/GCF_000007545.1_ASM754v1_genomic.fna.gz,CC0 GCF_000007545.1_ASM754v1_genomic.fna.gz.sig,fd958e3b5649bc03890517ff239970ea,21,DNA,0,10000,445,42,0,"NC_004631.1 Salmonella enterica subsp. enterica serovar Typhi Ty2, complete genome",../fasta/GCF_000007545.1_ASM754v1_genomic.fna.gz,CC0 GCF_000007545.1_ASM754v1_genomic.fna.gz.sig,8c22dff88a2239607762da00f7fd1725,31,DNA,0,10000,472,42,0,"NC_004631.1 Salmonella enterica subsp. enterica serovar Typhi Ty2, complete genome",../fasta/GCF_000007545.1_ASM754v1_genomic.fna.gz,CC0 GCF_000008105.1_ASM810v1_genomic.fna.gz.sig,9db6efc92a041e11713ccfa8597edae5,11,DNA,0,10000,150,42,0,"NC_006905.1 Salmonella enterica subsp. enterica serovar Choleraesuis str. SC-B67, complete genome",../fasta/GCF_000008105.1_ASM810v1_genomic.fna.gz,CC0 GCF_000008105.1_ASM810v1_genomic.fna.gz.sig,8996699a05d3e5a05fa3fe94bfa41431,21,DNA,0,10000,472,42,0,"NC_006905.1 Salmonella enterica subsp. enterica serovar Choleraesuis str. SC-B67, complete genome",../fasta/GCF_000008105.1_ASM810v1_genomic.fna.gz,CC0 GCF_000008105.1_ASM810v1_genomic.fna.gz.sig,85c3aeec6457c0b1d210472ddeb67714,31,DNA,0,10000,468,42,0,"NC_006905.1 Salmonella enterica subsp. enterica serovar Choleraesuis str. SC-B67, complete genome",../fasta/GCF_000008105.1_ASM810v1_genomic.fna.gz,CC0 GCF_000009505.1_ASM950v1_genomic.fna.gz.sig,0f35aeadda1532ed450bd6de1e73545d,11,DNA,0,10000,148,42,0,NC_011294.1 Salmonella enterica subsp. enterica serovar Enteritidis str. P125109 complete genome,../fasta/GCF_000009505.1_ASM950v1_genomic.fna.gz,CC0 GCF_000009505.1_ASM950v1_genomic.fna.gz.sig,405ae3300f28ca5fe5c223cbf7e28734,21,DNA,0,10000,471,42,0,NC_011294.1 Salmonella enterica subsp. enterica serovar Enteritidis str. P125109 complete genome,../fasta/GCF_000009505.1_ASM950v1_genomic.fna.gz,CC0 GCF_000009505.1_ASM950v1_genomic.fna.gz.sig,0842f7edb426fc4fa2701c107e678279,31,DNA,0,10000,461,42,0,NC_011294.1 Salmonella enterica subsp. enterica serovar Enteritidis str. P125109 complete genome,../fasta/GCF_000009505.1_ASM950v1_genomic.fna.gz,CC0 GCF_000009525.1_ASM952v1_genomic.fna.gz.sig,d883538a0c983a863fa4b6e5fcd19612,11,DNA,0,10000,148,42,0,NC_011274.1 Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91 complete genome,../fasta/GCF_000009525.1_ASM952v1_genomic.fna.gz,CC0 GCF_000009525.1_ASM952v1_genomic.fna.gz.sig,9133bd71b86628b38c665ab7e5eb8712,21,DNA,0,10000,457,42,0,NC_011274.1 Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91 complete genome,../fasta/GCF_000009525.1_ASM952v1_genomic.fna.gz,CC0 GCF_000009525.1_ASM952v1_genomic.fna.gz.sig,afadabf39aec247929e84a29fd797117,31,DNA,0,10000,461,42,0,NC_011274.1 Salmonella enterica subsp. enterica serovar Gallinarum str. 287/91 complete genome,../fasta/GCF_000009525.1_ASM952v1_genomic.fna.gz,CC0 GCF_000011885.1_ASM1188v1_genomic.fna.gz.sig,feef9e4d39fecd3d9292b76c0cc72b81,11,DNA,0,10000,155,42,0,"NC_006511.1 Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150, complete genome",../fasta/GCF_000011885.1_ASM1188v1_genomic.fna.gz,CC0 GCF_000011885.1_ASM1188v1_genomic.fna.gz.sig,cc80cb247b195ca3dfa0756257d882b6,21,DNA,0,10000,427,42,0,"NC_006511.1 Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150, complete genome",../fasta/GCF_000011885.1_ASM1188v1_genomic.fna.gz,CC0 GCF_000011885.1_ASM1188v1_genomic.fna.gz.sig,bb365606acbf08d183399f139af80c32,31,DNA,0,10000,459,42,0,"NC_006511.1 Salmonella enterica subsp. enterica serovar Paratyphi A str. ATCC 9150, complete genome",../fasta/GCF_000011885.1_ASM1188v1_genomic.fna.gz,CC0 GCF_000016045.1_ASM1604v1_genomic.fna.gz.sig,4cec832176c4831239faed42c0616ef4,11,DNA,0,10000,155,42,0,"NC_011080.1 Salmonella enterica subsp. enterica serovar Newport str. SL254, complete genome",../fasta/GCF_000016045.1_ASM1604v1_genomic.fna.gz,CC0 GCF_000016045.1_ASM1604v1_genomic.fna.gz.sig,43a9d80a4cd995779c7538a32088dd0e,21,DNA,0,10000,469,42,0,"NC_011080.1 Salmonella enterica subsp. enterica serovar Newport str. SL254, complete genome",../fasta/GCF_000016045.1_ASM1604v1_genomic.fna.gz,CC0 GCF_000016045.1_ASM1604v1_genomic.fna.gz.sig,d0cfbe22579f98fd5de2d41203589964,31,DNA,0,10000,480,42,0,"NC_011080.1 Salmonella enterica subsp. enterica serovar Newport str. SL254, complete genome",../fasta/GCF_000016045.1_ASM1604v1_genomic.fna.gz,CC0 GCF_000195995.1_ASM19599v1_genomic.fna.gz.sig,40df36a7eb411022be4b1d6a7af05496,11,DNA,0,10000,161,42,0,"NC_003198.1 Salmonella enterica subsp. enterica serovar Typhi str. CT18, complete genome",../fasta/GCF_000195995.1_ASM19599v1_genomic.fna.gz,CC0 GCF_000195995.1_ASM19599v1_genomic.fna.gz.sig,ffa92983f7e67454c407499cbfbabf88,21,DNA,0,10000,487,42,0,"NC_003198.1 Salmonella enterica subsp. enterica serovar Typhi str. CT18, complete genome",../fasta/GCF_000195995.1_ASM19599v1_genomic.fna.gz,CC0 GCF_000195995.1_ASM19599v1_genomic.fna.gz.sig,cb26db5716a213c9a6614021e7176c1d,31,DNA,0,10000,512,42,0,"NC_003198.1 Salmonella enterica subsp. enterica serovar Typhi str. CT18, complete genome",../fasta/GCF_000195995.1_ASM19599v1_genomic.fna.gz,CC0 sourmash-bio-sourmash-bc3c0f8/tests/test-data/gather/thermotoga-picklist.csv000066400000000000000000000040111474157046300274720ustar00rootroot00000000000000signature_file,md5,ksize,moltype,num,scaled,n_hashes,seed,with_abundance,name,filename,license GCF_000008545.1_ASM854v1_genomic.fna.gz.sig,74b928d3db1f7f033c0dcca6c6e52aea,11,DNA,0,10000,84,42,0,"NC_000853.1 Thermotoga maritima MSB8 chromosome, complete genome",../fasta/GCF_000008545.1_ASM854v1_genomic.fna.gz,CC0 GCF_000008545.1_ASM854v1_genomic.fna.gz.sig,ba9947e078cab29e20bc7d31bc1b9f0d,21,DNA,0,10000,192,42,0,"NC_000853.1 Thermotoga maritima MSB8 chromosome, complete genome",../fasta/GCF_000008545.1_ASM854v1_genomic.fna.gz,CC0 GCF_000008545.1_ASM854v1_genomic.fna.gz.sig,1bfe96d76ec9cdb60779a1a9223c424e,31,DNA,0,10000,187,42,0,"NC_000853.1 Thermotoga maritima MSB8 chromosome, complete genome",../fasta/GCF_000008545.1_ASM854v1_genomic.fna.gz,CC0 GCF_000016785.1_ASM1678v1_genomic.fna.gz.sig,328f7b0643bdb6c76135292b5afc8fa7,11,DNA,0,10000,82,42,0,"NC_009486.1 Thermotoga petrophila RKU-1, complete genome",../fasta/GCF_000016785.1_ASM1678v1_genomic.fna.gz,CC0 GCF_000016785.1_ASM1678v1_genomic.fna.gz.sig,a77789e831fcd2436c3b9e4e22fb173e,21,DNA,0,10000,190,42,0,"NC_009486.1 Thermotoga petrophila RKU-1, complete genome",../fasta/GCF_000016785.1_ASM1678v1_genomic.fna.gz,CC0 GCF_000016785.1_ASM1678v1_genomic.fna.gz.sig,50d8efd580ff2000cb38d1f8cc9cf9b4,31,DNA,0,10000,185,42,0,"NC_009486.1 Thermotoga petrophila RKU-1, complete genome",../fasta/GCF_000016785.1_ASM1678v1_genomic.fna.gz,CC0 GCF_000018945.1_ASM1894v1_genomic.fna.gz.sig,989f88420b193ef39c4dbe3b268e0049,11,DNA,0,10000,90,42,0,"NC_011978.1 Thermotoga neapolitana DSM 4359, complete genome",../fasta/GCF_000018945.1_ASM1894v1_genomic.fna.gz,CC0 GCF_000018945.1_ASM1894v1_genomic.fna.gz.sig,bebcd0dcc0ed3b120ad16c4e15805370,21,DNA,0,10000,188,42,0,"NC_011978.1 Thermotoga neapolitana DSM 4359, complete genome",../fasta/GCF_000018945.1_ASM1894v1_genomic.fna.gz,CC0 GCF_000018945.1_ASM1894v1_genomic.fna.gz.sig,4289d4241be8573145282352215ca3c4,31,DNA,0,10000,198,42,0,"NC_011978.1 Thermotoga neapolitana DSM 4359, complete genome",../fasta/GCF_000018945.1_ASM1894v1_genomic.fna.gz,CC0 sourmash-bio-sourmash-bc3c0f8/tests/test-data/genome-s10+s11.fa.gz000066400000000000000000010736701474157046300247360ustar00rootroot00000000000000oXgenome-s10+s11.fa\A%Kn$l/0",p܄a=GM"မ?umnuکObz?5?ۿ }?~rN)~~ ~t{oo}{_{{C3?~߫:~&kog?\rd>onn9Ku}y|?|ϭ|[ߣ6^uw knSU~^vw4F{Gn^{- ~ޖ}Mo¹߻Max ~[[_u-o#m̃cJ-Sީ;ͷ˩igwܷ)K۪}u .{_}te7RWW,WY[~.sw˿=(qsgǜ[_e ͫ־[[[ܒ8w#1B_õkrJs4pZš޿^gio~j+:#}QCL ߱7!ݓ^nI9mC/5qO> ȯQN'[z;7}s΋UjS2MWƷKl)-lse|E vW7,,ڵkw|(TCqku[{=JJ#}Sp6Sj"TrЮS*O2ϗc;Ԟ*6{L@Pw}' V9Ā>Bࣲ@v4LA[j Wg'9F*|"OS,-3lh@ hAQd|]Av*~8nB;LE[tl/sĀxOdAdCeh/N z!|ᢷ0ifeg0x$@WzK࠴7^󚏻L FOs>"t@;HWt9 AƤvi}9"^obc!#;V5E )LQz7 WP8`j m̥b\H8«^t})SriN$ijWXV)CD,ɊLS0bi%V34 *p?:?nIQD O4jN#f' FEJȆki69FBy % -B c:A꘾ gZcU Ɇ1i[Zp()v `DfYoK(NIllz1\xL pc"U[N~G.g+;b٬b5Rz9np{Z= w-~igA7“fJ{BWfl0\ Ŷ<Y!+}_@5mt 2-q#?Qp4(Lcu^nҮ~<.'8"BqKq /H[lؒqQҭNŞ7!;2IEYFgE@  yLO%L?:L,}L}BoX"Wnh~;Qfe'S1vjA #1Oz ÃeI}|zBj@TV|;,FD!| XدJŞOca1c1/}RBT=UdiLn7MܚD0xiiM'suͳ<~TwLT yc7#"NA0 +#{zHC25f7p؎+j$ 6(ck7VjL'Y}SYϞQUF=91'X;ހ`0y9*4/ -JqB$`P{<^= U!LeE0Ga uzk혹|6`s'AbR SĦ= C+' Z VFΓWFd9Z2O庥Dz"W,\V5;ݢJ5'J,59n $cNwN[}4ؗ.zUm}u"Uc eMpufČ8@)y6Q._`E!vz>Ɛ它4hGJM{tE 7I[nx[hqۊ#Af9LО&R]pp[*PM[ɹ@)$r$j2j0f dutaU:S4Bd)s ]"3)8&xc9A6D*kZO7jVn*i%Y0@L<80c-EOA8a90m;O[>ih誘$)ˣDF.HxJ `q<{ƅ$ @#df@q u&nW7;f(Fru30D'yjHyO:( cCfM7BgLkǩꝇU5Tu'+9=EyƞO&$P DE(ͮF>ue SvH+h6B(h;Q(x;hQm8!.Odkχꏆs (9:#*cJt l8`FV:#u͸`W&!D:2ۻ$VT*%G&<`%*-hu ICҽC0I*Ps4\:_LF"yrr:a F}A"~jC=  &QDZ4k݂M8up'^'6["1{^ !WCċ`Seio~&{3t`3EQ g l FWxYEVDPL6Iυ}IENͫHd!g0}FQUuluhX7MVIĭ@G9nf&p={5d D55pŽ/UGivg'߻NFRmI"A@Ǚ1ZPÏ?ɉtE_RFKAv1Co{(V!s5Ǩl'r @bUm2L]_JRt S뉣X,Fpum6go2qhyA(>n7|9и5}7S*|_&)10rL_+lQC| 03qic4GHlA{àl ؔ0)6nAIqv$\] ^2`GQQ֦*Ed i]x0Sr<ϡ;:لt1+Q6!m+Pjp$sywOuhdOek4Ƅ6 -? 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PCC 7120, AE009441,178306,Archaea,Crenarchaeota,Thermoprotei,Thermoproteales,Thermoproteaceae,Pyrobaculum,Pyrobaculum aerophilum,Pyrobaculum aerophilum str. IM2 AE009950,186497,Archaea,Euryarchaeota,Thermococci,Thermococcales,Thermococcaceae,Pyrococcus,Pyrococcus furiosus,Pyrococcus furiosus DSM 3638 AE009951,190304,Bacteria,Fusobacteria,Fusobacteriia,Fusobacteriales,Fusobacteriaceae,Fusobacterium,Fusobacterium nucleatum, AE010299,188937,Archaea,Euryarchaeota,Methanomicrobia,Methanosarcinales,Methanosarcinaceae,Methanosarcina,Methanosarcina acetivorans,Methanosarcina acetivorans C2A AE009439,190192,Archaea,Euryarchaeota,Methanopyri,Methanopyrales,Methanopyraceae,Methanopyrus,Methanopyrus kandleri,Methanopyrus kandleri AV19 NC_003911,246200,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Ruegeria,Ruegeria pomeroyi,Ruegeria pomeroyi DSS-3 AE006470,194439,Bacteria,Chlorobi,Chlorobia,Chlorobiales,Chlorobiaceae,Chlorobaculum,Chlorobaculum tepidum,Chlorobaculum tepidum TLS AE015928,226186,Bacteria,Bacteroidetes,Bacteroidia,Bacteroidales,Bacteroidaceae,Bacteroides,Bacteroides thetaiotaomicron,Bacteroides thetaiotaomicron VPI-5482 AL954747,228410,Bacteria,Proteobacteria,Betaproteobacteria,Nitrosomonadales,Nitrosomonadaceae,Nitrosomonas,Nitrosomonas europaea,Nitrosomonas europaea ATCC 19718 BX119912,243090,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Rhodopirellula,Rhodopirellula baltica,Rhodopirellula baltica SH 1 BX571656,273121,Bacteria,Proteobacteria,Epsilonproteobacteria,Campylobacterales,Helicobacteraceae,Wolinella,Wolinella succinogenes,Wolinella succinogenes DSM 1740 AE017180,243231,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfuromonadales,Geobacteraceae,Geobacter,Geobacter sulfurreducens,Geobacter sulfurreducens PCA AE017226,243275,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Spirochaetaceae,Treponema,Treponema denticola,Treponema denticola ATCC 35405 BX950229,267377,Archaea,Euryarchaeota,Methanococci,Methanococcales,Methanococcaceae,Methanococcus,Methanococcus maripaludis,Methanococcus maripaludis S2 AE017221,262724,Bacteria,Deinococcus-Thermus,Deinococci,Thermales,Thermaceae,Thermus,Thermus thermophilus,Thermus thermophilus HB27 BA000001,70601,Archaea,Euryarchaeota,Thermococci,Thermococcales,Thermococcaceae,Pyrococcus,Pyrococcus horikoshii,Pyrococcus horikoshii OT3 BA000023,273063,Archaea,Crenarchaeota,Thermoprotei,Sulfolobales,Sulfolobaceae,Sulfolobus,Sulfolobus tokodaii,Sulfolobus tokodaii str. 7 NC_007951,266265,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Burkholderiaceae,Paraburkholderia,Paraburkholderia xenovorans,Paraburkholderia xenovorans LB400 CP000492,290317,Bacteria,Chlorobi,Chlorobia,Chlorobiales,Chlorobiaceae,Chlorobium,Chlorobium phaeobacteroides,Chlorobium phaeobacteroides DSM 266 NC_008751,391774,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfovibrionales,Desulfovibrionaceae,Desulfovibrio,Desulfovibrio vulgaris,Desulfovibrio vulgaris DP4 CP000568,203119,Bacteria,Firmicutes,Clostridia,Clostridiales,Ruminococcaceae,Ruminiclostridium,Ruminiclostridium thermocellum,Ruminiclostridium thermocellum ATCC 27405 CP000561,410359,Archaea,Crenarchaeota,Thermoprotei,Thermoproteales,Thermoproteaceae,Pyrobaculum,Pyrobaculum calidifontis,Pyrobaculum calidifontis JCM 11548 CP000609,402880,Archaea,Euryarchaeota,Methanococci,Methanococcales,Methanococcaceae,Methanococcus,Methanococcus maripaludis,Methanococcus maripaludis C5 CP000607,290318,Bacteria,Chlorobi,Chlorobia,Chlorobiales,Chlorobiaceae,Chlorobium,Chlorobium phaeovibrioides,Chlorobium phaeovibrioides DSM 265 CP000660,340102,Archaea,Crenarchaeota,Thermoprotei,Thermoproteales,Thermoproteaceae,Pyrobaculum,Pyrobaculum arsenaticum,Pyrobaculum arsenaticum DSM 13514 CP000667,369723,Bacteria,Actinobacteria,Actinobacteria,Micromonosporales,Micromonosporaceae,Salinispora,Salinispora tropica,Salinispora tropica CNB-440 CP000679,351627,Bacteria,Firmicutes,Clostridia,Thermoanaerobacterales,Thermoanaerobacterales Family III. Incertae Sedis,Caldicellulosiruptor,Caldicellulosiruptor saccharolyticus,Caldicellulosiruptor saccharolyticus DSM 8903 CP000702,390874,Bacteria,Thermotogae,Thermotogae,Thermotogales,Thermotogaceae,Thermotoga,Thermotoga petrophila,Thermotoga petrophila RKU-1 CP000139,435590,Bacteria,Bacteroidetes,Bacteroidia,Bacteroidales,Bacteroidaceae,Bacteroides,Bacteroides vulgatus,Bacteroides vulgatus ATCC 8482 NC_009665,402882,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Shewanellaceae,Shewanella,Shewanella baltica,Shewanella baltica OS185 CP000816,453591,Archaea,Crenarchaeota,Thermoprotei,Desulfurococcales,Desulfurococcaceae,Ignicoccus,Ignicoccus hospitalis,Ignicoccus hospitalis KIN4/I CP000850,391037,Bacteria,Actinobacteria,Actinobacteria,Micromonosporales,Micromonosporaceae,Salinispora,Salinispora arenicola,Salinispora arenicola CNS-205 CP000909,324602,Bacteria,Chloroflexi,Chloroflexia,Chloroflexales,Chloroflexaceae,Chloroflexus,Chloroflexus aurantiacus,Chloroflexus aurantiacus J-10-fl CP000924,340099,Bacteria,Firmicutes,Clostridia,Thermoanaerobacterales,Thermoanaerobacteraceae,Thermoanaerobacter,Thermoanaerobacter pseudethanolicus,Thermoanaerobacter pseudethanolicus ATCC 33223 CP000969,126740,Bacteria,Thermotogae,Thermotogae,Thermotogales,Thermotogaceae,Thermotoga,Thermotoga sp. RQ2, CP001013,395495,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,,Leptothrix,Leptothrix cholodnii,Leptothrix cholodnii SP-6 CP001071,349741,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Akkermansiaceae,Akkermansia,Akkermansia muciniphila,Akkermansia muciniphila ATCC BAA-835 AP009380,431947,Bacteria,Bacteroidetes,Bacteroidia,Bacteroidales,Porphyromonadaceae,Porphyromonas,Porphyromonas gingivalis,Porphyromonas gingivalis ATCC 33277 NC_010730,436114,Bacteria,Aquificae,Aquificae,Aquificales,Hydrogenothermaceae,Sulfurihydrogenibium,Sulfurihydrogenibium sp. YO3AOP1, CP001097,290315,Bacteria,Chlorobi,Chlorobia,Chlorobiales,Chlorobiaceae,Chlorobium,Chlorobium limicola,Chlorobium limicola DSM 245 CP001110,324925,Bacteria,Chlorobi,Chlorobia,Chlorobiales,Chlorobiaceae,Pelodictyon,Pelodictyon phaeoclathratiforme,Pelodictyon phaeoclathratiforme BU-1 CP001130,380749,Bacteria,Aquificae,Aquificae,Aquificales,Aquificaceae,Hydrogenobaculum,Hydrogenobaculum sp. Y04AAS1, NZ_CH959311,52598,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Sulfitobacter,Sulfitobacter sp. EE-36, NZ_CH959317,314267,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Sulfitobacter,Sulfitobacter sp. NAS-14.1, CP001251,515635,Bacteria,Dictyoglomi,Dictyoglomia,Dictyoglomales,Dictyoglomaceae,Dictyoglomus,Dictyoglomus turgidum,Dictyoglomus turgidum DSM 6724 NC_011663,407976,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Shewanellaceae,Shewanella,Shewanella baltica,Shewanella baltica OS223 CP000916,309803,Bacteria,Thermotogae,Thermotogae,Thermotogales,Thermotogaceae,Thermotoga,Thermotoga neapolitana,Thermotoga neapolitana DSM 4359 NZ_DS996397,411464,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfovibrionales,Desulfovibrionaceae,Desulfovibrio,Desulfovibrio piger,Desulfovibrio piger ATCC 29098 CP001230,123214,Bacteria,Aquificae,Aquificae,Aquificales,Hydrogenothermaceae,Persephonella,Persephonella marina,Persephonella marina EX-H1 CP001472,240015,Bacteria,Acidobacteria,Acidobacteriia,Acidobacteriales,Acidobacteriaceae,Acidobacterium,Acidobacterium capsulatum,Acidobacterium capsulatum ATCC 51196 AP009153,379066,Bacteria,Gemmatimonadetes,Gemmatimonadetes,Gemmatimonadales,Gemmatimonadaceae,Gemmatimonas,Gemmatimonas aurantiaca,Gemmatimonas aurantiaca T-27 CP001941,439481,Archaea,Euryarchaeota,,,,Aciduliprofundum,Aciduliprofundum boonei,Aciduliprofundum boonei T469 NC_013968,309800,Archaea,Euryarchaeota,Halobacteria,Haloferacales,Haloferacaceae,Haloferax,Haloferax volcanii,Haloferax volcanii DS2 NZ_KE136524,226185,Bacteria,Firmicutes,Bacilli,Lactobacillales,Enterococcaceae,Enterococcus,Enterococcus faecalis,Enterococcus faecalis V583 NZ_KQ961402,542,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Zymomonas,Zymomonas mobilis, NZ_CP015081,243230,Bacteria,Deinococcus-Thermus,Deinococci,Deinococcales,Deinococcaceae,Deinococcus,Deinococcus radiodurans,Deinococcus radiodurans R1 NZ_ABZS01000228,432331,Bacteria,Aquificae,Aquificae,Aquificales,Hydrogenothermaceae,Sulfurihydrogenibium,Sulfurihydrogenibium yellowstonense,Sulfurihydrogenibium yellowstonense SS-5 NZ_JGWU01000001,1458259,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Alcaligenaceae,Bordetella,Bordetella bronchiseptica,Bordetella bronchiseptica D989 NZ_FWDH01000003,31899,Bacteria,Firmicutes,Clostridia,Thermoanaerobacterales,Thermoanaerobacterales Family III. Incertae Sedis,Caldicellulosiruptor,Caldicellulosiruptor bescii, NC_009972,316274,Bacteria,Chloroflexi,Chloroflexia,Herpetosiphonales,Herpetosiphonaceae,Herpetosiphon,Herpetosiphon aurantiacus,Herpetosiphon aurantiacus DSM 785 NC_005213,228908,Archaea,Nanoarchaeota,,Nanoarchaeales,Nanoarchaeaceae,Nanoarchaeum,Nanoarchaeum equitans,Nanoarchaeum equitans Kin4-M sourmash-bio-sourmash-bc3c0f8/tests/test-data/lca/separate.csv000066400000000000000000000033751474157046300246060ustar00rootroot00000000000000ID,status,superkingdom,phylum,class,order,family,genus,species TARA_ANE_MAG_00011,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TARA_ANE_MAG_00015,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TARA_ANE_MAG_00041,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TARA_ANE_MAG_00044,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TARA_ANE_MAG_00063,found,Archaea,Euryarchaeota,,,,, TARA_ANE_MAG_00068,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TARA_ANE_MAG_00069,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TARA_ANW_MAG_00005,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TARA_ANW_MAG_00020,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TARA_ANW_MAG_00034,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TARA_ANW_MAG_00051,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,, TARA_ANW_MAG_00083,found,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania, TARA_ANW_MAG_00084,found,Eukaryota,Cryptophyta,Cryptophyceae,Pyrenomonadales,Geminigeraceae,Guillardia, TARA_ANW_MAG_00085,found,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania, TARA_ASE_MAG_00007,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TARA_ASE_MAG_00009,found,Archaea,Euryarchaeota,,,,, TARA_ASE_MAG_00015,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TARA_ASE_MAG_00018,found,Archaea,Euryarchaeota,,,,, TARA_ASE_MAG_00028,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii sourmash-bio-sourmash-bc3c0f8/tests/test-data/lca/tara-delmont-SuppTable3.csv000066400000000000000000002304021474157046300273420ustar00rootroot00000000000000MAGs,Domain,Phylum,Class,Order,Family,Genus,Species TARA_PON_MAG_00061,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,na TARA_PSW_MAG_00136,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Bathycoccaceae,Ostreococcus,na TARA_ION_MAG_00072,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Bathycoccus,na TARA_MED_MAG_00110,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00119,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_MED_MAG_00139,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas,na TARA_PSW_MAG_00074,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_RED_MAG_00106,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_MED_MAG_00103,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_RED_MAG_00118,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Bathycoccaceae,Ostreococcus,na TARA_PSW_MAG_00129,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TARA_MED_MAG_00040,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ASE_MAG_00022,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,na,na TARA_ANE_MAG_00077,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,na,na,na TARA_ASW_MAG_00036,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_PON_MAG_00074,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Bathycoccus,na TARA_PSW_MAG_00048,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_SOC_MAG_00002,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,na,na TARA_ASE_MAG_00032,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_PSW_MAG_00131,Eukaryota,na,na,na,na,na,na TARA_RED_MAG_00114,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Bathycoccus,na TARA_PSE_MAG_00140,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Bathycoccus,na TARA_ASE_MAG_00036,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Bathycoccaceae,Ostreococcus,na TARA_MED_MAG_00026,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSW_MAG_00081,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_ION_MAG_00066,Eukaryota,na,na,na,na,na,na TARA_ANW_MAG_00073,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANE_MAG_00088,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Bathycoccus,na TARA_ANE_MAG_00093,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Bathycoccaceae,Ostreococcus,na TARA_RED_MAG_00062,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00045,Bacteria,Proteobacteria,na,na,na,na,na TARA_IOS_MAG_00022,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSE_MAG_00001,Bacteria,Bacteroidetes,na,na,na,na,na TARA_ANE_MAG_00096,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TARA_ANW_MAG_00017,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Leptospiraceae,na,na TARA_ANE_MAG_00086,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Bathycoccus,na TARA_IOS_MAG_00011,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_MED_MAG_00142,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas,na TARA_ASE_MAG_00031,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TARA_ASW_MAG_00035,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Bathycoccus,na TARA_ANW_MAG_00074,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_ANE_MAG_00026,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANE_MAG_00076,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_PSE_MAG_00136,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_ANE_MAG_00032,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_MED_MAG_00133,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_ANW_MAG_00052,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_ANW_MAG_00006,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,na,na,na TARA_MED_MAG_00105,Bacteria,Proteobacteria,na,na,na,na,na TARA_MED_MAG_00112,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_IOS_MAG_00045,Archaea,Euryarchaeota,na,na,na,na,na TARA_ASW_MAG_00011,Archaea,Euryarchaeota,na,na,na,na,na TARA_ASW_MAG_00032,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_ANE_MAG_00035,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_PSW_MAG_00057,Archaea,Euryarchaeota,na,na,na,na,na TARA_MED_MAG_00141,Eukaryota,na,na,na,na,na,na TARA_ANE_MAG_00101,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_ION_MAG_00074,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_ANW_MAG_00033,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00076,Bacteria,Proteobacteria,na,na,na,na,na TARA_ANW_MAG_00076,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ASW_MAG_00038,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_ANE_MAG_00002,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_MED_MAG_00070,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSW_MAG_00004,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax,na TARA_PON_MAG_00013,Bacteria,Proteobacteria,Deltaproteobacteria,na,na,na,na TARA_ION_MAG_00039,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ASE_MAG_00024,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Bathycoccus,na TARA_PSW_MAG_00075,Archaea,Euryarchaeota,na,na,na,na,na TARA_IOS_MAG_00061,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,na,na TARA_PSE_MAG_00007,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ION_MAG_00012,Bacteria,Cyanobacteria,Prochlorales,Prochlorales,Prochlorococcaceae,Prochlorococcus,Prochlorococcus_marinus TARA_ANW_MAG_00082,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ASW_MAG_00021,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSE_MAG_00137,Eukaryota,na,na,na,na,na,na TARA_PSW_MAG_00003,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ION_MAG_00027,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ANE_MAG_00087,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,na,na TARA_PSE_MAG_00073,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_PSE_MAG_00005,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,Olleya,na TARA_IOS_MAG_00038,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANW_MAG_00088,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_MED_MAG_00092,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_IOS_MAG_00027,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_ANE_MAG_00018,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ASW_MAG_00042,Eukaryota,Heterokonta,Pelagophyceae,Pelagomonadales,Pelagomonadaceae,Aureococcus,na TARA_MED_MAG_00101,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSW_MAG_00084,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANE_MAG_00091,Eukaryota,na,na,na,na,na,na TARA_MED_MAG_00041,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_ANW_MAG_00056,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_MED_MAG_00062,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANW_MAG_00049,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00011,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,na,na,na TARA_PSW_MAG_00066,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_PSE_MAG_00026,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00051,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ION_MAG_00035,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANW_MAG_00080,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_IOS_MAG_00006,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax,na TARA_PSW_MAG_00019,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00093,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_MED_MAG_00096,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00024,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_RED_MAG_00009,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSW_MAG_00027,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_MED_MAG_00023,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_ASE_MAG_00026,Eukaryota,Heterokonta,Pelagophyceae,Pelagomonadales,Pelagomonadaceae,Aureococcus,na TARA_ASE_MAG_00014,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_ION_MAG_00070,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,na,na TARA_ANE_MAG_00074,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,na,na,na TARA_IOS_MAG_00068,Eukaryota,na,na,na,na,na,na TARA_PSE_MAG_00014,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Flammeovirgaceae,na,na TARA_IOS_MAG_00039,Archaea,Euryarchaeota,na,na,na,na,na TARA_ASW_MAG_00029,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,na,na,na TARA_PON_MAG_00082,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Bathycoccaceae,Ostreococcus,na TARA_PSW_MAG_00017,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Mycobacteriaceae,Mycobacterium,na TARA_ANE_MAG_00090,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_ANE_MAG_00004,Bacteria,Bacteroidetes,na,na,na,na,na TARA_PSE_MAG_00027,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_IOS_MAG_00046,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_IOS_MAG_00024,Bacteria,Proteobacteria,na,na,na,na,na TARA_ANW_MAG_00001,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,na,na,na TARA_MED_MAG_00108,Bacteria,Cyanobacteria,Chroococcales,Chroococcales,Cyanobium,na,na TARA_RED_MAG_00116,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Bathycoccaceae,Ostreococcus,na TARA_ION_MAG_00017,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ASE_MAG_00004,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_MED_MAG_00140,Eukaryota,na,na,na,na,na,na TARA_IOS_MAG_00005,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSW_MAG_00041,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_RED_MAG_00001,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,Citromicrobium,na TARA_IOS_MAG_00067,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter,na TARA_MED_MAG_00104,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_RED_MAG_00096,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_ION_MAG_00071,Eukaryota,na,na,na,na,na,na TARA_RED_MAG_00123,Bacteria,na,na,na,na,na,na TARA_MED_MAG_00135,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ASW_MAG_00047,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_MED_MAG_00068,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANE_MAG_00040,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_IOS_MAG_00023,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANW_MAG_00015,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Leptospiraceae,na,na TARA_ANW_MAG_00035,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ANE_MAG_00017,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_PSE_MAG_00129,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Bathycoccaceae,Ostreococcus,na TARA_ANE_MAG_00007,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium,na TARA_SOC_MAG_00010,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ION_MAG_00005,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,Haliea,na TARA_IOS_MAG_00041,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_ANE_MAG_00080,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,na,na TARA_ASE_MAG_00006,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,na,na,na TARA_PSW_MAG_00086,Archaea,Euryarchaeota,na,na,na,na,na TARA_MED_MAG_00079,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00007,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ASW_MAG_00039,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_PSW_MAG_00056,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANW_MAG_00087,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ASW_MAG_00031,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter,na TARA_MED_MAG_00081,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_PSW_MAG_00098,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_RED_MAG_00104,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter,na TARA_MED_MAG_00137,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TARA_IOS_MAG_00008,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis,Oceanicaulis_alexandrii TARA_MED_MAG_00095,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00062,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,na,na,na TARA_ANE_MAG_00071,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSW_MAG_00115,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSW_MAG_00049,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_PSE_MAG_00035,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,na,na TARA_PSE_MAG_00037,Bacteria,na,na,na,na,na,na TARA_ASW_MAG_00015,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_ASW_MAG_00034,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_ASE_MAG_00016,Archaea,Euryarchaeota,na,na,na,na,na TARA_MED_MAG_00078,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_ANE_MAG_00070,Bacteria,Bacteroidetes,Cytophagia,na,na,na,na TARA_MED_MAG_00048,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,na,na,na TARA_IOS_MAG_00026,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSW_MAG_00133,Eukaryota,na,na,na,na,na,na TARA_ASW_MAG_00044,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas,na TARA_ION_MAG_00073,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_RED_MAG_00103,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,na,na TARA_ION_MAG_00056,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSW_MAG_00070,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_MED_MAG_00032,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,na,na,na TARA_IOS_MAG_00029,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00150,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Moraxellaceae,na,na TARA_ANE_MAG_00051,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_MED_MAG_00014,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,na,na,na TARA_PSW_MAG_00016,Bacteria,Proteobacteria,na,na,na,na,na TARA_MED_MAG_00029,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_RED_MAG_00112,Eukaryota,na,na,na,na,na,na TARA_ANE_MAG_00078,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ASE_MAG_00010,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSE_MAG_00092,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_MED_MAG_00059,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,na,na,na TARA_PSE_MAG_00078,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_ANE_MAG_00038,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_MED_MAG_00091,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANE_MAG_00059,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_ANW_MAG_00043,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANE_MAG_00009,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhizobiaceae,na,na TARA_ASE_MAG_00007,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANE_MAG_00042,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00022,Bacteria,Proteobacteria,na,na,na,na,na TARA_ASE_MAG_00019,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ION_MAG_00023,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,na,na,na TARA_PSE_MAG_00084,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_PSE_MAG_00116,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_IOS_MAG_00033,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_ANE_MAG_00069,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSW_MAG_00100,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSW_MAG_00135,Eukaryota,na,na,na,na,na,na TARA_PSE_MAG_00002,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis,na TARA_RED_MAG_00072,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSE_MAG_00142,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,na,na,na TARA_ANE_MAG_00005,Bacteria,na,na,na,na,na,na TARA_MED_MAG_00124,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ASW_MAG_00014,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSW_MAG_00040,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANE_MAG_00050,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,na,na,na TARA_PSE_MAG_00132,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas,na TARA_MED_MAG_00053,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANE_MAG_00028,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_ASW_MAG_00046,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_PSW_MAG_00077,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PON_MAG_00087,Bacteria,na,na,na,na,na,na TARA_ASW_MAG_00041,Eukaryota,na,na,na,na,na,na TARA_IOS_MAG_00076,Eukaryota,na,na,na,na,na,na TARA_PSW_MAG_00018,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ANE_MAG_00014,Bacteria,Proteobacteria,na,na,na,na,na TARA_IOS_MAG_00049,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ANE_MAG_00025,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSW_MAG_00043,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANW_MAG_00038,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_MED_MAG_00060,Archaea,Euryarchaeota,na,na,na,na,na TARA_ION_MAG_00043,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ION_MAG_00055,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_PSE_MAG_00047,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ASW_MAG_00002,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSE_MAG_00048,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,na,na,na TARA_IOS_MAG_00065,Bacteria,na,na,na,na,na,na TARA_ANW_MAG_00058,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,na,na,na TARA_ANW_MAG_00012,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_IOS_MAG_00060,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_RED_MAG_00055,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSE_MAG_00099,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_ASW_MAG_00018,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,na,na TARA_MED_MAG_00030,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ASE_MAG_00009,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANW_MAG_00054,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_PSE_MAG_00138,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,na,na TARA_PSW_MAG_00102,Archaea,Euryarchaeota,na,na,na,na,na TARA_RED_MAG_00040,Bacteria,na,na,na,na,na,na TARA_ANW_MAG_00064,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSE_MAG_00009,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANW_MAG_00051,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_ANE_MAG_00060,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_ANE_MAG_00024,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANE_MAG_00039,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_ASE_MAG_00017,Bacteria,Cyanobacteria,na,na,na,na,na TARA_PON_MAG_00079,Eukaryota,Haptophyta,Prymnesiophyceae,Phaeocystales,Phaeocystaceae,Phaeocystis,na TARA_IOS_MAG_00073,Eukaryota,Heterokonta,Pelagophyceae,Pelagomonadales,Pelagomonadaceae,Aureococcus,na TARA_IOS_MAG_00013,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00053,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_RED_MAG_00087,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00128,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_ASW_MAG_00025,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_IOS_MAG_00047,Archaea,Euryarchaeota,na,na,na,na,na TARA_MED_MAG_00008,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSW_MAG_00039,Bacteria,na,na,na,na,na,na TARA_PSE_MAG_00145,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_ASW_MAG_00037,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_ANW_MAG_00023,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSE_MAG_00075,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_RED_MAG_00098,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PON_MAG_00011,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_PON_MAG_00051,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSE_MAG_00016,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,na,na,na TARA_IOS_MAG_00004,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANE_MAG_00023,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_PSW_MAG_00083,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PON_MAG_00046,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_IOS_MAG_00003,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_RED_MAG_00090,Archaea,Euryarchaeota,na,na,na,na,na TARA_ASW_MAG_00008,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,na,na TARA_ANW_MAG_00029,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_MED_MAG_00012,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_IOS_MAG_00075,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_ION_MAG_00033,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANE_MAG_00048,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANW_MAG_00021,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANW_MAG_00075,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_PON_MAG_00012,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSE_MAG_00039,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_ANW_MAG_00024,Archaea,Euryarchaeota,na,na,na,na,na TARA_MED_MAG_00107,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_ASE_MAG_00018,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00116,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,na,na TARA_PON_MAG_00007,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANE_MAG_00053,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ASE_MAG_00020,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_PSE_MAG_00010,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobacteraceae,na,na TARA_MED_MAG_00025,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ASW_MAG_00003,Bacteria,Cyanobacteria,Chroococcales,Chroococcales,Cyanothece,Cyanothece,na TARA_ASE_MAG_00011,Archaea,Euryarchaeota,na,na,na,na,na TARA_ASW_MAG_00045,Eukaryota,na,na,na,na,na,na TARA_MED_MAG_00004,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_RED_MAG_00050,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Planctomyces,na TARA_PON_MAG_00006,Bacteria,Proteobacteria,Deltaproteobacteria,na,na,na,na TARA_MED_MAG_00031,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANE_MAG_00079,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_ASW_MAG_00019,Archaea,Euryarchaeota,na,na,na,na,na TARA_ION_MAG_00053,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,na,na TARA_PSE_MAG_00124,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ION_MAG_00044,Archaea,Euryarchaeota,na,na,na,na,na TARA_ION_MAG_00042,Bacteria,na,na,na,na,na,na TARA_PSW_MAG_00109,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSW_MAG_00047,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,na,na TARA_PSW_MAG_00078,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_IOS_MAG_00044,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_MED_MAG_00038,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00104,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,na,na TARA_PSE_MAG_00013,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_PSW_MAG_00091,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,na,na,na TARA_IOS_MAG_00036,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSW_MAG_00020,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,na,na,na TARA_PSW_MAG_00094,Archaea,Euryarchaeota,na,na,na,na,na TARA_MED_MAG_00042,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_MED_MAG_00021,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_MED_MAG_00121,Bacteria,Proteobacteria,na,na,na,na,na TARA_ASE_MAG_00027,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANW_MAG_00030,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_PSE_MAG_00110,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_ANW_MAG_00009,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,Haliea,na TARA_ANE_MAG_00098,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_SOC_MAG_00011,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,Polaribacter,na TARA_ANW_MAG_00085,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_ION_MAG_00004,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,na,na,na TARA_ASE_MAG_00025,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_SOC_MAG_00004,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ION_MAG_00007,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PSE_MAG_00079,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_ANE_MAG_00081,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_MED_MAG_00035,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_ASE_MAG_00029,Bacteria,Bacteroidetes,Sphingobacteriia,Sphingobacteriales,Saprospiraceae,na,na TARA_PSE_MAG_00006,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANW_MAG_00053,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00122,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Anaplasmataceae,na,na TARA_IOS_MAG_00070,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSW_MAG_00032,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobacteraceae,na,na TARA_ION_MAG_00011,Bacteria,Proteobacteria,na,na,na,na,na TARA_RED_MAG_00059,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_SOC_MAG_00006,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PON_MAG_00050,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSW_MAG_00061,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_IOS_MAG_00051,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_PSE_MAG_00123,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter,na TARA_RED_MAG_00051,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ANE_MAG_00034,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00083,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_PSW_MAG_00101,Archaea,Euryarchaeota,na,na,na,na,na TARA_RED_MAG_00029,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_RED_MAG_00026,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_ANE_MAG_00049,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00120,Bacteria,na,na,na,na,na,na TARA_ANW_MAG_00081,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_MED_MAG_00115,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSW_MAG_00106,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_RED_MAG_00045,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANE_MAG_00068,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ANE_MAG_00022,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PON_MAG_00016,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANE_MAG_00099,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas,na TARA_PSE_MAG_00101,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,na,na TARA_ASE_MAG_00008,Bacteria,Proteobacteria,Betaproteobacteria,Methylophilales,na,na,na TARA_ASW_MAG_00004,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_PSE_MAG_00018,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Flammeovirgaceae,na,na TARA_PSE_MAG_00023,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ION_MAG_00050,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_SOC_MAG_00001,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PSE_MAG_00015,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PON_MAG_00026,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSE_MAG_00088,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_RED_MAG_00038,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_PSE_MAG_00147,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_ASE_MAG_00035,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_ANW_MAG_00007,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ANE_MAG_00065,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANW_MAG_00045,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANW_MAG_00042,Bacteria,Proteobacteria,Betaproteobacteria,Methylophilales,na,na,na TARA_PON_MAG_00009,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_ASE_MAG_00033,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_RED_MAG_00015,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PON_MAG_00047,Archaea,Euryarchaeota,na,na,na,na,na TARA_ION_MAG_00058,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Pseudomonadaceae,Pseudomonas,Pseudomonas_mendocina TARA_ANW_MAG_00005,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00131,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_ASE_MAG_00037,Eukaryota,Heterokonta,Pelagophyceae,Pelagomonadales,Pelagomonadaceae,Aureococcus,na TARA_MED_MAG_00010,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Flammeovirgaceae,na,na TARA_RED_MAG_00006,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSE_MAG_00066,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_MED_MAG_00084,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_RED_MAG_00093,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PSE_MAG_00024,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,na,na TARA_ANE_MAG_00043,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_MED_MAG_00015,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,na,na,na TARA_ANW_MAG_00065,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_PSE_MAG_00083,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_MED_MAG_00119,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_RED_MAG_00025,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANW_MAG_00025,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_PSE_MAG_00074,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_MED_MAG_00082,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANW_MAG_00068,Bacteria,Cyanobacteria,Chroococcales,Chroococcales,Cyanobium,na,na TARA_ASW_MAG_00012,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,Maribacter,na TARA_IOS_MAG_00030,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00090,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,na,na TARA_ANW_MAG_00048,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_RED_MAG_00092,Bacteria,na,na,na,na,na,na TARA_MED_MAG_00018,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSE_MAG_00068,Bacteria,na,na,na,na,na,na TARA_PSW_MAG_00092,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANW_MAG_00031,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ION_MAG_00020,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ASE_MAG_00012,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00109,Archaea,Euryarchaeota,na,na,na,na,na TARA_ION_MAG_00028,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_RED_MAG_00063,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00046,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_PSW_MAG_00010,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PSW_MAG_00104,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_RED_MAG_00036,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Planctomyces,na TARA_PSE_MAG_00098,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_MED_MAG_00090,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,na,na TARA_IOS_MAG_00016,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_IOS_MAG_00025,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ION_MAG_00059,Bacteria,Verrucomicrobia,na,na,na,na,na TARA_MED_MAG_00020,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PON_MAG_00029,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_IOS_MAG_00001,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_MED_MAG_00019,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PON_MAG_00031,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,na,na TARA_SOC_MAG_00014,Eukaryota,na,na,na,na,na,na TARA_ANW_MAG_00083,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_MED_MAG_00046,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PON_MAG_00039,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSW_MAG_00029,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius,na TARA_MED_MAG_00052,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANW_MAG_00077,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_RED_MAG_00046,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_MED_MAG_00017,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,na,na TARA_IOS_MAG_00018,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_MED_MAG_00077,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ION_MAG_00025,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobacteraceae,Labrenzia,na TARA_PSW_MAG_00093,Bacteria,Spirochaetes,Spirochaetia,na,na,na,na TARA_ANW_MAG_00013,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ANE_MAG_00097,Eukaryota,Haptophyta,Prymnesiophyceae,Isochrysidales,Noelaerhabdaceae,Emiliania,na TARA_ANW_MAG_00034,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSW_MAG_00089,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,na,na TARA_RED_MAG_00057,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,na,na,na TARA_RED_MAG_00105,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,na,na TARA_ANE_MAG_00083,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_IOS_MAG_00020,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_RED_MAG_00010,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_MED_MAG_00083,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,na,na,na TARA_ASW_MAG_00013,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANE_MAG_00036,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSE_MAG_00077,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,na,na TARA_PSW_MAG_00028,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Spirochaetaceae,na,na TARA_PSE_MAG_00089,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ASW_MAG_00024,Archaea,Euryarchaeota,na,na,na,na,na TARA_ION_MAG_00045,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ASE_MAG_00021,Bacteria,Bacteroidetes,Sphingobacteriia,Sphingobacteriales,Saprospiraceae,na,na TARA_PSW_MAG_00082,Bacteria,na,na,na,na,na,na TARA_ANW_MAG_00046,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANE_MAG_00058,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_RED_MAG_00033,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_PSW_MAG_00030,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_RED_MAG_00042,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_PSW_MAG_00036,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,na,na,na TARA_PSE_MAG_00148,Bacteria,na,na,na,na,na,na TARA_PSW_MAG_00060,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00085,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Nisaea,na TARA_RED_MAG_00074,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSE_MAG_00106,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,na,na TARA_ION_MAG_00040,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANE_MAG_00057,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PON_MAG_00023,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_PSW_MAG_00053,Bacteria,na,na,na,na,na,na TARA_ANE_MAG_00015,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_ANE_MAG_00003,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_RED_MAG_00102,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANE_MAG_00019,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANW_MAG_00003,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_MED_MAG_00071,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00039,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_PSW_MAG_00068,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_MED_MAG_00099,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANE_MAG_00041,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PON_MAG_00036,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_MED_MAG_00100,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ASW_MAG_00010,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,na,na TARA_MED_MAG_00006,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_RED_MAG_00115,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ASE_MAG_00015,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSE_MAG_00096,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00108,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSE_MAG_00127,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSW_MAG_00046,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PON_MAG_00040,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSW_MAG_00114,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_IOS_MAG_00059,Bacteria,na,na,na,na,na,na TARA_RED_MAG_00117,Bacteria,Firmicutes,Bacilli,na,na,na,na TARA_ANE_MAG_00010,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_PON_MAG_00005,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_IOS_MAG_00035,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_IOS_MAG_00019,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ION_MAG_00048,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassospira,na TARA_PON_MAG_00071,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_MED_MAG_00044,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_MED_MAG_00080,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00073,Archaea,Euryarchaeota,na,na,na,na,na TARA_MED_MAG_00036,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_ANW_MAG_00002,Bacteria,Proteobacteria,Gammaproteobacteria,Xanthomonadales,na,na,na TARA_ANE_MAG_00020,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_ION_MAG_00034,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00042,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,na,na,na TARA_MED_MAG_00064,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,na,na,na TARA_ASW_MAG_00016,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,na,na TARA_PON_MAG_00030,Bacteria,Cyanobacteria,Chroococcales,Chroococcales,Cyanobium,na,na TARA_ANW_MAG_00067,Bacteria,na,na,na,na,na,na TARA_ION_MAG_00032,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,na,na TARA_ASE_MAG_00003,Bacteria,Bacteroidetes,Sphingobacteriia,Sphingobacteriales,Saprospiraceae,na,na TARA_PSE_MAG_00105,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSE_MAG_00144,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas,na TARA_ANE_MAG_00094,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSW_MAG_00058,Bacteria,na,na,na,na,na,na TARA_RED_MAG_00022,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_MED_MAG_00045,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,na,na TARA_RED_MAG_00064,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_ION_MAG_00060,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSW_MAG_00088,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_PSW_MAG_00085,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_RED_MAG_00052,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_MED_MAG_00028,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANW_MAG_00036,Bacteria,Proteobacteria,Gammaproteobacteria,Xanthomonadales,na,na,na TARA_PON_MAG_00077,Bacteria,na,na,na,na,na,na TARA_MED_MAG_00033,Bacteria,Proteobacteria,na,na,na,na,na TARA_ANE_MAG_00066,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_IOS_MAG_00012,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSE_MAG_00022,Bacteria,Proteobacteria,Gammaproteobacteria,Xanthomonadales,na,na,na TARA_PON_MAG_00049,Archaea,Euryarchaeota,na,na,na,na,na TARA_MED_MAG_00130,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_ANE_MAG_00092,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TARA_ANW_MAG_00016,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00060,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSW_MAG_00087,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_ION_MAG_00014,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_PSW_MAG_00006,Bacteria,na,na,na,na,na,na TARA_MED_MAG_00073,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSW_MAG_00059,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSE_MAG_00028,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PSW_MAG_00072,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ION_MAG_00031,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSE_MAG_00050,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ION_MAG_00047,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,na,na TARA_RED_MAG_00044,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_MED_MAG_00098,Bacteria,Bacteroidetes,na,na,na,na,na TARA_ANW_MAG_00026,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_ASW_MAG_00040,Eukaryota,na,na,na,na,na,na TARA_PSE_MAG_00064,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSE_MAG_00120,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,na,na,na TARA_PON_MAG_00010,Bacteria,Proteobacteria,na,na,na,na,na TARA_RED_MAG_00020,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Planctomyces,na TARA_IOS_MAG_00031,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_IOS_MAG_00057,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_IOS_MAG_00056,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_IOS_MAG_00066,Bacteria,Proteobacteria,Deltaproteobacteria,na,na,na,na TARA_IOS_MAG_00017,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_ANE_MAG_00037,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_ANE_MAG_00082,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_ION_MAG_00029,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae_3,Sphingobium,na TARA_PSE_MAG_00061,Archaea,Euryarchaeota,na,na,na,na,na TARA_RED_MAG_00084,Bacteria,na,na,na,na,na,na TARA_MED_MAG_00072,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_ANW_MAG_00019,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,na,na,na TARA_PON_MAG_00055,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00075,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00080,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_PSW_MAG_00012,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_RED_MAG_00016,Bacteria,Chloroflexi,na,na,na,na,na TARA_IOS_MAG_00063,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter,na TARA_PON_MAG_00027,Bacteria,Ignavibacteriae,Ignavibacteria,Ignavibacteriales,na,na,na TARA_MED_MAG_00002,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ION_MAG_00006,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PON_MAG_00070,Bacteria,Proteobacteria,Deltaproteobacteria,na,na,na,na TARA_RED_MAG_00002,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_RED_MAG_00068,Bacteria,na,na,na,na,na,na TARA_PON_MAG_00064,Bacteria,na,na,na,na,na,na TARA_PSW_MAG_00064,Bacteria,Proteobacteria,Deltaproteobacteria,na,na,na,na TARA_PSE_MAG_00081,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_PON_MAG_00001,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,na,na TARA_PSW_MAG_00026,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfobacterales,Desulfobacteraceae,na,na TARA_IOS_MAG_00053,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_ASW_MAG_00022,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhizobiaceae,na,na TARA_PSW_MAG_00103,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_RED_MAG_00061,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_PSW_MAG_00096,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,na,na TARA_ION_MAG_00064,Bacteria,Proteobacteria,na,na,na,na,na TARA_MED_MAG_00065,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANE_MAG_00001,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_RED_MAG_00007,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_MED_MAG_00134,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_RED_MAG_00028,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_PON_MAG_00056,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_PSW_MAG_00122,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_RED_MAG_00004,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PSW_MAG_00055,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANW_MAG_00020,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_IOS_MAG_00014,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSW_MAG_00044,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_RED_MAG_00069,Bacteria,na,na,na,na,na,na TARA_MED_MAG_00009,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00118,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_PSW_MAG_00038,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_PSW_MAG_00054,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ASE_MAG_00002,Bacteria,Proteobacteria,na,na,na,na,na TARA_PON_MAG_00058,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,Idiomarina,na TARA_MED_MAG_00094,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSE_MAG_00131,Bacteria,Proteobacteria,Epsilonproteobacteria,Campylobacterales,Campylobacteraceae,Arcobacter,na TARA_SOC_MAG_00012,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ION_MAG_00022,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSW_MAG_00065,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_IOS_MAG_00058,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ION_MAG_00026,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANW_MAG_00018,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSW_MAG_00002,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_RED_MAG_00048,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_MED_MAG_00122,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ASW_MAG_00043,Eukaryota,na,na,na,na,na,na TARA_MED_MAG_00005,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_MED_MAG_00129,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_MED_MAG_00086,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00057,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_IOS_MAG_00015,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANW_MAG_00011,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,na,na TARA_ANW_MAG_00062,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_PSE_MAG_00044,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_PSW_MAG_00071,Bacteria,Proteobacteria,Deltaproteobacteria,na,na,na,na TARA_ANW_MAG_00022,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Planctomyces,na TARA_ANE_MAG_00030,Archaea,Euryarchaeota,na,na,na,na,na TARA_RED_MAG_00030,Bacteria,Acidobacteria,na,na,na,na,na TARA_MED_MAG_00057,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,na,na,na TARA_PSW_MAG_00128,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhizobiaceae,na,na TARA_ANE_MAG_00013,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANE_MAG_00044,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSE_MAG_00139,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_PSE_MAG_00071,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00004,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,na,na,na TARA_IOS_MAG_00064,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_IOS_MAG_00042,Bacteria,Proteobacteria,na,na,na,na,na TARA_PON_MAG_00052,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ION_MAG_00015,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium,na TARA_PSE_MAG_00019,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ION_MAG_00036,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_MED_MAG_00106,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSE_MAG_00109,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00033,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSE_MAG_00020,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ION_MAG_00038,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_MED_MAG_00055,Bacteria,na,na,na,na,na,na TARA_PON_MAG_00053,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PSE_MAG_00130,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_ANE_MAG_00033,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_ANE_MAG_00027,Archaea,Euryarchaeota,na,na,na,na,na TARA_RED_MAG_00099,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ANE_MAG_00046,Bacteria,Proteobacteria,na,na,na,na,na TARA_MED_MAG_00136,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_MED_MAG_00087,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,na,na,na TARA_MED_MAG_00003,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ASW_MAG_00017,Archaea,Euryarchaeota,na,na,na,na,na TARA_ION_MAG_00041,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_PSW_MAG_00130,Bacteria,Planctomycetes,na,na,na,na,na TARA_PSE_MAG_00090,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSE_MAG_00094,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00058,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_RED_MAG_00109,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_MED_MAG_00027,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_PSW_MAG_00097,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PSE_MAG_00070,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANW_MAG_00004,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_RED_MAG_00080,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00022,Bacteria,Proteobacteria,na,na,na,na,na TARA_RED_MAG_00111,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PSE_MAG_00052,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_IOS_MAG_00077,Eukaryota,na,na,na,na,na,na TARA_PSE_MAG_00121,Bacteria,Proteobacteria,Deltaproteobacteria,na,na,na,na TARA_RED_MAG_00021,Archaea,Euryarchaeota,na,na,na,na,na TARA_MED_MAG_00126,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ANW_MAG_00060,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_SOC_MAG_00003,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSE_MAG_00118,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_RED_MAG_00110,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PON_MAG_00069,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSW_MAG_00013,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfovibrionales,Desulfovibrionaceae,Desulfovibrio,na TARA_IOS_MAG_00037,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ION_MAG_00046,Bacteria,na,na,na,na,na,na TARA_PON_MAG_00033,Archaea,Euryarchaeota,na,na,na,na,na TARA_RED_MAG_00035,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSE_MAG_00025,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,na,na TARA_ION_MAG_00018,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_IOS_MAG_00052,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_ANW_MAG_00014,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PSE_MAG_00062,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,na,na TARA_PSW_MAG_00107,Bacteria,Chloroflexi,na,na,na,na,na TARA_ASW_MAG_00026,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium,na TARA_PON_MAG_00042,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,Idiomarina,na TARA_PSE_MAG_00042,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Pseudomonadaceae,Pseudomonas,na TARA_IOS_MAG_00055,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_ANE_MAG_00061,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_PSW_MAG_00069,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ION_MAG_00024,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANW_MAG_00044,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_ASE_MAG_00028,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_IOS_MAG_00010,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSW_MAG_00023,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_RED_MAG_00047,Bacteria,na,na,na,na,na,na TARA_MED_MAG_00001,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_ANW_MAG_00032,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_PSE_MAG_00100,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassospira,na TARA_MED_MAG_00016,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PON_MAG_00041,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_ION_MAG_00057,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSE_MAG_00056,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSW_MAG_00123,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ANE_MAG_00031,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Moraxellaceae,Psychrobacter,na TARA_PSW_MAG_00105,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_ANW_MAG_00040,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,na,na TARA_PSE_MAG_00111,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,na,na TARA_PSW_MAG_00127,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PON_MAG_00072,Bacteria,Acidobacteria,na,na,na,na,na TARA_ION_MAG_00002,Bacteria,Proteobacteria,Alphaproteobacteria,Caulobacterales,Caulobacteraceae,Brevundimonas,na TARA_PON_MAG_00002,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_ASW_MAG_00023,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_RED_MAG_00086,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_RED_MAG_00075,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_ANW_MAG_00084,Eukaryota,Cryptophyta,Cryptophyceae,Pyrenomonadales,Geminigeraceae,Guillardia,na TARA_PSE_MAG_00091,Bacteria,Actinobacteria,Actinobacteria,na,na,na,na TARA_PSE_MAG_00032,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSE_MAG_00055,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANE_MAG_00006,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_IOS_MAG_00028,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PSE_MAG_00103,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius,na TARA_MED_MAG_00039,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PON_MAG_00015,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_PSE_MAG_00128,Bacteria,Proteobacteria,Gammaproteobacteria,Vibrionales,Vibrionaceae,Vibrio,na TARA_ASW_MAG_00001,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00043,Bacteria,na,na,na,na,na,na TARA_ANW_MAG_00057,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_RED_MAG_00089,Bacteria,na,na,na,na,na,na TARA_PSW_MAG_00067,Bacteria,Bacteroidetes,na,na,na,na,na TARA_MED_MAG_00047,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANW_MAG_00028,Bacteria,Candidate_Phyla_Radiation,na,na,na,na,na TARA_MED_MAG_00132,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,na,na TARA_PON_MAG_00085,Bacteria,Proteobacteria,na,na,na,na,na TARA_ANW_MAG_00050,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANE_MAG_00029,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ION_MAG_00067,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_MED_MAG_00034,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSE_MAG_00141,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_MED_MAG_00011,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_ION_MAG_00054,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00114,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_ANE_MAG_00016,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PON_MAG_00075,Bacteria,na,na,na,na,na,na TARA_IOS_MAG_00078,Eukaryota,na,na,na,na,na,na TARA_RED_MAG_00076,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_ANE_MAG_00052,Archaea,Euryarchaeota,na,na,na,na,na TARA_ANW_MAG_00010,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_PSE_MAG_00072,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_ANW_MAG_00027,Bacteria,Candidate_Phyla_Radiation,na,na,na,na,na TARA_PON_MAG_00068,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_MED_MAG_00127,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00088,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00082,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_MED_MAG_00097,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_IOS_MAG_00048,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_PSE_MAG_00125,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PON_MAG_00062,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,na,na TARA_RED_MAG_00043,Bacteria,Candidatus_Marinimicrobia ,na,na,na,na,na TARA_ANW_MAG_00039,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PSE_MAG_00093,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_PSW_MAG_00113,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PSE_MAG_00038,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ION_MAG_00016,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PSE_MAG_00112,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PSW_MAG_00033,Bacteria,Proteobacteria,na,na,na,na,na TARA_PON_MAG_00065,Bacteria,na,na,na,na,na,na TARA_ANW_MAG_00061,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PON_MAG_00019,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_ION_MAG_00009,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_PSE_MAG_00097,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Planctomyces,na TARA_RED_MAG_00085,Bacteria,na,na,na,na,na,na TARA_PSE_MAG_00126,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_PSE_MAG_00058,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_MED_MAG_00054,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_ANW_MAG_00059,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobacteraceae,na,na TARA_RED_MAG_00101,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_RED_MAG_00024,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_RED_MAG_00066,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_RED_MAG_00008,Bacteria,Acidobacteria,na,na,na,na,na TARA_PSE_MAG_00065,Bacteria,Firmicutes,Clostridia,Clostridiales,na,na,na TARA_MED_MAG_00013,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PON_MAG_00035,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANE_MAG_00055,Archaea,Euryarchaeota,na,na,na,na,na TARA_RED_MAG_00094,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_ANE_MAG_00063,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSE_MAG_00059,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00034,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_RED_MAG_00037,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PON_MAG_00081,Bacteria,Proteobacteria,na,na,na,na,na TARA_ASW_MAG_00033,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00023,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_MED_MAG_00043,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PON_MAG_00038,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANE_MAG_00064,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_RED_MAG_00071,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,na,na,na TARA_SOC_MAG_00007,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00018,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00134,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhizobiaceae,na,na TARA_PSE_MAG_00011,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00014,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_MED_MAG_00050,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_ASW_MAG_00020,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_RED_MAG_00019,Bacteria,Chloroflexi,na,na,na,na,na TARA_ION_MAG_00051,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Flammeovirgaceae,na,na TARA_ION_MAG_00052,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ION_MAG_00037,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Cyclobacteriaceae,Algoriphagus,na TARA_ANW_MAG_00070,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,na,na TARA_RED_MAG_00070,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_RED_MAG_00003,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00107,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_PON_MAG_00057,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00095,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_RED_MAG_00032,Bacteria,Proteobacteria,Deltaproteobacteria,na,na,na,na TARA_PSE_MAG_00080,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSW_MAG_00126,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfobacterales,Desulfobulbaceae,na,na TARA_PSW_MAG_00037,Bacteria,Proteobacteria,na,na,na,na,na TARA_IOS_MAG_00034,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ION_MAG_00049,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00114,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,na,na TARA_RED_MAG_00041,Bacteria,Chloroflexi,na,na,na,na,na TARA_RED_MAG_00081,Bacteria,Acidobacteria,na,na,na,na,na TARA_IOS_MAG_00040,Bacteria,Proteobacteria,Betaproteobacteria,na,na,na,na TARA_SOC_MAG_00008,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_ANE_MAG_00056,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_ANE_MAG_00012,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,na,na,na TARA_ANE_MAG_00011,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PON_MAG_00017,Bacteria,Candidate_Phyla_Radiation,na,na,na,na,na TARA_PON_MAG_00067,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSW_MAG_00110,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_RED_MAG_00065,Bacteria,na,na,na,na,na,na TARA_PSW_MAG_00124,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PSE_MAG_00133,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,na,na TARA_PON_MAG_00004,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,na,na,na TARA_PSW_MAG_00112,Bacteria,na,na,na,na,na,na TARA_PSE_MAG_00115,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSW_MAG_00011,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,na,na,na TARA_PSW_MAG_00014,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,na,na,na TARA_PON_MAG_00084,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_MED_MAG_00063,Bacteria,na,na,na,na,na,na TARA_PON_MAG_00054,Bacteria,na,na,na,na,na,na TARA_MED_MAG_00111,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_PSE_MAG_00051,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PON_MAG_00008,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,na,na,na TARA_PON_MAG_00024,Bacteria,Acidobacteria,na,na,na,na,na TARA_ANW_MAG_00055,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_RED_MAG_00005,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_ANE_MAG_00008,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PSW_MAG_00117,Bacteria,na,na,na,na,na,na TARA_SOC_MAG_00013,Eukaryota,na,na,na,na,na,na TARA_PSE_MAG_00017,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,na,na,na TARA_ION_MAG_00001,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PSW_MAG_00024,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_ANW_MAG_00069,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PON_MAG_00060,Bacteria,Proteobacteria,Deltaproteobacteria,na,na,na,na TARA_PSE_MAG_00053,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_MED_MAG_00056,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSW_MAG_00125,Bacteria,na,na,na,na,na,na TARA_PSE_MAG_00031,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_MED_MAG_00049,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Oxalobacteraceae,na,na TARA_RED_MAG_00100,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_ANW_MAG_00079,Eukaryota,na,na,na,na,na,na TARA_PON_MAG_00043,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_PSW_MAG_00132,Bacteria,na,na,na,na,na,na TARA_RED_MAG_00079,Archaea,Euryarchaeota,na,na,na,na,na TARA_IOS_MAG_00069,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_MED_MAG_00143,Eukaryota,na,na,na,na,na,na TARA_MED_MAG_00069,Bacteria,na,na,na,na,na,na TARA_PSW_MAG_00108,Bacteria,Proteobacteria,na,na,na,na,na TARA_MED_MAG_00138,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,na,na TARA_PSW_MAG_00118,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_PSE_MAG_00102,Bacteria,Proteobacteria,Deltaproteobacteria,na,na,na,na TARA_RED_MAG_00078,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_MED_MAG_00066,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_MED_MAG_00113,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_MED_MAG_00076,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_SOC_MAG_00005,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,na,na,na TARA_PON_MAG_00066,Bacteria,Acidobacteria,na,na,na,na,na TARA_PON_MAG_00076,Bacteria,Proteobacteria,Gammaproteobacteria,Xanthomonadales,na,na,na TARA_MED_MAG_00074,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_PSE_MAG_00087,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,na,na,na TARA_ANE_MAG_00073,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,na,na,na TARA_RED_MAG_00121,Bacteria,na,na,na,na,na,na TARA_PON_MAG_00083,Eukaryota,na,na,na,na,na,na TARA_PSE_MAG_00119,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius,na TARA_PON_MAG_00018,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,na,na,na TARA_ANE_MAG_00054,Bacteria,na,na,na,na,na,na TARA_ANW_MAG_00072,Bacteria,na,na,na,na,na,na TARA_PSE_MAG_00076,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00067,Bacteria,Proteobacteria,Betaproteobacteria,na,na,na,na TARA_RED_MAG_00091,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_RED_MAG_00097,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_MED_MAG_00089,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,na,na,na TARA_RED_MAG_00031,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_PSE_MAG_00021,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,na,na,na TARA_ANW_MAG_00041,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_MED_MAG_00125,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,na,na TARA_PON_MAG_00086,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Azospirillum,na TARA_IOS_MAG_00062,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_PSE_MAG_00085,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_MED_MAG_00102,Bacteria,Proteobacteria,Alphaproteobacteria,na,na,na,na TARA_MED_MAG_00061,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,na,na,na TARA_PSE_MAG_00122,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSW_MAG_00121,Bacteria,Proteobacteria,na,na,na,na,na TARA_MED_MAG_00116,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_MED_MAG_00117,Bacteria,Verrucomicrobia,Opitutae,Opitutales,na,na,na TARA_RED_MAG_00049,Archaea,Euryarchaeota,na,na,na,na,na TARA_PSW_MAG_00120,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Leptospiraceae,na,na TARA_PON_MAG_00063,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,na,na,na TARA_RED_MAG_00077,Bacteria,Chloroflexi,Dehalococcoidetes,na,na,na,na TARA_MED_MAG_00120,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_RED_MAG_00054,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSE_MAG_00086,Bacteria,Proteobacteria,na,na,na,na,na TARA_PSE_MAG_00054,Bacteria,Proteobacteria,Gammaproteobacteria,na,na,na,na TARA_PSE_MAG_00045,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_PSE_MAG_00041,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,na,na TARA_ANE_MAG_00062,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,na,na TARA_PON_MAG_00088,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,Myxococcaceae,na,na TARA_RED_MAG_00067,Bacteria,na,na,na,na,na,na TARA_PSE_MAG_00063,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,na,na,na TARA_MED_MAG_00123,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,na,na,na TARA_RED_MAG_00113,Bacteria,na,na,na,na,na,na TARA_PSW_MAG_00021,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,na,na,na TARA_RED_MAG_00082,Bacteria,na,na,na,na,na,na TARA_PON_MAG_00048,Bacteria,Verrucomicrobia,na,na,na,na,na TARA_PON_MAG_00044,Bacteria,Candidate_Phyla_Radiation,na,na,na,na,nasourmash-bio-sourmash-bc3c0f8/tests/test-data/lca/tully-genome-sigs.classify.csv000066400000000000000000006457071474157046300302150ustar00rootroot00000000000000ID,status,superkingdom,phylum,class,order,family,genus,species TOBG_ARS-1,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_ARS-10,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-100,nomatch,,,,,,, TOBG_ARS-1004,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_E, TOBG_ARS-1005,nomatch,,,,,,, TOBG_ARS-1007,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_ARS-1008,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,novelGenus_A, TOBG_ARS-1009,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_ARS-101,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_ARS-1011,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_ARS-1012,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_ARS-1013,found,Bacteria,Chloroflexi,Dehalococcoidia,Dehalococcoidales,novelFamily_A,, TOBG_ARS-1015,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_ARS-1016,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_ARS-1018,found,Archaea,,,,,, TOBG_ARS-1019,nomatch,,,,,,, TOBG_ARS-102,found,Archaea,Woesearchaeota,,,,, TOBG_ARS-1020,found,Archaea,Thaumarchaeota,novelClass_A,,,, TOBG_ARS-1021,nomatch,,,,,,, TOBG_ARS-1023,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Pseudoalteromonadaceae,, TOBG_ARS-1024,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Aestuariibacter,aggregatus TOBG_ARS-1025,found,Archaea,Euryarcheoata,,,,, TOBG_ARS-1026,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_ARS-1028,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_A,,, TOBG_ARS-1029,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_ARS-103,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-1030,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_ARS-1032,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_ARS-1033,nomatch,,,,,,, TOBG_ARS-1034,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_ARS-1035,found,Bacteria,Poribacteria,,,,, TOBG_ARS-1036,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_ARS-1037,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_ARS-1039,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Formosa, TOBG_ARS-104,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Zunongwangia,profunda TOBG_ARS-1040,disagree,,,,,,, TOBG_ARS-1041,found,Archaea,Woesearchaeota,,,,, TOBG_ARS-1043,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Colwelliaceae,, TOBG_ARS-105,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_ARS-106,nomatch,,,,,,, TOBG_ARS-108,found,Archaea,,,,,, TOBG_ARS-109,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-11,nomatch,,,,,,, TOBG_ARS-112,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Sutterellaceae,, TOBG_ARS-1126,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-113,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_ARS-1134,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_ARS-115,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,, TOBG_ARS-116,nomatch,,,,,,, TOBG_ARS-1160,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-1168,found,Bacteria,Chloroflexi,Dehalococcoidia,Dehalococcoidales,novelFamily_A,, TOBG_ARS-117,found,Bacteria,Planctomycetes,OM190,,,, TOBG_ARS-1172,found,Archaea,Nanoarchaoeta,,,,, TOBG_ARS-1176,found,Archaea,,,,,, TOBG_ARS-1178,found,Archaea,,,,,, TOBG_ARS-118,nomatch,,,,,,, TOBG_ARS-1180,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-1182,nomatch,,,,,,, TOBG_ARS-1183,found,Bacteria,Woesebacteria,,,,, TOBG_ARS-1193,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-1199,found,Archaea,Woesearchaeota,,,,, TOBG_ARS-12,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_ARS-120,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_ARS-1203,found,Archaea,,,,,, TOBG_ARS-121,disagree,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,, TOBG_ARS-1215,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_ARS-1224,found,Bacteria,Planctomycetes,Phycisphaerae,,,, TOBG_ARS-123,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_E, TOBG_ARS-124,found,Bacteria,Marinimicrobia,,,,, TOBG_ARS-1246,nomatch,,,,,,, TOBG_ARS-125,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-1279,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,,, TOBG_ARS-1285,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-1286,found,Archaea,Nanoarchaoeta,,,,, TOBG_ARS-1290,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-1297,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Micavibrio, TOBG_ARS-1298,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-13,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Bdellovibrionaceae,, TOBG_ARS-1301,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-1304,nomatch,,,,,,, TOBG_ARS-1307,found,Archaea,Diapherotrites,,,,, TOBG_ARS-1309,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-1310,found,Bacteria,Proteobacteria,novelClass_A,,,, TOBG_ARS-1318,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-1330,found,Bacteria,Chloroflexi,Dehalococcoidia,Dehalococcoidales,novelFamily_A,, TOBG_ARS-1333,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_ARS-1334,found,Archaea,,,,,, TOBG_ARS-1358,found,Archaea,Euryarcheoata,,,,, TOBG_ARS-1360,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-1386,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-1394,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-14,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_ARS-1403,found,Archaea,Nanoarchaoeta,,,,, TOBG_ARS-1414,found,Archaea,,,,,, TOBG_ARS-1419,found,Archaea,Woesearchaeota,,,,, TOBG_ARS-1420,found,Archaea,Nanoarchaoeta,,,,, TOBG_ARS-1426,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_ARS-1427,found,Archaea,Diapherotrites,,,,, TOBG_ARS-1434,found,Bacteria,Planctomycetes,OM190,,,, TOBG_ARS-1441,found,Archaea,Woesearchaeota,,,,, TOBG_ARS-15,found,Bacteria,Planctomycetes,OM190,,,, TOBG_ARS-16,nomatch,,,,,,, TOBG_ARS-17,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_ARS-18,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,Leeuwenhoekiella, TOBG_ARS-19,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_ARS-2,found,Bacteria,proteobacteria,Gammaproteobacteria,Pseudomonadales,pseudomonadaceae,Pseudomonas,stutzeri TOBG_ARS-20,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_ARS-21,found,Archaea,,,,,, TOBG_ARS-22,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Ponticaulis, TOBG_ARS-23,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-24,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_ARS-25,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_ARS-26,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Halomonas, TOBG_ARS-27,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_ARS-28,found,Bacteria,Poribacteria,,,,, TOBG_ARS-29,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_ARS-3,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Blastomonas, TOBG_ARS-30,found,Bacteria,Actinobacteria,Actinobacteria,Micrococcales,novelFamily_A,, TOBG_ARS-31,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_ARS-32,nomatch,,,,,,, TOBG_ARS-33,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Micavibrio, TOBG_ARS-34,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_ARS-35,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_ARS-36,found,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfobacterales,Desulfobacteraceae,Desulfobacter, TOBG_ARS-37,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,Halobacteriovorax, TOBG_ARS-38,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Oxalobacteraceae,Herbaspirillum,seropedicae TOBG_ARS-39,nomatch,,,,,,, TOBG_ARS-4,found,Bacteria,Proteobacteria,Alphaproteobacteria,Magnetococcales,,, TOBG_ARS-40,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_ARS-41,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_ARS-42,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_ARS-43,found,Archaea,Euryarcheoata,,,,, TOBG_ARS-44,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_ARS-45,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Bdellovibrionaceae,, TOBG_ARS-46,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_ARS-47,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_ARS-48,nomatch,,,,,,, TOBG_ARS-49,found,Archaea,Woesearchaeota,,,,, TOBG_ARS-5,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Pelagibaca, TOBG_ARS-50,found,Archaea,Pacearchaeota,,,,, TOBG_ARS-51,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_F, TOBG_ARS-52,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-53,found,Eukaryota,,,,,, TOBG_ARS-54,found,Archaea,,,,,, TOBG_ARS-55,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-56,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-57,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-58,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_ARS-59,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Geminicoccus, TOBG_ARS-6,nomatch,,,,,,, TOBG_ARS-60,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_ARS-61,found,Bacteria,Poribacteria,,,,, TOBG_ARS-62,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,Croceicoccus, TOBG_ARS-63,found,Bacteria,Planctomycetes,OM190,,,, TOBG_ARS-64,nomatch,,,,,,, TOBG_ARS-65,found,Bacteria,Planctomycetes,Phycisphaerae,Phycisphaerales,Phycisphaeraceae,novelGenus_B, TOBG_ARS-66,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_ARS-67,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_ARS-68,found,Bacteria,Planctomycetes,OM190,,,, TOBG_ARS-69,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_ARS-7,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,Marinomonas,mediterranea TOBG_ARS-70,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_ARS-71,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Micavibrio, TOBG_ARS-72,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_ARS-73,found,Archaea,Woesearchaeota,,,,, TOBG_ARS-733,nomatch,,,,,,, TOBG_ARS-74,found,Archaea,Woesearchaeota,,,,, TOBG_ARS-75,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Pedosphaera,OPB35soil, TOBG_ARS-76,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_ARS-77,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_ARS-78,nomatch,,,,,,, TOBG_ARS-79,found,Bacteria,Chloroflexi,Dehalococcoidia,Dehalococcoidales,novelFamily_A,, TOBG_ARS-8,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_ARS-80,nomatch,,,,,,, TOBG_ARS-81,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_ARS-82,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Morazellaceae,Psychrobacter, TOBG_ARS-83,nomatch,,,,,,, TOBG_ARS-84,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_ARS-85,found,Bacteria,Marinimicrobia,,,,, TOBG_ARS-86,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_ARS-87,found,Bacteria,Poribacteria,,,,, TOBG_ARS-88,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_ARS-89,found,Bacteria,Proteobacteria,Nitrospinae,,,, TOBG_ARS-9,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_ARS-90,found,Bacteria,Planctomycetes,OM190,,,, TOBG_ARS-91,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_ARS-92,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_ARS-93,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,, TOBG_ARS-94,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_ARS-95,found,Bacteria,Marinimicrobia,,,,, TOBG_ARS-96,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_J,, TOBG_ARS-97,nomatch,,,,,,, TOBG_ARS-98,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_F, TOBG_ARS-99,nomatch,,,,,,, TOBG_CPC-1,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_CPC-10,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_CPC-100,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_CPC-11,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_CPC-12,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,Haliea,salexigens TOBG_CPC-1232,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,Porticoccus, TOBG_CPC-13,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Chromatiaceae,Rheinheimera, TOBG_CPC-1326,nomatch,,,,,,, TOBG_CPC-1328,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,, TOBG_CPC-1337,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_B, TOBG_CPC-1357,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_CPC-1365,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_CPC-1369,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_CPC-1382,nomatch,,,,,,, TOBG_CPC-1397,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_CPC-1399,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_CPC-14,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,, TOBG_CPC-1446,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_CPC-1447,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,novel_familyE2,, TOBG_CPC-1462,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,, TOBG_CPC-1469,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_A,, TOBG_CPC-15,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Pseudoalteromonadaceae,, TOBG_CPC-16,nomatch,,,,,,, TOBG_CPC-17,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_CPC-18,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_CPC-19,disagree,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_CPC-2,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_CPC-20,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_CPC-21,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Tistrella, TOBG_CPC-22,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Halomonas, TOBG_CPC-23,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_CPC-24,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,Oceanospirillum, TOBG_CPC-25,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Maricaulis, TOBG_CPC-26,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_CPC-27,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_CPC-28,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_CPC-288,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_CPC-289,nomatch,,,,,,, TOBG_CPC-29,found,Bacteria,Proteobacteria,novelClass_A,,,, TOBG_CPC-290,nomatch,,,,,,, TOBG_CPC-291,nomatch,,,,,,, TOBG_CPC-293,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_CPC-294,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Zunongwangia,profunda TOBG_CPC-295,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_CPC-296,found,Bacteria,,,,,, TOBG_CPC-297,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_CPC-299,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_CPC-3,found,Bacteria,Planctomycetes,OM190,,,, TOBG_CPC-30,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_CPC-300,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_CPC-301,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_CPC-302,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Novosphingobium, TOBG_CPC-305,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_CPC-306,nomatch,,,,,,, TOBG_CPC-307,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_CPC-309,nomatch,,,,,,, TOBG_CPC-31,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Gimesia, TOBG_CPC-310,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_CPC-311,nomatch,,,,,,, TOBG_CPC-313,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_CPC-314,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Pseudoalteromonadaceae,, TOBG_CPC-315,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE4, TOBG_CPC-316,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_CPC-317,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_CPC-319,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_CPC-32,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,Parvibaculum, TOBG_CPC-320,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_CPC-322,nomatch,,,,,,, TOBG_CPC-323,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Formosa, TOBG_CPC-324,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_B,, TOBG_CPC-325,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,CandidatusPuniceispirillum, TOBG_CPC-326,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_CPC-327,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_CPC-33,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,idiomarina, TOBG_CPC-34,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_CPC-35,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_CPC-36,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_CPC-37,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Pseudoalteromonadaceae,, TOBG_CPC-38,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Stappia, TOBG_CPC-39,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_CPC-4,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseobacter, TOBG_CPC-40,disagree,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_CPC-41,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_D,, TOBG_CPC-42,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,Haliea, TOBG_CPC-43,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassopira, TOBG_CPC-44,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_CPC-45,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_CPC-46,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis, TOBG_CPC-47,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_CPC-48,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_CPC-49,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_CPC-5,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_CPC-50,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_CPC-51,found,Bacteria,Planctomycetes,OM190,,,, TOBG_CPC-52,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,DEV007,, TOBG_CPC-53,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_CPC-54,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Sphingorhabdus, TOBG_CPC-55,nomatch,,,,,,, TOBG_CPC-56,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_CPC-57,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_CPC-58,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_CPC-59,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_CPC-6,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Chromatiaceae,Rheinheimera, TOBG_CPC-60,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_CPC-61,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_CPC-62,found,Bacteria,proteobacteria,Gammaproteobacteria,Pseudomonadales,pseudomonadaceae,, TOBG_CPC-63,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_CPC-64,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,Bermanella, TOBG_CPC-65,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_CPC-66,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE6, TOBG_CPC-67,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Aequorivita, TOBG_CPC-68,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,Haliea,salexigens TOBG_CPC-69,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_CPC-7,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_CPC-70,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Aequorivita, TOBG_CPC-71,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_E, TOBG_CPC-72,found,Bacteria,Proteobacteria,Gammaproteobacteria,Salinisphaerales,Salinisphaeraceae,, TOBG_CPC-73,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_CPC-74,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE4, TOBG_CPC-75,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_CPC-76,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_CPC-77,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_CPC-78,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Maricaulis, TOBG_CPC-79,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_CPC-8,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_CPC-80,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,idiomarina, TOBG_CPC-81,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_CPC-82,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_CPC-83,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_CPC-84,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_CPC-85,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_CPC-86,found,Bacteria,Planctomycetes,Phycisphaerae,novelOrder_D,FS140-16B-02marinegroup,, TOBG_CPC-87,nomatch,,,,,,, TOBG_CPC-88,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,Sandracinaceae,Sandaracinus, TOBG_CPC-89,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_CPC-9,found,Bacteria,Planctomycetes,OM190,,,, TOBG_CPC-90,found,Bacteria,Actinobacteria,novelClass_A,,,, TOBG_CPC-91,found,Bacteria,Actinobacteria,novelClass_A,,,, TOBG_CPC-92,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_CPC-93,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,nove_genusE8, TOBG_CPC-94,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_CPC-95,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_CPC-96,found,Bacteria,Planctomycetes,Phycisphaerae,novelOrder_D,novelFamily_A,, TOBG_CPC-97,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,,, TOBG_CPC-98,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Winogradskyella, TOBG_CPC-99,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_EAC-1,disagree,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,, TOBG_EAC-10,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_EAC-100,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_EAC-101,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis, TOBG_EAC-102,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_EAC-103,nomatch,,,,,,, TOBG_EAC-104,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_G, TOBG_EAC-105,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_EAC-106,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_EAC-107,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_EAC-108,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Aequorivita, TOBG_EAC-109,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Altibacter, TOBG_EAC-11,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-110,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_EAC-111,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_EAC-112,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_EAC-113,found,Bacteria,SBR1093,,,,, TOBG_EAC-114,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_EAC-115,nomatch,,,,,,, TOBG_EAC-116,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Sphingobium, TOBG_EAC-117,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-118,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-119,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_EAC-12,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_EAC-120,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_EAC-121,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_A,,, TOBG_EAC-122,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-123,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_EAC-13,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_EAC-14,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Rhodopirellula, TOBG_EAC-15,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Aurantimonadaceae,Aurantimonas, TOBG_EAC-16,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Oceanibulbus, TOBG_EAC-17,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Aestuariibacter,aggregatus TOBG_EAC-1768,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_EAC-1775,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_C, TOBG_EAC-1785,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_EAC-1786,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-1795,found,Bacteria,Poribacteria,,,,, TOBG_EAC-18,disagree,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,,, TOBG_EAC-19,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_EAC-1924,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_EAC-1938,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_EAC-1972,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_EAC-1995,nomatch,,,,,,, TOBG_EAC-2,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Citromicrobium, TOBG_EAC-20,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,Methylophaga, TOBG_EAC-2003,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_EAC-2006,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Formosa, TOBG_EAC-2011,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_EAC-2021,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_EAC-2025,nomatch,,,,,,, TOBG_EAC-2037,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_EAC-2038,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,, TOBG_EAC-2047,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_EAC-2074,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-2087,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_EAC-2090,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_EAC-2092,nomatch,,,,,,, TOBG_EAC-2094,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-21,disagree,Bacteria,Proteobacteria,,,,, TOBG_EAC-2107,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_EAC-22,found,Archaea,Euryarcheoata,,,,, TOBG_EAC-23,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_EAC-24,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_EAC-25,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-26,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE1, TOBG_EAC-27,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_G, TOBG_EAC-28,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_EAC-29,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,novel_familyE2,, TOBG_EAC-3,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Zunongwangia,profunda TOBG_EAC-30,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_A,, TOBG_EAC-31,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Aequorivita, TOBG_EAC-32,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_EAC-33,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_EAC-34,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_EAC-35,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_EAC-36,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_EAC-37,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_EAC-38,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_EAC-39,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,Methylophaga, TOBG_EAC-4,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Muricauda, TOBG_EAC-40,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_EAC-41,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_EAC-42,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_EAC-43,found,Bacteria,Planctomycetes,Phycisphaerae,SM1A02,,, TOBG_EAC-44,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Halomonas, TOBG_EAC-45,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,Leeuwenhoekiella, TOBG_EAC-46,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_A,novelSpecies_B TOBG_EAC-47,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_EAC-48,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius, TOBG_EAC-49,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Alcaligenaceae,Pusillimonas, TOBG_EAC-5,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_EAC-50,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_EAC-51,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_EAC-52,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_EAC-53,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_EAC-54,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Nocardioidaceae,Nocardioides, TOBG_EAC-55,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,idiomarina, TOBG_EAC-56,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius, TOBG_EAC-57,disagree,Bacteria,Proteobacteria,,,,, TOBG_EAC-58,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Cellvibrionaceae,Marinimicrobium,agarilyticum TOBG_EAC-59,nomatch,,,,,,, TOBG_EAC-6,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Henriciella, TOBG_EAC-60,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,Parvibaculum, TOBG_EAC-61,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Hyphomicrobiaceae,Pelagibacterium,halotolerans TOBG_EAC-615,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_EAC-62,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_EAC-622,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,, TOBG_EAC-623,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_EAC-624,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-625,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_EAC-627,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_EAC-628,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_EAC-63,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Cyclobacteriaceae,Algoriphagus, TOBG_EAC-630,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_EAC-631,nomatch,,,,,,, TOBG_EAC-632,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_EAC-634,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Citromicrobium, TOBG_EAC-635,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_EAC-636,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_EAC-637,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_EAC-638,nomatch,,,,,,, TOBG_EAC-64,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_EAC-640,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_EAC-641,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_EAC-642,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_EAC-645,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Paracoccus, TOBG_EAC-646,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_EAC-647,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_EAC-649,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-65,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_E, TOBG_EAC-650,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_EAC-651,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_EAC-652,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_EAC-653,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_EAC-654,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_EAC-656,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_EAC-657,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_EAC-659,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_EAC-66,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Mesonia, TOBG_EAC-660,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_EAC-662,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Pedosphaera,OPB35soil, TOBG_EAC-663,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_EAC-669,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_EAC-67,disagree,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_EAC-670,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Dinoroseobacter, TOBG_EAC-671,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_EAC-673,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_EAC-674,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_EAC-675,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_EAC-676,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_EAC-678,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Halomonas, TOBG_EAC-68,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Gramella, TOBG_EAC-682,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-683,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Aurantimonadaceae,Martelella, TOBG_EAC-685,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,novel_familyE2,, TOBG_EAC-69,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_EAC-691,found,Archaea,Thaumarchaeota,novelClass_A,,,, TOBG_EAC-692,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_EAC-693,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_EAC-694,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_EAC-695,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseobacter, TOBG_EAC-7,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-70,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Croceibacter,atlanticus TOBG_EAC-703,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_EAC-704,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_EAC-705,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-707,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_EAC-708,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_EAC-709,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_EAC-71,disagree,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_EAC-710,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_EAC-712,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_EAC-713,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_EAC-714,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_EAC-72,found,Bacteria,Planctomycetes,Pla3,,,, TOBG_EAC-720,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_EAC-721,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_EAC-723,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-73,nomatch,,,,,,, TOBG_EAC-74,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cryomorphaceae,Owenweeksia, TOBG_EAC-75,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Micavibrio, TOBG_EAC-76,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Maritimibacter, TOBG_EAC-77,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,Parvibaculum, TOBG_EAC-78,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-79,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_A, TOBG_EAC-8,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_EAC-80,found,Bacteria,Marinimicrobia,,,,, TOBG_EAC-81,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_EAC-82,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_EAC-83,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_EAC-84,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_C,,, TOBG_EAC-85,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,Opitutaceae,, TOBG_EAC-86,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_EAC-87,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Tistrella, TOBG_EAC-88,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_EAC-89,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_EAC-9,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_EAC-90,nomatch,,,,,,, TOBG_EAC-91,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Hahellaceae,, TOBG_EAC-92,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_A,novelSpecies_A TOBG_EAC-93,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_EAC-94,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,Haliea, TOBG_EAC-95,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Xanthomarina, TOBG_EAC-96,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassopira, TOBG_EAC-97,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_EAC-98,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,Croceicoccus, TOBG_EAC-99,found,Bacteria,SBR1093,,,,, TOBG_IN-1,found,Bacteria,proteobacteria,Gammaproteobacteria,Pseudomonadales,pseudomonadaceae,Pseudomonas,stutzeri TOBG_IN-10,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_IN-1000,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_IN-1003,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_IN-1073,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_IN-1077,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_IN-1093,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_IN-11,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_IN-12,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,Methylophaga, TOBG_IN-1207,found,Bacteria,Marinimicrobia,,,,, TOBG_IN-1213,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_E, TOBG_IN-1214,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_IN-1219,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_IN-1222,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_E,, TOBG_IN-13,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Cyclobacteriaceae,Algoriphagus, TOBG_IN-1311,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_IN-14,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Maricaulis, TOBG_IN-15,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_IN-16,found,Bacteria,Proteobacteria,Alphaproteobacteria,Caulobacterales,Caulobacteraceae,Brevundimonas, TOBG_IN-17,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_B,novelSpecies_B TOBG_IN-18,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_IN-19,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassopira, TOBG_IN-2,found,Bacteria,proteobacteria,Gammaproteobacteria,Pseudomonadales,pseudomonadaceae,Pseudomonas,Xanthomarina TOBG_IN-20,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Citromicrobium, TOBG_IN-21,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sneathiellales,Sneathiellaceae,Sneathiella, TOBG_IN-22,found,Bacteria,Proteobacteria,Alphaproteobacteria,Caulobacterales,Caulobacteraceae,Brevundimonas, TOBG_IN-23,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_A,novelSpecies_A TOBG_IN-24,found,Bacteria,Proteobacteria,Gammaproteobacteria,Xanthomonadales,Xanthomonadaceae,Arenimonas, TOBG_IN-25,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_IN-26,nomatch,,,,,,, TOBG_IN-27,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Maricaulis, TOBG_IN-28,nomatch,,,,,,, TOBG_IN-29,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Spongiibacteraceae,spongiibacter,marinus TOBG_IN-3,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Morazellaceae,Acinetobacter, TOBG_IN-30,nomatch,,,,,,, TOBG_IN-31,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_IN-32,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_IN-33,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_IN-34,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_IN-35,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_IN-36,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,idiomarina, TOBG_IN-37,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_IN-38,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Alcaligenaceae,Pusillimonas, TOBG_IN-39,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE6, TOBG_IN-4,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Alcaligenaceae,Pusillimonas, TOBG_IN-40,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,, TOBG_IN-41,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_IN-42,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_IN-43,found,Bacteria,Actinobacteria,Actinobacteria,Micrococcales,novelFamily_A,, TOBG_IN-44,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_IN-45,nomatch,,,,,,, TOBG_IN-46,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_IN-47,disagree,,,,,,, TOBG_IN-48,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_IN-49,found,Bacteria,Marinimicrobia,,,,, TOBG_IN-5,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,Methylophaga, TOBG_IN-6,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_IN-7,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Alcaligenaceae,Pusillimonas, TOBG_IN-8,found,Bacteria,Proteobacteria,Alphaproteobacteria,Caulobacterales,Caulobacteraceae,Phenylobacterium, TOBG_IN-9,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_IN-912,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_IN-913,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_IN-918,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_IN-919,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_IN-921,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_IN-922,found,Bacteria,proteobacteria,Gammaproteobacteria,Pseudomonadales,pseudomonadaceae,Pseudomonas,stutzeri TOBG_IN-924,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_IN-926,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_IN-928,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_IN-929,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE1, TOBG_IN-930,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_IN-961,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_A,, TOBG_MED-1001,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_B, TOBG_MED-1003,nomatch,,,,,,, TOBG_MED-1008,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_MED-1017,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Agrococcus, TOBG_MED-1036,nomatch,,,,,,, TOBG_MED-104,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-1046,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_MED-1047,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_MED-105,found,Bacteria,Cyanobacteria,unassigned,Prochlorales,Prochlorococcaceae,Prochlorococcus, TOBG_MED-1076,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_B, TOBG_MED-1079,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_MED-1093,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_MED-1094,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_A,, TOBG_MED-1105,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-1116,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-1122,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_B,, TOBG_MED-1131,nomatch,,,,,,, TOBG_MED-1153,nomatch,,,,,,, TOBG_MED-1155,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,,, TOBG_MED-1178,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_MED-1190,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE6, TOBG_MED-1222,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_MED-1223,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_MED-1227,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_MED-1229,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,, TOBG_MED-1233,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_MED-124,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_MED-1247,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_E, TOBG_MED-1250,nomatch,,,,,,, TOBG_MED-1257,nomatch,,,,,,, TOBG_MED-1261,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_MED-1269,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_MED-1270,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_MED-139,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_MED-174,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_MED-191,nomatch,,,,,,, TOBG_MED-195,nomatch,,,,,,, TOBG_MED-203,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_MED-211,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_MED-215,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_MED-569,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_MED-580,nomatch,,,,,,, TOBG_MED-581,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-582,found,Bacteria,Proteobacteria,novelClass_A,,,, TOBG_MED-583,nomatch,,,,,,, TOBG_MED-584,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE3, TOBG_MED-585,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_MED-586,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-587,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,,, TOBG_MED-588,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Muricauda, TOBG_MED-589,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-590,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_MED-591,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_F,,, TOBG_MED-592,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Pseudoalteromonadaceae,, TOBG_MED-593,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,novelGenus_A, TOBG_MED-594,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_MED-595,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_MED-596,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,,, TOBG_MED-597,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,Opitutaceae,, TOBG_MED-598,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-599,found,Bacteria,Poribacteria,,,,, TOBG_MED-600,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-601,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Rhodopirellula, TOBG_MED-602,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-603,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,, TOBG_MED-604,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,, TOBG_MED-605,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-606,nomatch,,,,,,, TOBG_MED-607,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_MED-608,nomatch,,,,,,, TOBG_MED-609,found,Bacteria,Planctomycetes,Pla3,,,, TOBG_MED-610,nomatch,,,,,,, TOBG_MED-611,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE1, TOBG_MED-612,found,Bacteria,Proteobacteria,Gammaproteobacteria,unassigned,unassigned,CaThioglobus, TOBG_MED-613,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE3, TOBG_MED-614,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_MED-615,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE1, TOBG_MED-616,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_MED-617,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_MED-618,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-619,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_MED-620,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Nisaea, TOBG_MED-621,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_MED-622,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_MED-623,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_MED-624,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Ponticaulis, TOBG_MED-625,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_J,, TOBG_MED-626,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-627,found,Bacteria,Bacteroidetes,Saprospiria,Saprospirales,,, TOBG_MED-628,found,Bacteria,Actinobacteria,novelClass_A,,,, TOBG_MED-629,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_B,, TOBG_MED-630,found,Bacteria,Cyanobacteria,unassigned,Prochlorales,Prochlorococcaceae,Prochlorococcus, TOBG_MED-631,nomatch,,,,,,, TOBG_MED-632,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_MED-633,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseobacter, TOBG_MED-634,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,,, TOBG_MED-635,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_MED-636,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_D, TOBG_MED-637,found,Bacteria,Actinobacteria,Actinobacteria,Micrococcales,novelFamily_A,, TOBG_MED-638,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_MED-639,nomatch,,,,,,, TOBG_MED-640,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_MED-641,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_A,,, TOBG_MED-642,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-643,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_B, TOBG_MED-644,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_MED-645,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-646,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_MED-647,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-648,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-649,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_MED-650,nomatch,,,,,,, TOBG_MED-651,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_MED-652,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_MED-653,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Ponticaulis, TOBG_MED-654,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_MED-655,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_MED-656,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_A,,, TOBG_MED-657,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_MED-658,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_MED-659,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_MED-660,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_MED-661,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Rhodopirellula, TOBG_MED-662,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_MED-663,nomatch,,,,,,, TOBG_MED-664,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_MED-665,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Hyphomicrobiaceae,Filomicrobium, TOBG_MED-666,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_B,, TOBG_MED-667,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-668,nomatch,,,,,,, TOBG_MED-669,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-670,found,Bacteria,Kiritimatiellaeota,Kiritimatiellae,Kiritimatiellales,Kiritimatiellaceae,, TOBG_MED-671,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_MED-672,nomatch,,,,,,, TOBG_MED-673,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-674,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_MED-675,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-676,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_MED-677,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_MED-678,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_MED-679,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_MED-680,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-681,nomatch,,,,,,, TOBG_MED-682,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_MED-683,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE6, TOBG_MED-684,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_MED-685,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_MED-686,nomatch,,,,,,, TOBG_MED-687,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-688,nomatch,,,,,,, TOBG_MED-689,nomatch,,,,,,, TOBG_MED-690,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-691,nomatch,,,,,,, TOBG_MED-692,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_MED-693,found,Bacteria,Cyanobacteria,unassigned,Prochlorales,Prochlorococcaceae,Prochlorococcus, TOBG_MED-694,found,Bacteria,Proteobacteria,Alphaproteobacteria,Magnetococcales,,, TOBG_MED-695,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_D, TOBG_MED-696,found,Bacteria,Kiritimatiellaeota,Kiritimatiellae,Kiritimatiellales,Kiritimatiellaceae,, TOBG_MED-697,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Rhodopirellula, TOBG_MED-698,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-699,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_E,, TOBG_MED-700,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_MED-701,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_MED-702,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_MED-703,found,Bacteria,Planctomycetes,Phycisphaerae,JL-ENTP-F27,,, TOBG_MED-704,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_MED-705,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_MED-706,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_MED-707,nomatch,,,,,,, TOBG_MED-708,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_MED-709,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_MED-710,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_MED-711,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_MED-712,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-713,found,Bacteria,SBR1093,,,,, TOBG_MED-714,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_MED-715,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_MED-716,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_MED-717,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_MED-718,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_MED-719,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Pedosphaera,OPB35soil, TOBG_MED-720,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_MED-721,nomatch,,,,,,, TOBG_MED-722,nomatch,,,,,,, TOBG_MED-723,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_MED-724,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_MED-725,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE6, TOBG_MED-726,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_MED-727,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-728,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Bradyrhizobiaceae,Afipia, TOBG_MED-729,found,Bacteria,Proteobacteria,novelClass_A,,,, TOBG_MED-730,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE6, TOBG_MED-731,found,Bacteria,SBR1093,,,,, TOBG_MED-732,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_MED-733,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-734,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_MED-735,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_MED-736,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_MED-737,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-738,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_MED-739,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,novelGenus_A, TOBG_MED-740,found,Archaea,Thaumarchaeota,,,,, TOBG_MED-741,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,Coraliomargarita, TOBG_MED-742,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-743,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_MED-744,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,, TOBG_MED-745,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_MED-746,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-747,found,Bacteria,Bacteroidetes,,,,, TOBG_MED-748,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_MED-749,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_A, TOBG_MED-750,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_MED-751,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_MED-752,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Micavibrio, TOBG_MED-753,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_MED-754,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE3, TOBG_MED-755,nomatch,,,,,,, TOBG_MED-756,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Geminicoccus, TOBG_MED-757,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-758,found,Bacteria,Actinobacteria,novelClass_A,,,, TOBG_MED-759,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-760,nomatch,,,,,,, TOBG_MED-761,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-762,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_MED-763,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_MED-764,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-765,found,Bacteria,Proteobacteria,novelClass_B,,,, TOBG_MED-766,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_MED-767,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_MED-768,found,Bacteria,Proteobacteria,novelClass_B,,,, TOBG_MED-769,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-770,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_MED-771,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_E, TOBG_MED-772,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_MED-773,found,Bacteria,Actinobacteria,novelClass_A,,,, TOBG_MED-774,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_MED-775,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_MED-776,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,,, TOBG_MED-777,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_E,, TOBG_MED-778,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_MED-779,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_MED-780,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_MED-781,nomatch,,,,,,, TOBG_MED-782,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_MED-783,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_MED-784,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-785,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,marinegroup, TOBG_MED-786,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_MED-787,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-788,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-789,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-790,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_MED-791,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_MED-792,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_MED-793,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_MED-794,nomatch,,,,,,, TOBG_MED-795,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_MED-796,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_MED-797,nomatch,,,,,,, TOBG_MED-798,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_MED-800,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_MED-801,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,nove_genusE8, TOBG_MED-802,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,marinegroup, TOBG_MED-803,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Geminicoccus, TOBG_MED-804,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_MED-805,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_E, TOBG_MED-806,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-807,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_MED-808,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-809,found,Bacteria,Kiritimatiellaeota,Kiritimatiellae,Kiritimatiellales,Kiritimatiellaceae,, TOBG_MED-810,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-811,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_MED-812,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-813,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_MED-814,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-815,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-816,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_MED-817,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-818,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_MED-819,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_MED-820,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,,, TOBG_MED-821,nomatch,,,,,,, TOBG_MED-822,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_MED-823,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_MED-824,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-825,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE1, TOBG_MED-826,found,Bacteria,Proteobacteria,novelClass_A,,,, TOBG_MED-827,nomatch,,,,,,, TOBG_MED-828,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-829,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-830,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,, TOBG_MED-831,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_MED-832,nomatch,,,,,,, TOBG_MED-833,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_MED-834,nomatch,,,,,,, TOBG_MED-835,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_MED-836,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-837,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_MED-838,nomatch,,,,,,, TOBG_MED-839,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE6, TOBG_MED-840,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,novelGenus_A, TOBG_MED-841,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_MED-842,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_MED-843,nomatch,,,,,,, TOBG_MED-844,found,Bacteria,Actinobacteria,novelClass_A,,,, TOBG_MED-845,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_MED-846,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_MED-847,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_MED-848,nomatch,,,,,,, TOBG_MED-849,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_MED-850,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_MED-851,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_MED-852,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_MED-853,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_MED-854,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_A, TOBG_MED-855,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-856,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_MED-857,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_MED-858,nomatch,,,,,,, TOBG_MED-859,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_MED-861,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,Opitutaceae,, TOBG_MED-862,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_C,, TOBG_MED-863,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_MED-864,nomatch,,,,,,, TOBG_MED-865,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_MED-866,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-867,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_MED-868,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_MED-869,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-870,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_MED-871,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_MED-872,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_MED-873,nomatch,,,,,,, TOBG_MED-874,found,Archaea,Euryarcheoata,,,,, TOBG_MED-875,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_MED-876,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-877,nomatch,,,,,,, TOBG_MED-878,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_MED-879,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_MED-880,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_MED-881,nomatch,,,,,,, TOBG_MED-882,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_MED-883,found,Bacteria,Proteobacteria,novelClass_A,,,, TOBG_MED-884,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_MED-899,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_MED-921,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_MED-928,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_MED-932,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_MED-939,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_MED-943,nomatch,,,,,,, TOBG_MED-947,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_MED-950,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_MED-952,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_MED-955,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,nove_genusE8, TOBG_MED-957,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_MED-961,nomatch,,,,,,, TOBG_MED-965,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_MED-972,found,Bacteria,Marinimicrobia,,,,, TOBG_MED-995,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_MED-999,nomatch,,,,,,, TOBG_NAT-1,found,Bacteria,Poribacteria,,,,, TOBG_NAT-10,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Aurantimonadaceae,Martelella, TOBG_NAT-100,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NAT-101,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NAT-102,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_NAT-103,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_NAT-104,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_A,novelSpecies_B TOBG_NAT-105,found,Bacteria,Saccharibacteria,,,,, TOBG_NAT-106,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_NAT-107,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_J,, TOBG_NAT-108,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_NAT-109,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_NAT-11,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_NAT-110,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Muricauda,lutaonensis TOBG_NAT-111,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Muricauda,lutaonensis TOBG_NAT-112,found,Bacteria,Planctomycetes,Phycisphaerae,JL-ENTP-F27,,, TOBG_NAT-113,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Ponticaulis, TOBG_NAT-114,nomatch,,,,,,, TOBG_NAT-115,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_NAT-116,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Leifsonia, TOBG_NAT-117,found,Bacteria,Chloroflexi,Anaerolineae,Anaerolineales,Anaerolineaceae,, TOBG_NAT-118,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NAT-119,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NAT-12,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_NAT-120,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_NAT-121,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,Halioglobus, TOBG_NAT-122,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Ectothiorhodospiraceae,, TOBG_NAT-123,found,Bacteria,Chloroflexi,Anaerolineae,Anaerolineales,Anaerolineaceae,, TOBG_NAT-124,found,Bacteria,Chloroflexi,Anaerolineae,Anaerolineales,Anaerolineaceae,, TOBG_NAT-125,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NAT-126,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NAT-127,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,, TOBG_NAT-128,nomatch,,,,,,, TOBG_NAT-129,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Wenzhouxiangellaceae,, TOBG_NAT-13,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NAT-130,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_C, TOBG_NAT-131,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,Sandracinaceae,Sandaracinus, TOBG_NAT-132,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_NAT-133,found,Bacteria,Bacteroidetes,Saprospiria,Saprospirales,,, TOBG_NAT-134,found,Bacteria,Ignavibacteriae,NovelClass_B,,,, TOBG_NAT-135,nomatch,,,,,,, TOBG_NAT-136,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NAT-137,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_NAT-138,found,Bacteria,Actinobacteria,Actinobacteria,Micrococcales,novelFamily_A,, TOBG_NAT-139,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_NAT-14,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_G, TOBG_NAT-140,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NAT-141,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Micavibrio, TOBG_NAT-142,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_NAT-143,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_NAT-144,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_NAT-145,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,Coraliomargarita, TOBG_NAT-146,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_NAT-147,nomatch,,,,,,, TOBG_NAT-148,nomatch,,,,,,, TOBG_NAT-149,found,Bacteria,Planctomycetes,Phycisphaerae,novelOrder_D,novelFamily_A,, TOBG_NAT-15,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Zunongwangia,profunda TOBG_NAT-150,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_NAT-151,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_NAT-152,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Pseudozobellia, TOBG_NAT-153,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,, TOBG_NAT-154,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_NAT-155,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_E, TOBG_NAT-156,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_NAT-157,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,Cycloclasticus, TOBG_NAT-158,found,Bacteria,Proteobacteria,Alphaproteobacteria,Kordiimonadales,Kordiimonadaceae,Kordiimonas, TOBG_NAT-159,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_NAT-16,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_NAT-160,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_NAT-161,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_NAT-162,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_NAT-163,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_NAT-164,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_NAT-165,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_NAT-166,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NAT-167,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Yangia, TOBG_NAT-168,found,Archaea,Thaumarchaeota,novelClass_A,,,, TOBG_NAT-169,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Gordoniaceae,Gordonia, TOBG_NAT-17,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius, TOBG_NAT-170,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,Parvibaculum, TOBG_NAT-171,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NAT-172,nomatch,,,,,,, TOBG_NAT-173,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Aurantimonadaceae,Fulvimarina, TOBG_NAT-174,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_NAT-175,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_NAT-176,found,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Spirochaetaceae,novelGenus_A, TOBG_NAT-177,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_NAT-178,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Bdellovibrionaceae,, TOBG_NAT-179,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NAT-18,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_NAT-180,nomatch,,,,,,, TOBG_NAT-181,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_NAT-182,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_NAT-183,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_NAT-184,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-185,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_NAT-186,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_NAT-187,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_NAT-188,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_NAT-189,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_NAT-19,found,Bacteria,Planctomycetes,OM190,,,, TOBG_NAT-190,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_C,,, TOBG_NAT-191,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_NAT-192,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_C, TOBG_NAT-193,nomatch,,,,,,, TOBG_NAT-194,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_NAT-195,nomatch,,,,,,, TOBG_NAT-196,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_NAT-197,nomatch,,,,,,, TOBG_NAT-198,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-199,nomatch,,,,,,, TOBG_NAT-2,found,Bacteria,Planctomycetes,Pla3,,,, TOBG_NAT-20,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Phyllobacteriaceae,, TOBG_NAT-200,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_NAT-201,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_NAT-202,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_B,, TOBG_NAT-203,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-204,found,Archaea,Euryarcheoata,,,,, TOBG_NAT-205,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NAT-206,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_NAT-207,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_NAT-208,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_NAT-209,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,Altererythrobacter, TOBG_NAT-21,found,Bacteria,Proteobacteria,Alphaproteobacteria,Parvulalarculales,Parvularculaceae,Parvularcula, TOBG_NAT-210,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,Leeuwenhoekiella, TOBG_NAT-211,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-212,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Sphingomonas, TOBG_NAT-213,nomatch,,,,,,, TOBG_NAT-214,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_NAT-215,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NAT-216,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Sphingobium, TOBG_NAT-217,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-218,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_NAT-219,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-22,found,Bacteria,Planctomycetes,Phycisphaerae,Phycisphaerales,Phycisphaeraceae,novelGenus_A, TOBG_NAT-220,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-221,found,Bacteria,Proteobacteria,Nitrospinae,Nitrospinales,Nitrospinaceae,Nitrospina, TOBG_NAT-222,found,Bacteria,Chloroflexi,novelClass_F,,,, TOBG_NAT-223,disagree,,,,,,, TOBG_NAT-224,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-225,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NAT-226,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,,, TOBG_NAT-227,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NAT-228,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NAT-229,found,Archaea,Thaumarchaeota,,,,, TOBG_NAT-23,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,DEV007,, TOBG_NAT-230,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE4, TOBG_NAT-231,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_E, TOBG_NAT-232,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_NAT-233,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_NAT-234,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_NAT-235,nomatch,,,,,,, TOBG_NAT-236,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_NAT-237,found,Bacteria,Kiritimatiellaeota,Kiritimatiellae,Kiritimatiellales,Kiritimatiellaceae,, TOBG_NAT-238,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_A,, TOBG_NAT-239,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_E, TOBG_NAT-24,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NAT-240,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_NAT-241,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_NAT-242,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_NAT-243,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_NAT-244,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-245,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_NAT-246,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE4, TOBG_NAT-247,nomatch,,,,,,, TOBG_NAT-248,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Formosa, TOBG_NAT-249,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_NAT-25,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_E,, TOBG_NAT-250,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,, TOBG_NAT-251,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-252,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_NAT-253,found,Bacteria,Actinobacteria,Actinobacteria,Propionibacteriales,Nocardioidaceae,Pimelobacter, TOBG_NAT-254,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_NAT-255,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NAT-256,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_NAT-257,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_J, TOBG_NAT-258,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_NAT-259,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_NAT-26,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_NAT-260,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_NAT-261,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,, TOBG_NAT-262,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_A,,, TOBG_NAT-263,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_NAT-264,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_NAT-265,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_NAT-266,disagree,Archaea,Euryarcheoata,unassigned,unassigned,,, TOBG_NAT-267,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_NAT-268,nomatch,,,,,,, TOBG_NAT-269,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_B, TOBG_NAT-27,found,Bacteria,Actinobacteria,novelClass_A,,,, TOBG_NAT-270,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_B, TOBG_NAT-271,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_NAT-272,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_NAT-273,disagree,Bacteria,,,,,, TOBG_NAT-274,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-275,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_NAT-276,found,Bacteria,Proteobacteria,Gammaproteobacteria,Salinisphaerales,Salinisphaeraceae,Salinisphaera, TOBG_NAT-277,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_NAT-278,found,Bacteria,Proteobacteria,Nitrospinae,Nitrospinales,Nitrospinaceae,Nitrospina, TOBG_NAT-279,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_NAT-28,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,,, TOBG_NAT-280,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_NAT-281,disagree,Archaea,Euryarcheoata,unassigned,unassigned,,, TOBG_NAT-282,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,Leeuwenhoekiella, TOBG_NAT-283,nomatch,,,,,,, TOBG_NAT-284,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_NAT-288,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_NAT-29,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_NAT-290,found,Bacteria,Chloroflexi,Anaerolineae,Anaerolineales,Anaerolineaceae,, TOBG_NAT-291,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Phyllobacteriaceae,Nitratireductor, TOBG_NAT-292,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_NAT-3,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Ahrensia, TOBG_NAT-30,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Halomonas, TOBG_NAT-302,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_NAT-31,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_NAT-32,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,marinegroup, TOBG_NAT-33,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_NAT-333,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NAT-34,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_NAT-35,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_NAT-353,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_NAT-36,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_L,, TOBG_NAT-37,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_NAT-375,nomatch,,,,,,, TOBG_NAT-38,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-39,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_NAT-392,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_NAT-4,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NAT-40,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_NAT-41,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_NAT-417,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NAT-42,found,Archaea,Thaumarchaeota,,,,, TOBG_NAT-422,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-43,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE3, TOBG_NAT-430,found,Bacteria,SBR1093,,,,, TOBG_NAT-44,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_C, TOBG_NAT-441,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NAT-45,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NAT-453,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_NAT-46,found,Bacteria,Proteobacteria,Gammaproteobacteria,Salinisphaerales,Salinisphaeraceae,Salinisphaera, TOBG_NAT-47,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_NAT-472,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_NAT-478,nomatch,,,,,,, TOBG_NAT-48,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_NAT-483,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_NAT-484,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_NAT-49,found,Bacteria,Actinobacteria,Actinobacteria,Micrococcales,novelFamily_A,, TOBG_NAT-493,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-495,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-5,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Aequorivita, TOBG_NAT-50,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_NAT-501,disagree,Bacteria,,,,,, TOBG_NAT-503,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_D,,, TOBG_NAT-504,nomatch,,,,,,, TOBG_NAT-509,found,Bacteria,SBR1093,,,,, TOBG_NAT-51,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-510,found,Bacteria,SBR1093,,,,, TOBG_NAT-52,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-53,found,Bacteria,SBR1093,,,,, TOBG_NAT-535,found,Bacteria,novelPhylum_C,,,,, TOBG_NAT-54,nomatch,,,,,,, TOBG_NAT-545,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_NAT-549,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NAT-55,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NAT-56,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NAT-57,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_J,, TOBG_NAT-575,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_NAT-579,nomatch,,,,,,, TOBG_NAT-58,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_NAT-59,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_NAT-6,found,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Rhodothermaceae,, TOBG_NAT-60,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_NAT-608,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Micavibrio, TOBG_NAT-61,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_E, TOBG_NAT-62,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-63,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_C, TOBG_NAT-630,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_NAT-634,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_NAT-64,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NAT-65,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NAT-653,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NAT-66,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_NAT-661,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NAT-67,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_NAT-68,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_NAT-682,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Planktomarina,temperata TOBG_NAT-686,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE4, TOBG_NAT-69,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_G, TOBG_NAT-7,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Maritimibacter, TOBG_NAT-70,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_E, TOBG_NAT-71,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_NAT-72,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_NAT-73,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_NAT-74,found,Bacteria,Marinimicrobia,,,,, TOBG_NAT-75,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_NAT-76,nomatch,,,,,,, TOBG_NAT-77,found,Bacteria,Saccharibacteria,,,,, TOBG_NAT-78,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_J,, TOBG_NAT-79,found,Bacteria,Poribacteria,,,,, TOBG_NAT-8,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,Sandracinaceae,Sandaracinus, TOBG_NAT-80,nomatch,,,,,,, TOBG_NAT-81,found,Bacteria,Poribacteria,,,,, TOBG_NAT-82,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Thioclava, TOBG_NAT-83,nomatch,,,,,,, TOBG_NAT-84,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NAT-85,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,, TOBG_NAT-86,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_NAT-87,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_F,,, TOBG_NAT-88,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_B,, TOBG_NAT-89,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Ponticaulis, TOBG_NAT-9,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Confluentimicrobium, TOBG_NAT-90,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_NAT-91,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_NAT-92,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Mesonia, TOBG_NAT-93,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_NAT-94,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NAT-95,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NAT-96,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_NAT-97,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_NAT-98,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NAT-99,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_B,, TOBG_NP-1,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Halomonas, TOBG_NP-10,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseobacter, TOBG_NP-100,found,Bacteria,Proteobacteria,Gammaproteobacteria,Nevskiales,,, TOBG_NP-1000,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_NP-1001,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_NP-101,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_NP-102,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_NP-1026,found,Bacteria,Proteobacteria,Nitrospinae,,,, TOBG_NP-103,found,Bacteria,Planctomycetes,OM190,,,, TOBG_NP-1036,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,novel_familyE2,, TOBG_NP-1037,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_NP-104,found,Bacteria,Marinimicrobia,,,,, TOBG_NP-1044,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-1046,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,Coxiellaceae,, TOBG_NP-1048,found,Bacteria,Marinimicrobia,,,,, TOBG_NP-105,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_NP-1051,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_NP-1052,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE3, TOBG_NP-1057,nomatch,,,,,,, TOBG_NP-106,found,Archaea,Euryarcheoata,,,,, TOBG_NP-1069,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_NP-107,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_NP-1070,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,nove_genusE8, TOBG_NP-1071,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-1073,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassopira, TOBG_NP-108,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_NP-1080,nomatch,,,,,,, TOBG_NP-109,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Rubinisphaera, TOBG_NP-1098,found,Bacteria,Planctomycetes,OM190,,,, TOBG_NP-11,nomatch,,,,,,, TOBG_NP-110,nomatch,,,,,,, TOBG_NP-1106,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_NP-111,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NP-112,found,Archaea,Euryarcheoata,,,,, TOBG_NP-1124,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_NP-113,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_NP-114,nomatch,,,,,,, TOBG_NP-115,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_NP-1152,found,Bacteria,Marinimicrobia,,,,, TOBG_NP-1159,found,Archaea,Euryarcheoata,,,,, TOBG_NP-116,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_E,, TOBG_NP-1160,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NP-117,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_NP-1171,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_NP-118,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_NP-1180,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE4, TOBG_NP-119,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_NP-12,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_NP-120,found,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Spirochaetaceae,novelGenus_A, TOBG_NP-1202,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_NP-1207,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_NP-121,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Pseudoalteromonadaceae,, TOBG_NP-122,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_NP-123,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Citromicrobium, TOBG_NP-124,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Burkholderiaceae,Cupriavidus,metallidurans TOBG_NP-125,disagree,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NP-1256,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_NP-126,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NP-127,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_NP-1271,found,Bacteria,Marinimicrobia,,,,, TOBG_NP-1277,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NP-128,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_NP-1282,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_NP-1285,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_NP-129,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_NP-1295,found,Archaea,Woesearchaeota,,,,, TOBG_NP-1297,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_C,,, TOBG_NP-13,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_NP-130,found,Bacteria,Acidobacteria,novelClass_A,,,, TOBG_NP-1301,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_NP-1304,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_NP-131,nomatch,,,,,,, TOBG_NP-132,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NP-1321,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_B, TOBG_NP-1324,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_NP-133,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_NP-134,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NP-1344,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_NP-135,nomatch,,,,,,, TOBG_NP-136,nomatch,,,,,,, TOBG_NP-137,nomatch,,,,,,, TOBG_NP-1374,nomatch,,,,,,, TOBG_NP-1378,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_NP-138,found,Bacteria,Chloroflexi,Dehalococcoidia,Dehalococcoidales,novelFamily_A,, TOBG_NP-139,found,Bacteria,Chloroflexi,Anaerolineae,Anaerolineales,Anaerolineaceae,, TOBG_NP-1391,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-1395,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_NP-14,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_NP-140,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_NP-1400,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_NP-141,found,Bacteria,Marinimicrobia,,,,, TOBG_NP-142,found,Archaea,Thaumarchaeota,,,,, TOBG_NP-143,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_NP-144,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_NP-1444,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NP-145,nomatch,,,,,,, TOBG_NP-146,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_NP-1463,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_NP-1466,found,Archaea,Pacearchaeota,,,,, TOBG_NP-147,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_NP-1472,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NP-148,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_NP-149,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_NP-1496,nomatch,,,,,,, TOBG_NP-15,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_NP-150,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_NP-151,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NP-152,found,Bacteria,Marinimicrobia,,,,, TOBG_NP-1521,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_NP-153,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,,, TOBG_NP-155,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_NP-156,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_NP-16,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Sutterellaceae,, TOBG_NP-17,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_NP-18,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_NP-19,nomatch,,,,,,, TOBG_NP-2,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_NP-20,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NP-21,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-22,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_NP-23,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,marinegroup, TOBG_NP-24,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_NP-25,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Leifsonia, TOBG_NP-26,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Citromicrobium, TOBG_NP-27,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,, TOBG_NP-28,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Morazellaceae,Acinetobacter, TOBG_NP-29,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Mameliella, TOBG_NP-3,disagree,Bacteria,proteobacteria,Gammaproteobacteria,Pseudomonadales,pseudomonadaceae,Pseudomonas, TOBG_NP-30,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Thalassobius,gelatinovorus TOBG_NP-31,nomatch,,,,,,, TOBG_NP-32,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_NP-33,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Nocardioidaceae,Nocardioides, TOBG_NP-34,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Sphingobium, TOBG_NP-35,disagree,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,,, TOBG_NP-36,nomatch,,,,,,, TOBG_NP-37,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,,, TOBG_NP-38,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_NP-39,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_NP-4,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Comamonadaceae,Variovorax, TOBG_NP-40,found,Bacteria,Proteobacteria,Gammaproteobacteria,Salinisphaerales,Salinisphaeraceae,Salinisphaera, TOBG_NP-41,found,Bacteria,Poribacteria,,,,, TOBG_NP-42,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Pseudoalteromonadaceae,, TOBG_NP-43,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Sphingobium, TOBG_NP-44,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,, TOBG_NP-45,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_NP-46,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_NP-47,found,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Spirochaetaceae,novelGenus_A, TOBG_NP-48,disagree,,,,,,, TOBG_NP-49,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_NP-5,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_F, TOBG_NP-50,found,Bacteria,Proteobacteria,Nitrospinae,,,, TOBG_NP-51,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-52,found,Bacteria,Actinobacteria,Actinobacteria,Micrococcales,novelFamily_A,, TOBG_NP-53,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_B,, TOBG_NP-54,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_NP-55,found,Bacteria,Marinimicrobia,,,,, TOBG_NP-56,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Leifsonia, TOBG_NP-57,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_NP-58,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_NP-59,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-6,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Methylobacteriaceae,Methylobacterum,phyllosphaerae TOBG_NP-60,found,Bacteria,Poribacteria,,,,, TOBG_NP-61,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Planktomarina,temperata TOBG_NP-62,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,Leeuwenhoekiella, TOBG_NP-63,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-64,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_NP-65,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_NP-66,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,,, TOBG_NP-67,found,Bacteria,Saccharibacteria,,,,, TOBG_NP-68,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_NP-69,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_H, TOBG_NP-7,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseobacter, TOBG_NP-70,found,Bacteria,Saccharibacteria,,,,, TOBG_NP-71,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_NP-72,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_NP-73,nomatch,,,,,,, TOBG_NP-74,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,,, TOBG_NP-75,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,DEV007,, TOBG_NP-76,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_NP-77,found,Archaea,Bathyarchaeota,,,,, TOBG_NP-78,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_NP-79,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_NP-8,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,idiomarina, TOBG_NP-80,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NP-81,nomatch,,,,,,, TOBG_NP-82,nomatch,,,,,,, TOBG_NP-83,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_NP-84,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_NP-85,nomatch,,,,,,, TOBG_NP-86,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_F, TOBG_NP-87,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_I, TOBG_NP-88,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_NP-89,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_D,, TOBG_NP-9,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,Haliea,salexigens TOBG_NP-90,nomatch,,,,,,, TOBG_NP-91,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Aequorivita, TOBG_NP-92,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,Methylophaga, TOBG_NP-93,nomatch,,,,,,, TOBG_NP-931,nomatch,,,,,,, TOBG_NP-932,found,Bacteria,Marinimicrobia,,,,, TOBG_NP-933,nomatch,,,,,,, TOBG_NP-935,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_NP-936,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_B,, TOBG_NP-937,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NP-939,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NP-94,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_NP-941,disagree,Archaea,Euryarcheoata,unassigned,unassigned,,, TOBG_NP-942,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NP-943,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_NP-944,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,nove_genusE8, TOBG_NP-945,nomatch,,,,,,, TOBG_NP-946,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_NP-948,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-949,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-95,found,Bacteria,proteobacteria,Gammaproteobacteria,Pseudomonadales,pseudomonadaceae,, TOBG_NP-951,found,Bacteria,Proteobacteria,Zetaproteobacteria,,,, TOBG_NP-952,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_NP-954,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NP-955,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_NP-956,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_NP-958,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_NP-959,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_NP-96,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_NP-960,disagree,Bacteria,,,,,, TOBG_NP-961,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_NP-962,nomatch,,,,,,, TOBG_NP-964,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-965,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_NP-966,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_NP-969,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_NP-97,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_NP-970,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_NP-972,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_NP-973,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,,, TOBG_NP-975,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_NP-976,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_NP-977,found,Bacteria,Marinimicrobia,,,,, TOBG_NP-978,nomatch,,,,,,, TOBG_NP-98,nomatch,,,,,,, TOBG_NP-980,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_NP-981,nomatch,,,,,,, TOBG_NP-982,disagree,Bacteria,,,,,, TOBG_NP-983,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_NP-986,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_NP-987,nomatch,,,,,,, TOBG_NP-989,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_NP-99,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Gimesia, TOBG_NP-990,nomatch,,,,,,, TOBG_NP-992,nomatch,,,,,,, TOBG_NP-993,found,Bacteria,Acidobacteria,novelClass_A,,,, TOBG_NP-995,found,Bacteria,Chloroflexi,Anaerolineae,Anaerolineales,Anaerolineaceae,, TOBG_NP-996,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_NP-997,found,Bacteria,Planctomycetes,OM190,,,, TOBG_NP-998,nomatch,,,,,,, TOBG_RS-307,found,Bacteria,Actinobacteria,novelClass_A,,,, TOBG_RS-341,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_RS-342,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_RS-343,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_RS-344,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_RS-345,found,Bacteria,Actinobacteria,novelClass_A,,,, TOBG_RS-346,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_RS-347,found,Bacteria,Marinimicrobia,,,,, TOBG_RS-348,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_L,, TOBG_RS-349,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_RS-350,found,Bacteria,Marinimicrobia,,,,, TOBG_RS-351,found,Archaea,Thaumarchaeota,unassigned,unassigned,unassigned,Nitropelagicus, TOBG_RS-352,found,Archaea,Euryarcheoata,,,,, TOBG_RS-353,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_RS-354,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_RS-355,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Oxalobacteraceae,Herbaspirillum,seropedicae TOBG_RS-356,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_RS-357,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_RS-358,found,Archaea,Euryarcheoata,,,,, TOBG_RS-359,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_C,,, TOBG_RS-360,found,Bacteria,Chloroflexi,SAR202-1,,,, TOBG_RS-361,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-362,found,Bacteria,Chloroflexi,novelClass_A,,,, TOBG_RS-363,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_RS-364,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,,, TOBG_RS-365,found,Bacteria,Marinimicrobia,,,,, TOBG_RS-366,found,Archaea,,,,,, TOBG_RS-367,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Citromicrobium, TOBG_RS-368,nomatch,,,,,,, TOBG_RS-369,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_RS-370,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_B,, TOBG_RS-371,nomatch,,,,,,, TOBG_RS-372,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_RS-373,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_RS-374,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_E,, TOBG_RS-375,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_RS-376,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Pedosphaera,novelGenus_A, TOBG_RS-377,nomatch,,,,,,, TOBG_RS-378,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-379,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_RS-380,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_RS-381,found,Bacteria,Chloroflexi,novelClass_F,,,, TOBG_RS-382,nomatch,,,,,,, TOBG_RS-383,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_I, TOBG_RS-384,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_RS-385,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_C,,, TOBG_RS-386,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-387,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_RS-388,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_RS-389,found,Bacteria,Marinimicrobia,,,,, TOBG_RS-390,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_RS-391,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_RS-392,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_RS-393,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_RS-394,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_RS-395,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_I, TOBG_RS-396,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_RS-397,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_RS-398,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_E, TOBG_RS-399,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_I, TOBG_RS-400,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_RS-401,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_RS-402,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_RS-403,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_RS-404,found,Bacteria,Actinobacteria,Actinobacteria,Micrococcales,novelFamily_A,, TOBG_RS-405,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_RS-406,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_RS-407,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_RS-408,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_RS-409,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_RS-410,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_A,,, TOBG_RS-411,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_RS-412,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_RS-413,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_RS-414,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_E,, TOBG_RS-415,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,,, TOBG_RS-416,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_RS-417,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,, TOBG_RS-418,found,Bacteria,Marinimicrobia,,,,, TOBG_RS-419,found,Bacteria,Marinimicrobia,,,,, TOBG_RS-420,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_RS-421,found,Bacteria,Marinimicrobia,,,,, TOBG_RS-422,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_RS-423,found,Bacteria,Poribacteria,,,,, TOBG_RS-424,nomatch,,,,,,, TOBG_RS-425,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-426,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_RS-427,nomatch,,,,,,, TOBG_RS-428,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_RS-429,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_RS-430,found,Bacteria,Marinimicrobia,,,,, TOBG_RS-431,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_RS-432,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_RS-433,nomatch,,,,,,, TOBG_RS-434,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-435,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_RS-436,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_RS-437,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_RS-438,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_RS-439,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_E, TOBG_RS-440,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_RS-441,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_RS-442,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_RS-443,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_RS-444,nomatch,,,,,,, TOBG_RS-445,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_B,, TOBG_RS-446,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_RS-447,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_RS-448,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_RS-449,found,Bacteria,Chloroflexi,novelClass_B,,,, TOBG_RS-450,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_RS-451,nomatch,,,,,,, TOBG_RS-452,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_E,, TOBG_RS-453,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_RS-454,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_RS-455,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_RS-456,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_B,, TOBG_RS-457,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-458,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_RS-459,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_RS-460,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_RS-461,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_RS-462,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_RS-463,nomatch,,,,,,, TOBG_RS-464,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_B,, TOBG_RS-465,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_RS-466,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_RS-467,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_RS-468,nomatch,,,,,,, TOBG_RS-469,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_RS-470,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_RS-471,nomatch,,,,,,, TOBG_RS-472,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_RS-473,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_RS-474,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_RS-475,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_RS-476,nomatch,,,,,,, TOBG_RS-481,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_RS-495,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_RS-508,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_RS-510,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_RS-533,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_RS-535,found,Bacteria,Chloroflexi,novelClass_F,,,, TOBG_RS-551,nomatch,,,,,,, TOBG_RS-562,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_RS-570,nomatch,,,,,,, TOBG_RS-582,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_RS-590,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_RS-604,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_RS-605,nomatch,,,,,,, TOBG_RS-615,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,,, TOBG_RS-626,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,nove_genusE8, TOBG_RS-648,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_D,,, TOBG_RS-652,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_RS-654,found,Bacteria,Chloroflexi,SAR202-1,,,, TOBG_RS-659,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_RS-665,disagree,Bacteria,,,,,, TOBG_RS-668,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_RS-678,found,Archaea,Heimdallarchaeota,,,,, TOBG_RS-697,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-711,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_RS-723,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_F,,, TOBG_RS-726,nomatch,,,,,,, TOBG_RS-789,found,Bacteria,Marinimicrobia,,,,, TOBG_RS-797,nomatch,,,,,,, TOBG_RS-799,nomatch,,,,,,, TOBG_RS-813,nomatch,,,,,,, TOBG_RS-814,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_RS-815,found,Bacteria,,,,,, TOBG_RS-816,found,Bacteria,Marinimicrobia,,,,, TOBG_RS-817,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_E, TOBG_RS-818,disagree,Bacteria,,,,,, TOBG_RS-819,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_RS-820,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_RS-821,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_RS-822,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_RS-823,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE6, TOBG_RS-824,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_RS-825,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Pedosphaera,OPB35soil, TOBG_RS-826,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_SAT-1,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_SAT-10,found,Bacteria,Poribacteria,,,,, TOBG_SAT-100,nomatch,,,,,,, TOBG_SAT-101,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,Leeuwenhoekiella, TOBG_SAT-102,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_SAT-103,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SAT-104,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Xanthomarina, TOBG_SAT-105,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SAT-106,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Aequorivita, TOBG_SAT-107,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_H, TOBG_SAT-108,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Confluentimicrobium, TOBG_SAT-109,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_SAT-11,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_SAT-110,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Alcaligenaceae,Pusillimonas, TOBG_SAT-111,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Halomonas, TOBG_SAT-112,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,, TOBG_SAT-113,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Cobetia, TOBG_SAT-114,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Maribacter, TOBG_SAT-115,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hirschia, TOBG_SAT-116,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_SAT-117,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_SAT-118,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_SAT-119,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_SAT-12,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_G, TOBG_SAT-120,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_F, TOBG_SAT-121,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_SAT-122,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_SAT-123,nomatch,,,,,,, TOBG_SAT-124,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_A,,, TOBG_SAT-125,nomatch,,,,,,, TOBG_SAT-126,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-127,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SAT-128,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_SAT-1289,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SAT-129,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Flavobacterium, TOBG_SAT-1290,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-13,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SAT-130,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Flavobacterium, TOBG_SAT-1300,nomatch,,,,,,, TOBG_SAT-1301,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_C, TOBG_SAT-1302,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_C, TOBG_SAT-1308,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassopira, TOBG_SAT-1309,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Henriciella, TOBG_SAT-131,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-132,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SAT-1328,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-1329,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_SAT-133,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SAT-1331,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_SAT-1332,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_SAT-1333,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_E, TOBG_SAT-1339,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_SAT-134,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,idiomarina, TOBG_SAT-1340,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_SAT-1342,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SAT-1344,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SAT-1345,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_SAT-1346,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_SAT-1347,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_L,, TOBG_SAT-135,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_B,, TOBG_SAT-1356,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_SAT-1358,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SAT-1359,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SAT-136,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_SAT-1366,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_SAT-1367,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_A,,, TOBG_SAT-1369,found,Bacteria,,,,,, TOBG_SAT-137,found,Archaea,Thaumarchaeota,,,,, TOBG_SAT-138,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-1383,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Citromicrobium, TOBG_SAT-1384,found,Bacteria,,,,,, TOBG_SAT-1386,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Oceanibulbus, TOBG_SAT-1387,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_SAT-1388,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_B,, TOBG_SAT-139,nomatch,,,,,,, TOBG_SAT-1390,nomatch,,,,,,, TOBG_SAT-1391,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_SAT-14,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_G, TOBG_SAT-140,nomatch,,,,,,, TOBG_SAT-1401,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_SAT-1403,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SAT-1404,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_SAT-1405,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SAT-141,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SAT-1415,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_SAT-1416,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_SAT-142,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_SAT-1429,nomatch,,,,,,, TOBG_SAT-143,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_SAT-1430,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SAT-1431,found,Bacteria,novelPhylum_C,,,,, TOBG_SAT-1433,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SAT-1434,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-144,found,Bacteria,Proteobacteria,Nitrospinae,,,, TOBG_SAT-1446,found,Bacteria,novelPhylum_C,,,,, TOBG_SAT-1448,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SAT-1449,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-145,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_SAT-1450,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-1451,found,Bacteria,Poribacteria,,,,, TOBG_SAT-146,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Citromicrobium, TOBG_SAT-1460,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_SAT-1461,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-1462,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Pseudoalteromonadaceae,, TOBG_SAT-1464,found,Bacteria,Acidobacteria,novelClass_A,,,, TOBG_SAT-147,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Rhodopirellula, TOBG_SAT-1475,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_SAT-1476,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_SAT-148,nomatch,,,,,,, TOBG_SAT-1485,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SAT-1487,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_SAT-1488,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_B, TOBG_SAT-1489,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_SAT-149,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,, TOBG_SAT-15,found,Bacteria,Chloroflexi,SAR202-1,,,, TOBG_SAT-150,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_B,, TOBG_SAT-1504,found,Archaea,Euryarcheoata,,,,, TOBG_SAT-1505,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SAT-1507,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-1508,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_A,, TOBG_SAT-1509,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,, TOBG_SAT-151,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-1510,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_SAT-1517,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SAT-152,nomatch,,,,,,, TOBG_SAT-153,found,Bacteria,Bacteroidetes,Saprospiria,Saprospirales,,, TOBG_SAT-1530,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_A, TOBG_SAT-154,nomatch,,,,,,, TOBG_SAT-155,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,Opitutaceae,, TOBG_SAT-1551,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_SAT-1553,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_SAT-1554,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_SAT-156,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_SAT-1560,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_SAT-1561,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_SAT-1567,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_SAT-1568,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_SAT-157,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_E,, TOBG_SAT-1576,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_SAT-1578,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SAT-1579,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SAT-158,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_SAT-1581,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SAT-1582,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_SAT-1584,found,Bacteria,SBR1093,,,,, TOBG_SAT-1585,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_A, TOBG_SAT-159,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SAT-16,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Bdellovibrionaceae,, TOBG_SAT-160,nomatch,,,,,,, TOBG_SAT-1607,found,Bacteria,SBR1093,,,,, TOBG_SAT-1608,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SAT-1609,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-161,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_SAT-1610,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_C, TOBG_SAT-1611,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_SAT-1613,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_SAT-1614,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SAT-162,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_SAT-163,nomatch,,,,,,, TOBG_SAT-164,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_SAT-165,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_SAT-166,nomatch,,,,,,, TOBG_SAT-167,nomatch,,,,,,, TOBG_SAT-168,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_SAT-169,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-17,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_F,,, TOBG_SAT-170,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-171,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_C,,, TOBG_SAT-172,disagree,Bacteria,Verrucomicrobia,Verrucomicrobiae,,,, TOBG_SAT-173,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Winogradskyella, TOBG_SAT-174,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius, TOBG_SAT-175,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SAT-176,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-177,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-178,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_SAT-179,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_SAT-18,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SAT-180,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_SAT-181,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SAT-182,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SAT-183,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Morazellaceae,Acinetobacter, TOBG_SAT-184,found,Bacteria,Cyanobacteria,Chroococcales,unassigned,unassigned,CaAtelocyanobacterium, TOBG_SAT-185,found,Bacteria,Proteobacteria,Zetaproteobacteria,,,, TOBG_SAT-186,nomatch,,,,,,, TOBG_SAT-187,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassopira, TOBG_SAT-188,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_SAT-189,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SAT-19,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Muricauda, TOBG_SAT-190,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_SAT-191,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SAT-192,nomatch,,,,,,, TOBG_SAT-193,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-194,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis, TOBG_SAT-195,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Bdellovibrionaceae,, TOBG_SAT-196,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,Ilumatobacter, TOBG_SAT-197,found,Bacteria,Kiritimatiellaeota,Kiritimatiellae,Kiritimatiellales,Kiritimatiellaceae,, TOBG_SAT-198,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Confluentimicrobium, TOBG_SAT-199,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_SAT-2,nomatch,,,,,,, TOBG_SAT-20,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Alcaligenaceae,Pusillimonas, TOBG_SAT-200,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SAT-201,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SAT-202,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_SAT-203,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SAT-204,nomatch,,,,,,, TOBG_SAT-205,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SAT-206,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SAT-207,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_SAT-208,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SAT-209,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_SAT-21,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_SAT-210,nomatch,,,,,,, TOBG_SAT-211,found,Bacteria,SBR1093,,,,, TOBG_SAT-212,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_SAT-213,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SAT-214,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-215,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SAT-216,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Sphingobium, TOBG_SAT-217,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,Oleispira,antarctica TOBG_SAT-218,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_SAT-219,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_SAT-22,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Sphingomonas, TOBG_SAT-220,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SAT-221,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SAT-222,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-223,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_SAT-224,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Marinovum, TOBG_SAT-225,nomatch,,,,,,, TOBG_SAT-226,nomatch,,,,,,, TOBG_SAT-227,found,Bacteria,Proteobacteria,Alphaproteobacteria,Magnetococcales,,, TOBG_SAT-228,found,Bacteria,Chloroflexi,novelClass_F,,,, TOBG_SAT-229,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_SAT-23,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Maricaulis, TOBG_SAT-230,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Cellvibrionaceae,, TOBG_SAT-24,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-25,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_A,novelSpecies_A TOBG_SAT-2587,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Kangiellaceae,, TOBG_SAT-2594,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Micavibrio, TOBG_SAT-2595,nomatch,,,,,,, TOBG_SAT-26,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,Porticoccus,hydrocarbonoclasticus TOBG_SAT-2604,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_SAT-2610,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_SAT-2619,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-2654,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_SAT-2681,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_J,, TOBG_SAT-2699,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SAT-27,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Phyllobacteriaceae,Hoeflea, TOBG_SAT-2701,nomatch,,,,,,, TOBG_SAT-2727,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_SAT-2731,nomatch,,,,,,, TOBG_SAT-2734,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SAT-2750,nomatch,,,,,,, TOBG_SAT-2751,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-2755,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_SAT-2756,nomatch,,,,,,, TOBG_SAT-2766,found,Bacteria,Bacteroidetes,Bacteroidia,Bacteroidales,Lentimicrobiaceae,, TOBG_SAT-2781,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_SAT-2787,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SAT-2788,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_SAT-2797,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_SAT-28,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassopira, TOBG_SAT-2804,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_SAT-2814,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-2823,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_D, TOBG_SAT-2856,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_SAT-2866,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassopira, TOBG_SAT-29,nomatch,,,,,,, TOBG_SAT-2910,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SAT-2911,found,Bacteria,Deinococcus-Thermus,Deinococci,Deinococcales,Trueperaceae,, TOBG_SAT-2956,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-2963,nomatch,,,,,,, TOBG_SAT-2971,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SAT-2981,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,, TOBG_SAT-2985,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SAT-2989,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_SAT-2990,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_SAT-2998,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SAT-3,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SAT-30,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_B,, TOBG_SAT-3008,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_SAT-3021,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SAT-3034,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_A,, TOBG_SAT-3048,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_SAT-3076,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SAT-3078,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_SAT-31,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,Haliea,salexigens TOBG_SAT-32,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_SAT-33,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Micavibrio, TOBG_SAT-34,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE4, TOBG_SAT-35,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SAT-36,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SAT-37,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Rhodovulum, TOBG_SAT-38,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SAT-39,nomatch,,,,,,, TOBG_SAT-4,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius, TOBG_SAT-40,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Spongiibacteraceae,spongiibacter, TOBG_SAT-41,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SAT-42,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_SAT-43,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_D,, TOBG_SAT-44,nomatch,,,,,,, TOBG_SAT-45,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SAT-46,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_SAT-47,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_SAT-48,nomatch,,,,,,, TOBG_SAT-49,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_B,novelSpecies_A TOBG_SAT-5,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Aurantimonadaceae,Aurantimonas, TOBG_SAT-50,nomatch,,,,,,, TOBG_SAT-51,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,, TOBG_SAT-52,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Nocardioidaceae,Nocardioides, TOBG_SAT-53,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Phyllobacteriaceae,Hoeflea, TOBG_SAT-54,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Pseudooceanicola, TOBG_SAT-55,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Ahrensia, TOBG_SAT-56,found,Bacteria,Planctomycetes,Phycisphaerae,SM1A02,,, TOBG_SAT-57,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_SAT-58,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Halomonas, TOBG_SAT-59,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_B,novelSpecies_B TOBG_SAT-6,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,Methylophaga, TOBG_SAT-60,found,Bacteria,Proteobacteria,Gammaproteobacteria,Nevskiales,Sinobacteraceae,Polycyclovorans, TOBG_SAT-61,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Spongiibacteraceae,, TOBG_SAT-62,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Sutterellaceae,, TOBG_SAT-63,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_SAT-64,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Halomonas, TOBG_SAT-65,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SAT-66,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sneathiellales,Sneathiellaceae,Sneathiella, TOBG_SAT-67,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_A,novelSpecies_A TOBG_SAT-68,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,novel_genusE5, TOBG_SAT-69,nomatch,,,,,,, TOBG_SAT-7,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,Citromicrobium, TOBG_SAT-70,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_SAT-71,found,Archaea,Thaumarchaeota,,,,, TOBG_SAT-72,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_C,,, TOBG_SAT-73,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,,, TOBG_SAT-74,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_SAT-75,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_SAT-76,found,Bacteria,Marinimicrobia,,,,, TOBG_SAT-77,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SAT-78,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_L,, TOBG_SAT-79,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_SAT-8,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseobacter, TOBG_SAT-80,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,nove_genusE8, TOBG_SAT-81,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_E,, TOBG_SAT-82,found,Bacteria,Cyanobacteria,unassigned,Synechococcales,Synechococcaceae,Synechococcus, TOBG_SAT-83,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SAT-84,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_SAT-85,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Sutterellaceae,, TOBG_SAT-86,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Aequorivita, TOBG_SAT-87,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Gimesia, TOBG_SAT-88,found,Bacteria,proteobacteria,Gammaproteobacteria,Pseudomonadales,pseudomonadaceae,Pseudomonas,Xanthomarina TOBG_SAT-89,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-9,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-90,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Dinoroseobacter, TOBG_SAT-91,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_SAT-92,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,Methylophaga, TOBG_SAT-93,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,, TOBG_SAT-94,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SAT-95,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_SAT-96,nomatch,,,,,,, TOBG_SAT-97,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_SAT-98,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Aestuariibacter,aggregatus TOBG_SAT-99,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-1,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,, TOBG_SP-10,found,Bacteria,Planctomycetes,Phycisphaerae,Phycisphaerales,Phycisphaeraceae,novelGenus_A, TOBG_SP-100,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_L,, TOBG_SP-101,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SP-102,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-103,nomatch,,,,,,, TOBG_SP-104,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,,, TOBG_SP-105,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_B, TOBG_SP-106,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SP-107,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-108,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-109,found,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfovibrionales,Desulfovibrionaceae,Desulfovibrio, TOBG_SP-11,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Muricauda,lutaonensis TOBG_SP-110,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_SP-111,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_SP-112,found,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Rhodothermaceae,, TOBG_SP-113,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_E, TOBG_SP-114,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_SP-115,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_SP-116,found,Bacteria,Actinobacteria,novelClass_A,,,, TOBG_SP-117,found,Archaea,Euryarcheoata,,,,, TOBG_SP-118,nomatch,,,,,,, TOBG_SP-119,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,Fabibacter, TOBG_SP-12,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Pedosphaera,novelGenus_A, TOBG_SP-120,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_SP-121,found,Bacteria,Planctomycetes,Phycisphaerae,SM1A02,,, TOBG_SP-122,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_SP-123,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-124,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_SP-125,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_SP-126,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_SP-127,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_SP-128,found,Bacteria,Planctomycetes,Phycisphaerae,SM1A02,,, TOBG_SP-129,found,Bacteria,Proteobacteria,Gammaproteobacteria,Nevskiales,,, TOBG_SP-13,found,Bacteria,Chlamydiae,Chlamydiae,Chlamydiales,Waddliaceae,, TOBG_SP-130,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Leifsonia, TOBG_SP-131,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassopira, TOBG_SP-132,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_SP-133,found,Bacteria,proteobacteria,Gammaproteobacteria,Pseudomonadales,pseudomonadaceae,Pseudomonas,balearica TOBG_SP-134,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_SP-135,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_SP-136,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-137,found,Bacteria,Planctomycetes,Phycisphaerae,Urania1B19,,, TOBG_SP-138,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_SP-139,found,Archaea,Thaumarchaeota,,,,, TOBG_SP-14,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-140,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hirschia, TOBG_SP-141,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_E, TOBG_SP-142,found,Bacteria,Poribacteria,,,,, TOBG_SP-143,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SP-144,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_SP-145,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_SP-146,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Formosa, TOBG_SP-147,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_SP-148,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_SP-149,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Winogradskyella, TOBG_SP-15,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,0319-6G20,, TOBG_SP-150,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_A,,, TOBG_SP-151,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SP-152,found,Bacteria,,,,,, TOBG_SP-153,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_SP-154,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-155,nomatch,,,,,,, TOBG_SP-156,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,unassigned,Geminicoccus, TOBG_SP-157,found,Bacteria,Bacteroidetes,Saprospiria,Saprospirales,,, TOBG_SP-158,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Pseudooceanicola, TOBG_SP-159,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseobacter, TOBG_SP-16,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Tistrella, TOBG_SP-160,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SP-161,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Thalassobius,gelatinovorus TOBG_SP-162,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_D,, TOBG_SP-163,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_SP-164,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_SP-165,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_D,,, TOBG_SP-166,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_G, TOBG_SP-167,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,Leeuwenhoekiella, TOBG_SP-168,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Phyllobacteriaceae,Hoeflea, TOBG_SP-169,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Paracoccus, TOBG_SP-17,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Phyllobacteriaceae,, TOBG_SP-170,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_SP-171,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-172,found,Archaea,Euryarcheoata,,,,, TOBG_SP-173,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-174,found,Bacteria,novelPhylum_C,,,,, TOBG_SP-175,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_SP-176,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_D, TOBG_SP-177,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_SP-178,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_SP-179,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Hyphomicrobiaceae,Pelagibacterium,halotolerans TOBG_SP-18,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-180,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,Haliea,salexigens TOBG_SP-181,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Spongiibacteraceae,, TOBG_SP-182,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_SP-183,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-184,found,Bacteria,Planctomycetes,Phycisphaerae,SM1A02,,, TOBG_SP-185,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_SP-186,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Ponticaulis, TOBG_SP-187,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-188,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_SP-189,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SP-19,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-190,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_E, TOBG_SP-191,nomatch,,,,,,, TOBG_SP-192,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_SP-193,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,nove_genusE8, TOBG_SP-194,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_SP-195,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SP-196,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SP-197,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-198,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,novel_familyE2,, TOBG_SP-199,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_SP-2,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,Methylophaga, TOBG_SP-20,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_SP-200,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_I, TOBG_SP-201,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-202,found,Bacteria,Planctomycetes,Phycisphaerae,novelOrder_D,FS140-16B-02marinegroup,, TOBG_SP-203,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_B,, TOBG_SP-204,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_B,, TOBG_SP-205,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_SP-206,nomatch,,,,,,, TOBG_SP-207,nomatch,,,,,,, TOBG_SP-208,nomatch,,,,,,, TOBG_SP-209,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_SP-21,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,0319-6G20,, TOBG_SP-210,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_L,, TOBG_SP-211,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,Opitutaceae,, TOBG_SP-212,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_E,, TOBG_SP-213,found,Bacteria,Acidobacteria,novelClass_A,,,, TOBG_SP-214,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_SP-215,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_SP-216,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_SP-217,nomatch,,,,,,, TOBG_SP-218,nomatch,,,,,,, TOBG_SP-219,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SP-22,found,Bacteria,Epsilonbacteraeota,,,,, TOBG_SP-220,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_SP-221,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_SP-222,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_SP-223,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SP-224,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_C,,, TOBG_SP-225,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-226,found,Bacteria,Actinobacteria,Actinobacteria,Micrococcales,novelFamily_A,, TOBG_SP-227,nomatch,,,,,,, TOBG_SP-228,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SP-229,disagree,Bacteria,,,,,, TOBG_SP-23,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_E,, TOBG_SP-230,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SP-231,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_SP-232,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_SP-233,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_SP-234,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,novel_familyE2,, TOBG_SP-235,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,marinegroup, TOBG_SP-236,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Oceanibulbus, TOBG_SP-237,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_SP-238,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,novel_genusE2, TOBG_SP-239,found,Bacteria,Proteobacteria,Alphaproteobacteria,unclassifiedAlphaproteobacteria,SAR116cluster,, TOBG_SP-24,nomatch,,,,,,, TOBG_SP-240,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE6, TOBG_SP-241,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_SP-242,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_SP-243,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-244,disagree,Bacteria,Proteobacteria,,,,, TOBG_SP-245,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Stappia, TOBG_SP-246,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_SP-247,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Winogradskyella, TOBG_SP-248,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_SP-249,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_C,, TOBG_SP-25,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_A,,, TOBG_SP-250,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_E,, TOBG_SP-251,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Bdellovibrionaceae,, TOBG_SP-252,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Chromatiaceae,Rheinheimera, TOBG_SP-253,found,Bacteria,Chloroflexi,SAR202-1,,,, TOBG_SP-254,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-255,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_SP-256,nomatch,,,,,,, TOBG_SP-257,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_SP-258,found,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Spirochaetaceae,Spirochaeta, TOBG_SP-259,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,, TOBG_SP-26,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_SP-260,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_G, TOBG_SP-261,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-262,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SP-263,found,Bacteria,Proteobacteria,Betaproteobacteria,Nitrosomonadales,Nitrosomonadaceae,, TOBG_SP-264,disagree,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-265,found,Bacteria,Proteobacteria,Nitrospinae,Nitrospinales,Nitrospinaceae,Nitrospina, TOBG_SP-266,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_SP-267,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SP-268,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Aestuariibacter,aggregatus TOBG_SP-269,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_SP-27,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_SP-270,nomatch,,,,,,, TOBG_SP-271,nomatch,,,,,,, TOBG_SP-272,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_E,, TOBG_SP-273,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_E,,, TOBG_SP-274,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_SP-275,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-276,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-277,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_SP-278,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Mycobacteriaceae,Mycobacterium, TOBG_SP-279,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-28,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-280,nomatch,,,,,,, TOBG_SP-281,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SP-282,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_SP-283,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_E, TOBG_SP-284,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_SP-285,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-286,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-287,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-288,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,Cobetia, TOBG_SP-289,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SP-29,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_SP-290,nomatch,,,,,,, TOBG_SP-291,found,Bacteria,proteobacteria,Gammaproteobacteria,Pseudomonadales,pseudomonadaceae,Pseudomonas,Xanthomarina TOBG_SP-292,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_SP-293,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE3, TOBG_SP-294,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_C,,, TOBG_SP-295,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_SP-296,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_SP-2968,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,novelOrder_A,novelFamily_A,, TOBG_SP-297,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_C,, TOBG_SP-2970,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_SP-2971,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_SP-2972,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_SP-2974,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-2975,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,nove_genusE8, TOBG_SP-2976,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_E,, TOBG_SP-2977,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_SP-2978,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,Woeseia, TOBG_SP-2979,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SP-298,nomatch,,,,,,, TOBG_SP-2981,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_SP-2982,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-2983,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-2985,found,Bacteria,Planctomycetes,OM190,,,, TOBG_SP-2986,found,Bacteria,Proteobacteria,Nitrospinae,,,, TOBG_SP-2987,nomatch,,,,,,, TOBG_SP-2989,nomatch,,,,,,, TOBG_SP-299,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_SP-2992,nomatch,,,,,,, TOBG_SP-2993,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,Sandracinaceae,Sandaracinus, TOBG_SP-2995,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,marinegroup, TOBG_SP-2997,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_SP-2998,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_SP-2999,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,Halobacteriovorax, TOBG_SP-3,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-30,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_B,, TOBG_SP-300,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius, TOBG_SP-3001,found,Bacteria,Planctomycetes,Pla3,,,, TOBG_SP-3003,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_SP-3004,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_D, TOBG_SP-3005,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_SP-3006,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SP-3007,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_SP-3009,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-301,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SP-3010,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_SP-3012,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_SP-3013,found,Bacteria,Chloroflexi,novelClass_F,,,, TOBG_SP-3014,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_G,, TOBG_SP-3016,nomatch,,,,,,, TOBG_SP-3017,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-3018,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Cyclobacteriaceae,Algoriphagus, TOBG_SP-302,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Halieaceae,Haliea,salexigens TOBG_SP-3020,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_SP-3021,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,Altererythrobacter, TOBG_SP-3022,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_SP-3023,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-3024,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_A,, TOBG_SP-3025,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_A,, TOBG_SP-3027,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_L,, TOBG_SP-3028,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Formosa, TOBG_SP-3029,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SP-303,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SP-3030,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Mesonia, TOBG_SP-3031,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Henriciella, TOBG_SP-3034,found,Bacteria,Cyanobacteria,unassigned,Prochlorales,Prochlorococcaceae,Prochlorococcus, TOBG_SP-3035,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_G, TOBG_SP-3036,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_SP-3037,nomatch,,,,,,, TOBG_SP-3038,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_L,, TOBG_SP-304,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SP-3040,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_SP-3041,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SP-3042,nomatch,,,,,,, TOBG_SP-3044,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SP-3045,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SP-3046,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,nove_genusE8, TOBG_SP-3048,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-3049,found,Bacteria,Saccharibacteria,,,,, TOBG_SP-305,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SP-3050,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-3051,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_SP-3053,found,Bacteria,Proteobacteria,novelClass_B,,,, TOBG_SP-3054,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SP-3055,found,Bacteria,Proteobacteria,novelClass_A,,,, TOBG_SP-3056,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_SP-3058,nomatch,,,,,,, TOBG_SP-3059,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_A,, TOBG_SP-306,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_SP-3060,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-3061,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_D, TOBG_SP-3062,nomatch,,,,,,, TOBG_SP-3063,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_SP-3065,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_D, TOBG_SP-3066,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SP-3067,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_SP-3068,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_SP-3069,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-307,nomatch,,,,,,, TOBG_SP-3070,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SP-3072,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,Leeuwenhoekiella, TOBG_SP-3073,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_B,, TOBG_SP-3074,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_SP-3075,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_SP-3078,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_E, TOBG_SP-308,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-3081,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_F, TOBG_SP-3082,found,Bacteria,Planctomycetes,OM190,,,, TOBG_SP-3083,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_H, TOBG_SP-3084,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_SP-3086,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_E,, TOBG_SP-3087,nomatch,,,,,,, TOBG_SP-3088,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_L,, TOBG_SP-3089,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_SP-309,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_SP-3091,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_SP-3092,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_SP-3093,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_SP-3094,found,Bacteria,Proteobacteria,Gammaproteobacteria,Salinisphaerales,Salinisphaeraceae,Salinisphaera, TOBG_SP-3096,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_F,,, TOBG_SP-3097,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-3098,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_SP-31,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_D,, TOBG_SP-310,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-3100,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_SP-3101,nomatch,,,,,,, TOBG_SP-3102,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_C,,, TOBG_SP-3103,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-3105,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-3106,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_SP-3107,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_SP-3109,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SP-311,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_SP-3110,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Maricaulis, TOBG_SP-3111,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-3112,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,Legionellaceae,, TOBG_SP-3114,nomatch,,,,,,, TOBG_SP-3115,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I,, TOBG_SP-3117,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-3118,found,Bacteria,Planctomycetes,Phycisphaerae,JL-ENTP-F27,,, TOBG_SP-3119,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-312,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Spongiibacteraceae,spongiibacter, TOBG_SP-313,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-314,nomatch,,,,,,, TOBG_SP-315,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-316,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SP-317,found,Bacteria,novelPhylum_C,,,,, TOBG_SP-318,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SP-319,found,Bacteria,Chloroflexi,SAR202-1,,,, TOBG_SP-32,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_SP-320,found,Bacteria,Chloroflexi,SAR202-1,,,, TOBG_SP-321,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SP-322,disagree,,,,,,, TOBG_SP-323,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_C, TOBG_SP-324,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_E,, TOBG_SP-325,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_A,,, TOBG_SP-326,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_C, TOBG_SP-327,found,Bacteria,Chloroflexi,novelClass_F,,,, TOBG_SP-328,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-329,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-33,disagree,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-330,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_E,, TOBG_SP-331,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-332,found,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Spirochaetaceae,novelGenus_A, TOBG_SP-333,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_SP-334,nomatch,,,,,,, TOBG_SP-335,nomatch,,,,,,, TOBG_SP-336,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SP-337,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_SP-338,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_SP-339,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_E,, TOBG_SP-34,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_SP-340,found,Bacteria,Proteobacteria,Gammaproteobacteria,Acidiferrobacterales,Acidiferrobacteraceae,, TOBG_SP-341,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-342,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SP-343,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SP-344,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SP-345,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_SP-346,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_SP-347,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Sutterellaceae,, TOBG_SP-348,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_A, TOBG_SP-349,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SP-35,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_SP-350,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,idiomarina, TOBG_SP-351,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SP-352,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SP-353,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-354,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-355,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-356,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,,,, TOBG_SP-357,found,Bacteria,SBR1093,,,,, TOBG_SP-358,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Magnetovibrio, TOBG_SP-359,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-36,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Marinobacter, TOBG_SP-360,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-361,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_SP-362,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-37,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_SP-38,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_SP-39,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_C,, TOBG_SP-3966,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-3971,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-3980,nomatch,,,,,,, TOBG_SP-3982,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-3983,disagree,Bacteria,,,,,, TOBG_SP-3984,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SP-3985,nomatch,,,,,,, TOBG_SP-3992,found,Archaea,Thaumarchaeota,novelClass_A,,,, TOBG_SP-4,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,novelGenus_G, TOBG_SP-40,nomatch,,,,,,, TOBG_SP-4016,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SP-4021,nomatch,,,,,,, TOBG_SP-4023,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,Opitutaceae,, TOBG_SP-4025,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SP-4027,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,novel_genusE10, TOBG_SP-4032,found,Bacteria,Verrucomicrobia,Opitutae,Puniceicoccales,Puniceicoccaceae,marinegroup, TOBG_SP-4038,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_D,,, TOBG_SP-4039,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-4044,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_A,,, TOBG_SP-4045,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_SP-4060,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_G, TOBG_SP-4073,nomatch,,,,,,, TOBG_SP-4095,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-4097,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,Halobacteriovorax, TOBG_SP-41,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-4114,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SP-4145,nomatch,,,,,,, TOBG_SP-4150,found,Bacteria,CandidatePhylaRadiation,,,,, TOBG_SP-4154,found,Bacteria,novelPhylum_C,,,,, TOBG_SP-4170,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-4189,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_D,,, TOBG_SP-42,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_F,, TOBG_SP-4206,nomatch,,,,,,, TOBG_SP-4215,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SP-4220,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_D,,, TOBG_SP-4222,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_A,,, TOBG_SP-4225,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-4236,found,Bacteria,Proteobacteria,Gammaproteobacteria,novelOrder_B,,, TOBG_SP-4246,nomatch,,,,,,, TOBG_SP-4248,found,Bacteria,Chloroflexi,novelClass_A,,,, TOBG_SP-4251,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-4258,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,novel_familyE2,, TOBG_SP-4260,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,, TOBG_SP-4269,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-4272,found,Bacteria,Gemmatimonadetes,ÂGemmatimonadetes,Gemmatimonadales,,, TOBG_SP-4275,nomatch,,,,,,, TOBG_SP-4288,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobiaceae,novel_genusE7, TOBG_SP-4291,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_SP-4294,nomatch,,,,,,, TOBG_SP-43,found,Bacteria,Proteobacteria,Gammaproteobacteria,Thiotrichales,Piscirickettsiaceae,Cycloclasticus, TOBG_SP-4300,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,Pelagibacter, TOBG_SP-4355,found,Bacteria,Chloroflexi,SAR202-2,novelOrder_B,,, TOBG_SP-4356,found,Bacteria,Chloroflexi,novelClass_F,,,, TOBG_SP-4360,found,Bacteria,Chloroflexi,SAR202-2,,,, TOBG_SP-4365,found,Bacteria,Planctomycetes,Phycisphaerae,novelOrder_D,,, TOBG_SP-4366,found,Bacteria,Planctomycetes,Phycisphaerae,novelOrder_D,novelFamily_A,, TOBG_SP-4369,nomatch,,,,,,, TOBG_SP-4372,nomatch,,,,,,, TOBG_SP-4374,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SP-4379,found,Bacteria,,,,,, TOBG_SP-4380,found,Bacteria,Acidobacteria,Solibacteres,Solibacterales,,, TOBG_SP-4387,found,Bacteria,Planctomycetes,Phycisphaerae,Phycisphaerales,Phycisphaeraceae,novelGenus_B, TOBG_SP-4388,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-44,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_SP-45,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cytophagaceae,Leeuwenhoekiella, TOBG_SP-46,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_D,, TOBG_SP-47,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SP-48,nomatch,,,,,,, TOBG_SP-486,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,Pelagibacteraceae,, TOBG_SP-49,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_B, TOBG_SP-5,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,novelFamily_D,, TOBG_SP-50,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae,novel_genusE9, TOBG_SP-51,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-52,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,unassigned,Roseibacillus, TOBG_SP-53,nomatch,,,,,,, TOBG_SP-54,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SP-55,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_SP-56,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,Woeseiaceae,, TOBG_SP-57,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseobacter, TOBG_SP-58,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Hyphomonas, TOBG_SP-59,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,Porticoccus,hydrocarbonoclasticus TOBG_SP-6,found,Bacteria,Proteobacteria,Gammaproteobacteria,novel_order_E1,,, TOBG_SP-60,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_SP-61,nomatch,,,,,,, TOBG_SP-62,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Croceibacter,atlanticus TOBG_SP-63,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Crenotrichaceae,Balneola, TOBG_SP-64,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_SP-65,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_A, TOBG_SP-66,nomatch,,,,,,, TOBG_SP-67,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_B,, TOBG_SP-68,found,Bacteria,Proteobacteria,Deltaproteobacteria,SAR324cluster,,, TOBG_SP-69,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_B,,, TOBG_SP-7,nomatch,,,,,,, TOBG_SP-70,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Pseudozobellia, TOBG_SP-71,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SP-72,found,Bacteria,Chloroflexi,novelClass_E,novelOrder_C,,, TOBG_SP-73,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis, TOBG_SP-74,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Cryomorphaceae,Owenweeksia, TOBG_SP-75,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,Aequorivita, TOBG_SP-76,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Flavobacteriaceae,novelGenus_B,novelSpecies_A TOBG_SP-77,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Phyllobacteriaceae,Hoeflea, TOBG_SP-78,found,Bacteria,SBR1093,,,,, TOBG_SP-79,nomatch,,,,,,, TOBG_SP-8,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-80,nomatch,,,,,,, TOBG_SP-81,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_SP-82,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,Crocinitomicaceae,, TOBG_SP-83,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_SP-84,found,Bacteria,Bacteria,,,,, TOBG_SP-85,found,Bacteria,Verrucomicrobia,Opitutae,novelOrder_B,,, TOBG_SP-86,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,, TOBG_SP-87,found,Bacteria,Proteobacteria,Deltaproteobacteria,Bdellovibrionales,Halobacteriovoraceae,, TOBG_SP-88,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_H,, TOBG_SP-89,found,Bacteria,Marinimicrobia,,,,, TOBG_SP-9,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,novel_familyE2,, TOBG_SP-90,found,Bacteria,Actinobacteria,Acidimicrobidae,Acidimicrobiales,Acidimicrobiaceae,novelGenus_C, TOBG_SP-91,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SP-92,found,Bacteria,Acidobacteria,unassigned,unassigned,novelFamily_A,, TOBG_SP-93,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,idiomarina, TOBG_SP-94,found,Bacteria,Proteobacteria,Alphaproteobacteria,Pelagibacterales,novel_familyE1,, TOBG_SP-95,found,Bacteria,Bacteroidetes,Flavobacteria,Flavobacteriales,novelFamily_F,, TOBG_SP-96,found,Bacteria,Planctomycetes,Phycisphaerae,novelOrder_D,FS140-16B-02marinegroup,, TOBG_SP-97,found,Bacteria,Bacteroidetes,Sphingobacteria,Sphingobacteriales,Flammeovirgaceae,novelGenus_E, TOBG_SP-98,found,Bacteria,Proteobacteria,Gammaproteobacteria,Cellvibrionales,Porticoccaceae,novel_genusE3, TOBG_SP-99,found,Bacteria,Marinimicrobia,,,,, sourmash-bio-sourmash-bc3c0f8/tests/test-data/lca/tully-query.delmont-db.sigs.classify.csv000066400000000000000000003650761474157046300321330ustar00rootroot00000000000000ID,status,superkingdom,phylum,class,order,family,genus,species TOBG_ARS-1,nomatch,,,,,,, TOBG_ARS-10,nomatch,,,,,,, TOBG_ARS-100,nomatch,,,,,,, TOBG_ARS-1004,found,Archaea,Euryarchaeota,,,,, TOBG_ARS-1005,nomatch,,,,,,, TOBG_ARS-1007,nomatch,,,,,,, TOBG_ARS-1008,nomatch,,,,,,, TOBG_ARS-1009,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_ARS-101,nomatch,,,,,,, TOBG_ARS-1011,nomatch,,,,,,, TOBG_ARS-1012,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_ARS-1013,nomatch,,,,,,, TOBG_ARS-1015,disagree,Bacteria,Proteobacteria,,,,, TOBG_ARS-1016,nomatch,,,,,,, TOBG_ARS-1018,nomatch,,,,,,, TOBG_ARS-1019,nomatch,,,,,,, TOBG_ARS-102,nomatch,,,,,,, TOBG_ARS-1020,nomatch,,,,,,, TOBG_ARS-1021,nomatch,,,,,,, TOBG_ARS-1023,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas, TOBG_ARS-1024,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_ARS-1025,nomatch,,,,,,, TOBG_ARS-1026,nomatch,,,,,,, TOBG_ARS-1028,nomatch,,,,,,, TOBG_ARS-1029,nomatch,,,,,,, TOBG_ARS-103,nomatch,,,,,,, TOBG_ARS-1030,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_ARS-1032,nomatch,,,,,,, TOBG_ARS-1033,nomatch,,,,,,, TOBG_ARS-1034,nomatch,,,,,,, TOBG_ARS-1035,nomatch,,,,,,, TOBG_ARS-1036,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_ARS-1037,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_ARS-1039,nomatch,,,,,,, TOBG_ARS-104,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_ARS-1040,nomatch,,,,,,, TOBG_ARS-1041,nomatch,,,,,,, TOBG_ARS-1043,nomatch,,,,,,, TOBG_ARS-105,nomatch,,,,,,, TOBG_ARS-106,nomatch,,,,,,, TOBG_ARS-108,nomatch,,,,,,, TOBG_ARS-109,nomatch,,,,,,, TOBG_ARS-11,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_ARS-112,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,,, TOBG_ARS-1126,nomatch,,,,,,, TOBG_ARS-113,nomatch,,,,,,, TOBG_ARS-1134,found,Archaea,Euryarchaeota,,,,, TOBG_ARS-115,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,Idiomarina, TOBG_ARS-116,nomatch,,,,,,, TOBG_ARS-1160,nomatch,,,,,,, TOBG_ARS-1168,nomatch,,,,,,, TOBG_ARS-117,nomatch,,,,,,, TOBG_ARS-1172,nomatch,,,,,,, TOBG_ARS-1176,nomatch,,,,,,, TOBG_ARS-1178,nomatch,,,,,,, TOBG_ARS-118,found,Archaea,Euryarchaeota,,,,, TOBG_ARS-1180,nomatch,,,,,,, TOBG_ARS-1182,nomatch,,,,,,, TOBG_ARS-1183,nomatch,,,,,,, TOBG_ARS-1193,nomatch,,,,,,, TOBG_ARS-1199,nomatch,,,,,,, TOBG_ARS-12,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_ARS-120,nomatch,,,,,,, TOBG_ARS-1203,nomatch,,,,,,, TOBG_ARS-121,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_ARS-1215,nomatch,,,,,,, TOBG_ARS-1224,nomatch,,,,,,, TOBG_ARS-123,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_ARS-124,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_ARS-1246,nomatch,,,,,,, TOBG_ARS-125,nomatch,,,,,,, TOBG_ARS-1279,nomatch,,,,,,, TOBG_ARS-1285,nomatch,,,,,,, TOBG_ARS-1286,nomatch,,,,,,, TOBG_ARS-1290,nomatch,,,,,,, TOBG_ARS-1297,nomatch,,,,,,, TOBG_ARS-1298,nomatch,,,,,,, TOBG_ARS-13,found,Bacteria,Proteobacteria,,,,, TOBG_ARS-1301,nomatch,,,,,,, TOBG_ARS-1304,nomatch,,,,,,, TOBG_ARS-1307,nomatch,,,,,,, TOBG_ARS-1309,nomatch,,,,,,, TOBG_ARS-1310,nomatch,,,,,,, TOBG_ARS-1318,nomatch,,,,,,, TOBG_ARS-1330,nomatch,,,,,,, TOBG_ARS-1333,nomatch,,,,,,, TOBG_ARS-1334,nomatch,,,,,,, TOBG_ARS-1358,nomatch,,,,,,, TOBG_ARS-1360,nomatch,,,,,,, TOBG_ARS-1386,nomatch,,,,,,, TOBG_ARS-1394,nomatch,,,,,,, TOBG_ARS-14,nomatch,,,,,,, TOBG_ARS-1403,nomatch,,,,,,, TOBG_ARS-1414,nomatch,,,,,,, TOBG_ARS-1419,nomatch,,,,,,, TOBG_ARS-1420,nomatch,,,,,,, TOBG_ARS-1426,nomatch,,,,,,, TOBG_ARS-1427,nomatch,,,,,,, TOBG_ARS-1434,nomatch,,,,,,, TOBG_ARS-1441,nomatch,,,,,,, TOBG_ARS-15,nomatch,,,,,,, TOBG_ARS-16,nomatch,,,,,,, TOBG_ARS-17,found,Bacteria,Acidobacteria,,,,, TOBG_ARS-18,nomatch,,,,,,, TOBG_ARS-19,nomatch,,,,,,, TOBG_ARS-2,nomatch,,,,,,, TOBG_ARS-20,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_ARS-21,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_ARS-22,nomatch,,,,,,, TOBG_ARS-23,nomatch,,,,,,, TOBG_ARS-24,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_ARS-25,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_ARS-26,nomatch,,,,,,, TOBG_ARS-27,nomatch,,,,,,, TOBG_ARS-28,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_ARS-29,nomatch,,,,,,, TOBG_ARS-3,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_ARS-30,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,,, TOBG_ARS-31,nomatch,,,,,,, TOBG_ARS-32,nomatch,,,,,,, TOBG_ARS-33,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_ARS-34,nomatch,,,,,,, TOBG_ARS-35,nomatch,,,,,,, TOBG_ARS-36,nomatch,,,,,,, TOBG_ARS-37,nomatch,,,,,,, TOBG_ARS-38,nomatch,,,,,,, TOBG_ARS-39,nomatch,,,,,,, TOBG_ARS-4,nomatch,,,,,,, TOBG_ARS-40,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_ARS-41,nomatch,,,,,,, TOBG_ARS-42,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_ARS-43,nomatch,,,,,,, TOBG_ARS-44,found,Bacteria,,,,,, TOBG_ARS-45,nomatch,,,,,,, TOBG_ARS-46,nomatch,,,,,,, TOBG_ARS-47,found,Archaea,Euryarchaeota,,,,, TOBG_ARS-48,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_ARS-49,nomatch,,,,,,, TOBG_ARS-5,disagree,Bacteria,Proteobacteria,,,,, TOBG_ARS-50,nomatch,,,,,,, TOBG_ARS-51,nomatch,,,,,,, TOBG_ARS-52,nomatch,,,,,,, TOBG_ARS-53,nomatch,,,,,,, TOBG_ARS-54,nomatch,,,,,,, TOBG_ARS-55,nomatch,,,,,,, TOBG_ARS-56,nomatch,,,,,,, TOBG_ARS-57,nomatch,,,,,,, TOBG_ARS-58,nomatch,,,,,,, TOBG_ARS-59,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_ARS-6,nomatch,,,,,,, TOBG_ARS-60,found,Archaea,Euryarchaeota,,,,, TOBG_ARS-61,nomatch,,,,,,, TOBG_ARS-62,nomatch,,,,,,, TOBG_ARS-63,nomatch,,,,,,, TOBG_ARS-64,nomatch,,,,,,, TOBG_ARS-65,nomatch,,,,,,, TOBG_ARS-66,nomatch,,,,,,, TOBG_ARS-67,nomatch,,,,,,, TOBG_ARS-68,nomatch,,,,,,, TOBG_ARS-69,nomatch,,,,,,, TOBG_ARS-7,nomatch,,,,,,, TOBG_ARS-70,nomatch,,,,,,, TOBG_ARS-71,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_ARS-72,nomatch,,,,,,, TOBG_ARS-73,nomatch,,,,,,, TOBG_ARS-733,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Pseudomonadaceae,Pseudomonas,Pseudomonas_mendocina TOBG_ARS-74,nomatch,,,,,,, TOBG_ARS-75,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_ARS-76,nomatch,,,,,,, TOBG_ARS-77,nomatch,,,,,,, TOBG_ARS-78,found,Archaea,Euryarchaeota,,,,, TOBG_ARS-79,nomatch,,,,,,, TOBG_ARS-8,nomatch,,,,,,, TOBG_ARS-80,nomatch,,,,,,, TOBG_ARS-81,nomatch,,,,,,, TOBG_ARS-82,nomatch,,,,,,, TOBG_ARS-83,nomatch,,,,,,, TOBG_ARS-84,nomatch,,,,,,, TOBG_ARS-85,nomatch,,,,,,, TOBG_ARS-86,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_ARS-87,nomatch,,,,,,, TOBG_ARS-88,nomatch,,,,,,, TOBG_ARS-89,nomatch,,,,,,, TOBG_ARS-9,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_ARS-90,nomatch,,,,,,, TOBG_ARS-91,found,Archaea,Euryarchaeota,,,,, TOBG_ARS-92,nomatch,,,,,,, TOBG_ARS-93,nomatch,,,,,,, TOBG_ARS-94,found,Bacteria,Proteobacteria,,,,, TOBG_ARS-95,nomatch,,,,,,, TOBG_ARS-96,nomatch,,,,,,, TOBG_ARS-97,nomatch,,,,,,, TOBG_ARS-98,nomatch,,,,,,, TOBG_ARS-99,found,Archaea,Euryarchaeota,,,,, TOBG_CPC-1,nomatch,,,,,,, TOBG_CPC-10,nomatch,,,,,,, TOBG_CPC-100,nomatch,,,,,,, TOBG_CPC-11,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_CPC-12,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,Haliea, TOBG_CPC-1232,nomatch,,,,,,, TOBG_CPC-13,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_CPC-1326,nomatch,,,,,,, TOBG_CPC-1328,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_CPC-1337,nomatch,,,,,,, TOBG_CPC-1357,nomatch,,,,,,, TOBG_CPC-1365,nomatch,,,,,,, TOBG_CPC-1369,nomatch,,,,,,, TOBG_CPC-1382,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_CPC-1397,nomatch,,,,,,, TOBG_CPC-1399,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_CPC-14,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,Idiomarina, TOBG_CPC-1446,nomatch,,,,,,, TOBG_CPC-1447,nomatch,,,,,,, TOBG_CPC-1462,nomatch,,,,,,, TOBG_CPC-1469,nomatch,,,,,,, TOBG_CPC-15,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas, TOBG_CPC-16,nomatch,,,,,,, TOBG_CPC-17,nomatch,,,,,,, TOBG_CPC-18,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_CPC-19,nomatch,,,,,,, TOBG_CPC-2,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_CPC-20,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_CPC-21,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,,, TOBG_CPC-22,nomatch,,,,,,, TOBG_CPC-23,nomatch,,,,,,, TOBG_CPC-24,nomatch,,,,,,, TOBG_CPC-25,nomatch,,,,,,, TOBG_CPC-26,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_CPC-27,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_CPC-28,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_CPC-288,nomatch,,,,,,, TOBG_CPC-289,nomatch,,,,,,, TOBG_CPC-29,nomatch,,,,,,, TOBG_CPC-290,nomatch,,,,,,, TOBG_CPC-291,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_CPC-293,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_CPC-294,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_CPC-295,found,Bacteria,Proteobacteria,,,,, TOBG_CPC-296,found,Bacteria,,,,,, TOBG_CPC-297,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_CPC-299,nomatch,,,,,,, TOBG_CPC-3,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_CPC-30,nomatch,,,,,,, TOBG_CPC-300,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_CPC-301,nomatch,,,,,,, TOBG_CPC-302,nomatch,,,,,,, TOBG_CPC-305,nomatch,,,,,,, TOBG_CPC-306,nomatch,,,,,,, TOBG_CPC-307,nomatch,,,,,,, TOBG_CPC-309,found,Bacteria,Proteobacteria,Epsilonproteobacteria,Campylobacterales,Campylobacteraceae,Arcobacter, TOBG_CPC-31,nomatch,,,,,,, TOBG_CPC-310,nomatch,,,,,,, TOBG_CPC-311,nomatch,,,,,,, TOBG_CPC-313,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_CPC-314,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas, TOBG_CPC-315,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_CPC-316,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_CPC-317,nomatch,,,,,,, TOBG_CPC-319,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_CPC-32,nomatch,,,,,,, TOBG_CPC-320,nomatch,,,,,,, TOBG_CPC-322,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_CPC-323,nomatch,,,,,,, TOBG_CPC-324,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_CPC-325,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_CPC-326,nomatch,,,,,,, TOBG_CPC-327,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_CPC-33,nomatch,,,,,,, TOBG_CPC-34,nomatch,,,,,,, TOBG_CPC-35,nomatch,,,,,,, TOBG_CPC-36,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_CPC-37,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas, TOBG_CPC-38,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobacteraceae,, TOBG_CPC-39,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,, TOBG_CPC-4,nomatch,,,,,,, TOBG_CPC-40,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_CPC-41,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_CPC-42,nomatch,,,,,,, TOBG_CPC-43,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassospira, TOBG_CPC-44,nomatch,,,,,,, TOBG_CPC-45,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_CPC-46,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis,Oceanicaulis_alexandrii TOBG_CPC-47,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_CPC-48,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_CPC-49,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_CPC-5,nomatch,,,,,,, TOBG_CPC-50,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_CPC-51,nomatch,,,,,,, TOBG_CPC-52,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_CPC-53,found,Bacteria,Proteobacteria,,,,, TOBG_CPC-54,nomatch,,,,,,, TOBG_CPC-55,nomatch,,,,,,, TOBG_CPC-56,nomatch,,,,,,, TOBG_CPC-57,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_CPC-58,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_CPC-59,nomatch,,,,,,, TOBG_CPC-6,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_CPC-60,nomatch,,,,,,, TOBG_CPC-61,found,Bacteria,Proteobacteria,,,,, TOBG_CPC-62,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Pseudomonadaceae,Pseudomonas, TOBG_CPC-63,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,, TOBG_CPC-64,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_CPC-65,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_CPC-66,nomatch,,,,,,, TOBG_CPC-67,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_CPC-68,nomatch,,,,,,, TOBG_CPC-69,nomatch,,,,,,, TOBG_CPC-7,nomatch,,,,,,, TOBG_CPC-70,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_CPC-71,found,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Flammeovirgaceae,, TOBG_CPC-72,nomatch,,,,,,, TOBG_CPC-73,nomatch,,,,,,, TOBG_CPC-74,nomatch,,,,,,, TOBG_CPC-75,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_CPC-76,nomatch,,,,,,, TOBG_CPC-77,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_CPC-78,disagree,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_CPC-79,found,Bacteria,Bacteroidetes,,,,, TOBG_CPC-8,nomatch,,,,,,, TOBG_CPC-80,nomatch,,,,,,, TOBG_CPC-81,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_CPC-82,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_CPC-83,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_CPC-84,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_CPC-85,nomatch,,,,,,, TOBG_CPC-86,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_CPC-87,nomatch,,,,,,, TOBG_CPC-88,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,,, TOBG_CPC-89,nomatch,,,,,,, TOBG_CPC-9,nomatch,,,,,,, TOBG_CPC-90,nomatch,,,,,,, TOBG_CPC-91,nomatch,,,,,,, TOBG_CPC-92,nomatch,,,,,,, TOBG_CPC-93,nomatch,,,,,,, TOBG_CPC-94,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TOBG_CPC-95,nomatch,,,,,,, TOBG_CPC-96,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_CPC-97,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_CPC-98,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_CPC-99,nomatch,,,,,,, TOBG_EAC-1,nomatch,,,,,,, TOBG_EAC-10,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_EAC-100,found,Archaea,Euryarchaeota,,,,, TOBG_EAC-101,nomatch,,,,,,, TOBG_EAC-102,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_EAC-103,nomatch,,,,,,, TOBG_EAC-104,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_EAC-105,nomatch,,,,,,, TOBG_EAC-106,found,Archaea,Euryarchaeota,,,,, TOBG_EAC-107,found,Archaea,Euryarchaeota,,,,, TOBG_EAC-108,nomatch,,,,,,, TOBG_EAC-109,nomatch,,,,,,, TOBG_EAC-11,nomatch,,,,,,, TOBG_EAC-110,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_EAC-111,nomatch,,,,,,, TOBG_EAC-112,nomatch,,,,,,, TOBG_EAC-113,found,Bacteria,Proteobacteria,,,,, TOBG_EAC-114,nomatch,,,,,,, TOBG_EAC-115,nomatch,,,,,,, TOBG_EAC-116,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae_3,Sphingobium, TOBG_EAC-117,nomatch,,,,,,, TOBG_EAC-118,nomatch,,,,,,, TOBG_EAC-119,nomatch,,,,,,, TOBG_EAC-12,nomatch,,,,,,, TOBG_EAC-120,nomatch,,,,,,, TOBG_EAC-121,nomatch,,,,,,, TOBG_EAC-122,nomatch,,,,,,, TOBG_EAC-123,nomatch,,,,,,, TOBG_EAC-13,nomatch,,,,,,, TOBG_EAC-14,nomatch,,,,,,, TOBG_EAC-15,nomatch,,,,,,, TOBG_EAC-16,nomatch,,,,,,, TOBG_EAC-17,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_EAC-1768,found,Archaea,Euryarchaeota,,,,, TOBG_EAC-1775,nomatch,,,,,,, TOBG_EAC-1785,nomatch,,,,,,, TOBG_EAC-1786,nomatch,,,,,,, TOBG_EAC-1795,nomatch,,,,,,, TOBG_EAC-18,nomatch,,,,,,, TOBG_EAC-19,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_EAC-1924,nomatch,,,,,,, TOBG_EAC-1938,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_EAC-1972,nomatch,,,,,,, TOBG_EAC-1995,disagree,Bacteria,Proteobacteria,,,,, TOBG_EAC-2,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_EAC-20,nomatch,,,,,,, TOBG_EAC-2003,nomatch,,,,,,, TOBG_EAC-2006,nomatch,,,,,,, TOBG_EAC-2011,nomatch,,,,,,, TOBG_EAC-2021,nomatch,,,,,,, TOBG_EAC-2025,nomatch,,,,,,, TOBG_EAC-2037,nomatch,,,,,,, TOBG_EAC-2038,nomatch,,,,,,, TOBG_EAC-2047,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_EAC-2074,nomatch,,,,,,, TOBG_EAC-2087,nomatch,,,,,,, TOBG_EAC-2090,nomatch,,,,,,, TOBG_EAC-2092,nomatch,,,,,,, TOBG_EAC-2094,nomatch,,,,,,, TOBG_EAC-21,nomatch,,,,,,, TOBG_EAC-2107,nomatch,,,,,,, TOBG_EAC-22,nomatch,,,,,,, TOBG_EAC-23,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_EAC-24,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_EAC-25,nomatch,,,,,,, TOBG_EAC-26,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_EAC-27,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_EAC-28,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_EAC-29,disagree,Bacteria,,,,,, TOBG_EAC-3,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_EAC-30,nomatch,,,,,,, TOBG_EAC-31,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_EAC-32,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_EAC-33,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_EAC-34,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_EAC-35,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_EAC-36,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_EAC-37,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_EAC-38,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_EAC-39,nomatch,,,,,,, TOBG_EAC-4,nomatch,,,,,,, TOBG_EAC-40,nomatch,,,,,,, TOBG_EAC-41,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_EAC-42,nomatch,,,,,,, TOBG_EAC-43,disagree,Bacteria,Bacteroidetes,,,,, TOBG_EAC-44,nomatch,,,,,,, TOBG_EAC-45,nomatch,,,,,,, TOBG_EAC-46,nomatch,,,,,,, TOBG_EAC-47,nomatch,,,,,,, TOBG_EAC-48,nomatch,,,,,,, TOBG_EAC-49,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis,Oceanicaulis_alexandrii TOBG_EAC-5,nomatch,,,,,,, TOBG_EAC-50,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_EAC-51,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_EAC-52,nomatch,,,,,,, TOBG_EAC-53,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,, TOBG_EAC-54,nomatch,,,,,,, TOBG_EAC-55,nomatch,,,,,,, TOBG_EAC-56,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius, TOBG_EAC-57,nomatch,,,,,,, TOBG_EAC-58,nomatch,,,,,,, TOBG_EAC-59,nomatch,,,,,,, TOBG_EAC-6,nomatch,,,,,,, TOBG_EAC-60,nomatch,,,,,,, TOBG_EAC-61,nomatch,,,,,,, TOBG_EAC-615,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TOBG_EAC-62,found,Bacteria,Bacteroidetes,,,,, TOBG_EAC-622,nomatch,,,,,,, TOBG_EAC-623,nomatch,,,,,,, TOBG_EAC-624,nomatch,,,,,,, TOBG_EAC-625,nomatch,,,,,,, TOBG_EAC-627,nomatch,,,,,,, TOBG_EAC-628,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_EAC-63,found,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Cyclobacteriaceae,Algoriphagus, TOBG_EAC-630,nomatch,,,,,,, TOBG_EAC-631,nomatch,,,,,,, TOBG_EAC-632,nomatch,,,,,,, TOBG_EAC-634,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,Citromicrobium, TOBG_EAC-635,nomatch,,,,,,, TOBG_EAC-636,nomatch,,,,,,, TOBG_EAC-637,nomatch,,,,,,, TOBG_EAC-638,nomatch,,,,,,, TOBG_EAC-64,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_EAC-640,nomatch,,,,,,, TOBG_EAC-641,nomatch,,,,,,, TOBG_EAC-642,nomatch,,,,,,, TOBG_EAC-645,nomatch,,,,,,, TOBG_EAC-646,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_EAC-647,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_EAC-649,nomatch,,,,,,, TOBG_EAC-65,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_EAC-650,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_EAC-651,nomatch,,,,,,, TOBG_EAC-652,nomatch,,,,,,, TOBG_EAC-653,nomatch,,,,,,, TOBG_EAC-654,found,Bacteria,,,,,, TOBG_EAC-656,nomatch,,,,,,, TOBG_EAC-657,nomatch,,,,,,, TOBG_EAC-659,nomatch,,,,,,, TOBG_EAC-66,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_EAC-660,found,Archaea,Euryarchaeota,,,,, TOBG_EAC-662,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_EAC-663,nomatch,,,,,,, TOBG_EAC-669,nomatch,,,,,,, TOBG_EAC-67,nomatch,,,,,,, TOBG_EAC-670,nomatch,,,,,,, TOBG_EAC-671,found,Bacteria,Proteobacteria,,,,, TOBG_EAC-673,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,, TOBG_EAC-674,nomatch,,,,,,, TOBG_EAC-675,found,Archaea,Euryarchaeota,,,,, TOBG_EAC-676,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_EAC-678,nomatch,,,,,,, TOBG_EAC-68,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_EAC-682,nomatch,,,,,,, TOBG_EAC-683,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhizobiaceae,, TOBG_EAC-685,found,Bacteria,,,,,, TOBG_EAC-69,nomatch,,,,,,, TOBG_EAC-691,nomatch,,,,,,, TOBG_EAC-692,nomatch,,,,,,, TOBG_EAC-693,nomatch,,,,,,, TOBG_EAC-694,nomatch,,,,,,, TOBG_EAC-695,nomatch,,,,,,, TOBG_EAC-7,nomatch,,,,,,, TOBG_EAC-70,nomatch,,,,,,, TOBG_EAC-703,nomatch,,,,,,, TOBG_EAC-704,nomatch,,,,,,, TOBG_EAC-705,nomatch,,,,,,, TOBG_EAC-707,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_EAC-708,nomatch,,,,,,, TOBG_EAC-709,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_EAC-71,nomatch,,,,,,, TOBG_EAC-710,found,Archaea,Euryarchaeota,,,,, TOBG_EAC-712,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_EAC-713,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_EAC-714,nomatch,,,,,,, TOBG_EAC-72,nomatch,,,,,,, TOBG_EAC-720,found,Archaea,Euryarchaeota,,,,, TOBG_EAC-721,nomatch,,,,,,, TOBG_EAC-723,nomatch,,,,,,, TOBG_EAC-73,nomatch,,,,,,, TOBG_EAC-74,nomatch,,,,,,, TOBG_EAC-75,nomatch,,,,,,, TOBG_EAC-76,nomatch,,,,,,, TOBG_EAC-77,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobacteraceae,, TOBG_EAC-78,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_EAC-79,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_EAC-8,nomatch,,,,,,, TOBG_EAC-80,nomatch,,,,,,, TOBG_EAC-81,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_EAC-82,nomatch,,,,,,, TOBG_EAC-83,nomatch,,,,,,, TOBG_EAC-84,nomatch,,,,,,, TOBG_EAC-85,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_EAC-86,nomatch,,,,,,, TOBG_EAC-87,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,,, TOBG_EAC-88,nomatch,,,,,,, TOBG_EAC-89,found,Archaea,Euryarchaeota,,,,, TOBG_EAC-9,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_EAC-90,nomatch,,,,,,, TOBG_EAC-91,nomatch,,,,,,, TOBG_EAC-92,nomatch,,,,,,, TOBG_EAC-93,nomatch,,,,,,, TOBG_EAC-94,nomatch,,,,,,, TOBG_EAC-95,nomatch,,,,,,, TOBG_EAC-96,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassospira, TOBG_EAC-97,disagree,Bacteria,Proteobacteria,,,,, TOBG_EAC-98,nomatch,,,,,,, TOBG_EAC-99,found,Bacteria,Proteobacteria,,,,, TOBG_IN-1,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Pseudomonadaceae,Pseudomonas,Pseudomonas_mendocina TOBG_IN-10,found,Bacteria,Bacteroidetes,,,,, TOBG_IN-1000,nomatch,,,,,,, TOBG_IN-1003,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_IN-1073,nomatch,,,,,,, TOBG_IN-1077,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_IN-1093,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_IN-11,nomatch,,,,,,, TOBG_IN-12,nomatch,,,,,,, TOBG_IN-1207,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_IN-1213,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_IN-1214,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_IN-1219,nomatch,,,,,,, TOBG_IN-1222,disagree,,,,,,, TOBG_IN-13,found,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Cyclobacteriaceae,Algoriphagus, TOBG_IN-1311,nomatch,,,,,,, TOBG_IN-14,nomatch,,,,,,, TOBG_IN-15,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_IN-16,found,Bacteria,Proteobacteria,Alphaproteobacteria,Caulobacterales,Caulobacteraceae,Brevundimonas, TOBG_IN-17,nomatch,,,,,,, TOBG_IN-18,nomatch,,,,,,, TOBG_IN-19,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassospira, TOBG_IN-2,nomatch,,,,,,, TOBG_IN-20,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_IN-21,nomatch,,,,,,, TOBG_IN-22,nomatch,,,,,,, TOBG_IN-23,nomatch,,,,,,, TOBG_IN-24,nomatch,,,,,,, TOBG_IN-25,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_IN-26,nomatch,,,,,,, TOBG_IN-27,nomatch,,,,,,, TOBG_IN-28,nomatch,,,,,,, TOBG_IN-29,nomatch,,,,,,, TOBG_IN-3,nomatch,,,,,,, TOBG_IN-30,nomatch,,,,,,, TOBG_IN-31,found,Archaea,Euryarchaeota,,,,, TOBG_IN-32,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TOBG_IN-33,disagree,,,,,,, TOBG_IN-34,found,Archaea,Euryarchaeota,,,,, TOBG_IN-35,nomatch,,,,,,, TOBG_IN-36,nomatch,,,,,,, TOBG_IN-37,found,Archaea,Euryarchaeota,,,,, TOBG_IN-38,nomatch,,,,,,, TOBG_IN-39,nomatch,,,,,,, TOBG_IN-4,nomatch,,,,,,, TOBG_IN-40,nomatch,,,,,,, TOBG_IN-41,nomatch,,,,,,, TOBG_IN-42,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_IN-43,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,,, TOBG_IN-44,nomatch,,,,,,, TOBG_IN-45,nomatch,,,,,,, TOBG_IN-46,nomatch,,,,,,, TOBG_IN-47,disagree,,,,,,, TOBG_IN-48,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_IN-49,nomatch,,,,,,, TOBG_IN-5,nomatch,,,,,,, TOBG_IN-6,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_IN-7,nomatch,,,,,,, TOBG_IN-8,nomatch,,,,,,, TOBG_IN-9,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_IN-912,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_IN-913,found,Archaea,Euryarchaeota,,,,, TOBG_IN-918,found,Archaea,Euryarchaeota,,,,, TOBG_IN-919,disagree,,,,,,, TOBG_IN-921,nomatch,,,,,,, TOBG_IN-922,nomatch,,,,,,, TOBG_IN-924,nomatch,,,,,,, TOBG_IN-926,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_IN-928,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_IN-929,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_IN-930,found,Archaea,Euryarchaeota,,,,, TOBG_IN-961,nomatch,,,,,,, TOBG_MED-1001,found,Bacteria,Bacteroidetes,Cytophagia,,,, TOBG_MED-1003,nomatch,,,,,,, TOBG_MED-1008,nomatch,,,,,,, TOBG_MED-1017,nomatch,,,,,,, TOBG_MED-1036,nomatch,,,,,,, TOBG_MED-104,nomatch,,,,,,, TOBG_MED-1046,nomatch,,,,,,, TOBG_MED-1047,nomatch,,,,,,, TOBG_MED-105,nomatch,,,,,,, TOBG_MED-1076,nomatch,,,,,,, TOBG_MED-1079,nomatch,,,,,,, TOBG_MED-1093,found,Archaea,Euryarchaeota,,,,, TOBG_MED-1094,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-1105,nomatch,,,,,,, TOBG_MED-1116,nomatch,,,,,,, TOBG_MED-1122,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_MED-1131,nomatch,,,,,,, TOBG_MED-1153,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,Pelagibacteraceae,, TOBG_MED-1155,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,Oxalobacteraceae,, TOBG_MED-1178,nomatch,,,,,,, TOBG_MED-1190,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,,, TOBG_MED-1222,nomatch,,,,,,, TOBG_MED-1223,found,Archaea,Euryarchaeota,,,,, TOBG_MED-1227,nomatch,,,,,,, TOBG_MED-1229,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-1233,nomatch,,,,,,, TOBG_MED-124,nomatch,,,,,,, TOBG_MED-1247,nomatch,,,,,,, TOBG_MED-1250,nomatch,,,,,,, TOBG_MED-1257,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-1261,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-1269,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_MED-1270,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_MED-139,nomatch,,,,,,, TOBG_MED-174,nomatch,,,,,,, TOBG_MED-191,nomatch,,,,,,, TOBG_MED-195,found,Bacteria,Cyanobacteria,Chroococcales,Chroococcales,Cyanobium,, TOBG_MED-203,nomatch,,,,,,, TOBG_MED-211,found,Archaea,Euryarchaeota,,,,, TOBG_MED-215,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_MED-569,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_MED-580,nomatch,,,,,,, TOBG_MED-581,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-582,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,,, TOBG_MED-583,found,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Micromonas, TOBG_MED-584,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-585,nomatch,,,,,,, TOBG_MED-586,nomatch,,,,,,, TOBG_MED-587,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_MED-588,nomatch,,,,,,, TOBG_MED-589,nomatch,,,,,,, TOBG_MED-590,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_MED-591,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-592,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas, TOBG_MED-593,nomatch,,,,,,, TOBG_MED-594,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_MED-595,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-596,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-597,nomatch,,,,,,, TOBG_MED-598,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_MED-599,nomatch,,,,,,, TOBG_MED-600,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_MED-601,nomatch,,,,,,, TOBG_MED-602,nomatch,,,,,,, TOBG_MED-603,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_MED-604,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_MED-605,nomatch,,,,,,, TOBG_MED-606,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_MED-607,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_MED-608,nomatch,,,,,,, TOBG_MED-609,nomatch,,,,,,, TOBG_MED-610,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_MED-611,nomatch,,,,,,, TOBG_MED-612,nomatch,,,,,,, TOBG_MED-613,nomatch,,,,,,, TOBG_MED-614,nomatch,,,,,,, TOBG_MED-615,nomatch,,,,,,, TOBG_MED-616,nomatch,,,,,,, TOBG_MED-617,nomatch,,,,,,, TOBG_MED-618,nomatch,,,,,,, TOBG_MED-619,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_MED-620,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Nisaea, TOBG_MED-621,nomatch,,,,,,, TOBG_MED-622,nomatch,,,,,,, TOBG_MED-623,nomatch,,,,,,, TOBG_MED-624,nomatch,,,,,,, TOBG_MED-625,nomatch,,,,,,, TOBG_MED-626,nomatch,,,,,,, TOBG_MED-627,nomatch,,,,,,, TOBG_MED-628,nomatch,,,,,,, TOBG_MED-629,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_MED-630,nomatch,,,,,,, TOBG_MED-631,nomatch,,,,,,, TOBG_MED-632,nomatch,,,,,,, TOBG_MED-633,nomatch,,,,,,, TOBG_MED-634,nomatch,,,,,,, TOBG_MED-635,nomatch,,,,,,, TOBG_MED-636,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_MED-637,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,,, TOBG_MED-638,nomatch,,,,,,, TOBG_MED-639,nomatch,,,,,,, TOBG_MED-640,nomatch,,,,,,, TOBG_MED-641,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-642,nomatch,,,,,,, TOBG_MED-643,disagree,Bacteria,,,,,, TOBG_MED-644,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,,, TOBG_MED-645,nomatch,,,,,,, TOBG_MED-646,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_MED-647,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_MED-648,nomatch,,,,,,, TOBG_MED-649,nomatch,,,,,,, TOBG_MED-650,nomatch,,,,,,, TOBG_MED-651,nomatch,,,,,,, TOBG_MED-652,nomatch,,,,,,, TOBG_MED-653,nomatch,,,,,,, TOBG_MED-654,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-655,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_MED-656,nomatch,,,,,,, TOBG_MED-657,disagree,Bacteria,,,,,, TOBG_MED-658,nomatch,,,,,,, TOBG_MED-659,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_MED-660,nomatch,,,,,,, TOBG_MED-661,nomatch,,,,,,, TOBG_MED-662,nomatch,,,,,,, TOBG_MED-663,nomatch,,,,,,, TOBG_MED-664,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-665,nomatch,,,,,,, TOBG_MED-666,nomatch,,,,,,, TOBG_MED-667,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-668,nomatch,,,,,,, TOBG_MED-669,nomatch,,,,,,, TOBG_MED-670,nomatch,,,,,,, TOBG_MED-671,nomatch,,,,,,, TOBG_MED-672,nomatch,,,,,,, TOBG_MED-673,nomatch,,,,,,, TOBG_MED-674,nomatch,,,,,,, TOBG_MED-675,nomatch,,,,,,, TOBG_MED-676,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-677,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-678,nomatch,,,,,,, TOBG_MED-679,found,Archaea,Euryarchaeota,,,,, TOBG_MED-680,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-681,nomatch,,,,,,, TOBG_MED-682,nomatch,,,,,,, TOBG_MED-683,nomatch,,,,,,, TOBG_MED-684,nomatch,,,,,,, TOBG_MED-685,nomatch,,,,,,, TOBG_MED-686,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-687,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-688,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_MED-689,nomatch,,,,,,, TOBG_MED-690,nomatch,,,,,,, TOBG_MED-691,nomatch,,,,,,, TOBG_MED-692,nomatch,,,,,,, TOBG_MED-693,nomatch,,,,,,, TOBG_MED-694,nomatch,,,,,,, TOBG_MED-695,found,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Flammeovirgaceae,, TOBG_MED-696,nomatch,,,,,,, TOBG_MED-697,nomatch,,,,,,, TOBG_MED-698,nomatch,,,,,,, TOBG_MED-699,found,Archaea,Euryarchaeota,,,,, TOBG_MED-700,found,Archaea,Euryarchaeota,,,,, TOBG_MED-701,nomatch,,,,,,, TOBG_MED-702,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,, TOBG_MED-703,nomatch,,,,,,, TOBG_MED-704,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-705,found,Archaea,Euryarchaeota,,,,, TOBG_MED-706,found,Archaea,Euryarchaeota,,,,, TOBG_MED-707,nomatch,,,,,,, TOBG_MED-708,nomatch,,,,,,, TOBG_MED-709,nomatch,,,,,,, TOBG_MED-710,nomatch,,,,,,, TOBG_MED-711,nomatch,,,,,,, TOBG_MED-712,nomatch,,,,,,, TOBG_MED-713,found,Bacteria,Proteobacteria,,,,, TOBG_MED-714,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_MED-715,nomatch,,,,,,, TOBG_MED-716,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-717,nomatch,,,,,,, TOBG_MED-718,nomatch,,,,,,, TOBG_MED-719,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_MED-720,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-721,nomatch,,,,,,, TOBG_MED-722,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_MED-723,nomatch,,,,,,, TOBG_MED-724,nomatch,,,,,,, TOBG_MED-725,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,,, TOBG_MED-726,nomatch,,,,,,, TOBG_MED-727,nomatch,,,,,,, TOBG_MED-728,nomatch,,,,,,, TOBG_MED-729,nomatch,,,,,,, TOBG_MED-730,nomatch,,,,,,, TOBG_MED-731,found,Bacteria,Proteobacteria,,,,, TOBG_MED-732,found,Bacteria,Bacteroidetes,,,,, TOBG_MED-733,nomatch,,,,,,, TOBG_MED-734,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,, TOBG_MED-735,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-736,nomatch,,,,,,, TOBG_MED-737,nomatch,,,,,,, TOBG_MED-738,nomatch,,,,,,, TOBG_MED-739,nomatch,,,,,,, TOBG_MED-740,nomatch,,,,,,, TOBG_MED-741,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_MED-742,nomatch,,,,,,, TOBG_MED-743,disagree,,,,,,, TOBG_MED-744,nomatch,,,,,,, TOBG_MED-745,found,Bacteria,Proteobacteria,,,,, TOBG_MED-746,nomatch,,,,,,, TOBG_MED-747,found,Bacteria,Bacteroidetes,,,,, TOBG_MED-748,nomatch,,,,,,, TOBG_MED-749,nomatch,,,,,,, TOBG_MED-750,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_MED-751,nomatch,,,,,,, TOBG_MED-752,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_MED-753,nomatch,,,,,,, TOBG_MED-754,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-755,nomatch,,,,,,, TOBG_MED-756,nomatch,,,,,,, TOBG_MED-757,nomatch,,,,,,, TOBG_MED-758,nomatch,,,,,,, TOBG_MED-759,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-760,nomatch,,,,,,, TOBG_MED-761,nomatch,,,,,,, TOBG_MED-762,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-763,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-764,nomatch,,,,,,, TOBG_MED-765,found,Bacteria,Proteobacteria,,,,, TOBG_MED-766,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,,, TOBG_MED-767,nomatch,,,,,,, TOBG_MED-768,nomatch,,,,,,, TOBG_MED-769,nomatch,,,,,,, TOBG_MED-770,found,Archaea,Euryarchaeota,,,,, TOBG_MED-771,nomatch,,,,,,, TOBG_MED-772,nomatch,,,,,,, TOBG_MED-773,nomatch,,,,,,, TOBG_MED-774,nomatch,,,,,,, TOBG_MED-775,nomatch,,,,,,, TOBG_MED-776,nomatch,,,,,,, TOBG_MED-777,found,Archaea,Euryarchaeota,,,,, TOBG_MED-778,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-779,nomatch,,,,,,, TOBG_MED-780,nomatch,,,,,,, TOBG_MED-781,nomatch,,,,,,, TOBG_MED-782,nomatch,,,,,,, TOBG_MED-783,nomatch,,,,,,, TOBG_MED-784,nomatch,,,,,,, TOBG_MED-785,nomatch,,,,,,, TOBG_MED-786,nomatch,,,,,,, TOBG_MED-787,nomatch,,,,,,, TOBG_MED-788,nomatch,,,,,,, TOBG_MED-789,disagree,Bacteria,,,,,, TOBG_MED-790,found,Archaea,Euryarchaeota,,,,, TOBG_MED-791,nomatch,,,,,,, TOBG_MED-792,nomatch,,,,,,, TOBG_MED-793,nomatch,,,,,,, TOBG_MED-794,nomatch,,,,,,, TOBG_MED-795,nomatch,,,,,,, TOBG_MED-796,nomatch,,,,,,, TOBG_MED-797,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-798,nomatch,,,,,,, TOBG_MED-800,nomatch,,,,,,, TOBG_MED-801,nomatch,,,,,,, TOBG_MED-802,nomatch,,,,,,, TOBG_MED-803,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-804,nomatch,,,,,,, TOBG_MED-805,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_MED-806,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_MED-807,found,Archaea,Euryarchaeota,,,,, TOBG_MED-808,nomatch,,,,,,, TOBG_MED-809,found,Bacteria,,,,,, TOBG_MED-810,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-811,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-812,nomatch,,,,,,, TOBG_MED-813,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_MED-814,nomatch,,,,,,, TOBG_MED-815,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-816,nomatch,,,,,,, TOBG_MED-817,nomatch,,,,,,, TOBG_MED-818,nomatch,,,,,,, TOBG_MED-819,nomatch,,,,,,, TOBG_MED-820,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-821,nomatch,,,,,,, TOBG_MED-822,nomatch,,,,,,, TOBG_MED-823,nomatch,,,,,,, TOBG_MED-824,nomatch,,,,,,, TOBG_MED-825,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_MED-826,found,Bacteria,Proteobacteria,Betaproteobacteria,,,, TOBG_MED-827,nomatch,,,,,,, TOBG_MED-828,nomatch,,,,,,, TOBG_MED-829,nomatch,,,,,,, TOBG_MED-830,nomatch,,,,,,, TOBG_MED-831,nomatch,,,,,,, TOBG_MED-832,nomatch,,,,,,, TOBG_MED-833,nomatch,,,,,,, TOBG_MED-834,nomatch,,,,,,, TOBG_MED-835,nomatch,,,,,,, TOBG_MED-836,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_MED-837,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_MED-838,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-839,nomatch,,,,,,, TOBG_MED-840,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_MED-841,nomatch,,,,,,, TOBG_MED-842,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_MED-843,nomatch,,,,,,, TOBG_MED-844,nomatch,,,,,,, TOBG_MED-845,found,Bacteria,Cyanobacteria,,,,, TOBG_MED-846,nomatch,,,,,,, TOBG_MED-847,nomatch,,,,,,, TOBG_MED-848,nomatch,,,,,,, TOBG_MED-849,nomatch,,,,,,, TOBG_MED-850,nomatch,,,,,,, TOBG_MED-851,nomatch,,,,,,, TOBG_MED-852,nomatch,,,,,,, TOBG_MED-853,nomatch,,,,,,, TOBG_MED-854,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_MED-855,nomatch,,,,,,, TOBG_MED-856,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_MED-857,nomatch,,,,,,, TOBG_MED-858,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-859,nomatch,,,,,,, TOBG_MED-861,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_MED-862,nomatch,,,,,,, TOBG_MED-863,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_MED-864,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_MED-865,nomatch,,,,,,, TOBG_MED-866,nomatch,,,,,,, TOBG_MED-867,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_MED-868,nomatch,,,,,,, TOBG_MED-869,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-870,nomatch,,,,,,, TOBG_MED-871,nomatch,,,,,,, TOBG_MED-872,nomatch,,,,,,, TOBG_MED-873,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_MED-874,nomatch,,,,,,, TOBG_MED-875,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_MED-876,nomatch,,,,,,, TOBG_MED-877,nomatch,,,,,,, TOBG_MED-878,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,,, TOBG_MED-879,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,,, TOBG_MED-880,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_MED-881,nomatch,,,,,,, TOBG_MED-882,nomatch,,,,,,, TOBG_MED-883,nomatch,,,,,,, TOBG_MED-884,nomatch,,,,,,, TOBG_MED-899,nomatch,,,,,,, TOBG_MED-921,found,Archaea,Euryarchaeota,,,,, TOBG_MED-928,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-932,nomatch,,,,,,, TOBG_MED-939,nomatch,,,,,,, TOBG_MED-943,nomatch,,,,,,, TOBG_MED-947,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_MED-950,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_MED-952,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_MED-955,nomatch,,,,,,, TOBG_MED-957,nomatch,,,,,,, TOBG_MED-961,nomatch,,,,,,, TOBG_MED-965,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_MED-972,nomatch,,,,,,, TOBG_MED-995,nomatch,,,,,,, TOBG_MED-999,nomatch,,,,,,, TOBG_NAT-1,nomatch,,,,,,, TOBG_NAT-10,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhizobiaceae,, TOBG_NAT-100,nomatch,,,,,,, TOBG_NAT-101,nomatch,,,,,,, TOBG_NAT-102,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,,, TOBG_NAT-103,nomatch,,,,,,, TOBG_NAT-104,nomatch,,,,,,, TOBG_NAT-105,nomatch,,,,,,, TOBG_NAT-106,nomatch,,,,,,, TOBG_NAT-107,nomatch,,,,,,, TOBG_NAT-108,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-109,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NAT-11,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-110,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_NAT-111,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_NAT-112,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_NAT-113,nomatch,,,,,,, TOBG_NAT-114,nomatch,,,,,,, TOBG_NAT-115,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-116,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,, TOBG_NAT-117,found,Bacteria,Chloroflexi,,,,, TOBG_NAT-118,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_NAT-119,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-12,nomatch,,,,,,, TOBG_NAT-120,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-121,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,Haliea, TOBG_NAT-122,nomatch,,,,,,, TOBG_NAT-123,nomatch,,,,,,, TOBG_NAT-124,nomatch,,,,,,, TOBG_NAT-125,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NAT-126,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NAT-127,nomatch,,,,,,, TOBG_NAT-128,nomatch,,,,,,, TOBG_NAT-129,found,Bacteria,Proteobacteria,Gammaproteobacteria,Xanthomonadales,,, TOBG_NAT-13,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-130,nomatch,,,,,,, TOBG_NAT-131,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,,, TOBG_NAT-132,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NAT-133,nomatch,,,,,,, TOBG_NAT-134,found,Bacteria,Ignavibacteriae,Ignavibacteria,Ignavibacteriales,,, TOBG_NAT-135,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-136,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_NAT-137,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_NAT-138,nomatch,,,,,,, TOBG_NAT-139,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-14,disagree,,,,,,, TOBG_NAT-140,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_NAT-141,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_NAT-142,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NAT-143,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-144,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_NAT-145,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_NAT-146,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,,, TOBG_NAT-147,nomatch,,,,,,, TOBG_NAT-148,nomatch,,,,,,, TOBG_NAT-149,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_NAT-15,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_NAT-150,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-151,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_NAT-152,nomatch,,,,,,, TOBG_NAT-153,nomatch,,,,,,, TOBG_NAT-154,nomatch,,,,,,, TOBG_NAT-155,nomatch,,,,,,, TOBG_NAT-156,nomatch,,,,,,, TOBG_NAT-157,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,Haliea, TOBG_NAT-158,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_NAT-159,nomatch,,,,,,, TOBG_NAT-16,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,, TOBG_NAT-160,nomatch,,,,,,, TOBG_NAT-161,nomatch,,,,,,, TOBG_NAT-162,nomatch,,,,,,, TOBG_NAT-163,nomatch,,,,,,, TOBG_NAT-164,nomatch,,,,,,, TOBG_NAT-165,nomatch,,,,,,, TOBG_NAT-166,disagree,Bacteria,,,,,, TOBG_NAT-167,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_NAT-168,nomatch,,,,,,, TOBG_NAT-169,nomatch,,,,,,, TOBG_NAT-17,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius, TOBG_NAT-170,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,,, TOBG_NAT-171,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-172,nomatch,,,,,,, TOBG_NAT-173,nomatch,,,,,,, TOBG_NAT-174,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TOBG_NAT-175,nomatch,,,,,,, TOBG_NAT-176,found,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Leptospiraceae,, TOBG_NAT-177,found,Bacteria,Candidate_Phyla_Radiation,,,,, TOBG_NAT-178,found,Bacteria,Proteobacteria,,,,, TOBG_NAT-179,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_NAT-18,nomatch,,,,,,, TOBG_NAT-180,nomatch,,,,,,, TOBG_NAT-181,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NAT-182,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_NAT-183,nomatch,,,,,,, TOBG_NAT-184,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_NAT-185,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,,, TOBG_NAT-186,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-187,nomatch,,,,,,, TOBG_NAT-188,nomatch,,,,,,, TOBG_NAT-189,nomatch,,,,,,, TOBG_NAT-19,found,Bacteria,,,,,, TOBG_NAT-190,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_NAT-191,found,Bacteria,,,,,, TOBG_NAT-192,nomatch,,,,,,, TOBG_NAT-193,nomatch,,,,,,, TOBG_NAT-194,found,Bacteria,Proteobacteria,,,,, TOBG_NAT-195,nomatch,,,,,,, TOBG_NAT-196,nomatch,,,,,,, TOBG_NAT-197,nomatch,,,,,,, TOBG_NAT-198,nomatch,,,,,,, TOBG_NAT-199,nomatch,,,,,,, TOBG_NAT-2,nomatch,,,,,,, TOBG_NAT-20,nomatch,,,,,,, TOBG_NAT-200,nomatch,,,,,,, TOBG_NAT-201,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_NAT-202,nomatch,,,,,,, TOBG_NAT-203,nomatch,,,,,,, TOBG_NAT-204,nomatch,,,,,,, TOBG_NAT-205,nomatch,,,,,,, TOBG_NAT-206,nomatch,,,,,,, TOBG_NAT-207,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-208,nomatch,,,,,,, TOBG_NAT-209,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_NAT-21,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_NAT-210,nomatch,,,,,,, TOBG_NAT-211,nomatch,,,,,,, TOBG_NAT-212,nomatch,,,,,,, TOBG_NAT-213,nomatch,,,,,,, TOBG_NAT-214,found,Bacteria,Acidobacteria,,,,, TOBG_NAT-215,nomatch,,,,,,, TOBG_NAT-216,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae_3,Sphingobium, TOBG_NAT-217,nomatch,,,,,,, TOBG_NAT-218,nomatch,,,,,,, TOBG_NAT-219,nomatch,,,,,,, TOBG_NAT-22,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_NAT-220,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_NAT-221,nomatch,,,,,,, TOBG_NAT-222,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_NAT-223,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_NAT-224,nomatch,,,,,,, TOBG_NAT-225,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NAT-226,nomatch,,,,,,, TOBG_NAT-227,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_NAT-228,nomatch,,,,,,, TOBG_NAT-229,nomatch,,,,,,, TOBG_NAT-23,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius, TOBG_NAT-230,nomatch,,,,,,, TOBG_NAT-231,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_NAT-232,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NAT-233,nomatch,,,,,,, TOBG_NAT-234,nomatch,,,,,,, TOBG_NAT-235,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_NAT-236,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_NAT-237,found,Bacteria,,,,,, TOBG_NAT-238,nomatch,,,,,,, TOBG_NAT-239,nomatch,,,,,,, TOBG_NAT-24,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NAT-240,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NAT-241,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_NAT-242,nomatch,,,,,,, TOBG_NAT-243,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_NAT-244,nomatch,,,,,,, TOBG_NAT-245,nomatch,,,,,,, TOBG_NAT-246,nomatch,,,,,,, TOBG_NAT-247,nomatch,,,,,,, TOBG_NAT-248,disagree,Bacteria,,,,,, TOBG_NAT-249,found,Bacteria,Cyanobacteria,Chroococcales,Chroococcales,Cyanobium,, TOBG_NAT-25,nomatch,,,,,,, TOBG_NAT-250,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NAT-251,nomatch,,,,,,, TOBG_NAT-252,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NAT-253,nomatch,,,,,,, TOBG_NAT-254,nomatch,,,,,,, TOBG_NAT-255,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-256,nomatch,,,,,,, TOBG_NAT-257,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Planctomyces, TOBG_NAT-258,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_NAT-259,nomatch,,,,,,, TOBG_NAT-26,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_NAT-260,nomatch,,,,,,, TOBG_NAT-261,nomatch,,,,,,, TOBG_NAT-262,nomatch,,,,,,, TOBG_NAT-263,nomatch,,,,,,, TOBG_NAT-264,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_NAT-265,nomatch,,,,,,, TOBG_NAT-266,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-267,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_NAT-268,nomatch,,,,,,, TOBG_NAT-269,found,Bacteria,Bacteroidetes,Cytophagia,,,, TOBG_NAT-27,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-270,found,Bacteria,Bacteroidetes,Cytophagia,,,, TOBG_NAT-271,nomatch,,,,,,, TOBG_NAT-272,nomatch,,,,,,, TOBG_NAT-273,nomatch,,,,,,, TOBG_NAT-274,nomatch,,,,,,, TOBG_NAT-275,nomatch,,,,,,, TOBG_NAT-276,found,Bacteria,Proteobacteria,Gammaproteobacteria,Xanthomonadales,,, TOBG_NAT-277,nomatch,,,,,,, TOBG_NAT-278,nomatch,,,,,,, TOBG_NAT-279,nomatch,,,,,,, TOBG_NAT-28,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_NAT-280,found,Bacteria,,,,,, TOBG_NAT-281,nomatch,,,,,,, TOBG_NAT-282,nomatch,,,,,,, TOBG_NAT-283,found,Bacteria,Proteobacteria,Betaproteobacteria,Methylophilales,,, TOBG_NAT-284,nomatch,,,,,,, TOBG_NAT-288,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-29,nomatch,,,,,,, TOBG_NAT-290,nomatch,,,,,,, TOBG_NAT-291,nomatch,,,,,,, TOBG_NAT-292,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-3,found,Bacteria,Proteobacteria,Gammaproteobacteria,Xanthomonadales,,, TOBG_NAT-30,nomatch,,,,,,, TOBG_NAT-302,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-31,nomatch,,,,,,, TOBG_NAT-32,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_NAT-33,nomatch,,,,,,, TOBG_NAT-333,nomatch,,,,,,, TOBG_NAT-34,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NAT-35,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-353,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,,, TOBG_NAT-36,nomatch,,,,,,, TOBG_NAT-37,nomatch,,,,,,, TOBG_NAT-375,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_NAT-38,nomatch,,,,,,, TOBG_NAT-39,nomatch,,,,,,, TOBG_NAT-392,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_NAT-4,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NAT-40,nomatch,,,,,,, TOBG_NAT-41,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_NAT-417,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_NAT-42,nomatch,,,,,,, TOBG_NAT-422,nomatch,,,,,,, TOBG_NAT-43,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NAT-430,found,Bacteria,Proteobacteria,,,,, TOBG_NAT-44,nomatch,,,,,,, TOBG_NAT-441,nomatch,,,,,,, TOBG_NAT-45,nomatch,,,,,,, TOBG_NAT-453,nomatch,,,,,,, TOBG_NAT-46,found,Bacteria,Proteobacteria,Gammaproteobacteria,Xanthomonadales,,, TOBG_NAT-47,found,Bacteria,Proteobacteria,,,,, TOBG_NAT-472,nomatch,,,,,,, TOBG_NAT-478,nomatch,,,,,,, TOBG_NAT-48,nomatch,,,,,,, TOBG_NAT-483,nomatch,,,,,,, TOBG_NAT-484,nomatch,,,,,,, TOBG_NAT-49,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,,, TOBG_NAT-493,nomatch,,,,,,, TOBG_NAT-495,nomatch,,,,,,, TOBG_NAT-5,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_NAT-50,nomatch,,,,,,, TOBG_NAT-501,nomatch,,,,,,, TOBG_NAT-503,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_NAT-504,nomatch,,,,,,, TOBG_NAT-509,nomatch,,,,,,, TOBG_NAT-51,nomatch,,,,,,, TOBG_NAT-510,nomatch,,,,,,, TOBG_NAT-52,nomatch,,,,,,, TOBG_NAT-53,found,Bacteria,Proteobacteria,,,,, TOBG_NAT-535,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_NAT-54,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-545,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_NAT-549,nomatch,,,,,,, TOBG_NAT-55,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_NAT-56,nomatch,,,,,,, TOBG_NAT-57,nomatch,,,,,,, TOBG_NAT-575,nomatch,,,,,,, TOBG_NAT-579,nomatch,,,,,,, TOBG_NAT-58,nomatch,,,,,,, TOBG_NAT-59,nomatch,,,,,,, TOBG_NAT-6,nomatch,,,,,,, TOBG_NAT-60,nomatch,,,,,,, TOBG_NAT-608,nomatch,,,,,,, TOBG_NAT-61,disagree,Bacteria,,,,,, TOBG_NAT-62,nomatch,,,,,,, TOBG_NAT-63,nomatch,,,,,,, TOBG_NAT-630,nomatch,,,,,,, TOBG_NAT-634,nomatch,,,,,,, TOBG_NAT-64,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NAT-65,disagree,Bacteria,,,,,, TOBG_NAT-653,nomatch,,,,,,, TOBG_NAT-66,nomatch,,,,,,, TOBG_NAT-661,nomatch,,,,,,, TOBG_NAT-67,nomatch,,,,,,, TOBG_NAT-68,found,Archaea,Euryarchaeota,,,,, TOBG_NAT-682,nomatch,,,,,,, TOBG_NAT-686,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_NAT-69,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_NAT-7,nomatch,,,,,,, TOBG_NAT-70,found,Bacteria,Cyanobacteria,Chroococcales,Chroococcales,Cyanobium,, TOBG_NAT-71,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-72,nomatch,,,,,,, TOBG_NAT-73,nomatch,,,,,,, TOBG_NAT-74,nomatch,,,,,,, TOBG_NAT-75,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_NAT-76,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_NAT-77,found,Bacteria,Candidate_Phyla_Radiation,,,,, TOBG_NAT-78,nomatch,,,,,,, TOBG_NAT-79,nomatch,,,,,,, TOBG_NAT-8,nomatch,,,,,,, TOBG_NAT-80,nomatch,,,,,,, TOBG_NAT-81,nomatch,,,,,,, TOBG_NAT-82,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_NAT-83,nomatch,,,,,,, TOBG_NAT-84,nomatch,,,,,,, TOBG_NAT-85,nomatch,,,,,,, TOBG_NAT-86,nomatch,,,,,,, TOBG_NAT-87,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NAT-88,nomatch,,,,,,, TOBG_NAT-89,nomatch,,,,,,, TOBG_NAT-9,nomatch,,,,,,, TOBG_NAT-90,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NAT-91,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_NAT-92,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_NAT-93,nomatch,,,,,,, TOBG_NAT-94,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NAT-95,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NAT-96,nomatch,,,,,,, TOBG_NAT-97,found,Bacteria,Candidate_Phyla_Radiation,,,,, TOBG_NAT-98,nomatch,,,,,,, TOBG_NAT-99,nomatch,,,,,,, TOBG_NP-1,nomatch,,,,,,, TOBG_NP-10,nomatch,,,,,,, TOBG_NP-100,nomatch,,,,,,, TOBG_NP-1000,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_NP-1001,nomatch,,,,,,, TOBG_NP-101,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_NP-102,nomatch,,,,,,, TOBG_NP-1026,found,Bacteria,,,,,, TOBG_NP-103,found,Bacteria,,,,,, TOBG_NP-1036,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,,, TOBG_NP-1037,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NP-104,nomatch,,,,,,, TOBG_NP-1044,nomatch,,,,,,, TOBG_NP-1046,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_NP-1048,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_NP-105,nomatch,,,,,,, TOBG_NP-1051,nomatch,,,,,,, TOBG_NP-1052,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NP-1057,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NP-106,nomatch,,,,,,, TOBG_NP-1069,nomatch,,,,,,, TOBG_NP-107,nomatch,,,,,,, TOBG_NP-1070,nomatch,,,,,,, TOBG_NP-1071,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NP-1073,nomatch,,,,,,, TOBG_NP-108,nomatch,,,,,,, TOBG_NP-1080,nomatch,,,,,,, TOBG_NP-109,nomatch,,,,,,, TOBG_NP-1098,nomatch,,,,,,, TOBG_NP-11,nomatch,,,,,,, TOBG_NP-110,found,Archaea,Euryarchaeota,,,,, TOBG_NP-1106,nomatch,,,,,,, TOBG_NP-111,nomatch,,,,,,, TOBG_NP-112,nomatch,,,,,,, TOBG_NP-1124,nomatch,,,,,,, TOBG_NP-113,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_NP-114,nomatch,,,,,,, TOBG_NP-115,nomatch,,,,,,, TOBG_NP-1152,nomatch,,,,,,, TOBG_NP-1159,nomatch,,,,,,, TOBG_NP-116,found,Archaea,Euryarchaeota,,,,, TOBG_NP-1160,nomatch,,,,,,, TOBG_NP-117,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_NP-1171,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NP-118,nomatch,,,,,,, TOBG_NP-1180,nomatch,,,,,,, TOBG_NP-119,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_NP-12,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_NP-120,found,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Leptospiraceae,, TOBG_NP-1202,nomatch,,,,,,, TOBG_NP-1207,nomatch,,,,,,, TOBG_NP-121,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas, TOBG_NP-122,nomatch,,,,,,, TOBG_NP-123,nomatch,,,,,,, TOBG_NP-124,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_NP-125,disagree,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NP-1256,nomatch,,,,,,, TOBG_NP-126,nomatch,,,,,,, TOBG_NP-127,nomatch,,,,,,, TOBG_NP-1271,nomatch,,,,,,, TOBG_NP-1277,nomatch,,,,,,, TOBG_NP-128,nomatch,,,,,,, TOBG_NP-1282,nomatch,,,,,,, TOBG_NP-1285,found,Bacteria,Acidobacteria,,,,, TOBG_NP-129,nomatch,,,,,,, TOBG_NP-1295,nomatch,,,,,,, TOBG_NP-1297,nomatch,,,,,,, TOBG_NP-13,nomatch,,,,,,, TOBG_NP-130,nomatch,,,,,,, TOBG_NP-1301,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_NP-1304,nomatch,,,,,,, TOBG_NP-131,nomatch,,,,,,, TOBG_NP-132,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_NP-1321,nomatch,,,,,,, TOBG_NP-1324,nomatch,,,,,,, TOBG_NP-133,found,Bacteria,Acidobacteria,,,,, TOBG_NP-134,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NP-1344,nomatch,,,,,,, TOBG_NP-135,found,Bacteria,Proteobacteria,,,,, TOBG_NP-136,nomatch,,,,,,, TOBG_NP-137,found,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Bathycoccaceae,Ostreococcus, TOBG_NP-1374,nomatch,,,,,,, TOBG_NP-1378,nomatch,,,,,,, TOBG_NP-138,nomatch,,,,,,, TOBG_NP-139,nomatch,,,,,,, TOBG_NP-1391,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_NP-1395,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NP-14,found,Bacteria,Proteobacteria,,,,, TOBG_NP-140,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_NP-1400,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_NP-141,nomatch,,,,,,, TOBG_NP-142,nomatch,,,,,,, TOBG_NP-143,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_NP-144,nomatch,,,,,,, TOBG_NP-1444,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NP-145,nomatch,,,,,,, TOBG_NP-146,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_NP-1463,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_NP-1466,nomatch,,,,,,, TOBG_NP-147,nomatch,,,,,,, TOBG_NP-1472,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NP-148,nomatch,,,,,,, TOBG_NP-149,nomatch,,,,,,, TOBG_NP-1496,nomatch,,,,,,, TOBG_NP-15,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas, TOBG_NP-150,found,Bacteria,Proteobacteria,,,,, TOBG_NP-151,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NP-152,nomatch,,,,,,, TOBG_NP-1521,found,Archaea,Euryarchaeota,,,,, TOBG_NP-153,nomatch,,,,,,, TOBG_NP-155,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NP-156,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_NP-16,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,,, TOBG_NP-17,found,Bacteria,Cyanobacteria,Chroococcales,Chroococcales,Cyanobium,, TOBG_NP-18,nomatch,,,,,,, TOBG_NP-19,nomatch,,,,,,, TOBG_NP-2,nomatch,,,,,,, TOBG_NP-20,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NP-21,found,Bacteria,Proteobacteria,,,,, TOBG_NP-22,nomatch,,,,,,, TOBG_NP-23,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_NP-24,nomatch,,,,,,, TOBG_NP-25,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,, TOBG_NP-26,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_NP-27,nomatch,,,,,,, TOBG_NP-28,nomatch,,,,,,, TOBG_NP-29,nomatch,,,,,,, TOBG_NP-3,nomatch,,,,,,, TOBG_NP-30,nomatch,,,,,,, TOBG_NP-31,nomatch,,,,,,, TOBG_NP-32,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_NP-33,nomatch,,,,,,, TOBG_NP-34,disagree,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,,, TOBG_NP-35,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_NP-36,nomatch,,,,,,, TOBG_NP-37,nomatch,,,,,,, TOBG_NP-38,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NP-39,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_NP-4,nomatch,,,,,,, TOBG_NP-40,nomatch,,,,,,, TOBG_NP-41,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NP-42,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas, TOBG_NP-43,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae_3,Sphingobium, TOBG_NP-44,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,Idiomarina, TOBG_NP-45,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_NP-46,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TOBG_NP-47,found,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Leptospiraceae,, TOBG_NP-48,found,Archaea,Euryarchaeota,,,,, TOBG_NP-49,nomatch,,,,,,, TOBG_NP-5,nomatch,,,,,,, TOBG_NP-50,nomatch,,,,,,, TOBG_NP-51,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_NP-52,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,,, TOBG_NP-53,disagree,Bacteria,,,,,, TOBG_NP-54,nomatch,,,,,,, TOBG_NP-55,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_NP-56,nomatch,,,,,,, TOBG_NP-57,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_NP-58,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NP-59,nomatch,,,,,,, TOBG_NP-6,nomatch,,,,,,, TOBG_NP-60,nomatch,,,,,,, TOBG_NP-61,nomatch,,,,,,, TOBG_NP-62,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TOBG_NP-63,nomatch,,,,,,, TOBG_NP-64,nomatch,,,,,,, TOBG_NP-65,found,Archaea,Euryarchaeota,,,,, TOBG_NP-66,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,,, TOBG_NP-67,found,Bacteria,Candidate_Phyla_Radiation,,,,, TOBG_NP-68,nomatch,,,,,,, TOBG_NP-69,nomatch,,,,,,, TOBG_NP-7,nomatch,,,,,,, TOBG_NP-70,nomatch,,,,,,, TOBG_NP-71,nomatch,,,,,,, TOBG_NP-72,nomatch,,,,,,, TOBG_NP-73,found,Archaea,Euryarchaeota,,,,, TOBG_NP-74,nomatch,,,,,,, TOBG_NP-75,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NP-76,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_NP-77,nomatch,,,,,,, TOBG_NP-78,nomatch,,,,,,, TOBG_NP-79,nomatch,,,,,,, TOBG_NP-8,nomatch,,,,,,, TOBG_NP-80,nomatch,,,,,,, TOBG_NP-81,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_NP-82,nomatch,,,,,,, TOBG_NP-83,nomatch,,,,,,, TOBG_NP-84,nomatch,,,,,,, TOBG_NP-85,nomatch,,,,,,, TOBG_NP-86,nomatch,,,,,,, TOBG_NP-87,nomatch,,,,,,, TOBG_NP-88,nomatch,,,,,,, TOBG_NP-89,nomatch,,,,,,, TOBG_NP-9,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,Haliea, TOBG_NP-90,nomatch,,,,,,, TOBG_NP-91,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_NP-92,nomatch,,,,,,, TOBG_NP-93,nomatch,,,,,,, TOBG_NP-931,nomatch,,,,,,, TOBG_NP-932,nomatch,,,,,,, TOBG_NP-933,nomatch,,,,,,, TOBG_NP-935,found,Archaea,Euryarchaeota,,,,, TOBG_NP-936,found,Bacteria,Acidobacteria,,,,, TOBG_NP-937,nomatch,,,,,,, TOBG_NP-939,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NP-94,nomatch,,,,,,, TOBG_NP-941,found,Archaea,Euryarchaeota,,,,, TOBG_NP-942,found,Bacteria,,,,,, TOBG_NP-943,nomatch,,,,,,, TOBG_NP-944,nomatch,,,,,,, TOBG_NP-945,nomatch,,,,,,, TOBG_NP-946,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_NP-948,nomatch,,,,,,, TOBG_NP-949,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_NP-95,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Pseudomonadaceae,Pseudomonas, TOBG_NP-951,found,Bacteria,,,,,, TOBG_NP-952,nomatch,,,,,,, TOBG_NP-954,nomatch,,,,,,, TOBG_NP-955,found,Bacteria,Proteobacteria,,,,, TOBG_NP-956,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_NP-958,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NP-959,found,Bacteria,Proteobacteria,,,,, TOBG_NP-96,nomatch,,,,,,, TOBG_NP-960,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_NP-961,nomatch,,,,,,, TOBG_NP-962,nomatch,,,,,,, TOBG_NP-964,nomatch,,,,,,, TOBG_NP-965,found,Archaea,Euryarchaeota,,,,, TOBG_NP-966,nomatch,,,,,,, TOBG_NP-969,nomatch,,,,,,, TOBG_NP-97,found,Bacteria,Proteobacteria,,,,, TOBG_NP-970,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_NP-972,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_NP-973,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,,, TOBG_NP-975,found,Bacteria,,,,,, TOBG_NP-976,found,Archaea,Euryarchaeota,,,,, TOBG_NP-977,nomatch,,,,,,, TOBG_NP-978,nomatch,,,,,,, TOBG_NP-98,nomatch,,,,,,, TOBG_NP-980,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,,, TOBG_NP-981,nomatch,,,,,,, TOBG_NP-982,nomatch,,,,,,, TOBG_NP-983,found,Bacteria,Acidobacteria,,,,, TOBG_NP-986,nomatch,,,,,,, TOBG_NP-987,nomatch,,,,,,, TOBG_NP-989,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_NP-99,nomatch,,,,,,, TOBG_NP-990,disagree,Bacteria,,,,,, TOBG_NP-992,found,Bacteria,Proteobacteria,,,,, TOBG_NP-993,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_NP-995,found,Bacteria,,,,,, TOBG_NP-996,nomatch,,,,,,, TOBG_NP-997,disagree,Bacteria,,,,,, TOBG_NP-998,nomatch,,,,,,, TOBG_RS-307,nomatch,,,,,,, TOBG_RS-341,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_RS-342,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_RS-343,nomatch,,,,,,, TOBG_RS-344,nomatch,,,,,,, TOBG_RS-345,nomatch,,,,,,, TOBG_RS-346,nomatch,,,,,,, TOBG_RS-347,nomatch,,,,,,, TOBG_RS-348,nomatch,,,,,,, TOBG_RS-349,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_RS-350,nomatch,,,,,,, TOBG_RS-351,nomatch,,,,,,, TOBG_RS-352,nomatch,,,,,,, TOBG_RS-353,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_RS-354,nomatch,,,,,,, TOBG_RS-355,nomatch,,,,,,, TOBG_RS-356,found,Archaea,Euryarchaeota,,,,, TOBG_RS-357,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_RS-358,found,Archaea,Euryarchaeota,,,,, TOBG_RS-359,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_RS-360,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-361,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-362,found,Bacteria,Chloroflexi,,,,, TOBG_RS-363,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_RS-364,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_RS-365,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_RS-366,nomatch,,,,,,, TOBG_RS-367,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,Citromicrobium, TOBG_RS-368,nomatch,,,,,,, TOBG_RS-369,found,Bacteria,,,,,, TOBG_RS-370,nomatch,,,,,,, TOBG_RS-371,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-372,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_RS-373,found,Bacteria,,,,,, TOBG_RS-374,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_RS-375,found,Archaea,Euryarchaeota,,,,, TOBG_RS-376,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_RS-377,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_RS-378,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-379,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_RS-380,nomatch,,,,,,, TOBG_RS-381,nomatch,,,,,,, TOBG_RS-382,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_RS-383,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Planctomyces, TOBG_RS-384,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_RS-385,nomatch,,,,,,, TOBG_RS-386,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-387,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_RS-388,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_RS-389,nomatch,,,,,,, TOBG_RS-390,nomatch,,,,,,, TOBG_RS-391,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_RS-392,nomatch,,,,,,, TOBG_RS-393,found,Bacteria,Proteobacteria,,,,, TOBG_RS-394,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-395,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Planctomyces, TOBG_RS-396,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_RS-397,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_RS-398,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_RS-399,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Planctomyces, TOBG_RS-400,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_RS-401,nomatch,,,,,,, TOBG_RS-402,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_RS-403,nomatch,,,,,,, TOBG_RS-404,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,,, TOBG_RS-405,nomatch,,,,,,, TOBG_RS-406,found,Archaea,Euryarchaeota,,,,, TOBG_RS-407,found,Archaea,Euryarchaeota,,,,, TOBG_RS-408,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_RS-409,nomatch,,,,,,, TOBG_RS-410,nomatch,,,,,,, TOBG_RS-411,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii TOBG_RS-412,nomatch,,,,,,, TOBG_RS-413,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-414,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_RS-415,disagree,Bacteria,,,,,, TOBG_RS-416,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-417,nomatch,,,,,,, TOBG_RS-418,nomatch,,,,,,, TOBG_RS-419,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_RS-420,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-421,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_RS-422,nomatch,,,,,,, TOBG_RS-423,nomatch,,,,,,, TOBG_RS-424,nomatch,,,,,,, TOBG_RS-425,found,Bacteria,Proteobacteria,,,,, TOBG_RS-426,disagree,Bacteria,Proteobacteria,,,,, TOBG_RS-427,nomatch,,,,,,, TOBG_RS-428,nomatch,,,,,,, TOBG_RS-429,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_RS-430,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_RS-431,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_RS-432,found,Archaea,Euryarchaeota,,,,, TOBG_RS-433,found,Archaea,Euryarchaeota,,,,, TOBG_RS-434,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_RS-435,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-436,nomatch,,,,,,, TOBG_RS-437,nomatch,,,,,,, TOBG_RS-438,nomatch,,,,,,, TOBG_RS-439,nomatch,,,,,,, TOBG_RS-440,nomatch,,,,,,, TOBG_RS-441,nomatch,,,,,,, TOBG_RS-442,found,Bacteria,,,,,, TOBG_RS-443,found,Archaea,Euryarchaeota,,,,, TOBG_RS-444,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_RS-445,found,Bacteria,Acidobacteria,,,,, TOBG_RS-446,found,Bacteria,Proteobacteria,,,,, TOBG_RS-447,found,Bacteria,Acidobacteria,,,,, TOBG_RS-448,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_RS-449,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-450,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-451,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-452,nomatch,,,,,,, TOBG_RS-453,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-454,nomatch,,,,,,, TOBG_RS-455,nomatch,,,,,,, TOBG_RS-456,found,Bacteria,Acidobacteria,,,,, TOBG_RS-457,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-458,found,Bacteria,,,,,, TOBG_RS-459,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_RS-460,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_RS-461,nomatch,,,,,,, TOBG_RS-462,nomatch,,,,,,, TOBG_RS-463,nomatch,,,,,,, TOBG_RS-464,disagree,Bacteria,,,,,, TOBG_RS-465,nomatch,,,,,,, TOBG_RS-466,nomatch,,,,,,, TOBG_RS-467,nomatch,,,,,,, TOBG_RS-468,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_RS-469,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_RS-470,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_RS-471,nomatch,,,,,,, TOBG_RS-472,nomatch,,,,,,, TOBG_RS-473,nomatch,,,,,,, TOBG_RS-474,nomatch,,,,,,, TOBG_RS-475,nomatch,,,,,,, TOBG_RS-476,nomatch,,,,,,, TOBG_RS-481,nomatch,,,,,,, TOBG_RS-495,nomatch,,,,,,, TOBG_RS-508,nomatch,,,,,,, TOBG_RS-510,nomatch,,,,,,, TOBG_RS-533,nomatch,,,,,,, TOBG_RS-535,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-551,nomatch,,,,,,, TOBG_RS-562,nomatch,,,,,,, TOBG_RS-570,nomatch,,,,,,, TOBG_RS-582,nomatch,,,,,,, TOBG_RS-590,nomatch,,,,,,, TOBG_RS-604,found,Archaea,Euryarchaeota,,,,, TOBG_RS-605,found,Archaea,Euryarchaeota,,,,, TOBG_RS-615,disagree,Bacteria,,,,,, TOBG_RS-626,nomatch,,,,,,, TOBG_RS-648,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-652,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-654,nomatch,,,,,,, TOBG_RS-659,nomatch,,,,,,, TOBG_RS-665,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-668,nomatch,,,,,,, TOBG_RS-678,nomatch,,,,,,, TOBG_RS-697,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_RS-711,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_RS-723,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_RS-726,nomatch,,,,,,, TOBG_RS-789,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_RS-797,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_RS-799,nomatch,,,,,,, TOBG_RS-813,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-814,nomatch,,,,,,, TOBG_RS-815,nomatch,,,,,,, TOBG_RS-816,nomatch,,,,,,, TOBG_RS-817,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_RS-818,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-819,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-820,nomatch,,,,,,, TOBG_RS-821,found,Bacteria,Firmicutes,Bacilli,,,, TOBG_RS-822,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_RS-823,nomatch,,,,,,, TOBG_RS-824,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_RS-825,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_RS-826,found,Bacteria,Proteobacteria,,,,, TOBG_SAT-1,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_SAT-10,nomatch,,,,,,, TOBG_SAT-100,nomatch,,,,,,, TOBG_SAT-101,nomatch,,,,,,, TOBG_SAT-102,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-103,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SAT-104,nomatch,,,,,,, TOBG_SAT-105,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SAT-106,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SAT-107,nomatch,,,,,,, TOBG_SAT-108,nomatch,,,,,,, TOBG_SAT-109,nomatch,,,,,,, TOBG_SAT-11,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-110,nomatch,,,,,,, TOBG_SAT-111,nomatch,,,,,,, TOBG_SAT-112,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,Idiomarina, TOBG_SAT-113,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,, TOBG_SAT-114,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,Maribacter, TOBG_SAT-115,nomatch,,,,,,, TOBG_SAT-116,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-117,found,Bacteria,Bacteroidetes,,,,, TOBG_SAT-118,nomatch,,,,,,, TOBG_SAT-119,nomatch,,,,,,, TOBG_SAT-12,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SAT-120,nomatch,,,,,,, TOBG_SAT-121,nomatch,,,,,,, TOBG_SAT-122,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-123,nomatch,,,,,,, TOBG_SAT-124,nomatch,,,,,,, TOBG_SAT-125,nomatch,,,,,,, TOBG_SAT-126,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-127,nomatch,,,,,,, TOBG_SAT-128,nomatch,,,,,,, TOBG_SAT-1289,nomatch,,,,,,, TOBG_SAT-129,nomatch,,,,,,, TOBG_SAT-1290,nomatch,,,,,,, TOBG_SAT-13,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-130,nomatch,,,,,,, TOBG_SAT-1300,nomatch,,,,,,, TOBG_SAT-1301,nomatch,,,,,,, TOBG_SAT-1302,nomatch,,,,,,, TOBG_SAT-1308,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassospira, TOBG_SAT-1309,nomatch,,,,,,, TOBG_SAT-131,nomatch,,,,,,, TOBG_SAT-132,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SAT-1328,nomatch,,,,,,, TOBG_SAT-1329,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-133,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SAT-1331,nomatch,,,,,,, TOBG_SAT-1332,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_SAT-1333,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SAT-1339,found,Bacteria,Acidobacteria,,,,, TOBG_SAT-134,nomatch,,,,,,, TOBG_SAT-1340,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SAT-1342,nomatch,,,,,,, TOBG_SAT-1344,nomatch,,,,,,, TOBG_SAT-1345,nomatch,,,,,,, TOBG_SAT-1346,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SAT-1347,nomatch,,,,,,, TOBG_SAT-135,nomatch,,,,,,, TOBG_SAT-1356,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_SAT-1358,nomatch,,,,,,, TOBG_SAT-1359,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-136,nomatch,,,,,,, TOBG_SAT-1366,found,Bacteria,,,,,, TOBG_SAT-1367,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_SAT-1369,nomatch,,,,,,, TOBG_SAT-137,nomatch,,,,,,, TOBG_SAT-138,nomatch,,,,,,, TOBG_SAT-1383,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_SAT-1384,nomatch,,,,,,, TOBG_SAT-1386,nomatch,,,,,,, TOBG_SAT-1387,nomatch,,,,,,, TOBG_SAT-1388,nomatch,,,,,,, TOBG_SAT-139,nomatch,,,,,,, TOBG_SAT-1390,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-1391,found,Bacteria,Acidobacteria,,,,, TOBG_SAT-14,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SAT-140,nomatch,,,,,,, TOBG_SAT-1401,nomatch,,,,,,, TOBG_SAT-1403,nomatch,,,,,,, TOBG_SAT-1404,nomatch,,,,,,, TOBG_SAT-1405,nomatch,,,,,,, TOBG_SAT-141,nomatch,,,,,,, TOBG_SAT-1415,nomatch,,,,,,, TOBG_SAT-1416,found,Bacteria,Proteobacteria,,,,, TOBG_SAT-142,nomatch,,,,,,, TOBG_SAT-1429,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_SAT-143,nomatch,,,,,,, TOBG_SAT-1430,nomatch,,,,,,, TOBG_SAT-1431,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SAT-1433,nomatch,,,,,,, TOBG_SAT-1434,nomatch,,,,,,, TOBG_SAT-144,nomatch,,,,,,, TOBG_SAT-1446,nomatch,,,,,,, TOBG_SAT-1448,nomatch,,,,,,, TOBG_SAT-1449,nomatch,,,,,,, TOBG_SAT-145,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-1450,nomatch,,,,,,, TOBG_SAT-1451,nomatch,,,,,,, TOBG_SAT-146,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_SAT-1460,nomatch,,,,,,, TOBG_SAT-1461,nomatch,,,,,,, TOBG_SAT-1462,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas, TOBG_SAT-1464,nomatch,,,,,,, TOBG_SAT-147,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SAT-1475,found,Bacteria,Proteobacteria,,,,, TOBG_SAT-1476,nomatch,,,,,,, TOBG_SAT-148,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,,, TOBG_SAT-1485,nomatch,,,,,,, TOBG_SAT-1487,nomatch,,,,,,, TOBG_SAT-1488,found,Bacteria,Bacteroidetes,Cytophagia,,,, TOBG_SAT-1489,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-149,nomatch,,,,,,, TOBG_SAT-15,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-150,nomatch,,,,,,, TOBG_SAT-1504,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-1505,nomatch,,,,,,, TOBG_SAT-1507,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-1508,nomatch,,,,,,, TOBG_SAT-1509,nomatch,,,,,,, TOBG_SAT-151,nomatch,,,,,,, TOBG_SAT-1510,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-1517,nomatch,,,,,,, TOBG_SAT-152,nomatch,,,,,,, TOBG_SAT-153,found,Bacteria,Bacteroidetes,Sphingobacteriia,Sphingobacteriales,Saprospiraceae,, TOBG_SAT-1530,nomatch,,,,,,, TOBG_SAT-154,nomatch,,,,,,, TOBG_SAT-155,nomatch,,,,,,, TOBG_SAT-1551,nomatch,,,,,,, TOBG_SAT-1553,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-1554,nomatch,,,,,,, TOBG_SAT-156,nomatch,,,,,,, TOBG_SAT-1560,nomatch,,,,,,, TOBG_SAT-1561,nomatch,,,,,,, TOBG_SAT-1567,nomatch,,,,,,, TOBG_SAT-1568,nomatch,,,,,,, TOBG_SAT-157,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-1576,nomatch,,,,,,, TOBG_SAT-1578,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SAT-1579,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-158,nomatch,,,,,,, TOBG_SAT-1581,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SAT-1582,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SAT-1584,found,Bacteria,Proteobacteria,,,,, TOBG_SAT-1585,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SAT-159,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SAT-16,nomatch,,,,,,, TOBG_SAT-160,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-1607,found,Bacteria,Proteobacteria,,,,, TOBG_SAT-1608,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SAT-1609,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-161,nomatch,,,,,,, TOBG_SAT-1610,nomatch,,,,,,, TOBG_SAT-1611,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_SAT-1613,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SAT-1614,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SAT-162,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_SAT-163,nomatch,,,,,,, TOBG_SAT-164,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-165,nomatch,,,,,,, TOBG_SAT-166,nomatch,,,,,,, TOBG_SAT-167,nomatch,,,,,,, TOBG_SAT-168,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SAT-169,nomatch,,,,,,, TOBG_SAT-17,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SAT-170,nomatch,,,,,,, TOBG_SAT-171,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-172,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SAT-173,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SAT-174,nomatch,,,,,,, TOBG_SAT-175,nomatch,,,,,,, TOBG_SAT-176,nomatch,,,,,,, TOBG_SAT-177,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-178,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-179,nomatch,,,,,,, TOBG_SAT-18,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SAT-180,nomatch,,,,,,, TOBG_SAT-181,nomatch,,,,,,, TOBG_SAT-182,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-183,nomatch,,,,,,, TOBG_SAT-184,found,Bacteria,Cyanobacteria,Chroococcales,Chroococcales,Cyanothece,Cyanothece, TOBG_SAT-185,nomatch,,,,,,, TOBG_SAT-186,nomatch,,,,,,, TOBG_SAT-187,nomatch,,,,,,, TOBG_SAT-188,nomatch,,,,,,, TOBG_SAT-189,nomatch,,,,,,, TOBG_SAT-19,nomatch,,,,,,, TOBG_SAT-190,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-191,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-192,nomatch,,,,,,, TOBG_SAT-193,nomatch,,,,,,, TOBG_SAT-194,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis,Oceanicaulis_alexandrii TOBG_SAT-195,found,Bacteria,Proteobacteria,,,,, TOBG_SAT-196,nomatch,,,,,,, TOBG_SAT-197,found,Bacteria,,,,,, TOBG_SAT-198,nomatch,,,,,,, TOBG_SAT-199,nomatch,,,,,,, TOBG_SAT-2,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,, TOBG_SAT-20,nomatch,,,,,,, TOBG_SAT-200,nomatch,,,,,,, TOBG_SAT-201,nomatch,,,,,,, TOBG_SAT-202,nomatch,,,,,,, TOBG_SAT-203,nomatch,,,,,,, TOBG_SAT-204,nomatch,,,,,,, TOBG_SAT-205,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-206,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-207,nomatch,,,,,,, TOBG_SAT-208,nomatch,,,,,,, TOBG_SAT-209,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_SAT-21,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,, TOBG_SAT-210,nomatch,,,,,,, TOBG_SAT-211,found,Bacteria,,,,,, TOBG_SAT-212,nomatch,,,,,,, TOBG_SAT-213,nomatch,,,,,,, TOBG_SAT-214,nomatch,,,,,,, TOBG_SAT-215,nomatch,,,,,,, TOBG_SAT-216,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Sphingomonadaceae_3,Sphingobium, TOBG_SAT-217,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_SAT-218,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-219,nomatch,,,,,,, TOBG_SAT-22,nomatch,,,,,,, TOBG_SAT-220,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-221,nomatch,,,,,,, TOBG_SAT-222,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_SAT-223,nomatch,,,,,,, TOBG_SAT-224,nomatch,,,,,,, TOBG_SAT-225,nomatch,,,,,,, TOBG_SAT-226,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SAT-227,nomatch,,,,,,, TOBG_SAT-228,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SAT-229,nomatch,,,,,,, TOBG_SAT-23,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis, TOBG_SAT-230,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_SAT-24,nomatch,,,,,,, TOBG_SAT-25,nomatch,,,,,,, TOBG_SAT-2587,nomatch,,,,,,, TOBG_SAT-2594,nomatch,,,,,,, TOBG_SAT-2595,nomatch,,,,,,, TOBG_SAT-26,nomatch,,,,,,, TOBG_SAT-2604,nomatch,,,,,,, TOBG_SAT-2610,nomatch,,,,,,, TOBG_SAT-2619,nomatch,,,,,,, TOBG_SAT-2654,nomatch,,,,,,, TOBG_SAT-2681,nomatch,,,,,,, TOBG_SAT-2699,nomatch,,,,,,, TOBG_SAT-27,nomatch,,,,,,, TOBG_SAT-2701,nomatch,,,,,,, TOBG_SAT-2727,nomatch,,,,,,, TOBG_SAT-2731,nomatch,,,,,,, TOBG_SAT-2734,nomatch,,,,,,, TOBG_SAT-2750,nomatch,,,,,,, TOBG_SAT-2751,nomatch,,,,,,, TOBG_SAT-2755,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SAT-2756,nomatch,,,,,,, TOBG_SAT-2766,nomatch,,,,,,, TOBG_SAT-2781,nomatch,,,,,,, TOBG_SAT-2787,nomatch,,,,,,, TOBG_SAT-2788,nomatch,,,,,,, TOBG_SAT-2797,nomatch,,,,,,, TOBG_SAT-28,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassospira, TOBG_SAT-2804,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SAT-2814,nomatch,,,,,,, TOBG_SAT-2823,found,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Flammeovirgaceae,, TOBG_SAT-2856,nomatch,,,,,,, TOBG_SAT-2866,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassospira, TOBG_SAT-29,nomatch,,,,,,, TOBG_SAT-2910,nomatch,,,,,,, TOBG_SAT-2911,nomatch,,,,,,, TOBG_SAT-2956,nomatch,,,,,,, TOBG_SAT-2963,nomatch,,,,,,, TOBG_SAT-2971,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-2981,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SAT-2985,nomatch,,,,,,, TOBG_SAT-2989,nomatch,,,,,,, TOBG_SAT-2990,nomatch,,,,,,, TOBG_SAT-2998,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SAT-3,nomatch,,,,,,, TOBG_SAT-30,nomatch,,,,,,, TOBG_SAT-3008,nomatch,,,,,,, TOBG_SAT-3021,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SAT-3034,found,Bacteria,Bacteroidetes,Sphingobacteriia,Sphingobacteriales,Saprospiraceae,, TOBG_SAT-3048,nomatch,,,,,,, TOBG_SAT-3076,nomatch,,,,,,, TOBG_SAT-3078,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SAT-31,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,Haliea, TOBG_SAT-32,found,Eukaryota,Chlorophyta,Prasinophyceae,Mamiellales,Mamiellaceae,Bathycoccus, TOBG_SAT-33,nomatch,,,,,,, TOBG_SAT-34,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,, TOBG_SAT-35,nomatch,,,,,,, TOBG_SAT-36,nomatch,,,,,,, TOBG_SAT-37,nomatch,,,,,,, TOBG_SAT-38,nomatch,,,,,,, TOBG_SAT-39,nomatch,,,,,,, TOBG_SAT-4,nomatch,,,,,,, TOBG_SAT-40,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_SAT-41,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SAT-42,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SAT-43,nomatch,,,,,,, TOBG_SAT-44,found,Archaea,Euryarchaeota,,,,, TOBG_SAT-45,nomatch,,,,,,, TOBG_SAT-46,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,Microbacterium, TOBG_SAT-47,found,Bacteria,,,,,, TOBG_SAT-48,nomatch,,,,,,, TOBG_SAT-49,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SAT-5,nomatch,,,,,,, TOBG_SAT-50,nomatch,,,,,,, TOBG_SAT-51,nomatch,,,,,,, TOBG_SAT-52,nomatch,,,,,,, TOBG_SAT-53,nomatch,,,,,,, TOBG_SAT-54,nomatch,,,,,,, TOBG_SAT-55,nomatch,,,,,,, TOBG_SAT-56,nomatch,,,,,,, TOBG_SAT-57,nomatch,,,,,,, TOBG_SAT-58,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_SAT-59,nomatch,,,,,,, TOBG_SAT-6,nomatch,,,,,,, TOBG_SAT-60,nomatch,,,,,,, TOBG_SAT-61,nomatch,,,,,,, TOBG_SAT-62,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,,, TOBG_SAT-63,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_SAT-64,nomatch,,,,,,, TOBG_SAT-65,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,, TOBG_SAT-66,nomatch,,,,,,, TOBG_SAT-67,nomatch,,,,,,, TOBG_SAT-68,nomatch,,,,,,, TOBG_SAT-69,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_2,Pseudoalteromonadaceae,Pseudoalteromonas, TOBG_SAT-7,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,Citromicrobium, TOBG_SAT-70,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_SAT-71,nomatch,,,,,,, TOBG_SAT-72,nomatch,,,,,,, TOBG_SAT-73,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_SAT-74,nomatch,,,,,,, TOBG_SAT-75,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_SAT-76,nomatch,,,,,,, TOBG_SAT-77,nomatch,,,,,,, TOBG_SAT-78,nomatch,,,,,,, TOBG_SAT-79,nomatch,,,,,,, TOBG_SAT-8,nomatch,,,,,,, TOBG_SAT-80,nomatch,,,,,,, TOBG_SAT-81,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_SAT-82,nomatch,,,,,,, TOBG_SAT-83,nomatch,,,,,,, TOBG_SAT-84,nomatch,,,,,,, TOBG_SAT-85,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,,, TOBG_SAT-86,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SAT-87,nomatch,,,,,,, TOBG_SAT-88,nomatch,,,,,,, TOBG_SAT-89,nomatch,,,,,,, TOBG_SAT-9,nomatch,,,,,,, TOBG_SAT-90,nomatch,,,,,,, TOBG_SAT-91,nomatch,,,,,,, TOBG_SAT-92,nomatch,,,,,,, TOBG_SAT-93,nomatch,,,,,,, TOBG_SAT-94,nomatch,,,,,,, TOBG_SAT-95,nomatch,,,,,,, TOBG_SAT-96,nomatch,,,,,,, TOBG_SAT-97,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_SAT-98,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_SAT-99,nomatch,,,,,,, TOBG_SP-1,nomatch,,,,,,, TOBG_SP-10,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-100,nomatch,,,,,,, TOBG_SP-101,nomatch,,,,,,, TOBG_SP-102,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-103,nomatch,,,,,,, TOBG_SP-104,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-105,nomatch,,,,,,, TOBG_SP-106,nomatch,,,,,,, TOBG_SP-107,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-108,nomatch,,,,,,, TOBG_SP-109,found,Bacteria,Proteobacteria,Deltaproteobacteria,Desulfovibrionales,Desulfovibrionaceae,Desulfovibrio, TOBG_SP-11,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-110,nomatch,,,,,,, TOBG_SP-111,nomatch,,,,,,, TOBG_SP-112,nomatch,,,,,,, TOBG_SP-113,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-114,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-115,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-116,nomatch,,,,,,, TOBG_SP-117,nomatch,,,,,,, TOBG_SP-118,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobacteraceae,, TOBG_SP-119,found,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Flammeovirgaceae,, TOBG_SP-12,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_SP-120,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_SP-121,disagree,Bacteria,,,,,, TOBG_SP-122,nomatch,,,,,,, TOBG_SP-123,nomatch,,,,,,, TOBG_SP-124,nomatch,,,,,,, TOBG_SP-125,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-126,nomatch,,,,,,, TOBG_SP-127,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-128,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-129,nomatch,,,,,,, TOBG_SP-13,nomatch,,,,,,, TOBG_SP-130,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Microbacteriaceae,, TOBG_SP-131,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,Thalassospira, TOBG_SP-132,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_SP-133,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Pseudomonadaceae,Pseudomonas,Pseudomonas_mendocina TOBG_SP-134,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-135,nomatch,,,,,,, TOBG_SP-136,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,, TOBG_SP-137,nomatch,,,,,,, TOBG_SP-138,nomatch,,,,,,, TOBG_SP-139,nomatch,,,,,,, TOBG_SP-14,nomatch,,,,,,, TOBG_SP-140,nomatch,,,,,,, TOBG_SP-141,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-142,nomatch,,,,,,, TOBG_SP-143,nomatch,,,,,,, TOBG_SP-144,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,, TOBG_SP-145,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-146,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-147,nomatch,,,,,,, TOBG_SP-148,nomatch,,,,,,, TOBG_SP-149,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-15,found,Bacteria,Proteobacteria,,,,, TOBG_SP-150,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_SP-151,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SP-152,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Oceanospirillaceae,, TOBG_SP-153,nomatch,,,,,,, TOBG_SP-154,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-155,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,Planctomyces, TOBG_SP-156,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-157,nomatch,,,,,,, TOBG_SP-158,nomatch,,,,,,, TOBG_SP-159,nomatch,,,,,,, TOBG_SP-16,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,,, TOBG_SP-160,nomatch,,,,,,, TOBG_SP-161,nomatch,,,,,,, TOBG_SP-162,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-163,nomatch,,,,,,, TOBG_SP-164,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-165,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-166,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-167,nomatch,,,,,,, TOBG_SP-168,nomatch,,,,,,, TOBG_SP-169,nomatch,,,,,,, TOBG_SP-17,nomatch,,,,,,, TOBG_SP-170,nomatch,,,,,,, TOBG_SP-171,nomatch,,,,,,, TOBG_SP-172,nomatch,,,,,,, TOBG_SP-173,nomatch,,,,,,, TOBG_SP-174,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SP-175,nomatch,,,,,,, TOBG_SP-176,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-177,nomatch,,,,,,, TOBG_SP-178,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-179,nomatch,,,,,,, TOBG_SP-18,nomatch,,,,,,, TOBG_SP-180,nomatch,,,,,,, TOBG_SP-181,nomatch,,,,,,, TOBG_SP-182,nomatch,,,,,,, TOBG_SP-183,nomatch,,,,,,, TOBG_SP-184,nomatch,,,,,,, TOBG_SP-185,nomatch,,,,,,, TOBG_SP-186,nomatch,,,,,,, TOBG_SP-187,nomatch,,,,,,, TOBG_SP-188,nomatch,,,,,,, TOBG_SP-189,nomatch,,,,,,, TOBG_SP-19,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,, TOBG_SP-190,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-191,nomatch,,,,,,, TOBG_SP-192,nomatch,,,,,,, TOBG_SP-193,nomatch,,,,,,, TOBG_SP-194,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-195,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SP-196,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-197,nomatch,,,,,,, TOBG_SP-198,nomatch,,,,,,, TOBG_SP-199,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-2,nomatch,,,,,,, TOBG_SP-20,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales_2,Hyphomonadaceae,, TOBG_SP-200,nomatch,,,,,,, TOBG_SP-201,nomatch,,,,,,, TOBG_SP-202,nomatch,,,,,,, TOBG_SP-203,found,Bacteria,Acidobacteria,,,,, TOBG_SP-204,nomatch,,,,,,, TOBG_SP-205,nomatch,,,,,,, TOBG_SP-206,nomatch,,,,,,, TOBG_SP-207,nomatch,,,,,,, TOBG_SP-208,found,Archaea,Euryarchaeota,,,,, TOBG_SP-209,nomatch,,,,,,, TOBG_SP-21,nomatch,,,,,,, TOBG_SP-210,found,Archaea,Euryarchaeota,,,,, TOBG_SP-211,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_SP-212,nomatch,,,,,,, TOBG_SP-213,nomatch,,,,,,, TOBG_SP-214,nomatch,,,,,,, TOBG_SP-215,nomatch,,,,,,, TOBG_SP-216,disagree,Bacteria,,,,,, TOBG_SP-217,nomatch,,,,,,, TOBG_SP-218,nomatch,,,,,,, TOBG_SP-219,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SP-22,nomatch,,,,,,, TOBG_SP-220,nomatch,,,,,,, TOBG_SP-221,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_SP-222,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-223,nomatch,,,,,,, TOBG_SP-224,nomatch,,,,,,, TOBG_SP-225,nomatch,,,,,,, TOBG_SP-226,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,,, TOBG_SP-227,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-228,found,Bacteria,Proteobacteria,Gammaproteobacteria,Legionellales,,, TOBG_SP-229,nomatch,,,,,,, TOBG_SP-23,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_SP-230,nomatch,,,,,,, TOBG_SP-231,nomatch,,,,,,, TOBG_SP-232,nomatch,,,,,,, TOBG_SP-233,nomatch,,,,,,, TOBG_SP-234,disagree,Bacteria,,,,,, TOBG_SP-235,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_SP-236,nomatch,,,,,,, TOBG_SP-237,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_SP-238,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_SP-239,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-24,nomatch,,,,,,, TOBG_SP-240,nomatch,,,,,,, TOBG_SP-241,found,Bacteria,Verrucomicrobia,Opitutae,Opitutales,,, TOBG_SP-242,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-243,nomatch,,,,,,, TOBG_SP-244,found,Bacteria,Proteobacteria,Gammaproteobacteria,Xanthomonadales,,, TOBG_SP-245,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,Rhodobacteraceae,, TOBG_SP-246,found,Bacteria,Proteobacteria,Alphaproteobacteria,Sphingomonadales,Erythrobacteraceae,, TOBG_SP-247,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-248,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-249,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-25,nomatch,,,,,,, TOBG_SP-250,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-251,found,Bacteria,Proteobacteria,,,,, TOBG_SP-252,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-253,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-254,nomatch,,,,,,, TOBG_SP-255,nomatch,,,,,,, TOBG_SP-256,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-257,nomatch,,,,,,, TOBG_SP-258,found,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Spirochaetaceae,, TOBG_SP-259,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Idiomarinaceae,Idiomarina, TOBG_SP-26,nomatch,,,,,,, TOBG_SP-260,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-261,nomatch,,,,,,, TOBG_SP-262,nomatch,,,,,,, TOBG_SP-263,nomatch,,,,,,, TOBG_SP-264,disagree,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-265,nomatch,,,,,,, TOBG_SP-266,nomatch,,,,,,, TOBG_SP-267,nomatch,,,,,,, TOBG_SP-268,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,, TOBG_SP-269,nomatch,,,,,,, TOBG_SP-27,nomatch,,,,,,, TOBG_SP-270,nomatch,,,,,,, TOBG_SP-271,nomatch,,,,,,, TOBG_SP-272,found,Archaea,Euryarchaeota,,,,, TOBG_SP-273,nomatch,,,,,,, TOBG_SP-274,nomatch,,,,,,, TOBG_SP-275,nomatch,,,,,,, TOBG_SP-276,found,Bacteria,Candidatus_Marinimicrobia ,,,,, TOBG_SP-277,found,Archaea,Euryarchaeota,,,,, TOBG_SP-278,found,Bacteria,Actinobacteria,Actinobacteria,Actinomycetales,Mycobacteriaceae,Mycobacterium, TOBG_SP-279,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SP-28,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_SP-280,nomatch,,,,,,, TOBG_SP-281,nomatch,,,,,,, TOBG_SP-282,nomatch,,,,,,, TOBG_SP-283,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-284,nomatch,,,,,,, TOBG_SP-285,nomatch,,,,,,, TOBG_SP-286,nomatch,,,,,,, TOBG_SP-287,nomatch,,,,,,, TOBG_SP-288,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Halomonadaceae,, TOBG_SP-289,nomatch,,,,,,, TOBG_SP-29,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-290,nomatch,,,,,,, TOBG_SP-291,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,Pseudomonadaceae,Pseudomonas,Pseudomonas_mendocina TOBG_SP-292,found,Bacteria,Bacteroidetes,,,,, TOBG_SP-293,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-294,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-295,nomatch,,,,,,, TOBG_SP-296,nomatch,,,,,,, TOBG_SP-2968,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-297,nomatch,,,,,,, TOBG_SP-2970,found,Bacteria,,,,,, TOBG_SP-2971,disagree,Bacteria,,,,,, TOBG_SP-2972,nomatch,,,,,,, TOBG_SP-2974,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-2975,nomatch,,,,,,, TOBG_SP-2976,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-2977,nomatch,,,,,,, TOBG_SP-2978,nomatch,,,,,,, TOBG_SP-2979,nomatch,,,,,,, TOBG_SP-298,nomatch,,,,,,, TOBG_SP-2981,nomatch,,,,,,, TOBG_SP-2982,nomatch,,,,,,, TOBG_SP-2983,nomatch,,,,,,, TOBG_SP-2985,nomatch,,,,,,, TOBG_SP-2986,found,Bacteria,,,,,, TOBG_SP-2987,nomatch,,,,,,, TOBG_SP-2989,nomatch,,,,,,, TOBG_SP-299,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_SP-2992,nomatch,,,,,,, TOBG_SP-2993,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,,, TOBG_SP-2995,nomatch,,,,,,, TOBG_SP-2997,nomatch,,,,,,, TOBG_SP-2998,nomatch,,,,,,, TOBG_SP-2999,nomatch,,,,,,, TOBG_SP-3,nomatch,,,,,,, TOBG_SP-30,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-300,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Rhodobacteraceae,Roseovarius, TOBG_SP-3001,nomatch,,,,,,, TOBG_SP-3003,nomatch,,,,,,, TOBG_SP-3004,nomatch,,,,,,, TOBG_SP-3005,found,Bacteria,Proteobacteria,Deltaproteobacteria,,,, TOBG_SP-3006,nomatch,,,,,,, TOBG_SP-3007,nomatch,,,,,,, TOBG_SP-3009,nomatch,,,,,,, TOBG_SP-301,nomatch,,,,,,, TOBG_SP-3010,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3012,nomatch,,,,,,, TOBG_SP-3013,nomatch,,,,,,, TOBG_SP-3014,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3016,nomatch,,,,,,, TOBG_SP-3017,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-3018,found,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Cyclobacteriaceae,Algoriphagus, TOBG_SP-302,nomatch,,,,,,, TOBG_SP-3020,nomatch,,,,,,, TOBG_SP-3021,nomatch,,,,,,, TOBG_SP-3022,found,Bacteria,Acidobacteria,,,,, TOBG_SP-3023,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-3024,nomatch,,,,,,, TOBG_SP-3025,nomatch,,,,,,, TOBG_SP-3027,nomatch,,,,,,, TOBG_SP-3028,disagree,Bacteria,,,,,, TOBG_SP-3029,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-303,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3030,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-3031,nomatch,,,,,,, TOBG_SP-3034,nomatch,,,,,,, TOBG_SP-3035,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-3036,nomatch,,,,,,, TOBG_SP-3037,nomatch,,,,,,, TOBG_SP-3038,nomatch,,,,,,, TOBG_SP-304,nomatch,,,,,,, TOBG_SP-3040,nomatch,,,,,,, TOBG_SP-3041,nomatch,,,,,,, TOBG_SP-3042,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3044,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-3045,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-3046,nomatch,,,,,,, TOBG_SP-3048,nomatch,,,,,,, TOBG_SP-3049,nomatch,,,,,,, TOBG_SP-305,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-3050,nomatch,,,,,,, TOBG_SP-3051,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3053,found,Bacteria,Proteobacteria,,,,, TOBG_SP-3054,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3055,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,,, TOBG_SP-3056,found,Bacteria,,,,,, TOBG_SP-3058,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rickettsiales,,, TOBG_SP-3059,found,Archaea,Euryarchaeota,,,,, TOBG_SP-306,found,Bacteria,Proteobacteria,,,,, TOBG_SP-3060,nomatch,,,,,,, TOBG_SP-3061,nomatch,,,,,,, TOBG_SP-3062,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3063,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3065,nomatch,,,,,,, TOBG_SP-3066,nomatch,,,,,,, TOBG_SP-3067,nomatch,,,,,,, TOBG_SP-3068,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3069,disagree,Bacteria,Proteobacteria,,,,, TOBG_SP-307,nomatch,,,,,,, TOBG_SP-3070,nomatch,,,,,,, TOBG_SP-3072,nomatch,,,,,,, TOBG_SP-3073,nomatch,,,,,,, TOBG_SP-3074,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3075,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-3078,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-308,nomatch,,,,,,, TOBG_SP-3081,nomatch,,,,,,, TOBG_SP-3082,nomatch,,,,,,, TOBG_SP-3083,nomatch,,,,,,, TOBG_SP-3084,nomatch,,,,,,, TOBG_SP-3086,nomatch,,,,,,, TOBG_SP-3087,nomatch,,,,,,, TOBG_SP-3088,nomatch,,,,,,, TOBG_SP-3089,nomatch,,,,,,, TOBG_SP-309,nomatch,,,,,,, TOBG_SP-3091,found,Bacteria,,,,,, TOBG_SP-3092,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3093,nomatch,,,,,,, TOBG_SP-3094,nomatch,,,,,,, TOBG_SP-3096,found,Bacteria,Chloroflexi,,,,, TOBG_SP-3097,nomatch,,,,,,, TOBG_SP-3098,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-31,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-310,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_SP-3100,nomatch,,,,,,, TOBG_SP-3101,nomatch,,,,,,, TOBG_SP-3102,nomatch,,,,,,, TOBG_SP-3103,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-3105,nomatch,,,,,,, TOBG_SP-3106,nomatch,,,,,,, TOBG_SP-3107,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3109,nomatch,,,,,,, TOBG_SP-311,found,Bacteria,,,,,, TOBG_SP-3110,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis, TOBG_SP-3111,nomatch,,,,,,, TOBG_SP-3112,nomatch,,,,,,, TOBG_SP-3114,nomatch,,,,,,, TOBG_SP-3115,found,Archaea,Euryarchaeota,,,,, TOBG_SP-3117,nomatch,,,,,,, TOBG_SP-3118,nomatch,,,,,,, TOBG_SP-3119,nomatch,,,,,,, TOBG_SP-312,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales_3,Alteromonadaceae,, TOBG_SP-313,nomatch,,,,,,, TOBG_SP-314,nomatch,,,,,,, TOBG_SP-315,nomatch,,,,,,, TOBG_SP-316,nomatch,,,,,,, TOBG_SP-317,nomatch,,,,,,, TOBG_SP-318,nomatch,,,,,,, TOBG_SP-319,nomatch,,,,,,, TOBG_SP-32,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_SP-320,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-321,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-322,nomatch,,,,,,, TOBG_SP-323,nomatch,,,,,,, TOBG_SP-324,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,,, TOBG_SP-325,nomatch,,,,,,, TOBG_SP-326,nomatch,,,,,,, TOBG_SP-327,nomatch,,,,,,, TOBG_SP-328,nomatch,,,,,,, TOBG_SP-329,nomatch,,,,,,, TOBG_SP-33,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_SP-330,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-331,found,Bacteria,Proteobacteria,Gammaproteobacteria,Chromatiales,,, TOBG_SP-332,found,Bacteria,Spirochaetes,Spirochaetia,Spirochaetales,Leptospiraceae,, TOBG_SP-333,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_SP-334,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_SP-335,nomatch,,,,,,, TOBG_SP-336,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SP-337,nomatch,,,,,,, TOBG_SP-338,found,Archaea,Euryarchaeota,,,,, TOBG_SP-339,nomatch,,,,,,, TOBG_SP-34,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-340,found,Bacteria,Proteobacteria,,,,, TOBG_SP-341,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Cryomorphaceae,, TOBG_SP-342,nomatch,,,,,,, TOBG_SP-343,nomatch,,,,,,, TOBG_SP-344,nomatch,,,,,,, TOBG_SP-345,found,Bacteria,Proteobacteria,Gammaproteobacteria,Pseudomonadales,,, TOBG_SP-346,nomatch,,,,,,, TOBG_SP-347,found,Bacteria,Proteobacteria,Betaproteobacteria,Burkholderiales,,, TOBG_SP-348,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SP-349,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SP-35,found,Bacteria,Proteobacteria,,,,, TOBG_SP-350,nomatch,,,,,,, TOBG_SP-351,nomatch,,,,,,, TOBG_SP-352,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-353,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-354,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_SP-355,nomatch,,,,,,, TOBG_SP-356,found,Bacteria,,,,,, TOBG_SP-357,found,Bacteria,,,,,, TOBG_SP-358,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-359,nomatch,,,,,,, TOBG_SP-36,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alteromonadaceae_2,Marinobacter, TOBG_SP-360,nomatch,,,,,,, TOBG_SP-361,nomatch,,,,,,, TOBG_SP-362,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-37,nomatch,,,,,,, TOBG_SP-38,nomatch,,,,,,, TOBG_SP-39,nomatch,,,,,,, TOBG_SP-3966,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodospirillales,Rhodospirillaceae,, TOBG_SP-3971,nomatch,,,,,,, TOBG_SP-3980,nomatch,,,,,,, TOBG_SP-3982,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-3983,found,Bacteria,Proteobacteria,Alphaproteobacteria,,,, TOBG_SP-3984,nomatch,,,,,,, TOBG_SP-3985,nomatch,,,,,,, TOBG_SP-3992,nomatch,,,,,,, TOBG_SP-4,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-40,nomatch,,,,,,, TOBG_SP-4016,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-4021,nomatch,,,,,,, TOBG_SP-4023,nomatch,,,,,,, TOBG_SP-4025,nomatch,,,,,,, TOBG_SP-4027,nomatch,,,,,,, TOBG_SP-4032,nomatch,,,,,,, TOBG_SP-4038,nomatch,,,,,,, TOBG_SP-4039,nomatch,,,,,,, TOBG_SP-4044,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-4045,nomatch,,,,,,, TOBG_SP-4060,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-4073,nomatch,,,,,,, TOBG_SP-4095,nomatch,,,,,,, TOBG_SP-4097,nomatch,,,,,,, TOBG_SP-41,nomatch,,,,,,, TOBG_SP-4114,nomatch,,,,,,, TOBG_SP-4145,found,Archaea,Euryarchaeota,,,,, TOBG_SP-4150,nomatch,,,,,,, TOBG_SP-4154,nomatch,,,,,,, TOBG_SP-4170,nomatch,,,,,,, TOBG_SP-4189,nomatch,,,,,,, TOBG_SP-42,nomatch,,,,,,, TOBG_SP-4206,nomatch,,,,,,, TOBG_SP-4215,nomatch,,,,,,, TOBG_SP-4220,nomatch,,,,,,, TOBG_SP-4222,nomatch,,,,,,, TOBG_SP-4225,nomatch,,,,,,, TOBG_SP-4236,nomatch,,,,,,, TOBG_SP-4246,found,Bacteria,Chloroflexi,,,,, TOBG_SP-4248,found,Bacteria,Chloroflexi,,,,, TOBG_SP-4251,nomatch,,,,,,, TOBG_SP-4258,nomatch,,,,,,, TOBG_SP-4260,nomatch,,,,,,, TOBG_SP-4269,nomatch,,,,,,, TOBG_SP-4272,nomatch,,,,,,, TOBG_SP-4275,nomatch,,,,,,, TOBG_SP-4288,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhizobiales,,, TOBG_SP-4291,found,Bacteria,Acidobacteria,,,,, TOBG_SP-4294,nomatch,,,,,,, TOBG_SP-43,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-4300,nomatch,,,,,,, TOBG_SP-4355,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-4356,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-4360,nomatch,,,,,,, TOBG_SP-4365,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-4366,found,Bacteria,Planctomycetes,Planctomycetia,Planctomycetales,Planctomycetaceae,, TOBG_SP-4369,nomatch,,,,,,, TOBG_SP-4372,nomatch,,,,,,, TOBG_SP-4374,nomatch,,,,,,, TOBG_SP-4379,nomatch,,,,,,, TOBG_SP-4380,nomatch,,,,,,, TOBG_SP-4387,nomatch,,,,,,, TOBG_SP-4388,nomatch,,,,,,, TOBG_SP-44,found,Bacteria,Bacteroidetes,,,,, TOBG_SP-45,nomatch,,,,,,, TOBG_SP-46,nomatch,,,,,,, TOBG_SP-47,nomatch,,,,,,, TOBG_SP-48,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-486,nomatch,,,,,,, TOBG_SP-49,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SP-5,nomatch,,,,,,, TOBG_SP-50,nomatch,,,,,,, TOBG_SP-51,nomatch,,,,,,, TOBG_SP-52,found,Bacteria,Verrucomicrobia,Verrucomicrobiae,Verrucomicrobiales,Verrucomicrobiaceae,, TOBG_SP-53,nomatch,,,,,,, TOBG_SP-54,nomatch,,,,,,, TOBG_SP-55,nomatch,,,,,,, TOBG_SP-56,nomatch,,,,,,, TOBG_SP-57,nomatch,,,,,,, TOBG_SP-58,nomatch,,,,,,, TOBG_SP-59,nomatch,,,,,,, TOBG_SP-6,nomatch,,,,,,, TOBG_SP-60,nomatch,,,,,,, TOBG_SP-61,nomatch,,,,,,, TOBG_SP-62,nomatch,,,,,,, TOBG_SP-63,nomatch,,,,,,, TOBG_SP-64,found,Bacteria,Proteobacteria,Gammaproteobacteria,Oceanospirillales,Alcanivoracaceae,Alcanivorax, TOBG_SP-65,nomatch,,,,,,, TOBG_SP-66,nomatch,,,,,,, TOBG_SP-67,nomatch,,,,,,, TOBG_SP-68,nomatch,,,,,,, TOBG_SP-69,found,Bacteria,Chloroflexi,Dehalococcoidetes,,,, TOBG_SP-7,nomatch,,,,,,, TOBG_SP-70,nomatch,,,,,,, TOBG_SP-71,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-72,nomatch,,,,,,, TOBG_SP-73,found,Bacteria,Proteobacteria,Alphaproteobacteria,Rhodobacterales,Hyphomonadaceae,Oceanicaulis,Oceanicaulis_alexandrii TOBG_SP-74,nomatch,,,,,,, TOBG_SP-75,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-76,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,Flavobacteriaceae,, TOBG_SP-77,nomatch,,,,,,, TOBG_SP-78,found,Bacteria,Proteobacteria,,,,, TOBG_SP-79,nomatch,,,,,,, TOBG_SP-8,nomatch,,,,,,, TOBG_SP-80,nomatch,,,,,,, TOBG_SP-81,nomatch,,,,,,, TOBG_SP-82,found,Bacteria,Bacteroidetes,Flavobacteriia,Flavobacteriales,,, TOBG_SP-83,nomatch,,,,,,, TOBG_SP-84,found,Bacteria,Spirochaetes,Spirochaetia,,,, TOBG_SP-85,nomatch,,,,,,, TOBG_SP-86,nomatch,,,,,,, TOBG_SP-87,found,Bacteria,Proteobacteria,,,,, TOBG_SP-88,found,Archaea,Euryarchaeota,,,,, TOBG_SP-89,nomatch,,,,,,, TOBG_SP-9,found,Bacteria,Proteobacteria,Deltaproteobacteria,Myxococcales,,, TOBG_SP-90,found,Bacteria,Actinobacteria,Actinobacteria,,,, TOBG_SP-91,nomatch,,,,,,, TOBG_SP-92,nomatch,,,,,,, TOBG_SP-93,nomatch,,,,,,, TOBG_SP-94,nomatch,,,,,,, TOBG_SP-95,nomatch,,,,,,, TOBG_SP-96,disagree,Bacteria,,,,,, TOBG_SP-97,found,Bacteria,Bacteroidetes,Cytophagia,Cytophagales,Flammeovirgaceae,, TOBG_SP-98,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,, TOBG_SP-99,nomatch,,,,,,, sourmash-bio-sourmash-bc3c0f8/tests/test-data/leaves.sbt.json000066400000000000000000000022471474157046300244640ustar00rootroot00000000000000{"factory": {"args": [1,100000,4], "class": "GraphFactory"},"storage": {"args": {"path": ".sbt.v3"}, "backend": "FSStorage"}, "d": 2, "nodes": {"6": {"name": "6d6e87e1154e95b279e5e7db414bc37b", "metadata": "6d6e87e1154e95b279e5e7db414bc37b", "filename": "6d6e87e1154e95b279e5e7db414bc37b"}, "7": {"name": "60f7e23c24a8d94791cc7a8680c493f9", "metadata": "60f7e23c24a8d94791cc7a8680c493f9", "filename": "60f7e23c24a8d94791cc7a8680c493f9"}, "8": {"name": "0107d767a345eff67ecdaed2ee5cd7ba", "metadata": "0107d767a345eff67ecdaed2ee5cd7ba", "filename": "0107d767a345eff67ecdaed2ee5cd7ba"}, "9": {"name": "f71e78178af9e45e6f1d87a0c53c465c", "metadata": "f71e78178af9e45e6f1d87a0c53c465c", "filename": "f71e78178af9e45e6f1d87a0c53c465c"}, "10": {"name": "f0c834bc306651d2b9321fb21d3e8d8f", "metadata": "f0c834bc306651d2b9321fb21d3e8d8f", "filename": "f0c834bc306651d2b9321fb21d3e8d8f"}, "11": {"name": "4e94e60265e04f0763142e20b52c0da1", "metadata": "4e94e60265e04f0763142e20b52c0da1", "filename": "4e94e60265e04f0763142e20b52c0da1"}, "12": {"name": "b59473c94ff2889eca5d7165936e64b3", "metadata": "b59473c94ff2889eca5d7165936e64b3", "filename": "b59473c94ff2889eca5d7165936e64b3"}}, "version": 3} sourmash-bio-sourmash-bc3c0f8/tests/test-data/n10000/000077500000000000000000000000001474157046300223345ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/tests/test-data/n10000/GCF_000005845.2_ASM584v2_genomic.fna.gz.sig.gz000066400000000000000000002662271474157046300313760ustar00rootroot00000000000000/?XGCF_000005845.2_ASM584v2_genomic.fna.gz.sig|lq] c:yayD"%Bb P!eM7Tu*Ofds?'7^3&9sOb3>߼G{~;ڳW}2޷sܭV.\}|zq]i}>Oy^<vg7?)+p͚3䵯;v{l˫8m?<ͷ=l^o6ȶwĤz֒}xͦ}ys߶;[ΖϹO{M^T3&msfKy]=ODjك̞eBʗ5=o;0Z7'4}qCuy5oSV;69]k5ʃ}('>}K6[6F'nm:7FX]3ۻk 9!+O7O3ϡZCNks\ ;+د' 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Bacteroides thetaiotaomicron VPI-5482, complete 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1IS)Ĥt#)g(!Sn({j]сg'oK|gYqHVqhz%1D}yZZj}H?ڔ?2h7f֕}OUAghLJalÐ\)se;yFVg46uԴ/JھVjUsӜ<7!\۟d"!/|F{%!TG_o]8F*BބX[/w3浇u=\4KC3%uZ0DhoZSL]t^>(:N^w}MfPf Tj)|ZEH*tzfl]Koӛʠx Ie=LUL1q kuڅqY=j=`7dFo(}ڏ[~N$^u}9, | NygI3Twqb*,U{Đ26iзΙtk.G*abNs,x 0W4A/ðUhJ=*Gk igoR\ѕ-Q'F5lQS9"!Ȏzb;We&~^ ?m|F$YVܝG6A qp9V^W*^VҴܶ=Z'S:JƘ!&3a̜A( G˨hcZW}jߘ?(=l7ϻy B=zAUOozo/36l}禿ڞqZ (>} aZ{|˵ i{Ohq8U?xf  Mm㵑h[\Ϻ7q7 qv@O(uTa1 wكV𧫸ܛƒ!ήAJxnlW3R7?4=_` 8 X|[u. ijJrA NS<-p>4lZ*/i=0壖pY%aַ;/dr6)3z}~ABBEQ{VuU֋, qTؘ8z#sWM:GgﻷCkX%3PLV^_x>ӓ)-IkAw@-q+SL|/˄z/Y<|b8 8My?ZQvBgnx }-1 juS}.g͂LgPpo >w^/ n72" q~L^za󲾾wcDpgJqNX u9ȥ_W{WB[=$\2lf>a?еבΆ3&9<_lE <|xYϿ /o_O[ ?OuG_|mǿ?o?֟nOh!(sourmash-bio-sourmash-bc3c0f8/tests/test-data/shewanella.faa000066400000000000000000000016051474157046300243140ustar00rootroot00000000000000>WP_006079348.1 MULTISPECIES: glutamine--fructose-6-phosphate transaminase (isomerizing) [Shewanella] MCGIVGAVAQRDVAEILVEGLRRLEYRGYDSAGVAVIHNGELNRTRRVGKVQELSAALETDPLAGGTGIAHTRWATHGEP SERNAHPHLSEGDIAVVHNGIIENHNKLREMLKGLGYKFSSDTDTEVICHLVHHELKTNSTLLSAVQATVKQLEGAYGTV VIDRRDSERLVVARSGSPLVIGFGLGENFVASDQLALLPVTRSFAFLEEGDVAEVTRRSVSIFDLNGNAVEREVKESEIT HDAGDKGEYRHYMLKEIYEQPLALTRTIEGRIANKQVLDTAFGDNAAEFLKDIKHVQIIACGTSYHAGMAARYWLEDWAG VSCNVEIASEFRYRKSHLFPNSLLVTISQSGETADTLAAMRLAKEMGYKATLTICNAPGSSLVRESDMAYMMKAGAEIGV ASTKAFTVQLAGLLMLTAVIGRHNGMSEQMQADITQSLQSMPAKVEQALGLDAAIAELAEDFADKHHALFLGRGDQYPIA MEGALKLKEISYIHAEAYASGELKHGPLALIDADMPVIVVAPNNELLEKLKSNVEEVRARGGLMYVFADVDAEFESDDTM KVIPVPHCDIFMAPLIYTIPLQLLSYHVALIKGTDVDQPRNLAKSVTVE >WP_006079351.1 MULTISPECIES: hypothetical protein [Shewanella] MKGWLILALLAGALYYLYTETDKLDAPIAKTEAMVKKIENKVDSMTGTKIIKIDHKLAKVRTDIVERLSTLELEAFNQIP MTPESIADFKANYCGTMAPEHPVFSKDNQLYLCDHL sourmash-bio-sourmash-bc3c0f8/tests/test-data/short-protein.fa000066400000000000000000000016601474157046300246460ustar00rootroot00000000000000>shortProt MIAAQLLAYYFTELKDDQVKKIDKYLYAMRLSDETLIDIMTRFRKEMKNGLSRDFNPTAT VKMLPTFVRSIPDGSEKGDFIALDLGGSSFRILRVQVNHEKNQNVHMESEVYDTPENIVH GSGSQLFDHVAECLGDFMEKRKIKDKKLPVGFTFSFPCQQSKIDEAILITWTKRFKASGV EGADVVKLLNKAIKKRGDYDANIVAVVNDTVGTMMTCGYDDQHCEVGLIIGTGTNACYME ELRHIDLVEGDEGRMCINTEWGAFGDDGSLEDIRTEFDREIDRGSLNPGKQLFEKMVSGM YLGELVRLILVKMAKEGLLFEGRITPELLTRGKFNTSDVSAIEKNKEGLHNAKEILTRLG VEPSDDDCVSVQHVCTIVSFRSANLVAATLGAILNRLRDNKGTPRLRTTVGVDGSLYKTH PQYSRRFHKTLRRLVPDSDVRFLLSESGSGKGAAMVTAVAYRLAEQHRQIEETLAHFHLT KDMLLEVKKRMRAEMELGLRKQTHNNAVVKMLPSFVRRTPDGTENGDFLALDLGGTNFRV LLVKIRSGKKRTVEMHNKIYAIPIEIMQGTGEELFDHIVSCISDFLDYMGIKGPRMPLGF TFSFPCQQTSLDAGILITWTKGFKATDCVGHDVVTLLRDAIKRREEFDLDVVAVVNDTVG TMMTCAYEEPTCEVGLIVGTGSNACYMEEMKNVEMVEGDQGQMCINMEWGAFGDNGCLDD IRTHYDRLVDEYSLNAGKQRYEKMISGMYLGEIVRNILIDFTKKGFLFRGQISETLKTRG IFETKFLSQIESDRLALLQVRAILQQLGLNSTCDDSILVKTVCGVVSRRAAQLCGAGMAA VVDKIRENRGLDRLNVTVGVDGTLYKLHPHFSRIMHQTVKELSPKCNVSFLLSEDGSGKG AALITAVGVRLRTEASS sourmash-bio-sourmash-bc3c0f8/tests/test-data/short.bad.fa000066400000000000000000000020001474157046300237020ustar00rootroot00000000000000>shortName TCTGATCTCNGGATAAANAAGCGATCCCAGTACATTTGAATGCCCCCGAGTACTNACCTTGTGTGAACCCGATGGTNAATTTATTTTCTGAGNNNGATTCAGGAGANCACCACAACCTGTATGTACCTCCGATGCGCAGAGGCAACTCAGCCTGACTAGCTAGGAATGGCGGGGACGTGTTATATATGACTGATTATATGTGGAACTTAGGTGCCGCGAGCAAGATCTAGACACCCCCAATATTATTTTTCCGACCAACTTACCGGTGTTGCTCCGCCGTATACGTGGACAACTGTGGATACCGAGTCCTTGATAATCCTAATTGTACTCGGGACTTATCACTAGGACAGATGACCTTTGTCTTCTAAATTGGAGATAGTGTAATTTTTATTTAATCAAGATAATTTTGACACTTCTGCAGTGTGCGGACTATACGAGTTAGTGCTCTTTTACATCCGCGTATATCGCAACTTGGCCAATTTACGACGGGGGTGAATCGCGGAATATATCGAATCTCTGTTTAATTCATAAGATCGCACCACTTAATTGTAGACTCCTCGGCTTCGAAACACAGCTAAGGTGACTGTCGAACCCAGAACAGTTTTTAAAAAACGGCAACCTTGCCATAACACCAACATAGCAAACGCAGCCACGTCGTCCGTTCGCCCAAGGGTACATGATGCGTTACCTTAGTGAAGACTAAGCACCGTGTGTCGATAATCATTCTTAAGGTCCTAACATGTTTATACGGCAACCCCTTTGACACACGGCTTCGTTGGTGCGCTCTGGTGACAGGGCGGCCAGAATGCCGCTATCATAAAAACGTCAAAACAACACGGCTAGTTTGGCTGCTAAACAGAGTCTCGTATTCCCCTAATAGTGGCCGACGAACCTTTATTAGGCGAACCACCTGANTATAGTAATCGAATCGCGTGCAGAGTGGCTGCAGAGCCTGTTNTTCGCCGTGTGCTGTCTCCATGTAANCGTCGAAGCCTCCGGACNGCGCTCCAAN sourmash-bio-sourmash-bc3c0f8/tests/test-data/short.fa000066400000000000000000000017641474157046300231750ustar00rootroot00000000000000>shortName TCTGATCTCGGATAAACAAGCGATCCCAGTACATTTGAATGCCCCCGAGTACTACCTTGTGTGAACCCGATGGTAATTTATTTTCTGAGGATTCAGGAGACACCACAACCTGTATGTACCTCCGATGCGCAGAGGCAACTCAGCCTGACTAGCTAGGAATGGCGGGGACGTGTTATATATGACTGATTATATGTGGAACTTAGGTGCCGCGAGCAAGATCTAGACACCCCCAATATTATTTTTCCGACCAACTTACCGGTGTTGCTCCGCCGTATACGTGGACAACTGTGGATACCGAGTCCTTGATAATCCTAATTGTACTCGGGACTTATCACTAGGACAGATGACCTTTGTCTTCTAAATTGGAGATAGTGTAATTTTTATTTAATCAAGATAATTTTGACACTTCTGCAGTGTGCGGACTATACGAGTTAGTGCTCTTTTACATCCGCGTATATCGCAACTTGGCCAATTTACGACGGGGGTGAATCGCGGAATATATCGAATCTCTGTTTAATTCATAAGATCGCACCACTTAATTGTAGACTCCTCGGCTTCGAAACACAGCTAAGGTGACTGTCGAACCCAGAACAGTTTTTAAAAAACGGCAACCTTGCCATAACACCAACATAGCAAACGCAGCCACGTCGTCCGTTCGCCCAAGGGTACATGATGCGTTACCTTAGTGAAGACTAAGCACCGTGTGTCGATAATCATTCTTAAGGTCCTAACATGTTTATACGGCAACCCCTTTGACACACGGCTTCGTTGGTGCGCTCTGGTGACAGGGCGGCCAGAATGCCGCTATCATAAAAACGTCAAAACAACACGGCTAGTTTGGCTGCTAAACAGAGTCTCGTATTCCCCTAATAGTGGCCGACGAACCTTTATTAGGCGAACCACCTGATATAGTAATCGAATCGCGTGCAGAGTGGCTGCAGAGCCTGTTTTCGCCGTGTGCTGTCTCCATGTAACGTCGAAGCCTCCGGACGCGCTCCAA sourmash-bio-sourmash-bc3c0f8/tests/test-data/short.fa.msh.dump000066400000000000000000000466011474157046300247260ustar00rootroot00000000000000murmur64,42,31,short.fa,81188339440733424 91978956896026414 124650281852403290 128099623049835827 144122315219657026 201431308980010980 201712370616985488 205961730522259272 207247978848945480 211764368736119854 224818703640383824 279337963725221385 282031133607518380 308207593024427860 358157291173294434 382219487474240028 389509551693136316 394316682587112380 419091375111332644 425141571918060438 454721074838127424 477743804132329479 560820627468784485 606806605787776148 625217889903139371 649406983667682479 741052918615392521 750645005943143633 763155904808228137 830995918023649904 832698490625475241 863046388343728910 887270775809673993 889777820216232782 901580013236127792 920825504648011332 924605379572117297 947258985708348478 974047736328120627 980650803537148304 988832714191281888 990642519624860243 999516436546736964 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U rMy:" T r:x: S iZ0d R g6MT2 Q `#Ī P ^Բ O Zov N ZMKw M ZY@ L P]} K L[B̡ J L+uH I I?iz H F G G EG4@ F Chg E BX D ?b@ C  -ؙ = ,Dy_ < +wAp ;  - : / 9 D@] 8 ' 7 )K& 6 K 5 ƕ 4 }Tn 3 )p 2 #-# 1 t, 0 1= / 37 . ݜ - ] , ҆ + {ǝL *  ) + ( 寈b; ' %o & ΁@ޗ % N $ S]a # ? " 8A~ ! 3.6  xU  ک;b  O  ~    &FoM  2  ⣐  8  -  <},    O  wfaq  T)    i  4  8_;}  c~  zq  Q  6Xh  ]}=,  ~NGw:  }Q  z6ƴ/  uc?@  t   tJ$  tU  q  q0+  nh7- ~ nG'" } l9 | jq z { gHU- z dl y b| x _ c- w ]bV v [$ u Zb t T` s T{}- r TtN q S p Q>߿ o P=b n BqB m @c!; l @XvE k 9r" j 6d i 5+;~ h 5smi g 5X:jY f 1$ e 0 0~x d "D|l c  F, b  R,) a Z ` \^ _  -ғ ^  \bN ]  E| \  ir [ $ Z  Y :] X |r8 W %m V 2. U Rve T d S  R Bc Q  #I P Lfa O EB N RcǑ6 M Ndߞ L ?b] K A6Ò J m2' I }@ H 0 G G N>X = )m. < u< ; %<2\ :  < 9 ++ 8 kAX# 7 BO 6 ] 5 ߶-? 4 x[ 3 ;fԚ 2 ǡ] 1 \)"n 0  / T-J . OH - M , < + G D * d ) 0(*e ( "t@ ' @Q & & % ~3 $ ui # s~! 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A DP( A DP  A 2]  A J  A 1O @(@M (@QE( @QE @I`{@x(~ @%TKsourmash-bio-sourmash-bc3c0f8/tests/test-data/sqlite/lca-2.sqldb000066400000000000000000024200001474157046300247420ustar00rootroot00000000000000SQLite format 3@  .K  +4  , +} ;/indexsourmash_taxonomy_identsourmash_taxonomyCREATE UNIQUE INDEX sourmash_taxonomy_ident ON sourmash_taxonomy(ident)G //;tablesourmash_taxonomysourmash_taxonomyCREATE TABLE sourmash_taxonomy ( ident TEXT NOT NULL, superkingdom TEXT, phylum TEXT, class TEXT, order_ TEXT, family TEXT, genus TEXT, species TEXT, strain TEXT )3+_indexsourmash_sketch_idxsourmash_hashes CREATE INDEX sourmash_sketch_idx ON sourmash_hashes ( sketch_id ) 7+_indexsourmash_hashval_idx2sourmash_hashesCREATE INDEX sourmash_hashval_idx2 ON sourmash_hashes ( hashval ) 25+indexsourmash_hashval_idxsourmash_hashesCREATE INDEX sourmash_hashval_idx ON sourmash_hashes ( hashval, sketch_id ) t++tablesourmash_hashessourmash_hashesCREATE TABLE sourmash_hashes ( hashval INTEGER NOT NULL, sketch_id INTEGER NOT NULL, FOREIGN KEY (sketch_id) REFERENCES sourmash_sketches (id) )//[tablesourmash_sketchessourmash_sketchesCREATE TABLE sourmash_sketches (id INTEGER PRIMARY KEY, name TEXT, num INTEGER NOT NULL, scaled INTEGER NOT NULL, ksize INTEGER NOT NULL, filename TEXT, moltype TEXT NOT NULL, with_abundance BOOLEAN NOT NULL, md5sum TEXT NOT NULL, seed INTEGER NOT NULL, n_hashes INTEGER NOT NULL, internal_location TEXT, UNIQUE(internal_location, md5sum) )AU/indexsqlite_autoindex_sourmash_sketches_1sourmash_sketches//-tablesourmash_internalsourmash_internalCREATE TABLE sourmash_internal ( key TEXT UNIQUE, value TEXT )AU/indexsqlite_autoindex_sourmash_internal_1sourmash_internal 'SqliteLineage1.0)SqliteManifest1.0#SqliteIndex1.0 'SqliteLineage)SqliteManifest# SqliteIndex p wMNC_011663.1 Shewanella baltica OS223, complete genome63.faDNA38729c6374925585db28916b82a6f513*vp wMNC_009665.1 Shewanella baltica OS185, complete genome47.faDNA09a08691ce52952152f0e866a59f6261*9 %M38729c6374925585db28916b82a6f513$M 09a08691ce52952152f0e866a59f6261*4|vpjd^XRLF@:4.(" 32/1?0O/_.o-,+*/)?(O'_&o%$#"/!? 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H1;GrŇSaǷP؋N#s9nS} g!&XZ~fNr17`6S/Q@V*T?Jf%tE~36keݾ|ɜXNy+~>F,Ɲh ^탂Ǫj}0 bpwLJ 5w BTo7t+V/&VP*p7Q,KMr}b:#g|d~vs0™ {Q[c:<5= =-]9 j1NxZznQ+4Wnr'+xy=%e+ͱ> N?3@1`͇JhBg(Y1G\ɥE,ΛHW06e(d2b4xQ^66TU3r3ȔkN-{0nt\YR:B5(0χ{Wd7˥a7/)ʫ3f1ea^ DYjYUMOL j(k_t \pC(f)gZ-lp֩ˊ^i)W0S} 7dm/STq ~օ{Qb/vRϧsDR~ ;mt4hf':Kfc+Kޕ\^lf"tn9yAnZ&6&,ڛR^~dMCFhç32}*B̝*q(˺[ewu5[mq^0^}Ռ}8@+g+ӞH6*>hs3>4B{/ 8?>>Q&E oEԧϰ N0LJ]}X>BB}`P[zƸfq_v&Vqb=*9ϲN z|L'IT5dDڪƷXrerW&dM?(f]3l_V3N0ѯBoGlqؾyw:k"Mn' ?ۿ?WW_!{oW_-+=G{{7RtU~k"<0?_4?S߯?T8z{?IoQ}L-UR?߿}yw?7Mw~?_@+PK/T]JsSOURMASH-MANIFEST.csvKk@{Aױ/I S&^>FRiKk!4ć?示-լ\,/Ϋ_f%)9a1'?]} DV=D;tV#S[k NItçV#9f)%$TeH$5XtӦp;)-@Bq"YJ`n}Lތ~h|4jUerɢ*wkz sy9#ʌpc "\L5a{ɱ'իQ%{Wnjr2-qLhSZ ׄBWc!ùwY=d;v7kPK/TJMJM2$signatures/e5698329948ebe9b7abe87f8ad81be74.sig.gzPK/TOO2$Msignatures/13c8503bb708de0b2d1c3ad59ffd18f2.sig.gzPK/T1OO2$signatures/bd70e54acd9d9a96cbe3a00b744d7b89.sig.gzPK/T]Js$^SOURMASH-MANIFEST.csvPKcsourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/protozoa_genbank_lineage.csv000066400000000000000000000004741474157046300300620ustar00rootroot00000000000000ident,taxid,superkingdom,phylum,class,order,family,genus,species GCA_002754635,5855,Eukaryota,Apicomplexa,Aconoidasida,Haemosporida,Plasmodiidae,Plasmodium,Plasmodium vivax, GCA_000256725,1130821,Eukaryota,Apicomplexa,Conoidasida,Eucoccidiorida,Sarcocystidae,Toxoplasma,Toxoplasma gondii,Toxoplasma gondii TgCatPRC2 sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test-empty-line.taxonomy.csv000066400000000000000000000015611474157046300277470ustar00rootroot00000000000000ident,superkingdom,phylum,class,order,family,genus,species GCF_001881345.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Enterobacteriaceae,g__Escherichia,s__Escherichia coli GCF_009494285.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri GCF_013368705.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Phocaeicola,s__Phocaeicola vulgatus GCF_003471795.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri GCF_000017325.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica GCF_000021665.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test-empty-ranks-2.taxonomy.csv000066400000000000000000000013111474157046300302660ustar00rootroot00000000000000ident,superkingdom,phylum,class,order,family,genus,species GCF_001881345.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Enterobacteriaceae,, GCF_009494285.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella, GCF_013368705.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,,,, GCF_003471795.1,,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri GCF_000017325.1,d__Bacteria,,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica GCF_000021665.1,d__Bacteria,,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test-empty-ranks-2.taxonomy.db000066400000000000000000000300001474157046300300550ustar00rootroot00000000000000SQLite format 3@ .K yyW)windextaxonomy_identtaxonomyCREATE UNIQUE INDEX taxonomy_ident ON taxonomy(ident)+)tabletaxonomytaxonomyCREATE TABLE taxonomy ( ident TEXT NOT NULL, superkingdom TEXT, phylum TEXT, class TEXT, order_ TEXT, family TEXT, genus TEXT, species TEXT, strain TEXT )  F\ F '#!93/'7 GCF_000021665d__Bacteriaunassignedc__Gammaproteobacteriao__Enterobacteralesf__Shewanellaceaeg__Shewanellas__Shewanella baltica '#!93/'7 GCF_000017325d__Bacteriaunassignedc__Gammaproteobacteriao__Enterobacteralesf__Shewanellaceaeg__Shewanellas__Shewanella baltica '!+)-/'3 GCF_003471795unassignedp__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Bacteroidaceaeg__Prevotellas__Prevotella copri? '#+) GCF_013368705d__Bacteriap__Bacteroidotac__Bacteroidiam '#+)-/' GCF_009494285d__Bacteriap__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Bacteroidaceaeg__Prevotellaq '#/937 GCF_001881345d__Bacteriap__Proteobacteriac__Gammaproteobacteriao__Enterobacteralesf__Enterobacteriaceae 'GCF_000021665'GCF_000017325'GCF_003471795'GCF_013368705'GCF_009494285' GCF_001881345sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test-empty-ranks-3.taxonomy.csv000066400000000000000000000013051474157046300302720ustar00rootroot00000000000000ident,superkingdom,phylum,class,order,family,genus,species GCF_001881345.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Enterobacteriaceae,, GCF_009494285.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella GCF_013368705.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia, GCF_003471795.1,,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri GCF_000017325.1,d__Bacteria,,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica GCF_000021665.1,d__Bacteria,,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test-empty-ranks-3.taxonomy.db000066400000000000000000000300001474157046300300560ustar00rootroot00000000000000SQLite format 3@ .K yyW)windextaxonomy_identtaxonomyCREATE UNIQUE INDEX taxonomy_ident ON taxonomy(ident)+)tabletaxonomytaxonomyCREATE TABLE taxonomy ( ident TEXT NOT NULL, superkingdom TEXT, phylum TEXT, class TEXT, order_ TEXT, family TEXT, genus TEXT, species TEXT, strain TEXT )  F\ F '#!93/'7 GCF_000021665d__Bacteriaunassignedc__Gammaproteobacteriao__Enterobacteralesf__Shewanellaceaeg__Shewanellas__Shewanella baltica '#!93/'7 GCF_000017325d__Bacteriaunassignedc__Gammaproteobacteriao__Enterobacteralesf__Shewanellaceaeg__Shewanellas__Shewanella baltica '!+)-/'3 GCF_003471795unassignedp__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Bacteroidaceaeg__Prevotellas__Prevotella copri? '#+) GCF_013368705d__Bacteriap__Bacteroidotac__Bacteroidiam '#+)-/' GCF_009494285d__Bacteriap__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Bacteroidaceaeg__Prevotellaq '#/937 GCF_001881345d__Bacteriap__Proteobacteriac__Gammaproteobacteriao__Enterobacteralesf__Enterobacteriaceae 'GCF_000021665'GCF_000017325'GCF_003471795'GCF_013368705'GCF_009494285' GCF_001881345sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test-empty-ranks.taxonomy.csv000066400000000000000000000013661474157046300301410ustar00rootroot00000000000000ident,superkingdom,phylum,class,order,family,genus,species GCF_001881345.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Enterobacteriaceae,, GCF_009494285.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella, GCF_013368705.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,,,, GCF_003471795.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri GCF_000017325.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica GCF_000021665.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test-empty-ranks.taxonomy.db000066400000000000000000000300001474157046300277160ustar00rootroot00000000000000SQLite format 3@ .K yyW)windextaxonomy_identtaxonomyCREATE UNIQUE INDEX taxonomy_ident ON taxonomy(ident)+)tabletaxonomytaxonomyCREATE TABLE taxonomy ( ident TEXT NOT NULL, superkingdom TEXT, phylum TEXT, class TEXT, order_ TEXT, family TEXT, genus TEXT, species TEXT, strain TEXT )  6Z 6 '#/93/'7 GCF_000021665d__Bacteriap__Proteobacteriac__Gammaproteobacteriao__Enterobacteralesf__Shewanellaceaeg__Shewanellas__Shewanella baltica '#/93/'7 GCF_000017325d__Bacteriap__Proteobacteriac__Gammaproteobacteriao__Enterobacteralesf__Shewanellaceaeg__Shewanellas__Shewanella baltica '#+)-/'3 GCF_003471795d__Bacteriap__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Bacteroidaceaeg__Prevotellas__Prevotella copri? '#+) GCF_013368705d__Bacteriap__Bacteroidotac__Bacteroidiam '#+)-/' GCF_009494285d__Bacteriap__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Bacteroidaceaeg__Prevotellaq '#/937 GCF_001881345d__Bacteriap__Proteobacteriac__Gammaproteobacteriao__Enterobacteralesf__Enterobacteriaceae 'GCF_000021665'GCF_000017325'GCF_003471795'GCF_013368705'GCF_009494285' GCF_001881345sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test-missing-ranks.taxonomy.csv000066400000000000000000000015411474157046300304470ustar00rootroot00000000000000ident,superkingdom,phylum,class,order,a,b,c GCF_001881345.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Enterobacteriaceae,g__Escherichia,s__Escherichia coli GCF_009494285.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri GCF_013368705.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Phocaeicola,s__Phocaeicola vulgatus GCF_003471795.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri GCF_000017325.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica GCF_000021665.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test-strain.taxonomy.csv000066400000000000000000000016031474157046300271610ustar00rootroot00000000000000ident,superkingdom,phylum,class,order,family,genus,species,strain GCF_001881345.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Enterobacteriaceae,g__Escherichia,s__Escherichia coli,1 GCF_009494285.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri,2 GCF_013368705.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Phocaeicola,s__Phocaeicola vulgatus,3 GCF_003471795.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri,4 GCF_000017325.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica,5 GCF_000021665.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica,6 sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test.LIN-taxonomy.csv000066400000000000000000000005321474157046300263030ustar00rootroot00000000000000ident,lin GCF_001881345.1,0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 GCF_009494285.1,1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 GCF_013368705.1,2;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 GCF_003471795.1,1;0;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 GCF_000017325.1,1;0;1;0;0;0;0;0;0;0;0;0;0;1;0;0;0;0;0;0 GCF_000021665.1,1;0;1;0;0;0;0;0;0;0;0;0;0;1;1;0;0;0;0;0 sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test.ictv-taxonomy.csv000066400000000000000000000026011474157046300266250ustar00rootroot00000000000000ident,superkingdom,realm,subrealm,kingdom,subkingdom,phylum,subphylum,class,subclass,order,suborder,family,subfamily,genus,subgenus,species,name GCF_000017325,Viruses,Riboviria,,Orthornavirae,,Negarnaviricota,Haploviricotina,Monjiviricetes,,Mononegavirales,,Filoviridae,,Orthoebolavirus,,Orthoebolavirus zairense,Ebola virus GCF_000021665,Viruses,Riboviria,,Orthornavirae,,Negarnaviricota,Haploviricotina,Monjiviricetes,,Mononegavirales,,Filoviridae,,Orthoebolavirus,,Orthoebolavirus sudanense,Sudan virus GCF_009494285,Viruses,Riboviria,,Orthornavirae,,Negarnaviricota,Haploviricotina,Monjiviricetes,,Mononegavirales,,Filoviridae,,Orthoebolavirus,,Orthoebolavirus taiense,Taï Forest virus GCF_003471795,Viruses,Riboviria,,Orthornavirae,,Negarnaviricota,Haploviricotina,Monjiviricetes,,Mononegavirales,,Filoviridae,,Orthoebolavirus,,Orthoebolavirus restonense,Reston virus GCF_001881345,Viruses,Riboviria,,Orthornavirae,,Negarnaviricota,Haploviricotina,Monjiviricetes,,Mononegavirales,,Filoviridae,,Orthoebolavirus,,Orthoebolavirus bundibugyoense,Bundibugyo virus GCF_013368705,Viruses,Riboviria,,Orthornavirae,,Negarnaviricota,Haploviricotina,Monjiviricetes,,Mononegavirales,,Filoviridae,,Orthoebolavirus,,Orthoebolavirus bombaliense,Bombali virus GCF_013311112,Viruses,Riboviria,,Orthornavirae,,Negarnaviricota,Haploviricotina,Monjiviricetes,,Mononegavirales,,Filoviridae,,other-genus,,other-sp,other test sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test.ncbi-taxonomy.csv000066400000000000000000000021041474157046300265710ustar00rootroot00000000000000ident,taxid,superkingdom,phylum,class,order,family,genus,species,strain,taxpath GCF_001881345.1,562,Bacteria,Pseudomonadota,Gammaproteobacteria,Enterobacterales,Enterobacteriaceae,Escherichia,Escherichia coli,,2|1224|1236|91347|543|561|562| GCF_009494285.1,165179,Bacteria,Bacteroidota,Bacteroidia,Bacteroidales,Prevotellaceae,Prevotella,Prevotella copri,,2|976|200643|171549|171552|838|165179| GCF_013368705.1,821,Bacteria,Bacteroidota,Bacteroidia,Bacteroidales,Bacteroidaceae,Phocaeicola,Phocaeicola vulgatus,,2|976|200643|171549|815|909656|821| GCF_003471795.1,165179,Bacteria,Bacteroidota,Bacteroidia,Bacteroidales,Prevotellaceae,Prevotella,Prevotella copri,,2|976|200643|171549|171552|838|165179| GCF_000017325.1,402882,Bacteria,Pseudomonadota,Gammaproteobacteria,Alteromonadales,Shewanellaceae,Shewanella,Shewanella baltica,Shewanella baltica OS185,2|1224|1236|135622|267890|22|62322|402882 GCF_000021665.1,407976,Bacteria,Pseudomonadota,Gammaproteobacteria,Alteromonadales,Shewanellaceae,Shewanella,Shewanella baltica,Shewanella baltica OS223,2|1224|1236|135622|267890|22|62322|407976 sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test.taxonomy.csv000066400000000000000000000015601474157046300256650ustar00rootroot00000000000000ident,superkingdom,phylum,class,order,family,genus,species GCF_001881345.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Enterobacteriaceae,g__Escherichia,s__Escherichia coli GCF_009494285.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri GCF_013368705.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Phocaeicola,s__Phocaeicola vulgatus GCF_003471795.1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri GCF_000017325.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica GCF_000021665.1,d__Bacteria,p__Proteobacteria,c__Gammaproteobacteria,o__Enterobacterales,f__Shewanellaceae,g__Shewanella,s__Shewanella baltica sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test.taxonomy.db000066400000000000000000000300001474157046300254460ustar00rootroot00000000000000SQLite format 3@ .K yyW)windextaxonomy_identtaxonomyCREATE UNIQUE INDEX taxonomy_ident ON taxonomy(ident)+)tabletaxonomytaxonomyCREATE TABLE taxonomy ( ident TEXT NOT NULL, superkingdom TEXT, phylum TEXT, class TEXT, order_ TEXT, family TEXT, genus TEXT, species TEXT, strain TEXT )  k` K  '#/93/'7 GCF_000021665d__Bacteriap__Proteobacteriac__Gammaproteobacteriao__Enterobacteralesf__Shewanellaceaeg__Shewanellas__Shewanella baltica '#/93/'7 GCF_000017325d__Bacteriap__Proteobacteriac__Gammaproteobacteriao__Enterobacteralesf__Shewanellaceaeg__Shewanellas__Shewanella baltica '#+)-/'3 GCF_003471795d__Bacteriap__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Bacteroidaceaeg__Prevotellas__Prevotella copri '#+)-/); GCF_013368705d__Bacteriap__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Bacteroidaceaeg__Phocaeicolas__Phocaeicola vulgatus '#+)-/'3 GCF_009494285d__Bacteriap__Bacteroidotac__Bacteroidiao__Bacteroidalesf__Bacteroidaceaeg__Prevotellas__Prevotella copri '#/937)3 GCF_001881345d__Bacteriap__Proteobacteriac__Gammaproteobacteriao__Enterobacteralesf__Enterobacteriaceaeg__Escherichias__Escherichia coli 'GCF_000021665'GCF_000017325'GCF_003471795'GCF_013368705'GCF_009494285' GCF_001881345sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test1.gather.csv000066400000000000000000000033331474157046300253420ustar00rootroot00000000000000intersect_bp,f_orig_query,f_match,f_unique_to_query,f_unique_weighted,average_abund,median_abund,std_abund,name,filename,md5,f_match_orig,unique_intersect_bp,gather_result_rank,remaining_bp,query_name,query_md5,query_filename,query_bp,ksize,scaled,query_n_hashes 442000,0.08815317112086159,0.08438335242458954,0.08815317112086159,0.05815279361459521,1.6153846153846154,1.0,1.1059438185997785,"GCF_001881345.1 Escherichia coli strain=SF-596, ASM188134v1",/group/ctbrowngrp/gtdb/databases/ctb/gtdb-rs202.genomic.k31.sbt.zip,683df1ec13872b4b98d59e98b355b52c,0.042779713511420826,442000,0,4572000,test1,md5,test1.sig,5014000,31,1000,2507 390000,0.07778220981252493,0.10416666666666667,0.07778220981252493,0.050496823586903404,1.5897435897435896,1.0,0.8804995294906566,"GCF_009494285.1 Prevotella copri strain=iAK1218, ASM949428v1",/group/ctbrowngrp/gtdb/databases/ctb/gtdb-rs202.genomic.k31.sbt.zip,1266c86141e3a5603da61f57dd863ed0,0.052236806857755155,390000,1,4182000,test1,md5,test1.sig,5014000,31,1000,2507 138000,0.027522935779816515,0.024722321748477247,0.027522935779816515,0.015637726014008795,1.391304347826087,1.0,0.5702120455914782,"GCF_013368705.1 Bacteroides vulgatus strain=B33, ASM1336870v1",/group/ctbrowngrp/gtdb/databases/ctb/gtdb-rs202.genomic.k31.sbt.zip,7d5f4ba1d01c8c3f7a520d19faded7cb,0.012648945921173235,138000,2,4044000,test1,md5,test1.sig,5014000,31,1000,2507 338000,0.06741124850418827,0.013789581205311542,0.010769844435580374,0.006515719172503665,1.4814814814814814,1.0,0.738886568268889,"GCF_003471795.1 Prevotella copri strain=AM16-54, ASM347179v1",/group/ctbrowngrp/gtdb/databases/ctb/gtdb-rs202.genomic.k31.sbt.zip,0ebd36ff45fc2810808789667f4aad84,0.04337782340862423,54000,3,3990000,test1,md5,test1.sig,5014000,31,1000,2507 sourmash-bio-sourmash-bc3c0f8/tests/test-data/tax/test1.gather.v450.csv000066400000000000000000000046031474157046300260400ustar00rootroot00000000000000intersect_bp,f_orig_query,f_match,f_unique_to_query,f_unique_weighted,average_abund,median_abund,std_abund,filename,name,md5,f_match_orig,unique_intersect_bp,gather_result_rank,remaining_bp,query_filename,query_name,query_md5,query_bp,ksize,moltype,scaled,query_n_hashes,query_abundance,query_containment_ani,match_containment_ani,average_containment_ani,max_containment_ani,potential_false_negative,n_unique_weighted_found,sum_weighted_found,total_weighted_hashes 442000,0.08815317112086159,0.08438335242458954,0.08815317112086159,0.05815279361459521,1.6153846153846154,1.0,1.1059438185997785,/Users/t/dev/sourmash/gtdb-rs207.genomic.k31.zip,"GCF_001881345.1 Escherichia coli strain=SF-596, 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Shewanella baltica OS223, complete 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1), min_size=10, max_size=1000), st.lists(st.integers(min_value=0, max_value=2**64 - 1), min_size=10, max_size=1000), st.integers(min_value=10, max_value=1000), ) @example([1, 2], [3, 4], 2) def test_set_abundance_num_hypothesis(hashes, abundances, sketch_size): a = MinHash(sketch_size, 10, track_abundance=True) oracle = dict(zip(hashes, abundances)) a.set_abundances(oracle) mins = a.hashes size = min(sum(1 for v in oracle.values() if v > 0), sketch_size) assert len(mins) == size for k, v in mins.items(): assert oracle[k] == v @given( st.lists(st.integers(min_value=0, max_value=2**64 - 1), min_size=10, max_size=1000), st.lists(st.integers(min_value=0, max_value=2**64 - 1), min_size=10, max_size=1000), st.integers(min_value=1000, max_value=10000), ) @example([0], [0], 1000) def test_set_abundance_scaled_hypothesis(hashes, abundances, scaled): a = MinHash(0, 10, track_abundance=True, scaled=scaled) oracle = dict(zip(hashes, abundances)) a.set_abundances(oracle) max_hash = _get_max_hash_for_scaled(scaled) below_max_hash = sum(1 for (k, v) in oracle.items() if k <= max_hash and v > 0) mins = a.hashes assert len(mins) == below_max_hash for k, v in mins.items(): assert oracle[k] == v assert k <= max_hash assert v > 0 sourmash-bio-sourmash-bc3c0f8/tests/test_api.py000066400000000000000000000047041474157046300220170ustar00rootroot00000000000000import pytest import sourmash import sourmash_tst_utils as utils @utils.in_tempdir def test_sourmash_signature_api(c): e = sourmash.MinHash(n=1, ksize=20) sig = sourmash.SourmashSignature(e) with open(c.output("xxx.sig"), "w") as fp: sourmash.save_signatures([sig], fp) sig_x1 = sourmash.load_one_signature(c.output("xxx.sig")) sig_x2 = list(sourmash.load_file_as_signatures(c.output("xxx.sig")))[0] assert sig_x1 == sig assert sig_x2 == sig @utils.in_tempdir def test_load_index_0_no_file(c): with pytest.raises(ValueError) as exc: sourmash.load_file_as_index(c.output("does-not-exist")) assert "Error while reading signatures from " in str(exc.value) def test_load_index_1(): testfile = utils.get_test_data("prot/protein.sbt.zip") idx = sourmash.load_file_as_index(testfile) sigs = list(idx.signatures()) assert len(sigs) == 2 def test_load_index_2(): testfile = utils.get_test_data("prot/protein.lca.json.gz") idx = sourmash.load_file_as_index(testfile) sigs = list(idx.signatures()) assert len(sigs) == 2 def test_load_index_3(): testfile = utils.get_test_data("prot/protein/") idx = sourmash.load_file_as_index(testfile) sigs = list(idx.signatures()) assert len(sigs) == 2 def test_load_index_4(): testfile = utils.get_test_data("prot/all.zip") idx = sourmash.load_file_as_index(testfile) sigs = list(idx.signatures()) assert len(sigs) == 8 def test_load_index_4_b(): testfile = utils.get_test_data("prot/protein.zip") idx = sourmash.load_file_as_index(testfile) sigs = list(idx.signatures()) assert len(sigs) == 2 def test_load_fasta_as_signature(): # try loading a fasta file - should fail with informative exception testfile = utils.get_test_data("short.fa") with pytest.raises(Exception) as exc: sourmash.load_file_as_index(testfile) print(exc.value) assert ( f"Error while reading signatures from '{testfile}' - got sequences instead! Is this a FASTA/FASTQ file?" in str(exc.value) ) def test_load_and_search_sbt_api(): treefile = utils.get_test_data("prot/protein.sbt.zip") queryfile = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) tree = sourmash.load_sbt_index(treefile) query = sourmash.load_one_signature(queryfile) results = list(sourmash.search_sbt_index(tree, query, 0)) assert len(results) == 2 sourmash-bio-sourmash-bc3c0f8/tests/test_bugs.py000066400000000000000000000006541474157046300222060ustar00rootroot00000000000000import sourmash_tst_utils as utils @utils.in_tempdir def test_bug_803(c): # can we do a 'sourmash search' on an LCA database and a query with abundance? query = utils.get_test_data("track_abund/47.fa.sig") lca_db = utils.get_test_data("lca/47+63.lca.json") c.run_sourmash("search", query, lca_db, "--ignore-abundance") print(c) assert "NC_009665.1 Shewanella baltica OS185, complete genome" in str(c) sourmash-bio-sourmash-bc3c0f8/tests/test_cmd_signature.py000066400000000000000000005441631474157046300241020ustar00rootroot00000000000000""" Tests for the 'sourmash signature' command line. """ import csv import shutil import os import glob import gzip import pytest import screed import sourmash_tst_utils as utils import sourmash from sourmash.signature import ( load_signatures_from_json, save_signatures_to_json, load_one_signature_from_json, ) from sourmash.manifest import CollectionManifest from sourmash_tst_utils import SourmashCommandFailed ## command line tests def _write_file(runtmp, basename, lines, *, gz=False): loc = runtmp.output(basename) if gz: xopen = gzip.open else: xopen = open with xopen(loc, "wt") as fp: fp.write("\n".join(lines)) return loc def test_run_sourmash_signature_cmd(): status, out, err = utils.runscript("sourmash", ["signature"], fail_ok=True) assert "sourmash: error: argument cmd: invalid choice:" not in err assert "Manipulate signature files:" in out assert status != 0 # no args provided, ok ;) def test_run_sourmash_sig_cmd(): status, out, err = utils.runscript("sourmash", ["sig"], fail_ok=True) assert "sourmash: error: argument cmd: invalid choice:" not in err assert "Manipulate signature files:" in out assert status != 0 # no args provided, ok ;) def test_run_cat_via_parse_args(): # run a command ('sourmash.sig.cat') with args constructed via parse_args import sourmash.sig import sourmash.cli sig47 = utils.get_test_data("47.fa.sig") args = sourmash.cli.parse_args(["sig", "cat", sig47]) sourmash.sig.cat(args) def test_sig_merge_1_use_full_signature_in_cmd(runtmp): c = runtmp # merge of 47 & 63 should be union of mins sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") sig47and63 = utils.get_test_data("47+63.fa.sig") c.run_sourmash("signature", "merge", sig47, sig63) # stdout should be new signature out = c.last_result.out test_merge_sig = load_one_signature_from_json(sig47and63) actual_merge_sig = load_one_signature_from_json(out) print(test_merge_sig.minhash) print(actual_merge_sig.minhash) print(out) assert actual_merge_sig.minhash == test_merge_sig.minhash def test_sig_merge_1_fromfile_picklist(runtmp): c = runtmp # merge of 47 & 63 should be union of mins sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") sig47and63 = utils.get_test_data("47+63.fa.sig") from_file = _write_file(runtmp, "list.txt", [sig47, sig63]) picklist = _write_file(runtmp, "pl.csv", ["md5short", "09a08691", "38729c63"]) c.run_sourmash( "signature", "merge", "--from-file", from_file, "--picklist", f"{picklist}:md5short:md5short", ) # stdout should be new signature out = c.last_result.out test_merge_sig = load_one_signature_from_json(sig47and63) actual_merge_sig = load_one_signature_from_json(out) print(test_merge_sig.minhash) print(actual_merge_sig.minhash) print(out) assert actual_merge_sig.minhash == test_merge_sig.minhash def test_sig_merge_1_fromfile_picklist_gz(runtmp): # test with --from-file and gzipped picklist c = runtmp # merge of 47 & 63 should be union of mins sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") sig47and63 = utils.get_test_data("47+63.fa.sig") from_file = _write_file(runtmp, "list.txt", [sig47, sig63]) picklist = _write_file( runtmp, "pl.csv", ["md5short", "09a08691", "38729c63"], gz=True ) c.run_sourmash( "signature", "merge", "--from-file", from_file, "--picklist", f"{picklist}:md5short:md5short", ) # stdout should be new signature out = c.last_result.out test_merge_sig = load_one_signature_from_json(sig47and63) actual_merge_sig = load_one_signature_from_json(out) print(test_merge_sig.minhash) print(actual_merge_sig.minhash) print(out) assert actual_merge_sig.minhash == test_merge_sig.minhash @utils.in_tempdir def test_sig_merge_1(c): # merge of 47 & 63 should be union of mins sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") sig47and63 = utils.get_test_data("47+63.fa.sig") c.run_sourmash("sig", "merge", sig47, sig63) # stdout should be new signature out = c.last_result.out test_merge_sig = load_one_signature_from_json(sig47and63) actual_merge_sig = load_one_signature_from_json(out) print(test_merge_sig.minhash) print(actual_merge_sig.minhash) print(out) assert actual_merge_sig.minhash == test_merge_sig.minhash @utils.in_tempdir def test_sig_merge_1_multisig(c): # merge of 47 & 63 should be union of mins; here, sigs are in same file. multisig = utils.get_test_data("47+63-multisig.sig") sig47and63 = utils.get_test_data("47+63.fa.sig") c.run_sourmash("sig", "merge", multisig, "--flatten") # stdout should be new signature out = c.last_result.out test_merge_sig = load_one_signature_from_json(sig47and63) actual_merge_sig = load_one_signature_from_json(out) print(test_merge_sig.minhash) print(actual_merge_sig.minhash) print(out) assert actual_merge_sig.minhash == test_merge_sig.minhash @utils.in_tempdir def test_sig_merge_1_name(c): # check name arg sig2 = utils.get_test_data("2.fa.sig") sig63 = utils.get_test_data("63.fa.sig") assignedSigName = "SIG_NAME" outsig = c.output("merged2and63.sig") c.run_sourmash( "sig", "merge", sig2, sig63, "--dna", "-k", "31", "-o", "merged2and63.sig", "--name", assignedSigName, ) test_merge_sig = load_one_signature_from_json(outsig) print("outsig", outsig) print("xx_test_merge_sig.name", test_merge_sig.name) assert assignedSigName == test_merge_sig.name @utils.in_tempdir def test_sig_merge_1_ksize_moltype(c): # check ksize, moltype args sig2 = utils.get_test_data("2.fa.sig") sig63 = utils.get_test_data("63.fa.sig") sig2and63 = utils.get_test_data("2+63.fa.sig") c.run_sourmash("sig", "merge", sig2, sig63, "--dna", "-k", "31") # stdout should be new signature out = c.last_result.out test_merge_sig = load_one_signature_from_json(sig2and63) actual_merge_sig = load_one_signature_from_json(out) print(test_merge_sig.minhash) print(actual_merge_sig.minhash) print(out) assert actual_merge_sig.minhash == test_merge_sig.minhash @utils.in_tempdir def test_sig_merge_1_ksize_moltype_fail(c): # check ksize, moltype args sig2 = utils.get_test_data("2.fa.sig") sig63 = utils.get_test_data("63.fa.sig") utils.get_test_data("2+63.fa.sig") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash("sig", "merge", sig2, sig63) assert "ERROR when merging signature" in str(exc.value) @utils.in_tempdir def test_sig_merge_2(c): # merge of 47 with nothing should be 47 sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "merge", sig47) # stdout should be new signature out = c.last_result.out test_merge_sig = load_one_signature_from_json(sig47) actual_merge_sig = load_one_signature_from_json(out) print(out) assert actual_merge_sig.minhash == test_merge_sig.minhash @utils.in_tempdir def test_sig_merge_3_abund_ab_ok(c): # merge of 47 and 63 with abund should work sig47abund = utils.get_test_data("track_abund/47.fa.sig") sig63abund = utils.get_test_data("track_abund/63.fa.sig") c.run_sourmash("sig", "merge", sig47abund, sig63abund) load_one_signature_from_json(c.last_result.out) # CTB: should check that this merge did what we think it should do! @utils.in_tempdir def test_sig_merge_3_abund_ab(c): # merge of 47 with abund, with 63 without, should fail; and vice versa sig47 = utils.get_test_data("47.fa.sig") sig63abund = utils.get_test_data("track_abund/63.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "merge", sig47, sig63abund) print(c.last_result) assert ( "incompatible signatures: track_abundance is False in first sig, True in second" in c.last_result.err ) @utils.in_tempdir def test_sig_merge_3_abund_ba(c): # merge of 47 without abund, with 63 with, should fail sig47 = utils.get_test_data("47.fa.sig") sig63abund = utils.get_test_data("track_abund/63.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "merge", sig63abund, sig47) print(c.last_result) assert ( "incompatible signatures: track_abundance is True in first sig, False in second" in c.last_result.err ) @utils.in_tempdir def test_sig_filter_1(c): # test basic filtering sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") c.run_sourmash("sig", "filter", sig47, sig63) # stdout should be new signature out = c.last_result.out filtered_sigs = list(load_signatures_from_json(out)) filtered_sigs.sort(key=lambda x: str(x)) assert len(filtered_sigs) == 2 ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) assert filtered_sigs[0].minhash == ss47.minhash assert filtered_sigs[0].name == ss47.name assert filtered_sigs[1].minhash == ss63.minhash assert filtered_sigs[1].name == ss63.name @utils.in_tempdir def test_sig_filter_2(c): # test basic filtering sig47 = utils.get_test_data("track_abund/47.fa.sig") c.run_sourmash("sig", "filter", "-m", "2", "-M", "5", sig47) # stdout should be new signature out = c.last_result.out filtered_sig = load_one_signature_from_json(out) test_sig = load_one_signature_from_json(sig47) abunds = test_sig.minhash.hashes abunds = {k: v for (k, v) in abunds.items() if v >= 2 and v <= 5} assert abunds assert filtered_sig.minhash.hashes == abunds @utils.in_tempdir def test_sig_filter_3(c): # test basic filtering sig47 = utils.get_test_data("track_abund/47.fa.sig") c.run_sourmash("sig", "filter", "-m", "2", sig47) # stdout should be new signature out = c.last_result.out filtered_sig = load_one_signature_from_json(out) test_sig = load_one_signature_from_json(sig47) abunds = test_sig.minhash.hashes abunds = {k: v for (k, v) in abunds.items() if v >= 2} assert abunds assert filtered_sig.minhash.hashes == abunds @utils.in_tempdir def test_sig_filter_3_ksize_select(c): # test filtering with ksize selectiong psw_mag = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") c.run_sourmash("sig", "filter", "-m", "2", psw_mag, "-k", "31") # stdout should be new signature out = c.last_result.out filtered_sig = load_one_signature_from_json(out) test_sig = load_one_signature_from_json(psw_mag, ksize=31) abunds = test_sig.minhash.hashes abunds = {k: v for (k, v) in abunds.items() if v >= 2} assert abunds assert filtered_sig.minhash.hashes == abunds @utils.in_tempdir def test_sig_merge_flatten(c): # merge of 47 without abund, with 63 with, will succeed with --flatten sig47 = utils.get_test_data("47.fa.sig") sig63abund = utils.get_test_data("track_abund/63.fa.sig") sig47and63 = utils.get_test_data("47+63.fa.sig") c.run_sourmash("sig", "merge", sig63abund, sig47, "--flatten") print(c.last_result) out = c.last_result.out test_merge_sig = load_one_signature_from_json(sig47and63) actual_merge_sig = load_one_signature_from_json(out) print(test_merge_sig.minhash) print(actual_merge_sig.minhash) print(out) assert actual_merge_sig.minhash == test_merge_sig.minhash @utils.in_tempdir def test_sig_merge_flatten_2(c): # merge of 47 with abund, with 63 with, will succeed with --flatten sig47abund = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") sig47and63 = utils.get_test_data("47+63.fa.sig") c.run_sourmash("sig", "merge", sig63, sig47abund, "--flatten") print(c.last_result) out = c.last_result.out test_merge_sig = load_one_signature_from_json(sig47and63) actual_merge_sig = load_one_signature_from_json(out) print(test_merge_sig.minhash) print(actual_merge_sig.minhash) print(out) assert actual_merge_sig.minhash == test_merge_sig.minhash def test_sig_intersect_0(runtmp): # should print usage when no arguments c = runtmp with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "intersect") err = c.last_result.err assert "no signatures provided to intersect!?" in err def test_sig_intersect_1(runtmp): c = runtmp # intersect of 47 and 63 should be intersection of mins sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") sig47and63 = utils.get_test_data("47+63-intersect.fa.sig") c.run_sourmash("sig", "intersect", sig47, sig63) # stdout should be new signature out = c.last_result.out test_intersect_sig = load_one_signature_from_json(sig47and63) actual_intersect_sig = load_one_signature_from_json(out) print(test_intersect_sig.minhash) print(actual_intersect_sig.minhash) print(out) assert actual_intersect_sig.minhash == test_intersect_sig.minhash def test_sig_intersect_1_rename(runtmp): # intersect of 47 and 63 should be intersection of mins sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") sig47and63 = utils.get_test_data("47+63-intersect.fa.sig") runtmp.run_sourmash("sig", "intersect", sig47, sig63, "--set-name", "footest") # stdout should be new signature out = runtmp.last_result.out test_intersect_sig = load_one_signature_from_json(sig47and63) actual_intersect_sig = load_one_signature_from_json(out) print(test_intersect_sig.minhash) print(actual_intersect_sig.minhash) print(out) assert actual_intersect_sig.minhash == test_intersect_sig.minhash assert actual_intersect_sig.name == "footest" def test_sig_intersect_1_fromfile_picklist(runtmp): c = runtmp # intersect of 47 and 63 should be intersection of mins sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") sig47and63 = utils.get_test_data("47+63-intersect.fa.sig") from_file = _write_file(runtmp, "list.txt", [sig47, sig63]) picklist = _write_file(runtmp, "pl.csv", ["md5short", "09a08691", "38729c63"]) c.run_sourmash( "signature", "intersect", "--from-file", from_file, "--picklist", f"{picklist}:md5short:md5short", ) # stdout should be new signature out = c.last_result.out test_intersect_sig = load_one_signature_from_json(sig47and63) actual_intersect_sig = load_one_signature_from_json(out) print(test_intersect_sig.minhash) print(actual_intersect_sig.minhash) print(out) assert actual_intersect_sig.minhash == test_intersect_sig.minhash @utils.in_tempdir def test_sig_intersect_2(c): # intersect of 47 with abund and 63 with abund should be same # as without abund, i.e. intersect 'flattens' sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") sig47and63 = utils.get_test_data("47+63-intersect.fa.sig") c.run_sourmash("sig", "intersect", sig47, sig63) # stdout should be new signature out = c.last_result.out test_intersect_sig = load_one_signature_from_json(sig47and63) actual_intersect_sig = load_one_signature_from_json(out) print(test_intersect_sig.minhash) print(actual_intersect_sig.minhash) print(out) assert actual_intersect_sig.minhash == test_intersect_sig.minhash @utils.in_tempdir def test_sig_intersect_3(c): # use --abundances-from to preserve abundances from sig #47 sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") c.run_sourmash("sig", "intersect", "--abundances-from", sig47, sig63) # stdout should be new signature out = c.last_result.out actual_intersect_sig = load_one_signature_from_json(out) # actually do an intersection ourselves for the test mh47 = load_one_signature_from_json(sig47).minhash mh63 = load_one_signature_from_json(sig63).minhash mh47_abunds = mh47.hashes mh63_mins = set(mh63.hashes.keys()) # get the set of mins that are in common mh63_mins.intersection_update(mh47_abunds) # take abundances from mh47 & create new sig mh47_abunds = {k: mh47_abunds[k] for k in mh63_mins} test_mh = mh47.copy_and_clear() test_mh.set_abundances(mh47_abunds) print(actual_intersect_sig.minhash) print(out) assert actual_intersect_sig.minhash == test_mh @utils.in_tempdir def test_sig_intersect_4(c): # use --abundances-from to preserve abundances from sig #47 sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") c.run_sourmash("sig", "intersect", "--abundances-from", sig47, sig63) # stdout should be new signature out = c.last_result.out actual_intersect_sig = load_one_signature_from_json(out) # actually do an intersection ourselves for the test mh47 = load_one_signature_from_json(sig47).minhash mh63 = load_one_signature_from_json(sig63).minhash mh47_abunds = mh47.hashes mh63_mins = set(mh63.hashes.keys()) # get the set of mins that are in common mh63_mins.intersection_update(mh47_abunds) # take abundances from mh47 & create new sig mh47_abunds = {k: mh47_abunds[k] for k in mh63_mins} test_mh = mh47.copy_and_clear() test_mh.set_abundances(mh47_abunds) print(actual_intersect_sig.minhash) print(out) assert actual_intersect_sig.minhash == test_mh @utils.in_tempdir def test_sig_intersect_5(c): # use --abundances-from to preserve abundances from sig #47 # make sure that you can't specify a flat sig for --abundances-from sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "intersect", "--abundances-from", sig47, sig63) @utils.in_tempdir def test_sig_intersect_6_ksize_fail(c): # specify ksize to intersect 2.fa.sig with 47.fa.sig - 2.fa.sig contains # multiple ksizes. sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "intersect", sig2, sig47) @utils.in_tempdir def test_sig_intersect_6_ksize_succeed(c): # specify ksize to intersect 2.fa.sig with 47.fa.sig - 2.fa.sig contains # multiple ksizes. sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "intersect", "-k", "31", sig2, sig47) assert "loaded and intersected 2 signatures" in c.last_result.err @utils.in_tempdir def test_sig_intersect_7(c): # intersect of 47 and nothing should be self sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "intersect", sig47) # stdout should be new signature out = c.last_result.out test_intersect_sig = load_one_signature_from_json(sig47) actual_intersect_sig = load_one_signature_from_json(out) print(test_intersect_sig.minhash) print(actual_intersect_sig.minhash) print(out) assert actual_intersect_sig.minhash == test_intersect_sig.minhash @utils.in_tempdir def test_sig_intersect_8_multisig(c): # intersect of all the multisig stuff should be nothing sig47 = utils.get_test_data("47+63-multisig.sig") c.run_sourmash("sig", "intersect", sig47) # stdout should be new signature out = c.last_result.out actual_intersect_sig = load_one_signature_from_json(out) assert not len(actual_intersect_sig.minhash) def test_sig_inflate_1(runtmp): # basic inflate test - inflate 47 flat with 47 abund sig47_flat = utils.get_test_data("47.fa.sig") sig47_abund = utils.get_test_data("track_abund/47.fa.sig") runtmp.run_sourmash("sig", "inflate", sig47_abund, sig47_flat) # stdout should be new signature out = runtmp.last_result.out actual_inflate_sig = load_one_signature_from_json(out) actual_inflate_mh = actual_inflate_sig.minhash # should be identical to track_abund sig sig47 = load_one_signature_from_json(sig47_abund) mh47 = sig47.minhash assert actual_inflate_sig.name == sig47.name assert actual_inflate_mh == mh47 def test_sig_inflate_2(runtmp): # use abundances from sig #47 sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") runtmp.run_sourmash("sig", "inflate", sig47, sig63) # stdout should be new signature out = runtmp.last_result.out actual_inflate_sig = load_one_signature_from_json(out) # actually do an inflation ourselves for the test mh47 = load_one_signature_from_json(sig47).minhash mh63 = load_one_signature_from_json(sig63).minhash mh47_abunds = mh47.hashes mh63_mins = set(mh63.hashes.keys()) # get the set of mins that are in common mh63_mins.intersection_update(mh47_abunds) # take abundances from mh47 & create new sig mh47_abunds = {k: mh47_abunds[k] for k in mh63_mins} test_mh = mh47.copy_and_clear() test_mh.set_abundances(mh47_abunds) print(actual_inflate_sig.minhash) print(out) assert actual_inflate_sig.minhash == test_mh def test_sig_inflate_3(runtmp): # should fail on flat first sig sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("sig", "inflate", sig63, sig47) assert "has no abundances" in runtmp.last_result.err def test_sig_inflate_4_picklist(runtmp): # try out picklists sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") sig47_flat = utils.get_test_data("47.fa.sig") ss63 = load_one_signature_from_json(sig63, ksize=31) _write_file(runtmp, "pl.csv", ["md5", ss63.md5sum()]) print(ss63.md5sum()) runtmp.run_sourmash( "sig", "inflate", sig47, sig63, sig47_flat, "--picklist", "pl.csv:md5:md5" ) # stdout should be new signature out = runtmp.last_result.out actual_inflate_sig = load_one_signature_from_json(out) # actually do an inflation ourselves for the test mh47 = load_one_signature_from_json(sig47).minhash mh63 = load_one_signature_from_json(sig63).minhash mh47_abunds = mh47.hashes mh63_mins = set(mh63.hashes.keys()) # get the set of mins that are in common mh63_mins.intersection_update(mh47_abunds) # take abundances from mh47 & create new sig mh47_abunds = {k: mh47_abunds[k] for k in mh63_mins} test_mh = mh47.copy_and_clear() test_mh.set_abundances(mh47_abunds) print(actual_inflate_sig.minhash) print(out) assert actual_inflate_sig.minhash == test_mh def test_sig_inflate_5_bad_moltype(runtmp): # should fail when no signatures match moltype sig47 = utils.get_test_data("track_abund/47.fa.sig") prot = utils.get_test_data("prot/protein.zip") with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("sig", "inflate", sig47, prot) assert "no signatures to inflate" in runtmp.last_result.err def test_sig_subtract_1(runtmp): c = runtmp # subtract of 63 from 47 sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") c.run_sourmash("sig", "subtract", sig47, sig63) # stdout should be new signature out = c.last_result.out test1_sig = load_one_signature_from_json(sig47) test2_sig = load_one_signature_from_json(sig63) actual_subtract_sig = load_one_signature_from_json(out) mins = set(test1_sig.minhash.hashes.keys()) mins -= set(test2_sig.minhash.hashes.keys()) assert set(actual_subtract_sig.minhash.hashes.keys()) == set(mins) def test_sig_subtract_1_name(runtmp): # subtract of 63 from 47; rename sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") runtmp.run_sourmash("sig", "subtract", sig47, sig63, "--set-name", "footest") # stdout should be new signature out = runtmp.last_result.out test1_sig = load_one_signature_from_json(sig47) test2_sig = load_one_signature_from_json(sig63) actual_subtract_sig = load_one_signature_from_json(out) mins = set(test1_sig.minhash.hashes.keys()) mins -= set(test2_sig.minhash.hashes.keys()) assert set(actual_subtract_sig.minhash.hashes.keys()) == set(mins) assert actual_subtract_sig.name == "footest" def test_sig_subtract_1_sigzip(runtmp): c = runtmp # subtract of 63 from 47 sig47 = utils.get_test_data("47.fa.sig.zip") sig63 = utils.get_test_data("63.fa.sig.zip") c.run_sourmash("sig", "subtract", sig47, sig63) # stdout should be new signature out = c.last_result.out from sourmash import sourmash_args test1_sig = sourmash_args.load_one_signature(sig47) test2_sig = sourmash_args.load_one_signature(sig63) actual_subtract_sig = load_one_signature_from_json(out) mins = set(test1_sig.minhash.hashes.keys()) mins -= set(test2_sig.minhash.hashes.keys()) assert set(actual_subtract_sig.minhash.hashes.keys()) == set(mins) def test_sig_subtract_1_sigzip(runtmp): c = runtmp # subtract of 63 from 47 sig47 = utils.get_test_data("47.fa.sig.zip") sig63 = utils.get_test_data("63.fa.sig.zip") c.run_sourmash("sig", "subtract", sig47, sig63) # stdout should be new signature out = c.last_result.out from sourmash import sourmash_args test1_sig = sourmash_args.load_one_signature(sig47) test2_sig = sourmash_args.load_one_signature(sig63) actual_subtract_sig = sourmash.load_one_signature(out) mins = set(test1_sig.minhash.hashes.keys()) mins -= set(test2_sig.minhash.hashes.keys()) assert set(actual_subtract_sig.minhash.hashes.keys()) == set(mins) def test_sig_subtract_1_abund(runtmp): # subtract 63 from 47, with abundances borrowed from 47 c = runtmp sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") c.run_sourmash("sig", "subtract", sig47, sig63, "-A", sig47) # stdout should be new signature out = c.last_result.out test1_sig = load_one_signature_from_json(sig47) test2_sig = load_one_signature_from_json(sig63) actual_subtract_sig = load_one_signature_from_json(out) assert actual_subtract_sig.minhash.track_abundance mins = set(test1_sig.minhash.hashes.keys()) mins -= set(test2_sig.minhash.hashes.keys()) assert set(actual_subtract_sig.minhash.hashes.keys()) == set(mins) distinct_abunds = set() actual_sub_hashes = actual_subtract_sig.minhash.hashes sig47_hashes = test1_sig.minhash.hashes for h in mins: assert actual_sub_hashes[h] == sig47_hashes[h] distinct_abunds.add(sig47_hashes[h]) # this is really just to make sure that we have a sketch with some # abundances in it... assert max(distinct_abunds) > 1 def test_sig_subtract_1_abund_is_flat(runtmp): # subtract 63 from 47, with abundances borrowed from 47 c = runtmp sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") sig47_flat = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "subtract", sig47, sig63, "-A", sig47_flat) def test_sig_subtract_1_flatten(runtmp): # subtract 63 from 47, with abund signatures originally and --flatten c = runtmp sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") c.run_sourmash("sig", "subtract", sig47, sig63, "--flatten") # stdout should be new signature out = c.last_result.out test1_sig = load_one_signature_from_json(sig47) test2_sig = load_one_signature_from_json(sig63) actual_subtract_sig = load_one_signature_from_json(out) assert not actual_subtract_sig.minhash.track_abundance mins = set(test1_sig.minhash.hashes.keys()) mins -= set(test2_sig.minhash.hashes.keys()) assert set(actual_subtract_sig.minhash.hashes.keys()) == set(mins) def test_sig_subtract_1_multisig(runtmp): c = runtmp # subtract of everything from 47 sig47 = utils.get_test_data("47.fa.sig") multisig = utils.get_test_data("47+63-multisig.sig") c.run_sourmash("sig", "subtract", sig47, multisig, "--flatten") # stdout should be new signature out = c.last_result.out actual_subtract_sig = load_one_signature_from_json(out) assert not set(actual_subtract_sig.minhash.hashes.keys()) def test_sig_subtract_2(runtmp): c = runtmp # subtract of 63 from 47 should fail if 47 has abund sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "subtract", sig47, sig63) def test_sig_subtract_3(runtmp): c = runtmp # subtract of 63 from 47 should fail if 63 has abund sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "subtract", sig47, sig63) def test_sig_subtract_4_ksize_fail(runtmp): c = runtmp # subtract of 2 from 47 should fail without -k specified sig47 = utils.get_test_data("47.fa.sig") sig2 = utils.get_test_data("2.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "subtract", sig47, sig2) def test_sig_subtract_4_ksize_succeed(runtmp): c = runtmp # subtract of 2 from 47 should fail without -k specified sig47 = utils.get_test_data("47.fa.sig") sig2 = utils.get_test_data("2.fa.sig") c.run_sourmash("sig", "subtract", sig47, sig2, "-k", "31") assert "loaded and subtracted 1 signatures" in c.last_result.err def test_sig_subtract_5_bad_moltype(runtmp): # should fail when no matching sigs sig47 = utils.get_test_data("47.fa.sig") prot = utils.get_test_data("prot/protein.zip") with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("sig", "subtract", "-k", "31", sig47, prot) assert "no signatures to subtract" in runtmp.last_result.err def test_sig_rename_1(runtmp): c = runtmp # set new name for 47 sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "rename", sig47, "fiz bar") # stdout should be new signature out = c.last_result.out test_rename_sig = load_one_signature_from_json(sig47) actual_rename_sig = load_one_signature_from_json(out) print(test_rename_sig.minhash) print(actual_rename_sig.minhash) assert actual_rename_sig.minhash == test_rename_sig.minhash assert test_rename_sig.name != actual_rename_sig.name assert actual_rename_sig.name == "fiz bar" def test_sig_rename_1_fromfile_picklist(runtmp): c = runtmp # set new name for 47 sig47 = utils.get_test_data("47.fa.sig") from_file = _write_file(runtmp, "list.txt", [sig47]) picklist = _write_file(runtmp, "pl.csv", ["md5short", "09a08691"]) c.run_sourmash( "sig", "rename", "--from-file", from_file, "fiz bar", "--picklist", f"{picklist}:md5short:md5short", ) # stdout should be new signature out = c.last_result.out test_rename_sig = load_one_signature_from_json(sig47) actual_rename_sig = load_one_signature_from_json(out) print(test_rename_sig.minhash) print(actual_rename_sig.minhash) assert actual_rename_sig.minhash == test_rename_sig.minhash assert test_rename_sig.name != actual_rename_sig.name assert actual_rename_sig.name == "fiz bar" @utils.in_tempdir def test_sig_rename_1_multisig(c): # set new name for multiple signatures/files multisig = utils.get_test_data("47+63-multisig.sig") other_sig = utils.get_test_data("2.fa.sig") c.run_sourmash("sig", "rename", multisig, other_sig, "fiz bar") # stdout should be new signature out = c.last_result.out n = 0 for sig in load_signatures_from_json(out): assert sig.name == "fiz bar" n += 1 assert n == 9, n @utils.in_tempdir def test_sig_rename_1_multisig_ksize(c): # set new name for multiple signatures/files; select k=31 multisig = utils.get_test_data("47+63-multisig.sig") other_sig = utils.get_test_data("2.fa.sig") c.run_sourmash("sig", "rename", multisig, other_sig, "fiz bar", "-k", "31") # stdout should be new signature out = c.last_result.out n = 0 for sig in load_signatures_from_json(out): assert sig.name == "fiz bar" n += 1 assert n == 7, n @utils.in_tempdir def test_sig_rename_2_output_to_same(c): # change name of signature "in place", same output file sig47 = utils.get_test_data("47.fa.sig") inplace = c.output("inplace.sig") shutil.copyfile(sig47, inplace) print(inplace) c.run_sourmash("sig", "rename", "-d", inplace, "fiz bar", "-o", inplace) actual_rename_sig = load_one_signature_from_json(inplace) assert actual_rename_sig.name == "fiz bar" @utils.in_tempdir def test_sig_rename_3_file_dne(c): # rename on a file that does not exist should fail! with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "rename", "no-such-sig", "fiz bar") assert "Error while reading signatures from 'no-such-sig'" in c.last_result.err @utils.in_tempdir def test_sig_rename_3_file_dne_force(c): # rename on a file that does not exist should fail! c.run_sourmash("sig", "rename", "no-such-sig", "fiz bar", "-f") print(c.last_result.err) assert "Error while reading signatures from 'no-such-sig'" in c.last_result.err def test_sig_rename_4_pattern_include(runtmp): # test sig rename --include-db-pattern sigfiles = glob.glob(utils.get_test_data("prot/*.zip")) runtmp.sourmash( "sig", "rename", "--include", "shewanella", *sigfiles, "SHEWME", "-o", "out.zip" ) idx = sourmash.load_file_as_index(runtmp.output("out.zip")) names = [ss.name for ss in idx.signatures()] for n in names: assert n == "SHEWME" assert len(names) == 2 def test_sig_rename_4_pattern_exclude(runtmp): # test sig rename --exclude-db-pattern sigfiles = glob.glob(utils.get_test_data("prot/*.zip")) runtmp.sourmash( "sig", "rename", "--exclude", "shewanella", *sigfiles, "NOSHEW", "-o", "out.zip" ) idx = sourmash.load_file_as_index(runtmp.output("out.zip")) names = [ss.name for ss in idx.signatures()] for n in names: assert n == "NOSHEW" assert len(names) == 6 @utils.in_thisdir def test_sig_cat_1(c): # cat 47 to 47... sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "cat", sig47) # stdout should be same signature out = c.last_result.out test_cat_sig = load_one_signature_from_json(sig47) actual_cat_sig = load_one_signature_from_json(out) assert actual_cat_sig == test_cat_sig @utils.in_thisdir def test_sig_cat_1_no_unique(c): # cat 47 to 47... twice sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "cat", sig47, sig47) # stdout should be same signature out = c.last_result.out test_cat_sig = load_one_signature_from_json(sig47) actual_cat_sigs = load_signatures_from_json(out) for n, sig in enumerate(actual_cat_sigs): assert sig == test_cat_sig assert n == 1 # two signatures, but enumerate stops at 1. assert "encountered 1 MinHashes multiple times" in c.last_result.err @utils.in_thisdir def test_sig_cat_1_unique(c): # cat 47 to 47... twice... and get unique sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "cat", sig47, sig47, "--unique") # stdout should be same signature out = c.last_result.out err = c.last_result.err test_cat_sig = load_one_signature_from_json(sig47) actual_cat_sigs = load_signatures_from_json(out) for n, sig in enumerate(actual_cat_sigs): assert sig == test_cat_sig assert n == 0 # enumerate stops at 0, first sig. assert "encountered 1 MinHashes multiple times" in err assert "...and removed the duplicates, because --unique was specified." in err @utils.in_thisdir def test_sig_cat_2(c): # cat several sig47 = utils.get_test_data("47.fa.sig") sig47abund = utils.get_test_data("track_abund/47.fa.sig") multisig = utils.get_test_data("47+63-multisig.sig") c.run_sourmash("sig", "cat", sig47, sig47abund, multisig) # stdout should be same signatures out = c.last_result.out siglist = list(load_signatures_from_json(out)) print(len(siglist)) assert ( repr(siglist) == """[SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 57e2b22f), SourmashSignature('NC_009661.1 Shewanella baltica OS185 plasmid pS18501, complete sequence', bde81a41), SourmashSignature('NC_011663.1 Shewanella baltica OS223, complete genome', f033bbd8), SourmashSignature('NC_011664.1 Shewanella baltica OS223 plasmid pS22301, complete sequence', 87a9aec4), SourmashSignature('NC_011668.1 Shewanella baltica OS223 plasmid pS22302, complete sequence', 837bf2a7), SourmashSignature('NC_011665.1 Shewanella baltica OS223 plasmid pS22303, complete sequence', 485c3377)]""" ) @utils.in_tempdir def test_sig_cat_2_out(c): # cat several sig47 = utils.get_test_data("47.fa.sig") sig47abund = utils.get_test_data("track_abund/47.fa.sig") multisig = utils.get_test_data("47+63-multisig.sig") c.run_sourmash("sig", "cat", sig47, sig47abund, multisig, "-o", "out.sig") # stdout should be same signatures out = c.output("out.sig") siglist = list(load_signatures_from_json(out)) print(len(siglist)) assert ( repr(siglist) == """[SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 57e2b22f), SourmashSignature('NC_009661.1 Shewanella baltica OS185 plasmid pS18501, complete sequence', bde81a41), SourmashSignature('NC_011663.1 Shewanella baltica OS223, complete genome', f033bbd8), SourmashSignature('NC_011664.1 Shewanella baltica OS223 plasmid pS22301, complete sequence', 87a9aec4), SourmashSignature('NC_011668.1 Shewanella baltica OS223 plasmid pS22302, complete sequence', 837bf2a7), SourmashSignature('NC_011665.1 Shewanella baltica OS223 plasmid pS22303, complete sequence', 485c3377)]""" ) @utils.in_tempdir def test_sig_cat_2_out_inplace(c): # cat several; check that we can overwrite one of the input files. sig47 = utils.get_test_data("47.fa.sig") input_sig = c.output("inp.sig") shutil.copyfile(sig47, input_sig) sig47abund = utils.get_test_data("track_abund/47.fa.sig") multisig = utils.get_test_data("47+63-multisig.sig") # write out to input. c.run_sourmash("sig", "cat", input_sig, sig47abund, multisig, "-o", input_sig) # stdout should be same signatures out = input_sig siglist = list(load_signatures_from_json(out)) print(len(siglist)) assert ( repr(siglist) == """[SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 57e2b22f), SourmashSignature('NC_009661.1 Shewanella baltica OS185 plasmid pS18501, complete sequence', bde81a41), SourmashSignature('NC_011663.1 Shewanella baltica OS223, complete genome', f033bbd8), SourmashSignature('NC_011664.1 Shewanella baltica OS223 plasmid pS22301, complete sequence', 87a9aec4), SourmashSignature('NC_011668.1 Shewanella baltica OS223 plasmid pS22302, complete sequence', 837bf2a7), SourmashSignature('NC_011665.1 Shewanella baltica OS223 plasmid pS22303, complete sequence', 485c3377)]""" ) @utils.in_tempdir def test_sig_cat_3_filelist(c): # cat using a file list as input sig47 = utils.get_test_data("47.fa.sig") sig47abund = utils.get_test_data("track_abund/47.fa.sig") multisig = utils.get_test_data("47+63-multisig.sig") filelist = c.output("filelist") with open(filelist, "w") as f: f.write("\n".join((sig47, sig47abund, multisig))) c.run_sourmash("sig", "cat", filelist, "-o", "out.sig") # stdout should be same signatures out = c.output("out.sig") # make this a list, not a set, because a set will collapse identical # signatures. `sig cat` does not collapse identical signatures, although # the pathlist function will ignore duplicate files. siglist = list(load_signatures_from_json(out)) # verify the number of signatures matches what we expect to see based # on the input files all_sigs = [] all_sigs += list(load_signatures_from_json(sig47)) all_sigs += list(load_signatures_from_json(sig47abund)) all_sigs += list(load_signatures_from_json(multisig)) assert len(all_sigs) == len(siglist) # sort the signatures by something deterministic and unique siglist.sort(key=lambda x: x.md5sum()) assert ( repr(siglist) == """[SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('NC_011665.1 Shewanella baltica OS223 plasmid pS22303, complete sequence', 485c3377), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 57e2b22f), SourmashSignature('NC_011668.1 Shewanella baltica OS223 plasmid pS22302, complete sequence', 837bf2a7), SourmashSignature('NC_011664.1 Shewanella baltica OS223 plasmid pS22301, complete sequence', 87a9aec4), SourmashSignature('NC_009661.1 Shewanella baltica OS185 plasmid pS18501, complete sequence', bde81a41), SourmashSignature('NC_011663.1 Shewanella baltica OS223, complete genome', f033bbd8)]""" ) @utils.in_tempdir def test_sig_cat_4_filelist_with_dbs(c): # cat using a file list as input sig47 = utils.get_test_data("47.fa.sig") sig47abund = utils.get_test_data("track_abund/47.fa.sig") sbt = utils.get_test_data("v6.sbt.zip") filelist = c.output("filelist") with open(filelist, "w") as f: f.write("\n".join((sig47, sig47abund, sbt))) c.run_sourmash("sig", "cat", filelist, "-o", "out.sig") # stdout should be same signatures out = c.output("out.sig") siglist = list(load_signatures_from_json(out)) print(len(siglist)) # print("siglist: ",siglist) # print("\n") # verify the number of signatures matches what we expect to see based # on the input files all_sigs = [] all_sigs += list(load_signatures_from_json(sig47)) all_sigs += list(load_signatures_from_json(sig47abund)) all_sigs += list(sourmash.load_file_as_signatures(sbt)) assert len(all_sigs) == len(siglist) # sort the signatures by something deterministic and unique siglist.sort(key=lambda x: x.md5sum()) assert ( repr(siglist) == """[SourmashSignature('', 0107d767), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('', 4e94e602), SourmashSignature('', 60f7e23c), SourmashSignature('', 6d6e87e1), SourmashSignature('', b59473c9), SourmashSignature('', f0c834bc), SourmashSignature('', f71e7817)]""" ) @utils.in_tempdir def test_sig_cat_5_from_file(c): # cat using a file list as input sig47 = utils.get_test_data("47.fa.sig") sig47abund = utils.get_test_data("track_abund/47.fa.sig") sbt = utils.get_test_data("v6.sbt.zip") filelist = c.output("filelist") with open(filelist, "w") as f: f.write("\n".join((sig47, sig47abund, sbt))) c.run_sourmash("sig", "cat", "--from-file", filelist, "-o", "out.sig") # stdout should be same signatures out = c.output("out.sig") siglist = list(load_signatures_from_json(out)) print(len(siglist)) # print("siglist: ",siglist) # print("\n") # verify the number of signatures matches what we expect to see based # on the input files all_sigs = [] all_sigs += list(load_signatures_from_json(sig47)) all_sigs += list(load_signatures_from_json(sig47abund)) all_sigs += list(sourmash.load_file_as_signatures(sbt)) assert len(all_sigs) == len(siglist) # sort the signatures by something deterministic and unique siglist.sort(key=lambda x: x.md5sum()) assert ( repr(siglist) == """[SourmashSignature('', 0107d767), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691), SourmashSignature('', 4e94e602), SourmashSignature('', 60f7e23c), SourmashSignature('', 6d6e87e1), SourmashSignature('', b59473c9), SourmashSignature('', f0c834bc), SourmashSignature('', f71e7817)]""" ) def test_sig_cat_5_from_file_picklist(runtmp): c = runtmp # cat using a file list as input sig47 = utils.get_test_data("47.fa.sig") sbt = utils.get_test_data("v6.sbt.zip") filelist = c.output("filelist") with open(filelist, "w") as f: f.write("\n".join((sig47, sbt))) picklist = _write_file(runtmp, "pl.csv", ["md5short", "09a08691"]) c.run_sourmash( "sig", "cat", "--from-file", filelist, "--picklist", f"{picklist}:md5short:md5short", "-o", "out.sig", ) # stdout should be same signatures out = c.output("out.sig") siglist = list(load_signatures_from_json(out)) print(len(siglist)) # print("siglist: ",siglist) # print("\n") # verify the number of signatures matches what we expect to see based # on the input files all_sigs = [] all_sigs += list(load_signatures_from_json(sig47, ksize=31)) assert len(all_sigs) == len(siglist) # sort the signatures by something deterministic and unique siglist.sort(key=lambda x: x.md5sum()) assert ( repr(siglist) == """[SourmashSignature('NC_009665.1 Shewanella baltica OS185, complete genome', 09a08691)]""" ) def test_sig_cat_6_pattern_include(runtmp): # test --include-db-pattern sigfiles = glob.glob(utils.get_test_data("prot/*.zip")) runtmp.sourmash("sig", "cat", "--include", "shewanella", *sigfiles, "-o", "out.zip") idx = sourmash.load_file_as_index(runtmp.output("out.zip")) assert len(idx) == 2 names = [ss.name for ss in idx.signatures()] for n in names: assert "shewanella" in n.lower(), n def test_sig_cat_6_pattern_exclude(runtmp): # test --exclude-db-pattern sigfiles = glob.glob(utils.get_test_data("prot/*.zip")) runtmp.sourmash("sig", "cat", "--exclude", "shewanella", *sigfiles, "-o", "out.zip") idx = sourmash.load_file_as_index(runtmp.output("out.zip")) assert len(idx) == 18 names = [ss.name for ss in idx.signatures()] for n in names: assert "shewanella" not in n.lower(), n def test_sig_cat_6_pattern_exclude_no_manifest(runtmp): # test --exclude-db-pattern db = utils.get_test_data("v6.sbt.zip") with pytest.raises(SourmashCommandFailed) as e: runtmp.sourmash("sig", "cat", "--exclude", "shewanella", db, "-o", "out.zip") assert "require a manifest" in str(e) def test_sig_split_1(runtmp): c = runtmp # split 47 into 1 sig :) sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "split", sig47) outname = "09a08691.k=31.scaled=1000.DNA.dup=0.47.fa.sig" assert os.path.exists(c.output(outname)) test_split_sig = load_one_signature_from_json(sig47) actual_split_sig = load_one_signature_from_json(c.output(outname)) assert actual_split_sig == test_split_sig def test_sig_split_1_fromfile_picklist(runtmp): c = runtmp # split 47 into 1 sig :) sig47 = utils.get_test_data("47.fa.sig") from_file = _write_file(runtmp, "list.txt", [sig47]) picklist = _write_file(runtmp, "pl.csv", ["md5short", "09a08691"]) c.run_sourmash( "sig", "split", "--from-file", from_file, "--picklist", f"{picklist}:md5short:md5short", ) outname = "09a08691.k=31.scaled=1000.DNA.dup=0.47.fa.sig" assert os.path.exists(c.output(outname)) test_split_sig = load_one_signature_from_json(sig47) actual_split_sig = load_one_signature_from_json(c.output(outname)) assert actual_split_sig == test_split_sig @utils.in_tempdir def test_sig_split_1_overwrite(c): # check message about overwriting sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "split", sig47) outname = "09a08691.k=31.scaled=1000.DNA.dup=0.47.fa.sig" assert os.path.exists(c.output(outname)) c.run_sourmash("sig", "split", sig47) err = c.last_result.err print(err) assert "** overwriting existing file " + outname in err @utils.in_tempdir def test_sig_split_2(c): # split 47 twice sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "split", sig47, sig47) outname1 = "09a08691.k=31.scaled=1000.DNA.dup=0.47.fa.sig" outname2 = "09a08691.k=31.scaled=1000.DNA.dup=1.47.fa.sig" assert os.path.exists(c.output(outname1)) assert os.path.exists(c.output(outname2)) test_split_sig = load_one_signature_from_json(sig47) actual_split_sig = load_one_signature_from_json(c.output(outname1)) assert actual_split_sig == test_split_sig actual_split_sig = load_one_signature_from_json(c.output(outname2)) assert actual_split_sig == test_split_sig @utils.in_tempdir def test_sig_split_2_outdir(c): # split 47 twice, put in outdir sig47 = utils.get_test_data("47.fa.sig") outdir = c.output("sigout/") c.run_sourmash("sig", "split", sig47, sig47, "--outdir", outdir) outname1 = "sigout/09a08691.k=31.scaled=1000.DNA.dup=0.47.fa.sig" outname2 = "sigout/09a08691.k=31.scaled=1000.DNA.dup=1.47.fa.sig" assert os.path.exists(c.output(outname1)) assert os.path.exists(c.output(outname2)) test_split_sig = load_one_signature_from_json(sig47) actual_split_sig = load_one_signature_from_json(c.output(outname1)) assert actual_split_sig == test_split_sig actual_split_sig = load_one_signature_from_json(c.output(outname2)) assert actual_split_sig == test_split_sig @utils.in_tempdir def test_sig_split_2_output_dir(c): # split 47 twice, put in outdir via --output-dir instead of --outdir sig47 = utils.get_test_data("47.fa.sig") outdir = c.output("sigout/") c.run_sourmash("sig", "split", sig47, sig47, "--output-dir", outdir) outname1 = "sigout/09a08691.k=31.scaled=1000.DNA.dup=0.47.fa.sig" outname2 = "sigout/09a08691.k=31.scaled=1000.DNA.dup=1.47.fa.sig" assert os.path.exists(c.output(outname1)) assert os.path.exists(c.output(outname2)) test_split_sig = load_one_signature_from_json(sig47) actual_split_sig = load_one_signature_from_json(c.output(outname1)) assert actual_split_sig == test_split_sig actual_split_sig = load_one_signature_from_json(c.output(outname2)) assert actual_split_sig == test_split_sig @utils.in_tempdir def test_sig_split_3_multisig(c): # split 47 and 47+63-multisig.sig sig47 = utils.get_test_data("47.fa.sig") multisig = utils.get_test_data("47+63-multisig.sig") c.run_sourmash("sig", "split", sig47, multisig) outlist = [ "57e2b22f.k=31.scaled=1000.DNA.dup=0.none.sig", "bde81a41.k=31.scaled=1000.DNA.dup=0.none.sig", "f033bbd8.k=31.scaled=1000.DNA.dup=0.none.sig", "87a9aec4.k=31.scaled=1000.DNA.dup=0.none.sig", "837bf2a7.k=31.scaled=1000.DNA.dup=0.none.sig", "485c3377.k=31.scaled=1000.DNA.dup=0.none.sig", ] for filename in outlist: assert os.path.exists(c.output(filename)) def test_sig_split_3_multisig_sig_gz(runtmp): # split 47 and 47+63-multisig.sig with a .sig.gz extension c = runtmp sig47 = utils.get_test_data("47.fa.sig") multisig = utils.get_test_data("47+63-multisig.sig") c.run_sourmash("sig", "split", sig47, multisig, "-E", ".sig.gz") outlist = [ "57e2b22f.k=31.scaled=1000.DNA.dup=0.none.sig.gz", "bde81a41.k=31.scaled=1000.DNA.dup=0.none.sig.gz", "f033bbd8.k=31.scaled=1000.DNA.dup=0.none.sig.gz", "87a9aec4.k=31.scaled=1000.DNA.dup=0.none.sig.gz", "837bf2a7.k=31.scaled=1000.DNA.dup=0.none.sig.gz", "485c3377.k=31.scaled=1000.DNA.dup=0.none.sig.gz", ] for filename in outlist: assert os.path.exists(c.output(filename)) def test_sig_split_3_multisig_zip(runtmp): # split 47 and 47+63-multisig.sig with a .zip extension c = runtmp sig47 = utils.get_test_data("47.fa.sig") multisig = utils.get_test_data("47+63-multisig.sig") c.run_sourmash("sig", "split", sig47, multisig, "-E", ".zip") outlist = [ "57e2b22f.k=31.scaled=1000.DNA.dup=0.none.zip", "bde81a41.k=31.scaled=1000.DNA.dup=0.none.zip", "f033bbd8.k=31.scaled=1000.DNA.dup=0.none.zip", "87a9aec4.k=31.scaled=1000.DNA.dup=0.none.zip", "837bf2a7.k=31.scaled=1000.DNA.dup=0.none.zip", "485c3377.k=31.scaled=1000.DNA.dup=0.none.zip", ] for filename in outlist: assert os.path.exists(c.output(filename)) @utils.in_tempdir def test_sig_split_4_sbt_prot(c): # split sbt sbt1 = utils.get_test_data("prot/protein.sbt.zip") sbt2 = utils.get_test_data("prot/dayhoff.sbt.zip") sbt3 = utils.get_test_data("prot/hp.sbt.zip") c.run_sourmash("sig", "split", sbt1, sbt2, sbt3) outlist = [ "16869d2c.k=19.scaled=100.protein.dup=0.GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", "120d311c.k=19.scaled=100.protein.dup=0.GCA_001593935.1_ASM159393v1_protein.faa.gz.sig", "fbca5e52.k=19.scaled=100.dayhoff.dup=0.GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", "1cbd888b.k=19.scaled=100.dayhoff.dup=0.GCA_001593935.1_ASM159393v1_protein.faa.gz.sig", "ea2a1ad2.k=19.scaled=100.hp.dup=0.GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", "bb0e6d90.k=19.scaled=100.hp.dup=0.GCA_001593935.1_ASM159393v1_protein.faa.gz.sig", ] for filename in outlist: assert os.path.exists(c.output(filename)) @utils.in_tempdir def test_sig_split_4_lca_prot(c): # split lca lca1 = utils.get_test_data("prot/protein.lca.json.gz") lca2 = utils.get_test_data("prot/dayhoff.lca.json.gz") lca3 = utils.get_test_data("prot/hp.lca.json.gz") c.run_sourmash("sig", "split", lca1, lca2, lca3) print(c.last_result.out) print(c.last_result.err) outlist = [ "16869d2c.k=19.scaled=100.protein.dup=0.none.sig", "120d311c.k=19.scaled=100.protein.dup=0.none.sig", "fbca5e52.k=19.scaled=100.dayhoff.dup=0.none.sig", "1cbd888b.k=19.scaled=100.dayhoff.dup=0.none.sig", "ea2a1ad2.k=19.scaled=100.hp.dup=0.none.sig", "bb0e6d90.k=19.scaled=100.hp.dup=0.none.sig", ] for filename in outlist: assert os.path.exists(c.output(filename)) @utils.in_tempdir def test_sig_split_5_no_exist(c): # no such file with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "split", "foo") def test_sig_split_6_numsigs(runtmp): c = runtmp sigs11 = utils.get_test_data("genome-s11.fa.gz.sig") c.run_sourmash("sig", "split", sigs11) print(c.last_result.out) print(c.last_result.err) outlist = [ "1437d8ea.k=21.num=500.DNA.dup=0.genome-s11.fa.gz.sig", "37aea787.k=7.num=500.protein.dup=0.genome-s11.fa.gz.sig", "68c565be.k=30.num=500.DNA.dup=0.genome-s11.fa.gz.sig", "73b6df1c.k=10.num=500.protein.dup=0.genome-s11.fa.gz.sig", ] for filename in outlist: assert os.path.exists(c.output(filename)) def test_sig_extract_1(runtmp): c = runtmp # extract 47 from 47... :) sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "extract", sig47) # stdout should be new signature out = c.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_1_from_file(runtmp): # run sig extract with --from-file c = runtmp # extract 47 from 47... :) sig47 = utils.get_test_data("47.fa.sig") from_file = _write_file(runtmp, "list.txt", [sig47]) c.run_sourmash("sig", "extract", "--from-file", from_file) # stdout should be new signature out = c.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_2(runtmp): c = runtmp # extract matches to 47's md5sum from among several sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") c.run_sourmash("sig", "extract", sig47, sig63, "--md5", "09a0869") # stdout should be new signature out = c.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) print(test_extract_sig.minhash) print(actual_extract_sig.minhash) assert actual_extract_sig == test_extract_sig def test_sig_extract_2_zipfile(runtmp): c = runtmp # extract matches to 47's md5sum from among several in a zipfile all_zip = utils.get_test_data("prot/all.zip") sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "extract", all_zip, "--md5", "09a0869") # stdout should be new signature out = c.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) print(test_extract_sig.minhash) print(actual_extract_sig.minhash) assert actual_extract_sig == test_extract_sig def test_sig_extract_3(runtmp): c = runtmp # extract nothing (no md5 match) sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "extract", sig47, "--md5", "FOO") def test_sig_extract_4(runtmp): c = runtmp # extract matches to 47's name from among several signatures sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") c.run_sourmash("sig", "extract", sig47, sig63, "--name", "NC_009665.1") # stdout should be new signature out = c.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) print(test_extract_sig.minhash) print(actual_extract_sig.minhash) assert actual_extract_sig == test_extract_sig def test_sig_extract_5(runtmp): c = runtmp # extract nothing (no name match) sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "extract", sig47, "--name", "FOO") def test_sig_extract_5_to_zip(runtmp): c = runtmp # extract nothing (no name match) sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "extract", sig47, "--name", "FOO", "-o", "xyz.sig.zip") outfile = runtmp.output("xyz.sig.zip") assert os.path.exists(outfile) assert list(sourmash.load_file_as_signatures(outfile)) == [] def test_sig_extract_6(runtmp): c = runtmp # extract matches to several names from among several signatures sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") c.run_sourmash("sig", "extract", sig47, sig63, "--name", "Shewanella") # stdout should be new signature out = c.last_result.out siglist = load_signatures_from_json(out) siglist = list(siglist) assert len(siglist) == 2 def test_sig_extract_7(runtmp): c = runtmp # extract matches based on ksize sig2 = utils.get_test_data("2.fa.sig") c.run_sourmash("sig", "extract", sig2, "-k", "31") # stdout should be new signature out = c.last_result.out siglist = load_signatures_from_json(out) siglist = list(siglist) assert len(siglist) == 1 def test_sig_extract_7_no_ksize(runtmp): c = runtmp # extract all three matches when -k not specified sig2 = utils.get_test_data("2.fa.sig") c.run_sourmash("sig", "extract", sig2) # stdout should be new signature out = c.last_result.out siglist = load_signatures_from_json(out) siglist = list(siglist) assert len(siglist) == 3 def test_sig_extract_8_empty_picklist_fail(runtmp): # what happens with an empty picklist? sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # make empty picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline=""): pass picklist_arg = f"{picklist_csv}:md5full:md5" with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) err = runtmp.last_result.err print(err) assert "empty or improperly formatted pickfile" in err def test_sig_extract_8_nofile_picklist_fail(runtmp): # what happens when picklist file does not exist? sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # picklist file does not exist picklist_csv = runtmp.output("pick.csv") picklist_arg = f"{picklist_csv}:md5full:md5" with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) err = runtmp.last_result.err print(err) assert "must exist and be a regular file" in err def test_sig_extract_8_picklist_md5(runtmp): # extract 47 from 47, using a picklist w/full md5 sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5full:md5" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig err = runtmp.last_result.err print(err) assert "loaded 1 distinct values into picklist." in err assert "loaded 1 total that matched ksize & molecule type" in err assert "extracted 1 signatures from 2 file(s)" in err assert "for given picklist, found 1 matches to 1 distinct values" in err def test_sig_extract_8_picklist_md5_zipfile(runtmp): # extract 47 from a zipfile, using a picklist w/full md5 allzip = utils.get_test_data("prot/all.zip") sig47 = utils.get_test_data("47.fa.sig") utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5full:md5" runtmp.sourmash("sig", "extract", allzip, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig err = runtmp.last_result.err print(err) assert "loaded 1 distinct values into picklist." in err assert "loaded 1 total that matched ksize & molecule type" in err assert "extracted 1 signatures from 1 file(s)" in err assert "for given picklist, found 1 matches to 1 distinct values" in err def test_sig_extract_8_picklist_md5_lca_fail(runtmp): # try to extract 47 from an LCA database, using a picklist w/full md5; will # fail. allzip = utils.get_test_data("lca/47+63.lca.json") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="50a9274021e43eda8b2e77f8fa60ae8e", md5short="50a9274021e43eda8b2e77f8fa60ae8e"[:8], fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5full:md5" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", allzip, "--picklist", picklist_arg, "--md5", "50a9274021e4", ) # this happens b/c the implementation of 'extract' uses picklists, and # LCA databases don't support multiple picklists. print(runtmp.last_result.err) assert ( "This input collection doesn't support 'extract' with picklists or patterns." in runtmp.last_result.err ) def test_sig_extract_8_picklist_md5_include(runtmp): # extract 47 from 47, using a picklist w/full md5:: explicit include sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5full:md5:include" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig err = runtmp.last_result.err print(err) assert "loaded 1 distinct values into picklist." in err assert "loaded 1 total that matched ksize & molecule type" in err assert "extracted 1 signatures from 2 file(s)" in err assert "for given picklist, found 1 matches to 1 distinct values" in err def test_sig_extract_8_picklist_md5_exclude(runtmp): # extract 63 from 47,63 by excluding 47, using a picklist w/full md5 sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5full:md5:exclude" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig63) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig err = runtmp.last_result.err print(err) assert "loaded 1 distinct values into picklist." in err assert "loaded 1 total that matched ksize & molecule type" in err assert "extracted 1 signatures from 2 file(s)" in err assert "for given picklist, found 1 matches by excluding 1 distinct values" in err def test_sig_extract_8_picklist_md5_require_all(runtmp): # extract 47 from 47, using a picklist w/full md5; # confirm that check missing picklist val errors out on --picklist-require sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) w.writerow( dict( exactName="", md5full="BAD MD5", md5short="", fullIdent="", nodotIdent="", ) ) picklist_arg = f"{picklist_csv}:md5full:md5" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", sig47, sig63, "--picklist", picklist_arg, "--picklist-require-all", ) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig err = runtmp.last_result.err print(err) assert "loaded 2 distinct values into picklist." in err assert "loaded 1 total that matched ksize & molecule type" in err assert "extracted 1 signatures from 2 file(s)" in err assert "for given picklist, found 1 matches to 2 distinct values" in err assert "WARNING: 1 missing picklist values." in err assert "ERROR: failing because --picklist-require-all was set" in err def test_sig_extract_8_picklist_name(runtmp): # extract 47 from 47, using a picklist w/full md5 sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:exactName:name" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_name_exclude(runtmp): # exclude 47 based on name sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:exactName:name:exclude" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig63) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_ident(runtmp): # extract 47 from 47, using a picklist w/full md5 sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:fullIdent:ident" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_ident_exclude(runtmp): # exclude 47 based on ident sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:fullIdent:ident:exclude" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig63) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_ident_dot(runtmp): # extract 47 from 47, using a picklist w/full md5 sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:nodotIdent:identprefix" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_ident_dot_exclude(runtmp): # exlude 47 based on identprefix sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:nodotIdent:identprefix:exclude" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig63) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_md5_short(runtmp): # extract 47 from 47, using a picklist w/full md5 sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5short:md5prefix8" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_md5_short_exclude(runtmp): # exclude 47 based on md5prefix8 sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5short:md5prefix8:exclude" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig63) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_md5_short_alias(runtmp): # extract 47 from 47, using a picklist w/full md5 sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5short:md5short" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_md5_short_alias_exclude(runtmp): # exlude 47 based on md5prefix8 alias, md5short sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5short:md5short:exclude" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig63) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_md5_short_alias_with_md5_selector_nomatch(runtmp): # extract 47 from 47, using a picklist w/full md5 and also md5 selector sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5short:md5short" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", sig47, sig63, "--picklist", picklist_arg, "--md5", "XXX" ) # no match to md5 selector here err = runtmp.last_result.err assert "no matching signatures to save!" in err def test_sig_extract_8_picklist_md5_short_alias_with_md5_selector_nomatch_exclude( runtmp, ): # exclude 47 using a picklist w/full md5 and also md5 selector sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5short:md5short:exclude" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", sig47, sig63, "--picklist", picklist_arg, "--md5", "XXX" ) # no match to md5 selector here err = runtmp.last_result.err assert "no matching signatures to save!" in err def test_sig_extract_8_picklist_md5_short_alias_with_md5_selector(runtmp): # extract 47 from 47, using a picklist w/full md5 and also md5 selector sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5short:md5short" runtmp.sourmash( "sig", "extract", sig47, sig63, "--picklist", picklist_arg, "--md5", "09a08691ce5295215", ) # stdout should be new signature out = runtmp.last_result.out test_extract_sig = load_one_signature_from_json(sig47) actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig == test_extract_sig def test_sig_extract_8_picklist_md5_short_alias_with_md5_selector_exclude(runtmp): # exclude 47, using a picklist w/full md5; but try to select with md5 selector sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="09a08691ce52952152f0e866a59f6261", md5short="09a08691ce5295215", fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5short:md5short:exclude" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", sig47, sig63, "--picklist", picklist_arg, "--md5", "09a08691ce5295215", ) # NTP: do we want to emit a more informative "conflicting selectors" type of msg? err = runtmp.last_result.err print(err) assert "loaded 1 distinct values into picklist." in err assert "loaded 1 total that matched ksize & molecule type" in err assert "no matching signatures to save!" in err def test_sig_extract_8_picklist_md5_nomatch(runtmp): # use an empty picklist => no match sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5short"]) w.writeheader() picklist_arg = f"{picklist_csv}:md5short:md5prefix8" with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be new signature out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "no matching signatures to save!" in err assert runtmp.last_result.status != 0 def test_sig_extract_8_picklist_md5_nomatch_exclude(runtmp): # use an empty picklist to exclude => no match => include everything sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5short"]) w.writeheader() picklist_arg = f"{picklist_csv}:md5short:md5prefix8:exclude" runtmp.sourmash("sig", "extract", sig47, sig63, "--picklist", picklist_arg) # stdout should be both signatures out = runtmp.last_result.out extract_siglist = list(load_signatures_from_json(out)) print(len(extract_siglist)) s47 = sourmash.load_file_as_signatures(sig47) s63 = sourmash.load_file_as_signatures(sig63) actual_extract_siglist = list(s47) + list(s63) assert set(extract_siglist) == set(actual_extract_siglist) err = runtmp.last_result.err print(err) assert runtmp.last_result.status == 0 assert "loaded 0 distinct values into picklist." in err assert "loaded 2 total that matched ksize & molecule type" in err assert "extracted 2 signatures from 2 file(s)" in err assert "for given picklist, found 2 matches by excluding 0 distinct values" in err def test_sig_extract_9_picklist_md5_ksize_hp_select(runtmp): # test with -k and moltype selector sigdir = utils.get_test_data("prot/") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) picklist_arg = f"{picklist_csv}:md5:md5" runtmp.sourmash( "sig", "extract", sigdir, "--picklist", picklist_arg, "-k", "19", "--hp" ) # stdout should be new signature out = runtmp.last_result.out actual_extract_sig = load_one_signature_from_json(out) print(actual_extract_sig.md5sum) assert str(actual_extract_sig) == "GCA_001593925" assert actual_extract_sig.md5sum() == "ea2a1ad233c2908529d124a330bcb672" assert actual_extract_sig.minhash.ksize == 19 assert actual_extract_sig.minhash.moltype == "hp" def test_sig_extract_9_picklist_md5_ksize_hp_select_exclude(runtmp): # test picklist exclude with -k and moltype selector sigdir = utils.get_test_data("prot/") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) picklist_arg = f"{picklist_csv}:md5:md5:exclude" runtmp.sourmash( "sig", "extract", sigdir, "--picklist", picklist_arg, "-k", "19", "--hp" ) # stdout should be new signature out = runtmp.last_result.out actual_extract_sig = load_one_signature_from_json(out) print(actual_extract_sig.md5sum) assert str(actual_extract_sig) == "GCA_001593935" assert actual_extract_sig.md5sum() == "bb0e6d90df01b7bd5d0956a5f9e3ed12" assert actual_extract_sig.minhash.ksize == 19 assert actual_extract_sig.minhash.moltype == "hp" def test_sig_extract_10_picklist_md5_dups_and_empty(runtmp): # test empty picklist values, and duplicate picklist values sigdir = utils.get_test_data("prot/") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) w.writerow(dict(md5="")) picklist_arg = f"{picklist_csv}:md5:md5" runtmp.sourmash( "sig", "extract", sigdir, "--picklist", picklist_arg, "-k", "19", "--hp" ) # stdout should be new signature out = runtmp.last_result.out actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig.minhash.ksize == 19 assert actual_extract_sig.minhash.moltype == "hp" assert actual_extract_sig.md5sum() == "ea2a1ad233c2908529d124a330bcb672" err = runtmp.last_result.err print(err) assert "WARNING: 1 empty values in column 'md5' in picklist file" in err assert "WARNING: 1 values in picklist column 'md5' were not distinct" in err def test_sig_extract_10_picklist_md5_dups_and_empty_exclude(runtmp): # test empty picklist values, and duplicate picklist values for exclude sigdir = utils.get_test_data("prot/") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) w.writerow(dict(md5="")) picklist_arg = f"{picklist_csv}:md5:md5:exclude" runtmp.sourmash( "sig", "extract", sigdir, "--picklist", picklist_arg, "-k", "19", "--hp" ) # stdout should be new signature out = runtmp.last_result.out actual_extract_sig = load_one_signature_from_json(out) assert actual_extract_sig.minhash.ksize == 19 assert actual_extract_sig.minhash.moltype == "hp" assert actual_extract_sig.md5sum() == "bb0e6d90df01b7bd5d0956a5f9e3ed12" err = runtmp.last_result.err print(err) assert "WARNING: 1 empty values in column 'md5' in picklist file" in err assert "WARNING: 1 values in picklist column 'md5' were not distinct" in err def test_sig_extract_11_picklist_bad_coltype(runtmp): # test with invalid picklist coltype sigdir = utils.get_test_data("prot/") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) picklist_arg = f"{picklist_csv}:md5:BADCOLTYPE" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", sigdir, "--picklist", picklist_arg, "-k", "19", "--hp" ) err = runtmp.last_result.err print(err) assert "invalid picklist column type 'BADCOLTYPE'" in err def test_sig_extract_11_picklist_bad_coltype_exclude(runtmp): # test with invalid picklist coltype sigdir = utils.get_test_data("prot/") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) picklist_arg = f"{picklist_csv}:md5:BADCOLTYPE:exclude" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", sigdir, "--picklist", picklist_arg, "-k", "19", "--hp" ) err = runtmp.last_result.err print(err) assert "invalid picklist column type 'BADCOLTYPE'" in err def test_sig_extract_12_picklist_bad_argstr(runtmp): # test with invalid argument format to --picklist sigdir = utils.get_test_data("prot/") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) picklist_arg = f"{picklist_csv}" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", sigdir, "--picklist", picklist_arg, "-k", "19", "--hp" ) err = runtmp.last_result.err print(err) assert "invalid picklist argument" in err def test_sig_extract_12_picklist_bad_pickstyle(runtmp): # test with invalid argument format to --picklist sigdir = utils.get_test_data("prot/") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) picklist_arg = f"{picklist_csv}:md5:md5:XXX" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", sigdir, "--picklist", picklist_arg, "-k", "19", "--hp" ) err = runtmp.last_result.err print(err) assert ( "invalid picklist 'pickstyle' argument 4: 'XXX' must be 'include' or 'exclude'" in err ) def test_sig_extract_12_picklist_bad_colname(runtmp): # test with invalid picklist colname sigdir = utils.get_test_data("prot/") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) picklist_arg = f"{picklist_csv}:BADCOLNAME:md5" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", sigdir, "--picklist", picklist_arg, "-k", "19", "--hp" ) err = runtmp.last_result.err print(err) assert "column 'BADCOLNAME' not in pickfile" in err def test_sig_extract_12_picklist_bad_colname_exclude(runtmp): # test with invalid picklist colname sigdir = utils.get_test_data("prot/") # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="ea2a1ad233c2908529d124a330bcb672")) picklist_arg = f"{picklist_csv}:BADCOLNAME:md5:exclude" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "extract", sigdir, "--picklist", picklist_arg, "-k", "19", "--hp" ) err = runtmp.last_result.err print(err) assert "column 'BADCOLNAME' not in pickfile" in err def test_sig_extract_11_pattern_include(runtmp): # test --include-db-pattern sigfiles = glob.glob(utils.get_test_data("prot/*.zip")) runtmp.sourmash( "sig", "extract", "--include", "shewanella", *sigfiles, "-o", "out.zip" ) idx = sourmash.load_file_as_index(runtmp.output("out.zip")) assert len(idx) == 2 names = [ss.name for ss in idx.signatures()] for n in names: assert "shewanella" in n.lower(), n def test_sig_extract_11_pattern_exclude(runtmp): # test --exclude-db-pattern sigfiles = glob.glob(utils.get_test_data("prot/*.zip")) runtmp.sourmash( "sig", "extract", "--exclude", "shewanella", *sigfiles, "-o", "out.zip" ) idx = sourmash.load_file_as_index(runtmp.output("out.zip")) assert len(idx) == 18 names = [ss.name for ss in idx.signatures()] for n in names: assert "shewanella" not in n.lower(), n def test_sig_extract_identical_md5s(runtmp): # test that we properly handle different signatures with identical md5s sig47 = utils.get_test_data("47.fa.sig") ss = load_signatures_from_json(sig47) sig = list(ss)[0] new_sig = sig.to_mutable() new_sig.name = "foo" sig47foo = runtmp.output("foo.sig") # this was only a problem when the signatures are stored in the same file with open(sig47foo, "w") as fp: save_signatures_to_json([new_sig, sig], fp) runtmp.run_sourmash("sig", "extract", "--name", "foo", sig47foo) out = runtmp.last_result.out print(out) ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella" not in ss.name assert "foo" in ss.name assert ss.md5sum() == "09a08691ce52952152f0e866a59f6261" def test_sig_flatten_1(runtmp): c = runtmp # extract matches to several names from among several signatures & flatten sig47abund = utils.get_test_data("track_abund/47.fa.sig") sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "flatten", sig47abund, "--name", "Shewanella") # stdout should be new signature out = c.last_result.out siglist = load_signatures_from_json(out) siglist = list(siglist) assert len(siglist) == 1 test_flattened = load_one_signature_from_json(sig47) assert test_flattened.minhash == siglist[0].minhash assert test_flattened.name == siglist[0].name def test_sig_flatten_1_from_file(runtmp): c = runtmp # extract matches to several names from among several signatures & flatten sig47abund = utils.get_test_data("track_abund/47.fa.sig") sig47 = utils.get_test_data("47.fa.sig") from_file = _write_file(runtmp, "list.txt", [sig47abund]) picklist = _write_file(runtmp, "pl.csv", ["md5short", "09a08691"]) c.run_sourmash( "sig", "flatten", "--from-file", from_file, "--picklist", f"{picklist}:md5short:md5short", ) # stdout should be new signature out = c.last_result.out siglist = load_signatures_from_json(out) siglist = list(siglist) assert len(siglist) == 1 test_flattened = load_one_signature_from_json(sig47) assert test_flattened.minhash == siglist[0].minhash @utils.in_tempdir def test_sig_flatten_1_select_name(c): # extract matches to several names from among several signatures & flatten sig47abund = utils.get_test_data("track_abund/47.fa.sig") sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "flatten", sig2, sig47abund, "--name", "Shewanella") # stdout should be new signature out = c.last_result.out siglist = load_signatures_from_json(out) siglist = list(siglist) assert len(siglist) == 1 test_flattened = load_one_signature_from_json(sig47) assert test_flattened.minhash == siglist[0].minhash def test_sig_flatten_1_select_md5(runtmp): c = runtmp # extract matches to several names from among several signatures & flatten sig47abund = utils.get_test_data("track_abund/47.fa.sig") sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "flatten", sig2, sig47abund, "--md5", "09a08691c") # stdout should be new signature out = c.last_result.out siglist = load_signatures_from_json(out) siglist = list(siglist) assert len(siglist) == 1 test_flattened = load_one_signature_from_json(sig47) assert test_flattened.minhash == siglist[0].minhash def test_sig_flatten_2_ksize(runtmp): c = runtmp # flatten only one signature selected using ksize psw_mag = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") c.run_sourmash("sig", "flatten", psw_mag, "-k", "31") # stdout should be new signature out = c.last_result.out siglist = load_signatures_from_json(out) siglist = list(siglist) assert len(siglist) == 1 @utils.in_tempdir def test_sig_downsample_1_scaled(c): # downsample a scaled signature sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "downsample", "--scaled", "10000", sig47) # stdout should be new signature out = c.last_result.out test_downsample_sig = load_one_signature_from_json(sig47) actual_downsample_sig = load_one_signature_from_json(out) test_mh = test_downsample_sig.minhash.downsample(scaled=10000) assert actual_downsample_sig.minhash == test_mh assert actual_downsample_sig.name == test_downsample_sig.name @utils.in_tempdir def test_sig_downsample_1_scaled_downsample_multisig(c): # downsample many scaled signatures in one file multisig = utils.get_test_data("47+63-multisig.sig") c.run_sourmash("sig", "downsample", "--scaled", "10000", multisig) # stdout should be new signatures out = c.last_result.out for sig in load_signatures_from_json(out): assert sig.minhash.scaled == 10000 @utils.in_tempdir def test_sig_downsample_1_scaled_to_num(c): # downsample a scaled signature sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "downsample", "--num", "500", sig47) # stdout should be new signature out = c.last_result.out actual_downsample_sig = load_one_signature_from_json(out) actual_mins = actual_downsample_sig.minhash.hashes.keys() actual_mins = list(actual_mins) actual_mins.sort() test_downsample_sig = load_one_signature_from_json(sig47) test_mins = test_downsample_sig.minhash.hashes.keys() test_mins = list(test_mins) test_mins.sort() test_mins = test_mins[:500] # take 500 smallest assert actual_mins == test_mins def test_sig_downsample_check_num_bounds_negative(runtmp): c = runtmp sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "downsample", "--num", "-5", sig47) assert "ERROR: num value must be positive" in c.last_result.err def test_sig_downsample_check_num_bounds_less_than_minimum(runtmp): c = runtmp sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "downsample", "--num", "25", sig47) assert "WARNING: num value should be >= 50. Continuing anyway." in c.last_result.err def test_sig_downsample_check_num_bounds_more_than_maximum(runtmp): c = runtmp sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "downsample", "--num", "100000", sig47) assert ( "WARNING: num value should be <= 50000. Continuing anyway." in c.last_result.err ) @utils.in_tempdir def test_sig_downsample_1_scaled_to_num_fail(c): # downsample a scaled signature sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "downsample", "--num", "50000", sig47) @utils.in_tempdir def test_sig_downsample_1_scaled_empty(c): # downsample a scaled signature sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "downsample", sig47) @utils.in_tempdir def test_sig_downsample_2_num(c): # downsample a num signature sigs11 = utils.get_test_data("genome-s11.fa.gz.sig") c.run_sourmash("sig", "downsample", "--num", "500", "-k", "21", "--dna", sigs11) # stdout should be new signature out = c.last_result.out test_downsample_sig = load_one_signature_from_json( sigs11, ksize=21, select_moltype="DNA" ) actual_downsample_sig = load_one_signature_from_json(out) test_mh = test_downsample_sig.minhash.downsample(num=500) assert actual_downsample_sig.minhash == test_mh @utils.in_tempdir def test_sig_downsample_2_num_to_scaled(c): # downsample a num signature and convert it into a scaled sig sigs11 = utils.get_test_data("genome-s11.fa.gz.sig") c.run_sourmash( "sig", "downsample", "--scaled", "10000", "-k", "21", "--dna", sigs11 ) # stdout should be new signature out = c.last_result.out test_downsample_sig = load_one_signature_from_json( sigs11, ksize=21, select_moltype="DNA" ) actual_downsample_sig = load_one_signature_from_json(out) test_mins = test_downsample_sig.minhash.hashes.keys() actual_mins = actual_downsample_sig.minhash.hashes.keys() # select those mins that are beneath the new max hash... max_hash = actual_downsample_sig.minhash._max_hash test_mins_down = {k for k in test_mins if k < max_hash} assert test_mins_down == set(actual_mins) @utils.in_tempdir def test_sig_downsample_2_num_to_scaled_fail(c): # downsample a num signature and FAIL to convert it into a scaled sig # because new scaled is too low sigs11 = utils.get_test_data("genome-s11.fa.gz.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "sig", "downsample", "--scaled", "100", "-k", "21", "--dna", sigs11 ) @utils.in_tempdir def test_sig_downsample_2_num_and_scaled_both_fail(c): # cannot specify both --num and --scaled sigs11 = utils.get_test_data("genome-s11.fa.gz.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "sig", "downsample", "--scaled", "100", "--num", "50", "-k", "21", "--dna", sigs11, ) @utils.in_tempdir def test_sig_downsample_2_num_empty(c): # downsample a num signature sigs11 = utils.get_test_data("genome-s11.fa.gz.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("sig", "downsample", "-k", "21", "--dna", sigs11) def test_sig_describe_1(runtmp): c = runtmp # get basic info on a signature sig47 = utils.get_test_data("47.fa.sig") c.run_sourmash("sig", "describe", sig47) out = c.last_result.out print(c.last_result) expected_output = """\ signature: NC_009665.1 Shewanella baltica OS185, complete genome source file: 47.fa md5: 09a08691ce52952152f0e866a59f6261 k=31 molecule=DNA num=0 scaled=1000 seed=42 track_abundance=0 size: 5177 signature license: CC0 """.splitlines() for line in expected_output: assert line.strip() in out def test_sig_describe_1_fromfile_picklist(runtmp): c = runtmp # get basic info on a signature sig47 = utils.get_test_data("47.fa.sig") from_file = _write_file(runtmp, "list.txt", [sig47]) picklist = _write_file(runtmp, "pl.csv", ["md5short", "09a08691"]) c.run_sourmash( "sig", "describe", "--from-file", from_file, "--picklist", f"{picklist}:md5short:md5short", ) out = c.last_result.out print(c.last_result) expected_output = """\ signature: NC_009665.1 Shewanella baltica OS185, complete genome source file: 47.fa md5: 09a08691ce52952152f0e866a59f6261 k=31 molecule=DNA num=0 scaled=1000 seed=42 track_abundance=0 size: 5177 signature license: CC0 """.splitlines() for line in expected_output: assert line.strip() in out @utils.in_thisdir def test_sig_describe_protein(c): # test describe on a singleton protein signature testdata = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) c.run_sourmash("sig", "describe", testdata) assert ( "k=19 molecule=protein num=0 scaled=100 seed=42 track_abundance=0" in c.last_result.out ) @utils.in_thisdir def test_sig_describe_hp(c): # test describe on a singleton hp signature testdata = utils.get_test_data( "prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) c.run_sourmash("sig", "describe", testdata) assert ( "k=19 molecule=hp num=0 scaled=100 seed=42 track_abundance=0" in c.last_result.out ) @utils.in_thisdir def test_sig_describe_dayhoff(c): # test describe on a singleton dayhoff signature testdata = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) c.run_sourmash("sig", "describe", testdata) assert ( "k=19 molecule=dayhoff num=0 scaled=100 seed=42 track_abundance=0" in c.last_result.out ) def test_sig_describe_1_hp(runtmp): c = runtmp # get basic info on a signature testdata = utils.get_test_data("short.fa") # run four separate commands to make 4 different sets of sigs... c.sourmash("sketch", "dna", "-p", "k=21,k=30,num=500", "-o", "out.zip", testdata) c.sourmash( "sketch", "translate", "-p", "k=7,k=10,num=500", "-o", "out.zip", testdata ) c.sourmash( "sketch", "translate", "-p", "k=7,k=10,num=500,hp", "-o", "out.zip", testdata ) c.sourmash( "sketch", "translate", "-p", "k=7,k=10,num=500,dayhoff", "-o", "out.zip", testdata, ) # then combine into one .sig file c.sourmash("sig", "cat", "out.zip", "-o", "short.fa.sig") # & run sig describe c.run_sourmash("sig", "describe", "short.fa.sig") out = c.last_result.out print(c.last_result.out) # Add final trailing slash for this OS testdata_dirname = os.path.dirname(testdata) + os.sep location = c.location + os.sep expected_output = """\ --- signature filename: short.fa.sig signature: ** no name ** source file: short.fa md5: e45a080101751e044d6df861d3d0f3fd k=7 molecule=protein num=500 scaled=0 seed=42 track_abundance=0 size: 500 sum hashes: 500 signature license: CC0 --- signature filename: short.fa.sig signature: ** no name ** source file: short.fa md5: c027e96c3379d38942639219daa24fdc k=7 molecule=dayhoff num=500 scaled=0 seed=42 track_abundance=0 size: 500 sum hashes: 500 signature license: CC0 --- signature filename: short.fa.sig signature: ** no name ** source file: short.fa md5: 4b50ae79657d9dd07a1d543ba8b986a0 k=7 molecule=hp num=500 scaled=0 seed=42 track_abundance=0 size: 500 signature license: CC0 --- signature filename: short.fa.sig signature: ** no name ** source file: short.fa md5: 1136a8a68420bd93683e45cdaf109b80 k=21 molecule=DNA num=500 scaled=0 seed=42 track_abundance=0 size: 500 sum hashes: 500 signature license: CC0 --- signature filename: short.fa.sig signature: ** no name ** source file: short.fa md5: 4244d1612598af044e799587132f007e k=10 molecule=protein num=500 scaled=0 seed=42 track_abundance=0 size: 500 sum hashes: 500 signature license: CC0 --- signature filename: short.fa.sig signature: ** no name ** source file: short.fa md5: 396dcb7c1875f48ca31e0759bec72ee1 k=10 molecule=dayhoff num=500 scaled=0 seed=42 track_abundance=0 size: 500 sum hashes: 500 signature license: CC0 --- signature filename: short.fa.sig signature: ** no name ** source file: short.fa md5: 4c43878296459783dbd6a4a071ab7e9d k=10 molecule=hp num=500 scaled=0 seed=42 track_abundance=0 size: 500 sum hashes: 500 signature license: CC0 --- signature: ** no name ** source file: short.fa md5: 71f7c111c01785e5f38efad45b00a0e1 k=30 molecule=DNA num=500 scaled=0 seed=42 track_abundance=0 size: 500 sum hashes: 500 signature license: CC0 """.splitlines() for line in out.splitlines(): cleaned_line = line.strip().replace(testdata_dirname, "").replace(location, "") assert cleaned_line in expected_output, cleaned_line @utils.in_tempdir def test_sig_describe_1_multisig(c): # get basic info on multiple signatures in a single file sigs = utils.get_test_data("47+63-multisig.sig") c.run_sourmash("sig", "describe", sigs) out = c.last_result.out print(c.last_result) expected_output = """\ signature: NC_009665.1 Shewanella baltica OS185, complete genome signature: NC_009661.1 Shewanella baltica OS185 plasmid pS18501, complete sequence signature: NC_011663.1 Shewanella baltica OS223, complete genome signature: NC_011664.1 Shewanella baltica OS223 plasmid pS22301, complete sequence signature: NC_011668.1 Shewanella baltica OS223 plasmid pS22302, complete sequence signature: NC_011665.1 Shewanella baltica OS223 plasmid pS22303, complete sequence""".splitlines() for line in expected_output: assert line.strip() in out @utils.in_tempdir def test_sig_describe_1_sbt(c): # get basic info on multiple signatures in an SBT sigs = utils.get_test_data("prot/protein.sbt.zip") c.run_sourmash("sig", "describe", sigs) out = c.last_result.out print(c.last_result) expected_output = """\ signature: GCA_001593925 signature: GCA_001593935 """.splitlines() for line in expected_output: assert line.strip() in out @utils.in_tempdir def test_sig_describe_1_lca(c): # get basic info on multiple signatures in an LCA database sigs = utils.get_test_data("prot/protein.lca.json.gz") c.run_sourmash("sig", "describe", sigs) out = c.last_result.out print(c.last_result) expected_output = """\ signature: GCA_001593925 signature: GCA_001593935 """.splitlines() for line in expected_output: assert line.strip() in out @utils.in_tempdir def test_sig_describe_1_dir(c): # get basic info on multiple signatures in a directory sigs = utils.get_test_data("prot/protein/") c.run_sourmash("sig", "describe", sigs) out = c.last_result.out print(c.last_result) # make sure signature names, as well as full path to .sig file under # directory, show up in output. expected_output = """\ signature: GCA_001593925 signature: GCA_001593935 prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig prot/protein/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig """.splitlines() for line in expected_output: assert line.strip() in out @utils.in_tempdir def test_sig_describe_1_zipfile(c): # get basic info on multiple signatures in a zipfile sigs = utils.get_test_data("prot/all.zip") c.run_sourmash("sig", "describe", sigs) out = c.last_result.out print(c.last_result) expected_output = """\ k=19 molecule=dayhoff num=0 scaled=100 seed=42 track_abundance=0 k=19 molecule=dayhoff num=0 scaled=100 seed=42 track_abundance=0 k=19 molecule=hp num=0 scaled=100 seed=42 track_abundance=0 k=19 molecule=hp num=0 scaled=100 seed=42 track_abundance=0 k=19 molecule=protein num=0 scaled=100 seed=42 track_abundance=0 k=19 molecule=protein num=0 scaled=100 seed=42 track_abundance=0 """.splitlines() for line in expected_output: assert line.strip() in out def test_sig_describe_1_sig_abund(runtmp): # check output of sig describe on a sketch with abundances c = runtmp sigfile = utils.get_test_data("track_abund/47.fa.sig") c.run_sourmash("sig", "describe", sigfile) out = c.last_result.out print(c.last_result.out) expected_output = """\ signature: NC_009665.1 Shewanella baltica OS185, complete genome source file: podar-ref/47.fa md5: 09a08691ce52952152f0e866a59f6261 k=31 molecule=DNA num=0 scaled=1000 seed=42 track_abundance=1 size: 5177 sum hashes: 5292 signature license: CC0 """.splitlines() for line in expected_output: assert line.strip() in out @utils.in_thisdir def test_sig_describe_stdin(c): sig = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) with open(sig) as fp: data = fp.read() c.run_sourmash("sig", "describe", "-", stdin_data=data) assert "signature: GCA_001593925" in c.last_result.out @utils.in_tempdir def test_sig_describe_empty(c): sig = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) ss = sourmash.load_file_as_signatures(sig) ss = list(ss) assert len(ss) == 1 ss = ss[0] ss = ss.to_mutable() ss.name = "" ss.filename = "" outsig = c.output("xxx.sig") with open(outsig, "w") as fp: save_signatures_to_json([ss], fp) ss = sourmash.load_file_as_signatures(outsig) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert ss.name == "" assert ss.filename == "" c.run_sourmash("sig", "describe", outsig) print(c.last_result.out) assert "signature: ** no name **" in c.last_result.out assert "source file: ** no name **" in c.last_result.out def test_sig_describe_sqldb(runtmp): # make a sqldb and run fileinfo on it gcf_all = glob.glob(utils.get_test_data("gather/GCF*.sig")) sqldb = runtmp.output("some.sqldb") runtmp.sourmash("sig", "cat", "-k", "31", *gcf_all, "-o", sqldb) runtmp.sourmash("sig", "describe", sqldb) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "md5: 4289d4241be8573145282352215ca3c4" in out assert "md5: 85c3aeec6457c0b1d210472ddeb67714" in out def test_sig_describe_2_csv(runtmp): # output info in CSV spreadsheet c = runtmp sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") c.run_sourmash("sig", "describe", sig47, sig63, "--csv", "out.csv") expected_md5 = [ "09a08691ce52952152f0e866a59f6261", "38729c6374925585db28916b82a6f513", ] with open(c.output("out.csv")) as fp: r = csv.DictReader(fp) n = 0 for row, md5 in zip(r, expected_md5): assert row["md5"] == md5 n += 1 assert n == 2 def test_sig_describe_2_csv_gz(runtmp): # output info in CSV spreadsheet, gzipped c = runtmp sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") c.run_sourmash("sig", "describe", sig47, sig63, "--csv", "out.csv.gz") expected_md5 = [ "09a08691ce52952152f0e866a59f6261", "38729c6374925585db28916b82a6f513", ] with gzip.open(c.output("out.csv.gz"), "rt", newline="") as fp: r = csv.DictReader(fp) n = 0 for row, md5 in zip(r, expected_md5): assert row["md5"] == md5 n += 1 assert n == 2 def test_sig_describe_2_csv_abund(runtmp): # output info in CSV spreadsheet, for abund sig c = runtmp sig47 = utils.get_test_data("track_abund/47.fa.sig") c.run_sourmash("sig", "describe", sig47, "--csv", "out.csv") with open(c.output("out.csv")) as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 1 row = rows[0] assert row["signature_file"] == sig47 assert row["md5"] == "09a08691ce52952152f0e866a59f6261" assert row["ksize"] == "31" assert row["moltype"] == "DNA" assert row["num"] == "0" assert row["scaled"] == "1000" assert row["n_hashes"] == "5177" assert row["seed"] == "42" assert row["with_abundance"] == "1" assert row["name"] == "NC_009665.1 Shewanella baltica OS185, complete genome" assert row["filename"] == "podar-ref/47.fa" assert row["license"] == "CC0" assert row["sum_hashes"] == "5292" def test_sig_describe_2_csv_as_picklist(runtmp): # generate an output CSV from describe and then use it as a manifest # pickfile c = runtmp sig47 = utils.get_test_data("47.fa.sig") outcsv = runtmp.output("out.csv") c.run_sourmash("sig", "describe", sig47, "--csv", outcsv) c.run_sourmash("sig", "describe", sig47, "--picklist", f"{outcsv}::manifest") out = c.last_result.out print(c.last_result) expected_output = """\ signature: NC_009665.1 Shewanella baltica OS185, complete genome source file: 47.fa md5: 09a08691ce52952152f0e866a59f6261 k=31 molecule=DNA num=0 scaled=1000 seed=42 track_abundance=0 size: 5177 signature license: CC0 """.splitlines() for line in expected_output: assert line.strip() in out def test_sig_describe_2_include_db_pattern(runtmp): # test sig describe --include-db-pattern c = runtmp allzip = utils.get_test_data("prot/all.zip") c.run_sourmash("sig", "describe", allzip, "--include-db-pattern", "os185") out = c.last_result.out print(c.last_result) expected_output = """\ signature: NC_009665.1 Shewanella baltica OS185, complete genome source file: 47.fa md5: 09a08691ce52952152f0e866a59f6261 k=31 molecule=DNA num=0 scaled=1000 seed=42 track_abundance=0 size: 5177 signature license: CC0 """.splitlines() for line in expected_output: assert line.strip() in out def test_sig_describe_2_exclude_db_pattern(runtmp): # test sig describe --exclude-db-pattern c = runtmp allzip = utils.get_test_data("prot/all.zip") c.run_sourmash( "sig", "describe", allzip, "--dna", "-k", "31", "--exclude-db-pattern", "os223" ) out = c.last_result.out print(c.last_result) expected_output = """\ signature: NC_009665.1 Shewanella baltica OS185, complete genome source file: 47.fa md5: 09a08691ce52952152f0e866a59f6261 k=31 molecule=DNA num=0 scaled=1000 seed=42 track_abundance=0 size: 5177 signature license: CC0 """.splitlines() for line in expected_output: assert line.strip() in out def test_sig_describe_3_manifest_works(runtmp): # test on a manifest with relative paths, in proper location mf = utils.get_test_data("scaled/mf.csv") runtmp.sourmash("sig", "describe", mf, "--csv", "out.csv") out = runtmp.last_result.out print(out) with open(runtmp.output("out.csv"), newline="") as fp: r = csv.reader(fp) rows = list(r) assert len(rows) == 16 # 15 signatures, plus head def test_sig_describe_3_manifest_fails_when_moved(runtmp): # test on a manifest with relative paths, when in wrong place; # should fail, because actual signatures cannot be loaded now. # note: this tests lazy loading. mf = utils.get_test_data("scaled/mf.csv") shutil.copyfile(mf, runtmp.output("mf.csv")) with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "describe", "mf.csv") def test_sig_overlap(runtmp): c = runtmp # get overlap details sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") c.run_sourmash("sig", "overlap", sig47, sig63) out = c.last_result.out print(out) # md5s assert "09a08691ce52952152f0e866a59f6261" in out assert "38729c6374925585db28916b82a6f513" in out assert "similarity: 0.32069" in out assert "number of hashes in common: 2529" in out def test_sig_overlap_2(runtmp): c = runtmp # get overlap details sig47 = utils.get_test_data("47.fa.sig.zip") sig63 = utils.get_test_data("63.fa.sig.zip") c.run_sourmash("sig", "overlap", sig47, sig63) out = c.last_result.out print(out) # md5s assert "09a08691ce52952152f0e866a59f6261" in out assert "38729c6374925585db28916b82a6f513" in out assert "similarity: 0.32069" in out assert "number of hashes in common: 2529" in out @utils.in_tempdir def test_import_export_1(c): # check to make sure we can import what we've exported! inp = utils.get_test_data("genome-s11.fa.gz.sig") outp = c.output("export.json") c.run_sourmash("sig", "export", inp, "-o", outp, "-k", "21", "--dna") c.run_sourmash("sig", "import", outp) original = load_one_signature_from_json(inp, ksize=21, select_moltype="DNA") roundtrip = load_one_signature_from_json(c.last_result.out) assert original.minhash == roundtrip.minhash @utils.in_tempdir def test_import_export_1_by_md5(c): # check to make sure we can import what we've exported! inp = utils.get_test_data("genome-s11.fa.gz.sig") outp = c.output("export.json") c.run_sourmash("sig", "export", inp, "-o", outp, "--md5", "1437d8eae6") c.run_sourmash("sig", "import", outp) original = load_one_signature_from_json(inp, ksize=21, select_moltype="DNA") roundtrip = load_one_signature_from_json(c.last_result.out) assert original.minhash == roundtrip.minhash @utils.in_tempdir def test_import_export_2(c): # check to make sure we can import a mash JSON dump file. # NOTE: msh.json_dump file calculated like so: # mash sketch -s 500 -k 21 ./tests/test-data/genome-s11.fa.gz # mash info -d ./tests/test-data/genome-s11.fa.gz.msh > tests/test-data/genome-s11.fa.gz.msh.json_dump # sig1 = utils.get_test_data("genome-s11.fa.gz.sig") msh_sig = utils.get_test_data("genome-s11.fa.gz.msh.json_dump") c.run_sourmash("sig", "import", msh_sig) imported = load_one_signature_from_json(c.last_result.out) compare = load_one_signature_from_json(sig1, ksize=21, select_moltype="DNA") assert imported.minhash == compare.minhash def test_import_mash_csv_to_sig(runtmp): # test copied over from 'sourmash import_csv'. testdata1 = utils.get_test_data("short.fa.msh.dump") testdata2 = utils.get_test_data("short.fa") runtmp.sourmash("sig", "import", "--csv", testdata1, "-o", "xxx.sig") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=970", testdata2) runtmp.sourmash("search", "-k", "31", "short.fa.sig", "xxx.sig") print("RUNTEMP", runtmp) assert "1 matches" in runtmp.last_result.out assert "100.0% short.fa" in runtmp.last_result.out def test_sig_manifest_1_zipfile(runtmp): # make a manifest from a .zip file protzip = utils.get_test_data("prot/protein.zip") runtmp.sourmash("sig", "manifest", protzip, "-o", "SOURMASH-MANIFEST.csv") manifest_fn = runtmp.output("SOURMASH-MANIFEST.csv") with open(manifest_fn, newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_sig_manifest_1_zipfile_csv_gz(runtmp): # make a gzipped manifest from a .zip file protzip = utils.get_test_data("prot/protein.zip") runtmp.sourmash("sig", "manifest", protzip, "-o", "SOURMASH-MANIFEST.csv.gz") manifest_fn = runtmp.output("SOURMASH-MANIFEST.csv.gz") with gzip.open(manifest_fn, "rt", newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_sig_manifest_1_zipfile_already_exists(runtmp): # make a manifest from a .zip file; f protzip = utils.get_test_data("prot/protein.zip") mf_csv = runtmp.output("mf.csv") with open(mf_csv, "w") as fp: fp.write("hello, world") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "manifest", protzip, "-o", "mf.csv") def test_sig_manifest_1_zipfile_already_exists_force(runtmp): # make a manifest from a .zip file protzip = utils.get_test_data("prot/protein.zip") mf_csv = runtmp.output("mf.csv") with open(mf_csv, "w") as fp: fp.write("hello, world") runtmp.sourmash("sig", "manifest", protzip, "-o", "mf.csv", "-f") with open(mf_csv, newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_sig_manifest_1_zipfile_already_exists_sql(runtmp): # make a manifest from a .zip file protzip = utils.get_test_data("prot/protein.zip") mf_csv = runtmp.output("mf.mfsql") runtmp.sourmash("sig", "manifest", protzip, "-o", "mf.mfsql", "-F", "sql") runtmp.sourmash("sig", "manifest", protzip, "-o", "mf.mfsql", "-F", "sql", "-f") manifest = CollectionManifest.load_from_filename(mf_csv) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_sig_manifest_2_sigfile(runtmp): # make a manifest from a .sig file sigfile = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) runtmp.sourmash("sig", "manifest", sigfile, "-o", "SOURMASH-MANIFEST.csv") manifest_fn = runtmp.output("SOURMASH-MANIFEST.csv") with open(manifest_fn, newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 1 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list def test_sig_manifest_3_sbt(runtmp): # make a manifest from an SBT protzip = utils.get_test_data("prot/protein.sbt.zip") runtmp.sourmash("sig", "manifest", protzip, "-o", "SOURMASH-MANIFEST.csv") manifest_fn = runtmp.output("SOURMASH-MANIFEST.csv") with open(manifest_fn, newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_sig_manifest_4_lca(runtmp): # make a manifest from a .lca.json file sigfile = utils.get_test_data("prot/protein.lca.json.gz") runtmp.sourmash("sig", "manifest", sigfile, "-o", "SOURMASH-MANIFEST.csv") manifest_fn = runtmp.output("SOURMASH-MANIFEST.csv") with open(manifest_fn, newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_sig_manifest_5_dir(runtmp): # make a manifest from a directory sigfile = utils.get_test_data("prot/protein/") runtmp.sourmash("sig", "manifest", sigfile, "-o", "SOURMASH-MANIFEST.csv") manifest_fn = runtmp.output("SOURMASH-MANIFEST.csv") with open(manifest_fn, newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_sig_manifest_6_pathlist(runtmp): # make a manifest from a pathlist file sigfiles = utils.get_test_data("prot/protein/*.sig") sigfiles = glob.glob(sigfiles) pathlist = runtmp.output("pathlist.txt") with open(pathlist, "w") as fp: fp.write("\n".join(sigfiles)) runtmp.sourmash("sig", "manifest", pathlist, "-o", "SOURMASH-MANIFEST.csv") manifest_fn = runtmp.output("SOURMASH-MANIFEST.csv") with open(manifest_fn, newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list # note: the manifest output for pathlists will contain the locations # used in the pathlist. This is required by StandaloneManifestIndex. for row in manifest.rows: iloc = row["internal_location"] print(iloc) assert iloc.startswith("/"), iloc def test_sig_manifest_does_not_exist(runtmp): with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("sig", "manifest", "does-not-exist", "-o", "out.csv") assert ( "Cannot open 'does-not-exist' as a sourmash signature collection." in runtmp.last_result.err ) def test_sig_manifest_7_allzip_1(runtmp): # the rebuilt manifest w/o '-f' will miss dna-sig.noext allzip = utils.get_test_data("prot/all.zip") runtmp.sourmash("sig", "manifest", allzip, "-o", "xyz.csv") manifest_fn = runtmp.output("xyz.csv") with open(manifest_fn, newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 7 filenames = set(row["internal_location"] for row in manifest.rows) assert "dna-sig.noext" not in filenames def test_sig_manifest_7_allzip_2(runtmp): # the rebuilt manifest w/ '-f' will contain dna-sig.noext allzip = utils.get_test_data("prot/all.zip") runtmp.sourmash("sig", "manifest", allzip, "-o", "xyz.csv", "-f") manifest_fn = runtmp.output("xyz.csv") with open(manifest_fn, newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 8 filenames = set(row["internal_location"] for row in manifest.rows) assert "dna-sig.noext" in filenames def test_sig_manifest_7_allzip_3(runtmp): # the existing manifest contains 'dna-sig.noext' whther or not -f is # used. allzip = utils.get_test_data("prot/all.zip") runtmp.sourmash("sig", "manifest", allzip, "-o", "xyz.csv", "--no-rebuild") manifest_fn = runtmp.output("xyz.csv") with open(manifest_fn, newline="") as csvfp: manifest = CollectionManifest.load_from_csv(csvfp) assert len(manifest) == 8 filenames = set(row["internal_location"] for row in manifest.rows) assert "dna-sig.noext" in filenames def test_sig_manifest_8_sqldb(runtmp): # make a sqldb and then run sig manifest on it. gcf_all = glob.glob(utils.get_test_data("gather/GCF*.sig")) sqldb = runtmp.output("some.sqldb") runtmp.sourmash("sig", "cat", "-k", "31", *gcf_all, "-o", sqldb) # need to use '--no-rebuild-manifest' with 'sig manifest' on sqldb, # because it has a manifest but not the _signatures_with_internal # method to rebuild one ;) # so, this should fail... with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "manifest", sqldb, "-o", "mf.csv") # ...and this should succeed: runtmp.sourmash("sig", "manifest", sqldb, "-o", "mf.csv", "--no-rebuild") err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "manifest contains 12 signatures total." in err assert "wrote manifest to 'mf.csv'" in err mf = CollectionManifest.load_from_filename(runtmp.output("mf.csv")) assert len(mf) == 12 def test_sig_manifest_8_sqldb_out(runtmp): # make a zip and run manifest out on it to make a sql format manifest. gcf_all = glob.glob(utils.get_test_data("gather/GCF*.sig")) zipfile = runtmp.output("some.zip") runtmp.sourmash("sig", "cat", "-k", "31", *gcf_all, "-o", zipfile) # ...and this should succeed: runtmp.sourmash("sig", "manifest", zipfile, "-o", "mf.sqldb", "-F", "sql") err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "manifest contains 12 signatures total." in err assert "wrote manifest to 'mf.sqldb'" in err mf = CollectionManifest.load_from_filename(runtmp.output("mf.sqldb")) assert len(mf) == 12 def test_sig_kmers_1_dna(runtmp): # test sig kmers on dna seqfile = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "dna", seqfile, "-p", "scaled=1") ss = load_one_signature_from_json(runtmp.output("short.fa.sig")) mh = ss.minhash assert mh.moltype == "DNA" runtmp.sourmash( "sig", "kmers", "--sig", "short.fa.sig", "--seq", seqfile, "--save-kmers", "short.csv", "--save-sequences", "matched.fa", ) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 970" in err assert "found 970 distinct matching hashes (100.0%)" in err # check FASTA output assert os.path.exists(runtmp.output("matched.fa")) with screed.open(runtmp.output("matched.fa")) as f: records = list(f) assert len(records) == 1 assert len(records[0].sequence) == 1000, len(records[0].sequence) seq_mh = mh.copy_and_clear() for record in records: seq_mh.add_sequence(record.sequence) assert seq_mh.similarity(mh) == 1.0 # check CSV output w/k-mers and hashes etc assert os.path.exists(runtmp.output("short.csv")) with open(runtmp.output("short.csv"), newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 970 check_mh = mh.copy_and_clear() check_mh2 = mh.copy_and_clear() for row in rows: check_mh.add_sequence(row["kmer"]) check_mh2.add_hash(int(row["hashval"])) assert check_mh.similarity(mh) == 1.0 assert check_mh2.similarity(mh) == 1.0 def test_sig_kmers_1_dna_more_in_query(runtmp): # test sig kmers on dna, where query has more than matches seqfile = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "dna", seqfile, "-p", "scaled=1") ss = load_one_signature_from_json(runtmp.output("short.fa.sig")) mh = ss.minhash assert mh.moltype == "DNA" # make a new sequence for query, with more k-mers query_seqfile = runtmp.output("query.fa") with open(query_seqfile, "w") as fp: with screed.open(seqfile) as screed_iter: for record in screed_iter: fp.write(f">{record.name}\n{record.sequence}AGTTACGATC\n") runtmp.sourmash("sig", "kmers", "--sig", "short.fa.sig", "--seq", query_seqfile) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 970" in err # should only find 970 overlapping hashes here -- assert "found 970 distinct matching hashes (100.0%)" in err def test_sig_kmers_1_dna_empty_seq(runtmp): # test sig kmers with empty query seq seqfile = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "dna", seqfile, "-p", "scaled=1") ss = load_one_signature_from_json(runtmp.output("short.fa.sig")) mh = ss.minhash assert mh.moltype == "DNA" # make a new sequence for query, with more k-mers query_seqfile = runtmp.output("query.fa") with open(query_seqfile, "w"): pass with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "kmers", "--sig", "short.fa.sig", "--seq", query_seqfile) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "ERROR: no sequences searched!?" in err def test_sig_kmers_1_dna_empty_sig(runtmp): # test sig kmers with empty query sig seqfile = utils.get_test_data("short.fa") mh = sourmash.MinHash(ksize=31, n=0, scaled=1) ss = sourmash.SourmashSignature(mh, name="empty") with open(runtmp.output("empty.sig"), "w") as fp: save_signatures_to_json([ss], fp) with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "kmers", "--sig", "empty.sig", "--seq", seqfile) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "ERROR: no hashes in query signature!?" in err def test_sig_kmers_1_dna_single_sig(runtmp): # test sig kmers with a fabricated query sig with a single hash seqfile = utils.get_test_data("short.fa") mh = sourmash.MinHash(ksize=31, n=0, scaled=1) mh.add_hash(1070961951490202715) ss = sourmash.SourmashSignature(mh, name="small") with open(runtmp.output("small.sig"), "w") as fp: save_signatures_to_json([ss], fp) runtmp.sourmash("sig", "kmers", "--sig", "small.sig", "--seq", seqfile) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 1" in err assert "found 1 distinct matching hashes (100.0%)" in err def test_sig_kmers_1_dna_lowscaled(runtmp): # test sig kmers on dna with a scaled of 100, so not all k-mers seqfile = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "dna", seqfile, "-p", "scaled=100") ss = load_one_signature_from_json(runtmp.output("short.fa.sig")) mh = ss.minhash assert mh.moltype == "DNA" runtmp.sourmash( "sig", "kmers", "--sig", "short.fa.sig", "--seq", seqfile, "--save-kmers", "short.csv", "--save-sequences", "matched.fa", ) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 5" in err assert "found 5 distinct matching hashes (100.0%)" in err # check FASTA output assert os.path.exists(runtmp.output("matched.fa")) with screed.open(runtmp.output("matched.fa")) as f: records = list(f) assert len(records) == 1 assert len(records[0].sequence) == 1000, len(records[0].sequence) seq_mh = mh.copy_and_clear() for record in records: seq_mh.add_sequence(record.sequence) assert seq_mh.similarity(mh) == 1.0 # check CSV output w/k-mers and hashes etc assert os.path.exists(runtmp.output("short.csv")) with open(runtmp.output("short.csv"), newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 5 check_mh = mh.copy_and_clear() check_mh2 = mh.copy_and_clear() for row in rows: check_mh.add_sequence(row["kmer"]) check_mh2.add_hash(int(row["hashval"])) assert check_mh.similarity(mh) == 1.0 assert check_mh2.similarity(mh) == 1.0 def test_sig_kmers_1_dna_num(runtmp): # test sig kmers on dna with a scaled of 100, so not all k-mers seqfile = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "dna", seqfile, "-p", "num=50") ss = load_one_signature_from_json(runtmp.output("short.fa.sig")) mh = ss.minhash assert mh.moltype == "DNA" runtmp.sourmash( "sig", "kmers", "--sig", "short.fa.sig", "--seq", seqfile, "--save-kmers", "short.csv", "--save-sequences", "matched.fa", ) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 50" in err assert "found 50 distinct matching hashes (100.0%)" in err # check FASTA output assert os.path.exists(runtmp.output("matched.fa")) with screed.open(runtmp.output("matched.fa")) as f: records = list(f) assert len(records) == 1 assert len(records[0].sequence) == 1000, len(records[0].sequence) seq_mh = mh.copy_and_clear() for record in records: seq_mh.add_sequence(record.sequence) assert seq_mh.similarity(mh) == 1.0 # check CSV output w/k-mers and hashes etc assert os.path.exists(runtmp.output("short.csv")) with open(runtmp.output("short.csv"), newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 50 check_mh = mh.copy_and_clear() check_mh2 = mh.copy_and_clear() for row in rows: check_mh.add_sequence(row["kmer"]) check_mh2.add_hash(int(row["hashval"])) assert check_mh.similarity(mh) == 1.0 assert check_mh2.similarity(mh) == 1.0 def test_sig_kmers_1_dna_translate_protein(runtmp): # test sig kmers on dna seqfile = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "translate", seqfile, "-p", "scaled=1") ss = load_one_signature_from_json(runtmp.output("short.fa.sig")) mh = ss.minhash assert mh.moltype == "protein" runtmp.sourmash( "sig", "kmers", "--sig", "short.fa.sig", "--seq", seqfile, "--save-kmers", "short.csv", "--save-sequences", "matched.fa", "--translate", ) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 1942" in err assert "found 1942 distinct matching hashes (100.0%)" in err # check FASTA output assert os.path.exists(runtmp.output("matched.fa")) with screed.open(runtmp.output("matched.fa")) as f: records = list(f) assert len(records) == 1 assert len(records[0].sequence) == 1000, len(records[0].sequence) seq_mh = mh.copy_and_clear() for record in records: seq_mh.add_sequence(record.sequence) assert seq_mh.similarity(mh) == 1.0 # check CSV output w/k-mers and hashes etc assert os.path.exists(runtmp.output("short.csv")) with open(runtmp.output("short.csv"), newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 1942 check_mh = mh.copy_and_clear() check_mh2 = mh.copy_and_clear() for row in rows: check_mh.add_sequence(row["kmer"]) check_mh2.add_hash(int(row["hashval"])) assert check_mh.similarity(mh) == 1.0 assert check_mh2.similarity(mh) == 1.0 def test_sig_kmers_1_dna_translate_dayhoff(runtmp): # test sig kmers on dna seqfile = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "translate", seqfile, "-p", "scaled=1,dayhoff") ss = load_one_signature_from_json(runtmp.output("short.fa.sig")) mh = ss.minhash assert mh.moltype == "dayhoff" runtmp.sourmash( "sig", "kmers", "--sig", "short.fa.sig", "--seq", seqfile, "--save-kmers", "short.csv", "--save-sequences", "matched.fa", "--translate", ) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 1906" in err assert "found 1906 distinct matching hashes (100.0%)" in err # check FASTA output assert os.path.exists(runtmp.output("matched.fa")) with screed.open(runtmp.output("matched.fa")) as f: records = list(f) assert len(records) == 1 assert len(records[0].sequence) == 1000, len(records[0].sequence) seq_mh = mh.copy_and_clear() for record in records: seq_mh.add_sequence(record.sequence) assert seq_mh.similarity(mh) == 1.0 # check CSV output w/k-mers and hashes etc assert os.path.exists(runtmp.output("short.csv")) with open(runtmp.output("short.csv"), newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 1906 check_mh = mh.copy_and_clear() check_mh2 = mh.copy_and_clear() for row in rows: check_mh.add_sequence(row["kmer"]) check_mh2.add_hash(int(row["hashval"])) assert check_mh.similarity(mh) == 1.0 assert check_mh2.similarity(mh) == 1.0 def test_sig_kmers_1_dna_translate_hp(runtmp): # test sig kmers on dna seqfile = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "translate", seqfile, "-p", "scaled=1,hp") ss = load_one_signature_from_json(runtmp.output("short.fa.sig")) mh = ss.minhash assert mh.moltype == "hp" runtmp.sourmash( "sig", "kmers", "--sig", "short.fa.sig", "--seq", seqfile, "--save-kmers", "short.csv", "--save-sequences", "matched.fa", "--translate", ) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 1750" in err assert "found 1750 distinct matching hashes (100.0%)" in err # check FASTA output assert os.path.exists(runtmp.output("matched.fa")) with screed.open(runtmp.output("matched.fa")) as f: records = list(f) assert len(records) == 1 assert len(records[0].sequence) == 1000, len(records[0].sequence) seq_mh = mh.copy_and_clear() for record in records: seq_mh.add_sequence(record.sequence) assert seq_mh.similarity(mh) == 1.0 # check CSV output w/k-mers and hashes etc assert os.path.exists(runtmp.output("short.csv")) with open(runtmp.output("short.csv"), newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 1750 check_mh = mh.copy_and_clear() check_mh2 = mh.copy_and_clear() for row in rows: check_mh.add_sequence(row["kmer"]) check_mh2.add_hash(int(row["hashval"])) assert check_mh.similarity(mh) == 1.0 assert check_mh2.similarity(mh) == 1.0 def test_sig_kmers_2_protein(runtmp): # test out sig kmers on an faa file seqfile = utils.get_test_data("ecoli.faa") runtmp.sourmash("sketch", "protein", seqfile, "-p", "scaled=1") ss = load_one_signature_from_json(runtmp.output("ecoli.faa.sig")) mh = ss.minhash assert mh.moltype == "protein" runtmp.sourmash( "sig", "kmers", "--sig", "ecoli.faa.sig", "--seq", seqfile, "--save-kmers", "ecoli.csv", "--save-sequences", "matched.fa", ) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 1112" in err assert "found 1112 distinct matching hashes (100.0%)" in err # check FASTA output assert os.path.exists(runtmp.output("matched.fa")) with screed.open(runtmp.output("matched.fa")) as f: records = list(f) assert len(records) == 2 assert len(records[0].sequence) == 820, len(records[0].sequence) assert len(records[1].sequence) == 310, len(records[1].sequence) seq_mh = mh.copy_and_clear() for record in records: seq_mh.add_protein(record.sequence) assert seq_mh.similarity(mh) == 1.0 # check CSV output w/k-mers and hashes etc assert os.path.exists(runtmp.output("ecoli.csv")) with open(runtmp.output("ecoli.csv"), newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 1112 check_mh = mh.copy_and_clear() check_mh2 = mh.copy_and_clear() for row in rows: check_mh.add_protein(row["kmer"]) check_mh2.add_hash(int(row["hashval"])) assert check_mh.similarity(mh) == 1.0 assert check_mh2.similarity(mh) == 1.0 def test_sig_kmers_2_dayhoff(runtmp): # test out sig kmers on an faa file seqfile = utils.get_test_data("ecoli.faa") runtmp.sourmash("sketch", "protein", seqfile, "-p", "scaled=1,dayhoff") ss = load_one_signature_from_json(runtmp.output("ecoli.faa.sig")) mh = ss.minhash assert mh.moltype == "dayhoff" runtmp.sourmash( "sig", "kmers", "--sig", "ecoli.faa.sig", "--seq", seqfile, "--save-kmers", "ecoli.csv", "--save-sequences", "matched.fa", ) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 1100" in err assert "found 1100 distinct matching hashes (100.0%)" in err # check FASTA output assert os.path.exists(runtmp.output("matched.fa")) with screed.open(runtmp.output("matched.fa")) as f: records = list(f) assert len(records) == 2 assert len(records[0].sequence) == 820, len(records[0].sequence) assert len(records[1].sequence) == 310, len(records[1].sequence) seq_mh = mh.copy_and_clear() for record in records: seq_mh.add_protein(record.sequence) assert seq_mh.similarity(mh) == 1.0 # check CSV output w/k-mers and hashes etc assert os.path.exists(runtmp.output("ecoli.csv")) with open(runtmp.output("ecoli.csv"), newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 1100 check_mh = mh.copy_and_clear() check_mh2 = mh.copy_and_clear() for row in rows: check_mh.add_protein(row["kmer"]) check_mh2.add_hash(int(row["hashval"])) assert check_mh.similarity(mh) == 1.0 assert check_mh2.similarity(mh) == 1.0 def test_sig_kmers_2_hp(runtmp): # test out sig kmers on an faa file seqfile = utils.get_test_data("ecoli.faa") runtmp.sourmash("sketch", "protein", seqfile, "-p", "scaled=1,hp") ss = load_one_signature_from_json(runtmp.output("ecoli.faa.sig")) mh = ss.minhash assert mh.moltype == "hp" runtmp.sourmash( "sig", "kmers", "--sig", "ecoli.faa.sig", "--seq", seqfile, "--save-kmers", "ecoli.csv", "--save-sequences", "matched.fa", ) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "total hashes in merged signature: 1048" in err assert "found 1048 distinct matching hashes (100.0%)" in err # check FASTA output assert os.path.exists(runtmp.output("matched.fa")) with screed.open(runtmp.output("matched.fa")) as f: records = list(f) assert len(records) == 2 assert len(records[0].sequence) == 820, len(records[0].sequence) assert len(records[1].sequence) == 310, len(records[1].sequence) seq_mh = mh.copy_and_clear() for record in records: seq_mh.add_protein(record.sequence) assert seq_mh.similarity(mh) == 1.0 # check CSV output w/k-mers and hashes etc assert os.path.exists(runtmp.output("ecoli.csv")) with open(runtmp.output("ecoli.csv"), newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 1048 check_mh = mh.copy_and_clear() check_mh2 = mh.copy_and_clear() for row in rows: check_mh.add_protein(row["kmer"]) check_mh2.add_hash(int(row["hashval"])) assert check_mh.similarity(mh) == 1.0 assert check_mh2.similarity(mh) == 1.0 def test_sig_check_1(runtmp, abspath_relpath_v4): # basic check functionality sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", f"{picklist}::manifest", "-m", "mf.csv", abspath_relpath_v4, ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) # all should match. with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 idx = sourmash.load_file_as_index(out_mf) siglist = list(idx.signatures()) assert len(siglist) == 24 ksizes = set([ss.minhash.ksize for ss in siglist]) assert len(ksizes) == 3 assert 11 in ksizes assert 21 in ksizes assert 31 in ksizes def test_sig_check_1_fail_abspath_relpath(runtmp): # basic check functionality sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") with pytest.raises( SourmashCommandFailed, match="Cannot specify both --abspath and --relpath; pick one!", ): runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", f"{picklist}::manifest", "-m", "mf.csv", "--abspath", "--relpath", ) def test_sig_check_1_warn_abspath_relpath(runtmp, abspath_or_relpath): # warn that without -m, --abspath/--relpath are not helpful sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", f"{picklist}::manifest", abspath_or_relpath, ) err = runtmp.last_result.err assert ( " WARNING: --abspath and --relpath only have effects when saving a manifest" in err ) def test_sig_check_1_mf_csv_gz(runtmp, abspath_relpath_v4): # basic check functionality, with gzipped manifest output sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", f"{picklist}::manifest", "-m", "mf.csv.gz", abspath_relpath_v4, ) out_mf = runtmp.output("mf.csv.gz") assert os.path.exists(out_mf) # all should match. with gzip.open(out_mf, "rt", newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 idx = sourmash.load_file_as_index(out_mf) siglist = list(idx.signatures()) assert len(siglist) == 24 ksizes = set([ss.minhash.ksize for ss in siglist]) assert len(ksizes) == 3 assert 11 in ksizes assert 21 in ksizes assert 31 in ksizes def test_sig_check_1_gz(runtmp, abspath_relpath_v4): # basic check functionality with gzipped picklist sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") picklist_gz = runtmp.output("salmonella.csv.gz") with gzip.open(picklist_gz, "w") as outfp: with open(picklist, "rb") as infp: outfp.write(infp.read()) runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", "salmonella.csv.gz::manifest", "-m", "mf.csv", abspath_relpath_v4, ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) # all should match. with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 idx = sourmash.load_file_as_index(out_mf) siglist = list(idx.signatures()) assert len(siglist) == 24 ksizes = set([ss.minhash.ksize for ss in siglist]) assert len(ksizes) == 3 assert 11 in ksizes assert 21 in ksizes assert 31 in ksizes def test_sig_check_1_nofail(runtmp, abspath_relpath_v4): # basic check functionality with --fail-if-missing sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", f"{picklist}::manifest", "-m", "mf.csv", "--fail-if-missing", abspath_relpath_v4, ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) # all should match. with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 idx = sourmash.load_file_as_index(out_mf) siglist = list(idx.signatures()) assert len(siglist) == 24 ksizes = set([ss.minhash.ksize for ss in siglist]) assert len(ksizes) == 3 assert 11 in ksizes assert 21 in ksizes assert 31 in ksizes def test_sig_check_1_no_picklist(runtmp): # basic check functionality sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) utils.get_test_data("gather/salmonella-picklist.csv") with pytest.raises(SourmashCommandFailed) as exc: runtmp.sourmash("sig", "check", *sigfiles) assert "No picklist provided?! Exiting." in str(exc) @pytest.mark.parametrize( "column, coltype", ( ("md5", "md5"), ("md5", "md5prefix8"), ("name", "name"), ("name", "ident"), ("name", "identprefix"), ), ) def test_sig_check_1_column(runtmp, column, coltype, abspath_relpath_v4): # basic check functionality for various columns/coltypes sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", f"{picklist}:{column}:{coltype}", "-m", "mf.csv", "-o", "missing.csv", abspath_relpath_v4, ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) # all should match. with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 idx = sourmash.load_file_as_index(out_mf) siglist = list(idx.signatures()) assert len(siglist) == 24 ksizes = set([ss.minhash.ksize for ss in siglist]) assert len(ksizes) == 3 assert 11 in ksizes assert 21 in ksizes assert 31 in ksizes def test_sig_check_1_diff_col_name(runtmp, abspath_relpath_v4): # 'sig check' with 'name2' column instead of default name sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist-diffcolumn.csv") runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", f"{picklist}:name2:name", "-o", "missing.csv", "-m", "mf.csv", abspath_relpath_v4, ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) missing_csv = runtmp.output("missing.csv") assert os.path.exists(missing_csv) # should be 24 matching manifest rows with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 # internal locations should match sigfile_set = set(sigfiles) for row in mf.rows: assert row["internal_location"] in sigfile_set idx = sourmash.load_file_as_index(out_mf) siglist = list(idx.signatures()) assert len(siglist) == 24 ksizes = set([ss.minhash.ksize for ss in siglist]) assert len(ksizes) == 3 assert 11 in ksizes assert 21 in ksizes assert 31 in ksizes # should be one non-matching picklist row with open(missing_csv, newline="") as fp: rows = list(csv.reader(fp)) assert len(rows) == 2 # header row + data row assert rows[1][0] == "NOT THERE" def test_sig_check_1_diff_col_name_zip(runtmp, abspath_relpath_v4): # 'sig check' with 'name2' column instead of default name, on a zip file sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist-diffcolumn.csv") # first create a zip db runtmp.sourmash("sig", "cat", *sigfiles, "-o", "gcf.zip") # now run against this zip runtmp.sourmash( "sig", "check", "gcf.zip", "--picklist", f"{picklist}:name2:name", "-o", "missing.csv", "-m", "mf.csv", abspath_relpath_v4, ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) missing_csv = runtmp.output("missing.csv") assert os.path.exists(missing_csv) # should be 24 matching manifest rows with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 # internal locations should all point to zip ilocs = set(row["internal_location"] for row in mf.rows) assert len(ilocs) == 1 # can we get 'em? idx = sourmash.load_file_as_index(out_mf) siglist = list(idx.signatures()) assert len(siglist) == 24 ksizes = set([ss.minhash.ksize for ss in siglist]) assert len(ksizes) == 3 assert 11 in ksizes assert 21 in ksizes assert 31 in ksizes # should be one non-matching picklist row with open(missing_csv, newline="") as fp: rows = list(csv.reader(fp)) assert len(rows) == 2 # header row + data row assert rows[1][0] == "NOT THERE" def test_sig_check_1_diff_col_name_exclude(runtmp, abspath_relpath_v4): # 'sig check' with 'name2' column, :exclude picklist sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist-diffcolumn.csv") runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", f"{picklist}:name2:name:exclude", "-m", "mf.csv", abspath_relpath_v4, ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) # should be 12 matching manifest rows with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 12 idx = sourmash.load_file_as_index(out_mf) siglist = list(idx.signatures()) assert len(siglist) == 12 ksizes = set([ss.minhash.ksize for ss in siglist]) assert len(ksizes) == 3 assert 11 in ksizes assert 21 in ksizes assert 31 in ksizes def test_sig_check_1_ksize(runtmp, abspath_relpath_v4): # basic check functionality with selection for ksize sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") runtmp.sourmash( "sig", "check", *sigfiles, "-k", "31", "--picklist", f"{picklist}::manifest", "-m", "mf.csv", abspath_relpath_v4, ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) # 8 of the 24 should match. with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 8 idx = sourmash.load_file_as_index(out_mf) siglist = list(idx.signatures()) assert len(siglist) == 8 ksizes = set([ss.minhash.ksize for ss in siglist]) assert len(ksizes) == 1 assert 31 in ksizes def test_sig_check_1_ksize_output_sql(runtmp, abspath_relpath_v4): # basic check functionality with selection for ksize sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") runtmp.sourmash( "sig", "check", *sigfiles, "-k", "31", "--picklist", f"{picklist}::manifest", "-m", "mf.mfsql", "-F", "sql", abspath_relpath_v4, ) out_mf = runtmp.output("mf.mfsql") assert os.path.exists(out_mf) # 8 of the 24 should match. mf = CollectionManifest.load_from_filename(out_mf) assert len(mf) == 8 assert mf.conn # check that it's a sqlite manifest! hacky... idx = sourmash.load_file_as_index(out_mf) siglist = list(idx.signatures()) assert len(siglist) == 8 ksizes = set([ss.minhash.ksize for ss in siglist]) assert len(ksizes) == 1 assert 31 in ksizes def test_sig_check_2_output_missing(runtmp, abspath_relpath_v4): # output missing all as identical to input picklist sigfiles = utils.get_test_data("gather/combined.sig") picklist = utils.get_test_data("gather/salmonella-picklist.csv") runtmp.sourmash( "sig", "check", sigfiles, "--picklist", f"{picklist}::manifest", "-o", "missing.csv", "-m", "mf.csv", abspath_relpath_v4, ) out_csv = runtmp.output("missing.csv") assert os.path.exists(out_csv) mf_csv = runtmp.output("mf.csv") assert not os.path.exists(mf_csv) assert "not saving matching manifest" in runtmp.last_result.err # everything is missing with 'combined.sig' with open(out_csv, newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 24 def test_sig_check_2_output_missing_error_exit(runtmp): # output missing all as identical to input picklist sigfiles = utils.get_test_data("gather/combined.sig") picklist = utils.get_test_data("gather/salmonella-picklist.csv") # should error exit... with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "check", sigfiles, "--picklist", f"{picklist}::manifest", "-o", "missing.csv", "--fail", ) print(runtmp.last_result.out) print(runtmp.last_result.err) # ...and also output stuff! out_csv = runtmp.output("missing.csv") assert os.path.exists(out_csv) # everything is missing with 'combined.sig' with open(out_csv, newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 24 @pytest.mark.parametrize( "column, coltype", ( ("md5", "md5"), ("md5", "md5prefix8"), ("name", "name"), ("name", "ident"), ("name", "identprefix"), ), ) def test_sig_check_2_output_missing_column(runtmp, column, coltype, abspath_relpath_v4): # output missing all as identical to input picklist sigfiles = utils.get_test_data("gather/combined.sig") picklist = utils.get_test_data("gather/salmonella-picklist.csv") runtmp.sourmash( "sig", "check", sigfiles, "--picklist", f"{picklist}::manifest", "-o", "missing.csv", abspath_relpath_v4, ) out_csv = runtmp.output("missing.csv") assert os.path.exists(out_csv) # everything is missing with 'combined.sig' with open(out_csv, newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 24 def test_sig_check_2_output_missing_exclude(runtmp): # 'exclude' with '-o' shouldn't work sigfiles = utils.get_test_data("gather/combined.sig") picklist = utils.get_test_data("gather/salmonella-picklist.csv") with pytest.raises(SourmashCommandFailed) as exc: runtmp.sourmash( "sig", "check", sigfiles, "--picklist", f"{picklist}:name:name:exclude", "-o", "missing.csv", ) assert ( "** ERROR: Cannot use an 'exclude' picklist with '-o/--output-missing'" in str(exc) ) def test_sig_check_3_no_manifest(runtmp): # fail check when no manifest, by default sbt = utils.get_test_data("v6.sbt.zip") picklist = utils.get_test_data("v6.sbt.zip.mf.csv") with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("sig", "check", sbt, "--picklist", f"{picklist}::manifest") print(runtmp.last_result.out) print(runtmp.last_result.err) err = runtmp.last_result.err assert "sig check requires a manifest by default, but no manifest present." in err def test_sig_check_3_no_manifest_ok(runtmp, abspath_relpath_v4): # generate manifest if --no-require-manifest sbt = utils.get_test_data("v6.sbt.zip") picklist = utils.get_test_data("v6.sbt.zip.mf.csv") runtmp.run_sourmash( "sig", "check", sbt, "--no-require-manifest", "--picklist", f"{picklist}::manifest", abspath_relpath_v4, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "for given picklist, found 7 matches to 7 distinct values" in runtmp.last_result.err ) def test_sig_check_4_manifest_cwd_cwd(runtmp, abspath_relpath_v4): # check: manifest and sigs in cwd prot_zip = utils.get_test_data("prot/all.zip") shutil.copyfile(prot_zip, runtmp.output("prot.zip")) # generate a picklist, whatever runtmp.sourmash("sig", "manifest", "prot.zip", "-o", "picklist.csv") assert os.path.exists(runtmp.output("picklist.csv")) # use picklist with sig check to generate a manifest runtmp.sourmash( "sig", "check", "-m", "mf.csv", "--picklist", "picklist.csv::manifest", "prot.zip", abspath_relpath_v4, ) # check that it all works runtmp.sourmash("sig", "cat", "mf.csv") def test_sig_check_4_manifest_subdir_cwd(runtmp, abspath_or_relpath): # check: manifest in subdir and sigs in cwd. note, # fails with default v4 behavior. see #3008. prot_zip = utils.get_test_data("prot/all.zip") shutil.copyfile(prot_zip, runtmp.output("prot.zip")) os.mkdir(runtmp.output("mf_dir")) # generate a picklist, whatever runtmp.sourmash("sig", "manifest", "prot.zip", "-o", "picklist.csv") assert os.path.exists(runtmp.output("picklist.csv")) # use picklist with sig check to generate a manifest runtmp.sourmash( "sig", "check", "-m", "mf_dir/mf.csv", "--picklist", "picklist.csv::manifest", "prot.zip", abspath_or_relpath, ) print(runtmp.last_result.out) print(runtmp.last_result.err) # check that it all works runtmp.sourmash("sig", "cat", "mf_dir/mf.csv") def test_sig_check_4_manifest_cwd_subdir(runtmp, abspath_relpath_v4): # check: manifest in cwd and sigs in subdir prot_zip = utils.get_test_data("prot/all.zip") os.mkdir(runtmp.output("zip_dir")) shutil.copyfile(prot_zip, runtmp.output("zip_dir/prot.zip")) # generate a picklist, whatever runtmp.sourmash("sig", "manifest", "zip_dir/prot.zip", "-o", "picklist.csv") assert os.path.exists(runtmp.output("picklist.csv")) # use picklist with sig check to generate a manifest runtmp.sourmash( "sig", "check", "-m", "mf.csv", "--picklist", "picklist.csv::manifest", "zip_dir/prot.zip", abspath_relpath_v4, ) print(runtmp.last_result.out) print(runtmp.last_result.err) # check that it all works runtmp.sourmash("sig", "cat", "mf.csv") def test_sig_check_4_manifest_subdir_subdir(runtmp, abspath_or_relpath): # check: manifest and sigs in subdir. note, fails with default v4 behavior. # see #3008. prot_zip = utils.get_test_data("prot/all.zip") os.mkdir(runtmp.output("zip_dir")) shutil.copyfile(prot_zip, runtmp.output("zip_dir/prot.zip")) os.mkdir(runtmp.output("mf_dir")) # generate a picklist, whatever runtmp.sourmash("sig", "manifest", "zip_dir/prot.zip", "-o", "picklist.csv") assert os.path.exists(runtmp.output("picklist.csv")) # use picklist with sig check to generate a manifest runtmp.sourmash( "sig", "check", "-m", "mf_dir/mf.csv", "--picklist", "picklist.csv::manifest", "zip_dir/prot.zip", abspath_or_relpath, ) print(runtmp.last_result.out) print(runtmp.last_result.err) # check that it all works runtmp.sourmash("sig", "cat", "mf_dir/mf.csv") def test_sig_check_5_relpath(runtmp): # check path rewriting when sketches are in a subdir. # this will be the default behavior in v5 => remove --relpath. sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") os.mkdir(runtmp.output("mf_dir")) os.mkdir(runtmp.output("sigs_dir")) new_names = [] for f in sigfiles: basename = os.path.basename(f) filename = os.path.join("sigs_dir", basename) shutil.copyfile(f, runtmp.output(filename)) new_names.append(filename) runtmp.sourmash( "sig", "check", *new_names, "--picklist", f"{picklist}::manifest", "-m", "mf_dir/mf.csv", "--relpath", ) out_mf = runtmp.output("mf_dir/mf.csv") assert os.path.exists(out_mf) # all should match. with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 locations = [row["internal_location"] for row in mf.rows] expected_names = ["../" + f for f in new_names] assert set(locations).issubset(expected_names), (locations, expected_names) def test_sig_check_5_relpath_subdir(runtmp): # check path rewriting when both sigs and mf are in different subdirs. # this will be the default behavior in v5 => can remove --relpath then. sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") os.mkdir(runtmp.output("sigs_dir")) new_names = [] for f in sigfiles: basename = os.path.basename(f) filename = os.path.join("sigs_dir", basename) shutil.copyfile(f, runtmp.output(filename)) new_names.append(filename) runtmp.sourmash( "sig", "check", *new_names, "--picklist", f"{picklist}::manifest", "-m", "mf.csv", "--relpath", ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) # all should match. with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 locations = [row["internal_location"] for row in mf.rows] print("XXX", locations) print("YYY", new_names) expected_names = ["./" + f for f in new_names] assert set(locations).issubset(expected_names), (locations, expected_names) def test_sig_check_5_abspath(runtmp): # check path rewriting with `--abspath` => absolute paths. sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") for f in sigfiles: shutil.copyfile(f, runtmp.output(os.path.basename(f))) # strip off abspath sigfiles = [os.path.basename(f) for f in sigfiles] runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", f"{picklist}::manifest", "-m", "mf.csv", "--abspath", ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) # all should match. with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 locations = [row["internal_location"] for row in mf.rows] for k in locations: assert k.startswith("/") # absolute assert os.path.basename(k) in sigfiles # converts back to basic def test_sig_check_5_no_abspath(runtmp): # check path rewriting for default (--no-relpath --no-abspath) # this behavior will change in v5; specify `--no-abspath` then? sigfiles = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/salmonella-picklist.csv") for f in sigfiles: shutil.copyfile(f, runtmp.output(os.path.basename(f))) # strip off abspath sigfiles = [os.path.basename(f) for f in sigfiles] runtmp.sourmash( "sig", "check", *sigfiles, "--picklist", f"{picklist}::manifest", "-m", "mf.csv", # "--no-abspath" # => default behavior ) out_mf = runtmp.output("mf.csv") assert os.path.exists(out_mf) # all should match. with open(out_mf, newline="") as fp: mf = CollectionManifest.load_from_csv(fp) assert len(mf) == 24 locations = [row["internal_location"] for row in mf.rows] # no rewriting assert set(locations).issubset(sigfiles) sourmash-bio-sourmash-bc3c0f8/tests/test_cmd_signature_collect.py000066400000000000000000000515441474157046300256030ustar00rootroot00000000000000""" Tests for 'sourmash sig collect' """ import pytest import shutil import os.path import gzip import sourmash from sourmash.manifest import BaseCollectionManifest import sourmash_tst_utils as utils from sourmash_tst_utils import SourmashCommandFailed def test_sig_collect_0_nothing(runtmp, manifest_db_format, abspath_relpath_v4): # run with just output ext = "sqlmf" if manifest_db_format == "sql" else "csv" if manifest_db_format != "sql": return runtmp.sourmash( "sig", "collect", "-o", f"mf.{ext}", "-F", manifest_db_format, abspath_relpath_v4, ) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 0 def test_sig_collect_0_fail_abspath_relpath(runtmp, manifest_db_format): # check that it complains if both --abspath and --relpath are specified ext = "sqlmf" if manifest_db_format == "sql" else "csv" if manifest_db_format != "sql": return with pytest.raises( SourmashCommandFailed, match="Cannot specify both --abspath and --relpath; pick one!", ): runtmp.sourmash( "sig", "collect", "-o", f"mf.{ext}", "-F", manifest_db_format, "--abspath", "--relpath", ) def test_sig_collect_1_zipfile(runtmp, manifest_db_format, abspath_relpath_v4): # collect a manifest from a .zip file protzip = utils.get_test_data("prot/protein.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" runtmp.sourmash( "sig", "collect", protzip, "-o", f"mf.{ext}", "-F", manifest_db_format, abspath_relpath_v4, ) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_sig_collect_1_zipfile_csv_gz(runtmp, abspath_relpath_v4): # collect a manifest from a .zip file, save to csv.gz protzip = utils.get_test_data("prot/protein.zip") runtmp.sourmash( "sig", "collect", protzip, "-o", "mf.csv.gz", "-F", "csv", abspath_relpath_v4 ) manifest_fn = runtmp.output("mf.csv.gz") # gzip, yes? print("XXX", manifest_fn) with gzip.open(manifest_fn, "rt", newline="") as fp: fp.read() manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_sig_collect_1_zipfile_csv_gz_roundtrip(runtmp, abspath_relpath_v4): # collect a manifest from a .zip file, save to csv.gz; then load again protzip = utils.get_test_data("prot/protein.zip") runtmp.sourmash( "sig", "collect", protzip, "-o", "mf.csv.gz", "-F", "csv", abspath_relpath_v4 ) manifest_fn = runtmp.output("mf.csv.gz") # gzip, yes? print("XXX", manifest_fn) with gzip.open(manifest_fn, "rt", newline="") as fp: fp.read() manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list # can we read a csv.gz? runtmp.sourmash("sig", "collect", "mf.csv.gz", "-o", "mf2.csv", "-F", "csv") manifest_fn2 = runtmp.output("mf2.csv") manifest2 = BaseCollectionManifest.load_from_filename(manifest_fn2) assert len(manifest2) == 2 md5_list = [row["md5"] for row in manifest2.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_sig_collect_2_exists_fail(runtmp, manifest_db_format): # collect a manifest from two .zip files protzip = utils.get_test_data("prot/protein.zip") allzip = utils.get_test_data("prot/protein.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" runtmp.sourmash( "sig", "collect", protzip, "-o", f"mf.{ext}", "-F", manifest_db_format ) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list # now run with same filename - should fail with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "collect", allzip, "-o", manifest_fn, "-F", manifest_db_format ) def test_sig_collect_2_exists_merge(runtmp, manifest_db_format, abspath_relpath_v4): # collect a manifest from two .zip files protzip = utils.get_test_data("prot/protein.zip") allzip = utils.get_test_data("prot/all.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" runtmp.sourmash( "sig", "collect", protzip, "-o", f"mf.{ext}", "-F", manifest_db_format, abspath_relpath_v4, ) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list # now run with same filename - should merge runtmp.sourmash( "sig", "collect", allzip, "-o", manifest_fn, "-F", manifest_db_format, "--merge" ) manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 10 def test_sig_collect_2_exists_sql_merge_csv(runtmp, manifest_db_format): # try to merge csv into sql protzip = utils.get_test_data("prot/protein.zip") allzip = utils.get_test_data("prot/all.zip") ext = "sqlmf" # save as sql... runtmp.sourmash("sig", "collect", protzip, "-o", f"mf.{ext}", "-F", "sql") manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "collect", allzip, "-o", manifest_fn, "-F", "csv", "--merge" ) assert "ERROR loading" in runtmp.last_result.err def test_sig_collect_2_exists_csv_merge_sql(runtmp): # try to merge sql into csv protzip = utils.get_test_data("prot/protein.zip") allzip = utils.get_test_data("prot/all.zip") ext = "csv" # save as csv... runtmp.sourmash("sig", "collect", protzip, "-o", f"mf.{ext}", "-F", "csv") manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "collect", allzip, "-o", manifest_fn, "-F", "sql", "--merge" ) assert "ERROR loading" in runtmp.last_result.err def test_sig_collect_2_no_exists_merge(runtmp, manifest_db_format, abspath_relpath_v4): # test 'merge' when args.output doesn't already exist => warning utils.get_test_data("prot/protein.zip") allzip = utils.get_test_data("prot/all.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" manifest_fn = runtmp.output(f"mf.{ext}") # run with --merge but no previous: runtmp.sourmash( "sig", "collect", allzip, "-o", manifest_fn, "-F", manifest_db_format, "--merge", abspath_relpath_v4, ) manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 8 err = runtmp.last_result.err print(err) assert "WARNING: --merge-previous specified, but output file" in err def test_sig_collect_3_multiple(runtmp, manifest_db_format): # collect a manifest from two .zip files protzip = utils.get_test_data("prot/protein.zip") hpzip = utils.get_test_data("prot/hp.zip") dayzip = utils.get_test_data("prot/dayhoff.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" runtmp.sourmash( "sig", "collect", protzip, hpzip, dayzip, "-o", f"mf.{ext}", "-F", manifest_db_format, ) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 6 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list assert "ea2a1ad233c2908529d124a330bcb672" in md5_list assert "bb0e6d90df01b7bd5d0956a5f9e3ed12" in md5_list assert "fbca5e5211e4d58427997fd5c8343e9a" in md5_list assert "1cbd888bf910f83ad8f1715509183223" in md5_list locations = set([row["internal_location"] for row in manifest.rows]) assert protzip in locations assert hpzip in locations assert dayzip in locations assert len(locations) == 3, locations def test_sig_collect_3_multiple_use_fromfile(runtmp, manifest_db_format): # collect a manifest from two .zip files using --from-file protzip = utils.get_test_data("prot/protein.zip") hpzip = utils.get_test_data("prot/hp.zip") dayzip = utils.get_test_data("prot/dayhoff.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" fromfile = runtmp.output("fromfile.txt") with open(fromfile, "w") as fp: print(protzip, file=fp) print(hpzip, file=fp) print(dayzip, file=fp) runtmp.sourmash( "sig", "collect", "--from-file", "fromfile.txt", "-o", f"mf.{ext}", "-F", manifest_db_format, ) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 6 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list assert "ea2a1ad233c2908529d124a330bcb672" in md5_list assert "bb0e6d90df01b7bd5d0956a5f9e3ed12" in md5_list assert "fbca5e5211e4d58427997fd5c8343e9a" in md5_list assert "1cbd888bf910f83ad8f1715509183223" in md5_list locations = set([row["internal_location"] for row in manifest.rows]) assert protzip in locations assert hpzip in locations assert dayzip in locations assert len(locations) == 3, locations def test_sig_collect_4_multiple_from_sig(runtmp, manifest_db_format): # collect a manifest from sig files sig43 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ext = "sqlmf" if manifest_db_format == "sql" else "csv" runtmp.sourmash( "sig", "collect", sig43, sig63, "-o", f"mf.{ext}", "-F", manifest_db_format ) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "09a08691ce52952152f0e866a59f6261" in md5_list assert "38729c6374925585db28916b82a6f513" in md5_list locations = set([row["internal_location"] for row in manifest.rows]) assert sig43 in locations assert sig63 in locations assert len(locations) == 2, locations def test_sig_collect_4_multiple_from_sig_abspath(runtmp, manifest_db_format): # collect a manifest from sig files, forcing abspath sig43 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") shutil.copyfile(sig43, runtmp.output("47.fa.sig")) shutil.copyfile(sig63, runtmp.output("63.fa.sig")) ext = "sqlmf" if manifest_db_format == "sql" else "csv" runtmp.sourmash( "sig", "collect", "47.fa.sig", "63.fa.sig", "--abspath", "-o", f"mf.{ext}", "-F", manifest_db_format, ) print(runtmp.last_result.out) print(runtmp.last_result.err) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "09a08691ce52952152f0e866a59f6261" in md5_list assert "38729c6374925585db28916b82a6f513" in md5_list locations = set([row["internal_location"] for row in manifest.rows]) print(locations) assert len(locations) == 2, locations for xx in locations: assert xx.startswith("/") def test_sig_collect_4_multiple_no_abspath(runtmp, manifest_db_format): # collect a manifest from sig files, no abspath sig43 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # copy files to tmp, where they will not have full paths shutil.copyfile(sig43, runtmp.output("47.fa.sig")) shutil.copyfile(sig63, runtmp.output("63.fa.sig")) ext = "sqlmf" if manifest_db_format == "sql" else "csv" runtmp.sourmash( "sig", "collect", "47.fa.sig", "63.fa.sig", "-o", f"mf.{ext}", "-F", manifest_db_format, ) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "09a08691ce52952152f0e866a59f6261" in md5_list assert "38729c6374925585db28916b82a6f513" in md5_list locations = set([row["internal_location"] for row in manifest.rows]) print(locations) assert len(locations) == 2, locations assert "47.fa.sig" in locations assert "63.fa.sig" in locations runtmp.sourmash("sig", "cat", f"mf.{ext}") def test_sig_collect_4_multiple_subdir_subdir_no_abspath(runtmp, manifest_db_format): # collect a manifest from sig files, no abspath; use a subdir for sketches # this should work with default behavior. sig43 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # copy files to tmp, where they will not have full paths os.mkdir(runtmp.output("sigs_dir")) shutil.copyfile(sig43, runtmp.output("sigs_dir/47.fa.sig")) shutil.copyfile(sig63, runtmp.output("sigs_dir/63.fa.sig")) # put manifest in subdir too. os.mkdir(runtmp.output("mf_dir")) ext = "sqlmf" if manifest_db_format == "sql" else "csv" runtmp.sourmash( "sig", "collect", "sigs_dir/47.fa.sig", "sigs_dir/63.fa.sig", "-o", f"mf_dir/mf.{ext}", "-F", manifest_db_format, "--relpath", ) manifest_fn = runtmp.output(f"mf_dir/mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "09a08691ce52952152f0e866a59f6261" in md5_list assert "38729c6374925585db28916b82a6f513" in md5_list locations = set([row["internal_location"] for row in manifest.rows]) print(locations) assert len(locations) == 2, locations assert "../sigs_dir/47.fa.sig" in locations assert "../sigs_dir/63.fa.sig" in locations runtmp.sourmash("sig", "cat", f"mf_dir/mf.{ext}") def test_sig_collect_4_multiple_cwd_subdir_no_abspath(runtmp, manifest_db_format): # collect a manifest from sig files, no abspath; use a subdir for sketches # this should work with default behavior. sig43 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") # copy files to tmp, where they will not have full paths os.mkdir(runtmp.output("sigs_dir")) shutil.copyfile(sig43, runtmp.output("sigs_dir/47.fa.sig")) shutil.copyfile(sig63, runtmp.output("sigs_dir/63.fa.sig")) ext = "sqlmf" if manifest_db_format == "sql" else "csv" runtmp.sourmash( "sig", "collect", "sigs_dir/47.fa.sig", "sigs_dir/63.fa.sig", "-o", f"mf.{ext}", "-F", manifest_db_format, ) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "09a08691ce52952152f0e866a59f6261" in md5_list assert "38729c6374925585db28916b82a6f513" in md5_list locations = set([row["internal_location"] for row in manifest.rows]) print(locations) assert len(locations) == 2, locations assert "sigs_dir/47.fa.sig" in locations assert "sigs_dir/63.fa.sig" in locations runtmp.sourmash("sig", "cat", f"mf.{ext}") def test_sig_collect_5_no_manifest_sbt_fail(runtmp, manifest_db_format): # collect a manifest from files that don't have one sbt_zip = utils.get_test_data("v6.sbt.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "collect", sbt_zip, "-o", f"mf.{ext}", "-F", manifest_db_format ) def test_sig_collect_5_no_manifest_sbt_succeed( runtmp, manifest_db_format, abspath_relpath_v4 ): # generate a manifest from files that don't have one when --no-require sbt_zip = utils.get_test_data("v6.sbt.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" runtmp.sourmash( "sig", "collect", sbt_zip, "--no-require-manifest", "-o", f"mf.{ext}", "-F", manifest_db_format, abspath_relpath_v4, ) manifest_fn = runtmp.output(f"mf.{ext}") manifest = BaseCollectionManifest.load_from_filename(manifest_fn) assert len(manifest) == 7 locations = set([row["internal_location"] for row in manifest.rows]) assert len(locations) == 1, locations assert sbt_zip in locations def test_sig_collect_6_path_cwd_cwd(runtmp, manifest_db_format, abspath_relpath_v4): # check: manifest and sigs in cwd protzip = utils.get_test_data("prot/protein.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" protzip_path = "protein.zip" shutil.copyfile(protzip, runtmp.output(protzip_path)) mf_path = f"mf.{ext}" runtmp.sourmash( "sig", "collect", protzip_path, "-o", mf_path, "-F", manifest_db_format, abspath_relpath_v4, ) runtmp.sourmash("sig", "cat", mf_path) def test_sig_collect_6_path_cwd_subdir(runtmp, manifest_db_format, abspath_relpath_v4): # check: manifest in cwd, sigs in subdir protzip = utils.get_test_data("prot/protein.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" os.mkdir(runtmp.output("sigs_dir")) protzip_path = "sigs_dir/protein.zip" shutil.copyfile(protzip, runtmp.output(protzip_path)) mf_path = f"mf.{ext}" runtmp.sourmash( "sig", "collect", protzip_path, "-o", mf_path, "-F", manifest_db_format, abspath_relpath_v4, ) runtmp.sourmash("sig", "cat", mf_path) def test_sig_collect_6_path_subdir_cwd(runtmp, manifest_db_format, abspath_or_relpath): # check: manifest in cwd, sigs in subdir. note, fails with default v4 # behavior. see #3008. protzip = utils.get_test_data("prot/protein.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" protzip_path = "protein.zip" shutil.copyfile(protzip, runtmp.output(protzip_path)) os.mkdir(runtmp.output("mf_dir")) mf_path = f"mf_dir/mf.{ext}" runtmp.sourmash( "sig", "collect", protzip_path, "-o", mf_path, "-F", manifest_db_format, abspath_or_relpath, ) runtmp.sourmash("sig", "cat", mf_path) def test_sig_collect_6_path_subdir_subdir( runtmp, manifest_db_format, abspath_or_relpath ): # check: manifest and sigs in subdir. note, fails with default v4 # behavior. see #3008. protzip = utils.get_test_data("prot/protein.zip") ext = "sqlmf" if manifest_db_format == "sql" else "csv" os.mkdir(runtmp.output("sigs_dir")) protzip_path = "sigs_dir/protein.zip" shutil.copyfile(protzip, runtmp.output(protzip_path)) os.mkdir(runtmp.output("mf_dir")) mf_path = f"mf_dir/mf.{ext}" runtmp.sourmash( "sig", "collect", protzip_path, "-o", mf_path, "-F", manifest_db_format, abspath_or_relpath, ) runtmp.sourmash("sig", "cat", mf_path) sourmash-bio-sourmash-bc3c0f8/tests/test_cmd_signature_fileinfo.py000066400000000000000000000320211474157046300257360ustar00rootroot00000000000000""" Tests for the 'sourmash signature fileinfo' command line. """ import shutil import os import glob import json import pytest import sourmash_tst_utils as utils from sourmash_tst_utils import SourmashCommandFailed ## command line tests def test_fileinfo_1_sig(runtmp): # get basic info on a signature sig47 = utils.get_test_data("47.fa.sig") shutil.copyfile(sig47, runtmp.output("sig47.sig")) runtmp.run_sourmash("sig", "fileinfo", "sig47.sig") out = runtmp.last_result.out print(runtmp.last_result.out) expected_output = """\ path filetype: MultiIndex location: sig47.sig is database? no has manifest? yes num signatures: 1 total hashes: 5177 summary of sketches: 1 sketches with DNA, k=31, scaled=1000 5177 """.splitlines() for line in expected_output: assert line.strip() in out def test_fileinfo_1_sig_summarize(runtmp): # get basic info on a signature with 'summarize' as alias for fileinfo sig47 = utils.get_test_data("47.fa.sig") shutil.copyfile(sig47, runtmp.output("sig47.sig")) runtmp.run_sourmash("sig", "summarize", "sig47.sig") out = runtmp.last_result.out print(runtmp.last_result.out) expected_output = """\ path filetype: MultiIndex location: sig47.sig is database? no has manifest? yes num signatures: 1 total hashes: 5177 summary of sketches: 1 sketches with DNA, k=31, scaled=1000 5177 """.splitlines() for line in expected_output: assert line.strip() in out def test_fileinfo_1_sig_abund(runtmp): # get basic info on a signature with abundance sig47 = utils.get_test_data("track_abund/47.fa.sig") shutil.copyfile(sig47, runtmp.output("sig47.sig")) runtmp.run_sourmash("sig", "fileinfo", "sig47.sig") out = runtmp.last_result.out print(runtmp.last_result.out) expected_output = """\ path filetype: MultiIndex location: sig47.sig is database? no has manifest? yes num signatures: 1 5177 total hashes summary of sketches: 1 sketches with DNA, k=31, scaled=1000, abund 5177 total hashes """.splitlines() for line in expected_output: assert line.strip() in out def test_fileinfo_2_lca(runtmp): # get basic info on an LCA database prot = utils.get_test_data("prot/protein.lca.json.gz") shutil.copyfile(prot, runtmp.output("protein.lca.json.gz")) runtmp.run_sourmash("sig", "fileinfo", "protein.lca.json.gz") out = runtmp.last_result.out print(runtmp.last_result.out) expected_output = """\ path filetype: LCA_Database location: protein.lca.json.gz is database? yes has manifest? no num signatures: 2 total hashes: 8214 summary of sketches: 2 sketches with protein, k=19, scaled=100 8214 """.splitlines() for line in expected_output: assert line.strip() in out def test_fileinfo_3_sbt_zip(runtmp): # test on an SBT.zip prot = utils.get_test_data("prot/protein.sbt.zip") shutil.copyfile(prot, runtmp.output("protein.sbt.zip")) runtmp.run_sourmash("sig", "fileinfo", "protein.sbt.zip") out = runtmp.last_result.out print(runtmp.last_result.out) expected_output = """\ path filetype: SBT location: protein.sbt.zip is database? yes has manifest? yes num signatures: 2 total hashes: 8214 summary of sketches: 2 sketches with protein, k=19, scaled=100 8214 total hashes """.splitlines() for line in expected_output: assert line.strip() in out, line.strip() def test_fileinfo_4_zip(runtmp): # test on a ZipFileLinearIndex prot = utils.get_test_data("prot/all.zip") shutil.copyfile(prot, runtmp.output("all.zip")) runtmp.run_sourmash("sig", "fileinfo", "all.zip") out = runtmp.last_result.out print(runtmp.last_result.out) # 'location' will be fully resolved, ignore it for now expected_output = """\ path filetype: ZipFileLinearIndex is database? yes has manifest? yes num signatures: 8 total hashes: 31758 summary of sketches: 2 sketches with dayhoff, k=19, scaled=100 7945 total hashes 2 sketches with hp, k=19, scaled=100 5184 total hashes 2 sketches with protein, k=19, scaled=100 8214 total hashes 2 sketches with DNA, k=31, scaled=1000 10415 total hashes """.splitlines() for line in expected_output: assert line.strip() in out def test_fileinfo_4_zip_json_out(runtmp): # check --json-out prot = utils.get_test_data("prot/all.zip") shutil.copyfile(prot, runtmp.output("all.zip")) runtmp.run_sourmash("sig", "fileinfo", "all.zip", "--json-out") out = runtmp.last_result.out print(runtmp.last_result.out) # should succeed as loading as JSON, with correct info vals = json.loads(out) assert vals["has_manifest"] assert vals["is_database"] assert vals["num_sketches"] == 8 assert vals["path_filetype"] == "ZipFileLinearIndex" assert vals["total_hashes"] == 31758 d1 = { "ksize": 19, "moltype": "dayhoff", "scaled": 100, "num": 0, "abund": False, "count": 2, "n_hashes": 7945, } d2 = { "ksize": 19, "moltype": "hp", "scaled": 100, "num": 0, "abund": False, "count": 2, "n_hashes": 5184, } d3 = { "ksize": 19, "moltype": "protein", "scaled": 100, "num": 0, "abund": False, "count": 2, "n_hashes": 8214, } d4 = { "ksize": 31, "moltype": "DNA", "scaled": 1000, "num": 0, "abund": False, "count": 2, "n_hashes": 10415, } assert d1 in vals["sketch_info"] assert d2 in vals["sketch_info"] assert d3 in vals["sketch_info"] assert d4 in vals["sketch_info"] assert len(vals["sketch_info"]) == 4 def test_fileinfo_4_zip_rebuild(runtmp): # test --rebuild prot = utils.get_test_data("prot/all.zip") shutil.copyfile(prot, runtmp.output("all.zip")) runtmp.run_sourmash("sig", "fileinfo", "all.zip", "--rebuild") out = runtmp.last_result.out print(runtmp.last_result.out) # 'location' will be fully resolved, ignore it for now # CTB: note we're missing one of the 8 in the rebuilt, dna-sig.noext, # because it is not automatically included unless you load the zipfile # with traverse. This is intentional. expected_output = """\ path filetype: ZipFileLinearIndex is database? yes has manifest? yes num signatures: 8 total hashes: 26581 summary of sketches: 2 sketches with dayhoff, k=19, scaled=100 7945 total hashes 2 sketches with hp, k=19, scaled=100 5184 total hashes 2 sketches with protein, k=19, scaled=100 8214 total hashes 1 sketches with DNA, k=31, scaled=1000 5238 total hashes """.splitlines() for line in expected_output: assert line.strip() in out def test_fileinfo_5_dir(runtmp): # test on a directory sig47 = utils.get_test_data("47.fa.sig") os.mkdir(runtmp.output("subdir")) shutil.copyfile(sig47, runtmp.output("subdir/sig47.sig")) runtmp.run_sourmash("sig", "fileinfo", "subdir/") out = runtmp.last_result.out print(runtmp.last_result.out) expected_output = """\ path filetype: MultiIndex location: subdir/ is database? no has manifest? yes num signatures: 1 total hashes: 5177 summary of sketches: 1 sketches with DNA, k=31, scaled=1000 5177 total hashes """.splitlines() for line in expected_output: assert line.strip() in out def test_fileinfo_6_pathlist(runtmp): # test on a pathlist sig47 = utils.get_test_data("47.fa.sig") shutil.copyfile(sig47, runtmp.output("47.fa.sig")) with open(runtmp.output("pathlist.txt"), "w") as fp: fp.write("47.fa.sig\n") runtmp.run_sourmash("sig", "fileinfo", "pathlist.txt") out = runtmp.last_result.out print(runtmp.last_result.out) expected_output = """\ path filetype: MultiIndex location: pathlist.txt is database? no has manifest? yes num signatures: 1 total hashes: 5177 summary of sketches: 1 sketches with DNA, k=31, scaled=1000 5177 total hashes """.splitlines() for line in expected_output: assert line.strip() in out @pytest.mark.parametrize( "db", [ "v6.sbt.json", "v5.sbt.json", "v4.sbt.json", "v3.sbt.json", "v2.sbt.json", "v1.sbt.json", ], ) def test_fileinfo_7_sbt_json(runtmp, db): # test on multiple versions of SBT JSON files dbfile = utils.get_test_data(db) runtmp.run_sourmash("sig", "fileinfo", dbfile) out = runtmp.last_result.out print(runtmp.last_result.out) expected_output = f"""\ path filetype: SBT location: {dbfile} is database? yes has manifest? no num signatures: 7 total hashes: 3500 summary of sketches: 7 sketches with DNA, k=31, num=500 3500 total hashes """.splitlines() for line in expected_output: assert line.strip() in out, line.strip() def test_sig_fileinfo_stdin(runtmp): # test on stdin sig = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) with open(sig) as fp: data = fp.read() runtmp.run_sourmash("sig", "fileinfo", "-", stdin_data=data) out = runtmp.last_result.out print(out) expected_output = """\ path filetype: MultiIndex location: - is database? no has manifest? yes num signatures: 1 total hashes: 3409 summary of sketches: 1 sketches with protein, k=19, scaled=100 3409 total hashes """.splitlines() for line in expected_output: assert line.strip() in out, line.strip() def test_sig_fileinfo_does_not_exist(runtmp): # test on file that does not exist with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("sig", "fileinfo", "does-not-exist") assert ( "Cannot open 'does-not-exist' as a sourmash signature collection" in runtmp.last_result.err ) def test_sig_fileinfo_8_manifest_works(runtmp): # test on a manifest with relative paths, in proper location mf = utils.get_test_data("scaled/mf.csv") runtmp.sourmash("sig", "fileinfo", mf) out = runtmp.last_result.out print(out) assert "15 sketches with DNA, k=31, scaled=10000 717 total hashes" in out assert "num signatures: 15" in out assert "has manifest? yes" in out assert "is database? yes" in out assert "path filetype: StandaloneManifestIndex" in out def test_sig_fileinfo_8_manifest_works_when_moved(runtmp): # test on a manifest with relative paths, when in wrong place # note: this works, unlike 'describe', because all the necessary info # for 'fileinfo' is in the manifest. mf = utils.get_test_data("scaled/mf.csv") shutil.copyfile(mf, runtmp.output("mf.csv")) runtmp.sourmash("sig", "fileinfo", "mf.csv") out = runtmp.last_result.out print(out) assert "15 sketches with DNA, k=31, scaled=10000 717 total hashes" in out assert "num signatures: 15" in out assert "has manifest? yes" in out assert "is database? yes" in out assert "path filetype: StandaloneManifestIndex" in out def test_sig_fileinfo_9_sqldb_make(runtmp): # make a sqldb and run fileinfo on it gcf_all = glob.glob(utils.get_test_data("gather/GCF*.sig")) sqldb = runtmp.output("some.sqldb") runtmp.sourmash("sig", "cat", "-k", "31", *gcf_all, "-o", sqldb) runtmp.sourmash("sig", "fileinfo", sqldb) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "12 sketches with DNA, k=31, scaled=10000 4540 total hashes" in out def test_sig_fileinfo_9_sqldb_exists(runtmp): # run fileinfo on existing sqldb sqldb = utils.get_test_data("sqlite/index.sqldb") runtmp.sourmash("sig", "fileinfo", sqldb) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "path filetype: SqliteIndex" in out assert ( "2 sketches with DNA, k=31, scaled=1000 10415 total hashes" in out ) def test_sig_fileinfo_9_sql_manifest(runtmp): # run fileinfo on existing sqldb sqldb = utils.get_test_data("sqlite/prot.sqlmf") runtmp.sourmash("sig", "fileinfo", sqldb) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "path filetype: StandaloneManifestIndex" in out assert "num signatures: 7" in out assert "1 sketches with DNA, k=31, scaled=1000 5238 total hashes" in out assert "2 sketches with hp, k=19, scaled=100 5184 total hashes" in out assert "2 sketches with dayhoff, k=19, scaled=100 7945 total hashes" in out assert "2 sketches with protein, k=19, scaled=100 8214 total hashes" in out def test_sig_fileinfo_9_sql_lca_db(runtmp): # run fileinfo on existing sqldb sqldb = utils.get_test_data("sqlite/lca.sqldb") runtmp.sourmash("sig", "fileinfo", sqldb) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "path filetype: LCA_SqliteDatabase" in out assert "2 sketches with DNA, k=31, scaled=10000 1431 total hashes" in out sourmash-bio-sourmash-bc3c0f8/tests/test_cmd_signature_grep.py000066400000000000000000000306101474157046300251020ustar00rootroot00000000000000""" Tests for the 'sourmash signature grep' command line. """ import shutil import os import csv import gzip import pytest import sourmash_tst_utils as utils import sourmash from sourmash_tst_utils import SourmashCommandFailed from sourmash.signature import load_signatures_from_json, save_signatures_to_json ## command line tests def test_grep_1_sig_name(runtmp): # search on substring in name sig47 = utils.get_test_data("47.fa.sig") runtmp.run_sourmash("sig", "grep", "Shewanella", sig47) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella" in ss.name assert ss.md5sum() == "09a08691ce52952152f0e866a59f6261" def test_grep_1_sig_name_case_sensitive(runtmp): # search on substring in name sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("sig", "grep", "shewanella", sig47) def test_grep_1_sig_name_case_insensitive(runtmp): # search on substring in name, case insensitive sig47 = utils.get_test_data("47.fa.sig") runtmp.run_sourmash("sig", "grep", "-i", "shewanella", sig47) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella" in ss.name assert ss.md5sum() == "09a08691ce52952152f0e866a59f6261" def test_grep_1_sig_name_exclude(runtmp): # search on substring in name, case insensitive sig47 = utils.get_test_data("47.fa.sig") # no matches! with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("sig", "grep", "-v", "Shewanella", sig47) def test_grep_2_sig_md5(runtmp): # search on substring in md5 sig47 = utils.get_test_data("47.fa.sig") runtmp.run_sourmash("sig", "grep", "ce52952152f0", sig47) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert ss.md5sum() == "09a08691ce52952152f0e866a59f6261" def test_grep_2_sig_md5_case_sensitive(runtmp): # case sensitive no match sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("sig", "grep", "CE52952152f0", sig47) def test_grep_2_sig_md5_case_insensitive(runtmp): # search on substring in md5, case insensitive sig47 = utils.get_test_data("47.fa.sig") runtmp.run_sourmash("sig", "grep", "-i", "CE52952152f0", sig47) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert ss.md5sum() == "09a08691ce52952152f0e866a59f6261" def test_grep_3_filename(runtmp): # filename match sig47 = utils.get_test_data("47.fa.sig") runtmp.run_sourmash("sig", "grep", "47.fa", sig47) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "47.fa" in ss.filename assert ss.md5sum() == "09a08691ce52952152f0e866a59f6261" def test_grep_3_filename_regexp(runtmp): # search for a regexp on filename sig47 = utils.get_test_data("47.fa.sig") runtmp.run_sourmash("sig", "grep", "^47.fa", sig47) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "7.fa" in ss.filename assert ss.md5sum() == "09a08691ce52952152f0e866a59f6261" def test_grep_4_no_manifest(runtmp): # fail search when no manifest, by default sbt = utils.get_test_data("v6.sbt.zip") with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("sig", "grep", "e60265", sbt) print(runtmp.last_result.err) assert "ERROR on filename" in runtmp.last_result.err assert ( "sig grep requires a manifest by default, but no manifest present." in runtmp.last_result.err ) def test_grep_4_no_manifest_ok(runtmp): # generate manifest if --no-require-manifest sbt = utils.get_test_data("v6.sbt.zip") runtmp.run_sourmash("sig", "grep", "e60265", sbt, "--no-require-manifest") ss = load_signatures_from_json(runtmp.last_result.out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "e60265" in ss.md5sum() def test_grep_5_zip_include(runtmp): # search zip, include on case sensitive match to name allzip = utils.get_test_data("prot/all.zip") runtmp.run_sourmash("sig", "grep", "--dna", "OS223", allzip) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella baltica OS223" in ss.name assert ss.md5sum() == "38729c6374925585db28916b82a6f513" def test_grep_5_zip_include_picklist(runtmp): # search zip, include on case sensitive match to name allzip = utils.get_test_data("prot/all.zip") pickfile = runtmp.output("pick.csv") with open(pickfile, "w", newline="") as fp: w = csv.DictWriter(fp, fieldnames=["md5"]) w.writeheader() w.writerow(dict(md5="09a08691ce52952152f0e866a59f6261")) w.writerow(dict(md5="38729c6374925585db28916b82a6f513")) runtmp.run_sourmash( "sig", "grep", "--dna", "OS223", allzip, "--picklist", f"{pickfile}:md5:md5" ) out = runtmp.last_result.out print(out) err = runtmp.last_result.err print(err) assert "for given picklist, found 2 matches to 2 distinct values" in err ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella baltica OS223" in ss.name assert ss.md5sum() == "38729c6374925585db28916b82a6f513" def test_grep_5_zip_include_case_insensitive(runtmp): # search zip, include on case insensitive match to name allzip = utils.get_test_data("prot/all.zip") runtmp.run_sourmash("sig", "grep", "--dna", "-i", "os223", allzip) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella baltica OS223" in ss.name assert ss.md5sum() == "38729c6374925585db28916b82a6f513" def test_grep_5_zip_exclude(runtmp): # search zip, exclude on case-sensitive match allzip = utils.get_test_data("prot/all.zip") runtmp.run_sourmash("sig", "grep", "--dna", "-v", "OS185", allzip) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella baltica OS223" in ss.name assert ss.md5sum() == "38729c6374925585db28916b82a6f513" def test_grep_5_zip_exclude_case_insensitive(runtmp): # search zip, exclude on case-insensitive match allzip = utils.get_test_data("prot/all.zip") runtmp.run_sourmash("sig", "grep", "--dna", "-vi", "os185", allzip) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella baltica OS223" in ss.name assert ss.md5sum() == "38729c6374925585db28916b82a6f513" def test_grep_6_zip_manifest_csv(runtmp): # do --csv and use result as picklist allzip = utils.get_test_data("prot/all.zip") runtmp.run_sourmash("sig", "grep", "--dna", "OS223", allzip, "--csv", "match.csv") out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella baltica OS223" in ss.name assert ss.md5sum() == "38729c6374925585db28916b82a6f513" # now run cat with picklist runtmp.run_sourmash("sig", "cat", allzip, "--picklist", "match.csv::manifest") out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella baltica OS223" in ss.name assert ss.md5sum() == "38729c6374925585db28916b82a6f513" def test_grep_6_zip_manifest_csv_gz(runtmp): # do --csv and use result as picklist allzip = utils.get_test_data("prot/all.zip") runtmp.run_sourmash( "sig", "grep", "--dna", "OS223", allzip, "--csv", "match.csv.gz" ) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella baltica OS223" in ss.name assert ss.md5sum() == "38729c6374925585db28916b82a6f513" # check that match.csv.gz is a gzip file with gzip.open(runtmp.output("match.csv.gz"), "rt", newline="") as fp: fp.read() # now run cat with picklist runtmp.run_sourmash("sig", "cat", allzip, "--picklist", "match.csv.gz::manifest") out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella baltica OS223" in ss.name assert ss.md5sum() == "38729c6374925585db28916b82a6f513" def test_sig_grep_7_lca(runtmp): # extract 47 from an LCA database, with --no-require-manifest allzip = utils.get_test_data("lca/47+63.lca.json") sig47 = utils.get_test_data("47.fa.sig") runtmp.sourmash( "sig", "grep", "50a9274021e4", allzip, "--no-require-manifest", "-o", "matches.sig", ) match = sourmash.load_file_as_signatures(runtmp.output("matches.sig")) match = list(match)[0] ss47 = sourmash.load_file_as_signatures(sig47) ss47 = list(ss47)[0] ss47 = ss47.to_mutable() ss47.minhash = ss47.minhash.downsample(scaled=10000) assert ss47.minhash == match.minhash def test_sig_grep_7_picklist_md5_lca_fail(runtmp): # extract 47 from an LCA database, using a picklist w/full md5 => fail allzip = utils.get_test_data("lca/47+63.lca.json") # select on any of these attributes row = dict( exactName="NC_009665.1 Shewanella baltica OS185, complete genome", md5full="50a9274021e43eda8b2e77f8fa60ae8e", md5short="50a9274021e43eda8b2e77f8fa60ae8e"[:8], fullIdent="NC_009665.1", nodotIdent="NC_009665", ) # make picklist picklist_csv = runtmp.output("pick.csv") with open(picklist_csv, "w", newline="") as csvfp: w = csv.DictWriter(csvfp, fieldnames=row.keys()) w.writeheader() w.writerow(row) picklist_arg = f"{picklist_csv}:md5full:md5" with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sig", "grep", "50a92740", allzip, "--picklist", picklist_arg, "--no-require-manifest", ) # this happens b/c the implementation of 'grep' uses picklists, and # LCA databases don't support multiple picklists. print(runtmp.last_result.err) assert ( "This input collection doesn't support 'grep' with picklists." in runtmp.last_result.err ) def test_sig_grep_8_count(runtmp): zips = [ "prot/all.zip", "prot/dayhoff.sbt.zip", "prot/dayhoff.zip", "prot/hp.sbt.zip", "prot/hp.zip", "prot/protein.sbt.zip", "prot/protein.zip", ] zip_src = [utils.get_test_data(x) for x in zips] os.mkdir(runtmp.output("prot")) for src, dest in zip(zip_src, zips): shutil.copyfile(src, runtmp.output(dest)) runtmp.sourmash("sig", "grep", "-c", "0015939", *zips) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "(no signatures will be saved because of --silent/--count)." in err for line in """\ 6 matches: prot/all.zip 2 matches: prot/dayhoff.sbt.zip 2 matches: prot/dayhoff.zip 2 matches: prot/hp.sbt.zip 2 matches: prot/hp.zip 2 matches: prot/protein.sbt.zip 2 matches: prot/protein.zip """.splitlines(): assert line.strip() in out def test_sig_grep_identical_md5s(runtmp): # test that we properly handle different signatures with identical md5s sig47 = utils.get_test_data("47.fa.sig") ss = load_signatures_from_json(sig47) sig = list(ss)[0] new_sig = sig.to_mutable() new_sig.name = "foo" sig47foo = runtmp.output("foo.sig") # this was only a problem when the signatures are stored in the same file with open(sig47foo, "w") as fp: save_signatures_to_json([new_sig, sig], fp) runtmp.run_sourmash("sig", "grep", "-i", "foo", sig47foo) out = runtmp.last_result.out ss = load_signatures_from_json(out) ss = list(ss) assert len(ss) == 1 ss = ss[0] assert "Shewanella" not in ss.name assert "foo" in ss.name assert ss.md5sum() == "09a08691ce52952152f0e866a59f6261" sourmash-bio-sourmash-bc3c0f8/tests/test_compare.py000066400000000000000000000135101474157046300226670ustar00rootroot00000000000000import glob import os import numpy as np import pytest import sourmash from sourmash.compare import ( compare_all_pairs, compare_parallel, compare_serial, compare_serial_containment, compare_serial_max_containment, compare_serial_avg_containment, ) import sourmash_tst_utils as utils @pytest.fixture() def siglist(): demo_path = utils.get_test_data("demo") filenames = sorted(glob.glob(os.path.join(demo_path, "*.sig"))) sigs = [] for filename in filenames: sigs.extend(sourmash.load_file_as_signatures(filename)) return sigs @pytest.fixture() def scaled_siglist(): demo_path = utils.get_test_data("scaled") filenames = sorted(glob.glob(os.path.join(demo_path, "*.sig"))) sigfiles = ["2.fa.sig", "2+63.fa.sig", "47.fa.sig", "63.fa.sig"] filenames = [utils.get_test_data(c) for c in sigfiles] sigs = [] for filename in filenames: these_sigs = sourmash.load_file_as_signatures(filename, ksize=31) scaled_sigs = [s for s in these_sigs if s.minhash.scaled != 0] sigs.extend(scaled_sigs) return sigs @pytest.fixture() def ignore_abundance(track_abundance): return not track_abundance def test_compare_serial(siglist, ignore_abundance): similarities = compare_serial(siglist, ignore_abundance, downsample=False) true_similarities = np.array( [ [1.0, 0.356, 0.078, 0.086, 0.0, 0.0, 0.0], [0.356, 1.0, 0.072, 0.078, 0.0, 0.0, 0.0], [0.078, 0.072, 1.0, 0.074, 0.0, 0.0, 0.0], [0.086, 0.078, 0.074, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.382, 0.364], [0.0, 0.0, 0.0, 0.0, 0.382, 1.0, 0.386], [0.0, 0.0, 0.0, 0.0, 0.364, 0.386, 1.0], ] ) np.testing.assert_array_equal(similarities, true_similarities) def test_compare_parallel(siglist, ignore_abundance): similarities = compare_parallel( siglist, ignore_abundance, downsample=False, n_jobs=2 ) true_similarities = np.array( [ [1.0, 0.356, 0.078, 0.086, 0.0, 0.0, 0.0], [0.356, 1.0, 0.072, 0.078, 0.0, 0.0, 0.0], [0.078, 0.072, 1.0, 0.074, 0.0, 0.0, 0.0], [0.086, 0.078, 0.074, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.382, 0.364], [0.0, 0.0, 0.0, 0.0, 0.382, 1.0, 0.386], [0.0, 0.0, 0.0, 0.0, 0.364, 0.386, 1.0], ] ) np.testing.assert_array_equal(similarities, true_similarities) def test_compare_all_pairs(siglist, ignore_abundance): similarities_parallel = compare_all_pairs( siglist, ignore_abundance, downsample=False, n_jobs=2 ) similarities_serial = compare_serial(siglist, ignore_abundance, downsample=False) np.testing.assert_array_equal(similarities_parallel, similarities_serial) def test_compare_serial_jaccardANI(scaled_siglist, ignore_abundance): jANI = compare_serial( scaled_siglist, ignore_abundance, downsample=False, return_ani=True ) print(jANI) true_jaccard_ANI = np.array( [ [1.0, 0.978, 0.0, 0.0], [0.978, 1.0, 0.96973012, 0.99262776], [0.0, 0.96973012, 1.0, 0.97697011], [0.0, 0.99262776, 0.97697011, 1.0], ] ) np.testing.assert_array_almost_equal(jANI, true_jaccard_ANI, decimal=3) def test_compare_parallel_jaccardANI(scaled_siglist, ignore_abundance): jANI = compare_parallel( scaled_siglist, ignore_abundance, downsample=False, n_jobs=2, return_ani=True ) true_jaccard_ANI = np.array( [ [1.0, 0.978, 0.0, 0.0], [0.978, 1.0, 0.96973012, 0.99262776], [0.0, 0.96973012, 1.0, 0.97697011], [0.0, 0.99262776, 0.97697011, 1.0], ] ) np.testing.assert_array_almost_equal(jANI, true_jaccard_ANI, decimal=3) def test_compare_all_pairs_jaccardANI(scaled_siglist, ignore_abundance): similarities_parallel = compare_all_pairs( scaled_siglist, ignore_abundance, downsample=False, n_jobs=2, return_ani=True ) similarities_serial = compare_serial( scaled_siglist, ignore_abundance, downsample=False, return_ani=True ) np.testing.assert_array_equal(similarities_parallel, similarities_serial) def test_compare_serial_containmentANI(scaled_siglist): containment_ANI = compare_serial_containment(scaled_siglist, return_ani=True) print(containment_ANI) true_containment_ANI = np.array( [ [1, 0.966, 0.0, 0.0], [1, 1.0, 0.97715525, 1.0], [0.0, 0.96377054, 1.0, 0.97678608], [0.0, 0.98667513, 0.97715525, 1.0], ] ) np.testing.assert_array_almost_equal( containment_ANI, true_containment_ANI, decimal=3 ) def test_compare_serial_maxcontainmentANI(scaled_siglist): # check max_containment ANI max_containment_ANI = compare_serial_max_containment( scaled_siglist, return_ani=True ) print(max_containment_ANI) true_max_containment_ANI = np.array( [ [1.0, 1.0, 0.0, 0.0], [1.0, 1.0, 0.97715525, 1.0], [0.0, 0.97715525, 1.0, 0.97715525], [0.0, 1.0, 0.97715525, 1.0], ] ) np.testing.assert_array_almost_equal( max_containment_ANI, true_max_containment_ANI, decimal=3 ) def test_compare_serial_avg_containmentANI(scaled_siglist): # check avg_containment ANI avg_containment_ANI = compare_serial_avg_containment( scaled_siglist, return_ani=True ) print(avg_containment_ANI) true_avg_containment_ANI = np.array( [ [1.0, 0.983, 0.0, 0.0], [0.983, 1.0, 0.97046289, 0.99333757], [0.0, 0.97046289, 1.0, 0.97697067], [0.0, 0.99333757, 0.97697067, 1.0], ] ) np.testing.assert_array_almost_equal( avg_containment_ANI, true_avg_containment_ANI, decimal=3 ) sourmash-bio-sourmash-bc3c0f8/tests/test_deprecated.py000066400000000000000000000010421474157046300233360ustar00rootroot00000000000000from sourmash import signature import sourmash_tst_utils as utils def test_load_textmode(track_abundance): # ijson required a file in binary mode or bytes, # but we had an API example in the docs using 'rt'. # I fixed the docs, but I'm keeping this test here # to make sure we still support it =/ sigfile = utils.get_test_data("genome-s10+s11.sig") with open(sigfile) as sigfp: siglist = list(signature.load_signatures_from_json(sigfp)) loaded_sig = siglist[0] assert loaded_sig.name == "genome-s10+s11" sourmash-bio-sourmash-bc3c0f8/tests/test_distance_utils.py000066400000000000000000000367761474157046300242760ustar00rootroot00000000000000""" Tests for distance utils. """ import pytest import numpy as np from sourmash.distance_utils import ( containment_to_distance, get_exp_probability_nothing_common, handle_seqlen_nkmers, jaccard_to_distance, ANIResult, ciANIResult, jaccardANIResult, var_n_mutated, set_size_chernoff, set_size_exact_prob, ) def test_aniresult(): res = ANIResult(0.4, 0.1) assert res.dist == 0.4 assert res.ani == 0.6 assert res.p_nothing_in_common == 0.1 assert res.p_exceeds_threshold == True # check that they're equivalent res2 = ANIResult(0.4, 0.1) assert res == res2 res3 = ANIResult(0.5, 0) assert res != res3 assert res3.p_exceeds_threshold == False def test_aniresult_bad_distance(): """ Fail if distance is not between 0 and 1. """ with pytest.raises(Exception) as exc: ANIResult(1.1, 0.1) print("\n", str(exc.value)) assert "distance value 1.1000 is not between 0 and 1!" in str(exc.value) with pytest.raises(Exception) as exc: ANIResult(-0.1, 0.1) print("\n", str(exc.value)) assert "distance value -0.1000 is not between 0 and 1!" in str(exc.value) def test_jaccard_aniresult(): res = jaccardANIResult(0.4, 0.1, jaccard_error=0.03) assert res.dist == 0.4 assert res.ani is None assert res.p_nothing_in_common == 0.1 assert res.jaccard_error == 0.03 assert res.p_exceeds_threshold == True assert res.je_exceeds_threshold == True res3 = jaccardANIResult(0.4, 0.1, jaccard_error=0.03, je_threshold=0.1) assert res3.je_exceeds_threshold == False assert res3.ani == 0.6 def test_jaccard_aniresult_nojaccarderror(): # jaccard error is None with pytest.raises(Exception) as exc: jaccardANIResult(0.4, 0.1, None) print("\n", str(exc.value)) assert "Error: jaccard_error cannot be None." in str(exc.value) def test_ci_aniresult(): res = ciANIResult(0.4, 0.1, dist_low=0.3, dist_high=0.5) print(res) assert res.dist == 0.4 assert res.ani == 0.6 assert res.p_nothing_in_common == 0.1 assert res.ani_low == 0.5 assert res.ani_high == 0.7 res2 = ciANIResult(0.4, 0.1, dist_low=0.3, dist_high=0.5) assert res == res2 res3 = ciANIResult(0.4, 0.2, dist_low=0.3, dist_high=0.5) assert res != res3 def test_containment_to_distance_zero(): contain = 0 scaled = 1 nkmers = 10000 ksize = 21 res = containment_to_distance( contain, ksize, scaled, n_unique_kmers=nkmers, estimate_ci=True ) print(res) # check results exp_dist, exp_low, exp_high, pnc = 1.0, 1.0, 1.0, 1.0 exp_id, exp_idlow, exp_idhigh, pnc = 0.0, 0.0, 0.0, 1.0 assert res.dist == exp_dist assert res.dist_low == exp_low assert res.dist_high == exp_high assert res.p_nothing_in_common == pnc assert res.ani == exp_id assert res.ani_low == exp_idlow assert res.ani_high == exp_idhigh # check without returning ci res2 = containment_to_distance(contain, ksize, scaled, n_unique_kmers=nkmers) print(res2) exp_res = ciANIResult( dist=1.0, dist_low=1.0, dist_high=1.0, p_nothing_in_common=1.0, p_threshold=0.001, ) assert res2 == exp_res def test_containment_to_distance_one(): contain = 1 scaled = 1 nkmers = 10000 ksize = 21 res = containment_to_distance( contain, ksize, scaled, n_unique_kmers=nkmers, estimate_ci=True ) print(res) exp_dist, exp_low, exp_high, pnc = 0.0, 0.0, 0.0, 0.0 exp_id, exp_idlow, exp_idhigh, pnc = 1.0, 1.0, 1.0, 0.0 assert res.dist == exp_dist assert res.dist_low == exp_low assert res.dist_high == exp_high assert res.p_nothing_in_common == pnc assert res.ani == exp_id assert res.ani_low == exp_idlow assert res.ani_high == exp_idhigh # check without returning ci res = containment_to_distance(contain, ksize, scaled, n_unique_kmers=nkmers) assert res.dist == exp_dist assert res.ani == exp_id assert res.p_nothing_in_common == pnc assert res.ani_low == 1.0 assert res.ani_high == 1.0 def test_containment_to_distance_scaled1(): contain = 0.5 scaled = 1 nkmers = 10000 ksize = 21 res = containment_to_distance( contain, ksize, scaled, n_unique_kmers=nkmers, estimate_ci=True ) print(res) # check results assert res.dist == 0.032468221476108394 assert res.ani == 0.9675317785238916 assert res.dist_low == 0.028709912966405623 assert res.ani_high == 0.9712900870335944 assert res.dist_high == 0.03647860197289783 assert res.ani_low == 0.9635213980271021 assert res.p_nothing_in_common == 0.0 # without returning ci res2 = containment_to_distance(contain, ksize, scaled, n_unique_kmers=nkmers) assert (res2.dist, res2.ani, res2.p_nothing_in_common) == ( 0.032468221476108394, 0.9675317785238916, 0.0, ) assert (res2.dist, res2.ani, res2.p_nothing_in_common) == ( res.dist, res.ani, res.p_nothing_in_common, ) def test_containment_to_distance_scaled100(): contain = 0.1 scaled = 100 nkmers = 10000 ksize = 31 res = containment_to_distance( contain, ksize, scaled, n_unique_kmers=nkmers, estimate_ci=True ) print(res) # check results assert res.dist == 0.07158545548052564 assert res.dist_low == 0.05320779238601372 assert res.dist_high == 0.09055547672455365 assert res.p_nothing_in_common == 4.3171247410658655e-05 assert res.p_exceeds_threshold == False def test_containment_to_distance_scaled100_2(): contain = 0.5 scaled = 100 nkmers = 10000 ksize = 21 res = containment_to_distance( contain, ksize, scaled, n_unique_kmers=nkmers, estimate_ci=True ) print(res) # check results assert res.dist == 0.032468221476108394 assert res.dist_low == 0.023712063916639017 assert res.dist_high == 0.04309960543965866 assert res.p_exceeds_threshold == False def test_containment_to_distance_k10(): contain = 0.5 scaled = 100 nkmers = 10000 ksize = 10 res = containment_to_distance( contain, ksize, scaled, n_unique_kmers=nkmers, estimate_ci=True ) print(res) # check results assert res.dist == 0.06696700846319259 assert res.dist_low == 0.04982777541057476 assert res.dist_high == 0.08745108232411622 assert res.p_exceeds_threshold == False def test_containment_to_distance_confidence(): contain = 0.1 scaled = 100 nkmers = 10000 ksize = 31 confidence = 0.99 res = containment_to_distance( contain, ksize, scaled, confidence=confidence, n_unique_kmers=nkmers, estimate_ci=True, ) print(res) # check results assert res.dist == 0.07158545548052564 assert res.dist_low == 0.04802880300938562 assert res.dist_high == 0.09619930040790341 assert res.p_exceeds_threshold == False confidence = 0.90 res2 = containment_to_distance( contain, ksize, scaled, n_unique_kmers=nkmers, confidence=confidence, estimate_ci=True, ) print(res2) # check results assert res2.dist == res.dist assert round(res2.dist_low, 10) == round(0.05599435479247415, 10) assert res2.dist_high == 0.08758718871990222 assert res.p_exceeds_threshold == False def test_nkmers_to_bp_containment(): containment = 0.1 scaled = 100 bp_len = 10030 ksize = 31 nkmers = handle_seqlen_nkmers(ksize, sequence_len_bp=bp_len) print("nkmers_from_bp:", nkmers) confidence = 0.99 kmer_res = containment_to_distance( containment, ksize, scaled, confidence=confidence, n_unique_kmers=nkmers, estimate_ci=True, ) bp_res = containment_to_distance( containment, ksize, scaled, confidence=confidence, sequence_len_bp=bp_len, estimate_ci=True, ) print(f"\nkDIST: {kmer_res}") print(f"\nbpDIST:,{bp_res}") # check results assert kmer_res == bp_res assert kmer_res.dist == 0.07158545548052564 assert kmer_res.dist_low == 0.04802880300938562 assert kmer_res.dist_high == 0.09619930040790341 def test_jaccard_to_distance_zero(): jaccard = 0 scaled = 1 nkmers = 10000 ksize = 21 res = jaccard_to_distance(jaccard, ksize, scaled, n_unique_kmers=nkmers) print(res) # check results assert res.dist == 1.0 assert res.ani == 0.0 assert res.p_nothing_in_common == 1.0 assert res.jaccard_error == 0.0 def test_jaccard_to_distance_one(): jaccard = 1 scaled = 1 nkmers = 10000 ksize = 21 res = jaccard_to_distance(jaccard, ksize, scaled, n_unique_kmers=nkmers) print(res) # check results assert res.dist == 0.0 assert res.ani == 1.0 assert res.p_nothing_in_common == 0.0 assert res.jaccard_error == 0.0 def test_jaccard_to_distance_scaled(): # scaled value doesn't impact point estimate or jaccard error, just p_nothing_in_common jaccard = 0.5 scaled = 1 nkmers = 10000 ksize = 21 res = jaccard_to_distance(jaccard, ksize, scaled, n_unique_kmers=nkmers) print(res) # check results assert round(res.dist, 3) == round(0.019122659390482077, 3) assert res.ani is None assert res.p_exceeds_threshold == False assert res.jaccard_error == 0.00018351337045518042 assert res.je_exceeds_threshold == True scaled = 100 res2 = jaccard_to_distance(jaccard, ksize, scaled, n_unique_kmers=nkmers) print(res2) assert res2.dist == res.dist assert res2.jaccard_error == res.jaccard_error assert res2.p_nothing_in_common != res.p_nothing_in_common assert res2.p_exceeds_threshold == False def test_jaccard_to_distance_k31(): jaccard = 0.5 scaled = 100 nkmers = 10000 ksize = 31 res = jaccard_to_distance(jaccard, ksize, scaled, n_unique_kmers=nkmers) print(res) # check results assert res.je_exceeds_threshold == True assert res.ani is None assert res.p_exceeds_threshold == False res2 = jaccard_to_distance( jaccard, ksize, scaled, n_unique_kmers=nkmers, err_threshold=0.1 ) assert res2.je_exceeds_threshold == False assert res2.ani == 0.9870056455892898 def test_jaccard_to_distance_k31_2(): jaccard = 0.1 scaled = 100 nkmers = 10000 ksize = 31 res = jaccard_to_distance(jaccard, ksize, scaled, n_unique_kmers=nkmers) print(res) # check results assert res.ani == 0.9464928391768298 assert res.p_exceeds_threshold == False assert res.je_exceeds_threshold == False def test_nkmers_to_bp_jaccard(): jaccard = 0.1 scaled = 100 bp_len = 10030 ksize = 31 nkmers = handle_seqlen_nkmers(ksize, sequence_len_bp=bp_len) print("nkmers_from_bp:", nkmers) kmer_res = jaccard_to_distance(jaccard, ksize, scaled, n_unique_kmers=nkmers) bp_res = jaccard_to_distance(jaccard, ksize, scaled, sequence_len_bp=bp_len) print(f"\nkmer_res: {kmer_res}") print(f"\nbp_res: {bp_res}") # check results assert kmer_res == bp_res assert kmer_res.dist == 0.0535071608231702 assert kmer_res.p_exceeds_threshold == False assert kmer_res.je_exceeds_threshold == False def test_exp_prob_nothing_common(): dist = 0.25 ksize = 31 scaled = 10 bp_len = 1000030 nkmers = handle_seqlen_nkmers(ksize, sequence_len_bp=bp_len) print("nkmers_from_bp:", nkmers) nkmers_pnc = get_exp_probability_nothing_common( dist, ksize, scaled, n_unique_kmers=nkmers ) print(f"prob nothing in common: {nkmers_pnc}") bp_pnc = get_exp_probability_nothing_common( dist, ksize, scaled, sequence_len_bp=bp_len ) assert nkmers_pnc == bp_pnc == 7.437016945722123e-07 def test_containment_to_distance_tinytestdata_var0(): """ tiny test data to trigger the following: WARNING: Cannot estimate ANI confidence intervals from containment. Do your sketches contain enough hashes? Error: varN <0.0! """ contain = 0.9 scaled = 1 nkmers = 4 ksize = 31 res = containment_to_distance( contain, ksize, scaled, n_unique_kmers=nkmers, estimate_ci=True ) print(res) # check results assert res.dist == 0.003392957179023992 assert res.dist_low is None assert res.dist_high is None assert res.ani_low is None assert res.ani_high is None assert res.p_exceeds_threshold == False def test_var_n_mutated(): # check 0 r = 0 ksize = 31 nkmers = 200 var_n_mut = var_n_mutated(nkmers, ksize, r) print(f"var_n_mutated: {var_n_mut}") assert var_n_mut == 0 # check var 0.0 valuerror r = 10 ksize = 31 nkmers = 200 with pytest.raises(ValueError) as exc: var_n_mut = var_n_mutated(nkmers, ksize, r) assert "Error: varN <0.0!" in str(exc) # check successful r = 0.4 ksize = 31 nkmers = 200000 var_n_mut = var_n_mutated(nkmers, ksize, r) print(f"var_n_mutated: {var_n_mut}") assert var_n_mut == 0.10611425440741508 def test_handle_seqlen_nkmers(): bp_len = 10030 ksize = 31 # convert seqlen to nkmers nkmers = handle_seqlen_nkmers(ksize, sequence_len_bp=bp_len) assert nkmers == 10000 # if nkmers is provided, just use that nkmers = handle_seqlen_nkmers(ksize, sequence_len_bp=bp_len, n_unique_kmers=bp_len) assert nkmers == 10030 # if neither seqlen or nkmers provided, complain with pytest.raises(ValueError) as exc: nkmers = handle_seqlen_nkmers(ksize) assert ( "Error: distance estimation requires input of either 'sequence_len_bp' or 'n_unique_kmers'" ) in str(exc) def test_set_size_chernoff(): eps = 10 ** (-6) rel_error = 0.01 set_size = 1000000 s = 1 / 0.1 # I'm used to using a scale value between 0 and 1 value_from_mathematica = 0.928652 assert ( np.abs( set_size_chernoff(set_size, s, relative_error=rel_error) - value_from_mathematica ) < eps ) rel_error = 0.05 set_size = 10000 s = 1 value_from_mathematica = 0.999519 assert ( np.abs( set_size_chernoff(set_size, s, relative_error=rel_error) - value_from_mathematica ) < eps ) rel_error = 0.001 set_size = 10 s = 1 / 0.01 value_from_mathematica = -1 assert ( np.abs( set_size_chernoff(set_size, s, relative_error=rel_error) - value_from_mathematica ) < eps ) def test_set_size_exact_prob(): # values obtained from Mathematica # specifically: Probability[Abs[X*s - n]/n <= delta, # X \[Distributed] BinomialDistribution[n, 1/s]] // N set_size = 100 scaled = 2 relative_error = 0.05 prob = set_size_exact_prob(set_size, scaled, relative_error=relative_error) true_prob = 0.382701 np.testing.assert_array_almost_equal(true_prob, prob, decimal=3) set_size = 200 scaled = 5 relative_error = 0.15 prob = set_size_exact_prob(set_size, scaled, relative_error=relative_error) true_prob = 0.749858 np.testing.assert_array_almost_equal(true_prob, prob, decimal=3) set_size = 10 scaled = 10 relative_error = 0.10 prob = set_size_exact_prob(set_size, scaled, relative_error=relative_error) true_prob = 0.38742 np.testing.assert_array_almost_equal(true_prob, prob, decimal=3) set_size = 1000 scaled = 10 relative_error = 0.10 prob = set_size_exact_prob(set_size, scaled, relative_error=relative_error) true_prob = 0.73182 np.testing.assert_array_almost_equal(true_prob, prob, decimal=3) sourmash-bio-sourmash-bc3c0f8/tests/test_hll.py000066400000000000000000000074101474157046300220220ustar00rootroot00000000000000import gzip from tempfile import NamedTemporaryFile from screed.fasta import fasta_iter import pytest from sourmash.hll import HLL import sourmash_tst_utils as utils K = 21 # size of kmer ERR_RATE = 0.01 N_UNIQUE = 3356 TRANSLATE = {"A": "T", "C": "G", "T": "A", "G": "C"} def test_hll_add_python(): # test python code to count unique kmers using HyperLogLog. # use the lower level add() method, which accepts anything, # and compare to an exact count using collections.Counter filename = utils.get_test_data("ecoli.genes.fna") hll = HLL(ERR_RATE, K) counter = set() with open(filename) as f: for n, record in enumerate(fasta_iter(f)): sequence = record["sequence"] seq_len = len(sequence) for n in range(0, seq_len + 1 - K): kmer = sequence[n : n + K] rc = "".join(TRANSLATE[c] for c in kmer[::-1]) hll.add(kmer) if rc in counter: kmer = rc counter.update([kmer]) n_unique = len(counter) assert n_unique == N_UNIQUE assert abs(1 - float(hll.cardinality()) / N_UNIQUE) < ERR_RATE def test_hll_consume_string(): # test rust code to count unique kmers using HyperLogLog, # using screed to feed each read to the counter. filename = utils.get_test_data("ecoli.genes.fna") hll = HLL(ERR_RATE, K) n = 0 with open(filename) as f: for n, record in enumerate(fasta_iter(f), 1): hll.add_sequence(record["sequence"]) assert abs(1 - float(len(hll)) / N_UNIQUE) < ERR_RATE def test_hll_similarity_containment(): N_UNIQUE_H1 = 500741 N_UNIQUE_H2 = 995845 SIMILARITY = 0.502783 CONTAINMENT_H1 = 1.0 CONTAINMENT_H2 = 0.502783 INTERSECTION = 500838 hll1 = HLL(ERR_RATE, K) hll2 = HLL(ERR_RATE, K) hllu = HLL(ERR_RATE, K) filename = utils.get_test_data("genome-s10.fa.gz") with gzip.GzipFile(filename) as f: for n, record in enumerate(fasta_iter(f)): sequence = record["sequence"] seq_len = len(sequence) for n in range(0, seq_len + 1 - K): kmer = sequence[n : n + K] hll1.add(kmer) hllu.add(kmer) filename = utils.get_test_data("genome-s10+s11.fa.gz") with gzip.GzipFile(filename) as f: for n, record in enumerate(fasta_iter(f)): sequence = record["sequence"] seq_len = len(sequence) for n in range(0, seq_len + 1 - K): kmer = sequence[n : n + K] hll2.add(kmer) hllu.add(kmer) assert abs(1 - float(hll1.cardinality()) / N_UNIQUE_H1) < ERR_RATE assert abs(1 - float(hll2.cardinality()) / N_UNIQUE_H2) < ERR_RATE assert abs(1 - float(hll1.similarity(hll2)) / SIMILARITY) < ERR_RATE assert abs(1 - float(hll1.containment(hll2)) / CONTAINMENT_H1) < ERR_RATE assert abs(1 - float(hll2.containment(hll1)) / CONTAINMENT_H2) < ERR_RATE assert abs(1 - float(hll1.intersection(hll2)) / INTERSECTION) < ERR_RATE """ hll1.merge(hll2) assert abs(1 - float(hllu.similarity(hll1))) < ERR_RATE assert abs(1 - float(hllu.containment(hll1))) < ERR_RATE assert abs(1 - float(hllu.containment(hll2))) < ERR_RATE assert abs(1 - float(hll1.intersection(hllu)) / N_UNIQUE_U) < ERR_RATE """ def test_hll_save_load(): filename = utils.get_test_data("ecoli.genes.fna") hll = HLL(ERR_RATE, K) n = 0 with open(filename) as f: for n, record in enumerate(fasta_iter(f), 1): hll.add_sequence(record["sequence"]) assert abs(1 - float(len(hll)) / N_UNIQUE) < ERR_RATE with NamedTemporaryFile() as f: hll.save(f.name) new_hll = HLL.load(f.name) assert len(hll) == len(new_hll) sourmash-bio-sourmash-bc3c0f8/tests/test_index.py000066400000000000000000002041131474157046300223510ustar00rootroot00000000000000""" Tests for Index classes and subclasses. """ import pytest import glob import os import zipfile import shutil import sourmash from sourmash import load_one_signature, SourmashSignature from sourmash.index import ( LinearIndex, ZipFileLinearIndex, make_jaccard_search_query, CounterGather, LazyLinearIndex, MultiIndex, StandaloneManifestIndex, ) from sourmash.signature import load_one_signature_from_json, save_signatures_to_json from sourmash.index.revindex import RevIndex from sourmash.sbt import SBT, GraphFactory from sourmash import sourmash_args from sourmash.search import JaccardSearch, SearchType from sourmash.picklist import SignaturePicklist, PickStyle from sourmash_tst_utils import SourmashCommandFailed from sourmash.manifest import CollectionManifest import sourmash_tst_utils as utils def test_simple_index(n_children): # test basic SBT functionality factory = GraphFactory(5, 100, 3) root = SBT(factory, d=n_children) leaf1_mh = sourmash.MinHash(0, 5, scaled=1) leaf1_mh.add_sequence("AAAAA") leaf1_mh.add_sequence("AAAAT") leaf1_mh.add_sequence("AAAAC") leaf1_sig = SourmashSignature(leaf1_mh) root.insert(leaf1_sig) leaf2_mh = sourmash.MinHash(0, 5, scaled=1) leaf2_mh.add_sequence("AAAAA") leaf2_mh.add_sequence("AAAAT") leaf2_mh.add_sequence("AAAAG") leaf2_sig = SourmashSignature(leaf2_mh) root.insert(leaf2_sig) leaf3_mh = sourmash.MinHash(0, 5, scaled=1) leaf3_mh.add_sequence("AAAAA") leaf3_mh.add_sequence("AAAAT") leaf3_mh.add_sequence("CAAAA") leaf3_sig = SourmashSignature(leaf3_mh) root.insert(leaf3_sig) leaf4_mh = sourmash.MinHash(0, 5, scaled=1) leaf4_mh.add_sequence("AAAAA") leaf4_mh.add_sequence("CAAAA") leaf4_mh.add_sequence("GAAAA") leaf4_sig = SourmashSignature(leaf4_mh) root.insert(leaf4_sig) leaf5_mh = sourmash.MinHash(0, 5, scaled=1) leaf5_mh.add_sequence("AAAAA") leaf5_mh.add_sequence("AAAAT") leaf5_mh.add_sequence("GAAAA") leaf5_sig = SourmashSignature(leaf5_mh) root.insert(leaf5_sig) linear = LinearIndex() linear.insert(leaf1_sig) linear.insert(leaf2_sig) linear.insert(leaf3_sig) linear.insert(leaf4_sig) linear.insert(leaf5_sig) search_fn = make_jaccard_search_query(do_containment=True) kmers = ["AAAAA", "AAAAT", "AAAAG", "CAAAA", "GAAAA"] for kmer in kmers: search_mh = sourmash.MinHash(0, 5, scaled=1) search_mh.add_sequence(kmer) search_sig = sourmash.SourmashSignature(search_mh) linear_found = linear.find(search_fn, search_sig) linear_found = set(linear_found) tree_found = set(root.find(search_fn, search_sig)) assert tree_found assert tree_found == set(linear_found) def test_linear_index_prefetch_empty(): # check that an exception is raised upon for an empty LinearIndex sig2 = utils.get_test_data("2.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) lidx = LinearIndex() # since this is a generator, we need to actually ask for a value to # get exception raised. g = lidx.prefetch(ss2, threshold_bp=0) with pytest.raises(ValueError) as e: next(g) assert "no signatures to search" in str(e.value) def test_linear_index_prefetch_lazy(): # make sure that prefetch doesn't touch values 'til requested. class FakeSignature: @property def minhash(self): raise Exception("don't touch me!") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) fake = FakeSignature() lidx = LinearIndex() lidx.insert(ss47) lidx.insert(ss63) lidx.insert(fake) g = lidx.prefetch(ss47, threshold_bp=0) # first value: sr = next(g) assert sr.signature == ss47 # second value: sr = next(g) assert sr.signature == ss63 # third value: raises exception! with pytest.raises(Exception) as e: next(g) assert "don't touch me!" in str(e.value) def test_linear_index_search_subj_has_abundance(): # check that search signatures in the index are flattened appropriately. queryfile = utils.get_test_data("47.fa.sig") subjfile = utils.get_test_data("track_abund/47.fa.sig") qs = load_one_signature_from_json(queryfile) ss = load_one_signature_from_json(subjfile) linear = LinearIndex() linear.insert(ss) results = list(linear.search(qs, threshold=0)) assert len(results) == 1 # note: search returns _original_ signature, not flattened assert results[0].signature == ss def test_linear_index_gather_subj_has_abundance(): # check that target signatures in the index are flattened appropriately. queryfile = utils.get_test_data("47.fa.sig") subjfile = utils.get_test_data("track_abund/47.fa.sig") qs = load_one_signature_from_json(queryfile) ss = load_one_signature_from_json(subjfile) linear = LinearIndex() linear.insert(ss) result = linear.best_containment(qs, threshold=0) assert result # note: gather returns _original_ signature, not flattened assert result.signature == ss def test_index_search_subj_scaled_is_lower(): # check that subject sketches are appropriately downsampled for scaled # sketches. sigfile = utils.get_test_data( "scaled100/GCF_000005845.2_ASM584v2_genomic.fna.gz.sig.gz" ) ss = load_one_signature_from_json(sigfile) # double check :) assert ss.minhash.scaled == 100 # build a new query that has a scaled of 1000 qs = SourmashSignature(ss.minhash.downsample(scaled=1000)) # create Index to search linear = LinearIndex() linear.insert(ss) # search! results = list(linear.search(qs, threshold=0)) assert len(results) == 1 # original signature (not downsampled) is returned assert results[0].signature == ss def test_index_search_subj_num_is_lower(): # check that subject sketches are appropriately downsampled for num # sketches sigfile = utils.get_test_data("num/47.fa.sig") ss = load_one_signature_from_json(sigfile, ksize=31) # double check :) assert ss.minhash.num == 500 # build a new query that has a num of 250 qs = SourmashSignature(ss.minhash.downsample(num=250)) # create Index to search linear = LinearIndex() linear.insert(ss) # search! results = list(linear.search(qs, threshold=0)) assert len(results) == 1 # original signature (not downsampled) is returned assert results[0].signature == ss def test_index_search_query_num_is_lower(): # check that query sketches are appropriately downsampled for num. sigfile = utils.get_test_data("num/47.fa.sig") qs = load_one_signature_from_json(sigfile, ksize=31) # double check :) assert qs.minhash.num == 500 # build a new subject that has a num of 250 ss = SourmashSignature(qs.minhash.downsample(num=250)) # create Index to search linear = LinearIndex() linear.insert(ss) # search! results = list(linear.search(qs, threshold=0)) assert len(results) == 1 assert results[0].signature == ss def test_linear_index_search_abund(): # test Index.search_abund sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) lidx = LinearIndex() lidx.insert(ss47) lidx.insert(ss63) results = list(lidx.search_abund(ss47, threshold=0)) assert len(results) == 2 assert results[0].signature == ss47 assert results[1].signature == ss63 def test_linear_index_search_abund_downsample_query(): # test Index.search_abund with query with higher scaled sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) # forcibly downsample ss47 for the purpose of this test :) ss47 = ss47.to_mutable() ss47.minhash = ss63.minhash.downsample(scaled=2000) assert ss63.minhash.scaled != ss47.minhash.scaled lidx = LinearIndex() lidx.insert(ss47) lidx.insert(ss63) results = list(lidx.search_abund(ss47, threshold=0)) assert len(results) == 2 assert results[0].signature == ss47 assert results[1].signature == ss63 def test_linear_index_search_abund_downsample_subj(): # test Index.search_abund with subj with higher scaled sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) # forcibly downsample ss63 for the purpose of this test :) ss63 = ss63.to_mutable() ss63.minhash = ss63.minhash.downsample(scaled=2000) assert ss63.minhash.scaled != ss47.minhash.scaled lidx = LinearIndex() lidx.insert(ss47) lidx.insert(ss63) results = list(lidx.search_abund(ss47, threshold=0)) assert len(results) == 2 assert results[0].signature == ss47 assert results[1].signature == ss63 def test_linear_index_search_abund_requires_threshold(): # test that Index.search_abund requires a 'threshold' sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) lidx = LinearIndex() lidx.insert(ss47) lidx.insert(ss63) with pytest.raises(TypeError) as exc: list(lidx.search_abund(ss47, threshold=None)) assert "'search_abund' requires 'threshold'" in str(exc.value) def test_linear_index_search_abund_query_flat(): # test that Index.search_abund requires an abund query sig sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") ss47 = load_one_signature_from_json(sig47, ksize=31) ss63 = load_one_signature_from_json(sig63) lidx = LinearIndex() lidx.insert(ss47) lidx.insert(ss63) with pytest.raises(TypeError) as exc: list(lidx.search_abund(ss47, threshold=0)) assert "'search_abund' requires query signature with abundance information" in str( exc.value ) def test_linear_index_search_abund_subj_flat(): # test Index.search_abund requires an abund subj sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) lidx = LinearIndex() lidx.insert(ss47) lidx.insert(ss63) with pytest.raises(TypeError) as exc: list(lidx.search_abund(ss47, threshold=0)) assert ( "'search_abund' requires subject signatures with abundance information" in str(exc.value) ) def test_linear_index_save(runtmp): # test save output from LinearIndex => JSON sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) linear = LinearIndex() linear.insert(ss2) linear.insert(ss47) linear.insert(ss63) filename = runtmp.output("foo") linear.save(filename) si = set(sourmash.load_file_as_signatures(filename)) x = {ss2, ss47, ss63} print(len(si)) print(len(x)) print("si: ", si) print("x: ", x) assert si == x, si def test_linear_index_load(runtmp): # test .load class method of LinearIndex sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) filename = runtmp.output("foo") with open(filename, "w") as fp: save_signatures_to_json([ss2, ss47, ss63], fp) linear = LinearIndex.load(filename) x = {ss2, ss47, ss63} assert set(linear.signatures()) == x, linear.signatures assert linear.location == filename def test_linear_index_save_load(runtmp): # LinearIndex save/load round trip sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) linear = LinearIndex() linear.insert(ss2) linear.insert(ss47) linear.insert(ss63) filename = runtmp.output("foo") linear.save(filename) linear2 = LinearIndex.load(filename) # now, search for sig2 sr = linear2.search(ss2, threshold=1.0) print([s[1].name for s in sr]) assert len(sr) == 1 assert sr[0][1] == ss2 def test_linear_gather_threshold_1(): # test gather() method, in some detail sig2 = load_one_signature(utils.get_test_data("2.fa.sig"), ksize=31) sig47 = load_one_signature(utils.get_test_data("47.fa.sig"), ksize=31) sig63 = load_one_signature(utils.get_test_data("63.fa.sig"), ksize=31) linear = LinearIndex() linear.insert(sig47) linear.insert(sig63) linear.insert(sig2) # now construct query signatures with specific numbers of hashes -- # note, these signatures all have scaled=1000. mins = list(sorted(sig2.minhash.hashes.keys())) new_mh = sig2.minhash.copy_and_clear() # query with empty hashes assert not new_mh with pytest.raises(ValueError): linear.best_containment(SourmashSignature(new_mh)) # add one hash new_mh.add_hash(mins.pop()) assert len(new_mh) == 1 result = linear.best_containment(SourmashSignature(new_mh)) assert result # it's a namedtuple, so we can unpack like a tuple. containment, match_sig, name = result assert containment == 1.0 assert match_sig == sig2 assert name is None # check with a threshold -> should be no results. with pytest.raises(ValueError): linear.best_containment(SourmashSignature(new_mh), threshold_bp=5000) # add three more hashes => length of 4 new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) assert len(new_mh) == 4 result = linear.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig == sig2 assert name is None # check with a too-high threshold -> should be no results. with pytest.raises(ValueError): linear.best_containment(SourmashSignature(new_mh), threshold_bp=5000) def test_linear_gather_threshold_5(): # test gather() method above threshold sig2 = load_one_signature(utils.get_test_data("2.fa.sig"), ksize=31) sig47 = load_one_signature(utils.get_test_data("47.fa.sig"), ksize=31) sig63 = load_one_signature(utils.get_test_data("63.fa.sig"), ksize=31) linear = LinearIndex(filename="foo") linear.insert(sig47) linear.insert(sig63) linear.insert(sig2) # now construct query signatures with specific numbers of hashes -- # note, these signatures all have scaled=1000. mins = list(sorted(sig2.minhash.hashes.keys())) new_mh = sig2.minhash.copy_and_clear() # add five hashes for i in range(5): new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) # should get a result with no threshold (any match at all is returned) result = linear.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig == sig2 assert name == "foo" # now, check with a threshold_bp that should be meet-able. result = linear.best_containment(SourmashSignature(new_mh), threshold_bp=5000) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig == sig2 assert name == "foo" def test_linear_index_multik_select(): # test that LinearIndx can load multiple (three) ksizes, 21/31/51 sig2 = utils.get_test_data("2.fa.sig") siglist = sourmash.load_file_as_signatures(sig2) linear = LinearIndex() for ss in siglist: linear.insert(ss) # select most specifically linear2 = linear.select(ksize=31, moltype="DNA") assert len(linear2) == 1 # all are DNA: linear2 = linear.select(moltype="DNA") assert len(linear2) == 3 def test_linear_index_moltype_select(): # this loads two ksizes(21, 10), and two moltypes (DNA and protein) filename = utils.get_test_data("genome-s10+s11.sig") siglist = sourmash.load_file_as_signatures(filename) linear = LinearIndex() for ss in siglist: linear.insert(ss) # select most specific DNA linear2 = linear.select(ksize=30, moltype="DNA") assert len(linear2) == 1 # select most specific protein linear2 = linear.select(ksize=10, moltype="protein") assert len(linear2) == 1 # can leave off ksize, selects all ksizes linear2 = linear.select(moltype="DNA") assert len(linear2) == 2 # can leave off ksize, selects all ksizes linear2 = linear.select(moltype="protein") assert len(linear2) == 2 # select something impossible linear2 = linear.select(ksize=4) assert len(linear2) == 0 def test_linear_index_picklist_select(): # test LinearIndex.select with a picklist # this loads three ksizes, 21/31/51 sig2 = utils.get_test_data("2.fa.sig") siglist = sourmash.load_file_as_signatures(sig2) linear = LinearIndex() for ss in siglist: linear.insert(ss) # construct a picklist... picklist = SignaturePicklist("md5prefix8") picklist.init(["f3a90d4e"]) # select on picklist linear2 = linear.select(picklist=picklist) assert len(linear2) == 1 ss = list(linear2.signatures())[0] assert ss.minhash.ksize == 31 assert ss.md5sum().startswith("f3a90d4e55") def test_linear_index_picklist_select_exclude(): # test select with a picklist, but exclude # this loads three ksizes, 21/31/51 sig2 = utils.get_test_data("2.fa.sig") siglist = sourmash.load_file_as_signatures(sig2) linear = LinearIndex() for ss in siglist: linear.insert(ss) # construct a picklist... picklist = SignaturePicklist("md5prefix8", pickstyle=PickStyle.EXCLUDE) picklist.init(["f3a90d4e"]) # select on picklist linear2 = linear.select(picklist=picklist) assert len(linear2) == 2 md5s = set() ksizes = set() for ss in list(linear2.signatures()): md5s.add(ss.md5sum()) ksizes.add(ss.minhash.ksize) assert md5s == set( ["f372e47893edd349e5956f8b0d8dcbf7", "43f3b48e59443092850964d355a20ac0"] ) assert ksizes == set([21, 51]) def test_index_same_md5sum_fsstorage(runtmp): # check SBT directory 'save' with two signatures that have identical md5 c = runtmp testdata1 = utils.get_test_data("img/2706795855.sig") testdata2 = utils.get_test_data("img/638277004.sig") c.run_sourmash("index", "-k", "21", "zzz.sbt.json", testdata1, testdata2) assert c.last_result.status == 0 outfile = c.output("zzz.sbt.json") assert os.path.exists(outfile) storage = c.output(".sbt.zzz") assert len(glob.glob(storage + "/*")) == 4 def test_index_same_md5sum_sbt_zipstorage(runtmp): # check SBT zipfile 'save' with two signatures w/identical md5 c = runtmp testdata1 = utils.get_test_data("img/2706795855.sig") testdata2 = utils.get_test_data("img/638277004.sig") c.run_sourmash("index", "-k", "21", "zzz.sbt.zip", testdata1, testdata2) assert c.last_result.status == 0 outfile = c.output("zzz.sbt.zip") assert os.path.exists(outfile) zout = zipfile.ZipFile(outfile, mode="r") # should have 3 files, 1 internal and two sigs. We check for 4 because the # directory also shows in namelist() assert len([f for f in zout.namelist() if f.startswith(".sbt.zzz/")]) == 5 def test_zipfile_does_not_exist(runtmp): with pytest.raises(SourmashCommandFailed) as exc: runtmp.sourmash("sig", "describe", "no-exist.zip") # old behavior, pre PR #1777 assert "FileNotFoundError: SOURMASH-MANIFEST.csv" not in str(exc) assert not os.path.exists(runtmp.output("no-exist.zip")) # correct behavior assert "ERROR: Error while reading signatures from 'no-exist.zip'." in str(exc) def test_zipfile_protein_command_search(runtmp): # test command-line search/gather of zipfile with protein sigs c = runtmp sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/protein.zip") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out) assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out def test_zipfile_hp_command_search(runtmp): # test command-line search/gather of zipfile with hp sigs c = runtmp sigfile1 = utils.get_test_data( "prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/hp.zip") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out, "--threshold", "0.0") assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out def test_zipfile_dayhoff_command_search(runtmp): # test command-line search/gather of zipfile with dayhoff sigs c = runtmp sigfile1 = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/dayhoff.zip") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out, "--threshold", "0.0") assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out def test_zipfile_protein_command_search_combined(runtmp): # test command-line search/gather of combined zipfile with protein sigs c = runtmp sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/all.zip") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out) assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out def test_zipfile_hp_command_search_combined(runtmp): # test command-line search/gather of combined zipfile with hp sigs c = runtmp sigfile1 = utils.get_test_data( "prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/all.zip") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out, "--threshold", "0.0") assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out def test_zipfile_dayhoff_command_search_combined(runtmp): # test command-line search/gather of combined zipfile with dayhoff sigs c = runtmp sigfile1 = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/all.zip") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out, "--threshold", "0.0") assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out def test_zipfile_dayhoff_command_search_protein(runtmp): # test command-line search/gather of protein sigs in zipfile c = runtmp # with dayhoff query sigfile1 = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/protein.zip") with pytest.raises(SourmashCommandFailed): c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") print(c.last_result.out) print(c.last_result.err) assert "no compatible signatures found in " in c.last_result.err def test_zipfile_API_signatures(use_manifest): # return all of the .sig and .sig.gz files in all.zip zipfile_db = utils.get_test_data("prot/all.zip") zipidx = ZipFileLinearIndex.load(zipfile_db, use_manifest=use_manifest) siglist = list(zipidx.signatures()) if use_manifest: assert len(siglist) == 8 assert len(zipidx) == 8 else: assert len(siglist) == 7 assert len(zipidx) == 7 def test_zipfile_bool(): # make sure that zipfile __bool__ doesn't traverse all the signatures # by relying on __len__! # create fake class that overrides everything useful except for bool - class FakeZipFileLinearIndex(ZipFileLinearIndex): def __init__(self): pass def signatures(self): yield "a" raise Exception("don't touch me!") def __len__(self): raise Exception("don't call len!") # 'bool' should not touch __len__ or a second signature zf = FakeZipFileLinearIndex() assert bool(zf) # __len__ should raise an exception with pytest.raises(Exception) as exc: len(zf) assert "don't call len!" in str(exc.value) def test_zipfile_API_signatures_traverse_yield_all(use_manifest): # include dna-sig.noext, but not build.sh (cannot be loaded as signature) zipfile_db = utils.get_test_data("prot/all.zip") zipidx = ZipFileLinearIndex.load( zipfile_db, traverse_yield_all=True, use_manifest=use_manifest ) siglist = list(zipidx.signatures()) assert len(siglist) == 8 assert len(zipidx) == 8 # confirm that there are 12 files in there total, incl build.sh and dirs allfiles = zipidx.storage._filenames() print(allfiles) assert len(allfiles) == 13 def test_zipfile_API_signatures_traverse_yield_all_select(use_manifest): # include dna-sig.noext zipfile_db = utils.get_test_data("prot/all.zip") zipidx = ZipFileLinearIndex.load( zipfile_db, traverse_yield_all=True, use_manifest=use_manifest ) zipidx = zipidx.select(moltype="DNA") siglist = list(zipidx.signatures()) assert len(siglist) == 2 assert len(zipidx) == 2 def test_zipfile_API_signatures_traverse_yield_all_manifest(): # check that manifest len is correct zipfile_db = utils.get_test_data("prot/all.zip") zipidx = ZipFileLinearIndex.load( zipfile_db, traverse_yield_all=True, use_manifest=True ) assert len(zipidx) == 8, len(zipidx) assert len(zipidx.manifest) == 8, len(zipidx.manifest) zipidx = zipidx.select(moltype="DNA") siglist = list(zipidx.signatures()) assert len(siglist) == 2 assert len(zipidx) == 2 assert len(zipidx.manifest) == 2 def test_zipfile_API_signatures_select(use_manifest): # include dna-sig.noext zipfile_db = utils.get_test_data("prot/all.zip") zipidx = ZipFileLinearIndex.load(zipfile_db, use_manifest=use_manifest) ziplist_pre = LinearIndex(zipidx.signatures()) ziplist_pre = ziplist_pre.select(moltype="DNA") zipidx = zipidx.select(moltype="DNA") siglist = list(zipidx.signatures()) if use_manifest: assert len(siglist) == 2 assert len(zipidx) == 2 assert len(ziplist_pre) == 2 else: assert len(siglist) == 1 assert len(zipidx) == 1 assert len(ziplist_pre) == 1 def test_zipfile_API_signatures_select_abund_false(use_manifest): # check for abund=False (all signatures match b/c can convert) zipfile_db = utils.get_test_data("track_abund/track_abund.zip") zipidx = ZipFileLinearIndex.load(zipfile_db, use_manifest=use_manifest) ziplist_pre = LinearIndex(zipidx.signatures()) ziplist_pre = ziplist_pre.select(abund=False) zipidx = zipidx.select(abund=False) siglist = list(zipidx.signatures()) assert len(siglist) == 2 assert len(zipidx) == 2 assert len(ziplist_pre) == 2 def test_zipfile_API_signatures_select_abund_true(use_manifest): # find all abund=True (all signatures match, b/c abund) zipfile_db = utils.get_test_data("track_abund/track_abund.zip") zipidx = ZipFileLinearIndex.load(zipfile_db, use_manifest=use_manifest) ziplist_pre = LinearIndex(zipidx.signatures()) ziplist_pre = ziplist_pre.select(abund=True) zipidx = zipidx.select(abund=True) siglist = list(zipidx.signatures()) assert len(siglist) == 2 assert len(zipidx) == 2 assert len(ziplist_pre) == 2 def test_zipfile_API_signatures_select_abund_none(use_manifest): # find all abund=None (all signatures match, b/c no selection criteria) zipfile_db = utils.get_test_data("track_abund/track_abund.zip") zipidx = ZipFileLinearIndex.load(zipfile_db, use_manifest=use_manifest) ziplist_pre = LinearIndex(zipidx.signatures()) ziplist_pre = ziplist_pre.select(abund=None) zipidx = zipidx.select(abund=None) siglist = list(zipidx.signatures()) assert len(siglist) == 2 assert len(zipidx) == 2 assert len(ziplist_pre) == 2 def test_zipfile_API_signatures_select_twice(use_manifest): # include dna-sig.noext zipfile_db = utils.get_test_data("prot/all.zip") zipidx = ZipFileLinearIndex.load(zipfile_db, use_manifest=use_manifest) ziplist_pre = LinearIndex(zipidx.signatures()) ziplist_pre = ziplist_pre.select(moltype="DNA") ziplist_pre = ziplist_pre.select(ksize=31) zipidx = zipidx.select(moltype="DNA") zipidx = zipidx.select(ksize=31) siglist = list(zipidx.signatures()) if use_manifest: assert len(siglist) == 2 assert len(zipidx) == 2 assert len(ziplist_pre) == 2 else: assert len(siglist) == 1 assert len(zipidx) == 1 assert len(ziplist_pre) == 1 def test_zipfile_API_save(): # ZipFileLinearIndex.save is not implemented. zipfile_db = utils.get_test_data("prot/all.zip") zipidx = ZipFileLinearIndex.load(zipfile_db) with pytest.raises(NotImplementedError): zipidx.save("xxx") def test_zipfile_API_insert(): # ZipFileLinearIndex.insert is not implemented. zipfile_db = utils.get_test_data("prot/all.zip") zipidx = ZipFileLinearIndex.load(zipfile_db) with pytest.raises(NotImplementedError): # at some point probably want to change this to a real signature :) zipidx.insert(None) def test_zipfile_API_location(use_manifest): # test ZipFileLinearIndex.location property zipfile_db = utils.get_test_data("prot/all.zip") zipidx = ZipFileLinearIndex.load(zipfile_db, use_manifest=use_manifest) assert zipidx.location == zipfile_db def test_zipfile_load_file_as_signatures(use_manifest): # make sure that ZipFileLinearIndex.signatures works, and is generator from types import GeneratorType zipfile_db = utils.get_test_data("prot/all.zip") sigs = sourmash_args.load_file_as_signatures(zipfile_db, _use_manifest=use_manifest) # it's fine if this needs to change, but for now I want to make # sure that this is a generator. assert isinstance(sigs, GeneratorType) sigs = list(sigs) if use_manifest: assert len(sigs) == 8 else: assert len(sigs) == 7 def test_zipfile_load_file_as_signatures_traverse_yield_all(use_manifest): # test with --force, which loads all files from types import GeneratorType zipfile_db = utils.get_test_data("prot/all.zip") sigs = sourmash_args.load_file_as_signatures( zipfile_db, yield_all_files=True, _use_manifest=use_manifest ) # it's fine if this needs to change, but for now I want to make # sure that this is a generator. assert isinstance(sigs, GeneratorType) sigs = list(sigs) assert len(sigs) == 8 def test_zipfile_load_database_fail_if_not_zip(runtmp): # fail _load_database if not .zip c = runtmp zipfile_db = utils.get_test_data("prot/all.zip") badname = c.output("xyz.nada") shutil.copyfile(zipfile_db, badname) with pytest.raises(ValueError) as exc: sourmash_args.load_file_as_signatures(badname) assert "Error while reading signatures from" in str(exc.value) def test_multi_index_search(): # test MultiIndex.search sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) lidx1 = LinearIndex.load(sig2) lidx2 = LinearIndex.load(sig47) lidx3 = LinearIndex.load(sig63) # create MultiIndex with source location override lidx = MultiIndex.load([lidx1, lidx2, lidx3], ["A", None, "C"], None) lidx = lidx.select(ksize=31) # now, search for sig2 sr = lidx.search(ss2, threshold=1.0) print([s[1].name for s in sr]) assert len(sr) == 1 assert sr[0][1] == ss2 assert sr[0][2] == "A" # source override # search for sig47 with lower threshold; search order not guaranteed. sr = lidx.search(ss47, threshold=0.1) print([s[1].name for s in sr]) assert len(sr) == 2 sr.sort(key=lambda x: -x[0]) assert sr[0][1] == ss47 assert sr[0][2] == sig47 # source was set to None, so no override assert sr[1][1] == ss63 assert sr[1][2] == "C" # source override # search for sig63 with lower threshold; search order not guaranteed. sr = lidx.search(ss63, threshold=0.1) print([s[1].name for s in sr]) assert len(sr) == 2 sr.sort(key=lambda x: -x[0]) assert sr[0][1] == ss63 assert sr[0][2] == "C" # source override assert sr[1][1] == ss47 assert sr[1][2] == sig47 # source was set to None, so no override # search for sig63 with high threshold => 1 match sr = lidx.search(ss63, threshold=0.8) print([s[1].name for s in sr]) assert len(sr) == 1 sr.sort(key=lambda x: -x[0]) assert sr[0][1] == ss63 assert sr[0][2] == "C" # source override def test_multi_index_gather(): # test MultiIndex.best_containment sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47) load_one_signature_from_json(sig63) lidx1 = LinearIndex.load(sig2) lidx2 = LinearIndex.load(sig47) lidx3 = LinearIndex.load(sig63) # create MultiIndex with source location override lidx = MultiIndex.load([lidx1, lidx2, lidx3], ["A", None, "C"], None) lidx = lidx.select(ksize=31) match = lidx.best_containment(ss2) assert match assert match.score == 1.0 assert match.location == "A" match = lidx.best_containment(ss47) assert match assert match.score == 1.0 assert match.signature == ss47 assert match.location == sig47 # no source override def test_multi_index_signatures(): # test MultiIndex.signatures sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) lidx1 = LinearIndex.load(sig2) lidx2 = LinearIndex.load(sig47) lidx3 = LinearIndex.load(sig63) # create MultiIndex with source location override lidx = MultiIndex.load([lidx1, lidx2, lidx3], ["A", None, "C"], None) lidx = lidx.select(ksize=31) siglist = list(lidx.signatures()) assert len(siglist) == 3 assert ss2 in siglist assert ss47 in siglist assert ss63 in siglist def test_multi_index_create(): # test MultiIndex constructor mi = MultiIndex(None, None, prepend_location=False) assert len(mi) == 0 def test_multi_index_create_prepend(): # test MultiIndex constructor - location must be specified if # 'prepend_location is True with pytest.raises(ValueError): MultiIndex(None, None, prepend_location=True) def test_multi_index_load_from_directory(): # test MultiIndex loading from a directory. The full paths to the # signature files should be available via 'signatures_with_location()' dirname = utils.get_test_data("prot/protein") mi = MultiIndex.load_from_directory(dirname, force=False) assert mi.location == dirname sigs = list(mi.signatures()) assert len(sigs) == 2 # check to make sure that full paths to expected sig files are returned locs = [x[1] for x in mi.signatures_with_location()] endings = ( "GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", "GCA_001593935.1_ASM159393v1_protein.faa.gz.sig", ) for loc in locs: found = False for end in endings: if loc.endswith(end): found = True assert found, f"could not find full filename in locations for {end}" # also check internal locations and parent value -- assert mi.parent.endswith("prot/protein") ilocs = [x[1] for x in mi._signatures_with_internal()] assert endings[0] in ilocs, ilocs assert endings[1] in ilocs, ilocs def test_multi_index_load_from_directory_2(): # only load .sig files, currently; not the databases under that directory. dirname = utils.get_test_data("prot") mi = MultiIndex.load_from_directory(dirname, force=False) sigs = list(mi.signatures()) assert len(sigs) == 7 def test_multi_index_load_from_directory_3_simple_bad_file(runtmp): # check that force=False fails properly when confronted with non-JSON # files. with open(runtmp.output("badsig.sig"), "w") as fp: fp.write("bad content.") with pytest.raises(ValueError): MultiIndex.load_from_directory(runtmp.location, force=False) def test_multi_index_load_from_directory_3(runtmp): # check that force=False fails properly when confronted with non-JSON # files that are legit sourmash files... c = runtmp dirname = utils.get_test_data("prot") count = 0 for root, dirs, files in os.walk(dirname): for name in files: print(f"at {name}") fullname = os.path.join(root, name) copyto = c.output(f"file{count}.sig") shutil.copyfile(fullname, copyto) count += 1 with pytest.raises(ValueError): MultiIndex.load_from_directory(c.location, force=False) def test_multi_index_load_from_directory_3_yield_all_true(runtmp): # check that force works ok on a directory w/force=True # Note here that only .sig/.sig.gz files are loaded. c = runtmp dirname = utils.get_test_data("prot") count = 0 for root, dirs, files in os.walk(dirname): for name in files: print(f"at {name}") fullname = os.path.join(root, name) copyto = c.output(f"file{count}.something") shutil.copyfile(fullname, copyto) count += 1 mi = MultiIndex.load_from_directory(c.location, force=True) sigs = list(mi.signatures()) assert len(sigs) == 8 def test_multi_index_load_from_directory_3_yield_all_true_subdir(runtmp): # check that force works ok on subdirectories. # Note here that only .sig/.sig.gz files are loaded. c = runtmp dirname = utils.get_test_data("prot") target_dir = c.output("some_subdir") os.mkdir(target_dir) count = 0 for root, dirs, files in os.walk(dirname): for name in files: print(f"at {name}") fullname = os.path.join(root, name) copyto = os.path.join(target_dir, f"file{count}.something") shutil.copyfile(fullname, copyto) count += 1 mi = MultiIndex.load_from_directory(c.location, force=True) locations = set([row["internal_location"] for row in mi.manifest.rows]) print(locations) sigs = list(mi.signatures()) assert len(sigs) == 8 def test_multi_index_load_from_directory_3_sig_gz(runtmp): # check that we find .sig.gz files, too c = runtmp dirname = utils.get_test_data("prot") count = 0 for root, dirs, files in os.walk(dirname): for name in files: if not name.endswith(".sig"): # skip non .sig things continue print(f"at {name}") fullname = os.path.join(root, name) copyto = c.output(f"file{count}.sig.gz") shutil.copyfile(fullname, copyto) count += 1 mi = MultiIndex.load_from_directory(c.location, force=False) assert mi.location == c.location sigs = list(mi.signatures()) assert len(sigs) == 6 def test_multi_index_load_from_directory_3_check_traverse_fn(runtmp): # test the actual traverse function... eventually this test can be # removed, probably, as we consolidate functionality and test MultiIndex # better. dirname = utils.get_test_data("prot") files = list(sourmash_args.traverse_find_sigs([dirname])) assert len(files) == 7, files files = list(sourmash_args.traverse_find_sigs([dirname], True)) assert len(files) == 20, files # if this fails, check for extra files! def test_multi_index_load_from_directory_no_exist(): # raise ValueError on files that don't exist in load_from_directory dirname = utils.get_test_data("does-not-exist") with pytest.raises(ValueError): MultiIndex.load_from_directory(dirname, force=True) def test_multi_index_load_from_file_path(): # test that MultiIndex.load_from_path works fine sig2 = utils.get_test_data("2.fa.sig") mi = MultiIndex.load_from_path(sig2) assert len(mi) == 3 assert mi.location == sig2 def test_multi_index_load_from_file_path_no_exist(): # test that load_from_path fails on non-existent files filename = utils.get_test_data("does-not-exist") with pytest.raises(ValueError): MultiIndex.load_from_directory(filename, force=True) def test_multi_index_load_from_pathlist_no_exist(): # test that load_from_pathlist fails on non-existent files dirname = utils.get_test_data("does-not-exist") with pytest.raises(ValueError): MultiIndex.load_from_pathlist(dirname) def test_multi_index_load_from_pathlist_1(runtmp): # test functionality of MultiIndex.load_from_pathlist with .sig files c = runtmp dirname = utils.get_test_data("prot") files = list(sourmash_args.traverse_find_sigs([dirname])) assert len(files) == 7, files file_list = c.output("filelist.txt") with open(file_list, "w") as fp: print("\n".join(files), file=fp) mi = MultiIndex.load_from_pathlist(file_list) sigs = list(mi.signatures()) assert len(sigs) == 7 assert mi.location == file_list def test_multi_index_load_from_pathlist_2(runtmp): # create a pathlist file with _all_ files under dir, and try to load it. # this will fail on one of several CSV or .sh files in there. # CTB note: if you create extra files under this directory, # it will fail :) c = runtmp dirname = utils.get_test_data("prot") files = list(sourmash_args.traverse_find_sigs([dirname], True)) assert len(files) == 20, files # check there aren't extra files in here! file_list = c.output("filelist.txt") with open(file_list, "w") as fp: print("\n".join(files), file=fp) with pytest.raises(ValueError) as exc: MultiIndex.load_from_pathlist(file_list) print(str(exc)) assert "Error while reading signatures from" in str(exc) def test_multi_index_load_from_pathlist_3_zipfile(runtmp): # can we load zipfiles in a pathlist? yes please. c = runtmp zipfile = utils.get_test_data("prot/all.zip") file_list = c.output("filelist.txt") with open(file_list, "w") as fp: print(zipfile, file=fp) mi = MultiIndex.load_from_pathlist(file_list) assert len(mi) == 8 ## ## test a slightly outre version of JaccardSearch - this is a test of the ## JaccardSearch 'collect' protocol, in particular... ## class JaccardSearchBestOnly_ButIgnore(JaccardSearch): "A class that ignores certain results, but still does all the pruning." def __init__(self, ignore_list): super().__init__(SearchType.JACCARD, threshold=0.1) self.ignore_list = ignore_list # a collect function that _ignores_ things in the ignore_list def collect(self, score, match): print("in collect; current threshold:", self.threshold) for q in self.ignore_list: print("ZZZ", match, match.similarity(q)) if match.similarity(q) == 1.0: print("yes, found.") return False # update threshold if not perfect match, which could help prune. self.threshold = score return True def test_linear_index_gather_ignore(): # do we properly ignore exact matches in 'search' for LinearIndex? sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47, ksize=31) ss63 = load_one_signature_from_json(sig63, ksize=31) # construct an index... lidx = LinearIndex([ss2, ss47, ss63]) # ...now search with something that should ignore sig47, the exact match. search_fn = JaccardSearchBestOnly_ButIgnore([ss47]) results = list(lidx.find(search_fn, ss47)) results = [sr.signature for sr in results] def is_found(ss, xx): for q in xx: print(ss, ss.similarity(q)) if ss.similarity(q) == 1.0: return True return False assert not is_found(ss47, results) assert not is_found(ss2, results) assert is_found(ss63, results) def test_lca_index_gather_ignore(): # do we properly ignore exact matches in gather on an LCA DB? from sourmash.lca import LCA_Database sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47, ksize=31) ss63 = load_one_signature_from_json(sig63, ksize=31) # construct an index... db = LCA_Database(ksize=31, scaled=1000) db.insert(ss2) db.insert(ss47) db.insert(ss63) # ...now search with something that should ignore sig47, the exact match. search_fn = JaccardSearchBestOnly_ButIgnore([ss47]) results = list(db.find(search_fn, ss47)) results = [sr.signature for sr in results] def is_found(ss, xx): for q in xx: print(ss, ss.similarity(q)) if ss.similarity(q) == 1.0: return True return False assert not is_found(ss47, results) assert not is_found(ss2, results) assert is_found(ss63, results) def test_sbt_index_gather_ignore(): # do we properly ignore exact matches in gather on an SBT? sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47, ksize=31) ss63 = load_one_signature_from_json(sig63, ksize=31) # construct an index... factory = GraphFactory(5, 100, 3) db = SBT(factory, d=2) db.insert(ss2) db.insert(ss47) db.insert(ss63) # ...now search with something that should ignore sig47, the exact match. print(f"\n** trying to ignore {ss47}") search_fn = JaccardSearchBestOnly_ButIgnore([ss47]) results = list(db.find(search_fn, ss47)) results = [sr.signature for sr in results] def is_found(ss, xx): for q in xx: print("is found?", ss, ss.similarity(q)) if ss.similarity(q) == 1.0: return True return False assert not is_found(ss47, results) assert not is_found(ss2, results) assert is_found(ss63, results) def test_counter_gather_test_consume(): # open-box testing of CounterGather.consume(...) # (see test_index_protocol.py for generic CounterGather tests.) query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear() match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") match_mh_2 = query_mh.copy_and_clear() match_mh_2.add_many(range(7, 15)) match_ss_2 = SourmashSignature(match_mh_2, name="match2") match_mh_3 = query_mh.copy_and_clear() match_mh_3.add_many(range(13, 17)) match_ss_3 = SourmashSignature(match_mh_3, name="match3") # load up the counter counter = CounterGather(query_ss) counter.add(match_ss_1, location="loc a") counter.add(match_ss_2, location="loc b") counter.add(match_ss_3, location="loc c") ### ok, dig into actual counts... import pprint pprint.pprint(counter.counter) pprint.pprint(list(counter.signatures())) pprint.pprint(counter.locations) assert set(counter.signatures()) == set([match_ss_1, match_ss_2, match_ss_3]) assert list(sorted(counter.locations.values())) == ["loc a", "loc b", "loc c"] pprint.pprint(counter.counter.most_common()) assert list(counter.counter.most_common()) == [ ("26d4943627b33c446f37be1f5baf8d46", 10), ("f51cedec90ea666e0ebc11aa274eca61", 8), ("f331f8279113d77e42ab8efca8f9cc17", 4), ] ## round 1 cur_query = query_ss.minhash.to_mutable() (sr, intersect_mh) = counter.peek(cur_query) assert sr.signature == match_ss_1 assert len(intersect_mh) == 10 assert cur_query == query_ss.minhash counter.consume(intersect_mh) assert set(counter.signatures()) == set([match_ss_1, match_ss_2, match_ss_3]) assert list(sorted(counter.locations.values())) == ["loc a", "loc b", "loc c"] pprint.pprint(counter.counter.most_common()) assert list(counter.counter.most_common()) == [ ("f51cedec90ea666e0ebc11aa274eca61", 5), ("f331f8279113d77e42ab8efca8f9cc17", 4), ] ### round 2 cur_query.remove_many(intersect_mh.hashes) (sr, intersect_mh) = counter.peek(cur_query) assert sr.signature == match_ss_2 assert len(intersect_mh) == 5 assert cur_query != query_ss.minhash counter.consume(intersect_mh) assert set(counter.signatures()) == set([match_ss_1, match_ss_2, match_ss_3]) assert list(sorted(counter.locations.values())) == ["loc a", "loc b", "loc c"] pprint.pprint(counter.counter.most_common()) assert list(counter.counter.most_common()) == [ ("f331f8279113d77e42ab8efca8f9cc17", 2) ] ## round 3 cur_query.remove_many(intersect_mh.hashes) (sr, intersect_mh) = counter.peek(cur_query) assert sr.signature == match_ss_3 assert len(intersect_mh) == 2 assert cur_query != query_ss.minhash counter.consume(intersect_mh) assert set(counter.signatures()) == set([match_ss_1, match_ss_2, match_ss_3]) assert list(sorted(counter.locations.values())) == ["loc a", "loc b", "loc c"] pprint.pprint(counter.counter.most_common()) assert list(counter.counter.most_common()) == [] ## round 4 - nothing left! cur_query.remove_many(intersect_mh.hashes) results = counter.peek(cur_query) assert not results counter.consume(intersect_mh) assert set(counter.signatures()) == set([match_ss_1, match_ss_2, match_ss_3]) assert list(sorted(counter.locations.values())) == ["loc a", "loc b", "loc c"] assert list(counter.counter.most_common()) == [] def test_counter_gather_identical_md5sum(): # open-box testing of CounterGather.consume(...) # check what happens with identical matches w/different names query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear() match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") # same as match_mh_1 match_mh_2 = query_mh.copy_and_clear() match_mh_2.add_many(range(0, 10)) match_ss_2 = SourmashSignature(match_mh_2, name="match2") # identical md5sum assert match_ss_1.md5sum() == match_ss_2.md5sum() # load up the counter counter = CounterGather(query_ss) counter.add(match_ss_1, location="loc a") counter.add(match_ss_2, location="loc b") assert len(counter.siglist) == 1 stored_match = list(counter.siglist.values()).pop() assert stored_match.name == "match2" # CTB note: this behavior may be changed freely, as the protocol # tests simply specify that _one_ of the identical matches is # returned. See test_counter_gather_multiple_identical_matches. def test_lazy_index_1(): # test some basic features of LazyLinearIndex sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) lidx = LinearIndex() lidx.insert(ss2) lidx.insert(ss47) lidx.insert(ss63) lazy = LazyLinearIndex(lidx) lazy2 = lazy.select(ksize=31) assert len(list(lazy2.signatures())) == 3 results = lazy2.search(ss2, threshold=1.0) assert len(results) == 1 assert results[0].signature == ss2 def test_lazy_index_2(): # test laziness by adding a signature that raises an exception when # touched. class FakeSignature: @property def minhash(self): raise Exception("don't touch me!") lidx = LinearIndex() lidx.insert(FakeSignature()) lazy = LazyLinearIndex(lidx) lazy2 = lazy.select(ksize=31) sig_iter = lazy2.signatures() with pytest.raises(Exception) as e: list(sig_iter) assert str(e.value) == "don't touch me!" def test_lazy_index_3(): # make sure that you can't do multiple _incompatible_ selects. class FakeSignature: @property def minhash(self): raise Exception("don't touch me!") lidx = LinearIndex() lidx.insert(FakeSignature()) lazy = LazyLinearIndex(lidx) lazy2 = lazy.select(ksize=31) with pytest.raises(ValueError) as e: lazy2.select(ksize=21) assert str(e.value) == "cannot select on two different values for ksize" def test_lazy_index_4_bool(): # test some basic features of LazyLinearIndex sig2 = utils.get_test_data("2.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) # test bool false/true lidx = LinearIndex() lazy = LazyLinearIndex(lidx) assert not lazy lidx.insert(ss2) assert lazy def test_lazy_index_wraps_multi_index_location(): # check that 'location' works fine when MultiIndex is wrapped by # LazyLinearIndex. sigdir = utils.get_test_data("prot/protein/") sigzip = utils.get_test_data("prot/protein.zip") siglca = utils.get_test_data("prot/protein.lca.json.gz") sigsbt = utils.get_test_data("prot/protein.sbt.zip") db_paths = (sigdir, sigzip, siglca, sigsbt) dbs = [sourmash.load_file_as_index(db_path) for db_path in db_paths] mi = MultiIndex.load(dbs, db_paths, None) lazy = LazyLinearIndex(mi) mi2 = mi.select(moltype="protein") lazy2 = lazy.select(moltype="protein") for ss_tup, ss_lazy_tup in zip( mi2.signatures_with_location(), lazy2.signatures_with_location() ): assert ss_tup == ss_lazy_tup def test_revindex_index_search(): # confirm that RevIndex works sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) lidx = RevIndex(template=ss2.minhash) lidx.insert(ss2) lidx.insert(ss47) lidx.insert(ss63) # now, search for sig2 sr = lidx.search(ss2, threshold=1.0) print([s[1].name for s in sr]) assert len(sr) == 1 assert sr[0][1] == ss2 # search for sig47 with lower threshold; search order not guaranteed. sr = lidx.search(ss47, threshold=0.1) print([s[1].name for s in sr]) assert len(sr) == 2 sr.sort(key=lambda x: -x[0]) assert sr[0][1] == ss47 assert sr[1][1] == ss63 # search for sig63 with lower threshold; search order not guaranteed. sr = lidx.search(ss63, threshold=0.1) print([s[1].name for s in sr]) assert len(sr) == 2 sr.sort(key=lambda x: -x[0]) assert sr[0][1] == ss63 assert sr[1][1] == ss47 # search for sig63 with high threshold => 1 match sr = lidx.search(ss63, threshold=0.8) print([s[1].name for s in sr]) assert len(sr) == 1 sr.sort(key=lambda x: -x[0]) assert sr[0][1] == ss63 def test_revindex_gather(): # check that RevIndex.best_containment works. sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) lidx = RevIndex(template=ss2.minhash) lidx.insert(ss2) lidx.insert(ss47) lidx.insert(ss63) match = lidx.best_containment(ss2) assert match assert match.score == 1.0 assert match.signature == ss2 match = lidx.best_containment(ss47) assert match assert match.score == 1.0 assert match.signature == ss47 def test_revindex_gather_ignore(): # check that RevIndex gather ignores things properly. sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = load_one_signature_from_json(sig2, ksize=31) ss47 = load_one_signature_from_json(sig47, ksize=31) ss63 = load_one_signature_from_json(sig63, ksize=31) # construct an index... lidx = RevIndex(template=ss2.minhash, signatures=[ss2, ss47, ss63]) # ...now search with something that should ignore sig47, the exact match. search_fn = JaccardSearchBestOnly_ButIgnore([ss47]) results = list(lidx.find(search_fn, ss47)) results = [ss.signature for ss in results] def is_found(ss, xx): for q in xx: print(ss, ss.similarity(q)) if ss.similarity(q) == 1.0: return True return False assert not is_found(ss47, results) assert not is_found(ss2, results) assert is_found(ss63, results) def test_standalone_manifest_signatures(runtmp): # build a StandaloneManifestIndex and test 'signatures' method. ## first, build a manifest in memory using MultiIndex sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss47 = load_one_signature_from_json(sig47) ss63 = load_one_signature_from_json(sig63) lidx1 = LinearIndex.load(sig47) lidx2 = LinearIndex.load(sig63) mi = MultiIndex.load([lidx1, lidx2], [sig47, sig63], "") ## got a manifest! ok, now test out StandaloneManifestIndex mm = StandaloneManifestIndex(mi.manifest, None) siglist = [ss for ss in mm.signatures()] assert len(siglist) == 2 assert ss47 in siglist assert ss63 in siglist def test_standalone_manifest_signatures_prefix(runtmp): # try out 'prefix' for StandaloneManifestIndex ## first, build a manifest in memory using MultiIndex sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") load_one_signature_from_json(sig47) load_one_signature_from_json(sig63) lidx1 = LinearIndex.load(sig47) lidx2 = LinearIndex.load(sig63) mi = MultiIndex.load([lidx1, lidx2], [sig47, sig63], "") # ok, now remove the abspath prefix from iloc for row in mi.manifest.rows: row["internal_location"] = os.path.basename(row["internal_location"]) ## this should succeed! mm = StandaloneManifestIndex(mi.manifest, None, prefix=utils.get_test_data("")) assert len(list(mm.signatures())) == 2 def test_standalone_manifest_signatures_prefix_fail(runtmp): # give StandaloneManifest the wrong prefix ## first, build a manifest in memory using MultiIndex sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") load_one_signature_from_json(sig47) load_one_signature_from_json(sig63) lidx1 = LinearIndex.load(sig47) lidx2 = LinearIndex.load(sig63) print("XXX", lidx1.location) mi = MultiIndex.load([lidx1, lidx2], [sig47, sig63], "") # remove prefix from manifest for row in mi.manifest.rows: row["internal_location"] = os.path.basename(row["internal_location"]) ## got a manifest! ok, now test out StandaloneManifestIndex mm = StandaloneManifestIndex(mi.manifest, None, prefix=runtmp.output("foo")) # should fail with pytest.raises(ValueError) as exc: list(mm.signatures()) assert "Error while reading signatures from " in str(exc) assert "foo/47.fa.sig'" in str(exc) def test_standalone_manifest_load_from_dir(runtmp): # test loading a mf with relative directory paths from test-data mf = utils.get_test_data("scaled/mf.csv") idx = sourmash.load_file_as_index(mf) siglist = list(idx.signatures()) assert len(siglist) == 15 assert idx # should be 'True' assert len(idx) == 15 with pytest.raises(NotImplementedError): idx.insert() with pytest.raises(NotImplementedError): idx.save("foo") assert idx.location == mf def test_standalone_manifest_lazy_load(runtmp): # check that it's actually doing lazy loading orig_sig47 = utils.get_test_data("47.fa.sig") sig47 = runtmp.output("47.fa.sig") # build an external manifest shutil.copyfile(orig_sig47, sig47) # this is an abspath to sig47 runtmp.sourmash("sig", "manifest", sig47, "-o", "mf.csv") # should work to get signatures: idx = StandaloneManifestIndex.load(runtmp.output("mf.csv")) siglist = list(idx.signatures()) assert len(siglist) == 1 # now remove! os.unlink(sig47) # can still access manifest... assert len(idx) == 1 # ...but we should get an error when we call signatures. with pytest.raises(ValueError): list(idx.signatures()) # but put it back, and all is forgiven. yay! shutil.copyfile(orig_sig47, sig47) x = list(idx.signatures()) assert len(x) == 1 def test_standalone_manifest_lazy_load_2_prefix(runtmp): # check that it's actually doing lazy loading; supply explicit prefix orig_sig47 = utils.get_test_data("47.fa.sig") sig47 = runtmp.output("47.fa.sig") # build an external manifest # note, here use a relative path to 47.fa.sig; the manifest will contain # just '47.fa.sig' as the location shutil.copyfile(orig_sig47, sig47) runtmp.sourmash("sig", "manifest", "47.fa.sig", "-o", "mf.csv") # should work to get signatures: idx = StandaloneManifestIndex.load( runtmp.output("mf.csv"), prefix=runtmp.output("") ) siglist = list(idx.signatures()) assert len(siglist) == 1 # now remove! os.unlink(sig47) # can still access manifest... assert len(idx) == 1 # ...but we should get an error when we call signatures. with pytest.raises(ValueError): list(idx.signatures()) # but put it back, and all is forgiven. yay! shutil.copyfile(orig_sig47, sig47) x = list(idx.signatures()) assert len(x) == 1 def test_standalone_manifest_search(runtmp): # test a straight up 'search' query_sig = utils.get_test_data("scaled/genome-s12.fa.gz.sig") mf = utils.get_test_data("scaled/mf.csv") runtmp.sourmash("search", query_sig, mf) out = runtmp.last_result.out print(out) assert "100.0% d84ef28f" in out def test_standalone_manifest_prefetch_lazy(runtmp): # check that prefetch is actually doing lazy loading on manifest index. orig_sig47 = utils.get_test_data("47.fa.sig") sig47 = runtmp.output("47.fa.sig") orig_sig2 = utils.get_test_data("2.fa.sig") sig2 = runtmp.output("2.fa.sig") orig_sig63 = utils.get_test_data("63.fa.sig") sig63 = runtmp.output("63.fa.sig") shutil.copyfile(orig_sig47, sig47) runtmp.sourmash("sig", "manifest", sig47, "-o", "mf1.csv") shutil.copyfile(orig_sig2, sig2) runtmp.sourmash("sig", "manifest", sig2, "-o", "mf2.csv") shutil.copyfile(orig_sig63, sig63) runtmp.sourmash("sig", "manifest", sig63, "-o", "mf3.csv") # combine the manifests, manually for now... mf1 = CollectionManifest.load_from_filename(runtmp.output("mf1.csv")) assert len(mf1) == 1 mf2 = CollectionManifest.load_from_filename(runtmp.output("mf2.csv")) assert len(mf2) == 3 mf3 = CollectionManifest.load_from_filename(runtmp.output("mf3.csv")) assert len(mf3) == 1 mf = mf1 + mf2 + mf3 assert len(mf) == 5 mf.write_to_filename(runtmp.output("mf.csv")) # ok! now, remove the last signature, 'sig63'. os.unlink(sig63) # ...but loading the manifest should still work. idx = StandaloneManifestIndex.load(runtmp.output("mf.csv")) # double check - third load will fail. this relies on load order :shrug:. sig_iter = iter(idx.signatures()) ss = next(sig_iter) print(ss) assert "47.fa" in ss.filename for i in range(3): ss = next(sig_iter) print(i, ss) assert "2.fa" in ss.filename with pytest.raises(ValueError) as exc: ss = next(sig_iter) assert "Error while reading signatures from" in str(exc) assert "63.fa.sig" in str(exc) # ok! now test prefetch... should get one match legit, to 47, # and then no matches to 2, and then error. ss47 = load_one_signature_from_json(sig47) idx = idx.select(ksize=31) g = idx.prefetch(ss47, threshold_bp=0) # first value: sr = next(g) assert sr.signature == ss47 # second value should raise error. with pytest.raises(ValueError) as exc: sr = next(g) assert "Error while reading signatures from" in str(exc) assert "63.fa.sig" in str(exc) sourmash-bio-sourmash-bc3c0f8/tests/test_index_protocol.py000066400000000000000000001153411474157046300242760ustar00rootroot00000000000000""" Tests for the 'Index' class and protocol. All Index classes should support this functionality. """ import pytest import glob import sourmash from sourmash import SourmashSignature from sourmash.index import ( LinearIndex, ZipFileLinearIndex, LazyLinearIndex, MultiIndex, StandaloneManifestIndex, IndexSearchResult, ) from sourmash.index import CounterGather from sourmash.index.sqlite_index import SqliteIndex from sourmash.index.revindex import RevIndex from sourmash.sbt import SBT, GraphFactory from sourmash.manifest import CollectionManifest, BaseCollectionManifest from sourmash.lca.lca_db import LCA_Database, load_single_database from sourmash.minhash import flatten_and_intersect_scaled, flatten_and_downsample_scaled import sourmash_tst_utils as utils def _load_three_sigs(): # utility function - load & return these three sigs. sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) ss47 = sourmash.load_one_signature(sig47) ss63 = sourmash.load_one_signature(sig63) return [ss2, ss47, ss63] def build_linear_index(runtmp): ss2, ss47, ss63 = _load_three_sigs() lidx = LinearIndex() lidx.insert(ss2) lidx.insert(ss47) lidx.insert(ss63) return lidx def build_lazy_linear_index(runtmp): lidx = build_linear_index(runtmp) return LazyLinearIndex(lidx) def build_sbt_index(runtmp): ss2, ss47, ss63 = _load_three_sigs() factory = GraphFactory(5, 100, 3) root = SBT(factory, d=2) root.insert(ss2) root.insert(ss47) root.insert(ss63) return root def build_sbt_index_save_load(runtmp): root = build_sbt_index(runtmp) out = runtmp.output("xyz.sbt.zip") root.save(out) return sourmash.load_file_as_index(out) def build_zipfile_index(runtmp): from sourmash.save_load import SaveSignatures_ZipFile location = runtmp.output("index.zip") with SaveSignatures_ZipFile(location) as save_sigs: for ss in _load_three_sigs(): save_sigs.add(ss) idx = ZipFileLinearIndex.load(location) return idx def build_multi_index(runtmp): siglist = _load_three_sigs() lidx = LinearIndex(siglist) mi = MultiIndex.load([lidx], [None], None) return mi def build_standalone_manifest_index(runtmp): sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) ss47 = sourmash.load_one_signature(sig47) ss63 = sourmash.load_one_signature(sig63) siglist = [(ss2, sig2), (ss47, sig47), (ss63, sig63)] rows = [] rows.extend((CollectionManifest.make_manifest_row(ss, loc) for ss, loc in siglist)) mf = CollectionManifest(rows) mf_filename = runtmp.output("mf.csv") mf.write_to_filename(mf_filename) idx = StandaloneManifestIndex.load(mf_filename) return idx def build_lca_index(runtmp): siglist = _load_three_sigs() db = LCA_Database(31, 1000, "DNA") for ss in siglist: db.insert(ss) return db def build_lca_index_save_load(runtmp): db = build_lca_index(runtmp) outfile = runtmp.output("db.lca.json") db.save(outfile) return sourmash.load_file_as_index(outfile) def build_sqlite_index(runtmp): filename = runtmp.output("idx.sqldb") db = SqliteIndex.create(filename) siglist = _load_three_sigs() for ss in siglist: db.insert(ss) return db def build_revindex(runtmp): ss2, ss47, ss63 = _load_three_sigs() lidx = RevIndex(template=ss2.minhash) lidx.insert(ss2) lidx.insert(ss47) lidx.insert(ss63) return lidx def build_lca_index_save_load_sql(runtmp): db = build_lca_index(runtmp) outfile = runtmp.output("db.lca.json") db.save(outfile, format="sql") x = load_single_database(outfile) db_load = x[0] return db_load # # create a fixture 'index_obj' that is parameterized by all of these # building functions. # @pytest.fixture( params=[ build_linear_index, build_lazy_linear_index, build_sbt_index, build_zipfile_index, build_multi_index, build_standalone_manifest_index, build_lca_index, build_sbt_index_save_load, build_lca_index_save_load, build_sqlite_index, build_lca_index_save_load_sql, # build_revindex, ] ) def index_obj(request, runtmp): build_fn = request.param # build on demand return build_fn(runtmp) ### ### generic Index tests go here ### def test_index_search_exact_match(index_obj): # search for an exact match ss2, ss47, ss63 = _load_three_sigs() sr = index_obj.search(ss2, threshold=1.0) print([s[1].name for s in sr]) assert len(sr) == 1 assert sr[0].signature.minhash == ss2.minhash assert sr[0].score == 1.0 def test_index_search_lower_threshold(index_obj): # search at a lower threshold/multiple results with ss47 ss2, ss47, ss63 = _load_three_sigs() sr = index_obj.search(ss47, threshold=0.1) print([s[1].name for s in sr]) assert len(sr) == 2 sr.sort(key=lambda x: -x[0]) assert sr[0].signature.minhash == ss47.minhash assert sr[0].score == 1.0 assert sr[1].signature.minhash == ss63.minhash assert round(sr[1].score, 2) == 0.32 def test_index_search_lower_threshold_2(index_obj): # search at a lower threshold/multiple results with ss63 ss2, ss47, ss63 = _load_three_sigs() sr = index_obj.search(ss63, threshold=0.1) print([s[1].name for s in sr]) assert len(sr) == 2 sr.sort(key=lambda x: -x[0]) assert sr[0].signature.minhash == ss63.minhash assert sr[0].score == 1.0 assert sr[1].signature.minhash == ss47.minhash assert round(sr[1].score, 2) == 0.32 def test_index_search_higher_threshold_2(index_obj): # search at a higher threshold/one match ss2, ss47, ss63 = _load_three_sigs() # search for sig63 with high threshold => 1 match sr = index_obj.search(ss63, threshold=0.8) print([s[1].name for s in sr]) assert len(sr) == 1 sr.sort(key=lambda x: -x[0]) assert sr[0].signature.minhash == ss63.minhash assert sr[0].score == 1.0 def test_index_search_containment(index_obj): # search for containment at a low threshold/multiple results with ss63 ss2, ss47, ss63 = _load_three_sigs() sr = index_obj.search(ss63, do_containment=True, threshold=0.1) print([s[1].name for s in sr]) assert len(sr) == 2 sr.sort(key=lambda x: -x[0]) assert sr[0].signature.minhash == ss63.minhash assert sr[0].score == 1.0 assert sr[1].signature.minhash == ss47.minhash assert round(sr[1].score, 2) == 0.48 def test_index_signatures(index_obj): # signatures works? siglist = list(index_obj.signatures()) ss2, ss47, ss63 = _load_three_sigs() assert len(siglist) == 3 # check md5sums, since 'in' doesn't always work md5s = set(ss.md5sum() for ss in siglist) assert ss2.md5sum() in md5s assert ss47.md5sum() in md5s assert ss63.md5sum() in md5s def test_index_signatures_with_location(index_obj): # signatures_with_location works? siglist = list(index_obj.signatures_with_location()) ss2, ss47, ss63 = _load_three_sigs() assert len(siglist) == 3 # check md5sums, since 'in' doesn't always work md5s = set((ss.md5sum() for ss, loc in siglist)) assert ss2.md5sum() in md5s assert ss47.md5sum() in md5s assert ss63.md5sum() in md5s def test_index_len(index_obj): # len works? assert len(index_obj) == 3 def test_index_bool(index_obj): # bool works? assert bool(index_obj) def test_index_location(index_obj): # location works? assert str(index_obj.location) def test_index_manifest(index_obj): # manifest is either None or a BaseCollectionManifest manifest = index_obj.manifest if manifest is not None: assert isinstance(manifest, BaseCollectionManifest) def test_index_select_basic(index_obj): # select does the basic thing ok idx = index_obj.select( ksize=31, moltype="DNA", abund=False, containment=True, scaled=1000, num=0, picklist=None, ) assert len(idx) == 3 siglist = list(idx.signatures()) assert len(siglist) == 3 # check md5sums, since 'in' doesn't always work md5s = set(ss.md5sum() for ss in siglist) ss2, ss47, ss63 = _load_three_sigs() assert ss2.md5sum() in md5s assert ss47.md5sum() in md5s assert ss63.md5sum() in md5s def test_index_select_fail(index_obj): # non-int ksize - str with pytest.raises(ValueError): index_obj.select(ksize="31") # non-int ksize - float with pytest.raises(ValueError): index_obj.select(ksize=31.1) # case sensitive moltype with pytest.raises(ValueError): index_obj.select(moltype="dna") # unknown moltype with pytest.raises(ValueError): index_obj.select(moltype="foo") # non-int scaled with pytest.raises(ValueError): index_obj.select(scaled=1000.1) # non-int num with pytest.raises(ValueError): index_obj.select(num=1000.1) # non-bool abund with pytest.raises(ValueError): index_obj.select(abund=1) # extra parameters with pytest.raises(ValueError): index_obj.select(plausible_extra_parameter=5) def test_index_select_nada(index_obj): # select works ok when nothing matches! # CTB: currently this EITHER raises a ValueError OR returns an empty # Index object, depending on implementation. :think: # See: https://github.com/sourmash-bio/sourmash/issues/1940 try: idx = index_obj.select(ksize=21) except ValueError: idx = LinearIndex([]) assert len(idx) == 0 siglist = list(idx.signatures()) assert len(siglist) == 0 def test_index_prefetch(index_obj): # test basic prefetch ss2, ss47, ss63 = _load_three_sigs() # search for ss2 results = [] for result in index_obj.prefetch(ss2, threshold_bp=0): results.append(result) assert len(results) == 1 assert results[0].signature.minhash == ss2.minhash # search for ss47 - expect two results results = [] for result in index_obj.prefetch(ss47, threshold_bp=0): results.append(result) assert len(results) == 2 assert results[0].signature.minhash == ss47.minhash assert results[1].signature.minhash == ss63.minhash def test_index_best_containment(index_obj): # test basic containment search ss2, ss47, ss63 = _load_three_sigs() match = index_obj.best_containment(ss2) assert match assert match.score == 1.0 assert match.signature.minhash == ss2.minhash match = index_obj.best_containment(ss47) assert match assert match.score == 1.0 assert match.signature.minhash == ss47.minhash def test_index_best_containment_threshold_1(index_obj): # test best_containment() method, in some detail ss2, ss47, ss63 = _load_three_sigs() # now construct query signatures with specific numbers of hashes -- # note, these signatures all have scaled=1000. mins = list(sorted(ss2.minhash.hashes)) new_mh = ss2.minhash.copy_and_clear() # query with empty hashes assert not new_mh with pytest.raises(ValueError): index_obj.best_containment(SourmashSignature(new_mh)) # add one hash new_mh.add_hash(mins.pop()) assert len(new_mh) == 1 result = index_obj.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig.minhash == ss2.minhash # check with a threshold -> should be no results. with pytest.raises(ValueError): index_obj.best_containment(SourmashSignature(new_mh), threshold_bp=5000) # add three more hashes => length of 4 new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) assert len(new_mh) == 4 result = index_obj.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig.minhash == ss2.minhash # check with a too-high threshold -> should be no results. with pytest.raises(ValueError): index_obj.best_containment(SourmashSignature(new_mh), threshold_bp=5000) def test_best_containment_threshold_5(index_obj): # test gather() method, in some detail ss2, ss47, ss63 = _load_three_sigs() # now construct query signatures with specific numbers of hashes -- # note, these signatures all have scaled=1000. mins = list(sorted(ss2.minhash.hashes.keys())) new_mh = ss2.minhash.copy_and_clear() # add five hashes for i in range(5): new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) # should get a result with no threshold (any match at all is returned) result = index_obj.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig.minhash == ss2.minhash # now, check with a threshold_bp that should be meet-able. result = index_obj.best_containment(SourmashSignature(new_mh), threshold_bp=5000) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig.minhash == ss2.minhash ### ### CounterGather tests ### def create_basic_counter_gather(runtmp): "Construct a CounterGather class." return CounterGather class CounterGather_LinearIndex: """ Provides an (inefficient) CounterGather-style class, for protocol testing purposes. """ def __init__(self, orig_query): "Constructor - take a SourmashSignature that is the original query." orig_query_mh = orig_query.minhash if orig_query_mh.scaled == 0: raise ValueError # Index object used to actually track matches. self.idx = LinearIndex() self.orig_query_mh = orig_query_mh.copy().flatten() self.query_started = 0 self.scaled = orig_query_mh.scaled self.locations = {} def add(self, ss, *, location=None, require_overlap=True): "Insert potential match." if self.query_started: raise ValueError("cannot add more signatures to counter after peek/consume") # skip duplicates md5 = ss.md5sum() if md5 in self.locations: return # confirm that this match has an overlap... add_mh = ss.minhash.flatten() overlap = self.orig_query_mh.count_common(add_mh, downsample=True) # ...figure out what scaled we are operating at now... if overlap: self.downsample(add_mh.scaled) elif require_overlap: raise ValueError("no overlap between query and signature!?") # ...and add to the Index, while also tracking location! self.idx.insert(ss) self.locations[md5] = location def signatures(self): "Yield all signatures" return self.idx.signatures() def downsample(self, scaled): "Track highest scaled across all possible matches." if scaled > self.scaled: self.scaled = scaled return self.scaled def peek(self, cur_query_mh, *, threshold_bp=0): """ Find best match to current query within this CounterGather object. """ self.query_started = 1 scaled = self.downsample(cur_query_mh.scaled) cur_query_mh = flatten_and_downsample_scaled(cur_query_mh, scaled) # no hashes remaining? exit. if not self.orig_query_mh or not cur_query_mh: return [] # verify current query is a subset of the original. if cur_query_mh.contained_by(self.orig_query_mh, downsample=True) < 1: raise ValueError("current query not a subset of original query") # did we get a match? res = self.idx.peek(cur_query_mh, threshold_bp=threshold_bp) if not res: return [] sr, intersect_mh = res # got match - replace location & return. match = sr.signature md5 = match.md5sum() location = self.locations[md5] return IndexSearchResult(sr.score, match, location), intersect_mh def consume(self, *args, **kwargs): self.query_started = 1 return self.idx.consume(*args, **kwargs) class CounterGather_LCA: """ Provides an alternative implementation of a CounterGather-style class, based on LCA_Database. This is currently just for protocol and API testing purposes. """ def __init__(self, query): from sourmash.lca.lca_db import LCA_Database query_mh = query.minhash if query_mh.scaled == 0: raise ValueError("must use scaled MinHash") self.orig_query_mh = query_mh lca_db = LCA_Database(query_mh.ksize, query_mh.scaled, query_mh.moltype) self.db = lca_db self.siglist = {} self.locations = {} self.query_started = 0 def add(self, ss, *, location=None, require_overlap=True): "Add this signature into the counter." if self.query_started: raise ValueError("cannot add more signatures to counter after peek/consume") overlap = self.orig_query_mh.count_common(ss.minhash, True) if overlap: self.downsample(ss.minhash.scaled) elif require_overlap: raise ValueError("no overlap between query and signature!?") self.db.insert(ss) md5 = ss.md5sum() self.siglist[md5] = ss self.locations[md5] = location def signatures(self): "Yield all signatures." yield from self.siglist.values() def downsample(self, scaled): "Track highest scaled across all possible matches." if scaled > self.db.scaled: self.db.downsample_scaled(scaled) return self.db.scaled def peek(self, query_mh, *, threshold_bp=0): "Return next possible match." from sourmash import SourmashSignature self.query_started = 1 scaled = self.downsample(query_mh.scaled) query_mh = query_mh.downsample(scaled=scaled) if not self.orig_query_mh or not query_mh: return [] if query_mh.contained_by(self.orig_query_mh, downsample=True) < 1: raise ValueError("current query not a subset of original query") query_ss = SourmashSignature(query_mh) # returns search_result, intersect_mh try: result = self.db.best_containment(query_ss, threshold_bp=threshold_bp) except ValueError: result = None if not result: return [] cont = result.score match = result.signature intersect_mh = flatten_and_intersect_scaled(result.signature.minhash, query_mh) md5 = result.signature.md5sum() location = self.locations[md5] new_sr = IndexSearchResult(cont, match, location) return [new_sr, intersect_mh] def consume(self, intersect_mh): self.query_started = 1 @pytest.fixture( params=[ CounterGather, CounterGather_LinearIndex, CounterGather_LCA, ] ) def counter_gather_constructor(request): build_fn = request.param # build on demand return build_fn def test_counter_get_signatures(counter_gather_constructor): # test .signatures() method query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear() match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") match_mh_2 = query_mh.copy_and_clear() match_mh_2.add_many(range(10, 15)) match_ss_2 = SourmashSignature(match_mh_2, name="match2") match_mh_3 = query_mh.copy_and_clear() match_mh_3.add_many(range(15, 17)) match_ss_3 = SourmashSignature(match_mh_3, name="match3") counter = counter_gather_constructor(query_ss) counter.add(match_ss_1) counter.add(match_ss_2) counter.add(match_ss_3) siglist = list(counter.signatures()) assert len(siglist) == 3 assert match_ss_1 in siglist assert match_ss_2 in siglist assert match_ss_3 in siglist def _consume_all(query_mh, counter, threshold_bp=0): results = [] query_mh = query_mh.to_mutable() last_intersect_size = None while 1: result = counter.peek(query_mh, threshold_bp=threshold_bp) if not result: break sr, intersect_mh = result print(sr.signature.name, len(intersect_mh)) if last_intersect_size: assert len(intersect_mh) <= last_intersect_size last_intersect_size = len(intersect_mh) counter.consume(intersect_mh) query_mh.remove_many(intersect_mh.hashes) results.append((sr, len(intersect_mh))) return results def test_counter_gather_1(counter_gather_constructor): # check a contrived set of non-overlapping gather results, # generated via CounterGather query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear() match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") match_mh_2 = query_mh.copy_and_clear() match_mh_2.add_many(range(10, 15)) match_ss_2 = SourmashSignature(match_mh_2, name="match2") match_mh_3 = query_mh.copy_and_clear() match_mh_3.add_many(range(15, 17)) match_ss_3 = SourmashSignature(match_mh_3, name="match3") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(match_ss_1) counter.add(match_ss_2) counter.add(match_ss_3) results = _consume_all(query_ss.minhash, counter) expected = ( ["match1", 10], ["match2", 5], ["match3", 2], ) assert len(results) == len(expected), results for (sr, size), (exp_name, exp_size) in zip(results, expected): sr_name = sr.signature.name.split()[0] assert sr_name == exp_name assert size == exp_size def test_counter_gather_1_b(counter_gather_constructor): # check a contrived set of somewhat-overlapping gather results, # generated via CounterGather. Here the overlaps are structured # so that the gather results are the same as those in # test_counter_gather_1(), even though the overlaps themselves are # larger. query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear() match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") match_mh_2 = query_mh.copy_and_clear() match_mh_2.add_many(range(7, 15)) match_ss_2 = SourmashSignature(match_mh_2, name="match2") match_mh_3 = query_mh.copy_and_clear() match_mh_3.add_many(range(13, 17)) match_ss_3 = SourmashSignature(match_mh_3, name="match3") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(match_ss_1) counter.add(match_ss_2) counter.add(match_ss_3) results = _consume_all(query_ss.minhash, counter) expected = ( ["match1", 10], ["match2", 5], ["match3", 2], ) assert len(results) == len(expected), results for (sr, size), (exp_name, exp_size) in zip(results, expected): sr_name = sr.signature.name.split()[0] assert sr_name == exp_name assert size == exp_size def test_counter_gather_1_c_with_threshold(counter_gather_constructor): # check a contrived set of somewhat-overlapping gather results, # generated via CounterGather. Here the overlaps are structured # so that the gather results are the same as those in # test_counter_gather_1(), even though the overlaps themselves are # larger. # use a threshold, here. query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear() match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") match_mh_2 = query_mh.copy_and_clear() match_mh_2.add_many(range(7, 15)) match_ss_2 = SourmashSignature(match_mh_2, name="match2") match_mh_3 = query_mh.copy_and_clear() match_mh_3.add_many(range(13, 17)) match_ss_3 = SourmashSignature(match_mh_3, name="match3") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(match_ss_1) counter.add(match_ss_2) counter.add(match_ss_3) results = _consume_all(query_ss.minhash, counter, threshold_bp=3) expected = (["match1", 10], ["match2", 5]) assert len(results) == len(expected), results for (sr, size), (exp_name, exp_size) in zip(results, expected): sr_name = sr.signature.name.split()[0] assert sr_name == exp_name assert size == exp_size def test_counter_gather_1_d_diff_scaled(counter_gather_constructor): # test as above, but with different scaled. query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear().downsample(scaled=10) match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") match_mh_2 = query_mh.copy_and_clear().downsample(scaled=20) match_mh_2.add_many(range(7, 15)) match_ss_2 = SourmashSignature(match_mh_2, name="match2") match_mh_3 = query_mh.copy_and_clear().downsample(scaled=30) match_mh_3.add_many(range(13, 17)) match_ss_3 = SourmashSignature(match_mh_3, name="match3") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(match_ss_1) counter.add(match_ss_2) counter.add(match_ss_3) results = _consume_all(query_ss.minhash, counter) expected = ( ["match1", 10], ["match2", 5], ["match3", 2], ) assert len(results) == len(expected), results for (sr, size), (exp_name, exp_size) in zip(results, expected): sr_name = sr.signature.name.split()[0] assert sr_name == exp_name assert size == exp_size def test_counter_gather_1_d_diff_scaled_query(counter_gather_constructor): # test as above, but with different scaled for QUERY. query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) match_mh_1 = query_mh.copy_and_clear().downsample(scaled=10) match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") match_mh_2 = query_mh.copy_and_clear().downsample(scaled=20) match_mh_2.add_many(range(7, 15)) match_ss_2 = SourmashSignature(match_mh_2, name="match2") match_mh_3 = query_mh.copy_and_clear().downsample(scaled=30) match_mh_3.add_many(range(13, 17)) match_ss_3 = SourmashSignature(match_mh_3, name="match3") # downsample query now - query_ss = SourmashSignature(query_mh.downsample(scaled=100), name="query") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(match_ss_1) counter.add(match_ss_2) counter.add(match_ss_3) results = _consume_all(query_ss.minhash, counter) expected = ( ["match1", 10], ["match2", 5], ["match3", 2], ) assert len(results) == len(expected), results for (sr, size), (exp_name, exp_size) in zip(results, expected): sr_name = sr.signature.name.split()[0] assert sr_name == exp_name assert size == exp_size def test_counter_gather_1_e_abund_query(counter_gather_constructor): # test as above, but abund query query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1, track_abundance=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear().flatten() match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") match_mh_2 = query_mh.copy_and_clear().flatten() match_mh_2.add_many(range(7, 15)) match_ss_2 = SourmashSignature(match_mh_2, name="match2") match_mh_3 = query_mh.copy_and_clear().flatten() match_mh_3.add_many(range(13, 17)) match_ss_3 = SourmashSignature(match_mh_3, name="match3") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(match_ss_1) counter.add(match_ss_2) counter.add(match_ss_3) # must flatten before peek! results = _consume_all(query_ss.minhash.flatten(), counter) expected = ( ["match1", 10], ["match2", 5], ["match3", 2], ) assert len(results) == len(expected), results for (sr, size), (exp_name, exp_size) in zip(results, expected): sr_name = sr.signature.name.split()[0] assert sr_name == exp_name assert size == exp_size def test_counter_gather_1_f_abund_match(counter_gather_constructor): # test as above, but abund query query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1, track_abundance=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh.flatten(), name="query") match_mh_1 = query_mh.copy_and_clear() match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") match_mh_2 = query_mh.copy_and_clear() match_mh_2.add_many(range(7, 15)) match_ss_2 = SourmashSignature(match_mh_2, name="match2") match_mh_3 = query_mh.copy_and_clear() match_mh_3.add_many(range(13, 17)) match_ss_3 = SourmashSignature(match_mh_3, name="match3") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(match_ss_1) counter.add(match_ss_2) counter.add(match_ss_3) # must flatten before peek! results = _consume_all(query_ss.minhash.flatten(), counter) expected = ( ["match1", 10], ["match2", 5], ["match3", 2], ) assert len(results) == len(expected), results for (sr, size), (exp_name, exp_size) in zip(results, expected): sr_name = sr.signature.name.split()[0] assert sr_name == exp_name assert size == exp_size def test_counter_gather_2(counter_gather_constructor): # check basic set of gather results on semi-real data, # generated via CounterGather testdata_combined = utils.get_test_data("gather/combined.sig") testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_ss = sourmash.load_one_signature(testdata_combined, ksize=21) subject_sigs = [ (sourmash.load_one_signature(t, ksize=21), t) for t in testdata_sigs ] # load up the counter counter = counter_gather_constructor(query_ss) for ss, loc in subject_sigs: counter.add(ss, location=loc) results = _consume_all(query_ss.minhash, counter) expected = ( ["NC_003198.1", 487], ["NC_000853.1", 192], ["NC_011978.1", 169], ["NC_002163.1", 157], ["NC_003197.2", 152], ["NC_009486.1", 92], ["NC_006905.1", 76], ["NC_011080.1", 59], ["NC_011274.1", 42], ["NC_006511.1", 31], ["NC_011294.1", 7], ["NC_004631.1", 2], ) assert len(results) == len(expected) for (sr, size), (exp_name, exp_size) in zip(results, expected): sr_name = sr.signature.name.split()[0] print(sr_name, size) assert sr_name == exp_name assert size == exp_size def test_counter_gather_exact_match(counter_gather_constructor): # query == match query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") # load up the counter; provide a location override, too. counter = counter_gather_constructor(query_ss) counter.add(query_ss, location="somewhere over the rainbow") results = _consume_all(query_ss.minhash, counter) assert len(results) == 1 (sr, intersect_mh) = results[0] assert sr.score == 1.0 assert sr.signature == query_ss assert sr.location == "somewhere over the rainbow" def test_counter_gather_multiple_identical_matches(counter_gather_constructor): # test multiple identical matches being inserted, with only one return query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") # create counter... counter = counter_gather_constructor(query_ss) # now add multiple identical matches. match_mh = query_mh.copy_and_clear() match_mh.add_many(range(5, 15)) for name in "match1", "match2", "match3": match_ss = SourmashSignature(match_mh, name=name) counter.add(match_ss, location=name) results = _consume_all(query_ss.minhash, counter) assert len(results) == 1 sr, overlap_count = results[0] assert sr.score == 0.5 assert overlap_count == 10 # any one of the three is valid assert sr.location in ("match1", "match2", "match3") def test_counter_gather_add_after_peek(counter_gather_constructor): # cannot add after peek or consume query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(query_ss, location="somewhere over the rainbow") counter.peek(query_ss.minhash) with pytest.raises(ValueError): counter.add(query_ss, location="try again") def test_counter_gather_add_after_consume(counter_gather_constructor): # cannot add after peek or consume query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(query_ss, location="somewhere over the rainbow") counter.consume(query_ss.minhash) with pytest.raises(ValueError): counter.add(query_ss, location="try again") def test_counter_gather_consume_empty_intersect(counter_gather_constructor): # check that consume works fine when there is an empty signature. query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(query_ss, location="somewhere over the rainbow") # nothing really happens here :laugh:, just making sure there's no error counter.consume(query_ss.minhash.copy_and_clear()) def test_counter_gather_empty_initial_query(counter_gather_constructor): # check empty initial query query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear() match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(match_ss_1, require_overlap=False) assert counter.peek(query_ss.minhash) == [] def test_counter_gather_num_query(counter_gather_constructor): # check num query query_mh = sourmash.MinHash(n=500, ksize=31) query_mh.add_many(range(0, 10)) query_ss = SourmashSignature(query_mh, name="query") with pytest.raises(ValueError): counter_gather_constructor(query_ss) def test_counter_gather_empty_cur_query(counter_gather_constructor): # test empty cur query query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(query_ss, location="somewhere over the rainbow") cur_query_mh = query_ss.minhash.copy_and_clear() results = _consume_all(cur_query_mh, counter) assert results == [] def test_counter_gather_add_num_matchy(counter_gather_constructor): # test add num query query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") match_mh = sourmash.MinHash(n=500, ksize=31) match_mh.add_many(range(0, 20)) match_ss = SourmashSignature(match_mh, name="query") # load up the counter counter = counter_gather_constructor(query_ss) with pytest.raises(ValueError): counter.add(match_ss, location="somewhere over the rainbow") def test_counter_gather_bad_cur_query(counter_gather_constructor): # test cur query that is not subset of original query query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(query_ss, location="somewhere over the rainbow") cur_query_mh = query_ss.minhash.copy_and_clear() cur_query_mh.add_many(range(20, 30)) with pytest.raises(ValueError): counter.peek(cur_query_mh) def test_counter_gather_add_no_overlap(counter_gather_constructor): # check adding match with no overlap w/query query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 10)) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear() match_mh_1.add_many(range(10, 20)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") # load up the counter counter = counter_gather_constructor(query_ss) with pytest.raises(ValueError): counter.add(match_ss_1) assert counter.peek(query_ss.minhash) == [] def test_counter_gather_big_threshold(counter_gather_constructor): # check 'peek' with a huge threshold query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_mh.add_many(range(0, 20)) query_ss = SourmashSignature(query_mh, name="query") match_mh_1 = query_mh.copy_and_clear() match_mh_1.add_many(range(0, 10)) match_ss_1 = SourmashSignature(match_mh_1, name="match1") # load up the counter counter = counter_gather_constructor(query_ss) counter.add(match_ss_1) # impossible threshold: threshold_bp = 30 * query_ss.minhash.scaled results = counter.peek(query_ss.minhash, threshold_bp=threshold_bp) assert results == [] def test_counter_gather_empty_counter(counter_gather_constructor): # check empty counter query_mh = sourmash.MinHash(n=0, ksize=31, scaled=1) query_ss = SourmashSignature(query_mh, name="query") # empty counter! counter = counter_gather_constructor(query_ss) assert counter.peek(query_ss.minhash) == [] sourmash-bio-sourmash-bc3c0f8/tests/test_jaccard.py000066400000000000000000000174731474157046300226440ustar00rootroot00000000000000""" Legacy tests from when there were Estimator objects and not just MinHash objects. """ import pytest from sourmash import MinHash from sourmash.signature import load_signatures_from_json import sourmash_tst_utils as utils # below, 'track_abundance' is toggled to both True and False by py.test -- # see conftest.py. def test_jaccard_1(track_abundance): E1 = MinHash(n=5, ksize=20, track_abundance=track_abundance) E2 = MinHash(n=5, ksize=20, track_abundance=track_abundance) for i in [1, 2, 3, 4, 5]: E1.add_hash(i) for i in [1, 2, 3, 4, 6]: E2.add_hash(i) # here the union is [1, 2, 3, 4, 5] # and the intersection is [1, 2, 3, 4] => 4/5. assert round(E1.jaccard(E2), 2) == round(4 / 5.0, 2) assert round(E2.jaccard(E1), 2) == round(4 / 5.0, 2) def test_jaccard_2_difflen(track_abundance): E1 = MinHash(n=5, ksize=20, track_abundance=track_abundance) E2 = MinHash(n=5, ksize=20, track_abundance=track_abundance) for i in [1, 2, 3, 4, 5]: E1.add_hash(i) for i in [1, 2, 3, 4]: E2.add_hash(i) print(E1.jaccard(E2)) assert round(E1.jaccard(E2), 2) == 4 / 5.0 assert round(E2.jaccard(E1), 2) == 4 / 5.0 def test_common_1(track_abundance): E1 = MinHash(n=5, ksize=20, track_abundance=track_abundance) E2 = MinHash(n=5, ksize=20, track_abundance=track_abundance) for i in [1, 2, 3, 4, 5]: E1.add_hash(i) for i in [1, 2, 3, 4, 6]: E2.add_hash(i) assert E1.count_common(E2) == 4 assert E2.count_common(E1) == 4 def test_diff_seed(track_abundance): E1 = MinHash(n=5, ksize=20, track_abundance=track_abundance, seed=1) E2 = MinHash(n=5, ksize=20, track_abundance=track_abundance, seed=2) for i in [1, 2, 3, 4, 5]: E1.add_hash(i) for i in [1, 2, 3, 4, 6]: E2.add_hash(i) with pytest.raises(ValueError): E1.count_common(E2) def test_dna_mh(track_abundance): e1 = MinHash(n=5, ksize=4, track_abundance=track_abundance) e2 = MinHash(n=5, ksize=4, track_abundance=track_abundance) seq = "ATGGCAGTGACGATGCCAG" e1.add_sequence(seq) for i in range(len(seq) - 3): e2.add_kmer(seq[i : i + 4]) assert e1.hashes.keys() == e2.hashes.keys() print(e1.hashes.keys()) assert 726311917625663847 in e1.hashes.keys() assert 3697418565283905118 in e1.hashes.keys() def test_protein_mh(track_abundance): e1 = MinHash(n=5, ksize=2, is_protein=True, track_abundance=track_abundance) e2 = MinHash(n=5, ksize=2, is_protein=True, track_abundance=track_abundance) # ok, so this is confusing, but: we are adding _DNA_ kmers here, # and translating. so, add_sequence and add_kmer actually both add # 6-mers. seq = "ATGGCAGTGACGATGCCG" e1.add_sequence(seq) for i in range(len(seq) - 5): kmer = seq[i : i + 6] e2.add_kmer(kmer) assert e1.hashes.keys() == e2.hashes.keys() assert 901193879228338100 in e1.hashes.keys() def test_pickle(track_abundance): import pickle from io import BytesIO e1 = MinHash(n=5, ksize=6, is_protein=False, track_abundance=track_abundance) seq = "ATGGCAGTGACGATGCCG" e1.add_sequence(seq) e1.add_sequence(seq) fp = BytesIO() pickle.dump(e1, fp) fp2 = BytesIO(fp.getvalue()) e2 = pickle.load(fp2) assert e1.hashes == e2.hashes assert e1.num == e2.num assert e1.ksize == e2.ksize assert e1.is_protein == e2.is_protein assert e1.scaled == e2.scaled assert e1.scaled == 0 assert e1.seed == e2.seed def test_bad_construct_1(track_abundance): try: MinHash(ksize=6, is_protein=False, track_abundance=track_abundance) assert 0, "require n in constructor" except TypeError: pass def test_bad_construct_2(track_abundance): try: MinHash(n=100, is_protein=False, track_abundance=track_abundance) assert 0, "require ksize in constructor" except TypeError: pass def test_abund_similarity(): E1 = MinHash(n=5, ksize=20, track_abundance=True) E2 = MinHash(n=5, ksize=20, track_abundance=True) for i in [1]: E1.add_hash(i) for i in [1, 2]: E2.add_hash(i) assert round(E1.similarity(E1)) == 1.0 assert round(E1.similarity(E2), 2) == 0.5 assert round(E1.similarity(E1, ignore_abundance=True)) == 1.0 assert round(E1.similarity(E2, ignore_abundance=True), 2) == 0.5 def test_abund_similarity_zero(): E1 = MinHash(n=5, ksize=20, track_abundance=True) E2 = MinHash(n=5, ksize=20, track_abundance=True) for i in [1]: E1.add_hash(i) assert E1.similarity(E2) == 0.0 #### def test_jaccard_on_real_data(): afile = "n10000/GCF_000005845.2_ASM584v2_genomic.fna.gz.sig.gz" a = utils.get_test_data(afile) sig1 = list(load_signatures_from_json(a))[0] mh1 = sig1.minhash bfile = "n10000/GCF_000006945.1_ASM694v1_genomic.fna.gz.sig.gz" b = utils.get_test_data(bfile) sig2 = list(load_signatures_from_json(b))[0] mh2 = sig2.minhash assert mh1.similarity(mh2) == 0.0183 assert mh2.similarity(mh1) == 0.0183 mh1 = mh1.downsample(num=1000) mh2 = mh2.downsample(num=1000) assert mh1.similarity(mh2) == 0.011 assert mh2.similarity(mh1) == 0.011 mh1 = mh1.downsample(num=100) mh2 = mh2.downsample(num=100) assert mh1.similarity(mh2) == 0.01 assert mh2.similarity(mh1) == 0.01 mh1 = mh1.downsample(num=10) mh2 = mh2.downsample(num=10) assert mh1.similarity(mh2) == 0.0 assert mh2.similarity(mh1) == 0.0 def test_scaled_on_real_data(): afile = "scaled100/GCF_000005845.2_ASM584v2_genomic.fna.gz.sig.gz" a = utils.get_test_data(afile) sig1 = list(load_signatures_from_json(a))[0] mh1 = sig1.minhash bfile = "scaled100/GCF_000006945.1_ASM694v1_genomic.fna.gz.sig.gz" b = utils.get_test_data(bfile) sig2 = list(load_signatures_from_json(b))[0] mh2 = sig2.minhash assert round(mh1.similarity(mh2), 5) == 0.01644 assert round(mh2.similarity(mh1), 5) == 0.01644 mh1 = mh1.downsample(scaled=100) mh2 = mh2.downsample(scaled=100) assert round(mh1.similarity(mh2), 5) == 0.01644 assert round(mh2.similarity(mh1), 5) == 0.01644 mh1 = mh1.downsample(scaled=1000) mh2 = mh2.downsample(scaled=1000) assert round(mh1.similarity(mh2), 5) == 0.01874 assert round(mh2.similarity(mh1), 5) == 0.01874 mh1 = mh1.downsample(scaled=10000) mh2 = mh2.downsample(scaled=10000) assert mh1.similarity(mh2) == 0.01 assert mh2.similarity(mh1) == 0.01 def test_scaled_on_real_data_2(): afile = "scaled100/GCF_000005845.2_ASM584v2_genomic.fna.gz.sig.gz" a = utils.get_test_data(afile) sig1 = list(load_signatures_from_json(a))[0] mh1 = sig1.minhash bfile = "scaled100/GCF_000006945.1_ASM694v1_genomic.fna.gz.sig.gz" b = utils.get_test_data(bfile) sig2 = list(load_signatures_from_json(b))[0] mh2 = sig2.minhash assert round(mh1.similarity(mh2), 5) == 0.01644 assert round(mh2.similarity(mh1), 5) == 0.01644 mh1 = mh1.downsample(scaled=1000) mh2 = mh2.downsample(scaled=1000) assert round(mh1.similarity(mh2), 4) == 0.0187 assert round(mh2.similarity(mh1), 4) == 0.0187 mh1 = mh1.downsample(scaled=10000) mh2 = mh2.downsample(scaled=10000) assert round(mh1.similarity(mh2), 3) == 0.01 assert round(mh2.similarity(mh1), 3) == 0.01 mh1 = mh1.downsample(scaled=100000) mh2 = mh2.downsample(scaled=100000) assert round(mh1.similarity(mh2), 2) == 0.01 assert round(mh2.similarity(mh1), 2) == 0.01 def test_downsample_scaled_with_num(): afile = "scaled100/GCF_000005845.2_ASM584v2_genomic.fna.gz.sig.gz" a = utils.get_test_data(afile) sig1 = list(load_signatures_from_json(a))[0] mh1 = sig1.minhash with pytest.raises(ValueError) as exc: mh1.downsample(num=500) assert "cannot downsample a scaled MinHash using num" in str(exc.value) sourmash-bio-sourmash-bc3c0f8/tests/test_lca.py000066400000000000000000003377661474157046300220260ustar00rootroot00000000000000""" Tests for the 'sourmash lca' command line and high level API. """ import os import shutil import csv import pytest import glob from pathlib import Path import sourmash_tst_utils as utils import sourmash from sourmash import SourmashSignature, sourmash_args from sourmash.signature import save_signatures_to_json, load_one_signature_from_json from sourmash.search import make_jaccard_search_query from sourmash.lca import lca_utils from sourmash.lca.lca_utils import LineagePair from sourmash.picklist import SignaturePicklist, PickStyle from sourmash_tst_utils import SourmashCommandFailed def test_api_create_search(): # create a database and then search for result. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) assert len(lca_db) == 0 assert not lca_db count = lca_db.insert(ss) assert count == len(ss.minhash) assert len(lca_db) == 1 assert lca_db results = lca_db.search(ss, threshold=0.0) print(results) assert len(results) == 1 (similarity, match, filename) = results[0] assert match.minhash == ss.minhash def test_api_find_picklist_select(): # does 'find' respect picklists? sig47 = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) sig63 = load_one_signature_from_json(utils.get_test_data("63.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(sig47) lca_db.insert(sig63) # construct a picklist... picklist = SignaturePicklist("md5prefix8") picklist.init(["09a08691"]) # run a 'find' with sig63, should find 47 and 63 both. search_obj = make_jaccard_search_query(do_containment=True, threshold=0.0) results = list(lca_db.find(search_obj, sig63)) print(results) assert len(results) == 2 # now, select on picklist and do another find... lca_db = lca_db.select(picklist=picklist) results = list(lca_db.find(search_obj, sig63)) print(results) assert len(results) == 1 # and check that it is the expected one! ss = results[0].signature assert ss.minhash.ksize == 31 assert ss.md5sum().startswith("09a08691c") def test_api_find_picklist_select_exclude(): # does 'find' respect picklists? sig47 = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) sig63 = load_one_signature_from_json(utils.get_test_data("63.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(sig47) lca_db.insert(sig63) # construct a picklist... picklist = SignaturePicklist("md5prefix8", pickstyle=PickStyle.EXCLUDE) picklist.init(["09a08691"]) # run a 'find' with sig63, should find 47 and 63 both. search_obj = make_jaccard_search_query(do_containment=True, threshold=0.0) results = list(lca_db.find(search_obj, sig63)) print(results) assert len(results) == 2 # now, select on picklist and do another find... lca_db = lca_db.select(picklist=picklist) results = list(lca_db.find(search_obj, sig63)) print(results) assert len(results) == 1 # and check that it is the expected one! ss = results[0].signature assert ss.minhash.ksize == 31 assert ss.md5sum().startswith("38729c637") def test_api_create_insert(): # test some internal implementation stuff: create & then insert a sig. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(ss) ident = ss.name assert len(lca_db._ident_to_name) == 1 assert ident in lca_db._ident_to_name assert lca_db._ident_to_name[ident] == ident assert len(lca_db._ident_to_idx) == 1 assert lca_db._ident_to_idx[ident] == 0 assert len(lca_db._hashval_to_idx) == len(ss.minhash) assert len(lca_db._idx_to_ident) == 1 assert lca_db._idx_to_ident[0] == ident set_of_values = set() for vv in lca_db._hashval_to_idx.values(): set_of_values.update(vv) assert len(set_of_values) == 1 assert set_of_values == {0} assert not lca_db._idx_to_lid # no lineage added assert not lca_db._lid_to_lineage # no lineage added def test_api_create_insert_bad_ksize(): # can we insert a ksize=21 signature into a ksize=31 DB? hopefully not. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=21, scaled=1000) with pytest.raises(ValueError): lca_db.insert(ss) def test_api_create_insert_bad_ident(): # can we insert a signature with no/empty ident? ss1 = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) ss2 = load_one_signature_from_json(utils.get_test_data("63.fa.sig"), ksize=31) ss1 = ss1.to_mutable() ss2 = ss2.to_mutable() ss1.name = "" ss1.filename = "" ss2.name = "" ss2.filename = "" lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(ss1) lca_db.insert(ss2) # SUCCESS! # would fail, previously :) def test_api_create_insert_bad_scaled(): # can we insert a scaled=1000 signature into a scaled=500 DB? # hopefully not. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) assert ss.minhash.scaled == 1000 lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=500) with pytest.raises(ValueError): lca_db.insert(ss) def test_api_create_insert_bad_moltype(): # can we insert a DNAsignature into a protein DB? # hopefully not. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) assert ss.minhash.moltype == "DNA" lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=500, moltype="protein") with pytest.raises(ValueError): lca_db.insert(ss) def test_api_create_insert_ident(): # test some internal implementation stuff: signature inserted with # different ident than name. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(ss, ident="foo") ident = "foo" assert len(lca_db._ident_to_name) == 1 assert ident in lca_db._ident_to_name assert lca_db._ident_to_name[ident] == ss.name assert len(lca_db._ident_to_idx) == 1 assert lca_db._ident_to_idx[ident] == 0 assert len(lca_db._hashval_to_idx) == len(ss.minhash) assert len(lca_db._idx_to_ident) == 1 assert lca_db._idx_to_ident[0] == ident set_of_values = set() for vv in lca_db._hashval_to_idx.values(): set_of_values.update(vv) assert len(set_of_values) == 1 assert set_of_values == {0} assert not lca_db._idx_to_lid # no lineage added assert not lca_db._lid_to_lineage # no lineage added assert not lca_db._lineage_to_lid assert not lca_db._lid_to_idx def test_api_create_insert_two(): # check internal details if multiple signatures are inserted. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) ss2 = load_one_signature_from_json(utils.get_test_data("63.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(ss, ident="foo") lca_db.insert(ss2, ident="bar") ident = "foo" ident2 = "bar" assert len(lca_db._ident_to_name) == 2 assert ident in lca_db._ident_to_name assert ident2 in lca_db._ident_to_name assert lca_db._ident_to_name[ident] == ss.name assert lca_db._ident_to_name[ident2] == ss2.name assert len(lca_db._ident_to_idx) == 2 assert lca_db._ident_to_idx[ident] == 0 assert lca_db._ident_to_idx[ident2] == 1 combined_mins = set(ss.minhash.hashes.keys()) combined_mins.update(set(ss2.minhash.hashes.keys())) assert len(lca_db._hashval_to_idx) == len(combined_mins) assert len(lca_db._idx_to_ident) == 2 assert lca_db._idx_to_ident[0] == ident assert lca_db._idx_to_ident[1] == ident2 set_of_values = set() for vv in lca_db._hashval_to_idx.values(): set_of_values.update(vv) assert len(set_of_values) == 2 assert set_of_values == {0, 1} assert not lca_db._idx_to_lid # no lineage added assert not lca_db._lid_to_lineage # no lineage added assert not lca_db._lineage_to_lid assert not lca_db._lid_to_idx def test_api_create_insert_w_lineage(): # test some internal implementation stuff - insert signature w/lineage ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lineage = (LineagePair("rank1", "name1"), LineagePair("rank2", "name2")) lca_db.insert(ss, lineage=lineage) # basic ident stuff ident = ss.name assert len(lca_db._ident_to_name) == 1 assert ident in lca_db._ident_to_name assert lca_db._ident_to_name[ident] == ident assert len(lca_db._ident_to_idx) == 1 assert lca_db._ident_to_idx[ident] == 0 assert len(lca_db._hashval_to_idx) == len(ss.minhash) assert len(lca_db._idx_to_ident) == 1 assert lca_db._idx_to_ident[0] == ident # all hash values added set_of_values = set() for vv in lca_db._hashval_to_idx.values(): set_of_values.update(vv) assert len(set_of_values) == 1 assert set_of_values == {0} # check lineage stuff assert len(lca_db._idx_to_lid) == 1 assert lca_db._idx_to_lid[0] == 0 assert len(lca_db._lid_to_lineage) == 1 assert lca_db._lid_to_lineage[0] == lineage assert lca_db._lid_to_idx[0] == {0} assert len(lca_db._lineage_to_lid) == 1 assert lca_db._lineage_to_lid[lineage] == 0 def test_api_create_insert_w_bad_lineage(): # test some internal implementation stuff - insert signature w/bad lineage ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lineage = ([LineagePair("rank1", "name1"), LineagePair("rank2", "name2")],) with pytest.raises(ValueError): lca_db.insert(ss, lineage=lineage) def test_api_create_insert_w_bad_lineage_2(): # test some internal implementation stuff - insert signature w/bad lineage ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lineage = 1 # something non-iterable... with pytest.raises(ValueError): lca_db.insert(ss, lineage=lineage) def test_api_create_gather(): # create a database, and then run gather on it. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(ss) result = lca_db.best_containment(ss, threshold_bp=0) print(result) assert result (similarity, match, filename) = result assert match.minhash == ss.minhash def test_api_add_genome_lineage(): # LCA_Databases can store/retrieve arbitrary lineages/taxonomies. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) lineage = (LineagePair("rank1", "name1"), (LineagePair("rank2", "name2"))) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(ss, lineage=lineage) somehash = next(iter(ss.minhash.hashes.keys())) lineages = lca_db.get_lineage_assignments(somehash) assert len(lineages) == 1 assert lineage in lineages def test_api_insert_update(): # check that cached parts of LCA_Database are updated when a new # signature is inserted. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) ss2 = load_one_signature_from_json(utils.get_test_data("63.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(ss) all_mh = [x.minhash for x in lca_db.signatures()] assert ss.minhash in all_mh # see decorator @cached_property assert hasattr(lca_db, "_cache") assert lca_db._cache # inserting a signature should delete the cache lca_db.insert(ss2) assert not hasattr(lca_db, "_cache") # check that it's rebuilt etc. etc. all_mh = [x.minhash for x in lca_db.signatures()] assert ss.minhash in all_mh assert ss2.minhash in all_mh def test_api_insert_retrieve_check_name(): # check that signatures retrieved from LCA_Database objects have the # right name. ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(ss) sigs = list(lca_db.signatures()) assert len(sigs) == 1 retrieved_sig = sigs[0] assert retrieved_sig.name == ss.name assert retrieved_sig.minhash == ss.minhash def test_api_create_insert_two_then_scale(): # construct database, THEN downsample ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) ss2 = load_one_signature_from_json(utils.get_test_data("63.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(ss) lca_db.insert(ss2) # downsample everything to 5000 lca_db.downsample_scaled(5000) minhash = ss.minhash.downsample(scaled=5000) minhash2 = ss2.minhash.downsample(scaled=5000) # & check... combined_mins = set(minhash.hashes.keys()) combined_mins.update(set(minhash2.hashes.keys())) assert len(lca_db._hashval_to_idx) == len(combined_mins) def test_api_create_insert_two_then_scale_then_add(): # construct database, THEN downsample, then add another ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) ss2 = load_one_signature_from_json(utils.get_test_data("63.fa.sig"), ksize=31) lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) lca_db.insert(ss) # downsample everything to 5000 lca_db.downsample_scaled(5000) # insert another after downsample lca_db.insert(ss2) # now test - ss = ss.to_mutable() ss.minhash = ss.minhash.downsample(scaled=5000) ss2 = ss2.to_mutable() ss2.minhash = ss2.minhash.downsample(scaled=5000) # & check... combined_mins = set(ss.minhash.hashes.keys()) combined_mins.update(set(ss2.minhash.hashes.keys())) assert len(lca_db._hashval_to_idx) == len(combined_mins) def test_api_create_insert_scale_two(): # downsample while constructing database ss = load_one_signature_from_json(utils.get_test_data("47.fa.sig"), ksize=31) ss2 = load_one_signature_from_json(utils.get_test_data("63.fa.sig"), ksize=31) # downsample to 5000 while inserting: lca_db = sourmash.lca.LCA_Database(ksize=31, scaled=5000) count = lca_db.insert(ss) assert count == 1037 assert count == len(ss.minhash.downsample(scaled=5000)) lca_db.insert(ss2) # downsample sigs to 5000 minhash = ss.minhash.downsample(scaled=5000) minhash2 = ss2.minhash.downsample(scaled=5000) # & check... combined_mins = set(minhash.hashes.keys()) combined_mins.update(set(minhash2.hashes.keys())) assert len(lca_db._hashval_to_idx) == len(combined_mins) def test_load_single_db(): filename = utils.get_test_data("lca/delmont-1.lca.json") db, ksize, scaled = lca_utils.load_single_database(filename) print(db) assert ksize == 31 assert scaled == 10000 def test_load_single_db_empty(runtmp): # test load_single_database on an empty file; should raise ValueError empty = runtmp.output("empty.lca.json") with open(empty, "w"): pass with pytest.raises(ValueError) as exc: db, ksize, scaled = lca_utils.load_single_database(empty) assert f"'{empty}' is not an LCA database file." in str(exc.value) def test_databases(): filename1 = utils.get_test_data("lca/delmont-1.lca.json") filename2 = utils.get_test_data("lca/delmont-2.lca.json") dblist, ksize, scaled = lca_utils.load_databases([filename1, filename2]) print(dblist) assert len(dblist) == 2 assert ksize == 31 assert scaled == 10000 def test_databases_load_fail_on_no_JSON(): filename1 = utils.get_test_data("prot/protein.zip") with pytest.raises(ValueError) as exc: dblist, ksize, scaled = lca_utils.load_databases([filename1]) err = str(exc.value) print(err) assert f"'{filename1}' is not an LCA database file." in err def test_databases_load_fail_on_dir(): filename1 = utils.get_test_data("lca") with pytest.raises(ValueError) as exc: dblist, ksize, scaled = lca_utils.load_databases([filename1]) err = str(exc.value) print(err) assert f"'{filename1}' is not a file and cannot be loaded as an LCA database" in err assert "found 0 matches total;" not in err def test_databases_load_fail_on_not_exist(): filename1 = utils.get_test_data("does-not-exist") with pytest.raises(ValueError) as exc: dblist, ksize, scaled = lca_utils.load_databases([filename1]) err = str(exc.value) print(err) assert f"'{filename1}' is not a file and cannot be loaded as an LCA database" in err assert "found 0 matches total;" not in err def test_db_repr(): filename = utils.get_test_data("lca/delmont-1.lca.json") db, ksize, scaled = lca_utils.load_single_database(filename) assert repr(db) == f"LCA_Database('{filename}')" def test_lca_index_signatures_method(): # test 'signatures' method from base class Index filename = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(filename) siglist = list(db.signatures()) assert len(siglist) == 2 def test_lca_index_select(): # test 'select' method from Index base class. filename = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(filename) xx = db.select(ksize=31) assert xx == db xx = db.select(moltype="DNA") assert xx == db xx = db.select(abund=False) assert xx == db with pytest.raises(ValueError): db.select(ksize=21) with pytest.raises(ValueError): db.select(moltype="protein") with pytest.raises(ValueError): db.select(abund=True) def test_lca_index_select_picklist(): # test 'select' method from Index base class with a picklist. filename = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(filename) # construct a picklist... picklist = SignaturePicklist("md5prefix8") picklist.init(["50a92740"]) xx = db.select(picklist=picklist) assert xx == db siglist = list(db.signatures()) assert len(siglist) == 1 ss = siglist[0] assert ss.md5sum().startswith("50a92740") assert ss.minhash.ksize == 31 def test_lca_index_find_picklist_check_overlap(): # make sure 'find' works for picklists that exclude relevant signatures # (bug #1638) query_fn = utils.get_test_data("47.fa.sig") query_sig = load_one_signature_from_json(query_fn, ksize=31) db_fn = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(db_fn) # construct a picklist... picklist = SignaturePicklist("ident") picklist.init(["NC_009665.1"]) xx = db.select(picklist=picklist) assert xx == db results = list(db.search(query_sig, threshold=0.1)) assert len(results) == 1 def test_lca_index_select_picklist_exclude(): # test 'select' method from Index base class with a picklist. filename = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(filename) # construct a picklist... picklist = SignaturePicklist("md5prefix8", pickstyle=PickStyle.EXCLUDE) picklist.init(["50a92740"]) xx = db.select(picklist=picklist) assert xx == db siglist = list(db.signatures()) assert len(siglist) == 1 ss = siglist[0] assert ss.md5sum().startswith("e88dc390") assert ss.minhash.ksize == 31 def test_lca_index_select_picklist_twice(): # test 'select' method from Index base class with a picklist. filename = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(filename) # construct a picklist... picklist = SignaturePicklist("md5prefix8") picklist.init(["50a92740"]) xx = db.select(picklist=picklist) assert xx == db with pytest.raises(ValueError) as exc: xx = db.select(picklist=picklist) assert "we do not (yet) support multiple picklists for LCA databases" in str(exc) def test_search_db_scaled_gt_sig_scaled(): dbfile = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(dbfile) sig = load_one_signature_from_json(utils.get_test_data("47.fa.sig")) results = db.search(sig, threshold=0.01, ignore_abundance=True) match_sig = results[0][1] minhash = sig.minhash.downsample(scaled=10000) assert minhash == match_sig.minhash def test_search_db_scaled_lt_sig_scaled(): dbfile = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(dbfile) sig = load_one_signature_from_json(utils.get_test_data("47.fa.sig")) sig = sig.to_mutable() sig.minhash = sig.minhash.downsample(scaled=100000) results = db.search(sig, threshold=0.01, ignore_abundance=True) print(results) assert results[0].score == 1.0 match = results[0].signature orig_sig = load_one_signature_from_json(utils.get_test_data("47.fa.sig")) assert orig_sig.minhash.jaccard(match.minhash, downsample=True) == 1.0 def test_gather_db_scaled_gt_sig_scaled(): dbfile = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(dbfile) sig = load_one_signature_from_json(utils.get_test_data("47.fa.sig")) result = db.best_containment(sig, threshold=0.01, ignore_abundance=True) match_sig = result[1] minhash = sig.minhash.downsample(scaled=10000) assert minhash == match_sig.minhash def test_gather_db_scaled_lt_sig_scaled(): dbfile = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(dbfile) sig = load_one_signature_from_json(utils.get_test_data("47.fa.sig")) sig_minhash = sig.minhash.downsample(scaled=100000) result = db.best_containment(sig, threshold=0.01, ignore_abundance=True) match_sig = result[1] minhash = match_sig.minhash.downsample(scaled=100000) assert sig_minhash == minhash def test_db_lineage_to_lid(): dbfile = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(dbfile) d = db._lineage_to_lid items = list(d.items()) items.sort() assert len(items) == 2 print(items) lin1 = items[0][0][-1] assert lin1.rank == "strain" assert lin1.name == "Shewanella baltica OS185" lin1 = items[1][0][-1] assert lin1.rank == "strain" assert lin1.name == "Shewanella baltica OS223" def test_db_lid_to_idx(): dbfile = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(dbfile) d = db._lid_to_idx items = list(d.items()) items.sort() assert len(items) == 2 print(items) assert items == [(32, {32}), (48, {48})] def test_db_idx_to_ident(): dbfile = utils.get_test_data("lca/47+63.lca.json") db, ksize, scaled = lca_utils.load_single_database(dbfile) d = db._idx_to_ident items = list(d.items()) items.sort() assert len(items) == 2 print(items) assert items == [(32, "NC_009665"), (48, "NC_011663")] ## command line tests def test_run_sourmash_lca(): status, out, err = utils.runscript("sourmash", ["lca"], fail_ok=True) assert status != 0 # no args provided, ok ;) def test_basic_index(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/delmont-1.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, "delmont-1", input_sig, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db), lca_db assert ( "Building LCA database with ksize=31 scaled=10000 moltype=DNA" in runtmp.last_result.err ) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "1 identifiers used out of 1 distinct identifiers in spreadsheet." in runtmp.last_result.err ) def test_basic_index_twice(runtmp, lca_db_format): # run 'lca index' twice. taxcsv = utils.get_test_data("lca/delmont-1.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, "delmont-1", input_sig, "-F", lca_db_format] runtmp.sourmash(*cmd) with pytest.raises(SourmashCommandFailed): cmd = ["lca", "index", taxcsv, "delmont-1", input_sig, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "already exists. Not overwriting." in runtmp.last_result.err def test_basic_index_bad_spreadsheet(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/bad-spreadsheet.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db), lca_db assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "1 identifiers used out of 1 distinct identifiers in spreadsheet." in runtmp.last_result.err ) def test_basic_index_broken_spreadsheet(runtmp, lca_db_format): # duplicate identifiers in this spreadsheet taxcsv = utils.get_test_data("lca/bad-spreadsheet-2.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig, "-F", lca_db_format] with pytest.raises(SourmashCommandFailed): runtmp.sourmash(*cmd) assert runtmp.last_result.status != 0 assert ( "multiple lineages for identifier TARA_ASE_MAG_00031" in runtmp.last_result.err ) def test_basic_index_too_many_strains_too_few_species(runtmp, lca_db_format): # explicit test for #841, where 'n_species' wasn't getting counted # if lineage was at strain level resolution. taxcsv = utils.get_test_data("lca/podar-lineage.csv") input_sig = utils.get_test_data("47.fa.sig") lca_db = runtmp.output(f"out.lca.{lca_db_format}") cmd = [ "lca", "index", taxcsv, lca_db, input_sig, "-C", "3", "--split-identifiers", "-F", lca_db_format, ] runtmp.sourmash(*cmd) assert "error: fewer than 20% of lineages" not in runtmp.last_result.err assert runtmp.last_result.status == 0 def test_basic_index_too_few_species(runtmp, lca_db_format): # spreadsheets with too few species should be flagged, unless -f specified taxcsv = utils.get_test_data("lca/tully-genome-sigs.classify.csv") # (these don't really matter, should break on load spreadsheet) input_sig = utils.get_test_data("47.fa.sig") lca_db = runtmp.output(f"out.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig, "-C", "3", "-F", lca_db_format] with pytest.raises(SourmashCommandFailed): runtmp.sourmash(*cmd) assert ( '"ERROR: fewer than 20% of lineages have species-level resolution' not in runtmp.last_result.err ) assert runtmp.last_result.status != 0 def test_basic_index_require_taxonomy(runtmp, lca_db_format): # no taxonomy in here taxcsv = utils.get_test_data("lca/bad-spreadsheet-3.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = [ "lca", "index", "--require-taxonomy", taxcsv, lca_db, input_sig, "-F", lca_db_format, ] with pytest.raises(SourmashCommandFailed): runtmp.sourmash(*cmd) assert runtmp.last_result.status != 0 assert ( "ERROR: no hash values found - are there any signatures?" in runtmp.last_result.err ) def test_basic_index_column_start(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/delmont-3.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", "-C", "3", taxcsv, lca_db, input_sig, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "1 identifiers used out of 1 distinct identifiers in spreadsheet." in runtmp.last_result.err ) def test_index_empty_sketch_name(runtmp, lca_db_format): c = runtmp # create two signatures with empty 'name' attributes cmd = [ "sketch", "dna", utils.get_test_data("genome-s12.fa.gz"), utils.get_test_data("genome-s11.fa.gz"), ] c.run_sourmash(*cmd) sig1 = c.output("genome-s11.fa.gz.sig") assert os.path.exists(sig1) sig2 = c.output("genome-s12.fa.gz.sig") assert os.path.exists(sig2) outfile = f"zzz.lca.{lca_db_format}" # can we insert them both? taxcsv = utils.get_test_data("lca/delmont-1.csv") cmd = ["lca", "index", taxcsv, outfile, sig1, sig2, "-F", lca_db_format] c.run_sourmash(*cmd) assert os.path.exists(c.output(outfile)) print(c.last_result.out) print(c.last_result.err) assert "WARNING: no lineage provided for 2 sig" in c.last_result.err def test_basic_index_and_classify_with_tsv_and_gz(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/delmont-1.tsv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") if lca_db_format == "json": lca_db = runtmp.output("delmont-1.lca.json.gz") else: lca_db = runtmp.output("delmont-1.lca.sql") cmd = [ "lca", "index", "--tabs", "--no-header", taxcsv, lca_db, input_sig, "-F", lca_db_format, ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "1 identifiers used out of 1 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = ["lca", "classify", "--db", lca_db, "--query", input_sig] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "ID,status,superkingdom,phylum,class,order,family,genus,species" in runtmp.last_result.out ) assert ( "TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii" in runtmp.last_result.out ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_basic_index_and_classify(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/delmont-1.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "1 identifiers used out of 1 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = ["lca", "classify", "--db", lca_db, "--query", input_sig] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "ID,status,superkingdom,phylum,class,order,family,genus,species" in runtmp.last_result.out ) assert ( "TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii" in runtmp.last_result.out ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_basic_index_and_classify_dup_lineage(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/tara-delmont-SuppTable3.csv") input_sig1 = utils.get_test_data("lca/TARA_ASE_MAG_00007.sig") input_sig2 = utils.get_test_data("lca/TARA_ANW_MAG_00005.sig") lca_db = runtmp.output(f"delmont-dup.lca.{lca_db_format}") cmd = [ "lca", "index", taxcsv, lca_db, input_sig1, input_sig2, "-F", lca_db_format, "-f", ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) cmd = ["lca", "classify", "--db", lca_db, "--query", input_sig1] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "TARA_ASE_MAG_00007,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,,," in runtmp.last_result.out ) cmd = ["lca", "classify", "--db", lca_db, "--query", input_sig2] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "TARA_ANW_MAG_00005,found,Bacteria,Proteobacteria,Gammaproteobacteria,,,,," in runtmp.last_result.out ) def test_index_traverse(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/delmont-1.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") in_dir = runtmp.output("sigs") os.mkdir(in_dir) shutil.copyfile(input_sig, os.path.join(in_dir, "q.sig")) cmd = ["lca", "index", taxcsv, lca_db, in_dir, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "1 identifiers used out of 1 distinct identifiers in spreadsheet." in runtmp.last_result.err ) assert "WARNING: 1 duplicate signatures." not in runtmp.last_result.err def test_index_traverse_force(runtmp, lca_db_format): c = runtmp # test the use of --force to load all files, not just .sig taxcsv = utils.get_test_data("lca/delmont-1.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = c.output(f"delmont-1.lca.{lca_db_format}") in_dir = c.output("sigs") os.mkdir(in_dir) # name signature .txt instead of .sig: shutil.copyfile(input_sig, os.path.join(in_dir, "q.txt")) # use --force cmd = ["lca", "index", taxcsv, lca_db, in_dir, "-f", "-F", lca_db_format] c.run_sourmash(*cmd) out = c.last_result.out err = c.last_result.err print(out) print(err) assert os.path.exists(lca_db) assert "** assuming column 'MAGs' is identifiers in spreadsheet" in err assert "** assuming column 'Domain' is superkingdom in spreadsheet" in err assert "1 identifiers used out of 1 distinct identifiers in spreadsheet." in err assert "WARNING: 1 duplicate signatures." not in err def test_index_from_file_cmdline_sig(runtmp, lca_db_format): c = runtmp taxcsv = utils.get_test_data("lca/delmont-1.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = c.output(f"delmont-1.lca.{lca_db_format}") file_list = c.output("sigs.list") with open(file_list, "w") as fp: print(input_sig, file=fp) cmd = [ "lca", "index", taxcsv, lca_db, input_sig, "--from-file", file_list, "-F", lca_db_format, ] c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert os.path.exists(lca_db) assert "** assuming column 'MAGs' is identifiers in spreadsheet" in err assert "** assuming column 'Domain' is superkingdom in spreadsheet" in err assert "1 identifiers used out of 1 distinct identifiers in spreadsheet." in err assert "WARNING: 1 duplicate signatures." in err def test_index_from_file(runtmp, lca_db_format): c = runtmp taxcsv = utils.get_test_data("lca/delmont-1.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = c.output(f"delmont-1.lca.{lca_db_format}") file_list = c.output("sigs.list") with open(file_list, "w") as fp: print(input_sig, file=fp) cmd = [ "lca", "index", taxcsv, lca_db, "--from-file", file_list, "-F", lca_db_format, ] c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert os.path.exists(lca_db) assert "** assuming column 'MAGs' is identifiers in spreadsheet" in err assert "** assuming column 'Domain' is superkingdom in spreadsheet" in err assert "1 identifiers used out of 1 distinct identifiers in spreadsheet." in err def test_index_fail_on_num(runtmp, lca_db_format): c = runtmp # lca index should yield a decent error message when attempted on 'num' sigfile = utils.get_test_data("num/63.fa.sig") taxcsv = utils.get_test_data("lca/podar-lineage.csv") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "lca", "index", taxcsv, f"xxx.lca.{lca_db_format}", sigfile, "-C", "3", "-F", lca_db_format, ) err = c.last_result.err print(err) assert "ERROR: cannot insert signature " in err assert "ERROR: cannot downsample signature; is it a scaled signature?" in err def test_index_traverse_real_spreadsheet_no_report(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/tara-delmont-SuppTable3.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig, "-f", "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "1 identifiers used out of 957 distinct identifiers in spreadsheet." in runtmp.last_result.err ) assert "WARNING: no signatures for 956 spreadsheet rows." in runtmp.last_result.err assert "WARNING: 105 unused lineages." in runtmp.last_result.err assert ( "(You can use --report to generate a detailed report.)" in runtmp.last_result.err ) def test_index_traverse_real_spreadsheet_report(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/tara-delmont-SuppTable3.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") report_loc = runtmp.output("report.txt") cmd = [ "lca", "index", taxcsv, lca_db, input_sig, "--report", report_loc, "-f", "-F", lca_db_format, ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "1 identifiers used out of 957 distinct identifiers in spreadsheet." in runtmp.last_result.err ) assert "WARNING: no signatures for 956 spreadsheet rows." in runtmp.last_result.err assert "WARNING: 105 unused lineages." in runtmp.last_result.err assert ( "(You can use --report to generate a detailed report.)" not in runtmp.last_result.err ) assert os.path.exists(report_loc) def test_single_classify(runtmp): # run a basic 'classify', check output. db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") cmd = ["lca", "classify", "--db", db1, "--query", input_sig] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii" in runtmp.last_result.out ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_single_classify_zip_query(runtmp): # run 'classify' with a query in a zipfile db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") query_ss = load_one_signature_from_json(input_sig, ksize=31) query_zipfile = runtmp.output("query.zip") with sourmash_args.SaveSignaturesToLocation(query_zipfile) as save_sig: save_sig.add(query_ss) cmd = ["lca", "classify", "--db", db1, "--query", query_zipfile] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii" in runtmp.last_result.out ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_single_classify_to_output(runtmp): db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") cmd = [ "lca", "classify", "--db", db1, "--query", input_sig, "-o", runtmp.output("outfile.txt"), ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) with open(runtmp.output("outfile.txt")) as fp: outdata = fp.read() assert ( "TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii" in outdata ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_single_classify_to_output_no_name(runtmp): db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") ss = load_one_signature_from_json(input_sig, ksize=31) outsig_filename = runtmp.output("q.sig") with open(outsig_filename, "w") as fp: # remove name from signature here -- new_sig = sourmash.SourmashSignature(ss.minhash, filename="xyz") save_signatures_to_json([new_sig], fp) cmd = [ "lca", "classify", "--db", db1, "--query", outsig_filename, "-o", runtmp.output("outfile.txt"), ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) with open(runtmp.output("outfile.txt")) as fp: outdata = fp.read() print((outdata,)) assert ( "xyz,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii" in outdata ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_single_classify_empty(runtmp): db1 = utils.get_test_data("lca/both.lca.json") input_sig = utils.get_test_data("GCF_000005845.2_ASM584v2_genomic.fna.gz.sig") cmd = ["lca", "classify", "--db", db1, "--query", input_sig] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "GCF_000005845,nomatch,,,,,,,," in runtmp.last_result.out assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_single_classify_traverse(runtmp): db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") in_dir = runtmp.output("sigs") os.mkdir(in_dir) shutil.copyfile(input_sig, os.path.join(in_dir, "q.sig")) cmd = ["lca", "classify", "--db", db1, "--query", input_sig] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii" in runtmp.last_result.out ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_multi_query_classify_traverse(runtmp): # both.lca.json is built from both dir and dir2 db1 = utils.get_test_data("lca/both.lca.json") dir1 = utils.get_test_data("lca/dir1") dir2 = utils.get_test_data("lca/dir2") cmd = ["lca", "classify", "--db", db1, "--query", dir1, dir2] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) with open(utils.get_test_data("lca/classify-by-both.csv")) as fp: fp_lines = fp.readlines() out_lines = runtmp.last_result.out.splitlines() fp_lines.sort() out_lines.sort() assert len(fp_lines) == len(out_lines) for line1, line2 in zip(fp_lines, out_lines): assert line1.strip() == line2.strip(), (line1, line2) @utils.in_tempdir def test_multi_query_classify_query_from_file(c): # both.lca.json is built from both dir and dir2 db1 = utils.get_test_data("lca/both.lca.json") dir1_glob = utils.get_test_data("lca/dir1/*.sig") dir1_files = glob.glob(dir1_glob) dir2_glob = utils.get_test_data("lca/dir2/*.sig") dir2_files = glob.glob(dir2_glob) file_list = c.output("file.list") with open(file_list, "w") as fp: print("\n".join(dir1_files), file=fp) print("\n".join(dir2_files), file=fp) cmd = ["lca", "classify", "--db", db1, "--query-from-file", file_list] c.run_sourmash(*cmd) out = c.last_result.out with open(utils.get_test_data("lca/classify-by-both.csv")) as fp: fp_lines = fp.readlines() out_lines = out.splitlines() fp_lines.sort() out_lines.sort() assert len(fp_lines) == len(out_lines) for line1, line2 in zip(fp_lines, out_lines): assert line1.strip() == line2.strip(), (line1, line2) @utils.in_tempdir def test_multi_query_classify_query_from_file_and_query(c): # both.lca.json is built from both dir and dir2 db1 = utils.get_test_data("lca/both.lca.json") dir1_glob = utils.get_test_data("lca/dir1/*.sig") dir1_files = glob.glob(dir1_glob) dir2_glob = utils.get_test_data("lca/dir2/*.sig") dir2_files = glob.glob(dir2_glob) file_list = c.output("file.list") with open(file_list, "w") as fp: print("\n".join(dir1_files[1:]), file=fp) # leave off first one print("\n".join(dir2_files), file=fp) cmd = [ "lca", "classify", "--db", db1, "--query", dir1_files[0], "--query-from-file", file_list, ] c.run_sourmash(*cmd) out = c.last_result.out with open(utils.get_test_data("lca/classify-by-both.csv")) as fp: fp_lines = fp.readlines() out_lines = out.splitlines() fp_lines.sort() out_lines.sort() assert len(fp_lines) == len(out_lines) for line1, line2 in zip(fp_lines, out_lines): assert line1.strip() == line2.strip(), (line1, line2) def test_multi_db_multi_query_classify_traverse(runtmp): # two halves of both.lca.json, see above test. db1 = utils.get_test_data("lca/dir1.lca.json") db2 = utils.get_test_data("lca/dir2.lca.json") dir1 = utils.get_test_data("lca/dir1") dir2 = utils.get_test_data("lca/dir2") cmd = ["lca", "classify", "--db", db1, db2, "--query", dir1, dir2] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) with open(utils.get_test_data("lca/classify-by-both.csv")) as fp: fp_lines = fp.readlines() out_lines = runtmp.last_result.out.splitlines() fp_lines.sort() out_lines.sort() assert len(fp_lines) == len(out_lines) for line1, line2 in zip(fp_lines, out_lines): assert line1.strip() == line2.strip(), (line1, line2) def test_unassigned_internal_index_and_classify(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/delmont-4.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "1 identifiers used out of 1 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = ["lca", "classify", "--db", lca_db, "--query", input_sig] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "ID,status,superkingdom,phylum,class,order,family,genus,species" in runtmp.last_result.out ) assert ( "TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,Gammaproteobacteria,unassigned,Alteromonadaceae,unassigned,Alteromonas_macleodii" in runtmp.last_result.out ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_unassigned_last_index_and_classify(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/delmont-5.csv") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "1 identifiers used out of 1 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = ["lca", "classify", "--db", lca_db, "--query", input_sig] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "ID,status,superkingdom,phylum,class,order,family,genus,species" in runtmp.last_result.out ) assert ( "TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,,,\r\n" in runtmp.last_result.out ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_index_and_classify_internal_unassigned_multi(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/delmont-6.csv") input_sig1 = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") input_sig2 = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig1, input_sig2, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "2 identifiers used out of 2 distinct identifiers in spreadsheet." in runtmp.last_result.err ) # classify input_sig1 cmd = ["lca", "classify", "--db", lca_db, "--query", input_sig1] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "ID,status,superkingdom,phylum,class,order,family,genus,species" in runtmp.last_result.out ) assert ( "TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,unassigned,unassigned,Alteromonadaceae,,,\r\n" in runtmp.last_result.out ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err # classify input_sig2 cmd = ["lca", "classify", "--db", lca_db, "--query", input_sig2] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "ID,status,superkingdom,phylum,class,order,family,genus,species" in runtmp.last_result.out ) assert ( "TARA_PSW_MAG_00136,found,Eukaryota,Chlorophyta,Prasinophyceae,unassigned,unassigned,Ostreococcus,,\r\n" in runtmp.last_result.out ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 1 LCA databases" in runtmp.last_result.err def test_classify_majority_vote_1(runtmp, lca_db_format): # classify merged signature using lca should yield no results c = runtmp # build database taxcsv = utils.get_test_data("lca/delmont-6.csv") input_sig1 = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") input_sig2 = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") lca_db = c.output(f"delmont-1.lca.{lca_db_format}") c.run_sourmash( "lca", "index", taxcsv, lca_db, input_sig1, input_sig2, "-F", lca_db_format ) print(c.last_command) print(c.last_result.out) print(c.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in c.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in c.last_result.err ) assert ( "2 identifiers used out of 2 distinct identifiers in spreadsheet." in c.last_result.err ) # merge input_sig1 and input_sig2 c.run_sourmash( "signature", "merge", input_sig1, input_sig2, "-k", "31", "--flatten", "-o", "sig1and2.sig", ) sig1and2 = c.output("sig1and2.sig") # lca classify should yield no results c.run_sourmash("lca", "classify", "--db", lca_db, "--query", sig1and2) print(c.last_command) print(c.last_result.out) print(c.last_result.err) assert ( "ID,status,superkingdom,phylum,class,order,family,genus,species" in c.last_result.out ) assert "disagree,,,,,,,," in c.last_result.out assert "classified 1 signatures total" in c.last_result.err assert "loaded 1 LCA databases" in c.last_result.err def test_classify_majority_vote_2(runtmp, lca_db_format): # classify same signature with same database using --majority # should yield results c = runtmp # build database taxcsv = utils.get_test_data("lca/delmont-6.csv") input_sig1 = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") input_sig2 = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") lca_db = c.output(f"delmont-1.lca.{lca_db_format}") c.run_sourmash( "lca", "index", taxcsv, lca_db, input_sig1, input_sig2, "-F", lca_db_format ) print(c.last_command) print(c.last_result.out) print(c.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in c.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in c.last_result.err ) assert ( "2 identifiers used out of 2 distinct identifiers in spreadsheet." in c.last_result.err ) # merge input_sig1 and input_sig2 c.run_sourmash( "signature", "merge", input_sig1, input_sig2, "-k", "31", "--flatten", "-o", "sig1and2.sig", ) sig1and2 = c.output("sig1and2.sig") # majority vote classify c.run_sourmash("lca", "classify", "--db", lca_db, "--query", sig1and2, "--majority") print(c.last_command) print(c.last_result.out) print(c.last_result.err) assert ( "ID,status,superkingdom,phylum,class,order,family,genus,species" in c.last_result.out ) assert ( "found,Eukaryota,Chlorophyta,Prasinophyceae,unassigned,unassigned,Ostreococcus" in c.last_result.out ) assert "classified 1 signatures total" in c.last_result.err assert "loaded 1 LCA databases" in c.last_result.err def test_classify_majority_vote_3(runtmp, lca_db_format): # classify signature with nothing in counts c = runtmp # build database taxcsv = utils.get_test_data("lca/delmont-6.csv") input_sig1 = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") input_sig2 = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") lca_db = c.output(f"delmont-1.lca.{lca_db_format}") c.run_sourmash( "lca", "index", taxcsv, lca_db, input_sig1, input_sig2, "-F", lca_db_format ) print(c.last_command) print(c.last_result.out) print(c.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in c.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in c.last_result.err ) assert ( "2 identifiers used out of 2 distinct identifiers in spreadsheet." in c.last_result.err ) # obtain testdata '47.fa.sig' testdata1 = utils.get_test_data("47.fa.sig") # majority vote classify c.run_sourmash( "lca", "classify", "--db", lca_db, "--query", testdata1, "--majority" ) print(c.last_command) print(c.last_result.out) print(c.last_result.err) assert ( "ID,status,superkingdom,phylum,class,order,family,genus,species" in c.last_result.out ) assert "nomatch,,,,,,,," in c.last_result.out assert "classified 1 signatures total" in c.last_result.err assert "loaded 1 LCA databases" in c.last_result.err def test_multi_db_classify(runtmp): db1 = utils.get_test_data("lca/delmont-1.lca.json") db2 = utils.get_test_data("lca/delmont-2.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") cmd = ["lca", "classify", "--db", db1, db2, "--query", input_sig] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "ID,status,superkingdom,phylum,class,order,family,genus,species" in runtmp.last_result.out ) assert ( "TARA_ASE_MAG_00031,found,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,,,," in runtmp.last_result.out ) assert "classified 1 signatures total" in runtmp.last_result.err assert "loaded 2 LCA databases" in runtmp.last_result.err def test_classify_unknown_hashes(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca-root/tax.csv") input_sig1 = utils.get_test_data("lca-root/TARA_MED_MAG_00029.fa.sig") input_sig2 = utils.get_test_data("lca-root/TOBG_MED-875.fna.gz.sig") lca_db = runtmp.output(f"lca-root.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig2, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "1 identifiers used out of 2 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = ["lca", "classify", "--db", lca_db, "--query", input_sig1] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "(root)" not in runtmp.last_result.out assert ( "TARA_MED_MAG_00029,found,Archaea,Euryarcheoata,unassigned,unassigned,novelFamily_I" in runtmp.last_result.out ) def test_single_summarize(runtmp): db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") cmd = ["lca", "summarize", "--db", db1, "--query", input_sig] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "loaded 1 signatures from 1 files total." in runtmp.last_result.err assert ( "100.0% 200 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales" in runtmp.last_result.out ) def test_single_summarize_singleton(runtmp): db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") cmd = [ "lca", "summarize", "--db", db1, "--query", input_sig, ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "loaded 1 signatures from 1 files total." in runtmp.last_result.err assert ( "100.0% 200 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales" in runtmp.last_result.out ) assert ( "test-data/lca/TARA_ASE_MAG_00031.sig:5b438c6c TARA_ASE_MAG_00031" in runtmp.last_result.out ) @utils.in_tempdir def test_single_summarize_traverse(c): db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") in_dir = c.output("sigs") os.mkdir(in_dir) shutil.copyfile(input_sig, os.path.join(in_dir, "q.sig")) cmd = ["lca", "summarize", "--db", db1, "--query", in_dir] c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert "loaded 1 signatures from 1 files total." in err assert ( "100.0% 200 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales" in out ) @utils.in_tempdir def test_single_summarize_singleton_traverse(c): db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") in_dir = c.output("sigs") os.mkdir(in_dir) shutil.copyfile(input_sig, os.path.join(in_dir, "q.sig")) cmd = ["lca", "summarize", "--db", db1, "--query", in_dir] c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert "loaded 1 signatures from 1 files total." in err assert ( "100.0% 200 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales" in out ) assert "q.sig:5b438c6c TARA_ASE_MAG_00031" in out def test_single_summarize_to_output(runtmp): db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") in_dir = runtmp.output("sigs") os.mkdir(in_dir) shutil.copyfile(input_sig, os.path.join(in_dir, "q.sig")) cmd = [ "lca", "summarize", "--db", db1, "--query", input_sig, "-o", runtmp.output("output.txt"), ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) with open(runtmp.output("output.txt")) as fp: outdata = fp.read() assert "loaded 1 signatures from 1 files total." in runtmp.last_result.err assert "200,Bacteria,Proteobacteria,Gammaproteobacteria" in outdata def test_single_summarize_to_output_check_filename(runtmp): db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") in_dir = runtmp.output("sigs") os.mkdir(in_dir) shutil.copyfile(input_sig, os.path.join(in_dir, "q.sig")) cmd = [ "lca", "summarize", "--db", db1, "--query", os.path.join(in_dir, "q.sig"), "-o", runtmp.output("output.txt"), ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) outdata = Path(runtmp.output("output.txt")).read_text() assert "loaded 1 signatures from 1 files total." in runtmp.last_result.err assert ( "count,superkingdom,phylum,class,order,family,genus,species,strain,filename,sig_name,sig_md5,total_counts\n" in outdata ) assert ( "200,Bacteria,Proteobacteria,Gammaproteobacteria,Alteromonadales,Alteromonadaceae,Alteromonas,Alteromonas_macleodii,," + os.path.join(in_dir, "q.sig") + ",TARA_ASE_MAG_00031,5b438c6c858cdaf9e9b05a207fa3f9f0,200.0\n" in outdata ) print(outdata) def test_summarize_unknown_hashes_to_output_check_total_counts(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca-root/tax.csv") input_sig1 = utils.get_test_data("lca-root/TARA_MED_MAG_00029.fa.sig") input_sig2 = utils.get_test_data("lca-root/TOBG_MED-875.fna.gz.sig") lca_db = runtmp.output(f"lca-root.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig2, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "1 identifiers used out of 2 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = ["lca", "summarize", "--db", lca_db, "--query", input_sig1, "-o", "out.csv"] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "(root)" not in runtmp.last_result.out assert ( "11.5% 27 Archaea;Euryarcheoata;unassigned;unassigned;novelFamily_I" in runtmp.last_result.out ) with open(runtmp.output("out.csv"), newline="") as fp: r = csv.DictReader(fp) rows = list(r) pairs = [(row["count"], row["total_counts"]) for row in rows] pairs = [(float(x), float(y)) for x, y in pairs] pairs = set(pairs) assert pairs == {(27.0, 234.0)} def test_single_summarize_scaled(runtmp): db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") in_dir = runtmp.output("sigs") os.mkdir(in_dir) shutil.copyfile(input_sig, os.path.join(in_dir, "q.sig")) cmd = ["lca", "summarize", "--db", db1, "--query", input_sig, "--scaled", "100000"] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "loaded 1 signatures from 1 files total." in runtmp.last_result.err assert "100.0% 27 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales" def test_single_summarize_scaled_zip_query(runtmp): # check zipfile as query db1 = utils.get_test_data("lca/delmont-1.lca.json") input_sig = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") query_ss = load_one_signature_from_json(input_sig, ksize=31) query_zipfile = runtmp.output("query.zip") with sourmash_args.SaveSignaturesToLocation(query_zipfile) as save_sig: save_sig.add(query_ss) cmd = [ "lca", "summarize", "--db", db1, "--query", query_zipfile, "--scaled", "100000", ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "loaded 1 signatures from 1 files total." in runtmp.last_result.err assert "100.0% 27 Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales" def test_multi_summarize_with_unassigned_singleton(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca/delmont-6.csv") input_sig1 = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") input_sig2 = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig1, input_sig2, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "2 identifiers used out of 2 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = [ "lca", "summarize", "--db", lca_db, "--query", input_sig1, input_sig2, "--ignore-abundance", ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "loaded 2 signatures from 2 files total." in runtmp.last_result.err out_lines = runtmp.last_result.out.splitlines() def remove_line_startswith(x, check=None): for line in out_lines: if line.startswith(x): out_lines.remove(line) if check: # make sure the check value is in there assert check in line return line assert 0, f"couldn't find {x}" # note, proportions/percentages are now per-file remove_line_startswith( "100.0% 200 Bacteria ", "TARA_ASE_MAG_00031.sig:5b438c6c" ) remove_line_startswith( "100.0% 200 Bacteria;Proteobacteria;unassigned;unassigned " ) remove_line_startswith("100.0% 1231 Eukaryota;Chlorophyta ") remove_line_startswith( "100.0% 1231 Eukaryota ", "TARA_PSW_MAG_00136.sig:db50b713" ) remove_line_startswith("100.0% 200 Bacteria;Proteobacteria ") remove_line_startswith("100.0% 200 Bacteria;Proteobacteria;unassigned ") remove_line_startswith("100.0% 1231 Eukaryota;Chlorophyta;Prasinophyceae ") remove_line_startswith( "100.0% 200 Bacteria;Proteobacteria;unassigned;unassigned;Alteromonadaceae " ) remove_line_startswith( "100.0% 1231 Eukaryota;Chlorophyta;Prasinophyceae;unassigned;unassigned " ) remove_line_startswith( "100.0% 1231 Eukaryota;Chlorophyta;Prasinophyceae;unassigned " ) remove_line_startswith( "100.0% 1231 Eukaryota;Chlorophyta;Prasinophyceae;unassigned;unassigned;Ostreococcus " ) assert not out_lines def test_multi_summarize_with_zip_unassigned_singleton(runtmp, lca_db_format): # test summarize on multiple queries, in a zipfile. taxcsv = utils.get_test_data("lca/delmont-6.csv") input_sig1 = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") input_sig2 = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig1, input_sig2, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "2 identifiers used out of 2 distinct identifiers in spreadsheet." in runtmp.last_result.err ) query_zipfile = runtmp.output("query.zip") with sourmash_args.SaveSignaturesToLocation(query_zipfile) as save_sig: input_sig1 = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") sig1 = load_one_signature_from_json(input_sig1, ksize=31) input_sig2 = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") sig2 = load_one_signature_from_json(input_sig2, ksize=31) save_sig.add(sig1) save_sig.add(sig2) cmd = [ "lca", "summarize", "--db", lca_db, "--query", "query.zip", "--ignore-abundance", ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "loaded 2 signatures from 1 files total." in runtmp.last_result.err out_lines = runtmp.last_result.out.splitlines() def remove_line_startswith(x, check=None): for line in out_lines: if line.startswith(x): out_lines.remove(line) if check: # make sure the check value is in there assert check in line return line assert 0, f"couldn't find {x}" # note, proportions/percentages are now per-file remove_line_startswith("100.0% 200 Bacteria ", ":5b438c6c") remove_line_startswith( "100.0% 200 Bacteria;Proteobacteria;unassigned;unassigned " ) remove_line_startswith("100.0% 1231 Eukaryota;Chlorophyta ") remove_line_startswith("100.0% 1231 Eukaryota ", ":db50b713") remove_line_startswith("100.0% 200 Bacteria;Proteobacteria ") remove_line_startswith("100.0% 200 Bacteria;Proteobacteria;unassigned ") remove_line_startswith("100.0% 1231 Eukaryota;Chlorophyta;Prasinophyceae ") remove_line_startswith( "100.0% 200 Bacteria;Proteobacteria;unassigned;unassigned;Alteromonadaceae " ) remove_line_startswith( "100.0% 1231 Eukaryota;Chlorophyta;Prasinophyceae;unassigned;unassigned " ) remove_line_startswith( "100.0% 1231 Eukaryota;Chlorophyta;Prasinophyceae;unassigned " ) remove_line_startswith( "100.0% 1231 Eukaryota;Chlorophyta;Prasinophyceae;unassigned;unassigned;Ostreococcus " ) assert not out_lines def test_summarize_to_root(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca-root/tax.csv") input_sig1 = utils.get_test_data("lca-root/TARA_MED_MAG_00029.fa.sig") input_sig2 = utils.get_test_data("lca-root/TOBG_MED-875.fna.gz.sig") lca_db = runtmp.output(f"lca-root.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig1, input_sig2, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "2 identifiers used out of 2 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = [ "lca", "summarize", "--db", lca_db, "--query", input_sig2, "--ignore-abundance", ] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "78.6% 99 Archaea" in runtmp.last_result.out assert "21.4% 27 (root)" in runtmp.last_result.out def test_summarize_unknown_hashes(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca-root/tax.csv") input_sig1 = utils.get_test_data("lca-root/TARA_MED_MAG_00029.fa.sig") input_sig2 = utils.get_test_data("lca-root/TOBG_MED-875.fna.gz.sig") lca_db = runtmp.output(f"lca-root.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig2, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "1 identifiers used out of 2 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = ["lca", "summarize", "--db", lca_db, "--query", input_sig1] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "(root)" not in runtmp.last_result.out assert ( "11.5% 27 Archaea;Euryarcheoata;unassigned;unassigned;novelFamily_I" in runtmp.last_result.out ) def test_summarize_to_root_abund(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca-root/tax.csv") input_sig1 = utils.get_test_data("lca-root/TARA_MED_MAG_00029.fa.sig") input_sig2 = utils.get_test_data("lca-root/TOBG_MED-875.fna.gz.sig") lca_db = runtmp.output(f"lca-root.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig1, input_sig2, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "2 identifiers used out of 2 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = ["lca", "summarize", "--db", lca_db, "--query", input_sig2] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "78.9% 101 Archaea" in runtmp.last_result.out assert "21.1% 27 (root)" in runtmp.last_result.out def test_summarize_unknown_hashes_abund(runtmp, lca_db_format): taxcsv = utils.get_test_data("lca-root/tax.csv") input_sig1 = utils.get_test_data("lca-root/TARA_MED_MAG_00029.fa.sig") input_sig2 = utils.get_test_data("lca-root/TOBG_MED-875.fna.gz.sig") lca_db = runtmp.output(f"lca-root.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig2, "-F", lca_db_format] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "1 identifiers used out of 2 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = ["lca", "summarize", "--db", lca_db, "--query", input_sig1] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "(root)" not in runtmp.last_result.out assert ( "11.5% 27 Archaea;Euryarcheoata;unassigned;unassigned;novelFamily_I" in runtmp.last_result.out ) @utils.in_thisdir def test_summarize_abund_hmp(c): # test lca summarize --with-abundance on some real data queryfile = utils.get_test_data("hmp-sigs/G36354.sig.gz") dbname = utils.get_test_data("hmp-sigs/G36354-matches.lca.json.gz") c.run_sourmash("lca", "summarize", "--db", dbname, "--query", queryfile) assert ( "32.1% 1080 p__Firmicutes;c__Bacilli;o__Lactobacillales" in c.last_result.out ) @utils.in_thisdir def test_summarize_abund_fake_no_abund(c): # test lca summarize on some known/fake data; see docs for explanation. queryfile = utils.get_test_data("fake-abund/query.sig.gz") dbname = utils.get_test_data("fake-abund/matches.lca.json.gz") c.run_sourmash( "lca", "summarize", "--db", dbname, "--query", queryfile, "--ignore-abundance" ) assert ( "NOTE: discarding abundances in query, since --ignore-abundance" in c.last_result.err ) assert "79.6% 550 Bacteria" in c.last_result.out assert "20.4% 141 Archaea" in c.last_result.out @utils.in_thisdir def test_summarize_abund_fake_yes_abund(c): # test lca summarize abundance weighting on some known/fake data queryfile = utils.get_test_data("fake-abund/query.sig.gz") dbname = utils.get_test_data("fake-abund/matches.lca.json.gz") c.run_sourmash("lca", "summarize", "--db", dbname, "--query", queryfile) assert "43.2% 563 Bacteria" in c.last_result.out assert "56.8% 740 Archaea" in c.last_result.out def test_rankinfo_on_multi(runtmp): db1 = utils.get_test_data("lca/dir1.lca.json") db2 = utils.get_test_data("lca/dir2.lca.json") cmd = ["lca", "rankinfo", db1, db2] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) lines = runtmp.last_result.out.splitlines() lines.remove("superkingdom: 0 (0.0%)") lines.remove("phylum: 464 (12.8%)") lines.remove("class: 533 (14.7%)") lines.remove("order: 1050 (29.0%)") lines.remove("family: 695 (19.2%)") lines.remove("genus: 681 (18.8%)") lines.remove("species: 200 (5.5%)") lines.remove("strain: 0 (0.0%)") assert not lines def test_rankinfo_on_single(runtmp): db1 = utils.get_test_data("lca/both.lca.json") cmd = ["lca", "rankinfo", db1] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) lines = runtmp.last_result.out.splitlines() lines.remove("superkingdom: 0 (0.0%)") lines.remove("phylum: 464 (12.8%)") lines.remove("class: 533 (14.7%)") lines.remove("order: 1050 (29.0%)") lines.remove("family: 695 (19.2%)") lines.remove("genus: 681 (18.8%)") lines.remove("species: 200 (5.5%)") lines.remove("strain: 0 (0.0%)") assert not lines def test_rankinfo_no_tax(runtmp, lca_db_format): # note: TARA_PSW_MAG_00136 is _not_ in delmont-1.csv. taxcsv = utils.get_test_data("lca/delmont-1.csv") input_sig = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") lca_db = runtmp.output(f"delmont-1.lca.{lca_db_format}") cmd = ["lca", "index", taxcsv, lca_db, input_sig, "-F", lca_db_format] runtmp.sourmash(*cmd) print("cmd:", cmd) print("out:", runtmp.last_result.out) print("err:", runtmp.last_result.err) assert os.path.exists(lca_db) assert ( "** assuming column 'MAGs' is identifiers in spreadsheet" in runtmp.last_result.err ) assert ( "** assuming column 'Domain' is superkingdom in spreadsheet" in runtmp.last_result.err ) assert ( "0 identifiers used out of 1 distinct identifiers in spreadsheet." in runtmp.last_result.err ) cmd = ["lca", "rankinfo", lca_db] runtmp.sourmash(*cmd) def test_rankinfo_with_min(runtmp): db1 = utils.get_test_data("lca/dir1.lca.json") db2 = utils.get_test_data("lca/dir2.lca.json") cmd = ["lca", "rankinfo", db1, db2, "--minimum-num", "1"] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) lines = runtmp.last_result.out.splitlines() lines.remove("superkingdom: 0 (0.0%)") lines.remove("phylum: 464 (12.8%)") lines.remove("class: 533 (14.7%)") lines.remove("order: 1050 (29.0%)") lines.remove("family: 695 (19.2%)") lines.remove("genus: 681 (18.8%)") lines.remove("species: 200 (5.5%)") lines.remove("strain: 0 (0.0%)") assert not lines def test_rankinfo_with_min_2(runtmp): db1 = utils.get_test_data("lca/dir1.lca.json") db2 = utils.get_test_data("lca/dir2.lca.json") cmd = ["lca", "rankinfo", db1, db2, "--minimum-num", "2"] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "(no hashvals with lineages found)" in runtmp.last_result.err def test_compare_csv(runtmp): a = utils.get_test_data("lca/classify-by-both.csv") b = utils.get_test_data("lca/tara-delmont-SuppTable3.csv") cmd = ["lca", "compare_csv", a, b, "-f"] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "loaded 106 distinct lineages, 957 rows" in runtmp.last_result.err assert "missing 937 assignments in classify spreadsheet." in runtmp.last_result.err assert ( "20 total assignments, 0 differ between spreadsheets." in runtmp.last_result.err ) def test_compare_csv_real(runtmp): a = utils.get_test_data("lca/tully-genome-sigs.classify.csv") b = utils.get_test_data("lca/tully-query.delmont-db.sigs.classify.csv") cmd = ["lca", "compare_csv", a, b, "--start-column=3", "-f"] runtmp.sourmash(*cmd) print(cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "loaded 87 distinct lineages, 2631 rows" in runtmp.last_result.err assert "missing 71 assignments in classify spreadsheet." in runtmp.last_result.err assert "missing 1380 assignments in custom spreadsheet." in runtmp.last_result.err assert "(these will not be evaluated any further)" in runtmp.last_result.err assert ( "987 total assignments, 889 differ between spreadsheets." in runtmp.last_result.err ) assert ( "296 are compatible (one lineage is ancestor of another." in runtmp.last_result.err ) assert ( "593 are incompatible (there is a disagreement in the trees)." in runtmp.last_result.err ) assert "164 incompatible at rank superkingdom" in runtmp.last_result.err assert "255 incompatible at rank phylum" in runtmp.last_result.err assert "107 incompatible at rank class" in runtmp.last_result.err assert "54 incompatible at rank order" in runtmp.last_result.err assert "13 incompatible at rank family" in runtmp.last_result.err assert "0 incompatible at rank genus" in runtmp.last_result.err assert "0 incompatible at rank species" in runtmp.last_result.err def test_incompat_lca_db_ksize_2_fail(runtmp, lca_db_format): # test on gather - create a database with ksize of 25 => fail # because of incompatibility. c = runtmp testdata1 = utils.get_test_data("lca/TARA_ASE_MAG_00031.fa.gz") c.run_sourmash( "sketch", "dna", "-p", "k=25,scaled=1000", testdata1, "-o", "test_db.sig" ) print(c) c.run_sourmash( "lca", "index", utils.get_test_data( "lca/delmont-1.csv", ), f"test.lca.{lca_db_format}", "test_db.sig", "-k", "25", "--scaled", "10000", "-F", lca_db_format, ) print(c) # this should fail: the LCA database has ksize 25, and the query sig has # no compatible ksizes. with pytest.raises(SourmashCommandFailed): c.run_sourmash( "gather", utils.get_test_data("lca/TARA_ASE_MAG_00031.sig"), f"test.lca.{lca_db_format}", ) err = c.last_result.err print(err) if lca_db_format == "sql": assert "no compatible signatures found in 'test.lca.sql'" in err else: assert "ERROR: cannot use 'test.lca.json' for this query." in err assert ( "ksize on this database is 25; this is different from requested ksize of 31" ) def test_incompat_lca_db_ksize_2_nofail(runtmp, lca_db_format): # test on gather - create a database with ksize of 25, no fail # because of --no-fail-on-empty-databases c = runtmp testdata1 = utils.get_test_data("lca/TARA_ASE_MAG_00031.fa.gz") c.run_sourmash( "sketch", "dna", "-p", "k=25,scaled=1000", testdata1, "-o", "test_db.sig" ) print(c) c.run_sourmash( "lca", "index", utils.get_test_data( "lca/delmont-1.csv", ), f"test.lca.{lca_db_format}", "test_db.sig", "-k", "25", "--scaled", "10000", "-F", lca_db_format, ) print(c) # this should not fail despite mismatched ksize, b/c of --no-fail flag. c.run_sourmash( "gather", utils.get_test_data("lca/TARA_ASE_MAG_00031.sig"), f"test.lca.{lca_db_format}", "--no-fail-on-empty-database", ) err = c.last_result.err print(err) if lca_db_format == "sql": assert "no compatible signatures found in 'test.lca.sql'" in err else: assert "ERROR: cannot use 'test.lca.json' for this query." in err assert ( "ksize on this database is 25; this is different from requested ksize of 31" ) def test_lca_index_empty(runtmp, lca_db_format): c = runtmp # test lca index with an empty taxonomy CSV, followed by a load & gather. sig2file = utils.get_test_data("2.fa.sig") sig47file = utils.get_test_data("47.fa.sig") sig63file = utils.get_test_data("63.fa.sig") sig63 = load_one_signature_from_json(sig63file, ksize=31) # create an empty spreadsheet with open(c.output("empty.csv"), "w") as fp: fp.write( "accession,superkingdom,phylum,class,order,family,genus,species,strain" ) # index! c.run_sourmash( "lca", "index", "empty.csv", "xxx", sig2file, sig47file, sig63file, "--scaled", "1000", "-F", lca_db_format, ) # can we load and search? lca_db_filename = c.output(f"xxx.lca.{lca_db_format}") db, ksize, scaled = lca_utils.load_single_database(lca_db_filename) result = db.best_containment(sig63) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig.minhash == sig63.minhash assert name == lca_db_filename def test_lca_gather_threshold_1(): # test gather() method, in some detail; see same tests for sbt. sig2file = utils.get_test_data("2.fa.sig") sig47file = utils.get_test_data("47.fa.sig") sig63file = utils.get_test_data("63.fa.sig") sig2 = load_one_signature_from_json(sig2file, ksize=31) sig47 = load_one_signature_from_json(sig47file, ksize=31) sig63 = load_one_signature_from_json(sig63file, ksize=31) # construct LCA Database db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) db.insert(sig2) db.insert(sig47) db.insert(sig63) # now construct query signatures with specific numbers of hashes -- # note, these signatures all have scaled=1000. mins = list(sorted(sig2.minhash.hashes.keys())) new_mh = sig2.minhash.copy_and_clear() # query with empty hashes assert not new_mh with pytest.raises(ValueError): db.best_containment(SourmashSignature(new_mh)) # add one hash new_mh.add_hash(mins.pop()) assert len(new_mh) == 1 result = db.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig.minhash == sig2.minhash assert name is None # check with a threshold -> should be no results. with pytest.raises(ValueError): db.best_containment(SourmashSignature(new_mh), threshold_bp=5000) # add three more hashes => length of 4 new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) assert len(new_mh) == 4 result = db.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig.minhash == sig2.minhash assert name is None # check with a too-high threshold -> should be no results. with pytest.raises(ValueError): db.best_containment(SourmashSignature(new_mh), threshold_bp=5000) def test_lca_gather_threshold_5(): # test gather() method, in some detail; see same tests for sbt. sig2file = utils.get_test_data("2.fa.sig") sig47file = utils.get_test_data("47.fa.sig") sig63file = utils.get_test_data("63.fa.sig") sig2 = load_one_signature_from_json(sig2file, ksize=31) sig47 = load_one_signature_from_json(sig47file, ksize=31) sig63 = load_one_signature_from_json(sig63file, ksize=31) # construct LCA Database db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) db.insert(sig2) db.insert(sig47) db.insert(sig63) # now construct query signatures with specific numbers of hashes -- # note, these signatures both have scaled=1000. mins = list(sorted(sig2.minhash.hashes.keys())) new_mh = sig2.minhash.copy_and_clear() # add five hashes for i in range(5): new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) # should get a result with no threshold (any match at all is returned) result = db.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig.minhash == sig2.minhash assert name is None # now, check with a threshold_bp that should be meet-able. result = db.best_containment(SourmashSignature(new_mh), threshold_bp=5000) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig.minhash == sig2.minhash assert name is None def test_gather_multiple_return(): sig2file = utils.get_test_data("2.fa.sig") sig47file = utils.get_test_data("47.fa.sig") sig63file = utils.get_test_data("63.fa.sig") sig2 = load_one_signature_from_json(sig2file, ksize=31) sig47 = load_one_signature_from_json(sig47file, ksize=31) sig63 = load_one_signature_from_json(sig63file, ksize=31) # construct LCA Database db = sourmash.lca.LCA_Database(ksize=31, scaled=1000) db.insert(sig2) db.insert(sig47) db.insert(sig63) # now, run gather. how many results do we get, and are they in the # right order? result = db.best_containment(sig63) print(result) assert result assert result.score == 1.0 def test_lca_db_protein_build(): # test programmatic creation of LCA database with protein sigs in it sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/protein/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) sig1 = load_one_signature_from_json(sigfile1) sig2 = load_one_signature_from_json(sigfile2) db = sourmash.lca.LCA_Database(ksize=19, scaled=100, moltype="protein") assert db.insert(sig1) assert db.insert(sig2) # check reconstruction -- mh_list = [x.minhash for x in db.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db.search(sig1, threshold=0.0) assert len(results) == 2 result = db.best_containment(sig2) assert result.score == 1.0 @utils.in_tempdir def test_lca_db_protein_save_load(c): # test save/load of programmatically created db with protein sigs in it sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/protein/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) sig1 = load_one_signature_from_json(sigfile1) sig2 = load_one_signature_from_json(sigfile2) db = sourmash.lca.LCA_Database(ksize=19, scaled=100, moltype="protein") assert db.insert(sig1) assert db.insert(sig2) db.save(c.output("xxx.lca.json")) del db x = sourmash.lca.lca_db.load_single_database(c.output("xxx.lca.json")) db2 = x[0] assert db2.moltype == "protein" # check reconstruction -- mh_list = [x.minhash for x in db2.signatures()] assert len(mh_list) == 2 print("XXX", mh_list[0].ksize) print("YYY", sig1.minhash.ksize) assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db2.search(sig1, threshold=0.0) assert len(results) == 2 result = db2.best_containment(sig2) assert result.score == 1.0 def test_lca_db_protein_command_index(runtmp, lca_db_format): # test command-line creation of LCA database with protein sigs c = runtmp sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/protein/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) lineages = utils.get_test_data("prot/gtdb-subset-lineages.csv") db_out = c.output(f"protein.lca.{lca_db_format}") c.run_sourmash( "lca", "index", lineages, db_out, sigfile1, sigfile2, "-C", "2", "--split-identifiers", "--require-taxonomy", "--scaled", "100", "-k", "19", "--protein", "-F", lca_db_format, ) x = sourmash.lca.lca_db.load_single_database(db_out) db2 = x[0] assert db2.moltype == "protein" sig1 = load_one_signature_from_json(sigfile1) sig2 = load_one_signature_from_json(sigfile2) # check reconstruction -- mh_list = [x.minhash for x in db2.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db2.search(sig1, threshold=0.0) assert len(results) == 2 result = db2.best_containment(sig2) assert result.score == 1.0 @utils.in_thisdir def test_lca_db_protein_command_search(c): # test command-line search/gather of LCA database with protein sigs # (LCA database created as above) sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/protein.lca.json.gz") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out) assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out def test_lca_db_hp_build(): # test programmatic creation of LCA database with hp sigs in it sigfile1 = utils.get_test_data( "prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/hp/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) sig1 = load_one_signature_from_json(sigfile1) sig2 = load_one_signature_from_json(sigfile2) db = sourmash.lca.LCA_Database(ksize=19, scaled=100, moltype="hp") assert db.insert(sig1) assert db.insert(sig2) # check reconstruction -- mh_list = [x.minhash for x in db.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db.search(sig1, threshold=0.0) assert len(results) == 2 result = db.best_containment(sig2) assert result.score == 1.0 @utils.in_tempdir def test_lca_db_hp_save_load(c): # test save/load of programmatically created db with hp sigs in it sigfile1 = utils.get_test_data( "prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/hp/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) sig1 = load_one_signature_from_json(sigfile1) sig2 = load_one_signature_from_json(sigfile2) db = sourmash.lca.LCA_Database(ksize=19, scaled=100, moltype="hp") assert db.insert(sig1) assert db.insert(sig2) db.save(c.output("xxx.lca.json")) del db x = sourmash.lca.lca_db.load_single_database(c.output("xxx.lca.json")) db2 = x[0] assert db2.moltype == "hp" # check reconstruction -- mh_list = [x.minhash for x in db2.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db2.search(sig1, threshold=0.0) assert len(results) == 2 result = db2.best_containment(sig2) assert result.score == 1.0 def test_lca_db_hp_command_index(runtmp, lca_db_format): # test command-line creation of LCA database with hp sigs c = runtmp sigfile1 = utils.get_test_data( "prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/hp/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) lineages = utils.get_test_data("prot/gtdb-subset-lineages.csv") db_out = c.output(f"hp.lca.{lca_db_format}") c.run_sourmash( "lca", "index", lineages, db_out, sigfile1, sigfile2, "-C", "2", "--split-identifiers", "--require-taxonomy", "--scaled", "100", "-k", "19", "--hp", "-F", lca_db_format, ) x = sourmash.lca.lca_db.load_single_database(db_out) db2 = x[0] assert db2.moltype == "hp" sig1 = load_one_signature_from_json(sigfile1) sig2 = load_one_signature_from_json(sigfile2) # check reconstruction -- mh_list = [x.minhash for x in db2.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db2.search(sig1, threshold=0.0) assert len(results) == 2 result = db2.best_containment(sig2) assert result.score == 1.0 @utils.in_thisdir def test_lca_db_hp_command_search(c): # test command-line search/gather of LCA database with hp sigs # (LCA database created as above) sigfile1 = utils.get_test_data( "prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/hp.lca.json.gz") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out, "--threshold", "0.0") assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out def test_lca_db_dayhoff_build(): # test programmatic creation of LCA database with dayhoff sigs in it sigfile1 = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/dayhoff/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) sig1 = load_one_signature_from_json(sigfile1) sig2 = load_one_signature_from_json(sigfile2) db = sourmash.lca.LCA_Database(ksize=19, scaled=100, moltype="dayhoff") assert db.insert(sig1) assert db.insert(sig2) # check reconstruction -- mh_list = [x.minhash for x in db.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db.search(sig1, threshold=0.0) assert len(results) == 2 result = db.best_containment(sig2) assert result.score == 1.0 @utils.in_tempdir def test_lca_db_dayhoff_save_load(c): # test save/load of programmatically created db with dayhoff sigs in it sigfile1 = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/dayhoff/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) sig1 = load_one_signature_from_json(sigfile1) sig2 = load_one_signature_from_json(sigfile2) db = sourmash.lca.LCA_Database(ksize=19, scaled=100, moltype="dayhoff") assert db.insert(sig1) assert db.insert(sig2) db.save(c.output("xxx.lca.json")) del db x = sourmash.lca.lca_db.load_single_database(c.output("xxx.lca.json")) db2 = x[0] assert db2.moltype == "dayhoff" # check reconstruction -- mh_list = [x.minhash for x in db2.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db2.search(sig1, threshold=0.0) assert len(results) == 2 result = db2.best_containment(sig2) assert result.score == 1.0 def test_lca_db_dayhoff_command_index(runtmp, lca_db_format): # test command-line creation of LCA database with dayhoff sigs c = runtmp sigfile1 = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/dayhoff/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) lineages = utils.get_test_data("prot/gtdb-subset-lineages.csv") db_out = c.output(f"dayhoff.lca.{lca_db_format}") c.run_sourmash( "lca", "index", lineages, db_out, sigfile1, sigfile2, "-C", "2", "--split-identifiers", "--require-taxonomy", "--scaled", "100", "-k", "19", "--dayhoff", "-F", lca_db_format, ) x = sourmash.lca.lca_db.load_single_database(db_out) db2 = x[0] assert db2.moltype == "dayhoff" sig1 = load_one_signature_from_json(sigfile1) sig2 = load_one_signature_from_json(sigfile2) # check reconstruction -- mh_list = [x.minhash for x in db2.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db2.search(sig1, threshold=0.0) assert len(results) == 2 result = db2.best_containment(sig2) assert result.score == 1.0 @utils.in_thisdir def test_lca_db_dayhoff_command_search(c): # test command-line search/gather of LCA database with dayhoff sigs # (LCA database created as above) sigfile1 = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/dayhoff.lca.json.gz") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out, "--threshold", "0.0") assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out def test_lca_index_with_picklist(runtmp, lca_db_format): gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) outdb = runtmp.output(f"gcf.lca.{lca_db_format}") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") # create an empty spreadsheet with open(runtmp.output("empty.csv"), "w") as fp: fp.write( "accession,superkingdom,phylum,class,order,family,genus,species,strain" ) runtmp.sourmash( "lca", "index", "empty.csv", outdb, *gcf_sigs, "-k", "21", "--picklist", f"{picklist}:md5:md5", "-F", lca_db_format, ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "for given picklist, found 3 matches to 9 distinct values" in err assert "WARNING: 6 missing picklist values." assert "WARNING: no lineage provided for 3 signatures" in err siglist = list(sourmash.load_file_as_signatures(outdb)) assert len(siglist) == 3 for ss in siglist: assert "Thermotoga" in ss.name def test_lca_index_with_picklist_exclude(runtmp, lca_db_format): gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) outdb = runtmp.output(f"gcf.lca.{lca_db_format}") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") # create an empty spreadsheet with open(runtmp.output("empty.csv"), "w") as fp: fp.write( "accession,superkingdom,phylum,class,order,family,genus,species,strain" ) runtmp.sourmash( "lca", "index", "empty.csv", outdb, *gcf_sigs, "-k", "21", "--picklist", f"{picklist}:md5:md5:exclude", "-F", lca_db_format, ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) siglist = list(sourmash.load_file_as_signatures(outdb)) assert len(siglist) == 9 for ss in siglist: assert "Thermotoga" not in ss.name def test_lca_index_select_with_picklist(runtmp, lca_db_format): # check what happens with picklists after index gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) outdb = runtmp.output(f"gcf.lca.{lca_db_format}") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") # create an empty spreadsheet with open(runtmp.output("empty.csv"), "w") as fp: fp.write( "accession,superkingdom,phylum,class,order,family,genus,species,strain" ) runtmp.sourmash( "lca", "index", "empty.csv", outdb, *gcf_sigs, "-k", "21", "-F", lca_db_format ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) idx = sourmash.load_file_as_index(outdb) picklist_obj = SignaturePicklist.from_picklist_args(f"{picklist}:md5:md5") picklist_obj.load() idx = idx.select(picklist=picklist_obj) siglist = list(idx.signatures()) assert len(siglist) == 3 for ss in siglist: assert "Thermotoga" in ss.name def test_lca_index_select_with_picklist_exclude(runtmp, lca_db_format): # check what happens with picklists after index gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) outdb = runtmp.output(f"gcf.lca.{lca_db_format}") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") # create an empty spreadsheet with open(runtmp.output("empty.csv"), "w") as fp: fp.write( "accession,superkingdom,phylum,class,order,family,genus,species,strain" ) runtmp.sourmash( "lca", "index", "empty.csv", outdb, *gcf_sigs, "-k", "21", "-F", lca_db_format ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) idx = sourmash.load_file_as_index(outdb) picklist_obj = SignaturePicklist.from_picklist_args(f"{picklist}:md5:md5:exclude") picklist_obj.load() idx = idx.select(picklist=picklist_obj) siglist = list(idx.signatures()) assert len(siglist) == 9 for ss in siglist: assert "Thermotoga" not in ss.name def test_lca_jaccard_ordering(): # this tests a tricky situation where for three sketches A, B, C, # |A intersect B| is greater than |A intersect C| # _but_ # |A jaccard B| is less than |A intersect B| a = sourmash.MinHash(ksize=31, n=0, scaled=2) b = a.copy_and_clear() c = a.copy_and_clear() a.add_many([1, 2, 3, 4]) b.add_many([1, 2, 3] + list(range(10, 30))) c.add_many([1, 5]) def _intersect(x, y): return x.intersection_and_union_size(y)[0] print("a intersect b:", _intersect(a, b)) print("a intersect c:", _intersect(a, c)) print("a jaccard b:", a.jaccard(b)) print("a jaccard c:", a.jaccard(c)) assert _intersect(a, b) > _intersect(a, c) assert a.jaccard(b) < a.jaccard(c) # thresholds to use: assert a.jaccard(b) < 0.15 assert a.jaccard(c) > 0.15 # now - make signatures, try out :) ss_a = sourmash.SourmashSignature(a, name="A") ss_b = sourmash.SourmashSignature(b, name="B") ss_c = sourmash.SourmashSignature(c, name="C") db = sourmash.lca.LCA_Database(ksize=31, scaled=2) db.insert(ss_a) db.insert(ss_b) db.insert(ss_c) sr = db.search(ss_a, threshold=0.15) print(sr) assert len(sr) == 2 assert sr[0].signature == ss_a assert sr[0].score == 1.0 assert sr[1].signature == ss_c assert sr[1].score == 0.2 def test_lca_db_protein_save_twice(runtmp, lca_db_format): # test save twice sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/protein/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) sig1 = load_one_signature_from_json(sigfile1) sig2 = load_one_signature_from_json(sigfile2) db = sourmash.lca.LCA_Database(ksize=19, scaled=100, moltype="protein") assert db.insert(sig1) assert db.insert(sig2) db.save(runtmp.output("xxx"), format=lca_db_format) with pytest.raises(ValueError): db.save(runtmp.output("xxx"), format=lca_db_format) sourmash-bio-sourmash-bc3c0f8/tests/test_lca_db_protocol.py000066400000000000000000000074351474157046300243770ustar00rootroot00000000000000""" Test the behavior of LCA databases. New LCA database classes should support all of this functionality. """ import pytest import sourmash_tst_utils as utils import sourmash from sourmash.tax.tax_utils import MultiLineageDB from sourmash.lca.lca_db import LCA_Database, load_single_database def build_inmem_lca_db(runtmp): # test in-memory LCA_Database sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/protein/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) ss1 = sourmash.load_one_signature(sigfile1) ss2 = sourmash.load_one_signature(sigfile2) lineages_file = utils.get_test_data("prot/gtdb-subset-lineages.csv") lineages = MultiLineageDB.load([lineages_file]) db = LCA_Database(ksize=19, scaled=100, moltype="protein") ident1 = ss1.name.split(" ")[0].split(".")[0] assert lineages[ident1] db.insert(ss1, ident=ident1, lineage=lineages[ident1]) ident2 = ss2.name.split(" ")[0].split(".")[0] assert lineages[ident2] db.insert(ss2, ident=ident2, lineage=lineages[ident2]) return db def build_json_lca_db(runtmp): # test saved/loaded JSON database db = build_inmem_lca_db(runtmp) db_out = runtmp.output("protein.lca.json") db.save(db_out, format="json") x = load_single_database(db_out) db_load = x[0] return db_load def build_sql_lca_db(runtmp): # test saved/loaded SQL database db = build_inmem_lca_db(runtmp) db_out = runtmp.output("protein.lca.json") db.save(db_out, format="sql") x = load_single_database(db_out) db_load = x[0] return db_load @pytest.fixture(params=[build_inmem_lca_db, build_json_lca_db, build_sql_lca_db]) def lca_db_obj(request, runtmp): build_fn = request.param return build_fn(runtmp) def test_get_lineage_assignments(lca_db_obj): # test get_lineage_assignments for a specific hash lineages = lca_db_obj.get_lineage_assignments(178936042868009693) assert len(lineages) == 1 lineage = lineages[0] x = [] for tup in lineage: if tup[0] != "strain" or tup[1]: # ignore empty strain x.append((tup[0], tup[1])) assert x == [ ("superkingdom", "d__Archaea"), ("phylum", "p__Crenarchaeota"), ("class", "c__Bathyarchaeia"), ("order", "o__B26-1"), ("family", "f__B26-1"), ("genus", "g__B26-1"), ("species", "s__B26-1 sp001593925"), ] def test_hashvals(lca_db_obj): # test getting individual hashvals hashvals = set(lca_db_obj.hashvals) assert 178936042868009693 in hashvals def test_get_identifiers_for_hashval(lca_db_obj): # test getting identifiers belonging to individual hashvals idents = lca_db_obj.get_identifiers_for_hashval(178936042868009693) idents = list(idents) assert len(idents) == 1 ident = idents[0] assert ident == "GCA_001593925" def test_get_identifiers_for_hashval_2(lca_db_obj): # test systematic hashval => identifiers all_idents = set() for hashval in lca_db_obj.hashvals: idents = lca_db_obj.get_identifiers_for_hashval(hashval) # idents = list(idents) all_idents.update(idents) all_idents = list(all_idents) print(all_idents) assert len(all_idents) == 2 assert "GCA_001593925" in all_idents assert "GCA_001593935" in all_idents def test_downsample_scaled(lca_db_obj): # check the downsample_scaled method assert lca_db_obj.scaled == 100 lca_db_obj.downsample_scaled(500) assert lca_db_obj.scaled == 500 def test_downsample_scaled_fail(lca_db_obj): # check the downsample_scaled method - should fail if lower scaled. assert lca_db_obj.scaled == 100 with pytest.raises(ValueError): lca_db_obj.downsample_scaled(50) sourmash-bio-sourmash-bc3c0f8/tests/test_lca_functions.py000066400000000000000000000334571474157046300241040ustar00rootroot00000000000000""" Tests for functions in lca submodule. """ import pytest from sourmash.lca import lca_utils from sourmash.lca.lca_utils import ( LineagePair, build_tree, find_lca, taxlist, count_lca_for_assignments, zip_lineage, display_lineage, make_lineage, is_lineage_match, pop_to_rank, ) class FakeLCA_Database: def __init__(self): self._assignments = {} def _set_lineage_assignment(self, hashval, assignment): self._assignments[hashval] = assignment def get_lineage_assignments(self, hashval): if hashval in self._assignments: return self._assignments[hashval] else: return None def test_taxlist_1(): assert list(taxlist()) == [ "superkingdom", "phylum", "class", "order", "family", "genus", "species", "strain", ] def test_taxlist_2(): assert list(taxlist(include_strain=False)) == [ "superkingdom", "phylum", "class", "order", "family", "genus", "species", ] def test_zip_lineage_1(): x = [LineagePair("superkingdom", "a"), LineagePair("phylum", "b")] assert zip_lineage(x) == ["a", "b", "", "", "", "", "", ""] def test_zip_lineage_2(): x = [LineagePair("superkingdom", "a"), LineagePair("phylum", "b")] assert zip_lineage(x, truncate_empty=True) == ["a", "b"] def test_zip_lineage_3(): x = [ LineagePair("superkingdom", "a"), LineagePair(None, ""), LineagePair("class", "c"), ] assert zip_lineage(x) == ["a", "", "c", "", "", "", "", ""] def test_zip_lineage_3_truncate(): x = [ LineagePair("superkingdom", "a"), LineagePair(None, ""), LineagePair("class", "c"), ] assert zip_lineage(x, truncate_empty=True) == ["a", "", "c"] def test_zip_lineage_4(): x = [LineagePair("superkingdom", "a"), LineagePair("class", "c")] with pytest.raises(ValueError) as e: zip_lineage(x) assert "incomplete lineage at phylum - is class instead" in str(e.value) def test_display_lineage_1(): x = [LineagePair("superkingdom", "a"), LineagePair("phylum", "b")] assert display_lineage(x) == "a;b", display_lineage(x) def test_display_lineage_2(): x = [ LineagePair("superkingdom", "a"), LineagePair(None, ""), LineagePair("class", "c"), ] assert display_lineage(x) == "a;;c", display_lineage(x) def test_build_tree(): tree = build_tree([[LineagePair("rank1", "name1"), LineagePair("rank2", "name2")]]) assert tree == {LineagePair("rank1", "name1"): {LineagePair("rank2", "name2"): {}}} def test_build_tree_2(): tree = build_tree( [ [LineagePair("rank1", "name1"), LineagePair("rank2", "name2a")], [LineagePair("rank1", "name1"), LineagePair("rank2", "name2b")], ] ) assert tree == { LineagePair("rank1", "name1"): { LineagePair("rank2", "name2a"): {}, LineagePair("rank2", "name2b"): {}, } } def test_build_tree_3(): # empty 'rank2' name tree = build_tree([[LineagePair("rank1", "name1"), LineagePair("rank2", "")]]) assert tree == {LineagePair("rank1", "name1"): {}} def test_build_tree_4(): tree = build_tree( [ [LineagePair("rank1", "name1"), LineagePair("rank2", "name2a")], ] ) tree = build_tree( [ [LineagePair("rank1", "name1"), LineagePair("rank2", "name2b")], ], tree, ) assert tree == { LineagePair("rank1", "name1"): { LineagePair("rank2", "name2a"): {}, LineagePair("rank2", "name2b"): {}, } } def test_build_tree_5(): with pytest.raises(ValueError): build_tree([]) def test_find_lca(): tree = build_tree([[LineagePair("rank1", "name1"), LineagePair("rank2", "name2")]]) lca = find_lca(tree) assert lca == ( ( LineagePair("rank1", "name1"), LineagePair("rank2", "name2"), ), 0, ) def test_find_lca_2(): tree = build_tree( [ [LineagePair("rank1", "name1"), LineagePair("rank2", "name2a")], [LineagePair("rank1", "name1"), LineagePair("rank2", "name2b")], ] ) lca = find_lca(tree) assert lca == ((LineagePair("rank1", "name1"),), 2) def test_find_lca_3(): lin1 = lca_utils.make_lineage("a;b;c") lin2 = lca_utils.make_lineage("a;b") tree = build_tree([lin1, lin2]) lca, reason = find_lca(tree) assert lca == lin1 # find most specific leaf node def test_gather_assignments_1(): # test basic mechanics of gather_assignments function hashval = 12345678 lin = lca_utils.make_lineage("a;b;c") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin])) assignments = lca_utils.gather_assignments([hashval], [db]) print(assignments) assert assignments[hashval] == set([lin]) def test_gather_assignments_2(): # test basic mechanics of gather_assignments function with two lineages hashval = 12345678 lin = lca_utils.make_lineage("a;b;c") lin2 = lca_utils.make_lineage("a;b;d") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin, lin2])) assignments = lca_utils.gather_assignments([hashval], [db]) print(assignments) assert assignments[hashval] == set([lin, lin2]) def test_gather_assignments_3(): # test basic mechanics of gather_assignments function with two lineages # and two hashvals hashval = 12345678 hashval2 = 87654321 lin = lca_utils.make_lineage("a;b;c") lin2 = lca_utils.make_lineage("a;b;d") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin, lin2])) db._set_lineage_assignment(hashval2, set([lin])) assignments = lca_utils.gather_assignments([hashval, hashval2], [db]) print(assignments) assert assignments[hashval] == set([lin, lin2]) assert assignments[hashval2] == set([lin]) def test_count_lca_for_assignments_1(): # test basic mechanics of gather_assignments function hashval = 12345678 lin = lca_utils.make_lineage("a;b;c") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin])) assignments = lca_utils.gather_assignments([hashval], [db]) counts = count_lca_for_assignments(assignments) print(counts) assert len(counts) == 1 assert counts[lin] == 1 def test_count_lca_for_assignments_2(): # test basic mechanics of gather_assignments function with two lineages hashval = 12345678 lin = lca_utils.make_lineage("a;b;c") lin2 = lca_utils.make_lineage("a;b;d") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin, lin2])) assignments = lca_utils.gather_assignments([hashval], [db]) counts = count_lca_for_assignments(assignments) print(counts) assert counts[lin] == 0 assert counts[lin2] == 0 assert len(counts) == 1 lca_lin = lca_utils.make_lineage("a;b") assert counts[lca_lin] == 1 def test_count_lca_for_assignments_3(): # test basic mechanics of gather_assignments function with two lineages # and two hashvals hashval = 12345678 hashval2 = 87654321 lin = lca_utils.make_lineage("a;b;c") lin2 = lca_utils.make_lineage("a;b;d") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin, lin2])) db._set_lineage_assignment(hashval2, set([lin])) assignments = lca_utils.gather_assignments([hashval, hashval2], [db]) counts = count_lca_for_assignments(assignments) print(counts) assert len(counts) == 2 assert counts[lin] == 1 assert counts[lin2] == 0 lca_lin = lca_utils.make_lineage("a;b") assert counts[lca_lin] == 1 def test_count_lca_for_assignments_abund_1(): # test basic mechanics of gather_assignments function hashval = 12345678 hashval_counts = {} hashval_counts[hashval] = 3 lin = lca_utils.make_lineage("a;b;c") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin])) assignments = lca_utils.gather_assignments(hashval_counts.keys(), [db]) counts = count_lca_for_assignments(assignments, hashval_counts) print(counts) assert len(counts) == 1 assert counts[lin] == 3 def test_count_lca_for_assignments_abund_2(): # test basic mechanics of gather_assignments function with two lineages hashval = 12345678 hashval_counts = {} hashval_counts[hashval] = 3 lin = lca_utils.make_lineage("a;b;c") lin2 = lca_utils.make_lineage("a;b;d") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin, lin2])) assignments = lca_utils.gather_assignments(hashval_counts, [db]) counts = count_lca_for_assignments(assignments, hashval_counts) print(counts) assert counts[lin] == 0 assert counts[lin2] == 0 assert len(counts) == 1 lca_lin = lca_utils.make_lineage("a;b") assert counts[lca_lin] == 3 # yes! def test_count_lca_for_assignments_abund_3(): # test basic mechanics of gather_assignments function with two lineages # and two hashvals hashval = 12345678 hashval2 = 87654321 hashval_counts = {} hashval_counts[hashval] = 2 hashval_counts[hashval2] = 5 lin = lca_utils.make_lineage("a;b;c") lin2 = lca_utils.make_lineage("a;b;d") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin, lin2])) db._set_lineage_assignment(hashval2, set([lin])) assignments = lca_utils.gather_assignments(hashval_counts, [db]) counts = count_lca_for_assignments(assignments, hashval_counts) print(counts) assert len(counts) == 2 assert counts[lin] == 5 # makes sense assert counts[lin2] == 0 # makes sense lca_lin = lca_utils.make_lineage("a;b") assert counts[lca_lin] == 2 # yes! def test_count_lca_for_assignments_abund_4(): # test basic mechanics of gather_assignments function with three lineages # and three hashvals hashval = 12345678 hashval2 = 87654321 hashval3 = 34567891 hashval_counts = {} hashval_counts[hashval] = 2 hashval_counts[hashval2] = 5 hashval_counts[hashval3] = 3 lin = lca_utils.make_lineage("a;b;c") lin2 = lca_utils.make_lineage("a;b;d") lin3 = lca_utils.make_lineage("a;b;d;e") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin, lin2])) # lca: a;b db._set_lineage_assignment(hashval2, set([lin])) # lca: a;b;c db._set_lineage_assignment(hashval3, set([lin2, lin3])) # a;b;d;e assignments = lca_utils.gather_assignments(hashval_counts, [db]) counts = count_lca_for_assignments(assignments, hashval_counts) print(counts) assert len(counts) == 3 assert counts[lin] == 5 # makes sense b/c hashval2 assert counts[lin2] == 0 # a;b;d (lin2) + a;b;d;e (lin3) -->a;b;d;e (lin3) only assert counts[lin3] == 3 # hashval3 lca_lin = lca_utils.make_lineage("a;b") assert counts[lca_lin] == 2 # yes, b/c hashval def test_count_lca_for_assignments_abund_5(): # test basic mechanics of gather_assignments function with two lineages # and two hashvals when linages match but one has lower taxo detail hashval = 12345678 hashval2 = 87654321 hashval_counts = {} hashval_counts[hashval] = 2 hashval_counts[hashval2] = 5 lin = lca_utils.make_lineage("a;b;d") lin2 = lca_utils.make_lineage("a;b;d;e") db = FakeLCA_Database() db._set_lineage_assignment(hashval, set([lin, lin2])) db._set_lineage_assignment(hashval2, set([lin])) assignments = lca_utils.gather_assignments(hashval_counts, [db]) counts = count_lca_for_assignments(assignments, hashval_counts) print(counts) assert len(counts) == 2 assert counts[lin] == 5 # makes sense assert counts[lin2] == 2 # lin+lin2 yield just lin2 def test_is_lineage_match_1(): # basic behavior: match at order and above, but not at family or below. lin1 = make_lineage("d__a;p__b;c__c;o__d;f__e") lin2 = make_lineage("d__a;p__b;c__c;o__d;f__f") assert is_lineage_match(lin1, lin2, "superkingdom") assert is_lineage_match(lin1, lin2, "phylum") assert is_lineage_match(lin1, lin2, "class") assert is_lineage_match(lin1, lin2, "order") assert not is_lineage_match(lin1, lin2, "family") assert not is_lineage_match(lin1, lin2, "genus") assert not is_lineage_match(lin1, lin2, "species") def test_is_lineage_match_2(): # match at family, and above, levels; no genus or species to match lin1 = make_lineage("d__a;p__b;c__c;o__d;f__f") lin2 = make_lineage("d__a;p__b;c__c;o__d;f__f") assert is_lineage_match(lin1, lin2, "superkingdom") assert is_lineage_match(lin1, lin2, "phylum") assert is_lineage_match(lin1, lin2, "class") assert is_lineage_match(lin1, lin2, "order") assert is_lineage_match(lin1, lin2, "family") assert not is_lineage_match(lin1, lin2, "genus") assert not is_lineage_match(lin1, lin2, "species") def test_is_lineage_match_3(): # one lineage is empty lin1 = make_lineage("") lin2 = make_lineage("d__a;p__b;c__c;o__d;f__f") assert not is_lineage_match(lin1, lin2, "superkingdom") assert not is_lineage_match(lin1, lin2, "family") assert not is_lineage_match(lin1, lin2, "order") assert not is_lineage_match(lin1, lin2, "class") assert not is_lineage_match(lin1, lin2, "phylum") assert not is_lineage_match(lin1, lin2, "genus") assert not is_lineage_match(lin1, lin2, "species") def test_pop_to_rank_1(): # basic behavior - pop to order? lin1 = make_lineage("d__a;p__b;c__c;o__d") lin2 = make_lineage("d__a;p__b;c__c;o__d;f__f") print(lin1) print(pop_to_rank(lin2, "order")) assert pop_to_rank(lin2, "order") == lin1 def test_pop_to_rank_2(): # what if we're already above rank? lin2 = make_lineage("d__a;p__b;c__c;o__d;f__f") print(pop_to_rank(lin2, "species")) assert pop_to_rank(lin2, "species") == lin2 sourmash-bio-sourmash-bc3c0f8/tests/test_manifest.py000066400000000000000000000150241474157046300230510ustar00rootroot00000000000000""" Tests for manifest code in databases, etc. """ import pytest from io import StringIO import sourmash from sourmash import index, sourmash_args import sourmash_tst_utils as utils def test_generate_manifest(): # test basic manifest-generating functionality. protzip = utils.get_test_data("prot/protein.zip") loader = sourmash.load_file_as_index(protzip) rows = [] siglist = [] for sig, loc in loader._signatures_with_internal(): row = index.CollectionManifest.make_manifest_row(sig, loc) rows.append(row) siglist.append(sig) manifest = index.CollectionManifest(rows) assert len(manifest) == len(rows) assert len(manifest) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list for sig in siglist: assert sig in manifest def test_manifest_operations(): # test basic manifest operations - += protzip = utils.get_test_data("prot/protein.zip") loader = sourmash.load_file_as_index(protzip) rows = [] siglist = [] for sig, loc in loader._signatures_with_internal(): row = index.CollectionManifest.make_manifest_row(sig, loc) rows.append(row) siglist.append(sig) manifest = index.CollectionManifest(rows) manifest2 = index.CollectionManifest(rows) manifest += manifest2 assert len(manifest) == 2 * len(rows) assert len(manifest) == 4 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list def test_manifest_operations_fail(): # should not be able to add a manifest to itself - not only makes # no sense, but it means you're modifying a generator in place, sometimes. protzip = utils.get_test_data("prot/protein.zip") loader = sourmash.load_file_as_index(protzip) rows = [] siglist = [] for sig, loc in loader._signatures_with_internal(): row = index.CollectionManifest.make_manifest_row(sig, loc) rows.append(row) siglist.append(sig) manifest = index.CollectionManifest(rows) with pytest.raises(Exception): manifest += manifest def test_manifest_to_picklist(): # test manifest/picklist interaction basics protzip = utils.get_test_data("prot/protein.zip") loader = sourmash.load_file_as_index(protzip) rows = [] siglist = [] for sig, loc in loader._signatures_with_internal(): row = index.CollectionManifest.make_manifest_row(sig, loc) rows.append(row) siglist.append(sig) manifest = index.CollectionManifest(rows) picklist = manifest.to_picklist() assert len(picklist.pickset) == len(manifest) new_manifest = manifest.select_to_manifest(picklist=picklist) assert len(new_manifest) == len(manifest) def test_manifest_compare(): # test saving and loading manifests protzip = utils.get_test_data("prot/protein.zip") loader = sourmash.load_file_as_index(protzip) manifest = loader.manifest # equal rows = list(manifest.rows) equal_mf = index.CollectionManifest(rows) assert equal_mf == manifest # not equal / shorter rows = list(manifest.rows) rows = rows[:-1] short_mf = index.CollectionManifest(rows) assert short_mf != manifest # not equal / diff values rows = list(manifest.rows) rows[0] = dict(rows[0]) rows[0]["internal_location"] += ".foo" short_mf = index.CollectionManifest(rows) assert short_mf != manifest def test_save_load_manifest(): # test saving and loading manifests protzip = utils.get_test_data("prot/protein.zip") loader = sourmash.load_file_as_index(protzip) rows = [] siglist = [] for sig, loc in loader._signatures_with_internal(): row = index.CollectionManifest.make_manifest_row(sig, loc) rows.append(row) siglist.append(sig) manifest = index.CollectionManifest(rows) # now, on to CSV fp = StringIO() manifest.write_csv_header(fp) manifest.write_to_csv(fp) rfp = StringIO(fp.getvalue()) manifest2 = index.CollectionManifest.load_from_csv(rfp) assert len(manifest) == len(manifest2) pick1 = manifest.to_picklist() pick2 = manifest2.to_picklist() # manifest 1 in manifest2? for row in manifest.rows: assert pick2.matches_manifest_row(row) # manifest 2 in manifest? for row in manifest2.rows: assert pick1.matches_manifest_row(row) # equal? assert manifest == manifest2 # not equal / shorter rows = list(manifest.rows) rows = rows[1:] short_mf = index.CollectionManifest(rows) assert short_mf != manifest # not equal / diff values rows = list(manifest.rows) rows[0] = dict(rows[0]) rows[0]["internal_location"] += ".foo" short_mf = index.CollectionManifest(rows) assert short_mf != manifest def test_manifest_to_picklist_bug(runtmp): # this tests a fun combination of things that led to a bug. # tl;dr we only want to iterate once across a generator... # ref #2762 all_zip = utils.get_test_data("prot/all.zip") idx = sourmash_args.load_file_as_index(all_zip) assert len(idx) == 8 manifest = sourmash_args.get_manifest(idx) assert len(manifest) == 8 def filter_fn(row): # match? keep = False if "09a0869" in row["md5"]: keep = True return keep sub_manifest = manifest.filter_rows(filter_fn) sub_picklist = sub_manifest.to_picklist() idx = idx.select(picklist=sub_picklist) assert len(idx) == 1 print(idx) x = list(idx.signatures()) assert len(x) def test_generate_manifest_iterate_once(): # we should only iterate across manifest rows once protzip = utils.get_test_data("prot/protein.zip") loader = sourmash.load_file_as_index(protzip) siglist = [] for sig, loc in loader._signatures_with_internal(): siglist.append(sig) # build generator function => will not allow iteration twice def genfn(): for sig, loc in loader._signatures_with_internal(): row = index.CollectionManifest.make_manifest_row(sig, loc) yield row manifest = index.CollectionManifest(genfn()) assert len(manifest) == 2 assert len(manifest._md5_set) == 2 md5_list = [row["md5"] for row in manifest.rows] assert "16869d2c8a1d29d1c8e56f5c561e585e" in md5_list assert "120d311cc785cc9d0df9dc0646b2b857" in md5_list for sig in siglist: assert sig in manifest sourmash-bio-sourmash-bc3c0f8/tests/test_manifest_protocol.py000066400000000000000000000137751474157046300250050ustar00rootroot00000000000000""" Tests for the 'CollectionManifest' class and protocol. All subclasses of BaseCollectionManifest should support this functionality. """ import pytest import sourmash_tst_utils as utils import sourmash from sourmash.manifest import BaseCollectionManifest, CollectionManifest from sourmash.index.sqlite_index import SqliteCollectionManifest def build_simple_manifest(runtmp): # load and return the manifest from prot/all.zip filename = utils.get_test_data("prot/all.zip") idx = sourmash.load_file_as_index(filename) mf = idx.manifest assert len(mf) == 8 return mf def build_sqlite_manifest(runtmp): # return the manifest from prot/all.zip filename = utils.get_test_data("prot/all.zip") idx = sourmash.load_file_as_index(filename) mf = idx.manifest # build sqlite manifest from this 'un mfdb = runtmp.output("test.sqlmf") return SqliteCollectionManifest.load_from_manifest(mf, dbfile=mfdb) def save_load_manifest(runtmp): # save/load the manifest from a CSV. mf = build_simple_manifest(runtmp) mf_csv = runtmp.output("mf.csv") mf.write_to_filename(mf_csv) load_mf = CollectionManifest.load_from_filename(mf_csv) return load_mf @pytest.fixture( params=[build_simple_manifest, save_load_manifest, build_sqlite_manifest] ) def manifest_obj(request, runtmp): build_fn = request.param return build_fn(runtmp) ### ### generic CollectionManifeset tests go here ### def test_manifest_len(manifest_obj): # check that 'len' works assert len(manifest_obj) == 8 def test_manifest_rows(manifest_obj): # check that '.rows' property works rows = list(manifest_obj.rows) assert len(rows) == 8 required_keys = set(BaseCollectionManifest.required_keys) for row in rows: kk = set(row.keys()) assert required_keys.issubset(kk) def test_manifest_bool(manifest_obj): # check that 'bool' works assert bool(manifest_obj) def test_make_manifest_row(manifest_obj): # build a manifest row from a signature sig47 = utils.get_test_data("47.fa.sig") ss = sourmash.load_one_signature(sig47) row = manifest_obj.make_manifest_row(ss, "foo", include_signature=False) assert "signature" not in row assert row["internal_location"] == "foo" assert row["md5"] == ss.md5sum() assert row["md5short"] == ss.md5sum()[:8] assert row["ksize"] == 31 assert row["moltype"] == "DNA" assert row["num"] == 0 assert row["scaled"] == 1000 assert row["n_hashes"] == len(ss.minhash) assert not row["with_abundance"] assert row["name"] == ss.name assert row["filename"] == ss.filename def test_manifest_create_manifest(manifest_obj): # test the 'create_manifest' method sig47 = utils.get_test_data("47.fa.sig") ss = sourmash.load_one_signature(sig47) def yield_sigs(): yield ss, "fiz" new_mf = manifest_obj.create_manifest(yield_sigs(), include_signature=False) assert len(new_mf) == 1 new_row = list(new_mf.rows)[0] row = manifest_obj.make_manifest_row(ss, "fiz", include_signature=False) required_keys = BaseCollectionManifest.required_keys for k in required_keys: assert new_row[k] == row[k], k def test_manifest_select_to_manifest(manifest_obj): # do some light testing of 'select_to_manifest' new_mf = manifest_obj.select_to_manifest(moltype="DNA") assert len(new_mf) == 2 def test_manifest_locations(manifest_obj): # check the 'locations' method locs = set( [ "dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", "dayhoff/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig", "hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", "hp/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig", "protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig", "protein/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig", "dna-sig.noext", "dna-sig.sig.gz", ] ) assert set(manifest_obj.locations()) == locs def test_manifest___contains__(manifest_obj): # check the 'in' operator sigfile = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) ss = sourmash.load_one_signature(sigfile) assert ss in manifest_obj sigfile2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sigfile2, ksize=31) assert ss2 not in manifest_obj def test_manifest_to_picklist(manifest_obj): # test 'to_picklist' picklist = manifest_obj.to_picklist() mf = manifest_obj.select_to_manifest(picklist=picklist) assert mf == manifest_obj def test_manifest_filter_rows(manifest_obj): # test filter_rows def filter_fn(x): return "OS223" in x["name"] mf = manifest_obj.filter_rows(filter_fn) assert len(mf) == 1 row = list(mf.rows)[0] assert row["name"] == "NC_011663.1 Shewanella baltica OS223, complete genome" def test_manifest_filter_cols(manifest_obj): # test filter_rows def col_filter_fn(x): return "OS223" in x[0] mf = manifest_obj.filter_on_columns(col_filter_fn, ["name"]) assert len(mf) == 1 row = list(mf.rows)[0] assert row["name"] == "NC_011663.1 Shewanella baltica OS223, complete genome" def test_manifest_iadd(manifest_obj): # test the 'create_manifest' method sig47 = utils.get_test_data("47.fa.sig") ss = sourmash.load_one_signature(sig47) def yield_sigs(): yield ss, "fiz" new_mf = manifest_obj.create_manifest(yield_sigs(), include_signature=False) assert len(new_mf) == 1 new_mf += manifest_obj assert len(new_mf) == len(manifest_obj) + 1 def test_manifest_add(manifest_obj): # test the 'create_manifest' method sig47 = utils.get_test_data("47.fa.sig") ss = sourmash.load_one_signature(sig47) def yield_sigs(): yield ss, "fiz" new_mf = manifest_obj.create_manifest(yield_sigs(), include_signature=False) assert len(new_mf) == 1 new_mf2 = new_mf + manifest_obj assert len(new_mf2) == len(manifest_obj) + len(new_mf) sourmash-bio-sourmash-bc3c0f8/tests/test_minhash.py000066400000000000000000003173561474157046300227070ustar00rootroot00000000000000# This file is part of sourmash, https://github.com/sourmash-bio/sourmash/, and is # Copyright (C) 2016, The Regents of the University of California. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided # with the distribution. # # * Neither the name of the Michigan State University nor the names # of its contributors may be used to endorse or promote products # derived from this software without specific prior written # permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # # Contact: titus@idyll.org # pylint: disable=missing-docstring,protected-access import itertools import pickle import math import numpy as np import pytest import screed import sourmash from sourmash.minhash import ( MinHash, FrozenMinHash, hash_murmur, _get_scaled_for_max_hash, _get_max_hash_for_scaled, translate_codon, ) from sourmash import signature import sourmash_tst_utils as utils # add: # * get default params from Python # * keyword args for minhash constructor # * trap error from handing protein/non-DNA to a DNA MH # * fail on untagged/unloaded countgraph # * nan on empty minhash # * define equals scaled2 = _get_scaled_for_max_hash(2**63) assert scaled2 == 2 scaled4 = _get_scaled_for_max_hash(2**62) assert scaled4 == 4 scaled8 = _get_scaled_for_max_hash(2**61) assert scaled8 == 8 def _kmers_from_all_coding_frames(sequence, ksize): """Mimic the internal rust code for translation of DNA into aa. For each frame, yield all fwd k-mers, then all reverse k-mers from that frame. Then do next frame. """ seqrc = screed.rc(sequence) for frame in (0, 1, 2): # get forward k-mers for start in range(0, len(sequence) - ksize + 1 - frame, 3): kmer = sequence[start + frame : start + frame + ksize] yield kmer # get rc k-mers for start in range(0, len(seqrc) - ksize + 1 - frame, 3): kmer = seqrc[start + frame : start + frame + ksize] yield kmer def _hash_fwd_only(mh_translate, seq): "Return the first hashval only, for coding frame +1." assert len(seq) == mh_translate.ksize * 3 xx = mh_translate.seq_to_hashes(seq)[0] return xx def test_basic_dna(track_abundance): # verify that MHs of size 1 stay size 1, & act properly as bottom sketches. mh = MinHash(1, 4, track_abundance=track_abundance) assert mh.moltype == "DNA" mh.add_sequence("ATGC") a = mh.hashes mh.add_sequence("GCAT") # this will not get added; hash > ATGC b = mh.hashes print(a, b) assert list(a) == list(b) assert len(b) == 1 assert list(a)[0] == list(b)[0] == 12415348535738636339 def test_div_zero(track_abundance): # verify that empty MHs do not yield divide by zero errors for similarity mh = MinHash(1, 4, track_abundance=track_abundance) mh2 = mh.copy_and_clear() mh.add_sequence("ATGC") assert mh.similarity(mh2) == 0 assert mh2.similarity(mh) == 0 def test_div_zero_contained(track_abundance): # verify that empty MHs do not yield divide by zero errors for contained_by mh = MinHash(0, 4, scaled=1, track_abundance=track_abundance) mh2 = mh.copy_and_clear() mh.add_sequence("ATGC") assert mh.contained_by(mh2) == 0 assert mh2.contained_by(mh) == 0 def test_contained_requires_scaled(track_abundance): # test that contained_by requires scaled signatures mh1 = MinHash(1, 4, track_abundance=track_abundance) mh2 = MinHash(0, 4, scaled=1, track_abundance=track_abundance) mh1.add_sequence("ATGC") mh2.add_sequence("ATGC") with pytest.raises(TypeError): mh2.contained_by(mh1) with pytest.raises(TypeError): mh1.contained_by(mh2) def test_contained_requires_scaled_2(track_abundance): # test that max_containment requires scaled signatures mh1 = MinHash(1, 4, track_abundance=track_abundance) mh2 = MinHash(0, 4, scaled=1, track_abundance=track_abundance) mh1.add_sequence("ATGC") mh2.add_sequence("ATGC") with pytest.raises(TypeError): mh2.max_containment(mh1) with pytest.raises(TypeError): mh1.max_containment(mh2) def test_contained_requires_scaled_3(track_abundance): # test that avg_containment requires scaled signatures mh1 = MinHash(1, 4, track_abundance=track_abundance) mh2 = MinHash(0, 4, scaled=1, track_abundance=track_abundance) mh1.add_sequence("ATGC") mh2.add_sequence("ATGC") with pytest.raises(TypeError): mh2.avg_containment(mh1) with pytest.raises(TypeError): mh1.avg_containment(mh2) def test_bytes_dna(track_abundance): mh = MinHash(1, 4, track_abundance=track_abundance) mh.add_sequence("ATGC") mh.add_sequence(b"ATGC") mh.add_sequence("ATGC") a = mh.hashes mh.add_sequence("GCAT") # this will not get added; hash > ATGC mh.add_sequence(b"GCAT") # this will not get added; hash > ATGC mh.add_sequence("GCAT") # this will not get added; hash > ATGC b = mh.hashes print(a, b) assert list(a) == list(b) assert len(b) == 1 def test_add_long_seqs_force(): # Test for (All kmers are invalid) mh = sourmash.minhash.MinHash(n=0, ksize=21, scaled=10, seed=42) seq = "ACGTN" * 100000 mh.seq_to_hashes(seq, force=True) assert len(mh.hashes) == 0 def test_seq_to_hashes(track_abundance): mh = sourmash.minhash.MinHash( n=0, ksize=21, scaled=1, track_abundance=track_abundance ) seq = "ATGAGAGACGATAGACAGATGACC" mh.add_sequence(seq) golden_hashes = mh.hashes # New seq to hashes without adding to the sketch new_hashes = mh.seq_to_hashes(seq) assert set(golden_hashes) == set(new_hashes) def test_seq_to_hashes_protein_1(track_abundance, dayhoff): mh = MinHash( 10, 2, is_protein=True, dayhoff=dayhoff, hp=False, track_abundance=track_abundance, ) prot_seq = "AGYYG" mh.add_protein(prot_seq) golden_hashes = mh.hashes # New seq to hashes without adding to the sketch new_hashes = mh.seq_to_hashes(prot_seq, is_protein=True) assert set(golden_hashes) == set(new_hashes) def test_seq_to_hashes_protein_2(track_abundance): mh = sourmash.minhash.MinHash( n=0, ksize=21, scaled=1, track_abundance=track_abundance ) seq = "ATGAGAGACGATAGACAGATGACC" with pytest.raises(ValueError): mh.seq_to_hashes(seq, is_protein=True) def test_seq_to_hashes_translated(track_abundance): mh_protein = MinHash(10, 2, is_protein=True, track_abundance=track_abundance) seq = "ACTGAC" mh_protein.add_sequence(seq) golden_hashes = mh_protein.hashes # New seq to hashes without adding to the sketch new_hashes = mh_protein.seq_to_hashes(seq) assert set(golden_hashes) == set(new_hashes) def test_seq_to_hashes_bad_kmers_as_zeroes_1(): mh = sourmash.minhash.MinHash(n=0, ksize=21, scaled=1) seq = "ATGAGAGACGATAGACAGATGACN" # New seq to hashes without adding to the sketch hashes = mh.seq_to_hashes(seq, force=True, bad_kmers_as_zeroes=True) assert len(hashes) == len(seq) - 21 + 1 def test_seq_to_hashes_bad_kmers_as_zeroes_2(): mh = sourmash.minhash.MinHash(n=0, ksize=21, scaled=1) seq = "ATGAGAGACGATAGACAGATGACN" with pytest.raises(ValueError): mh.seq_to_hashes(seq, bad_kmers_as_zeroes=True) def test_seq_to_hashes_translated_short(): mh = MinHash(0, 2, is_protein=True, dayhoff=True, hp=False, scaled=1) hashes = mh.seq_to_hashes("ACTGA") assert len(hashes) == 0 def test_bytes_protein_dayhoff(track_abundance, dayhoff): # verify that we can hash protein/aa sequences mh = MinHash( 10, 2, is_protein=True, dayhoff=dayhoff, hp=False, track_abundance=track_abundance, ) expected_moltype = "protein" if dayhoff: expected_moltype = "dayhoff" assert mh.moltype == expected_moltype mh.add_protein("AGYYG") mh.add_protein("AGYYG") mh.add_protein(b"AGYYG") assert len(mh.hashes) == 4 def test_protein_dayhoff(track_abundance, dayhoff): # verify that we can hash protein/aa sequences mh = MinHash( 10, 2, is_protein=True, dayhoff=dayhoff, hp=False, track_abundance=track_abundance, ) mh.add_protein("AGYYG") assert len(mh.hashes) == 4 def test_bytes_protein_hp(track_abundance, hp): # verify that we can hash protein/aa sequences mh = MinHash( 10, 2, is_protein=True, dayhoff=False, hp=hp, track_abundance=track_abundance ) expected_moltype = "protein" if hp: expected_moltype = "hp" assert mh.moltype == expected_moltype mh.add_protein("AGYYG") mh.add_protein("AGYYG") mh.add_protein(b"AGYYG") if hp: assert len(mh.hashes) == 1 else: assert len(mh.hashes) == 4 def test_protein_hp(track_abundance, hp): # verify that we can hash protein/aa sequences mh = MinHash( 10, 2, is_protein=True, dayhoff=False, hp=hp, track_abundance=track_abundance ) mh.add_protein("AGYYG") if hp: assert len(mh.hashes) == 1 else: assert len(mh.hashes) == 4 def test_module_translate_codon(track_abundance): # Ensure that translation occurs properly - module level function tests assert "S" == translate_codon("TCT") assert "S" == translate_codon("TC") assert "X" == translate_codon("T") with pytest.raises(ValueError): translate_codon("") translate_codon("TCTA") def test_dayhoff(track_abundance): # verify that we can hash to dayhoff-encoded protein/aa sequences mh_dayhoff = MinHash( 10, 2, is_protein=True, dayhoff=True, hp=False, track_abundance=track_abundance ) mh_dayhoff.add_sequence("ACTGAC") assert len(mh_dayhoff.hashes) == 2 # verify that dayhoff-encoded hashes are different from protein/aa hashes mh_protein = MinHash(10, 2, is_protein=True, track_abundance=track_abundance) mh_protein.add_sequence("ACTGAC") assert len(mh_protein.hashes) == 2 print(mh_protein.hashes) print(mh_dayhoff.hashes) assert mh_protein.hashes != mh_dayhoff.hashes def test_dayhoff_2(track_abundance): mh = MinHash(0, 7, scaled=1, dayhoff=True, track_abundance=1) # first, check protein -> dayhoff hashes via minhash mh.add_protein("CADHIFC") assert len(mh) == 1 hashval = list(mh.hashes)[0] assert hashval == hash_murmur("abcdefa") # also check seq_to_hashes hashes = list(mh.seq_to_hashes("CADHIFC", is_protein=True)) assert hashval == hashes[0] # do we handle stop codons properly? mh = mh.copy_and_clear() mh.add_protein("CADHIF*") assert len(mh) == 1 hashval = list(mh.hashes)[0] assert hashval == hash_murmur("abcdef*") # again, check seq_to_hashes hashes = list(mh.seq_to_hashes("CADHIF*", is_protein=True)) assert hashval == hashes[0] def test_hp(track_abundance): # verify that we can hash to hp-encoded protein/aa sequences mh_hp = MinHash( 10, 2, is_protein=True, dayhoff=False, hp=True, track_abundance=track_abundance ) assert mh_hp.moltype == "hp" mh_hp.add_sequence("ACTGAC") assert len(mh_hp.hashes) == 2 # verify that hp-encoded hashes are different from protein/aa hashes mh_protein = MinHash(10, 2, is_protein=True, track_abundance=track_abundance) mh_protein.add_sequence("ACTGAC") assert len(mh_protein.hashes) == 2 assert mh_protein.hashes != mh_hp.hashes def test_hp_2(track_abundance): mh = MinHash(0, 3, scaled=1, hp=True, track_abundance=track_abundance) mh.add_protein("ANA") assert len(mh) == 1 hashval = list(mh.hashes)[0] assert hashval == hash_murmur("hph") # also check seq_to_hashes hashes = list(mh.seq_to_hashes("ANA", is_protein=True)) assert hashval == hashes[0] mh = mh.copy_and_clear() mh.add_protein("AN*") assert len(mh) == 1 hashval = list(mh.hashes)[0] assert hashval == hash_murmur("hp*") # also check seq_to_hashes hashes = list(mh.seq_to_hashes("AN*", is_protein=True)) assert hashval == hashes[0] def test_protein_short(track_abundance): # verify that we can hash protein/aa sequences mh = MinHash(10, 9, is_protein=True, track_abundance=track_abundance) mh.add_protein("AG") assert len(mh.hashes) == 0, mh.hashes def test_size_limit(track_abundance): # test behavior with size limit of 3 mh = MinHash(3, 4, track_abundance=track_abundance) mh.add_hash(10) mh.add_hash(20) mh.add_hash(30) assert list(sorted(mh.hashes)) == [10, 20, 30] mh.add_hash(5) # -> should push 30 off end assert list(sorted(mh.hashes)) == [5, 10, 20] def test_scaled(track_abundance): # test behavior with scaled: cannot add hashes above max hash. scaled = 2**3 print("XX", scaled, _get_max_hash_for_scaled(scaled)) mh = MinHash(0, 4, track_abundance=track_abundance, scaled=scaled) assert mh._max_hash == 2**61 # 2**64 / 2**3 mh.add_hash(10) mh.add_hash(20) mh.add_hash(30) assert list(sorted(mh.hashes)) == [10, 20, 30] mh.add_hash(2**62) assert list(sorted(mh.hashes)) == [10, 20, 30] mh.add_hash(2**63) assert list(sorted(mh.hashes)) == [10, 20, 30] def test_no_scaled(track_abundance): # no 'scaled', num=0 - should fail with pytest.raises(ValueError): MinHash(0, 4, track_abundance=track_abundance) def test_max_hash_conversion(): SCALED = 100000 max_hash = _get_max_hash_for_scaled(SCALED) new_scaled = _get_scaled_for_max_hash(max_hash) assert new_scaled == SCALED def test_max_hash_and_scaled_zero(): max_hash = _get_max_hash_for_scaled(0) new_scaled = _get_scaled_for_max_hash(0) assert max_hash == new_scaled assert max_hash == 0 def test_max_hash_and_scaled_error(track_abundance): # test behavior when supplying both max_hash and scaled with pytest.raises(ValueError): MinHash(0, 4, track_abundance=track_abundance, max_hash=35, scaled=5) def test_max_hash_cannot_limit(track_abundance): # make sure you can't set both n and scaled. with pytest.raises(ValueError): MinHash( 2, 4, track_abundance=track_abundance, scaled=_get_scaled_for_max_hash(1) ) def test_no_downsample_scaled_if_n(track_abundance): # make sure you can't set max_n and then downsample scaled mh = MinHash(2, 4, track_abundance=track_abundance) with pytest.raises(ValueError) as excinfo: mh.downsample(scaled=100000000) assert "cannot downsample a num MinHash using scaled" in str(excinfo.value) def test_scaled_num_both(track_abundance): # make sure you can't set both max_n and scaled. with pytest.raises(ValueError): MinHash(2, 4, track_abundance=track_abundance, scaled=2) def test_mh_jaccard_similarity(): # check actual Jaccard value for a non-trivial case a = MinHash(0, 20, scaled=scaled2, track_abundance=False) b = MinHash(0, 20, scaled=scaled2, track_abundance=False) a.add_many([1, 3, 5, 8]) b.add_many([1, 3, 5, 6, 8, 10]) assert a.similarity(b) == 4.0 / 6.0 def test_mh_similarity_downsample_jaccard_value(): # check jaccard value after downsampling # max_hash = 50 a = MinHash(0, 20, scaled=scaled2, track_abundance=False) # max_hash = 100 b = MinHash(0, 20, scaled=scaled8, track_abundance=False) a.add_many([1, 3, 5, 8, 2**62]) b.add_many([1, 3, 5, 6, 8, 10, 2**62]) # the hash=70 will be truncated by downsampling assert a.similarity(b, downsample=True) == 4.0 / 6.0 def test_mh_angular_similarity(): # check actual angular similarity for a non-trivial case, taken from: # https://www.sciencedirect.com/topics/computer-science/cosine-similarity # note: angular similarity is 1 - 2*(acos(sim) / pi), when elements # are always positive (https://en.wikipedia.org/wiki/Cosine_similarity) a = MinHash(0, 20, scaled=scaled2, track_abundance=True) b = MinHash(0, 20, scaled=scaled2, track_abundance=True) a.set_abundances({1: 5, 3: 3, 5: 2, 8: 2}) b.set_abundances({1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1}) cos_sim = 0.9356 angular_sim = 1 - 2 * math.acos(cos_sim) / math.pi assert round(angular_sim, 4) == 0.7703 assert round(a.similarity(b), 4) == round(angular_sim, 4) def test_mh_angular_similarity_2(): # check actual angular similarity for a second non-trivial case a = MinHash(0, 20, scaled=scaled2, track_abundance=True) b = MinHash(0, 20, scaled=scaled2, track_abundance=True) a.set_abundances({1: 5, 3: 3, 5: 2, 8: 2, 70: 70}) b.set_abundances({1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1, 70: 70}) assert round(a.similarity(b), 4) == 0.9728 # ignore_abundance => jaccard assert a.similarity(b, ignore_abundance=True) == 5.0 / 7.0 def test_mh_similarity_downsample_angular_value(): # test downsample=True argument to MinHash.similarity # max_hash = 50 a = MinHash(0, 20, scaled=scaled2, track_abundance=True) # max_hash = 100 b = MinHash(0, 20, scaled=scaled8, track_abundance=True) a.set_abundances({1: 5, 3: 3, 5: 2, 8: 2, 2**62: 70}) b.set_abundances({1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1, 2**62: 70}) # the hash=70 will be truncated by downsampling sim = a.similarity(b, downsample=True) assert round(sim, 4) == 0.7703 # with ignore_abundance, will be equal to jaccard jaccard = a.similarity(b, downsample=True, ignore_abundance=True) assert jaccard == 4.0 / 6.0 def test_mh_angular_similarity_fail(): # raise TypeError if calling angular_similarity directly and # one or both sketches do not have abundance info a = MinHash(0, 20, scaled=scaled2, track_abundance=True) b = MinHash(0, 20, scaled=scaled2, track_abundance=False) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} a.set_abundances(a_values) b.add_many(b_values.keys()) # one sketch lacks track_abundance with pytest.raises(TypeError) as exc: a.angular_similarity(b) print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) # both sketches lack track abundance a = MinHash(0, 20, scaled=scaled2, track_abundance=False) a.add_many(a_values.keys()) with pytest.raises(TypeError) as exc: a.angular_similarity(b) print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) def test_mh_similarity_downsample_true(track_abundance): # verify sim(a, b) == sim(b, a), with and without ignore_abundance # max_hash = 50 a = MinHash(0, 20, scaled=scaled2, track_abundance=track_abundance) # max_hash = 100 b = MinHash(0, 20, scaled=scaled8, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) # downsample=True => no error; values should match either way x = a.similarity(b, ignore_abundance=True, downsample=True) y = b.similarity(a, ignore_abundance=True, downsample=True) assert x == y # downsample=True => no error; values should match either way x = a.similarity(b, ignore_abundance=False, downsample=True) y = b.similarity(a, ignore_abundance=False, downsample=True) assert x == y def test_mh_similarity_downsample_errors(track_abundance): # test downsample=False (default) argument to MinHash.similarity # max_hash = 50 a = MinHash(0, 20, scaled=scaled2, track_abundance=track_abundance) # max_hash = 100 b = MinHash(0, 20, scaled=scaled8, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) # error, incompatible max hash with pytest.raises(ValueError) as e: a.similarity(b, ignore_abundance=True) # downsample=False assert "mismatch in scaled; comparison fail" in str(e.value) with pytest.raises(ValueError) as e: a.similarity(b, ignore_abundance=False) # downsample=False assert "mismatch in scaled; comparison fail" in str(e.value) with pytest.raises(ValueError) as e: b.similarity(a, ignore_abundance=True) # downsample=False assert "mismatch in scaled; comparison fail" in str(e.value) with pytest.raises(ValueError) as e: b.similarity(a, ignore_abundance=False) # downsample=false assert "mismatch in scaled; comparison fail" in str(e.value) def test_basic_dna_bad(track_abundance): # test behavior on bad DNA mh = MinHash(1, 4, track_abundance=track_abundance) with pytest.raises(ValueError) as e: mh.add_sequence("ATGR") print(e) assert "invalid DNA character in input k-mer: ATGR" in str(e.value) def test_basic_dna_bad_2(track_abundance): # test behavior on bad DNA mh = MinHash(1, 6, track_abundance=track_abundance) with pytest.raises(ValueError): mh.add_protein("YYYY") def test_basic_dna_bad_force(track_abundance): # test behavior on bad DNA; use 100 so multiple hashes get added. mh = MinHash(100, 4, track_abundance=track_abundance) assert len(mh.hashes) == 0 mh.add_sequence("ATGN", True) # ambiguous kmer skipped. assert len(mh.hashes) == 0 mh.add_sequence("AATGN", True) # but good k-mers still used. assert len(mh.hashes) == 1 mh.add_sequence("AATG", True) # checking that right kmer was added assert len(mh.hashes) == 1 # (only 1 hash <- this is a dup) def test_basic_dna_bad_force_2(track_abundance): # test behavior on bad DNA mh = MinHash(100, 4, track_abundance=track_abundance) assert len(mh.hashes) == 0 mh.add_sequence("AAGNCGG", True) # ambiguous kmers skipped. assert len(mh.hashes) == 0 mh.add_sequence("AATGNGCGG", True) # ambiguous kmers skipped. assert len(mh.hashes) == 2 mh.add_sequence("AATG", True) # checking that right kmers were added mh.add_sequence("GCGG", True) assert len(mh.hashes) == 2 # (only 2 hashes should be there) def test_consume_lowercase(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) b = MinHash(20, 10, track_abundance=track_abundance) a.add_sequence("TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA".lower()) b.add_sequence("TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA") assert round(a.similarity(b), 3) == 1.0 assert round(b.similarity(b), 3) == 1.0 assert round(b.similarity(a), 3) == 1.0 assert round(a.similarity(a), 3) == 1.0 def test_similarity_1(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) b = MinHash(20, 10, track_abundance=track_abundance) a.add_sequence("TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA") b.add_sequence("TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA") assert round(a.similarity(b), 3) == 1.0 assert round(b.similarity(b), 3) == 1.0 assert round(b.similarity(a), 3) == 1.0 assert round(a.similarity(a), 3) == 1.0 # add same sequence again b.add_sequence("TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA") assert round(a.similarity(b), 3) == 1.0 assert round(b.similarity(b), 3) == 1.0 assert round(b.similarity(a), 3) == 1.0 assert round(a.similarity(a), 3) == 1.0 b.add_sequence("GATTGGTGCACACTTAACTGGGTGCCGCGCTGGTGCTGATCCATGAAGTT") x = a.similarity(b) assert x >= 0.3, x x = b.similarity(a) assert x >= 0.3, x assert round(a.similarity(a), 3) == 1.0 assert round(b.similarity(b), 3) == 1.0 def test_copy(track_abundance): a = MinHash(20, 21, track_abundance=track_abundance) a.add_hash(5) b = a.copy() assert a == b a.add_hash(6) assert a != b def test_frozen_copy(track_abundance): a = MinHash(20, 21, track_abundance=track_abundance) a.add_hash(5) b = a.copy() assert 5 in b.hashes a.add_hash(6) assert 6 not in b.hashes def test_mh_copy(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) a.add_sequence("TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA") b = a.__copy__() assert round(b.similarity(a), 3) == 1.0 def test_mh_len(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) assert len(a) == 0 a.add_sequence("TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA") assert len(a) == 20 def test_mh_len_2(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 40, 2): a.add_hash(i) assert list(sorted(a.hashes)) == list(range(0, 40, 2)) def test_mh_unsigned_long_long(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) a.add_hash(9227159859419181011) # too big for a C long int. assert 9227159859419181011 in a.hashes def test_mh_count_common(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 40, 2): a.add_hash(i) b = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 80, 4): b.add_hash(i) assert a.count_common(b) == 10 assert b.count_common(a) == 10 def test_mh_count_common_diff_protein(track_abundance): a = MinHash(20, 5, is_protein=False, track_abundance=track_abundance) b = MinHash(20, 5, is_protein=True, track_abundance=track_abundance) with pytest.raises(ValueError): a.count_common(b) def test_mh_count_common_diff_maxhash(track_abundance): a = MinHash( 0, 5, is_protein=False, track_abundance=track_abundance, scaled=scaled8, ) b = MinHash( 0, 5, is_protein=True, track_abundance=track_abundance, scaled=scaled4, ) with pytest.raises(ValueError): a.count_common(b) def test_mh_count_common_diff_seed(track_abundance): a = MinHash(20, 5, is_protein=False, track_abundance=track_abundance, seed=1) b = MinHash(20, 5, is_protein=True, track_abundance=track_abundance, seed=2) with pytest.raises(ValueError): a.count_common(b) def test_mh_count_common_diff_ksize(track_abundance): a = MinHash(20, 5, track_abundance=track_abundance) b = MinHash(20, 6, track_abundance=track_abundance) with pytest.raises(ValueError): a.count_common(b) def test_mh_count_common_notmh(track_abundance): a = MinHash(20, 5, track_abundance=track_abundance) b = set() with pytest.raises(TypeError): a.count_common(b) def test_mh_downsample_num_error(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) with pytest.raises(ValueError): a.downsample(num=30) def test_mh_jaccard_asymmetric_num(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 40, 2): a.add_hash(i) # different size: 10 b = MinHash(10, 10, track_abundance=track_abundance) for i in range(0, 80, 4): b.add_hash(i) assert a.count_common(b) == 10 assert b.count_common(a) == 10 # with 'jaccard', this will raise an error b/c different num with pytest.raises(TypeError): a.jaccard(b) a = a.downsample(num=10) # CTB note: this used to be 'compare', is now 'jaccard' assert a.jaccard(b) == 0.5 assert b.jaccard(a) == 0.5 def test_mh_merge_typeerror(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) with pytest.raises(TypeError): a.merge(set()) def test_mh_merge(track_abundance): # test merging two identically configured minhashes a = MinHash(100, 10, track_abundance=track_abundance) for i in range(0, 40, 2): a.add_hash(i) b = MinHash(100, 10, track_abundance=track_abundance) for i in range(0, 80, 4): b.add_hash(i) c = a.__copy__() c.merge(b) d = b.__copy__() d.merge(a) assert len(c) == len(d) assert list(sorted(c.hashes.items())) == list(sorted(d.hashes.items())) assert round(c.similarity(d), 3) == 1.0 assert round(d.similarity(c), 3) == 1.0 def test_mh_merge_empty_num(track_abundance): # test merging two identically configured minhashes, one empty a = MinHash(100, 10, track_abundance=track_abundance) b = MinHash(100, 10, track_abundance=track_abundance) for i in range(0, 80, 4): b.add_hash(i) c = a.__copy__() c.merge(b) d = b.__copy__() d.merge(a) assert len(c) assert len(c) == len(d) assert list(sorted(c.hashes.items())) == list(sorted(d.hashes.items())) assert round(c.similarity(d), 3) == 1.0 assert round(d.similarity(c), 3) == 1.0 def test_mh_merge_empty_scaled(track_abundance): # test merging two identically configured minhashes, one empty a = MinHash(0, 10, scaled=1, track_abundance=track_abundance) b = MinHash(0, 10, scaled=1, track_abundance=track_abundance) for i in range(0, 80, 4): b.add_hash(i) c = a.__copy__() c.merge(b) d = b.__copy__() d.merge(a) assert len(c) assert len(c) == len(d) assert list(sorted(c.hashes.items())) == list(sorted(d.hashes.items())) assert round(c.similarity(d), 3) == 1.0 assert round(d.similarity(c), 3) == 1.0 def test_mh_merge_check_length(track_abundance): a = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 40, 2): a.add_hash(i) b = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 80, 4): b.add_hash(i) c = a.__copy__() c.merge(b) assert len(c.hashes) == 20 def test_mh_merge_check_length2(track_abundance): # merged MH doesn't have full number of elements a = MinHash(4, 10, track_abundance=track_abundance) a.add_hash(3) a.add_hash(1) a.add_hash(4) b = MinHash(4, 10, track_abundance=track_abundance) b.add_hash(3) b.add_hash(1) b.add_hash(4) c = a.__copy__() c.merge(b) assert len(c.hashes) == 3 def test_mh_asymmetric_merge(track_abundance): # test merging two asymmetric (different size) MHs a = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 40, 2): a.add_hash(i) # different size: 10 b = MinHash(10, 10, track_abundance=track_abundance) for i in range(0, 80, 4): b.add_hash(i) c = a.__copy__() c.merge(b) d = b.__copy__() d.merge(a) assert len(a) == 20 assert len(b) == 10 assert len(c) == len(a) assert len(d) == len(b) # can't use jaccard on different nums without downsampling with pytest.raises(TypeError): d.jaccard(a) a = a.downsample(num=d.num) if track_abundance: assert round(d.similarity(a), 3) == 0.795 else: assert round(d.similarity(a), 3) == 1.0 c = c.downsample(num=b.num) if track_abundance: assert round(c.similarity(b), 3) == 0.436 else: assert c.similarity(b) == 0.5 def test_mh_inplace_concat_asymmetric(track_abundance): # test merging two asymmetric (different size) MHs a = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 40, 2): a.add_hash(i) # different size: 10 b = MinHash(10, 10, track_abundance=track_abundance) for i in range(0, 80, 4): b.add_hash(i) c = a.__copy__() c += b d = b.__copy__() d += a assert len(a) == 20 assert len(b) == 10 assert len(c) == len(a) assert len(d) == len(b) try: d.similarity(a) except TypeError as exc: assert "must have same num" in str(exc) a = a.downsample(num=d.num) if track_abundance: assert round(d.similarity(a), 3) == 0.795 # see: d += a, above. else: assert d.similarity(a) == 1.0 # see: d += a, above. c = c.downsample(num=b.num) if track_abundance: assert round(c.similarity(b), 3) == 0.436 else: assert c.similarity(b) == 0.5 def test_mh_inplace_concat(track_abundance): # test merging two identically configured minhashes a = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 40, 2): a.add_hash(i) b = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 80, 4): b.add_hash(i) c = a.__copy__() c += b d = b.__copy__() d += a assert len(c) == len(d) assert c.hashes == d.hashes assert round(c.similarity(d), 3) == 1.0 assert round(d.similarity(c), 3) == 1.0 def test_mh_merge_diff_protein(track_abundance): a = MinHash(20, 5, is_protein=False, track_abundance=track_abundance) b = MinHash(20, 5, is_protein=True, track_abundance=track_abundance) with pytest.raises(ValueError): a.merge(b) def test_mh_merge_diff_ksize(track_abundance): a = MinHash(20, 5, track_abundance=track_abundance) b = MinHash(20, 6, track_abundance=track_abundance) with pytest.raises(ValueError): a.merge(b) def test_mh_similarity_diff_protein(track_abundance): a = MinHash(20, 5, is_protein=False, track_abundance=track_abundance) b = MinHash(20, 5, is_protein=True, track_abundance=track_abundance) with pytest.raises(ValueError): a.similarity(b) def test_mh_similarity_diff_ksize(track_abundance): a = MinHash(20, 5, track_abundance=track_abundance) b = MinHash(20, 6, track_abundance=track_abundance) with pytest.raises(ValueError): a.similarity(b) def test_mh_similarity_diff_seed(track_abundance): a = MinHash(20, 5, track_abundance=track_abundance, seed=1) b = MinHash(20, 5, track_abundance=track_abundance, seed=2) with pytest.raises(ValueError): a.similarity(b) def test_mh_compare_diff_max_hash(track_abundance): a = MinHash(0, 5, track_abundance=track_abundance, scaled=scaled2) b = MinHash(0, 5, track_abundance=track_abundance, scaled=scaled4) with pytest.raises(ValueError): a.similarity(b) def test_mh_concat_diff_protein(track_abundance): a = MinHash(20, 5, is_protein=False, track_abundance=track_abundance) b = MinHash(20, 5, is_protein=True, track_abundance=track_abundance) with pytest.raises(ValueError): a += b def test_mh_concat_diff_ksize(track_abundance): a = MinHash(20, 5, track_abundance=track_abundance) b = MinHash(20, 6, track_abundance=track_abundance) with pytest.raises(ValueError): a += b def test_mh_concat_diff_max_hash(track_abundance): a = MinHash(0, 5, track_abundance=track_abundance, scaled=scaled8) b = MinHash(0, 5, track_abundance=track_abundance, scaled=scaled4) with pytest.raises(ValueError): a += b def test_mh_concat_diff_seed(track_abundance): a = MinHash(20, 5, track_abundance=track_abundance, seed=1) b = MinHash(20, 5, track_abundance=track_abundance, seed=2) with pytest.raises(ValueError): a += b def test_short_sequence(track_abundance): a = MinHash(20, 5, track_abundance=track_abundance) a.add_sequence("GGGG") # adding a short sequence should fail silently assert len(a.hashes) == 0 def test_bytes_murmur(): x = hash_murmur("ACG") assert x == 1731421407650554201 x = hash_murmur(b"ACG") assert x == 1731421407650554201 x = hash_murmur("ACG") assert x == 1731421407650554201 def test_murmur(): x = hash_murmur("ACG") assert x == 1731421407650554201 try: x = hash_murmur() assert 0, "hash_murmur requires an argument" except TypeError: pass x = hash_murmur("ACG", 42) assert x == 1731421407650554201 y = hash_murmur("ACG", 43) assert y != x def test_abundance_simple(): a = MinHash(20, 5, is_protein=False, track_abundance=True) a.add_sequence("AAAAA") assert list(a.hashes) == [2110480117637990133] assert a.hashes == {2110480117637990133: 1} a.add_sequence("AAAAA") assert list(a.hashes) == [2110480117637990133] assert a.hashes == {2110480117637990133: 2} def test_add_hash_with_abundance(): a = MinHash(20, 5, is_protein=False, track_abundance=True) a.add_hash_with_abundance(10, 1) assert a.hashes == {10: 1} a.add_hash_with_abundance(20, 2) assert a.hashes == {10: 1, 20: 2} a.add_hash_with_abundance(10, 2) assert a.hashes == {10: 3, 20: 2} def test_add_hash_with_abundance_2(): a = MinHash(20, 5, is_protein=False, track_abundance=False) with pytest.raises(RuntimeError) as e: a.add_hash_with_abundance(10, 1) assert "track_abundance=True when constructing" in e.value.args[0] def test_clear(): a = MinHash(20, 5, is_protein=False, track_abundance=True) a.add_hash(10) assert a.hashes == {10: 1} a.clear() assert a.hashes == {} def test_clear_2(): a = MinHash(20, 5, is_protein=False, track_abundance=False) a.add_hash(10) assert list(a.hashes) == [10] a.clear() assert list(a.hashes) == [] def test_abundance_simple_2(): a = MinHash(20, 5, is_protein=False, track_abundance=True) b = MinHash(20, 5, is_protein=False, track_abundance=True) a.add_sequence("AAAAA") assert list(a.hashes) == [2110480117637990133] assert a.hashes == {2110480117637990133: 1} a.add_sequence("AAAAA") assert list(a.hashes) == [2110480117637990133] assert a.hashes == {2110480117637990133: 2} b.add_sequence("AAAAA") assert a.count_common(b) == 1 def test_abundance_count_common(): a = MinHash(20, 5, is_protein=False, track_abundance=True) b = MinHash(20, 5, is_protein=False, track_abundance=False) a.add_sequence("AAAAA") a.add_sequence("AAAAA") assert list(a.hashes) == [2110480117637990133] assert a.hashes == {2110480117637990133: 2} b.add_sequence("AAAAA") b.add_sequence("GGGGG") assert a.count_common(b) == 1 assert a.count_common(b) == b.count_common(a) assert list(sorted(b.hashes)) == [2110480117637990133, 10798773792509008305] def test_abundance_similarity(): a = MinHash(20, 10, track_abundance=True) b = MinHash(20, 10, track_abundance=False) a.add_sequence("TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA") b.add_sequence("TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA") assert round(a.similarity(b), 3) == 1.0 assert round(b.similarity(b), 3) == 1.0 assert round(b.similarity(a), 3) == 1.0 assert round(a.similarity(a), 3) == 1.0 # add same sequence again b.add_sequence("TGCCGCCCAGCACCGGGTGACTAGGTTGAGCCATGATTAACCTGCAATGA") assert round(a.similarity(b), 3) == 1.0 assert round(b.similarity(b), 3) == 1.0 assert round(b.similarity(a), 3) == 1.0 assert round(a.similarity(a), 3) == 1.0 b.add_sequence("GATTGGTGCACACTTAACTGGGTGCCGCGCTGGTGCTGATCCATGAAGTT") x = a.similarity(b) assert x >= 0.3, x x = b.similarity(a) assert x >= 0.3, x assert round(a.similarity(a), 3) == 1.0 assert round(b.similarity(b), 3) == 1.0 def test_set_abundance(): a = MinHash(20, 10, track_abundance=False) with pytest.raises(RuntimeError) as e: a.set_abundances({1: 3, 2: 4}) assert "track_abundance=True when constructing" in e.value.args[0] def test_set_abundance_2(): datapath = utils.get_test_data("genome-s12.fa.gz.sig") sig = sourmash.load_one_signature(datapath, ksize=30, select_moltype="dna") new_mh = sig.minhash.copy_and_clear() mins = sig.minhash.hashes mins = {k: 1 for k in mins} new_mh.track_abundance = True new_mh.set_abundances(mins) assert set(new_mh.hashes) == set(mins) def test_set_abundance_clear(): # on empty minhash, clear should have no effect a = MinHash(20, 5, is_protein=False, track_abundance=True) b = MinHash(20, 5, is_protein=False, track_abundance=True) a.set_abundances({1: 3, 2: 4}, clear=True) b.set_abundances({1: 3, 2: 4}, clear=False) assert list(sorted(a.hashes)) == list(sorted(b.hashes)) def test_set_abundance_clear_2(): # default should be clear=True a = MinHash(20, 5, is_protein=False, track_abundance=True) a.add_hash(10) assert a.hashes == {10: 1} a.set_abundances({20: 2}) assert a.hashes == {20: 2} def test_set_abundance_clear_3(): a = MinHash(20, 5, is_protein=False, track_abundance=True) a.add_hash(10) assert a.hashes == {10: 1} a.set_abundances({20: 1, 30: 4}, clear=False) assert a.hashes == {10: 1, 20: 1, 30: 4} def test_set_abundance_clear_4(): # setting the abundance of an already set hash should add # the abundances together a = MinHash(20, 5, is_protein=False, track_abundance=True) a.set_abundances({20: 2, 10: 1}, clear=False) # should also sort the hashes assert a.hashes == {10: 1, 20: 2} a.set_abundances({20: 1, 10: 2}, clear=False) assert a.hashes == {10: 3, 20: 3} def test_clear_abundance_on_zero(): mh = sourmash.minhash.MinHash(n=0, ksize=31, scaled=1, track_abundance=True) mh.set_abundances({1: 5, 2: 3, 3: 5}) mh.set_abundances({1: 0}, clear=False) assert 1 not in dict(mh.hashes) assert dict(mh.hashes)[2] == 3 assert dict(mh.hashes)[3] == 5 assert len(mh) == 2 with pytest.raises(ValueError): mh.set_abundances({2: -1}) # Test on clear = True with pytest.raises(ValueError): mh.set_abundances({2: -1}, clear=False) assert len(mh) == 2 # Assert that nothing was affected def test_reset_abundance_initialized(): a = MinHash(1, 4, track_abundance=True) a.add_sequence("ATGC") # If we had a minhash with abundances and drop it, this shouldn't fail. # Convert from Abundance to Regular MinHash a.track_abundance = False assert list(a.hashes) == [12415348535738636339] def test_set_abundance_initialized(): a = MinHash(1, 4, track_abundance=False) a.add_sequence("ATGC") with pytest.raises(RuntimeError) as e: a.track_abundance = True assert ( "Can only set track_abundance=True if the MinHash is empty" in e.value.args[0] ) def test_set_abundance_num(): a = MinHash(2, 10, track_abundance=True) a.set_abundances({1: 3, 2: 4}) assert a.hashes == {1: 3, 2: 4} def test_mh_copy_and_clear(track_abundance): # test basic creation of new, empty MinHash a = MinHash(20, 10, track_abundance=track_abundance) for i in range(0, 40, 2): a.add_hash(i) b = a.copy_and_clear() assert a.ksize == b.ksize assert b.num == a.num assert b._max_hash == 0 assert not b.is_protein assert b.track_abundance == track_abundance assert b.seed == a.seed assert len(b.hashes) == 0 assert a.scaled == b.scaled assert b.scaled == 0 def test_mh_copy_and_clear_with_max_hash(track_abundance): # test basic creation of new, empty MinHash w/max_hash param set a = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled8) for i in range(44, 64): a.add_hash(2**i) b = a.copy_and_clear() assert a.ksize == b.ksize assert b.num == a.num assert b._max_hash == 2**61 assert not b.is_protein assert b.track_abundance == track_abundance assert b.seed == a.seed assert len(b.hashes) == 0 assert a.scaled == b.scaled assert b.scaled != 0 def test_scaled_property(track_abundance): scaled = 10000 a = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled) assert a.scaled == scaled def test_pickle_protein(track_abundance): # check that protein/etc ksize is handled properly during serialization. a = MinHash( 0, 10, track_abundance=track_abundance, is_protein=True, scaled=scaled8, ) for i in range(44, 64): a.add_hash(2**i) b = pickle.loads(pickle.dumps(a)) assert a.ksize == b.ksize assert b.num == a.num assert b._max_hash == a._max_hash assert b._max_hash == 2**61 assert b.is_protein assert b.track_abundance == track_abundance assert b.seed == a.seed assert len(b.hashes) == len(a.hashes) assert len(b.hashes) == 18 assert a.scaled == b.scaled assert b.scaled != 0 def test_pickle_dayhoff(track_abundance): # check that dayhoff ksize is handled properly during serialization. a = MinHash( 0, 10, track_abundance=track_abundance, dayhoff=True, scaled=scaled8, ) for i in range(44, 64): a.add_hash(2**i) b = pickle.loads(pickle.dumps(a)) assert a.ksize == b.ksize assert b.num == a.num assert b._max_hash == a._max_hash assert b._max_hash == 2**61 assert b.dayhoff assert b.track_abundance == track_abundance assert b.seed == a.seed assert len(b.hashes) == len(a.hashes) assert len(b.hashes) == 18 assert a.scaled == b.scaled assert b.scaled != 0 def test_pickle_hp(track_abundance): # check that hp ksize is handled properly during serialization. a = MinHash( 0, 10, track_abundance=track_abundance, hp=True, scaled=scaled8, ) for i in range(44, 64): a.add_hash(2**i) b = pickle.loads(pickle.dumps(a)) assert a.ksize == b.ksize assert b.num == a.num assert b._max_hash == a._max_hash assert b._max_hash == 2**61 assert b.hp assert b.track_abundance == track_abundance assert b.seed == a.seed assert len(b.hashes) == len(a.hashes) assert len(b.hashes) == 18 assert a.scaled == b.scaled assert b.scaled != 0 def test_pickle_max_hash(track_abundance): a = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled8) for i in range(44, 64): a.add_hash(2**i) b = pickle.loads(pickle.dumps(a)) assert a.ksize == b.ksize assert b.num == a.num assert b._max_hash == a._max_hash assert b._max_hash == 2**61 assert not b.is_protein assert b.track_abundance == track_abundance assert b.seed == a.seed assert len(b.hashes) == len(a.hashes) assert len(b.hashes) == 18 assert a.scaled == b.scaled assert b.scaled != 0 def test_pickle_scaled(track_abundance): a = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled8) for i in range(44, 64): a.add_hash(2**i) b = pickle.loads(pickle.dumps(a)) assert a.ksize == b.ksize assert b.num == a.num assert b._max_hash == a._max_hash assert b._max_hash == 2**61 assert not b.is_protein assert b.track_abundance == track_abundance assert b.seed == a.seed assert len(b.hashes) == len(a.hashes) assert len(b.hashes) == 18 assert a.scaled == b.scaled assert b.scaled != 0 def test_minhash_abund_add(): # this targets part of bug #319, a segfault caused by invalidation of # std::vector iterators upon vector resizing - in this case, there # was also a bug in inserting into the middle of mins when scaled was set. a = MinHash(0, 10, track_abundance=True, scaled=scaled8) n = 0 for i in range(10, 0, -1): a.add_hash(i) n += 1 assert len(a.hashes) == n print(len(a.hashes)) def test_minhash_abund_capacity_increase(): # this targets bug #319, a segfault caused by invalidation of # std::vector iterators upon vector resizing. # this should set capacity to 1000 - see KmerMinHash constructor call # to 'reserve' when n > 0 for specific parameter. a = MinHash(0, 10, track_abundance=True, scaled=scaled8) # 1001 is dependent on the value passed to reserve (currently 1000). for i in range(1001, 0, -1): a.add_hash(i) def test_minhash_abund_merge_flat(): # this targets a segfault caused by trying to compute similarity # of a signature with abundance and a signature without abundance. # the correct behavior for now is to calculate simple Jaccard, # i.e. 'flatten' both of them. a = MinHash(0, 10, track_abundance=True, scaled=scaled8) b = MinHash(0, 10, scaled=scaled8) for i in range(0, 10, 2): a.add_hash(i) for j in range(0, 10, 3): b.add_hash(i) # these crashed, previously. assert a.similarity(b) == 0.2 assert b.similarity(a) == 0.2 def test_minhash_abund_merge_flat_2(): # this targets a segfault caused by trying to merge # a signature with abundance and a signature without abundance. a = MinHash(0, 10, track_abundance=True, scaled=scaled2) b = MinHash(0, 10, scaled=scaled2) for i in range(0, 10, 2): a.add_hash(i) for j in range(0, 10, 3): b.add_hash(i) a.merge(b) def test_distance_matrix(track_abundance): import numpy siglist = [ next(signature.load_signatures_from_json(utils.get_test_data(f))) for f in utils.SIG_FILES ] D1 = numpy.zeros([len(siglist), len(siglist)]) D2 = numpy.zeros([len(siglist), len(siglist)]) for i, E in enumerate(siglist): for j, E2 in enumerate(siglist): if i < j: continue similarity = E.similarity(E2, track_abundance) D2[i][j] = similarity D2[j][i] = similarity for i, E in enumerate(siglist): for j, E2 in enumerate(siglist): D1[i][j] = E.similarity(E2, track_abundance) assert numpy.array_equal(D1, D2) def test_remove_many(track_abundance): a = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled2) a.add_many(list(range(0, 100, 2))) orig_sig = signature.SourmashSignature(a) orig_md5 = orig_sig.md5sum() a.remove_many(list(range(0, 100, 3))) new_sig = signature.SourmashSignature(a) new_md5 = new_sig.md5sum() assert orig_md5 == "f1cc295157374f5c07cfca5f867188a1" assert new_md5 == "dd93fa319ef57f4a019c59ee1a8c73e2" assert orig_md5 != new_md5 assert len(a) == 33 assert all(c % 6 != 0 for c in a.hashes) def test_remove_minhash(track_abundance): original_mh = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled8) added_mh = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled8) tested_mh = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled8) original_mh.add_many([2**i for i in range(62)]) added_mh.add_many([2**63]) # contains original in it tested_mh.add_many([2**i for i in range(63)]) # original + added # Now we should expect tested_minhash == original_minhash # Note we are passing a MinHash object instead of an iterable object tested_mh.remove_many(added_mh) # Assertion original_sig = signature.SourmashSignature(original_mh) tested_sig = signature.SourmashSignature(tested_mh) # Should pass if the hashes list in the same order assert original_mh.hashes == tested_mh.hashes assert len(original_mh) == len(tested_mh) assert original_sig.md5sum() == tested_sig.md5sum() def test_add_many(track_abundance): a = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled8) b = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled8) a.add_many(list(range(0, 100, 2))) a.add_many(list(range(0, 100, 2))) # => abundance = 2 assert len(a) == 50 assert all(c % 2 == 0 for c in a.hashes) for h in range(0, 100, 2): b.add_hash(h) b.add_hash(h) assert len(b) == 50 assert a == b def test_set_abundances_huge(): max_hash = _get_max_hash_for_scaled(2**31) a = MinHash(0, 10, track_abundance=True, scaled=_get_scaled_for_max_hash(max_hash)) hashes = [2**i for i in range(64)] abundances = itertools.repeat(2) a.set_abundances(dict(zip(hashes, abundances))) def test_try_change_hashes(track_abundance): a = MinHash(0, 10, track_abundance=track_abundance, scaled=scaled8) MinHash(0, 10, track_abundance=track_abundance, scaled=scaled8) a.add_many(list(range(0, 100, 2))) h = a.hashes with pytest.raises(RuntimeError): h[5] = 10 def test_flatten(): # test behavior with scaled scaled = scaled8 mh = MinHash(0, 4, track_abundance=True, scaled=scaled) assert mh._max_hash == 2**61 mh.add_hash(10) mh.add_hash(10) mh.add_hash(10) mh.add_hash(20) mh.add_hash(20) mh.add_hash(30) mh.add_hash(30) mh.add_hash(30) assert mh.hashes[10] == 3 assert mh.hashes[20] == 2 assert mh.hashes[30] == 3 mh2 = mh.flatten() assert mh2.hashes[10] == 1 assert mh2.hashes[20] == 1 assert mh2.hashes[30] == 1 assert len(mh2) == 3 def test_inflate(): # test behavior of inflate function scaled = scaled2 mh = MinHash(0, 4, track_abundance=False, scaled=scaled) mh2 = MinHash(0, 4, track_abundance=True, scaled=scaled) assert mh._max_hash == 2**63 mh.add_hash(10) mh.add_hash(20) mh.add_hash(30) assert mh.hashes[10] == 1 assert mh.hashes[20] == 1 assert mh.hashes[30] == 1 mh2.add_hash(10) mh2.add_hash(10) mh2.add_hash(10) mh2.add_hash(20) mh2.add_hash(20) mh2.add_hash(30) mh2.add_hash(30) mh2.add_hash(30) assert mh2.hashes[10] == 3 assert mh2.hashes[20] == 2 assert mh2.hashes[30] == 3 mh3 = mh.inflate(mh2) assert mh3.hashes[10] == 3 assert mh3.hashes[20] == 2 assert mh3.hashes[30] == 3 def test_inflate_error(): # test behavior of inflate function with 'self' as an abund sketch scaled = scaled2 mh = MinHash(0, 4, track_abundance=True, scaled=scaled) mh2 = MinHash(0, 4, track_abundance=True, scaled=scaled) assert mh._max_hash == 2**63 mh.add_hash(10) mh.add_hash(20) mh.add_hash(30) assert mh.hashes[10] == 1 assert mh.hashes[20] == 1 assert mh.hashes[30] == 1 mh2.add_hash(10) mh2.add_hash(10) mh2.add_hash(10) mh2.add_hash(20) mh2.add_hash(20) mh2.add_hash(30) mh2.add_hash(30) mh2.add_hash(30) assert mh2.hashes[10] == 3 assert mh2.hashes[20] == 2 assert mh2.hashes[30] == 3 with pytest.raises(ValueError) as exc: mh = mh.inflate(mh2) assert ( "inflate operates on a flat MinHash and takes a MinHash object with track_abundance=True" in str(exc.value) ) def test_inflate_not_a_subset(): # test behavior of inflate function when 'from_mh' is not a subset. scaled = scaled2 mh = MinHash(0, 4, track_abundance=False, scaled=scaled) mh2 = MinHash(0, 4, track_abundance=True, scaled=scaled) assert mh._max_hash == 2**63 mh.add_hash(10) mh.add_hash(20) mh.add_hash(30) assert mh.hashes[10] == 1 assert mh.hashes[20] == 1 assert mh.hashes[30] == 1 mh2.add_hash(10) mh2.add_hash(10) mh2.add_hash(10) mh2.add_hash(30) mh2.add_hash(30) mh2.add_hash(30) assert mh2.hashes[10] == 3 assert 20 not in mh2.hashes assert mh2.hashes[30] == 3 mh3 = mh.inflate(mh2) assert mh3.hashes[10] == 3 assert 20 not in mh3.hashes # should intersect, in this case. assert mh3.hashes[30] == 3 def test_add_kmer(track_abundance): # test add_kmer method mh1 = MinHash(0, 4, scaled=1, track_abundance=track_abundance) mh2 = MinHash(0, 4, scaled=1, track_abundance=track_abundance) mh1.add_sequence("ATGCGTGC") a = mh1.hashes mh2.add_kmer("ATGC") mh2.add_kmer("TGCG") mh2.add_kmer("GCGT") mh2.add_kmer("CGTG") mh2.add_kmer("GTGC") b = mh2.hashes assert set(a.items()) == set(b.items()) def test_add_kmer_too_long(track_abundance): # test add_kmer method - should only take length k mh1 = MinHash(0, 4, scaled=1, track_abundance=track_abundance) with pytest.raises(ValueError): mh1.add_kmer("ATGCGTGC") def test_get_mins_deprecated(track_abundance): mh = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mins = (28945103950853965, 74690756200987412, 82962372765557409) mh.add_many(mins) mh.add_many(mins) mh.add_many(mins) mh.add_many(mins) with pytest.warns(DeprecationWarning): assert set(mh.get_mins()) == set(mins) if track_abundance: d = mh.get_mins(with_abundance=True) for k in mins: assert d[k] == 4 assert len(d) == len(mins) def test_get_hashes_deprecated(track_abundance): mh = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mins = (28945103950853965, 74690756200987412, 82962372765557409) mh.add_many(mins) mh.add_many(mins) mh.add_many(mins) mh.add_many(mins) with pytest.warns(DeprecationWarning): assert set(mh.get_hashes()) == set(mins) def test_downsample_num(track_abundance): # test downsample(num=...) function mh = MinHash(10, 21, track_abundance=track_abundance) for i in range(20): mh.add_hash(i) assert mh.num == 10 assert len(mh) == 10 assert list(sorted(mh.hashes)) == list(range(10)) mh2 = mh.downsample(num=5) assert mh2.num == 5 assert len(mh2) == 5 assert list(sorted(mh2.hashes)) == list(range(5)) def test_downsample_scaled(track_abundance): # test downsample(scaled...) method mh = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mins = ( 1, 2, 3, 9223372036854775808 + 1, 9223372036854775808 + 2, 9223372036854775808 + 3, ) mh.add_many(mins) assert len(mh) == 6 assert list(sorted(mh.hashes)) == list(mins) mh2 = mh.downsample(scaled=2) print(mh._max_hash, mh2._max_hash) assert len(mh2) == 3 assert list(sorted(mh2.hashes)) == list(mins[:3]) def test_is_molecule_type_1(track_abundance): mh = MinHash(1, 21, track_abundance=track_abundance) assert mh.moltype == "DNA" assert mh.is_dna assert not mh.is_protein assert not mh.hp assert not mh.dayhoff def test_is_molecule_type_2(track_abundance): mh = MinHash(1, 21, track_abundance=track_abundance, is_protein=True) assert mh.moltype == "protein" assert not mh.is_dna assert mh.is_protein assert not mh.hp assert not mh.dayhoff def test_is_molecule_type_3(track_abundance): mh = MinHash(1, 21, track_abundance=track_abundance, hp=True) assert mh.moltype == "hp" assert not mh.is_dna assert not mh.is_protein assert mh.hp assert not mh.dayhoff def test_is_molecule_type_4(track_abundance): mh = MinHash(1, 21, track_abundance=track_abundance, dayhoff=True) assert mh.moltype == "dayhoff" assert not mh.is_dna assert not mh.is_protein assert not mh.hp assert mh.dayhoff def test_addition_num_incompatible(): mh1 = MinHash(10, 21) mh2 = MinHash(20, 21) mh1.add_hash(0) mh2.add_hash(1) with pytest.raises(TypeError) as exc: mh1 + mh2 assert "incompatible num values: self=10 other=20" in str(exc.value) def test_addition_abund(): mh1 = MinHash(10, 21, track_abundance=True) mh2 = MinHash(10, 21, track_abundance=True) mh1.set_abundances({0: 1}) mh2.set_abundances({0: 3}) mh3 = mh1 + mh2 hashcounts = mh3.hashes assert len(hashcounts) == 1 assert hashcounts[0] == 4 def test_addition_noabund(): mh1 = MinHash(10, 21, track_abundance=False) mh2 = MinHash(10, 21, track_abundance=False) mh1.add_hash(0) mh2.add_hash(0) mh3 = mh1 + mh2 hashcounts = mh3.hashes assert len(hashcounts) == 1 assert hashcounts[0] == 1 def test_iaddition_abund(): mh1 = MinHash(10, 21, track_abundance=True) mh2 = MinHash(10, 21, track_abundance=True) mh1.set_abundances({0: 1}) mh2.set_abundances({0: 3}) mh1 += mh2 hashcounts = mh1.hashes assert len(hashcounts) == 1 assert hashcounts[0] == 4 hashcounts2 = mh2.hashes assert len(hashcounts2) == 1 assert hashcounts2[0] == 3 def test_iaddition_noabund(): mh1 = MinHash(10, 21, track_abundance=False) mh2 = MinHash(10, 21, track_abundance=False) mh1.add_hash(0) mh2.add_hash(0) mh1 += mh2 hashcounts = mh1.hashes assert len(hashcounts) == 1 assert hashcounts[0] == 1 def test_intersection_1_num(): mh1 = MinHash(10, 21) mh2 = MinHash(10, 21) mh1.add_hash(0) mh1.add_hash(1) mh2.add_hash(0) mh2.add_hash(2) mh3 = mh1.intersection(mh2) print("mh.intersection INTERSECTION HASHES:", set(mh3.hashes)) assert len(mh3) == 1 assert 0 in mh3.hashes def test_and_operator(): mh1 = MinHash(20, 21) mh1.add_hash(5) mh1.add_hash(6) mh2 = MinHash(20, 21) mh2.add_hash(6) mh2.add_hash(7) print("\n \n mh1 EQUALS ", mh1.hashes, "\n mh2 EQUALS", mh2.hashes) mh3 = mh1.intersection(mh2) mh4 = mh1 & mh2 print( "\n Intersection hashes (mh3): ", mh3.hashes, "\n '&' hashes: (mh4)", mh4.hashes ) assert mh3 assert mh3 == mh4 def test_intersection_2_scaled(): mh1 = MinHash(0, 21, scaled=1) mh2 = MinHash(0, 21, scaled=1) mh1.add_hash(0) mh1.add_hash(1) mh2.add_hash(0) mh2.add_hash(2) mh3 = mh1.intersection(mh2) print(set(mh3.hashes)) assert len(mh3) == 1 assert 0 in mh3.hashes def test_intersection_3_abundance_error(): # cannot intersect abundance MinHash mh1 = MinHash(0, 21, scaled=1, track_abundance=True) mh2 = MinHash(0, 21, scaled=1, track_abundance=True) with pytest.raises(TypeError) as exc: mh1.intersection(mh2) assert str(exc.value) == "can only intersect flat MinHash objects" def test_intersection_4_incompatible_ksize(): # cannot intersect incompatible ksize etc mh1 = MinHash(500, 21) mh2 = MinHash(500, 31) with pytest.raises(ValueError) as exc: mh1.intersection(mh2) assert str(exc.value) == "different ksizes cannot be compared" def test_intersection_5_incompatible(): # cannot intersect with non-MinHash objects mh1 = MinHash(0, 21, scaled=1) with pytest.raises(TypeError) as exc: mh1.intersection(set()) assert str(exc.value) == "can only intersect MinHash objects" def test_intersection_6_full_num(): # intersection of two "full" num objects is correct mh1 = MinHash(20, 21) mh2 = MinHash(20, 21) for i in range(100): mh1.add_hash(i) for i in range(0, 100, 2): mh2.add_hash(i) # they are both full: assert len(mh1) == 20 assert len(mh2) == 20 # intersection is symmetric: mh3 = mh1.intersection(mh2) mh4 = mh2.intersection(mh1) assert mh3 == mh4 # everything in intersection is in both: for k in mh3.hashes: assert k in mh1.hashes assert k in mh2.hashes assert mh1.intersection_and_union_size(mh2) == (10, 20) def test_intersection_7_full_scaled(): # intersection of two scaled objects is correct mh1 = MinHash(0, 21, scaled=100) mh2 = MinHash(0, 21, scaled=100) for i in range(100): mh1.add_hash(i) for i in range(0, 200, 2): mh2.add_hash(i) # they both have everything: assert len(mh1) == 100 assert len(mh2) == 100 # intersection is symmetric: mh3 = mh1.intersection(mh2) mh4 = mh2.intersection(mh1) assert mh3 == mh4 # everything in intersection is in both: for k in mh3.hashes: assert k in mh1.hashes assert k in mh2.hashes assert mh1.intersection_and_union_size(mh2) == (50, 150) def test_intersection_and_union_8_incompatible_ksize(): # cannot intersect different ksizes mh1 = MinHash(0, 21, scaled=1) mh2 = MinHash(0, 31, scaled=1) with pytest.raises(TypeError) as exc: mh1.intersection_and_union_size(mh2) assert "incompatible MinHash objects" in str(exc) def test_merge_abund(): mh1 = MinHash(10, 21, track_abundance=True) mh2 = MinHash(10, 21, track_abundance=True) mh1.set_abundances({0: 1}) mh2.set_abundances({0: 3}) ret = mh1.merge(mh2) assert ret is None print("MH1 EQUALS ", mh1.hashes) hashcounts = mh1.hashes assert len(hashcounts) == 1 assert hashcounts[0] == 4 hashcounts2 = mh2.hashes assert len(hashcounts2) == 1 assert hashcounts2[0] == 3 def test_merge_noabund(): mh1 = MinHash(10, 21, track_abundance=False) mh2 = MinHash(10, 21, track_abundance=False) mh1.add_hash(0) mh2.add_hash(0) ret = mh1.merge(mh2) assert ret is None hashcounts = mh1.hashes assert len(hashcounts) == 1 assert hashcounts[0] == 1 def test_merge_full_num(): # merge/union of two "full" num objects is correct mh1 = MinHash(20, 21) mh2 = MinHash(20, 21) for i in range(100): mh1.add_hash(i) for i in range(0, 100, 2): mh2.add_hash(i) # they are both full: assert len(mh1) == 20 assert len(mh2) == 20 # add is symmetric: mh3 = mh1 + mh2 mh4 = mh2 + mh1 assert mh3 == mh4 # merge is full assert len(mh3) == 20 # everything in union is in at least one for k in mh3.hashes: assert k in mh1.hashes or k in mh2.hashes def test_merge_scaled(): # merge/union of two reasonably full scaled objects is correct mh1 = MinHash(0, 21, scaled=100) mh2 = MinHash(0, 21, scaled=100) for i in range(100): mh1.add_hash(i) for i in range(0, 200, 2): mh2.add_hash(i) assert len(mh1) == 100 assert len(mh2) == 100 # merge contains all the things mh3 = mh1 + mh2 assert len(mh3) == 150 # everything in either one is in union for k in mh1.hashes: assert k in mh3.hashes for k in mh2.hashes: assert k in mh3.hashes def test_add_is_symmetric(): mh1 = MinHash(20, 21) mh1.add_hash(5) mh2 = MinHash(20, 21) mh2.add_hash(6) print("\n mh1 EQUALS ", mh1.hashes, "\n mh2 EQUALS", mh2.hashes) mh3 = mh1 + mh2 mh4 = mh2 + mh1 print("\n mh3 EQUALS ", mh3.hashes, "\n mh4 EQUALS", mh4.hashes) # if mh3 != 0, then it is "true", so it passes assert mh3 assert mh3 == mh4 def test_or_equals_add(): mh1 = MinHash(20, 21) mh1.add_hash(5) mh2 = MinHash(20, 21) mh2.add_hash(6) print("\n mh1 EQUALS ", mh1.hashes, "\n mh2 EQUALS", mh2.hashes) mh3 = mh1 + mh2 mh4 = mh1 | mh2 print("\n mh3 EQUALS ", mh3.hashes, "\n mh4 EQUALS", mh4.hashes) assert mh3 assert mh3 == mh4 def test_max_containment(): mh1 = MinHash(0, 21, scaled=1, track_abundance=False) mh2 = MinHash(0, 21, scaled=1, track_abundance=False) mh1.add_many((1, 2, 3, 4)) mh2.add_many((1, 5)) assert mh1.contained_by(mh2) == 1 / 4 assert mh2.contained_by(mh1) == 1 / 2 assert mh1.max_containment(mh2) == 1 / 2 assert mh2.max_containment(mh1) == 1 / 2 def test_max_containment_empty(): mh1 = MinHash(0, 21, scaled=1, track_abundance=False) mh2 = MinHash(0, 21, scaled=1, track_abundance=False) mh1.add_many((1, 2, 3, 4)) assert mh1.contained_by(mh2) == 0 assert mh2.contained_by(mh1) == 0 assert mh1.max_containment(mh2) == 0 assert mh2.max_containment(mh1) == 0 def test_max_containment_equal(): mh1 = MinHash(0, 21, scaled=1, track_abundance=False) mh2 = MinHash(0, 21, scaled=1, track_abundance=False) mh1.add_many((1, 2, 3, 4)) mh2.add_many((1, 2, 3, 4)) assert mh1.contained_by(mh2) == 1 assert mh2.contained_by(mh1) == 1 assert mh1.max_containment(mh2) == 1 assert mh2.max_containment(mh1) == 1 def test_avg_containment(): mh1 = MinHash(0, 21, scaled=1, track_abundance=False) mh2 = MinHash(0, 21, scaled=1, track_abundance=False) mh1.add_many((1, 2, 3, 4)) mh2.add_many((1, 5)) assert mh1.contained_by(mh2) == 1 / 4 assert mh2.contained_by(mh1) == 1 / 2 assert mh1.avg_containment(mh2) == 0.375 assert mh2.avg_containment(mh1) == 0.375 def test_avg_containment_empty(): mh1 = MinHash(0, 21, scaled=1, track_abundance=False) mh2 = MinHash(0, 21, scaled=1, track_abundance=False) mh1.add_many((1, 2, 3, 4)) assert mh1.contained_by(mh2) == 0 assert mh2.contained_by(mh1) == 0 assert mh1.avg_containment(mh2) == 0 assert mh2.avg_containment(mh1) == 0 def test_avg_containment_equal(): mh1 = MinHash(0, 21, scaled=1, track_abundance=False) mh2 = MinHash(0, 21, scaled=1, track_abundance=False) mh1.add_many((1, 2, 3, 4)) mh2.add_many((1, 2, 3, 4)) assert mh1.contained_by(mh2) == 1 assert mh2.contained_by(mh1) == 1 assert mh1.avg_containment(mh2) == 1 assert mh2.avg_containment(mh1) == 1 def test_frozen_and_mutable_1(track_abundance): # mutable minhashes -> mutable minhashes creates new copy mh1 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh2 = mh1.to_mutable() mh1.add_hash(10) assert 10 not in mh2.hashes def test_frozen_and_mutable_2(track_abundance): # check that mutable -> frozen are separate mh1 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh1.add_hash(10) mh2 = mh1.to_frozen() assert 10 in mh2.hashes mh1.add_hash(11) assert 11 not in mh2.hashes def test_frozen_and_mutable_3(track_abundance): # check that mutable -> frozen -> mutable are all separate from each other mh1 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh1.add_hash(10) mh2 = mh1.to_frozen() assert 10 in mh2.hashes mh1.add_hash(11) assert 11 not in mh2.hashes mh3 = mh2.to_mutable() mh3.add_hash(12) assert 12 not in mh2.hashes assert 12 not in mh1.hashes def test_dna_kmers(): # test seq_to_hashes for dna -> dna mh = MinHash(0, ksize=31, scaled=1) # DNA seq = "ATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGTGTTGCCGATATTCTGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCTGGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATTTTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCGTCGATCAGGAATTTGCCCAAATAAAACATGTCCTGCATGGCATTAGTTTGTTGGGGCAGTGCCCGGATAGCATCAACGCTGCGCTGATTTGCCGTGGCGAGAAAATGTCGATCGCCATTATGGCCGGCGTATTAGAAGCGCGCGGTCACAACGTTACTGTTATCGATCCGGTCGAAAAACTGCTGGCAGTGGGGCATTACCTCGAATCTACCGTCGATATTGCTGAGTCCACCCGCCGTATTGCGGCAAGCCGCATTCCGGCTGATCACATGGTGCTGAT" # first calculate seq to hashes hashes = mh.seq_to_hashes(seq) # then calculate all hashes for the sequence mh.add_sequence(seq) # identical? assert set(hashes) == set(mh.hashes) # k-mer by k-mer? for i in range(0, len(seq) - 31 + 1): # calculate each k-mer kmer = seq[i : i + 31] # add to minhash obj single_mh = mh.copy_and_clear() single_mh.add_sequence(kmer) assert len(single_mh) == 1 # also calculate via seq_to_hashes hashvals = mh.seq_to_hashes(kmer) assert len(hashvals) == 1 hashval = hashvals[0] # confirm it all matches assert hashval == list(single_mh.hashes)[0] assert hashval == hashes[i] def test_dna_kmers_2(): # test kmers_and_hashes for dna -> dna mh = MinHash(0, ksize=31, scaled=1) # DNA seq = "ATGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGTGTTGCCGATATTCTGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCTGGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATTTTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCGTCGATCAGGAATTTGCCCAAATAAAACATGTCCTGCATGGCATTAGTTTGTTGGGGCAGTGCCCGGATAGCATCAACGCTGCGCTGATTTGCCGTGGCGAGAAAATGTCGATCGCCATTATGGCCGGCGTATTAGAAGCGCGCGGTCACAACGTTACTGTTATCGATCCGGTCGAAAAACTGCTGGCAGTGGGGCATTACCTCGAATCTACCGTCGATATTGCTGAGTCCACCCGCCGTATTGCGGCAAGCCGCATTCCGGCTGATCACATGGTGCTGAT" # k-mer by k-mer? for kmer, hashval in mh.kmers_and_hashes(seq): # add to minhash obj single_mh = mh.copy_and_clear() single_mh.add_sequence(kmer) assert len(single_mh) == 1 # confirm it all matches assert hashval == list(single_mh.hashes)[0] def test_dna_kmers_3_bad_dna(): # test kmers_and_hashes for dna -> dna, with some bad k-mers in there mh = MinHash(0, ksize=31, scaled=1) # DNA seq = "NTGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGTGTTGCCGATATTCTGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCTGGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATTTTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCGTCGATCAGGAATTTGCCCAAATAAAACATGTCCTGCATGGCATTAGTTTGTTGGGGCAGTGCCCGGATAGCATCAACGCTGCGCTGATTTGCCGTGGCGAGAAAATGTCGATCGCCATTATGGCCGGCGTATTAGAAGCGCGCGGTCACAACGTTACTGTTATCGATCCGGTCGAAAAACTGCTGGCAGTGGGGCATTACCTCGAATCTACCGTCGATATTGCTGAGTCCACCCGCCGTATTGCGGCAAGCCGCATTCCGGCTGATCACATGGTGCTGAT" with pytest.raises(ValueError) as exc: list(mh.kmers_and_hashes(seq)) assert "invalid DNA character in input k-mer: NTGCGAGTGT" in str(exc) def test_dna_kmers_4_bad_dna(): # test kmers_and_hashes for bad dna -> dna, using force mh = MinHash(0, ksize=31, scaled=1) # DNA seq = "NTGCGAGTGTTGAAGTTCGGCGGTACATCAGTGGCAAATGCAGAACGTTTTCTGCGTGTTGCCGATATTCTGGAAAGCAATGCCAGGCAGGGGCAGGTGGCCACCGTCCTCTCTGCCCCCGCCAAAATCACCAACCACCTGGTGGCGATGATTGAAAAAACCATTAGCGGCCAGGATGCTTTACCCAATATCAGCGATGCCGAACGTATTTTTGCCGAACTTTTGACGGGACTCGCCGCCGCCCAGCCGGGGTTCCCGCTGGCGCAATTGAAAACTTTCGTCGATCAGGAATTTGCCCAAATAAAACATGTCCTGCATGGCATTAGTTTGTTGGGGCAGTGCCCGGATAGCATCAACGCTGCGCTGATTTGCCGTGGCGAGAAAATGTCGATCGCCATTATGGCCGGCGTATTAGAAGCGCGCGGTCACAACGTTACTGTTATCGATCCGGTCGAAAAACTGCTGGCAGTGGGGCATTACCTCGAATCTACCGTCGATATTGCTGAGTCCACCCGCCGTATTGCGGCAAGCCGCATTCCGGCTGATCACATGGTGCTGAT" # k-mer by k-mer? found_bad_kmer = False for kmer, hashval in mh.kmers_and_hashes(seq, force=True): # add to minhash obj single_mh = mh.copy_and_clear() if hashval is None: assert kmer == seq[:31] # first k-mer is baaaaad. found_bad_kmer = True continue # if the below code raises an exception, it's because the above # 'if' statement was not triggered (but should have been :) single_mh.add_sequence(kmer) assert len(single_mh) == 1 assert hashval == list(single_mh.hashes)[0] assert found_bad_kmer, "there is one bad k-mer in here" def test_protein_kmers(): # test seq_to_hashes for protein -> protein mh = MinHash(0, ksize=7, is_protein=True, scaled=1) seq = "MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGRFADKLPSEPRENIVYQCWERFCQELGKQIPVAMTLEKNMPIGSGLGSSACSVVAALMAMNEHCGKPLNDTRLLALMGELEGRISGSIHYDNVAPCFLGGMQLMIEENDIISQQVPGFDEWLWVLAYPGIKVSTAEARAILPAQYRRQDCIAHGRHLAGFIHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVADWLGKNYLQNQEGFVHICRLDTAGARVLEN*" # first calculate seq to hashes hashes = mh.seq_to_hashes(seq, is_protein=True) # then calculate all hashes for the sequence mh.add_protein(seq) # identical? assert set(hashes) == set(mh.hashes) # k-mer by k-mer? for i in range(0, len(seq) - 7 + 1): # calculate each k-mer kmer = seq[i : i + 7] # add to minhash obj single_mh = mh.copy_and_clear() single_mh.add_protein(kmer) assert len(single_mh) == 1 # also calculate via seq_to_hashes hashvals = mh.seq_to_hashes(kmer, is_protein=True) assert len(hashvals) == 1 hashval = hashvals[0] # confirm it all matches assert hashval == list(single_mh.hashes)[0] assert hashval == hashes[i] def test_protein_kmers_2(): # test kmers_and_hashes for protein -> protein mh = MinHash(0, ksize=7, is_protein=True, scaled=1) seq = "MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGRFADKLPSEPRENIVYQCWERFCQELGKQIPVAMTLEKNMPIGSGLGSSACSVVAALMAMNEHCGKPLNDTRLLALMGELEGRISGSIHYDNVAPCFLGGMQLMIEENDIISQQVPGFDEWLWVLAYPGIKVSTAEARAILPAQYRRQDCIAHGRHLAGFIHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVADWLGKNYLQNQEGFVHICRLDTAGARVLEN*" # k-mer by k-mer? for kmer, hashval in mh.kmers_and_hashes(seq, is_protein=True): # add to minhash obj single_mh = mh.copy_and_clear() single_mh.add_protein(kmer) assert len(single_mh) == 1 # confirm it all matches assert hashval == list(single_mh.hashes)[0] def test_dayhoff_kmers(): # test seq_to_hashes for protein -> dayhoff mh = MinHash(0, ksize=7, dayhoff=True, scaled=1) seq = "MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGRFADKLPSEPRENIVYQCWERFCQELGKQIPVAMTLEKNMPIGSGLGSSACSVVAALMAMNEHCGKPLNDTRLLALMGELEGRISGSIHYDNVAPCFLGGMQLMIEENDIISQQVPGFDEWLWVLAYPGIKVSTAEARAILPAQYRRQDCIAHGRHLAGFIHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVADWLGKNYLQNQEGFVHICRLDTAGARVLEN*" # first calculate seq to hashes hashes = mh.seq_to_hashes(seq, is_protein=True) # then calculate all hashes for the sequence mh.add_protein(seq) # identical? assert set(hashes) == set(mh.hashes) # k-mer by k-mer? for i in range(0, len(seq) - 7 + 1): # calculate each k-mer kmer = seq[i : i + 7] # add to minhash obj single_mh = mh.copy_and_clear() single_mh.add_protein(kmer) assert len(single_mh) == 1 # also calculate via seq_to_hashes hashvals = mh.seq_to_hashes(kmer, is_protein=True) assert len(hashvals) == 1 hashval = hashvals[0] # confirm it all matches assert hashval == list(single_mh.hashes)[0] assert hashval == hashes[i] def test_dayhoff_kmers_2(): # test kmers_and_hashes for protein -> dayhoff mh = MinHash(0, ksize=7, dayhoff=True, scaled=1) seq = "MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGRFADKLPSEPRENIVYQCWERFCQELGKQIPVAMTLEKNMPIGSGLGSSACSVVAALMAMNEHCGKPLNDTRLLALMGELEGRISGSIHYDNVAPCFLGGMQLMIEENDIISQQVPGFDEWLWVLAYPGIKVSTAEARAILPAQYRRQDCIAHGRHLAGFIHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVADWLGKNYLQNQEGFVHICRLDTAGARVLEN*" # k-mer by k-mer? for kmer, hashval in mh.kmers_and_hashes(seq, is_protein=True): # add to minhash obj single_mh = mh.copy_and_clear() single_mh.add_protein(kmer) assert len(single_mh) == 1 # confirm it all matches assert hashval == list(single_mh.hashes)[0] def test_hp_kmers(): # test hashes_to_seq for protein -> hp mh = MinHash(0, ksize=7, hp=True, scaled=1) seq = "MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGRFADKLPSEPRENIVYQCWERFCQELGKQIPVAMTLEKNMPIGSGLGSSACSVVAALMAMNEHCGKPLNDTRLLALMGELEGRISGSIHYDNVAPCFLGGMQLMIEENDIISQQVPGFDEWLWVLAYPGIKVSTAEARAILPAQYRRQDCIAHGRHLAGFIHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVADWLGKNYLQNQEGFVHICRLDTAGARVLEN*" # first calculate seq to hashes hashes = mh.seq_to_hashes(seq, is_protein=True) # then calculate all hashes for the sequence mh.add_protein(seq) # identical? assert set(hashes) == set(mh.hashes) # k-mer by k-mer? for i in range(0, len(seq) - 7 + 1): # calculate each k-mer kmer = seq[i : i + 7] # add to minhash obj single_mh = mh.copy_and_clear() single_mh.add_protein(kmer) assert len(single_mh) == 1 # also calculate via seq_to_hashes hashvals = mh.seq_to_hashes(kmer, is_protein=True) assert len(hashvals) == 1 hashval = hashvals[0] # confirm it all matches assert hashval == list(single_mh.hashes)[0] assert hashval == hashes[i] def test_hp_kmers_2(): # test kmers_and_hashes for protein -> hp mh = MinHash(0, ksize=7, hp=True, scaled=1) seq = "MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGRFADKLPSEPRENIVYQCWERFCQELGKQIPVAMTLEKNMPIGSGLGSSACSVVAALMAMNEHCGKPLNDTRLLALMGELEGRISGSIHYDNVAPCFLGGMQLMIEENDIISQQVPGFDEWLWVLAYPGIKVSTAEARAILPAQYRRQDCIAHGRHLAGFIHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVADWLGKNYLQNQEGFVHICRLDTAGARVLEN*" # k-mer by k-mer? for kmer, hashval in mh.kmers_and_hashes(seq, is_protein=True): # add to minhash obj single_mh = mh.copy_and_clear() single_mh.add_protein(kmer) assert len(single_mh) == 1 # confirm it all matches assert hashval == list(single_mh.hashes)[0] def test_translate_protein_hashes(): # test seq_to_hashes for dna -> protein mh = MinHash(0, ksize=7, is_protein=True, scaled=1) mh_translate = mh.copy() dna = "atggttaaagtttatgccccggcttccagtgccaatatgagcgtcgggtttgatgtgctcggggcggcggtgacacctgttgatggtgcattgctcggagatgtagtcacggttgaggcggcagagacattcagtctcaacaacctcggacgctttgccgataagctgccgtcagaaccacgggaaaatatcgtttatcagtgctgggagcgtttttgccaggaactgggtaagcaaattccagtggcgatgaccctggaaaagaatatgccgatcggttcgggcttaggctccagtgcctgttcggtggtcgcggcgctgatggcgatgaatgaacactgcggcaagccgcttaatgacactcgtttgctggctttgatgggcgagctggaaggccgtatctccggcagcattcattacgacaacgtggcaccgtgttttctcggtggtatgcagttgatgatcgaagaaaacgacatcatcagccagcaagtgccagggtttgatgagtggctgtgggtgctggcgtatccggggattaaagtctcgacggcagaagccagggctattttaccggcgcagtatcgccgccaggattgcattgcgcacgggcgacatctggcaggcttcattcacgcctgctattcccgtcagcctgagcttgccgcgaagctgatgaaagatgttatcgctgaaccctaccgtgaacggttactgccaggcttccggcaggcgcggcaggcggtcgcggaaatcggcgcggtagcgagcggtatctccggctccggcccgaccttgttcgctctgtgtgacaagccggaaaccgcccagcgcgttgccgactggttgggtaagaactacctgcaaaatcaggaaggttttgttcatatttgccggctggatacggcgggcgcacgagtactggaaaactaa" prot = "MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGRFADKLPSEPRENIVYQCWERFCQELGKQIPVAMTLEKNMPIGSGLGSSACSVVAALMAMNEHCGKPLNDTRLLALMGELEGRISGSIHYDNVAPCFLGGMQLMIEENDIISQQVPGFDEWLWVLAYPGIKVSTAEARAILPAQYRRQDCIAHGRHLAGFIHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVADWLGKNYLQNQEGFVHICRLDTAGARVLEN*" hashes_translate = mh_translate.seq_to_hashes(dna) hashes_prot = mh_translate.seq_to_hashes(prot, is_protein=True) # one is a subset of other, b/c of six frame translation assert set(hashes_prot).issubset(set(hashes_translate)) assert not set(hashes_translate).issubset(set(hashes_prot)) def test_translate_protein_hashes_2(): # test kmers_and_hashes for dna -> protein mh_translate = MinHash(0, ksize=7, is_protein=True, scaled=1) dna = "atggttaaagtttatgccccggcttccagtgccaatatgagcgtcgggtttgatgtgctcggggcggcggtgacacctgttgatggtgcattgctcggagatgtagtcacggttgaggcggcagagacattcagtctcaacaacctcggacgctttgccgataagctgccgtcagaaccacgggaaaatatcgtttatcagtgctgggagcgtttttgccaggaactgggtaagcaaattccagtggcgatgaccctggaaaagaatatgccgatcggttcgggcttaggctccagtgcctgttcggtggtcgcggcgctgatggcgatgaatgaacactgcggcaagccgcttaatgacactcgtttgctggctttgatgggcgagctggaaggccgtatctccggcagcattcattacgacaacgtggcaccgtgttttctcggtggtatgcagttgatgatcgaagaaaacgacatcatcagccagcaagtgccagggtttgatgagtggctgtgggtgctggcgtatccggggattaaagtctcgacggcagaagccagggctattttaccggcgcagtatcgccgccaggattgcattgcgcacgggcgacatctggcaggcttcattcacgcctgctattcccgtcagcctgagcttgccgcgaagctgatgaaagatgttatcgctgaaccctaccgtgaacggttactgccaggcttccggcaggcgcggcaggcggtcgcggaaatcggcgcggtagcgagcggtatctccggctccggcccgaccttgttcgctctgtgtgacaagccggaaaccgcccagcgcgttgccgactggttgggtaagaactacctgcaaaatcaggaaggttttgttcatatttgccggctggatacggcgggcgcacgagtactggaaaactaa".upper() # does everything match? check! k_and_h = list(mh_translate.kmers_and_hashes(dna)) for idx, kmer in enumerate(_kmers_from_all_coding_frames(dna, 21)): k, h = k_and_h[idx] assert kmer == k assert _hash_fwd_only(mh_translate, kmer) == h def test_translate_hp_hashes(): # test seq_to_hashes for dna -> protein -> hp mh = MinHash(0, ksize=7, hp=True, scaled=1) mh_translate = mh.copy() dna = "atggttaaagtttatgccccggcttccagtgccaatatgagcgtcgggtttgatgtgctcggggcggcggtgacacctgttgatggtgcattgctcggagatgtagtcacggttgaggcggcagagacattcagtctcaacaacctcggacgctttgccgataagctgccgtcagaaccacgggaaaatatcgtttatcagtgctgggagcgtttttgccaggaactgggtaagcaaattccagtggcgatgaccctggaaaagaatatgccgatcggttcgggcttaggctccagtgcctgttcggtggtcgcggcgctgatggcgatgaatgaacactgcggcaagccgcttaatgacactcgtttgctggctttgatgggcgagctggaaggccgtatctccggcagcattcattacgacaacgtggcaccgtgttttctcggtggtatgcagttgatgatcgaagaaaacgacatcatcagccagcaagtgccagggtttgatgagtggctgtgggtgctggcgtatccggggattaaagtctcgacggcagaagccagggctattttaccggcgcagtatcgccgccaggattgcattgcgcacgggcgacatctggcaggcttcattcacgcctgctattcccgtcagcctgagcttgccgcgaagctgatgaaagatgttatcgctgaaccctaccgtgaacggttactgccaggcttccggcaggcgcggcaggcggtcgcggaaatcggcgcggtagcgagcggtatctccggctccggcccgaccttgttcgctctgtgtgacaagccggaaaccgcccagcgcgttgccgactggttgggtaagaactacctgcaaaatcaggaaggttttgttcatatttgccggctggatacggcgggcgcacgagtactggaaaactaa" prot = "MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGRFADKLPSEPRENIVYQCWERFCQELGKQIPVAMTLEKNMPIGSGLGSSACSVVAALMAMNEHCGKPLNDTRLLALMGELEGRISGSIHYDNVAPCFLGGMQLMIEENDIISQQVPGFDEWLWVLAYPGIKVSTAEARAILPAQYRRQDCIAHGRHLAGFIHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVADWLGKNYLQNQEGFVHICRLDTAGARVLEN*" hashes_translate = mh_translate.seq_to_hashes(dna) hashes_prot = mh_translate.seq_to_hashes(prot, is_protein=True) # one is a subset of other, b/c of six frame translation assert set(hashes_prot).issubset(set(hashes_translate)) assert not set(hashes_translate).issubset(set(hashes_prot)) def test_translate_hp_hashes_2(): # test kmers_and_hashes for dna -> protein -> hp mh_translate = MinHash(0, ksize=7, hp=True, scaled=1) dna = "atggttaaagtttatgccccggcttccagtgccaatatgagcgtcgggtttgatgtgctcggggcggcggtgacacctgttgatggtgcattgctcggagatgtagtcacggttgaggcggcagagacattcagtctcaacaacctcggacgctttgccgataagctgccgtcagaaccacgggaaaatatcgtttatcagtgctgggagcgtttttgccaggaactgggtaagcaaattccagtggcgatgaccctggaaaagaatatgccgatcggttcgggcttaggctccagtgcctgttcggtggtcgcggcgctgatggcgatgaatgaacactgcggcaagccgcttaatgacactcgtttgctggctttgatgggcgagctggaaggccgtatctccggcagcattcattacgacaacgtggcaccgtgttttctcggtggtatgcagttgatgatcgaagaaaacgacatcatcagccagcaagtgccagggtttgatgagtggctgtgggtgctggcgtatccggggattaaagtctcgacggcagaagccagggctattttaccggcgcagtatcgccgccaggattgcattgcgcacgggcgacatctggcaggcttcattcacgcctgctattcccgtcagcctgagcttgccgcgaagctgatgaaagatgttatcgctgaaccctaccgtgaacggttactgccaggcttccggcaggcgcggcaggcggtcgcggaaatcggcgcggtagcgagcggtatctccggctccggcccgaccttgttcgctctgtgtgacaagccggaaaccgcccagcgcgttgccgactggttgggtaagaactacctgcaaaatcaggaaggttttgttcatatttgccggctggatacggcgggcgcacgagtactggaaaactaa" dna = dna.upper() # does everything match? check! k_and_h = list(mh_translate.kmers_and_hashes(dna)) for idx, kmer in enumerate(_kmers_from_all_coding_frames(dna, 21)): k, h = k_and_h[idx] assert kmer == k assert _hash_fwd_only(mh_translate, kmer) == h def test_translate_dayhoff_hashes(): # test seq_to_hashes for dna -> protein -> dayhoff mh = MinHash(0, ksize=7, dayhoff=True, scaled=1) mh_translate = mh.copy() dna = "atggttaaagtttatgccccggcttccagtgccaatatgagcgtcgggtttgatgtgctcggggcggcggtgacacctgttgatggtgcattgctcggagatgtagtcacggttgaggcggcagagacattcagtctcaacaacctcggacgctttgccgataagctgccgtcagaaccacgggaaaatatcgtttatcagtgctgggagcgtttttgccaggaactgggtaagcaaattccagtggcgatgaccctggaaaagaatatgccgatcggttcgggcttaggctccagtgcctgttcggtggtcgcggcgctgatggcgatgaatgaacactgcggcaagccgcttaatgacactcgtttgctggctttgatgggcgagctggaaggccgtatctccggcagcattcattacgacaacgtggcaccgtgttttctcggtggtatgcagttgatgatcgaagaaaacgacatcatcagccagcaagtgccagggtttgatgagtggctgtgggtgctggcgtatccggggattaaagtctcgacggcagaagccagggctattttaccggcgcagtatcgccgccaggattgcattgcgcacgggcgacatctggcaggcttcattcacgcctgctattcccgtcagcctgagcttgccgcgaagctgatgaaagatgttatcgctgaaccctaccgtgaacggttactgccaggcttccggcaggcgcggcaggcggtcgcggaaatcggcgcggtagcgagcggtatctccggctccggcccgaccttgttcgctctgtgtgacaagccggaaaccgcccagcgcgttgccgactggttgggtaagaactacctgcaaaatcaggaaggttttgttcatatttgccggctggatacggcgggcgcacgagtactggaaaactaa" prot = "MVKVYAPASSANMSVGFDVLGAAVTPVDGALLGDVVTVEAAETFSLNNLGRFADKLPSEPRENIVYQCWERFCQELGKQIPVAMTLEKNMPIGSGLGSSACSVVAALMAMNEHCGKPLNDTRLLALMGELEGRISGSIHYDNVAPCFLGGMQLMIEENDIISQQVPGFDEWLWVLAYPGIKVSTAEARAILPAQYRRQDCIAHGRHLAGFIHACYSRQPELAAKLMKDVIAEPYRERLLPGFRQARQAVAEIGAVASGISGSGPTLFALCDKPETAQRVADWLGKNYLQNQEGFVHICRLDTAGARVLEN*" hashes_translate = mh_translate.seq_to_hashes(dna) hashes_prot = mh_translate.seq_to_hashes(prot, is_protein=True) # one is a subset of other, b/c of six frame translation assert set(hashes_prot).issubset(set(hashes_translate)) assert not set(hashes_translate).issubset(set(hashes_prot)) def test_translate_dayhoff_hashes_2(): # test kmers_and_hashes for dna -> protein -> dayhoff mh_translate = MinHash(0, ksize=7, dayhoff=True, scaled=1) dna = "atggttaaagtttatgccccggcttccagtgccaatatgagcgtcgggtttgatgtgctcggggcggcggtgacacctgttgatggtgcattgctcggagatgtagtcacggttgaggcggcagagacattcagtctcaacaacctcggacgctttgccgataagctgccgtcagaaccacgggaaaatatcgtttatcagtgctgggagcgtttttgccaggaactgggtaagcaaattccagtggcgatgaccctggaaaagaatatgccgatcggttcgggcttaggctccagtgcctgttcggtggtcgcggcgctgatggcgatgaatgaacactgcggcaagccgcttaatgacactcgtttgctggctttgatgggcgagctggaaggccgtatctccggcagcattcattacgacaacgtggcaccgtgttttctcggtggtatgcagttgatgatcgaagaaaacgacatcatcagccagcaagtgccagggtttgatgagtggctgtgggtgctggcgtatccggggattaaagtctcgacggcagaagccagggctattttaccggcgcagtatcgccgccaggattgcattgcgcacgggcgacatctggcaggcttcattcacgcctgctattcccgtcagcctgagcttgccgcgaagctgatgaaagatgttatcgctgaaccctaccgtgaacggttactgccaggcttccggcaggcgcggcaggcggtcgcggaaatcggcgcggtagcgagcggtatctccggctccggcccgaccttgttcgctctgtgtgacaagccggaaaccgcccagcgcgttgccgactggttgggtaagaactacctgcaaaatcaggaaggttttgttcatatttgccggctggatacggcgggcgcacgagtactggaaaactaa" dna = dna.upper() # does everything match? check! k_and_h = list(mh_translate.kmers_and_hashes(dna)) for idx, kmer in enumerate(_kmers_from_all_coding_frames(dna, 21)): k, h = k_and_h[idx] assert kmer == k assert _hash_fwd_only(mh_translate, kmer) == h def test_containment(track_abundance): "basic containment test. note: containment w/abundance ignores abundance." mh1 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh2 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh1.add_many((1, 2, 3, 4)) mh1.add_many((1, 2)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) assert mh1.contained_by(mh2) == 1 / 4 assert mh2.contained_by(mh1) == 1 / 2 def test_sum_abundances(track_abundance): "test sum_abundances" mh1 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh2 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh1.add_many((1, 2, 3, 4)) mh1.add_many((1, 2)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) if track_abundance: assert mh1.sum_abundances == 6 assert mh2.sum_abundances == 6 else: assert mh1.sum_abundances is None assert mh2.sum_abundances is None def test_mean_abundance(track_abundance): "test mean_abundance" mh1 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh2 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh1.add_many((1, 2, 3, 4)) mh1.add_many((1, 2)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) if track_abundance: assert mh1.mean_abundance == 1.5 assert mh2.mean_abundance == 3 else: assert not mh1.mean_abundance assert not mh2.mean_abundance def test_median_abundance(track_abundance): "test median_abundance" mh1 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh2 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh1.add_many((1, 2, 3, 4)) mh1.add_many((1, 2)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) if track_abundance: assert mh1.median_abundance == 1.5 assert mh2.median_abundance == 3 else: assert not mh1.median_abundance assert not mh2.median_abundance def test_std_abundance(track_abundance): "test std_abundance" mh1 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh2 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh1.add_many((1, 2, 3, 4)) mh1.add_many((1, 2)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) if track_abundance: assert mh1.std_abundance == 0.5 assert mh2.std_abundance == 0.0 else: assert not mh1.std_abundance assert not mh2.std_abundance def test_unique_dataset_hashes(track_abundance): "test total_hashes approximation" mh1 = MinHash(0, 21, scaled=1, track_abundance=track_abundance) mh2 = MinHash(4, 21, track_abundance=track_abundance) mh1.add_many((1, 2, 3, 4)) mh1.add_many((1, 2)) mh2.add_many((1, 5)) assert mh1.unique_dataset_hashes == 4 with pytest.raises(TypeError) as exc: mh2.unique_dataset_hashes assert "can only approximate unique_dataset_hashes for scaled MinHashes" in str(exc) def test_containment_ANI(): f1 = utils.get_test_data("2.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2, ksize=31).minhash m1_cont_m2 = mh1.containment_ani(mh2, estimate_ci=True) m2_cont_m1 = mh2.containment_ani(mh1, estimate_ci=True) print("\nmh1 contained by mh2", m1_cont_m2) print("mh2 contained by mh1", m2_cont_m1) assert (round(m1_cont_m2.ani, 3), m1_cont_m2.ani_low, m1_cont_m2.ani_high) == ( 1.0, 1.0, 1.0, ) assert ( round(m2_cont_m1.ani, 3), round(m2_cont_m1.ani_low, 3), round(m2_cont_m1.ani_high, 3), ) == (0.966, 0.965, 0.967) m1_mc_m2 = mh1.max_containment_ani(mh2, estimate_ci=True) m2_mc_m1 = mh2.max_containment_ani(mh1, estimate_ci=True) print("mh1 max containment", m1_mc_m2) print("mh2 max containment", m2_mc_m1) m1_mc_m2.size_is_inaccurate = False m2_mc_m1.size_is_inaccurate = False assert m1_mc_m2 == m2_mc_m1 assert ( round(m1_mc_m2.ani, 3), round(m1_mc_m2.ani_low, 3), round(m1_mc_m2.ani_high, 3), ) == (1.0, 1.0, 1.0) def test_containment_ANI_precalc_containment(): f1 = utils.get_test_data("47+63.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2, ksize=31).minhash # precalc containments and assert same results s1c = mh1.contained_by(mh2) s2c = mh2.contained_by(mh1) mc = max(s1c, s2c) assert mh1.containment_ani(mh2, estimate_ci=True) == mh1.containment_ani( mh2, containment=s1c, estimate_ci=True ) assert mh2.containment_ani(mh1) == mh2.containment_ani(mh1, containment=s2c) assert mh1.max_containment_ani(mh2) == mh2.max_containment_ani(mh1) assert mh1.max_containment_ani(mh2) == mh1.max_containment_ani( mh2, max_containment=mc ) assert mh1.max_containment_ani(mh2) == mh2.max_containment_ani( mh1, max_containment=mc ) def test_avg_containment_ani(): f1 = utils.get_test_data("47+63.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2, ksize=31).minhash # check average_containment_ani ac_m1 = mh1.avg_containment_ani(mh2) ac_m2 = mh2.avg_containment_ani(mh1) assert ( ac_m1 == ac_m2 == (mh1.containment_ani(mh2).ani + mh2.containment_ani(mh1).ani) / 2 ) def test_containment_ANI_downsample(): f2 = utils.get_test_data("2+63.fa.sig") f3 = utils.get_test_data("47+63.fa.sig") mh2 = sourmash.load_one_signature(f2, ksize=31).minhash mh3 = sourmash.load_one_signature(f3, ksize=31).minhash # check that downsampling works properly print(mh2.scaled) mh2 = mh2.downsample(scaled=1100) assert mh2.scaled != mh3.scaled ds_s3c = mh2.containment_ani(mh3, downsample=True) ds_s4c = mh3.containment_ani(mh2, downsample=True) mc_w_ds_1 = mh2.max_containment_ani(mh3, downsample=True) mc_w_ds_2 = mh3.max_containment_ani(mh2, downsample=True) print(ds_s3c) with pytest.raises(ValueError) as e: mh2.containment_ani(mh3) assert "ValueError: mismatch in scaled; comparison fail" in e with pytest.raises(ValueError) as e: mh2.max_containment_ani(mh3) assert "ValueError: mismatch in scaled; comparison fail" in e mh3 = mh3.downsample(scaled=1100) assert mh2.scaled == mh3.scaled ds_s3c_manual = mh2.containment_ani(mh3) ds_s4c_manual = mh3.containment_ani(mh2) ds_mc_manual = mh2.max_containment_ani(mh3) assert ds_s3c == ds_s3c_manual assert ds_s4c == ds_s4c_manual assert mc_w_ds_1 == mc_w_ds_2 == ds_mc_manual ac_m2 = mh2.avg_containment_ani(mh3) ac_m3 = mh3.avg_containment_ani(mh2) assert ac_m2 == ac_m3 == (ds_s3c.ani + ds_s4c.ani) / 2 def test_jaccard_ANI(): f1 = utils.get_test_data("2.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2).minhash print("\nJACCARD_ANI", mh1.jaccard_ani(mh2)) m1_jani_m2 = mh1.jaccard_ani(mh2) m2_jani_m1 = mh2.jaccard_ani(mh1) assert m1_jani_m2 == m2_jani_m1 assert ( m1_jani_m2.ani, m1_jani_m2.p_nothing_in_common, m1_jani_m2.jaccard_error, ) == (0.9783711630110239, 0.0, 3.891666770716877e-07) def test_jaccard_ANI_untrustworthy(): f1 = utils.get_test_data("2.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2).minhash print("\nJACCARD_ANI", mh1.jaccard_ani(mh2)) m1_jani_m2 = mh1.jaccard_ani(mh2, err_threshold=1e-7) # since size is inaccurate on 2.fa.sig, need to override to be able to get ani m1_jani_m2.size_is_inaccurate = False assert m1_jani_m2.ani is None assert m1_jani_m2.je_exceeds_threshold == True assert m1_jani_m2.je_threshold == 1e-7 def test_jaccard_ANI_precalc_jaccard(): f1 = utils.get_test_data("2.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2).minhash # precalc jaccard and assert same result jaccard = mh1.jaccard(mh2) print("\nJACCARD_ANI", mh1.jaccard_ani(mh2, jaccard=jaccard)) assert ( mh1.jaccard_ani(mh2) == mh1.jaccard_ani(mh2, jaccard=jaccard) == mh2.jaccard_ani(mh1, jaccard=jaccard) ) wrong_jaccard = jaccard - 0.1 assert mh1.jaccard_ani(mh2) != mh1.jaccard_ani(mh2, jaccard=wrong_jaccard) def test_jaccard_ANI_downsample(): f1 = utils.get_test_data("2.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2).minhash print(mh1.scaled) mh1 = mh1.downsample(scaled=2000) assert mh1.scaled != mh2.scaled with pytest.raises(ValueError) as e: mh1.jaccard_ani(mh2) assert "ValueError: mismatch in scaled; comparison fail" in e ds_s1c = mh1.jaccard_ani(mh2, downsample=True) ds_s2c = mh2.jaccard_ani(mh1, downsample=True) mh2 = mh2.downsample(scaled=2000) assert mh1.scaled == mh2.scaled ds_j_manual = mh1.jaccard_ani(mh2) assert ds_s1c == ds_s2c == ds_j_manual def test_containment_ani_ci_tiny_testdata(): """ tiny test data to trigger the following: WARNING: Cannot estimate ANI confidence intervals from containment. Do your sketches contain enough hashes? Error: varN <0.0! """ mh1 = MinHash(0, 21, scaled=1, track_abundance=False) mh2 = MinHash(0, 21, scaled=1, track_abundance=False) mh1.add_many((1, 3, 4)) mh2.add_many((1, 2, 3, 4)) m2_cani_m1 = mh2.containment_ani(mh1, estimate_ci=True) print(m2_cani_m1) # from the formula ANI = c^(1/k) for c=3/4 and k=21 np.testing.assert_almost_equal(m2_cani_m1.ani, 0.986394259982259, decimal=3) m2_cani_m1.size_is_inaccurate = False assert m2_cani_m1.ani_low is None assert m2_cani_m1.ani_high is None def test_containment_num_fail(): f1 = utils.get_test_data("num/47.fa.sig") f2 = utils.get_test_data("num/63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2, ksize=31).minhash with pytest.raises(TypeError) as exc: mh1.contained_by(mh2) print(str(exc)) assert "Error: can only calculate containment for scaled MinHashes" in str(exc) with pytest.raises(TypeError) as exc: mh1.max_containment(mh2) assert "Error: can only calculate containment for scaled MinHashes" in str(exc) with pytest.raises(TypeError) as exc: mh1.avg_containment(mh2) assert "Error: can only calculate containment for scaled MinHashes" in str(exc) def test_ANI_num_fail(): f1 = utils.get_test_data("num/47.fa.sig") f2 = utils.get_test_data("num/63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2, ksize=31).minhash with pytest.raises(TypeError) as exc: mh1.containment_ani(mh2) print(str(exc)) assert "Error: can only calculate ANI for scaled MinHashes" in str(exc) with pytest.raises(TypeError) as exc: mh2.containment_ani(mh1, estimate_ci=True) assert "Error: can only calculate ANI for scaled MinHashes" in str(exc) with pytest.raises(TypeError) as exc: mh1.max_containment_ani(mh2) assert "Error: can only calculate ANI for scaled MinHashes" in str(exc) with pytest.raises(TypeError) as exc: mh1.avg_containment_ani(mh2) assert "Error: can only calculate ANI for scaled MinHashes" in str(exc) with pytest.raises(TypeError) as exc: mh1.jaccard_ani(mh2) assert "Error: can only calculate ANI for scaled MinHashes" in str(exc) def test_minhash_set_size_estimate_is_accurate(): f1 = utils.get_test_data("2.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2).minhash mh1_ds = mh1.downsample(scaled=100000) # check accuracy using default thresholds (rel_err= 0.2, confidence=0.95) assert mh1.size_is_accurate() == True assert mh1_ds.size_is_accurate() == False assert mh2.size_is_accurate() == True # change rel err assert mh1.size_is_accurate(relative_error=0.5) == True assert mh2.size_is_accurate(relative_error=0.0001) == False # change prob assert mh1.size_is_accurate(confidence=0.5) == True assert mh1.size_is_accurate(relative_error=0.001, confidence=1) == False # check that relative error and confidence must be between 0 and 1 with pytest.raises(ValueError) as exc: mh2.size_is_accurate(relative_error=-1) assert ( "Error: relative error and confidence values must be between 0 and 1." in str(exc) ) with pytest.raises(ValueError) as exc: mh2.size_is_accurate(confidence=-1) assert ( "Error: relative error and confidence values must be between 0 and 1." in str(exc) ) with pytest.raises(ValueError) as exc: mh2.size_is_accurate(relative_error=-1, confidence=-1) assert ( "Error: relative error and confidence values must be between 0 and 1." in str(exc) ) def test_minhash_ani_inaccurate_size_est(): # TODO: It's actually really tricky to get the set size to be inaccurate. Eg. For a scale factor of 10000, # you would need f1 = utils.get_test_data("2.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash mh2 = sourmash.load_one_signature(f2).minhash # downsample mh1_ds = mh1.downsample(scaled=100000) mh2_ds = mh2.downsample(scaled=100000) assert mh1.size_is_accurate(relative_error=0.05, confidence=0.95) == True assert mh1.size_is_accurate() == True assert mh1_ds.size_is_accurate() == False assert mh2.size_is_accurate() == True assert round(mh1.jaccard_ani(mh2).ani, 3) == 0.978 m2_ca_m1 = mh2.containment_ani(mh1) assert round(m2_ca_m1.ani, 3) == 0.966 assert m2_ca_m1.size_is_inaccurate == False m1_ca_m2_ds = mh1_ds.containment_ani(mh2_ds) print(m1_ca_m2_ds) assert m1_ca_m2_ds.ani is None # 0.987 assert m1_ca_m2_ds.size_is_inaccurate == True def test_size_num_fail(): f1 = utils.get_test_data("num/47.fa.sig") mh1 = sourmash.load_one_signature(f1, ksize=31).minhash with pytest.raises(TypeError) as exc: mh1.size_is_accurate() print(str(exc)) assert "Error: can only estimate dataset size for scaled MinHashes" in str(exc) sourmash-bio-sourmash-bc3c0f8/tests/test_nodegraph.py000066400000000000000000000053341474157046300232150ustar00rootroot00000000000000from tempfile import NamedTemporaryFile import pytest from sourmash.nodegraph import ( Nodegraph, extract_nodegraph_info, calc_expected_collisions, ) import sourmash_tst_utils as utils def test_nodegraph_to_khmer_basic(): pytest.importorskip("khmer") ng_file = utils.get_test_data(".sbt.v3/internal.0") sourmash_ng = Nodegraph.load(ng_file) khmer_sm_ng = sourmash_ng.to_khmer_nodegraph() assert sourmash_ng.ksize() == khmer_sm_ng.ksize() def test_nodegraph_khmer_compare(): khmer = pytest.importorskip("khmer") khmer_ng = khmer.Nodegraph(3, 23, 6) khmer_ng.count("ACG") khmer_ng.count("TTA") khmer_ng.count("CGA") sm_ng = Nodegraph(3, 23, 6) sm_ng.count("ACG") sm_ng.count("TTA") sm_ng.count("CGA") assert sm_ng.ksize() == khmer_ng.ksize() assert sm_ng.hashsizes() == khmer_ng.hashsizes() assert sm_ng.get("ACG") assert sm_ng.get("TTA") assert sm_ng.get("CGA") assert khmer_ng.get("ACG") assert khmer_ng.get("TTA") assert khmer_ng.get("CGA") def test_nodegraph_same_file(): khmer = pytest.importorskip("khmer") try: load_nodegraph = khmer.load_nodegraph except AttributeError: load_nodegraph = khmer.Nodegraph.load ng_file = utils.get_test_data(".sbt.v3/internal.0") with open(ng_file, "rb") as f: ng_data = f.read() sourmash_ng = Nodegraph.load(ng_file) khmer_sm_ng = sourmash_ng.to_khmer_nodegraph() khmer_ng = load_nodegraph(ng_file) with ( NamedTemporaryFile() as f1, NamedTemporaryFile() as f2, NamedTemporaryFile() as f3, ): sourmash_ng.save(f1.name) khmer_sm_ng.save(f2.name) khmer_ng.save(f3.name) f1.seek(0) sm_data = f1.read() f2.seek(0) kh_sm_data = f2.read() f3.seek(0) kh_data = f3.read() assert extract_nodegraph_info(f1.name) == extract_nodegraph_info(f2.name) assert extract_nodegraph_info(f3.name) == extract_nodegraph_info(f2.name) assert extract_nodegraph_info(f1.name) == extract_nodegraph_info(f3.name) assert sm_data == kh_data assert sm_data == kh_sm_data assert ng_data == sm_data assert ng_data == kh_data assert ng_data == kh_sm_data def test_nodegraph_expected_collisions(): ng_file = utils.get_test_data(".sbt.v3/internal.0") sourmash_ng = Nodegraph.load(ng_file) assert calc_expected_collisions(sourmash_ng) == 3.412442571740036e-07 def test_nodegraph_expected_collisions_error(): ng_file = utils.get_test_data(".sbt.v3/internal.0") sourmash_ng = Nodegraph.load(ng_file) with pytest.raises(SystemExit): calc_expected_collisions(sourmash_ng, max_false_pos=1e-8) sourmash-bio-sourmash-bc3c0f8/tests/test_np_utils.py000066400000000000000000000010211474157046300230700ustar00rootroot00000000000000import numpy as np from sourmash import SourmashSignature import sourmash from sourmash.np_utils import to_memmap def test_memmap(): e1 = sourmash.MinHash(n=1, ksize=20) sig1 = SourmashSignature(e1) e2 = sourmash.MinHash(n=1, ksize=25) sig2 = SourmashSignature(e2) siglist = [sig1, sig2] memmapped, filename = to_memmap(np.array(siglist)) # Assert that the data didn't change as a result of memory-mapping np.testing.assert_array_equal(memmapped, siglist) assert filename.endswith(".mmap") sourmash-bio-sourmash-bc3c0f8/tests/test_picklist.py000066400000000000000000000066101474157046300230660ustar00rootroot00000000000000""" Tests for the picklist API. """ import pytest import sourmash import copy import sourmash_tst_utils as utils from sourmash import picklist from sourmash.picklist import SignaturePicklist from sourmash.index import LinearIndex, MultiIndex from sourmash.index.sqlite_index import SqliteIndex def test_load_empty_picklist_fail(): empty = utils.get_test_data("picklist/empty.csv") pl = SignaturePicklist("manifest", pickfile=empty) with pytest.raises(ValueError): pl.load(allow_empty=False) def test_load_empty_picklist_allow(): empty = utils.get_test_data("picklist/empty.csv") pl = SignaturePicklist("manifest", pickfile=empty) pl.load(allow_empty=True) def test_dup_md5_picked(runtmp): # load a sig, duplicate, and see if a picklist gets the right one sig47 = utils.get_test_data("47.fa.sig") ss = sourmash.load_file_as_signatures(sig47) sig = list(ss)[0] # save a manifest with one entry xl = LinearIndex([sig]) ml = MultiIndex.load([xl], [None], None) print(ml.manifest.rows) assert len(ml.manifest) == 1 mf_csv = runtmp.output("select.csv") ml.manifest.write_to_filename(mf_csv) # now make an index to select against, with an identical signature # (but diff name) new_sig = sig.to_mutable() new_sig.name = "foo" xl = LinearIndex([sig, new_sig]) ml2 = MultiIndex.load([xl], [None], None) assert len(ml2) == 2 # create a picklist... pl = SignaturePicklist("manifest", pickfile=mf_csv) print(pl.load()) print("loaded:", len(pl.pickset)) # use in select ml3 = ml2.select(picklist=pl) print("picked:", len(ml3)) assert len(pl.pickset) == len(ml3) def test_dup_md5_picked_mf_to_picklist(runtmp): # load a sig, duplicate, and see if a picklist gets the right one # uses an in memory picklist sig47 = utils.get_test_data("47.fa.sig") ss = sourmash.load_file_as_signatures(sig47) sig = list(ss)[0] # save a manifest with one entry xl = LinearIndex([sig]) ml = MultiIndex.load([xl], [None], None) print(ml.manifest.rows) assert len(ml.manifest) == 1 pl = ml.manifest.to_picklist() # now make an index to select against, with an identical signature # (but diff name) new_sig = sig.to_mutable() new_sig.name = "foo" xl = LinearIndex([sig, new_sig]) ml2 = MultiIndex.load([xl], [None], None) assert len(ml2) == 2 # use picklist in select ml3 = ml2.select(picklist=pl) print("picked:", len(ml3)) assert len(pl.pickset) == len(ml3) def test_dup_md5_picked_mf_to_picklist_sqlite(runtmp): # load a sig, duplicate, and see if a picklist gets the right one # use a sqlite db with its own to_picklist behavior. sig47 = utils.get_test_data("47.fa.sig") ss = sourmash.load_file_as_signatures(sig47) sig = list(ss)[0] # save a manifest with one entry xl = SqliteIndex.create(":memory:") xl.insert(sig) print(xl.manifest.rows) assert len(xl.manifest) == 1 pl = xl.manifest.to_picklist() # now make an index to select against, with an identical signature # (but diff name) new_sig = sig.to_mutable() new_sig.name = "foo" xl = LinearIndex([sig, new_sig]) ml2 = MultiIndex.load([xl], [None], None) assert len(ml2) == 2 # use picklist in select ml3 = ml2.select(picklist=pl) print("picked:", len(ml3)) assert len(pl.pickset) == len(ml3) sourmash-bio-sourmash-bc3c0f8/tests/test_plugin_framework.py000066400000000000000000000373701474157046300246260ustar00rootroot00000000000000""" Test the plugin framework in sourmash.plugins, which uses importlib.metadata entrypoints. """ import sys import pytest import collections import sourmash from sourmash.logging import set_quiet import sourmash_tst_utils as utils from sourmash import plugins from sourmash.index import LinearIndex from sourmash.save_load import Base_SaveSignaturesToLocation, SaveSignaturesToLocation _Dist = collections.namedtuple("_Dist", ["version"]) class FakeEntryPoint: """ A class that stores a name and an object to be returned on 'load()'. Mocks the EntryPoint class used by importlib.metadata. """ module = "test_plugin_framework" dist = _Dist("0.1") group = "groupfoo" def __init__(self, name, load_obj, *, error_on_import=None): self.name = name self.load_obj = load_obj self.error_on_import = error_on_import def load(self): if self.error_on_import is not None: raise self.error_on_import("as requested") return self.load_obj # # Test basic features of the load_from plugin hook. # class Test_EntryPointBasics_LoadFrom: def get_some_sigs(self, location, *args, **kwargs): ss2 = utils.get_test_data("2.fa.sig") ss47 = utils.get_test_data("47.fa.sig") ss63 = utils.get_test_data("63.fa.sig") sig2 = sourmash.load_one_signature(ss2, ksize=31) sig47 = sourmash.load_one_signature(ss47, ksize=31) sig63 = sourmash.load_one_signature(ss63, ksize=31) lidx = LinearIndex([sig2, sig47, sig63], location) return lidx get_some_sigs.priority = 1 def setup_method(self): self.saved_plugins = plugins._plugin_load_from plugins._plugin_load_from = [ FakeEntryPoint("test_load", self.get_some_sigs), FakeEntryPoint( "test_load", self.get_some_sigs, error_on_import=ModuleNotFoundError ), ] def teardown_method(self): plugins._plugin_load_from = self.saved_plugins def test_load_1(self): ps = list(plugins.get_load_from_functions()) assert len(ps) == 1 def test_load_2(self, runtmp): fake_location = runtmp.output("passed-through location") idx = sourmash.load_file_as_index(fake_location) print(idx, idx.location) assert len(idx) == 3 assert idx.location == fake_location class Test_EntryPoint_LoadFrom_Priority: def get_some_sigs(self, location, *args, **kwargs): ss2 = utils.get_test_data("2.fa.sig") ss47 = utils.get_test_data("47.fa.sig") ss63 = utils.get_test_data("63.fa.sig") sig2 = sourmash.load_one_signature(ss2, ksize=31) sig47 = sourmash.load_one_signature(ss47, ksize=31) sig63 = sourmash.load_one_signature(ss63, ksize=31) lidx = LinearIndex([sig2, sig47, sig63], location) return lidx get_some_sigs.priority = 5 def set_called_flag_1(self, location, *args, **kwargs): # high priority 1, raise ValueError print("setting flag 1") self.was_called_flag_1 = True raise ValueError set_called_flag_1.priority = 1 def set_called_flag_2(self, location, *args, **kwargs): # high priority 2, return None print("setting flag 2") self.was_called_flag_2 = True return None set_called_flag_2.priority = 2 def set_called_flag_3(self, location, *args, **kwargs): # lower priority 10, should not be called print("setting flag 3") self.was_called_flag_3 = True return None set_called_flag_3.priority = 10 def setup_method(self): self.saved_plugins = plugins._plugin_load_from plugins._plugin_load_from = [ FakeEntryPoint("test_load", self.get_some_sigs), FakeEntryPoint("test_load_2", self.set_called_flag_1), FakeEntryPoint("test_load_3", self.set_called_flag_2), FakeEntryPoint("test_load_4", self.set_called_flag_3), ] self.was_called_flag_1 = False self.was_called_flag_2 = False self.was_called_flag_3 = False def teardown_method(self): plugins._plugin_load_from = self.saved_plugins def test_load_1(self): ps = list(plugins.get_load_from_functions()) assert len(ps) == 4 assert not self.was_called_flag_1 assert not self.was_called_flag_2 assert not self.was_called_flag_3 def test_load_2(self, runtmp): fake_location = runtmp.output("passed-through location") idx = sourmash.load_file_as_index(fake_location) print(idx, idx.location) assert len(idx) == 3 assert idx.location == fake_location assert self.was_called_flag_1 assert self.was_called_flag_2 assert not self.was_called_flag_3 # # Test basic features of the save_to plugin hook. # class FakeSaveClass(Base_SaveSignaturesToLocation): """ A fake save class that just records what was sent to it. """ priority = 50 def __init__(self, location): super().__init__(location) self.keep = [] @classmethod def matches(cls, location): if location: return location.endswith(".this-is-a-test") def add(self, ss): super().add(ss) self.keep.append(ss) class FakeSaveClass_HighPriority(FakeSaveClass): priority = 1 class Test_EntryPointBasics_SaveTo: # test the basics def setup_method(self): self.saved_plugins = plugins._plugin_save_to plugins._plugin_save_to = [ FakeEntryPoint("test_save", FakeSaveClass), FakeEntryPoint( "test_save", FakeSaveClass, error_on_import=ModuleNotFoundError ), ] def teardown_method(self): plugins._plugin_save_to = self.saved_plugins def test_save_1(self): ps = list(plugins.get_save_to_functions()) print(ps) assert len(ps) == 1 def test_save_2(self, runtmp): # load some signatures to save ss2 = utils.get_test_data("2.fa.sig") ss47 = utils.get_test_data("47.fa.sig") ss63 = utils.get_test_data("63.fa.sig") sig2 = sourmash.load_one_signature(ss2, ksize=31) sig47 = sourmash.load_one_signature(ss47, ksize=31) sig63 = sourmash.load_one_signature(ss63, ksize=31) # build a fake location that matches the FakeSaveClass # extension fake_location = runtmp.output("out.this-is-a-test") # this should use the plugin architecture to return an object # of type FakeSaveClass, with the three signatures in it. x = SaveSignaturesToLocation(fake_location) with x as save_sig: save_sig.add(sig2) save_sig.add(sig47) save_sig.add(sig63) print(len(x)) print(x.keep) assert isinstance(x, FakeSaveClass) assert x.keep == [sig2, sig47, sig63] class Test_EntryPointPriority_SaveTo: # test that priority is observed def setup_method(self): self.saved_plugins = plugins._plugin_save_to plugins._plugin_save_to = [ FakeEntryPoint("test_save", FakeSaveClass), FakeEntryPoint("test_save2", FakeSaveClass_HighPriority), ] def teardown_method(self): plugins._plugin_save_to = self.saved_plugins def test_save_1(self): ps = list(plugins.get_save_to_functions()) print(ps) assert len(ps) == 2 def test_save_2(self, runtmp): # load some signatures to save ss2 = utils.get_test_data("2.fa.sig") ss47 = utils.get_test_data("47.fa.sig") ss63 = utils.get_test_data("63.fa.sig") sig2 = sourmash.load_one_signature(ss2, ksize=31) sig47 = sourmash.load_one_signature(ss47, ksize=31) sig63 = sourmash.load_one_signature(ss63, ksize=31) # build a fake location that matches the FakeSaveClass # extension fake_location = runtmp.output("out.this-is-a-test") # this should use the plugin architecture to return an object # of type FakeSaveClass, with the three signatures in it. x = SaveSignaturesToLocation(fake_location) with x as save_sig: save_sig.add(sig2) save_sig.add(sig47) save_sig.add(sig63) print(len(x)) print(x.keep) assert isinstance(x, FakeSaveClass_HighPriority) assert x.keep == [sig2, sig47, sig63] assert x.priority == 1 # # Test basic features of the save_to plugin hook. # class FakeCommandClass(plugins.CommandLinePlugin): """ A fake CLI class. """ command = "nifty" description = "do somethin' nifty" def __init__(self, parser): super().__init__(parser) parser.add_argument("arg1") parser.add_argument("--other", action="store_true") parser.add_argument("--do-fail", action="store_true") def main(self, args): super().main(args) print(f"hello, world! argument is: {args.arg1}") print(f"other is {args.other}") if args.do_fail: return 1 return 0 class Test_EntryPointBasics_Command: # test the basics def setup_method(self): _ = plugins.get_cli_script_plugins() self.saved_plugins = plugins._plugin_cli plugins._plugin_cli_once = False plugins._plugin_cli = [FakeEntryPoint("test_command", FakeCommandClass)] def teardown_method(self): plugins._plugin_cli = self.saved_plugins def test_empty(self, runtmp): # empty out script plugins... plugins._plugin_cli = [] with pytest.raises(utils.SourmashCommandFailed): runtmp.sourmash("scripts") out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "(No script plugins detected!)" in out def test_cmd_0(self, runtmp): # test default output with some plugins with pytest.raises(utils.SourmashCommandFailed): runtmp.sourmash("scripts") out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "do somethin' nifty" in out assert "sourmash scripts nifty" in out def test_cmd_1(self): # test descriptions ps = list(plugins.get_cli_scripts_descriptions()) print(ps) assert len(ps) == 1 descr0 = ps[0] assert "do somethin' nifty" in descr0 assert "sourmash scripts nifty" in descr0 def test_cmd_2(self): # test get_cli_script_plugins function ps = list(plugins.get_cli_script_plugins()) print(ps) assert len(ps) == 1 def test_cmd_3(self, runtmp): # test ability to run 'nifty' ;) with pytest.raises(utils.SourmashCommandFailed): runtmp.sourmash("scripts", "nifty") out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "nifty: error: the following arguments are required: arg1" in err assert "usage: nifty [-h] [-q] [-d] [--other] [--do-fail] arg1" in err def test_cmd_4(self, runtmp): # test basic argument parsing etc runtmp.sourmash("scripts", "nifty", "--other", "some arg") out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "other is True" in out assert "hello, world! argument is: some arg" in out def test_cmd_5(self, runtmp): # test exit code passthru with pytest.raises(utils.SourmashCommandFailed): runtmp.sourmash("scripts", "nifty", "--do-fail", "some arg") status = runtmp.last_result.status out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) print(status) assert "other is False" in out assert "hello, world! argument is: some arg" in out class FakeCommandClass_Second(plugins.CommandLinePlugin): """ A fake CLI class. """ command = "more_nifty" description = "do somethin' else nifty" def __init__(self, parser): super().__init__(parser) parser.add_argument("arg1") parser.add_argument("--other", action="store_true") parser.add_argument("--do-fail", action="store_true") def main(self, args): super().main(args) print(f"hello, world! argument is: {args.arg1}") print(f"other is {args.other}") if args.do_fail: return 1 return 0 class FakeCommandClass_Broken_1: """ A fake CLI class. """ # command = 'more_nifty' # no command def __init__(self, parser): assert 0 def main(self, args): assert 0 class FakeCommandClass_Broken_2: """ A fake CLI class. """ command = "broken" # no description def __init__(self, parser): pass def main(self, args): return 0 class Test_EntryPointBasics_TwoCommands: # test a second command def setup_method(self): _ = plugins.get_cli_script_plugins() self.saved_plugins = plugins._plugin_cli plugins._plugin_cli_once = False plugins._plugin_cli = [ FakeEntryPoint("test_command", FakeCommandClass), FakeEntryPoint("test_command2", FakeCommandClass_Second), FakeEntryPoint("test_command3", FakeCommandClass_Broken_1), FakeEntryPoint("test_command4", FakeCommandClass_Broken_2), FakeEntryPoint( "error-on-import", FakeCommandClass, error_on_import=ModuleNotFoundError ), ] def teardown_method(self): plugins._plugin_cli = self.saved_plugins def test_cmd_0(self, runtmp): # test default output for a few plugins with pytest.raises(utils.SourmashCommandFailed): runtmp.sourmash("scripts") out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "do somethin' nifty" in out assert "sourmash scripts nifty" in out assert "do somethin' else nifty" in out assert "sourmash scripts more_nifty" in out def test_cmd_1(self, runtmp): # test 'nifty' runtmp.sourmash("scripts", "nifty", "some arg") status = runtmp.last_result.status out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) print(status) assert "other is False" in out assert "hello, world! argument is: some arg" in out def test_cmd_2(self, runtmp): # test 'more_nifty' runtmp.sourmash("scripts", "more_nifty", "some arg") status = runtmp.last_result.status out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) print(status) assert "other is False" in out assert "hello, world! argument is: some arg" in out def test_sourmash_info(self, runtmp): # test 'sourmash info -v' => shows the plugins runtmp.sourmash("info", "-v") out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) expected = """ groupfoo test_plugin_framework 0.1 test_command groupfoo test_plugin_framework 0.1 test_command2 groupfoo test_plugin_framework 0.1 test_command3 groupfoo test_plugin_framework 0.1 test_command4 """.splitlines() for line in expected: assert line in err def test_cli_scripts_getattr_fail(): # test scripts.__getattr__ w/fail from sourmash.cli import scripts with pytest.raises(AttributeError): scripts.ThisAttrDoesNotExist def test_cli_scripts_getattr_succ(): # test scripts.__getattr__ w/success from sourmash.cli import scripts scripts.subparser sourmash-bio-sourmash-bc3c0f8/tests/test_prefetch.py000066400000000000000000001002621474157046300230420ustar00rootroot00000000000000""" Tests for `sourmash prefetch` command-line and API functionality. """ import os import csv import gzip import pytest import glob import random from sourmash.search import PrefetchResult import sourmash_tst_utils as utils import sourmash from sourmash_tst_utils import SourmashCommandFailed from sourmash import SourmashSignature, sourmash_args from sourmash.signature import ( save_signatures_to_json, load_signatures_from_json, load_one_signature_from_json, ) def approx_eq(val1, val2): if val1 == "": return val1 == val2 return round(float(val1), 3) == round(float(val2), 3) def test_prefetch_basic(runtmp, linear_gather): c = runtmp # test a basic prefetch sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") c.run_sourmash("prefetch", "-k", "31", sig47, sig63, sig2, sig47, linear_gather) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "WARNING: no output(s) specified! Nothing will be saved from this prefetch!" in c.last_result.err ) assert "selecting specified query k=31" in c.last_result.err assert ( "loaded query: NC_009665.1 Shewanella baltica... (k=31, DNA)" in c.last_result.err ) assert ( "query sketch has scaled=1000; will be dynamically downsampled as needed" in c.last_result.err ) err = c.last_result.err assert "loaded 5 total signatures from 3 locations." in err assert "after selecting signatures compatible with search, 3 remain." in err assert "total of 2 matching signatures." in c.last_result.err assert ( "of 5177 distinct query hashes, 5177 were found in matches above threshold." in c.last_result.err ) assert "a total of 0 query hashes remain unmatched." in c.last_result.err def test_prefetch_select_query_ksize(runtmp, linear_gather): # test prefetch where query and subject db both have multiple ksizes c = runtmp ss = utils.get_test_data("GCF_000005845.2_ASM584v2_genomic.fna.gz.sig") c.run_sourmash("prefetch", ss, ss, linear_gather) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "of 4476 distinct query hashes, 4476 were found in matches above threshold." in c.last_result.err ) def test_prefetch_subject_scaled_is_larger(runtmp, linear_gather): # test prefetch where subject scaled is larger c = runtmp # make a query sketch with scaled=1000 fa = utils.get_test_data("genome-s10.fa.gz") c.run_sourmash("sketch", "dna", fa, "-o", "query.sig") assert os.path.exists(runtmp.output("query.sig")) # this has a scaled of 10000, from same genome: against1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") against2 = utils.get_test_data("scaled/all.sbt.zip") against3 = utils.get_test_data("scaled/all.lca.json") # run against large scaled, then small (self) c.run_sourmash( "prefetch", "query.sig", against1, against2, against3, "query.sig", linear_gather, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "total of 8 matching signatures." in c.last_result.err assert ( "of 48 distinct query hashes, 48 were found in matches above threshold." in c.last_result.err ) assert ( "final scaled value (max across query and all matches) is 10000" in c.last_result.err ) def test_prefetch_subject_scaled_is_larger_outsigs(runtmp, linear_gather): # test prefetch where subject scaled is larger -- output sigs c = runtmp # make a query sketch with scaled=1000 fa = utils.get_test_data("genome-s10.fa.gz") c.run_sourmash("sketch", "dna", fa, "-o", "query.sig") assert os.path.exists(runtmp.output("query.sig")) # this has a scaled of 10000, from same genome: against1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") against2 = utils.get_test_data("scaled/all.sbt.zip") against3 = utils.get_test_data("scaled/all.lca.json") # run against large scaled, then small (self) c.run_sourmash( "prefetch", "query.sig", against1, against2, against3, "query.sig", linear_gather, "--save-matches", "matches.sig", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "total of 8 matching signatures." in c.last_result.err assert ( "of 48 distinct query hashes, 48 were found in matches above threshold." in c.last_result.err ) assert ( "final scaled value (max across query and all matches) is 10000" in c.last_result.err ) # make sure non-downsampled sketches were saved. matches = sourmash.load_file_as_signatures(runtmp.output("matches.sig")) scaled_vals = set([match.minhash.scaled for match in matches]) assert 1000 in scaled_vals assert 10000 in scaled_vals assert len(scaled_vals) == 2 def test_prefetch_query_abund(runtmp, linear_gather): c = runtmp # test a basic prefetch w/abund query sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") c.run_sourmash("prefetch", "-k", "31", sig47, sig63, sig2, sig47, linear_gather) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "WARNING: no output(s) specified! Nothing will be saved from this prefetch!" in c.last_result.err ) assert "selecting specified query k=31" in c.last_result.err assert ( "loaded query: NC_009665.1 Shewanella baltica... (k=31, DNA)" in c.last_result.err ) assert ( "query sketch has scaled=1000; will be dynamically downsampled as needed" in c.last_result.err ) assert "total of 2 matching signatures." in c.last_result.err assert ( "of 5177 distinct query hashes, 5177 were found in matches above threshold." in c.last_result.err ) assert "a total of 0 query hashes remain unmatched." in c.last_result.err def test_prefetch_subj_abund(runtmp, linear_gather): c = runtmp # test a basic prefetch w/abund signature. sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") c.run_sourmash("prefetch", "-k", "31", sig47, sig63, sig2, sig47, linear_gather) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "WARNING: no output(s) specified! Nothing will be saved from this prefetch!" in c.last_result.err ) assert "selecting specified query k=31" in c.last_result.err assert ( "loaded query: NC_009665.1 Shewanella baltica... (k=31, DNA)" in c.last_result.err ) assert ( "query sketch has scaled=1000; will be dynamically downsampled as needed" in c.last_result.err ) assert "total of 2 matching signatures." in c.last_result.err assert ( "of 5177 distinct query hashes, 5177 were found in matches above threshold." in c.last_result.err ) assert "a total of 0 query hashes remain unmatched." in c.last_result.err def test_prefetch_csv_out(runtmp, linear_gather): c = runtmp # test a basic prefetch, with CSV output sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") csvout = c.output("out.csv") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "-o", csvout, linear_gather ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(csvout) expected_intersect_bp = [2529000, 5177000] with open(csvout, newline="") as fp: r = csv.DictReader(fp) for row, expected in zip(r, expected_intersect_bp): print(row) assert int(row["intersect_bp"]) == expected def test_prefetch_csv_gz_out(runtmp, linear_gather): c = runtmp # test a basic prefetch, with CSV output to a .gz file sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") csvout = c.output("out.csv.gz") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "-o", csvout, linear_gather ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(csvout) expected_intersect_bp = [2529000, 5177000] with gzip.open(csvout, "rt", newline="") as fp: r = csv.DictReader(fp) for row, expected in zip(r, expected_intersect_bp): print(row) assert int(row["intersect_bp"]) == expected def test_prefetch_matches(runtmp, linear_gather): c = runtmp # test a basic prefetch, with --save-matches sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") matches_out = c.output("matches.sig") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "--save-matches", matches_out, linear_gather, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(matches_out) sigs = sourmash.load_file_as_index(matches_out) expected_matches = [sig63, sig47] for match, expected in zip(sigs.signatures(), expected_matches): ss = load_one_signature_from_json(expected, ksize=31) assert match == ss def test_prefetch_matches_to_dir(runtmp, linear_gather): c = runtmp # test a basic prefetch, with --save-matches to a directory sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss63 = load_one_signature_from_json(sig63) ss47 = load_one_signature_from_json(sig47) matches_out = c.output("matches_dir/") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "--save-matches", matches_out, linear_gather, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(matches_out) assert os.path.isdir(matches_out) sigs = sourmash.load_file_as_signatures(matches_out) match_sigs = list(sigs) assert ss63 in match_sigs assert ss47 in match_sigs assert len(match_sigs) == 2 def test_prefetch_matches_to_sig_gz(runtmp, linear_gather): c = runtmp import gzip # test a basic prefetch, with --save-matches to a sig.gz file sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss63 = load_one_signature_from_json(sig63) ss47 = load_one_signature_from_json(sig47) matches_out = c.output("matches.sig.gz") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "--save-matches", matches_out, linear_gather, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(matches_out) assert os.path.isfile(matches_out) with gzip.open(matches_out, "rt") as fp: # can we read this as a gz file? fp.read() sigs = sourmash.load_file_as_signatures(matches_out) match_sigs = list(sigs) assert ss63 in match_sigs assert ss47 in match_sigs assert len(match_sigs) == 2 def test_prefetch_matches_to_zip(runtmp, linear_gather): c = runtmp # test a basic prefetch, with --save-matches to a zipfile import zipfile sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss63 = load_one_signature_from_json(sig63) ss47 = load_one_signature_from_json(sig47) matches_out = c.output("matches.zip") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "--save-matches", matches_out, linear_gather, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(matches_out) assert os.path.isfile(matches_out) with zipfile.ZipFile(matches_out, "r") as fp: # can we read this as a .zip file? for zi in fp.infolist(): pass sigs = sourmash.load_file_as_signatures(matches_out) match_sigs = list(sigs) assert ss63 in match_sigs assert ss47 in match_sigs assert len(match_sigs) == 2 def test_prefetch_matching_hashes(runtmp, linear_gather): c = runtmp # test a basic prefetch, with --save-matches utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") matches_out = c.output("matches.sig") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, "--save-matching-hashes", matches_out, linear_gather, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(matches_out) ss47 = load_one_signature_from_json(sig47, ksize=31) ss63 = load_one_signature_from_json(sig63, ksize=31) matches = set(ss47.minhash.hashes) & set(ss63.minhash.hashes) intersect = ss47.minhash.copy_and_clear() intersect.add_many(matches) ss = load_one_signature_from_json(matches_out) assert ss.name.endswith("-known") assert ss.minhash == intersect def test_prefetch_nomatch_hashes(runtmp, linear_gather): c = runtmp # test a basic prefetch, with --save-matches sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") nomatch_out = c.output("unmatched_hashes.sig") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, "--save-unmatched-hashes", nomatch_out, linear_gather, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(nomatch_out) ss47 = load_one_signature_from_json(sig47, ksize=31) ss63 = load_one_signature_from_json(sig63, ksize=31) remain = ss47.minhash.to_mutable() remain.remove_many(ss63.minhash.hashes) ss = load_one_signature_from_json(nomatch_out) assert ss.name.endswith("-unknown") assert ss.minhash == remain def test_prefetch_no_num_query(runtmp, linear_gather): c = runtmp # can't do prefetch with num signatures for query sig47 = utils.get_test_data("num/47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("prefetch", "-k", "31", sig47, sig63, sig47, linear_gather) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status != 0 def test_prefetch_no_num_subj(runtmp, linear_gather): c = runtmp # can't do prefetch with num signatures for query; no matches! sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("num/63.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("prefetch", "-k", "31", sig47, sig63, linear_gather) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status != 0 assert ( "ERROR in prefetch: after picklists and patterns, no signatures to search!?" in c.last_result.err ) def test_prefetch_db_fromfile(runtmp, linear_gather): c = runtmp # test a basic prefetch sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") from_file = c.output("from-list.txt") with open(from_file, "w") as fp: print(sig63, file=fp) print(sig2, file=fp) print(sig47, file=fp) c.run_sourmash( "prefetch", "-k", "31", sig47, linear_gather, "--db-from-file", from_file ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "WARNING: no output(s) specified! Nothing will be saved from this prefetch!" in c.last_result.err ) assert "selecting specified query k=31" in c.last_result.err assert ( "loaded query: NC_009665.1 Shewanella baltica... (k=31, DNA)" in c.last_result.err ) assert ( "query sketch has scaled=1000; will be dynamically downsampled as needed" in c.last_result.err ) assert "total of 2 matching signatures." in c.last_result.err assert ( "of 5177 distinct query hashes, 5177 were found in matches above threshold." in c.last_result.err ) assert "a total of 0 query hashes remain unmatched." in c.last_result.err def test_prefetch_no_db(runtmp, linear_gather): c = runtmp # test a basic prefetch with no databases/signatures sig47 = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("prefetch", "-k", "31", sig47, linear_gather) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status != 0 assert "ERROR: no databases or signatures to search!?" in c.last_result.err def test_prefetch_check_scaled_bounds_negative(runtmp, linear_gather): c = runtmp sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "--scaled", "-5", linear_gather, ) assert "ERROR: scaled value must be positive" in str(exc.value) def test_prefetch_check_scaled_bounds_less_than_minimum(runtmp, linear_gather): c = runtmp sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "--scaled", "50", linear_gather, ) assert "WARNING: scaled value should be >= 100. Continuing anyway." in str( exc.value ) def test_prefetch_check_scaled_bounds_more_than_maximum(runtmp, linear_gather): c = runtmp sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "--scaled", "1e9", linear_gather, ) assert "WARNING: scaled value should be <= 1e6. Continuing anyway." in str( exc.value ) def test_prefetch_downsample_scaled(runtmp, linear_gather): c = runtmp # test --scaled sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "--scaled", "1e5", linear_gather, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "downsampling query from scaled=1000 to 10000" in c.last_result.err def test_prefetch_downsample_multiple(runtmp, linear_gather): # test multiple different downsamplings in prefetch code query_sig = utils.get_test_data("GCF_000006945.2-s500.sig") # load in the hashes and do split them into four bins, randomly. ss = load_one_signature_from_json(query_sig) hashes = list(ss.minhash.hashes) random.seed(a=1) # fix seed so test is reproducible random.shuffle(hashes) # split into 4 bins: mh_bins = [ss.minhash.copy_and_clear() for i in range(4)] for i, hashval in enumerate(hashes): mh_bins[i % 4].add_hash(hashval) # downsample with different scaleds; initial scaled is 500, note. mh_bins[0] = mh_bins[0].downsample(scaled=750) mh_bins[1] = mh_bins[1].downsample(scaled=600) mh_bins[2] = mh_bins[2].downsample(scaled=1000) mh_bins[3] = mh_bins[3].downsample(scaled=650) gathersigs = [] for i in range(4): binsig = SourmashSignature(mh_bins[i], name=f"bin{i}") with open(runtmp.output(f"bin{i}.sig"), "wb") as fp: save_signatures_to_json([binsig], fp) gathersigs.append(f"bin{i}.sig") runtmp.sourmash("prefetch", linear_gather, query_sig, *gathersigs) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "final scaled value (max across query and all matches) is 1000" in runtmp.last_result.err ) def test_prefetch_empty(runtmp, linear_gather): c = runtmp # test --scaled sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "--scaled", "1e9", linear_gather, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status != 0 assert "no query hashes!? exiting." in c.last_result.err def test_prefetch_basic_many_sigs(runtmp, linear_gather): c = runtmp # test what happens with many (and duplicate) signatures sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") manysigs = [sig63, sig2, sig47] * 5 c.run_sourmash("prefetch", "-k", "31", sig47, *manysigs, linear_gather) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "total of 10 matching signatures so far." in c.last_result.err assert "total of 10 matching signatures." in c.last_result.err assert ( "of 5177 distinct query hashes, 5177 were found in matches above threshold." in c.last_result.err ) assert "a total of 0 query hashes remain unmatched." in c.last_result.err def test_prefetch_with_picklist(runtmp): # test 'sourmash prefetch' with picklists gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") runtmp.sourmash( "prefetch", metag_sig, *gcf_sigs, "--picklist", f"{picklist}:md5:md5" ) err = runtmp.last_result.err print(err) assert "for given picklist, found 3 matches to 9 distinct values" in err # these are the different ksizes assert "WARNING: 6 missing picklist values." in err out = runtmp.last_result.out print(out) assert "total of 3 matching signatures." in err assert ( "of 1466 distinct query hashes, 453 were found in matches above threshold." in err ) assert "a total of 1013 query hashes remain unmatched." in err def test_prefetch_with_picklist_exclude(runtmp): # test 'sourmash prefetch' with picklists, exclude gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") runtmp.sourmash( "prefetch", metag_sig, *gcf_sigs, "--picklist", f"{picklist}:md5:md5:exclude" ) err = runtmp.last_result.err print(err) assert "for given picklist, found 9 matches by excluding 9 distinct values" in err # these are the different ksizes out = runtmp.last_result.out print(out) assert "total of 9 matching signatures." in err assert ( "of 1466 distinct query hashes, 1013 were found in matches above threshold." in err ) assert "a total of 453 query hashes remain unmatched." in err def test_prefetch_with_pattern_include(runtmp): # test 'sourmash prefetch' with --include-db-pattern gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash("prefetch", metag_sig, *gcf_sigs, "--include", "thermotoga") err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "total of 3 matching signatures." in err assert ( "of 1466 distinct query hashes, 453 were found in matches above threshold." in err ) assert "a total of 1013 query hashes remain unmatched." in err def test_prefetch_with_pattern_exclude(runtmp): # test 'sourmash prefetch' with --exclude-db-pattern gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash("prefetch", metag_sig, *gcf_sigs, "--exclude", "thermotoga") err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "total of 9 matching signatures." in err assert ( "of 1466 distinct query hashes, 1013 were found in matches above threshold." in err ) assert "a total of 453 query hashes remain unmatched." in err def test_prefetch_output_with_abundance(runtmp, prefetch_gather, linear_gather): c = runtmp query = utils.get_test_data("gather-abund/reads-s10x10-s11.sig") against = utils.get_test_data("gather-abund/genome-s10.fa.gz.sig") c.run_sourmash( "prefetch", linear_gather, query, against, "--save-matching-hashes", c.output("match-hash.sig"), "--save-unmatched-hashes", c.output("nomatch-hash.sig"), ) print(c.last_result.out) assert os.path.exists(c.output("match-hash.sig")) ss = list(sourmash.load_file_as_signatures(c.output("match-hash.sig")))[0] assert ss.minhash.track_abundance assert os.path.exists(c.output("nomatch-hash.sig")) ss = list(sourmash.load_file_as_signatures(c.output("nomatch-hash.sig")))[0] assert ss.minhash.track_abundance def test_prefetch_ani_csv_out(runtmp, linear_gather): c = runtmp # test a basic prefetch, with CSV output sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") csvout = c.output("out.csv") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "-o", csvout, linear_gather ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(csvout) prefetch_result_names = PrefetchResult.prefetch_write_cols exp1 = { "q_ani": "0.9771552502238963", "m_ani": "0.9767860811200507", "ac_ani": "0.9769706656719734", "mc_ani": "0.9771552502238963", "pfn": "False", } exp2 = { "q_ani": "1.0", "m_ani": "1.0", "ac_ani": "1.0", "mc_ani": "1.0", "pfn": "False", } expected_ani_vals = [exp1, exp2] with open(csvout, newline="") as fp: r = csv.DictReader(fp) for row, expected in zip(r, expected_ani_vals): print(row) assert prefetch_result_names == list(row.keys()) assert approx_eq(row["query_containment_ani"], expected["q_ani"]) assert approx_eq(row["match_containment_ani"], expected["m_ani"]) assert approx_eq(row["max_containment_ani"], expected["mc_ani"]) assert approx_eq(row["average_containment_ani"], expected["ac_ani"]) assert row["potential_false_negative"] == expected["pfn"] def test_prefetch_ani_csv_out_estimate_ci(runtmp, linear_gather): c = runtmp # test a basic prefetch, with CSV output sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") csvout = c.output("out.csv") c.run_sourmash( "prefetch", "-k", "31", sig47, sig63, sig2, sig47, "-o", csvout, linear_gather, "--estimate-ani-ci", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(csvout) prefetch_result_names_ci = PrefetchResult.prefetch_write_cols_ci exp1 = { "q_ani": "0.9771552502238963", "m_ani": "0.9767860811200507", "q_ani_low": "0.9762537506990911", "q_ani_high": "0.9780336875157754", "m_ani_low": "0.9758801604653301", "m_ani_high": "0.9776692390768575", "ac_ani": "0.9769706656719734", "mc_ani": "0.9771552502238963", "pfn": "False", } exp2 = { "q_ani": "1.0", "m_ani": "1.0", "q_ani_low": "1.0", "q_ani_high": "1.0", "m_ani_low": "1.0", "m_ani_high": "1.0", "ac_ani": "1.0", "mc_ani": "1.0", "pfn": "False", } expected_ani_vals = [exp1, exp2] with open(csvout, newline="") as fp: r = csv.DictReader(fp) for row, expected in zip(r, expected_ani_vals): print(row) assert prefetch_result_names_ci == list(row.keys()) assert approx_eq(row["query_containment_ani"], expected["q_ani"]) assert approx_eq(row["query_containment_ani_low"], expected["q_ani_low"]) assert approx_eq(row["query_containment_ani_high"], expected["q_ani_high"]) assert approx_eq(row["match_containment_ani"], expected["m_ani"]) assert approx_eq(row["match_containment_ani_low"], expected["m_ani_low"]) assert approx_eq(row["match_containment_ani_high"], expected["m_ani_high"]) assert approx_eq(row["max_containment_ani"], expected["mc_ani"]) assert approx_eq(row["average_containment_ani"], expected["ac_ani"]) assert row["potential_false_negative"] == expected["pfn"] def test_prefetch_ani_containment_asymmetry(runtmp): # test contained_by asymmetries, viz #2215 query_sig = utils.get_test_data("47.fa.sig") merged_sig = utils.get_test_data("47-63-merge.sig") runtmp.sourmash("prefetch", query_sig, merged_sig, "-o", "query-in-merged.csv") runtmp.sourmash("prefetch", merged_sig, query_sig, "-o", "merged-in-query.csv") with sourmash_args.FileInputCSV(runtmp.output("query-in-merged.csv")) as r: query_in_merged = list(r)[0] with sourmash_args.FileInputCSV(runtmp.output("merged-in-query.csv")) as r: merged_in_query = list(r)[0] assert query_in_merged["query_containment_ani"] == "1.0" assert query_in_merged["match_containment_ani"] == "0.9865155060423993" assert query_in_merged["average_containment_ani"] == "0.9932577530211997" assert merged_in_query["match_containment_ani"] == "1.0" assert merged_in_query["query_containment_ani"] == "0.9865155060423993" assert merged_in_query["average_containment_ani"] == "0.9932577530211997" sourmash-bio-sourmash-bc3c0f8/tests/test_rustobj.py000066400000000000000000000005641474157046300227360ustar00rootroot00000000000000import pytest from sourmash.utils import RustObject from sourmash.minhash import to_bytes def test_rustobj_init(): with pytest.raises(TypeError): RustObject() def test_to_bytes(): with pytest.raises(TypeError): to_bytes([9882]) assert to_bytes(98) == bytes([98]) assert to_bytes("abc") == b"abc" assert to_bytes(b"abc") == b"abc" sourmash-bio-sourmash-bc3c0f8/tests/test_sbt.py000066400000000000000000001171031474157046300220340ustar00rootroot00000000000000"Test SBT code." import json import shutil import os import pytest import sourmash from sourmash import load_one_signature, SourmashSignature, load_file_as_signatures from sourmash.exceptions import IndexNotSupported from sourmash.sbt import SBT, GraphFactory, Leaf, Node from sourmash.sbtmh import SigLeaf, load_sbt_index from sourmash.sbt_storage import FSStorage, RedisStorage, IPFSStorage, ZipStorage from sourmash.search import make_jaccard_search_query from sourmash.picklist import SignaturePicklist, PickStyle import sourmash_tst_utils as utils def test_simple(runtmp, n_children): factory = GraphFactory(5, 100, 3) root = SBT(factory, d=n_children) leaf1 = Leaf("a", factory()) leaf1.data.count("AAAAA") leaf1.data.count("AAAAT") leaf1.data.count("AAAAC") leaf2 = Leaf("b", factory()) leaf2.data.count("AAAAA") leaf2.data.count("AAAAT") leaf2.data.count("AAAAG") leaf3 = Leaf("c", factory()) leaf3.data.count("AAAAA") leaf3.data.count("AAAAT") leaf3.data.count("CAAAA") leaf4 = Leaf("d", factory()) leaf4.data.count("AAAAA") leaf4.data.count("CAAAA") leaf4.data.count("GAAAA") leaf5 = Leaf("e", factory()) leaf5.data.count("AAAAA") leaf5.data.count("AAAAT") leaf5.data.count("GAAAA") root.add_node(leaf1) root.add_node(leaf2) root.add_node(leaf3) root.add_node(leaf4) root.add_node(leaf5) # return True if leaf node contains nodegraph w/kmer def search_kmer(leaf, kmer): return leaf.data.get(kmer) leaves = [leaf1, leaf2, leaf3, leaf4, leaf5] kmers = ["AAAAA", "AAAAT", "AAAAG", "CAAAA", "GAAAA"] # define an exhaustive search function that looks in all the leaf nodes. def search_kmer_in_list(kmer): x = [] for l in leaves: if l.data.get(kmer): x.append(l) return set(x) # for all k-mers, ensure that tree._find_nodes matches the exhaustive # search. for kmer in kmers: assert set(root._find_nodes(search_kmer, kmer)) == search_kmer_in_list(kmer) print("-----") print([x.metadata for x in root._find_nodes(search_kmer, "AAAAA")]) print([x.metadata for x in root._find_nodes(search_kmer, "AAAAT")]) print([x.metadata for x in root._find_nodes(search_kmer, "AAAAG")]) print([x.metadata for x in root._find_nodes(search_kmer, "CAAAA")]) print([x.metadata for x in root._find_nodes(search_kmer, "GAAAA")]) # save SBT to a directory and then reload root.save(runtmp.output("demo")) root = SBT.load(runtmp.output("demo")) for kmer in kmers: new_result = {str(r) for r in root._find_nodes(search_kmer, kmer)} print(*new_result, sep="\n") assert new_result == {str(r) for r in search_kmer_in_list(kmer)} def test_longer_search(n_children): ksize = 5 factory = GraphFactory(ksize, 100, 3) root = SBT(factory, d=n_children) leaf1 = Leaf("a", factory()) leaf1.data.count("AAAAA") leaf1.data.count("AAAAT") leaf1.data.count("AAAAC") leaf2 = Leaf("b", factory()) leaf2.data.count("AAAAA") leaf2.data.count("AAAAT") leaf2.data.count("AAAAG") leaf3 = Leaf("c", factory()) leaf3.data.count("AAAAA") leaf3.data.count("AAAAT") leaf3.data.count("CAAAA") leaf4 = Leaf("d", factory()) leaf4.data.count("AAAAA") leaf4.data.count("CAAAA") leaf4.data.count("GAAAA") leaf5 = Leaf("e", factory()) leaf5.data.count("AAAAA") leaf5.data.count("AAAAT") leaf5.data.count("GAAAA") root.add_node(leaf1) root.add_node(leaf2) root.add_node(leaf3) root.add_node(leaf4) root.add_node(leaf5) def kmers(k, seq): for start in range(len(seq) - k + 1): yield seq[start : start + k] def search_transcript(node, seq, threshold): presence = [node.data.get(kmer) for kmer in kmers(ksize, seq)] if sum(presence) >= int(threshold * (len(seq) - ksize + 1)): return 1 return 0 try1 = [x.metadata for x in root._find_nodes(search_transcript, "AAAAT", 1.0)] assert set(try1) == set(["a", "b", "c", "e"]), try1 # no 'd' try2 = [x.metadata for x in root._find_nodes(search_transcript, "GAAAAAT", 0.6)] assert set(try2) == set(["a", "b", "c", "d", "e"]) try3 = [x.metadata for x in root._find_nodes(search_transcript, "GAAAA", 1.0)] assert set(try3) == set(["d", "e"]), try3 # @pytest.mark.parametrize("old_version", ["v1", "v2", "v3", "v4", "v5"]) @pytest.mark.parametrize("old_version", ["v3", "v4", "v5"]) def test_tree_old_load(old_version): tree_old = SBT.load( utils.get_test_data(f"{old_version}.sbt.json"), leaf_loader=SigLeaf.load ) tree_cur = SBT.load(utils.get_test_data("v6.sbt.json"), leaf_loader=SigLeaf.load) testdata1 = utils.get_test_data(utils.SIG_FILES[0]) to_search = load_one_signature(testdata1) print(list(tree_old.leaves())) # note: earlier versions of this test did containment on # the num MinHash in `to_search`, which doesn't work properly. # (See test_sbt_no_containment_on_num for test). So, to # fix the test for the new search API, we had to adjust # the threshold. search_obj = make_jaccard_search_query(threshold=0.05) results_old = {str(s.signature) for s in tree_old.find(search_obj, to_search)} results_cur = {str(s.signature) for s in tree_cur.find(search_obj, to_search)} assert results_old == results_cur assert len(results_old) == 4 def test_load_future(tmpdir): with open(str(tmpdir.join("v9999.sbt.json")), "w") as f: json.dump({"version": 9999}, f) with pytest.raises(IndexNotSupported) as excinfo: SBT.load(str(tmpdir.join("v9999.sbt.json"))) assert "index format is not supported" in str(excinfo.value) def test_tree_save_load(runtmp, n_children): factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=n_children) for f in utils.SIG_FILES: sig = load_one_signature(utils.get_test_data(f)) leaf = SigLeaf(os.path.basename(f), sig) tree.add_node(leaf) to_search = leaf print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) old_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*old_result, sep="\n") tree.save(runtmp.output("demo")) tree = SBT.load(runtmp.output("demo"), leaf_loader=SigLeaf.load) print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) new_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*new_result, sep="\n") assert old_result == new_result def test_search_minhashes(): factory = GraphFactory(31, 1e5, 4) tree = SBT(factory) for f in utils.SIG_FILES: sig = load_one_signature(utils.get_test_data(f)) leaf = SigLeaf(os.path.basename(f), sig) tree.add_node(leaf) to_search = next(iter(tree.leaves())) # this fails if 'search_obj' is calc containment and not similarity. search_obj = make_jaccard_search_query(threshold=0.08) results = tree.find(search_obj, to_search.data) n = 0 for n, sr in enumerate(results): assert to_search.data.jaccard(sr.signature) >= 0.08 assert n == 1 def test_binary_nary_tree(): factory = GraphFactory(31, 1e5, 4) trees = {} trees[2] = SBT(factory) trees[5] = SBT(factory, d=5) trees[10] = SBT(factory, d=10) n_leaves = 0 for f in utils.SIG_FILES: sig = load_one_signature(utils.get_test_data(f)) leaf = SigLeaf(os.path.basename(f), sig) for tree in trees.values(): tree.add_node(leaf) to_search = leaf n_leaves += 1 assert all([len(list(t.leaves())) == n_leaves for t in trees.values()]) results = {} print("*" * 60) print(f"{to_search.metadata}:") for d, tree in trees.items(): search_obj = make_jaccard_search_query(threshold=0.1) results[d] = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*results[2], sep="\n") assert results[2] == results[5] assert results[5] == results[10] def test_sbt_combine(n_children): factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=n_children) tree_1 = SBT(factory, d=n_children) tree_2 = SBT(factory, d=n_children) n_leaves = 0 for f in utils.SIG_FILES: sig = load_one_signature(utils.get_test_data(f)) leaf = SigLeaf(os.path.basename(f), sig) tree.add_node(leaf) if n_leaves < 4: tree_1.add_node(leaf) else: tree_2.add_node(leaf) n_leaves += 1 tree_1.combine(tree_2) t1_leaves = {str(l) for l in tree_1.leaves()} t_leaves = {str(l) for l in tree.leaves()} assert len(t1_leaves) == n_leaves assert len(t_leaves) == len(t1_leaves) assert t1_leaves == t_leaves to_search = load_one_signature(utils.get_test_data(utils.SIG_FILES[0])) search_obj = make_jaccard_search_query(threshold=0.1) t1_result = {str(s.signature) for s in tree_1.find(search_obj, to_search)} tree_result = {str(s.signature) for s in tree.find(search_obj, to_search)} assert t1_result == tree_result # TODO: save and load both trees # check if adding a new node will use the next empty position next_empty = 0 for n, (d, _) in enumerate(tree_1): if n != d: next_empty = n break if not next_empty: next_empty = n + 1 tree_1.add_node(SigLeaf(to_search.name, to_search)) assert tree_1.next_node == next_empty def test_sbt_fsstorage(runtmp): factory = GraphFactory(31, 1e5, 4) # with utils.TempDirectory() as location: tree = SBT(factory) for f in utils.SIG_FILES: sig = load_one_signature(utils.get_test_data(f)) leaf = SigLeaf(os.path.basename(f), sig) tree.add_node(leaf) to_search = leaf print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) old_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*old_result, sep="\n") with FSStorage(runtmp.location, ".fstree") as storage: tree.save(runtmp.output("tree.sbt.json"), storage=storage) tree = SBT.load(runtmp.output("tree.sbt.json"), leaf_loader=SigLeaf.load) print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) new_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*new_result, sep="\n") assert old_result == new_result assert os.path.exists(runtmp.output(tree.storage.subdir)) assert os.path.exists(runtmp.output(".fstree")) def test_sbt_zipstorage(tmpdir): # create tree, save to a zip, then load and search. factory = GraphFactory(31, 1e5, 4) tree = SBT(factory) for f in utils.SIG_FILES: sig = next(load_file_as_signatures(utils.get_test_data(f))) leaf = SigLeaf(os.path.basename(f), sig) tree.add_node(leaf) to_search = leaf print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) old_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*old_result, sep="\n") with ZipStorage(str(tmpdir.join("tree.sbt.zip")), mode="w") as storage: tree.save(str(tmpdir.join("tree.sbt.json")), storage=storage) with ZipStorage(str(tmpdir.join("tree.sbt.zip"))) as storage: tree = SBT.load( str(tmpdir.join("tree.sbt.json")), leaf_loader=SigLeaf.load, storage=storage ) print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) new_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*new_result, sep="\n") assert old_result == new_result def test_sbt_ipfsstorage(runtmp): ipfshttpclient = pytest.importorskip("ipfshttpclient") factory = GraphFactory(31, 1e5, 4) tree = SBT(factory) for f in utils.SIG_FILES: sig = load_one_signature(utils.get_test_data(f)) leaf = SigLeaf(os.path.basename(f), sig) tree.add_node(leaf) to_search = leaf print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) old_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*old_result, sep="\n") try: with IPFSStorage() as storage: tree.save(runtmp.output("tree.sbt.json"), storage=storage) except ipfshttpclient.exceptions.ConnectionError: pytest.xfail("ipfs not installed/functioning probably") with IPFSStorage() as storage: tree = SBT.load( runtmp.output("tree.sbt.json"), leaf_loader=SigLeaf.load, storage=storage ) print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) new_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*new_result, sep="\n") assert old_result == new_result def test_sbt_redisstorage(runtmp): redis = pytest.importorskip("redis") factory = GraphFactory(31, 1e5, 4) tree = SBT(factory) for f in utils.SIG_FILES: sig = load_one_signature(utils.get_test_data(f)) leaf = SigLeaf(os.path.basename(f), sig) tree.add_node(leaf) to_search = leaf print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) old_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*old_result, sep="\n") try: with RedisStorage() as storage: tree.save(runtmp.output("tree.sbt.json"), storage=storage) except redis.exceptions.ConnectionError: pytest.xfail("Couldn't connect to redis server") with RedisStorage() as storage: tree = SBT.load( runtmp.output("tree.sbt.json"), leaf_loader=SigLeaf.load, storage=storage ) print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) new_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*new_result, sep="\n") assert old_result == new_result def test_save_zip(tmpdir): # load from zipped SBT, save to zipped SBT, and then search. testdata = utils.get_test_data("v6.sbt.zip") testsbt = tmpdir.join("v6.sbt.zip") newsbt = tmpdir.join("new.sbt.zip") shutil.copyfile(testdata, str(testsbt)) tree = SBT.load(str(testsbt), leaf_loader=SigLeaf.load) tree.save(str(newsbt)) assert newsbt.exists() new_tree = SBT.load(str(newsbt), leaf_loader=SigLeaf.load) assert isinstance(new_tree.storage, ZipStorage) assert new_tree.storage.list_sbts() == ["new.sbt.json"] to_search = load_one_signature(utils.get_test_data(utils.SIG_FILES[0])) print("*" * 60) print(f"{to_search}:") search_obj = make_jaccard_search_query(threshold=0.1) old_result = {str(s.signature) for s in tree.find(search_obj, to_search)} new_result = {str(s.signature) for s in new_tree.find(search_obj, to_search)} print(*new_result, sep="\n") assert old_result == new_result assert len(new_result) == 2 def test_load_zip(tmpdir): # search zipped SBT testdata = utils.get_test_data("v6.sbt.zip") testsbt = tmpdir.join("v6.sbt.zip") shutil.copyfile(testdata, str(testsbt)) tree = SBT.load(str(testsbt), leaf_loader=SigLeaf.load) to_search = load_one_signature(utils.get_test_data(utils.SIG_FILES[0])) print("*" * 60) print(f"{to_search}:") search_obj = make_jaccard_search_query(threshold=0.1) new_result = {str(s.signature) for s in tree.find(search_obj, to_search)} print(*new_result, sep="\n") assert len(new_result) == 2 def test_load_zip_uncompressed(tmpdir): # uncompress zipped SBT into a tmpdir and search unpacked SBT import zipfile testdata = utils.get_test_data("v6.sbt.zip") testsbt = tmpdir.join("v6.sbt.json") with zipfile.ZipFile(testdata, "r") as z: z.extractall(str(tmpdir)) tree = SBT.load(str(testsbt), leaf_loader=SigLeaf.load) to_search = load_one_signature(utils.get_test_data(utils.SIG_FILES[0])) print("*" * 60) print(f"{to_search}:") search_obj = make_jaccard_search_query(threshold=0.1) new_result = {str(s.signature) for s in tree.find(search_obj, to_search)} print(*new_result, sep="\n") assert len(new_result) == 2 def test_tree_repair(): tree_repair = SBT.load( utils.get_test_data("leaves.sbt.json"), leaf_loader=SigLeaf.load ) tree_cur = SBT.load(utils.get_test_data("v3.sbt.json"), leaf_loader=SigLeaf.load) testdata1 = utils.get_test_data(utils.SIG_FILES[0]) to_search = load_one_signature(testdata1) search_obj = make_jaccard_search_query(threshold=0.1) results_repair = {str(s.signature) for s in tree_repair.find(search_obj, to_search)} results_cur = {str(s.signature) for s in tree_cur.find(search_obj, to_search)} assert results_repair == results_cur assert len(results_repair) == 2 def test_tree_repair_insert(): tree_repair = SBT.load( utils.get_test_data("leaves.sbt.json"), leaf_loader=SigLeaf.load ) for f in utils.SIG_FILES: sig = load_one_signature(utils.get_test_data(f)) leaf = SigLeaf(os.path.basename(f), sig) tree_repair.add_node(leaf) for pos, node in tree_repair: # Every parent of a node must be an internal node (and not a leaf), # except for node 0 (the root), whose parent is None. if pos != 0: assert isinstance(tree_repair.parent(pos).node, Node) # Leaf nodes can't have children if isinstance(node, Leaf): assert all(c.node is None for c in tree_repair.children(pos)) def test_save_sparseness(runtmp, n_children): factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=n_children) for f in utils.SIG_FILES: sig = load_one_signature(utils.get_test_data(f)) leaf = SigLeaf(os.path.basename(f), sig) tree.add_node(leaf) to_search = leaf print("*" * 60) print(f"{to_search.metadata}:") search_obj = make_jaccard_search_query(threshold=0.1) old_result = {str(s.signature) for s in tree.find(search_obj, to_search.data)} print(*old_result, sep="\n") tree.save(runtmp.output("demo"), sparseness=1.0) tree_loaded = SBT.load(runtmp.output("demo"), leaf_loader=SigLeaf.load) assert all(not isinstance(n, Node) for _, n in tree_loaded) print("*" * 60) print(f"{to_search.metadata}:") new_result = { str(s.signature) for s in tree_loaded.find(search_obj, to_search.data) } print(*new_result, sep="\n") assert old_result == new_result for pos, node in tree_loaded: # Every parent of a node must be an internal node (and not a leaf), # except for node 0 (the root), whose parent is None. if pos != 0: assert isinstance(tree_loaded.parent(pos).node, Node) # Leaf nodes can't have children if isinstance(node, Leaf): assert all(c.node is None for c in tree_loaded.children(pos)) def test_sbt_as_index_select(): # test 'select' method from Index base class. factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=2) sig47 = load_one_signature(utils.get_test_data("47.fa.sig")) sig63 = load_one_signature(utils.get_test_data("63.fa.sig")) tree.insert(sig47) tree.insert(sig63) xx = tree.select(ksize=31) assert xx == tree xx = tree.select(moltype="DNA") assert xx == tree xx = tree.select(abund=False) assert xx == tree with pytest.raises(ValueError): tree.select(ksize=21) with pytest.raises(ValueError): tree.select(moltype="protein") with pytest.raises(ValueError): tree.select(abund=True) def test_sbt_as_index_select_picklist(): # test 'select' method from Index base class with a picklist factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=2) sig47 = load_one_signature(utils.get_test_data("47.fa.sig")) sig63 = load_one_signature(utils.get_test_data("63.fa.sig")) tree.insert(sig47) tree.insert(sig63) # construct a picklist... picklist = SignaturePicklist("md5prefix8") picklist.init(["09a08691"]) # select on picklist tree = tree.select(picklist=picklist) siglist = list(tree.signatures()) assert len(siglist) == 1 ss = siglist[0] assert ss.minhash.ksize == 31 assert ss.md5sum().startswith("09a08691c") def test_sbt_as_index_select_picklist_exclude(): # test 'select' method from Index base class with a picklist, exclude factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=2) sig47 = load_one_signature(utils.get_test_data("47.fa.sig")) sig63 = load_one_signature(utils.get_test_data("63.fa.sig")) tree.insert(sig47) tree.insert(sig63) # construct a picklist... picklist = SignaturePicklist("md5prefix8", pickstyle=PickStyle.EXCLUDE) picklist.init(["09a08691"]) # select on picklist tree = tree.select(picklist=picklist) siglist = list(tree.signatures()) assert len(siglist) == 1 ss = siglist[0] assert ss.minhash.ksize == 31 assert ss.md5sum().startswith("38729c637") def test_sbt_as_index_find_picklist(): # test 'select' method from Index base class with a picklist factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=2) sig47 = load_one_signature(utils.get_test_data("47.fa.sig")) sig63 = load_one_signature(utils.get_test_data("63.fa.sig")) tree.insert(sig47) tree.insert(sig63) # construct a picklist... picklist = SignaturePicklist("md5prefix8") picklist.init(["09a08691"]) # run a 'find' with sig63, should find 47 and 63 both. search_obj = make_jaccard_search_query(do_containment=True, threshold=0.0) results = list(tree.find(search_obj, sig63)) print(results) assert len(results) == 2 # now, select on picklist and do another find... tree = tree.select(picklist=picklist) results = list(tree.find(search_obj, sig63)) print(results) assert len(results) == 1 # and check that it is the expected one! ss = results[0].signature assert ss.minhash.ksize == 31 assert ss.md5sum().startswith("09a08691c") def test_sbt_as_index_find_picklist_exclude(): # test 'select' method from Index base class with a picklist factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=2) sig47 = load_one_signature(utils.get_test_data("47.fa.sig")) sig63 = load_one_signature(utils.get_test_data("63.fa.sig")) tree.insert(sig47) tree.insert(sig63) # construct a picklist... picklist = SignaturePicklist("md5prefix8", pickstyle=PickStyle.EXCLUDE) picklist.init(["09a08691"]) # run a 'find' with sig63, should find 47 and 63 both. search_obj = make_jaccard_search_query(do_containment=True, threshold=0.0) results = list(tree.find(search_obj, sig63)) print(results) assert len(results) == 2 # now, select on picklist and do another find... tree = tree.select(picklist=picklist) results = list(tree.find(search_obj, sig63)) print(results) assert len(results) == 1 # and check that it is the expected one! ss = results[0].signature assert ss.minhash.ksize == 31 assert ss.md5sum().startswith("38729c637") def test_sbt_as_index_find_picklist_twice(): # test 'select' method from Index base class with a picklist factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=2) sig47 = load_one_signature(utils.get_test_data("47.fa.sig")) sig63 = load_one_signature(utils.get_test_data("63.fa.sig")) tree.insert(sig47) tree.insert(sig63) # construct a picklist... picklist = SignaturePicklist("md5prefix8") picklist.init(["09a08691"]) # run a 'find' with sig63, should find 47 and 63 both. search_obj = make_jaccard_search_query(do_containment=True, threshold=0.0) results = list(tree.find(search_obj, sig63)) print(results) assert len(results) == 2 # now, select twice on picklists... tree = tree.select(picklist=picklist) with pytest.raises(ValueError): tree = tree.select(picklist=picklist) assert "we do not (yet) support multiple picklists for SBT databases" in str( exc ) def test_sbt_as_index_signatures(): # test 'signatures' method from Index base class. factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=2) sig47 = load_one_signature(utils.get_test_data("47.fa.sig")) sig63 = load_one_signature(utils.get_test_data("63.fa.sig")) tree.insert(sig47) tree.insert(sig63) xx = list(tree.signatures()) assert len(xx) == 2 assert sig47 in xx assert sig63 in xx def test_sbt_gather_threshold_1(): # test gather() method, in some detail factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=2) sig2 = load_one_signature(utils.get_test_data("2.fa.sig"), ksize=31) sig47 = load_one_signature(utils.get_test_data("47.fa.sig"), ksize=31) sig63 = load_one_signature(utils.get_test_data("63.fa.sig"), ksize=31) tree.insert(sig47) tree.insert(sig63) tree.insert(sig2) # now construct query signatures with specific numbers of hashes -- # note, these signatures all have scaled=1000. mins = list(sorted(sig2.minhash.hashes.keys())) new_mh = sig2.minhash.copy_and_clear() # query with empty hashes assert not new_mh with pytest.raises(ValueError): tree.best_containment(SourmashSignature(new_mh)) # add one hash new_mh.add_hash(mins.pop()) assert len(new_mh) == 1 result = tree.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig == sig2 assert name is None # check with a threshold -> should be no results. with pytest.raises(ValueError): tree.best_containment(SourmashSignature(new_mh), threshold_bp=5000) # add three more hashes => length of 4 new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) assert len(new_mh) == 4 result = tree.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig == sig2 assert name is None # check with a too-high threshold -> should be no results. print("len mh", len(new_mh)) with pytest.raises(ValueError): tree.best_containment(SourmashSignature(new_mh), threshold_bp=5000) def test_sbt_gather_threshold_5(): # test gather() method above threshold factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=2) sig2 = load_one_signature(utils.get_test_data("2.fa.sig"), ksize=31) sig47 = load_one_signature(utils.get_test_data("47.fa.sig"), ksize=31) sig63 = load_one_signature(utils.get_test_data("63.fa.sig"), ksize=31) tree.insert(sig47) tree.insert(sig63) tree.insert(sig2) # now construct query signatures with specific numbers of hashes -- # note, these signatures all have scaled=1000. mins = list(sorted(sig2.minhash.hashes.keys())) new_mh = sig2.minhash.copy_and_clear() # add five hashes for i in range(5): new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) new_mh.add_hash(mins.pop()) # should get a result with no threshold (any match at all is returned) result = tree.best_containment(SourmashSignature(new_mh)) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig == sig2 assert name is None # now, check with a threshold_bp that should be meet-able. tree.best_containment(SourmashSignature(new_mh), threshold_bp=5000) assert result containment, match_sig, name = result assert containment == 1.0 assert match_sig == sig2 assert name is None @utils.in_tempdir def test_gather_single_return(c): # test gather() number of returns sig2file = utils.get_test_data("2.fa.sig") sig47file = utils.get_test_data("47.fa.sig") sig63file = utils.get_test_data("63.fa.sig") sig2 = load_one_signature(sig2file, ksize=31) sig47 = load_one_signature(sig47file, ksize=31) sig63 = load_one_signature(sig63file, ksize=31) # construct SBT Database factory = GraphFactory(31, 1e5, 4) tree = SBT(factory, d=2) tree.insert(sig2) tree.insert(sig47) tree.insert(sig63) # now, run gather. how many results do we get, and are they in the # right order? result = tree.best_containment(sig63) print(result) assert result assert result.score == 1.0 def test_sbt_jaccard_ordering(runtmp): # this tests a tricky situation where for three sketches A, B, C, # |A intersect B| is greater than |A intersect C| # _but_ # |A jaccard B| is less than |A intersect B| a = sourmash.MinHash(ksize=31, n=0, scaled=2) b = a.copy_and_clear() c = a.copy_and_clear() a.add_many([1, 2, 3, 4]) b.add_many([1, 2, 3] + list(range(10, 30))) c.add_many([1, 5]) def _intersect(x, y): return x.intersection_and_union_size(y)[0] print("a intersect b:", _intersect(a, b)) print("a intersect c:", _intersect(a, c)) print("a jaccard b:", a.jaccard(b)) print("a jaccard c:", a.jaccard(c)) assert _intersect(a, b) > _intersect(a, c) assert a.jaccard(b) < a.jaccard(c) # thresholds to use: assert a.jaccard(b) < 0.15 assert a.jaccard(c) > 0.15 # now - make signatures, try out :) ss_a = sourmash.SourmashSignature(a, name="A") ss_b = sourmash.SourmashSignature(b, name="B") ss_c = sourmash.SourmashSignature(c, name="C") factory = GraphFactory(31, 1e5, 4) db = SBT(factory, d=2) db.insert(ss_a) db.insert(ss_b) db.insert(ss_c) sr = db.search(ss_a, threshold=0.15) print(sr) assert len(sr) == 2 assert sr[0].signature == ss_a assert sr[0].score == 1.0 assert sr[1].signature == ss_c assert sr[1].score == 0.2 def test_sbt_protein_command_index(runtmp): c = runtmp # test command-line creation of SBT database with protein sigs sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/protein/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) db_out = c.output("protein.sbt.zip") c.run_sourmash( "index", db_out, sigfile1, sigfile2, "--scaled", "100", "-k", "19", "--protein" ) # check to make sure .sbt.protein directory doesn't get created assert not os.path.exists(c.output(".sbt.protein")) db2 = load_sbt_index(db_out) sig1 = sourmash.load_one_signature(sigfile1) sig2 = sourmash.load_one_signature(sigfile2) # check reconstruction -- mh_list = [x.minhash for x in db2.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db2.search( sig1, threshold=0.0, ignore_abundance=True, do_containment=False, best_only=False, ) assert len(results) == 2 result = db2.best_containment(sig2) assert result.score == 1.0 assert result.location == db2._location assert result.location == db_out @utils.in_tempdir def test_sbt_protein_search_no_threshold(c): # test the '.search' method on SBTs w/no threshold sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/protein/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) db_out = c.output("protein.sbt.zip") c.run_sourmash( "index", db_out, sigfile1, sigfile2, "--scaled", "100", "-k", "19", "--protein" ) db2 = load_sbt_index(db_out) sig1 = sourmash.load_one_signature(sigfile1) # and search, gather with pytest.raises(TypeError) as exc: db2.search(sig1) assert "'search' requires 'threshold'" in str(exc) @utils.in_thisdir def test_sbt_protein_command_search(c): # test command-line search/gather of SBT database with protein sigs sigfile1 = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/protein.sbt.zip") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out) assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out @utils.in_tempdir def test_sbt_hp_command_index(c): # test command-line creation of SBT database with hp sigs sigfile1 = utils.get_test_data( "prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/hp/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) db_out = c.output("hp.sbt.zip") c.run_sourmash( "index", db_out, sigfile1, sigfile2, "--scaled", "100", "-k", "19", "--hp" ) db2 = load_sbt_index(db_out) sig1 = sourmash.load_one_signature(sigfile1) sig2 = sourmash.load_one_signature(sigfile2) # check reconstruction -- mh_list = [x.minhash for x in db2.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db2.search( sig1, threshold=0.0, ignore_abundance=True, do_containment=False, best_only=False, ) assert results result = db2.best_containment(sig2) assert result.score == 1.0 assert result.location == db2._location assert result.location == db_out @utils.in_thisdir def test_sbt_hp_command_search(c): # test command-line search/gather of SBT database with hp sigs sigfile1 = utils.get_test_data( "prot/hp/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/hp.sbt.zip") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out, "--threshold", "0.0") assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out @utils.in_tempdir def test_sbt_dayhoff_command_index(c): # test command-line creation of SBT database with dayhoff sigs sigfile1 = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) sigfile2 = utils.get_test_data( "prot/dayhoff/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) db_out = c.output("dayhoff.sbt.zip") c.run_sourmash( "index", db_out, sigfile1, sigfile2, "--scaled", "100", "-k", "19", "--dayhoff" ) db2 = load_sbt_index(db_out) sig1 = sourmash.load_one_signature(sigfile1) sig2 = sourmash.load_one_signature(sigfile2) # check reconstruction -- mh_list = [x.minhash for x in db2.signatures()] assert len(mh_list) == 2 assert sig1.minhash in mh_list assert sig2.minhash in mh_list # and search, gather results = db2.search( sig1, threshold=0.0, ignore_abundance=True, do_containment=False, best_only=False, ) assert len(results) == 2 result = db2.best_containment(sig2) assert result.score == 1.0 assert result.location == db2._location assert result.location == db_out @utils.in_thisdir def test_sbt_dayhoff_command_search(c): # test command-line search/gather of SBT database with dayhoff sigs sigfile1 = utils.get_test_data( "prot/dayhoff/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) db_out = utils.get_test_data("prot/dayhoff.sbt.zip") c.run_sourmash("search", sigfile1, db_out, "--threshold", "0.0") assert "2 matches" in c.last_result.out c.run_sourmash("gather", sigfile1, db_out, "--threshold", "0.0") assert "found 1 matches total" in c.last_result.out assert "the recovered matches hit 100.0% of the query" in c.last_result.out def test_sbt_node_cache(): tree = SBT.load( utils.get_test_data("v6.sbt.json"), leaf_loader=SigLeaf.load, cache_size=1 ) testdata1 = utils.get_test_data(utils.SIG_FILES[0]) to_search = load_one_signature(testdata1) # note: earlier versions of this test did containment on # the num MinHash in `to_search`, which doesn't work properly. # (See test_sbt_no_containment_on_num for test). So, to # fix the test for the new search API, we had to adjust # the threshold. search_obj = make_jaccard_search_query(threshold=0.05) results = list(tree.find(search_obj, to_search)) assert len(results) == 4 assert tree._nodescache.currsize == 1 assert tree._nodescache.currsize == 1 def test_sbt_no_containment_on_num(): tree = SBT.load( utils.get_test_data("v6.sbt.json"), leaf_loader=SigLeaf.load, cache_size=1 ) testdata1 = utils.get_test_data(utils.SIG_FILES[0]) to_search = load_one_signature(testdata1) search_obj = make_jaccard_search_query(do_containment=True, threshold=0.05) with pytest.raises(TypeError) as exc: list(tree.find(search_obj, to_search)) assert "this search requires a scaled signature" in str(exc) def test_build_sbt_zip_with_dups(runtmp): dups_data = utils.get_test_data("duplicate-sigs") all_sigs = set(sourmash.load_file_as_signatures(dups_data)) assert len(all_sigs) == 4 runtmp.run_sourmash("index", "dups.sbt.zip", dups_data) outfile = runtmp.output("dups.sbt.zip") sbt_sigs = set(sourmash.load_file_as_signatures(outfile)) assert len(sbt_sigs) == 4 assert all_sigs == sbt_sigs def test_build_sbt_zip_with_dups_exists(runtmp): dups_data = utils.get_test_data("duplicate-sigs") all_sigs = set(sourmash.load_file_as_signatures(dups_data)) assert len(all_sigs) == 4 runtmp.run_sourmash("index", "dups.sbt.zip", dups_data) outfile = runtmp.output("dups.sbt.zip") # run again, to see what happens :) runtmp.run_sourmash("index", "dups.sbt.zip", dups_data) outfile = runtmp.output("dups.sbt.zip") sbt_sigs = set(sourmash.load_file_as_signatures(outfile)) assert len(sbt_sigs) == 4 assert all_sigs == sbt_sigs def test_build_sbt_json_with_dups(runtmp): dups_data = utils.get_test_data("duplicate-sigs") all_sigs = set(sourmash.load_file_as_signatures(dups_data)) assert len(all_sigs) == 4 runtmp.run_sourmash("index", "dups.sbt.json", dups_data) outfile = runtmp.output("dups.sbt.json") sbt_sigs = set(sourmash.load_file_as_signatures(outfile)) assert len(sbt_sigs) == 4 assert all_sigs == sbt_sigs def test_build_sbt_json_with_dups_exists(runtmp): dups_data = utils.get_test_data("duplicate-sigs") all_sigs = set(sourmash.load_file_as_signatures(dups_data)) assert len(all_sigs) == 4 runtmp.run_sourmash("index", "dups.sbt.json", dups_data) outfile = runtmp.output("dups.sbt.json") # run again, see what happens! runtmp.run_sourmash("index", "dups.sbt.json", dups_data) outfile = runtmp.output("dups.sbt.json") sbt_sigs = set(sourmash.load_file_as_signatures(outfile)) assert len(sbt_sigs) == 4 assert all_sigs == sbt_sigs def test_load_fail_on_file_not_dir(runtmp): # make sure the load function raises a ValueError for {filename}/sbt, # rather than a NotADirectoryError filename = runtmp.output("foo") with open(filename, "w") as fp: fp.write("something") with pytest.raises(ValueError): SBT.load(runtmp.output("foo/bar.sbt.json")) sourmash-bio-sourmash-bc3c0f8/tests/test_search.py000066400000000000000000000631021474157046300225100ustar00rootroot00000000000000"Tests for search.py code." import pytest import numpy as np import sourmash_tst_utils as utils from sourmash import search, SourmashSignature, MinHash, load_one_signature from sourmash.search import ( make_jaccard_search_query, make_containment_query, SearchResult, PrefetchResult, GatherResult, ) from sourmash.index import LinearIndex def test_make_jaccard_search_query(): search_obj = make_jaccard_search_query(threshold=0) assert search_obj.score_fn == search_obj.score_jaccard assert not search_obj.require_scaled assert search_obj.threshold == 0 def test_make_jaccard_search_query_cont(): search_obj = make_jaccard_search_query(do_containment=True, threshold=0) assert search_obj.score_fn == search_obj.score_containment assert search_obj.require_scaled assert search_obj.threshold == 0 def test_make_jaccard_search_query_max_cont(): search_obj = make_jaccard_search_query(do_max_containment=True, threshold=0) assert search_obj.score_fn == search_obj.score_max_containment assert search_obj.require_scaled assert search_obj.threshold == 0 def test_make_jaccard_search_query_best_only(): search_obj = make_jaccard_search_query(best_only=True) assert search_obj.score_fn == search_obj.score_jaccard assert not search_obj.require_scaled assert type(search_obj) is search.JaccardSearchBestOnly def test_make_jaccard_search_query_no_threshold_none(): search_obj = make_jaccard_search_query(threshold=None) assert search_obj.score_fn == search_obj.score_jaccard assert not search_obj.require_scaled assert search_obj.threshold == 0 def test_make_jaccard_search_query_cont_and_max_cont(): with pytest.raises(TypeError) as exc: make_jaccard_search_query(do_containment=True, do_max_containment=True) assert ( str(exc.value) == "'do_containment' and 'do_max_containment' cannot both be True" ) def test_cont_requires_scaled(): search_obj = make_jaccard_search_query(do_containment=True) assert search_obj.require_scaled mh = MinHash(n=500, ksize=31) with pytest.raises(TypeError) as exc: search_obj.check_is_compatible(SourmashSignature(mh)) assert str(exc.value) == "this search requires a scaled signature" def test_search_requires_flat(): search_obj = make_jaccard_search_query() mh = MinHash(n=500, ksize=31, track_abundance=True) with pytest.raises(TypeError) as exc: search_obj.check_is_compatible(SourmashSignature(mh)) assert str(exc.value) == "this search cannot be done with an abund signature" def test_score_jaccard_similarity(): search_obj = make_jaccard_search_query() assert search_obj.score_fn(None, 100, None, 200) == 0.5 def test_score_jaccard_containment(): search_obj = make_jaccard_search_query(do_containment=True) assert search_obj.score_fn(100, 50, None, 0) == 0.5 def test_score_jaccard_containment_zero_query_size(): search_obj = make_jaccard_search_query(do_containment=True) assert search_obj.score_fn(0, 100, None, None) == 0 def test_score_jaccard_max_containment_1(): search_obj = make_jaccard_search_query(do_max_containment=True) assert search_obj.score_fn(150, 75, 100, None) == 0.75 def test_score_jaccard_max_containment_2(): search_obj = make_jaccard_search_query(do_max_containment=True) assert search_obj.score_fn(100, 75, 150, None) == 0.75 def test_score_jaccard_max_containment_zero_query_size(): search_obj = make_jaccard_search_query(do_containment=True) assert search_obj.score_fn(0, 100, None, None) == 0 def test_collect(): search_obj = make_jaccard_search_query(threshold=0) search_obj.collect(1.0, None) assert search_obj.threshold == 0 def test_collect_best_only(): search_obj = make_jaccard_search_query(threshold=0, best_only=True) search_obj.collect(1.0, None) assert search_obj.threshold == 1.0 def test_make_containment_query(): # test basic make_containment_query call mh = MinHash(n=0, ksize=31, scaled=1000) for i in range(100): mh.add_hash(i) search_obj = make_containment_query(mh, 5e4) assert search_obj.score_fn == search_obj.score_containment assert search_obj.require_scaled assert search_obj.threshold == 0.5 def test_make_containment_query_no_threshold(): # test basic make_containment_query call mh = MinHash(n=0, ksize=31, scaled=1000) for i in range(100): mh.add_hash(i) search_obj = make_containment_query(mh, None) assert search_obj.score_fn == search_obj.score_containment assert search_obj.require_scaled assert search_obj.threshold == 0 def test_make_containment_query_num_minhash(): # will fail on non-scaled minhash mh = MinHash(n=500, ksize=31) for i in range(100): mh.add_hash(i) with pytest.raises(TypeError) as exc: make_containment_query(mh, 5e4) assert str(exc.value) == "query signature must be calculated with scaled" def test_make_containment_query_empty_minhash(): # will fail on non-scaled minhash mh = MinHash(n=0, ksize=31, scaled=1000) for i in range(100): mh.add_hash(i) with pytest.raises(TypeError) as exc: make_containment_query(mh, -1) assert str(exc.value) == "threshold_bp must be non-negative" def test_make_containment_query_high_threshold(): # will fail on non-scaled minhash mh = MinHash(n=0, ksize=31, scaled=1000) for i in range(100): mh.add_hash(i) # effective threshold > 1; raise ValueError with pytest.raises(ValueError): make_containment_query(mh, 200000) class FakeIndex(LinearIndex): _signatures = [] filename = "something_or_other" def __init__(self, validator_fn): self.validator = validator_fn def find(self, search_fn, query, *args, **kwargs): if self.validator: self.validator(search_fn, query, args, kwargs) else: assert 0, "what are we even doing here?" return [] def test_index_search_passthru(): # check that kwargs are passed through from 'search' to 'find' query = None def validate_kwarg_passthru(search_fn, query, args, kwargs): assert "this_kw_arg" in kwargs assert kwargs["this_kw_arg"] == 5 idx = FakeIndex(validate_kwarg_passthru) idx.search(query, threshold=0.0, this_kw_arg=5) def test_index_containment_passthru(): # check that kwargs are passed through from 'search' to 'find' query = None def validate_kwarg_passthru(search_fn, query, args, kwargs): assert "this_kw_arg" in kwargs assert kwargs["this_kw_arg"] == 5 idx = FakeIndex(validate_kwarg_passthru) idx.search(query, threshold=0.0, this_kw_arg=5) def test_search_with_abund_query(): mh = MinHash(n=0, ksize=31, scaled=1, track_abundance=True) query = SourmashSignature(mh) with pytest.raises(TypeError): search.search_databases_with_abund_query( query, [], threshold=0, do_containment=True ) with pytest.raises(TypeError): search.search_databases_with_abund_query( query, [], threshold=0, do_max_containment=True ) def test_scaledSearchResult(): # check that values get stored/calculated correctly ss47_file = utils.get_test_data("47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") ss4763 = ss4763.to_mutable() ss4763.filename = ss4763_file scaled = ss47.minhash.scaled res = SearchResult( ss47, ss4763, cmp_scaled=scaled, similarity=ss47.contained_by(ss4763) ) assert res.query_name == ss47.name assert res.match_name == ss4763.name assert res.query_scaled == ss47.minhash.scaled == 1000 assert res.match_scaled == ss4763.minhash.scaled == 1000 assert res.cmp_scaled == 1000 assert res.query_abundance == ss47.minhash.track_abundance assert res.match_abundance == ss4763.minhash.track_abundance # assert res.query_bp == len(ss47.minhash) * scaled # assert res.match_bp == len(ss4763.minhash) * scaled assert res.ksize == 31 assert res.moltype == "DNA" assert res.query_filename == "47.fa" assert res.match_filename == ss4763_file assert res.query_md5 == ss47.md5sum() assert res.match_md5 == ss4763.md5sum() # assert res.query_n_hashes == len(ss47.minhash) # assert res.match_n_hashes == len(ss4763.minhash) assert res.md5 == ss4763.md5sum() assert res.name == ss4763.name assert res.filename == ss4763.filename queryc_ani = ss47.containment_ani(ss4763) matchc_ani = ss4763.containment_ani(ss47) # check that we _can_ get avg_containment_ani assert res.cmp.avg_containment_ani == np.mean([queryc_ani.ani, matchc_ani.ani]) def test_numSearchResult(): # check that values get stored/calculated correctly ss47_file = utils.get_test_data("num/47.fa.sig") ss63_file = utils.get_test_data("num/63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss63 = load_one_signature(ss63_file, ksize=31, select_moltype="dna") ss63 = ss63.to_mutable() ss63.filename = ss63_file assert ss47.minhash.num and ss63.minhash.num res = SearchResult(ss47, ss63, similarity=ss47.jaccard(ss63)) print(res.cmp_num) assert res.mh1.num assert res.cmp.cmp_num == 500 assert res.query_name == ss47.name assert res.match_name == ss63.name assert res.query_num == ss47.minhash.num == 500 assert res.match_num == ss63.minhash.num == 500 assert res.query_abundance == ss47.minhash.track_abundance assert res.match_abundance == ss63.minhash.track_abundance assert res.ksize == 31 assert res.moltype == "DNA" assert res.query_filename == "47.fa" assert res.match_filename == ss63_file assert res.query_md5 == ss47.md5sum() assert res.match_md5 == ss63.md5sum() assert res.md5 == ss63.md5sum() assert res.name == ss63.name assert res.filename == ss63.filename # check that we can't get ani with pytest.raises(TypeError) as exc: res.estimate_search_ani() assert ("ANI can only be estimated from scaled signatures.") in str(exc) # get result as dictionary (of just items to write) resD = res.resultdict assert resD["filename"] == res.filename assert resD["name"] == res.name assert resD["similarity"] == res.similarity def test_SearchResult_incompatible_sigs(): ss47_file = utils.get_test_data("num/47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") with pytest.raises(TypeError) as exc: SearchResult(ss47, ss4763, similarity=10) print(str(exc)) assert "Error: Both sketches must be 'num' or 'scaled'." in str(exc) def test_SearchResult_notsigs(): ss47_file = utils.get_test_data("num/47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") with pytest.raises(AttributeError) as exc: SearchResult(ss47_file, ss4763_file, similarity=10) print(str(exc)) assert "'str' object has no attribute 'minhash'" in str(exc) def test_SearchResult_no_similarity(): # check that values get stored/calculated correctly ss47_file = utils.get_test_data("47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") with pytest.raises(ValueError) as exc: SearchResult(ss47, ss4763) print(str(exc)) assert "Error: Must provide 'similarity' for SearchResult." in str(exc) def test_PrefetchResult(): # check that values get stored/calculated correctly ss47_file = utils.get_test_data("47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") ss4763 = ss4763.to_mutable() ss4763.filename = ss4763_file scaled = ss47.minhash.scaled intersect_mh = ss47.minhash.intersection(ss4763.minhash) intersect_bp = len(intersect_mh) * scaled jaccard = ss4763.jaccard(ss47) max_containment = ss4763.max_containment(ss47) f_match_query = ss47.contained_by(ss4763) f_query_match = ss4763.contained_by(ss47) queryc_ani = ss47.containment_ani(ss4763) matchc_ani = ss4763.containment_ani(ss47) res = PrefetchResult(ss47, ss4763, cmp_scaled=scaled) assert res.query_name == ss47.name assert res.match_name == ss4763.name assert res.query_scaled == ss47.minhash.scaled == 1000 assert res.match_scaled == ss4763.minhash.scaled == 1000 assert res.cmp_scaled == 1000 assert res.query_abundance == ss47.minhash.track_abundance assert res.match_abundance == ss4763.minhash.track_abundance assert res.query_bp == len(ss47.minhash) * scaled assert res.match_bp == len(ss4763.minhash) * scaled assert res.ksize == 31 assert res.moltype == "DNA" assert res.query_filename == "47.fa" assert res.match_filename == ss4763_file assert res.query_md5 == ss47.md5sum() assert res.match_md5 == ss4763.md5sum() assert res.query_n_hashes == len(ss47.minhash) assert res.match_n_hashes == len(ss4763.minhash) assert res.md5 == ss4763.md5sum() assert res.name == ss4763.name assert res.intersect_bp == intersect_bp assert res.jaccard == jaccard assert res.max_containment == max_containment assert res.f_query_match == f_query_match assert res.f_match_query == f_match_query # check ani assert res.query_containment_ani == queryc_ani.ani print(queryc_ani.ani) print(matchc_ani.ani) assert res.match_containment_ani == matchc_ani.ani assert res.max_containment_ani == max(queryc_ani.ani, matchc_ani.ani) assert res.average_containment_ani == np.mean([queryc_ani.ani, matchc_ani.ani]) assert res.potential_false_negative == False def test_PrefetchResult_incompatible_sigs(): ss47_file = utils.get_test_data("num/47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") with pytest.raises(TypeError) as exc: PrefetchResult(ss47, ss4763) print(str(exc)) assert ( "Error: prefetch and gather results must be between scaled signatures." in str(exc) ) def test_GatherResult(): # check that values get stored/calculated correctly ss47_file = utils.get_test_data("track_abund/47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") ss4763 = ss4763.to_mutable() ss4763.filename = ss4763_file scaled = ss47.minhash.scaled intersect_mh = ss47.minhash.flatten().intersection(ss4763.minhash) remaining_mh = ss4763.minhash.to_mutable() remaining_mh.remove_many(intersect_mh) intersect_bp = len(intersect_mh) * scaled max_containment = ss4763.max_containment(ss47) ss47.contained_by(ss4763) orig_query_abunds = ss47.minhash.hashes queryc_ani = ss47.containment_ani(ss4763) matchc_ani = ss4763.containment_ani(ss47) # make some fake vals to check gather_result_rank = 1 sum_abunds = 1000 res = GatherResult( ss47, ss4763, cmp_scaled=scaled, gather_querymh=remaining_mh, gather_result_rank=gather_result_rank, total_weighted_hashes=sum_abunds, orig_query_len=len(ss47.minhash), orig_query_abunds=orig_query_abunds, ) assert res.query_name == ss47.name assert res.match_name == ss4763.name assert res.query_scaled == ss47.minhash.scaled == 1000 assert res.match_scaled == ss4763.minhash.scaled == 1000 assert res.cmp_scaled == 1000 assert res.query_abundance == ss47.minhash.track_abundance assert res.match_abundance == ss4763.minhash.track_abundance assert res.query_bp == len(ss47.minhash) * scaled assert res.match_bp == len(ss4763.minhash) * scaled assert res.ksize == 31 assert res.moltype == "DNA" assert res.query_filename == "podar-ref/47.fa" assert res.match_filename == ss4763_file assert res.query_md5 == ss47.md5sum() assert res.match_md5 == ss4763.md5sum() assert res.query_n_hashes == len(ss47.minhash) assert res.match_n_hashes == len(ss4763.minhash) assert res.query_bp == ss47.minhash.unique_dataset_hashes assert res.match_bp == ss4763.minhash.unique_dataset_hashes assert res.md5 == ss4763.md5sum() assert res.name == ss4763.name assert res.match_filename == ss4763.filename assert res.intersect_bp == intersect_bp assert res.max_containment == max_containment # check that we can write prefetch result directly from gather pf = PrefetchResult(ss47, ss4763, cmp_scaled=scaled) assert pf.prefetchresultdict == res.prefetchresultdict # check ani assert res.query_containment_ani == queryc_ani.ani print(queryc_ani.ani) print(matchc_ani.ani) assert res.match_containment_ani == matchc_ani.ani assert res.max_containment_ani == max(queryc_ani.ani, matchc_ani.ani) assert res.average_containment_ani == np.mean([queryc_ani.ani, matchc_ani.ani]) assert res.potential_false_negative == False # get write dict version of GatherResult resD = res.gatherresultdict assert resD["intersect_bp"] == res.intersect_bp def test_GatherResult_ci(): # check that values get stored/calculated correctly ss47_file = utils.get_test_data("track_abund/47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") ss4763 = ss4763.to_mutable() ss4763.filename = ss4763_file scaled = ss47.minhash.scaled intersect_mh = ss47.minhash.flatten().intersection(ss4763.minhash) remaining_mh = ss4763.minhash.to_mutable() remaining_mh.remove_many(intersect_mh) orig_query_abunds = ss47.minhash.hashes queryc_ani = ss47.containment_ani(ss4763, estimate_ci=True) matchc_ani = ss4763.containment_ani(ss47, estimate_ci=True) # make some fake vals to check gather_result_rank = 1 sum_abunds = 1000 res = GatherResult( ss47, ss4763, cmp_scaled=scaled, gather_querymh=remaining_mh, gather_result_rank=gather_result_rank, total_weighted_hashes=sum_abunds, orig_query_len=len(ss47.minhash), orig_query_abunds=orig_query_abunds, estimate_ani_ci=True, ) # check that we can write prefetch result directly from gather pf = PrefetchResult(ss47, ss4763, cmp_scaled=scaled, estimate_ani_ci=True) assert pf.prefetchresultdict == res.prefetchresultdict # check ani assert res.query_containment_ani == queryc_ani.ani print(queryc_ani.ani) print(matchc_ani.ani) assert res.match_containment_ani == matchc_ani.ani assert res.match_containment_ani_low == matchc_ani.ani_low assert res.match_containment_ani_high == matchc_ani.ani_high assert res.max_containment_ani == max(queryc_ani.ani, matchc_ani.ani) assert res.average_containment_ani == np.mean([queryc_ani.ani, matchc_ani.ani]) assert res.potential_false_negative == False # get write dict version of GatherResult resD = res.gatherresultdict assert resD["intersect_bp"] == res.intersect_bp assert resD["match_containment_ani_low"] == res.match_containment_ani_low def test_GatherResult_incompatible_sigs(): ss47_file = utils.get_test_data("num/47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") orig_query_abunds = ss47.minhash.hashes with pytest.raises(TypeError) as exc: GatherResult( ss47, ss4763, cmp_scaled=1, gather_querymh=ss47.minhash, gather_result_rank=1, total_weighted_hashes=1, orig_query_len=len(ss47.minhash), orig_query_abunds=orig_query_abunds, ) print(str(exc)) assert ( "Error: prefetch and gather results must be between scaled signatures." in str(exc) ) def test_GatherResult_incomplete_input_cmpscaled(): ss47_file = utils.get_test_data("47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") orig_query_abunds = ss47.minhash.hashes with pytest.raises(ValueError) as exc: GatherResult( ss47, ss4763, cmp_scaled=None, gather_querymh=ss47.minhash, gather_result_rank=1, total_weighted_hashes=1, orig_query_len=len(ss47.minhash), orig_query_abunds=orig_query_abunds, ) print(str(exc)) assert ( "Error: must provide comparison scaled value ('cmp_scaled') for GatherResult" in str(exc) ) def test_GatherResult_incomplete_input_gathermh(): ss47_file = utils.get_test_data("47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") orig_query_abunds = ss47.minhash.hashes with pytest.raises(ValueError) as exc: GatherResult( ss47, ss4763, cmp_scaled=1000, gather_querymh=None, gather_result_rank=1, total_weighted_hashes=1, orig_query_len=len(ss47.minhash), orig_query_abunds=orig_query_abunds, ) print(str(exc)) assert ( "Error: must provide current gather sketch (remaining hashes) for GatherResult" in str(exc) ) def test_GatherResult_incomplete_input_gather_result_rank(): ss47_file = utils.get_test_data("47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") orig_query_abunds = ss47.minhash.hashes with pytest.raises(ValueError) as exc: GatherResult( ss47, ss4763, cmp_scaled=1000, gather_querymh=ss47.minhash, gather_result_rank=None, total_weighted_hashes=1, orig_query_len=len(ss47.minhash), orig_query_abunds=orig_query_abunds, ) print(str(exc)) assert "Error: must provide 'gather_result_rank' to GatherResult" in str(exc) def test_GatherResult_incomplete_input_total_weighted_hashes(): ss47_file = utils.get_test_data("47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") orig_query_abunds = ss47.minhash.hashes with pytest.raises(ValueError) as exc: GatherResult( ss47, ss4763, cmp_scaled=1000, gather_querymh=ss47.minhash, gather_result_rank=1, total_weighted_hashes=None, orig_query_len=len(ss47.minhash), orig_query_abunds=orig_query_abunds, ) print(str(exc)) assert ( "Error: must provide sum of all abundances ('total_weighted_hashes') to GatherResult" in str(exc) ) with pytest.raises(ValueError) as exc: GatherResult( ss47, ss4763, cmp_scaled=1000, gather_querymh=ss47.minhash, gather_result_rank=1, total_weighted_hashes=0, orig_query_len=len(ss47.minhash), orig_query_abunds=orig_query_abunds, ) print(str(exc)) assert ( "Error: must provide sum of all abundances ('total_weighted_hashes') to GatherResult" in str(exc) ) def test_GatherResult_incomplete_input_orig_query_abunds(): ss47_file = utils.get_test_data("47.fa.sig") ss4763_file = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(ss47_file, ksize=31, select_moltype="dna") ss4763 = load_one_signature(ss4763_file, ksize=31, select_moltype="dna") orig_query_abunds = None with pytest.raises(ValueError) as exc: GatherResult( ss47, ss4763, cmp_scaled=1000, gather_querymh=ss47.minhash, gather_result_rank=1, total_weighted_hashes=1, orig_query_len=len(ss47.minhash), orig_query_abunds=orig_query_abunds, ) print(str(exc)) assert ( "Error: must provide original query abundances ('orig_query_abunds') to GatherResult" in str(exc) ) orig_query_abunds = {} with pytest.raises(ValueError) as exc: GatherResult( ss47, ss4763, cmp_scaled=1000, gather_querymh=ss47.minhash, gather_result_rank=1, total_weighted_hashes=1, orig_query_len=len(ss47.minhash), orig_query_abunds=orig_query_abunds, ) print(str(exc)) assert ( "Error: must provide original query abundances ('orig_query_abunds') to GatherResult" in str(exc) ) sourmash-bio-sourmash-bc3c0f8/tests/test_signature.py000066400000000000000000000510341474157046300232450ustar00rootroot00000000000000import os import pytest import sourmash from sourmash.signature import ( SourmashSignature, FrozenSourmashSignature, ) from sourmash.signature import ( save_signatures_to_json, load_signatures_from_json, load_one_signature_from_json, ) import sourmash_tst_utils as utils from sourmash.minhash import MinHash, FrozenMinHash from sourmash_tst_utils import SourmashCommandFailed def test_minhash_copy(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) SourmashSignature(e, name="foo") f = e.copy() assert e == f def test_sig_copy(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig1 = SourmashSignature(e, name="foo") sig2 = sig1.copy() assert sig1 == sig2 def test_sig_copy_frozen(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig1 = SourmashSignature(e, name="foo") sig2 = sig1.copy() assert sig1 == sig2 with pytest.raises(TypeError) as e: sig2.minhash.add_hash(5) assert "FrozenMinHash does not support modification" in str(e.value) def test_sig_copy_frozen_mutable(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig1 = SourmashSignature(e, name="foo") sig1.minhash = sig1.minhash.to_mutable() sig2 = sig1.copy() assert sig1 == sig2 with pytest.raises(TypeError) as e: sig2.minhash.add_hash(5) assert "FrozenMinHash does not support modification" in str(e.value) def test_compare(track_abundance): # same content, same name -> equal e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) SourmashSignature(e, name="foo") f = MinHash(n=1, ksize=20, track_abundance=track_abundance) f.add_kmer("AT" * 10) SourmashSignature(f, name="foo") assert e == f def test_compare_ne(track_abundance): # same content, different names -> different e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig1 = SourmashSignature(e, name="foo") f = MinHash(n=1, ksize=20, track_abundance=track_abundance) f.add_kmer("AT" * 10) sig2 = SourmashSignature(f, name="bar") assert sig1 != sig2 def test_compare_ne2(track_abundance): # same content, different filename -> different e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig1 = SourmashSignature(e, name="foo", filename="a") f = MinHash(n=1, ksize=20, track_abundance=track_abundance) f.add_kmer("AT" * 10) sig2 = SourmashSignature(f, name="foo", filename="b") assert sig1 != sig2 assert sig2 != sig1 def test_compare_ne2_reverse(track_abundance): # same content, one has filename, other does not -> different e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig1 = SourmashSignature(e, name="foo") f = MinHash(n=1, ksize=20, track_abundance=track_abundance) f.add_kmer("AT" * 10) sig2 = SourmashSignature(f, filename="b") assert sig2 != sig1 assert sig1 != sig2 def test_hashable(track_abundance): # check: can we use signatures as keys in dictionaries and sets? e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig = SourmashSignature(e) x = set() x.add(sig) def test_str(track_abundance): # signatures should be printable e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig = SourmashSignature(e) print(sig) assert repr(sig) == "SourmashSignature('', 59502a74)" sig._name = "fizbar" assert repr(sig) == "SourmashSignature('fizbar', 59502a74)" def test_roundtrip(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig = SourmashSignature(e) s = save_signatures_to_json([sig]) siglist = list(load_signatures_from_json(s)) sig2 = siglist[0] assert sig.similarity(sig2) == 1.0 assert sig2.similarity(sig) == 1.0 assert isinstance(sig, SourmashSignature) assert not isinstance(sig, FrozenSourmashSignature) assert isinstance(sig2, FrozenSourmashSignature) assert isinstance(e, MinHash) assert isinstance(sig.minhash, FrozenMinHash) assert isinstance(sig2.minhash, FrozenMinHash) def test_roundtrip_mutable_frozen(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig = SourmashSignature(e) assert isinstance(sig.minhash, FrozenMinHash) sig.minhash = sig.minhash.to_mutable() sig2 = sig.to_frozen() assert isinstance(sig2.minhash, FrozenMinHash) def test_load_signature_ksize_nonint(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) sig = SourmashSignature(e) s = save_signatures_to_json([sig]) siglist = list(load_signatures_from_json(s, ksize="20")) sig2 = siglist[0] assert sig.similarity(sig2) == 1.0 assert sig2.similarity(sig) == 1.0 def test_roundtrip_empty(track_abundance): # edge case, but: empty minhash? :) e = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig = SourmashSignature(e) s = save_signatures_to_json([sig]) siglist = list(load_signatures_from_json(s)) sig2 = siglist[0] assert sig.similarity(sig2) == 0 assert sig2.similarity(sig) == 0 def test_roundtrip_scaled(track_abundance): e = MinHash(n=0, ksize=20, track_abundance=track_abundance, max_hash=2**61) e.add_hash(5) sig = SourmashSignature(e) s = save_signatures_to_json([sig]) siglist = list(load_signatures_from_json(s)) sig2 = siglist[0] e2 = sig2.minhash assert e.scaled == e2.scaled assert sig.similarity(sig2) == 1.0 assert sig2.similarity(sig) == 1.0 def test_roundtrip_seed(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance, seed=10) e.add_hash(5) sig = SourmashSignature(e) s = save_signatures_to_json([sig]) siglist = list(load_signatures_from_json(s)) sig2 = siglist[0] e2 = sig2.minhash assert e.seed == e2.seed assert sig.similarity(sig2) == 1.0 assert sig2.similarity(sig) == 1.0 def test_similarity_downsample(track_abundance): e = MinHash(n=0, ksize=20, track_abundance=track_abundance, max_hash=2**63) f = MinHash(n=0, ksize=20, track_abundance=track_abundance, max_hash=2**61) e.add_hash(1) e.add_hash(2**62) assert len(e.hashes) == 2 f.add_hash(1) f.add_hash(2**62) # should be discarded due to max_hash assert len(f.hashes) == 1 ee = SourmashSignature(e) ff = SourmashSignature(f) with pytest.raises(ValueError) as e: # mismatch in max_hash ee.similarity(ff) assert "mismatch in scaled; comparison fail" in str(e.value) x = ee.similarity(ff, downsample=True) assert round(x, 1) == 1.0 def test_add_sequence_bad_dna(track_abundance): # test add_sequence behavior on bad DNA mh = MinHash(n=1, ksize=21) sig = SourmashSignature(mh) with pytest.raises(ValueError) as e: sig.add_sequence("N" * 21, force=False) assert "invalid DNA character in input k-mer: NNNNNNNNNNNNNNNNNNNNN" in str(e.value) def test_md5(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_hash(5) sig = SourmashSignature(e) assert sig.md5sum() == "eae27d77ca20db309e056e3d2dcd7d69", sig.md5sum() def test_str_1(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig = SourmashSignature(e, name="foo") assert str(sig) == "foo" def test_str_2(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig = SourmashSignature(e, filename="foo.txt") assert str(sig) == "foo.txt" def test_str_3(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig = SourmashSignature(e, name="foo", filename="foo.txt") assert str(sig) == "foo" def test_name_4(track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig = SourmashSignature(e) assert str(sig) == sig.md5sum()[:8] def test_save_load_multisig(track_abundance): e1 = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig1 = SourmashSignature(e1) e2 = MinHash(n=1, ksize=25, track_abundance=track_abundance) sig2 = SourmashSignature(e2) x = save_signatures_to_json([sig1, sig2]) y = list(load_signatures_from_json(x)) print(x) assert len(y) == 2 assert sig1 in y # order not guaranteed, note. assert sig2 in y assert sig1 != sig2 def test_load_one_fail_nosig(track_abundance): x = save_signatures_to_json([]) print((x,)) with pytest.raises(ValueError): load_one_signature_from_json(x) def test_load_one_succeed(track_abundance): e1 = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig1 = SourmashSignature(e1) x = save_signatures_to_json([sig1]) y = load_one_signature_from_json(x) assert sig1 == y def test_load_one_fail_multisig(track_abundance): e1 = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig1 = SourmashSignature(e1) e2 = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig2 = SourmashSignature(e2) x = save_signatures_to_json([sig1, sig2]) with pytest.raises(ValueError): load_one_signature_from_json(x) def test_save_minified(track_abundance): e1 = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig1 = SourmashSignature(e1, name="foo") e2 = MinHash(n=1, ksize=25, track_abundance=track_abundance) sig2 = SourmashSignature(e2, name="bar baz") x = save_signatures_to_json([sig1, sig2]) assert b"\n" not in x assert len(x.split(b"\n")) == 1 y = list(load_signatures_from_json(x)) assert len(y) == 2 assert any(sig.name == "foo" for sig in y) assert any(sig.name == "bar baz" for sig in y) def test_load_minified(track_abundance): sigfile = utils.get_test_data("genome-s10+s11.sig") sigs = load_signatures_from_json(sigfile) minified = save_signatures_to_json(sigs) with open(sigfile) as f: orig_file = f.read() assert len(minified) < len(orig_file) assert b"\n" not in minified def test_load_compressed(track_abundance): e1 = MinHash(n=1, ksize=20, track_abundance=track_abundance) sig1 = SourmashSignature(e1) x = save_signatures_to_json([sig1], compression=5) y = load_one_signature_from_json(x) assert sig1 == y sigfile = utils.get_test_data("genome-s10+s11.sig.gz") load_signatures_from_json(sigfile) def test_binary_fp(tmpdir, track_abundance): e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) path = tmpdir.join("1.sig") with open(str(path), "wb") as fp: sig = SourmashSignature(e) save_signatures_to_json([sig], fp) def test_load_signatures_from_json_no_file_do_raise(tmpdir): path = tmpdir.join("dne.sig") siglist = load_signatures_from_json(path, do_raise=True) with pytest.raises(Exception): list(siglist) def test_load_signatures_from_json_no_file_do_not_raise(tmpdir): path = tmpdir.join("dne.sig") siglist = load_signatures_from_json(path) siglist = list(siglist) assert not siglist def test_max_containment(): mh1 = MinHash(0, 21, scaled=1, track_abundance=False) mh2 = MinHash(0, 21, scaled=1, track_abundance=False) mh1.add_many((1, 2, 3, 4)) mh2.add_many((1, 5)) ss1 = SourmashSignature(mh1) ss2 = SourmashSignature(mh2) assert ss1.contained_by(ss2) == 1 / 4 assert ss2.contained_by(ss1) == 1 / 2 assert ss1.max_containment(ss2) == 1 / 2 assert ss2.max_containment(ss1) == 1 / 2 def test_max_containment_empty(): mh1 = MinHash(0, 21, scaled=1, track_abundance=False) mh2 = MinHash(0, 21, scaled=1, track_abundance=False) mh1.add_many((1, 2, 3, 4)) ss1 = SourmashSignature(mh1) ss2 = SourmashSignature(mh2) assert ss1.contained_by(ss2) == 0 assert ss2.contained_by(ss1) == 0 assert ss1.max_containment(ss2) == 0 assert ss2.max_containment(ss1) == 0 def test_max_containment_equal(): mh1 = MinHash(0, 21, scaled=1, track_abundance=False) mh2 = MinHash(0, 21, scaled=1, track_abundance=False) mh1.add_many((1, 2, 3, 4)) mh2.add_many((1, 2, 3, 4)) ss1 = SourmashSignature(mh1) ss2 = SourmashSignature(mh2) assert ss1.contained_by(ss2) == 1 assert ss2.contained_by(ss1) == 1 assert ss1.max_containment(ss2) == 1 assert ss2.max_containment(ss1) == 1 def test_containment_ANI(): f1 = utils.get_test_data("2.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") ss1 = sourmash.load_one_signature_from_json(f1, ksize=31) ss2 = sourmash.load_one_signature_from_json(f2, ksize=31) s1_cont_s2 = ss1.containment_ani(ss2, estimate_ci=True) s2_cont_s1 = ss2.containment_ani(ss1, estimate_ci=True) print("\nss1 contained by ss2", s1_cont_s2) print("ss2 contained by ss1", s2_cont_s1) assert (round(s1_cont_s2.ani, 3), s1_cont_s2.ani_low, s1_cont_s2.ani_high) == ( 1.0, 1.0, 1.0, ) assert ( round(s2_cont_s1.ani, 3), round(s2_cont_s1.ani_low, 3), round(s2_cont_s1.ani_high, 3), ) == (0.966, 0.965, 0.967) s1_mc_s2 = ss1.max_containment_ani(ss2, estimate_ci=True) s2_mc_s1 = ss2.max_containment_ani(ss1, estimate_ci=True) print("mh1 max containment", s1_mc_s2) print("mh2 max containment", s2_mc_s1) s1_mc_s2.size_is_inaccurate = False s2_mc_s1.size_is_inaccurate = False assert s1_mc_s2 == s2_mc_s1 assert ( round(s1_mc_s2.ani, 3), round(s1_mc_s2.ani_low, 3), round(s1_mc_s2.ani_high, 3), ) == (1.0, 1.0, 1.0) def test_containment_ANI_precalc_containment(): f1 = utils.get_test_data("47+63.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") ss1 = sourmash.load_one_signature_from_json(f1, ksize=31) ss2 = sourmash.load_one_signature_from_json(f2, ksize=31) # precalc containments and assert same results s1c = ss1.contained_by(ss2) s2c = ss2.contained_by(ss1) mc = max(s1c, s2c) assert ss1.containment_ani(ss2, estimate_ci=True) == ss1.containment_ani( ss2, containment=s1c, estimate_ci=True ) assert ss2.containment_ani(ss1) == ss2.containment_ani(ss1, containment=s2c) assert ss1.max_containment_ani(ss2) == ss2.max_containment_ani(ss1) assert ss1.max_containment_ani(ss2) == ss1.max_containment_ani( ss2, max_containment=mc ) assert ss1.max_containment_ani(ss2) == ss2.max_containment_ani( ss1, max_containment=mc ) def test_avg_containment(): f1 = utils.get_test_data("47+63.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") ss1 = sourmash.load_one_signature_from_json(f1, ksize=31) ss2 = sourmash.load_one_signature_from_json(f2, ksize=31) # check average_containment_ani ac_s1 = ss1.avg_containment(ss2) ac_s2 = ss2.avg_containment(ss1) assert ( ac_s1 == ac_s2 == (ss1.contained_by(ss2) + ss2.contained_by(ss1)) / 2 == 0.6619979467456603 ) def test_avg_containment_ani(): f1 = utils.get_test_data("47+63.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") ss1 = sourmash.load_one_signature_from_json(f1, ksize=31) ss2 = sourmash.load_one_signature_from_json(f2, ksize=31) # check average_containment_ani ac_s1 = ss1.avg_containment_ani(ss2) ac_s2 = ss2.avg_containment_ani(ss1) assert ( ac_s1 == ac_s2 == (ss1.containment_ani(ss2).ani + ss2.containment_ani(ss1).ani) / 2 ) def test_containment_ANI_downsample(): f2 = utils.get_test_data("2+63.fa.sig") f3 = utils.get_test_data("47+63.fa.sig") ss2 = sourmash.load_one_signature_from_json(f2, ksize=31) ss3 = sourmash.load_one_signature_from_json(f3, ksize=31) # check that downsampling works properly print(ss2.minhash.scaled) ss2 = ss2.to_mutable() ss2.minhash = ss2.minhash.downsample(scaled=2000) assert ss2.minhash.scaled != ss3.minhash.scaled ds_s3c = ss2.containment_ani(ss3, downsample=True) ds_s4c = ss3.containment_ani(ss2, downsample=True) mc_w_ds_1 = ss2.max_containment_ani(ss3, downsample=True) mc_w_ds_2 = ss3.max_containment_ani(ss2, downsample=True) with pytest.raises(ValueError) as e: ss2.containment_ani(ss3) assert "ValueError: mismatch in scaled; comparison fail" in e with pytest.raises(ValueError) as e: ss2.max_containment_ani(ss3) assert "ValueError: mismatch in scaled; comparison fail" in e ss3 = ss3.to_mutable() ss3.minhash = ss3.minhash.downsample(scaled=2000) assert ss2.minhash.scaled == ss3.minhash.scaled ds_s3c_manual = ss2.containment_ani(ss3) ds_s4c_manual = ss3.containment_ani(ss2) ds_mc_manual = ss2.max_containment_ani(ss3) assert ds_s3c == ds_s3c_manual assert ds_s4c == ds_s4c_manual assert mc_w_ds_1 == mc_w_ds_2 == ds_mc_manual def test_jaccard_ANI(): f1 = utils.get_test_data("2.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") ss1 = sourmash.load_one_signature_from_json(f1, ksize=31) ss2 = sourmash.load_one_signature_from_json(f2) print("\nJACCARD_ANI", ss1.jaccard_ani(ss2)) s1_jani_s2 = ss1.jaccard_ani(ss2) s2_jani_s1 = ss2.jaccard_ani(ss1) assert s1_jani_s2 == s2_jani_s1 assert ( s1_jani_s2.ani, s1_jani_s2.p_nothing_in_common, s1_jani_s2.jaccard_error, ) == (0.9783711630110239, 0.0, 3.891666770716877e-07) def test_jaccard_ANI_untrustworthy(): f1 = utils.get_test_data("2.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") ss1 = sourmash.load_one_signature_from_json(f1, ksize=31) ss2 = sourmash.load_one_signature_from_json(f2) print("\nJACCARD_ANI", ss1.jaccard_ani(ss2)) s1_jani_s2 = ss1.jaccard_ani(ss2, err_threshold=1e-7) # since size is inaccurate on 2.fa.sig, need to override to be able to get ani s1_jani_s2.size_is_inaccurate = False assert s1_jani_s2.ani is None assert s1_jani_s2.je_exceeds_threshold == True assert s1_jani_s2.je_threshold == 1e-7 def test_jaccard_ANI_precalc_jaccard(): f1 = utils.get_test_data("47+63.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") ss1 = sourmash.load_one_signature_from_json(f1, ksize=31) ss2 = sourmash.load_one_signature_from_json(f2) # precalc jaccard and assert same result jaccard = ss1.jaccard(ss2) print("\nJACCARD_ANI", ss1.jaccard_ani(ss2, jaccard=jaccard)) assert ( ss1.jaccard_ani(ss2) == ss1.jaccard_ani(ss2, jaccard=jaccard) == ss2.jaccard_ani(ss1, jaccard=jaccard) ) wrong_jaccard = jaccard - 0.1 assert ss1.jaccard_ani(ss2) != ss1.jaccard_ani(ss2, jaccard=wrong_jaccard) def test_jaccard_ANI_downsample(): f1 = utils.get_test_data("47+63.fa.sig") f2 = utils.get_test_data("2+63.fa.sig") ss1 = sourmash.load_one_signature_from_json(f1, ksize=31) ss2 = sourmash.load_one_signature_from_json(f2) print(ss1.minhash.scaled) ss1 = ss1.to_mutable() ss1.minhash = ss1.minhash.downsample(scaled=2000) assert ss1.minhash.scaled != ss2.minhash.scaled with pytest.raises(ValueError) as e: ss1.jaccard_ani(ss2) assert "ValueError: mismatch in scaled; comparison fail" in e ds_s1c = ss1.jaccard_ani(ss2, downsample=True) ds_s2c = ss2.jaccard_ani(ss1, downsample=True) ss2 = ss2.to_mutable() ss2.minhash = ss2.minhash.downsample(scaled=2000) assert ss1.minhash.scaled == ss2.minhash.scaled ds_j_manual = ss1.jaccard_ani(ss2) assert ds_s1c == ds_s2c == ds_j_manual def test_frozen_signature_update_1(track_abundance): # setting .name should fail on a FrozenSourmashSignature e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) ss = SourmashSignature(e, name="foo").to_frozen() with pytest.raises(ValueError): ss.name = "foo2" def test_frozen_signature_update_2(track_abundance): # setting .minhash should fail on a FrozenSourmashSignature e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) e2 = e.copy_and_clear() ss = SourmashSignature(e, name="foo").to_frozen() with pytest.raises(ValueError): ss.minhash = e2 def test_frozen_signature_update_3(track_abundance): # setting .minhash should succeed with update() context manager e = MinHash(n=1, ksize=20, track_abundance=track_abundance) e.add_kmer("AT" * 10) ss = SourmashSignature(e, name="foo").to_frozen() with ss.update() as ss2: ss2.name = "foo2" assert ss2.name == "foo2" sourmash-bio-sourmash-bc3c0f8/tests/test_sketchcomparison.py000066400000000000000000001153001474157046300246150ustar00rootroot00000000000000""" Tests for the 'SketchComparison' classes. """ import numpy as np import pytest from sourmash import load_one_signature from sourmash.minhash import MinHash from sourmash.sketchcomparison import FracMinHashComparison, NumMinHashComparison import sourmash_tst_utils as utils # can we parameterize scaled too (so don't need separate downsample tests?) def test_FracMinHashComparison(track_abundance): # build FracMinHash Comparison and check values a = MinHash(0, 21, scaled=1, track_abundance=track_abundance) b = MinHash(0, 21, scaled=1, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) # build FracMinHashComparison cmp = FracMinHashComparison(a, b) assert cmp.mh1 == a assert cmp.mh2 == b assert cmp.ignore_abundance == False assert cmp.cmp_scaled == 1 assert cmp.ksize == 21 assert cmp.moltype == "DNA" assert cmp.mh1_containment_in_mh2 == a.contained_by(b) assert cmp.mh2_containment_in_mh1 == b.contained_by(a) assert cmp.avg_containment == a.avg_containment(b) assert cmp.max_containment == a.max_containment(b) assert cmp.jaccard == a.jaccard(b) == b.jaccard(a) intersect_mh = a.flatten().intersection(b.flatten()) assert cmp.intersect_mh == intersect_mh == b.flatten().intersection(a.flatten()) assert cmp.total_unique_intersect_hashes == 4 assert cmp.pass_threshold # default threshold is 0; this should pass if track_abundance: assert ( cmp.angular_similarity == a.angular_similarity(b) == b.angular_similarity(a) ) assert ( cmp.cosine_similarity == a.angular_similarity(b) == b.angular_similarity(a) ) assert ( cmp.weighted_intersection(from_mh=cmp.mh1).hashes == intersect_mh.inflate(a).hashes ) assert ( cmp.weighted_intersection(from_mh=cmp.mh2).hashes == intersect_mh.inflate(b).hashes ) assert ( cmp.weighted_intersection(from_abundD=a_values).hashes == intersect_mh.inflate(a).hashes ) assert ( cmp.weighted_intersection(from_abundD=b_values).hashes == intersect_mh.inflate(b).hashes ) else: with pytest.raises(TypeError) as exc: cmp.angular_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) with pytest.raises(TypeError) as exc: cmp.cosine_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) assert cmp.weighted_intersection(from_mh=cmp.mh1).hashes == intersect_mh.hashes assert cmp.weighted_intersection(from_mh=cmp.mh2).hashes == intersect_mh.hashes def test_FracMinHashComparison_downsample(track_abundance): # build FracMinHash Comparison and check values a = MinHash(0, 21, scaled=1, track_abundance=track_abundance) b = MinHash(0, 21, scaled=1, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) cmp_scaled = 2 ds_a = a.downsample(scaled=cmp_scaled) ds_b = b.downsample(scaled=cmp_scaled) # build FracMinHashComparison cmp = FracMinHashComparison(a, b, cmp_scaled=cmp_scaled) assert cmp.mh1 == a assert cmp.mh2 == b assert cmp.mh1_cmp == ds_a assert cmp.mh2_cmp == ds_b assert cmp.ignore_abundance == False assert cmp.cmp_scaled == cmp_scaled assert cmp.ksize == 21 assert cmp.moltype == "DNA" assert cmp.mh1_containment_in_mh2 == ds_a.contained_by(ds_b) assert cmp.mh2_containment_in_mh1 == ds_b.contained_by(ds_a) assert cmp.avg_containment == ds_a.avg_containment(ds_b) assert cmp.max_containment == ds_a.max_containment(ds_b) assert cmp.jaccard == ds_a.jaccard(ds_b) == ds_b.jaccard(ds_a) intersect_mh = ds_a.flatten().intersection(ds_b.flatten()) assert ( cmp.intersect_mh == intersect_mh == ds_b.flatten().intersection(ds_a.flatten()) ) assert cmp.total_unique_intersect_hashes == 8 assert cmp.pass_threshold # default threshold is 0; this should pass if track_abundance: assert ( cmp.angular_similarity == ds_a.angular_similarity(ds_b) == ds_b.angular_similarity(ds_a) ) assert ( cmp.cosine_similarity == ds_a.angular_similarity(ds_b) == ds_b.angular_similarity(ds_a) ) assert ( cmp.weighted_intersection(from_mh=cmp.mh1_cmp).hashes == intersect_mh.inflate(ds_a).hashes ) assert ( cmp.weighted_intersection(from_mh=cmp.mh2_cmp).hashes == intersect_mh.inflate(ds_b).hashes ) assert ( cmp.weighted_intersection(from_abundD=cmp.mh1_cmp.hashes).hashes == intersect_mh.inflate(ds_a).hashes ) assert ( cmp.weighted_intersection(from_abundD=cmp.mh2_cmp.hashes).hashes == intersect_mh.inflate(ds_b).hashes ) else: with pytest.raises(TypeError) as exc: cmp.angular_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) with pytest.raises(TypeError) as exc: cmp.cosine_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) assert ( cmp.weighted_intersection(from_mh=cmp.mh1_cmp).hashes == intersect_mh.hashes ) assert ( cmp.weighted_intersection(from_mh=cmp.mh2_cmp).hashes == intersect_mh.hashes ) def test_FracMinHashComparison_autodownsample(track_abundance): # build FracMinHash Comparison and check values a = MinHash(0, 21, scaled=1, track_abundance=track_abundance) b = MinHash(0, 21, scaled=2, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) cmp_scaled = 2 ds_a = a.downsample(scaled=cmp_scaled) ds_b = b.downsample(scaled=cmp_scaled) # build FracMinHashComparison cmp = FracMinHashComparison(a, b) assert cmp.mh1 == a assert cmp.mh2 == b assert cmp.mh1_cmp == ds_a assert cmp.mh2_cmp == ds_b assert cmp.ignore_abundance == False assert cmp.cmp_scaled == cmp_scaled assert cmp.ksize == 21 assert cmp.moltype == "DNA" assert cmp.mh1_containment_in_mh2 == ds_a.contained_by(ds_b) assert cmp.mh2_containment_in_mh1 == ds_b.contained_by(ds_a) assert cmp.avg_containment == ds_a.avg_containment(ds_b) assert cmp.max_containment == ds_a.max_containment(ds_b) assert cmp.jaccard == ds_a.jaccard(ds_b) == ds_b.jaccard(ds_a) intersect_mh = ds_a.flatten().intersection(ds_b.flatten()) assert ( cmp.intersect_mh == intersect_mh == ds_b.flatten().intersection(ds_a.flatten()) ) assert cmp.total_unique_intersect_hashes == 8 assert cmp.pass_threshold # default threshold is 0; this should pass if track_abundance: assert ( cmp.angular_similarity == ds_a.angular_similarity(ds_b) == ds_b.angular_similarity(ds_a) ) assert ( cmp.cosine_similarity == ds_a.angular_similarity(ds_b) == ds_b.angular_similarity(ds_a) ) assert ( cmp.weighted_intersection(from_mh=cmp.mh1_cmp).hashes == intersect_mh.inflate(ds_a).hashes ) assert ( cmp.weighted_intersection(from_mh=cmp.mh2_cmp).hashes == intersect_mh.inflate(ds_b).hashes ) assert ( cmp.weighted_intersection(from_abundD=a_values).hashes == intersect_mh.inflate(a).hashes ) assert ( cmp.weighted_intersection(from_abundD=b_values).hashes == intersect_mh.inflate(b).hashes ) else: with pytest.raises(TypeError) as exc: cmp.angular_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) with pytest.raises(TypeError) as exc: cmp.cosine_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) assert ( cmp.weighted_intersection(from_mh=cmp.mh1_cmp).hashes == intersect_mh.hashes ) assert ( cmp.weighted_intersection(from_mh=cmp.mh2_cmp).hashes == intersect_mh.hashes ) def test_FracMinHashComparison_ignore_abundance(track_abundance): # build FracMinHash Comparison and check values a = MinHash(0, 21, scaled=1, track_abundance=track_abundance) b = MinHash(0, 21, scaled=1, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) cmp_scaled = 2 ds_a = a.flatten().downsample(scaled=cmp_scaled) ds_b = b.flatten().downsample(scaled=cmp_scaled) # build FracMinHashComparison cmp = FracMinHashComparison(a, b, cmp_scaled=cmp_scaled, ignore_abundance=True) assert cmp.mh1 == a assert cmp.mh2 == b assert cmp.mh1_cmp == ds_a assert cmp.mh2_cmp == ds_b assert cmp.ignore_abundance == True assert cmp.cmp_scaled == cmp_scaled assert cmp.ksize == 21 assert cmp.moltype == "DNA" assert cmp.mh1_containment_in_mh2 == ds_a.contained_by(ds_b) assert cmp.mh2_containment_in_mh1 == ds_b.contained_by(ds_a) assert cmp.avg_containment == ds_b.avg_containment(ds_a) assert cmp.max_containment == ds_a.max_containment(ds_b) assert cmp.jaccard == ds_a.jaccard(ds_b) == ds_b.jaccard(ds_a) intersect_mh = ds_a.flatten().intersection(ds_b.flatten()) assert ( cmp.intersect_mh == intersect_mh == ds_b.flatten().intersection(ds_a.flatten()) ) assert cmp.total_unique_intersect_hashes == 8 assert cmp.pass_threshold # default threshold is 0; this should pass # with ignore_abundance = True, all of these should not be usable. Do we want errors, or ""/None? with pytest.raises(TypeError) as exc: cmp.angular_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) with pytest.raises(TypeError) as exc: cmp.cosine_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) assert not cmp.mh1_cmp.track_abundance assert not cmp.mh2_cmp.track_abundance assert cmp.weighted_intersection(from_mh=cmp.mh1_cmp).hashes == intersect_mh.hashes assert cmp.weighted_intersection(from_mh=cmp.mh2_cmp).hashes == intersect_mh.hashes def test_FracMinHashComparison_fail_threshold(track_abundance): # build FracMinHash Comparison and check values a = MinHash(0, 21, scaled=1, track_abundance=track_abundance) b = MinHash(0, 21, scaled=1, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) cmp_scaled = 2 ds_a = a.flatten().downsample(scaled=cmp_scaled) ds_b = b.flatten().downsample(scaled=cmp_scaled) # build FracMinHashComparison cmp = FracMinHashComparison(a, b, cmp_scaled=cmp_scaled, threshold_bp=40) assert cmp.mh1 == a assert cmp.mh2 == b assert cmp.ignore_abundance == False assert cmp.cmp_scaled == cmp_scaled assert cmp.ksize == 21 assert cmp.moltype == "DNA" assert cmp.mh1_containment_in_mh2 == ds_a.contained_by(ds_b) assert cmp.mh2_containment_in_mh1 == ds_b.contained_by(ds_a) assert cmp.avg_containment == ds_a.avg_containment(ds_b) assert cmp.max_containment == ds_a.max_containment(ds_b) assert cmp.jaccard == a.jaccard(b) == b.jaccard(a) intersect_mh = ds_a.flatten().intersection(ds_b.flatten()) assert ( cmp.intersect_mh == intersect_mh == ds_b.flatten().intersection(ds_a.flatten()) ) assert cmp.total_unique_intersect_hashes == 8 assert not cmp.pass_threshold # threshold is 40; this should fail def test_FracMinHashComparison_potential_false_negative(): f1 = utils.get_test_data("scaled100/GCF_000005845.2_ASM584v2_genomic.fna.gz.sig.gz") f2 = utils.get_test_data("scaled100/GCF_000006945.1_ASM694v1_genomic.fna.gz.sig.gz") f3 = utils.get_test_data( "scaled100/GCF_000783305.1_ASM78330v1_genomic.fna.gz.sig.gz" ) a = load_one_signature(f1, ksize=21).minhash b = load_one_signature(f2).minhash c = load_one_signature(f3).minhash assert a.size_is_accurate() == True assert b.size_is_accurate() == True assert c.size_is_accurate() == True # build FracMinHashComparison cmp = FracMinHashComparison(a, b) # check ani, potential false negative cmp.estimate_jaccard_ani() assert cmp.jaccard_ani == a.jaccard_ani(b).ani == b.jaccard_ani(a).ani print(cmp.jaccard_ani) assert ( cmp.potential_false_negative == a.jaccard_ani(b).p_exceeds_threshold == b.jaccard_ani(a).p_exceeds_threshold ) assert cmp.potential_false_negative == False assert ( cmp.jaccard_ani_untrustworthy == a.jaccard_ani(b).je_exceeds_threshold == b.jaccard_ani(a).je_exceeds_threshold ) cmp.estimate_ani_from_mh1_containment_in_mh2() a_cont_ani_manual = a.containment_ani(b) assert cmp.ani_from_mh1_containment_in_mh2 == a_cont_ani_manual.ani print(a_cont_ani_manual.p_exceeds_threshold) assert cmp.potential_false_negative == a_cont_ani_manual.p_exceeds_threshold assert cmp.potential_false_negative == False cmp.estimate_ani_from_mh2_containment_in_mh1() b_cont_ani_manual = b.containment_ani(a) assert cmp.ani_from_mh2_containment_in_mh1 == b_cont_ani_manual.ani assert cmp.potential_false_negative == b_cont_ani_manual.p_exceeds_threshold assert cmp.potential_false_negative == False cmp.estimate_max_containment_ani() mc_ani_manual = a.max_containment_ani(b) assert ( cmp.max_containment_ani == max(a.containment_ani(b).ani, b.containment_ani(a).ani) == mc_ani_manual.ani ) assert cmp.potential_false_negative == mc_ani_manual.p_exceeds_threshold assert cmp.avg_containment_ani == np.mean( [a.containment_ani(b).ani, b.containment_ani(a).ani] ) assert cmp.potential_false_negative == False # downsample to where it becomes a potential false negative cmp = FracMinHashComparison(a, b, cmp_scaled=16000) cmp.estimate_ani_from_mh1_containment_in_mh2() assert cmp.potential_false_negative == True def test_FracMinHashComparison_incompatible_ksize(track_abundance): a = MinHash(0, 31, scaled=1, track_abundance=track_abundance) b = MinHash(0, 21, scaled=2, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) with pytest.raises(TypeError) as exc: FracMinHashComparison(a, b) print(str(exc)) assert "Error: Cannot compare incompatible sketches." in str(exc) def test_FracMinHashComparison_incompatible_moltype(track_abundance): a = MinHash(0, 31, scaled=1, track_abundance=track_abundance) b = MinHash(0, 31, scaled=2, is_protein=True, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) with pytest.raises(TypeError) as exc: FracMinHashComparison(a, b) print(str(exc)) assert "Error: Cannot compare incompatible sketches." in str(exc) def test_FracMinHashComparison_incompatible_sketchtype(track_abundance): a = MinHash(0, 31, scaled=1, track_abundance=track_abundance) b = MinHash(10, 31, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) with pytest.raises(TypeError) as exc: FracMinHashComparison(a, b) print(str(exc)) assert "Error: Both sketches must be 'num' or 'scaled'." in str(exc) def test_FracMinHashComparison_incompatible_cmp_scaled(track_abundance): # pass in too low of a cmp_scaled value a = MinHash(0, 31, scaled=1, track_abundance=track_abundance) b = MinHash(0, 31, scaled=10, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) with pytest.raises(ValueError) as exc: FracMinHashComparison(a, b, cmp_scaled=1) print(str(exc)) assert "new scaled 1 is lower than current sample scaled 10" in str(exc) def test_FracMinHashComparison_redownsample_without_scaled(track_abundance): a = MinHash(0, 31, scaled=1, track_abundance=track_abundance) b = MinHash(0, 31, scaled=10, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) cmp = FracMinHashComparison(a, b) assert cmp.cmp_scaled == 10 with pytest.raises(ValueError) as exc: # try to redownsample without passing in cmp_scaled cmp.downsample_and_handle_ignore_abundance() print(str(exc)) assert "Error: must pass in a comparison scaled or num value." in str(exc) def test_NumMinHashComparison(track_abundance): # build FracMinHash Comparison and check values a = MinHash(10, 21, scaled=0, track_abundance=track_abundance) b = MinHash(10, 21, scaled=0, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) assert a.num and b.num and not a.scaled and not b.scaled # build NumMinHashComparison cmp = NumMinHashComparison(a, b) assert cmp.mh1 == a assert cmp.mh2 == b assert cmp.ignore_abundance == False assert cmp.cmp_num == 10 assert cmp.ksize == 21 assert cmp.moltype == "DNA" assert cmp.jaccard == a.jaccard(b) == b.jaccard(a) intersect_mh = a.flatten().intersection(b.flatten()) assert cmp.intersect_mh == intersect_mh == b.flatten().intersection(a.flatten()) if track_abundance: assert ( cmp.angular_similarity == a.angular_similarity(b) == b.angular_similarity(a) ) assert ( cmp.cosine_similarity == a.angular_similarity(b) == b.angular_similarity(a) ) else: with pytest.raises(TypeError) as exc: cmp.angular_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) with pytest.raises(TypeError) as exc: cmp.cosine_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) def test_NumMinHashComparison_downsample(track_abundance): # build FracMinHash Comparison and check values a = MinHash(10, 21, scaled=0, track_abundance=track_abundance) b = MinHash(10, 21, scaled=0, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) assert a.num and b.num and not a.scaled and not b.scaled cmp_num = 5 ds_a = a.downsample(num=cmp_num) ds_b = b.downsample(num=cmp_num) # build NumMinHashComparison cmp = NumMinHashComparison(a, b, cmp_num=cmp_num) assert cmp.mh1 == a assert cmp.mh2 == b assert cmp.ignore_abundance == False assert cmp.cmp_num == cmp_num assert cmp.ksize == 21 assert cmp.moltype == "DNA" assert cmp.jaccard == ds_a.jaccard(ds_b) == ds_b.jaccard(ds_a) intersect_mh = ds_a.flatten().intersection(ds_b.flatten()) assert ( cmp.intersect_mh == intersect_mh == ds_b.flatten().intersection(ds_a.flatten()) ) if track_abundance: assert ( cmp.angular_similarity == ds_a.angular_similarity(ds_b) == ds_b.angular_similarity(ds_a) ) assert ( cmp.cosine_similarity == ds_a.angular_similarity(ds_b) == ds_b.angular_similarity(ds_a) ) else: with pytest.raises(TypeError) as exc: cmp.angular_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) with pytest.raises(TypeError) as exc: cmp.cosine_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) def test_NumMinHashComparison_autodownsample(track_abundance): # build FracMinHash Comparison and check values a = MinHash(10, 21, scaled=0, track_abundance=track_abundance) b = MinHash(5, 21, scaled=0, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) assert a.num and b.num and not a.scaled and not b.scaled cmp_num = 5 ds_a = a.downsample(num=cmp_num) ds_b = b.downsample(num=cmp_num) # build NumMinHashComparison cmp = NumMinHashComparison(a, b) assert cmp.mh1 == a assert cmp.mh2 == b assert cmp.ignore_abundance == False assert cmp.cmp_num == cmp_num assert cmp.ksize == 21 assert cmp.moltype == "DNA" assert cmp.jaccard == ds_a.jaccard(ds_b) == ds_b.jaccard(ds_a) intersect_mh = ds_a.flatten().intersection(ds_b.flatten()) assert ( cmp.intersect_mh == intersect_mh == ds_b.flatten().intersection(ds_a.flatten()) ) if track_abundance: assert ( cmp.angular_similarity == ds_a.angular_similarity(ds_b) == ds_b.angular_similarity(ds_a) ) assert ( cmp.cosine_similarity == ds_a.angular_similarity(ds_b) == ds_b.angular_similarity(ds_a) ) else: with pytest.raises(TypeError) as exc: cmp.angular_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) with pytest.raises(TypeError) as exc: cmp.cosine_similarity print(str(exc)) assert ( "Error: Angular (cosine) similarity requires both sketches to track hash abundance." in str(exc) ) def test_NumMinHashComparison_incompatible_ksize(track_abundance): a_num = MinHash(20, 31, track_abundance=track_abundance) b_num = MinHash(10, 21, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a_num.set_abundances(a_values) b_num.set_abundances(b_values) else: a_num.add_many(a_values.keys()) b_num.add_many(b_values.keys()) # build NumMinHashComparison with pytest.raises(TypeError) as exc: NumMinHashComparison(a_num, b_num) print(str(exc)) assert "Error: Cannot compare incompatible sketches." in str(exc) def test_NumMinHashComparison_incompatible_moltype(track_abundance): a_num = MinHash(20, 31, track_abundance=track_abundance) b_num = MinHash(10, 31, is_protein=True, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a_num.set_abundances(a_values) b_num.set_abundances(b_values) else: a_num.add_many(a_values.keys()) b_num.add_many(b_values.keys()) with pytest.raises(TypeError) as exc: NumMinHashComparison(a_num, b_num) print(str(exc)) assert "Error: Cannot compare incompatible sketches." in str(exc) def test_NumMinHashComparison_incompatible_sketchtype(track_abundance): a = MinHash(0, 31, scaled=1, track_abundance=track_abundance) b = MinHash(10, 31, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) with pytest.raises(TypeError) as exc: NumMinHashComparison(a, b) print(str(exc)) assert "Error: Both sketches must be 'num' or 'scaled'." in str(exc) def test_NumMinHashComparison_redownsample_without_num(track_abundance): a = MinHash(10, 31, track_abundance=track_abundance) b = MinHash(5, 31, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) cmp = NumMinHashComparison(a, b) with pytest.raises(ValueError) as exc: # try to redownsample without passing in cmp_num cmp.downsample_and_handle_ignore_abundance() print(str(exc)) assert "Error: must pass in a comparison scaled or num value." in str(exc) def test_NumMinHashComparison_incompatible_cmp_num(track_abundance): # pass in too high of a cmp_num value a = MinHash(200, 31, track_abundance=track_abundance) b = MinHash(100, 31, track_abundance=track_abundance) a_values = {1: 5, 3: 3, 5: 2, 8: 2} b_values = {1: 3, 3: 2, 5: 1, 6: 1, 8: 1, 10: 1} if track_abundance: a.set_abundances(a_values) b.set_abundances(b_values) else: a.add_many(a_values.keys()) b.add_many(b_values.keys()) with pytest.raises(ValueError) as exc: NumMinHashComparison(a, b, cmp_num=150) print(str(exc)) assert "new sample num is higher than current sample num" in str(exc) def test_FracMinHashComparison_ANI(track_abundance): # need real mh here, small test data fails if track_abundance: f1 = utils.get_test_data("track_abund/47.fa.sig") f2 = utils.get_test_data("track_abund/63.fa.sig") else: f1 = utils.get_test_data("47.fa.sig") f2 = utils.get_test_data("63.fa.sig") a = load_one_signature(f1, ksize=31).minhash b = load_one_signature(f2, ksize=31).minhash cmp = FracMinHashComparison(a, b) # check jaccard ani cmp.estimate_jaccard_ani() assert cmp.jaccard_ani == a.jaccard_ani(b).ani == b.jaccard_ani(a).ani assert ( cmp.potential_false_negative == a.jaccard_ani(b).p_exceeds_threshold == b.jaccard_ani(a).p_exceeds_threshold ) assert ( cmp.jaccard_ani_untrustworthy == a.jaccard_ani(b).je_exceeds_threshold == b.jaccard_ani(a).je_exceeds_threshold ) cmp.estimate_ani_from_mh1_containment_in_mh2() a_cont_ani_manual = a.containment_ani(b) assert cmp.ani_from_mh1_containment_in_mh2 == a_cont_ani_manual.ani assert cmp.potential_false_negative == a_cont_ani_manual.p_exceeds_threshold # assert cmp.mh1_containment_ani_low is None # assert cmp.mh1_containment_ani_high is None cmp.estimate_ani_from_mh2_containment_in_mh1() b_cont_ani_manual = b.containment_ani(a) assert cmp.ani_from_mh2_containment_in_mh1 == b_cont_ani_manual.ani assert cmp.potential_false_negative == b_cont_ani_manual.p_exceeds_threshold # assert cmp.mh2_containment_ani_low is None # assert cmp.mh2_containment_ani_high is None cmp.estimate_max_containment_ani() mc_ani_manual = a.max_containment_ani(b) assert ( cmp.max_containment_ani == max(a.containment_ani(b).ani, b.containment_ani(a).ani) == mc_ani_manual.ani ) assert cmp.potential_false_negative == mc_ani_manual.p_exceeds_threshold # assert cmp.max_containment_ani_low is None # assert cmp.max_containment_ani_high is None assert cmp.avg_containment_ani == np.mean( [a.containment_ani(b).ani, b.containment_ani(a).ani] ) def test_FracMinHashComparison_ANI_provide_similarity(track_abundance): # need real mh here, small test data fails if track_abundance: f1 = utils.get_test_data("track_abund/47.fa.sig") f2 = utils.get_test_data("track_abund/63.fa.sig") else: f1 = utils.get_test_data("47.fa.sig") f2 = utils.get_test_data("63.fa.sig") a = load_one_signature(f1, ksize=31).minhash b = load_one_signature(f2, ksize=31).minhash cmp = FracMinHashComparison(a, b) # check jaccard ani jaccard = a.jaccard(b) cmp.estimate_jaccard_ani(jaccard=jaccard) assert cmp.jaccard_ani == a.jaccard_ani(b).ani == b.jaccard_ani(a).ani assert ( cmp.potential_false_negative == a.jaccard_ani(b).p_exceeds_threshold == b.jaccard_ani(a).p_exceeds_threshold ) assert ( cmp.jaccard_ani_untrustworthy == a.jaccard_ani(b).je_exceeds_threshold == b.jaccard_ani(a).je_exceeds_threshold ) a_cont = a.contained_by(b) b_cont = b.contained_by(a) mc = a.max_containment(b) cmp.estimate_ani_from_mh1_containment_in_mh2(containment=a_cont) a_cont_ani_manual = a.containment_ani(b) assert cmp.ani_from_mh1_containment_in_mh2 == a_cont_ani_manual.ani assert cmp.potential_false_negative == a_cont_ani_manual.p_exceeds_threshold cmp.estimate_ani_from_mh2_containment_in_mh1(containment=b_cont) b_cont_ani_manual = b.containment_ani(a) assert cmp.ani_from_mh2_containment_in_mh1 == b_cont_ani_manual.ani assert cmp.potential_false_negative == b_cont_ani_manual.p_exceeds_threshold cmp.estimate_max_containment_ani(max_containment=mc) mc_ani_manual = a.max_containment_ani(b) assert ( cmp.max_containment_ani == max(a.containment_ani(b).ani, b.containment_ani(a).ani) == mc_ani_manual.ani ) assert cmp.potential_false_negative == mc_ani_manual.p_exceeds_threshold assert cmp.avg_containment_ani == np.mean( [a.containment_ani(b).ani, b.containment_ani(a).ani] ) def test_FracMinHashComparison_ANI_estimate_CI(track_abundance): # need real mh here, small test data fails if track_abundance: f1 = utils.get_test_data("track_abund/47.fa.sig") f2 = utils.get_test_data("track_abund/63.fa.sig") else: f1 = utils.get_test_data("47.fa.sig") f2 = utils.get_test_data("63.fa.sig") a = load_one_signature(f1, ksize=31).minhash b = load_one_signature(f2, ksize=31).minhash cmp = FracMinHashComparison(a, b, estimate_ani_ci=True) cmp.estimate_jaccard_ani() assert cmp.jaccard_ani == a.jaccard_ani(b).ani == b.jaccard_ani(a).ani assert ( cmp.potential_false_negative == a.jaccard_ani(b).p_exceeds_threshold == b.jaccard_ani(a).p_exceeds_threshold ) assert ( cmp.jaccard_ani_untrustworthy == a.jaccard_ani(b).je_exceeds_threshold == b.jaccard_ani(a).je_exceeds_threshold ) cmp.estimate_ani_from_mh1_containment_in_mh2() a_cont_ani_manual = a.containment_ani(b, estimate_ci=True) assert cmp.ani_from_mh1_containment_in_mh2 == a_cont_ani_manual.ani assert cmp.potential_false_negative == a_cont_ani_manual.p_exceeds_threshold assert cmp.ani_from_mh1_containment_in_mh2_low == a_cont_ani_manual.ani_low assert cmp.ani_from_mh1_containment_in_mh2_high == a_cont_ani_manual.ani_high cmp.estimate_ani_from_mh2_containment_in_mh1() b_cont_ani_manual = b.containment_ani(a, estimate_ci=True) assert cmp.ani_from_mh2_containment_in_mh1 == b_cont_ani_manual.ani assert cmp.potential_false_negative == b_cont_ani_manual.p_exceeds_threshold assert cmp.ani_from_mh2_containment_in_mh1_low == b_cont_ani_manual.ani_low assert cmp.ani_from_mh2_containment_in_mh1_high == b_cont_ani_manual.ani_high cmp.estimate_max_containment_ani() mc_ani_manual = a.max_containment_ani(b, estimate_ci=True) assert ( cmp.max_containment_ani == max(a.containment_ani(b).ani, b.containment_ani(a).ani) == mc_ani_manual.ani ) assert cmp.potential_false_negative == mc_ani_manual.p_exceeds_threshold assert cmp.max_containment_ani_low == mc_ani_manual.ani_low assert cmp.max_containment_ani_high == mc_ani_manual.ani_high def test_FracMinHashComparison_ANI_estimate_CI_ci99(track_abundance): # need real mh here, small test data fails if track_abundance: f1 = utils.get_test_data("track_abund/47.fa.sig") f2 = utils.get_test_data("track_abund/63.fa.sig") else: f1 = utils.get_test_data("47.fa.sig") f2 = utils.get_test_data("63.fa.sig") a = load_one_signature(f1, ksize=31).minhash b = load_one_signature(f2, ksize=31).minhash cmp = FracMinHashComparison(a, b, estimate_ani_ci=True, ani_confidence=0.99) # check containment ani cmp.estimate_ani_from_mh1_containment_in_mh2() a_cont_ani_manual = a.containment_ani(b, estimate_ci=True, confidence=0.99) assert cmp.ani_from_mh1_containment_in_mh2 == a_cont_ani_manual.ani assert cmp.potential_false_negative == a_cont_ani_manual.p_exceeds_threshold assert cmp.ani_from_mh1_containment_in_mh2_low == a_cont_ani_manual.ani_low assert cmp.ani_from_mh1_containment_in_mh2_high == a_cont_ani_manual.ani_high cmp.estimate_ani_from_mh2_containment_in_mh1() b_cont_ani_manual = b.containment_ani(a, estimate_ci=True, confidence=0.99) assert cmp.ani_from_mh2_containment_in_mh1 == b_cont_ani_manual.ani assert cmp.potential_false_negative == b_cont_ani_manual.p_exceeds_threshold assert cmp.ani_from_mh2_containment_in_mh1_low == b_cont_ani_manual.ani_low assert cmp.ani_from_mh2_containment_in_mh1_high == b_cont_ani_manual.ani_high cmp.estimate_max_containment_ani() mc_ani_manual = a.max_containment_ani(b, estimate_ci=True, confidence=0.99) assert ( cmp.max_containment_ani == max(a.containment_ani(b).ani, b.containment_ani(a).ani) == mc_ani_manual.ani ) assert cmp.potential_false_negative == mc_ani_manual.p_exceeds_threshold assert cmp.max_containment_ani_low == mc_ani_manual.ani_low assert cmp.max_containment_ani_high == mc_ani_manual.ani_high def test_FracMinHashComparison_ANI_downsample(track_abundance): # need real mh here, small test data fails if track_abundance: f1 = utils.get_test_data("track_abund/47.fa.sig") f2 = utils.get_test_data("track_abund/63.fa.sig") else: f1 = utils.get_test_data("47.fa.sig") f2 = utils.get_test_data("63.fa.sig") a = load_one_signature(f1, ksize=31).minhash b = load_one_signature(f2, ksize=31).minhash cmp = FracMinHashComparison(a, b, cmp_scaled=1100, estimate_ani_ci=True) # now manually downsample a = a.downsample(scaled=1100) b = b.downsample(scaled=1100) # check jaccard ani cmp.estimate_jaccard_ani() assert cmp.jaccard_ani == a.jaccard_ani(b).ani == b.jaccard_ani(a).ani assert ( cmp.potential_false_negative == a.jaccard_ani(b).p_exceeds_threshold == b.jaccard_ani(a).p_exceeds_threshold ) assert ( cmp.jaccard_ani_untrustworthy == a.jaccard_ani(b).je_exceeds_threshold == b.jaccard_ani(a).je_exceeds_threshold ) cmp.estimate_ani_from_mh1_containment_in_mh2() a_cont_ani_manual = a.containment_ani(b, estimate_ci=True) assert cmp.ani_from_mh1_containment_in_mh2 == a_cont_ani_manual.ani assert cmp.potential_false_negative == a_cont_ani_manual.p_exceeds_threshold assert cmp.ani_from_mh1_containment_in_mh2_low == a_cont_ani_manual.ani_low assert cmp.ani_from_mh1_containment_in_mh2_high == a_cont_ani_manual.ani_high cmp.estimate_ani_from_mh2_containment_in_mh1() b_cont_ani_manual = b.containment_ani(a, estimate_ci=True) assert cmp.ani_from_mh2_containment_in_mh1 == b_cont_ani_manual.ani assert cmp.potential_false_negative == b_cont_ani_manual.p_exceeds_threshold assert cmp.ani_from_mh2_containment_in_mh1_low == b_cont_ani_manual.ani_low assert cmp.ani_from_mh2_containment_in_mh1_high == b_cont_ani_manual.ani_high cmp.estimate_max_containment_ani() mc_ani_manual = a.max_containment_ani(b, estimate_ci=True) assert ( cmp.max_containment_ani == max(a.containment_ani(b).ani, b.containment_ani(a).ani) == mc_ani_manual.ani ) assert cmp.potential_false_negative == mc_ani_manual.p_exceeds_threshold assert cmp.max_containment_ani_low == mc_ani_manual.ani_low assert cmp.max_containment_ani_high == mc_ani_manual.ani_high sourmash-bio-sourmash-bc3c0f8/tests/test_sourmash.py000066400000000000000000010265451474157046300231170ustar00rootroot00000000000000""" Tests for the 'sourmash' command line. """ import os import gzip import shutil import screed import glob import json import csv import pytest import zipfile import random import warnings from pathlib import Path import numpy import sourmash_tst_utils as utils import sourmash from sourmash import MinHash, sourmash_args from sourmash.sbt import SBT, Node from sourmash.sbtmh import SigLeaf, load_sbt_index from sourmash.search import SearchResult, GatherResult from sourmash.signature import load_one_signature_from_json as load_one_signature from sourmash.signature import load_signatures_from_json try: import matplotlib matplotlib.use("Agg") except ImportError: pass from sourmash import signature from sourmash import VERSION from sourmash.sourmash_args import load_pathlist_from_file from sourmash_tst_utils import SourmashCommandFailed def test_citation_file(): import yaml thisdir = os.path.dirname(__file__) citation_file = os.path.join(thisdir, "../CITATION.cff") with open(citation_file) as fp: x = yaml.safe_load(fp) assert ( x["title"] == "sourmash v4: A multitool to quickly search, compare, and analyze genomic and metagenomic data sets" ), x def test_run_sourmash(): status, out, err = utils.runscript("sourmash", [], fail_ok=True) assert status != 0 # no args provided, ok ;) def test_run_sourmash_badcmd(): status, out, err = utils.runscript("sourmash", ["foobarbaz"], fail_ok=True) assert status != 0 # bad arg! assert "cmd: invalid choice" in err def test_run_sourmash_subcmd_help(): status, out, err = utils.runscript("sourmash", ["sbt"], fail_ok=True) print(out) print(err) assert status != 0 # should fail assert "invalid choice:" in err assert "'sbt' (choose from" in err # should not have printed a Traceback assert any("Traceback" not in o for o in (err, out)) def test_sourmash_info(): status, out, err = utils.runscript("sourmash", ["info"], fail_ok=False) # no output to stdout assert not out assert "sourmash version" in err assert "loaded from path" in err assert VERSION in err def test_sourmash_info_verbose(): status, out, err = utils.runscript("sourmash", ["info", "-v"]) # no output to stdout assert not out assert "khmer version" in err assert "screed version" in err assert "loaded from path" in err def test_load_pathlist_from_file_does_not_exist(): from sourmash.sourmash_args import load_pathlist_from_file with pytest.raises(ValueError) as e: load_pathlist_from_file("") assert "file '' does not exist" in str(e.value) @utils.in_tempdir def test_load_pathlist_from_file_empty(c): file_list = c.output("file_list") with open(file_list, "w") as fp: fp.write("") with pytest.raises(ValueError) as e: load_pathlist_from_file(file_list) assert "pathlist is empty" in str(e.value) @utils.in_tempdir def test_load_pathlist_from_file_badly_formatted(c): file_list = c.output("file_list") with open(file_list, "w") as fp: fp.write("{'a':1}") with pytest.raises(ValueError) as e: load_pathlist_from_file(file_list) assert "file '{'a':1}' inside the pathlist does not exist" in str(e.value) @utils.in_tempdir def test_load_pathlist_from_file_badly_formatted_2(c): file_list = c.output("file_list") sig1 = utils.get_test_data("compare/genome-s10.fa.gz.sig") with open(file_list, "w") as fp: fp.write(sig1 + "\n") fp.write("{'a':1}") with pytest.raises(ValueError) as e: load_pathlist_from_file(file_list) assert "file '{'a':1}' inside the pathlist does not exist" in str(e.value) @utils.in_tempdir def test_load_pathlist_from_file_duplicate(c): file_list = c.output("file_list") sig1 = utils.get_test_data("compare/genome-s10.fa.gz.sig") with open(file_list, "w") as fp: fp.write(sig1 + "\n") fp.write(sig1 + "\n") check = load_pathlist_from_file(file_list) print(check) assert len(check) == 1 def test_compare_serial(runtmp): # try doing a compare serially c = runtmp testsigs = utils.get_test_data("genome-s1*.sig") testsigs = glob.glob(testsigs) assert len(testsigs) == 4 c.run_sourmash("compare", "-o", "cmp", "-k", "21", "--dna", *testsigs) cmp_outfile = c.output("cmp") assert os.path.exists(cmp_outfile) cmp_out = numpy.load(cmp_outfile) sigs = [] for fn in testsigs: sigs.append(load_one_signature(fn, ksize=21, select_moltype="dna")) cmp_calc = numpy.zeros([len(sigs), len(sigs)]) for i, si in enumerate(sigs): for j, sj in enumerate(sigs): cmp_calc[i][j] = si.similarity(sj) sigs = [] for fn in testsigs: sigs.append(load_one_signature(fn, ksize=21, select_moltype="dna")) assert (cmp_out == cmp_calc).all() def test_compare_serial_distance(runtmp): # try doing a compare serially, with --distance output c = runtmp testsigs = utils.get_test_data("genome-s1*.sig") testsigs = glob.glob(testsigs) c.run_sourmash("compare", "-o", "cmp", "-k", "21", "--dna", *testsigs, "--distance") cmp_outfile = c.output("cmp") assert os.path.exists(cmp_outfile) cmp_out = numpy.load(cmp_outfile) sigs = [] for fn in testsigs: sigs.append(load_one_signature(fn, ksize=21, select_moltype="dna")) cmp_calc = numpy.zeros([len(sigs), len(sigs)]) for i, si in enumerate(sigs): for j, sj in enumerate(sigs): cmp_calc[i][j] = 1 - si.similarity(sj) sigs = [] for fn in testsigs: sigs.append(load_one_signature(fn, ksize=21, select_moltype="dna")) assert (cmp_out == cmp_calc).all() def test_compare_parallel(runtmp): # try doing a compare parallel c = runtmp testsigs = utils.get_test_data("genome-s1*.sig") testsigs = glob.glob(testsigs) c.run_sourmash( "compare", "-o", "cmp", "-k", "21", "--dna", "--processes", "2", *testsigs ) cmp_outfile = c.output("cmp") assert os.path.exists(cmp_outfile) cmp_out = numpy.load(cmp_outfile) sigs = [] for fn in testsigs: sigs.append(load_one_signature(fn, ksize=21, select_moltype="dna")) cmp_calc = numpy.zeros([len(sigs), len(sigs)]) for i, si in enumerate(sigs): for j, sj in enumerate(sigs): cmp_calc[i][j] = si.similarity(sj) sigs = [] for fn in testsigs: sigs.append(load_one_signature(fn, ksize=21, select_moltype="dna")) assert (cmp_out == cmp_calc).all() def test_compare_do_serial_compare_with_from_file(runtmp): # try doing a compare serial c = runtmp testsigs = utils.get_test_data("genome-s1*.sig") testsigs = glob.glob(testsigs) file_list = c.output("file.list") with open(file_list, "w") as fp: print("\n".join(testsigs), file=fp) c.run_sourmash( "compare", "-o", "cmp", "-k", "21", "--dna", "--from-file", file_list ) cmp_outfile = c.output("cmp") assert os.path.exists(cmp_outfile) cmp_out = numpy.load(cmp_outfile) sigs = [] for fn in testsigs: sigs.append(load_one_signature(fn, ksize=21, select_moltype="dna")) cmp_calc = numpy.zeros([len(sigs), len(sigs)]) for i, si in enumerate(sigs): for j, sj in enumerate(sigs): cmp_calc[i][j] = si.similarity(sj) sigs = [] for fn in testsigs: sigs.append(load_one_signature(fn, ksize=21, select_moltype="dna")) assert numpy.array_equal(numpy.sort(cmp_out.flat), numpy.sort(cmp_calc.flat)) def test_compare_do_basic_compare_using_rna_arg(runtmp): # try doing a basic compare using --rna instead of --dna c = runtmp testsigs = utils.get_test_data("genome-s1*.sig") testsigs = glob.glob(testsigs) c.run_sourmash("compare", "-o", "cmp", "-k", "21", "--rna", *testsigs) cmp_outfile = c.output("cmp") assert os.path.exists(cmp_outfile) cmp_out = numpy.load(cmp_outfile) sigs = [] for fn in testsigs: sigs.append(load_one_signature(fn, ksize=21, select_moltype="dna")) cmp_calc = numpy.zeros([len(sigs), len(sigs)]) for i, si in enumerate(sigs): for j, sj in enumerate(sigs): cmp_calc[i][j] = si.similarity(sj) assert (cmp_out == cmp_calc).all() def test_compare_do_basic_using_nucleotide_arg(runtmp): # try doing a basic compare using --nucleotide instead of --dna/--rna c = runtmp testsigs = utils.get_test_data("genome-s1*.sig") testsigs = glob.glob(testsigs) c.run_sourmash("compare", "-o", "cmp", "-k", "21", "--nucleotide", *testsigs) cmp_outfile = c.output("cmp") assert os.path.exists(cmp_outfile) cmp_out = numpy.load(cmp_outfile) sigs = [] for fn in testsigs: sigs.append(load_one_signature(fn, ksize=21, select_moltype="dna")) cmp_calc = numpy.zeros([len(sigs), len(sigs)]) for i, si in enumerate(sigs): for j, sj in enumerate(sigs): cmp_calc[i][j] = si.similarity(sj) assert (cmp_out == cmp_calc).all() def test_compare_quiet(runtmp): # test 'compare -q' has no output c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "translate", "-p", "k=31,num=500", testdata1, testdata2) c.run_sourmash("compare", "short.fa.sig", "short2.fa.sig", "--csv", "xxx", "-q") assert not c.last_result.out assert not c.last_result.err def test_compare_do_traverse_directory_parse_args(runtmp): # test 'compare' on a directory, using sourmash.cli.parse_args. import sourmash.commands import sourmash.cli args = sourmash.cli.parse_args( ["compare", "-k", "21", "--dna", utils.get_test_data("compare")] ) sourmash.commands.compare(args) def test_compare_do_traverse_directory(runtmp): # test 'compare' on a directory c = runtmp c.run_sourmash("compare", "-k 21", "--dna", utils.get_test_data("compare")) print(c.last_result.out) assert "genome-s10.fa.gz" in c.last_result.out assert "genome-s11.fa.gz" in c.last_result.out def test_compare_do_traverse_directory_compare_force(runtmp): # test 'compare' on a directory, with -f c = runtmp sig1 = utils.get_test_data("compare/genome-s10.fa.gz.sig") sig2 = utils.get_test_data("compare/genome-s11.fa.gz.sig") newdir = c.output("newdir") os.mkdir(newdir) shutil.copyfile(sig1, os.path.join(newdir, "sig1")) shutil.copyfile(sig2, os.path.join(newdir, "sig2")) c.run_sourmash("compare", "-k 21", "--dna", newdir, "-f") print(c.last_result.out) assert "genome-s10.fa.gz" in c.last_result.out assert "genome-s11.fa.gz" in c.last_result.out def test_compare_output_csv(runtmp): # test 'sourmash compare --csv' c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) c.run_sourmash("compare", "short.fa.sig", "short2.fa.sig", "--csv", "xxx") with open(c.output("xxx")) as fp: r = iter(csv.reader(fp)) row = next(r) print(row) row = next(r) print(row) assert float(row[0]) == 1.0 assert float(row[1]) == 0.93 row = next(r) assert float(row[0]) == 0.93 assert float(row[1]) == 1.0 # exactly three lines with pytest.raises(StopIteration): next(r) def test_compare_output_csv_gz(runtmp): # test 'sourmash compare --csv' with a .gz file c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) c.run_sourmash("compare", "short.fa.sig", "short2.fa.sig", "--csv", "xxx.gz") with gzip.open(c.output("xxx.gz"), "rt", newline="") as fp: r = iter(csv.reader(fp)) row = next(r) print(row) row = next(r) print(row) assert float(row[0]) == 1.0 assert float(row[1]) == 0.93 row = next(r) assert float(row[0]) == 0.93 assert float(row[1]) == 1.0 # exactly three lines with pytest.raises(StopIteration): next(r) def test_compare_downsample(runtmp): # test 'compare' with implicit downsampling c = runtmp testdata1 = utils.get_test_data("short.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=200", testdata1) testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=100", testdata2) c.run_sourmash("compare", "short.fa.sig", "short2.fa.sig", "--csv", "xxx") print(c.last_result.status, c.last_result.out, c.last_result.err) assert "downsampling to scaled value of 200" in c.last_result.err with open(c.output("xxx")) as fp: lines = fp.readlines() assert len(lines) == 3 assert lines[1].startswith("1.0,0.6666") assert lines[2].startswith("0.6666") def test_compare_downsample_scaled(runtmp): # test 'compare' with explicit --scaled downsampling c = runtmp testdata1 = utils.get_test_data("short.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=200", testdata1) testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=100", testdata2) c.run_sourmash( "compare", "short.fa.sig", "short2.fa.sig", "--csv", "xxx", "--scaled", "300" ) print(c.last_result.status, c.last_result.out, c.last_result.err) assert "downsampling to scaled value of 300" in c.last_result.err with open(c.output("xxx")) as fp: lines = fp.readlines() assert len(lines) == 3 assert lines[1].startswith("1.0,0.0") assert lines[2].startswith("0.0") def test_compare_downsample_scaled_too_low(runtmp): # test 'compare' with explicit --scaled downsampling, but lower than min c = runtmp testdata1 = utils.get_test_data("short.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=200", testdata1) testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=100", testdata2) c.run_sourmash( "compare", "short.fa.sig", "short2.fa.sig", "--csv", "xxx", "--scaled", "100" ) print(c.last_result.status, c.last_result.out, c.last_result.err) assert "downsampling to scaled value of 200" in c.last_result.err assert ( "WARNING: --scaled specified 100, but max scaled of sketches is 200" in c.last_result.err ) with open(c.output("xxx")) as fp: lines = fp.readlines() assert len(lines) == 3 assert lines[1].startswith("1.0,0.6666") assert lines[2].startswith("0.6666") def test_compare_downsample_scaled_fail_num(runtmp): # test 'compare' with explicit --scaled downsampling; fail on num sketch c = runtmp testdata1 = utils.get_test_data("short.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,num=20", testdata1) testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=100", testdata2) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "compare", "short.fa.sig", "short2.fa.sig", "--csv", "xxx", "--scaled", "300", ) print(c.last_result.status, c.last_result.out, c.last_result.err) assert "cannot mix scaled signatures with num signatures" in c.last_result.err def test_compare_downsample_scaled_fail_all_num(runtmp): # test 'compare' with explicit --scaled downsampling; fail on all num sketches c = runtmp testdata1 = utils.get_test_data("short.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,num=20", testdata1) testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,num=30", testdata2) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "compare", "short.fa.sig", "short2.fa.sig", "--csv", "xxx", "--scaled", "300", ) print(c.last_result.status, c.last_result.out, c.last_result.err) assert ( "ERROR: cannot specify --scaled with non-scaled signatures." in c.last_result.err ) def test_compare_output_multiple_k(runtmp): # test 'compare' when given multiple k-mer sizes -> should fail c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "translate", "-p", "k=21,num=500", testdata1) c.run_sourmash("sketch", "translate", "-p", "k=31,num=500", testdata2) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "compare", "short.fa.sig", "short2.fa.sig", "--csv", "xxx", fail_ok=True ) print(c.last_result.status, c.last_result.out, c.last_result.err) assert c.last_result.status == -1 assert "multiple k-mer sizes loaded; please specify one" in c.last_result.err assert "(saw k-mer sizes 21, 31)" in c.last_result.err def test_compare_output_multiple_moltype(runtmp): # 'compare' should fail when given multiple moltypes c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=21,num=500", testdata1) c.run_sourmash("sketch", "translate", "-p", "k=21,num=500", testdata2) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "compare", "short.fa.sig", "short2.fa.sig", "--csv", "xxx", fail_ok=True ) assert c.last_result.status == -1 print(c.last_result.err) assert "multiple molecule types loaded;" in c.last_result.err def test_compare_dayhoff(runtmp): # test 'compare' works with dayhoff moltype c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "translate", "-p", "k=21,num=500", "--dayhoff", testdata1) assert c.last_result.status == 0 c.run_sourmash("sketch", "translate", "-p", "k=21,num=500", "--dayhoff", testdata2) assert c.last_result.status == 0 c.run_sourmash( "compare", "short.fa.sig", "short2.fa.sig", "--dayhoff", "--csv", "xxx" ) true_out = """[1. 0.94] [0.94 1. ] min similarity in matrix: 0.940""".splitlines() for line in c.last_result.out: cleaned_line = line.split("...")[-1].strip() cleaned_line in true_out assert c.last_result.status == 0 def test_compare_hp(runtmp): # test that 'compare' works with --hp moltype c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "translate", "-p", "k=21,num=500", "--hp", testdata1) assert c.last_result.status == 0 c.run_sourmash("sketch", "translate", "-p", "k=21,num=500", "--hp", testdata2) assert c.last_result.status == 0 c.run_sourmash("compare", "short.fa.sig", "short2.fa.sig", "--hp", "--csv", "xxx") true_out = """[1. 0.94] [0.94 1. ] min similarity in matrix: 0.940""".splitlines() for line in c.last_result.out: cleaned_line = line.split("...")[-1].strip() cleaned_line in true_out assert c.last_result.status == 0 def _load_compare_matrix_and_sigs(compare_csv, sigfiles, *, ksize=31): # load in the output of 'compare' together with sigs # load compare CSV with open(compare_csv, newline="") as fp: r = iter(csv.reader(fp)) headers = next(r) mat = numpy.zeros((len(headers), len(headers))) for i, row in enumerate(r): for j, val in enumerate(row): mat[i][j] = float(val) print(mat) # load in all the input signatures idx_to_sig = {} for idx, filename in enumerate(sigfiles): ss = load_one_signature(filename, ksize=ksize) idx_to_sig[idx] = ss return mat, idx_to_sig def test_compare_containment(runtmp): # test compare --containment c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) c.run_sourmash( "compare", "--containment", "-k", "31", "--csv", "output.csv", *testdata_sigs ) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): ss_j = idx_to_sig[j] containment = ss_j.contained_by(ss_i) containment = round(containment, 3) mat_val = round(mat[i][j], 3) assert containment == mat_val, (i, j) def test_compare_containment_distance(runtmp): # test compare --containment --distance-matrix c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) c.run_sourmash( "compare", "--containment", "--distance-matrix", "-k", "31", "--csv", "output.csv", *testdata_sigs, ) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): ss_j = idx_to_sig[j] containment = 1 - ss_j.contained_by(ss_i) containment = round(containment, 3) mat_val = round(mat[i][j], 3) assert containment == mat_val, (i, j) def test_compare_max_containment(runtmp): # test compare --max-containment c = runtmp testdata_glob = utils.get_test_data("scaled/*.sig") testdata_sigs = glob.glob(testdata_glob) c.run_sourmash( "compare", "--max-containment", "-k", "31", "--csv", "output.csv", *testdata_sigs, ) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): ss_j = idx_to_sig[j] containment = ss_j.max_containment(ss_i) containment = round(containment, 3) mat_val = round(mat[i][j], 3) assert containment == mat_val, (i, j) def test_compare_avg_containment(runtmp): # test compare --avg-containment c = runtmp testdata_glob = utils.get_test_data("scaled/*.sig") testdata_sigs = glob.glob(testdata_glob) c.run_sourmash( "compare", "--avg-containment", "-k", "31", "--csv", "output.csv", *testdata_sigs, ) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): ss_j = idx_to_sig[j] containment = ss_j.avg_containment(ss_i) containment = round(containment, 3) mat_val = round(mat[i][j], 3) assert containment == mat_val, (i, j) def test_compare_max_containment_and_containment(runtmp): # make sure that can't specify both --max-containment and --containment c = runtmp testdata_glob = utils.get_test_data("scaled/*.sig") testdata_sigs = glob.glob(testdata_glob) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "compare", "--max-containment", "-k", "31", "--containment", "--csv", "output.csv", *testdata_sigs, ) print(c.last_result.err) assert ( "ERROR: cannot specify more than one containment argument!" in c.last_result.err ) def test_compare_avg_containment_and_containment(runtmp): # make sure that can't specify both --avg-containment and --containment c = runtmp testdata_glob = utils.get_test_data("scaled/*.sig") testdata_sigs = glob.glob(testdata_glob) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "compare", "--avg-containment", "-k", "31", "--containment", "--csv", "output.csv", *testdata_sigs, ) print(c.last_result.err) assert ( "ERROR: cannot specify more than one containment argument!" in c.last_result.err ) def test_compare_avg_containment_and_max_containment(runtmp): # make sure that can't specify both --avg-containment and --max-containment c = runtmp testdata_glob = utils.get_test_data("scaled/*.sig") testdata_sigs = glob.glob(testdata_glob) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "compare", "--avg-containment", "-k", "31", "--max-containment", "--csv", "output.csv", *testdata_sigs, ) print(c.last_result.err) assert ( "ERROR: cannot specify more than one containment argument!" in c.last_result.err ) def test_compare_containment_abund_flatten_warning(runtmp): # check warning message about ignoring abund signatures c = runtmp s47 = utils.get_test_data("track_abund/47.fa.sig") s63 = utils.get_test_data("track_abund/63.fa.sig") c.run_sourmash("compare", "--containment", "-k", "31", s47, s63) print(c.last_result.out) print(c.last_result.err) assert ( "NOTE: --containment, --max-containment, --avg-containment, and --estimate-ani ignore signature abundances." in c.last_result.err ) def test_compare_ani_abund_flatten(runtmp): # check warning message about ignoring abund signatures c = runtmp s47 = utils.get_test_data("track_abund/47.fa.sig") s63 = utils.get_test_data("track_abund/63.fa.sig") c.run_sourmash("compare", "--estimate-ani", "-k", "31", s47, s63) print(c.last_result.out) print(c.last_result.err) assert ( "NOTE: --containment, --max-containment, --avg-containment, and --estimate-ani ignore signature abundances." in c.last_result.err ) def test_compare_containment_require_scaled(runtmp): # check warning message about scaled signatures & containment c = runtmp s47 = utils.get_test_data("num/47.fa.sig") s63 = utils.get_test_data("num/63.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("compare", "--containment", "-k", "31", s47, s63, fail_ok=True) assert ( "must use scaled signatures with --containment, --max-containment, and --avg-containment" in c.last_result.err ) assert c.last_result.status != 0 def test_do_plot_comparison(runtmp): # make sure 'plot' outputs files ;) c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) c.run_sourmash("compare", "short.fa.sig", "short2.fa.sig", "-o", "cmp") c.run_sourmash("plot", "cmp") assert os.path.exists(c.output("cmp.dendro.png")) assert os.path.exists(c.output("cmp.matrix.png")) def test_do_plot_comparison_2_pdf(runtmp): # test plot --pdf c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "translate", "-p", "k=31,num=500", testdata1, testdata2) c.run_sourmash("compare", "short.fa.sig", "short2.fa.sig", "-o", "cmp") c.run_sourmash("plot", "cmp", "--pdf") assert os.path.exists(c.output("cmp.dendro.pdf")) assert os.path.exists(c.output("cmp.matrix.pdf")) def test_do_plot_comparison_3(runtmp): # test plot --labels c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "translate", "-p", "k=31,num=500", testdata1, testdata2) c.run_sourmash("compare", "short.fa.sig", "short2.fa.sig", "-o", "cmp") c.run_sourmash("plot", "cmp", "--labels") assert os.path.exists(c.output("cmp.dendro.png")) assert os.path.exists(c.output("cmp.matrix.png")) def test_do_plot_comparison_4_output_dir(runtmp): # test plot --output-dir c = runtmp output_dir = c.output("xyz_test") testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "translate", "-p", "k=31,num=500", testdata1, testdata2) c.run_sourmash("compare", "short.fa.sig", "short2.fa.sig", "-o", "cmp") c.run_sourmash("plot", "cmp", "--labels", "--output-dir", output_dir) assert os.path.exists(os.path.join(output_dir, "cmp.dendro.png")) assert os.path.exists(os.path.join(output_dir, "cmp.matrix.png")) def test_do_plot_comparison_5_force(runtmp): # test -f to force display of something that's not a distance matrix c = runtmp D = numpy.zeros([2, 2]) D[0, 0] = 5 with open(c.output("cmp"), "wb") as fp: numpy.save(fp, D) with open(c.output("cmp.labels.txt"), "w") as fp: fp.write("a\nb\n") c.run_sourmash("plot", "cmp", "--labels", "-f") print(c.last_result.status, c.last_result.out, c.last_result.err) assert c.last_result.status == 0 def test_do_plot_comparison_4_fail_not_distance(runtmp): # plot should fail when not a distance matrix c = runtmp D = numpy.zeros([2, 2]) D[0, 0] = 5 with open(c.output("cmp"), "wb") as fp: numpy.save(fp, D) with open(c.output("cmp.labels.txt"), "w") as fp: fp.write("a\nb\n") with pytest.raises(SourmashCommandFailed): c.run_sourmash("plot", "cmp", "--labels", fail_ok=True) print(c.last_result.status, c.last_result.out, c.last_result.err) assert c.last_result.status != 0 def test_plot_6_labels_default(runtmp): # plot --labels is default testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) runtmp.sourmash("plot", "cmp", "--labels") print(runtmp.last_result.out) expected = """\ 0\tgenome-s10 1\tgenome-s11 2\tgenome-s12 3\tgenome-s10+s11""" assert expected in runtmp.last_result.out def test_plot_6_labels(runtmp): # specifing --labels gives the right result testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) runtmp.sourmash("plot", "cmp", "--labels") print(runtmp.last_result.out) expected = """\ 0\tgenome-s10 1\tgenome-s11 2\tgenome-s12 3\tgenome-s10+s11""" assert expected in runtmp.last_result.out def test_plot_6_indices(runtmp): # test plot --indices testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) runtmp.sourmash("plot", "cmp", "--indices") print(runtmp.last_result.out) expected = """\ 0\t1 1\t2 2\t3 3\t4""" assert expected in runtmp.last_result.out def test_plot_6_no_labels(runtmp): # test plot --no-labels testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) runtmp.sourmash("plot", "cmp", "--no-labels") print(runtmp.last_result.out) expected = """\ 0\t1 1\t2 2\t3 3\t4""" assert expected in runtmp.last_result.out def test_plot_6_no_indices(runtmp): # test plot --no-labels testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) runtmp.sourmash("plot", "cmp", "--no-labels") print(runtmp.last_result.out) expected = """\ 0\t1 1\t2 2\t3 3\t4""" assert expected in runtmp.last_result.out def test_plot_6_no_labels_no_indices(runtmp): # test plot --no-labels --no-indices testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) runtmp.sourmash("plot", "cmp", "--no-labels", "--no-indices") print((runtmp.last_result.out,)) expected = """\ 0\t 1\t 2\t 3\t""" assert expected in runtmp.last_result.out def test_plot_6_indices_labels(runtmp): # check that --labels --indices => --labels testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) runtmp.sourmash("plot", "cmp", "--labels", "--indices") print(runtmp.last_result.out) expected = """\ 0\tgenome-s10 1\tgenome-s11 2\tgenome-s12 3\tgenome-s10+s11""" assert expected in runtmp.last_result.out def test_plot_override_labeltext(runtmp): # test overriding labeltext testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) with open(runtmp.output("new.labels.txt"), "w") as fp: fp.write("a\nb\nc\nd\n") runtmp.sourmash("plot", "cmp", "--labeltext", "new.labels.txt") print(runtmp.last_result.out) assert "loading labels from text file 'new.labels.txt'" in runtmp.last_result.err expected = """\ 0\ta 1\tb 2\tc 3\td""" assert expected in runtmp.last_result.out def test_plot_override_labeltext_fail(runtmp): # test failed override of labeltext testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) with open(runtmp.output("new.labels.txt"), "w") as fp: fp.write("a\nb\nc\n") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("plot", "cmp", "--labeltext", "new.labels.txt") print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status != 0 assert "loading labels from text file 'new.labels.txt'" in runtmp.last_result.err assert "3 labels != matrix size, exiting" in runtmp.last_result.err def test_plot_reordered_labels_csv(runtmp): # test 'plot --csv' & correct ordering of labels c = runtmp ss2 = utils.get_test_data("2.fa.sig") ss47 = utils.get_test_data("47.fa.sig") ss63 = utils.get_test_data("63.fa.sig") c.run_sourmash("compare", "-k", "31", "-o", "cmp", ss2, ss47, ss63) c.run_sourmash("plot", "cmp", "--csv", "neworder.csv") with open(c.output("neworder.csv"), newline="") as fp: r = csv.DictReader(fp) akker_vals = set() for row in r: akker_vals.add( row["CP001071.1 Akkermansia muciniphila ATCC BAA-835, complete genome"] ) assert "1.0" in akker_vals assert "0.0" in akker_vals assert len(akker_vals) == 2 def test_plot_reordered_labels_csv_gz(runtmp): # test 'plot --csv' with a .gz output c = runtmp ss2 = utils.get_test_data("2.fa.sig") ss47 = utils.get_test_data("47.fa.sig") ss63 = utils.get_test_data("63.fa.sig") c.run_sourmash("compare", "-k", "31", "-o", "cmp", ss2, ss47, ss63) c.run_sourmash("plot", "cmp", "--csv", "neworder.csv.gz") with gzip.open(c.output("neworder.csv.gz"), "rt", newline="") as fp: r = csv.DictReader(fp) akker_vals = set() for row in r: akker_vals.add( row["CP001071.1 Akkermansia muciniphila ATCC BAA-835, complete genome"] ) assert "1.0" in akker_vals assert "0.0" in akker_vals assert len(akker_vals) == 2 def test_plot_subsample_1(runtmp): # test plotting with --subsample testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) runtmp.sourmash("plot", "cmp", "--subsample", "3") print(runtmp.last_result.out) expected = """\ 0\tgenome-s10+s11 1\tgenome-s12 2\tgenome-s10""" assert expected in runtmp.last_result.out def test_plot_subsample_2(runtmp): # test plotting --subsample with --subsample-seed testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") runtmp.sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) runtmp.sourmash("plot", "cmp", "--subsample", "3", "--subsample-seed=2") print(runtmp.last_result.out) expected = """\ 0\tgenome-s12 1\tgenome-s10+s11 2\tgenome-s11""" assert expected in runtmp.last_result.out def test_compare_and_plot_labels_from_to(runtmp): # test doing compare --labels-to and plot --labels-from. testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") labels_csv = runtmp.output("label.csv") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", "--labels-to", labels_csv, ) runtmp.sourmash("plot", "cmp", "--labels-from", labels_csv) print(runtmp.last_result.out) assert "loading labels from CSV file" in runtmp.last_result.err expected = """\ 0\tgenome-s10 1\tgenome-s11 2\tgenome-s12 3\tgenome-s10+s11""" assert expected in runtmp.last_result.out def test_compare_and_plot_labels_from_changed(runtmp): # test 'plot --labels-from' with changed labels testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") labels_csv = utils.get_test_data("compare/labels_from-test.csv") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) runtmp.sourmash("plot", "cmp", "--labels-from", labels_csv) print(runtmp.last_result.out) assert "loading labels from CSV file" in runtmp.last_result.err expected = """\ 0\tgenome-s10-CHANGED 1\tgenome-s11-CHANGED 2\tgenome-s12-CHANGED 3\tgenome-s10+s11-CHANGED""" assert expected in runtmp.last_result.out def test_compare_and_plot_labels_from_error(runtmp): # 'plot --labels-from ... --labeltext ...' should fail testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") labels_csv = utils.get_test_data("compare/labels_from-test.csv") runtmp.run_sourmash( "compare", testdata1, testdata2, testdata3, testdata4, "-o", "cmp", "-k", "21", "--dna", ) with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "plot", "cmp", "--labels-from", labels_csv, "--labeltext", labels_csv, fail_ok=True, ) err = runtmp.last_result.err assert "ERROR: cannot supply both --labeltext and --labels-from" in err @utils.in_tempdir def test_search_query_sig_does_not_exist(c): testdata1 = utils.get_test_data("short.fa") c.run_sourmash("sketch", "translate", "-p", "k=31,num=500", testdata1) with pytest.raises(SourmashCommandFailed): c.run_sourmash("search", "short2.fa.sig", "short.fa.sig", fail_ok=True) print(c.last_result.status, c.last_result.out, c.last_result.err) assert c.last_result.status == -1 assert "Cannot open query file 'short2.fa.sig'" in c.last_result.err assert len(c.last_result.err.split("\n\r")) < 5 @utils.in_tempdir def test_search_subject_sig_does_not_exist(c): testdata1 = utils.get_test_data("short.fa") c.run_sourmash("sketch", "translate", "-p", "k=31,num=500", testdata1) with pytest.raises(SourmashCommandFailed): c.run_sourmash("search", "short.fa.sig", "short2.fa.sig", fail_ok=True) print(c.last_result.status, c.last_result.out, c.last_result.err) assert c.last_result.status == -1 assert "Error while reading signatures from 'short2.fa.sig'" in c.last_result.err @utils.in_tempdir def test_search_second_subject_sig_does_not_exist(c): testdata1 = utils.get_test_data("short.fa") c.run_sourmash("sketch", "translate", "-p", "k=31,num=500", testdata1) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "search", "short.fa.sig", "short.fa.sig", "short2.fa.sig", fail_ok=True ) print(c.last_result.status, c.last_result.out, c.last_result.err) assert c.last_result.status == -1 assert "Error while reading signatures from 'short2.fa.sig'." in c.last_result.err @utils.in_tempdir def test_search(c): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) c.run_sourmash("search", "short.fa.sig", "short2.fa.sig") print(c.last_result.status, c.last_result.out, c.last_result.err) assert "1 matches" in c.last_result.out assert "93.0%" in c.last_result.out def test_search_ignore_abundance(runtmp): # note: uses num signatures. testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500,abund", testdata1, testdata2) # Make sure there's different percent matches when using or # not using abundance runtmp.sourmash("search", "short.fa.sig", "short2.fa.sig") out1 = runtmp.last_result.out print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "81.5%" in runtmp.last_result.out runtmp.sourmash("search", "--ignore-abundance", "short.fa.sig", "short2.fa.sig") out2 = runtmp.last_result.out print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "93.0%" in runtmp.last_result.out # Make sure results are different! assert out1 != out2 def test_search_abund_subj_flat(runtmp): # test Index.search_abund requires an abund subj sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") with pytest.raises(SourmashCommandFailed) as exc: runtmp.sourmash("search", sig47, sig63) assert ( "'search_abund' requires subject signatures with abundance information" in str(exc.value) ) def test_search_abund_csv(runtmp): # test search with abundance signatures, look at CSV output testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,scaled=1,abund", testdata1, testdata2) runtmp.sourmash("search", "short.fa.sig", "short2.fa.sig", "-o", "xxx.csv") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "82.7%" in runtmp.last_result.out with open(runtmp.output("xxx.csv"), newline="") as fp: r = csv.DictReader(fp) row = next(r) print(row) assert float(row["similarity"]) == 0.8266277454288367 assert row["md5"] == "bf752903d635b1eb83c53fe4aae951db" assert row["filename"].endswith("short2.fa.sig") assert row["md5"] == "bf752903d635b1eb83c53fe4aae951db" assert row["query_filename"].endswith("short.fa") assert row["query_name"] == "" assert row["query_md5"] == "9191284a" assert row["filename"] == "short2.fa.sig", row["filename"] @utils.in_tempdir def test_search_csv(c): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) c.run_sourmash("search", "short.fa.sig", "short2.fa.sig", "-o", "xxx.csv") print(c.last_result.status, c.last_result.out, c.last_result.err) csv_file = c.output("xxx.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert float(row["similarity"]) == 0.93 assert row["filename"].endswith("short2.fa.sig") assert row["md5"] == "914591cd1130aa915fe0c0c63db8f19d" assert row["query_filename"].endswith("short.fa") assert row["query_name"] == "" assert row["query_md5"] == "e26a306d" @utils.in_tempdir def test_search_lca_db(c): # can we do a 'sourmash search' on an LCA database? query = utils.get_test_data("47.fa.sig") lca_db = utils.get_test_data("lca/47+63.lca.json") c.run_sourmash("search", query, lca_db) print(c) assert "NC_009665.1 Shewanella baltica OS185, complete genome" in str(c) def test_search_query_db_md5(runtmp): # pull a search query out of a database with an md5sum db = utils.get_test_data("prot/protein.sbt.zip") runtmp.run_sourmash("search", db, db, "--md5", "16869d2c8a1") assert "100.0% GCA_001593925" in str(runtmp) def test_gather_query_db_md5(runtmp, linear_gather, prefetch_gather): # pull a search query out of a database with an md5sum db = utils.get_test_data("prot/protein.sbt.zip") runtmp.run_sourmash( "gather", db, db, "--md5", "16869d2c8a1", linear_gather, prefetch_gather ) assert "340.9 kbp 100.0% 100.0% GCA_001593925" in str(runtmp) def test_gather_query_db_md5_ambiguous(runtmp, linear_gather, prefetch_gather): c = runtmp # what if we give an ambiguous md5 prefix? db = utils.get_test_data("prot/protein.sbt.zip") with pytest.raises(SourmashCommandFailed): c.run_sourmash("gather", db, db, "--md5", "1", linear_gather, prefetch_gather) err = c.last_result.err assert "Error! Multiple signatures start with md5 '1'" in err def test_gather_lca_db(runtmp, linear_gather, prefetch_gather): # can we do a 'sourmash gather' on an LCA database? query = utils.get_test_data("47+63.fa.sig") lca_db = utils.get_test_data("lca/47+63.lca.json") runtmp.sourmash("gather", query, lca_db, linear_gather, prefetch_gather) print(runtmp) out = runtmp.last_result.out assert "NC_009665.1 Shewanella baltica OS185" in out assert "WARNING: final scaled was 10000, vs query scaled of 1000" in out def test_gather_csv_output_filename_bug(runtmp, linear_gather, prefetch_gather): c = runtmp # check a bug where the database filename in the output CSV was incorrect query = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") lca_db_1 = utils.get_test_data("lca/delmont-1.lca.json") lca_db_2 = utils.get_test_data("lca/delmont-2.lca.json") c.run_sourmash( "gather", query, lca_db_1, lca_db_2, "-o", "out.csv", linear_gather, prefetch_gather, ) with open(c.output("out.csv")) as fp: r = csv.DictReader(fp) row = next(r) assert row["filename"] == lca_db_1 def test_compare_no_such_file(runtmp): # 'compare' fails on nonexistent files c = runtmp with pytest.raises(SourmashCommandFailed): c.run_sourmash("compare", "nosuchfile.sig") assert "Error while reading signatures from 'nosuchfile.sig'." in c.last_result.err def test_compare_no_such_file_force(runtmp): # can still run compare on nonexistent with -f c = runtmp with pytest.raises(SourmashCommandFailed): c.run_sourmash("compare", "nosuchfile.sig", "-f") print(c.last_result.err) assert "Error while reading signatures from 'nosuchfile.sig'." def test_compare_no_matching_sigs(runtmp): # compare fails when no sketches found with desired ksize c = runtmp query = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") with pytest.raises(SourmashCommandFailed): c.last_result.status, c.last_result.out, c.last_result.err = c.run_sourmash( "compare", "-k", "100", query, fail_ok=True ) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status assert ( "warning: no signatures loaded at given ksize/molecule type" in c.last_result.err ) assert "no signatures found! exiting." in c.last_result.err def test_compare_deduce_molecule(runtmp): # deduce DNA vs protein from query, if it is unique testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "translate", "-p", "k=10,num=500", testdata1, testdata2) runtmp.sourmash("compare", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "min similarity in matrix: 0.91" in runtmp.last_result.out def test_compare_choose_molecule_dna(runtmp): # choose molecule type with --dna, ignoring protein testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash( "sketch", "dna", "-p", "k=30,num=500", testdata1, testdata2, "-o", "sigs.zip" ) runtmp.sourmash( "sketch", "translate", "-p", "k=10,num=500", testdata1, testdata2, "-o", "sigs.zip", ) runtmp.sourmash("compare", "--dna", "sigs.zip") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "min similarity in matrix: 0.938" in runtmp.last_result.out def test_compare_choose_molecule_protein(runtmp): # choose molecule type with --protein, ignoring DNA testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash( "sketch", "dna", "-p", "k=30,num=500", testdata1, testdata2, "-o", "sigs.zip" ) runtmp.sourmash( "sketch", "translate", "-p", "k=10,num=500", testdata1, testdata2, "-o", "sigs.zip", ) runtmp.sourmash("compare", "--protein", "sigs.zip") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "min similarity in matrix: 0.91" in runtmp.last_result.out def test_compare_no_choose_molecule_fail(runtmp): # choose molecule type testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=30,num=500", testdata1) runtmp.sourmash("sketch", "protein", "-p", "k=30,num=500", testdata2) with pytest.raises(SourmashCommandFailed): runtmp.sourmash("compare", "short.fa.sig", "short2.fa.sig") assert "multiple molecule types loaded; please specify" in runtmp.last_result.err assert runtmp.last_result.status != 0 def test_compare_deduce_ksize(runtmp): # deduce ksize, if it is unique testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=29,num=500", testdata1, testdata2) runtmp.sourmash("compare", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "min similarity in matrix: 0.938" in runtmp.last_result.out def test_search_deduce_molecule(runtmp): # deduce DNA vs protein from query, if it is unique testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "translate", "-p", "k=10,num=500", testdata1, testdata2) runtmp.sourmash("search", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "(k=10, protein)" in runtmp.last_result.err def test_search_deduce_ksize(runtmp): # deduce ksize from query, if it is unique testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "translate", "-p", "k=23,num=500", testdata1, testdata2) runtmp.sourmash("search", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "k=23" in runtmp.last_result.err def test_do_sourmash_index_multik_fail(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "translate", "-p", "k=31,num=500", testdata1) runtmp.sourmash("sketch", "translate", "-p", "k=32,num=500", testdata2) with pytest.raises(SourmashCommandFailed): runtmp.sourmash("index", "zzz", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert runtmp.last_result.status == -1 def test_do_sourmash_index_multimol_fail(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "translate", testdata1) runtmp.sourmash("sketch", "translate", "-p", "k=30,num=500", testdata2) with pytest.raises(SourmashCommandFailed): runtmp.sourmash("index", "zzz", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert runtmp.last_result.status == -1 def test_do_sourmash_index_multinum_fail(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "translate", "-p", "k=31,num=500", testdata1) runtmp.sourmash("sketch", "translate", "-p", "k=31,num=1000", testdata2) with pytest.raises(SourmashCommandFailed): runtmp.sourmash("index", "zzz", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert runtmp.last_result.status == -1 assert ( "trying to build an SBT with incompatible signatures." in runtmp.last_result.err ) def test_do_sourmash_index_multiscaled_fail(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=10", testdata1) runtmp.sourmash("sketch", "dna", "-p", "scaled=1", testdata2) with pytest.raises(SourmashCommandFailed): runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert runtmp.last_result.status == -1 assert ( "trying to build an SBT with incompatible signatures." in runtmp.last_result.err ) @utils.in_tempdir def test_do_sourmash_index_multiscaled_rescale(c): # test sourmash index --scaled testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "scaled=10", testdata1) c.run_sourmash("sketch", "dna", "-p", "scaled=1", testdata2) c.run_sourmash( "index", "zzz", "short.fa.sig", "short2.fa.sig", "-k", "31", "--scaled", "10" ) print(c) assert c.last_result.status == 0 @utils.in_tempdir def test_do_sourmash_index_multiscaled_rescale_fail(c): # test sourmash index --scaled with invalid rescaling (10 -> 5) testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "scaled=10", testdata1) c.run_sourmash("sketch", "dna", "-p", "scaled=1", testdata2) # this should fail: cannot go from a scaled value of 10 to 5 with pytest.raises(SourmashCommandFailed) as e: c.run_sourmash( "index", "zzz", "short.fa.sig", "short2.fa.sig", "-k", "31", "--scaled", "5" ) print(e.value) assert c.last_result.status == -1 assert "new scaled 5 is lower than current sample scaled 10" in c.last_result.err def test_do_sourmash_sbt_search_output(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) runtmp.sourmash("index", "zzz", "short.fa.sig", "short2.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("search", "short.fa.sig", "zzz", "-o", "foo") output = Path(runtmp.output("foo")).read_text() print(output) assert "e26a306d26512" in output assert "914591cd1130aa915" in output # check against a bug in sbt search triggered by incorrect max Jaccard # calculation. def test_do_sourmash_sbt_search_check_bug(runtmp): # mins: 431 testdata1 = utils.get_test_data("sbt-search-bug/nano.sig") # mins: 6264 testdata2 = utils.get_test_data("sbt-search-bug/bacteroides.sig") runtmp.sourmash("index", "zzz", testdata1, testdata2, "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("search", testdata1, "zzz") assert "1 matches" in runtmp.last_result.out tree = load_sbt_index(runtmp.output("zzz.sbt.zip")) assert tree._nodes[0].metadata["min_n_below"] == 431 def test_do_sourmash_sbt_search_empty_sig(runtmp): # mins: 431 testdata1 = utils.get_test_data("sbt-search-bug/nano.sig") # mins: 0 testdata2 = utils.get_test_data("sbt-search-bug/empty.sig") runtmp.sourmash("index", "zzz", testdata1, testdata2, "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("search", testdata1, "zzz") assert "1 matches" in runtmp.last_result.out tree = load_sbt_index(runtmp.output("zzz.sbt.zip")) assert tree._nodes[0].metadata["min_n_below"] == 1 def test_do_sourmash_sbt_move_and_search_output(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) runtmp.sourmash( "index", "zzz.sbt.json", "short.fa.sig", "short2.fa.sig", "-k", "31" ) assert os.path.exists(runtmp.output("zzz.sbt.json")) print(runtmp.last_result.out) with open(runtmp.output("zzz.sbt.json")) as fp: d = json.load(fp) assert d["storage"]["args"]["path"] == ".sbt.zzz" newpath = runtmp.output("subdir") os.mkdir(newpath) # move both JSON file and subdirectory. shutil.move(runtmp.output("zzz.sbt.json"), newpath) shutil.move(runtmp.output(".sbt.zzz"), newpath) status, out, err = utils.runscript( "sourmash", ["search", "../short.fa.sig", "zzz.sbt.json", "-o", "foo"], in_directory=newpath, ) output = Path(os.path.join(newpath, "foo")).read_text() print(output) assert "914591cd1130aa91" in output assert "e26a306d2651" in output def test_search_deduce_ksize_and_select_appropriate(runtmp): # deduce ksize from query and select correct signature from DB testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "translate", "-p", "k=24,num=500", testdata1) # The DB contains signatres for multiple ksizes runtmp.sourmash( "sketch", "translate", "-p", "k=23,num=500", "-p", "k=24,num=500", testdata2 ) runtmp.sourmash("search", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "k=24" in runtmp.last_result.err def test_search_deduce_ksize_not_unique(runtmp): # deduce ksize from query, fail because it is not unique testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash( "sketch", "translate", "-p", "k=23,num=500", "-p", "k=25,num=500", testdata1, testdata2, ) with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert runtmp.last_result.status == -1 assert "2 signatures matching ksize" in runtmp.last_result.err @utils.in_tempdir def test_search_deduce_ksize_no_match(c): # no matching sigs in search sig list testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "translate", "-p", "k=23,num=500", testdata1) c.run_sourmash("sketch", "translate", "-p", "k=25,num=500", testdata2) with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash("search", "short.fa.sig", "short2.fa.sig") assert "no compatible signatures found in 'short2.fa.sig'" in str(exc.value) def test_search_deduce_ksize_vs_user_specified(runtmp): # user specified ksize is not available testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "translate", "-p", "k=23,num=500", testdata1, testdata2) with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", "-k", "24", "short.fa.sig", "short2.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert runtmp.last_result.status == -1 assert "0 signatures matching ksize" in runtmp.last_result.err def test_search_containment(runtmp): # search with --containment in signatures testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=1", testdata1, testdata2) runtmp.sourmash("search", "short.fa.sig", "short2.fa.sig", "--containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "95.6%" in runtmp.last_result.out def test_search_containment_abund(runtmp): "Construct some signatures with abund, make sure that containment complains" # build minhashes mh1 = MinHash(0, 21, scaled=1, track_abundance=True) mh2 = MinHash(0, 21, scaled=1, track_abundance=True) mh1.add_many((1, 2, 3, 4)) mh1.add_many((1, 2)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) # build signatures x = sourmash.SourmashSignature(mh1, name="a") y = sourmash.SourmashSignature(mh2, name="b") # save! with open(runtmp.output("a.sig"), "w") as fp: signature.save_signatures_to_json([x], fp) with open(runtmp.output("b.sig"), "w") as fp: signature.save_signatures_to_json([y], fp) # run sourmash search --containment with pytest.raises(SourmashCommandFailed) as exc: runtmp.sourmash("search", "a.sig", "b.sig", "-o", "xxx.csv", "--containment") assert ( "ERROR: cannot do containment searches on an abund signature; maybe specify --ignore-abundance?" in str(exc) ) # run sourmash search --max-containment with pytest.raises(SourmashCommandFailed) as exc: runtmp.sourmash( "search", "a.sig", "b.sig", "-o", "xxx.csv", "--max-containment" ) assert ( "ERROR: cannot do containment searches on an abund signature; maybe specify --ignore-abundance?" in str(exc) ) def test_search_containment_abund_ignore(runtmp): "Construct some signatures with abund, check containment + ignore abund" # build minhashes mh1 = MinHash(0, 21, scaled=1, track_abundance=True) mh2 = MinHash(0, 21, scaled=1, track_abundance=True) mh1.add_many((1, 2, 3, 4)) mh1.add_many((1, 2)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) mh2.add_many((1, 5)) # build signatures x = sourmash.SourmashSignature(mh1, name="a") y = sourmash.SourmashSignature(mh2, name="b") # save! with open(runtmp.output("a.sig"), "w") as fp: signature.save_signatures_to_json([x], fp) with open(runtmp.output("b.sig"), "w") as fp: signature.save_signatures_to_json([y], fp) # run sourmash search runtmp.sourmash( "search", "a.sig", "b.sig", "-o", "xxx.csv", "--containment", "--ignore-abundance", ) # check results with open(runtmp.output("xxx.csv")) as fp: r = csv.DictReader(fp) row = next(r) similarity = row["similarity"] print(f"search output: similarity is {similarity}") print(mh1.contained_by(mh2)) assert float(similarity) == mh1.contained_by(mh2) assert float(similarity) == 0.25 def test_search_containment_sbt(runtmp): # search with --containment in an SBT testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=1", testdata1, testdata2) runtmp.sourmash("index", "-k", "31", "zzz", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("search", "short.fa.sig", "zzz", "--containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "95.6%" in runtmp.last_result.out def test_search_containment_s10(runtmp): # check --containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/genome-s10-small.fa.gz.sig") runtmp.sourmash("search", q1, q2, "--containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "16.7%" in runtmp.last_result.out def test_search_containment_s10_no_max(run): # check --containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/genome-s10-small.fa.gz.sig") with pytest.raises(SourmashCommandFailed): run.run_sourmash("search", q1, q2, "--containment", "--max-containment") print(run.last_result.out) print(run.last_result.err) assert ( "ERROR: cannot specify both --containment and --max-containment!" in run.last_result.err ) def test_search_max_containment_s10_pairwise(runtmp): # check --max-containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/genome-s10-small.fa.gz.sig") runtmp.sourmash("search", q1, q2, "--max-containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "100.0%" in runtmp.last_result.out def test_search_containment_s10_siglist(runtmp): # check --containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/*.sig") q2 = glob.glob(q2) runtmp.sourmash("search", q1, *q2, "--containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "3 matches" in runtmp.last_result.out assert " 16.7% ../genome-s10-small.fa.gz" in runtmp.last_result.out assert "100.0% ../genome-s10.fa.gz" in runtmp.last_result.out assert "100.0% ../genome-s10+s11.fa.gz" in runtmp.last_result.out def test_search_max_containment_s10_siglist(runtmp): # check --max-containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/*.sig") q2 = glob.glob(q2) runtmp.sourmash("search", q1, *q2, "--max-containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "3 matches" in runtmp.last_result.out assert "100.0% ../genome-s10-small.fa.gz" in runtmp.last_result.out assert "100.0% ../genome-s10.fa.gz" in runtmp.last_result.out assert "100.0% ../genome-s10+s11.fa.gz" in runtmp.last_result.out def test_search_containment_s10_sbt(runtmp): # check --containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/all.sbt.zip") runtmp.sourmash("search", q1, q2, "--containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "3 matches" in runtmp.last_result.out assert "100.0% ../genome-s10+s11.fa.gz" in runtmp.last_result.out assert "100.0% ../genome-s10.fa.gz" in runtmp.last_result.out assert " 16.7% ../genome-s10-small.fa.gz" in runtmp.last_result.out def test_search_containment_s10_sbt_best_only(runtmp): # check --containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/all.sbt.zip") runtmp.sourmash("search", q1, q2, "--containment", "--best-only") print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "100.0% " in runtmp.last_result.out ) # there are at least two perfect matches! assert runtmp.last_result.status == 0 def test_search_containment_s10_sbt_empty(runtmp): # check --containment for s10/s10-small at absurd scaled/empty mh q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/all.sbt.zip") runtmp.sourmash("search", q1, q2, "--scaled", "1e7", "--containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "0 matches" in runtmp.last_result.out def test_search_max_containment_s10_sbt(runtmp): # check --max-containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/all.sbt.zip") runtmp.sourmash("search", q1, q2, "--max-containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "3 matches" in runtmp.last_result.out assert "100.0% ../genome-s10-small.fa.gz" in runtmp.last_result.out assert "100.0% ../genome-s10.fa.gz" in runtmp.last_result.out assert "100.0% ../genome-s10+s11.fa.gz" in runtmp.last_result.out def test_search_max_containment_s10_sbt_best_only(runtmp): # check --max-containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/all.sbt.zip") runtmp.sourmash("search", q1, q2, "--max-containment", "--best-only") print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 def test_search_max_containment_s10_sbt_empty(runtmp): # check --max-containment for s10/s10-small at absurd scaled/empty mh. q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/all.sbt.zip") runtmp.sourmash("search", q1, q2, "--scaled", "1e7", "--max-containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "0 matches" in runtmp.last_result.out def test_search_containment_s10_lca(runtmp): # check --containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/all.lca.json") runtmp.sourmash("search", q1, q2, "--containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "3 matches" in runtmp.last_result.out assert "100.0% 455c2f95" in runtmp.last_result.out assert "100.0% 684aa226" in runtmp.last_result.out assert " 16.7% 7f7835d2" in runtmp.last_result.out def test_search_max_containment_s10_lca(runtmp): # check --max-containment for s10/s10-small q1 = utils.get_test_data("scaled/genome-s10.fa.gz.sig") q2 = utils.get_test_data("scaled/all.lca.json") runtmp.sourmash("search", q1, q2, "--max-containment") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "3 matches" in runtmp.last_result.out assert "100.0% 455c2f95" in runtmp.last_result.out assert "100.0% 684aa226" in runtmp.last_result.out assert "100.0% 7f7835d2" in runtmp.last_result.out def test_search_gzip(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) data = Path(runtmp.output("short.fa.sig")).read_bytes() with gzip.open(runtmp.output("zzz.gz"), "wb") as fp: fp.write(data) data = Path(runtmp.output("short2.fa.sig")).read_bytes() with gzip.open(runtmp.output("yyy.gz"), "wb") as fp: fp.write(data) runtmp.sourmash("search", "zzz.gz", "yyy.gz") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "93.0%" in runtmp.last_result.out def test_search_2(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") runtmp.sourmash( "sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2, testdata3 ) runtmp.sourmash("search", "short.fa.sig", "short2.fa.sig", "short3.fa.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "2 matches" in runtmp.last_result.out assert "93.0%" in runtmp.last_result.out assert "89.6%" in runtmp.last_result.out def test_search_3(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") runtmp.sourmash( "sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2, testdata3 ) runtmp.sourmash( "search", "-n", "1", "short.fa.sig", "short2.fa.sig", "short3.fa.sig" ) print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "2 matches above threshold 0.080; showing first 1:" in runtmp.last_result.out def test_search_4(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") runtmp.sourmash( "sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2, testdata3 ) runtmp.sourmash( "search", "-n", "0", "short.fa.sig", "short2.fa.sig", "short3.fa.sig" ) print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "2 matches above threshold 0.080:" in runtmp.last_result.out assert "short2.fa" in runtmp.last_result.out assert "short3.fa" in runtmp.last_result.out def test_search_5_num_results(runtmp): query = utils.get_test_data("gather/combined.sig") against = glob.glob(utils.get_test_data("gather/GCF*.sig")) runtmp.sourmash("search", "-n", "5", query, *against) print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert ( "12 matches above threshold 0.080; showing first 5:" in runtmp.last_result.out ) def test_index_check_scaled_bounds_negative(runtmp): with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "index", "zzz", "short.fa.sig", "short2.fa.sig", "-k", "31", "--scaled", "-5", "--dna", ) print(runtmp.last_result.err) assert "ERROR: scaled value must be positive" in runtmp.last_result.err def test_index_check_scaled_bounds_less_than_minimum(runtmp): with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "index", "zzz", "short.fa.sig", "short2.fa.sig", "-k", "31", "--scaled", "50", "--dna", ) assert ( "WARNING: scaled value should be >= 100. Continuing anyway." in runtmp.last_result.err ) def test_index_check_scaled_bounds_more_than_maximum(runtmp): with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "index", "zzz", "short.fa.sig", "short2.fa.sig", "-k", "31", "--scaled", "1e9", "--dna", ) assert ( "WARNING: scaled value should be <= 1e6. Continuing anyway." in runtmp.last_result.err ) @utils.in_tempdir def test_index_metagenome_fromfile(c): # test index --from-file testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") # construct a file list with open(c.output("sig.list"), "w") as fp: fp.write("\n".join(testdata_sigs)) cmd = [ "index", "gcf_all", testdata_sigs[0], "-k", "21", "--from-file", c.output("sig.list"), ] c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) cmd = f"search {query_sig} gcf_all -k 21" cmd = cmd.split() c.run_sourmash(*cmd) out = c.last_result.out print(out) print(c.last_result.err) assert ( " 33.2% NC_003198.1 Salmonella enterica subsp. enterica serovar T..." in out ) assert "12 matches above threshold 0.080; showing first 3:" in out @utils.in_tempdir def test_index_metagenome_fromfile_no_cmdline_sig(c): # test index --from-file testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") # construct a file list with open(c.output("sig.list"), "w") as fp: fp.write("\n".join(testdata_sigs)) cmd = ["index", "gcf_all", "-k", "21", "--from-file", c.output("sig.list")] c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) cmd = f"search {query_sig} gcf_all -k 21" cmd = cmd.split() c.run_sourmash(*cmd) out = c.last_result.out print(out) print(c.last_result.err) assert ( " 33.2% NC_003198.1 Salmonella enterica subsp. enterica serovar T" in out ) assert "12 matches above threshold 0.080; showing first 3:" in out def test_search_metagenome(runtmp): testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash("search", query_sig, "gcf_all", "-k", "21") print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( " 33.2% NC_003198.1 Salmonella enterica subsp. enterica serovar T" in runtmp.last_result.out ) assert ( "12 matches above threshold 0.080; showing first 3:" in runtmp.last_result.out ) def test_search_metagenome_traverse(runtmp): testdata_dir = utils.get_test_data("gather") query_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash("search", query_sig, testdata_dir, "-k", "21") print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( " 33.2% NC_003198.1 Salmonella enterica subsp. enterica serovar T" in runtmp.last_result.out ) assert ( "13 matches above threshold 0.080; showing first 3:" in runtmp.last_result.out ) def test_search_metagenome_traverse_check_csv(runtmp): # this test confirms that the CSV 'filename' output for signatures loaded # via directory traversal properly contains the actual path to the # signature file from which the signature was loaded. testdata_dir = utils.get_test_data("gather") query_sig = utils.get_test_data("gather/combined.sig") out_csv = runtmp.output("out.csv") runtmp.sourmash("search", query_sig, testdata_dir, "-k", "21", "-o", out_csv) print(runtmp.last_result.out) print(runtmp.last_result.err) with open(out_csv) as fp: prefix_len = len(testdata_dir) r = csv.DictReader(fp) for row in r: print(row) filename = row["filename"] assert filename.startswith(testdata_dir), filename # should have full path to file sig was loaded from assert len(filename) > prefix_len assert ( " 33.2% NC_003198.1 Salmonella enterica subsp. enterica serovar T" in runtmp.last_result.out ) assert ( "13 matches above threshold 0.080; showing first 3:" in runtmp.last_result.out ) @utils.in_thisdir def test_search_incompatible(c): num_sig = utils.get_test_data("num/47.fa.sig") scaled_sig = utils.get_test_data("47.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("search", scaled_sig, num_sig, fail_ok=True) assert c.last_result.status != 0 print(c.last_result.out) print(c.last_result.err) assert "no compatible signatures found in " in c.last_result.err @utils.in_tempdir def test_search_traverse_incompatible(c): # build a directory with some signatures in it, search for compatible # signatures. searchdir = c.output("searchme") os.mkdir(searchdir) num_sig = utils.get_test_data("num/47.fa.sig") scaled_sig = utils.get_test_data("47.fa.sig") shutil.copyfile(num_sig, c.output("searchme/num.sig")) shutil.copyfile(scaled_sig, c.output("searchme/scaled.sig")) c.run_sourmash("search", scaled_sig, c.output("searchme")) assert ( "100.0% NC_009665.1 Shewanella baltica OS185, complete genome" in c.last_result.out ) def test_search_check_scaled_bounds_negative(runtmp): testdata_glob = utils.get_test_data("gather/GCF*.sig") glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", query_sig, "gcf_all", "-k", "21", "--scaled", "-5") assert "ERROR: scaled value must be positive" in runtmp.last_result.err def test_search_check_scaled_bounds_less_than_minimum(runtmp): testdata_glob = utils.get_test_data("gather/GCF*.sig") glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", query_sig, "gcf_all", "-k", "21", "--scaled", "50") assert ( "WARNING: scaled value should be >= 100. Continuing anyway." in runtmp.last_result.err ) def test_search_check_scaled_bounds_more_than_maximum(runtmp): testdata_glob = utils.get_test_data("gather/GCF*.sig") glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", query_sig, "gcf_all", "-k", "21", "--scaled", "1e9") assert ( "WARNING: scaled value should be <= 1e6. Continuing anyway." in runtmp.last_result.err ) # explanation: you cannot downsample a scaled SBT to match a scaled # signature, so make sure that when you try such a search, it fails! # (you *can* downsample a signature to match an SBT.) def test_search_metagenome_sbt_downsample_fail(runtmp): # test downsample on SBT => failure, with --fail-on-empty-databases testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "search", query_sig, "gcf_all", "-k", "21", "--scaled", "100000" ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == -1 assert "ERROR: cannot use 'gcf_all' for this query." in runtmp.last_result.err assert ( "search scaled value 100000 is less than database scaled value of 10000" in runtmp.last_result.err ) def test_search_metagenome_sbt_downsample_nofail(runtmp): # test downsample on SBT => failure but ok with --no-fail-on-empty-database testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash( "search", query_sig, "gcf_all", "-k", "21", "--scaled", "100000", "--no-fail-on-empty-database", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert "ERROR: cannot use 'gcf_all' for this query." in runtmp.last_result.err assert ( "search scaled value 100000 is less than database scaled value of 10000" in runtmp.last_result.err ) assert "0 matches" in runtmp.last_result.out def test_search_metagenome_downsample_containment(runtmp): testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash( "search", query_sig, "gcf_all", "-k", "21", "--scaled", "100000", "--containment", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( " 32.9% NC_003198.1 Salmonella enterica subsp. enterica serovar T" in runtmp.last_result.out ) assert ( "12 matches above threshold 0.080; showing first 3:" in runtmp.last_result.out ) @utils.in_tempdir def test_search_metagenome_downsample_index(c): # does same search as search_metagenome_downsample_containment but # rescales during indexing testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") # downscale during indexing, rather than during search. c.run_sourmash("index", "gcf_all", *testdata_sigs, "-k", "21", "--scaled", "100000") assert os.path.exists(c.output("gcf_all.sbt.zip")) c.run_sourmash("search", query_sig, "gcf_all", "-k", "21", "--containment") print(c) assert ( " 32.9% NC_003198.1 Salmonella enterica subsp. enterica serovar T" in str(c) ) assert ( " 29.7% NC_003197.2 Salmonella enterica subsp. enterica serovar T" in str(c) ) assert "12 matches above threshold 0.080; showing first 3:" in str(c) def test_search_with_picklist(runtmp): # test 'sourmash search' with picklists gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") runtmp.sourmash( "search", metag_sig, *gcf_sigs, "--containment", "-k", "21", "--picklist", f"{picklist}:md5:md5", ) err = runtmp.last_result.err print(err) assert "for given picklist, found 3 matches to 9 distinct values" in err # these are the different ksizes assert "WARNING: 6 missing picklist values." in err out = runtmp.last_result.out print(out) assert "3 matches" in out assert "13.1% NC_000853.1 Thermotoga" in out assert "13.0% NC_009486.1 Thermotoga" in out assert "12.8% NC_011978.1 Thermotoga" in out def test_search_with_picklist_exclude(runtmp): # test 'sourmash search' with picklists gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") runtmp.sourmash( "search", metag_sig, *gcf_sigs, "--containment", "-k", "21", "--picklist", f"{picklist}:md5:md5:exclude", ) err = runtmp.last_result.err print(err) assert "for given picklist, found 9 matches by excluding 9 distinct values" in err # these are the different ksizes out = runtmp.last_result.out print(out) assert "9 matches above threshold 0.080; showing first 3:" in out assert "33.2% NC_003198.1 Salmonella" in out assert "33.1% NC_003197.2 Salmonella" in out assert "32.2% NC_006905.1 Salmonella" in out def test_search_with_pattern_include(runtmp): # test 'sourmash search' with --include-db-pattern gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash( "search", metag_sig, *gcf_sigs, "--containment", "-k", "21", "--include", "thermotoga", ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "3 matches" in out assert "13.1% NC_000853.1 Thermotoga" in out assert "13.0% NC_009486.1 Thermotoga" in out assert "12.8% NC_011978.1 Thermotoga" in out def test_search_with_pattern_exclude(runtmp): # test 'sourmash search' with --exclude-db-pattern gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash( "search", metag_sig, *gcf_sigs, "--containment", "-k", "21", "--exclude", "thermotoga", ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "9 matches above threshold 0.080; showing first 3:" in out assert "33.2% NC_003198.1 Salmonella" in out assert "33.1% NC_003197.2 Salmonella" in out assert "32.2% NC_006905.1 Salmonella" in out def test_search_empty_db_fail(runtmp): # search should fail on empty db with --fail-on-empty-database query = utils.get_test_data("2.fa.sig") against = utils.get_test_data("47.fa.sig") against2 = utils.get_test_data("lca/47+63.lca.json") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", query, against, against2, "-k", "51") err = runtmp.last_result.err assert "no compatible signatures found in " in err def test_search_empty_db_nofail(runtmp): # search should not fail on empty db with --no-fail-on-empty-database query = utils.get_test_data("2.fa.sig") against = utils.get_test_data("47.fa.sig") against2 = utils.get_test_data("lca/47+63.lca.json") runtmp.sourmash( "search", query, against, against2, "-k", "51", "--no-fail-on-empty-data" ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "no compatible signatures found in " in err assert ( "ksize on this database is 31; this is different from requested ksize of 51" in err ) assert "loaded 50 total signatures from 2 locations" in err assert "after selecting signatures compatible with search, 0 remain." in err def test_mash_csv_to_sig(runtmp): testdata1 = utils.get_test_data("short.fa.msh.dump") testdata2 = utils.get_test_data("short.fa") runtmp.sourmash("import_csv", testdata1, "-o", "xxx.sig") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=970", testdata2) runtmp.sourmash("search", "-k", "31", "short.fa.sig", "xxx.sig") print(runtmp.last_result.status, runtmp.last_result.out, runtmp.last_result.err) assert "1 matches" in runtmp.last_result.out assert "100.0% short.fa" in runtmp.last_result.out def test_do_sourmash_index_bad_args(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "index", "zzz", "short.fa.sig", "short2.fa.sig", "-k", "31", "--dna", "--protein", ) print(runtmp.last_result.out, runtmp.last_result.err) assert ( "cannot specify more than one of --dna/--rna/--nucleotide/--protein/--hp/--dayhoff" in runtmp.last_result.err ) assert runtmp.last_result.status != 0 def test_do_sourmash_sbt_search(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) runtmp.sourmash("index", "zzz", "short.fa.sig", "short2.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("search", "short.fa.sig", "zzz") print(runtmp.last_result.out) assert "short.fa" in runtmp.last_result.out assert "short2.fa" in runtmp.last_result.out def test_do_sourmash_sbt_search_wrong_ksize(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash( "sketch", "translate", "-p", "k=31,num=500", "-p", "k=51,num=500", testdata1, testdata2, ) runtmp.sourmash("index", "zzz", "short.fa.sig", "short2.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", "-k", "51", "short.fa.sig", "zzz") assert runtmp.last_result.status == -1 print(runtmp.last_result.out) print(runtmp.last_result.err) assert "ERROR: cannot use 'zzz' for this query." in runtmp.last_result.err assert ( "search ksize 51 is different from database ksize 31" in runtmp.last_result.err ) def test_do_sourmash_sbt_search_multiple(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) runtmp.sourmash("index", "zzz", "short.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("index", "zzz2", "short2.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz2.sbt.zip")) runtmp.sourmash("search", "short.fa.sig", "zzz", "zzz2") print(runtmp.last_result.out) assert "short.fa" in runtmp.last_result.out assert "short2.fa" in runtmp.last_result.out def test_do_sourmash_sbt_search_and_sigs(runtmp): # search an SBT and a signature at same time. testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) runtmp.sourmash("index", "zzz", "short.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("search", "short.fa.sig", "zzz", "short2.fa.sig") print(runtmp.last_result.out) assert "short.fa" in runtmp.last_result.out assert "short2.fa" in runtmp.last_result.out def test_do_sourmash_sbt_search_downsample(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,scaled=10", testdata1, testdata2) testdata1 = utils.get_test_data("short.fa") runtmp.sourmash( "sketch", "dna", "-p", "k=31,scaled=5", "-o", "query.sig", testdata1 ) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("search", "query.sig", "zzz") print(runtmp.last_result.out) assert "short.fa" in runtmp.last_result.out assert "short2.fa" in runtmp.last_result.out def test_do_sourmash_sbt_search_downsample_2(runtmp): testdata1 = utils.get_test_data("lca-root/TARA_MED_MAG_00029.fa.sig") testdata2 = utils.get_test_data("lca-root/TOBG_MED-875.fna.gz.sig") sbtname = "foo" runtmp.sourmash("index", "-k", "31", sbtname, testdata2) assert runtmp.last_result.status == 0 with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "search", testdata1, sbtname, "--scaled=100000", "--threshold=0.01" ) assert runtmp.last_result.status == -1 print(runtmp.last_result.out) print(runtmp.last_result.err) assert "ERROR: cannot use 'foo' for this query." in runtmp.last_result.err assert ( "search scaled value 100000 is less than database scaled value of 2000" in runtmp.last_result.err ) @utils.in_tempdir def test_do_sourmash_index_abund(c): # 'sourmash index' should flatten signatures w/track_abund. testdata2 = utils.get_test_data("lca-root/TOBG_MED-875.fna.gz.sig") with open(testdata2): ss = load_one_signature(testdata2, ksize=31) assert ss.minhash.track_abundance == True sbtname = "foo" c.run_sourmash("index", "-k", "31", sbtname, testdata2) for kk in sourmash.load_file_as_signatures(c.output(sbtname)): assert kk.minhash.track_abundance == False def test_do_sourmash_index_single(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("search", "short.fa.sig", "zzz") print(runtmp.last_result.out) assert "short.fa" in runtmp.last_result.out def test_do_sourmash_sbt_search_selectprot(runtmp): # index should fail when run on signatures with multiple types testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") args = ["sketch", "dna", "-p", "k=30,num=500", testdata1, testdata2] runtmp.sourmash(*args) args = ["index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig"] with pytest.raises(SourmashCommandFailed): runtmp.sourmash(*args) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status != 0 def test_do_sourmash_search_multimoltype_query(runtmp): # 'search' should fail if multiple sigs are given as query, due to # multiple molecule types. testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") # first, calculate signatures with multiple molecule types args = [ "sketch", "translate", testdata1, testdata2, "-p", "protein", "-p", "dayhoff", ] runtmp.sourmash(*args) # now, index one of 'em args = ["index", "zzz", "short.fa.sig", "short2.fa.sig", "--protein"] runtmp.sourmash(*args) # output exists, yes? assert os.path.exists(runtmp.output("zzz.sbt.zip")) # now, try searching. Should raise error. args = ["search", "short.fa.sig", "zzz"] with pytest.raises(SourmashCommandFailed): runtmp.sourmash(*args) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "need exactly one" in runtmp.last_result.err def test_do_sourmash_index_traverse(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) runtmp.sourmash("index", "-k", "31", "zzz", ".") assert os.path.exists(runtmp.output("zzz.sbt.zip")) assert "loaded 2 sigs; saving SBT under" in runtmp.last_result.err runtmp.sourmash("search", "short.fa.sig", "zzz") print(runtmp.last_result.out) assert "short.fa" in runtmp.last_result.out assert "short2.fa" in runtmp.last_result.out @utils.in_tempdir def test_do_sourmash_index_traverse_force(c): # test loading of files that don't end with .sig with -f testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") outdir = c.output("sigs") os.mkdir(outdir) out1 = os.path.join(outdir, "short1") out2 = os.path.join(outdir, "short2") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=5", "-o", out1, testdata1) c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=5", "-o", out2, testdata2) c.run_sourmash("index", "-k", "31", "zzz", ".", "-f") err = c.last_result.err assert os.path.exists(c.output("zzz.sbt.zip")) assert "loaded 2 sigs; saving SBT under" in err c.run_sourmash("search", out1, "zzz") out = c.last_result.out print(out) assert "short.fa" in out assert "short2.fa" in out def test_do_sourmash_index_sparseness(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) runtmp.sourmash("index", "-k", "31", "zzz.sbt.json", ".", "--sparseness", "1.0") assert os.path.exists(runtmp.output("zzz.sbt.json")) assert "loaded 2 sigs; saving SBT under" in runtmp.last_result.err runtmp.sourmash("search", "short.fa.sig", "zzz.sbt.json") print(runtmp.last_result.out) assert len(glob.glob(runtmp.output(".sbt.zzz/*"))) == 3 assert not glob.glob(runtmp.output(".sbt.zzz/*internal*")) assert "short.fa" in runtmp.last_result.out assert "short2.fa" in runtmp.last_result.out def test_do_sourmash_sbt_combine(runtmp): files = [utils.get_test_data(f) for f in utils.SIG_FILES] runtmp.sourmash("index", "-k", "31", "zzz", *files) assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("sbt_combine", "joined", "zzz.sbt.zip", "zzz.sbt.zip") assert os.path.exists(runtmp.output("joined.sbt.zip")) filename = os.path.splitext(os.path.basename(utils.SIG_FILES[0]))[0] runtmp.sourmash("search", files[0], "zzz") print(runtmp.last_result.out) # we get notification of signature loading, too - so notify + result. assert runtmp.last_result.out.count(filename) == 1 runtmp.sourmash("search", files[0], "joined") print(runtmp.last_result.out) assert runtmp.last_result.out.count(filename) == 1 def test_do_sourmash_index_append(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") runtmp.sourmash( "sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2, testdata3 ) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) sbt_name = runtmp.output( "zzz", ) sig_loc = runtmp.output("short3.fa.sig") runtmp.sourmash("search", sig_loc, sbt_name) print(runtmp.last_result.out) assert "short.fa" in runtmp.last_result.out assert "short2.fa" in runtmp.last_result.out assert "short3.fa" not in runtmp.last_result.out runtmp.sourmash("index", "-k", "31", "--append", "zzz", "short3.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) sbt_name = runtmp.output( "zzz", ) sig_loc = runtmp.output("short3.fa.sig") runtmp.sourmash("search", "--threshold", "0.95", sig_loc, sbt_name) print(runtmp.last_result.out) assert "short.fa" not in runtmp.last_result.out assert "short2.fa" in runtmp.last_result.out assert "short3.fa" in runtmp.last_result.out def test_do_sourmash_sbt_search_otherdir(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) runtmp.sourmash("index", "-k", "31", "xxx/zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("xxx/zzz.sbt.zip")) sbt_name = runtmp.output( "xxx/zzz", ) sig_loc = runtmp.output("short.fa.sig") runtmp.sourmash("search", sig_loc, sbt_name) print(runtmp.last_result.out) assert "short.fa" in runtmp.last_result.out assert "short2.fa" in runtmp.last_result.out def test_do_sourmash_sbt_search_scaled_vs_num_1(runtmp): # should not work: scaled query against num tree testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1) runtmp.sourmash("sketch", "dna", "-p", "scaled=1000", testdata2) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) sbt_name = runtmp.output( "zzz", ) sig_loc = runtmp.output("short2.fa.sig") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", sig_loc, sbt_name) assert runtmp.last_result.status == -1 print(runtmp.last_result.out) print(runtmp.last_result.err) assert "ERROR: cannot use '" in runtmp.last_result.err assert ( "this database was created with 'num' MinHash sketches, not 'scaled'" in runtmp.last_result.err ) def test_do_sourmash_sbt_search_scaled_vs_num_2(runtmp): # should not work: num query against scaled tree testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1) runtmp.sourmash("sketch", "dna", "-p", "scaled=1000", testdata2) runtmp.sourmash("index", "-k", "31", "zzz", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) sbt_name = runtmp.output( "zzz", ) sig_loc = runtmp.output("short.fa.sig") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", sig_loc, sbt_name) assert runtmp.last_result.status == -1 print(runtmp.last_result.out) print(runtmp.last_result.err) assert "ERROR: cannot use '" in runtmp.last_result.err assert ( "this database was created with 'scaled' MinHash sketches, not 'num'" in runtmp.last_result.err ) def test_do_sourmash_sbt_search_scaled_vs_num_3(runtmp): # should not work: scaled query against num signature testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1) runtmp.sourmash("sketch", "dna", "-p", "scaled=1000", testdata2) sig_loc = runtmp.output("short.fa.sig") sig_loc2 = runtmp.output("short2.fa.sig") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", sig_loc, sig_loc2) assert runtmp.last_result.status == -1 print(runtmp.last_result.out) print(runtmp.last_result.err) assert "no compatible signatures found in " in runtmp.last_result.err def test_do_sourmash_sbt_search_scaled_vs_num_4(runtmp): # should not work: num query against scaled signature testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1) runtmp.sourmash("sketch", "dna", "-p", "scaled=1000", testdata2) sig_loc = runtmp.output("short.fa.sig") sig_loc2 = runtmp.output("short2.fa.sig") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", sig_loc2, sig_loc) assert runtmp.last_result.status == -1 print(runtmp.last_result.out) print(runtmp.last_result.err) assert "no compatible signatures found in " in runtmp.last_result.err def test_do_sourmash_check_search_vs_actual_similarity(runtmp): files = [utils.get_test_data(f) for f in utils.SIG_FILES] runtmp.sourmash("index", "-k", "31", "zzz", *files) assert os.path.exists(runtmp.output("zzz.sbt.zip")) os.path.splitext(os.path.basename(utils.SIG_FILES[0]))[0] runtmp.sourmash("search", files[0], "zzz") assert runtmp.last_result.status == 0 def test_do_sourmash_check_sbt_filenames(runtmp): files = [utils.get_test_data(f) for f in utils.SIG_FILES] runtmp.sourmash("index", "-k", "31", "zzz.sbt.json", *files) assert os.path.exists(runtmp.output("zzz.sbt.json")) sig_names = set() sig_md5s = set() for f in files: sig = load_one_signature(f) sig_names.add(sig.name) sig_md5s.add(sig.md5sum()) sbt_files = glob.glob(runtmp.output(".sbt.zzz/*")) assert len(sbt_files) == 14 for f in sbt_files: if "internal" in f or f.endswith("zzz.manifest.csv"): continue f = os.path.basename(f) assert f not in sig_names assert f in sig_md5s def test_do_sourmash_sbt_search_bestonly(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("search", "--best-only", "short.fa.sig", "zzz") print(runtmp.last_result.out) assert "short.fa" in runtmp.last_result.out def test_do_sourmash_sbt_search_bestonly_scaled(runtmp): # as currently implemented, the query signature will be automatically # downsampled to match the tree. testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=1", testdata1, testdata2) runtmp.sourmash( "index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig", "--scaled", "10" ) assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("search", "--best-only", "short.fa.sig", "zzz") print(runtmp.last_result.out) assert "short.fa" in runtmp.last_result.out def test_sbt_search_order_dependence(runtmp): testdata1 = utils.get_test_data("genome-s10.fa.gz") testdata2 = utils.get_test_data("genome-s11.fa.gz") testdata3 = utils.get_test_data("genome-s12.fa.gz") testdata4 = utils.get_test_data("genome-s10+s11.fa.gz") runtmp.sourmash( "sketch", "dna", "-p", "k=21,scaled=10000", "-p", "k=31,scaled=10000", testdata1, testdata2, testdata3, testdata4, ) runtmp.sourmash( "index", "-k", "21", "134", "genome-s10+s11.fa.gz.sig", "genome-s11.fa.gz.sig", "genome-s12.fa.gz.sig", ) runtmp.sourmash( "search", "-k", "21", "genome-s11.fa.gz.sig", "134", "--best-only", "-k", "21", "--dna", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "100.0%" in runtmp.last_result.out def test_sbt_search_order_dependence_2(runtmp): # *should* return the same result as test_sbt_search_order_dependence, # but does not due to a bug. testdata1 = utils.get_test_data("genome-s10.fa.gz") testdata2 = utils.get_test_data("genome-s11.fa.gz") testdata3 = utils.get_test_data("genome-s12.fa.gz") testdata4 = utils.get_test_data("genome-s10+s11.fa.gz") runtmp.sourmash( "sketch", "dna", "-p", "k=21,scaled=10000", "-p", "k=31,scaled=10000", testdata1, testdata2, testdata3, testdata4, ) runtmp.sourmash( "index", "-k", "21", "314", "genome-s11.fa.gz.sig", "genome-s10+s11.fa.gz.sig", "genome-s12.fa.gz.sig", ) runtmp.sourmash( "search", "-k", "21", "genome-s11.fa.gz.sig", "314", "--best-only", "--dna" ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "100.0%" in runtmp.last_result.out def test_compare_with_abundance_1(runtmp): # create two signatures E1 = MinHash(ksize=5, n=5, is_protein=False, track_abundance=True) E2 = MinHash(ksize=5, n=5, is_protein=False, track_abundance=True) E1.add_sequence("ATGGA") E2.add_sequence("ATGGA") s1 = signature.SourmashSignature(E1, filename="e1", name="e1") s2 = signature.SourmashSignature(E2, filename="e2", name="e2") with open(runtmp.output("e1.sig"), "w") as f: signature.save_signatures_to_json([s1], f) with open(runtmp.output("e2.sig"), "w") as f: signature.save_signatures_to_json([s2], f) runtmp.sourmash("search", "e1.sig", "e2.sig", "-k", "5") assert "100.0%" in runtmp.last_result.out def test_compare_with_abundance_2(runtmp): # create two signatures E1 = MinHash(ksize=5, n=5, is_protein=False, track_abundance=True) E2 = MinHash(ksize=5, n=5, is_protein=False, track_abundance=True) E1.add_sequence("ATGGA") E1.add_sequence("ATGGA") E2.add_sequence("ATGGA") s1 = signature.SourmashSignature(E1, filename="e1", name="e1") s2 = signature.SourmashSignature(E2, filename="e2", name="e2") with open(runtmp.output("e1.sig"), "w") as f: signature.save_signatures_to_json([s1], f) with open(runtmp.output("e2.sig"), "w") as f: signature.save_signatures_to_json([s2], f) runtmp.sourmash("search", "e1.sig", "e2.sig", "-k", "5") assert "100.0%" in runtmp.last_result.out def test_compare_with_abundance_3(runtmp): # create two signatures E1 = MinHash(ksize=5, n=5, is_protein=False, track_abundance=True) E2 = MinHash(ksize=5, n=5, is_protein=False, track_abundance=True) E1.add_sequence("ATGGA") E1.add_sequence("GGACA") E1.add_sequence("ATGGA") E2.add_sequence("ATGGA") s1 = signature.SourmashSignature(E1, filename="e1", name="e1") s2 = signature.SourmashSignature(E2, filename="e2", name="e2") with open(runtmp.output("e1.sig"), "w") as f: signature.save_signatures_to_json([s1], f) with open(runtmp.output("e2.sig"), "w") as f: signature.save_signatures_to_json([s2], f) runtmp.sourmash("search", "e1.sig", "e2.sig", "-k", "5") assert "70.5%" in runtmp.last_result.out def test_compare_with_picklist(runtmp): # test 'sourmash compare' with picklists gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/thermotoga-picklist.csv") runtmp.sourmash( "compare", *gcf_sigs, "-k", "21", "--picklist", f"{picklist}:md5:md5" ) err = runtmp.last_result.err out = runtmp.last_result.out print(runtmp.last_result.out) print(runtmp.last_result.err) assert "for given picklist, found 3 matches to 9 distinct values" in err assert "WARNING: 6 missing picklist values." in err assert "NC_009486.1 The..." in out assert "NC_000853.1 The..." in out assert "NC_011978.1 The..." in out def test_compare_with_picklist_exclude(runtmp): # test 'sourmash compare' with picklists - exclude gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/thermotoga-picklist.csv") runtmp.sourmash( "compare", *gcf_sigs, "-k", "21", "--picklist", f"{picklist}:md5:md5:exclude" ) err = runtmp.last_result.err out = runtmp.last_result.out print(runtmp.last_result.out) print(runtmp.last_result.err) assert "for given picklist, found 9 matches by excluding 9 distinct values" in err assert "NC_004631.1 Sal..." in out assert "NC_006905.1 Sal..." in out assert "NC_003198.1 Sal..." in out assert "NC_002163.1 Cam..." in out assert "NC_011294.1 Sal..." in out def test_compare_with_pattern_include(runtmp): # test 'sourmash compare' with --include-db-pattern gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) runtmp.sourmash("compare", *gcf_sigs, "-k", "21", "--include", "thermotoga") out = runtmp.last_result.out print(runtmp.last_result.out) print(runtmp.last_result.err) assert "NC_009486.1 The..." in out assert "NC_000853.1 The..." in out assert "NC_011978.1 The..." in out def test_compare_with_pattern_exclude(runtmp): # test 'sourmash compare' with picklists - exclude gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) runtmp.sourmash("compare", *gcf_sigs, "-k", "21", "--exclude", "thermotoga") out = runtmp.last_result.out print(runtmp.last_result.out) print(runtmp.last_result.err) assert "NC_004631.1 Sal..." in out assert "NC_006905.1 Sal..." in out assert "NC_003198.1 Sal..." in out assert "NC_002163.1 Cam..." in out assert "NC_011294.1 Sal..." in out def test_gather(runtmp, linear_gather, prefetch_gather): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=10", testdata1, testdata2) runtmp.sourmash("sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", testdata2) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "-o", "foo.csv", "--threshold-bp=1", linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "0.9 kbp 100.0% 100.0%" in runtmp.last_result.out def test_gather_csv(runtmp, linear_gather, prefetch_gather): # test 'gather -o csvfile' testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash( "sketch", "dna", "-p", "scaled=10", "--name-from-first", testdata1, testdata2 ) runtmp.sourmash( "sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", "--name-from-first", testdata2, ) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "-o", "foo.csv", "--threshold-bp=1", linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) csv_file = runtmp.output("foo.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert float(row["intersect_bp"]) == 910 assert float(row["unique_intersect_bp"]) == 910 assert float(row["remaining_bp"]) == 0 assert float(row["f_orig_query"]) == 1.0 assert float(row["f_unique_to_query"]) == 1.0 assert float(row["f_match"]) == 1.0 assert row["filename"] == "zzz" assert row["name"] == "tr1 4" assert row["md5"] == "c9d5a795eeaaf58e286fb299133e1938" assert row["gather_result_rank"] == "0" assert row["query_filename"].endswith("short2.fa") assert row["query_name"] == "tr1 4" assert row["query_md5"] == "c9d5a795" assert row["query_bp"] == "910" assert row["query_abundance"] == "False" assert row["n_unique_weighted_found"] == "" def test_gather_csv_gz(runtmp, linear_gather, prefetch_gather): # test 'gather -o csvfile.gz' testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash( "sketch", "dna", "-p", "scaled=10", "--name-from-first", testdata1, testdata2 ) runtmp.sourmash( "sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", "--name-from-first", testdata2, ) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "-o", "foo.csv.gz", "--threshold-bp=1", linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) csv_file = runtmp.output("foo.csv.gz") with gzip.open(csv_file, "rt", newline="") as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert float(row["intersect_bp"]) == 910 assert float(row["unique_intersect_bp"]) == 910 assert float(row["remaining_bp"]) == 0 assert float(row["f_orig_query"]) == 1.0 assert float(row["f_unique_to_query"]) == 1.0 assert float(row["f_match"]) == 1.0 assert row["filename"] == "zzz" assert row["name"] == "tr1 4" assert row["md5"] == "c9d5a795eeaaf58e286fb299133e1938" assert row["gather_result_rank"] == "0" assert row["query_filename"].endswith("short2.fa") assert row["query_name"] == "tr1 4" assert row["query_md5"] == "c9d5a795" assert row["query_bp"] == "910" def test_gather_abund_x_abund(runtmp, prefetch_gather, linear_gather): sig47 = utils.get_test_data("track_abund/47.fa.sig") sig63 = utils.get_test_data("track_abund/63.fa.sig") runtmp.sourmash("gather", sig47, sig63, linear_gather, prefetch_gather) assert ( "2.5 Mbp 49.2% 48.3% 1.0 NC_011663.1" in runtmp.last_result.out ) def test_gather_multiple_sbts(runtmp, prefetch_gather, linear_gather): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=10", testdata1, testdata2) runtmp.sourmash("sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", testdata2) runtmp.sourmash("index", "zzz", "short.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("index", "zzz2", "short2.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "zzz2", "-o", "foo.csv", "--threshold-bp=1", linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "0.9 kbp 100.0% 100.0%" in runtmp.last_result.out def test_gather_multiple_sbts_save_prefetch(runtmp, linear_gather): # test --save-prefetch with multiple databases testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=10", testdata1, testdata2) runtmp.sourmash("sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", testdata2) runtmp.sourmash("index", "zzz", "short.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("index", "zzz2", "short2.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "zzz2", "-o", "foo.csv", "--save-prefetch", "out.zip", "--threshold-bp=1", linear_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "0.9 kbp 100.0% 100.0%" in runtmp.last_result.out assert os.path.exists(runtmp.output("out.zip")) def test_gather_multiple_sbts_save_prefetch_csv(runtmp, linear_gather): # test --save-prefetch-csv with multiple databases testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=10", testdata1, testdata2) runtmp.sourmash("sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", testdata2) runtmp.sourmash("index", "zzz", "short.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("index", "zzz2", "short2.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "zzz2", "-o", "foo.csv", "--save-prefetch-csv", "prefetch.csv", "--threshold-bp=1", linear_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "0.9 kbp 100.0% 100.0%" in runtmp.last_result.out assert os.path.exists(runtmp.output("prefetch.csv")) with open(runtmp.output("prefetch.csv")) as f: output = f.read() print((output,)) assert "870,0.925531914893617,0.9666666666666667" in output def test_gather_multiple_sbts_save_prefetch_csv_gz(runtmp, linear_gather): # test --save-prefetch-csv to a .gz file, with multiple databases testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=10", testdata1, testdata2) runtmp.sourmash("sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", testdata2) runtmp.sourmash("index", "zzz", "short.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("index", "zzz2", "short2.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "zzz2", "-o", "foo.csv", "--save-prefetch-csv", "prefetch.csv.gz", "--threshold-bp=1", linear_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "0.9 kbp 100.0% 100.0%" in runtmp.last_result.out assert os.path.exists(runtmp.output("prefetch.csv.gz")) with gzip.open(runtmp.output("prefetch.csv.gz"), "rt", newline="") as f: output = f.read() print((output,)) assert "870,0.925531914893617,0.9666666666666667" in output def test_gather_multiple_sbts_save_prefetch_and_prefetch_csv(runtmp, linear_gather): # test --save-prefetch-csv with multiple databases testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=10", testdata1, testdata2) runtmp.sourmash("sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", testdata2) runtmp.sourmash("index", "zzz", "short.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash("index", "zzz2", "short2.fa.sig", "-k", "31") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "zzz2", "-o", "foo.csv", "--save-prefetch", "out.zip", "--save-prefetch-csv", "prefetch.csv", "--threshold-bp=1", linear_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "0.9 kbp 100.0% 100.0%" in runtmp.last_result.out assert os.path.exists(runtmp.output("prefetch.csv")) with open(runtmp.output("prefetch.csv")) as f: output = f.read() print((output,)) assert "870,0.925531914893617,0.9666666666666667" in output assert os.path.exists(runtmp.output("out.zip")) def test_gather_sbt_and_sigs(runtmp, linear_gather, prefetch_gather): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,scaled=10", testdata1, testdata2) runtmp.sourmash("sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", testdata2) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "short2.fa.sig", "-o", "foo.csv", linear_gather, prefetch_gather, "--threshold-bp=1", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "0.9 kbp 100.0% 100.0%" in runtmp.last_result.out def test_gather_file_output(runtmp, linear_gather, prefetch_gather): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "scaled=10", testdata1, testdata2) runtmp.sourmash("sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", testdata2) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "--threshold-bp=500", linear_gather, prefetch_gather, "-o", "foo.out", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "0.9 kbp 100.0% 100.0%" in runtmp.last_result.out with open(runtmp.output("foo.out")) as f: output = f.read() print((output,)) assert "910,1.0,1.0" in output def test_gather_f_match_orig(runtmp, linear_gather, prefetch_gather): import copy testdata_combined = utils.get_test_data("gather/combined.sig") testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) runtmp.sourmash( "gather", testdata_combined, "-o", "out.csv", *testdata_sigs, linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) combined_sig = load_one_signature(testdata_combined, ksize=21) remaining_mh = combined_sig.minhash.to_mutable() def approx_equal(a, b, n=5): return round(a, n) == round(b, n) with open(runtmp.output("out.csv")) as fp: r = csv.DictReader(fp) for n, row in enumerate(r): print(n, row["f_match"], row["f_match_orig"]) # each match is completely in the original query assert row["f_match_orig"] == "1.0" # double check -- should match 'search --containment'. # (this is kind of useless for a 1.0 contained_by, I guess) filename = row["filename"] match = load_one_signature(filename, ksize=21) assert match.contained_by(combined_sig) == 1.0 # check other fields, too. f_orig_query = float(row["f_orig_query"]) f_match_orig = float(row["f_match_orig"]) f_match = float(row["f_match"]) f_unique_to_query = float(row["f_unique_to_query"]) # f_orig_query is the containment of the query by the match. # (note, this only works because containment is 100% in combined). assert approx_equal(combined_sig.contained_by(match), f_orig_query) # just redoing above, for completeness; this is always 1.0 for # this data set. assert approx_equal(match.contained_by(combined_sig), f_match_orig) # f_match is how much of the match is in the unallocated hashes assert approx_equal(match.minhash.contained_by(remaining_mh), f_match) # f_unique_to_query is how much of the match is unique wrt # the original query. a = set(remaining_mh.hashes.keys()) b = set(match.minhash.hashes.keys()) n_intersect = len(a.intersection(b)) f_intersect = n_intersect / float(len(combined_sig.minhash)) assert approx_equal(f_unique_to_query, f_intersect) # now, subtract current match from remaining... and iterate! remaining_mh.remove_many(match.minhash.hashes.keys()) def test_gather_nomatch(runtmp, linear_gather, prefetch_gather): testdata_query = utils.get_test_data( "gather/GCF_000006945.2_ASM694v2_genomic.fna.gz.sig" ) testdata_match = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") out_csv = runtmp.output("results.csv") runtmp.sourmash( "gather", testdata_query, testdata_match, "-o", out_csv, linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "No matches found for --threshold-bp at 50.0 kbp." in runtmp.last_result.err assert not os.path.exists(out_csv) def test_gather_nomatch_create_empty(runtmp, linear_gather, prefetch_gather): testdata_query = utils.get_test_data( "gather/GCF_000006945.2_ASM694v2_genomic.fna.gz.sig" ) testdata_match = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") out_csv = runtmp.output("results.csv") runtmp.sourmash( "gather", testdata_query, testdata_match, "-o", out_csv, "--create-empty-results", linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "No matches found for --threshold-bp at 50.0 kbp." in runtmp.last_result.err assert os.path.exists(out_csv) with open(out_csv) as fp: data = fp.read() assert not data def test_gather_abund_nomatch(runtmp, linear_gather, prefetch_gather): testdata_query = utils.get_test_data("gather-abund/reads-s10x10-s11.sig") testdata_match = utils.get_test_data( "gather/GCF_000006945.2_ASM694v2_genomic.fna.gz.sig" ) runtmp.sourmash( "gather", testdata_query, testdata_match, linear_gather, prefetch_gather ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "No matches found for --threshold-bp at 50.0 kbp." in runtmp.last_result.err def test_gather_metagenome_3_thermo(runtmp): # test gather matches in more detail. match1 = "gather/GCF_000016785.1_ASM1678v1_genomic.fna.gz.sig" match2 = "gather/GCF_000018945.1_ASM1894v1_genomic.fna.gz.sig" match3 = "gather/GCF_000008545.1_ASM854v1_genomic.fna.gz.sig" match1 = utils.get_test_data(match1) match2 = utils.get_test_data(match2) match3 = utils.get_test_data(match3) query_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash( "gather", query_sig, match1, match2, match3, "-k", "21", "--threshold-bp=0", "-o", "match3.csv", ) print(runtmp.last_result.out) print(runtmp.last_result.err) outfile = runtmp.output("match3.csv") with sourmash_args.FileInputCSV(outfile) as r: rows = list(r) assert len(rows) == 3 # first row row = rows[0] assert row["name"].startswith("NC_000853.1 ") f_match = float(row["f_match"]) f_unique_to_query = round(float(row["f_unique_to_query"]), 5) unique_intersect_bp = int(row["unique_intersect_bp"]) remaining_bp = int(row["remaining_bp"]) assert f_match == 1.0 assert f_unique_to_query == round(0.13096862, 5) assert unique_intersect_bp == 1920000 assert remaining_bp == 12740000 # second row row = rows[1] assert row["name"].startswith("NC_011978.1 ") f_match = float(row["f_match"]) f_unique_to_query = round(float(row["f_unique_to_query"]), 5) unique_intersect_bp = int(row["unique_intersect_bp"]) remaining_bp = int(row["remaining_bp"]) assert round(f_match, 5) == round(0.898936170212766, 5) assert f_unique_to_query == round(0.115279, 5) assert unique_intersect_bp == 1690000 assert remaining_bp == 11050000 # third row row = rows[2] assert row["name"].startswith("NC_009486.1 ") f_match = float(row["f_match"]) f_unique_to_query = round(float(row["f_unique_to_query"]), 5) unique_intersect_bp = int(row["unique_intersect_bp"]) remaining_bp = int(row["remaining_bp"]) assert round(f_match, 5) == round(0.4842105, 5) assert f_unique_to_query == round(0.0627557, 5) assert unique_intersect_bp == 920000 assert remaining_bp == 10130000 def test_gather_metagenome(runtmp): testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash("gather", query_sig, "gcf_all", "-k", "21", "--threshold-bp=0") print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 12 matches total" in runtmp.last_result.out assert "the recovered matches hit 100.0% of the query" in runtmp.last_result.out assert all( ( "4.9 Mbp 33.2% 100.0%" in runtmp.last_result.out, "NC_003198.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) assert all( ( "4.7 Mbp 0.5% 1.5%" in runtmp.last_result.out, "NC_011294.1 Salmonella enterica subs" in runtmp.last_result.out, ) ) @utils.in_tempdir def test_gather_metagenome_num_results(c): # set a threshold on the number of results to be reported by gather testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) cmd = f"gather {query_sig} gcf_all -k 21 --num-results 10" cmd = cmd.split(" ") c.run_sourmash(*cmd) print(c.last_result.out) print(c.last_result.err) out = c.last_result.out assert "found 10 matches total" in out assert "(truncated gather because --num-results=10)" in out assert "the recovered matches hit 99.4% of the query" in out assert all( ( "4.9 Mbp 33.2% 100.0%" in out, "NC_003198.1 Salmonella enterica subsp" in out, ) ) assert "4.3 Mbp 2.1% 7.3% NC_006511.1 Salmonella enterica subsp" in out def test_gather_metagenome_threshold_bp(runtmp, linear_gather, prefetch_gather): # set a threshold on the gather output testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash( "gather", query_sig, "gcf_all", "-k", "21", "--threshold-bp", "2e6", linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 1 matches total" in runtmp.last_result.out assert "found less than 2.0 Mbp in common. => exiting" in runtmp.last_result.err assert "the recovered matches hit 33.2% of the query" in runtmp.last_result.out assert all( ( "4.9 Mbp 33.2% 100.0%" in runtmp.last_result.out, "NC_003198.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) def test_gather_metagenome_threshold_bp_low(runtmp, linear_gather, prefetch_gather): # set a threshold on the gather output => too low testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash( "gather", query_sig, "gcf_all", "-k", "21", "--threshold-bp", "1", linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 12 matches total" in runtmp.last_result.out assert "found less than 1 bp in common. => exiting" in runtmp.last_result.err assert "the recovered matches hit 100.0% of the query" in runtmp.last_result.out def test_gather_metagenome_threshold_bp_too_high( runtmp, linear_gather, prefetch_gather ): # set a threshold on the gather output => no results testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash( "gather", query_sig, "gcf_all", "-k", "21", "--threshold-bp", "5e6", linear_gather, prefetch_gather, ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "No matches found for --threshold-bp at 5.0 Mbp." in err def test_multigather_metagenome(runtmp): testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash( "multigather", "--query", query_sig, "--db", "gcf_all", "-k", "21", "--threshold-bp=0", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 12 matches total" in runtmp.last_result.out assert "the recovered matches hit 100.0% of the query" in runtmp.last_result.out assert all( ( "4.9 Mbp 33.2% 100.0%" in runtmp.last_result.out, "NC_003198.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) assert all( ( "4.7 Mbp 0.5% 1.5%" in runtmp.last_result.out, "NC_011294.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) def test_multigather_check_scaled_bounds_negative(runtmp): c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) cmd = f"multigather --query {query_sig} --db gcf_all -k 21 --scaled -5 --threshold-bp=0" cmd = cmd.split(" ") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash(*cmd) assert "ERROR: scaled value must be positive" in str(exc.value) def test_multigather_check_scaled_bounds_less_than_minimum(runtmp): c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) cmd = f"multigather --query {query_sig} --db gcf_all -k 21 --scaled 50 --threshold-bp=0" cmd = cmd.split(" ") # Note: this is the value error that is emitted, but we want the Warning from below to be generated instead. (ValueError: new scaled 50.0 is lower than current sample scaled 10000) with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash(*cmd) assert "WARNING: scaled value should be >= 100. Continuing anyway." in str( exc.value ) def test_multigather_check_scaled_bounds_more_than_maximum(runtmp): c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) cmd = f"multigather --query {query_sig} --db gcf_all -k 21 --scaled 1e9 --threshold-bp=0" cmd = cmd.split(" ") c.run_sourmash(*cmd) assert ( "WARNING: scaled value should be <= 1e6. Continuing anyway." in c.last_result.err ) def test_multigather_metagenome_query_from_file(runtmp): # test multigather --query-from-file c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) # make list w/query sig query_list = c.output("query.list") with open(query_list, "w") as fp: print(query_sig, file=fp) cmd = f"multigather --query-from-file {query_list} --db gcf_all -k 21 --threshold-bp=0" cmd = cmd.split(" ") c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert "found 12 matches total" in out assert "the recovered matches hit 100.0% of the query" in out assert all( ( "4.9 Mbp 33.2% 100.0%" in out, "NC_003198.1 Salmonella enterica subsp" in out, ) ) assert all( ( "4.7 Mbp 0.5% 1.5%" in out, "NC_011294.1 Salmonella enterica subsp" in out, ) ) def test_multigather_metagenome_output(runtmp): # test multigather CSV output has more than one output line c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) cmd = f"multigather --query {query_sig} --db gcf_all -k 21 --threshold-bp=0" cmd = cmd.split(" ") c.run_sourmash(*cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) output_csv = runtmp.output("b92dbf45dd57867cbec2321ccfa55af8.csv") assert os.path.exists(output_csv) with open(output_csv, newline="") as fp: x = fp.readlines() assert len(x) == 13 def test_multigather_metagenome_output_outdir(runtmp): # test multigather CSV output to different location c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) # create output directory outdir = runtmp.output("savehere") os.mkdir(outdir) cmd = f"multigather --query {query_sig} --db gcf_all -k 21 --threshold-bp=0 --output-dir {outdir}" cmd = cmd.split(" ") c.run_sourmash(*cmd) output_csv = runtmp.output("savehere/b92dbf45dd57867cbec2321ccfa55af8.csv") assert os.path.exists(output_csv) with open(output_csv, newline="") as fp: x = fp.readlines() assert len(x) == 13 def test_multigather_metagenome_query_with_sbt(runtmp): # multigather should work with an SBT as a query c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all.sbt.zip"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) cmd = "multigather --query gcf_all.sbt.zip --db gcf_all.sbt.zip -k 21 --threshold-bp=0" cmd = cmd.split(" ") c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert "conducted gather searches on 12 signatures" in err assert "the recovered matches hit 100.0% of the query" in out assert all( ( "4.7 Mbp 100.0% 100.0%" in out, "NC_011080.1 Salmonella enterica subsp" in out, ) ) assert all( ( "4.5 Mbp 100.0% 100.0%" in out, "NC_004631.1 Salmonella enterica subsp" in out, ) ) assert all( ( "1.6 Mbp 100.0% 100.0%" in out, "NC_002163.1 Campylobacter jejuni subs" in out, ) ) assert all( ( "1.9 Mbp 100.0% 100.0%" in out, "NC_000853.1 Thermotoga maritima MSB8 " in out, ) ) def test_multigather_metagenome_query_with_lca(runtmp): # make sure that LCA databases can be used as queries c = runtmp testdata_glob = utils.get_test_data("47*.fa.sig") testdata_sigs = glob.glob(testdata_glob) lca_db = utils.get_test_data("lca/47+63.lca.json") cmd = ["index", "47+63.sbt.zip"] cmd.extend(testdata_sigs) cmd.extend(["-k", "31"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("47+63.sbt.zip")) cmd = f"multigather --query {lca_db} --db 47+63.sbt.zip -k 31 --threshold-bp=0" cmd = cmd.split(" ") c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert "conducted gather searches on 2 signatures" in err assert "the recovered matches hit 100.0% of the query" in out # assert '5.1 Mbp 100.0% 64.9% 491c0a81' in out assert "5.5 Mbp 100.0% 69.4% 491c0a81" in out def test_multigather_metagenome_query_on_lca_db(runtmp): # test multigather against LCA databases c = runtmp testdata_sig1 = utils.get_test_data("47.fa.sig") testdata_sig2 = utils.get_test_data("63.fa.sig") lca_db = utils.get_test_data("lca/47+63.lca.json") cmd = f"multigather --query {testdata_sig1} {testdata_sig2} --db {lca_db} -k 31 --threshold-bp=0" cmd = cmd.split(" ") c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert "conducted gather searches on 2 signatures" in err assert "the recovered matches hit 100.0% of the query" in out assert all( ( "5.1 Mbp 100.0% 100.0%" in out, "NC_009665.1 Shewanella baltica OS185," in out, ) ) assert all( ( "5.5 Mbp 100.0% 100.0%" in out, "NC_011663.1 Shewanella baltica OS223," in out, ) ) def test_multigather_metagenome_query_with_sbt_addl_query(runtmp): # throw in an additional (duplicate) query c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) another_query = utils.get_test_data( "gather/GCF_000195995.1_ASM19599v1_genomic.fna.gz.sig" ) cmd = ["index", "gcf_all.sbt.zip"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) cmd = f"multigather --query {another_query} gcf_all.sbt.zip --db gcf_all.sbt.zip -k 21 --threshold-bp=0 --force-allow-overwrite-output" cmd = cmd.split(" ") c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert "conducted gather searches on 13 signatures" in err assert "the recovered matches hit 100.0% of the query" in out # check for matches to some of the sbt signatures assert all( ( "4.7 Mbp 100.0% 100.0%" in out, "NC_011080.1 Salmonella enterica subsp" in out, ) ) assert all( ( "4.5 Mbp 100.0% 100.0%" in out, "NC_004631.1 Salmonella enterica subsp" in out, ) ) assert all( ( "1.6 Mbp 100.0% 100.0%" in out, "NC_002163.1 Campylobacter jejuni subs" in out, ) ) assert all( ( "1.9 Mbp 100.0% 100.0%" in out, "NC_000853.1 Thermotoga maritima MSB8 " in out, ) ) # check additional query sig assert all( ( "4.9 Mbp 100.0% 100.0%" in out, "NC_003198.1 Salmonella enterica subsp" in out, ) ) def test_multigather_metagenome_query_with_sbt_addl_query_fail_overwrite(runtmp): # provide multiple identical queries - fails c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) another_query = utils.get_test_data( "gather/GCF_000195995.1_ASM19599v1_genomic.fna.gz.sig" ) utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all.sbt.zip"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) cmd = f"multigather --query {another_query} {another_query} --db gcf_all.sbt.zip -k 21 --threshold-bp=0" cmd = cmd.split(" ") with pytest.raises(SourmashCommandFailed): c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert ( "ERROR: detected overwritten outputs! 'GCF_000195995.1_ASM19599v1_genomic.fna.gz' has already been used. Failing." in err ) def test_multigather_metagenome_query_with_sbt_addl_query_fail_overwrite_force(runtmp): # provide multiple identical queries - fails -> overwrite with --force c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) another_query = utils.get_test_data( "gather/GCF_000195995.1_ASM19599v1_genomic.fna.gz.sig" ) utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all.sbt.zip"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) cmd = f"multigather --query {another_query} {another_query} --db gcf_all.sbt.zip -k 21 --threshold-bp=0 --force-allow-overwrite-output" cmd = cmd.split(" ") c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert ( "ERROR: detected overwritten outputs! 'GCF_000195995.1_ASM19599v1_genomic.fna.gz' has already been used. Failing." in err ) assert "continuing because --force-allow-overwrite was specified" in err def test_multigather_metagenome_sbt_query_from_file_with_addl_query(runtmp): # test what happens when we use SBT in a from-file. c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) another_query = utils.get_test_data( "gather/GCF_000195995.1_ASM19599v1_genomic.fna.gz.sig" ) testdata_sigs.remove(another_query) cmd = ["index", "gcf_all.sbt.zip"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) # make list w/query sbt query_list = c.output("query.list") with open(query_list, "w") as fp: print("gcf_all.sbt.zip", file=fp) cmd = f"multigather --query {another_query} --query-from-file {query_list} --db gcf_all.sbt.zip {another_query} -k 21 --threshold-bp=0" cmd = cmd.split(" ") c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert "conducted gather searches on 12 signatures" in err assert "the recovered matches hit 100.0% of the query" in out # check for matches to some of the sbt signatures assert all( ( "4.7 Mbp 100.0% 100.0%" in out, "NC_011080.1 Salmonella enterica subsp" in out, ) ) assert all( ( "4.5 Mbp 100.0% 100.0%" in out, "NC_004631.1 Salmonella enterica subsp" in out, ) ) assert all( ( "1.6 Mbp 100.0% 100.0%" in out, "NC_002163.1 Campylobacter jejuni subs" in out, ) ) assert all( ( "1.9 Mbp 100.0% 100.0%" in out, "NC_000853.1 Thermotoga maritima MSB8 " in out, ) ) # check additional query sig assert all( ( "4.9 Mbp 100.0% 100.0%" in out, "NC_003198.1 Salmonella enterica subsp" in out, ) ) def test_multigather_metagenome_sbt_query_from_file_incorrect(runtmp): # use the wrong type of file with --query-from-file c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all.sbt.zip"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) # incorrectly query with sbt using `--query-from-file` cmd = "multigather --query-from-file gcf_all.sbt.zip --db gcf_all.sbt.zip -k 21 --threshold-bp=0" cmd = cmd.split(" ") with pytest.raises(SourmashCommandFailed): c.run_sourmash(*cmd) print(c.last_result.out) print(c.last_result.err) def test_multigather_metagenome_lca_query_from_file(runtmp): # putting an LCA database in a file for a query should work c = runtmp testdata_glob = utils.get_test_data("47*.fa.sig") testdata_sigs = glob.glob(testdata_glob) lca_db = utils.get_test_data("lca/47+63.lca.json") cmd = ["index", "47+63.sbt.zip"] cmd.extend(testdata_sigs) cmd.extend(["-k", "31"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("47+63.sbt.zip")) # make list w/query sig query_list = c.output("query.list") with open(query_list, "w") as fp: print(lca_db, file=fp) cmd = f"multigather --query-from-file {query_list} --db 47+63.sbt.zip -k 31 --threshold-bp=0" cmd = cmd.split(" ") c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) assert "conducted gather searches on 2 signatures" in err assert "the recovered matches hit 100.0% of the query" in out # assert '5.1 Mbp 100.0% 64.9% 491c0a81' in out assert "5.5 Mbp 100.0% 69.4% 491c0a81" in out def test_multigather_metagenome_query_from_file_with_addl_query(runtmp): # test multigather --query-from-file and --query too c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) c.run_sourmash(*cmd) assert os.path.exists(c.output("gcf_all.sbt.zip")) # make list w/query sig query_list = c.output("query.list") with open(query_list, "w") as fp: print(query_sig, file=fp) another_query = utils.get_test_data( "gather/GCF_000195995.1_ASM19599v1_genomic.fna.gz.sig" ) cmd = f"multigather --query-from-file {query_list} --query {another_query} --db gcf_all -k 21 --threshold-bp=0" cmd = cmd.split(" ") c.run_sourmash(*cmd) out = c.last_result.out print(out) err = c.last_result.err print(err) # first gather query assert "found 12 matches total" in out assert "the recovered matches hit 100.0% of the query" in out assert all( ( "4.9 Mbp 33.2% 100.0%" in out, "NC_003198.1 Salmonella enterica subsp" in out, ) ) assert all( ( "4.7 Mbp 0.5% 1.5%" in out, "NC_011294.1 Salmonella enterica subsp" in out, ) ) # second gather query assert "4.9 Mbp 100.0% 100.0% NC_003198.1 Salmonella enterica subsp" in out assert "found 1 matches total;" in out assert "the recovered matches hit 100.0% of the query" in out def test_multigather_metagenome_output_unique_empty_filename(runtmp): # test multigather CSV output with -U/--output-add-query-md5sum # NOTE: source file of 'combined.sig' is '-' c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) testdata_sigs_arg = " ".join(testdata_sigs) query_sig = utils.get_test_data("gather/combined.sig") cmd = f"multigather --query {query_sig} --db {testdata_sigs_arg} -k 21 --threshold-bp=0 -U" cmd = cmd.split(" ") c.run_sourmash(*cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) output_csv = runtmp.output("b92dbf45dd57867cbec2321ccfa55af8.csv") assert os.path.exists(output_csv) with open(output_csv, newline="") as fp: x = fp.readlines() assert len(x) == 13 def test_multigather_metagenome_output_unique(runtmp): # test multigather CSV output with -U/--output-add-query-md5sum # with a file that has a filename ;) c = runtmp testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) testdata_sigs_arg = " ".join(testdata_sigs) # change 'filename' on 'combined.sig' to something else orig_query_sig = utils.get_test_data("gather/combined.sig") sketch = load_one_signature(orig_query_sig) ss = signature.SourmashSignature(sketch.minhash, filename="named_query") query_sig = runtmp.output("the_query.sig") with open(query_sig, "w") as f: signature.save_signatures_to_json([ss], f) cmd = f"multigather --query {query_sig} --db {testdata_sigs_arg} -k 21 --threshold-bp=0 -U" cmd = cmd.split(" ") c.run_sourmash(*cmd) print(runtmp.last_result.out) print(runtmp.last_result.err) # check that output filename has 'named_query' and md5sum in it: output_csv = runtmp.output("named_query.b92dbf45dd57867cbec2321ccfa55af8.csv") assert os.path.exists(output_csv) with open(output_csv, newline="") as fp: x = fp.readlines() assert len(x) == 13 def test_gather_metagenome_traverse(runtmp, linear_gather, prefetch_gather): # set up a directory $location/gather that contains # everything in the 'tests/test-data/gather' directory # *except* the query sequence, which is 'combined.sig'. testdata_dir = utils.get_test_data("gather") copy_testdata = runtmp.output("somesigs") shutil.copytree(testdata_dir, copy_testdata) os.unlink(os.path.join(copy_testdata, "combined.sig")) query_sig = utils.get_test_data("gather/combined.sig") # now, feed in the new directory -- runtmp.sourmash( "gather", query_sig, copy_testdata, "-k", "21", "--threshold-bp=0", linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 12 matches total" in runtmp.last_result.out assert "the recovered matches hit 100.0% of the query" in runtmp.last_result.out assert all( ( "4.9 Mbp 33.2% 100.0%" in runtmp.last_result.out, "NC_003198.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) assert all( ( "4.7 Mbp 0.5% 1.5%" in runtmp.last_result.out, "NC_011294.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) def test_gather_metagenome_traverse_check_csv(runtmp, linear_gather, prefetch_gather): # this test confirms that the CSV 'filename' output for signatures loaded # via directory traversal properly contains the actual path to the # signature file from which the signature was loaded. # set up a directory $location/gather that contains # everything in the 'tests/test-data/gather' directory # *except* the query sequence, which is 'combined.sig'. testdata_dir = utils.get_test_data("gather") copy_testdata = runtmp.output("somesigs") shutil.copytree(testdata_dir, copy_testdata) os.unlink(os.path.join(copy_testdata, "combined.sig")) query_sig = utils.get_test_data("gather/combined.sig") out_csv = runtmp.output("out.csv") # now, feed in the new directory -- runtmp.sourmash( "gather", query_sig, copy_testdata, "-k", "21", "--threshold-bp=0", "-o", out_csv, linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) with open(out_csv) as fp: prefix_len = len(copy_testdata) r = csv.DictReader(fp) for row in r: filename = row["filename"] assert filename.startswith(copy_testdata), filename # should have full path to file sig was loaded from assert len(filename) > prefix_len assert "found 12 matches total" in runtmp.last_result.out assert "the recovered matches hit 100.0% of the query" in runtmp.last_result.out assert all( ( "4.9 Mbp 33.2% 100.0%" in runtmp.last_result.out, "NC_003198.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) assert all( ( "4.7 Mbp 0.5% 1.5%" in runtmp.last_result.out, "NC_011294.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) @utils.in_tempdir def test_gather_traverse_incompatible(c): searchdir = c.output("searchme") os.mkdir(searchdir) num_sig = utils.get_test_data("num/47.fa.sig") scaled_sig = utils.get_test_data("47.fa.sig") shutil.copyfile(num_sig, c.output("searchme/num.sig")) shutil.copyfile(scaled_sig, c.output("searchme/scaled.sig")) c.run_sourmash("gather", scaled_sig, c.output("searchme")) print(c.last_result.out) print(c.last_result.err) assert ( "5.2 Mbp 100.0% 100.0% NC_009665.1 Shewanella baltica OS185," in c.last_result.out ) def test_gather_metagenome_output_unassigned(runtmp): testdata_glob = utils.get_test_data("gather/GCF_000195995*g") testdata_sigs = glob.glob(testdata_glob)[0] query_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash( "gather", query_sig, testdata_sigs, "-k", "21", "--output-unassigned=unassigned.sig", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 1 matches total" in runtmp.last_result.out assert "the recovered matches hit 33.2% of the query" in runtmp.last_result.out assert all( ( "4.9 Mbp 33.2% 100.0%" in runtmp.last_result.out, "NC_003198.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) # now examine unassigned testdata2_glob = utils.get_test_data("gather/GCF_000009505.1*.sig") testdata2_sigs = glob.glob(testdata2_glob)[0] runtmp.sourmash( "gather", "unassigned.sig", testdata_sigs, testdata2_sigs, "-k", "21" ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert all( ( "1.3 Mbp 13.6% 28.2%" in runtmp.last_result.out, "NC_011294.1" in runtmp.last_result.out, ) ) def test_gather_metagenome_output_unassigned_as_zip(runtmp): testdata_glob = utils.get_test_data("gather/GCF_000195995*g") testdata_sigs = glob.glob(testdata_glob)[0] query_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash( "gather", query_sig, testdata_sigs, "-k", "21", "--output-unassigned=unassigned.sig.zip", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 1 matches total" in runtmp.last_result.out assert "the recovered matches hit 33.2% of the query" in runtmp.last_result.out assert all( ( "4.9 Mbp 33.2% 100.0%" in runtmp.last_result.out, "NC_003198.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) assert zipfile.is_zipfile(runtmp.output("unassigned.sig.zip")) # now examine unassigned testdata2_glob = utils.get_test_data("gather/GCF_000009505.1*.sig") testdata2_sigs = glob.glob(testdata2_glob)[0] runtmp.sourmash( "gather", "unassigned.sig.zip", testdata_sigs, testdata2_sigs, "-k", "21" ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert all( ( "1.3 Mbp 13.6% 28.2%" in runtmp.last_result.out, "NC_011294.1" in runtmp.last_result.out, ) ) def test_gather_metagenome_output_unassigned_none(runtmp): # test what happens when there's nothing unassigned to output testdata_glob = utils.get_test_data("gather/GCF_*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash( "gather", query_sig, *testdata_sigs, "-k", "21", "--output-unassigned=unassigned.sig", "--threshold=0", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 12 matches total" in runtmp.last_result.out assert "the recovered matches hit 100.0% of the query" in runtmp.last_result.out assert all( ( "4.9 Mbp 33.2% 100.0%" in runtmp.last_result.out, "NC_003198.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) assert all( ( "4.5 Mbp 0.1% 0.4%" in runtmp.last_result.out, "NC_004631.1 Salmonella enterica subsp" in runtmp.last_result.out, ) ) # now examine unassigned assert not os.path.exists(runtmp.output("unassigned.sig")) assert ( "no unassigned hashes to save with --output-unassigned!" in runtmp.last_result.err ) def test_gather_metagenome_output_unassigned_nomatches( runtmp, prefetch_gather, linear_gather ): c = runtmp # test --output-unassigned when there are no matches query_sig = utils.get_test_data("2.fa.sig") against_sig = utils.get_test_data("47.fa.sig") c.run_sourmash( "gather", query_sig, against_sig, "--output-unassigned", "foo.sig", linear_gather, prefetch_gather, ) print(c.last_result.out) assert "No matches found for --threshold-bp at 50.0 kbp." in c.last_result.err x = load_one_signature(query_sig, ksize=31) y = load_one_signature(c.output("foo.sig")) assert x.minhash == y.minhash def test_gather_metagenome_output_unassigned_nomatches_protein( runtmp, linear_gather, prefetch_gather ): c = runtmp # test --output-unassigned with protein signatures query_sig = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) against_sig = utils.get_test_data( "prot/protein/GCA_001593935.1_ASM159393v1_protein.faa.gz.sig" ) c.run_sourmash( "gather", query_sig, against_sig, "--output-unassigned", "foo.sig", linear_gather, prefetch_gather, ) print(c.last_result.out) assert "No matches found for --threshold-bp at 50.0 kbp." in c.last_result.err c.run_sourmash("sig", "describe", c.output("foo.sig")) print(c.last_result.out) x = load_one_signature(query_sig, ksize=57) y = load_one_signature(c.output("foo.sig")) assert x.minhash == y.minhash assert y.minhash.moltype == "protein" def test_gather_check_scaled_bounds_negative(runtmp, prefetch_gather, linear_gather): testdata_glob = utils.get_test_data("gather/GCF*.sig") glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "gather", query_sig, prefetch_gather, linear_gather, "gcf_all", "-k", "21", "--scaled", "-5", "--threshold-bp", "50000", ) assert "ERROR: scaled value must be positive" in runtmp.last_result.err def test_gather_check_scaled_bounds_less_than_minimum( runtmp, prefetch_gather, linear_gather ): testdata_glob = utils.get_test_data("gather/GCF*.sig") glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "gather", query_sig, prefetch_gather, linear_gather, "gcf_all", "-k", "21", "--scaled", "50", "--threshold-bp", "50000", ) assert ( "WARNING: scaled value should be >= 100. Continuing anyway." in runtmp.last_result.err ) def test_gather_check_scaled_bounds_more_than_maximum( runtmp, prefetch_gather, linear_gather ): testdata_glob = utils.get_test_data("gather/GCF*.sig") glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "gather", query_sig, prefetch_gather, linear_gather, "-k", "21", "--scaled", "1e9", "--threshold-bp", "50000", ) assert ( "WARNING: scaled value should be <= 1e6. Continuing anyway." in runtmp.last_result.err ) def test_gather_metagenome_downsample(runtmp, prefetch_gather, linear_gather): # downsample w/scaled of 100,000 testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash( "gather", query_sig, "gcf_all", "-k", "21", "--scaled", "100000", prefetch_gather, linear_gather, "--threshold-bp", "50000", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 11 matches total" in runtmp.last_result.out assert "the recovered matches hit 100.0% of the query" in runtmp.last_result.out assert all( ( "5.2 Mbp 32.9% 100.0%" in runtmp.last_result.out, "NC_003198.1" in runtmp.last_result.out, ) ) assert all( ( "4.1 Mbp 0.6% 2.4%" in runtmp.last_result.out, "4.1 Mbp 4.4% 17.1%" in runtmp.last_result.out, ) ) def test_gather_query_downsample(runtmp, linear_gather, prefetch_gather): testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) print(testdata_sigs) query_sig = utils.get_test_data("GCF_000006945.2-s500.sig") runtmp.sourmash( "gather", "-k", "31", linear_gather, prefetch_gather, query_sig, *testdata_sigs ) print(runtmp.last_result.out) print(runtmp.last_result.err) err = runtmp.last_result.err assert "loaded 36 total signatures from 12 locations." in err assert "after selecting signatures compatible with search, 12 remain." in err assert all( ( "4.9 Mbp 100.0% 100.0%" in runtmp.last_result.out, "NC_003197.2" in runtmp.last_result.out, ) ) assert ( "WARNING: final scaled was 10000, vs query scaled of 500" in runtmp.last_result.out ) def test_gather_query_downsample_explicit(runtmp, linear_gather, prefetch_gather): # do an explicit downsampling to fix `test_gather_query_downsample` testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("GCF_000006945.2-s500.sig") runtmp.sourmash( "gather", "-k", "31", "--scaled", "10000", linear_gather, prefetch_gather, query_sig, *testdata_sigs, ) print(runtmp.last_result.out) print(runtmp.last_result.err) err = runtmp.last_result.err assert "loaded 36 total signatures from 12 locations." in err assert "after selecting signatures compatible with search, 12 remain." in err assert all( ( "4.9 Mbp 100.0% 100.0%" in runtmp.last_result.out, "NC_003197.2" in runtmp.last_result.out, ) ) def test_gather_downsample_multiple(runtmp, linear_gather, prefetch_gather): # test multiple different downsamplings in gather code query_sig = utils.get_test_data("GCF_000006945.2-s500.sig") # load in the hashes and do split them into four bins, randomly. ss = load_one_signature(query_sig) hashes = list(ss.minhash.hashes) random.seed(a=1) # fix seed so test is reproducible random.shuffle(hashes) # split into 4 bins: mh_bins = [ss.minhash.copy_and_clear() for i in range(4)] for i, hashval in enumerate(hashes): mh_bins[i % 4].add_hash(hashval) # downsample with different scaleds; initial scaled is 500, note. mh_bins[0] = mh_bins[0].downsample(scaled=750) mh_bins[1] = mh_bins[1].downsample(scaled=600) mh_bins[2] = mh_bins[2].downsample(scaled=1000) mh_bins[3] = mh_bins[3].downsample(scaled=650) gathersigs = [] for i in range(4): binsig = signature.SourmashSignature(mh_bins[i], name=f"bin{i}") with open(runtmp.output(f"bin{i}.sig"), "wb") as fp: sourmash.save_signatures_to_json([binsig], fp) gathersigs.append(f"bin{i}.sig") runtmp.sourmash( "gather", "-k", "31", linear_gather, prefetch_gather, query_sig, *gathersigs ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "WARNING: final scaled was 1000, vs query scaled of 500" in runtmp.last_result.out ) def test_gather_with_picklist(runtmp, linear_gather, prefetch_gather): # test 'sourmash gather' with picklists gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") runtmp.sourmash( "gather", metag_sig, *gcf_sigs, "--threshold-bp=0", "-k", "21", "--picklist", f"{picklist}:md5:md5", linear_gather, prefetch_gather, ) err = runtmp.last_result.err print(err) assert "for given picklist, found 3 matches to 9 distinct values" in err # these are the different ksizes assert "WARNING: 6 missing picklist values." in err out = runtmp.last_result.out print(out) assert "found 3 matches total;" in out assert "1.9 Mbp 13.1% 100.0% NC_000853.1 Thermotoga" in out assert "1.9 Mbp 11.5% 89.9% NC_011978.1 Thermotoga" in out assert "1.9 Mbp 6.3% 48.4% NC_009486.1 Thermotoga" in out def test_gather_with_picklist_exclude(runtmp, linear_gather, prefetch_gather): # test 'sourmash gather' with picklists - exclude gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") runtmp.sourmash( "gather", metag_sig, *gcf_sigs, "--threshold-bp=0", "-k", "21", "--picklist", f"{picklist}:md5:md5:exclude", linear_gather, prefetch_gather, ) err = runtmp.last_result.err print(err) assert "for given picklist, found 9 matches by excluding 9 distinct values" in err # these are the different ksizes out = runtmp.last_result.out print(out) assert "found 9 matches total;" in out assert "4.9 Mbp 33.2% 100.0% NC_003198.1 Salmonella enterica subsp" in out assert "1.6 Mbp 10.7% 100.0% NC_002163.1 Campylobacter jejuni subs" in out assert "4.8 Mbp 10.4% 31.3% NC_003197.2 Salmonella enterica subsp" in out assert "4.7 Mbp 5.2% 16.1% NC_006905.1 Salmonella enterica subsp" in out assert "4.7 Mbp 4.0% 12.6% NC_011080.1 Salmonella enterica subsp" in out assert "4.6 Mbp 2.9% 9.2% NC_011274.1 Salmonella enterica subsp" in out assert "4.3 Mbp 2.1% 7.3% NC_006511.1 Salmonella enterica subsp" in out assert "4.7 Mbp 0.5% 1.5% NC_011294.1 Salmonella enterica subsp" in out assert "4.5 Mbp 0.1% 0.4% NC_004631.1 Salmonella enterica subsp" in out def test_gather_with_pattern_include(runtmp, linear_gather, prefetch_gather): # test 'sourmash gather' with --include-db-pattern gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash( "gather", metag_sig, *gcf_sigs, "--threshold-bp=0", "-k", "21", "--include", "thermotoga", linear_gather, prefetch_gather, ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "found 3 matches total;" in out assert "1.9 Mbp 13.1% 100.0% NC_000853.1 Thermotoga" in out assert "1.9 Mbp 11.5% 89.9% NC_011978.1 Thermotoga" in out assert "1.9 Mbp 6.3% 48.4% NC_009486.1 Thermotoga" in out def test_gather_with_pattern_exclude(runtmp, linear_gather, prefetch_gather): # test 'sourmash gather' with --exclude gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") runtmp.sourmash( "gather", metag_sig, *gcf_sigs, "--threshold-bp=0", "-k", "21", "--exclude", "thermotoga", linear_gather, prefetch_gather, ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "found 9 matches total;" in out assert "4.9 Mbp 33.2% 100.0% NC_003198.1 Salmonella enterica subsp" in out assert "1.6 Mbp 10.7% 100.0% NC_002163.1 Campylobacter jejuni subs" in out assert "4.8 Mbp 10.4% 31.3% NC_003197.2 Salmonella enterica subsp" in out assert "4.7 Mbp 5.2% 16.1% NC_006905.1 Salmonella enterica subsp" in out assert "4.7 Mbp 4.0% 12.6% NC_011080.1 Salmonella enterica subsp" in out assert "4.6 Mbp 2.9% 9.2% NC_011274.1 Salmonella enterica subsp" in out assert "4.3 Mbp 2.1% 7.3% NC_006511.1 Salmonella enterica subsp" in out assert "4.7 Mbp 0.5% 1.5% NC_011294.1 Salmonella enterica subsp" in out assert "4.5 Mbp 0.1% 0.4% NC_004631.1 Salmonella enterica subsp" in out def test_gather_save_matches(runtmp, linear_gather, prefetch_gather): testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash( "gather", query_sig, "gcf_all", "-k", "21", "--save-matches", "save.sigs", linear_gather, prefetch_gather, "--threshold-bp", "0", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 12 matches total" in runtmp.last_result.out assert "the recovered matches hit 100.0% of the query" in runtmp.last_result.out assert os.path.exists(runtmp.output("save.sigs")) def test_gather_save_matches_and_save_prefetch(runtmp, linear_gather): testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) query_sig = utils.get_test_data("gather/combined.sig") cmd = ["index", "gcf_all"] cmd.extend(testdata_sigs) cmd.extend(["-k", "21"]) runtmp.sourmash(*cmd) assert os.path.exists(runtmp.output("gcf_all.sbt.zip")) runtmp.sourmash( "gather", query_sig, "gcf_all", "-k", "21", "--save-matches", "save.sigs", "--save-prefetch", "save2.sigs", linear_gather, "--threshold-bp", "0", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 12 matches total" in runtmp.last_result.out assert "the recovered matches hit 100.0% of the query" in runtmp.last_result.out matches_save = runtmp.output("save.sigs") prefetch_save = runtmp.output("save2.sigs") assert os.path.exists(matches_save) assert os.path.exists(prefetch_save) matches = list(sourmash.load_file_as_signatures(matches_save)) prefetch = list(sourmash.load_file_as_signatures(prefetch_save)) assert set(matches) == set(prefetch) @utils.in_tempdir def test_gather_error_no_sigs_traverse(c): # test gather applied to a directory query = utils.get_test_data( "prot/protein/GCA_001593925.1_ASM159392v1_protein.faa.gz.sig" ) emptydir = c.output("") with pytest.raises(SourmashCommandFailed): c.run_sourmash("gather", query, emptydir) err = c.last_result.err print(err) assert f"Error while reading signatures from '{emptydir}'" in err def test_gather_error_no_cardinality_query(runtmp, linear_gather, prefetch_gather): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,num=500", testdata1, testdata2) testdata3 = utils.get_test_data("short3.fa") runtmp.sourmash("sketch", "translate", "-p", "k=31,num=500", testdata3) runtmp.sourmash("index", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "gather", "short3.fa.sig", "zzz", linear_gather, prefetch_gather ) assert runtmp.last_result.status == -1 assert "query signature needs to be created with --scaled" in runtmp.last_result.err def test_gather_deduce_ksize(runtmp, prefetch_gather, linear_gather): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "dna", "-p", "k=23,scaled=10", testdata1, testdata2) runtmp.sourmash( "sketch", "dna", "-p", "k=23,scaled=10", "-o", "query.fa.sig", testdata2 ) runtmp.sourmash("index", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", prefetch_gather, linear_gather, "--threshold-bp=1", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "0.9 kbp 100.0% 100.0%" in runtmp.last_result.out def test_gather_deduce_moltype(runtmp, linear_gather, prefetch_gather): # gather should automatically figure out ksize testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash("sketch", "translate", "-p", "k=10,scaled=10", testdata1, testdata2) runtmp.sourmash( "sketch", "translate", "-p", "k=10,scaled=10", "-o", "query.fa.sig", testdata2 ) runtmp.sourmash("index", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", linear_gather, prefetch_gather, "--threshold-bp=1", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "1.9 kbp 100.0% 100.0%" in runtmp.last_result.out def test_gather_abund_1_1(runtmp, linear_gather, prefetch_gather): # check gather with a hand-constructed abundance-weighted query, mark 1 c = runtmp # # make r1.fa with 2x coverage of genome s10 # make r2.fa with 20x coverage of genome s10. # make r3.fa with 2x coverage of genome s11. # # nullgraph/make-reads.py -S 1 -r 200 -C 2 tests/test-data/genome-s10.fa.gz > r1.fa # nullgraph/make-reads.py -S 1 -r 200 -C 20 tests/test-data/genome-s10.fa.gz > r2.fa # nullgraph/make-reads.py -S 1 -r 200 -C 2 tests/test-data/genome-s11.fa.gz > r3.fa # # make signature s10-s11 with r1 and r3, i.e. 1:1 abundance # make signature s10x10-s11 with r2 and r3, i.e. 10:1 abundance # # ./sourmash compute -k 21 --scaled 1000 --merge=1-1 -o reads-s10-s11.sig r[13].fa --track-abundance # ./sourmash compute -k 21 --scaled 1000 --merge=10-1 -o reads-s10x10-s11.sig r[23].fa --track-abundance query = utils.get_test_data("gather-abund/reads-s10-s11.sig") against_list = ["genome-s10", "genome-s11", "genome-s12"] against_list = ["gather-abund/" + i + ".fa.gz.sig" for i in against_list] against_list = [utils.get_test_data(i) for i in against_list] status, out, err = c.run_sourmash( "gather", query, *against_list, linear_gather, prefetch_gather ) print(out) print(err) # when we project s10-s11 (r1+r3), 1:1 abundance, # onto s10 and s11 genomes with gather, we get: # * approximately 50% of each query matching (first column, p_query) # * approximately 80% of subject genomes contents being matched # (this is due to the low coverage of 2 used to build queries) # * approximately 2.0 abundance (third column, avg_abund) assert "49.6% 78.5% 1.8 tests/test-data/genome-s10.fa.gz" in out assert "50.4% 80.0% 1.9 tests/test-data/genome-s11.fa.gz" in out assert "genome-s12.fa.gz" not in out assert "the recovered matches hit 100.0% of the abundance-weighted query" in out assert "the recovered matches hit 100.0% of the query k-mers (unweighted)" in out def test_gather_abund_10_1(runtmp, prefetch_gather, linear_gather): # check gather with a hand-constructed abundance-weighted query c = runtmp # see comments in test_gather_abund_1_1, above. # nullgraph/make-reads.py -S 1 -r 200 -C 2 tests/test-data/genome-s10.fa.gz > r1.fa # nullgraph/make-reads.py -S 1 -r 200 -C 20 tests/test-data/genome-s10.fa.gz > r2.fa # nullgraph/make-reads.py -S 1 -r 200 -C 2 tests/test-data/genome-s11.fa.gz > r3.fa # ./sourmash compute -k 21 --scaled 1000 --merge=1-1 -o reads-s10-s11.sig r[13].fa --track-abundance # ./sourmash compute -k 21 --scaled 1000 --merge=10-1 -o reads-s10x10-s11.sig r[23].fa --track-abundance query = utils.get_test_data("gather-abund/reads-s10x10-s11.sig") against_list = ["genome-s10", "genome-s11", "genome-s12"] against_list = ["gather-abund/" + i + ".fa.gz.sig" for i in against_list] against_list = [utils.get_test_data(i) for i in against_list] status, out, err = c.run_sourmash( "gather", query, "-o", "xxx.csv", *against_list, linear_gather, prefetch_gather ) print(out) print(err) # when we project s10x10-s11 (r2+r3), 10:1 abundance, # onto s10 and s11 genomes with gather, we get: # * approximately 91% of s10 matching # * approximately 9% of s11 matching # * approximately 100% of the high coverage genome being matched, # with only 80% of the low coverage genome # * approximately 2.0 abundance (third column, avg_abund) for s11, # and (very) approximately 20x abundance for genome s10. assert "91.0% 100.0% 14.5 tests/test-data/genome-s10.fa.gz" in out assert "9.0% 80.0% 1.9 tests/test-data/genome-s11.fa.gz" in out assert "genome-s12.fa.gz" not in out assert "the recovered matches hit 100.0% of the abundance-weighted query" in out # check the calculations behind the above output by looking into # the CSV. with open(c.output("xxx.csv")) as fp: r = csv.DictReader(fp) overlaps = [] unique_overlaps = [] f_weighted_list = [] average_abunds = [] remaining_bps = [] n_weighted_list = [] sum_weighted_list = [] total_weighted_list = [] for n, row in enumerate(r): assert int(row["gather_result_rank"]) == n # other than f_weighted, these are all 'flat' numbers - no abunds. overlap = float(row["intersect_bp"]) remaining_bp = float(row["remaining_bp"]) unique_overlap = float(row["unique_intersect_bp"]) f_weighted = float(row["f_unique_weighted"]) average_abund = float(row["average_abund"]) overlaps.append(overlap) unique_overlaps.append(unique_overlap) f_weighted_list.append(f_weighted) average_abunds.append(average_abund) remaining_bps.append(remaining_bp) # also track weighted calculations n_weighted_list.append(float(row["n_unique_weighted_found"])) sum_weighted_list.append(float(row["sum_weighted_found"])) total_weighted_list.append(float(row["total_weighted_hashes"])) weighted_calc = [] for overlap, average_abund in zip(overlaps, average_abunds): prod = overlap * average_abund weighted_calc.append(prod) # @CTB redundant terms with below? total_weighted = sum(weighted_calc) for prod, f_weighted in zip(weighted_calc, f_weighted_list): assert prod / total_weighted == f_weighted, (prod, f_weighted) query_sig = load_one_signature(query) query_mh = query_sig.minhash total_bp_analyzed = sum(unique_overlaps) + remaining_bps[-1] total_query_bp = len(query_mh) * query_mh.scaled assert total_bp_analyzed == total_query_bp # running sum of n_weighted_list should match sum_weighted_list sofar_sum = 0 for i in range(len(n_weighted_list)): n_weighted = n_weighted_list[i] sum_weighted = sum_weighted_list[i] sofar_sum += n_weighted assert sum_weighted == sofar_sum # weighted list should all be the same, and should match sum_weighted_list # for this query, since 100% found. assert min(total_weighted_list) == max(total_weighted_list) assert min(total_weighted_list) == 7986 assert sum_weighted_list[-1] == 7986 # check/verify calculations for f_weighted - for i in range(len(n_weighted_list)): n_weighted = n_weighted_list[i] f_weighted = f_weighted_list[i] assert f_weighted == n_weighted / 7986 def test_gather_abund_10_1_ignore_abundance(runtmp, linear_gather, prefetch_gather): # check gather with an abundance-weighted query, then flattened with # --ignore-abund c = runtmp # see comments in test_gather_abund_1_1, above. # nullgraph/make-reads.py -S 1 -r 200 -C 2 tests/test-data/genome-s10.fa.gz > r1.fa # nullgraph/make-reads.py -S 1 -r 200 -C 20 tests/test-data/genome-s10.fa.gz > r2.fa # nullgraph/make-reads.py -S 1 -r 200 -C 2 tests/test-data/genome-s11.fa.gz > r3.fa # ./sourmash compute -k 21 --scaled 1000 --merge=1-1 -o reads-s10-s11.sig r[13].fa --track-abundance # ./sourmash compute -k 21 --scaled 1000 --merge=10-1 -o reads-s10x10-s11.sig r[23].fa --track-abundance query = utils.get_test_data("gather-abund/reads-s10x10-s11.sig") against_list = ["genome-s10", "genome-s11", "genome-s12"] against_list = ["gather-abund/" + i + ".fa.gz.sig" for i in against_list] against_list = [utils.get_test_data(i) for i in against_list] status, out, err = c.run_sourmash( "gather", query, "--ignore-abundance", *against_list, linear_gather, prefetch_gather, "-o", c.output("results.csv"), ) print(out) print(err) assert "the recovered matches hit 100.0% of the abundance-weighted query" not in out assert "the recovered matches hit 100.0% of the query k-mers (unweighted)" in out # when we project s10x10-s11 (r2+r3), 10:1 abundance, # onto s10 and s11 genomes with gather --ignore-abundance, we get: # * approximately 50% of s10 and s11 matching (first column) # * approximately 100% of the high coverage genome being matched, # with only 80% of the low coverage genome assert all(("57.2% 100.0%", "tests/test-data/genome-s10.fa.gz" in out)) assert all(("42.8% 80.0%", "tests/test-data/genome-s11.fa.gz" in out)) assert "genome-s12.fa.gz" not in out with open(c.output("results.csv")) as fp: r = csv.DictReader(fp) some_results = False for row in r: some_results = True assert row["average_abund"] == "" assert row["median_abund"] == "" assert row["std_abund"] == "" assert row["query_abundance"] == "False", row["query_abundance"] assert row["n_unique_weighted_found"] == "" assert some_results def test_gather_output_unassigned_with_abundance( runtmp, prefetch_gather, linear_gather ): # check --output-unassigned with an abund query # @CTB: could add check on sum weighted etc. c = runtmp query = utils.get_test_data("gather-abund/reads-s10x10-s11.sig") against = utils.get_test_data("gather-abund/genome-s10.fa.gz.sig") c.run_sourmash( "gather", query, against, "--output-unassigned", c.output("unassigned.sig"), linear_gather, prefetch_gather, ) assert os.path.exists(c.output("unassigned.sig")) nomatch = load_one_signature(c.output("unassigned.sig")) assert nomatch.minhash.track_abundance query_ss = load_one_signature(query) against_ss = load_one_signature(against) # unassigned should have nothing that is in the database nomatch_mh = nomatch.minhash for hashval in against_ss.minhash.hashes: assert hashval not in nomatch_mh.hashes # unassigned should have abundances from original query, if not in database for hashval, abund in query_ss.minhash.hashes.items(): if hashval not in against_ss.minhash.hashes: assert nomatch_mh.hashes[hashval] == abund def test_gather_empty_db_fail(runtmp, linear_gather, prefetch_gather): # gather should fail on empty db with --fail-on-empty-database query = utils.get_test_data("2.fa.sig") against = utils.get_test_data("47.fa.sig") against2 = utils.get_test_data("lca/47+63.lca.json") with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "gather", query, against, against2, "-k", "51", linear_gather, prefetch_gather, ) err = runtmp.last_result.err assert "no compatible signatures found in " in err def test_gather_empty_db_nofail(runtmp, prefetch_gather, linear_gather): # gather should not fail on empty db with --no-fail-on-empty-database query = utils.get_test_data("2.fa.sig") against = utils.get_test_data("47.fa.sig") against2 = utils.get_test_data("lca/47+63.lca.json") runtmp.sourmash( "gather", query, against, against2, "-k", "51", "--no-fail-on-empty-data", linear_gather, prefetch_gather, ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "no compatible signatures found in " in err assert ( "ksize on this database is 31; this is different from requested ksize of 51" in err ) assert "loaded 50 total signatures from 2 locations" in err assert "after selecting signatures compatible with search, 0 remain." in err def test_multigather_output_unassigned_with_abundance(runtmp, sig_save_extension_abund): c = runtmp query = utils.get_test_data("gather-abund/reads-s10x10-s11.sig") against = utils.get_test_data("gather-abund/genome-s10.fa.gz.sig") cmd = f"multigather --query {query} --db {against} -E {sig_save_extension_abund}".split() c.run_sourmash(*cmd) print(c.last_result.out) print(c.last_result.err) out = c.last_result.out assert "the recovered matches hit 91.0% of the abundance-weighted query." in out assert "the recovered matches hit 57.2% of the query k-mers (unweighted)." in out assert os.path.exists(c.output(f"r3.fa.unassigned{sig_save_extension_abund}")) nomatch = sourmash.load_file_as_signatures( c.output(f"r3.fa.unassigned{sig_save_extension_abund}") ) nomatch = list(nomatch)[0] assert nomatch.minhash.track_abundance query_ss = load_one_signature(query) against_ss = load_one_signature(against) # unassigned should have nothing that is in the database nomatch_mh = nomatch.minhash for hashval in against_ss.minhash.hashes: assert hashval not in nomatch_mh.hashes # unassigned should have abundances from original query, if not in database for hashval, abund in query_ss.minhash.hashes.items(): if hashval not in against_ss.minhash.hashes: assert nomatch_mh.hashes[hashval] == abund def test_multigather_empty_db_fail(runtmp): # multigather should fail on empty db with --fail-on-empty-database query = utils.get_test_data("2.fa.sig") against = utils.get_test_data("47.fa.sig") against2 = utils.get_test_data("lca/47+63.lca.json") with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "multigather", "--query", query, "--db", against, against2, "-k", "51" ) err = runtmp.last_result.err assert "no compatible signatures found in " in err def test_multigather_empty_db_nofail(runtmp): # multigather should not fail on empty db with --no-fail-on-empty-database query = utils.get_test_data("2.fa.sig") against = utils.get_test_data("47.fa.sig") against2 = utils.get_test_data("lca/47+63.lca.json") runtmp.sourmash( "multigather", "--query", query, "--db", against, against2, "-k", "51", "--no-fail-on-empty-data", ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "no compatible signatures found in " in err assert ( "ksize on this database is 31; this is different from requested ksize of 51" in err ) assert "conducted gather searches on 0 signatures" in err assert "loaded 50 total signatures from 2 locations" in err assert "after selecting signatures compatible with search, 0 remain." in err def test_multigather_nomatch(runtmp): testdata_query = utils.get_test_data( "gather/GCF_000006945.2_ASM694v2_genomic.fna.gz.sig" ) testdata_match = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") runtmp.sourmash( "multigather", "--query", testdata_query, "--db", testdata_match, "-k", "31" ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 0 matches total" in runtmp.last_result.out assert "the recovered matches hit 0.0% of the query" in runtmp.last_result.out def test_multigather_abund_nomatch(runtmp): testdata_query = utils.get_test_data("gather-abund/reads-s10x10-s11.sig") testdata_match = utils.get_test_data( "gather/GCF_000006945.2_ASM694v2_genomic.fna.gz.sig" ) runtmp.sourmash("multigather", "--query", testdata_query, "--db", testdata_match) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "found 0 matches total" in runtmp.last_result.out assert "the recovered matches hit 0.0% of the query" in runtmp.last_result.out def test_sbt_categorize(runtmp): testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") # all four in the current directory for categorize . shutil.copyfile(testdata1, runtmp.output("1.sig")) shutil.copyfile(testdata2, runtmp.output("2.sig")) shutil.copyfile(testdata3, runtmp.output("3.sig")) shutil.copyfile(testdata4, runtmp.output("4.sig")) # omit 3 args = ["index", "--dna", "-k", "21", "zzz", "1.sig", "2.sig"] runtmp.sourmash(*args) # categorize all of the ones that were copied to 'location' args = ["categorize", "zzz", ".", "--ksize", "21", "--dna", "--csv", "out.csv"] runtmp.sourmash(*args) print(runtmp.last_result.out) print(runtmp.last_result.err) # mash dist genome-s10.fa.gz genome-s10+s11.fa.gz # yields 521/1000 ==> ~0.5 assert "for genome-s10+s11, found: 0.50 genome-s10" in runtmp.last_result.err out_csv = Path(runtmp.output("out.csv")).read_text() print(out_csv) assert "4.sig,genome-s10+s11,genome-s10,0.504" in out_csv def test_sbt_categorize_ignore_abundance_1(runtmp): # --- Categorize without ignoring abundance --- query = utils.get_test_data("gather-abund/reads-s10x10-s11.sig") against_list = ["reads-s10-s11"] against_list = ["gather-abund/" + i + ".sig" for i in against_list] against_list = [utils.get_test_data(i) for i in against_list] # omit 3 args = ["index", "--dna", "-k", "21", "thebestdatabase"] + against_list runtmp.sourmash(*args) args = [ "categorize", "thebestdatabase", "--ksize", "21", "--dna", "--csv", "out3.csv", query, ] with pytest.raises(SourmashCommandFailed): runtmp.sourmash(*args) assert runtmp.last_result.status != 0 print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "ERROR: this search cannot be done on signatures calculated with abundance." in runtmp.last_result.err ) assert "ERROR: please specify --ignore-abundance." in runtmp.last_result.err def test_sbt_categorize_ignore_abundance_3(runtmp): # --- Now categorize with ignored abundance --- query = utils.get_test_data("gather-abund/reads-s10x10-s11.sig") against_list = ["reads-s10-s11"] against_list = ["gather-abund/" + i + ".sig" for i in against_list] against_list = [utils.get_test_data(i) for i in against_list] # omit 3 args = ["index", "--dna", "-k", "21", "thebestdatabase"] + against_list runtmp.sourmash(*args) args = [ "categorize", "--ignore-abundance", "--ksize", "21", "--dna", "--csv", "out4.csv", "thebestdatabase", query, ] runtmp.sourmash(*args) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "for 1-1, found: 0.88 1-1" in runtmp.last_result.err out_csv4 = Path(runtmp.output("out4.csv")).read_text() assert "reads-s10x10-s11.sig,1-1,1-1,0.87699" in out_csv4 def test_sbt_categorize_already_done(runtmp): testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") shutil.copyfile(testdata1, runtmp.output("1.sig")) shutil.copyfile(testdata2, runtmp.output("2.sig")) shutil.copyfile(testdata3, runtmp.output("3.sig")) shutil.copyfile(testdata4, runtmp.output("4.sig")) # omit 3 args = ["index", "--dna", "-k", "21", "zzz", "1.sig", "2.sig"] runtmp.sourmash(*args) with open(runtmp.output("in.csv"), "w") as fp: fp.write("./4.sig,genome-s10.fa.gz,0.50") args = [ "categorize", "zzz", "./2.sig", "./4.sig", "--ksize", "21", "--dna", "--load-csv", "in.csv", ] runtmp.sourmash(*args) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "for genome-s11.fa.gz, no match found" assert "for s10+s11, found: 0.50 genome-s10.fa.gz" not in runtmp.last_result.err def test_sbt_categorize_already_done_traverse(runtmp): testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") testdata4 = utils.get_test_data("genome-s10+s11.sig") shutil.copyfile(testdata1, runtmp.output("1.sig")) shutil.copyfile(testdata2, runtmp.output("2.sig")) shutil.copyfile(testdata3, runtmp.output("3.sig")) shutil.copyfile(testdata4, runtmp.output("4.sig")) # omit 3 args = ["index", "--dna", "-k", "21", "zzz", "1.sig", "2.sig"] runtmp.sourmash(*args) with open(runtmp.output("in.csv"), "w") as fp: fp.write("./4.sig,genome-s10.fa.gz,0.50") args = ["categorize", "zzz", ".", "--ksize", "21", "--dna", "--load-csv", "in.csv"] runtmp.sourmash(*args) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "for genome-s11.fa.gz, no match found" assert "for s10+s11, found: 0.50 genome-s10.fa.gz" not in runtmp.last_result.err def test_sbt_categorize_multiple_ksizes_moltypes(runtmp): # 'categorize' works fine with multiple moltypes/ksizes testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") testdata2 = utils.get_test_data("genome-s11.fa.gz.sig") testdata3 = utils.get_test_data("genome-s12.fa.gz.sig") shutil.copyfile(testdata1, runtmp.output("1.sig")) shutil.copyfile(testdata2, runtmp.output("2.sig")) shutil.copyfile(testdata3, runtmp.output("3.sig")) args = ["index", "--dna", "-k", "21", "zzz", "1.sig", "2.sig"] runtmp.sourmash(*args) args = ["categorize", "zzz", "."] runtmp.sourmash(*args) def test_watch_check_num_bounds_negative(runtmp): # check that watch properly outputs error on negative num c = runtmp testdata0 = utils.get_test_data("genome-s10.fa.gz") testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") shutil.copyfile(testdata1, c.output("1.sig")) c.run_sourmash("index", "--dna", "-k", "21", "zzz", "1.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash("watch", "--ksize", "21", "-n", "-5", "--dna", "zzz", testdata0) assert "ERROR: num value must be positive" in c.last_result.err def test_watch_check_num_bounds_less_than_minimum(runtmp): # check that watch properly outputs warnings on small num c = runtmp testdata0 = utils.get_test_data("genome-s10.fa.gz") testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") shutil.copyfile(testdata1, c.output("1.sig")) c.run_sourmash("index", "--dna", "-k", "21", "zzz", "1.sig") c.run_sourmash("watch", "--ksize", "21", "-n", "25", "--dna", "zzz", testdata0) assert "WARNING: num value should be >= 50. Continuing anyway." in c.last_result.err def test_watch_check_num_bounds_more_than_maximum(runtmp): # check that watch properly outputs warnings on large num c = runtmp testdata0 = utils.get_test_data("genome-s10.fa.gz") testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") shutil.copyfile(testdata1, c.output("1.sig")) c.run_sourmash("index", "--dna", "-k", "21", "zzz", "1.sig") c.run_sourmash("watch", "--ksize", "21", "-n", "100000", "--dna", "zzz", testdata0) assert ( "WARNING: num value should be <= 50000. Continuing anyway." in c.last_result.err ) def test_watch(runtmp): # check basic watch functionality c = runtmp testdata0 = utils.get_test_data("genome-s10.fa.gz") testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") shutil.copyfile(testdata1, c.output("1.sig")) c.run_sourmash("index", "--dna", "-k", "21", "zzz", "1.sig") c.run_sourmash("watch", "--ksize", "21", "--dna", "zzz", testdata0) print(c.last_result.out) print(c.last_result.err) assert "FOUND: genome-s10, at 1.000" in c.last_result.out def test_watch_deduce_ksize(runtmp): # check that watch guesses ksize automatically from database c = runtmp testdata0 = utils.get_test_data("genome-s10.fa.gz") c.run_sourmash("sketch", "dna", "-p", "k=29,num=500", "-o", "1.sig", testdata0) c.run_sourmash("index", "--dna", "-k", "29", "zzz", "1.sig") c.run_sourmash("watch", "--dna", "zzz", testdata0) print(c.last_result.out) print(c.last_result.err) assert "Computing signature for k=29" in c.last_result.err assert "genome-s10.fa.gz, at 1.000" in c.last_result.out def test_watch_coverage(runtmp): # check output details/coverage of found testdata0 = utils.get_test_data("genome-s10.fa.gz") testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") shutil.copyfile(testdata1, runtmp.output("1.sig")) args = ["index", "--dna", "-k", "21", "zzz", "1.sig"] runtmp.sourmash(*args) with open(runtmp.output("query.fa"), "w") as fp: record = list(screed.open(testdata0))[0] for start in range(0, len(record), 100): fp.write(f">{start}\n{record.sequence[start : start + 500]}\n") args = ["watch", "--ksize", "21", "--dna", "zzz", "query.fa"] runtmp.sourmash(*args) print(runtmp.last_result.out) print(runtmp.last_result.err) assert "FOUND: genome-s10, at 1.000" in runtmp.last_result.out def test_watch_output_sig(runtmp): # test watch --output testdata0 = utils.get_test_data("genome-s10.fa.gz") testdata1 = utils.get_test_data("genome-s10.fa.gz.sig") shutil.copyfile(testdata1, runtmp.output("1.sig")) args = ["index", "--dna", "-k", "21", "zzz", "1.sig"] runtmp.sourmash(*args) with open(runtmp.output("query.fa"), "w") as fp: record = list(screed.open(testdata0))[0] for start in range(0, len(record), 100): fp.write(f">{start}\n{record.sequence[start : start + 500]}\n") args = [ "watch", "--ksize", "21", "--dna", "zzz", "query.fa", "-o", "out.sig", "--name", "xyzfoo", ] runtmp.sourmash(*args) print(runtmp.last_result.out) print(runtmp.last_result.err) out_sig = runtmp.output("out.sig") assert os.path.exists(out_sig) siglist = list(sourmash.load_file_as_signatures(out_sig)) assert len(siglist) == 1 assert siglist[0].filename == "stdin" assert siglist[0].name == "xyzfoo" def test_storage_convert(runtmp): testdata = utils.get_test_data("v2.sbt.json") shutil.copyfile(testdata, runtmp.output("v2.sbt.json")) shutil.copytree( os.path.join(os.path.dirname(testdata), ".sbt.v2"), runtmp.output(".sbt.v2") ) testsbt = runtmp.output("v2.sbt.json") original = SBT.load(testsbt, leaf_loader=SigLeaf.load) args = ["storage", "convert", "-b", "ipfs", testsbt] try: runtmp.sourmash(*args) except SourmashCommandFailed: pass if runtmp.last_result.status: if "ipfshttpclient.ConnectionError" in runtmp.last_result.err: raise pytest.xfail("ipfs probably not running") if "No module named 'ipfshttpclient'" in runtmp.last_result.err: raise pytest.xfail("ipfshttpclient module not installed") print("NO FAIL; KEEP ON GOING!") ipfs = SBT.load(testsbt, leaf_loader=SigLeaf.load) assert len(original) == len(ipfs) assert all( n1[1].name == n2[1].name for (n1, n2) in zip(sorted(original), sorted(ipfs)) ) args = [ "storage", "convert", "-b", """'ZipStorage("{}")'""".format(runtmp.output("v2.sbt.zip")), testsbt, ] runtmp.sourmash(*args) tar = SBT.load(testsbt, leaf_loader=SigLeaf.load) assert len(original) == len(tar) assert all( n1[1].name == n2[1].name for (n1, n2) in zip(sorted(original), sorted(tar)) ) print("it all worked!!") def test_storage_convert_identity(runtmp): testdata = utils.get_test_data("v2.sbt.json") shutil.copyfile(testdata, runtmp.output("v2.sbt.json")) shutil.copytree( os.path.join(os.path.dirname(testdata), ".sbt.v2"), runtmp.output(".sbt.v2") ) testsbt = runtmp.output("v2.sbt.json") original = SBT.load(testsbt, leaf_loader=SigLeaf.load) args = ["storage", "convert", "-b", "fsstorage", testsbt] runtmp.sourmash(*args) identity = SBT.load(testsbt, leaf_loader=SigLeaf.load) assert len(original) == len(identity) assert all( n1[1].name == n2[1].name for (n1, n2) in zip(sorted(original), sorted(identity)) ) def test_storage_convert_fsstorage_newpath(runtmp): testdata = utils.get_test_data("v2.sbt.json") shutil.copyfile(testdata, runtmp.output("v2.sbt.json")) shutil.copytree( os.path.join(os.path.dirname(testdata), ".sbt.v2"), runtmp.output(".sbt.v2") ) testsbt = runtmp.output("v2.sbt.json") original = SBT.load(testsbt, leaf_loader=SigLeaf.load) args = [ "storage", "convert", "-b", "fsstorage({})".format(runtmp.output("v3")), testsbt, ] runtmp.sourmash(*args) identity = SBT.load(testsbt, leaf_loader=SigLeaf.load) assert len(original) == len(identity) assert all( n1[1].name == n2[1].name for (n1, n2) in zip(sorted(original), sorted(identity)) ) def test_migrate(runtmp): testdata = utils.get_test_data("v3.sbt.json") shutil.copyfile(testdata, runtmp.output("v3.sbt.json")) shutil.copytree( os.path.join(os.path.dirname(testdata), ".sbt.v3"), runtmp.output(".sbt.v3") ) testsbt = runtmp.output("v3.sbt.json") original = SBT.load(testsbt, leaf_loader=SigLeaf.load) runtmp.sourmash("migrate", testsbt) identity = SBT.load(testsbt, leaf_loader=SigLeaf.load) assert len(original) == len(identity) assert all( n1[1].name == n2[1].name for (n1, n2) in zip(sorted(original), sorted(identity)) ) assert "this is an old index version" not in runtmp.last_result.err assert all( "min_n_below" in node.metadata for node in identity if isinstance(node, Node) ) def test_license_cc0(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "translate", "-p", "k=31", testdata1) sigfile = runtmp.output("short.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert str(sig).endswith("short.fa") assert sig.license == "CC0" def test_license_non_cc0(runtmp): testdata1 = utils.get_test_data("short.fa") with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sketch", "translate", "-p", "k=31", "--license", "GPL", testdata1 ) assert runtmp.last_result.status != 0 print(runtmp.last_result.out) print(runtmp.last_result.err) assert "sourmash only supports CC0" in runtmp.last_result.err def test_license_load_non_cc0(): sigfile = utils.get_test_data("bad-license.sig") try: next(load_signatures_from_json(sigfile, do_raise=True)) except Exception as e: assert "sourmash only supports CC0-licensed signatures" in str(e) @utils.in_tempdir def test_do_sourmash_index_zipfile(c): testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) c.run_sourmash("index", "-k", "31", "zzz.sbt.zip", *testdata_sigs) outfile = c.output("zzz.sbt.zip") assert os.path.exists(outfile) print(c) assert c.last_result.status == 0 assert "Finished saving SBT index, available at" in c.last_result.err # look internally at the zip file with zipfile.ZipFile(outfile) as zf: content = zf.namelist() assert len(content) == 26 assert len([c for c in content if "internal" in c]) == 11 assert ".sbt.zzz/" in content sbts = [c for c in content if c.endswith(".sbt.json")] assert len(sbts) == 1 assert sbts[0] == "zzz.sbt.json" @utils.in_tempdir def test_do_sourmash_index_zipfile_append(c): testdata_glob = utils.get_test_data("gather/GCF*.sig") testdata_sigs = glob.glob(testdata_glob) half_point = int(len(testdata_sigs) / 2) first_half = testdata_sigs[:half_point] second_half = testdata_sigs[half_point:] print(first_half) print(second_half) # should be no overlap assert not set(first_half).intersection(set(second_half)) with warnings.catch_warnings(record=True) as record: c.run_sourmash("index", "-k", "31", "zzz.sbt.zip", *first_half) # UserWarning is raised when there are duplicated entries in the zipfile assert not record, record outfile = c.output("zzz.sbt.zip") assert os.path.exists(outfile) print(c) assert c.last_result.status == 0 assert "Finished saving SBT index, available at" in c.last_result.err with warnings.catch_warnings(record=True) as record: c.run_sourmash("index", "--append", "-k", "31", "zzz.sbt.zip", *second_half) # UserWarning is raised when there are duplicated entries in the zipfile print(record) # assert not record, record print(c) assert c.last_result.status == 0 assert "Finished saving SBT index, available at" in c.last_result.err # look internally at the zip file with zipfile.ZipFile(outfile) as zf: content = zf.namelist() print(content) assert len(content) == 26 assert len([c for c in content if "internal" in c]) == 11 assert ".sbt.zzz/" in content sbts = [c for c in content if c.endswith(".sbt.json")] assert len(sbts) == 1 assert sbts[0] == "zzz.sbt.json" def test_index_with_picklist(runtmp): # test 'sourmash index' with picklists gcf_sig_dir = utils.get_test_data("gather/") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") output_db = runtmp.output("thermo.sbt.zip") runtmp.sourmash( "index", output_db, gcf_sig_dir, "-k", "31", "--picklist", f"{picklist}:md5:md5" ) err = runtmp.last_result.err print(err) assert "for given picklist, found 3 matches to 9 distinct values" in err # these are the different ksizes assert "WARNING: 6 missing picklist values." in err # verify: siglist = list(sourmash.load_file_as_signatures(output_db)) assert len(siglist) == 3 for ss in siglist: assert "Thermotoga" in ss.name def test_index_with_picklist_exclude(runtmp): # test 'sourmash index' with picklists - exclude gcf_sig_dir = utils.get_test_data("gather/") picklist = utils.get_test_data("gather/thermotoga-picklist.csv") output_db = runtmp.output("thermo-exclude.sbt.zip") runtmp.sourmash( "index", output_db, gcf_sig_dir, "-k", "31", "--picklist", f"{picklist}:md5:md5:exclude", ) err = runtmp.last_result.err print(err) assert "for given picklist, found 9 matches by excluding 9 distinct values" in err # verify: siglist = list(sourmash.load_file_as_signatures(output_db)) assert len(siglist) == 9 for ss in siglist: assert "Thermotoga" not in ss.name def test_index_matches_search_with_picklist(runtmp): # test 'sourmash index' with picklists gcf_sig_dir = utils.get_test_data("gather/") glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/thermotoga-picklist.csv") metag_sig = utils.get_test_data("gather/combined.sig") output_db = runtmp.output("thermo.sbt.zip") runtmp.sourmash("index", output_db, gcf_sig_dir, "-k", "21") print(runtmp.last_result.out) print(runtmp.last_result.err) # verify: siglist = list(sourmash.load_file_as_signatures(output_db)) assert len(siglist) > 3 # all signatures included... n_thermo = 0 for ss in siglist: if "Thermotoga" in ss.name: n_thermo += 1 assert n_thermo == 3 runtmp.sourmash( "search", metag_sig, output_db, "--containment", "-k", "21", "--picklist", f"{picklist}:md5:md5", ) err = runtmp.last_result.err print(err) assert "for given picklist, found 3 matches to 9 distinct values" in err # these are the different ksizes assert "WARNING: 6 missing picklist values." in err out = runtmp.last_result.out print(out) assert "3 matches" in out assert "13.1% NC_000853.1 Thermotoga" in out assert "13.0% NC_009486.1 Thermotoga" in out assert "12.8% NC_011978.1 Thermotoga" in out def test_index_matches_search_with_picklist_exclude(runtmp): # test 'sourmash index' with picklists - exclude gcf_sig_dir = utils.get_test_data("gather/") glob.glob(utils.get_test_data("gather/GCF*.sig")) picklist = utils.get_test_data("gather/thermotoga-picklist.csv") metag_sig = utils.get_test_data("gather/combined.sig") output_db = runtmp.output("thermo-exclude.sbt.zip") runtmp.sourmash("index", output_db, gcf_sig_dir, "-k", "21") print(runtmp.last_result.out) print(runtmp.last_result.err) # verify: siglist = list(sourmash.load_file_as_signatures(output_db)) assert len(siglist) > 3 # all signatures included... n_thermo = 0 for ss in siglist: if "Thermotoga" in ss.name: n_thermo += 1 assert n_thermo == 3 runtmp.sourmash( "search", metag_sig, output_db, "--containment", "-k", "21", "--picklist", f"{picklist}:md5:md5:exclude", ) err = runtmp.last_result.err print(err) assert "for given picklist, found 10 matches by excluding 9 distinct values" in err ### NTP: FIX REPORTING assert "WARNING: -1 missing picklist values" out = runtmp.last_result.out print(out) assert "10 matches above threshold 0.080; showing first 3:" in out assert "100.0% -" in out assert "33.2% NC_003198.1 Salmonella" in out assert "33.1% NC_003197.2 Salmonella" in out def test_gather_with_prefetch_picklist(runtmp, linear_gather): # test 'gather' using a picklist taken from 'sourmash prefetch' output gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") prefetch_csv = runtmp.output("prefetch-out.csv") runtmp.sourmash("prefetch", metag_sig, *gcf_sigs, "-k", "21", "-o", prefetch_csv) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "total of 12 matching signatures." in err assert ( "of 1466 distinct query hashes, 1466 were found in matches above threshold." in err ) # now, do a gather with the results runtmp.sourmash( "gather", metag_sig, *gcf_sigs, linear_gather, "-k", "21", "--picklist", f"{prefetch_csv}:match_md5:md5short", ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "found 11 matches total;" in out assert "the recovered matches hit 99.9% of the query" in out assert "4.9 Mbp 33.2% 100.0% NC_003198.1 " in out assert "1.9 Mbp 13.1% 100.0% NC_000853.1 " in out def test_gather_with_prefetch_picklist_2_prefetch(runtmp, linear_gather): # test 'gather' using a picklist taken from 'sourmash prefetch' output # using ::prefetch gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") prefetch_csv = runtmp.output("prefetch-out.csv") runtmp.sourmash("prefetch", metag_sig, *gcf_sigs, "-k", "21", "-o", prefetch_csv) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "total of 12 matching signatures." in err assert ( "of 1466 distinct query hashes, 1466 were found in matches above threshold." in err ) # now, do a gather with the results runtmp.sourmash( "gather", metag_sig, *gcf_sigs, linear_gather, "-k", "21", "--picklist", f"{prefetch_csv}::prefetch", ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "found 11 matches total;" in out assert "the recovered matches hit 99.9% of the query" in out assert "4.9 Mbp 33.2% 100.0% NC_003198.1 " in out assert "1.9 Mbp 13.1% 100.0% NC_000853.1 " in out def test_gather_with_prefetch_picklist_3_gather(runtmp, linear_gather): # test 'gather' using a picklist taken from 'sourmash gather' output, # using ::gather. # (this doesn't really do anything useful, but it's an ok test :) gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") gather_csv = runtmp.output("gather-out.csv") runtmp.sourmash("gather", metag_sig, *gcf_sigs, "-k", "21", "-o", gather_csv) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "found 11 matches total;" in out assert "the recovered matches hit 99.9% of the query" in out assert "4.9 Mbp 33.2% 100.0% NC_003198.1 " in out assert "1.9 Mbp 13.1% 100.0% NC_000853.1 " in out # now, do another gather with the results runtmp.sourmash( "gather", metag_sig, *gcf_sigs, linear_gather, "-k", "21", "--picklist", f"{gather_csv}::gather", ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "found 11 matches total;" in out assert "the recovered matches hit 99.9% of the query" in out assert "4.9 Mbp 33.2% 100.0% NC_003198.1 " in out assert "1.9 Mbp 13.1% 100.0% NC_000853.1 " in out def test_gather_with_prefetch_picklist_3_gather_badcol(runtmp): # test 'gather' using a picklist taken from 'sourmash gather' output, # using ::gather. # (this doesn't really do anything useful, but it's an ok test :) gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") gather_csv = runtmp.output("gather-out.csv") runtmp.sourmash("gather", metag_sig, *gcf_sigs, "-k", "21", "-o", gather_csv) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "found 11 matches total;" in out assert "the recovered matches hit 99.9% of the query" in out assert "4.9 Mbp 33.2% 100.0% NC_003198.1 " in out assert "1.9 Mbp 13.1% 100.0% NC_000853.1 " in out # now, do another gather with the results, but with a bad picklist # parameter with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "gather", metag_sig, *gcf_sigs, "-k", "21", "--picklist", f"{gather_csv}:FOO:gather", ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "ERROR: could not load picklist." in err assert "no column name allowed for coltype 'gather'" in err def test_gather_with_prefetch_picklist_4_manifest(runtmp, linear_gather): # test 'gather' using a picklist taken from 'sourmash sig manifest' # output, using ::manifest. # (this doesn't really do anything useful, but it's an ok test :) gather_dir = utils.get_test_data("gather/") metag_sig = utils.get_test_data("gather/combined.sig") manifest_csv = runtmp.output("manifest.csv") runtmp.sourmash("sig", "manifest", gather_dir, "-o", manifest_csv) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) # now, do a gather on the manifest runtmp.sourmash( "gather", metag_sig, gather_dir, linear_gather, "-k", "21", "--picklist", f"{manifest_csv}::manifest", ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "found 1 matches total;" in out assert "the recovered matches hit 100.0% of the query" in out # the query sig itself is in there, so :shrug: that matches at 100% assert "14.7 Mbp 100.0% 100.0% -" in out def test_gather_with_prefetch_picklist_4_manifest_excl(runtmp, linear_gather): # test 'gather' using a picklist taken from 'sourmash sig manifest' # output, using ::manifest. # (this doesn't really do anything useful, but it's an ok test :) gather_dir = utils.get_test_data("gather/") metag_sig = utils.get_test_data("gather/combined.sig") manifest_csv = runtmp.output("manifest.csv") runtmp.sourmash("sig", "manifest", gather_dir, "-o", manifest_csv) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) # now, do a gather on the manifest runtmp.sourmash( "gather", metag_sig, gather_dir, linear_gather, "-k", "21", "--picklist", f"{manifest_csv}::manifest:exclude", ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) # excluded everything, so nothing to match! assert "No matches found for --threshold-bp at 50.0 kbp." in runtmp.last_result.err def test_gather_with_prefetch_picklist_5_search(runtmp): # test 'gather' using a picklist taken from 'sourmash prefetch' output # using ::prefetch gcf_sigs = glob.glob(utils.get_test_data("gather/GCF*.sig")) metag_sig = utils.get_test_data("gather/combined.sig") search_csv = runtmp.output("search-out.csv") runtmp.sourmash( "search", "--containment", metag_sig, *gcf_sigs, "-k", "21", "-o", search_csv ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "12 matches above threshold 0.080; showing first 3:" in out assert " 33.2% NC_003198.1 Salmonella enterica subsp." in out # now, do a gather with the results runtmp.sourmash( "gather", metag_sig, *gcf_sigs, "-k", "21", "--picklist", f"{search_csv}::search", ) err = runtmp.last_result.err print(err) out = runtmp.last_result.out print(out) assert "found 11 matches total;" in out assert "the recovered matches hit 99.9% of the query" in out assert "4.9 Mbp 33.2% 100.0% NC_003198.1 " in out assert "1.9 Mbp 13.1% 100.0% NC_000853.1 " in out def test_gather_scaled_1(runtmp, linear_gather, prefetch_gather): # test gather on a sig indexed with scaled=1 inp = utils.get_test_data("short.fa") outp = runtmp.output("out.sig") # prepare a signature with a scaled of 1 runtmp.sourmash("sketch", "dna", "-p", "scaled=1,k=31", inp, "-o", outp) # run with a low threshold runtmp.sourmash("gather", outp, outp, "--threshold-bp", "0") print(runtmp.last_result.out) print("---") print(runtmp.last_result.err) assert "1.0 kbp 100.0% 100.0%" in runtmp.last_result.out assert "found 1 matches total;" in runtmp.last_result.out def test_standalone_manifest_search(runtmp): # test loading/searching a manifest file from the command line. sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") dirname = runtmp.output("somedir") os.mkdir(dirname) subdir = runtmp.output("somedir/subdir") os.mkdir(subdir) shutil.copyfile(sig47, os.path.join(dirname, "47.fa.sig")) shutil.copyfile(sig63, os.path.join(subdir, "63.fa.sig")) # for now, the output manifest must be within top level dir for # CLI stuff to work properly. mf = os.path.join(dirname, "mf.csv") # build manifest... runtmp.sourmash("sig", "manifest", dirname, "-o", mf) # ...and now use for a search! runtmp.sourmash("search", sig47, mf) out = runtmp.last_result.out print(out) print(runtmp.last_result.err) assert "100.0% NC_009665.1 Shewanella baltica OS185, complete genome" in out def test_standalone_manifest_search_fail(runtmp): # test loading/searching a manifest file from the command line; should # fail if manifest is not located within tld. sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") dirname = runtmp.output("somedir") os.mkdir(dirname) subdir = runtmp.output("somedir/subdir") os.mkdir(subdir) shutil.copyfile(sig47, os.path.join(dirname, "47.fa.sig")) shutil.copyfile(sig63, os.path.join(subdir, "63.fa.sig")) # for now, the output manifest must be within top level dir for # CLI stuff to work properly. here we intentionally break this, # for testing purposes. mf = runtmp.output("mf.csv") # build manifest... runtmp.sourmash("sig", "manifest", dirname, "-o", mf) # ...and now use for a search! with pytest.raises(SourmashCommandFailed): runtmp.sourmash("search", sig47, mf) def test_search_ani_jaccard(runtmp): c = runtmp sig47 = utils.get_test_data("47.fa.sig") sig4763 = utils.get_test_data("47+63.fa.sig") c.run_sourmash("search", sig47, sig4763, "-o", "xxx.csv") print(c.last_result.status, c.last_result.out, c.last_result.err) search_result_names = SearchResult.search_write_cols csv_file = c.output("xxx.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names == list(row.keys()) assert float(row["similarity"]) == 0.6564798376870403 assert row["filename"].endswith("47+63.fa.sig") assert row["md5"] == "491c0a81b2cfb0188c0d3b46837c2f42" assert row["query_filename"].endswith("47.fa") assert ( row["query_name"] == "NC_009665.1 Shewanella baltica OS185, complete genome" ) assert row["query_md5"] == "09a08691" assert row["ani"] == "0.992530907924384" def test_search_ani_jaccard_error_too_high(runtmp): c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=1", testdata1, testdata2) c.run_sourmash("search", "short.fa.sig", "short2.fa.sig", "-o", "xxx.csv") print(c.last_result.status, c.last_result.out, c.last_result.err) search_result_names = SearchResult.search_write_cols csv_file = c.output("xxx.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names == list(row.keys()) assert float(row["similarity"]) == 0.9288577154308617 assert row["filename"].endswith("short2.fa.sig") assert row["md5"] == "bf752903d635b1eb83c53fe4aae951db" assert row["query_filename"].endswith("short.fa") assert row["query_name"] == "" assert row["query_md5"] == "9191284a" # assert row['ani'] == "0.9987884602947684" assert row["ani"] == "" assert ( "WARNING: Jaccard estimation for at least one of these comparisons is likely inaccurate. Could not estimate ANI for these comparisons." in c.last_result.err ) def test_searchabund_no_ani(runtmp): c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=10,abund", testdata1, testdata2) c.run_sourmash("search", "short.fa.sig", "short2.fa.sig", "-o", "xxx.csv") print(c.last_result.status, c.last_result.out, c.last_result.err) csv_file = c.output("xxx.csv") search_result_names = SearchResult.search_write_cols with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names == list(row.keys()) assert float(row["similarity"]) == 0.8224046424612483 assert row["md5"] == "c9d5a795eeaaf58e286fb299133e1938" assert row["filename"].endswith("short2.fa.sig") assert row["query_filename"].endswith("short.fa") assert row["query_name"] == "" assert row["query_md5"] == "b5cc464c" assert row["ani"] == "" # do we want empty column to appear?? def test_search_ani_containment(runtmp): c = runtmp testdata1 = utils.get_test_data("2+63.fa.sig") testdata2 = utils.get_test_data("47+63.fa.sig") c.run_sourmash("search", "--containment", testdata1, testdata2, "-o", "xxx.csv") print(c.last_result.status, c.last_result.out, c.last_result.err) search_result_names = SearchResult.search_write_cols csv_file = c.output("xxx.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names == list(row.keys()) assert float(row["similarity"]) == 0.6597808288197506 assert row["filename"].endswith("47+63.fa.sig") assert row["md5"] == "491c0a81b2cfb0188c0d3b46837c2f42" assert row["query_name"] == "" assert row["query_md5"] == "832a45e8" assert row["ani"] == "0.9866751346467802" # search other direction c.run_sourmash("search", "--containment", testdata2, testdata1, "-o", "xxxx.csv") print(c.last_result.status, c.last_result.out, c.last_result.err) csv_file = c.output("xxxx.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names == list(row.keys()) assert float(row["similarity"]) == 0.6642150646715699 assert row["filename"].endswith("2+63.fa.sig") assert row["md5"] == "832a45e85bdca6eaef5d73047e3e6321" assert row["query_name"] == "" assert row["query_md5"] == "491c0a81" assert row["ani"] == "0.9868883523107224" def test_search_ani_containment_asymmetry(runtmp): # test contained_by asymmetries, viz #2215 query_sig = utils.get_test_data("47.fa.sig") merged_sig = utils.get_test_data("47-63-merge.sig") runtmp.sourmash( "search", query_sig, merged_sig, "-o", "query-in-merged.csv", "--containment" ) runtmp.sourmash( "search", merged_sig, query_sig, "-o", "merged-in-query.csv", "--containment" ) with sourmash_args.FileInputCSV(runtmp.output("query-in-merged.csv")) as r: query_in_merged = list(r)[0] with sourmash_args.FileInputCSV(runtmp.output("merged-in-query.csv")) as r: merged_in_query = list(r)[0] assert query_in_merged["ani"] == "1.0" assert merged_in_query["ani"] == "0.9865155060423993" def test_search_ani_containment_fail(runtmp): c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=10", testdata1, testdata2) c.run_sourmash( "search", "--containment", "short.fa.sig", "short2.fa.sig", "-o", "xxx.csv" ) print(c.last_result.status, c.last_result.out, c.last_result.err) search_result_names = SearchResult.search_write_cols csv_file = c.output("xxx.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names == list(row.keys()) assert round(float(row["similarity"]), 3) == 0.967 assert row["ani"] == "0.998906999319701" # With PR #2268, this error message should not appear # assert "WARNING: size estimation for at least one of these sketches may be inaccurate. ANI values will not be reported for these comparisons." in c.last_result.err def test_search_ani_containment_estimate_ci(runtmp): # test ANI confidence intervals, based on (asymmetric) containment c = runtmp testdata1 = utils.get_test_data("2+63.fa.sig") testdata2 = utils.get_test_data("47+63.fa.sig") c.run_sourmash( "search", "--containment", testdata1, testdata2, "-o", "xxx.csv", "--estimate-ani-ci", ) print(c.last_result.status, c.last_result.out, c.last_result.err) search_result_names_ci = SearchResult.search_write_cols_ci csv_file = c.output("xxx.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names_ci == list(row.keys()) assert float(row["similarity"]) == 0.6597808288197506 assert row["filename"].endswith("47+63.fa.sig") assert row["md5"] == "491c0a81b2cfb0188c0d3b46837c2f42" assert row["query_name"] == "" assert row["query_md5"] == "832a45e8" assert row["ani"] == "0.9866751346467802" assert row["ani_low"] == "0.9861576758035308" # "0.9861559138341189" assert row["ani_high"] == "0.9871770716451368" # "0.9871787293232042" # search other direction c.run_sourmash( "search", "--containment", testdata2, testdata1, "-o", "xxxx.csv", "--estimate-ani-ci", ) print(c.last_result.status, c.last_result.out, c.last_result.err) csv_file = c.output("xxxx.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names_ci == list(row.keys()) assert float(row["similarity"]) == 0.6642150646715699 assert row["filename"].endswith("2+63.fa.sig") assert row["md5"] == "832a45e85bdca6eaef5d73047e3e6321" assert row["query_name"] == "" assert row["query_md5"] == "491c0a81" assert row["ani"] == "0.9868883523107224" assert row["ani_low"] == "0.986374049720872" # "0.9863757952722036" assert row["ani_high"] == "0.9873870188726516" # "0.9873853776786775" def test_search_ani_max_containment(runtmp): c = runtmp testdata1 = utils.get_test_data("2+63.fa.sig") testdata2 = utils.get_test_data("47+63.fa.sig") c.run_sourmash("search", "--max-containment", testdata1, testdata2, "-o", "xxx.csv") print(c.last_result.status, c.last_result.out, c.last_result.err) csv_file = c.output("xxx.csv") search_result_names = SearchResult.search_write_cols with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names == list(row.keys()) assert float(row["similarity"]) == 0.6642150646715699 assert row["filename"].endswith("47+63.fa.sig") assert row["md5"] == "491c0a81b2cfb0188c0d3b46837c2f42" assert row["query_name"] == "" assert row["query_md5"] == "832a45e8" assert row["ani"] == "0.9868883523107224" def test_search_ani_max_containment_estimate_ci(runtmp): # test ANI confidence intervals, based on (symmetric) max-containment c = runtmp testdata1 = utils.get_test_data("2+63.fa.sig") testdata2 = utils.get_test_data("47+63.fa.sig") c.run_sourmash( "search", "--max-containment", testdata1, testdata2, "-o", "xxx.csv", "--estimate-ani-ci", ) print(c.last_result.status, c.last_result.out, c.last_result.err) csv_file = c.output("xxx.csv") search_result_names_ci = SearchResult.search_write_cols_ci with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names_ci == list(row.keys()) assert float(row["similarity"]) == 0.6642150646715699 assert row["filename"].endswith("47+63.fa.sig") assert row["md5"] == "491c0a81b2cfb0188c0d3b46837c2f42" assert row["query_name"] == "" assert row["query_md5"] == "832a45e8" assert row["ani"] == "0.9868883523107224" assert row["ani_low"] == "0.986374049720872" assert row["ani_high"] == "0.9873870188726516" def test_search_jaccard_ani_downsample(runtmp): c = runtmp sig47 = utils.get_test_data("47.fa.sig") sig4763 = utils.get_test_data("47+63.fa.sig") ss47 = load_one_signature(sig47) ss4763 = load_one_signature(sig4763) print(f"SCALED: sig1: {ss47.minhash.scaled}, sig2: {ss4763.minhash.scaled}") c.run_sourmash("search", sig47, sig4763, "-o", "xxx.csv") print(c.last_result.status, c.last_result.out, c.last_result.err) search_result_names = SearchResult.search_write_cols search_result_names_ci = SearchResult.search_write_cols_ci csv_file = c.output("xxx.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert search_result_names == list(row.keys()) assert search_result_names_ci != list(row.keys()) assert float(row["similarity"]) == 0.6564798376870403 assert row["filename"].endswith("47+63.fa.sig") assert row["md5"] == "491c0a81b2cfb0188c0d3b46837c2f42" assert row["query_filename"].endswith("47.fa") assert ( row["query_name"] == "NC_009665.1 Shewanella baltica OS185, complete genome" ) assert row["query_md5"] == "09a08691" assert row["ani"] == "0.992530907924384" # downsample one and check similarity and ANI ds_sig47 = c.output("ds_sig47.sig") c.run_sourmash("sig", "downsample", sig47, "--scaled", "2000", "-o", ds_sig47) c.run_sourmash("search", ds_sig47, sig4763, "-o", "xxx.csv") # csv_file = c.output("xxx.csv") with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert round(float(row["similarity"]), 3) == round(0.6634517766497462, 3) assert round(float(row["ani"]), 3) == 0.993 # downsample manually and assert same ANI ss47_ds = load_one_signature(ds_sig47) print("SCALED:", ss47_ds.minhash.scaled, ss4763.minhash.scaled) ani_info = ss47_ds.jaccard_ani(ss4763, downsample=True) print(ani_info) assert round(ani_info.ani, 3) == 0.993 assert (1 - round(ani_info.dist, 3)) == 0.993 def test_gather_ani_csv(runtmp, linear_gather, prefetch_gather): testdata1 = utils.get_test_data("63.fa.sig") testdata2 = utils.get_test_data("47+63.fa.sig") runtmp.sourmash("index", "-k", "31", "zzz", testdata2) assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", testdata1, "zzz", "-o", "foo.csv", "--threshold-bp=1", linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) csv_file = runtmp.output("foo.csv") gather_result_names = GatherResult.gather_write_cols gather_result_names_ci = GatherResult.gather_write_cols_ci with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert gather_result_names == list(row.keys()) assert gather_result_names_ci != list(row.keys()) assert float(row["intersect_bp"]) == 5238000.0 assert float(row["unique_intersect_bp"]) == 5238000.0 assert float(row["remaining_bp"]) == 0.0 assert float(row["f_orig_query"]) == 1.0 assert float(row["f_unique_to_query"]) == 1.0 assert float(row["f_match"]) == 0.6642150646715699 assert row["filename"] == "zzz" assert row["md5"] == "491c0a81b2cfb0188c0d3b46837c2f42" assert row["gather_result_rank"] == "0" assert row["query_md5"] == "38729c63" assert row["query_bp"] == "5238000" assert row["query_containment_ani"] == "1.0" assert round(float(row["match_containment_ani"]), 3) == 0.987 assert round(float(row["average_containment_ani"]), 3) == 0.993 assert round(float(row["max_containment_ani"]), 3) == 1.0 assert row["potential_false_negative"] == "False" def test_gather_ani_csv_estimate_ci(runtmp, linear_gather, prefetch_gather): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") runtmp.sourmash( "sketch", "dna", "-p", "scaled=10", "--name-from-first", testdata1, testdata2 ) runtmp.sourmash( "sketch", "dna", "-p", "scaled=10", "-o", "query.fa.sig", "--name-from-first", testdata2, ) runtmp.sourmash("index", "-k", "31", "zzz", "short.fa.sig", "short2.fa.sig") assert os.path.exists(runtmp.output("zzz.sbt.zip")) runtmp.sourmash( "gather", "query.fa.sig", "zzz", "-o", "foo.csv", "--threshold-bp=1", "--estimate-ani-ci", linear_gather, prefetch_gather, ) print(runtmp.last_result.out) print(runtmp.last_result.err) csv_file = runtmp.output("foo.csv") gather_result_names = GatherResult.gather_write_cols_ci with open(csv_file) as fp: reader = csv.DictReader(fp) row = next(reader) print(row) assert gather_result_names == list(row.keys()) assert float(row["intersect_bp"]) == 910 assert float(row["unique_intersect_bp"]) == 910 assert float(row["remaining_bp"]) == 0 assert float(row["f_orig_query"]) == 1.0 assert float(row["f_unique_to_query"]) == 1.0 assert float(row["f_match"]) == 1.0 assert row["filename"] == "zzz" assert row["name"] == "tr1 4" assert row["md5"] == "c9d5a795eeaaf58e286fb299133e1938" assert row["gather_result_rank"] == "0" assert row["query_filename"].endswith("short2.fa") assert row["query_name"] == "tr1 4" assert row["query_md5"] == "c9d5a795" assert row["query_bp"] == "910" assert row["query_containment_ani"] == "1.0" assert row["query_containment_ani_low"] == "1.0" assert row["query_containment_ani_high"] == "1.0" assert row["match_containment_ani"] == "1.0" assert row["match_containment_ani_low"] == "1.0" assert row["match_containment_ani_high"] == "1.0" assert row["average_containment_ani"] == "1.0" assert row["max_containment_ani"] == "1.0" assert row["potential_false_negative"] == "False" def test_compare_containment_ani(runtmp): # test compare --containment --ani c = runtmp sigfiles = ["2.fa.sig", "2+63.fa.sig", "47.fa.sig", "63.fa.sig"] testdata_sigs = [utils.get_test_data(c) for c in sigfiles] c.run_sourmash( "compare", "--containment", "-k", "31", "--ani", "--csv", "output.csv", *testdata_sigs, ) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): mat_val = round(mat[i][j], 3) print(mat_val) if i == j: assert 1 == mat_val else: ss_j = idx_to_sig[j] containment_ani = ss_j.containment_ani(ss_i).ani if containment_ani is not None: containment_ani = round(containment_ani, 3) else: containment_ani = 0.0 mat_val = round(mat[i][j], 3) assert containment_ani == mat_val # , (i, j) print(c.last_result.err) print(c.last_result.out) assert ( "WARNING: Some of these sketches may have no hashes in common based on chance alone (false negatives). Consider decreasing your scaled value to prevent this." in c.last_result.err ) def test_compare_containment_ani_asymmetry(runtmp): # very specifically test asymmetry of ANI in containment matrices ;) c = runtmp import numpy sigfiles = ["47.fa.sig", "47-63-merge.sig"] testdata_sigs = [utils.get_test_data(c) for c in sigfiles] c.run_sourmash( "compare", "--containment", "-k", "31", "--ani", "--csv", "output.csv", *testdata_sigs, ) # load the matrix output of compare --containment --estimate-ani with open(c.output("output.csv")) as fp: r = iter(csv.reader(fp)) headers = next(r) mat = numpy.zeros((len(headers), len(headers))) for i, row in enumerate(r): for j, val in enumerate(row): mat[i][j] = float(val) print(mat) # load in all the input signatures idx_to_sig = {} for idx, filename in enumerate(testdata_sigs): ss = load_one_signature(filename, ksize=31) idx_to_sig[idx] = ss # check explicit containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): mat_val = round(mat[i][j], 6) print(mat_val) if i == j: assert 1 == mat_val else: ss_j = idx_to_sig[j] containment_ani = ss_j.containment_ani(ss_i).ani if containment_ani is not None: containment_ani = round(containment_ani, 6) else: containment_ani = 0.0 mat_val = round(mat[i][j], 6) assert containment_ani == mat_val # , (i, j) print(c.last_result.err) print(c.last_result.out) def test_compare_jaccard_ani(runtmp): c = runtmp sigfiles = ["47.fa.sig", "47-63-merge.sig"] testdata_sigs = [utils.get_test_data(c) for c in sigfiles] c.run_sourmash( "compare", "--containment", "-k", "31", "--ani", "--csv", "output.csv", *testdata_sigs, ) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): mat_val = round(mat[i][j], 6) print(mat_val) if i == j: assert 1 == mat_val else: ss_j = idx_to_sig[j] containment_ani = ss_j.containment_ani(ss_i).ani if containment_ani is not None: containment_ani = round(containment_ani, 6) else: containment_ani = 0.0 mat_val = round(mat[i][j], 6) assert containment_ani == mat_val # , (i, j) print(c.last_result.err) print(c.last_result.out) def test_compare_jaccard_protein_parallel_ani_bug(runtmp): # this checks a bug that occurred with serialization of protein minhash # in parallel situations. See #2262. c = runtmp sigfile = utils.get_test_data("prot/protein.zip") c.run_sourmash("compare", "--ani", "-p", "2", "--csv", "output.csv", sigfile) print(c.last_result.err) print(c.last_result.out) def test_compare_containment_ani_asymmetry_distance(runtmp): # very specifically test asymmetry of ANI in containment matrices ;) # ...calculated with --distance c = runtmp sigfiles = ["47.fa.sig", "47-63-merge.sig"] testdata_sigs = [utils.get_test_data(c) for c in sigfiles] c.run_sourmash( "compare", "--containment", "-k", "31", "--distance-matrix", "--ani", "--csv", "output.csv", *testdata_sigs, ) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): mat_val = round(mat[i][j], 6) print(mat_val) if i == j: assert 0 == mat_val else: ss_j = idx_to_sig[j] containment_ani = 1 - ss_j.containment_ani(ss_i).ani if containment_ani is not None: containment_ani = round(containment_ani, 6) else: containment_ani = 1 mat_val = round(mat[i][j], 6) assert containment_ani == mat_val # , (i, j) print(c.last_result.err) print(c.last_result.out) def test_compare_jaccard_ani(runtmp): c = runtmp sigfiles = ["2.fa.sig", "2+63.fa.sig", "47.fa.sig", "63.fa.sig"] testdata_sigs = [utils.get_test_data(c) for c in sigfiles] c.run_sourmash( "compare", "-k", "31", "--estimate-ani", "--csv", "output.csv", *testdata_sigs ) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit calculations against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): mat_val = round(mat[i][j], 3) print(mat_val) if i == j: assert 1 == mat_val else: ss_j = idx_to_sig[j] jaccard_ani = ss_j.jaccard_ani(ss_i).ani if jaccard_ani is not None: jaccard_ani = round(jaccard_ani, 3) else: jaccard_ani = 0.0 print(jaccard_ani) assert jaccard_ani == mat_val # , (i, j) print(c.last_result.err) print(c.last_result.out) assert ( "WARNING: Some of these sketches may have no hashes in common based on chance alone (false negatives). Consider decreasing your scaled value to prevent this." in c.last_result.err ) def test_compare_jaccard_ani_jaccard_error_too_high(runtmp): c = runtmp testdata1 = utils.get_test_data("short.fa") sig1 = c.output("short.fa.sig") testdata2 = utils.get_test_data("short2.fa") sig2 = c.output("short2.fa.sig") c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=1", "-o", sig1, testdata1) c.run_sourmash("sketch", "dna", "-p", "k=31,scaled=1", "-o", sig2, testdata2) testdata_sigs = [sig1, sig2] c.run_sourmash( "compare", "-k", "31", "--estimate-ani", "--csv", "output.csv", "short.fa.sig", "short2.fa.sig", ) print(c.last_result.status, c.last_result.out, c.last_result.err) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): mat_val = round(mat[i][j], 3) print(mat_val) if i == j: assert 1 == mat_val else: ss_j = idx_to_sig[j] jaccard_ani = ss_j.jaccard_ani(ss_i).ani if jaccard_ani is not None: jaccard_ani = round(jaccard_ani, 3) else: jaccard_ani = 0.0 print(jaccard_ani) assert jaccard_ani == mat_val # , (i, j) assert ( "WARNING: Jaccard estimation for at least one of these comparisons is likely inaccurate. Could not estimate ANI for these comparisons." in c.last_result.err ) def test_compare_max_containment_ani(runtmp): c = runtmp sigfiles = ["2.fa.sig", "2+63.fa.sig", "47.fa.sig", "63.fa.sig"] testdata_sigs = [utils.get_test_data(c) for c in sigfiles] c.run_sourmash( "compare", "--max-containment", "-k", "31", "--estimate-ani", "--csv", "output.csv", *testdata_sigs, ) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): mat_val = round(mat[i][j], 3) print(mat_val) if i == j: assert 1 == mat_val else: ss_j = idx_to_sig[j] containment_ani = ss_j.max_containment_ani(ss_i).ani if containment_ani is not None: containment_ani = round(containment_ani, 3) else: containment_ani = 0.0 assert containment_ani == mat_val, (i, j) print(c.last_result.err) print(c.last_result.out) assert ( "WARNING: Some of these sketches may have no hashes in common based on chance alone (false negatives). Consider decreasing your scaled value to prevent this." in c.last_result.err ) def test_compare_avg_containment_ani(runtmp): # test compare --avg-containment --ani c = runtmp sigfiles = ["2.fa.sig", "2+63.fa.sig", "47.fa.sig", "63.fa.sig"] testdata_sigs = [utils.get_test_data(c) for c in sigfiles] c.run_sourmash( "compare", "--avg-containment", "-k", "31", "--estimate-ani", "--csv", "output.csv", *testdata_sigs, ) # load the matrix output mat, idx_to_sig = _load_compare_matrix_and_sigs( c.output("output.csv"), testdata_sigs ) # check explicit avg containment against output of compare for i in range(len(idx_to_sig)): ss_i = idx_to_sig[i] for j in range(len(idx_to_sig)): mat_val = round(mat[i][j], 3) print(mat_val) if i == j: assert 1 == mat_val else: ss_j = idx_to_sig[j] containment_ani = ss_j.avg_containment_ani(ss_i) if containment_ani is not None: containment_ani = round(containment_ani, 3) else: containment_ani = 0.0 assert containment_ani == mat_val, (i, j) print(c.last_result.err) print(c.last_result.out) assert ( "WARNING: Some of these sketches may have no hashes in common based on chance alone (false negatives). Consider decreasing your scaled value to prevent this." in c.last_result.err ) def test_compare_ANI_require_scaled(runtmp): # check that compare with containment requires scaled sketches c = runtmp s47 = utils.get_test_data("num/47.fa.sig") s63 = utils.get_test_data("num/63.fa.sig") # containment and estimate ANI will give this error with pytest.raises(SourmashCommandFailed): c.run_sourmash( "compare", "--containment", "--estimate-ani", "-k", "31", s47, s63, fail_ok=True, ) assert ( "must use scaled signatures with --containment, --max-containment, and --avg-containment" in c.last_result.err ) assert c.last_result.status != 0 # jaccard + estimate ANI will give this error with pytest.raises(SourmashCommandFailed): c.run_sourmash("compare", "--estimate-ani", "-k", "31", s47, s63, fail_ok=True) assert "must use scaled signatures with --estimate-ani" in c.last_result.err assert c.last_result.status != 0 sourmash-bio-sourmash-bc3c0f8/tests/test_sourmash_args.py000066400000000000000000000631721474157046300241270ustar00rootroot00000000000000""" Tests for functions in sourmash_args module. """ import sys import os import pytest import gzip import zipfile import io import contextlib import csv import argparse import shutil import json import sourmash_tst_utils as utils import sourmash from sourmash import sourmash_args, manifest from sourmash.index import LinearIndex from sourmash.cli.utils import add_ksize_arg from sourmash.signature import load_signatures_from_json, save_signatures_to_json def test_save_signatures_api_none(): # save to sigfile sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) with sourmash_args.SaveSignaturesToLocation(None) as save_sig: print(repr(save_sig)) save_sig.add(ss2) save_sig.add(ss47) # nothing to test - no output! def test_save_signatures_to_location_1_sig(runtmp): # save to sigfile.sig sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) outloc = runtmp.output("foo.sig") with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss2) save_sig.add(ss47) saved = list(sourmash.load_file_as_signatures(outloc)) assert ss2 in saved assert ss47 in saved assert len(saved) == 2 def test_save_signatures_to_location_1_stdout(): # save to stdout sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) output_capture = io.StringIO() with contextlib.redirect_stdout(output_capture): with sourmash_args.SaveSignaturesToLocation("-") as save_sig: save_sig.add(ss2) save_sig.add(ss47) output = output_capture.getvalue() saved = list(load_signatures_from_json(output)) assert ss2 in saved assert ss47 in saved assert len(saved) == 2 def test_save_signatures_to_location_1_sig_is_default(runtmp): # save to sigfile.txt sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) outloc = runtmp.output("foo.txt") with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss2) save_sig.add(ss47) saved = list(load_signatures_from_json(outloc)) assert ss2 in saved assert ss47 in saved assert len(saved) == 2 def test_save_signatures_to_location_1_sig_gz(runtmp): # save to sigfile.gz sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) outloc = runtmp.output("foo.sig.gz") with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss2) save_sig.add(ss47) # can we open as a .gz file? with gzip.open(outloc, "r") as fp: print(save_sig) fp.read() saved = list(sourmash.load_file_as_signatures(outloc)) assert ss2 in saved assert ss47 in saved assert len(saved) == 2 def test_save_signatures_to_location_1_zip(runtmp): # save to sigfile.zip sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) outloc = runtmp.output("foo.zip") with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss2) save_sig.add(ss47) # can we open as a .zip file? with zipfile.ZipFile(outloc, "r") as zf: assert list(zf.infolist()) saved = list(sourmash.load_file_as_signatures(outloc)) assert ss2 in saved assert ss47 in saved assert len(saved) == 2 def test_save_signatures_to_location_1_zip_bad(runtmp): # try saving to bad sigfile.zip sig2 = utils.get_test_data("2.fa.sig") sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") sourmash.load_one_signature(sig47, ksize=31) outloc = runtmp.output("foo.zip") # create bad zip: with open(outloc, "w"): pass # now check for error with pytest.raises(ValueError) as exc: with sourmash_args.SaveSignaturesToLocation(outloc): pass assert "cannot be opened as a zip file" in str(exc) def test_save_signatures_to_location_1_zip_dup(runtmp): # save to sigfile.zip sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) outloc = runtmp.output("foo.zip") with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss2) save_sig.add(ss47) # here we have to change the names so the sig content is different; # exact duplicates will not be saved, otherwise. ss2 = ss2.to_mutable() ss2.name = "different name for ss2" save_sig.add(ss2) ss47 = ss47.to_mutable() ss47.name = "different name for ss47" save_sig.add(ss47) # can we open as a .zip file? with zipfile.ZipFile(outloc, "r") as zf: assert list(zf.infolist()) saved = list(sourmash.load_file_as_signatures(outloc)) assert ss2 in saved assert ss47 in saved assert len(saved) == 4 def test_save_signatures_to_location_2_zip_add(runtmp): # create sigfile.zip; then, add a new signature. sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) # add only ss2 outloc = runtmp.output("foo.zip") with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss2) # can we open as a .zip file? with zipfile.ZipFile(outloc, "r") as zf: assert list(zf.infolist()) saved = list(sourmash.load_file_as_signatures(outloc)) assert ss2 in saved assert len(saved) == 1 # now, re-open and add ss47. with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss47) # updated file should contain both. saved = list(sourmash.load_file_as_signatures(outloc)) print(saved) assert ss47 in saved assert ss2 in saved def test_save_signatures_to_location_2_zip_add_dup(runtmp): # create sigfile.zip; then, add a new signature, plus a ~duplicate. sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) # add only ss2 outloc = runtmp.output("foo.zip") with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss2) # can we open as a .zip file? with zipfile.ZipFile(outloc, "r") as zf: assert list(zf.infolist()) saved = list(sourmash.load_file_as_signatures(outloc)) assert ss2 in saved assert len(saved) == 1 # now, re-open and add ss47, plus a slightly renamed ss2. with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss47) # add ss2; here we have to change the names so the sig content is # different exact duplicates will not be saved, otherwise. import copy ss2copy = ss2.to_mutable() ss2copy.name = "different name for ss2" save_sig.add(ss2copy) # updated file should contain all three. saved = list(sourmash.load_file_as_signatures(outloc)) print(len(saved), saved) assert ss47 in saved assert ss2 in saved assert ss2copy in saved def test_save_signatures_to_location_3_zip_add_fail(runtmp): # create sigfile.zip using zipfile, then try to add to it (& fail) sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") sourmash.load_one_signature(sig47, ksize=31) # add only ss2, using zipfile API outloc = runtmp.output("foo.zip") with zipfile.ZipFile(outloc, "x") as zf: with zf.open("xyz.sig", "w") as fp: save_signatures_to_json([ss2], fp=fp, compression=1) # verify it can be loaded, yada yada saved = list(sourmash.load_file_as_signatures(outloc)) print(len(saved), saved) assert ss2 in saved # now, try to open existing file with SaveSignaturesToLocation... with pytest.raises(ValueError) as exc: with sourmash_args.SaveSignaturesToLocation(outloc): pass assert "Cannot add to existing zipfile" in str(exc) def test_save_signatures_to_location_3_zip_add_with_manifest(runtmp): # create sigfile.zip using zipfile, then try to add to it (& fail) sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) # add only ss2, using zipfile API; add manifest manually. outloc = runtmp.output("foo.zip") with zipfile.ZipFile(outloc, "x") as zf: with zf.open("xyz.sig", "w") as fp: save_signatures_to_json([ss2], fp=fp, compression=1) # make a manifest row... row = manifest.CollectionManifest.make_manifest_row( ss2, "xyz.sig", include_signature=False ) # construct & save manifest mf = manifest.CollectionManifest([row]) mf_name = "SOURMASH-MANIFEST.csv" manifest_fp = io.StringIO() mf.write_to_csv(manifest_fp, write_header=True) manifest_data = manifest_fp.getvalue().encode("utf-8") with zf.open(mf_name, "w") as fp: fp.write(manifest_data) # fini! made our artisanal hand-crafted zipfile. Now... # verify it can be loaded, yada yada saved = list(sourmash.load_file_as_signatures(outloc)) print(len(saved), saved) assert ss2 in saved # now, try to open existing file with SaveSignaturesToLocation... # ...should succeed! with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss47) # updated file should contain both saved = list(sourmash.load_file_as_signatures(outloc)) print(len(saved), saved) assert ss47 in saved assert ss2 in saved def test_save_signatures_to_location_1_dirout(runtmp): # save to sigout/ (directory) sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) outloc = runtmp.output("sigout/") with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss2) save_sig.add(ss47) assert os.path.isdir(outloc) saved = list(sourmash.load_file_as_signatures(outloc)) assert ss2 in saved assert ss47 in saved assert len(saved) == 2 def test_save_signatures_to_location_1_dirout_bug_2751(runtmp): # check for 2x compressed sig files sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) outloc = runtmp.output("sigout/") with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss2) save_sig.add(ss47) assert os.path.isdir(outloc) print(os.listdir(outloc)) outloc2 = runtmp.output("sigout/09a08691ce52952152f0e866a59f6261.sig.gz") with gzip.open(outloc2, "r") as fp: data = fp.read() print(data) _ = json.loads(data) def test_save_signatures_to_location_1_dirout_duplicate(runtmp): # save to sigout/ (directory) sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) outloc = runtmp.output("sigout/") with sourmash_args.SaveSignaturesToLocation(outloc) as save_sig: print(save_sig) save_sig.add(ss2) save_sig.add(ss47) save_sig.add(ss2) save_sig.add(ss47) assert os.path.isdir(outloc) saved = list(sourmash.load_file_as_signatures(outloc)) assert ss2 in saved assert ss47 in saved assert len(saved) == 4 def test_load_empty_zipfile(runtmp): outloc = runtmp.output("empty.zip") with sourmash_args.SaveSignaturesToLocation(outloc): pass sigiter = sourmash.load_file_as_signatures(outloc) assert list(sigiter) == [] def test_load_many_sigs_empty_file(runtmp): # make sure load_many_signatures behaves properly on empty file outloc = runtmp.output("empty.sig") with open(outloc, "w"): pass progress = sourmash_args.SignatureLoadingProgress() with contextlib.redirect_stderr(io.StringIO()) as errfp: with pytest.raises(SystemExit): for ss, sigloc in sourmash_args.load_many_signatures([outloc], progress): pass err = errfp.getvalue() print(err) assert f"ERROR: Error while reading signatures from '{outloc}'." in err assert "(continuing)" not in err def test_load_many_sigs_empty_file_force(runtmp): # make sure load_many_signatures behaves properly on empty file w/force outloc = runtmp.output("empty.sig") with open(outloc, "w"): pass progress = sourmash_args.SignatureLoadingProgress() with contextlib.redirect_stderr(io.StringIO()) as errfp: for ss, sigloc in sourmash_args.load_many_signatures( [outloc], progress, force=True ): pass err = errfp.getvalue() print(err) assert f"ERROR: Error while reading signatures from '{outloc}'." in err assert "(continuing)" in err def test_get_manifest_1(): # basic get_manifest retrieves a manifest sig47 = utils.get_test_data("47.fa.sig") idx = sourmash.load_file_as_index(sig47) manifest = sourmash_args.get_manifest(idx) assert len(manifest) == 1 def test_get_manifest_2_cannot_build(): # test what happens when get_manifest cannot build manifest sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47) idx = LinearIndex([ss47]) with pytest.raises(SystemExit): sourmash_args.get_manifest(idx) def test_get_manifest_2_cannot_buildno_require(): # test what happens when get_manifest cannot build manifest sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47) idx = LinearIndex([ss47]) m = sourmash_args.get_manifest(idx, require=False) assert m is None def test_get_manifest_3_build(): # check that manifest is building sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47) class FakeIndex(LinearIndex): was_called = 0 def _signatures_with_internal(self): self.was_called = 1 return [(ss47, "fakeiloc")] idx = FakeIndex([sig47]) assert not idx.was_called m = sourmash_args.get_manifest(idx) assert idx.was_called print(m) assert len(m) == 1 assert m.rows[0]["internal_location"] == "fakeiloc" def test_get_manifest_3_build_2(): # check that manifest is building, but only when asked sig47 = utils.get_test_data("47.fa.sig") ss47 = sourmash.load_one_signature(sig47) class FakeIndex(LinearIndex): manifest = None was_called = 0 def _signatures_with_internal(self): self.was_called = 1 return [(ss47, "fakeiloc")] idx = FakeIndex([sig47]) assert not idx.was_called m = sourmash_args.get_manifest(idx) assert idx.was_called # now set and ask again, should not be called idx.manifest = m idx.was_called = 0 m2 = sourmash_args.get_manifest(idx) assert not idx.was_called assert m == m2 # now, force rebuild m3 = sourmash_args.get_manifest(idx, rebuild=True) assert idx.was_called assert m == m3 class FakeArgs: picklist = None include_db_pattern = None exclude_db_pattern = None def test_pattern_0(): # test neither --include nor --exclude args = FakeArgs() args.picklist = None args.include_db_pattern = None args.exclude_db_pattern = None pattern_search = sourmash_args.load_include_exclude_db_patterns(args) assert pattern_search is None def test_pattern_1(): # test just --include-pattern handling args = FakeArgs() args.picklist = None args.include_db_pattern = "foo" args.exclude_db_pattern = None pattern_search = sourmash_args.load_include_exclude_db_patterns(args) assert pattern_search(["foo", "bar", "baz"]) assert not pattern_search(["bar", "bif"]) def test_pattern_2(): # test just --exclude-pattern handling args = FakeArgs() args.picklist = None args.exclude_db_pattern = "foo" args.include_db_pattern = None pattern_search = sourmash_args.load_include_exclude_db_patterns(args) assert not pattern_search(["foo", "bar", "baz"]) assert pattern_search(["bar", "baz", "bif"]) def test_pattern_3(): # test with --picklist and --exclude: should fail args = FakeArgs() args.picklist = True args.exclude_db_pattern = "foo" args.include_db_pattern = None with pytest.raises(SystemExit): sourmash_args.load_include_exclude_db_patterns(args) def test_pattern_4(): # test with --picklist and --include: should fail args = FakeArgs() args.picklist = True args.include_db_pattern = "foo" args.exclude_db_pattern = None with pytest.raises(SystemExit): sourmash_args.load_include_exclude_db_patterns(args) def test_pattern_5(): # test with --include and --exclude: should fail args = FakeArgs() args.picklist = None args.exclude_db_pattern = "foo" args.include_db_pattern = "bar" with pytest.raises(SystemExit): sourmash_args.load_include_exclude_db_patterns(args) def test_fileinput_csv_1_plain(): # test basic CSV input testfile = utils.get_test_data("tax/test.taxonomy.csv") with sourmash_args.FileInputCSV(testfile) as r: rows = list(r) assert len(rows) == 6 def test_fileinput_csv_1_no_such_file(runtmp): # test fail to load file noexistfile = runtmp.output("does-not-exist.csv") with pytest.raises(FileNotFoundError): with sourmash_args.FileInputCSV(noexistfile): pass def test_fileinput_csv_2_gz(runtmp): # test basic CSV input from gz file testfile = utils.get_test_data("tax/test.taxonomy.csv") gzfile = runtmp.output("test.csv.gz") with gzip.open(gzfile, "wt") as outfp: with open(testfile, newline="") as infp: outfp.write(infp.read()) with sourmash_args.FileInputCSV(gzfile) as r: rows = list(r) assert len(rows) == 6 def test_fileinput_csv_2_gz_not_csv(runtmp): # test basic CSV input from gz file that's not CSV - works gzfile = runtmp.output("test.csv.gz") with gzip.open(gzfile, "wt") as outfp: outfp.write("hello world!") with sourmash_args.FileInputCSV(gzfile) as r: assert r.fieldnames == ["hello world!"] def test_fileinput_csv_2_gz_bad_version_header(runtmp): # test basic CSV input from gz file with bad version header # currently this works; not clear to me how it should fail :grin: gzfile = runtmp.output("test.csv.gz") with gzip.open(gzfile, "wt") as outfp: outfp.write("# excelsior\nhello world!") with sourmash_args.FileInputCSV(gzfile) as r: assert r.fieldnames == ["hello world!"] print(r.version_info) assert r.version_info == ["excelsior"] def test_fileinput_csv_2_zip(runtmp): # test CSV input from zip file, with component filename testfile = utils.get_test_data("tax/test.taxonomy.csv") zf_file = runtmp.output("test.zip") with zipfile.ZipFile(zf_file, "w") as outzip: with open(testfile, "rb") as infp: with outzip.open("XYZ.csv", "w") as outfp: outfp.write(infp.read()) with sourmash_args.FileInputCSV(zf_file, default_csv_name="XYZ.csv") as r: rows = list(r) assert len(rows) == 6 print(rows) def test_fileinput_csv_3_load_manifest(): # test loading a manifest from a zipfile collection, using # FileInputCSV. testfile = utils.get_test_data("prot/all.zip") with sourmash_args.FileInputCSV( testfile, default_csv_name="SOURMASH-MANIFEST.csv" ) as r: rows = list(r) assert len(rows) == 8 assert r.version_info == ["SOURMASH-MANIFEST-VERSION", "1.0"] def test_fileinput_csv_3_load_manifest_no_default(): # test loading a manifest from a zipfile collection, using # FileInputCSV, but with no default_csv_name - should fail testfile = utils.get_test_data("prot/all.zip") with pytest.raises(csv.Error): with sourmash_args.FileInputCSV(testfile) as r: print(r.fieldnames) def test_fileinput_csv_3_load_manifest_zipfile_obj(): # test loading a manifest from an open zipfile obj, using # FileInputCSV. testfile = utils.get_test_data("prot/all.zip") with zipfile.ZipFile(testfile, "r") as zf: with sourmash_args.FileInputCSV( testfile, default_csv_name="SOURMASH-MANIFEST.csv", zipfile_obj=zf ) as r: rows = list(r) assert len(rows) == 8 assert r.version_info == ["SOURMASH-MANIFEST-VERSION", "1.0"] def test_fileinput_csv_3_load_manifest_zipfile_obj_no_defualt(): # test loading a manifest from an open zipfile obj, using # FileInputCSV, but with no default csv name => should fail. testfile = utils.get_test_data("prot/all.zip") with zipfile.ZipFile(testfile, "r") as zf: with pytest.raises(ValueError): with sourmash_args.FileInputCSV(testfile, zipfile_obj=zf): pass def test_fileoutput_csv_1(runtmp): # test basic behavior outfile = runtmp.output("xxx.csv") with sourmash_args.FileOutputCSV(outfile) as fp: w = csv.writer(fp) w.writerow(["a", "b", "c"]) w.writerow(["x", "y", "z"]) with open(outfile, newline="") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 1 row = rows[0] assert row["a"] == "x" assert row["b"] == "y" assert row["c"] == "z" def test_fileoutput_csv_1_gz(runtmp): # test basic behavior => gz outfile = runtmp.output("xxx.csv.gz") with sourmash_args.FileOutputCSV(outfile) as fp: w = csv.writer(fp) w.writerow(["a", "b", "c"]) w.writerow(["x", "y", "z"]) with gzip.open(outfile, "rt") as fp: r = csv.DictReader(fp) rows = list(r) assert len(rows) == 1 row = rows[0] assert row["a"] == "x" assert row["b"] == "y" assert row["c"] == "z" def test_fileoutput_csv_2_stdout(): # test '-' and 'None' go to sys.stdout with sourmash_args.FileOutputCSV("-") as fp: assert fp == sys.stdout with sourmash_args.FileOutputCSV(None) as fp: assert fp == sys.stdout def test_add_ksize_arg_no_default(): # test behavior of cli.utils.add_ksize_arg p = argparse.ArgumentParser() add_ksize_arg(p) args = p.parse_args() assert args.ksize is None def test_add_ksize_arg_no_default_specify(): # test behavior of cli.utils.add_ksize_arg p = argparse.ArgumentParser() add_ksize_arg(p) args = p.parse_args(["-k", "21"]) assert args.ksize == 21 def test_add_ksize_arg_default_31(): # test behavior of cli.utils.add_ksize_arg p = argparse.ArgumentParser() add_ksize_arg(p, default=31) args = p.parse_args() assert args.ksize == 31 def test_add_ksize_arg_default_31_specify(): # test behavior of cli.utils.add_ksize_arg p = argparse.ArgumentParser() add_ksize_arg(p, default=31) args = p.parse_args(["-k", "21"]) assert args.ksize == 21 def test_bug_2370(runtmp): # bug - manifest loading code does not catch gzip.BadGzipFile sigfile = utils.get_test_data("63.fa.sig") # copy sigfile over to a .gz file without compressing it - shutil.copyfile(sigfile, runtmp.output("not_really_gzipped.gz")) # try running sourmash_args.load_file_as_index # runtmp.sourmash('sig', 'describe', runtmp.output('not_really_gzipped.gz')) sourmash_args.load_file_as_index(runtmp.output("not_really_gzipped.gz")) def test_load_one_signature_1(runtmp): # test the sourmash_args.load_one_signature function sigfile = utils.get_test_data("63.fa.sig.zip") ss = sourmash_args.load_one_signature(sigfile, ksize=31) assert ss.name.startswith("NC_011663.1 ") def test_load_one_signature_2_fail(runtmp): # test the sourmash_args.load_one_signature function on failure - no sig sigfile = utils.get_test_data("63.fa.sig.zip") with pytest.raises(ValueError) as exc: sourmash_args.load_one_signature(sigfile, ksize=21) assert "expected exactly one." in str(exc) def test_load_one_signature_3_fail(runtmp): # test the sourmash_args.load_one_signature function on failure - many sigs sigfile = utils.get_test_data("prot/all.zip") with pytest.raises(ValueError) as exc: sourmash_args.load_one_signature(sigfile) assert "more than one signature" in str(exc) sourmash-bio-sourmash-bc3c0f8/tests/test_sourmash_compute.py000066400000000000000000000765631474157046300246570ustar00rootroot00000000000000""" Tests for sourmash compute command-line functionality. """ import os import gzip import shutil import screed import glob import json import csv import pytest import sourmash_tst_utils as utils import sourmash from sourmash import MinHash from sourmash.sbt import SBT, Node from sourmash.sbtmh import SigLeaf, load_sbt_index from sourmash.command_compute import ComputeParameters from sourmash.cli.compute import subparser from sourmash.cli import SourmashParser from sourmash import signature from sourmash import VERSION from sourmash_tst_utils import SourmashCommandFailed from sourmash.signature import load_signatures_from_json def test_do_sourmash_compute(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", testdata1], in_directory=location ) sigfile = os.path.join(location, "short.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert str(sig).endswith("short.fa") def test_do_sourmash_compute_check_num_bounds_negative(runtmp): c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") sigfile = c.output("short.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "compute", "-k", "31", "--num-hashes", "-5", "-o", sigfile, "--merge", '"name"', testdata1, testdata2, testdata3, ) assert "ERROR: num value must be positive" in c.last_result.err def test_do_sourmash_compute_check_num_bounds_less_than_minimum(runtmp): c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") sigfile = c.output("short.fa.sig") c.run_sourmash( "compute", "-k", "31", "--num-hashes", "25", "-o", sigfile, "--merge", '"name"', testdata1, testdata2, testdata3, ) assert "WARNING: num value should be >= 50. Continuing anyway." in c.last_result.err def test_do_sourmash_compute_check_num_bounds_more_than_maximum(runtmp): c = runtmp testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") sigfile = c.output("short.fa.sig") c.run_sourmash( "compute", "-k", "31", "--num-hashes", "100000", "-o", sigfile, "--merge", '"name"', testdata1, testdata2, testdata3, ) assert ( "WARNING: num value should be <= 50000. Continuing anyway." in c.last_result.err ) @utils.in_tempdir def test_do_sourmash_compute_outdir(c): testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", testdata1, "--outdir", c.location] ) sigfile = os.path.join(c.location, "short.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert str(sig).endswith("short.fa") def test_do_sourmash_compute_output_valid_file(): """Trigger bug #123""" with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") sigfile = os.path.join(location, "short.fa.sig") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", "-o", sigfile, testdata1, testdata2, testdata3], in_directory=location, ) assert os.path.exists(sigfile) assert not out # stdout should be empty # is it valid json? with open(sigfile) as f: data = json.load(f) filesigs = [sig["filename"] for sig in data] assert all( testdata in filesigs for testdata in (testdata1, testdata2, testdata3) ) def test_do_sourmash_compute_output_stdout_valid(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", "-o", "-", testdata1, testdata2, testdata3], in_directory=location, ) # is it valid json? data = json.loads(out) filesigs = [sig["filename"] for sig in data] assert all( testdata in filesigs for testdata in (testdata1, testdata2, testdata3) ) @utils.in_tempdir def test_do_sourmash_compute_output_and_name_valid_file(c): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") sigfile = c.output("short.fa.sig") c.run_sourmash( "compute", "-k", "31", "-o", sigfile, "--merge", '"name"', testdata1, testdata2, testdata3, ) assert os.path.exists(sigfile) assert ( "calculated 1 signature for 4 sequences taken from 3 files" in c.last_result.err ) # is it valid json? with open(sigfile) as f: data = json.load(f) assert len(data) == 1 sigfile_merged = c.output("short.all.fa.sig") c.run_sourmash( "compute", "-k", "31", "-o", sigfile_merged, "--merge", '"name"', testdata1, testdata2, testdata3, ) with open(sigfile_merged) as f: data_merged = json.load(f) assert data[0]["signatures"][0]["mins"] == data_merged[0]["signatures"][0]["mins"] @utils.in_tempdir def test_do_sourmash_compute_output_and_name_valid_file_outdir(c): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") sigfile = os.path.join(c.location, "short.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "compute", "-k", "31", "-o", sigfile, "--merge", '"name"', testdata1, testdata2, testdata3, "--outdir", c.location, ) errmsg = c.last_result.err assert "ERROR: --output-dir doesn't make sense with -o/--output" in errmsg def test_do_sourmash_compute_singleton(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", "--singleton", testdata1], in_directory=location, ) sigfile = os.path.join(location, "short.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert sig.name.endswith("shortName") def test_do_sourmash_compute_name(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", "--merge", "foo", testdata1, "-o", "foo.sig"], in_directory=location, ) sigfile = os.path.join(location, "foo.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert sig.name == "foo" status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", "--name", "foo", testdata1, "-o", "foo2.sig"], in_directory=location, ) sigfile2 = os.path.join(location, "foo2.sig") assert os.path.exists(sigfile2) sig2 = next(load_signatures_from_json(sigfile)) assert sig2.name == "foo" assert sig.name == sig2.name def test_do_sourmash_compute_name_fail_no_output(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", "--merge", "foo", testdata1], in_directory=location, fail_ok=True, ) assert status == -1 def test_do_sourmash_compute_merge_fail_no_output(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", "--merge", "foo", testdata1], in_directory=location, fail_ok=True, ) assert status == -1 status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", "--name", "foo", testdata1], in_directory=location, fail_ok=True, ) assert status == -1 def test_do_sourmash_compute_name_from_first(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short3.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "31", "--name-from-first", testdata1], in_directory=location, ) sigfile = os.path.join(location, "short3.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert sig.name == "firstname" def test_do_sourmash_compute_multik(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,31", testdata1], in_directory=location ) outfile = os.path.join(location, "short.fa.sig") assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 21 in ksizes assert 31 in ksizes assert len(ksizes) == 2 def test_do_sourmash_compute_multik_with_protein(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,30", "--protein", testdata1], in_directory=location, ) outfile = os.path.join(location, "short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 4 ksizes = set([x.minhash.ksize for x in siglist]) assert 21 in ksizes assert 30 in ksizes assert 7 in ksizes assert 10 in ksizes assert len(ksizes) == 4 def test_do_sourmash_compute_multik_with_dayhoff(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,30", "--dayhoff", "--no-dna", testdata1], in_directory=location, ) assert ( "Computing only Dayhoff-encoded protein (and not nucleotide) " "signatures." in err ) outfile = os.path.join(location, "short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes assert all(x.minhash.dayhoff for x in siglist) assert len(ksizes) == 2 def test_do_sourmash_compute_multik_with_dayhoff_and_dna(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,30", "--dayhoff", testdata1], in_directory=location, ) outfile = os.path.join(location, "short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 4 ksizes = set([x.minhash.ksize for x in siglist]) assert 21 in ksizes assert 30 in ksizes assert 7 in ksizes assert 10 in ksizes assert sum(x.minhash.moltype == "DNA" for x in siglist) == 2 assert sum(x.minhash.moltype == "dayhoff" for x in siglist) == 2 assert len(ksizes) == 4 def test_do_sourmash_compute_multik_with_hp(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,30", "--hp", "--no-dna", testdata1], in_directory=location, ) assert ( "Computing only hp-encoded protein (and not nucleotide) signatures." in err ) outfile = os.path.join(location, "short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes assert all(x.minhash.hp for x in siglist) assert len(ksizes) == 2 def test_do_sourmash_compute_multik_with_hp_and_dna(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,30", "--hp", testdata1], in_directory=location, ) outfile = os.path.join(location, "short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 4 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes assert 21 in ksizes assert 30 in ksizes assert len(ksizes) == 4 def test_do_sourmash_compute_multik_with_dayhoff_dna_protein(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,30", "--dayhoff", "--protein", testdata1], in_directory=location, ) outfile = os.path.join(location, "short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 6 ksizes = set([x.minhash.ksize for x in siglist]) assert 21 in ksizes assert 30 in ksizes assert 7 in ksizes assert 10 in ksizes assert sum(x.minhash.moltype == "DNA" for x in siglist) == 2 assert sum(x.minhash.moltype == "dayhoff" for x in siglist) == 2 assert sum(x.minhash.moltype == "protein" for x in siglist) == 2 assert len(ksizes) == 4 def test_do_sourmash_compute_multik_with_dayhoff_hp_dna_protein(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,30", "--dayhoff", "--hp", "--protein", testdata1], in_directory=location, ) outfile = os.path.join(location, "short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 8 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes assert 21 in ksizes assert 30 in ksizes assert sum(x.minhash.moltype == "DNA" for x in siglist) == 2 assert sum(x.minhash.moltype == "dayhoff" for x in siglist) == 2 assert sum(x.minhash.moltype == "hp" for x in siglist) == 2 # 2 = dayhoff, 2 = hp = 4 protein assert sum(x.minhash.moltype == "protein" for x in siglist) == 2 assert len(ksizes) == 4 def test_do_sourmash_compute_multik_with_nothing(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,31", "--no-protein", "--no-dna", testdata1], in_directory=location, fail_ok=True, ) outfile = os.path.join(location, "short.fa.sig") assert not os.path.exists(outfile) def test_do_sourmash_compute_multik_protein_bad_ksize(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "20,32", "--protein", "--no-dna", testdata1], in_directory=location, fail_ok=True, ) outfile = os.path.join(location, "short.fa.sig") assert not os.path.exists(outfile) assert "protein ksizes must be divisible by 3" in err @utils.in_tempdir def test_do_sourmash_compute_multik_only_protein(c): # check sourmash compute with only protein, no nucl testdata1 = utils.get_test_data("short.fa") c.run_sourmash("compute", "-k", "21,30", "--protein", "--no-dna", testdata1) outfile = os.path.join(c.location, "short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes assert len(ksizes) == 2 def test_do_sourmash_compute_multik_protein_input_bad_ksize(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short-protein.fa") status, out, err = utils.runscript( "sourmash", [ "compute", "-k", "20,32", "--protein", "--no-dna", "--input-is-protein", testdata1, ], in_directory=location, fail_ok=True, ) os.path.join(location, "short-protein.fa.sig") assert status != 0 assert "protein ksizes must be divisible by 3" in err @utils.in_tempdir def test_do_sourmash_compute_multik_only_protein_no_rna(c): # test --no-rna as well (otherwise identical to previous test) testdata1 = utils.get_test_data("short.fa") c.run_sourmash("compute", "-k", "21,30", "--protein", "--no-rna", testdata1) outfile = os.path.join(c.location, "short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes assert len(ksizes) == 2 def test_do_sourmash_compute_protein_bad_sequences(): """Proper error handling when Ns in dna sequence""" with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.bad.fa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,30", "--protein", "--no-dna", testdata1], in_directory=location, ) outfile = os.path.join(location, "short.bad.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes assert len(ksizes) == 2 def test_do_sourmash_compute_multik_input_is_protein(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("ecoli.faa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,30", "--input-is-protein", testdata1], in_directory=location, ) outfile = os.path.join(location, "ecoli.faa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes assert len(ksizes) == 2 moltype = set([x.minhash.moltype == "protein" for x in siglist]) assert len(moltype) == 1 assert True in moltype def test_do_sourmash_compute_multik_outfile(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") outfile = os.path.join(location, "FOO.xxx") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,31", testdata1, "-o", outfile], in_directory=location, ) assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 21 in ksizes assert 31 in ksizes def test_do_sourmash_compute_with_scaled_1(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") outfile = os.path.join(location, "FOO.xxx") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,31", "--scaled", "1", testdata1, "-o", outfile], in_directory=location, ) assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 scaled_vals = [x.minhash.scaled for x in siglist] assert len(scaled_vals) == 2 assert set(scaled_vals) == {1} def test_do_sourmash_compute_with_scaled_2(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") outfile = os.path.join(location, "FOO.xxx") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,31", "--scaled", "2", testdata1, "-o", outfile], in_directory=location, ) assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 max_hashes = [x.minhash._max_hash for x in siglist] assert len(max_hashes) == 2 assert set(max_hashes) == set([int(2**64 / 2.0)]) def test_do_sourmash_compute_with_scaled(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") outfile = os.path.join(location, "FOO.xxx") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,31", "--scaled", "100", testdata1, "-o", outfile], in_directory=location, ) assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 max_hashes = [x.minhash._max_hash for x in siglist] assert len(max_hashes) == 2 assert set(max_hashes) == set([int(2**64 / 100.0)]) def test_do_sourmash_compute_with_bad_scaled(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") outfile = os.path.join(location, "FOO.xxx") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,31", "--scaled", "-1", testdata1, "-o", outfile], in_directory=location, fail_ok=True, ) assert status != 0 assert "--scaled value must be >= 1" in err status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,31", "--scaled", "1000.5", testdata1, "-o", outfile], in_directory=location, fail_ok=True, ) assert status != 0 assert "--scaled value must be integer value" in err status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,31", "--scaled", "1e9", testdata1, "-o", outfile], in_directory=location, ) assert status == 0 assert "WARNING: scaled value is nonsensical!?" in err def test_do_sourmash_compute_with_seed(): with utils.TempDirectory() as location: testdata1 = utils.get_test_data("short.fa") outfile = os.path.join(location, "FOO.xxx") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21,31", "--seed", "43", testdata1, "-o", outfile], in_directory=location, ) assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 seeds = [x.minhash.seed for x in siglist] assert len(seeds) == 2 assert set(seeds) == set([43]) def test_do_sourmash_check_protein_comparisons(): # this test checks 2 x 2 protein comparisons with E. coli genes. with utils.TempDirectory() as location: testdata1 = utils.get_test_data("ecoli.faa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21", "--input-is-protein", "--singleton", testdata1], in_directory=location, ) sig1 = os.path.join(location, "ecoli.faa.sig") assert os.path.exists(sig1) testdata2 = utils.get_test_data("ecoli.genes.fna") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21", "--protein", "--no-dna", "--singleton", testdata2], in_directory=location, ) sig2 = os.path.join(location, "ecoli.genes.fna.sig") assert os.path.exists(sig2) # I'm not sure why load_signatures_from_json is randomizing order, but ok. x = list(load_signatures_from_json(sig1)) sig1_aa, sig2_aa = sorted(x, key=lambda x: x.name) x = list(load_signatures_from_json(sig2)) sig1_trans, sig2_trans = sorted(x, key=lambda x: x.name) name1 = sig1_aa.name.split()[0] assert name1 == "NP_414543.1" name2 = sig2_aa.name.split()[0] assert name2 == "NP_414544.1" name3 = sig1_trans.name.split()[0] assert name3 == "gi|556503834:2801-3733" name4 = sig2_trans.name.split()[0] assert name4 == "gi|556503834:337-2799" print(name1, name3, round(sig1_aa.similarity(sig1_trans), 3)) print(name2, name3, round(sig2_aa.similarity(sig1_trans), 3)) print(name1, name4, round(sig1_aa.similarity(sig2_trans), 3)) print(name2, name4, round(sig2_aa.similarity(sig2_trans), 3)) assert round(sig1_aa.similarity(sig1_trans), 3) == 0.0 assert round(sig2_aa.similarity(sig1_trans), 3) == 0.166 assert round(sig1_aa.similarity(sig2_trans), 3) == 0.174 assert round(sig2_aa.similarity(sig2_trans), 3) == 0.0 @utils.in_tempdir def test_do_sourmash_check_knowngood_dna_comparisons(c): # this test checks against a known good signature calculated # by utils/compute-dna-mh-another-way.py testdata1 = utils.get_test_data("ecoli.genes.fna") c.run_sourmash("compute", "-k", "21", "--singleton", "--dna", testdata1) sig1 = c.output("ecoli.genes.fna.sig") assert os.path.exists(sig1) x = list(load_signatures_from_json(sig1)) sig1, sig2 = sorted(x, key=lambda x: x.name) print(sig1.name) print(sig2.name) knowngood = utils.get_test_data("benchmark.dna.sig") good = list(load_signatures_from_json(knowngood))[0] assert sig2.similarity(good) == 1.0 @utils.in_tempdir def test_do_sourmash_check_knowngood_dna_comparisons_use_rna(c): # check the --rna flag; otherwise identical to previous test. testdata1 = utils.get_test_data("ecoli.genes.fna") c.run_sourmash("compute", "-k", "21", "--singleton", "--rna", testdata1) sig1 = c.output("ecoli.genes.fna.sig") assert os.path.exists(sig1) x = list(load_signatures_from_json(sig1)) sig1, sig2 = sorted(x, key=lambda x: x.name) knowngood = utils.get_test_data("benchmark.dna.sig") good = list(load_signatures_from_json(knowngood))[0] assert sig2.similarity(good) == 1.0 def test_do_sourmash_check_knowngood_input_protein_comparisons(): # this test checks against a known good signature calculated # by utils/compute-input-prot-another-way.py with utils.TempDirectory() as location: testdata1 = utils.get_test_data("ecoli.faa") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21", "--input-is-protein", "--singleton", testdata1], in_directory=location, ) sig1 = os.path.join(location, "ecoli.faa.sig") assert os.path.exists(sig1) x = list(load_signatures_from_json(sig1)) sig1_aa, sig2_aa = sorted(x, key=lambda x: x.name) knowngood = utils.get_test_data("benchmark.input_prot.sig") good_aa = list(load_signatures_from_json(knowngood))[0] assert sig1_aa.similarity(good_aa) == 1.0 def test_do_sourmash_check_knowngood_protein_comparisons(): # this test checks against a known good signature calculated # by utils/compute-prot-mh-another-way.py with utils.TempDirectory() as location: testdata1 = utils.get_test_data("ecoli.genes.fna") status, out, err = utils.runscript( "sourmash", ["compute", "-k", "21", "--singleton", "--protein", "--no-dna", testdata1], in_directory=location, ) sig1 = os.path.join(location, "ecoli.genes.fna.sig") assert os.path.exists(sig1) x = list(load_signatures_from_json(sig1)) sig1_trans, sig2_trans = sorted(x, key=lambda x: x.name) knowngood = utils.get_test_data("benchmark.prot.sig") good_trans = list(load_signatures_from_json(knowngood))[0] assert sig2_trans.similarity(good_trans) == 1.0 def test_compute_parameters(): args_list = [ "compute", "-k", "21,31", "--singleton", "--protein", "--no-dna", "input_file", ] parser = SourmashParser(prog="sourmash") subp = parser.add_subparsers(title="instruction", dest="cmd", metavar="cmd") subparser(subp) args = parser.parse_args(args_list) params = ComputeParameters.from_args(args) assert params.ksizes == [21, 31] assert params.protein == True assert params.dna == False assert params.seed == 42 assert params.dayhoff == False assert params.hp == False assert params.num_hashes == 500 assert params.scaled == 0 assert params.track_abundance == False sourmash-bio-sourmash-bc3c0f8/tests/test_sourmash_sketch.py000066400000000000000000002123141474157046300244460ustar00rootroot00000000000000""" Tests for sourmash sketch command-line functionality. """ import os import gzip import shutil import screed import glob import json import csv import pytest import sourmash_tst_utils as utils import sourmash from sourmash import MinHash from sourmash.sbt import SBT, Node from sourmash.sbtmh import SigLeaf, load_sbt_index from sourmash.command_sketch import ComputeParameters from sourmash.cli import SourmashParser from sourmash import manifest from sourmash import signature from sourmash import VERSION ### from sourmash.command_sketch import _signatures_for_sketch_factory from sourmash_tst_utils import SourmashCommandFailed from sourmash.signature import load_one_signature_from_json as load_one_signature from sourmash.signature import load_signatures_from_json def test_do_sourmash_sketch_check_scaled_bounds_negative(runtmp): testdata1 = utils.get_test_data("short.fa") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sketch", "translate", "-p", "scaled=-5", testdata1) assert "ERROR: scaled value must be positive" in runtmp.last_result.err def test_do_sourmash_sketch_check_scaled_bounds_less_than_minimum(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "translate", "-p", "scaled=50", testdata1) assert ( "WARNING: scaled value should be >= 100. Continuing anyway." in runtmp.last_result.err ) def test_do_sourmash_sketch_check_scaled_bounds_more_than_maximum(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "translate", "-p", "scaled=1000000000", testdata1) assert ( "WARNING: scaled value should be <= 1e6. Continuing anyway." in runtmp.last_result.err ) def test_do_sourmash_sketch_check_num_bounds_negative(runtmp): testdata1 = utils.get_test_data("short.fa") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sketch", "translate", "-p", "num=-5", testdata1) assert "ERROR: num value must be positive" in runtmp.last_result.err def test_do_sourmash_sketch_check_num_bounds_less_than_minimum(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "translate", "-p", "num=25", testdata1) assert ( "WARNING: num value should be >= 50. Continuing anyway." in runtmp.last_result.err ) def test_do_sourmash_sketch_check_num_bounds_more_than_maximum(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "translate", "-p", "num=100000", testdata1) assert ( "WARNING: num value should be <= 50000. Continuing anyway." in runtmp.last_result.err ) def test_empty_factory(): with pytest.raises(ValueError): _signatures_for_sketch_factory([], None) def test_no_default_moltype_factory_nonempty(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["k=31"], None) def test_factory_no_default_moltype_dna(): factory = _signatures_for_sketch_factory(["dna"], None) params_list = list(factory.get_compute_params()) assert len(params_list) == 1 params = params_list[0] assert params.dna def test_factory_no_default_moltype_protein(): factory = _signatures_for_sketch_factory(["protein"], None) params_list = list(factory.get_compute_params()) assert len(params_list) == 1 params = params_list[0] assert params.protein def test_factory_dna_nosplit(): factory = _signatures_for_sketch_factory(["k=31,k=51"], "dna") params_list = list(factory.get_compute_params(split_ksizes=False)) assert len(params_list) == 1 params = params_list[0] assert params.ksizes == [31, 51] def test_factory_dna_split(): factory = _signatures_for_sketch_factory(["k=31,k=51"], "dna") params_list = list(factory.get_compute_params(split_ksizes=True)) assert len(params_list) == 2 params = params_list[0] assert params.ksizes == [31] params = params_list[1] assert params.ksizes == [51] def test_factory_protein_nosplit(): factory = _signatures_for_sketch_factory(["k=10,k=9"], "protein") params_list = list(factory.get_compute_params(split_ksizes=False)) assert len(params_list) == 1 params = params_list[0] assert params.ksizes == [30, 27] def test_factory_protein_split(): factory = _signatures_for_sketch_factory(["k=10,k=9"], "protein") params_list = list(factory.get_compute_params(split_ksizes=True)) assert len(params_list) == 2 params = params_list[0] assert params.ksizes == [30] params = params_list[1] assert params.ksizes == [27] def test_factory_dna_equal(): factory1 = _signatures_for_sketch_factory(["dna"], None) params_list1 = list(factory1.get_compute_params()) assert len(params_list1) == 1 params1 = params_list1[0] factory2 = _signatures_for_sketch_factory([], "dna") params_list2 = list(factory2.get_compute_params()) assert len(params_list2) == 1 params2 = params_list2[0] assert params1 == params2 assert repr(params1) == repr(params2) def test_factory_protein_equal(): factory1 = _signatures_for_sketch_factory(["protein"], None) params_list1 = list(factory1.get_compute_params()) assert len(params_list1) == 1 params1 = params_list1[0] factory2 = _signatures_for_sketch_factory([], "protein") params_list2 = list(factory2.get_compute_params()) assert len(params_list2) == 1 params2 = params_list2[0] assert params1 == params2 assert repr(params1) == repr(params2) def test_factory_dna_multi_ksize_eq(): factory1 = _signatures_for_sketch_factory(["k=21,k=31,dna"], None) params_list1 = list(factory1.get_compute_params()) assert len(params_list1) == 1 params1 = params_list1[0] factory2 = _signatures_for_sketch_factory(["k=21,k=31"], "dna") params_list2 = list(factory2.get_compute_params()) assert len(params_list2) == 1 params2 = params_list2[0] assert params1 == params2 assert repr(params1) == repr(params2) def test_factory_protein_multi_ksize_eq(): factory1 = _signatures_for_sketch_factory(["k=10,k=11,protein"], None) params_list1 = list(factory1.get_compute_params()) assert len(params_list1) == 1 params1 = params_list1[0] factory2 = _signatures_for_sketch_factory(["k=10,k=11"], "protein") params_list2 = list(factory2.get_compute_params()) assert len(params_list2) == 1 params2 = params_list2[0] assert params1 == params2 assert repr(params1) == repr(params2) def test_dna_defaults(): factory = _signatures_for_sketch_factory([], "dna") params_list = list(factory.get_compute_params()) assert len(params_list) == 1 params = params_list[0] assert params.ksizes == [31] assert params.num_hashes == 0 assert params.scaled == 1000 assert not params.track_abundance assert params.seed == 42 assert params.dna assert not params.dayhoff assert not params.hp assert not params.protein siglist = factory() sig = siglist[0] sig.minhash def test_dna_multiple_ksize(): factory = _signatures_for_sketch_factory(["k=21,k=31,k=51"], "dna") params_list = list(factory.get_compute_params()) assert len(params_list) == 1 params = params_list[0] assert params.ksizes == [21, 31, 51] assert params.num_hashes == 0 assert params.scaled == 1000 assert not params.track_abundance assert params.seed == 42 assert params.dna assert not params.dayhoff assert not params.hp assert not params.protein from sourmash.save_load import _get_signatures_from_rust siglist = factory() ksizes = set() for ss in _get_signatures_from_rust(siglist): ksizes.add(ss.minhash.ksize) assert ksizes == {21, 31, 51} def test_dna_override_1(): factory = _signatures_for_sketch_factory(["k=21,scaled=2000,abund"], "dna") params_list = list(factory.get_compute_params()) assert len(params_list) == 1 params = params_list[0] assert params.ksizes == [21] assert params.num_hashes == 0 assert params.scaled == 2000 assert params.track_abundance assert params.seed == 42 assert params.dna assert not params.dayhoff assert not params.hp assert not params.protein def test_scaled_param_requires_equal(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["k=21,scaled"], "dna") def test_k_param_requires_equal(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["k"], "dna") def test_k_param_requires_equal_2(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["k="], "dna") def test_seed_param_requires_equal(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["seed="], "dna") def test_num_param_requires_equal(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["num="], "dna") def test_dna_override_bad_1(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["k=21,scaledFOO=2000,abund"], "dna") def test_dna_override_bad_2(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["k=21,protein"], "dna") def test_protein_defaults(): factory = _signatures_for_sketch_factory([], "protein") params_list = list(factory.get_compute_params()) assert len(params_list) == 1 params = params_list[0] assert params.ksizes == [30] # x3 for now assert params.num_hashes == 0 assert params.scaled == 200 assert not params.track_abundance assert params.seed == 42 assert not params.dna assert not params.dayhoff assert not params.hp assert params.protein def test_protein_override_bad_2(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["k=21,dna"], "protein") def test_protein_override_bad_rust_foo(): # mimic 'sourmash sketch protein -p dna' factory = _signatures_for_sketch_factory([], "protein") # reach in and avoid error checking to construct a bad params_list. factory.params_list = [("dna", {})] # now, get sigs... siglist = factory() assert len(siglist) == 1 sig = siglist[0] print(sig.minhash.ksize) # try adding something testdata1 = utils.get_test_data("ecoli.faa") with screed.open(testdata1) as f: record = next(iter(f)) with pytest.raises(ValueError) as exc: sig.add_protein(record.sequence) assert "invalid DNA character in input k-mer: MRVLKFGGTS" in str(exc) def test_dayhoff_defaults(): factory = _signatures_for_sketch_factory([], "dayhoff") params_list = list(factory.get_compute_params()) assert len(params_list) == 1 params = params_list[0] assert params.ksizes == [48] # x3 for now assert params.num_hashes == 0 assert params.scaled == 200 assert not params.track_abundance assert params.seed == 42 assert not params.dna assert params.dayhoff assert not params.hp assert not params.protein def test_dayhoff_override_bad_2(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["k=21,dna"], "dayhoff") def test_hp_defaults(): factory = _signatures_for_sketch_factory([], "hp") params_list = list(factory.get_compute_params()) assert len(params_list) == 1 params = params_list[0] assert params.ksizes == [126] # x3 for now assert params.num_hashes == 0 assert params.scaled == 200 assert not params.track_abundance assert params.seed == 42 assert not params.dna assert not params.dayhoff assert params.hp assert not params.protein def test_hp_override_bad_2(): with pytest.raises(ValueError): _signatures_for_sketch_factory(["k=21,dna"], "hp") def test_multiple_moltypes(): params_foo = [ "k=20,num=500,protein", "k=19,num=400,dayhoff,abund", "k=30,scaled=200,hp", "k=30,scaled=200,seed=58", ] factory = _signatures_for_sketch_factory(params_foo, "protein") params_list = list(factory.get_compute_params()) assert len(params_list) == 4 params = params_list[0] assert params.ksizes == [60] # x3, for now. assert params.num_hashes == 500 assert params.scaled == 0 assert not params.track_abundance assert params.seed == 42 assert not params.dna assert not params.dayhoff assert not params.hp assert params.protein params = params_list[1] assert params.ksizes == [57] # x3, for now. assert params.num_hashes == 400 assert params.scaled == 0 assert params.track_abundance assert params.seed == 42 assert not params.dna assert params.dayhoff assert not params.hp assert not params.protein params = params_list[2] assert params.ksizes == [90] # x3, for now. assert params.num_hashes == 0 assert params.scaled == 200 assert not params.track_abundance assert params.seed == 42 assert not params.dna assert not params.dayhoff assert params.hp assert not params.protein params = params_list[3] assert params.ksizes == [90] # x3, for now. assert params.num_hashes == 0 assert params.scaled == 200 assert not params.track_abundance assert params.seed == 58 assert not params.dna assert not params.dayhoff assert not params.hp assert params.protein @pytest.mark.parametrize( "input_param_str, expected_output", [ ("protein", "protein,k=10,scaled=200"), ("dna", "dna,k=31,scaled=1000"), ("hp", "hp,k=42,scaled=200"), ("dayhoff", "dayhoff,k=16,scaled=200"), ("dna,seed=52", "dna,k=31,scaled=1000,seed=52"), ("dna,num=500", "dna,k=31,num=500"), ("scaled=1100,dna", "dna,k=31,scaled=1100"), ("dna,abund", "dna,k=31,scaled=1000,abund"), ], ) def test_compute_parameters_to_param_str(input_param_str, expected_output): factory = _signatures_for_sketch_factory([input_param_str], None) params_list = list(factory.get_compute_params()) assert len(params_list) == 1 params = params_list[0] actual_output_str = params.to_param_str() assert actual_output_str == expected_output, (actual_output_str, expected_output) def test_manifest_row_to_compute_parameters_1(): # test ComputeParameters.from_manifest_row with moltype 'DNA' row = dict(moltype="DNA", ksize=21, num=0, scaled=1000, with_abundance=1) p = ComputeParameters.from_manifest_row(row) assert p.dna assert not p.protein assert not p.dayhoff assert not p.hp assert p.moltype == "DNA" assert p.num_hashes == 0 assert p.scaled == 1000 assert p.ksizes == [21] assert p.track_abundance assert p.seed == 42 def test_manifest_row_to_compute_parameters_2(): # test ComputeParameters.from_manifest_row with moltype 'protein' row = dict(moltype="protein", ksize=10, num=0, scaled=200, with_abundance=1) p = ComputeParameters.from_manifest_row(row) assert not p.dna assert p.protein assert p.moltype == "protein" assert not p.dayhoff assert not p.hp assert p.num_hashes == 0 assert p.scaled == 200 assert p.ksizes == [30] assert p.track_abundance assert p.seed == 42 def test_manifest_row_to_compute_parameters_3(): # test ComputeParameters.from_manifest_row with moltype 'dayhoff' row = dict(moltype="dayhoff", ksize=12, num=0, scaled=200, with_abundance=0) p = ComputeParameters.from_manifest_row(row) assert not p.dna assert not p.protein assert p.dayhoff assert p.moltype == "dayhoff" assert not p.hp assert p.num_hashes == 0 assert p.scaled == 200 assert p.ksizes == [36] assert not p.track_abundance assert p.seed == 42 def test_manifest_row_to_compute_parameters_4(): # test ComputeParameters.from_manifest_row with moltype 'hp' row = dict(moltype="hp", ksize=32, num=0, scaled=200, with_abundance=0) p = ComputeParameters.from_manifest_row(row) assert not p.dna assert not p.protein assert not p.dayhoff assert p.hp assert p.moltype == "hp" assert p.num_hashes == 0 assert p.scaled == 200 assert p.ksizes == [96] assert not p.track_abundance assert p.seed == 42 def test_bad_compute_parameters(): p = ComputeParameters( ksizes=[31], seed=42, dna=0, protein=0, dayhoff=0, hp=0, num_hashes=0, track_abundance=True, scaled=1000, ) with pytest.raises(AssertionError): p.moltype ### command line tests @utils.in_thisdir def test_do_sourmash_sketchdna_empty(c): with pytest.raises(SourmashCommandFailed): c.run_sourmash("sketch", "dna") assert ( "error: no input filenames provided! nothing to do - exiting." in c.last_result.err ) @utils.in_thisdir def test_do_sourmash_sketchprotein_empty(c): with pytest.raises(SourmashCommandFailed): c.run_sourmash("sketch", "protein") assert ( "error: no input filenames provided! nothing to do - exiting." in c.last_result.err ) @utils.in_thisdir def test_do_sourmash_sketchtranslate_empty(c): with pytest.raises(SourmashCommandFailed): c.run_sourmash("sketch", "translate") assert ( "error: no input filenames provided! nothing to do - exiting." in c.last_result.err ) def test_do_sourmash_sketchdna(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "dna", testdata1) sigfile = runtmp.output("short.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert str(sig).endswith("short.fa") def test_do_sourmash_sketchdna_check_sequence_succeed(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "dna", testdata1, "--check-sequence") sigfile = runtmp.output("short.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert str(sig).endswith("short.fa") def test_do_sourmash_sketchdna_check_sequence_fail(runtmp): testdata1 = utils.get_test_data("shewanella.faa") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sketch", "dna", testdata1, "--check-sequence") err = runtmp.last_result.err print(err) assert "ERROR when reading from " in err assert ( "invalid DNA character in input k-mer: MCGIVGAVAQRDVAEILVEGLRRLEYRGYDS" in err ) def test_do_sourmash_sketchdna_check_sequence_fail_singleton(runtmp): testdata1 = utils.get_test_data("shewanella.faa") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sketch", "dna", testdata1, "--check-sequence", "--singleton") err = runtmp.last_result.err print(err) assert "ERROR when reading from " in err assert ( "invalid DNA character in input k-mer: MCGIVGAVAQRDVAEILVEGLRRLEYRGYDS" in err ) def test_do_sourmash_sketchdna_from_file(runtmp): testdata1 = utils.get_test_data("short.fa") file_list = runtmp.output("filelist.txt") with open(file_list, "w") as fp: print(testdata1, file=fp) runtmp.sourmash("sketch", "dna", "--from-file", file_list) sigfile = runtmp.output("short.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert str(sig).endswith("short.fa") @utils.in_tempdir def test_do_sourmash_sketchdna_noinput(c): data = "" cmd = ["sketch", "dna", "-", "-o", c.output("xxx.sig")] c.run_sourmash(*cmd, stdin_data=data) print(c.last_result.out) print(c.last_result.err) sigfile = c.output("xxx.sig") assert not os.path.exists(sigfile) assert "no sequences found" in c.last_result.err @utils.in_tempdir def test_do_sourmash_sketchdna_noinput_singleton(c): data = "" cmd = ["sketch", "dna", "-", "-o", c.output("xxx.sig"), "--singleton"] c.run_sourmash(*cmd, stdin_data=data) sigfile = c.output("xxx.sig") assert not os.path.exists(sigfile) assert "no sequences found" in c.last_result.err @utils.in_tempdir def test_do_sourmash_sketchdna_noinput_merge(c): data = "" cmd = ["sketch", "dna", "-", "-o", c.output("xxx.sig"), "--merge", "name"] c.run_sourmash(*cmd, stdin_data=data) sigfile = c.output("xxx.sig") assert not os.path.exists(sigfile) assert "no sequences found" in c.last_result.err @utils.in_tempdir def test_do_sourmash_sketchdna_outdir(c): testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["sketch", "dna", testdata1, "--outdir", c.location] ) sigfile = os.path.join(c.location, "short.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert str(sig).endswith("short.fa") @utils.in_tempdir def test_do_sourmash_sketchdna_output_dir(c): # test via --output-dir not --outdir testdata1 = utils.get_test_data("short.fa") status, out, err = utils.runscript( "sourmash", ["sketch", "dna", testdata1, "--output-dir", c.location] ) sigfile = os.path.join(c.location, "short.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert str(sig).endswith("short.fa") def test_do_sourmash_sketchdna_output_valid_file(runtmp): """Trigger bug #123""" testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") sigfile = runtmp.output("short.fa.sig") runtmp.sourmash("sketch", "dna", "-o", sigfile, testdata1, testdata2, testdata3) assert os.path.exists(sigfile) assert not runtmp.last_result.out # stdout should be empty # is it valid json? with open(sigfile) as f: data = json.load(f) filesigs = [sig["filename"] for sig in data] assert all(testdata in filesigs for testdata in (testdata1, testdata2, testdata3)) def test_do_sourmash_sketchdna_output_zipfile(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") outfile = runtmp.output("shorts.zip") runtmp.sourmash("sketch", "dna", "-o", outfile, testdata1, testdata2, testdata3) assert os.path.exists(outfile) assert not runtmp.last_result.out # stdout should be empty sigs = list(sourmash.load_file_as_signatures(outfile)) assert len(sigs) == 3 def test_do_sourmash_sketchdna_output_stdout_valid(runtmp): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") runtmp.sourmash("sketch", "dna", "-o", "-", testdata1, testdata2, testdata3) # is it valid json? data = json.loads(runtmp.last_result.out) filesigs = [sig["filename"] for sig in data] assert all(testdata in filesigs for testdata in (testdata1, testdata2, testdata3)) @utils.in_tempdir def test_do_sourmash_sketchdna_output_and_name_valid_file(c): # test --merge of multiple input files testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") sigfile = c.output("short.fa.sig") c.run_sourmash( "sketch", "dna", "-p", "num=500", "-o", sigfile, "--merge", '"name"', testdata1, testdata2, testdata3, ) assert os.path.exists(sigfile) assert ( "calculated 1 signature for 4 sequences taken from 3 files" in c.last_result.err ) # is it valid json? with open(sigfile) as f: data = json.load(f) assert len(data) == 1 sigfile_merged = c.output("short.all.fa.sig") c.run_sourmash( "sketch", "dna", "-p", "num=500", "-o", sigfile_merged, "--merge", '"name"', testdata1, testdata2, testdata3, ) with open(sigfile_merged) as f: data_merged = json.load(f) assert data[0]["signatures"][0]["mins"] == data_merged[0]["signatures"][0]["mins"] @utils.in_tempdir def test_do_sourmash_sketchdna_output_and_name_valid_file_outdir(c): testdata1 = utils.get_test_data("short.fa") testdata2 = utils.get_test_data("short2.fa") testdata3 = utils.get_test_data("short3.fa") sigfile = os.path.join(c.location, "short.fa.sig") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "sketch", "dna", "-o", sigfile, "--merge", '"name"', testdata1, testdata2, testdata3, "--outdir", c.location, ) errmsg = c.last_result.err assert "ERROR: --output-dir doesn't make sense with -o/--output" in errmsg def test_do_sourmash_sketchdna_singleton(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "dna", "--singleton", testdata1) sigfile = runtmp.output("short.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert str(sig).endswith("shortName") def test_do_sourmash_sketchdna_name(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "dna", "--merge", "foo", testdata1, "-o", "foo.sig") sigfile = runtmp.output("foo.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert sig.name == "foo" runtmp.sourmash("sketch", "dna", "--name", "foo", testdata1, "-o", "foo2.sig") sigfile2 = runtmp.output("foo2.sig") assert os.path.exists(sigfile2) sig2 = next(load_signatures_from_json(sigfile)) assert sig2.name == "foo" assert sig.name == sig2.name def test_do_sourmash_sketchdna_name_fail_no_output(runtmp): testdata1 = utils.get_test_data("short.fa") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sketch", "dna", "--merge", "foo", testdata1) assert runtmp.last_result.status == -1 def test_do_sourmash_sketchdna_fail_no_output(runtmp): testdata1 = utils.get_test_data("short.fa") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sketch", "dna", "--merge", "foo", testdata1) assert runtmp.last_result.status == -1 with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sketch", "dna", "--name", "foo", testdata1) assert runtmp.last_result.status == -1 def test_do_sourmash_sketchdna_name_from_first(runtmp): testdata1 = utils.get_test_data("short3.fa") runtmp.sourmash("sketch", "dna", "--name-from-first", testdata1) sigfile = runtmp.output("short3.fa.sig") assert os.path.exists(sigfile) sig = next(load_signatures_from_json(sigfile)) assert sig.name == "firstname" def test_do_sourmash_sketchdna_multik(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "dna", "-p", "k=31,k=21", testdata1) outfile = runtmp.output("short.fa.sig") assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 21 in ksizes assert 31 in ksizes def test_do_sourmash_sketchdna_multik_output(runtmp, sig_save_extension): testdata1 = utils.get_test_data("short.fa") outfile = runtmp.output(f"out.{sig_save_extension}") runtmp.sourmash("sketch", "dna", "-p", "k=31,k=21", testdata1, "-o", outfile) print("saved to file/path with extension:", outfile) assert os.path.exists(outfile) siglist = list(sourmash.load_file_as_signatures(outfile)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 21 in ksizes assert 31 in ksizes def test_do_sketch_dna_override_protein_fail(runtmp): testdata1 = utils.get_test_data("short.fa") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sketch", "dna", "-p", "k=7,num=500,protein", testdata1) assert runtmp.last_result.status != 0 assert ( "Error creating signatures: Incompatible sketch type" in runtmp.last_result.err ) def test_do_sketch_protein_override_dna_fail(runtmp): testdata1 = utils.get_test_data("short.fa") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sketch", "protein", "-p", "k=7,num=500,dna", testdata1) assert runtmp.last_result.status != 0 assert ( "Error creating signatures: Incompatible sketch type" in runtmp.last_result.err ) def test_do_sketch_translate_multik_with_protein(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "translate", "-p", "k=7,k=10,num=500", testdata1) outfile = runtmp.output("short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes def test_do_sketch_translate_multik_with_protein_from_file(runtmp): testdata1 = utils.get_test_data("short.fa") file_list = runtmp.output("filelist.txt") with open(file_list, "w") as fp: print(testdata1, file=fp) runtmp.sourmash( "sketch", "translate", "-p", "k=7,k=10,num=500", "--from-file", file_list ) outfile = runtmp.output("short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes def test_do_sketch_translate_multik_with_dayhoff(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash( "sketch", "translate", "-p", "k=7,k=10,num=500", "--dayhoff", testdata1 ) outfile = runtmp.output("short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes assert all(x.minhash.dayhoff for x in siglist) def test_do_sketch_translate_multik_with_hp(runtmp): testdata1 = utils.get_test_data("short.fa") runtmp.sourmash("sketch", "translate", "-p", "k=7,k=10,num=500", "--hp", testdata1) outfile = runtmp.output("short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes assert all(x.minhash.hp for x in siglist) @utils.in_tempdir def test_do_sourmash_sketch_translate_multik_only_protein(c): # check sourmash sketch_translate with only protein, no nucl testdata1 = utils.get_test_data("short.fa") c.run_sourmash("sketch", "translate", "-p", "k=7,k=10,num=500", testdata1) outfile = os.path.join(c.location, "short.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes def test_do_sourmash_sketch_translate_bad_sequences(runtmp): """Proper error handling when Ns in dna sequence""" testdata1 = utils.get_test_data("short.bad.fa") runtmp.sourmash("sketch", "translate", "-p", "k=7,k=10,num=500", testdata1) outfile = runtmp.output("short.bad.fa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes def test_do_sketch_protein_multik_input(runtmp): testdata1 = utils.get_test_data("ecoli.faa") runtmp.sourmash("sketch", "protein", "-p", "k=7,k=10,num=500", testdata1) outfile = runtmp.output("ecoli.faa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes moltype = set([x.minhash.moltype == "protein" for x in siglist]) assert len(moltype) == 1 assert True in moltype def test_do_sketch_protein_multik_input_from_file(runtmp): testdata1 = utils.get_test_data("ecoli.faa") file_list = runtmp.output("filelist.txt") with open(file_list, "w") as fp: print(testdata1, file=fp) runtmp.sourmash( "sketch", "protein", "-p", "k=7,k=10,num=500", "--from-file", file_list ) outfile = runtmp.output("ecoli.faa.sig") assert os.path.exists(outfile) with open(outfile) as fp: sigdata = fp.read() siglist = list(load_signatures_from_json(sigdata)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 7 in ksizes assert 10 in ksizes moltype = set([x.minhash.moltype == "protein" for x in siglist]) assert len(moltype) == 1 assert True in moltype def test_do_sourmash_sketchdna_multik_outfile(runtmp): testdata1 = utils.get_test_data("short.fa") outfile = runtmp.output("FOO.xxx") runtmp.sourmash("sketch", "dna", "-p", "k=21,k=31", testdata1, "-o", outfile) assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 ksizes = set([x.minhash.ksize for x in siglist]) assert 21 in ksizes assert 31 in ksizes def test_do_sourmash_sketchdna_with_scaled_1(runtmp): testdata1 = utils.get_test_data("short.fa") outfile = runtmp.output("FOO.xxx") runtmp.sourmash( "sketch", "dna", "-p", "k=21,k=31,scaled=1", testdata1, "-o", outfile ) assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 scaled_vals = [x.minhash.scaled for x in siglist] assert len(scaled_vals) == 2 assert set(scaled_vals) == {1} def test_do_sourmash_sketchdna_with_scaled_2(runtmp): testdata1 = utils.get_test_data("short.fa") outfile = runtmp.output("FOO.xxx") runtmp.sourmash( "sketch", "dna", "-p", "k=21,k=31,scaled=2", testdata1, "-o", outfile ) assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 max_hashes = [x.minhash._max_hash for x in siglist] assert len(max_hashes) == 2 assert set(max_hashes) == set([int(2**64 / 2.0)]) def test_do_sourmash_sketchdna_with_scaled(runtmp): testdata1 = utils.get_test_data("short.fa") outfile = runtmp.output("FOO.xxx") runtmp.sourmash( "sketch", "dna", "-p", "k=21,k=31,scaled=100", testdata1, "-o", outfile ) assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 max_hashes = [x.minhash._max_hash for x in siglist] assert len(max_hashes) == 2 assert set(max_hashes) == set([int(2**64 / 100.0)]) def test_do_sourmash_sketchdna_with_bad_scaled(runtmp): testdata1 = utils.get_test_data("short.fa") outfile = runtmp.output("FOO.xxx") with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sketch", "dna", "-p", "k=21,k=31,scaled=-1", testdata1, "-o", outfile ) assert runtmp.last_result.status != 0 print(runtmp.last_result.err) assert "ERROR: scaled value must be positive" in runtmp.last_result.err with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sketch", "dna", "-p", "k=21,k=31,scaled=1000.5", testdata1, "-o", outfile ) assert runtmp.last_result.status != 0 assert "cannot parse scaled='1000.5' as an integer" in runtmp.last_result.err runtmp.sourmash( "sketch", "dna", "-p", "k=21,k=31,scaled=1000000000", testdata1, "-o", outfile ) assert runtmp.last_result.status == 0 print("XXX") print(runtmp.last_result.err) assert ( "WARNING: scaled value should be <= 1e6. Continuing anyway." in runtmp.last_result.err ) def test_do_sketch_with_seed(runtmp): testdata1 = utils.get_test_data("short.fa") outfile = runtmp.output("FOO.xxx") runtmp.sourmash( "sketch", "dna", "-p", "k=21,k=31,seed=43", testdata1, "-o", outfile ) assert os.path.exists(outfile) siglist = list(load_signatures_from_json(outfile)) assert len(siglist) == 2 seeds = [x.minhash.seed for x in siglist] assert len(seeds) == 2 assert set(seeds) == set([43]) def test_do_sourmash_check_protein_comparisons(runtmp): # this test checks 2 x 2 protein comparisons with E. coli genes. testdata1 = utils.get_test_data("ecoli.faa") runtmp.sourmash("sketch", "protein", "-p", "k=7,num=500", "--singleton", testdata1) sig1 = runtmp.output("ecoli.faa.sig") assert os.path.exists(sig1) testdata2 = utils.get_test_data("ecoli.genes.fna") runtmp.sourmash( "sketch", "translate", "-p", "k=7,num=500", "--singleton", testdata2 ) sig2 = runtmp.output("ecoli.genes.fna.sig") assert os.path.exists(sig2) # I'm not sure why load_signatures_from_json is randomizing order, but ok. x = list(load_signatures_from_json(sig1)) sig1_aa, sig2_aa = sorted(x, key=lambda x: x.name) x = list(load_signatures_from_json(sig2)) sig1_trans, sig2_trans = sorted(x, key=lambda x: x.name) name1 = sig1_aa.name.split()[0] assert name1 == "NP_414543.1" name2 = sig2_aa.name.split()[0] assert name2 == "NP_414544.1" name3 = sig1_trans.name.split()[0] assert name3 == "gi|556503834:2801-3733" name4 = sig2_trans.name.split()[0] assert name4 == "gi|556503834:337-2799" print(name1, name3, round(sig1_aa.similarity(sig1_trans), 3)) print(name2, name3, round(sig2_aa.similarity(sig1_trans), 3)) print(name1, name4, round(sig1_aa.similarity(sig2_trans), 3)) print(name2, name4, round(sig2_aa.similarity(sig2_trans), 3)) assert round(sig1_aa.similarity(sig1_trans), 3) == 0.0 assert round(sig2_aa.similarity(sig1_trans), 3) == 0.166 assert round(sig1_aa.similarity(sig2_trans), 3) == 0.174 assert round(sig2_aa.similarity(sig2_trans), 3) == 0.0 @utils.in_tempdir def test_do_sourmash_check_knowngood_dna_comparisons(c): # this test checks against a known good signature calculated # by utils/compute-dna-mh-another-way.py testdata1 = utils.get_test_data("ecoli.genes.fna") c.run_sourmash("sketch", "dna", "-p", "k=21,num=500", "--singleton", testdata1) sig1 = c.output("ecoli.genes.fna.sig") assert os.path.exists(sig1) x = list(load_signatures_from_json(sig1)) sig1, sig2 = sorted(x, key=lambda x: x.name) print(sig1.name) print(sig2.name) knowngood = utils.get_test_data("benchmark.dna.sig") good = list(load_signatures_from_json(knowngood))[0] assert sig2.similarity(good) == 1.0 @utils.in_tempdir def test_do_sourmash_check_knowngood_dna_comparisons_use_rna(c): # check the rna ; otherwise identical to previous test. testdata1 = utils.get_test_data("ecoli.genes.fna") c.run_sourmash("sketch", "rna", "-p", "k=21,num=500", "--singleton", testdata1) sig1 = c.output("ecoli.genes.fna.sig") assert os.path.exists(sig1) x = list(load_signatures_from_json(sig1)) sig1, sig2 = sorted(x, key=lambda x: x.name) knowngood = utils.get_test_data("benchmark.dna.sig") good = list(load_signatures_from_json(knowngood))[0] assert sig2.similarity(good) == 1.0 def test_do_sourmash_check_knowngood_input_protein_comparisons(runtmp): # this test checks against a known good signature calculated # by utils/compute-input-prot-another-way.py testdata1 = utils.get_test_data("ecoli.faa") runtmp.sourmash("sketch", "protein", "-p", "k=7,num=500", "--singleton", testdata1) sig1 = runtmp.output("ecoli.faa.sig") assert os.path.exists(sig1) x = list(load_signatures_from_json(sig1)) sig1_aa, sig2_aa = sorted(x, key=lambda x: x.name) knowngood = utils.get_test_data("benchmark.input_prot.sig") good_aa = list(load_signatures_from_json(knowngood))[0] assert sig1_aa.similarity(good_aa) == 1.0 def test_do_sourmash_check_knowngood_protein_comparisons(runtmp): # this test checks against a known good signature calculated # by utils/compute-prot-mh-another-way.py testdata1 = utils.get_test_data("ecoli.genes.fna") runtmp.sourmash( "sketch", "translate", "-p", "k=7,num=500", "--singleton", testdata1 ) sig1 = runtmp.output("ecoli.genes.fna.sig") assert os.path.exists(sig1) x = list(load_signatures_from_json(sig1)) sig1_trans, sig2_trans = sorted(x, key=lambda x: x.name) knowngood = utils.get_test_data("benchmark.prot.sig") good_trans = list(load_signatures_from_json(knowngood))[0] assert sig2_trans.similarity(good_trans) == 1.0 def test_do_sourmash_singleton_multiple_files_no_out_specified(runtmp): # this test checks that --singleton -o works testdata1 = utils.get_test_data("ecoli.faa") testdata2 = utils.get_test_data("shewanella.faa") runtmp.sourmash( "sketch", "protein", "-p", "k=7", "--singleton", testdata1, testdata2 ) print(runtmp.last_result.err) assert ( "saved 2 signature(s) to 'ecoli.faa.sig'. Note: signature license is CC0." in runtmp.last_result.err ) assert ( "saved 2 signature(s) to 'shewanella.faa.sig'. Note: signature license is CC0." in runtmp.last_result.err ) assert "calculated 2 signatures for 2 sequences in" in runtmp.last_result.err sig1 = runtmp.output("ecoli.faa.sig") assert os.path.exists(sig1) sig2 = runtmp.output("shewanella.faa.sig") assert os.path.exists(sig2) x = list(load_signatures_from_json(sig1)) for ss in x: print(ss.name) y = list(load_signatures_from_json(sig2)) for ss in y: print(ss.name) assert len(x) == 2 assert len(y) == 2 idents = [ss.name.split()[0] for ss in x] print(idents) assert set(["NP_414543.1", "NP_414544.1"]) == set(idents) idents = [ss.name.split()[0] for ss in y] print(idents) assert set(["WP_006079348.1", "WP_006079351.1"]) == set(idents) def test_do_sourmash_singleton_multiple_files_output(runtmp): # this test checks that --singleton -o works testdata1 = utils.get_test_data("ecoli.faa") testdata2 = utils.get_test_data("shewanella.faa") runtmp.sourmash( "sketch", "protein", "-p", "k=7", "--singleton", testdata1, testdata2, "-o", "output.sig", ) print(runtmp.last_result.err) assert ( "saved 4 signature(s) to 'output.sig'. Note: signature license is CC0." in runtmp.last_result.err ) assert "calculated 2 signatures for 2 sequences in" in runtmp.last_result.err assert "calculated 4 signatures for 2 sequences in" not in runtmp.last_result.err sig1 = runtmp.output("output.sig") assert os.path.exists(sig1) x = list(load_signatures_from_json(sig1)) for ss in x: print(ss.name) assert len(x) == 4 idents = [ss.name.split()[0] for ss in x] print(idents) assert set( ["NP_414543.1", "NP_414544.1", "WP_006079348.1", "WP_006079351.1"] ) == set(idents) def test_do_sourmash_singleton_multiple_files_output_zip(runtmp): # this test checks that --singleton -o works testdata1 = utils.get_test_data("ecoli.faa") testdata2 = utils.get_test_data("shewanella.faa") runtmp.sourmash( "sketch", "protein", "-p", "k=7", "--singleton", testdata1, testdata2, "-o", "output.zip", ) print(runtmp.last_result.err) assert ( "saved 4 signature(s) to 'output.zip'. Note: signature license is CC0." in runtmp.last_result.err ) sig1 = runtmp.output("output.zip") assert os.path.exists(sig1) x = list(sourmash.load_file_as_signatures(sig1)) for ss in x: print(ss.name) assert len(x) == 4 idents = [ss.name.split()[0] for ss in x] print(idents) assert set( ["NP_414543.1", "NP_414544.1", "WP_006079348.1", "WP_006079351.1"] ) == set(idents) def test_protein_with_stop_codons(runtmp): # compare protein seq with/without stop codons, via cli and also python # apis testdata1 = utils.get_test_data("ecoli.faa") with screed.open(testdata1) as f: ecoli_seq = [record.sequence for record in f] # first, via CLI w/o stop codons runtmp.sourmash("sketch", "protein", "-p", "k=7,scaled=1", testdata1) sig1 = runtmp.output("ecoli.faa.sig") assert os.path.exists(sig1) x = load_one_signature(sig1) cli_mh1 = x.minhash # second, via CLI w/stop codons ecoli_stop = runtmp.output("ecoli.stop.faa") with open(ecoli_stop, "w") as fp: for seq in ecoli_seq: fp.write(f">seq\n{seq}*\n") runtmp.sourmash("sketch", "protein", "-p", "k=7,scaled=1", ecoli_stop) sig2 = runtmp.output("ecoli.stop.faa.sig") assert os.path.exists(sig2) x = load_one_signature(sig2) cli_mh2 = x.minhash # now calculate sketch with MinHash... py_mh1 = MinHash(n=0, ksize=7, is_protein=True, scaled=1) for seq in ecoli_seq: py_mh1.add_protein(seq) # now calculate sketch with MinHash and stop codons... py_mh2 = MinHash(n=0, ksize=7, is_protein=True, scaled=1) for seq in ecoli_seq: py_mh2.add_protein(seq + "*") # and, last, calculate hashes separately with seq_to_hashes h_mh1 = MinHash(n=0, ksize=7, is_protein=True, scaled=1) h_mh2 = MinHash(n=0, ksize=7, is_protein=True, scaled=1) for seq in ecoli_seq: h = h_mh1.seq_to_hashes(seq, is_protein=1) h_mh1.add_many(h) h = h_mh2.seq_to_hashes(seq + "*", is_protein=1) h_mh2.add_many(h) # check! assert cli_mh1 == py_mh1 assert cli_mh2 == py_mh2 assert cli_mh1 == h_mh1 assert cli_mh2 == h_mh2 assert cli_mh1.contained_by(cli_mh2) == 1.0 assert py_mh1.contained_by(cli_mh2) == 1.0 assert h_mh1.contained_by(h_mh2) == 1.0 assert cli_mh2.contained_by(cli_mh1) < 1 assert py_mh2.contained_by(cli_mh1) < 1 assert h_mh2.contained_by(h_mh1) < 1 def test_hp_with_stop_codons(runtmp): # compare hp seq with/without stop codons, via cli and also python # apis testdata1 = utils.get_test_data("ecoli.faa") with screed.open(testdata1) as f: ecoli_seq = [record.sequence for record in f] # first, via CLI w/o stop codons runtmp.sourmash("sketch", "protein", "-p", "k=7,scaled=1,hp", testdata1) sig1 = runtmp.output("ecoli.faa.sig") assert os.path.exists(sig1) x = load_one_signature(sig1) cli_mh1 = x.minhash # second, via CLI w/stop codons ecoli_stop = runtmp.output("ecoli.stop.faa") with open(ecoli_stop, "w") as fp: for seq in ecoli_seq: fp.write(f">seq\n{seq}*\n") runtmp.sourmash("sketch", "protein", "-p", "k=7,scaled=1,hp", ecoli_stop) sig2 = runtmp.output("ecoli.stop.faa.sig") assert os.path.exists(sig2) x = load_one_signature(sig2) cli_mh2 = x.minhash # now calculate sketch with MinHash... py_mh1 = MinHash(n=0, ksize=7, hp=True, scaled=1) for seq in ecoli_seq: py_mh1.add_protein(seq) # now calculate sketch with MinHash and stop codons... py_mh2 = MinHash(n=0, ksize=7, hp=True, scaled=1) for seq in ecoli_seq: py_mh2.add_protein(seq + "*") # and, last, calculate hashes separately with seq_to_hashes h_mh1 = MinHash(n=0, ksize=7, hp=True, scaled=1) h_mh2 = MinHash(n=0, ksize=7, hp=True, scaled=1) for seq in ecoli_seq: h = h_mh1.seq_to_hashes(seq, is_protein=1) h_mh1.add_many(h) h = h_mh2.seq_to_hashes(seq + "*", is_protein=1) h_mh2.add_many(h) # check! assert cli_mh1 == py_mh1 assert cli_mh2 == py_mh2 assert cli_mh1 == h_mh1 assert cli_mh2 == h_mh2 assert cli_mh1.contained_by(cli_mh2) == 1.0 assert py_mh1.contained_by(cli_mh2) == 1.0 assert h_mh1.contained_by(h_mh2) == 1.0 assert cli_mh2.contained_by(cli_mh1) < 1 assert py_mh2.contained_by(cli_mh1) < 1 assert h_mh2.contained_by(h_mh1) < 1 def test_dayhoff_with_stop_codons(runtmp): # compare dayhoff seq with/without stop codons, via cli and also python # apis testdata1 = utils.get_test_data("ecoli.faa") with screed.open(testdata1) as f: ecoli_seq = [record.sequence for record in f] # first, via CLI w/o stop codons runtmp.sourmash("sketch", "protein", "-p", "k=7,scaled=1,dayhoff", testdata1) sig1 = runtmp.output("ecoli.faa.sig") assert os.path.exists(sig1) x = load_one_signature(sig1) cli_mh1 = x.minhash # second, via CLI w/stop codons ecoli_stop = runtmp.output("ecoli.stop.faa") with open(ecoli_stop, "w") as fp: for seq in ecoli_seq: fp.write(f">seq\n{seq}*\n") runtmp.sourmash("sketch", "protein", "-p", "k=7,scaled=1,dayhoff", ecoli_stop) sig2 = runtmp.output("ecoli.stop.faa.sig") assert os.path.exists(sig2) x = load_one_signature(sig2) cli_mh2 = x.minhash # now calculate sketch with MinHash... py_mh1 = MinHash(n=0, ksize=7, dayhoff=True, scaled=1) for seq in ecoli_seq: py_mh1.add_protein(seq) # now calculate sketch with MinHash and stop codons... py_mh2 = MinHash(n=0, ksize=7, dayhoff=True, scaled=1) for seq in ecoli_seq: py_mh2.add_protein(seq + "*") # and, last, calculate hashes separately with seq_to_hashes h_mh1 = MinHash(n=0, ksize=7, dayhoff=True, scaled=1) h_mh2 = MinHash(n=0, ksize=7, dayhoff=True, scaled=1) for seq in ecoli_seq: h = h_mh1.seq_to_hashes(seq, is_protein=1) h_mh1.add_many(h) h = h_mh2.seq_to_hashes(seq + "*", is_protein=1) h_mh2.add_many(h) # check! assert cli_mh1 == py_mh1 assert cli_mh2 == py_mh2 assert cli_mh1 == h_mh1 assert cli_mh2 == h_mh2 assert cli_mh1.contained_by(cli_mh2) == 1.0 assert py_mh1.contained_by(cli_mh2) == 1.0 assert h_mh1.contained_by(h_mh2) == 1.0 assert cli_mh2.contained_by(cli_mh1) < 1 assert py_mh2.contained_by(cli_mh1) < 1 assert h_mh2.contained_by(h_mh1) < 1 ### test sourmash sketch fromfile def test_fromfile_dna(runtmp): # does it run? yes, hopefully. test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "dna", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(runtmp.output("out.zip")) idx = sourmash.load_file_as_index(runtmp.output("out.zip")) siglist = list(idx.signatures()) assert len(siglist) == 1 ss = siglist[0] assert ss.name == "GCA_903797575 Salmonella enterica" assert ss.minhash.moltype == "DNA" assert "** 1 total requested; output 1, skipped 0" in runtmp.last_result.err def test_fromfile_dna_csv_gz(runtmp): # test with a gzipped csv test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) # gzip the CSV file with open(runtmp.output("sketch_fromfile/salmonella.csv"), "rb") as infp: with gzip.open(runtmp.output("salmonella.csv.gz"), "w") as outfp: outfp.write(infp.read()) runtmp.sourmash( "sketch", "fromfile", "salmonella.csv.gz", "-o", "out.zip", "-p", "dna" ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(runtmp.output("out.zip")) idx = sourmash.load_file_as_index(runtmp.output("out.zip")) siglist = list(idx.signatures()) assert len(siglist) == 1 ss = siglist[0] assert ss.name == "GCA_903797575 Salmonella enterica" assert ss.minhash.moltype == "DNA" assert "** 1 total requested; output 1, skipped 0" in runtmp.last_result.err def test_fromfile_dna_empty(runtmp): # test what happens on empty files. test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) # zero out the file with gzip.open( runtmp.output("sketch_fromfile/GCA_903797575.1_PARATYPHIC668_genomic.fna.gz"), "w", ): pass # now what happens? with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "dna", ) print(runtmp.last_result.out) err = runtmp.last_result.err print(err) assert "ERROR: no sequences found in " in err def test_fromfile_dna_check_sequence_succeed(runtmp): # does it run? yes, hopefully. test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "dna", "--check-sequence", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(runtmp.output("out.zip")) idx = sourmash.load_file_as_index(runtmp.output("out.zip")) siglist = list(idx.signatures()) assert len(siglist) == 1 ss = siglist[0] assert ss.name == "GCA_903797575 Salmonella enterica" assert ss.minhash.moltype == "DNA" assert "** 1 total requested; output 1, skipped 0" in runtmp.last_result.err def test_fromfile_dna_check_sequence_fail(runtmp): # does it run? yes, hopefully. test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella-badseq.csv", "-o", "out.zip", "-p", "dna", "--check-sequence", ) print(runtmp.last_result.out) err = runtmp.last_result.err print(err) assert "ERROR when reading from " in err assert ( "invalid DNA character in input k-mer: MTNILKLFSRKAGEPLDSLAVKSVRQHLSGD" in err ) def test_fromfile_dna_and_protein(runtmp): # does it run and produce DNA _and_ protein signatures? test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "dna", "-p", "protein", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(runtmp.output("out.zip")) idx = sourmash.load_file_as_index(runtmp.output("out.zip")) siglist = list(idx.signatures()) assert len(siglist) == 2 prot_sig = [ss for ss in siglist if ss.minhash.moltype == "protein"] assert len(prot_sig) == 1 prot_sig = prot_sig[0] assert prot_sig.name == "GCA_903797575 Salmonella enterica" dna_sig = [ss for ss in siglist if ss.minhash.moltype == "DNA"] assert len(dna_sig) == 1 dna_sig = dna_sig[0] assert dna_sig.name == "GCA_903797575 Salmonella enterica" assert "** 2 total requested; output 2, skipped 0" in runtmp.last_result.err def test_fromfile_dna_and_protein_and_hp_and_dayhoff(runtmp): # does it run and produce DNA _and_ protein signatures? test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "dna", "-p", "dna,k=25", "-p", "protein", "-p", "hp", "-p", "dayhoff", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(runtmp.output("out.zip")) idx = sourmash.load_file_as_index(runtmp.output("out.zip")) siglist = list(idx.signatures()) assert len(siglist) == 5 prot_sig = [ss for ss in siglist if ss.minhash.moltype == "protein"] assert len(prot_sig) == 1 prot_sig = prot_sig[0] assert prot_sig.name == "GCA_903797575 Salmonella enterica" prot_sig = [ss for ss in siglist if ss.minhash.moltype == "hp"] assert len(prot_sig) == 1 prot_sig = prot_sig[0] assert prot_sig.name == "GCA_903797575 Salmonella enterica" prot_sig = [ss for ss in siglist if ss.minhash.moltype == "dayhoff"] assert len(prot_sig) == 1 prot_sig = prot_sig[0] assert prot_sig.name == "GCA_903797575 Salmonella enterica" dna_sig = [ss for ss in siglist if ss.minhash.moltype == "DNA"] assert len(dna_sig) == 2 dna_sig = dna_sig[0] assert dna_sig.name == "GCA_903797575 Salmonella enterica" assert "** 5 total requested; output 5, skipped 0" in runtmp.last_result.err def test_fromfile_dna_and_protein_noname(runtmp): # nothing in the name column test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella-noname.csv", "-o", "out.zip", "-p", "dna", "-p", "protein", ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "ERROR: 1 entries have blank 'name's? Exiting!" in err def test_fromfile_dna_and_protein_dup_name(runtmp): # duplicate names test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "dna", "-p", "protein", ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "GCA_903797575 Salmonella enterica" not in err assert "ERROR: 1 entries have duplicate 'name' records. Exiting!" in err def test_fromfile_dna_and_protein_dup_name_report(runtmp): # duplicate names test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "sketch_fromfile/salmonella.csv", "--report-duplicated", "-o", "out.zip", "-p", "dna", "-p", "protein", ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "GCA_903797575 Salmonella enterica" in err assert "ERROR: 1 entries have duplicate 'name' records. Exiting!" in err def test_fromfile_dna_and_protein_missing(runtmp): # test what happens when missing protein. test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella-missing.csv", "-o", "out.zip", "-p", "protein", ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert ( "WARNING: fromfile entry 'GCA_903797575 Salmonella enterica' is missing a proteome" in err ) assert "** ERROR: we cannot build some of the requested signatures." in err assert "** 1 total signatures (for 1 names) cannot be built." in err def test_fromfile_dna_and_protein_missing_ignore(runtmp): # test what happens when missing protein + --ignore-missing test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella-missing.csv", "-o", "out.zip", "-p", "protein", "--ignore-missing", ) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert ( "WARNING: fromfile entry 'GCA_903797575 Salmonella enterica' is missing a proteome" in err ) assert "** ERROR: we cannot build some of the requested signatures." in err assert "** 1 total signatures (for 1 names) cannot be built." in err assert "** (continuing past this error because --ignore-missing was set)" in err assert "** 1 new signatures to build from 0 files;" in err def test_fromfile_no_overwrite(runtmp): # test --force-output-already-exists test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "dna", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(runtmp.output("out.zip")) # now run again; will fail since already exists with pytest.raises(SourmashCommandFailed): runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "protein", ) err = runtmp.last_result.err assert "ERROR: output location 'out.zip' already exists!" in err assert "Use --force-output-already-exists if you want to overwrite/append." in err def test_fromfile_force_overwrite(runtmp): # test --force-output-already-exists test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "dna", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(runtmp.output("out.zip")) # now run again, with --force runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "protein", "--force-output", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(runtmp.output("out.zip")) idx = sourmash.load_file_as_index(runtmp.output("out.zip")) siglist = list(idx.signatures()) assert len(siglist) == 2 names = list(set([ss.name for ss in siglist])) assert names[0] == "GCA_903797575 Salmonella enterica" assert "** 1 total requested; output 1, skipped 0" in runtmp.last_result.err def test_fromfile_need_params(runtmp): # check that we need a -p test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) with pytest.raises(SourmashCommandFailed) as exc: runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip" ) print(str(exc)) assert ( "Error creating signatures: No default moltype and none specified in param string" in str(exc) ) def test_fromfile_seed_not_allowed(runtmp): # check that we cannot adjust 'seed' test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) with pytest.raises(SourmashCommandFailed) as exc: runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "dna,seed=43", ) print(str(exc)) assert "ERROR: cannot set 'seed' in 'sketch fromfile'" in str(exc) def test_fromfile_license_not_allowed(runtmp): # check that license is CC0 test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) with pytest.raises(SourmashCommandFailed) as exc: runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-o", "out.zip", "-p", "dna", "--license", "BSD", ) print(str(exc)) assert "sourmash only supports CC0-licensed signatures" in str(exc) def test_fromfile_dna_and_protein_csv_output(runtmp): # does it run and produce DNA _and_ protein signatures? test_inp = utils.get_test_data("sketch_fromfile") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "--output-csv", "out.csv", "-p", "dna", "-p", "protein", ) print(runtmp.last_result.out) print(runtmp.last_result.err) assert os.path.exists(runtmp.output("out.csv")) with open(runtmp.output("out.csv"), newline="") as fp: r = csv.DictReader(fp) # filename,sketchtype,output_index,name,param_strs x = [] for row in r: x.append(row) x.sort(key=lambda x: x["filename"]) assert len(x) == 2 assert x[0]["sketchtype"] == "dna" assert x[0]["param_strs"] == "-p dna,k=31,scaled=1000" assert ( x[0]["filename"] == "sketch_fromfile/GCA_903797575.1_PARATYPHIC668_genomic.fna.gz" ) assert x[1]["sketchtype"] == "protein" assert x[1]["param_strs"] == "-p protein,k=10,scaled=200" assert ( x[1]["filename"] == "sketch_fromfile/GCA_903797575.1_PARATYPHIC668_protein.faa.gz" ) # same name... assert x[0]["name"] == x[1]["name"] == "GCA_903797575 Salmonella enterica" # ...different output index. assert x[1]["output_index"] != x[0]["output_index"] def test_fromfile_dna_and_protein_already_exists(runtmp): # does it properly ignore existing (--already-done) sigs? test_inp = utils.get_test_data("sketch_fromfile") already_done = utils.get_test_data("sketch_fromfile/salmonella-dna-protein.zip") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-p", "dna", "-p", "protein", "--already-done", already_done, "--output-manifest", "matching.csv", ) print(runtmp.last_result.out) err = runtmp.last_result.err print(err) assert "Loaded 1 pre-existing names from manifest(s)" in err assert "Read 1 rows, requesting that 2 signatures be built." in err assert "** 0 new signatures to build from 0 files;" in err assert "** Nothing to build. Exiting!" in err assert ( "output 2 already-done signatures to 'matching.csv' in manifest format." in err ) mf = manifest.CollectionManifest.load_from_filename(runtmp.output("matching.csv")) assert len(mf) == 2 def test_fromfile_dna_and_protein_partly_already_exists(runtmp): # does it properly ignore existing (--already-done) sigs? test_inp = utils.get_test_data("sketch_fromfile") already_done = utils.get_test_data("sketch_fromfile/salmonella-dna-protein.zip") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella-mult.csv", "-p", "dna", "-p", "protein", "--already-done", already_done, ) print(runtmp.last_result.out) err = runtmp.last_result.err print(err) assert "Loaded 1 pre-existing names from manifest(s)" in err assert "Read 2 rows, requesting that 4 signatures be built." in err assert "** 2 new signatures to build from 2 files;" in err assert "** 2 already exist, so skipping those." in err assert "** 4 total requested; output 2, skipped 2" in err def test_fromfile_dna_and_protein_already_exists_noname(runtmp): # check that no name in already_exists is handled test_inp = utils.get_test_data("sketch_fromfile") already_done = utils.get_test_data("sketch_fromfile/salmonella-dna-protein.zip") shutil.copytree(test_inp, runtmp.output("sketch_fromfile")) # run rename to get rid of names runtmp.sourmash("sig", "rename", already_done, "", "-o", "already-done.zip") runtmp.sourmash( "sketch", "fromfile", "sketch_fromfile/salmonella.csv", "-p", "dna", "-p", "protein", "--already-done", "already-done.zip", ) print(runtmp.last_result.out) err = runtmp.last_result.err print(err) assert "Loaded 0 pre-existing names from manifest(s)" in err assert "Read 1 rows, requesting that 2 signatures be built." in err assert "** 2 new signatures to build from 2 files;" in err assert "** 2 total requested; output 2, skipped 0" in err sourmash-bio-sourmash-bc3c0f8/tests/test_sqlite_index.py000066400000000000000000000712511474157046300237370ustar00rootroot00000000000000"Tests for SqliteIndex, SqliteCollectionManifest, and LCA_SqliteDatabase" import os import pytest import shutil import sqlite3 import sourmash from sourmash.exceptions import IndexNotSupported from sourmash.index.sqlite_index import ( SqliteIndex, load_sqlite_index, SqliteCollectionManifest, LCA_SqliteDatabase, ) from sourmash.index import StandaloneManifestIndex from sourmash import load_one_signature, SourmashSignature from sourmash.picklist import SignaturePicklist, PickStyle from sourmash.manifest import CollectionManifest from sourmash.tax.tax_utils import MultiLineageDB import sourmash_tst_utils as utils from sourmash_tst_utils import SourmashCommandFailed from sourmash import sqlite_utils def test_sqlite_index_prefetch_empty(): # check that an exception is raised upon for an empty database sig2 = utils.get_test_data("2.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) sqlidx = SqliteIndex.create(":memory:") # since this is a generator, we need to actually ask for a value to # get exception raised. g = sqlidx.prefetch(ss2, threshold_bp=0) with pytest.raises(ValueError) as e: next(g) assert "no signatures to search" in str(e.value) def test_sqlite_index_bad_version(runtmp): # create a sqlite database with a bad index version in the # sourmash_internal table, see what happens :) dbfile = runtmp.output("xyz.sqldb") conn = sqlite3.connect(dbfile) c = conn.cursor() SqliteIndex._create_tables(c) # 0.9 doesn't exist/is bad version c.execute( "UPDATE sourmash_internal SET value=? WHERE key=?", ("0.9", "SqliteIndex") ) conn.commit() with pytest.raises(IndexNotSupported): sourmash.load_file_as_index(dbfile) def test_sqlite_index_bad_version_unique(runtmp): # try to insert duplicate sqlite index info into sourmash_internal; fail dbfile = runtmp.output("xyz.sqldb") conn = sqlite3.connect(dbfile) c = conn.cursor() SqliteIndex._create_tables(c) # can't insert duplicate key with pytest.raises(sqlite3.IntegrityError): c.execute( "INSERT INTO sourmash_internal (value, key) VALUES (?, ?)", ("1.1", "SqliteIndex"), ) def test_index_search_subj_scaled_is_lower(): # check that subject sketches are appropriately downsampled sigfile = utils.get_test_data( "scaled100/GCF_000005845.2_ASM584v2_genomic.fna.gz.sig.gz" ) ss = sourmash.load_one_signature(sigfile) # double check :) assert ss.minhash.scaled == 100 # build a new query that has a scaled of 1000 qs = SourmashSignature(ss.minhash.downsample(scaled=1000)) # create Index to search sqlidx = SqliteIndex.create(":memory:") sqlidx.insert(ss) # search! results = list(sqlidx.search(qs, threshold=0)) assert len(results) == 1 # original signature (not downsampled) is returned assert results[0].signature == ss def test_sqlite_index_save_load(runtmp): sig2 = utils.get_test_data("2.fa.sig") sig47 = utils.get_test_data("47.fa.sig") sig63 = utils.get_test_data("63.fa.sig") ss2 = sourmash.load_one_signature(sig2, ksize=31) ss47 = sourmash.load_one_signature(sig47) ss63 = sourmash.load_one_signature(sig63) filename = runtmp.output("foo") sqlidx = SqliteIndex.create(filename) sqlidx.insert(ss2) sqlidx.insert(ss47) sqlidx.insert(ss63) sqlidx.close() sqlidx2 = SqliteIndex.load(filename) # now, search for sig2 sr = sqlidx2.search(ss2, threshold=1.0) print([s[1].name for s in sr]) assert len(sr) == 1 assert sr[0][1] == ss2 def test_sqlite_index_multik_select(): # this loads three ksizes, 21/31/51 sig2 = utils.get_test_data("2.fa.sig") siglist = sourmash.load_file_as_signatures(sig2) sqlidx = SqliteIndex.create(":memory:") for ss in siglist: sqlidx.insert(ss) # select most specifically sqlidx2 = sqlidx.select(ksize=31, moltype="DNA") assert len(sqlidx2) == 1 # all are DNA: sqlidx2 = sqlidx.select(moltype="DNA") assert len(sqlidx2) == 3 def test_sqlite_index_num_select(): # this will fail on 'num' select, which is not allowed sqlidx = SqliteIndex.create(":memory:") with pytest.raises(ValueError): sqlidx.select(num=100) def test_sqlite_index_abund_select(): # this will fail on 'track_abundance' select, which is not allowed sqlidx = SqliteIndex.create(":memory:") with pytest.raises(ValueError): sqlidx.select(track_abundance=True) def test_sqlite_index_insert_num_fail(): # cannot insert 'num' signatures sqlidx = SqliteIndex.create(":memory:") sig47 = utils.get_test_data("num/47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) assert ss47.minhash.num != 0 with pytest.raises(ValueError) as exc: sqlidx.insert(ss47) assert "cannot store 'num' signatures in SqliteIndex" in str(exc) def test_sqlite_index_insert_abund_fail(): # cannot insert 'num' signatures sqlidx = SqliteIndex.create(":memory:") sig47 = utils.get_test_data("track_abund/47.fa.sig") ss47 = sourmash.load_one_signature(sig47, ksize=31) with pytest.raises(ValueError) as exc: sqlidx.insert(ss47) assert "cannot store signatures with abundance in SqliteIndex" in str(exc) def test_sqlite_index_moltype_multi_fail(): # check that we cannot store sigs with multiple scaled values. # this loads multiple ksizes (19, 31) and moltypes (DNA, protein, hp, etc) filename = utils.get_test_data("prot/all.zip") siglist = sourmash.load_file_as_signatures(filename) siglist = list(siglist) sqlidx = SqliteIndex.create(":memory:") sqlidx.insert(siglist[0]) assert sqlidx.scaled == 100 with pytest.raises(ValueError) as exc: for ss in siglist: sqlidx.insert(ss) assert "this database can only store scaled values=100" in str(exc) def test_sqlite_index_picklist_select(): # test select with a picklist # this loads three ksizes, 21/31/51 sig2 = utils.get_test_data("2.fa.sig") siglist = sourmash.load_file_as_signatures(sig2) sqlidx = SqliteIndex.create(":memory:") for ss in siglist: sqlidx.insert(ss) # construct a picklist... picklist = SignaturePicklist("md5prefix8") picklist.init(["f3a90d4e"]) # select on picklist sqlidx2 = sqlidx.select(picklist=picklist) assert len(sqlidx2) == 1 ss = list(sqlidx2.signatures())[0] assert ss.minhash.ksize == 31 assert ss.md5sum().startswith("f3a90d4e55") def test_sqlite_index_picklist_select_exclude(): # test select with a picklist, but exclude # this loads three ksizes, 21/31/51 sig2 = utils.get_test_data("2.fa.sig") siglist = sourmash.load_file_as_signatures(sig2) sqlidx = SqliteIndex.create(":memory:") for ss in siglist: sqlidx.insert(ss) # construct a picklist... picklist = SignaturePicklist("md5prefix8", pickstyle=PickStyle.EXCLUDE) picklist.init(["f3a90d4e"]) # select on picklist sqlidx2 = sqlidx.select(picklist=picklist) assert len(sqlidx2) == 2 md5s = set() ksizes = set() for ss in list(sqlidx2.signatures()): md5s.add(ss.md5sum()) ksizes.add(ss.minhash.ksize) assert md5s == set( ["f372e47893edd349e5956f8b0d8dcbf7", "43f3b48e59443092850964d355a20ac0"] ) assert ksizes == set([21, 51]) def test_sqlite_jaccard_ordering(): # this tests a tricky situation where for three sketches A, B, C, # |A intersect B| is greater than |A intersect C| # _but_ # |A jaccard B| is less than |A intersect B| a = sourmash.MinHash(ksize=31, n=0, scaled=2) b = a.copy_and_clear() c = a.copy_and_clear() a.add_many([1, 2, 3, 4]) b.add_many([1, 2, 3] + list(range(10, 30))) c.add_many([1, 5]) def _intersect(x, y): return x.intersection_and_union_size(y)[0] print("a intersect b:", _intersect(a, b)) print("a intersect c:", _intersect(a, c)) print("a jaccard b:", a.jaccard(b)) print("a jaccard c:", a.jaccard(c)) assert _intersect(a, b) > _intersect(a, c) assert a.jaccard(b) < a.jaccard(c) # thresholds to use: assert a.jaccard(b) < 0.15 assert a.jaccard(c) > 0.15 # now - make signatures, try out :) ss_a = sourmash.SourmashSignature(a, name="A") ss_b = sourmash.SourmashSignature(b, name="B") ss_c = sourmash.SourmashSignature(c, name="C") sqlidx = SqliteIndex.create(":memory:") sqlidx.insert(ss_a) sqlidx.insert(ss_b) sqlidx.insert(ss_c) sr = sqlidx.search(ss_a, threshold=0.15) print(sr) assert len(sr) == 2 assert sr[0].signature == ss_a assert sr[0].score == 1.0 assert sr[1].signature == ss_c assert sr[1].score == 0.2 def test_sqlite_index_scaled1(): # check on scaled=1 storage. sqlidx = SqliteIndex.create(":memory:") mh1 = sourmash.MinHash(0, 31, scaled=1) mh1.add_hash(2**64 - 1) mh1.add_hash(2**64 - 2) mh1.add_hash(2**64 - 3) ss1 = sourmash.SourmashSignature(mh1, name="ss 1") mh2 = sourmash.MinHash(0, 31, scaled=1) mh2.add_hash(2**64 - 1) mh2.add_hash(2**64 - 2) mh2.add_hash(2**64 - 3) mh2.add_hash(0) mh2.add_hash(1) mh2.add_hash(2) ss2 = sourmash.SourmashSignature(mh2, name="ss 2") sqlidx.insert(ss1) sqlidx.insert(ss2) # check jaccard search results = list(sqlidx.search(ss1, threshold=0)) print(results) assert len(results) == 2 assert results[0].signature == ss1 assert results[0].score == 1.0 assert results[1].signature == ss2 assert results[1].score == 0.5 results = list(sqlidx.search(ss1, threshold=0, do_containment=True)) print(results) assert results[0].signature == ss1 assert results[0].score == 1.0 assert results[1].signature == ss2 assert results[1].score == 1.0 # minhashes retrieved successfully? assert len(results[0].signature.minhash) == 3 assert len(results[1].signature.minhash) == 6 def test_sqlite_index_load_existing(): # try loading an existing sqlite index filename = utils.get_test_data("sqlite/index.sqldb") sqlidx = sourmash.load_file_as_index(filename) assert isinstance(sqlidx, SqliteIndex) siglist = list(sqlidx.signatures()) assert len(siglist) == 2 def test_sqlite_index_create_load_existing(runtmp): # try creating then loading an existing sqlite index; create from CLI filename = runtmp.output("idx.sqldb") sig1 = utils.get_test_data("47.fa.sig") sig2 = utils.get_test_data("63.fa.sig") runtmp.sourmash("sig", "cat", sig1, sig2, "-o", filename) sqlidx = sourmash.load_file_as_index(filename) assert isinstance(sqlidx, SqliteIndex) siglist = list(sqlidx.signatures()) assert len(siglist) == 2 def test_sqlite_index_create_load_insert_existing(runtmp): # try creating, loading, inserting into an existing sqlite index filename = runtmp.output("idx.sqldb") sig1 = utils.get_test_data("47.fa.sig") sig2 = utils.get_test_data("63.fa.sig") sig3 = utils.get_test_data("2.fa.sig") runtmp.sourmash("sig", "cat", sig1, sig2, "-o", filename) sqlidx = sourmash.load_file_as_index(filename) assert isinstance(sqlidx, SqliteIndex) siglist = list(sqlidx.signatures()) assert len(siglist) == 2 ss3 = sourmash.load_one_signature(sig3, ksize=31) sqlidx.insert(ss3) sqlidx.commit() runtmp.sourmash("sig", "describe", filename) print(runtmp.last_result.out) assert "md5: f3a90d4e5528864a5bcc8434b0d0c3b1" in runtmp.last_result.out def test_sqlite_index_create_load_insert_existing_cli(runtmp): # try creating, loading, inserting into an existing sqlite index from cli # (aka "append" to existing database) filename = runtmp.output("idx.sqldb") sig1 = utils.get_test_data("47.fa.sig") sig2 = utils.get_test_data("63.fa.sig") sig3 = utils.get_test_data("2.fa.sig") runtmp.sourmash("sig", "cat", sig1, sig2, "-o", filename) sqlidx = sourmash.load_file_as_index(filename) assert isinstance(sqlidx, SqliteIndex) siglist = list(sqlidx.signatures()) assert len(siglist) == 2 # add a third runtmp.sourmash("sig", "cat", sig3, "-o", filename, "-k", "31") siglist = list(sqlidx.signatures()) assert len(siglist) == 3 def test_sqlite_manifest_bad_version(runtmp): # create a sqlite database with a bad manifest version in the # sourmash_internal table, see what happens :) dbfile = runtmp.output("xyz.sqlmf") conn = sqlite3.connect(dbfile) c = conn.cursor() SqliteCollectionManifest._create_tables(c) # 0.9 doesn't exist/bad version c.execute( "UPDATE sourmash_internal SET value=? WHERE key=?", ("0.9", "SqliteManifest") ) conn.commit() with pytest.raises(IndexNotSupported): CollectionManifest.load_from_filename(dbfile) def test_sqlite_manifest_bad_version_unique(runtmp): # try to insert duplicate sqlite manifest info into sourmash_internal; fail dbfile = runtmp.output("xyz.sqldb") conn = sqlite3.connect(dbfile) c = conn.cursor() SqliteCollectionManifest._create_tables(c) # can't insert duplicate key with pytest.raises(sqlite3.IntegrityError): c.execute( "INSERT INTO sourmash_internal (value, key) VALUES (?, ?)", ("1.1", "SqliteManifest"), ) def test_sqlite_manifest_basic(): # test some features of the SQLite-based manifest. sig2 = load_one_signature(utils.get_test_data("2.fa.sig"), ksize=31) sig47 = load_one_signature(utils.get_test_data("47.fa.sig"), ksize=31) sig63 = load_one_signature(utils.get_test_data("63.fa.sig"), ksize=31) sqlidx = SqliteIndex.create(":memory:") # empty manifest tests manifest = sqlidx.manifest assert not manifest assert len(manifest) == 0 sqlidx.insert(sig47) sqlidx.insert(sig63) # ok, more full manifest tests! assert manifest assert len(manifest) == 2 assert sig47 in manifest assert sig2 not in manifest # check that we can get a "standard" manifest out standard_mf = CollectionManifest.load_from_manifest(sqlidx.manifest) assert len(standard_mf) == 2 picklist = manifest.to_picklist() assert sig47 in picklist assert sig2 not in picklist def test_sqlite_manifest_round_trip(): # check that we can go from regular mf -> sqlite mf -> regular again. sig2 = load_one_signature(utils.get_test_data("2.fa.sig"), ksize=31) sig47 = load_one_signature(utils.get_test_data("47.fa.sig"), ksize=31) sig63 = load_one_signature(utils.get_test_data("63.fa.sig"), ksize=31) rows = [] rows.append( CollectionManifest.make_manifest_row(sig47, None, include_signature=False) ) rows.append( CollectionManifest.make_manifest_row(sig63, None, include_signature=False) ) nosql_mf = CollectionManifest(rows) sqlite_mf = SqliteCollectionManifest.load_from_manifest(nosql_mf) # test roundtrip round_mf = CollectionManifest.load_from_manifest(sqlite_mf) assert len(round_mf) == 2 print(round_mf.rows, nosql_mf.rows) assert round_mf == nosql_mf for mf in (nosql_mf, sqlite_mf, round_mf): picklist = mf.to_picklist() assert sig47 in picklist assert sig2 not in picklist def test_sqlite_manifest_create(runtmp): # test creation and summarization of a manifest of prot.zip zipfile = utils.get_test_data("prot/all.zip") # create manifest runtmp.sourmash("sig", "manifest", "-F", "sql", zipfile, "-o", "mf.sqlmf") sqlmf = runtmp.output("mf.sqlmf") assert os.path.exists(sqlmf) # verify it's loadable as the right type idx = load_sqlite_index(sqlmf) assert isinstance(idx, StandaloneManifestIndex) # summarize runtmp.sourmash("sig", "fileinfo", "mf.sqlmf") out = runtmp.last_result.out print(out) assert "2 sketches with dayhoff, k=19, scaled=100 7945 total hashes" in out assert "2 sketches with hp, k=19, scaled=100 5184 total hashes" in out assert "2 sketches with protein, k=19, scaled=100 8214 total hashes" in out assert "1 sketches with DNA, k=31, scaled=1000 5238 total hashes" in out assert "path filetype: StandaloneManifestIndex" in out assert "location: mf.sqlmf" in out assert "is database? yes" in out assert "has manifest? yes" in out assert "num signatures: 7" in out def test_sqlite_manifest_create_noload_sigs(runtmp): # sigs should not be loadable from manifest this way... zipfile = utils.get_test_data("prot/all.zip") # create manifest runtmp.sourmash("sig", "manifest", "-F", "sql", zipfile, "-o", "mf.sqlmf") # 'describe' should not be able to load the sqlmf b/c prefix is wrong with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "describe", "mf.sqlmf") def test_sqlite_manifest_create_yesload_sigs(runtmp): # should be able to load after copying files zipfile = utils.get_test_data("prot/all.zip") shutil.copytree(utils.get_test_data("prot"), runtmp.output("prot")) # create manifest runtmp.sourmash("sig", "manifest", "-F", "sql", zipfile, "-o", "prot/mf.sqlmf") # 'describe' should now be able to load the sqlmf, which is cool runtmp.sourmash("sig", "describe", "prot/mf.sqlmf") print(runtmp.last_result.out) def test_sqlite_manifest_num(runtmp): # should be able to produce sql manifests with 'num' sketches in them numsig = utils.get_test_data("num/47.fa.sig") # create mf runtmp.sourmash("sig", "manifest", "-F", "sql", numsig, "-o", "mf.sqlmf") # do summarize: runtmp.sourmash("sig", "summarize", "mf.sqlmf") out = runtmp.last_result.out print(out) assert "1 sketches with DNA, k=21, num=500 500 total hashes" in out assert "1 sketches with DNA, k=31, num=500 500 total hashes" in out assert "1 sketches with DNA, k=51, num=500 500 total hashes" in out def test_sqlite_manifest_num_select(runtmp): # should be able to _select_ sql manifests with 'num' sketches in them numsig = utils.get_test_data("num/47.fa.sig") # create mf runtmp.sourmash("sig", "manifest", "-F", "sql", numsig, "-o", "mf.sqlmf") # load as index idx = sourmash.load_file_as_index(runtmp.output("mf.sqlmf")) # select print(list(idx.manifest.rows)) idx = idx.select(num=500) print(list(idx.manifest.rows)) assert len(idx) == 3 def test_sqlite_manifest_locations(runtmp): # check what locations returns... may return too many, that's ok. prot = utils.get_test_data("prot") runtmp.sourmash("sig", "manifest", "-F", "sql", prot, "-o", "mf.sqlmf") # load as index idx = sourmash.load_file_as_index(runtmp.output("mf.sqlmf")) picklist = SignaturePicklist("identprefix") picklist.pickset = set(["GCA_001593925"]) idx = idx.select(picklist=picklist) sql_locations = set(idx.manifest.locations()) row_locations = set(row["internal_location"] for row in idx.manifest.rows) assert sql_locations.issuperset(row_locations) assert "dna-sig.sig.gz" in sql_locations # this is unnecessary... assert "dna-sig.sig.gz" not in row_locations # ...this is correct :) def test_sqlite_manifest_create_insert(runtmp): # try out creating a sqlite manifest and then running cli on it mfname = runtmp.output("some.sqlmf") mf = SqliteCollectionManifest.create(mfname) sigfile = utils.get_test_data("47.fa.sig") ss = sourmash.load_one_signature(sigfile) mf._insert_row(mf.conn.cursor(), mf.make_manifest_row(ss, "some.sig")) mf.conn.commit() # copy sig in since we want it to resolve... shutil.copyfile(sigfile, runtmp.output("some.sig")) # 'describe' should work here, to resolve actual sigs. runtmp.sourmash("sig", "describe", mfname) print(runtmp.last_result.out) assert "md5: 09a08691ce52952152f0e866a59f6261" in runtmp.last_result.out def test_sqlite_manifest_create_insert_2(runtmp): # try out creating a sqlite manifest from cli and then _insert_row into it # copy sig in since we want it to resolve... sigfile = utils.get_test_data("47.fa.sig") shutil.copyfile(sigfile, runtmp.output("some.sig")) runtmp.sourmash("sig", "manifest", "some.sig", "-F", "sql", "-o", "some.sqlmf") mfname = runtmp.output("some.sqlmf") mf = CollectionManifest.load_from_filename(mfname) ss = sourmash.load_one_signature(runtmp.output("some.sig")) mf._insert_row(mf.conn.cursor(), mf.make_manifest_row(ss, "some.sig")) mf.conn.commit() # 'describe' should work here, to resolve actual sigs. runtmp.sourmash("sig", "describe", mfname) print(runtmp.last_result.out) assert "md5: 09a08691ce52952152f0e866a59f6261" in runtmp.last_result.out def test_sqlite_manifest_existing(runtmp): # try out an existing sqlite manifest prefix = runtmp.output("protdir") mf = runtmp.output("protdir/prot.sqlmf") shutil.copytree(utils.get_test_data("prot"), prefix) shutil.copyfile(utils.get_test_data("sqlite/prot.sqlmf"), mf) runtmp.sourmash("sig", "describe", mf) print(runtmp.last_result.out) def test_sqlite_manifest_existing_insert(runtmp): # try out an existing sqlite manifest - insert into it prefix = runtmp.output("protdir") shutil.copytree(utils.get_test_data("prot"), prefix) mfname = runtmp.output("protdir/prot.sqlmf") shutil.copyfile(utils.get_test_data("sqlite/prot.sqlmf"), mfname) mf = CollectionManifest.load_from_filename(mfname) assert isinstance(mf, SqliteCollectionManifest) sigfile = utils.get_test_data("47.fa.sig") ss = sourmash.load_one_signature(sigfile) mf._insert_row(mf.conn.cursor(), mf.make_manifest_row(ss, "some.sig")) mf.conn.commit() # copy sig in since we want it to resolve... shutil.copyfile(sigfile, runtmp.output("protdir/some.sig")) # 'describe' should work here. runtmp.sourmash("sig", "describe", mfname) print(runtmp.last_result.out) def test_sqlite_manifest_existing_mf_only(runtmp): # try out an existing sqlite manifest, but without underlying files -> fail mf = runtmp.output("prot.sqlmf") shutil.copyfile(utils.get_test_data("sqlite/prot.sqlmf"), mf) # 'fileinfo' should work... runtmp.sourmash("sig", "fileinfo", mf) print(runtmp.last_result.out) assert "num signatures: 7" in runtmp.last_result.out # ...but 'describe' should fail, since it needs actual sigs. with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "describe", mf) print(runtmp.last_result.err) assert "ERROR: Error while reading signatures from" in runtmp.last_result.err def test_sqlite_manifest_existing_mfonly_insert(runtmp): # try out an existing sqlite manifest - insert into it, but fail describe mfname = runtmp.output("prot.sqlmf") shutil.copyfile(utils.get_test_data("sqlite/prot.sqlmf"), mfname) mf = CollectionManifest.load_from_filename(mfname) assert isinstance(mf, SqliteCollectionManifest) sigfile = utils.get_test_data("47.fa.sig") ss = sourmash.load_one_signature(sigfile) mf._insert_row(mf.conn.cursor(), mf.make_manifest_row(ss, sigfile)) mf.conn.commit() # 'fileinfo' should work... runtmp.sourmash("sig", "fileinfo", mfname) print(runtmp.last_result.out) assert "num signatures: 8" in runtmp.last_result.out # ...but 'describe' should fail, since it needs actual sigs. with pytest.raises(SourmashCommandFailed): runtmp.sourmash("sig", "describe", mfname) def test_sqlite_manifest_load_existing_index(): # try loading an existing sqlite index as a manifest filename = utils.get_test_data("sqlite/index.sqldb") mf = CollectionManifest.load_from_filename(filename) assert isinstance(mf, SqliteCollectionManifest) assert len(mf) == 2 def test_sqlite_manifest_load_existing_index_insert_fail(): # try loading an existing sqlite index as a manifest; insert should fail filename = utils.get_test_data("sqlite/index.sqldb") mf = CollectionManifest.load_from_filename(filename) assert isinstance(mf, SqliteCollectionManifest) assert len(mf) == 2 # try insert - should fail sigfile = utils.get_test_data("47.fa.sig") ss = sourmash.load_one_signature(sigfile) with pytest.raises(Exception) as exc: mf._insert_row(mf.conn.cursor(), mf.make_manifest_row(ss, sigfile)) assert "must use SqliteIndex.insert to add to this manifest" in str(exc) def test_sqlite_manifest_create_load_empty(runtmp): # try creating an empty manifest, then loading mfname = runtmp.output("some.sqlmf") mf = SqliteCollectionManifest.create(mfname) mf.close() mf2 = load_sqlite_index(mfname) assert len(mf2) == 0 def test_sqlite_lca_db_load_existing(): # try loading an existing sqlite index filename = utils.get_test_data("sqlite/lca.sqldb") sqlidx = sourmash.load_file_as_index(filename) assert isinstance(sqlidx, LCA_SqliteDatabase) siglist = list(sqlidx.signatures()) assert len(siglist) == 2 def test_sqlite_lca_db_select(): # try loading an existing sqlite index filename = utils.get_test_data("sqlite/lca.sqldb") sqlidx = sourmash.load_file_as_index(filename) assert isinstance(sqlidx, LCA_SqliteDatabase) sqlidx2 = sqlidx.select(ksize=31) list(sqlidx2.hashvals) # only on LCA_SqliteDatabase assert isinstance(sqlidx2, LCA_SqliteDatabase) def test_sqlite_lca_db_create_load_existing(runtmp): # try creating (from CLI) then loading (from API) an LCA db filename = runtmp.output("lca.sqldb") sig1 = utils.get_test_data("lca/TARA_ASE_MAG_00031.sig") sig2 = utils.get_test_data("lca/TARA_PSW_MAG_00136.sig") runtmp.sourmash("sig", "flatten", sig1, sig2, "-o", filename, "-k", "31") # load tax tax_csv = utils.get_test_data("sqlite/delmont-6.csv") runtmp.sourmash("tax", "prepare", "-t", tax_csv, "-o", filename, "-F", "sql") sqlidx = sourmash.load_file_as_index(filename) assert isinstance(sqlidx, LCA_SqliteDatabase) siglist = list(sqlidx.signatures()) assert len(siglist) == 2 def test_sqlite_lca_db_load_empty(runtmp): # try creating then loading an _empty_ LCA_SqliteDatabase dbname = runtmp.output("empty.sqldb") # create empty SqliteIndex... runtmp.sourmash("sig", "cat", "-o", dbname) assert os.path.exists(dbname) # ...and create empty sourmash_taxonomy tables in there... empty_tax = utils.get_test_data("scaled/empty-lineage.csv") runtmp.sourmash("tax", "prepare", "-F", "sql", "-t", empty_tax, "-o", dbname) runtmp.sourmash("sig", "describe", dbname) assert "loaded 0 signatures" in runtmp.last_result.err def test_sqlite_lca_db_create_readonly(runtmp): # try running 'prepare' on a read-only sqlite db, check error message. dbname = runtmp.output("empty.sqldb") # create empty SqliteIndex... runtmp.sourmash("sig", "cat", "-o", dbname) assert os.path.exists(dbname) # make it read only... from stat import S_IREAD, S_IRGRP, S_IROTH os.chmod(dbname, S_IREAD | S_IRGRP | S_IROTH) # ...and try creating empty sourmash_taxonomy tables in there... empty_tax = utils.get_test_data("scaled/empty-lineage.csv") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("tax", "prepare", "-F", "sql", "-t", empty_tax, "-o", dbname) err = runtmp.last_result.err print(err) assert "taxonomy table already exists in" not in err assert "attempt to write a readonly database" in err def test_sqlite_lca_db_try_load_sqlite_index(): # try loading a SqliteIndex with no tax tables from .load classmethod dbname = utils.get_test_data("sqlite/index.sqldb") with pytest.raises(ValueError) as exc: LCA_SqliteDatabase.load(dbname) assert "not a taxonomy database" in str(exc) def test_sqlite_lca_db_supply_lineage_db(): # try creating an LCA_SqliteDatabase object with a separate lineage DB. dbname = utils.get_test_data("sqlite/index.sqldb") tax_csv = utils.get_test_data("sqlite/shewanella-lineage.csv") lineage_db = MultiLineageDB.load([tax_csv]) db = LCA_SqliteDatabase(dbname, lineage_db=lineage_db) hashval = next(iter(db.hashvals)) lineages = db.get_lineage_assignments(hashval) print(lineages) assert lineages[0][0].rank == "superkingdom" assert lineages[0][0].name == "d__Bacteria" assert lineages[0][-1].rank == "species" assert lineages[0][-1].name == "s__Shewanella baltica" assert lineages[1][0].rank == "superkingdom" assert lineages[1][0].name == "d__Bacteria" assert lineages[0][-1].rank == "species" assert lineages[0][-1].name == "s__Shewanella baltica" def test_bad_sqlite_internal_version(): # check get_sourmash_internal dbname = utils.get_test_data("sqlite/index.sqldb") conn = sqlite_utils.open_sqlite_db(dbname) c = conn.cursor() with pytest.raises(Exception): sqlite_utils.add_sourmash_internal(c, "SqliteIndex", "0.9") sourmash-bio-sourmash-bc3c0f8/tests/test_tax.py000066400000000000000000006377241474157046300220600ustar00rootroot00000000000000""" Tests for the 'sourmash tax' command line and high level API. """ import os import csv import pytest import gzip from collections import Counter from pathlib import Path import sourmash import sourmash_tst_utils as utils from sourmash.tax import tax_utils from sourmash.lca import lca_utils from sourmash_tst_utils import SourmashCommandFailed from sourmash import sqlite_utils from sourmash.exceptions import IndexNotSupported from sourmash import sourmash_args ## command line tests def test_run_sourmash_tax(): status, out, err = utils.runscript("sourmash", ["tax"], fail_ok=True) assert status != 0 # no args provided, ok ;) def test_metagenome_stdout_0(runtmp): # test basic metagenome c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") c.run_sourmash("tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,superkingdom,0.204,d__Bacteria,md5,test1.sig,0.131,1024000" in c.last_result.out ) assert ( "test1,superkingdom,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,phylum,0.116,d__Bacteria;p__Bacteroidota,md5,test1.sig,0.073,582000" in c.last_result.out ) assert ( "test1,phylum,0.088,d__Bacteria;p__Proteobacteria,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,phylum,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,class,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia,md5,test1.sig,0.073,582000" in c.last_result.out ) assert ( "test1,class,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,class,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,order,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales,md5,test1.sig,0.073,582000" in c.last_result.out ) assert ( "test1,order,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,order,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,family,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae,md5,test1.sig,0.073,582000" in c.last_result.out ) assert ( "test1,family,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,family,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,genus,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella,md5,test1.sig,0.057,444000" in c.last_result.out ) assert ( "test1,genus,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,genus,0.028,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola,md5,test1.sig,0.016,138000" in c.last_result.out ) assert ( "test1,genus,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) assert ( "test1,species,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,species,0.028,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola vulgatus,md5,test1.sig,0.016,138000" in c.last_result.out ) assert ( "test1,species,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) def test_metagenome_stdout_0_db(runtmp): # test basic metagenome with sqlite database c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.db") c.run_sourmash("tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,superkingdom,0.204,d__Bacteria,md5,test1.sig,0.131,1024000" in c.last_result.out ) assert ( "test1,superkingdom,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,phylum,0.116,d__Bacteria;p__Bacteroidota,md5,test1.sig,0.073,582000" in c.last_result.out ) assert ( "test1,phylum,0.088,d__Bacteria;p__Proteobacteria,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,phylum,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,class,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia,md5,test1.sig,0.073,582000" in c.last_result.out ) assert ( "test1,class,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,class,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,order,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales,md5,test1.sig,0.073,582000" in c.last_result.out ) assert ( "test1,order,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,order,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,family,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae,md5,test1.sig,0.073,582000" in c.last_result.out ) assert ( "test1,family,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,family,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,genus,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella,md5,test1.sig,0.057,444000" in c.last_result.out ) assert ( "test1,genus,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,genus,0.028,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola,md5,test1.sig,0.016,138000" in c.last_result.out ) assert ( "test1,genus,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) assert ( "test1,species,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,species,0.028,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola vulgatus,md5,test1.sig,0.016,138000" in c.last_result.out ) assert ( "test1,species,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) def test_metagenome_summary_csv_out(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" sum_csv = csv_base + ".summarized.csv" csvout = runtmp.output(sum_csv) outdir = os.path.dirname(csvout) runtmp.run_sourmash( "tax", "metagenome", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-dir", outdir, ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert os.path.exists(csvout) sum_gather_results = [x.rstrip() for x in Path(csvout).read_text().splitlines()] assert f"saving 'csv_summary' output to '{csvout}'" in runtmp.last_result.err assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in sum_gather_results[0] ) assert ( "test1,superkingdom,0.2042281611487834,d__Bacteria,md5,test1.sig,0.13080306238801107,1024000" in sum_gather_results[1] ) assert ( "test1,superkingdom,0.7957718388512166,unclassified,md5,test1.sig,0.8691969376119889,3990000" in sum_gather_results[2] ) assert ( "test1,phylum,0.11607499002792182,d__Bacteria;p__Bacteroidota,md5,test1.sig,0.07265026877341586,582000" in sum_gather_results[3] ) assert ( "test1,phylum,0.08815317112086159,d__Bacteria;p__Proteobacteria,md5,test1.sig,0.05815279361459521,442000" in sum_gather_results[4] ) assert ( "test1,phylum,0.7957718388512166,unclassified,md5,test1.sig,0.8691969376119889,3990000" in sum_gather_results[5] ) assert ( "test1,class,0.11607499002792182,d__Bacteria;p__Bacteroidota;c__Bacteroidia,md5,test1.sig,0.07265026877341586,582000" in sum_gather_results[6] ) assert ( "test1,class,0.08815317112086159,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria,md5,test1.sig,0.05815279361459521,442000" in sum_gather_results[7] ) assert ( "test1,class,0.7957718388512166,unclassified,md5,test1.sig,0.8691969376119889,3990000" in sum_gather_results[8] ) assert ( "test1,order,0.11607499002792182,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales,md5,test1.sig,0.07265026877341586,582000" in sum_gather_results[9] ) assert ( "test1,order,0.08815317112086159,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales,md5,test1.sig,0.05815279361459521,442000" in sum_gather_results[10] ) assert ( "test1,order,0.7957718388512166,unclassified,md5,test1.sig,0.8691969376119889,3990000" in sum_gather_results[11] ) assert ( "test1,family,0.11607499002792182,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae,md5,test1.sig,0.07265026877341586,582000" in sum_gather_results[12] ) assert ( "test1,family,0.08815317112086159,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae,md5,test1.sig,0.05815279361459521,442000" in sum_gather_results[13] ) assert ( "test1,family,0.7957718388512166,unclassified,md5,test1.sig,0.8691969376119889,3990000" in sum_gather_results[14] ) assert ( "test1,genus,0.0885520542481053,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella,md5,test1.sig,0.05701254275940707,444000" in sum_gather_results[15] ) assert ( "test1,genus,0.08815317112086159,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia,md5,test1.sig,0.05815279361459521,442000" in sum_gather_results[16] ) assert ( "test1,genus,0.027522935779816515,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola,md5,test1.sig,0.015637726014008795,138000" in sum_gather_results[17] ) assert ( "test1,genus,0.7957718388512166,unclassified,md5,test1.sig,0.8691969376119889,3990000" in sum_gather_results[18] ) assert ( "test1,species,0.0885520542481053,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.05701254275940707,444000" in sum_gather_results[19] ) assert ( "test1,species,0.08815317112086159,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli,md5,test1.sig,0.05815279361459521,442000" in sum_gather_results[20] ) assert ( "test1,species,0.027522935779816515,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola vulgatus,md5,test1.sig,0.015637726014008795,138000" in sum_gather_results[21] ) assert ( "test1,species,0.7957718388512166,unclassified,md5,test1.sig,0.8691969376119889,3990000" in sum_gather_results[22] ) def test_metagenome_summary_csv_out_empty_gather_force(runtmp): # test multiple -g, empty -g file, and --force g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" sum_csv = csv_base + ".summarized.csv" csvout = runtmp.output(sum_csv) outdir = os.path.dirname(csvout) gather_empty = runtmp.output("g.csv") with open(gather_empty, "w") as fp: fp.write("") print("g_csv: ", gather_empty) runtmp.run_sourmash( "tax", "metagenome", "--gather-csv", g_csv, "-g", gather_empty, "--taxonomy-csv", tax, "-o", csv_base, "--output-dir", outdir, "-f", ) sum_gather_results = [x.rstrip() for x in Path(csvout).read_text().splitlines()] assert f"saving 'csv_summary' output to '{csvout}'" in runtmp.last_result.err assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in sum_gather_results[0] ) assert ( "test1,superkingdom,0.2042281611487834,d__Bacteria,md5,test1.sig,0.13080306238801107,1024000" in sum_gather_results[1] ) def test_metagenome_kreport_out(runtmp): # test 'kreport' kraken output format g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" sum_csv = csv_base + ".kreport.txt" csvout = runtmp.output(sum_csv) outdir = os.path.dirname(csvout) runtmp.run_sourmash( "tax", "metagenome", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-dir", outdir, "-F", "kreport", ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert os.path.exists(csvout) kreport_results = [ x.rstrip().split("\t") for x in Path(csvout).read_text().splitlines() ] assert f"saving 'kreport' output to '{csvout}'" in runtmp.last_result.err print(kreport_results) assert ["13.08", "1605999", "0", "D", "", "d__Bacteria"] == kreport_results[0] assert [ "86.92", "10672000", "10672000", "U", "", "unclassified", ] == kreport_results[1] assert ["7.27", "892000", "0", "P", "", "p__Bacteroidota"] == kreport_results[2] assert ["5.82", "714000", "0", "P", "", "p__Proteobacteria"] == kreport_results[3] assert ["7.27", "892000", "0", "C", "", "c__Bacteroidia"] == kreport_results[4] assert [ "5.82", "714000", "0", "C", "", "c__Gammaproteobacteria", ] == kreport_results[5] assert ["7.27", "892000", "0", "O", "", "o__Bacteroidales"] == kreport_results[6] assert ["5.82", "714000", "0", "O", "", "o__Enterobacterales"] == kreport_results[7] assert ["7.27", "892000", "0", "F", "", "f__Bacteroidaceae"] == kreport_results[8] assert ["5.82", "714000", "0", "F", "", "f__Enterobacteriaceae"] == kreport_results[ 9 ] assert ["5.70", "700000", "0", "G", "", "g__Prevotella"] == kreport_results[10] assert ["5.82", "714000", "0", "G", "", "g__Escherichia"] == kreport_results[11] assert ["1.56", "192000", "0", "G", "", "g__Phocaeicola"] == kreport_results[12] assert [ "5.70", "700000", "700000", "S", "", "s__Prevotella copri", ] == kreport_results[13] assert [ "5.82", "714000", "714000", "S", "", "s__Escherichia coli", ] == kreport_results[14] assert [ "1.56", "192000", "192000", "S", "", "s__Phocaeicola vulgatus", ] == kreport_results[15] def test_metagenome_kreport_ncbi_taxid_out(runtmp): # test NCBI taxid output from kreport g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.ncbi-taxonomy.csv") csv_base = "out" sum_csv = csv_base + ".kreport.txt" csvout = runtmp.output(sum_csv) outdir = os.path.dirname(csvout) runtmp.run_sourmash( "tax", "metagenome", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-dir", outdir, "-F", "kreport", ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert os.path.exists(csvout) kreport_results = [ x.rstrip().split("\t") for x in Path(csvout).read_text().splitlines() ] assert f"saving 'kreport' output to '{csvout}'" in runtmp.last_result.err print(kreport_results) assert ["13.08", "1605999", "0", "D", "2", "Bacteria"] == kreport_results[0] assert [ "86.92", "10672000", "10672000", "U", "", "unclassified", ] == kreport_results[1] assert ["7.27", "892000", "0", "P", "976", "Bacteroidota"] == kreport_results[2] assert ["5.82", "714000", "0", "P", "1224", "Pseudomonadota"] == kreport_results[3] assert ["7.27", "892000", "0", "C", "200643", "Bacteroidia"] == kreport_results[4] assert [ "5.82", "714000", "0", "C", "1236", "Gammaproteobacteria", ] == kreport_results[5] assert ["7.27", "892000", "0", "O", "171549", "Bacteroidales"] == kreport_results[6] assert ["5.82", "714000", "0", "O", "91347", "Enterobacterales"] == kreport_results[ 7 ] assert ["5.70", "700000", "0", "F", "171552", "Prevotellaceae"] == kreport_results[ 8 ] assert ["5.82", "714000", "0", "F", "543", "Enterobacteriaceae"] == kreport_results[ 9 ] assert ["1.56", "192000", "0", "F", "815", "Bacteroidaceae"] == kreport_results[10] assert ["5.70", "700000", "0", "G", "838", "Prevotella"] == kreport_results[11] assert ["5.82", "714000", "0", "G", "561", "Escherichia"] == kreport_results[12] assert ["1.56", "192000", "0", "G", "909656", "Phocaeicola"] == kreport_results[13] assert [ "5.70", "700000", "700000", "S", "165179", "Prevotella copri", ] == kreport_results[14] assert [ "5.82", "714000", "714000", "S", "562", "Escherichia coli", ] == kreport_results[15] assert [ "1.56", "192000", "192000", "S", "821", "Phocaeicola vulgatus", ] == kreport_results[16] def test_metagenome_kreport_out_lemonade(runtmp): # test 'kreport' kraken output format against lemonade output g_csv = utils.get_test_data("tax/lemonade-MAG3.x.gtdb.csv") tax = utils.get_test_data("tax/lemonade-MAG3.x.gtdb.matches.tax.csv") csv_base = "out" sum_csv = csv_base + ".kreport.txt" csvout = runtmp.output(sum_csv) outdir = os.path.dirname(csvout) runtmp.run_sourmash( "tax", "metagenome", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-dir", outdir, "-F", "kreport", ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert os.path.exists(csvout) kreport_results = [ x.rstrip().split("\t") for x in Path(csvout).read_text().splitlines() ] assert f"saving 'kreport' output to '{csvout}'" in runtmp.last_result.err print(kreport_results) assert ["5.35", "116000", "0", "D", "", "d__Bacteria"] == kreport_results[0] assert ["94.65", "2054000", "2054000", "U", "", "unclassified"] == kreport_results[ 1 ] assert ["5.35", "116000", "0", "P", "", "p__Bacteroidota"] == kreport_results[2] assert ["5.35", "116000", "0", "C", "", "c__Chlorobia"] == kreport_results[3] assert ["5.35", "116000", "0", "O", "", "o__Chlorobiales"] == kreport_results[4] assert ["5.35", "116000", "0", "F", "", "f__Chlorobiaceae"] == kreport_results[5] assert ["5.35", "116000", "0", "G", "", "g__Prosthecochloris"] == kreport_results[6] assert [ "5.35", "116000", "116000", "S", "", "s__Prosthecochloris vibrioformis", ] == kreport_results[7] def test_metagenome_kreport_out_fail(runtmp): # kreport cannot be generated with gather results from < v4.5.0 g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" sum_csv = csv_base + ".kreport.txt" csvout = runtmp.output(sum_csv) outdir = os.path.dirname(csvout) with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "metagenome", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-dir", outdir, "-F", "kreport", ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( "ERROR: cannot produce 'kreport' format from gather results before sourmash v4.5.0" in runtmp.last_result.err ) def test_metagenome_bioboxes_stdout(runtmp): # test CAMI bioboxes format output g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.ncbi-taxonomy.csv") runtmp.run_sourmash( "tax", "metagenome", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-F", "bioboxes", ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert "# Taxonomic Profiling Output" in runtmp.last_result.out assert "@SampleID:test1" in runtmp.last_result.out assert "@Version:0.10.0" in runtmp.last_result.out assert ( "@Ranks:superkingdom|phylum|class|order|family|genus|species|strain" in runtmp.last_result.out ) assert "@__program__:sourmash" in runtmp.last_result.out assert "2 superkingdom 2 Bacteria 13.08" in runtmp.last_result.out assert ( "976 phylum 2|976 Bacteria|Bacteroidota 7.27" in runtmp.last_result.out ) assert ( "1224 phylum 2|1224 Bacteria|Pseudomonadota 5.82" in runtmp.last_result.out ) assert ( "200643 class 2|976|200643 Bacteria|Bacteroidota|Bacteroidia 7.27" in runtmp.last_result.out ) assert ( "1236 class 2|1224|1236 Bacteria|Pseudomonadota|Gammaproteobacteria 5.82" in runtmp.last_result.out ) assert ( "171549 order 2|976|200643|171549 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales 7.27" in runtmp.last_result.out ) assert ( "91347 order 2|1224|1236|91347 Bacteria|Pseudomonadota|Gammaproteobacteria|Enterobacterales 5.82" in runtmp.last_result.out ) assert ( "171552 family 2|976|200643|171549|171552 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Prevotellaceae 5.70" in runtmp.last_result.out ) assert ( "543 family 2|1224|1236|91347|543 Bacteria|Pseudomonadota|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae 5.82" in runtmp.last_result.out ) assert ( "815 family 2|976|200643|171549|815 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Bacteroidaceae 1.56" in runtmp.last_result.out ) assert ( "838 genus 2|976|200643|171549|171552|838 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Prevotellaceae|Prevotella 5.70" in runtmp.last_result.out ) assert ( "561 genus 2|1224|1236|91347|543|561 Bacteria|Pseudomonadota|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae|Escherichia 5.82" in runtmp.last_result.out ) assert ( "909656 genus 2|976|200643|171549|815|909656 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Bacteroidaceae|Phocaeicola 1.56" in runtmp.last_result.out ) assert ( "165179 species 2|976|200643|171549|171552|838|165179 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Prevotellaceae|Prevotella|Prevotella copri 5.70" in runtmp.last_result.out ) assert ( "562 species 2|1224|1236|91347|543|561|562 Bacteria|Pseudomonadota|Gammaproteobacteria|Enterobacterales|Enterobacteriaceae|Escherichia|Escherichia coli 5.82" in runtmp.last_result.out ) assert ( "821 species 2|976|200643|171549|815|909656|821 Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Bacteroidaceae|Phocaeicola|Phocaeicola vulgatus 1.56" in runtmp.last_result.out ) def test_metagenome_bioboxes_outfile(runtmp): # test CAMI bioboxes format output g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.ncbi-taxonomy.csv") csv_base = "out" sum_csv = csv_base + ".bioboxes.profile" csvout = runtmp.output(sum_csv) outdir = os.path.dirname(csvout) runtmp.run_sourmash( "tax", "metagenome", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-F", "bioboxes", "-o", csv_base, "--output-dir", outdir, ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 bb_results = [x.rstrip().split("\t") for x in Path(csvout).read_text().splitlines()] assert f"saving 'bioboxes' output to '{csvout}'" in runtmp.last_result.err print(bb_results) assert ["# Taxonomic Profiling Output"] == bb_results[0] assert ["@SampleID:test1"] == bb_results[1] assert ["2", "superkingdom", "2", "Bacteria", "13.08"] == bb_results[6] assert [ "838", "genus", "2|976|200643|171549|171552|838", "Bacteria|Bacteroidota|Bacteroidia|Bacteroidales|Prevotellaceae|Prevotella", "5.70", ] == bb_results[16] def test_metagenome_krona_tsv_out(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" kr_csv = csv_base + ".krona.tsv" csvout = runtmp.output(kr_csv) outdir = os.path.dirname(csvout) print("csvout: ", csvout) runtmp.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-format", "krona", "--rank", "genus", "--output-dir", outdir, ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert os.path.exists(csvout) assert f"saving 'krona' output to '{csvout}'" in runtmp.last_result.err gn_krona_results = [ x.rstrip().split("\t") for x in Path(csvout).read_text().splitlines() ] print("species krona results: \n", gn_krona_results) assert [ "fraction", "superkingdom", "phylum", "class", "order", "family", "genus", ] == gn_krona_results[0] assert [ "0.0885520542481053", "d__Bacteria", "p__Bacteroidota", "c__Bacteroidia", "o__Bacteroidales", "f__Bacteroidaceae", "g__Prevotella", ] == gn_krona_results[1] assert [ "0.08815317112086159", "d__Bacteria", "p__Proteobacteria", "c__Gammaproteobacteria", "o__Enterobacterales", "f__Enterobacteriaceae", "g__Escherichia", ] == gn_krona_results[2] assert [ "0.027522935779816515", "d__Bacteria", "p__Bacteroidota", "c__Bacteroidia", "o__Bacteroidales", "f__Bacteroidaceae", "g__Phocaeicola", ] == gn_krona_results[3] assert [ "0.7957718388512166", "unclassified", "unclassified", "unclassified", "unclassified", "unclassified", "unclassified", ] == gn_krona_results[4] def test_metagenome_lineage_summary_out(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" lin_csv = csv_base + ".lineage_summary.tsv" csvout = runtmp.output(lin_csv) outdir = os.path.dirname(csvout) print("csvout: ", csvout) runtmp.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-format", "lineage_summary", "--rank", "genus", "--output-dir", outdir, ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert os.path.exists(csvout) assert f"saving 'lineage_summary' output to '{csvout}'" in runtmp.last_result.err gn_lineage_summary = [ x.rstrip().split("\t") for x in Path(csvout).read_text().splitlines() ] print("species lineage summary results: \n", gn_lineage_summary) assert ["lineage", "test1"] == gn_lineage_summary[0] assert [ "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola", "0.027522935779816515", ] == gn_lineage_summary[1] assert [ "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella", "0.0885520542481053", ] == gn_lineage_summary[2] assert [ "d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia", "0.08815317112086159", ] == gn_lineage_summary[3] assert ["unclassified", "0.7957718388512166"] == gn_lineage_summary[4] def test_metagenome_human_format_out(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" csvout = runtmp.output(csv_base + ".human.txt") outdir = os.path.dirname(csvout) print("csvout: ", csvout) runtmp.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-format", "human", "--rank", "genus", "--output-dir", outdir, ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert os.path.exists(csvout) assert f"saving 'human' output to '{csvout}'" in runtmp.last_result.err with open(csvout) as fp: outp = fp.readlines() assert len(outp) == 6 outp = [x.strip() for x in outp] print(outp) assert outp[0] == "sample name proportion cANI lineage" assert outp[1] == "----------- ---------- ---- -------" assert outp[2] == "test1 86.9% - unclassified" assert ( outp[3] == "test1 5.8% 92.5% d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia" ) assert ( outp[4] == "test1 5.7% 92.5% d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella" ) assert ( outp[5] == "test1 1.6% 89.1% d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola" ) def test_metagenome_no_taxonomy_fail(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash("tax", "metagenome", "-g", g_csv) assert "error: the following arguments are required: -t/--taxonomy-csv" in str( exc.value ) def test_metagenome_no_rank_lineage_summary(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-format", "lineage_summary", ) print(str(exc.value)) assert ( "Rank (--rank) is required for krona, lineage_summary output formats." in str(exc.value) ) def test_metagenome_no_rank_krona(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-format", "krona", ) print(str(exc.value)) assert ( "Rank (--rank) is required for krona, lineage_summary output formats." in str(exc.value) ) def test_metagenome_bad_rank_krona(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-format", "krona", "--rank", "NotARank", ) print(str(exc.value)) assert ( "Invalid '--rank'/'--position' input: 'NotARank'. Please choose: 'strain', 'species', 'genus', 'family', 'order', 'class', 'phylum', 'superkingdom'" in runtmp.last_result.err ) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-format", "krona", "--rank", "5", ) print(str(exc.value)) assert ( "Invalid '--rank'/'--position' input: '5'. Please choose: 'strain', 'species', 'genus', 'family', 'order', 'class', 'phylum', 'superkingdom'" in runtmp.last_result.err ) def test_metagenome_ictv(runtmp): # test basic metagenome c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.ictv-taxonomy.csv") c.run_sourmash("tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--ictv") print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 print(c.last_result.out) assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,realm,0.204,Riboviria,md5,test1.sig,0.131,1024000,0.950,0" in c.last_result.out ) assert ( "test1,realm,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) assert ( "test1,kingdom,0.204,Riboviria;;Orthornavirae,md5,test1.sig,0.131,1024000,0.950,0" in c.last_result.out ) assert ( "test1,kingdom,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) assert ( "test1,phylum,0.204,Riboviria;;Orthornavirae;;Negarnaviricota,md5,test1.sig,0.131,1024000,0.950,0" in c.last_result.out ) assert ( "test1,phylum,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) assert ( "test1,subphylum,0.204,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina,md5,test1.sig,0.131,1024000,0.950,0" in c.last_result.out ) assert ( "test1,subphylum,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) assert ( "test1,class,0.204,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes,md5,test1.sig,0.131,1024000,0.950,0" in c.last_result.out ) assert ( "test1,class,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) assert ( "test1,order,0.204,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales,md5,test1.sig,0.131,1024000,0.950,0" in c.last_result.out ) assert ( "test1,order,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) assert ( "test1,family,0.204,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae,md5,test1.sig,0.131,1024000,0.950,0" in c.last_result.out ) assert ( "test1,family,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) assert ( "test1,genus,0.204,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus,md5,test1.sig,0.131,1024000,0.950,0" in c.last_result.out ) assert ( "test1,genus,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) assert ( "test1,species,0.088,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus bundibugyoense,md5,test1.sig,0.058,442000,0.925,0" in c.last_result.out ) assert ( "test1,species,0.078,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus taiense,md5,test1.sig,0.050,390000,0.921,0" in c.last_result.out ) assert ( "test1,species,0.028,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus bombaliense,md5,test1.sig,0.016,138000,0.891,0" in c.last_result.out ) assert ( "test1,species,0.011,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus restonense,md5,test1.sig,0.007,54000,0.864,0" in c.last_result.out ) assert ( "test1,species,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) assert ( "test1,name,0.088,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus bundibugyoense;Bundibugyo virus,md5,test1.sig,0.058,442000,0.925,0" in c.last_result.out ) assert ( "test1,name,0.078,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus taiense;Taï Forest virus,md5,test1.sig,0.050,390000,0.921,0" in c.last_result.out ) assert ( "test1,name,0.028,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus bombaliense;Bombali virus,md5,test1.sig,0.016,138000,0.891,0" in c.last_result.out ) assert ( "test1,name,0.011,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus restonense;Reston virus,md5,test1.sig,0.007,54000,0.864,0" in c.last_result.out ) assert ( "test1,name,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) def test_genome_no_rank_krona(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "-o", csv_base, "--output-format", "krona", ) assert "ERROR: Rank (--rank) is required for krona output formats" in str(exc.value) def test_metagenome_rank_not_available(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--rank", "strain" ) print(str(exc.value)) assert c.last_result.status == -1 assert ( "No taxonomic information provided for rank strain: cannot summarize at this rank" in str(exc.value) ) def test_metagenome_duplicated_taxonomy_fail(runtmp): c = runtmp # write temp taxonomy with duplicates taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") duplicated_csv = runtmp.output("duplicated_taxonomy.csv") with open(duplicated_csv, "w") as dup: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax.append(tax[1] + "FOO") # add first tax_assign again dup.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", duplicated_csv ) assert "cannot read taxonomy" in str(exc.value) assert "multiple lineages for identifier GCF_001881345" in str(exc.value) def test_metagenome_duplicated_taxonomy_force(runtmp): c = runtmp # write temp taxonomy with duplicates taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") duplicated_csv = runtmp.output("duplicated_taxonomy.csv") with open(duplicated_csv, "w") as dup: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax.append(tax[1]) # add first tax_assign again dup.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", duplicated_csv, "--force" ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) # same as stdout test - just check the first few lines assert c.last_result.status == 0 assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,superkingdom,0.204,d__Bacteria,md5,test1.sig,0.131,1024000" in c.last_result.out ) assert ( "test1,superkingdom,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) assert ( "test1,phylum,0.116,d__Bacteria;p__Bacteroidota,md5,test1.sig,0.073,582000" in c.last_result.out ) assert ( "test1,phylum,0.088,d__Bacteria;p__Proteobacteria,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,phylum,0.796,unclassified,md5,test1.sig,0.869,3990000" in c.last_result.out ) def test_metagenome_missing_taxonomy(runtmp): c = runtmp # write temp taxonomy with missing entry taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") subset_csv = runtmp.output("subset_taxonomy.csv") with open(subset_csv, "w") as subset: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] subset.write("\n".join(tax[:4])) g_csv = utils.get_test_data("tax/test1.gather.csv") c.run_sourmash("tax", "metagenome", "-g", g_csv, "--taxonomy-csv", subset_csv) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "The following are missing from the taxonomy information: GCF_003471795" in c.last_result.err ) assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,superkingdom,0.193,d__Bacteria,md5,test1.sig,0.124,970000" in c.last_result.out ) assert ( "test1,superkingdom,0.807,unclassified,md5,test1.sig,0.876,4044000" in c.last_result.out ) assert ( "test1,phylum,0.105,d__Bacteria;p__Bacteroidota,md5,test1.sig,0.066,528000" in c.last_result.out ) assert ( "test1,phylum,0.088,d__Bacteria;p__Proteobacteria,md5,test1.sig,0.058,442000" in c.last_result.out ) assert ( "test1,phylum,0.807,unclassified,md5,test1.sig,0.876,4044000" in c.last_result.out ) assert ( "test1,class,0.105,d__Bacteria;p__Bacteroidota;c__Bacteroidia,md5,test1.sig,0.066,528000" in c.last_result.out ) def test_metagenome_missing_fail_taxonomy(runtmp): c = runtmp # write temp taxonomy with missing entry taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") subset_csv = runtmp.output("subset_taxonomy.csv") with open(subset_csv, "w") as subset: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] subset.write("\n".join(tax[:4])) g_csv = utils.get_test_data("tax/test1.gather.csv") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", subset_csv, "--fail-on-missing-taxonomy", ) print(str(exc.value)) assert "ident 'GCF_003471795' is not in the taxonomy database." in str(exc.value) assert "Failing, as requested via --fail-on-missing-taxonomy" in str(exc.value) assert c.last_result.status == -1 def test_metagenome_multiple_taxonomy_files_missing(runtmp): c = runtmp # write temp taxonomy with duplicates taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") # gather against mult databases g_csv = utils.get_test_data("tax/test1_x_gtdbrs202_genbank_euks.gather.csv") c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", taxonomy_csv, "--force" ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert ( "of 6 gather results, lineage assignments for 2 results were missed" in c.last_result.err ) assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "multtest,superkingdom,0.204,d__Bacteria,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.131,1024000" in c.last_result.out ) assert ( "multtest,superkingdom,0.796,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.869,3990000" in c.last_result.out ) assert ( "multtest,phylum,0.116,d__Bacteria;p__Bacteroidota,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.073,582000" in c.last_result.out ) assert ( "multtest,phylum,0.088,d__Bacteria;p__Proteobacteria,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.058,442000" in c.last_result.out ) assert ( "multtest,phylum,0.796,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.869,3990000" in c.last_result.out ) assert ( "multtest,class,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.073,582000" in c.last_result.out ) assert ( "multtest,class,0.088,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.058,442000" in c.last_result.out ) assert ( "multtest,class,0.796,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.869,3990000" in c.last_result.out ) def test_metagenome_multiple_taxonomy_files(runtmp): c = runtmp # write temp taxonomy with duplicates taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") protozoa_genbank = utils.get_test_data("tax/protozoa_genbank_lineage.csv") bacteria_refseq = utils.get_test_data("tax/bacteria_refseq_lineage.csv") # gather against mult databases g_csv = utils.get_test_data("tax/test1_x_gtdbrs202_genbank_euks.gather.csv") c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", taxonomy_csv, protozoa_genbank, bacteria_refseq, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "multtest,superkingdom,0.204,Bacteria,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.131,1024000" in c.last_result.out ) assert ( "multtest,superkingdom,0.051,Eukaryota,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.245,258000" in c.last_result.out ) assert ( "multtest,superkingdom,0.744,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.624,3732000" in c.last_result.out ) assert ( "multtest,phylum,0.116,Bacteria;Bacteroidetes,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.073,582000" in c.last_result.out ) assert ( "multtest,phylum,0.088,Bacteria;Proteobacteria,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.058,442000" in c.last_result.out ) assert ( "multtest,phylum,0.051,Eukaryota;Apicomplexa,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.245,258000" in c.last_result.out ) assert ( "multtest,phylum,0.744,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.624,3732000" in c.last_result.out ) assert ( "multtest,class,0.116,Bacteria;Bacteroidetes;Bacteroidia,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.073,582000" in c.last_result.out ) def test_metagenome_multiple_taxonomy_files_multiple_taxonomy_args(runtmp): c = runtmp # pass in mult tax files using mult tax arguments taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") protozoa_genbank = utils.get_test_data("tax/protozoa_genbank_lineage.csv") bacteria_refseq = utils.get_test_data("tax/bacteria_refseq_lineage.csv") # gather against mult databases g_csv = utils.get_test_data("tax/test1_x_gtdbrs202_genbank_euks.gather.csv") c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", taxonomy_csv, "-t", protozoa_genbank, "-t", bacteria_refseq, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "multtest,superkingdom,0.204,Bacteria,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.131,1024000" in c.last_result.out ) assert ( "multtest,superkingdom,0.051,Eukaryota,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.245,258000" in c.last_result.out ) assert ( "multtest,superkingdom,0.744,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.624,3732000" in c.last_result.out ) assert ( "multtest,phylum,0.116,Bacteria;Bacteroidetes,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.073,582000" in c.last_result.out ) assert ( "multtest,phylum,0.088,Bacteria;Proteobacteria,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.058,442000" in c.last_result.out ) assert ( "multtest,phylum,0.051,Eukaryota;Apicomplexa,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.245,258000" in c.last_result.out ) assert ( "multtest,phylum,0.744,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.624,3732000" in c.last_result.out ) assert ( "multtest,class,0.116,Bacteria;Bacteroidetes;Bacteroidia,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.073,582000" in c.last_result.out ) def test_metagenome_multiple_taxonomy_files_multiple_taxonomy_args_empty_force(runtmp): # pass in mult tax files using mult tax arguments, with one empty, # and use --force c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") protozoa_genbank = utils.get_test_data("tax/protozoa_genbank_lineage.csv") bacteria_refseq = utils.get_test_data("tax/bacteria_refseq_lineage.csv") tax_empty = runtmp.output("t.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") with open(tax_empty, "w") as fp: fp.write("") print("t_csv: ", tax_empty) # gather against mult databases g_csv = utils.get_test_data("tax/test1_x_gtdbrs202_genbank_euks.gather.csv") c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", taxonomy_csv, "-t", protozoa_genbank, "-t", bacteria_refseq, "-t", tax_empty, "--force", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "multtest,superkingdom,0.204,Bacteria,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.131,1024000" in c.last_result.out ) assert ( "multtest,superkingdom,0.051,Eukaryota,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.245,258000" in c.last_result.out ) assert ( "multtest,superkingdom,0.744,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.624,3732000" in c.last_result.out ) assert ( "multtest,phylum,0.116,Bacteria;Bacteroidetes,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.073,582000" in c.last_result.out ) assert ( "multtest,phylum,0.088,Bacteria;Proteobacteria,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.058,442000" in c.last_result.out ) assert ( "multtest,phylum,0.051,Eukaryota;Apicomplexa,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.245,258000" in c.last_result.out ) assert ( "multtest,phylum,0.744,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.624,3732000" in c.last_result.out ) assert ( "multtest,class,0.116,Bacteria;Bacteroidetes;Bacteroidia,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.073,582000" in c.last_result.out ) def test_metagenome_empty_gather_results(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") # creates empty gather result g_csv = runtmp.output("g.csv") with open(g_csv, "w") as fp: fp.write("") print("g_csv: ", g_csv) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash("tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax) assert f"Cannot read gather results from '{g_csv}'. Is file empty?" in str( exc.value ) assert runtmp.last_result.status == -1 def test_metagenome_bad_gather_header(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") bad_g_csv = runtmp.output("g.csv") # creates bad gather result bad_g = [ x.replace("query_bp", "nope") + "\n" for x in Path(g_csv).read_text().splitlines() ] with open(bad_g_csv, "w") as fp: fp.writelines(bad_g) print("bad_gather_results: \n", bad_g) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash("tax", "metagenome", "-g", bad_g_csv, "--taxonomy-csv", tax) print(str(exc.value)) assert "is missing columns needed for taxonomic summarization." in str(exc.value) assert runtmp.last_result.status == -1 def test_metagenome_empty_tax_lineage_input(runtmp): # test an empty tax CSV tax_empty = runtmp.output("t.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") with open(tax_empty, "w") as fp: fp.write("") print("t_csv: ", tax_empty) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax_empty ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status != 0 assert "cannot read taxonomy assignments from" in str(exc.value) def test_metagenome_empty_tax_lineage_input_force(runtmp): # test an empty tax CSV with --force tax_empty = runtmp.output("t.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") with open(tax_empty, "w") as fp: fp.write("") print("t_csv: ", tax_empty) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax_empty, "--force" ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status != 0 assert "ERROR: No taxonomic assignments loaded" in str(exc.value) def test_metagenome_perfect_match_warning(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") perfect_g_csv = runtmp.output("g.csv") # create a perfect gather result with open(g_csv) as fp: r = csv.DictReader(fp, delimiter=",") header = r.fieldnames print(header) with open(perfect_g_csv, "w") as out_fp: w = csv.DictWriter(out_fp, header) w.writeheader() for n, row in enumerate(r): if n == 0: # make a perfect match row["f_unique_to_query"] = 1.0 else: # set the rest to 0 row["f_unique_to_query"] = 0.0 w.writerow(row) print(row) runtmp.run_sourmash("tax", "metagenome", "-g", perfect_g_csv, "--taxonomy-csv", tax) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert ( "WARNING: 100% match! Is query 'test1' identical to its database match, 'GCF_001881345'?" in runtmp.last_result.err ) def test_metagenome_over100percent_error(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") perfect_g_csv = runtmp.output("g.csv") # create a perfect gather result with open(g_csv) as fp: r = csv.DictReader(fp, delimiter=",") header = r.fieldnames print(header) with open(perfect_g_csv, "w") as out_fp: w = csv.DictWriter(out_fp, header) w.writeheader() for n, row in enumerate(r): if n == 0: row["f_unique_to_query"] = 1.0 # let the rest stay as they are (should be > 100% match now) w.writerow(row) print(row) with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "metagenome", "-g", perfect_g_csv, "--taxonomy-csv", tax ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == -1 assert ( "fraction is > 100% of the query! This should not be possible." in runtmp.last_result.err ) def test_metagenome_gather_duplicate_query(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # different filename, contents identical to test1 g_res2 = runtmp.output("test2.gather.csv") with open(g_res2, "w") as fp: fp.write(Path(g_res).read_text()) with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "metagenome", "--gather-csv", g_res, g_res2, "--taxonomy-csv", taxonomy_csv, ) assert c.last_result.status == -1 print(str(exc.value)) assert ( "Gather query test1 was found in more than one CSV. Cannot load from " in str(exc.value) ) def test_metagenome_gather_duplicate_query_force(runtmp): # do not load same query from multiple files. c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # different filename, contents identical to test1 g_res2 = runtmp.output("test2.gather.csv") with open(g_res2, "w") as fp: fp.write(Path(g_res).read_text()) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "metagenome", "--gather-csv", g_res, g_res2, "--taxonomy-csv", taxonomy_csv, "--force", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == -1 assert "Gather query test1 was found in more than one CSV." in c.last_result.err assert "Cannot force past duplicated gather query. Exiting." in c.last_result.err def test_metagenome_two_queries_human_output(runtmp): # do not load same query from multiple files. c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # make a second query with same output g_res2 = runtmp.output("test2.gather.csv") with open(g_res2, "w") as fp: for line in Path(g_res).read_text().splitlines(): line = line.replace("test1", "test2") + "\n" fp.write(line) c.run_sourmash( "tax", "metagenome", "--gather-csv", g_res, g_res2, "--taxonomy-csv", taxonomy_csv, "-F", "human", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "test1 86.9% - unclassified" in c.last_result.out assert ( "test1 5.8% 92.5% d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli" in c.last_result.out ) assert "test2 86.9% - unclassified" in c.last_result.out assert ( "test2 5.8% 92.5% d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli" in c.last_result.out ) assert "test2 5.7% 92.5% d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri" assert "test2 1.6% 89.1% d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola vulgatus" def test_metagenome_two_queries_csv_summary_output(runtmp): # remove single-query outputs when working with multiple queries c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # make a second query with same output g_res2 = runtmp.output("test2.gather.csv") with open(g_res2, "w") as fp: for line in Path(g_res).read_text().splitlines(): line = line.replace("test1", "test2") + "\n" fp.write(line) csv_summary_out = runtmp.output("tst.summarized.csv") c.run_sourmash( "tax", "metagenome", "--gather-csv", g_res, g_res2, "--taxonomy-csv", taxonomy_csv, "-F", "csv_summary", "--rank", "phylum", "-o", "tst", ) assert os.path.exists(csv_summary_out) assert c.last_result.status == 0 assert "loaded results for 2 queries from 2 gather CSVs" in c.last_result.err assert ( f"saving 'csv_summary' output to '{os.path.basename(csv_summary_out)}'" in runtmp.last_result.err ) sum_gather_results = [ x.rstrip() for x in Path(csv_summary_out).read_text().splitlines() ] assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in sum_gather_results[0] ) # check both queries exist in csv_summary results; check several assert ( "test1,superkingdom,0.2042281611487834,d__Bacteria,md5,test1.sig,0.13080306238801107,1024000,0.9500482567175479,0" in sum_gather_results[1] ) assert ( "test2,superkingdom,0.2042281611487834,d__Bacteria,md5,test2.sig,0.13080306238801107,1024000,0.9500482567175479,0" in sum_gather_results[23] ) assert ( "test2,phylum,0.11607499002792182,d__Bacteria;p__Bacteroidota,md5,test2.sig,0.07265026877341586,582000" in sum_gather_results[25] ) assert ( "test2,phylum,0.08815317112086159,d__Bacteria;p__Proteobacteria,md5,test2.sig,0.05815279361459521,442000" in sum_gather_results[26] ) assert ( "test2,phylum,0.7957718388512166,unclassified,md5,test2.sig,0.8691969376119889,3990000" in sum_gather_results[27] ) assert ( "test2,class,0.11607499002792182,d__Bacteria;p__Bacteroidota;c__Bacteroidia,md5,test2.sig,0.07265026877341586,582000" in sum_gather_results[28] ) assert ( "test2,class,0.08815317112086159,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria,md5,test2.sig,0.05815279361459521,442000" in sum_gather_results[29] ) assert ( "test2,class,0.7957718388512166,unclassified,md5,test2.sig,0.8691969376119889,3990000" in sum_gather_results[30] ) assert ( "test2,order,0.11607499002792182,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales,md5,test2.sig,0.07265026877341586,582000" in sum_gather_results[31] ) assert ( "test2,order,0.08815317112086159,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales,md5,test2.sig,0.05815279361459521,442000" in sum_gather_results[32] ) assert ( "test2,order,0.7957718388512166,unclassified,md5,test2.sig,0.8691969376119889,3990000" in sum_gather_results[33] ) assert ( "test2,family,0.11607499002792182,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae,md5,test2.sig,0.07265026877341586,582000" in sum_gather_results[34] ) assert ( "test2,family,0.08815317112086159,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae,md5,test2.sig,0.05815279361459521,442000" in sum_gather_results[35] ) assert ( "test2,family,0.7957718388512166,unclassified,md5,test2.sig,0.8691969376119889,3990000" in sum_gather_results[36] ) assert ( "test2,genus,0.0885520542481053,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella,md5,test2.sig,0.05701254275940707,444000" in sum_gather_results[37] ) assert ( "test2,genus,0.08815317112086159,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia,md5,test2.sig,0.05815279361459521,442000" in sum_gather_results[38] ) assert ( "test2,genus,0.027522935779816515,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola,md5,test2.sig,0.015637726014008795,138000" in sum_gather_results[39] ) assert ( "test2,genus,0.7957718388512166,unclassified,md5,test2.sig,0.8691969376119889,3990000" in sum_gather_results[40] ) assert ( "test2,species,0.0885520542481053,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test2.sig,0.05701254275940707,444000" in sum_gather_results[41] ) assert ( "test2,species,0.08815317112086159,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli,md5,test2.sig,0.05815279361459521,442000" in sum_gather_results[42] ) assert ( "test2,species,0.027522935779816515,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola vulgatus,md5,test2.sig,0.015637726014008795,138000" in sum_gather_results[43] ) def test_metagenome_two_queries_with_single_query_output_formats_fail(runtmp): # fail on multiple queries with single query output formats c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # make a second query with same output g_res2 = runtmp.output("test2.gather.csv") with open(g_res2, "w") as fp: for line in Path(g_res).read_text().splitlines(): line = line.replace("test1", "test2") + "\n" fp.write(line) runtmp.output("tst.summarized.csv") bioboxes_out = runtmp.output("tst.bioboxes.out") kreport_out = runtmp.output("tst.kreport.txt") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "metagenome", "--gather-csv", g_res, g_res2, "--taxonomy-csv", taxonomy_csv, "-F", "bioboxes", "kreport", "--rank", "phylum", "-o", "tst", ) print(str(exc.value)) assert not os.path.exists(bioboxes_out) assert not os.path.exists(kreport_out) assert c.last_result.status == -1 assert "loaded results for 2 queries from 2 gather CSVs" in c.last_result.err assert ( "WARNING: found results for multiple gather queries. Can only output multi-query result formats: skipping bioboxes, kreport" in c.last_result.err ) assert "ERROR: No output formats remaining." in c.last_result.err def test_metagenome_two_queries_skip_single_query_output_formats(runtmp): # remove single-query outputs when working with multiple queries c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # make a second query with same output g_res2 = runtmp.output("test2.gather.csv") with open(g_res2, "w") as fp: for line in Path(g_res).read_text().splitlines(): line = line.replace("test1", "test2") + "\n" fp.write(line) csv_summary_out = runtmp.output("tst.summarized.csv") kreport_out = runtmp.output("tst.kreport.txt") bioboxes_out = runtmp.output("tst.bioboxes.txt") lineage_summary_out = runtmp.output("tst.lineage_summary.tsv") c.run_sourmash( "tax", "metagenome", "--gather-csv", g_res, g_res2, "--taxonomy-csv", taxonomy_csv, "-F", "csv_summary", "bioboxes", "kreport", "lineage_summary", "--rank", "phylum", "-o", "tst", ) assert not os.path.exists(kreport_out) assert not os.path.exists(bioboxes_out) assert os.path.exists(csv_summary_out) assert os.path.exists(lineage_summary_out) assert c.last_result.status == 0 assert "loaded results for 2 queries from 2 gather CSVs" in c.last_result.err assert ( "WARNING: found results for multiple gather queries. Can only output multi-query result formats: skipping bioboxes, kreport" in c.last_result.err ) assert ( f"saving 'csv_summary' output to '{os.path.basename(csv_summary_out)}'" in runtmp.last_result.err ) sum_gather_results = [ x.rstrip() for x in Path(csv_summary_out).read_text().splitlines() ] assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in sum_gather_results[0] ) # check both queries exist in csv_summary results assert ( "test1,superkingdom,0.2042281611487834,d__Bacteria,md5,test1.sig,0.13080306238801107,1024000,0.9500482567175479,0" in sum_gather_results[1] ) assert ( "test2,superkingdom,0.2042281611487834,d__Bacteria,md5,test2.sig,0.13080306238801107,1024000,0.9500482567175479,0" in sum_gather_results[23] ) def test_metagenome_two_queries_krona(runtmp): # for now, we enable multi-query krona. Is this desired? c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # make a second query with same output g_res2 = runtmp.output("test2.gather.csv") with open(g_res2, "w") as fp: for line in Path(g_res).read_text().splitlines(): line = line.replace("test1", "test2") + "\n" fp.write(line) c.run_sourmash( "tax", "metagenome", "--gather-csv", g_res, g_res2, "--taxonomy-csv", taxonomy_csv, "-F", "krona", "--rank", "superkingdom", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "WARNING: results from more than one query found. Krona summarization not recommended." in c.last_result.err ) assert ( "Percentage assignment will be normalized by the number of queries to maintain range 0-100%" in c.last_result.err ) assert "fraction superkingdom" in c.last_result.out assert "0.2042281611487834 d__Bacteria" in c.last_result.out assert "0.7957718388512166 unclassified" in c.last_result.out def test_metagenome_gather_duplicate_filename(runtmp): # test that a duplicate filename is properly flagged, when passed in # twice to a single -g argument. c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") c.run_sourmash( "tax", "metagenome", "--gather-csv", g_res, g_res, "--taxonomy-csv", taxonomy_csv, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert f"ignoring duplicated reference to file: {g_res}" assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,superkingdom,0.204,d__Bacteria,md5,test1.sig,0.131,1024000" in c.last_result.out ) def test_metagenome_gather_duplicate_filename_2(runtmp): # test that a duplicate filename is properly flagged, with -g a -g b c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") c.run_sourmash( "tax", "metagenome", "--gather-csv", g_res, "-g", g_res, "--taxonomy-csv", taxonomy_csv, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert f"ignoring duplicated reference to file: {g_res}" assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,superkingdom,0.204,d__Bacteria,md5,test1.sig,0.131,1024000" in c.last_result.out ) def test_metagenome_gather_duplicate_filename_from_file(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") g_from_file = runtmp.output("tmp-from-file.txt") with open(g_from_file, "w") as f_csv: f_csv.write(f"{g_res}\n") f_csv.write(f"{g_res}\n") c.run_sourmash( "tax", "metagenome", "--from-file", g_from_file, "--taxonomy-csv", taxonomy_csv ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert f"ignoring duplicated reference to file: {g_res}" assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,superkingdom,0.204,d__Bacteria,md5,test1.sig,0.131,1024000" in c.last_result.out ) def test_genome_empty_gather_results(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") # creates empty gather result g_csv = runtmp.output("g.csv") with open(g_csv, "w") as fp: fp.write("") print("g_csv: ", g_csv) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash("tax", "genome", "-g", g_csv, "--taxonomy-csv", tax) assert runtmp.last_result.status == -1 print(runtmp.last_result.err) print(runtmp.last_result.out) assert f"Cannot read gather results from '{g_csv}'. Is file empty?" in str( exc.value ) def test_genome_bad_gather_header(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") bad_g_csv = runtmp.output("g.csv") # creates bad gather result bad_g = [ x.replace("f_unique_to_query", "nope") + "\n" for x in Path(g_csv).read_text().splitlines() ] with open(bad_g_csv, "w") as fp: fp.writelines(bad_g) print("bad_gather_results: \n", bad_g) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash("tax", "genome", "-g", bad_g_csv, "--taxonomy-csv", tax) assert "is missing columns needed for taxonomic summarization." in str(exc.value) assert runtmp.last_result.status == -1 def test_genome_empty_tax_lineage_input(runtmp): # test an empty tax csv tax_empty = runtmp.output("t.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") with open(tax_empty, "w") as fp: fp.write("") print("t_csv: ", tax_empty) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash("tax", "genome", "-g", g_csv, "--taxonomy-csv", tax_empty) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status != 0 assert "cannot read taxonomy assignments from" in str(exc.value) def test_genome_rank_stdout_0(runtmp): # test basic genome c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") c.run_sourmash( "tax", "genome", "--gather-csv", g_csv, "--taxonomy-csv", tax, "--rank", "species", "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) def test_genome_rank_stdout_0_db(runtmp): # test basic genome with sqlite database c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.db") c.run_sourmash( "tax", "genome", "--gather-csv", g_csv, "--taxonomy-csv", tax, "--rank", "species", "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) # too stringent of containment threshold: c.run_sourmash( "tax", "genome", "--gather-csv", g_csv, "--taxonomy-csv", tax, "--rank", "species", "--containment-threshold", "1.0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "test1,below_threshold,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000," in c.last_result.out ) def test_genome_rank_csv_0(runtmp): # test basic genome - output csv c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" cl_csv = csv_base + ".classifications.csv" csvout = runtmp.output(cl_csv) outdir = os.path.dirname(csvout) print("csvout: ", csvout) c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--rank", "species", "-o", csv_base, "--containment-threshold", "0", "--output-dir", outdir, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert f"saving 'classification' output to '{csvout}'" in runtmp.last_result.err assert c.last_result.status == 0 cl_results = [x.rstrip() for x in Path(csvout).read_text().splitlines()] assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in cl_results[0] ) assert ( "test1,match,species,0.0885520542481053,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.05701254275940707,444000" in cl_results[1] ) def test_genome_rank_krona(runtmp): # test basic genome - output csv c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" cl_csv = csv_base + ".krona.tsv" csvout = runtmp.output(cl_csv) outdir = os.path.dirname(csvout) print("csvout: ", csvout) c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--rank", "species", "-o", csv_base, "--containment-threshold", "0", "--output-format", "krona", "--output-dir", outdir, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert f"saving 'krona' output to '{csvout}'" in runtmp.last_result.err assert c.last_result.status == 0 kr_results = [x.rstrip().split("\t") for x in Path(csvout).read_text().splitlines()] print(kr_results) assert [ "fraction", "superkingdom", "phylum", "class", "order", "family", "genus", "species", ] == kr_results[0] assert [ "0.0885520542481053", "d__Bacteria", "p__Bacteroidota", "c__Bacteroidia", "o__Bacteroidales", "f__Bacteroidaceae", "g__Prevotella", "s__Prevotella copri", ] == kr_results[1] def test_genome_rank_human_output(runtmp): # test basic genome - output csv c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" csvout = runtmp.output(csv_base + ".human.txt") outdir = os.path.dirname(csvout) print("csvout: ", csvout) c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--rank", "species", "-o", csv_base, "--containment-threshold", "0", "--output-format", "human", "--output-dir", outdir, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert f"saving 'human' output to '{csvout}'" in runtmp.last_result.err assert c.last_result.status == 0 with open(csvout) as fp: outp = fp.readlines() print(outp) assert len(outp) == 3 outp = [x.strip() for x in outp] assert outp[0] == "sample name status proportion cANI lineage" assert outp[1] == "----------- ------ ---------- ---- -------" assert ( outp[2] == "test1 match 5.7% 92.5% d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri" ) def test_genome_rank_lineage_csv_output(runtmp): # test basic genome - output csv c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csv_base = "out" csvout = runtmp.output(csv_base + ".lineage.csv") outdir = os.path.dirname(csvout) print("csvout: ", csvout) c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--rank", "species", "-o", csv_base, "--containment-threshold", "0", "--output-format", "lineage_csv", "--output-dir", outdir, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert f"saving 'lineage_csv' output to '{csvout}'" in runtmp.last_result.err assert c.last_result.status == 0 with open(csvout) as fp: outp = fp.readlines() assert len(outp) == 2 outp = [x.strip() for x in outp] assert outp[0] == "ident,superkingdom,phylum,class,order,family,genus,species" assert ( outp[1] == "test1,d__Bacteria,p__Bacteroidota,c__Bacteroidia,o__Bacteroidales,f__Bacteroidaceae,g__Prevotella,s__Prevotella copri" ) def test_genome_gather_from_file_rank(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") g_from_file = runtmp.output("tmp-from-file.txt") with open(g_from_file, "w") as f_csv: f_csv.write(f"{g_res}\n") c.run_sourmash( "tax", "genome", "--from-file", g_from_file, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) def test_genome_gather_two_files(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # make test2 results (identical to test1 except query_name and filename) g_res2 = runtmp.output("test2.gather.csv") test2_results = [ x.replace("test1", "test2") + "\n" for x in Path(g_res).read_text().splitlines() ] with open(g_res2, "w") as fp: fp.writelines(test2_results) c.run_sourmash( "tax", "genome", "-g", g_res, g_res2, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) assert ( "test2,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test2.sig,0.057,444000" in c.last_result.out ) def test_genome_gather_two_files_empty_force(runtmp): # make test2 results (identical to test1 except query_name and filename) # add an empty file too, with --force -> should work c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") g_empty_csv = runtmp.output("g_empty.csv") with open(g_empty_csv, "w") as fp: fp.write("") print("g_csv: ", g_empty_csv) g_res2 = runtmp.output("test2.gather.csv") test2_results = [ x.replace("test1", "test2") + "\n" for x in Path(g_res).read_text().splitlines() ] with open(g_res2, "w") as fp: fp.writelines(test2_results) c.run_sourmash( "tax", "genome", "-g", g_res, g_res2, "-g", g_empty_csv, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", "--force", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) assert ( "test2,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test2.sig,0.057,444000" in c.last_result.out ) def test_genome_gather_two_files_one_classif_fail(runtmp): # if one query cant be classified still get classif for second # no --force = fail but still write file c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # make test2 results (identical to test1 except query_name and filename) g_res2 = runtmp.output("test2.gather.csv") test2_results = [ x.replace("test1", "test2") + "\n" for x in Path(g_res).read_text().splitlines() ] test2_results[1] = test2_results[1].replace( "0.08815317112086159", "1.1" ) # make test2 f_unique_to_query sum to >1 for line in test2_results: print(line) with open(g_res2, "w") as fp: fp.writelines(test2_results) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "genome", "-g", g_res, g_res2, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == -1 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) assert "test2" not in c.last_result.out assert ( "ERROR: Summarized fraction is > 100% of the query! This should not be possible. Please check that your input files come directly from a single gather run per query." in c.last_result.err ) def test_genome_gather_two_files_one_classif(runtmp): # if one query cant be classified, still get classif for second c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # make test2 results (identical to test1 except query_name and filename) g_res2 = runtmp.output("test2.gather.csv") test2_results = [ x.replace("test1", "test2") + "\n" for x in Path(g_res).read_text().splitlines() ] test2_results[1] = test2_results[1].replace( "0.08815317112086159", "1.1" ) # make test2 f_unique_to_query sum to >1 for line in test2_results: print(line) with open(g_res2, "w") as fp: fp.writelines(test2_results) c.run_sourmash( "tax", "genome", "-g", g_res, g_res2, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", "--force", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) assert "test2" not in c.last_result.out assert ( "ERROR: Summarized fraction is > 100% of the query! This should not be possible. Please check that your input files come directly from a single gather run per query." in c.last_result.err ) def test_genome_gather_duplicate_filename(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") c.run_sourmash( "tax", "genome", "--gather-csv", g_res, "-g", g_res, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert f"ignoring duplicated reference to file: {g_res}" assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) def test_genome_gather_from_file_duplicate_filename(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") g_from_file = runtmp.output("tmp-from-file.txt") with open(g_from_file, "w") as f_csv: f_csv.write(f"{g_res}\n") f_csv.write(f"{g_res}\n") c.run_sourmash( "tax", "genome", "--from-file", g_from_file, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert f"ignoring duplicated reference to file: {g_res}" assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) def test_genome_gather_from_file_duplicate_query(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # different filename, contents identical to test1 g_res2 = runtmp.output("test2.gather.csv") with open(g_res2, "w") as fp: fp.write(Path(g_res).read_text()) g_from_file = runtmp.output("tmp-from-file.txt") with open(g_from_file, "w") as f_csv: f_csv.write(f"{g_res}\n") f_csv.write(f"{g_res2}\n") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "--from-file", g_from_file, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", ) assert c.last_result.status == -1 print(str(exc.value)) assert ( "Gather query test1 was found in more than one CSV. Cannot load from " in str(exc.value) ) def test_genome_gather_from_file_duplicate_query_force(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") # different filename, contents identical to test1 g_res2 = runtmp.output("test2.gather.csv") with open(g_res2, "w") as fp: fp.write(Path(g_res).read_text()) g_from_file = runtmp.output("tmp-from-file.txt") with open(g_from_file, "w") as f_csv: f_csv.write(f"{g_res}\n") f_csv.write(f"{g_res2}\n") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "genome", "--from-file", g_from_file, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", "--force", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == -1 assert "Gather query test1 was found in more than one CSV." in c.last_result.err assert "Cannot force past duplicated gather query. Exiting." in c.last_result.err def test_genome_gather_cli_and_from_file(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") g_from_file = runtmp.output("tmp-from-file.txt") # make test2 results (identical to test1 except query_name) g_res2 = runtmp.output("test2.gather.csv") test2_results = [ x.replace("test1", "test2") + "\n" for x in Path(g_res).read_text().splitlines() ] with open(g_res2, "w") as fp: fp.writelines(test2_results) # write test2 csv to a text file for input g_from_file = runtmp.output("tmp-from-file.txt") with open(g_from_file, "w") as f_csv: f_csv.write(f"{g_res2}\n") c.run_sourmash( "tax", "genome", "-g", g_res, "--from-file", g_from_file, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) assert ( "test2,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test2.sig,0.057,444000" in c.last_result.out ) def test_genome_gather_cli_and_from_file_duplicate_filename(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") g_from_file = runtmp.output("tmp-from-file.txt") # also write test1 csv to a text file for input g_from_file = runtmp.output("tmp-from-file.txt") with open(g_from_file, "w") as f_csv: f_csv.write(f"{g_res}\n") c.run_sourmash( "tax", "genome", "-g", g_res, "--from-file", g_from_file, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert f"ignoring duplicated reference to file: {g_res}" in c.last_result.err assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) def test_genome_gather_from_file_below_threshold(runtmp): # What do we want the results from this to be? I think I initially thought we shouldn't report anything, # but wouldn't a "below_threshold" + superkingdom result (here, 0.204) be helpful information? c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") g_from_file = runtmp.output("tmp-from-file.txt") with open(g_from_file, "w") as f_csv: f_csv.write(f"{g_res}\n") c.run_sourmash( "tax", "genome", "--from-file", g_from_file, "--taxonomy-csv", taxonomy_csv, "--containment-threshold", "1", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "query_name,status,rank,fraction,lineage" in c.last_result.out assert "test1,below_threshold,superkingdom,0.204," in c.last_result.out def test_genome_gather_two_queries(runtmp): """ This checks for initial bug where classification would only happen for one genome per rank when doing --containment-threshold classification """ c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/47+63_x_gtdb-rs202.gather.csv") # split 47+63 into two fake queries: q47, q63 g_res2 = runtmp.output("two-queries.gather.csv") q2_results = [x + "\n" for x in Path(g_res).read_text().splitlines()] # rename queries q2_results[1] = q2_results[1].replace("47+63", "q47") q2_results[2] = q2_results[2].replace("47+63", "q63") with open(g_res2, "w") as fp: for line in q2_results: print(line) fp.write(line) c.run_sourmash( "tax", "genome", "-g", g_res2, "--taxonomy-csv", taxonomy_csv, "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "query_name,status,rank,fraction,lineage" in c.last_result.out assert ( "q63,match,species,0.336,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Shewanellaceae;g__Shewanella;s__Shewanella baltica,491c0a81," in c.last_result.out ) assert ( "q47,match,species,0.664,d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Shewanellaceae;g__Shewanella;s__Shewanella baltica," in c.last_result.out ) def test_genome_gather_ictv(runtmp): """ test genome classification with ictv taxonomy """ c = runtmp taxonomy_csv = utils.get_test_data("tax/test.ictv-taxonomy.csv") g_res = utils.get_test_data("tax/47+63_x_gtdb-rs202.gather.csv") c.run_sourmash( "tax", "genome", "-g", g_res, "--taxonomy-csv", taxonomy_csv, "--containment-threshold", "0", "--ictv", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "query_name,status,rank,fraction,lineage" in c.last_result.out assert ( "47+63,match,name,0.664,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus sudanense;Sudan virus,491c0a81,,0.664,5238000,0.987" in c.last_result.out ) def test_genome_gather_ictv_twoqueries(runtmp): """ test genome classification with ictv taxonomy """ c = runtmp taxonomy_csv = utils.get_test_data("tax/test.ictv-taxonomy.csv") g_res = utils.get_test_data("tax/47+63_x_gtdb-rs202.gather.csv") # split 47+63 into two fake queries: q47, q63 g_res2 = runtmp.output("two-queries.gather.csv") q2_results = [x + "\n" for x in Path(g_res).read_text().splitlines()] # rename queries q2_results[1] = q2_results[1].replace("47+63", "q47") q2_results[2] = q2_results[2].replace("47+63", "q63") with open(g_res2, "w") as fp: for line in q2_results: print(line) fp.write(line) c.run_sourmash( "tax", "genome", "-g", g_res2, "--taxonomy-csv", taxonomy_csv, "--containment-threshold", "0", "--ictv", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 print(c.last_result.out) assert "query_name,status,rank,fraction,lineage" in c.last_result.out assert ( "q47,match,name,0.664,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus sudanense;Sudan virus,491c0a81,,0.664,5238000,0.987" in c.last_result.out ) assert ( "q63,match,name,0.336,Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus zairense;Ebola virus,491c0a81,,0.336,2648000,0.965" in c.last_result.out ) def test_genome_gather_ictv_fail(runtmp): """ test genome classification with ictv taxonomy """ c = runtmp taxonomy_csv = utils.get_test_data("tax/test.ictv-taxonomy.csv") tax2_csv = runtmp.output("ictv-taxfail") # copy taxonomy csv to new file, but remove one of the columns with open(taxonomy_csv) as inF: with open(tax2_csv, "w") as outF: for line in inF.readlines(): line = line.rsplit(",", 1)[0] outF.write(f"{line}\n") g_res = utils.get_test_data("tax/47+63_x_gtdb-rs202.gather.csv") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", g_res, "--taxonomy-csv", tax2_csv, "--containment-threshold", "0", "--ictv", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status != 0 print(c.last_result.out) assert "Not all taxonomy ranks present" in str(exc.value) def test_genome_rank_duplicated_taxonomy_fail(runtmp): c = runtmp # write temp taxonomy with duplicates taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") duplicated_csv = runtmp.output("duplicated_taxonomy.csv") with open(duplicated_csv, "w") as dup: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax.append(tax[1] + "FOO") # add first tax_assign again dup.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", duplicated_csv, "--rank", "species", ) assert "cannot read taxonomy assignments" in str(exc.value) assert "multiple lineages for identifier GCF_001881345" in str(exc.value) def test_genome_rank_duplicated_taxonomy_fail_lineages(runtmp): # write temp taxonomy with duplicates => lineages-style file c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") taxdb = tax_utils.LineageDB.load(taxonomy_csv) for k, v in taxdb.items(): print(k, v) lineage_csv = runtmp.output("lin.csv") with open(lineage_csv, "w", newline="") as fp: w = csv.writer(fp) w.writerow(["name", "lineage"]) for k, v in taxdb.items(): linstr = lca_utils.display_lineage(v) w.writerow([k, linstr]) # duplicate each row, changing something (truncate species, here) v = v[:-1] linstr = lca_utils.display_lineage(v) w.writerow([k, linstr]) with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash("tax", "summarize", lineage_csv) print(c.last_result.out) print(c.last_result.err) assert "cannot read taxonomy assignments" in str(exc.value) assert "multiple lineages for identifier GCF_001881345" in str(exc.value) def test_genome_rank_duplicated_taxonomy_force(runtmp): # write temp taxonomy with duplicates c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") duplicated_csv = runtmp.output("duplicated_taxonomy.csv") with open(duplicated_csv, "w") as dup: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax.append(tax[1]) # add first tax_assign again dup.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", duplicated_csv, "--rank", "species", "--force", "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) def test_genome_missing_taxonomy_ignore_threshold(runtmp): c = runtmp # write temp taxonomy with missing entry taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") subset_csv = runtmp.output("subset_taxonomy.csv") with open(subset_csv, "w") as subset: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax = [tax[0]] + tax[2:] # remove the best match (1st tax entry) subset.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", subset_csv, "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "The following are missing from the taxonomy information: GCF_001881345" in c.last_result.err ) assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) def test_genome_missing_taxonomy_recover_with_second_tax_file(runtmp): c = runtmp # write temp taxonomy with missing entry taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") subset_csv = runtmp.output("subset_taxonomy.csv") with open(subset_csv, "w") as subset: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax = [tax[0]] + tax[2:] # remove the best match (1st tax entry) subset.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", subset_csv, "-t", taxonomy_csv, "--containment-threshold", "0", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "The following are missing from the taxonomy information: GCF_001881345" not in c.last_result.err ) assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) def test_genome_missing_taxonomy_ignore_rank(runtmp): c = runtmp # write temp taxonomy with missing entry taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") subset_csv = runtmp.output("subset_taxonomy.csv") with open(subset_csv, "w") as subset: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax = [tax[0]] + tax[2:] # remove the best match (1st tax entry) subset.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", subset_csv, "--rank", "species" ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "The following are missing from the taxonomy information: GCF_001881345" in c.last_result.err ) assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,below_threshold,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) def test_genome_multiple_taxonomy_files(runtmp): c = runtmp # write temp taxonomy with missing entry taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") subset_csv = runtmp.output("subset_taxonomy.csv") with open(subset_csv, "w") as subset: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax = [tax[0]] + tax[2:] # remove the best match (1st tax entry) subset.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") # using mult -t args c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", subset_csv, "-t", taxonomy_csv ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "The following are missing from the taxonomy information: GCF_001881345" not in c.last_result.err ) assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,family,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae,md5,test1.sig,0.073,582000," in c.last_result.out ) # using single -t arg c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", subset_csv, taxonomy_csv ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "The following are missing from the taxonomy information: GCF_001881345" not in c.last_result.err ) assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,family,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae,md5,test1.sig,0.073,582000," in c.last_result.out ) def test_genome_multiple_taxonomy_files_empty_force(runtmp): c = runtmp # write temp taxonomy with missing entry, as well as an empty file, # and use force taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") subset_csv = runtmp.output("subset_taxonomy.csv") with open(subset_csv, "w") as subset: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax = [tax[0]] + tax[2:] # remove the best match (1st tax entry) subset.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") empty_tax = runtmp.output("tax_empty.txt") with open(empty_tax, "w") as fp: fp.write("") # using mult -t args c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", subset_csv, "-t", taxonomy_csv, "-t", empty_tax, "--force", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "The following are missing from the taxonomy information: GCF_001881345" not in c.last_result.err ) assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,family,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae,md5,test1.sig,0.073,582000," in c.last_result.out ) def test_genome_missing_taxonomy_fail_threshold(runtmp): c = runtmp # write temp taxonomy with missing entry taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") subset_csv = runtmp.output("subset_taxonomy.csv") with open(subset_csv, "w") as subset: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax = [tax[0]] + tax[2:] # remove the best match (1st tax entry) subset.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", subset_csv, "--fail-on-missing-taxonomy", "--containment-threshold", "0", ) print(str(exc.value)) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert "ident 'GCF_001881345' is not in the taxonomy database." in str(exc.value) assert "Failing, as requested via --fail-on-missing-taxonomy" in str(exc.value) assert c.last_result.status == -1 def test_genome_missing_taxonomy_fail_rank(runtmp): c = runtmp # write temp taxonomy with missing entry taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") subset_csv = runtmp.output("subset_taxonomy.csv") with open(subset_csv, "w") as subset: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax = [tax[0]] + tax[2:] # remove the best match (1st tax entry) subset.write("\n".join(tax)) g_csv = utils.get_test_data("tax/test1.gather.csv") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", subset_csv, "--fail-on-missing-taxonomy", "--rank", "species", ) print(str(exc.value)) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert "ident 'GCF_001881345' is not in the taxonomy database." in str(exc.value) assert "Failing, as requested via --fail-on-missing-taxonomy" in str(exc.value) assert c.last_result.status == -1 def test_genome_rank_not_available(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--rank", "strain", "--containment-threshold", "0", ) print(str(exc.value)) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == -1 assert ( "No taxonomic information provided for rank strain: cannot classify at this rank" in str(exc.value) ) def test_genome_empty_gather_results_with_header_single(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") gather_results = [x for x in Path(g_csv).read_text().splitlines()] empty_gather_with_header = runtmp.output("g_header.csv") # write temp empty gather results (header only) with open(empty_gather_with_header, "w") as fp: fp.write(gather_results[0]) with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", empty_gather_with_header, "--taxonomy-csv", taxonomy_csv, ) print(str(exc.value)) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == -1 assert f"No gather results loaded from {empty_gather_with_header}." in str( exc.value ) assert "Exiting." in str(exc.value) def test_genome_empty_gather_results_single(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") # write temp empty gather results empty_tax = runtmp.output("tax_header.csv") with open(empty_tax, "w") as fp: fp.write("") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash("tax", "genome", "-g", empty_tax, "--taxonomy-csv", taxonomy_csv) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == -1 assert f"Cannot read gather results from '{empty_tax}'. Is file empty?" in str( exc.value ) assert "Exiting." in c.last_result.err def test_genome_empty_gather_results_single_force(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") # write temp empty gather results (header only) empty_tax = runtmp.output("tax_header.csv") with open(empty_tax, "w") as fp: fp.write("") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", empty_tax, "--taxonomy-csv", taxonomy_csv, "--force" ) print(str(exc.value)) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == -1 assert ( "--force is set. Attempting to continue to next set of gather results." in str(exc.value) ) assert "No results for classification. Exiting." in str(exc.value) def test_genome_empty_gather_results_with_empty_csv_force(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") # write temp empty gather results empty_tax = runtmp.output("tax_empty.txt") with open(empty_tax, "w") as fp: fp.write("") g_from_file = runtmp.output("tmp-from-csv.csv") with open(g_from_file, "w") as f_csv: f_csv.write(f"{empty_tax}\n") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", empty_tax, "--from-file", g_from_file, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--force", ) print(str(exc.value)) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == -1 assert ( "--force is set. Attempting to continue to next set of gather results." in str(exc.value) ) assert "No results for classification. Exiting." in str(exc.value) def test_genome_empty_gather_results_with_csv_force(runtmp): c = runtmp taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") g_res = utils.get_test_data("tax/test1.gather.csv") g_from_file = runtmp.output("tmp-from-file.txt") with open(g_from_file, "w") as f_csv: f_csv.write(f"{g_res}\n") # write temp empty gather results empty_tax = runtmp.output("tax_empty.csv") with open(empty_tax, "w") as fp: fp.write("") c.run_sourmash( "tax", "genome", "-g", empty_tax, "--from-file", g_from_file, "--taxonomy-csv", taxonomy_csv, "--rank", "species", "--containment-threshold", "0", "--force", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "--force is set. Attempting to continue to next set of gather results." in c.last_result.err ) assert "loaded results for 1 queries from 1 gather CSVs" in c.last_result.err assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) def test_genome_containment_threshold_bounds(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") below_threshold = "-1" with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", tax, "--taxonomy-csv", tax, "--containment-threshold", below_threshold, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert "ERROR: Argument must be >0 and <1" in str(exc.value) above_threshold = "1.1" with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--containment-threshold", above_threshold, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert "ERROR: Argument must be >0 and <1" in str(exc.value) def test_genome_containment_threshold_type(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") not_a_float = "str" with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--containment-threshold", not_a_float, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert "ERROR: Must be a floating point number" in str(exc.value) def test_genome_over100percent_error(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") perfect_g_csv = runtmp.output("g.csv") # create an impossible gather result with open(g_csv) as fp: r = csv.DictReader(fp, delimiter=",") header = r.fieldnames print(header) with open(perfect_g_csv, "w") as out_fp: w = csv.DictWriter(out_fp, header) w.writeheader() for n, row in enumerate(r): if n == 0: row["f_unique_to_query"] = 1.1 w.writerow(row) print(row) with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("tax", "genome", "-g", perfect_g_csv, "--taxonomy-csv", tax) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == -1 assert ( "fraction is > 100% of the query! This should not be possible." in runtmp.last_result.err ) def test_genome_ani_threshold_input_errors(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather_old.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") below_threshold = "-1" with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", tax, "--taxonomy-csv", tax, "--ani-threshold", below_threshold, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert "ERROR: Argument must be >0 and <1" in str(exc.value) above_threshold = "1.1" with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--ani-threshold", above_threshold, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert "ERROR: Argument must be >0 and <1" in str(exc.value) not_a_float = "str" with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--ani-threshold", not_a_float, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert "ERROR: Must be a floating point number" in str(exc.value) def test_genome_ani_threshold(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--ani-threshold", "0.93" ) # note: I think this was previously a bug, if 0.95 produced the result below... print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) assert ( "test1,match,family,0.116,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae,md5,test1.sig,0.073,582000,0.93" in c.last_result.out ) # more lax threshold c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--ani-threshold", "0.9" ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "test1,match,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000" in c.last_result.out ) # too stringent of threshold (using rank) c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--ani-threshold", "1.0", "--rank", "species", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert ( "test1,below_threshold,species,0.089,d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri,md5,test1.sig,0.057,444000,0.92" in c.last_result.out ) def test_genome_ani_oldgather(runtmp): # now fail if using gather <4.4 c = runtmp g_csv = utils.get_test_data("tax/test1.gather_old.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") with pytest.raises(SourmashCommandFailed) as exc: c.run_sourmash("tax", "genome", "-g", g_csv, "--taxonomy-csv", tax) assert ( "is missing columns needed for taxonomic summarization. Please run gather with sourmash >= 4.4." in str(exc.value) ) assert c.last_result.status == -1 def test_genome_ani_lemonade_classify(runtmp): # test a complete MAG classification with lemonade MAG from STAMPS 2022 # (real data!) c = runtmp ## first run gather genome = utils.get_test_data("tax/lemonade-MAG3.sig.gz") matches = utils.get_test_data("tax/lemonade-MAG3.x.gtdb.matches.zip") c.run_sourmash("gather", genome, matches, "--threshold-bp=5000", "-o", "gather.csv") print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 this_gather_file = c.output("gather.csv") this_gather = Path(this_gather_file).read_text().splitlines() assert len(this_gather) == 4 ## now run 'tax genome' with human output taxonomy_file = utils.get_test_data("tax/lemonade-MAG3.x.gtdb.matches.tax.csv") c.run_sourmash( "tax", "genome", "-g", this_gather_file, "-t", taxonomy_file, "--ani", "0.8", "-F", "human", ) output = c.last_result.out assert ( "MAG3_1 match 5.3% 91.0% d__Bacteria;p__Bacteroidota;c__Chlorobia;o__Chlorobiales;f__Chlorobiaceae;g__Prosthecochloris;s__Prosthecochloris vibrioformis" in output ) # aaand classify to lineage_csv c.run_sourmash( "tax", "genome", "-g", this_gather_file, "-t", taxonomy_file, "--ani", "0.8", "-F", "lineage_csv", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) output = c.last_result.out assert "ident,superkingdom,phylum,class,order,family,genus,species" in output assert ( "MAG3_1,d__Bacteria,p__Bacteroidota,c__Chlorobia,o__Chlorobiales,f__Chlorobiaceae,g__Prosthecochloris,s__Prosthecochloris vibrioformis" in output ) def test_genome_ani_lemonade_classify_estimate_ani_ci(runtmp): # test a complete MAG classification with lemonade MAG from STAMPS 2022 # (real data!) c = runtmp ## first run gather genome = utils.get_test_data("tax/lemonade-MAG3.sig.gz") matches = utils.get_test_data("tax/lemonade-MAG3.x.gtdb.matches.zip") c.run_sourmash( "gather", genome, matches, "--threshold-bp=5000", "-o", "gather.csv", "--estimate-ani", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 this_gather_file = c.output("gather.csv") this_gather = Path(this_gather_file).read_text().splitlines() assert len(this_gather) == 4 ## now run 'tax genome' with human output taxonomy_file = utils.get_test_data("tax/lemonade-MAG3.x.gtdb.matches.tax.csv") c.run_sourmash( "tax", "genome", "-g", this_gather_file, "-t", taxonomy_file, "--ani", "0.8", "-F", "human", ) output = c.last_result.out assert ( "MAG3_1 match 5.3% 91.0% d__Bacteria;p__Bacteroidota;c__Chlorobia;o__Chlorobiales;f__Chlorobiaceae;g__Prosthecochloris;s__Prosthecochloris vibrioformis" in output ) # aaand classify to lineage_csv c.run_sourmash( "tax", "genome", "-g", this_gather_file, "-t", taxonomy_file, "--ani", "0.8", "-F", "lineage_csv", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) output = c.last_result.out assert "ident,superkingdom,phylum,class,order,family,genus,species" in output assert ( "MAG3_1,d__Bacteria,p__Bacteroidota,c__Chlorobia,o__Chlorobiales,f__Chlorobiaceae,g__Prosthecochloris,s__Prosthecochloris vibrioformis" in output ) def test_metagenome_no_gather_csv(runtmp): # test tax metagenome with no -g taxonomy_file = utils.get_test_data("tax/lemonade-MAG3.x.gtdb.matches.tax.csv") with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("tax", "metagenome", "-t", taxonomy_file) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) def test_genome_no_gather_csv(runtmp): # test tax genome with no -g taxonomy_file = utils.get_test_data("tax/lemonade-MAG3.x.gtdb.matches.tax.csv") with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("tax", "genome", "-t", taxonomy_file) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) def test_annotate_no_gather_csv(runtmp): # test tax annotate with no -g taxonomy_file = utils.get_test_data("tax/lemonade-MAG3.x.gtdb.matches.tax.csv") with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash("tax", "annotate", "-t", taxonomy_file) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) def test_genome_LIN(runtmp): # test basic genome with LIN taxonomy c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "--ani-threshold", "0.93", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank,query_ani_at_rank" in c.last_result.out ) assert ( "test1,below_threshold,0,0.089,1,md5,test1.sig,0.057,444000,0.925" in c.last_result.out ) c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "--ani-threshold", "0.924", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank,query_ani_at_rank" in c.last_result.out ) assert ( "test1,match,19,0.088,0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0,md5,test1.sig,0.058,442000,0.925" in c.last_result.out ) c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "--rank", "4" ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank,query_ani_at_rank" in c.last_result.out ) assert ( "test1,below_threshold,4,0.088,0;0;0;0;0,md5,test1.sig,0.058,442000,0.925" in c.last_result.out ) def test_genome_LIN_lingroups(runtmp): # test basic genome with LIN taxonomy c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") lg_file = runtmp.output("test.lg.csv") with open(lg_file, "w") as out: out.write("lin,name\n") out.write("0;0;0,lg1\n") out.write("1;0;0,lg2\n") out.write("2;0;0,lg3\n") out.write("1;0;1,lg3\n") # write a 19 so we can check the end out.write("0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0,lg4\n") c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "--lingroup", lg_file, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank,query_ani_at_rank" in c.last_result.out ) assert ( "test1,below_threshold,2,0.088,0;0;0,md5,test1.sig,0.058,442000,0.925" in c.last_result.out ) c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "--lingroup", lg_file, "--ani-threshold", "0.924", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,status,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank,query_ani_at_rank" in c.last_result.out ) assert ( "test1,match,19,0.088,0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0,md5,test1.sig,0.058,442000,0.925" in c.last_result.out ) def test_annotate_0(runtmp): # test annotate basics c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csvout = runtmp.output("test1.gather.with-lineages.csv") out_dir = os.path.dirname(csvout) c.run_sourmash( "tax", "annotate", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-o", out_dir ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(csvout) lin_gather_results = [x.rstrip() for x in Path(csvout).read_text().splitlines()] print("\n".join(lin_gather_results)) assert f"saving 'annotate' output to '{csvout}'" in runtmp.last_result.err assert "lineage" in lin_gather_results[0] assert ( "d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli" in lin_gather_results[1] ) assert ( "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri" in lin_gather_results[2] ) assert ( "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola vulgatus" in lin_gather_results[3] ) assert ( "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri" in lin_gather_results[4] ) def test_annotate_gzipped_gather(runtmp): # test annotate basics c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") # rewrite gather_csv as gzipped csv gz_gather = runtmp.output("test1.gather.csv.gz") with open(g_csv, "rb") as f_in, gzip.open(gz_gather, "wb") as f_out: f_out.writelines(f_in) tax = utils.get_test_data("tax/test.taxonomy.csv") csvout = runtmp.output("test1.gather.with-lineages.csv") out_dir = os.path.dirname(csvout) c.run_sourmash( "tax", "annotate", "--gather-csv", gz_gather, "--taxonomy-csv", tax, "-o", out_dir, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(csvout) lin_gather_results = [x.rstrip() for x in Path(csvout).read_text().splitlines()] print("\n".join(lin_gather_results)) assert f"saving 'annotate' output to '{csvout}'" in runtmp.last_result.err assert "lineage" in lin_gather_results[0] assert ( "d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli" in lin_gather_results[1] ) assert ( "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri" in lin_gather_results[2] ) assert ( "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola vulgatus" in lin_gather_results[3] ) assert ( "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri" in lin_gather_results[4] ) def test_annotate_0_ictv(runtmp): # test annotate basics c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.ictv-taxonomy.csv") csvout = runtmp.output("test1.gather.with-lineages.csv") out_dir = os.path.dirname(csvout) c.run_sourmash( "tax", "annotate", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-o", out_dir, "--ictv", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(csvout) gather_results = [x.rstrip() for x in Path(csvout).read_text().splitlines()] print("\n".join(gather_results)) assert f"saving 'annotate' output to '{csvout}'" in runtmp.last_result.err assert "lineage" in gather_results[0] assert ( "Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus bundibugyoense;Bundibugyo virus" in gather_results[1] ) assert ( "Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus taiense;Taï Forest virus" in gather_results[2] ) assert ( "Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus bombaliense;Bombali virus" in gather_results[3] ) assert ( "Riboviria;;Orthornavirae;;Negarnaviricota;Haploviricotina;Monjiviricetes;;Mononegavirales;;Filoviridae;;Orthoebolavirus;;Orthoebolavirus restonense;Reston virus" in gather_results[4] ) def test_annotate_0_LIN(runtmp): # test annotate basics c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") csvout = runtmp.output("test1.gather.with-lineages.csv") out_dir = os.path.dirname(csvout) c.run_sourmash( "tax", "annotate", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-o", out_dir, "--lins", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(csvout) lin_gather_results = [x.rstrip() for x in Path(csvout).read_text().splitlines()] print("\n".join(lin_gather_results)) assert f"saving 'annotate' output to '{csvout}'" in runtmp.last_result.err assert "lineage" in lin_gather_results[0] assert "0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0" in lin_gather_results[1] assert "1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0" in lin_gather_results[2] assert "2;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0" in lin_gather_results[3] assert "1;0;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0" in lin_gather_results[4] def test_annotate_gather_argparse(runtmp): # test annotate with two gather CSVs, second one empty, and --force. # this tests argparse handling w/extend. c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csvout = runtmp.output("test1.gather.with-lineages.csv") out_dir = os.path.dirname(csvout) g_empty_csv = runtmp.output("g_empty.csv") with open(g_empty_csv, "w") as fp: fp.write("") print("g_csv: ", g_empty_csv) c.run_sourmash( "tax", "annotate", "--gather-csv", g_csv, "-g", g_empty_csv, "--taxonomy-csv", tax, "-o", out_dir, "--force", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert os.path.exists(csvout) lin_gather_results = [x.rstrip() for x in Path(csvout).read_text().splitlines()] print("\n".join(lin_gather_results)) assert f"saving 'annotate' output to '{csvout}'" in runtmp.last_result.err assert "lineage" in lin_gather_results[0] assert ( "d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli" in lin_gather_results[1] ) def test_annotate_0_db(runtmp): # test annotate with sqlite db c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.db") csvout = runtmp.output("test1.gather.with-lineages.csv") out_dir = os.path.dirname(csvout) c.run_sourmash( "tax", "annotate", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-o", out_dir ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 lin_gather_results = [x.rstrip() for x in Path(csvout).read_text().splitlines()] print("\n".join(lin_gather_results)) assert f"saving 'annotate' output to '{csvout}'" in runtmp.last_result.err assert "lineage" in lin_gather_results[0] assert ( "d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Enterobacterales;f__Enterobacteriaceae;g__Escherichia;s__Escherichia coli" in lin_gather_results[1] ) assert ( "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri" in lin_gather_results[2] ) assert ( "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Phocaeicola;s__Phocaeicola vulgatus" in lin_gather_results[3] ) assert ( "d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella copri" in lin_gather_results[4] ) def test_annotate_empty_gather_results(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") # creates empty gather result g_csv = runtmp.output("g.csv") with open(g_csv, "w") as fp: fp.write("") print("g_csv: ", g_csv) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash("tax", "annotate", "-g", g_csv, "--taxonomy-csv", tax) assert f"Cannot read from '{g_csv}'. Is file empty?" in str(exc.value) assert runtmp.last_result.status == -1 def test_annotate_prefetch_or_other_header(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") alt_csv = runtmp.output("g.csv") for alt_col in ["match_name", "ident", "accession"]: # modify 'name' to other acceptable id_columns result alt_g = [ x.replace("name", alt_col) + "\n" for x in Path(g_csv).read_text().splitlines() ] with open(alt_csv, "w") as fp: fp.writelines(alt_g) runtmp.run_sourmash("tax", "annotate", "-g", alt_csv, "--taxonomy-csv", tax) assert runtmp.last_result.status == 0 print(runtmp.last_result.out) print(runtmp.last_result.err) assert ( f"Starting annotation on '{alt_csv}'. Using ID column: '{alt_col}'" in runtmp.last_result.err ) assert f"Annotated 4 of 4 total rows from '{alt_csv}'" in runtmp.last_result.err def test_annotate_bad_header(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") bad_g_csv = runtmp.output("g.csv") # creates bad gather result bad_g = [ x.replace("name", "nope") + "\n" for x in Path(g_csv).read_text().splitlines() ] with open(bad_g_csv, "w") as fp: fp.writelines(bad_g) # print("bad_gather_results: \n", bad_g) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash("tax", "annotate", "-g", bad_g_csv, "--taxonomy-csv", tax) assert ( f"ERROR: Cannot find taxonomic identifier column in '{bad_g_csv}'. Tried: name, match_name, ident, accession" in str(exc.value) ) assert runtmp.last_result.status == -1 print(runtmp.last_result.out) print(runtmp.last_result.err) def test_annotate_no_tax_matches(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") bad_g_csv = runtmp.output("g.csv") # mess up tax idents bad_g = [ x.replace("GCF_", "GGG_") + "\n" for x in Path(g_csv).read_text().splitlines() ] with open(bad_g_csv, "w") as fp: fp.writelines(bad_g) # print("bad_gather_results: \n", bad_g) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash("tax", "annotate", "-g", bad_g_csv, "--taxonomy-csv", tax) assert f"ERROR: Could not annotate any rows from '{bad_g_csv}'" in str(exc.value) assert runtmp.last_result.status == -1 print(runtmp.last_result.out) print(runtmp.last_result.err) runtmp.run_sourmash( "tax", "annotate", "-g", bad_g_csv, "--taxonomy-csv", tax, "--force" ) assert runtmp.last_result.status == 0 assert f"Could not annotate any rows from '{bad_g_csv}'" in runtmp.last_result.err assert ( "--force is set. Attempting to continue to next file." in runtmp.last_result.err ) print(runtmp.last_result.out) print(runtmp.last_result.err) def test_annotate_missed_tax_matches(runtmp): tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") bad_g_csv = runtmp.output("g.csv") with open(g_csv) as gather_lines, open(bad_g_csv, "w") as fp: for n, line in enumerate(gather_lines): if n > 2: # mess up tax idents of lines 3, 4 line = line.replace("GCF_", "GGG_") fp.write(line) # print("bad_gather_results: \n", bad_g) runtmp.run_sourmash("tax", "annotate", "-g", bad_g_csv, "--taxonomy-csv", tax) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert f"Annotated 2 of 4 total rows from '{bad_g_csv}'." in runtmp.last_result.err def test_annotate_empty_tax_lineage_input(runtmp): tax_empty = runtmp.output("t.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") with open(tax_empty, "w") as fp: fp.write("") print("t_csv: ", tax_empty) with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash("tax", "annotate", "-g", g_csv, "--taxonomy-csv", tax_empty) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status != 0 assert "cannot read taxonomy assignments from" in str(exc.value) def test_annotate_empty_tax_lineage_input_recover_with_second_taxfile(runtmp): tax_empty = runtmp.output("t.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") with open(tax_empty, "w") as fp: fp.write("") print("t_csv: ", tax_empty) runtmp.run_sourmash( "tax", "annotate", "-g", g_csv, "-t", tax_empty, "--taxonomy-csv", tax, "--force", ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 def test_annotate_empty_tax_lineage_input_recover_with_second_taxfile_2(runtmp): # test with empty tax second, to check on argparse handling tax_empty = runtmp.output("t.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") g_csv = utils.get_test_data("tax/test1.gather.csv") with open(tax_empty, "w") as fp: fp.write("") print("t_csv: ", tax_empty) runtmp.run_sourmash( "tax", "annotate", "-g", g_csv, "--taxonomy-csv", tax, "-t", tax_empty, "--force", ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 def test_tax_prepare_1_csv_to_csv(runtmp, keep_identifiers, keep_versions): # CSV -> CSV; same assignments tax = utils.get_test_data("tax/test.taxonomy.csv") taxout = runtmp.output("out.csv") args = [] if keep_identifiers: args.append("--keep-full-identifiers") if keep_versions: args.append("--keep-identifier-versions") # this is an error - can't strip versions if not splitting identifiers if keep_identifiers and not keep_versions: with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-o", taxout, "-F", "csv", *args ) return runtmp.run_sourmash("tax", "prepare", "-t", tax, "-o", taxout, "-F", "csv", *args) assert os.path.exists(taxout) db1 = tax_utils.MultiLineageDB.load( [tax], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) db2 = tax_utils.MultiLineageDB.load([taxout]) assert set(db1) == set(db2) def test_tax_prepare_1_combine_csv(runtmp): # multiple CSVs to a single combined CSV tax1 = utils.get_test_data("tax/test.taxonomy.csv") tax2 = utils.get_test_data("tax/protozoa_genbank_lineage.csv") taxout = runtmp.output("out.csv") runtmp.sourmash("tax", "prepare", "-t", tax1, tax2, "-F", "csv", "-o", taxout) out = runtmp.last_result.out err = runtmp.last_result.err assert not out assert "...loaded 8 entries" in err out = Path(taxout).read_text().splitlines() assert len(out) == 9 def test_tax_prepare_1_csv_to_csv_empty_ranks(runtmp, keep_identifiers, keep_versions): # CSV -> CSV; same assignments, even when trailing ranks are empty tax = utils.get_test_data("tax/test-empty-ranks.taxonomy.csv") taxout = runtmp.output("out.csv") args = [] if keep_identifiers: args.append("--keep-full-identifiers") if keep_versions: args.append("--keep-identifier-versions") # this is an error - can't strip versions if not splitting identifiers if keep_identifiers and not keep_versions: with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-o", taxout, "-F", "csv", *args ) return runtmp.run_sourmash("tax", "prepare", "-t", tax, "-o", taxout, "-F", "csv", *args) assert os.path.exists(taxout) db1 = tax_utils.MultiLineageDB.load( [tax], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) db2 = tax_utils.MultiLineageDB.load([taxout]) assert set(db1) == set(db2) def test_tax_prepare_1_csv_to_csv_empty_file(runtmp, keep_identifiers, keep_versions): # CSV -> CSV with an empty input file and --force # tests argparse extend tax = utils.get_test_data("tax/test-empty-ranks.taxonomy.csv") tax_empty = runtmp.output("t.csv") taxout = runtmp.output("out.csv") with open(tax_empty, "w") as fp: fp.write("") print("t_csv: ", tax_empty) args = [] if keep_identifiers: args.append("--keep-full-identifiers") if keep_versions: args.append("--keep-identifier-versions") # this is an error - can't strip versions if not splitting identifiers if keep_identifiers and not keep_versions: with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-o", taxout, "-F", "csv", *args ) return runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-t", tax_empty, "-o", taxout, "-F", "csv", *args, "--force", ) assert os.path.exists(taxout) db1 = tax_utils.MultiLineageDB.load( [tax], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) db2 = tax_utils.MultiLineageDB.load([taxout]) assert set(db1) == set(db2) def test_tax_prepare_1_csv_to_csv_empty_ranks_2( runtmp, keep_identifiers, keep_versions ): # CSV -> CSV; same assignments for situations with empty internal ranks tax = utils.get_test_data("tax/test-empty-ranks-2.taxonomy.csv") taxout = runtmp.output("out.csv") args = [] if keep_identifiers: args.append("--keep-full-identifiers") if keep_versions: args.append("--keep-identifier-versions") # this is an error - can't strip versions if not splitting identifiers if keep_identifiers and not keep_versions: with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-o", taxout, "-F", "csv", *args ) return runtmp.run_sourmash("tax", "prepare", "-t", tax, "-o", taxout, "-F", "csv", *args) assert os.path.exists(taxout) db1 = tax_utils.MultiLineageDB.load( [tax], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) db2 = tax_utils.MultiLineageDB.load([taxout]) assert set(db1) == set(db2) def test_tax_prepare_1_csv_to_csv_empty_ranks_3( runtmp, keep_identifiers, keep_versions ): # CSV -> CSV; same assignments for situations with empty internal ranks tax = utils.get_test_data("tax/test-empty-ranks-3.taxonomy.csv") taxout = runtmp.output("out.csv") args = [] if keep_identifiers: args.append("--keep-full-identifiers") if keep_versions: args.append("--keep-identifier-versions") # this is an error - can't strip versions if not splitting identifiers if keep_identifiers and not keep_versions: with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-o", taxout, "-F", "csv", *args ) return runtmp.run_sourmash("tax", "prepare", "-t", tax, "-o", taxout, "-F", "csv", *args) assert os.path.exists(taxout) db1 = tax_utils.MultiLineageDB.load( [tax], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) db2 = tax_utils.MultiLineageDB.load([taxout]) assert set(db1) == set(db2) def test_tax_prepare_2_csv_to_sql(runtmp, keep_identifiers, keep_versions): # CSV -> SQL; same assignments? tax = utils.get_test_data("tax/test.taxonomy.csv") taxout = runtmp.output("out.db") args = [] if keep_identifiers: args.append("--keep-full-identifiers") if keep_versions: args.append("--keep-identifier-versions") # this is an error - can't strip versions if not splitting identifiers if keep_identifiers and not keep_versions: with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-o", taxout, "-F", "sql", *args ) return runtmp.run_sourmash("tax", "prepare", "-t", tax, "-o", taxout, "-F", "sql", *args) assert os.path.exists(taxout) db1 = tax_utils.MultiLineageDB.load( [tax], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) db2 = tax_utils.MultiLineageDB.load([taxout]) assert set(db1) == set(db2) # cannot overwrite - with pytest.raises(SourmashCommandFailed) as exc: runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-o", taxout, "-F", "sql", *args ) assert "taxonomy table already exists" in str(exc.value) def test_tax_prepare_2_csv_to_sql_empty_ranks(runtmp, keep_identifiers, keep_versions): # CSV -> SQL with some empty ranks in the taxonomy file tax = utils.get_test_data("tax/test-empty-ranks.taxonomy.csv") taxout = runtmp.output("out.db") args = [] if keep_identifiers: args.append("--keep-full-identifiers") if keep_versions: args.append("--keep-identifier-versions") # this is an error - can't strip versions if not splitting identifiers if keep_identifiers and not keep_versions: with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-o", taxout, "-F", "sql", *args ) return runtmp.run_sourmash("tax", "prepare", "-t", tax, "-o", taxout, "-F", "sql", *args) assert os.path.exists(taxout) db1 = tax_utils.MultiLineageDB.load( [tax], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) db2 = tax_utils.MultiLineageDB.load([taxout]) assert set(db1) == set(db2) def test_tax_prepare_3_db_to_csv(runtmp): # SQL -> CSV; same assignments taxcsv = utils.get_test_data("tax/test.taxonomy.csv") taxdb = utils.get_test_data("tax/test.taxonomy.db") taxout = runtmp.output("out.csv") runtmp.run_sourmash("tax", "prepare", "-t", taxdb, "-o", taxout, "-F", "csv") assert os.path.exists(taxout) with open(taxout) as fp: print(fp.read()) db1 = tax_utils.MultiLineageDB.load( [taxcsv], keep_full_identifiers=False, keep_identifier_versions=False ) db2 = tax_utils.MultiLineageDB.load([taxout]) db3 = tax_utils.MultiLineageDB.load( [taxdb], keep_full_identifiers=False, keep_identifier_versions=False ) assert set(db1) == set(db2) assert set(db1) == set(db3) def test_tax_prepare_3_db_to_csv_gz(runtmp): # SQL -> CSV; same assignments taxcsv = utils.get_test_data("tax/test.taxonomy.csv") taxdb = utils.get_test_data("tax/test.taxonomy.db") taxout = runtmp.output("out.csv.gz") runtmp.run_sourmash("tax", "prepare", "-t", taxdb, "-o", taxout, "-F", "csv") assert os.path.exists(taxout) with gzip.open(taxout, "rt") as fp: print(fp.read()) db1 = tax_utils.MultiLineageDB.load( [taxcsv], keep_full_identifiers=False, keep_identifier_versions=False ) db2 = tax_utils.MultiLineageDB.load([taxout]) db3 = tax_utils.MultiLineageDB.load( [taxdb], keep_full_identifiers=False, keep_identifier_versions=False ) assert set(db1) == set(db2) assert set(db1) == set(db3) def test_tax_prepare_2_csv_to_sql_empty_ranks_2( runtmp, keep_identifiers, keep_versions ): # CSV -> SQL with some empty internal ranks in the taxonomy file tax = utils.get_test_data("tax/test-empty-ranks-2.taxonomy.csv") taxout = runtmp.output("out.db") args = [] if keep_identifiers: args.append("--keep-full-identifiers") if keep_versions: args.append("--keep-identifier-versions") # this is an error - can't strip versions if not splitting identifiers if keep_identifiers and not keep_versions: with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-o", taxout, "-F", "sql", *args ) return runtmp.run_sourmash("tax", "prepare", "-t", tax, "-o", taxout, "-F", "sql", *args) assert os.path.exists(taxout) db1 = tax_utils.MultiLineageDB.load( [tax], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) db2 = tax_utils.MultiLineageDB.load([taxout]) assert set(db1) == set(db2) def test_tax_prepare_2_csv_to_sql_empty_ranks_3( runtmp, keep_identifiers, keep_versions ): # CSV -> SQL with some empty internal ranks in the taxonomy file tax = utils.get_test_data("tax/test-empty-ranks-3.taxonomy.csv") taxout = runtmp.output("out.db") args = [] if keep_identifiers: args.append("--keep-full-identifiers") if keep_versions: args.append("--keep-identifier-versions") # this is an error - can't strip versions if not splitting identifiers if keep_identifiers and not keep_versions: with pytest.raises(SourmashCommandFailed): runtmp.run_sourmash( "tax", "prepare", "-t", tax, "-o", taxout, "-F", "sql", *args ) return runtmp.run_sourmash("tax", "prepare", "-t", tax, "-o", taxout, "-F", "sql", *args) assert os.path.exists(taxout) db1 = tax_utils.MultiLineageDB.load( [tax], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) db2 = tax_utils.MultiLineageDB.load([taxout]) assert set(db1) == set(db2) def test_tax_prepare_3_db_to_csv_empty_ranks(runtmp): # SQL -> CSV; same assignments, with empty ranks taxcsv = utils.get_test_data("tax/test-empty-ranks.taxonomy.csv") taxdb = utils.get_test_data("tax/test-empty-ranks.taxonomy.db") taxout = runtmp.output("out.csv") runtmp.run_sourmash("tax", "prepare", "-t", taxdb, "-o", taxout, "-F", "csv") assert os.path.exists(taxout) with open(taxout) as fp: print(fp.read()) db1 = tax_utils.MultiLineageDB.load( [taxcsv], keep_full_identifiers=False, keep_identifier_versions=False ) db2 = tax_utils.MultiLineageDB.load([taxout]) db3 = tax_utils.MultiLineageDB.load( [taxdb], keep_full_identifiers=False, keep_identifier_versions=False ) assert set(db1) == set(db2) assert set(db1) == set(db3) def test_tax_prepare_3_db_to_csv_empty_ranks_2(runtmp): # SQL -> CSV; same assignments, with empty ranks taxcsv = utils.get_test_data("tax/test-empty-ranks-2.taxonomy.csv") taxdb = utils.get_test_data("tax/test-empty-ranks-2.taxonomy.db") taxout = runtmp.output("out.csv") runtmp.run_sourmash("tax", "prepare", "-t", taxdb, "-o", taxout, "-F", "csv") assert os.path.exists(taxout) with open(taxout) as fp: print(fp.read()) db1 = tax_utils.MultiLineageDB.load( [taxcsv], keep_full_identifiers=False, keep_identifier_versions=False ) db2 = tax_utils.MultiLineageDB.load([taxout]) db3 = tax_utils.MultiLineageDB.load( [taxdb], keep_full_identifiers=False, keep_identifier_versions=False ) assert set(db1) == set(db2) assert set(db1) == set(db3) def test_tax_prepare_3_db_to_csv_empty_ranks_3(runtmp): # SQL -> CSV; same assignments, with empty ranks taxcsv = utils.get_test_data("tax/test-empty-ranks-3.taxonomy.csv") taxdb = utils.get_test_data("tax/test-empty-ranks-3.taxonomy.db") taxout = runtmp.output("out.csv") runtmp.run_sourmash("tax", "prepare", "-t", taxdb, "-o", taxout, "-F", "csv") assert os.path.exists(taxout) with open(taxout) as fp: print(fp.read()) db1 = tax_utils.MultiLineageDB.load( [taxcsv], keep_full_identifiers=False, keep_identifier_versions=False ) db2 = tax_utils.MultiLineageDB.load([taxout]) db3 = tax_utils.MultiLineageDB.load( [taxdb], keep_full_identifiers=False, keep_identifier_versions=False ) assert set(db1) == set(db2) assert set(db1) == set(db3) def test_tax_prepare_sqlite_lineage_version(runtmp): # test bad sourmash_internals version for SqliteLineage taxcsv = utils.get_test_data("tax/test.taxonomy.csv") taxout = runtmp.output("out.db") runtmp.run_sourmash("tax", "prepare", "-t", taxcsv, "-o", taxout, "-F", "sql") assert os.path.exists(taxout) # set bad version conn = sqlite_utils.open_sqlite_db(taxout) c = conn.cursor() c.execute("UPDATE sourmash_internal SET value='0.9' WHERE key='SqliteLineage'") conn.commit() conn.close() with pytest.raises(IndexNotSupported): tax_utils.MultiLineageDB.load([taxout]) def test_tax_prepare_sqlite_no_lineage(): # no lineage table at all sqldb = utils.get_test_data("sqlite/index.sqldb") with pytest.raises(ValueError): tax_utils.MultiLineageDB.load([sqldb]) def test_tax_grep_exists(runtmp): # test that 'tax grep' exists with pytest.raises(SourmashCommandFailed): runtmp.sourmash("tax", "grep") err = runtmp.last_result.err assert "usage:" in err def test_tax_grep_search_shew(runtmp): # test 'tax grep Shew' taxfile = utils.get_test_data("tax/test.taxonomy.csv") runtmp.sourmash("tax", "grep", "Shew", "-t", taxfile) out = runtmp.last_result.out err = runtmp.last_result.err lines = [x.strip() for x in out.splitlines()] lines = [x.split(",") for x in lines] assert lines[0][0] == "ident" assert lines[1][0] == "GCF_000017325.1" assert lines[2][0] == "GCF_000021665.1" assert len(lines) == 3 assert "searching 1 taxonomy files for 'Shew'" in err assert "found 2 matches; saved identifiers to picklist" in err def test_tax_grep_search_shew_out(runtmp): # test 'tax grep Shew', save result to a file taxfile = utils.get_test_data("tax/test.taxonomy.csv") runtmp.sourmash("tax", "grep", "Shew", "-t", taxfile, "-o", "pick.csv") err = runtmp.last_result.err out = Path(runtmp.output("pick.csv")).read_text() lines = [x.strip() for x in out.splitlines()] lines = [x.split(",") for x in lines] assert lines[0][0] == "ident" assert lines[1][0] == "GCF_000017325.1" assert lines[2][0] == "GCF_000021665.1" assert len(lines) == 3 assert "searching 1 taxonomy files for 'Shew'" in err assert "found 2 matches; saved identifiers to picklist" in err def test_tax_grep_search_shew_sqldb_out(runtmp): # test 'tax grep Shew' on a sqldb, save result to a file taxfile = utils.get_test_data("tax/test.taxonomy.db") runtmp.sourmash("tax", "grep", "Shew", "-t", taxfile, "-o", "pick.csv") err = runtmp.last_result.err out = Path(runtmp.output("pick.csv")).read_text() lines = [x.strip() for x in out.splitlines()] lines = [x.split(",") for x in lines] assert lines[0][0] == "ident" assert lines[1][0] == "GCF_000017325" assert lines[2][0] == "GCF_000021665" assert len(lines) == 3 assert "searching 1 taxonomy files for 'Shew'" in err assert "found 2 matches; saved identifiers to picklist" in err def test_tax_grep_search_shew_lowercase(runtmp): # test 'tax grep shew' (lowercase), save result to a file taxfile = utils.get_test_data("tax/test.taxonomy.csv") runtmp.sourmash("tax", "grep", "shew", "-t", taxfile, "-o", "pick.csv") err = runtmp.last_result.err assert "searching 1 taxonomy files for 'shew'" in err assert "found 0 matches; saved identifiers to picklist" in err runtmp.sourmash("tax", "grep", "-i", "shew", "-t", taxfile, "-o", "pick.csv") err = runtmp.last_result.err assert "searching 1 taxonomy files for 'shew'" in err assert "found 2 matches; saved identifiers to picklist" in err out = Path(runtmp.output("pick.csv")).read_text() lines = [x.strip() for x in out.splitlines()] lines = [x.split(",") for x in lines] assert lines[0][0] == "ident" assert lines[1][0] == "GCF_000017325.1" assert lines[2][0] == "GCF_000021665.1" assert len(lines) == 3 def test_tax_grep_search_shew_out_use_picklist(runtmp): # test 'tax grep Shew', output to a picklist, use picklist taxfile = utils.get_test_data("tax/test.taxonomy.csv") dbfile = utils.get_test_data("tax/gtdb-tax-grep.sigs.zip") runtmp.sourmash("tax", "grep", "Shew", "-t", taxfile, "-o", "pick.csv") runtmp.sourmash( "sig", "cat", dbfile, "--picklist", "pick.csv:ident:ident", "-o", "pick-out.zip" ) all_sigs = sourmash.load_file_as_index(dbfile) assert len(all_sigs) == 3 pick_sigs = sourmash.load_file_as_index(runtmp.output("pick-out.zip")) assert len(pick_sigs) == 2 names = [ss.name.split()[0] for ss in pick_sigs.signatures()] assert len(names) == 2 assert "GCF_000017325.1" in names assert "GCF_000021665.1" in names def test_tax_grep_search_shew_invert(runtmp): # test 'tax grep -v Shew' taxfile = utils.get_test_data("tax/test.taxonomy.csv") runtmp.sourmash("tax", "grep", "-v", "Shew", "-t", taxfile) out = runtmp.last_result.out err = runtmp.last_result.err assert ( "-v/--invert-match specified; returning only lineages that do not match." in err ) lines = [x.strip() for x in out.splitlines()] lines = [x.split(",") for x in lines] assert lines[0][0] == "ident" assert lines[1][0] == "GCF_001881345.1" assert lines[2][0] == "GCF_003471795.1" assert len(lines) == 5 assert "searching 1 taxonomy files for 'Shew'" in err assert "found 4 matches; saved identifiers to picklist" in err all_names = set([x[0] for x in lines]) assert "GCF_000017325.1" not in all_names assert "GCF_000021665.1" not in all_names def test_tax_grep_search_shew_invert_select_phylum(runtmp): # test 'tax grep -v Shew -r phylum' taxfile = utils.get_test_data("tax/test.taxonomy.csv") runtmp.sourmash("tax", "grep", "-v", "Shew", "-t", taxfile, "-r", "phylum") out = runtmp.last_result.out err = runtmp.last_result.err assert ( "-v/--invert-match specified; returning only lineages that do not match." in err ) assert "limiting matches to phylum" lines = [x.strip() for x in out.splitlines()] lines = [x.split(",") for x in lines] assert lines[0][0] == "ident" assert len(lines) == 7 assert "searching 1 taxonomy files for 'Shew'" in err assert "found 6 matches; saved identifiers to picklist" in err all_names = set([x[0] for x in lines]) assert "GCF_000017325.1" in all_names assert "GCF_000021665.1" in all_names def test_tax_grep_search_shew_invert_select_bad_rank(runtmp): # test 'tax grep -v Shew -r badrank' - should fail taxfile = utils.get_test_data("tax/test.taxonomy.csv") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("tax", "grep", "-v", "Shew", "-t", taxfile, "-r", "badrank") err = runtmp.last_result.err print(err) assert "error: argument -r/--rank: invalid choice:" in err def test_tax_grep_search_shew_count(runtmp): # test 'tax grep Shew --count' taxfile = utils.get_test_data("tax/test.taxonomy.csv") runtmp.sourmash("tax", "grep", "Shew", "-t", taxfile, "-c") out = runtmp.last_result.out err = runtmp.last_result.err assert not out.strip() assert "searching 1 taxonomy files for 'Shew'" in err assert "found 2 matches; saved identifiers to picklist" not in err def test_tax_grep_multiple_csv(runtmp): # grep on multiple CSVs tax1 = utils.get_test_data("tax/test.taxonomy.csv") tax2 = utils.get_test_data("tax/protozoa_genbank_lineage.csv") taxout = runtmp.output("out.csv") runtmp.sourmash("tax", "grep", "Toxo|Gamma", "-t", tax1, tax2, "-o", taxout) out = runtmp.last_result.out err = runtmp.last_result.err assert not out assert "found 4 matches" in err lines = Path(taxout).read_text().splitlines() assert len(lines) == 5 names = set([x.split(",")[0] for x in lines]) assert "GCA_000256725" in names assert "GCF_000017325.1" in names assert "GCF_000021665.1" in names assert "GCF_001881345.1" in names def test_tax_grep_multiple_csv_empty_force(runtmp): # grep on multiple CSVs, one empty, with --force tax1 = utils.get_test_data("tax/test.taxonomy.csv") tax2 = utils.get_test_data("tax/protozoa_genbank_lineage.csv") tax_empty = runtmp.output("t.csv") taxout = runtmp.output("out.csv") with open(tax_empty, "w") as fp: fp.write("") print("t_csv: ", tax_empty) runtmp.sourmash( "tax", "grep", "Toxo|Gamma", "-t", tax1, tax2, "-t", tax_empty, "-o", taxout, "--force", ) out = runtmp.last_result.out err = runtmp.last_result.err assert not out assert "found 4 matches" in err lines = Path(taxout).read_text().splitlines() assert len(lines) == 5 names = set([x.split(",")[0] for x in lines]) assert "GCA_000256725" in names assert "GCF_000017325.1" in names assert "GCF_000021665.1" in names assert "GCF_001881345.1" in names def test_tax_grep_duplicate_csv(runtmp): # grep on duplicates => should collapse to uniques on identifiers tax1 = utils.get_test_data("tax/test.taxonomy.csv") taxout = runtmp.output("out.csv") runtmp.sourmash("tax", "grep", "Gamma", "-t", tax1, tax1, "-o", taxout) out = runtmp.last_result.out err = runtmp.last_result.err assert not out assert "found 3 matches" in err lines = Path(taxout).read_text().splitlines() assert len(lines) == 4 names = set([x.split(",")[0] for x in lines]) assert "GCF_000017325.1" in names assert "GCF_000021665.1" in names assert "GCF_001881345.1" in names def test_tax_summarize(runtmp): # test basic operation with summarize taxfile = utils.get_test_data("tax/test.taxonomy.csv") runtmp.sourmash("tax", "summarize", taxfile) out = runtmp.last_result.out assert "number of distinct taxonomic lineages: 6" in out assert "rank superkingdom: 1 distinct taxonomic lineages" in out assert "rank phylum: 2 distinct taxonomic lineages" in out assert "rank class: 2 distinct taxonomic lineages" in out assert "rank order: 2 distinct taxonomic lineages" in out assert "rank family: 3 distinct taxonomic lineages" in out assert "rank genus: 4 distinct taxonomic lineages" in out assert "rank species: 4 distinct taxonomic lineages" in out def test_tax_summarize_multiple(runtmp): # test basic operation with summarize on multiple files tax1 = utils.get_test_data("tax/bacteria_refseq_lineage.csv") tax2 = utils.get_test_data("tax/protozoa_genbank_lineage.csv") runtmp.sourmash("tax", "summarize", tax1, tax2) out = runtmp.last_result.out assert "number of distinct taxonomic lineages: 6" in out assert "rank superkingdom: 2 distinct taxonomic lineages" in out assert "rank phylum: 3 distinct taxonomic lineages" in out assert "rank class: 4 distinct taxonomic lineages" in out assert "rank order: 4 distinct taxonomic lineages" in out assert "rank family: 5 distinct taxonomic lineages" in out assert "rank genus: 5 distinct taxonomic lineages" in out assert "rank species: 5 distinct taxonomic lineages" in out def test_tax_summarize_empty_line(runtmp): # test basic operation with summarize on a file w/empty line taxfile = utils.get_test_data("tax/test-empty-line.taxonomy.csv") runtmp.sourmash("tax", "summarize", taxfile) out = runtmp.last_result.out assert "number of distinct taxonomic lineages: 6" in out assert "rank superkingdom: 1 distinct taxonomic lineages" in out assert "rank phylum: 2 distinct taxonomic lineages" in out assert "rank class: 2 distinct taxonomic lineages" in out assert "rank order: 2 distinct taxonomic lineages" in out assert "rank family: 3 distinct taxonomic lineages" in out assert "rank genus: 4 distinct taxonomic lineages" in out assert "rank species: 4 distinct taxonomic lineages" in out def test_tax_summarize_empty(runtmp): # test failure on empty file taxfile = runtmp.output("no-exist") with pytest.raises(SourmashCommandFailed): runtmp.sourmash("tax", "summarize", taxfile) err = runtmp.last_result.err assert "ERROR while loading taxonomies" in err def test_tax_summarize_csv(runtmp): # test basic operation w/csv output taxfile = utils.get_test_data("tax/test.taxonomy.csv") runtmp.sourmash("tax", "summarize", taxfile, "-o", "ranks.csv") out = runtmp.last_result.out err = runtmp.last_result.err assert "number of distinct taxonomic lineages: 6" in out assert "saved 18 lineage counts to 'ranks.csv'" in err csv_out = runtmp.output("ranks.csv") with sourmash_args.FileInputCSV(csv_out) as r: # count number across ranks as a cheap consistency check c = Counter() for row in r: val = row["lineage_count"] c[val] += 1 assert c["3"] == 7 assert c["2"] == 5 assert c["1"] == 5 def test_tax_summarize_on_annotate(runtmp): # test summarize on output of annotate basics g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.taxonomy.csv") csvout = runtmp.output("test1.gather.with-lineages.csv") out_dir = os.path.dirname(csvout) runtmp.run_sourmash( "tax", "annotate", "--gather-csv", g_csv, "--taxonomy-csv", tax, "-o", out_dir ) print(runtmp.last_result.status) print(runtmp.last_result.out) print(runtmp.last_result.err) assert runtmp.last_result.status == 0 assert os.path.exists(csvout) # so far so good - now see if we can run summarize! runtmp.run_sourmash("tax", "summarize", csvout) out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "number of distinct taxonomic lineages: 4" in out assert "rank superkingdom: 1 distinct taxonomic lineages" in out assert "rank phylum: 2 distinct taxonomic lineages" in out assert "rank class: 2 distinct taxonomic lineages" in out assert "rank order: 2 distinct taxonomic lineages" in out assert "rank family: 2 distinct taxonomic lineages" in out assert "rank genus: 3 distinct taxonomic lineages" in out assert "rank species: 3 distinct taxonomic lineages" in out def test_tax_summarize_strain_csv(runtmp): # test basic operation w/csv output on taxonomy with strains taxfile = utils.get_test_data("tax/test-strain.taxonomy.csv") runtmp.sourmash("tax", "summarize", taxfile, "-o", "ranks.csv") out = runtmp.last_result.out err = runtmp.last_result.err assert "number of distinct taxonomic lineages: 6" in out assert "saved 24 lineage counts to 'ranks.csv'" in err csv_out = runtmp.output("ranks.csv") with sourmash_args.FileInputCSV(csv_out) as r: # count number across ranks as a cheap consistency check c = Counter() for row in r: print(row) val = row["lineage_count"] c[val] += 1 print(list(c.most_common())) assert c["3"] == 7 assert c["2"] == 5 assert c["6"] == 1 assert c["1"] == 11 def test_tax_summarize_strain_csv_with_lineages(runtmp): # test basic operation w/csv output on lineages-style file w/strain csv taxfile = utils.get_test_data("tax/test-strain.taxonomy.csv") lineage_csv = runtmp.output("lin-with-strains.csv") taxdb = tax_utils.LineageDB.load(taxfile) with open(lineage_csv, "w", newline="") as fp: w = csv.writer(fp) w.writerow(["name", "lineage"]) for k, v in taxdb.items(): linstr = lca_utils.display_lineage(v) w.writerow([k, linstr]) runtmp.sourmash("tax", "summarize", lineage_csv, "-o", "ranks.csv") out = runtmp.last_result.out err = runtmp.last_result.err assert "number of distinct taxonomic lineages: 6" in out assert "saved 24 lineage counts to" in err csv_out = runtmp.output("ranks.csv") with sourmash_args.FileInputCSV(csv_out) as r: # count number across ranks as a cheap consistency check c = Counter() for row in r: print(row) val = row["lineage_count"] c[val] += 1 print(list(c.most_common())) assert c["3"] == 7 assert c["2"] == 5 assert c["6"] == 1 assert c["1"] == 11 def test_tax_summarize_ictv(runtmp): # test basic operation w/csv output on lineages-style file w/strain csv taxfile = utils.get_test_data("tax/test.ictv-taxonomy.csv") lineage_csv = runtmp.output("ictv-lins.csv") taxdb = tax_utils.LineageDB.load(taxfile) with open(lineage_csv, "w", newline="") as fp: w = csv.writer(fp) w.writerow(["name", "lineage"]) for k, v in taxdb.items(): linstr = lca_utils.display_lineage(v) w.writerow([k, linstr]) runtmp.sourmash("tax", "summarize", lineage_csv, "-o", "ranks.csv", "--ictv") out = runtmp.last_result.out err = runtmp.last_result.err assert "number of distinct taxonomic lineages: 7" in out assert "saved 14 lineage counts to" in err csv_out = runtmp.output("ranks.csv") with sourmash_args.FileInputCSV(csv_out) as r: # count number across ranks as a cheap consistency check c = Counter() for row in r: print(row) val = row["lineage_count"] c[val] += 1 print(list(c.most_common())) print(c) assert c["1"] == 8 assert c["7"] == 5 assert c["6"] == 1 def test_tax_summarize_LINS(runtmp): # test basic operation w/LINs taxfile = utils.get_test_data("tax/test.LIN-taxonomy.csv") lineage_csv = runtmp.output("annotated-lin.csv") taxdb = tax_utils.LineageDB.load(taxfile, lins=True) with open(lineage_csv, "w", newline="") as fp: w = csv.writer(fp) w.writerow(["name", "lineage"]) for k, v in taxdb.items(): lin = tax_utils.LINLineageInfo(lineage=v) linstr = lin.display_lineage(truncate_empty=False) print(linstr) w.writerow([k, linstr]) runtmp.sourmash("tax", "summarize", lineage_csv, "-o", "ranks.csv", "--lins") out = runtmp.last_result.out err = runtmp.last_result.err print(out) print(err) assert "number of distinct taxonomic lineages: 6" in out assert "saved 91 lineage counts to" in err csv_out = runtmp.output("ranks.csv") with sourmash_args.FileInputCSV(csv_out) as r: # count number across ranks as a cheap consistency check c = Counter() for row in r: print(row) val = row["lineage_count"] c[val] += 1 print(list(c.most_common())) assert c["1"] == 77 assert c["2"] == 1 assert c["3"] == 11 assert c["4"] == 2 def test_metagenome_LIN(runtmp): # test basic metagenome with LIN taxonomy c = runtmp g_csv = utils.get_test_data("tax/test1.gather.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") c.run_sourmash("tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins") print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in c.last_result.out ) # 0th rank/position assert "test1,0,0.089,1,md5,test1.sig,0.057,444000,0.925,0" in c.last_result.out assert "test1,0,0.088,0,md5,test1.sig,0.058,442000,0.925,0" in c.last_result.out assert "test1,0,0.028,2,md5,test1.sig,0.016,138000,0.891,0" in c.last_result.out assert ( "test1,0,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) # 1st rank/position assert "test1,1,0.089,1;0,md5,test1.sig,0.057,444000,0.925,0" in c.last_result.out assert "test1,1,0.088,0;0,md5,test1.sig,0.058,442000,0.925,0" in c.last_result.out assert "test1,1,0.028,2;0,md5,test1.sig,0.016,138000,0.891,0" in c.last_result.out assert ( "test1,1,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) # 2nd rank/position assert "test1,2,0.088,0;0;0,md5,test1.sig,0.058,442000,0.925,0" in c.last_result.out assert "test1,2,0.078,1;0;0,md5,test1.sig,0.050,390000,0.921,0" in c.last_result.out assert "test1,2,0.028,2;0;0,md5,test1.sig,0.016,138000,0.891,0" in c.last_result.out assert "test1,2,0.011,1;0;1,md5,test1.sig,0.007,54000,0.864,0" in c.last_result.out assert ( "test1,2,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) # 19th rank/position assert ( "test1,19,0.088,0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0,md5,test1.sig,0.058,442000,0.925,0" in c.last_result.out ) assert ( "test1,19,0.078,1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0,md5,test1.sig,0.050,390000,0.921,0" in c.last_result.out ) assert ( "test1,19,0.028,2;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0,md5,test1.sig,0.016,138000,0.891,0" in c.last_result.out ) assert ( "test1,19,0.011,1;0;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0,md5,test1.sig,0.007,54000,0.864,0" in c.last_result.out ) assert ( "test1,19,0.796,unclassified,md5,test1.sig,0.869,3990000,,0" in c.last_result.out ) def test_metagenome_LIN_lingroups(runtmp): # test lingroups output c = runtmp g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") lg_file = runtmp.output("test.lg.csv") with open(lg_file, "w") as out: out.write("lin,name\n") out.write("0;0;0,lg1\n") out.write("1;0;0,lg2\n") out.write("2;0;0,lg3\n") out.write("1;0;1,lg3\n") # write a 19 so we can check the end out.write("1;0;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0,lg4\n") c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "--lingroup", lg_file, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "Read 5 lingroup rows and found 5 distinct lingroup prefixes." in c.last_result.err ) assert "name lin percent_containment num_bp_contained" in c.last_result.out assert "lg1 0;0;0 5.82 714000" in c.last_result.out assert "lg2 1;0;0 5.05 620000" in c.last_result.out assert "lg3 2;0;0 1.56 192000" in c.last_result.out assert "lg3 1;0;1 0.65 80000" in c.last_result.out assert ( "lg4 1;0;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0 0.65 80000" in c.last_result.out ) def test_metagenome_LIN_lingroups_summary(runtmp): # test lingroups summary file. Can no longer output stdout, b/c will produce 2 files c = runtmp csv_base = "out" sum_csv = csv_base + ".summarized.csv" csvout = runtmp.output(sum_csv) outdir = os.path.dirname(csvout) g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") lg_file = runtmp.output("test.lg.csv") with open(lg_file, "w") as out: out.write("lin,name\n") out.write("0;0;0,lg1\n") out.write("1;0;0,lg2\n") out.write("2;0;0,lg3\n") out.write("1;0;1,lg3\n") # write a 19 so we can check the end out.write("1;0;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0,lg4\n") c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "--lingroup", lg_file, "-o", csv_base, "--output-dir", outdir, "-F", "csv_summary", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert ( "Read 5 lingroup rows and found 5 distinct lingroup prefixes." in c.last_result.err ) assert os.path.exists(csvout) sum_gather_results = [x.rstrip() for x in Path(csvout).read_text().splitlines()] print(sum_gather_results) assert f"saving 'csv_summary' output to '{csvout}'" in runtmp.last_result.err assert ( "query_name,rank,fraction,lineage,query_md5,query_filename,f_weighted_at_rank,bp_match_at_rank" in sum_gather_results[0] ) assert ( "test1,2,0.08815317112086159,lg1,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.05815279361459521,442000,0.9246458342627294,6139" in sum_gather_results[1] ) assert ( "test1,2,0.07778220981252493,lg2,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.050496823586903404,390000,0.920920083987624,6139" in sum_gather_results[2] ) assert ( "test1,2,0.027522935779816515,lg3,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.015637726014008795,138000,0.8905689983332759,6139" in sum_gather_results[3] ) assert ( "test1,2,0.010769844435580374,lg3,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.006515719172503665,54000,0.8640181883213995,6139" in sum_gather_results[4] ) assert ( "test1,2,0.7957718388512166,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.8691969376119889,3990000,,6139" in sum_gather_results[5] ) assert ( "test1,19,0.010769844435580374,lg4,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.006515719172503665,54000,0.8640181883213995,6139" in sum_gather_results[6] ) assert ( "test1,19,0.7957718388512166,unclassified,9687eeed,outputs/abundtrim/HSMA33MX.abundtrim.fq.gz,0.8691969376119889,3990000,,6139" in sum_gather_results[7] ) def test_metagenome_LIN_human_summary_no_lin_position(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "-F", "human" ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "sample name proportion cANI lineage" in c.last_result.out assert "----------- ---------- ---- -------" in c.last_result.out assert "test1 86.9% - unclassified" in c.last_result.out assert ( "test1 5.8% 92.5% 0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0" in c.last_result.out ) assert ( "test1 5.0% 92.1% 1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0" in c.last_result.out ) assert ( "test1 1.6% 89.1% 2;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0" in c.last_result.out ) assert ( "test1 0.7% 86.4% 1;0;1;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0" in c.last_result.out ) def test_metagenome_LIN_human_summary_lin_position_5(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "-F", "human", "--lin-position", "5", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "sample name proportion cANI lineage" in c.last_result.out assert "----------- ---------- ---- -------" in c.last_result.out assert "test1 86.9% - unclassified" in c.last_result.out assert "test1 5.8% 92.5% 0;0;0;0;0;0" in c.last_result.out assert "test1 5.0% 92.1% 1;0;0;0;0;0" in c.last_result.out assert "test1 1.6% 89.1% 2;0;0;0;0;0" in c.last_result.out assert "test1 0.7% 86.4% 1;0;1;0;0;0" in c.last_result.out def test_metagenome_LIN_krona_lin_position_5(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "-F", "krona", "--lin-position", "5", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status == 0 assert "fraction 0 1 2 3 4 5" in c.last_result.out assert "0.08815317112086159 0 0 0 0 0 0" in c.last_result.out assert "0.07778220981252493 1 0 0 0 0 0" in c.last_result.out assert "0.027522935779816515 2 0 0 0 0 0" in c.last_result.out assert "0.010769844435580374 1 0 1 0 0 0" in c.last_result.out assert ( "0.7957718388512166 unclassified unclassified unclassified unclassified unclassified unclassified" in c.last_result.out ) def test_metagenome_LIN_krona_bad_rank(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "-F", "krona", "--lin-position", "strain", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status != 0 assert ( "Invalid '--rank'/'--position' input: 'strain'. '--lins' is specified. Rank must be an integer corresponding to a LIN position." in c.last_result.err ) def test_metagenome_LIN_lingroups_empty_lg_file(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") lg_file = runtmp.output("test.lg.csv") with open(lg_file, "w") as out: out.write("") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "--lingroup", lg_file, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status != 0 assert ( f"Cannot read lingroups from '{lg_file}'. Is file empty?" in c.last_result.err ) def test_metagenome_LIN_lingroups_bad_cli_inputs(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") lg_file = runtmp.output("test.lg.csv") with open(lg_file, "w") as out: out.write("") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "-F", "lingroup", ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status != 0 assert ( "Must provide lingroup csv via '--lingroup' in order to output a lingroup report." in c.last_result.err ) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "-F", "lingroup" ) print(c.last_result.err) assert c.last_result.status != 0 assert ( "Must enable LIN taxonomy via '--lins' in order to use lingroups." in c.last_result.err ) with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lingroup", lg_file, ) print(c.last_result.err) assert c.last_result.status != 0 with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "-F", "bioboxes", ) print(c.last_result.err) assert c.last_result.status != 0 assert ( "ERROR: The following outputs are incompatible with '--lins': : bioboxes, kreport" in c.last_result.err ) def test_metagenome_mult_outputs_stdout_fail(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "-F", "kreport", "csv_summary", ) print(c.last_result.err) assert c.last_result.status != 0 assert ( "Writing to stdout is incompatible with multiple output formats ['kreport', 'csv_summary']" in c.last_result.err ) def test_genome_mult_outputs_stdout_fail(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "genome", "-g", g_csv, "--taxonomy-csv", tax, "-F", "lineage_csv", "csv_summary", ) print(c.last_result.err) assert c.last_result.status != 0 assert ( "Writing to stdout is incompatible with multiple output formats ['lineage_csv', 'csv_summary']" in c.last_result.err ) def test_metagenome_LIN_lingroups_lg_only_header(runtmp): c = runtmp g_csv = utils.get_test_data("tax/test1.gather.v450.csv") tax = utils.get_test_data("tax/test.LIN-taxonomy.csv") lg_file = runtmp.output("test.lg.csv") with open(lg_file, "w") as out: out.write("lin,name\n") with pytest.raises(SourmashCommandFailed): c.run_sourmash( "tax", "metagenome", "-g", g_csv, "--taxonomy-csv", tax, "--lins", "--lingroup", lg_file, ) print(c.last_result.status) print(c.last_result.out) print(c.last_result.err) assert c.last_result.status != 0 assert f"No lingroups loaded from {lg_file}" in c.last_result.err sourmash-bio-sourmash-bc3c0f8/tests/test_tax_utils.py000066400000000000000000004765251474157046300233000ustar00rootroot00000000000000""" Tests for functions in taxonomy submodule. """ import pytest from pytest import approx import os from os.path import basename import gzip from pathlib import Path import sourmash_tst_utils as utils from sourmash.tax.tax_utils import ( ascending_taxlist, get_ident, load_gather_results, collect_gather_csvs, check_and_load_gather_csvs, LineagePair, QueryInfo, GatherRow, TaxResult, QueryTaxResult, SummarizedGatherResult, ClassificationResult, AnnotateTaxResult, BaseLineageInfo, RankLineageInfo, LINLineageInfo, ICTVRankLineageInfo, aggregate_by_lineage_at_rank, format_for_krona, write_krona, write_lineage_sample_frac, read_lingroups, LineageTree, LineageDB, LineageDB_Sqlite, MultiLineageDB, filter_row, NCBI_RANKS, ICTV_RANKS, ) # utility functions for testing def make_mini_taxonomy(tax_info, LIN=False): # pass in list of tuples: (name, lineage) taxD = {} for name, lin in tax_info: if LIN: lineage = LINLineageInfo(lineage_str=lin) else: lineage = RankLineageInfo(lineage_str=lin) taxD[name] = lineage.filled_lineage return taxD def make_mini_taxonomy_with_taxids(tax_info, LIN=False): taxD = {} for name, lin, taxids in tax_info: if LIN: lineage = LINLineageInfo(lineage_str=lin) else: ranks = RankLineageInfo.ranks txs = taxids.split(";") lns = lin.split(";") lineage_tups = [] for n, taxname in enumerate(lns): rk = ranks[n] tx = txs[n] this_lineage = LineagePair(rk, name=taxname, taxid=tx) lineage_tups.append(this_lineage) lineage = RankLineageInfo(lineage=lineage_tups) taxD[name] = lineage.filled_lineage return taxD def make_GatherRow(gather_dict=None, exclude_cols=[]): """Load artificial gather row (dict) into GatherRow class""" # default contains just the essential cols gatherD = { "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "name": "gA", "f_unique_weighted": 0.2, "f_unique_to_query": 0.1, "query_bp": 100, "unique_intersect_bp": 20, "remaining_bp": 1, "ksize": 31, "scaled": 1, } if gather_dict is not None: gatherD.update(gather_dict) for col in exclude_cols: gatherD.pop(col) fgatherD = filter_row(gatherD, GatherRow) gatherRaw = GatherRow(**fgatherD) return gatherRaw def make_TaxResult( gather_dict=None, taxD=None, keep_full_ident=False, keep_ident_version=False, skip_idents=None, LIN=False, ): """Make TaxResult from artificial gather row (dict)""" gRow = make_GatherRow(gather_dict) taxres = TaxResult( raw=gRow, keep_full_identifiers=keep_full_ident, keep_identifier_versions=keep_ident_version, lins=LIN, ) if taxD is not None: taxres.get_match_lineage(tax_assignments=taxD, skip_idents=skip_idents) return taxres def make_QueryTaxResults( gather_info, taxD=None, single_query=False, keep_full_ident=False, keep_ident_version=False, skip_idents=None, summarize=False, classify=False, classify_rank=None, c_thresh=0.1, ani_thresh=None, LIN=False, ): """Make QueryTaxResult(s) from artificial gather information, formatted as list of gather rows (dicts)""" gather_results = {} this_querytaxres = None for gather_infoD in gather_info: taxres = make_TaxResult( gather_infoD, taxD=taxD, keep_full_ident=keep_full_ident, keep_ident_version=keep_ident_version, skip_idents=skip_idents, LIN=LIN, ) query_name = taxres.query_name # add to matching QueryTaxResult or create new one if not this_querytaxres or not this_querytaxres.is_compatible(taxres): # get existing or initialize new this_querytaxres = gather_results.get( query_name, QueryTaxResult(taxres.query_info, lins=LIN) ) this_querytaxres.add_taxresult(taxres) # print('missed_ident?', taxres.missed_ident) gather_results[query_name] = this_querytaxres if summarize: for query_name, qres in gather_results.items(): qres.build_summarized_result() if classify: for query_name, qres in gather_results.items(): qres.build_classification_result( rank=classify_rank, containment_threshold=c_thresh, ani_threshold=ani_thresh, ) # for convenience: If working with single query, just return that QueryTaxResult. if single_query: if len(gather_results.keys()) > 1: raise ValueError("You passed in results for more than one query") else: return next(iter(gather_results.values())) return gather_results ## tests def test_ascending_taxlist_1(): assert list(ascending_taxlist()) == [ "strain", "species", "genus", "family", "order", "class", "phylum", "superkingdom", ] def test_ascending_taxlist_2(): assert list(ascending_taxlist(include_strain=False)) == [ "species", "genus", "family", "order", "class", "phylum", "superkingdom", ] def test_QueryInfo_basic(): "basic functionality of QueryInfo dataclass" qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) assert qInf.query_name == "q1" assert isinstance(qInf.query_n_hashes, int) assert isinstance(qInf.ksize, int) assert isinstance(qInf.scaled, int) assert qInf.total_weighted_hashes == 200 assert qInf.total_weighted_bp == 2000 def test_QueryInfo_no_hash_info(): "QueryInfo dataclass for older gather results without query_n_hashes or total_weighted_hashes" qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", ksize=31, scaled=10, ) assert qInf.query_name == "q1" assert qInf.query_n_hashes == 0 assert qInf.total_weighted_hashes == 0 assert qInf.total_weighted_bp == 0 def test_QueryInfo_missing(): "check that required args" with pytest.raises(TypeError) as exc: QueryInfo( query_name="q1", query_filename="f1", query_bp="100", query_n_hashes="10", ksize=31, scaled=10, total_weighted_hashes=200, ) print(str(exc)) assert "missing 1 required positional argument: 'query_md5'" in str(exc) def test_SummarizedGatherResult(): "basic functionality of SummarizedGatherResult dataclass" qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) sgr = SummarizedGatherResult( rank="phylum", fraction=0.2, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) print(sgr) assert sgr.rank == "phylum" sumD = sgr.as_summary_dict(query_info=qInf) print(sumD) assert sumD == { "rank": "phylum", "fraction": "0.2", "lineage": "a;b", "f_weighted_at_rank": "0.3", "bp_match_at_rank": "30", "query_ani_at_rank": None, "query_name": "q1", "query_md5": "md5", "query_filename": "f1", "total_weighted_hashes": "200", } hD = sgr.as_human_friendly_dict(query_info=qInf) print(hD) assert hD == { "rank": "phylum", "fraction": "0.200", "lineage": "a;b", "f_weighted_at_rank": "30.0%", "bp_match_at_rank": "30", "query_ani_at_rank": "- ", "query_name": "q1", "query_md5": "md5", "query_filename": "f1", "total_weighted_hashes": "200", } krD = sgr.as_kreport_dict(query_info=qInf) print(krD) assert krD == { "ncbi_taxid": None, "sci_name": "b", "rank_code": "P", "num_bp_assigned": "0", "percent_containment": "30.00", "num_bp_contained": "600", } lD = sgr.as_lineage_dict(ranks=RankLineageInfo().ranks, query_info=qInf) print(lD) assert lD == { "ident": "q1", "superkingdom": "a", "phylum": "b", "class": "", "order": "", "family": "", "genus": "", "species": "", "strain": "", } cami = sgr.as_cami_bioboxes() print(cami) assert cami == [None, "phylum", None, "a|b", "30.00"] def test_SummarizedGatherResult_withtaxids(): "basic functionality of SummarizedGatherResult dataclass" qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) lin = [ LineagePair(rank="superkingdom", name="a", taxid="1"), LineagePair(rank="phylum", name="b", taxid=2), ] sgr = SummarizedGatherResult( rank="phylum", fraction=0.2, lineage=RankLineageInfo(lineage=lin), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) print(sgr) assert sgr.rank == "phylum" sumD = sgr.as_summary_dict(query_info=qInf) print(sumD) assert sumD == { "rank": "phylum", "fraction": "0.2", "lineage": "a;b", "f_weighted_at_rank": "0.3", "bp_match_at_rank": "30", "query_ani_at_rank": None, "query_name": "q1", "query_md5": "md5", "query_filename": "f1", "total_weighted_hashes": "200", } hD = sgr.as_human_friendly_dict(query_info=qInf) print(hD) assert hD == { "rank": "phylum", "fraction": "0.200", "lineage": "a;b", "f_weighted_at_rank": "30.0%", "bp_match_at_rank": "30", "query_ani_at_rank": "- ", "query_name": "q1", "query_md5": "md5", "query_filename": "f1", "total_weighted_hashes": "200", } krD = sgr.as_kreport_dict(query_info=qInf) print(krD) assert krD == { "ncbi_taxid": "2", "sci_name": "b", "rank_code": "P", "num_bp_assigned": "0", "percent_containment": "30.00", "num_bp_contained": "600", } lD = sgr.as_lineage_dict(ranks=RankLineageInfo().ranks, query_info=qInf) print(lD) assert lD == { "ident": "q1", "superkingdom": "a", "phylum": "b", "class": "", "order": "", "family": "", "genus": "", "species": "", "strain": "", } cami = sgr.as_cami_bioboxes() print(cami) assert cami == ["2", "phylum", "1|2", "a|b", "30.00"] def test_SummarizedGatherResult_LINs(): "SummarizedGatherResult with LINs" qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) sgr = SummarizedGatherResult( rank="phylum", fraction=0.2, lineage=LINLineageInfo(lineage_str="0;0;1"), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) lgD = sgr.as_lingroup_dict(query_info=qInf, lg_name="lg_name") print(lgD) assert lgD == { "name": "lg_name", "lin": "0;0;1", "percent_containment": "30.00", "num_bp_contained": "600", } lgD = sgr.as_lingroup_dict(query_info=qInf, lg_name="lg_name") print(lgD) assert lgD == { "name": "lg_name", "lin": "0;0;1", "percent_containment": "30.00", "num_bp_contained": "600", } with pytest.raises(ValueError) as exc: sgr.as_kreport_dict(query_info=qInf) print(str(exc)) assert "Cannot produce 'kreport' with LIN taxonomy." in str(exc) with pytest.raises(ValueError) as exc: sgr.as_cami_bioboxes() print(str(exc)) assert "Cannot produce 'bioboxes' with LIN taxonomy." in str(exc) def test_SummarizedGatherResult_set_query_ani(): "Check ANI estimation within SummarizedGatherResult dataclass" qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) sgr = SummarizedGatherResult( rank="phylum", fraction=0.2, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) sgr.set_query_ani(query_info=qInf) print(sgr.query_ani_at_rank) assert sgr.query_ani_at_rank == approx(0.949, rel=1e-3) # ANI can be calculated with query_bp OR query_n_hashes. Remove each and check the results are identical qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes=0, ksize="31", scaled="10", total_weighted_hashes="200", ) sgr = SummarizedGatherResult( rank="phylum", fraction=0.2, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) sgr.set_query_ani(query_info=qInf) print(sgr.query_ani_at_rank) assert sgr.query_ani_at_rank == approx(0.949, rel=1e-3) # try without query_bp qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp=0, query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) sgr = SummarizedGatherResult( rank="phylum", fraction=0.2, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) sgr.set_query_ani(query_info=qInf) print(sgr.query_ani_at_rank) assert sgr.query_ani_at_rank == approx(0.949, rel=1e-3) def test_SummarizedGatherResult_greater_than_1(): "basic functionality of SummarizedGatherResult dataclass" # fraction > 1 with pytest.raises(ValueError) as exc: SummarizedGatherResult( rank="phylum", fraction=0.3, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=1.2, bp_match_at_rank=30, ) print(str(exc)) assert "> 100% of the query!" in str(exc) # f_weighted > 1 with pytest.raises(ValueError) as exc: SummarizedGatherResult( rank="phylum", fraction=1.2, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) print(str(exc)) assert "> 100% of the query!" in str(exc) def test_SummarizedGatherResult_0_fraction(): with pytest.raises(ValueError) as exc: SummarizedGatherResult( rank="phylum", fraction=-0.1, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) err_msg = "Summarized fraction is <=0% of the query! This should not occur." assert err_msg in str(exc) # assert cr.status == 'nomatch' with pytest.raises(ValueError) as exc: SummarizedGatherResult( rank="phylum", fraction=0.1, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0, bp_match_at_rank=30, ) print(str(exc)) assert err_msg in str(exc) def test_SummarizedGatherResult_species_kreport(): "basic functionality of SummarizedGatherResult dataclass" qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) sgr = SummarizedGatherResult( rank="species", fraction=0.2, lineage=RankLineageInfo(lineage_str="a;b;c;d;e;f;g"), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) print(sgr) assert sgr.rank == "species" krD = sgr.as_kreport_dict(query_info=qInf) print(krD) assert krD == { "ncbi_taxid": None, "sci_name": "g", "rank_code": "S", "num_bp_assigned": "600", "percent_containment": "30.00", "num_bp_contained": "600", } def test_SummarizedGatherResult_summary_dict_limit_float(): "basic functionality of SummarizedGatherResult dataclass" qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) sgr = SummarizedGatherResult( rank="phylum", fraction=0.123456, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.345678, bp_match_at_rank=30, ) print(sgr) assert sgr.rank == "phylum" sumD = sgr.as_summary_dict(query_info=qInf) print(sumD) assert sumD == { "rank": "phylum", "fraction": "0.123456", "lineage": "a;b", "f_weighted_at_rank": "0.345678", "bp_match_at_rank": "30", "query_ani_at_rank": None, "query_name": "q1", "query_md5": "md5", "query_filename": "f1", "total_weighted_hashes": "200", } sumD = sgr.as_summary_dict(query_info=qInf, limit_float=True) print(sumD) assert sumD == { "rank": "phylum", "fraction": "0.123", "lineage": "a;b", "f_weighted_at_rank": "0.346", "bp_match_at_rank": "30", "query_ani_at_rank": None, "query_name": "q1", "query_md5": "md5", "query_filename": "f1", "total_weighted_hashes": "200", } def test_ClassificationResult(): "basic functionality of ClassificationResult dataclass" qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) cr = ClassificationResult( rank="phylum", fraction=0.2, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, query_ani_at_rank=0.97, ) cr.set_status(query_info=qInf, containment_threshold=0.1) assert cr.status == "match" print(cr.query_ani_at_rank) assert cr.query_ani_at_rank == approx(0.949, rel=1e-3) cr.set_status(query_info=qInf, containment_threshold=0.35) assert cr.status == "below_threshold" lD = cr.as_lineage_dict(ranks=RankLineageInfo().ranks, query_info=qInf) print(lD) assert lD == { "ident": "q1", "superkingdom": "a", "phylum": "b", "class": "", "order": "", "family": "", "genus": "", "species": "", "strain": "", } def test_ClassificationResult_greater_than_1(): "basic functionality of SummarizedGatherResult dataclass" # fraction > 1 with pytest.raises(ValueError) as exc: ClassificationResult( rank="phylum", fraction=0.3, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=1.2, bp_match_at_rank=30, ) print(str(exc)) assert "> 100% of the query!" in str(exc) # f_weighted > 1 with pytest.raises(ValueError) as exc: ClassificationResult( rank="phylum", fraction=1.2, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) print(str(exc)) assert "> 100% of the query!" in str(exc) def test_ClassificationResult_0_fraction(): with pytest.raises(ValueError) as exc: ClassificationResult( rank="phylum", fraction=-0.1, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, ) err_msg = "Summarized fraction is <=0% of the query! This should not occur." assert err_msg in str(exc) # assert cr.status == 'nomatch' with pytest.raises(ValueError) as exc: ClassificationResult( rank="phylum", fraction=0.1, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0, bp_match_at_rank=30, ) print(str(exc)) assert err_msg in str(exc) def test_ClassificationResult_build_krona_result(): QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) cr = ClassificationResult( rank="phylum", fraction=0.2, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, query_ani_at_rank=0.97, ) # cr.set_status(query_info=qInf, rank='phylum') kr, ukr = cr.build_krona_result(rank="phylum") print(kr) assert kr == (0.2, "a", "b") print(ukr) assert ukr == (0.8, "unclassified", "unclassified") def test_ClassificationResult_build_krona_result_no_rank(): qInf = QueryInfo( query_name="q1", query_md5="md5", query_filename="f1", query_bp="100", query_n_hashes="10", ksize="31", scaled="10", total_weighted_hashes="200", ) cr = ClassificationResult( rank="phylum", fraction=0.2, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.3, bp_match_at_rank=30, query_ani_at_rank=0.97, ) cr.set_status(query_info=qInf, containment_threshold=0.1) def test_GatherRow_old_gather(): # gather does not contain query_name column gA = {"name": "gA.1 name"} with pytest.raises(TypeError) as exc: make_GatherRow(gA, exclude_cols=["query_bp"]) print(str(exc)) assert "__init__() missing 1 required positional argument: 'query_bp'" in str(exc) def test_GatherRow_match_name_not_name(): # gather contains match_name but not name column gA = {"match_name": "gA.1 name"} grow = make_GatherRow(gA, exclude_cols=["name"]) print(grow) assert grow.name == "gA.1 name" def test_GatherRow_extra_cols(): # gather contains extra columns gA = {"not-a-col": "nope"} grow = make_GatherRow(gA) assert isinstance(grow, GatherRow) def test_get_ident_default(): ident = "GCF_001881345.1" n_id = get_ident(ident) assert n_id == "GCF_001881345" def test_TaxResult_get_ident_default(): gA = {"name": "GCF_001881345.1"} # gather result with match name as GCF_001881345.1 taxres = make_TaxResult(gA) print(taxres.match_ident) assert taxres.match_ident == "GCF_001881345" def test_AnnotateTaxResult_get_ident_default(): gA = {"name": "GCF_001881345.1"} # gather result with match name as GCF_001881345.1 taxres = AnnotateTaxResult(raw=gA) print(taxres.match_ident) assert taxres.match_ident == "GCF_001881345" def test_AnnotateTaxResult_get_ident_idcol(): gA = { "name": "n1", "match_name": "n2", "ident": "n3", "accession": "n4", } # gather result with match name as GCF_001881345.1 taxres = AnnotateTaxResult(raw=gA) print(taxres.match_ident) assert taxres.match_ident == "n1" taxres = AnnotateTaxResult(raw=gA, id_col="match_name") print(taxres.match_ident) assert taxres.match_ident == "n2" taxres = AnnotateTaxResult(raw=gA, id_col="ident") print(taxres.match_ident) assert taxres.match_ident == "n3" taxres = AnnotateTaxResult(raw=gA, id_col="accession") print(taxres.match_ident) assert taxres.match_ident == "n4" def test_AnnotateTaxResult_get_ident_idcol_fail(): gA = { "name": "n1", "match_name": "n2", "ident": "n3", "accession": "n4", } # gather result with match name as GCF_001881345.1 with pytest.raises(ValueError) as exc: AnnotateTaxResult(raw=gA, id_col="NotACol") print(str(exc)) assert "ID column 'NotACol' not found." in str(exc) def test_get_ident_split_but_keep_version(): ident = "GCF_001881345.1 secondname" n_id = get_ident(ident, keep_identifier_versions=True) assert n_id == "GCF_001881345.1" def test_TaxResult_get_ident_split_but_keep_version(): gA = {"name": "GCF_001881345.1 secondname"} taxres = make_TaxResult(gA, keep_ident_version=True) print("raw ident: ", taxres.raw.name) print("keep_full?: ", taxres.keep_full_identifiers) print("keep_version?: ", taxres.keep_identifier_versions) print("final ident: ", taxres.match_ident) assert taxres.match_ident == "GCF_001881345.1" def test_AnnotateTaxResult_get_ident_split_but_keep_version(): gA = {"name": "GCF_001881345.1 secondname"} taxres = AnnotateTaxResult(gA, keep_identifier_versions=True) print("raw ident: ", taxres.raw["name"]) print("keep_full?: ", taxres.keep_full_identifiers) print("keep_version?: ", taxres.keep_identifier_versions) print("final ident: ", taxres.match_ident) assert taxres.match_ident == "GCF_001881345.1" def test_get_ident_no_split(): ident = "GCF_001881345.1 secondname" n_id = get_ident(ident, keep_full_identifiers=True) assert n_id == "GCF_001881345.1 secondname" def test_TaxResult_get_ident_keep_full(): gA = {"name": "GCF_001881345.1 secondname"} taxres = make_TaxResult(gA, keep_full_ident=True) print("raw ident: ", taxres.raw.name) print("keep_full?: ", taxres.keep_full_identifiers) print("keep_version?: ", taxres.keep_identifier_versions) print("final ident: ", taxres.match_ident) assert taxres.match_ident == "GCF_001881345.1 secondname" def test_AnnotateTaxResult_get_ident_keep_full(): gA = {"name": "GCF_001881345.1 secondname"} taxres = AnnotateTaxResult(gA, keep_full_identifiers=True) print("raw ident: ", taxres.raw["name"]) print("keep_full?: ", taxres.keep_full_identifiers) print("keep_version?: ", taxres.keep_identifier_versions) print("final ident: ", taxres.match_ident) assert taxres.match_ident == "GCF_001881345.1 secondname" def test_collect_gather_csvs(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") from_file = runtmp.output("tmp-from-file.txt") with open(from_file, "w") as fp: fp.write(f"{g_csv}\n") gather_files = collect_gather_csvs([g_csv], from_file=from_file) print("gather_files: ", gather_files) assert len(gather_files) == 1 assert basename(gather_files[0]) == "test1.gather.csv" def test_check_and_load_gather_csvs_empty(runtmp): g_res = runtmp.output("empty.gather.csv") with open(g_res, "w") as fp: fp.write("") csvs = [g_res] # load taxonomy csv taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") tax_assign = MultiLineageDB.load([taxonomy_csv], keep_full_identifiers=1) print(tax_assign) # check gather results and missing ids with pytest.raises(Exception) as exc: check_and_load_gather_csvs(csvs, tax_assign) assert "Cannot read gather results from" in str(exc.value) def test_check_and_load_gather_csvs_with_empty_force(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") # make gather results with taxonomy name not in tax_assign g_res2 = runtmp.output("gA.gather.csv") g_results = [ x.replace("GCF_001881345.1", "gA") + "\n" for x in Path(g_csv).read_text().splitlines() ] with open(g_res2, "w") as fp: fp.writelines(g_results) # make empty gather results g_res3 = runtmp.output("empty.gather.csv") with open(g_res3, "w") as fp: fp.write("") csvs = [g_res2, g_res3] # load taxonomy csv taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") tax_assign = MultiLineageDB.load( [taxonomy_csv], keep_full_identifiers=False, keep_identifier_versions=False ) print(tax_assign) # check gather results and missing ids gather_results = check_and_load_gather_csvs(csvs, tax_assign, force=True) assert len(gather_results) == 1 q_res = gather_results[0] assert len(q_res.raw_taxresults) == 4 assert q_res.n_missed == 1 assert "gA" in q_res.missed_idents assert q_res.n_skipped == 0 def test_check_and_load_gather_lineage_csvs_empty(runtmp): # try loading an empty annotated gather file g_res = runtmp.output("empty.gather-tax.csv") with open(g_res, "w") as fp: fp.write("") with pytest.raises(ValueError) as exc: LineageDB.load_from_gather_with_lineages(g_res) assert "cannot read taxonomy assignments" in str(exc.value) def test_check_and_load_gather_lineage_csvs_bad_header(runtmp): # test on file with wrong headers g_res = runtmp.output("empty.gather-tax.csv") with open(g_res, "w", newline="") as fp: fp.write("x,y,z") with pytest.raises(ValueError) as exc: LineageDB.load_from_gather_with_lineages(g_res) assert ( "Expected headers 'name'/'match_name' and 'lineage' not found. Is this a with-lineages file?" in str(exc.value) ) def test_check_and_load_gather_lineage_csvs_dne(runtmp): # test loading with-lineage file that does not exist g_res = runtmp.output("empty.gather-tax.csv") with pytest.raises(ValueError) as exc: LineageDB.load_from_gather_with_lineages(g_res) assert "does not exist" in str(exc.value) def test_check_and_load_gather_lineage_csvs_isdir(runtmp): # test loading a with-lineage file that is actually a directory g_res = runtmp.output("empty.gather-tax.csv") os.mkdir(g_res) with pytest.raises(ValueError) as exc: LineageDB.load_from_gather_with_lineages(g_res) assert "is a directory" in str(exc.value) def test_check_and_load_gather_lineage_csvs_name(runtmp): # test loading a with-lineage file that has 'name', not 'match_name' g_res = utils.get_test_data("tax/test1.gather.with-lineages.csv") lins = LineageDB.load_from_gather_with_lineages(g_res) assert len(lins) == 4 def test_check_and_load_gather_lineage_csvs_match_name(runtmp): # test loading a with-lineage file that has 'match_name' instead of 'name' g_res = utils.get_test_data("tax/test1.gather.with-lineages.csv") out_lins = runtmp.output("match-name.lineages.csv") with open(g_res) as f_in: first_line = f_in.readline().replace("name", "match_name") with open(out_lins, "w") as f_out: f_out.write(first_line) for line in f_in: f_out.write(line) lins = LineageDB.load_from_gather_with_lineages(out_lins) assert len(lins) == 4 def test_check_and_load_gather_csvs_fail_on_missing(runtmp): g_csv = utils.get_test_data("tax/test1.gather.csv") # make gather results with taxonomy name not in tax_assign g_res2 = runtmp.output("gA.gather.csv") g_results = [ x.replace("GCF_001881345.1", "gA") + "\n" for x in Path(g_csv).read_text().splitlines() ] with open(g_res2, "w") as fp: fp.writelines(g_results) csvs = [g_res2] # load taxonomy csv taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") tax_assign = MultiLineageDB.load([taxonomy_csv], keep_full_identifiers=1) print(tax_assign) # check gather results and missing ids with pytest.raises(ValueError) as exc: check_and_load_gather_csvs( csvs, tax_assign, fail_on_missing_taxonomy=True, force=True ) assert "Failing, as requested via --fail-on-missing-taxonomy" in str(exc) def test_load_gather_results(): taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") tax_assign = MultiLineageDB.load( [taxonomy_csv], keep_full_identifiers=False, keep_identifier_versions=False ) gather_csv = utils.get_test_data("tax/test1.gather.csv") gather_results, header = load_gather_results(gather_csv, tax_assignments=tax_assign) assert len(gather_results) == 1 for query_name, res in gather_results.items(): assert query_name == "test1" assert len(res.raw_taxresults) == 4 def test_load_gather_results_gzipped(runtmp): gather_csv = utils.get_test_data("tax/test1.gather.csv") taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") tax_assign = MultiLineageDB.load( [taxonomy_csv], keep_full_identifiers=False, keep_identifier_versions=False ) gather_csv = utils.get_test_data("tax/test1.gather.csv") # rewrite gather_csv as gzipped csv gz_gather = runtmp.output("g.csv.gz") with open(gather_csv, "rb") as f_in, gzip.open(gz_gather, "wb") as f_out: f_out.writelines(f_in) # gather_results, header, seen_queries = load_gather_results(gz_gather) gather_results, header = load_gather_results(gz_gather, tax_assignments=tax_assign) assert len(gather_results) == 1 for query_name, res in gather_results.items(): assert query_name == "test1" assert len(res.raw_taxresults) == 4 def test_load_gather_results_bad_header(runtmp): taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") tax_assign = MultiLineageDB.load( [taxonomy_csv], keep_full_identifiers=False, keep_identifier_versions=False ) g_csv = utils.get_test_data("tax/test1.gather.csv") bad_g_csv = runtmp.output("g.csv") # creates bad gather result bad_g = [ x.replace("f_unique_to_query", "nope") + "\n" for x in Path(g_csv).read_text().splitlines() ] with open(bad_g_csv, "w") as fp: fp.writelines(bad_g) print("bad_gather_results: \n", bad_g) with pytest.raises(ValueError) as exc: gather_results, header = load_gather_results( bad_g_csv, tax_assignments=tax_assign ) assert ( f"'{bad_g_csv}' is missing columns needed for taxonomic summarization" in str(exc.value) ) def test_load_gather_results_empty(runtmp): taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") tax_assign = MultiLineageDB.load( [taxonomy_csv], keep_full_identifiers=False, keep_identifier_versions=False ) empty_csv = runtmp.output("g.csv") # creates empty gather result with open(empty_csv, "w") as fp: fp.write("") with pytest.raises(ValueError) as exc: gather_results, header = load_gather_results( empty_csv, tax_assignments=tax_assign ) assert f"Cannot read gather results from '{empty_csv}'. Is file empty?" in str( exc.value ) def test_load_taxonomy_csv(): taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") tax_assign = MultiLineageDB.load([taxonomy_csv]) print("taxonomy assignments: \n", tax_assign) assert list(tax_assign.keys()) == [ "GCF_001881345.1", "GCF_009494285.1", "GCF_013368705.1", "GCF_003471795.1", "GCF_000017325.1", "GCF_000021665.1", ] assert len(tax_assign) == 6 # should have read 6 rows def test_load_taxonomy_csv_LIN(): taxonomy_csv = utils.get_test_data("tax/test.LIN-taxonomy.csv") tax_assign = MultiLineageDB.load([taxonomy_csv], lins=True) print("taxonomy assignments: \n", tax_assign) assert list(tax_assign.keys()) == [ "GCF_001881345.1", "GCF_009494285.1", "GCF_013368705.1", "GCF_003471795.1", "GCF_000017325.1", "GCF_000021665.1", ] # assert list(tax_assign.keys()) == ["GCF_000010525.1", "GCF_000007365.1", "GCF_000007725.1", "GCF_000009605.1", "GCF_000021065.1", "GCF_000021085.1"] assert len(tax_assign) == 6 # should have read 6 rows print(tax_assign.available_ranks) assert tax_assign.available_ranks == {str(x) for x in range(0, 20)} def test_load_taxonomy_csv_LIN_fail(): taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") with pytest.raises(ValueError) as exc: MultiLineageDB.load([taxonomy_csv], lins=True) assert ( f"'lin' column not found: cannot read LIN taxonomy assignments from {taxonomy_csv}." in str(exc.value) ) def test_load_taxonomy_csv_LIN_mismatch_in_taxfile(runtmp): taxonomy_csv = utils.get_test_data("tax/test.LIN-taxonomy.csv") mimatchLIN_csv = runtmp.output("mmLIN-taxonomy.csv") with open(mimatchLIN_csv, "w") as mm: tax21 = [] tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] for n, taxline in enumerate(tax): if n == 2: # add ;0 to a LIN taxlist = taxline.split(",") taxlist[1] += ";0" # add 21st position to LIN tax21.append(",".join(taxlist)) else: tax21.append(taxline) mm.write("\n".join(tax21)) with pytest.raises(ValueError) as exc: MultiLineageDB.load([mimatchLIN_csv], lins=True) assert ( "For taxonomic summarization, all LIN assignments must use the same number of LIN positions." in str(exc.value) ) def test_load_taxonomy_csv_gzip(runtmp): # test loading a gzipped taxonomy csv file taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") tax_gz = runtmp.output("tax.csv.gz") with gzip.open(tax_gz, "wt") as outfp: with open(taxonomy_csv) as infp: data = infp.read() outfp.write(data) tax_assign = MultiLineageDB.load([tax_gz]) print("taxonomy assignments: \n", tax_assign) assert list(tax_assign.keys()) == [ "GCF_001881345.1", "GCF_009494285.1", "GCF_013368705.1", "GCF_003471795.1", "GCF_000017325.1", "GCF_000021665.1", ] assert len(tax_assign) == 6 # should have read 6 rows def test_load_taxonomy_csv_split_id(): taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") tax_assign = MultiLineageDB.load( [taxonomy_csv], keep_full_identifiers=0, keep_identifier_versions=False ) print("taxonomy assignments: \n", tax_assign) assert list(tax_assign.keys()) == [ "GCF_001881345", "GCF_009494285", "GCF_013368705", "GCF_003471795", "GCF_000017325", "GCF_000021665", ] assert len(tax_assign) == 6 # should have read 6 rows def test_load_taxonomy_csv_with_ncbi_id(runtmp): taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") upd_csv = runtmp.output("updated_taxonomy.csv") with open(upd_csv, "w") as new_tax: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] ncbi_id = "ncbi_id after_space" fake_lin = [ncbi_id] + ["sk", "phy", "cls", "ord", "fam", "gen", "sp"] ncbi_tax = ",".join(fake_lin) tax.append(ncbi_tax) new_tax.write("\n".join(tax)) tax_assign = MultiLineageDB.load([upd_csv], keep_full_identifiers=True) print("taxonomy assignments: \n", tax_assign) assert list(tax_assign.keys()) == [ "GCF_001881345.1", "GCF_009494285.1", "GCF_013368705.1", "GCF_003471795.1", "GCF_000017325.1", "GCF_000021665.1", "ncbi_id after_space", ] assert len(tax_assign) == 7 # should have read 7 rows def test_load_taxonomy_csv_split_id_ncbi(runtmp): taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") upd_csv = runtmp.output("updated_taxonomy.csv") with open(upd_csv, "w") as new_tax: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] ncbi_id = "ncbi_id after_space" fake_lin = [ncbi_id] + ["sk", "phy", "cls", "ord", "fam", "gen", "sp"] ncbi_tax = ",".join(fake_lin) tax.append(ncbi_tax) new_tax.write("\n".join(tax)) tax_assign = MultiLineageDB.load( [upd_csv], keep_full_identifiers=False, keep_identifier_versions=False ) print("taxonomy assignments: \n", tax_assign) assert list(tax_assign.keys()) == [ "GCF_001881345", "GCF_009494285", "GCF_013368705", "GCF_003471795", "GCF_000017325", "GCF_000021665", "ncbi_id", ] assert len(tax_assign) == 7 # should have read 7 rows # check for non-sensical args. with pytest.raises(ValueError): tax_assign = MultiLineageDB.load( [upd_csv], keep_full_identifiers=1, keep_identifier_versions=False ) def test_load_taxonomy_csv_duplicate(runtmp): taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") duplicated_csv = runtmp.output("duplicated_taxonomy.csv") with open(duplicated_csv, "w") as dup: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax.append(tax[1] + "FOO") # add first tax_assign again print(tax[-1]) dup.write("\n".join(tax)) with pytest.raises(Exception) as exc: MultiLineageDB.load([duplicated_csv]) assert "cannot read taxonomy assignments" in str(exc.value) assert "multiple lineages for identifier GCF_001881345.1" in str(exc.value) def test_load_taxonomy_csv_duplicate_force(runtmp): taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") duplicated_csv = runtmp.output("duplicated_taxonomy.csv") with open(duplicated_csv, "w") as dup: tax = [x.rstrip() for x in Path(taxonomy_csv).read_text().splitlines()] tax.append(tax[1]) # add first tax_assign again dup.write("\n".join(tax)) # now force tax_assign = MultiLineageDB.load([duplicated_csv], force=True) print("taxonomy assignments: \n", tax_assign) assert list(tax_assign.keys()) == [ "GCF_001881345.1", "GCF_009494285.1", "GCF_013368705.1", "GCF_003471795.1", "GCF_000017325.1", "GCF_000021665.1", ] def test_format_for_krona_summarization(): """test format for krona""" # make gather results # make mini taxonomy gA_tax = ("gA", "a;b") gB_tax = ("gB", "a;c") taxD = make_mini_taxonomy([gA_tax, gB_tax]) gather_results = [ { "query_name": "queryA", "name": "gA", "f_unique_weighted": 0.2, "f_unique_to_query": 0.2, "unique_intersect_bp": 50, }, { "query_name": "queryA", "name": "gB", "f_unique_weighted": 0.3, "f_unique_to_query": 0.3, "unique_intersect_bp": 30, }, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, summarize=True, single_query=True ) kres, header = format_for_krona([q_res], "superkingdom") assert header == ["fraction", "superkingdom"] print("krona_res: ", kres) assert kres == [(0.5, "a"), (0.5, "unclassified")] kres, header = format_for_krona([q_res], "phylum") assert header == ["fraction", "superkingdom", "phylum"] assert kres == [ (0.3, "a", "c"), (0.2, "a", "b"), (0.5, "unclassified", "unclassified"), ] def test_format_for_krona_classification(): """test format for krona""" # make gather results # make mini taxonomy gA_tax = ("gA", "a;b") gB_tax = ("gB", "a;c") taxD = make_mini_taxonomy([gA_tax, gB_tax]) gather_results = [ { "query_name": "queryA", "name": "gA", "f_unique_weighted": 0.2, "f_unique_to_query": 0.2, "unique_intersect_bp": 50, }, { "query_name": "queryA", "name": "gB", "f_unique_weighted": 0.3, "f_unique_to_query": 0.3, "unique_intersect_bp": 30, }, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, classify=True, single_query=True ) kres, header = format_for_krona([q_res], "superkingdom", classification=True) assert header == ["fraction", "superkingdom"] print("krona_res: ", kres) assert kres == [(0.5, "a")] # , (0.5, 'unclassified')] kres, header = format_for_krona([q_res], "phylum", classification=True) assert header == ["fraction", "superkingdom", "phylum"] assert kres == [(0.3, "a", "c")] # , (0.7, 'unclassified', 'unclassified')] def test_format_for_krona_improper_rank(): """test format for krona""" # make gather results # make mini taxonomy gA_tax = ("gA", "a;b") gB_tax = ("gB", "a;c") taxD = make_mini_taxonomy([gA_tax, gB_tax]) gather_results = [ { "query_name": "queryA", "name": "gA", "f_unique_weighted": 0.2, "f_unique_to_query": 0.2, "unique_intersect_bp": 50, }, { "query_name": "queryA", "name": "gB", "f_unique_weighted": 0.3, "f_unique_to_query": 0.3, "unique_intersect_bp": 30, }, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, summarize=True, single_query=True ) with pytest.raises(ValueError) as exc: format_for_krona([q_res], "NotARank") print(str(exc)) assert "Rank 'NotARank' not present in summarized ranks." in str(exc) def test_format_for_krona_summarization_two_queries(): """test format for krona with multiple queries (normalize by n_queries)""" # make gather results # make mini taxonomy gA_tax = ("gA", "a;b") gB_tax = ("gB", "a;c") taxD = make_mini_taxonomy([gA_tax, gB_tax]) gather_results = [ { "query_name": "queryA", "name": "gA", "f_unique_weighted": 0.2, "f_unique_to_query": 0.2, "unique_intersect_bp": 50, }, { "query_name": "queryA", "name": "gB", "f_unique_weighted": 0.3, "f_unique_to_query": 0.3, "unique_intersect_bp": 30, }, { "query_name": "queryB", "name": "gB", "f_unique_weighted": 0.5, "f_unique_to_query": 0.5, "unique_intersect_bp": 50, }, ] gres = make_QueryTaxResults(gather_info=gather_results, taxD=taxD, summarize=True) kres, header = format_for_krona(list(gres.values()), "superkingdom") assert header == ["fraction", "superkingdom"] print("krona_res: ", kres) assert kres == [(0.5, "a"), (0.5, "unclassified")] kres, header = format_for_krona(list(gres.values()), "phylum") assert header == ["fraction", "superkingdom", "phylum"] assert kres == [ (0.4, "a", "c"), (0.1, "a", "b"), (0.5, "unclassified", "unclassified"), ] def test_write_krona(runtmp): """test two matches, equal f_unique_to_query""" krona_results = [(0.5, "a", "b", "c"), (0.5, "a", "b", "d")] header = ["fraction", "superkingdom", "phylum", "class"] outk = runtmp.output("outkrona.tsv") with open(outk, "w") as out_fp: write_krona(header, krona_results, out_fp) kr = [x.strip().split("\t") for x in Path(outk).read_text().splitlines()] print("krona_results_from_file: \n", kr) assert kr[0] == ["fraction", "superkingdom", "phylum", "class"] assert kr[1] == ["0.5", "a", "b", "c"] assert kr[2] == ["0.5", "a", "b", "d"] def test_write_lineage_sample_frac(runtmp): outfrac = runtmp.output("outfrac.csv") sample_names = ["sample1", "sample2"] sk_linD = {"a": {"sample1": "0.500", "sample2": "0.700"}} with open(outfrac, "w") as out_fp: write_lineage_sample_frac(sample_names, sk_linD, out_fp) frac_lines = [x.strip().split("\t") for x in Path(outfrac).read_text().splitlines()] print("csv_lines: ", frac_lines) assert frac_lines == [["lineage", "sample1", "sample2"], ["a", "0.500", "0.700"]] phy_linD = {"a;b": {"sample1": "0.500"}, "a;c": {"sample2": "0.700"}} with open(outfrac, "w") as out_fp: write_lineage_sample_frac(sample_names, phy_linD, out_fp) frac_lines = [x.strip().split("\t") for x in Path(outfrac).read_text().splitlines()] print("csv_lines: ", frac_lines) assert frac_lines == [ ["lineage", "sample1", "sample2"], ["a;b", "0.500", "0"], ["a;c", "0", "0.700"], ] def test_write_lineage_sample_frac_format_lineage(runtmp): outfrac = runtmp.output("outfrac.csv") sample_names = ["sample1", "sample2"] sk_lineage = "a" print(sk_lineage) sk_linD = {sk_lineage: {"sample1": "0.500", "sample2": "0.700"}} with open(outfrac, "w") as out_fp: write_lineage_sample_frac(sample_names, sk_linD, out_fp) frac_lines = [x.strip().split("\t") for x in Path(outfrac).read_text().splitlines()] print("csv_lines: ", frac_lines) assert frac_lines == [["lineage", "sample1", "sample2"], ["a", "0.500", "0.700"]] phy_lineage = "a;b" print(phy_lineage) phy2_lineage = "a;c" print(phy2_lineage) phy_linD = {phy_lineage: {"sample1": "0.500"}, phy2_lineage: {"sample2": "0.700"}} with open(outfrac, "w") as out_fp: write_lineage_sample_frac(sample_names, phy_linD, out_fp) frac_lines = [x.strip().split("\t") for x in Path(outfrac).read_text().splitlines()] print("csv_lines: ", frac_lines) assert frac_lines == [ ["lineage", "sample1", "sample2"], ["a;b", "0.500", "0"], ["a;c", "0", "0.700"], ] def test_tax_multi_load_files(runtmp): # test loading various good and bad files taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") taxonomy_csv2 = utils.get_test_data("tax/test-strain.taxonomy.csv") badcsv = utils.get_test_data("tax/47+63_x_gtdb-rs202.gather.csv") db = MultiLineageDB.load([taxonomy_csv]) assert len(db) == 6 assert "strain" not in db.available_ranks db = MultiLineageDB.load([taxonomy_csv2]) assert len(db) == 6 assert "strain" in db.available_ranks assert db["GCF_001881345.1"][0].rank == "superkingdom" # load a string rather than a list with pytest.raises(TypeError): MultiLineageDB.load(badcsv) # load a bad CSV with pytest.raises(ValueError): MultiLineageDB.load([badcsv]) # load a directory with pytest.raises(ValueError): MultiLineageDB.load([runtmp.output("")]) # file does not exist with pytest.raises(ValueError): MultiLineageDB.load([runtmp.output("no-such-file")]) def test_tax_sql_load_new_file(runtmp): # test loading a newer-format sql file with sourmash_internals table taxonomy_db = utils.get_test_data("sqlite/test.taxonomy.db") db = MultiLineageDB.load([taxonomy_db]) print(list(db.keys())) assert len(db) == 6 assert "strain" not in db.available_ranks assert db["GCF_001881345"][0].rank == "superkingdom" def test_tax_multi_load_files_shadowed(runtmp): # test loading various good and bad files taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") taxonomy_csv2 = utils.get_test_data("tax/test-strain.taxonomy.csv") taxonomy_db = utils.get_test_data("tax/test.taxonomy.db") db = MultiLineageDB.load( [taxonomy_csv, taxonomy_csv2, taxonomy_db], keep_full_identifiers=False, keep_identifier_versions=False, ) assert len(db.shadowed_identifiers()) == 6 # we should have everything including strain assert set(RankLineageInfo().taxlist) == set(db.available_ranks) db = MultiLineageDB.load( [taxonomy_csv, taxonomy_db], keep_full_identifiers=False, keep_identifier_versions=False, ) assert len(db.shadowed_identifiers()) == 6 assert set(RankLineageInfo().taxlist[:-1]) == set(db.available_ranks) def test_tax_multi_save_files(runtmp, keep_identifiers, keep_versions): # test save taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") if keep_identifiers and not keep_versions: with pytest.raises(ValueError): db = MultiLineageDB.load( [taxonomy_csv], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) return db = MultiLineageDB.load( [taxonomy_csv], keep_full_identifiers=keep_identifiers, keep_identifier_versions=keep_versions, ) out_db = runtmp.output("out.db") out_csv = runtmp.output("out.csv") out2_csv = runtmp.output("out2.csv") # can't save to fp with sql with open(out_csv, "w") as fp: with pytest.raises(ValueError): db.save(fp, "sql") # these should all work... with open(out_csv, "w") as fp: db.save(fp, "csv") db.save(out2_csv, "csv") db.save(out_db, "sql") # ...and be equal db1 = db.load([out_db]) db2 = db.load([out_csv]) db3 = db.load([out2_csv]) def strip_strain(it): for k, v in it: if v[-1].rank == "strain": v = v[:-1] yield k, v import pprint db_items = list(strip_strain(db.items())) db1_items = list(strip_strain(db1.items())) db2_items = list(strip_strain(db2.items())) db3_items = list(strip_strain(db3.items())) pprint.pprint(db_items) print("XXX") pprint.pprint(list(db1_items)) print("XXX") pprint.pprint(list(db2_items)) assert set(db_items) == set(db1_items) assert set(db_items) == set(db2_items) assert set(db_items) == set(db3_items) def test_lineage_db_csv_load(runtmp): # test LineageDB.load taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") taxonomy_csv2 = utils.get_test_data("tax/test-strain.taxonomy.csv") badcsv = utils.get_test_data("tax/47+63_x_gtdb-rs202.gather.csv") badcsv2 = utils.get_test_data("tax/test-missing-ranks.taxonomy.csv") db = LineageDB.load(taxonomy_csv) assert len(db) == 6 assert "strain" not in db.available_ranks db = LineageDB.load(taxonomy_csv2) assert len(db) == 6 assert "strain" in db.available_ranks # load the wrong kind of csv with pytest.raises(ValueError): LineageDB.load(badcsv) # load a bad CSV with pytest.raises(ValueError): LineageDB.load(badcsv2) # load a directory with pytest.raises(ValueError): LineageDB.load(runtmp.output("")) # file does not exist with pytest.raises(ValueError): LineageDB.load(runtmp.output("no-such-file")) # construct a CSV with bad headers with open(runtmp.output("xxx.csv"), "w", newline="") as fp: fp.write("x,y,z\n") with pytest.raises(ValueError): LineageDB.load(runtmp.output("xxx.csv")) def test_lineage_db_sql_load(runtmp): # test LineageDB_sqlite.load taxonomy_db = utils.get_test_data("tax/test.taxonomy.db") taxonomy_csv = utils.get_test_data("tax/test.taxonomy.csv") db = LineageDB_Sqlite.load(taxonomy_db) assert bool(db) assert len(db) == 6 db.available_ranks assert "strain" not in db.available_ranks assert db["GCF_001881345"][0].rank == "superkingdom" with pytest.raises(KeyError): db["foo"] # load any kind of CSV with pytest.raises(ValueError): LineageDB_Sqlite.load(taxonomy_csv) # load a directory with pytest.raises(ValueError): LineageDB_Sqlite.load(runtmp.output("")) # file does not exist with pytest.raises(ValueError): LineageDB_Sqlite.load(runtmp.output("no-such-file")) def test_LineagePair(): lin = LineagePair(rank="rank1", name="name1") print(lin) assert lin.rank == "rank1" assert lin.name == "name1" assert lin.taxid is None def test_LineagePair_1(): lin = LineagePair(rank="rank1", name="name1", taxid=1) assert lin.rank == "rank1" assert lin.name == "name1" assert lin.taxid == 1 print(lin) def test_BaseLineageInfo_init_empty(): ranks = ["A", "B", "C"] taxinf = BaseLineageInfo(ranks=ranks) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage() == [ "", "", "", ] # this is a bit odd, but it's what preserves empty ranks... print(taxinf.filled_lineage) assert taxinf.filled_lineage == () assert taxinf.lowest_lineage_name is None assert taxinf.lowest_lineage_taxid is None assert taxinf.filled_ranks == () assert taxinf.name_at_rank("A") is None assert taxinf.lowest_rank is None assert taxinf.display_lineage() == "" assert taxinf.display_lineage(null_as_unclassified=True) == "unclassified" def test_BaseLineageInfo_init_lineage_str(): x = "a;b;c" ranks = ["A", "B", "C"] taxinf = BaseLineageInfo(lineage_str=x, ranks=ranks) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage() == ["a", "b", "c"] print(taxinf.filled_lineage) assert taxinf.filled_lineage == ( LineagePair(rank="A", name="a", taxid=None), LineagePair(rank="B", name="b", taxid=None), LineagePair(rank="C", name="c", taxid=None), ) assert taxinf.lowest_lineage_name == "c" assert taxinf.lowest_rank == "C" assert taxinf.name_at_rank("A") == "a" def test_BaseLineageInfo_init_lineage_str_comma_sep(): x = "a,b,c" ranks = ["A", "B", "C"] taxinf = BaseLineageInfo(lineage_str=x, ranks=ranks) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage() == ["a", "b", "c"] print(taxinf.filled_lineage) assert taxinf.lowest_lineage_name == "c" def test_BaseLineageInfo_init_lineage_tups(): ranks = ["A", "B", "C"] lin_tups = (LineagePair(rank="A", name="a"), LineagePair(rank="C", name="b")) taxinf = BaseLineageInfo(lineage=lin_tups, ranks=ranks) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage() == ["a", "", "b"] def test_BaseLineageInfo_init_lca_lineage_tups(): ranks = ["A", "B", "C"] lin_tups = (LineagePair(rank="A", name="a"), LineagePair(rank="C", name="b")) taxinf = BaseLineageInfo(lineage=lin_tups, ranks=ranks) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage() == ["a", "", "b"] def test_BaseLineageInfo_init_no_ranks(): x = "a;b;c" lin_tups = ( LineagePair(rank="rank2", name="name1"), LineagePair(rank="rank1", name="name1"), ) with pytest.raises(TypeError) as exc: BaseLineageInfo(lineage_str=x) print(exc) assert "__init__() missing 1 required positional argument: 'ranks'" in str(exc) with pytest.raises(TypeError) as exc: BaseLineageInfo(lineage=lin_tups) print(exc) assert "__init__() missing 1 required positional argument: 'ranks'" in str(exc) def test_BaseLineageInfo_init_with_wrong_ranks(): ranks = ["A", "B", "C"] lin_tups = [LineagePair(rank="rank1", name="name1")] with pytest.raises(ValueError) as exc: BaseLineageInfo(lineage=lin_tups, ranks=ranks) print(str(exc)) assert "Rank 'rank1' not present in A, B, C" in str(exc) def test_BaseLineageInfo_init_not_lineagepair(): ranks = ["A", "B", "C"] lin_tups = (("rank1", "name1"),) with pytest.raises(ValueError) as exc: BaseLineageInfo(lineage=lin_tups, ranks=ranks) print(str(exc)) assert "is not tax_utils LineagePair" in str(exc) def test_RankLineageInfo_taxlist(): taxinf = RankLineageInfo() taxranks = ( "superkingdom", "phylum", "class", "order", "family", "genus", "species", "strain", ) assert taxinf.taxlist == taxranks assert taxinf.ascending_taxlist == taxranks[::-1] def test_RankLineageInfo_init_lineage_str(): x = "a;b;c" taxinf = RankLineageInfo(lineage_str=x) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage() == ["a", "b", "c", "", "", "", "", ""] def test_LINLineageInfo_init_empty(): taxinf = LINLineageInfo() assert taxinf.n_lin_positions == 0 assert taxinf.zip_lineage() == [] assert taxinf.display_lineage() == "" assert taxinf.filled_ranks == () assert taxinf.n_filled_pos == 0 def test_LINLineageInfo_init_n_pos(): n_pos = 5 taxinf = LINLineageInfo(n_lin_positions=n_pos) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.n_lin_positions == 5 assert taxinf.zip_lineage() == ["", "", "", "", ""] assert taxinf.filled_ranks == () assert taxinf.n_filled_pos == 0 def test_LINLineageInfo_init_n_pos_and_lineage_str(): x = "0;0;1" n_pos = 5 taxinf = LINLineageInfo(lineage_str=x, n_lin_positions=n_pos) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.n_lin_positions == 5 assert taxinf.zip_lineage() == ["0", "0", "1", "", ""] assert taxinf.filled_ranks == ("0", "1", "2") assert taxinf.n_filled_pos == 3 def test_LINLineageInfo_init_n_pos_and_lineage_str_fail(): x = "0;0;1" n_pos = 2 with pytest.raises(ValueError) as exc: LINLineageInfo(lineage_str=x, n_lin_positions=n_pos) print(str(exc)) assert ( "Provided 'n_lin_positions' has fewer positions than provided 'lineage_str'." in str(exc) ) def test_LINLineageInfo_init_lineage_str_only(): x = "0,0,1" taxinf = LINLineageInfo(lineage_str=x) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.n_lin_positions == 3 assert taxinf.zip_lineage() == ["0", "0", "1"] assert taxinf.filled_ranks == ("0", "1", "2") assert taxinf.n_filled_pos == 3 def test_LINLineageInfo_init_not_lineagepair(): lin_tups = (("rank1", "name1"),) with pytest.raises(ValueError) as exc: LINLineageInfo(lineage=lin_tups) print(str(exc)) assert "is not tax_utils LineagePair" in str(exc) def test_LINLineageInfo_init_lineagepair(): lin_tups = ( LineagePair("rank1", "name1"), LineagePair("rank2", None), ) taxinf = LINLineageInfo(lineage=lin_tups) print(taxinf.lineage) assert taxinf.n_lin_positions == 2 assert taxinf.zip_lineage() == ["name1", ""] assert taxinf.zip_lineage(truncate_empty=True) == ["name1"] assert taxinf.filled_ranks == ("rank1",) assert taxinf.ranks == ("rank1", "rank2") assert taxinf.n_filled_pos == 1 def test_lca_LINLineageInfo_diff_n_pos(): x = "0;0;1" y = "0" lin1 = LINLineageInfo(lineage_str=x) lin2 = LINLineageInfo(lineage_str=y) assert lin1.is_compatible(lin2) assert lin2.is_compatible(lin1) lca_from_lin1 = lin1.find_lca(lin2) lca_from_lin2 = lin2.find_lca(lin1) assert lca_from_lin1 == lca_from_lin2 assert lca_from_lin1.display_lineage(truncate_empty=True) == "0" def test_lca_LINLineageInfo_no_lca(): x = "0;0;1" y = "12;0;1" lin1 = LINLineageInfo(lineage_str=x) lin2 = LINLineageInfo(lineage_str=y) assert lin1.is_compatible(lin2) assert lin2.is_compatible(lin1) lca_from_lin1 = lin1.find_lca(lin2) lca_from_lin2 = lin2.find_lca(lin1) assert lca_from_lin1 == lca_from_lin2 is None def test_lca_RankLineageInfo_no_lca(): x = "a;b;c" y = "d;e;f;g" lin1 = RankLineageInfo(lineage_str=x) lin2 = RankLineageInfo(lineage_str=y) assert lin1.is_compatible(lin2) assert lin2.is_compatible(lin1) lca_from_lin1 = lin1.find_lca(lin2) lca_from_lin2 = lin2.find_lca(lin1) assert lca_from_lin1 == lca_from_lin2 is None def test_ICTVLineageInfo_ranks_input_ignored(): # check that ranks cannot be changed taxinfo = ICTVRankLineageInfo(ranks=["one", "two"]) assert taxinfo.taxlist == ICTV_RANKS def test_ICTVLineageInfo_lineagedict_input(): # check that ranks cannot be changed dummy_names = [f"name{i}" for i in range(1, len(ICTV_RANKS) + 1)] input_lindict = dict(zip(ICTV_RANKS, dummy_names)) taxinfo = ICTVRankLineageInfo(lineage_dict=input_lindict) print(taxinfo.display_lineage()) assert taxinfo.display_lineage() == ";".join(dummy_names) assert taxinfo.taxlist == ICTV_RANKS def test_incompatibility_LINLineageInfo_RankLineageInfo(): x = "a;b;c" lin1 = RankLineageInfo(lineage_str=x) lin2 = LINLineageInfo(lineage_str=x) assert not lin1.is_compatible(lin2) assert not lin2.is_compatible(lin1) def test_RankLineageInfo_init_lineage_str_with_ranks_as_list(): x = "a;b;c" taxranks = [ "superkingdom", "phylum", "class", "order", "family", "genus", "species", ] taxinf = RankLineageInfo(lineage_str=x, ranks=taxranks) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage() == ["a", "b", "c", "", "", "", ""] def test_RankLineageInfo_init_lineage_tups(): x = ( LineagePair(rank="superkingdom", name="a"), LineagePair(rank="phylum", name="b"), ) taxinf = RankLineageInfo(lineage=x) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage() == ["a", "b", "", "", "", "", "", ""] def test_RankLineageInfo_init_lineage_dict_fail(): ranks = ["A", "B", "C"] lin_tups = (LineagePair(rank="A", name="a"), LineagePair(rank="C", name="b")) with pytest.raises(ValueError) as exc: RankLineageInfo(ranks=ranks, lineage_dict=lin_tups) print(str(exc)) assert "is not dictionary" in str(exc) def test_RankLineageInfo_init_lineage_dict(): x = {"rank1": "name1", "rank2": "name2"} taxinf = RankLineageInfo(lineage_dict=x, ranks=["rank1", "rank2"]) print("ranks: ", taxinf.ranks) print("lineage: ", taxinf.lineage) print("zipped lineage: ", taxinf.zip_lineage()) assert taxinf.zip_lineage() == ["name1", "name2"] def test_RankLineageInfo_init_lineage_dict_default_ranks(): x = {"superkingdom": "a", "phylum": "b"} taxinf = RankLineageInfo(lineage_dict=x) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage() == ["a", "b", "", "", "", "", "", ""] def test_RankLineageInfo_init_lineage_dict_withtaxpath(): x = {"rank1": "name1", "rank2": "name2", "taxpath": "1|2"} taxinf = RankLineageInfo(lineage_dict=x, ranks=["rank1", "rank2"]) print("ranks: ", taxinf.ranks) print("lineage: ", taxinf.lineage) print("zipped lineage: ", taxinf.zip_lineage()) print("zipped taxids: ", taxinf.zip_taxid()) assert taxinf.zip_lineage() == ["name1", "name2"] assert taxinf.zip_taxid() == ["1", "2"] assert taxinf.lowest_lineage_taxid == "2" assert taxinf.lowest_lineage_name == "name2" def test_RankLineageInfo_init_lineage_str_lineage_dict_test_eq(): x = "a;b;c" ranks = ["A", "B", "C"] rankD = {"A": "a", "B": "b", "C": "c"} lin1 = RankLineageInfo(lineage_str=x, ranks=ranks) lin2 = RankLineageInfo(lineage_dict=rankD, ranks=ranks) assert lin1 == lin2 def test_RankLineageInfo_init_lineage_dict_missing_rank(): x = {"superkingdom": "name1", "class": "name2"} taxinf = RankLineageInfo(lineage_dict=x) print("ranks: ", taxinf.ranks) print("lineage: ", taxinf.lineage) print("zipped lineage: ", taxinf.zip_lineage()) assert taxinf.zip_lineage() == ["name1", "", "name2", "", "", "", "", ""] assert taxinf.zip_lineage(truncate_empty=True) == ["name1", "", "name2"] def test_RankLineageInfo_init_lineage_dict_missing_rank_with_taxpath(): x = {"superkingdom": "name1", "class": "name2", "taxpath": "1||2"} taxinf = RankLineageInfo(lineage_dict=x) print("ranks: ", taxinf.ranks) print("lineage: ", taxinf.lineage) print("zipped lineage: ", taxinf.zip_lineage()) assert taxinf.zip_lineage() == ["name1", "", "name2", "", "", "", "", ""] assert taxinf.zip_taxid() == ["1", "", "2", "", "", "", "", ""] def test_RankLineageInfo_init_lineage_dict_name_taxpath_mismatch(): # If there's no name, we don't report the taxpath, because lineage is not "filled". # Is this desired behavior? x = {"superkingdom": "name1", "taxpath": "1||2"} taxinf = RankLineageInfo(lineage_dict=x) print("ranks: ", taxinf.ranks) print("lineage: ", taxinf.lineage) print("zipped lineage: ", taxinf.zip_lineage()) assert taxinf.zip_lineage() == ["name1", "", "", "", "", "", "", ""] assert taxinf.zip_taxid() == ["1", "", "", "", "", "", "", ""] def test_RankLineageInfo_init_lineage_dict_name_taxpath_missing_taxids(): # If there's no name, we don't report the taxpath, because lineage is not "filled". # Is this desired behavior? x = {"superkingdom": "name1", "phylum": "name2", "class": "name3", "taxpath": "|2"} taxinf = RankLineageInfo(lineage_dict=x) print("ranks: ", taxinf.ranks) print("lineage: ", taxinf.lineage) print("zipped lineage: ", taxinf.zip_lineage()) print("zipped taxids: ", taxinf.zip_taxid()) assert taxinf.zip_lineage() == ["name1", "name2", "name3", "", "", "", "", ""] assert taxinf.zip_taxid() == ["", "2", "", "", "", "", "", ""] def test_RankLineageInfo_init_lineage_dict_taxpath_too_long(): x = {"superkingdom": "name1", "class": "name2", "taxpath": "1||2||||||||||"} with pytest.raises(ValueError) as exc: RankLineageInfo(lineage_dict=x) print(str(exc)) assert "Number of NCBI taxids (13) exceeds number of ranks (8)" in str(exc) def test_RankLineageInfo_init_lineage_str_lineage_dict_test_eq(): x = "a;b;c" rankD = {"superkingdom": "a", "phylum": "b", "class": "c"} lin1 = RankLineageInfo(lineage_str=x) lin2 = RankLineageInfo(lineage_dict=rankD) print("lin1: ", lin1) print("lin2: ", lin2) assert lin1 == lin2 def test_RankLineageInfo_init_lineage_str_1_truncate(): x = "a;b;c" taxinf = RankLineageInfo(lineage_str=x) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage(truncate_empty=True) == ["a", "b", "c"] def test_RankLineageInfo_init_lineage_str_2(): x = "a;b;;c" taxinf = RankLineageInfo(lineage_str=x) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage() == ["a", "b", "", "c", "", "", "", ""] def test_RankLineageInfo_init_lineage_str_2_truncate(): x = "a;b;;c" taxinf = RankLineageInfo(lineage_str=x) print(taxinf.lineage) print(taxinf.lineage_str) assert taxinf.zip_lineage(truncate_empty=True) == ["a", "b", "", "c"] def test_RankLineageInfo_init_lineage_with_incorrect_rank(): x = [ LineagePair("superkingdom", "a"), LineagePair("NotARank", ""), LineagePair("class", "c"), ] with pytest.raises(ValueError) as exc: RankLineageInfo(lineage=x) print(str(exc)) assert "Rank 'NotARank' not present in " in str(exc) def test_zip_lineage_1(): x = [LineagePair("superkingdom", "a"), LineagePair("phylum", "b")] taxinf = RankLineageInfo(lineage=x) print("ranks: ", taxinf.ranks) print("zipped lineage: ", taxinf.zip_lineage()) assert taxinf.zip_lineage() == ["a", "b", "", "", "", "", "", ""] def test_zip_lineage_2(): x = [LineagePair("superkingdom", "a"), LineagePair("phylum", "b")] taxinf = RankLineageInfo(lineage=x) print("ranks: ", taxinf.ranks) print("zipped lineage: ", taxinf.zip_lineage(truncate_empty=True)) assert taxinf.zip_lineage(truncate_empty=True) == ["a", "b"] def test_zip_lineage_3(): x = [ LineagePair("superkingdom", "a"), LineagePair(None, ""), LineagePair("class", "c"), ] taxinf = RankLineageInfo(lineage=x) assert taxinf.zip_lineage() == ["a", "", "c", "", "", "", "", ""] def test_zip_lineage_3_truncate(): x = [ LineagePair("superkingdom", "a"), LineagePair(None, ""), LineagePair("class", "c"), ] taxinf = RankLineageInfo(lineage=x) assert taxinf.zip_lineage(truncate_empty=True) == ["a", "", "c"] def test_zip_lineage_4(): x = [LineagePair("superkingdom", "a"), LineagePair("class", "c")] taxinf = RankLineageInfo(lineage=x) assert taxinf.zip_lineage(truncate_empty=True) == ["a", "", "c"] def test_display_lineage_1(): x = [LineagePair("superkingdom", "a"), LineagePair("phylum", "b")] taxinf = RankLineageInfo(lineage=x) assert taxinf.display_lineage() == "a;b" def test_display_lineage_2(): x = [ LineagePair("superkingdom", "a"), LineagePair(None, ""), LineagePair("class", "c"), ] taxinf = RankLineageInfo(lineage=x) assert taxinf.display_lineage() == "a;;c" def test_display_taxid_1(): x = [LineagePair("superkingdom", "a", 1), LineagePair("phylum", "b", 2)] taxinf = RankLineageInfo(lineage=x) print(taxinf) assert taxinf.display_taxid() == "1;2" def test_display_taxid_2(): x = [ LineagePair("superkingdom", "name1", 1), LineagePair(None, ""), LineagePair("class", "name2", 2), ] taxinf = RankLineageInfo(lineage=x) print(taxinf) assert taxinf.display_taxid() == "1;;2" def test_is_lineage_match_1(): # basic behavior: match at order and above, but not at family or below. lin1 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__e") lin2 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") print(lin1.lineage) assert lin1.is_compatible(lin2) assert lin1.is_lineage_match(lin2, "superkingdom") assert lin2.is_lineage_match(lin1, "superkingdom") assert lin1.is_lineage_match(lin2, "phylum") assert lin2.is_lineage_match(lin1, "phylum") assert lin1.is_lineage_match(lin2, "class") assert lin2.is_lineage_match(lin1, "class") assert lin1.is_lineage_match(lin2, "order") assert lin2.is_lineage_match(lin1, "order") assert not lin1.is_lineage_match(lin2, "family") assert not lin2.is_lineage_match(lin1, "family") assert not lin1.is_lineage_match(lin2, "genus") assert not lin2.is_lineage_match(lin1, "genus") assert not lin1.is_lineage_match(lin2, "species") assert not lin2.is_lineage_match(lin1, "species") lca_from_lin1 = lin1.find_lca(lin2) print(lca_from_lin1.display_lineage()) lca_from_lin2 = lin2.find_lca(lin1) assert lca_from_lin1 == lca_from_lin2 assert lca_from_lin1.display_lineage() == "d__a;p__b;c__c;o__d" def test_is_lineage_match_2(): # match at family, and above, levels; no genus or species to match lin1 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") lin2 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") assert lin1.is_compatible(lin2) assert lin1.is_lineage_match(lin2, "superkingdom") assert lin2.is_lineage_match(lin1, "superkingdom") assert lin1.is_lineage_match(lin2, "phylum") assert lin2.is_lineage_match(lin1, "phylum") assert lin1.is_lineage_match(lin2, "class") assert lin2.is_lineage_match(lin1, "class") assert lin1.is_lineage_match(lin2, "order") assert lin2.is_lineage_match(lin1, "order") assert lin1.is_lineage_match(lin2, "family") assert lin2.is_lineage_match(lin1, "family") assert not lin1.is_lineage_match(lin2, "genus") assert not lin2.is_lineage_match(lin1, "genus") assert not lin1.is_lineage_match(lin2, "species") assert not lin2.is_lineage_match(lin1, "species") lca_from_lin1 = lin1.find_lca(lin2) print(lca_from_lin1.display_lineage()) lca_from_lin2 = lin2.find_lca(lin1) assert lca_from_lin1 == lca_from_lin2 assert lca_from_lin1.display_lineage() == "d__a;p__b;c__c;o__d;f__f" def test_is_lineage_match_3(): # one lineage is empty lin1 = RankLineageInfo() lin2 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") assert lin1.is_compatible(lin2) assert not lin1.is_lineage_match(lin2, "superkingdom") assert not lin2.is_lineage_match(lin1, "superkingdom") assert not lin1.is_lineage_match(lin2, "phylum") assert not lin2.is_lineage_match(lin1, "phylum") assert not lin1.is_lineage_match(lin2, "class") assert not lin2.is_lineage_match(lin1, "class") assert not lin1.is_lineage_match(lin2, "order") assert not lin2.is_lineage_match(lin1, "order") assert not lin1.is_lineage_match(lin2, "family") assert not lin2.is_lineage_match(lin1, "family") assert not lin1.is_lineage_match(lin2, "genus") assert not lin2.is_lineage_match(lin1, "genus") assert not lin1.is_lineage_match(lin2, "species") assert not lin2.is_lineage_match(lin1, "species") def test_is_lineage_match_incorrect_ranks(): # test comparison with incompatible ranks taxranks = ( "superkingdom", "phylum", "class", "order", "family", "genus", "species", "strain", ) lin1 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__e", ranks=taxranks[::-1]) lin2 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") print(lin1.lineage) assert not lin1.is_compatible(lin2) with pytest.raises(ValueError) as exc: lin1.is_lineage_match(lin2, "superkingdom") print(str(exc)) assert "Cannot compare lineages from taxonomies with different ranks." in str(exc) def test_is_lineage_match_improper_rank(): # test comparison with incompatible ranks lin1 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__e") lin2 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") print(lin1.lineage) assert lin1.is_compatible(lin2) with pytest.raises(ValueError) as exc: lin1.is_lineage_match(lin2, "NotARank") print(str(exc)) assert "Desired Rank 'NotARank' not available for this lineage" in str(exc) def test_pop_to_rank_1(): # basic behavior - pop to order? lin1 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d") lin2 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") print(lin1) popped = lin2.pop_to_rank("order") print(popped) assert popped == lin1 def test_pop_to_rank_2(): # what if we're already above rank? lin2 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") print(lin2.pop_to_rank("species")) assert lin2.pop_to_rank("species") == lin2 def test_pop_to_rank_rank_not_avail(): lin1 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") with pytest.raises(ValueError) as exc: lin1.pop_to_rank("NotARank") print(str(exc)) assert "Desired Rank 'NotARank' not available for this lineage" in str(exc) def test_lineage_at_rank_norank(): lin1 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") with pytest.raises(TypeError) as exc: lin1.lineage_at_rank() print(str(exc)) assert "lineage_at_rank() missing 1 required positional argument: 'rank'" in str( exc ) def test_lineage_at_rank_rank_not_avail(): lin1 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") with pytest.raises(ValueError) as exc: lin1.lineage_at_rank("NotARank") print(str(exc)) assert "Desired Rank 'NotARank' not available for this lineage" in str(exc) def test_lineage_at_rank_1(): lin1 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") print(lin1.lineage_at_rank("superkingdom")) assert lin1.lineage_at_rank("superkingdom") == ( LineagePair(rank="superkingdom", name="d__a", taxid=None), ) print(lin1.lineage_at_rank("class")) assert lin1.lineage_at_rank("class") == ( LineagePair(rank="superkingdom", name="d__a", taxid=None), LineagePair(rank="phylum", name="p__b", taxid=None), LineagePair(rank="class", name="c__c", taxid=None), ) def test_lineage_at_rank_below_rank(): lin1 = RankLineageInfo(lineage_str="d__a;p__b;c__c;o__d;f__f") print(lin1.lineage_at_rank("superkingdom")) # if rank is not provided, we only return the filled lineage, to follow original pop_to_rank behavior. print(lin1.lineage_at_rank("genus")) assert lin1.lineage_at_rank("genus") == ( LineagePair(rank="superkingdom", name="d__a", taxid=None), LineagePair(rank="phylum", name="p__b", taxid=None), LineagePair(rank="class", name="c__c", taxid=None), LineagePair(rank="order", name="o__d", taxid=None), LineagePair(rank="family", name="f__f", taxid=None), ) def test_TaxResult_get_match_lineage_1(): gA_tax = ("gA", "a;b;c") taxD = make_mini_taxonomy([gA_tax]) gA = {"name": "gA.1 name"} taxres = make_TaxResult(gA) taxres.get_match_lineage(tax_assignments=taxD) assert taxres.lineageInfo.display_lineage() == "a;b;c" def test_AnnotateTaxResult_get_match_lineage_1(): gA_tax = ("gA", "a;b;c") taxD = make_mini_taxonomy([gA_tax]) gA = {"name": "gA.1 name"} taxres = AnnotateTaxResult(gA) taxres.get_match_lineage(tax_assignments=taxD) assert taxres.lineageInfo.display_lineage() == "a;b;c" assert taxres.row_with_lineages() == {"name": "gA.1 name", "lineage": "a;b;c"} def test_TaxResult_get_match_lineage_skip_ident(): gA_tax = ("gA", "a;b;c") taxD = make_mini_taxonomy([gA_tax]) gA = {"name": "gA.1 name"} taxres = make_TaxResult(gA) taxres.get_match_lineage(tax_assignments=taxD, skip_idents=["gA"]) print("skipped_ident?: ", taxres.skipped_ident) print("missed_ident?: ", taxres.missed_ident) assert taxres.skipped_ident == True assert taxres.lineageInfo == RankLineageInfo() assert taxres.lineageInfo.display_lineage() == "" assert ( taxres.lineageInfo.display_lineage(null_as_unclassified=True) == "unclassified" ) def test_TaxResult_get_match_lineage_missed_ident_fail_on_missing(): gA_tax = ("gA.1", "a;b;c") taxD = make_mini_taxonomy([gA_tax]) gA = {"name": "gA.1 name"} taxres = make_TaxResult(gA) taxres.get_match_lineage(tax_assignments=taxD, skip_idents=["gB"]) print("skipped_ident?: ", taxres.skipped_ident) print("missed_ident?: ", taxres.missed_ident) assert taxres.skipped_ident == False assert taxres.missed_ident == True assert taxres.lineageInfo == RankLineageInfo() assert taxres.lineageInfo.display_lineage() == "" assert ( taxres.lineageInfo.display_lineage(null_as_unclassified=True) == "unclassified" ) def test_TaxResult_get_match_lineage_missed_ident_fail_on_missing(): gA_tax = ("gA.1", "a;b;c") taxD = make_mini_taxonomy([gA_tax]) gA = {"name": "gA.1 name"} taxres = make_TaxResult(gA) with pytest.raises(ValueError) as exc: taxres.get_match_lineage( tax_assignments=taxD, skip_idents=["gB"], fail_on_missing_taxonomy=True ) print(str(exc)) assert "Error: ident 'gA' is not in the taxonomy database." in str(exc) def test_QueryTaxResult(): "basic functionality: initialize and add a taxresult" tax_info = [("gA", "a;b;c")] taxD = make_mini_taxonomy(tax_info=tax_info) taxres = make_TaxResult(taxD=taxD) # initialize q_res = QueryTaxResult(taxres.query_info) assert q_res.ranks == [] assert q_res.ascending_ranks == [] q_res.add_taxresult(taxres) # check that new querytaxres is compatible with taxres assert q_res.is_compatible(taxres) # check that a few thngs were set properly and/or are not yet set. assert q_res.query_name == "q1" assert q_res.query_info.query_bp == 100 assert len(q_res.raw_taxresults) == 1 assert q_res.skipped_idents == set() assert q_res.missed_idents == set() assert q_res.summarized_lineage_results == {} taxranks = ( "superkingdom", "phylum", "class", "order", "family", "genus", "species", "strain", ) assert q_res.ranks == taxranks assert q_res.ascending_ranks == taxranks[::-1] def test_QueryTaxResult_add_incompatible(): "initialize and try to add incompatible taxresult" tax_info = [("gA", "a;b;c")] taxD = make_mini_taxonomy(tax_info=tax_info) taxres = make_TaxResult(taxD=taxD) taxres2 = make_TaxResult({"query_name": "q2"}, taxD=taxD) # initialize q_res = QueryTaxResult(taxres.query_info) # check that new querytaxres is compatible with taxres and not taxres2 assert q_res.is_compatible(taxres) assert not q_res.is_compatible(taxres2) q_res.add_taxresult(taxres) with pytest.raises(ValueError) as exc: q_res.add_taxresult(taxres2) print(str(exc)) assert "Error: Cannot add TaxResult: query information does not match." in str(exc) def test_QueryTaxResult_add_without_tax_info(): "initialize and add a taxresult with missed ident" taxres = make_TaxResult() # do not add taxonomic info # initialize q_res = QueryTaxResult(taxres.query_info) print("attempted to add lineage info?: ", taxres.match_lineage_attempted) with pytest.raises(ValueError) as exc: q_res.add_taxresult(taxres) print(str(exc)) assert ( "Error: Cannot add TaxResult. Please use get_match_lineage() to add taxonomic lineage information first." in str(exc) ) def test_QueryTaxResult_add_skipped_ident(): "initialize and add a taxresult with skipped ident" gA_tax = ("gA", "a;b;c") taxD = make_mini_taxonomy([gA_tax]) taxres = make_TaxResult(taxD=taxD, skip_idents=["gA"]) # taxres.get_match_lineage(tax_assignments=taxD, skip_idents=['gA']) # initialize q_res = QueryTaxResult(taxres.query_info) q_res.add_taxresult(taxres) assert len(q_res.skipped_idents) == 1 assert len(q_res.raw_taxresults) == 1 assert q_res.missed_idents == set() assert q_res.summarized_lineage_results == {} def test_QueryTaxResult_add_missed_ident(): "initialize and add a taxresult with missed ident" gA_tax = ("gB", "a;b;c") taxD = make_mini_taxonomy([gA_tax]) taxres = make_TaxResult(taxD=taxD) # initialize q_res = QueryTaxResult(taxres.query_info) # add taxonomic info to taxres q_res.add_taxresult(taxres) assert len(q_res.missed_idents) == 1 assert len(q_res.raw_taxresults) == 1 assert q_res.skipped_idents == set() assert q_res.summarized_lineage_results == {} def test_QueryTaxResult_track_missed_and_skipped(): "make sure missed and skipped idents are being tracked" # make taxonomy tax_info = [("gA", "a;b;c"), ("gB", "a;b;d")] taxD = make_mini_taxonomy(tax_info=tax_info) # make results taxres = make_TaxResult() taxres2 = make_TaxResult({"name": "gB"}) # skipped taxres3 = make_TaxResult({"name": "gB"}) # skipped taxres4 = make_TaxResult({"name": "gC"}) # skipped taxres5 = make_TaxResult({"name": "gD"}) # missed taxres6 = make_TaxResult({"name": "gE"}) # missed # initialize q_res = QueryTaxResult(taxres.query_info) # add taxonomic info to taxres, add to q_res for n, tr in enumerate([taxres, taxres2, taxres3, taxres4, taxres5, taxres6]): tr.get_match_lineage(tax_assignments=taxD, skip_idents=["gB", "gC"]) print("num: ", n) print("skipped?: ", tr.skipped_ident) print("missed?: ", tr.missed_ident) q_res.add_taxresult(tr) assert len(q_res.raw_taxresults) == 6 print(q_res.n_skipped) print(q_res.n_missed) assert q_res.n_missed == 2 assert q_res.n_skipped == 3 assert "gB" in q_res.skipped_idents assert len(q_res.skipped_idents) == 2 assert "gD" in q_res.missed_idents assert q_res.summarized_lineage_results == {} def test_QueryTaxResult_track_missed_and_skipped_using_fn(): "make sure missed and skipped idents are being tracked. Same as above but use helper fn." taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [ {}, {"name": "gB"}, {"name": "gB"}, {"name": "gC"}, {"name": "gD"}, {"name": "gE"}, ] gres = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, skip_idents=["gB", "gC"] ) # should have 6 results for default query 'q1' print(gres.keys()) q_res = next(iter(gres.values())) assert len(q_res.raw_taxresults) == 6 print(q_res.n_skipped) print(q_res.n_missed) assert q_res.n_missed == 2 assert q_res.n_skipped == 3 assert "gB" in q_res.skipped_idents assert len(q_res.skipped_idents) == 2 assert "gD" in q_res.missed_idents assert q_res.summarized_lineage_results == {} def test_QueryTaxResult_summarize_up_ranks_1(): "basic functionality: summarize up ranks" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] gres = make_QueryTaxResults(gather_info=gather_results, taxD=taxD) assert len(gres.keys()) == 1 q_res = next(iter(gres.values())) # now summarize up the ranks q_res.summarize_up_ranks() assert len(q_res.raw_taxresults) == 2 # print(q_res.sum_uniq_weighted.values()) # print(q_res.sum_uniq_weighted['superkingdom']) assert list(q_res.sum_uniq_weighted.keys()) == ["class", "phylum", "superkingdom"] assert q_res.sum_uniq_weighted["superkingdom"] == { RankLineageInfo(lineage_str="a"): approx(0.4) } assert q_res.sum_uniq_to_query["superkingdom"] == { RankLineageInfo(lineage_str="a"): approx(0.2) } assert q_res.sum_uniq_bp["superkingdom"] == {RankLineageInfo(lineage_str="a"): 40} assert q_res.sum_uniq_weighted["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.4) } assert q_res.sum_uniq_to_query["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.2) } assert q_res.sum_uniq_bp["phylum"] == {RankLineageInfo(lineage_str="a;b"): 40} assert q_res.sum_uniq_weighted["class"] == { RankLineageInfo(lineage_str="a;b;c"): approx(0.2), RankLineageInfo(lineage_str="a;b;d"): approx(0.2), } assert q_res.sum_uniq_to_query["class"] == { RankLineageInfo(lineage_str="a;b;c"): approx(0.1), RankLineageInfo(lineage_str="a;b;d"): approx(0.1), } assert q_res.sum_uniq_bp["class"] == { RankLineageInfo(lineage_str="a;b;c"): 20, RankLineageInfo(lineage_str="a;b;d"): 20, } def test_QueryTaxResult_summarize_up_ranks_2(): "summarize up ranks: different values" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [ {}, { "name": "gB", "f_unique_weighted": 0.1, "f_unique_to_query": 0.05, "unique_intersect_bp": 10, }, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) # now summarize up the ranks q_res.summarize_up_ranks() assert len(q_res.raw_taxresults) == 2 print(q_res.sum_uniq_weighted.values()) print(q_res.sum_uniq_weighted["superkingdom"]) assert q_res.sum_uniq_weighted["superkingdom"] == { RankLineageInfo(lineage_str="a"): approx(0.3) } assert q_res.sum_uniq_to_query["superkingdom"] == { RankLineageInfo(lineage_str="a"): approx(0.15) } assert q_res.sum_uniq_bp["superkingdom"] == {RankLineageInfo(lineage_str="a"): 30} assert q_res.sum_uniq_weighted["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.3) } assert q_res.sum_uniq_to_query["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.15) } assert q_res.sum_uniq_bp["phylum"] == {RankLineageInfo(lineage_str="a;b"): 30} assert q_res.sum_uniq_weighted["class"] == { RankLineageInfo(lineage_str="a;b;c"): approx(0.2), RankLineageInfo(lineage_str="a;b;d"): approx(0.1), } assert q_res.sum_uniq_to_query["class"] == { RankLineageInfo(lineage_str="a;b;c"): approx(0.1), RankLineageInfo(lineage_str="a;b;d"): approx(0.05), } assert q_res.sum_uniq_bp["class"] == { RankLineageInfo(lineage_str="a;b;c"): 20, RankLineageInfo(lineage_str="a;b;d"): 10, } def test_QueryTaxResult_summarize_up_ranks_missing_lineage(): "basic functionality: summarize up ranks" taxD = make_mini_taxonomy([("gA", "a;b;c")]) gather_results = [{}, {"name": "gB"}] gres = make_QueryTaxResults(gather_info=gather_results, taxD=taxD) assert len(gres.keys()) == 1 q_res = next(iter(gres.values())) # now summarize up the ranks q_res.summarize_up_ranks() assert len(q_res.raw_taxresults) == 2 # print(q_res.sum_uniq_weighted.values()) print(q_res.sum_uniq_weighted["superkingdom"]) assert q_res.sum_uniq_weighted["superkingdom"] == { RankLineageInfo(lineage_str="a"): approx(0.2) } assert q_res.sum_uniq_to_query["superkingdom"] == { RankLineageInfo(lineage_str="a"): approx(0.1) } assert q_res.sum_uniq_bp["superkingdom"] == {RankLineageInfo(lineage_str="a"): 20} assert q_res.sum_uniq_weighted["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.2) } assert q_res.sum_uniq_to_query["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.1) } assert q_res.sum_uniq_bp["phylum"] == {RankLineageInfo(lineage_str="a;b"): 20} assert q_res.sum_uniq_weighted["class"] == { RankLineageInfo(lineage_str="a;b;c"): approx(0.2) } assert q_res.sum_uniq_to_query["class"] == { RankLineageInfo(lineage_str="a;b;c"): approx(0.1) } assert q_res.sum_uniq_bp["class"] == {RankLineageInfo(lineage_str="a;b;c"): 20} def test_QueryTaxResult_summarize_up_ranks_skipped_lineage(): "basic functionality: summarize up ranks" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] gres = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, skip_idents=["gB"] ) assert len(gres.keys()) == 1 q_res = next(iter(gres.values())) # now summarize up the ranks q_res.summarize_up_ranks() assert len(q_res.raw_taxresults) == 2 assert list(q_res.sum_uniq_weighted.keys()) == ["class", "phylum", "superkingdom"] # print(q_res.sum_uniq_weighted.values()) print(q_res.sum_uniq_weighted["superkingdom"]) assert q_res.sum_uniq_weighted["superkingdom"] == { RankLineageInfo(lineage_str="a"): approx(0.2) } assert q_res.sum_uniq_to_query["superkingdom"] == { RankLineageInfo(lineage_str="a"): approx(0.1) } assert q_res.sum_uniq_bp["superkingdom"] == {RankLineageInfo(lineage_str="a"): 20} assert q_res.sum_uniq_weighted["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.2) } assert q_res.sum_uniq_to_query["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.1) } assert q_res.sum_uniq_bp["phylum"] == {RankLineageInfo(lineage_str="a;b"): 20} assert q_res.sum_uniq_weighted["class"] == { RankLineageInfo(lineage_str="a;b;c"): approx(0.2) } assert q_res.sum_uniq_to_query["class"] == { RankLineageInfo(lineage_str="a;b;c"): approx(0.1) } assert q_res.sum_uniq_bp["class"] == {RankLineageInfo(lineage_str="a;b;c"): 20} def test_QueryTaxResult_summarize_up_ranks_perfect_match(): "summarize up ranks: different values" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{"f_unique_to_query": 1.0}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) # now summarize up the ranks q_res.summarize_up_ranks() assert len(q_res.raw_taxresults) == 1 print(q_res.sum_uniq_weighted.values()) print(q_res.sum_uniq_to_query["superkingdom"]) assert list(q_res.sum_uniq_to_query["superkingdom"].values()) == [1.0] assert "gA" in q_res.perfect_match def test_QueryTaxResult_summarize_up_ranks_already_summarized(): "summarize up ranks: error, already summarized" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{"f_unique_to_query": 1.0}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) # now summarize up the ranks q_res.summarize_up_ranks() with pytest.raises(ValueError) as exc: q_res.summarize_up_ranks() print(str(exc)) assert "Error: already summarized" in str(exc) def test_QueryTaxResult_summarize_up_ranks_already_summarized_force(): "summarize up ranks: already summarized but force" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [ {}, { "name": "gB", "f_unique_weighted": 0.1, "f_unique_to_query": 0.05, "unique_intersect_bp": 10, }, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) # now summarize up the ranks q_res.summarize_up_ranks() q_res.summarize_up_ranks(force_resummarize=True) assert list(q_res.sum_uniq_weighted.keys()) == ["class", "phylum", "superkingdom"] # check that all results are still good assert len(q_res.raw_taxresults) == 2 assert q_res.sum_uniq_weighted["superkingdom"] == { RankLineageInfo(lineage_str="a"): approx(0.3) } assert q_res.sum_uniq_weighted["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.3) } assert q_res.sum_uniq_to_query["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.15) } assert q_res.sum_uniq_bp["phylum"] == {RankLineageInfo(lineage_str="a;b"): 30} assert q_res.sum_uniq_to_query["class"] == { RankLineageInfo(lineage_str="a;b;c"): approx(0.1), RankLineageInfo(lineage_str="a;b;d"): approx(0.05), } assert q_res.sum_uniq_weighted["class"] == { RankLineageInfo(lineage_str="a;b;c"): approx(0.2), RankLineageInfo(lineage_str="a;b;d"): approx(0.1), } assert q_res.sum_uniq_bp["class"] == { RankLineageInfo(lineage_str="a;b;c"): 20, RankLineageInfo(lineage_str="a;b;d"): 10, } def test_QueryTaxResult_summarize_up_ranks_single_rank(): "summarize up ranks: different values" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [ {}, { "name": "gB", "f_unique_weighted": 0.1, "f_unique_to_query": 0.05, "unique_intersect_bp": 10, }, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) # now summarize up the ranks q_res.summarize_up_ranks(single_rank="phylum") assert len(q_res.raw_taxresults) == 2 assert list(q_res.sum_uniq_weighted.keys()) == ["phylum"] print(q_res.sum_uniq_weighted.keys()) print(q_res.sum_uniq_weighted.values()) print(q_res.sum_uniq_weighted["phylum"]) assert q_res.sum_uniq_weighted["phylum"] == { RankLineageInfo(lineage_str="a;b"): approx(0.3) } assert list(q_res.sum_uniq_to_query["phylum"].values()) == [approx(0.15)] assert list(q_res.sum_uniq_bp["phylum"].values()) == [30] assert q_res.summarized_ranks == ["phylum"] def test_QueryTaxResult_summarize_up_ranks_single_rank_not_available(): "summarize up ranks: different values" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [ {}, { "name": "gB", "f_unique_weighted": 0.1, "f_unique_to_query": 0.05, "unique_intersect_bp": 10, }, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) # now summarize up the ranks with pytest.raises(ValueError) as exc: q_res.summarize_up_ranks(single_rank="NotARank") print(str(exc)) assert ( "Error: rank 'NotARank' not in available ranks (strain, species, genus, family, order, class, phylum, superkingdom)" in str(exc) ) def test_QueryTaxResult_summarize_up_ranks_single_rank_not_filled(): "summarize up ranks: different values" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [ {}, { "name": "gB", "f_unique_weighted": 0.1, "f_unique_to_query": 0.05, "unique_intersect_bp": 10, }, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) # now summarize up the ranks with pytest.raises(ValueError) as exc: q_res.summarize_up_ranks(single_rank="species") print(str(exc)) assert "Error: rank 'species' was not available for any matching lineages." in str( exc ) def test_QueryTaxResult_build_summarized_result_1(): "basic functionality: build summarized_result" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.build_summarized_result() print(q_res.summarized_lineage_results.keys()) sk = [ SummarizedGatherResult( rank="superkingdom", fraction=0.2, f_weighted_at_rank=0.4, lineage=RankLineageInfo(lineage_str="a"), bp_match_at_rank=40, query_ani_at_rank=approx(0.95, rel=1e-2), ), SummarizedGatherResult( rank="superkingdom", fraction=0.8, f_weighted_at_rank=0.6, lineage=RankLineageInfo(), bp_match_at_rank=60, query_ani_at_rank=None, ), ] print(q_res.summarized_lineage_results["superkingdom"]) assert q_res.summarized_lineage_results["superkingdom"] == sk print(q_res.summarized_lineage_results["phylum"]) phy = [ SummarizedGatherResult( rank="phylum", fraction=0.2, f_weighted_at_rank=0.4, lineage=RankLineageInfo(lineage_str="a;b"), bp_match_at_rank=40, query_ani_at_rank=approx(0.95, rel=1e-2), ), SummarizedGatherResult( rank="phylum", fraction=0.8, f_weighted_at_rank=0.6, lineage=RankLineageInfo(), bp_match_at_rank=60, query_ani_at_rank=None, ), ] assert q_res.summarized_lineage_results["phylum"] == phy print(q_res.summarized_lineage_results["class"]) cl = [ SummarizedGatherResult( rank="class", fraction=0.1, f_weighted_at_rank=0.2, lineage=RankLineageInfo(lineage_str="a;b;c"), bp_match_at_rank=20, query_ani_at_rank=approx(0.93, rel=1e-2), ), SummarizedGatherResult( rank="class", fraction=0.1, f_weighted_at_rank=0.2, lineage=RankLineageInfo(lineage_str="a;b;d"), bp_match_at_rank=20, query_ani_at_rank=approx(0.93, rel=1e-2), ), SummarizedGatherResult( rank="class", fraction=0.8, f_weighted_at_rank=0.6, lineage=RankLineageInfo(), bp_match_at_rank=60, query_ani_at_rank=None, ), ] assert q_res.summarized_lineage_results["class"] == cl assert q_res.total_f_weighted["phylum"] == approx(0.4) assert q_res.total_f_classified["class"] == approx(0.2) assert q_res.total_bp_classified["superkingdom"] == 40 def test_QueryTaxResult_build_summarized_result_2(): """test two queries, build summarized result for each""" # make mini taxonomy gA_tax = ("gA", "a;b") gB_tax = ("gB", "a;c") taxD = make_mini_taxonomy([gA_tax, gB_tax]) # make gather results gather_results = [ { "query_name": "queryA", "name": "gA", "f_unique_weighted": 0.5, "f_unique_to_query": 0.5, "unique_intersect_bp": 50, }, { "query_name": "queryA", "name": "gB", "f_unique_weighted": 0.4, "f_unique_to_query": 0.3, "unique_intersect_bp": 30, }, { "query_name": "queryB", "name": "gB", "f_unique_weighted": 0.3, "f_unique_to_query": 0.3, "unique_intersect_bp": 30, }, ] gres = make_QueryTaxResults(gather_info=gather_results, taxD=taxD) for query_name, q_res in gres.items(): q_res.build_summarized_result() # summarize and build result sk = q_res.summarized_lineage_results["superkingdom"] phy = q_res.summarized_lineage_results["phylum"] assert len(sk) == 2 assert sk[0].lineage == RankLineageInfo(lineage_str="a") print(phy) if query_name == "queryA": # check superkingdom results assert sk[0].fraction == approx(0.8) assert sk[0].f_weighted_at_rank == approx(0.9) assert sk[0].bp_match_at_rank == 80 assert sk[1].fraction == approx(0.2) assert sk[1].f_weighted_at_rank == approx(0.1) assert sk[1].bp_match_at_rank == 20 assert sk[1].lineage == RankLineageInfo() # check phylum results assert len(phy) == 3 assert phy[0].fraction == approx(0.5) assert phy[0].f_weighted_at_rank == approx(0.5) assert phy[0].bp_match_at_rank == 50 assert phy[0].lineage == RankLineageInfo(lineage_str="a;b") assert phy[1].fraction == approx(0.3) assert phy[1].f_weighted_at_rank == approx(0.4) assert phy[1].bp_match_at_rank == 30 assert phy[1].lineage == RankLineageInfo(lineage_str="a;c") assert phy[2].fraction == approx(0.2) assert phy[2].f_weighted_at_rank == approx(0.1) assert phy[2].bp_match_at_rank == 20 assert phy[2].lineage == RankLineageInfo() if query_name == "queryB": # check superkingdom results assert sk[0].fraction == approx(0.3) assert sk[0].f_weighted_at_rank == approx(0.3) assert sk[0].bp_match_at_rank == 30 assert sk[1].fraction == approx(0.7) assert sk[1].f_weighted_at_rank == approx(0.7) assert sk[1].bp_match_at_rank == 70 assert sk[1].lineage == RankLineageInfo() # check phylum results assert len(phy) == 2 assert phy[0].fraction == approx(0.3) assert phy[0].f_weighted_at_rank == approx(0.3) assert phy[0].bp_match_at_rank == 30 assert phy[0].lineage == RankLineageInfo(lineage_str="a;c") assert phy[1].fraction == approx(0.7) assert phy[1].f_weighted_at_rank == approx(0.7) assert phy[1].bp_match_at_rank == 70 assert phy[1].lineage == RankLineageInfo() def test_QueryTaxResult_build_summarized_result_missing_lineage(): "build summarized_result with missing lineage" taxD = make_mini_taxonomy([("gA", "a;b;c")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.build_summarized_result() print(q_res.summarized_lineage_results.keys()) print(q_res.summarized_lineage_results["superkingdom"]) sk = [ SummarizedGatherResult( rank="superkingdom", fraction=0.1, f_weighted_at_rank=0.2, lineage=RankLineageInfo(lineage_str="a"), bp_match_at_rank=20, query_ani_at_rank=approx(0.928, rel=1e-2), ), SummarizedGatherResult( rank="superkingdom", fraction=0.9, lineage=RankLineageInfo(), f_weighted_at_rank=0.8, bp_match_at_rank=80, query_ani_at_rank=None, ), ] assert q_res.summarized_lineage_results["superkingdom"] == sk print(q_res.summarized_lineage_results["phylum"]) phy = [ SummarizedGatherResult( rank="phylum", fraction=0.1, f_weighted_at_rank=0.2, lineage=RankLineageInfo(lineage_str="a;b"), bp_match_at_rank=20, query_ani_at_rank=approx(0.928, rel=1e-2), ), SummarizedGatherResult( rank="phylum", fraction=0.9, lineage=RankLineageInfo(), f_weighted_at_rank=0.8, bp_match_at_rank=80, query_ani_at_rank=None, ), ] assert q_res.summarized_lineage_results["phylum"] == phy print(q_res.summarized_lineage_results["class"]) cl = [ SummarizedGatherResult( rank="class", fraction=0.1, lineage=RankLineageInfo(lineage_str="a;b;c"), f_weighted_at_rank=0.2, bp_match_at_rank=20, query_ani_at_rank=approx(0.928, rel=1e-2), ), SummarizedGatherResult( rank="class", fraction=0.9, lineage=RankLineageInfo(), f_weighted_at_rank=0.8, bp_match_at_rank=80, query_ani_at_rank=None, ), ] assert q_res.summarized_lineage_results["class"] == cl assert q_res.total_f_weighted["phylum"] == approx(0.2) assert q_res.total_f_classified["class"] == approx(0.1) assert q_res.total_bp_classified["superkingdom"] == 20 def test_QueryTaxResult_build_summarized_result_skipped_lineage(): "build summarized_result with skipped lineage" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, skip_idents=["gB"] ) q_res.build_summarized_result() print(q_res.summarized_lineage_results.keys()) print(q_res.summarized_lineage_results["superkingdom"]) sk = [ SummarizedGatherResult( rank="superkingdom", fraction=0.1, f_weighted_at_rank=0.2, lineage=RankLineageInfo(lineage_str="a"), bp_match_at_rank=20, query_ani_at_rank=approx(0.928, rel=1e-2), ), SummarizedGatherResult( rank="superkingdom", fraction=0.9, lineage=RankLineageInfo(), f_weighted_at_rank=0.8, bp_match_at_rank=80, query_ani_at_rank=None, ), ] assert q_res.summarized_lineage_results["superkingdom"] == sk print(q_res.summarized_lineage_results["phylum"]) phy = [ SummarizedGatherResult( rank="phylum", fraction=0.1, lineage=RankLineageInfo(lineage_str="a;b"), f_weighted_at_rank=0.2, bp_match_at_rank=20, query_ani_at_rank=approx(0.928, rel=1e-2), ), SummarizedGatherResult( rank="phylum", fraction=0.9, lineage=RankLineageInfo(), f_weighted_at_rank=0.8, bp_match_at_rank=80, query_ani_at_rank=None, ), ] assert q_res.summarized_lineage_results["phylum"] == phy print(q_res.summarized_lineage_results["class"]) cl = [ SummarizedGatherResult( rank="class", fraction=0.1, lineage=RankLineageInfo(lineage_str="a;b;c"), f_weighted_at_rank=0.2, bp_match_at_rank=20, query_ani_at_rank=approx(0.928, rel=1e-2), ), SummarizedGatherResult( rank="class", fraction=0.9, lineage=RankLineageInfo(), f_weighted_at_rank=0.8, bp_match_at_rank=80, query_ani_at_rank=None, ), ] assert q_res.summarized_lineage_results["class"] == cl assert q_res.total_f_weighted["phylum"] == approx(0.2) assert q_res.total_f_classified["class"] == approx(0.1) assert q_res.total_bp_classified["superkingdom"] == 20 def test_QueryTaxResult_build_summarized_result_over100percent(): "summarize up ranks: different values" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB", "f_unique_to_query": 0.95}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) # now summarize up the ranks assert len(q_res.raw_taxresults) == 2 with pytest.raises(ValueError) as exc: q_res.build_summarized_result() print(str(exc)) assert ( "Summarized fraction is > 100% of the query! This should not be possible" in str(exc) ) def test_build_summarized_result_rank_fail_not_available_resummarize(): "build classification result" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.summarize_up_ranks("superkingdom") with pytest.raises(ValueError) as exc: q_res.build_summarized_result(single_rank="order") print(str(exc)) assert "Error: rank 'order' not in summarized rank(s), superkingdom" in str(exc) def test_aggregate_by_lineage_at_rank(): """test aggregate by lineage at rank""" # make mini taxonomy gA_tax = ("gA", "a;b") gB_tax = ("gB", "a;c") taxD = make_mini_taxonomy([gA_tax, gB_tax]) # make gather results gather_results = [ { "query_name": "queryA", "name": "gA", "f_unique_weighted": 0.5, "f_unique_to_query": 0.4, "unique_intersect_bp": 50, }, { "query_name": "queryA", "name": "gB", "f_unique_weighted": 0.3, "f_unique_to_query": 0.3, "unique_intersect_bp": 30, }, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) summarized, all_queries = aggregate_by_lineage_at_rank( [q_res], rank="phylum", by_query=False ) print(summarized) assert summarized == {"a;b": 0.4, "a;c": 0.3, "unclassified": approx(0.3, rel=1e-2)} assert all_queries == ["queryA"] def test_aggregate_by_lineage_at_rank_not_available(): """test aggregate by lineage at rank""" # make mini taxonomy gA_tax = ("gA", "a;b") gB_tax = ("gB", "a;c") taxD = make_mini_taxonomy([gA_tax, gB_tax]) # make gather results gather_results = [ { "query_name": "queryA", "name": "gA", "f_unique_weighted": 0.5, "f_unique_to_query": 0.4, "unique_intersect_bp": 50, }, { "query_name": "queryA", "name": "gB", "f_unique_weighted": 0.3, "f_unique_to_query": 0.3, "unique_intersect_bp": 30, }, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) with pytest.raises(ValueError) as exc: aggregate_by_lineage_at_rank([q_res], rank="species", by_query=False) print(str(exc)) assert "Error: rank 'species' not available for aggregation." in str(exc) def test_aggregate_by_lineage_at_rank_by_query(): """test two queries, aggregate by lineage at rank by query""" # make mini taxonomy gA_tax = ("gA", "a;b") gB_tax = ("gB", "a;c") taxD = make_mini_taxonomy([gA_tax, gB_tax]) # make gather results gather_results = [ { "query_name": "queryA", "name": "gA", "f_unique_weighted": 0.2, "f_unique_to_query": 0.2, "unique_intersect_bp": 50, }, { "query_name": "queryA", "name": "gB", "f_unique_weighted": 0.3, "f_unique_to_query": 0.3, "unique_intersect_bp": 30, }, { "query_name": "queryB", "name": "gB", "f_unique_weighted": 0.4, "f_unique_to_query": 0.4, "unique_intersect_bp": 30, }, ] gres = make_QueryTaxResults(gather_info=gather_results, taxD=taxD, summarize=True) # check by query summarized, all_queries = aggregate_by_lineage_at_rank( gres.values(), rank="superkingdom", by_query=True ) print(summarized) assert summarized == { "a": {"queryA": 0.5, "queryB": 0.4}, "unclassified": {"queryA": 0.5, "queryB": 0.6}, } # assert summarized == {'a': {'queryA': approx(0.1, rel=1e-2), 'queryB': 0.7}} assert all_queries == ["queryA", "queryB"] summarized, all_queries = aggregate_by_lineage_at_rank( gres.values(), rank="phylum", by_query=True ) print(summarized) assert summarized == { "a;c": {"queryA": 0.3, "queryB": 0.4}, "a;b": {"queryA": 0.2}, "unclassified": {"queryA": 0.5, "queryB": 0.6}, } def test_build_classification_result_containment_threshold_fail(): "classification result: improper containment threshold" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) with pytest.raises(ValueError) as exc: q_res.build_classification_result(containment_threshold=1.2) print(str(exc)) assert "Containment threshold must be between 0 and 1 (input value: 1.2)." in str( exc ) with pytest.raises(ValueError) as exc: q_res.build_classification_result(containment_threshold=-0.1) print(str(exc)) assert "Containment threshold must be between 0 and 1 (input value: -0.1)." in str( exc ) def test_build_classification_result_containment_threshold(): "basic functionality: build classification result using containment threshold" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.build_classification_result(containment_threshold=0.1) print("classif: ", q_res.classification_result) assert q_res.classification_result.status == "match" assert q_res.classification_result.rank == "class" assert q_res.classification_result.fraction == 0.1 assert q_res.classification_result.lineage == RankLineageInfo(lineage_str="a;b;c") assert q_res.classification_result.f_weighted_at_rank == 0.2 assert q_res.classification_result.bp_match_at_rank == 20 assert q_res.classification_result.query_ani_at_rank == approx(0.928, rel=1e-2) q_res.build_classification_result(containment_threshold=0.2) print("classif: ", q_res.classification_result) assert q_res.classification_result.status == "match" assert q_res.classification_result.rank == "phylum" assert q_res.classification_result.lineage == RankLineageInfo(lineage_str="a;b") assert q_res.classification_result.f_weighted_at_rank == 0.4 assert q_res.classification_result.fraction == 0.2 assert q_res.classification_result.bp_match_at_rank == 40 assert q_res.classification_result.query_ani_at_rank == approx(0.95, rel=1e-2) q_res.build_classification_result(containment_threshold=1.0) print("classif: ", q_res.classification_result) assert q_res.classification_result.status == "below_threshold" assert q_res.classification_result.rank == "superkingdom" assert q_res.classification_result.fraction == 0.2 assert q_res.classification_result.lineage == RankLineageInfo(lineage_str="a") assert q_res.classification_result.f_weighted_at_rank == 0.4 assert q_res.classification_result.bp_match_at_rank == 40 assert q_res.classification_result.query_ani_at_rank == approx(0.95, rel=1e-2) def test_build_classification_result_ani_threshold(): "basic functionality: build classification result" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.build_classification_result(ani_threshold=0.92) print("classif: ", q_res.classification_result) assert q_res.classification_result.status == "match" assert q_res.classification_result.rank == "class" assert q_res.classification_result.fraction == 0.1 assert q_res.classification_result.lineage == RankLineageInfo(lineage_str="a;b;c") assert q_res.classification_result.f_weighted_at_rank == 0.2 assert q_res.classification_result.bp_match_at_rank == 20 assert q_res.classification_result.query_ani_at_rank == approx(0.928, rel=1e-2) q_res.build_classification_result(ani_threshold=0.94) # should classify at phylum print("classif: ", q_res.classification_result) assert q_res.classification_result.status == "match" assert q_res.classification_result.rank == "phylum" assert q_res.classification_result.fraction == 0.2 assert q_res.classification_result.lineage == RankLineageInfo(lineage_str="a;b") assert q_res.classification_result.f_weighted_at_rank == 0.4 assert q_res.classification_result.bp_match_at_rank == 40 assert q_res.classification_result.query_ani_at_rank == approx(0.95, rel=1e-2) # superk result, but doesn't meet ANI threshold q_res.build_classification_result(ani_threshold=0.96) print("classif: ", q_res.classification_result) assert q_res.classification_result.status == "below_threshold" assert q_res.classification_result.rank == "superkingdom" assert q_res.classification_result.fraction == 0.2 assert q_res.classification_result.lineage == RankLineageInfo(lineage_str="a") assert q_res.classification_result.f_weighted_at_rank == 0.4 assert q_res.classification_result.bp_match_at_rank == 40 assert q_res.classification_result.query_ani_at_rank == approx(0.95, rel=1e-2) def test_build_classification_result_ani_threshold_fail(): "classification result: improper ANI threshold" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) with pytest.raises(ValueError) as exc: q_res.build_classification_result(ani_threshold=1.2) print(str(exc)) assert "ANI threshold must be between 0 and 1 (input value: 1.2)." in str(exc) with pytest.raises(ValueError) as exc: q_res.build_classification_result(ani_threshold=-0.1) print(str(exc)) assert "ANI threshold must be between 0 and 1 (input value: -0.1)." in str(exc) def test_build_classification_result_rank_fail_not_filled(): "classification result: rank not available (wasn't filled in tax lineage matches)" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) with pytest.raises(ValueError) as exc: q_res.build_classification_result(rank="order") print(str(exc)) assert "Error: rank 'order' was not available for any matching lineages." in str( exc ) def test_build_classification_result_rank_fail_not_available_resummarize(): "classification result: rank not available (wasn't summarized)" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.summarize_up_ranks("superkingdom") with pytest.raises(ValueError) as exc: q_res.build_classification_result(rank="order") print(str(exc)) assert "Error: rank 'order' not in summarized rank(s), superkingdom" in str(exc) def test_build_classification_result_rank_fail_not_available(): "classification result: rank not available" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) with pytest.raises(ValueError) as exc: q_res.build_classification_result(rank="NotARank") print(str(exc)) assert ( "Error: rank 'NotARank' not in available ranks (strain, species, genus, family, order, class, phylum, superkingdom)" in str(exc) ) def test_build_classification_result_rank_containment_threshold(): "classification result - rank and containment threshold (default)" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.build_classification_result(rank="class") print("classif: ", q_res.classification_result) assert q_res.classification_result.status == "match" assert q_res.classification_result.rank == "class" assert q_res.classification_result.fraction == 0.1 assert q_res.classification_result.lineage == RankLineageInfo(lineage_str="a;b;c") assert q_res.classification_result.f_weighted_at_rank == 0.2 assert q_res.classification_result.bp_match_at_rank == 20 assert q_res.classification_result.query_ani_at_rank == approx(0.928, rel=1e-2) q_res.build_classification_result(rank="class", containment_threshold=0.4) assert q_res.classification_result.status == "below_threshold" assert q_res.classification_result.rank == "class" assert q_res.classification_result.fraction == 0.1 assert q_res.classification_result.lineage == RankLineageInfo(lineage_str="a;b;c") assert q_res.classification_result.f_weighted_at_rank == 0.2 assert q_res.classification_result.bp_match_at_rank == 20 assert q_res.classification_result.query_ani_at_rank == approx(0.928, rel=1e-2) def test_build_classification_result_rank_ani_threshold(): "classification result with rank and ANI threshold" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.build_classification_result(rank="class", ani_threshold=0.92) assert q_res.classification_result.status == "match" assert q_res.classification_result.rank == "class" assert q_res.classification_result.fraction == 0.1 assert q_res.classification_result.lineage == RankLineageInfo(lineage_str="a;b;c") assert q_res.classification_result.f_weighted_at_rank == 0.2 assert q_res.classification_result.bp_match_at_rank == 20 assert q_res.classification_result.query_ani_at_rank == approx(0.928, rel=1e-2) q_res.build_classification_result(rank="class", ani_threshold=0.95) assert q_res.classification_result.status == "below_threshold" assert q_res.classification_result.rank == "class" assert q_res.classification_result.fraction == 0.1 assert q_res.classification_result.lineage == RankLineageInfo(lineage_str="a;b;c") assert q_res.classification_result.f_weighted_at_rank == 0.2 assert q_res.classification_result.bp_match_at_rank == 20 assert q_res.classification_result.query_ani_at_rank == approx(0.928, rel=1e-2) def test_krona_classified(): "basic functionality: build classification result using containment threshold" taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.build_classification_result() assert q_res.krona_classified is None q_res.build_classification_result(rank="phylum") # , force_resummarize=True) print(q_res.krona_classified) assert q_res.krona_classified == (0.2, "a", "b") assert q_res.krona_unclassified == (0.8, "unclassified", "unclassified") q_res.build_classification_result(rank="superkingdom") print(q_res.krona_classified) assert q_res.krona_classified == (0.2, "a") assert q_res.krona_unclassified == (0.8, "unclassified") # make sure this goes back to None if we reclassify without rank q_res.build_classification_result() assert q_res.krona_classified is None assert q_res.krona_unclassified is None assert q_res.krona_header == [] def test_make_krona_header_basic(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] phy_header = ["fraction", "superkingdom", "phylum"] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.build_classification_result(rank="phylum") print(q_res.krona_classified) print(q_res.krona_header) assert q_res.krona_header == phy_header hd = q_res.make_krona_header("phylum") print("header: ", hd) assert hd == phy_header def test_make_krona_header_basic_1(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] class_header = ["fraction", "superkingdom", "phylum", "class"] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True ) q_res.build_classification_result(rank="class") assert q_res.krona_header == class_header hd = q_res.make_krona_header(min_rank="class") print("header: ", hd) assert hd == class_header def test_make_krona_header_fail(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) with pytest.raises(ValueError) as exc: q_res.make_krona_header("order") assert "Rank 'order' not present in summarized ranks." in str(exc.value) with pytest.raises(ValueError) as exc: q_res.make_krona_header("NotARank") assert "Rank 'NotARank' not present in summarized ranks." in str(exc.value) def test_make_human_summary(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) hs = q_res.make_human_summary(display_rank="superkingdom") print(hs) assert hs == [ { "rank": "superkingdom", "fraction": "0.800", "lineage": "unclassified", "f_weighted_at_rank": "60.0%", "bp_match_at_rank": "60", "query_ani_at_rank": "- ", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "superkingdom", "fraction": "0.200", "lineage": "a", "f_weighted_at_rank": "40.0%", "bp_match_at_rank": "40", "query_ani_at_rank": "94.9%", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, ] def test_make_human_summary_2(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) hs = q_res.make_human_summary(display_rank="phylum") print(hs) assert hs == [ { "rank": "phylum", "fraction": "0.800", "lineage": "unclassified", "f_weighted_at_rank": "60.0%", "bp_match_at_rank": "60", "query_ani_at_rank": "- ", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "phylum", "fraction": "0.200", "lineage": "a;b", "f_weighted_at_rank": "40.0%", "bp_match_at_rank": "40", "query_ani_at_rank": "94.9%", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, ] def test_make_human_summary_classification(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, classify=True, classify_rank="superkingdom", ) hs = q_res.make_human_summary(display_rank="superkingdom", classification=True) print(hs) assert hs == [ { "rank": "superkingdom", "fraction": "0.200", "lineage": "a", "f_weighted_at_rank": "40.0%", "bp_match_at_rank": "40", "query_ani_at_rank": "94.9%", "status": "match", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", } ] def test_make_human_summary_classification_2(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, classify=True, classify_rank="phylum", ) hs = q_res.make_human_summary(display_rank="phylum", classification=True) print(hs) assert hs == [ { "rank": "phylum", "fraction": "0.200", "lineage": "a;b", "f_weighted_at_rank": "40.0%", "bp_match_at_rank": "40", "query_ani_at_rank": "94.9%", "status": "match", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", } ] def test_make_full_summary(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) header, fs = q_res.make_full_summary() assert header == [ "query_name", "rank", "fraction", "lineage", "query_md5", "query_filename", "f_weighted_at_rank", "bp_match_at_rank", "query_ani_at_rank", "total_weighted_hashes", ] print(fs) assert fs == [ { "rank": "superkingdom", "fraction": "0.2", "lineage": "a", "f_weighted_at_rank": "0.4", "bp_match_at_rank": "40", "query_ani_at_rank": approx(0.949, rel=1e-3), "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "superkingdom", "fraction": "0.8", "lineage": "unclassified", "f_weighted_at_rank": "0.6", "bp_match_at_rank": "60", "query_ani_at_rank": None, "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "phylum", "fraction": "0.2", "lineage": "a;b", "f_weighted_at_rank": "0.4", "bp_match_at_rank": "40", "query_ani_at_rank": approx(0.949, rel=1e-3), "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "phylum", "fraction": "0.8", "lineage": "unclassified", "f_weighted_at_rank": "0.6", "bp_match_at_rank": "60", "query_ani_at_rank": None, "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "class", "fraction": "0.1", "lineage": "a;b;c", "f_weighted_at_rank": "0.2", "bp_match_at_rank": "20", "query_ani_at_rank": approx(0.928, rel=1e-3), "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "class", "fraction": "0.1", "lineage": "a;b;d", "f_weighted_at_rank": "0.2", "bp_match_at_rank": "20", "query_ani_at_rank": approx(0.928, rel=1e-3), "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "class", "fraction": "0.8", "lineage": "unclassified", "f_weighted_at_rank": "0.6", "bp_match_at_rank": "60", "query_ani_at_rank": None, "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, ] header, fs = q_res.make_full_summary(limit_float=True) assert header == [ "query_name", "rank", "fraction", "lineage", "query_md5", "query_filename", "f_weighted_at_rank", "bp_match_at_rank", "query_ani_at_rank", "total_weighted_hashes", ] print(fs) assert fs == [ { "rank": "superkingdom", "fraction": "0.200", "lineage": "a", "f_weighted_at_rank": "0.400", "bp_match_at_rank": "40", "query_ani_at_rank": "0.949", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "superkingdom", "fraction": "0.800", "lineage": "unclassified", "f_weighted_at_rank": "0.600", "bp_match_at_rank": "60", "query_ani_at_rank": None, "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "phylum", "fraction": "0.200", "lineage": "a;b", "f_weighted_at_rank": "0.400", "bp_match_at_rank": "40", "query_ani_at_rank": "0.949", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "phylum", "fraction": "0.800", "lineage": "unclassified", "f_weighted_at_rank": "0.600", "bp_match_at_rank": "60", "query_ani_at_rank": None, "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "class", "fraction": "0.100", "lineage": "a;b;c", "f_weighted_at_rank": "0.200", "bp_match_at_rank": "20", "query_ani_at_rank": "0.928", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "class", "fraction": "0.100", "lineage": "a;b;d", "f_weighted_at_rank": "0.200", "bp_match_at_rank": "20", "query_ani_at_rank": "0.928", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, { "rank": "class", "fraction": "0.800", "lineage": "unclassified", "f_weighted_at_rank": "0.600", "bp_match_at_rank": "60", "query_ani_at_rank": None, "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", "total_weighted_hashes": "0", }, ] def test_make_full_summary_summarization_fail(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=False ) with pytest.raises(ValueError) as exc: q_res.make_full_summary() print(str(exc)) assert "not summarized yet" in str(exc) def test_make_full_summary_classification(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, classify=True ) header, fs = q_res.make_full_summary(classification=True) assert header == [ "query_name", "status", "rank", "fraction", "lineage", "query_md5", "query_filename", "f_weighted_at_rank", "bp_match_at_rank", "query_ani_at_rank", ] print(fs) assert fs == [ { "rank": "class", "fraction": "0.1", "lineage": "a;b;c", "f_weighted_at_rank": "0.2", "bp_match_at_rank": "20", "query_ani_at_rank": approx(0.928, rel=1e-3), "status": "match", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", } ] def test_make_full_summary_classification_limit_float(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, classify=True ) header, fs = q_res.make_full_summary(classification=True, limit_float=True) assert header == [ "query_name", "status", "rank", "fraction", "lineage", "query_md5", "query_filename", "f_weighted_at_rank", "bp_match_at_rank", "query_ani_at_rank", ] print(fs) assert fs == [ { "rank": "class", "fraction": "0.100", "lineage": "a;b;c", "f_weighted_at_rank": "0.200", "bp_match_at_rank": "20", "query_ani_at_rank": "0.928", "status": "match", "query_name": "q1", "query_md5": "md5", "query_filename": "query_fn", } ] def test_make_full_summary_classification_fail(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) with pytest.raises(ValueError) as exc: q_res.make_full_summary(classification=True) print(str(exc)) assert "not classified yet" in str(exc) def test_make_kreport_results(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;c;d;e;f;g")]) # need to go down to species to check that `num_bp_assigned` is happening correctly gather_results = [ {"total_weighted_hashes": 100}, {"name": "gB", "total_weighted_hashes": 100}, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) header, krepD = q_res.make_kreport_results() print(krepD) assert krepD == [ { "num_bp_assigned": "0", "percent_containment": "40.00", "num_bp_contained": "40", "rank_code": "D", "sci_name": "a", "ncbi_taxid": None, }, { "num_bp_assigned": "60", "percent_containment": "60.00", "num_bp_contained": "60", "sci_name": "unclassified", "rank_code": "U", "ncbi_taxid": None, }, { "num_bp_assigned": "0", "percent_containment": "40.00", "num_bp_contained": "40", "rank_code": "P", "sci_name": "b", "ncbi_taxid": None, }, { "num_bp_assigned": "0", "percent_containment": "40.00", "num_bp_contained": "40", "rank_code": "C", "sci_name": "c", "ncbi_taxid": None, }, { "num_bp_assigned": "0", "percent_containment": "20.00", "num_bp_contained": "20", "rank_code": "O", "sci_name": "d", "ncbi_taxid": None, }, { "num_bp_assigned": "0", "percent_containment": "20.00", "num_bp_contained": "20", "rank_code": "F", "sci_name": "e", "ncbi_taxid": None, }, { "num_bp_assigned": "0", "percent_containment": "20.00", "num_bp_contained": "20", "rank_code": "G", "sci_name": "f", "ncbi_taxid": None, }, { "num_bp_assigned": "20", "percent_containment": "20.00", "num_bp_contained": "20", "rank_code": "S", "sci_name": "g", "ncbi_taxid": None, }, ] def test_make_kreport_results_with_taxids(): taxD = make_mini_taxonomy_with_taxids( [("gA", "a;b;c", "1;2;3"), ("gB", "a;b;c;d;e;f;g", "1;2;3;4;5;6;7")] ) print(taxD) # need to go down to species to check that `num_bp_assigned` is happening correctly gather_results = [ {"total_weighted_hashes": 100}, {"name": "gB", "total_weighted_hashes": 100}, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) header, krepD = q_res.make_kreport_results() print(krepD) assert krepD == [ { "num_bp_assigned": "0", "percent_containment": "40.00", "num_bp_contained": "40", "rank_code": "D", "sci_name": "a", "ncbi_taxid": "1", }, { "num_bp_assigned": "60", "percent_containment": "60.00", "num_bp_contained": "60", "sci_name": "unclassified", "rank_code": "U", "ncbi_taxid": None, }, { "num_bp_assigned": "0", "percent_containment": "40.00", "num_bp_contained": "40", "rank_code": "P", "sci_name": "b", "ncbi_taxid": "2", }, { "num_bp_assigned": "0", "percent_containment": "40.00", "num_bp_contained": "40", "rank_code": "C", "sci_name": "c", "ncbi_taxid": "3", }, { "num_bp_assigned": "0", "percent_containment": "20.00", "num_bp_contained": "20", "rank_code": "O", "sci_name": "d", "ncbi_taxid": "4", }, { "num_bp_assigned": "0", "percent_containment": "20.00", "num_bp_contained": "20", "rank_code": "F", "sci_name": "e", "ncbi_taxid": "5", }, { "num_bp_assigned": "0", "percent_containment": "20.00", "num_bp_contained": "20", "rank_code": "G", "sci_name": "f", "ncbi_taxid": "6", }, { "num_bp_assigned": "20", "percent_containment": "20.00", "num_bp_contained": "20", "rank_code": "S", "sci_name": "g", "ncbi_taxid": "7", }, ] def test_make_kreport_results_fail(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=False ) with pytest.raises(ValueError) as exc: q_res.make_kreport_results() print(str(exc)) assert "not summarized yet" in str(exc) def test_make_kreport_results_fail_pre_v450(): taxD = make_mini_taxonomy([("gA", "a;b;c"), ("gB", "a;b;d")]) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) with pytest.raises(ValueError) as exc: q_res.make_kreport_results() print(str(exc)) assert ( "cannot produce 'kreport' format from gather results before sourmash v4.5.0" in str(exc) ) def test_make_cami_results_with_taxids(): taxD = make_mini_taxonomy_with_taxids( [("gA", "a;b;c", "1;2;3"), ("gB", "a;b;c;d;e;f;g", "1;2;3;4;5;6;7")] ) print(taxD) # need to go down to species to check that `num_bp_assigned` is happening correctly gather_results = [ {"total_weighted_hashes": 100}, {"name": "gB", "total_weighted_hashes": 100}, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True ) header, camires = q_res.make_cami_bioboxes() print(camires) assert camires == [ ["1", "superkingdom", "1", "a", "40.00"], ["2", "phylum", "1|2", "a|b", "40.00"], ["3", "class", "1|2|3", "a|b|c", "40.00"], ["4", "order", "1|2|3|4", "a|b|c|d", "20.00"], ["5", "family", "1|2|3|4|5", "a|b|c|d|e", "20.00"], ["6", "genus", "1|2|3|4|5|6", "a|b|c|d|e|f", "20.00"], ["7", "species", "1|2|3|4|5|6|7", "a|b|c|d|e|f|g", "20.00"], ] def test_make_lingroup_results(): taxD = make_mini_taxonomy( [("gA", "1;0;0"), ("gB", "1;0;1"), ("gC", "1;1;0")], LIN=True ) print(taxD) lingroupD = {"1": "lg1", "1;0": "lg2", "1;1": "lg3"} print(lingroupD) gather_results = [ {"total_weighted_hashes": 100}, {"name": "gB", "total_weighted_hashes": 100}, {"name": "gC", "total_weighted_hashes": 100}, ] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True, LIN=True, ) print(q_res.summarized_lineage_results) header, lgD = q_res.make_lingroup_results(LINgroupsD=lingroupD) print(header) assert header == ["name", "lin", "percent_containment", "num_bp_contained"] # order may change, just check that each lg entry is present in list of results lg1 = { "percent_containment": "60.00", "num_bp_contained": "60", "lin": "1", "name": "lg1", } lg2 = { "percent_containment": "40.00", "num_bp_contained": "40", "lin": "1;0", "name": "lg2", } lg3 = { "percent_containment": "20.00", "num_bp_contained": "20", "lin": "1;1", "name": "lg3", } assert lg1 in lgD assert lg2 in lgD assert lg3 in lgD def test_make_lingroup_results_fail_pre_v450(): taxD = make_mini_taxonomy( [("gA", "1;0;0"), ("gB", "1;0;1"), ("gC", "1;1;0")], LIN=True ) gather_results = [{}, {"name": "gB"}] q_res = make_QueryTaxResults( gather_info=gather_results, taxD=taxD, single_query=True, summarize=True, LIN=True, ) lingroupD = {"1": "lg1", "1;0": "lg2", "1;1": "lg3"} with pytest.raises(ValueError) as exc: q_res.make_lingroup_results(lingroupD) print(str(exc)) assert ( "cannot produce 'lingroup' format from gather results before sourmash v4.5.0" in str(exc) ) def test_read_lingroups(runtmp): lg_file = runtmp.output("test.lg.csv") with open(lg_file, "w") as out: out.write("lin,name\n") out.write("1,lg1\n") out.write("1;0,lg2\n") out.write("1;1,lg3\n") lgD = read_lingroups(lg_file) assert lgD == {"1": "lg1", "1;0": "lg2", "1;1": "lg3"} def test_read_lingroups_empty_file(runtmp): lg_file = runtmp.output("test.lg.csv") with open(lg_file, "w") as out: out.write("") with pytest.raises(ValueError) as exc: read_lingroups(lg_file) print(str(exc)) assert f"Cannot read lingroups from '{lg_file}'. Is file empty?" in str(exc) def test_read_lingroups_only_header(runtmp): lg_file = runtmp.output("test.lg.csv") with open(lg_file, "w") as out: out.write("lin,name\n") with pytest.raises(ValueError) as exc: read_lingroups(lg_file) print(str(exc)) assert f"No lingroups loaded from {lg_file}" in str(exc) def test_read_lingroups_bad_header(runtmp): lg_file = runtmp.output("test.lg.csv") with open(lg_file, "w") as out: out.write("LINgroup_pfx,LINgroup_nm\n") with pytest.raises(ValueError) as exc: read_lingroups(lg_file) print(str(exc)) assert f"'{lg_file}' must contain the following columns: 'name', 'lin'." in str(exc) def test_LineageTree_init(): x = "a;b" lin1 = RankLineageInfo(lineage_str=x) print(lin1) tree = LineageTree([lin1]) assert tree.tree == { LineagePair("superkingdom", "a"): {LineagePair("phylum", "b"): {}} } def test_LineageTree_init_mult(): x = "a;b" y = "a;c" lin1 = RankLineageInfo(lineage_str=x) lin2 = RankLineageInfo(lineage_str=y) print(lin1) from sourmash.tax.tax_utils import LineageTree tree = LineageTree([lin1, lin2]) assert tree.tree == { LineagePair(rank="superkingdom", name="a", taxid=None): { LineagePair(rank="phylum", name="b", taxid=None): {}, LineagePair(rank="phylum", name="c", taxid=None): {}, } } def test_LineageTree_init_and_add_lineage(): x = "a;b" y = "a;c" lin1 = RankLineageInfo(lineage_str=x) lin2 = RankLineageInfo(lineage_str=y) print(lin1) from sourmash.tax.tax_utils import LineageTree tree = LineageTree([lin1]) assert tree.tree == { LineagePair("superkingdom", "a"): {LineagePair("phylum", "b"): {}} } tree.add_lineage(lin2) assert tree.tree == { LineagePair(rank="superkingdom", name="a", taxid=None): { LineagePair(rank="phylum", name="b", taxid=None): {}, LineagePair(rank="phylum", name="c", taxid=None): {}, } } def test_LineageTree_init_and_add_lineages(): x = "a;b" y = "a;c" lin1 = RankLineageInfo(lineage_str=x) lin2 = RankLineageInfo(lineage_str=y) print(lin1) from sourmash.tax.tax_utils import LineageTree tree = LineageTree([lin1]) assert tree.tree == { LineagePair("superkingdom", "a"): {LineagePair("phylum", "b"): {}} } tree.add_lineages([lin2]) assert tree.tree == { LineagePair(rank="superkingdom", name="a", taxid=None): { LineagePair(rank="phylum", name="b", taxid=None): {}, LineagePair(rank="phylum", name="c", taxid=None): {}, } } def test_build_tree_RankLineageInfo(): x = "a;b" lin1 = RankLineageInfo(lineage_str=x) print(lin1) tree = LineageTree([lin1]) assert tree.tree == { LineagePair("superkingdom", "a"): {LineagePair("phylum", "b"): {}} } def test_build_tree_LINLineageInfo(): x = "0;3" lin1 = LINLineageInfo(lineage_str=x) print(lin1) tree = LineageTree([lin1]) assert tree.tree == {LineagePair("0", "0"): {LineagePair("1", "3"): {}}} def test_build_tree_2(): x = "a;b" y = "a;c" lin1 = RankLineageInfo(lineage_str=x) lin2 = RankLineageInfo(lineage_str=y) print(lin1) print(lin2) tree = LineageTree([lin1, lin2]) assert tree.tree == { LineagePair("superkingdom", "a"): { LineagePair("phylum", "b"): {}, LineagePair("phylum", "c"): {}, } } def test_build_tree_2_LineagePairs(): # build tree from LineagePairs tree = LineageTree( [ [LineagePair("superkingdom", "a"), LineagePair("phylum", "b")], [LineagePair("superkingdom", "a"), LineagePair("phylum", "c")], ] ) assert tree.tree == { LineagePair("superkingdom", "a"): { LineagePair("phylum", "b"): {}, LineagePair("phylum", "c"): {}, } } def test_build_tree_3(): # empty phylum name x = "a;" lin1 = RankLineageInfo(lineage_str=x) tree = LineageTree([lin1]) assert tree.tree == {LineagePair("superkingdom", "a"): {}} def test_build_tree_3_LineagePairs(): # empty phylum name: LineagePair input lin1 = ( LineagePair("superkingdom", "a", "3"), LineagePair("phylum", "", ""), ) tree = LineageTree([lin1]) assert tree.tree == {LineagePair("superkingdom", "a", "3"): {}} def test_build_tree_5(): with pytest.raises(ValueError): LineageTree([]) def test_build_tree_5b(): with pytest.raises(ValueError): LineageTree("") def test_build_tree_iterable(): with pytest.raises(ValueError) as exc: LineageTree(RankLineageInfo()) assert ( "Must pass in an iterable containing LineagePair or LineageInfo objects" in str(exc) ) def test_find_lca(): x = "a;b" lin1 = RankLineageInfo(lineage_str=x) tree = LineageTree([lin1]) lca = tree.find_lca() assert lca == ( ( LineagePair("superkingdom", "a"), LineagePair("phylum", "b"), ), 0, ) def test_find_lca_LineagePairs(): tree = LineageTree([[LineagePair("rank1", "name1"), LineagePair("rank2", "name2")]]) lca = tree.find_lca() assert lca == ( ( LineagePair("rank1", "name1"), LineagePair("rank2", "name2"), ), 0, ) def test_find_lca_2(): x = "a;b" y = "a;c" lin1 = RankLineageInfo(lineage_str=x) lin2 = RankLineageInfo(lineage_str=y) tree = LineageTree([lin1, lin2]) lca = tree.find_lca() assert lca == ((LineagePair("superkingdom", "a"),), 2) def test_find_lca_LIN(): x = "5;6" y = "5;10" lin1 = LINLineageInfo(lineage_str=x) lin2 = LINLineageInfo(lineage_str=y) tree = LineageTree([lin1, lin2]) lca = tree.find_lca() assert lca == ((LineagePair("0", "5"),), 2) print(lca) def test_find_lca_2_LineagePairs(): tree = LineageTree( [ [LineagePair("rank1", "name1"), LineagePair("rank2", "name2a")], [LineagePair("rank1", "name1"), LineagePair("rank2", "name2b")], ] ) lca = tree.find_lca() assert lca == ((LineagePair("rank1", "name1"),), 2) def test_find_lca_3(): lin1 = RankLineageInfo(lineage_str="a;b;c") lin2 = RankLineageInfo(lineage_str="a;b") tree = LineageTree([lin1, lin2]) lca, reason = tree.find_lca() assert lca == lin1.filled_lineage # find most specific leaf node print(lca) def test_build_tree_with_initial(): x = "a;b;c" y = "a;b;d" z = "a;e" lin1 = RankLineageInfo(lineage_str=x) lin2 = RankLineageInfo(lineage_str=y) lin3 = RankLineageInfo(lineage_str=z) tree = LineageTree([lin1, lin2]) lca = tree.find_lca() print(lca) assert lca == ( ( LineagePair(rank="superkingdom", name="a", taxid=None), LineagePair(rank="phylum", name="b", taxid=None), ), 2, ) tree.add_lineages([lin3]) lca2 = tree.find_lca() print(lca2) assert lca2 == ((LineagePair("superkingdom", "a"),), 2) def test_LineageTree_find_ordered_paths(): x = "a;b;c" y = "a;b;d" z = "a;e" lin1 = RankLineageInfo(lineage_str=x) lin2 = RankLineageInfo(lineage_str=y) lin3 = RankLineageInfo(lineage_str=z) tree = LineageTree([lin1, lin2, lin3]) paths = tree.ordered_paths() print(paths) assert paths == [ ( LineagePair(rank="superkingdom", name="a", taxid=None), LineagePair(rank="phylum", name="e", taxid=None), ), ( LineagePair(rank="superkingdom", name="a", taxid=None), LineagePair(rank="phylum", name="b", taxid=None), LineagePair(rank="class", name="c", taxid=None), ), ( LineagePair(rank="superkingdom", name="a", taxid=None), LineagePair(rank="phylum", name="b", taxid=None), LineagePair(rank="class", name="d", taxid=None), ), ] def test_LineageTree_find_ordered_paths_include_internal(): x = "a;b;c" y = "a;b;d" z = "a;e" lin1 = RankLineageInfo(lineage_str=x) lin2 = RankLineageInfo(lineage_str=y) lin3 = RankLineageInfo(lineage_str=z) tree = LineageTree([lin1, lin2, lin3]) paths = tree.ordered_paths(include_internal=True) print(paths) assert paths == [ (LineagePair(rank="superkingdom", name="a", taxid=None),), ( LineagePair(rank="superkingdom", name="a", taxid=None), LineagePair(rank="phylum", name="e", taxid=None), ), ( LineagePair(rank="superkingdom", name="a", taxid=None), LineagePair(rank="phylum", name="b", taxid=None), ), ( LineagePair(rank="superkingdom", name="a", taxid=None), LineagePair(rank="phylum", name="b", taxid=None), LineagePair(rank="class", name="c", taxid=None), ), ( LineagePair(rank="superkingdom", name="a", taxid=None), LineagePair(rank="phylum", name="b", taxid=None), LineagePair(rank="class", name="d", taxid=None), ), ] sourmash-bio-sourmash-bc3c0f8/tests/test_test_framework.py000066400000000000000000000003371474157046300243000ustar00rootroot00000000000000import pytest import sourmash_tst_utils as utils from sourmash_tst_utils import SourmashCommandFailed def test_failed_sourmash_exception(runtmp): with pytest.raises(SourmashCommandFailed): runtmp.sourmash("") sourmash-bio-sourmash-bc3c0f8/tox.ini000066400000000000000000000131531474157046300200040ustar00rootroot00000000000000[tox] isolated_build = true skip_missing_interpreters = true env_list = py311, py312, py310, coverage, docs, package_description fix_lint, hypothesis, khmer, khmer_master min_version = 3.27 [testenv] description = run the tests with pytest under {basepython} deps = pip>=19.3.1 extras = storage test commands = pytest \ --cov "{envsitepackagesdir}/sourmash" \ --cov-config "{toxinidir}/tox.ini" \ --cov-report= \ --junitxml {toxworkdir}/junit.{envname}.xml \ {posargs:doc tests} package = wheel pass_env = TOXENV CURL_CA_BUNDLE http_proxy https_proxy no_proxy REQUESTS_CA_BUNDLE SSL_CERT_FILE PYTEST_* PIP_CACHE_DIR CI PYTHONDEVMODE PYTHONTRACEMALLOC LIBCLANG_PATH BINDGEN_EXTRA_CLANG_ARGS NIX_LD set_env = PIP_DISABLE_VERSION_CHECK = 1 COVERAGE_FILE = {env:COVERAGE_FILE:{toxworkdir}/.coverage.{envname}} VIRTUALENV_NO_DOWNLOAD = 1 PIP_EXTRA_INDEX_URL = https://antocuni.github.io/pypy-wheels/manylinux2010 wheel_build_env = .pkg [testenv:.pkg] pass_env = LIBCLANG_PATH BINDGEN_EXTRA_CLANG_ARGS NIX_* [testenv:pypy3] deps = pip>=19.3.1 psutil<=5.6.7 [testenv:hypothesis] commands = pytest \ --cov "{envsitepackagesdir}/sourmash" \ --cov-config "{toxinidir}/tox.ini" \ --cov-report= \ --junitxml {toxworkdir}/junit.{envname}.xml \ --run-hypothesis \ --hypothesis-show-statistics \ --hypothesis-profile ci \ {posargs:doc tests} [testenv:khmer] basepython = python3.10 deps = khmer commands = pytest \ --cov "{envsitepackagesdir}/sourmash" \ --cov-config "{toxinidir}/tox.ini" \ --cov-report= \ --junitxml {toxworkdir}/junit.{envname}.xml \ -k test_nodegraph \ {posargs:doc tests} [testenv:khmer_master] basepython = python3.10 deps = git+https://github.com/dib-lab/khmer.git\#egg commands = pytest \ --cov "{envsitepackagesdir}/sourmash" \ --cov-config "{toxinidir}/tox.ini" \ --cov-report= \ --junitxml {toxworkdir}/junit.{envname}.xml \ -k test_nodegraph \ {posargs:doc tests} [testenv:asv] description = run asv for benchmarking (compare current commit with latest) deps = asv==0.6.2 virtualenv changedir = {toxinidir} commands = asv machine --yes asv continuous latest HEAD {posargs} [testenv:docs] description = invoke sphinx-build to build the HTML docs basepython = python3.10 extras = doc commands = sphinx-build -d "{toxworkdir}/docs_doctree" doc "{toxworkdir}/docs_out" --color -bhtml {posargs} python -c 'import pathlib; print("documentation available under file://\{0\}".format(pathlib.Path(r"{toxworkdir}") / "docs_out" / "index.html"))' allowlist_externals = pandoc change_dir = {toxinidir} pass_env = HOME LC_ALL LOCALE_* [testenv:package_description] description = check that the long description is valid basepython = python3.10 skip_install = true deps = pip>=19.1 readme-renderer[md]>=24 twine>=1.12.1 extras = commands = pip wheel -w {envtmpdir}/build --no-deps . twine check {envtmpdir}/build/* change_dir = {toxinidir} [testenv:mypy] description = run mypy checker basepython = python3.10 deps = mypy commands = mypy src/sourmash pass_env = {[testenv]pass_env} PROGRAMDATA [testenv:fix_lint] description = format the code base to adhere to our styles, and complain about what we cannot do automatically basepython = python3.10 skip_install = true deps = pre-commit>=2 extras = lint commands = pre-commit run --all-files --show-diff-on-failure {posargs} python -c 'import pathlib; print("hint: run \{\} install to add checks as pre-commit hook".format(pathlib.Path(r"{envdir}") / "bin" / "pre-commit"))' pass_env = {[testenv]pass_env} PROGRAMDATA PRE_COMMIT_HOME [testenv:coverage] description = [run locally after tests]: combine coverage data and create report; generates a diff coverage against origin/latest (can be changed by setting DIFF_AGAINST env var) skip_install = true deps = {[testenv]deps} coverage>=5.0.1 diff_cover parallel_show_output = true commands = coverage combine coverage report -i -m coverage xml -i -o {toxworkdir}/coverage.xml coverage html -i -d {toxworkdir}/htmlcov diff-cover --compare-branch {env:DIFF_AGAINST:origin/latest} {toxworkdir}/coverage.xml depends = py312, py311, py310, pypy3 pass_env = {[testenv]pass_env} DIFF_AGAINST set_env = COVERAGE_FILE={toxworkdir}/.coverage [testenv:X] description = print the positional arguments passed in with echo commands = echo {posargs} [testenv:dev] description = dev environment with all deps at {envdir} usedevelop = true deps = {[testenv]deps} extras = doc storage test commands = python -m pip list --format=columns python -c "print(r'{envpython}')" [coverage:run] branch = true parallel = true [coverage:report] skip_covered = True show_missing = True exclude_lines = \#\s*pragma: no cover ^\s*raise AssertionError\b ^\s*raise NotImplementedError\b ^\s*return NotImplemented\b ^\s*raise$ ^if __name__ == ['"]__main__['"]:$ [coverage:paths] source = src/sourmash/ tests/ */.tox/*/lib/python*/site-packages/sourmash */.tox/pypy*/site-packages/sourmash */.tox\*\Lib\site-packages\sourmash */src/sourmash *\src\sourmash */tests *\tests [gh-actions] python = 3.10: py310, docs, package_description, coverage 3.11: py311, coverage 3.12: py312, coverage [flake8] max-complexity = 22 max-line-length = 99 ignore = E203, W503, C901, E402, B011 [pep8] max-line-length = 99 sourmash-bio-sourmash-bc3c0f8/utils/000077500000000000000000000000001474157046300176265ustar00rootroot00000000000000sourmash-bio-sourmash-bc3c0f8/utils/.gitignore000066400000000000000000000001031474157046300216100ustar00rootroot00000000000000ftp_list-strongyl.txt ftp_list.csv ftp_list.csv.bak sra_result.csv sourmash-bio-sourmash-bc3c0f8/utils/README.md000066400000000000000000000015041474157046300211050ustar00rootroot00000000000000# Misc utility scripts ## Misc scripts * trim-noV.sh - a script to do trimming of short reads. requires khmer >= 2.0. ## Debugging and testing scripts * check-tree.py - exhaustively confirm the results of a search on an SBT. * compute-dna-mh-another-way.py - a separate implementation of MinHash signature calculation for DNA. * compute-input-prot-another-way.py - a separate implementation of MinHash signature calculation for proteins. * compute-prot-mh-another-way.py - a separate implementation of MinHash signature computing for 6-frame translations of DNA into amino acid space. CTB 1/2019 ## Formula implementations * cardinality_estimate_confidence.py - a function that will tell you if the sketch size is too small to trust the estimate `sketch_size * scale` as an estimate of the number of distinct k-mers. DMK 5/2022 sourmash-bio-sourmash-bc3c0f8/utils/cardinality_estimate_confidence.py000066400000000000000000000103601474157046300265530ustar00rootroot00000000000000# This is a simple function designed to raise a warning whenever the sketch size is too small # for the set being estimated. See the discussion in issue #1798 import numpy as np def set_size_chernoff(set_size, scale, relative_error=0.05): """ Computes the probability that the estimate: sketch_size * scaled deviates from the true set_size by more than relative_error. This relies on the fact that the sketch_size is binomially distributed with parameters sketch_size and 1/scale. The two-sided Chernoff bounds are used. @param set_size: The number of distinct k-mers in the given set @param relative_error: the desired relative error (defaults to 5%) @return: float (the upper bound probability) """ upper_bound = 1 - 2 * np.exp(-(relative_error**2) * set_size / (scale * 3)) return upper_bound def get_set_size(scale, num_sketches): """ This returns the expected number of distinct k-mers from the scale size and number of sketches. @param scale: the scale factor used @param num_sketches: the actual size of the FracMinHash sketch @return: int (the expected number of distinct k-mers in the original set of k-mers) """ # TODO: replace with HLL when that gets implemented return int(np.floor(scale * num_sketches)) def set_size_estimate_is_accurate( scale, num_sketches, relative_error=0.05, confidence=0.95 ): set_size = get_set_size(scale, num_sketches) probability = set_size_chernoff(set_size, scale, relative_error) if probability >= confidence: return True else: return False def test_set_size_chernoff(): eps = 10 ** (-6) rel_error = 0.01 set_size = 1000000 s = 1 / 0.1 # I'm used to using a scale value between 0 and 1 value_from_mathematica = 0.928652 assert ( np.abs(set_size_chernoff(set_size, s, rel_error) - value_from_mathematica) < eps ) rel_error = 0.05 set_size = 10000 s = 1 value_from_mathematica = 0.999519 assert ( np.abs(set_size_chernoff(set_size, s, rel_error) - value_from_mathematica) < eps ) rel_error = 0.001 set_size = 10 s = 1 / 0.01 value_from_mathematica = -1 assert ( np.abs(set_size_chernoff(set_size, s, rel_error) - value_from_mathematica) < eps ) def test_set_size_estimate_is_accurate(): 10 ** (-6) rel_error = 0.05 set_size = 1000000 s = 1 / 0.1 # I'm used to using a scale value between 0 and 1 num_sketches = set_size / s # idealized case confidence = 0.95 assert ( set_size_estimate_is_accurate( scale=s, num_sketches=num_sketches, relative_error=rel_error, confidence=confidence, ) is True ) confidence = set_size_chernoff(set_size=set_size, scale=s, relative_error=rel_error) assert ( set_size_estimate_is_accurate( scale=s, num_sketches=num_sketches, relative_error=rel_error, confidence=confidence, ) is True ) # Horrible values assert ( set_size_estimate_is_accurate( scale=10000, num_sketches=num_sketches, relative_error=0, confidence=1 ) is False ) # Less horrible, but still bad values confidence = set_size_chernoff(set_size=set_size, scale=s, relative_error=rel_error) assert ( set_size_estimate_is_accurate( scale=s, num_sketches=num_sketches, relative_error=rel_error, confidence=confidence * 2, ) is False ) # one where the confidence is negative rel_error = 0.001 set_size = 10 s = 100 num_sketches = set_size / s assert ( set_size_estimate_is_accurate( scale=s, num_sketches=num_sketches, relative_error=rel_error, confidence=confidence, ) is False ) assert ( set_size_estimate_is_accurate( scale=s, num_sketches=0, relative_error=rel_error, confidence=confidence ) is False ) def run_tests(): test_set_size_chernoff() test_set_size_estimate_is_accurate() if __name__ == "__main__": print("Running tests") run_tests() print("Tests completed successfully") sourmash-bio-sourmash-bc3c0f8/utils/check-tree.py000066400000000000000000000012541474157046300222140ustar00rootroot00000000000000#! /usr/bin/env python """ Check SBT search by taking every leaf node in a tree and checking to make sure we can find it. """ import argparse import sourmash from sourmash.sbtmh import search_minhashes THRESHOLD = 0.08 def main(): p = argparse.ArgumentParser() p.add_argument("sbt") args = p.parse_args() db = sourmash.sbtmh.load_sbt_index(args.sbt) threshold = THRESHOLD for leaf in db.leaves(): query = leaf.data matches = db.find(search_minhashes, query, threshold) matches = list([x.data for x in matches]) if query not in matches: print(query) assert 0 if __name__ == "__main__": main() sourmash-bio-sourmash-bc3c0f8/utils/compute-dna-mh-another-way.py000077500000000000000000000035001474157046300252530ustar00rootroot00000000000000#! /usr/bin/env python """ Use the MurmurHash library mmh3 and separate Python code to calculate a MinHash signature for input DNA sequence, as a way to do an external check on our C++ implementation. The output of this is used in test_sourmash.py to verify our C++ code. """ __complementTranslation = {"A": "T", "C": "G", "G": "C", "T": "A", "N": "N"} def complement(s): """ Return complement of 's'. """ c = "".join(__complementTranslation[n] for n in s) return c def reverse(s): """ Return reverse of 's'. """ r = "".join(reversed(s)) return r def kmers(seq, k): for start in range(len(seq) - k + 1): yield seq[start : start + k] ### K = 21 import sys import screed import mmh3 import sourmash print("imported sourmash:", sourmash, file=sys.stderr) import sourmash.signature record = next(iter(screed.open(sys.argv[1]))) print("loaded", record.name, file=sys.stderr) revcomp = reverse(complement(record.sequence)) mh = sourmash.MinHash(ksize=K, n=500, is_protein=False) # # compute the actual hashes to insert by breaking down the sequence # into k-mers and applying MurmurHash to each one; here, the only # interesting thing that is done by add_hash is to keep only the # (numerically) lowest n=500 hashes. # # this method of hash computation is exactly how sourmash does it # internally, and should be approximately the same as what mash does. # for fwd_kmer in kmers(record.sequence, K): rev_kmer = reverse(complement(fwd_kmer)) if fwd_kmer < rev_kmer: kmer = fwd_kmer else: kmer = rev_kmer hash = mmh3.hash64(kmer, seed=42)[0] # convert to unsigned int if negative if hash < 0: hash += 2**64 mh.add_hash(hash) s = sourmash.signature.SourmashSignature("", mh, name=record.name) print(sourmash.signature.save_signatures([s])) sourmash-bio-sourmash-bc3c0f8/utils/compute-input-prot-another-way.py000077500000000000000000000052351474157046300262370ustar00rootroot00000000000000#! /usr/bin/env python """ Use the MurmurHash library mmh3 and separate Python code to calculate a MinHash signature for input protein sequence, as a way to do an external check on our C++ implementation. The output of this is used in test_sourmash.py to verify our C++ code. """ dna_to_aa = { "TTT": "F", "TTC": "F", "TTA": "L", "TTG": "L", "TCT": "S", "TCC": "S", "TCA": "S", "TCG": "S", "TAT": "Y", "TAC": "Y", "TAA": "*", "TAG": "*", "TGA": "*", "TGT": "C", "TGC": "C", "TGG": "W", "CTT": "L", "CTC": "L", "CTA": "L", "CTG": "L", "CCT": "P", "CCC": "P", "CCA": "P", "CCG": "P", "CAT": "H", "CAC": "H", "CAA": "Q", "CAG": "Q", "CGT": "R", "CGC": "R", "CGA": "R", "CGG": "R", "ATT": "I", "ATC": "I", "ATA": "I", "ATG": "M", "ACT": "T", "ACC": "T", "ACA": "T", "ACG": "T", "AAT": "N", "AAC": "N", "AAA": "K", "AAG": "K", "AGT": "S", "AGC": "S", "AGA": "R", "AGG": "R", "GTT": "V", "GTC": "V", "GTA": "V", "GTG": "V", "GCT": "A", "GCC": "A", "GCA": "A", "GCG": "A", "GAT": "D", "GAC": "D", "GAA": "E", "GAG": "E", "GGT": "G", "GGC": "G", "GGA": "G", "GGG": "G", } __complementTranslation = {"A": "T", "C": "G", "G": "C", "T": "A", "N": "N"} def complement(s): """ Return complement of 's'. """ c = "".join(__complementTranslation[n] for n in s) return c def reverse(s): """ Return reverse of 's'. """ r = "".join(reversed(s)) return r def peptides(seq, start): for i in range(start, len(seq), 3): yield dna_to_aa.get(seq[i : i + 3], "X") def translate(seq): for i in range(3): pep = peptides(seq, i) yield "".join(pep) revcomp = reverse(complement(seq)) for i in range(3): pep = peptides(revcomp, i) yield "".join(pep) def kmers(seq, k): for start in range(len(seq) - k + 1): yield seq[start : start + k] ### K = 21 import sys import screed import mmh3 import sourmash print("imported sourmash:", sourmash, file=sys.stderr) import sourmash.signature record = next(iter(screed.open(sys.argv[1]))) print("loaded", record.name, file=sys.stderr) mh = sourmash.MinHash(ksize=K, n=500, is_protein=True) prot_ksize = int(K / 3) for kmer in kmers(record.sequence, prot_ksize): hash = mmh3.hash64(kmer, seed=42)[0] # convert to unsigned int if negative if hash < 0: hash += 2**64 mh.add_hash(hash) s = sourmash.signature.SourmashSignature("", mh, name=record.name) print(sourmash.signature.save_signatures([s])) sourmash-bio-sourmash-bc3c0f8/utils/compute-prot-mh-another-way.py000077500000000000000000000053311474157046300255010ustar00rootroot00000000000000#! /usr/bin/env python """ Use the MurmurHash library mmh3 and separate Python code to calculate a MinHash signature for translated protein sequence, as a way to do an external check on our C++ implementation. The output of this is used in test_sourmash.py to verify our C++ code. """ dna_to_aa = { "TTT": "F", "TTC": "F", "TTA": "L", "TTG": "L", "TCT": "S", "TCC": "S", "TCA": "S", "TCG": "S", "TAT": "Y", "TAC": "Y", "TAA": "*", "TAG": "*", "TGA": "*", "TGT": "C", "TGC": "C", "TGG": "W", "CTT": "L", "CTC": "L", "CTA": "L", "CTG": "L", "CCT": "P", "CCC": "P", "CCA": "P", "CCG": "P", "CAT": "H", "CAC": "H", "CAA": "Q", "CAG": "Q", "CGT": "R", "CGC": "R", "CGA": "R", "CGG": "R", "ATT": "I", "ATC": "I", "ATA": "I", "ATG": "M", "ACT": "T", "ACC": "T", "ACA": "T", "ACG": "T", "AAT": "N", "AAC": "N", "AAA": "K", "AAG": "K", "AGT": "S", "AGC": "S", "AGA": "R", "AGG": "R", "GTT": "V", "GTC": "V", "GTA": "V", "GTG": "V", "GCT": "A", "GCC": "A", "GCA": "A", "GCG": "A", "GAT": "D", "GAC": "D", "GAA": "E", "GAG": "E", "GGT": "G", "GGC": "G", "GGA": "G", "GGG": "G", } __complementTranslation = {"A": "T", "C": "G", "G": "C", "T": "A", "N": "N"} def complement(s): """ Return complement of 's'. """ c = "".join(__complementTranslation[n] for n in s) return c def reverse(s): """ Return reverse of 's'. """ r = "".join(reversed(s)) return r def peptides(seq, start): for i in range(start, len(seq), 3): yield dna_to_aa.get(seq[i : i + 3], "X") def translate(seq): for i in range(3): pep = peptides(seq, i) yield "".join(pep) revcomp = reverse(complement(seq)) for i in range(3): pep = peptides(revcomp, i) yield "".join(pep) def kmers(seq, k): for start in range(len(seq) - k + 1): yield seq[start : start + k] ### K = 21 import sys import screed import mmh3 import sourmash print("imported sourmash:", sourmash, file=sys.stderr) import sourmash.signature record = next(iter(screed.open(sys.argv[1]))) print("loaded", record.name, file=sys.stderr) mh = sourmash.MinHash(ksize=K, n=500, is_protein=True) prot_ksize = int(K / 3) for trans in translate(record.sequence): for kmer in kmers(trans, prot_ksize): hash = mmh3.hash64(kmer, seed=42)[0] # convert to unsigned int if negative if hash < 0: hash += 2**64 mh.add_hash(hash) s = sourmash.signature.SourmashSignature("", mh, name=record.name) print(sourmash.signature.save_signatures([s])) sourmash-bio-sourmash-bc3c0f8/utils/toml-to-zenodo-json.py000077500000000000000000000011561474157046300240440ustar00rootroot00000000000000#! /usr/bin/env python import sys import argparse import tomllib import pprint import json def main(): p = argparse.ArgumentParser() p.add_argument("toml_file") p.add_argument("-o", "--output-json", required=True) args = p.parse_args() with open(args.toml_file, "rb") as fp: d = tomllib.load(fp) # pprint.pprint(d) out_d = {} creators = [] for author_d in d["project"]["authors"]: creators.append(author_d) out_d["creators"] = creators with open(args.output_json, "w") as fp: json.dump(out_d, fp) if __name__ == "__main__": sys.exit(main()) sourmash-bio-sourmash-bc3c0f8/utils/trim-noV.sh000066400000000000000000000001461474157046300216760ustar00rootroot00000000000000for i in *.fastq.gz; do trim-low-abund.py -C 4 -Z 20 -M 1e9 -k 21 $i --gzip -o $i.trimnoV.fq.gz; done

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