pax_global_header00006660000000000000000000000064147704607240014525gustar00rootroot0000000000000052 comment=eaf4c47949f67554c0b678982d77b6c70db1e21e refnx-0.1.53/000077500000000000000000000000001477046072400127355ustar00rootroot00000000000000refnx-0.1.53/.gitattributes000066400000000000000000000004771477046072400156400ustar00rootroot00000000000000# Auto detect text files and perform LF normalization *.py text=auto eol=lf *.pyx text=auto eol=lf *.cpp text=auto eol=lf *.h text=auto eol=lf *.rst text=auto eol=lf *.txt text=auto eol=lf *.yml text=auto eol=lf *.md text=auto eol=lf # Force git to look at some files as binary *.png binary *.pdf binary *.hdf binary refnx-0.1.53/.github/000077500000000000000000000000001477046072400142755ustar00rootroot00000000000000refnx-0.1.53/.github/dependabot.yml000066400000000000000000000002311477046072400171210ustar00rootroot00000000000000version: 2 updates: - package-ecosystem: github-actions directory: / schedule: interval: daily commit-message: prefix: "MAINT" refnx-0.1.53/.github/workflows/000077500000000000000000000000001477046072400163325ustar00rootroot00000000000000refnx-0.1.53/.github/workflows/artifacts.yml000066400000000000000000000005471477046072400210430ustar00rootroot00000000000000name: 'Delete old artifacts' on: schedule: - cron: '0 0 * * 0' jobs: delete-artifacts: runs-on: ubuntu-latest steps: - uses: kolpav/purge-artifacts-action@04c636a505f26ebc82f8d070b202fb87ff572b10 # v1.0 with: token: ${{ secrets.GITHUB_TOKEN }} expire-in: 7days # Setting this to 0 will delete all artifacts refnx-0.1.53/.github/workflows/build_wheels.yml.bak000066400000000000000000000013641477046072400222630ustar00rootroot00000000000000name: Build Wheels on: [pull_request] jobs: build_wheels: name: Build wheels on ${{ matrix.os }} runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-22.04, windows-2019, macos-11] steps: - uses: actions/checkout@v4 - name: build wheels uses: pypa/cibuildwheel@v2.15.0 env: # only build a subset of wheels to check that the wheel build works CIBW_BUILD: cp310-* CIBW_TEST_COMMAND: pytest --pyargs refnx.reflect.test.test_reflect CIBW_ARCHS_MACOS: "x86_64 arm64" CIBW_ENVIRONMENT_MACOS: MACOSX_DEPLOYMENT_TARGET="10.13" - uses: actions/upload-artifact@v3 with: name: refnx-wheels path: ./wheelhouse/*.whl refnx-0.1.53/.github/workflows/pythonpackage.yml000066400000000000000000000334431477046072400217210ustar00rootroot00000000000000name: Test on: push: branches: - main tags: - "v*" pull_request: branches: - main workflow_dispatch: permissions: contents: read jobs: ############################################################################### test_linux: runs-on: ubuntu-22.04 strategy: fail-fast: true max-parallel: 3 matrix: python-version: ['3.10', '3.11', '3.12', '3.13'] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0 with: python-version: ${{ matrix.python-version }} cache: 'pip' # caching pip dependencies allow-prereleases: true - name: setup apt dependencies run: | sudo apt-get update sudo apt-get install xvfb qt6-base-dev libhdf5-serial-dev libnetcdf-dev build-essential sudo apt-get install '^libxcb.*-dev' libx11-xcb-dev libglu1-mesa-dev libxrender-dev libxi-dev libxkbcommon-dev libxkbcommon-x11-dev python -m pip install --upgrade pip - name: Test with pytest env: MPLBACKEND: agg run: | python -m pip install --upgrade pip python -m pip install wheel build python -m build python -m pip install dist/*.whl pip install -r .requirements.txt # uses xvfb for GUI part of the test pushd tools xvfb-run pytest --pyargs refnx popd # check that refnx gui starts # python tools/app/check_app_starts.py refnx # run vendored ptemcee tests pytest --pyargs refnx._lib.ptemcee.tests - name: Make sdist if: ${{ matrix.python-version == '3.9' }} run: | git clean -xdf python setup.py sdist - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 if: ${{ matrix.python-version == '3.9' }} with: name: refnx-wheel-linux-${{ matrix.python-version }} path: dist/ # linux_wheels: # # runs-on: ubuntu-latest # strategy: # max-parallel: 2 # matrix: # PLAT: ["manylinux2014_x86_64"] # # steps: # - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 # - name: Make Linux Wheel # run: | # docker run --rm -e="PLAT=${{ matrix.PLAT }}" -v $(pwd):/io quay.io/pypa/${{ matrix.PLAT }} /bin/bash /io/tools/build_manylinux_wheels.sh # # - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 # with: # name: refnx-wheel # path: dist/ ############################################################################### test_macos_intel: runs-on: macos-13 strategy: fail-fast: true max-parallel: 1 matrix: python-version: [ '3.12' ] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - run: mkdir -p dist - uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0 with: python-version: ${{ matrix.python-version }} cache: 'pip' # caching pip dependencies allow-prereleases: true - name: Make wheel shell: bash run: | python -m pip install --upgrade pip python -m pip install wheel delocate build python -m build . -v - name: Install package and test with pytest shell: bash env: MPLBACKEND: agg run: | pip install -r .requirements.txt pushd dist python -m pip install --only-binary=refnx --no-index --find-links=. refnx pytest --pyargs refnx popd - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 with: name: refnx-wheel-macos-${{ matrix.python-version }}-intel path: dist/ test_macos: runs-on: macos-14 strategy: fail-fast: true max-parallel: 3 matrix: python-version: ['3.10', '3.11', '3.12'] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - run: mkdir -p dist - uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0 with: python-version: ${{ matrix.python-version }} cache: 'pip' # caching pip dependencies allow-prereleases: true - name: install compilers shell: bash run: | brew install llvm - name: Make wheel shell: bash env: ARCHFLAGS: "-arch arm64" _PYTHON_HOST_PLATFORM: macosx-11.0-arm64 run: | sudo xcode-select -s /Applications/Xcode_15.3.app export PATH="$PATH:/opt/homebrew/opt/llvm/bin" export CPPFLAGS="-I/opt/homebrew/opt/llvm/include" export LDFLAGS="-L/opt/homebrew/opt/llvm/lib/c++ -Wl,-rpath,/opt/homebrew/opt/llvm/lib/c++ -L/opt/homebrew/opt/llvm/lib" python -m pip install --upgrade pip python -m pip install wheel delocate build python -m build . -v # so that libomp is distributed with wheel delocate-wheel -v dist/refnx*.whl - name: Install package and test with pytest shell: bash env: MPLBACKEND: agg run: | python -m pip install -r .requirements.txt pushd dist python -m pip install --only-binary=refnx --no-index --find-links=. refnx pytest --pyargs refnx popd - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 with: name: refnx-wheel-macos-${{ matrix.python-version }} path: dist/ test_macos_app: needs: test_macos runs-on: macos-14 strategy: fail-fast: true steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0 with: python-version: '3.12' allow-prereleases: true - run: mkdir -p dist - name: Download wheel uses: actions/download-artifact@cc203385981b70ca67e1cc392babf9cc229d5806 # v4.1.9 with: pattern: refnx-wheel-macos-3.12 merge-multiple: true path: dist - name: Make frozen GUI executable run: | # make app in virtualenv python -m venv app source app/bin/activate python -m pip install --upgrade --upgrade-strategy eager -r tools/app/requirements.txt python -m pip install scipy pushd dist ls python -m pip install --only-binary=refnx --no-index --find-links=. refnx popd python -m pip install pyinstaller psutil # compileall in an effort to speedup pyinstaller GUI start python -m compileall pushd tools/app pyinstaller motofit.spec # check to see that the app starts python check_app_starts.py dist/refnx.app/Contents/MacOS/refnx popd printenv - name: Sign app and create dmg if: github.repository == 'refnx/refnx' && (github.event.pull_request.merged == true || startsWith(github.ref, 'refs/tags')) env: MACOS_CERTIFICATE: ${{ secrets.MACOS_CERTIFICATE_ISA }} MACOS_CERTIFICATE_PWD: ${{ secrets.MACOS_CERTIFICATE_ISA_PWD }} run: | pushd tools/app echo $MACOS_CERTIFICATE | base64 --decode > certificate.p12 ls -al certificate.p12 security create-keychain -p DloaAcYP build.keychain security default-keychain -s build.keychain security unlock-keychain -p DloaAcYP build.keychain security import certificate.p12 -k build.keychain -P $MACOS_CERTIFICATE_PWD -T /usr/bin/codesign security set-key-partition-list -S apple-tool:,apple:,codesign: -s -k DloaAcYP build.keychain >/dev/null security find-identity -p codesigning codesign --verify --options=runtime --entitlements entitlements.plist --timestamp --deep --verbose=4 --force --sign "Developer ID Application: The International Scattering Alliance (8CX8K63BQM)" dist/refnx.app cp ../../refnx/reflect/_app/icons/Motofit.icns . sips -i Motofit.icns DeRez -only icns Motofit.icns > icns.rsrc hdiutil create dist/refnx.dmg -srcfolder dist/refnx.app -ov -format UDZO Rez -append icns.rsrc -o dist/refnx.dmg SetFile -a C dist/refnx.dmg codesign -s "Developer ID Application: The International Scattering Alliance (8CX8K63BQM)" dist/refnx.dmg mv dist/refnx.dmg ../../dist/ popd # xcrun notarytool submit --apple-id "$APPLEID" --password "$APP_PASSWORD" --team-id 8CX8K63BQM --wait refnx.dmg - name: Notarize DMG if: github.repository == 'refnx/refnx' && (github.event.pull_request.merged == true || startsWith(github.ref, 'refs/tags')) uses: lando/notarize-action@b5c3ef16cf2fbcf2af26dc58c90255ec242abeed # v2.0.2 with: product-path: "dist/refnx.dmg" primary-bundle-id: "com.refnx.refnx" appstore-connect-username: ${{ secrets.NOTARIZATION_USERNAME }} appstore-connect-password: ${{ secrets.NOTARIZATION_PASSWORD }} appstore-connect-team-id: 8CX8K63BQM verbose: True - name: Staple Release Build if: github.repository == 'refnx/refnx' && (github.event.pull_request.merged == true || startsWith(github.ref, 'refs/tags')) uses: BoundfoxStudios/action-xcode-staple@1e2200b448c6ed4dd44b963ff17d3e340fc6b064 # v1 with: product-path: "dist/refnx.dmg" - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 with: name: refnx-app-macos-${{ matrix.python-version }} path: dist/*.dmg ############################################################################### test_win: runs-on: windows-latest strategy: fail-fast: true max-parallel: 3 matrix: python-version: ['3.10', '3.11', '3.12'] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0 with: python-version: ${{ matrix.python-version }} cache: 'pip' # caching pip dependencies allow-prereleases: true - run: pip install -r .requirements.txt - run: mkdir -p dist - name: Make wheel run: | python -m pip install --upgrade pip python -m pip install wheel python -m pip wheel . --no-deps -w dist - name: Install package and test with pytest if: ${{ matrix.python-version == '3.10' }} env: PYOPENCL_CTX: 0 run: | cd dist python -m pip install --only-binary=refnx --no-index --find-links=. refnx # python -m pip install pytools mako cffi # choco install opencl-intel-cpu-runtime # python -m pip install --only-binary=pyopencl --find-links http://www.silx.org/pub/wheelhouse/ --trusted-host www.silx.org pyopencl pytest --pyargs refnx cd .. - name: Check refnx gui starts if: ${{ matrix.python-version == '3.10' }} run: | # check that refnx gui starts pip install psutil python tools/app/check_app_starts.py refnx - name: Make frozen GUI executable if: ${{ matrix.python-version == '3.10' }} run: | # make app in virtualenv pip uninstall -y h5py python -m venv app app\Scripts\activate.bat python -m pip install --upgrade --upgrade-strategy eager -r tools/app/requirements.txt cd dist python -m pip install --only-binary=refnx --no-index --find-links=. refnx cd .. # fix for multiprocessing on Py3.9 is not merged in PyInstaller # if you are on Py3.7 you can just pip install pyinstaller # pip install git+https://github.com/andyfaff/pyinstaller.git@gh4865 cd tools\app pyinstaller motofit.spec move dist\motofit.exe ..\..\dist\ cd ..\.. - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 with: name: refnx-exe-win-${{ matrix.python-version }} path: dist/ ############################################################################### build_doc: runs-on: ubuntu-latest strategy: max-parallel: 1 matrix: python-version: ["3.10"] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0 with: python-version: ${{ matrix.python-version }} - name: setup apt dependencies run: | sudo apt-get update sudo apt-get install pandoc - name: Build documentation run: | python -m pip install --upgrade pip python -m pip install . python -m pip install wheel cd doc python -m pip install -r requirements.txt make html ############################################################################### lint: runs-on: ubuntu-latest strategy: max-parallel: 1 matrix: python-version: ["3.10"] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0 with: python-version: ${{ matrix.python-version }} - name: Lint with flake8 and black run: | python -m pip install ruff black # stop the build if there are Python syntax errors or undefined names # the ignores are taken care of by black ruff check refnx black --check refnx - name: clang-format run: | sudo apt update sudo apt install clang-format cd src clang-format --Werror -n *.cpp *.h *.c cd pnr clang-format --Werror -n *.cc *.h refnx-0.1.53/.github/workflows/release.yml000066400000000000000000000116141477046072400205000ustar00rootroot00000000000000# This action releases refnx on PyPI for every version tagged commit (e.g. v0.0.1) name: PyPI/Github Release on: push: tags: - "v*" jobs: build_wheels: runs-on: ${{ matrix.os }} if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') strategy: matrix: os: [windows-latest, macos-14] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: build wheels uses: pypa/cibuildwheel@42728e866bbc80d544a70825bd9990b9a26f1a50 # v2.23.1 env: CIBW_TEST_COMMAND: pytest --pyargs refnx.reflect.tests.test_reflect CIBW_ARCHS_MACOS: "x86_64 arm64" CIBW_ENVIRONMENT_MACOS: MACOSX_DEPLOYMENT_TARGET="10.13" - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 with: name: wheels-${{ matrix.os }} path: ./wheelhouse/*.whl build_linux_x86_64_wheels: runs-on: ${{ matrix.os }} if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') strategy: matrix: os: [ubuntu-latest] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: build wheels uses: pypa/cibuildwheel@42728e866bbc80d544a70825bd9990b9a26f1a50 # v2.23.1 env: CIBW_TEST_COMMAND: pytest --pyargs refnx.reflect.tests.test_reflect CIBW_BUILD: "*-manylinux_x86_64" - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 with: name: wheels-manylinux path: ./wheelhouse/*.whl build_linux_musl_wheels: runs-on: ${{ matrix.os }} if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') strategy: matrix: os: [ubuntu-latest] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: build wheels uses: pypa/cibuildwheel@42728e866bbc80d544a70825bd9990b9a26f1a50 # v2.23.1 env: CIBW_TEST_COMMAND: pytest --pyargs refnx.reflect.tests.test_reflect CIBW_BUILD: "*-musllinux_x86_64" - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 with: name: wheels-musllinux path: ./wheelhouse/*.whl make_sdist: name: Make sdist runs-on: ubuntu-latest steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Build sdist run: pipx run build --sdist - uses: actions/upload-artifact@4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1 # v4.6.1 with: name: wheels-sdist path: dist/*.tar.gz check-version: runs-on: ubuntu-latest needs: [build_linux_musl_wheels, build_linux_x86_64_wheels, build_wheels, make_sdist] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0 with: python-version: 3.11 - uses: actions/download-artifact@cc203385981b70ca67e1cc392babf9cc229d5806 # v4.1.9 with: pattern: wheels-* merge-multiple: true path: dist - name: Check version run: | python -m pip install numpy scipy orsopy ls dist python -m pip install --only-binary=refnx --no-index --find-links=dist refnx cd dist RNX_VERSION="$(python -c "import refnx;print(refnx.version.release)")" cd .. if [ $RNX_VERSION == "True" ]; then echo "It's a release version of refnx" else echo "This is not a release version of refnx" exit 1 fi pypi-publish: name: Upload release to PyPI runs-on: ubuntu-latest needs: [check-version] environment: name: pypi url: https://pypi.org/p/refnx permissions: id-token: write # IMPORTANT: this permission is mandatory for trusted publishing steps: - uses: actions/download-artifact@cc203385981b70ca67e1cc392babf9cc229d5806 # v4.1.9 with: pattern: wheels-* merge-multiple: true path: dist - name: Upload to PyPI uses: pypa/gh-action-pypi-publish@76f52bc884231f62b9a034ebfe128415bbaabdfc # v1.12.4 with: # repository-url: https://test.pypi.org/legacy/ skip_existing: true release-github: runs-on: ubuntu-latest needs: [ pypi-publish ] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - uses: actions/download-artifact@cc203385981b70ca67e1cc392babf9cc229d5806 # v4.1.9 with: pattern: wheels-* merge-multiple: true path: dist - uses: ncipollo/release-action@440c8c1cb0ed28b9f43e4d1d670870f059653174 # v1.16.0 with: artifacts: "dist/refnx*.tar.gz" token: ${{ secrets.GITHUB_TOKEN }} allowUpdates: true generateReleaseNotes: true refnx-0.1.53/.gitignore000066400000000000000000000007111477046072400147240ustar00rootroot00000000000000.vscode/ *.pyc .idea .DS_Store build/ dist/ *.o *.obj *.pyd *.so .eggs *.icns .tox refnx.egg-info/ __pycache__ .ipynb_checkpoints/ refnx.egg-info/ src/_cevent.c src/_cevent.cpp src/_cevent2.c src/_creflect.c src/_creflect.cpp src/_cyreflect.c src/_cyreflect.cpp src/_cutil.c build.log doc/_build/ t.txt t .cache/ refnx/version.py benchmarks/.asv # testing artifacts PLP*dat PLP*xml c_PLP0000708.dat c_PLP0000708.xml offspec.xml test1.dat test.dat test.xml refnx-0.1.53/.requirements.txt000066400000000000000000000004001477046072400162710ustar00rootroot00000000000000numpy numba; python_version != "3.12" cython scipy orsopy h5py pandas xlrd pytest ipywidgets IPython matplotlib traitlets PyQt6-Qt6==6.6.1 PyQt6==6.6.1 PyQt6-sip==13.6.0 qtpy uncertainties setuptools attrs corner tqdm periodictable pytensor pymc pytest-qt refnx-0.1.53/.travis.yml.bak000066400000000000000000000052561477046072400156120ustar00rootroot00000000000000sudo: false dist: xenial language: python matrix: include: # - os: linux # env: PY=3.7.3 - os: osx language: generic env: PY=3.7 addons: apt: packages: - libhdf5-serial-dev services: - xvfb #before_script: # - | # if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then # export DISPLAY=:99.0 # sh -e /etc/init.d/xvfb start # sleep 3 # give xvfb some time to start # fi before_install: - echo $TRAVIS_OS_NAME - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then wget https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O miniconda.sh; elif [[ "$TRAVIS_OS_NAME" == "linux" ]]; then wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh; fi - bash miniconda.sh -b -p $HOME/miniconda - export PATH="$HOME/miniconda/bin:$PATH" - hash -r - conda update --yes conda # Useful for debugging any issues with conda - conda info -a - conda config --add channels conda-forge - conda config --set channel_priority strict - conda create --yes -n conda-refnx python=$PY - source activate conda-refnx - conda install --yes -c conda-forge numpy scipy h5py cython pandas xlrd pytest ipywidgets IPython matplotlib traitlets pyqt - conda install --yes -c conda-forge numpydoc sphinx jupyter pandoc nbconvert pyopencl - pip install uncertainties attrs corner nbsphinx jupyter_sphinx sphinx_rtd_theme tqdm pytest-qt periodictable - pip install git+https://github.com/pymc-devs/pymc3 # enable OpenMP support for Apple-clang - | if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew update brew install libomp export CC=/usr/bin/clang export CXX=/usr/bin/clang++ export CXXFLAGS="$CXXFLAGS -Xpreprocessor -fopenmp" export CFLAGS="$CFLAGS -Xpreprocessor -fopenmp" export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include" export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include" export LDFLAGS="$LDFLAGS -L/usr/local/opt/libomp/lib -lomp" export DYLD_LIBRARY_PATH=/usr/local/opt/libomp/lib fi script: - export MPLBACKEND=agg # put cwd in the top of the directory stack - pushd . # install and test from the sdist - python setup.py sdist - cd dist - pip install *.tar.gz - python -c 'import refnx;refnx.test()' # restore the working directory to the root refnx directory - popd - sphinx-build -b html doc doc/html notifications: # Perhaps we should have status emails sent to the mailing list, but # let's wait to see what people think before turning that on. email: false refnx-0.1.53/CHANGELOG.txt000066400000000000000000000703571477046072400150010ustar00rootroot00000000000000Details of changes made to refnx ================================ 0.1.1 ----- - removal of Python 2.7 support - added azure pipelines for faster windows CI - remove `uncertainties` as a package dependency (it's still used for testing) - remove `six` as a package dependency - add `refnx.util.refplot` for quick plotting of reflectometry datasets - fixed various deprecation warnings - added the ability to mask (hide) points in a `refnx.dataset.Data1D` dataset 0.1.2 ----- - added save/load model buttons in the interactive reflectometry modeller. - removed `from __future__ import ...` statements as refnx is now solely Py3 - added `cython: language_level=3` statements to cython code - marked cython extensions as c++ language - smarter (faster) cataloging of NeXUS files - removed pandas as a strict dependency, as it's only required for reduction, not analysis - improved documentation of the ManualBeamFinder - adding a pyqt based GUI (alpha state) - improved __repr__ of many classes - Start the GUI via a 'refnx' console command, via a setup.py entry_point. 0.1.3 ----- - GUI machinery can now use Components other than Slab, such as LipidLeaflet (already added). New Components may require extra shim code to be written for them. Specifically how they're to be displayed, and a default way of initialising the Component (which may require a dialogue). - The 'Link equivalent parameters' action has been added, enabling equivalent parameters on other datasets to be linked. This greatly aids setup of multiple contrast datasets. All the datasets to be linked must have the same type of structure. - The initialisation of a LipidLeaflet is made much easier by using a library of lipid head/volumes and scattering lengths for popular lipids which are presented to the user in an initialisation dialogue. - The refnx paper is accepted and the article and manuscript file are included in the repository. - The pyqt GUI to refnx can be made into standalone executables for Windows, macOS. - Fixed a bug that meant most reflectivity calculations were single-threaded instead of multi-threaded. - Added MixedReflectModel to the pyqt GUI, allowing one to model 'patchy' systems, i.e. incoherent averaging of reflectivities. - BACKWARDS INCOMPATIBLE CHANGE: the slabs properties of `Component` and `Structure` have now been changed to methods, taking the optional `structure` kwd parameter. The reason for this is so each `Component` knows what kind of `Structure` it is in. - The Spline Component can be used within the pyqt GUI. - In the pyqt gui Components can be re-ordered within their host structure by drag/drop. Dragging to other Structures copies the Component to that structure. - Added the Stack Component. A Stack contains a series of Components, and the Stack.repeats attribute can be used to produce a multilayer repeat structure. - Folded in a reduction pyqt gui for Platypus data. The app was already in the slim directory. It's now available from refnx.reduce.gui 0.1.4 ----- - fixed bug in reflectivity calculation with significant non-zero absorption. The wavevector calculation was using the wrong branch of the complex sqrt. 0.1.5 ----- - fixed font size in pyqt GUI. - script export from pyqt GUI can use either multiprocessing or MPI for parallelisation during MCMC sampling. - speeded up reflectivity calculation, following on from changes made in 0.1.4 (if the imaginary part of a complex sqrt argument is very small, then the C++ calculation takes a lot longer). - added a plot method to PlatypusNexus - refactor util.PoolWrapper to util.MapWrapper - allow the number of Stack repeats to be fittable. - GUI option to only display those parameters (and datasets) that are going to be varied in a fit. - update testimonials.bib - "to code" button in Jupyter interactive modeller respects the transform popup. 0.1.6 ----- - When parameters are linked in the refnx GUI only the dataset containing the master parameter was being updated (reflectivity/SLD curves) when the master was changed. Now all datasets that have parameters linked to the master parameter (a constraint) are updated. - When a dataset/component/structure containing a master parameter (i.e. a parameter to which other parameters are constrained to) is removed, the GUI now unlinks those dependent parameters from the master parameter. - display number of iterations in GUI progress box. If fit is aborted put best fit so far into Objective that's being fitted. - fixed crash resulting from the use of a comma when entering a floating point number. Entering '3,1' would crash the gui, using '3.1' would work but be displayed as '3,1'. The use of a dot as a decimal point is now enforced. 0.1.7 ----- - print human readable output when fitting with the Jupyter interactive modeller. - added shgo and dual_annealing minimiser options to the refnx gui. - SLD calculator retains state between viewings. - Added dialogue to adjust optimisation parameters. - Fixed bug in export of MCMC code fragment, Gui would crash. - Autocorrelation plot produced from mcmc.py code fragment, this can be used to judge how much to thin the chains by. - `refnx.analysis` now possesses a standalone function, `autocorrelation_chain` for calculating chain autocorrelation. Previously the calculation had to be done using a CurveFitter instance. - Function for calculating autocorrelation time made visible as: `refnx.analysis.integrated_time`. You should pass the autocorrelation array to this function. - GUI can now export an ASCII file representing the model SLD curve. - BUG: when the GUI saves a model it should pickle a ReflectModel. It wasn't doing this, it was pickling a DataObject. This has now been fixed, but the fix affects back compatibility. - *GUI can now do MCMC sampling* - Add option to change context of Mapwrapper (spawn/fork/forkserver) - BUG: when loading (not refreshing) a dataset that was already loaded, the associated model was lost. (GH331) - Added a progress bar for batch fitting. - Speeded up batch fitting 0.1.8 ----- - When GUI experiment file is loaded the correct fitting algorithm wasn't being set correctly (GH338). - Prevent crash when trying to refresh a stale dataset (i.e. is no longer in its original location. - Produce autocorrelation graph when sampling in GUI. - Added links to ptemcee and emcee in the optimisation parameters window. - GUI produces corner plot after MCMC sampling - BUG fix for rebinning code (doesn't affect analysis) - Different interfacial roughness types can be specified between all Components in a Structure. The available types are: Erf (Error Function, default), Tanh, Sinusoidal, Exponential, Step, Linear. User specifiable interfaces can be created by subclassing Interface. - A fix for the PyQt5 interface using 4K screens on Windows was made. Previously the GUI elements and fonts were being displayed in a much too small fashion. - Made various dialogues in the PyQt5 GUI window modal (to prevent them being lost). - fixed bugs if cancel was pressed during the MCMC folder dialogue phase. - made loading of experiment file back compatible. 0.1.9 ----- - Added DOTAP, h-DOPC, 18:1 Diether PC to lipids database - Updated SLIM reduction software to cope with the new monobloc detector - Improve speed of resolution smearing by using splev/splrep - Added shell scripts to build manylinux and macOS wheels (as well as test them). - Some minor optimisations for calculation of `Interval.logp`. - Some minor optimisations for various calculations in `Objective`. - Fixed potential for crashes in ManualBeamFinder if controls specified regions that went outside the detector region. - cythonized contract_by_area, resulting in huge speedup for microsliced structures. - Enabled parallel calculation of reflectivity using OpenMP. Tests show that it should be ~20% faster than the previous calculation in C. - tqdm progress bar for ptemcee sampling (if tqdm is installed). - Cleaned up the _creflect module. Threaded reflectivity calculation in that extension now uses std::thread instead of pthreads (POSIX) or WinAPI (windows). 0.1.10 ------ - Prevent spurious benchmark package installation. - Fixed bug when loading a MTFT file saved in a previous version of refnx. - event mode data reduction sped up by an order of magnitude - Added neutron transmission calculator (if periodictable is present) - event file reader can now read any ANSTO packedbin file (reduction). - Align SLD plots around a specific interface in a slab representation. Useful if plotting many samples at the same time. - Add MaterialSLD object that is constructed from a chemical formula and mass density. This enables the use of specific materials to describe layers, e.g. MaterialSLD('SiO2', density=2.2). - Components can be multiplied by an integer to make them repeat. - Add _open_mp_helpers to MANIFEST.in (gh381). - update bundled vendored emcee 0.1.11 ------ - Fixed a bug in the GUI that prevented load/save of mixed area models. - Fixed a bug in the resynthesis of data. - Fixed a bug in the loading of event data from the Platypus monobloc detector. - Added a FresnelTransform. - Document inequality constraints. - Made refnx SLD calculator GUI more fault tolerant to incorrect formula. - Add example Jupyter notebook for batch reduction. - Autoscaling for Panalytical XRR reduction. - Fixed bug in drag/drop within a Stack in the GUI. - Added a MixedSlab Component which is constituted from several individual Scatterers. The SLD of the MixedSlab is weighted by their volume fractions. 0.1.12 ------ - allow master and slave chopper parameters to be ignored in PLP reduction. - tunable t_0 offset in PLP/SPZ reduction. - possibly_create_parameter accepts default bounds/vary/constraint. This fixes a bug in MixedSlab. - Don't require numpy be installed before setup.py can run. - Speed up reflectivity calculation if there is solvation. - reflect.choose_dq_type for finding out fastest mode of resolution smearing. - User can now select resolution smearing approach in ReflectModel. Choosing between 'pointwise' or 'constant'. - Add progress bar for Curvefitter.fit() (requires tqdm being installed). - A few micro-optimisations. - Optimized pickling/unpickling of Bounds instances. This can lead to a huge performance increase (~ 40%) when doing parallel sampling. - MCMC sampling initialisation made reproducible 0.1.13 ------ - synthesising of datasets (Data1D.synthesise) can now be repeatable by providing a seed. - use Github Actions to test and build macOS wheels across all the Pythons. - The _creflect.abeles reflectivity calculation can release the GIL for a large part of its calculation now. This will enable parallelisation using either Processes or Threads. Processes still have the edge on speed at the moment. - change reduction code for ReflectNexus.phase_angle to use degrees, not radians. (previously angles were returned in a mix of degrees/radians) - Speed of reflectivity calculation is improved between 10 and 20 %. This is achieved by the use of C99 complex arithmetic instead of C++ std::complex. This improvement does not apply to Windows because it doesn't have a C99 standard conformant compiler. - Document how to save a model. - macOS wheels (and CI testing) have the cyreflect openmp option activated. - An openCL reflectivity calculation backend is added that can use a GPU. - Added a `reflect.use_reflect_backend` function to choose between the backends used to calculate reflectivity. 0.1.14 ------ This will be a bug fix release over 0.1.13. The Linux wheels weren't tested enough and the default reflectivity calculation backend didn't work correctly in 0.1.13. - fix default backend calculation. 0.1.15 ------ - Build standalone GUI as part of continuous integration runs. - Fix couple of GUI warnings when started from a terminal. - pyinstaller plist settings adjusted to make retina compatible and include refnx version number. - use Github Actions to test and build Linux wheels across all the Pythons. - Compensate for dq_type being added to ReflectModel (older .mtft files wouldn't load into GUI). - Refactor test_reflect to challenge all reflectivity calculation backends. - refnx.reflect.available_backends lists all reflectivity calculation backends. - Add pytest fixture to download test data. This dramatically reduces the size of the package. - Compensate for _stderr being added to Parameter (older .mtft files wouldn't load properly into GUI). - Fixed progress bar for parallel tempered sampling. - Make sdist 0.1.16 ------ - build more Linux wheels. - vendored ptemcee (bleeding edge), so it's no longer necessary to install it. - use -funsafe-math-optimizations for reflectivity calculation. - fixed bug when retrieving log-probabilities from PTMCMC sampling. - fixed encoding call for Py3 (.decode doesn't exist for Py3). - improve pymc3 model creation. pymc3 offers different ways to carry out MCMC sampling. This may lead to pymc3 being included as a sampling method in Curvefitter. - implement `invcdf` method for Bounds objects. This will allow creation of prior transforms from [0, 1) to the original range specification. - BREAKING CHANGE: `objective.model.logp` and `Objective.logp_extra` are now included in the log-likelihood (`Objective.logl`) instead of log-prior (`Objective.logp`). This makes it easier to work with pymc3 and dynesty. - Add `Objective.prior_transform` that converts random variates in a unit hypercube to parameter values, according to their prior distributions. - manual_beam_find can be sent a name to be displayed on the window title. - ReductionOptions dict is now used for specifying options for reduction and processing of reflectometry data. This will permit different settings to be used for datasets measured in different configurations. - Add AutoReducer, an object that watches a directory for modified/created files and automatically reduces them. 0.1.17 ------ - Drop Python 3.6 for new development (as per https://numpy.org/neps/nep-0029-deprecation_policy.html) - Pin setuptools version on RTD to allow the docs to build. - Add a tutorial to the docs on model selection. - Update emcee vendored code. - Fix repr of Model - Fix several warnings during test run. - Add error bar plotting to refnx GUI. - Improved the specular ridge finding methodology for data reduction. 0.1.18 ------ - SPZ batch reduction - remove some roadblocks to JAXification (float casting in Parameter.value setter) - volume fraction of solvent can now be used when constructing a Slab from a Scatterer. - Fully remove traces from graph when opening new experiments. - Store reduction_options when processing spectra. - RuntimeError if a log transform creates non-finite numbers. - ReflectModels can now use a q_offset to correct for possible angular misalignments. - `possibly_create_parameter` and `is_parameter` now use `BaseParameter` for their operation, rather than `Parameter`. Thus _BinaryOp and _UnaryOp can be given to these operations and work. This enables construction of various objects (SLD/Slab/etc) from constrained parameters, rather than setting a constraint afterwards. - MixedSlab is now allowed to be the first/last Component in a Structure. 0.1.19 ------ - MCMC sampling in GUI now plots parameter value vs step number, so you can see how many samples to burn - Fixed GUI crash when trying to make a snapshot with a name that has already been used. - Give BaseObjective a default weighted attribute, enabling it to be used in GlobalObjective. - Fallback to numerical estimation of Hessian/Covariance matrix if an Objective cannot calculate a residuals array (e.g. mixed use of BaseObjective/Objective in a GlobalObjective). - Vendor scipy.stats.qmc. - Use Latin Hyper Cube sampling to initialise MCMC walkers when initialising with prior. This ensures a good distribution of walkers and no clustering. 0.1.20 ------ - Make default Spline.dz.bounds.lb > 0, so that crashes aren't experienced when a dz is set to 0. GH549 - Add the shgo keyword parameters 'n' and 'iters'. - Add max_delta_z to sld_profile so that the point density in an SLD plot can be changed. - Don't use tqdm progress bar when fitting in GUI if sys.stderr is None. 0.1.21 ------ - check that refnx/analysis doesn't get in the way of handling multidimensional data. - add a header to datafiles during reduction. - make requirements.txt available in the refnx GUI. - add units attribute to Parameter. This is displayed as a tooltip in the GUI. - GUI and refnx command line entry point now reads sys.argv to see if GUI should open an experiment file. - Associate the mtft file extension on macOS with the GUI app, meaning you can double click on a mtft file and it'll open with the app. The mtft files also display with the GUI icon. - BUG: an error was experienced whilst reprocessing an existing chain in the refnx GUI. - BUG: fix gh336, remember the dq/q choice on reloading an experiment file. Previously the GUI reverted to constant dq/q on a reload, even if it was deselected. 0.1.22 ------ - Added polarisation reduction (Oliver Paull) - Introduce Objective.auxiliary_params. These are extra parameters that are modified during the calculation of Objective statistics, and are varied during a fit, but may not be directly part of Model calculating a signal. Their main purpose is to aid in creating constraints. - Add example to show how to co-refine non-spinflip NR data. - Silenced warning during test from slow reflectivity calculation, which erroneously gave the impression that the C based kernel wasn't available. 0.1.23 ------ - bugfix: remove printing of auxiliary parameters in Objective.setp. A remnant print function was in that method. However, those parameters aren't present in a global objective, which caused an AttributeError. 0.1.24 ------ - use oldest-supported-numpy to ensure that older numpy versions are used to build refnx wheels. This will allow refnx to be used with more recent versions as well as the oldest version supported on a given Python version. Previously refnx wheels were built with the newest numpy available at build time, and were probably not compatible with older numpy versions. - Rejig the build process to make macOS universal wheels. 0.1.25 ------ - Add NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION define to cython extension building to remove warnings. This requires a different mode of access to the array data. - Fixed drag and drop of datasets onto refnx GUI. - Add OrsoDataset and a generalised load_data function. - refnx GUI removes horizontal scrollbar. - Fixed crash when trying to access version numbers when saving expt file. 0.1.26 ------ - change wheel builder to make 3.10 wheels. - check that pyinstaller based macOS standalone app runs in CI. - update install documentation - workaround for difficulty using orsopy on Python 3.10 - added fields to ReflectReduce output dict, including omega, reflected_beam, direct_beam. 0.1.27 ------ - Add a little machinery for energy dispersive calculations. This is implemented by adding a wavelength attribute to Structure. The Structure.slabs method then works out the Slab representation for that particular wavelength. The wavelength attribute should be used by the Component.slabs method when asking for the SLD of a Scatterer. The Slab and MixedSlab Components have been altered to permit this. User provided components should work as they currently do, but probably have to be rewritten to permit energy dispersive operation. For energy dispersive operation classes that inherit Scatterer should override the newly introduced Scatterer.complex() method. 0.1.28 ------ - add name="refnx" to pyproject.toml. 0.1.29 ------ - Don't pickle the manual_beam_finder. - Fixed a bug when inserting a Component into a Stack in the refnx GUI. If it was appended to the end of the Stack Component addition worked. Inserting a Component into the middle of the Stack appeared to put it in the correct place in the GUI, but inserted it in the wrong place in the Structure. - Added Lagrangian multipliers to GlobalObjective, allowing the user to modulate the contribution of individual log-likelihoods to the whole. This may be of use when co-refining Neutron/X-ray/Ellipsometry datasets. 0.1.30 ------ - First appearance of Parameter.set_constraint(). - Previously Parameter constraints were limited to algebraic Python expressions. Now Parameter.set_constraint can be given a callable which is evaluated at runtime, allowing more sophisticated constraints to be developed. (N.B this is separate to the inequality constraints that can be made during curvefitting using the NonLinearConstraint machinery). - Add a Parratt recursion for calculating reflectivity, it seems to be a little faster. - Ported the pymc wrapper from pymc3 --> pymc (v4), which was released in Jun2022. - added sequence_to_parameters function. - added LipidLeaflet.make_constraint. 0.1.31 ------ - Amend setup.py package discovery - Change the manual beam finder box to green - When a Parameters object contains a _UnaryOp or _BinaryOp the dependencies of those objects are queried for varying parameters when asking for Parameters.varying_parameters() 0.1.32 ------- - event mode reduction for SPZ - Display SLD profile for first structure in MixedReflectModel - Github: changed the default branch from master to main. This is done to match other repositories behaviour and to reduce cognitive load. - Added a Parameter.corner method to plot the posterior distribution for a given Parameter. - Fixed a bug in MixedSlab 0.1.33 ------ - upgrade to PyQt6. PyQt6 is currently only available as a wheel from PyPI, to be installed by e.g. pip. There are currently no conda packages for PyQt6. PyQt6 enables the gui to be used on macosx_arm64, there are no PyQt5 wheels available on that platform. - further modification to use qtpy. qtpy is a shim that allows either PyQt6 or Pyside6 to be used to run the refnx GUI. qtpy is a new dependency for running the GUI. - add Objective._generate_generative_mcmc to yield fit curves corresponding to MCMC samples. - add Structure._generate_sld_profile_mcmc to yield sld profiles corresponding to MCMC samples. 0.1.34 ------ - Fix bug in ManualBeamFinder - Fix bug in refnx GUI regarding checkbox selection. 0.1.35 ------ - only extract requested keys from catalogue - enable cython3 operations - change install docs to recommend use of refnx[all] - update SPZ reduction for new detector translation 0.1.36 ------ - rudimentary HDF <-> Data1D interconversion. This is functionality that might be subject to change, so the functions are prefixed by "_". - added TOF simulation to reduction code. - print out the fitting statistic in a tqdm progress bar. 0.1.37 ------ - automated the release to PyPI via Github Actions. - removed setup.cfg, transferring all information to pyproject.toml. - fixed various scipy DeprecationWarnings related to keyword only arguments. - converted various os.path to pathlib.Path - stopped writing xml output (nobody uses it?) - added detailed resolution kernel calculation (writes out an HDF file). Refactor detailed kernel calculator. - removed np.asfarray usage, it's deprecated. - updated emcee vendored code. - silence RuntimeWarning if the foreground width is found to be wider than predicted. - fix pandas warnings in batchreduction. - wholesale conversion of os.path to pathlib.Path. Hopefully not too many bugs introduced as a result of this. 0.1.38 ------ - allow detailed Q resolution kernel to work with q_offset. - added lopx_hipx to ReductionOptions to allow one to specify the foreground region. - fix path concatenation issue in reduce_stitch, str or Path should be allowed. 0.1.40 ------ - remove roadblocks for building wheels for cp312. - update vendored emcee. - change str.format to f-strings. 0.1.41 ------ - bump minimum Python version to 3.9. - remove oldest-supported-numpy from list of build requirements. - add "numba_parratt" reflectometry kernel. If numba is installed this kernel has greater calculation speed (in calculating the reflectivity) under some circumstances. - fixed bug when exporting parameters from refnx GUI. - use optionally installed black to format code fragment exported from refnx GUI. - update jax reflectivity kernel to fix deprecated functions. 0.1.42 ------ - scale log-prior in log-posterior calculation using a multiplier. This allows one to balance relative size of likelihood and prior. - added Structure.from_slabs classmethod. - fixed bug when trying to print a Parameter. 0.1.43 ------ - Store the fname as a PurePath in Data1D. This is because pickled datafiles from one OS may not be unpickleable on another OS. e.g. Posix can't unpickle WindowsPath and vice versa. - modified `util.general.neutron_transmission` to be able to select which cross sections are used for transmission calculation. - We revised the ReflectReduce.write_offspecular to output an ascii file with four columns, qz,qx,m_ref, m_ref_err. - ReflectModelTL added. Carries out reflectivity for wavelength dependent scattering length density profiles. 0.1.44 ------ - build against numpy=2 - update gh runners 0.1.45 ------ - Stop repeat append of a Parameter to Objective.model.parameters when the Objective uses lnsigma, alpha, or auxiliary_params - reenable pymc tests and update so that it works with pymc5 - add pymc/emcee/dynesty example to show how to use all three packages to sample a posterior. - add jax function for smeared reflectivity. - optimisation of ReflectModelTL. - add refnx.reflect._cyreflect.abeles_vectorised, a vectorised reflectivity calculator with optional parallelisation 0.1.46 ------ - Fix bug in ReflectModelTL when numpy2 is installed. - Added an example of incoherent summing in the documentation. - Added `reflect.FunctionalForm` for Functional profiles. This was previously only present in refnx-models. 0.1.47 ------ - added vectorised reflectivity calculator, `refnx.reflect._creflect.abeles_vectorised`. This is mainly of use for external programs, it's not used internally yet. - Fix Parameter constraints that themselves depended on Parameters constrained with functions. 0.1.48 ------ - Improve documentation for resolution smearing options in ReflectModel. - Added refnx.reflect.create_occupancy, a helper function for creating occupancy (volume fraction) profiles. - Added codesigning for macOS thanks to the International Scattering Association (ISA). This means that the refnx app open a lot more easily on macOS, as it's not stopped by Gatekeeper. 0.1.49 ------ - rudimentary exporter of a Structure to an ORSO model language file, `Structure.to_orso()`. Can only export Structures made of Slabs at the moment. - fixed bug in SpatzNexus.chod(). Repeat calls to this method would result in increasing values because an array value wasn't being dereferenced. - Fix several DeprecationWarnings emitted during tests. - Fix numpy deprecation warning for `Parameters.__array__`. This method is used to quickly extract the parameter set into a numpy array. This magic method now requires the copy and dtype keywords, which we ignore in our implementation. 0.1.50 ------ - put black configuration in pyproject.toml - put ruff configuration in pyproject.toml - fix bug in ManualBeamFinder, must be a matplotlib thing - Add LipidLeafletGuest, a LipidLeaflet that can have another molecule (e.g. cholesterol) in the tail region. - add refnx.reflect.possibly_create_scatterer. - remove refnx._lib._qmc, changing to scipy.stats.qmc. - transition vendored ptemcee to use np.random.Generator. - convert use of scipy.integrate.quadrature to scipy.integrate.quad. - Appropriate unit conversion when loading ORSO data (1/nm to 1/angstrom). 0.1.51 ------ - Modify to allow loading of .ORB ORSO files. - Capability for calculating PNR theoretical curves. - Fix some tests for new periodictable release. - Stop using deprecated setuptools module. - Analysis for polarised neutron reflectometry. 0.1.52 ------ - relax reflection kernel accuracy for i386. Necessary for Debian build. - add Orso file output to `reduce.reduce_stitch`. - Improve performance of ReflectModelTL, laying groundwork for footprint correction. 0.1.53 ------ - Reduction - fallback to normalising by time if no beam monitor counts are present. - renamed test directories from refnx..test to refnx..tests. This is to prevent name shadowing, but also to bring it into line with e.g. scipy/numpy that use that nomenclature. This might mean downstream packages who use files from those directories have to do some renaming. refnx-0.1.53/INSTALL_CONTRIBUTE.md000066400000000000000000000143601477046072400161270ustar00rootroot00000000000000# refnx - Installation and Development Instructions refnx is a python package for analysis of neutron and X-ray reflectometry data. It can also be used as a generalised curvefitting tool. It uses Markov Chain Monte Carlo to obtain posterior distributions for curvefitting problems. -------------- # Installation *refnx* has been tested on Python 3.9, 3.10, 3.11, and 3.12. It requires the *numpy, scipy, cython* packages to work. Additional features require the *pytest, pandas, qtpy, pyqt6, h5py, xlrd, attrs, tqdm, matplotlib, pymc, pytensor* packages. To build the bleeding edge code you will need to have access to a C-compiler to build a couple of Python extensions. C-compilers should be installed on Linux. On OSX you will need to install Xcode and the command line tools. On Windows you will need to install the correct [Visual Studio compiler][Visual-studio-compiler] for your Python version. In the current version of *refnx* the *emcee* and *ptemcee* packages are vendored by *refnx*. That is, *refnx* possesses it's own private copy of the package, and there is no need to those packages separately. ## Installation into a *conda* environment Perhaps the easiest way to create a scientific computing environment is to use the [miniconda][miniconda] package manager. Once *conda* has been installed the first step is to create a *conda* environment. ### Creating a conda environment 1) In a shell window create a conda environment and install the dependencies. The **-n** flag indicates that the environment is called *refnx*. ```conda create -n refnx python=3.7 numpy scipy cython pandas h5py xlrd pytest tqdm attrs``` 2) Activate the environment that we're going to be working in: ``` # on OSX conda activate refnx # on windows conda activate refnx ``` ### Installing into a conda environment from source The latest source code can be obtained from either [PyPi][PyPi] or [Github][github-refnx]. You can also build the package from within the refnx git repository (see later in this document). 1) In a shell window navigate into the source directory and build the package. If you are on Windows you'll need to start a Visual Studio command window. ``` python setup.py build python setup.py install ``` 2) Run the tests, they should all work. ``` python setup.py test ``` ### Installing into a conda environment from a released version 1) There are pre-built versions on *conda-forge*, but they're not necessarily at the bleeding edge: ```conda install -c conda-forge refnx``` 2) Start up a Python interpreter and make sure the tests run: ``` >>> import refnx >>> refnx.test() ``` ----------------------- ## Development Workflow These instructions outline the workflow for contributing to refnx development. The refnx community welcomes all contributions that will improve the package. The following instructions are based on use of a command line *git* client. *Git* is a distributed version control program. An example of [how to contribute to the numpy project][numpy-contrib] is a useful reference. ### Setting up a local git repository 1) Create an account on [github](https://github.com/). 2) On the [refnx github][github-refnx] page fork the *refnx* repository to your own github account. Forking means that now you have your own personal repository of the *refnx* code. 3) Now we will make a local copy of your personal repository on your local machine: ``` # is your github username git clone https://github.com//refnx.git ``` 4) Add the *refnx* remote repository, we're going to refer to the remote with the *upstream* name: ``` git remote add upstream https://github.com/refnx/refnx.git ``` 5) List the remote repositories that your local repository knows about: ``` git remote -v ``` ### Keeping your local and remote repositories up to date The main *refnx* repository may be a lot more advanced than your fork, or your local copy, of the git repository. 1) To update your repositories you need to fetch the changes from the main *refnx* repository: ``` git fetch upstream ``` 2) Now update the local branch you're on by rebasing against the *refnx* main branch: ``` git rebase upstream/main ``` 3) Push your updated local branch to the remote fork on github. You have to specify the remote branch you're pushing to. Here we push to the *main* branch: ``` git push origin main ``` ### Adding a feature The git repository is automatically on the main branch to start with. However, when developing features that you'd like to contribute to the *refnx* project you'll need to do it on a feature branch. 1) Create a feature branch and check it out: ``` git branch my_feature_name git checkout my_feature_name ``` 2) Once you're happy with the changes you've made you should check that the tests still work: ``` python setup.py test ``` 3) If the performance of what you've added/changed may be critical, then consider writing a benchmark. The benchmarks use the *asv* package and are run as: ``` cd benchmarks pip install asv asv run asv publish asv preview ``` For an example benchmark look at one of the files in the *benchmarks* directory. 4) Now commit the changes. You'll have to supply a commit message that outlines the changes you made. The commit message should follow the [numpy guidelines][numpy-contib] ``` git commit -a ``` 5) Now you need to push those changes on the *my_feature_branch* branch to *your* fork of the refnx repository on github: ``` git push origin my_feature_branch ``` 6) On the main [refnx][github-refnx] repository you should be able to create a pull request (PR). The PR says that you'd like the *refnx* project to include the changes you made. 7) Once the automated tests have passed, and the *refnx* maintainers are happy with the changes you've made then the PR is merged. You can then delete the feature branch on github, and delete your local feature branch: ``` git branch -D my_feature_branch ``` [PyPi]: [github-refnx]: [Visual-studio-compiler]: [miniconda]: [numpy-contrib]: refnx-0.1.53/LICENSE000066400000000000000000000030001477046072400137330ustar00rootroot00000000000000Copyright 2015-2024 A. R. J. Nelson, Australian Nuclear Science and Technology Organisation Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. 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. 3. Neither the name of the copyright holder 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. refnx-0.1.53/MANIFEST.in000066400000000000000000000001561477046072400144750ustar00rootroot00000000000000# All source files recursive-include refnx * recursive-include src * exclude src/refcalc.o prune */__pycache__refnx-0.1.53/README.md000066400000000000000000000015511477046072400142160ustar00rootroot00000000000000refnx ===== ![Github Action](https://github.com/refnx/refnx/workflows/Lint%20+%20Test/badge.svg) [![Build Status](https://dev.azure.com/refnx/refnx/_apis/build/status/refnx.refnx?branchName=master)](https://dev.azure.com/refnx/refnx/_build/latest?definitionId=1&branchName=master) [![Build status](https://ci.appveyor.com/api/projects/status/gv6965vuqnuufx9u?svg=true)](https://ci.appveyor.com/project/andyfaff/refnx) [![Documentation Status](https://readthedocs.org/projects/refnx/badge/?version=latest)](https://refnx.readthedocs.io/en/latest/?badge=latest) [![DOI](https://zenodo.org/badge/23189/refnx/refnx.svg)](https://zenodo.org/badge/latestdoi/23189/refnx/refnx) [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/refnx/refnx-binder.git/master) Neutron and X-ray reflectometry analysis in Python. Documentation at https://refnx.readthedocs.io.refnx-0.1.53/appveyor.yml.bak000066400000000000000000000060261477046072400160650ustar00rootroot00000000000000environment: # SDK v7.0 MSVC Express 2008's SetEnv.cmd script will fail if the # /E:ON and /V:ON options are not enabled in the batch script interpreter # See: http://stackoverflow.com/a/13751649/163740 CMD_IN_ENV: "cmd /E:ON /V:ON /C obvci_appveyor_python_build_env.cmd" # Workaround for https://github.com/conda/conda-build/issues/636 PYTHONIOENCODING: "UTF-8" matrix: # Note: Because we have to separate the py2 and py3 components due to compiler version, we have a race condition for non-python packages. # Not sure how to resolve this, but maybe we should be tracking the VS version in the build string anyway? - TARGET_ARCH: x64 CONDA_NPY: 118 PYTHON_VERSION: 3.8 CONDA_INSTALL_LOCN: C:\\Miniconda38-x64 # We always use a 64-bit machine, but can build x86 distributions # with the TARGET_ARCH variable (which is used by CMD_IN_ENV). platform: - x64 init: - "ECHO %PYTHON_VERSION% %CONDA_INSTALL_LOCN%" - "ECHO %PYTHON% %PYTHON_VERSION% %PYTHON_ARCH%" - "ECHO \"%APPVEYOR_SCHEDULED_BUILD%\"" # cancel build if newer one is submitted; complicated # details for getting this to work are credited to JuliaLang # developers - ps: if ($env:APPVEYOR_PULL_REQUEST_NUMBER -and $env:APPVEYOR_BUILD_NUMBER -ne ((Invoke-RestMethod ` https://ci.appveyor.com/api/projects/$env:APPVEYOR_ACCOUNT_NAME/$env:APPVEYOR_PROJECT_SLUG/history?recordsNumber=50).builds | ` Where-Object pullRequestId -eq $env:APPVEYOR_PULL_REQUEST_NUMBER)[0].buildNumber) { ` raise "There are newer queued builds for this pull request, skipping build." } install: - "set PATH=%CONDA_INSTALL_LOCN%;%CONDA_INSTALL_LOCN%\\Scripts;%PATH%" # Set the CONDA_NPY, although it has no impact on the actual build. We need this because of a test within conda-build. - cmd: set CONDA_NPY=19 # Remove cygwin (and therefore the git that comes with it). - cmd: rmdir C:\cygwin /s /q # Add path, activate `conda` and update conda. - conda config --set always_yes yes --set changeps1 no # - cmd: call %CONDA_INSTALL_LOCN%\Scripts\activate.bat - cmd: set PYTHONUNBUFFERED=1 # Add our channels. - conda config --set show_channel_urls true - conda update --yes --quiet conda # create conda environment - conda create --yes -n test python=%PYTHON_VERSION% - conda info --envs # Configure the VM. - activate test - conda install --yes --quiet -c conda-forge numpy scipy h5py cython traitlets ipywidgets xlrd pandas pytest pyqt - pip install corner uncertainties matplotlib IPython pytest-qt periodictable pyqt6 attrs # - pip install git+https://github.com/pymc-devs/pymc3 build_script: # Build the compiled extension - pip install -e . test_script: # Run the project tests - cmd: pytest after_test: # If tests are successful, create binary packages for the project. - python setup.py bdist_wheel - ps: "ls dist" artifacts: # Archive the generated packages in the ci.appveyor.com build report. - path: dist\*.whlrefnx-0.1.53/azure-pipelines.yml.bak000066400000000000000000000033071477046072400173330ustar00rootroot00000000000000# Python package # Create and test a Python package on multiple Python versions. # Add steps that analyze code, save the dist with the build record, publish to a PyPI-compatible index, and more: # https://docs.microsoft.com/azure/devops/pipelines/languages/python jobs: - job: 'Test' pool: vmImage: 'windows-2022' strategy: matrix: Python310: python.version: '3.10' maxParallel: 4 steps: - task: UsePythonVersion@0 inputs: versionSpec: '$(python.version)' architecture: 'x64' - script: | python -m pip install --upgrade pip python -m pip install wheel pip install numpy scipy orsopy cython traitlets ipython ipywidgets pandas h5py xlrd pytest tqdm corner uncertainties matplotlib pyqt6 pytest-qt periodictable attrs # - script: pip install git+https://github.com/pymc-devs/pymc3 displayName: 'Install dependencies' - script: | python setup.py bdist_wheel displayName: 'make wheel' - script: | cd dist pip install --only-binary=refnx --no-index --find-links=. refnx pip install pytest pytest --pyargs refnx cd .. displayName: 'pytest' - script: | pip uninstall -y pandas h5py xlrd pytest pytest-qt # more recent versions of setuptools don't work with PyInstaller pip install setuptools==44 pyinstaller pyinstaller tools/app/motofit.spec displayName: 'Frozen refnx GUI' condition: in(variables['python.version'], '3.10') - task: CopyFiles@2 inputs: contents: dist/** targetFolder: $(Build.ArtifactStagingDirectory) - task: PublishBuildArtifacts@1 inputs: pathtoPublish: $(Build.ArtifactStagingDirectory) artifactName: refnx_wheels refnx-0.1.53/benchmarks/000077500000000000000000000000001477046072400150525ustar00rootroot00000000000000refnx-0.1.53/benchmarks/Benchmark.ipynb000066400000000000000000000236621477046072400200200ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "9cb3497a-ef29-4b3f-a230-bcee166acad9", "metadata": {}, "outputs": [], "source": [ "import psutil\n", "import platform\n", "import os.path\n", "import numpy as np\n", "import pickle\n", "from multiprocessing import Pool\n", "import refnx\n", "from refnx.analysis import CurveFitter, Objective, Parameter, process_chain\n", "import refnx.reflect\n", "\n", "from refnx.reflect import (\n", " SLD,\n", " Slab,\n", " Structure,\n", " ReflectModel,\n", " reflectivity,\n", " use_reflect_backend,\n", " available_backends,\n", ")\n", "from refnx.dataset import ReflectDataset as RD" ] }, { "cell_type": "code", "execution_count": 2, "id": "e822533f-099e-4299-8c00-552ed3b19395", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python version: 3.12.0\n", "numpy version: 1.26.3\n", "refnx version: 0.1.43.dev0+74a19fd\n", "\n", "System: Darwin\n", "Release: 23.2.0\n", "Version: Darwin Kernel Version 23.2.0: Wed Nov 15 21:54:55 PST 2023; root:xnu-10002.61.3~2/RELEASE_ARM64_T8122\n", "Machine: arm64\n", "Processor: arm\n", "Physical cores: 8\n", "Total cores: 8\n", "Total: 16.00GB\n", "Available: 6.69GB\n" ] } ], "source": [ "def get_size(bytes, suffix=\"B\"):\n", " \"\"\"\n", " Scale bytes to its proper format\n", " e.g:\n", " 1253656 => '1.20MB'\n", " 1253656678 => '1.17GB'\n", " \"\"\"\n", " factor = 1024\n", " for unit in [\"\", \"K\", \"M\", \"G\", \"T\", \"P\"]:\n", " if bytes < factor:\n", " return f\"{bytes:.2f}{unit}{suffix}\"\n", " bytes /= factor\n", "\n", "\n", "uname = platform.uname()\n", "print(f\"Python version: {platform.python_version()}\")\n", "print(f\"numpy version: {np.version.version}\")\n", "print(f\"refnx version: {refnx.version.version}\")\n", "print()\n", "print(f\"System: {uname.system}\")\n", "print(f\"Release: {uname.release}\")\n", "print(f\"Version: {uname.version}\")\n", "print(f\"Machine: {uname.machine}\")\n", "print(f\"Processor: {uname.processor}\")\n", "\n", "# number of cores\n", "print(\"Physical cores:\", psutil.cpu_count(logical=False))\n", "print(\"Total cores:\", psutil.cpu_count(logical=True))\n", "\n", "svmem = psutil.virtual_memory()\n", "print(f\"Total: {get_size(svmem.total)}\")\n", "print(f\"Available: {get_size(svmem.available)}\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "46403961-87aa-49dc-b204-c5db9287a6d8", "metadata": {}, "outputs": [], "source": [ "q = np.linspace(0.005, 0.5, 2000)\n", "layers = np.array(\n", " [\n", " [0, 2.07, 0, 3],\n", " [50, 3.47, 0.0001, 4],\n", " [200, -0.5, 1e-5, 5],\n", " [50, 1, 0, 3],\n", " [0, 6.36, 0, 3],\n", " ]\n", ")" ] }, { "cell_type": "markdown", "id": "9c2c0c48-1b77-44a7-acaf-62e1e1e0b73c", "metadata": {}, "source": [ "## test reflectometry backend speed\n", "### Threaded calculation" ] }, { "cell_type": "code", "execution_count": 4, "id": "bdafc001-a6a3-48fb-8611-738c7b9ede8a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "backend='python'\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/andrew/Documents/Andy/programming/refnx/refnx/reflect/reflect_model.py:254: UserWarning: Using the SLOW reflectivity calculation.\n", " warnings.warn(\"Using the SLOW reflectivity calculation.\")\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "383 µs ± 619 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "backend='c'\n", "102 µs ± 352 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n", "backend='c_parratt'\n", "95 µs ± 153 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n", "backend='py_parratt'\n", "331 µs ± 1.18 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" ] } ], "source": [ "for backend in available_backends():\n", " print(f\"{backend=}\")\n", " with use_reflect_backend(backend) as f:\n", " %timeit f(q, layers)" ] }, { "cell_type": "markdown", "id": "a33682c3-f904-437d-965e-4f15fc8bcb74", "metadata": {}, "source": [ "### Unthreaded calculation" ] }, { "cell_type": "code", "execution_count": 5, "id": "38759c56-ad52-4d4a-94db-56833bf1e81b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "backend='python'\n", "382 µs ± 155 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "backend='c'\n", "215 µs ± 451 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", "backend='c_parratt'\n", "188 µs ± 310 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n", "backend='py_parratt'\n", "331 µs ± 1.23 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" ] } ], "source": [ "for backend in available_backends():\n", " print(f\"{backend=}\")\n", " with use_reflect_backend(backend) as f:\n", " %timeit f(q, layers, threads=1)" ] }, { "cell_type": "markdown", "id": "f6d5db6c-1968-413e-94b4-6597c5beb476", "metadata": {}, "source": [ "## Test resolution smearing speed\n", "### Constant dq/q" ] }, { "cell_type": "code", "execution_count": 6, "id": "faa39f6e-c6ef-441f-b8a7-bbfbec9ce585", "metadata": {}, "outputs": [], "source": [ "q = np.geomspace(0.005, 0.5, 200)" ] }, { "cell_type": "code", "execution_count": 7, "id": "59edacf3-c09f-450d-9864-8096014e541f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "227 µs ± 283 ns per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" ] } ], "source": [ "%timeit reflectivity(q, layers)" ] }, { "cell_type": "markdown", "id": "6f8fe052-772f-4d52-a4bc-2c29223c462f", "metadata": {}, "source": [ "### Pointwise dq/q" ] }, { "cell_type": "code", "execution_count": 8, "id": "a1282205-874c-4b7a-b61c-5d7a5ce756ac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "171 µs ± 158 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" ] } ], "source": [ "dq = 0.05 * q\n", "%timeit reflectivity(q, layers, dq=dq)" ] }, { "cell_type": "markdown", "id": "96a9240b-97cf-47c2-8231-d5939b266155", "metadata": {}, "source": [ "## Test sampling speed" ] }, { "cell_type": "code", "execution_count": 9, "id": "05614277-4f2b-434a-9b69-7e24e3bbb3c7", "metadata": {}, "outputs": [], "source": [ "pth = os.path.dirname(os.path.abspath(refnx.reflect.__file__))\n", "e361 = RD(os.path.join(pth, \"test\", \"e361r.txt\"))\n", "\n", "sio2 = SLD(3.47, name=\"SiO2\")\n", "si = SLD(2.07, name=\"Si\")\n", "d2o = SLD(6.36, name=\"D2O\")\n", "polymer = SLD(1, name=\"polymer\")\n", "\n", "# e361 is an older dataset, but well characterised\n", "structure361 = si | sio2(10, 4) | polymer(200, 3) | d2o(0, 3)\n", "model361 = ReflectModel(structure361, bkg=2e-5)\n", "\n", "model361.scale.vary = True\n", "model361.bkg.vary = True\n", "model361.scale.range(0.1, 2)\n", "model361.bkg.range(0, 5e-5)\n", "model361.dq = 5.0\n", "\n", "# d2o\n", "structure361[-1].sld.real.vary = True\n", "structure361[-1].sld.real.range(6, 6.36)\n", "\n", "p = structure361[1].thick\n", "structure361[1].thick.vary = True\n", "structure361[1].thick.range(5, 20)\n", "structure361[2].thick.vary = True\n", "structure361[2].thick.range(100, 220)\n", "\n", "structure361[2].sld.real.vary = True\n", "structure361[2].sld.real.range(0.2, 1.5)\n", "\n", "# e361.x_err = None\n", "np.random.seed(1)\n", "\n", "objective = Objective(model361, e361)\n", "fitter = CurveFitter(objective, nwalkers=200)\n", "fitter.initialise(\"jitter\")\n", "model361.threads = 1" ] }, { "cell_type": "code", "execution_count": 10, "id": "550365da-2ee3-43d4-a153-f4a795806b97", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.97 s ± 38.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit fitter.sample(100, pool=-1, verbose=False)" ] }, { "cell_type": "code", "execution_count": 11, "id": "ef4976b5-67fd-48a8-961d-86f386cf5bf3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "16.3 ms ± 11.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%timeit process_chain(objective, fitter.chain);" ] }, { "cell_type": "code", "execution_count": null, "id": "997611ae-8c7c-4f8e-a67d-715b7ea3ba80", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.0" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.53/benchmarks/__init__.py000066400000000000000000000001121477046072400171550ustar00rootroot00000000000000 import numpy as np import random np.random.seed(1234) random.seed(1234) refnx-0.1.53/benchmarks/asv.conf.json000066400000000000000000000133211477046072400174620ustar00rootroot00000000000000{ // The version of the config file format. Do not change, unless // you know what you are doing. "version": 1, // The name of the project being benchmarked "project": "refnx", // The project's homepage "project_url": "https://github.com/refnx/refnx/", // The URL or local path of the source code repository for the // project being benchmarked "repo": "..", // List of branches to benchmark. If not provided, defaults to "master" // (for git) or "default" (for mercurial). "branches": ["main"], // for git // The DVCS being used. If not set, it will be automatically // determined from "repo" by looking at the protocol in the URL // (if remote), or by looking for special directories, such as // ".git" (if local). "dvcs": "git", // The tool to use to create environments. May be "conda", // "virtualenv" or other value depending on the plugins in use. // If missing or the empty string, the tool will be automatically // determined by looking for tools on the PATH environment // variable. "environment_type": "conda", // timeout in seconds for installing any dependencies in environment // defaults to 10 min //"install_timeout": 600, // the base URL to show a commit for the project. "show_commit_url": "https://github.com/refnx/refnx/commit/", // The Pythons you'd like to test against. If not provided, defaults // to the current version of Python used to run `asv`. // "pythons": ["2.7", "3.3"], // The matrix of dependencies to test. Each key is the name of a // package (in PyPI) and the values are version numbers. An empty // list or empty string indicates to just test against the default // (latest) version. null indicates that the package is to not be // installed. If the package to be tested is only available from // PyPi, and the 'environment_type' is conda, then you can preface // the package name by 'pip+', and the package will be installed via // pip (with all the conda available packages installed first, // followed by the pip installed packages). // "matrix": { "numpy": [], "Cython": [], "attrs": [], "scipy": [], "tqdm": [], }, // Combinations of libraries/python versions can be excluded/included // from the set to test. Each entry is a dictionary containing additional // key-value pairs to include/exclude. // // An exclude entry excludes entries where all values match. The // values are regexps that should match the whole string. // // An include entry adds an environment. Only the packages listed // are installed. The 'python' key is required. The exclude rules // do not apply to includes. // // In addition to package names, the following keys are available: // // - python // Python version, as in the *pythons* variable above. // - environment_type // Environment type, as above. // - sys_platform // Platform, as in sys.platform. Possible values for the common // cases: 'linux2', 'win32', 'cygwin', 'darwin'. // // "exclude": [ // {"python": "3.2", "sys_platform": "win32"}, // skip py3.2 on windows // {"environment_type": "conda", "six": null}, // don't run without six on conda // ], // // "include": [ // // additional env for python2.7 // {"python": "2.7", "numpy": "1.8"}, // // additional env if run on windows+conda // {"platform": "win32", "environment_type": "conda", "python": "2.7", "libpython": ""}, // ], // The directory (relative to the current directory) that benchmarks are // stored in. If not provided, defaults to "benchmarks" "benchmark_dir": "benchmarks", // The directory (relative to the current directory) to cache the Python // environments in. If not provided, defaults to "env" "env_dir": ".asv/env", // The directory (relative to the current directory) that raw benchmark // results are stored in. If not provided, defaults to "results". "results_dir": ".asv/results", // The directory (relative to the current directory) that the html tree // should be written to. If not provided, defaults to "html". "html_dir": ".asv/html", // The number of characters to retain in the commit hashes. // "hash_length": 8, // `asv` will cache wheels of the recent builds in each // environment, making them faster to install next time. This is // number of builds to keep, per environment. "build_cache_size": 5 // The commits after which the regression search in `asv publish` // should start looking for regressions. Dictionary whose keys are // regexps matching to benchmark names, and values corresponding to // the commit (exclusive) after which to start looking for // regressions. The default is to start from the first commit // with results. If the commit is `null`, regression detection is // skipped for the matching benchmark. // // "regressions_first_commits": { // "some_benchmark": "352cdf", // Consider regressions only after this commit // "another_benchmark": null, // Skip regression detection altogether // } // The thresholds for relative change in results, after which `asv // publish` starts reporting regressions. Dictionary of the same // form as in ``regressions_first_commits``, with values // indicating the thresholds. If multiple entries match, the // maximum is taken. If no entry matches, the default is 5%. // // "regressions_thresholds": { // "some_benchmark": 0.01, // Threshold of 1% // "another_benchmark": 0.5, // Threshold of 50% // } } refnx-0.1.53/benchmarks/benchmarks/000077500000000000000000000000001477046072400171675ustar00rootroot00000000000000refnx-0.1.53/benchmarks/benchmarks/__init__.py000066400000000000000000000000001477046072400212660ustar00rootroot00000000000000refnx-0.1.53/benchmarks/benchmarks/analysis.py000066400000000000000000000032561477046072400213720ustar00rootroot00000000000000import os.path import numpy as np from .common import Benchmark from refnx.analysis import CurveFitter, Objective, Parameter, Model from refnx.dataset import Data1D def line(x, params, *args, **kwds): p_arr = np.array(params) return p_arr[0] + x * p_arr[1] class curvefitter(Benchmark): repeat = 3 def setup(self): # Reproducible results! np.random.seed(123) m_true = -0.9594 b_true = 4.294 f_true = 0.534 m_ls = -1.1040757010910947 b_ls = 5.4405552502319505 # Generate some synthetic data from the model. N = 50 x = np.sort(10 * np.random.rand(N)) y_err = 0.1 + 0.5 * np.random.rand(N) y = m_true * x + b_true y += np.abs(f_true * y) * np.random.randn(N) y += y_err * np.random.randn(N) data = Data1D(data=(x, y, y_err)) p = Parameter(b_ls, 'b', vary=True, bounds=(-100, 100)) p |= Parameter(m_ls, 'm', vary=True, bounds=(-100, 100)) model = Model(p, fitfunc=line) self.objective = Objective(model, data) self.mcfitter = CurveFitter(self.objective) self.mcfitter_t = CurveFitter(self.objective, ntemps=20) self.mcfitter.initialise('prior') self.mcfitter_t.initialise('prior') def time_sampler(self): # to get an idea of how fast the actual sampling is. # i.e. the overhead of objective.lnprob, objective.lnprior, etc self.mcfitter.sampler.run_mcmc(self.mcfitter._state, 100) def time_sampler_pool(self): # see how the multiprocessing in curvefitter performs # automatically use all the cores available self.mcfitter_t.sample(20, pool=-1) refnx-0.1.53/benchmarks/benchmarks/common.py000066400000000000000000000044201477046072400210310ustar00rootroot00000000000000""" Airspeed Velocity benchmark utilities """ import sys import re import time import textwrap import subprocess class Benchmark(object): """ Base class with sensible options """ pass def run_monitored(code): """ Run code in a new Python process, and monitor peak memory usage. Returns ------- duration : float Duration in seconds (including Python startup time) peak_memusage : float Peak memory usage (rough estimate only) in bytes """ if not sys.platform.startswith('linux'): raise RuntimeError("Peak memory monitoring only works on Linux") code = textwrap.dedent(code) process = subprocess.Popen([sys.executable, '-c', code]) peak_memusage = -1 start = time.time() while True: ret = process.poll() if ret is not None: break with open('/proc/%d/status' % process.pid, 'r') as f: procdata = f.read() m = re.search('VmRSS:\s*(\d+)\s*kB', procdata, re.S | re.I) if m is not None: memusage = float(m.group(1)) * 1e3 peak_memusage = max(memusage, peak_memusage) time.sleep(0.01) process.wait() duration = time.time() - start if process.returncode != 0: raise AssertionError("Running failed:\n%s" % code) return duration, peak_memusage def get_mem_info(): """Get information about available memory""" if not sys.platform.startswith('linux'): raise RuntimeError("Memory information implemented only for Linux") info = {} with open('/proc/meminfo', 'r') as f: for line in f: p = line.split() info[p[0].strip(':').lower()] = float(p[1]) * 1e3 return info def set_mem_rlimit(max_mem=None): """ Set address space rlimit """ import resource if max_mem is None: mem_info = get_mem_info() max_mem = int(mem_info['memtotal'] * 0.7) cur_limit = resource.getrlimit(resource.RLIMIT_AS) if cur_limit[0] > 0: max_mem = min(max_mem, cur_limit[0]) resource.setrlimit(resource.RLIMIT_AS, (max_mem, cur_limit[1])) def with_attributes(**attrs): def decorator(func): for key, value in attrs.items(): setattr(func, key, value) return func return decorator refnx-0.1.53/benchmarks/benchmarks/reflect.py000066400000000000000000000065301477046072400211710ustar00rootroot00000000000000import os.path import numpy as np import pickle from .common import Benchmark from refnx.analysis import CurveFitter, Objective, Parameter import refnx.reflect from refnx.reflect._creflect import abeles as c_abeles from refnx.reflect._reflect import abeles from refnx.reflect import SLD, Slab, Structure, ReflectModel, reflectivity from refnx.dataset import ReflectDataset as RD class Abeles(Benchmark): def setup(self): self.q = np.linspace(0.005, 0.5, 50000) self.layers = np.array([[0, 2.07, 0, 3], [50, 3.47, 0.0001, 4], [200, -0.5, 1e-5, 5], [50, 1, 0, 3], [0, 6.36, 0, 3]]) self.repeat = 20 self.number = 10 def time_cabeles(self): c_abeles(self.q, self.layers) def time_abeles(self): abeles(self.q, self.layers) def time_reflectivity_constant_dq_q(self): reflectivity(self.q, self.layers) def time_reflectivity_pointwise_dq(self): reflectivity(self.q, self.layers, dq=0.05 * self.q) class Reflect(Benchmark): timeout = 120. # repeat = 2 def setup(self): pth = os.path.dirname(os.path.abspath(refnx.reflect.__file__)) e361 = RD(os.path.join(pth, 'test', 'e361r.txt')) sio2 = SLD(3.47, name='SiO2') si = SLD(2.07, name='Si') d2o = SLD(6.36, name='D2O') polymer = SLD(1, name='polymer') # e361 is an older dataset, but well characterised structure361 = si | sio2(10, 4) | polymer(200, 3) | d2o(0, 3) model361 = ReflectModel(structure361, bkg=2e-5) model361.scale.vary = True model361.bkg.vary = True model361.scale.range(0.1, 2) model361.bkg.range(0, 5e-5) model361.dq = 5. # d2o structure361[-1].sld.real.vary = True structure361[-1].sld.real.range(6, 6.36) self.p = structure361[1].thick structure361[1].thick.vary = True structure361[1].thick.range(5, 20) structure361[2].thick.vary = True structure361[2].thick.range(100, 220) structure361[2].sld.real.vary = True structure361[2].sld.real.range(0.2, 1.5) self.structure361 = structure361 self.model361 = model361 # e361.x_err = None self.objective = Objective(self.model361, e361) self.fitter = CurveFitter(self.objective, nwalkers=200) self.fitter.initialise('jitter') def time_reflect_emcee(self): # test how fast the emcee sampler runs in serial mode self.fitter.sampler.run_mcmc(self.fitter._state, 30) def time_reflect_sampling_parallel(self): # discrepancies in different runs may be because of different numbers # of processors self.model361.threads = 1 self.fitter.sample(30, pool=-1) def time_pickle_objective(self): # time taken to pickle an objective s = pickle.dumps(self.objective) pickle.loads(s) def time_pickle_model(self): # time taken to pickle a model s = pickle.dumps(self.model361) pickle.loads(s) def time_pickle_model(self): # time taken to pickle a parameter s = pickle.dumps(self.p) pickle.loads(s) def time_structure_slabs(self): self.structure361.slabs() refnx-0.1.53/benchmarks/run.py000066400000000000000000000034741477046072400162400ustar00rootroot00000000000000#!/usr/bin/env python """ run.py [options] ASV_COMMAND.. Convenience wrapper around the ``asv`` command; just sets environment variables and chdirs to the correct place etc. """ import os import sys import subprocess import json import shutil import argparse import sysconfig import errno EXTRA_PATH = [''] def main(): class ASVHelpAction(argparse.Action): nargs = 0 def __call__(self, parser, namespace, values, option_string=None): sys.exit(run_asv(['--help'])) p = argparse.ArgumentParser(usage=__doc__.strip()) p.add_argument('--help-asv', nargs=0, action=ASVHelpAction, help="""show ASV help""") p.add_argument('asv_command', nargs=argparse.REMAINDER) args = p.parse_args() sys.exit(run_asv(args.asv_command)) def run_asv(args): cwd = os.path.abspath(os.path.dirname(__file__)) repo_dir = os.path.join(cwd, 'refnx') cmd = ['asv'] + list(args) env = dict(os.environ) # Inject ccache/f90cache paths if sys.platform.startswith('linux'): env['PATH'] = os.pathsep.join(EXTRA_PATH + env.get('PATH', '').split(os.pathsep)) # Check refnx version if in dev mode; otherwise clone and setup results # repository if args and (args[0] == 'dev' or '--python=same' in args): import refnx print("Running benchmarks for refnx version %s at %s" % (refnx.__version__, refnx.__file__)) # Run try: return subprocess.call(cmd, env=env, cwd=cwd) except OSError as err: if err.errno == errno.ENOENT: print("Error when running '%s': %s\n" % (" ".join(cmd), str(err),)) print("You need to install Airspeed Velocity https://spacetelescope.github.io/asv/") print("to run refnx benchmarks") return 1 raise if __name__ == "__main__": sys.exit(main()) refnx-0.1.53/doc/000077500000000000000000000000001477046072400135025ustar00rootroot00000000000000refnx-0.1.53/doc/Makefile000066400000000000000000000163551477046072400151540ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. 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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 clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext 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 " 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)" clean: rm -rf $(BUILDDIR)/* html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. 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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." devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/refnx" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/refnx" @echo "# devhelp" epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. 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The message catalogs are in $(BUILDDIR)/locale." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." 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." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." coverage: $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage @echo "Testing of coverage in the sources finished, look at the " \ "results in $(BUILDDIR)/coverage/python.txt." xml: $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml @echo @echo "Build finished. The XML files are in $(BUILDDIR)/xml." pseudoxml: $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml @echo @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." refnx-0.1.53/doc/NSF2.ipynb000066400000000000000000001337171477046072400152710ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "4879c2ee-e89d-42db-974e-a9572851ef20", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Analysing non-spin flip data (mark2)\n", "`refnx` recently added the ability to perform polarised neutron reflectometry analysis in v0.1.51. Here we analyse datasets that uses magnetic films and PNR as an extra contrast. The datasets of interest have the structure:\n", "\n", "`Si | SiO2 | Permalloy | Au | 2-mercaptoethanol | D2O`" ] }, { "cell_type": "code", "execution_count": 1, "id": "e30f7cbb-3f89-4c85-98eb-88a9d63af161", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# some necessary imports\n", "from importlib import resources\n", "\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import refnx\n", "from refnx.analysis import Parameter, Objective, CurveFitter, GlobalObjective\n", "from refnx.reflect import (\n", " SLD,\n", " Slab,\n", " Structure,\n", " MagneticSlab,\n", " PolarisedReflectModel,\n", " SpinChannel,\n", ")\n", "from refnx.dataset import Data1D" ] }, { "cell_type": "code", "execution_count": 2, "id": "bbed1272-9a1b-4c3c-8b13-4af3cc79ad55", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# create datasets from the NSF PNR data\n", "with resources.path(refnx.reflect) as pth:\n", " dd = \"c_PLP0007882.dat\"\n", " uu = \"c_PLP0007885.dat\"\n", "\n", " file_path_uu = pth / f\"tests/{uu}\"\n", " file_path_dd = pth / f\"tests/{dd}\"\n", "\n", "data_uu = Data1D(file_path_uu)\n", "data_dd = Data1D(file_path_dd)" ] }, { "cell_type": "code", "execution_count": 3, "id": "42b8e1c0-fb82-4464-bc83-1361154085ac", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# create SLD (Scattering Length Density) objects for each of the materials\n", "si = SLD(2.07, name=\"Si\")\n", "sio2 = SLD(3.47, name=\"SiO2\")\n", "au = SLD(4.66, name=\"Au\")\n", "mercapto = SLD(3.49, name=\"2-mercaptoethanol\")\n", "d2o = SLD(6.35, name=\"d2o\")\n", "\n", "py = SLD(9.0, name=\"permalloy\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "0f72701b-509e-4cb0-a1a9-b72895a6ad5e", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# Now make Slabs that describe each layer. These can either be made from SLD objects,\n", "# or by using the `Slab` constructor directly.\n", "\n", "# sio2 slab has a thickness of 20 and roughness of 4 with the Si fronting medium\n", "sio2_l = sio2(20, 4)\n", "\n", "au_l = au(215, 4)\n", "mercapto_l = mercapto(8, 4)\n", "d2o_l = d2o(0, 4)" ] }, { "cell_type": "markdown", "id": "41824d88-3b7c-469f-808d-f714f8fad19f", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now let's make the Permalloy layer. To do this we need to utilise a `MagneticSlab` Component. The value of 1.75 represents a magnetic SLD correction of $1.75\\times 10^{-6}\\\\A^{-2}$. The value of `thetaM=90` (degrees) represents the angle of the magnetic moment in the plane of the sample. For the applied magnetic field to be the plane of the sample `Aguide=270` or `90`. For the magnetic moment to be parallel or antiparallel to the applied field `thetaM=90` or `270` degrees respectively." ] }, { "cell_type": "code", "execution_count": 5, "id": "b3b7ac26-f168-463f-aa78-9ac41b8e54fd", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# now make the Py layer\n", "py_thickness = Parameter(50, name=\"Py thickness\")\n", "py_roughness = Parameter(5, name=\"Py roughness\")\n", "\n", "\n", "py_l = MagneticSlab(py_thickness, py, py_roughness, 1.75, 90.0, name=\"Py slab\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "e59f21af-9882-4477-bf22-dd2099ea5df8", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "s = si | sio2_l | py_l | au_l | mercapto_l | d2o_l" ] }, { "cell_type": "code", "execution_count": 7, "id": "2866af82-074d-4e97-be9b-0290597ad1eb", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# Note that we're using the same structure to describe both spin channels.\n", "model_dd = PolarisedReflectModel(s, spin=SpinChannel.DOWN_DOWN, Aguide=270)\n", "model_uu = PolarisedReflectModel(s, spin=SpinChannel.UP_UP, Aguide=270)" ] }, { "cell_type": "code", "execution_count": 8, "id": "c9d39562-d2e2-4d85-ac8c-86d26047ca7c", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "objective_dd = Objective(model_dd, data_dd)\n", "objective_uu = Objective(model_uu, data_uu)\n", "\n", "global_objective = GlobalObjective([objective_dd, objective_uu])" ] }, { "cell_type": "code", "execution_count": 9, "id": "a50782f1-4a2f-4db5-b059-d10002c159cc", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# select the parameters to be fitted and their bounds\n", "model_uu.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model_uu.bkg.setp(vary=True, bounds=(1e-7, 5e-6))\n", "model_dd.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model_dd.bkg.setp(vary=True, bounds=(1e-7, 5e-6))\n", "\n", "sio2_l.thick.setp(vary=True, bounds=(10, 25))\n", "sio2_l.rough.setp(vary=True, bounds=(1, 8))\n", "\n", "py_thickness.setp(vary=True, bounds=(38, 55))\n", "py_roughness.setp(vary=True, bounds=(1, 8))\n", "py.real.setp(vary=True, bounds=(9, 9.5))\n", "py_l.rhoM.setp(vary=True, bounds=(1.5, 3.0))\n", "\n", "au_l.thick.setp(vary=True, bounds=(200, 240))\n", "au_l.rough.setp(vary=True, bounds=(1, 8))\n", "au.real.setp(vary=True, bounds=(4.5, 4.66))\n", "\n", "mercapto_l.thick.setp(vary=True, bounds=(5, 15))\n", "mercapto_l.rough.setp(vary=True, bounds=(1, 8))\n", "mercapto.real.setp(vary=True, bounds=(3, 4))\n", "\n", "d2o_l.rough.setp(vary=True, bounds=(1, 8))\n", "d2o.real.setp(vary=True, bounds=(6.2, 6.36))" ] }, { "cell_type": "code", "execution_count": 10, "id": "30855431-e791-43a8-84ac-b68940b67587", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-1271.4413157228305: : 62it [01:21, 1.31s/it]\n" ] } ], "source": [ "fitter = CurveFitter(global_objective)\n", "fitter.fit(\"differential_evolution\", seed=1);" ] }, { "cell_type": "code", "execution_count": 11, "id": "eb94add9-fa9b-42d9-9734-15e1ceb75879", "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "plt.scatter(data_dd.x, data_dd.y, label=\"dd\", s=4)\n", "plt.plot(data_dd.x, objective_dd.generative())\n", "\n", "plt.scatter(data_uu.x, data_uu.y, label=\"uu\", s=4)\n", "plt.plot(data_uu.x, objective_uu.generative())\n", "\n", "plt.ylabel(\"R\")\n", "plt.xlabel(\"Q / $\\\\AA^{-1}$\")\n", "plt.yscale(\"log\")\n", "plt.xscale(\"log\")\n", "plt.legend();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.53/doc/_images/000077500000000000000000000000001477046072400151065ustar00rootroot00000000000000refnx-0.1.53/doc/_images/gui.png000066400000000000000000005011171477046072400164050ustar00rootroot00000000000000PNG  IHDRO iCCPICC ProfileHTY7Z )7Az^C@B 1!Gp,"蠈cDlbaPl'(`ATT8{v͹~{9/A:,@?KI`@TdaaA}$;fSjlp[@$Ya<$%+ nLD{!<@ڋJA[<>SL6[9m%Nj&Ik2)R9˴y"A:sy2ų{"N Ñ'm2O e{:bYfgM d/CZ'9pi}'b{Ӣnea<LDq~ĸ3?; ܑ@(q`1`.BV`'(U`?8 n{1` D*CB>PAP ćJhT @P5T .Bנ! 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[10.1103/PhysRevB.58.R13419](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.58.R13419) for further details" ] }, { "cell_type": "code", "execution_count": 124, "id": "9e92ec6d-a347-4600-81a2-0528e19e9a24", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from refnx.analysis import Parameter\n", "from refnx.reflect import SLD, Slab, FunctionalForm, ReflectModel" ] }, { "cell_type": "markdown", "id": "b088cb09-7e7b-4afa-860c-6f1c4064ac11", "metadata": {}, "source": [ "`FunctionalForm` requires a callable of signature `profile(z, extent, left_sld, right_sld, **kwds)`. `kwds` is used to supply parameters describing the shape of the profile. `left_sld`, `right_sld` provide the SLDs of the structure to the left and right of the Component. `extent` is the total width of the Component. `z` is an array provided to `profile`, a list of distances at which the function needs to return a (possibly complex) SLD. `profile` needs to return a tuple `(sld, vfsolv)`, where `sld` is an array of the same shape as `z`, and `vfsolv` is the volume fraction of solvent at each point in z. If `sld` already incorporates a solvent contribution, then return `(sld, None)`.\n", "\n", "For further details see [FunctionalForm](https://refnx.readthedocs.io/en/latest/refnx.reflect.html#refnx.reflect.FunctionalForm). " ] }, { "cell_type": "code", "execution_count": 119, "id": "a8eea8c4-1f56-4d92-b69e-075f89346c56", "metadata": {}, "outputs": [], "source": [ "def rho(z, extent, left_sld, right_sld, d=2.72, sigma_t=1.0, sigma_bar=0.46, offset=5):\n", " # d, sigma_t, sigma_bar are parameters that describe the shape of the profile\n", " def term(n):\n", " sigma_n = n * sigma_bar**2 + sigma_t**2\n", " prefactor = d / sigma_n / np.sqrt(2 * np.pi)\n", " return prefactor * np.exp(-0.5 * ((z - offset - n * d) / sigma_n) ** 2)\n", "\n", " _rho = np.zeros_like(z)\n", " for i in range(0, 20):\n", " _rho += term(i)\n", " return left_sld + _rho * (right_sld - left_sld), None" ] }, { "cell_type": "code", "execution_count": 120, "id": "43d93801-086b-4d6a-b33d-573b3eca7328", "metadata": {}, "outputs": [], "source": [ "air = SLD(0)\n", "d2o = SLD(6.36)\n", "sigma_bar = Parameter(0.46, \"sigma_bar\")\n", "d = Parameter(2.72, \"d\")\n", "sigma_t = Parameter(1.0, \"sigma_t\")\n", "offset = Parameter(\n", " 5\n", ") # don't allow me to vary. It's used because rho needs to be evaluated at negative distances of z.\n", "\n", "f = FunctionalForm(\n", " 20,\n", " rho,\n", " microslab_max_thickness=0.1,\n", " sigma_bar=sigma_bar,\n", " d=d,\n", " sigma_t=sigma_t,\n", " offset=offset,\n", ")" ] }, { "cell_type": "code", "execution_count": 121, "id": "060cbd52-b37e-49b3-974f-16bc07ebcef0", "metadata": {}, "outputs": [], "source": [ "s = air | f | d2o(0, 0)" ] }, { "cell_type": "code", "execution_count": 122, "id": "fc33fb43-9f1f-46d1-8e01-2f476b144250", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s.plot();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.53/doc/conf.py000066400000000000000000000246401477046072400150070ustar00rootroot00000000000000#!/usr/bin/env python3 #!/usr/bin/env python3 # -*- coding: utf-8 -*- # # refnx documentation build configuration file, created by # sphinx-quickstart on Fri Oct 23 10:21:57 2015. # # 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 shlex import re # 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.intersphinx', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.autosummary', 'sphinx.ext.napoleon', 'myst_nb', 'jupyter_sphinx', 'sphinxcontrib.bibtex', 'sphinxcontrib.jquery', 'sphinx_rtd_theme', ] bibtex_bibfiles = ["../testimonials.bib"] # myst_nb settings jupyter_execute_notebooks = "off" myst_enable_extensions = [ "amsmath", "colon_fence", "deflist", "dollarmath", "html_image", ] # 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' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = 'refnx' copyright = '2015-2024, Andrew Nelson' author = 'Andrew Nelson' # 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. import refnx # The short X.Y version. version = "1.0" #re.sub(r'\.dev-.*$', r'.dev', refnx.__version__) # The full version, including alpha/beta/rc tags. release = "1.0"#refnx.__version__ intersphinx_mapping = {'py': ('https://docs.python.org/3', None), 'numpy': ('https://numpy.org/doc/stable/', None), 'scipy': ('https://docs.scipy.org/doc/scipy/', None), 'matplotlib': ('https://matplotlib.org/stable/', None), } extlinks = { 'scipydoc' : ('https://docs.scipy.org/doc/scipy/reference/generated/%s.html', ''), 'numpydoc' : ('https://docs.scipy.org/doc/numpy/reference/generated/numpy.%s.html', ''), } numpydoc_show_class_members = False # 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. exclude_patterns = ['_build', '**.ipynb_checkpoints'] # 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 = False # 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 # -- 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 = 'sphinx_rtd_theme' # 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 = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # 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 (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = '_images/scattering.png' # 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 '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # 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 = {} # 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' #html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value #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 = 'refnxdoc' # -- 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, 'refnx.tex', 'refnx Documentation', 'Andrew Nelson', '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, 'refnx', 'refnx 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, 'refnx', 'refnx Documentation', author, 'refnx', '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 # Example configuration for intersphinx: refer to the Python standard library. #intersphinx_mapping = {'https://docs.python.org/': None} # -------------------------- nbsphinx options-------------------------------- # time out when evaluating notebooks nbsphinx_timeout = 1800refnx-0.1.53/doc/emcee_pymc_dynesty.ipynb000066400000000000000000045002541477046072400204440ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Using different MC packages for Bayesian sampling" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "refnx can work with a variety of MC packages for inference. This notebook will demonstrate the use of various packages:\n", "\n", "- [emcee](https://emcee.readthedocs.io/en/stable/) (vendored into refnx), \n", "- [pymc](https://www.pymc.io/welcome.html)\n", "- [dynesty](https://dynesty.readthedocs.io/en/stable/)\n", "\n", "An excellent reference to see how to use a wide range of packages for statistical inference is [https://mattpitkin.github.io/samplers-demo/pages/samplers-samplers-everywhere](https://mattpitkin.github.io/samplers-demo/pages/samplers-samplers-everywhere)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "from importlib import resources\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy\n", "\n", "import refnx\n", "from refnx.dataset import ReflectDataset, Data1D\n", "from refnx.analysis import (\n", " Transform,\n", " CurveFitter,\n", " Objective,\n", " Model,\n", " Parameter,\n", " pymc_model,\n", " process_chain,\n", ")\n", "from refnx.reflect import SLD, Slab, ReflectModel\n", "\n", "import pymc as pm\n", "import dynesty\n", "import arviz as az" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "It's important to note down the versions of the software that you're using, in order for the analysis to be reproducible." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "refnx: 0.1.53.dev0+19c4b26\n", "scipy: 1.15.2\n", "numpy: 2.1.3\n" ] } ], "source": [ "print(\n", " f\"refnx: {refnx.version.version}\\n\"\n", " f\"scipy: {scipy.version.version}\\n\"\n", " f\"numpy: {np.version.version}\"\n", ")" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The dataset we're going to use as an example is distributed with every install. The following cell determines its location." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "with resources.path(refnx.analysis) as pth:\n", " DATASET_NAME = \"c_PLP0011859_q.txt\"\n", " file_path = pth / f\"tests/{DATASET_NAME}\"\n", "\n", "data = ReflectDataset(file_path)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### The Structure" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "si = SLD(2.07, name=\"Si\")\n", "sio2 = SLD(3.47, name=\"SiO2\")\n", "film = SLD(2.0, name=\"film\")\n", "d2o = SLD(6.36, name=\"d2o\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# first number is thickness, second number is roughness\n", "# a native oxide layer\n", "sio2_layer = sio2(30, 3)\n", "\n", "# the film of interest\n", "film_layer = film(250, 3)\n", "\n", "# layer for the solvent\n", "d2o_layer = d2o(0, 3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "sio2_layer.thick.setp(bounds=(15, 50), vary=True)\n", "sio2_layer.rough.setp(bounds=(1, 15), vary=True)\n", "\n", "film_layer.thick.setp(bounds=(200, 300), vary=True)\n", "film_layer.sld.real.setp(bounds=(0.1, 3), vary=True)\n", "film_layer.rough.setp(bounds=(1, 15), vary=True)\n", "\n", "d2o_layer.rough.setp(vary=True, bounds=(1, 15))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "structure = si | sio2_layer | film_layer | d2o_layer" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(sio2_layer.parameters)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### ReflectModel, Objective, Curvefitter" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "model = ReflectModel(structure, bkg=3e-6, dq=5.0)\n", "model.scale.setp(bounds=(0.6, 1.2), vary=True)\n", "model.bkg.setp(bounds=(1e-9, 9e-6), vary=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "objective = Objective(model, data, transform=Transform(\"logY\"))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-564.9911383231366: : 53it [00:02, 18.49it/s] \n" ] } ], "source": [ "fitter = CurveFitter(objective)\n", "fitter.fit(\"differential_evolution\", target=\"nlpost\");" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "objective.plot()\n", "plt.legend()\n", "plt.xlabel(\"Q\")\n", "plt.ylabel(\"logR\")\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### emcee" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now lets do a MCMC sampling of the curvefitting system. First we do sampling to burn-in the system. We'll also checkout the autocorrelation time of the system. We'll then discard the burn-in samples because the initial chain might not be representative of an equilibrated system (i.e. distributed around the mean with the correct covariance)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [01:52<00:00, 26.78it/s]\n" ] } ], "source": [ "fitter.sample(3000, pool=-1);" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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IK8JIl+7KSBtzRgAAiCvCSBeuMwIAgDUII11SnZ236fEHwwqFjcWtAQAgcRBGuqR0VUYkqiMAAMQTYaRL9xVYJeaNAAAQT4SRLna7LVIdaQsQRgAAiBfCyFFSnF1hhMoIAABxQxg5CpURAADijzBylO7KSCthBACAuCGMHCWFa40AABB3hJGjRIZpCCMAAMQNYeQoDNMAABB/hJGjUBkBACD+CCNH6a6MtFMZAQAgbggjR2GYBgCA+COMHIVhGgAA4o8wchSW9gIAEH+EkaMcGaYJWtwSAAASB2HkKEeGacIWtwQAgMRBGDlK5EZ5TGAFACBuCCNHSY3ctZdhGgAA4oUwchQ3d+0FACDuCCNHYc4IAADxRxg5SmSYhtU0AADEDWHkKG4uegYAQNwRRo7CahoAAOKPMHIULgcPAED8EUaO0j1npCNk1BFiEisAAPFAGDlK95wRifvTAAAQL4SRo7iS7LLZOv/NvBEAAOKDMHIUm82mVOaNAAAQV4SRY0RW1BBGAACIC8LIMbrnjbQyTAMAQFwQRo6RyrVGAACIK8LIMVKcSZKojAAAEC+EkWOkuzorIy1+7k8DAEA8EEaOkdZVGWkmjAAAEBeEkWOkubqHaQgjAADEA2HkGGldwzTNfuaMAAAQD4SRY0QqIwzTAAAQF4SRY3TPGWlhmAYAgLggjByjuzLCMA0AAPFBGDlG99JehmkAAIgPwsgxUlnaCwBAXJ1SGFmyZImKiorkdrtVXFystWvX9uu4ZcuWyWaz6cYbbzyVt42LdBdzRgAAiKeow8jy5ctVVlamRYsWaf369Zo6dapmz56tmpqaEx63a9cuffe739Wll156yo2Nh+5707QyZwQAgLiIOow89thjuv3223Xbbbdp0qRJWrp0qVJTU/Xss8/2eUwoFNItt9yihx56SGPGjDmtBsfakQmsVEYAAIiHqMJIIBDQunXrVFpaeuQF7HaVlpaqvLy8z+N+9KMfKTc3V1/72tdOvaVxEhmmIYwAABAXSdHsXFdXp1AoJK/X22O71+vV1q1bez1mzZo1euaZZ1RRUdHv9/H7/fL7/ZH/9vl80TTztKR2r6bpCCkcNrLbbXF7bwAAElFMV9M0NTXp1ltv1dNPP62cnJx+H7d48WJlZmZGHoWFhTFsZU/dlRFjpLYO5o0AABBrUVVGcnJy5HA4VF1d3WN7dXW18vLyjtt/x44d2rVrl+bMmRPZFg6HO984KUnbtm3T2LFjjztuwYIFKisri/y3z+eLWyBJSXbIZusMIy3+YGQOCQAAiI2oKiNOp1MzZszQqlWrItvC4bBWrVqlkpKS4/afOHGiNm3apIqKisjj85//vK688kpVVFT0GTBcLpc8Hk+PR7zYbLajLglPZQQAgFiL+s/+srIyzZ8/XzNnztSsWbP0+OOPq6WlRbfddpskad68eSooKNDixYvldrs1efLkHsdnZWVJ0nHbzyRpLoea/UEmsQIAEAdRh5G5c+eqtrZWCxcuVFVVlaZNm6aVK1dGJrXu2bNHdvvgvrBr59CMnzACAEAc2IwxxupGnIzP51NmZqYaGxvjMmQz54k12rS/Uc/+40z93UTvyQ8AAADH6e/39+AuYcRIWtfyXu7cCwBA7BFGetE9gZU79wIAEHuEkV5wSXgAAOKHMNILwggAAPFDGOmFx90ZRpraCSMAAMQaYaQXGZEw0mFxSwAAOPsRRnqR4U6WRGUEAIB4IIz0IoNhGgAA4oYw0osjlRGGaQAAiDXCSC+ojAAAED+EkV50hxEfYQQAgJgjjPTCwzANAABxQxjpRXdlxB8MKxAMW9waAADOboSRXqR3XYFVojoCAECsEUZ6keSwK9XZeedeJrECABBbhJE+sKIGAID4IIz0gWuNAAAQH4SRPrC8FwCA+CCM9IHKCAAA8UEY6QNzRgAAiA/CSB88hBEAAOKCMNIHhmkAAIgPwkgfMlxURgAAiAfCSB+654w0+wkjAADEEmGkD93DND6GaQAAiCnCSB9YTQMAQHwQRvrgSemqjLRRGQEAIJYII33ISu0MI42EEQAAYoow0ofsVKckqaGtQ8YYi1sDAMDZizDSh8yuYZpQ2KiJFTUAAMQMYaQP7mSH3Mmd3dPYylANAACxQhg5gayUrqEawggAADFDGDmB7kmsDW0Bi1sCAMDZizByApEwQmUEAICYIYycwJFhGiojAADECmHkBKiMAAAQe4SRE8iMzBkhjAAAECuEkRNgNQ0AALFHGDmBI5eEZ84IAACxQhg5geyuMHKYyggAADFDGDmBTFbTAAAQc4SRE+DOvQAAxB5h5ASOXtrLnXsBAIgNwsgJZKd2DtMEw0a+du7cCwBALBBGTsCd7FCa0yFJqm9h3ggAALFAGDmJoekuSdKhZr/FLQEA4OxEGDmJoemdQzV1zVRGAACIBcLISQxN66yMMEwDAEBsEEZOIqerMsIwDQAAsUEYOYnuYZpDVEYAAIgJwshJdA/T1FEZAQAgJggjJxGpjDCBFQCAmCCMnERO99LeFiojAADEAmHkJKiMAAAQW6cURpYsWaKioiK53W4VFxdr7dq1fe779NNP69JLL1V2drays7NVWlp6wv3PNJGlva0BhcLcnwYAgIEWdRhZvny5ysrKtGjRIq1fv15Tp07V7NmzVVNT0+v+q1ev1k033aQ333xT5eXlKiws1DXXXKP9+/efduPjIbvrZnnGSIdbqY4AADDQbCbK29EWFxfrwgsv1JNPPilJCofDKiws1De/+U3dd999Jz0+FAopOztbTz75pObNm9ev9/T5fMrMzFRjY6M8Hk80zR0Q03/0Fx1u7dBr37pME/Iy4v7+AAAMRv39/o6qMhIIBLRu3TqVlpYeeQG7XaWlpSovL+/Xa7S2tqqjo0NDhgzpcx+/3y+fz9fjYSXuTwMAQOxEFUbq6uoUCoXk9Xp7bPd6vaqqqurXa9x7773Kz8/vEWiOtXjxYmVmZkYehYWF0TRzwA1N67o/DRc+AwBgwMV1Nc0jjzyiZcuW6eWXX5bb7e5zvwULFqixsTHy2Lt3bxxbebwcKiMAAMRMUjQ75+TkyOFwqLq6usf26upq5eXlnfDY//iP/9Ajjzyiv/71r5oyZcoJ93W5XHK5XNE0LaaO3LmXMAIAwECLqjLidDo1Y8YMrVq1KrItHA5r1apVKikp6fO4Rx99VD/+8Y+1cuVKzZw589Rba5HcjM5gVNtEGAEAYKBFVRmRpLKyMs2fP18zZ87UrFmz9Pjjj6ulpUW33XabJGnevHkqKCjQ4sWLJUn//u//roULF+r5559XUVFRZG5Jenq60tPTB/BUYifX0zmkVO0jjAAAMNCiDiNz585VbW2tFi5cqKqqKk2bNk0rV66MTGrds2eP7PYjBZdf/OIXCgQC+spXvtLjdRYtWqQf/vCHp9f6OPFGwki7xS0BAODsE/V1Rqxg9XVGtlb5dO3j7yg7NVkbFl4T9/cHAGAwisl1RhJVXldl5HBrh/zBkMWtAQDg7EIY6YfMlGQ5kzq7qoZ5IwAADCjCSD/YbDZ5PZ0ramqamDcCAMBAIoz0kzeDFTUAAMQCYaSfulfUVDVSGQEAYCARRvqJ5b0AAMQGYaSf8jI754wcpDICAMCAIoz0U0FWqiRpf0ObxS0BAODsQhjpp4LsFEnS/sOEEQAABhJhpJ9GdIWR6qZ2BYJhi1sDAMDZgzDST0PTnHIn22WMdLCR6ggAAAOFMNJPNptN+VkM1QAAMNAII1Eo6Aoj+5jECgDAgCGMRGEEk1gBABhwhJEodFdGWN4LAMDAIYxEoXt5777DrRa3BACAswdhJAqjhqZJknbVEUYAABgohJEojB2WLkmq8rWrqb3D4tYAAHB2IIxEITMlWcMyOu9Rs7O2xeLWAABwdiCMRGnssM6hmu01zRa3BACAswNhJErjcjuHanbUEkYAABgIhJEodc8boTICAMDAIIxEicoIAAADizASpe7KyO5DreoIcfdeAABOF2EkSsMz3Up1OhQMG+0+xPVGAAA4XYSRKNlstkh15LPqJotbAwDA4EcYOQUT8zIkSVsO+ixuCQAAgx9h5BRMyvdIkj45SGUEAIDTRRg5BecO7wwjVEYAADh9hJFT0B1G9je0qbGNe9QAAHA6CCOnIDMlWQVZKZKojgAAcLoII6eouzryyQHCCAAAp4MwcoqmjMiUJFXsbbC2IQAADHKEkVM0c1S2JOnDXfUWtwQAgMGNMHKKpo3MksNu04HGdu1vaLO6OQAADFqEkVOU6kzSeV3XG/mgkuoIAACnijByGi4ZlyNJ+uuWaotbAgDA4EUYOQ3XTPJKklZvq5U/GLK4NQAADE6EkdMwdUSWcjNcavYH9e6OQ1Y3BwCAQYkwchrsdpuuOa+zOvLqxoMWtwYAgMGJMHKa5kzJlyS9trlK7R0M1QAAEC3CyGm6sGiIhme61eQPavW2WqubAwDAoEMYOU12u01zpnZWR/53/T6LWwMAwOBDGBkA/2fGCEnSqi3VOtjIBdAAAIgGYWQAjPdmqHj0EIWN9Nv391jdHAAABhXCyAC5tWSUJOn5tXuZyAoAQBQIIwPkmkl5KshKUV2zX8+9u8vq5gAAMGgQRgaIM8musqvPkSQ99eZ2NbQGLG4RAACDA2FkAN04vUAT8zLkaw/qqdU7rG4OAACDAmFkADnsNt177URJ0q//VqntNU0WtwgAgDMfYWSAXTFhmK6amKuOkNF9/7tJobCxukkAAJzRCCMDzGaz6Uc3Tlaa06EPdx/WI3/eYnWTAAA4oxFGYqAgK0WPfmWqJOnpdyr1503cRA8AgL6cUhhZsmSJioqK5Ha7VVxcrLVr155w/xdffFETJ06U2+3W+eefrxUrVpxSYweTG6YM1x2Xj5Uk3ffSJh1o4MqsAAD0Juowsnz5cpWVlWnRokVav369pk6dqtmzZ6umpqbX/d99913ddNNN+trXvqYNGzboxhtv1I033qjNmzefduPPdN+55hxNHZGpxrYOlb1QwfwRAAB6YTPGRPUNWVxcrAsvvFBPPvmkJCkcDquwsFDf/OY3dd999x23/9y5c9XS0qI//elPkW0XXXSRpk2bpqVLl/brPX0+nzIzM9XY2CiPxxNNcy23q65F1//8HbUGQvrny8ZowfXnWt0kAADior/f30nRvGggENC6deu0YMGCyDa73a7S0lKVl5f3ekx5ebnKysp6bJs9e7ZeeeWVPt/H7/fL7/dH/tvn80XTzDNKUU6aHv3KFN39/Ab98u2dmpTv0RemFVjdLABAFFr8Qe1vaNP+w23a19CmqsY2+dqC2lPfqj31rapqbFeq06GcdJdyMpzKSXcpO9WpFKdDHcGw6lsDqva1q8bnlzPJrgx3ktKcSUpxOpTmTJI72a5AyMiVZFdBVopSnA6lJDuU4nTInWxXayAkf0dYwXBYwbBRMGTUEQorFDYKho38wbDqmv1qaA3I3xFWoOu57DSnhqW7lJWarLCRQuGwgqHOY0Lhztdo7wirNRDU/defq8IhqZb0b1RhpK6uTqFQSF6vt8d2r9errVu39npMVVVVr/tXVVX1+T6LFy/WQw89FE3Tzmh/PyVfWw76tOTNHbr/pU2aVpilUUPTrG4WgAFmjNHm/T7tO9yqIWlOHW7t0KfVTeoIhTWtMEsXjRmqNFdUv3aPs+9wq97cVqsDDW1KSXYoFDZat/uwdtQ2q9kf1LjcdF0wMluXnzNMXo9bW6t8qvH5Vd8aUH1zQN5Mt7wel0YNSdMFo7KU6jy99hytqb1DtU1+1bcE1NYRUtHQNBVkpchutw3Ye5xIiz+oLQd9qvK1q6qxXdW+dlX7/AoZI5fDrqxUp4ZluJSf5VZmSrLsNptsNulQc0AVextU7WtXRyisFn9I9S0BGRl1hIwOtwbU0Npx0vdv6wjpUEtA26rjcLIx8PVLxwyOMBIvCxYs6FFN8fl8KiwstLBFp6/s6gn6oPKw1u6q1zd/u0Ev/HOJ3MkOq5uFGAuHTdx+EZ+MMUZ76lvV1B5UIBSW02HXucM9cpwh7RvMWgNB/emjg3ru3V365GDfldxkh00zRmXrsnOG6epzvRrvzej3e1T72vX4Xz/V8g/26kTTzzbsadCGPQ16Zk3lSV8zyW5TUU6a7DapNRBSe0dIHneycjJcmlKQqYvGDNVFY4cq/QQBqrGtQ3/86IB+X7FfH+4+rGMH/tOcDl0xMVdfmJqvyycMkyvp9H7vNfuD+tv2Ou2qa1FTe1BGRmEjfbS3QR/sqldHKHZz8zzuJBVkp6ogKyUSaIZnpqgoJ1X5mSlqDYR0qMWvuma/6poCamgLqC0QVnKSTZkpycrzuJWb4VZHOKzm9qDaAiG1BIKRvk922NXsD6rG1662jpDaOsJqCwTlD4aVkuyQK9mhJLtNSXabkh12JTlscthtSrbblZxk05A0l4amOeVOtivZYZfDblN9S0C1TX41tHXIYbMpydF5vMNu7/pfW1d1xqER2Skx67uTiSqM5OTkyOFwqLq6Z+yrrq5WXl5er8fk5eVFtb8kuVwuuVyuaJp2xnPYbfrPr07TDT9/Rxv3Neq7L36kJ26aLpuNL4KzTcXeBj39zk59UFmv2ma/hnWVa202aXROmr44vUCl53rjFlLCYaNXKvbrqdU7tL2mucdz5xdk6lfzZ8rrccelLWcDfzCkj/Y26o8fHdCe+lY1tnXos+omtQQ679adkuzQxOEZamjtUKrToYl5HjnsUvnOQ9pb36b3dtbrvZ31enTlNt04LV9f+9wYTS7w9Pq7oKapXZv2NWptZb3+//Ldauu6I/iFRdmaXJCpFn9QxkhTCrN0Xr5H6a4kfXLAp/d2HtLqbbVq9gd1jjddhUNSlZ3qVHaqU1W+NtU2BfTJgUYdaGw/7jNR1xzQzroWra2s16/WVCrZYdPMUUN02TnDlJPuVLLDrjRXkto7QlpbWa+X1u+LnLvUGT6GprvkTLJrz6FWtQRCenXjQb268aA87iR9flq+/uWKccrPOvEXX12zXys2HdTO2hY1tnVE+mP97oZIP/Qmz+NW4ZAUeT1u5Xnc8nrcSnLYFOgaKqnx+bX/cJtaAkGFTWdIz3AnaXJBpkbnpMnpsCvF6dCQNKfsNltkSKUgK0UZ7uT+fUgQtVOawDpr1iw98cQTkjonsI4cOVJ33313nxNYW1tb9cc//jGy7eKLL9aUKVMSYgLrscp3HNKtz7yvYNjoW6Xj9a3Sc6xuEqJU1+zXtqomDc90a8ywdDW0BvTOZ3V6+9NavbvjkPb3Yxl36blePTZ3qjz9+OXW2Nqh9ysPadehFh1u7VCex60vzxhxwr9Ww2Gjv3xSpfd21utv2+v0WdcXTrKj8y80d7JDVY3tCoaN8jxuff3S0fr7KfnyelyDKiCHwkZbDvq0t75Vn9U0KxQ28qQkqzA7RZPyPcpOdSoYNspwJZ1S+Osu+2/e36jNB3z6+IBP22uaev3re9TQVM29sFA3zxqprFTncc8bY7TrUKve+axWb26t0ZvbaiPPFWSl6JJxQzVjVLZsNptCYaNVW6r11y09VyleMDJLC64/VxcWDYn6XHprz77DbdpT3ypJSnU65E52yNfWoX2H2/Th7nqt2V6nvfUn/zyPz03XP8ws1A1ThvcIGcFQWB8f8OmPHx3QHzceULWvcy6gM8muWy8apWsn52nKiMxItaSpvUPv7azXKxv26/VPqhUIhXt9v9E5aZoyIlNZKcmy2Wwyxmjk0DT93cRcFQ1NHVSf4bNdf7+/ow4jy5cv1/z58/XLX/5Ss2bN0uOPP64XXnhBW7duldfr1bx581RQUKDFixdL6lzae/nll+uRRx7RDTfcoGXLlunhhx/W+vXrNXny5AE9mcFi+Qd7dO//bpIkPfqVKfqHmYN7CCpRNLZ16IGXN2nFpoORMrnX49Kh5oCCR9XNk+w2fX5avv5hZqFGDklVbZNfzf6gOkJhvfNZnf7ve7sVCIblSrKraGia8rPcGjU0TXmZbtU2+XW4JaAhaU752jv0t+29hxuvx6V/vWq8ikcP0Zic9MgXrTFGr31cpV+8tVMf7W2I7J/hStIdV4zVvJJRkb/u9ta3at6za1VZ1xLZL83p0OfG5+iuK8dpyoisge/E02SM0faaZr1fWa/3dh7SO5/VRf5qPhF3sl1D01xqbOuQw26T1+PSiOxUTS7IVG2TXwca2uRr71Buhktej1vtHSFt3NeobdVNxw07SFJ2arIuP2eYLh6b0xl+hqTo3DxPVIFn8/5G/fLtnfrLx1XyB3v/0rXZOr/oJxdk6upzvbp2cl5cv2i7A9Rb22r0fmW92jpCCoaMmvxBuZPsKshO0Zemj9Al44aetF2hsNF7Ow/pv1Z9prWV9ZHtriR7589Js/+4eRlTR2TqorFDNTTNKWOkoekujR2WpmmFWQSOQSJmYUSSnnzySf30pz9VVVWVpk2bpp///OcqLi6WJF1xxRUqKirSc889F9n/xRdf1A9+8APt2rVL48eP16OPPqrrr79+wE9mMPnJq5/o6Xc6x3RvmlWou64cpxHZ1kwcGgihsIn53IO1lfX695VbtWl/o9KcjshEwGZ/UK3+kHI9Lk3My9Ck/Ex9cXqBRucMzCThxrYOPbumUv/3vd2qbwlIkkYOSVVVY3vkL7dzvOm6/Jxh+tz4YZoxKvuEVYuN+xr0r7/doF2HWvvdhlFDUzW9MEvp7iS9/Wld5K9ZScpwJ2nqiCyd483Q5gONkV/0aU6H/s/MQk0a7tHs8/KUmXp8FaY1ENQrGw5o2Qd7tHl/YyRk2WzSVRNzNS43Q1dPytX0wuyTftEaY1Tb5NdH+xr1500HVdcSUJ7Hpasn5enS8TknnCNV1diu/12/T41tHRqa5lRBdoo87mQNz3SrJRDS2spDWlt5WOv3HI78f3D0+Y/LTdeYnHSlOO1qaO3QjtoWfVbd1CMkniqvx6XzCzJ1Xn6mzsv36NzhHo3IThmwL8O2QEjvVx5S+Y5D+mhfg9zJDtkkjc5J1y0XjdTYYekD8j5nCmOMVm+r1Qsf7tXaynodOub/z4KsFM0+L09fuqBAkwsyLWolBkpMw0i8nY1hJBw2+tnr27TkzR2SJKfDrm/+3Tj98+Vj5Uw686/S//GBRr344T5t3Neg/Q1tqvb5leZ0aMqILBXlpEYmT4XCRinJDl0yLkcXjx2qJMeJz622ya9/X7lVb39aq6HpLo0akqqRQ1O1rapJb31ae8Jjj3XVxFw9/KXzT3k+RH1LQK9/UqWfr9oeqU6MzknTf86dpmmFWfK1d2jLAZ/ys1KinoFujNHOupbOZYKH27SztlkHfe3Kz3RrSJpLh1sDsttsKhk7VBeMzOoxVt3eEdIzayq1eluNNu1vVHtHz7+qnQ67vn7paP3jxUXKjeLcA8GwPq1u0q/e2alXKg70eG5MTpp+ftP0Hl8Oja0dWvr2Dr3zWa2a2oOq8fn7HMtPdTp08dgc2W2Sr71DBxvb1dDaoZx0pxx2m7bXNJ9wUubR3Ml2XTAyWxcWDdGl43M0rTCr189VINi5DNJht+lAQ7sOtwaUnepUayCohtYOfbSvQXvr2zQsw6URWSnypCSppskfWaJZOCRVJWOHKjeD+TSx0l3pOtjYrrxMt4ZnupmXcZYhjAwSaz6r08/fOFK2nJiXoSdvnq5xuf2fZR9rBxratL2mWdW+du2tb9Vbn9X1GALorzyPWzdMGa4xw9I0dUSWJuRlKLnrS6QtENKzf6vUkje3qzXQ+xea3SZ9ddZI/dMlRQqbzoqIMZ0z3FOcDu2tb9O2Kp/e+rRWqz+tlTHSsAyX7r9+ooqGpmlImlOF2akn/Qt/075GLfrDZq3fc+QcRw5J1XdnT9D1k/NOGqjiqSPUGSAq9jZo96HO5aTXTx6ukUNPr8r20d4Gfbj7sDbua9AbW2rU5A/KmWTXvddOVOm5udpa1aSFv98cmQPQzW6TRmSn6upJXk3My9DHB3z6y8dVOtDYftL3nDkqW9MKs1Tb7Ne+w21qbg/qYGObnEkOTRmRqYvHDtUFo7I1OT9zUAR2AISRQcUYoz98dEA//MPHOtzaoTSnQ1+/dIzaOkLqCIV1jjdDV07IVV5m/P5CawuE9Lt1e/U/5buPm20vdc6LuHZynq45L08FWSkqzE5RQ1uH1lbW63BLQB2hsDrCRsl2m2qa/Hrt4yodPmY82Jlk16ghqUpzJamy7siM+akjMvWt0nNkZLT7UOcFhbJTnZozNb/fQy/ba5p112/Wa1t1U4/tI4ekal7JKE0tzFJrIKSqxjYl2e0qykmV3WbTc+/u0h8/OhD5K32CN0Ofn5av/++iUcpMScy/2BpbO/SdFyuOm0wpdVZM7ikdr4KsFA1Nd6kgK+W4oGCM0ab9jfpg12G5kuzypCRraFrnRaEONfvlaw9qWmFWXD/fAOKDMDII1TX7dffz6/XezvrjnrPbpLuvHKd/vWp8zP8y37y/Ud/87YbIxEaH3aaxw9Lk9biVn5mi8d50fWFagYZl9H/5tT8Y0msfV+uDynrtOtSij/Y2yNce7LHPiOwUfeeac3TjtIIBGY9v8Qf11OrtWr2tVg2tHapr9vc5UfBYfz9luBb+/aSohjnOZsYYPbOmUs+v3aMDDW1y2Gy6uXikyq6eoBQn18sB0DvCyCAVDIX13Lu7tG73YeVlupXssOuDXfXa0DVkMHVEpm6YMlzJDruMkbJSk+VKcmhoulNZqckaNSTttL4cXt14UN9+oUKBYFh5Hrf++fIx+vKMEf1aghqN7ln6Bxra1OwPKifdqSkjsiLDNrHQGghq2dq9WrO9TlsP+uRJSVZepluBYFi7D7XqcGtAV07I1Z1XjGXi3EkYY1jNAOCkCCNnmd9X7NcDL29Wsz940n0z3Ema4M3QuNx0hcJGB7vG60vGDtWtJaP6DBYrNh3U3c+vV9h0Tv587B+m9boCAwCA/iCMnIVqmtq1bO1e7ahtVjBkJFvneH4gGFZtsz8y/n4iOelO3XPVeM29cGRkbN8Yo1VbanTnb9apI2T0DzNH6JEvTTljLmMOABicCCMJyJjOixEdaGjT1oNN2lnbLJvNpoKsFPmDIf363V3aWds5DyQzJVmThntUkJ2iD3fVR655ccOU4fr5V6dzvxIAwGkjjOA4gWBYyz7Yoyfe2K7app5LMpPsNn35ghH68Y2TWTYJABgQ/f3+PiPv2ovYcCbZNa+kSHMvLNS2qiZt2t+oQ80BjRmWpisn5J72rc0BADgVfPskIFdS55VSz8R7jwAAEg/1eAAAYCnCCAAAsBRhBAAAWIowAgAALEUYAQAAliKMAAAASxFGAACApQgjAADAUoQRAABgKcIIAACwFGEEAABYijACAAAsRRgBAACWGhR37TXGSJJ8Pp/FLQEAAP3V/b3d/T3el0ERRpqamiRJhYWFFrcEAABEq6mpSZmZmX0+bzMniytngHA4rAMHDigjI0M2m23AXtfn86mwsFB79+6Vx+MZsNc9G9FX0aG/+o++6j/6qv/oq/6LZV8ZY9TU1KT8/HzZ7X3PDBkUlRG73a4RI0bE7PU9Hg8f1n6ir6JDf/UffdV/9FX/0Vf9F6u+OlFFpBsTWAEAgKUIIwAAwFIJHUZcLpcWLVokl8tldVPOePRVdOiv/qOv+o++6j/6qv/OhL4aFBNYAQDA2SuhKyMAAMB6hBEAAGApwggAALAUYQQAAFgqocPIkiVLVFRUJLfbreLiYq1du9bqJsXVD3/4Q9lsth6PiRMnRp5vb2/XXXfdpaFDhyo9PV1f/vKXVV1d3eM19uzZoxtuuEGpqanKzc3V9773PQWDwXifSky8/fbbmjNnjvLz82Wz2fTKK6/0eN4Yo4ULF2r48OFKSUlRaWmpPvvssx771NfX65ZbbpHH41FWVpa+9rWvqbm5ucc+Gzdu1KWXXiq3263CwkI9+uijsT61AXeyvvrHf/zH4z5r1157bY99EqGvFi9erAsvvFAZGRnKzc3VjTfeqG3btvXYZ6B+7lavXq0LLrhALpdL48aN03PPPRfr0xtw/emvK6644rjP1h133NFjn0Tor1/84heaMmVK5MJlJSUl+vOf/xx5/oz/XJkEtWzZMuN0Os2zzz5rPv74Y3P77bebrKwsU11dbXXT4mbRokXmvPPOMwcPHow8amtrI8/fcccdprCw0Kxatcp8+OGH5qKLLjIXX3xx5PlgMGgmT55sSktLzYYNG8yKFStMTk6OWbBggRWnM+BWrFhhHnjgAfPSSy8ZSebll1/u8fwjjzxiMjMzzSuvvGI++ugj8/nPf96MHj3atLW1Rfa59tprzdSpU817771n3nnnHTNu3Dhz0003RZ5vbGw0Xq/X3HLLLWbz5s3mt7/9rUlJSTG//OUv43WaA+JkfTV//nxz7bXX9vis1dfX99gnEfpq9uzZ5te//rXZvHmzqaioMNdff70ZOXKkaW5ujuwzED93O3fuNKmpqaasrMx88skn5oknnjAOh8OsXLkyrud7uvrTX5dffrm5/fbbe3y2GhsbI88nSn/94Q9/MK+++qr59NNPzbZt28z9999vkpOTzebNm40xZ/7nKmHDyKxZs8xdd90V+e9QKGTy8/PN4sWLLWxVfC1atMhMnTq11+caGhpMcnKyefHFFyPbtmzZYiSZ8vJyY0znF5DdbjdVVVWRfX7xi18Yj8dj/H5/TNseb8d+wYbDYZOXl2d++tOfRrY1NDQYl8tlfvvb3xpjjPnkk0+MJPPBBx9E9vnzn/9sbDab2b9/vzHGmKeeespkZ2f36K97773XTJgwIcZnFDt9hZEvfOELfR6TqH1VU1NjJJm33nrLGDNwP3ff//73zXnnndfjvebOnWtmz54d61OKqWP7y5jOMHLPPff0eUwi91d2drb51a9+NSg+Vwk5TBMIBLRu3TqVlpZGttntdpWWlqq8vNzClsXfZ599pvz8fI0ZM0a33HKL9uzZI0lat26dOjo6evTRxIkTNXLkyEgflZeX6/zzz5fX643sM3v2bPl8Pn388cfxPZE4q6ysVFVVVY/+yczMVHFxcY/+ycrK0syZMyP7lJaWym636/3334/sc9lll8npdEb2mT17trZt26bDhw/H6WziY/Xq1crNzdWECRN055136tChQ5HnErWvGhsbJUlDhgyRNHA/d+Xl5T1eo3ufwf777dj+6vab3/xGOTk5mjx5shYsWKDW1tbIc4nYX6FQSMuWLVNLS4tKSkoGxedqUNwob6DV1dUpFAr16HRJ8nq92rp1q0Wtir/i4mI999xzmjBhgg4ePKiHHnpIl156qTZv3qyqqio5nU5lZWX1OMbr9aqqqkqSVFVV1Wsfdj93Nus+v97O/+j+yc3N7fF8UlKShgwZ0mOf0aNHH/ca3c9lZ2fHpP3xdu211+pLX/qSRo8erR07duj+++/Xddddp/LycjkcjoTsq3A4rG9961u65JJLNHnyZEkasJ+7vvbx+Xxqa2tTSkpKLE4ppnrrL0m6+eabNWrUKOXn52vjxo269957tW3bNr300kuSEqu/Nm3apJKSErW3tys9PV0vv/yyJk2apIqKijP+c5WQYQSdrrvuusi/p0yZouLiYo0aNUovvPDCoPnhw+Dw1a9+NfLv888/X1OmTNHYsWO1evVqXXXVVRa2zDp33XWXNm/erDVr1ljdlEGhr/76xje+Efn3+eefr+HDh+uqq67Sjh07NHbs2Hg301ITJkxQRUWFGhsb9bvf/U7z58/XW2+9ZXWz+iUhh2lycnLkcDiOm0lcXV2tvLw8i1plvaysLJ1zzjnavn278vLyFAgE1NDQ0GOfo/soLy+v1z7sfu5s1n1+J/oM5eXlqaampsfzwWBQ9fX1Cd+HY8aMUU5OjrZv3y4p8frq7rvv1p/+9Ce9+eabGjFiRGT7QP3c9bWPx+MZlH9o9NVfvSkuLpakHp+tROkvp9OpcePGacaMGVq8eLGmTp2q//qv/xoUn6uEDCNOp1MzZszQqlWrItvC4bBWrVqlkpISC1tmrebmZu3YsUPDhw/XjBkzlJyc3KOPtm3bpj179kT6qKSkRJs2berxJfL666/L4/Fo0qRJcW9/PI0ePVp5eXk9+sfn8+n999/v0T8NDQ1at25dZJ833nhD4XA48guzpKREb7/9tjo6OiL7vP7665owYcKgG3aIxr59+3To0CENHz5cUuL0lTFGd999t15++WW98cYbxw07DdTPXUlJSY/X6N5nsP1+O1l/9aaiokKSeny2EqW/jhUOh+X3+wfH5+q0p8AOUsuWLTMul8s899xz5pNPPjHf+MY3TFZWVo+ZxGe773znO2b16tWmsrLS/O1vfzOlpaUmJyfH1NTUGGM6l4KNHDnSvPHGG+bDDz80JSUlpqSkJHJ891Kwa665xlRUVJiVK1eaYcOGnTVLe5uamsyGDRvMhg0bjCTz2GOPmQ0bNpjdu3cbYzqX9mZlZZnf//73ZuPGjeYLX/hCr0t7p0+fbt5//32zZs0aM378+B7LVRsaGozX6zW33nqr2bx5s1m2bJlJTU0dVMtVjTlxXzU1NZnvfve7pry83FRWVpq//vWv5oILLjDjx4837e3tkddIhL668847TWZmplm9enWPpaitra2RfQbi5657Ceb3vvc9s2XLFrNkyZJBt1TVmJP31/bt282PfvQj8+GHH5rKykrz+9//3owZM8ZcdtllkddIlP667777zFtvvWUqKyvNxo0bzX333WdsNpv5y1/+Yow58z9XCRtGjDHmiSeeMCNHjjROp9PMmjXLvPfee1Y3Ka7mzp1rhg8fbpxOpykoKDBz584127dvjzzf1tZm/uVf/sVkZ2eb1NRU88UvftEcPHiwx2vs2rXLXHfddSYlJcXk5OSY73znO6ajoyPepxITb775ppF03GP+/PnGmM7lvQ8++KDxer3G5XKZq666ymzbtq3Haxw6dMjcdNNNJj093Xg8HnPbbbeZpqamHvt89NFH5nOf+5xxuVymoKDAPPLII/E6xQFzor5qbW0111xzjRk2bJhJTk42o0aNMrfffvtxwT8R+qq3PpJkfv3rX0f2GaifuzfffNNMmzbNOJ1OM2bMmB7vMVicrL/27NljLrvsMjNkyBDjcrnMuHHjzPe+970e1xkxJjH665/+6Z/MqFGjjNPpNMOGDTNXXXVVJIgYc+Z/rmzGGHP69RUAAIBTk5BzRgAAwJmDMAIAACxFGAEAAJYijAAAAEsRRgAAgKUIIwAAwFKEEQAAYCnCCAAAsBRhBAAAWIowAgAALEUYAQAAliKMAAAAS/0/M+TxsVdoLN4AAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(fitter.acf()[:, 4])\n", "fitter.reset()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We then follow up with a production run, only saving 1 in 200 samples. This is to remove autocorrelation. We save 15 steps, giving a total of 15 * 200 samples (200 walkers is the default)." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [01:51<00:00, 26.89it/s]\n" ] } ], "source": [ "res = fitter.sample(15, nthin=200, pool=-1)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "This seems to be an effective sampling rate of ~15 * 200 / 111 = 27 samples/sec\n", "In the final output of the sampling each varying parameter is given a set of statistics. `Parameter.value` is the median of the chain samples. `Parameter.stderr` is half the [15, 85] percentile, representing a standard deviation." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 4906563104\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 919.592754626952\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(objective)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "A corner plot shows the covariance between parameters. You need to install the *matplotlib* and *corner* packages to create these graphs." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "objective.corner();" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Once we've done the sampling we can look at the variation in the model at describing the data. In this example there isn't much spread." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "objective.plot(samples=300);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "In a similar manner we can look at the spread in SLD profiles consistent with the data." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "structure.plot(samples=300)\n", "plt.ylim(2.2, 6);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Sampling with pymc" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "`pymc` is also an excellent Bayesian package. `refnx` has some features built in to work with `pymc` models. You'll need to install `pymc` and `arviz` to run this section." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "from refnx.analysis import pymc_model\n", "import pymc as pm\n", "import arviz as az" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "To do the sampling we're going to use the `DEMetropolis` stepper. `refnx` can use NUTS, but it seems to work very slowly with reflectometry datasets (via a [black-box model](https://www.pymc.io/projects/examples/en/latest/howto/blackbox_external_likelihood_numpy.html)). More investigations need to be done on the refnx codebase to try and improve NUTS performance." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Population sampling (10 chains)\n", "DEMetropolis: [p0, p1, p2, p3, p4, p5, p6, p7]\n", "Attempting to parallelize chains to all cores. You can turn this off with `pm.sample(cores=1)`.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fd9e911272c84f278cdcf00131a229c2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Population parallelization failed. Falling back to sequential stepping of chains.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b720cc0375be4efa9a191ce69f350647",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 10 chains for 1_000 tune and 10_000 draw iterations (10_000 + 100_000 draws total) took 91 seconds.\n"
     ]
    }
   ],
   "source": [
    "with pymc_model(objective) as _model:\n",
    "    starter = {\n",
    "        f\"p{n}\": par.value for n, par in enumerate(objective.varying_parameters())\n",
    "    }\n",
    "    trace = pm.sample(draws=10000, chains=10, initvals=starter, step=pm.DEMetropolis())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
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       "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "az.plot_posterior(trace);" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "az.plot_autocorr(trace, combined=True, max_lag=1000);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Given that the autocorrelation time is ~100, this corresponds to something like 10 * 10000 / 100 = 1000 independent samples.\n", "The time taken on my machine was ~91 sec, corresponding to an effective sampling rate of ~11 samples/sec. There is room to tweak both sampling runs to slightly speed them up.\n", "\n", "Note how all the parameters are labelled `p0, p1, ..., pn`. Each of those parameters correspond to a Parameter in `Objective.varying_parameters()`. Compared to the inbuilt processing one would have to do some manual processing. Let's work out some stats for `p4`, which corresponds to a layer thickness." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(objective.varying_parameters()[4])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10, 10000)\n" ] }, { "data": { "text/plain": [ "(np.float64(259.04226306323346), np.float64(0.22410364549520523))" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# grab hold of the MCMC chain for that parameter\n", "chain = trace.posterior[\"p4\"].data\n", "print(chain.shape)\n", "\n", "\n", "# work out some of the quantile statistics.\n", "def process_chain_for_parameter(chain):\n", " quantiles = np.quantile(chain, [0.158, 0.5, 0.842])\n", " return quantiles[1], 0.5 * (quantiles[-1] - quantiles[0])\n", "\n", "\n", "process_chain_for_parameter(chain)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# Let's create a function to update the best fit.\n", "def update_objective_from_trace(objective, trace):\n", " vpars = objective.varying_parameters()\n", " for i, vpar in enumerate(vpars):\n", " median, sd = process_chain_for_parameter(trace.posterior[f\"p{i}\"])\n", " vpar.value = median\n", " vpar.stderr = sd\n", "\n", "\n", "update_objective_from_trace(objective, trace)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 4906563104\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 919.5947878408159\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(objective)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The parameter values from `pymc` and `emcee` are effectively the same." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Sampling with dynesty" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import dynesty" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "You can find out how to estimate posteriors with dynesty using this page, https://dynesty.readthedocs.io/en/stable/dynamic.html. It's best to use the dynamic nested sampler, then you need to reweight the samples with the weights. Dynesty provides a utility function for that.\n", "You can also use dynesty to perform model comparison by looking at the evidence term." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "26049it [01:01, 424.53it/s, batch: 4 | bound: 9 | nc: 1 | ncall: 126612 | eff(%): 20.460 | loglstar: 563.732 < 570.929 < 568.848 | logz: 536.538 +/- 0.196 | stop: 0.945] \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(26049, 8)\n" ] } ], "source": [ "nested_sampler = dynesty.DynamicNestedSampler(\n", " objective.logl, objective.prior_transform, ndim=len(objective.varying_parameters())\n", ")\n", "nested_sampler.run_nested()\n", "# process the samples\n", "chain = nested_sampler.results.samples_equal()\n", "\n", "# another way of processing the samples (reweighting is needed)\n", "logZdynesty = nested_sampler.results.logz[-1] # value of logZ\n", "weights = np.exp(nested_sampler.results.logwt - logZdynesty)\n", "chain = dynesty.utils.resample_equal(nested_sampler.results.samples, weights)\n", "\n", "print(chain.shape)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The size of the chain resulting from the `samples_equal` method is not equal to the number of effective samples. One can estimate the effective number of posterior samples resulting from a run using the following:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "effective number of samples: 26048\n" ] } ], "source": [ "def ess(weights):\n", " \"\"\"\n", " Estimate the effective sample size from the weights.\n", "\n", " Args:\n", " weights (array_like): an array of weights values for each nested sample\n", "\n", " Returns:\n", " int: the effective sample size\n", " \"\"\"\n", "\n", " N = len(weights)\n", " w = weights / weights.sum()\n", " ess = N / (1.0 + ((N * w - 1) ** 2).sum() / N)\n", "\n", " return int(ess)\n", "\n", "\n", "print(\"effective number of samples: \", ess(np.exp(weights)))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The effective sampling rate for dynesty seems to be ~26048/61 ~ 427 samples/sec.\n", "\n", "Let's process the chain to put the statistics into the objective. Here we'll use the `process_chain` utility function that's designed for use with emcee chains. This function assumes that the chain has shape `(nsteps, nwalkers, nvars)`. The chain from dynesty has shape `(nsamples, nvars)`, so we can fake the dynesty chain into looking like an emcee chain by putting an extra axis in." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "process_chain(objective, chain[:, None, :]);" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 4906563104\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 919.5873733827214\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(objective)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Conclusions\n", "\n", "Hopefully you've found it useful to see how the three sampling packages can be used to obtain posterior distributions for the parameter set. All have high performance. The `emcee` sampler will probably stay the default, but it may be useful to look into both `pymc` and `dynesty` to see what useful (unique) features they may offer - especially if `dynesty` appears to have a much faster sampling rate." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.53/doc/energy_dispersive.ipynb000066400000000000000000001071201477046072400202740ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "6eeef840-c338-4ec9-ae10-a9e75b658053", "metadata": {}, "source": [ "## Energy and angular dispersive analysis\n", "\n", "`refnx` able to deal with reflectivity from systems containing energy dispersive materials, i.e. those whose optical properties change as a function of wavelength. For neutrons this mainly corresponds to elements with strong absorption effects. The treatment below is also able to deal with energy dispersive X-ray measurements" ] }, { "cell_type": "code", "execution_count": 1, "id": "f2d9a637-f1a9-47c4-8ce7-dd9475032b38", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from refnx.reflect import ReflectModelTL, ReflectModel, SLD, MaterialSLD\n", "from refnx.util import q, xray_wavelength" ] }, { "cell_type": "markdown", "id": "b8ad07e9-8658-433a-995c-742ec780b91d", "metadata": {}, "source": [ "Start off by creating a `MaterialSLD`. This is a variant of `Scatterer`, whose optical properties are controlled by a formula, mass density (g/cc), and whether the material is being used for neutron or X-ray calculation.\n", "\n", "Here we'll calculate the SLD of the material at two different wavelengths." ] }, { "cell_type": "code", "execution_count": 2, "id": "9b54ac43-b365-4862-b22e-6d6392d5544c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SLD: (6.544686245235056+0.7318962182191739j) at 2.8 Angstrom\n", "SLD: (6.471928664642279+0.648390380281317j) at 18.0 Angstrom\n" ] } ], "source": [ "gdgao_disp = MaterialSLD(\"GdGa5O12\", 7, probe='neutron') # can be 'x-ray'\n", "\n", "gdgao_disp.wavelength = 2.8 # Angstrom\n", "print(f\"SLD: {complex(gdgao_disp)} at {gdgao_disp.wavelength} Angstrom\")\n", "gdgao_disp.wavelength = 18.\n", "print(f\"SLD: {complex(gdgao_disp)} at {gdgao_disp.wavelength} Angstrom\")" ] }, { "cell_type": "markdown", "id": "38228996-5edc-43c5-9019-54f92838e3b3", "metadata": {}, "source": [ "For comparison let's create a non-dispersive version. By non-dispersive we mean that the optical properties don't change as a function of wavelength" ] }, { "cell_type": "code", "execution_count": 3, "id": "d976c1fe-a739-4446-8e84-5b66edd87fbf", "metadata": {}, "outputs": [], "source": [ "gdgao_nondisp = SLD(6.5 + 0.68j)" ] }, { "cell_type": "markdown", "id": "2d5078ff-1f36-4d03-a2dd-0029af54563c", "metadata": {}, "source": [ "The `MaterialSLD.density` attribute can be allowed to vary during a fit." ] }, { "cell_type": "code", "execution_count": 4, "id": "8d37781c-c504-446d-b40c-92fa9817ae00", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Parameters: '' \n", "\n" ] } ], "source": [ "print(gdgao_disp.parameters)" ] }, { "cell_type": "markdown", "id": "c2ac79a0-0a43-441d-a782-b2e1cb419268", "metadata": {}, "source": [ "In comparison, with the non-dispersive analogue one can allow the real and imaginary part of the SLD to vary." ] }, { "cell_type": "code", "execution_count": 5, "id": "0d240af4-fb86-4f68-9564-42c38da109f0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n" ] } ], "source": [ "print(gdgao_nondisp.parameters)" ] }, { "cell_type": "markdown", "id": "5a66c1e4-8a88-4480-934b-b50696d3fc6c", "metadata": {}, "source": [ "Now we create two `Structure`s that are ostensibly the same, but one has a dispersive material in it, the other a non-dispersive analogue." ] }, { "cell_type": "code", "execution_count": 6, "id": "42637c4e-1c87-480d-b6ea-5754bc9d58e4", "metadata": {}, "outputs": [], "source": [ "air = SLD(0.0)\n", "si = SLD(2.07)\n", "\n", "s_disp = air | gdgao_disp(300, 5) | si(0, 3)\n", "s_nondisp = air | gdgao_nondisp(300, 5) | si(0, 3)" ] }, { "cell_type": "markdown", "id": "851691b9-7e3e-421b-89db-84260f0efc5c", "metadata": {}, "source": [ "Now we generate `theta`/`wavelength` arrays, with a corresponding Q value.\n", "Subsequently we create a `ReflectModelTL` and a `ReflectModel`. `ReflectModelTL` is a variant of `ReflectModel`. Instead of calculating reflectivity as a function of Q (a. la. `ReflectModel`), it calculates as a function of incident angle and wavelength" ] }, { "cell_type": "code", "execution_count": 7, "id": "f816cf11-9180-4f89-b1b3-cfbd137a59a3", "metadata": {}, "outputs": [], "source": [ "npnts = 201\n", "theta = np.ones(npnts) * 0.65\n", "wavelength = np.geomspace(2.8, 18, npnts)\n", "qq = q(theta, wavelength)\n", "\n", "model_disp = ReflectModelTL(s_disp)\n", "model_nondisp = ReflectModel(s_nondisp)" ] }, { "cell_type": "markdown", "id": "6b0a42b5-0a46-4875-bad0-f7fb63019651", "metadata": {}, "source": [ "Now let's compare the reflectivity from the dispersive and non-dispersive analogues. The reflectivities are almost identical, the energy dispersive absorption effect has little effect in this case." ] }, { "cell_type": "code", "execution_count": 8, "id": "a199c625-2073-4ffa-956d-8fdab09ce99d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(qq, model_nondisp(qq), label='nondisp')\n", "\n", "# note how we provide theta and wavelength to the ReflectModelTL object.\n", "plt.plot(qq, model_disp(np.c_[theta, wavelength]), label='disp')\n", "plt.yscale('log')\n", "plt.xscale('log')\n", "plt.legend();" ] }, { "cell_type": "markdown", "id": "25f47802-b6c0-4b41-8748-74f17d940c36", "metadata": {}, "source": [ "To reassure ourselves let's loko at the slab representation of the the dispersive `Structure` at two different wavelengths. We can see that the real and imaginary components of the SLD (second column) do change, just not by much." ] }, { "cell_type": "code", "execution_count": 9, "id": "274ac51a-006e-404a-8d16-f20bc7575051", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 0. 0. 0. ]\n", " [300. 6.54468625 0.73189622 5. 0. ]\n", " [ 0. 2.07 0. 3. 0. ]]\n", "\n", "[[ 0. 0. 0. 0. 0. ]\n", " [300. 6.47192866 0.64839038 5. 0. ]\n", " [ 0. 2.07 0. 3. 0. ]]\n" ] } ], "source": [ "print(s_disp.slabs(wavelength=2.8))\n", "print()\n", "print(s_disp.slabs(wavelength=18.0))" ] }, { "cell_type": "markdown", "id": "c0da8e77-1d80-4cd2-b172-43cc3f47bc7e", "metadata": {}, "source": [ "For comparison here is the non-dispersive system." ] }, { "cell_type": "code", "execution_count": 10, "id": "76fb6a62-3892-45a6-b3b4-170178edadf1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 0. 0. 0. ]\n", " [300. 6.5 0.68 5. 0. ]\n", " [ 0. 2.07 0. 3. 0. ]]\n", "\n", "[[ 0. 0. 0. 0. 0. ]\n", " [300. 6.5 0.68 5. 0. ]\n", " [ 0. 2.07 0. 3. 0. ]]\n" ] } ], "source": [ "print(s_nondisp.slabs(wavelength=2.8))\n", "print()\n", "print(s_nondisp.slabs(wavelength=18.0))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.5" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.53/doc/environment.yml000066400000000000000000000005671477046072400166010ustar00rootroot00000000000000channels: - conda-forge dependencies: - python==3.9 - pip: - nbsphinx - jupyter-sphinx - sphinx_rtd_theme - tqdm - corner - periodictable - pandoc - scipy - numpy - sphinx - pandas - numpydoc - h5py - nbconvert - ipywidgets - setuptools - cython - jupyter - matplotlib - pytest - xlrd refnx-0.1.53/doc/examples.rst000066400000000000000000000011061477046072400160500ustar00rootroot00000000000000Examples ======== .. toctree:: :maxdepth: 2 reflectometry_global.ipynb inequality_constraints.ipynb model_selection.ipynb lipid.ipynb occupancy.ipynb Batch fitting analytical.ipynb Freeform modelling with maximum entropy incoherent_sum.ipynb nsf.ipynb NSF2.ipynb emcee_pymc_dynesty.ipynb using_mpi.ipynb energy_dispersive.ipynb refnx-0.1.53/doc/faq.rst000066400000000000000000000104051477046072400150030ustar00rootroot00000000000000.. _faq_chapter: ==================================== Frequently Asked Questions ==================================== .. _mailing list: https://groups.google.com/group/refnx .. _github issues: https://github.com/refnx/refnx/issues .. _van Well et al: https://doi.org/10.1016/j.physb.2004.11.058 .. _Nelson et al: https://doi.org/10.1107/S1600576714009595 A list of common questions. What's the best way to ask for help or submit a bug report? ----------------------------------------------------------- If you have questions on the use of refnx please use the `mailing list`_. If you find a bug in the code or documentation, use `GitHub Issues`_. How should I cite refnx? ------------------------ The full reference for the refnx paper is: "Nelson, A.R.J. & Prescott, S.W. (2019). J. Appl. Cryst. 52, https://doi.org/10.1107/S1600576718017296." How is instrumental resolution smearing handled? ------------------------------------------------ There are a variety of ways that you can account for instrumental resolution smearing in refnx. The easiest is if the fractional instrumental resolution, :math:`\frac{dQ}{Q}`, is constant. When setting up :class:`refnx.reflect.ReflectModel` the fractional resolution can be specified, and the reflectivity that it calculates is automatically smeared. For a given :math:`Q` value the :math:`dQ` (found by multiplying the fractional resolution by :math:`Q`) value refers to the Full Width at Half Maximum (FWHM) of a Gaussian approximation to the instrumental resolution. This Gaussian distribution is convolved with the unsmeared model to compare with the data. The second way of using the resolution function is for the :math:`dQ` values for each data point to be read in via from a data file (e.g. the 4th column of a text file). In this way point-by-point resolution smearing is achieved. The last way of specifying instrumental resolution is for a full resolution kernel to be provided for each data point. A resolution kernel is a probability distribution that describes the distribution of possible :math:`Q` vectors for each data point. The first two options are typically used, only more advanced users will ever need to apply the last option. For further details on instrumental resolution functions it's a good idea to read the papers by `van Well et al`_, and `Nelson et al`_. What are the units of scattering length density? ------------------------------------------------ If the scattering length density of a material is :math:`(124.88 + 12.85j)\times 10^{-6} A^{-2}` (the X-ray SLD for Au), then you would use 124.88 as the real part and 12.85 as the imaginary part. What are the 'fronting' and 'backing' media? -------------------------------------------- The 'fronting' and 'backing' media are infinite. The 'fronting' medium carries the incident beam of radiation, whilst the 'backing' medium will carry the transmitted beam away from the interface. How do I open the standalone app on macOS Catalina? ---------------------------------------------------- macOS Catalina expects all apps to be code-signed and notarised for them to be able to run via 'double-clicking' in the finder. The project is working towards fulfilling those conditions, but in the meantime you can still open the standalone motofit.app by right-clicking and selecting 'open'. Can I save models/objectives to file? ----------------------------------------- I'm assuming that you have a :class:`refnx.reflect.ReflectModel` or :class:`refnx.analysis.Objective` that you'd like to save to file. The easiest way to do this is via serialisation to a Python pickle:: import pickle # save with open('my_objective.pkl', 'wb+') as f: pickle.dump(objective, f) # load with open('my_objective.pkl', 'rb') as f: restored_objective = pickle.load(f) The saved pickle files are in a binary format, and are not human readable. It may also be useful to save the representation, :code:`repr(objective)`. How do I install pyqt6? ----------------------- PyQt6 and qtpy is needed for the refnx GUI. The `pyqt6` and `qtpy` packages are currently available from PyPI and can be installed as `pip install pyqt6 qtpy`. However, pyqt6 is not currently available via conda-forge. You can use conda to install most of the refnx dependencies, but you will need to use `pip` to install pyqt6. refnx-0.1.53/doc/getting_started.ipynb000066400000000000000000043421251477046072400177470ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Getting started" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Fitting a data to a user defined model" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "*refnx* can examine most curve fitting problems. Here we demonstrate a fit to a simple user defined model. This line example is taken from the [emcee documentation](http://emcee.readthedocs.io/en/stable/user/line.html) and the reader is referred to that link for more detailed explanation. The errorbars are underestimated, and the modelling will account for that." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "To use *refnx* we need first need to create a dataset. We create a synthetic dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "\n", "rng = np.random.default_rng(1220289787)\n", "\n", "# Choose the \"true\" parameters.\n", "m_true = -0.9594\n", "b_true = 4.294\n", "f_true = 0.534\n", "\n", "N = 50\n", "x = np.sort(10 * rng.uniform(size=N))\n", "yerr = 0.1 + 0.5 * rng.uniform(size=N)\n", "y = m_true * x + b_true\n", "y += np.abs(f_true * y) * rng.normal(size=N)\n", "y += yerr * rng.normal(size=N)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We create a `Data1D` object from this synthetic data:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "from refnx.dataset import Data1D\n", "\n", "data = Data1D(data=(x, y, yerr))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Then we need to set up a generative model. Firstly we write a fit-function that returns our straight line model. The `Parameter` objects describe the parameters we're going to use in the fit. We give the parameters values, names, and specify their limits. The parameters are combined into a `Parameters` set, `p`, using the or operator.\n", "Then we create a `Model` object from our parameter set and the fit-function. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "from refnx.analysis import Parameter, Model\n", "\n", "\n", "def line(x, params, *args, **kwds):\n", " p_arr = np.array(params)\n", " return p_arr[0] + x * p_arr[1]\n", "\n", "\n", "# the model needs parameters\n", "p = Parameter(1, \"b\", vary=True, bounds=(0, 10))\n", "p |= Parameter(-2, \"m\", vary=True, bounds=(-5, 0.5))\n", "\n", "model = Model(p, fitfunc=line)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We set lower and upper limits on each of the parameters. This means that the log-prior probability from those parameters is described by a uniform distribution. Only solutions which have finite probability (i.e. lie between the limits) will be considered by the fit/sampler." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-2.3025850929940455 -inf\n" ] } ], "source": [ "# ln(1 / 10)\n", "# a value lying outside the limits is not possible\n", "print(p[0].logp(1), p[0].logp(-1))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "It's not required to give each parameter bounds unless the fit method requires it. The bounds do not have to be from a uniform distribution, any of the `scipy.stats.rv_continuous` distributions can be used:\n", "\n", "```\n", "import scipy.stats as stats\n", "# a normal distribution of mean 5 and standard deviation 1.\n", "p[0].bounds = stats.norm(5, 1)\n", "```" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now we create an `Objective` from the model and the data. We use an extra parameter, `lnsigma`, to describe the underestimated error bars. Objectives use the model and data to calculate statistics about the curve fitting system." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "631.1788336329323 -6.405228458030841 -440.09147816937787 -446.4967066274087\n" ] } ], "source": [ "from refnx.analysis import Objective\n", "\n", "lnf = Parameter(0, \"lnf\", vary=True, bounds=(-10, 1))\n", "objective = Objective(model, data, lnsigma=lnf)\n", "print(objective.chisqr(), objective.logp(), objective.logl(), objective.logpost())" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Then a `CurveFitter` is created from the `Objective`. This is responsible for doing all the curvefitting/Bayesian sampling. Let's do a quick fit using Differential Evolution." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "61.78490690835281: : 20it [00:00, 436.03it/s] \n" ] } ], "source": [ "from refnx.analysis import CurveFitter\n", "\n", "fitter = CurveFitter(objective)\n", "fitter.fit(\"differential_evolution\");" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "In the final output of the sampling each varying parameter is given a set of statistics. `Parameter.value` is the median of the chain samples. `Parameter.stderr` is half the [15, 85] percentile, representing a standard deviation." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 4491965312\n", "Dataset = , 50 points\n", "datapoints = 50\n", "chi2 = 45.73166955271531\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: None \n", "\n", "\n", "\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print(objective)\n", "objective.plot();" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The trouble with a single fit that minimises $\\chi^2$ is that it doesn't reveal the range of solutions that are consistent with the data. It also assumes that the parameter uncertainties will be normally distributed and uni-modal. To investigate the parameter probability distributions we need to use MCMC to sample the posterior probability distribution of the system. \n", "\n", "Note: `pool=` specifies that no parallelisation is done during sampling. On platforms that use `spawn` for multiprocessing special precautions [must be used](https://docs.python.org/3/library/multiprocessing.html#the-spawn-and-forkserver-start-methods) when using `pool > 1`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:05<00:00, 189.77it/s]\n" ] } ], "source": [ "fitter.sample(1000, pool=1);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Once the sampling is done we burn/discard some of the initial steps because the initial locations of the walkers won't be around their 'equilibrium' position. We thin out the chain to reduce auto-correlation between successive steps." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Parameters: None \n", "\n", "\n", "\n" ] } ], "source": [ "from refnx.analysis import process_chain\n", "\n", "process_chain(objective, fitter.chain, nburn=300, nthin=100, flatchain=True)\n", "print(objective.parameters)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now we can see the range of solutions that are consistent with the data:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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kUikuvPBCbr31Vr7xjW+M97fbI9UXcgATMDFNY9SordVqxeVy7TaYqAv3VCjI5/O4XC4CgQCxWIwnn0wDVqDM6aeX8Pv9lEolfWSTyWQYHh7WwScUChGNRnnhb/+WN5Ys4eTf/54Zq1cz9/e/p+Wxx1h/6aX0f/CDBEMhyuWyPkZSd+NYrVZKJRMwKJclmAghhDgwjfsr2DXXXMMFF1zAWWed9bafm8/nSSQSo97GQzV8OKgGBwPTNPWbxWLRFRGv10skEtFHOSqQqDDi8Xjw+/34/X6CwSDTpk0DmoAAYMHj8eiv6/f7sViqj9XV1WEYBslkksHBQbZv305nZyfDdXXcf/XV3HvFFcTq6/HEYhz53e8y95OfJL5yJUNDQ6TTaXK5HIlEglQqxfLlJfr6LICF3l6T226TYxwhhBAHnnGtkPz617/mpZde4oUXXtijz7/xxhu54YYbxvMpaRaLFbBiGBWs1uqdMqonQwUTdSuv1WrF7XbrsV7VxKp+3WazYbVaKRaLVP9IA4AfrzcGgMvlIpFI4HQ6MQyDXC6H1+vF4/EQj8fJ5/PE43HS6TTRaJTOI47gjkMO4ciVKznu4YepX7+euuuuY9t73kPXVVeRmz4dv99PR4fJdddFADV1Y3DVVSbveU+F9naplgghhDhwjNurVkdHB5/5zGe44447cLlce/R7vvjFLxKPx/VbR0fHeD09Te0MUVtS1UitaZp62kaN4fr9fqLRKKFQCI/Hg81m07+mqiWG4Qe8QJS2tja9IM3r9eJyucjn89hsNvx+P1arlXA4jMvlolCo3onT3d3Npk2bSBWL/PXcc7n9i19k7THHYJgmMx56iMUXXohj+XL6urpYs6ZEpTJ6BLhcNtiwQZpehRBCHFjGrULy4osv0t/fz9FHH60fK5fLPPHEE3z/+98nn89jtVpH/R51FDIRDMPA4XBgmqZelqaOW1RviaqWWK1WvF4vmUyGdDpNoVDQX8dqdVMqOTEMOx5PFpfLhc/n00dQqkKSz+fxeDx6fFdN6SQSCb011u/309jYyEN///e8duKJnPb731Pf2clhy5cz8sADpC+6Hovl+lGhxGo1aWvLU6nY9c+1u90lQgghxGQyboHkzDPP5LXXXhv12CWXXML8+fP5/Oc/v0sYmSwMw9Br3GuDSS11RKOCiRrLzefz2O1FSiUDq9VNMOggl8vpaRyv10s8Htd342QyGYrFIl6vl3K5jNVq1btMksmk3gIbCASozJjB//3TP3Hon//M0j/8gXBnJ+/51j/y/KwH+eDm2+hgOmDyjW8M4vMVSac9uvKzNxf2CSGEEPvTuAUSv9/P4YcfPuoxr9dLNBrd5fHJqDaYVCoVisXiqB4TqAYT0zQJBoP4fD5SqRQ2WxbIY7MVsFrB4/HgdrvJ5XIYhqFvFE4kEthsNh06ANxutz5CKhQKDA0NMTw8TDKZJJlMEgqFqJx8MuuPPJIT/vhHFj31FMds/hNrmceN/APf4UIuuCBCKuWkUCjg8XgoFov6Th21sE2qJUIIISab/bKHZDIql0e/fzOGYehqiBq73V0wsVqtBINB7HYPkMFuz+J05igUCpimqe+lUZWQQCCgbxS22+3k83lyuZxeE18ul2loaNA3Dw8PD+vL/qLRKI//7d/y6nHHccY999C6aRNf51Y+yV2M/OYqMhdcgHvH7hKXy0WpVKqurXc49O6SqVAt2dO/YyGEEBNvSq6OX7ECLrts58e33QaXXrpnv7ezE9auLTNjRpGWlp3BRPVpzJwJvb0lIpEir76a0TcB5/P5URtWS6WSHuFNJpPE43FSqZReYa8qGdlsFtM0SSaTpNNpLBaLbpJtbGzEYbfz/Ode4TvcTxsxAHoXLmTjpz+N44gjCAQC2Gw2PB6P3ihrt+/sLzlYg8m7+TsWQgjx7shdNnugsxOmT4dKZedjVits3Vq9E+atrFgBl18OpgmGAcuXV/j7vy+Mqpi0txsMDVmpr6+weXOJYrFIJlMNJuo+HBVIKpWKvl04nU6TSCR08EilUpRKpVFHRiq85HI53R8SDof5znca8VDgizzKF6yrsJXLVKxWNp13Hr1XXom/tRWXy6XX3Kv1+CqMjMcxzk03QSIBgcD+35L6bv6OhRBCvHt78/o95Y5sNmwY/UIF1ZL+xo1v/WLV2QlXXFENI1B9f/XVFs47z8W0adVgsWIFDA3ZAYOBAZNf/MLKxRfvHAtWUzmqZ8Riseh19GoaJ5VKEYvFcLvdZLNZkskk5XIZh8OhqyOpVIpkMsnIyAj5fB5IkSHClzmfps8dx6n33cfcN95g7v330/rEE2z85CcZ+tCH8AeD5PN5PeGjlr2NxzHORK5t39u/YyGEEBNnygWSuXPBYtn1X89z5rz173vrFzkL/f0Orr22usK9yuDaa22ccUaR9vbR+0rU0UwmkxlVnVBNtOpzBgYGdH9JMpnc8VyrS9qcTqeuvMAQkARidDjquPsf/oHpa9Zw5v33Ex0YYOH3vsfgvfey/tprsZxwAqVSiVwupxtf1c3C+7LpdSL7N/b271gIIcTEOTgbCN5Cayvccsvox5Yvf/t/OasXuVq1L3LVwLLrkrItW6xUKhX9Yu92uwmHw9TX1xMOh/ViNJvNhsVi0ffiBAIBZs6cSVtbG4FAgEgkgs/nwzRNfdeOz+cjHA4DPiAPDLBp0yY6OztZO2MGP/7MZ1h5wQXknU7qNm9m6bJlNP/LvzCydi2JRIKhoSGGhoaIx+O6V6VSqYzaWrs3VqyA3t7qf/f2Vj/en/b271gIIcTEmXI9JEpzc/XFsqkJenr27PeM7SG59dadjZK771sw2bChRHPzziVr6o+7eiletcckkUjoikmxWNSNr4ZhEI/HqVQqJBIJBgYGdL+J6kUxDINvfzsNlIAkH/pQVm+C9Xg8NDU1EUinOe2hhzj8xRcBKLjdrPvoR0lefDGeYBCXy4Xb7da7Umw2m27SfafHOJOpf2Nv/o6FEEK8e9JD8g6ovWzvZD/bpZfCOedUj2nmzBn9Aqv+VV472bF8ucHMmXYqlerIcO2StdqKid1up1Qq6UqFCialUgmfz6fvzPF4PAwPD4+6/6baj+IFioBBOOwilUrpXSfpdJq6ujru/sAH+Ovxx3Pm3XfT3NXFwttvJ/7ww6z71KfInH46Pp+PfD6v95bs7e6SydS/sTd/x0IIISbGlK2QtLbubLrs7Nx3X/ft/lVeWwEBdEOpYRiUy2UKhYLe/JpKpfTnq89Vo8IjIyP6xt+77qpO8LjddpYuzVMul4nH4+RyOUqlkp6uiUajREIhjnjpJU754x/xptMAdB57LFuvvRbH/Pm6pyQQCOgx4XdSLZlMFZLx+jsWQgjx1qRCMgm83b/K7XY7drtdBw31Il9bMXE4HGSz1Xtwao9oyuUyfr9fj/D6/X7i8Tgf/3hcr5svFHZufM3n83qqR/WKJOrqSM2fzxvz53PqypUc8+yztL7wAk2XXsqmD36Q/ksvxVNXR7FY1PfpeL1erFbrHq2g332lSPo3hBBCvDWpkOzjfz2/0687tmJSe0RSLpfJZDJkMhkdTLLZLJVKRa+dz+VypFIp4vE4Q0NDelRYHbFUKhVSqRSJRIJisagvEayvrycajdI8PMx7HniAmZs3A5CORlnzyU+SPPdcItEoLpcLv99PIBDA4XDoO4je7hhnd5Wi/b2bRCokQggxMaRCcgBSFRO1ll694JfLZQzD0M2pmUxGv1fL0SwWC8FgEIfDgcfjIRAIMDIyom8OzmQy+muoSwDVcVBXVxeJRIJBv5/uCy9k4caNnPXgg4SGhlj8n/9J3wMPsP6aa8gefbS+CDAYDOL1evdoBf3nPrczfCgTuZtECCHE5CaBZJJQ9+VUKhUKhcJug4nX6yWdTus9JalUSt9/o3aTuN1uQqEQw8PDeipHra1XYUL1oaiL+1KpFLH6ejZedx1Ln3qKE1atonH1auqvvZYt555L95VXkp82jXw+j9frJRgM4vF49DHO7qolEjiEEEK8ExJIJhm1i0Sti1fGBhM1pquOYwqFAg6HA5fLpd8HAgEGBweJxWIUi0Xi8Tg2m41wOEw2m9W/b3BwcGcwWbyYlxct4uyHH2bBG28w+8EHaXnySdZ/4hP0/s3fEKmvJ5fL4ff7CYVCOBwOAN34KjcJCyGE2BsSSCYpi8WC0+ncbTAxTROfz6fHc9VxjNoAqyZlHA4HXq+XSCTC4OAgVquVYrFIMpnE4XBQV1dHJpPRY8QDAwOMjIwwFI3S8973smDxYs598EEa+vtZ9MMfMvyHP7DummvInHCC7l2JRCL4/f53tbtkvCxbtuuxkRBCiMlJAskkVxtMSqWSfry2YuJ2u/Vtvuqem1wuh8/nw+l06h6TcDjM0NCQnsCpnaJRO02y2Sw9PT3VRtlQiPUXX8zJb7zBKX/6E5GtWznhc59j28kns+3qq8nOmEGpVCKTyeDz+fTOlDc7xtnf5NhICCEOHBJI9qN3M2VisVhwOBz6pmBlbDBRwUA1sBaLRbxerx4V9ng8RKNRenp6dIOrqrioqoq6VVjdQDzY2sqLV1/NOatWcfRf/8r0J59k2vPPs/6jH6X3ox8l2NCgp4Ci0eioY5zJUi0RQggxuU3Zsd/xGkF9q6+7L8dQVTBR4UQd5aiV9OpIJZFIkEwmd9wKXB0DVhM4yWSSrq4uMpkMhUKBYrGoP09teVWVjkAgQF1dHQsyGc5/8EHauroASDY2svqKK8ideSaBQEAHHp/Pt8sxjozhCiHE1LA3r99TNpBMhPF4QTZNU48Mw85gYrFYRu0xicfjuwSTZDKpg0lPT49epKYmfdSek0KhoHefRCIR6qNRTti0ibMffRR/KgVA11FHsemaa7Adeig+n49gMKgvDlShpr3dQne3IYFECCEOcrKHZAoyDAObzabvnTEMQ4cUwzD0RI7H49H/xxGLxSiVSnqviFotH4/H6evrI5/P694Vl8tFMpnUVZf+/n5isRgDoRAvXXIJ5/7lLxz/3HO0vPwyTVdeyYb3vpfuSy4hP22a7i2JRCL6GAcMpuAl00IIId6GBJKDxFsFE4vFMiqYBAIBHUwMwyAcDuP3+3E6nQSDQQYHBxkcHCSVSmG324lEIuRyOaC6WVYFk3Q6Td9hh/H0oYfygccfZ97mzcy/5x7aV61izcUX0/u+9xEMhchms0QiEfJ5L+CgVCpjmhPf9CqEEGLykEBykFHBxGazUSqVsFgsVCoVKjtuu1MNriqYJJNJRkZGAIhGo6RSKaxWK9FolN7eXoaHh8lms5TLZUKhkD7qUevqs9ksCa+Xbeecw3H9/bz30UeJxmIc873v0b9jTDh15JH88pduhoaigEFfn8mtt1a47DLZXSKEEKJKekj2o4lq6iyVSpTLZR1MVDhRPSbpdJp4PE48HtdNsmrKplQq0dPTw+DgIKVSiVQqpSsv6kI/dT9OIBCgIRDg3DVrOPPZZ3EUi5iGwZqTzuHUJ3/GIPX6OVmtJhs2lGhvt8g0jhBCHGSkh0TsVm3FpDaYmKap78rx+Xz6KCeZTI7aT9LS0kJdXR39/f3YbDZ9E7CaoimXy2SzWUZGRkgmk9zR3s4zc+bwoeee48jVq1nw5B9ZxyF8ma+znCspY6NcNnjllTRNTU7sdvuk2V0ihBBiYkggmUJUMFHbX9VxTu0eEp/PRyKRIJFIkEqldH9JMpnEZrNRV1dHZ2envm3Y7/eTyWSwWq3kcjmy2SylUom0282Wo4/mmPnz+dtVT9M21MsPuJYruIXr+B/+bDkFl6uTvj4foVAIj8ejQ4lUS4QQYuqRQLIflcuj308UNUFjmiaFQkEHE5vNplfSBwIBHUwSiQQ2mw2Xy0U6ndbvOzo6sNlsepmaurlY3ZOTy+VY5XLx1/PO4ui/OLhw7d0cwaus4jRemHEWyb6L6AnO08EnFApht9uxWq1yjCOEEFOM9JDsJytWwGWX7fz4ttvg0ksn7vnUMk2TYrGod5ioY53aHhN1M7BpmmQyGRKJBKVSiWQyyfbt20mn0xQKBbLZLMVikUKhQCqVwjAMvSH28d9H+TfWchV/xopJ0W5n3Yc+RO9FF+GvrycSiRCJRHC73TgcDh1K5BhHCCEOLLIYbZLq7ITp02FHLykAVits3VptdJ0sdhdM1DbYXC5HJpNheHiYRCIBoCdtCoUC8Xic3t5eHUxqt7/mcjlM02TlSifg5ghK3DntRWZ3dwOQqKtj9WWXkTnrLMI7QkkgEMDr9eojHKmWCCHEgUOaWiepDRtGhxGoHtts3Di5AomallFr6Q3DwGq16t4TNS6cSqUYGhrCNE28Xi+JRAKLxYLP52N4eJj+/n59jJPJZIDqRA9kgBKvYONLJ5/MaX19fPQvfyE0OMjx3/oW3fffz/prriG7aBHJZJJQKEQoFMLhcGC1WnW1ZLzW/gshhJg4UiEZJ7Uvmh/5yIFRIRmrtkqijnDUGnl1lDM4OEgmk8E0TeLxOMPDw1QqFXp6ehgZGaFUKumL9+66Kw8UgAJnnmmvbo+12fj41q2c/eqr2MplyhYL6887j65PfpJAa6u+Q0fdJGy1WmlrM2QFvRBCTGJSIZlEbrpp586RZcvglltG95AsXz65wwjsuv21VCrp/7ZarbjdbrxeL+l0mqGhIb2qPpFIYBgGzc3NdHZ2Eo/HsdlsQB5IAVbK5TzxeJy8y8XNbW080NjIFWvWsGjbNg594AGmP/kkr33843R+8IOk02nq6uoIhUK4XC5KJStgoVyWYxwhhDhYSIVknOxuCVpzM/T2QlMT9PRM7PPbG6piUvtWLBb1XhIVTNLpNPl8Xm+Bzefz9PX18d3vJoEskOCCC0oUCgXy+TymaeJwOPD5fBw/MsIlr75KYywGQP+sWay5+mosS5YQCoV46KFWPve5ENU7cUxuvRUuu0yaXoUQYjKRCskksrsRX6t19PsDTW3FpFKp6IqJGvd1OBx4PB4SiQTxeBy73Y7f7ycWi2G324Ec0APYCQSqAcZqtZLP5ykUCsRiMVa6XLxw0kl8sKODD69eTcPmzTR87nNsOPlk/vqRz/D5zx9ONYwAGFx1lcl73lOhvV2qJUIIcSCTQDIOVqyoVkKg+n7Fiskz4rsvqGZXtb+kNpiokV2fz0cqlWJkZAS73b4jIQ8DbiBFJNKjbxK22+3k83ny+bye0rmjuZk/NTZy6YYNnLxlC3OffJLmp17k05UOfsA1lLADUC4brF1bpLl553MSQghx4JFAso91dsIVV4x+7Mor4ZxzJub5jCcVTFRfieotsVqteDwevfk1mUwyNDQEOKhWSfqZNcvN4OAgTqeTbDard5YUi0VdMcna7XzzkEOIbZnG/7KWxZUhvsf1etvro5yFxWJSVxejUPDq6o1arLY3ZIJHCCEmhgSSfeytRnwPZioIqDHhUqmEzWbD7XbjdDrxer1cc02a/v4hrFYnra3Vu3AikQgDAwO4XC4cDodeQ6/2oQwPD/M8LpawhEvo4Tu2jSworeFPnM3v+QCvfeIKcrkg27eHiUaj+P1+vXV2b6olY5uRhRBC7B8SSPaxuXPBYtl1xHfOnIl7TvvT2FCijnKcTidf+IKbbNarx4U9Ho/uNQmFQgwODjIyMoLb7SaVSpHP53fcTGxQIcYKIiTPOIETHu7mWlbzIe7m/b+4n9dHLqDrk58klUoRDAZpbGzE6XRisViw2+2y6VUIIQ4AEkj2sdbWA3PEd1+zWCw4HA7sdjvFYlEvVnM6nXqVfCqV0vfmjIyM4PF4CIfD+lbhXC6Hw+GgOplTAjJ0Z6x8yVvHLytL+W9zHSfkBjjynntIrFrFK5/4BMn3v59MJkNDQwPBYJBKpYLdbt/jFfST5b4hIYSYamTsd5zsbsR3d6PAU0XtWnq1LC2bzZLL5YjH4/p24cHBQWKxGMlkkoGBATKZDD/9aYJqKMlw4ollfRzkcjo5fXiYK9avp37HRtjOefNYf801uI89llAopNfQ1x4pvZnJfN+QEEIcSCbd2O+NN97I73//e9auXYvb7Wbp0qV8+9vf5pBDDhnPbzsp7G7Ed9mynQ2TU03tWnpVMXG73boK4vf7SSQS+P1+RkZGGB4exuPxEIvFqE7mxAEnDkcCq9VavT8nkeCPPh/PLFnCxzs7+fDmzbSuX8+0669nzWmn0XHFFSRmzCASidDQ0IDT6aRcLu/2GOetmpGnWnVLCCEmwrgGklWrVnHNNddw7LHHUiqV+Jd/+Rfe8573sHr1arxe73h+60lJmiRHB5NyuYzNZsPpdJLP53E6nQSDQXw+H8FgUB/fgA/wAiOEw1ZyuRzZbJZMJkOxWKQ3m+VHjY08UF/PNVu2sLSnh8Mee4yZzz3HXz/8YTovuoh4PE5TUxPhcFg3vVa3x1YdKPcNCSHEwWq/HtkMDAzQ0NDAqlWrOOWUU9728w/kI5upfDzzTpimSaVSoVAo6M2tqVRKjwLH43HOOWcEGACGWLYsweDgIKlUSm+EzWazVCoVTNPE4/FwXCLBdZs20ZZMAtDX2srrV1yB8/TTCYer0zjBYBCLxaJHhQ+UG5mFEOJAMOmObMaKx+MARCKR3f66Wo6lqGvuD0RT+XjmnVC7TNTYr2p6zefzuFwuvF4vhlGHac4Aumlr26ZvFR4aGiKbzWK328lmsxSLRdLpNE84HLx4xBH8XX8/n9i8mcbOThq/8hXWH3ccW666isT8+dTV1eljnFKpxLRpdm65xTLlm5GFEGKi7LcKSaVS4f3vfz+xWIw///nPu/2cr33ta9xwww27PH4gVkjE3lEVE7Ug7cc/hmXLAqi7az796VdZvPgV+vr6iMfjDA0N0dfXR6FQIJ1Ok8vl9PZYu91Ok8XCldu3c05XFxYg73Dw6vvfT/ySSwg1NFBfX09dXZ0eVW5ttdLXZxyw9w0JIcRksDcVkv0WSK6++moefPBB/vznP9P6Jv/s3F2FpK2tTQLJFGSaJtu3V5g1y0KlsrMB1WIx+e1vX8Bu72NkZISenh49mTM8PEw6nSabzZLP5ymVSlQqFTweD/OTSf5x2zYO3VGlG6qr4/VLL4XzziMUCtHY2EggEGD2bCu9vTZaWqxy1CaEEHtp0h7ZXHvttdx///088cQTbxpGAF2uF8IwDDZvtu7SaFqpGORyrcyZ4yIYDOL1emloaKC3txe/36/DSSqVolwu64DyAnDVwoWc3dfHNdu3Ex0c5NRvf5vN993Hhk99iuQRR9DU1EQuFwE8VCoVymXLW44JCyGE2HfGNZCYpsmnP/1p7rrrLh5//HFmzpw5nt9OHGR2v/XW5IgjvAQCZTwej16qFggECIfDhMNh+vqq1ZORkREMwyCXy2EYBiPxOL/zeHjyqKO4pLOTD3d1MWv1ato/8xleOfNMNl1xBR/5SBsORxNNTRGKRZueyJFtr0IIMb7G9cjmU5/6FL/85S+55557Ru0eCQaDuN3ut/39B/KUjdg3dres7OKLq8vRMpmMXjGfTCYZHh4mFovR3d3N0NAQ/f39DAwMMDw8TKFQIJPJUKlUyOVyeDweDjFNrt+6lWNHRgBI+P389cILKXzoQ9Q3NOhjnNqlahJMhBDi7U26HpI3+3/eP/nJT7j44ovf9vdLIBGw+623gN76mslkSCaT1WVp8bhudu3s7GRwcJD+/n6GhoYYGRkhm81SKBT0zcJOh4PTUyk+u30707JZADpmzOD1K67As3Qp9fX1/N//NVEqeQgGrfzTP1VDyX/9lyG3AgshxJuYdIHk3ZJAIuDtd7qoqRwVTHK5HIlEdV/J8PAwAwMDdHZ20t/fz/DwMMlkknQ6TbFYBKBQKFDn83FhXx//0NWFq1ymYhi8evzxdFxxBR+49BAqlWai0QidnQ6sViszZljp7rbInhkhhNiNSdvUKsR4slgsOJ1OHA4HbrebTCaDy+XC7/dTX19PIBAgGo3S399PR0cH/f39DA4O6mBit9sZzmT4b6+X+w8/nM92dXHm4CBHPvMM815+mSsrH+AWPkY83sbISBOBQIBKxQZYMU0LYJnoPwIhhDjgSSARBw3DMPSNwh6Ph3Q6jdvtxuv1EovF8Pv91NXV0dXVRXd3N4ODg/T29uqNrxaLhc3FIp9tauLEujr+qaODOek0P+TXXMljfLp0MW+8cTbNzc0UCi2AC9O0Ui6be3ybsBBCiN2TQLIf3HQT0m+wH+0umHg8HkKhEIODg/j9fpqamujs7NRTOf39/fr24VKpxOOmyUvz5vGRWIxPbungCPp5gv/gjWUPsv7yyxkZOQJopVCIUiw6sdlsOpRIMBFCiHdOekj2A7nX5t15t39+pmnqSRx1KV9fXx8DAwPEYjG2bdtGf3+/XrKmGl8rlQqVSoWh9S6+QR9X0Ffd9mqzcUPpbG7i7ylZZ7NtWwvBYBCHw4HFYsFisfC971kkhAohpizpIRFiN2orJvl8nkQigcfjGbVQbWhoiLq6Ojo6OnQzbDqdplQqMUSGqwmwnCC3B3o5IpHgmzzIpTzN9eWP8teX38f0GTNobm7G7XZjt9v57ndNursNWlosEkiEEGIPSCARU8bYYBKLxXQw6erqIhQK0dDQQE9PDx0dHfT09DA0NASYQIW/UuZD0Sgfr6vjqs2dzCbOvdzC+s8+yquXXELf8cczc+ZMwuEwlYobsGGaJqYp/SVCCPF2JJCIKUcFk8bGRvL5PG63G5/PR0NDA/39/YTDYerq6qivr6ezs5NXXwUIAOvZvKWXO2ZW+C9m8C+k+Ed6mLdpE7O/8hWeP+EEnr3oItoPP5x0egYQolJx6E2z0vgqhBBvTgLJflAuj34v3plly3Y2Be9LtRWTbDaL2+0mGAxSV1fHwMAA0WiUjo6zgfOojvaWga9imneQpsK/YufHzOX28AAnjYxwwlNPcdjLL/PU+95HKvle4HByuRbyeS92u11/T2l8FUKIXUlT6zjb3erzSy+duOcj3pxpmqTTaX0h3xtvxHnf+xZhmrXhoczChe/jtdfSQDOwmZaWbt5vs/HF/n7admx7fYZmPs3HeZFTWbfuEOrq6nR/iQokFovsLxFCHJz25vVb/j/iOOrshCuuGP3YlVfKpM1kZRgGPp+PxsZG6urqyGZbx4QRACul0qeAx4BfA8+QzX6c+8plTg2H+e9p08hYrZxAD8/zXW7lyzx77728/PLL9Pf3k8lkKJfLmKZJuVymMvY6YyGEmKKkQjKOVq6EM87Y/eOnnbbfn454hzo7Yfp0k0qlNpRUMAx2bGhVyjQ1HY/TOUC5XKbFMLimI8XfU720L+NwsOr00+n/8IeZu2ABs2bNwufz4XK59NGN9JcIIQ4mUiGZZObOhbFVeasV5syZmOcj3pnWVrjlFoPqlA2Aic0WGxNGAKxEIsdhsVioVCr0GAafwMmJTOMlPHgKBc576CHO/vzn2bpiBStXrmTz5s2MjIyQz+cxTZNKpaIrJ0IIMRVJIBlH1Re00Y8tX159XBwYLr0UmppU5cLE43FisYwODYZR4dhjwyxatIi2trYdocLgaeBYgnypvp6Yzca04WE+/uMfs+ArX+H53/yG5557jq6uLlKplL7oTy1jk2AihJhq5MhmP2huht5eaGqCnp6JfjbinQqHIRbb+fEnPlHhZz8zgGr15J//eQNHHPEXNm3aRGdnJ9u3b+ePf+wBBoEiDQ0QtVj4l0KBjw0PYwUKViurjj+ezR/+MLMPP5x58+YRDoe5+WY3yaSxY8OrNL4KIQ5Me/P6LYFkP5DV8Qeuzk5oaxv9mNUKkQgMDJQJhdK89lqCRCJBd3c3HR0dbN68mW98I001sLxENPoalUoFu93OIsPgm6kUx6bTAAz6fDxyzjlkzj2XQxcs4LzzZpJI+Jk2zc327dXvJxM5QogDjayOF2If27Bh18fKZajGeCteb4CmJg9+v59AIEBTUxOvvXYccCjVE9EK0eh3KJWWE4/HebFS4XyXiw+7XHwlkaA5leJjd97J2mef5fEPfpBE4mRgAdlsI6VSAJvNpidypPFVCHEwk392CfEW5s7d9TGrFWw1Ud5msxEMBmlqasIw2vjv/17Azv9pWdiw4Z845JAzOeSQQ3A6nRgWC3cUCpwQDPI/oRB5w2B+VxeX/+AH/Bc3EuT3jIw8w/bt24nFYpR3bNST/hIhxMFMAokQb6G1FUKh0Y8tX14NJWPZbDb6+4NjxoSrI8KzZ5/DEUccwdFHH017eztOp5N4qcRXTJNT6up4yOPBapp8lr+ynm/ySX7Egw88wMsvv0xXVxeZTEaHERVM9qWbboKvfa36XgghJoL0kOwHN92EXEV/AFM9QLCzMfnN+oKqu0ugNi9YrSZ33/0K+fwmbr65gy1bVjM8vJX29n66u7vJZDJYrVbeY7FwQyzNAqoTN5ujUR664AJsJ53EUUcdRVNTE6FQaNT+kn3VXyJ9TkKIfUl6SCYpCSEHtmXLqtWDZHL3lZFaatS79rqA73+/xJIlLSSTAR59tAXTbAK24vWuJZuN0tbWTU9PDw/k89yNi2tx8TXSzBoa4uqf/YynnnmGx9//fhoWLWLRokU0NzcTDAax2+3SXyKEOGhIhUSIPTC2gvB2FYWxo96maZLNZmltTTMykgC6gO3ARj760bX09PSwZcsWOjp8QBsNbOe/Pdv5f5kMABmbjYeOO47N730vcxYsYN68edTV1REIVBtflb0NJgdjhUQqk0JMHKmQCDFJqEqKem8YBh6PB6fTCXgBH9XL+dp4z3vaWb9+PcnkR+jouAqw0k+Zi/KfYkXg1/xHNstRxSIffPppul59lT+edx6bTziBRYsWMWPGDCKRCB6PB5vNRqVSkRuFd7jppp0hSwKJEJOfBBIh9iOr1Qp4ACfgIxz2sXRpIy7XHP7jP05iZ5+5lXL5BzyZf5SlRgeXuFz8W6FASyrFpb/9LS8+8wyr/uZv2LRoEYcffjjTp08nEAjgdDqxWq2Ypin7S4QQBxQJJEJMAIvFSqXixel00dzsweGo380dOTamTTuFwcHfcXupxP8ZBjc4nVyZz3NMZycLf/QjHl64kKc+8AE2zZ7NggULaGlpwefz8YMfOEinrQQCZdn4KoQ4IEggEWIvLFu2sz/h3bBarQSDQRYvLmGxjL5Z2DAqHHNMkMHBY9i2bRt9fX0sKxb5kWHwfauV00sl3vvKKxy/fj33nnQSj555JrPnzGH+/PnceGM98biXxkY7119vlcZXIcSkJ4FEiD0wNoDsbU/Cjh1neixYfTxjhm3HdE71Yj4w+dd/3c5hhy1h06Y61qxZw/bt29m8eTObBgc5p1zm/VYr3zVNpmezfPKRR3jj5Zf5wznn0L1kCfH4XGA2hUKUbNaxo3elSoKJEGIykkAixB7YF02RK1ZUJ29q9fZWH7/00urbl75k0NsLdXUFPvtZP6nUCcycOZOWlhY2bNhAfX093d3dbNy4kbtHRviTzcb1psnnikUOGxxk/h138KdnnyXCxximyMhIHcPDLQQCAex2O06nc7eNryoYqfdCCLG/ycGyEPtBZydcccXuf+3KK3eO2qqpHKfTSTQapbGxkVmzZnHKKadw3nnnce6553L88cdz0kknccQRR2B4PPxbucwiu53fWixYgXM2bWI9P+QqOrGwmG98o5s1a9YwMjJCKpUil8vpba+VSmVUUFIBSQgh9jcJJELsBxs2jN7eWqtcho0bd/9rLpeLSCRCQ0MDc+fO5dRTT+WCCy7g9NNP59hjj+XEE09k3rx59NjtfNxi4UxLI6+ykCjD/IhP8SLHsvbWHPfe+xLPPfccW7duJZ1Ok8lkKBQKfPnLFS6/fPQqotqAJIQQ+4sc2QixH8ydCxbL7kOJ1Qpz5rz577Varfh8PtxuNx6Ph0AgwLRp05g5cyavv/46TU1NtLW1sWXLFlZtmcHRPMhV3MzX+TJH8gpPcDqPrDiMVb3H0nHEEcybN4/Zs2cTiUT4wQ88mKZ31PdTAam1dR//IQghxFuQQCLEfrC7lfLK8uV79uJvtVrx+/2jgsmMGTNYu3Ytq1evpq6ujrq6Is8+a/ADruXX/D++wZe4gls4e+ANTvrZGu4+7DCePuccurq6mDt3LoXCLKp7UYya72MyezajHhNCiPEmgUSI/aTatDq6sbWpqfr4O2Gz2fD7/Xg8HrxeL6FQiOnTpzNz5kzWrVtHsXgLL754BUPUcTXf5xZ6+F/u5cRKhY+99hpdGzfy6yVLePyUU8hmtwCnALNQ0z3f/36J5maDSsVyQO8vkUZdIQ4sEkiEGAdvtqdENa2q45uxl/Xt6X4TwzCw2WyEQiHcbjder5e6ujpmzpzJ3LmbuOSSrwMl4AW2Rf7CScNwIfAfQEs2yz8+/jgvvPEGv+Q0NtAPHAIcit8f4KMfdZHL2bHb7VitViyWAy+Y7K5R950GPyHE/iWX6wmxH6lL7FQg2VeX2ZmmST6fJ5VKMTQ0xPz5HcBaYB3vfe9Wtm3bRmdnJ8WREf4VWAY4gAIG32MWX+cMUhwKzOS11+YQiURwu93Y7TuDidVqPSD2l3R2wvTpo/t1rFbYulX6YoTYX+RyPSGmKMMwcLlc2O12vF4vX/pSiO7uJlKpNo49dgM//ekGcrltZNnMDa4OfpLL8V/A+Zj8M5v4e7bxeY7gF5zJk0/2MGvWLKZPn04oFNJf12azvaNgMlG37e5uokkadYWY/KRCIsR+NF4VklqmaVKpVMjlcgwPDzM4OMjRR28A1gFrOOOMPrZu3UpPTw+nZz18jwxzyQLwND7uO/sEnEuXMn36dGbNmkVTUxORSASHw6GrJTabbZeNr2MDiPpZx+NnfCtSIRFi4u3N6/d+ORj+wQ9+wIwZM3C5XCxZsoTnn39+f3xbISYtnw+++tXxqRwYhoHVasXj8dDc3MycOXPweo8D3gOcxfHHH88xxxxDS8tX+AN9HM4IX+DfSWFjKSn+/ZFHOOaWW3j54Yd5/vnneeGFF1i/fj0jIyOk02ny+Tz5fJ5CoUC5pmP0ppvghhuq7yeSmmiqtaeTTEKIiTPuFZL/+7//4xOf+AQ333wzS5Ys4Xvf+x6//e1vWbduHQ0NDW/5e6VCIg42E1E1qFQqTJtWoq8vg9c7xD33bOXFF/v4whf+36gbhpvZzreZz9/vqJbELRZ+MXcuG846ixlz5tDW1kZraytNTU14vV6cTqc+wrHZbEyfbh31s01UhUT/PM3VhtamJujp2f/fX4ipbFL2kNx0001cfvnlXHLJJQDcfPPNPPDAA/z4xz/mC1/4wnh/eyEmlX11S/A7YbFYsNkcgA2v182RR4bo6EiNCiMAPbTzCd7DzdzD/wJHVypcs24dG7Zu5R/tp/Ba3QeYMSPD9df3MXPmTD3ho+7HKZctgJ3JsgBaTTCNnWQSQkxO4xpICoUCL774Il/84hf1YxaLhbPOOotnnnlml89XpWAlkUiM59MTYr/bn82du6oGk1AoxIknerBYTCqV2ubUMuBmcN48zh8e5v2Dg3wTmJvPc2/+EX6TCvFPW/+DW7wv88EPPk9rayvt7e16i2yx6AKgUIBKZddgMlFNrkKIA8O4/lNmcHCQcrlMY2PjqMcbGxvpHXvtKXDjjTcSDAb1W1tb23g+PSGmHNVfMmeOi5tvNgF1YmuyYMHznHTSYZxyyinMOeQQHp01i8X+Q/g+n6KMhY/wW9aygKMfeIUHf/8cL7zwAi+88AKbN29m+fISQ0NewMnAgINbbilTqRSBnZ2lk6XHRAgxOU2O2uoOX/ziF4nH4/qto6Njop+SEAclwzC4/HILjY0mUCIcTnPvvQ3827+dyPHHH8/ZZ5/Nqaeeiqf1TD7NDziKl3mcU/GQ5d+4gZse+iWZO+5g5WOP8ac/reXLX25k56p5g2uvtVEoFIAClUqRypvdLCiEEDuM65FNXV0dVquVvr6+UY/39fXR1NS0y+c7nU6cTud4PiUhpqQ3W6Nus1kAA7fbQ2trK6FQiLa2NjZs2EBjYyM+X4I1ayq8xiJOZyUf4Td8h3+ivdzJN9eu5ent27lpuoFpXjnm+xnkcjnASrlso1CwUanYqP4b6N39O0iOfoQ4OI1rhcThcHDMMcfw6KOP6scqlQqPPvooJ5xwwnh+ayHEDrtboz6agWFYcDiq/SWtra0cddRRLFmyhNNOm8PHPraS6tGLwW/4MEsCH+TfDYMcsDST4ddrbucmPkOAuP6KFkuFbLYHiFMopMjlcpTLOaoVk9K7qpjI0Y8QB6dxP7JZtmwZt956Kz/96U9Zs2YNV199Nel0Wk/dCCHGT2cnXHHF6MeuvHL3Y7iqv8ThcFBXV8e8efNYvHgxn/mMD7gf+B3wJQ47bg2/PuwwTm9o4B7DwAZcz/+wnnl8khUYFDnnnLuoVF4H+kilRojHq8EEclQqBUqlEqXSuwsmQoiDy7iP/X70ox9lYGCAr3zlK/T29nLkkUfyxz/+cZdGVyHEvrc3a9TVZXoWi4Xm5maCwSDLltXR2dlNLjefE04IsnHjRrZt28Y/b9/Oz7q6+Pd0mvn0s4LLuJLPcuNfPw78CLBQKlX4n/9ZSzabAXwUCk4yGQcOh0OvozcMY48v8JNbfIU4OMnqeCEOYm+3Rv3tlpeZpolpmhQKBZLJJAMDA/T09LB161a6u7tZu3YtmzZtIjU8zDkbtvBVSqj/pf6Ei/kC36KfRgyjgmn+AQji8TSxZo0Tl8uF0+kcdU/O2wWTFSvgsst2fnzbbW9+i+9EL2YTYiqblIvRhBATR61Rr30Rr12j/naL2gzDwDAMnE4nNpsNn89HKBSivr6ejo4O6uvrmTlzJmvWrOGmDUHuoI8bKXMJ3VzC7XyI33MDX+V/zU9TYg1gJ5Nppbu7lbq6OtxuNzabTV/gZ7fbMU1zt8HkzY6fzjlH1sILcTCQCokQU8C+WqNe3chaJp/PMzIyQmdnJ729vXR1dfHpT28BNgIZlvA1/pfPcix/AWAN87mORv7E8YCd//3fRtrb22lubiYSiehAoiomdrtdX96ngsnKlXDGGbs+p5Ur4bTTdn1cpnGEmDhSIRFC7Na+WqOuQoLVasVut+P1eqmrq9txL1Ud0AxsJX348yx5/Wku5ud8iy9wKGt5hLXcRZh/5D9YseIhzjhjFQsWLKClpYWmpibC4TDFYhGHwzHqKEdVTObOtehbkmt/rjlzdv9cJYQIcWCRQCKEeEfUMY7D4dC3CgeDQa67LkxPTxOFQiPHHrudL33pC/wEk99zIl+lnU/zAz7I3ZzHg/znX/+RlY4tdHR0MHPmTA499FBaWlpoaGggGAxSKBRwOBy4XC5sNht2u53mZpObbza44oqdRzlyi68QBw85shFiChjPBk/TNCmVSuRyOZLJJH19fQwMDHDOOZ3AVqrbBb7Goazmf7iOs6juJeow3Hy3KcSaww+nqbmZGTNmMHfuXJqbm0fdKGy323G5XLoq09pq0Ndn0NRkkVt8hZik5MhGCLHfGYaB3W7XgcHtdhMOh7HboxSLDUAMw6iwxlzA2TzCB7ibm1jGTHMr3+vJ8tTgIN9pa+P5/n46Ozs55JBDaG1tpa2tjbq6Orxe76ijnOqNwlZ9OeCejgsLISY3CSRCiH3CYrHoaRyn04nP52NkxIfFMsSnPvUq3//+IsDC3byf1W0vc1nse1yTTHJiscjxmzfz874+fj53Lv39/bS0tDB37lxmzJhBS0sLoVAIt9tNoVCgWHQADsplB6Zpo1wuv6M9JkKIyUkCiRBinzEMQ+8UcTrtgBO/38/VV8f4/vefB/LAK5xxwQCPblnKyo4Ortq8mffnclycTvP+V17hps5OVs2dS09PD1u2bGHu3Lm0tbXR0tJCOBymVHID1WBSu2BNgokQBzYJJEKIfa46iVM9WnG57DQ3u/H7/SSTg8AiTjopSmNjI+sjEf67qYnfb9zI57u7ObRU4huDg7wWi/HvjY2sTafp7e1lxowZ+u2ii6ZRKPioq/OQThd1A6zT6cRqtUowEeIAJYFEiCng7RagjR8LNpsLv9+G220nmfTg9cY49NAAzc3NtLe3s379ejaEQnyqv58z1q3j04ODLCyV+HVXF78fGeF/W1rIZrP09fXR1dXFYYe1MWPGDKZNm0Y67cflclEulymVSjgcDt3PIsFEiAOLBBIhpoCJ3slhs9mw2byAHa/XSWurH4/Hg8/no6mpidbWVrZs2cIL4TAX9vby8dWr+Vg6zYcyGc7ZuJEfDA3x+/Z2PcXT09Oj+0taWlrw+XzY7XaKxSJ2u12CiRAHIAkkQoj9wjAMwInVaicQqDa+er1evF4v0WiUadOm0dDQwPbt2/lFNMpD27dz3YYNLC4W+efhYf4uHufG/n5Wz5xJLBbTRzk9PT20tbXR2NiI1+vF5XLpqRy19VWCiRCTnwQSIcR+ZbFY9MIzq9WK1+slmUzi8Xhobm5m8+bNNDc3s7GxkX+dNo3jNmzg2o4OZpbL3NLdzWPDw3y3tZXN7e0MDw/T09NDf38/TU1NzJgxg2g0itfrpVQqYbPZcDhG3ywswUSIyUkCiRBiQthsNrxer65iuN1u/dba2kpTUxNbt25lXTjMJ9vb+ds1a7hocJAzcjlO2riRFf39/HzHMU5/fz9tbW0MDg4ybdo0XW3xeDyUSiXd+KqCicViedNL/IQQE0MCiRBiXOxJI626SVgdqzidTtxut95j0traSnNzMxs2bODuUIhHOju5et06TslkuDqR4ENr1vCdwUGebG8nlUoxMDBAf38/g4ODNDQ00NbWRjQaxe12Uy6XKRQKujojwUSIyUUCiRBiXIxtpH2rgGKxWPB4PBSLRaxWK263m1Qqhcvlor6+nra2NlpbW9m0aRP/0dzMfZs28enNm2kvFvnP3l5ejMW4sbmZ7e3txONx+vr6aG9vZ2hoiMbGRlpbWwmHw7hcLkql0qibha1WqwQTISYBuctGCDGpmKZJLpfTRy2xWIx8Ps/IyAgdHR1s3bqV9evX0799O+evW8fFXV14TJMK8Gu/n1unT8fe3EwgECASidDe3q5vJG5vbycYDOowMrbxVd1mLMFEiHdH7rIRQhzwDMPA7XbraondbieVSuFwOAgEqvtL6uvr6Zoxgyfq6ljV0cEla9Zw7sgIH08muWD1av67r48/7DjGSaVSBINBpk+fzvDwMA0NDUybNo1IJKIbX2srJhaLZVTFRL0JIcaXBBIhxKSkKheFQgGLxYLb7SaTyeB0OgkGg7S0tNDQ0MDmzZu5pbGR+zZu5LqNGzkkm+UrAwN8NB7nW9OmsX7aNILBIMlkkvr6emKxmJ7KaWpqIhqNUiqVdDhRwaS6bdY6KpRIMBFi/EggEUJMamoyJp/PY7VacblcOBwOPB4P0WhUL1Zb19zMP7e3c/KaNVy2bRuHFAr8ZOtW/jA0xP+2tdGdy5FIJBgeHqa+vp5kMsng4CCNjY00NjbqYFIul3UYUqPCtUc5EkyEGB8SSIQQk56qkKiKid1uJ51O43A48Hq9NDQ0VJeqTZ/Oq5EIV82axYdffZUP9vdzfjLJ6WvWsLy3l9/NnEk2myWVSjE8PExLSwvJZFKHlIaGhh0X+JX01lcJJkLsHxJIhBAHDBUM1DGOy+XC6XTicDjw+Xw0NjbS0NBAR0cH9zY389DatVy9di1HJ5N8dniYD6dS/EdjIy9Mm0Y6nSaRSDAwMEBDQwOxWIyhoSEaGhqor68nGAxSLpd3G0xU06sEEyH2HQkkQogDitpdosKB2sbqdDr1MU5jYyP19fVsbGjgxvZ2Fq1ZwxUbNtBaKPA/HR38eXiY/2huZrClhUwmQywWI5FIEI1GSSQS9PX10dLSQigUIhQK6cv7aidyJJgIsW9JIBFCHJBUP0mpVNLTOPl8Xk/MRCIRGhoamD17Nq81NHDdzJlc8Oqr/L/OTk5Kp1mycSN3jIzw45YWyuUyyWSSoaEhhoeHaWpqIp1O4/f7mTZt2i7BRIUgq9U6an+JBBMh9p4EEiHEAcswDF2xKBQKWK1WHA4HmUxGH+M0NDQQCoXo7u7miaYmntq6lYtefpmTh4e5eGiI98bj/PfwMH+aNg2AWCxGMpkkHA7T0tJCOp0mGAzS1NREOBzG5/PpILS7YDI2nAgh9owEEiHEAU/1k5RKJb1HRI3vut1uAoEA9fX1hMNhOtvbuaW5mUfWrePS115jei7H1zs7+X+xGN+aNo2tDQ2Ypsnw8DDpdJpAIKCbX0OhEA0NDXodvdPppFwu6wqNzWbDMIzdVk2EEG9NAokQ4qCh7qdRbzabjWKxiMPhwOVyEQqF9P6RTdOm8cX2dk579VUu3LSJw1Ipfr5+PfcOD/ODlhby9fXk83ncbjfJZJJIJKKPcgYHB6mrq6Ourg6Px4PdbqdSqehwIsFEiHdOAokQ4qCiqiNWq3VUQFC9JX6/n2AwSF1dHdOnT+e15mY+s3kzH/7LXzi3v5/3Dw5yxsgIt7W08NvGRirRKFu3btVHOdFolGg0qkeH6+rq9FGOOsI5UILJTTftvF9o7N1DQuxvcpeNEOKgValUqFQqFItFPcKby+XI5XKkUilisRidnZ10dnayfv16IuvWccnLLzM/lQJgi9vNd9vbWdPSgtPpxGKxEI1GdXWkrq4Or9dLOBwmGo0SCATw+/2jRoTHVm0mUzBpbYWuLmhpgc7OCX0q4iAjd9kIIUSN2iBQLBb1MY7aX+L1evH5fESj0er+kunTuXH6dBa/9hoXrV7NzGyW769bx6qBAf67vZ1kXR3Dw8MkEgn9FgwGyeVyxGIxotEokUgEr9eL3+8fVS1RxzoWi0W/nyzBRIjJQAKJEOKgp45x1Gp4i8Wiqxc2mw23200wGNTbWje3tPD5efM4//nnee+2bZw6PMzxsRi/am3lZ83NOMNhBgYG9GV96XSaSCRCoVAgmUwSCAR09UQd5ag9JrXHOBMdTMrl0e+FmEgSSIQQU4Y6PlHHJmOncfx+P4FAoFotaWlhVUsLq954g0/85S8cNTLCxdu3c15fH9+fOZMn6uvx+f10d3czNDREU1MT8XicaDRKOp0mHo8TDAZpaGjA7Xbj8/moVCo6mNTeKjwRwWTFCujtrf53b2/140svHfdvu99If8yBR3pIhBBTkqqWqDfVX5LP5/UlfN3d3XR1dbF1yxZmvPQS//DqqzTmcgC8HArxP7Nns9Xvx+fzYbVa8Xg8ehonHA4TiURwOBy6EdbpdOLz+fTuFBVMxt4qXPvxeOjshOnToVLZ+ZjVClu3VvtKDgbSHzOxpIdECCH20NhqiQoI6nZhj8eD1+ulrq6OpqYmNtbX843DD+fEp57igxs2cFQsxq0vvsi9LS38ePp0iqEQ6XSabDZLIpGgrq6OeDxOXV0dpVKJ4eFhHUxcLhder3dUf4l6LrVL1sYrmGzYMDqMQPXYZuPGgyeQiAOPBBIhxJRlsVh046nqLVF35WQyGVwul56ciUQidHZ28npzM8+98QYffvZZTuzt5UNdXZzR18ePZ8/mwWnT8AYCJJNJkskkiURCr6CPRqOUSiXi8TiRSIRQKITL5dJHORaLBbvdrp/beAaTuXPBYtm1QjJnzj758kLsFQkkQogpz2q1YrFYKJVKGIZBuVzG5/NRLBb1nTler5dAIEAkEqG7ro7fzZjBqldf5cLnnmN6Os2ydet4b3c3P5w/nzXhMG63m6GhIWKxGPX19aTTaXw+H3V1deTzeb35NZ/P6zX3pmnqSaDa57avg0lrK9xyC1x22c7Hli+X6oiYWNJDIoQQNVS1RG1eBcjlcmQyGYrFIiMjI8TjcXp6eti+fTu9HR0c+cwz/N3rr+MrlQD4U3Mzt82ZQyoQwG63YxiG7iWpq6vTi9m8Xi9er1ePCqvgU7vDxGq1Auyyx0T997vR3FxtaG1qgp6ed/fnNtlID8nEmlQ9JFu3buXrX/86jz32GL29vUybNo2LLrqIf/3Xf8XhcIzXtxVCiHdlbLXENE29tySTyWC323WQaGhooKOxkS11dXzjqKM4+/HHOX3LFs7q6WFpfz+/nD2b+2bOhB0jx729vSSTSerq6vQFfpFIRB/rRCIRva7e7XaP6jExDEPf1bO7yZy9+1lHvxdiIo1bIFm7di2VSoXly5czZ84cXn/9dS6//HLS6TTf+c53xuvbCiHEu6ZuEVbVEsMwqFQq+qZftVzN4/HgdDppbGxkW1MTjzY18ezq1fztqlXMj8W4bP16zu3o4OZDD+WlxkY8Hg/pdFof3zQ0NOijm2KxSCqVwu/3U19fTzabxe1260sDa3eYqLBktVr3STARYjLYr0c2//mf/8mPfvQjNm/evEefL0c2QoiJZpqmPsJRb4ZhkMlkyOVylEol+vv7yWQy9Pb20tnZyUBfH7Oefpq/+8tfCOfzADxbV8ePFy6kz+/XEz5Wq1X3lQSDQX2Uo45u6uvrsdvtuFwuHYTsdrueylFhRPWWvNNgcjDv6jiYj6MOBJPqyGZ3VHf5m8nn8+R3/I8Xqj+QEEJMJMMwsNlsuqdEHeN4vV6cTie5XI6mpiay2Swul4toNEpfXx+d0Sg3Hn44Jz3+OOevX8/xg4Mc/fjj3DVjBr+dN4+K14tpmsTjcTKZjA4kqVSKcDhMOBymUCjor5nP5/XRUalU0uEE0Gvxay8U3JNgcrCFEOVgX/p2sNpvFZKNGzdyzDHH8J3vfIfLL798t5/zta99jRtuuGGXx6VCIoSYLNQxjmmaupejVCqRTqcpFoskk0mGh4dJJpN0dXXR09ODfdMmznv4YY7c8So56HRy+4IFPNnait3h0MHB7XYTCoUIh8N4vd5RF/b5fD6CwSAOhwOn04nT6cThcGC1WnHs+BqqeqOmdPY0mBxMpsLStwPB3lRI3nEg+cIXvsC3v/3tt/ycNWvWMH/+fP1xV1cXp556Kqeddhq33Xbbm/6+3VVI2traJJAIISYV0zQplUo6kKggkM1myWazFItFhoaGSKfTxGIxtm/fzkB/P00vvMAHn3iCpkwGgNdCIW5btIjuhgZdeQH05I3f7ycUCuk3NXrs8/n0ynsVUGw2mw4mqpIzFYPJypVwxhm7f/y00/b705my9ksgGRgYYGho6C0/Z9asWXqSpru7m9NOO43jjz+e22+/HYvFssffS3pIhBCT2dhqiXpMBZNcLsfw8DCZTIb+/n46OzuJ9/Vx5KOPcv4rr+AqlykDf2xr4zeLFlHw+wGoVCqYpqkXqgUCAQKBAMFgkHA4jMfjIRgM6qZXq9Wqqyaqx6Q2mKgek6kQTCZrheRg7tfZnf0SSN6Jrq4uTj/9dI455hh+8Ytf6Hn6PSWBRAgx2dVWS9SbYRjk83kymQyFQoFUKsXIyAjJZJL+/n66u7spb93KGQ89xAlbtwKQtNn4xfz5rJw7F7vbrftESqWSrpDUhhKfz6ePc9xuN06nUx/fuFwuPZVjsVh0MKltelUTOwejFStGL3277baJ7yGZantRJlUg6erq4rTTTmP69On89Kc/HRVGmpqa9uhrSCARQhwo1CSOOsZRRzCqWlIoFIjH4yQSCeLxON3d3fT39+N/+WU+8NhjTI/FANjs93Pr4Yezua1NN9Oq7a1ut1sf5QSDQaLRqD7G8fv9ukqidpfUfqxGhNU22IM9mEy2KRsJJG9v3KZsHnnkETZu3MjGjRtpHVMnm8TLYYUQYq+o4xC13VX9/zm1q0QtVVMf+3w+otEoveEwPz3sMOatWsUFzzzDrGSSG595hlXbtvGbY44hGQrp24jL5TKFQoFkMkk6nSaZTBIMBolEInqHSSgU0kc4pVJp1HSOmsRRjbhqydrBGExk6duBR1bHCyHEPqZGhGt7SwAKhQLpdHrUMU4mk2FwcJDe3l5yXV0sffBBTl69GguQs1r5zezZrDz6aEynE9M0KRaLGIahL/5T1RF1pBMKhfD5fPj9fj2Fo45vdhdMKpXKqN4S9d8HuslWkZhsz2e8TaoKiRBCTFXqxb32PhzTNHXTaTabxWaz4fV6icVi+gimLxTi+cZGXlm9mvMefJC5fX18Yv16zu7s5MeHH866efNwOp26wjE4OEgymSQQCJDNZvnNb1KYZopQKMCVV4bw+/14d+w7KRaLlEolstmsrtKoAKJ+fexFfgdDMBEHDgkkQggxDmpf1Es7Lt1TL/ZqqVo2m8Vut5NOp/VxTiQSocfv585582h58kkuePxxmjMZ/vX553lpyxZuP/JIMu3t5PN5rFYrxWKRWCxGIpHgkUciQApIcsEFKYLBoG6E9Xq9GIahj34ymQwejweHw6ErJiqY1B7lSDAR+4sEEiGEGEdj78VRbDabbkS1WCw4nU5dufD7/QwMDDDg93Pb4sUc+Yc/cPrLL3P0wACL/vQn7ps1iweOPhoCAT1BUw09Q1QDSZienozejp1IJAiFQgSDQXw+H8ViUQcTtb6+9obhSqUiwUTsdxJIhBBiP6hteq3sWJJRe4yjml7dbjeJRELvGhkIBll90UWsXbqU0++9l4XbtvHBTZs4rbOTnx92GC8ffjgen49UKrXjO+WBAXp6qg2v2WwWn89HJpMhkUhw331hisUAdXV+PvWpEqVSSY8Yq2Ci3mqDSe29ORJMxHiQQCKEEPvJ2HtxAD0i7PV6cblc5HI5HA4HmUwGh8OB1+slFAoxEAjwyIwZvPjss5z30EM0JhJc99JLrN2yhR8feST5OXMAO9VAYoxaZR8Oh0mlUgQCAVasSAMhDMPPP/xDtWJSKpWoVCrEYrG3DCaqEVb9HBJMxL4kgUQIIfYz1fQ6tlpitVrxer16OsbpdOJ2u7Hb7foYJ+b3c8fRR3PIgw9yxtNPM39khG+tXMmjmzZxN0czQAtQwWqt3lKcSqXI5/O4dyxbqx7pxDHNIF1d1T6TUCiEy+XSzycWi2G1WvWkjsViwW6377b5dbIGk2XLdm5GFQcGCSRCCDEBdlctURwOB6FQiHQ6jcPh0Mc4DoeDYDDIoNdL58c/zi3HH8/S++7j2DVrOHv7dtbTzVdZwA85Eo+neluwOpJJJpMUi0XABxSADF1dGZLJpJ7UUWvpi8XiqGDi8/nweDyYpnnABJPJtp5d/RWP+asWNSSQCCHEOHure0zerFpisVjw+/2USiUcDgcOhwOPx4Pb7cbj8ZBIJBhwuXiurY03Xn2VM+++m7aBAf6bV7mcLfzuxSPoX7gQj8dDLpfDNE1yuRzVMJIDfPT350mlUiQSCcLhMOl0Wu80UcHENE0SiQSpVOpNg8nY5td3ek3IwW7FiurWWKi+X7Fi4lfZT0ayGE0IIcbZni7Fql2opqjtqWqZWqlUIh6P66OYoaEhhoeHSYyM8MY/reSbPEo9OQCenjaN3x53HJX2dmw2G+l0muXLS4ABOLn+eocOGR6PR98kHA6H9VSOw+HQY8sqbLjdbnw+nw4hpmnu9iI/CSaT97K/8SaL0YQQ4gBWu1BN3TujqiXqGCeXy2Gz2fB4PCSTSZxOJ16vl2Gvl+v5IL9jCV/jHq41XmJpdzfH3Hsv9xxyCH86+mg8dXWAlWqFxNQBR923k8/nicfjpNNp/YISiUQIBAJYrVY9uqxuNFbBBKpjzOqiQdgZrqZ6MNmwYXQYgeqxzcaNB3cg2RsSSIQQYhKp3fmhjnHUZIvFYtG7StQxjlqu5vF4gBgxXHyWy/F9Zi2n3Hknczs6+MiaNZy+dSt3HH00sAjwAiVcLqsOF4VCQW9xNU1TH+Wk02mCwaB+U8GkUChQqVT07/F4PPq5Azq41AYTFbimkrlzwWLZtUIyZ87EPafJSgKJEEJMQmObXtXxCDBqGsfhcOB0OkkkElitHsrl6UAPjqM9/GnuXN54+mlOufde6pNJPvvUUyxkPZ9hMW9wKE6nTS9IU82vlUqFQqGA2+2mWCySyWQYGRkhHA4TCASIRqN6+kb93lQqpYOJ2+3WwUMd5ahgUtv8OlWCSWsr3HILXHbZzseWL5fqyO5ID4kQQoyzd3uxmnphV02vhmHoF3RVrbjttgrLlgWo9oeYXH/9apYuXUMikSAzOMjhDzzAiU89hb1cpoTBD1jA+o8twtPSolfKFwoFffRS2yvicrnw+/34fD59S7GayFHr62Fnj4nH48HlcukpHPX81edMxWDS3FxtaG1qgp6eiX42429vXr8lkAghxDjbVze97q5aYhgGHR0mc+bYqFR2vrhbLCa/+91fcLuHdBPsK3c7OPX+u/hb7gJgxObjN0fO57XFi/EGAhiGoSdyKpWK3kHidrvx+/06bITDYbxeL9FolGg0itvtxjAMCoXCqIZWdWdP7ThwbYPsVAomctvv25MjGyGEOEDUjgirygPApk2WUWEEoFIxiMfraW+3EAgE2LAhy/cfOJ3/5e85i0f4bz7DgtIarvzLX9i0aRM/P/ZYUgsX6luA8/k8pVJJVz+KxaIe9c3lcrhcLtLpNMlkklAoRCQS0b+3UCjooxqbzYbb7dYVE1WBqb0FeSoFE/HmJJAIIcQBpLa3RL3NmlXecd/Mzhdzq9XkiCO8hEImyWSSdDqMaVYrFX/ibI7gFa7hB/y77YvMHhnhaw8/zKq1a7ln6VIc06fjdrupVCrY7XYKhQL5fB6Hw0G5XNZbXYvFIolEQt82HIlECIfD2O12LBYL+XyeSqWiJ3ncbvdue0yAURM5qnoiphYJJEIIMc7GY0unelE3DIO2tgo//GGZq66yonpIfvjDMvPn+ygUHLhcLhYuzGGxmDq0lLDzv5braLve5PRHfsbRf/0rp27fznHd3dy5YAEvLF2KKxDAZrNht9t102smkyGfz5PL5fD5fHo6J51OE4vFCIfD1NfXEwwGcTqdehRYbY1VwURVTNTFfSq4SDCZuqSHRAghxtGKFaMnLG67bd9v6VQv6C0tFfr7DRoaDLq6dv6aChK3327hc58Lo0LLZz7zOiecsJpUKkVo/XqO+/nPadvRcdnt9/OLxYvpWrgQv99PuVytwqhgoYKKaZr6Lhy73Y7X6yUYDBIIBGhoaMDn8+F0OvUkj2qYtdvt+q4eNSpcu3/FMAz+538spNM2QiEr//iPB3YwkR6StyeBRAghxsn+3NJpmiatrRW6uys0N5ts27ZzEkf1m+Tzedra8sTjJVyuEVatSpLJZEilUsRiMXKZDG0rV7L03nvxZzIA/GXaNH57/PGYs2fjdrspFAp6WVs6ndY9Iqrh1e12Y7PZ8Hq9RCIR3V+igomqtKhgosaWVcUEqDmKMunrM2hqstDRUe0xmUz35bwTEkjenhzZCCHEONmfWzqrxzfqyKYCVCdlal/EXS4XLpedeLyA01khErHjcrlwu904nU7i8Th9553Hb487jkX33MNRTz7J4u5ujrj7bh487DD+tHgxwZYWvVPE5/ORz+dJJpO43W69ZM3j8VAqlUin03rBWigUIhqN4vV6cblc+nhGLWXL5/O4XC6cTqc+ygETKGOaZQqFnT/LZLvIT+wbEkiEEGKcTMyWTsuO0Vtzly2v1TcDsOBw2AiHq0cmyWRSX96XSCRIezys/uQn2XTGGSz+2c+YvWkT73/tNU7atIlfH300GxcvJhgKUSwW9WV/qrfE7XbrKojX66Wvr0+PHcfjcUKhEPX19bjdbt0kWy6XyeVyFAoFHA6HDiaVihWwUS5Xg0ltI68Ek4OPBBIhhBgnE7WlsxpADN30qvaXVF+8q4HEZrPj99v0cUkymcRmq37s8XhIpVIkHQ5WffGLbPjLXzju178mEovxqT//mbXr1vHz446jcOih+lbg2l0klUqFdDpNJpPB4/FgsVjo6ekhFouRTCb17cJqh8nugsmddwYZGKguehscNPnZz0pcckllVCipDSa1y+LEgUl6SIQQYpztry2db9anUPsCPmOGQU+PQXOzwfbtO7eoFgoFvVckl8vpyZlEIkEikYBcjrn33MMxDz+Mo1SiYhisnDePPxx/PJ62NqxWK6VSSQeDXC6HYRg4nU6AUdtbnU4nkUgEv99PXV0ddXV1utpRqVTo6DA58cSWXcaY160r0NZm6MVt6uWr9ihnsgYT6SF5e1IhEUKIcaYuu52oS29rR4Sr/SUVwMAwdj6uXtBdLheZTIZEIoHdbt/Rd1J9bMNHP0rHGWew6Oc/Z/4rr3DmunUs2bqV3x91FM8feSQNzc16aZvH46FcLpPP57FYLLpXRI0C9/b2Mjw8TCqVYnh4mPr6er3DpLPTtcuit3LZYM2aIs3NFj0SrILJ2Ev8JmMwWbYMEgmQf1u/OQkkQggxBaiV7rWhRB3j1P6azWbTky/JZJJUKoXD4SCdTuPz+Rh2OHh+2TI2v/EGx/z0pzT29fGJZ5/l9PXr+dUJJzBw6KEEAgG9Ij4QCJDP50mlUrrxNZfL6SOevr4+HUxGRkaIRCK0tdWN2pkC1QpJY2OSVMqqbztWwcRisehKj3o/2YLJsmUT/QwmPwkkQggxhRhGtY+k+kK9a9OraZr6xV7tCUmlUjidTjKZDA6Ho9qgeuSR/HHePGb/6U8cfffdtA0P888PPMAzq1dzz9KlWNrb8fl8ZLNZbDYb0WiUXC6nN75ms1mKxaL+Hj09PSQSCeLxOJFInK9+Fb761WmonSnf+laMxsYi2Wx1Ikf1vqitsGO3v6r3FosFu90+aYKJeHMSSIQQYsqpjghbrcaoY4/aUFJ7jON0OnWwcDqd2O12HA4HeZ+PLeefT88pp3DoHXew4KmnOGHLFo7u6OC+RYt45vjjCTY26tuE1e9XjauVSkXfe6OOchKJBKlUiiOPjAGdQDNer52LLjKoVHauzB87laOCiary1AaTsVM5EkwmJwkkQggxhdT2MqjwAYxqFB17jOP1ekcd46iFZrlcDofDQdLt5q9XXcXGM89k8c9+RuvWrXz4pZc4ecMGfrd0KRvnz6exqYlisairIlarlWQyicVi0SPDDocDt9ut78eBNDBEJhMikajD7/fvco+PCibqGEfdUKyCCaAXsalgImvpJycJJEIIcZDYk8bJsb0MKnioo5vaaomqkpimid1u1yvfXS4XqVSKbDaLy+XS4STldPLoV7/KzGee4ahf/YrGZJJrHnqI11ev5vcnn0y2vZ1QKKT7SDweD4ZhkEql9H05lUoFm82Gx+MBhoA4phlj8+YE0WiUYDCog4mqgABks1m9XM3hcOiKCaArJrtrgJVgMnnI2K8QQoyzA2XkU62YH7tQDdBhRb2Y53I5PY2jbv3NZDLkcrnqTpNslnl33smihx/GWqlQslh49PDDefSEE3DW1xMIBMjlcgB6c2s2mwXQ9+T86U82ikUPbrfJ3/2dS6+jr6+vx+/34/f7AfRzUj0khmHsNpion7G2EqQqJgfyWvrJSO6yEUKISehACSRKbSipPcJRL/jq8VKpRC6X05tY1S3Aap18NpvF293NottvZ8brrwMQd7u564QTeHXhQkKRCHa7nWKxCIDX6yWdTmOaJsViEa/Xi2maevS4XC7j8Xjw+XxEo1Hq6uoIh8O4XC4qO9bh1lZNDMPA7XZjt9ux2WyjqiFjfz4JJvuWBBIhhJiEbrpp51HKgTT+WfuiDYw6wqkNLYVCQVdLcrkcpVJJ/3c8HqdYLDLt5Zc56mc/Izw4CMCmxkZ+e/LJpOfPJxgM6v4SVR3J5/MUi0UsFsuONfIVvF6vXrTmcDgIBoNEo1FCoRDhcBi3262fb7lcHvWzeDwebDabbmxVoUP9DOrnU70nEkzeHQkkQggh9qnaisjujnFqJ1lyuZze9prP58lkMmSzWTKZDOl0mko2y7wHH2Th3XfjKBSoAE8vWMAfli7F2tREOBymUChQKBRwuVwYhqGDiephUWO8brcbqAaTcDisj3EikYgOMMAuRznqNuKxoaM2xEgwefckkAghhBgXtaFkT45xEokEyWSSYrFINpvV6+gTiQSeWIzDf/5z5jz7LAAZh4MHjjuOF5cswRMIEAgEyGQyeuNrsVgkn89TKpVwOBz6GMftduvA4HQ6CYfDNDQ06MZXu92uqzu1wcRiseB2u/XytLcLJrXNr9IAu2ckkAghhBg3b1YtGduXoY5x1JGN6i1JpVKUSiVGRkYolUpE1qzh6Ntvp76jA4CucJi7Tz+drkMOIRAIYLPZyOfzOjSowFMsFnE4HAB61FeFBo/HQygUorGxkUAggN/v12vmoToCrL6W2otSu9W19nPH3pcjwWTPSSARQggx7t6qWgKM2pRaKBT0CvraYKImc8qFAjMfe4wjfvMb3Ok0AC/OmsUfTj+dQnMz4XCYUqlEqVTS22NLpRKFQkE/5nK5sFgsOBwOvafE7/cTCoVobm7G5/Ph8/mw7rhMSI3+1o40q9+rqiW1xzS7Cya122HFriZtIMnn8yxZsoRXXnmFl19+mSOPPHKPfp8EEiGEmJxqA0nti7UKJiqU7O4Yp1Kp6HFhVUVx53LMu+MO5j/2GBbTpGCz8adjjuGJJUtwhcOEw2F9/GO1WvF6vRQKBYrFIpVKRS9EU+FCVW+8Xi8NDQ1Eo1ECgQA+n2/UinkVTAB9j8+bBRP1M6ufVYLJm5u0geQzn/kMGzZs4MEHH5RAIoQQB5HaUKJe3GuncWqPcVQAicVi5HI5isUiqVRKV1EKhQK+LVs46vbbmbZuHQBDfj/3nnoq6xYswL8jUOTzeSqVij5uUY2v6hhGjfh6PB5dwfH5fDQ3NxMKhfD7/fh8PoC3DCbqDhxVWVGhY3fBZPTlhWJSBpIHH3yQZcuWceedd3LYYYdJIBFCiIOMCiRjX6iBXZpe1TFOKpXS0ziFQoF0Oq1vBS4WCrQ99xyLfvYz/CMjAKxtbeXeM88k0dpKIBDAbreTy+V0gyqgF62p9fSGYejQAmC32/H7/TQ2NhIKhXTFRP0M6oZi9bzVivyxwWTsz6V+XgkmO026QNLX18cxxxzD3XffTV1dHTNnznzLQJLP58nn8/rjRCJBW1ubBBIhhDgAjG16rT3C2d0xTj6fJ5FIkEgk9MV66lgmlUrhLJWY+dvfsuD++7GVSpQNgyeOOILHTzkFSySix4TV91L9JarHRFVLAD1GrB4LBoM0NDQQCAQIhUL619VSNhUoaqd4ahtfFfV71FvtUc9UDiaTKpCYpsn555/PiSeeyJe+9CW2bt36toHka1/7GjfccMMuj0sgEUKIA0dtKIGd47O7O8ZR0ziJRIJsNquXrJXLZb1czdvfz8Kf/IT2l14CIOl2c//Spby2eDFev59AIKBXzzudTr25NZ/Pj+pvsdlsuFwu/bHdbicUChGNRolEIvj9fl1ZUffqqEChwoUKJmP7S2onjSSY7KdA8oUvfIFvf/vbb/k5a9as4eGHH+Y3v/kNq1atwmq17lEgkQqJEEIcHN5qRLj2Xhz166qPRPWSqCVrqs+kUqnQ8Ne/sujHPybU0wPA9oYG7nvPe+ibOVOHCfW11CgwoIOJeg5qlbz62OVyEQ6HiUajhMNh/H4/t9ziJJk0cblKXHutqSshqpFVHQXtaTBRlZWpsmRtvwSSgYEBhoaG3vJzZs2axUc+8hHuu+++UalQ3a544YUX8tOf/vRtv5f0kAghxIHtrZpegVGbXsvlsj7GSaVSukpSLBYplUrE43HsQNvdd3P4nXfi2NEz8tz8+Tx8xhkYzc14PB59+3C5XMbr9WKxWCiXy7p/pTaUqNDicDhwuVwEAgEaGxs59dQQ2ayHSMTBmjU7G19rg4nqMVFBo3b0ufYoR32sfu6pEEwm1ZHN9u3bSSQS+uPu7m7OOeccfve737FkyRJaW1vf9mtIIBFCiANf7fFNbRPo2E2vtcc4+XxeT+OUSiXS6TSlUolkMkkul8OXSjH39tuZ88QTAGTtdh454QReOvFErG43wWCw+viOMWG3241hGHoaJ5/P89BDVioVG16vg/e9b+eEjc/n42Mf8wFNuN0hNmxw6w2xKpgAowJGbTBRP5/6nDcLJur3Hoz25vXbNl5Ppr29fdTHqpN59uzZexRGhBBCHBzUcUXtMrJyuTwqlNRuQHW5XNjtdlwuF8lkkng8js1m0+GiWCyScjpZ/Y//yPZzz2XhrbdSv2UL73/iCZa89hr3nHEGPQsX4vP58Pv9ulFWTeSoo5SVK0tAATA5+2wDh8NBpVIhFosBGSBNNhuiv7+6jl49r9rwpAJKuVwetTFWqQ0otc2vtSFMdplUjVsgEUIIIWrVNrWqgKLCQe3G19oqirrFN5lM6k2tqVQKqB73xA45hKe+8x3aHnuMBT//OY0jI1xx55289tJL/PE976GvsZG6ujrdX5JOp7Hb7TtuDbYDRaCEaaKPhqq/VgLiQIYNG+L6nhy/34/H48Fut+swogKWGkNWy9XUz6h+lto/g7HBpLYJdqoGk/0WSGbMmME4nQ4JIYQ4QLxVtUT1aKj17+pjdRGe2+0mkUjoz02lUjqgbDvjDHqXLmX2L37BvIceYuGWLcy/7TZWLV7MU6ecgisa1f0klUqFeDwO+KmGEjuGkdd9jtV9JhXADZSIxWJkMhlGRkb01lev17tLMKntg1H37aiKye4mjmD3PTZTNZhIhUQIIcR+V1spUFURVSVRAWV3xzgqgKjeBLUMzev1kkgkWH/VVXSffz6HLl9O8+uvc9Zzz3HMG2/wh9NOY9Pixbg9Hnw+H3a7HVDBw4vP59MVkuqCNAvV4xwAF8VikXg8Ti6XY3h4mEgkQl1dHX6/Xz+32uOb2uOY2mCift7aPSew+6mk2mbZqUACiRBCiAlR29QKjHoxHjsqXHv0YbPZcLvdpFIpXSVJp9P4fD5M0yTe0sKL3/wm9U89xYIVKwgPDnLh/fez6eWXue/ssxmcNQuv1wu4qAaSNKmUTU/oFAoFqkc2ZaBaMVGhKJfL6U2z8XicaDRKNBrVY8cqRNSONY89ylFhrPbnVD8boAOZ+pypEkwkkAghhJhQtcc4Y48vxr4ol8tlHVTUorN4PI5hGHg8HtLpNFarlWw2S9dxxzG4eDHTf/tb5t91F7O7urjupz/lmUWLWHXWWUAYCAJeLJbq7cM2WzWYqMdhkEolqb+mul249i6ekZGRUcvVVEWktlKigonVah01clxbJaodKYZdg4k6zjpYg4kEEiGEEJNCbdPr2OqBOs5R/SW1oURVSOLxOBaLhXw+r3tPUqkUmz7+cfrPPZfZN9/M9Oef58RXXuHIdevYzkncyglUCGG3u3VAeOyxmcCZVI9tKvzlL6s44YTVlMtlAJLJJB6PR/ex5PN5MpkMw8PDumLi9Xr1DcS1Uznq5yuVSthsNux2+6hemtqwoYJJbWhRa+0PxmCyX2773Vuyh0QIIaam2mpJbYVABZGxL/JqyiWVSpFOpymXy/om4Uqlopthw3/9K4fdcguhjg4AXqaBT3M25//7AtxuN4VCA1/84scwzZ2LyyyWCt/61v/h9Y7oxldV/VA3ClcqFX15n9/v1z0mbrdbX85XLpf1BX61O0xqg4lSWzGp7TOp/bXJvP11Uu0hEUIIIfbW7qolu2t6rT3G8Xq92O12PY0DUCqVyGQyBINBisUigwsX8vT3v0/LfffRftuvOIp+/swdvPSLQ3nkrLPYkGkYFUYAKhULPT1eDjssoy8GVKPL6XRaH/OYpkk8Hiefz5NOpxkZGaG+vp5QKITH49FHTKp5tnZD7e4qJiqI7e5Yp/bXasPNgUwCiRBCiElpdyPCb9b0qlbQq8vv1DGO6gtRl/bZ7XYymQy3ua7h1/w73+DLXM6tHL1mDYdt3MgfjlrLL/gQedz6eVgsFbzeHtLpPF6vd0clpaCDEVSPcRwOBw6Hg3w+Tzab1Uc5g4ODNDU1EQwG9Qiz3W6nWCzqlfa1wUT1mNSOQtfekfNmwUT9OR2owUSObIQQQkx6tevn1Qtx7Xr2tzrGSafT+m6cdDpNZyd85CPHUalUX+SP4iX+l09zIk8D0OVp4orMLfyB9wEVLrxwFSeeuBaAXC6HzWbD6/Vis9koFovk8zt3mKjn6PV69XPx+Xz6npz6+nq99VXtGymVSro/pbY3Rr2NPboZWxkZu+ME2GVj7P4mRzZCCCEOSrXVEvVxbdPrWx3j/OAHNoaHXdhsSS6+2GBkxKnDCMDLHM1J/Jm7/vYrvOfR79MS6+UB3s8DLOR6zmbhwgbKZS9OpxO3260v/fN4PFitVv74Rw/pdAW7vcD73lcNCmrax+l0kslkyGazZDIZUqkUoVBI3y6spm1sNpsOJqpi8mbHOLWTN7VTRyqIqcdVSDpQRoalQiKEEOKAsifVElUhMU2T6dNNenpK1NWVePXVNBs2ZDn99JmjQonFYnL77Y/T6Blm5h13MP2ue3FQpoCFp5Ycy1Onnoq7oQGXy4XT6dQbWU3T5LOfdVEdES6zfHl+1OZZNcZrt9sxTVP3kbjdbiKRCKFQiFAopKshtRf4qd9rGMaoY5zavSUqgKifvXYqaWzFZH8Gk715/Z58rblCCCHEWxh7GV/ti796QQd09aH6UmfDZnMSCARYuDDMt741Aqh/j5t87nMbaW2FitfL+ksv5XBu5g8ciYMKpz/3HNf98Ie0PP44/X19JJNJ7HY7Pp8Ph8MB5IEYkMPlcumV8rBzdFfdVgwQj8eJxWL09vayZcsWOjo6GBkZIZ/P62kdVRVRlZJCoUA2m9W9K7AzjCm1215re07Un0ltM+1kJIFECCHEAan2zpfau2HUe9XkWalYASuVigW73Y7H4+HKK20EAsNANy7Xei68MIfb7cbr9eL1etlAExfwLd7Ll0g1NhJIpfh/99/PJ3/yE+yvvUZfX59ukq1WR6qr6NV0j/o+qupRvbAP/eumaTIyMkIsFqOnp4dt27bR0dFBLBYjl8thmiYOh2O3wSSXy5HP53XAqa0Y1X68u8mcSqUyaYOJBBIhhBAHLBVGaoNJ7f6SW2+t0NcHYKW/38btt1ePLlwuFy6XG/DidAZwOqvVk53VjQBg8AAn8dQtt7DmE5+g5HQyvbOTa26/nbN+9zviGzcSi8WoXsLXCtRhtVqJxWJks1kAXC4XXq9XhwI19ptKpXQfzNDQEP39/XR1delgEo/Hd6ywB6fTOSqYFItFHUxUuIBdKya1Tb7qz2p3waQ2zEwk6SERQghx0FCVAYCODpPZs62jekWsVti8ucK0aRXa2yv09FSory+zdm11d0g6naZYLHLUUUXK5SxQ4uGHq8ckzoEBZt96K+1PPglA1uXip7Mu5NrVP6SMA6hwySXPcMIJq/VCNq/Xq3tISqWS7jsxDINCoaAbb1WoqAYlF8FgUDe/qkqLWsimemNU74y6J6e2qbf22Eb9eajQVnt/kPpYHTHtK9JDIoQQYkpTL9AAGzcao8IIQLkMmzerm4SrRzk2mx2v10swGNT30VSPWIJYrdWeEJfLRa6ujrVf+hJPf+tbxGfMwJ3LcdXqFbzMMZzGSsDC7befwMiIV4/6ZjIZEokE2WxW952oUV61tySTyZDP5/UOk2QyOapi0tPTQyqV0scstZf0qf0luVyOXC5HqVSiWCzuchyjqjGqYXbsn9lkIGO/QgghDiqq6XXevAoWizmmQmIyc2YFsOwIJBagjGFUcLlceqna9de7GBhI4HZb8XhyZDIZLBYLxWKRzDHH8OwPfkD55pUsue/HLOR1VnIGv+Hv+CfzO2zaZBAIZPXXKxaLelGamrAZuxDNMAwSiQR2ux2n00k+nyeXy+mR4Xg8rgOT2+3GYqn2w6jfr6om6mZip9Opw9nYlfS1C+ZUNWUykCMbIYQQB63bbjO5/HIAAzBZvrzMpZdWA0p7u0F3t4WWFti+vTKq50I1kKbTaTKZjF6AppagZbNZBgacXHfR4fwbX+EqbsZKhQxunjjhFJ475Uh89fWEw2H9wq82uKpQogKFChJQPV4pFovY7XYcDgflclmPCnu91cqLGhV2u92jpoxUMKntqXE6nXoiaXeVEDXVU50W2nf25vVbAokQQoiDWnMz9PZWaGw06erauX59+nSD7m6YNs1CV9fOysHYUVl1aV8ul6NSqegpl3w+z/33N/Jf/7WARbzK/3Adp/IEALFwmAfPOos1hxxCU3OzPmYplUr6WEXdvaNuMFaPq+OVQqGgqyzlchmHw6HDTDAYJBAIEA6HdegARlVd9jSYWCwWPQW0r0ggEUIIIcZobYWuLpg2zWT79p0L1drboafHYNo06OjYeYPu2OmUcrmsp1oSiQSlUolcLkehUKBQKHDKKTbAASR54R/v57Cf/AT38DAAW2bP5r6zzybV1qbDg8vl0kc4AA6HA7/fr49Ostms3rKqqieq36RYLOJ0OvH7/TgcDkKhEH6/n1AopI9p1HNW4aQ2iDgcjl2CidoGuy/J6nghhBDiTey6fh7AxDTZ8bbzfpjaYxC1Al5Ny6h+ElXdsFrzlMtpoEji/PN56uSTab/jDmbfdRczN23imi1beGHJEh496STsdXWEQiFcLpeevMlmszpoeL1ePB7PqEVmDodDr6N3uVyYpsnQ0BAOh4NCoUAqlSKZTBIMBgmHw3oip/YCv9qjIbUVVk3kTBYSSIQQQhzUli2DRALUP9RVI+f115v68dpbc8ced9Q2gKpL61QwsdlsOBwOstn8joBSpOD10nHllfScey7zli+n+fnnOf6ZZ1j42ms8etZZvHj44UTr6/H5fHora+0xkN/v12GnWCzqqRkVQFR4KZfLDA8Pk0qlyGazpNNpHUxCoZC+I6f2nhwVTNQ9OWriZ19XSPaGHNkIIYSYssbeiwOMuhumdgPs2GMcFSJuuinN8HAep7PEJz6R1f0fKgD4/vxnDr/tNnxdXQD0tLVx39ln0zt9OtFoFI/Ho7+PWnam9pHUBoVcLqe/pgpJtb0fqsfE7/fj9/t1n4kKJipslUolfTxlGAYejwe/379P/1ylh0QIIYTYC7UL1cYGE1Upqb0rZ+y203Q6rY9e1J0zahGazWajksvR9NvfMvdXv8K+Y4vra4sX88dTT6VcV0c0GtWr4lUoKZfLuN1uXC6XXopWGypU8FFVFvUcvV6v3jzr8/kIBoN6/0ntxYNqGZvH4yEUCu3TP08JJEIIIcReertqSe2W09ptqbVNr5lMRje7qkBSu6zM0tfHrNtuo/2xxwDIu1w8ecYZPHPMMbgDASKRiF4TryowqofF7XbraRr19VWoUFUPl8ulqyc+n083wKqKidvt1j0p6rm73W6pkLwdCSRCCCH2t7eqlrzZMY56cVe7RlQwUaFE7S5RvSrOl19mwc03E9qwAYDhxkb+eN55rJ8+XW+L9Xq9OtCohWcejwePx6P7QAA9rVP7XFwuV3Xd/Y5mXFUp8Xq9o5pqa8POviSBRAghhNgHdneDrgol8PbHOMViUa+EVyFFPa6+biGXo/7++zn05z/HGY8DsHHhQv5wxhmkGhoIh8P4fD59zKLGgS0Wi26IHbvbRFVKVJXEbrfrhlir1ToqlKgKitfrlQrJ25FAIoQQYiLtabVkd8c4pVJJr37P5/M6kKiKiRrBNUdGaP/xj5nxwANYKhVKNhsvnHYaTyxdiuH1UldXp6d5VNApl8u6V0RN/gD6+6jgks/n9X4S0zSx2+268dXr9RIIBKirq6OxsXGf/rlJIBFCCCH2sT2tlsDbH+Ooj9VxTqFQ2Bl0Vq/m0OXLqX/lFQCS4TCPnXceL8+ZQyQa1cvQgFFHQV6vVzfEws6laPl8Xi9GK5VK+tctFgs2mw23243X62XGjBnMmTNnn/6ZSSARQgghxsnYakltKBlbLRl7jKP2h9RO4mSz2VENr6ZpUiwUCK1cyaG33Yanvx+ArrlzefC88+ipq9PHOB6PB8MwSCaT+hbg2mOc2jX4uVxu1O4R9TzV5tZDDz2UhQsX7tM/KwkkQgghxDhSweHNXjprqyW7O8apDSMqKKhpGhUe7HY7pWSSljvuYPbvfoe1UKBiGLx28sk8ctJJFH0+IpGIroyoUJPNZrFYLHi9Xn0jcO1CNPX9TNPUVRLDMGhvb+eUU07Zp39OEkiEEEKI/WDsMU6t2hFh2PUYRx3VqN4SNSasjmBU42ulUsHYvp25t9zCtKeeAiDn9fLn88/nL0ccgd3lIhKJ6OOX2vt11CV8aqtsuVzGMAxSqdQulwguWrSIY489dp/++UggEUIIIfaj2mOcscYe49SGADWNk8/ndZBQ72uDiWEYlMtlPM8+y4Lly/Fv2wbAYHs7D7/vfWxpbiYUCuH1evUor9qHonaMeDwe3fiqFqqpikypVGLBggUcf/zx+/TPRQKJEEIIsZ+9VbUEdn+Moz5fjeyqEKFCiqqiqCMXgEI6TdNddzHvjjuwp9MAbFiyhEfOOosRt1sf43i93lE3EgO4XC59Y7CqmKhqyowZMzjxxBP36Z+JBBIhhBBigoytltQ2vtZWS9Tn1r7VTuOoUKICidovonaMlHt7mbViBW0PP4xhmhSdTv5y3nk8efTR4HQSCoX0/TT5fF4HHdM0CQQCWCwWfWswwJw5czjssMP26Z+FBBIhhBBiAr2TEeHdHeOoykZtP4harqaOeVSzqu2vf+Wwm28mtHYtAPHGRp780Id4vb0dh8Oh19DX9peovSQOhwO3243NZqOtrY0jjzxyn/45SCARQgghJoG36y2pbXp9q2OcdDpNpVLRW1pV1URXV0olQvffz/yf/ATnyAgA2484gj+dfz6DO7ax+nw+3G43druddDqtj4Tcbjf33OPA45nHkUcexbJl++7nl0AihBBCTBJv11uyu2Oc2t0l6rhGHbmoXSbqv9XIsM1mozA4yPSf/YwZ996LpVSibLPx+tln8/iJJ5KxWIhEIrjdbgKBAJVKhXQ6TbFY5NJLy8AhtLScQmfnvvvZ9+b12/L2n7L3HnjgAZYsWYLb7SYcDvOBD3xgPL+dEEIIMWkYhoHVah11ZFOrdk8J7NygqiZi1LFKIBDQVQ71ptbBq14QZ309HdddxzPLl9N/zDFYSyWOePBBLv/Od1iyaRPxWIyhoSEGBgbI5XL6BmBwArt/fvubbby+8J133snll1/ON7/5Tc444wxKpRKvv/76eH07IYQQYlJS98i8WbVEXYSn+ktUiFGfr+6rcTgcehrHbrfrYxybzUahUOAXv7CSTs/Dcdh3uPaClSy49VY8PT2csWIFh82dy+N/+7ds2dE4GwgEdtzwGwaa9t8fxlsYlyObUqnEjBkzuOGGG7j00kv3+uvIkY0QQogD3U03QSIBgQB89rNv3lsCo5teYddjHHVxnjpyUT0l+XyepUvL5HIlLJYif/6zjVwsRstvfsOsX/0KWz5PxTBYf9pprDrrLOI2G4FAgKuv9gKzaWlZenAe2bz00kt0dXVhsVg46qijaG5u5rzzznvbCkk+nyeRSIx6E0IIIQ5kN90EN9xQfW+xWHYJHcAuW13HHuNYLBasVitOpxOn00kwGNQr4tVbpWIHnFgsDgzDwBEI0PfJT/Ls7bfTfdppWEyT+StXcvE3v8kpr71GPp0GBoD0/v0DeRPjEkg2b94MwNe+9jW+9KUvcf/99xMOhznttNMYHh5+09934403EgwG9VtbW9t4PD0hhBBiwqjjmdqG1trx4NpxYPX5qrdEBRO73Y7X6yUQCOhgYrf7qPaEVDe2OhzVYELr/2/v3mOjKvc1jj9rpp1pwbanQC+U0tpKt8itgRY4XOJOhEAMEIkGQzZyqpBsTUqk1BCLBIkRKJdI3KBCaxoSs8XbH4iaLQkCQciRiyBEolCIIAUpBQ92SpECM+v8UWbolIItMH2nM99PMmlndWbWr2+arifvei+ZOrlkifasWKGGnBy5Gxv13//+t/7nX//S4zovydn5jdCGDgWSsrKyoOlKbT2OHj0aaMSFCxfqmWeeUUFBgTZs2CDLsvTZZ5/d8fMXLFig+vr6wKOmpub+fjsAAMJU60GvrdcsaWvQq9PpDPSYxMTEyO12Kz4+XklJSXK54iR1k8vVPbDGiH8/G9u2pccf14HKSv1UXKzrDz2k5Joa7dRKbdQKOWyviSYI0qFBra+88oqef/75u74mNzdX586dkyQNGDAgcNztdis3N1enT5++43v9XVEAAESLloNe2xpf0nrQq//h3zDP4XDI6/UqNtYp6apcrj/VrVtzj8r169fldDrV1NTU3Cvjcun/pk/X/z7xhHKqqpT5n691TQ/pus98L0mHAklKSopSUlL+8nUFBQVyu906duyYxo4dK0m6fv26Tp06pezs7HurFACACOXvLblTKPH3lrQMJTExMUG3dppvATkUG+tSfPzVwGuuXbsmy7J048YNJSQk6MqVK4pJS9N7g9/Wlv8s0Xmlq7ZWqqqS7mMeyn0LybTfxMREvfTSS1q8eLH69u2r7OxsrVq1SpI0bdq0UJwSAICwdHNvvMDXu7nTFGHLsgLHWy6o5g8oDodDPp9Xkku2HaP4eEtut1uNjY2B9Ur+/PNPXbt2Td26ddPp0z6tWtVPvhZrkLz4ojRxopSZ+aB+844J2cJoq1at0vTp0zVz5kwNHz5cv/76q7Zv367k5ORQnRIAgLBSVSXV1jZ/7++F+CttLajWetBry7EllmVpwwaH6upiJMXq4kWXPvqom2JjYwOLqjkcjhaDX2N14cJ/yecLnunj9UonTjyQX/uesHQ8AAAhcOaMlJ0ttezscDqlU6fa3wvRurekZSjx++03hx5+2Gp1HlvV1dfVp48dWIK+qalJV69e1Y0bN1RTY2vcuEeCQklHa7ubsFmHBACAaHf8eHAYkTreC9HWTJzWs3GOHfO1cR5Lp041z8aJjY1VXFxcYBn6uLg4ZWc7tXTpRUm3gk1FhbnbNRKBBACAkMjLkxytrrJOp9SvX8c/q/WCav5QYlnWzfME3+xwOqW//e3WomoOh0Nut1sulyuwj80//vGnunc/K6le6elmB7RKBBIAAEIiM1OqrAw+dj+9EK0XVPP3lvTta2n9elu3ejvswHn8M238C6vFxsbK7XYrLi5OiYmJcrliJdlymp/1yxgSAABCqXfv5gGt6enSzWW6HoiWU4Qty1Lv3lJdna20NOns2dv3xWm5Aqz/+6ysa7pwIU59+riM72UTst1+AQCAAr0PD7oXovWCajExkmTdPM+tKcItH/7l57035yA7nW6Fy82S8KgCAAB02O1ThJtv3dxpXxz/e/y3cSzLIcm67XNNIJAAANDF+Vdp9Ws56NX/vOXaJf73WFaMuuTmegAAIDw1hw+n/D0ebU0Rbqu3JFwQSAAAiCCWdWsmjtS+3pJwwKBWAAAiTOvN+lrOxpFujS0pKbHU0GApKcl8TwmBBACAECotlTweKdSrV7R1Hv9MnJaPW5vx+TRvXvMgWGcYLETCOiQAAESB1vvi+MNKy9s5Dwp72QAAgDa1niLsDyfhMrCVQAIAQBRpvfy8f5E00wgkAABEmZa9JY7WOwAawqBWAACiVLiEEYkeEgAAEAYIJAAAwDgCCQAAMI5AAgAAjCOQAAAA4wgkAADAOKb9AgAQhlavvrU3TWmp6WpCj0ACAEAYWr1aOntW6tMnOgIJt2wAAIBxBBIAAGAcgQQAABhHIAEAIAz5N+ENk814Q45AAgBAmKmqkmprm7+vrW1+HukIJAAAhJEzZ6R//jP42IsvNh+PZAQSAADCyPHjks8XfMzrlU6cMFNPZyGQAAAQRvLyJEerq7PTKfXrZ6aezkIgAQAgjGRmSpWVwccqKpqPRzICCQAAYWb2bCk9vfn79PTm55GOQAIAQBhyOoO/RjoCCQAAMC5kgaS6ulpPPfWUevXqpcTERI0dO1Y7duwI1ekAAEAXFrJAMnnyZN24cUPbt2/XgQMHlJ+fr8mTJ6vWv9ILAADATTGh+NCLFy/q+PHjqqqq0pAhQyRJy5cv13vvvacjR44o3T9SBwAAtKm0VPJ4pMRE05V0jpAEkp49e+rRRx/VBx98oGHDhsntdquiokKpqakqKCi44/uamprU1NQUeO7xeEJRHgAAYa+0tOPvWb36Voi5l/ebFJJAYlmWvvnmG02dOlUJCQlyOBxKTU3Vli1blJycfMf3lZeX64033ghFSQAARLzVq6WzZ6U+fbpeIOnQGJKysjJZlnXXx9GjR2XbtoqLi5Wamqpdu3Zp3759mjp1qqZMmaJz587d8fMXLFig+vr6wKOmpua+f0EAABD+LNu27fa++MKFC/r999/v+prc3Fzt2rVLEyZM0KVLl5TY4uZXXl6eZs+erbKysnadz+PxKCkpSfX19UGfAwAAbpeZeauHxORmfPdy/e7QLZuUlBSlpKT85euuXLkiSXK0Wozf4XDI13rHIAAAEPVCMu131KhRSk5OVlFRkQ4fPqzq6mrNnz9fJ0+e1KRJk0JxSgAAop7XG/y1KwlJIOnVq5e2bNmiy5cv64knnlBhYaF2796tzZs3Kz8/PxSnBAAgqlVVSf6lvmprm593JR0aQ9LZGEMCAMBfO3NGys6WWo6KcDqlU6fM7BJ8L9dv9rIBAKCLO348OIxIzbdtTpwwU8+9IJAAANDF5eVJreaRyOmU+vUzU8+9IJAAANDFZWZKlZXBxyoqzNyuuVcEEgAAIsDs2ZJ/q7j09ObnXQmBBACACOF0Bn/tSggkAADAOAIJAAAwjkACAACM69BeNgAAIHyVlkoej9QV1xIlkAAAECFKS01XcO+4ZQMAAIwjkAAAAOMIJAAAwDgCCQAAMI5AAgAAjCOQAAAA4wgkAADAOAIJAAAwjkACAACMI5AAAADjCCQAAMA4AgkAADCOQAIAAIwjkAAAAONiTBdwN7ZtS5I8Ho/hSgAAQHv5r9v+63h7hHUgaWhokCT17dvXcCUAAKCjGhoalJSU1K7XWnZH4ksn8/l8+u2335SQkCDLsh7oZ3s8HvXt21c1NTVKTEx8oJ+NttHmZtDunY8273y0uRl3anfbttXQ0KCMjAw5HO0bHRLWPSQOh0OZmZkhPUdiYiJ/vJ2MNjeDdu98tHnno83NaKvd29sz4segVgAAYByBBAAAGBe1gcTtdmvx4sVyu92mS4katLkZtHvno807H21uxoNs97Ae1AoAAKJD1PaQAACA8EEgAQAAxhFIAACAcQQSAABgHIEEAAAYF5WB5N1339XDDz+suLg4jRw5Uvv27TNdUkQrLy/X8OHDlZCQoNTUVE2dOlXHjh0zXVZUWb58uSzLUklJielSIt7Zs2f13HPPqWfPnoqPj9fgwYP1/fffmy4rYnm9Xi1atEg5OTmKj4/XI488ojfffLNDm7rhr3377beaMmWKMjIyZFmWPv/886Cf27at119/Xb1791Z8fLzGjx+v48ePd+gcURdIPvnkE5WWlmrx4sU6ePCg8vPzNXHiRNXV1ZkuLWLt3LlTxcXF2rNnj7Zu3arr169rwoQJamxsNF1aVNi/f78qKio0ZMgQ06VEvEuXLmnMmDGKjY3V119/rZ9++klvvfWWkpOTTZcWsVasWKF169bpnXfe0c8//6wVK1Zo5cqVWrt2renSIkpjY6Py8/P17rvvtvnzlStXas2aNVq/fr327t2r7t27a+LEibp69Wr7T2JHmREjRtjFxcWB516v187IyLDLy8sNVhVd6urqbEn2zp07TZcS8RoaGuy8vDx769at9t///nd77ty5pkuKaK+++qo9duxY02VElUmTJtmzZs0KOvb000/bM2bMMFRR5JNkb9q0KfDc5/PZ6enp9qpVqwLH/vjjD9vtdtsfffRRuz83qnpIrl27pgMHDmj8+PGBYw6HQ+PHj9d3331nsLLoUl9fL0nq0aOH4UoiX3FxsSZNmhT0N4/Q+eKLL1RYWKhp06YpNTVVQ4cO1fvvv2+6rIg2evRobdu2TdXV1ZKkw4cPa/fu3XryyScNVxY9Tp48qdra2qD/M0lJSRo5cmSHrq1hvdvvg3bx4kV5vV6lpaUFHU9LS9PRo0cNVRVdfD6fSkpKNGbMGA0aNMh0ORHt448/1sGDB7V//37TpUSNX375RevWrVNpaalee+017d+/Xy+//LJcLpeKiopMlxeRysrK5PF41L9/fzmdTnm9Xi1dulQzZswwXVrUqK2tlaQ2r63+n7VHVAUSmFdcXKwjR45o9+7dpkuJaDU1NZo7d662bt2quLg40+VEDZ/Pp8LCQi1btkySNHToUB05ckTr168nkITIp59+qg8//FAbN27UwIEDdejQIZWUlCgjI4M272Ki6pZNr1695HQ6df78+aDj58+fV3p6uqGqosecOXP01VdfaceOHcrMzDRdTkQ7cOCA6urqNGzYMMXExCgmJkY7d+7UmjVrFBMTI6/Xa7rEiNS7d28NGDAg6Nhjjz2m06dPG6oo8s2fP19lZWWaPn26Bg8erJkzZ2revHkqLy83XVrU8F8/7/faGlWBxOVyqaCgQNu2bQsc8/l82rZtm0aNGmWwsshm27bmzJmjTZs2afv27crJyTFdUsQbN26cfvzxRx06dCjwKCws1IwZM3To0CE5nU7TJUakMWPG3Dalvbq6WtnZ2YYqinxXrlyRwxF8KXM6nfL5fIYqij45OTlKT08PurZ6PB7t3bu3Q9fWqLtlU1paqqKiIhUWFmrEiBF6++231djYqBdeeMF0aRGruLhYGzdu1ObNm5WQkBC4p5iUlKT4+HjD1UWmhISE28bodO/eXT179mTsTgjNmzdPo0eP1rJly/Tss89q3759qqysVGVlpenSItaUKVO0dOlSZWVlaeDAgfrhhx+0evVqzZo1y3RpEeXy5cs6ceJE4PnJkyd16NAh9ejRQ1lZWSopKdGSJUuUl5ennJwcLVq0SBkZGZo6dWr7T/IAZwJ1GWvXrrWzsrJsl8tljxgxwt6zZ4/pkiKapDYfGzZsMF1aVGHab+f48ssv7UGDBtlut9vu37+/XVlZabqkiObxeOy5c+faWVlZdlxcnJ2bm2svXLjQbmpqMl1aRNmxY0eb/8eLiops226e+rto0SI7LS3Ndrvd9rhx4+xjx4516ByWbbOcHQAAMCuqxpAAAIDwRCABAADGEUgAAIBxBBIAAGAcgQQ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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "objective.plot(samples=300);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Fitting a neutron reflectometry dataset" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We start off with all the relevant imports we'll need." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "from importlib import resources\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy\n", "\n", "import refnx\n", "from refnx.dataset import ReflectDataset, Data1D\n", "from refnx.analysis import Transform, CurveFitter, Objective, Model, Parameter\n", "from refnx.reflect import SLD, Slab, ReflectModel" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "It's important to note down the versions of the software that you're using, in order for the analysis to be reproducible." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "refnx: 0.1.53.dev0+19c4b26\n", "scipy: 1.15.2\n", "numpy: 2.1.3\n" ] } ], "source": [ "print(\n", " f\"refnx: {refnx.version.version}\\n\"\n", " f\"scipy: {scipy.version.version}\\n\"\n", " f\"numpy: {np.version.version}\"\n", ")" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Loading/Creating a dataset" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "*refnx* reads 2, 3, or 4 column plain-text files using `Data1D` or `ReflectDataset`.\n", "\n", "\n", "| columns | data |\n", "|---------|-------------------------|\n", "| 2 | $x, y$ |\n", "| 3 | $x, y, y_{err}$ |\n", "| 4 |$x, y, y_{err}, x_{err}$ |\n", "\n", "\n", "$y_{err}$ being the standard deviation of the measured $y$ data, $x_{err}$ being the uncertainty in $x$.\n", "\n", "In a reflectometry context $x$ is the momentum transfer $Q$ ($A^{-1}$), $y_{err}$ is the uncertainty in the reflectivity, and $x_{err}$ is the full width at half maximum (FWHM) of the Gaussian approximation to the resolution function, $dQ$.\n", "\n", "The dataset we're going to use as an example is distributed with every install. The following cell determines its location." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "with resources.path(refnx.analysis) as pth:\n", " DATASET_NAME = \"c_PLP0011859_q.txt\"\n", " file_path = pth / f\"tests/{DATASET_NAME}\"" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "`ReflectDataset` uses a file path to load the data. However, you can also make a dataset directly from numerical arrays." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "data = ReflectDataset(file_path)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Creating an interfacial Structure" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "`Structure` objects describe the interface of interest. They are made by assembling a series of `Components` (the simplest `Component` being a `Slab`. However, the first step is to create `SLD` objects that represent each of the materials:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "si = SLD(2.07, name=\"Si\")\n", "sio2 = SLD(3.47, name=\"SiO2\")\n", "film = SLD(2.0, name=\"film\")\n", "d2o = SLD(6.36, name=\"d2o\")" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "`Slab`s are created from these `SLD`s to represent each layer in the system." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# first number is thickness, second number is roughness\n", "# a native oxide layer\n", "sio2_layer = sio2(30, 3)\n", "\n", "# the film of interest\n", "film_layer = film(250, 3)\n", "\n", "# layer for the solvent\n", "d2o_layer = d2o(0, 3)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "A `Slab` has the following parameters, which are all accessible as attributes:\n", "\n", " - `Slab.thick`\n", " - `Slab.sld.real`\n", " - `Slab.sld.imag`\n", " - `Slab.rough`\n", " - `Slab.vfsolv`\n", " \n", "We need to specify which parameters are going to vary in a fit, and what the limits are on those parameters. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "sio2_layer.thick.setp(bounds=(15, 50), vary=True)\n", "sio2_layer.rough.setp(bounds=(1, 15), vary=True)\n", "\n", "film_layer.thick.setp(bounds=(200, 300), vary=True)\n", "film_layer.sld.real.setp(bounds=(0.1, 3), vary=True)\n", "film_layer.rough.setp(bounds=(1, 15), vary=True)\n", "\n", "d2o_layer.rough.setp(vary=True, bounds=(1, 15))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now we assemble the `Structure` from the `Components`." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "structure = si | sio2_layer | film_layer | d2o_layer" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(sio2_layer.parameters)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "`Structure` has a `sld_profile` method to return the SLD profile. Let's also plot that." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(*structure.sld_profile())\n", "plt.ylabel(\"SLD /$10^{-6} \\\\AA^{-2}$\")\n", "plt.xlabel(\"distance / $\\\\AA$\");" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### `ReflectModel` calculates the generative model." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "A `ReflectModel` is made from the `Structure` and is responsible for calculating the reflectivity of the system. `ReflectModel` performs resolution smearing, applies scaling factor and adds a Q-independent constant background. It can use constant `dq/q`, point-by-point, and full resolution kernel smearing. The resolution parameter, `dq`, can be fitted, but this will only be valid if your dataset didn't supply the instrument resolution." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "model = ReflectModel(structure, bkg=3e-6, dq=5.0)\n", "model.scale.setp(bounds=(0.6, 1.2), vary=True)\n", "model.bkg.setp(bounds=(1e-9, 9e-6), vary=True)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Let's quickly have a look at the model generated by the structure:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q = np.linspace(0.005, 0.3, 1001)\n", "plt.plot(q, model(q))\n", "plt.xlabel(\"Q\")\n", "plt.ylabel(\"Reflectivity\")\n", "plt.yscale(\"log\")" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### `Objective` combines the model and data, calculating statistics" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "An `Objective` is made from a model and dataset. Here we use a `Transform` to fit as logY vs X." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "objective = Objective(model, data, transform=Transform(\"logY\"))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The `Objective` can calculate statistics for the fitting system. Note how the log-probability is the sum of the log-prior and log-likelihood." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "34376.69092305149 -5.013178251637257 -16157.42806574066 -16162.441243992296\n" ] } ], "source": [ "print(objective.chisqr(), objective.logp(), objective.logl(), objective.logpost())" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### `CurveFitter` does the fitting/sampling" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The final setup step is to create a `CurveFitter` from the `Objective`. These objects do the fitting/sampling. Let's do an initial fit with differential evolution." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-568.738983169657: : 52it [00:02, 19.65it/s] \n" ] } ], "source": [ "fitter = CurveFitter(objective)\n", "fitter.fit(\"differential_evolution\");" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "An `Objective` has a plot method, which is a quick visualisation. You need matplotlib installed to create a graph. You can see that the fit looks good." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "objective.plot()\n", "plt.legend()\n", "plt.xlabel(\"Q\")\n", "plt.ylabel(\"logR\")\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Let's see the results of the fit. For the case of DifferentialEvolution uncertainties are estimated by estimating the Hessian/Covariance matrix." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 4510182416\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 922.9880511274863\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(objective)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now lets do a MCMC sampling of the curvefitting system. First we do 400 samples which we then discard (burn). These samples are discarded because the initial chain might not be representative of an equilibrated system (i.e. distributed around the mean with the correct covariance)." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:19<00:00, 20.36it/s]\n" ] } ], "source": [ "fitter.sample(400, pool=-1)\n", "fitter.reset()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We then follow up with a production run, only saving 1 in 100 samples. This is to remove autocorrelation. We save 15 steps, giving a total of 15 * 200 samples (200 walkers is the default)." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1500/1500 [01:17<00:00, 19.37it/s]\n" ] } ], "source": [ "res = fitter.sample(15, nthin=100, pool=-1)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "In the final output of the sampling each varying parameter is given a set of statistics. `Parameter.value` is the median of the chain samples. `Parameter.stderr` is half the [15, 85] percentile, representing a standard deviation." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 4510182416\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 919.5964109463828\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(objective)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "A corner plot shows the covariance between parameters. You need to install the *matplotlib* and *corner* packages to create these graphs." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "objective.corner();" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Once we've done the sampling we can look at the variation in the model at describing the data. In this example there isn't much spread." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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krVvt58CC1RdeiFaLREREah8FkSoKds8ZFayKiIiERkGkknTPGRERkapTEKmkwHvO+HpEQCusioiIhEpBpAoGDoQNRT0indlMPAWAVlgVEREJlYJIFWRkwB6rDbkk48RLFzbhcECXLtFumYiISO2gIFIFLhfccqtVqmDV64X3349yw0RERGoJBZEqcLvhpZfKFqyOGKGZMyIiIqFQEKmC7Gx7WffAe84ELvXudsPixQonIiIigRREqiDwnjOns674vbfesp+zsqB9exgwANq1g+nTa7qVIiIisUtBpApcLrjxRlhNDwC6s4ZEjgLw97/Dv/9t35PG67W3NwbGjYNJk6LUYBERkRijIFJFV10F22nHTtrQgELOYSVgh4/Bg0tCSDK5dGQLDjxMmaIwIiIiAgoiVWavsGqxlPMA6MuSUu834jDPk8l+WrCFzmSTwcV8xJQpMGNGzbdXREQkliiIVJHLBY89BkvpA8AFfFb8npNC/sGvyeQF4vDgwUEntvIfLmMAixg3TgWsIiJSvymIVIP27eEDBgEwkI9oyBHA8By3cwXvcJREBvIhyeSxkOtoQCF/50aamf3MmaMwIiIi9ZeCSDX5mrPZRnsacZTreJU/8keGk4UHB9ezgEUM5AhJDOVF1nI6rdnLAzzIgw/aQSYrK9qfQEREpOZZxhgT7UaUJy8vj5SUFHJzc0lOTo52c8rldkN6OozjUR5lQqn3RvEcsxlV6rWL+YiPuITjNKA937ObNjidsG2bPdQjIiJSm4Vz/VaPSDVwueCKK+BZ7mA1Zxe/Pp5pZUIIwCIG8gX9iOcEd/AsAB4PbNpUY00WERGJCQoi1eSBB+AwjTmfz/k1C+jOtzzG+HK3f4LRAGSShZNCQDfLExGR+kdBpJr07g1Dh9phZCG/Zi3dS71//vkwfHjJz28zmH20oA27GchHWJZuliciIvWPgkg1mjcP5s4N/t4TT8CcOXDppfbPJ4jn79wIwPUswBgYNUozaEREpH5REKlmw4bZPSP+hg61e0zAnh1jWfaf/8kvAbicd7Hwqk5ERETqHQWRCJg3D5YtgyeftJ/nzSt5z+WCe++1//w555NLMq3IoTfLsSzViYiISP0S0SBy5ZVX0q5dOxITE2nTpg233HILO3fujOQpY0bv3vD735f0hPi7+267V6SQBrzHzwG4gn8TuxOpRUREIiOiQeSiiy5i4cKFbNy4kddff53Nmzdz7bXXRvKUtYJ/r8i/uQKwh2cAnnoqWq0SERGpeTW6oNlbb73F1VdfTUFBAQ0aNDjp9rVlQbPKWL4czj0X2rCTnbTFg4NmHCDfSmb7di1sJiIitVdMLmj2448/8vLLL9OvX79yQ0hBQQF5eXmlHnVVfr79vItT2EwnnHg5j6UYA0uWVLyviIhIXRHxIDJ+/HiSkpJo0aIF27dv58033yx326lTp5KSklL8SE9Pj3TzoiYjo+TPn3M+UHLn3o8/jkaLREREal7YQWTChAlYllXhY8OGDcXbjx07llWrVvHBBx/gdDq59dZbKW80aOLEieTm5hY/duzYUflPFuNcLnjsMfvPviByPp8D6I68IiJSb4RdI5KTk8P+/fsr3KZTp07Ex8eXed3tdpOens6XX35J3759T3quulwj4nP77fDprPWs53SO0JCmHOQE8SxeDP37R7t1IiIi4Qvn+h0X7sFTU1NJTU2tVMO8Xi9g14KIbdIkmD2rGz/SjOYcoDtrWG39ROuJiIhIvRCxGpGvvvqKP//5z6xevZrvv/+ejz/+mBtuuIHOnTuH1BtSr1gWq+gJQA9WF6+8KiIiUtdFLIg0atSIN954g4svvpiuXbuSmZnJWWedxaeffkpCQkKkTlvrZGeDMbCaHgD0ZBVer5Z6FxGR+iHsoZlQnXnmmXys6R8nlZEBDges8to9Ij1ZBcCKFaoRERGRuk/3mokylwumTYNVRT0iZ/M1Fl4mTNDMGRERqfsURGJAr16wkW4cJZEm5NOZzboTr4iI1AsKIjGgcWPwEMe3nAmUDM8kJUWzVSIiIpGnIBIDfMu9+wpWz+RbAA4fjlKDREREaoiCSAxo3Nh+Xs9pAHRlI6AeERERqfsURGKAr0dkA90A6Ia9RL56REREpK5TEIkBvim8viByKt/hwMOKFVFumIiISIQpiMQA3xTe7bTjKIkkUkB7vtcUXhERqfMURGJEr17gxcl3nArAaawPawqv2w2LFyu4iIhI7aIgEiMyMsCySteJWBYnvfmd2w033wzt2sGAAdC+PWRl1UCDRUREqoGCSIwJLFitSFYWpKfDyy/b96sB8Hph+HBYvjySrRQREakeCiIxwnfzO18QOY31GANPPRV8e7fbDhwWXoYyj/nczB+ZTHP2A3DuueoZERGR2KcgEiN8QzO+GpEu2MUhTz4ZvO7jyy8BDC8ylHncxs28zGQeZAW9aIu9w4gRqhkREZHYpiASI1wuuPde2ExnANLYQ2MOlVuwumgRjGIWt/A3jtOAGdzLZjrRkW0sZAgOPBgDS5bU8AcREREJg4JIDLn7bjjkaEoOLQHozGaczrIFq243vDz7MFO4D4BxPMZYZnAxi8ijCf1YwjDmAbB/f01+AhERkfAoiMQQlwtmz4ZNRb0iXdjIrFn26/6ys+FGXqYFP7KZTvyZOwH4ng78ickATOJhnBTy29+qVkRERGKXgkgM2oTdBdKFzUHfb5xkuJM/A/Asd+AhDoAhQ+Cv/IYcWtKRbVzChxgDo0apVkRERGKTgkgMcbth5EjYRAYAXdgSNEQ8dtNCzuJbjtCQudxW/Pp558FRGvEyNwFwG3MBwloYTUREpCYpiMSQ7Gx7HZCSHpFNZULEdddBt0323Xk/4FIO0gwApxPOP9/eZh7DALiKN2nGjwB89FHNfAYREZFwKIjEEN/N73xBpDObcThKilWXL4fXXoMreRuAt7iyeN977oHevWHMGPiaHnzNWSRwnKv5FwBTp2p4RkREYo+CSAzxFatuKSpWTcdNvPco779vv//ZZ9CGnfRmBV4s3uEXxfvefXfJs2XBv7gagEHYO3u9Gp4REZHYoyASYwYNgv204CApAHRiS/HCZBdcABfwXwBW0ZO9tAbgvvtKZta4XPDoo/A+lwJwCR/iwFOqZ0VERCRWKIjEmC+/BINVqk7EtzDZN99AX+wVyr6kHwDXXgsPP1z6GGPHwjXT+nCQFJpzgN4sxxiKe1ZERERihYJIjPIPImAvTDZyJPQrCiJL6Itl2UvAB/Prm+L4iIsBe3hG03hFRCQWKYjEmH797BoP/yDicEDz5hDvPUpPVgF2EDGm/LqP7Gz4iEsAuIDPAE3jFRGR2KMgEmNcLpgzBzbREYAuZDN7th1QzmE5DShkF2lso0OFdR+NG8NS+gDQm+VYeAFISqqRjyEiIhISBZEYlJkJm5gAQBc+ITPTfr0vSwG7NwQsjCn/GPn5sIYzOUJDUsijK/baI4cPR7LlIiIi4VEQiVH//eFrANrhgePHeeqp0vUhQIVDMxkZYBxxrOQcAPrwlWbOiIhIzFEQiVGONm3Ix8IJ/HHY48yYYYpnzPiCSLA78/r41iT5it6AHUQ0c0ZERGKNgkiMynrBYhNnAbDsle50YBtp7OE4DYp7OUaMKHtnXn+DBsFXRXUiviCimTMiIhJLFERiUMnN73wzZ7bSjy8BeyGzYzQEYMCAio+TnQ3LOA+AM/mWBhzXzBkREYkpNRJECgoK6NGjB5ZlsXr16po4Za0W7OZ3gcMylgV9+1Z8nIwMcFvtyKMJDSgkg2wAVqyIXNtFRETCUSNBZNy4cZxyyik1cao6ISPDDhrZZABwOuuKe0SW0BeHw57iW9GwDNjvT3vUYi1nANCdNQBMmKDhGRERiQ0RDyL/+c9/+OCDD5gxY0akT1XnrKAXAANZxE+KFjIbOqc7339P8ZTek+nVi+IgcgZrgdAXNnO7YfFihRYREYmciAaRPXv2MGLECObPn0+jRo0ieao6JTvbnpq7hu4conHx69+QxuXDzzhpT4i/jAxYy2lASRCpaLaNT1YWtG9v16G0awfTp4f9MURERE4qYkHEGMOwYcO4/fbb6dWrV0j7FBQUkJeXV+pRH/mGZrw4+Se/LH79ZX7H8uXhHcvlgjV8CNhDM04nzJpV8bCOr1g20XuYa3iNK8xbPDDuKJMmVebTiIiIlC/sIDJhwgQsy6rwsWHDBp555hkOHTrExIkTQz721KlTSUlJKX6kp6eH27w6weWCe++1//wwk/iW7nzMRfyVOzjvPLu3IhwfuO0dupDN0k+OnXRY56mnoJt3Ld9xKq9xHW9xFavoyYIp2WiETUREqpNlTEULhZeVk5PD/v37K9ymU6dODBkyhLfffhvLsopf93g8OJ1ObrrpJl588cUy+xUUFFBQUFD8c15eHunp6eTm5pKcnBxOM2s9t9seGvF6y77ndMK2bScvVvXJet7wqxHNacZBzmYlv3v+J+WGkeXL4aJz81lDdzrwPbtIowEnaMl+1nEa57KMDTsahzU8JCIi9UteXh4pKSkhXb/DDiKh2r59e6mhlZ07dzJo0CBee+01+vTpgyuEK1k4H6QuGjYMguQ1wC4i7d//5MfwBZr/en/KT/mS63mF15zXBw0yWVkwfDhMZQITeJStdOAcVpJAASvoRVt28ggTWX/zI8yfX8UPJyIidVY41++I1Yi0a9eO7t27Fz9OPfVUADp37hxSCKnv3G546aXy3w/1LrqBa5J0ZnPQWTNut71SaxPy+C1/AeD3zOQAzdlNm+LX7uFJPvrbLg3RiIhItdDKqjHKN3OmPKHeRTcjAxyO0kEk2M3vfOcbxjySOcR6uvE2g4vff4srWcJ5NOQYI5jD+PGa1isiIlVXY0GkQ4cOGGPo0aNHTZ2yVsvIKP+9cO6i67v53WY6AvYqrcFufuc73438HYC/8FtMqX8eFk/zOwBGMQvLW6il4kVEpMrUIxKjXC4YMyb4e6NHh16oCvbN7zbRGbB7RMq7+Z2LHZzHV3ixeI1rAXv9kPvus99/nWvYRwvaspOf8amWihcRkSpTEIlhd99t9374czjs18ORnQ2bsWt02rKThhwpUyeSnQ1X8SYAX9KP3bQB7JVZH37YDiMniOcNfgXAEF5l/HjCXtdERETEn4JIDPMNqzid9s9Op/1zuLW+GRlw0GrOAZoC0IktZVZXXbECLila+OzfXFF8Pt82F19sP7/KdQD8ijcwXm+l1jURERHxURCJcZmZ9pohixfbz6HeY8afywWz51hsphMAnclm6tSSQON2wx/Ge7iQTwFYhJ06pk0r2cZX9PopF5JHE1qRQ09W4fUGH+YREREJhYJILeBy2WuGVGXWc2YmbMKeatOFLUyYUNKTkZ0NZ5qvaUouuSSzip6APSzj34Zp0+zhGV9Q+TnvAaHfRE9ERCSQgkg94XbDpqL6js5sLtWTkZEB52AXe3xFHzzEBb0xni+YvM8gAAbyERDeLB4RERF/cdFugNSM7GzYUrSWSCe2ACU9GZs3Q09WA7CaHjgcwW+M5xue+cTbH4DzWEoDjjNuYryWfBcRkUpRj0g9kZEBW2kHQEe2AnYxalKSfaddXxDxDcsMGlT2GL7i2U2OruTQkoYc4xxWMnWqClZFRKRyFETqCZcLRk211xLpwDbiHF5mzYL8fMDr4Sy+AeweEa+3/JqPzExYstTic34KwAV8poJVERGpNAWReuT6MekUAgkc55v315OZaU/bzeA7kjjCERryHacGrQ/xl58Pn3MBAH1ZAqhgVUREKkdBpD6Ji2N70R9HXNIdtxsmTIAefA3AN5yFF2epabvBZGTAcn4CwDmsBDhpeBEREQlGQaSe2UoCAB1pVnxn3p6sAgg6bTcYlwuG/7kXXizasYM0a0/Q4lYREZGTURCpR7KyYCs3AdCR37FihT0LpodfoWqoPRsnEpuwgW4A9DTLwm6L220v0qa6EhGR+k1BpJ5wu+3ZMduK7sLbke+ZOBEenWaKe0S+sc4KqWfDd6wV2F0nvVgVVrHqjBnQvj0MGGA/a8aNiEj9pSBST/iGYbYWB5GteDzw4Uu304ocPFj8+dOzQ1pC3nesrzkbgO6sCblYddIkeGDsYS7xvkc/vsB4vYwcqZ4REZH6SkGknvAtRuYfRJxOiFuTA8AGTqNP/4Yh9U74jrWWMwA4g7UhDelMnw7vTVnONjryHpfxBeeznN608O7l4Yer9PFERKSWUhCpJ3yLkW132EHEhZtHHzpOj6IC1VX0DHk9EN+x1jvsIHIq3/HoQ8crHNJxu+HP47bxIZfSihxyaEk+SZzD/1jExbw46yiTJlXLRxURkVpEQaQeycyEpdvSyMeBEy9Z911Iz6Kpu6vpAYS+HkhmJtw1zUUuyTSgkHl/2FBhb8pTT8HjjKEZB1lGbzqxhZ6sYjetOZM1TGQqU6bY9SMiIlJ/KIjUM650iw04AehqckrNmIHQ1wNxu2H8BMtveGZdub0pbjcsnvEV1/I6HhxkkkU+TdhEBnfwLADjeRQXOxg3TvUiIiL1iYJIPZOVBesZAkAfrqELm4GKb3YXjK9gdQ3dAbtOpLzelOxsuJ05ACzgetZwZvF7b/ArPuFCEjjOXTyDMXbviYiI1A8KIvWIb9rtek4H4DbmAbCN9vxICyD4ze6CCSxY7c4awF4yPtCHr23kBl4B4C/8FrD3vesuAIsZjAFgJLNJIp8nn1SviIhIfaEgUo/4ejG+4SwAWrMXgE/oD1Dhze4CuVwwbRqs8Zs5A/aS8f4hwu0G91+WkMQR1nEaX9IPgEcfhaeftotj3+VysulCU3L5Jf/UfWtEROoRBZF6xNeLsZiL8Ph99Yu5CAj/fjG9esHaoqGZLmwikaNlQsRTT8FVvA3Aa1wLWMX7gr2uiOVw8DduBuAGXtF9a0RE6hEFkXrE14txhCTe4RfFr7/Hz7Gs0OtDfDIyIMdqzX6a48DQjQ2lQoTbDX+ecZRBvA/Am1wFlA48vqnAr3AdAJfwITMn7dN9a0RE6gkFkXrG1xMxgWk8w52cxjr20hoIvT7Ex+WC2XOs4uGZ0/m2VJjJzobz+ZzGHMZNW/5XdMfee+4pHXgyM2Hk9NP5Hz1pQCH/+9O/tOy7iEg9oSBSz2RkgGXZBau/4xk2cBoAxlSuLiMzE9azEoDTmVoqzGRkwM/4BICPGQBYOBxw992lj+F2w/jx8BZXAnAZ72vZdxGRekJBpJ5xueCaa8q+XpW6jHVF03FP47RSN7F7/324gC8A+IwLcDjsYZjAYRdfEe1/uAywh2cc3hOaxisiUg8oiNQzbje88UbZ16dNC68+xP946/gTAKezrniZ+OXL4c4Rx+jDV4AdRCD48I+vl2YFvdhHC5qSS1+WaBqviEg9oCBSz/h6HwL5akcqc7z1fjNnGnAcjwc+/xx6mv/RkGPk0JKNdC13erDLBffeC16cfMglAFzEYk3jFRGpBxRE6hnfFF5/VRmWyciA3dYp5JJMHB4yyMbhgPPPh/P5LwCfcz5gVXieu++22/VffgbYRa6axisiUvcpiNQzvumylr2cR6Wm7ZY53hyL9XQDSoZnnn0WLuBzoKQ+pKLz+Nr1OecB0JclTHu4UNN4RUTquLhoN0BqXmamXauxaZPd41DVi/2gQfABp3MeyziN9QC89KKHJ/gSqLg+JLBdB/afzY/jm9GcA/xj4gqapZ5HZubJ2+B228NEjRtDfr7dU6MQIyIS+yLaI9KhQwcsyyr1mDZtWiRPKSFyuaB//+q5WGdnw7ritUTWAfbdeJtzgHySWEXPkJaPd7th/EQHX/BTAC7gy5Cm8c6YAe3bw4ABcO659nO7djB9epU/moiIRFjEh2YefPBBdu3aVfy4y77TmdQhGRmwnlOBkiByQVF9yBL64iEupHoPXyGtXVNi14l4vRXfjXf6dBg7FozXS2+WcQ2vkcYujIFx4+yQIiIisSviQaRJkyakpaUVP5KSkiJ9SqlhLhfcOtVeS6QrG3FyolR9CIQ2Pdg3jde3z/l8Dphyp/H6FkJLZS+f0J9l9OE1rmMLnbifBwHDuHGaAiwiEssiHkSmTZtGixYt6NmzJ9OnT6ewsLDcbQsKCsjLyyv1kNrh1+Pac4AEEjhOT1ZzAZ8BJaEilOnBvmm8KzmHoyTSihy6srHcabzZ2ZBgjvIJ/fkZn5FPEus4jYYc40EmM5GpGAMPP1ydn1RERKpTRIPI7373OxYsWMDixYsZNWoUjzzyCOPGjSt3+6lTp5KSklL8SE9Pj2TzpBq5dzr4L5cCMJzncfEDx2nAV/QJaxru3XdDoSOBZZwLwAV8Vu7+7doV8DB/4HTWs5M29GIFZ7CG3/MkAI9wHxfyCbNmaYhGRCRmmTCNHz/eABU+1q9fH3TfrKwsExcXZ44dOxb0/WPHjpnc3Nzix44dOwxgcnNzw22m1LCPPzbmbp40xr5tjTFgPuRiA8ZMnx7esZ5/3piHmGgMmLncYsaMMWbHjrLbdQfjKTrXZbxjHA5j+vSxTz+LEcaA+ZozjZMTxuEIfgwREal+ubm5IV+/LWOMCSe45OTksH///gq36dSpE/Hx8WVeX7t2Ld27d2fDhg107dr1pOfKy8sjJSWF3NxckpOTw2mm1DC3G65IX81qeha/dgN/ZwE3sHixPUMnHJdbLXmX/XxHBl35rvg+Nb6pvIcPH+bNxr24kQ28yrUM4VUeewxuuMGeMdPM7Oc7TqUFP3I7f2UWt1eqHSIiEr5wrt9hB5GqePnll7n11lvZt28fzZo1O+n2CiK1y/THDK3HD+VW5rOTNnRiC4XORLZtC3+a8I5v9pF+dipgF6PuIxWnk+JjdbaG8x1zceKlJ/9jNT2L33/lFXvGzF08zdPczWY6cZrjO7Z879TaIiIiNSCc63fEakSWLFnCzJkz+frrr9myZQsvv/wy99xzDzfffHNIIURqn7HjLPZOf4mfsJLzWMpxK7HSq7Zu2t+StZwO2KusAsVFq2433E4KTry8x6DiXhjf+2PH2tN651r/x36a05ktDPYOUggREYlBEQsiCQkJLFiwgAsvvJAzzjiDKVOmcM899zB79uxInVJiwJgx8NaOn/DS4nZs305Iq6IGk5EBX9IXgJ/yBVByT5yuHW9gKPMBeJY7ivdxOEqKWseMgfXbG/NXGgPwe1ZoGq+ISAyK2BLvP/nJT1i6dGmkDi8xzOWq+oqtLhcsdW5hhAf68wlOp2HWLIv334efF/6KVixgJ234D5cV7/Poo6XP63LB7l/OwPPP67mAXDLSNzLysa6MHVu1tomISPXRTe8kZv01OwuA3izj4p6/ZNAgGDHCMJwXAJjLbXiKsvRjj9m9IP7cbvjrm9fxPvZNbobxklZbFRGJMQoiErPiO3ZkDfY/0uQVb/LUU+AyOxjE+wC8wP8BsHAhQXs5fEvGz+U2AG7lJRx4GD9eq62KiMQKBRGJaR8UPV9KI2bM8HIbc3FgWMQAttAZpxP69g2+r2/J+LcZzI80Ix03F7MopBvwiYhIzVAQkZj2i5kzAfgVTtqwm9/yFwCysKtg77mn/HoUl8uuGykgkb9zIwDDmBfSSq9uNyxeDMuX28/qQRERiQwFEYlpXzS8g2y60IJD7KQtrdnLRk7lVa7D4bCXhK+Ibyrvi9wKwC/5J7+86FOys8sPFzNmQPv2MGAAnHuu/dyuHdx3n0KJiEh1UxCRmOV2w/Db43iYSaVeH8t0CmnA6NGhzc4ZMwaGPNqLb+lOQ47R/KP1DBhgh42srNLbTppkhxevt/TrxsAjj5SEkunTq/jhREQEqOGVVcOllVXrt8WL7Qu/k0Ke4m46sI0l9GUK92FZFtu3hxZE3G47dPze+ziPM4al9KEv9tRyhwO+/94+zvTpMG6cASzS2c5QXuRUvuMH2jKPYWykW6njTp9edqaOiIjE8BLv4VIQqd/cbrv3Idi/0HBCgC/QtGIPP9CWODycxjo2cBpgH+fuu6FdO4MxFqN4jhmMoTGHSx3nfh4s6p2xgNIhRkRESsTEEu8iVeVywZw5ZV8PtmZIRTIy7NCwl9a8y+UA3Mbc4vcffxyee86DMRZ/YArP8Rsac5gv6McfeJi3uQKAh3iApygpSvF6YcmSyn02ERGxqUdEYp7bXXLB79u3cj0QWVkwYgQMNm/yJleTSzId2coBmhdvcydP80xR0JjEQzzCH7hogINPPjGM8v6VZ7gLJ17+jyzmFq1hAnYw0mqtIiIlNDQjEoTbDV98Vki3G8/hbL7hESZyH48AMJi3+Ce/xImX+3mQRxz3M22aHTDcbnvdkQ8uGsgjLOIoifRmOWvpXnxs1YuIiJRQEBGpwKN9n2P80t9whIZcw+s050fmchvxnGA2I7idWXy1zKJ379L7GY+H/8S5uJzdfMW59ONLvDiB0OtF3G57xdeMDNWWiEjdpRoRkQrctHAU7/JzGnGU/3A5L3Mz8ZxgIddxB89isDh8uOx+ltNJ67fe5iAp9GEZd/Ln4vdCWa01K6tkfZJ27Up6W0RE6jMFEal3XOkWu559g5e5CYBcknmMsdzAKxTSoMKVV88Z3Ivx2F0lU7iPdnwPcNLVWpcvh5EjS9YnMaZk4bTAtUxEROoTBRGplzJ/25ALd/yNT98+yPQ/HGSi4zG8OHE6YdasiodNnit8j//SlsYc5i/8FgtvhftkZUGfPmUXSQP7tVGj1DMiIvWXgojUWy4XXHhFCg9Psfj+e3u9kW3bIDOz4v0sp5ONo39NAfAL3mUIGbRv7ykVJvzvVTNypAm6FoqPx6Ob8IlI/aViVZFKesCyeBDYSyqnsZ4DVnOmTYO9e+GJJ8AYC/AATtLYRVc28j3t+Z72GL/fASwLFiyAfv1UwCoidYOKVUVqwLXLd7KGM2hFDk/zO4yB8ePh8cctjLFw4OEmXmENZ7CLU/iEi9hKJ3aQzs28CNi/AxgDv/61XcCqehERqW8UREQqaf+hNoxgDl4sbuLvPM9wkjhMIke5joV8y5n8jVs4g3V4sdhKBwqIpy07mc8w3uQqEjhWfDxjVC8iIvWPgohIJWVkwDLHefwfL+DBQSYv8CPNOUAzFvJrTmc9P9KMiTxCc36kE1tJJo/Xe4/mKIlcydu8wa9KhRGPJ/xl4331KAowIlIbKYiIVJLLBbNnW7zIUH7JP9lMJ+I5QSIFuGnLH5lMR7YyjYnk0hQAjzOBds8+zhXWOxyhIZfzH57jdnzDNGAP04Q6ROO/NommAotIbaRiVZEqcLvtAGBPzTV0YgtOPGTThcCcb1n2TfwyM+3A8I/hH/AfLsOJlxHM5nlGFG/rdNozeCoqXi197tD3ExGJNBWritSQ7Gz/IGCxhc5kcyoLFzrYscMeMlm2zH7evr1kanBmJryw41Ke79AfgGe4i578r/i4J5vS6/V62bDBU2ZtEk0FFpHaRkFEpAoyMuz7zPhzOkvuEty/P/TubT8H9lK4XDBq84e8RSKJFPA619CUA4B9zKSk0tsbY9i2bRtDhgzB6XRyySXtsacHlwi2n4hILFMQEakCu07EHnYB+/lkK7OW4nBw5Y872UJjOrKNl7kJJyfweu3VWLOywOPxsGXLFi688EJ6duxIh1df5X80IIc95NGQV7mGq/gX4C21n4hIbaAaEZFq4HbbQyJdulSuPsP99tu0uPIqGmKYyzD+jxcAC4fD8Mtf3sObr7/K3SQyma00Ifh/sq9wPSOYw2Ea43DA0qWUuYOwiEhNUI2ISA3zDcNUtkjUNXgw/7p+CoU4uY15vM41dGIzTby5NHq9gG9pwgy20ATDN5xJJs9zJt/Qm2XMYDQniOMGFrCU80hlL14vnHuu7vArIrFPPSIiMcLthvvTX+B5RuCk7B3ycmjJWKbzEreWWiIeoB+fs5Bf05adLKcXF7GYwzQufn/MGLj77kr21rjtotyMDM3GEZHQqEdEpBZyueD85/+Pc1jJx1xU/Pou0pjAVLqwiRcZViaEAHzJ+VzEx+TQkt6s4DWuxUlh8fszZlRuCfmsLHu/AQO0BL2IRIZ6RERizIwZMHasIYnDFBJHAQmAVeE+Tif861/w0OCv+JgBJHGEGdzLWGaU2c63zsgDD8D338PWrdCxI6SkHGDs2Hw8Hg9paWl8+20i552ndUpEJHzhXL8VRERiSLBFykLx/PMlC6X9Z/hCXuPXANzMfF7m5lLbLl4MmzfD8OG+VwzxHOdy/k06L9GEFWzgYt7hOQpoVOZcixfb9TAiIuWJmaGZd955hz59+tCwYUOaNWvG1VdfHcnTidR6pRdIK80K0ily7bVw//2lF0qbuWMITyT+AoAsMhnIh8XbO50wfz4MH27//pFMLg8zCTcu/sm1PM1bTGEnrzOfXbTlESYST0Gp/bt0OfnnCHb/G90TR0SCiViPyOuvv86IESN45JFHGDBgAIWFhaxZs4YhQ4aEfAz1iEh9E6xHxDcV95tv/HsxSnpBgvJ6edWZxHUc4zCN+Dnv8Tk/5fLL9/Puuy1wYLiJl3mMcaSxB4AduPiSfhwjkQF8TDp2YljJT7ieBWwig759IS7OHspp3x4efLDsqbOyYMQI+27CvmXtoexr5bb9JHxDSuWdX0SiL6zrt4mAEydOmLZt25rnn3++SsfJzc01gMnNza2mlonEvuefN8a+ZNsP//+M7r/fmKFD7eeTKTx82Pyn6CCFYJ6jpRnMP83t/MWso1vxCdbT1fyS142TE8XndFBofsVrJocWxoA5QIrpy39LtSuwbcYYs2OHMQ5H6W0cjrKvOZ32ttX5dyMisSOc63dEekSWLVtGnz59eOGFF3j66afZvXs3PXr0YPr06XTv3j3k46hHROqrBx6w703Trl3Vfus/kZvLu2lpXHXsWJn3DtCUaUzgSX7PCRKC7t8WNwu5jn4s5TCNuJp/8RGXFL8fWLy6eLE9wyYUt9xi946E2rOhm/yJ1B7hXL/jItGALVu2APDHP/6RJ554gg4dOvD444/Tv39/vvvuO5o3bx50v4KCAgoKSsaj8/LyItE8kZhXXUMODVJSuCI/nyevm0e3f75KMw6ST2MWcTHPcgeHHU14802LTz+Fxx+3+xn83TDGxVrXQnJ/P5LLeI93+AU38ApvcA1QcpM9XxDw3XvHPyxYlhewMKZ0kcv8+SV/bt/+5EM1wepnAs8vIrVQOF0t48ePN0CFj/Xr15uXX37ZAGbWrFnF+x47dsy0bNnSPPfcc+Uef/LkyUGPqaEZkcoLNlwSbGhjxw5jFi82Ztky+9k3dLJjhzEJ1jHzD64rGuZxmGG8UDTE4i01xJKfn29+97uvjYut5rf82bzN5WYbSWY7SWYLHcxL3Gx+xaumIYfDHqop73Nce21oQ1UiUnMiNjSTk5PD/v37K9ymU6dOfPHFFwwYMIDPPvuM888/v/i9Pn36MHDgQKZMmRJ032A9Iunp6RqaEamC8oZLbrkFXnoptGNkZcHI4YXM4naGY69q9neu54k2pzDotkTS0tJ45403sD75ltuBK9iPs4Lj7aQN9/MQ8xiGt2jLUKYF+xfCBqqweDcGPPEE5OVBcjKMHh3t1ohEVtTXEcnLy6NVq1Y8++yzZBb9n+HEiRO4XC4eeughRo4cGfJxVCMiUjXVVVvxwAOwYf1herzWhQnsxgEcBTYCB4mnN4YkThRv/zmteJu97OrcmSNeLz9ubcplXMwQFtKe7QB8zVmMZDYrneeybZsVUnvcbliyBK6/vnbVi7RpA7t3Q1oa7NoV7daIRFbUa0SSk5O5/fbbmTx5Munp6bRv357p06cDcN1110XilCJSDpcLZs8uPfV31qzwL9h23UoSR45s5sHrr2fQ22/TF+gBwHHA7ul4jWv5K7/hO0c3Nm0qpF07e7kitxs6dXIwyfswv+Uv3M9DnM03LKEvf/acxn/fuoOrhg6lUaNGWEEWTTHG4PV6adnyBImJhXi9jUu9X5V6kUj1VviOu369HULAfs7Kiu3eG5GaFLF1RE6cOMHEiROZP38+R48epU+fPsycOZMzzjgj5GOoR0Sk+lTXTByf39y+ka/mPknr41fQiv2spgffcBb+y9EHzozJyioJRC3Yw+NczlD+B8BB4Jm4OOLuvJM1e68iPv4UGjY8QV7eQTZ+8z9OfPs+Z7GRM9lBBhYWl+Ahjl20YQW9WGH1Yv7KNE4/I4UGDRoEDTPliVRvhe+4gWK990ZiS20c1ov60Ex1URARiV0uF/zwQ/nvW1bpWg5fDYcvEKWnG3r1eos/X53JExzjTA4Xb7sJJ9mk0IR8WuKlC4Uhdd9uwsmKTu1pddddnDZkCC1atjxpKPEPR/7tDEewC0XgcQNpqfzQ1cYLcThO9vl8/621bVt7ViZWEBGRiCvvt32fwCASrBfAvlgbLAzXspDx3Mk5BC+I/5FmfMNZfEt3NrCS4ywljhQ6cxa9sDiX5TTiaPH264A1p59O3GWX0fXGG2nepg1xcXHEx8djWRbTpx9n506LuXObl5pa7HQavvnmEO3aOXA4HCQkJDBzpoO8PEhJsUpdKHwXkJkzITe35EJxsnsGVaVHpDZflP3bDqF/jtp4IQ5H4OcL/I5rY32RgoiIRNTJfttv1gwOHCj7un8vQPCLtYcUDtKHFbTlB3JJ4SBN2UhXfqAtvmEfh8OwYMFS8vLyGDHiEoxxkEQ+V/BvrucVLuOtUku0HQU2WBa58fEcTUzEMoYTeU4akUJD2tCQozTkKI04QgIF5MUdI7exg6MtWuDt1o1p7/TkW/rzY+Jp7NnbhLi4OJxOJx06OP0uDBatWsHYsfZy/PPnl3crL8Pzz1tBe11CCRn+F63Ro6MTSiobhvzbDuWHi8DjRzKIxEKwC/x8/j//6U+V77GL5mdTEBGRiKnot/3GjeHee+33pkypeFZLOKuwBrN4sd3jEuwYyfyGq/gHV3OA84FWlT9NKQdw8kOzZA6mplLYpg3zP80glzM4SheO0RAwWGzD4hAWp2MBHpwcoRFHaEguhRxp2IwR9zTm6LEEmjSxe12OHo0jOdnB00872b3bok0b2LGj5Lz+Q0vp6RY7d8Ipp9g/+/7sf4G2LKtSF6FQ96lsGPLfz+Mp/7f8wAtzYI9AYDurcsGNhd6WwM/na1NaGuzdG/y/o4ULwwut5X22SIUVBRERiZjyAsTQoXDWWeXXSAT+Jney4YuK+P5nDMGOYbB7TrzAE8C/OZX1dGEvLYBmpODhQY6SxFEacoRGHCWRozTiKA04zqO04E3aAh2A7nSkOw3pykbi8ITf2CCOYbGPBHJoSA6N2UcKObQkh1QO0YQTNOKuexrgSEzEkZAA8fFYRY9Rv0viBI04gRMaxHPoRCOsRo358MtGWI0bYxo2xBkfT5cuDnJz7eGkbdss4uLsKhvLssrUzDgcDizLol072LvXonVru47Hx7e9b9/0dPuC2aaN/b7vz/7hyX8/nzZt7AtrkyZw6JBV9D0ZZs82ZGaWbO9ylQSsP/3JXjvGt+2cOTB5cukA5r99KGHCv12RDiInu9AH++9k0iQ7mFTUs3jzzRUP5wT7bKFsU10UREQkYsJZl+RkY9snG+Ipj3+oqfgYvlBiuPrqd2jf/iOeeqor8Jsg234KfAfsB7YBO2jfPpfvv/8UcBJPAV3ZSHe+piMTaMcvSMdNY/KLh3bsM1p+j104OUJDjtEID02BRuF/3LAddzg45I3jMA04QjyNW8djGjQAh4OcA3GcMA6suDi8DgcnvE4c8XGc0s7JVyviKMSJByf9B8ZjOZ0YpxPi4rDi4sDpxIqP56WX4ykkgUIsPFgUEoeHOEaPddr/GBo0wCraz9GgAcTFsXR5Y+YvaI4H3zniivZzYhxOfnHlQawGThKT4nj5H04OHXUS3zCew8dSOW4a4MFplyw7HDRoeIi8w9CkicXKVQ569LQ4eMhBcrKDb9c6sBwOu0ip6NlyOu2fLYuZMyE/3+69+/3voVMnOHgQmjaFrVstnnrKKvU+lA1UwYqfy3utQwfYt8+iZUv7rtH+fvjBolu3RLxey28fU1RbZf+7tWutStcwrV9/jJ/+FHJyIDW15G7U/j9D2dd8P7dqZZXaZu9eB23bxiuIBKMgIhKbQp1pEkq3b0VFr82aQUJC6feDhZqTFc6C/T/4yy4zvPuu7zfx0u/dfbcdpKZPhz17DImJhQwefIJXX00KcrT7gYcqPiEAhdj9KiXTixoCqbSmJS5S2UYq+2kJpHIWqfQmiSM04DjxLKcB24kHGgDxNKABZ9GAE8RzvPg5kb004jiNMJRXlSIlvFiYomeH0+KExxcaIS7eouA4eHFggMRG9jOWhSkKMqWegcNHi+6j5LBI8v1T8Qsle/fZfzZFNURYFvmHwGMsjHFy9FhS8fuhPDdrfoykpONs3+F7Hdq3g23bS7br0ME+z9atJft16myxeXPJz1262O37bpPFEs5mRttXFUSCURARiV3VVcn/xBN2L8vMmaVn2ViW3UXdtKkdZlatgp49g4ea8o4RKl+QCq2HxmD3mrQgMNAEM3LkK6SlbeDgwYPk5+ezbNmZrFlzF+AEPMBEYCuwoOg1n8AQ0x9YXM5ZPMAIEplLEpDEEBpxP0kcI4k8kphGHB8SV3QG/+fSr6UQR0uc5BBHXpltnTQmjkycGL9+jRM48RY9LyGOLUH2a0YcZxf3gwR/zi56TipqR3nbFRbFBKku/6EZI9r+qCASjIKISOyq7rHl6ljPozJDPc2awY8/ll+zkpxsB6HgSoZ+SpTuRt+yxdCmjQdjDG43ZGQ0KNUVX3KMYP4MrCcjo4C8vCbs2fM4lNvv4QF+WvS8lIpDTTD/B8ymJCCNBF4I2KY/5Yehis7TFvg+oE2+z10IjCo6V7DtPEXbxhVtOx+Lm3FgYVGIg9ux+BCLzjjYhMUuHEVHtqD4z4HPFmk4WIqFA0dRP4nFCRxcgMWeINtXdCxb4J/t55ZAOhY7gH1B3r8cGIuFA/D45oUV9dGARSFwAxb7/Pb7ORa/B5xF78/E4v2A416KxV1+2zyDxYcB29hyAHdbE7UgEpEl3kWk7vOfLVEdMjNLivTS0iq3BLr/MSoOECUSiub5ZmcHL5yNj7fbE3zop6SI0xi79+bgwZJ3Z82yaNfOd8myx+nLnqO8EGKAkbRqZTh40JCTY0hKOsHhw/Hl7OME/gUcofSFHOz/1f8V2IK9HH8h9gU+EWgKHMWy7sAYh9+xZtO6dTJxcXvxeI5z7JgXh6MpP/7opfwwFEdGxuUkJi6lsLAQr9dLYWEhxhgOHJjAgQNTKQkUDwOrgC00berm4MHGwJlB2u4ErsO+XB4GlmJwFpUNx0HRTRgrDlDBdAPal/P6nhD2D0Uo4e5d4GugC7AJGATMouTvaRQUhRBbW+DflP57GkjpANi26Lj+21wG9AUaA9kB22aQVj112JWiICIilRKJdQmcztLPVTlGkybQqFHFtSODB5esa5KRYdc2BgYFy6q4PUOH2jMbfMNG06eXH6bKO0cwyckWeXnxOBwl52/a1P5c5X+mNOwCR1OqwNEONZeRlgYejyEnBxo3tsjPd+Lr0Sm9PYCTPXum0KzZCTyeQvLyvCQlGZ544hCjRycX7+cfihwOQ9++4zDmGA0bevF6PRw6dJyEhEIyMz1ceOFy7PBzBLv35jzAw/z5hsGDnUBKkGN68Xp/BeRjT8QOFlT8/zwH6A4c5P77DQ89VFh0TMOYMR5mzPAW/ZyMZXn9whdYlgdjLgUGA3sYMeIgc+Z4sGdgwbBhMG+eKT7eLbfA/Pm+ny1uusnw8stgh44U4C+UhDYnljWbQYMcvPfePny9aFdeaXjrLV8LfsIVV+zl3//OLPoud3LZZfuxQ4R9v6V9+85kxYpgQfN6YDUAPXuezapVwbb5qqg9Hrp2fQKAjRtHA87o3gPJxLDc3FwDmNzc3Gg3RURqwOOPGzN5sv1cHce4/35jLMsYu3Kk9CMtrey+zz8ffNshQ4y5556yx3I6jdmxo/Qx2ra132vbNnj7yjuH/yM5ufTPTZuWHNN3fN9rwR6B7fRtW9E+5T38z+n7TGlpwdrpNbNne0ybNh4D9rP/nz0e/58LTZs2hQZOmDZtTphZswoNeIuP4//nWbNOmLS04waOm9TU48bh8IbQ7mOmVavDJj8/36Sm5huwnw8dOmRatMgzkGeaN881Tz99uNS5brjhWKmfn3rqkElO3m8gxzRunGP27t1rGjbcZ+CAadhwX9Cfk5L2GthrEhMPBG3b66/vN0lJuw3sNklJu8zOnTvNhAk7zZ132s87d+40SUk7Dew0DRu6jdvtNjt27Ch+LFu2s8zfgcPhNQ7HVgObjNO5yfz3v1uC/D2V/tmyvGW2CfbvubLCuX6rRkRE6qyKFk0rr7alVSt7iqM/3/Tk998/eR1LODOFAoePAod2grXZf0jM1/sSjG8NirQ0++eTzSoK5GtLsFVQK1qYrKLVU4Mt8R5s8TufYAuYpaSU/g4CbyXg/3cV2NZgP/u+i9RU2L8/cFq6oWVL2LMn+Jomt95qeOml0uuhTJ5st7lVK3varn89kK9m6LzzStZeCfZv0OWq+P2sLBg5svR5J02y12hp1creN3CbUAqrofrugRTW9bt6sk9kqEdERKpixw5jHI7yf9MPpmXL4NsvXmy/7+sNCNajEir/Hgb/4z3+uDG33lpx70Sw4wQ+nM6S44baC+J0GpOaWtIW/zYG9ohU9r1gPv449M/r4/93FtjD5N97FNiein4u73tv1sx+Tk0t/99SsL/3tm3Ltu3558u2P5hQegYr+jsIPE+wtjscZV+LVo+Ipp2LSJ3lcsHs2aVfGzLEXpmzvN6KuCCVc05nyboL1VHHMnp0SRv8jzd6tN074Cjn/8yecgoKmzYt/fOsWSXHraiHJXCf+PiStlTEv/1V5aubCYf/31lmZkmvT1oaJWt5hCkurmw7LKtkZdOcnJPX9ng8UFhY8nNg23xTxH29U766jECjR8Mf/1jx36//dzRyZOn3Ro2ye1J828THl/3vYPbssq/NmlW5GzFWWfVkn8hQj4iIVIdwejEef9yYwYOD/4ZpTGi/5Ycj2PEqqiPxb4v/b86Bn7G83hL/h6/GI3CfwN6Miv7+qtojEuzzBvZqVOWc4fwc2I7y6otC7REJPH6wHrrK9kKcrCdn8eLya3v8v8fq6OELJpzrt2bNiEidF04vxujRZW+97l8HUt3TloMdz38acqBRo2DQIPs3V//fmJ+wJ0EUf8bRoyte5C0tzd42L6/0Pr62+I53+HBJr0qwmRXV8fcROHV77Njq/TuuiH/7/dtR3n1e/A0dCi++WPLzrFl2T1F5gk0R93hg06bK90T4enICb7ng68HzF+y/g+ro4asqBRERkSDK+x90dU9bLu945V0YQr1w+Y57xhmlizt9RajBju/flunT7efAoR3/IBSs/b7hI48nvItb4BBVZfmfPxSB5/K1IyEh+AW+eXN7mCYtDebNswuY/QPrpEnlnz/Y9O3yQkMo7S6vgDdqQyyVpCAiInVedfdiRFO4F67A3gan8+R1I/51DIEqCkKB9Q+BtSvVpbzvM1jvTWU5nXYNReAF3tfjERhUnc7g9R/+bfXVLFVHaAgMUMEWA6wt/+4VRESkzovE4ms1pexqrcEvXBVddCrq3Qncx+0uW/wYeKxgQSjYfqEWyoarvO8zWO9NamrlzxNstd/yhl48nuBFo4F3pa6OFYSDCfYd15Z/9woiIiJBRPu3yWDrhVR04Qr1ouN/3GD7lLfUvU95Qaii/UIdJqms0aPh66/hpZfKntd/Fkuox/L/3kOtoSgsDL3+IxbqMmKJgoiISBDR/m2yokLU6jpuMBUtQ19REKpov1CWD69K8PMV5v7tb2XrL4JNxz7ZsSojnKLRaAn2dxztwA0KIiIi4idYHUNFBa4V7ecvsMg1UFWDX3n1FxXNYqmM8i7c5dWUxFLRaLC/42gHbij/FooiIlJPVXaRMP/9AvmGKSIp2AJi1S1wsTH/xd1q4vx1kXpERETquMp0v1e2jqG87WtqmKKm6y/Km/6r+o/QKYiIiNRx0eh+D3W2j1SPWKj1qCwFERERqVBlLnJJSZCYWP3TVGtaJC7wkTpmbaUgIiIS46L9225lL3J1YZgiEhf42hwaIkFBREQkxunCJXWZZs2IiIhI1CiIiIiISNRoaEZERMqobF2K/36+FWFrUrTraSR8CiIiIlJGZetSgi1NX5NO1m7ffW8idf8bBaHwRWxo5pNPPsGyrKCP5cuXR+q0IiIiQWVl2dOJoeT+N9UtcOVVObmIBZF+/fqxa9euUo/hw4fTsWNHevXqFanTioiIlOHxwMiRpV8bNcq+WZ5EV8SGZuLj40nzu+nAiRMnePPNN7nrrruwLCtSpxURESmjsLDsnYF997/Riq/RVWM1Im+99Rb79+/ntttuK3ebgoICCgoKin/Oy8uriaaJiEgExEK9hK8WxLLA4SgdRmrq/jdSsRoLIllZWQwaNAhXBdFz6tSp/OlPf6qpJomISARFu07CvyYkJweGDoUXXyx5X/e/iQ2WMcaEs8OECRN49NFHK9xm/fr1dOvWrfhnt9tN+/btWbhwIddcc025+wXrEUlPTyc3N5dklSCLiEiI3G5o375sD0jz5nYoSUuDXbui1766Li8vj5SUlJCu32H3iNx7770MGzaswm06depU6ue5c+fSokULrrzyygr3S0hIICEhIdwmiYiIlJKdHbwmxPerd22+/01dE3YQSU1NJTU1NeTtjTHMnTuXW2+9lQYNGoR7OhERkbBlZASvCYnT6lkxJ+JLvH/88cds3bqV4cOHR/pUIiIigF37MXt26ddmzVJPSCyKeBDJysqiX79+pWpGREREIi0z064FAfs5MzO67ZHgIt5J9fe//z3SpxAREQnK1wOinpDYpbvvioiISNQoiIiIiEjUKIiIiIhI1CiIiIiISNQoiIiIiEjUKIiIiIhI1CiIiIiISNQoiIiIiEjUKIiIiIhI1CiIiIiISNQoiIiIiEjUKIiIiIhI1CiIiIiISNQoiIiIiEjUKIiIiIhI1CiIiIiISNQoiIiIiEjUKIiIiIhI1CiIiIiISNQoiIiIiEjUKIiIiIhI1MRFuwEiIiI1ZfRoyMuD5ORot0R8FERERKTeGD062i2QQBqaERERkahREBEREZGoURARERGRqFEQERERkahRsaqIiNRZmiUT+xRERESkztIsmdinoRkRERGJGgURERERiZqIBZHvvvuOq666ipYtW5KcnMz555/P4sWLI3U6ERERqYUiFkSuuOIKCgsL+fjjj1m5ciVnn302V1xxBbt3747UKUVERKSWiUgQ2bdvH9nZ2UyYMIGzzjqLjIwMpk2bxpEjR1izZk0kTikiIiK1UESCSIsWLejatSsvvfQShw8fprCwkFmzZtGqVSvOOeecSJxSREREaqGITN+1LIuPPvqIq6++miZNmuBwOGjVqhXvvfcezZo1K3e/goICCgoKin/Oy8uLRPNEREQkRoTVIzJhwgQsy6rwsWHDBowx3HHHHbRq1YrPPvuMZcuWcfXVVzN48GB27dpV7vGnTp1KSkpK8SM9Pb3KH1BERERil2WMMaFunJOTw/79+yvcplOnTnz22WdceumlHDhwgGS/5ewyMjLIzMxkwoQJQfcN1iOSnp5Obm5uqeOIiIhI7MrLyyMlJSWk63dYQzOpqamkpqaedLsjR44A4HCU7nBxOBx4vd5y90tISCAhISGcJomIiEgtFpFi1b59+9KsWTOGDh3K119/zXfffcfYsWPZunUrv/jFLyJxShEREamFIhJEWrZsyXvvvUd+fj4DBgygV69efP7557z55pucffbZkTiliIiI1EJh1YjUtHDGmERERCQ2hHP91r1mREREJGoiso5IdfF11mg9ERERkdrDd90OZdAlpoPIoUOHALSeiIiISC106NAhUlJSKtwmpmtEvF4vO3fupEmTJliWFda+vjVIduzYofqSKNL3EDv0XcQOfRexQd9D5BhjOHToEKecckqZpTwCxXSPiMPhwOVyVekYycnJ+gcWA/Q9xA59F7FD30Vs0PcQGSfrCfFRsaqIiIhEjYKIiIiIRE2dDSIJCQlMnjxZS8ZHmb6H2KHvInbou4gN+h5iQ0wXq4qIiEjdVmd7RERERCT2KYiIiIhI1CiIiIiISNQoiIiIiEjU1Oog8uyzz9KhQwcSExPp06cPy5Ytq3D7V199lW7dupGYmMiZZ57Ju+++W0MtrdvC+R7Wrl3LNddcQ4cOHbAsi5kzZ9ZcQ+uBcL6LOXPmcMEFF9CsWTOaNWvGwIEDT/rfkIQmnO/hjTfeoFevXjRt2pSkpCR69OjB/Pnza7C1dVu41wmfBQsWYFkWV199dWQbKGBqqQULFpj4+HjzwgsvmLVr15oRI0aYpk2bmj179gTd/osvvjBOp9M89thjZt26dWbSpEmmQYMG5ttvv63hltct4X4Py5YtM2PGjDGvvPKKSUtLM08++WTNNrgOC/e7uPHGG82zzz5rVq1aZdavX2+GDRtmUlJSjNvtruGW1y3hfg+LFy82b7zxhlm3bp3ZtGmTmTlzpnE6nea9996r4ZbXPeF+Fz5bt241bdu2NRdccIG56qqraqax9VitDSLnnnuuueOOO4p/9ng85pRTTjFTp04Nuv2QIUPML37xi1Kv9enTx4waNSqi7azrwv0e/LVv315BpBpV5bswxpjCwkLTpEkT8+KLL0aqifVCVb8HY4zp2bOnmTRpUiSaV69U5rsoLCw0/fr1M88//7wZOnSogkgNqJVDM8ePH2flypUMHDiw+DWHw8HAgQNZsmRJ0H2WLFlSanuAQYMGlbu9nFxlvgeJjOr4Lo4cOcKJEydo3rx5pJpZ51X1ezDGsGjRIjZu3MjPfvazSDa1zqvsd/Hggw/SqlUrMjMza6KZQozf9K48+/btw+Px0Lp161Kvt27dmg0bNgTdZ/fu3UG33717d8TaWddV5nuQyKiO72L8+PGccsopZQK7hK6y30Nubi5t27aloKAAp9PJX/7yFy655JJIN7dOq8x38fnnn5OVlcXq1atroIXiUyuDiIhUr2nTprFgwQI++eQTEhMTo92ceqdJkyasXr2a/Px8Fi1axOjRo+nUqRP9+/ePdtPqjUOHDnHLLbcwZ84cWrZsGe3m1Cu1Moi0bNkSp9PJnj17Sr2+Z88e0tLSgu6TlpYW1vZycpX5HiQyqvJdzJgxg2nTpvHRRx9x1llnRbKZdV5lvweHw0GXLl0A6NGjB+vXr2fq1KkKIlUQ7nexefNmtm3bxuDBg4tf83q9AMTFxbFx40Y6d+4c2UbXU7WyRiQ+Pp5zzjmHRYsWFb/m9XpZtGgRffv2DbpP3759S20P8OGHH5a7vZxcZb4HiYzKfhePPfYYDz30EO+99x69evWqiabWadX134TX66WgoCASTaw3wv0uunXrxrfffsvq1auLH1deeSUXXXQRq1evJj09vSabX79Eu1q2shYsWGASEhLMvHnzzLp168zIkSNN06ZNze7du40xxtxyyy1mwoQJxdt/8cUXJi4uzsyYMcOsX7/eTJ48WdN3q0G430NBQYFZtWqVWbVqlWnTpo0ZM2aMWbVqlcnOzo7WR6gzwv0upk2bZuLj481rr71mdu3aVfw4dOhQtD5CnRDu9/DII4+YDz74wGzevNmsW7fOzJgxw8TFxZk5c+ZE6yPUGeF+F4E0a6Zm1NogYowxzzzzjGnXrp2Jj4835557rlm6dGnxexdeeKEZOnRoqe0XLlxoTj31VBMfH2/OOOMM884779Rwi+umcL6HrVu3GqDM48ILL6z5htdB4XwX7du3D/pdTJ48ueYbXseE8z3cd999pkuXLiYxMdE0a9bM9O3b1yxYsCAKra6bwr1O+FMQqRmWMcZEqzdGRERE6rdaWSMiIiIidYOCiIiIiESNgoiIiIhEjYKIiIiIRI2CiIiIiESNgoiIiIhEjYKIiIiIRI2CiIiIiESNgoiIiIhEjYKIiIiIRI2CiIiIiESNgoiIiIhEzf8D1H9yvWutR5gAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "objective.plot(samples=300);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "In a similar manner we can look at the spread in SLD profiles consistent with the data." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "structure.plot(samples=300)\n", "plt.ylim(2.2, 6);" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Fitting the BornAgain example" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "[BornAgain](https://www.bornagainproject.org/) is another program for fitting reflectometry and GISAS data. The following cells repeat the analysis in their [specular fitting example]( https://github.com/scgmlz/BornAgain/blob/master/Examples/python/fitting/ex03_ExtendedExamples/specular/FitSpecularBasics.py). The simulated thickness of the Titanium layer is 30 Angstrom, but we'll start the fit with a value of 50." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# necessary imports\n", "import numpy as np\n", "from refnx.util import q\n", "from refnx.analysis import Objective, CurveFitter, Transform\n", "from refnx.dataset import Data1D\n", "from refnx.reflect import SLD, Stack, ReflectModel" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# first grab the data from the BornAgain repository.\n", "# The data was originally created in genx.\n", "import requests as req\n", "import io\n", "import gzip\n", "\n", "url = (\n", " \"https://jugit.fz-juelich.de/mlz/bornagain/-/raw/main/\"\n", " \"testdata/specular/genx_alternating_layers.dat.gz?inline=false\"\n", ")\n", "f = gzip.open(io.BytesIO(req.get(url).content))\n", "dataset = np.loadtxt(f, usecols=(0, 1), skiprows=3).T\n", "\n", "# data is saved as two_theta, convert to Q.\n", "dataset[0] = q(dataset[0] / 2, 1.54)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# make a dataset\n", "data = Data1D(data=dataset)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# make the structure\n", "air = SLD(0)\n", "si = SLD(2.0704) # silicon substrate\n", "ni = SLD(9.4245) # nickel\n", "ti = SLD(-1.9493) # titanium\n", "\n", "# make the layers\n", "ti_layer = ti(50)\n", "ni_layer = ni(70)\n", "\n", "# Make a multilayer by using a Stack Component\n", "stack = Stack()\n", "stack |= ti_layer\n", "stack |= ni_layer\n", "stack.repeats.value = 10\n", "\n", "structure = air | stack | si(0, 0)\n", "\n", "# put the Structure in a Model\n", "model = ReflectModel(structure, bkg=0, dq=0)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# we're only going to fit the Titanium thickness\n", "ti_layer.thick.setp(vary=True, bounds=(10, 60))\n", "\n", "# now do the fit\n", "objective = Objective(model, data, transform=Transform(\"logY\"))\n", "fitter = CurveFitter(objective)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "1.0115394037801482e-07: : 25it [00:00, 96.44it/s]\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# now fit and plot\n", "fitter.fit(\"differential_evolution\")\n", "fig, ax = objective.plot()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.023074484566843e-07\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(objective.chisqr())\n", "print(ti_layer)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.53/doc/gui.rst000066400000000000000000000012551477046072400150230ustar00rootroot00000000000000.. _gui_chapter: === GUI === .. _YouTube: https://www.youtube.com/channel/UCvhOxwZsdFMGqSzasE0ZSOw .. _github: https://github.com/refnx/refnx/releases/latest *refnx* offers a sophisticated *PyQt* graphical user interface to analyse data, with pre-built executables available on `github`_. The gui can also be started from the interpreter (requiring the *qtpy, pyqt6, periodictable, matplotlib* packages to be installed): :: >>> from refnx.reflect import gui >>> gui() There are tutorials on how to use the PyQt interface on `YouTube`_. Suggestions for more tutorials are welcomed. .. image:: _images/gui.png :width: 400 :alt: The *refnx* front end GUI.refnx-0.1.53/doc/incoherent_sum.ipynb000066400000000000000000002644771477046072400176130ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "af01a16e-b5aa-474f-abe7-d87d325d660a", "metadata": {}, "source": [ "# Incoherent Summing for patchy surfaces\n", "\n", "If you have patchy areas on a surface (> coherence length of neutron) you may want to average the reflectivity signals from those different areas, a process called incoherent summing. In contrast if the lateral inhomogeneity lengthscale is less than the coherence length you want to be laterally averaging the scattering length density profile.\n", "\n", "This example demonstrates the incoherent summing using `MixedReflectModel`. The steps are to first set up `Structure` for each of the patchy areas. Don't forget that you can re-use objects across different structures to enforce constraints/reduce parameterisation.\n", "\n", "The example I'll create is a simple polymer layer on top of a silicon wafer, but it's the same process different systems. Incoherent averaging is used for: patchy lipid bilayers, thickness gradients of films across a surface, etc. I sometimes use ellipsometric thickness mapping to guide the incoherent summing in an NR analysis. \n", "\n", "This is a good paper that demonstrates incoherent averaging:\n", "\n", "> [Gresham, Isaac J., et al. \"Geometrical confinement modulates the thermoresponse of a poly (N-isopropylacrylamide) brush.\" Macromolecules 54.5 (2021): 2541-2550.](https://pubs.acs.org/doi/epdf/10.1021/acs.macromol.0c02775)" ] }, { "cell_type": "code", "execution_count": 1, "id": "c18bb6b1-2511-468f-8176-d04ee3756058", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from refnx.analysis import Parameter\n", "from refnx.reflect import (\n", " ReflectModel,\n", " SLD,\n", " Slab,\n", " Structure,\n", " MixedReflectModel,\n", " LipidLeaflet,\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "id": "dbdf44af-4f51-4de2-9eae-627260d15b52", "metadata": {}, "outputs": [], "source": [ "# SLDs\n", "air = SLD(0.0)\n", "sio2 = SLD(3.47)\n", "polymer = SLD(1.0)\n", "si = SLD(2.07)" ] }, { "cell_type": "code", "execution_count": 3, "id": "8c6a6550-8eb5-45f0-8250-96ca0f60d79e", "metadata": {}, "outputs": [], "source": [ "# sio2 slab is common over all areas\n", "sio2_layer = Slab(25, sio2, 3)\n", "\n", "# the si/sio2 roughness is common across all areas\n", "si_roughness = Parameter(3.0)" ] }, { "cell_type": "markdown", "id": "a4bb9746-fbae-48a9-9f54-de17a2950b2e", "metadata": {}, "source": [ "We're going to assume that the polymer coated area has two different thicknesses, one of which is 50% of the other. This kind of information can often be determined by ellipsometry. More complex thickness variations can be modelled with analytical thickness distributions, e.g. convex or concave domes. Note that the constraint is automatically propagated, i.e. if you change `polymer_thickness_0.value`, then this will be propagated to `polymer_thickness_1`." ] }, { "cell_type": "code", "execution_count": 4, "id": "1f92cd2a-023b-4133-a9c3-ba572d089d99", "metadata": {}, "outputs": [], "source": [ "polymer_thickness_0 = Parameter(200.0)\n", "# the thickness constraint is applied automatically\n", "polymer_thickness_1 = polymer_thickness_0 * 0.5\n", "\n", "# polymer_thickness_1 = Parameter(constraint=polymer_thickness_0 * 0.5) # an alternate way of enforcing the constraint\n", "\n", "polymer_l_0 = Slab(polymer_thickness_0, polymer, 4)\n", "polymer_l_1 = Slab(polymer_thickness_1, polymer, 4)" ] }, { "cell_type": "code", "execution_count": 5, "id": "9532d4dc-f631-48cf-aa64-0302fc2ad0e5", "metadata": {}, "outputs": [], "source": [ "structure_bare = air | sio2 | si(np.inf, si_roughness)\n", "structure0 = air | polymer_l_0 | sio2 | si(np.inf, si_roughness)\n", "structure1 = air | polymer_l_1 | sio2 | si(np.inf, si_roughness)" ] }, { "cell_type": "markdown", "id": "7ba5bd10-8ccc-4cac-9905-1ec8b08de62c", "metadata": {}, "source": [ "Once we have the structures that we wish to model we can set up the `MixedReflectModel` to incoherently sum the areas. `MixedReflectModel` is very similar to `ReflectModel` in the way it adds background, performs resolution smearing, etc." ] }, { "cell_type": "code", "execution_count": 6, "id": "7acd8241-3b90-4118-8740-f762cb335e32", "metadata": {}, "outputs": [], "source": [ "model = MixedReflectModel(\n", " [structure_bare, structure0, structure1], scales=(0.2, 0.5, 1.3), bkg=1e-8\n", ")" ] }, { "cell_type": "code", "execution_count": 7, "id": "48f1803b-1b67-4637-9fa5-ce38652652dd", "metadata": {}, "outputs": [ { "data": { "image/png": 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ClQqknZvvZR+7joiIiAbE5W6j6dOnY/r06Rd8/8UXX8RDDz2E+fPnAwBWrlyJTz/9FK+++ioWLlwIACgpKbnoOaxWK2bOnImFCxfiyiuvvOS+Vuv3ix6aTL45KHZikgYlVU3Yf6oJt6cnSF0OERGR33Lr00Y2mw3FxcXIzc39/gQKBXJzc7Ft27Y+HUMURcybNw/XX3897r///kvuv2TJEmg0GufLV7uYuse97D3FlhciIqKBcGt4qa+vh91uh1ar7bFdq9WipqamT8fYsmULVq9ejXXr1iEjIwMZGRnYv3//BfdftGgRmpubna+qqqoBXYOnTEyKBAAcPN0Mu0OUthgiIiI/5nNPG1199dVwOPo+jb5arYZarYbBYIDBYIDdbvdgdf03IjYMwYFKWGx2VNabMSI2XOqSiIiI/JJbW15iYmKgVCphNBp7bDcajYiLi3PnqXrJz89HaWkpdu7c6dHz9JdSIWB84rlBu+w6IiIi6je3hheVSoXMzEwUFhY6tzkcDhQWFiInJ8edp/JLExIjAQB7q5okrYOIiMifudxtZDabUVFR4fy5srISJSUliI6ORnJyMgoKCpCXl4fJkycjOzsby5cvh8VicT595Cm+3m0EAJcnRwJbgOKTZ6UuhYiIyG8Joii6NHp048aNmDp1aq/teXl5WLVqFQBgxYoVWLp0KWpqapCRkYGXXnoJer3eLQVfislkgkajQXNzMyIiIrxyzr4609yGnCVfQyEA+56ZhjC1zw05IiIikoQrf79dDi++6octL+Xl5T4ZXgDgque+xummNvz753pcPTJG6nKIiIh8givhxedWle4vXx+w221yahQAYNeJRokrISIi8k+yCS/+YnJqNACg+ATHvRAREfWHbMKLwWBAWloasrKypC7loiandLW87D5xFp32vs9nQ0RERF1kE178pdtolDYc4eoAWGx2HK5pkbocIiIivyOb8OIvlAoBk861vrDriIiIyHUMLxLo7jraeZyDdomIiFwlm/DiL2NeACB7WNeg3e+ONUImT6oTERF5jWzCi7+MeQGAjORIBAcqUW+2otxolrocIiIivyKb8OJP1AFKZJ1rfdlSUS9xNURERP6F4UUiV102BADDCxERkasYXiRy1YiupQG2VzZyvhciIiIXyCa8+NOAXQBIi49AZEggzNZO7D3VLHU5REREfkM24cWfBuwCgEIh4MpzXUffHqmTuBoiIiL/IZvw4o+uGxULAPjmcK3ElRAREfkPhhcJXTdmKABg76lm1La0S1wNERGRf2B4kVBseBDSkzQAgI2H2XVERETUF7IJL/42YLfb9WO0AIDCw0aJKyEiIvIPsgkv/jZgt9sNY7vGvXx7pB7WTrvE1RAREfk+2YQXfzUuIQLaCDVabXZsPdogdTlEREQ+j+FFYoIg4Ka0OADAp/vOSFwNERGR72N48QG3TYwHAHxxsIZdR0RERJfA8OIDslKjoY1Qo6W9E9+Wc60jIiKii2F48QEKhYBbJyQAAD7eVy1xNURERL6N4cVH3Jbe1XX0VakR7R3sOiIiIroQhhcfcbkuEklRwbDY7PjiYI3U5RAREfks2YQXf52krpsgCJidmQQAWL2zSuJqiIiIfJdswou/TlL3Q3dP1kEQgK1HG3CiwSJ1OURERD5JNuFFDhIjg3HNyK7FGtfsYusLERHR+TC8+JifZOkAAO/tOoVOu0PiaoiIiHwPw4uPuWGsFjFhKtS2WPFlKRdrJCIi+jGGFx+jClDgp9nJAIBXvj0mcTVERES+h+HFB92fkwKVUoHdJ5tQfOKs1OUQERH5FIYXHxQbHoSZl3fNuPtPtr4QERH14HPhpampCZMnT0ZGRgbGjx+PV155ReqSJPHzq4cD6FqskY9NExERfc/nwkt4eDiKiopQUlKC7du349lnn0VDQ4PUZXnd6LhwXDNqKBwisHLTUanLISIi8hk+F16USiVCQkIAAFarFaIoQhRFiauSxoLrRwDoemy6qrFV4mqIiIh8g8vhpaioCDNmzEBCQgIEQcC6det67WMwGJCamoqgoCDo9Xrs2LHDpXM0NTUhPT0dSUlJePzxxxETE+NqmbKQlRqNKSNj0OkQseLrCqnLISIi8gkuhxeLxYL09HQYDIbzvr969WoUFBRg8eLF2L17N9LT0zFt2jTU1tY69+kez/LjV3V1NQAgMjISe/fuRWVlJd5++20YjYN3vpP/zh0FAHh/9ymOfSEiIgIgiAPokxEEAWvXrsXMmTOd2/R6PbKysrBixQoAgMPhgE6nw4IFC7Bw4UKXz/HrX/8a119/PWbPnn3e961WK6xWq/Nnk8kEnU6H5uZmREREuHw+XzT31R0oKq/DrEmJePGeDKnLISIicjuTyQSNRtOnv99uHfNis9lQXFyM3Nzc70+gUCA3Nxfbtm3r0zGMRiNaWloAAM3NzSgqKsLo0aMvuP+SJUug0WicL51ON7CL8EGP3tjV+rJ2z2mUVpskroaIiEhabg0v9fX1sNvt0Gq1PbZrtVrU1NT06RgnTpzAlClTkJ6ejilTpmDBggWYMGHCBfdftGgRmpubna+qKvktaJiui8SM9ASIIvDsZ4cG7QBmIiIiAAiQuoAfy87ORklJSZ/3V6vVUKvVMBgMMBgMsNvtnitOQk9MG40vDtRgc0U9NpXX4brRsVKXREREJAm3trzExMRAqVT2GmBrNBoRFxfnzlP1kp+fj9LSUuzcudOj55GKLjoEeVemAACWfHYYdgdbX4iIaHBya3hRqVTIzMxEYWGhc5vD4UBhYSFycnLceapeDAYD0tLSkJWV5dHzSOmRqSOhCQ5EmbEF7xfLr3uMiIioL1wOL2azGSUlJc6uncrKSpSUlODkyZMAgIKCArzyyit4/fXXcejQITz88MOwWCyYP3++Wwv/Mbm3vACAJiQQ/3XDSADAsg3lMFs7Ja6IiIjI+1we87Jr1y5MnTrV+XNBQQEAIC8vD6tWrcKcOXNQV1eHp59+GjU1NcjIyMD69et7DeKl/rn/ihS8ue04jje0wvBNBX578xipSyIiIvKqAc3z4kt+OGC3vLxcVvO8/NhXpUY8+MYuqJQKbPh/1yA1JlTqkoiIiAbElXleZBNeurly8f5KFEXkvbYTReV1yB2rxT/zJktdEhER0YBINkkdeYcgCHj6tjQEKAR8dciIovI6qUsiIiLyGtmEl8HwtNEPjYgNQ96VqQCA339Sig67Q9qCiIiIvITdRn6sua0D1y/biAaLDU/floYHrh4mdUlERET9wm6jQUITHIjHpnWt+/Tnr8rRYLZe4hNERET+j+HFz90zWYdxCRFoae/Esg3lUpdDRETkcbIJL4NtzEs3pULAM7ePAwC8u/MkDpxulrgiIiIiz+KYF5n4r3f24KO91chKjcKaX+ZAEASpSyIiIuozjnkZhBZOH4OgQAV2Hj+LT/adkbocIiIij2F4kYmEyGD8+roRAIAlnx1Cm80ucUVERESeIZvwMljHvPzQL64ZjqSoYFQ3t+Nvm45KXQ4REZFHcMyLzHy+/wwefms31AEKFD56LZKiQqQuiYiI6JI45mUQu3l8HK4YHg1rpwPPfnZI6nKIiIjcjuFFZgRBwOIZ46AQgM/212Dr0XqpSyIiInIrhhcZGhsfgfv0KQCA339cik6ue0RERDLC8CJTBTeOgiY4EIdrWvDOziqpyyEiInIb2YQXPm3UU1SoCo/eNAoA8MKGMjS12iSuiIiIyD34tJGMddoduPWlzSgztuA+fTL+dOcEqUsiIiI6Lz5tRACAAKUCv7+ja92jt3ecRElVk7QFERERuQHDi8zphw/BrEmJEEXgyXX7YXfIqqGNiIgGIYaXQWDR9LEIDwrAgdMmvLX9hNTlEBERDQjDyyAwNFyNJ6aNBgAs/aIMtS3tEldERETUfwwvg8S9+hRMSNSgpb0T//tRqdTlEBER9RvDyyChVAhYMmsClAoBn+4/g0/2VUtdEhERUb/IJrxwnpdLG5+oQf51lwEAnlp3AHUtVokrIiIich3neRlkbJ0O3GHYgkNnTMgdq8UrczMhCILUZRER0SDHeV7oglQBCrxwdzpUSgW+OmTE34uOSV0SERGRSxheBqG0hAgsvj0NAPD8+sPYWsGVp4mIyH8wvAxS92YnY3ZmEhwisOCdPTjZ0Cp1SURERH3C8DJICYKAP84cj3EJEWiw2PCzf21HTTPnfyEiIt/H8DKIBQUq8dq8LKQMCcHJxlbMXrkVlfUWqcsiIiK6KIaXQS42IghvPahH6pAQnDrbhttXbMan+85AZg+hERGRjPhseGltbUVKSgoee+wxqUuRvaSoELz3qysxOSUKLe2dyH97N+av2oniE2cZYoiIyOcESF3AhfzpT3/CFVdcIXUZg8bQcDXe+cUV+MtXR/D3oqPYWFaHjWV1GBYTiqtHxGBUXDiiQ1SIDAlEsEoJpSBAIQhQKND1T0GAUgEEKBTQRgQhWKWU+pKIiEimfDK8HDlyBIcPH8aMGTNw4MABqcsZNAKVCjw2bTRmTUrEyk1Hsa6kGpX1ln6Ng4kNVyMtIQJZqdG4ZuRQjE+M4GR4RETkFi53GxUVFWHGjBlISEiAIAhYt25dr30MBgNSU1MRFBQEvV6PHTt2uHSOxx57DEuWLHG1NHKT4UPD8PzsdOx+6kb87b5J+MU1w5E7Vovs1GiMjA2DLjoYiZHBiNcEITZcjZgwNaJDz7XKBHa1uNS2WLGxrA5LvyjDjBWbMXXZRqz4+ggaLTaJr46IiPydyy0vFosF6enpeOCBBzBr1qxe769evRoFBQVYuXIl9Ho9li9fjmnTpqGsrAyxsbEAgIyMDHR2dvb67IYNG7Bz506MGjUKo0aNwtatW/txSeQuYeoATJ8Qj+kT4vv8GVEUYWrrREVdC/adasbWow3YfKQexxtasWxDOVZ8U4Gf6VPwyPUjEBmi8mD1REQkVwNa20gQBKxduxYzZ850btPr9cjKysKKFSsAAA6HAzqdDgsWLMDChQsvecxFixbh3//+N5RKJcxmMzo6OvDoo4/i6aefPu/+VqsVVuv3CwyaTCbodDqubeRDLNZOrD9Qg9e2VuLAaRMAQBMciIXTx+AnWTp2JxERkXRrG9lsNhQXFyM3N/f7EygUyM3NxbZt2/p0jCVLlqCqqgrHjx/HsmXL8NBDD10wuHTvr9FonC+dTjfg6yD3ClUH4K7MJHz8yNV4bX4WRmvD0dzWgUUf7OfkeERE5DK3hpf6+nrY7XZotdoe27VaLWpqatx5KqdFixahubnZ+aqqqvLIeWjgBEHA1NGx+Ow3U/DkrWMRFKjAlooG3PrSt9jC9ZWIiKiPfPJpo27z5s275D5qtRpqtRoGgwEGgwF2u93zhdGAKBUCHpwyHDeM1eLXb+3GoTMm3P+v7fjDzPG4T58idXlEROTj3NryEhMTA6VSCaPR2GO70WhEXFycO0/VS35+PkpLS7Fz506PnofcZ1hMKNb++krMmpQIhwj8bu0BPL/+MCfGIyKii3JreFGpVMjMzERhYaFzm8PhQGFhIXJyctx5ql4MBgPS0tKQlZXl0fOQewUFKvHC3en4f7mjAAB/3XgUf/z0EAMMERFdkMvdRmazGRUVFc6fKysrUVJSgujoaCQnJ6OgoAB5eXmYPHkysrOzsXz5clgsFsyfP9+thf9Yfn4+8vPznaOVyX8IgoDf5I5EdJgKT607gH9troQoAk/dNpZPIhERUS8uh5ddu3Zh6tSpzp8LCgoAAHl5eVi1ahXmzJmDuro6PP3006ipqUFGRgbWr1/faxAv0Y/df0UKAhQCFn2wH69uqcSQMBXyp46QuiwiIvIxA5rnxZf8cMBueXk553nxY//aXIk/fFIKAHj+rom4J4uPvxMRyZ0r87zIJrx0c+XiyXc9v/4w/rrxKAIUAt56UA/98CFSl0RERB4k2SR1RO7y+LTRuG1iPDodIh5+azeqGlulLomIiHyEbMILnzaSF0EQsHR2OiYkatBoseGXbxajvYNz+BAREbuNyMedaW7DrS9tRqPFhvv0yfjTnROkLomIiDyA3UYkG/GaYPx5TgYEAXhr+0l8tLda6pKIiEhiDC/k864dNRSPnHtk+ndr9+N0U5vEFRERkZRkE1445kXefnPDSFyeHImW9k48tmYvHA5Z9XYSEZELOOaF/Mbxegum/+VbtHXY8eStY/HglOFSl0RERG7CMS8kS6kxoXjqtjQAwLINZTjZwMeniYgGI4YX8is/zdbhiuHRaO9w4Hfr9nMBRyKiQUg24YVjXgYHQRCwZNZEqAIU+PZIPdaVnJa6JCIi8jKOeSG/ZPimAku/KEN0qApfFVyL6FCV1CUREdEAcMwLyd5DU4ZjtDYcjRYb/vhpqdTlEBGRFzG8kF9SBSiw5K4JEATgg92nsflIvdQlERGRlzC8kN+alByFuVekAACeXLcf1k6ufURENBgwvJBfe2zaaMSGq3G8oRX//LZS6nKIiMgLZBNe+LTR4BQeFIhFt4wBAKz4ugJnmrl0ABGR3PFpI/J7oihi9sptKD5xFjPSE/DyTy+XuiQiInIRnzaiQUUQBPzv7eMgCMDHe6ux/ViD1CUREZEHMbyQLIxP1OCn2ckAgMUfHUSn3SFxRURE5CkMLyQbj980GprgQByuacHbO05KXQ4REXkIwwvJRlSoCo/dNAoA8MKGcjRabBJXREREnsDwQrJyrz4FY+Mj0NzWgaVflEldDhEReQDDC8mKUtE1eBcA3t15EvtPNUtcERERuZtswgvneaFu2cOicUdGAkQRWPzRATgcspoNgIho0OM8LyRLNc3tuP6FjWi12fHC3em4KzNJ6pKIiOgiOM8LDXpxmiAsuH4kAOC59YfR0t4hcUVEROQuDC8kWw9cnYphMaGoa7Hi5a8rpC6HiIjchOGFZEsdoMTTM9IAAK9urkRFrVniioiIyB0YXkjWpo6ORe7YWHQ6RDy5bj8H7xIRyQDDC8ne4hnjEByoxHfHGvHOTs68S0Tk7xheSPZ00SF4bNpoAMCSzw6juqlN4oqIiGggfDK8pKamYuLEicjIyMDUqVOlLodkYN6VqZiUHAmztROLPtgPmc0QQEQ0qPhkeAGArVu3oqSkBN98843UpZAMKBUCnp89EaoABTaV1+FfmyulLomIiPrJZ8MLkbuNiA3HU7eOBQD83/rDKKlqkrYgIiLqF5fDS1FREWbMmIGEhAQIgoB169b12sdgMCA1NRVBQUHQ6/XYsWOHS+cQBAHXXnstsrKy8NZbb7laItEF/eyKFNwyIQ4ddhGPvL0bTa1ceZqIyN+4HF4sFgvS09NhMBjO+/7q1atRUFCAxYsXY/fu3UhPT8e0adNQW1vr3CcjIwPjx4/v9aqurgYAbN68GcXFxfjoo4/w7LPPYt++ff28PKKeBEHAklkToYsOxqmzbXjw9V1o77BLXRYREblgQGsbCYKAtWvXYubMmc5ter0eWVlZWLFiBQDA4XBAp9NhwYIFWLhwocvnePzxxzFu3DjMmzfvvO9brVZYrVbnzyaTCTqdjmsb0UWV1bRg9sqtaGnvRO5YLVb+bBIClOxFJSKSimRrG9lsNhQXFyM3N/f7EygUyM3NxbZt2/p0DIvFgpaWFgCA2WzG119/jXHjxl1w/yVLlkCj0ThfOp1uYBdBg8LouHD8c+5kqAIU+OqQEQVr9sLayRYYIiJ/4NbwUl9fD7vdDq1W22O7VqtFTU1Nn45hNBpx9dVXIz09HVdccQXmzp2LrKysC+6/aNEiNDc3O19VVVUDugYaPPTDh+Cln1wOpULAR3urMfdfO9DcygUciYh8XYDUBfzY8OHDsXfv3j7vr1aroVarYTAYYDAYYLfz/56p724eH4fX5mXh12/txvbKRsz86xa8cE86JiVHSV0aERFdgFtbXmJiYqBUKmE0GntsNxqNiIuLc+epesnPz0dpaSl27tzp0fOQ/FwzaijefzgHCZogVNZbcNfftuIPn5TCYu2UujQiIjoPt4YXlUqFzMxMFBYWOrc5HA4UFhYiJyfHnacicqsxcRH47DdTMGtSIkQR+NfmSkx5/hus3HQUrTaGGCIiX+JyeDGbzSgpKUFJSQkAoLKyEiUlJTh5smvBu4KCArzyyit4/fXXcejQITz88MOwWCyYP3++Wwv/MYPBgLS0tIuOjyG6mMgQFV68JwOvzc9C6pAQNFpseO7zw7jqua+x7IsyGE3tUpdIRETox6PSGzduPO96Q3l5eVi1ahUAYMWKFVi6dClqamqQkZGBl156CXq93i0FX4orj1oRXUin3YF1JdV4+esjONHQCgAIVAq4bWICHrhqGCYkaSSukIhIXlz5+z2geV58EcMLuVOn3YEvS414dUsldh4/69yelRqFB64ahhvTtJwfhojIDQZlePnh00bl5eUML+R2+0414bUtx/Hx3mp0Orr+s0mMDMbPrx6G+65IhjpAKXGFRET+a1CGl25seSFPM5ra8ea2E3hr+wmcPTcvTGJkMB69aRRmZiRCoRAkrpCIyP8wvDC8kBe0d9jxn92n8FLhERhNXUtUTEzS4E8zJ/jkmBhrpx3tNgciggMgCAxYRORbBmV4YbcRSaXNZseqrcfx128q0GLthEIA5uak4rFpoxGm9v48kHaHiH2nmrCloh5bjzbg1Nk2NFpsMJ+btyZEpURSVDCSokIwMUmD2ybGY0RsuNfrJCL6oUEZXrqx5YWkUtvSjj9+cggf7e1aHT0xMhjP3TUBU0YO9cr5D9eY8Oa2E/h4bzVM7a7NTTMmLhwz0hNwb3YyokJVHqqQiOjCGF4YXkhC3x6pw/+s3Y+qxjYAwE+zdVh0y1hEBAW6/VwOh4gvDtbgtS3HseN4o3N7eFAArrxsCK4eORRj48IRHarCkFA11IEKnGlux6mzrTje0IpvDteiqLzOOQA5IigA/3XDSMzNSYUqgE9REZH3DMrwwm4j8iUWayeWflGGVVuPAwDiNUFYMmsCrhsd65bjOxwi1h+swV++OoIyY9cq7EqFgGnjtLhPnwL9sOg+P8Ld1GrD+gM1WLX1OA7XdB1rWEwofnfLWOSmaS/xaSIi9xiU4aUbW17Il3x3rAG//c8+50R3d2cmYeH0MRgSpu7X8c4XWsKDAjDvylTcp09BnCao37XaHSLe21WFZRvKUW/uGoB8/xUpeOq2NLbCEJHHMbwwvJAPabV1YtkX5XhtayVEEQhXB+CR60dg3lWpfZ4bxn6ue+jHoeWBq4bhgauHQRPsvi4ps7UTLxcewT++PQZRBC5PjsTf7sscUDAiIroUhheGF/JBu4434pmPD+LAaRMAIEEThHuydLhrUhJ00SHn/czJhlas2VWF94tPoebc2kqeCi0/9s3hWvzm3T0wtXciJkyFl386CTmXDfHY+YhocGN4YXghH+VwiPhgz2ks/eKwc24YoGt+GF1UCOI0QQhQCqgwmlFe2+Ic9AsAkSGByMtJ9Xho+aETDRb86t+7ceiMCYFKAX+7L5PjYIjIIwZleOGAXfIn7R12rD9Qg//sPoXNFfW40H+FggBcPSIGc7J0uDFNK8kSBG02OwrWlODzAzUIVAow3DsJN42L83odRCRvgzK8dGPLC/mbM81t2H2iCTWmdhhN7WjvsGNEbBhGxoZj9LnHnKXWaXfgv1eX4JN9ZxCgELDi3km4eTwDDBG5D8MLwwuR23XaHXj0vb34sKQaAQoBL//0ckyfEC91WUQkE678/ebzj0TUJwFKBV68JwN3Xp6IToeI37xbgh2VjZf+IBGRmzG8EFGfKRUClt2djpvHxcFmd+AXb+7CsTqz1GUR0SDD8EJELlEqBPx5TgbSdZFoau3AA6t2otFik7osIhpEZBNeDAYD0tLSkJWVJXUpRLIXrFLin3MnIykqGMcbWvHQG7vQ3mGXuiwiGiQ4YJeI+q2itgWz/roVpvZO3J6egL/8JAOCIEhdFhH5IQ7YJSKvGBEbjpX3ZyJAIeCjvdX41+ZKqUsiokGA4YWIBuTKy2Lw1G1pAIBnPzuErRX1EldERHLH8EJEAzY3JwV3TUqCQwTy396NU2dbpS6JiGSM4YWIBkwQBPzpzvEYnxiBs60d+NW/izmAl4g8huGFiNwiKFCJlT/LRHSoCgdOm/A/a/dDZs8DEJGPYHghIrdJigrBinsvh1Ih4IPdp/H61uNSl0REMiSb8MJ5Xoh8w5WXxWDR9DEAgD98egjfHWuQuCIikhvO80JEbieKIv57dQk+LKnGkFAVPl5wNRIig6Uui4h8GOd5ISJJCYKA52ZNxNj4CDRYbHiYA3iJyI0YXojII4JVSvzj/kxEhgRi76lm/M8HHMBLRO7B8EJEHqOLDsHLPz03gHfPaSz5/LDUJRGRDDC8EJFHTRk5FM/NmgAA+EfRMazcdFTiiojI3zG8EJHH3T1Zh/+5pesJpOc+P4w1O6skroiI/BnDCxF5xS+uuQy/vHY4AGDhB/uw/kCNxBURkb/yyfBSWVmJqVOnIi0tDRMmTIDFYpG6JCJyg4U3j8E9k7vWQPr1W8X4+6ajHMRLRC7zyfAyb948/P73v0dpaSk2bdoEtVotdUlE5AaCIODZOydgzmQdHCKw5PPD+K93S9Bq65S6NCLyIz4XXg4ePIjAwEBMmTIFABAdHY2AgACJqyIidwlQKvDcXRPwhzvGIUAh4OO91Zj1162oauRK1ETUNy6Hl6KiIsyYMQMJCQkQBAHr1q3rtY/BYEBqaiqCgoKg1+uxY8eOPh//yJEjCAsLw4wZMzBp0iQ8++yzrpZIRD5OEATcn5OKd35xBWLC1Dhc04Kblxfhuc8Po8Fslbo8j+i0O1BrakejxYaW9g60d9jhcLDLjKg/XG7SsFgsSE9PxwMPPIBZs2b1en/16tUoKCjAypUrodfrsXz5ckybNg1lZWWIjY0FAGRkZKCzs3cz8YYNG9DZ2Ylvv/0WJSUliI2Nxc0334ysrCzceOON/bg8IvJlWanR+HjBVXjk7T0oPnEWKzcdxetbj+M+fTJ+cc1wxEYESV1iv7V32LG3qgk7jzdie2Ujdp84C4ut9yzDKqUCGcmRuClNixvTtEgZEipBtUT+ZUBrGwmCgLVr12LmzJnObXq9HllZWVixYgUAwOFwQKfTYcGCBVi4cOElj7lt2zY888wz+OKLLwAAS5cuBQA8/vjj593farXCav3+/9RMJhN0Oh3XNiLyI6IoovBQLV7++gj2nmoG0PVHPXtYNK4bPRTXjY7FZUNDIQiCxJVenMMh4puyWqzaehzbjzXCZnf0eF8QgEt9447WhuOmcVrcOjEeY+L4HUaDhytrG7l1MInNZkNxcTEWLVrk3KZQKJCbm4tt27b16RhZWVmora3F2bNnodFoUFRUhF/+8pcX3H/JkiX43//93wHXTkTSEQQBuWla3DA2FpvK6/Dy1xUoPnEWmyvqsbmiHn/89BB00cG4cngMsoZFIzs1GrroYJ8JMxZrJ/6z+xRe23IclfXfPx05NFyN7NRoZKVGIXvYEIyOCwcAdNgd6LA7YOt0oKmtA0Xlddhw0IgdxxtRZmxBmbEFL39dgbszk/DEzWMwNJwPLRD9kFtbXqqrq5GYmIitW7ciJyfHud8TTzyBTZs2Yfv27X067ueff44nnngCoijipptuwosvvnjBfdnyQiQ/oijiaJ0FG8tqsam87rytGNoINfTDhjhbZqJDVV6vs95sxSvfHsM720/C1N7VFR4eFICfZidjTpYOw2Ncay1qarXh68O1+PxADb4sNXYdTx2A3+SORN6VqQhU+twzFkRuI1nLi7tMnz4d06dP79O+arUaarUaBoMBBoMBdjtXriXyd4IgYERsGEbEhuHBKcNhsXZie2UDtlc2YmdlI/afbobRZMVHe6vx0d5qCAJwuS4S14+Jxc3j4zAiNtyj9TW12vDKt8fw2pbjaD03jmVYTCjmX5WKuyYlIVTdv6/WyBAVZk1KwqxJSSg+cRbPfHQQ+08344+fHsK7O6uweEYapowc6s5LIfJLbm15sdlsCAkJwfvvv99jHExeXh6amprw4YcfDrTeS3IluRGRf2qz2VFS1YTNFXUoPFSLwzUtPd4flxCBOzISMCM9AfGaYLedt6W9A69uPo5/fnsMLdaulpaJSRosuH4kbhgTC4XCvd1YDoeI94qr8Pz6MjRYbACAuzOT8IeZ4xEUqHTruYik5srfb48M2M3OzsbLL78MoGvAbnJyMh555JE+Ddjtrx+2vJSXlzO8EA0i1U1t+KasFl+VGvHtkXp0nnsEWRCA7NRo3JGRiFsmxCEypH9dS5X1Fry9/QTW7DqF5rYOAMCYuHAU3DgKN6ZpPT72prmtA8u/KsfrW4/DIQKXJ0fi7z/L9OunsYh+zKPhxWw2o6KiAgBw+eWX48UXX8TUqVMRHR2N5ORkrF69Gnl5efj73/+O7OxsLF++HGvWrMHhw4eh1Wr7f1V9xJYXosHtrMWGzw6cwYcl1dhR2ejcHqgUcO2oWNyRkYAbxsYiRHXxrp0OuwNflhrx1vYT2FLR4Nx+2dBQ/L8bR+GW8fFub2m5lG+P1OGRt/egua0DcRFB+Pv9mUjXRXq1BiJP8Wh42bhxI6ZOndpre15eHlatWgUAWLFiBZYuXYqamhpkZGTgpZdegl6vd+U0/cbwQkTdTje14ZO91fiwpBqlZ0zO7YIAJEeHYGRsOEZpwzBSG4YOu4iqxlacbGzFiYZWHKszOwfhCgIwdXQsfnZFMq4dFQull0PLDx2vt+DBN3ahotYMdYACz8+eiDsyEiWrh8hdvNZt5EvYbUREF3PE2IKPzgWZk31ciiAmTI2fZOnwk2wdkqJCPFxh37W0d+A375bg68O1AIBfXXsZHp82WtJQRTRQgzK8dGPLCxFdjCiKqDfbcKS2BUeMZpQbW1BRa4YqQIHk6BDnSxcdgtFx4T77eLLdIWLZhjL8beNRAMDt6Ql44Z50n62X6FIYXhheiGiQWLfnNB57by86HSJyx8Zixb2T+CQS+SVX/n7LJqIbDAakpaUhKytL6lKIiLxm5uWJeGXuZKgDFPjqUC3mv7YTZmvvteOI5IQtL0REMvDdsQb8fNVOWGx2ZOgisWp+Vr8fDSeSwqBseSEiGsyuGD4Ebz90BSJDAlFS1YSf/OM71La0S10WkUcwvBARyUS6LhKrf5GDoeFqHK5pwT0rt+FkQ9+erCLyJ7IJLxzzQkQEjI4Lx/u/ykFSVDCON7Ri1t+2YN+pJqnLInIrjnkhIpIho6kd817biUNnTAhRKWG4dxKmjomVuiyiC+KYFyKiQU4bEYQ1v7wCU0bGoNVmx4Nv7MLqnSelLovILRheiIhkKjwoEK/Oy8KsSYmwO0T89j/78ecvyyGzBncahGQTXjjmhYiot0ClAi/cnY4F148AAPyl8AgefW8v54Ihv8YxL0REg8Rb20/gqXUH4BCBeE0Q/nDHeOSmaaUuiwgAx7wQEdF53KdPwb8f1CNlSAjONLfjwTd2If+t3ag1cT4Y8i9seSEiGmTabHb8pfAIXvn2GOwOEeFBAfifW8ZizmQdFDJbmbq5rQMnG1pxotGCEw2tzn+vaW5HTJgaqTGhGBYTitQhoUiNCUHqkFCEqgOkLntQ4sKMDC9ERJd0sLoZiz7Yj32nmgEA6UkazJ6swy3j4zAkTC1xdf1j7bSjqLwen+yrxrdH6tFosbl8jKHhalx52RDMzUnBpOQoCIK8Ap2vYnhheCEi6hO7Q8RrWyrxwoZytHXYAQBKhYCrRsRgxsR43DQuDprgQImrvDhbpwObK+rwyb4z+PKgES0/GowcE6ZGypAQpESHIHlICFKGhCBeE4zaFitO1FtQ2WDB8XoLjje09go74xIiMDcnBbenJyJYxdW6PWlQhheDwQCDwQC73Y7y8nKGFyIiF9Sa2vFhSTU+2luN/aebndtVSgWuGjEEE5MiMS4hAuMSNUjQBEneGuFwiPiusgHr9pzGFweNaG7rcL4XFxGEWybE45YJcRgbH+FSN1BzWwfKjS14f9cprCs5DWunAwCgCQ7EnCwdfqZPQfKQELdfDw3S8NKNLS9ERANTWW/Bx3u7gkxFrbnX+1EhgUhLiMC4BE1XoEmIwLCYMCi9MF7maJ0Za3efxto9p3G6qc25fWi4GreMj8Nt6QnITI5yy9idplYb1uyqwpvfnUBVY9e5BAG4YYwWC6ePwYjYsAGfg77H8MLwQkQ0YKIo4nBNC7YdbcDBahMOVjejotaMTkfvPxvBgUqMiQ8/F2a6Qs0obTiCAgfe1dLUasPH+87gP8WnUFLV5NweHhSA2ybG4/b0RGQPi/ZYeLI7RGwsq8Xr206gqLwOABCoFPCray9D/tQRbrlGYnhheCEi8pD2DjuOGM04WN3sDDSHzrQ4x8v8UIBCwIjYsB6tNKO14YgIDrxg0DBbO3HojAkHTncd/8DpZhypNcN+LjApFQKuGRmDuzKTkDtW6/XgcLTOjD9+UopvyrpCTHJ0CH5/xzhcN5rrRg0UwwvDCxGR19gdIirrLThY3YzSapMz1Jxt7bjgZ0JUSoSpAxAeFICwoECEqZU409SOygYLzvdXaUxcOGZnJuH2jATEhgd58GouTRRFfHGwBs98VIqac3Pk3DohHk/PSIM2Qtra/BnDC8MLEZGkRFHEmeZ2Z5A5WG1CabWpxziVC4nXBPXofhqXqEFiZLAXqnaN2dqJP39Zjte2VMIhAmHqADx60yjMzUn1yvgfuWF4YXghIvJJtk4HzNZOmNs70WLt6PpneyfM1k5EhaowPiHC7+aYOXC6GU+uO+AcjzMhUYMlsyZgfKJG2sL8DMMLwwsREXmRwyHinZ0n8X+fH4apvRNKhYAHpwzDf98wivPD9NGgXNuIq0oTEZFUFAoB9+lT8NWj1+LWCfGwO0T8fdMx3PyXImypqJe6PNlhywsREZGbfVlqxFPrDjgH9M7OTMLvbhmLqFCVxJX5rkHZ8kJEROQrbkzT4suCazA3JwWCALxffAq5L27ChyWnIbM2A0mw5YWIiMiDik80YuF/9uPIudmKxydG4FfXXobp4+P5VNIPcMAuwwsREfkQW6cDKzcdxd82HnVO6JcyJAQPTRmO2ZlJnKUXDC8ML0RE5JMaLTa8se04Vm09jqZzk/jFhKkw/6ph+NkVKT6/grcnMbwwvBARkQ9rtXVi9c4q/PPbSufEfWHqANwzWYepY4YiMyUKIaq+r4YtBwwvDC9EROQHOuwOfLKvGis3HkOZscW5PUAhYEKSBvphQ6AfHo3JKVEID5J3q4xfh5eysjLMmTOnx8/vvPMOZs6c2afPM7wQEZG/EUURG8vq8PG+amw/1thrGQWFAIxP1GBySjSGDw1F6pBQpMaEIF4T7PVBv2ZrJ1qtnYh18zpOfh1efshsNiM1NRUnTpxAaGhonz7D8EJERP6uqrEV2ysbsf1YA7ZXNuJkY+t591MpFdBFB2NYTChShoQiZUgI4iKCoI0IQpwmCDFh6n6Hm1ZbJypqzSg3mnHE2IIyYwuOGM043dSG29MT8NJPLx/IJfbiyt9vn+5Q++ijj3DDDTf0ObgQERHJgS46BLroEMzOTAIAVDe1YXtlA/afMuFEgwXHGyw42dgKm92Bo3UWHK2znPc4CgEYGq6G9lygiQ1XI1CpgCiKcIiAiHP/FLtaf0QRqDdbUV7bglNn2867wjfQtY+UXA4vRUVFWLp0KYqLi3HmzBmsXbu2V5eOwWDA0qVLUVNTg/T0dLz88svIzs52ubg1a9Zg7ty5Ln+OiIhIThIig3Hn5Um48weNHXaHiOqmNhxvsOB4QyuO13cFmlpTO4wmK+rMVtgdIowmK4wmK4Bml887JFSFkdowjNaGY6Q2HKO04RilDUNkiLQzBbscXiwWC9LT0/HAAw9g1qxZvd5fvXo1CgoKsHLlSuj1eixfvhzTpk1DWVkZYmNjAQAZGRno7Ozs9dkNGzYgISEBQFfz0datW/Huu++6WiIREZHsKRWCs4Vmysje79sdIhrMVtScCzNGUztqW6xwOEQoBACCAIUACDj3TwEQBAHhQQEYGdsVUnx1he8BjXkRBKFXy4ter0dWVhZWrFgBAHA4HNDpdFiwYAEWLlzY52O/+eab+OKLL/Dvf//7ovtZrVZYrd83X5lMJuh0Oo55ISIi8iOSrW1ks9lQXFyM3Nzc70+gUCA3Nxfbtm1z6Vhr1qzp8dTRhSxZsgQajcb50ul0LtdNRERE/sOt4aW+vh52ux1arbbHdq1Wi5qamj4fp7m5GTt27MC0adMuue+iRYvQ3NzsfFVVVblcNxEREfkPn3zaSKPRwGg09mlftVoNtVoNg8EAg8EAu93u4eqIiIhISm5teYmJiYFSqewVPIxGI+Li4tx5ql7y8/NRWlqKnTt3evQ8REREJC23hheVSoXMzEwUFhY6tzkcDhQWFiInJ8edpyIiIqJByuVuI7PZjIqKCufPlZWVKCkpQXR0NJKTk1FQUIC8vDxMnjwZ2dnZWL58OSwWC+bPn+/Wwn+M3UZERESDg8uPSm/cuBFTp07ttT0vLw+rVq0CAKxYscI5SV1GRgZeeukl6PV6txR8KVwegIiIyP/IZm0jV/yw5aW8vJzhhYiIyI8MyvDSjS0vRERE/keySeqIiIiIPE024cVgMCAtLQ1ZWVlSl0JEREQexG4jIiIikhy7jYiIiEi2fHJ5gIHobkgymUwSV0JERER91f13uy8dQrIJL92PSttsNgDg6tJERER+qKWlBRqN5qL7yG7Mi8PhQHV1NcLDwyEIwiX3N5lM0Ol0qKqq4hiZPuI9cx3vmet4z1zHe+Y63jPXeeqeiaKIlpYWJCQkQKG4+KgW2bS8dFMoFEhKSnL5cxEREfzFdRHvmet4z1zHe+Y63jPX8Z65zhP37FItLt04YJeIiIj8CsMLERER+ZVBH17UajUWL14MtVotdSl+g/fMdbxnruM9cx3vmet4z1znC/dMdgN2iYiISN4GfcsLERER+ReGFyIiIvIrDC9ERETkVxheiIiIyK/IMrwYDAakpqYiKCgIer0eO3bsuOj+7733HsaMGYOgoCBMmDABn332WY/3RVHE008/jfj4eAQHByM3NxdHjhzx5CV4nbvv2bx58yAIQo/XzTff7MlL8CpX7tfBgwdx1113ITU1FYIgYPny5QM+pj9y9z175plnev2OjRkzxoNX4H2u3LNXXnkFU6ZMQVRUFKKiopCbm9trf36X9dSXeyb37zLAtXv2wQcfYPLkyYiMjERoaCgyMjLw5ptv9tjHK79nosy8++67okqlEl999VXx4MGD4kMPPSRGRkaKRqPxvPtv2bJFVCqV4vPPPy+WlpaKTz75pBgYGCju37/fuc9zzz0najQacd26deLevXvF22+/XRw2bJjY1tbmrcvyKE/cs7y8PPHmm28Wz5w543w1NjZ665I8ytX7tWPHDvGxxx4T33nnHTEuLk7885//POBj+htP3LPFixeL48aN6/E7VldX5+Er8R5X79m9994rGgwGcc+ePeKhQ4fEefPmiRqNRjx16pRzH36X9dSXeybn7zJRdP2effPNN+IHH3wglpaWihUVFeLy5ctFpVIprl+/3rmPN37PZBdesrOzxfz8fOfPdrtdTEhIEJcsWXLe/e+55x7x1ltv7bFNr9eLv/zlL0VRFEWHwyHGxcWJS5cudb7f1NQkqtVq8Z133vHAFXifu++ZKHb9B3/HHXd4pF6puXq/figlJeW8f4gHckx/4Il7tnjxYjE9Pd2NVfqWgf5OdHZ2iuHh4eLrr78uiiK/y/rix/dMFOX9XSaK7vnuufzyy8Unn3xSFEXv/Z7JqtvIZrOhuLgYubm5zm0KhQK5ubnYtm3beT+zbdu2HvsDwLRp05z7V1ZWoqampsc+Go0Ger3+gsf0J564Z902btyI2NhYjB49Gg8//DAaGhrcfwFe1p/7JcUxfYknr+/IkSNISEjA8OHDcd999+HkyZMDLdcnuOOetba2oqOjA9HR0QD4XdYXP75n3eT4XQYM/J6JoojCwkKUlZXhmmuuAeC93zNZhZf6+nrY7XZotdoe27VaLWpqas77mZqamovu3/1PV47pTzxxzwDg5ptvxhtvvIHCwkL83//9HzZt2oTp06fDbre7/yK8qD/3S4pj+hJPXZ9er8eqVauwfv16/O1vf0NlZSWmTJmClpaWgZYsOXfcs9/+9rdISEhw/hHhd9ml/fieAfL9LgP6f8+am5sRFhYGlUqFW2+9FS+//DJuvPFGAN77PZPdqtLkG37yk584/33ChAmYOHEiLrvsMmzcuBE33HCDhJWRXEyfPt357xMnToRer0dKSgrWrFmDn//85xJWJr3nnnsO7777LjZu3IigoCCpy/ELF7pn/C7rLTw8HCUlJTCbzSgsLERBQQGGDx+O6667zms1yKrlJSYmBkqlEkajscd2o9GIuLi4834mLi7uovt3/9OVY/oTT9yz8xk+fDhiYmJQUVEx8KIl1J/7JcUxfYm3ri8yMhKjRo3y+98xYGD3bNmyZXjuueewYcMGTJw40bmd32UXdqF7dj5y+S4D+n/PFAoFRowYgYyMDDz66KOYPXs2lixZAsB7v2eyCi8qlQqZmZkoLCx0bnM4HCgsLEROTs55P5OTk9NjfwD48ssvnfsPGzYMcXFxPfYxmUzYvn37BY/pTzxxz87n1KlTaGhoQHx8vHsKl0h/7pcUx/Ql3ro+s9mMo0eP+v3vGND/e/b888/jD3/4A9avX4/Jkyf3eI/fZed3sXt2PnL5LgPc99+mw+GA1WoF4MXfM7cN/fUR7777rqhWq8VVq1aJpaWl4i9+8QsxMjJSrKmpEUVRFO+//35x4cKFzv23bNkiBgQEiMuWLRMPHTokLl68+LyPSkdGRooffvihuG/fPvGOO+6Q3eOF7rxnLS0t4mOPPSZu27ZNrKysFL/66itx0qRJ4siRI8X29nZJrtGdXL1fVqtV3LNnj7hnzx4xPj5efOyxx8Q9e/aIR44c6fMx/Z0n7tmjjz4qbty4UaysrBS3bNki5ubmijExMWJtba3Xr88TXL1nzz33nKhSqcT333+/x2O9LS0tPfbhd1nf75ncv8tE0fV79uyzz4obNmwQjx49KpaWlorLli0TAwICxFdeecW5jzd+z2QXXkRRFF9++WUxOTlZVKlUYnZ2tvjdd98537v22mvFvLy8HvuvWbNGHDVqlKhSqcRx48aJn376aY/3HQ6H+NRTT4larVZUq9XiDTfcIJaVlXnjUrzGnfestbVVvOmmm8ShQ4eKgYGBYkpKivjQQw/J5g+xKLp2vyorK0UAvV7XXnttn48pB+6+Z3PmzBHj4+NFlUolJiYminPmzBErKiq8eEWe58o9S0lJOe89W7x4sXMffpe5ds8Gw3eZKLp2z373u9+JI0aMEIOCgsSoqCgxJydHfPfdd3sczxu/Z4IoiqL72nGIiIiIPEtWY16IiIhI/hheiIiIyK8wvBAREZFfYXghIiIiv8LwQkRERH6F4YWIiIj8CsMLERER+RWGFyIiIvIrDC9ERETkVxheiIiIyK8wvBAREZFfYXghIiIiv/L/Acsuzar/hjP4AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "q = np.geomspace(0.007, 0.3, 201)\n", "plt.plot(q, model(q))\n", "plt.yscale(\"log\");" ] }, { "cell_type": "markdown", "id": "c28e4f07-52b9-4ad1-a714-ed7f2bbfb7a2", "metadata": {}, "source": [ "If you were paying attention you'll see that the reflectivity below the critical edge is greater than 1. This is a consequence of \n", "`MixedReflectModel.scales` adding up to more than 1. Let's adjust the scales and replot, with the reflectivities from the different areas also displayed.\n", "Note that the scales are applied in the same order that the individual structures were supplied to `MixedReflectModel`." ] }, { "cell_type": "code", "execution_count": 8, "id": "8c58ba92-9eda-40d3-8ab0-d61d0b30919c", "metadata": {}, "outputs": [], "source": [ "# Each of the scales is a Parameter, and are collectively held in a `Parameters` object that can be indexed.\n", "model.scales[0].value = 0.2 # structure_bare\n", "model.scales[1].value = 0.5 # structure0\n", "model.scales[2].value = 0.3 # structure1" ] }, { "cell_type": "code", "execution_count": 9, "id": "700f1d74-64c0-4b3d-ac71-ae81e516c5ef", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(q, model(q), label=\"incoherent sum\")\n", "plt.plot(q, structure_bare.reflectivity(q), label=\"bare\")\n", "plt.plot(q, structure0.reflectivity(q), label=\"s0\")\n", "plt.plot(q, structure1.reflectivity(q), label=\"s1\")\n", "plt.yscale(\"log\")\n", "plt.legend();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.53/doc/index.rst000066400000000000000000000044071477046072400153500ustar00rootroot00000000000000.. refnx documentation master file, created by sphinx-quickstart on Fri Oct 23 10:21:57 2015. refnx - Neutron and X-ray reflectometry analysis in Python ========================================================== .. _refnx github repository: http://github.com/refnx/refnx .. _github: https://github.com/refnx/refnx/releases/latest .. _scipy.optimize: http://docs.scipy.org/doc/scipy/reference/optimize.html .. _emcee: http://emcee.readthedocs.io/en/stable/ .. _refnx YouTube channel: https://www.youtube.com/channel/UCvhOxwZsdFMGqSzasE0ZSOw *refnx* is a flexible, powerful, Python package for generalised curvefitting analysis, specifically neutron and X-ray reflectometry data. It uses several `scipy.optimize`_ algorithms for fitting data, and estimating parameter uncertainties. As well as the scipy algorithms *refnx* uses the `emcee`_ Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler for Bayesian parameter estimation. Reflectometry analysis uses a modular and object oriented approach to model parameterisation. Models are made up by sequences of components, frequently slabs of uniform scattering length density, but other components are available, including splines for freeform modelling of a scattering length density profile. These components allow the parameterisation of a model in terms of physically relevant parameters. The Bayesian nature of the package allows the specification of prior probabilities for the model, so parameter bounds can be described in terms of probability distribution functions. These priors not only applicable to any parameter, but can apply to any other user-definable knowledge about the system (such as adsorbed amount). Co-refinement of multiple contrast datasets is straightforward, with sharing of joint parameters across each model. Various tutorials are available from the `refnx YouTube channel`_, and there are GUI programs available on `github`_ as well. The refnx package is free software, using a BSD licence. If you are interested in participating in this project please use the `refnx github repository`_, all contributions are welcomed. .. toctree:: :maxdepth: 2 installation getting_started.ipynb gui examples faq testimonials modules * :ref:`genindex` * :ref:`modindex` refnx-0.1.53/doc/inequality_constraints.ipynb000066400000000000000000003050551477046072400213700ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Inequality constraints with *refnx*" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Simple equality constraints can use the mechanisms outlined in this notebook, but are better expressed using the `Parameter.constraint` mechanism, or by sharing `Parameter` objects. It is sometimes also possible to implement different parameterisation of the model to use physically relevant values.\n", "\n", "The following processes can be used to make inequality constraints with *refnx*. The dataset is reflectivity from a clean silicon wafer with a native oxide layer." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "%matplotlib inline\n", "from importlib import resources\n", "import numpy as np\n", "\n", "import refnx\n", "from refnx.dataset import ReflectDataset\n", "from refnx.reflect import SLD, MaterialSLD, ReflectModel\n", "from refnx.analysis import Objective, CurveFitter" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "with resources.path(refnx.dataset) as pth:\n", " DATASET_NAME = 'c_PLP0000708.dat'\n", " file_path = pth / f\"tests/{DATASET_NAME}\"\n", "\n", "data = ReflectDataset(file_path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "air = SLD(0)\n", "sio2 = MaterialSLD('SiO2', 2.2)\n", "si = MaterialSLD('Si', 2.33)\n", "s = air | sio2(15, 3) | si(0, 3)\n", "\n", "model = ReflectModel(s, bkg=3e-8)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# model.bkg.setp(vary=True, bounds=(0, 1e-6))\n", "# model.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "\n", "# sio2 layer\n", "s[1].rough.setp(vary=True, bounds=(0, 10))\n", "s[1].thick.setp(vary=True, bounds=(0, 20))\n", "\n", "# si/sio2 interface\n", "s[-1].rough.setp(vary=True, bounds=(0, 10))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "objective = Objective(model, data)\n", "fitter = CurveFitter(objective)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-22.303297934635626: : 23it [00:00, 76.34it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Structure: \n", "solvent: None\n", "reverse structure: False\n", "contract: 0\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n" ] } ], "source": [ "fitter.fit('differential_evolution', rng=1234)\n", "print(s)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%% md\n" } }, "source": [ "## Inequality constraints with `differential_evolution`" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%% md\n" } }, "source": [ "*Simple equality constraints can use the following mechanism, but are better expressed using the `Parameter.constraint` mechanism, or by sharing `Parameter` objects. It is sometimes also possible to implement different parameterisation of the model to use physically relevant values.*\n", "\n", "We see that the thickness of the SiO2 layer is 12.45 and the roughness of the air/SiO2 interface is 4.77. Let's make a constraint that the roughness can't be more than a quarter of the layer thickness. In optimisation such constraints are expressed as inequalities:\n", "\n", "$$t > 4\\sigma$$\n", "\n", "We need to rearrange so that all variables are on one side, we do the rearrangement like this so there is no divide by 0:\n", "\n", "$$t - 4\\sigma > 0$$\n", "\n", "Now we create a callable object (has the `__call__` magic method) that encodes this inequality. We're going to create the object with the parameters we want to constrain (`pars`), so we can refer to them later. We'll also store the objective because we'll need to update it with the fitting parameters." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "class DEC(object):\n", " def __init__(self, pars, objective):\n", " # we'll store the parameters and objective in this object\n", " # this will be necessary for pickling in the future\n", " self.pars = pars\n", " self.objective = objective\n", "\n", " def __call__(self, x):\n", " # we need to update the varying parameters in the\n", " # objective first\n", " self.objective.setp(x)\n", " return float(self.pars[0] - 4*self.pars[1])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%% md\n" } }, "source": [ "Now lets create an instance of that object, using the parameters we want to constrain. Following that we set up a `scipy.optimize.NonlinearConstraint` for use with `differential_evolution`. Note that we want the constraint calculation to be greater than 0." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "pars = (s[1].thick, s[1].rough)\n", "dec = DEC(pars, objective)\n", "\n", "from scipy.optimize import NonlinearConstraint\n", "constraint = NonlinearConstraint(dec, 0, np.inf)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%% md\n" } }, "source": [ "Now do the fit with the added constraint. Note that you can have more than one constraint." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-22.201646155358837: : 32it [00:00, 102.48it/s]/Users/andrew/miniforge3/envs/dev3/lib/python3.13/site-packages/scipy/optimize/_differentiable_functions.py:552: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", " self.H.update(delta_x, delta_g)\n", "/Users/andrew/miniforge3/envs/dev3/lib/python3.13/site-packages/scipy/optimize/_differentiable_functions.py:317: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n", " self.H.update(self.x - self.x_prev, self.g - self.g_prev)\n", "-22.201646155358837: : 32it [00:00, 60.63it/s] " ] }, { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Structure: \n", "solvent: None\n", "reverse structure: False\n", "contract: 0\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "fitter.fit('differential_evolution', constraints=(constraint,), rng=1234)\n", "print(s)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%% md\n" } }, "source": [ "## Inequality constraints during MCMC sampling" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%% md\n" } }, "source": [ "If we want to implement that inequality constraint during sampling we can add an extra log-probability term to the `Objective`. This log-probability term will return 0 if the inequality is satisfied, but `-np.inf` if not." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "class LogpExtra(object):\n", " def __init__(self, pars):\n", " # we'll store the parameters and objective in this object\n", " # this will be necessary for pickling in the future\n", " self.pars = pars\n", "\n", " def __call__(self, model, data):\n", " if float(self.pars[0] - 4*self.pars[1]) > 0:\n", " return 0\n", " return -np.inf" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "lpe = LogpExtra(pars)\n", "\n", "# set the log_extra attribute of the Objective with our extra log-probability term.\n", "objective.logp_extra = lpe" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%% md\n" } }, "source": [ "Lets check what happens to the probabilities with the specified inequality." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "14.602783132959756\n", "Now exceed the inequality\n", "-inf\n" ] } ], "source": [ "print(s[1].thick)\n", "print(s[1].rough)\n", "print(objective.logpost())\n", "\n", "print(\"Now exceed the inequality\")\n", "s[1].rough.value = 5.\n", "print(objective.logpost())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%% md\n" } }, "source": [ "Now let's MCMC sample the system. There will be a user warning because some walkers have initial starting points which disobey the inequality. Normally one would sample for a far longer time, and thin more appropriately. However, the purpose of the following is to produce a corner plot that demonstrates the inequality - note the sharp dropoff in the probability distribution for the roughness. The roughness doesn't like to go much higher than ~2.5, which is around a quarter of the optimal layer thickness of ~10." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/200 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "s[1].rough.value = 2.\n", "fitter.initialise('covar')\n", "fitter.sample(20, nthin=10, pool=1, random_state=1234);\n", "objective.corner();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.53/doc/installation.rst000066400000000000000000000073261477046072400167450ustar00rootroot00000000000000.. _installation_chapter: ==================================== Installation ==================================== .. _Visual Studio compiler: https://wiki.python.org/moin/WindowsCompilers .. _miniforge: https://github.com/conda-forge/miniforge .. _github: https://github.com/refnx/refnx .. _homebrew: https://brew.sh/ *refnx* has been tested on Python 3.9, 3.10, 3.11, 3.12, 3.13. It requires the *numpy, scipy, cython* packages to work. Additional features require the *pytest, h5py, xlrd, uncertainties, attrs, matplotlib, Jupyter,* *ipywidgets, traitlets, tqdm, pandas, qtpy, pyqt6, periodictable, pymc, pytensor* packages. To build the bleeding edge code you will need to have access to a C-compiler to build a couple of Python extensions. C-compilers should be installed on Linux. On OSX you will need to install Xcode and the command line tools. On Windows you will need to install the correct `Visual Studio compiler`_ for your Python version. Installation into a *conda* environment ======================================= Perhaps the easiest way to create a scientific computing environment is to use the `miniforge`_ package manager. Once *conda* has been installed the first step is to create a *conda* environment. Creating a conda environment ============================ 1. In a shell window create a conda environment and install the dependencies. Note that not all of these dependencies are essential, but they are required to run the full refnx test suite. The **-n** flag indicates that the environment is called *refnx*. :: conda create -n refnx python=3.12 2. Activate the environment that we're going to be working in: :: # on OSX conda activate refnx # on windows activate refnx 3. Install the remaining dependencies: :: python -m pip install "refnx[all]" # the quotes are required if you're using zsh Installing with pip =================== There are refnx wheels available for macOS/Windows/Linux on PyPI. Using the [all] modifier means that all refnx's optional dependencies will also be installed. :: # install refnx and all optional dependencies python -m pip install "refnx[all]" # the quotes are required if you're using zsh # alternatively just install refnx itself python -m pip install refnx Installing into a conda environment from a released version =========================================================== 1. There are pre-built versions on *conda-forge*: :: conda install -c conda-forge refnx 2. Start up a Python interpreter and make sure the tests run: :: >>> import refnx >>> refnx.test() Installing from source ======================= The latest source code can be obtained from `github`_. You can build the package from within the refnx git repository. 1. [macOS only] If you wish to enable the parallelised calculation of reflectivity with OpenMP, then you will need to install *libomp*. This is easily achieved via `homebrew`_, and the setting of environment variables. The refnx kernel that uses OpenMP is not normally any better than the default C version. :: brew install libomp export CC=clang export CXX=clang++ export CXXFLAGS="$CXXFLAGS -Xpreprocessor -fopenmp" export CFLAGS="$CFLAGS -I/usr/local/opt/libomp/include" export CXXFLAGS="$CXXFLAGS -I/usr/local/opt/libomp/include" export LDFLAGS="$LDFLAGS -L/usr/local/opt/libomp/lib -lomp" export DYLD_LIBRARY_PATH=/usr/local/opt/libomp/lib 2. In a shell window navigate into the source directory and build the package. If you are on Windows you'll need to start a Visual Studio command window. :: pip install . 3. Run the tests, they should all work. :: python setup.py test refnx-0.1.53/doc/lipid.ipynb000066400000000000000000003726601477046072400156640ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysing lipid membrane data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This Jupyter notebook demonstrates the utility of the *refnx* for:\n", "\n", " - the co-refinement of three contrast variation datasets of a DMPC (1,2-dimyristoyl-sn-glycero-3-phosphocholine) bilayer measured at the solid-liquid interface with a common model\n", " - the use of the `LipidLeaflet` component to parameterise the model in terms of physically relevant parameters\n", " - the use of Bayesian Markov Chain Monte Carlo (MCMC) to investigate the Posterior distribution of the curvefitting system.\n", " - the intrinsic usefulness of Jupyter notebooks to facilitate reproducible research in scientific data analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step in most Python scripts is to import modules and functions that are going to be used" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# use matplotlib for plotting\n", "%matplotlib inline\n", "from importlib import resources\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "import refnx, scipy\n", "\n", "# the analysis module contains the curvefitting engine\n", "from refnx.analysis import CurveFitter, Objective, Parameter, GlobalObjective, process_chain\n", "\n", "# the reflect module contains functionality relevant to reflectometry\n", "from refnx.reflect import SLD, ReflectModel, Structure, LipidLeaflet\n", "\n", "# the ReflectDataset object will contain the data\n", "from refnx.dataset import ReflectDataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order for the analysis to be exactly reproducible the same package versions must be used. The *conda* packaging manager, and *pip*, can be used to ensure this is the case." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('0.1.53.dev0+19c4b26', '1.15.2')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# version numbers used in this analysis\n", "refnx.version.version, scipy.version.version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `ReflectDataset` class is used to represent a dataset. They can be constructed by supplying a filename" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "with resources.path(refnx.analysis) as pth:\n", " data_d2o = ReflectDataset(pth / \"tests\" / \"c_PLP0016596.dat\")\n", " data_d2o.name = \"d2o\"\n", "\n", " data_hdmix = ReflectDataset(pth / \"tests\" / 'c_PLP0016601.dat')\n", " data_hdmix.name = \"hdmix\"\n", " \n", " data_h2o = ReflectDataset(pth / \"tests\" / 'c_PLP0016607.dat')\n", " data_h2o.name = \"h2o\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `SLD` object is used to represent the Scattering Length Density of a material. It has `real` and `imag` attributes because the SLD is a complex number, with the imaginary part accounting for absorption. The units of SLD are $10^{-6} \\mathring{A}^{-2}$\n", "\n", "The `real` and `imag` attributes are `Parameter` objects. These `Parameter` objects contain the: parameter value, whether it allowed to vary, any interparameter constraints, and bounds applied to the parameter. The bounds applied to a parameter are probability distributions which encode the log-prior probability of the parameter having a certain value." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "si = SLD(2.07 + 0j)\n", "sio2 = SLD(3.47 + 0j)\n", "\n", "# the following represent the solvent contrasts used in the experiment\n", "d2o = SLD(6.36 + 0j)\n", "h2o = SLD(-0.56 + 0j)\n", "hdmix = SLD(2.07 + 0j)\n", "\n", "# We want the `real` attribute parameter to vary in the analysis, and we want to apply\n", "# uniform bounds. The `setp` method of a Parameter is a way of changing many aspects of\n", "# Parameter behaviour at once.\n", "d2o.real.setp(vary=True, bounds=(6.1, 6.36))\n", "d2o.real.name='d2o SLD'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `LipidLeaflet` class is used to describe a single lipid leaflet in our interfacial model. A leaflet consists of a head and tail group region. Since we are studying a bilayer then inner and outer `LipidLeaflet`'s are required." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Parameter for the area per molecule each DMPC molecule occupies at the surface. We\n", "# use the same area per molecule for the inner and outer leaflets.\n", "apm = Parameter(56, 'area per molecule', vary=True, bounds=(52, 65))\n", "\n", "# the sum of scattering lengths for the lipid head and tail in Angstrom.\n", "b_heads = Parameter(6.01e-4, 'b_heads')\n", "b_tails = Parameter(-2.92e-4, 'b_tails')\n", "\n", "# the volume occupied by the head and tail groups in cubic Angstrom.\n", "v_heads = Parameter(319, 'v_heads')\n", "v_tails = Parameter(782, 'v_tails')\n", "\n", "# the head and tail group thicknesses.\n", "inner_head_thickness = Parameter(9, 'inner_head_thickness', vary=True, bounds=(4, 11))\n", "outer_head_thickness = Parameter(9, 'outer_head_thickness', vary=True, bounds=(4, 11))\n", "tail_thickness = Parameter(14, 'tail_thickness', vary=True, bounds=(10, 17))\n", "\n", "# finally construct a `LipidLeaflet` object for the inner and outer leaflets.\n", "# Note that here the inner and outer leaflets use the same area per molecule,\n", "# same tail thickness, etc, but this is not necessary if the inner and outer\n", "# leaflets are different.\n", "inner_leaflet = LipidLeaflet(apm,\n", " b_heads, v_heads, inner_head_thickness,\n", " b_tails, v_tails, tail_thickness,\n", " 3, 3)\n", "\n", "# we reverse the monolayer for the outer leaflet because the tail groups face upwards\n", "outer_leaflet = LipidLeaflet(apm,\n", " b_heads, v_heads, outer_head_thickness,\n", " b_tails, v_tails, tail_thickness,\n", " 3, 0, reverse_monolayer=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Slab` Component represents a layer of uniform scattering length density of a given thickness in our interfacial model. Here we make `Slabs` from `SLD` objects, but other approaches are possible." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Slab constructed from SLD object.\n", "sio2_slab = sio2(15, 3)\n", "sio2_slab.thick.setp(vary=True, bounds=(2, 30))\n", "sio2_slab.thick.name = 'sio2 thickness'\n", "sio2_slab.rough.setp(vary=True, bounds=(0, 7))\n", "sio2_slab.rough.name = name='sio2 roughness'\n", "sio2_slab.vfsolv.setp(0.1, vary=True, bounds=(0., 0.5))\n", "sio2_slab.vfsolv.name = 'sio2 solvation'\n", "\n", "solv_roughness = Parameter(3, 'bilayer/solvent roughness')\n", "solv_roughness.setp(vary=True, bounds=(0, 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once all the `Component`s have been constructed we can chain them together to compose a `Structure` object. The `Structure` object represents the interfacial structure of our system. We create different `Structure`s for each contrast. It is important to note that each of the `Structure`s share many components, such as the `LipidLeaflet` objects. This means that parameters used to construct those components are shared between all the `Structure`s, which enables co-refinement of multiple datasets. An alternate way to carry this out would be to apply constraints to underlying parameters, but this way is clearer. Note that the final component for each structure is a `Slab` created from the solvent `SLD`s, we give those slabs a zero thickness." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "s_d2o = si | sio2_slab | inner_leaflet | outer_leaflet | d2o(0, solv_roughness)\n", "s_hdmix = si | sio2_slab | inner_leaflet | outer_leaflet | hdmix(0, solv_roughness)\n", "s_h2o = si | sio2_slab | inner_leaflet | outer_leaflet | h2o(0, solv_roughness)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Structure`s created in the previous step describe the interfacial structure, these structures are used to create `ReflectModel` objects that know how to apply resolution smearing, scaling factors and background." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "model_d2o = ReflectModel(s_d2o)\n", "model_hdmix = ReflectModel(s_hdmix)\n", "model_h2o = ReflectModel(s_h2o)\n", "\n", "model_d2o.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "\n", "model_d2o.bkg.setp(vary=True, bounds=(-1e-6, 1e-6))\n", "model_hdmix.bkg.setp(vary=True, bounds=(-1e-6, 1e-6))\n", "model_h2o.bkg.setp(vary=True, bounds=(-1e-6, 1e-6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An `Objective` is constructed from a `ReflectDataset` and `ReflectModel`. Amongst other things `Objective`s can calculate chi-squared, log-likelihood probability, log-prior probability, etc. We then combine all the individual `Objective`s into a `GlobalObjective`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "objective_d2o = Objective(model_d2o, data_d2o)\n", "objective_hdmix = Objective(model_hdmix, data_hdmix)\n", "objective_h2o = Objective(model_h2o, data_h2o)\n", "\n", "global_objective = GlobalObjective([objective_d2o, objective_hdmix, objective_h2o])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `CurveFitter` object can perform least squares fitting, or MCMC sampling on the `Objective` used to construct it." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "fitter = CurveFitter(global_objective)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll just do a normal least squares fit here. MCMC sampling is left as an exercise for the reader." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-2743.7355775855553: : 70it [00:12, 5.84it/s]/Users/andrew/miniforge3/envs/dev3/lib/python3.13/site-packages/scipy/optimize/_numdiff.py:596: RuntimeWarning: invalid value encountered in subtract\n", " df = fun(x1) - f0\n", "-2743.7355775855553: : 70it [00:12, 5.67it/s]\n" ] } ], "source": [ "fitter.fit('differential_evolution');" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "global_objective.plot()\n", "plt.yscale('log')\n", "plt.xlabel('Q / $\\\\AA^{-1}$')\n", "plt.ylabel('Reflectivity')\n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can display out what the fit parameters are by printing out an objective:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "\n", "\n", "--Global Objective--\n", "________________________________________________________________________________\n", "Objective - 4884365728\n", "Dataset = d2o\n", "datapoints = 137\n", "chi2 = 411.1660258149095\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 4884252304\n", "Dataset = hdmix\n", "datapoints = 97\n", "chi2 = 114.40210335558325\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 4884252624\n", "Dataset = h2o\n", "datapoints = 104\n", "chi2 = 254.21572280767197\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(global_objective)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's example the scattering length density profile for each of the systems:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(*s_d2o.sld_profile(), label='d2o')\n", "ax.plot(*s_hdmix.sld_profile(), label='hdmix')\n", "ax.plot(*s_h2o.sld_profile(), label='h2o')\n", "\n", "ax.set_ylabel(\"$\\\\rho$ / $10^{-6} \\\\AA^{-2}$\")\n", "ax.set_xlabel(\"z / $\\\\AA$\")\n", "ax.legend();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.53/doc/make.bat000066400000000000000000000155031477046072400151130ustar00rootroot00000000000000@ECHO OFF REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set BUILDDIR=_build set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% . set I18NSPHINXOPTS=%SPHINXOPTS% . if NOT "%PAPER%" == "" ( set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% ) if "%1" == "" goto help if "%1" == "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. devhelp to make HTML files and a Devhelp project echo. epub to make an epub echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter echo. text to make text files echo. man to make manual pages echo. texinfo to make Texinfo files echo. gettext to make PO message catalogs echo. changes to make an overview over 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 goto end ) if "%1" == "clean" ( for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i del /q /s %BUILDDIR%\* goto end ) REM Check if sphinx-build is available and fallback to Python version if any %SPHINXBUILD% 2> nul if errorlevel 9009 goto sphinx_python goto sphinx_ok :sphinx_python set SPHINXBUILD=python -m sphinx.__init__ %SPHINXBUILD% 2> nul if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.http://sphinx-doc.org/ exit /b 1 ) :sphinx_ok if "%1" == "html" ( %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/html. goto end ) if "%1" == "dirhtml" ( %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. goto end ) if "%1" == "singlehtml" ( %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml if errorlevel 1 exit /b 1 echo. echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. goto end ) if "%1" == "pickle" ( %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the pickle files. goto end ) if "%1" == "json" ( %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can process the JSON files. goto end ) if "%1" == "htmlhelp" ( %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run HTML Help Workshop with the ^ .hhp project file in %BUILDDIR%/htmlhelp. goto end ) if "%1" == "qthelp" ( %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp if errorlevel 1 exit /b 1 echo. echo.Build finished; now you can run "qcollectiongenerator" with the ^ .qhcp project file in %BUILDDIR%/qthelp, like this: echo.^> qcollectiongenerator %BUILDDIR%\qthelp\refnx.qhcp echo.To view the help file: echo.^> assistant -collectionFile %BUILDDIR%\qthelp\refnx.ghc goto end ) if "%1" == "devhelp" ( %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp if errorlevel 1 exit /b 1 echo. echo.Build finished. goto end ) if "%1" == "epub" ( %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub if errorlevel 1 exit /b 1 echo. echo.Build finished. The epub file is in %BUILDDIR%/epub. goto end ) if "%1" == "latex" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex if errorlevel 1 exit /b 1 echo. echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. goto end ) if "%1" == "latexpdf" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex cd %BUILDDIR%/latex make all-pdf cd %~dp0 echo. echo.Build finished; the PDF files are in %BUILDDIR%/latex. goto end ) if "%1" == "latexpdfja" ( %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex cd %BUILDDIR%/latex make all-pdf-ja cd %~dp0 echo. echo.Build finished; the PDF files are in %BUILDDIR%/latex. goto end ) if "%1" == "text" ( %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text if errorlevel 1 exit /b 1 echo. echo.Build finished. The text files are in %BUILDDIR%/text. goto end ) if "%1" == "man" ( %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man if errorlevel 1 exit /b 1 echo. echo.Build finished. The manual pages are in %BUILDDIR%/man. goto end ) if "%1" == "texinfo" ( %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo if errorlevel 1 exit /b 1 echo. echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. goto end ) if "%1" == "gettext" ( %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale if errorlevel 1 exit /b 1 echo. echo.Build finished. The message catalogs are in %BUILDDIR%/locale. goto end ) if "%1" == "changes" ( %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes if errorlevel 1 exit /b 1 echo. echo.The overview file is in %BUILDDIR%/changes. goto end ) if "%1" == "linkcheck" ( %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck if errorlevel 1 exit /b 1 echo. echo.Link check complete; look for any errors in the above output ^ or in %BUILDDIR%/linkcheck/output.txt. goto end ) if "%1" == "doctest" ( %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest if errorlevel 1 exit /b 1 echo. echo.Testing of doctests in the sources finished, look at the ^ results in %BUILDDIR%/doctest/output.txt. goto end ) if "%1" == "coverage" ( %SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage if errorlevel 1 exit /b 1 echo. echo.Testing of coverage in the sources finished, look at the ^ results in %BUILDDIR%/coverage/python.txt. goto end ) if "%1" == "xml" ( %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml if errorlevel 1 exit /b 1 echo. echo.Build finished. The XML files are in %BUILDDIR%/xml. goto end ) if "%1" == "pseudoxml" ( %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml if errorlevel 1 exit /b 1 echo. echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. goto end ) :end refnx-0.1.53/doc/model_selection.ipynb000066400000000000000000001134611477046072400177200ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Model selection using refnx and dynesty\n", "\n", "refnx + dynesty can be used to obtain the Bayesian evidence, which allows you to perform model selection." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import dynesty\n", "\n", "from refnx.analysis import Objective, Model, Parameter, Parameters\n", "from refnx.dataset import Data1D\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def gauss(x, p):\n", " A, loc, sd = p\n", " y = A * np.exp(-((x - loc) / sd)**2)\n", "\n", " return y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll synthesise some experimental data from two Gaussians with a linear background. We'll also add on some noise." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "x = np.linspace(3, 7, 250)\n", "rng = np.random.default_rng(0)\n", "\n", "y = 4 + 10 * x + gauss(x, [200, 5, 0.5]) + gauss(x, [60, 5.8, 0.2])\n", "dy = np.sqrt(y)\n", "y += dy * rng.normal(size=np.size(y))\n", "\n", "data = Data1D((x, y, dy))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data.plot();" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# this is our model that we want to fit.\n", "# It will have a linear background and a number of Gaussian peaks\n", "# The parameters for the background and each of the Gaussian peaks\n", "# will be held in separate entries in `p`.\n", "def n_gauss(x, p):\n", " y = np.zeros_like(x)\n", " \n", " # background parameters\n", " a, b = np.array(p[0])\n", " y += a + b*x\n", " \n", " for i in range(1, len(p)):\n", " g_pars = p[i]\n", " A, loc, sd = np.array(g_pars)\n", " y += gauss(x, [A, loc, sd])\n", " return y" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "5907it [00:05, 1075.69it/s, +500 | bound: 14 | nc: 1 | ncall: 28160 | eff(%): 22.752 | loglstar: -inf < -4233.325 < inf | logz: -4244.268 +/- 0.198 | dlogz: 0.001 > 0.509]\n", "12557it [00:18, 696.48it/s, +500 | bound: 48 | nc: 1 | ncall: 53297 | eff(%): 24.499 | loglstar: -inf < -1048.585 < inf | logz: -1072.989 +/- 0.313 | dlogz: 0.001 > 0.509]\n", "17403it [11:47, 24.60it/s, +500 | bound: 2186 | nc: 1 | ncall: 1701762 | eff(%): 1.052 | loglstar: -inf < -913.031 < inf | logz: -947.230 +/- 0.372 | dlogz: 0.001 > 0.509]\n", "19222it [02:40, 119.67it/s, +500 | bound: 261 | nc: 1 | ncall: 461894 | eff(%): 4.270 | loglstar: -inf < -911.813 < inf | logz: -949.640 +/- 0.392 | dlogz: 0.001 > 0.509]\n" ] } ], "source": [ "# the overall parameter set\n", "pars = Parameters(name=\"overall_parameters\")\n", "\n", "# parameters for the background\n", "bkg_pars = Parameters(name='bkg') \n", "intercept = Parameter(1, name='intercept', bounds=(0, 200), vary=True)\n", "gradient = Parameter(1, name='gradient', bounds=(-20, 250), vary=True)\n", "bkg_pars.extend([intercept, gradient])\n", "\n", "pars.append(bkg_pars)\n", "\n", "# now go through and add in gaussian peaks and calculate the log-evidence\n", "model = Model(pars, n_gauss)\n", "logz = []\n", "for i in range(4):\n", " if i:\n", " A = Parameter(5, name=f\"A{i}\", bounds=(40, 250), vary=True)\n", " loc = Parameter(5, name=f\"loc{i}\", bounds=(3, 7), vary=True)\n", " sd = Parameter(5, name=f\"sd{i}\", bounds=(0.1, 2), vary=True)\n", " g_pars = Parameters(data=[A, loc, sd], name=f\"gauss{i}\")\n", " \n", " pars.append(g_pars)\n", " \n", " objective = Objective(model, data)\n", " nested_sampler = dynesty.NestedSampler(\n", " objective.logl,\n", " objective.prior_transform,\n", " ndim=len(pars.varying_parameters())\n", " )\n", " nested_sampler.run_nested()\n", " logz.append(nested_sampler.results.logz[-1])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[-4244.267669942061, -1072.989448001302, -947.2295781758878, -949.6400447012933]\n" ] } ], "source": [ "print(logz)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(logz)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The log-evidence points to the use of 2 Gaussians to fit the data. There is a sufficient increase in evidence over 1 Gaussian. However, 3 Gaussians is not justified, the logz term does not increase." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.53/doc/modules.rst000066400000000000000000000001041477046072400156770ustar00rootroot00000000000000API reference ============= .. toctree:: :maxdepth: 4 refnx refnx-0.1.53/doc/nsf.ipynb000066400000000000000000004517351477046072400153520ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "8faef39e", "metadata": {}, "source": [ "# Analysing non-spinflip polarised NR data\n", "\n", "Currently (5/Aug/2021) `refnx` does not have the ability to fully analyse polarised neutron reflectometry data. However, it can be used to analyse the non-spinflip channels of a polarised neutron reflectometry measurement. Here we demonstrate how to do this using auxiliary `Parameter`. The datasets of interest have the structure:\n", "\n", "`Si | SiO2 | Permalloy | Au | 2-mercaptoethanol | D2O`" ] }, { "cell_type": "code", "execution_count": 1, "id": "c7d349ad", "metadata": {}, "outputs": [], "source": [ "# some necessary imports\n", "from importlib import resources\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import refnx\n", "from refnx.analysis import Parameter, Objective, CurveFitter, GlobalObjective\n", "from refnx.reflect import SLD, Slab, Structure, ReflectModel\n", "from refnx.dataset import Data1D\n", "from refnx._lib import flatten" ] }, { "cell_type": "code", "execution_count": 2, "id": "f06c6737", "metadata": {}, "outputs": [], "source": [ "# create datasets from the NSF PNR data\n", "with resources.path(refnx.reflect) as pth:\n", " dd = \"c_PLP0007882.dat\"\n", " uu = \"c_PLP0007885.dat\"\n", "\n", " file_path_uu = pth / f\"tests/{uu}\"\n", " file_path_dd = pth / f\"tests/{dd}\"\n", "\n", "data_uu = Data1D(file_path_uu)\n", "data_dd = Data1D(file_path_dd)" ] }, { "cell_type": "code", "execution_count": 3, "id": "131aed72", "metadata": {}, "outputs": [], "source": [ "# create SLD (Scattering Length Density) objects for each of the materials\n", "si = SLD(2.07, name=\"Si\")\n", "sio2 = SLD(3.47, name=\"SiO2\")\n", "au = SLD(4.66, name=\"Au\")\n", "mercapto = SLD(3.49, name=\"2-mercaptoethanol\")\n", "d2o = SLD(6.35, name=\"d2o\")\n", "\n", "# to describe the Permalloy layer we're going to create parameters to describe the nuclear\n", "# and magnetic parts of the SLD. Instead of using the magnetic moment and angle we\n", "# could just use a magnetic SLD\n", "\n", "nuclear_py = Parameter(9.0, name=\"Py nuclear part\")\n", "mag_moment_py = Parameter(600, name=\"Py emu/cc\")\n", "angle = Parameter(0, name=\"angle\", bounds=(0, 90.))\n", "\n", "# Now create two SLD objects for the Permalloy layer, one for the UU channel, one for the DD channel.\n", "# don't worry that the SLD is set to zero to start with\n", "\n", "py_dd = SLD(0.0, name=\"Py DD SLD\")\n", "py_uu = SLD(0.0, name=\"Py UU SLD\")\n", "\n", "# Now we make constraints for the SLD objects. Each SLD object has two parameters,\n", "# SLD.real and SLD.imag. The conversion factor of 2.85e-3 converts the magnetic moment\n", "# from emu/cc to 10**-6 Å**-2\n", "py_dd.real.constraint = nuclear_py - mag_moment_py * 2.85e-3 * np.cos(angle*np.pi/180)\n", "py_uu.real.constraint = nuclear_py + mag_moment_py * 2.85e-3 * np.cos(angle*np.pi/180)" ] }, { "cell_type": "code", "execution_count": 4, "id": "5a4b88bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((7.29+0j), (10.71+0j))" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's check on the SLDs. Observe that the SLDs obey the constraints\n", "\n", "complex(py_dd), complex(py_uu)" ] }, { "cell_type": "code", "execution_count": 5, "id": "93678e27", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((7.005+0j), (10.995000000000001+0j))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# let's try altering the magnetic part and see if the values are updated in the SLDs.\n", "# `magnetic_Fe` is a Parameter, and Parameter values are modified like this:\n", "\n", "mag_moment_py.value = 700\n", "\n", "# note how both the SLD objects are updated.\n", "\n", "complex(py_dd), complex(py_uu)" ] }, { "cell_type": "code", "execution_count": 6, "id": "d28c798d", "metadata": {}, "outputs": [], "source": [ "# Now make Slabs that describe each layer. These can either be made from SLD objects,\n", "# or by using the `Slab` constructor directly.\n", "\n", "# sio2 slab has a thickness of 20 and roughness of 4 with the Si fronting medium\n", "sio2_l = sio2(20, 4)\n", "\n", "au_l = au(215, 4)\n", "mercapto_l = mercapto(8, 4)\n", "d2o_l = d2o(0, 4)\n", "\n", "# now make the Fe layers for each of the spin channels. Note that we create\n", "# Parameter for the thickness and roughness which will be shared over both spin channels.\n", "\n", "py_thickness = Parameter(50, name=\"Py thickness\")\n", "py_roughness = Parameter(5, name=\"Py roughness\")\n", "\n", "py_dd_l = Slab(py_thickness, py_dd, py_roughness, name=\"Py dd slab\")\n", "py_uu_l = Slab(py_thickness, py_uu, py_roughness, name=\"Py uu slab\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "f18b1435", "metadata": {}, "outputs": [], "source": [ "# now we make structures for each of the spin channels\n", "# note that we use the same `sio2_l` for each of the structures. This\n", "# will share the same sio2 thickness and roughness in the structures\n", "# and will reduce parameter numbers in a fit\n", "\n", "s_dd = si | sio2_l | py_dd_l | au_l | mercapto_l | d2o_l\n", "s_uu = si | sio2_l | py_uu_l | au_l | mercapto_l | d2o_l" ] }, { "cell_type": "code", "execution_count": 8, "id": "b0454b46", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30 30\n", "36\n" ] } ], "source": [ "# The total number of parameters is reduced by sharing Parameter/SLD/Slab objects\n", "# over the two structures.\n", "\n", "# what are the number of parameters in each of the structures?\n", "print(len(list(flatten(s_dd.parameters))), len(list(flatten(s_uu.parameters))))\n", "\n", "# now what are the number of unique parameters in both parameter sets?\n", "combined_set = set(flatten(s_dd.parameters)).union(set(flatten(s_uu.parameters)))\n", "print(len(combined_set))\n", "\n", "# this shows that the unique number of parameters over both datasets is 36, reduced from 60.\n", "# i.e. there are parameters that are joint over both datasets" ] }, { "cell_type": "code", "execution_count": 9, "id": "9fe3999a", "metadata": {}, "outputs": [], "source": [ "# now place the Structures into a ReflectModel. ReflectModel applies resolution smearing, etc.\n", "\n", "model_dd = ReflectModel(s_dd)\n", "model_uu = ReflectModel(s_uu)" ] }, { "cell_type": "code", "execution_count": 10, "id": "aaec7067", "metadata": {}, "outputs": [], "source": [ "objective_dd = Objective(model_dd, data_dd, \n", " auxiliary_params=(nuclear_py, mag_moment_py, angle))\n", "objective_uu = Objective(model_uu, data_uu, \n", " auxiliary_params=(nuclear_py, mag_moment_py, angle))\n", "\n", "global_objective = GlobalObjective([objective_dd, objective_uu])" ] }, { "cell_type": "code", "execution_count": 11, "id": "9eb96e15", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "plt.scatter(data_dd.x, data_dd.y, label=\"dd\", s=4)\n", "plt.plot(data_dd.x, objective_dd.generative())\n", "\n", "plt.scatter(data_uu.x, data_uu.y, label=\"uu\", s=4)\n", "plt.plot(data_uu.x, objective_uu.generative())\n", "\n", "plt.ylabel(\"R\")\n", "plt.xlabel(\"Q / $\\\\AA^{-1}$\")\n", "plt.yscale('log')\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 12, "id": "40bc9afe", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(*s_dd.sld_profile(), label='dd')\n", "plt.plot(*s_uu.sld_profile(), label=\"uu\")\n", "plt.ylabel(\"SLD\")\n", "plt.xlabel(\"z / $\\\\AA$\")\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 13, "id": "2142473a", "metadata": {}, "outputs": [], "source": [ "# select the parameters to be fitted and their bounds\n", "model_uu.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model_uu.bkg.setp(vary=True, bounds=(1e-7, 5e-6))\n", "model_dd.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model_dd.bkg.setp(vary=True, bounds=(1e-7, 5e-6))\n", "\n", "sio2_l.thick.setp(vary=True, bounds=(10, 25))\n", "sio2_l.rough.setp(vary=True, bounds=(1, 8))\n", "\n", "py_thickness.setp(vary=True, bounds=(38, 55))\n", "py_roughness.setp(vary=True, bounds=(1, 8))\n", "nuclear_py.setp(vary=True, bounds=(8.5, 9.5))\n", "mag_moment_py.setp(vary=True, bounds=(500, 800))\n", "\n", "au_l.thick.setp(vary=True, bounds=(200, 240))\n", "au_l.rough.setp(vary=True, bounds=(1, 8))\n", "au.real.setp(vary=True, bounds=(4.5, 4.66))\n", "\n", "mercapto_l.thick.setp(vary=True, bounds=(5, 15))\n", "mercapto_l.rough.setp(vary=True, bounds=(1, 8))\n", "mercapto.real.setp(vary=True, bounds=(3, 4))\n", "\n", "d2o_l.rough.setp(vary=True, bounds=(1, 8))\n", "d2o.real.setp(vary=True, bounds=(6.2, 6.36))" ] }, { "cell_type": "code", "execution_count": 14, "id": "49da0113", "metadata": {}, "outputs": [], "source": [ "fitter = CurveFitter(global_objective)" ] }, { "cell_type": "code", "execution_count": 15, "id": "b7676bec", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-1273.3106680570752: : 76it [00:11, 6.45it/s]\n" ] } ], "source": [ "fitter.fit('differential_evolution', seed=1);" ] }, { "cell_type": "code", "execution_count": 16, "id": "22e8f804", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "plt.scatter(data_dd.x, data_dd.y, label=\"dd\", s=4)\n", "plt.plot(data_dd.x, objective_dd.generative())\n", "\n", "plt.scatter(data_uu.x, data_uu.y, label=\"uu\", s=4)\n", "plt.plot(data_uu.x, objective_uu.generative())\n", "\n", "plt.ylabel(\"R\")\n", "plt.xlabel(\"Q / $\\\\AA^{-1}$\")\n", "plt.yscale('log')\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 17, "id": "2858d05d", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(*s_dd.sld_profile(), label='dd')\n", "plt.plot(*s_uu.sld_profile(), label=\"uu\")\n", "plt.ylabel(\"SLD\")\n", "plt.xlabel(\"z / $\\\\AA$\")\n", "plt.legend();" ] }, { "cell_type": "markdown", "id": "b4730494", "metadata": {}, "source": [ "By printing out the objectives we can see what the parameters are. Here we see that the Permalloy magnetic moment is 615 emu/cc, with a nuclear SLD of $9.15\\times10^{-6}Å^{-2}$" ] }, { "cell_type": "code", "execution_count": 18, "id": "c206bbbb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 5009606032\n", "Dataset = c_PLP0007882\n", "datapoints = 94\n", "chi2 = 296.0254482329964\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Py dd slab' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Py DD SLD' \n", ">\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Au' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Au' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '2-mercaptoethanol'\n", "\n", "________________________________________________________________________________\n", "Parameters: '2-mercaptoethanol'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: None \n", "\n", "\n", "\n" ] } ], "source": [ "print(objective_dd)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.53/doc/occupancy.ipynb000066400000000000000000004714431477046072400165460ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "b72083eb-dbfe-4de5-87da-aa575ce85154", "metadata": {}, "source": [ "# Creating occupancy/volume fraction profiles\n", "\n", "One of the ways of graphically representing interfacial structure is via occupancy/volume fraction profile graphs. Here we will demonstrate how to do this in `refnx`, using a supported lipid bilayer as an example." ] }, { "cell_type": "code", "execution_count": 1, "id": "c6c29ddb-be2d-4b6d-abad-1a1da0de6007", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "from refnx.analysis import Parameter\n", "from refnx.reflect import SLD, LipidLeaflet, create_occupancy" ] }, { "cell_type": "code", "execution_count": 2, "id": "03b02c5a-2455-4ac4-b65b-478a65340503", "metadata": {}, "outputs": [], "source": [ "si = SLD(2.07)\n", "d2o = SLD(6.36)\n", "sio2 = SLD(3.47)\n", "\n", "# these values are roughly correct for DMPC.\n", "\n", "apm = Parameter(56, \"area per molecule\")\n", "# the sum of scattering lengths for the lipid head and tail in Angstrom.\n", "b_heads = Parameter(6.01e-4, \"b_heads\")\n", "b_tails = Parameter(-2.92e-4, \"b_tails\")\n", "\n", "# the volume occupied by the head and tail groups in cubic Angstrom.\n", "v_heads = Parameter(319, \"v_heads\")\n", "v_tails = Parameter(782, \"v_tails\")\n", "\n", "# the head and tail group thicknesses.\n", "inner_head_thickness = Parameter(9, \"inner_head_thickness\")\n", "outer_head_thickness = Parameter(9, \"outer_head_thickness\")\n", "tail_thickness = Parameter(14, \"tail_thickness\")\n", "\n", "# finally construct a `LipidLeaflet` object for the inner and outer leaflets.\n", "# Note that here the inner and outer leaflets use the same area per molecule,\n", "# same tail thickness, etc, but this is not necessary if the inner and outer\n", "# leaflets are different.\n", "inner_leaflet = LipidLeaflet(\n", " apm, b_heads, v_heads, inner_head_thickness, b_tails, v_tails, tail_thickness, 3, 3\n", ")\n", "# we reverse the monolayer for the outer leaflet because the tail groups face upwards\n", "outer_leaflet = LipidLeaflet(\n", " apm,\n", " b_heads,\n", " v_heads,\n", " outer_head_thickness,\n", " b_tails,\n", " v_tails,\n", " tail_thickness,\n", " 3,\n", " 3,\n", " reverse_monolayer=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "id": "4e8c4fd4-d0e2-477d-8119-f323cf5514e5", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# create the structure and visualise the SLD.\n", "s = si | sio2(15, 3) | inner_leaflet | outer_leaflet | d2o(0, 3)\n", "\n", "s.plot();" ] }, { "cell_type": "markdown", "id": "e24139e5-48a5-4da6-83cf-23ff16c1b7ed", "metadata": {}, "source": [ "This SLD plot is fine, but now we want to see the occupancy/volume fraction profile" ] }, { "cell_type": "code", "execution_count": 4, "id": "1ca42956-ff0b-4017-919d-dd1718dfb4be", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "z, vfps = create_occupancy(s)\n", "\n", "labels = [\"Si\", \"SiO2\", \"inner head\", \"inner tail\", \"outer tail\", \"outer head\", \"water\"]\n", "\n", "for vfp, label in zip(vfps, labels):\n", " plt.plot(z, vfp, label=label)\n", "\n", "plt.legend()\n", "plt.ylabel(\"$\\\\phi(z)$\")\n", "plt.xlabel(\"$z/ \\\\AA$\");" ] }, { "cell_type": "markdown", "id": "87e9f510-6daf-4c18-955e-4aaa68a793ab", "metadata": {}, "source": [ "This looks a bit clumpy, perhaps we want to plot the tails together and the head regions together, to simplify the plot. Unfortunately this has to be done manually at the moment. Here we specify which occupancy profiles we want to group together. The head groups are in `vfps[2]` and in `vfps[5]`, and the tails are in `vfps[3]`, `vfps[4]`. This example is relatively straightforward. It might be more difficult if your `Structure` or `Component`s have more complexity." ] }, { "cell_type": "code", "execution_count": 5, "id": "79059ce9-d7a4-403c-8dba-bbbb9b9a725b", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "groups = [(0,), (1,), (2, 5), (3, 4), (6,)]\n", "labels = [\"Si\", \"SiO2\", \"head\", \"tail\", \"water\"]\n", "\n", "for g, label in zip(groups, labels):\n", " vfp = np.take(\n", " vfps, g, axis=0\n", " ) # the take function is used to extract indices from a numpy array.\n", " if len(vfp.shape) > 1:\n", " vfp = np.sum(vfp, axis=0)\n", " plt.plot(z, vfp, label=label)\n", "\n", "plt.legend()\n", "plt.ylabel(\"$\\\\phi(z)$\")\n", "plt.xlabel(\"$z/ \\\\AA$\");" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.53/doc/reflectometry_global.ipynb000066400000000000000000044443211477046072400207650ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Co-refinement of multiple contrast datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A demonstration of how to do co-refinement of several datasets with *refnx*." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from importlib import resources\n", "\n", "from refnx.dataset import ReflectDataset\n", "from refnx.analysis import Transform, CurveFitter, Objective, GlobalObjective, Parameter\n", "from refnx.reflect import SLD, ReflectModel\n", "import refnx\n", "\n", "import scipy\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "refnx: 0.1.53.dev0+19c4b26\n", "scipy: 1.15.2\n", "numpy: 2.1.3\n" ] } ], "source": [ "print(f'refnx: {refnx.version.version}\\nscipy: {scipy.version.version}\\nnumpy: {np.version.version}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are datasets used in refnx testing, distributed with every refnx install." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "with resources.path(refnx.analysis) as pth:\n", " e361 = ReflectDataset(pth / 'tests' / 'e361r.txt')\n", " e365 = ReflectDataset(pth / 'tests' / 'e365r.txt')\n", " e366 = ReflectDataset(pth / 'tests' / 'e366r.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make some `SLD` objects to represent all the materials." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "si = SLD(2.07, 'Si')\n", "sio2 = SLD(3.47, 'SiO2')\n", "polymer = SLD(2.0, 'polymer')\n", "d2o = SLD(6.36, 'D2O')\n", "h2o = SLD(-0.56, 'H2O')\n", "cm3 = SLD(3.5, 'cm3.5')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `SLD`s are used to create `Slab`s." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "sio2_l = sio2(30, 3)\n", "polymer_l = polymer(250, 3)\n", "\n", "# we're going to share the water/polymer roughness across all 3 datasets\n", "water_poly_rough = Parameter(3, 'water_poly_rough')\n", "d2o_l = d2o(0, water_poly_rough)\n", "h2o_l = h2o(0, water_poly_rough)\n", "cm3_l = cm3(0, water_poly_rough)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set the limits for the parameters we wish to vary. Each contrast uses the same polymer SLD. We account for contrast change using the volume fraction of solvent." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "sio2_l.thick.setp(vary=True, bounds=(1, 50))\n", "\n", "polymer_l.thick.setp(vary=True, bounds=(200, 300))\n", "polymer_l.sld.real.setp(vary=True, bounds=(0.1, 2))\n", "polymer_l.vfsolv.setp(vary=True, bounds=(0, 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create a different `Structure` for each contrast of interest. It's important to note here that the `Structure`s all share the same `si`, `sio2_l`, `polymer_l` objects. This means the `Structure`s all share the same parameters. The only thing that's different is the solvent contrast. By default the `Structure` object solvates with the SLD of the last slab. This behaviour can be modified by changing the `Structure.solvent` attribute." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "structure361 = si | sio2_l | polymer_l | d2o_l\n", "structure365 = si | sio2_l | polymer_l | cm3_l\n", "structure366 = si | sio2_l | polymer_l | h2o_l" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a `ReflectModel` from the `Structure`. These are responsible for calculating the generative model, doing resolution smearing, applying a scale factor, and adding a Q-independent constant background." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "model361 = ReflectModel(structure361)\n", "model365 = ReflectModel(structure365)\n", "model366 = ReflectModel(structure366)\n", "\n", "model361.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model361.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))\n", "model365.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model365.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))\n", "model366.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Objective`s are created from the datasets and the model. Here we also add a `Transform` to fit as logR vs Q." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "objective361 = Objective(model361, e361, transform=Transform('logY'))\n", "objective365 = Objective(model365, e365, transform=Transform('logY'))\n", "objective366 = Objective(model366, e366, transform=Transform('logY'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A `GlobalObjective` is formed from the individual `Objective`s. This means that they're all analysed together." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "global_objective = GlobalObjective([objective361, objective365, objective366])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the `CurveFitter` and fit with differential evolution." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-185.7522343032894: : 47it [00:07, 6.37it/s] \n" ] } ], "source": [ "# create the fit instance\n", "fitter = CurveFitter(global_objective)\n", "fitter.fit('differential_evolution');" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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yZcoUfHx88PHxwdfXF9c8+dZatGiByWTi/PnzBAUF3XBda2trOnTowKlTp/I97+joWJEfr1AyUlPREsLB/OefmDPiUpJo3M4OXFzUsUxBCSFEzeLqC0M+yv/ckDkWmXoCaNmyJba2tkRERNCkSZN8Nz8/vwLfY97VlJGRAUD37t25cOFCboAEcOLECXQ6Xb6ppbwMBgOHDh3Cx8engj9RychITUVzDwRXvTpONUGWCWysShaN162rSitcvAhNm1q2n0IIISpXx9Hw13S1SNixnnpsIc7Ozrz88stMmDABo9FIjx49SExMZOvWrbi4uODl5UVsbCzBwcE4OTlx5MgRJk6cSPfu3XN3OI0aNYq33nqLRx99lGnTphEXF8fEiRN57LHHsLdXpXzefPNNbrnlFpo0acKVK1d4//33OXfuHE888YTFPltRJKipaK6+MOJjmP0IZAFJwKNzShaNe3nBqVMyUiOEEDWVTp//3oLeeustvLy8mDlzJqdPn8bNzY2OHTvy6quvYjAYmD9/PhMmTCAjIwM/Pz9CQ0OZNGlS7vudnJxYt24dzz33HJ07d8bDw4MRI0Ywffr03DaXL1/mySefJCYmhjp16tCpUye2bdtGy5YtLf75CiJBjSVoGrjqIM4IScbi25vJDighhBAVRNM0xo8fz/jx4wt8fdu2bcWeo3nz5qxbt67Q12fPns3s2bOLPMfZs2eLvU5FkTU1Fc28bc8tJ6NigkEtFE6MKv69OUFNxLGzRCemF9NYCCGEEHlJUFPRzNv23HOGFhOMJd62dyRbrUjf+M9hur+zgSW7IyzZUyGEEJUtZCz0nqTuRYWT6aeKZt6255ETL8YbS7RtLzoxnaWRmbQCPFITMZrg1bDD9GrqhY+rveX7LYQQwvK6javqHtRoMlJT0czb9jxz4sV4Y4m27Z2JSyXOXu0F90q7AoDBZOJsXJoFOyuEEELUHBLUWELH0TBxvTpO1EO7B4p9S4CnIxddVHkFn6RLAOg1jUaeDhbrphBCCFGTSFBjKa2CwdZWZQg+vLPY5j6u9ox64FYAfJMuYWfMZkZoa5l6EkIIIUpIghpLOfAduGap4/cHlKj+012DOmN0cEBvMrL5vsaMDPa3cCeFEEKImkOCGkswb+s2LxaOyy7Ztm5NQ9ekCQB1Y89bto9CCCFEDSO7nyzBvK07N6jJs627uMzCgYFw8KDKLCyEEKJGWXRkEalZqThaO/Jwq4erujs1jgQ1lmDe1l03J1fNpZyswhf2Q0DPot+bM1IjQY0QQtQ8i48u5mLaReo61JWgxgJk+skSXH3htjegbs6P96IBTCZY/0bxU1DmoCY83JI9FEIIUYvFx8czaNAg6tevj62tLX5+fowbN46kpKR87TIyMnjttddo2LAhtra2NGrUiAULFuRrc+XKFcaOHYuPjw+2trY0bdqUVatWVebHySUjNZZSv4OaftKAq0CyCVxKMAUVGKjuZaRGCCGEheh0Ou666y6mT5+Ol5cXp06dYuzYsSQkJPDDDz/kthsxYgSxsbF8/fXXNGnShOjoaIzGazUNMzMz6d+/P3Xr1mXp0qX4+vpy7tw53NzcCr12ZmYmNjY2FvlcEtRYinsgWOtVYBNnhFgDuFoXm1k4d6Tm9GkwGEBv+UquQgghKofBaMh3b0lGo5F3332XL7/8kpiYGJo2bcqUKVMYPnw4derUYcyYMbltGzZsyLPPPsv777+f+9zq1avZtGkTp0+fxt3dHYBGjRrlu8aCBQtISEhg27ZtWFtbF9imT58+tG7dGisrK7777jvatGnDxo0bLfKZZfrJUsyZhb1zgpJYU4kyC9OgAdjYQFYWe7f8K4UthRCihgg7GUb81XgA4q/GE3YyzKLXmzlzJosXL+aLL77gyJEjTJgwgQcffJBNmzbd0PbChQuEhYXRu3fv3OeWL19O586dee+99/D19aVp06a8/PLLpKen52sTEhLC2LFjqVevHq1bt2bGjBkYDPmDtkWLFmFjY8PWrVv54osvLPaZZaTGkjqOhnuPw+EZ4HybelwcvZ6k+n64nA3ng8//ZEdANDND20jOGiGEqMZiUmOYtn1avuembZ9Gt/rd8Hb0rvDrZWRkMGPGDNavX09ISAgAjRs3ZsuWLcybNy83eLn//vtZtmwZ6enpDBkyhK+++ir3HKdPn2bLli3Y2dnx22+/ERcXx7PPPkt8fDwLFy7MbbNhwwYeeOABVq1axalTp3j22WfJyspi6tSpuecKCgrivffeq/DPeT0ZqbG0kD7q/vCxEjWPTkxnjz5nmO9KdG5hSxmxEUKI6isiKQKjyZjvOaPJSGRypEWud+rUKdLS0ujfvz9OTk65t8WLFxOeZyPK7Nmz2bdvH8uWLSM8PJwXX3zxWv+MRjRN4/vvv6dLly4MHjyYDz/8kEWLFuWO1hiNRurWrcuXX35Jp06dGDlyJK+99toNozGdOnWyyOe8nozUWFqHDur+1ClISgIXlyKbn4lL5aybDwANL0cD1wpbSskEIYSonvxd/NFpunyBjU7T4efsZ5HrpaSkALBy5Up8ffMve7C1tc099vb2xtvbm+bNm+Pu7k7Pnj2ZMmUKPj4++Pj44Ovri6ura277Fi1aYDKZOH/+PEFBQfj4+GBtbY0+z/rPFi1aEBMTk29BsKOjo0U+5/VkpMbSPD3VOhmAPxcVu6U7wNORc+4qqAm4fAGQwpZCCFHdeTt6MzVkar7npoZMtcjUE0DLli2xtbUlIiKCJk2a5Lv5+RUcSJl3NWVkZADQvXt3Lly4kBsgAZw4cQKdTkeDnO+17t27c+rUqXw7ok6cOIGPj4/FdjgVRYKaytDYU91/MxHmtC6yDpSPqz197+oFQJO4CPSaJoUthRCiBggNCsXDzgMADzsPQoNCLXYtZ2dnXn75ZSZMmMCiRYsIDw9n3759fPLJJyxatIhVq1axcOFCDh8+zNmzZ1m5ciXPPPMM3bt3z929NGrUKDw8PHj00Uc5evQomzdvZuLEiTz22GPY26vvpDFjxpCQkMD48eM5ceIEK1euZMaMGYwdO9Zin60oMv1kaYlRoP2njmOMqnzCihcgsF+hO6H6DO8H4yDgSjRbnu+Kj49H5fVXCCGExeh1+nz3lvTWW2/h5eXFzJkzOX36NG5ubnTs2JFXX30Vg8HA/PnzmTBhAhkZGfj5+REaGsqkSZNy3+/k5MS6det47rnn6Ny5Mx4eHowYMYLp06fntvHz82PNmjVMmDCBtm3b4uvry/jx43nllVcs/vkKIkGNpSWEg3fOgFh0zha34upA1a0Lnp5ocXH4XDgLEtQIIYQoJU3TGD9+POPHjy/w9W3bthV7jubNm7Nu3boi24SEhLBjx45CX//777+LvU5FkeknS3MPhPoqIREXjZBlUscX9hf+Hk2DNm3U8aFDlu2fEEIIUUNIUGNprr4QOg0cNTABMTmjNcXVgWrdWt1LUCOEEDXG6JajGdNuDKNbliBvmSg1mX6qDL4dwVcPJ7IhygB+VsVPQZlHag4frrx+CiGEsCipzG1ZMlJTGdwDoX5O/HghZ6RG0xddBypnpMZw8CDbwuMk+Z4QQghRDAlqKoOrLwwfp47PG1RAU1wdqFatANDHxPDsx+vo/s4GluyOsHxfhRBC3MBkMlV1F2q8ivgZS1BTWR54Td1fNsGDfxdbByraZM15l7oANLt0TsolCCFEFTBXnk5LS6vintR85p+x+WdeFrKmprK4uUGLFvDff/BfBAS2LbL5mbhU0rwa0iDpIk3jzrHTv42USxBCiEqm1+txc3Pj4sWLADg4OKBpWhX3qmYxmUykpaVx8eJF3Nzc8pVcKC0JairTLbeooGb1z9CzQ5HTTwGejvzu1ZDbwnfT4uJZQMolCCFEVfD2VqUMzIGNsAw3N7fcn3VZSVBTmernzBeu+gG8VsCQjwqdhvJxtafdkD6wYyktL56WcglCCFFFNE3Dx8eHunXrkpWVVdXdqZGuL4pZVhLUVJbEKIj/VR1HGcBgKLZcQrd7B8Br0Db+HFte6oGPZ9EVvoUQQliOXq+vkC9eYTmyULiyJISDF2ADZAKXjNdy1RQmMBBcXNBlZqhyCUIIIYQolAQ1lcU9EPR6aJAT5UcYis9Vo9NBhw7qeN8+y/dRCCGEqMYkqKksrr5qDY1fzla1SGPxuWoAOnZU93v3WrR7QgghRHUnQU1l6jgaXligjhPrFZurBoBOndS9jNQIIYQQRZKgprL1u1NNK0VEQlQRBS3NzCM1Bw6oxcVCCCGEKJAENZXN2RnatVPHW7cW375pU3B0hLQ0OH7csn0TQgghqrFqE9S8/fbbdOvWDQcHB9zc3Kq6O+XTvbu6//NntdW7KHo9tG8PwIk/N0mZBCGEEKIQ1SaoyczM5N5772XMmDFV3ZXy8zWq+zW/wZzWsG9xkc1PNGgKwOaf1khhSyGEEKIQ1SaomTZtGhMmTKBNmzZV3ZXySYyCi9+p4xgjXM1JwlfIiE10YjpfprgB0CY2XApbCiGEEIWoNkFNWWRkZJCUlJTvVuUSwsEFcNPABJw3FJmE70xcKgfrBQLQKjYcndGQW9hSCCGEENfU6KBm5syZuLq65t78/PyquksqCZ+mg4Y5FSrOZReZhC/A05HTnn6kWtvhlJlO44QoKWwphBBCFKBKg5pJkyahaVqRt2PHjpX5/JMnTyYxMTH3FhkZWYG9LyNzEr6GOUn4zhWdhM/H1Z63h7fnkHcQAB2jT0hhSyGEEKIAVVrQ8qWXXuKRRx4psk3jxkWUESiGra0ttra2ZX6/xXQcDVMDYHkviNFByxFFNh8Z7E/KPbfBx4eY6p2CY7B/JXVUCCGEqD6qNKjx8vLCy8urKrtQdTr0AG9viImBXbugV68imzv17AYfz8bxgGQWFkIIIQpSbdbUREREcODAASIiIjAYDBw4cIADBw6QkpJS1V0rG027Fshs3lx8+65d1f3Bg5AuO5+EEEKI61WboOb111+nQ4cOTJ06lZSUFDp06ECHDh3Ys2dPVXet7MxBzZplxSfha9BAjexkZ3Poj42ypVsIIYS4TrUJar755htMJtMNtz59+lR118rO84q637kHPmhVdBI+TSOqqcrR8/sXv0kSPiGEEOI61SaoqXESo+Do+2AHZAEXsopNwvcjPgC0iz4hSfiEEEKI60hQU1USwkEz5clXU3wSvv0+qlxC+wuqsKUk4RNCCCGukaCmquQm4dOrx+cMxSbhO1S/KUY0/BNj8Uy9LEn4hBBCiDwkqKkq5iR8jWzU44hsGPxhkUn4Xru/Kye9VI6azheOSxI+IYQQIg8JaqpSx9Ew8zA4O0EGoCu6WOfIYH/8Bt8KwCy/NEZKEj4hhBAilwQ1Vc3dH3r3UcebNhXb3KF3TwCc9lfjrexCCCGEBUhQczPo3Vvdr/q1+Hw1t9yi7nfvhqwsy/ZLCCGEqEYkqKkAMakx7IreRUxqTNlOUC8nK/LWbfBhMflqmjUDNzeVVfjgwbJdTwghhKiBJKgpp7CTYQxYOoDH1z7OgKUDCDsZVroTJEbByTlgA1wFYrKLzFeDTpc7WnN6+TrJUyOEEELkkKCmHGJSY5i2fRomTACYMDFt+7TSjdgkhIMub76a7CLz1QAc8m8JwMGlqyWzsBBCCJFDgppyiEiKwGgy5nvOaDISmRxZ8pNcn6/mbNH5aqIT03k/sQ4AHaOOSWZhIYQQIocENeXg7+KPTsv/I9RpOvyc/Up+kuvz1ZwzwB2F56s5E5fKPp9m+ZLwSWZhIYQQQoKacvF29GZqyNTcwEan6ZgaMhVvR+/SnajjaHjnEDjaw1UTWHcotGmApyNpdg4c92oIQKeo/ySzsBBCCAFYVXUHqrvQoFC6ed/CiX1ryPBwo039kLKdyKMh9OwNq1fD339Du3YFNvNxtWdmaBv2r25Bi0tnCY76j1snPy2ZhYUQQtR6MlJTAWx69qFXn8dY9tULZdsBZdanj7pf9yec2VzoDqiRwf4MfmYYAKO5IJmFhRBCCCSoKbeY1Bi2O1wCoElURtl2QJmZg5q/1sI3d8Kc1oXmrHHr3xcAmwP74erVsnRdCCGEqFEkqCmniKQITvraAhB0XgUXpd4BZRZYLydfjQlijWAyFp6zJiAA6tWDzEzYIyUThBBCCAlqysnfxZ/TDdR6liZRGUAZdkCZJUeAn3lrd7a6LyxnjaZB9+4AnFu+VrZ0CyGEqPUkqCknb0dvBg/5HwAB0RlYGSjbDihQOWsaWavjcwZ1X0TOmv1+Kgnfid/WShI+IYQQtZ4ENRVgUJ+nMTo6YJNtYm27TwkNCi3biVx94cGJ6vicAUw6GDKnwJw10YnpvHnZnITvP4xGkyThE0IIUatJUFMRdDp0rVoDcGXv1rIXtgR4YAo42EO6CQb+onLYFOBMXCqH6zbmqpUNHulJBCaclyR8QgghajUJairIWX8nANavms3AXweWfVu3tTV076GO9/5XaLMAT0cMVtYc8GkKQKfzkoRPCCFE7SZBTQWISY3hF2sVgDQ5n4HRZCz7tm64trV706ZCm5iT8O31awVAl6ijzAhtLUn4hBBC1FoS1FSACt3WDdeCmo3r4cr5QpuNDPbn/hfvB+Du1DOShE8IIUStJkFNBfB38eeUnxoh8b+YiW2mqtx9OO5w2U6oP6YKWCQkwmstC03AB+B+Wx/QNKxOh0NMOdbyCCGEENWcBDUVwNvRm4f6vsxlJz06EwReUPlq5uybU/opqMQoWP0S+OfkqzmTVXgCPgBXV2jbVh1v3Vq2DyCEEELUABLUVJBWnq052cAOgCblmYJKCFeZhP1zao1GZBeegM+sR87C4i1bStttIYQQosaQoKaCqCkoFdSY19WUKbOweyBoOmiYM1ITYQB0hSbgA3KDmpS//pY8NUIIIWotCWoqiLejN8173QtA0PkMNLSyZRZ29YUhH0EDG/Wnk2yCDq8WmIDPbLlTAAB2hw/S/82VkllYCCFErSRBTQVKbNYQuDZSU2YdR8PEw9BBJfTjslehTaMT03lhaxznXepiZTLSLuq4ZBYWQghRK0lQU0FiUmN4NeE7AOpeycYlJat8uWpcfaH/nep48+ZCm52JS8Vogl3mfDWRRySzsBBCiFpJgpoKEpEUQYqdxnkvVZAyKPJq+XLVAPTsqe7/+afQJgGejug02N1ABTXB549IZmEhhBC1kgQ1FcTfxR+dpsvdAdX0fEbZFgrn1b07aBqcOgXR0QU2MWcW3uOvpqo6XjjGO0OaSmZhIYQQtY4ENRXE29GbqSFTOZmThK/p+YyyLRTOy9UV2rVTx0WM1owM9mfxew+R5e6JXXYm93Kx7NcUQgghqikJaipQaFAo9w6fDsDtyfVp4NSgfBW74doUVBHragB83Byw7tOrRG2FEEKImkiCmgpWp4sKLLTDR3hy9WMMWDqg7BW7AXrlBCpFjNSUqa0QQghRw0hQU8FifJy4aq1hn2miwcVMTJjKtwvKPFJz6BBcvlx0W3NQs2ULGAxlu54QQghRTUlQU8EiUqM4ZV4sHFkBFbvr1YOmTcFkKr62U9u24OICSUlw8GDZrieEEEJUUxLUVDB/F39O5pRLaBZZjnIJeZVgazcAer3aMQWyrkYIIUStI0FNBfN29KZxz7sBNVJT5nIJeZVwsTBwbQpKghohhBC1jAQ1FtDuttEAdL/kxNrhawkNCi3fCc2Byp49kFZMpuC8ozomU/muK4QQQlQjEtRYQk5uGdvz0WhXEst/vkaNwNcXsrNhx46i23bujMnODi5d4uKef8t/bSGEEKKakKDGAsLiNnLBQ5VLmPTp4PJt6QaVVdg8WrP8O0iMKrTpkoOx7KgbBMBHU76Sit1CCCFqDQlqKlhMagzTtk/jeM5i4aYR6eXb0m3WUAVJLF8Mc1rDvsU3NIlOTGdy2CF2+qmSCV0jDknFbiGEELWGBDUVLCIpAqPJyHF/FdQ0j0gvf2HLxChI/E0dRxog2wArXrhhxMZcsXuHfxsAbok4hMFolIrdQgghagUJaiqYubDlfw1VDahmEWoHVLm2dCeEgxdgB2QDMUYwGSDhdL5m5ord++s3J0NvTd3UywRdjpKK3UIIIWoFCWoqmLmwpXmkJuh8BvpsA9subCv7Sd0DQaeHBlbq8fls0PTg3jhfM3PF7mxrW/b6tgBgZp04qdgthBCiVpCgxgK61e9GtKctSfY6rA0mGl/IKN+6GldfGPIR+JmDGiMMmaOev87IYH+2TOpLg9DBAHQ+IzughBBC1A4S1FhARFIERs2UO1rT4tzV8q+r6Tgaxi9Ux4n11ONC+Lja4z/sDvXg778lX40QQohaQYIaCzCvqzmWs66mecTV8pdKAOg7WG3vjjwP0dFFtw0OBnt7uHQJjh4t33WFEEKIakCCGgswr6sxLxZueS69/KUSQBWrbK22a7N9e9FtbW2v1YHauLF81xVCCCGqAQlqLCQ0KJQXn/wOgPZREBp4d8WcOCRE3RcX1AD07avuJagRQghRC0hQY0GeHbpjsrdDl5rGpX/Lsfspr7IENX//DUZjxVxfCCGEuElJUGNBYWeWc7C+BsAHX9xf/nIJcC2o2bMHMjOLbtu5Mzg6QkICHDpU/msLIYQQNzEJaizEXC7hcIBaV9PqbAWVS2jaFNzdISMDDhwouq21dW7V7jNL/5ByCUIIIWo0CWosxFwuITeoOVMB5RJA7X665RZ1XIIpqH+bdADg5JI/6P7OBilwKYQQosaSoMZCzNu6j+QENS3OpWNtLGe5BLMSrquJTkxnarIXoOpAaQaDFLgUQghRY0lQYyHmbd3nvO1IsdNhn2niPc9Hyr+tG6BbN3VfTFBzJi6Vg3UDuWznjEtGKu0unMBgMkmBSyGEEDVStQhqzp49y+OPP05AQAD29vYEBgYydepUMotbKFvFQoNCWTNiHcaOagqo8YmE8q+pAejSBXQ6iIiACxcKbRbg6Qh6PVsbtQeg95l96DVNClwKIYSokapFUHPs2DGMRiPz5s3jyJEjzJ49my+++IJXX321qrtWLG9Hb2JaNgBgzx+fMfDXgeXfBeXkBG3aqOMiRmvMBS7/CegIQK+z+5gR2loKXAohhKiRqkVQM2jQIBYuXMiAAQNo3LgxQ4cO5eWXXyYsrAK2SFtYTGoMX9ruB6B1zmLhCtkFVcJ1NSOD/Xlx1lgA2secZGSgU/muK4QQQtykqkVQU5DExETc3d2LbJORkUFSUlK+W2WLSIrg38aqsGXQ+avYZRgrZhdUKZLw1WsZBK1aoRmNsH59+a4rhBBC3KSqZVBz6tQpPvnkE55++uki282cORNXV9fcm59fBew8KiV/F38uethy0c0Ka4PaBQVwOO5w+U6cNwlfRkbx7QcMUPdr1pTvukIIIcRNqkqDmkmTJqFpWpG3Y8eO5XtPVFQUgwYN4t577+XJJ58s8vyTJ08mMTEx9xYZWc7RkTLwdvTmhU4TOBioFue2O6V2Hs3ZN6d8U1BNmoCHu8oq/E8JApWBA9X9mjVgMpX9ukIIIcRNyqoqL/7SSy/xyCOPFNmmcePGuccXLlygb9++dOvWjS+//LLY89va2mJra1vebpZbK49W/BNoz217k2ifE9SYp6DKvMV7/7fgngTxwIf3gfsX0HF04e179QI7O4iKgqNHoVWrsl1XCCGEuElVaVDj5eWFl5dXidpGRUXRt29fOnXqxMKFC9Hpqs/Mmb+LPweaOQGxdDiZpkZKNI3DcYcJ9g4u/QkTo2DFePDRwUngQjaseAEC+4Grb8HvsbdXgc3atWq0RoIaIYQQNUy1iAyioqLo06cP/v7+zJo1i0uXLhETE0NMTAXkfKkE3o7e3HbPK6TbaLgnGwiIVmtgyjwFlRAOJiPU16vHFwxgMkDC6aLflzMFdfm3FZJVWAghRI1TLYKadevWcerUKf766y8aNGiAj49P7q26aOHdjkON1bqaTifyT0GVmnsgaLprQU2cEbJ04N64yLf9Wb8tAA7bt9L/zZVSB0oIIUSNUi2CmkceeQSTyVTgrbrwd/FnfzNHADoeTwVAp+nKVgvK1ReGfAQu1uCkgQkIHFv41BOqDtTYA1c56+aDrSGL3qf2SB0oIYQQNUq1CGpqAm9Hb1rdpbagdzyRhobG1JCpZV8o3HE0vHAIuuZs7U6pW2TzM3GpGNFY3UzVjbr9xDapAyWEEKJGkaCmEvUYPhGTXo9vfBbru3xNaFBo+U7o6gs9c/LP7NlTZNMAT0d0Gqxq1h2AvuG7ccjOlDpQQgghagwJaiqTszNaly4A1N36b8Wcs3NndV9MUGOuA3XIJ4jzLl44Zl3ly3pxUgdKCCFEjSFBTWUzJ8Fbu7Ziztepk7o/dgxSUopsOjLYn22T+6ELVSNEPQ5urpg+CCGEEDcBCWoqm7lcwfr1YDCU/3ze3uDrq3Lf7N9fbHMfV3vqP/GQerBiRclKLAghhBDVgAQ1lS04GNzc4PLlYqeMSsw8BbV3b8nah4SAjw8kJsJff1VMH4QQQogqJkFNZbOygn791HFFFZc0T0GVNEjS6eCeewCIXfi9bOsWQghRI0hQUxVy1tUk//Fr+YpampV2pAbY2LonADZ/rKDP22skEZ8QQohqT4KaKvBnkFpLY7/3ICMW3krYybDyndA8UnP8OCQlFds8OjGdp846EOvkTp2ryQw8tlUS8QkhhKj2JKipZDGpMUyK+JyjDe2wMsJtuxOZtn1a+UZs6tYFP78SLxY+E5dKlqbn+/a3A/DI3hWSiE8IIUS1V+FBzZ6KWvxaQ0UkRWA0GVl1ixsAg3deKXsNqLxKMQVlTsT3Y7tBZOqs6HjhOO1jTkkiPiGEENVamYKalJQU0tPzT1UcOHCAIUOG0LVr1wrpWE3l7+KPTtOxuosLAJ2Pp+GTkF22GlB5lWKxsDkRX4KzOyub9wBgdtwWScQnhBCiWitVUBMZGUlISAiurq64urry4osvkpaWxujRo+natSuOjo5s27bNUn2tEbwdvZkaMpVLnnbsbapGRj6I7VL2GlBmpVwsPDLYny2T+tLkzckABKxfAZcula8PQgghRBUqVVAzceJErl69ykcffUSPHj346KOP6N27Ny4uLoSHh/PTTz/JSE0JhAaFsmbYGjweGwdA41U7y78LyjxSc+KEyj9TAj6u9rQZNkDlzsnIgPnzy9cHIYQQogqVKqjZvHkzn3/+OePGjeOnn37CZDLxwAMPMHfuXBo0aGCpPtZI3o7eHO7TnGwdOB46zlMf9yrfLihPT2jYUB2XYLFwPs89p+4//1wyDAshhKi2ShXUxMbGEhAQAEDdunVxcHDg9ttvt0jHarqY1BheOz6HrW2cAbj/r/jy74Jq10rdbyllluARI1S5hfPnYfr0sl9fCCGEqEKlXiis0+nyHdvY2FRoh2oL8y6oRYM8ALhn82XcEjPLvgtq32JI2aCOf3lXPS4pW1suv/sBAKaZM2HfvrL1QQghhKhCpQpqTCYTTZs2xd3dHXd3d1JSUujQoUPuY/NNFM+8C2p3c0cOBdhjl2Vi1Lp47PR2pT9ZYhSsGA91NfU41gArXlDPl8CS3RF0+s+VP5r1QDMYuDLyQcjMLH0/hBBCiCpkVZrGCxcutFQ/ah3zLqg3tr3BgsGezP40kvv/imfgr/cxsd9bhAaFlvxkCeFgMkI9vXp8yQjZ2ZBwGlx9i3xrdGI6k8MOYTTB1P7PEBJxEI9T/5E89U2cZ8pUlBBCiOqjVEHNww8/bKl+1EqhQaEEuQXxkHEUZ7xjCYjJZGxYLNMcphHkFkR6djr+Lv7Fb/d2DwRNB64msAUygHgN3BsX24czcakYTeo43tGNN257mk9WvI/jrHdh2N3XtooLIYQQNzkpk1DF0rPTMehg5oM+AIxaH0+bkyk8sOoBHl/7OAN/HVj8rihXXxjyEeisro3W1L+/2FEauJZd2GxFi16saRqCLjsb+vSBsHLWpRJCCCEqSZmCmjp16tywjsbd3R0PDw98fX3p3bu3TFWVkHltzfbWzizr7obOBNMWRGGVpYpeGk3Gku2K6jgaXjgEvYaox8muJbq+ObuwXlORjV6nI/WzeWT06gOpqTBsGEyZAkZjWT+iEEIIUSnKFNS8/vrr6HQ67rjjDqZNm8a0adO444470Ol0jB07lqZNmzJmzBjmSzK3YpnX1ug0He/d7028i57ACxn878cYVaASSl4bytUXegxSxwcPlrgP5uzCPz55C1sm9SXLxZXWt0zgq853qQbTp0NQELz1FkRElPYjCiGEEJVCM5lyvjlLYdiwYfTv359nnnkm3/Pz5s1j7dq1/Prrr3zyySd8+eWXHDp0qMI6W15JSUm4urqSmJiIi4tLVXcnn5jUGNaeXcueL6fw8ccqcPhoWF2+GlIXHTrWDF9TslIK27dDt25Qvz5ElWz3U17Riel0f2dD7jqbew5v4M31X+CckVPBW9OgeXNo1w7at1f3bdqo62laoecVQgghyqqk399lCmqcnJw4cOAATZo0yff8qVOnaN++PSkpKYSHh9O2bVtSU1NL33sLuZmDGlCBzcBfB3Lf2ktM/j4agE/vrovjGzN4pN3jJTtJcjKYP1tcHHh4lKoP28LjGDV/Z77n7DOv8rnDWUI2r8D2n00Fvs/oVoeklm3Q9b8Nl7vuVMGOTpZsCSGEKL+Sfn+X6VvH3d2dFStW3PD8ihUrcvPUpKam4uzsXJbT11rmqagf+3vy5RAvAMb+fpFHJi6BixdLdhJnZ8jJ+kwZRsmuXzgMkG5jxyPZzWnRfSLLVu2GVas4+Oz/WNGiJyc9/DBoOnRXLuO2bTMu016Hjh3JqO9L8v9Nhfh4ohPT2RYeR3RiesEXFUIIISpAqbZ0m02ZMoUxY8awceNGunTpAsDu3btZtWoVX3zxBQDr1q2jd+/eFdfTWiI0KJRu9bsROTCSK8u34/bCJFi3Djp0gCVLoEeP4k/Sti2cOaPW1fTpU6rrmxcOvxp2GMN1g3hGE0zYFEvS3a2Y6mLEOLQXALbZmTSJj6TT+aP0OrOPkIhDOMbGYPv2m2S+/x5/tbqVz7sOJ9qtLq8Mak6bBq4EeDri42pfqr4JIYQQRSnT9BPA1q1bmTt3LsePHwegWbNmPPfcc3Tr1q1CO1iRbvbppwIdPgz33gvHjoFeD2+/DS+/DHo9MakxRCRF3JjL5vXX1aLexx+Hr74q02WjE9NZeTCa6Sv/K/V7bbKzGHRiK0/u+o02seEApFrb8V7vh1nc8Q5Mmg6dBjND2zAy2L9M/RNCCFF7WHRNTXVVLYMagJQUePpp+OEH9bhbN9a+fh8TY7/CaDKi03RMDZl6LQvx0qUqEAoOhl27ynzZ6xcNl5rJRNfIw7z4z3d0PX8EgF0NWvK/28dz1t0XHfDxqA50alhHRm2EEEIUyuJBjcFg4Pfff+e//9Rv8q1atWLo0KHo9fqy9bgSVNugBtT27q+/hhdfhORkrlprfBJaj+8GemDUaeg0HWuG5eyQOnECmjUDe3u1cLgcfyZLdkcUOBWVl5bzn8KaaCYjDxxYzaS/F+KUmU6SrSNP3/Ma2xu2BZBRGyGEEEWyaFBz6tQpBg8eTFRUFM2aNQPg+PHj+Pn5sXLlSgIDA8vecwuq1kENandUzNFdNHjxDTy37AfgQBN7Jo7xI8bDhgUDFxDsHQwGg1ownJ4Ox49D06blum50Yjp7z17m+Z/25xu10QGfjOpAx4Z1ADgbl8bBqCu89+dxDCYTek3j7g71+X3/BQwmE76JF5mzYhbBUUfJ1Fnxyu3P81vrWwHQaxpbJvWVERshhBA3sGhQM3jwYEwmE99//33ubqf4+HgefPBBdDodK1euLHvPLag6BzVhJ8OYtn0aRpMRzQTDNl3mpZ+icbpqJN5Zz8RxDZnx2tZra2u6dIHdu+GXX2D48ArpQ95RG72mMSO0dYGjK9GJ6ZyNS6ORpwM+rva5jw9GXWHOikO8/8eH3HnsHwDe6zWaz0JGAPDjk7cQEli6LehCCCFqPosGNY6OjuzYsYM2bdrke/7ff/+le/fupKSklL7HlaC6BjXm/DVG07VSBRoa9S9l8uHcc7Q8dxWjlR7d7DkwdqxKgvf447BggSpx8OabFdaX6wOWsrx/7+l4osa8wNM7fwVgSv9n+L7jnbK+RgghRIEsmqfG1taW5OTkG55PSUnBxsamLKcURYhIisgX0ACYMDFh+Eekb1hL+shQdNkGeO45Lt03hJj4s2pbN5SqXEJJ+LjaExLoUebAw8fVnjs7NMDtkw+Z3eMBAKatm8ftx7Yw7of9dH9nA0t2SykGIYQQpVemoObOO+/kqaeeYufOnZhMJkwmEzt27OCZZ55h6NChFd3HWs9c9DIvnaajnVc7OjXuif2PSzk46WEMGnj9vJJTfdvwj3OcaljBQU1FGRnsz30rvuTsvQ+hw8SHf8wi5NxBjCZ4NeywJOoTQghRamUKaj7++GMCAwMJCQnBzs4OOzs7unXrRpMmTZgzZ04Fd1GYMw1rap8RGhpTQ6bmrp+JSYvloRb7GfNSI9JsNHocSkEz/zmcOcOekxuLr/JdBXzcHLgw/X3+bNoNW0M288Km0yQuAoPJxMqD0RLYCCGEKJVy5ak5depU7pbuFi1a3FAL6mZTXdfUmMWkxhCZHImfs1++ZHu7onfx+FpVG6rr0RTmzj6HXZaJLBsrrDOzefD/GnOwiSNvdHvjWi6bm0R0Yjp9p69m0ZLX6Rp5mOOe/tw1+kOuWtvJVm8hhBCABRYKv/jiiyW++IcffljitpWpugc1hbl+IXH3g8l8/HEENtnqj/ath3z4uZ9H/lw2N5EluyP44Nt/WLlgHF6pV/ih3SBeHTQOkK3eQgghSv79XeLaT/v37y9RO03Tim8kKpR5esq85Xt7O1dWvfMwQyd+g84E921I4Od+HhhNRiKTI2+6oGZksD+9moayO8DIoBcfZtS/q9nasB0rW/TEYDJxNi5NghohhBDFkjIJNUje6SmADXe3YtT6BABee8KXP3q4s2b42psuqDGLTkwn7PZHGLv9Z5JsHBj86MdE1/GRkRohhKjlLLqlW9ycvB29CfYOxtvRG+9sA/XaXCuPMGXRBT44GIt3tqEKe1g0H1d7vD6YyZ4GLXHJTGPmmk+ZcU8rCWiEEEKUiAQ1NVVCOP3sr2IehrPLMnHbN3Fw9ubc4m02IqQx/st+wmhjS8+z+xlwcCPbwuNkJ5QQQohiSVBTU7kHgo0ezT3nj9hRgwQjvPZR4ZUnbxJ1O7ZB9/oUAAzjJzDm4/WSlE8IIUSxJKipqVx9YchH4JXzR9zWGqytYOUaeP/9qu1bCUQ/NY6THn54pl3hlU3fSFI+IYQQxZKgpibrOBruHKOOm90Ln8xVx6+9BocPV12/SuBMUhaTc7Z1j/p3DZ3PH8ndCSWEEEIURIKamq5DV3V/6hw89RRX7xwI2dlkPv4IGI1FvrUqBXg6ss+vFT+2HQDA/234Gj3QyNOhajsmhBDipiVBTU3XqpW6P3KEsJNhDOl3jhQ7HTa79nLgzWeqtm9F8HG1Z2ZoG2b3Hk2qtR3to0+w2DVCdkIJIYQolAQ1NV3z5qDTweXLfLp6CjF1rPhoeD0Amrz7NRdPliypYlUYGezPsrdCSXjmOQC6L/iQ6Lgk2Q0lhBCiQBLU1HR2dhAYCEBAlAoEfr7VnX8D7XG6asT6+QlV2bti+bja4/f2FKhbF06d4otRrzBq/k7ZDSWEEOIGEtTUBi1bAhAUlQmAUacx7RFfsvRQZ/Um+O23quxd8ZydSfzfZADGbf0Jx4w02Q0lhBDiBhLU1AY562pGZLdCQ9XmOuVnT/iTqmJ35piniI05VWXdK4mjd4zkdJ36eKVd4fE9ywBkN5QQQoh8JKipDXKCmoDzaawdvpYFAxewdvhajo29l3P1bLCJjSNsTHfCToZVcUcL18jHjTk9HwDg0T3LcchMR69pshtKCCFELglqagPzDqijR/F2qEewdzAAU/fN5ONhatHw6NVxfLJmCjGpMVXVyyL5uNrTY/IYztapT52ryYz6dw0zQlvLbighhBC5JKipDZo1y90BRYwKWiKSIjCajKwNduFIIzscrxp5bHkskcmRVdzZwo24JQC3aa8BMPm/lYxsW6+KeySEEOJmIkFNbZBnBxRHjgDg7+KPTtOBpjHnXm8ARm5MoFFCVXWyZNyeehzq10cfHQ3fflvV3RFCCHETkaCmtsiThA/A29GbqSFT0dDY0cqJHS0dsck24fXu3PJfKzEKzmxW9xXN1hZeegmA7HfeZduJWNkBJYQQApCgpvbIs67GLDQoNHfhcJPPl6onv/22fHWh9i2GOa1h0RB1v29xOTpdiKeeIsPFDavwU3w/cbbkrBFCCAFIUFN75OSqMY/UmHk7ehPsHYxnr0EwbBiYTKrgZVkkRsGK8ZBtgAsGOJ0J7z8Ln7wNH74Gpw+V80Mo0QY9n7cdDMCzO37BaDRJzhohhBDVJ6gZOnQo/v7+2NnZ4ePjw0MPPcSFCxequlvVR97pJ5Op4DZvvw16PSxfDtu3l/4aCeFwKhPmpsD8VFicBj+lwvP/By/NgGZtYeRtcPp0uaaozsSlsrDjENKsbWl18TRdIw9LzhohhBDVJ6jp27cvP//8M8ePH+fXX38lPDyc4cOHV3W3qg/zDqgrVyA6uvA2Dz+sjt95p3Tnv3gRXpkN36XBFRPYAl468NVBgB7q6iAb+PkvCGoC3QPgg8FlmqIK8HQk2cGZ31r1BWD0vj8kZ40QQgg0k6mwX9tvbsuXL+fuu+8mIyMDa2vrAttkZGSQkZGR+zgpKQk/Pz8SExNxcXGprK7ePJo1gxMnYN06uO22gtscP66KYAL899+146J8/Sm8NBkSk0HToIsN9LUBWz1gVG1MJjhrgG0ZcMqgnrMDHnSEBjbwwiFw9S3xR1myO4JvvviDPxeMJVvTsWr5Nobe2bXE7xdCCFF9JCUl4erqWuz3d7UZqckrISGB77//nm7duhUa0ADMnDkTV1fX3Jufn18l9vImVMi6mnyaNYOhQ9Xxhx8Wf85PJ8IT41RA462HRa/DmnB4aiU8sR60nL9imgYBVvCAMzzjCL56uAosToUzGZBwulQfZWSwPws+fJSk4BCsTEaG7lpZqvcLIYSoeapVUPPKK6/g6OiIh4cHERERLFu2rMj2kydPJjExMfcWGXnzJparFNdt6y5ITGoMRx+9Qz1YvBhiYws/33974H8fqOP21vCkA5z+WD0O6AkNOsGQj0DTq+c0PfR/A7ytYbSDmpbKBL5Pg3/2lXqNjY+rPS4TVZVxwxfz2H40ShYLCyFELValQc2kSZPQNK3I27Fjx3LbT5w4kf3797N27Vr0ej2jR4+mqNkzW1tbXFxc8t1qtQK2decVdjKMAUsHMPLKxxxsbA8ZGTC3kLw1WVnw0GOQZgJvHdxhBzoNTIb8oy4dR6uppYf/UPfdx6tAx9YKRjlAU2u11ubRF+B/g0q/xubuu0nzqof+0kV+nDRHtncLIUQtVqVrai5dukR8fHyRbRo3boyNjc0Nz58/fx4/Pz+2bdtGSEhIia5X0jm5GuvgQWjXDlxdVckETct9KSY1hoG/DsRoUmtgbtudyOxPIzG610EXEQmOjvnPNXEizJqlFgQ/5QTu5mkmfcnWxyRG5QQ/NjC0JxzOAg0V6ATZlniNTXRiOj8OeZIX//mevfWbM+yhWeg1jS2T+kpdKCGEqCGqxZoaLy8vmjdvXuStoIAGwGhUX755FwKLYjRvrjLyJibC/i35XjLXgjLb0MmFiLo26BIuw8KF+c+zbJkKaACmPwceOeuaND0MmVOyBb+uvmqKigy4xw7aWYMJWJYOKVklXmNzJi6VH9sOIlNnRacLx2hx8bRs7xZCiFqqWqyp2blzJ3PnzuXAgQOcO3eODRs2cP/99xMYGFjiURoBHP4J6mar47cG5pvmya0FlcOo0/h2kKd68OGHYMjZsRQTA488oo5feAFe/jj/9FLH0aXrk3ugyo1zh53aAp5igj8yoE5Aid4e4OlIvHMd1jfpAsCIg+tke7cQQtRS1SKocXBwICwsjH79+tGsWTMef/xx2rZty6ZNm7C1ta3q7lUP5my/vjmLds9nw4oXchfmmmtBmQMbnaaj9UuzwMMDzpyBsDD1vvfeU7luOnaEd99Vz5lHXUqxJTuXq69aY2NjBaH26m/ksSz4dU2JEvT5uNozM7QNS9sNBODuI3/zzp1BMvUkhBC1ULXNU1MWtXpNzZnNqh7TkSxYmg4+OrUW5uE/cqaBlJjUGCKTI/Fz9sPb0RumToU334SePWHpUmjUCNLTYeF7cM+osgUyBTGvsfluDbz+NtjbwhM24K6pbeFDPipyFCg6IQX3Vs2wjbkAS5bAiBEV0y8hhBBVrlqsqRGVyD1QBQcNckZqYoyQpQP3xvmamWtBeTt6qyeeekplIv7nH1UTKj0d6uvhzFswu1XFFaw0j/a8Og16hEB6BoSlgdEEJmO+UaWC+Lg7YfvEYwBkzJvPtvA42d4thBC1jAQ1tYV5msdFB86aWpTb+NniR1p8feGOnLw1i75R971scnZOmYoNNkpNr4eZE9SuqigDbMtUz1+/Vbwgjz4KgPWGv5g4a4Vs7xZCiFpGgprapONoePEodMuZbkpwK9n7nnxS3WdlQ10Nmlpde60kwUZptekGt+cs9N2SAekmtbPqulGl60V7+LDNvy06TAw7/BdGE1K9WwghahEJamobV1/od6c63rGjZO8JCbmW0ybQOl9+m5IEG2Xq42ufQj0dZABbM0u0VfxMXCpL2vYH4N5D69FMRtneLYQQtYgENbXRLbeo++3bVaHJ4nz++bV2hib5yx6UNC9NaXV+BD5aoI73aeDTv9i3BHg6srZZN5JsHfFLjOWWiMOyvVsIIWoR2f1UG6WlgYuLyj1z7hz4+xfeNjkZY6OG6BIuY9I0NJMJDmwFF4MaobFEQGNmMkGPHrBtG4wZA599VuxbluyOwPTkU9z37xp+adMf49dfMTK4iM8nhBDipie7n0ThHBxUuQQodgrq0Nvj0CVc5oy3Ddtb5pRK+Hll2fPSlIamwcyZ6nj+fAgPL/YtI4P9uW3mywAMO7uDka08LdlDIYQQNxEJamor8xRUEUFNzJVIvOb/CMCCwV4s7VMHAMOCryE72+JdBKBXLxg0SF3v1YklquTtOfBWaNQIXXKyKukghBCiVpCgprYqQVCT+v1CvBOyiHOxYmWIKxs7OBPvrEcfEwsrV1ZSR4EZM9T9z7/Be4OLr+St08GDD6rjb7+1fP+EEELcFCSoqa3MQc2+fVBQUVCTCf/5vwDww23uZFnryLbSsbynOwBX5r5PTGpM5fS1cV1olVM08++MEiXj46GH1P3atRAba/EuCiGEqHoS1NRWTZqouk4ZGfDvvze+vnEj1v8eJtvWiqV91bSTzmTi8h3BADht2Mp9C/sRdjLM8n1NCIc+OdXaj2XDFWPx+XGaNoUuXdRi6B9/tHwfhRBCVDkJamorTSt6CuqDDwCwaqvj58sXWRAdy3cXYljkcI4jjeywMsJtuy4zbfs0y4/YuAeClzU0ztlKvjezZPlxckZrsr5ZJGUThBCiFpCgpjYrLKg5ehRWrcoJfGzwNhgIvppBuk6HUdNY1dUNgNt3JmI0GYlMjrRsP80lHjrnVGTflwUD3y9+99V992HUW2H97wFen/mLlE0QQogaToKa2qywoObDD9X9HQPB41pJBP+sbHQmE2u6umLUoNOJNOonZOPn7Gf5vnYcDZ8dBW8vSDPBafti3xJt7ciGgI4AhB7ZIGUThBCihpOgpjYLDlajMWfOwH33wSefwF9/XdsxNOn/1AgJqiyCt8HI1AaDuOhuw76mKkvvrJg8Fb0tzaMhPPucOi5BIr4zcamEtboVgLuObJKyCUIIUcNJUFObubpCnz7qeMkSeP55uO02yMxUozjduqkRkglH4OE/YMIRQm+bxdrha/F8VAUXbdYdrtw+P/EEWFnB1q0FL3DOI8DTkQ1BXUiydcQ3+ZKUTRBCiBpOgpra7s8/1ejMW2/B7beDm5vK8/LGG9cKV7r65ssg7O3oTaPHX1bBxf79cPx45fXXxwdCQ9Xx558X3dTVnmkjOrGyeQ9ATUHNCG2Nj2vxU1dCCCGqH6n9JPIzGiE9HRwdi287eLAKiqZOVUFQZdm0SY0wOTpCVJQacSpC3J/r8RzcH6OTE7rYWFUmQgghRLUhtZ9E2eh0JQtoAO6/X93/+GPJqn1XlF69oGVLSE0tUcbg3LIJKSlSNkEIIWowCWpqu8SoEtVTKtDdd4OdHZw4oTITVxZNg2efVccfzYIr54tuL2UThBCiVpCgpjbbtxhmt4JFQ9R9UfWUCuLsDHfeqY4rO2tvGyuwAk6dg1daFN93KZsghBA1ngQ1tVViFKwYD5injUyqntL5vaUbuTFPQf30k1qPUxkSo2DjJGiak0PnSGbxtaCaNoWuXaVsghBC1GAS1NRWCeGqMGReJgN81a90IzeDB6sRm6go2LnTMn29nrnvLXOKXB7NAmN20bWgAEaPBiD18y+JvlL1uWqiE9NzyzfkPRZCCFE2VsU3ETWSeyBouhsDm+tHbgL7FV2OwM4O7rhDjdT89huEhFiow3mY+x6UMwV12QQxFFsLKqx5T263ssXxxH+MHvcZI8aPYGSwv0W7Gp2Yzpm4VAI8HfNtJV+yO4LJYYcwmsypDdVPXqfBzNA2Fu+XEELURBLU1FbmekrLnyu8jbkSdnE1lu6551pQ8+671/LbWEpu359XU1BHsyGjV5H9jE5M5+V1ERia9+Tew+sZ9e+f/C+sJb2aelksb03ewMUlM42Pggx0TI3mdBqsO3iZtg5uHKnXmCy9de57jCaY/OshHG2t6NSwjuTUEUKIUpCgpjbrOBquREJiJNi5wa55+UduSlIJG4jt1REvWxt0p07BkSPQurXl+mzWcbQaRfL8Cp57A/75T20rLySgOhOXitEE33e4nXsPr+fO//7hzVuf5GxcmkUCh+jEdN7+cSdP71tJ6OENBMafR5czCtYB+CqnXby9C8ta9mFpm9s4Wk/9rI3AuB/2y6iNEEKUkqypqe1ufQ3u+QJufyenzlMeQ+YUO0oTdjKM/mtC+ae5qqB99Ku3LdTRArj6wqMvg709nD5d5LbyAE9HdBoc8GnK0boB2BqyGH5kg2VKJly5Qvb/TeGfzx7llU2LCIqPRIeJ8y51WdekC5sCOvKvdxDx9i54pCfx2N7lrPrmecK+fYmml87mnkYKcAohROnISI24Ju/IjaufelyEmNQYpm2fhgkTf3V0pve/yWi//07M2zGVV+TS0VGt6Vm6FH75BTp1KrCZj6s9M0Pb8GrYYb5vfztvr/2M8eEbcHGxq9j+7NhB9oiR+EVGAHDKvQFf3DKcjY07E+/olq+p3mig55n9DD/8F/1PbqfjheOsWPQCs3s8yJdd7sGo0+cW4JRpKCGEKJ6USRBltit6F4+vfRyAOknZbBx/DL0J/t31O+2C76q8jvzyC4wYAQEBEB5e5Jqe6MR0Is/E0rlHa3SpqfD339C7d/n7YDTCrFkYX30NnSGbc27evNv7EdY0C8Gg6dGhFgLn/Z9NB3wyqgMdG9ZBFxMNTz1Nvc3rAdjt25KnQl8j0cGVj0d1kPU1QohaTcokCIvzd/FHp6m/QpddrNjXVE3lBGwsunp2hRs8WE1BnTlTbGZjH1d7urRvhO6BB9QTc+eW//rJyTBkCLzyCjpDNiua9+TORz5iVfMemDQ9n47qwNbJt/LOsDbocwIuvaYxc1gb7mhbHx9Xe+o1a0y9v9eya8r7JNs6EBx1lB9/fBWPlMuM+2E/3d/ZwJLdEeXvqxBC1GAS1Igy83b0ZmrIVLScTckbOqnCki4r/6rcjpinoAB+/rlk7xk7Vo3oLF0Ku3eX/dpxcXDrrbBqFQZbO14Z9BzPDf0fybaqfpYRcHe0xcfVnpHB/myZ1Jcfn7yFLZP63rgAWNPo8ubLXN28hate3jSPO8dPP07COylO1tcIIUQJSFAjyiU0KJS1w9eyYOACHv2/nGKRW7bApUuV25ERI9T9j98WXwsKoG3ba6UTXn65bAU5IyOhZ0/YswejuwdbF/zKL+0H5pv+0mtavsXIPq72hAR6FDmV5NWlA0d+Ws55Fy8CE6L4+YdX8Eq5nLu+RgghRMEkqBHl5u3oTbB3MHVbBEPHjmp9yfLllduJ+lfUsvfI6JLVggKYPl0lD9y8ufTVu0+ehB494Ngx0ur6MGDYdEYfNOXbVa7XNGaEti7TWpj6nVpz3wPvctbNB//EWOYuewdbowEHG51kHhZCiEJIUCMq1j33qPvffqu8ayZGwfr/QWDOZr5TWcXXggKiXTyJfGyMevC//0FWVsmud+QI9OoFERGkNGzMgGFvc8rdD1ALgTUTfDqqQ8FTTCXk42rPc4/247ERb5Bk40DX80f46tCP3PPZNkbN3ylrbIQQogAS1IiKZQ5q1q1TC2grg7kWlDmoCc++lg25EEt2R9Bt5gYG2YQQ5+CmRl6++KLYS13atJWsnr0gJob/vBrRZ8g0zrvUzdcm7zqa8hgZ7M/37z3EhY/nAdBz9U/cdWiDuoassRFCiBtIUCMqVsuWXPH3gsxMVn82vnKuaa4FZQ5qIg2QqSs0G3J0YjqTww5hAlJtHZjdYxQAxjemwalThV5m68zPsRs4AOvLCRzwCeK++2cS51jnhnbXr6MpDx9Xe5o//SCR414GYOaaubSKDQeQNTZCCHEdCWpEhQo79RthrdSiW8PvYYSdDLP8Rc21oNz1UEdTQyV1Hy00G7K5ZILZT+0G8p9XI3QJ8dC1K2zYkP8NycmkPfAQ3V99FueMNHY2aMWDI98m0d75hnNrGmVeR1MUqzffYGNgZ+yyM5mzYhY22VkVGjwJIURNIEGNqDDmDMMbO6ov+54Hk3n7nzeISY2x/MU7joYJR+D2oepxeOHrY8wlE8wMOj2PjXiTzE6dISEBBgyAN9+Ezz+Ht96C9u1x+OE7jGh8EjKSB+57mxTb/MGEDrWOZtukWy1Sq8mnjiNXvviaOAc3guIjeXbnUosET0IIUZ1JUCMqTERSBEaTkYOBDsQ763FJM9LheDKRyZGV0wFXXxj+sDpeu7bQZuaSCeZdSpoGLzzcG5t/NsMDD4DBAFOnwrPPwuuvw+nTZDfw4/4HZvJBr4fI1uevLqJp5EukZyn33NYW/dyPARi/82dGOqZY7FpCCFEdSe0nUWHMGYaNOiN/d3Bh2ObL9NuXjJ+zX+V14tZbQa+HEyfg7Flo1KjAZiOD/enV1IuzcWk08nS4Fox8+y3ccgv8/ju4uICnJzRqhNWzzxJ6MoldYYdyt21PGtSctg3c8r/fwuo88iCE/Yz2xx9kPvY4e75dRkBdZxmxEUIIpPaTqGBhJ8OYum0qvfcnMfejCNJ8PHGIulhkPaYK16MHbN0KX34JTz5ZoaeOTky/MRCqbJGRZDVvgXVaKv/Xfww/dLqDmaFtLDLtJYQQNwOp/SSqRGhQKE+3fRqPISPJsrPBIToODhyo3E4MGKDui5iCKkp0YnqhCe5KkhHY0qJdPJne/UEAXvrnO5zSU2R7txBCIEGNsIBxHcYxrd97WA8arJ4obbbe8jIHNevXQ3Z2id5iDmTmbQ6n28wNjJq/k24zNzBvU/hNl8H3TFwq37UfzAkPf+pcTWbMjqWyvVsIIZCgRljS3Xer+8oOajp3Bjc3uHIF9uwptrk5Ed+o+TuZueoY5vlYEzDzz2O5Ac7NksE3wNMRk17PO30eAeCxPctokBwn27uFELWeBDXCcu64A3Q6Nf109mzlXdfKCm67TR0XMwWVNxFfUUzcPBl8zbu3NjYJZqdfa2wNWXx9egVn4lJviv4JIURVkaBGWI6np1q0C5Vf4LKE62quT8RXlJtpimdksD/bJvfDYc6HAAStDuPNt3+6qUaUhBCisklQIyzrrrvUfWVPQfXvr+537IDExEKbXZ+Iryg3WwZfH1d7PPv1YGXzHugwMXHz4ptqREkIISqbBDXCsnKCGsOmjczf9F7lXbdRI2jaVCXS++uvQpsVlIhv8u3N+fHJW5g8uHm+5ys0g29iFJzZXGwl8eKciUvl/Z4PYdB09AvfTcvY0zfViJIQQlQmSb4nLCrM+C9tGtgSdD6D0z98Qlj9JoQGhVbOxfv3V0n4/v4bQgu/ZmGJ+EICPRjarn7F56XZtxjCngOdSa05GvqxKvNQBgGejkR4+LKyeQ+G/reZMTt+4YW7J91UI0pCCFFZJKgRFmOuBTWmowtB5y8xYFciL2yfRrf63fB29LZ8B3r1gk8/hX/+Kbapj6t9gUFLYc+XWvw52LgStv8Lv3wN5w3q/776elj/FDydAYNDwcOjVKc1jzTNi72Xof9tZvDxreiCnSTDsBCiVpLpJ2Ex5lpQq25xBaDHoWRcEzMrrxZUuybq/t9/i1xXY3FvjwG/ALh3LHz4JUQa1HaqLOCcAbZmwOinwN8PPpwJRmOpTj8y2J+vZj/O5T63oTcZuWP1dxb5GEIIcbOToEZYjLkW1Jn6dhxpZIe1AQbtTqqcWlD7FsOPt0IdDUwmWPym5a95PYMBnn8G/u8LSDeBvQYtrWCIHTzvBGMcYagddLQGDx2kpcNLr8ItbUq9Bd7H1Z46b01VDxYtggsXKv7zCCHETU6CGmEx3o7eTA1RX7QrurkB8MxBR8tPPSVGwYrxgAka5syw/vwZJEYRk3ieXdG7iEmNsez1D6yEgf3gk3nquR428LIT3OsAHW3A3Qrq6qGDHQxxgLGOMMhWTUntPgqtW8HbL8OV8yW/bo8e0L07ZGbC7NkW+WhCCHEzk6BGWFRoUCgedh6s7uqGQQfu/56Ekycte9GEcEjKJmVTJhlnDeq5bVcxejbE282POl16seK+tmxc+k6pp3qKtW8x/F9z6DcU/toEdjYw3AH62ZG7d1zTw+Pr4eE/YPjXQE7Z7662MMYJ/PWQmgb/9wGEBqlzltTkyer+iy/g8uWK/WxCCHGTk6BGWNyjrR9lRK/niO7eVj3x/feWuZDJBFu2wMsfYJyTgtPfV7G9khO0GEGXrQKcoPMZPPnHJfreOxmDjze8+iqkVcAW6MQoCHsefkyDBCO4avCwLbwwEzAnw9FgyBxo0AkCeoJfV9Dy/G/oroNHHOBWW/V441WYOqbkW78HD4a2bSElBebOLf9nEkKIakQzmUwlzKda/ZW0dLmwkB9+gAcegMBANVqjlTDrXUlcvEjEXb3x33Es96m9TR1Y2rsOr3wfg1uagQlj/fi3iQPBx1Lpsz+ZHoeScU7PCXoaN4Yvv4R+/crehzOb4f7+sDMTnDR42hGcdGpExr0xJJxW966++d+3bzEsfx6uL9aw8SpszlTx0GfT4ZnXStaPH3+EUaPUTqrISLCXnVBCiOqtpN/fEtSIypOaCvXqqftt2yAkpFyni0mNISIpgsD/LuIw6mHsYxPIsNL4o5sbP/Zz53hD9WX+wdwIBuxJ4uNh9Zg/xCv3/TYGWG/3AnUmTVNf/gCPPgqzZoG7e+k7tHwJ3HWfOh5lD0HWaqrphUM3BjLXS4xSQY+1A3x9G5iMauTpj6uwLwtsbVXJh169iu9HdjYEBanFxvPnwxNPlP6zCCHETaSk39/VbvopIyOD9u3bo2kaBw4cqOruiNJwdIRhwwA49tH/8dmBz1h0ZFGJ3z53/1xe2/Iac/fPJexkGAN+6c9fE+/GZcAQ7GMTOONtw8hpgbzxmG9uQAOwt5kjAKExXmg500AaGq/1nEadUY/DkSMwbpwaOVq4kMyWzYjbubF0n+3KFRj7sjruZKMCGvNUU3EBDag2AT3VtNSQj9R7NQ3usIfeHSEjA+68A3ZsKP5cVlbq8wB89JEKjoQQohaodiM148eP5+TJk/z555/s37+f9u3bl/i9MlJzE1i3DgYMINHJij5zmuLq5MXfI/8u9m1hJ8OYum1q7mPrbJj2dSRDtqv8M2s7uzDlcV/S7PW5bTQ0TJhoce4qP089BS4uxEQeJTLtAn7Ofjfswvp7yQwavvAmATEZXHHUs2/xDG4N/V/xnykxCh57HMLWqKm1TasgM7bgqaaSMo/cuDeGI3/C/c9AhAG8dfD7fOj6WJFvjzkXjVeLJujT01SZiFtvLVs/KlF0Yjpn4lIJ8HSU5IFCiHxK+v1drTIK//nnn6xdu5Zff/2VP//8s6q7I8ri1ltJr1sH14uX6XEohb876Ag7GVZg6YRFRxaRmpWK0WRk/qH5uc9bZZt454tIBuxJIksPH47w5rsBHvnW6Og0Hd/d/h1XDVfxc6gPH7SEpCS8wy/i3SE4t93c/XOJTo3G2dqZHzOW4DSlMZ99cJZ2p9Pp8sBk4pc3w6P/XYV/nn2L4d0xEJam1r68dj/4NgWalu/n5OqrbolRsG4i3GsPn6VCjBFeHQthAwsNmJbsjmDSr4d4s3kfHtq/ivPT3qXBTRLUFBa4LNkdweSwQxhNapPYzNA2jAz2r8KeCiGqo2oz/RQbG8uTTz7Jt99+i4NDyeraZGRkkJSUlO8mqlbM1Uv83FGNpoz8KwFMJqZtn1Zg3piFhxfy+b+f8+N/P2I0qQW9eoOJd+apgCbTSmP8cw35bqAnmqbLN7U0NWQqbbzaEOwdjLeLL3Trpk6ap2RC2Mkw5h2cx/Lw5Xx/7HuMJiNJjnqemtiI3c0ccLpqxG3oSDW6VJDEKPjpOViRUxG7uw1EfFruIpX5JISr9TVOOpXHBmDTVdhZ8DRUdGI6k8MOYQK+6TQEgPqb13Hx36MV16cyWrI7gm4zNzBq/k66zdzAkt0RwLU+G3PGjI0mqTQuhCibahHUmEwmHnnkEZ555hk6d+5c4vfNnDkTV1fX3JufXyVkshVFikiKYGnvOmTpNXocTmHItitqJObg/HxrbMJOhhF/NR6ApKwkNDT0BhMzvjzPwN1JZOk1Jozz45/2zgC80e0N1g5fy4KBC1g7fO2NIz89e6r7nKDGXJeqIGn2ep59sRFb2jijv5oBd96pimJeLyEctl9V2YLr6qCPLZgMatqoorgHXtvy3cYagqzAALw2W2Usvs6ZuNTc4CDcw49NAR3RYSL7o08qrk9lkDfYArXPyxy45O2zmVQaF0KURZUGNZMmTULTtCJvx44d45NPPiE5OZnJ5sRiJTR58mQSExNzb5GRlVRzSBTK38WfiPr2fHa32oU0+btofBKyWXtuLZ//+zkLDy8sMODQG0y8Pf88g3cmkqXX2P3pK7QYPZGhgUN5uu3ThAaF4u3orUZmCspYnDeoMZly61IV5qqtjrgf58Pdd6sMvcOH31i6wKou7M5Ux31tQa+p3U7ujcv40ymAq2/+hcN32oOjPezZDx9/fEPzAE/H3Bx/AAs7DQXA+9cfIDm54vpVQtGJ6WwLj2PvucsYjSZ6nNnPpI0LeHHztzyx4xfS58yl5brfuf3ENnqe2YdH6hUA9JomlcaFEKVWpQuFL126RHx8fJFtGjduzIgRI1ixYgVanjUTBoMBvV7PAw88wKJFJdtBIwuFbw5hJ8N485/XWTTjNO3C0znVuRGhzzpiyvk2fqTVI3xz5Jvc9nYZRt77PJK+B5Ix6HWsfv9x7pjwZekuevUquLqqAOXECWLqOzPw14H5AhudpuP+ZveTnJWMj6MP4zqMg/R0FRDt3auS2m3bpnZxAbz/Pvzvf+Cpg2cdVdAx9BPoOLq8P6Ib5V04/Muf8OSTKv/MwYPQpEm+pkt2RzAp7BAmE+gwcuDHF3CJOK2S8Y0dW/F9yyPvmpnNJy4x6ddD2GRlcM+RjTy2ZzlN4yOKfH+qtR0f97ifwLdfY0RI4A3nlAXEQtRONSpPTURERL71MBcuXGDgwIEsXbqUrl270qBBgxKdR4Kam8fc/XPJ/O8wzz22AOuMLKY/5MOSfh6AWhOjoWHEiFtyNnPnnKNdeDomOzu0JUtg6NCyXbRnT5Vx+Kuv4PHHCTsZxhvb3sCECQ2NN7q9UeCCZc6fh86dITZWjdj8/LMKjgICIDoaJt4DIXXB1Q9uLWGCvPIwmaB/f7WracAAWLPmhibRiemcjUujkacDPt9+Dc89B02bwn//gc4yA7R5F/tqOf18bM8ynt3+Mx7p6v/fFBt7VjbrQYa1DT197AmwM6nsxykpZF+IxupMztRdq1bw2WcssW8kC4iFEDUrqLne2bNnCQgIkC3dNcC5t16i4esfkmajMfytJkTWU4thH2n1COv+/pLPPzhLQEwmma5O2KxcrQo2ltWrr8LMmfDww/DNN4BaWxOZHFngFm+4tjuqw8l0hj89F7Ky4K23wNtbjZb4+sLp02BjU/Z+lUV4OLRoofqzcSP06VN42+RkaNAAkpJgxQq1RqiCRSem0/2dDblrY+wzr/L+qjnceXwLAOdd6rKw0xCC33wZVx8vFWxdP+piMsHixfDyyxAXB8CvrW9lRp/HiHd0A9S01JZJfWXERohapsYm3xM1i+0LL7GrhSMOmSbe++I8j/9xiYk/xfDs7J38+k40ATGZJHnXwWb7rvIFNAC9e6v7jRtzE9IVtQ4n7+6oabp17Hv9cfXClCnwv/Hq+KWXKj+gAZUP5+FR6njSxKIT7Dk7w9NPq+N337VId/Iu9m1wJYaw717mzuNbyNRZMaX/M/R+ej7fdA2lXZuGhAR6FByUaJoKOI8fh2eewaRpDDu8gQ3zn2bkv2o0ShYQCyGKUi2DmkaNGmEymUo1SiNuTt7O9Yn/bBYpdjpan0nnhaWxjF4dh/0Pv+AYnwxt2+Ky97AalSiv7t1Br4OICPj+5SKbFrRY+VH/baQOzylTcDkNbIFgx/L3qyz2LQaXMJVpauce+KToz8MLL6jga8sW2Lq1wrtjXqDc7ewBViyaQItLZ7nk6MYDo2bwbcc7QW/FjNDWJRthcXeHzz8nfv3fHKoXiGtGKu+u/oQH9q+SBcRCiCJVy6BG1Cy393mGDe8/xZFeTTk69BaYOFGNKHz7rfoCrl+/Yi50Igzq5yw2//kzFRgUoqDdUUaTkf/a/XctZaWjButfqdi8NCWRGAUrxoOzBl1yRolmfgSXi9jdV78+jM5ZwGyB0RofV3sW+sSz6Jep1LmazL8+QWxfsoaP547jxydvYcukvqVeC+N5ay/++30dn90yHIA3133BN14xMvUkhChUtVxTU1aypqYWS4yCOa1hQ5qqfN3GCoY5F1psMiY15sbdUejYuPE07otSrjUcYgcfrVV1myrLmc2wSCXWI80IH6VAJjB3Gox9vfD3nTgBzZurqapDh6B164rr086dqhRDWhpxt99F1sKF+NSrUyGnjr6Shv7Jp6i79Hu14+vvv6FLlwo5txCiepA1NULkZc7MG5AzzHLGAMbsQhPleTt6MzVkav4sxe2fx31XhmrQIOd/nTVXIUlf4DksJm9CPgcdhORkGv5osarQXZimTXMLivLeexXXn2PH4I47IC0NBg7E8/efKyygAfBxc6DuDwth4EC1xf7OO9VCaSGEuI4ENaJ2MAcCDfSgB1JMEK8VmSgvNCg0f5Zi7zvhhBq5OXSPCxkNrdQIydj/FZjd12LyJuQDFdS4OsHJcPjuuwLf8uHa47y45ADf9x6pnvjhBzh3rvx9OX9ebSuPj1ejJ0uXWmbhtLU1/PILdOgAly7BoEHqXggh8pCgRtQO5kDASgP/nJEVp7uLraKdb3fU4sVgMPBvoD2jOjXgrnGNyXK0U+t+Zs2y/GfIq+NomHAEHv4DJh2F/8upYD5t2g2jNUt2R/DxhlOE7Y/itfP2xAR3V0HYhx+Wrw+XL5PVfwBERpId1BRWrgQnp/KdsyjOzuoaDRvCqVMqX1Ga7IQSQlwjQY2oPTqOBsd60ChnCupUasnfazKRPX8eAL/2UlMrUV42TL/PU70+darailyZXH3VWh5XX5Up2MtLlXJYujS3ibnmUl4TA29XB/Pn5+aDKbWsLGIH3In1sf+IdvKg922TWHKmEgIMHx/480+oUwd27ODqiPvYdiJWil8KIQAJakRt0/15CH1UHW/cCMbC6z/ls2ULVifDSbXTsbqra+7TYT1cSexzC2RkqGR8JT1fRbO3v1YC4YMPcvPWFFQs8h//dqS0aqvWpxRQP6pYJhNpTzxFvT3bSLGx59F73yDKpW7lVdZu0QKWLcNgbYPdyhXsfWR8vqrfQojaS4IaUbt0GwdPf6zqNyUkqF1AJfHVVwCs7upGut21hcE6nZ6Mz3LO988/avSjqjz7LNjZwZ49udXIry9wCaDX6cia+D/14IMPSj/C9MEHOCz+BoOmY9zQ/3GsbgBQuYnxott25qVBzwMwZscvNL94uvKCKiHETUuCGlH7WFtDr5wkehs2FN/+yhW1SBVwHzcx30tTQ6ZSt0UwvP22euJ//4OoSs5bY3boS+ilAgzzGh8fV3tmhrbBXAtW02BGaGvqPHR/7hZsRo1StaxK4vff1WcEpvd7gr8Dg3NfqszEeGfiUvm9ZR9WNe2GlcnIO39+AoZsyTYsRC0nQY2onW69Vd2XJKj58Uc1VdO6NX2HvcLTbZ9maOBQnm779LUCmOPGQdeuqr7S2LFFly2whH2LYfN7EJQzBbNiRe4IzMhgf7ZNupUfn7yFbZNuVUnwdDq18NndHfbtg//7v+KvsX07PPCA+mxjxtD87VdvCJYKTIyXGKVy61RgkkLzCNTU/s+QZOtIu5iTPLZ3hWQbFqKWk+R7onbau1dV3nZxUduRrawKb9upk/rinzMHxo8vvN3hw9Cxoyoy+csvqqJ3ZTAnFjQnCvwpDY5nw6MPwoJvi37v77/DPfeo43Xr4LbbCm63eDE89ZRaOzRggNqFZGWVvxp4QQHNvsWw/HnABGgw9GO1YPvKFbXgd/9+tUYmJETl0SlFBfEluyN4Newwww+s5t3Vn5Bta4fV0SPQuPBt+kKI6qlGV+kuKwlqRC6DATw91Zfrzp2FZ6jdt08FNTY2cOECeHgUfd6pU+HNN6FePTh6VI2EWFreDMMA57LhmzSwtYHI82pXVFGeeQbmzVM7i/btUxXIzbKz1XTT7Nnq8V13qfIVzs7F9+v6YCvBCCcMkNYBtu+6MVFgnTpqtOuWW1SQ061bsVvEoxPTOXsplY6PDsN2y2bo3x/WrCF3CEkIUSNIRmEhiqLXQ58+6rioKah5ahs3oaHFBzQAr76qRh5iY1URycqQN8MwqDw89fWQkQmffVbo26IT09kWHkf01LdV+YToaFUjqmVLVSfqk09g8OBrAc2UKRAWVrKABlQWZ6MBDmTCZynwSQqsSYd/tqmApnlTuO9OCOmqdm9dvgyrV8Mbb6jswY0bq5GkIvi42hPSxBPbBV+Bra0abSokAaEQouaTkRpRe33yCTz/vPrtfu3aG18/fBjat1ejOps2XVtcXJwdO1RFcKMRfvsN7r67IntdsOuneewegMmfqVGac+dU0JDHkt0RTPr1kLk1n7ezZtCb41XJg+s5OMCiRaWfTrtwEm5vBQez1GMNaGgFT/0ftHSA/W9f6+/tH4JVO/Wz274d/t4AUdHqfY8+qqb+ivt/duZMFVS6u8N//0HduqXrbyWITkznTFwqAZ6OUphTiFKQ6acCSFAj8jl2TI2qaBqsX39t8TCoxbC33aZGce65R41QlMbkyfDOO+qL9fDh4qeAKkJilKpl5d5YJRls0kQFNPPnwxNP5DaLTkyn+zsb8uWv0WsaWyb1xedqktoSbr4ZjTBjBrRrx4drj3P+cjoN6tjz4oBmRfdjx3oY/zYcP6mCmd620MUWRn4Cgf3yT0sBaPprxUX3LYaw5+Dvq7A1Z1dWo0YqsCoqsMzKUuukDh5UO7q+/75MP0ZLWbI7gslhhzCaQKfBzNA2pa5cLkRtJUFNASSoETd46in1pV+/vvoyNE8xLV0K996r8r4cPQoBAYWeYu7+uUSnRuPj6MO4DuPUkxkZ6gv28GE1wvHzz5W/zuODD+Dll6FVK5WPJ+f628LjGDV/5w3Nf3zyFkICC55iW7I7gld+vZbT591hhXwh71sM05+BFemQDXi6wTfzoWU9FWy5+t64Bsjs4T9Um7wBz7ls+P0qXDGq/k+cqNYs2doW/Jl371ZrcoxGjn79I3WG3XVTjIgUGUjeBP0T4mYna2qEKInZs6FZM7UI+Ikn1AhNWhq89JJ6/X//KzKgCTsZxryD81gevpx5B+cRdjJnRMfWVo0sWFmpAGnJkkr4MNcJugJ21nDkiJpeO3oUfv2VFn+v5J4jGxl+aD3DD62n0/mjOGddLXQ7dEGlFgpMdJcYBTPHwG85AU1jPTxigh4h18o5wI1rgECN1Lg3vlZN3ayhFTzjCCPuUH82772nFnWfPFnwZw4O5vh9jwHgMuF5bpu28qbINFxQZufKTFYoRG1RxD5WIWoBR0eVh+aWW9Si1Hnz1ILZiAjw94dXXin0rTGpMUzbPi3fc9O2T+NEwgmSs5LVyM3//Z9a+Prss9C7t9phVBn2LYaVs8HDCFHAHXfkVhKvA8y+rrlJ09BWNVNb0vv3V6NLOTuPivpCzjfKsHwJhOV8SXeyhsF2oDOpKbG8hUPNxUXzrgEaMudaG02XP7Cxs4Iv58GoPaoUxcGD0LcvbN58w/bt6MR0Qr0Hsdp1BX6JsTy37SdetXWgV1OvKh0RMefVqX85hlaxp3HIuopjdiatfjoMxkwVSGdlQb9+apF0Kba2CyGukeknIUCN2Lz4oppuArh6tdhcM7uid/H42seLPO2bwVO456F31VbpXr3UiElhUycV4epV+GYeTH8Jogz5X7O1VQufHR1Br+fMlQwyMzLxjTqNU/zF/G2dnGDkSHjsMaJbtqf7uxuLnjrZvRv69oHUNGhlBaH2auFI3rUy18u7Bijv64XltgGIiVFf/EePqmrdmzer4DOHeWrt1lO7WPDrm2Tp9Ax+5GPefHVkoVNrleLECc6+MBm/1b+jzxuwFSC7cSBW48aqBdJubpXTPyFucrKmpgAS1IhCGY1q+/KaNepxxyDYc7zIdTAxqTEM/HUgxiK+pHSajvUtP8br1iGQnAwPPaSmpSp6fc3ly6o0wrx5KpkgqMnlZlYQb4SLRrhvCPy4vMC3/756L78tWEmHC8e46+jfBFyOzn0tq2Vrdjz2Ag9drJfb73xrao4fhx49VMXvrq3gtgiwyvl8Qz+5FpCURmEBT2IUHN8FD7wMp05DYKAKbOrXB/KvXfkybDoDTu5gp19r/A/uxMetCrINHz8O06fDDz/kFjtNadUWay9PbF2dVYDp6MiJZAN7/rvAncf+wSUjp3q8gwM8+KDKUN22beX3XYibiAQ1BZCgRhQpJgaaNoA0A4z3hw/Owba5kJEMts6qGOZ1wk6GMXXb1CJPu2DgAoIPXlZBk8GgEvS98UbF9PnqVfj0U1V76vJl9VwDX2gWz8VOtpytY0OTE+m4L0wBezuVjO+6fDs3LGI1mehy/gj3HlzPHcf/wSErA4CoVh0JG/EcWbeEXNv9dOkSBAerXVadO6vdYrs/hsRIcPWDW1+rmM8J+Udwkkzwsx1EXVI5dv7+WyU8JGe7etgh6l+5yPqvxmCfnaEyIj/0UMX1pRgX9x5Ee/MtPP8IQzNXbh8yBF5/Xf2c8sj787fPvMrdR//mkX0raHbpXG6bK917k/HlfOq1DKq0zyDEzUSCmgJIUCOKtG8x/DAWsoA6OjXK8Nd0SI1VW6QnnijwbebdT87Wzvx4/Md8Izc6TceaYWtYemIpvj+u4p73/1QvLFqkEtyVldGofvt/7TW1/gegdWu1M2joUMI2vsK086sxaho6o5FNb1/CLfwSa57uw8mnh13bpUXhu6EAXK6m8PTOX3lsz3IVHAAMHQrvv6+2jJtHt5o0gW3bLLd1/frsxABXgF+d4PwF9dk3blRZoiG3fEOrbz/DZdrramv98eOVMp2zbcandHp9ArYGlZ8nqld/fD+cqTJTF9S+oJ+/ycQf7U24fD2P+hv+xMpk5JKjGwfnfEW/J4ZZ+iMIcdOR3U9ClEZiFKwYD046FdCAGhVIjVXHqbEq6CnAuA7jeLvH20zqOompIflHbaaGTGXbhW3MOziP11tF8vVg9aXLE0/AX3+Vra9//aV+23/oIRXQNGgACxbAgQNwzz3EXL3EtAvrMOZMFRl1Omb1Vet42v+8ha/3fXFtlxbXFrEWJNnJwIe396DvmFnEjnxILWBdvlxNhwwcqAIae3uVZNCSuXiu3xUF4AYsnqUWXx8+rGpSXbkC5GQaDvTA5dVX1EjOxYslK9pZHiYTyVPeoNtr47A1ZPFPw/bc8fAcenV7gegmLQt9W0E/f71Oh6FHD/oEj6Hfk1/wn1cjvFKv0PPp+0ic84llP4cQ1ZgENUJAwV+aXDeIueKFYitNhwaF4mGnpnc87DzoVr9bvh1SHw2vx5ourmqny8CBKu9KauoN55m7fy6vbXmNufvn5j7+eNGTnOveUiUF3L9fZdidORNOnFCLSvV6ACKSIm5Y57OqqwtxLlbUu5zN4B1XmLZ9GjGpMYAKAGaGtsnXXgOsXXfj2OQdHBrOJ6Xjl6ycPFhtDx80SOXhWb9eNZ4wQY2UWFJh28A79lJBnpeX+pnceaeqqG5mY3OtVMRnn6lCppaQmQmPPYbzdPVnPT/4bh4eMY0j3k2K3bpt/vlfX/E8NdOA0QTn6tQn9MFZ/NGsBzbGbFwnPK/qdWVmWuazCFGNyfSTqN3Ma2ZMRvhnVgGBzXUe/kPlXCnCoiOLSM1KxdHakRbuLW7YIWWTaWTtnw3w+C2nNEOjRvDFFyrIIf86HfsMIy9FBeH721+EHE5BbwKjlR7ds2P56i4fztgk50/6R+ELmB9beYkJv8QS7W7NkHeC+HzINwR7B+e+/uHa42yMXobeJgZX6wbsTfkGTbv2z4N5Ks07RVOjHzmjImgajBmjMiiXtC5UWRS1K+rgQbW7LDFR1en6+efcIA+ABx6AH34gpmkb3n9jIb4eTkVnRS6Ny5dh2DDYuBGTTsfrtz3Dtx0G575caJK9CxdUOYc6dcDXl2gbJ84mXM2teF7QWqexO5fy8ubFaCaTWpy9dGnuWiIharKSfn9LnhpRu23/FJIvgHP9nNwpz+V5USPfaI05QVwxHm71cO5xTGoMOk2XL8DItrUi69tF8Pdelb/m7Fk1+tGrF+l13cmO38x4Bx31ErLoty8Zh4yjue9d29mFT+71IXRwez7a+2Hu8/Wd6hMaFAqAt6M3U0Om5lvArKHxfX8P7vsrAZ+ELB5cn4DfKL98/W4UcISz0d9CJpB54wYto8lI5JWzeD/6ugpogoJUfp9vv1WjIH/8AV9+mRucVbiOo1WJhYJ2RbVtC8uWqSmosDBVTPTjj699iFmzyFy2Au8Th7D7ZgEfdxiMbx378pcpOH1a5QA6dgycnNB+/pnWnq3Qwg5hMl0bdfHJSIZVG/OXoIiOzncqH2trfHx8wNcXfH3xufVWPujXnRf/ilTn0mn4v/8m2sV7VRmILVvUNOS6dSrIFELISI2o5d5vmn8hcN7H/f4vf5BTxu3J1++QmtZtWm4AQkqKqn798ce5W36vF+llzR/d3PgjxI0Ib7U2RkPDRAGjKI7euc/lLd9Q36k+U7dNZcjWy8yYH0WWkwPWZ87lLqwt6fb0rUf74fTubLC1gs/GwGMfq+mfJ5+EM2dUw4cfhg8/VIUlK9vPP8N996nsw+++qzJCoxYOzxs+gTfWzyPR1pHBj35CjFu98pUp2L5dLZqOi1PrmlauzN16HZ2YztnYZJpvW0udObPg339vfL9Op7akJyerqu4F/VNsY0P67XdwbvAwXEOH4OOZ8+/W8eOqUOqxY2o7++bN6lxC1FCy+6kAEtSIfPYtvjFouZqUfwv39UFPGfVZ0of4q/F42Hnw98i/b2xw9Cjs3k1SbATfbf8U57RssnUaf3V24d9A+3zDJtcHNGZ3BtyJTqe7YTrKbO7+ucQkX2D8U9/idfy8qlD+0UdA4YkEzdfS0PgyeSC3PDdLvXC3HbSzUT+zK5EQcxp+/Q9+26G+nOvWVUHF6NGVlx03MUqtjfppI7z6hnruu+/ggQfYFh7HQ/O28fviF2kTG06Eaz1GjnqHD18aUrakfEuWkP3QaKyyMolt0pJ6m9bl5srBYFDB1VtvqeklUH9+zZqp7e+dO6tb+/aQdVn12dkf0vUQFQX/fA+r58ORLIjNE2TWratGaB59VAVPcXHQp49a51RAIkIhahIJagogQY3IVdAW4YKy3xaTp6ak8q6zyTs9VZDrR3aGBg5lefi1pHkvdnqROfvm5BtVuT7QyTcadL3161UpBCsr9aXbpEmBIzU6Tcd3t3/HVcNVAs4l49lviMoYfIsNDLTLvXK0XkektRV+Wdn4NBgL7y1VIwig6jR98om6t6R9i4le+cK1fpzoCt+tVp9x0SLi3bx4ZeEW6l+JYfzWn/BITyLR1hHrYaE4NPIDb+/8t3r11ELs65lManH2ayr/zromXXl+yETeuD+YkR3qw08/qWR7x4+r9m5uaiH1uHE3jlwVtEbo+grmMQb4NxvCneFS3LX3hoZycdIUzhms6fDg3ViFn1Lb6jdvrrxSHGUQnZjOmbhUAjwdpZCnJZgDe/fAgrN4V2MS1BRAghqRq6hK0cUsBLaE64Oe6yt/Xz/Sc33gU5LpqHxuvx1Wr+bUbe1ZOHVwvikqs9zAKD5ejTCcOaOKVD7ggHkPcpiTI9M83VU+HJOJqfFXCB39D3yzVOXMSUlRJ3vkkWsVwytaYhRhX3XhrTpu+MVm0eJcOo8fu0zT/xzg4qWyn9fHB7p2vXZr2xaefxZ++BmArzrfxYy+j2HU6el/ahefH/hBBRegApgXX1TBjKtrgX0uMKge9hUsffTG9qN+h5Np8M03as2Q0YhB0/FLm9v4sd1Avl8/G6fo89CypUpEaMnt9aWV80W7PNKO8asumUM43ims0rsom6IW0hchJjWGiKQI/F38b/j3orDXinqPpUhQUwAJakSuko7UVJLipqcKGukxBz5Gk5E/Tv9xw3sWDFyQb3dTvlEn596Y2rdHMxp5eHIA+5o5Mq3bNC6kXMgXTJGdrRb+btgAjRrCiMvEOOmJsLbC3mjkwfreuflwAHQmE2tumYF386FqIeykSSqbr1n37vDUU3DvvSq/TXlcvQq7dpH86zwO/7WMdqfSccgoZE2QXg/NmpHhVY8UO0cc7Kyx37BerWdxc1M7ia5cUVmlY2KuBWOFMLa35s0+T7Iy6xamrfuCwSe2qRc8PFSF93Hjit4JVlhQPfwb+PWxIv9eXtqxl/2jxzHg5A71Y7Cy4bdWtzIi9gD6mBg1rbVhg9pVVdX2LSZm1YtEWOlokGlgTsaj/GzoC+TfFVZjRnCqaqSkBP+eFRSIhJ0MY9q2NzBiQofG1G5v5I7whp0M441tb+ROP7+R81rYyTCmbZ+G0WREp+mYGjI19z0xMQeIiN6Dv09nvL3bV+hHlKCmABLUiHwKWlNTljpF5VTkQuISKGzq6IaRmjzrg2Ke3cz2wS25Z/Nl0mw0Zj5Yn+W93FkzfO2196SkqF1EX3+tahTt2EFYzDe5mYo1kwlTATWsFnR/l+Am17Y0s327qku1bFlupXDc3FTywMceU6MLNjZFf8iUFJWP5/hxlWjvn39g584bcrWk2Wic8LfjmL89Xfs9SEC3e+C559R77OzU7ixzuYQzZzD06oH+/AWymwVhtXmLWrcCKtg5cEBdY8MGtcMoOzvftUyAUdOhNxnJRuPqc+NxmvFWbnXzXAV90RX1JRT+V+G/cSdGcfjwPp5YcZn65y/yyqZv6Hr+CADZjk6q5FZKipruW7eu4Cm0ypIzgjbNow6YIORwMpM2XiLrvCc2mVnYZWfgbg1Go5GrGdlk6a34zysAtwF9aTOsF/g5gG8ronG/FvCQUKlBQ6m+pPctVgk8TUaVU2nIR/n+PSnsXBUSCOQEyTF69QuHf1Y23gZD7shzQQFKt/rdGLh0AMa8I7xorBmuUk0UNh394J8PFvhvzbbts65lMTeZmNpgEKG3zSrb5ymABDUFkKBG3KCCFgKXVYkDkmIUGxhdF8DtunUiEw7/wAefRnDLUZX8788urtRbHEZHv1vUFu1331WLUQF+/ZWYgd2K3yGV849igX2PjoaFC2H+fLWNPS8PD7WWxcdH3by9VVLC48fV7fz5gi/o7U16ty7MdtjD3mYOnPK1xajT8vcjMVEVhvwjZzRr/Hh4/33Czq7gq98ns3DmaepdziYxyA/X6bPgzBnSjh0i69RxnCJi0M5fQGc0ghPQzRZiDXA0W5XTyMvJCXr2hL591a1DB/j3+8IDlKKmCwoq6JmnvcGkMTn7CX7O7kOf03t4ZdNiWlzK2X2maWrtT0gIrF5daYFNvi9nXX0uf/8+e7+bR6cTabimGEqd6dUEGB01TtVryJJGt+Hpm8KYBqvQbKA00yuF9reYKZSw9S8X+iV9w3uLGSkp7FxFXaNUoz55Ash8U8FP7CTGSl9g8PJu50lM3DPzhlMt6P4uJkfPAjcOTOw8kff3vH/D87M6T+Z/u2fcOGo76LsKG7GRPDVClET3569NyVQi83RSfHr8DUGC0WQkMjmyVEFNaFBovqmjfAGNuQREHv6b55DiV5+nXm7Eo3/GMS4sltt3JWLoPxoys9UWY1CLT995B0JDiYjeVXBAY14kYYKp3d8ovN8+PvDqq2pKav16FdysWKGyE8fHq9uRI4V/SE9PtYOoeXP1hd2rFzRpgr2m0fxkGD9tm5rbland8vYjBea8BC2bwHtz4KOPyNy7i09GJhJX14bHXglgwTtnqHcyEkaOBOD6et6H6gfScmAMV49mY384G50BTFaQMOAOHOwdsN+0UQWAf/6pbqCCCe80aKQHPz146lVW6sB+6kuqqLw7rr43VidfMZ4YvS73N/EZfM1mQ1v+Dgxm8ISHaLHvT/VnFZWT9Xr7doz+/uhuv11lsM7IUFN2ee+zs9U0oKOjqgqeUzUca8BJDwHNILDVtWDTNguSzt3wRbvi93FsWreMzsfSqHskBWIzqQPcdt3PMUsPlx1cyHKwxdPpMin2OhwyTZzP8CEj3QqflDjcMpLQG9Sfoz7VRLPTZ3n99Ffqr5oGqT5WmAL0OJ0eC+91A+8mhf+dKURRUyigAjRzsAFg1DSmnV9Nt5gH2ZZ8+sZpGStPMBlvHClJOE1M+qUCzxV0pGuh1/C+cJCYlROIsNbjn2XA+47ZRY76xFjpmebpkRu4GDWNaZ7udLPSExG9L19AA2DEhBZ/Cp3JdEMg4pedDS7+6NBuCIQ6ODYo8D2m+JP5njP3ITJ6b4VPQxVHghpRu5VjR1N5LDy8kPir8bjZut2QnE+n6fBz9ivi3QUraBs3UGAJCO/sLKYGjWLqye9ZcIcXu5s7Mn9hCo6ROV+IjRqpitIPPaR2EAH+Lv4F9vX+5veTnJWcG0xdv8j5BjqdSpI3YIDKzXP5shrFiY5W61nMx3Z2Kogx34rIexMaFEq3+t2ITI7Ez9nvWkCTdzTEXoNZz8O0hdhs2c4PR6154Tl/jgbY88QrAUz+7gItrf3Yah9DpJc1kXVtiKxrg3OKgTvXuNPqm9M45gxsb2rnRNr/7uf2UV+q61yOhK1r4N9I2LFP7UJKSoIk4ESeaStnDdYMhY4hKjhr1kwtJE69DHFXVWBhb68+e1qaWudz5Qr8t5kd4bDayh7XFAOnLmv0jkljWeobOCSn4Twr7trUXt4fdWKi2pFVUTTASQM3HZkN25BmcsUu+iy3nz3HkALi3Qw9/NvEgdVd3Vgb7Eyysw2zWn1C0qZRvOnRLHdU4f/iLvNB3Fs0sAnnZONf8bicxcBdSfTbl0TrM+nYZamfu2YCxwvZcCEbtmbAklbQqxfJvbpyrktTPEP64e2Sf1Tj+lGVmNSY3IAG1C8R07ZNo5tz49wv4IjoPQV+Sf977m+mHVuQuyjfhIlp296g28DFbHN2yjdS8nr8FYa5Nybi9J/YZprwvJKJXaYJK4O6RV38lo4JqVgbVLCW5KAn2UFH9P7lbD/0BW/4+Vwbddn4KqE5wXC+0Z1/1ehOg1YjCgxcIpMj8c/OKjAQaefkz9T4yzeM7nj7dIJsA1Pj4q977TJtUq4wNS4h/+aAuATat21YcIDkU3ARV0uSoEaIShZ2Moz4q/EAXMm4csOW7akhUyt2R4G5btJ1Q+OhrR7igr2TCkDa+uD48mi19qVhQxXM2NjAhrdVHho3P7xvfe2GTMXX/4YbdjKMeQfn5T6u71Sf5Mzkwrez63Rq6snDI1/9qGuBUQrjOoSU6GN6O3rn/7nljlDl1hmAlG9h/XKyH3wCn5PhLH77NGuDXYisa8OqbnXQ93uc2Re+It7Filv3JfHSkhjahacDqhL6ljZOLBrkyY6Wjuiyd9IhNQbv42uv/VZtNOA9bTa0/Q3+WQMzh8OZbLU1O8UEySbYvk/dSukW4BauXPfsmdwjgwaXna2Id7UiwdqbuleyaJwQRe7XjLW1WkTcsyeEhJBgayD2ajzeOhfqGG3VdF/8BVjzFmQYVX/NfTa6q51kJtTjZAM2kQcobCVUmo1GWmN/LrUJYiuHybLW6HjyKsO6PkCrC/9yn84JqywTmTYaRk1jumcdmiSd5py1NUZN45K7Dd8N8uS7QZ7YZRh5/Fc3Rm7dQZ1U9Xc4xU7DhIbz1UxYvx7n9etpDVy11kjy98WlcQvw9uZ0ViyHInehN5qIN0Ib95Y4aLbMjj6DzqhiNKMOsvQaxo9CwL8L+LWllSGJcVGxpNvoSLXTkWKvI81OR530szSLTSPFXkeqvZ4kBx0Gncbxw5tZHWvL8IMJ+MZl4XU5i7pXsrn8SQ86J8SxK7mgheenGVTgT28qHYABdrEkOuqJcbcm2sOauCPPYd28Jetif6Ghpw0XPG3IsNEx7fxqvmsyuNBfjrzt6xccvAT2JxQd3Vb+f3v3Ht9kdf8B/PMkadL0lvQSmrT0yn1AkSIw6hxDqoA3NtjAn05EtLLpJqI4YVMr6qRchk7ndD/nZVPHZcpvuulQBEEt5TLFC1ChhTJa2pTS0jZt06RNzu+PpKGhSUqhbZrk83698qJ9bjlPDu3z7Tnfc85SlIfJkdLRIqRJBso+wRxTE3JazK5pEvQ2GwAJc5rNyDFXnttuBzDkauTXHezSldbfrTQAc2qI+pW3HJoYZQzqLfXeJ+e7VBeTFO3lHG8tMb11b77yg7ptBerM17D9uCxU/Wg6DB97XuDSLgEy529Gq0LCP8ZNwN/m1ONYcrjbca9csRrl7+R5zGWAJtm9pagVwKj7AJHhmB/o22/RdvRb2E2NUFjaIDe3Ohbj7DyzdGQkoNWiJUqFb+1GmCLkaIiU43SsAqdjw/CTafchbshY3HTgftTEyGGTO0MYATSVrsD4E6fwxuaHEdHmnlDdHhGObWNV2J4dg1qNArdO/Bmu+s6NQO1B4N1FqJbLUFcLJP/XiphTbUBDLFDuPjzeLgFN4REIt1qg9NBKdCHa5BLa5YBdJkGhUEMepkS9zQSbDFDYBZRtAqo2AWX7wH1MdXR5dscqB6xhEmwyCSqFEuERWrTYmlArLJABULfaEdMmQWFp7/ZaHUzhMjRGyaGN16MlJhqHreVoUUmwKGXISp6ITE2Go4vx9FGYy/ehWZIQKQTUgy8H4p3ddm1moK0ZiIoFIrWO4NfeChx41fFDIJcczR8KGTDzcaChFPj6dUDh3DfpZ0DKlUBrK+ori9FQXYLweQuQOMFzyHaxmFNDNAB5WkHbLuy4Ju0axKvjERkW2TdvnL3A0eLSUA5oUroPaDzk4XTkg3gLJrzdW72lHgBQ21qLLSVbfAYoHV0Dna0sWomcpBzsrtzdpRXI5ygxZwuVUSady3Oww5m/ooHho33413N3I+zwEaiPt0NWYkJKQzWSG6uhsAucjZJj01VxeG3MAlRL2YhMKoB03lxA4c1nXAEN4MxliNcip+pz6J15My+3lqOhrhSauKG4I+c3rvPPjUhJOJebMfRHQFsb/nfP71Fur0WiZjB+Mf4XaGw24nYPAeOiuQtwrOoLGE+c96tcAhKUx/HN6HbMX5mGl1cfx6AGGyzaKCiiYqCoqMSsva2YtbfBecJDzpdDovN1jiOgqdCF4cuhETiWpELOwSZMPOJYfdwUocQffhiLrZO0GNTQjonHk9BYNRaGxhokmWoxKzkM4Y0NQF0d7LVnIOrqIBdAmE0gzAY4QoNmAM1I8F6jAIB2GWCTSwhrE5A5z6yOVeBYsgqSAGKabUg3RyGi9ixk7Z6DrTalhEqtAo2RClgVEmxyCco2O1RtdgxT6KAQYYDVCpu5GVJTE2RW74HGhQQ0AKC0AUqbcJa4FWg0IgJd87d6IrrVjuhWO3CmHJEAprrt3eb2ndr5AgB8tRvA7p6/4fsPdt32xnoAjnXotM4XtGOBXg5qLhSDGqJ+5C0vJS8rr+8nsbrqN90f08FDHg6EI/HR20gMT/d2vu4CFG+B0Vc1X3kNdrx+bppkbLnijq6jSzrKL5Ph+iUvnlsN29n9L7fboG89gZyZVUgzJOP/Mu/AiTMtKG6S4Zkvn3IliOZPyYdZCvecIKlQQA9H4PJMiTOnpfY/iNWNwpxhc1zBm1tuRqfP5rmyc3P7dHw2+VPy3RJUXd2UXnImDKIRZsNWnJBUuH1FJl5eUwZ9XRNalUq8em0CVG12XFZqRmSrHWqLHTq7GvKWVkjOYfJVcWE4lKHG4XQ1mofOx3spH0LTZMMvt1RjydunAQAWuRyvTpiNF3Pm44170jCkbC/W7ZJjvz4d6FQt+rzvupajkAHYcuQtrNuRjwhzOxR2CUsv+yVmpOQ6WhVsNpwxGWG01iIxLg26uBRApcK7pz7Eys8LYFUAkiTDisF346qC15H40W7oz7ZDbgfW/o8eH0yKwdCyn0But0Eh+xvGlbYg3WhFmtGCdKMF+rPtCLMKpJ1uQ9chbABwbqRdp3XeYY+OhiwjA02RAMRR1MbLEBUpw+awa/EvRQ7mjqvEFyc2IdrcDrVFQHX6O1CbdFh0mQ5ScxO27zsGlbUV4bY2KGztUNrbMS4xAkq7DW2tFliFBKUqDGFyGc5aG3CquRICQLtcQlu7DlahRbtMgfT4Whyz10IACG+zY4QUC114kmMaApPJMdqvqcnzWmJ9RZLcV8D1NUdTH2NQQ9SPPK2g3es5NL3BSx6Or1XKPd3b+boLULwFfUKIHo8SMzYbsbJym3srSuU25DQb3c4pO9MMe6ff/zaZHLYILRbWHMfo8AZAo4ZBo8YU3IRrh/7ALRnZ2Gz0OEokxZDts9XpYoI3b8nQesPlXXImHq2thzl3AlaXOkZindSrcPvyTPxp3Qmknq7Dne87Wl3++MNBeG+KFpDL8cHcD3Cy8SQWv78IYTYBs+rcIOzI4gjc9/ZpzP2kDmE2R9fTP6dosXb8Y6iIGorVc8dizNBUxOuysHzHDrfyyyUJ6Qnu7RFzRvwYOYO/1zWx2ykBWV1abG4c9FNMGp6LclM5vjwux6/frULihMVYPeQIUt8xIcNoxZoXK/DAR0r88gdqnIw3YFlCK564Jt4tIXlT5RJsan4CpnqBP8mjEWGxQ2UVaAuTYAmT4c5Jv0SMNhFQq/FpZQtWH2xGuSYRjeFRKPhxFr4/XIe5BX9HqlSNE/ZEGBEPuSRh1qK7YDs0H6u3fwabJQHaWAlPDxOIajmFmk+KMNp4DBl1pyDrnNDrjJ/CnK/qqDjYx4yFYfp0mFIN+HmxESUxo2EV8a7P8rPl0zDafATlVZ8jxTABOk95K3Y7UFPjGAl36hRQWelIvm9qcuRONTc7EtE7vm5vB1QqRx6dUun+tUbjmMhRq3X82/F1XJwjFy421pHYPkAwqCHqZ3OGzcGzXzzrmkG4JxPt9Tpva1tpkh2Th3XOqbnhmW7ny+h8b1qVFo3Wxh4FKBP1Ez0GfZcNuqzHo8S8BQ5ugdCO32LE6SokYSIqnY/RefKPsUrxZ8jLhCMPV3uuu+78ZGR9pB75OY+5l9c5nHyfhyHwHe9/scFbl2RoANAkY860VZjy3lJUhMkxuM0Gw3VPwzhiKtaW/tYVcFUMUmLuk8Owvf4mKNf8DoNr6vHUS6dw57/OoOahe6BXD3KWQ4LurBWpp61IrbZiyCkLri98GhFtFgDArnHRePbHidCNWYrJ1hzM0apdyx0YNGqsmjMWy7d8AyEcf7w/NWeMx1mCPd5LN/SReoh2Deb/cwcEACPi8V78QizJexUVe1uRvKMFiaVWvF72MN687FocvuJGbDVvRkWYHElWO35vWYj/KEdjhXoxntK/jGHRElYm6ByTSTq7AGOcP49VDWbcVrAD9k5F/PWWg/hs+TQsmfMDxz2i0z0qgbwzFbjl2/2Qf/wxVFWnXOfpnK/uJDbVAXt2AXt2IRXAewCsMgX+MuF6FEzPO/dZai7znYQrkznWL0tMBLKze/QZBzoGNUR+cPuY210jgvyq8Nlzkw+eP7y9p3k4Tp3vLVoZ3eMAxducOz1t4fIWOLgCoS/+CnyyBvEACsNfx0NtefjEluUIaKROf013nlvGA2/l9fX+3lrsLiZ4AwBkL4BhyHQYOs15o2841WVY7orGesTcdTuQkwjTyiVQf9aKzCoLMu9bD/zhXejtduz/7wnIbF27EA8YRmD19J9g5n1T8fzIMV4/+/kTU/H94TqcONOC9ISIXl/24PyWtc22afjEloWnH4zBfZmluPvDTbjq+H+w6PN3Yf5KBfudCzDxzhvwbnMi3v73GQACb9uvwvemz8OcFAty1NH43f5jaG6OxYmyZGCY5/cBAJsQOHGmxXWP5cerMOw/uxC7+n7HRIfNze45MkOGOCZhHD8euyKT8atSOaojtJBLEp6aMwYpcRG4+c/7AAARVjNG1PwXI2tOYInODP1/jwJffw1lQwNmfG8UrncuKUG+cfQTUajqp2UiPCUEX+zSED0a/eTrfTzMAGuHDG8kP4IFp1Z2vdBFLnTqbf0cX/fT3TkXrNPU+W7DcjuvL9UqgD1WoMgCdBogZY9Qoyk1CbsRhxOaVOwbPBo7hkyEXCZzrdfkL64cqE5Pro5umU+O1mD521/jirIv8cBnb2B8pXO19KgoYMkSGPPuRlm7yi3Y2rT/JB56+xvXtVY7F9r09j67bxmKxF3bgHffBT7+2DGxYYeUFOCHPwRuvNGxCOx5i5lWNZjdgj1f92LQqB15MeXljm4g/QDrou5nXCbBAwY1RE7epnW/8gFHLs353VF9oKcBSq++zyUsKNlTxmaj19yR3jynCy+B2+tJj+C2yvMCtxY7kPYrIOsHjlmk9XpAkrBp/0m3rqSCOQNjZW1f5XIFDvFqGAo/dkwi+YVzXqCYGOCOO4CpU4EpU1ClivYZVGzafxLL3/4ao43HcHXpXiyo+QqxRw+7F2bUKOBHP3K8JkxwT5i9xHuhcxjUeMCghsjJ20M9XAu01vttLax+0+2Ckv5f6LRXdJonxw4Jy9vuxCe2LBSq7nXvYvMRuJ3fujBQXHC5hHAspvroo8A337jtMqem4/3oDBxIHoWKmEHQNZ/FoKY63JoaBn1zHVBZCdvxMsjPdJqjRyYDcnKA2bOBG25wzArdX/cSwhjUeMCghsjJ00O9YwGnDoH8ML8Qvrrfdvz2XC5RT4bCD0QNp1BbXowb3qxEpXMUzTz5x3hK8TIUkh29sThkQLDbHcHN1q3A7t2OdcYu9PEXGQnMmOHoVrr2WkB3IWm/1JsY1HjAoIaok/Mf6ucHNZfY7RIQgil48WH3sTO4+aW9btv0qMXLN8Rj9JjLgruOvamvB/buxcG3/o36j3YhrqUBp6PikDp6CDLHDQeSkhyLeCYlAVlZA2rYcijijMJE5Fv2AmD7k47RT+GxQOtZ9/3dTLYXFII4kOksIyESMgluuSM1UgLixkwDNGqs//AIKs6aMThWjfuvufTulICg1QIzZmDMjBmu7p8R7P4JeAxqiELZFfc65qkRduDTdT2abI8CR8f8MZ1H+XTMebJp/0k8u6PUtT05Vh1yiaoG5wSLFPgY1BCFss4jnLQpPZ5sjwLH/ImpOHXWjFP1ZiQ7J8yrajBjxRb35NlfbzmI7w/X8SFPAYlBDRE5dO6OikwM/sTREHR+15KvCeYY1FAgYlBDROd0dEep/LcgHfUfT7k2ntZqIgoUDGqI6Jw+nnCPBhZfuTZEgYhBDRFRCPOUa0MUqBjUEBGFuJAZxk1BT+bvAhARERH1BgY1REREFBQY1BAREVFQCJigJj09HZIkub0KCgr8XSwiIiIaIAIqUfjxxx9HXl6e6/voaM6lQURERA4BFdRER0dDr9f7uxhEREQ0AAVM9xMAFBQUID4+HuPHj8fatWvR3t7u83iLxYLGxka3FxEREQWngGmpuffee5GdnY24uDjs3r0bK1asQFVVFdavX+/1nFWrVmHlypX9WEoiIiLyF0kIIbo/rG8sX74cq1ev9nlMcXExRo4c2WX7K6+8gsWLF6OpqQkqlcrjuRaLBRaLxfV9Y2MjUlJS0NDQgJiYmEsrPBEREfWLxsZGaDSabp/ffg1qampqUFtb6/OYzMxMKJXKLtsPHTqEMWPG4Ntvv8WIERc2G+aFfihEREQ0cFzo89uv3U86nQ46ne6izv3yyy8hk8kwaNCgXi4VERERBaKAyKkpKirC3r17MW3aNERHR6OoqAhLly7FT3/6U8TGxvq7eERERDQABERQo1KpsHHjRjz22GOwWCzIyMjA0qVLcf/99/u7aERERDRABERQk52djT179vi7GERERDSABURQ01s6cqI5Xw0REVHg6Hhudze2KaSCGpPJBABISUnxc0mIiIiop0wmEzQajdf9fh3S3d/sdjsqKysRHR0NSZLc9nXMYVNeXs7h3gMc6yowsJ4CA+spcIRyXQkhYDKZkJSUBJnM+2IIIdVSI5PJMHjwYJ/HxMTEhNx/lkDFugoMrKfAwHoKHKFaV75aaDoE1NpPRERERN4wqCEiIqKgwKDGSaVSIT8/3+s6UjRwsK4CA+spMLCeAgfrqnshlShMREREwYstNURERBQUGNQQERFRUGBQQ0REREGBQQ0REREFhaAOap5//nmkp6cjPDwckydPxr59+3we//e//x0jR45EeHg4xo4di/fff99tvxACjz76KAwGA9RqNXJzc1FSUtKXtxASerueFi5cCEmS3F4zZ87sy1sIGT2pq0OHDmHu3LlIT0+HJEl45plnLvmadGF6u54ee+yxLj9TI0eO7MM7CA09qaeXXnoJV155JWJjYxEbG4vc3Nwux/MZBUAEqY0bNwqlUileeeUVcejQIZGXlye0Wq2orq72eHxhYaGQy+VizZo14vDhw+Lhhx8WYWFh4ptvvnEdU1BQIDQajfjHP/4hvvrqK3HjjTeKjIwMYTab++u2gk5f1NNtt90mZs6cKaqqqlyvurq6/rqloNXTutq3b59YtmyZ2LBhg9Dr9eLpp5++5GtS9/qinvLz88Xo0aPdfqZqamr6+E6CW0/r6eabbxbPP/+8OHDggCguLhYLFy4UGo1GVFRUuI7hM0qIoA1qJk2aJO655x7X9zabTSQlJYlVq1Z5PH7evHniuuuuc9s2efJksXjxYiGEEHa7Xej1erF27VrX/vr6eqFSqcSGDRv64A5CQ2/XkxCOoGb27Nl9Ut5Q1tO66iwtLc3jw/JSrkme9UU95efni3HjxvViKelS/++3t7eL6Oho8Ze//EUIwWdUh6DsfrJarfj888+Rm5vr2iaTyZCbm4uioiKP5xQVFbkdDwAzZsxwHV9WVgaj0eh2jEajweTJk71ek3zri3rqsHPnTgwaNAgjRozAz3/+c9TW1vb+DYSQi6krf1wz1PXlZ1pSUoKkpCRkZmbilltuwcmTJy+1uCGrN+qppaUFbW1tiIuLA8BnVIegDGrOnDkDm82GxMREt+2JiYkwGo0ezzEajT6P7/i3J9ck3/qingBg5syZ+Otf/4rt27dj9erV2LVrF2bNmgWbzdb7NxEiLqau/HHNUNdXn+nkyZPx2muvYevWrXjhhRdQVlaGK6+8EiaT6VKLHJJ6o54eeughJCUluYIYPqMcQmqVbgoNN910k+vrsWPHIisrC0OGDMHOnTsxffp0P5aMKDDNmjXL9XVWVhYmT56MtLQ0bN68GXfccYcfSxaaCgoKsHHjRuzcuRPh4eH+Ls6AEpQtNQkJCZDL5aiurnbbXl1dDb1e7/EcvV7v8/iOf3tyTfKtL+rJk8zMTCQkJKC0tPTSCx2iLqau/HHNUNdfn6lWq8Xw4cP5M3WRLqWe1q1bh4KCAnz44YfIyspybeczyiEogxqlUokJEyZg+/btrm12ux3bt2/HlClTPJ4zZcoUt+MBYNu2ba7jMzIyoNfr3Y5pbGzE3r17vV6TfOuLevKkoqICtbW1MBgMvVPwEHQxdeWPa4a6/vpMm5qacOzYMf5MXaSLrac1a9bgiSeewNatW3H55Ze77eMzysnfmcp9ZePGjUKlUonXXntNHD58WNx1111Cq9UKo9EohBDi1ltvFcuXL3cdX1hYKBQKhVi3bp0oLi4W+fn5Hod0a7Va8c4774ivv/5azJ49O+SGy/W23q4nk8kkli1bJoqKikRZWZn46KOPRHZ2thg2bJhobW31yz0Gi57WlcViEQcOHBAHDhwQBoNBLFu2TBw4cECUlJRc8DWp5/qinh544AGxc+dOUVZWJgoLC0Vubq5ISEgQp0+f7vf7CxY9raeCggKhVCrFW2+95Ta03mQyuR0T6s+ooA1qhBDiueeeE6mpqUKpVIpJkyaJPXv2uPZNnTpV3HbbbW7Hb968WQwfPlwolUoxevRo8d5777ntt9vt4pFHHhGJiYlCpVKJ6dOniyNHjvTHrQS13qynlpYWcc011widTifCwsJEWlqayMvL40Oyl/SkrsrKygSALq+pU6de8DXp4vR2Pc2fP18YDAahVCpFcnKymD9/vigtLe3HOwpOPamntLQ0j/WUn5/vOobPKCEkIYTwQwMRERERUa8KypwaIiIiCj0MaoiIiCgoMKghIiKioMCghoiIiIICgxoiIiIKCgxqiIiIKCgwqCEiIqKgwKCGiIiIggKDGiIiIgoKDGqIKKCVl5dj0aJFSEpKglKpRFpaGpYsWYLa2lp/F42I+hmDGiIKWMePH8fll1+OkpISbNiwAaWlpXjxxRddqx3X1dX5u4hE1I+49hMRBaxZs2bh4MGDOHr0KNRqtWu70WjEkCFDsGDBArzwwgt+LCER9Se21BBRQKqrq8MHH3yAu+++2y2gAQC9Xo9bbrkFmzZtAv9uIwodDGqIKCCVlJRACIFRo0Z53D9q1CicPXsWNTU1/VwyIvIXBjVEFNC6a4lRKpX9VBIi8jcGNUQUkIYOHQpJklBcXOxxf3FxMXQ6HbRabf8WjIj8hkENEQWk+Ph4XH311fjjH/8Is9nsts9oNOLNN9/EwoUL/VM4IvILjn4iooBVUlKCnJwcjBo1Ck8++SQyMjJw6NAhPPjgg1AoFPj0008RFRXl72ISUT9hSw0RBaxhw4Zh//79yMzMxLx585CWloZZs2Zh+PDhKCwsZEBDFGLYUkNEQSU/Px/r16/Htm3b8N3vftffxSGifsSghoiCzquvvoqGhgbce++9kMnYIE0UKhjUEBERUVDgnzBEREQUFBjUEBERUVBgUENERERBgUENERERBQUGNURERBQUGNQQERFRUGBQQ0REREGBQQ0REREFBQY1REREFBT+H+PW3Myjp9eRAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "global_objective.plot()\n", "plt.legend()\n", "plt.xlabel('Q')\n", "plt.ylabel('logR');" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "\n", "\n", "--Global Objective--\n", "________________________________________________________________________________\n", "Objective - 4673437776\n", "Dataset = e361r\n", "datapoints = 99\n", "chi2 = 552.9546248326391\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'D2O' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'D2O' \n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 4673260112\n", "Dataset = e365r\n", "datapoints = 99\n", "chi2 = 400.7510922157482\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'cm3.5' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'cm3.5' \n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 4673260752\n", "Dataset = e366r\n", "datapoints = 99\n", "chi2 = 376.89103792013174\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'H2O' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'H2O' \n", "\n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(global_objective)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we're going to do some MCMC sampling. We discard the first 400 steps, then save 1 in every 100 steps, for a total of 30 saved steps." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:26<00:00, 15.37it/s]\n", "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3000/3000 [03:16<00:00, 15.27it/s]\n" ] } ], "source": [ "fitter.sample(400, random_state=1)\n", "fitter.sampler.reset()\n", "fitter.sample(30, nthin=100, random_state=1);" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "global_objective.corner();" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.53/doc/refnx.analysis.rst000066400000000000000000000001671477046072400172040ustar00rootroot00000000000000refnx.analysis ============== .. automodule:: refnx.analysis :members: :undoc-members: :show-inheritance: refnx-0.1.53/doc/refnx.dataset.rst000066400000000000000000000001641477046072400170030ustar00rootroot00000000000000refnx.dataset ============= .. automodule:: refnx.dataset :members: :undoc-members: :show-inheritance: refnx-0.1.53/doc/refnx.reduce.rst000066400000000000000000000001611477046072400166220ustar00rootroot00000000000000refnx.reduce ============ .. automodule:: refnx.reduce :members: :undoc-members: :show-inheritance: refnx-0.1.53/doc/refnx.reflect.rst000066400000000000000000000003631477046072400170030ustar00rootroot00000000000000refnx.reflect ============= .. automodule:: refnx.reflect :members: :undoc-members: :show-inheritance: :special-members: :exclude-members: __dict__,__weakref__, __repr__, __module__, __init__, __abstractmethods__, __copy__refnx-0.1.53/doc/refnx.rst000066400000000000000000000003551477046072400153610ustar00rootroot00000000000000refnx - Neutron and X-ray reflectometry analysis in Python ========================================================== Modules ------- .. toctree:: refnx.analysis refnx.reflect refnx.dataset refnx.reduce refnx.util refnx-0.1.53/doc/refnx.util.rst000066400000000000000000000001531477046072400163310ustar00rootroot00000000000000refnx.util ========== .. automodule:: refnx.util :members: :undoc-members: :show-inheritance: refnx-0.1.53/doc/requirements.txt000066400000000000000000000003501477046072400167640ustar00rootroot00000000000000nbsphinx jupyter-sphinx sphinxcontrib-bibtex sphinxcontrib-jquery jinja2 sphinx_rtd_theme # tqdm corner periodictable pandoc scipy numpy sphinx myst_nb pandas numpydoc h5py nbconvert ipywidgets cython jupyter matplotlib pytest xlrd refnx-0.1.53/doc/testimonials.rst000066400000000000000000000010421477046072400167440ustar00rootroot00000000000000.. _testimonials: Testimonials ------------ Please cite the *refnx* paper if you use it for data analysis in your own publications. Its full reference is: "Nelson, A.R.J. & Prescott, S.W. (2019). J. Appl. Cryst. 52, https://doi.org/10.1107/S1600576718017296." Please `let us know `_ if your work should be included in this list or `fork the repository `_ and add it yourself. .. bibliography:: ../testimonials.bib :style: unsrt :all: :cited: :list: enumerated refnx-0.1.53/doc/using_mpi.ipynb000066400000000000000000000164451477046072400165510ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "1e240c11-442b-4c09-b245-f286907ec7a7", "metadata": {}, "source": [ "# Using `refnx` on a cluster with MPI\n", "\n", "`refnx` can be used on a compute cluster, typically when you want to do a largish MCMC sampling run. You will need to install these packages in the Python environment:\n", "\n", "- refnx\n", "- numpy\n", "- cython\n", "- schwimmbad\n", "- mpi4py\n", "- scipy\n", "\n", "For this specific example you'll also need the `corner` and `matplotlib` packages. Setting up a Python environment on your cluster can have difficulties, so contact your helpful cluster administrator if you need help.\n", "\n", "You would typically start the code running with something along the lines of:\n", "\n", "```\n", "mpiexec -n 8 python cf.py # requests parallelisation over 8 processes\n", "```\n", "\n", "(assuming the script is saved as `cf.py`). This call might be started using a scheduler, such as PBS. Use of that is outside the bounds of this tutorial. Again, your cluster admin would be able to help there.\n", "This file would generate a text file called `steps.chain` which would then be further processed to give an output that's useful.\n", "\n", "When you start modifying this example for your purposes you should begin by tailoring the `setup` function to return an `refnx.analysis.Objective` for your system." ] }, { "cell_type": "markdown", "id": "d47ad3e9-3759-447d-a4d5-d204f116ba7e", "metadata": {}, "source": [ "```python\n", "import sys\n", "from importlib import resources\n", "import refnx\n", "from schwimmbad import MPIPool\n", "\n", "from refnx.reflect import SLD, Slab, ReflectModel\n", "from refnx.dataset import ReflectDataset\n", "from refnx.analysis import (Objective, CurveFitter, Transform, GlobalObjective)\n", "\n", "\n", "def setup():\n", " # Tailor this function for your own system\n", " \n", " # load the data.\n", " with resources.path(refnx.analysis) as pth:\n", " DATASET_NAME = pth / 'tests' / 'c_PLP0011859_q.txt'\n", "\n", " # load the data\n", " data = ReflectDataset(DATASET_NAME)\n", "\n", " # the materials we're using\n", " si = SLD(2.07, name='Si')\n", " sio2 = SLD(3.47, name='SiO2')\n", " film = SLD(2, name='film')\n", " d2o = SLD(6.36, name='d2o')\n", "\n", " structure = si | sio2(30, 3) | film(250, 3) | d2o(0, 3)\n", " structure[1].thick.setp(vary=True, bounds=(15., 50.))\n", " structure[1].rough.setp(vary=True, bounds=(1., 6.))\n", " structure[2].thick.setp(vary=True, bounds=(200, 300))\n", " structure[2].sld.real.setp(vary=True, bounds=(0.1, 3))\n", " structure[2].rough.setp(vary=True, bounds=(1, 6))\n", "\n", " model = ReflectModel(structure, bkg=9e-6, scale=1.)\n", " model.bkg.setp(vary=True, bounds=(1e-8, 1e-5))\n", " model.scale.setp(vary=True, bounds=(0.9, 1.1))\n", " \n", " # model.threads controls the parallelisation of the reflectivity calculation\n", " # because we're parallelising the MCMC calculation we don't want oversubscription\n", " # of the computer, so we only calculate the reflectivity with one thread.\n", " model.threads = 1\n", " \n", " # fit on a logR scale, but use weighting\n", " objective = Objective(model, data, transform=Transform('logY'),\n", " use_weights=True)\n", "\n", " return objective\n", "\n", "\n", "def structure_plot(obj, samples=0):\n", " # plot sld profiles\n", " import matplotlib.pyplot as plt\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", "\n", " if isinstance(obj, GlobalObjective):\n", " if samples > 0:\n", " savedparams = np.array(obj.parameters)\n", " for pvec in obj.parameters.pgen(ngen=samples):\n", " obj.setp(pvec)\n", " for o in obj.objectives:\n", " if hasattr(o.model, 'structure'):\n", " ax.plot(*o.model.structure.sld_profile(),\n", " color=\"k\", alpha=0.01)\n", "\n", " # put back saved_params\n", " obj.setp(savedparams)\n", "\n", " for o in obj.objectives:\n", " if hasattr(o.model, 'structure'):\n", " ax.plot(*o.model.structure.sld_profile(), zorder=20)\n", "\n", " ax.set_ylabel('SLD / $10^{-6}\\\\AA^{-2}$')\n", " ax.set_xlabel(\"z / $\\\\AA$\")\n", "\n", " elif isinstance(obj, Objective) and hasattr(obj.model, 'structure'):\n", " fig, ax = obj.model.structure.plot(samples=samples)\n", "\n", " fig.savefig('steps_sld.png', dpi=1000)\n", "\n", "\n", "if __name__ == \"__main__\":\n", " with MPIPool() as pool:\n", " if not pool.is_master():\n", " pool.wait()\n", " sys.exit(0)\n", " # buffering so the program doesn't try to write to the file\n", " # constantly\n", " with open('steps.chain', 'w', buffering=500000) as f:\n", " objective = setup()\n", " # Create the fitter and fit\n", " fitter = CurveFitter(objective, nwalkers=300)\n", " fitter.initialise('prior')\n", " fitter.fit('differential_evolution')\n", " # Collect 200 saved steps, which are thinned/separated by 10 steps.\n", " fitter.sample(200, pool=pool.map, f=f, verbose=False, nthin=10);\n", " f.flush()\n", "\n", " # the following section is only necessary if you want to make some pretty graphs\n", " try:\n", " # create graphs of reflectivity and SLD profiles\n", " import matplotlib\n", " import matplotlib.pyplot as plt\n", " matplotlib.use('agg')\n", "\n", " fig, ax = objective.plot(samples=1000)\n", " ax.set_ylabel('R')\n", " ax.set_xlabel(\"Q / $\\\\AA$\")\n", " fig.savefig('steps.png', dpi=1000)\n", "\n", " structure_plot(objective, samples=1000)\n", "\n", " # corner plot\n", " fig = objective.corner()\n", " fig.savefig('steps_corner.png')\n", "\n", " # plot the Autocorrelation function of the chain\n", " fig = plt.figure()\n", " ax = fig.add_subplot(111)\n", " ax.plot(fitter.acf())\n", " ax.set_ylabel('autocorrelation')\n", " ax.set_xlabel('step')\n", " fig.savefig('steps-autocorrelation.png')\n", " except ImportError:\n", " pass\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.53/examples/000077500000000000000000000000001477046072400145535ustar00rootroot00000000000000refnx-0.1.53/examples/CurveFitter_EMCEE.ipynb000066400000000000000000000232271477046072400207640ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demonstration of MCMC non-linear regression with EMCEE and refnx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`refnx` is a package that can be used for non-linear regression (curvefitting). Here I demonstrate how it can be used to analyse Gaussian curve dataset, with Bayesian MCMC sampling of the posterior distributions of the parameters. This is a very robust way of estimating parameter uncertainties. I will also do the analysis with the `emcee` package for comparison\n", "\n", "The first step is all the imports." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import emcee\n", "import corner\n", "from scipy.optimize import leastsq\n", "from refnx.analysis import CurveFitter, Parameter, Parameters, Model, Objective, process_chain\n", "from refnx.dataset import Data1D\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First step is to load some data in." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = Data1D('gauss_data.txt')\n", "plt.errorbar(data.x, data.y, yerr=data.y_err, fmt='.k')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the fit function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def gauss(x, p, *args):\n", " # p is a Parameters instance. A quick way of getting all the numerical values out\n", " # is making it into array. However, there alternate ways of access:\n", " # e.g. p['bkg'].value or p[0].value.\n", " p0 = np.array(p)\n", " return p0[0] + p0[1] * np.exp(-((x - p0[2]) / p0[3])**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up initial parameter guesses and lower and upper bounds. The last step is to create a `refnx.Parameters` instance." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bkg = Parameter(0.1, 'bkg', vary=True, bounds=(-1, 1))\n", "amp = Parameter(20, 'amp', vary=True, bounds=(0, 30))\n", "mu = Parameter(0.1, 'mu', vary=True, bounds=(-5, 5))\n", "wid = Parameter(0.1, 'wid', vary=True, bounds=(0.001, 2))\n", "\n", "# to get numerical values out of p0 you have to use np.array(p0), or refer to each Parameter\n", "# by using p0['bkg'].value or p0[0].value.\n", "p0 = bkg | amp | mu | wid" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse with emcee\n", "\n", "To start with we'll do the analysis with the `emcee` package. Then we'll repeat the analysis with `refnx.analysis.CurveFitter`. \n", "\n", "The following functions have to be defined for `emcee`. The log-likelihood, the uniform log-prior and the overall log-posterior probability." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bounds_varying = np.array([[-1, 0, -5, 0.001], [1, 30, 5, 2]]).T\n", "\n", "def residuals(theta):\n", " resid = (gauss(data.x, theta) - data.y) / data.y_err\n", " return resid\n", " \n", "def lnlike(theta):\n", " # log likelihood\n", " return -0.5 * (np.sum(residuals(theta) ** 2))\n", "\n", "def lnprior(theta):\n", " # uniform prior\n", " if (np.any(theta > bounds_varying[:, 1])\n", " or np.any(theta < bounds_varying[:, 0])):\n", " return -np.inf\n", " return 0\n", "\n", "def lnpost(theta):\n", " lp = lnprior(theta)\n", " if not np.isfinite(lp):\n", " return -np.inf\n", " return lp + lnlike(theta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets fit the data with least squares first." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "result = leastsq(residuals, p0, full_output=True)\n", "best_fit = result[0]\n", "best_errors = np.sqrt(np.diag(result[1]))\n", "for mean, std in zip(best_fit, best_errors):\n", " print(\"{:<12g} +/- {:<10g}\".format(mean, std))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up the walkers for `emcee`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ndim, nwalkers = 4, 100\n", "pos = np.array([np.array(p0) * (1 + 1e-2 * np.random.randn(ndim))\n", " for i in range(nwalkers)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the `emcee` sampler" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sampler = emcee.EnsembleSampler(nwalkers, ndim, lnpost)\n", "a = sampler.run_mcmc(pos, 1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Discard 100 burn in steps for each walker and flatten the chain." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "chain = sampler.chain[:, 100:, :].reshape(-1, 4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse with CurveFitter\n", "\n", "Now we're going to do the analysis using a `refnx.analysis.CurveFitter` instance, it should be a lot simpler than the direct approach above. First setup the curvefitter." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# first setup a model\n", "model = Model(p0, fitfunc=gauss)\n", "\n", "# an objective is composed of a model and data\n", "objective = Objective(model, data)\n", "\n", "# a fitter is constructed\n", "fitter = CurveFitter(objective, nwalkers=100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First of all do a least-squares fit, to get a starting point for the sampling." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "res_leastsq = fitter.fit('least_squares')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now do the MCMC sampling with CurveFitter instead. There are 100 walkers, we do 1000 steps on each walker. We parallelise using 4 threads. After the sampling discard the first 100 steps of each walker and take every 5th step" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fitter.sample(1000, pool=4)\n", "res_sampling = process_chain(objective, fitter.chain, nburn=100, nthin=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following plot shows the posterior distributions for each parameter" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "b = corner.corner(fitter.sampler.flatchain)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But what about the fits, are they good?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.errorbar(data.x, data.y, yerr=data.y_err, fmt=\".\")\n", "\n", "saved_state = np.array(p0)\n", "# plot a selection of the samples\n", "for pars in objective.pgen(500):\n", " # could also use:\n", " # >>> objective.setp(pars)\n", " # then to calculate the model:\n", " # >>> model(data.x)\n", " plt.plot(data.x, objective.generative(pars), color=\"k\", alpha=0.02)\n", "\n", "plt.plot(data.x, gauss(data.x, p0), color='r', label='sampling')\n", "objective.setp(saved_state)\n", "\n", "# the leastsq fit overlies the sampling\n", "# plt.plot(data.x, gauss(data.x, best_fit), color='g', label='leastsq')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following fit parameters are obtained. Lets compare them to the least squares output." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(\"Curvefitter.sampling\")\n", "print(objective)\n", "\n", "print(\"\\nleastsq\")\n", "print(\"-------\")\n", "print(best_fit, '\\n', best_errors)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 } refnx-0.1.53/examples/analytical_profiles/000077500000000000000000000000001477046072400205775ustar00rootroot00000000000000refnx-0.1.53/examples/analytical_profiles/brushes/000077500000000000000000000000001477046072400222525ustar00rootroot00000000000000refnx-0.1.53/examples/analytical_profiles/brushes/brushes.txt000066400000000000000000000002221477046072400244620ustar00rootroot00000000000000For a detailed explanation of how brushes can be analysed with refnx please see https://github.com/refnx/refnx-models/tree/master/polymer_brushes refnx-0.1.53/examples/auto_reducer.py000077500000000000000000000017631477046072400176200ustar00rootroot00000000000000""" Auto reduce reflectometry files """ #!/usr/bin/env python import os import sys import time import argparse from refnx.reduce import AutoReducer def dir_path(path): if os.path.isdir(path): return path else: raise argparse.ArgumentTypeError(f"readable_dir:{path} is not a valid path") if __name__ == "__main__": parser = argparse.ArgumentParser(description="Auto-reduce reflectometry files.") parser.add_argument("file_list", nargs='+') parser.add_argument("-p", "--path", type=dir_path, default="./", help="path to datafiles") parser.add_argument("-s", "--scale", help="scale factor", type=float, default=1.0) args = parser.parse_args() files = args.file_list pth = args.path files = [os.path.join(pth, file) for file in files] print(f"Path: {pth}") print(f"Reducing against: {files}, with scale: {args.scale}") ar = AutoReducer(files, data_folder=pth, scale=args.scale) while True: time.sleep(10.) refnx-0.1.53/examples/experiment.mtft000066400000000000000000006562021477046072400176420ustar00rootroot00000000000000(dp0 S'params_store_model.params_store' p1 ccopy_reg _reconstructor p2 (cdatastore ParametersStore p3 c__builtin__ object p4 Ntp5 Rp6 (dp7 S'displayOtherThanReflect' p8 I00 sS'parameters' p9 ccollections OrderedDict p10 ((lp11 (lp12 S'theoretical' p13 aclmfit.parameter Parameters p14 ((lp15 (lp16 S'nlayers' p17 ag2 (clmfit.parameter Parameter p18 g4 Ntp19 Rp20 (g17 cnumpy.core.multiarray scalar p21 (cnumpy dtype p22 (S'f8' p23 I0 I1 tp24 Rp25 (I3 S'<' p26 NNNI-1 I-1 I0 tp27 bS'\x00\x00\x00\x00\x00\x00\xf0?' p28 tp29 Rp30 I00 NF-inf Finf I0 Ng30 tp31 baa(lp32 S'scale' p33 ag2 (g18 g4 Ntp34 Rp35 (g33 g21 (g25 S'\x06\x87E\x84;\xfa\xef?' p36 tp37 Rp38 I01 NF-inf Finf g21 (g25 S'\xd0uyC\x1a~T?' p39 tp40 Rp41 (dp42 S'sigma_back' p43 g21 (g25 S'\xf2\\@\xbf\xb4W\xae\xbf' p44 tp45 Rp46 sS'thick0' p47 g21 (g25 S"Hk'\xbaA\x0c\x92?" p48 tp49 Rp50 sS'sigma0' p51 g21 (g25 S'2\xbc_\xd1^\x8b\xaa?' p52 tp53 Rp54 sS'bkg' p55 g21 (g25 S'(8e4\xf2\xa6\xa1?' p56 tp57 Rp58 sS'SLDback' p59 g21 (g25 S'6' p114 tp115 Rp116 I01 NF-inf Finf g21 (g25 S'2\x1ee\x12I\x83[>' p117 tp118 Rp119 (dp120 g43 g21 (g25 S'\xecLJb}9\xea\xbf' p121 tp122 Rp123 sg47 g21 (g25 S'\xef;\xee=v\xeb\xeb?' p124 tp125 Rp126 sg33 g21 (g25 S'\xf0#e4\xf2\xa6\xa1?' p127 tp128 Rp129 sg59 g21 (g25 S'|\x87:\xa4V\xef\x98?' p130 tp131 Rp132 sg51 g21 (g25 S'\xb8\xab[\xd8\xa9\xe6\xec?' p133 tp134 Rp135 sg21 (g25 S"'q\x1a\x1fR\xfdi>" p136 tp137 Rp138 tp139 baa(lp140 g43 ag2 (g18 g4 Ntp141 Rp142 (g43 g21 (g25 S'\x1a\xb8uK\x08P\x1a@' p143 tp144 Rp145 I01 NF-inf Finf g21 (g25 S'\xa5\x18\x83\x02\x12\x00\xf5?' p146 tp147 Rp148 (dp149 g47 g21 (g25 S'"\xf8:\x0c<\xf1\xee\xbf' p150 tp151 Rp152 sg51 g21 (g25 S'\xd0\xff\x8c+\x9d\x0e\xef\xbf' p153 tp154 Rp155 sg33 g21 (g25 S'\xbeG@\xbf\xb4W\xae\xbf' p156 tp157 Rp158 sg55 g21 (g25 S'\xd7LJb}9\xea\xbf' p159 tp160 Rp161 sg59 g21 (g25 S'/\xda\xe9 o\xce\xa7\xbf' p162 tp163 Rp164 sg21 (g25 S'\x10\xe7\xc9\xc3\xb2P\x1a@' p165 tp166 Rp167 tp168 baa(lp169 g47 ag2 (g18 g4 Ntp170 Rp171 (g47 g21 (g25 S'\xeb\xae\x90VNN%@' p172 tp173 Rp174 I01 NF-inf Finf g21 (g25 S'\x83\x9e\xfd\x8eC\x03\xe5?' p175 tp176 Rp177 (dp178 g43 g21 (g25 S'\x15\xf8:\x0c<\xf1\xee\xbf' p179 tp180 Rp181 sg33 g21 (g25 S"\xca?'\xbaA\x0c\x92?" p182 tp183 Rp184 sg51 g21 (g25 S'\xe6\x97\xf4\xd2 \x99\xef?' p185 tp186 Rp187 sg55 g21 (g25 S'\xdf;\xee=v\xeb\xeb?' p188 tp189 Rp190 sg59 g21 (g25 S'\x7fE\xc9\rL\xdd\xa4?' p191 tp192 Rp193 sg21 (g25 S'h`\xe1\x9f&N%@' p194 tp195 Rp196 tp197 baa(lp198 S'SLD0' p199 ag2 (g18 g4 Ntp200 Rp201 (g199 g21 (g25 S'\xc3\xf5(\\\x8f\xc2\x0b@' p202 tp203 Rp204 I00 NF-inf Finf I0 Ng204 tp205 baa(lp206 S'iSLD0' p207 ag2 (g18 g4 Ntp208 Rp209 (g207 I0 I00 NF-inf Finf I0 NI0 tp210 baa(lp211 g51 ag2 (g18 g4 Ntp212 Rp213 (g51 g21 (g25 S'\x92\n\x8dg\xcb\xa2\x02@' p214 tp215 Rp216 I01 NF-inf Finf g21 (g25 S'\x92\x8dK\xca\xbe\xf8\xe7?' p217 tp218 Rp219 (dp220 g43 g21 (g25 S'\xd3\xff\x8c+\x9d\x0e\xef\xbf' p221 tp222 Rp223 sg47 g21 (g25 S'\xf5\x97\xf4\xd2 \x99\xef?' p224 tp225 Rp226 sg33 g21 (g25 S'O\xa6_\xd1^\x8b\xaa?' p227 tp228 Rp229 sg55 g21 (g25 S'\xb2\xab[\xd8\xa9\xe6\xec?' p230 tp231 Rp232 sg59 g21 (g25 S'\x9cy\xbd\xaf]\xdb\xa0?' p233 tp234 Rp235 sg21 (g25 S'~voX\x15\xa2\x02@' p236 tp237 Rp238 tp239 baatp240 Rp241 aa(lp242 Vcoef_c_PLP0000708 p243 ag14 ((lp244 (lp245 g17 ag2 (g18 g4 Ntp246 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p738 tp739 bsg663 Nsbaatp740 Rp741 sbsS'plugins' p742 g10 ((lp743 (lp744 S'default' p745 a(crefnx.analysis.reflectivity ReflectivityFitFunction p746 S'' p747 tp748 aatp749 Rp750 sS'settings' p751 g2 (cView ProgramSettings p752 g4 Ntp753 Rp754 (dp755 S'usedq' p756 I01 sS'current_dataset_name' p757 Vc_PLP0000708 p758 sS'transformdata' p759 S'logY' p760 sS'quad_order' p761 I17 sS'current_model_name' p762 Vtheoretical p763 sS'useerrors' p764 I01 sS'fit_plugin' p765 g746 sS'resolution' p766 I5 sS'fitting_algorithm' p767 S'DE' p768 sbsS'history' p769 VSession started at: Sat Jan 10 19:38:55 2015\u000a___________________________________________________\u000afitting to: c_PLP0000708\u000aDE logY\u000a[[Variables]]\u000a SLD0: 3.47 (fixed)\u000a SLDback: 2.07 (fixed)\u000a SLDfront: 0 (fixed)\u000a bkg: 2.5460e-08 (init= 1e-07)\u000a iSLD0: 0 (fixed)\u000a iSLDback: 0 (fixed)\u000a iSLDfront: 0 (fixed)\u000a nlayers: 1 (fixed)\u000a scale: 1.02776490 (init= 1)\u000a sigma0: 1.50865496 (init= 5)\u000a sigma_back: 8.96316867 (init= 5)\u000a thick0: 9.43233911 (init= 25)\u000a[[Correlations]] (unreported correlations are < 0.100)\u000a___________________________________________________\u000a___________________________________________________\u000afitting to: c_PLP0000708\u000aDE logY\u000a[[Variables]]\u000a SLD0: 3.47 (fixed)\u000a SLDback: 2.10012647 (init= 2.07)\u000a SLDfront: 0 (fixed)\u000a bkg: 2.6717e-10 (init= 2.546023e-08)\u000a iSLD0: 0 (fixed)\u000a iSLDback: 0 (fixed)\u000a iSLDfront: 0 (fixed)\u000a nlayers: 1 (fixed)\u000a scale: 0.99914376 (init= 1.027765)\u000a sigma0: 0.03420463 (init= 1.508655)\u000a sigma_back: 9.40460955 (init= 8.963169)\u000a thick0: 9.01947491 (init= 9.432339)\u000a[[Correlations]] (unreported correlations are < 0.100)\u000a___________________________________________________\u000a___________________________________________________\u000afitting to: c_PLP0000708\u000aLM logY\u000a[[Variables]]\u000a SLD0: 3.47 (fixed)\u000a SLDback: 2.10017649 +/- 0.002931 (0.14%) (init= 2.100126)\u000a SLDfront: 0 (fixed)\u000a bkg: 4.8409e-08 +/- 9.12e-08 (188.44%) (init= 2.671706e-10)\u000a iSLD0: 0 (fixed)\u000a iSLDback: 0 (fixed)\u000a iSLDfront: 0 (fixed)\u000a nlayers: 1 (fixed)\u000a scale: 0.99929591 +/- 0.004452 (0.45%) (init= 0.9991438)\u000a sigma0: 2.32914227 +/- 2.665627 (114.45%) (init= 0.03420463)\u000a sigma_back: 6.57880693 +/- 4.670020 (70.99%) (init= 9.40461)\u000a thick0: 10.6526384 +/- 2.336976 (21.94%) (init= 9.019475)\u000a[[Correlations]] (unreported correlations are < 0.100)\u000a C(sigma0, thick0) = 0.987 \u000a C(bkg, sigma0) = 0.903 \u000a C(bkg, thick0) = 0.872 \u000a___________________________________________________\u000a___________________________________________________\u000afitting to: c_PLP0000708\u000aLM logY\u000a[[Variables]]\u000a nlayers: 1 (fixed)\u000a scale: 0.99929595 +/- 0.001250 (0.13%) (init= 0.9992959)\u000a SLDfront: 0 (fixed)\u000a iSLDfront: 0 (fixed)\u000a SLDback: 2.10017651 +/- 0.000823 (0.04%) (init= 2.100176)\u000a iSLDback: 0 (fixed)\u000a bkg: 4.8418e-08 +/- 2.56e-08 (52.92%) (init= 4.840928e-08)\u000a sigma_back: 6.57815664 +/- 1.312517 (19.95%) (init= 6.578807)\u000a thick0: 10.6529414 +/- 0.656648 (6.16%) (init= 10.65264)\u000a SLD0: 3.47 (fixed)\u000a iSLD0: 0 (fixed)\u000a sigma0: 2.32948952 +/- 0.749114 (32.16%) (init= 2.329142)\u000a[[Correlations]] (unreported correlations are < 0.100)\u000a C(thick0, sigma0) = 0.987 \u000a C(sigma_back, sigma0) = -0.971 \u000a C(sigma_back, thick0) = -0.967 \u000a C(bkg, sigma0) = 0.903 \u000a C(bkg, thick0) = 0.872 \u000a C(bkg, sigma_back) = -0.820 \u000a C(scale, SLDback) = -0.535 \u000a___________________________________________________\u000a p770 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com.apple.print.PrintSettings.PMLastPage 2147483647 com.apple.print.ticket.stateFlag 0 com.apple.print.PrintSettings.PMPageRange com.apple.print.ticket.creator com.apple.jobticket com.apple.print.ticket.itemArray com.apple.print.PrintSettings.PMPageRange 1 2147483647 com.apple.print.ticket.stateFlag 0 com.apple.print.ticket.APIVersion 00.20 com.apple.print.ticket.type com.apple.print.PrintSettingsTicket ^Graph*@@??WDashSettings#  !0\26Normal@ Geneva<HHHH$$0\26Normal@ Geneva<HHHH$$4444440 ,SNormal@ Geneva<HHHH$$4 4 4 4 4 4 homeawds1Macintosh HD:Users:anz:Documents:Andy:programming:refnx:examples:global_fitting_motofit: Macintosh HD1H+a/global_fitting_motofitsNq examples1M^a/(dBA ^Macintosh HD:Users:anz:Documents:Andy:programming:refnx:examples:global_fitting_motofit.global_fitting_motofit Macintosh HDJUsers/anz/Documents/Andy/programming/refnx/examples/global_fitting_motofit/ motoMPIawds1Macintosh HD:Users:anz:Documents:Andy:programming:refnx:examples:global_fitting_motofit: Macintosh HD1H+a/global_fitting_motofitsNq examples1M^a/(dBA ^Macintosh HD:Users:anz:Documents:Andy:programming:refnx:examples:global_fitting_motofit.global_fitting_motofit Macintosh HDJUsers/anz/Documents/Andy/programming/refnx/examples/global_fitting_motofit/ RecentWindowsGlobal Reflectometry AnalysisMOTOFIT_globalreflectometry.ipfReflectivityReflectivity PanelScattering length densityTable0: 4Misc_EndXOPState_StartData Browser PGizmo anz ectometry.ipfRePeakFunctions2ctivity PanelScAbelesg len` ble0:base64  HDF5(d SOCKIT b@p.XMLutils^b;'.rsrcZIPdy/programming/rGenCurvefit  ng_motInterpolate@ gb`.jobtieasyHttp` 4XOPState_EndV_FlagV_fiterrorV_gausspoints1@bloadMotofitPackage() plotCalcref() Motofit_GR#init_fitting() Motofit_GR#init_fitting() Motofit_GR#init_fitting() Motofit_GR#init_fitting() Motofit_GR#init_fitting() _________________________________________________________________ Global Fitting e361r_R vs e361r_q w[0] = 2.000000 +/- 0 w[1] = 1.000000 +/- 0 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = 6.411011 +/- 0.00241586 w[5] = 0.000000 +/- 0 w[6] = 0.000013 +/- 4.91189e-07 w[7] = 4.000000 +/- 0 w[8] = 10.649409 +/- 0.293464 w[9] = 3.470000 +/- 0 w[10] = 0.000000 +/- 0 w[11] = 3.000000 +/- 0 w[12] = 212.174845 +/- 0.19969 w[13] = 0.462346 +/- 0.0144887 w[14] = 0.000000 +/- 0 w[15] = 3.000000 +/- 0 _________________________________________________________________ _________________________________________________________________ Global Fitting e365r_R vs e365r_q w[0] = 2.000000 +/- 0 w[1] = 0.899760 +/- 0.0318868 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = 3.572482 +/- 0.0230132 w[5] = 0.000000 +/- 0 w[6] = 0.000016 +/- 3.82877e-07 w[7] = 4.000000 +/- 0 w[8] = 10.649409 +/- 0.293464 w[9] = 3.470000 +/- 0 w[10] = 0.000000 +/- 0 w[11] = 3.000000 +/- 0 w[12] = 212.174845 +/- 0.19969 w[13] = 0.382092 +/- 0.0418078 w[14] = 0.000000 +/- 0 w[15] = 3.000000 +/- 0 _________________________________________________________________ _________________________________________________________________ Global Fitting e366r_R vs e366r_q w[0] = 2.000000 +/- 0 w[1] = 0.894982 +/- 0.0712748 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = -0.551582 +/- 0.113992 w[5] = 0.000000 +/- 0 w[6] = 0.000016 +/- 4.32547e-07 w[7] = 4.000000 +/- 0 w[8] = 10.649409 +/- 0.293464 w[9] = 3.470000 +/- 0 w[10] = 0.000000 +/- 0 w[11] = 3.000000 +/- 0 w[12] = 212.174845 +/- 0.19969 w[13] = 0.176707 +/- 0.0828097 w[14] = 0.000000 +/- 0 w[15] = 3.000000 +/- 0 _________________________________________________________________ _________________________________________________________________ Global Fitting e361r_R vs e361r_q w[0] = 2.000000 +/- 0 w[1] = 1.000000 +/- 0 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = 6.357716 +/- 0.00463148 w[5] = 0.000000 +/- 0 w[6] = 0.000013 +/- 4.77725e-07 w[7] = 4.000000 +/- 0 w[8] = 9.835674 +/- 0.266634 w[9] = 3.470000 +/- 0 w[10] = 0.000000 +/- 0 w[11] = 3.000000 +/- 0 w[12] = 211.781262 +/- 0.190209 w[13] = 0.383379 +/- 0.015601 w[14] = 0.000000 +/- 0 w[15] = 3.000000 +/- 0 _________________________________________________________________ _________________________________________________________________ Global Fitting e365r_R vs e365r_q w[0] = 2.000000 +/- 0 w[1] = 1.000000 +/- 0 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = 3.481883 +/- 0.010562 w[5] = 0.000000 +/- 0 w[6] = 0.000017 +/- 4.05502e-07 w[7] = 4.000000 +/- 0 w[8] = 9.835674 +/- 0.266634 w[9] = 3.470000 +/- 0 w[10] = 0.000000 +/- 0 w[11] = 3.000000 +/- 0 w[12] = 211.781262 +/- 0.190209 w[13] = 0.423765 +/- 0.0167166 w[14] = 0.000000 +/- 0 w[15] = 3.000000 +/- 0 _________________________________________________________________ _________________________________________________________________ Global Fitting e366r_R vs e366r_q w[0] = 2.000000 +/- 0 w[1] = 1.000000 +/- 0 w[2] = 2.070000 +/- 0 w[3] = 0.000000 +/- 0 w[4] = -0.560000 +/- 0 w[5] = 0.000000 +/- 0 w[6] = 0.000016 +/- 4.05176e-07 w[7] = 4.000000 +/- 0 w[8] = 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O![?QĖ]x:`(L@Z#Az_n'7ۉ W兠AFTͺ_1?#?/p?K?OD奇(HǶ&=xz(/cDE*Z\Ya ^x[\}?t0zW*2^E?$v!X?bQU=տwzL-9ӾckjNkb`7zU0~%'^?%*ڕ쿻ɨߚѿ _W?Gcᰙ@ҿonY@H/O?#?&2Yx?)?xLO?UE?hЩ?7C4? 5 @0N&@[?yȿ}?#i? old_genoptimise@ 3 !iterations@@popsize$@recomb?k_mffffff?fittol?GCF_continue?,hƈtƈt thosebeingvaried ????g!@@@ A0A`ApAAAA  ƈtƈt(limitsdialog_selwave ????h@@@@@@@@@@@@@@@@@@@@kƈtЈt(limitsdialog_listwave ????i 457101114151619206.361.3314e-0510.649212.170.462353.57251.5827e-050.382091.5511e-050.1767160000000006.362.6628e-0521.298424.340.92477.1453.1654e-050.764183.1022e-050.35342Parametercoeflower_limupper_lim %+28BISZ[\]^_`abcdhrx~awdtՈtlimitsForThoseBeingVaried ????j@@2_7NbA+C$l?ף@98MC?8w>oՈtՈt*GENcurvefitlimits????k@ p@@>IL5@pz@$?@z@ ?)t?@C?n? WM_WaveSelectorList@ 3 ! *5// Platform=Macintosh, IGORVersion=6.370, architecture=Intel, systemTextEncoding="macintosh", historyTextEncoding="macintosh", procwinTextEncoding="macintosh" Silent 101 // use | as bitwise or -- not comment. NewPath/Z motoMPI "Macintosh HD:Users:anz:Documents:Andy:programming:refnx:examples:global_fitting_motofit:" DefaultFont "Geneva" MoveWindow/P 5,44,505,339 MoveWindow/C 2,860,1846,1074 Table0() SLDgraph() reflectivitygraph() globalreflectometrypanel() reflectivitypanel() KillStrings/Z root:gWMSetNextTextFilesTextEncoding Window reflectivitypanel() : Panel PauseUpdate; Silent 1 // building window... NewPanel /K=1 /W=(873,44,1464,628) as "Reflectivity Panel" ModifyPanel cbRGB=(43520,43520,43520) ListBox baseparams_tab0,pos={22,266},size={205,72},proc=motofit#moto_GUI_listbox ListBox baseparams_tab0,fSize=12 ListBox baseparams_tab0,listWave=root:packages:motofit:reflectivity:baselayerparams ListBox baseparams_tab0,selWave=root:packages:motofit:reflectivity:baselayerparams_selwave ListBox baseparams_tab0,mode= 6,editStyle= 2,widths={80,80,20} ListBox layerparams_tab0,pos={22,341},size={544,197},proc=motofit#moto_GUI_listbox ListBox layerparams_tab0,fSize=12 ListBox layerparams_tab0,listWave=root:packages:motofit:reflectivity:layerparams ListBox layerparams_tab0,selWave=root:packages:motofit:reflectivity:layerparams_selwave ListBox layerparams_tab0,mode= 5,editStyle= 2 ListBox layerparams_tab0,widths={60,60,21,60,21,60,21,60,21} CheckBox usemultilayer_tab0,pos={407,308},size={116,16},proc=motofit#moto_GUI_check,title="make multilayer?" CheckBox usemultilayer_tab0,fSize=12,value= 0 PopupMenu coefwave_tab0,pos={236,270},size={176,20},bodyWidth=139,proc=motofit#moto_GUI_PopMenu,title="Model" PopupMenu coefwave_tab0,fSize=12 PopupMenu coefwave_tab0,mode=1,popvalue="coef_theoretical_R",value= #"motofit#moto_useable_coefs()" ValDisplay Chisquare_tab0,pos={252,304},size={132,20},title="\\F'Symbol'c\\M\\S2" ValDisplay Chisquare_tab0,fSize=14,fStyle=0,limits={0,0,0},barmisc={0,1000} ValDisplay Chisquare_tab0,value= _NUM:1 Button Savecoefwave_tab0,pos={421,265},size={68,31},proc=motofit#moto_GUI_button,title="Save" Button Savecoefwave_tab0,fSize=12 Button loadcoefwave_tab0,pos={495,265},size={62,30},proc=motofit#moto_GUI_button,title="Load" Button loadcoefwave_tab0,fSize=12 GroupBox group0_tab0,pos={14,28},size={555,74},title="Dataset" GroupBox group1_tab0,pos={14,107},size={554,48},title="Plotting" GroupBox group2_tab0,pos={16,160},size={554,80},title="Fitting" GroupBox group3_tab0,pos={16,245},size={554,297},title="Model" Slider slider0_tab0,pos={8,544},size={564,16},proc=motofit#moto_GUI_slider Slider slider0_tab0,help={"adjust a parameter by moving the slider"} Slider slider0_tab0,userdata(whichparam)= A"Ch[s4G@>Z+/TPcJF?4AL@r#LcATKnLDffo0BllCVAS,ai@ruF.BlnV]@UX=hCghU#Ec>H-@;U'IEc6.R0frl`Cb7@" Slider slider0_tab0,fSize=12,fColor=(43690,43690,43690) Slider slider0_tab0,valueColor=(43690,43690,43690) Slider slider0_tab0,limits={0.5,1.5,0.1},variable= root:data:theoretical:V_Flag,vert= 0,ticks= 0 Button loaddatas_tab0,pos={32,50},size={108,43},proc=motofit#moto_GUI_button,title="\\f04l\\f00oad data" Button loaddatas_tab0,fColor=(65280,32512,16384) PopupMenu dataset_tab0,pos={163,62},size={192,20},bodyWidth=145,proc=motofit#moto_GUI_PopMenu,title="dataset" PopupMenu dataset_tab0,fSize=12 PopupMenu dataset_tab0,mode=2,popvalue="_none_",value= #"motofit#Moto_fittable_datasets()" Button Savefitwave_tab0,pos={382,56},size={167,31},proc=motofit#moto_GUI_button,title="Save fits" Button Savefitwave_tab0,fSize=12 PopupMenu plotype_tab0,pos={24,129},size={133,20},bodyWidth=104,proc=motofit#moto_GUI_PopMenu,title="type" PopupMenu plotype_tab0,help={"you can change the plot type to whatever you want."} PopupMenu plotype_tab0,fSize=12 PopupMenu plotype_tab0,mode=1,popvalue="logR vs Q",value= #"\"logR vs Q;R vs Q;RQ^4 vs Q;RQ^2 vs Q\"" SetVariable res_tab0,pos={199,129},size={160,19},proc=motofit#moto_GUI_setvariable,title="resolution dq/q %" SetVariable res_tab0,help={"Enter the resolution, dq/q in terms of a percentage. Use dq/q=0 to start with"} SetVariable res_tab0,fSize=12,limits={0,20,0.5},value= _NUM:5,live= 1 Button Addcursor_tab0,pos={430,123},size={79,29},proc=motofit#moto_GUI_button,title="Add cursor" Button Addcursor_tab0,fSize=12 Button Dofit_tab0,pos={30,182},size={111,48},proc=motofit#moto_GUI_button,title="Do \\f04f\\f00it" Button Dofit_tab0,help={"Performs the fit"},fColor=(65280,32512,16384) PopupMenu Typeoffit_tab0,pos={147,195},size={150,20},bodyWidth=150 PopupMenu Typeoffit_tab0,mode=4,popvalue="Genetic+MC_Analysis",value= #"\"Genetic;Levenberg-Marquardt;Genetic + LM;Genetic+MC_Analysis\"" CheckBox usedQwave_tab0,pos={307,187},size={99,16},proc=motofit#moto_GUI_check,title="use dQ wave?" CheckBox usedQwave_tab0,fSize=12,value= 1 CheckBox useerrors_tab0,pos={307,205},size={111,16},proc=motofit#moto_GUI_check,title="use error wave?" CheckBox useerrors_tab0,fSize=12,value= 1 CheckBox fitcursors_tab0,pos={423,205},size={140,16},proc=motofit#moto_GUI_check,title="Fit between cursors?" CheckBox fitcursors_tab0,help={"To get the cursors on the graph press Ctrl-I. This enables you to fit over a selected x-range"} CheckBox fitcursors_tab0,fSize=12,value= 0 CheckBox useconstraint_tab0,pos={423,188},size={137,16},proc=motofit#moto_GUI_check,title="Fit with constraints?" CheckBox useconstraint_tab0,fSize=12,value= 0 SetVariable FT_lowQ_tab2,pos={73,53},size={142,19},bodyWidth=60,disable=1,proc=motofit#moto_GUI_setvariable,title="low Q for FFT" SetVariable FT_lowQ_tab2,fSize=12,limits={0.005,1,0.01},value= _NUM:0.005 SetVariable FT_hiQ_tab2,pos={68,77},size={147,19},bodyWidth=60,disable=1,proc=motofit#moto_GUI_setvariable,title="high Q for FFT" SetVariable FT_hiQ_tab2,fSize=12,limits={0.005,1,0.01},value= _NUM:0.5 SetVariable fringe_tab2,pos={255,77},size={213,19},disable=1,title="layer thickness spacing" SetVariable fringe_tab2,fSize=12,limits={0,0,0},value= _NUM:0 SetVariable numfringe_tab2,pos={281,53},size={193,19},disable=1,proc=motofit#moto_GUI_setvariable,title="number of fringes" SetVariable numfringe_tab2,fSize=12,limits={0,100,1},value= _NUM:0 Button Addconstraint_tab1,pos={36,52},size={119,29},disable=1,proc=motofit#moto_GUI_button,title="Add constraint" Button Addconstraint_tab1,fSize=10 Button removeconstraint_tab1,pos={36,95},size={119,30},disable=1,proc=motofit#moto_GUI_button,title="Remove constraint" Button removeconstraint_tab1,fSize=10 Button allon_tab3,pos={300,68},size={100,20},disable=1,proc=motofit#moto_GUI_button,title="toggle on" Button alloff_tab3,pos={413,68},size={100,20},disable=1,proc=motofit#moto_GUI_button,title="toggle off" ListBox plot_tab3,pos={26,96},size={535,345},disable=1,proc=motofit#moto_GUI_listbox ListBox plot_tab3,listWave=root:packages:motofit:reflectivity:plot_listwave ListBox plot_tab3,selWave=root:packages:motofit:reflectivity:plot_selwave ListBox plot_tab3,mode= 5,userColumnResize= 1 TabControl refpanel,pos={3,1},size={575,571},proc=motofit#moto_GUI_tab TabControl refpanel,tabLabel(0)="Fit",tabLabel(1)="Constraints" TabControl refpanel,tabLabel(2)="thickness estimation" TabControl refpanel,tabLabel(3)="plot control",value= 0 SetWindow kwTopWin,hook(moto_GUI)=moto_GUI_hook String fldrSav0= GetDataFolder(1) SetDataFolder root:packages:motofit:reflectivity:ft: Display/W=(0.1,0.3,0.9,0.9)/HOST=# /HIDE=1 fftoutput SetDataFolder fldrSav0 RenameWindow #,FFToutput SetActiveSubwindow ## EndMacro Window globalreflectometrypanel() : Panel PauseUpdate; Silent 1 // building window... NewPanel /K=1 /W=(406,356,962,1012) as "Global Reflectometry Analysis" TabControl globalpaneltab,pos={5,7},size={544,573},proc=Motofit_GR#globalpanel_GUI_tab TabControl globalpaneltab,tabLabel(0)="Datasets",tabLabel(1)="Coefficients" TabControl globalpaneltab,value= 1 Button adddataset_tab0,pos={20,35},size={72,31},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="Add\rdataset" Button adddataset_tab0,fSize=11 Button removedataset_tab0,pos={97,35},size={72,31},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="Remove\rdataset" Button removedataset_tab0,fSize=11 Button changelayers_tab0,pos={174,35},size={72,31},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="change\rlayers" Button changelayers_tab0,fSize=11 Button linkparameter_tab0,pos={326,37},size={100,30},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="link selection" Button linkparameter_tab0,fSize=11 Button unlinkparameter_tab0,pos={434,37},size={100,30},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="unlink selection" Button unlinkparameter_tab0,fSize=11 ListBox datasetparams_tab0,pos={17,72},size={526,499},disable=1,proc=Motofit_GR#globalpanel_GUI_listbox ListBox datasetparams_tab0,fSize=12,frame=3 ListBox datasetparams_tab0,listWave=root:packages:motofit:reflectivity:globalfitting:datasets_listwave ListBox datasetparams_tab0,selWave=root:packages:motofit:reflectivity:globalfitting:datasets_selwave ListBox datasetparams_tab0,colorWave=root:packages:motofit:reflectivity:globalfitting:M_colors ListBox datasetparams_tab0,mode= 10,widths={164,156,103,217},userColumnResize= 1 ListBox coefficients_tab1,pos={17,72},size={526,499},proc=Motofit_GR#globalpanel_GUI_listbox ListBox coefficients_tab1,fSize=11 ListBox coefficients_tab1,listWave=root:packages:motofit:reflectivity:globalfitting:coefficients_listwave ListBox coefficients_tab1,selWave=root:packages:motofit:reflectivity:globalfitting:coefficients_selwave ListBox coefficients_tab1,mode= 6,widths={60},userColumnResize= 1 ListBox coefficients_tab1,clickEventModifiers= 4 Button do_global_fit,pos={184,600},size={80,40},proc=Motofit_GR#globalpanel_GUI_button,title="Fit" Button do_global_fit,fSize=12 Button simulate,pos={276,600},size={80,40},proc=Motofit_GR#globalpanel_GUI_button,title="Simulate" Button simulate,fSize=12 Button savesetup_tab0,pos={451,600},size={70,20},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="Save setup" Button savesetup_tab0,fSize=10 Button loadsetup_tab0,pos={451,622},size={70,20},disable=1,proc=Motofit_GR#globalpanel_GUI_button,title="Load setup" Button loadsetup_tab0,fSize=10 Slider slider0_tab1,pos={22,589},size={517,16},proc=Motofit_GR#globalpanel_GUI_slider Slider slider0_tab1,userdata(whichparam)= "row-4;col-5" Slider slider0_tab1,limits={-1,1,0.2},value= 0,vert= 0,ticks= 0 ValDisplay Chi2_tab1,pos={223,42},size={100,17},title="\\F'Symbol'c\\M\\S2" ValDisplay Chi2_tab1,fSize=12,limits={0,0,0},barmisc={0,1000} ValDisplay Chi2_tab1,value= _NUM:3.78398563933925 EndMacro Window reflectivitygraph() : Graph PauseUpdate; Silent 1 // building window... String fldrSav0= GetDataFolder(1) SetDataFolder root:data:e361r: Display /W=(10,44,560,342)/K=1 ::theoretical:theoretical_R vs ::theoretical:theoretical_q as "Reflectivity" AppendToGraph e361r_R vs e361r_q AppendToGraph ::e365r:e365r_R vs ::e365r:e365r_q AppendToGraph ::e366r:e366r_R vs ::e366r:e366r_q AppendToGraph fit_e361r_R vs fit_e361r_q AppendToGraph ::e365r:fit_e365r_R vs ::e365r:fit_e365r_q AppendToGraph ::e366r:fit_e366r_R vs ::e366r:fit_e366r_q SetDataFolder fldrSav0 ModifyGraph mode(e361r_R)=3,mode(e365r_R)=3,mode(e366r_R)=3 ModifyGraph marker(theoretical_R)=8,marker(e361r_R)=8,marker(e365r_R)=8,marker(e366r_R)=8 ModifyGraph lSize(theoretical_R)=2 ModifyGraph rgb(theoretical_R)=(0,0,0),rgb(e361r_R)=(18724,65535,0),rgb(e365r_R)=(0,43690,65535) ModifyGraph rgb(e366r_R)=(65535,34327,0),rgb(fit_e361r_R)=(18724,65535,0),rgb(fit_e365r_R)=(0,43690,65535) ModifyGraph rgb(fit_e366r_R)=(65535,34327,0) ModifyGraph fSize=12 ModifyGraph standoff(left)=0 Label left "R" Label bottom "Q (\\S-1\\M)" ErrorBars/T=0 e361r_R Y,wave=(:data:e361r:e361r_E,:data:e361r:e361r_E) ErrorBars/T=0 e365r_R Y,wave=(:data:e365r:e365r_E,:data:e365r:e365r_E) ErrorBars/T=0 e366r_R Y,wave=(:data:e366r:e366r_E,:data:e366r:e366r_E) ControlBar 50 PopupMenu plotype_tab0,pos={167,7},size={143,20},bodyWidth=100,proc=motofit#moto_GUI_PopMenu,title="Plot type" PopupMenu plotype_tab0,mode=1,popvalue="logR vs Q",value= #"\"logR vs Q;R vs Q;RQ^4 vs Q;RQ^2 vs Q\"" Button Autoscale,pos={9,5},size={73,24},proc=motofit#moto_GUI_button,title="Autoscale" Button Autoscale,fSize=10 Button ChangeQrange,pos={87,5},size={73,24},proc=motofit#moto_GUI_button,title="Q range" Button ChangeQrange,fSize=10 Button Snapshot,pos={320,5},size={73,24},proc=motofit#moto_GUI_button,title="snapshot" Button Snapshot,fSize=10 Button restore,pos={395,5},size={73,24},proc=motofit#moto_GUI_button,title="restore" Button restore,fSize=10 Button refreshdata,pos={472,5},size={73,24},proc=motofit#moto_GUI_button,title="refresh" Button refreshdata,fSize=10 CheckBox appendresiduals,pos={10,32},size={100,15},proc=motofit#moto_GUI_check,title="Append residuals" CheckBox appendresiduals,fSize=10,value= 0 EndMacro Window SLDgraph() : Graph PauseUpdate; Silent 1 // building window... String fldrSav0= GetDataFolder(1) SetDataFolder root:data:theoretical: Display /W=(10,365,560,591)/K=1 sld_theoretical_R,::e361r:SLD_e361r_R,::e365r:SLD_e365r_R as "Scattering length density" AppendToGraph ::e366r:SLD_e366r_R SetDataFolder fldrSav0 ModifyGraph lSize=2 ModifyGraph rgb(sld_theoretical_R)=(0,0,0),rgb(SLD_e361r_R)=(18724,65535,0),rgb(SLD_e365r_R)=(0,43690,65535) ModifyGraph rgb(SLD_e366r_R)=(65535,34327,0) ModifyGraph fSize=12 Label left "SLD (10\\S-6\\M \\S-2\\M)" Label bottom "distance from interface ()" EndMacro Window Table0() : Table PauseUpdate; Silent 1 // building window... Edit/W=(5,44,510,251) ModifyTable format=1 EndMacro o#pragma rtGlobals=3 // Use modern global access method and strict wave access. #include "MOTOFIT_all_at_once" refnx-0.1.53/examples/global_fitting_motofit/README000066400000000000000000000002031477046072400221530ustar00rootroot00000000000000This example script takes a set of output files from the corefinement setup of Motofit (MotoMPI) and runs it with the refnx packagerefnx-0.1.53/examples/global_fitting_motofit/e361r.txt000066400000000000000000000063741477046072400227130ustar00rootroot000000000000000.010109 1.0034 0.0094945 0.00050463 0.01065 0.97225 0.0086708 0.00053146 0.011192 0.98372 0.0083194 0.00055644 0.011733 1.0342 0.0082585 0.00058513 0.012275 1.0154 0.0076366 0.00061382 0.012997 0.99216 0.0070227 0.00064589 0.013539 1.0046 0.0067381 0.00067643 0.014261 0.99416 0.006279 0.00071221 0.014983 0.67153 0.0048674 0.00074614 0.015705 0.35359 0.0033644 0.00078377 0.016607 0.22776 0.0025849 0.00082294 0.01751 0.15822 0.0019988 0.00087322 0.018593 0.10422 0.0015267 0.00092319 0.019495 0.076868 0.0012544 0.00097347 0.020579 0.055009 0.0010107 0.0010234 0.021481 0.040873 0.00082455 0.0010737 0.022564 0.029023 0.00066089 0.0011237 0.024008 0.018142 0.0004868 0.0011989 0.025633 0.011538 0.00036445 0.0012739 0.027077 0.0076838 0.00028001 0.0013492 0.028521 0.0057328 0.00022902 0.0014226 0.029965 0.0041516 0.00013134 0.0014978 0.03159 0.0034595 9.9397e-05 0.0015746 0.033034 0.0031528 9.0775e-05 0.001648 0.034478 0.0029971 8.4669e-05 0.0017233 0.036103 0.00316 8.3229e-05 0.0017983 0.037547 0.0031109 7.9381e-05 0.0018735 0.038991 0.0030434 7.5547e-05 0.0019488 0.040615 0.003049 7.2983e-05 0.0020237 0.042059 0.0029684 6.9403e-05 0.002099 0.043503 0.0027884 6.4658e-05 0.0021724 0.045128 0.0024531 5.8821e-05 0.0022492 0.046572 0.0021525 5.3315e-05 0.0023245 0.048016 0.0017791 4.6874e-05 0.0023997 0.04946 0.0014628 4.1274e-05 0.0024713 0.051085 0.0011376 3.5329e-05 0.0025481 0.052529 0.00088726 3.0275e-05 0.0026233 0.053973 0.00069964 2.6122e-05 0.0026986 0.055597 0.00047966 2.0985e-05 0.0027754 0.057041 0.00033789 1.7035e-05 0.002847 0.058485 0.00022997 1.3635e-05 0.0029222 0.06011 0.00017335 1.1489e-05 0.002999 0.061554 0.0001763 1.1306e-05 0.0030743 0.062998 0.00017343 1.096e-05 0.0031495 0.064622 0.00022163 1.2167e-05 0.0032245 0.066066 0.00022976 1.2117e-05 0.0032979 0.06751 0.00024547 1.2264e-05 0.0033731 0.069135 0.00028256 1.2876e-05 0.0034499 0.070579 0.00032159 1.3489e-05 0.0035233 0.072023 0.00031782 1.3131e-05 0.0035986 0.073467 0.00033734 1.3304e-05 0.003672 0.075091 0.00033519 1.3005e-05 0.0037488 0.076535 0.00031096 1.2254e-05 0.003824 0.077979 0.00027131 1.1229e-05 0.0038975 0.079604 0.0002403 1.0362e-05 0.0039742 0.081047 0.00018693 8.9476e-06 0.0040495 0.082491 0.00016258 8.1858e-06 0.0041229 0.084477 0.00010162 6.2806e-06 0.0042231 0.086462 6.5975e-05 4.9174e-06 0.0043234 0.088628 4.7223e-05 4.0454e-06 0.0044251 0.090433 3.9405e-05 3.6043e-06 0.004522 0.092599 3.7471e-05 3.4425e-06 0.0046238 0.094584 4.4874e-05 3.7049e-06 0.004724 0.096569 6.2564e-05 4.3059e-06 0.0048242 0.098555 8.3657e-05 4.8985e-06 0.0049226 0.10054 8.8552e-05 4.9385e-06 0.0050228 0.10253 9.7242e-05 5.0707e-06 0.0051231 0.10559 9.9722e-05 4.9951e-06 0.0052751 0.10848 8.3608e-05 4.4476e-06 0.0054219 0.11155 6.1624e-05 3.7079e-06 0.0055739 0.11462 4.2962e-05 3.0089e-06 0.0057259 0.1175 3.238e-05 2.5415e-06 0.0058727 0.12057 2.1377e-05 1.9997e-06 0.0060248 0.12346 2.4942e-05 2.1205e-06 0.0061715 0.12653 2.9365e-05 2.2547e-06 0.0063236 0.1296 3.6973e-05 2.4845e-06 0.0064737 0.13248 4.5666e-05 2.7125e-06 0.0066224 0.13555 3.933e-05 2.4552e-06 0.0067744 0.13862 3.6904e-05 2.3321e-06 0.0069245 0.14151 2.7705e-05 1.9706e-06 0.0070732 0.14457 2.3851e-05 1.7881e-06 0.0072233 0.14746 1.693e-05 1.4673e-06 0.0073738 0.15053 1.4813e-05 1.3432e-06 0.0075221 0.15359 2.0346e-05 1.5527e-06 0.0076741refnx-0.1.53/examples/global_fitting_motofit/e361r_pilot.txt000066400000000000000000000003551477046072400241130ustar00rootroot00000000000000stuff value hold lowlim hilim smearedabeles log10chisquared 2 1 0 0 1 1 0 0 2.07 1 0 0 0 1 0 0 6.3024 0 6.0 6.36 0 0 0 0 1.3798e-05 0 0 6e-05 4 1 0 0 10.208 0 0 30 3.47 1 0 0 0 0 0 0 3 1 0 0 212.71 0 0 420 0.44599 0 0 1.3 0 0 0 0 3 1 0 0refnx-0.1.53/examples/global_fitting_motofit/e365r.txt000066400000000000000000000051601477046072400227070ustar00rootroot000000000000000.0099283 0.30595 0.0040195 0.00049641 0.010289 0.26833 0.0029101 0.00051446 0.011011 0.19821 0.0022815 0.00055057 0.011372 0.17949 0.0020772 0.00056862 0.012094 0.13725 0.0016884 0.00060472 0.012997 0.1036 0.0013326 0.00064985 0.01408 0.077181 0.0010638 0.000704 0.014983 0.057562 0.0008426 0.00074913 0.016066 0.043969 0.0006914 0.00080329 0.016968 0.032673 0.00055253 0.00084841 0.019134 0.017386 0.0003556 0.00095672 0.02112 0.0085938 0.00022338 0.001056 0.023106 0.0042309 0.00014211 0.0011553 0.025091 0.00195 8.7889e-05 0.0012546 0.027077 0.00081153 5.1717e-05 0.0013538 0.029063 0.00036616 3.1872e-05 0.0014531 0.031048 0.00034338 2.8853e-05 0.0015524 0.033034 0.00051014 3.329e-05 0.0016517 0.035019 0.0007302 3.7709e-05 0.001751 0.037005 0.0009503 4.0907e-05 0.0018503 0.038991 0.0011156 4.2096e-05 0.0019495 0.040976 0.001064 3.9074e-05 0.0020488 0.042962 0.0010034 3.5948e-05 0.0021481 0.045128 0.00087652 3.2201e-05 0.0022564 0.047114 0.00073642 2.8155e-05 0.0023557 0.049099 0.00054692 2.3297e-05 0.002455 0.051085 0.00037025 1.8243e-05 0.0025542 0.05307 0.00024836 1.4376e-05 0.0026535 0.055056 0.00013019 9.9128e-06 0.0027528 0.057041 7.3e-05 7.0576e-06 0.0028521 0.059027 3.01e-05 4.272e-06 0.0029513 0.061012 3.66e-05 4.5898e-06 0.0030506 0.062998 3.5e-05 4.3483e-06 0.0031499 0.064983 7.8e-05 6.4093e-06 0.0032492 0.066969 9.91e-05 7.053e-06 0.0033484 0.069135 0.00012223 7.627e-06 0.0034567 0.07112 0.00013512 7.8193e-06 0.003556 0.073106 0.00014595 7.9306e-06 0.0036553 0.075091 0.00013131 7.3031e-06 0.0037546 0.077077 0.0001215 6.8341e-06 0.0038538 0.079062 0.0001047 6.1649e-06 0.0039531 0.081047 7.78e-05 5.1647e-06 0.0040524 0.083033 5.57e-05 4.2534e-06 0.0041516 0.085018 4.19e-05 3.5824e-06 0.0042509 0.087004 2.57e-05 2.7118e-06 0.0043502 0.088989 1.82e-05 2.2191e-06 0.0044495 0.090974 1.66e-05 2.0659e-06 0.0045487 0.09296 2.23e-05 2.3695e-06 0.004648 0.095126 3e-05 2.7044e-06 0.0047563 0.097111 2.91e-05 2.6094e-06 0.0048555 0.099096 4.42e-05 3.1919e-06 0.0049548 0.10108 5.35e-05 3.4466e-06 0.0050541 0.10397 4.85e-05 3.1832e-06 0.0051985 0.10704 4.69e-05 3.0394e-06 0.0053519 0.11011 3.8e-05 2.655e-06 0.0055053 0.11299 2.87e-05 2.2402e-06 0.0056496 0.11606 2.35e-05 1.9747e-06 0.005803 0.11913 1.88e-05 1.719e-06 0.0059564 0.12202 1.94e-05 1.703e-06 0.0061008 0.12508 2.69e-05 1.9756e-06 0.0062542 0.12797 2.29e-05 1.7791e-06 0.0063986 0.13104 2.56e-05 1.8421e-06 0.006552 0.13411 2.9e-05 1.9153e-06 0.0067053 0.13699 2.99e-05 1.9078e-06 0.0068497 0.14006 2.51e-05 1.7197e-06 0.0070031 0.14313 2.25e-05 1.583e-06 0.0071565 0.14602 1.96e-05 1.4547e-06 0.0073008 0.14908 1.89e-05 1.3971e-06 0.0074542 0.15197 2.38e-05 1.5485e-06 0.0075985refnx-0.1.53/examples/global_fitting_motofit/e365r_pilot.txt000066400000000000000000000003521477046072400241140ustar00rootroot00000000000000stuff value hold lowlim hilim smearedabeles log10chisquared 2 0 0 0 1 1 0 0 2.07 0 0 0 0 0 0 0 3.374 0 0 6.94 0 0 0 0 1.6413e-05 0 0 6e-05 4 0 0 0 10.208 0 0 30 3.47 0 0 0 0 0 0 0 3 0 0 0 212.71 0 0 420 0.44076 0 0 1.3 0 0 0 0 3 0 0 0refnx-0.1.53/examples/global_fitting_motofit/e366r.txt000066400000000000000000000051771477046072400227200ustar00rootroot000000000000000.0099283 0.015845 0.00093441 0.00049641 0.010289 0.012501 0.00064038 0.00051446 0.011011 0.010141 0.00052443 0.00055057 0.011372 0.0092412 0.00047856 0.00056862 0.012094 0.0082654 0.0004178 0.00060472 0.012997 0.0056552 0.00031192 0.00064985 0.01408 0.0045741 0.00025885 0.000704 0.014983 0.0040526 0.00022318 0.00074913 0.016066 0.0036273 0.00019553 0.00080329 0.016968 0.00373 0.0001866 0.00084841 0.019134 0.002566 0.00013591 0.00095672 0.02112 0.0028301 0.00012829 0.001056 0.023106 0.0023669 0.00010651 0.0011553 0.025091 0.0022101 9.3974e-05 0.0012546 0.027077 0.0015573 7.2677e-05 0.0013538 0.029063 0.0013097 6.2009e-05 0.0014531 0.031048 0.00089022 4.732e-05 0.0015524 0.033034 0.00059942 3.6374e-05 0.0016517 0.035019 0.00050994 3.16e-05 0.001751 0.037005 0.00030359 2.282e-05 0.0018503 0.038991 0.0002169 1.8213e-05 0.0019495 0.040976 0.00014288 1.3992e-05 0.0020488 0.042962 0.00010078 1.1069e-05 0.0021481 0.045128 8.83e-05 9.9212e-06 0.0022564 0.047114 9.5e-05 9.8691e-06 0.0023557 0.049099 0.00010321 9.9087e-06 0.002455 0.051085 0.00010571 9.6462e-06 0.0025542 0.05307 0.00011195 9.5508e-06 0.0026535 0.055056 0.00011972 9.5212e-06 0.0027528 0.057041 0.00012241 9.2885e-06 0.0028521 0.059027 9.75e-05 7.9932e-06 0.0029513 0.061012 0.00010275 7.9355e-06 0.0030506 0.062998 8.01e-05 6.7568e-06 0.0031499 0.064983 6.44e-05 5.8373e-06 0.0032492 0.066969 4.36e-05 4.6167e-06 0.0033484 0.069135 4.02e-05 4.3104e-06 0.0034567 0.07112 3.51e-05 3.9135e-06 0.003556 0.073106 2.3e-05 3.0487e-06 0.0036553 0.075091 2.61e-05 3.1695e-06 0.0037546 0.077077 3.31e-05 3.5074e-06 0.0038538 0.079062 3.27e-05 3.4063e-06 0.0039531 0.081047 3.23e-05 3.314e-06 0.0040524 0.083033 2.9e-05 3.0535e-06 0.0041516 0.085018 3.64e-05 3.36e-06 0.0042509 0.087004 3.59e-05 3.2668e-06 0.0043502 0.088989 2.7e-05 2.7546e-06 0.0044495 0.090974 3.22e-05 2.9506e-06 0.0045487 0.09296 3.49e-05 3.0159e-06 0.004648 0.095126 2.65e-05 2.5621e-06 0.0047563 0.097111 2.78e-05 2.5768e-06 0.0048555 0.099096 2.27e-05 2.2814e-06 0.0049548 0.10108 2.18e-05 2.1775e-06 0.0050541 0.10397 2.09e-05 2.0736e-06 0.0051985 0.10704 2.23e-05 2.089e-06 0.0053519 0.11011 2.13e-05 1.9846e-06 0.0055053 0.11299 2.82e-05 2.2387e-06 0.0056496 0.11606 2.07e-05 1.8635e-06 0.005803 0.11913 2.53e-05 2.0174e-06 0.0059564 0.12202 2.45e-05 1.941e-06 0.0061008 0.12508 2.39e-05 1.8745e-06 0.0062542 0.12797 2.19e-05 1.7576e-06 0.0063986 0.13104 2.08e-05 1.671e-06 0.006552 0.13411 2.14e-05 1.6636e-06 0.0067053 0.13699 2.1e-05 1.6069e-06 0.0068497 0.14006 2.41e-05 1.6894e-06 0.0070031 0.14313 2.26e-05 1.6019e-06 0.0071565 0.14602 2.17e-05 1.5344e-06 0.0073008 0.14908 2.2e-05 1.5165e-06 0.0074542 0.15197 2.13e-05 1.4682e-06 0.0075985refnx-0.1.53/examples/global_fitting_motofit/e366r_pilot.txt000066400000000000000000000003511477046072400241140ustar00rootroot00000000000000stuff value hold lowlim hilim smearedabeles log10chisquared 2 0 0 0 1 1 0 0 2.07 0 0 0 0 0 0 0 -0.56 1 -1.0 0 0 0 0 0 1.5481e-05 0 0 6e-05 4 0 0 0 10.208 0 0 30 3.47 0 0 0 0 0 0 0 3 0 0 0 212.71 0 0 420 0.3146 0 0 1.3 0 0 0 0 3 0 0 0refnx-0.1.53/examples/global_fitting_motofit/global_fitting_from_motofit.py000066400000000000000000000313371477046072400274310ustar00rootroot00000000000000#!/usr/bin/env python """ global_fitting_from_motofit.py [OPTIONS] [-- ARGS] Sets and executes up a global fitting environment supplied by Motofit in IGOR. Examples:: $ python global_fitting_from_motofit.py global_pilot_file """ import sys import numbers import time from argparse import ArgumentParser from copy import deepcopy from multiprocessing import Pool import numpy as np from refnx.analysis import (CurveFitter, ReflectivityFitFunction, GlobalFitter, to_parameters, Transform, values, names) from refnx.dataset import ReflectDataset def global_fitter_setup(global_pilot_file, dqvals=5.0): # Parse the global_fitter setup from Igor. # TODO deal with user generated non-slab models. with open(global_pilot_file, 'r') as f: data_files = f.readline().split() pilot_files = f.readline().split() constraints = np.loadtxt(global_pilot_file, skiprows=2, dtype=int) # open the datafiles datasets = [] for data_file in data_files: dataset = ReflectDataset(data_file) datasets.append(dataset) # deal with the individual pilot files parameters = [] for pilot_file in pilot_files: pars = np.loadtxt(pilot_file, skiprows=4) # lets just assume for now that the data has resolution info # and that we're doing a slab model. pv = pars[:, 0][:] varies = (pars[:, 1].astype(int) == 0) # workout bounds, and account for the fact that MotofitMPI # doesn't set bounds for parameters that are fixed bounds = [] for idx in range(np.size(pv)): if not varies[idx]: bounds.append((0, 2 * pv[idx])) else: bounds.append(pars[idx, 2:4]) P = to_parameters(pv, varies=varies, bounds=bounds) parameters.append(P) # now create CurveFitting instances T = Transform('logY') fitters = [] for parameter, dataset in zip(parameters, datasets): t_data_y, t_data_yerr = T.transform(dataset.x, dataset.y, dataset.y_err) if isinstance(dqvals, numbers.Real): _dqvals = float(dqvals) else: _dqvals = dataset.x_err c = CurveFitter(ReflectivityFitFunction(T.transform, workers=True), (dataset.x, t_data_y, t_data_yerr), parameter, fcn_kws={'dqvals': _dqvals}) fitters.append(c) # create globalfitter # setup constraints unique, indices = np.unique(constraints, return_index=True) # TODO assertions for checking linkage integrity n_datasets = len(datasets) def is_unique(row, col): ravelled_idx = row * n_datasets + col return ravelled_idx in indices cons = [] for col in range(n_datasets): for row, val in enumerate(parameters[col]): if constraints[row, col] == -1 or is_unique(row, col): continue # so it's not unique, but which parameter does it depend on? # find location of master parameter master = np.extract(unique == constraints[row, col], indices)[0] m_col = master % n_datasets m_row = (master - m_col) // n_datasets constraint = 'd%u:p%u = d%u:p%u' % (col, row, m_col, m_row) cons.append(constraint) # also have to rejig the bounds because MotoMPI doesn't # set bounds for those that aren't unique. But this is bad for # lmfit because it'll clip them. par = fitters[col].params['p%u' % row] m_par = fitters[m_col].params['p%u' % m_row] par.min = m_par.min par.max = m_par.max global_fitter = GlobalFitter(fitters, constraints=cons) # # update the constraints # global_fitter.params.update_constraints() return global_fitter def _mcmc(args, global_fitter): # sample via Markov Chain Monte Carlo pos = None if args.chain_input is not None: pos = np.load(args.chain_input) # do the sampling chunk_size = 50 n_remaining = args.steps done = 0 sys.stdout.write("----------------------\n") sys.stdout.write("Starting MCMC\n") sys.stdout.write("----------------------\n") start = time.time() reuse_sampler = False while n_remaining > 0: todo = min(chunk_size, n_remaining) res = global_fitter.emcee(nwalkers=args.walkers, steps=todo, ntemps=args.ntemps, burn=0, thin=1, workers=args.nprocesses, pos=pos, reuse_sampler=reuse_sampler) reuse_sampler = True n_remaining -= todo done += todo pos = res.chain # write raw chain in npy format. It is unburnt and unthinned np.save(args.chain_output, res.chain) sys.stdout.write( "{0:^7} steps, {1:^7} seconds\n".format(done, time.time() - start) ) # thin and burn the chain. chain = res.chain[..., args.burn::args.thin, :] res.chain = np.copy(chain) return res def __resample_mc_iterator(args): global_fitter, seed = args gf = deepcopy(global_fitter) np.random.seed(seed) res = gf._resample_mc(1, 'differential_evolution') return res.mc def _resample_mc(args, global_fitter): # do the sampling by Resampling Monte Carlo sys.stdout.write("----------------------\n") sys.stdout.write("Starting resampling MC\n") sys.stdout.write("----------------------\n") start = time.time() # do a single fit first output = global_fitter.fit('differential_evolution') chunksize = 5 * args.nprocesses remaining = args.steps done = 0 mcs = [] with Pool(args.nprocesses) as pool: while remaining > 0: todo = min(remaining, chunksize) # seeding the random number generator seeds = range(done, done + todo) gfs = [global_fitter] * todo results = pool.map(__resample_mc_iterator, zip(gfs, seeds)) mcs.append(results) done += todo remaining -= todo sys.stdout.write("{0:^7} steps, {1:^7} seconds\n".format(done, time.time() - start)) mc = np.squeeze(np.vstack(mcs)) quantiles = np.percentile(mc, [15.87, 50, 84.13], axis=0) params = output.params for i, var_name in enumerate(output.var_names): std_l, median, std_u = quantiles[:, i] params[var_name].value = median params[var_name].stderr = 0.5 * (std_u - std_l) params[var_name].correl = {} params.update_constraints() # work out correlation coefficients corrcoefs = np.corrcoef(mc.T) for i, var_name in enumerate(output.var_names): for j, var_name2 in enumerate(output.var_names): if i != j: output.params[var_name].correl[var_name2] = corrcoefs[i, j] output.mc = mc output.chain = mc output.errorbars = True output.nvarys = len(output.var_names) return output def main(argv): parser = ArgumentParser(usage=__doc__.lstrip()) parser.add_argument("global_pilot_file", help="The name of the global pilot file") parser.add_argument("--walkers", "-w", type=int, default=100, help="How many MCMC walkers? Default=100") parser.add_argument("--steps", "-s", type=int, default=2000, help="How many MCMC steps? Default=2000") parser.add_argument("--ntemps", '-T', type=int, default=1, help="How many parallel tempering temperatures? " "Default=1") parser.add_argument("--burn", "-b", type=int, default=500, help="How many initial MCMC steps do you want to " "burn? Default=500") parser.add_argument("--thin", "-t", type=int, default=20, help="Thins the chain by accepting 1 in every 'thin'. " "Default=20") parser.add_argument("--resample", "-r", default=False, action='store_true', help="Don't do Markov Chain Monte Carlo, do resampling " "MC instead. All MCMC parameters are ignored if " "this option is specified.") parser.add_argument("--qres", "-q", type=float, default=5.0, help="Constant dq/q resolution. Default=5") parser.add_argument("--pointqres", "-p", action="store_true", default=False, help="Use point by point resolution smearing. " "Default=False") parser.add_argument("--chain_input", "-i", type=str, help="Initialise/restart emcee with this RAW chain. " "This file is a numpy array (.npy) that would've " "originally been saved by the --chain_output " "option.") parser.add_argument("--chain_output", "-c", default='raw_chain.npy', type=str, help="Specify filename for unthinned, unburnt RAW " "chain. The file is saved as a numpy (.npy) " "array. You can use this file if you'd like to do " "the burn/thin procedure yourself. You can also" " use this file to restart the sampling. The array" " has shape (walkers, steps, dims), where dims " "represents the number of parameters you are " "varying. If ntemps is > 1 then the array has " "shape (ntemps, walkers, steps, dims). " "Default=raw_chain.npy") parser.add_argument("--output", "-o", type=str, default='iterations', help="Output file for burnt and thinned MCMC chain. " "This is only written once the sampling has " "finished") parser.add_argument("--nprocesses", "-n", type=int, default=1, help="How many processes for parallelisation? Default=1") args = parser.parse_args(argv) # set up global fitting if args.pointqres: dqvals = None else: dqvals = args.qres if args.nprocesses < 1: args.nprocesses = 1 global_fitter = global_fitter_setup(args.global_pilot_file, dqvals=dqvals) # do the Monte Carlo if not args.resample: # By Markov Chain Monte Carlo if args.thin < 1: sys.stdout.write("Can't have thin < 1, setting 'thin' to 1.\n") args.thin = 1 if (args.burn < 0) or (args.burn > args.steps): sys.stdout.write("Can't burn < 0 or burn > steps, setting 'burn' to 1.\n") args.burn = 1 res = _mcmc(args, global_fitter) else: # By resampling res = _resample_mc(args, global_fitter) # write the iterations. _write_results(args.output, res) sys.stdout.write("\nFinished MCMC\n") sys.stdout.write("-------------\n") sys.stdout.write(fit_report(res.params)) sys.stdout.write("\n-----------------------------------------------------\n") def filter_dependent_params(params, output): # filters dependent parameters from the MonteCarlo output # i.e. reject all parameters where expr is not None. independent = [] for idx, param in enumerate(params): if param.expr is not None: independent.append(idx) arr = np.zeros((np.size(output, 0), idx)) arr = output[:, idx] return arr def _write_results(f, emcee_result): # the flatchain is what we're interested in. # make an output array # hopefully the chain has been burned and thinned enough. output = np.zeros((np.size(emcee_result.flatchain, 0), len(emcee_result.params))) gen = pgen(emcee_result.params, emcee_result.flatchain) for row in output: pars = next(gen) row[:] = values(pars)[:] np.savetxt(f, output, header=' '.join(names(emcee_result.params))) def pgen(parameters, flatchain, idx=None): # generator for all the different parameters from a flatchain. if idx is None: idx = range(np.size(flatchain, 0)) for i in idx: for var_name in flatchain.columns: parameters[var_name].value = flatchain.iloc[i][var_name] yield parameters if __name__ == "__main__": main(argv=sys.argv[1:]) refnx-0.1.53/examples/global_fitting_motofit/global_pilot000066400000000000000000000002741477046072400236750ustar00rootroot00000000000000e361r.txt e365r.txt e366r.txt e361r_pilot.txt e365r_pilot.txt e366r_pilot.txt 0 0 0 1 13 17 2 2 2 3 3 3 4 14 18 3 3 3 5 15 19 6 6 6 7 7 7 8 8 8 3 3 3 9 9 9 10 10 10 11 16 20 3 3 3 12 12 12refnx-0.1.53/examples/interactive_fitter/000077500000000000000000000000001477046072400204455ustar00rootroot00000000000000refnx-0.1.53/examples/interactive_fitter/interactive_reflectometry_modeller.ipynb000066400000000000000000000016271477046072400306620ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib qt\n", "from refnx.reflect import Motofit" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "app = Motofit()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "app()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } refnx-0.1.53/examples/mpi_parallelisation.py000066400000000000000000000102011477046072400211470ustar00rootroot00000000000000#!/bin/bash """ Using refnx in a highly parallelised environment using mpi. You'll need to install: - refnx - numpy - cython - schwimmbad - mpi4py Usage ----- mpiexec -n 4 python mpi_parallelisation.py """ # Start off by importing necessary packages import sys import os.path import refnx from schwimmbad import MPIPool from refnx.reflect import SLD, Slab, ReflectModel from refnx.dataset import ReflectDataset from refnx.analysis import (Objective, CurveFitter, Transform, GlobalObjective) def setup(): # load the data. DATASET_NAME = os.path.join(refnx.__path__[0], 'analysis', 'test', 'c_PLP0011859_q.txt') # load the data data = ReflectDataset(DATASET_NAME) # the materials we're using si = SLD(2.07, name='Si') sio2 = SLD(3.47, name='SiO2') film = SLD(2, name='film') d2o = SLD(6.36, name='d2o') structure = si | sio2(30, 3) | film(250, 3) | d2o(0, 3) structure[1].thick.setp(vary=True, bounds=(15., 50.)) structure[1].rough.setp(vary=True, bounds=(1., 6.)) structure[2].thick.setp(vary=True, bounds=(200, 300)) structure[2].sld.real.setp(vary=True, bounds=(0.1, 3)) structure[2].rough.setp(vary=True, bounds=(1, 6)) model = ReflectModel(structure, bkg=9e-6, scale=1.) model.bkg.setp(vary=True, bounds=(1e-8, 1e-5)) model.scale.setp(vary=True, bounds=(0.9, 1.1)) model.threads = 1 # fit on a logR scale, but use weighting objective = Objective(model, data, transform=Transform('logY'), use_weights=True) return objective def structure_plot(obj, samples=0): # plot sld profiles import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111) if isinstance(obj, GlobalObjective): if samples > 0: savedparams = np.array(obj.parameters) for pvec in obj.parameters.pgen(ngen=samples): obj.setp(pvec) for o in obj.objectives: if hasattr(o.model, 'structure'): ax.plot(*o.model.structure.sld_profile(), color="k", alpha=0.01) # put back saved_params obj.setp(savedparams) for o in obj.objectives: if hasattr(o.model, 'structure'): ax.plot(*o.model.structure.sld_profile(), zorder=20) ax.set_ylabel('SLD / $10^{-6}\\AA^{-2}$') ax.set_xlabel("z / $\\AA$") elif isinstance(obj, Objective) and hasattr(obj.model, 'structure'): fig, ax = obj.model.structure.plot(samples=samples) fig.savefig('steps_sld.png', dpi=1000) if __name__ == "__main__": with MPIPool() as pool: if not pool.is_master(): pool.wait() sys.exit(0) # buffering so the program doesn't try to write to the file # constantly with open('steps.chain', 'w', buffering=500000) as f: objective = setup() # Create the fitter and fit fitter = CurveFitter(objective, nwalkers=300) fitter.initialise('prior') fitter.fit('differential_evolution') # thin by 10 so we have a smaller filesize fitter.sample(100, pool=pool.map, f=f, verbose=False, nthin=10); f.flush() try: # create graphs of reflectivity and SLD profiles import matplotlib import matplotlib.pyplot as plt matplotlib.use('agg') fig, ax = objective.plot(samples=1000) ax.set_ylabel('R') ax.set_xlabel("Q / $\\AA$") fig.savefig('steps.png', dpi=1000) structure_plot(objective, samples=1000) # corner plot fig = objective.corner() fig.savefig('steps_corner.png') # plot the Autocorrelation function of the chain fig = plt.figure() ax = fig.add_subplot(111) ax.plot(fitter.acf()) ax.set_ylabel('autocorrelation') ax.set_xlabel('step') fig.savefig('steps-autocorrelation.png') except ImportError: pass refnx-0.1.53/examples/platypus_reduction.ipynb000066400000000000000000000140031477046072400215510ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example Platypus reduction using `refnx`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from refnx.reduce import PlatypusReduce, reduce_stitch\n", "from refnx.dataset import ReflectDataset, Data1D\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_directory = '../refnx/reduce/test/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This command reduces and stitches multiple files together. The\n", "dataset is saved in the current working directory, if `save==True`. Use of `data_folder`\n", "is not necessary if the data is in the current directory. The\n", "first list is a list of the reflected beam files. The second is a\n", "list of the direct beam run files." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataset, fname = reduce_stitch([708, 709, 710],\n", " [711, 711, 711],\n", " data_folder=data_directory,\n", " rebin_percent=3,\n", " save=True)\n", "print(fname)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.errorbar(dataset.x, dataset.y, dataset.y_err)\n", "plt.yscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can reduce files individually. A `PlatypusReduce` object is created with the direct beam run. You need to create different `PlatypusReduce` objects for each direct beam used." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reducer = PlatypusReduce('PLP0000711.nx.hdf', data_folder=data_directory)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_data = reducer.reduce('PLP0000708.nx.hdf', rebin_percent=3, save=True)\n", "# alternatively\n", "reduced_data = reducer('PLP0000708.nx.hdf', rebin_percent=3, save=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`reduced_data` is a tuple. The first entry is a `ReflectDataset`, the second is a dict which contains all the reduced data. Additionally, the files are saved if you use the `save=True` keyword. You can get the filenames from `reduced_data[1]['fname']`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_data[1]['fname']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following items are present in `reduced_data[1]`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_data[1].keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`nspectra` is the number of detector images, N.\n", "\n", "`m_ref` is the 2D offspecular map. `m_ref.shape=(N, T, Y)`. `T` and `Y` are the number of wavelength and y-bins (2theta) respectively.\n", "\n", "`qz` and `qy` are the corresponding Q values for `m_ref`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Batch reduction" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from refnx.reduce import BatchReducer\n", "b = BatchReducer('reduction.xlsx', data_folder=data_directory)\n", "b.reduce()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Event mode reduction\n", "If you wish to reduce event mode data use the following:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduce_event = PlatypusReduce('PLP0011613.nx.hdf',\n", " data_folder=data_directory)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now use the ReducePlatypus object to reduce eventmode data. `eventmode` specifies the timebins for the event mode." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_event = reduce_event('PLP0011641.nx.hdf',\n", " rebin_percent=2,\n", " eventmode=[0, 900, 1800])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_event[1].keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are now two specular reflectivity curves produced, because there were two time bins specified." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(reduced_event[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data is saved in the following files:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "reduced_event[1]['fname']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 } refnx-0.1.53/examples/reduction.xlsx000066400000000000000000000210651477046072400174730ustar00rootroot00000000000000PK! k;[Content_Types].xml (̔J1&m /K'g:sZ۷7V)młnr/2eKHh@9_mӇ5gHked|~6# jo⍔X&$(wLFU ߿uzyWyxCnuQW1ZS+ʠ;u ,.Fے,ؚۄ|4h`*ѣrC|i\ܑԠCpJc @ΊRS_,epbnqG?J́ @Z[vcz(U S',?PK!4o) _rels/.rels ( 0 ;]6"nv>֔|{{t x I~$/yOr~`#!KRd47$e5! oPM~Iz%!='lNnV!C:}}PLQ5\d 귍mnAәc&~Dz"u$,؍w19,>PK!; xl/_rels/workbook.xml.rels (j0 }qne:A[&Q-o?mi %] 7϶vVA 殨m|XB7΢ #T,)0ݓl5EC&PJv:? ek]h}C翵]Y9\~j "Q+dc0YEu%a8gÙ1<,@7E49F{,^هuRLs0ɒ0&[?10?äȋ}PK!KGxl/workbook.xmlSM0W|'HVVvu vMa|=fhiIftz#_Vu:ۜ&.%$f5s/NXpªU\'t%*X*tDGHY7}+Nj~VK)=s^<'Y픦q<蔫:f~?tSf FH_Ɨ wwb L ;H6v;xGP5F-cLO dfC;z[.>.PZ7$6w^PƦjSizo (g6܈e1ZWhJ;L^5A;K~3j[n1^f\kmqH5}"6y7kV, 񳣑 k#jn]^go;]xL2pԽ fJب1M5G[ILHuel}h>b^n`jd7)]j׵d :2mg_s$KE4/ќ-M>!p`aZ隶bk43-Cg1b>)2Β<)e0o@ZٍrﮊRgr'P{ ]ΔDž9P`"hZ Ƈ#fW a6FHP('*,軁˒LDUĕ{N.i h P n$Cw=X. 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KThcg絜;f)J IW՞NKYһaz7oO5C>Æ(ʷ7\LS?sO#<Ω#lY_iIԹΙ{Ž$$Iq9//;%l9KԲTn1)(~Cע WkPK-! k;[Content_Types].xmlPK-!4o) t_rels/.relsPK-!; `xl/_rels/workbook.xml.relsPK-!KGxl/workbook.xmlPK-!6}8& xl/theme/theme1.xmlPK-!a`vxl/worksheets/sheet2.xmlPK-!a`v-xl/worksheets/sheet3.xmlPK-!Vo$uxl/sharedStrings.xmlPK-!ө xl/styles.xmlPK-!xEExl/worksheets/sheet1.xmlPK refnx-0.1.53/examples/referenceAnalysisScript.py000066400000000000000000000031211477046072400217510ustar00rootroot00000000000000import numpy as np import matplotlib.pyplot as plt import matplotlib from refnx.dataset import ReflectDataset from refnx.analysis import CurveFitter, Objective, Transform from refnx.reflect import ReflectModel, SLD matplotlib.pyplot.rcParams['figure.figsize'] = (10.0, 10.0) matplotlib.pyplot.rcParams['figure.dpi'] = 600 DATASET_NAME = 'c_PLP0011859_q.txt' # load the data data = ReflectDataset(DATASET_NAME) si = SLD(2.07, name='Si') sio2 = SLD(3.47, name='SiO2') film = SLD(2, name='film') d2o = SLD(6.36, name='d2o') structure = si | sio2(30, 3) | film(250, 3) | d2o(0, 3) structure[1].thick.setp(vary=True, bounds=(15., 50.)) structure[1].rough.setp(vary=True, bounds=(1., 6.)) structure[2].thick.setp(vary=True, bounds=(200, 300)) structure[2].sld.real.setp(vary=True, bounds=(0.1, 3)) structure[2].rough.setp(vary=True, bounds=(1, 6)) model = ReflectModel(structure, bkg=9e-6, scale=1.) model.bkg.setp(vary=True, bounds=(1e-8, 1e-5)) model.scale.setp(vary=True, bounds=(0.9, 1.1)) # fit on a logR scale, but use weighting objective = Objective(model, data, transform=Transform('logY'), use_weights=True) # create the fit instance fitter = CurveFitter(objective) # do the fit res = fitter.fit(method='differential_evolution') # see the fit results print(objective) fig = plt.figure() ax = fig.add_subplot(2, 1, 1) ax.scatter(data.x, data.y, label=DATASET_NAME) ax.semilogy() ax.plot(data.x, model.model(data.x, x_err=data.x_err), label='fit') plt.xlabel('Q') plt.ylabel('logR') plt.legend() ax2 = fig.add_subplot(2, 1, 2) z, rho_z = structure.sld_profile() ax2.plot(z, rho_z) refnx-0.1.53/examples/reflectometry_analysis.ipynb000066400000000000000000001731611477046072400224160ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "# start off with the necessary imports\n", "import os.path\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import refnx\n", "from refnx.dataset import ReflectDataset\n", "from refnx.analysis import Transform, CurveFitter, Objective\n", "from refnx.reflect import SLD, Slab, ReflectModel" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.1.46.dev0+8a21b67\n" ] } ], "source": [ "# what is the refnx version\n", "# it's import to record this for reproducing the analysis\n", "import refnx\n", "print(refnx.version.version)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# this is a dataset used in refnx testing, distributed with every refnx install\n", "pth = os.path.dirname(refnx.__file__)\n", "\n", "DATASET_NAME = 'c_PLP0011859_q.txt'\n", "\n", "# load the data\n", "data = ReflectDataset(os.path.join(pth, 'analysis/test/', DATASET_NAME))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# set up a series of SLD objects, representing each of the materials\n", "si = SLD(2.07, name='Si')\n", "sio2 = SLD(3.47, name='SiO2')\n", "film = SLD(2.0, name='film')\n", "d2o = SLD(6.36, name='d2o')\n", "\n", "# Slab objects are created from SLD objects in this way\n", "# this creates a native oxide layer\n", "sio2_layer = sio2(30, 3)\n", "# we can set limits on each of the parameters in a slab\n", "sio2_layer.thick.setp(bounds=(15, 50), vary=True)\n", "sio2_layer.rough.setp(bounds=(1, 15), vary=True)\n", "\n", "# create a layer for the layer of interest\n", "film_layer = film(250, 3)\n", "film_layer.thick.setp(bounds=(200, 300), vary=True)\n", "film_layer.sld.real.setp(bounds=(0.1, 3), vary=True)\n", "film_layer.rough.setp(bounds=(1, 15), vary=True)\n", "\n", "# and a layer for the solvent\n", "d2o_layer = d2o(0, 3)\n", "d2o_layer.rough.setp(vary=True, bounds=(1, 15))\n", "\n", "# a Structure is composed from a series of Components. In this\n", "# case all the components are Slab's.\n", "structure = si | sio2_layer | film_layer | d2o_layer" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n" ] } ], "source": [ "# a Slab has the following parameters, which are all accessible as attributes:\n", "# Slab.thick, Slab.sld.real, Slab.sld.imag, Slab.rough\n", "print(sio2_layer.parameters)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# a ReflectModel is made from the Structure.\n", "# ReflectModel calculates smeared reflectivity, applies scaling factor and background\n", "model = ReflectModel(structure, bkg=3e-6)\n", "model.scale.setp(bounds=(0.6, 1.2), vary=True)\n", "model.bkg.setp(bounds=(1e-9, 9e-6), vary=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# an Objective is made from a Model and a Data. Here we use a Transform to fit as logY vs X.\n", "objective = Objective(model, data, transform=Transform('logY'))\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-569.0075570121729: : 50it [00:02, 20.23it/s] \n" ] } ], "source": [ "# CurveFitters do the fitting/sampling\n", "fitter = CurveFitter(objective)\n", "\n", "# do an initial fit with differential evolution\n", "res = fitter.fit('differential_evolution')" ] }, { "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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iIuVFoaacJCdDVpZVUwNQncO4XJBjZXcREREpQwo15eTkgqqeUBPJEQCqV/dXiURERIKbQk05cc8S7W5+cocazSgsIiJSPhRqykliItjtuZufHA7NKCwiIlJeFGrKSVwc3HZb7pqaW2/VkG4REZHyolBTTpxOmD07d03N7Nka/SQiIlJeFGrKSd7RT5EcISsLnn/ezwUTEREJUgo15SQx0VqVO29H4eeeU22NiIhIeVCoKSdxcfDgg9k1NTWwhkNprhoREZHyoVBTju67D1KpBUAtDgJW7Y1GQImIiJQ9hZpytp+6ANThH8AKNSIiIlL2FGrKUXIy7KcOAHXZD1idh9X8JCIiUvYUaspRjRr5a2pASyWIiIiUB4WacpSeDv+crKnJGWq0VIKIiEjZU6gpRzlramqRioNMQDU1IiIi5UGhphylp8PBk6OfAGpzAFBNjYiISHlQqClHNWqAixAOnAw27s7CqqkREREpewo15Sjdmm+PfdQDoD5/A6qpERERKQ8KNeXIHV520giARuwEssOOiIiIlB2FmnL055/W4y4aAtCQXYDmqRERESkPCjXl6IILrMe8NTX79vmrRCIiIsFLoaYcdewI112XP9SMHauVukVERMqaQk056949f6gB+Pxzf5VIREQkOCnUlLPdu72HmpQUf5VIREQkOAVMqBk7dixdunQhMjKSWrVq+bs4PrvyyuxQ05Bd2MgCoFcvf5ZKREQk+ARMqDl+/DjXX389d999t7+LUiwNGlijnzJxEMZxGrDb87qIiIiUnYAJNU888QQPPPAAZ555ps/HZGRkkJaWlmuraEuXQibV+IsEAJqzGYBlyyq8KCIiIkEtYEJNSYwbN47o6GjPFh8fX+Fl2G+tjMBmmgPZoebTTyu8KCIiIkEtqEPNyJEjSU1N9Ww7duyo8DLUtRbpZgvNgOxQ8/bbGtYtIiJSlvwaakaMGIHNZit027BhQ4nPHxYWRlRUVK6tonXpYj26a2qasQWArCzNLCwiIlKWQvz54Q8++CD9+/cvdJ9mzZpVTGHKSVwcPPwwrH86d/MTaLVuERGRsuTXUBMTE0NMTIw/i1AhevSAL57O3fwEWq1bRESkLPk11BTH9u3b+eeff9i+fTsul4vVq1cD0KJFC2rUqOHfwhWhRg3YRAsA6rGf2vzDAeqopkZERKQMBUxH4VGjRtGuXTtGjx5Neno67dq1o127dvzyyy/+LlqRfvkFDlOD7Vijr05jPQArV/qzVCIiIsElYELNa6+9hjEm39atWzd/F61Iu6359ljPaUB2qPnyS3+VSEREJPgETKgJZFdeaT2u43QATmcdYC1qqWHdIiIiZUOhpgJ07AgXXpg/1BijmYVFRETKikJNBbnxxvzNT5A947CIiIiUjkJNBalbNzvUJLCd6qR7XhcREZHSU6ipIJGR8A91SeEUAFphzZQcEeHPUomIiAQPhZoK8uef1qO7tqY1awF4801/lUhERCS4KNRUkAsusB7X0hrI7iw8d65GQImIiJQFhZoK4h4B5Q417poajYASEREpGwo1FejGG2ENZwBwBms8r2sElIiISOkp1FSgunWza2qa8Bc1OOR5XUREREpHoaYCdekCB6jDLhoA2f1qOnf2Z6lERESCg0KNH+Rtgpozx5+lERERCQ4KNRVo6VLr0d0E5Q41w4ZpBJSIiEhpKdT4Qd6aGo2AEhERKT2FmgrUpYv16A417mHdoBFQIiIipaVQU4Hi4mDQoOzVuhuymzpYaUYjoEREREpHoaaCDRgA6dRkGwlAdm1NkyZ+LJSIiEgQUKipYFu3Wo95m6BeecVfJRIREQkOCjV+kncE1LRpGgElIiJSGgo1FSxvZ2GNgBIRESkbCjUVLC4O+vTJ2/xkANi0yX/lEhERCXQKNX5w5pmwgVa4sFOP/ZzCHgAyMvxcMBERkQCmUOMHV14Jx4hgM82B7CaozEx/lkpERCSwKdT4QQNrPct8I6DGj1dnYRERkZJSqPGD5GTrMe8IKJdL/WpERERKSqHGDxITrce8I6AAqlf3R4lEREQCn0KNH8TFwUMPZdfU5BwBdfiwHwsmIiISwBRq/KRrV/iTUzlBCFEcIp4dAKSn+7lgIiIiAUqhxk/+/BNOEMpGWgLZTVDqUyMiIlIyCjV+csEF1mPuJij49lt/lUhERCSwKdT4SceOcMUV+TsLf/EFrFjhz5KJiIgEpoAINdu2bSMpKYmmTZsSERFB8+bNGT16NMePH/d30Url4ou9j4BassRfJRIREQlcIf4ugC82bNhAVlYW06ZNo0WLFqxZs4aBAwdy+PBhnnnmGX8Xr8QuuACmnmx+Oo312HGRhYPzzvNzwURERAJQQISayy67jMsuu8zzvFmzZmzcuJGXX345oEMNwGaac4wwIjlKU7aymRb+LpKIiEhACojmJ29SU1OpU6dOoftkZGSQlpaWa6tMPvsMsnCwjtOB3P1qREREpHgCMtRs2rSJF198kcGDBxe637hx44iOjvZs8fHxFVRC37jXgMq7XEJsrL9KJCIiErj8GmpGjBiBzWYrdNuwYUOuY3bu3Mlll13G9ddfz8CBAws9/8iRI0lNTfVsO3bsKM+vU2xXXgk2G56amtNYD2gCPhERkZKwGWOMvz5879697N+/v9B9mjVrRmhoKAC7du2iW7dunHvuubz22mvY7cXLZGlpaURHR5OamkpUVFSJy12WJk2CJcM+5mP+xa+0oz2/4nDAtm3WcgoiIiJVna/3b792FI6JiSEmJsanfXfu3MlFF11E+/btefXVV4sdaCqrDh1gJqcB0IoN2MjC5bKzaZNCjYiISHEExOinnTt30q1bNxISEnjmmWfYu3ev573YAO+AUqOGNQLqONWI5CiN2c5fNNFq3SIiIsUUEKHmm2++YdOmTWzatIm4PNUXfmw9KxNbt4KLEJJJpDXraMUG/qIJ27ZZsw6LiIiIbwKiDad///4YY7xugc7dpWj9ySYod2fhIroaiYiISB4BEWqCWd261mPeUON+XURERHyjUONnXbpYw7o30ArIDjV//eXPUomIiAQehRo/i4uDCRPy19QMGwZOpz9LJiIiElgUaiqBhATYSEsA6rGfuuzDGFi2zM8FExERCSAKNZXA/v1whOpsIwFQZ2EREZGSUKipBNRZWEREpPQUaiqBpk2tx7yh5rff/FUiERGRwKNQUwm4F7DMOwJq3Dh1FhYREfGVQk0lkJhoDeten2MNKICsLNi0yZ8lExERCRwKNZVAXByMHJkdaprwF5EcBuDbb/1ZMhERkcChUFNJ9OgB+6nHXuoB0JKNgJqgREREfKVQU0nUqGE95u0srCYoERER3yjUVBLuzsJ5Q43dDi1a+KtUIiIigUOhppJITLQCjHsElLuz8IQJVp8bERERKZxCTSURFwfTp+evqVmzxp+lEhERCRwKNZVImzbZoSaRZBxk8vrrsGKFnwsmIiISABRqKpHFi2EH8RwmklBO0JzNACxZ4ueCiYiIBACFmkrk1FPBYM/Xr+azz3Lv53TC3LnWpuHeIiIilhB/F0CyVa9uPa7nNNrzK6exnk+5mgUL4PPPrfdXroRhw8CY7ONmzoSkJP+UWUREpLJQqKlEEhOtR3dNzems87x31VW5g0xOAwZY/XE6dizvEoqIiFRean6qROLi4Oab4VfOBqAzyzzvZQcaQx8+YiZJTOE+zuR3ADp1glmzKrjAIiIilYjNmIL+/g8+aWlpREdHk5qaSlRUlL+L45XTCa3jU/mHOjjIoiE72U1DAGxk8Tr9uI03Pftn4uBOXmE2t2O3w19/aV4bEREJLr7ev1VTUwmlEc1q2gJwIT94Xh/Do9zGm5wghCncx+f0IgQXr3An5/EjWVkwY4Y6D4uISNWkUFPJJCdbj9/TDYArsYY+3cksHmYcAEnM4gGmcCWf8TY3EYKLmQwghBM8+SQkJKgpSkREqh6FmkrG3Vn4XW4A4Bo+ZAAzmMpdADzBKGZz+8m9bdzNy/xNDK3YyEBmANYimIMHq8ZGRESqFoWaSiYuzgokK+jIcs4hgmPMYBDVyOQtbuZxHs+1fxrRPMVjADzIs9hxAeByaXVvERGpWhRqKqFHHwWw0Z/X2ElDsrDxCndwB68Ctnz7v8Kd7KcOzdlCL74AwOHQ6t4iIlK1KNRUQnFxMHEibOA0mrCN2hwgiVc4QajX/Y9QndfoD8DtvAHArbdqFJSIiFQtCjWV1NChMGkSZFKNNKLzvf/ZZ3DZZdnPX6cfYHUsjuYgb76pPjUiIlK1KNRUYg89BDt2QNeuuV/v1w9694avvoKmTa3X/qANazmdMI5zGV+rT42IiFQ5CjWVXFwcfP89/PwzPPec9fjaa9nvjx+f/e/PuBKA3nyO3a4+NSIiUrUo1ASIjh3h/vvzr+/UpQvYTvYd/pzeAFzOV3Q7P7NiCygiIuJnARNqrrrqKho3bkx4eDgNGjTgtttuY9euXf4ult/FxcGECda/f+Jc9lOHuvzD8R+WaRI+ERGpUgIm1Fx00UXMnTuXjRs38sEHH7B582auu+46fxerUujQwXp0EcJ8LgXgIhZqEj4REalSQvxdAF898MADnn8nJCQwYsQI+vTpw4kTJ6hWrZrXYzIyMsjIyPA8T0tLK/dy+oN7FmKAxVzATczhAhYD2ZPwaXi3iIgEu4Cpqcnpn3/+4a233qJLly4FBhqAcePGER0d7dni4+MrsJQVJy4O+va1/r2YCwDozDIcWP1q1GFYRESqgoAKNcOHD6d69erUrVuX7du388knnxS6/8iRI0lNTfVsO3bsqKCSVryHHrIe19Kag0RTg8OcxW+eTsQiIiLBzq+hZsSIEdhstkK3DRs2ePYfOnQoq1atYv78+TgcDm6//XaMMQWePywsjKioqFxbsOrYES69FAx2lnAeAOfzI8ZovhoREakabKawVFDO9u7dy/79+wvdp1mzZoSG5l8ewOl0Eh8fz9KlS+ncubNPn5eWlkZ0dDSpqalBGXCcTkhIgKFZ4xnPSOZyPTcwl0mTsmtyREREAo2v92+/dhSOiYkhJiamRMdmZWUB5OoIXNXFxVmT8c0bZg2HascqAEaMgBtvVGdhEREJbgEx+mn58uWsWLGC888/n9q1a7N582Yee+wxmjdv7nMtTVXRoQOMpx0AiWyiJmkcckVpBJSIiAS9gOgoHBkZyYcffsjFF19My5YtSUpKok2bNixatIiwsDB/F69SSUyEg/a6bMca6XUWvwHwyy/+LJWIiEj582ufmooW7H1q3CZNgpbDruIqPuM/PM+L/AeHA7ZtU22NiIgEHl/v3wFRUyPF8/ffsOpkE1RbVgNo1W4REQl6CjVBxumEZ5+F1bQFsjsLOxyahE9ERIKbQk2QSU4GY7JralqzlhBO0KOHmp5ERCS4KdQEmcREsNlgO41JpzqhnKAZW/j2Wy1sKSIiwU2hJsjExcGDD1ozC2+kJQCt2KA+NSIiEvQUaoLQffeB3Q4baAVYoUZ9akREJNgp1AShuDi47bbcoebWW9WnRkREgptCTRByOmH27Nyh5s03i9enxumEhQvVD0dERAKHQk0QSk6GrKzcocblMj73qZk1y1oYs3t363HWrHIsrIiISBlRqAlCiYlWn5pkEsnCRm0OUp+/i1wqwemEl1+GgQOtUATW4+DBqrEREZHKT6EmCLlX684gnK00BazamhEjCg4ns2ZB48bwf/8Htcw/NGMzYK2g4XLBsmUVVHgREZESUqgJUh06WI++DOt2Oq3aGZtxMYmH+Jv6bKYFv3I2ifwJQN++aoYSEZHKTaEmSNWoYT0mkwhAczYDUL16/n3dsxA/ySge4llCcOHCTjtW8y09iOFvAAYNUjOUiIhUXgo1QSo93XrchDU5TQusKprDh/PvW6MGnMEfjGA8AP15lUbsZAMtacwOnuc+wOpfown8RESkslKoCVLuzsI5Q01BE/Clp8MwJuIgi/e5ltfpzx5iuZm3cWHnJubQjl8B+N//KvJbiIiI+E6hJkjFxcH06bDVnh1qxj1tvE7At35hCjfwLgDjGeF5fRVn8w43ATCUSQC89x6sWFHOhRcRESkBhZoglpQEg8YmkImDSI7y/Ijd+Tr7rlgBO596hVBOsJTOrMTqYfzvf8O118JzPADAv/iIaA4CsGRJRX4LERER35R5qPmlqMlQpMI4nTD0kVD+IgGAZmZTrjlnZs2Cc86BPnxkPSfJc+w118Dw4fArZ7OG1oSTQV/mAvDttxX7PURERHxRolCTnp7O0aNHc722evVqrrzySjp16lQmBZPSc88snLNfjXtYt3sYd0N20pFfyMLG5/T2HFu9OnTsCFdcYeM1+gPQn9cA+OILNUGJiEjlU6xQs2PHDjp37kx0dDTR0dEMGTKEI0eOcPvtt9OpUyeqV6/O0qVLy6usUkyJiWCz5Q41NpvVWdg9jLs3nwOwnE78zSmeY92jpC6+GN7mZgDO5SfqsRdQE5SIiFQ+xQo1Q4cO5dixYzz//POcf/75PP/883Tt2pWoqCg2b97MnDlzVFNTCblDjXuuGsgOPD2ZB5CrlibnKKkLLoDdNOQ32mDHcAnfAHDeeRVUeBERER8VK9T88MMPvPzyy9x7773MmTMHYwy33HIL//3vf4nzNqxG/MpdG5OzpsYYeP55a3TU7bcZumDVrH1PN8AaBj5tGp5RUh07Qr9+MI+egBWCbDb4/fcK/zoiIiKFshljjK87OxwOdu3axSmnWM0UNWrUYOXKlbRs2bLcCliW0tLSiI6OJjU1laioKH8Xp9w5ndZ6Tq3MOtbRmlSiqMVBHA4by5bBjZ22stk04zjViCKN47Zwli+3gkze89zR+Du+MT3YTSwN2YXDYWPbNrwOERcRESlLvt6/i91R2G635/p3aGhoyUoo5S4uDh58ELbQjCxsRJNGPfbhcsGPP8K5xqqlWUU7MgjHGO8zDicnww/mfA4TSQNSOIM1Ba4jJSIi4i/FCjXGGE499VTq1KlDnTp1SE9Pp127dp7n7k0qj/vugxP2cJxYVSrumYX374fOWEtvL6ULQIEzDicmQqY9jOVY/aXO5ScANHpfREQqk5Di7Pzqq6+WVzmknLhnFt48oAWN2cGptk1cM64zI0bAzydDzTI6AzB+vPfmpLg4673lwzrRnYWcw8/MZCAjRsCNN6oJSkREKodihZp+/fqVVzmkHCUlQfqCFvD2Ql68bxMrO0B41mHO4jcgO9R06FDwOTp0gOdP1tR0YjmApwlKoUZERCoDLZNQRWQ2sdqVQrZtokYN6MgKQnCxgzicxAPWhHsFSUyEFTYr1LRmLdVJL7C5SkRExB+KVVPjVrt2bWw2W77XbTYb4eHhtGjRgv79+3PHHXeUuoBSerNmwVfjWvA+8MfHmxh1JLs/jbuWBrx3EnaLi4NLbm/A9tfjacwOOvALTW7tploaERGpNEpUUzNq1Cjsdju9evXiiSee4IknnqBXr17Y7XbuueceTj31VO6++25mzJhR1uWVYnI6YdAg+NNkT8A3fz6e+WncoaaoWhenE2bPhp85B4Bz+JnZs7PXkRIREfG3EtXU/Pjjj4wZM4a77ror1+vTpk1j/vz5fPDBB7Rp04YXXniBgQMHlklBpWTc6z9tpjkA9dhPLQ7kG/n0wAOF941xn+dXzuY6PqANv5OVZU3kN2lSuX8NERGRIpWopmbevHn06NEj3+sXX3wx8+ZZ0+5fccUVbNmypXSl8yIjI4O2bdtis9lYvXp1mZ8/2CQmWrMEH6E6u2gAwBV8ST32c4wwVtEOu90a+l3UeWw2WMMZgNWvBuC551RbIyIilUOJQk2dOnX47LPP8r3+2WefeeapOXz4MDVr1ixd6bwYNmwYDRs2LPPzBiv3cGzIDiRDmAzAL3QgyxHK9OlFj2ByT+S3ltYAnMZ67Lg0CZ+IiFQaJWp+euyxx7j77rtZuHAh55xj9bFYsWIFX375JVOnTgXgm2++oWvXrmVXUuCrr77yNG999dVXZXruYOYeqr2MzlzKN7TnVwAcPS9h20zfh2Tfdx8892xTjpgIIjlKczazxXGqzyOgVqyAxYutRTLzLsUgIiJSWiUKNQMHDuT000/nv//9Lx9++CEALVu2ZNGiRXTpYvXRePDBB8uulMCePXsYOHAgH3/8MZGRkT4dk5GRQUZGhud5WlpamZYpULibjhaY7ozmSc/rzf7Tm1OKMXopLg6mzbCzbsDpdGAlbexrGD7tVJ9CUf/+8Prr2c/79YPXXvP9s0VERIpSolADcN5553HeeeeVZVkKZIyhf//+3HXXXXTo0IFt27b5dNy4ceN44oknyrdwAeQHLuQ32nAWv7OAi9iX3p4uzuJNnpeUBIe/PgPeX8msB9YSnXRNkcesWAHzX9/FDEbRjlWsoCOPvj6GFffUU42NiIiUmRKHGpfLxccff8z69esBaN26NVdddRUOh8Pnc4wYMYIJEyYUus/69euZP38+hw4dYuTIkcUq48iRIxkyZIjneVpaGvHx8cU6RzBITgZrLXYblzKfTiznBy4k9QarBmfGDCus+Kp6pzPgfTi2cg2HfAhFM5/azVK60IS/AGjPr1zKfEY9upw358WU+HuJiIjkZDPGut0Vx6ZNm7jiiivYuXMnLVu2BGDjxo3Ex8fzxRdf0Lx5c5/Os3fvXvbv31/oPs2aNaNv37589tlnuSb8c7lcOBwObrnlFl7P2a5RCF+XLg82TickJFhDsr1xOGDbNt9rbObd/xU9n7+CNbTmLPsapk8vOBSt+Nmwq1MfruZTkmnBJIYygvE0YytfcjlLH/6CMWPzT+QoIiLi5uv9u0Sh5oorrsAYw1tvveUZ7bR//35uvfVW7HY7X3zxRclL7sX27dtz9YfZtWsXPXv25P3336dTp07E+Xg3rqqhBuCRR+Dppwt+f+FC6Nat6PM4ndCt8RY2meYcI4xIjmB32L2Golmz4JUBS1jC+RynGmfzK2s5g9NZy0raE04Gl/Ml7R+5nDFjSvPtREQkmPl6/y5R89OiRYv46aefPIEGoG7duowfP75c+tk0btw41/MaNWoA0Lx5c58DTVVXt27B79ntvq/hlJwM20xjThBCOBk0ZBc7XXH5FrZ0OmHgQPiAZwB4nX6sPTmkfB2teZF/M5RneJzHOXfs5dSqBQ89VMIvJyIiQgnnqQkLC+PQoUP5Xk9PTyc0NLTUhZKyd8EFBb83cqTvTU+JiZBlC2ErTQFowSZstvyh6Pnnob5J4Wo+AWAyQ3K9P4mhZBBKJ36mPb8wfLgm8RMRkdIpUajp3bs3gwYNYvny5RhjMMbw008/cdddd3HVVVeVdRnzadKkCcYY2rZtW+6fFSw6doRLL/X+npfJoYvkXnbBHWpycjrh2WfhGj7EjuEnOrGB03Lts5f6vMf1AAxiOllZmsRPRERKp0Sh5oUXXqB58+Z07tyZ8PBwwsPD6dKlCy1atGDKlCllXEQpK7NmWU1NORW1kGVe7pFUm7AOasGmfIHEvc/1vAfgCS82G8ycafXvAXiFOwEr/IRwgm+/Ldn3EhERgRL2qalVqxaffPIJmzZt8gzpPu2002hRnLujVLi4OJg+3errYowVMqZNK948Ne61pDZlZYeavMEoMRGibWlcYBYD8CHXYLPB8uW5ZxIeP/ZC9lCfU/ib7ixg7Nie6lsjIiIl5nOoyTnfizcLFy70/Hvy5MklL5GUq6Qk6NnTqllp0aJ4gQayg9Eng1pAlhVq8gajefPgQrOIEFwk04Lt9qbMmJ470Fx8MYwdG8JH/Iu7mMaVfMZ8ejJ8ONx4Y/HLJSIi4nOoWbVqlU/72fJ2sJBKJy6udKEhKQl6tWgB3aB12CbqXWoA67o7nTBoEEzGakv6FqvDTs+euc9xcgAbX9CLu5jGZXwN4GnKUqgREZHi8jnU5KyJEflqfRP6YaNaxmHOSdjDUzNiSUqy+tNkZcG5/ATAIrp6DSrp6dbjQi7iONVowWZakMwWe2Kx+viIiIi4laijsFRtTicMuCeM7VjzBzUzmxg82Ho9MRFCyKQNvwOwkvZe58Fx9805TA2WYi2C2o3vmTBBtTQiIlIyCjVSbO7amC00A6ApW3G5skdAtWIDERwjjZpspjne5qx2981xOGAx1iQ6dyYu5sYbK+pbiIhIsFGokWJz17LkDDXuEVDJydCOXwFYRTsMdmsIuJc5aJKSrDWnTrnuQgBikxeTkGANPRcRESkuhRopNnctyzabFWqa27Z4RkD98gucnSPUQNFz4Tz0QWcycdCUbTTIcnqaskRERIpDoUZKJCkJHnjBWirhxo5bSEqygsiIEdAOa6Tcr5wNwPjxBfeTSU6GQ6YGv9MGgHP4OVdTloiIiK8UaqTE6na0amrM1q04nSdnEs7KyhdqOnQo+BzupqxfsHbqyIpiLbApIiLiplAjJfbWMivUhO7dSWLjDH75BRJtm4niEEcJZwOtimx6cjdl/YI1M18HfsEYawI/ERGR4lCokRJxOuH2IfVIpzp2DPHmL0aOhGdutvrT/MZZ4AjxaRmGnj1hpc2qqbFCjfG5X43TCXPnWpv64YiIVG0KNVIiycmQZWyeEVDN2ILLBWe5rFATf1U7tm2z+t74cq7fzRlkEEptDtKEbT71q3nmGWjcGG64wdri42HoUIUbEZGqSqFGSiTvsO5mbMHhgOitVn+ayPPO9nkSvcREyLJXYwOtADiDNUU2W02aZAWYOLOdZxnC5/TiPqbw0jNHaNzYel9ERKoWhRopEc+wbqwRUM3Zwq23GE4st2pqLhl+ts/zzbjPtcZ2JgBn2tYwblzBzVZOJwwfDqexjp84lyE8Ry++ZAoP8DWXEWEOM2wYPPpoqb+miIgEEIUaKbGkJOj3lFWdcucFf/L97B3UYz8nCOEPzijWfDNJSRB/2RkAnGH+YMSIgifhe/55cJgTvMsNNGQ3f3AGo3iCVKK4kMVM5S4Axo61mqhERKRqUKiRUql93ukA2Nato62xamnWcAbHCSvWfDNOJ0z62qqpOYM1ZGXhNRQ5nfDss/AfXuBM1rCXenRnAU8xit58ThY2buNNumEtwDpsmPrYiIhUFQo1UipvrW4NQNT+LZzHEsD3mYRzcncWBmvtqBBOeA1FyclWLc0QJgMwgvHsI4bTToMfucBTSzOOkQAYY9XsiIhI8FOokRJzOuH2B2PYR13sGAYyA4AVdMRux6fh3G6JieC0NSaNmoRyglP5s8DVva+1fUQjdpHCKczmNux2mD8fHnkEnmA0GYRyLss5l2UAPPecamtERKoChRopMfewbnfNTC1SAVhAd8Caf8ZXcXEwfYaNNVi1NWfyh9dJ+ObMgTvNTACmMRiXPZTp063jx4yBfw0+hTe5FYD7mQKgZRdERKoIhRopMfew7k+5yvOak0b8yalkZRU/SPTsCWvI7ldjTO5+NZMmwbih++nOAgBmc5vnOLdHH4X/2e4F4Go+IYrUYjWDiYhI4FKokRKLi4PbboMPuYZMHAC8yL8BW4nWb0pOhj9O1tScwRogu5bFPYz7Sj4jBBe/0YbNtMgXnuLi4P+mt2UdpxFOBtfaPip0eLiIiAQPhRopMacTZs+GXTSiN5/Ti8+ZyHDA6qBbXImJsNY9Vw1/ANmdjZOTrXNezSeAFaQAr+EpaYCNf3reDMCN5u1Ch4eLiEjwUKiREktOhqws69/zuIwv6eV5z5jiNz/FxUHSZGs0VVO2UtN+2NPZODERHLYsurIIgK+4HIAJE/LXwjidkDT/BgAuYiE1slIZNEidhUVEgp1CjZSYu0+NNyXtx3LL/TG46tXHjuGDMes9/WXi4uCV+36jDgdIoyarbWczcSI89FD+cyQnw58mkY2cSjUy6cG3ZGVpaLeISLBTqJESi4uD8eO9vzd+fMn7seypZ/WreevhNTRubDUdzZoFq6d8D8BiLmDshBCGDvV+fGIi2Gzwxcmao158AWhot4hIsFOokVLp0KF4rxfF6YQPNlhNUO4RUIMGWZt7luDv6cbIkQUHlLg4ePBB+JIrALicrwCjod0iIkFOoUZKxVsTVGmGUOccAdWatcDJfjtZLi7kBwAWclGRAeW++2Cp7XyOEUYDUjiVPzW0W0QkyCnUSKm4V9i22aznNlvxZhLOKzER1tlyD+sGaMdqapFKKlGspm2RASUuDl6cEc5yzgXgItuiUpVLREQqP4UaKbWkJNi+HRYutB6Tkkp+rrg46Pu4tUhmPE6iOQhAV74HrP40xh7iU0BJSoJ293cFYGSXRcWa4djptL6P+uCIiAQOhRopE3Fx0K1b2dSEnHlBLXZgncjdBNXtZKhZyEWA70sw/OiwQo1jySIaxxuf5qt55hlISIDu3SE+3vtq4SIiUvkETKhp0qQJNpst1za+oKE3EtASE2Ftjn41DjI9/Wm+p5vPSzA4ndB38rkcpxpx7KQZm4ucr2bSJBg6NHv+HbCa19yjsEREpPIKmFAD8OSTT7J7927P9u9//9vfRZJyEBcHjS61RkCdxW+0ZTXRpHGQaJ/607glJ8NhE8lyOgHQlUWFzlfjXoqhAbt4g9v4nTP5nq5cyrx861CJiEjlE1ChpmbNmsTGxnq26tWrF7p/RkYGaWlpuTYJDGcOtDr4dmcBF50cyv0DF5KFgwce8K2Zyz1fzSKsJij3bMQFzVeTnAxnmt/4nTbcxpucyRq68gPzuIxHGIPLBcuWlc33ExGRshdQoWb8+PHUrVuXdu3aMWnSJDIzMwvdf9y4cURHR3u2+Pj4CiqplFqPHmTZHZzGBu5nCmD1p7HbreHavnDPV5M31BQ0HPzU2DTe5zrqsZ9VtKU3n/E8/wFgDI9xC29yww1WE5WIiFQ+NmNKsvRgxZs8eTJnn302derUYenSpYwcOZI77riDyZMnF3hMRkYGGRkZnudpaWnEx8eTmppKVFRURRRbSsjphL/iz+M8lgJwghDi2cFDk2K9Lo1Q2HlOa3yYf0wtqpFJE7ayw96Ev/7KX9uTfttd1HhzGn/RmHas4gB1ABjHCEYwgYNEcyp/spf6TJrkfYkGEREpe2lpaURHRxd5//ZrTc2IESPydf7Nu23YsAGAIUOG0K1bN9q0acNdd93Fs88+y4svvpgrtOQVFhZGVFRUrk0Cw9Kl8A43eZ5/wLXsIZaEhOKdJy4Opsyozi9YUxxfyA8YA/Pm5d7vvaeTCX9zJgD9eZ2kh+rw888wahQ8wlhWcja1SGUcIwGr743614iIVC5+DTUPPvgg69evL3Rr1qyZ12M7depEZmYm27Ztq9hCS4V5iXu4ndeZwQCGMbHE5+nZExZzIWA1QeXt9Ot0wvFHniAEF19wBd/TjeeegwYNYOBAMDYH/+ZFAO7gVVqywecRWCIiUnFC/PnhMTExxMTElOjY1atXY7fbqV+/fhmXSiqDLl3AZrMx29zObG4HrOUYOncu/rmSk+F7ujKMiZ6h4e5+NXFx8NpTOxjBHAAe46lc73frBhMmwLBhXfiYq+nDJzzEM9zlmKklF0REKpmA6Ci8bNkypkyZwm+//caWLVt46623eOCBB7j11lupXbu2v4sn5SAuDmbMsNaRAutx+vSSTe6XmAjLbOeRhY1ENtGQnZ5h4U4nOKa/TAguFtKNVZzt+Tx3aBk61Ooc/IxtGAC3MZvZE3ZpyQURkUomIEJNWFgYc+bMoWvXrrRu3ZqxY8fywAMPMH36dH8XTcpRUhJs22YtV7BtW8mXX4iLg2dmRLOS9gBcxjzPMgub1xxlINbv0QsnRzoB+YaNP/QQzNnehX0tzyOM41y4YZqWURARqWQCZvRTWfC197QEp9QHnyB68uMc7dmHiK8/AuCrvq9y+Xt3so0EWrAJFyHY7XgdHTVrFnw7YA7vcBM7aUgCf2HsIUyfXrr1rkREpHABMfpJpCJF33IlABGL58OxYzh3GGLfewGwOiW7TnYxmzAhf6BxOmHQIPiQf/E3MTRiF735nKwszTQsIlJZKNRI1dGuHTRqBEeOwNdf8+HgebRjNUeIYBbZVS0dOuQ/NDnZWg/qOGG8wp0A3MVUoODJ/EREpGIp1EjVYbNx6MqbATh6Y396f/V/AEzlLs9EewWtK5WYaI2+ApjBQAAuZT5N2eLzWlQiIlK+FGqkypg1C5pMHcF+6hCRkUoztrKLBjzO4559ClpXKi7OGn3lcMAWmjOPS7FjGMgMxo0relSW0wlz51rbihWok7GISDlQR2GpEpxOSEiwmpBuYA7P8iA7acSDPMuPXABYi19u3154QHE6raamnf/9iFs+uIY91KcxOxgzMZShQ/Pvm5wM330HTz8Nef9Ls9ms/jt5jxMRkdx8vX/7dfI9kYri7hMD8C438i435ttn4sSia1zc7/f8sDfdaEgjdnE1HzNsWF82b4ZHH7X2mTXL6liclQWRHOYuXuc01pNBGO9wE7/SHmNg2DByHSciIiWn5iepEhITrZqRgkyc6PsClcnJcNxUYyYDgOwOw9OmWbVBkyZlB5o+fMRfJPA/7uHf/JeHeJaVdGAONxDOUc9xjRtbQUhEREpOzU9SZUyaZNWM5GS3w08/QceOvp/H6bRCSCOzg200wUEWZ/AHazkDsMKTMTCEZ3kWKyltojnvcT0J/EVf5hKCi+/pSm8+5zA1PGXxNj+OiEhVp3lqRPJwL3fgrrGx2azOv8UJNGCFjgkTwEk8H/EvAKZwP2D9fWAMDGe8J9C8yL20YgMPM45beJuLWEgaNenGopPDw63jsrLg+efL4IuKiFRRqqmRKsfd2bdFi9LVijzzDLw8dAtrOZ1wMrib/zGdQUxiKEN4DoBHGMPTPAJYIWrQIKhXDxaOXcpCuhHKCe7lRV7iXqBkNUciIsHO1/u3Qo1IKTidsOyyJ7h+7eMApFGTKA4BMIJxTGAENhu8+661wrg7RDmd8N2VU+i3+gGOEUYbfieZUwEr/MyYoaUXRETc1PwkUgHi4qDzF4/yJKNwYSeKQxygFn15lwmMAKzmqJiY3LVCcXFw8af3MY+ehJPBDAZiI8uzv69LLzidmvNGRMRNoUaklOISHERMfILmbKY9v1Cfv3mPvp73C5pxOC7exsHxU0mnOl35gQHM9Lzny9ILs2ZZHZa7d4f4eKu/kIhIVaZQI1IGhg6Feyc1YZWtPZlU87xus1lDtgvqu3PD8Cb8M2QsAJMYSkN2AgUHIbcVK6z+OTkbj4cNs/r5iIhUVepTI1KG3J2Qq1eHw4d97IzscvH3qedRf8tyPuEqrrF9zPgJNs9Mw+6ZiRMTsyf2GzDA+6k0LFxEgpE6CnuhUCOV1po1uNqejcN1giE8yxTbEGbMgAMHrBoY93+lj93wJ4fnfk4Ts5V0arCA7nxPt1y1Q3PnWiOs3CFIRCTQKdR4oVAjlZXTCeMbv8R/zb1k4uAm3uF9rve8fwE/8ChjuJRv8h37B2dwG7P5jbbYbFaTV1aWVWszfbpGUYlI4FOo8UKhRiqrhQuhe3fDa/SnH28A8B7XsZnm9OBbOrASgCxszOdSVtOWU9jDVXxKXf7hONW4kTl8xDW5zutwwLZtqrERkcCmBS1FAkhiItjtNgZkzSSFWIYzket53/P+cay1piYyjL9o4nm9Pn/zfeIATkv+jDncyJV8xnx6et53j6JSqBGRqkA1NSKVxKxZMHCg1X+mC0vowbc0Yicr6MgnXM1e6uc7Zu5cqFfbxd5LbqIv73GYSDqx3LMOVUk6DuftmCwi4m9qfvJCoUYquxUr4NxzrT4xRXE3LQG0aHycz80V9OA7NtCSjqwgnZoAzJzpe7+anMFKMxuLSGWhGYVFAlB6uvdAM3euNbme/eR/sQ5H9vw3cXHw0oxQbuYddhBHKzYyiyTcC2UWZ3binHPfFGdmYxGRykChRqQSsfrW5H7N4bDWjXroIaspaeFCq4YmZw1KUhL8uiOGJf+ZywlC6Mt7/JsXAatfzbJlhXyoywUpKexYvI3QrKP53ir0WBGRSkShRqQSiYuzhmHbbNbzvDMSx8VBt27e+7rExcH5Qzsz1GZNK/wsD3IhiwC44QaraSmX7dth+HBcsQ2hQQM639yUPZzCS/wfzdjs2c3rsSIilZD61IhUQu6ZiX2akTiPWTMNNQbeyA3MJZ3qXMp8ltHF0wfHduQwx8dOosm7E7BlHPMcl0EoYRwHIJUobuIdvuIKQEPDRcS/1KdGJIAVViNTlKQBNkJmv8Y39KAGh/mGS5jAMK5wfcqv14yhWsumNH3jCWwZx/iBC/gXH1KN40RwlIv5lnW1uhBNGp/Tm8FMBaxmqIcfLtvvKCJS1lRTIxKEnE5o1fgIH5o+Xmch3kwzRjCe97kOsOV6rxrH+S//ZhDTycLGtXzAx/wLgN694bPPKuIbiIhkU02NSBUWFwfPz4ikJ/Poxed8Qw9+piMf0Yc7eIVWbDi5DIMt37EnCOUupvIyd2HH8DY304mfAPj8c+jQAUaNKnnZnE6rs7NGVYlIWdOMwiJBKikJ/vrLxnff9eLSpb2KdazBxt+PvshnY5xcyed8xL9oz0p205CVK2HlSkhIyB6B5Z6wr0YNa1h63on73O+vXJm9QKfmwRGRsqbmJ5Eg5nRa4cOXyfxycjisodzdz0lnKZ05kzX8RCe6sojjhHn22bYN5s3LnrDPLZqDTLh+Jd3Oz+S7ZZHcP+dcTuRYSTzn56gDsogURc1PIkJysu+BJu8w8o4dYdTEGvThY/6hNueynLe5GQeZgNV5+L77cgeazizlXfqSQiyD3+tBy/su4//mXMhWmjCax6lJWq7PdK9NJSJSFgIq1HzxxRd06tSJiIgIateuTZ8+ffxdJJFKzdtkfpAdYNwcDli+3Orrsn17dpPQ0KFw96TmXM97ZBDKtXzI6/TDQSY2G3z4oRVomrKFd7iRpZxHX94jnAw204xVtGUfdWnELh7nCX6hA2fyu+dz7XaruaqoPjajRkG/ftl9efI+Lw318REJIiZAvP/++6Z27drm5ZdfNhs3bjRr16417777brHOkZqaagCTmppaTqUUqXxmzjTGih7WNnOmtdls1nObzXpemB07jJn1r0/NcUKMAbOBU80AZpjbeN28zm3mBA5jwLiwmZncac5ilYEsA8ZUI8PcwDtmG42NAXOEcHM97+YqU2HlyFv+fv3yf5/S/GyK83MQEf/w9f4dEH1qMjMzadKkCU888QRJpehVqD41UlWNGmXVwDRuDE8+ab1Wkgn+9s38mKiHBhKaui/fe19xGSMYz++c5fXYOuxnNrdxBV+RhY27mMoMBuXaJ28fG1/6BJW0X463c6uPj0jl5Ov9OyBGP/3666/s3LkTu91Ou3btSElJoW3btkyaNIkzzjijwOMyMjLIyMjwPE9LSytwX5Fg5g4yObkXwyyOegP68EbGxWy69zl6Mo90arCDeKZyF6vsHejfH2psgKVL8x/bsnNdll/8OWf9dg+NPpvKdAYTTSrPMNSzj7uPjbtcvvQJch8zfbq1NlZCgvfvm5e3c+f9fBEJLAERarZs2QLA448/zuTJk2nSpAnPPvss3bp1488//6ROnTpejxs3bhxPPPFERRZVJKg5nXDHf2qSxSieIneHlpnTs/viuGuGtmyBZs1y1hDZce74H+M/q8UIxjOJYdTiII8yBrDhcFg1R27uPkFZWVCfPXRhKREc5SgRbKMJq2kL2BgzBr77Lvu4nMPNC5Lz3G42GyxYYM3mLCIBqEIawwowfPhwAxS6rV+/3rz11lsGMNOmTfMce+zYMVOvXj0zderUAs9/7Ngxk5qa6tl27NihPjUipbBggcnXFwaMue224p1n5kxjhjPOc4LZ3GLqk5KrT8uzzxoz+f6/zJzWT5q1nOb1gzfRzDzJo6Yue3O95XBY/YB8KUfe08bGFu+7+MuzzxozerT1KBLsAqJPzd69e9m/f3+h+zRr1owlS5bQvXt3Fi9ezPnnn+95r1OnTvTo0YOxY8f69HnqUyNSOmXZD2XUKGj+zVRu++n/sGM4FlqT8EH9WPRXEzh4kMgl39A+62fsZP8v6nirMwltVJ/0PYexr/mNSI4CcIBaPM7j/I//I/PkfDgLF/pW4zJqlFXLk7PJbObMyj8pYIMGkJICsbGwe7e/SyNSvny+f1dIxCql1NRUExYWZmbm+DPu+PHjpn79+rlqb3w5D6qpESkVb6OpSmXJEmM6dPBeBQTmOy4y3ye9Ycz+/Z5DFiwwpjqHzI28bVZxlmff1bQxHfjZ55oaY6z97HZTopoefynzayBSyQVETU1x3H///bz//vu88sorJCQkMGnSJD777DM2bNhA7dq1fTqHampEyoa30VSlkpUF77/PH7N+5vf5u8nCzgK68w2XsJM4T23Q3LmQlmbtPnas9WjHRRKzeJqHqcd+XNj5suk97Lz9Ye56PLbIj164ELp39/56SfvWTJ5slTMqCoYMKdk5Cjqv0wlTpuSewVmjtqQ4yuv3szz5ev8OmFBz4sQJRo4cyezZszl69CidOnViypQptG7d2udzKNSIVF6jRsGYMblv1jktXAg33ZTd5DJmDAwYkP3+i6P2ct2yIcR+8yYARwkn4j+D4IorePrbc9i0uzppzjR6tHKSkPYH1f9aR+iurcQe20ZqylEOU51UollFO5ZyHm3+73zqNY8u0f/0S9s0VNBNx31eb0oTwqqaQLypl6W4ONi5Exo1CpxJJ4Mu1JQFhRqRyismBvbln/4GsGoixo+3Zjh2mznTGsKds8Zo1ix4e8B3jOFROp9cWbykMghlQdgVXD77ZujdGyIiCt3ffaNcv96qUcpZTl/65+S80U6enP+mM2tW7hCXU1WsqckbTIoTVALxpu4rX34OgdgfS6HGC4Uakcqrfn3Yu9f7exMnwogRhXdQzt2J2XAp87mVNzmXn0gke4Gp/dRhDWewhjPYRAu20YSj9hpcf8Vh7Pv/Js75E012LM51DDVrsrblv/itxXUcaH0+9zyav8nbfaPMy1vg8HbjyXmjdbly33SKmoSwNB2bA7XWIm8wKU5QCeZQ4+275bzG0dG5w3FJQrc/fk+CavI9EQlus2Z5DzSdO0OPHtChQ9ET5eWeTM/GfHoyn56AtWo4QDo1cHn7314WjHjQmiMnIWEQWcAZ/MHNvM3NvE3Coe20/uUNWv/yhrX/nNbQtClr9tTjMDWItB/jhf1HsHGECI4SzjGOE8oxwklzRbHm6gTi7m4KLVtCmzZMnhztufG4bxAul/V46JB18wAr2NxwA4SHFxxoYmMLvin5ciPKWSsE3vcvzxtaaWpcSvoZ5fn5/r75u3+P3I/uMu3caf2u/P137v0HD7ZqPO12339PKnP4VU2NiPhVQbUQMTHZ/wP2ZSi5L0sqFMR9ruTk/J2GbWTRmWXczNv04Fta8mfxPyCPvxxN+dV1FptqtGXoG234aHVTkp5szAFqA7YijwdrosD778/+/u4bac5/e2vGyitnU4TD4X3/ktRs+HpzL2mNS94mlMKaVPKes7DvXNpaHH/WAuVtonTXwrjLVK+e9yZe9+uFlbmoGqDyCqRuqqkRkYBQ0FII112X/e+4OGsZhJz/w542LXeTjrd9fJXzXHlnGTbYWcp5LOU8wJrZePa/V3BpmxTGPbgPV1o6R4jkKBEcJYIjRJJBGNU4QTjHqMM/nF79L/pfuBXWroXt20lwbSWBrZD+MVwD/8LajhFGOjVIpwYZhOHC4dmwO8jICiGVaPZRj7MuqkfrWvUgrC53P1aPDQfqQ/1T2Gs/hXUptWnYyO71r/acZs3K7nickgK1amW/l/MGVZi8+3kLVGV9g8tb7v79cz+fNavg2qvCvnNJVHTNTEGf53TCoNxLqTF4MPTsmX39bbb8v98OB4R4SQLFreVzhxp/1+aopkZE/Ko4E/r50sGxsL45OUVFWf/Tznuuwjrk5i1fx44Fj0bKKTbW6uTsdMLrz/3DmfxOW1ZzFr9xBmtozHZO4e+iT+SjE4SwzxbDHlOfdGpwhEhato0goVUkREZCRASHXJFMnhrJkZNBzB3MjhBJRO0IMhyR/LUvkqj6ERy1RbJlTyS1G0SwcUek9QM4Kedf8OD938WpcSmqpsOXGrmctVhDhmR/RkwM7N/v/Vhfa2ry3uyLqmkqbugpav+Czv/33/Dyy/n3f+gheOaZ7Of9+sHrr2c/nzkTRo/O/519qUErTb+m4lJNjYgEBF9qYdyGDi269uC667z/z71fP/jwQ6vPijtkeDtXUpLVx2D5cpg/3/tnuFxWX5eCAo3NZvUF+uYb63lKCjz1FBw8CFCHRXRjEd1yHRPOUU5hD9U5TA3SCSMDBy7sZHnqa6qRyQ09D9IobB/dztgH+/ax9Zd97Ph1L/X5m/r8TR0OUI1MGpjdNCBHWlt9cjupJjC6gJ8hB3L8O2fW2g2EQKYjlMxqEYTXjuTHvyM4QhhZf4dxglDSCYV9YRw3oRwiFNv+MLgtFEJDISws1/bL76HckhJGBmFkpISx6M4weqeHsY8Qog474FOHFaBybHt/d9A5y0EmIblqsnJtxsEHz4VQP9ZBrHHgSnFQBwfH9zqIzLFfJiFkYcfXJj8oujYib+2Yt9qMwppsilvbkbO/TN5aGLvdej+nN9+0wt3evdn9sR59NHeZvSmq1q+yUE2NiFQKZTXMtLCaH/fkfb781VyaPjreRmsVpkYNSE/Pfu6uRfIm53w03spYjeOegBPDXqpzmAiOnqyLOUJs1FEu6nSEo/8cYf3Ko556mpz75Px3pO0oEeaIZ0mKYOTCjgsHoeEODmc4yDQOjM1Brbon22ZyhKqt2x1kuBzYQhy0PM3Bmg0Ojp4IwVHNQaPGDjZuzg5NrVo7+HOTdc5q4SFc1svBp186OHLURniEjT59bHz0sY30o3YiImxcd72Nue/bOHzERkSkjRtvtFkJ2d1uZLPxxps20g/biKxuo39/G6++ZuPQYRvVq9vo1NnO/G9tGKytQwcbP/+S/TwLOwYbERE2Dh+1UbOmjQu72fj0s+x9ru5jp30HG+Mn2khNsxEVBT162PjgQzAnw9+119o45xwYM9bGwTQb0VHw2CgbTz4JB9NsLD3lGn5KaVKm10hDur1QqBGpvMqyb0JBHSZLex63yEg4csT7MbGx8Pbb3mcpBqsPh1Vjk1vO5jB359W88jbLFTQbcmFyNg0VVBbvDOEc84SdJvWPsnjeEa6+5Aip+47TsO5xQk0Gh/45TijHCSODUKx/R9gyeHRoBtERxyEjg5VLMziwJ4NtGzM8++bcQsjEgYsQXLRr4yLU4bKqCE5uqQdc7N+T6alzqRHh4vjR/HU27vM4KEEylRK7ue483t53aZmeU81PIhJQyrJjobtK3V3zU9I5XHKex62gWpSc/TgSE/M3BbhVrw7VquXv9+M+p8Nh/Sy8TeSXt1musM/Jy1uAqV7dGi7uS78gsHGMCI4RwT/UxVQD2sLPIZACxFrriOL1VAb27oE2bazvdnUBc/p4069d9nFu0UCrQkY/uUOhewhzVpbxNOOF2l3E1HHxz76cocflCVH2HM/HPuFi6kuZ7P/bRYMYF1986qLPldaxsfVczH3HxS03uji4P5PaNV0cPpQ/WEVFujh2xEXdqEwmjnPx2MMuUlMNtaMNTzxuePxxQ1qqoXZUFo89ahg7xpCWZqgVZRg5wjB+vOFQmiG6puHi7ll8+om7TsVwVW/DokWG9EOGqBqG/9ybxUv/NaSnW8/vvsvwx++G+fOzj7n04iyOZxiOZxiOHjWsXZP9nnu7slcWixYaDh+BiDBDRoY5+RuQ/XjZpYYffoBjxwwR4XD9dYYP3jccPQa7aeDbxS0P5bT2VKWkBS1Fqo5nnzVm9GjrsSzOEx1d4JqbXheVzLvoZFSU9Rgba0y9et7P0a9f/vLGxmYf503ez/G2xcbmPk+jRta/GzXK/netWkWfJ+fWqJFvn533GGMK/kybrfDjcl7TnN8h5zkbNcr9Xb0t/unet6jN4cg+l7fPyfk8Njb/wqh5v0/Ozy7qfL6c31v5vP3eF/Q7VNhirkV97qRJ+X93y3ORVV/v3wo1IiI+ePZZY26/3fvNr3Zt78e4bybuQOPeevfOf8MraGXwvDe54nyOOzTkDQ/u540a5b4Jus9T2OY+1tvNrqhjihtACjsu78/F/T369s1/c817U/c11EB2APUlhOQtv7drnDeE5C1bQWUtKAjnLZ83hf0OFbTie2HXZeLEoq99Wa907+v9W81PIiI+GDLE6pj75pv5m3vCwrwf4x75nLe56quvrM7EOdeyKmjEl7spqrBugO7PqVnT6u/jboZxDyOfMiX3/jmbofLOHFyY2ByLnmdk+Nbs5W4OKqjvjrvsDkf+ZsPCjsv7c3Ffn4SE3PsNHmyN9sn5WQU179ls1i05Z9m8zeFSkJzlr10bDhzI/b7LBZmZ2c99mW/HLSTE+xwzNlv2uUvCW1Nt3nKB9Z57H28zfOeVd8bviqJQIyLiI2/Dz6+8svirY7tc1o0h542ioH4/vvQ1ynmDdwcTd9+chQvhueeKV768/W/cz48dy379wIH8IcCbHFPa+CRnyCmMt5+Lt4kc8waJvMf+8EPBK79Pm2bN4VIc7nKHhRUeQo4fzz9ZXs75YyB/IMv7u3frrdnHFDbpYFHBOOfPvKBJ/HKWw5e+XA6HtexIRbNX/EeKiASupKTsGovYWPj004KDx5Ah8MAD2TcyN/f/8H29gRdlyBB4/HHv5XDfgLzJ+9f9kCHWTfyxx3J/x+rVrX/7PlKq8Jl6c8554v7Msugo7u27FlXbkremKOf3LmkHc/f5pk/P/dqtt2Z3EN+717fajpyBLGf5YmJg9uzc+w8e7H3Su8J+P/LyJRi6w31O/frlfl5QzWN5U02NiEgxFbc2oXVr3yYXLAt5/yovbPmIvH/de2uKctf4/PYbvPFG7uONyW5mcd9sczZ95a09gvxNG9HRuT/XW62TrwqayDE11bclH9zfN+djaeRs2vEWQnwpS95A5i6XMUUv8loS3mphvJUjb7PVa6/BvHmlH3FYWgo1IiLlrKAh5r70lykub3+Nexua7uZeH6iwG2FB/Ykcjuz+RDlDgDsIubnDictV8PpE7s8vTh8fb8pqOL9bcWfSzXtNCwsheeVdwmDatIJn+y2oj01JmnxylrmgYOitGS5vACzLQFhSCjUiIsVUkjDi7X/4FbnoX0E3Gl//ui/Ozc7bZ4D3zsWFfX5Jp+Yvzs21sGuZt1YpZ8fdws7nTUEhpE6d7CUL8tZ2uD/X2+d762NT0hrAvGX2FgyL27fIXxRqRESKyV8rEJeH4vx1X5KbXc5g4K1zcUGfX5JAURIFXcuCapXcHWaLq6AQ4v75eQti3j7/4YetcBQVVfa1UnnLm/OxPGoVy4NCjYhIFZJ3ZFNxh5IXVguS9xhvI2ny8vb5BY3AqVPH+nd5Lqro/g7eVr32NpLK1/MVFEIKCoWZmd5rtbp3zz3arqKafAIlyCvUiIhUAH//pZvz8ydNKpuh5HnPm/cYbyNp8nYu9vb5BY3AcY8cKmz4cmm5v4PTaQWuojrM+nq+nOfI+ViQsuwzU178/TvtjUKNiEgF8PdfugWNbCrL8+ZV0Egab52Lizour8I6OJfFzbY0fYjKQln2mSkvea99ZQg5CjUiIlIuShoMChuG7lZYB+OyCpAV0WHW28zIhTVXVWb+Du6gUCMiIuWopMEg7xwv+/f7pymmvPuseKvtqMjPDzYKNSIi4rOyGs5enONCQyt/U0ywqQxNSSWhUCMiIj7zVxNDoDXFFKSsw0J5hY/K0JRUEgo1IiJVjL//Ci/p5wdDU0xZh4VADR/lRaFGRKSK8feN0N+fL8FLq3SLiIhIUFCoERERkaCgUCMiIiJBQX1qRESkXJW0Y7C/OzRXhjL4+/MDjUKNiIiUq5J2DK4MHYqLKoN7cc3yWmSzMvwMAklAND99//332Gw2r9uKFSv8XTwREamCZs2y5s2B7EU2xb8CItR06dKF3bt359oGDBhA06ZN6dChg7+LJyIiVYzLBYMG5X5t8GBrdW/xn4BofgoNDSU2Ntbz/MSJE3zyySf8+9//xmaz+bFkIiJSUSpD/xJ3M1NGRv5VxAtbZFMqRkCEmrw+/fRT9u/fzx133FHofhkZGWRkZHiep6WllXfRRESknPi7f0nO5qYDB8BmA2Oy36+oRTalYAHR/JTXrFmz6NmzJ3FFxOFx48YRHR3t2eLj4yuohCIiEkyczvzNTXlpkU3/82uoGTFiRIEdgN3bhg0bch3jdDqZN28eST6sZjZy5EhSU1M9244dO8rrq4iISBBLTs7f3GQM1K5t/TuQF9kMJn5tfnrwwQfp379/ofs0a9Ys1/NXX32VunXrctVVVxV5/rCwMMLCwkpTRBERERITwW7PHWwcDnDfYgJ5kc1g4tdQExMTQ0xMjM/7G2N49dVXuf3226lWrVo5lkxERCRbXBxMnw4DBmS/Nm0ajB7tvzJJfgHVp2bBggVs3bqVATl/q0RERCpAUpLVzARqbqqsAirUzJo1iy5dutCqVSt/F0VERKogdzOTmpsqp4Aa0v3222/7uwgiIiJSSQVUTY2IiIhIQRRqREREJCgo1IiIiEhQUKgRERGRoKBQIyIiIkFBoUZERESCgkKNiIiIBAWFGhEREQkKCjUiIiISFBRqREREJCgo1IiIiEhQUKgRERGRoKBQIyIiIkFBoUZERESCgkKNiIiIBAWFGhEREQkKCjUiIiISFBRqREREJCgo1IiIiEhQUKgRERGRoKBQIyIiIkEhxN8FEBERCVRDhkBaGkRF+bskAgo1IiIiJTZkiL9LIDmp+UlERESCgkKNiIiIBAWFGhEREQkKCjUiIiISFNRRWERExEca7VS5KdSIiIj4SKOdKjc1P4mIiEhQUKgRERGRoBAwoebPP//k6quvpl69ekRFRXH++eezcOFCfxdLREREKomACTW9e/cmMzOTBQsWsHLlSs466yx69+5NSkqKv4smIiIilUBAhJp9+/aRnJzMiBEjaNOmDYmJiYwfP54jR46wZs0afxdPREREKoGACDV169alZcuWvPHGGxw+fJjMzEymTZtG/fr1ad++fYHHZWRkkJaWlmsTERGR4BQQQ7ptNhvffvstffr0oWbNmtjtdurXr8/XX39N7dq1Czxu3LhxPPHEExVYUhEREfEXv9bUjBgxApvNVui2YcMGjDHcc8891K9fn8WLF/Pzzz/Tp08frrzySnbv3l3g+UeOHElqaqpn27FjRwV+OxEREalINmOM8deH7927l/379xe6T7NmzVi8eDGXXnopBw4cICrHNI6JiYkkJSUxYsQInz4vLS2N6OhoUlNTc51HREREKi9f799+bX6KiYkhJiamyP2OHDkCgN2eu2LJbreTlZVVLmUTERGRwBIQHYU7d+5M7dq16devH7/99ht//vknQ4cOZevWrfTq1cvfxRMREZFKICBCTb169fj6669JT0+ne/fudOjQgR9//JFPPvmEs846y9/FExERkUrAr31qKpr61IiIiAQeX+/fAVFTIyIiIlKUgJinpqy4K6U0CZ+IiEjgcN+3i2pcqlKh5tChQwDEx8f7uSQiIiJSXIcOHSI6OrrA96tUn5qsrCx27dpFzZo1sdlsPh+XlpZGfHw8O3bsUF8cP9O1qDx0LSoHXYfKQ9ei/BhjOHToEA0bNsw3vUtOVaqmxm63ExcXV+Ljo6Ki9ItaSehaVB66FpWDrkPloWtRPgqroXFTR2EREREJCgo1IiIiEhQUanwQFhbG6NGjCQsL83dRqjxdi8pD16Jy0HWoPHQt/K9KdRQWERGR4KWaGhEREQkKCjUiIiISFBRqREREJCgo1IiIiEhQUKg56aWXXqJJkyaEh4fTqVMnfv7550L3f++992jVqhXh4eGceeaZfPnllxVU0uBXnGuxdu1arr32Wpo0aYLNZmPKlCkVV9AgV5zrMGPGDC644AJq165N7dq16dGjR5H/DYnvinMtPvzwQzp06ECtWrWoXr06bdu2Zfbs2RVY2uBW3HuF25w5c7DZbPTp06d8C1jVGTFz5swxoaGh5pVXXjFr1641AwcONLVq1TJ79uzxuv+SJUuMw+EwEydONOvWrTOPPvqoqVatmvnjjz8quOTBp7jX4ueffzYPPfSQeeedd0xsbKx57rnnKrbAQaq41+Hmm282L730klm1apVZv3696d+/v4mOjjZOp7OCSx58instFi5caD788EOzbt06s2nTJjNlyhTjcDjM119/XcElDz7FvRZuW7duNY0aNTIXXHCBufrqqyumsFWUQo0x5pxzzjH33HOP57nL5TINGzY048aN87p/3759Ta9evXK91qlTJzN48OByLWdVUNxrkVNCQoJCTRkpzXUwxpjMzExTs2ZN8/rrr5dXEauM0l4LY4xp166defTRR8ujeFVKSa5FZmam6dKli5k5c6bp16+fQk05q/LNT8ePH2flypX06NHD85rdbqdHjx4sW7bM6zHLli3LtT9Az549C9xffFOSayFlryyuw5EjRzhx4gR16tQpr2JWCaW9FsYYvvvuOzZu3MiFF15YnkUNeiW9Fk8++ST169cnKSmpIopZ5VWpBS292bdvHy6Xi1NOOSXX66eccgobNmzwekxKSorX/VNSUsqtnFVBSa6FlL2yuA7Dhw+nYcOG+cK/FE9Jr0VqaiqNGjUiIyMDh8PB//73Py655JLyLm5QK8m1+PHHH5k1axarV6+ugBIKKNSISBkbP348c+bM4fvvvyc8PNzfxamSatasyerVq0lPT+e7775jyJAhNGvWjG7duvm7aFXGoUOHuO2225gxYwb16tXzd3GqjCofaurVq4fD4WDPnj25Xt+zZw+xsbFej4mNjS3W/uKbklwLKXuluQ7PPPMM48eP59tvv6VNmzblWcwqoaTXwm6306JFCwDatm3L+vXrGTdunEJNKRT3WmzevJlt27Zx5ZVXel7LysoCICQkhI0bN9K8efPyLXQVVOX71ISGhtK+fXu+++47z2tZWVl89913dO7c2esxnTt3zrU/wDfffFPg/uKbklwLKXslvQ4TJ07kqaee4uuvv6ZDhw4VUdSgV1b/TWRlZZGRkVEeRawyinstWrVqxR9//MHq1as921VXXcVFF13E6tWriY+Pr8jiVx3+7qlcGcyZM8eEhYWZ1157zaxbt84MGjTI1KpVy6SkpBhjjLntttvMiBEjPPsvWbLEhISEmGeeecasX7/ejB49WkO6y0hxr0VGRoZZtWqVWbVqlWnQoIF56KGHzKpVq0xycrK/vkJQKO51GD9+vAkNDTXvv/++2b17t2c7dOiQv75C0CjutXj66afN/PnzzebNm826devMM888Y0JCQsyMGTP89RWCRnGvRV4a/VT+FGpOevHFF03jxo1NaGioOeecc8xPP/3kea9r166mX79+ufafO3euOfXUU01oaKhp3bq1+eKLLyq4xMGrONdi69atBsi3de3ateILHmSKcx0SEhK8XofRo0dXfMGDUHGuxSOPPGJatGhhwsPDTe3atU3nzp3NnDlz/FDq4FTce0VOCjXlz2aMMf6qJRIREREpK1W+T42IiIgEB4UaERERCQoKNSIiIhIUFGpEREQkKCjUiIiISFBQqBEREZGgoFAjIiIiQUGhRkRERIKCQo2IiIgEBYUaEQloO3bs4M4776Rhw4aEhoaSkJDAfffdx/79+/1dNBGpYAo1IhKwtmzZQocOHUhOTuadd95h06ZNTJ061bNy8j///OPvIopIBdLaTyISsC6//HLWrFnDn3/+SUREhOf1lJQUmjdvzu23387LL7/sxxKKSEVSTY2IBKR//vmHefPm8X//93+5Ag1AbGwst9xyC++++y76u02k6lCoEZGAlJycjDGG0047zev7p512GgcOHGDv3r0VXDIR8ReFGhEJaEXVxISGhlZQSUTE3xRqRCQgtWjRApvNxvr1672+v379emJiYqhVq1bFFkxE/EahRkQCUt26dbnkkkv43//+x9GjR3O9l5KSwltvvUX//v39UzgR8QuNfhKRgJWcnEyXLl047bTTGDNmDE2bNmXt2rUMHTqUkJAQFi9eTI0aNfxdTBGpIKqpEZGAlZiYyIoVK2jWrBl9+/YlISGByy+/nFNPPZUlS5Yo0IhUMaqpEZGgMnr0aCZPnsw333zDueee6+/iiEgFUqgRkaDz6quvkpqayn/+8x/sdlVIi1QVCjUiIiISFPQnjIiIiAQFhRoREREJCgo1IiIiEhQUakRERCQoKNSIiIhIUFCoERERkaCgUCMiIiJBQaFGREREgoJCjYiIiASF/wdsDuuSKSp4HQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# an Objective has a plot method, which is a quick visualisation. You need\n", "# matplotlib installed to create a graph\n", "objective.plot()\n", "plt.legend()\n", "plt.xlabel('Q')\n", "plt.ylabel('logR')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from refnx.reflect._code_fragment import code_fragment\n", "cf = code_fragment(objective)\n", "\n", "with open(\"cf.py\", 'w') as f:\n", " f.write(cf)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "________________________________________________________________________________\n", "Objective - 4733931824\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 920.5781637868356\n", "Weighted = True\n", "Transform = Transform('logY')\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'film' \n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Structure has a sld_profile method to return the SLD profile. Let's also plot that.\n", "plt.plot(*structure.sld_profile())\n", "\n", "# and print out the results of the fit. For the case of DifferentialEvolution uncertainties\n", "# are estimated by estimating the Hessian/Covariance matrix\n", "print(objective)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# now lets do a MCMC sampling of the curvefitting system\n", "# first we do 400 samples which we then discard. These samples are\n", "# discarded because the initial chain might not be representative\n", "# of an equilibrated system (i.e. distributed around the mean with\n", "# the correct covariance).\n", "fitter.sample(400)\n", "fitter.reset()\n", "# now do a production run, only saving 1 in 100 samples. This is to\n", "# remove autocorrelation. We save 30 steps, giving a total of 30 * 200\n", "# samples (200 walkers is the default).\n", "res = fitter.sample(30, nthin=100, pool=4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# now let's look at the final output of the sampling. Each varying\n", "# parameter is given a set of statistics. `Parameter.value` is the\n", "# median of the chain samples. `Parameter.stderr` is half the [15, 85]\n", "# percentile, representing a standard deviation.\n", "print(objective)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# a corner plot shows the covariance between parameters\n", "objective.corner();" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# once we've done the sampling we can look at the variation in the model\n", "# at describing the data. In this example there isn't much spread.\n", "objective.plot(samples=100);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# in a similar manner we can look at the spread in SLD profiles\n", "# consistent with the data. The objective.pgen generator yields\n", "# parameter sets from the chain.\n", "\n", "# but first we'll save the parameters in an array.\n", "saved_params = np.array(objective.parameters)\n", "\n", "z, true_sld = structure.sld_profile()\n", "\n", "for pvec in objective.pgen(ngen=500):\n", " objective.setp(pvec)\n", " zs, sld = structure.sld_profile()\n", " plt.plot(zs, sld, color='k', alpha=0.05)\n", "\n", "# put back saved_params\n", "objective.setp(saved_params)\n", "\n", "plt.plot(z, true_sld, lw=1, color='r')\n", "plt.ylim(2.2, 6)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.53/examples/reflectometry_global.ipynb000066400000000000000000000125631477046072400220310ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates how to do co-refinement of several datasets with *refnx*" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "from __future__ import print_function, division\n", "\n", "import os.path\n", "\n", "from refnx.dataset import ReflectDataset\n", "from refnx.analysis import Transform, CurveFitter, Objective, GlobalObjective, Parameter\n", "from refnx.reflect import SLD, ReflectModel\n", "\n", "import corner\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# what is the refnx version\n", "# it's import to record this for reproducing the analysis\n", "import refnx\n", "print(refnx.version.version)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# these are datasets used in refnx testing, distributed with every refnx install\n", "pth = os.path.dirname(refnx.__file__)\n", "\n", "# load the data\n", "e361 = ReflectDataset(os.path.join(pth, 'analysis/test/', 'e361r.txt'))\n", "e365 = ReflectDataset(os.path.join(pth, 'analysis/test/', 'e365r.txt'))\n", "e366 = ReflectDataset(os.path.join(pth, 'analysis/test/', 'e366r.txt'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "si = SLD(2.07, 'Si')\n", "sio2 = SLD(3.47, 'SiO2')\n", "polymer = SLD(2.0, 'polymer')\n", "d2o = SLD(6.36, 'D2O')\n", "h2o = SLD(-0.56, 'H2O')\n", "cm3 = SLD(3.5, 'cm3.5')\n", "\n", "sio2_l = sio2(30, 3)\n", "sio2_l.thick.setp(vary=True, bounds=(1, 50))\n", "\n", "# Each contrast uses the same polymer SLD. We account for contrast change\n", "# using the volume fraction of solvent.\n", "polymer_l = polymer(250, 3)\n", "polymer_l.thick.setp(vary=True, bounds=(200, 300))\n", "polymer_l.sld.real.setp(vary=True, bounds=(0.1, 2))\n", "polymer_l.vfsolv.setp(vary=True, bounds=(0, 1))\n", "\n", "# we're going to share the water/polymer roughness across all 3 datasets\n", "water_poly_rough = Parameter(3, 'water_poly_rough')\n", "d2o_l = d2o(0, water_poly_rough)\n", "h2o_l = h2o(0, water_poly_rough)\n", "cm3_l = cm3(0, water_poly_rough)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "structure361 = si | sio2_l | polymer_l | d2o_l\n", "structure365 = si | sio2_l | polymer_l | cm3_l\n", "structure366 = si | sio2_l | polymer_l | h2o_l" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model361 = ReflectModel(structure361)\n", "model365 = ReflectModel(structure365)\n", "model366 = ReflectModel(structure366)\n", "\n", "model361.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model361.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))\n", "model365.scale.setp(vary=True, bounds=(0.9, 1.1))\n", "model365.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))\n", "model366.bkg.setp(vary=True, bounds=(0.9e-8, 3e-5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "objective361 = Objective(model361, e361, transform=Transform('logY'))\n", "objective365 = Objective(model365, e365, transform=Transform('logY'))\n", "objective366 = Objective(model366, e366, transform=Transform('logY'))\n", "\n", "global_objective = GlobalObjective([objective361, objective365, objective366])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# create the fit instance\n", "fitter = CurveFitter(global_objective)\n", "\n", "res = fitter.fit('differential_evolution')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "global_objective.plot()\n", "plt.legend()\n", "plt.xlabel('Q')\n", "plt.ylabel('logR');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(global_objective)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fitter.sample(400, random_state=1)\n", "fitter.sampler.reset()\n", "res = fitter.sample(30, nthin=100, random_state=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "global_objective.corner();" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(global_objective)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 } refnx-0.1.53/experimental/000077500000000000000000000000001477046072400154325ustar00rootroot00000000000000refnx-0.1.53/experimental/jax_ReflectModel.ipynb000066400000000000000000000235331477046072400217120ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "32926a7d-9929-4169-a067-e1c954c361d4", "metadata": {}, "outputs": [], "source": [ "from jax import config\n", "config.update(\"jax_enable_x64\", True)" ] }, { "cell_type": "code", "execution_count": 2, "id": "f03ff9cb-d047-48cb-ae11-e917729f4780", "metadata": {}, "outputs": [], "source": [ "from refnx._lib import flatten\n", "import numpy as np\n", "from refnx.analysis import Parameter, Model, Parameters, Objective\n", "from refnx.dataset import Data1D\n", "from refnx._lib import flatten, unique\n", "import jax.numpy as jnp\n", "from jax import grad, jit, vmap\n", "from jax import random" ] }, { "cell_type": "code", "execution_count": 3, "id": "e6a5e37d-e538-4513-97ce-5e47802f25e8", "metadata": {}, "outputs": [], "source": [ "np.random.seed(123)\n", "\n", "# Choose the \"true\" parameters.\n", "m_true = -0.9594\n", "b_true = 4.294\n", "f_true = 0.534\n", "\n", "# Generate some synthetic data from the model.\n", "N = 50000\n", "x = np.sort(10 * np.random.rand(N))\n", "yerr = 0.1 + 0.5 * np.random.rand(N)\n", "y = m_true * x + b_true\n", "y += np.abs(f_true * y) * np.random.randn(N)\n", "y += yerr * np.random.randn(N)\n", "\n", "data = Data1D(data=(x, y))" ] }, { "cell_type": "code", "execution_count": 4, "id": "e9e3727a-3abe-4974-96a1-7753b98d2ea7", "metadata": {}, "outputs": [], "source": [ "def make_evaluator(objective):\n", " # pars = list(flatten(objective.parameters))\n", " vpars = objective.varying_parameters()\n", " \n", " def func(pvs):\n", " for vpar, pv in zip(vpars, pvs):\n", " vpar._value = pv\n", " return objective.logl()\n", "\n", " return func, grad(func)" ] }, { "cell_type": "code", "execution_count": 5, "id": "422a3637-ad1a-42dd-985a-2d9ed160912f", "metadata": {}, "outputs": [], "source": [ "m = Parameter(1)\n", "c = Parameter(0)" ] }, { "cell_type": "code", "execution_count": 6, "id": "b58ba184-8d1d-4948-ac9d-5f9ca343daad", "metadata": {}, "outputs": [], "source": [ "class Line(Model):\n", " def __init__(self, pars):\n", " self._parameters = Parameters(pars)\n", " self.fitfunc = None\n", " self.fcn_args = None\n", " self.fcn_kwds = None\n", " self.pars = pars\n", "\n", " def model(self, x, p=None, x_err=None):\n", " if p is not None:\n", " self.parameters.pvals = np.array(p)\n", "\n", " return self.parameters[0].value * x + self.parameters[1].value" ] }, { "cell_type": "code", "execution_count": 7, "id": "fde02db3-636e-4fad-a441-3bb1de442c4a", "metadata": {}, "outputs": [], "source": [ "l = Line([m, c])\n", "m.vary = True" ] }, { "cell_type": "code", "execution_count": 8, "id": "29812390-4eab-4759-837b-e3ffaf71a687", "metadata": {}, "outputs": [], "source": [ "objective = Objective(l, data)" ] }, { "cell_type": "code", "execution_count": 9, "id": "2bf39752-c0cf-4c82-89e3-16d3b2f25ba7", "metadata": {}, "outputs": [], "source": [ "f, g = make_evaluator(objective)" ] }, { "cell_type": "code", "execution_count": 10, "id": "d4e469c6-f398-4d5e-bbc3-92d0521aa8be", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Array(-4659324.74119686, dtype=float64)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f(jnp.array([2.0]))" ] }, { "cell_type": "code", "execution_count": 11, "id": "82ade689-0d4b-4245-bacd-2fae0e52bfa8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Array([1144222.39350771], dtype=float64)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g(jnp.array([-1.0]))" ] }, { "cell_type": "code", "execution_count": 12, "id": "77f60053-7d4b-4408-bd45-19cbd5f337d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-1622769.8377402509" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "objective.logl([1.0, 0.0])" ] }, { "cell_type": "code", "execution_count": 13, "id": "d32cc40f-ac5a-4db1-8e4a-22210d9e3d09", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(numpy.float64, numpy.float64)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(l.parameters[0]._value), type(l.parameters[1]._value)" ] }, { "cell_type": "code", "execution_count": 14, "id": "518d5dfe-48af-40d7-899a-715d91dc80c2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[Array(1144222.39350771, dtype=float64, weak_type=True)]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g([-1.])" ] }, { "cell_type": "code", "execution_count": 15, "id": "fd9c2219-0085-4edb-93c4-b602ea4d6f45", "metadata": {}, "outputs": [], "source": [ "# %timeit g(jnp.array([1.0]))" ] }, { "cell_type": "code", "execution_count": 16, "id": "c5b8ffe3-45d7-4490-8f65-13d67e37ad28", "metadata": {}, "outputs": [], "source": [ "from scipy.optimize._numdiff import approx_derivative" ] }, { "cell_type": "code", "execution_count": 17, "id": "796978c1-a6e1-4acb-b94e-a10fb89510b5", "metadata": {}, "outputs": [], "source": [ "# %timeit approx_derivative(objective.logl, [-1.])" ] }, { "cell_type": "code", "execution_count": 18, "id": "ea1e7b38-764f-406b-ada1-6e5ee1e90cfe", "metadata": {}, "outputs": [], "source": [ "from refnx.reflect import reflect_model, Slab, SLD, Structure, ReflectModel, abeles, use_reflect_backend\n", "from refnx.reflect._jax_reflect import abeles_jax\n", "reflect_model.kernel = abeles_jax" ] }, { "cell_type": "code", "execution_count": 19, "id": "a19af87c-30b8-465d-96ed-51d4982f8165", "metadata": {}, "outputs": [], "source": [ "air = SLD(0.0)\n", "si = SLD(2.07)\n", "sio2 = SLD(3.47)\n", "s = air | sio2(15, 3) | si(0, 3)\n", "s[-2].thick.setp(vary=True, bounds=(10, 20))\n", "s[-2].rough.setp(vary=True, bounds=(1, 6))\n", "model = ReflectModel(s)\n", "model.scale.setp(vary=True)\n", "model.bkg.setp(vary=True)\n", "sio2.real.setp(vary=True)\n", "si.real.setp(vary=True)\n", "s[-1].rough.setp(vary=True)\n", "s[-2].rough.setp(vary=True)\n", "data = Data1D('c_PLP0000708.dat')\n", "objective = Objective(model, data)\n", "arr = np.array(objective.varying_parameters())\n", "sarr = np.array(objective.parameters)" ] }, { "cell_type": "code", "execution_count": 20, "id": "3f755b1e-7534-42f9-846a-91e1112ee973", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.88 ms ± 10.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "with use_reflect_backend('c'):\n", " model.threads=1\n", " objective.setp(np.copy(arr))\n", " %timeit approx_derivative(objective.logl, arr, method='2-point')" ] }, { "cell_type": "code", "execution_count": 20, "id": "9667e66b-8f1b-4e3e-b590-e6828ab245b3", "metadata": {}, "outputs": [], "source": [ "reflect_model.kernel = abeles_jax\n", "f, g = make_evaluator(objective)" ] }, { "cell_type": "code", "execution_count": 21, "id": "a5fec654-b6a7-4ab2-82f1-a9400972ca59", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11.8 ms ± 304 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit g(arr)" ] }, { "cell_type": "code", "execution_count": null, "id": "e22d02b1-7af1-4b95-9ee4-01b05ddb87e7", "metadata": {}, "outputs": [], "source": [ "for p in flatten(objective.parameters):\n", " print(type(p.value))" ] }, { "cell_type": "markdown", "id": "a55960f8-6896-489b-8c83-f87bcb935eb2", "metadata": {}, "source": [ "The basic timings for calculating d(Objective.logl) with finite differences vs `jax.grad` are clear. It's better to calculate the gradient using finite differences than use autograd.\n", "\n", "\n", "| method | Time | \n", "|--------|------|\n", "| finite differences | 3.88 ms |\n", "| jax.grad | 11.8 ms |\n", "\n", "\n", "I think the reason the difference is so stark is that the finite differences is due to the speed of the underlying reflectivity kernel. finite differences uses a C based kernel that is very fast (even when single threaded). In comparison `jax.grad` has to use a (jitted) JAX kernel which is way slower than the C-kernel. The speed comparison is a factor of 3! This means it's not worth using JAX for gradient estimation when trying to do NUTS sampling with `pymc`." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.0" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.53/experimental/jax_abeles.ipynb000066400000000000000000000215131477046072400205740ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "# os.environ['XLA_FLAGS'] = '--xla_dump_to=/tmp/foo'\n", "# import jax.numpy as jnp\n", "# from jax import grad, jacfwd, jacrev, jit\n", "# from jax.config import config\n", "# from jax.ops import index, index_add, index_update\n", "# config.update(\"jax_enable_x64\", True)\n", "from functools import reduce\n", "from scipy.optimize._numdiff import approx_derivative\n", "import matplotlib.pyplot as plt\n", "%load_ext line_profiler\n", "\n", "TINY = 1e-30\n", "q = np.linspace(0.01, 0.5, 1001)\n", "w = np.array([[0, 2.07, 0, 0],\n", " [100, 3.47, 0.0001, 3],\n", " [500, -0.5, 0.00001, 3],\n", " [0, 6.36, 0.0, 3]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def abeles(layers, q, bkg=0):\n", " qvals = np.asfarray(q)\n", " flatq = qvals.ravel()\n", "\n", " nlayers = layers.shape[0] - 2\n", " npnts = flatq.size\n", "\n", " kn = np.zeros((npnts, nlayers + 2), np.complex128)\n", " mi00 = np.ones((npnts, nlayers + 1), np.complex128)\n", "\n", " sld = np.zeros(nlayers + 2, np.complex128)\n", "\n", " # addition of TINY is to ensure the correct branch cut\n", " # in the complex sqrt calculation of kn.\n", " sld[1:] += (\n", " (layers[1:, 1] - layers[0, 1]) + 1j * (np.abs(layers[1:, 2]) + TINY)\n", " ) * 1.0e-6\n", "\n", " # kn is a 2D array. Rows are Q points, columns are kn in a layer.\n", " # calculate wavevector in each layer, for each Q point.\n", " kn[:] = np.sqrt(flatq[:, np.newaxis] ** 2.0 / 4.0 - 4.0 * np.pi * sld)\n", "\n", " # reflectances for each layer\n", " # rj.shape = (npnts, nlayers + 1)\n", " rj = kn[:, :-1] - kn[:, 1:]\n", " rj /= kn[:, :-1] + kn[:, 1:]\n", " rj *= np.exp(-2.0 * kn[:, :-1] * kn[:, 1:] * layers[1:, 3] ** 2)\n", "\n", " # characteristic matrices for each layer\n", " # miNN.shape = (npnts, nlayers + 1)\n", " if nlayers:\n", " mi00[:, 1:] = np.exp(kn[:, 1:-1] * 1j * np.fabs(layers[1:-1, 0]))\n", " mi11 = 1.0 / mi00\n", " mi10 = rj * mi00\n", " mi01 = rj * mi11\n", "\n", " # initialise matrix total\n", " mrtot00 = mi00[:, 0]\n", " mrtot01 = mi01[:, 0]\n", " mrtot10 = mi10[:, 0]\n", " mrtot11 = mi11[:, 0]\n", "# return mi00, mi01, mi10, mi11\n", "\n", " # propagate characteristic matrices\n", " for idx in range(1, nlayers + 1):\n", " # matrix multiply mrtot by characteristic matrix\n", " p0 = mrtot00 * mi00[:, idx] + mrtot10 * mi01[:, idx]\n", " p1 = mrtot00 * mi10[:, idx] + mrtot10 * mi11[:, idx]\n", " mrtot00 = p0\n", " mrtot10 = p1\n", "\n", " p0 = mrtot01 * mi00[:, idx] + mrtot11 * mi01[:, idx]\n", " p1 = mrtot01 * mi10[:, idx] + mrtot11 * mi11[:, idx]\n", "\n", " mrtot01 = p0\n", " mrtot11 = p1\n", " \n", "# return mrtot00, mrtot01, mrtot10, mrtot11\n", "\n", " r = mrtot01 / mrtot00\n", " reflectivity = r * np.conj(r)\n", " reflectivity += bkg\n", " return np.real(np.reshape(reflectivity, qvals.shape))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def abeles2(layers, q, bkg=0):\n", " qvals = np.asfarray(q)\n", " flatq = qvals.ravel()\n", " q2 = flatq**2 / 4.0\n", "\n", " nlayers = layers.shape[0] - 2\n", " npnts = flatq.size\n", "\n", " kn = np.zeros((npnts, nlayers + 2), np.complex128)\n", "# mi00 = np.ones((npnts, nlayers + 1), np.complex128)\n", " mi = np.zeros((npnts, nlayers + 1, 2, 2), np.complex128)\n", " mi[:, :, 0, 0] = 1.0\n", "\n", " sld = np.zeros(nlayers + 2, np.complex128)\n", "\n", " # addition of TINY is to ensure the correct branch cut\n", " # in the complex sqrt calculation of kn.\n", " sld[1:] += (\n", " (layers[1:, 1] - layers[0, 1]) + 1j * (np.abs(layers[1:, 2]) + TINY)\n", " ) * 1.0e-6\n", "\n", " # kn is a 2D array. Rows are Q points, columns are kn in a layer.\n", " # calculate wavevector in each layer, for each Q point.\n", " kn[:] = np.sqrt(q2[:, np.newaxis] - 4.0 * np.pi * sld)\n", "\n", " # reflectances for each layer\n", " # rj.shape = (npnts, nlayers + 1)\n", " rj = kn[:, :-1] - kn[:, 1:]\n", " rj /= kn[:, :-1] + kn[:, 1:]\n", " rj *= np.exp(-2.0 * kn[:, :-1] * kn[:, 1:] * layers[1:, 3] ** 2)\n", "\n", " # characteristic matrices for each layer\n", " # miNN.shape = (npnts, nlayers + 1)\n", " if nlayers:\n", " mi[:, 1:, 0, 0] = np.exp(kn[:, 1:-1] * 1j * np.fabs(layers[1:-1, 0]))\n", " mi[:, :, 1, 1] = 1.0 / mi[:, :, 0, 0]\n", " mi[:, :, 1, 0] = rj * mi[:, :, 0, 0]\n", " mi[:, :, 0, 1] = rj * mi[:, :, 1, 1]\n", "\n", "# stk = [np.squeeze(v) for v in np.hsplit(mi, nlayers + 1)]\n", "# mrtot = np.copy(stk[0])\n", "# mrtot = np.copy(mi[:, 0])\n", "# for idx in range(1, nlayers + 1):\n", "# mrtot[:] = np.matmul(mrtot[:], mi[:, idx])\n", "\n", "# for sub in stk[1:]:\n", "# mrtot = np.matmul(np.copy(mrtot), sub)\n", "# mrtot = reduce(np.matmul, stk[1:], stk[0])\n", "\n", " # initialise matrix total\n", " mrtot00 = mi[:, 0, 0, 0]\n", " mrtot01 = mi[:, 0, 0, 1]\n", " mrtot10 = mi[:, 0, 1, 0]\n", " mrtot11 = mi[:, 0, 1, 1]\n", " \n", "# # propagate characteristic matrices\n", " for idx in range(1, nlayers + 1):\n", " # matrix multiply mrtot by characteristic matrix\n", " p0 = mrtot00 * mi[:, idx, 0, 0] + mrtot10 * mi[:, idx, 0, 1]\n", " p1 = mrtot00 * mi[:, idx, 1, 0] + mrtot10 * mi[:, idx, 1, 1]\n", " mrtot00 = p0\n", " mrtot10 = p1\n", "\n", " p0 = mrtot01 * mi[:, idx, 0, 0] + mrtot11 * mi[:, idx, 0, 1]\n", " p1 = mrtot01 * mi[:, idx, 1, 0] + mrtot11 * mi[:, idx, 1, 1]\n", "\n", " mrtot01 = p0\n", " mrtot11 = p1\n", "\n", " r = mrtot01 / mrtot00\n", " reflectivity = r * np.conj(r)\n", " reflectivity += bkg\n", " return np.real(np.reshape(reflectivity, qvals.shape))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.testing.assert_allclose(abeles2(w, q), abeles(w, q))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.testing.assert_allclose(abeles2(w, q)[:, :, 0, 0], abeles(w, q)[0])\n", "np.testing.assert_allclose(abeles2(w, q)[:, :, 0, 1], abeles(w, q)[1])\n", "np.testing.assert_allclose(abeles2(w, q)[:, :, 1, 0], abeles(w, q)[2])\n", "np.testing.assert_allclose(abeles2(w, q)[:, :, 1, 1], abeles(w, q)[3])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%timeit abeles2(w, q)\n", "%timeit abeles(w, q)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(q, abeles2(w, q), label='new')\n", "plt.plot(q, abeles(w, q))\n", "plt.yscale('log')\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%lprun -f abeles abeles(w, q)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = np.random.uniform(size=100).reshape(25, 2, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.dot.reduce()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.multiply.identity" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.0" } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.53/experimental/jax_smear_reflect.ipynb000066400000000000000000000157411477046072400221620ustar00rootroot00000000000000{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "3597ab8d-b9b0-4a80-9852-293a1ab9f3b5", "metadata": {}, "outputs": [], "source": [ "from jax import config\n", "config.update(\"jax_enable_x64\", True)" ] }, { "cell_type": "code", "execution_count": 62, "id": "b632211d-77c3-4cce-bd58-ffec8f24f19b", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from functools import partial\n", "from scipy.optimize._numdiff import approx_derivative\n", "import jax.numpy as jnp\n", "from jax import jit, grad\n", "from refnx.reflect._jax_reflect import abeles_jax, jabeles\n", "from refnx.reflect import abeles\n", "from refnx.reflect.reflect_model import gauss_legendre, _smeared_kernel_pointwise, available_backends, get_reflect_backend\n", "\n", "_FWHM = 2 * np.sqrt(2 * np.log(2.0))\n", "_INTLIMIT = 3.5\n", "\n", "q = np.linspace(0.01, 0.5, 1000)\n", "w = np.array([[0, 2.07, 0, 0],\n", " [100, 3.47, 0, 3],\n", " [500, -0.5, 0.00001, 3],\n", " [0, 6.36, 0, 3]])" ] }, { "cell_type": "code", "execution_count": 11, "id": "d56c9dcf-02ff-4725-a171-4668df562620", "metadata": {}, "outputs": [], "source": [ "np.testing.assert_allclose(jabeles(q, w), abeles(q, w))" ] }, { "cell_type": "code", "execution_count": 51, "id": "4fe930c6-3997-41ec-a92c-a1055bab6c66", "metadata": {}, "outputs": [], "source": [ "def jax_smeared_kernel_pointwise(qvals, w, dqvals, quad_order=17, threads=-1):\n", " # get the gauss-legendre weights and abscissae\n", " abscissa, weights = gauss_legendre(quad_order)\n", "\n", " # get the normal distribution at that point\n", " prefactor = 1.0 / np.sqrt(2 * np.pi)\n", "\n", " def gauss(x):\n", " return np.exp(-0.5 * x * x)\n", "\n", " gaussvals = prefactor * gauss(abscissa * _INTLIMIT)\n", "\n", " # integration between -3.5 and 3.5 sigma\n", " va = qvals - _INTLIMIT * dqvals / _FWHM\n", " vb = qvals + _INTLIMIT * dqvals / _FWHM\n", "\n", " va = va[:, np.newaxis]\n", " vb = vb[:, np.newaxis]\n", "\n", " qvals_for_res = (np.atleast_2d(abscissa) * (vb - va) + vb + va) / 2.0\n", " smeared_rvals = jabeles(qvals_for_res, w)\n", "\n", " smeared_rvals = np.reshape(smeared_rvals, (qvals.size, abscissa.size))\n", "\n", " smeared_rvals *= np.atleast_2d(gaussvals * weights)\n", " return np.sum(smeared_rvals, 1) * _INTLIMIT\n", "\n", "smeared = jit(jax_smeared_kernel_pointwise)" ] }, { "cell_type": "code", "execution_count": 52, "id": "20f1f760-3385-4d5b-a3bb-be7b37294daf", "metadata": {}, "outputs": [], "source": [ "np.testing.assert_allclose(smeared(q, w, 0.05 * q), _smeared_kernel_pointwise(q, w, 0.05 * q))" ] }, { "cell_type": "code", "execution_count": 53, "id": "ca304005-5107-4739-879b-06eb8653f75c", "metadata": {}, "outputs": [], "source": [ "data = abeles(q, w)" ] }, { "cell_type": "code", "execution_count": 76, "id": "cb67e912-0e25-432e-b9b6-389aa19ddee1", "metadata": {}, "outputs": [], "source": [ "def chi2(q, w):\n", " return np.sum((smeared(q, w, 0.05 * q) - data)**2)\n", "\n", "def chi2_2(q, w):\n", " w = np.reshape(w, (-1, 4))\n", " return np.sum((smeared(q, w, 0.05 * q) - data)**2) \n", "\n", "def chi2_3(q, w):\n", " w = np.reshape(w, (-1, 4))\n", " return np.sum((_smeared_kernel_pointwise(q, w, 0.05 * q) - data)**2) " ] }, { "cell_type": "code", "execution_count": 71, "id": "69f59574-9bce-4b78-aa1c-24ed5625a867", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12.7 ms ± 40.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "gsmeared = grad(chi2, argnums=1)\n", "%timeit gsmeared(q, w)" ] }, { "cell_type": "code", "execution_count": 70, "id": "cbf62d18-2f6e-4476-8636-1f0f74e51787", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "110 ms ± 218 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "part_chi2 = partial(chi2_2, q)\n", "%timeit approx_derivative(part_chi2, w.ravel())" ] }, { "cell_type": "code", "execution_count": 77, "id": "8cd7a3f6-7435-4cf8-a2cc-9171b815913e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15.4 ms ± 54.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "part_chi3 = partial(chi2_3, q)\n", "%timeit approx_derivative(part_chi3, w.ravel())" ] }, { "cell_type": "code", "execution_count": 72, "id": "20b1713e-8297-44d1-a679-89b9de6c58f9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "264 µs ± 1.13 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" ] } ], "source": [ "%timeit abeles_jax(q, w)" ] }, { "cell_type": "code", "execution_count": 73, "id": "7696a4a8-ca4f-49cf-9a56-43a06cb2afe5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "70.5 µs ± 257 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" ] } ], "source": [ "%timeit abeles(q, w)" ] }, { "cell_type": "code", "execution_count": 74, "id": "4bade5ea-3be4-4406-9aff-335a4421af86", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "463 µs ± 1.06 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" ] } ], "source": [ "%timeit _smeared_kernel_pointwise(q, w, 0.05 * q)" ] }, { "cell_type": "code", "execution_count": 75, "id": "20af83cf-631d-4d0f-9186-629c5e0d8507", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.27 ms ± 3.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%timeit smeared(q, w, 0.05 * q)" ] }, { "cell_type": "code", "execution_count": null, "id": "505a4ae6-14be-48fa-88b1-176683640868", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.0" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.53/paper/000077500000000000000000000000001477046072400140445ustar00rootroot00000000000000refnx-0.1.53/paper/.gitignore000066400000000000000000000001501477046072400160300ustar00rootroot00000000000000*.aux *.bbl *.blg *.fdb_latexmk *.fls *.log *.out *.synctex.gz *.backup *.orig manuscript.pdf reply.pdf refnx-0.1.53/paper/LICENCE000066400000000000000000000443321477046072400150370ustar00rootroot00000000000000Attribution 4.0 International ======================================================================= Creative Commons Corporation ("Creative Commons") is not a law firm and does not provide legal services or legal advice. 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This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 UK: England & Wales License</a>. 2019-02-01 true 10.1107/S1600576718017296 10.1107/S1600576718017296 noindex 2019-02-01 true iucr.org iucr.org 10.1107/S1600576718017296 VoR doi:10.1107/S1600576718017296 J. Appl. Cryst (2019). 52 [doi:10.1107/S1600576718017296] computer programs application/pdf refnx: neutron and X-ray reflectometry analysis in Python en refnx is a model-based neutron and X-ray reflectometry data analysis package written in Python. It is cross platform and has been tested on Linux, macOS and Windows. Its graphical user interface is browser based, through a Jupyter notebook. Model construction is modular, being composed from a series of components that each describe a subset of the interface, parameterized in terms of physically relevant parameters (volume fraction of a polymer, lipid area per molecule etc.). The model and data are used to create an objective, which is used to calculate the residuals, log-likelihood and log-prior probabilities of the system. Objectives are combined to perform co-refinement of multiple data sets and mixed-area models. Prior knowledge of parameter values is encoded as probability distribution functions or bounds on all parameters in the system. Additional prior probability terms can be defined for sets of components, over and above those available from the parameters alone. Algebraic parameter constraints are available. The software offers a choice of fitting approaches, including least-squares (global and gradient-based optimizers) and a Bayesian approach using a Markov-chain Monte Carlo algorithm to investigate the posterior distribution of the model parameters. The Bayesian approach is useful for examining parameter covariances, model selection and variability in the resulting scattering length density profiles. The package is designed to facilitate reproducible research; its use in Jupyter notebooks, and subsequent distribution of those notebooks as supporting information, permits straightforward reproduction of analyses. 2019-02-01 International Union of Crystallography NEUTRON REFLECTOMETRY X-RAY REFLECTOMETRY BAYESIAN ANALYSIS COMPUTER MODELLING REFNX Nelson, A.R.J. 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ȺPws \$TBmgPԋB >Gm1Q\=Kqϋz5Hl1@GQcݓm]PBݑ|,~c%zB[i "4JwYɋJʝw]A=i3QJE}+kW4W{}3| ^ e)^Ӣth̟0 0J_ kݚdHnU@ŶaSb &IENDB`refnx-0.1.53/paper/components.svg000066400000000000000000001245771477046072400167720ustar00rootroot00000000000000 image/svg+xml ObjectiveCalculates: log-prior,log-likelihood, χ2 Data1D ReflectModel: ModelCalculates generative model of Structure.Applies resolution smearing CurveFitterPerforms least squares fitting, MCMC sampling of log-posterior distribution StructureDescribes the interface,comprised of a series ofComponents GlobalObjective: Objectiveoptionally combine several Objectives Parametercalculate log-prior vary: bool bounds: Bounds value: float background: Parameter resolution: Parameter scale: Parameter Measurement Parameters Slab: Component Slab: Component Componentdescribes sectionof interface thickness: Parameter sld.real: Parameter sld.imag: Parameter roughness: Parameter vfsolv: Parameter refnx-0.1.53/paper/main.bib000066400000000000000000000377251477046072400154640ustar00rootroot00000000000000% Encoding: UTF-8 @article{campbell2018, author = {Richard A. Campbell and Yussif Saaka and Yanan Shao and Yuri Gerelli and Robert Cubitt and Ewa Nazaruk and Dorota Matyszewska and M. Jayne Lawrence}, title = {Structure of surfactant and phospholipid monolayers at the air/water interface modeled from neutron reflectivity data}, journal = {Journal of Colloid and Interface Science}, year = 2018, volume = 531, pages = {98-108}, doi = {10.1016/j.jcis.2018.07.022} } @article{emcee, author = {{Foreman-Mackey}, D. and {Hogg}, D.~W. and {Lang}, D. and {Goodman}, J.}, title = {emcee: The MCMC Hammer}, journal = {Publications of the Astronomical Society of the Pacific}, year = 2013, volume = 125, pages = {306-312}, eprint = {1202.3665}, doi = {10.1086/670067} } @article{pauw2013, author = {{Pauw}, B.~R}, title = {Everything SAXS: small-angle scattering pattern collection and correction}, journal = {Journal of Physics: Condensed Matter}, year = 2013, volume = 25, pages = {383201}, doi = {10.1088/0953-8984/26/23/239501} } @article{corner, Author = {Daniel Foreman-Mackey}, Doi = {10.21105/joss.00024}, Title = {corner.py: Scatterplot matrices in {P}ython}, Journal = {The Journal of Open Source Software}, Year = 2016, volume = 1, number = 2, pages = 24, XUrl = {http://dx.doi.org/10.5281/zenodo.45906} } @Article{Majkrzak1999, author = {Charles Majkrzak}, title = {Neutron Reflectometry Studies of Thin Films and Multilayered Materials}, journal = {Acta Physica Polonica}, year = {1999}, volume = {96}, number = {1}, pages = {81}, } @Article{Heinrich2009, author = {Frank Heinrich and Tiffany Ng and David J. Vanderah and Prabhanshu Shekhar and Mihaela Mihailescu and Hirsh Nanda and Mathias Losche}, title = {A New Lipid Anchor for Sparsely Tethered Bilayer Lipid Membranes}, journal = {Langmuir}, year = {2009}, volume = {25}, number = {7}, pages = {4219-4229}, doi = {10.1021/la8033275}, } @Article{Helliwell2017, doi = {10.1107/s2052252517013690}, Xurl = {https://doi.org/10.1107%2Fs2052252517013690}, year = 2017, month = {oct}, publisher = {International Union of Crystallography ({IUCr})}, volume = {4}, number = {6}, pages = {714--722}, author = {John R. Helliwell and Brian McMahon and J. Mitchell Guss and Loes M. J. Kroon-Batenburg}, title = {The science is in the data}, journal = {{IUCrJ}} } @article{Chirigati2013, author = {Fernando Seabra Chirigati and Matthias Troyer and Dennis E. Shasha and Juliana Freire}, title = {A Computational Reproducibility Benchmark}, journal = {{IEEE} Data Eng. Bull.}, volume = {36}, number = {4}, pages = {54--59}, year = {2013}, Xurl = {http://sites.computer.org/debull/A13dec/p54.pdf}, timestamp = {Thu, 11 Aug 2016 11:10:44 +0200}, biburl = {https://dblp.org/rec/bib/journals/debu/ChirigatiTSF13}, bibsource = {dblp computer science bibliography, https://dblp.org} } @Misc{Kienzle2011, author = {P. A. Kienzle and J. Krycka and N. Patel and I. Sahin}, title = {Refl1D -- depth profile modelling}, Xhowpublished = {http://reflectometry.org/danse/}, year = {2011}, url = {http://reflectometry.org/danse/docs/refl1d/}, version = {0.7.9a2}, } @Conference{Kluyver:2016aa, author = {Thomas Kluyver and Benjamin Ragan-Kelley and Fernando P{\'e}rez and Brian Granger and Matthias Bussonnier and Jonathan Frederic and Kyle Kelley and Jessica Hamrick and Jason Grout and Sylvain Corlay and Paul Ivanov and Dami{\'a}n Avila and Safia Abdalla and Carol Willing}, title = {Jupyter Notebooks -- a publishing format for reproducible computational workflows}, booktitle = {Positioning and Power in Academic Publishing: Players, Agents and Agendas}, year = {2016}, editor = {F. Loizides and B. Schmidt}, pages = {87 - 90}, organization = {IOS Press}, } @Misc{ipywidgets, author = {{Project Jupyter Contributors}}, title = {ipywidgets}, Xhowpublished = {https://github.com/jupyter-widgets/ipywidgets}, year = {2015-2016}, url = {https://github.com/jupyter-widgets/ipywidgets}, } @Article{Moeller2017a, author = {Möller, Steffen and Prescott, Stuart W. and Wirzenius, Lars and Reinholdtsen, Petter and Chapman, Brad and Prins, Pjotr and Soiland-Reyes, Stian and Klötzl, Fabian and Bangnacani, Andrea and Kalaš, Matús and Tille, Andreas and Crusoe, Michael R}, title = {{Robust cross-platform workflows: how technical and scientific communities collaborate to develop, test and share best practices for data analysis}}, journal = {Data Science and Engineering}, year = {2017}, volume = {2}, pages = {232--244}, month = DEC, abstract = {Information integration and workflow technologies for data analysis have always been major fields of investigation in bioinformatics. A range of popular workflow suites are available to support analyses in computational biology. Commercial providers tend to offer prepared applications remote to their clients. However, for most academic environments with local expertise, novel data collection techniques or novel data analysis, it is essential to have all the flexibility of open-source tools and open-source workflow descriptions. Workflows in data-driven science such as computational biology have considerably gained in complexity. New tools or new releases with additional features arrive at an enormous pace, and new reference data or concepts for quality control are emerging. A well-abstracted workflow and the exchange of the same across work groups have an enormous impact on the efficiency of research and the further development of the field. High-throughput sequencing adds to the avalanche of data available in the field; efficient computation and, in particular, parallel execution motivate the transition from traditional scripts and Makefiles to workflows. We here review the extant software development and distribution model with a focus on the role of integration testing and discuss the effect of common workflow language on distributions of open-source scientific software to swiftly and reliably provide the tools demanded for the execution of such formally described workflows. It is contended that, alleviated from technical differences for the execution on local machines, clusters or the cloud, communities also gain the technical means to test workflow-driven interaction across several software packages.}, doi = {10.1007/s41019-017-0050-4}, file = {Moeller2017a.pdf:Moeller2017a.pdf:PDF}, owner = {stuart}, timestamp = {2017.11.24}, } @Article{Stark2018, author = {Philip Stark}, title = {Before reproducibility must come preproducibility}, journal = {Nature}, year = {2018}, volume = {557}, pages = {613}, doi = {10.1038/d41586-018-05256-0}, } @Article{Trewhella:jc5010, author = {Trewhella, Jill and Duff, Anthony P. and Durand, Dominique and Gabel, Frank and Guss, J. Mitchell and Hendrickson, Wayne A. and Hura, Greg L. and Jacques, David A. and Kirby, Nigel M. and Kwan, Ann H. and P{\'{e}}rez, Javier and Pollack, Lois and Ryan, Timothy M. and Sali, Andrej and Schneidman-Duhovny, Dina and Schwede, Torsten and Svergun, Dmitri I. and Sugiyama, Masaaki and Tainer, John A. and Vachette, Patrice and Westbrook, John and Whitten, Andrew E.}, title = {{2017 publication guidelines for structural modelling of small-angle scattering data from biomolecules in solution: an update}}, journal = {Acta Crystallographica Section D}, year = {2017}, volume = {73}, number = {9}, pages = {710--728}, month = {Sep}, abstract = {In 2012, preliminary guidelines were published addressing sample quality, data acquisition and reduction, presentation of scattering data and validation, and modelling for biomolecular small-angle scattering (SAS) experiments. Bio{\-}molecular SAS has since continued to grow and authors have increasingly adopted the preliminary guidelines. In parallel, integrative/hybrid determination of biomolecular structures is a rapidly growing field that is expanding the scope of structural biology. For SAS to contribute maximally to this field, it is essential to ensure open access to the information required for evaluation of the quality of SAS samples and data, as well as the validity of SAS-based structural models. To this end, the preliminary guidelines for data presentation in a publication are reviewed and updated, and the deposition of data and associated models in a public archive is recommended. These guidelines and recommendations have been prepared in consultation with the members of the International Union of Crystallography (IUCr) Small-Angle Scattering and Journals Commissions, the Worldwide Protein Data Bank (wwPDB) Small-Angle Scattering Validation Task Force and additional experts in the field.}, doi = {10.1107/S2059798317011597}, keywords = {small-angle scattering, SAXS, SANS, biomolecular structure, proteins, DNA, RNA, structural modelling, hybrid structural modelling, publication guidelines, integrative structural biology}, Xurl = {https://doi.org/10.1107/S2059798317011597}, } @Misc{conda, author = {{Continuum Analytics}}, title = {Conda -- Package, dependency and environment management for any language}, year = {2017}, url = {https://conda.io/docs/}, } @Article{Nelson2006, author = {Andrew Nelson}, title = {Co-refinement of multiple-contrast neutron/{X}-ray reflectivity data using MOTOFIT}, journal = {Journal of Applied Crystallography}, year = {2006}, volume = {39}, pages = {273-276}, doi = {10.1107/S0021889806005073}, } @Book{Heavens1955, title = {Optical Properties of Thin Films}, publisher = {Butterworth: London}, year = {1955}, author = {Heavens, O}, } @Article{Nevot1980, author = {Névot, L. and Croce, P.}, title = {Caractérisation des surfaces par réflexion rasante de rayons {X}. Application à l'étude du polissage de quelques verres silicates}, journal = {Rev. Phys. Appl.}, year = {1980}, volume = {15}, number = {761-769}, doi = {10.1051/rphysap:01980001503076100}, } @Article{Nelson2014, author = {Andrew Nelson and Charles Dewhurst}, title = {Toward a detailed resolution smearing kernel for time-of-Flight neutron reflectometers.}, journal = {Journal of Applied Crystallography}, year = {2014}, volume = {47}, pages = {1162}, doi = {10.1107/S1600576714009595}, owner = {anz}, timestamp = {2014.05.22}, } @Article{Gerelli2016, author = {Yuri Gerelli}, title = {Aurore: new software for neutron reflectivity data analysis}, journal = {Journal of Applied Crystallography}, year = {2016}, volume = {49}, pages = {330-339}, doi = {10.1107/S1600576716002466}, } @Misc{refnx, author = {Andrew Nelson and Stuart W. Prescott}, title = {refnx - Neutron and {X}-ray reflectometry analysis in {P}ython}, Xhowpublished = {https://www.github.com/refnx/refnx}, year = {2018}, doi = {10.5281/zenodo.1345464}, url = {https://www.github.com/refnx/refnx}, } @Misc{Jones2001-2017, author = {Eric Jones and Travis Oliphant and Pearu Peterson and others}, title = {{SciPy}: Open source scientific tools for {P}ython}, Xhowpublished = {https://www.scipy.org/}, year = {2001-2017}, url = {http://www.scipy.org/}, } @Article{Wood2017, author = {Mary Wood and Stuart Clarke}, title = {Neutron Reflectometry for Studying Corrosion and Corrosion Inhibition}, journal = {Metals}, year = {2017}, volume = {7}, number = {8}, pages = {304}, doi = {10.3390/met7080304}, } @Book{Daillant2009, title = {{X}-ray and Neutron Reflectivity: Principles and Applications}, publisher = {Springer Verlag}, year = {2009}, editor = {Jean Daillant and Alain Gibaud}, volume = {770}, series = {Lecture Notes in Physics}, isbn = {978-3-540-88588-7}, } @Article{Bjorck2007, author = {Matts Bjorck and Gabriella Andersson}, title = {{GenX}: an extensible {X}-ray reflectivity refinement program utilizing differential evolution}, journal = {Journal of Applied Crystallography}, year = {2007}, volume = {40}, pages = {1174-1178}, doi = {10.1107/S0021889807045086}, } @Book{Sivia2006, title = {Data Analysis: A Bayesian Tutorial}, publisher = {Oxford Science Publications}, year = {2006}, author = {Devinderjit Sivia and John Skilling}, isbn = {978-0198568322}, } @Article{Hogg2010, author = {David W. Hogg and Jo Bovy and Dustin Lang}, title = {Data analysis recipes: Fitting a model to data}, journal = {ArXiv e-prints}, year = {2010}, Xvolume = {arXiv:1008.4686}, month = aug, archiveprefix = {arXiv}, url = {arXiv:1008.4686}, eprint = {1008.4686}, } @article{Hogg2018a, author={David W. Hogg and Daniel Foreman-Mackey}, title={Data Analysis Recipes: Using Markov Chain Monte Carlo}, journal={The Astrophysical Journal Supplement Series}, volume={236}, number={1}, pages={11}, url={http://stacks.iop.org/0067-0049/236/i=1/a=11}, year={2018}, abstract={Markov Chain Monte Carlo (MCMC) methods for sampling probability density functions (combined with abundant computational resources) have transformed the sciences, especially in performing probabilistic inferences, or fitting models to data. In this primarily pedagogical contribution, we give a brief overview of the most basic MCMC method and some practical advice for the use of MCMC in real inference problems. We give advice on method choice, tuning for performance, methods for initialization, tests of convergence, troubleshooting, and use of the chain output to produce or report parameter estimates with associated uncertainties. We argue that autocorrelation time is the most important test for convergence, as it directly connects to the uncertainty on the sampling estimate of any quantity of interest. We emphasize that sampling is a method for doing integrals; this guides our thinking about how MCMC output is best used.} } @Article{ptemcee, author = {Will Vousden and Will M. Farr and Ilya Mandel}, title = {Dynamic temperature selection for parallel-tempering in Markov chain Monte Carlo simulations}, journal = {Monthly Notices of the Royal Astronomical Society}, year = {2016}, volume = {455}, pages = {1919-1937}, doi = {10.1093/mnras/stv2422}, } @Misc{Nelson2018, author = {Andrew Nelson and Stuart W. Prescott}, title = {Online reflectivity fitting with refnx}, Xhowpublished = {https://mybinder.org/v2/gh/refnx/refnx-binder.git/master}, year = {2018}, url = {https://mybinder.org/v2/gh/refnx/refnx-binder.git/master}, } @Article{Well2005, author = {Ad van Well and H. Fredrikze}, title = {On the resolution and intensity of a time-of-flight neutron reflectometer}, journal = {Physica B}, year = {2005}, volume = {357}, pages = {204-207}, doi = {10.1016/j.physb.2004.11.058}, } @InCollection{Millman2014, chapter = {Developing open source scientific practice}, booktitle = {Implementing Reproducible Research}, publisher = {Chapman \& Hall}, year = {2014}, author = {K. Jarrod Millman and Fernando Perez}, editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng}, } @Article{Hughes2016, author = {Arwel V. Hughes and Fillip Ciesielski and Antreas C. Kalli and Luke A. Clifton and Timothy R. Charlton and Mark S.P. Sansom and John R. P. Webster}, title = {On the interpretation of reflectivity data from lipid bilayers in terms of molecular-dynamics models}, journal = {Acta Crystallographica D}, year = {2016}, volume = {72}, pages = {1226-1240}, doi = {10.1107/S2059798316016235}, } @Comment{jabref-meta: databaseType:bibtex;} refnx-0.1.53/paper/manuscript.tex000066400000000000000000001016321477046072400167560ustar00rootroot00000000000000\documentclass[pdf,preprint]{article} \RequirePackage{graphicx} % \usepackage[colorlinks,urlcolor=black]{hyperref} %\renewcommand{\harvardurl}[1]{\url{#1}} %\newcommand{\url}[1]{#1} \usepackage[utf8]{inputenc} \usepackage{authblk} \usepackage{amsmath} \usepackage{siunitx} \sisetup{ separate-uncertainty = true, bracket-numbers = false, product-units = single, multi-part-units = single, } \usepackage[version=4]{mhchem} \begin{document} \title{refnx -- Neutron and X-ray reflectometry analysis in Python} \author[1]{Andrew~R.J.~Nelson} \author[2]{Stuart~W. Prescott} \affil[1]{ANSTO, Locked Bag 2001, Kirrawee DC, NSW 2232, Australia} \affil[2]{School of Chemical Engineering, University of New South Wales, Sydney, NSW, 2052, Australia} %\date{\today} \newcommand{\refnx}{\emph{refnx}} \newcommand{\Objective}{\texttt{Objective}} \newcommand{\GlobalObjective}{\texttt{GlobalObjective}} \newcommand{\Parameter}{\texttt{Parameter}} \newcommand{\Structure}{\texttt{Structure}} \newcommand{\Slab}{\texttt{Slab}} \newcommand{\Component}{\texttt{Component}} \newcommand{\LipidLeaflet}{\texttt{LipidLeaflet}} \newcommand{\Transform}{\texttt{Transform}} \newcommand{\DataD}{\texttt{Data1D}} \newcommand{\ReflectModel}{\texttt{ReflectModel}} \newcommand{\CurveFitter}{\texttt{CurveFitter}} \newcommand{\Spline}{\texttt{Spline}} \newcommand{\conda}{\emph{conda}} \newcommand{\corner}{\emph{corner}} \newcommand{\MixedReflectModel}{\texttt{MixedReflectModel}} \newcommand{\pip}{\emph{pip}} \newcommand{\emcee}{\emph{emcee}} \newcommand{\ptemcee}{\emph{ptemcee}} \newcommand{\NumPy}{\emph{NumPy}} \newcommand{\SciPy}{\emph{SciPy}} \newcommand{\Cython}{\emph{Cython}} \newcommand{\Jupyter}{\emph{Jupyter}} \newcommand{\ipywidgets}{\emph{ipywidgets}} \maketitle \hyphenation{Lipid-Leaflet} %\begin{synopsis} %The refnx Python modules for neutron and X-ray reflectometry data analysis are %introduced. An sample analysis illustrates a Bayesian approach using a %Markov Chain Monte Carlo algorithm to understand the confidence in the fit parameters. %\end{synopsis} \begin{abstract} \refnx\ is a model-based neutron and X-ray reflectometry data analysis package written in Python. It is cross platform, and has been tested on Linux, macOS, and Windows. Its graphical user interface is browser-based, through a \Jupyter\ notebook. Model construction is modular, being composed from a series of components that each describe a subset of the interface, parameterised in terms of physically relevant parameters (volume fraction of a polymer, lipid area per molecule, etc). The model and data are used to create an objective, which is used to calculate residuals, log-likelihood, and log-prior probabilities of the system. Objectives are combined to perform co-refinement of multiple datasets, and mixed-area models. Prior knowledge of parameter values is encoded as probability distribution functions or bounds on all parameters in the system. Additional prior probability terms can be defined for sets of components, over and above those available from the parameters alone. Algebraic parameter constraints are available. A choice of fitting approaches is available, including least-squares (global and gradient-based optimizers) and a Bayesian approach using Markov Chain Monte Carlo to investigate the posterior distribution of the model parameters. The Bayesian approach is useful in examining parameter covariances, model selection, and variability in the resulting scattering length density profiles. The package is designed to facilitate reproducible research; its use in \Jupyter\ notebooks, and subsequent distribution of those notebooks as supporting information, permits straightforward reproduction of analyses. \end{abstract} \section{Introduction} The use of specular X-ray and neutron reflectometry for the morphological characterisation of thin films on the approximate size range 10 to \SI{5000}{\angstrom} has grown remarkably over the past years \cite{Wood2017, Daillant2009}. Most neutron and X-ray sources have instruments to perform reflectometry measurements, and there is an ongoing need for accessible software programs for users of those instruments to analyse their data in a straightforward fashion, including the co-refinement of multiple contrast datasets. Several programs are available for this purpose, with a variety of different features \cite{Nelson2006,Bjorck2007,Kienzle2011,Gerelli2016,Hughes2016}. These programs typically create a model of the interface, and either incrementally refine the model against the data using least-squares methods, or use Bayesian approaches \cite{Sivia2006,Kienzle2011,Hogg2010} to examine the posterior probability distribution of the parameters (i.e.\ the statistical variation of the parameters in a model). Given the number of publications arising from the reflectometry technique, it is vital that both the experiments and analyses are reproducible. Reproducibility in research is an underlying principle of science; unfortunately, it is not always possible to reproduce the results of others \cite{Stark2018}, because there is frequently not enough information provided in journal articles to repeat the analyses. Even if the datasets and software packages used to analyse them are supplied in supporting information (most often they are not), a comprehensive, ordered, set of instructions or a codified workflow would need to be provided \cite{Moeller2017a}. One example for addressing this reproducibility issue is the set of guidelines from the small-angle scattering community for the deposition of data and and associated models \cite{Trewhella:jc5010, pauw2013}. Here, we outline a new reflectometry analysis package, \refnx\ (version number 0.1 is used in this paper \cite{refnx}), that helps address the reproducibility issue for the reflectometry community\footnote{We do not mean that other programs are irreproducible, rather that the information provided in journal articles is often lacking.} by creating a scripted analysis workflow that is readily published alongside the publication, such as we have done with this paper (see the Supporting Information). The \refnx\ Python package is specifically designed for use in \Jupyter\ notebooks \cite{Kluyver:2016aa}, which provide a literate programming environment that mixes executable code cells, rich documentation of the steps that were performed, and the computational output. By including the analysis, as performed by the authors, in such a notebook, and appending it as supporting information along with the data, readers are empowered to replicate the exact data analysis and potentially extend the analysis, provided they have set up the same computing environment \cite{Millman2014}. Setting up the computing environment is simplified using the \conda\ package manager \cite{conda}, and an environment file (although other approaches are available). \section{Method} \refnx\ is written in Python with an extensible object-oriented design, Figure~\ref{fig:components}, in which the user creates a model of the sample based on what they know about its composition, with refinement of that model against the data. As with \emph{Motofit} \cite{Nelson2006} it calculates reflectivity using the Abeles method \cite{Heavens1955} for specular reflection from a stratified medium. Detailed documentation for \refnx\ is available on-line\footnote{https://refnx.readthedocs.io/} and is distributed with the package. \begin{figure} \includegraphics[width=85mm]{components.pdf} \caption{Schematic showing the relationship between classes that make up a typical reflectometry curve-fitting problem. The key step for the user is assembling materials (\Component) such as a `Slab: Component' (a \Component\ that is a \Slab) and encoding prior knowledge into each \Parameter\ that describes that \Component.} \label{fig:components} \end{figure} The building block of the analysis is the \Parameter\ object which represents a model value (e.g. the SLD of the material), whether it is allowed to vary in a fit, and a bounds attribute. The bounds are a probability distribution representing pre-existing knowledge of a parameter's value, called a prior probability. A prior might be a simple uniform distribution that specifies a lower and upper bound (e.g. volume fraction is in the interval $[0, 1]$), or a normal distribution that represents an experimentally derived value and associated uncertainty (e.g. thickness is $\SI{100+-4}{\angstrom}$). Any of the \emph{scipy.stats} \cite{Jones2001-2017} continuous distributions, or other distributions created by the user, can be used for this purpose. Algebraic relationships between \Parameter\ objects can be applied to permit more sophisticated constraints that can cross between \Component\ objects (e.g. the sum of the thicknesses of several layers is known to some uncertainty). \subsection{Structure representation} The \Structure\ object represents the interfacial model, assembled from individual \Component\ objects in series. Each \Component\ represents a subset of the interface and selected attributes of the \Component\ can be described by physically relevant \Parameter\ objects. The simplest and most familiar \Component\ is a \Slab, which has a uniform scattering length density (SLD), thickness, roughness, and volume fraction of solvent. The simplest models are simply a series of \Slab\ objects. More sophisticated components include \LipidLeaflet\ (a lipid monolayer, or one-half of a lipid bilayer) and \Spline\ (for free-form modelling of an SLD profile using spline interpolation). It is straightforward to develop/modify new components for different structural functionality, a consequence of the program design. To include further prior knowledge of the real sample into the model, each \Component\ can additionally contribute to the prior probability in addition to its constituent \Parameter\ objects. This is useful when a \Component\ has a derived value, such as surface excess, which is already known. To calculate the reflectivity from series of \Component\ objects that form the model, each \Component\ has a \emph{slabs} property that represents a discretised `slice' approximation to a continuous SLD profile for its particular region of the interface. A \Slab\ object has a single slice because it is a single thickness of uniform SLD. A \LipidLeaflet\ is made of two slices (head/tail regions), but the \Spline\ has many thin slices approximating the smooth curve. Each of these slices has uniform SLD, with the N\'{e}vot--Croce approach being used to describe the roughness between them \cite{Nevot1980}. The \Structure\ object is used to construct a \ReflectModel\ object. This object is responsible for calculating the resolution smeared reflectivity of the \Structure, scaling the data, and adding a $Q$-independent constant background (via the scale and background \Parameter\ objects). There are different types of smearing available: constant $\mathrm{d}Q/Q$, point-by-point resolution smearing read from the dataset of interest, or via a smearing probability kernel of arbitrary shape \cite{Nelson2014}. The constant $\mathrm{d}Q/Q$ and point-by-point smearing use Gaussian convolution, with $\mathrm{d}Q$ representing the full width half maximum (FWHM) of a Gaussian approximation to the instrument resolution function \cite{Well2005}. \subsection{Model/data comparison} The \Objective\ class is the comparator of the predicted and measured reflectivities, using the \ReflectModel\ and a dataset, \DataD, to calculate $\chi^2$, log-likelihood (Equation~\ref{eqn:2}), log-prior, residuals, and the generative model. The \DataD\ object has \emph{x, x\_err, y, y\_err} attributes to represent $Q$, $\mathrm{d}Q$, $R$, $\mathrm{d}R$. As is standard for many reflectometry data files, the \DataD\ object reads a three or four column plain-text datafile. A three column dataset represents $Q$ (\si{\per\angstrom}), $R$, $\mathrm{d}R$ (1 standard deviation). A four column dataset represents $Q$ (\si{\per\angstrom}), $R$, $\mathrm{d}R$, $\mathrm{d}Q$ (\si{\per\angstrom}). $\mathrm{d}R$ is the uncertainty in reflectivity, and $\mathrm{d}Q$ specifies the FWHM of the instrument resolution function, for each datapoint. Extending \DataD\ would allow other formats to be read - at the moment there is no standardised data format for reflectometry. One example of this could be a wavelength dispersive file using ($\Omega$, $\lambda$)-data instead of $Q$, such as that used in energy scanned X-ray reflectometry, or sometimes produced by wavelength dispersive neutron reflectometers. In such a case \ReflectModel\ could be subclassed to make full use of this energy dispersive information. Creation of a standardised data format for reflectometry would facilitate ingestion of data, and allow other important information, such as experimental metadata, to be used. An \Objective\ can be given a \Transform\ object to permit fitting as $\log_{10} R$ vs $Q$, $RQ^4$ vs $Q$; the default (no \Transform) is $R$ vs $Q$. Several \Objective\ objects can be combined to form a \GlobalObjective\ for co-refinement. The object-oriented nature allows reuse of \Parameter and \Component\ objects, and this is the basis for linking parameters between samples for co-refinement. For a comprehensive demonstration of multiple contrast co-refinement, see the annotated notebook in the supporting information. \subsection{Statistical comparison and model refinement} The \Objective\ statistics are used directly by the \CurveFitter\ class to perform least-square fitting with the functionality provided by the \SciPy\ package (Differential Evolution, Levenberg--Marquardt, LBFGSB - Limited Broyden--Fletcher--Goldfarb--Shanno with bounds). Additional \SciPy\ solvers can be added relatively simply and it would be possible for other minimisers to use \Objective\ directly. \CurveFitter\ can also perform Bayesian Markov Chain Monte Carlo (MCMC) sampling of the system, examining the posterior probability distribution of the parameters, Equation~\ref{eqn:1}. The posterior distribution is proportional to the product of the prior probability and the likelihood (or the sum of the log-probabilities): % \begin{gather} \label{eqn:1}\ p(\theta | D, I) = \frac{p(\theta | I)\times p(D | \theta, I)}{p(D | I)}\\ p(D | \theta, I) = -\frac{1}{2} \sum_n \left[\left(\frac{y_n - y_{\mathrm{model},n}} {\sigma_n}\right)^2 + \log(2\pi\sigma_n^2)\right]\label{eqn:2} \end{gather} % The prior, $p(\theta | I)$, is the probability distribution function for a parameter, $\theta$, given pre-existing knowledge of the system, $I$, as outlined above. The likelihood (Equation~\ref{eqn:2}), $p(D | \theta, I)$, is the probability of the observed data, $D$, given the model parameters and other prior information. It is calculated from the measured data, $y_n$ (with uncertainties $\sigma_n$), and the generative model, $y_{\mathrm{model},n}$. The likelihoods that are used here assume that the measurement uncertainties are normally distributed, Equation~\ref{eqn:2}. However, other types of measurement uncertainties (e.g. Poissonian) could be implemented by a subclass of \Objective\ overriding the log-likelihood method. The model evidence, $p(D | I)$, is a normalising factor. The posterior probability, $p(\theta | D, I)$, describes the distribution of parameter values consistent with the data and prior information. In the simplest form, this is akin to a confidence interval for a parameter derived by least-squares analysis. However, when parameters are correlated, or two models give similar quality of fit (`multi-modality'), a simple confidence interval can be misleading. The posterior probability is derived by encoding the likelihood and prior distributions and then using an MCMC algorithm (via the \emcee\ and \ptemcee\ packages) to perform affine invariant ensemble sampling \cite{emcee, ptemcee}. At the end of an MCMC run, the parameter set possesses a number of samples (called a `chain'); the samples reveal the distribution and covariance of the parameters, the spread of the model-predicted measurements around the data, and in a reflectometry context, the range of SLD profiles that are consistent with the data. The chain statistics are used to update each \Parameter\ value, and assign a standard uncertainty. For the sampling, these represent the median and half the $[15.87, 84.13]$ percentile range respectively; the latter approximating the standard deviation for a normally distributed statistic. The \ptemcee\ package is a variant (a `fork' in open-source software development terms) of the \emcee\ package that has been extended to implement the parallel tempering algorithm for characterisation of multi-modal probability distributions; different modes can be traversed by chain populations at higher `temperatures', while individual modes are efficiently explored by chains at lower `temperatures' \cite{ptemcee}. Having multiple populations in the parallel tempering algorithm allows the sampler to escape local maxima, greatly aiding it's ability to explore the most probable regions of the posterior. \ptemcee\ is also able to estimate the log-evidence term (the denominator in Equation~\ref{eqn:2}), which is useful when calculating the Bayes factor for model comparison. Parallelisation of the sampling is automatic, making full use of multi-core machines, and can use MPI on a cluster for yet greater parallelisation. Visualisation of the samples produced by MCMC sampling is performed using the \corner\ package for scatter plot matrices \cite{corner}, which gives a representation of the probability distribution function for each individual parameter and also the covariance for each pair of parameters. As will be seen later, the plot for two normally distributed and uncorrelated parameters is isotropic, while covariant parameters show significant anisotropy. An evaluation of the impact of hard bounds can also be made by looking for plots where the bounds are clearly truncating the distribution function, allowing the bounds to be re-evaluated and adjusted if necessary. \subsection{User interface} A significant motivation in the development of \refnx\ has been the facilitation of reproducible analysis by helping the user describe \emph{how} the analysis was performed. A few lines of computer code is an incredibly powerful description, conveying the details with precision that is hard to match in written text, as well as being incredibly concise. Example analyses within the \refnx\ code base are often sufficient to complete the task. These few lines of Python code can be further extended to produce publication quality plots saved and ready to import into the next publication, or used in a loop for batch fitting purposes. While Python is a popular language for instruction and for data analysis, meaning that the relatively few lines of code required to complete a \refnx\ analysis of a set of experiments is not a huge hurdle, a simpler graphical user interface (GUI) is also provided. The browser-based GUI is available for fitting within a \Jupyter\ notebook, Figure~\ref{fig:gui}, leveraging the \ipywidgets\ modules \cite{ipywidgets}. The GUI has a `To code' button that turns the current model into the few lines of code required to perform the analysis without using the GUI, thus providing the desired instructions for the reproducible analysis. The ability to generate analysis code allows also makes it a stepping point for building more advanced models independently. The current GUI is able to use slab based models for fitting a single dataset; a fully functional web-based reflectometry analysis notebook is currently available \cite{Nelson2018}. If desired, it is possible to execute Jupyter notebooks in batch mode or to run the generated Python code within a Python program to complete batch mode fitting of larger data sets. The \refnx\ repository contains a growing set of examples of different uses of the \refnx\ package. \begin{figure} \includegraphics[width=85mm]{./supporting_information/gui.png} \caption{Screenshot of the \Jupyter/\ipywidgets\ GUI; this \Jupyter\ notebook is available in the supporting information.} \label{fig:gui} \end{figure} \section{Example data analysis with a lipid bilayer} Neutron reflectometry is an ideal technique for the study of biologically relevant lipid membrane mimics and their interactions with proteins, etc. Multiple contrast variation measurements are necessary to reduce modelling ambiguity (due to loss of phase information in the scattering experiment) and improve the ability to determine the structure of various components in the system. The gold standard approach for analysis of these datasets is co-refinement with a common model, and to parameterise the model in terms of chemically relevant parameters, such as the area per molecule \cite{campbell2018}. Sometimes a patchy coverage (distinct to low area per molecule) necessitates the use of an (incoherent) sum of reflectivities from different areas. \refnx\ has functionality for all these requirements, such as the \LipidLeaflet\ component for describing the head and tail groups of a lipid leaflet, and \MixedReflectModel\ to account for patchiness. The parameters used in the \LipidLeaflet\ component are: area per molecule ($A$), thicknesses for each of the head and tail regions ($t_x$), sums of scattering lengths of the head and tail regions ($b_x$), partial volumes of the head and tail groups ($V_x$), roughness between head and tail region, and SLDs of the solvents for the head and tail group ($\rho_{x,\mathrm{solv}}$). The overall SLD of each of the head and tail group regions are given by: \begin{gather} \label{eqn:3} \phi_{x} = \frac{V_x}{At_x}\\ \rho_x = \phi_{x} \frac{b_x}{V_x} + (1 - \phi_{x})\rho_{x,\mathrm{solv}} \label{eqn:4} \end{gather} The approach used in \LipidLeaflet\ component ensures that there is a 1:1 correspondence of heads to tails. By default the head and tail solvents are assumed to be the same as the solvent that is used throughout the \Structure. This will be the case when using \LipidLeaflet\ for a solid-liquid reflectometry experiment. However, at the air-liquid, or liquid-liquid interfaces the solvent for the head and tail region may be different, and it is possible to use different solvent SLDs for each. We note that the \LipidLeaflet\ component may also be used to describe other amphiphiles adsorbing at an interface. Here, \LipidLeaflet\ is used to co-refine three contrasts (\ce{D_2O}, \ce{Si} contrast match [hdmix, SLD=\SI{2.07E-6}{\per\square\angstrom}], and \ce{H_2O}) of a 1,2-dimyristoyl-sn-glycero-3-phospho\-choline (DMPC) bilayer at the solid-liquid interface, Figure~\ref{fig:global_fit}.\footnote{The validity of \LipidLeaflet\ does depend on the area per molecule being equal for the headgroup and tailgroup regions, as pointed out by Gerelli \cite{Gerelli2016}, which can be violated if there are guest molecules that insert in the membrane.} Two \LipidLeaflet\ objects are required to describe the inner and outer leaflets of a bilayer, hence, the component contains an attribute which can reverse the direction of one of the leaflets. The use of individual objects to describe each leaflet leads to great flexibility; it becomes easy to model asymmetric bilayers (inner leaflet can be a different lipid to the outer lipid), and one can model interstitial water layers between the leaflets as well. The \Jupyter\ notebook used for the analysis, \emph{lipid.ipynb}, is available in the supporting information. The corner plot (Figure~\ref{fig:corner}) produced from the MCMC analysis shows the covariance between parameters, with an area per molecule of \SI{57.0 \pm0.15}{\square\angstrom}. Figure~\ref{fig:global_fit} shows the probability distribution of the generative model around the data and in the SLD profile. These families of plausible fits that are obtained by plotting a subset of samples from the MCMC chain. The spread in SLD profiles is used to determine what range of structures is consistent with the data. Multi-modalities in these SLD profiles can be due to statistical uncertainties, the $Q$ ranges measured, and the loss of phase information in NR \cite{Majkrzak1999, Heinrich2009}. \begin{figure} \centering \label{fig:global_fit}% \includegraphics[width=100mm]{./supporting_information/global_fit.pdf}% \includegraphics[width=100mm]{./supporting_information/d2o_sld_spread.pdf} \caption{a) Neutron reflectivity from a DMPC bilayer supported on a silicon crystal, measured at three contrasts, with 500 samples from the posterior distribution in grey and median of the distribution in red. Data for the contrast matched (HD\textsubscript{mix}) and \ce{H2O} contrast offset by 0.1 and 0.01 respectively. b) SLD profile of the \ce{D2O} model showing 500 samples from the posterior distribution, as well as the median in red. It is seen that the uncertainty in the reflectivity at high $Q$ is associated with an uncertainty in SLD profile at the lipid-\ce{D2O} interface.} \end{figure} \begin{figure} \includegraphics[width=120mm]{./supporting_information/corner.pdf} \caption{Corner plot for the varying parameters of DMPC bilayers supported on a silicon crystal, measured at three contrasts. The sampling took $\sim$\SI{33}{\minute} on a \SI{2.8}{GHz} quad-core computer for 20 saved steps, corresponding to 4000 samples, with the steps being thinned by a factor of 400. A larger scale image is available in supporting information.} \label{fig:corner} \end{figure} \section{Distribution and Modification} Each submodule in \refnx\ possesses its own unit testing code for checking that the functions and classes in the module operate correctly, both individually and collectively. For example, there are tests that check that the reflectivity of a model is calculated correctly, or that the behaviour of a function is correct for the different possible inputs and code paths through it. Since the test suite is an integral part of the package each installation is testable. In addition, there is a benchmarking suite to track changes in performance, specifically the speed of critical calculations, over time. This development approach is important to providing assurances to the community that the code is tested and works. The source code for \refnx\ is held in a publicly accessible version controlled git repository \cite{refnx}. User contributions may be made using the standard GitHub workflow in which contributors create their own `fork' of the main \refnx\ repository, and create a feature branch to which they make modifications. They then submit a pull request (PR) against the main repository. The modifications made in the PR are checked on continuous integration (CI) web-services that run the test suite against a matrix of Python versions on the macOS, Linux and Windows operating systems. Features are merged into the main repository if all tests pass, and if manual code review concludes that the changes are scientifically correct, of sufficiently high standard, and useful. When a sufficient number of features have accumulated, a new release is made. Successive releases have an incrementing semantic version number which can be obtained from the installed package, with each release being given its own Digital Object Identifier (DOI). We encourage users to submit models for inclusion in a user-contributed models repository (refnx-models\footnote{https://github.com/refnx/refnx-models}). We will work with users to develop a suitable way of documenting and sharing their models. The recommended way of using \refnx\ is from a \conda\ environment, which offers package, dependency and environment management \cite{conda}, using the pre-compiled distributables on the \refnx\ conda-forge channel. These distributables are made as part of the release process using the same CI web-services used to test the code. The matrix of distributables covers the major Python versions currently in use, across the macOS, Windows, and Linux operating systems. Alternatively the package can be installed from source, either directly from the git repository, or via \pip\ from the version uploaded to PyPI.\footnote{https://pypi.python.org/pypi/refnx; the installation command is `\texttt{pip install refnx}'} Building from source requires a C compiler and the \Cython\ and \NumPy\ packages to be installed; further dependencies should be installed to run the test suite to verify that compilation and installation was successful. \refnx\ is released under the BSD permissive open source licence. In addition, all of the dependencies of \refnx\ are released under open source licences which means that use is free of cost to the end user and, more importantly, the user is free to modify, improve, and inspect this software. \section{Comments on reproducibility of analyses} In order for a given scattering analysis to be fully reproducible by others, a general set of conditions need to be met \cite{Helliwell2017, Moeller2017a}: \begin{itemize} \item the processed datasets used in the analysis need to be deposited with a journal article, or be freely available. Ideally the raw datasets, and the means to create the processed datasets should also be made available. \item the exact software environment needs to be recreatable. \item the exact ordered set of steps taken during the analysis needs to be listed. \end{itemize} Each of these points is often inadequately addressed in the literature. For example, the use of different software versions may change the output of an analysis, or the use of a GUI program may preclude recording the full set of steps, or options, applied by a user \cite{Chirigati2013}. Whilst it is unable to meet the first criterion by itself, the use of \refnx\ in a \Jupyter\ notebook can fulfil the other two requirements, providing a little care is taken. As we have already noted, the ordered set of steps to perform the analysis is the \Jupyter\ notebook in which the analysis was performed and this is an artefact able to be archived. The exact software environment can be recreated by noting down the versions of the software packages used during an analysis (\refnx, \SciPy, \NumPy, Python, etc). At a later date those exact versions can be installed in the same Python version using one of: the \conda\ package manager, by installing from the source at a given version tag in the git repository, or by \pip. \conda\ can use environment files to recreate a specific setup. An alternative way of recreating the environment is by using a virtual machine, or other container environment such as Docker; the strengths and weaknesses of various software distribution practices and the relationship with reproducible science has been discussed in detail elsewhere \cite{Moeller2017a}. The usefulness of open-source software in a git (or other version controlled) repository must be emphasised here \cite{Moeller2017a}. With closed source or proprietary software, the ability to return to a specific software version/environment can be frustrated, and different versions can have modifications that can unknowingly change the output of an analysis. In addition reduced accessibility (due to cost, etc) to the wider scientific community can also hinder reproducibility. Moreover, there are important ramifications for verifiability \cite{Chirigati2013}. \refnx\ is based on a fully open software stack, with good unit test coverage. The user can run tests for each component and inspect parts for correctness. For example, the behaviour of the reflectivity calculation in \refnx\ is checked from first principles in the test suite; and can be done now and in several years time. If problems are discovered, they can be corrected. With a fully or partially closed-source program such checking is much harder, as one does not possess full knowledge of what happens inside. \section{Conclusions}\label{conclusions} \refnx\ is a powerful tool for least-squares or Bayesian analysis of neutron and X-ray reflectometry data that is ideally usable for reproducible research with \Jupyter\ notebooks, and has been built with extensibility in mind. Its features include: MCMC sampling of posterior distribution for parameters, structural models constructed from modular components with physically relevant parameterisation, algebraic inter-parameter constraints, mixed area models, co-refinement of multiple datasets, probability distributions for parameter bounds used directly for log-prior terms, and a (\Jupyter) \ipywidgets\ GUI. \section*{Acknowledgements:} We acknowledge Anton Le Brun (ANSTO) for the provision of the lipid bilayer datasets in the example, James Hester (ANSTO) for comments made on the draft manuscript, and Andrew McCluskey (Bath University) and Isaac Gresham (UNSW) for important feedback on \refnx\ development. \section{Supporting information} \noindent \textbf{gui.ipynb} - \Jupyter\ notebook used to create the GUI screenshot.\\ \textbf{lipid.ipynb} - \Jupyter\ notebook used for the lipid analysis example.\\ \textbf{lipid.pdf} - PDF view of the \Jupyter\ notebook used for the lipid analysis example.\\ \textbf{corner.pdf} - larger scale image of the corner plot.\\ \textbf{c\_PLP0016596.dat}, \textbf{c\_PLP0016601.dat}, \textbf{c\_PLP0016607.dat} - example lipid datasets.\\ \textbf{reduction.ipynb} - notebook for reducing example datasets.\\ \textbf{raw\_data.zip} - raw files for the example datasets.\\ \textbf{refnx-paper.yml} - \conda\ environment file to reproduce the analysis environment in this paper. \bibliographystyle{abbrv} \bibliography{main} \end{document} refnx-0.1.53/paper/supporting_information/000077500000000000000000000000001477046072400206635ustar00rootroot00000000000000refnx-0.1.53/paper/supporting_information/DMPC.png000066400000000000000000000214601477046072400221170ustar00rootroot00000000000000PNG  IHDRX{msRGBYiTXtXML:com.adobe.xmp 1 L'Y!IDATx흯GJ" 8@`ud 8H8b@TDB[gg;{zvt̽39?3wT(cO?O>-Xt̙ɓ'+-E@D`}X L ~8|)666 D/^l#ݾ}B[D X)xAeSJ! 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"import refnx, scipy\n", "from refnx.reflect import Motofit\n", "from refnx.dataset import ReflectDataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('0.1.0', '1.1.0')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# version numbers used in this analysis\n", "refnx.version.version, scipy.version.version" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pth = os.path.join(os.path.dirname(refnx.__file__), 'analysis', 'test')\n", "data_d2o = ReflectDataset(os.path.join(pth, 'c_PLP0011859_q.txt'))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "app = Motofit()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", 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