pax_global_header00006660000000000000000000000064147555005250014522gustar00rootroot0000000000000052 comment=fca4775c26dc82708b5535e32cb4d021f3f3584a refnx-0.1.52/000077500000000000000000000000001475550052500127315ustar00rootroot00000000000000refnx-0.1.52/.gitattributes000066400000000000000000000004771475550052500156340ustar00rootroot00000000000000# 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-0.1.52/.github/000077500000000000000000000000001475550052500142715ustar00rootroot00000000000000refnx-0.1.52/.github/dependabot.yml000066400000000000000000000002311475550052500171150ustar00rootroot00000000000000version: 2 updates: - package-ecosystem: github-actions directory: / schedule: interval: daily commit-message: prefix: "MAINT" refnx-0.1.52/.github/workflows/000077500000000000000000000000001475550052500163265ustar00rootroot00000000000000refnx-0.1.52/.github/workflows/artifacts.yml000066400000000000000000000005471475550052500210370ustar00rootroot00000000000000name: '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-0.1.52/.github/workflows/build_wheels.yml.bak000066400000000000000000000013641475550052500222570ustar00rootroot00000000000000name: 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-0.1.52/.github/workflows/pythonpackage.yml000066400000000000000000000334401475550052500217120ustar00rootroot00000000000000name: 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: [3.9, '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 - 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@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08 # v4.6.0 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@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08 # v4.6.0 # 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: ${{ matrix.python-version }} cache: 'pip' # caching pip dependencies allow-prereleases: true - name: Make wheel shell: bash run: | python -m pip install --upgrade pip python -m pip install wheel delocate build python -m build . -v - name: Install package and test with pytest shell: bash env: MPLBACKEND: agg run: | pip install -r .requirements.txt pushd dist python -m pip install --only-binary=refnx --no-index --find-links=. refnx pytest --pyargs refnx popd - uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08 # v4.6.0 with: name: refnx-wheel-macos-${{ 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@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08 # v4.6.0 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@fa0a91b85d4f404e444e00e005971372dc801d16 # v4.1.8 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: ${{ secrets.NOTARIZATION_PASSWORD }} appstore-connect-team-id: 8CX8K63BQM verbose: True - name: Staple Release Build if: github.repository == 'refnx/refnx' && (github.event.pull_request.merged == true || startsWith(github.ref, 'refs/tags')) uses: BoundfoxStudios/action-xcode-staple@1e2200b448c6ed4dd44b963ff17d3e340fc6b064 # v1 with: product-path: "dist/refnx.dmg" - uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08 # v4.6.0 with: name: refnx-app-macos-${{ 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: ${{ matrix.python-version == '3.10' }} run: | # make app in virtualenv pip uninstall -y h5py python -m venv app app\Scripts\activate.bat python -m pip install --upgrade --upgrade-strategy eager -r tools/app/requirements.txt cd dist python -m pip install --only-binary=refnx --no-index --find-links=. refnx cd .. # fix for multiprocessing on Py3.9 is not merged in PyInstaller # if you are on Py3.7 you can just pip install pyinstaller # pip install git+https://github.com/andyfaff/pyinstaller.git@gh4865 cd tools\app pyinstaller motofit.spec move dist\motofit.exe ..\..\dist\ cd ..\.. - uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08 # v4.6.0 with: name: refnx-exe-win-${{ 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-0.1.52/.github/workflows/release.yml000066400000000000000000000116111475550052500204710ustar00rootroot00000000000000# 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: ${{ matrix.os }} if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') strategy: matrix: os: [windows-latest, macos-14] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: build wheels uses: pypa/cibuildwheel@ee63bf16da6cddfb925f542f2c7b59ad50e93969 # v2.22.0 env: CIBW_TEST_COMMAND: pytest --pyargs refnx.reflect.test.test_reflect CIBW_ARCHS_MACOS: "x86_64 arm64" CIBW_ENVIRONMENT_MACOS: MACOSX_DEPLOYMENT_TARGET="10.13" - uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08 # v4.6.0 with: name: wheels-${{ 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@ee63bf16da6cddfb925f542f2c7b59ad50e93969 # v2.22.0 env: CIBW_TEST_COMMAND: pytest --pyargs refnx.reflect.test.test_reflect CIBW_BUILD: "*-manylinux_x86_64" - uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08 # v4.6.0 with: name: wheels-manylinux path: ./wheelhouse/*.whl build_linux_musl_wheels: runs-on: ${{ matrix.os }} if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags') strategy: matrix: os: [ubuntu-latest] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: build wheels uses: pypa/cibuildwheel@ee63bf16da6cddfb925f542f2c7b59ad50e93969 # v2.22.0 env: CIBW_TEST_COMMAND: pytest --pyargs refnx.reflect.test.test_reflect CIBW_BUILD: "*-musllinux_x86_64" - uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08 # v4.6.0 with: name: wheels-musllinux path: ./wheelhouse/*.whl make_sdist: name: Make sdist runs-on: ubuntu-latest steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Build sdist run: pipx run build --sdist - uses: actions/upload-artifact@65c4c4a1ddee5b72f698fdd19549f0f0fb45cf08 # v4.6.0 with: name: wheels-sdist path: dist/*.tar.gz check-version: runs-on: ubuntu-latest needs: [build_linux_musl_wheels, build_linux_x86_64_wheels, build_wheels, make_sdist] steps: - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2 - name: Set up Python uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0 with: python-version: 3.11 - uses: actions/download-artifact@fa0a91b85d4f404e444e00e005971372dc801d16 # v4.1.8 with: pattern: wheels-* merge-multiple: true path: dist - name: Check version run: | python -m pip install numpy scipy orsopy ls dist python -m pip install --only-binary=refnx --no-index --find-links=dist refnx cd dist RNX_VERSION="$(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@fa0a91b85d4f404e444e00e005971372dc801d16 # v4.1.8 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@fa0a91b85d4f404e444e00e005971372dc801d16 # v4.1.8 with: pattern: wheels-* merge-multiple: true path: dist - uses: ncipollo/release-action@cdcc88a9acf3ca41c16c37bb7d21b9ad48560d87 # v1.15.0 with: artifacts: "dist/refnx*.tar.gz" token: ${{ secrets.GITHUB_TOKEN }} allowUpdates: true generateReleaseNotes: true refnx-0.1.52/.gitignore000066400000000000000000000007111475550052500147200ustar00rootroot00000000000000.vscode/ *.pyc .idea .DS_Store build/ dist/ *.o *.obj *.pyd *.so .eggs *.icns .tox refnx.egg-info/ __pycache__ .ipynb_checkpoints/ refnx.egg-info/ src/_cevent.c src/_cevent.cpp src/_cevent2.c src/_creflect.c src/_creflect.cpp src/_cyreflect.c src/_cyreflect.cpp src/_cutil.c build.log doc/_build/ t.txt t .cache/ refnx/version.py benchmarks/.asv # testing artifacts PLP*dat PLP*xml c_PLP0000708.dat c_PLP0000708.xml offspec.xml test1.dat test.dat test.xml refnx-0.1.52/.requirements.txt000066400000000000000000000004001475550052500162650ustar00rootroot00000000000000numpy numba; python_version != "3.12" cython scipy orsopy h5py pandas xlrd pytest ipywidgets IPython matplotlib traitlets PyQt6-Qt6==6.6.1 PyQt6==6.6.1 PyQt6-sip==13.6.0 qtpy uncertainties setuptools attrs corner tqdm periodictable pytensor pymc pytest-qt refnx-0.1.52/.travis.yml.bak000066400000000000000000000052561475550052500156060ustar00rootroot00000000000000sudo: false dist: xenial language: python matrix: include: # - os: linux # env: PY=3.7.3 - os: osx language: generic env: PY=3.7 addons: apt: packages: - libhdf5-serial-dev services: - xvfb #before_script: # - | # if [[ "$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-0.1.52/CHANGELOG.txt000066400000000000000000000675371475550052500150030ustar00rootroot00000000000000Details 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. refnx-0.1.52/INSTALL_CONTRIBUTE.md000066400000000000000000000143601475550052500161230ustar00rootroot00000000000000# 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-0.1.52/LICENSE000066400000000000000000000030001475550052500137270ustar00rootroot00000000000000Copyright 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-0.1.52/MANIFEST.in000066400000000000000000000001561475550052500144710ustar00rootroot00000000000000# All source files recursive-include refnx * recursive-include src * exclude src/refcalc.o prune */__pycache__refnx-0.1.52/README.md000066400000000000000000000015511475550052500142120ustar00rootroot00000000000000refnx ===== ![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-0.1.52/appveyor.yml.bak000066400000000000000000000060261475550052500160610ustar00rootroot00000000000000environment: # 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-0.1.52/azure-pipelines.yml.bak000066400000000000000000000033071475550052500173270ustar00rootroot00000000000000# 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-0.1.52/benchmarks/000077500000000000000000000000001475550052500150465ustar00rootroot00000000000000refnx-0.1.52/benchmarks/Benchmark.ipynb000066400000000000000000000236621475550052500200140ustar00rootroot00000000000000{ "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-0.1.52/benchmarks/__init__.py000066400000000000000000000001121475550052500171510ustar00rootroot00000000000000 import numpy as np import random np.random.seed(1234) random.seed(1234) refnx-0.1.52/benchmarks/asv.conf.json000066400000000000000000000133211475550052500174560ustar00rootroot00000000000000{ // 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-0.1.52/benchmarks/benchmarks/000077500000000000000000000000001475550052500171635ustar00rootroot00000000000000refnx-0.1.52/benchmarks/benchmarks/__init__.py000066400000000000000000000000001475550052500212620ustar00rootroot00000000000000refnx-0.1.52/benchmarks/benchmarks/analysis.py000066400000000000000000000032561475550052500213660ustar00rootroot00000000000000import 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-0.1.52/benchmarks/benchmarks/common.py000066400000000000000000000044201475550052500210250ustar00rootroot00000000000000""" 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-0.1.52/benchmarks/benchmarks/reflect.py000066400000000000000000000065301475550052500211650ustar00rootroot00000000000000import 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-0.1.52/benchmarks/run.py000066400000000000000000000034741475550052500162340ustar00rootroot00000000000000#!/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-0.1.52/doc/000077500000000000000000000000001475550052500134765ustar00rootroot00000000000000refnx-0.1.52/doc/Makefile000066400000000000000000000163551475550052500151500ustar00rootroot00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. 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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-0.1.52/doc/NSF2.ipynb000066400000000000000000001325411475550052500152570ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "4879c2ee-e89d-42db-974e-a9572851ef20", "metadata": {}, "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": {}, "outputs": [], "source": [ "# some necessary imports\n", "import os.path\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": {}, "outputs": [], "source": [ "# create datasets from the NSF PNR data\n", "pth = os.path.dirname(refnx.__file__)\n", "dd = \"c_PLP0007882.dat\"\n", "uu = \"c_PLP0007885.dat\"\n", "\n", "file_path_uu = os.path.join(pth, \"reflect\", \"test\", uu)\n", "file_path_dd = os.path.join(pth, \"reflect\", \"test\", 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": {}, "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": {}, "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": {}, "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": {}, "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": {}, "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": {}, "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": {}, "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": {}, "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": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-1271.4413157228305: : 62it [02:51, 2.76s/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": {}, "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": "3.12.5" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.52/doc/_images/000077500000000000000000000000001475550052500151025ustar00rootroot00000000000000refnx-0.1.52/doc/_images/gui.png000066400000000000000000005011171475550052500164010ustar00rootroot00000000000000PNG  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-0.1.52/doc/conf.py000066400000000000000000000244151475550052500150030ustar00rootroot00000000000000#!/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', ] jupyter_execute_notebooks = "off" bibtex_bibfiles = ["../testimonials.bib"] # 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 = re.sub(r'\.dev-.*$', r'.dev', refnx.__version__) # The full version, including alpha/beta/rc tags. release = 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-0.1.52/doc/emcee_pymc_dynesty.ipynb000066400000000000000000046523721475550052500204510ustar00rootroot00000000000000{ "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": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING (pytensor.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n" ] } ], "source": [ "import os.path\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: 0.1.46.dev0+8a21b67\n", "scipy: 1.13.0\n", "numpy: 1.26.4\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": [ "pth = os.path.dirname(refnx.__file__)\n", "DATASET_NAME = \"c_PLP0011859_q.txt\"\n", "file_path = os.path.join(pth, \"analysis\", \"test\", DATASET_NAME)\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": [ "-565.142272770164: : 44it [00:02, 18.99it/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 [02:55<00:00, 17.12it/s]\n" ] } ], "source": [ "fitter.sample(3000, pool=8);" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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", "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 [04:07<00:00, 12.12it/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 / 210 = 15 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 - 12912394064\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 919.5916726366621\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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       "
\n" ], "text/plain": [ "\n" ] }, "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 106 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 ~80 sec, corresponding to an effective sampling rate of ~13 samples/sec. Note that the emcee was sampling at an effective rate of ~15 samples/sec. *The two rates are effectively the same*. 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": [ "(259.04153520751777, 0.22965548622113374)" ] }, "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 - 12912394064\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 919.5912197764426\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": [ "26170it [00:59, 440.14it/s, batch: 4 | bound: 11 | nc: 1 | ncall: 129925 | eff(%): 20.022 | loglstar: 563.557 < 570.721 < 569.132 | logz: 537.081 +/- 0.193 | stop: 0.921] \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "(26170, 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: 26169\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 ~28000/50 ~ 570 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 - 12912394064\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 919.588838133723\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": "3.12.3" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 4 }refnx-0.1.52/doc/energy_dispersive.ipynb000066400000000000000000001071201475550052500202700ustar00rootroot00000000000000{ "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-0.1.52/doc/environment.yml000066400000000000000000000005671475550052500165750ustar00rootroot00000000000000channels: - 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-0.1.52/doc/examples.rst000066400000000000000000000011061475550052500160440ustar00rootroot00000000000000Examples ======== .. 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-0.1.52/doc/faq.rst000066400000000000000000000104051475550052500147770ustar00rootroot00000000000000.. _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-0.1.52/doc/getting_started.ipynb000066400000000000000000042462021475550052500177420ustar00rootroot00000000000000{ "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": [ "61.78059283607564: : 20it [00:00, 454.06it/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 - 4829246960\n", "Dataset = , 50 points\n", "datapoints = 50\n", "chi2 = 45.7316726401658\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 [00:04<00:00, 220.15it/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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", 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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": [ "import os.path\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: 0.1.45\n", "scipy: 1.14.0\n", "numpy: 1.26.4\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": [ "pth = os.path.dirname(refnx.__file__)\n", "DATASET_NAME = \"c_PLP0011859_q.txt\"\n", "file_path = os.path.join(pth, \"analysis\", \"test\", 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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crBspl4vkRQMGDNDy5cu1ZcsWbd++XT/+8Y/Vp08fnXfeeYqMDN3SFAAATtE8TTp0+12kU0hefIYNG6Zhw4apsLBQaWlpXRETAADogGMrL6Gsw6nZ5MmTAxkHAADopHDYlFHqRPJijAlkHAAAoJOqm7YG6EblpXWh3AgEAIATUXkBAACOQuUFAAA4CpWXk2B6NAAA9lJTF/qbMkqdSF7Wr18fyDgAAEAn+dZ58YTwpowSw0YAAISM5nVeQvvj/ZQXqfM5dOiQXnnlFa1evVqFhYWSpLS0NJ1//vmaMWOGevfuHbAgAQDAyfkbdqm8HO/zzz/Xaaedpvnz5yshIUETJkzQhAkTlJCQoPnz5+v000/X2rVrAx0rAABog79hl8rL8X72s5/p+uuv1wsvvHDcei/GGN1222362c9+ptWrVwckSAAAcHLhUnnpUPLy5ZdfasGCBa0uVOdyuTRnzhydffbZnQ4OAAC0X7hUXjp0dWlpafr73/9+wuN///vflZqa2uGgAADAqQuXjRk7VHmZO3eubr31Vq1bt04TJ070JyoHDhzQsmXL9NJLL+l3v/tdQAMFAABt802V7hbii9R1KHmZPXu2kpOT9e///u/6z//8TzU0NN6syMhIjRo1SgsWLNA//dM/BTRQAADQNiovJ3HDDTfohhtuUF1dnQ4dOiRJSk5OVnR0dMCCAwAA7UflpZ2io6OVnp4eiFgAAEAnhEvlpUtSsz179ujmm2/uiqcGAAAnEC4r7HbJ1R05ckSvvfZaVzw1AAA4gRr/sFFoV146NGy0ePHiNo9/++23HQoGAAB0nL/yQs/L8a655hq5XC4ZY054TmsL2AEAgK7jb9il5+V46enpevfdd+X1elv9+uKLLwIdJwAAOIlwqbx06OpGjRqldevWnfD4yaoyAAAg8Hw9LzTstuLee+/V+eeff8LjgwcP1qefftrhoI71+OOPy+Vy6a677grI8wEAEKqq69mY8YQuuOCCNo/36NFDF154YYcCOtbnn3+uF198USNGjOj0cwEAEMrqG7xq8DaOelB5acVDDz3U5rBRIJSXl2vatGl66aWX1LNnzy59LQAAnM5XdZFCv/LSoeRl7969mjJlivr27atZs2bpww8/VG1tbUADmz17ti6//HJNmjSpzfNqampUWlra4gsAgHDj63eRJHcklZfjvPLKKyosLNSbb76puLg43XXXXUpOTtYPf/hDvf766zpy5Einglq4cKG++OIL5eTknPTcnJwcJSQk+L8yMzM79doAADiRr/LijopQRERoL1fS4dQsIiJCF1xwgZ588klt27ZNeXl5GjNmjF588UVlZGRowoQJ+t3vfqd9+/ad0vPu2bNHd955p/785z+rW7duJz1/3rx5Kikp8X/t2bOno5cEAIBjhctMIykAGzP6nHHGGTrjjDN033336eDBg1q8eLF/Jd65c+e2+3nWrVunoqIinXPOOf7HGhoatGLFCv3Hf/yHampqFBnZPJbn8Xjk8XgCdRkAADhSuGzKKAUweTlW7969NXPmTM2cOfOUf3fixInatGlTi8duuukmnX766br//vtbJC4AAKCRf3XdEF+gTuqi5KUz4uLilJ2d3eKxHj16qFevXsc9DgAAGoXLjtJSF+0qDQAAgqs6THaUlmxYeWnN8uXLrQ4BAABbo/ICAAAchYbddvB6vVqwYIHeffdd7d69Wy6XS1lZWfrRj36kn/zkJ3K5QnuOOQAAdhJODbsdukJjjK666ir99Kc/1b59+zR8+HCdeeaZ+u677zRjxgxde+21gY4TAAC0gcrLSSxYsEArVqzQsmXL9P3vf7/FsU8++UTXXHONXn/9dd14440BCRIAALSthspL29588039/Oc/Py5xkaSLL75YDzzwgP785z93OjgAANA+4VR56VDysnHjRl122WUnPD5lyhR9+eWXHQ4KAACcGv/2AFReWnfkyBGlpqae8HhqaqqOHj3a4aAAAMCp8W3MGA7rvHQoeWloaFBU1InbZSIjI1VfX9/hoAAAwKnxzzYKg3VeOtSwa4zRjBkzTrghYk1NTaeCAgAAp6baP2wU+pWXDiUv06dPP+k5zDQCACB4quvCZ9ioQ8nLq6++Gug4AABAJ7BIXSft3btXt956a1c8NQAAaIW/YZep0h1z+PBhvfzyy13x1AAAoBXhtKt06NeWAAAIA6ywCwAAHKWKygsAAHCS5tlGof/R3qHZRtddd12bx4uLizvytAAAoIP867yEQcNuh5KXhISEkx5nnRcAAIInnBp2WecFAIAQ0Ly3UegPG3XoClevXq0lS5a0eOz1119XVlaWUlJSdOutt7JFAAAAQeL1GtWyMWPbHn30UW3evNn/86ZNmzRz5kxNmjRJDzzwgN5//33l5OQELEgAAHBiNU2Ji0TyckJffvmlJk6c6P954cKFGjNmjF566SXdfffdmj9/vt56662ABQkAAE7M1+8ihceu0h26wqNHjyo1NdX/c25urqZMmeL/+bzzztOePXs6Hx0AADip6vrG5CUqwqWoSJKXVqWmpmrXrl2SpNraWn3xxRf63ve+5z9eVlam6OjowEQIAADaFE47SksdTF5+8IMf6IEHHtD//d//ad68eerevbsuuOAC//GNGzdq0KBBAQsSAACcWDjtKC11cKr0Y489puuuu04XXnihYmNj9dprr8ntdvuPv/LKK5o8eXLAggQAACcWTgvUSR1MXpKTk7VixQqVlJQoNjZWkZEtb9bbb7+t2NjYgAQIAADaFk5bA0gdTF58TrTSblJSUmeeFgAAnAJfwy49LwAAwBFqwmhrAInkBQAAxwu3YaPwuEoAAEKYf7ZRmDTskrwAAOBw4bSjtNTJhl1Yq8FrtO9olXYfrlBZdb3qGryqbfCqvsFIklwuySUpoukblySXy9X0T99xlzxREeod51H/Xj3UO85j5SUBADrAt6O0J0yGjUheHKi4slbPfbpD76zbq6OVdQF97mHp8fqXCwfqqpEZcrlcAX1uAEDX8FVeYqi8wI52HarQja/kac+RKkmSOypC/ZO6q2cPt9yREYqObN7XwhhJMjJG8hoj0/RY4z+N/5yqugYVlVVr79EqbSko1Z0LN2jl9kN64ocjFBFBAgMAdhdu2wOQvDhIZW29Zr72ufYcqVK/pO56+MphuvC03gHbhOtoRa1eX/2d5n+yXW+v26u0hG66Z/LQgDw3AKDrhNv2AOFxlSHi3z7+Rt8erFBafDf9z6zzNfGM1IDuHtqzh1t3Thqip340QpL0/PKd+uZAWcCeHwDQNWrqmW0EGzpYVqM/5X0nScq5bniXNtZed05fXTIsVfVeo2f+tr3LXgcAEBjhNmxE8uIQf1rznarrvDorM1EXDe3d5a83Z9JpkqSPNheqqLS6y18PANBxDBvBdowxWvzlfknSjPMHBGUW0LCMeJ3bv6fqvUbvfLG3y18PANBx/l2lqbzALjbvL9WuQxXyREVo0rDUoL3uNWf3kSQt21oUtNcEAJw6ho1gO3/bekCS9P2hKYr1BG+C2MWnp0iSvsg/qiMVtUF7XQDAqfHvKh0VHh/r4XGVDrfm28OSpPFDkoP6uhmJMTojPV7GSCu+ORjU1wYAtB+VF9hKdV2DvsgvliSNHdQr6K9/ftNrrvvuaNBfGwDQPjVhtrcRyYvNrc8vVm29VylxHg1M7hH01z+nX09JjUNHAAB7qmK2Eexk495iSdKo/j0t2WvonP6JkqSvC8tUWVsf9NcHAJxcuO0qTfJic5v3l0qSsvskWPL66QkxSovvpgav0aa9JZbEAABom7/nhRV2YQdf7W9MGM7MiLcsBl/i9HUhWwUAgN0YY5pnGzFsBKtV1NRr16EKSdKZGdZUXiRpaFqsJGkb+xwBgO3UNnhlTOP3LFIHy31dWCZjpNR4T5fuZXQyp6XGSZK+ofICALbjGzKSqLzABnYWlUtqTh6sMjSt8fW3HSiT8aX3AABb8E2Tdrkkd2R4fKyHx1U61M6DjcnLoN6xlsYxMDlWUREulVXXq6CETRoBwE6Obda1YlaqFUhebKw5eQn++i7HckdFqF+v7pLk78EBANhDuDXrSiQvtrbzYGOiYHXlRZKyejUmULsPk7wAgJ2E2xovEsmLbdXWe5V/pFKSNCjF+uSlf1Py8t3hSosjAQAcK9z2NZJIXmxrz9FKNXiNergjlWLhTCOfAcmNw0a7GTYCAFvxVV48YbKjtETyYlt7j1ZJkvr27G6LBqz+DBsBgC0xbATb2OdPXmIsjqTRgKaG3e8OV8rrZbo0ANhFdb1v2Ch8PtLD50odZu/Rxt6SPjZJXvokxigywqWaeq+KymqsDgcA0KS6trHy0t0dZXEkwUPyYlP7ihsrL30S7ZG8REVGKC2+myRpXzFNuwBgF5W19ZKkGDfDRrDYvmN6XuzCVwXaV8xCdQBgF5VNPS8x9LzAar6GXbsMG0nNVSBfYgUAsF6Vf9iI5AUWqq336kBZY3XDLsNGUnMs+4tJXgDALnzJC8NGsFRhSbWMaZyznxzrtjocvwxf5YXkBQBsg2Ej2MKxM43ssMaLj28Ii8oLANgHw0awhb02m2nkQ88LANhP82wjpkpb5vnnn9eIESMUHx+v+Ph4jR07Vh9++KHVYQWV3Rao88lIbJwqXVZTr/KaeoujAQBIUlXT3kbdGTayTt++ffX4449r3bp1Wrt2rS6++GJdffXV2rx5s9WhBc1eG06TlhoXQIrzNGb2B0qZLg0AdlDFOi/Wu/LKK/WDH/xAQ4YM0Wmnnabf/OY3io2N1Zo1a6wOLWh8i8DZbdhIklLiGzeJJHkBAHuoDMPZRrYeIGtoaNDbb7+tiooKjR07ttVzampqVFPTvFx9aWlpsMLrMv7VdW02bCRJqfHdtPNghYpK2SIAAOzA37DLsJG1Nm3apNjYWHk8Ht12221atGiRhg0b1uq5OTk5SkhI8H9lZmYGOdrA8nqNCksaqxoZNqy8pDZtEVBI5QUAbKGqjr2NbGHo0KHasGGD8vLyNGvWLE2fPl1btmxp9dx58+appKTE/7Vnz54gRxtYRytrVdfQuGtzSpzH4miO50teGDYCAHtoHjay5Ud6l7BlmuZ2uzV48GBJ0qhRo/T555/rmWee0YsvvnjcuR6PRx6P/T7kO+pA03BMcqxb0ZH2eyOmNvW8MGwEAPbQvMKuLT/Su4T9Ph1b4fV6W/S1hDJfRSMlrpvFkbSOygsA2Ed9g1e1DeE3Vdp2adq8efM0ZcoU9evXT2VlZXrjjTe0fPlyffTRR1aHFhS+pCAtwa7JS9NsozKSFwCwmq/fRWK2kaWKiop04403qqCgQAkJCRoxYoQ++ugjXXLJJVaHFhS+YSNfkmA3vorQgdIaGWNstX0BAIQb35CRy9W4H164sF3y8vLLL1sdgqV8FQ27Dhv51nmprfequLJOPXvYZ+NIAAg3lcdMkw6n/5kMnzTNIYqaho18vSV244mKVM/u0ZIYOgIAq1WGYbOuRPJiO4X+5MWew0bSsU274dFEDQB21bzGS/j0u0gkL7bT3PNiz8qLxIwjALAL347SJC+wTH2DV4fKG5OXFFtXXnxrvZC8AICVKmoak5ceHoaNYJFD5bUyRoqMcCm5h52TF4aNAMAOKmoYNoLFmheo8ygiwr5d4ynsbwQAtlDRNGwUS+UFVvEnLzbud5Gk1DiGjQDADsoZNoLVfMlLqg03ZDyWL7kqKmPYCACs5Ot5ofICyzhhppHUvNv1wbIaeb3G4mgAIHz5el56eOh5gUUOOGCNF0nq3ZS81HuNjlTWWhwNAIQvZhvBcgfKfNOk7V15iY6MUK+mbQGKmHEEAJahYReW8zXAptk8eZGaqy9FbBEAAJYp90+VJnmBRQ7YfF+jY9G0CwDWa27YpecFFqiua9DRyjpJ9u95kVo27QIArEHPCyzlSwLcURFKiIm2OJqTS2GtFwCwnK/nheQFljh2ppHLZd/VdX38yQuVFwCwjG+qNA27sIR/jZc4+/e7SM19OSQvAGAdVtiFpZzUrCs173p9gGEjALBEXYNXtfVeSVIPNmaEFQ6UOSx5iWuuvBjDKrsAEGy+Zl2JygsscqDEtymj/WcaSc3rvNTWe1VaVX+SswEAgVZR29jv4o6KUHRkeH2ch9fV2lhBU/KSnuCMyku36EjFd2vM9FmoDgCCr7w6PFfXlUhebKPQQavr+rBQHQBYp7S6cW0wJyyvEWgkLzZgjPFXXjISYyyOpv18i+lReQGA4CutakxefFXwcELyYgNHK+v8HeNO6XmRjmnaZXNGAAg6X+UlnsoLrFBQUiVJSo51yxPlnOluLFQHANbxTZaIo/ICKxQUN/W7OKRZ18c344i1XgAg+JqHjai8wAIF/mZd5/S7SDTsAoCVyprWeWHYCJYobBo2ykh0VuWFnaUBwDo07MJSvplGThs2YmdpALAODbuwVKHDFqjz8Q0bVdQ2tFimGgDQ9XwNu/S8wBK+5MVpPS+xnij/ZmD0vQBAcDVXXhg2QpAZY7S/qefFaZUX6ZimXYaOACComG0Ey5RU1am6rnGBOqf1vEjN06WpvABAcJVWM9sIFvE16yb1cKtbtHMWqPNJYa0XAAg6YwyVF1inud/FeVUXSUplrRcACLqqugbVe40kVtiFBQocOtPIxxf3/uIqiyMBgPBxpKJWkuSOjFB3t/Oq9p1F8mKxfcWVkqR0hy1Q5+PbBduXhAEAup4veUnq4ZbL5bI4muAjebFY/pHGikX/pB4WR9IxvspLAZUXAAiaw8ckL+GI5MVi+UcaKy+ZSc5a48XHV3k5UFajhqbxVwBA1zpK8gIr7fUnL90tjqRjkmM9iopwqcFrVFTG0BEABMMRkhdYpaKm3l/6c2ryEhnh8s842l9M8gIAwcCwESyz52hj1aVn92hHz9P37YZdUELfCwAEw5HyxuSlF8kLgi3/sLOHjHx8fS/7jpK8AEAwHKlsTF56krwg2HYdqpAk9e/lzJlGPpk9G5MvXyUJANC1fD0vVF4QdDsPlkuSBveOtTiSzunXVDnyTfsGAHQtGnZhmR1FjcnLoBSHV16akpc9R6i8AEAwHC5v3JKlVyzJC4LIGKOdBxuHjQY5vPLiW6Nm39Eq1noBgC5WXdfg31E6OdZjcTTWIHmxyOGKWpVU1cnlkrKSnV15SU+IUVSES7UNXnaXBoAu5tvQNyY6Ugkxzp2p2hkkLxbZfqBxyKhPYoy6RTt7U63ICJf69GysvuQzdAQAXerYDX3DcV8jieTFMlsKSiVJZ6THWxxJYPiadnc3zaACAHSNwtLGyRFO3dA3EEheLLJ5f4kkKTsjweJIAsPXt+ObQQUA6Bq+yktavDP3xAsEkheLbN7XWHnJ7hMalZfBKY3Ji28GFQCgaxQUNw8bhSuSFwtU1TZoe1GZJCm7T2hUXnzJi28GFQCga/grLyQvCKZN+0rkNVLvOI9S4kJjmptv2GjP0UpV1zVYHA0AhC5/zwvJC4Jp9c7DkqTRWUkh0ymeHOtWQky0jKHvBQC6ijHGvy+eb5ZnOCJ5scDqbw9JksYO7GVxJIHjcrk0NC1OkrRlf6nF0QBAaDpUXqvS6nq5XNIAh++L1xkkL0FWVdugL/KLJUnnDwqd5EWShjf173y1r8TiSAAgNPkq25k9uzt+jbDOIHkJsmVfH1BtvVeZSTGOX1n3H43o25i8bCJ5AYAu4UteBvUOrc+PU0XyEmTvf7lfknTliIyQ6Xfx8c2c2lJQqvoGr8XRAEDo2VkUGnvidRbJSxAVlFTpk6+LJElXjsywOJrAy+rVQ3GeKFXXefV1YZnV4QBAyPFXXlJIXhAkzy/fqboGozFZSSGzLcCxIiJcGp2VJEn6bMchi6MBgNDi9Rp9ubdYUuhsLdNRJC9BkvftYf1pzXeSpDsnDrE4mq4zbnCyJGklyQsABNS3h8pVXFmnbtEROjOD5AVdyOs1+svGAv30tbXyGumH5/TV+U0f8KHIl7x8vvuIqmpZrA4AAmXt7qOSpJF9ExUdGd4f31FWB+AURypq9epnu+Q1Rl4jeY2RMVKD1/i/bzzWeLyhwaigtFpfF5SqqKxGkjR6QJJ+dfWZFl9J1zotNVaZSTHac6RKH28p1NVn9bE6JAAICXm7jkiSzhuQZHEk1iN5aafiylo9+8mODv1urCdKN48boH/9/uCQn5fvcrl07Vl9NP+THXr3i30kLwAQANV1DVq65YAk6YIhoVu9by+Sl3ZKiInWjPMHyOWSIlwuRTT909X0fWRE8/e+4ylx3dS/V3eNzEwM+aTlWNee01fzP9mh3G8OamtBadg3lgFAZ/1t6wGV19SrT2IMlRfZMHnJycnRu+++q6+//loxMTE6//zz9cQTT2jo0KGWxtUr1qNHrgrtIZ9AyUruoctHpOsvGwv02w+26rWbRisiIrTWtAGAYGnwGj336U5J0jVnZ/DfU9mwYTc3N1ezZ8/WmjVrtHTpUtXV1Wny5MmqqKiwOjScgnsuOU3uqAj93/ZD+u0HW+X1GqtDAgBHembZdm0tKFV8tyjNHD/Q6nBswWWMsfWnysGDB5WSkqLc3FxNmDDhpOeXlpYqISFBJSUlio9nuMJKb6/do3vf2ShJOjMjXpedmabsvglKiIlWrCdK7sgIHbvIsEvNPzh58eGu+IsyCvyTdk2cXfCcXRBo18TZBU8a1v/eu+A5nXI/jVRaXaf8w5X6y6YC5X5zUJL0+HXD9c+j+wX+BW3iVD6/bTds9I9KShr3yUlKan2Mr6amRjU1Nf6fS0vZ0dgurj83UxEulx587ytt3l+qzew2DQCnLCrCpfsvOz2kE5dTZevKi9fr1VVXXaXi4mKtXLmy1XMeeeQRPfroo8c9TuXFPo5U1Grxhn1al1+s7QfKVF5Tr4qaetXWN+9/dOyb8Nh35LH/p2RM4Csyx1Z7AvJ8AXy6QBefAr2XVkCfLeD/XgP8fIG+d7xPOvZcAa/I2vffaw93pPr0jNHwPom6/ty+YbGX0alUXmydvMyaNUsffvihVq5cqb59+7Z6TmuVl8zMTJIXAAAcJCSGjW6//XYtWbJEK1asOGHiIkkej0cejyeIkQEAACvZLnkxxuhnP/uZFi1apOXLlysrK8vqkAAAgI3YLnmZPXu23njjDb333nuKi4tTYWGhJCkhIUExMTEWRwcAAKxmu56XEzWLvfrqq5oxY8ZJf5+p0gAAOI+je15slksBAACbsd0KuwAAAG0heQEAAI5C8gIAAByF5AUAADgKyQsAAHAUkhcAAOAoJC8AAMBRSF4AAICjkLwAAABHsd0Ku53lW6G3tLTU4kgAAEB7+T6327PSfsglL2VlZZKkzMxMiyMBAACnqqysTAkJCW2eY7uNGTvL6/Vq//79iouLO+Emj3ZVWlqqzMxM7dmzh00lW8H9aRv3p23cn7Zxf06Me9O2QN0fY4zKysqUkZGhiIi2u1pCrvISERGhvn37Wh1Gp8THx/MH0gbuT9u4P23j/rSN+3Ni3Ju2BeL+nKzi4kPDLgAAcBSSFwAA4CgkLzbi8Xj08MMPy+PxWB2KLXF/2sb9aRv3p23cnxPj3rTNivsTcg27AAAgtFF5AQAAjkLyAgAAHIXkBQAAOArJCwAAcBSSFxt57rnnNGDAAHXr1k1jxozR3//+d6tDCrpHHnlELperxdfpp5/uP15dXa3Zs2erV69eio2N1Q9/+EMdOHDAwoi71ooVK3TllVcqIyNDLpdL//u//9viuDFGDz30kNLT0xUTE6NJkyZp+/btLc45cuSIpk2bpvj4eCUmJmrmzJkqLy8P4lV0nZPdnxkzZhz3frrssstanBOq9ycnJ0fnnXee4uLilJKSomuuuUbbtm1rcU57/p7y8/N1+eWXq3v37kpJSdG9996r+vr6YF5Kl2jP/bnooouOe//cdtttLc4J1fvz/PPPa8SIEf6F58aOHasPP/zQf9zq9w7Ji03893//t+6++249/PDD+uKLLzRy5EhdeumlKioqsjq0oDvzzDNVUFDg/1q5cqX/2Jw5c/T+++/r7bffVm5urvbv36/rrrvOwmi7VkVFhUaOHKnnnnuu1eNPPvmk5s+frxdeeEF5eXnq0aOHLr30UlVXV/vPmTZtmjZv3qylS5dqyZIlWrFihW699dZgXUKXOtn9kaTLLrusxfvpzTffbHE8VO9Pbm6uZs+erTVr1mjp0qWqq6vT5MmTVVFR4T/nZH9PDQ0Nuvzyy1VbW6tVq1bptdde04IFC/TQQw9ZcUkB1Z77I0m33HJLi/fPk08+6T8Wyvenb9++evzxx7Vu3TqtXbtWF198sa6++mpt3rxZkg3eOwa2MHr0aDN79mz/zw0NDSYjI8Pk5ORYGFXwPfzww2bkyJGtHisuLjbR0dHm7bff9j+2detWI8msXr06SBFaR5JZtGiR/2ev12vS0tLMU0895X+suLjYeDwe8+abbxpjjNmyZYuRZD7//HP/OR9++KFxuVxm3759QYs9GP7x/hhjzPTp083VV199wt8Jp/tTVFRkJJnc3FxjTPv+nj744AMTERFhCgsL/ec8//zzJj4+3tTU1AT3ArrYP94fY4y58MILzZ133nnC3wmn+2OMMT179jR//OMfbfHeofJiA7W1tVq3bp0mTZrkfywiIkKTJk3S6tWrLYzMGtu3b1dGRoYGDhyoadOmKT8/X5K0bt061dXVtbhPp59+uvr16xeW92nXrl0qLCxscT8SEhI0ZswY//1YvXq1EhMTde655/rPmTRpkiIiIpSXlxf0mK2wfPlypaSkaOjQoZo1a5YOHz7sPxZO96ekpESSlJSUJKl9f0+rV6/W8OHDlZqa6j/n0ksvVWlpqf//wEPFP94fnz//+c9KTk5Wdna25s2bp8rKSv+xcLk/DQ0NWrhwoSoqKjR27FhbvHdCbmNGJzp06JAaGhpa/EuWpNTUVH399dcWRWWNMWPGaMGCBRo6dKgKCgr06KOP6oILLtBXX32lwsJCud1uJSYmtvid1NRUFRYWWhOwhXzX3Nr7xnessLBQKSkpLY5HRUUpKSkpLO7ZZZddpuuuu05ZWVnauXOnfv7zn2vKlClavXq1IiMjw+b+eL1e3XXXXRo3bpyys7MlqV1/T4WFha2+v3zHQkVr90eS/t//+3/q37+/MjIytHHjRt1///3atm2b3n33XUmhf382bdqksWPHqrq6WrGxsVq0aJGGDRumDRs2WP7eIXmBrUyZMsX//YgRIzRmzBj1799fb731lmJiYiyMDE70z//8z/7vhw8frhEjRmjQoEFavny5Jk6caGFkwTV79mx99dVXLfrH0OxE9+fY3qfhw4crPT1dEydO1M6dOzVo0KBghxl0Q4cO1YYNG1RSUqJ33nlH06dPV25urtVhSaJh1xaSk5MVGRl5XKf2gQMHlJaWZlFU9pCYmKjTTjtNO3bsUFpammpra1VcXNzinHC9T75rbut9k5aWdlzTd319vY4cORKW92zgwIFKTk7Wjh07JIXH/bn99tu1ZMkSffrpp+rbt6//8fb8PaWlpbX6/vIdCwUnuj+tGTNmjCS1eP+E8v1xu90aPHiwRo0apZycHI0cOVLPPPOMLd47JC824Ha7NWrUKC1btsz/mNfr1bJlyzR27FgLI7NeeXm5du7cqfT0dI0aNUrR0dEt7tO2bduUn58flvcpKytLaWlpLe5HaWmp8vLy/Pdj7NixKi4u1rp16/znfPLJJ/J6vf7/EIeTvXv36vDhw0pPT5cU2vfHGKPbb79dixYt0ieffKKsrKwWx9vz9zR27Fht2rSpRYK3dOlSxcfHa9iwYcG5kC5ysvvTmg0bNkhSi/dPqN6f1ni9XtXU1NjjvdPpll8ExMKFC43H4zELFiwwW7ZsMbfeeqtJTExs0akdDu655x6zfPlys2vXLvPZZ5+ZSZMmmeTkZFNUVGSMMea2224z/fr1M5988olZu3atGTt2rBk7dqzFUXedsrIys379erN+/Xojyfzbv/2bWb9+vfnuu++MMcY8/vjjJjEx0bz33ntm48aN5uqrrzZZWVmmqqrK/xyXXXaZOfvss01eXp5ZuXKlGTJkiJk6dapVlxRQbd2fsrIyM3fuXLN69Wqza9cu87e//c2cc845ZsiQIaa6utr/HKF6f2bNmmUSEhLM8uXLTUFBgf+rsrLSf87J/p7q6+tNdna2mTx5stmwYYP561//anr37m3mzZtnxSUF1Mnuz44dO8yvfvUrs3btWrNr1y7z3nvvmYEDB5oJEyb4nyOU788DDzxgcnNzza5du8zGjRvNAw88YFwul/n444+NMda/d0hebOTZZ581/fr1M26324wePdqsWbPG6pCC7oYbbjDp6enG7XabPn36mBtuuMHs2LHDf7yqqsr867/+q+nZs6fp3r27ufbaa01BQYGFEXetTz/91Eg67mv69OnGmMbp0g8++KBJTU01Ho/HTJw40Wzbtq3Fcxw+fNhMnTrVxMbGmvj4eHPTTTeZsrIyC64m8Nq6P5WVlWby5Mmmd+/eJjo62vTv39/ccsstx/0PQajen9buiyTz6quv+s9pz9/T7t27zZQpU0xMTIxJTk4299xzj6mrqwvy1QTeye5Pfn6+mTBhgklKSjIej8cMHjzY3HvvvaakpKTF84Tq/bn55ptN//79jdvtNr179zYTJ070Jy7GWP/ecRljTOfrNwAAAMFBzwsAAHAUkhcAAOAoJC8AAMBRSF4AAICjkLwAAABHIXkBAACOQvICAAAcheQFAAA4CskLAABwFJIXAADgKCQvANAJS5YsUVZWlkaPHq3t27dbHQ4QFtjbCAA6YejQoXruuee0efNmrV69WgsXLrQ6JCDkUXkB0C4XXXSR7rrrruO+D3e9evXS4MGDNWDAALndbqvDAcJClNUBAHCed999V9HR0e0696KLLtJZZ52lp59+umuDCrCbbrpJffr00a9//euTnjdo0CClpqbqq6++ClJ0QHij8gLglCUlJSkuLs7qMLpMQ0ODlixZoquuuqrN8+rr6/XMM8/ovvvuU3l5uXr27BmkCIHwRvIC4DgVFRW68cYbFRsbq/T0dP3+979vcfwfh43eeecdDR8+XDExMerVq5cmTZqkiooKzZgxQ7m5uXrmmWfkcrnkcrm0e/duSdJf//pXjR8/XomJierVq5euuOIK7dy5s8Vr3HHHHbrvvvuUlJSktLQ0PfLIIy3i8Hq9evLJJzV48GB5PB7169dPv/nNb1ocz8nJUVZWlmJiYjRy5Ei98847J73+VatWKTo6Wuedd16b573wwgsaOHCgZs+erbKyMn377bcnfW4AnUfyAuA49957r3Jzc/Xee+/p448/1vLly/XFF1+0em5BQYGmTp2qm2++WVu3btXy5ct13XXXyRijZ555RmPHjtUtt9yigoICFRQUKDMzU1JjgnT33Xdr7dq1WrZsmSIiInTttdfK6/X6n/u1115Tjx49lJeXpyeffFK/+tWvtHTpUv/xefPm6fHHH9eDDz6oLVu26I033lBqaqr/eE5Ojl5//XW98MIL2rx5s+bMmaMf//jHys3NbfP6Fy9erCuvvFIul+uE5xw5ckSPPfaYnnjiCfXt21cJCQnasGFDe24vgM4yAHCMsrIy43a7zVtvveV/7PDhwyYmJsbceeedxhhjLrzwQv/369atM5LM7t27W32+Y89ty8GDB40ks2nTJv/vjR8/vsU55513nrn//vuNMcaUlpYaj8djXnrppVafr7q62nTv3t2sWrWqxeMzZ840U6dObTOWIUOGmCVLlrR5zu23327+5V/+xf/z2LFjzYMPPtjm7wAIDBp2AbSwc+dO1dbWasyYMf7HkpKSNHTo0FbPHzlypCZOnKjhw4fr0ksv1eTJk/WjH/3opP0f27dv10MPPaS8vDwdOnTIX3HJz89Xdna2JGnEiBEtfic9PV1FRUWSpK1bt6qmpkYTJ05s9fl37NihyspKXXLJJS0er62t1dlnn33CuLZu3ar9+/ef8HklacuWLfrTn/6krVu3+h/Lzs6m8gIECckLgE6JjIzU0qVLtWrVKn388cd69tln9Ytf/EJ5eXnKyso64e9deeWV6t+/v1566SVlZGTI6/UqOztbtbW1/nP+cUaTy+XyJzkxMTFtxlVeXi5J+stf/qI+ffq0OObxeE74e4sXL9Yll1yibt26nfCcOXPmqLi4WH379vU/5vV6/UNiALoWPS8AWhg0aJCio6OVl5fnf+zo0aP65ptvTvg7LpdL48aN06OPPqr169fL7XZr0aJFkiS3262GhoYW5x8+fFjbtm3TL3/5S02cOFFnnHGGjh49ekpxDhkyRDExMVq2bFmrx4cNGyaPx6P8/HwNHjy4xVdbScZ7772nq6+++oTHlyxZonXr1mn9+vXasGGD/+vll19Wfn7+KV8HgFNH5QVAC7GxsZo5c6buvfde9erVSykpKfrFL36hiIjW/18nLy9Py5Yt0+TJk5WSkqK8vDwdPHhQZ5xxhiRpwIABysvL0+7duxUbG6ukpCT17NlTvXr10h/+8Aelp6crPz9fDzzwwCnF2a1bN91///2677775Ha7NW7cOB08eFCbN2/WzJkzFRcXp7lz52rOnDnyer0aP368SkpK9Nlnnyk+Pl7Tp08/7jmLioq0du1aLV68uNXXrKur0z333KN7771XZ511Votj8fHxkqQvv/xSF1100SldC4BTQ/IC4DhPPfWUysvLdeWVVyouLk733HOPSkpKWj03Pj5eK1as0NNPP63S0lL1799fv//97zVlyhRJ0ty5czV9+nQNGzZMVVVV2rVrlwYMGKCFCxfqjjvuUHZ2toYOHar58+ef8of+gw8+qKioKD300EPav3+/0tPTddttt/mPP/bYY+rdu7dycnL07bffKjExUeecc45+/vOft/p877//vkaPHq3k5ORWjz/77LMqLi7W7bffftyxzMxMde/eXRs2bCB5AboYexsBQJOrrrpK48eP13333Wd1KADaQM8LADQZP368pk6danUYAE6CygsAAHAUKi8AAMBRSF4AAICjkLwAAABHIXkBAACOQvICAAAcheQFAAA4CskLAABwFJIXAADgKCQvAADAUf4/qm256pDWFXsAAAAASUVORK5CYII=", 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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": [ "-570.0766572838461: : 48it [00:02, 23.88it/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 - 5093927312\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 920.5357647881516\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 [00:26<00:00, 15.32it/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 [01:38<00:00, 15.28it/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 - 5093927312\n", "Dataset = c_PLP0011859_q\n", "datapoints = 408\n", "chi2 = 919.6010315269684\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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", 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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": 32, "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": 33, "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": [ "## 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": [ "1.0116103027947699e-07: : 27it [00:00, 128.72it/s]\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "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": [ "2.0230744864939112e-07\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(objective.chisqr())\n", "print(ti_layer)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (1519389772.py, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m Cell \u001b[0;32mIn[41], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m data.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "data." ] }, { "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": "3.12.3" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.52/doc/gui.rst000066400000000000000000000012551475550052500150170ustar00rootroot00000000000000.. _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-0.1.52/doc/incoherent_sum.ipynb000066400000000000000000002644771475550052500176070ustar00rootroot00000000000000{ "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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", "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-0.1.52/doc/index.rst000066400000000000000000000044071475550052500153440ustar00rootroot00000000000000.. 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-0.1.52/doc/inequality_constraints.ipynb000066400000000000000000002774271475550052500213770ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Inequality constraints with *refnx*" ] }, { "cell_type": "markdown", "metadata": {}, "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": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import os.path\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\n", "\n", "np.random.seed(1)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "pth = os.path.dirname(refnx.__file__)\n", "DATASET_NAME = 'c_PLP0000708.dat'\n", "file_path = os.path.join(pth, 'dataset', 'test', DATASET_NAME)\n", "\n", "data = ReflectDataset(file_path)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "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": {}, "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": {}, "outputs": [], "source": [ "objective = Objective(model, data)\n", "fitter = CurveFitter(objective)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-24.62789421854312: : 18it [00:00, 112.74it/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')\n", "print(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inequality constraints with `differential_evolution`" ] }, { "cell_type": "markdown", "metadata": {}, "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": {}, "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": {}, "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": {}, "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": {}, "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": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "-24.51333095283976: : 33it [00:00, 146.53it/s] /Users/andrew/miniconda3/envs/dev3/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:551: 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/miniconda3/envs/dev3/lib/python3.12/site-packages/scipy/optimize/_differentiable_functions.py:316: 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", "-24.51333095283976: : 33it [00:00, 90.88it/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": [ "np.random.seed(1)\n", "fitter.fit('differential_evolution', constraints=(constraint,))\n", "print(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inequality constraints during MCMC sampling" ] }, { "cell_type": "markdown", "metadata": {}, "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": {}, "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": {}, "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": {}, "source": [ "Lets check what happens to the probabilities with the specified inequality." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "16.92539558662436\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": {}, "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": {}, "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);\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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 4 } refnx-0.1.52/doc/installation.rst000066400000000000000000000073261475550052500167410ustar00rootroot00000000000000.. _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-0.1.52/doc/lipid.ipynb000066400000000000000000002416521475550052500156540ustar00rootroot00000000000000{ "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", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os.path\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": [ "('0.1.23.dev0+1b59a5a', '1.7.1')" ] }, "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": [ "pth = os.path.join(os.path.dirname(refnx.__file__), 'analysis', 'test')\n", "\n", "data_d2o = ReflectDataset(os.path.join(pth, 'c_PLP0016596.dat'))\n", "data_d2o.name = \"d2o\"\n", "\n", "data_hdmix = ReflectDataset(os.path.join(pth, 'c_PLP0016601.dat'))\n", "data_hdmix.name = \"hdmix\"\n", "\n", "data_h2o = ReflectDataset(os.path.join(pth, '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": [ "53it [00:26, 1.67it/s]/Users/andrew/miniconda3/envs/dev3/lib/python3.8/site-packages/scipy/optimize/_numdiff.py:557: RuntimeWarning: invalid value encountered in subtract\n", " df = fun(x) - f0\n", "53it [00:26, 1.98it/s]\n" ] } ], "source": [ "fitter.fit('differential_evolution');" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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", "--Global Objective--\n", "________________________________________________________________________________\n", "Objective - 140312336727488\n", "Dataset = d2o\n", "datapoints = 137\n", "chi2 = 417.78640890990016\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", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \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", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 140312336636128\n", "Dataset = hdmix\n", "datapoints = 97\n", "chi2 = 116.09164939809303\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", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \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", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 140312336633968\n", "Dataset = h2o\n", "datapoints = 104\n", "chi2 = 251.58933827912017\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", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \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", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "\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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 2 } refnx-0.1.52/doc/make.bat000066400000000000000000000155031475550052500151070ustar00rootroot00000000000000@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-0.1.52/doc/model_selection.ipynb000066400000000000000000001134611475550052500177140ustar00rootroot00000000000000{ "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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cmeUG7flmdsDMVgSPR6LWGWdm75lZmZndb0HNZjPrbWbFZrY2+KnSnQl67PUN7Nhfw4wrVBpbROKT6JFCMTDW3c8E1gA/iepb5+5nB4+bo9ofBr4NFASPoqD9x8Aidy8AFgXPpYUipbHXUTRmAGcPzg07HBFJEwklBXd/yd3rgqdLgLzjjTezgUAPd1/i7g48CVwddE8BngiWn4hqlxZ4aPE6DtQe5ocqjS0iJyCZ1xS+BcyPej7UzN42s1fN7MKgbRBQHjWmPGgD6O/uW4Plj4D+Tb2RmU0zsxIzK6msrExS+JmjfGc1/71kI18eN5gRJ3cLOxwRSSPNfrXVzBYCA2J03eXuc4MxdwF1wFNB31ZgiLvvMLNxwPNmNibeoNzdzcyP0z8bmA1QWFjY5LhsdW/xGszgjssLwg5FRNJMs0nB3Scer9/MbgA+D1wWnBLC3Q8Bh4LlZWa2DhgJVPDJU0x5QRvANjMb6O5bg9NM209wLkKkNPZzb1cw7cJhDOyp0tgicmISvfuoCJgOXOXu1VHt/cysXbA8jMgF5fXB6aE9ZnZucNfR9cDcYLV5wNRgeWpUu5yAWQtW061jDt+doNLYInLiEq2M9iDQESgObnlcEtxpdBHwMzOrBeqBm929KljnFuAPQGci1yAarkP8EviLmd0EbAS+kmBsWafkwyoWrtrOjyaPIreLSmOLyIlLKCm4+4gm2p8Fnm2irwQYG6N9B3BZIvFkM3dn5ourVRpbRBKibzRniJdXb+etD3dyu0pji0gClBQywOGgNHZ+ny58VaWxRSQBSgoZYO6KCkq3qTS2iCROW5A0d6juMPcWr2HsoB58TqWxRSRBSgpp7k9LN0VKY09WaWwRSZySQhrbd6iOB18u4/zhfbhQpbFFJAmUFNLYo6+vj5TGLlJpbBFJDiWFNPXxvkP8/rX1XDF2AGepNLaIJImSQpp6aHEZB+vqVRpbRJJKSSENba6q5qklm/jyuDyG91NpbBFJHiWFNHRfQ2nsiSPDDkVEMoySQppZ/dEenltRwQ0X5DOgZ6ewwxGRDKOkkGZmvVhK9445fPdilcYWkeRTUkgjb31YxaLV27l5wnCVxhaRVqGkkCbcnZnzV3Ny947ceP7QsMMRkQylpJAmFq3aTsnGndw+sYDOHdqFHY6IZCglhTRwuN6ZtaCUoX278pVClcYWkdajpJAGnn+7oTT2SJXGFpFWpS1MimsojX3GoJ5cOValsUWkdSkppLinlmyiYtcBpheNUmlsEWl1SgopbO/BWh5cXMYFI/pwYUG/sMMRkSyQcFIws5+b2btmtsLMXjKzU4J2M7P7zaws6D8nap2pZrY2eEyNah9nZu8F69xvWV4P+tHXN1C1v4bpk0eHHYqIZIlkHCnMcvcz3f1s4O/AT4P2K4CC4DENeBjAzHoDdwOfAcYDd5tZr2Cdh4FvR61XlIT40tLH+w7x6OvrufIMlcYWkbaTcFJw9z1RT7sCHixPAZ70iCVArpkNBCYDxe5e5e47gWKgKOjr4e5L3N2BJ4GrE40vXT34cqQ09p2TVBpbRNpOTjJexMx+AVwP7AYuCZoHAZujhpUHbcdrL4/RHuv9phE5+mDIkCGJTyDFbK6q5qmlG/lKoUpji0jbiutIwcwWmtn7MR5TANz9LncfDDwF3NaaAQfvN9vdC929sF+/zLsAe2/xGk4y4/bLVBpbRNpWXEcK7j4xztd7CniByDWDCiD667d5QVsFMOGo9leC9rwY47PKqq17eH5FBd+5aLhKY4tIm0vG3UcFUU+nAKuD5XnA9cFdSOcCu919K7AAmGRmvYILzJOABUHfHjM7N7jr6HpgbqLxpZtZC1QaW0TCk4xrCr80s1FAPbARuDlofwG4EigDqoEbAdy9ysx+DrwVjPuZu1cFy7cAfwA6A/ODR9Z4c0MVL6/ezoyi0fTs0j7scEQkCyWcFNz9S020O3BrE32PA4/HaC8BxiYaUzpyd2a+uJr+PTpyw/n5YYcjIllK32hOEQtXbWfZxp3cftlIlcYWkdAoKaSASGns1Qzr25WvFOY1v4KISCtRUkgBz71dwZpt+7hz0ihyVBpbREKkLVDIDtYe5r6G0thnDAg7HBHJckoKIXtqaaQ09oyi0WR5/T8RSQFKCiHae7CWhxaX8dkRfflsQd+wwxERUVII0+8bSmMXqeidiKQGJYWQVO6NlMb+3BkDOTMvN+xwREQAJYXQPPjyWg7V1XPnJBW9E5HUoaQQgk07qvnTm5v4SuFghqk0toikECWFENxbXEq7k4w7JhY0P1hEpA0pKbSxD7bsYe47W7jxgqH076HS2CKSWpQU2tisBavp3jGHmy9SaWwRST1KCm1o6fodLC6t5JZLRqg0toikJCWFNhJdGnvqeflhhyMiEpOSQhsp/mAbyzft4o6JKo0tIqlLSaENREpjlzKsb1e+PE6lsUUkdSkptIG/Li9n7fZ9/HCySmOLSGrTFqqVNZTGPjOvJ1eMVWlsEUltSgqt7L+XbGTL7oMqjS0iaUFJoRXtCUpjX1jQlwtGqDS2iKS+hJKCmf3czN41sxVm9pKZnRK0TzCz3UH7CjP7adQ6RWZWamZlZvbjqPahZrY0aH/azDokElsqePS19eysrmX65NFhhyIiEpdEjxRmufuZ7n428Hfgp1F9r7v72cHjZwBm1g54CLgCOB34mpmdHoyfCdzn7iOAncBNCcYWqsq9h3j0nxv43JkDOSOvZ9jhiIjEJaGk4O57op52BbyZVcYDZe6+3t1rgDnAFIucbL8UeCYY9wRwdSKxhe2BoDT2DyfpD+iISPpI+JqCmf3CzDYD3+CTRwrnmdk7ZjbfzMYEbYOAzVFjyoO2PsAud687qr2p95xmZiVmVlJZWZnoFJJu045q/rR0E1/99GCG9u0adjgiInFrNimY2UIzez/GYwqAu9/l7oOBp4DbgtWWA6e6+1nAA8DzyQza3We7e6G7F/br1y+ZL50U9xSXktPOuP0ylcYWkfSS09wAd58Y52s9BbwA3B19WsndXzCz35pZX6ACGBy1Tl7QtgPINbOc4GihoT3trNyym7krtnDLhOEqjS0iaSfRu4+id4WnAKuD9gHBdQLMbHzwPjuAt4CC4E6jDsB1wDx3d2AxcG3wWlOBuYnEFpZZC0rp2bk937lYpbFFJP00e6TQjF+a2SigHtgI3By0Xwt818zqgAPAdcGGv87MbgMWAO2Ax919ZbDODGCOmf0H8DbwWIKxtbkl63fwSmklP7liND07qzS2iKQfi2yr01dhYaGXlJSEHQbuzjUP/4utuw7yyo8m0Km9KqGKSOoys2XuXnh0u77RnCQvfbCNtzft4o6JBUoIIpK2lBSSoLE0dr+uXKvS2CKSxpQUkuDZ5eWUbd/HjyapNLaIpDdtwRJ0sPYwvy5ew1l5PSlSaWwRSXNKCglSaWwRySRKCgnYc7CWB4PS2OerNLaIZAAlhQT8/rX17KquZUaRSmOLSGZQUmih7XsP8ujrG/j8mQMZO0ilsUUkMygptNADi8qoPazS2CKSWZQUWmDjjv38+c1Iaex8lcYWkQyipNAC97y0hvbtTlJpbBHJOEoKJ2jllt3Me2cL3/psPierNLaIZBglhRP0qxcjpbGnXaTS2CKSeZQUTsAb63bw6ppKbr1kuEpji0hGUlKIk7sz88XVDOzZievPyw87HBGRVqGkEKcFK7exYrNKY4tIZlNSiEPd4Xr+86VShvfrypfOUWlsEclcSgpx+Ovyikhp7MkqjS0imU1buGYcrD3MfQvXcNbgXCaPUWlsEclsSgrN+OMbG9m6+yAzikapNLaIZDwlhePYc7CWh14p46KR/Th/uEpji0jmS1pSMLM7zczNrG/w3MzsfjMrM7N3zeycqLFTzWxt8Jga1T7OzN4L1rnfQt41n/1qpDT29Mkqeici2SEpScHMBgOTgE1RzVcABcFjGvBwMLY3cDfwGWA8cLeZ9QrWeRj4dtR6RcmIryW27znIY//cwBfOOkWlsUUkayTrSOE+YDrgUW1TgCc9YgmQa2YDgclAsbtXuftOoBgoCvp6uPsSd3fgSeDqJMV3wu5/eS21h+u58/KRYYUgItLmEk4KZjYFqHD3d47qGgRsjnpeHrQdr708Rnus95xmZiVmVlJZWZngDI714cf7mfPmZq4br9LYIpJdcuIZZGYLgVj3Y94F/DuRU0dtxt1nA7MBCgsLvZnhJ+ye4khp7O+pNLaIZJm4koK7T4zVbmZnAEOBd4JrwnnAcjMbD1QAg6OG5wVtFcCEo9pfCdrzYoxvU+9X7OZv72zhtktGcHJ3lcYWkeyS0Okjd3/P3U9293x3zydyyuccd/8ImAdcH9yFdC6w2923AguASWbWK7jAPAlYEPTtMbNzg7uOrgfmJhJfS/xqQSm5Xdoz7eJhbf3WIiKhi+tIoYVeAK4EyoBq4EYAd68ys58DbwXjfubuVcHyLcAfgM7A/ODRZv617mNeW1PJXVeeRo9OKo0tItknqUkhOFpoWHbg1ibGPQ48HqO9BBibzJjiFSmNXcrAnp345nmnhhGCiEjo9I3mwIKVH/HO5l18f+JIlcYWkaylpECkNPasBaWMOLkb15wT8y5YEZGsoKQAPLu8nHWV+/nhJJXGFpHslvVbwIO1h7mveC1nD85l8pj+YYcjIhKqrE8KT77xIR/tOciMotEqjS0iWS+rk8LuA7U8tHgdF4/sx3nD+4QdjohI6LI6Kcx+bR27D9QyvUilsUVEIIuTQkNp7KvOOoUxp6g0togIZHFS+M2itdQddu6cpNLYIiINsjYpDO7dhWkXDePUPiqNLSLSoDVrH6W0my8eHnYIIiIpJ2uPFERE5FhKCiIi0khJQUREGikpiIhIIyUFERFppKQgIiKNlBRERKSRkoKIiDSyyJ9STl9mVglsbOHqfYGPkxhOmDJlLpkyD9BcUlWmzCXReZzq7v2Obkz7pJAIMytx98Kw40iGTJlLpswDNJdUlSlzaa156PSRiIg0UlIQEZFG2Z4UZocdQBJlylwyZR6guaSqTJlLq8wjq68piIjIJ2X7kYKIiERRUhARkUZZkRTMrMjMSs2szMx+HKO/o5k9HfQvNbP8EMJsVhzzuMHMKs1sRfD4P2HEGQ8ze9zMtpvZ+030m5ndH8z1XTM7p61jjEcc85hgZrujPpOftnWM8TKzwWa22Mw+MLOVZnZ7jDEp/7nEOY+0+FzMrJOZvWlm7wRz+X8xxiR3++XuGf0A2gHrgGFAB+Ad4PSjxtwCPBIsXwc8HXbcLZzHDcCDYcca53wuAs4B3m+i/0pgPmDAucDSsGNu4TwmAH8PO8445zIQOCdY7g6sifE7lvKfS5zzSIvPJfh37hYstweWAuceNSap269sOFIYD5S5+3p3rwHmAFOOGjMFeCJYfga4zMysDWOMRzzzSBvu/hpQdZwhU4AnPWIJkGtmA9smuvjFMY+04e5b3X15sLwXWAUMOmpYyn8ucc4jLQT/zvuCp+2Dx9F3ByV1+5UNSWEQsDnqeTnH/oI0jnH3OmA30KdNootfPPMA+FJwWP+MmQ1um9BaRbzzTQfnBYf/881sTNjBxCM4BfEpInum0dLqcznOPCBNPhcza2dmK4DtQLG7N/mZJGP7lQ1JIZv8Dch39zOBYo7sPUh4lhOpMXMW8ADwfLjhNM/MugHPAne4+56w42mpZuaRNp+Lux9297OBPGC8mY1tzffLhqRQAUTvMecFbTHHmFkO0BPY0SbRxa/Zebj7Dnc/FDx9FBjXRrG1hng+t5Tn7nsaDv/d/QWgvZn1DTmsJplZeyIb0qfc/a8xhqTF59LcPNLtcwFw913AYqDoqK6kbr+yISm8BRSY2VAz60DkQsy8o8bMA6YGy9cCL3tw1SaFNDuPo87tXkXkXGq6mgdcH9ztci6w2923hh3UiTKzAQ3nd81sPJH/c6m2wwFE7iwCHgNWufu9TQxL+c8lnnmky+diZv3MLDdY7gxcDqw+alhSt185LV0xXbh7nZndBiwgcgfP4+6+0sx+BpS4+zwiv0B/NLMyIhcNrwsv4tjinMf3zOwqoI7IPG4ILeBmmNmfidwB0tfMyoG7iVxEw90fAV4gcqdLGVAN3BhOpMcXxzyuBb5rZnXAAeC6FNzhaHAB8E3gveAcNsC/A0MgrT6XeOaRLp/LQOAJM2tHJHH9xd3/3prbL5W5EBGRRtlw+khEROKkpCAiIo2UFEREpJGSgoiINFJSEBGRRkoKIiLSSElBREQa/X/ZvcSc1L8rPgAAAABJRU5ErkJggg==\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-0.1.52/doc/modules.rst000066400000000000000000000001041475550052500156730ustar00rootroot00000000000000API reference ============= .. toctree:: :maxdepth: 4 refnx refnx-0.1.52/doc/nsf.ipynb000066400000000000000000002576401475550052500153450ustar00rootroot00000000000000{ "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", "import os.path\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", "pth = os.path.dirname(refnx.__file__)\n", "dd = 'c_PLP0007882.dat'\n", "uu = 'c_PLP0007885.dat'\n", "\n", "file_path_uu = os.path.join(pth, 'reflect', 'test', uu)\n", "file_path_dd = os.path.join(pth, 'reflect', 'test', 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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lDaqxdu+JkmU0dGp2zeLwBu3cVkqVKo8HChGZA6wEWohIgohMMMakA/cBS4BtwOfGmC1uuJbHFgUsri6Na7Ah4SQX00s4mS5zFdqgCjDvXqvfQimlSoHH51F4gy/Mo8i0ZMsR7py1jgCsGdu5d8Yrsk+ug11LoWF3mLDELWVUSinw7jyKUuOTNYrImgDYcdqjoiR2/Wz9PrAKziWVPD+llCqEXwUKX+ujAKhZKZiqjm1ULxn5VBxdxmO9bQI/P1fy/JRSqhB+FSh81ZA29ahWIYinR1xW8syGToUpJ6BOa2vHvC9uK3meSilVAL8KFL7Y9AQQE1mDUxfSiPr3YibPi3NPpke3Wb+3fAN+2M+klPIdfhUofLHpCTzQTwGOJijH0h/bF7snT6WUyoNfBQpfrVE0DqtIhSAbgpv6KcBqgpqcBKHVYe5YWPCQe/JVSqlc/CpQ+GqNQkQYeFkdwiqH8MzI/PspJs+Lo9njixn4ym80e9yFZipbIKScth6vm+m+AiullBO/ChS+rGezMJLOXmRn4tk8X588L45Zq+LJMIYdR8+SYQyzVsUTOXERTSYuyj9oODdBHfrTM4VXSpVrGihKSc9m1gKBg15ZlueH/ier4rMehwblfFsMBfRtDJ0K/9oLVerCvHsgPdVtZVZKKfCzQOGrfRQADWtWBC790J88L44mExdl7URxfecI/np2CNueGUzretmbHo3p6ryGYi4VasDw1yBxKzxXGxY87IE7UEqVV34VKHy1jyJT5gf/NR2zt974dFV8VpDY9fwQXr6hPQAVgm0seuBy7r8qCoCdiWcL7rdoPoisJqh103U5cqWU2/hVoPB1r4/pCMBX6xOYPC+ORz/fgN3x2uiYhgTacr4dIsIjA5rTKyqM1XuTc26AlJcuE8gKFs+Fw4cD4MxRnWehlCoRXRSwlEVOXHRJWgCw58Wh+Z6z//h5rpz6Kxl2w99dWVhwSg3ICkFASFWwBcH5ZKsv48xRqxO8sP0wlFLlSrlYFLAs6Ncy/JK0m7s3LvCcRmEV+cegFgCM6ljQjrEOXcZb+1fUbAIIVIuA88cBA2cOA3ZYO80KGEopVQi/qlGIyHBgeFRU1O07d+70dnHytTfpHHfOimXX0bMuLz1+PjWdXi/+TKWQQA6fTGFst0aFn/d0TTAZVtCIGQdrp0PNSEjea7VQiQ3sGVaTldYulCr38qtR+FWgyOTLTU8l8drSnbyydAcANhF2v3B1wScsehRiZ1hBIncgOL4b3uiU/TwzmGjAUKrc0qYnP3Brz8YEBojrS4EMnQpPJef94R/WDDrflv3cZFhBRSmlctFAUYZUrxjMLT0isQUID/WPLnmGw1+DSUegqqPfI3qgVQt5uqb1Wyml0EBR5ozu0pB0u+HOWetcWw+qMEEV4L5YCIuGo3FWP4bWLpRSTjRQlDEt6lahXUQ1YuNPFD6vwlXBFeGad+H0QagVld1foZRSlIFAISKtRORdEflSRO72dnl8wfWdIwDrzXPbsuURMdDrQUjaAWPmaqe2UiqLRwOFiEwXkUQRicuVPlhEtovILhGZWFAexphtxpi7gBuBXp4sb1kxon19gm0B3Nor0qWhtS7r+ziEt4YFD8DFvFe5VUqVP56uUcwEBjsniIgNeAsYArQGxohIaxFpKyILc/2EO84ZASwCdCs3rE7t/q3Dmb/hEOkZ9sJPcFVgiNXBfeYwrHrHffkqpco0jwYKY8wyIDlXcldglzFmjzEmFZgLjDTGbDbGDMv1k+jIZ74xZghwc37XEpE7RCRWRGKPHTvmqVvyGSPa1+f4uVRW78395y2hhl2h5TBY8TrMu1dHQCmlvNJH0QA44PQ8wZGWJxHpKyKvi8h7FFCjMMa8b4yJMcbE1K5d232l9VF9W4RTMdjGlPlb3DP6ydlVkyH1LGz4VEdAKaV8vzPbGPOrMeYBY8ydxpi3CjrWl/ejcLfQIBv9WtVhZ+JZ941+yhTeEtqPBQkAAnQElFLlnDcCxUHAeReeCEeaKqKhbesBbh79lKnvRAgIhA5jdQSUUuWcNwLFWiBaRJqISDBwEzDfHRn7+sZF7ta3RW0qBdsY3dWFBQKLqnpDa7HAjXMgaZd781ZKlSmeHh47B1gJtBCRBBGZYIxJB+4DlgDbgM+NMVvcdL1y0/QE2c1P38cdJjW9aKOfJs+LK7xv4/KHrZFQv71YwpIqpcoyT496GmOMqWeMCTLGRBhjpjnSFxtjmhtjmhljnnfj9cpVjQLg2k4NOHE+jZ+2uba3RGaA+GRVfOF9G5XDoesdsPlLSNzmphIrpcoan+/MLoryVqMA6B1dm7pVQ3lu0bZ8awjOtYfZq/eTYQwGa6nyQvs2ej0IwZVhzhgdKqtUOVWsQCEiASKS75wGbymPNQpbgHB95wgOnryQbw0hMzh8siqesMrBCNC3eW22PDOo8L6NijWhxz1wYq8OlVWqnCpw4yIRqQrcizXPYT7wI1b/wqPARmPMyNIopKvKyg537rYv6Rx9X/4VAf7m2FZ19ur9NAuvxO7Ec9SqEszR0xcBiAqvTNLZi5w8n0ZYpWDm3NGdWSvjmb16f/675l04CS9HQ0YqxEwAkfw3RFJKlVnF2uFORL4FTmB1SPcDwrE20XzQGLPBM0UtOX/d4a4go99byZHTKSx95ApaPvE9Gbne1x5Nw3hkYHNiGtcg3W5YviuJf3y5iZDAAA6duICdQnbNi50OCx+GAc/A0qezt1h9ys0zw5VSXlPcHe6aGmNuM8a8B4zBWptpkC8HifJq/OVNiD9+noc+28BNXRsSAFSrEARAZFhFPp7QlS6RNRERgmwB9G0Rzoe3xJB09iI1KwcXPhej8zhoNQJ+egZaDs1eijz3Rke68ZFSfqewQJGW+cAYkwEkGGNSPFuk4iuPndmZBl1Wl0lXt2LRpsMknLhArSohnLqQxoTLm/Dzo30Jsl36VrdvWJ1XR3cg6Wwq13WOKLi/QgRGvAFV6sPhDfDPPVazU+yMnH0Xzs81aCjlFwoLFO1F5LTj5wzQLvOxiJwujQIWRXnszHZ2e5+mPDKgOb/tOEa96hX4+p6eTB7WmoAAyfecwW3qcWefpny5PoFdiWcKnl9RoTpcPx1OHYTPb4FDG6xahfNGR87PcwcRpVSZVGAfRVlVHvsoMhlj2Jt0jsiwSgUGCGfJ51Lp9eLPDGlbl2//PESGMYX3VyyZBGnnIaIrdL4N6raBGpEQ6hSkFz2as9M793OllE8pVmd2WVWeA0VxPbNgKx+t3MeIdvWZv/FQ/iOgMl04aS3vseYDSN6dnR5aHVqPhKuesCbsOXu6pnaCK+XD8gsUgd4ojKc4DY/1dlHKnDv6NGXWqn1UDg3MvybhrEJ16H43dL0TjsZZ8yxOxFszuDd8CnFfwxX/hG53QWCwdU5mc5SuRqtUmaI1CpXl8a838dX6g/zxrysJrxKaNZO70NpFbkk7raapnUsgLBrGLb60dqGU8jnFHR6rypE7+zQjPcPOzOX7gOwZ3UXe66JWNNz8OYz9HE7uh2/vBT/8QqJUeaGBQmWJrFWJK5rXZv7GQxhjGNutkWvrQeWn+SAY+Bzs/AHWvO/ewiqlSo1f9VGokhvSph6/fLWJLYdO8+yoNiXf56Lr7bBrKfwwGSIvhzqXuaegSqlS41c1ivI84c5d+reuQ4DAki1H3JOhCIx8yxo2++UESLvgnnyVUqXGrwJFeZ9w5w41KwXTrUkY38W5KVAAVK4N17wDx7bB0inZ6TpzW6kywa8ChXKPIW3rsivxLLsSz7gv06j+0OlWa7LeuSQrTWduK1UmaKBQlxjYui4A37uzVgHQ/R5rqfI/P7Ge517+QynlkzRQqEvUrRZKx0bV+d5d/RSZwltC48utWoXdbi3j8VSyLuehlI/TQKHyNKRNXeIOnuZA8nn3ZtxlPJyMh90/uTdfpZTHlIlAISKVRCRWRIZ5uyzlxeDL6gFFH/3kvPpsnivRthwOlcJh7TR3Flcp5UEeDRQiMl1EEkUkLlf6YBHZLiK7RGSiC1n9C/jcM6VUeWkUVpHmdSrz6/ZjAAUvP+7EeTZ3njO7A4Oh09+t5T1OHvDkLSil3MTTNYqZwGDnBBGxAW8BQ7B2zBsjIq1FpK2ILMz1Ey4iA4CtQKKHy6py6RNdmzX7krmQmuHych7Os7nzndnd+TZrSY91Mz1WdqWU+3h8UUARiQQWGmPaOJ73AKYYYwY5nj8OYIx5IZ/znwcqYQWVC8A1xhh7HsfdAdwB0KhRo87x8fHuv5ly5rcdx7h1+ho+Gt+VpVuP5rlAYFEWDsxx7Pln4eB6eHhL9uqySimv8qVFARsAzm0OCY60PBljJhljHgJmAx/kFSQcx70PPA2sDw7WDx536BpZk+DAAH7fcYxnR7Vh9wtXXxIMirJwYI5jYybAuUT4a4Gniq+UcpMy0ZkNYIyZaYxZWMgxOjPbjSoE2+gaWZPfdyblSHfuryjKwoE5jo3qB9Uawca52QfoTG2lfJI3AsVBoKHT8whHWonpWk/u1zu6FtuPnuHo6ZSsNOeaQX41jbzkODbABi2GwN7fIc2Rt87UVsoneSNQrAWiRaSJiAQDNwHz3ZGx1ijcr3d0bYActYr8ahHnU9P575K/WLz5MHa7C31f0QMg/QLE/2E915naSvkkj3Zmi8gcoC9QCzgKPGWMmSYiVwOvAjZgujHmeTddL3Mr1Nt37tzpjizLPbvd0PU/P9ErKozXbuqY73EHT17g9o9i2Xr4NAAt6lThgX7RDGlTl4AAyfuktAvw/yKh8zgY8qIHSq+UKgqvdGYbY8YYY+oZY4KMMRHGmGmO9MXGmObGmGbuChKOfLVG4WYBAULv6Fr8sTMp31rCuvhkRr75BweSzzPt1hheu6kD6XY7985ez6i3l5OSlpF35kEVrD0qdv1Y/AJqv4ZSHldmOrNdoX0UntE7uhbHz6Vm1Rac/bI9kTHvr6ZSSCDf3NuTfq3qMLJDA354+Ar+c01bNiWc4pNVBQxVjhoAx3dB8t7iFU77NZTyOL8KFFqj8IzLo2sBsHTb0RzpyedS+ccXG2lauxLf3tuLqPAqWa/ZAqw+jF5RYbzz627OXUzPO/PoAdbvXUsvfS13bSGv2oP2ayjlcX4VKLRG4RnhVUK5skVt3v5lN2v3JWelT54Xx6kLabx6UweqV8x77spjA1tw/FwqM5bnU2MIawY1msDOPJqfctcW8qo96Aq0SnmcXwUKrVF4ziujO9CgRgXunLWO/cfPs2DjIRZtPsxD/ZvTsm7VfM/r2KgG/VvV4b1lezh1Pi3vg6IHwN5l2cNkM+WuLWjtQSmv8PgSHt4QExNjYmNjvV0Mv7Pn2FmueXsFtauEkHT2Io3DKvHVXT0ItBX8fWPb4dNc/frv3H1FM/45uOWlB+z4AWbfAH/72pqIp5TyCl9awsNjtOnJs5rWrsw7N3diX9I5LqRmMPWG9oUGCYBW9aoyrF19Zizfx7EzF7PSM2d4PxNXE2whefdTFIeOhFLKrfwqUGjTk+f1jKrF9Nu68MEtMUSFV3b5vIf7R5OaYeedX3dnpWXO8P5obaI1TDavfori0JFQSrmVXwUKVTr6NK9Nn+a1i3RO09qVGdCqDt/FHSazuTPHDO/oAXB8J5zYV/ICal+GUm4V6O0CqPKjd/NafL/lCHuSztGsdmWeHdUme42opFBgolWr6Hp7yS40dKqOglLKjfyqRqF9FL7t8ihrPsbyXUmXvhjWDGo2hW267LhSvsavAoX2Ufi2RjUrElGjAn/szCNQiED7sbD3N0jeU/qFU0rly68ChfJtIsLlUbVYuec4GXmtG9XxZqtvYd1HpV84pVS+NFCoUtUrqhZnUtLZfDCP5sGq9aH5YNjwKaSnupSf8yZKSinP0EChSlXPZmFAPv0UYI1UOncMti92Kb+ibMWqlCoevwoU2pnt+8Iqh9CqXtW8+ykAml0F1RrCOtfmQBRlK9ZSY7eDPZ+l1ZUqg/xqeKwxZgGwICYmpoTjK5UnXR4Vxkcr4rmQmkGFYFvOFwNs0OkW+OV5q1O7ZtMC88oxxNYXpKXAh/0haTtUb2QteFgjEmo2sR5HdIHKRZuDopS3+VWNQpUNvaJqkZphJzY+Oe8DOv7N6tRe/3GO5DLRH/Hrf+DoZugwFupcBmePwqbPYMm/Ye4YeLsbJO1yz7USt1nDie129+SnVD78qkahyoauTWoSZBP+2JWUtSd3Dpmd2n9+An3/DYHBkJ7KibWf85htD7+s6QQjL7OG1PqSA2tgxRvQ6VYY/lp2ujFwPhkSt8AX4+CTa2HCj1ClTvGuY8+AlW/Cz89BRirUaQP9noTogb73N1F+QVePVV4x+r2VnEtNZ+H9vfM+IHNF2QHPwNlE2DgHzh/Pfr1GE2g/BtrfBDUal06hC5J6Ht69HDLS4J4VEFIl7+MS1sFHw6BWNNy2KP/j8nMiHubdDfHLodVwaHE1/PYSnNgLjXpAv6egcY+S348ql8rF6rGq7OgVVYsth05z4lw+w2Cj+lmd2j8+Cavfhca94OavYOJ+GPUOVIuwmnleawfL/lvwxUpjNdmfnoHk3TDyzYI//CM6ww0fwZE4+PwWl4cBYwxsmAPv9ILDm6y/wY2zrCau+9bC0P9ZfTozBsOnN1r5K+UmPh8oRKSviPwuIu+KSF9vl0e5R6+oWhgDK/dk1xJy9EEE2GDU2zDoBXhkG4yeBdH9IbSa9eF420J4aDO0HAa//r8c7f6X9GV4ejXZfX/A6negy+3Q9IrCj28+EEa8Drt/hvn3W0GgIOeOW0Fl3l1Qty3cvdz6G2Q2M9mCoMsEeGCDVaM4sMqq3Xx9J6SeK/HtKeXRQCEi00UkUUTicqUPFpHtIrJLRCYWko0BzgKhQIKnyqpKV/uIalQOCWTl7uxAkXtOxOSNNWk2P5LJSxPzzqR6Ixj2CgRVgCWP55uPR1eTvXgW5t1jNYUNeNr18zr+Da58AjbNhaVTLn09PRUOrIXlr8E7PWD7d9D/aStA5tfUFlwRej8CD26EXg/C5s9h7thLdw5Uqog83Zk9E3gTyBq+IiI24C1gANYH/1oRmQ/YgBdynT8e+N0Y85uI1AH+B9zs4TKrUhBoC6B1vapsO3w6K21st0bMXr0/a06E8wd+vkNgK4dD34nWqKIdS6D5oEvy8ehqsj8+CSf3w7jFEFypaOf2eQzOHILlr0LFMKjTGvavgviVcDAW0h0f8PU7wt++smoTrqhQwwpatVtY/Rlf3Go1UwXmva+5UoXxeGe2iEQCC40xbRzPewBTjDGDHM8fBzDG5A4SufMJBmYbY67P5/U7gDsAGjVq1Dk+Pt5t96A848lv4/h6/UE2TxmI5DFaZ/K8uKwP/ALnSmSkWW339jS4ZxUEhhS7TC5fE2D3LzBrFHS/Fwb/p3gXtGdYzUp/LbSeiw3qtbM6pht1h4bdiz86CmDtNFj0CLQeBddNA5ubvhsuetRqyosZp0u6+xFf6sxuABxwep7gSMuTiFwrIu8Bs7BqJ3kyxrwPPA2sDw7Wb05lQcu6VTl7MZ2EExfyfP3ZUW3Y/cLVhX9g24Jg8AtWZ+6qd0pUJpeXBLFnwOLHICwK+k12LfO8OtUDbHDdh1bn9N/nWZ31d/xq3U/rkcUPEpnXStwKA5+HrfPg23tdn3NR2AAA3UWwXPH5zmxjzNfGmDuNMaONMb8WcqwuM16GtKxnjQ7668iZkmcW1Q9aDLVGQJ0+XPjxx3bAN3dbzTxOXF4SZNt8OL4LrnrC6iNxRX4frkEVrM7pZldCiOvbyxb4Ye58rZ73ZfeHLHqk8M5zgLXTCw4EuotgueKNQHEQaOj0PMKRVmK61lPZ0qKOI1A49VOUyKDnrWaovDqHne35Dab1h42zreGk39xtzdXAxVqMMfD7/6zaRKsRrpevoA/X4gzhLehbfe5r9XkMLn/EWkNryb8vDRZ2uzVhcOkUeLMr4Kh5VIuA9bPgzNGcxw+dCk8la7NTOeGNmdlrgWgRaYIVIG4CxnqhHMrLKoUE0jisontqFGCtp9TzPvh9KlRrAD0fgArVcx6zfhYsfAjComH8D9a37BVvwl+LrCakmPFWc1BBdv8ERzbBiDcKP9bJ5LRxzE7px9i0Rjyb+0XnD31XP3xjxmX3E+SWuwNfxJq9nXYBVr0N8Sus+RjR/aByXdjxvbVqr9ggspdVwzmxzxogMP8+K496HawZ880HQr2OEODzDRLKTTzamS0ic4C+QC3gKPCUMWaaiFwNvIo10mm6MeZ5d15XZ2aXHXfOimXn0bP8/FjfQo+12w0HTpyncVgBo4tSz8H8ByDuS2vORa8HodtdEFgBfn4G/njFWqH2hpnW62A1Qy1+FPYug3rt4doPrBFD+Zkx1OoPeXBjkUYSNXt8MRnGYBNh9wtX53yxtDqHjbEC5bqZ2WkhVSGqvzXLO7q/NWrK+fijcVbA2LEEEtYCBiqFw8BnrZnxedHO7jIpv85sv1rCQ0SGA8OjoqJu37lzp7eLo1zwyo87eP3nnWx9evClK8nmMmX+Fmau2Mffuzdm0tBWhAYVcPzhTdYKtDu+h0q1IbyVFQg6j4Or/8vkBdtzjm4yBrZ8DYv/ac2svnMZhFYFco2E6nwepg2wJgL2uKdI91qkEVWezM9uh/f7WrWi5oOKNnT23HHYtRTWvA9HNlt/p/CWlx73dE2rhiQ2q4lKlQnlIlBk0hpF2fF93GHu+mQ9397bi/YNq+d73PJdSdz84WpaOeZeNK9TmdfHdKRl3aoFX+DAGmt5jX1/WN+Ae9wHIjSduAg7VifdnheHZh+/fxXMuBpaj4DrZ4BIzppA24/hwGp4OK7o8ybcrMAaiqedTYS3u0PVBvB/P10aaLRGUSb50vBYpbK0qmd90P91JP8O7VMX0njsi400rV2Jr+/uyUfju5J8Lo0Rby7noxX7KPDLTsOucOsCmBiP6XEfa/ad4JHPNmTN22gbkWuEXKPuVl/Flm8gdhqQPRLq4XZpsOM7qymrgCBx0/sraTJxEf/+erOLf4Xi8eqmTZXDYfjrVq3k1zymQGlnt1/xq0Cho57KnoY1KlIx2Ma2w/l3aD+9YAuJZy7yvxs7UCHYxhXNa/P9Q73p1SyMp+Zv4eHPNpCekf/8gJR0Ox+uPU7///3Gje+t5MetRxnTrSGDLqvD5oOnWLsvV9NIzwchagB8/284vDFrJNR9wQsguDJ0zX9frBW7kli1JxkDLFyzjVmr4rmY7pnd7goaoVUqe3e0GmYtRbL81UuGGSv/4leBQudRlD0BAUKLulXyrVF8H3eEr9cf5N6+zejg1DRVq3II02/rwmMDmzNvwyEe/GwDaXkEi0MnL3DDuyt5btE2qlUI4qXr27F6Uj+eG9WWl29oT8OaFXlo7gZOnU9zLhRc8661rMYXt0HKaUjeC3FfQefboGLNPMt67mI6//xqE5FymGlB/yVKDjJ5XhxX/vdXPvFgwMiLp/cSzwxEz6bfYq3y+82dcLGIo9dcGRJcGiv/qkL5VaBQZVPLulX568iZS5qQks5eZNI3m2nToCr3XRV9yXkiwn1XRTPp6lYs2nSY+2f/SWp6drCI3ZfMiDf/YG/SOT64JYav7+nFjTENqRhsjQqvEhrE6zd15OjpFCZ+vSnn9SvVguunWfs/LHgQVrwOAYFWH0c+XvzuLw6evMB/2x6iX+AmvuqVwKwJXalbLZQnHAHj+zgXJgO6gaebpTID0czY43DNe3DqAHz/eOEnOnNldrfOAPcJfhUotOmpbGpVrwonz6dx9PTFHOlPzd/CmYvp/O/GDgQH5v9P9fY+TXlyWGu+33KEe2ev52J6BnPW7GfMB6uoHBLIlS1qc9esdXk2w7RvWJ3HBrXgu7gjzF17IOeLjXvCVZOs0VCxM6yNkqrWy7MMK3YnMWtVPON6NqHL2CfhqWRk2FR6R9fmq7t78vH4rlStEMQjn2/kdEpannnkqYBv1Ha7ybMWBYVPHCxp01SOQNS4B/R6CP6cZc1HcZUrs7t1BrhP0FFPyuvW7E3mxvdWMmNcF65sEQ7AxgMnGfnWch7sF83DA5q7lM/HK/fx5LdbaBxWkfjj5+kdXYs3x3Si07M/Fjg6yG433DJ9DbHxySxst4KorW9lj9ax2+HT62HPr9YGQWHNLjn/3MV0Br+2DJsI3z3YJ99hvpsSTjLizeU8Nbw143o1ce2Pk88wU2MMD8zdwLIdx3igXzR/7964wGCam9tHTKWnwodXWcun3LPS6uxWZY6OelI+q0XdzKU8stu4X/5hOzUrBXN7n6Yu53NLj0j+c01bEk5c4PbeTZg5rivVKgYV2gwTECD878b2VAwO5L719UmxB2Q3dQQEwE2z4d7VeQYJgJe+/4uEExd46fr2Bc4FaRdRnY6NqjNrZTx2u4tf0PL5Rr1g02EWbDxEzUrBPLtwK4NfXcbSrUcLHgHmxO1NU4HB1kTFi2esCY9++AW0XDPG+M0PMBx4PyoqyqiypecLP5n7Z683xhizYleSafyvheaDZbuLldeF1PTCD1r4iDFTali/HX7edtQ0/tdC88akW3OkFySzrFPmx7l0/DfrE0zjfy00v21PzEp74pvNpunEReaJbza7lEfi6RTT4eklZsSbf5i09Azz07Yj5sqXfzGN/7XQjP1gpdl66JRL+XjEireMeaqq+ebD54t0T8o3ALEmj89Wv6pRGB31VGa1dIx8Msbw8g/bqVs1lL91z2cnt0IUOGM7Ux6dpFe2DKdnszC+rHYr5uqXs4/Np5/gfGo6//xqI43DKvLPQXnMTs7DkLZ1qVU5mI9W7MtKK8oIJWMMT8zbzLnUDKbe0I5AWwBXtazDkof6MGV4a7YcOs3Q13/n8a83cezMxULzy0+x+zC63QVN+jBg/6s04IjHRl0Vm46iKha/ChSq7GpZrwq7j53j+7gjrIs/wQP9ol37wC+ufJp0RnVowL7j59mY4DQgIp+RN/9dsp2EExf4byFNTs5CAm2M7dqIn7cnsv/4eaBozUDzNx5iyZajPDKgOVHhVbLSg2wB3NarCb89diW39WzCF7EJ9P/fbxw8mfdeH4Up9vDagAAY9Q4BtiDeDnqNF5v/BacPFasMReJqAPDnUVQeDIIaKJRPaFm3Khl2w6R5cTQOq8gNMRGevWA+M4cHtalLsC2AeX86rXyfR1BJOnuRT1ftZ3RMQ7o2yXteRX7GdmtMgAifrLZ2YXR14lzimRSemr+FDg2rc3vvvPtuqlUM4snhrVn0QG8upGXw8pLtRSpbdhlL0IdRLYIKN7xLmwonuCH+afhfK3itA8y7FzbMtlaldXcfhqsBwJ9HUXkwCHpjmXGlLtHKsYlR8rlUnhremiCbd77DVKsQxFUtw1m46TBPDG1FoC0gzz23Z6/eT2qGnf/bNAZCBxdpqYq61UIZfFldPlt7gIf7Ny+wNpL5zf7TVfEcPZ3C+dQMXr6hPbaAS7eOddaibhUmXN6Ed37dzfheTS5dqqQQz45qU7KFC1sNt1ajPbIZ4pfDvuWwfRFs+MR6vWqEtZx5y2HWsXlshVskBS257szF/dPdvYBjqXD1b1AMWqNQPiEyrBIhgQG0rFuF4e3qe7UsozrWJ+nsRVbsPp7n6xfTM5i1Kp6+ARuJkoRifYO7pUdjTl1I49sNBe/ZlfnNvmdULX7YepTHBjYnKty1XfDu7tuMmpWCeW7RVpdHQ7nCGMO5i+mF5xlgg/odoMe9MGY2/GMP3L0ChvwXImJg98/w+d/hk+usWkZJuHltKU/PbPcID66v5Vc1Cqdlxr1dFFVEgbYAXh/TkSa1KhFQyLdlT+vbIpwqoYHM23CQPs1rX/L6ok2HOXbmIuPaGNhdvGaMrk1q0rJuFT5aGc/oLg2zFinM7dlRbbj/qigGvLKMTo2qM+Fy14cLVw0N4uH+0Uz+dgtLtyUyoLVr+29n2A3T/tjDrsSznL6QzumUNOsn8/GFNOwG/t69cdG+bQcEQJ3LrJ9ud1j7jq+dBj89DW/3gCv/Dd3uBlv2x5K3vtmP7dYo67pKJ9wplad/fbmJhZsOEfvEgBxNQ8YYhr/5Bylpdn58uE++H/CumLNmP49/vZnP7+yRbz+HMYbbP17H7zuPsfjB3jSrXYQ9tYG0DDuDXl0GBpY83MelJr2nF2xhxvJ91KkaQrUKQVQNDaJqhSCqhgY6fgex/egZlm47ytd396RjoxqF5lmgUwmw6DFrZd567a1Vaet3ALy8lHo5pBPulCqCkR3qcy41g5/+yrlXdGz8CeIOnmZcr8gSBYnMa1QNDeSjlfvyfP18ajqv/LiDpduO8tjAFkUOEmCNhvr3kFbsSTrHnDWFN6PMXL6XGcv3Mb5XE1b/uz8/PHwFX97dk+m3deHVmzryzMg2PDaoBa+M7kB4lRCemBdHhquTB/NTLQLGzIEbPoIzR+CDq+CHJyD1vMud6qWyWq4PXru0aKBQKg/dmoZRp2oI8/7MObRz+h97qVYhiGs7RpT4A6JicCCjuzRkSdwRjpxKyUq/kJrBB8v20OelX3j9511c3bYu4y93ccmPPPRrFU73pjV5denOAteZWrr1KM8s3MqA1nWYNLRVgXlWDglk8jBr3sYnq+KLXbYsInDZKGsGfMe/wYo34O3uPNvmaIFrVmXyZp9Cka5dRudxaKBQKg+2AGFE+/r8tiORk+dTAUg4cZ4lW44wpmsjKgTb3PLh9Lfuja081uwnJS2DD3/fQ++XfuH5xdtoVa8qX93dg7dv7lzoKKeCiAhPDG1N8rlU3v5ld57HxB08xf1z/qRNg2q8dlMHl643tG09ekfX4uUl20k8k1Lo8S6pUANGvA63LQJbEHxyLXx7X6HDab25iVORrl1G53H4fKAQkQAReV5E3hCRW71dHlV+jOzQgLQMw6LN1tLgH6+MR0S4pYc1Y9wdH06NwypxZYtwPlqxj94v/cJzi7bRom5lvrirB7MmdKNz46LN0chPmwbVuLZjA6Yv30vCifM5Xjt08gLjZ66lZqVgPrw1JmsZ9sKICE+PuIyL6XZeWPyXW8qZJfJyuGs5dL/HpVVpC1st15OKdO0yOo/Do4FCRKaLSKKIxOVKHywi20Vkl4hMLCSbkUAEkAYkeKqsSuV2Wf2qNKtdiW//PMS5i+nMWbOfwW3qUr96BcB9H04TLm/CqQtpRNWuzGd3dOfT/+tOl0j3BAhnjw1qgWDNKM90JiWN8TPXciE1gxnjuhBeJbRIeTatXZk7r2jKN38eZGU+w4mLLSgUBjwLNZvBL/+xVvIt68roFrGerlHMBAY7J4iIDXgLGAK0BsaISGsRaSsiC3P9hAMtgBXGmEeAuz1cXqWyiAijOjRgzb5k3vh5F2dS0hnv6vLgRdArqhYbnhzAnDu6061pmNvzz1S/egX+r3cTvt1wiI0HTpKWYefe2X+yK/Es7/ytM83rVCk8kzzce2UUDWtWYPK3cTk2jnILWyD0fRwSt8DWb9ybt6/zof4MjwYKY8wyINeGxHQFdhlj9hhjUoG5wEhjzGZjzLBcP4lYtYgTjnNLby9JpbCanwDe/W037RtWp1Oj6h65TvWKwR7JN7e7rmhGrcrBPL9oG09+u4VlO47x/DVtuDy6VrHzDA2yMWX4ZexKPMv05XvdWFqHNtdC7VbwywuQke7+/EtRkQZA+FB/hjf6KBoAzluJJTjS8vM1MEhE3gCW5XeQiNwhIrEiEnvs2DH3lFSVe43CKmYFh/FuGBLrbVVCg3iof3PW7Etmzpr93NO3GaO7lLwDuF+rOgxsXYfXlu4s9kKE+QqwWZPxju+EzV+UKCtvD2Ut0gAIH+rP8PnObGPMeWPMBGPM/caYtwo47n3gaWB9cHDpfDtT5cMdfZrSO7oWQ9rkvQ1qWXNTl4Z0iazB6JiGPDawhdvyfXJ4awyGZxZsKfRYYwzr4k/w6/ZEUtJcaChoNRzqtoPfXoSMImwlm4u3l+YocABEWoq1NtbmL+GnZ9m6YwfrM5ry6/40OBLn1c2gPD4zW0QigYXGmDaO5z2AKcaYQY7njwMYY15w1zV1ZrZSBTPGeKR29Pavu3jp++3MuK0LV7a8dDvUA8nn+Xr9Qb7+M4F4xzLrlUMC6dcqnCFt6nFF89r5L5K4YwnMvhGGvwadbytW+VxeEmTRo9kL7Lm74zktxaodJf4Fx7bBse2QuA1O7AXj6OMRG7sy6nCKSnSQXdjEQFg0XHaNNd8kvHXJF1LMQ34zs70RKAKBHUA/4CCwFhhrjCn8a0jh18pc6+n2nTt3ljQ7pVQRpabbGfLaMtIyDD883IfQIBunU9L4bvNhvlp/kDV7rS7LHk3DuLZTA2pVCWFJ3BGWbDnCifNpVAiycVXLcIa0rcuVLcKpFOI0VNcY+LC/NXv7gfUQGOK5G8lnr/JiMcYKBFu/hb8WQuLWHAGBsGZQu6X1E97S6o8Ja8bkhTuZvXo/t3eqwsTIHbB1Huz7wzq3VnNoPcoKHOGt3BY0vBIoRGQO0BeoBRwFnjLGTBORq4FXARsw3RjzvJuup4FCKS9bsTuJsR+s5rpOEaRl2Fmy5QgX0+00rVWJ6zpHMLJDfSJqVMxxTnqGndV7k1m8+TBLthwl6exFQgID6NuiNtd3bpi9oOHuX2DWKGsF2m53eO4mSlqjMAaObLKCw9Zv4fguQKBRD2uOiFNAKFLAO5sI2+bDlnnW8u2ZQeOya3jjSBte3RRYogUUvVaj8AZtelLKux6c+yffbjhEtQpBjGhfn2s7NaBDw+ouNXdl2A2x+5L5Lu4I38Ud5ujpi7xwbVvGdG1kfQDPHGp98D6wAYIrFppfqTEGDq7LDg4n460aQ+Tl0HoEtBwOVVxbwdclZ45aQWPrt1ZNA8NOewMmpt/JV/95sFhZlotAoTUKpXzDuYvp/Ln/JF2a1CAksPhb2qZl2Lnj41h+23GMt2/uxOA29axNkGZeDQOfg573u7HURS1cCpw5BCfiYecPsHU+nE6AgCBoegW0HgkthkIlz82NyXLmKAs+e4/a+xfze5v/8I8brypWNuUiUGTSGoVSvs/VjuULqRn8bdpqNiecYub4LvRsVgtmXQOHN8KDGyGkeBMFC5R+0aq1nD4Mpw/CmcPW3t+nDzkeH4QLJ7KPt4VAVD9oNQJaDLbWrCqDNFAopXxKUfaaOHk+lRvfW8mhkynMvaM7bcwu+PAquOoJ6PMP9xYseQ98cj0k51pAsVI4VK0HVepD1frW46oNrMcNOnsmYJWy/AKF7nCnlPKKouwiV71iMB+P78Z176zgthlr+OKunjRpPsRajrzL7VChunsKlbDOGoJrMmDk21Znc9X6ULkuBJbf+Vlao1BKlRm7j53lhndXUjHYxrfXVSXsk37Q559w1aSSZ779e/hyHFSqDX/7CmpFlzzPMqZc7HAnIsNF5P1Tp055uyhKKQ9oVrsyM8d14cS5VG5eeJ60FiNg1TtwroQr18ZOh7ljrKGm/7e0aEHChxbv8xS/ChTGmAXGmDuqVavm7aIopTykXUR13vt7DLuPneWfSVdjUs/CiteKl5kx8NOzsPBhiOpvbZhU+dIZ5QXyocX7PMWv+iiUUuXD5dG1eGV0B+6f8yeja15Ft9XvI93vLdo8hfRUWPAAbJwDnW6Boa9Yy5oXVcy47Ml5xWSM4fCpFHYcPcOOo2fYfuQsOxPPcOzMRXpH12J4+/r0aBpGoM073+21j0IpVWbNWhXPtG9/5KeQfxJQoRrS4mpoORTTtC8msAIG60PYAHbHZ12ACLbUMwR8cSvs+QWunGSNnCriMhh2uyHDGOzGIAgiVt4BQoETC5POXmTHkTNsP3qGHUfPWsHhyBnOXMxeQj28SgjN61ShaoVAlu1I4uzFdMIqBTO4TV2GtatP1yY1S7Q9bn7KxfBYnXCnVPnz6tIdrP55HqNtv3JVwJ9UlfOcNyH8Zm/HDxkx/GzvyCkqZx0fzglmBr9EtCQwKeN25pm+BASATYSAAMEWINgcH/QZxpBhN9lBwZ6dVhgREDKDh+MJ5NjcqXrFIJrXqUKLOlVoXrcKzcMr07xOFWpUyh5hlZKWwa/bE1mw6TA/b0vkQloG4VVCuLptPYa3r0fHhjUIcFPQKBeBIpPWKJQqP4wxfLnOWo3WZtJoeHo9Ucm/EZX8G5VTj2HHxsHqndlbqy/HKzWj319PEZp+mgXNX2BP9e5k2K3aRobdERQcj7NrCI7gEZD52CmoOH6DVcPIrLkYY5XLbsBg/bYbAwbqVA2leZ0qNK9bmdqVQ4q0iu/51HR+2pbIwk2H+GX7MVLT7dSvFsrQdvUY1q4+7SKqlWhVYA0USqnyxW6HQ39aK7b+tRCSdljplevAzV9AvfbeLV8JnUlJY+m2oyzceJhlO4+RlmFoVLMib4zpSPuG1YuVpwYKpVT5dmwH7F9hjW6qFuHt0rjVqfNpLNlyhEWbD/Pq6A45mq6KQgOFUkqpAumEO6WUUsXiV4FCJ9wppZT7+VWgUEop5X4aKJRSShVIA4VSSqkCaaBQSilVIA0USimlCqSBQimlVIH8csKdiBwD4vN5uRaQVIrF8RR/uA9/uAfQ+/A1eh/F19gYUzt3ol8GioKISGxeMw/LGn+4D3+4B9D78DV6H+6nTU9KKaUKpIFCKaVUgcpjoHjf2wVwE3+4D3+4B9D78DV6H25W7voolFJKFU15rFEopZQqAg0USimlCuRXgUJEBovIdhHZJSIT83g9REQ+c7y+WkQinV573JG+XUQGlWrBc5axWPcgIpEickFENjh+3i31wucsZ2H30UdE1otIuohcn+u1W0Vkp+Pn1tIr9aVKeB8ZTu/H/NIr9aVcuI9HRGSriGwSkZ9EpLHTaz7xfpTwHsrSe3GXiGx2lPUPEWnt9Jp3PqeMMX7xA9iA3UBTIBjYCLTOdcw9wLuOxzcBnzket3YcHwI0ceRjK2P3EAnEeft9KMJ9RALtgI+B653SawJ7HL9rOB7XKGv34XjtrLffiyLcx5VARcfju53+XfnE+1GSeyiD70VVp8cjgO8dj732OeVPNYquwC5jzB5jTCowFxiZ65iRwEeOx18C/UREHOlzjTEXjTF7gV2O/EpbSe7BlxR6H8aYfcaYTYA917mDgB+NMcnGmBPAj8Dg0ih0HkpyH77Elfv4xRhz3vF0FZC5qbSvvB8luQdf4sp9nHZ6WgnIHHHktc8pfwoUDYADTs8THGl5HmOMSQdOAWEunlsaSnIPAE1E5E8R+U1Eenu6sAUoyd/TV94Ld5QlVERiRWSViIxya8mKpqj3MQH4rpjnekpJ7gHK2HshIveKyG7gJeCBopzrCYGlcRFVKg4DjYwxx0WkMzBPRC7L9e1Ela7GxpiDItIU+FlENhtjdnu7UAURkb8BMcAV3i5LceVzD2XqvTDGvAW8JSJjgScAr/bV+VON4iDQ0Ol5hCMtz2NEJBCoBhx38dzSUOx7cFRHjwMYY9ZhtV8293iJ81aSv6evvBclLosx5qDj9x7gV6CjOwtXBC7dh4j0ByYBI4wxF4tybikoyT2UuffCyVxgVDHPdR9vd+646werdrQHq5Mns5PoslzH3EvOjuDPHY8vI2cn0R6805ldknuonVlmrI6yg0BNX30vnI6dyaWd2XuxOk5rOB6XxfuoAYQ4HtcCdpKr09KX7gPrg3M3EJ0r3SfejxLeQ1l7L6KdHg8HYh2PvfY5Vep/KA+/CVcDOxz/WCY50p7B+nYBEAp8gdUJtAZo6nTuJMd524EhZe0egOuALcAGYD0w3Mffiy5YbaznsGp1W5zOHe+4v13AuLJ4H0BPYLPjP/ZmYIKP38dS4Kjj388GYL6vvR/FvYcy+F685vR/+RecAom3Pqd0CQ+llFIF8qc+CqWUUh6ggUIppVSBNFAopZQqkAYKpZRSBdJAoZRSqkAaKJRSShVIA4VSSqkCaaBQyktE5A3HXhZdXDi2qYhME5EvS6NsSjnTQKGUF4hIJSAcuBMYVtjxxlqWeoLHC6ZUHnT1WKWKQEQigLewNpGxAYuBR43TAnS5jn8XmGWMWe6cbow5JyL1sBaoa+R0fFvghVzZjDfGJLrtJpQqIq1RKOUixwZRXwPzjDHRQDRQAWvPgPx0x9pEJ3deYUBF4AyQnplujNlsjBmW60eDhPIqDRRKue4qIMUYMwPAGJMBPAzcIiKVcx8sIq2AHY7jcnsCeBlr8bfLCruwiIQ5aicdReTxEtyDUkWmTU9Kue4yYJ1zgjHmtIjsA6KwVvt0NgT4PncmIhKJtaLpI8DljnxXFHRhY+01clfxiq1UyWiNQinPGUQegQJ4DnjGWEs3b8OFGoVS3qQ1CqVctxW43jlBRKoCdbH2B3BOrwhUN8YcypXeAbgWuFxE3sLaX2SzB8usVIlpjUIp1/0EVBSRWwBExAZMBd40xlzIdeyVWJvO5Pb/sDaoiTTGRALt0RqF8nEaKJRykaOp6BrgehHZibWjnd0Y83weh1/SPyEiVwEVjTFLnfI8ClQWkZqeK7lSJaM73ClVTCLSE5gDXGOMWZ/rtfVAN2NMmlcKp5QbaaBQSilVIG16UkopVSANFEoppQqkgUIppVSBNFAopZQqkAYKpZRSBdJAoZRSqkAaKJRSShXo/wMZQRsGbmgFbAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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": [ "77it [00:51, 1.51it/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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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 - 140698445453824\n", "Dataset = c_PLP0007882\n", "datapoints = 94\n", "chi2 = 301.3613165434196\n", "Weighted = True\n", "Transform = None\n", "________________________________________________________________________________\n", "Parameters: None \n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '' \n", "________________________________________________________________________________\n", "Parameters: 'instrument parameters'\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Structure - ' \n", "________________________________________________________________________________\n", "Parameters: 'Si' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Py dd slab' \n", "\n", ">\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'Au' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: '2-mercaptoethanol'\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'd2o' \n", "\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(objective_dd)" ] }, { "cell_type": "code", "execution_count": null, "id": "67844b6d", "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.52/doc/occupancy.ipynb000066400000000000000000004714431475550052500165420ustar00rootroot00000000000000{ "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-0.1.52/doc/reflectometry_global.ipynb000066400000000000000000032216011475550052500207520ustar00rootroot00000000000000{ "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", "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", "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: 0.1.25.dev0+3805297\n", "scipy: 1.7.1\n", "numpy: 1.20.3\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": [ "pth = os.path.dirname(refnx.__file__)\n", "\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": "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": [ "49it [00:16, 2.95it/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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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "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": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_______________________________________________________________________________\n", "\n", "--Global Objective--\n", "________________________________________________________________________________\n", "Objective - 140332980321968\n", "Dataset = e361r\n", "datapoints = 99\n", "chi2 = 564.3594634863424\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", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'D2O' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 140332980320480\n", "Dataset = e365r\n", "datapoints = 99\n", "chi2 = 403.876417319329\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", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'cm3.5' \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Objective - 140332980322256\n", "Dataset = e366r\n", "datapoints = 99\n", "chi2 = 362.86669265391066\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", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'SiO2' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'polymer' \n", "\n", "\n", "\n", "\n", "\n", "________________________________________________________________________________\n", "Parameters: 'H2O' \n", "\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 [01:26<00:00, 4.63it/s]\n", "100%|███████████████████████████████████████| 3000/3000 [11:26<00:00, 4.37it/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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\n", 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" ] }, "metadata": { "needs_background": "light" }, "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 1 } refnx-0.1.52/doc/refnx.analysis.rst000066400000000000000000000001671475550052500172000ustar00rootroot00000000000000refnx.analysis ============== .. automodule:: refnx.analysis :members: :undoc-members: :show-inheritance: refnx-0.1.52/doc/refnx.dataset.rst000066400000000000000000000001641475550052500167770ustar00rootroot00000000000000refnx.dataset ============= .. automodule:: refnx.dataset :members: :undoc-members: :show-inheritance: refnx-0.1.52/doc/refnx.reduce.rst000066400000000000000000000001611475550052500166160ustar00rootroot00000000000000refnx.reduce ============ .. automodule:: refnx.reduce :members: :undoc-members: :show-inheritance: refnx-0.1.52/doc/refnx.reflect.rst000066400000000000000000000003631475550052500167770ustar00rootroot00000000000000refnx.reflect ============= .. automodule:: refnx.reflect :members: :undoc-members: :show-inheritance: :special-members: :exclude-members: __dict__,__weakref__, __repr__, __module__, __init__, __abstractmethods__, __copy__refnx-0.1.52/doc/refnx.rst000066400000000000000000000003551475550052500153550ustar00rootroot00000000000000refnx - Neutron and X-ray reflectometry analysis in Python ========================================================== Modules ------- .. toctree:: refnx.analysis refnx.reflect refnx.dataset refnx.reduce refnx.util refnx-0.1.52/doc/refnx.util.rst000066400000000000000000000001531475550052500163250ustar00rootroot00000000000000refnx.util ========== .. automodule:: refnx.util :members: :undoc-members: :show-inheritance: refnx-0.1.52/doc/requirements.txt000066400000000000000000000003501475550052500167600ustar00rootroot00000000000000nbsphinx 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-0.1.52/doc/testimonials.rst000066400000000000000000000010421475550052500167400ustar00rootroot00000000000000.. _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-0.1.52/doc/using_mpi.ipynb000066400000000000000000000167721475550052500165500ustar00rootroot00000000000000{ "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", "import os.path\n", "\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", " DATASET_NAME = os.path.join(refnx.__path__[0],\n", " 'analysis',\n", " 'test',\n", " '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", "```" ] }, { "cell_type": "markdown", "id": "b40748db-95db-4b34-881e-dde9efaf7aa6", "metadata": {}, "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 } refnx-0.1.52/examples/000077500000000000000000000000001475550052500145475ustar00rootroot00000000000000refnx-0.1.52/examples/CurveFitter_EMCEE.ipynb000066400000000000000000000232271475550052500207600ustar00rootroot00000000000000{ "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-0.1.52/examples/analytical_profiles/000077500000000000000000000000001475550052500205735ustar00rootroot00000000000000refnx-0.1.52/examples/analytical_profiles/brushes/000077500000000000000000000000001475550052500222465ustar00rootroot00000000000000refnx-0.1.52/examples/analytical_profiles/brushes/brushes.txt000066400000000000000000000002221475550052500244560ustar00rootroot00000000000000For a detailed explanation of how brushes can be analysed with refnx please see https://github.com/refnx/refnx-models/tree/master/polymer_brushes refnx-0.1.52/examples/auto_reducer.py000077500000000000000000000017631475550052500176140ustar00rootroot00000000000000""" 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-0.1.52/examples/experiment.mtft000066400000000000000000006562021475550052500176360ustar00rootroot00000000000000(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-0.1.52/examples/global_fitting_motofit/README000066400000000000000000000002031475550052500221470ustar00rootroot00000000000000This example script takes a set of output files from the corefinement setup of Motofit (MotoMPI) and runs it with the refnx packagerefnx-0.1.52/examples/global_fitting_motofit/e361r.txt000066400000000000000000000063741475550052500227070ustar00rootroot000000000000000.010109 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-0.1.52/examples/global_fitting_motofit/e361r_pilot.txt000066400000000000000000000003551475550052500241070ustar00rootroot00000000000000stuff 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-0.1.52/examples/global_fitting_motofit/e365r.txt000066400000000000000000000051601475550052500227030ustar00rootroot000000000000000.0099283 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-0.1.52/examples/global_fitting_motofit/e365r_pilot.txt000066400000000000000000000003521475550052500241100ustar00rootroot00000000000000stuff 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-0.1.52/examples/global_fitting_motofit/e366r.txt000066400000000000000000000051771475550052500227140ustar00rootroot000000000000000.0099283 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-0.1.52/examples/global_fitting_motofit/e366r_pilot.txt000066400000000000000000000003511475550052500241100ustar00rootroot00000000000000stuff 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-0.1.52/examples/global_fitting_motofit/global_fitting_from_motofit.py000066400000000000000000000313371475550052500274250ustar00rootroot00000000000000#!/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-0.1.52/examples/global_fitting_motofit/global_pilot000066400000000000000000000002741475550052500236710ustar00rootroot00000000000000e361r.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-0.1.52/examples/interactive_fitter/000077500000000000000000000000001475550052500204415ustar00rootroot00000000000000refnx-0.1.52/examples/interactive_fitter/interactive_reflectometry_modeller.ipynb000066400000000000000000000016271475550052500306560ustar00rootroot00000000000000{ "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-0.1.52/examples/mpi_parallelisation.py000066400000000000000000000102011475550052500211430ustar00rootroot00000000000000#!/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-0.1.52/examples/platypus_reduction.ipynb000066400000000000000000000140031475550052500215450ustar00rootroot00000000000000{ "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-0.1.52/examples/reduction.xlsx000066400000000000000000000210651475550052500174670ustar00rootroot00000000000000PK! 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-0.1.52/examples/referenceAnalysisScript.py000066400000000000000000000031211475550052500217450ustar00rootroot00000000000000import 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-0.1.52/examples/reflectometry_analysis.ipynb000066400000000000000000001731611475550052500224120ustar00rootroot00000000000000{ "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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", "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-0.1.52/examples/reflectometry_global.ipynb000066400000000000000000000125631475550052500220250ustar00rootroot00000000000000{ "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-0.1.52/experimental/000077500000000000000000000000001475550052500154265ustar00rootroot00000000000000refnx-0.1.52/experimental/jax_ReflectModel.ipynb000066400000000000000000000235331475550052500217060ustar00rootroot00000000000000{ "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-0.1.52/experimental/jax_abeles.ipynb000066400000000000000000000215131475550052500205700ustar00rootroot00000000000000{ "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-0.1.52/experimental/jax_smear_reflect.ipynb000066400000000000000000000157411475550052500221560ustar00rootroot00000000000000{ "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-0.1.52/paper/000077500000000000000000000000001475550052500140405ustar00rootroot00000000000000refnx-0.1.52/paper/.gitignore000066400000000000000000000001501475550052500160240ustar00rootroot00000000000000*.aux *.bbl *.blg *.fdb_latexmk *.fls *.log *.out *.synctex.gz *.backup *.orig manuscript.pdf reply.pdf refnx-0.1.52/paper/LICENCE000066400000000000000000000443321475550052500150330ustar00rootroot00000000000000Attribution 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-0.1.52/paper/components.svg000066400000000000000000001245771475550052500167660ustar00rootroot00000000000000 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-0.1.52/paper/main.bib000066400000000000000000000377251475550052500154600ustar00rootroot00000000000000% 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-0.1.52/paper/manuscript.tex000066400000000000000000001016321475550052500167520ustar00rootroot00000000000000\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-0.1.52/paper/supporting_information/000077500000000000000000000000001475550052500206575ustar00rootroot00000000000000refnx-0.1.52/paper/supporting_information/DMPC.png000066400000000000000000000214601475550052500221130ustar00rootroot00000000000000PNG  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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