diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml index 7f0205b5..688118ed 100644 --- a/.github/workflows/build_wheel.yml +++ b/.github/workflows/build_wheel.yml @@ -11,12 +11,14 @@ jobs: strategy: fail-fast: true matrix: - platform: [windows-latest, ubuntu-latest, macos-13, macos-14] + platform: [windows-latest, ubuntu-latest, macos-15-intel, macos-14] env: CIBW_SKIP: 'pp*' CIBW_ARCHS: 'auto64' + CIBW_MANYLINUX_X86_64_IMAGE: 'manylinux_2_28' CIBW_PROJECT_REQUIRES_PYTHON: '>=3.10' CIBW_TEST_REQUIRES: 'pytest' + MACOSX_DEPLOYMENT_TARGET: '14.0' defaults: run: shell: bash -l {0} @@ -24,19 +26,19 @@ jobs: runs-on: ${{ matrix.platform }} steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v5 with: submodules: true - - name: Set up Python version ${{ matrix.version }} - uses: actions/setup-python@v4 + - name: Set up Python version + uses: actions/setup-python@v6 with: python-version: "3.x" - name: Install OMP (MacOS Intel) - if: matrix.platform == 'macos-13' + if: matrix.platform == 'macos-15-intel' run: | - brew install llvm libomp - echo "export CC=/usr/local/opt/llvm/bin/clang" >> ~/.bashrc - echo "export CXX=/usr/local/opt/llvm/bin/clang++" >> ~/.bashrc + brew install llvm@20 libomp + echo "export CC=/usr/local/opt/llvm@20/bin/clang" >> ~/.bashrc + echo "export CXX=/usr/local/opt/llvm@20/bin/clang++" >> ~/.bashrc echo "export CFLAGS=\"$CFLAGS -I/usr/local/opt/libomp/include\"" >> ~/.bashrc echo "export CXXFLAGS=\"$CXXFLAGS -I/usr/local/opt/libomp/include\"" >> ~/.bashrc echo "export LDFLAGS=\"$LDFLAGS -Wl,-rpath,/usr/local/opt/libomp/lib -L/usr/local/opt/libomp/lib -lomp\"" >> ~/.bashrc @@ -44,9 +46,9 @@ jobs: - name: Install OMP (MacOS M1) if: matrix.platform == 'macos-14' run: | - brew install llvm libomp - echo "export CC=/opt/homebrew/opt/llvm/bin/clang" >> ~/.bashrc - echo "export CXX=/opt/homebrew/opt/llvm/bin/clang++" >> ~/.bashrc + brew install llvm@20 libomp + echo "export CC=/opt/homebrew/opt/llvm@20/bin/clang" >> ~/.bashrc + echo "export CXX=/opt/homebrew/opt/llvm@20/bin/clang++" >> ~/.bashrc echo "export CFLAGS=\"$CFLAGS -I/opt/homebrew/opt/libomp/include\"" >> ~/.bashrc echo "export CXXFLAGS=\"$CXXFLAGS -I/opt/homebrew/opt/libomp/include\"" >> ~/.bashrc echo "export LDFLAGS=\"$LDFLAGS -Wl,-rpath,/opt/homebrew/opt/libomp/lib -L/opt/homebrew/opt/libomp/lib -lomp\"" >> ~/.bashrc @@ -65,7 +67,7 @@ jobs: export PATH="$pythonLocation:$PATH" CIBW_TEST_COMMAND='cd ${pwd}/tmp && python -m pytest tests' echo "CIBW_TEST_COMMAND=${CIBW_TEST_COMMAND}" >> $GITHUB_ENV - python -m pip install cibuildwheel==2.16.5 + python -m pip install cibuildwheel python -m cibuildwheel --output-dir ./wheelhouse - uses: actions/upload-artifact@v4 with: diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 2c0cbe3c..8ac33f95 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -15,6 +15,8 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 + with: + submodules: true - name: Build sdist run: | pip install pybind11 diff --git a/.github/workflows/run_ruff.yml b/.github/workflows/run_ruff.yml index 5fc5b79f..64637b07 100644 --- a/.github/workflows/run_ruff.yml +++ b/.github/workflows/run_ruff.yml @@ -7,8 +7,6 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: chartboost/ruff-action@v1 - - uses: chartboost/ruff-action@v1 - with: - args: 'format --check' + - uses: astral-sh/ruff-action@v3 + - run: ruff format --check \ No newline at end of file diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml index 8911dfdc..7dce1346 100644 --- a/.github/workflows/run_tests.yml +++ b/.github/workflows/run_tests.yml @@ -19,32 +19,42 @@ jobs: strategy: fail-fast: false matrix: - os: [ubuntu-latest, windows-latest, macos-13] + platform: [windows-latest, ubuntu-latest, macos-15-intel, macos-latest] version: ["3.10", "3.13"] defaults: run: shell: bash -l {0} - runs-on: ${{ matrix.os }} + runs-on: ${{ matrix.platform}} steps: - - uses: actions/checkout@v4 + - uses: actions/checkout@v5 with: submodules: true - name: Set up Python version ${{ matrix.version }} - uses: actions/setup-python@v4 + uses: actions/setup-python@v6 with: python-version: ${{ matrix.version }} - - name: Install OMP (MacOS) - if: runner.os == 'macOS' + - name: Install OMP (MacOS Intel) + if: matrix.platform == 'macos-15-intel' run: | - brew install llvm libomp - echo "export CC=/usr/local/opt/llvm/bin/clang" >> ~/.bashrc - echo "export CXX=/usr/local/opt/llvm/bin/clang++" >> ~/.bashrc + brew install llvm@20 libomp + echo "export CC=/usr/local/opt/llvm@20/bin/clang" >> ~/.bashrc + echo "export CXX=/usr/local/opt/llvm@20/bin/clang++" >> ~/.bashrc echo "export CFLAGS=\"$CFLAGS -I/usr/local/opt/libomp/include\"" >> ~/.bashrc echo "export CXXFLAGS=\"$CXXFLAGS -I/usr/local/opt/libomp/include\"" >> ~/.bashrc echo "export LDFLAGS=\"$LDFLAGS -Wl,-rpath,/usr/local/opt/libomp/lib -L/usr/local/opt/libomp/lib -lomp\"" >> ~/.bashrc source ~/.bashrc + - name: Install OMP (MacOS M1) + if: matrix.platform == 'macos-latest' + run: | + brew install llvm@20 libomp + echo "export CC=/opt/homebrew/opt/llvm@20/bin/clang" >> ~/.bashrc + echo "export CXX=/opt/homebrew/opt/llvm@20/bin/clang++" >> ~/.bashrc + echo "export CFLAGS=\"$CFLAGS -I/opt/homebrew/opt/libomp/include\"" >> ~/.bashrc + echo "export CXXFLAGS=\"$CXXFLAGS -I/opt/homebrew/opt/libomp/include\"" >> ~/.bashrc + echo "export LDFLAGS=\"$LDFLAGS -Wl,-rpath,/opt/homebrew/opt/libomp/lib -L/opt/homebrew/opt/libomp/lib -lomp\"" >> ~/.bashrc + source ~/.bashrc - name: Install OMP (Linux) if: runner.os == 'Linux' run: | @@ -53,5 +63,5 @@ jobs: - name: Install and Test with pytest run: | export PATH="$pythonLocation:$PATH" - python -m pip install -e .[Dev,Orso] - pytest tests/ --cov=RATapi --cov-report=term + python -m pip install -e .[dev,orso] + pytest tests/ --cov=ratapi --cov-report=term diff --git a/.gitignore b/.gitignore index fde40baa..51a84ffe 100644 --- a/.gitignore +++ b/.gitignore @@ -40,3 +40,6 @@ dist/* # Jupyter notebook checkpoints .ipynb_checkpoints/* + +# Lock file for uv env +uv.lock diff --git a/RATapi/__init__.py b/RATapi/__init__.py deleted file mode 100644 index 31f83c85..00000000 --- a/RATapi/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -"""RATapi is a Python package for modelling, fitting and optimising reflectivity problems.""" - -from contextlib import suppress - -import RATapi.examples as examples -from RATapi import events, models -from RATapi.classlist import ClassList -from RATapi.controls import Controls -from RATapi.outputs import BayesResults, Results -from RATapi.project import Project -from RATapi.run import run -from RATapi.utils import convert, plotting - -with suppress(ImportError): # orsopy is an optional dependency - from RATapi.utils import orso as orso - -__all__ = [ - "examples", - "models", - "events", - "ClassList", - "Controls", - "BayesResults", - "Results", - "Project", - "run", - "plotting", - "convert", -] diff --git a/RATapi/examples/__init__.py b/RATapi/examples/__init__.py deleted file mode 100644 index 86de2334..00000000 --- a/RATapi/examples/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -"""Usage examples for the Python RAT API.""" - -from RATapi.examples.absorption.absorption import absorption -from RATapi.examples.convert_rascal_project.convert_rascal import convert_rascal -from RATapi.examples.domains.domains_custom_layers import domains_custom_layers -from RATapi.examples.domains.domains_custom_XY import domains_custom_XY -from RATapi.examples.domains.domains_standard_layers import domains_standard_layers -from RATapi.examples.normal_reflectivity.DSPC_custom_layers import DSPC_custom_layers -from RATapi.examples.normal_reflectivity.DSPC_custom_XY import DSPC_custom_XY -from RATapi.examples.normal_reflectivity.DSPC_data_background import DSPC_data_background -from RATapi.examples.normal_reflectivity.DSPC_function_background import DSPC_function_background -from RATapi.examples.normal_reflectivity.DSPC_standard_layers import DSPC_standard_layers - -__all__ = [ - "absorption", - "domains_custom_layers", - "domains_custom_XY", - "domains_standard_layers", - "DSPC_custom_layers", - "DSPC_custom_XY", - "DSPC_standard_layers", - "DSPC_data_background", - "DSPC_function_background", - "convert_rascal", -] diff --git a/RATapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb b/RATapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb deleted file mode 100644 index 0adac7eb..00000000 --- a/RATapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb +++ /dev/null @@ -1,865 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "4b988c4a-3a09-4b75-8a87-8ba8402635ba", - "metadata": {}, - "outputs": [], - "source": [ - "import pathlib\n", - "\n", - "import numpy as np\n", - "from IPython.display import Code\n", - "\n", - "import RATapi as RAT\n", - "from RATapi.models import Parameter" - ] - }, - { - "cell_type": "markdown", - "id": "793d9c50-698e-438b-87f7-85e3a9f11d6b", - "metadata": {}, - "source": [ - "# Custom Layers Example for Supported DSPC layer\n", - "\n", - "Example of using Custom layers to model a DSPC supported bilayer.\n", - "Start by making the project and setting it to a custom layers type:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9a60cd45-0e1d-448a-b4bd-4c02bd6a3475", - "metadata": {}, - "outputs": [], - "source": [ - "problem = RAT.Project(name=\"Orso lipid example - custom layers\", model=\"custom layers\", geometry=\"substrate/liquid\")" - ] - }, - { - "cell_type": "markdown", - "id": "9cc56e51-3d52-460a-bbb1-6d68571887c6", - "metadata": {}, - "source": [ - "For a custom layers model, rather than being forced to define our layers as \\[Thick SLD Rough.... etc\\], we can parameterise however we like and then use a function to calculate the \\[d $\\rho$ $\\sigma$\\] arrangement for each layer. So for example, if the volume of lipid tails are known (from the literature), then all we need is the Area per molecule, because then:\n", - "\n", - "$$\n", - "d = \\frac{V}{APM},\n", - "$$\n", - "where d is the thickness and V is the volume.\n", - "\n", - "Likewise, the SLD is:\n", - "$$\n", - "\\rho = \\frac{\\sum_{i}n_{i}b_{i}}{V},\n", - "$$\n", - "\n", - "as usual.\n", - "\n", - "In this folder there is a pre-prepared Python custom model for a DSPC on a Silicon substrate. We can display it here to see what we mean:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "9038b77f-e3fc-4946-87fe-af4addf8ee84", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
import numpy as np\n",
-       "\n",
-       "\n",
-       "def custom_bilayer_DSPC(params, bulk_in, bulk_out, contrast):\n",
-       "    """CUSTOMBILAYER RAT Custom Layer Model File.\n",
-       "\n",
-       "    This file accepts 3 vectors containing the values for params, bulk in and bulk out.\n",
-       "    The final parameter is an index of the contrast being calculated.\n",
-       "\n",
-       "    The function should output a matrix of layer values, in the form...\n",
-       "\n",
-       "    Output = [thick 1, SLD 1, Rough 1, Percent Hydration 1, Hydrate how 1\n",
-       "              ....\n",
-       "              thick n, SLD n, Rough n, Percent Hydration n, Hydration how n]\n",
-       "\n",
-       "    The "hydrate how" parameter decides if the layer is hydrated with Bulk out or Bulk in phases.\n",
-       "    Set to 1 for Bulk out, zero for Bulk in.\n",
-       "    Alternatively, leave out hydration and just return...\n",
-       "\n",
-       "    Output = [thick 1, SLD 1, Rough 1,\n",
-       "              ....\n",
-       "              thick n, SLD n, Rough n]\n",
-       "\n",
-       "    The second output parameter should be the substrate roughness.\n",
-       "    """\n",
-       "    sub_rough = params[0]\n",
-       "    oxide_thick = params[1]\n",
-       "    oxide_hydration = params[2]\n",
-       "    lipidAPM = params[3]\n",
-       "    headHydration = params[4]\n",
-       "    bilayerHydration = params[5]\n",
-       "    bilayerRough = params[6]\n",
-       "    waterThick = params[7]\n",
-       "\n",
-       "    # We have a constant SLD for the bilayer\n",
-       "    oxide_SLD = 3.41e-6\n",
-       "\n",
-       "    # Now make the lipid layers\n",
-       "    # Use known lipid volume and compositions to make the layers\n",
-       "\n",
-       "    # define all the neutron b's.\n",
-       "    bc = 0.6646e-4  # Carbon\n",
-       "    bo = 0.5843e-4  # Oxygen\n",
-       "    bh = -0.3739e-4  # Hydrogen\n",
-       "    bp = 0.513e-4  # Phosphorus\n",
-       "    bn = 0.936e-4  # Nitrogen\n",
-       "\n",
-       "    # Now make the lipid groups\n",
-       "    COO = (4 * bo) + (2 * bc)\n",
-       "    GLYC = (3 * bc) + (5 * bh)\n",
-       "    CH3 = (2 * bc) + (6 * bh)\n",
-       "    PO4 = (1 * bp) + (4 * bo)\n",
-       "    CH2 = (1 * bc) + (2 * bh)\n",
-       "    CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n",
-       "\n",
-       "    # Group these into heads and tails:\n",
-       "    Head = CHOL + PO4 + GLYC + COO\n",
-       "    Tails = (34 * CH2) + (2 * CH3)\n",
-       "\n",
-       "    # We need volumes for each. Use literature values:\n",
-       "    vHead = 319\n",
-       "    vTail = 782\n",
-       "\n",
-       "    # We use the volumes to calculate the SLDs\n",
-       "    SLDhead = Head / vHead\n",
-       "    SLDtail = Tails / vTail\n",
-       "\n",
-       "    # We calculate the layer thickness' from the volumes and the APM\n",
-       "    headThick = vHead / lipidAPM\n",
-       "    tailThick = vTail / lipidAPM\n",
-       "\n",
-       "    # Manually deal with hydration for layers in this example.\n",
-       "    oxSLD = (oxide_hydration * bulk_out[contrast]) + ((1 - oxide_hydration) * oxide_SLD)\n",
-       "    headSLD = (headHydration * bulk_out[contrast]) + ((1 - headHydration) * SLDhead)\n",
-       "    tailSLD = (bilayerHydration * bulk_out[contrast]) + ((1 - bilayerHydration) * SLDtail)\n",
-       "\n",
-       "    # Make the layers\n",
-       "    oxide = [oxide_thick, oxSLD, sub_rough]\n",
-       "    water = [waterThick, bulk_out[contrast], bilayerRough]\n",
-       "    head = [headThick, headSLD, bilayerRough]\n",
-       "    tail = [tailThick, tailSLD, bilayerRough]\n",
-       "\n",
-       "    output = np.array([oxide, water, head, tail, tail, head])\n",
-       "\n",
-       "    return output, sub_rough\n",
-       "
\n" - ], - "text/latex": [ - "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", - "\\PY{k+kn}{import} \\PY{n+nn}{numpy} \\PY{k}{as} \\PY{n+nn}{np}\n", - "\n", - "\n", - "\\PY{k}{def} \\PY{n+nf}{custom\\PYZus{}bilayer\\PYZus{}DSPC}\\PY{p}{(}\\PY{n}{params}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}in}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{,} \\PY{n}{contrast}\\PY{p}{)}\\PY{p}{:}\n", - "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}CUSTOMBILAYER RAT Custom Layer Model File.}\n", - "\n", - "\\PY{l+s+sd}{ This file accepts 3 vectors containing the values for params, bulk in and bulk out.}\n", - "\\PY{l+s+sd}{ The final parameter is an index of the contrast being calculated.}\n", - "\n", - "\\PY{l+s+sd}{ The function should output a matrix of layer values, in the form...}\n", - "\n", - "\\PY{l+s+sd}{ Output = [thick 1, SLD 1, Rough 1, Percent Hydration 1, Hydrate how 1}\n", - "\\PY{l+s+sd}{ ....}\n", - "\\PY{l+s+sd}{ thick n, SLD n, Rough n, Percent Hydration n, Hydration how n]}\n", - "\n", - "\\PY{l+s+sd}{ The \\PYZdq{}hydrate how\\PYZdq{} parameter decides if the layer is hydrated with Bulk out or Bulk in phases.}\n", - "\\PY{l+s+sd}{ Set to 1 for Bulk out, zero for Bulk in.}\n", - "\\PY{l+s+sd}{ Alternatively, leave out hydration and just return...}\n", - "\n", - "\\PY{l+s+sd}{ Output = [thick 1, SLD 1, Rough 1,}\n", - "\\PY{l+s+sd}{ ....}\n", - "\\PY{l+s+sd}{ thick n, SLD n, Rough n]}\n", - "\n", - "\\PY{l+s+sd}{ The second output parameter should be the substrate roughness.}\n", - "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", - " \\PY{n}{sub\\PYZus{}rough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\n", - " \\PY{n}{oxide\\PYZus{}thick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\n", - " \\PY{n}{oxide\\PYZus{}hydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{2}\\PY{p}{]}\n", - " \\PY{n}{lipidAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n", - " \\PY{n}{headHydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n", - " \\PY{n}{bilayerHydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{5}\\PY{p}{]}\n", - " \\PY{n}{bilayerRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{6}\\PY{p}{]}\n", - " \\PY{n}{waterThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{7}\\PY{p}{]}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} We have a constant SLD for the bilayer}\n", - " \\PY{n}{oxide\\PYZus{}SLD} \\PY{o}{=} \\PY{l+m+mf}{3.41e\\PYZhy{}6}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Now make the lipid layers}\n", - " \\PY{c+c1}{\\PYZsh{} Use known lipid volume and compositions to make the layers}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} define all the neutron b\\PYZsq{}s.}\n", - " \\PY{n}{bc} \\PY{o}{=} \\PY{l+m+mf}{0.6646e\\PYZhy{}4} \\PY{c+c1}{\\PYZsh{} Carbon}\n", - " \\PY{n}{bo} \\PY{o}{=} \\PY{l+m+mf}{0.5843e\\PYZhy{}4} \\PY{c+c1}{\\PYZsh{} Oxygen}\n", - " \\PY{n}{bh} \\PY{o}{=} \\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.3739e\\PYZhy{}4} \\PY{c+c1}{\\PYZsh{} Hydrogen}\n", - " \\PY{n}{bp} \\PY{o}{=} \\PY{l+m+mf}{0.513e\\PYZhy{}4} \\PY{c+c1}{\\PYZsh{} Phosphorus}\n", - " \\PY{n}{bn} \\PY{o}{=} \\PY{l+m+mf}{0.936e\\PYZhy{}4} \\PY{c+c1}{\\PYZsh{} Nitrogen}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Now make the lipid groups}\n", - " \\PY{n}{COO} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{4} \\PY{o}{*} \\PY{n}{bo}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)}\n", - " \\PY{n}{GLYC} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{3} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{5} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)}\n", - " \\PY{n}{CH3} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{6} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)}\n", - " \\PY{n}{PO4} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bp}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{4} \\PY{o}{*} \\PY{n}{bo}\\PY{p}{)}\n", - " \\PY{n}{CH2} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)}\n", - " \\PY{n}{CHOL} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{5} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{12} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bn}\\PY{p}{)}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Group these into heads and tails:}\n", - " \\PY{n}{Head} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{n}{COO}\n", - " \\PY{n}{Tails} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{34} \\PY{o}{*} \\PY{n}{CH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\\PY{p}{)}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} We need volumes for each. Use literature values:}\n", - " \\PY{n}{vHead} \\PY{o}{=} \\PY{l+m+mi}{319}\n", - " \\PY{n}{vTail} \\PY{o}{=} \\PY{l+m+mi}{782}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} We use the volumes to calculate the SLDs}\n", - " \\PY{n}{SLDhead} \\PY{o}{=} \\PY{n}{Head} \\PY{o}{/} \\PY{n}{vHead}\n", - " \\PY{n}{SLDtail} \\PY{o}{=} \\PY{n}{Tails} \\PY{o}{/} \\PY{n}{vTail}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} We calculate the layer thickness\\PYZsq{} from the volumes and the APM}\n", - " \\PY{n}{headThick} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{lipidAPM}\n", - " \\PY{n}{tailThick} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{lipidAPM}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Manually deal with hydration for layers in this example.}\n", - " \\PY{n}{oxSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{oxide\\PYZus{}hydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{oxide\\PYZus{}hydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{oxide\\PYZus{}SLD}\\PY{p}{)}\n", - " \\PY{n}{headSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{headHydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{headHydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{SLDhead}\\PY{p}{)}\n", - " \\PY{n}{tailSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerHydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerHydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{SLDtail}\\PY{p}{)}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Make the layers}\n", - " \\PY{n}{oxide} \\PY{o}{=} \\PY{p}{[}\\PY{n}{oxide\\PYZus{}thick}\\PY{p}{,} \\PY{n}{oxSLD}\\PY{p}{,} \\PY{n}{sub\\PYZus{}rough}\\PY{p}{]}\n", - " \\PY{n}{water} \\PY{o}{=} \\PY{p}{[}\\PY{n}{waterThick}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - " \\PY{n}{head} \\PY{o}{=} \\PY{p}{[}\\PY{n}{headThick}\\PY{p}{,} \\PY{n}{headSLD}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - " \\PY{n}{tail} \\PY{o}{=} \\PY{p}{[}\\PY{n}{tailThick}\\PY{p}{,} \\PY{n}{tailSLD}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - "\n", - " \\PY{n}{output} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{p}{[}\\PY{n}{oxide}\\PY{p}{,} \\PY{n}{water}\\PY{p}{,} \\PY{n}{head}\\PY{p}{,} \\PY{n}{tail}\\PY{p}{,} \\PY{n}{tail}\\PY{p}{,} \\PY{n}{head}\\PY{p}{]}\\PY{p}{)}\n", - "\n", - " \\PY{k}{return} \\PY{n}{output}\\PY{p}{,} \\PY{n}{sub\\PYZus{}rough}\n", - "\\end{Verbatim}\n" - ], - "text/plain": [ - "import numpy as np\n", - "\n", - "\n", - "def custom_bilayer_DSPC(params, bulk_in, bulk_out, contrast):\n", - " \"\"\"CUSTOMBILAYER RAT Custom Layer Model File.\n", - "\n", - " This file accepts 3 vectors containing the values for params, bulk in and bulk out.\n", - " The final parameter is an index of the contrast being calculated.\n", - "\n", - " The function should output a matrix of layer values, in the form...\n", - "\n", - " Output = [thick 1, SLD 1, Rough 1, Percent Hydration 1, Hydrate how 1\n", - " ....\n", - " thick n, SLD n, Rough n, Percent Hydration n, Hydration how n]\n", - "\n", - " The \"hydrate how\" parameter decides if the layer is hydrated with Bulk out or Bulk in phases.\n", - " Set to 1 for Bulk out, zero for Bulk in.\n", - " Alternatively, leave out hydration and just return...\n", - "\n", - " Output = [thick 1, SLD 1, Rough 1,\n", - " ....\n", - " thick n, SLD n, Rough n]\n", - "\n", - " The second output parameter should be the substrate roughness.\n", - " \"\"\"\n", - " sub_rough = params[0]\n", - " oxide_thick = params[1]\n", - " oxide_hydration = params[2]\n", - " lipidAPM = params[3]\n", - " headHydration = params[4]\n", - " bilayerHydration = params[5]\n", - " bilayerRough = params[6]\n", - " waterThick = params[7]\n", - "\n", - " # We have a constant SLD for the bilayer\n", - " oxide_SLD = 3.41e-6\n", - "\n", - " # Now make the lipid layers\n", - " # Use known lipid volume and compositions to make the layers\n", - "\n", - " # define all the neutron b's.\n", - " bc = 0.6646e-4 # Carbon\n", - " bo = 0.5843e-4 # Oxygen\n", - " bh = -0.3739e-4 # Hydrogen\n", - " bp = 0.513e-4 # Phosphorus\n", - " bn = 0.936e-4 # Nitrogen\n", - "\n", - " # Now make the lipid groups\n", - " COO = (4 * bo) + (2 * bc)\n", - " GLYC = (3 * bc) + (5 * bh)\n", - " CH3 = (2 * bc) + (6 * bh)\n", - " PO4 = (1 * bp) + (4 * bo)\n", - " CH2 = (1 * bc) + (2 * bh)\n", - " CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n", - "\n", - " # Group these into heads and tails:\n", - " Head = CHOL + PO4 + GLYC + COO\n", - " Tails = (34 * CH2) + (2 * CH3)\n", - "\n", - " # We need volumes for each. Use literature values:\n", - " vHead = 319\n", - " vTail = 782\n", - "\n", - " # We use the volumes to calculate the SLDs\n", - " SLDhead = Head / vHead\n", - " SLDtail = Tails / vTail\n", - "\n", - " # We calculate the layer thickness' from the volumes and the APM\n", - " headThick = vHead / lipidAPM\n", - " tailThick = vTail / lipidAPM\n", - "\n", - " # Manually deal with hydration for layers in this example.\n", - " oxSLD = (oxide_hydration * bulk_out[contrast]) + ((1 - oxide_hydration) * oxide_SLD)\n", - " headSLD = (headHydration * bulk_out[contrast]) + ((1 - headHydration) * SLDhead)\n", - " tailSLD = (bilayerHydration * bulk_out[contrast]) + ((1 - bilayerHydration) * SLDtail)\n", - "\n", - " # Make the layers\n", - " oxide = [oxide_thick, oxSLD, sub_rough]\n", - " water = [waterThick, bulk_out[contrast], bilayerRough]\n", - " head = [headThick, headSLD, bilayerRough]\n", - " tail = [tailThick, tailSLD, bilayerRough]\n", - "\n", - " output = np.array([oxide, water, head, tail, tail, head])\n", - "\n", - " return output, sub_rough" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Code(filename='custom_bilayer_DSPC.py', language='python')" - ] - }, - { - "cell_type": "markdown", - "id": "002b67c8-1091-4544-9325-58227a012e4e", - "metadata": {}, - "source": [ - "We need to add the parameters we are going to need to define the model (note that Substrate Roughness always exists as parameter 0 as before, and that we are setting a Gaussian prior on the Head Hydration here)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "70494ef9-6cc5-47dc-9d02-6506645de46b", - "metadata": {}, - "outputs": [], - "source": [ - "parameter_list = [\n", - " Parameter(name=\"Oxide Thickness\", min=5.0, value=20.0, max=60.0, fit=True),\n", - " Parameter(name=\"Oxide Hydration\", min=0.0, value=0.2, max=0.5, fit=True),\n", - " Parameter(name=\"Lipid APM\", min=45.0, value=55.0, max=65.0, fit=True),\n", - " Parameter(name=\"Head Hydration\", min=0.0, value=0.2, max=0.5, fit=True, prior_type='gaussian', mu=0.3, sigma=0.03),\n", - " Parameter(name=\"Bilayer Hydration\", min=0.0, value=0.1, max=0.2, fit=True),\n", - " Parameter(name=\"Bilayer Roughness\", min=2.0, value=4.0, max=8.0, fit=True),\n", - " Parameter(name=\"Water Thickness\", min=0.0, value=2.0, max=10.0, fit=True)\n", - "]\n", - "\n", - "problem.parameters.extend(parameter_list)\n", - "problem.parameters.set_fields(0, min=1.0, max=10.0)" - ] - }, - { - "cell_type": "markdown", - "id": "a11897b0-244b-46c2-8bcd-a3d65bd8fc5c", - "metadata": {}, - "source": [ - "Need to add the relevant Bulk SLD's. Change the bulk in from air to silicon, and add two additional water contrasts:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "453fe3d2-162a-42bb-91ee-b1d020ffd29e", - "metadata": {}, - "outputs": [], - "source": [ - "# Change the bulk in from air to silicon:\n", - "problem.bulk_in.set_fields(0, name=\"Silicon\", min=2.07e-6, value=2.073e-6, max=2.08e-6, fit=False)\n", - "\n", - "problem.bulk_out.append(name=\"SLD SMW\", min=1.0e-6, value=2.073e-6, max=3.0e-6, fit=True)\n", - "problem.bulk_out.append(name=\"SLD H2O\", min=-0.6e-6, value=-0.56e-6, max=-0.3e-6, fit=True)\n", - "\n", - "problem.bulk_out.set_fields(0, min=5.0e-6, fit=True)" - ] - }, - { - "cell_type": "markdown", - "id": "d767523b-70ab-42a9-b28f-cd013a8b177e", - "metadata": {}, - "source": [ - "Now add the datafiles. We have three datasets we need to consider - the bilayer against D2O, Silicon Matched water and H2O. Load these datafiles in and put them in the data block:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "fa4c1b96-3a1b-4aa6-8d61-68f24b0cb482", - "metadata": {}, - "outputs": [], - "source": [ - "# Read in the datafiles\n", - "data_path = pathlib.Path(\"../data\")\n", - "D2O_data = np.loadtxt(data_path / \"c_PLP0016596.dat\", delimiter=\",\")\n", - "SMW_data = np.loadtxt(data_path / \"c_PLP0016601.dat\", delimiter=\",\")\n", - "H2O_data = np.loadtxt(data_path / \"c_PLP0016607.dat\", delimiter=\",\")\n", - "\n", - "# Add the data to the project - note this data has a resolution 4th column\n", - "problem.data.append(name=\"Bilayer / D2O\", data=D2O_data, data_range=[0.013, 0.37])\n", - "problem.data.append(name=\"Bilayer / SMW\", data=SMW_data, data_range=[0.013, 0.32996])\n", - "problem.data.append(name=\"Bilayer / H2O\", data=H2O_data, data_range=[0.013, 0.33048])" - ] - }, - { - "cell_type": "markdown", - "id": "e60cd052-54f9-41b4-ab8b-6d4dde1c50fa", - "metadata": {}, - "source": [ - "Add the custom file to the project:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "2e649c26-b32b-4c79-8ae7-fa701c87e6c2", - "metadata": {}, - "outputs": [], - "source": [ - "problem.custom_files.append(name=\"DSPC Model\", filename=\"custom_bilayer_DSPC.py\", language=\"python\", path=pathlib.Path.cwd().resolve())" - ] - }, - { - "cell_type": "markdown", - "id": "19a57f11-3d3c-49c5-b7a6-52bf449a3878", - "metadata": {}, - "source": [ - "Also, add the relevant background parameters - one each for each contrast:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "5d51954f-469a-4044-9a7d-1b6e30474a6b", - "metadata": {}, - "outputs": [], - "source": [ - "problem.background_parameters.set_fields(0, name=\"Background parameter D2O\", min=1.0e-10, max=1.0e-5, value=1.0e-07, fit=True)\n", - "\n", - "problem.background_parameters.append(name=\"Background parameter SMW\", min=1.0e-10, value=1.0e-7, max=1.0e-5, fit=True)\n", - "problem.background_parameters.append(name=\"Background parameter H2O\", min=1.0e-10, value=1.0e-7, max=1.0e-5, fit=True)\n", - "\n", - "# And add the two new constant backgrounds\n", - "problem.backgrounds.append(name=\"Background SMW\", type=\"constant\", source=\"Background parameter SMW\")\n", - "problem.backgrounds.append(name=\"Background H2O\", type=\"constant\", source=\"Background parameter H2O\")\n", - "\n", - "# And edit the other one\n", - "problem.backgrounds.set_fields(0, name=\"Background D2O\", source=\"Background parameter D2O\")\n", - "\n", - "# Finally modify some of the other parameters to be more suitable values for a solid / liquid experiment\n", - "problem.scalefactors.set_fields(0, value=1.0, min=0.5, max=2.0, fit=True)" - ] - }, - { - "cell_type": "markdown", - "id": "a69a6d51-202a-4834-a6be-5c30f67d9107", - "metadata": {}, - "source": [ - "We need to use the data resolution (i.e. the fourth column of our datafiles). Do do this, we need to add a 'Data' resolution object to our resolutions table" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b1e4d313-8450-459b-b60e-868fe82f06b0", - "metadata": {}, - "outputs": [], - "source": [ - "problem.resolutions.append(name=\"Data Resolution\", type=\"data\")" - ] - }, - { - "cell_type": "markdown", - "id": "ddde7088-1382-4f56-9e05-6f1683ec2260", - "metadata": {}, - "source": [ - "Now add the three contrasts as before:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "efc7b351-2112-40c4-862b-a47e4570d173", - "metadata": {}, - "outputs": [], - "source": [ - "problem.contrasts.append(\n", - " name=\"Bilayer / D2O\",\n", - " background=\"Background D2O\",\n", - " resolution=\"Data Resolution\",\n", - " scalefactor=\"Scalefactor 1\",\n", - " bulk_out=\"SLD D2O\",\n", - " bulk_in=\"Silicon\",\n", - " data=\"Bilayer / D2O\",\n", - " model=[\"DSPC Model\"],\n", - ")\n", - "\n", - "problem.contrasts.append(\n", - " name=\"Bilayer / SMW\",\n", - " background=\"Background SMW\",\n", - " resolution=\"Data Resolution\",\n", - " scalefactor=\"Scalefactor 1\",\n", - " bulk_out=\"SLD SMW\",\n", - " bulk_in=\"Silicon\",\n", - " data=\"Bilayer / SMW\",\n", - " model=[\"DSPC Model\"],\n", - ")\n", - "\n", - "problem.contrasts.append(\n", - " name=\"Bilayer / H2O\",\n", - " background=\"Background H2O\",\n", - " resolution=\"Data Resolution\",\n", - " scalefactor=\"Scalefactor 1\",\n", - " bulk_out=\"SLD H2O\",\n", - " bulk_in=\"Silicon\",\n", - " data=\"Bilayer / H2O\",\n", - " model=[\"DSPC Model\"],\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "89f110e4-c3f8-488d-91d5-4f5fb5fbe9d7", - "metadata": {}, - "source": [ - "Note that the model is simply the custom file we've just added to the project.\n", - "\n", - "Look at the complete model definition before sending it to RAT:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "ee889e55-8357-4363-860d-fb1c13bb8e8b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Name: ----------------------------------------------------------------------------------------------\n", - "\n", - "Orso lipid example - custom layers\n", - "\n", - "Calculation: ---------------------------------------------------------------------------------------\n", - "\n", - "non polarised\n", - "\n", - "Model: ---------------------------------------------------------------------------------------------\n", - "\n", - "custom layers\n", - "\n", - "Geometry: ------------------------------------------------------------------------------------------\n", - "\n", - "substrate/liquid\n", - "\n", - "Parameters: ----------------------------------------------------------------------------------------\n", - "\n", - "+-------+---------------------+------+-------+------+------+------------+-----+-------+\n", - "| index | name | min | value | max | fit | prior type | mu | sigma |\n", - "+-------+---------------------+------+-------+------+------+------------+-----+-------+\n", - "| 0 | Substrate Roughness | 1.0 | 3.0 | 10.0 | True | uniform | 0.0 | inf |\n", - "| 1 | Oxide Thickness | 5.0 | 20.0 | 60.0 | True | uniform | 0.0 | inf |\n", - "| 2 | Oxide Hydration | 0.0 | 0.2 | 0.5 | True | uniform | 0.0 | inf |\n", - "| 3 | Lipid APM | 45.0 | 55.0 | 65.0 | True | uniform | 0.0 | inf |\n", - "| 4 | Head Hydration | 0.0 | 0.2 | 0.5 | True | gaussian | 0.3 | 0.03 |\n", - "| 5 | Bilayer Hydration | 0.0 | 0.1 | 0.2 | True | uniform | 0.0 | inf |\n", - "| 6 | Bilayer Roughness | 2.0 | 4.0 | 8.0 | True | uniform | 0.0 | inf |\n", - "| 7 | Water Thickness | 0.0 | 2.0 | 10.0 | True | uniform | 0.0 | inf |\n", - "+-------+---------------------+------+-------+------+------+------------+-----+-------+\n", - "\n", - "Bulk In: -------------------------------------------------------------------------------------------\n", - "\n", - "+-------+---------+----------+-----------+----------+-------+------------+-----+-------+\n", - "| index | name | min | value | max | fit | prior type | mu | sigma |\n", - "+-------+---------+----------+-----------+----------+-------+------------+-----+-------+\n", - "| 0 | Silicon | 2.07e-06 | 2.073e-06 | 2.08e-06 | False | uniform | 0.0 | inf |\n", - "+-------+---------+----------+-----------+----------+-------+------------+-----+-------+\n", - "\n", - "Bulk Out: ------------------------------------------------------------------------------------------\n", - "\n", - "+-------+---------+--------+-----------+----------+------+------------+-----+-------+\n", - "| index | name | min | value | max | fit | prior type | mu | sigma |\n", - "+-------+---------+--------+-----------+----------+------+------------+-----+-------+\n", - "| 0 | SLD D2O | 5e-06 | 6.35e-06 | 6.35e-06 | True | uniform | 0.0 | inf |\n", - "| 1 | SLD SMW | 1e-06 | 2.073e-06 | 3e-06 | True | uniform | 0.0 | inf |\n", - "| 2 | SLD H2O | -6e-07 | -5.6e-07 | -3e-07 | True | uniform | 0.0 | inf |\n", - "+-------+---------+--------+-----------+----------+------+------------+-----+-------+\n", - "\n", - "Scalefactors: --------------------------------------------------------------------------------------\n", - "\n", - "+-------+---------------+-----+-------+-----+------+------------+-----+-------+\n", - "| index | name | min | value | max | fit | prior type | mu | sigma |\n", - "+-------+---------------+-----+-------+-----+------+------------+-----+-------+\n", - "| 0 | Scalefactor 1 | 0.5 | 1.0 | 2.0 | True | uniform | 0.0 | inf |\n", - "+-------+---------------+-----+-------+-----+------+------------+-----+-------+\n", - "\n", - "Background Parameters: -----------------------------------------------------------------------------\n", - "\n", - "+-------+--------------------------+-------+-------+-------+------+------------+-----+-------+\n", - "| index | name | min | value | max | fit | prior type | mu | sigma |\n", - "+-------+--------------------------+-------+-------+-------+------+------------+-----+-------+\n", - "| 0 | Background parameter D2O | 1e-10 | 1e-07 | 1e-05 | True | uniform | 0.0 | inf |\n", - "| 1 | Background parameter SMW | 1e-10 | 1e-07 | 1e-05 | True | uniform | 0.0 | inf |\n", - "| 2 | Background parameter H2O | 1e-10 | 1e-07 | 1e-05 | True | uniform | 0.0 | inf |\n", - "+-------+--------------------------+-------+-------+-------+------+------------+-----+-------+\n", - "\n", - "Backgrounds: ---------------------------------------------------------------------------------------\n", - "\n", - "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", - "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", - "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", - "| 0 | Background D2O | constant | Background parameter D2O | | | | |\n", - "| 1 | Background SMW | constant | Background parameter SMW | | | | |\n", - "| 2 | Background H2O | constant | Background parameter H2O | | | | |\n", - "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", - "\n", - "Resolution Parameters: -----------------------------------------------------------------------------\n", - "\n", - "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", - "| index | name | min | value | max | fit | prior type | mu | sigma |\n", - "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", - "| 0 | Resolution Param 1 | 0.01 | 0.03 | 0.05 | False | uniform | 0.0 | inf |\n", - "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", - "\n", - "Resolutions: ---------------------------------------------------------------------------------------\n", - "\n", - "+-------+-----------------+----------+--------------------+---------+---------+---------+---------+\n", - "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", - "+-------+-----------------+----------+--------------------+---------+---------+---------+---------+\n", - "| 0 | Resolution 1 | constant | Resolution Param 1 | | | | |\n", - "| 1 | Data Resolution | data | | | | | |\n", - "+-------+-----------------+----------+--------------------+---------+---------+---------+---------+\n", - "\n", - "Custom Files: --------------------------------------------------------------------------------------\n", - "\n", - "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------+\n", - "| index | name | filename | function name | language | path |\n", - "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------+\n", - "| 0 | DSPC Model | custom_bilayer_DSPC.py | custom_bilayer_DSPC | python | /mnt/c/Users/gnn85523/projects/python-RAT/RATapi/examples/non_polarised |\n", - "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------+\n", - "\n", - "Data: ----------------------------------------------------------------------------------------------\n", - "\n", - "+-------+---------------+-----------------------+------------------+----------------------+\n", - "| index | name | data | data range | simulation range |\n", - "+-------+---------------+-----------------------+------------------+----------------------+\n", - "| 0 | Simulation | [] | [] | [0.005, 0.7] |\n", - "| 1 | Bilayer / D2O | Data array: [146 x 4] | [0.013, 0.37] | [0.0057118, 0.39606] |\n", - "| 2 | Bilayer / SMW | Data array: [97 x 4] | [0.013, 0.32996] | [0.0076029, 0.32996] |\n", - "| 3 | Bilayer / H2O | Data array: [104 x 4] | [0.013, 0.33048] | [0.0063374, 0.33048] |\n", - "+-------+---------------+-----------------------+------------------+----------------------+\n", - "\n", - "Contrasts: -----------------------------------------------------------------------------------------\n", - "\n", - "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+------------+\n", - "| index | name | data | background | background action | bulk in | bulk out | scalefactor | resolution | resample | model |\n", - "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+------------+\n", - "| 0 | Bilayer / D2O | Bilayer / D2O | Background D2O | add | Silicon | SLD D2O | Scalefactor 1 | Data Resolution | False | DSPC Model |\n", - "| 1 | Bilayer / SMW | Bilayer / SMW | Background SMW | add | Silicon | SLD SMW | Scalefactor 1 | Data Resolution | False | DSPC Model |\n", - "| 2 | Bilayer / H2O | Bilayer / H2O | Background H2O | add | Silicon | SLD H2O | Scalefactor 1 | Data Resolution | False | DSPC Model |\n", - "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+------------+\n", - "\n", - "\n" - ] - } - ], - "source": [ - "print(problem)" - ] - }, - { - "cell_type": "markdown", - "id": "861b6e03-773a-46c3-b3fd-0df47c99d27e", - "metadata": {}, - "source": [ - "To run it, we need to make a controls block" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "154a33df-06b9-4035-aa4c-a0e095c1bb06", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "+------------------+-----------+\n", - "| Property | Value |\n", - "+------------------+-----------+\n", - "| procedure | calculate |\n", - "| parallel | single |\n", - "| calcSldDuringFit | False |\n", - "| resampleMinAngle | 0.9 |\n", - "| resampleNPoints | 50 |\n", - "| display | iter |\n", - "+------------------+-----------+\n" - ] - } - ], - "source": [ - "controls = RAT.Controls()\n", - "print(controls)" - ] - }, - { - "cell_type": "markdown", - "id": "384f0a34-1a2b-40f7-a945-6d44db9391ab", - "metadata": {}, - "source": [ - ". . . and send this to RAT" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d5d9a782-0fb1-40b6-b1fa-86307abe32a6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "Elapsed time is 0.020 seconds\n", - "\n", - "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "problem, results = RAT.run(problem, controls)\n", - "RAT.plotting.plot_ref_sld(problem, results)" - ] - } - ], - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/RATapi/utils/__init__.py b/RATapi/utils/__init__.py deleted file mode 100644 index f229eb5c..00000000 --- a/RATapi/utils/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Additional utilities for RATapi.""" diff --git a/README.md b/README.md index e4566708..46cc348e 100644 --- a/README.md +++ b/README.md @@ -12,11 +12,11 @@ To install in local directory: matlabengine is an optional dependency only required for Matlab custom functions. The version of matlabengine should match the version of Matlab installed on the machine. This can be installed as shown below: - pip install -e .[Matlab-2023a] + pip install -e .[matlab-2023a] Development dependencies can be installed as shown below - pip install -e .[Dev] + pip install -e .[dev] To build wheel: diff --git a/cpp/RAT b/cpp/RAT index 589c9871..a5d4d80b 160000 --- a/cpp/RAT +++ b/cpp/RAT @@ -1 +1 @@ -Subproject commit 589c987180f33f1e265537044a6497ef51f4ef09 +Subproject commit a5d4d80ba63185846db678c3ebd9a85bdd82921d diff --git a/cpp/includes/defines.h b/cpp/includes/defines.h index 02b5fa97..097ad0eb 100644 --- a/cpp/includes/defines.h +++ b/cpp/includes/defines.h @@ -588,8 +588,8 @@ parallel : str How the calculation should be parallelised (This uses the Parallel Computing Toolbox). Can be 'single', 'contrasts' or 'points'. procedure : str Which procedure RAT should execute. Can be 'calculate', 'simplex', 'de', 'ns', or 'dream'. -calcSldDuringFit : bool - Whether SLD will be calculated during fit (for live plotting etc.) +numSimulationPoints : int + The number of points used for a reflectivity simulation where no data is present. resampleMinAngle : float The upper threshold on the angle between three sampled points for resampling, in units of radians over pi. resampleNPoints : int @@ -662,7 +662,7 @@ struct Control { real_T nMCMC {}; real_T propScale {}; real_T nsTolerance {}; - boolean_T calcSldDuringFit {}; + real_T numSimulationPoints {}; real_T resampleMinAngle {}; real_T resampleNPoints {}; real_T updateFreq {}; diff --git a/cpp/rat.cpp b/cpp/rat.cpp index 3335e09a..957ba4fa 100644 --- a/cpp/rat.cpp +++ b/cpp/rat.cpp @@ -317,7 +317,7 @@ RAT::Controls createControlsStruct(const Control& control) control_struct.nMCMC = control.nMCMC; control_struct.propScale = control.propScale; control_struct.nsTolerance = control.nsTolerance; - control_struct.calcSldDuringFit = control.calcSldDuringFit; + control_struct.numSimulationPoints = control.numSimulationPoints; control_struct.updateFreq = control.updateFreq; control_struct.updatePlotFreq = control.updatePlotFreq; control_struct.nSamples = control.nSamples; @@ -332,6 +332,7 @@ RAT::Controls createControlsStruct(const Control& control) control_struct.resampleNPoints = control.resampleNPoints; stringToRatBoundedArray(control.boundHandling, control_struct.boundHandling.data, control_struct.boundHandling.size); control_struct.adaptPCR = control.adaptPCR; + control_struct.calcSLD = false; stringToRatBoundedArray(control.IPCFilePath, control_struct.IPCFilePath.data, control_struct.IPCFilePath.size); return control_struct; @@ -524,8 +525,8 @@ OutputBayesResult OutputBayesResultsFromStruct(const RAT::BayesResults results) bayesResults.confidenceIntervals.percentile65 = pyArrayFromRatArray2d(results.confidenceIntervals.percentile65); bayesResults.confidenceIntervals.mean = pyArrayFromRatArray2d(results.confidenceIntervals.mean); - bayesResults.nestedSamplerOutput.logZ = results.nestedSamplerOutput.LogZ; - bayesResults.nestedSamplerOutput.logZErr = results.nestedSamplerOutput.LogZErr; + bayesResults.nestedSamplerOutput.logZ = results.nestedSamplerOutput.logZ; + bayesResults.nestedSamplerOutput.logZErr = results.nestedSamplerOutput.logZErr; bayesResults.nestedSamplerOutput.nestSamples = pyArrayFromRatArray2d(results.nestedSamplerOutput.nestSamples); bayesResults.nestedSamplerOutput.postSamples = pyArrayFromRatArray2d(results.nestedSamplerOutput.postSamples); @@ -909,7 +910,7 @@ PYBIND11_MODULE(rat_core, m) { .def_readwrite("nMCMC", &Control::nMCMC) .def_readwrite("propScale", &Control::propScale) .def_readwrite("nsTolerance", &Control::nsTolerance) - .def_readwrite("calcSldDuringFit", &Control::calcSldDuringFit) + .def_readwrite("numSimulationPoints", &Control::numSimulationPoints) .def_readwrite("resampleMinAngle", &Control::resampleMinAngle) .def_readwrite("resampleNPoints", &Control::resampleNPoints) .def_readwrite("updateFreq", &Control::updateFreq) @@ -927,9 +928,9 @@ PYBIND11_MODULE(rat_core, m) { return py::make_tuple(ctrl.parallel, ctrl.procedure, ctrl.display, ctrl.xTolerance, ctrl.funcTolerance, ctrl.maxFuncEvals, ctrl.maxIterations, ctrl.populationSize, ctrl.fWeight, ctrl.crossoverProbability, ctrl.targetValue, ctrl.numGenerations, ctrl.strategy, ctrl.nLive, ctrl.nMCMC, ctrl.propScale, - ctrl.nsTolerance, ctrl.calcSldDuringFit, ctrl.resampleMinAngle, ctrl.resampleNPoints, - ctrl.updateFreq, ctrl.updatePlotFreq, ctrl.nSamples, ctrl.nChains, ctrl.jumpProbability, ctrl.pUnitGamma, - ctrl.boundHandling, ctrl.adaptPCR, ctrl.IPCFilePath); + ctrl.nsTolerance, ctrl.numSimulationPoints, ctrl.resampleMinAngle, ctrl.resampleNPoints, + ctrl.updateFreq, ctrl.updatePlotFreq, ctrl.nSamples, ctrl.nChains, ctrl.jumpProbability, + ctrl.pUnitGamma, ctrl.boundHandling, ctrl.adaptPCR, ctrl.IPCFilePath); }, [](py::tuple t) { // __setstate__ if (t.size() != 29) @@ -955,7 +956,7 @@ PYBIND11_MODULE(rat_core, m) { ctrl.nMCMC = t[14].cast(); ctrl.propScale = t[15].cast(); ctrl.nsTolerance = t[16].cast(); - ctrl.calcSldDuringFit = t[17].cast(); + ctrl.numSimulationPoints = t[17].cast(); ctrl.resampleMinAngle = t[18].cast(); ctrl.resampleNPoints = t[19].cast(); ctrl.updateFreq = t[20].cast(); diff --git a/pyproject.toml b/pyproject.toml index 35febb57..ab6724ae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,10 +2,61 @@ requires = [ 'setuptools>=61', 'wheel', - 'pybind11>=2.4', + 'pybind11>=2.4, <=2.13.6', ] build-backend = 'setuptools.build_meta' +[project] +name = "ratapi" +version = "0.0.0.dev14" +description = "Python extension for the Reflectivity Analysis Toolbox (RAT)" +readme = "README.md" +requires-python = ">=3.10" +dependencies = [ + "matplotlib>=3.8.3", + "numpy>=1.20", + "prettytable>=3.9.0", + "pydantic>=2.7.2", + "scipy>=1.13.1", + "strenum>=0.4.15 ; python_full_version < '3.11'", + "tqdm>=4.66.5", +] + +[project.urls] +Documentation = "https://rascalsoftware.github.io/RAT/" +Repository = "https://github.com/RascalSoftware/python-RAT" + +[project.optional-dependencies] +dev = [ + "pytest>=7.4.0", + "pytest-cov>=4.1.0", + "ruff>=0.4.10" +] +orso = [ + "orsopy>=1.2.1", + "pint>=0.24.4" +] +matlab_latest = ["matlabengine"] +matlab_2025b = ["matlabengine == 25.2.*"] +matlab_2025a = ["matlabengine == 25.1.2"] +matlab_2024b = ["matlabengine == 24.2.2"] +matlab_2024a = ["matlabengine == 24.1.4"] +matlab_2023b = ["matlabengine == 23.2.3"] +matlab_2023a = ["matlabengine == 9.14.3"] + +[tool.uv] +conflicts = [ + [ + { extra = "matlab_latest" }, + { extra = "matlab_2025b" }, + { extra = "matlab_2025a" }, + { extra = "matlab_2024b" }, + { extra = "matlab_2024a" }, + { extra = "matlab_2023b" }, + { extra = "matlab_2023a" }, + ], +] + [tool.ruff] line-length = 120 extend-exclude = ["*.ipynb"] @@ -24,7 +75,8 @@ ignore = ["SIM103", # needless bool "D105", # undocumented __init__ "D107", # undocumented magic method "D203", # blank line before class docstring - "D213"] # multi line summary should start at second line + "D213", # multi line summary should start at second line + "UP038"] # non pep604 isinstance - to be removed # ignore docstring lints in the tests and install script [tool.ruff.lint.per-file-ignores] @@ -38,4 +90,5 @@ mark-parentheses = false [tool.ruff.lint.pydocstyle] convention = "numpy" - +[tool.ruff.lint.isort] +known-first-party = ["ratapi.rat_core"] diff --git a/ratapi/__init__.py b/ratapi/__init__.py new file mode 100644 index 00000000..98683c7e --- /dev/null +++ b/ratapi/__init__.py @@ -0,0 +1,29 @@ +"""ratapi is a Python package for modelling, fitting and optimising reflectivity problems.""" + +from contextlib import suppress + +import ratapi.examples as examples +from ratapi import events, models +from ratapi.classlist import ClassList +from ratapi.controls import Controls +from ratapi.outputs import BayesResults, Results +from ratapi.project import Project +from ratapi.run import run +from ratapi.utils import convert, plotting + +with suppress(ImportError): # orsopy is an optional dependency + from ratapi.utils import orso as orso + +__all__ = [ + "examples", + "models", + "events", + "ClassList", + "Controls", + "BayesResults", + "Results", + "Project", + "run", + "plotting", + "convert", +] diff --git a/RATapi/classlist.py b/ratapi/classlist.py similarity index 97% rename from RATapi/classlist.py rename to ratapi/classlist.py index f0a61d3b..29637a5f 100644 --- a/RATapi/classlist.py +++ b/ratapi/classlist.py @@ -5,7 +5,7 @@ import importlib import warnings from collections.abc import Sequence -from typing import Any, Generic, TypeVar, Union +from typing import Any, Generic, TypeVar import numpy as np import prettytable @@ -38,7 +38,7 @@ class ClassList(collections.UserList, Generic[T]): """ - def __init__(self, init_list: Union[Sequence[T], T] = None, name_field: str = "name") -> None: + def __init__(self, init_list: Sequence[T] | T = None, name_field: str = "name") -> None: self.name_field = name_field # Set input as list if necessary @@ -114,7 +114,7 @@ def __str__(self): output = str(self.data) return output - def __getitem__(self, index: Union[int, slice, str, T]) -> T: + def __getitem__(self, index: int | slice | str | T) -> T: """Get an item by its index, name, a slice, or the object itself.""" if isinstance(index, (int, slice)): return self.data[index] @@ -262,12 +262,12 @@ def insert(self, index: int, obj: T = None, **kwargs) -> None: self._validate_name_field(kwargs) self.data.insert(index, self._class_handle(**kwargs)) - def remove(self, item: Union[T, str]) -> None: + def remove(self, item: T | str) -> None: """Remove an object from the ClassList using either the object itself or its ``name_field`` value.""" item = self._get_item_from_name_field(item) self.data.remove(item) - def count(self, item: Union[T, str]) -> int: + def count(self, item: T | str) -> int: """Return the number of times an object appears in the ClassList. This method can use either the object itself or its ``name_field`` value. @@ -276,7 +276,7 @@ def count(self, item: Union[T, str]) -> int: item = self._get_item_from_name_field(item) return self.data.count(item) - def index(self, item: Union[T, str], offset: bool = False, *args) -> int: + def index(self, item: T | str, offset: bool = False, *args) -> int: """Return the index of a particular object in the ClassList. This method can use either the object itself or its ``name_field`` value. @@ -309,7 +309,7 @@ def union(self, other: Sequence[T]) -> None: ] ) - def set_fields(self, index: Union[int, slice, str, T], **kwargs) -> None: + def set_fields(self, index: int | slice | str | T, **kwargs) -> None: """Assign the values of an existing object's attributes using keyword arguments.""" self._validate_name_field(kwargs) pydantic_object = False @@ -519,7 +519,7 @@ def _check_classes(self, input_list: Sequence[T]) -> None: f"In the input list:\n{newline.join(error for error in error_list)}\n" ) - def _get_item_from_name_field(self, value: Union[T, str]) -> Union[T, str]: + def _get_item_from_name_field(self, value: T | str) -> T | str: """Return the object with the given value of the ``name_field`` attribute in the ClassList. Parameters @@ -577,11 +577,12 @@ def _determine_class_handle(input_list: Sequence[T]): @classmethod def __get_pydantic_core_schema__(cls, source: Any, handler): # import here so that the ClassList can be instantiated and used without Pydantic installed + from typing import get_args, get_origin + from pydantic import ValidatorFunctionWrapHandler from pydantic.types import ( core_schema, # import core_schema through here rather than making pydantic_core a dependency ) - from typing_extensions import get_args, get_origin # if annotated with a class, get the item type of that class origin = get_origin(source) diff --git a/RATapi/controls.py b/ratapi/controls.py similarity index 91% rename from RATapi/controls.py rename to ratapi/controls.py index d696fb57..9dd815b5 100644 --- a/RATapi/controls.py +++ b/ratapi/controls.py @@ -5,7 +5,6 @@ import tempfile import warnings from pathlib import Path -from typing import Union import prettytable from pydantic import ( @@ -17,10 +16,17 @@ model_validator, ) -from RATapi.utils.custom_errors import custom_pydantic_validation_error -from RATapi.utils.enums import BoundHandling, Display, Parallel, Procedures, Strategies - -common_fields = ["procedure", "parallel", "calcSldDuringFit", "resampleMinAngle", "resampleNPoints", "display"] +from ratapi.utils.custom_errors import custom_pydantic_validation_error +from ratapi.utils.enums import BoundHandling, Display, Parallel, Procedures, Strategies + +common_fields = [ + "procedure", + "parallel", + "numSimulationPoints", + "resampleMinAngle", + "resampleNPoints", + "display", +] update_fields = ["updateFreq", "updatePlotFreq"] fields = { "calculate": common_fields, @@ -50,8 +56,8 @@ class Controls(BaseModel, validate_assignment=True, extra="forbid", use_attribut parallel: Parallel = Parallel.Single """How the calculation should be parallelised. Can be 'single', 'contrasts' or 'points'.""" - calcSldDuringFit: bool = False - """Whether SLD will be calculated during fit (for live plotting etc.)""" + numSimulationPoints: int = Field(500, ge=2) + """The number of points used for reflectivity simulations where no data is supplied.""" resampleMinAngle: float = Field(0.9, le=1, gt=0) """The upper threshold on the angle between three sampled points for resampling, in units of radians over pi.""" @@ -200,7 +206,7 @@ def __str__(self) -> str: def initialise_IPC(self): """Set up the inter-process communication file.""" IPC_obj, self._IPCFilePath = tempfile.mkstemp() - os.write(IPC_obj, b"0") + os.write(IPC_obj, b"\x00") os.close(IPC_obj) return None @@ -215,7 +221,7 @@ def sendStopEvent(self): """ if os.path.isfile(self._IPCFilePath): with open(self._IPCFilePath, "wb") as f: - f.write(b"1") + f.write(b"\x01") else: warnings.warn("An IPC file was not initialised.", UserWarning, stacklevel=2) return None @@ -224,24 +230,22 @@ def delete_IPC(self): """Delete the inter-process communication file.""" with contextlib.suppress(FileNotFoundError): os.remove(self._IPCFilePath) + self._IPCFilePath = "" return None - def save(self, path: Union[str, Path], filename: str = "controls"): + def save(self, filepath: str | Path = "./controls.json"): """Save a controls object to a JSON file. Parameters ---------- - path : str or Path - The directory in which the controls object will be written. - filename : str - The name for the JSON file containing the controls object. - + filepath : str or Path + The path to where the controls file will be written. """ - file = Path(path, f"{filename.removesuffix('.json')}.json") - file.write_text(self.model_dump_json()) + filepath = Path(filepath).with_suffix(".json") + filepath.write_text(self.model_dump_json()) @classmethod - def load(cls, path: Union[str, Path]) -> "Controls": + def load(cls, path: str | Path) -> "Controls": """Load a controls object from file. Parameters diff --git a/RATapi/events.py b/ratapi/events.py similarity index 86% rename from RATapi/events.py rename to ratapi/events.py index c2348ecc..2383159c 100644 --- a/RATapi/events.py +++ b/ratapi/events.py @@ -1,12 +1,12 @@ """Hooks for connecting to run callback events.""" import os -from typing import Callable, Union +from collections.abc import Callable -from RATapi.rat_core import EventBridge, EventTypes, PlotEventData, ProgressEventData +from ratapi.rat_core import EventBridge, EventTypes, PlotEventData, ProgressEventData -def notify(event_type: EventTypes, data: Union[str, PlotEventData, ProgressEventData]) -> None: +def notify(event_type: EventTypes, data: str | PlotEventData | ProgressEventData) -> None: """Call registered callbacks with data when event type has been triggered. Parameters @@ -22,7 +22,7 @@ def notify(event_type: EventTypes, data: Union[str, PlotEventData, ProgressEvent callback(data) -def get_event_callback(event_type: EventTypes) -> list[Callable[[Union[str, PlotEventData, ProgressEventData]], None]]: +def get_event_callback(event_type: EventTypes) -> list[Callable[[str | PlotEventData | ProgressEventData], None]]: """Return all callbacks registered for the given event type. Parameters @@ -39,7 +39,7 @@ def get_event_callback(event_type: EventTypes) -> list[Callable[[Union[str, Plot return list(__event_callbacks[event_type]) -def register(event_type: EventTypes, callback: Callable[[Union[str, PlotEventData, ProgressEventData]], None]) -> None: +def register(event_type: EventTypes, callback: Callable[[str | PlotEventData | ProgressEventData], None]) -> None: """Register a new callback for the event type. Parameters diff --git a/ratapi/examples/__init__.py b/ratapi/examples/__init__.py new file mode 100644 index 00000000..b85aebed --- /dev/null +++ b/ratapi/examples/__init__.py @@ -0,0 +1,25 @@ +"""Usage examples for the Python RAT API.""" + +from ratapi.examples.absorption.absorption import absorption +from ratapi.examples.convert_rascal_project.convert_rascal import convert_rascal +from ratapi.examples.domains.domains_custom_layers import domains_custom_layers +from ratapi.examples.domains.domains_custom_XY import domains_custom_XY +from ratapi.examples.domains.domains_standard_layers import domains_standard_layers +from ratapi.examples.normal_reflectivity.DSPC_custom_layers import DSPC_custom_layers +from ratapi.examples.normal_reflectivity.DSPC_custom_XY import DSPC_custom_XY +from ratapi.examples.normal_reflectivity.DSPC_data_background import DSPC_data_background +from ratapi.examples.normal_reflectivity.DSPC_function_background import DSPC_function_background +from ratapi.examples.normal_reflectivity.DSPC_standard_layers import DSPC_standard_layers + +__all__ = [ + "absorption", + "domains_custom_layers", + "domains_custom_XY", + "domains_standard_layers", + "DSPC_custom_layers", + "DSPC_custom_XY", + "DSPC_standard_layers", + "DSPC_data_background", + "DSPC_function_background", + "convert_rascal", +] diff --git a/RATapi/examples/absorption/__init__.py b/ratapi/examples/absorption/__init__.py similarity index 100% rename from RATapi/examples/absorption/__init__.py rename to ratapi/examples/absorption/__init__.py diff --git a/RATapi/examples/absorption/absorption.ipynb b/ratapi/examples/absorption/absorption.ipynb similarity index 99% rename from RATapi/examples/absorption/absorption.ipynb rename to ratapi/examples/absorption/absorption.ipynb index 10f3cda0..696103bc 100644 --- a/RATapi/examples/absorption/absorption.ipynb +++ b/ratapi/examples/absorption/absorption.ipynb @@ -11,8 +11,8 @@ "import numpy as np\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT\n", - "from RATapi.models import Parameter" + "import ratapi as RAT\n", + "from ratapi.models import Parameter" ] }, { diff --git a/RATapi/examples/absorption/absorption.py b/ratapi/examples/absorption/absorption.py similarity index 98% rename from RATapi/examples/absorption/absorption.py rename to ratapi/examples/absorption/absorption.py index 63b1ba3e..5d100aff 100644 --- a/RATapi/examples/absorption/absorption.py +++ b/ratapi/examples/absorption/absorption.py @@ -4,7 +4,7 @@ import numpy as np -import RATapi as RAT +import ratapi as RAT def absorption(): @@ -108,7 +108,7 @@ def absorption(): name="DPPC absorption", filename="volume_thiol_bilayer.py", language="python", - path=pathlib.Path(__file__).parent.resolve(), + path=pathlib.Path(__file__).parent, ) # Finally add the contrasts diff --git a/RATapi/examples/absorption/volume_thiol_bilayer.py b/ratapi/examples/absorption/volume_thiol_bilayer.py similarity index 91% rename from RATapi/examples/absorption/volume_thiol_bilayer.py rename to ratapi/examples/absorption/volume_thiol_bilayer.py index 4398f2b6..2b9f7fd4 100644 --- a/RATapi/examples/absorption/volume_thiol_bilayer.py +++ b/ratapi/examples/absorption/volume_thiol_bilayer.py @@ -23,6 +23,9 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast): The second output parameter should be the substrate roughness. """ + # Note - The first contrast number is 1 (not 0) so be careful if you use + # this variable for array indexing. + subRough = params[0] alloyThick = params[1] alloySLDUp = params[2] @@ -92,11 +95,11 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast): # Correct head SLD based on hydration thiolHeadHydr = thiolHeadHydr / 100 - sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast]) + sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast - 1]) # Now correct both the SLDs for the coverage parameter - sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast]) - sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast]) + sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast - 1]) + sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast - 1]) SAMTAILS = [thickTail, sldTail, 0, goldRough] SAMHEAD = [thickHead, sldHead, 0, goldRough] @@ -113,7 +116,7 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast): sldHead = sumbHead / vHead thickHead = vHead / bilayerAPM bilHeadHydr = bilHeadHydr / 100 - sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast]) + sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast - 1]) sldTail = sumbTail / vTail thickTail = vTail / bilayerAPM @@ -121,9 +124,9 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast): sldMe = sumbMe / vMe thickMe = vMe / bilayerAPM - sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast]) - sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast]) - sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast]) + sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast - 1]) + sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast - 1]) + sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast - 1]) BILTAILS = [thickTail, sldTail, 0, bilayerRough] BILHEAD = [thickHead, sldHead, 0, bilayerRough] @@ -131,11 +134,11 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast): BILAYER = [BILHEAD, BILTAILS, BILME, BILME, BILTAILS, BILHEAD] - CW = [cwThick, bulk_out[contrast], 0, bilayerRough] + CW = [cwThick, bulk_out[contrast - 1], 0, bilayerRough] - if contrast == 0 or contrast == 2: + if contrast == 2 or contrast == 4: output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER] - else: + elif contrast == 1 or contrast == 3: output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER] return output, subRough diff --git a/RATapi/examples/bayes_benchmark/bayes_benchmark.ipynb b/ratapi/examples/bayes_benchmark/bayes_benchmark.ipynb similarity index 97% rename from RATapi/examples/bayes_benchmark/bayes_benchmark.ipynb rename to ratapi/examples/bayes_benchmark/bayes_benchmark.ipynb index 79e4ae95..bb9bba9f 100644 --- a/RATapi/examples/bayes_benchmark/bayes_benchmark.ipynb +++ b/ratapi/examples/bayes_benchmark/bayes_benchmark.ipynb @@ -41,8 +41,8 @@ "source": [ "import numpy as np\n", "\n", - "import RATapi as RAT\n", - "from RATapi.models import Parameter, Background, Resolution, Data, Contrast" + "import ratapi as RAT\n", + "from ratapi.models import Parameter, Background, Resolution, Data, Contrast" ] }, { @@ -188,7 +188,7 @@ "back_param = project.background_parameters[0]\n", "background = np.linspace(back_param.min, back_param.max, 30)\n", "\n", - "controls = RAT.Controls(procedure=\"calculate\", calcSldDuringFit=True, display=\"off\")\n", + "controls = RAT.Controls(procedure=\"calculate\", display=\"off\")\n", "\n", "# function to calculate exp(-chi_squared / 2) for a given pair of roughness/background values\n", "def calculate_posterior(roughness_index: int, background_index: int) -> float:\n", @@ -263,7 +263,7 @@ "source": [ "import matplotlib.pyplot as plt\n", "from matplotlib import colormaps\n", - "import RATapi.utils.plotting as RATplot\n", + "import ratapi.utils.plotting as RATplot\n", "\n", "fig, axes = plt.subplots(3, 2, figsize=(6, 9))\n", "\n", @@ -339,13 +339,6 @@ "fig.tight_layout()\n", "fig.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -364,7 +357,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.16" } }, "nbformat": 4, diff --git a/RATapi/examples/bayes_benchmark/bayes_benchmark.py b/ratapi/examples/bayes_benchmark/bayes_benchmark.py similarity index 98% rename from RATapi/examples/bayes_benchmark/bayes_benchmark.py rename to ratapi/examples/bayes_benchmark/bayes_benchmark.py index 80ac9a24..ee6ea96e 100644 --- a/RATapi/examples/bayes_benchmark/bayes_benchmark.py +++ b/ratapi/examples/bayes_benchmark/bayes_benchmark.py @@ -24,9 +24,9 @@ import matplotlib.pyplot as plt import numpy as np -import RATapi as RAT -import RATapi.utils.plotting as RATplot -from RATapi.models import Background, Contrast, Data, Parameter, Resolution +import ratapi as RAT +import ratapi.utils.plotting as RATplot +from ratapi.models import Background, Contrast, Data, Parameter, Resolution PWD = Path(__file__).parents[0] @@ -250,7 +250,7 @@ def bayes_benchmark_3d(grid_size: int) -> (RAT.outputs.BayesResults, Calculation scale_param = problem.scalefactors[0] scalefactor = np.linspace(scale_param.min, scale_param.max, grid_size) - controls = RAT.Controls(procedure="calculate", calcSldDuringFit=True, display="off") + controls = RAT.Controls(procedure="calculate", display="off") def calculate_posterior(roughness_index: int, background_index: int, scalefactor_index: int) -> float: """Calculate the posterior for an item in the roughness, background, and scalefactor vectors. diff --git a/RATapi/examples/convert_rascal_project/Model_IIb.m b/ratapi/examples/convert_rascal_project/Model_IIb.m similarity index 100% rename from RATapi/examples/convert_rascal_project/Model_IIb.m rename to ratapi/examples/convert_rascal_project/Model_IIb.m diff --git a/RATapi/examples/convert_rascal_project/Model_IIb.py b/ratapi/examples/convert_rascal_project/Model_IIb.py similarity index 89% rename from RATapi/examples/convert_rascal_project/Model_IIb.py rename to ratapi/examples/convert_rascal_project/Model_IIb.py index d7c61526..2e15d5d1 100644 --- a/RATapi/examples/convert_rascal_project/Model_IIb.py +++ b/ratapi/examples/convert_rascal_project/Model_IIb.py @@ -5,6 +5,9 @@ def Model_IIb(params, bulk_in, bulk_out, contrast): """Calculate layer parameters for a monolayer volume model at two deuterations.""" + # Note - The first contrast number is 1 (not 0) so be careful if you use + # this variable for array indexing. Same applies to the domain number. + # converted from matlab file Model_IIb.m Roughness, APM, thickHead, theta = params @@ -49,7 +52,7 @@ def Model_IIb(params, bulk_in, bulk_out, contrast): vTail = 2 * (16 * vCH2) + 2 * (vCH3) # make SLDs - thisMask = deut[contrast] + thisMask = deut[contrast - 1] if thisMask[0] == 0: thisWater = (H2O * 0.9249) + (D2O * 0.0871) @@ -57,7 +60,7 @@ def Model_IIb(params, bulk_in, bulk_out, contrast): thisWater = D2O # Calculate mole fraction of D2O from the bulk SLD - d2o_molfr = (1 / D2O - H2O) * ((bulk_out[contrast] / 0.036182336306) - H2O) + d2o_molfr = (1 / D2O - H2O) * ((bulk_out[contrast - 1] / 0.036182336306) - H2O) thisWater = (d2o_molfr * D2O) + ((1 - d2o_molfr) * H2O) if thisMask[1] == 0: diff --git a/RATapi/examples/convert_rascal_project/R1monolayerVolumeModel.mat b/ratapi/examples/convert_rascal_project/R1monolayerVolumeModel.mat similarity index 100% rename from RATapi/examples/convert_rascal_project/R1monolayerVolumeModel.mat rename to ratapi/examples/convert_rascal_project/R1monolayerVolumeModel.mat diff --git a/RATapi/examples/convert_rascal_project/__init__.py b/ratapi/examples/convert_rascal_project/__init__.py similarity index 100% rename from RATapi/examples/convert_rascal_project/__init__.py rename to ratapi/examples/convert_rascal_project/__init__.py diff --git a/RATapi/examples/convert_rascal_project/convert_rascal.ipynb b/ratapi/examples/convert_rascal_project/convert_rascal.ipynb similarity index 96% rename from RATapi/examples/convert_rascal_project/convert_rascal.ipynb rename to ratapi/examples/convert_rascal_project/convert_rascal.ipynb index fbae2d5b..aebd749a 100644 --- a/RATapi/examples/convert_rascal_project/convert_rascal.ipynb +++ b/ratapi/examples/convert_rascal_project/convert_rascal.ipynb @@ -24,7 +24,7 @@ "metadata": {}, "outputs": [], "source": [ - "from RATapi.utils.convert import r1_to_project\n", + "from ratapi.utils.convert import r1_to_project\n", "\n", "project = r1_to_project(\"R1monolayerVolumeModel.mat\")\n", "print(project)" @@ -99,7 +99,7 @@ "metadata": {}, "outputs": [], "source": [ - "from RATapi.examples import DSPC_standard_layers\n", + "from ratapi.examples import DSPC_standard_layers\n", "lipid_bilayer_project = DSPC_standard_layers()[0]\n", "print(lipid_bilayer_project)" ] @@ -121,7 +121,7 @@ "metadata": {}, "outputs": [], "source": [ - "from RATapi.utils.convert import project_to_r1\n", + "from ratapi.utils.convert import project_to_r1\n", "from pprint import pp # for printing the struct\n", "\n", "# save to a file called lipid_bilayer.mat\n", diff --git a/RATapi/examples/convert_rascal_project/convert_rascal.py b/ratapi/examples/convert_rascal_project/convert_rascal.py similarity index 98% rename from RATapi/examples/convert_rascal_project/convert_rascal.py rename to ratapi/examples/convert_rascal_project/convert_rascal.py index 2a41b61f..15179b35 100644 --- a/RATapi/examples/convert_rascal_project/convert_rascal.py +++ b/ratapi/examples/convert_rascal_project/convert_rascal.py @@ -3,7 +3,7 @@ import pathlib from pprint import pp -import RATapi as RAT +import ratapi as RAT # convert R1 project to Project class diff --git a/RATapi/examples/data/D2O_spin_down.dat b/ratapi/examples/data/D2O_spin_down.dat similarity index 100% rename from RATapi/examples/data/D2O_spin_down.dat rename to ratapi/examples/data/D2O_spin_down.dat diff --git a/RATapi/examples/data/D2O_spin_up.dat b/ratapi/examples/data/D2O_spin_up.dat similarity index 100% rename from RATapi/examples/data/D2O_spin_up.dat rename to ratapi/examples/data/D2O_spin_up.dat diff --git a/RATapi/examples/data/DSPC_D2O.dat b/ratapi/examples/data/DSPC_D2O.dat similarity index 100% rename from RATapi/examples/data/DSPC_D2O.dat rename to ratapi/examples/data/DSPC_D2O.dat diff --git a/RATapi/examples/data/DSPC_SMW.dat b/ratapi/examples/data/DSPC_SMW.dat similarity index 100% rename from RATapi/examples/data/DSPC_SMW.dat rename to ratapi/examples/data/DSPC_SMW.dat diff --git a/RATapi/examples/data/H2O_spin_down.dat b/ratapi/examples/data/H2O_spin_down.dat similarity index 100% rename from RATapi/examples/data/H2O_spin_down.dat rename to ratapi/examples/data/H2O_spin_down.dat diff --git a/RATapi/examples/data/H2O_spin_up.dat b/ratapi/examples/data/H2O_spin_up.dat similarity index 100% rename from RATapi/examples/data/H2O_spin_up.dat rename to ratapi/examples/data/H2O_spin_up.dat diff --git a/RATapi/examples/data/__init__.py b/ratapi/examples/data/__init__.py similarity index 100% rename from RATapi/examples/data/__init__.py rename to ratapi/examples/data/__init__.py diff --git a/RATapi/examples/data/c_PLP0011859_q.ort b/ratapi/examples/data/c_PLP0011859_q.ort similarity index 100% rename from RATapi/examples/data/c_PLP0011859_q.ort rename to ratapi/examples/data/c_PLP0011859_q.ort diff --git a/RATapi/examples/data/c_PLP0016596.dat b/ratapi/examples/data/c_PLP0016596.dat similarity index 100% rename from RATapi/examples/data/c_PLP0016596.dat rename to ratapi/examples/data/c_PLP0016596.dat diff --git a/RATapi/examples/data/c_PLP0016601.dat b/ratapi/examples/data/c_PLP0016601.dat similarity index 100% rename from RATapi/examples/data/c_PLP0016601.dat rename to ratapi/examples/data/c_PLP0016601.dat diff --git a/RATapi/examples/data/c_PLP0016607.dat b/ratapi/examples/data/c_PLP0016607.dat similarity index 100% rename from RATapi/examples/data/c_PLP0016607.dat rename to ratapi/examples/data/c_PLP0016607.dat diff --git a/RATapi/examples/data/d2o_background_data.dat b/ratapi/examples/data/d2o_background_data.dat similarity index 100% rename from RATapi/examples/data/d2o_background_data.dat rename to ratapi/examples/data/d2o_background_data.dat diff --git a/RATapi/examples/domains/__init__.py b/ratapi/examples/domains/__init__.py similarity index 100% rename from RATapi/examples/domains/__init__.py rename to ratapi/examples/domains/__init__.py diff --git a/RATapi/examples/domains/alloy_domains.py b/ratapi/examples/domains/alloy_domains.py similarity index 82% rename from RATapi/examples/domains/alloy_domains.py rename to ratapi/examples/domains/alloy_domains.py index 33454dcc..90da84fd 100644 --- a/RATapi/examples/domains/alloy_domains.py +++ b/ratapi/examples/domains/alloy_domains.py @@ -7,6 +7,9 @@ def alloy_domains(params, bulkIn, bulkOut, contrast, domain): Simple custom model for testing incoherent summing. Simple two layer of permalloy / gold, with up/down domains. """ + # Note - The first contrast number is 1 (not 0) so be careful if you use + # this variable for array indexing. Same applies to the domain number. + # Split up the parameters subRough = params[0] alloyThick = params[1] @@ -23,9 +26,9 @@ def alloy_domains(params, bulkIn, bulkOut, contrast, domain): gold = [goldThick, goldSLD, goldRough] # Make the model depending on which domain we are looking at - if domain == 0: + if domain == 1: output = [alloyUp, gold] - else: + elif domain == 2: output = [alloyDn, gold] return output, subRough diff --git a/RATapi/examples/domains/domains_XY_model.py b/ratapi/examples/domains/domains_XY_model.py similarity index 66% rename from RATapi/examples/domains/domains_XY_model.py rename to ratapi/examples/domains/domains_XY_model.py index 8aeb8c77..6a288ba2 100644 --- a/RATapi/examples/domains/domains_XY_model.py +++ b/ratapi/examples/domains/domains_XY_model.py @@ -1,12 +1,16 @@ """Custom model file for the domains custom XY example.""" -import math +from math import sqrt import numpy as np +from scipy.special import erf def domains_XY_model(params, bulk_in, bulk_out, contrast, domain): """Calculate the SLD profile for a domains custom XY model.""" + # Note - The first contrast number is 1 (not 0) so be careful if you use + # this variable for array indexing. Same applies to the domain number. + # Split up the parameters for convenience subRough = params[0] oxideThick = params[1] @@ -19,13 +23,13 @@ def domains_XY_model(params, bulk_in, bulk_out, contrast, domain): z = np.arange(0, 141) # Make the volume fraction distribution for our Silicon substrate - [vfSilicon, siSurf] = makeLayer(z, -25, 50, 1, subRough, subRough) + [vfSilicon, siSurf] = make_layer(z, -25, 50, 1, subRough, subRough) # ... and the Oxide ... - [vfOxide, oxSurface] = makeLayer(z, siSurf, oxideThick, 1, subRough, subRough) + [vfOxide, oxSurface] = make_layer(z, siSurf, oxideThick, 1, subRough, subRough) # ... and also our layer. - [vfLayer, laySurface] = makeLayer(z, oxSurface, layerThick, 1, subRough, layerRough) + [vfLayer, laySurface] = make_layer(z, oxSurface, layerThick, 1, subRough, layerRough) # Everything that is not already occupied will be filled will water totalVF = vfSilicon + vfOxide + vfLayer @@ -36,13 +40,13 @@ def domains_XY_model(params, bulk_in, bulk_out, contrast, domain): oxSLD = vfOxide * 3.41e-6 # Layer SLD depends on whether we are calculating the domain or not - if domain == 0: + if domain == 1: laySLD = vfLayer * layerSLD - else: + elif domain == 2: laySLD = vfLayer * domainSLD # ... and finally the water SLD. - waterSLD = vfWater * bulk_out[contrast] + waterSLD = vfWater * bulk_out[contrast - 1] # Make the total SLD by just adding them all up totalSLD = siSLD + oxSLD + laySLD + waterSLD @@ -53,7 +57,7 @@ def domains_XY_model(params, bulk_in, bulk_out, contrast, domain): return SLD, subRough -def makeLayer(z, prevLaySurf, thickness, height, Sigma_L, Sigma_R): +def make_layer(z, prevLaySurf, thickness, height, Sigma_L, Sigma_R): """Produce a layer, with a defined thickness, height and roughness. Each side of the layer has its own roughness value. @@ -63,12 +67,9 @@ def makeLayer(z, prevLaySurf, thickness, height, Sigma_L, Sigma_R): right = prevLaySurf + thickness # Make our heaviside - a = (z - left) / ((2**0.5) * Sigma_L) - b = (z - right) / ((2**0.5) * Sigma_R) - - erf_a = np.array([math.erf(value) for value in a]) - erf_b = np.array([math.erf(value) for value in b]) + erf_left = erf((z - left) / (sqrt(2) * Sigma_L)) + erf_right = erf((z - right) / (sqrt(2) * Sigma_R)) - VF = np.array((height / 2) * (erf_a - erf_b)) + VF = np.array((0.5 * height) * (erf_left - erf_right)) return VF, right diff --git a/RATapi/examples/domains/domains_custom_XY.ipynb b/ratapi/examples/domains/domains_custom_XY.ipynb similarity index 98% rename from RATapi/examples/domains/domains_custom_XY.ipynb rename to ratapi/examples/domains/domains_custom_XY.ipynb index fa8f317d..0a153e9d 100644 --- a/RATapi/examples/domains/domains_custom_XY.ipynb +++ b/ratapi/examples/domains/domains_custom_XY.ipynb @@ -9,8 +9,8 @@ "import pathlib\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT\n", - "from RATapi.models import Parameter" + "import ratapi as RAT\n", + "from ratapi.models import Parameter" ] }, { diff --git a/RATapi/examples/domains/domains_custom_XY.py b/ratapi/examples/domains/domains_custom_XY.py similarity index 97% rename from RATapi/examples/domains/domains_custom_XY.py rename to ratapi/examples/domains/domains_custom_XY.py index 1eb7222d..38d4f031 100644 --- a/RATapi/examples/domains/domains_custom_XY.py +++ b/ratapi/examples/domains/domains_custom_XY.py @@ -2,7 +2,7 @@ import pathlib -import RATapi as RAT +import ratapi as RAT def domains_custom_XY(): @@ -35,7 +35,7 @@ def domains_custom_XY(): name="Domain Layer", filename="domains_XY_model.py", language="python", - path=pathlib.Path(__file__).parent.resolve(), + path=pathlib.Path(__file__).parent, ) # Make contrasts diff --git a/RATapi/examples/domains/domains_custom_layers.ipynb b/ratapi/examples/domains/domains_custom_layers.ipynb similarity index 99% rename from RATapi/examples/domains/domains_custom_layers.ipynb rename to ratapi/examples/domains/domains_custom_layers.ipynb index 03179d54..3016e977 100644 --- a/RATapi/examples/domains/domains_custom_layers.ipynb +++ b/ratapi/examples/domains/domains_custom_layers.ipynb @@ -9,8 +9,8 @@ "import pathlib\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT\n", - "from RATapi.models import Parameter" + "import ratapi as RAT\n", + "from ratapi.models import Parameter" ] }, { diff --git a/RATapi/examples/domains/domains_custom_layers.py b/ratapi/examples/domains/domains_custom_layers.py similarity index 96% rename from RATapi/examples/domains/domains_custom_layers.py rename to ratapi/examples/domains/domains_custom_layers.py index 99c2d372..92b5e037 100644 --- a/RATapi/examples/domains/domains_custom_layers.py +++ b/ratapi/examples/domains/domains_custom_layers.py @@ -2,7 +2,7 @@ import pathlib -import RATapi as RAT +import ratapi as RAT def domains_custom_layers(): @@ -32,7 +32,7 @@ def domains_custom_layers(): name="Alloy domains", filename="alloy_domains.py", language="python", - path=pathlib.Path(__file__).parent.resolve(), + path=pathlib.Path(__file__).parent, ) # Make a contrast diff --git a/RATapi/examples/domains/domains_standard_layers.ipynb b/ratapi/examples/domains/domains_standard_layers.ipynb similarity index 99% rename from RATapi/examples/domains/domains_standard_layers.ipynb rename to ratapi/examples/domains/domains_standard_layers.ipynb index 2b5a29f0..300207d3 100644 --- a/RATapi/examples/domains/domains_standard_layers.ipynb +++ b/ratapi/examples/domains/domains_standard_layers.ipynb @@ -6,8 +6,8 @@ "metadata": {}, "outputs": [], "source": [ - "import RATapi as RAT\n", - "from RATapi.models import Layer, Parameter" + "import ratapi as RAT\n", + "from ratapi.models import Layer, Parameter" ] }, { diff --git a/RATapi/examples/domains/domains_standard_layers.py b/ratapi/examples/domains/domains_standard_layers.py similarity index 99% rename from RATapi/examples/domains/domains_standard_layers.py rename to ratapi/examples/domains/domains_standard_layers.py index 9e821a8d..9a251233 100644 --- a/RATapi/examples/domains/domains_standard_layers.py +++ b/ratapi/examples/domains/domains_standard_layers.py @@ -1,6 +1,6 @@ """An example using domains with standard layers.""" -import RATapi as RAT +import ratapi as RAT def domains_standard_layers(): diff --git a/RATapi/examples/extras/two_contrast_example.py b/ratapi/examples/extras/two_contrast_example.py similarity index 99% rename from RATapi/examples/extras/two_contrast_example.py rename to ratapi/examples/extras/two_contrast_example.py index ca441e14..86811056 100644 --- a/RATapi/examples/extras/two_contrast_example.py +++ b/ratapi/examples/extras/two_contrast_example.py @@ -2,7 +2,7 @@ import numpy as np -import RATapi as RAT +import ratapi as RAT def two_contrast_example(): diff --git a/RATapi/examples/languages/__init__.py b/ratapi/examples/languages/__init__.py similarity index 100% rename from RATapi/examples/languages/__init__.py rename to ratapi/examples/languages/__init__.py diff --git a/RATapi/examples/languages/custom_bilayer.cpp b/ratapi/examples/languages/custom_bilayer.cpp similarity index 87% rename from RATapi/examples/languages/custom_bilayer.cpp rename to ratapi/examples/languages/custom_bilayer.cpp index ad25b18f..2cf0ca81 100644 --- a/RATapi/examples/languages/custom_bilayer.cpp +++ b/ratapi/examples/languages/custom_bilayer.cpp @@ -13,6 +13,8 @@ extern "C" { LIB_EXPORT void custom_bilayer(std::vector& params, std::vector& bulkIn, std::vector& bulkOut, int contrast, std::vector& output, double* outputSize, double* rough) { + // Note - The first contrast number is 1 (not 0) so be careful if you use + // this variable for array indexing. double subRough = params[0]; double oxideThick = params[1]; double oxideHydration = params[2]; @@ -65,9 +67,9 @@ extern "C" { // Manually deal with hydration for layers in // this example. - double oxSLD = (oxideHydration * bulkOut[contrast]) + ((1 - oxideHydration) * oxideSLD); - double headSLD = (headHydration * bulkOut[contrast]) + ((1 - headHydration) * SLDhead); - double tailSLD = (bilayerHydration * bulkOut[contrast]) + ((1 - bilayerHydration) * SLDtail); + double oxSLD = (oxideHydration * bulkOut[contrast-1]) + ((1 - oxideHydration) * oxideSLD); + double headSLD = (headHydration * bulkOut[contrast-1]) + ((1 - headHydration) * SLDhead); + double tailSLD = (bilayerHydration * bulkOut[contrast-1]) + ((1 - bilayerHydration) * SLDtail); // Make the layers // oxide... @@ -77,7 +79,7 @@ extern "C" { // Water... output.push_back(waterThick); - output.push_back(bulkOut[contrast]); + output.push_back(bulkOut[contrast-1]); output.push_back(bilayerRough); // Heads... diff --git a/RATapi/examples/languages/custom_bilayer.m b/ratapi/examples/languages/custom_bilayer.m similarity index 100% rename from RATapi/examples/languages/custom_bilayer.m rename to ratapi/examples/languages/custom_bilayer.m diff --git a/RATapi/examples/languages/custom_bilayer.py b/ratapi/examples/languages/custom_bilayer.py similarity index 79% rename from RATapi/examples/languages/custom_bilayer.py rename to ratapi/examples/languages/custom_bilayer.py index 1777b358..bed82ca3 100644 --- a/RATapi/examples/languages/custom_bilayer.py +++ b/ratapi/examples/languages/custom_bilayer.py @@ -5,6 +5,9 @@ def custom_bilayer(params, bulk_in, bulk_out, contrast): """Calculate the layer parameters for a custom bilayer model.""" + # Note - The first contrast number is 1 (not 0) so be careful if you use + # this variable for array indexing. + sub_rough = params[0] oxide_thick = params[1] oxide_hydration = params[2] @@ -54,13 +57,13 @@ def custom_bilayer(params, bulk_in, bulk_out, contrast): tailThick = vTail / lipidAPM # Manually deal with hydration for layers in this example. - oxSLD = (oxide_hydration * bulk_out[contrast]) + ((1 - oxide_hydration) * oxide_SLD) - headSLD = (headHydration * bulk_out[contrast]) + ((1 - headHydration) * SLDhead) - tailSLD = (bilayerHydration * bulk_out[contrast]) + ((1 - bilayerHydration) * SLDtail) + oxSLD = (oxide_hydration * bulk_out[contrast - 1]) + ((1 - oxide_hydration) * oxide_SLD) + headSLD = (headHydration * bulk_out[contrast - 1]) + ((1 - headHydration) * SLDhead) + tailSLD = (bilayerHydration * bulk_out[contrast - 1]) + ((1 - bilayerHydration) * SLDtail) # Make the layers oxide = [oxide_thick, oxSLD, sub_rough] - water = [waterThick, bulk_out[contrast], bilayerRough] + water = [waterThick, bulk_out[contrast - 1], bilayerRough] head = [headThick, headSLD, bilayerRough] tail = [tailThick, tailSLD, bilayerRough] diff --git a/RATapi/examples/languages/run_custom_file_languages.py b/ratapi/examples/languages/run_custom_file_languages.py similarity index 89% rename from RATapi/examples/languages/run_custom_file_languages.py rename to ratapi/examples/languages/run_custom_file_languages.py index f91c0317..2f6025ae 100644 --- a/RATapi/examples/languages/run_custom_file_languages.py +++ b/ratapi/examples/languages/run_custom_file_languages.py @@ -5,13 +5,12 @@ import setup_problem -import RATapi as RAT +import ratapi as RAT -path = pathlib.Path(__file__).parent.resolve() +path = pathlib.Path(__file__).parent project = setup_problem.make_example_problem() controls = RAT.Controls() -controls.calcSldDuringFit = True # Python start = time.time() diff --git a/RATapi/examples/languages/setup_problem.py b/ratapi/examples/languages/setup_problem.py similarity index 98% rename from RATapi/examples/languages/setup_problem.py rename to ratapi/examples/languages/setup_problem.py index 4eea0965..dbf300db 100644 --- a/RATapi/examples/languages/setup_problem.py +++ b/ratapi/examples/languages/setup_problem.py @@ -4,7 +4,7 @@ import numpy as np -import RATapi as RAT +import ratapi as RAT def make_example_problem(): @@ -55,7 +55,7 @@ def make_example_problem(): name="DSPC Model", filename="custom_bilayer.py", language="python", - path=pathlib.Path(__file__).parent.resolve(), + path=pathlib.Path(__file__).parent, ) # Also, add the relevant background parameters - one each for each contrast: diff --git a/RATapi/examples/normal_reflectivity/DSPC_custom_XY.py b/ratapi/examples/normal_reflectivity/DSPC_custom_XY.py similarity index 98% rename from RATapi/examples/normal_reflectivity/DSPC_custom_XY.py rename to ratapi/examples/normal_reflectivity/DSPC_custom_XY.py index 27b15ffe..72511977 100644 --- a/RATapi/examples/normal_reflectivity/DSPC_custom_XY.py +++ b/ratapi/examples/normal_reflectivity/DSPC_custom_XY.py @@ -4,7 +4,7 @@ import numpy as np -import RATapi as RAT +import ratapi as RAT def DSPC_custom_XY(): @@ -75,7 +75,7 @@ def DSPC_custom_XY(): name="DSPC Model", filename="custom_XY_DSPC.py", language="python", - path=pathlib.Path(__file__).parent.resolve(), + path=pathlib.Path(__file__).parent, ) # Also, add the relevant background parameters - one each for each contrast: diff --git a/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb b/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb new file mode 100644 index 00000000..b90c4aa3 --- /dev/null +++ b/ratapi/examples/normal_reflectivity/DSPC_custom_layers.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "4b988c4a-3a09-4b75-8a87-8ba8402635ba", + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "import numpy as np\n", + "from IPython.display import Code\n", + "\n", + "import ratapi as RAT\n", + "from ratapi.models import Parameter" + ] + }, + { + "cell_type": "markdown", + "id": "793d9c50-698e-438b-87f7-85e3a9f11d6b", + "metadata": {}, + "source": [ + "# Custom Layers Example for Supported DSPC layer\n", + "\n", + "Example of using Custom layers to model a DSPC supported bilayer.\n", + "Start by making the project and setting it to a custom layers type:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a60cd45-0e1d-448a-b4bd-4c02bd6a3475", + "metadata": {}, + "outputs": [], + "source": [ + "problem = RAT.Project(name=\"Orso lipid example - custom layers\", model=\"custom layers\", geometry=\"substrate/liquid\")\n", + "problem.show_priors()" + ] + }, + { + "cell_type": "markdown", + "id": "9cc56e51-3d52-460a-bbb1-6d68571887c6", + "metadata": {}, + "source": [ + "For a custom layers model, rather than being forced to define our layers as \\[Thick SLD Rough.... etc\\], we can parameterise however we like and then use a function to calculate the \\[d $\\rho$ $\\sigma$\\] arrangement for each layer. So for example, if the volume of lipid tails are known (from the literature), then all we need is the Area per molecule, because then:\n", + "\n", + "$$\n", + "d = \\frac{V}{APM},\n", + "$$\n", + "where d is the thickness and V is the volume.\n", + "\n", + "Likewise, the SLD is:\n", + "$$\n", + "\\rho = \\frac{\\sum_{i}n_{i}b_{i}}{V},\n", + "$$\n", + "\n", + "as usual.\n", + "\n", + "In this folder there is a pre-prepared Python custom model for a DSPC on a Silicon substrate. We can display it here to see what we mean:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9038b77f-e3fc-4946-87fe-af4addf8ee84", + "metadata": {}, + "outputs": [], + "source": [ + "Code(filename='custom_bilayer_DSPC.py', language='python')" + ] + }, + { + "cell_type": "markdown", + "id": "002b67c8-1091-4544-9325-58227a012e4e", + "metadata": {}, + "source": [ + "We need to add the parameters we are going to need to define the model (note that Substrate Roughness always exists as parameter 0 as before, and that we are setting a Gaussian prior on the Head Hydration here)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70494ef9-6cc5-47dc-9d02-6506645de46b", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_list = [\n", + " Parameter(name=\"Oxide Thickness\", min=5.0, value=20.0, max=60.0, fit=True),\n", + " Parameter(name=\"Oxide Hydration\", min=0.0, value=0.2, max=0.5, fit=True),\n", + " Parameter(name=\"Lipid APM\", min=45.0, value=55.0, max=65.0, fit=True),\n", + " Parameter(name=\"Head Hydration\", min=0.0, value=0.2, max=0.5, fit=True, prior_type='gaussian', mu=0.3, sigma=0.03),\n", + " Parameter(name=\"Bilayer Hydration\", min=0.0, value=0.1, max=0.2, fit=True),\n", + " Parameter(name=\"Bilayer Roughness\", min=2.0, value=4.0, max=8.0, fit=True),\n", + " Parameter(name=\"Water Thickness\", min=0.0, value=2.0, max=10.0, fit=True)\n", + "]\n", + "\n", + "problem.parameters.extend(parameter_list)\n", + "problem.parameters.set_fields(0, min=1.0, max=10.0)" + ] + }, + { + "cell_type": "markdown", + "id": "a11897b0-244b-46c2-8bcd-a3d65bd8fc5c", + "metadata": {}, + "source": [ + "Need to add the relevant Bulk SLD's. Change the bulk in from air to silicon, and add two additional water contrasts:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "453fe3d2-162a-42bb-91ee-b1d020ffd29e", + "metadata": {}, + "outputs": [], + "source": [ + "# Change the bulk in from air to silicon:\n", + "problem.bulk_in.set_fields(0, name=\"Silicon\", min=2.07e-6, value=2.073e-6, max=2.08e-6, fit=False)\n", + "\n", + "problem.bulk_out.append(name=\"SLD SMW\", min=1.0e-6, value=2.073e-6, max=3.0e-6, fit=True)\n", + "problem.bulk_out.append(name=\"SLD H2O\", min=-0.6e-6, value=-0.56e-6, max=-0.3e-6, fit=True)\n", + "\n", + "problem.bulk_out.set_fields(0, min=5.0e-6, fit=True)" + ] + }, + { + "cell_type": "markdown", + "id": "d767523b-70ab-42a9-b28f-cd013a8b177e", + "metadata": {}, + "source": [ + "Now add the datafiles. We have three datasets we need to consider - the bilayer against D2O, Silicon Matched water and H2O. Load these datafiles in and put them in the data block:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa4c1b96-3a1b-4aa6-8d61-68f24b0cb482", + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the datafiles\n", + "data_path = pathlib.Path(\"../data\")\n", + "D2O_data = np.loadtxt(data_path / \"c_PLP0016596.dat\", delimiter=\",\")\n", + "SMW_data = np.loadtxt(data_path / \"c_PLP0016601.dat\", delimiter=\",\")\n", + "H2O_data = np.loadtxt(data_path / \"c_PLP0016607.dat\", delimiter=\",\")\n", + "\n", + "# Add the data to the project - note this data has a resolution 4th column\n", + "problem.data.append(name=\"Bilayer / D2O\", data=D2O_data, data_range=[0.013, 0.37])\n", + "problem.data.append(name=\"Bilayer / SMW\", data=SMW_data, data_range=[0.013, 0.32996])\n", + "problem.data.append(name=\"Bilayer / H2O\", data=H2O_data, data_range=[0.013, 0.33048])" + ] + }, + { + "cell_type": "markdown", + "id": "e60cd052-54f9-41b4-ab8b-6d4dde1c50fa", + "metadata": {}, + "source": [ + "Add the custom file to the project:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e649c26-b32b-4c79-8ae7-fa701c87e6c2", + "metadata": {}, + "outputs": [], + "source": [ + "problem.custom_files.append(name=\"DSPC Model\", filename=\"custom_bilayer_DSPC.py\", language=\"python\", path=pathlib.Path.cwd().resolve())" + ] + }, + { + "cell_type": "markdown", + "id": "19a57f11-3d3c-49c5-b7a6-52bf449a3878", + "metadata": {}, + "source": [ + "Also, add the relevant background parameters - one each for each contrast:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d51954f-469a-4044-9a7d-1b6e30474a6b", + "metadata": {}, + "outputs": [], + "source": [ + "problem.background_parameters.set_fields(0, name=\"Background parameter D2O\", min=1.0e-10, max=1.0e-5, value=1.0e-07, fit=True)\n", + "\n", + "problem.background_parameters.append(name=\"Background parameter SMW\", min=1.0e-10, value=1.0e-7, max=1.0e-5, fit=True)\n", + "problem.background_parameters.append(name=\"Background parameter H2O\", min=1.0e-10, value=1.0e-7, max=1.0e-5, fit=True)\n", + "\n", + "# And add the two new constant backgrounds\n", + "problem.backgrounds.append(name=\"Background SMW\", type=\"constant\", source=\"Background parameter SMW\")\n", + "problem.backgrounds.append(name=\"Background H2O\", type=\"constant\", source=\"Background parameter H2O\")\n", + "\n", + "# And edit the other one\n", + "problem.backgrounds.set_fields(0, name=\"Background D2O\", source=\"Background parameter D2O\")\n", + "\n", + "# Finally modify some of the other parameters to be more suitable values for a solid / liquid experiment\n", + "problem.scalefactors.set_fields(0, value=1.0, min=0.5, max=2.0, fit=True)" + ] + }, + { + "cell_type": "markdown", + "id": "a69a6d51-202a-4834-a6be-5c30f67d9107", + "metadata": {}, + "source": [ + "We need to use the data resolution (i.e. the fourth column of our datafiles). Do do this, we need to add a 'Data' resolution object to our resolutions table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1e4d313-8450-459b-b60e-868fe82f06b0", + "metadata": {}, + "outputs": [], + "source": [ + "problem.resolutions.append(name=\"Data Resolution\", type=\"data\")" + ] + }, + { + "cell_type": "markdown", + "id": "ddde7088-1382-4f56-9e05-6f1683ec2260", + "metadata": {}, + "source": [ + "Now add the three contrasts as before:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "efc7b351-2112-40c4-862b-a47e4570d173", + "metadata": {}, + "outputs": [], + "source": [ + "problem.contrasts.append(\n", + " name=\"Bilayer / D2O\",\n", + " background=\"Background D2O\",\n", + " resolution=\"Data Resolution\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_out=\"SLD D2O\",\n", + " bulk_in=\"Silicon\",\n", + " data=\"Bilayer / D2O\",\n", + " model=[\"DSPC Model\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"Bilayer / SMW\",\n", + " background=\"Background SMW\",\n", + " resolution=\"Data Resolution\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_out=\"SLD SMW\",\n", + " bulk_in=\"Silicon\",\n", + " data=\"Bilayer / SMW\",\n", + " model=[\"DSPC Model\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"Bilayer / H2O\",\n", + " background=\"Background H2O\",\n", + " resolution=\"Data Resolution\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_out=\"SLD H2O\",\n", + " bulk_in=\"Silicon\",\n", + " data=\"Bilayer / H2O\",\n", + " model=[\"DSPC Model\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "89f110e4-c3f8-488d-91d5-4f5fb5fbe9d7", + "metadata": {}, + "source": [ + "Note that the model is simply the custom file we've just added to the project.\n", + "\n", + "Look at the complete model definition before sending it to RAT:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee889e55-8357-4363-860d-fb1c13bb8e8b", + "metadata": {}, + "outputs": [], + "source": [ + "print(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "861b6e03-773a-46c3-b3fd-0df47c99d27e", + "metadata": {}, + "source": [ + "To run it, we need to make a controls block" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "154a33df-06b9-4035-aa4c-a0e095c1bb06", + "metadata": {}, + "outputs": [], + "source": [ + "controls = RAT.Controls()\n", + "print(controls)" + ] + }, + { + "cell_type": "markdown", + "id": "384f0a34-1a2b-40f7-a945-6d44db9391ab", + "metadata": {}, + "source": [ + ". . . and send this to RAT" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5d9a782-0fb1-40b6-b1fa-86307abe32a6", + "metadata": {}, + "outputs": [], + "source": [ + "problem, results = RAT.run(problem, controls)\n", + "RAT.plotting.plot_ref_sld(problem, results)" + ] + } + ], + "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": "3.10.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/RATapi/examples/normal_reflectivity/DSPC_custom_layers.py b/ratapi/examples/normal_reflectivity/DSPC_custom_layers.py similarity index 98% rename from RATapi/examples/normal_reflectivity/DSPC_custom_layers.py rename to ratapi/examples/normal_reflectivity/DSPC_custom_layers.py index 66a53589..8de14903 100644 --- a/RATapi/examples/normal_reflectivity/DSPC_custom_layers.py +++ b/ratapi/examples/normal_reflectivity/DSPC_custom_layers.py @@ -4,7 +4,7 @@ import numpy as np -import RATapi as RAT +import ratapi as RAT def DSPC_custom_layers(): @@ -52,7 +52,7 @@ def DSPC_custom_layers(): name="DSPC Model", filename="custom_bilayer_DSPC.py", language="python", - path=pathlib.Path(__file__).parent.resolve(), + path=pathlib.Path(__file__).parent, ) # Also, add the relevant background parameters - one each for each contrast: diff --git a/RATapi/examples/normal_reflectivity/DSPC_custom_xy.ipynb b/ratapi/examples/normal_reflectivity/DSPC_custom_xy.ipynb similarity index 99% rename from RATapi/examples/normal_reflectivity/DSPC_custom_xy.ipynb rename to ratapi/examples/normal_reflectivity/DSPC_custom_xy.ipynb index 8925865c..0c0472c9 100644 --- a/RATapi/examples/normal_reflectivity/DSPC_custom_xy.ipynb +++ b/ratapi/examples/normal_reflectivity/DSPC_custom_xy.ipynb @@ -12,8 +12,8 @@ "import numpy as np\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT\n", - "from RATapi.models import Parameter" + "import ratapi as RAT\n", + "from ratapi.models import Parameter" ] }, { diff --git a/RATapi/examples/normal_reflectivity/DSPC_data_background.py b/ratapi/examples/normal_reflectivity/DSPC_data_background.py similarity index 99% rename from RATapi/examples/normal_reflectivity/DSPC_data_background.py rename to ratapi/examples/normal_reflectivity/DSPC_data_background.py index e4b6869f..11a14730 100644 --- a/RATapi/examples/normal_reflectivity/DSPC_data_background.py +++ b/ratapi/examples/normal_reflectivity/DSPC_data_background.py @@ -4,7 +4,7 @@ import numpy as np -import RATapi as RAT +import ratapi as RAT def DSPC_data_background(): diff --git a/RATapi/examples/normal_reflectivity/DSPC_function_background.py b/ratapi/examples/normal_reflectivity/DSPC_function_background.py similarity index 98% rename from RATapi/examples/normal_reflectivity/DSPC_function_background.py rename to ratapi/examples/normal_reflectivity/DSPC_function_background.py index 54cc9d47..e6a87b69 100644 --- a/RATapi/examples/normal_reflectivity/DSPC_function_background.py +++ b/ratapi/examples/normal_reflectivity/DSPC_function_background.py @@ -4,7 +4,7 @@ import numpy as np -import RATapi as RAT +import ratapi as RAT def DSPC_function_background(): @@ -148,7 +148,7 @@ def DSPC_function_background(): name="D2O Background Function", filename="background_function.py", language="python", - path=pathlib.Path(__file__).parent.resolve(), + path=pathlib.Path(__file__).parent, ) problem.background_parameters.append(name="Fn Ao", min=5e-7, value=8e-6, max=5e-5) diff --git a/RATapi/examples/normal_reflectivity/DSPC_standard_layers.ipynb b/ratapi/examples/normal_reflectivity/DSPC_standard_layers.ipynb similarity index 99% rename from RATapi/examples/normal_reflectivity/DSPC_standard_layers.ipynb rename to ratapi/examples/normal_reflectivity/DSPC_standard_layers.ipynb index 050b7497..3110cfe5 100644 --- a/RATapi/examples/normal_reflectivity/DSPC_standard_layers.ipynb +++ b/ratapi/examples/normal_reflectivity/DSPC_standard_layers.ipynb @@ -11,8 +11,8 @@ "\n", "import numpy as np\n", "\n", - "import RATapi as RAT\n", - "from RATapi.models import Layer, Parameter" + "import ratapi as RAT\n", + "from ratapi.models import Layer, Parameter" ] }, { diff --git a/RATapi/examples/normal_reflectivity/DSPC_standard_layers.py b/ratapi/examples/normal_reflectivity/DSPC_standard_layers.py similarity index 99% rename from RATapi/examples/normal_reflectivity/DSPC_standard_layers.py rename to ratapi/examples/normal_reflectivity/DSPC_standard_layers.py index f4cc2ce4..6962f036 100644 --- a/RATapi/examples/normal_reflectivity/DSPC_standard_layers.py +++ b/ratapi/examples/normal_reflectivity/DSPC_standard_layers.py @@ -4,7 +4,7 @@ import numpy as np -import RATapi as RAT +import ratapi as RAT def DSPC_standard_layers(): diff --git a/RATapi/examples/normal_reflectivity/__init__.py b/ratapi/examples/normal_reflectivity/__init__.py similarity index 100% rename from RATapi/examples/normal_reflectivity/__init__.py rename to ratapi/examples/normal_reflectivity/__init__.py diff --git a/RATapi/examples/normal_reflectivity/background_function.py b/ratapi/examples/normal_reflectivity/background_function.py similarity index 100% rename from RATapi/examples/normal_reflectivity/background_function.py rename to ratapi/examples/normal_reflectivity/background_function.py diff --git a/RATapi/examples/normal_reflectivity/custom_XY_DSPC.py b/ratapi/examples/normal_reflectivity/custom_XY_DSPC.py similarity index 80% rename from RATapi/examples/normal_reflectivity/custom_XY_DSPC.py rename to ratapi/examples/normal_reflectivity/custom_XY_DSPC.py index dc1d1013..c57adecf 100644 --- a/RATapi/examples/normal_reflectivity/custom_XY_DSPC.py +++ b/ratapi/examples/normal_reflectivity/custom_XY_DSPC.py @@ -1,12 +1,16 @@ """A custom XY model for a supported DSPC bilayer.""" -import math +from math import sqrt import numpy as np +from scipy.special import erf def custom_XY_DSPC(params, bulk_in, bulk_out, contrast): """Calculate the continuous SLD of a supported DSPC bilayer using volume restricted distribution functions.""" + # Note - The first contrast number is 1 (not 0) so be careful if you use + # this variable for array indexing. + # Split up the parameters subRough = params[0] oxideThick = params[1] @@ -51,10 +55,10 @@ def custom_XY_DSPC(params, bulk_in, bulk_out, contrast): z = np.arange(0, 141) # Make our Silicon substrate - vfSilicon, siSurf = layer(z, -25, 50, 1, subRough, subRough) + vfSilicon, siSurf = make_layer(z, -25, 50, 1, subRough, subRough) # Add the Oxide - vfOxide, oxSurface = layer(z, siSurf, oxideThick, 1, subRough, subRough) + vfOxide, oxSurface = make_layer(z, siSurf, oxideThick, 1, subRough, subRough) # We fill in the water at the end, but there may be a hydration layer between the bilayer and the oxide, # so we start the bilayer stack an appropriate distance away @@ -65,15 +69,15 @@ def custom_XY_DSPC(params, bulk_in, bulk_out, contrast): headThick = vHead / lipidAPM # ... and make a box for the volume fraction (1 for now, we correct for coverage later) - vfHeadL, headLSurface = layer(z, watSurface, headThick, 1, bilayerRough, bilayerRough) + vfHeadL, headLSurface = make_layer(z, watSurface, headThick, 1, bilayerRough, bilayerRough) # ... also do the same for the tails # We'll make both together, so the thickness will be twice the volume tailsThick = (2 * vTail) / lipidAPM - vfTails, tailsSurf = layer(z, headLSurface, tailsThick, 1, bilayerRough, bilayerRough) + vfTails, tailsSurf = make_layer(z, headLSurface, tailsThick, 1, bilayerRough, bilayerRough) # Finally the upper head ... - vfHeadR, headSurface = layer(z, tailsSurf, headThick, 1, bilayerRough, bilayerRough) + vfHeadR, headSurface = make_layer(z, tailsSurf, headThick, 1, bilayerRough, bilayerRough) # Making the model # We've created the volume fraction profiles corresponding to each of the groups. @@ -108,18 +112,18 @@ def custom_XY_DSPC(params, bulk_in, bulk_out, contrast): sldHeadL = vfHeadL * sld_Value_Head sldHeadR = vfHeadR * sld_Value_Head sldTails = vfTails * sld_Value_Tails - sldWat = vfWat * bulk_out[contrast] + sldWat = vfWat * bulk_out[contrast - 1] # Put this all together totSLD = sldSilicon + sldOxide + sldHeadL + sldTails + sldHeadR + sldWat # Make the SLD array for output - SLD = [[a, b] for (a, b) in zip(z, totSLD)] + SLD = np.column_stack((z, totSLD)) return SLD, subRough -def layer(z, prevLaySurf, thickness, height, Sigma_L, Sigma_R): +def make_layer(z, prevLaySurf, thickness, height, Sigma_L, Sigma_R): """Produce a layer, with a defined thickness, height and roughness. Each side of the layer has its own roughness value. @@ -129,12 +133,9 @@ def layer(z, prevLaySurf, thickness, height, Sigma_L, Sigma_R): right = prevLaySurf + thickness # Make our heaviside - a = (z - left) / ((2**0.5) * Sigma_L) - b = (z - right) / ((2**0.5) * Sigma_R) - - erf_a = np.array([math.erf(value) for value in a]) - erf_b = np.array([math.erf(value) for value in b]) + erf_left = erf((z - left) / (sqrt(2) * Sigma_L)) + erf_right = erf((z - right) / (sqrt(2) * Sigma_R)) - VF = np.array((height / 2) * (erf_a - erf_b)) + VF = np.array((0.5 * height) * (erf_left - erf_right)) return VF, right diff --git a/RATapi/examples/normal_reflectivity/custom_bilayer_DSPC.py b/ratapi/examples/normal_reflectivity/custom_bilayer_DSPC.py similarity index 84% rename from RATapi/examples/normal_reflectivity/custom_bilayer_DSPC.py rename to ratapi/examples/normal_reflectivity/custom_bilayer_DSPC.py index 005147ff..8646de71 100644 --- a/RATapi/examples/normal_reflectivity/custom_bilayer_DSPC.py +++ b/ratapi/examples/normal_reflectivity/custom_bilayer_DSPC.py @@ -25,6 +25,8 @@ def custom_bilayer_DSPC(params, bulk_in, bulk_out, contrast): The second output parameter should be the substrate roughness. """ + # Note - The first contrast number is 1 (not 0) so be careful if you use + # this variable for array indexing. sub_rough = params[0] oxide_thick = params[1] oxide_hydration = params[2] @@ -72,13 +74,13 @@ def custom_bilayer_DSPC(params, bulk_in, bulk_out, contrast): tailThick = vTail / lipidAPM # Manually deal with hydration for layers in this example. - oxSLD = (oxide_hydration * bulk_out[contrast]) + ((1 - oxide_hydration) * oxide_SLD) - headSLD = (headHydration * bulk_out[contrast]) + ((1 - headHydration) * SLDhead) - tailSLD = (bilayerHydration * bulk_out[contrast]) + ((1 - bilayerHydration) * SLDtail) + oxSLD = (oxide_hydration * bulk_out[contrast - 1]) + ((1 - oxide_hydration) * oxide_SLD) + headSLD = (headHydration * bulk_out[contrast - 1]) + ((1 - headHydration) * SLDhead) + tailSLD = (bilayerHydration * bulk_out[contrast - 1]) + ((1 - bilayerHydration) * SLDtail) # Make the layers oxide = [oxide_thick, oxSLD, sub_rough] - water = [waterThick, bulk_out[contrast], bilayerRough] + water = [waterThick, bulk_out[contrast - 1], bilayerRough] head = [headThick, headSLD, bilayerRough] tail = [tailThick, tailSLD, bilayerRough] diff --git a/RATapi/examples/orso_integration/orso_integration.ipynb b/ratapi/examples/orso_integration/orso_integration.ipynb similarity index 90% rename from RATapi/examples/orso_integration/orso_integration.ipynb rename to ratapi/examples/orso_integration/orso_integration.ipynb index aa6f0797..03ec671e 100644 --- a/RATapi/examples/orso_integration/orso_integration.ipynb +++ b/ratapi/examples/orso_integration/orso_integration.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "``python-RAT`` contains some integration with ``orsopy``, allowing for convenient interaction with the ``.ort`` file format. This integration is available through the `RATapi.utils.orso` submodule." + "``python-RAT`` contains some integration with ``orsopy``, allowing for convenient interaction with the ``.ort`` file format. This integration is available through the `ratapi.utils.orso` submodule." ] }, { @@ -20,7 +20,7 @@ "metadata": {}, "outputs": [], "source": [ - "import RATapi.utils.orso" + "import ratapi.utils.orso" ] }, { @@ -31,7 +31,7 @@ "\n", "The [ORSO model description format](https://www.reflectometry.org/advanced_and_expert_level/file_format/simple_model) allows the description of a standard slab model as a one-line string, provided that all the layer materials are defined [in the ORSO SLD database](https://slddb.esss.dk/slddb/).\n", "\n", - "The function `RATapi.utils.orso.orso_model_to_rat` function can read a model and return an `ORSOSample` dataclass, which gives bulk in and bulk out parameters for the model, a list of all layers defined in the model, and all the parameters needed to define those layers as RAT models. \n", + "The function `ratapi.utils.orso.orso_model_to_rat` function can read a model and return an `ORSOSample` dataclass, which gives bulk in and bulk out parameters for the model, a list of all layers defined in the model, and all the parameters needed to define those layers as RAT models. \n", "\n", "**Note:** the ORSO format gives the thicknesses of materials in *nanometres*. When we convert them to RAT parameters, the units will be converted to Angstroms.\n", "\n", @@ -45,7 +45,7 @@ "outputs": [], "source": [ "# create the RAT parameters and layers from this model\n", - "sample = RATapi.utils.orso.orso_model_to_rat(\"air | Ni 100 | SiO2 0.5 | Si\")\n", + "sample = ratapi.utils.orso.orso_model_to_rat(\"air | Ni 100 | SiO2 0.5 | Si\")\n", "print(sample)" ] }, @@ -62,7 +62,7 @@ "metadata": {}, "outputs": [], "source": [ - "sample = RATapi.utils.orso.orso_model_to_rat(\"vacuum | B4C 100 | SiO2 0.5 | Si\", absorption=True)\n", + "sample = ratapi.utils.orso.orso_model_to_rat(\"vacuum | B4C 100 | SiO2 0.5 | Si\", absorption=True)\n", "print(sample)" ] }, @@ -81,7 +81,7 @@ "metadata": {}, "outputs": [], "source": [ - "sample = RATapi.utils.orso.orso_model_to_rat(\"air | 5 ( Si 7 | Fe 7 ) | Si\")\n", + "sample = ratapi.utils.orso.orso_model_to_rat(\"air | 5 ( Si 7 | Fe 7 ) | Si\")\n", "print(sample)" ] }, @@ -110,7 +110,7 @@ "import pathlib\n", "data_path = pathlib.Path(\"../data\")\n", "\n", - "orso_data = RATapi.utils.orso.ORSOProject(data_path / \"c_PLP0011859_q.ort\")\n", + "orso_data = ratapi.utils.orso.ORSOProject(data_path / \"c_PLP0011859_q.ort\")\n", "print(orso_data)" ] }, @@ -129,12 +129,12 @@ "metadata": {}, "outputs": [], "source": [ - "from RATapi.models import Background, Contrast, Parameter, Resolution\n", + "from ratapi.models import Background, Contrast, Parameter, Resolution\n", "\n", "dataset = orso_data.data[0]\n", "sample = orso_data.samples[0]\n", "\n", - "project = RATapi.Project(\n", + "project = ratapi.Project(\n", " name = \"Example Project\",\n", " geometry = \"substrate/liquid\",\n", " parameters = sample.parameters,\n", @@ -158,9 +158,9 @@ " )]\n", ")\n", "\n", - "controls = RATapi.Controls()\n", - "project, results = RATapi.run(project, controls)\n", - "RATapi.plotting.plot_ref_sld(project, results)" + "controls = ratapi.Controls()\n", + "project, results = ratapi.run(project, controls)\n", + "ratapi.plotting.plot_ref_sld(project, results)" ] } ], diff --git a/RATapi/inputs.py b/ratapi/inputs.py similarity index 91% rename from RATapi/inputs.py rename to ratapi/inputs.py index 757aa78a..bdfaddad 100644 --- a/RATapi/inputs.py +++ b/ratapi/inputs.py @@ -3,15 +3,14 @@ import importlib import os import pathlib -from typing import Callable, Union +from collections.abc import Callable import numpy as np -import RATapi -import RATapi.controls -import RATapi.wrappers -from RATapi.rat_core import Checks, Control, NameStore, ProblemDefinition -from RATapi.utils.enums import Calculations, Languages, LayerModels, TypeOptions +import ratapi +import ratapi.wrappers +from ratapi.rat_core import Checks, Control, NameStore, ProblemDefinition +from ratapi.utils.enums import Calculations, Languages, LayerModels, TypeOptions parameter_field = { "parameters": "params", @@ -24,7 +23,7 @@ } -def get_python_handle(file_name: str, function_name: str, path: Union[str, pathlib.Path] = "") -> Callable: +def get_python_handle(file_name: str, function_name: str, path: str | pathlib.Path = "") -> Callable: """Get the function handle from a function defined in a python module located anywhere within the filesystem. Parameters @@ -78,12 +77,19 @@ def get_handle(self, index: int): """ custom_file = self.files[index] full_path = os.path.join(custom_file["path"], custom_file["filename"]) + + if not os.path.isfile(full_path): + raise FileNotFoundError(f"The custom file ({custom_file['name']}) does not have a valid path.") + + if not custom_file["function_name"] and custom_file["language"] != Languages.Matlab: + raise ValueError(f"The custom file ({custom_file['name']}) does not have a valid function name.") + if custom_file["language"] == Languages.Python: file_handle = get_python_handle(custom_file["filename"], custom_file["function_name"], custom_file["path"]) elif custom_file["language"] == Languages.Matlab: - file_handle = RATapi.wrappers.MatlabWrapper(full_path).getHandle() + file_handle = ratapi.wrappers.MatlabWrapper(full_path).getHandle() elif custom_file["language"] == Languages.Cpp: - file_handle = RATapi.wrappers.DylibWrapper(full_path, custom_file["function_name"]).getHandle() + file_handle = ratapi.wrappers.DylibWrapper(full_path, custom_file["function_name"]).getHandle() return file_handle @@ -113,7 +119,7 @@ def __len__(self): return len(self.files) -def make_input(project: RATapi.Project, controls: RATapi.Controls) -> tuple[ProblemDefinition, Control]: +def make_input(project: ratapi.Project, controls: ratapi.Controls) -> tuple[ProblemDefinition, Control]: """Construct the inputs required for the compiled RAT code using the data defined in the input project and controls. Parameters @@ -137,7 +143,7 @@ def make_input(project: RATapi.Project, controls: RATapi.Controls) -> tuple[Prob return problem, cpp_controls -def make_problem(project: RATapi.Project) -> ProblemDefinition: +def make_problem(project: ratapi.Project) -> ProblemDefinition: """Construct the problem input required for the compiled RAT code. Parameters @@ -174,12 +180,12 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition: raise ValueError( f"All backgrounds must have a source defined. For a {background.type} type background, " f"the source must be defined in " - f'"{RATapi.project.values_defined_in[f"backgrounds.{background.type}.source"]}"' + f'"{ratapi.project.values_defined_in[f"backgrounds.{background.type}.source"]}"' ) if resolution.source == "" and resolution.type != TypeOptions.Data: raise ValueError( f"Constant resolutions must have a source defined. The source must be defined in " - f'"{RATapi.project.values_defined_in[f"resolutions.{resolution.type}.source"]}"' + f'"{ratapi.project.values_defined_in[f"resolutions.{resolution.type}.source"]}"' ) # Set contrast parameters according to model type @@ -303,7 +309,7 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition: problem.numberOfContrasts = len(project.contrasts) problem.geometry = project.geometry problem.useImaginary = project.absorption - problem.repeatLayers = [1] * len(project.contrasts) + problem.repeatLayers = [contrast.repeat_layers for contrast in project.contrasts] problem.contrastBackgroundParams = contrast_background_params problem.contrastBackgroundTypes = contrast_background_types problem.contrastBackgroundActions = [contrast.background_action for contrast in project.contrasts] @@ -347,34 +353,34 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition: ] problem.fitParams = [ param.value - for class_list in RATapi.project.parameter_class_lists + for class_list in ratapi.project.parameter_class_lists for param in getattr(project, class_list) if param.fit ] problem.fitLimits = [ [param.min, param.max] - for class_list in RATapi.project.parameter_class_lists + for class_list in ratapi.project.parameter_class_lists for param in getattr(project, class_list) if param.fit ] problem.priorNames = [ - param.name for class_list in RATapi.project.parameter_class_lists for param in getattr(project, class_list) + param.name for class_list in ratapi.project.parameter_class_lists for param in getattr(project, class_list) ] problem.priorValues = [ [prior_id[param.prior_type], param.mu, param.sigma] - for class_list in RATapi.project.parameter_class_lists + for class_list in ratapi.project.parameter_class_lists for param in getattr(project, class_list) ] # Names problem.names = NameStore() - for class_list in RATapi.project.parameter_class_lists: + for class_list in ratapi.project.parameter_class_lists: setattr(problem.names, parameter_field[class_list], [param.name for param in getattr(project, class_list)]) problem.names.contrasts = [contrast.name for contrast in project.contrasts] # Checks problem.checks = Checks() - for class_list in RATapi.project.parameter_class_lists: + for class_list in ratapi.project.parameter_class_lists: setattr( problem.checks, parameter_field[class_list], [int(element.fit) for element in getattr(project, class_list)] ) @@ -384,16 +390,16 @@ def make_problem(project: RATapi.Project) -> ProblemDefinition: return problem -def get_layer_details(project: RATapi.Project) -> list[int]: +def get_layer_details(project: ratapi.Project) -> list[int]: """Get parameter indices for all layers defined in the project.""" - hydrate_id = {"bulk in": 1, "bulk out": 2} + hydrate_id = {"bulk in": 0, "bulk out": 1} layer_details = [] # Get the thickness, SLD, roughness fields from the appropriate model if project.absorption: - layer_fields = list(RATapi.models.AbsorptionLayer.model_fields.keys())[1:-2] + layer_fields = list(ratapi.models.AbsorptionLayer.model_fields.keys())[1:-2] else: - layer_fields = list(RATapi.models.Layer.model_fields.keys())[1:-2] + layer_fields = list(ratapi.models.Layer.model_fields.keys())[1:-2] for layer in project.layers: for field in layer_fields: @@ -413,7 +419,7 @@ def get_layer_details(project: RATapi.Project) -> list[int]: return layer_details -def make_resample(project: RATapi.Project) -> list[int]: +def make_resample(project: ratapi.Project) -> list[int]: """Construct the "resample" field of the problem input required for the compiled RAT code. Parameters @@ -430,7 +436,7 @@ def make_resample(project: RATapi.Project) -> list[int]: return [contrast.resample for contrast in project.contrasts] -def make_data_present(project: RATapi.Project) -> list[int]: +def make_data_present(project: ratapi.Project) -> list[int]: """Construct the "dataPresent" field of the problem input required for the compiled RAT code. Parameters @@ -535,7 +541,7 @@ def append_data_background(data: np.array, background: np.array) -> np.array: return np.hstack((data, np.zeros((data.shape[0], 4 - data.shape[1])), background[:, 1:])) -def make_controls(input_controls: RATapi.Controls) -> Control: +def make_controls(input_controls: ratapi.Controls) -> Control: """Convert the controls object to the format required by the compiled RAT code. Parameters @@ -553,7 +559,7 @@ def make_controls(input_controls: RATapi.Controls) -> Control: controls.procedure = input_controls.procedure controls.parallel = input_controls.parallel - controls.calcSldDuringFit = input_controls.calcSldDuringFit + controls.numSimulationPoints = input_controls.numSimulationPoints controls.resampleMinAngle = input_controls.resampleMinAngle controls.resampleNPoints = input_controls.resampleNPoints controls.display = input_controls.display @@ -583,8 +589,6 @@ def make_controls(input_controls: RATapi.Controls) -> Control: controls.pUnitGamma = input_controls.pUnitGamma controls.boundHandling = input_controls.boundHandling controls.adaptPCR = input_controls.adaptPCR - # IPC - controls.IPCFilePath = "" controls.IPCFilePath = input_controls._IPCFilePath diff --git a/RATapi/models.py b/ratapi/models.py similarity index 88% rename from RATapi/models.py rename to ratapi/models.py index af557419..ba0a1e99 100644 --- a/RATapi/models.py +++ b/ratapi/models.py @@ -2,14 +2,14 @@ import pathlib import warnings -from itertools import count +from contextlib import suppress from typing import Any import numpy as np import prettytable from pydantic import BaseModel, Field, ValidationInfo, field_validator, model_validator -from RATapi.utils.enums import BackgroundActions, Hydration, Languages, Priors, TypeOptions +from ratapi.utils.enums import BackgroundActions, Hydration, Languages, Priors, TypeOptions try: from enum import StrEnum @@ -18,14 +18,41 @@ # Create a counter for each model -background_number = count(1) -contrast_number = count(1) -custom_file_number = count(1) -data_number = count(1) -domain_contrast_number = count(1) -layer_number = count(1) -parameter_number = count(1) -resolution_number = count(1) +background_number = ["Background", 0] +contrast_number = ["Contrast", 0] +custom_file_number = ["Custom File", 0] +data_number = ["Data", 0] +domain_contrast_number = ["Domain Contrast", 0] +layer_number = ["Layer", 0] +parameter_number = ["Parameter", 0] +resolution_number = ["Resolution", 0] + +_model_counter = { + "Background": background_number, + "Contrast": contrast_number, + "ContrastWithRatio": contrast_number, + "CustomFile": custom_file_number, + "Data": data_number, + "DomainContrast": domain_contrast_number, + "Layer": layer_number, + "AbsorptionLayer": layer_number, + "Parameter": parameter_number, + "ProtectedParameter": parameter_number, + "Resolution": resolution_number, +} + + +def _model_name_factory(model_name: str) -> str: + """Generate a unique name for model using a global counter. + + Parameters + ---------- + model_name : str + The name of the model class. + """ + title, number = _model_counter[model_name] + _model_counter[model_name][1] += 1 + return f"New {title} {(number + 1)}" class RATModel(BaseModel, validate_assignment=True, extra="forbid"): @@ -38,6 +65,25 @@ def __repr__(self): ) return f"{self.__repr_name__()}({fields_repr})" + @field_validator("name", mode="after", check_fields=False) + @classmethod + def update_counter(cls, name: str) -> str: + """Update the auto name counter if a similar name is manually given. + + Parameters + ---------- + name : str + The name of the model. + """ + title, number = _model_counter[cls.__name__] + prefix = f"New {title} " + if name.startswith(prefix): + with suppress(ValueError): + new_number = int(name[len(prefix) :]) + if new_number > number: + _model_counter[cls.__name__][1] = new_number + return name + def __str__(self): table = prettytable.PrettyTable() table.field_names = [key.replace("_", " ") for key in self.display_fields] @@ -116,7 +162,7 @@ class Background(Signal): """ - name: str = Field(default_factory=lambda: f"New Background {next(background_number)}", min_length=1) + name: str = Field(default_factory=lambda: _model_name_factory("Background"), min_length=1) @model_validator(mode="after") def check_unsupported_parameters(self): @@ -163,6 +209,8 @@ class Contrast(RATModel): The name of the instrument resolution for this contrast. resample : bool Whether adaptive resampling should be used for interface microslicing. + repeat_layers : int + For standard layers, the number of times the set of layers defined in the model should be repeated. model : list[str] If this is a standard layers model, this should be a list of layer names that make up the slab model for this contrast. @@ -171,7 +219,7 @@ class Contrast(RATModel): """ - name: str = Field(default_factory=lambda: f"New Contrast {next(contrast_number)}", min_length=1) + name: str = Field(default_factory=lambda: _model_name_factory("Contrast"), min_length=1) data: str = "" background: str = "" background_action: BackgroundActions = BackgroundActions.Add @@ -180,6 +228,7 @@ class Contrast(RATModel): scalefactor: str = "" resolution: str = "" resample: bool = False + repeat_layers: int = Field(default=1, gt=0) model: list[str] = [] @model_validator(mode="before") @@ -208,6 +257,7 @@ def __str__(self): self.scalefactor, self.resolution, self.resample, + self.repeat_layers, model_entry, ] ) @@ -238,6 +288,8 @@ class ContrastWithRatio(RATModel): The name of the instrument resolution for this contrast. resample : bool Whether adaptive resampling should be used for interface microslicing. + repeat_layers : int + For standard layers, the number of times the set of layers defined in the model should be repeated. domain_ratio : str The name of the domain ratio parameter describing how the first domain should be weighted relative to the second. @@ -249,7 +301,7 @@ class ContrastWithRatio(RATModel): """ - name: str = Field(default_factory=lambda: f"New Contrast {next(contrast_number)}", min_length=1) + name: str = Field(default_factory=lambda: _model_name_factory("ContrastWithRatio"), min_length=1) data: str = "" background: str = "" background_action: BackgroundActions = BackgroundActions.Add @@ -258,6 +310,7 @@ class ContrastWithRatio(RATModel): scalefactor: str = "" resolution: str = "" resample: bool = False + repeat_layers: int = Field(default=1, gt=0) domain_ratio: str = "" model: list[str] = [] @@ -276,6 +329,8 @@ def __str__(self): self.scalefactor, self.resolution, self.resample, + self.repeat_layers, + self.domain_ratio, model_entry, ] ) @@ -300,7 +355,7 @@ class CustomFile(RATModel): """ - name: str = Field(default_factory=lambda: f"New Custom File {next(custom_file_number)}", min_length=1) + name: str = Field(default_factory=lambda: _model_name_factory("CustomFile"), min_length=1) filename: str = "" function_name: str = "" language: Languages = Languages.Python @@ -339,7 +394,7 @@ class Data(RATModel, arbitrary_types_allowed=True): """ - name: str = Field(default_factory=lambda: f"New Data {next(data_number)}", min_length=1) + name: str = Field(default_factory=lambda: _model_name_factory("Data"), min_length=1) data: np.ndarray = np.empty([0, 3]) data_range: list[float] = Field(default=[], min_length=2, max_length=2) simulation_range: list[float] = Field(default=[], min_length=2, max_length=2) @@ -444,7 +499,7 @@ class DomainContrast(RATModel): """ - name: str = Field(default_factory=lambda: f"New Domain Contrast {next(domain_contrast_number)}", min_length=1) + name: str = Field(default_factory=lambda: _model_name_factory("DomainContrast"), min_length=1) model: list[str] = [] def __str__(self): @@ -474,7 +529,7 @@ class Layer(RATModel, populate_by_name=True): """ - name: str = Field(default_factory=lambda: f"New Layer {next(layer_number)}", min_length=1) + name: str = Field(default_factory=lambda: _model_name_factory("Layer"), min_length=1) thickness: str SLD: str = Field(validation_alias="SLD_real") roughness: str @@ -513,7 +568,7 @@ class AbsorptionLayer(RATModel, populate_by_name=True): """ - name: str = Field(default_factory=lambda: f"New Layer {next(layer_number)}", min_length=1) + name: str = Field(default_factory=lambda: _model_name_factory("AbsorptionLayer"), min_length=1) thickness: str SLD_real: str = Field(validation_alias="SLD") SLD_imaginary: str @@ -546,7 +601,7 @@ class Parameter(RATModel): """ - name: str = Field(default_factory=lambda: f"New Parameter {next(parameter_number)}", min_length=1) + name: str = Field(default_factory=lambda: _model_name_factory("Parameter"), min_length=1) min: float = 0.0 value: float = 0.0 max: float = 0.0 @@ -629,7 +684,7 @@ class Resolution(Signal): """ - name: str = Field(default_factory=lambda: f"New Resolution {next(resolution_number)}", min_length=1) + name: str = Field(default_factory=lambda: _model_name_factory("Resolution"), min_length=1) @field_validator("type") @classmethod diff --git a/RATapi/outputs.py b/ratapi/outputs.py similarity index 96% rename from RATapi/outputs.py rename to ratapi/outputs.py index e4c54c34..c1f5c0ed 100644 --- a/RATapi/outputs.py +++ b/ratapi/outputs.py @@ -3,12 +3,12 @@ import json from dataclasses import dataclass from pathlib import Path -from typing import Any, Optional, Union +from typing import Any, Union import numpy as np -import RATapi.rat_core -from RATapi.utils.enums import Procedures +import ratapi.rat_core +from ratapi.utils.enums import Procedures bayes_results_subclasses = [ "predictionIntervals", @@ -244,7 +244,7 @@ def __str__(self): output += get_field_string(key, value, 100) return output - def save(self, filepath: Union[str, Path] = "./results.json"): + def save(self, filepath: str | Path = "./results.json"): """Save the Results object to a JSON file. Parameters @@ -258,7 +258,7 @@ def save(self, filepath: Union[str, Path] = "./results.json"): filepath.write_text(json.dumps(json_dict)) @classmethod - def load(cls, path: Union[str, Path]) -> Union["Results", "BayesResults"]: + def load(cls, path: str | Path) -> Union["Results", "BayesResults"]: """Load a Results object from file. Parameters @@ -538,7 +538,20 @@ class BayesResults(Results): nestedSamplerOutput: NestedSamplerOutput chain: np.ndarray - def save(self, filepath: Union[str, Path] = "./results.json"): + def from_procedure(self) -> Procedures: + """Return the procedure that created the result. + + Returns + ------- + procedure: Procedures + The procedure that created the result. + """ + samples = self.nestedSamplerOutput.nestSamples + if samples.shape == (1, 2) and not np.any(samples): + return Procedures.DREAM + return Procedures.NS + + def save(self, filepath: str | Path = "./results.json"): """Save the BayesResults object to a JSON file. Parameters @@ -574,7 +587,7 @@ def save(self, filepath: Union[str, Path] = "./results.json"): filepath.write_text(json.dumps(json_dict)) -def write_core_results_fields(results: Union[Results, BayesResults], json_dict: Optional[dict] = None) -> dict: +def write_core_results_fields(results: Results | BayesResults, json_dict: dict | None = None) -> dict: """Modify the values of the fields that appear in both Results and BayesResults when saving to a json file. Parameters @@ -683,18 +696,18 @@ def read_bayes_results_fields(results_dict: dict) -> dict: def make_results( procedure: Procedures, - output_results: RATapi.rat_core.OutputResult, - bayes_results: Optional[RATapi.rat_core.OutputBayesResult] = None, -) -> Union[Results, BayesResults]: + output_results: ratapi.rat_core.OutputResult, + bayes_results: ratapi.rat_core.OutputBayesResult | None = None, +) -> Results | BayesResults: """Initialise a python Results or BayesResults object using the outputs from a RAT calculation. Parameters ---------- procedure : Procedures The procedure used by the calculation. - output_results : RATapi.rat_core.OutputResult + output_results : ratapi.rat_core.OutputResult The C++ output results from the calculation. - bayes_results : Optional[RATapi.rat_core.OutputBayesResult] + bayes_results : Optional[ratapi.rat_core.OutputBayesResult] The optional extra C++ Bayesian output results from a Bayesian calculation. Returns diff --git a/RATapi/project.py b/ratapi/project.py similarity index 87% rename from RATapi/project.py rename to ratapi/project.py index c35e92a6..b8ca49f2 100644 --- a/RATapi/project.py +++ b/ratapi/project.py @@ -5,10 +5,11 @@ import functools import json import warnings +from collections.abc import Callable from enum import Enum from pathlib import Path from textwrap import indent -from typing import Annotated, Any, Callable, Union +from typing import Annotated, Any, get_args, get_origin import numpy as np from pydantic import ( @@ -21,12 +22,11 @@ field_validator, model_validator, ) -from typing_extensions import get_args, get_origin -import RATapi.models -from RATapi.classlist import ClassList -from RATapi.utils.custom_errors import custom_pydantic_validation_error -from RATapi.utils.enums import Calculations, Geometries, LayerModels, Priors, TypeOptions +import ratapi.models +from ratapi.classlist import ClassList +from ratapi.utils.custom_errors import custom_pydantic_validation_error +from ratapi.utils.enums import Calculations, Geometries, LayerModels, Priors, TypeOptions # note for these discriminators that the before-validator discriminate_ambiguous_dicts @@ -35,7 +35,7 @@ def discriminate_layers(layer_input): """Union discriminator for layers.""" if isinstance(layer_input, collections.abc.Sequence): # if classlist is empty, just label it as no absorption and it'll get fixed in post_init - if len(layer_input) > 0 and isinstance(layer_input[0], RATapi.models.AbsorptionLayer): + if len(layer_input) > 0 and isinstance(layer_input[0], ratapi.models.AbsorptionLayer): return "abs" return "no_abs" @@ -44,7 +44,7 @@ def discriminate_contrasts(contrast_input): """Union discriminator for contrasts.""" if isinstance(contrast_input, collections.abc.Sequence): # if classlist is empty, just label it as no ratio and it'll get fixed in post_init - if len(contrast_input) > 0 and isinstance(contrast_input[0], RATapi.models.ContrastWithRatio): + if len(contrast_input) > 0 and isinstance(contrast_input[0], ratapi.models.ContrastWithRatio): return "ratio" return "no_ratio" @@ -145,11 +145,11 @@ class Project(BaseModel, validate_assignment=True, extra="forbid", use_attribute absorption: bool = False """Whether imaginary SLD (absorption) should be accounted for.""" - parameters: ClassList[RATapi.models.Parameter] = ClassList() + parameters: ClassList[ratapi.models.Parameter] = ClassList() """The list of parameters used in the layers of a model.""" - bulk_in: ClassList[RATapi.models.Parameter] = ClassList( - RATapi.models.Parameter( + bulk_in: ClassList[ratapi.models.Parameter] = ClassList( + ratapi.models.Parameter( name="SLD Air", min=0.0, value=0.0, @@ -162,8 +162,8 @@ class Project(BaseModel, validate_assignment=True, extra="forbid", use_attribute ) """The list of parameters for SLD of the entry interfaces of a model.""" - bulk_out: ClassList[RATapi.models.Parameter] = ClassList( - RATapi.models.Parameter( + bulk_out: ClassList[ratapi.models.Parameter] = ClassList( + ratapi.models.Parameter( name="SLD D2O", min=6.2e-6, value=6.35e-6, @@ -176,8 +176,8 @@ class Project(BaseModel, validate_assignment=True, extra="forbid", use_attribute ) """The list of parameters for SLD of the exit interfaces of a model.""" - scalefactors: ClassList[RATapi.models.Parameter] = ClassList( - RATapi.models.Parameter( + scalefactors: ClassList[ratapi.models.Parameter] = ClassList( + ratapi.models.Parameter( name="Scalefactor 1", min=0.02, value=0.23, @@ -190,8 +190,8 @@ class Project(BaseModel, validate_assignment=True, extra="forbid", use_attribute ) """The list of parameters for scale factors to handle systematic error in model data.""" - domain_ratios: ClassList[RATapi.models.Parameter] = ClassList( - RATapi.models.Parameter( + domain_ratios: ClassList[ratapi.models.Parameter] = ClassList( + ratapi.models.Parameter( name="Domain Ratio 1", min=0.4, value=0.5, @@ -204,8 +204,8 @@ class Project(BaseModel, validate_assignment=True, extra="forbid", use_attribute ) """The list of parameters for weighting between domains of a domains model.""" - background_parameters: ClassList[RATapi.models.Parameter] = ClassList( - RATapi.models.Parameter( + background_parameters: ClassList[ratapi.models.Parameter] = ClassList( + ratapi.models.Parameter( name="Background Param 1", min=1e-7, value=1e-6, @@ -218,13 +218,13 @@ class Project(BaseModel, validate_assignment=True, extra="forbid", use_attribute ) """The list of parameters for models of backgrounds.""" - backgrounds: ClassList[RATapi.models.Background] = ClassList( - RATapi.models.Background(name="Background 1", type=TypeOptions.Constant, source="Background Param 1"), + backgrounds: ClassList[ratapi.models.Background] = ClassList( + ratapi.models.Background(name="Background 1", type=TypeOptions.Constant, source="Background Param 1"), ) """The list of models for background noise in the project.""" - resolution_parameters: ClassList[RATapi.models.Parameter] = ClassList( - RATapi.models.Parameter( + resolution_parameters: ClassList[ratapi.models.Parameter] = ClassList( + ratapi.models.Parameter( name="Resolution Param 1", min=0.01, value=0.03, @@ -237,21 +237,21 @@ class Project(BaseModel, validate_assignment=True, extra="forbid", use_attribute ) """The list of parameters for models of resolutions.""" - resolutions: ClassList[RATapi.models.Resolution] = ClassList( - RATapi.models.Resolution(name="Resolution 1", type=TypeOptions.Constant, source="Resolution Param 1"), + resolutions: ClassList[ratapi.models.Resolution] = ClassList( + ratapi.models.Resolution(name="Resolution 1", type=TypeOptions.Constant, source="Resolution Param 1"), ) """The list of models for instrument resolution in the project.""" - custom_files: ClassList[RATapi.models.CustomFile] = ClassList() + custom_files: ClassList[ratapi.models.CustomFile] = ClassList() """Handles for custom files used by the project.""" - data: ClassList[RATapi.models.Data] = ClassList() + data: ClassList[ratapi.models.Data] = ClassList() """Experimental data for a model.""" - layers: Union[ - Annotated[ClassList[RATapi.models.Layer], Tag("no_abs")], - Annotated[ClassList[RATapi.models.AbsorptionLayer], Tag("abs")], - ] = Field( + layers: ( + Annotated[ClassList[ratapi.models.Layer], Tag("no_abs")] + | Annotated[ClassList[ratapi.models.AbsorptionLayer], Tag("abs")] + ) = Field( default=ClassList(), discriminator=Discriminator( discriminate_layers, @@ -262,13 +262,13 @@ class Project(BaseModel, validate_assignment=True, extra="forbid", use_attribute ) """The layers of a standard layer model.""" - domain_contrasts: ClassList[RATapi.models.DomainContrast] = ClassList() + domain_contrasts: ClassList[ratapi.models.DomainContrast] = ClassList() """The groups of layers required by each domain in a domains model.""" - contrasts: Union[ - Annotated[ClassList[RATapi.models.Contrast], Tag("no_ratio")], - Annotated[ClassList[RATapi.models.ContrastWithRatio], Tag("ratio")], - ] = Field( + contrasts: ( + Annotated[ClassList[ratapi.models.Contrast], Tag("no_ratio")] + | Annotated[ClassList[ratapi.models.ContrastWithRatio], Tag("ratio")] + ) = Field( default=ClassList(), discriminator=Discriminator( discriminate_contrasts, @@ -290,11 +290,11 @@ def discriminate_ambiguous_dicts(cls, data: Any) -> Any: # pydantic docs says data can be anything, but i can't see anywhere where it isn't a dict. # if it's not a dict, just return and let the library handle it if isinstance(data, dict): - layer_model = RATapi.models.AbsorptionLayer if data.get("absorption", False) else RATapi.models.Layer + layer_model = ratapi.models.AbsorptionLayer if data.get("absorption", False) else ratapi.models.Layer if data.get("calculation", Calculations.Normal) == Calculations.Domains: - contrast_model = RATapi.models.ContrastWithRatio + contrast_model = ratapi.models.ContrastWithRatio else: - contrast_model = RATapi.models.Contrast + contrast_model = ratapi.models.Contrast # note we aren't modifying the layers and contrasts in-place: # if a ClassList of dicts is passed, in-place conversion would make the ClassList heterogenous @@ -327,7 +327,7 @@ def check_layers(cls, value: ClassList, info: ValidationInfo): else: model_name = "Layer" other_model = "AbsorptionLayer" - model = getattr(RATapi.models, model_name) + model = getattr(ratapi.models, model_name) if not all(isinstance(element, model) for element in value): raise ValueError( f'"The layers attribute contains {other_model}s, ' @@ -347,7 +347,7 @@ def check_contrasts(cls, value: ClassList, info: ValidationInfo): else: model_name = "Contrast" error_word = "with" - model = getattr(RATapi.models, model_name) + model = getattr(ratapi.models, model_name) if not all(isinstance(element, model) for element in value): raise ValueError( f'"The contrasts attribute contains contrasts {error_word} ratio, ' @@ -361,8 +361,8 @@ def model_post_init(self, __context: Any) -> None: """Set up the Class to protect against disallowed modification. We initialise the class handle in the ClassLists for empty data fields, set protected parameters, get names of - all defined parameters, determine the contents of the "model" field in contrasts, - and wrap ClassList routines to control revalidation. + all defined parameters, determine the contents of the "model" field in contrasts, and wrap ClassList routines + to control revalidation. """ # Ensure all ClassLists have the correct _class_handle defined for field in (fields := Project.model_fields): @@ -375,27 +375,27 @@ def model_post_init(self, __context: Any) -> None: layers_field = self.layers if not hasattr(layers_field, "_class_handle"): if self.absorption: - layers_field._class_handle = RATapi.models.AbsorptionLayer + layers_field._class_handle = ratapi.models.AbsorptionLayer else: - layers_field._class_handle = RATapi.models.Layer + layers_field._class_handle = ratapi.models.Layer contrast_field = self.contrasts if not hasattr(contrast_field, "_class_handle"): if self.calculation == Calculations.Domains: - contrast_field._class_handle = RATapi.models.ContrastWithRatio + contrast_field._class_handle = ratapi.models.ContrastWithRatio else: - contrast_field._class_handle = RATapi.models.Contrast + contrast_field._class_handle = ratapi.models.Contrast if "Substrate Roughness" not in [name.title() for name in self.parameters.get_names()]: self.parameters.insert( 0, - RATapi.models.ProtectedParameter( + ratapi.models.ProtectedParameter( name="Substrate Roughness", min=1.0, value=3.0, max=5.0, fit=True, - prior_type=RATapi.models.Priors.Uniform, + prior_type=ratapi.models.Priors.Uniform, mu=0.0, sigma=np.inf, ), @@ -404,10 +404,10 @@ def model_post_init(self, __context: Any) -> None: # If substrate roughness is included as a standard parameter replace it with a protected parameter substrate_roughness_values = self.parameters["Substrate Roughness"].model_dump() self.parameters.remove("Substrate Roughness") - self.parameters.insert(0, RATapi.models.ProtectedParameter(**substrate_roughness_values)) + self.parameters.insert(0, ratapi.models.ProtectedParameter(**substrate_roughness_values)) if "Simulation" not in [name.title() for name in self.data.get_names()]: - self.data.insert(0, RATapi.models.Data(name="Simulation", simulation_range=[0.005, 0.7])) + self.data.insert(0, ratapi.models.Data(name="Simulation", simulation_range=[0.005, 0.7])) self._all_names = self.get_all_names() self._contrast_model_field = self.get_contrast_model_field() @@ -454,6 +454,33 @@ def set_layers(self) -> "Project": self.layers.data = [] return self + @model_validator(mode="after") + def set_repeat_layers(self) -> "Project": + """If we are not using a standard layers model, warn that the repeat layers setting is not valid.""" + if self.model != LayerModels.StandardLayers: + for contrast in self.contrasts: + if "repeat_layers" in contrast.model_fields_set and contrast.repeat_layers != 1: + warnings.warn( + 'For a custom layers or custom XY calculation, the "repeat_layers" setting for each ' + "contrast is not valid - resetting to 1.", + stacklevel=2, + ) + contrast.repeat_layers = 1 + return self + + @model_validator(mode="after") + def set_resample(self) -> "Project": + """If we are using a custom XY model, warn that the resample setting for each contrast must always be True.""" + if self.model == LayerModels.CustomXY: + for contrast in self.contrasts: + if "resample" in contrast.model_fields_set and contrast.resample is False: + warnings.warn( + 'For a custom XY calculation, "resample" must be True for each contrast - resetting to True.', + stacklevel=2, + ) + contrast.resample = True + return self + @model_validator(mode="after") def set_calculation(self) -> "Project": """Apply the calc setting to the project.""" @@ -461,28 +488,28 @@ def set_calculation(self) -> "Project": handle = self.contrasts._class_handle.__name__ if self.calculation == Calculations.Domains and handle == "Contrast": for contrast in self.contrasts: - contrast_list.append(RATapi.models.ContrastWithRatio(**contrast.model_dump())) + contrast_list.append(ratapi.models.ContrastWithRatio(**contrast.model_dump())) self.contrasts.data = contrast_list self.domain_ratios.data = [ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Domain Ratio 1", min=0.4, value=0.5, max=0.6, fit=False, - prior_type=RATapi.models.Priors.Uniform, + prior_type=ratapi.models.Priors.Uniform, mu=0.0, sigma=np.inf, ), ] - self.contrasts._class_handle = RATapi.models.ContrastWithRatio + self.contrasts._class_handle = ratapi.models.ContrastWithRatio elif self.calculation != Calculations.Domains and handle == "ContrastWithRatio": for contrast in self.contrasts: contrast_params = contrast.model_dump() del contrast_params["domain_ratio"] - contrast_list.append(RATapi.models.Contrast(**contrast_params)) + contrast_list.append(ratapi.models.Contrast(**contrast_params)) self.contrasts.data = contrast_list - self.contrasts._class_handle = RATapi.models.Contrast + self.contrasts._class_handle = ratapi.models.Contrast return self @model_validator(mode="after") @@ -531,16 +558,16 @@ def set_absorption(self) -> "Project": for layer in self.layers: layer_params = layer.model_dump() layer_params["SLD_imaginary"] = "" - layer_list.append(RATapi.models.AbsorptionLayer(**layer_params)) + layer_list.append(ratapi.models.AbsorptionLayer(**layer_params)) self.layers.data = layer_list - self.layers._class_handle = RATapi.models.AbsorptionLayer + self.layers._class_handle = ratapi.models.AbsorptionLayer elif not self.absorption and handle == "AbsorptionLayer": for layer in self.layers: layer_params = layer.model_dump() del layer_params["SLD_imaginary"] - layer_list.append(RATapi.models.Layer(**layer_params)) + layer_list.append(ratapi.models.Layer(**layer_params)) self.layers.data = layer_list - self.layers._class_handle = RATapi.models.Layer + self.layers._class_handle = ratapi.models.Layer return self @model_validator(mode="after") @@ -550,7 +577,7 @@ def update_renamed_models(self) -> "Project": old_names = self._all_names[class_list] new_names = getattr(self, class_list).get_names() if len(old_names) == len(new_names): - name_diff = [(old, new) for (old, new) in zip(old_names, new_names) if old != new] + name_diff = [(old, new) for (old, new) in zip(old_names, new_names, strict=False) if old != new] for old_name, new_name in name_diff: for field in fields_to_update: project_field = getattr(self, field.attribute) @@ -613,7 +640,7 @@ def check_protected_parameters(self) -> "Project": """Protected parameters should not be deleted. If this is attempted, raise an error.""" for class_list in parameter_class_lists: protected_parameters = [ - param.name for param in getattr(self, class_list) if isinstance(param, RATapi.models.ProtectedParameter) + param.name for param in getattr(self, class_list) if isinstance(param, ratapi.models.ProtectedParameter) ] # All previously existing protected parameters should be present in new list if not all(element in protected_parameters for element in self._protected_parameters[class_list]): @@ -649,7 +676,7 @@ def get_all_protected_parameters(self): """Record the protected parameters defined in the project.""" return { class_list: [ - param.name for param in getattr(self, class_list) if isinstance(param, RATapi.models.ProtectedParameter) + param.name for param in getattr(self, class_list) if isinstance(param, ratapi.models.ProtectedParameter) ] for class_list in parameter_class_lists } @@ -847,7 +874,7 @@ def write_script(self, obj_name: str = "problem", script: str = "project_script. def write_item(item): """Write a model item as a string of a dictionary.""" - if isinstance(item, RATapi.models.Data): + if isinstance(item, ratapi.models.Data): item_str = "{" + f"'name': '{item.name}'," if not np.any(item.data): # if array is empty, e.g. in simulation data item_str += "'data': empty([0, 3]), " @@ -860,7 +887,7 @@ def write_item(item): item_str += "}" return item_str - if isinstance(item, RATapi.models.CustomFile): + if isinstance(item, ratapi.models.CustomFile): # convert file paths to strings because pathlib gets too specific item_str = ( "{" @@ -868,7 +895,8 @@ def write_item(item): + f"'filename': '{item.filename}', " + f"'function_name': '{item.function_name}', " + f"'language': '{str(item.language)}', " - + f"'path': '{str(item.path)}'" + # Raw string to ensure backslash is not interpreted as escape + + f"'path': r'{str(try_relative_to(item.path, script_path.parent))}'" + "}" ) return item_str @@ -882,10 +910,10 @@ def classlist_script(name, classlist): script_path.write_text( f"""# THIS FILE IS GENERATED FROM RAT VIA THE "WRITE_SCRIPT" ROUTINE. IT IS NOT PART OF THE RAT CODE. -import RATapi +import ratapi from numpy import array, empty, inf -{obj_name} = RATapi.Project( +{obj_name} = ratapi.Project( name="{self.name}", calculation="{self.calculation}", model="{self.model}", @@ -899,7 +927,7 @@ def classlist_script(name, classlist): + "\n)" ) - def save(self, filepath: Union[str, Path] = "./project.json"): + def save(self, filepath: str | Path = "./project.json"): """Save a project to a JSON file. Parameters @@ -928,12 +956,16 @@ def make_data_dict(item): elif field == "custom_files": def make_custom_file_dict(item): - return { + file_dict = { "name": item.name, "filename": item.filename, "language": item.language, - "path": try_relative_to(item.path, filepath), + "path": try_relative_to(item.path, filepath.parent), } + if item.name != item.function_name: + file_dict["function_name"] = item.function_name + + return file_dict json_dict["custom_files"] = [make_custom_file_dict(file) for file in attr] @@ -945,7 +977,7 @@ def make_custom_file_dict(item): filepath.write_text(json.dumps(json_dict)) @classmethod - def load(cls, path: Union[str, Path]) -> "Project": + def load(cls, path: str | Path) -> "Project": """Load a project from file. Parameters @@ -968,7 +1000,7 @@ def load(cls, path: Union[str, Path]) -> "Project": # file paths are saved as relative to the project directory for file in model_dict["custom_files"]: if not Path(file["path"]).is_absolute(): - file["path"] = Path(path, file["path"]) + file["path"] = Path(path.parent, file["path"]).resolve() return cls.model_validate(model_dict) @@ -1034,11 +1066,13 @@ def try_relative_to(path: Path, relative_to: Path) -> str: """ path = Path(path) relative_to = Path(relative_to) - if path.is_relative_to(relative_to): + if not path.is_absolute(): + return str(path) + elif path.is_relative_to(relative_to): return str(path.relative_to(relative_to)) else: warnings.warn( - "Could not save custom file path as relative to the project directory, " + "Could not write custom file path as relative to the project directory, " "which means that it may not work on other devices. If you would like to share your project, " "make sure your custom files are in a subfolder of the project save location.", stacklevel=2, diff --git a/RATapi/run.py b/ratapi/run.py similarity index 87% rename from RATapi/run.py rename to ratapi/run.py index 476daa39..d973d068 100644 --- a/RATapi/run.py +++ b/ratapi/run.py @@ -4,10 +4,10 @@ from tqdm.auto import tqdm -import RATapi.rat_core -from RATapi.inputs import make_input -from RATapi.outputs import make_results -from RATapi.utils.enums import Display +import ratapi.rat_core +from ratapi.inputs import make_input +from ratapi.outputs import make_results +from ratapi.utils.enums import Display class ProgressBar: @@ -32,7 +32,7 @@ def __init__(self, display=True): def __enter__(self): if self.display: - RATapi.events.register(RATapi.events.EventTypes.Progress, self.updateProgress) + ratapi.events.register(ratapi.events.EventTypes.Progress, self.updateProgress) return self @@ -57,7 +57,7 @@ def __exit__(self, _exc_type, _exc_val, _traceback): print("") # Print new line after bar if self.display: - RATapi.events.clear(RATapi.events.EventTypes.Progress, self.updateProgress) + ratapi.events.clear(ratapi.events.EventTypes.Progress, self.updateProgress) class TextOutput: @@ -75,7 +75,7 @@ def __init__(self, display=True): def __enter__(self): if self.display: - RATapi.events.register(RATapi.events.EventTypes.Message, self.printMessage) + ratapi.events.register(ratapi.events.EventTypes.Message, self.printMessage) return self @@ -92,7 +92,7 @@ def printMessage(self, msg): def __exit__(self, _exc_type, _exc_val, _traceback): if self.display: - RATapi.events.clear(RATapi.events.EventTypes.Message, self.printMessage) + ratapi.events.clear(ratapi.events.EventTypes.Message, self.printMessage) def run(project, controls): @@ -116,7 +116,7 @@ def run(project, controls): start = time.time() with ProgressBar(display=display_on), TextOutput(display=display_on): - problem_definition, output_results, bayes_results = RATapi.rat_core.RATMain( + problem_definition, output_results, bayes_results = ratapi.rat_core.RATMain( problem_definition, cpp_controls, ) @@ -128,7 +128,7 @@ def run(project, controls): results = make_results(controls.procedure, output_results, bayes_results) # Update parameter values in project - for class_list in RATapi.project.parameter_class_lists: + for class_list in ratapi.project.parameter_class_lists: for index, value in enumerate(getattr(problem_definition, parameter_field[class_list])): getattr(project, class_list)[index].value = value diff --git a/ratapi/utils/__init__.py b/ratapi/utils/__init__.py new file mode 100644 index 00000000..56b312d4 --- /dev/null +++ b/ratapi/utils/__init__.py @@ -0,0 +1 @@ +"""Additional utilities for ratapi.""" diff --git a/RATapi/utils/convert.py b/ratapi/utils/convert.py similarity index 94% rename from RATapi/utils/convert.py rename to ratapi/utils/convert.py index 7c94ab50..7b3a44f7 100644 --- a/RATapi/utils/convert.py +++ b/ratapi/utils/convert.py @@ -4,18 +4,17 @@ from collections.abc import Iterable from os import PathLike from pathlib import Path -from typing import Union -from numpy import array, empty +from numpy import array, empty, ndarray from scipy.io.matlab import MatlabOpaque, loadmat -from RATapi import Project, wrappers -from RATapi.classlist import ClassList -from RATapi.models import Background, Contrast, CustomFile, Data, Layer, Parameter, Resolution -from RATapi.utils.enums import Geometries, Languages, LayerModels +from ratapi import Project, wrappers +from ratapi.classlist import ClassList +from ratapi.models import Background, Contrast, CustomFile, Data, Layer, Parameter, Resolution +from ratapi.utils.enums import Geometries, Languages, LayerModels -def r1_to_project(filename: Union[str, PathLike]) -> Project: +def r1_to_project(filename: str | PathLike) -> Project: """Read a RasCAL1 project struct as a Python `Project`. Parameters @@ -43,7 +42,7 @@ def r1_to_project(filename: Union[str, PathLike]) -> Project: layer_model = LayerModels.CustomXY layer_model = LayerModels(layer_model) - def zip_if_several(*params) -> Union[tuple, list[tuple]]: + def zip_if_several(*params) -> tuple | list[tuple]: """Zips parameters if necessary, but can handle single-item parameters. Examples @@ -64,7 +63,7 @@ def zip_if_several(*params) -> Union[tuple, list[tuple]]: """ if all(isinstance(param, Iterable) and not isinstance(param, str) for param in params): - return zip(*params) + return zip(*params, strict=False) return [params] def read_param(names, constrs, values, fits): @@ -228,6 +227,12 @@ def fix_invalid_constraints(name: str, constrs: tuple[float, float], value: floa if isinstance(mat_project["resolNames"], str): mat_project["resolNames"] = [mat_project["resolNames"]] + if isinstance(mat_project["contrastNames"], (ndarray, list)) and len( + dict.fromkeys(mat_project["contrastNames"]) + ) != len(mat_project["contrastNames"]): + # contrast names are not unique so create unique ones + mat_project["contrastNames"] = [f"Contrast {i + 1}" for i in range(len(mat_project["contrastNames"]))] + contrasts = ClassList( [ Contrast( @@ -292,7 +297,14 @@ def fix_invalid_constraints(name: str, constrs: tuple[float, float], value: floa if Path(custom_filepath).suffix != ".m": custom_filepath += ".m" model_name = Path(custom_filepath).stem - custom_file = ClassList([CustomFile(name=model_name, filename=custom_filepath, language=Languages.Matlab)]) + # Assume the custom file is in the same directory as the mat file + custom_file = ClassList( + [ + CustomFile( + name=model_name, filename=custom_filepath, language=Languages.Matlab, path=Path(filename).parent + ) + ] + ) layers = ClassList() for contrast in contrasts: contrast.model = [model_name] @@ -319,8 +331,8 @@ def fix_invalid_constraints(name: str, constrs: tuple[float, float], value: floa def project_to_r1( - project: Project, filename: Union[str, PathLike] = "RAT_project", return_struct: bool = False -) -> Union[dict, None]: + project: Project, filename: str | PathLike = "RAT_project", return_struct: bool = False +) -> dict | None: """Convert a RAT Project to a RasCAL1 project struct. Parameters diff --git a/RATapi/utils/custom_errors.py b/ratapi/utils/custom_errors.py similarity index 94% rename from RATapi/utils/custom_errors.py rename to ratapi/utils/custom_errors.py index 425cf9ef..83bf084f 100644 --- a/RATapi/utils/custom_errors.py +++ b/ratapi/utils/custom_errors.py @@ -1,13 +1,11 @@ """Defines routines for custom error handling in RAT.""" -from typing import Optional - import pydantic_core def custom_pydantic_validation_error( error_list: list[pydantic_core.ErrorDetails], - custom_error_msgs: Optional[dict[str, str]] = None, + custom_error_msgs: dict[str, str] | None = None, ) -> list[pydantic_core.ErrorDetails]: """Give Pydantic errors a better custom message with extraneous information removed. diff --git a/RATapi/utils/enums.py b/ratapi/utils/enums.py similarity index 96% rename from RATapi/utils/enums.py rename to ratapi/utils/enums.py index ff14b340..313f04c7 100644 --- a/RATapi/utils/enums.py +++ b/ratapi/utils/enums.py @@ -1,6 +1,4 @@ -"""The Enum values used in the parameters of various RATapi classes and functions.""" - -from typing import Union +"""The Enum values used in the parameters of various ratapi classes and functions.""" try: from enum import StrEnum @@ -73,26 +71,26 @@ class Strategies(RATEnum): """The base vector is random.""" LocalToBest = "local to best" - """The base vector is a combination of one randomly-selected local solution + """The base vector is a combination of one randomly-selected local solution and the best solution of the previous iteration.""" BestWithJitter = "best jitter" """The base vector is the best solution of the previous iteration, with a small random perturbation applied.""" RandomWithPerVectorDither = "vector dither" - """The base vector is random, with a random scaling factor applied to each mutant. + """The base vector is random, with a random scaling factor applied to each mutant. This scaling factor is different for each mutant.""" RandomWithPerGenerationDither = "generation dither" - """The base vector is random, with a random scaling factor applied to each mutant. + """The base vector is random, with a random scaling factor applied to each mutant. This scaling factor is the same for every mutant, and randomised every generation.""" RandomEitherOrAlgorithm = "either or" - """The base vector is randomly chosen from either a pure random mutation, + """The base vector is randomly chosen from either a pure random mutation, or a pure recombination of parent parameter values.""" @classmethod - def _missing_(cls, value: Union[int, str]): + def _missing_(cls, value: int | str): # legacy compatibility with strategies being 1-indexed ints under the hood if isinstance(value, int): if value < 1 or value > 6: diff --git a/RATapi/utils/orso.py b/ratapi/utils/orso.py similarity index 95% rename from RATapi/utils/orso.py rename to ratapi/utils/orso.py index 50550619..2d0345fc 100644 --- a/RATapi/utils/orso.py +++ b/ratapi/utils/orso.py @@ -4,14 +4,13 @@ from itertools import count from pathlib import Path from textwrap import shorten -from typing import Union import orsopy import prettytable from orsopy.fileio import load_orso -from RATapi import ClassList -from RATapi.models import AbsorptionLayer, Data, Layer, Parameter +from ratapi import ClassList +from ratapi.models import AbsorptionLayer, Data, Layer, Parameter class ORSOProject: @@ -26,7 +25,7 @@ class ORSOProject: """ - def __init__(self, filepath: Union[str, Path], absorption: bool = False): + def __init__(self, filepath: str | Path, absorption: bool = False): ort_data = load_orso(filepath) datasets = [Data(name=dataset.info.data_source.sample.name, data=dataset.data) for dataset in ort_data] # orso datasets in the same file can have repeated names! @@ -75,7 +74,7 @@ class ORSOSample: bulk_in: Parameter bulk_out: Parameter parameters: ClassList[Parameter] - layers: Union[ClassList[Layer], ClassList[AbsorptionLayer]] + layers: ClassList[Layer] | ClassList[AbsorptionLayer] model: list[str] def __str__(self): @@ -94,8 +93,8 @@ def __str__(self): def orso_model_to_rat( - model: Union[orsopy.fileio.model_language.SampleModel, str], absorption: bool = False -) -> Union[ORSOSample, None]: + model: orsopy.fileio.model_language.SampleModel | str, absorption: bool = False +) -> ORSOSample | None: """Get information from an ORSO SampleModel object. Parameters diff --git a/RATapi/utils/plotting.py b/ratapi/utils/plotting.py similarity index 79% rename from RATapi/utils/plotting.py rename to ratapi/utils/plotting.py index e6d925a3..6f9debb7 100644 --- a/RATapi/utils/plotting.py +++ b/ratapi/utils/plotting.py @@ -2,12 +2,14 @@ import copy import types +from collections.abc import Callable from functools import partial, wraps from math import ceil, floor, sqrt from statistics import stdev -from typing import Callable, Literal, Optional, Union +from typing import Literal import matplotlib +import matplotlib.figure import matplotlib.pyplot as plt import matplotlib.transforms as mtransforms import numpy as np @@ -15,13 +17,13 @@ from scipy.ndimage import gaussian_filter1d from scipy.stats import gaussian_kde, lognorm, norm -import RATapi -import RATapi.inputs -import RATapi.outputs -from RATapi.rat_core import PlotEventData, makeSLDProfile +import ratapi +import ratapi.inputs +import ratapi.outputs +from ratapi.rat_core import PlotEventData, makeSLDProfile -def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool): +def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool, shift_value: float): """Extract the plot data for the sld, ref, error plot lines. Parameters @@ -33,6 +35,8 @@ def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool Controls whether Q^4 is plotted on the reflectivity plot show_error_bar : bool, default: True Controls whether the error bars are shown + shift_value : float + A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts Returns ------- @@ -42,9 +46,14 @@ def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool """ results = {"ref": [], "error": [], "sld": [], "sld_resample": []} - for i, (r, data, sld) in enumerate(zip(event_data.reflectivity, event_data.shiftedData, event_data.sldProfiles)): + if shift_value < 0 or shift_value > 100: + raise ValueError("Parameter `shift_value` must be between 0 and 100") + + for i, (r, data, sld) in enumerate( + zip(event_data.reflectivity, event_data.shiftedData, event_data.sldProfiles, strict=False) + ): # Calculate the divisor - div = 1 if i == 0 and not q4 else 2 ** (4 * (i + 1)) + div = 10 ** (i / 100 * shift_value) q4_data = 1 if not q4 or not event_data.dataPresent[i] else data[:, 0] ** 4 mult = q4_data / div @@ -54,22 +63,25 @@ def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool if event_data.dataPresent[i]: sd_x = data[:, 0] sd_y, sd_e = map(lambda x: x * mult, (data[:, 1], data[:, 2])) + errors = np.zeros(len(sd_e)) if show_error_bar: - errors = np.zeros(len(sd_e)) valid = sd_y - sd_e >= 0 errors[valid] = sd_e[valid] valid |= sd_y < 0 + else: + valid = np.ones(len(sd_e)).astype(bool) + sd_e = errors - results["error"].append([sd_x[valid], sd_y[valid], sd_e[valid]]) + results["error"].append([sd_x[valid], sd_y[valid], sd_e[valid]]) results["sld"].append([]) for j in range(len(sld)): results["sld"][-1].append([sld[j][:, 0], sld[j][:, 1]]) + results["sld_resample"].append([]) if event_data.resample[i] == 1 or event_data.modelType == "custom xy": layers = event_data.resampledLayers[i][0] - results["sld_resample"].append([]) for j in range(len(event_data.resampledLayers[i])): layer = event_data.resampledLayers[i][j] if layers.shape[1] == 4: @@ -87,204 +99,17 @@ def _extract_plot_data(event_data: PlotEventData, q4: bool, show_error_bar: bool return results -class PlotSLDWithBlitting: - """Create a SLD plot that uses blitting to get faster draws. - - The blit plot stores the background from an - initial draw then updates the foreground (lines and error bars) if the background is not changed. - - Parameters - ---------- - data : PlotEventData - The plot event data that contains all the information - to generate the ref and sld plots - fig : matplotlib.pyplot.figure, optional - The figure class that has two subplots - linear_x : bool, default: False - Controls whether the x-axis on reflectivity plot uses the linear scale - q4 : bool, default: False - Controls whether Q^4 is plotted on the reflectivity plot - show_error_bar : bool, default: True - Controls whether the error bars are shown - show_grid : bool, default: False - Controls whether the grid is shown - show_legend : bool, default: True - Controls whether the legend is shown - """ - - def __init__( - self, - data: PlotEventData, - fig: Optional[matplotlib.pyplot.figure] = None, - linear_x: bool = False, - q4: bool = False, - show_error_bar: bool = True, - show_grid: bool = False, - show_legend: bool = True, - ): - self.figure = fig - self.linear_x = linear_x - self.q4 = q4 - self.show_error_bar = show_error_bar - self.show_grid = show_grid - self.show_legend = show_legend - self.updatePlot(data) - self.event_id = self.figure.canvas.mpl_connect("resize_event", self.resizeEvent) - - def __del__(self): - self.figure.canvas.mpl_disconnect(self.event_id) - - def resizeEvent(self, _event): - """Ensure the background is updated after a resize event.""" - self.__background_changed = True - - def update(self, data: PlotEventData): - """Update the foreground, if background has not changed otherwise it updates full plot. - - Parameters - ---------- - data : PlotEventData - The plot event data that contains all the information - to generate the ref and sld plots - """ - if self.__background_changed: - self.updatePlot(data) - else: - self.updateForeground(data) - - def __setattr__(self, name, value): - super().__setattr__(name, value) - if name in ["figure", "linear_x", "q4", "show_error_bar", "show_grid", "show_legend"]: - self.__background_changed = True - - def setAnimated(self, is_animated: bool): - """Set the animated property of foreground plot elements. - - Parameters - ---------- - is_animated : bool - Indicates if the animated property should been set. - """ - for line in self.figure.axes[0].lines: - line.set_animated(is_animated) - for line in self.figure.axes[1].lines: - line.set_animated(is_animated) - for container in self.figure.axes[0].containers: - container[2][0].set_animated(is_animated) - - def adjustErrorBar(self, error_bar_container, x, y, y_error): - """Adjust the error bar data. - - Parameters - ---------- - error_bar_container : Tuple - Tuple containing the artist of the errorbar i.e. (data line, cap lines, bar lines) - x : np.ndarray - The shifted data x axis data - y : np.ndarray - The shifted data y axis data - y_error : np.ndarray - The shifted data y axis error data - """ - line, _, (bars_y,) = error_bar_container - - line.set_data(x, y) - x_base = x - y_base = y - - y_error_top = y_base + y_error - y_error_bottom = y_base - y_error - - new_segments_y = [np.array([[x, yt], [x, yb]]) for x, yt, yb in zip(x_base, y_error_top, y_error_bottom)] - bars_y.set_segments(new_segments_y) - - def updatePlot(self, data: PlotEventData): - """Update the full plot. - - Parameters - ---------- - data : PlotEventData - The plot event data that contains all the information - to generate the ref and sld plots - """ - if self.figure is not None: - self.figure.clf() - self.figure = plot_ref_sld_helper( - data, - self.figure, - linear_x=self.linear_x, - q4=self.q4, - show_error_bar=self.show_error_bar, - show_grid=self.show_grid, - show_legend=self.show_legend, - animated=True, - ) - - self.bg = self.figure.canvas.copy_from_bbox(self.figure.bbox) - for line in self.figure.axes[0].lines: - self.figure.axes[0].draw_artist(line) - for line in self.figure.axes[1].lines: - self.figure.axes[1].draw_artist(line) - for container in self.figure.axes[0].containers: - self.figure.axes[0].draw_artist(container[2][0]) - self.figure.canvas.blit(self.figure.bbox) - self.setAnimated(False) - self.__background_changed = False - - def updateForeground(self, data: PlotEventData): - """Update the plot foreground only. - - Parameters - ---------- - data : PlotEventData - The plot event data that contains all the information - to generate the ref and sld plots - """ - self.setAnimated(True) - self.figure.canvas.restore_region(self.bg) - plot_data = _extract_plot_data(data, self.q4, self.show_error_bar) - - offset = 2 if self.show_error_bar else 1 - for i in range( - 0, - len(self.figure.axes[0].lines), - ): - self.figure.axes[0].lines[i].set_data(plot_data["ref"][i // offset][0], plot_data["ref"][i // offset][1]) - self.figure.axes[0].draw_artist(self.figure.axes[0].lines[i]) - - i = 0 - for j in range(len(plot_data["sld"])): - for sld in plot_data["sld"][j]: - self.figure.axes[1].lines[i].set_data(sld[0], sld[1]) - self.figure.axes[1].draw_artist(self.figure.axes[1].lines[i]) - i += 1 - - if plot_data["sld_resample"]: - for resampled in plot_data["sld_resample"][j]: - self.figure.axes[1].lines[i].set_data(resampled[0], resampled[1]) - self.figure.axes[1].draw_artist(self.figure.axes[1].lines[i]) - i += 1 - - for i, container in enumerate(self.figure.axes[0].containers): - self.adjustErrorBar(container, plot_data["error"][i][0], plot_data["error"][i][1], plot_data["error"][i][2]) - self.figure.axes[0].draw_artist(container[2][0]) - self.figure.axes[0].draw_artist(container[0]) - - self.figure.canvas.blit(self.figure.bbox) - self.figure.canvas.flush_events() - self.setAnimated(False) - - def plot_ref_sld_helper( data: PlotEventData, - fig: Optional[matplotlib.pyplot.figure] = None, + fig: matplotlib.figure.Figure, delay: bool = True, - confidence_intervals: Union[dict, None] = None, + confidence_intervals: dict | None = None, linear_x: bool = False, q4: bool = False, show_error_bar: bool = True, show_grid: bool = False, show_legend: bool = True, + shift_value: float = 100, animated=False, ): """Clear the previous plots and updates the ref and SLD plots. @@ -294,8 +119,8 @@ def plot_ref_sld_helper( data : PlotEventData The plot event data that contains all the information to generate the ref and sld plots - fig : matplotlib.pyplot.figure, optional - The figure class that has two subplots + fig : matplotlib.figure.Figure + The figure object that has two subplots delay : bool, default: True Controls whether to delay 0.005s after plot is created confidence_intervals : dict or None, default None @@ -311,23 +136,19 @@ def plot_ref_sld_helper( Controls whether the grid is shown show_legend : bool, default: True Controls whether the legend is shown + shift_value : float, default: 100 + A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts animated : bool, default: False Controls whether the animated property of foreground plot elements should be set. - Returns - ------- - fig : matplotlib.pyplot.figure - The figure class that has two subplots - """ preserve_zoom = False - if fig is None: - fig = plt.subplots(1, 2)[0] - elif len(fig.axes) != 2: + if len(fig.axes) != 2: fig.clf() fig.subplots(1, 2) - fig.subplots_adjust(wspace=0.3) + + fig.subplots_adjust(wspace=0.3, hspace=0) ref_plot: plt.Axes = fig.axes[0] sld_plot: plt.Axes = fig.axes[1] @@ -339,7 +160,7 @@ def plot_ref_sld_helper( ref_plot.cla() sld_plot.cla() - plot_data = _extract_plot_data(data, q4, show_error_bar) + plot_data = _extract_plot_data(data, q4, show_error_bar, shift_value) for i, name in enumerate(data.contrastNames): ref_plot.plot(plot_data["ref"][i][0], plot_data["ref"][i][1], label=name, linewidth=1, animated=animated) color = ref_plot.get_lines()[-1].get_color() @@ -347,12 +168,12 @@ def plot_ref_sld_helper( # Plot confidence intervals if required if confidence_intervals is not None: # Calculate the divisor - div = 1 if i == 0 and not q4 else 2 ** (4 * (i + 1)) + div = 10 ** (i / 100 * shift_value) ref_min, ref_max = confidence_intervals["reflectivity"][i] mult = (1 if not q4 else plot_data["ref"][i][0] ** 4) / div ref_plot.fill_between(plot_data["ref"][i][0], ref_min * mult, ref_max * mult, alpha=0.6, color="grey") - if data.dataPresent[i] and show_error_bar: + if data.dataPresent[i]: # Plot the errorbars ref_plot.errorbar( x=plot_data["error"][i][0], @@ -380,15 +201,14 @@ def plot_ref_sld_helper( sld_min, sld_max = confidence_intervals["sld"][i][j] sld_plot.fill_between(plot_data["sld"][i][j][0], sld_min, sld_max, alpha=0.6, color="grey") - if plot_data["sld_resample"]: - for j in range(len(plot_data["sld_resample"][i])): - sld_plot.plot( - plot_data["sld_resample"][i][j][0], - plot_data["sld_resample"][i][j][1], - color=color, - linewidth=1, - animated=animated, - ) + for j in range(len(plot_data["sld_resample"][i])): + sld_plot.plot( + plot_data["sld_resample"][i][j][0], + plot_data["sld_resample"][i][j][1], + color=color, + linewidth=1, + animated=animated, + ) # Format the axis ref_plot.set_yscale("log") @@ -413,13 +233,12 @@ def plot_ref_sld_helper( if delay: plt.pause(0.005) - return fig - def plot_ref_sld( - project: RATapi.Project, - results: Union[RATapi.outputs.Results, RATapi.outputs.BayesResults], + project: ratapi.Project, + results: ratapi.outputs.Results | ratapi.outputs.BayesResults, block: bool = False, + fig: matplotlib.figure.Figure | None = None, return_fig: bool = False, bayes: Literal[65, 95, None] = None, linear_x: bool = False, @@ -427,7 +246,8 @@ def plot_ref_sld( show_error_bar: bool = True, show_grid: bool = False, show_legend: bool = True, -) -> Union[plt.Figure, None]: + shift_value: float = 100, +) -> plt.Figure | None: """Plot the reflectivity and SLD profiles. Parameters @@ -438,6 +258,8 @@ def plot_ref_sld( The result from the calculation block : bool, default: False Indicates the plot should block until it is closed + fig : matplotlib.figure.Figure, optional + The figure object that has two subplots return_fig : bool, default False If True, return the figure instead of displaying it. bayes : 65, 95 or None, default None @@ -454,6 +276,8 @@ def plot_ref_sld( Controls whether the grid is shown show_legend : bool, default: True Controls whether the legend is shown + shift_value : float, default: 100 + A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts Returns ------- @@ -471,13 +295,13 @@ def plot_ref_sld( data.shiftedData = results.shiftedData data.sldProfiles = copy.deepcopy(results.sldProfiles) data.resampledLayers = results.resampledLayers - data.dataPresent = RATapi.inputs.make_data_present(project) + data.dataPresent = ratapi.inputs.make_data_present(project) data.subRoughs = results.contrastParams.subRoughs - data.resample = RATapi.inputs.make_resample(project) + data.resample = ratapi.inputs.make_resample(project) data.contrastNames = [contrast.name for contrast in project.contrasts] if bayes: - if isinstance(results, RATapi.outputs.BayesResults): + if isinstance(results, ratapi.outputs.BayesResults): # the predictionIntervals data consists of 5 rows: # row 0: min with 95% confidence # row 1: min with 65% confidence @@ -501,10 +325,12 @@ def plot_ref_sld( ], } # For a shaded plot, use the mean values from predictionIntervals - for reflectivity, mean_reflectivity in zip(data.reflectivity, results.predictionIntervals.reflectivity): + for reflectivity, mean_reflectivity in zip( + data.reflectivity, results.predictionIntervals.reflectivity, strict=False + ): reflectivity[:, 1] = mean_reflectivity[2] - for sldProfile, mean_sld_profile in zip(data.sldProfiles, results.predictionIntervals.sld): - for sld, mean_sld in zip(sldProfile, mean_sld_profile): + for sldProfile, mean_sld_profile in zip(data.sldProfiles, results.predictionIntervals.sld, strict=False): + for sld, mean_sld in zip(sldProfile, mean_sld_profile, strict=False): sld[:, 1] = mean_sld[2] else: raise ValueError( @@ -513,25 +339,232 @@ def plot_ref_sld( else: confidence_intervals = None - figure = plt.subplots(1, 2)[0] + if fig is None: + fig = plt.subplots(1, 2)[0] + elif len(fig.axes) != 2: + fig.clf() + fig.subplots(1, 2) plot_ref_sld_helper( data, - figure, + fig, confidence_intervals=confidence_intervals, linear_x=linear_x, q4=q4, show_error_bar=show_error_bar, show_grid=show_grid, show_legend=show_legend, + shift_value=shift_value, ) if return_fig: - return figure + return fig plt.show(block=block) +class BlittingSupport: + """Create a SLD plot that uses blitting to get faster draws. + + The blit plot stores the background from an + initial draw then updates the foreground (lines and error bars) if the background is not changed. + + Parameters + ---------- + data : PlotEventData + The plot event data that contains all the information + to generate the ref and sld plots + fig : matplotlib.figure.Figure, optional + The figure class that has two subplots + linear_x : bool, default: False + Controls whether the x-axis on reflectivity plot uses the linear scale + q4 : bool, default: False + Controls whether Q^4 is plotted on the reflectivity plot + show_error_bar : bool, default: True + Controls whether the error bars are shown + show_grid : bool, default: False + Controls whether the grid is shown + show_legend : bool, default: True + Controls whether the legend is shown + shift_value : float, default: 100 + A value between 0 and 100 that controls the spacing between the reflectivity plots for each of the contrasts + """ + + def __init__( + self, + data, + fig=None, + linear_x: bool = False, + q4: bool = False, + show_error_bar: bool = True, + show_grid: bool = False, + show_legend: bool = True, + shift_value: float = 100, + ): + self.figure = fig + self.linear_x = linear_x + self.q4 = q4 + self.show_error_bar = show_error_bar + self.show_grid = show_grid + self.show_legend = show_legend + self.shift_value = shift_value + self.update_plot(data) + self.event_id = self.figure.canvas.mpl_connect("resize_event", self.resizeEvent) + + def __del__(self): + self.figure.canvas.mpl_disconnect(self.event_id) + + def resizeEvent(self, _event): + """Ensure the background is updated after a resize event.""" + self.__background_changed = True + + def update(self, data): + """Update the foreground, if background has not changed otherwise it updates full plot. + + Parameters + ---------- + data : PlotEventData + The plot event data that contains all the information + to generate the ref and sld plots + """ + if self.__background_changed: + self.update_plot(data) + else: + self.update_foreground(data) + + def __setattr__(self, name, value): + old_value = getattr(self, name, None) + if value == old_value: + return + + super().__setattr__(name, value) + if name in ["figure", "linear_x", "q4", "show_error_bar", "show_grid", "show_legend", "shift_value"]: + self.__background_changed = True + + def set_animated(self, is_animated: bool): + """Set the animated property of foreground plot elements. + + Parameters + ---------- + is_animated : bool + Indicates if the animated property should be set. + """ + for line in self.figure.axes[0].lines: + line.set_animated(is_animated) + for line in self.figure.axes[1].lines: + line.set_animated(is_animated) + for container in self.figure.axes[0].containers: + container[2][0].set_animated(is_animated) + + def adjust_error_bar(self, error_bar_container, x, y, y_error): + """Adjust the error bar data. + + Parameters + ---------- + error_bar_container : Tuple + Tuple containing the artist of the errorbar i.e. (data line, cap lines, bar lines) + x : np.ndarray + The shifted data x axis data + y : np.ndarray + The shifted data y axis data + y_error : np.ndarray + The shifted data y axis error data + """ + line, _, (bars_y,) = error_bar_container + + line.set_data(x, y) + x_base = x + y_base = y + + y_error_top = y_base + y_error + y_error_bottom = y_base - y_error + + new_segments_y = [ + np.array([[x, yt], [x, yb]]) for x, yt, yb in zip(x_base, y_error_top, y_error_bottom, strict=False) + ] + bars_y.set_segments(new_segments_y) + + def update_plot(self, data): + """Update the full plot. + + Parameters + ---------- + data : PlotEventData + The plot event data that contains all the information + to generate the ref and sld plots + """ + if self.figure is not None: + self.figure.clf() + + self.figure.tight_layout() + plot_ref_sld_helper( + data, + self.figure, + linear_x=self.linear_x, + q4=self.q4, + show_error_bar=self.show_error_bar, + show_grid=self.show_grid, + show_legend=self.show_legend, + shift_value=self.shift_value, + animated=True, + ) + self.figure.canvas.draw() + self.bg = self.figure.canvas.copy_from_bbox(self.figure.bbox) + for line in self.figure.axes[0].lines: + self.figure.axes[0].draw_artist(line) + for line in self.figure.axes[1].lines: + self.figure.axes[1].draw_artist(line) + for container in self.figure.axes[0].containers: + self.figure.axes[0].draw_artist(container[2][0]) + self.figure.canvas.blit(self.figure.bbox) + self.set_animated(False) + self.__background_changed = False + + def update_foreground(self, data): + """Update the plot foreground only. + + Parameters + ---------- + data : PlotEventData + The plot event data that contains all the information + to generate the ref and sld plots + """ + self.set_animated(True) + self.figure.canvas.restore_region(self.bg) + plot_data = _extract_plot_data(data, self.q4, self.show_error_bar, self.shift_value) + + offset = 2 + for i in range( + 0, + len(self.figure.axes[0].lines), + ): + self.figure.axes[0].lines[i].set_data(plot_data["ref"][i // offset][0], plot_data["ref"][i // offset][1]) + self.figure.axes[0].draw_artist(self.figure.axes[0].lines[i]) + + i = 0 + for j in range(len(plot_data["sld"])): + for sld in plot_data["sld"][j]: + self.figure.axes[1].lines[i].set_data(sld[0], sld[1]) + self.figure.axes[1].draw_artist(self.figure.axes[1].lines[i]) + i += 1 + + for resampled in plot_data["sld_resample"][j]: + self.figure.axes[1].lines[i].set_data(resampled[0], resampled[1]) + self.figure.axes[1].draw_artist(self.figure.axes[1].lines[i]) + i += 1 + + for i, container in enumerate(self.figure.axes[0].containers): + self.adjust_error_bar( + container, plot_data["error"][i][0], plot_data["error"][i][1], plot_data["error"][i][2] + ) + self.figure.axes[0].draw_artist(container[2][0]) + self.figure.axes[0].draw_artist(container[0]) + + self.figure.canvas.blit(self.figure.bbox) + self.figure.canvas.flush_events() + self.set_animated(False) + + class LivePlot: """Create a plot that gets updates from the plot event during a calculation. @@ -545,12 +578,13 @@ class LivePlot: def __init__(self, block=False): self.block = block self.closed = False + self.blit_plot = None def __enter__(self): self.figure = plt.subplots(1, 2)[0] self.figure.canvas.mpl_connect("close_event", self._setCloseState) self.figure.show() - RATapi.events.register(RATapi.events.EventTypes.Plot, self.plotEvent) + ratapi.events.register(ratapi.events.EventTypes.Plot, self.plotEvent) return self.figure @@ -570,10 +604,13 @@ def plotEvent(self, event): """ if not self.closed and self.figure.number in plt.get_fignums(): - plot_ref_sld_helper(event, self.figure) + if self.blit_plot is None: + self.blit_plot = BlittingSupport(event, self.figure) + else: + self.blit_plot.update(event) def __exit__(self, _exc_type, _exc_val, _traceback): - RATapi.events.clear(RATapi.events.EventTypes.Plot, self.plotEvent) + ratapi.events.clear(ratapi.events.EventTypes.Plot, self.plotEvent) if not self.closed and self.figure.number in plt.get_fignums(): plt.show(block=self.block) @@ -593,7 +630,7 @@ def assert_bayesian(name: str): def decorator(func: Callable): @wraps(func) def inner(results, *args, **kwargs): - if isinstance(results, RATapi.outputs.BayesResults): + if isinstance(results, ratapi.outputs.BayesResults): return func(results, *args, **kwargs) raise ValueError(f"{name} plots are only available for the results of Bayesian analysis (NS or DREAM)") @@ -602,7 +639,7 @@ def inner(results, *args, **kwargs): return decorator -def name_to_index(param: Union[str, int], names: list[str]): +def name_to_index(param: str | int, names: list[str]): """Convert parameter names to indices.""" if isinstance(param, str): if param not in names: @@ -618,13 +655,15 @@ def name_to_index(param: Union[str, int], names: list[str]): @assert_bayesian("Corner") def plot_corner( - results: RATapi.outputs.BayesResults, - params: Union[list[Union[int, str]], None] = None, + results: ratapi.outputs.BayesResults, + params: list[int | str] | None = None, smooth: bool = True, block: bool = False, + fig: matplotlib.figure.Figure | None = None, return_fig: bool = False, - hist_kwargs: Union[dict, None] = None, - hist2d_kwargs: Union[dict, None] = None, + hist_kwargs: dict | None = None, + hist2d_kwargs: dict | None = None, + progress_callback: Callable[[int, int], None] | None = None, ): """Create a corner plot from a Bayesian analysis. @@ -639,6 +678,8 @@ def plot_corner( Whether to apply Gaussian smoothing to the corner plot. block : bool, default False Whether Python should block until the plot is closed. + fig : matplotlib.figure.Figure, optional + The figure object to use for plot. return_fig: bool, default False If True, return the figure as an object instead of showing it. hist_kwargs : dict @@ -647,6 +688,9 @@ def plot_corner( hist2d_kwargs : dict Extra keyword arguments to pass to the 2d histograms. Default is {'density': True, 'bins': 25} + progress_callback: Union[Callable[[int, int], None], None] + Callback function for providing progress during plot creation + First argument is current completed sub plot and second is total number of sub plots Returns ------- @@ -668,24 +712,32 @@ def plot_corner( hist2d_kwargs = {} num_params = len(params) + total_count = num_params + (num_params**2 - num_params) // 2 + + if fig is None: + fig, axes = plt.subplots(num_params, num_params, figsize=(11, 10), subplot_kw={"visible": False}) + else: + fig.clf() + axes = fig.subplots(num_params, num_params, subplot_kw={"visible": False}) - fig, axes = plt.subplots(num_params, num_params, figsize=(11, 10)) # i is row, j is column - for i, row_param in enumerate(params): - for j, col_param in enumerate(params): - current_axes: Axes = axes[i][j] + current_count = 0 + for i in range(num_params): + for j in range(i + 1): + row_param = params[i] + col_param = params[j] + current_axes: Axes = axes if isinstance(axes, matplotlib.axes.Axes) else axes[i][j] current_axes.tick_params(which="both", labelsize="medium") current_axes.xaxis.offsetText.set_fontsize("small") current_axes.yaxis.offsetText.set_fontsize("small") - + current_axes.set_visible(True) if i == j: # diagonal: histograms plot_one_hist(results, param=row_param, smooth=smooth, axes=current_axes, **hist_kwargs) elif i > j: # lower triangle: 2d histograms plot_contour( results, x_param=col_param, y_param=row_param, smooth=smooth, axes=current_axes, **hist2d_kwargs ) - elif i < j: # upper triangle: no plot - current_axes.set_visible(False) + # remove label if on inside of corner plot if j != 0: current_axes.get_yaxis().set_visible(False) @@ -698,6 +750,9 @@ def plot_corner( current_axes.yaxis.offset_text_position = "center" current_axes.set_ylabel("") current_axes.set_xlabel("") + if progress_callback is not None: + current_count += 1 + progress_callback(current_count, total_count) if return_fig: return fig plt.show(block=block) @@ -705,12 +760,12 @@ def plot_corner( @assert_bayesian("Histogram") def plot_one_hist( - results: RATapi.outputs.BayesResults, - param: Union[int, str], + results: ratapi.outputs.BayesResults, + param: int | str, smooth: bool = True, - sigma: Union[float, None] = None, + sigma: float | None = None, estimated_density: Literal["normal", "lognor", "kernel", None] = None, - axes: Union[Axes, None] = None, + axes: Axes | None = None, block: bool = False, return_fig: bool = False, **hist_settings, @@ -856,12 +911,12 @@ def _y_update_offset_text_position(axis, _bboxes, bboxes2): @assert_bayesian("Contour") def plot_contour( - results: RATapi.outputs.BayesResults, - x_param: Union[int, str], - y_param: Union[int, str], + results: ratapi.outputs.BayesResults, + x_param: int | str, + y_param: int | str, smooth: bool = True, - sigma: Union[tuple[float], None] = None, - axes: Union[Axes, None] = None, + sigma: tuple[float] | None = None, + axes: Axes | None = None, block: bool = False, return_fig: bool = False, **hist2d_settings, @@ -870,7 +925,7 @@ def plot_contour( Parameters ---------- - results : RATapi.outputs.BayesResults + results : ratapi.outputs.BayesResults The results of a Bayesian analysis. x_param : int The index or name of the parameter on the x-axis. @@ -929,7 +984,12 @@ def plot_contour( plt.show(block=block) -def panel_plot_helper(plot_func: Callable, indices: list[int]) -> matplotlib.figure.Figure: +def panel_plot_helper( + plot_func: Callable, + indices: list[int], + fig: matplotlib.figure.Figure | None = None, + progress_callback: Callable[[int, int], None] | None = None, +) -> matplotlib.figure.Figure: """Generate a panel-based plot from a single plot function. Parameters @@ -938,6 +998,11 @@ def panel_plot_helper(plot_func: Callable, indices: list[int]) -> matplotlib.fig A function which plots one parameter on an Axes object, given its index. indices : list[int] The list of indices to pass into ``plot_func``. + fig : matplotlib.figure.Figure, optional + The figure object to use for plot. + progress_callback: Union[Callable[[int, int], None], None] + Callback function for providing progress during plot creation + First argument is current completed sub plot and second is total number of sub plots Returns ------- @@ -947,15 +1012,21 @@ def panel_plot_helper(plot_func: Callable, indices: list[int]) -> matplotlib.fig """ nplots = len(indices) nrows, ncols = ceil(sqrt(nplots)), round(sqrt(nplots)) - fig = plt.subplots(nrows, ncols, figsize=(11, 10))[0] - axs = fig.get_axes() - for plot_num, index in enumerate(indices): - plot_func(axs[plot_num], index) - - # blank unused plots - for i in range(nplots, len(axs)): - axs[i].set_visible(False) + if fig is None: + fig = plt.subplots(nrows, ncols, figsize=(11, 10), subplot_kw={"visible": False})[0] + else: + fig.clf() + fig.subplots(nrows, ncols, subplot_kw={"visible": False}) + axs = fig.get_axes() + for index, plot_num in enumerate(indices): + axs[index].tick_params(which="both", labelsize="medium") + axs[index].xaxis.offsetText.set_fontsize("small") + axs[index].yaxis.offsetText.set_fontsize("small") + axs[index].set_visible(True) + plot_func(axs[index], plot_num) + if progress_callback is not None: + progress_callback(index, nplots) fig.tight_layout() return fig @@ -963,15 +1034,16 @@ def panel_plot_helper(plot_func: Callable, indices: list[int]) -> matplotlib.fig @assert_bayesian("Histogram") def plot_hists( - results: RATapi.outputs.BayesResults, - params: Union[list[Union[int, str]], None] = None, + results: ratapi.outputs.BayesResults, + params: list[int | str] | None = None, smooth: bool = True, - sigma: Union[float, None] = None, - estimated_density: Union[ - dict[Literal["normal", "lognor", "kernel", None]], Literal["normal", "lognor", "kernel", None] - ] = None, + sigma: float | None = None, + estimated_density: dict[Literal["normal", "lognor", "kernel", None]] + | Literal["normal", "lognor", "kernel", None] = None, block: bool = False, + fig: matplotlib.figure.Figure | None = None, return_fig: bool = False, + progress_callback: Callable[[int, int], None] | None = None, **hist_settings, ): """Plot marginalised posteriors for several parameters from a Bayesian analysis. @@ -1004,8 +1076,13 @@ def plot_hists( e.g. to apply 'normal' to all unset parameters, set `estimated_density = {'default': 'normal'}`. block : bool, default False Whether Python should block until the plot is closed. + fig : matplotlib.figure.Figure, optional + The figure object to use for plot. return_fig: bool, default False If True, return the figure as an object instead of showing it. + progress_callback: Union[Callable[[int, int], None], None] + Callback function for providing progress during plot creation + First argument is current completed sub plot and second is total number of sub plots hist_settings : Settings passed to `np.histogram`. By default, the settings passed are `bins = 25` and `density = True`. @@ -1026,7 +1103,7 @@ def plot_hists( if estimated_density is not None: - def validate_dens_type(dens_type: Union[str, None], param: str): + def validate_dens_type(dens_type: str | None, param: str): """Check estimated density is a supported type.""" if dens_type not in [None, "normal", "lognor", "kernel"]: raise ValueError( @@ -1058,11 +1135,13 @@ def validate_dens_type(dens_type: Union[str, None], param: str): i, smooth=smooth, sigma=sigma, - estimated_density=estimated_density.get(i, None), + estimated_density=estimated_density.get(i), axes=ax, **hist_settings, ), params, + fig, + progress_callback, ) if return_fig: return fig @@ -1071,17 +1150,19 @@ def validate_dens_type(dens_type: Union[str, None], param: str): @assert_bayesian("Chain") def plot_chain( - results: RATapi.outputs.BayesResults, - params: Union[list[Union[int, str]], None] = None, + results: ratapi.outputs.BayesResults, + params: list[int | str] | None = None, maxpoints: int = 15000, block: bool = False, + fig: matplotlib.figure.Figure | None = None, return_fig: bool = False, + progress_callback: Callable[[int, int], None] | None = None, ): """Plot the MCMC chain for each parameter of a Bayesian analysis. Parameters ---------- - results : RATapi.outputs.BayesResults + results : ratapi.outputs.BayesResults The results of a Bayesian analysis. params : list[int], default None The indices or names of a subset of parameters if required. @@ -1090,8 +1171,13 @@ def plot_chain( The maximum number of points to plot for each parameter. block : bool, default False Whether Python should block until the plot is closed. + fig : matplotlib.figure.Figure, optional + The figure object to use for plot. return_fig: bool, default False If True, return the figure as an object instead of showing it. + progress_callback: Union[Callable[[int, int], None], None] + Callback function for providing progress during plot creation + First argument is current completed sub plot and second is total number of sub plots Returns ------- @@ -1100,7 +1186,7 @@ def plot_chain( """ chain = results.chain - nsimulations, nplots = chain.shape + nsimulations, _ = chain.shape # skip is to evenly distribute points plotted # all points will be plotted if maxpoints < nsimulations skip = max(floor(nsimulations / maxpoints), 1) @@ -1115,15 +1201,15 @@ def plot_chain( def plot_one_chain(axes: Axes, i: int): axes.plot(range(0, nsimulations, skip), chain[:, i][0:nsimulations:skip]) - axes.set_title(results.fitNames[i]) + axes.set_title(results.fitNames[i], fontsize="small") - fig = panel_plot_helper(plot_one_chain, params) + fig = panel_plot_helper(plot_one_chain, params, fig, progress_callback) if return_fig: return fig plt.show(block=block) -def plot_bayes(project: RATapi.Project, results: RATapi.outputs.BayesResults): +def plot_bayes(project: ratapi.Project, results: ratapi.outputs.BayesResults): """Plot the results of a Bayesian analysis with confidence information. This produces an unshaded reflectivity/SLD plot, a reflectivity/SLD plot with shaded 95% confidence @@ -1140,7 +1226,7 @@ def plot_bayes(project: RATapi.Project, results: RATapi.outputs.BayesResults): Indicates the plot should block until it is closed """ - if isinstance(results, RATapi.outputs.BayesResults): + if isinstance(results, ratapi.outputs.BayesResults): plot_ref_sld(project, results) plot_ref_sld(project, results, bayes=95) plot_hists(results) diff --git a/RATapi/wrappers.py b/ratapi/wrappers.py similarity index 68% rename from RATapi/wrappers.py rename to ratapi/wrappers.py index ab5f5fa6..38f276ac 100644 --- a/RATapi/wrappers.py +++ b/ratapi/wrappers.py @@ -1,13 +1,14 @@ -"""Wrappers for the interface between RATapi and MATLAB custom files.""" +"""Wrappers for the interface between ratapi and MATLAB custom files.""" +import os import pathlib +from collections.abc import Callable from contextlib import suppress -from typing import Callable import numpy as np from numpy.typing import ArrayLike -import RATapi.rat_core +import ratapi.rat_core def start_matlab(): @@ -20,10 +21,14 @@ def start_matlab(): """ future = None - with suppress(ImportError): - import matlab.engine + if os.environ.get("DELAY_MATLAB_START", "0") == "0": + with suppress(ImportError): + import atexit + + import matlab.engine - future = matlab.engine.start_matlab(background=True) + future = matlab.engine.start_matlab(background=True) + atexit.register(lambda: future.result()) return future @@ -39,10 +44,11 @@ class MatlabWrapper: """ loader = start_matlab() + loader_error_message = "matlabengine is required to use MatlabWrapper" def __init__(self, filename: str) -> None: if self.loader is None: - raise ImportError("matlabengine is required to use MatlabWrapper") from None + raise ImportError(self.loader_error_message) from None self.engine = self.loader.result() path = pathlib.Path(filename) @@ -72,10 +78,10 @@ def handle(*args): np.array(args[0], "float"), # params np.array(args[1], "float"), # bulk in np.array(args[2], "float"), # bulk out - float(args[3] + 1), # contrast + float(args[3]), # contrast ] if len(args) > 4: - matlab_args.append(float(args[4] + 1)) # domain number + matlab_args.append(float(args[4])) # domain number output, sub_rough = getattr(self.engine, self.function_name)( *matlab_args, @@ -86,6 +92,30 @@ def handle(*args): return handle +def use_shared_matlab(name, custom_error_message): + """Connect asynchronously to shared MATLAB engine instance with the given name. + + Parameters + ---------- + name : str + The name of shared MATLAB engine instance + custom_error_message : str + The custom error message in case of failed connection + + Returns + ------- + future : matlab.engine.futureresult.FutureResult + A future used to get the actual matlab engine. + + """ + with suppress(ImportError): + import matlab.engine + + MatlabWrapper.loader = matlab.engine.connect_matlab(name, background=True) + MatlabWrapper.loader_error_message = custom_error_message + return MatlabWrapper.loader + + class DylibWrapper: """Creates a python callback for a function in dynamic library. @@ -99,7 +129,7 @@ class DylibWrapper: """ def __init__(self, filename, function_name) -> None: - self.engine = RATapi.rat_core.DylibEngine(filename, function_name) + self.engine = ratapi.rat_core.DylibEngine(filename, function_name) def getHandle(self) -> Callable[[ArrayLike, ArrayLike, ArrayLike, int, int], tuple[ArrayLike, float]]: """Return a wrapper for the custom dynamic library function. diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index cee9a790..00000000 --- a/requirements.txt +++ /dev/null @@ -1,14 +0,0 @@ -numpy >= 1.20 -scipy >= 1.13.1 -prettytable >= 3.9.0 -pybind11 >= 2.4 -pydantic >= 2.7.2 -pytest >= 7.4.0 -pytest-cov >= 4.1.0 -matplotlib >= 3.8.3 -StrEnum >= 0.4.15; python_version < '3.11' -ruff >= 0.4.10 -scipy >= 1.13.1 -tqdm >= 4.66.5 -orsopy >= 1.2.1 -pint >= 0.24.4 diff --git a/setup.py b/setup.py index 4da0357b..9f70ff77 100644 --- a/setup.py +++ b/setup.py @@ -8,8 +8,7 @@ from setuptools.command.build_clib import build_clib from setuptools.command.build_ext import build_ext -__version__ = "0.0.0.dev6" -PACKAGE_NAME = "RATapi" +PACKAGE_NAME = "ratapi" with open("README.md") as f: LONG_DESCRIPTION = f.read() @@ -19,7 +18,7 @@ ext_modules = [ Extension( - "RATapi.rat_core", + "ratapi.rat_core", sources=["cpp/rat.cpp", *glob("cpp/RAT/*.c*")], include_dirs=[ # Path to pybind11 headers @@ -61,8 +60,8 @@ class BuildExt(build_ext): """A custom build extension for adding compiler-specific options.""" c_opts = { - "msvc": ["/EHsc"], - "unix": ["-fopenmp", "-std=c++11"], + "msvc": ["/O2", "/EHsc", "/openmp"], + "unix": ["-O2", "-fopenmp", "-std=c++11"], } l_opts = { "msvc": [], @@ -70,8 +69,8 @@ class BuildExt(build_ext): } if sys.platform == "darwin": - darwin_opts = ["-stdlib=libc++", "-mmacosx-version-min=10.9"] - c_opts["unix"] = [*darwin_opts, "-fopenmp"] + darwin_opts = ["-stdlib=libc++"] + c_opts["unix"] = [*darwin_opts, "-fopenmp", "-O2"] l_opts["unix"] = [*darwin_opts, "-lomp"] def build_extensions(self): @@ -121,7 +120,7 @@ def build_libraries(self, libraries): compiler_type = self.compiler.compiler_type if compiler_type == "msvc": - compile_args = ["/EHsc", "/LD"] + compile_args = ["/EHsc", "/LD", "-D_DISABLE_CONSTEXPR_MUTEX_CONSTRUCTOR"] else: compile_args = ["-std=c++11", "-fPIC"] @@ -152,38 +151,11 @@ def build_libraries(self, libraries): setup( name=PACKAGE_NAME, - version=__version__, - author="", - author_email="", - url="https://github.com/RascalSoftware/python-RAT", - description="Python extension for the Reflectivity Analysis Toolbox (RAT)", - long_description=LONG_DESCRIPTION, - long_description_content_type="text/markdown", - packages=find_packages(), + packages=find_packages(exclude=("tests",)), include_package_data=True, - package_data={"": [get_shared_object_name(libevent[0])], "RATapi.examples": ["data/*.dat"]}, + package_data={"": [get_shared_object_name(libevent[0])], "ratapi.examples": ["data/*.dat"]}, cmdclass={"build_clib": BuildClib, "build_ext": BuildExt}, libraries=[libevent], ext_modules=ext_modules, - python_requires=">=3.10", - install_requires=[ - "numpy >= 1.20", - "prettytable >= 3.9.0", - "pydantic >= 2.7.2", - "matplotlib >= 3.8.3", - "scipy >= 1.13.1", - "tqdm >= 4.66.5", - ], - extras_require={ - ':python_version < "3.11"': ["StrEnum >= 0.4.15"], - "Dev": ["pytest>=7.4.0", "pytest-cov>=4.1.0", "ruff>=0.4.10"], - "Orso": ["orsopy>=1.2.1", "pint>=0.24.4"], - "Matlab_latest": ["matlabengine"], - "Matlab_2025a": ["matlabengine == 25.1.*"], - "Matlab_2024b": ["matlabengine == 24.2.2"], - "Matlab_2024a": ["matlabengine == 24.1.4"], - "Matlab_2023b": ["matlabengine == 23.2.3"], - "Matlab_2023a": ["matlabengine == 9.14.3"], - }, zip_safe=False, ) diff --git a/tests/conftest.py b/tests/conftest.py index c109c92d..0f3b3569 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -2,13 +2,16 @@ import tempfile from pathlib import Path +import matplotlib + +matplotlib.use("Agg") import numpy as np import pytest -import RATapi -import RATapi.classlist -import RATapi.outputs -import RATapi.rat_core +import ratapi +import ratapi.classlist +import ratapi.outputs +import ratapi.rat_core @pytest.fixture @@ -23,7 +26,7 @@ def input_project(): """A cut-down version of the input Project object for a reflectivity calculation set out in "DSPC_standard_layers.py". """ - project = RATapi.Project( + project = ratapi.Project( name="original_dspc_bilayer", calculation="normal", model="standard layers", @@ -269,7 +272,7 @@ def input_project(): @pytest.fixture def reflectivity_calculation_output_results(): """The C++ results object for a reflectivity calculation of the project set out in "DSPC_standard_layers.py".""" - results = RATapi.rat_core.OutputResult() + results = ratapi.rat_core.OutputResult() results.reflectivity = [ np.array( [ @@ -867,10 +870,10 @@ def reflectivity_calculation_output_results(): ], ] results.resampledLayers = [[np.array([[0.0, 0.0, 0.0]])], [np.array([[0.0, 0.0, 0.0]])]] - results.calculationResults = RATapi.rat_core.Calculation() + results.calculationResults = ratapi.rat_core.Calculation() results.calculationResults.chiValues = np.array([202.83057377, 1641.4024969]) results.calculationResults.sumChi = 1844.2330706690975 - results.contrastParams = RATapi.rat_core.ContrastParams() + results.contrastParams = ratapi.rat_core.ContrastParams() results.contrastParams.scalefactors = np.array([0.1, 0.15]) results.contrastParams.bulkIn = np.array([2.073e-06, 2.073e-06]) results.contrastParams.bulkOut = np.array([5.98e-06, 2.21e-06]) @@ -925,7 +928,7 @@ def reflectivity_calculation_output_results(): @pytest.fixture def reflectivity_calculation_results(): """The python results object for a reflectivity calculation of the project set out in "DSPC_standard_layers.py".""" - return RATapi.outputs.Results( + return ratapi.outputs.Results( reflectivity=[ np.array( [ @@ -1523,11 +1526,11 @@ def reflectivity_calculation_results(): ], ], resampledLayers=[[np.array([[0.0, 0.0, 0.0]])], [np.array([[0.0, 0.0, 0.0]])]], - calculationResults=RATapi.outputs.CalculationResults( + calculationResults=ratapi.outputs.CalculationResults( chiValues=np.array([202.83057377, 1641.4024969]), sumChi=1844.2330706690975, ), - contrastParams=RATapi.outputs.ContrastParams( + contrastParams=ratapi.outputs.ContrastParams( scalefactors=np.array([0.1, 0.15]), bulkIn=np.array([2.073e-06, 2.073e-06]), bulkOut=np.array([5.98e-06, 2.21e-06]), @@ -1586,7 +1589,7 @@ def dream_output_results(): This optimisation used the parameters: nSamples=1, nChains=1. However, the calculationResults, contrastParams, and fitParams are taken from an optimisation with the parameters: nSamples=50000, nChains=10. """ - results = RATapi.rat_core.OutputResult() + results = ratapi.rat_core.OutputResult() results.reflectivity = [ np.array( [ @@ -2192,10 +2195,10 @@ def dream_output_results(): ], ] results.resampledLayers = [[np.array([[0.0, 0.0, 0.0]])], [np.array([[0.0, 0.0, 0.0]])]] - results.calculationResults = RATapi.rat_core.Calculation() + results.calculationResults = ratapi.rat_core.Calculation() results.calculationResults.chiValues = (np.array([4.6077885, 7.00028098]),) results.calculationResults.sumChi = 11.608069475997699 - results.contrastParams = RATapi.rat_core.ContrastParams() + results.contrastParams = ratapi.rat_core.ContrastParams() results.contrastParams.scalefactors = np.array([0.1, 0.15]) results.contrastParams.bulkIn = np.array([2.073e-06, 2.073e-06]) results.contrastParams.bulkOut = np.array([6.01489149e-06, 1.59371685e-06]) @@ -2253,8 +2256,8 @@ def dream_bayes(): This optimisation used the parameters: nSamples=1, nChains=1. """ - bayes = RATapi.rat_core.OutputBayesResult() - bayes.predictionIntervals = RATapi.rat_core.PredictionIntervals() + bayes = ratapi.rat_core.OutputBayesResult() + bayes.predictionIntervals = ratapi.rat_core.PredictionIntervals() bayes.predictionIntervals.reflectivity = [ np.array( [ @@ -3821,7 +3824,7 @@ def dream_bayes(): 1.46133559e16, ], ) - bayes.confidenceIntervals = RATapi.rat_core.ConfidenceIntervals() + bayes.confidenceIntervals = ratapi.rat_core.ConfidenceIntervals() bayes.confidenceIntervals.percentile65 = np.array( [ [ @@ -3934,7 +3937,7 @@ def dream_bayes(): ], ], ) - bayes.dreamParams = RATapi.rat_core.DreamParams() + bayes.dreamParams = ratapi.rat_core.DreamParams() bayes.dreamParams.nParams = 18.0 bayes.dreamParams.nChains = 1.0 bayes.dreamParams.nGenerations = 1.0 @@ -3954,7 +3957,7 @@ def dream_bayes(): bayes.dreamParams.IO = 0 bayes.dreamParams.storeOutput = 0 bayes.dreamParams.R = np.array([[0.0]]) - bayes.dreamOutput = RATapi.rat_core.DreamOutput() + bayes.dreamOutput = ratapi.rat_core.DreamOutput() bayes.dreamOutput.allChains = np.array( [ [ @@ -4011,7 +4014,7 @@ def dream_bayes(): ], ) bayes.dreamOutput.CR = np.array([[1.00000000, 0.33333333, 0.33333333, 0.33333333]]) - bayes.nestedSamplerOutput = RATapi.rat_core.NestedSamplerOutput() + bayes.nestedSamplerOutput = ratapi.rat_core.NestedSamplerOutput() bayes.nestedSamplerOutput.logZ = 0.0 bayes.nestedSamplerOutput.nestSamples = np.array([[0.0, 0.0]]) bayes.nestedSamplerOutput.postSamples = np.array([[0.0, 0.0]]) @@ -4070,7 +4073,7 @@ def dream_results(): This optimisation used the parameters: nSamples=1, nChains=1. However, the calculationResults, contrastParams, and fitParams are taken from an optimisation with the parameters: nSamples=50000, nChains=10. """ - return RATapi.outputs.BayesResults( + return ratapi.outputs.BayesResults( reflectivity=[ np.array( [ @@ -4676,11 +4679,11 @@ def dream_results(): ], ], resampledLayers=[[np.array([[0.0, 0.0, 0.0]])], [np.array([[0.0, 0.0, 0.0]])]], - calculationResults=RATapi.outputs.CalculationResults( + calculationResults=ratapi.outputs.CalculationResults( chiValues=np.array([4.6077885, 7.00028098]), sumChi=11.608069475997699, ), - contrastParams=RATapi.outputs.ContrastParams( + contrastParams=ratapi.outputs.ContrastParams( scalefactors=np.array([0.1, 0.15]), bulkIn=np.array([2.073e-06, 2.073e-06]), bulkOut=np.array([6.01489149e-06, 1.59371685e-06]), @@ -4729,7 +4732,7 @@ def dream_results(): "D2O", "SMW", ], - predictionIntervals=RATapi.outputs.PredictionIntervals( + predictionIntervals=ratapi.outputs.PredictionIntervals( reflectivity=[ np.array( [ @@ -6297,7 +6300,7 @@ def dream_results(): ], ), ), - confidenceIntervals=RATapi.outputs.ConfidenceIntervals( + confidenceIntervals=ratapi.outputs.ConfidenceIntervals( percentile65=np.array( [ [ @@ -6411,7 +6414,7 @@ def dream_results(): ], ), ), - dreamParams=RATapi.outputs.DreamParams( + dreamParams=ratapi.outputs.DreamParams( nParams=18.0, nChains=1.0, nGenerations=1.0, @@ -6432,7 +6435,7 @@ def dream_results(): storeOutput=False, R=np.array([[0.0]]), ), - dreamOutput=RATapi.outputs.DreamOutput( + dreamOutput=ratapi.outputs.DreamOutput( allChains=np.array( [ [ @@ -6490,7 +6493,7 @@ def dream_results(): ), CR=np.array([[1.0, 0.33333333, 0.33333333, 0.33333333]]), ), - nestedSamplerOutput=RATapi.outputs.NestedSamplerOutput( + nestedSamplerOutput=ratapi.outputs.NestedSamplerOutput( logZ=0.0, logZErr=0.0, nestSamples=np.array([[0.0, 0.0]]), @@ -6543,17 +6546,29 @@ def dream_results(): ) +@pytest.fixture +def nested_sampler_results(dream_results): + results = dream_results + results.nestedSamplerOutput = ratapi.outputs.NestedSamplerOutput( + logZ=-28.99992503667041, + logZErr=0.3391187711291207, + nestSamples=np.ones((100, 9)), + postSamples=np.ones((100, 10)), + ) + return results + + @pytest.fixture def r1_default_project(): """The Project corresponding to the data in R1defaultProject.mat.""" - project = RATapi.Project( + project = ratapi.Project( name="defaultProject", calculation="normal", model="standard layers", geometry="air/substrate", absorption=False, - parameters=RATapi.ClassList( - RATapi.models.Parameter( + parameters=ratapi.ClassList( + ratapi.models.Parameter( name="Substrate Roughness", min=3.0, value=4.844363132849221, @@ -6564,8 +6579,8 @@ def r1_default_project(): sigma=np.inf, ) ), - bulk_in=RATapi.ClassList( - RATapi.models.Parameter( + bulk_in=ratapi.ClassList( + ratapi.models.Parameter( name="Air", min=0.0, value=0.0, @@ -6576,8 +6591,8 @@ def r1_default_project(): sigma=np.inf, ) ), - bulk_out=RATapi.ClassList( - RATapi.models.Parameter( + bulk_out=ratapi.ClassList( + ratapi.models.Parameter( name="D2O", min=6.3e-06, value=6.35e-06, @@ -6588,8 +6603,8 @@ def r1_default_project(): sigma=np.inf, ) ), - scalefactors=RATapi.ClassList( - RATapi.models.Parameter( + scalefactors=ratapi.ClassList( + ratapi.models.Parameter( name="Scalefactor 1", min=0.009999999776482582, value=0.10141560336360426, @@ -6600,11 +6615,11 @@ def r1_default_project(): sigma=np.inf, ) ), - backgrounds=RATapi.ClassList( - RATapi.models.Background(name="Background 1", type="constant", source="Background parameter 1") + backgrounds=ratapi.ClassList( + ratapi.models.Background(name="Background 1", type="constant", source="Background parameter 1") ), - background_parameters=RATapi.ClassList( - RATapi.models.Parameter( + background_parameters=ratapi.ClassList( + ratapi.models.Parameter( name="Background parameter 1", min=5e-08, value=3.069003361230152e-06, @@ -6615,11 +6630,11 @@ def r1_default_project(): sigma=np.inf, ) ), - resolutions=RATapi.ClassList( - RATapi.models.Resolution(name="Resolution 1", type="constant", source="Resolution parameter 1") + resolutions=ratapi.ClassList( + ratapi.models.Resolution(name="Resolution 1", type="constant", source="Resolution parameter 1") ), - resolution_parameters=RATapi.ClassList( - RATapi.models.Parameter( + resolution_parameters=ratapi.ClassList( + ratapi.models.Parameter( name="Resolution parameter 1", min=0.01, value=0.03, @@ -6711,15 +6726,15 @@ def r1_default_project(): @pytest.fixture def r1_monolayer(): """The Project file corresponding to the data in R1monolayerVolumeModel.mat.""" - project = RATapi.Project( + project = ratapi.Project( name="monolayerVolumeModel", calculation="normal", model="custom layers", geometry="air/substrate", absorption=False, - parameters=RATapi.ClassList( + parameters=ratapi.ClassList( [ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Substrate Roughness", min=1.0, value=2.9979642781948908, @@ -6729,7 +6744,7 @@ def r1_monolayer(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Area per molecule", min=47.0, value=53.052680457664785, @@ -6739,7 +6754,7 @@ def r1_monolayer(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Head Thickness", min=7.0, value=12.276333836779942, @@ -6749,7 +6764,7 @@ def r1_monolayer(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Theta", min=0.0, value=28.870541049836262, @@ -6761,8 +6776,8 @@ def r1_monolayer(): ), ] ), - bulk_in=RATapi.ClassList( - RATapi.models.Parameter( + bulk_in=ratapi.ClassList( + ratapi.models.Parameter( name="Air", min=0.0, value=0.0, @@ -6774,9 +6789,9 @@ def r1_monolayer(): ) ), bulk_out=( - RATapi.ClassList( + ratapi.ClassList( [ - RATapi.models.Parameter( + ratapi.models.Parameter( name="D2O", min=6.3e-06, value=6.35e-06, @@ -6786,7 +6801,7 @@ def r1_monolayer(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="ACMW", min=-5e-07, value=0.0, @@ -6799,8 +6814,8 @@ def r1_monolayer(): ] ) ), - scalefactors=RATapi.ClassList( - RATapi.models.Parameter( + scalefactors=ratapi.ClassList( + ratapi.models.Parameter( name="Scalefactor 1", min=0.1, value=0.2272676786810902, @@ -6811,15 +6826,15 @@ def r1_monolayer(): sigma=np.inf, ) ), - backgrounds=RATapi.ClassList( + backgrounds=ratapi.ClassList( [ - RATapi.models.Background(name="Background D2O", type="constant", source="Background parameter 1"), - RATapi.models.Background(name="Background ACMW", type="constant", source="Background parameter 2"), + ratapi.models.Background(name="Background D2O", type="constant", source="Background parameter 1"), + ratapi.models.Background(name="Background ACMW", type="constant", source="Background parameter 2"), ] ), - background_parameters=RATapi.ClassList( + background_parameters=ratapi.ClassList( [ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Background parameter 1", min=1e-07, value=2.2653463958223856e-06, @@ -6829,7 +6844,7 @@ def r1_monolayer(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Background parameter 2", min=1e-07, value=5.7431759430575025e-06, @@ -6841,11 +6856,11 @@ def r1_monolayer(): ), ] ), - resolutions=RATapi.ClassList( - RATapi.models.Resolution(name="Resolution 1", type="constant", source="Resolution parameter 1") + resolutions=ratapi.ClassList( + ratapi.models.Resolution(name="Resolution 1", type="constant", source="Resolution parameter 1") ), - resolution_parameters=RATapi.ClassList( - RATapi.models.Parameter( + resolution_parameters=ratapi.ClassList( + ratapi.models.Parameter( name="Resolution parameter 1", min=0.01, value=0.03, @@ -6856,9 +6871,13 @@ def r1_monolayer(): sigma=np.inf, ) ), - custom_files=RATapi.ClassList( - RATapi.models.CustomFile( - name="Model_IIb", filename="Model_IIb.m", function_name="Model_IIb", language="matlab", path="" + custom_files=ratapi.ClassList( + ratapi.models.CustomFile( + name="Model_IIb", + filename="Model_IIb.m", + function_name="Model_IIb", + language="matlab", + path=Path(__file__).parent / "test_data", ) ), ) @@ -7376,14 +7395,14 @@ def r1_monolayer(): @pytest.fixture def r1_monolayer_8_contrasts(): """The Project equivalent of the R1 Monolayer_8_contrasts RasCAL-1 project.""" - return RATapi.Project( + return ratapi.Project( name="20nM_data", calculation="normal", model="standard layers", geometry="air/substrate", absorption=False, parameters=[ - RATapi.models.ProtectedParameter( + ratapi.models.ProtectedParameter( name="Substrate Roughness", min=3.0, value=6.990825828311747, @@ -7393,7 +7412,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Tails thick", min=12.0, value=18.769067940891517, @@ -7403,7 +7422,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Deuterated tails SLD", min=5e-06, value=6.935587727961928e-06, @@ -7413,7 +7432,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Tails roughness", min=3.0, value=3.0000000000074776, @@ -7423,7 +7442,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Hydrogenated tails SLD", min=-6e-07, value=-2.1907853109709215e-07, @@ -7433,7 +7452,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Head thickness", min=7.0, value=7.000000000117902, @@ -7443,7 +7462,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Deuterated head SLD", min=3e-06, value=5.855129143529369e-06, @@ -7453,7 +7472,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Head roughness", min=3.0, value=3.0000000000000258, @@ -7463,7 +7482,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Hydrogenated head SLD", min=1e-06, value=1.8079398141440577e-06, @@ -7473,7 +7492,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Head hydration", min=0.0, value=9.33740417574095, @@ -7485,7 +7504,7 @@ def r1_monolayer_8_contrasts(): ), ], bulk_in=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Air", min=0.0, value=0.0, @@ -7497,7 +7516,7 @@ def r1_monolayer_8_contrasts(): ) ], bulk_out=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="D2O", min=6.3e-06, value=6.349999999999999e-06, @@ -7507,7 +7526,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="ACMW", min=-5e-07, value=3.492898018624419e-08, @@ -7519,7 +7538,7 @@ def r1_monolayer_8_contrasts(): ), ], scalefactors=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Scalefactor 1", min=0.1, value=0.23251357931599084, @@ -7532,7 +7551,7 @@ def r1_monolayer_8_contrasts(): ], domain_ratios=[], background_parameters=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Background parameter 1", min=1e-07, value=2.889465920816701e-06, @@ -7542,7 +7561,7 @@ def r1_monolayer_8_contrasts(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Background parameter 2", min=1e-07, value=5.172884539063037e-06, @@ -7554,19 +7573,19 @@ def r1_monolayer_8_contrasts(): ), ], backgrounds=[ - RATapi.models.Background( + ratapi.models.Background( name="Background D2O", type="constant", source="Background parameter 1", ), - RATapi.models.Background( + ratapi.models.Background( name="Background ACMW", type="constant", source="Background parameter 2", ), ], resolution_parameters=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Resolution parameter 1", min=0.01, value=0.029999999999999964, @@ -7578,7 +7597,7 @@ def r1_monolayer_8_contrasts(): ) ], resolutions=[ - RATapi.models.Resolution( + ratapi.models.Resolution( name="Resolution 1", type="constant", source="Resolution parameter 1", @@ -7586,7 +7605,7 @@ def r1_monolayer_8_contrasts(): ], custom_files=[], data=[ - RATapi.models.Data( + ratapi.models.Data( name="d70acmw20", data=np.array( [ @@ -7646,7 +7665,7 @@ def r1_monolayer_8_contrasts(): data_range=[0.051793, 0.58877], simulation_range=[0.051793, 0.58877], ), - RATapi.models.Data( + ratapi.models.Data( name="d70d2o20", data=np.array( [ @@ -7706,7 +7725,7 @@ def r1_monolayer_8_contrasts(): data_range=[0.051793, 0.58877], simulation_range=[0.051793, 0.58877], ), - RATapi.models.Data( + ratapi.models.Data( name="d13acmw20", data=np.array( [ @@ -7766,7 +7785,7 @@ def r1_monolayer_8_contrasts(): data_range=[0.051793, 0.58877], simulation_range=[0.051793, 0.58877], ), - RATapi.models.Data( + ratapi.models.Data( name="d13d2o20", data=np.array( [ @@ -7826,7 +7845,7 @@ def r1_monolayer_8_contrasts(): data_range=[0.051793, 0.58877], simulation_range=[0.051793, 0.58877], ), - RATapi.models.Data( + ratapi.models.Data( name="d83acmw20", data=np.array( [ @@ -7886,7 +7905,7 @@ def r1_monolayer_8_contrasts(): data_range=[0.051793, 0.58877], simulation_range=[0.051793, 0.58877], ), - RATapi.models.Data( + ratapi.models.Data( name="d83d2o20", data=np.array( [ @@ -7946,7 +7965,7 @@ def r1_monolayer_8_contrasts(): data_range=[0.051793, 0.58877], simulation_range=[0.051793, 0.58877], ), - RATapi.models.Data( + ratapi.models.Data( name="hd2o20", data=np.array( [ @@ -8008,7 +8027,7 @@ def r1_monolayer_8_contrasts(): ), ], layers=[ - RATapi.models.Layer( + ratapi.models.Layer( name="Deuterated tails", thickness="Tails thick", SLD="Deuterated tails SLD", @@ -8016,7 +8035,7 @@ def r1_monolayer_8_contrasts(): hydration="", hydrate_with="bulk out", ), - RATapi.models.Layer( + ratapi.models.Layer( name="Hydrogenated tails", thickness="Tails thick", SLD="Hydrogenated tails SLD", @@ -8024,7 +8043,7 @@ def r1_monolayer_8_contrasts(): hydration="", hydrate_with="bulk out", ), - RATapi.models.Layer( + ratapi.models.Layer( name="Deuterated heads", thickness="Head thickness", SLD="Deuterated head SLD", @@ -8032,7 +8051,7 @@ def r1_monolayer_8_contrasts(): hydration="Head hydration", hydrate_with="bulk out", ), - RATapi.models.Layer( + ratapi.models.Layer( name="Hydrogenated heads", thickness="Head thickness", SLD="Hydrogenated head SLD", @@ -8043,7 +8062,7 @@ def r1_monolayer_8_contrasts(): ], domain_contrasts=[], contrasts=[ - RATapi.models.Contrast( + ratapi.models.Contrast( name="d70, acmw", data="d70acmw20", background="Background ACMW", @@ -8055,7 +8074,7 @@ def r1_monolayer_8_contrasts(): resample=False, model=["Deuterated tails", "Hydrogenated heads"], ), - RATapi.models.Contrast( + ratapi.models.Contrast( name="d70 d2o", data="d70d2o20", background="Background D2O", @@ -8067,7 +8086,7 @@ def r1_monolayer_8_contrasts(): resample=False, model=["Deuterated tails", "Hydrogenated heads"], ), - RATapi.models.Contrast( + ratapi.models.Contrast( name="d13 acmw", data="d13acmw20", background="Background ACMW", @@ -8079,7 +8098,7 @@ def r1_monolayer_8_contrasts(): resample=False, model=["Hydrogenated tails", "Deuterated heads"], ), - RATapi.models.Contrast( + ratapi.models.Contrast( name="d13 d2o", data="d13d2o20", background="Background D2O", @@ -8091,7 +8110,7 @@ def r1_monolayer_8_contrasts(): resample=False, model=["Hydrogenated tails", "Deuterated heads"], ), - RATapi.models.Contrast( + ratapi.models.Contrast( name="d83 acmw", data="d83acmw20", background="Background ACMW", @@ -8103,7 +8122,7 @@ def r1_monolayer_8_contrasts(): resample=False, model=["Deuterated tails", "Deuterated heads"], ), - RATapi.models.Contrast( + ratapi.models.Contrast( name="d83 d2o", data="d83d2o20", background="Background D2O", @@ -8115,7 +8134,7 @@ def r1_monolayer_8_contrasts(): resample=False, model=["Deuterated tails", "Deuterated heads"], ), - RATapi.models.Contrast( + ratapi.models.Contrast( name="fully h, D2O", data="hd2o20", background="Background D2O", @@ -8137,14 +8156,14 @@ def r1_orso_polymer(): # the test data is BIG (400 lines) so it's easier to just load it in orso_poly_data = np.loadtxt(Path(__file__).parent / "test_data/orso_poly.dat") - return RATapi.Project( + return ratapi.Project( name="orsoPolymerExample", calculation="normal", model="standard layers", geometry="air/substrate", absorption=False, parameters=[ - RATapi.models.ProtectedParameter( + ratapi.models.ProtectedParameter( name="Substrate Roughness", min=3.0, value=4.844363132849221, @@ -8156,7 +8175,7 @@ def r1_orso_polymer(): ) ], bulk_in=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Air", min=0.0, value=0.0, @@ -8168,7 +8187,7 @@ def r1_orso_polymer(): ) ], bulk_out=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="D2O", min=6.3e-06, value=6.35e-06, @@ -8180,7 +8199,7 @@ def r1_orso_polymer(): ) ], scalefactors=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Scalefactor 1", min=0.05, value=0.10141560336360426, @@ -8193,7 +8212,7 @@ def r1_orso_polymer(): ], domain_ratios=[], background_parameters=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Background parameter 1", min=5e-08, value=3.069003361230152e-06, @@ -8205,14 +8224,14 @@ def r1_orso_polymer(): ) ], backgrounds=[ - RATapi.models.Background( + ratapi.models.Background( name="Background 1", type="constant", source="Background parameter 1", ) ], resolution_parameters=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Resolution parameter 1", min=0.01, value=0.03, @@ -8224,7 +8243,7 @@ def r1_orso_polymer(): ) ], resolutions=[ - RATapi.models.Resolution( + ratapi.models.Resolution( name="Resolution 1", type="constant", source="Resolution parameter 1", @@ -8232,7 +8251,7 @@ def r1_orso_polymer(): ], custom_files=[], data=[ - RATapi.models.Data( + ratapi.models.Data( name="polymerData", data=orso_poly_data, data_range=[0.0080602, 0.46555], @@ -8242,7 +8261,7 @@ def r1_orso_polymer(): layers=[], domain_contrasts=[], contrasts=[ - RATapi.models.Contrast( + ratapi.models.Contrast( name="Chain-d, acmw", data="polymerData", background="Background 1", @@ -8263,14 +8282,14 @@ def r1_motofit_bench_mark(): """The project from the R1motofitBenchMark RasCAL-1 project file.""" moto_data = np.loadtxt(Path(__file__).parent / "test_data/moto.dat") - return RATapi.Project( + return ratapi.Project( name="motofitBenchMark", calculation="normal", model="standard layers", geometry="air/substrate", absorption=False, parameters=[ - RATapi.models.ProtectedParameter( + ratapi.models.ProtectedParameter( name="Substrate Roughness", min=1.0, value=3.0, @@ -8280,7 +8299,7 @@ def r1_motofit_bench_mark(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="sub rough", min=3.0, value=3.9949146424129665, @@ -8290,7 +8309,7 @@ def r1_motofit_bench_mark(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Thick", min=0.0, value=33.2791896400743, @@ -8300,7 +8319,7 @@ def r1_motofit_bench_mark(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="SLD 1", min=0.0, value=1.074484187182878e-06, @@ -8310,7 +8329,7 @@ def r1_motofit_bench_mark(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="SLD 2", min=9e-06, value=1.0658506835478824e-05, @@ -8320,7 +8339,7 @@ def r1_motofit_bench_mark(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Thick2", min=100.0, value=498.6676783112137, @@ -8330,7 +8349,7 @@ def r1_motofit_bench_mark(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Rough 1", min=2.0, value=4.563688983733924, @@ -8340,7 +8359,7 @@ def r1_motofit_bench_mark(): mu=0.0, sigma=np.inf, ), - RATapi.models.Parameter( + ratapi.models.Parameter( name="Rough 2", min=2.0, value=4.410704485333302, @@ -8352,7 +8371,7 @@ def r1_motofit_bench_mark(): ), ], bulk_in=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Air", min=0.0, value=0.0, @@ -8364,7 +8383,7 @@ def r1_motofit_bench_mark(): ) ], bulk_out=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="D2O", min=2e-05, value=2.01e-05, @@ -8376,7 +8395,7 @@ def r1_motofit_bench_mark(): ) ], scalefactors=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Scalefactor 1", min=0.99, value=0.9999894027309877, @@ -8389,7 +8408,7 @@ def r1_motofit_bench_mark(): ], domain_ratios=[], background_parameters=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Background parameter 1", min=5e-08, value=1.306895319301746e-07, @@ -8401,14 +8420,14 @@ def r1_motofit_bench_mark(): ) ], backgrounds=[ - RATapi.models.Background( + ratapi.models.Background( name="Background 1", type="constant", source="Background parameter 1", ) ], resolution_parameters=[ - RATapi.models.Parameter( + ratapi.models.Parameter( name="Resolution parameter 1", min=0.0, value=0.0, @@ -8420,7 +8439,7 @@ def r1_motofit_bench_mark(): ) ], resolutions=[ - RATapi.models.Resolution( + ratapi.models.Resolution( name="Resolution 1", type="constant", source="Resolution parameter 1", @@ -8428,7 +8447,7 @@ def r1_motofit_bench_mark(): ], custom_files=[], data=[ - RATapi.models.Data( + ratapi.models.Data( name="mFitBench", data=moto_data, data_range=[0.02, 0.59188], @@ -8436,7 +8455,7 @@ def r1_motofit_bench_mark(): ) ], layers=[ - RATapi.models.Layer( + ratapi.models.Layer( name="New Layer 0", thickness="Thick", SLD="SLD 1", @@ -8444,7 +8463,7 @@ def r1_motofit_bench_mark(): hydration="", hydrate_with="bulk out", ), - RATapi.models.Layer( + ratapi.models.Layer( name="New Layer 1", thickness="Thick2", SLD="SLD 2", @@ -8455,7 +8474,7 @@ def r1_motofit_bench_mark(): ], domain_contrasts=[], contrasts=[ - RATapi.models.Contrast( + ratapi.models.Contrast( name="Chain-d, acmw", data="mFitBench", background="Background 1", @@ -8474,47 +8493,55 @@ def r1_motofit_bench_mark(): @pytest.fixture def dspc_standard_layers(): """The project from the DSPC Standard Layers example.""" - project, _ = RATapi.examples.DSPC_standard_layers() + project, _ = ratapi.examples.DSPC_standard_layers() return project @pytest.fixture def dspc_custom_layers(): """The project from the DSPC Custom Layers example.""" - project, _ = RATapi.examples.DSPC_custom_layers() + project, _ = ratapi.examples.DSPC_custom_layers() return project @pytest.fixture def dspc_custom_xy(): """The project from the DSPC Custom XY example.""" - project, _ = RATapi.examples.DSPC_custom_XY() + project, _ = ratapi.examples.DSPC_custom_XY() return project @pytest.fixture def domains_standard_layers(): """The project from the domains Standard Layers example.""" - project, _ = RATapi.examples.domains_standard_layers() + project, _ = ratapi.examples.domains_standard_layers() return project @pytest.fixture def domains_custom_layers(): """The project from the domains Custom Layers example.""" - project, _ = RATapi.examples.domains_custom_layers() + project, _ = ratapi.examples.domains_custom_layers() return project @pytest.fixture def domains_custom_xy(): """The project from the domains Custom XY example.""" - project, _ = RATapi.examples.domains_custom_XY() + project, _ = ratapi.examples.domains_custom_XY() return project @pytest.fixture def absorption(): """The project from the absorption example.""" - project, _ = RATapi.examples.absorption() + project, _ = ratapi.examples.absorption() + return project + + +@pytest.fixture +def absorption_different_function(): + """The project from the absorption example with a function name different from filename.""" + project, _ = ratapi.examples.absorption() + project.custom_files[0].function_name = "test_func" return project diff --git a/tests/test_classlist.py b/tests/test_classlist.py index 3fa874ab..98d9440c 100644 --- a/tests/test_classlist.py +++ b/tests/test_classlist.py @@ -5,12 +5,12 @@ import warnings from collections import deque from collections.abc import Iterable, Sequence -from typing import Any, Union +from typing import Any import prettytable import pytest -from RATapi.classlist import ClassList +from ratapi.classlist import ClassList from tests.utils import InputAttributes, SubInputAttributes @@ -611,7 +611,7 @@ def test_insert_kwargs_same_name(two_name_class_list: ClassList, new_values: dic (InputAttributes(name="Bob")), ], ) -def test_remove(two_name_class_list: ClassList, remove_value: Union[object, str]) -> None: +def test_remove(two_name_class_list: ClassList, remove_value: object | str) -> None: """We should be able to remove an object either by the value of the name_field or by specifying the object itself. """ @@ -626,7 +626,7 @@ def test_remove(two_name_class_list: ClassList, remove_value: Union[object, str] (InputAttributes(name="Eve")), ], ) -def test_remove_not_present(two_name_class_list: ClassList, remove_value: Union[object, str]) -> None: +def test_remove_not_present(two_name_class_list: ClassList, remove_value: object | str) -> None: """If we remove an object not included in the ClassList we should raise a ValueError.""" with pytest.raises(ValueError, match=re.escape("list.remove(x): x not in list")): two_name_class_list.remove(remove_value) @@ -641,7 +641,7 @@ def test_remove_not_present(two_name_class_list: ClassList, remove_value: Union[ (InputAttributes(name="Eve"), 0), ], ) -def test_count(two_name_class_list: ClassList, count_value: Union[object, str], expected_count: int) -> None: +def test_count(two_name_class_list: ClassList, count_value: object | str, expected_count: int) -> None: """We should be able to determine the number of times an object is in the ClassList using either the object itself or its name_field value. """ @@ -655,7 +655,7 @@ def test_count(two_name_class_list: ClassList, count_value: Union[object, str], (InputAttributes(name="Bob"), 1), ], ) -def test_index(two_name_class_list: ClassList, index_value: Union[object, str], expected_index: int) -> None: +def test_index(two_name_class_list: ClassList, index_value: object | str, expected_index: int) -> None: """We should be able to find the index of an object in the ClassList either by its name_field value or by specifying the object itself. """ @@ -671,7 +671,7 @@ def test_index(two_name_class_list: ClassList, index_value: Union[object, str], ) def test_index_offset( two_name_class_list: ClassList, - index_value: Union[object, str], + index_value: object | str, offset: int, expected_index: int, ) -> None: @@ -688,7 +688,7 @@ def test_index_offset( (InputAttributes(name="Eve")), ], ) -def test_index_not_present(two_name_class_list: ClassList, index_value: Union[object, str]) -> None: +def test_index_not_present(two_name_class_list: ClassList, index_value: object | str) -> None: """If we try to find the index of an object not included in the ClassList we should raise a ValueError.""" # with pytest.raises(ValueError, match=f"'{index_value}' is not in list") as e: with pytest.raises(ValueError): @@ -741,7 +741,7 @@ def test_extend_empty_classlist(extended_list: Sequence, one_name_class_list: Cl ], ) def test_set_fields( - two_name_class_list: ClassList, index: Union[int, str], new_values: dict[str, Any], expected_classlist: ClassList + two_name_class_list: ClassList, index: int | str, new_values: dict[str, Any], expected_classlist: ClassList ) -> None: """We should be able to set field values in an element of a ClassList using keyword arguments.""" class_list = two_name_class_list @@ -963,7 +963,7 @@ def test__check_classes_different_classes(input_list: Sequence) -> None: def test__get_item_from_name_field( two_name_class_list: ClassList, value: str, - expected_output: Union[object, str], + expected_output: object | str, ) -> None: """When we input the name_field value of an object defined in the ClassList, we should return the object. If the value is not the name_field of an object defined in the ClassList, we should return the value. @@ -1044,7 +1044,7 @@ class NestedModel(pydantic.BaseModel): submodels_list = [{"i": 3, "s": "hello", "f": 3.0}, {"i": 4, "s": "hi", "f": 3.14}] model = NestedModel(submodels=submodels_list) - for submodel, exp_dict in zip(model.submodels, submodels_list): + for submodel, exp_dict in zip(model.submodels, submodels_list, strict=False): for key, value in exp_dict.items(): assert getattr(submodel, key) == value diff --git a/tests/test_controls.py b/tests/test_controls.py index 5c4400a3..5a83bccc 100644 --- a/tests/test_controls.py +++ b/tests/test_controls.py @@ -3,13 +3,14 @@ import contextlib import os import tempfile -from typing import Any, Union +from pathlib import Path +from typing import Any import pydantic import pytest -from RATapi.controls import Controls, fields -from RATapi.utils.enums import BoundHandling, Display, Parallel, Procedures, Strategies +from ratapi.controls import Controls, fields +from ratapi.utils.enums import BoundHandling, Display, Parallel, Procedures, Strategies @pytest.fixture @@ -45,6 +46,30 @@ def test_extra_property_error() -> None: controls.test = 1 +@pytest.mark.parametrize( + "inputs", + [ + {"parallel": Parallel.Contrasts, "resampleMinAngle": 0.66}, + {"procedure": "simplex"}, + {"procedure": "dream", "nSamples": 504, "nChains": 1200}, + {"procedure": "de", "crossoverProbability": 0.45, "strategy": Strategies.RandomEitherOrAlgorithm}, + {"procedure": "ns", "nMCMC": 4, "propScale": 0.6}, + ], +) +def test_save_load(inputs): + """Test that saving and loading controls returns the same controls.""" + + original_controls = Controls(**inputs) + with tempfile.TemporaryDirectory() as tmp: + # ignore relative path warnings + path = Path(tmp, "controls.json") + original_controls.save(path) + converted_controls = Controls.load(path) + + for field in Controls.model_fields: + assert getattr(converted_controls, field) == getattr(original_controls, field) + + class TestCalculate: """Tests the Calculate class.""" @@ -55,16 +80,16 @@ def setup_class(self): @pytest.fixture def table_str(self): table_str = ( - "+------------------+-----------+\n" - "| Property | Value |\n" - "+------------------+-----------+\n" - "| procedure | calculate |\n" - "| parallel | single |\n" - "| calcSldDuringFit | False |\n" - "| resampleMinAngle | 0.9 |\n" - "| resampleNPoints | 50 |\n" - "| display | iter |\n" - "+------------------+-----------+" + "+---------------------+-----------+\n" + "| Property | Value |\n" + "+---------------------+-----------+\n" + "| procedure | calculate |\n" + "| parallel | single |\n" + "| numSimulationPoints | 500 |\n" + "| resampleMinAngle | 0.9 |\n" + "| resampleNPoints | 50 |\n" + "| display | iter |\n" + "+---------------------+-----------+" ) return table_str @@ -73,7 +98,7 @@ def table_str(self): "control_property, value", [ ("parallel", Parallel.Single), - ("calcSldDuringFit", False), + ("numSimulationPoints", 500), ("resampleMinAngle", 0.9), ("resampleNPoints", 50), ("display", Display.Iter), @@ -88,7 +113,7 @@ def test_calculate_property_values(self, control_property: str, value: Any) -> N "control_property, value", [ ("parallel", Parallel.Points), - ("calcSldDuringFit", True), + ("numSimulationPoints", 10), ("resampleMinAngle", 0.2), ("resampleNPoints", 1), ("display", Display.Notify), @@ -183,14 +208,6 @@ def test_calculate_parallel_validation(self, value: Any) -> None: with pytest.raises(pydantic.ValidationError, match="Input should be 'single', 'points' or 'contrasts'"): self.calculate.parallel = value - @pytest.mark.parametrize("value", [5.0, 12]) - def test_calculate_calcSldDuringFit_validation(self, value: Union[int, float]) -> None: - """Tests the calcSldDuringFit setter validation in Calculate class.""" - with pytest.raises( - pydantic.ValidationError, match="Input should be a valid boolean, unable to interpret input" - ): - self.calculate.calcSldDuringFit = value - @pytest.mark.parametrize("value", ["test", "iterate", True, 1, 3.0]) def test_calculate_display_validation(self, value: Any) -> None: """Tests the display setter validation in Calculate class.""" @@ -212,22 +229,22 @@ def setup_class(self): @pytest.fixture def table_str(self): table_str = ( - "+------------------+---------+\n" - "| Property | Value |\n" - "+------------------+---------+\n" - "| procedure | simplex |\n" - "| parallel | single |\n" - "| calcSldDuringFit | False |\n" - "| resampleMinAngle | 0.9 |\n" - "| resampleNPoints | 50 |\n" - "| display | iter |\n" - "| xTolerance | 1e-06 |\n" - "| funcTolerance | 1e-06 |\n" - "| maxFuncEvals | 10000 |\n" - "| maxIterations | 1000 |\n" - "| updateFreq | 1 |\n" - "| updatePlotFreq | 20 |\n" - "+------------------+---------+" + "+---------------------+---------+\n" + "| Property | Value |\n" + "+---------------------+---------+\n" + "| procedure | simplex |\n" + "| parallel | single |\n" + "| numSimulationPoints | 500 |\n" + "| resampleMinAngle | 0.9 |\n" + "| resampleNPoints | 50 |\n" + "| display | iter |\n" + "| xTolerance | 1e-06 |\n" + "| funcTolerance | 1e-06 |\n" + "| maxFuncEvals | 10000 |\n" + "| maxIterations | 1000 |\n" + "| updateFreq | 1 |\n" + "| updatePlotFreq | 20 |\n" + "+---------------------+---------+" ) return table_str @@ -236,7 +253,7 @@ def table_str(self): "control_property, value", [ ("parallel", Parallel.Single), - ("calcSldDuringFit", False), + ("numSimulationPoints", 500), ("resampleMinAngle", 0.9), ("resampleNPoints", 50), ("display", Display.Iter), @@ -257,7 +274,7 @@ def test_simplex_property_values(self, control_property: str, value: Any) -> Non "control_property, value", [ ("parallel", Parallel.Points), - ("calcSldDuringFit", True), + ("numSimulationPoints", 10), ("resampleMinAngle", 0.2), ("resampleNPoints", 1), ("display", Display.Notify), @@ -349,7 +366,7 @@ def test_set_non_simplex_properties(self, wrong_property: str, value: Any) -> No ("maxIterations", -50), ], ) - def test_simplex_property_errors(self, control_property: str, value: Union[float, int]) -> None: + def test_simplex_property_errors(self, control_property: str, value: float | int) -> None: """Tests the property errors of Simplex class.""" with pytest.raises(pydantic.ValidationError, match="Input should be greater than 0"): setattr(self.simplex, control_property, value) @@ -374,7 +391,7 @@ def table_str(self): "+----------------------+---------------+\n" "| procedure | de |\n" "| parallel | single |\n" - "| calcSldDuringFit | False |\n" + "| numSimulationPoints | 500 |\n" "| resampleMinAngle | 0.9 |\n" "| resampleNPoints | 50 |\n" "| display | iter |\n" @@ -395,7 +412,7 @@ def table_str(self): "control_property, value", [ ("parallel", Parallel.Single), - ("calcSldDuringFit", False), + ("numSimulationPoints", 500), ("resampleMinAngle", 0.9), ("resampleNPoints", 50), ("display", Display.Iter), @@ -416,7 +433,7 @@ def test_de_property_values(self, control_property: str, value: Any) -> None: "control_property, value", [ ("parallel", Parallel.Points), - ("calcSldDuringFit", True), + ("numSimulationPoints", 10), ("resampleMinAngle", 0.2), ("resampleNPoints", 1), ("display", Display.Notify), @@ -521,7 +538,7 @@ def test_de_crossoverProbability_error(self, value: int, msg: str) -> None: def test_de_targetValue_numGenerations_populationSize_error( self, control_property: str, - value: Union[int, float], + value: int | float, ) -> None: """Tests the targetValue, numGenerations, populationSize setter error in DE class.""" with pytest.raises(pydantic.ValidationError, match="Input should be greater than or equal to 1"): @@ -542,20 +559,20 @@ def setup_class(self): @pytest.fixture def table_str(self): table_str = ( - "+------------------+--------+\n" - "| Property | Value |\n" - "+------------------+--------+\n" - "| procedure | ns |\n" - "| parallel | single |\n" - "| calcSldDuringFit | False |\n" - "| resampleMinAngle | 0.9 |\n" - "| resampleNPoints | 50 |\n" - "| display | iter |\n" - "| nLive | 150 |\n" - "| nMCMC | 0 |\n" - "| propScale | 0.1 |\n" - "| nsTolerance | 0.1 |\n" - "+------------------+--------+" + "+---------------------+--------+\n" + "| Property | Value |\n" + "+---------------------+--------+\n" + "| procedure | ns |\n" + "| parallel | single |\n" + "| numSimulationPoints | 500 |\n" + "| resampleMinAngle | 0.9 |\n" + "| resampleNPoints | 50 |\n" + "| display | iter |\n" + "| nLive | 150 |\n" + "| nMCMC | 0 |\n" + "| propScale | 0.1 |\n" + "| nsTolerance | 0.1 |\n" + "+---------------------+--------+" ) return table_str @@ -564,7 +581,7 @@ def table_str(self): "control_property, value", [ ("parallel", Parallel.Single), - ("calcSldDuringFit", False), + ("numSimulationPoints", 500), ("resampleMinAngle", 0.9), ("resampleNPoints", 50), ("display", Display.Iter), @@ -583,7 +600,7 @@ def test_ns_property_values(self, control_property: str, value: Any) -> None: "control_property, value", [ ("parallel", Parallel.Points), - ("calcSldDuringFit", True), + ("numSimulationPoints", 10), ("resampleMinAngle", 0.2), ("resampleNPoints", 1), ("display", Display.Notify), @@ -676,7 +693,7 @@ def test_set_non_ns_properties(self, wrong_property: str, value: Any) -> None: ("nLive", -500, 1), ], ) - def test_ns_setter_error(self, control_property: str, value: Union[int, float], bound: int) -> None: + def test_ns_setter_error(self, control_property: str, value: int | float, bound: int) -> None: """Tests the nMCMC, nsTolerance, nLive setter error in NS class.""" with pytest.raises(pydantic.ValidationError, match=f"Input should be greater than or equal to {bound}"): setattr(self.ns, control_property, value) @@ -708,22 +725,22 @@ def setup_class(self): @pytest.fixture def table_str(self): table_str = ( - "+------------------+---------+\n" - "| Property | Value |\n" - "+------------------+---------+\n" - "| procedure | dream |\n" - "| parallel | single |\n" - "| calcSldDuringFit | False |\n" - "| resampleMinAngle | 0.9 |\n" - "| resampleNPoints | 50 |\n" - "| display | iter |\n" - "| nSamples | 20000 |\n" - "| nChains | 10 |\n" - "| jumpProbability | 0.5 |\n" - "| pUnitGamma | 0.2 |\n" - "| boundHandling | reflect |\n" - "| adaptPCR | True |\n" - "+------------------+---------+" + "+---------------------+---------+\n" + "| Property | Value |\n" + "+---------------------+---------+\n" + "| procedure | dream |\n" + "| parallel | single |\n" + "| numSimulationPoints | 500 |\n" + "| resampleMinAngle | 0.9 |\n" + "| resampleNPoints | 50 |\n" + "| display | iter |\n" + "| nSamples | 20000 |\n" + "| nChains | 10 |\n" + "| jumpProbability | 0.5 |\n" + "| pUnitGamma | 0.2 |\n" + "| boundHandling | reflect |\n" + "| adaptPCR | True |\n" + "+---------------------+---------+" ) return table_str @@ -732,7 +749,7 @@ def table_str(self): "control_property, value", [ ("parallel", Parallel.Single), - ("calcSldDuringFit", False), + ("numSimulationPoints", 500), ("resampleMinAngle", 0.9), ("resampleNPoints", 50), ("display", Display.Iter), @@ -753,7 +770,7 @@ def test_dream_property_values(self, control_property: str, value: Any) -> None: "control_property, value", [ ("parallel", Parallel.Points), - ("calcSldDuringFit", True), + ("numSimulationPoints", 10), ("resampleMinAngle", 0.2), ("resampleNPoints", 1), ("display", Display.Notify), @@ -874,7 +891,7 @@ def test_initialise_IPC() -> None: assert test_controls._IPCFilePath != "" with open(test_controls._IPCFilePath, "rb") as f: file_content = f.read() - assert file_content == b"0" + assert file_content == b"\x00" os.remove(test_controls._IPCFilePath) @@ -883,7 +900,7 @@ def test_sendStopEvent(IPC_controls) -> None: IPC_controls.sendStopEvent() with open(IPC_controls._IPCFilePath, "rb") as f: file_content = f.read() - assert file_content == b"1" + assert file_content == b"\x01" def test_sendStopEvent_empty_file() -> None: diff --git a/tests/test_convert.py b/tests/test_convert.py index 8dc3ecdb..0cbd621b 100644 --- a/tests/test_convert.py +++ b/tests/test_convert.py @@ -7,8 +7,8 @@ import pytest -import RATapi -from RATapi.utils.convert import project_to_r1, r1_to_project +import ratapi +from ratapi.utils.convert import project_to_r1, r1_to_project TEST_DIR_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data") @@ -19,10 +19,10 @@ def dspc_bilayer(): with some changes to make it compatible with R1. """ - project, _ = RATapi.examples.DSPC_standard_layers() + project, _ = ratapi.examples.DSPC_standard_layers() # change parameters to standardise arguments not in R1 - for class_list in RATapi.project.parameter_class_lists: + for class_list in ratapi.project.parameter_class_lists: params = getattr(project, class_list) for param in params: param.prior_type = "uniform" @@ -65,10 +65,17 @@ def test_r1_to_project(file, project, path_type, request): # assert statements have to be more careful due to R1 missing features # e.g. R1 doesn't support background parameter names, mu, sigma... - for class_list in RATapi.project.class_lists: + for class_list in ratapi.project.class_lists: assert getattr(output_project, class_list) == getattr(expected_project, class_list) +def test_r1_with_non_unique_contrast_names(): + """Test that R1 to Project class conversion returns the expected Project.""" + output_project = r1_to_project(pathlib.Path(TEST_DIR_PATH, "nonUniqueContrast.mat")) + assert output_project.contrasts[0].name == "Contrast 1" + assert output_project.contrasts[1].name == "Contrast 2" + + @pytest.mark.parametrize( "project", [ @@ -91,11 +98,11 @@ def mock_load(ignored_filename, **ignored_settings): """Load the generated R1 struct instead of reading a file.""" return {"problem": r1_struct} - monkeypatch.setattr("RATapi.utils.convert.loadmat", mock_load, raising=True) + monkeypatch.setattr("ratapi.utils.convert.loadmat", mock_load, raising=True) - converted_project = r1_to_project(project) + converted_project = r1_to_project(pathlib.Path(__file__).parent / "test_data" / project) - for class_list in RATapi.project.class_lists: + for class_list in ratapi.project.class_lists: assert getattr(converted_project, class_list) == getattr(original_project, class_list) diff --git a/tests/test_custom_errors.py b/tests/test_custom_errors.py index 7793ff85..92dbcab8 100644 --- a/tests/test_custom_errors.py +++ b/tests/test_custom_errors.py @@ -5,7 +5,7 @@ import pytest from pydantic import ValidationError, create_model -import RATapi.utils.custom_errors +import ratapi.utils.custom_errors @pytest.fixture @@ -43,7 +43,7 @@ def test_custom_pydantic_validation_error( try: TestModel(int_field="string", str_field=5) except ValidationError as exc: - custom_error_list = RATapi.utils.custom_errors.custom_pydantic_validation_error(exc.errors(), custom_errors) + custom_error_list = ratapi.utils.custom_errors.custom_pydantic_validation_error(exc.errors(), custom_errors) with pytest.raises(ValidationError, match=re.escape(expected_error_message)): raise ValidationError.from_exception_data("TestModel", custom_error_list) diff --git a/tests/test_data/ORSO/test0.layers b/tests/test_data/ORSO/test0.layers new file mode 100644 index 00000000..f52fa9ae --- /dev/null +++ b/tests/test_data/ORSO/test0.layers @@ -0,0 +1,4 @@ +0.000000000000000000e+00 2.069999999999999840e+00 0.000000000000000000e+00 0.000000000000000000e+00 +1.000000000000000000e+02 3.450000000000000178e+00 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a/tests/test_data/make_orso_project.py b/tests/test_data/make_orso_project.py deleted file mode 100644 index 7e8662e6..00000000 --- a/tests/test_data/make_orso_project.py +++ /dev/null @@ -1,181 +0,0 @@ -import pathlib - -import numpy as np - -import RATapi as RAT -from RATapi.utils.enums import BackgroundActions - -DATA_PATH = pathlib.Path(__file__).parents[0] / "ORSO_data" -sld_values = np.loadtxt(DATA_PATH / "test_3_sld.dat") - - -def make_orso_project(): - """Set up a project with all the ORSO validation data included.""" - orso_project = RAT.Project(absorption=True) - - orso_project.parameters.set_fields("Substrate Roughness", min=0.0) - orso_project.background_parameters.set_fields(0, min=0.0, value=0.0) - orso_project.resolution_parameters.set_fields(0, min=0.0, value=0.0) - orso_project.scalefactors.set_fields(0, max=1.0, value=1.0) - - orso_project.bulk_in.extend( - [ - RAT.models.Parameter(name="Bulk In 0", value=2.07e-6), - RAT.models.Parameter(name="Bulk In 1", value=0.0), - RAT.models.Parameter(name="Bulk In 2", value=0.0), - RAT.models.Parameter(name="Bulk In 3", value=0.0), - RAT.models.Parameter(name="Bulk In 6", value=2.07e-6), - RAT.models.Parameter(name="Bulk In 7", value=0.0), - ] - ) - - orso_project.bulk_out.extend( - [ - RAT.models.Parameter(name="Bulk Out 0", value=6.0e-6), - RAT.models.Parameter(name="Bulk Out 1", value=2.0704e-6), - RAT.models.Parameter(name="Bulk Out 2", value=6.36e-6), - RAT.models.Parameter(name="Bulk Out 3", value=6.36e-6), - RAT.models.Parameter(name="Bulk Out 6", value=6.36e-6), - RAT.models.Parameter(name="Bulk Out 7", value=6.36e-6), - ] - ) - - orso_project.data.extend( - [ - RAT.models.Data(name="Data 0", data=np.loadtxt(DATA_PATH / "test_0.dat")), - RAT.models.Data(name="Data 1", data=np.loadtxt(DATA_PATH / "test_1.dat")), - RAT.models.Data(name="Data 2", data=np.loadtxt(DATA_PATH / "test_2.dat")), - RAT.models.Data(name="Data 3", data=np.loadtxt(DATA_PATH / "test_3.dat")), - RAT.models.Data(name="Data 6", data=np.loadtxt(DATA_PATH / "test_6.dat")), - RAT.models.Data(name="Data 7", data=np.loadtxt(DATA_PATH / "test_7.dat")), - ] - ) - - orso_project.contrasts.append( - name="ORSO Contrast", - background="Background 1", - background_action=BackgroundActions.Add, - scalefactor="Scalefactor 1", - resolution="Resolution 1", - resample=False, - ) - - # Now set up parameters and layers for each test - - # Test 0 - orso_project.parameters.append(name="Test 0 Layer 1 Thickness", value=100.0) - orso_project.parameters.append(name="Test 0 Layer 1 SLD real", value=3.45e-6) - orso_project.parameters.append(name="Test 0 Layer 1 SLD imaginary", value=1.0e-7) - orso_project.parameters.append(name="Test 0 Layer 1 Roughness", value=3.0) - - orso_project.layers.append( - name="Test 0 Layer 1", - thickness="Test 0 Layer 1 Thickness", - SLD_real="Test 0 Layer 1 SLD real", - SLD_imaginary="Test 0 Layer 1 SLD imaginary", - roughness="Test 0 Layer 1 Roughness", - ) - - orso_project.parameters.append(name="Test 0 Layer 2 Thickness", value=200.0) - orso_project.parameters.append(name="Test 0 Layer 2 SLD real", value=5.0e-6) - orso_project.parameters.append(name="Test 0 Layer 2 SLD imaginary", value=1.0e-8) - orso_project.parameters.append(name="Test 0 Layer 2 Roughness", value=1.0) - - orso_project.layers.append( - name="Test 0 Layer 2", - thickness="Test 0 Layer 2 Thickness", - SLD_real="Test 0 Layer 2 SLD real", - SLD_imaginary="Test 0 Layer 2 SLD imaginary", - roughness="Test 0 Layer 2 Roughness", - ) - - # Test 1 - orso_project.parameters.append(name="Test 1 Layer 1 Thickness", value=30.0) - orso_project.parameters.append(name="Test 1 Layer 1 SLD real", value=-1.9493e-6) - orso_project.parameters.append(name="Test 1 Layer 1 SLD imaginary", value=0.0) - orso_project.parameters.append(name="Test 1 Layer 1 Roughness", value=0.0) - - orso_project.layers.append( - name="Test 1 Layer 1", - thickness="Test 1 Layer 1 Thickness", - SLD_real="Test 1 Layer 1 SLD real", - SLD_imaginary="Test 1 Layer 1 SLD imaginary", - roughness="Test 1 Layer 1 Roughness", - ) - - orso_project.parameters.append(name="Test 1 Layer 2 Thickness", value=70.0) - orso_project.parameters.append(name="Test 1 Layer 2 SLD real", value=9.4245e-6) - orso_project.parameters.append(name="Test 1 Layer 2 SLD imaginary", value=0.0) - orso_project.parameters.append(name="Test 1 Layer 2 Roughness", value=0.0) - - orso_project.layers.append( - name="Test 1 Layer 2", - thickness="Test 1 Layer 2 Thickness", - SLD_real="Test 1 Layer 2 SLD real", - SLD_imaginary="Test 1 Layer 2 SLD imaginary", - roughness="Test 1 Layer 2 Roughness", - ) - - # Test 2 - orso_project.parameters.append(name="Test 2 Layer 1 Thickness", value=0.0) - orso_project.parameters.append(name="Test 2 Layer 1 SLD real", value=0.0) - orso_project.parameters.append(name="Test 2 Layer 1 SLD imaginary", value=0.0) - orso_project.parameters.append(name="Test 2 Layer 1 Roughness", value=0.0) - - orso_project.layers.append( - name="Test 2 Layer 1", - thickness="Test 2 Layer 1 Thickness", - SLD_real="Test 2 Layer 1 SLD real", - SLD_imaginary="Test 2 Layer 1 SLD imaginary", - roughness="Test 2 Layer 1 Roughness", - ) - - # Test 3 - orso_project.parameters.append(name="Test 3 Thickness", value=0.025) - orso_project.parameters.append(name="Test 3 SLD imaginary", value=0.0) - orso_project.parameters.append(name="Test 3 Roughness", value=0.0) - - for i, sld in enumerate(sld_values): - orso_project.parameters.append(name=f"Test 3 Layer {i} SLD real", value=sld) - orso_project.layers.append( - name=f"Test 3 Layer {i}", - thickness="Test 3 Thickness", - SLD_real=f"Test 3 Layer {i} SLD real", - SLD_imaginary="Test 3 SLD imaginary", - roughness="Test 3 Roughness", - ) - - # Test 6 - orso_project.parameters.append(name="Test 6 Layer 1 Thickness", value=1200.0) - orso_project.parameters.append(name="Test 6 Layer 1 SLD real", value=4.66e-6) - orso_project.parameters.append(name="Test 6 Layer 1 SLD imaginary", value=1.6e-8) - orso_project.parameters.append(name="Test 6 Layer 1 Roughness", value=10.0) - - orso_project.layers.append( - name="Test 6 Layer 1", - thickness="Test 6 Layer 1 Thickness", - SLD_real="Test 6 Layer 1 SLD real", - SLD_imaginary="Test 6 Layer 1 SLD imaginary", - roughness="Test 6 Layer 1 Roughness", - ) - - # Test 7 - orso_project.parameters.append(name="Test 7 Layer 1 Thickness", value=1200.0) - orso_project.parameters.append(name="Test 7 Layer 1 SLD real", value=4.66e-6) - orso_project.parameters.append(name="Test 7 Layer 1 SLD imaginary", value=1.6e-8) - orso_project.parameters.append(name="Test 7 Layer 1 Roughness", value=10.0) - - orso_project.layers.append( - name="Test 7 Layer 1", - thickness="Test 7 Layer 1 Thickness", - SLD_real="Test 7 Layer 1 SLD real", - SLD_imaginary="Test 7 Layer 1 SLD imaginary", - roughness="Test 7 Layer 1 Roughness", - ) - - return orso_project - - -if __name__ == "__main__": - orso_project = make_orso_project() - orso_project.save(pathlib.Path(__file__).parents[0] / "orso_validation.json") diff --git a/tests/test_data/nonUniqueContrast.mat b/tests/test_data/nonUniqueContrast.mat new file mode 100644 index 00000000..41375134 Binary files /dev/null and b/tests/test_data/nonUniqueContrast.mat differ diff --git a/tests/test_data/orso_validation.json b/tests/test_data/orso_validation.json deleted file mode 100644 index 2e8f0d9f..00000000 --- a/tests/test_data/orso_validation.json +++ /dev/null @@ -1 +0,0 @@ -{"name": "", "calculation": "normal", "model": "standard layers", "geometry": "air/substrate", "absorption": true, "parameters": [{"name": "Substrate Roughness", "min": 0.0, "value": 3.0, "max": 5.0, "fit": true, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 0 Layer 1 Thickness", "min": 100.0, "value": 100.0, "max": 100.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 0 Layer 1 SLD real", "min": 3.45e-06, "value": 3.45e-06, "max": 3.45e-06, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 0 Layer 1 SLD imaginary", "min": 1e-07, "value": 1e-07, "max": 1e-07, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 0 Layer 1 Roughness", "min": 3.0, "value": 3.0, "max": 3.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 0 Layer 2 Thickness", "min": 200.0, "value": 200.0, "max": 200.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 0 Layer 2 SLD real", "min": 5e-06, "value": 5e-06, "max": 5e-06, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 0 Layer 2 SLD imaginary", "min": 1e-08, "value": 1e-08, "max": 1e-08, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 0 Layer 2 Roughness", "min": 1.0, "value": 1.0, "max": 1.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 1 Layer 1 Thickness", "min": 30.0, "value": 30.0, "max": 30.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 1 Layer 1 SLD real", "min": -1.9493e-06, "value": -1.9493e-06, "max": -1.9493e-06, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 1 Layer 1 SLD imaginary", "min": 0.0, "value": 0.0, "max": 0.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 1 Layer 1 Roughness", "min": 0.0, "value": 0.0, "max": 0.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 1 Layer 2 Thickness", "min": 70.0, "value": 70.0, "max": 70.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 1 Layer 2 SLD real", "min": 9.4245e-06, "value": 9.4245e-06, "max": 9.4245e-06, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 1 Layer 2 SLD imaginary", "min": 0.0, "value": 0.0, "max": 0.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 1 Layer 2 Roughness", "min": 0.0, "value": 0.0, "max": 0.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 2 Layer 1 Thickness", "min": 0.0, "value": 0.0, "max": 0.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 2 Layer 1 SLD real", "min": 0.0, "value": 0.0, "max": 0.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 2 Layer 1 SLD imaginary", "min": 0.0, "value": 0.0, "max": 0.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 2 Layer 1 Roughness", "min": 0.0, "value": 0.0, "max": 0.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Thickness", "min": 0.025, "value": 0.025, "max": 0.025, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 SLD imaginary", "min": 0.0, "value": 0.0, "max": 0.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Roughness", "min": 0.0, "value": 0.0, "max": 0.0, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 0 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 1 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 2 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 3 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 4 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 5 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 6 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 7 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 8 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 9 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 10 SLD real", "min": 3.530509218307998e-22, "value": 3.530509218307998e-22, "max": 3.530509218307998e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 11 SLD real", "min": 7.061018436615996e-22, "value": 7.061018436615996e-22, "max": 7.061018436615996e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 12 SLD real", "min": 7.061018436615996e-22, "value": 7.061018436615996e-22, "max": 7.061018436615996e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 13 SLD real", "min": 7.061018436615996e-22, "value": 7.061018436615996e-22, "max": 7.061018436615996e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 14 SLD real", "min": 7.061018436615996e-22, "value": 7.061018436615996e-22, "max": 7.061018436615996e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 15 SLD real", "min": 7.061018436615996e-22, "value": 7.061018436615996e-22, "max": 7.061018436615996e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 16 SLD real", "min": 7.061018436615996e-22, "value": 7.061018436615996e-22, "max": 7.061018436615996e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 17 SLD real", "min": 7.061018436615996e-22, "value": 7.061018436615996e-22, "max": 7.061018436615996e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 18 SLD real", "min": 7.061018436615996e-22, "value": 7.061018436615996e-22, "max": 7.061018436615996e-22, "fit": false, "prior_type": "uniform", "mu": 0.0, "sigma": Infinity, "show_priors": false}, {"name": "Test 3 Layer 19 SLD real", 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{"name": "Test 3 Layer 1100", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1100 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1101", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1101 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1102", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1102 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1103", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1103 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1104", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1104 SLD 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{"name": "Test 3 Layer 1658", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1658 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1659", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1659 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1660", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1660 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1661", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1661 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1662", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1662 SLD 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{"name": "Test 3 Layer 1689", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1689 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1690", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1690 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1691", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1691 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1692", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1692 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1693", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1693 SLD 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{"name": "Test 3 Layer 1751", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1751 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1752", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1752 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1753", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1753 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1754", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1754 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1755", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1755 SLD 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"thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1773 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1774", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1774 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1775", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1775 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1776", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1776 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1777", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1777 SLD real", "SLD_imaginary": "Test 3 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{"name": "Test 3 Layer 1782", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1782 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1783", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1783 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1784", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1784 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1785", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1785 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1786", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1786 SLD 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{"name": "Test 3 Layer 1844", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1844 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1845", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1845 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1846", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1846 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1847", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1847 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1848", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1848 SLD 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{"name": "Test 3 Layer 1875", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1875 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1876", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1876 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1877", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1877 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1878", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1878 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1879", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1879 SLD 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{"name": "Test 3 Layer 1906", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1906 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1907", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1907 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1908", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1908 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1909", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1909 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1910", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1910 SLD 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"hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1915", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1915 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1916", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1916 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1917", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1917 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1918", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1918 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1919", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1919 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1920", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1920 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1921", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1921 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1922", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1922 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1923", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1923 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1924", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1924 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1925", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1925 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1926", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1926 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1927", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1927 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1928", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1928 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1929", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1929 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1930", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1930 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1931", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1931 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1932", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1932 SLD real", "SLD_imaginary": "Test 3 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{"name": "Test 3 Layer 1937", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1937 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1938", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1938 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1939", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1939 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1940", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1940 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1941", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1941 SLD 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"hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1946", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1946 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1947", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1947 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1948", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1948 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1949", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1949 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1950", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1950 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1951", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1951 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1952", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1952 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1953", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1953 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1954", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1954 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1955", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1955 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1956", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1956 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1957", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1957 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1958", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1958 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1959", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1959 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1960", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1960 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1961", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1961 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1962", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1962 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1963", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1963 SLD real", "SLD_imaginary": "Test 3 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{"name": "Test 3 Layer 1968", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1968 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1969", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1969 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1970", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1970 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1971", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1971 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1972", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1972 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1973", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1973 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1974", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1974 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1975", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1975 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1976", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1976 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1977", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1977 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1978", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1978 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1979", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1979 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1980", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1980 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1981", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1981 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1982", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1982 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1983", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1983 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1984", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1984 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1985", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1985 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1986", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1986 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1987", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1987 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1988", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1988 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1989", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1989 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1990", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1990 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1991", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1991 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1992", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1992 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1993", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1993 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1994", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1994 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1995", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1995 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1996", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1996 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1997", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1997 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1998", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1998 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 1999", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 1999 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 3 Layer 2000", "thickness": "Test 3 Thickness", "SLD_real": "Test 3 Layer 2000 SLD real", "SLD_imaginary": "Test 3 SLD imaginary", "roughness": "Test 3 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 6 Layer 1", "thickness": "Test 6 Layer 1 Thickness", "SLD_real": "Test 6 Layer 1 SLD real", "SLD_imaginary": "Test 6 Layer 1 SLD imaginary", "roughness": "Test 6 Layer 1 Roughness", "hydration": "", "hydrate_with": "bulk out"}, {"name": "Test 7 Layer 1", "thickness": "Test 7 Layer 1 Thickness", "SLD_real": "Test 7 Layer 1 SLD real", "SLD_imaginary": "Test 7 Layer 1 SLD imaginary", "roughness": "Test 7 Layer 1 Roughness", "hydration": "", "hydrate_with": "bulk out"}], "domain_contrasts": [], "contrasts": [{"name": "ORSO Contrast", "data": "", "background": "Background 1", "background_action": "add", "bulk_in": "", "bulk_out": "", "scalefactor": "Scalefactor 1", "resolution": "Resolution 1", "resample": false, "model": []}]} \ No newline at end of file diff --git a/tests/test_enums.py b/tests/test_enums.py index 9da1f8c0..3d07c308 100644 --- a/tests/test_enums.py +++ b/tests/test_enums.py @@ -1,10 +1,10 @@ """Tests the enums module.""" -from typing import Callable +from collections.abc import Callable import pytest -from RATapi.utils.enums import ( +from ratapi.utils.enums import ( BackgroundActions, BoundHandling, Calculations, diff --git a/tests/test_events.py b/tests/test_events.py index 7baa3e32..06b43c39 100644 --- a/tests/test_events.py +++ b/tests/test_events.py @@ -4,7 +4,7 @@ import numpy as np import pytest -import RATapi.events +import ratapi.events def test_event_register() -> None: @@ -12,69 +12,69 @@ def test_event_register() -> None: second_callback = mock.Mock() with pytest.raises(ValueError): - RATapi.events.register("Message", first_callback) + ratapi.events.register("Message", first_callback) - RATapi.events.register(RATapi.events.EventTypes.Message, first_callback) - result = RATapi.events.get_event_callback(RATapi.events.EventTypes.Message) + ratapi.events.register(ratapi.events.EventTypes.Message, first_callback) + result = ratapi.events.get_event_callback(ratapi.events.EventTypes.Message) assert result == [first_callback] - RATapi.events.register(RATapi.events.EventTypes.Plot, second_callback) - assert RATapi.events.get_event_callback(RATapi.events.EventTypes.Plot) == [second_callback] + ratapi.events.register(ratapi.events.EventTypes.Plot, second_callback) + assert ratapi.events.get_event_callback(ratapi.events.EventTypes.Plot) == [second_callback] - RATapi.events.register(RATapi.events.EventTypes.Message, second_callback) + ratapi.events.register(ratapi.events.EventTypes.Message, second_callback) # the list is not guaranteed to be in the same order as inputted hence the set - assert set(RATapi.events.get_event_callback(RATapi.events.EventTypes.Message)) == {first_callback, second_callback} + assert set(ratapi.events.get_event_callback(ratapi.events.EventTypes.Message)) == {first_callback, second_callback} - RATapi.events.clear(RATapi.events.EventTypes.Message, second_callback) - result = RATapi.events.get_event_callback(RATapi.events.EventTypes.Message) + ratapi.events.clear(ratapi.events.EventTypes.Message, second_callback) + result = ratapi.events.get_event_callback(ratapi.events.EventTypes.Message) assert result == [first_callback] - result = RATapi.events.get_event_callback(RATapi.events.EventTypes.Plot) + result = ratapi.events.get_event_callback(ratapi.events.EventTypes.Plot) assert result == [second_callback] - RATapi.events.clear() - assert RATapi.events.get_event_callback(RATapi.events.EventTypes.Plot) == [] - assert RATapi.events.get_event_callback(RATapi.events.EventTypes.Message) == [] + ratapi.events.clear() + assert ratapi.events.get_event_callback(ratapi.events.EventTypes.Plot) == [] + assert ratapi.events.get_event_callback(ratapi.events.EventTypes.Message) == [] def test_event_notify() -> None: first_callback = mock.Mock() second_callback = mock.Mock() third_callback = mock.Mock() - RATapi.events.register(RATapi.events.EventTypes.Message, first_callback) - RATapi.events.register(RATapi.events.EventTypes.Plot, second_callback) - RATapi.events.register(RATapi.events.EventTypes.Progress, third_callback) + ratapi.events.register(ratapi.events.EventTypes.Message, first_callback) + ratapi.events.register(ratapi.events.EventTypes.Plot, second_callback) + ratapi.events.register(ratapi.events.EventTypes.Progress, third_callback) - RATapi.events.notify(RATapi.events.EventTypes.Message, "Hello World") + ratapi.events.notify(ratapi.events.EventTypes.Message, "Hello World") first_callback.assert_called_once_with("Hello World") second_callback.assert_not_called() - data = RATapi.events.PlotEventData() - RATapi.events.notify(RATapi.events.EventTypes.Plot, data) + data = ratapi.events.PlotEventData() + ratapi.events.notify(ratapi.events.EventTypes.Plot, data) first_callback.assert_called_once() second_callback.assert_called_once_with(data) - data = RATapi.events.ProgressEventData() - RATapi.events.notify(RATapi.events.EventTypes.Progress, data) + data = ratapi.events.ProgressEventData() + ratapi.events.notify(ratapi.events.EventTypes.Progress, data) first_callback.assert_called_once() second_callback.assert_called_once() third_callback.assert_called_once_with(data) - RATapi.events.clear() - RATapi.events.notify(RATapi.events.EventTypes.Message, "Hello World") - RATapi.events.notify(RATapi.events.EventTypes.Plot, data) + ratapi.events.clear() + ratapi.events.notify(ratapi.events.EventTypes.Message, "Hello World") + ratapi.events.notify(ratapi.events.EventTypes.Plot, data) assert first_callback.call_count == 1 assert second_callback.call_count == 1 assert third_callback.call_count == 1 def test_event_data_pickle(): - data = RATapi.events.ProgressEventData() + data = ratapi.events.ProgressEventData() data.message = "Hello" data.percent = 0.5 pickled_data = pickle.loads(pickle.dumps(data)) assert pickled_data.message == data.message assert pickled_data.percent == data.percent - data = RATapi.events.PlotEventData() + data = ratapi.events.PlotEventData() data.modelType = "custom layers" data.dataPresent = np.ones(2) data.subRoughs = np.ones((20, 2)) diff --git a/tests/test_examples.py b/tests/test_examples.py index de2a4e5a..532a5854 100644 --- a/tests/test_examples.py +++ b/tests/test_examples.py @@ -5,7 +5,7 @@ import pytest -import RATapi.examples as examples +import ratapi.examples as examples @pytest.mark.parametrize( diff --git a/tests/test_inputs.py b/tests/test_inputs.py index d3ed276e..242d7480 100644 --- a/tests/test_inputs.py +++ b/tests/test_inputs.py @@ -2,15 +2,17 @@ import pathlib import pickle +import tempfile +from unittest.mock import patch import numpy as np import pytest -import RATapi -import RATapi.wrappers -from RATapi.inputs import FileHandles, check_indices, make_controls, make_input, make_problem -from RATapi.rat_core import Checks, Control, NameStore, ProblemDefinition -from RATapi.utils.enums import ( +import ratapi +import ratapi.wrappers +from ratapi.inputs import FileHandles, check_indices, make_controls, make_input, make_problem +from ratapi.rat_core import Checks, Control, NameStore, ProblemDefinition +from ratapi.utils.enums import ( BackgroundActions, BoundHandling, Calculations, @@ -26,8 +28,8 @@ @pytest.fixture def standard_layers_project(): """Add parameters to the default project for a normal calculation.""" - test_project = RATapi.Project( - data=RATapi.ClassList([RATapi.models.Data(name="Test Data", data=np.array([[1.0, 1.0, 1.0]]))]), + test_project = ratapi.Project( + data=ratapi.ClassList([ratapi.models.Data(name="Test Data", data=np.array([[1.0, 1.0, 1.0]]))]), ) test_project.parameters.append(name="Test Thickness") test_project.parameters.append(name="Test SLD") @@ -60,9 +62,9 @@ def standard_layers_project(): @pytest.fixture def domains_project(): """Add parameters to the default project for a domains calculation.""" - test_project = RATapi.Project( + test_project = ratapi.Project( calculation=Calculations.Domains, - data=RATapi.ClassList([RATapi.models.Data(name="Test Data", data=np.array([[1.0, 1.0, 1.0]]))]), + data=ratapi.ClassList([ratapi.models.Data(name="Test Data", data=np.array([[1.0, 1.0, 1.0]]))]), ) test_project.parameters.append(name="Test Thickness") test_project.parameters.append(name="Test SLD") @@ -93,7 +95,7 @@ def domains_project(): @pytest.fixture def custom_xy_project(): """Add parameters to the default project for a normal calculation and use the custom xy model.""" - test_project = RATapi.Project(model=LayerModels.CustomXY) + test_project = ratapi.Project(model=LayerModels.CustomXY) test_project.parameters.append(name="Test Thickness") test_project.parameters.append(name="Test SLD") test_project.parameters.append(name="Test Roughness") @@ -175,7 +177,7 @@ def standard_layers_problem(test_names, test_checks): problem.numberOfContrasts = 1 problem.numberOfLayers = 1 problem.repeatLayers = [1] - problem.layersDetails = [[2, 3, 4, float("NaN"), 2]] + problem.layersDetails = [[2, 3, 4, float("NaN"), 1]] problem.contrastLayers = [[1]] problem.numberOfDomainContrasts = 0 problem.domainContrastLayers = [] @@ -243,7 +245,7 @@ def domains_problem(test_names, test_checks): problem.numberOfContrasts = 1 problem.numberOfLayers = 1 problem.repeatLayers = [1] - problem.layersDetails = [[2, 3, 4, float("NaN"), 2]] + problem.layersDetails = [[2, 3, 4, float("NaN"), 1]] problem.contrastLayers = [[2, 1]] problem.numberOfDomainContrasts = 2 problem.domainContrastLayers = [[1], [1]] @@ -306,7 +308,7 @@ def custom_xy_problem(test_names, test_checks): problem.contrastResolutionTypes = ["constant"] problem.contrastCustomFiles = [1] problem.contrastDomainRatios = [0] - problem.resample = [False] + problem.resample = [True] problem.dataPresent = [0] problem.data = [np.empty([0, 6])] problem.dataLimits = [[0.0, 0.0]] @@ -343,7 +345,7 @@ def custom_xy_problem(test_names, test_checks): [1, 0.0, np.inf], ] problem.customFiles = FileHandles( - [RATapi.models.CustomFile(name="Test Custom File", filename="cpp_test.dll", language="cpp")] + [ratapi.models.CustomFile(name="Test Custom File", filename="cpp_test.dll", language="cpp")] ) problem.names = test_names problem.checks = test_checks @@ -359,9 +361,9 @@ def standard_layers_controls(): controls = Control() controls.procedure = Procedures.Calculate controls.parallel = Parallel.Single - controls.calcSldDuringFit = False + controls.numSimulationPoints = 500 controls.resampleMinAngle = 0.9 - controls.resampleNPoints = 50.0 + controls.resampleNPoints = 50 controls.display = Display.Iter controls.xTolerance = 1.0e-6 controls.funcTolerance = 1.0e-6 @@ -397,7 +399,7 @@ def custom_xy_controls(): controls = Control() controls.procedure = Procedures.Calculate controls.parallel = Parallel.Single - controls.calcSldDuringFit = False + controls.numSimulationPoints = 500 controls.resampleMinAngle = 0.9 controls.resampleNPoints = 50.0 controls.display = Display.Iter @@ -455,7 +457,7 @@ def test_make_input(test_project, test_problem, test_controls, request) -> None: test_problem = request.getfixturevalue(test_problem) test_controls = request.getfixturevalue(test_controls) - problem, controls = make_input(test_project, RATapi.Controls()) + problem, controls = make_input(test_project, ratapi.Controls()) problem = pickle.loads(pickle.dumps(problem)) check_problem_equal(problem, test_problem) @@ -599,7 +601,7 @@ def test_undefined_background(test_project, request): ValueError, match=f"All backgrounds must have a source defined. For a {background.type} type " f"background, the source must be defined in " - f'"{RATapi.project.values_defined_in[f"backgrounds.{background.type}.source"]}"', + f'"{ratapi.project.values_defined_in[f"backgrounds.{background.type}.source"]}"', ): make_problem(test_project) @@ -614,7 +616,7 @@ def test_undefined_resolution(test_project, request): with pytest.raises( ValueError, match=f"Constant resolutions must have a source defined. The source must be defined in " - f'"{RATapi.project.values_defined_in[f"resolutions.{resolution.type}.source"]}"', + f'"{ratapi.project.values_defined_in[f"resolutions.{resolution.type}.source"]}"', ): make_problem(test_project) @@ -640,7 +642,7 @@ def test_append_data_background(): data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) background = np.array([[1, 10, 11], [4, 12, 13], [7, 14, 15]]) - result = RATapi.inputs.append_data_background(data, background) + result = ratapi.inputs.append_data_background(data, background) np.testing.assert_allclose(result, np.array([[1, 2, 3, 0, 10, 11], [4, 5, 6, 0, 12, 13], [7, 8, 9, 0, 14, 15]])) @@ -649,7 +651,7 @@ def test_append_data_background_res(): data = np.array([[1, 2, 3, 4], [4, 5, 6, 6], [7, 8, 9, 72]]) background = np.array([[1, 10, 11], [4, 12, 13], [7, 14, 15]]) - result = RATapi.inputs.append_data_background(data, background) + result = ratapi.inputs.append_data_background(data, background) np.testing.assert_allclose(result, np.array([[1, 2, 3, 4, 10, 11], [4, 5, 6, 6, 12, 13], [7, 8, 9, 72, 14, 15]])) @@ -659,22 +661,46 @@ def test_append_data_background_error(): background = np.array([[56, 10, 11], [41, 12, 13], [7, 14, 15]]) with pytest.raises(ValueError, match=("The q-values of the data and background must be equal.")): - RATapi.inputs.append_data_background(data, background) + ratapi.inputs.append_data_background(data, background) def test_get_python_handle(): - path = pathlib.Path(__file__).parent.resolve() - assert RATapi.inputs.get_python_handle("utils.py", "dummy_function", path).__code__ == dummy_function.__code__ + path = pathlib.Path(__file__).parent + assert ratapi.inputs.get_python_handle("utils.py", "dummy_function", path).__code__ == dummy_function.__code__ def test_make_controls(standard_layers_controls) -> None: """The controls object should contain the full set of controls parameters, with the appropriate set defined by the input controls. """ - controls = make_controls(RATapi.Controls()) + controls = make_controls(ratapi.Controls()) check_controls_equal(controls, standard_layers_controls) +@patch("ratapi.wrappers.MatlabWrapper") +def test_file_handles(wrapper): + handle = FileHandles([ratapi.models.CustomFile(name="Test Custom File", filename="cpp_test.dll", language="cpp")]) + + with pytest.raises(FileNotFoundError, match="The custom file \\(Test Custom File\\) does not have a valid path."): + handle.get_handle(0) + + with tempfile.NamedTemporaryFile("w", suffix=".dll") as f: + tmp_file = pathlib.Path(f.name) + handle.files[0]["path"] = tmp_file.parent + handle.files[0]["filename"] = tmp_file.name + handle.files[0]["function_name"] = "" + # No function name should throw exception + with pytest.raises( + ValueError, match="The custom file \\(Test Custom File\\) does not have a valid function name." + ): + handle.get_handle(0) + + # Matlab does not need function name + handle.files[0]["language"] = "matlab" + handle.get_handle(0) + wrapper.assert_called() + + def check_problem_equal(actual_problem, expected_problem) -> None: """Compare two instances of the "problem" object for equality.""" scalar_fields = [ @@ -736,11 +762,12 @@ def check_problem_equal(actual_problem, expected_problem) -> None: # Data field is a numpy array assert [ - actual_data == expected_data for (actual_data, expected_data) in zip(actual_problem.data, expected_problem.data) + actual_data == expected_data + for (actual_data, expected_data) in zip(actual_problem.data, expected_problem.data, strict=False) ] # Need to account for "NaN" entries in layersDetails and contrastCustomFiles field - for actual_layer, expected_layer in zip(actual_problem.layersDetails, expected_problem.layersDetails): + for actual_layer, expected_layer in zip(actual_problem.layersDetails, expected_problem.layersDetails, strict=False): assert (actual_layer == expected_layer) or ["NaN" if np.isnan(el) else el for el in actual_layer] == [ "NaN" if np.isnan(el) else el for el in expected_layer ] @@ -755,7 +782,7 @@ def check_controls_equal(actual_controls, expected_controls) -> None: controls_fields = [ "procedure", "parallel", - "calcSldDuringFit", + "numSimulationPoints", "resampleMinAngle", "resampleNPoints", "display", diff --git a/tests/test_models.py b/tests/test_models.py index 703e1f38..4343b442 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -1,30 +1,30 @@ """Test the pydantic models.""" import re -from typing import Callable +from collections.abc import Callable import numpy as np import pydantic import pytest -import RATapi.models +import ratapi.models @pytest.mark.parametrize( ["model", "model_name", "model_params"], [ - (RATapi.models.Background, "Background", {}), - (RATapi.models.Contrast, "Contrast", {}), - (RATapi.models.CustomFile, "Custom File", {}), - (RATapi.models.Data, "Data", {}), - (RATapi.models.DomainContrast, "Domain Contrast", {}), + (ratapi.models.Background, "Background", {}), + (ratapi.models.Contrast, "Contrast", {}), + (ratapi.models.CustomFile, "Custom File", {}), + (ratapi.models.Data, "Data", {}), + (ratapi.models.DomainContrast, "Domain Contrast", {}), ( - RATapi.models.Layer, + ratapi.models.Layer, "Layer", {"thickness": "Test Thickness", "SLD": "Test SLD", "roughness": "Test Roughness"}, ), - (RATapi.models.Parameter, "Parameter", {}), - (RATapi.models.Resolution, "Resolution", {}), + (ratapi.models.Parameter, "Parameter", {}), + (ratapi.models.Resolution, "Resolution", {}), ], ) def test_default_names(model: Callable, model_name: str, model_params: dict) -> None: @@ -32,28 +32,37 @@ def test_default_names(model: Callable, model_name: str, model_params: dict) -> format: "New ". """ model_1 = model(**model_params) + prefix = f"New {model_name} " + assert model_1.name.startswith(prefix) + index = int(model_1.name[len(prefix) :]) + model_2 = model(**model_params) model_3 = model(name="Given Name", **model_params) model_4 = model(**model_params) - assert model_1.name == f"New {model_name} 1" - assert model_2.name == f"New {model_name} 2" + assert model_1.name == f"New {model_name} {index}" + assert model_2.name == f"New {model_name} {index + 1}" assert model_3.name == "Given Name" - assert model_4.name == f"New {model_name} 3" + assert model_4.name == f"New {model_name} {index + 2}" + + # If user adds name in similar format. The next auto number will take it into account. + model(name=f"{prefix}{index + 20}", **model_params) + model_5 = model(**model_params) + assert model_5.name == f"New {model_name} {index + 21}" @pytest.mark.parametrize( ["model", "model_params"], [ - (RATapi.models.Background, {}), - (RATapi.models.Contrast, {}), - (RATapi.models.ContrastWithRatio, {}), - (RATapi.models.CustomFile, {}), - (RATapi.models.Data, {}), - (RATapi.models.DomainContrast, {}), - (RATapi.models.Layer, {"thickness": "Test Thickness", "SLD": "Test SLD", "roughness": "Test Roughness"}), + (ratapi.models.Background, {}), + (ratapi.models.Contrast, {}), + (ratapi.models.ContrastWithRatio, {}), + (ratapi.models.CustomFile, {}), + (ratapi.models.Data, {}), + (ratapi.models.DomainContrast, {}), + (ratapi.models.Layer, {"thickness": "Test Thickness", "SLD": "Test SLD", "roughness": "Test Roughness"}), ( - RATapi.models.AbsorptionLayer, + ratapi.models.AbsorptionLayer, { "thickness": "Test Thickness", "SLD_real": "Test SLD", @@ -61,8 +70,8 @@ def test_default_names(model: Callable, model_name: str, model_params: dict) -> "roughness": "Test Roughness", }, ), - (RATapi.models.Parameter, {}), - (RATapi.models.Resolution, {}), + (ratapi.models.Parameter, {}), + (ratapi.models.Resolution, {}), ], ) class TestModels: @@ -104,7 +113,7 @@ def test_data_eq() -> None: """If we use the Data.__eq__ method with an object that is not a pydantic BaseModel, we should return "NotImplemented". """ - assert RATapi.models.Data().__eq__("data") == NotImplemented + assert ratapi.models.Data().__eq__("data") == NotImplemented @pytest.mark.parametrize( @@ -117,7 +126,7 @@ def test_data_dimension(input_data: np.ndarray[float]) -> None: """The "data" field of the "Data" model should be a two-dimensional numpy array with at least three values in the second dimension. """ - test_data = RATapi.models.Data(data=input_data) + test_data = ratapi.models.Data(data=input_data) assert (test_data.data == input_data).all() @@ -136,7 +145,7 @@ def test_data_too_few_dimensions(input_data: np.ndarray[float]) -> None: pydantic.ValidationError, match='1 validation error for Data\ndata\n Value error, "data" must have at least two dimensions', ): - RATapi.models.Data(data=input_data) + ratapi.models.Data(data=input_data) @pytest.mark.parametrize( @@ -155,7 +164,7 @@ def test_data_too_few_values(input_data: np.ndarray[float]) -> None: pydantic.ValidationError, match='1 validation error for Data\ndata\n Value error, "data" must have at least three columns', ): - RATapi.models.Data(data=input_data) + ratapi.models.Data(data=input_data) @pytest.mark.parametrize( @@ -166,8 +175,8 @@ def test_data_too_few_values(input_data: np.ndarray[float]) -> None: ) def test_data_ranges(input_range: list[float]) -> None: """The "data_range" and "simulation_range" fields of the "Data" model should contain exactly two values.""" - assert RATapi.models.Data(data_range=input_range).data_range == input_range - assert RATapi.models.Data(simulation_range=input_range).simulation_range == input_range + assert ratapi.models.Data(data_range=input_range).data_range == input_range + assert ratapi.models.Data(simulation_range=input_range).simulation_range == input_range @pytest.mark.parametrize( @@ -188,7 +197,7 @@ def test_two_values_in_data_range(input_range: list[float]) -> None: f"at {'least' if len(input_range) < 2 else 'most'} 2 items " f"after validation, not {len(input_range)}", ): - RATapi.models.Data(data_range=input_range) + ratapi.models.Data(data_range=input_range) @pytest.mark.parametrize( @@ -209,7 +218,7 @@ def test_two_values_in_simulation_range(input_range: list[float]) -> None: f"have at {'least' if len(input_range) < 2 else 'most'} 2 items " f"after validation, not {len(input_range)}", ): - RATapi.models.Data(simulation_range=input_range) + ratapi.models.Data(simulation_range=input_range) @pytest.mark.parametrize( @@ -228,14 +237,14 @@ def test_min_max_in_range(field: str) -> None: match=f"1 validation error for Data\n{field}\n Value error, {field} " f'"min" value is greater than the "max" value', ): - RATapi.models.Data(**{field: [1.0, 0.0]}) + ratapi.models.Data(**{field: [1.0, 0.0]}) def test_default_ranges() -> None: """If "data" is specified but either the "data_range" or "simulation_range" fields are not, we set the ranges to the minimum and maximum values of the first column of the data. """ - test_data = RATapi.models.Data(data=np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]])) + test_data = ratapi.models.Data(data=np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]])) assert test_data.data_range == [1.0, 3.0] assert test_data.simulation_range == [1.0, 3.0] @@ -260,7 +269,7 @@ def test_data_range(test_range) -> None: f"the min/max values of the data: [1.0, 3.0]", ), ): - RATapi.models.Data(data=np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]]), data_range=test_range) + ratapi.models.Data(data=np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]]), data_range=test_range) @pytest.mark.parametrize( @@ -284,7 +293,7 @@ def test_simulation_range(test_range) -> None: f"[1.0, 3.0]", ), ): - RATapi.models.Data(data=np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]]), simulation_range=test_range) + ratapi.models.Data(data=np.array([[1.0, 0.0, 0.0], [3.0, 0.0, 0.0]]), simulation_range=test_range) @pytest.mark.parametrize( @@ -305,7 +314,7 @@ def test_parameter_range(minimum: float, value: float, maximum: float) -> None: f"{float(value)} is not within the defined range: " f"{float(minimum)} <= value <= {float(maximum)}", ): - RATapi.models.Parameter(min=minimum, value=value, max=maximum) + ratapi.models.Parameter(min=minimum, value=value, max=maximum) def test_layer_bad_imaginary_SLD(): @@ -318,7 +327,7 @@ def test_layer_bad_imaginary_SLD(): " Use the AbsorptionLayer class instead." ), ): - RATapi.models.Layer(name="My Layer", SLD_imaginary="bad sld") + ratapi.models.Layer(name="My Layer", SLD_imaginary="bad sld") def test_contrast_bad_ratio(): @@ -331,14 +340,14 @@ def test_contrast_bad_ratio(): " Use the ContrastWithRatio class instead." ), ): - RATapi.models.Contrast(name="My Contrast", domain_ratio="bad ratio") + ratapi.models.Contrast(name="My Contrast", domain_ratio="bad ratio") @pytest.mark.parametrize( ["model", "type", "values"], [ - (RATapi.models.Background, "function", ["val1", "val2", "val3", "val4", "val5"]), - (RATapi.models.Resolution, "constant", ["", "", "", "", ""]), + (ratapi.models.Background, "function", ["val1", "val2", "val3", "val4", "val5"]), + (ratapi.models.Resolution, "constant", ["", "", "", "", ""]), ], ) def test_type_change_clear(model, type, values): @@ -363,10 +372,10 @@ def test_type_change_clear(model, type, values): @pytest.mark.parametrize( ["model", "signal_type", "values"], [ - (RATapi.models.Background, "constant", ["value_1", "value_2", "value_3", "value_4", "value_5"]), - (RATapi.models.Background, "data", ["value_2", "value_3", "value_4", "value_5"]), - (RATapi.models.Resolution, "constant", ["value_1", "value_2", "value_3", "value_4", "value_5"]), - (RATapi.models.Resolution, "data", ["value_1", "value_2", "value_3", "value_4", "value_5"]), + (ratapi.models.Background, "constant", ["value_1", "value_2", "value_3", "value_4", "value_5"]), + (ratapi.models.Background, "data", ["value_2", "value_3", "value_4", "value_5"]), + (ratapi.models.Resolution, "constant", ["value_1", "value_2", "value_3", "value_4", "value_5"]), + (ratapi.models.Resolution, "data", ["value_1", "value_2", "value_3", "value_4", "value_5"]), ], ) def test_unsupported_parameters_error(model, signal_type, values): diff --git a/tests/test_orso_utils.py b/tests/test_orso_utils.py index 958abd88..60f04494 100644 --- a/tests/test_orso_utils.py +++ b/tests/test_orso_utils.py @@ -1,4 +1,4 @@ -"""Tests for the RATapi.utils.orso module.""" +"""Tests for the ratapi.utils.orso module.""" import os from io import StringIO @@ -8,9 +8,9 @@ import pytest from orsopy.fileio.model_language import SampleModel -from RATapi.examples.bayes_benchmark.bayes_benchmark import get_project -from RATapi.project import Project -from RATapi.utils.orso import ORSOProject, orso_model_to_rat +from ratapi.examples.bayes_benchmark.bayes_benchmark import get_project +from ratapi.project import Project +from ratapi.utils.orso import ORSOProject, orso_model_to_rat TEST_DIR_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data") @@ -36,6 +36,7 @@ def prist(): ], ) @pytest.mark.parametrize("absorption", [True, False]) +@pytest.mark.skip(reason="orsopy database website (https://slddb.esss.dk/slddb/) is not available") def test_orso_model_to_rat(model, absorption): """Test that orso_model_to_rat gives the expected parameters, layers and model.""" @@ -72,6 +73,7 @@ def test_orso_model_to_rat(model, absorption): "prist5_10K_m_025.Rqz.ort", ], ) +@pytest.mark.skip(reason="orsopy database website (https://slddb.esss.dk/slddb/) is not available") def test_load_ort_data(test_data): """Test that .ort data is loaded correctly.""" # manually get the test data for comparison @@ -93,7 +95,7 @@ def test_load_ort_data(test_data): actual_data = ORSOProject(Path(TEST_DIR_PATH, test_data)).data assert len(actual_data) == len(expected_data) - for actual_dataset, expected_dataset in zip(actual_data, expected_data): + for actual_dataset, expected_dataset in zip(actual_data, expected_data, strict=False): np.testing.assert_array_equal(actual_dataset.data, expected_dataset) @@ -104,6 +106,7 @@ def test_load_ort_data(test_data): ["prist5_10K_m_025.Rqz.ort", "prist.json"], ], ) +@pytest.mark.skip(reason="orsopy database website (https://slddb.esss.dk/slddb/) is not available") def test_load_ort_project(test_data, expected_data): """Test that a project with model data is loaded correctly.""" ort_data = ORSOProject(Path(TEST_DIR_PATH, test_data)) @@ -115,5 +118,5 @@ def test_load_ort_project(test_data, expected_data): assert sample.parameters == exp_project.parameters[1:] assert sample.layers == exp_project.layers - for data, exp_data in zip(ort_data.data, exp_project.data[1:]): + for data, exp_data in zip(ort_data.data, exp_project.data[1:], strict=False): np.testing.assert_array_equal(data.data, exp_data.data) diff --git a/tests/test_orso_validation.py b/tests/test_orso_validation.py index 99c2e449..a7ed22f5 100644 --- a/tests/test_orso_validation.py +++ b/tests/test_orso_validation.py @@ -1,46 +1,84 @@ +import os import pathlib +import tempfile +import numpy as np import pytest -import RATapi as RAT -from RATapi.utils.plotting import plot_ref_sld +import ratapi as RAT -PROJECT_PATH = pathlib.Path(__file__).parents[0] / "test_data" +TEST_FUNC = """import pathlib +import numpy as np -@pytest.fixture(scope="module") -def orso_project(): - """Load a project with all the ORSO validation data included.""" - return RAT.Project.load(pathlib.Path(PROJECT_PATH, "orso_validation.json")) +def run_func(params, bulk_in, bulk_out, contrast): + layers = np.loadtxt("{layers_file_path}") + + # Change the units to Å + layers[:, 1:3] = layers[:, 1:3] * 1e-6 + + # Returns layers only, bulk in and bulk out added to project + return layers[1:-1, :], params[0] +""" @pytest.mark.parametrize( - ["test_index", "substrate_roughness", "layer_model"], - [ - (0, 5.0, ["Test 0 Layer 1", "Test 0 Layer 2"]), - (1, 0.0, ["Test 1 Layer 1", "Test 1 Layer 2"] * 10), - (2, 3.0, ["Test 2 Layer 1"]), - (3, 0.0, [f"Test 3 Layer {i}" for i in range(2001)]), - (6, 3.0, ["Test 6 Layer 1"]), - (7, 3.0, ["Test 7 Layer 1"]), - ], + "layer_index", + [0, 1, 2, 3, 6, 7], ) -def test_orso_validation(orso_project, substrate_roughness, test_index, layer_model): - orso_project.parameters.set_fields("Substrate Roughness", value=substrate_roughness) +def test_orso_validation(layer_index): + data_path = pathlib.Path(__file__).parent / "test_data" / "ORSO" + + problem = RAT.Project(name="test", model="custom layers", absorption=True) + problem.scalefactors.set_fields(0, min=1, value=1, max=1) + problem.background_parameters.set_fields(0, min=0, value=0, max=0) + problem.resolution_parameters.set_fields(0, min=0, value=0, max=0) + + # Write a custom file that reads the ORSO layers. + filename = data_path / f"test{layer_index}.layers" + with tempfile.NamedTemporaryFile("w+", suffix=".py", delete=False) as f: + f.write(TEST_FUNC.format(layers_file_path=filename.as_posix())) + f.flush() + + layers = np.loadtxt(filename) + sub_rough = layers[-1, -1] + + # Change the units to Å + bulk_in = layers[0, 1] * 1e-6 + bulk_out = layers[-1, 1] * 1e-6 + + problem.parameters.set_fields(0, min=sub_rough, value=sub_rough, max=sub_rough) + problem.bulk_in.set_fields(0, name="Bulk In", min=bulk_in, value=bulk_in, max=bulk_in, fit=False) + problem.bulk_out.set_fields(0, name="Bulk Out", min=bulk_out, value=bulk_out, max=bulk_out, fit=False) - orso_project.contrasts.set_fields( - "ORSO Contrast", - data=f"Data {test_index}", - bulk_in=f"Bulk In {test_index}", - bulk_out=f"Bulk Out {test_index}", - model=layer_model, - ) + data = np.loadtxt(data_path / f"test_{layer_index}.dat") + problem.data.append(name="Data", data=data) - controls = RAT.Controls(procedure="calculate") - output_project, results = RAT.run(orso_project, controls) + problem.custom_files.append( + name="Model", + filename=pathlib.Path(f.name).name, + language="python", + path=pathlib.Path(f.name).parent, + function_name="run_func", + ) - plot_ref_sld(output_project, results) + problem.contrasts.append( + name="ORSO Contrast", + background="Background 1", + background_action="add", + resolution="Resolution 1", + scalefactor="Scalefactor 1", + bulk_out="Bulk Out", + bulk_in="Bulk In", + data="Data", + resample=False, + model=["Model"], + ) - total_error = sum((results.reflectivity[0][:, 1] - results.shiftedData[0][:, 1]) ** 2) + controls = RAT.Controls() + problem, results = RAT.run(problem, controls) + total_error = sum((results.reflectivity[0][:, 1] - results.shiftedData[0][:, 1]) ** 2) - assert total_error < 1e-10 + assert total_error < 1e-10 + f.close() + os.remove(f.name) diff --git a/tests/test_outputs.py b/tests/test_outputs.py index 0fce4697..7d2afff9 100644 --- a/tests/test_outputs.py +++ b/tests/test_outputs.py @@ -9,10 +9,10 @@ import numpy as np import pytest -import RATapi -import RATapi.outputs -import RATapi.rat_core -from RATapi.utils.enums import Procedures +import ratapi +import ratapi.outputs +import ratapi.rat_core +from ratapi.utils.enums import Procedures from tests.utils import check_results_equal @@ -167,7 +167,7 @@ def test_get_field_string(test_value, array_limit, expected_field_string) -> Non """For the string representation of output classes, we represent multidimensional and large arrays by their shape, with other variables printed normally. """ - field_string = RATapi.outputs.get_field_string("test_field", test_value, array_limit) + field_string = ratapi.outputs.get_field_string("test_field", test_value, array_limit) assert field_string == expected_field_string @@ -186,11 +186,24 @@ def test_make_results(test_procedure, test_output_results, test_bayes, test_resu test_bayes = request.getfixturevalue(test_bayes) test_results = request.getfixturevalue(test_results) - results = RATapi.outputs.make_results(test_procedure, test_output_results, test_bayes) + results = ratapi.outputs.make_results(test_procedure, test_output_results, test_bayes) check_results_equal(test_results, results) +@pytest.mark.parametrize( + ["test_procedure", "test_results"], + [ + (Procedures.NS, "nested_sampler_results"), + (Procedures.DREAM, "dream_results"), + ], +) +def test_results_procedure(test_procedure, test_results, request) -> None: + """Test that bayes results object return correct procedure.""" + test_output_results = request.getfixturevalue(test_results) + assert test_output_results.from_procedure() == test_procedure + + @pytest.mark.parametrize( ["test_output_results", "test_str"], [ @@ -208,7 +221,7 @@ def test_results_str(test_output_results, test_str, request) -> None: assert test_output_results.__str__() == test_str -@pytest.mark.parametrize("result_class", [RATapi.Results, RATapi.BayesResults]) +@pytest.mark.parametrize("result_class", [ratapi.Results, ratapi.BayesResults]) @pytest.mark.parametrize("test_results", ["reflectivity_calculation_results", "dream_results"]) def test_save_load(result_class, test_results, request): """Test that saving and loading an output object returns the same object.""" diff --git a/tests/test_plotting.py b/tests/test_plotting.py index abeb4177..31049a28 100644 --- a/tests/test_plotting.py +++ b/tests/test_plotting.py @@ -9,9 +9,9 @@ from matplotlib.collections import PolyCollection, QuadMesh from matplotlib.patches import Rectangle -import RATapi.utils.plotting as RATplot -from RATapi.events import notify -from RATapi.rat_core import EventTypes, PlotEventData +import ratapi.utils.plotting as RATplot +from ratapi.events import notify +from ratapi.rat_core import EventTypes, PlotEventData TEST_DIR_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), "test_data") @@ -50,7 +50,8 @@ def fig(request) -> plt.figure: """Creates the fixture for the tests.""" plt.close("all") figure = plt.subplots(1, 2)[0] - return RATplot.plot_ref_sld_helper(fig=figure, data=domains_data() if request.param else data()) + RATplot.plot_ref_sld_helper(fig=figure, data=domains_data() if request.param else data()) + return figure @pytest.fixture @@ -68,7 +69,8 @@ def bayes_fig(request) -> plt.figure: for sld in dat.sldProfiles ], } - return RATplot.plot_ref_sld_helper(data=dat, fig=figure, confidence_intervals=confidence_intervals) + RATplot.plot_ref_sld_helper(data=dat, fig=figure, confidence_intervals=confidence_intervals) + return figure @pytest.mark.parametrize("fig", [False, True], indirect=True) @@ -120,8 +122,7 @@ def test_ref_sld_color_formatting(fig: plt.figure) -> None: assert sld_plot.get_lines()[i].get_color() == sld_plot.get_lines()[i + 1].get_color() -@pytest.mark.parametrize("bayes", [65, 95]) -def test_ref_sld_bayes(fig, bayes_fig, bayes): +def test_ref_sld_bayes(fig, bayes_fig): """Test that shading is correctly added to the figure when confidence intervals are supplied.""" # the shading is of type PolyCollection for axes in fig.axes: @@ -132,12 +133,12 @@ def test_ref_sld_bayes(fig, bayes_fig, bayes): assert any(isinstance(comp, PolyCollection) for comp in components) -@patch("RATapi.utils.plotting.makeSLDProfile") +@patch("ratapi.utils.plotting.makeSLDProfile") def test_sld_profile_function_call(mock: MagicMock) -> None: """Tests the makeSLDProfile function called with correct args. """ - RATplot.plot_ref_sld_helper(data()) + RATplot.plot_ref_sld_helper(data(), plt.subplots(1, 2)[0]) assert mock.call_count == 3 assert mock.call_args_list[0].args[0] == 2.07e-06 @@ -156,7 +157,7 @@ def test_sld_profile_function_call(mock: MagicMock) -> None: assert mock.call_args_list[2].args[4] == 1 -@patch("RATapi.utils.plotting.makeSLDProfile") +@patch("ratapi.utils.plotting.makeSLDProfile") def test_live_plot(mock: MagicMock) -> None: plot_data = data() @@ -183,7 +184,7 @@ def test_live_plot(mock: MagicMock) -> None: assert mock.call_args_list[2].args[4] == 1 -@patch("RATapi.utils.plotting.plot_ref_sld_helper") +@patch("ratapi.utils.plotting.plot_ref_sld_helper") def test_plot_ref_sld(mock: MagicMock, input_project, reflectivity_calculation_results) -> None: RATplot.plot_ref_sld(input_project, reflectivity_calculation_results) mock.assert_called_once() @@ -193,10 +194,14 @@ def test_plot_ref_sld(mock: MagicMock, input_project, reflectivity_calculation_r assert figure.axes[0].get_subplotspec().get_gridspec().get_geometry() == (1, 2) assert len(figure.axes) == 2 - for reflectivity, reflectivity_results in zip(data.reflectivity, reflectivity_calculation_results.reflectivity): + for reflectivity, reflectivity_results in zip( + data.reflectivity, reflectivity_calculation_results.reflectivity, strict=False + ): assert (reflectivity == reflectivity_results).all() - for sldProfile, result_sld_profile in zip(data.sldProfiles, reflectivity_calculation_results.sldProfiles): - for sld, sld_results in zip(sldProfile, result_sld_profile): + for sldProfile, result_sld_profile in zip( + data.sldProfiles, reflectivity_calculation_results.sldProfiles, strict=False + ): + for sld, sld_results in zip(sldProfile, result_sld_profile, strict=False): assert (sld == sld_results).all() assert data.modelType == input_project.model @@ -211,12 +216,12 @@ def test_plot_ref_sld(mock: MagicMock, input_project, reflectivity_calculation_r def test_ref_sld_subplot_correction(): """Test that if an incorrect number of subplots is corrected in the figure helper.""" fig = plt.subplots(1, 3)[0] - ref_sld_fig = RATplot.plot_ref_sld_helper(data=data(), fig=fig) - assert ref_sld_fig.axes[0].get_subplotspec().get_gridspec().get_geometry() == (1, 2) - assert len(ref_sld_fig.axes) == 2 + RATplot.plot_ref_sld_helper(data=data(), fig=fig) + assert fig.axes[0].get_subplotspec().get_gridspec().get_geometry() == (1, 2) + assert len(fig.axes) == 2 -@patch("RATapi.utils.plotting.plot_ref_sld_helper") +@patch("ratapi.utils.plotting.plot_ref_sld_helper") def test_plot_ref_sld_bayes_validation(mock, input_project, reflectivity_calculation_results, dream_results): """Test that plot_ref_sld correctly throws errors for bad Bayesian input.""" RATplot.plot_ref_sld(input_project, dream_results) @@ -388,7 +393,7 @@ def test_corner(dream_results, params): @pytest.mark.parametrize( "params", [None, [2, 3], [1, 5, "D2O"], ["Bilayer Heads Thickness", "Bilayer Heads Hydration", "D2O"]] ) -@patch("RATapi.plotting.panel_plot_helper") +@patch("ratapi.plotting.panel_plot_helper") def test_hist_panel(mock_panel_helper: MagicMock, params, dream_results): """Test chain panel name-to-index (panel helper has already been tested)""" fig = RATplot.plot_hists(dream_results, params, return_fig=True) @@ -412,7 +417,7 @@ def test_hist_panel(mock_panel_helper: MagicMock, params, dream_results): ({"default": "normal", 1: "kernel"}, "DEFAULT_WITH_1CHANGE_DICT"), ], ) -@patch("RATapi.plotting.plot_one_hist") +@patch("ratapi.plotting.plot_one_hist") def test_standardise_est_dens(mock_plot_hist: MagicMock, input, expected_dict, dream_results): """Test estimated density is correctly standardised.""" _ = RATplot.plot_hists(dream_results, estimated_density=input, return_fig=True) @@ -445,7 +450,7 @@ def test_est_dens_error(dream_results, input): @pytest.mark.parametrize( "params", [None, [2, 3], [1, 5, "D2O"], ["Bilayer Heads Thickness", "Bilayer Heads Hydration", "D2O"]] ) -@patch("RATapi.plotting.panel_plot_helper") +@patch("ratapi.plotting.panel_plot_helper") def test_chain_panel(mock_panel_helper: MagicMock, params, dream_results): """Test chain panel name-to-index (panel helper has already been tested)""" # return fig just to avoid plt.show() being called @@ -458,9 +463,9 @@ def test_chain_panel(mock_panel_helper: MagicMock, params, dream_results): assert param == (dream_results.fitNames.index(param) if isinstance(param, str) else param) -@patch("RATapi.plotting.plot_ref_sld") -@patch("RATapi.plotting.plot_hists") -@patch("RATapi.plotting.plot_corner") +@patch("ratapi.plotting.plot_ref_sld") +@patch("ratapi.plotting.plot_hists") +@patch("ratapi.plotting.plot_corner") def test_bayes_calls( mock_corner: MagicMock, mock_hists: MagicMock, mock_ref_sld: MagicMock, input_project, dream_results ): @@ -481,20 +486,12 @@ def test_bayes_validation(input_project, reflectivity_calculation_results): @pytest.mark.parametrize("data", [data(), domains_data()]) def test_extract_plot_data(data) -> None: - plot_data = RATplot._extract_plot_data(data, False, True) + plot_data = RATplot._extract_plot_data(data, False, True, 50) assert len(plot_data["ref"]) == len(data.reflectivity) assert len(plot_data["sld"]) == len(data.shiftedData) + with pytest.raises(ValueError, match=r"Parameter `shift_value` must be between 0 and 100"): + RATplot._extract_plot_data(data, False, True, -0.1) -@patch("RATapi.utils.plotting.plot_ref_sld_helper") -def test_blit_plot(plot_helper, fig: plt.figure) -> None: - plot_helper.return_value = fig - event_data = data() - new_plot = RATplot.PlotSLDWithBlitting(event_data) - assert plot_helper.call_count == 1 - new_plot.update(event_data) - assert plot_helper.call_count == 1 # foreground only is updated so no call to plot helper - new_plot.show_grid = False - new_plot.figure = plt.subplots(1, 2)[0] - new_plot.update(event_data) # plot properties have changed so update should call plot_helper - assert plot_helper.call_count == 2 + with pytest.raises(ValueError, match=r"Parameter `shift_value` must be between 0 and 100"): + RATplot._extract_plot_data(data, False, True, 100.5) diff --git a/tests/test_project.py b/tests/test_project.py index ee6d6c59..35d5864e 100644 --- a/tests/test_project.py +++ b/tests/test_project.py @@ -4,16 +4,16 @@ import re import tempfile import warnings +from collections.abc import Callable from pathlib import Path -from typing import Callable +from typing import get_args, get_origin import numpy as np import pydantic import pytest -from typing_extensions import get_args, get_origin -import RATapi -from RATapi.utils.enums import Calculations, LayerModels, TypeOptions +import ratapi +from ratapi.utils.enums import Calculations, LayerModels, TypeOptions layer_params = {"thickness": "Test Thickness", "SLD": "Test SLD", "roughness": "Test Roughness"} absorption_layer_params = { @@ -24,28 +24,28 @@ } model_classes = { - "parameters": RATapi.models.Parameter, - "bulk_in": RATapi.models.Parameter, - "bulk_out": RATapi.models.Parameter, - "scalefactors": RATapi.models.Parameter, - "domain_ratios": RATapi.models.Parameter, - "background_parameters": RATapi.models.Parameter, - "resolution_parameters": RATapi.models.Parameter, - "backgrounds": RATapi.models.Background, - "resolutions": RATapi.models.Resolution, - "custom_files": RATapi.models.CustomFile, - "data": RATapi.models.Data, - "layers": RATapi.models.Layer, - "domain_contrasts": RATapi.models.DomainContrast, - "contrasts": RATapi.models.Contrast, + "parameters": ratapi.models.Parameter, + "bulk_in": ratapi.models.Parameter, + "bulk_out": ratapi.models.Parameter, + "scalefactors": ratapi.models.Parameter, + "domain_ratios": ratapi.models.Parameter, + "background_parameters": ratapi.models.Parameter, + "resolution_parameters": ratapi.models.Parameter, + "backgrounds": ratapi.models.Background, + "resolutions": ratapi.models.Resolution, + "custom_files": ratapi.models.CustomFile, + "data": ratapi.models.Data, + "layers": ratapi.models.Layer, + "domain_contrasts": ratapi.models.DomainContrast, + "contrasts": ratapi.models.Contrast, } @pytest.fixture def test_project(): """Add parameters to the default project, so each ClassList can be tested properly.""" - test_project = RATapi.Project( - data=RATapi.ClassList([RATapi.models.Data(name="Simulation", data=np.array([[1.0, 1.0, 1.0]]))]), + test_project = ratapi.Project( + data=ratapi.ClassList([ratapi.models.Data(name="Simulation", data=np.array([[1.0, 1.0, 1.0]]))]), ) test_project.parameters.append(name="Test Thickness") test_project.parameters.append(name="Test SLD") @@ -141,8 +141,8 @@ def test_classlists(test_project) -> None: """The ClassLists in the "Project" model should contain instances of the models given by the dictionary "model_in_classlist". """ - for model in (fields := RATapi.Project.model_fields): - if get_origin(fields[model].annotation) == RATapi.ClassList: + for model in (fields := ratapi.Project.model_fields): + if get_origin(fields[model].annotation) == ratapi.ClassList: class_list = getattr(test_project, model) assert class_list._class_handle == get_args(fields[model].annotation)[0] @@ -151,7 +151,7 @@ def test_classlists_specific_cases() -> None: """The ClassLists in the "Project" model should contain instances of specific models given various non-default options. """ - project = RATapi.Project(calculation=Calculations.Domains, absorption=True) + project = ratapi.Project(calculation=Calculations.Domains, absorption=True) assert project.layers._class_handle.__name__ == "AbsorptionLayer" assert project.contrasts._class_handle.__name__ == "ContrastWithRatio" @@ -159,15 +159,15 @@ def test_classlists_specific_cases() -> None: @pytest.mark.parametrize( ["input_model", "model_params"], [ - (RATapi.models.Background, {}), - (RATapi.models.Contrast, {}), - (RATapi.models.ContrastWithRatio, {}), - (RATapi.models.CustomFile, {}), - (RATapi.models.Data, {}), - (RATapi.models.DomainContrast, {}), - (RATapi.models.Layer, layer_params), - (RATapi.models.AbsorptionLayer, absorption_layer_params), - (RATapi.models.Resolution, {}), + (ratapi.models.Background, {}), + (ratapi.models.Contrast, {}), + (ratapi.models.ContrastWithRatio, {}), + (ratapi.models.CustomFile, {}), + (ratapi.models.Data, {}), + (ratapi.models.DomainContrast, {}), + (ratapi.models.Layer, layer_params), + (ratapi.models.AbsorptionLayer, absorption_layer_params), + (ratapi.models.Resolution, {}), ], ) def test_initialise_wrong_classes(input_model: Callable, model_params: dict) -> None: @@ -178,14 +178,14 @@ def test_initialise_wrong_classes(input_model: Callable, model_params: dict) -> "Value error, This ClassList only supports elements of type Parameter. In the input list:\n" f" index 0 is of type {input_model.__name__}", ): - RATapi.Project(parameters=RATapi.ClassList(input_model(**model_params))) + ratapi.Project(parameters=ratapi.ClassList(input_model(**model_params))) @pytest.mark.parametrize( ["input_model", "model_params", "absorption", "actual_model_name"], [ - (RATapi.models.Layer, layer_params, True, "AbsorptionLayer"), - (RATapi.models.AbsorptionLayer, absorption_layer_params, False, "Layer"), + (ratapi.models.Layer, layer_params, True, "AbsorptionLayer"), + (ratapi.models.AbsorptionLayer, absorption_layer_params, False, "Layer"), ], ) def test_initialise_wrong_layers( @@ -203,18 +203,18 @@ def test_initialise_wrong_layers( f'"The layers attribute contains {input_model.__name__}s, but the absorption parameter is ' f'{absorption}. The attribute should be a ClassList of {actual_model_name} instead."', ): - RATapi.Project(absorption=absorption, layers=RATapi.ClassList(input_model(**model_params))) + ratapi.Project(absorption=absorption, layers=ratapi.ClassList(input_model(**model_params))) @pytest.mark.parametrize( ["input_model", "calculation", "actual_model_name"], [ - (RATapi.models.Contrast, Calculations.Domains, "ContrastWithRatio"), - (RATapi.models.ContrastWithRatio, Calculations.Normal, "Contrast"), + (ratapi.models.Contrast, Calculations.Domains, "ContrastWithRatio"), + (ratapi.models.ContrastWithRatio, Calculations.Normal, "Contrast"), ], ) def test_initialise_wrong_contrasts( - input_model: RATapi.models.RATModel, calculation: Calculations, actual_model_name: str + input_model: ratapi.models.RATModel, calculation: Calculations, actual_model_name: str ) -> None: """If the "Project" model is initialised with the incorrect contrast model given the value of calculation, we should raise a ValidationError. @@ -226,16 +226,16 @@ def test_initialise_wrong_contrasts( f' Value error, "The contrasts attribute contains contrasts {word} ratio, ' f'but the calculation is {calculation}"', ): - RATapi.Project(calculation=calculation, contrasts=RATapi.ClassList(input_model())) + ratapi.Project(calculation=calculation, contrasts=ratapi.ClassList(input_model())) @pytest.mark.parametrize( "calculation, model", - [(Calculations.Domains, RATapi.models.ContrastWithRatio), (Calculations.Normal, RATapi.models.Contrast)], + [(Calculations.Domains, ratapi.models.ContrastWithRatio), (Calculations.Normal, ratapi.models.Contrast)], ) -def test_initialise_ambiguous_contrasts(calculation: Calculations, model: RATapi.models.RATModel): +def test_initialise_ambiguous_contrasts(calculation: Calculations, model: ratapi.models.RATModel): """If a sequence of dictionaries is passed to 'contrasts', convert them to the correct model for the calculation.""" - proj = RATapi.Project(calculation=calculation, contrasts=RATapi.ClassList([{"name": "Contrast 1"}])) + proj = ratapi.Project(calculation=calculation, contrasts=ratapi.ClassList([{"name": "Contrast 1"}])) assert proj.contrasts._class_handle == model @@ -243,14 +243,14 @@ def test_initialise_without_substrate_roughness() -> None: """If the "Project" model is initialised without "Substrate Roughness" as a parameter, add it as a protected parameter to the front of the "parameters" ClassList. """ - project = RATapi.Project(parameters=RATapi.ClassList(RATapi.models.Parameter(name="Test Parameter"))) - assert project.parameters[0] == RATapi.models.ProtectedParameter( + project = ratapi.Project(parameters=ratapi.ClassList(ratapi.models.Parameter(name="Test Parameter"))) + assert project.parameters[0] == ratapi.models.ProtectedParameter( name="Substrate Roughness", min=1.0, value=3.0, max=5.0, fit=True, - prior_type=RATapi.models.Priors.Uniform, + prior_type=ratapi.models.Priors.Uniform, mu=0.0, sigma=np.inf, ) @@ -259,38 +259,38 @@ def test_initialise_without_substrate_roughness() -> None: @pytest.mark.parametrize( "input_parameter", [ - RATapi.models.Parameter(name="Substrate Roughness"), - RATapi.models.Parameter(name="SUBSTRATE ROUGHNESS"), - RATapi.models.Parameter(name="substrate roughness"), + ratapi.models.Parameter(name="Substrate Roughness"), + ratapi.models.Parameter(name="SUBSTRATE ROUGHNESS"), + ratapi.models.Parameter(name="substrate roughness"), ], ) -def test_initialise_without_protected_substrate_roughness(input_parameter: RATapi.models.Parameter) -> None: +def test_initialise_without_protected_substrate_roughness(input_parameter: ratapi.models.Parameter) -> None: """If the "Project" model is initialised without "Substrate Roughness" as a protected parameter, add it to the front of the "parameters" ClassList. """ - project = RATapi.Project(parameters=RATapi.ClassList(input_parameter)) - assert project.parameters[0] == RATapi.models.ProtectedParameter(name=input_parameter.name) + project = ratapi.Project(parameters=ratapi.ClassList(input_parameter)) + assert project.parameters[0] == ratapi.models.ProtectedParameter(name=input_parameter.name) def test_initialise_without_simulation() -> None: """If the "Project" model is initialised without "Simulation" in the "data" ClassList, add it to the front of the "data" ClassList. """ - project = RATapi.Project(parameters=RATapi.ClassList(RATapi.models.Parameter(name="Test Parameter"))) - assert project.data[0] == RATapi.models.Data(name="Simulation", simulation_range=[0.005, 0.7]) + project = ratapi.Project(parameters=ratapi.ClassList(ratapi.models.Parameter(name="Test Parameter"))) + assert project.data[0] == ratapi.models.Data(name="Simulation", simulation_range=[0.005, 0.7]) @pytest.mark.parametrize( ["field", "model_type", "wrong_input_model", "model_params"], [ - ("backgrounds", "Background", RATapi.models.Resolution, {}), - ("contrasts", "Contrast", RATapi.models.Layer, layer_params), - ("domain_contrasts", "DomainContrast", RATapi.models.Parameter, {}), - ("custom_files", "CustomFile", RATapi.models.Data, {}), - ("data", "Data", RATapi.models.Contrast, {}), - ("layers", "Layer", RATapi.models.DomainContrast, {}), - ("parameters", "Parameter", RATapi.models.CustomFile, {}), - ("resolutions", "Resolution", RATapi.models.Background, {}), + ("backgrounds", "Background", ratapi.models.Resolution, {}), + ("contrasts", "Contrast", ratapi.models.Layer, layer_params), + ("domain_contrasts", "DomainContrast", ratapi.models.Parameter, {}), + ("custom_files", "CustomFile", ratapi.models.Data, {}), + ("data", "Data", ratapi.models.Contrast, {}), + ("layers", "Layer", ratapi.models.DomainContrast, {}), + ("parameters", "Parameter", ratapi.models.CustomFile, {}), + ("resolutions", "Resolution", ratapi.models.Background, {}), ], ) def test_assign_wrong_classes( @@ -312,14 +312,14 @@ def test_assign_wrong_classes( f" index 0 is of type {wrong_input_model.__name__}" ), ): - setattr(test_project, field, RATapi.ClassList(wrong_input_model(**model_params))) + setattr(test_project, field, ratapi.ClassList(wrong_input_model(**model_params))) @pytest.mark.parametrize( ["wrong_input_model", "model_params", "absorption", "actual_model_name"], [ - (RATapi.models.Layer, layer_params, True, "AbsorptionLayer"), - (RATapi.models.AbsorptionLayer, absorption_layer_params, False, "Layer"), + (ratapi.models.Layer, layer_params, True, "AbsorptionLayer"), + (ratapi.models.AbsorptionLayer, absorption_layer_params, False, "Layer"), ], ) def test_assign_wrong_layers( @@ -329,26 +329,26 @@ def test_assign_wrong_layers( actual_model_name: str, ) -> None: """If we assign incorrect classes to the "Project" model, we should raise a ValidationError.""" - project = RATapi.Project(absorption=absorption) + project = ratapi.Project(absorption=absorption) with pytest.raises( pydantic.ValidationError, match=f"1 validation error for Project\nlayers\n Value error, " f'"The layers attribute contains {wrong_input_model.__name__}s, but the absorption parameter is ' f'{absorption}. The attribute should be a ClassList of {actual_model_name} instead."', ): - project.layers = RATapi.ClassList(wrong_input_model(**model_params)) + project.layers = ratapi.ClassList(wrong_input_model(**model_params)) @pytest.mark.parametrize( ["wrong_input_model", "calculation", "actual_model_name"], [ - (RATapi.models.Contrast, Calculations.Domains, "ContrastWithRatio"), - (RATapi.models.ContrastWithRatio, Calculations.Normal, "Contrast"), + (ratapi.models.Contrast, Calculations.Domains, "ContrastWithRatio"), + (ratapi.models.ContrastWithRatio, Calculations.Normal, "Contrast"), ], ) def test_assign_wrong_contrasts(wrong_input_model: Callable, calculation: Calculations, actual_model_name: str) -> None: """If we assign incorrect classes to the "Project" model, we should raise a ValidationError.""" - project = RATapi.Project(calculation=calculation) + project = ratapi.Project(calculation=calculation) word = "without" if calculation == Calculations.Domains else "with" with pytest.raises( pydantic.ValidationError, @@ -356,7 +356,7 @@ def test_assign_wrong_contrasts(wrong_input_model: Callable, calculation: Calcul f' Value error, "The contrasts attribute contains contrasts {word} ratio, but the calculation is ' f'{calculation}"', ): - project.contrasts = RATapi.ClassList(wrong_input_model()) + project.contrasts = ratapi.ClassList(wrong_input_model()) @pytest.mark.parametrize( @@ -395,7 +395,7 @@ def test_wrapped_routines(test_project) -> None: "extend", "set_fields", ] - for class_list in RATapi.project.class_lists: + for class_list in ratapi.project.class_lists: attribute = getattr(test_project, class_list) for methodName in wrapped_methods: assert hasattr(getattr(attribute, methodName), "__wrapped__") @@ -422,7 +422,7 @@ def test_set_domain_contrasts(project_parameters: dict) -> None: """If we are not running a domains calculation with standard layers, the "domain_contrasts" field of the model should always be empty. """ - project = RATapi.Project(**project_parameters) + project = ratapi.Project(**project_parameters) assert project.domain_contrasts == [] project.domain_contrasts.append(name="New Domain Contrast") assert project.domain_contrasts == [] @@ -437,17 +437,57 @@ def test_set_domain_contrasts(project_parameters: dict) -> None: ) def test_set_layers(project_parameters: dict) -> None: """If we are not using a standard layers model, the "layers" field of the model should always be empty.""" - project = RATapi.Project(**project_parameters) + project = ratapi.Project(**project_parameters) assert project.layers == [] project.layers.append(name="New Layer", thickness="Test Thickness", SLD="Test SLD", roughness="Test Roughness") assert project.layers == [] +@pytest.mark.parametrize( + "project_parameters", + [ + ( + { + "model": LayerModels.CustomLayers, + "contrasts": [ratapi.models.Contrast(name="Test Contrast", repeat_layers=2)], + } + ), + ({"model": LayerModels.CustomXY, "contrasts": [ratapi.models.Contrast(name="Test Contrast", repeat_layers=2)]}), + ], +) +def test_set_repeat_layers(project_parameters: dict) -> None: + """If we are using a custom layers of custom XY model, the "resample" field of all the contrasts should always + be 1.""" + with pytest.warns( + match='For a custom layers or custom XY calculation, the "repeat_layers" setting for each ' + "contrast is not valid - resetting to 1." + ): + project = ratapi.Project(**project_parameters) + assert all(contrast.repeat_layers == 1 for contrast in project.contrasts) + + +@pytest.mark.parametrize( + "project_parameters", + [ + ({"model": LayerModels.CustomXY, "contrasts": [ratapi.models.Contrast(name="Test Contrast")]}), + ], +) +def test_set_resample(project_parameters: dict) -> None: + """If we are using a custom XY model, the "resample" field of all the contrasts should always be True.""" + project = ratapi.Project(**project_parameters) + assert all(contrast.resample for contrast in project.contrasts) + with pytest.warns( + match='For a custom XY calculation, "resample" must be True for each contrast - resetting to True.' + ): + project.contrasts.append(name="New Contrast", resample=False) + assert all(contrast.resample for contrast in project.contrasts) + + @pytest.mark.parametrize( ["input_calculation", "input_contrast", "new_calculation", "new_contrast_model", "num_domain_ratios"], [ - (Calculations.Normal, RATapi.models.Contrast, Calculations.Domains, "ContrastWithRatio", 1), - (Calculations.Domains, RATapi.models.ContrastWithRatio, Calculations.Normal, "Contrast", 0), + (Calculations.Normal, ratapi.models.Contrast, Calculations.Domains, "ContrastWithRatio", 1), + (Calculations.Domains, ratapi.models.ContrastWithRatio, Calculations.Normal, "Contrast", 0), ], ) def test_set_calculation( @@ -460,7 +500,7 @@ def test_set_calculation( """When changing the value of the calculation option, the "contrasts" ClassList should switch to using the appropriate Contrast model. """ - project = RATapi.Project(calculation=input_calculation, contrasts=RATapi.ClassList(input_contrast())) + project = ratapi.Project(calculation=input_calculation, contrasts=ratapi.ClassList(input_contrast())) project.calculation = new_calculation assert project.calculation is new_calculation @@ -525,13 +565,13 @@ def test_check_contrast_model_length( """If we are not running a domains calculation with standard layers, the "domain_contrasts" field of the model should always be empty. """ - test_domain_ratios = RATapi.ClassList(RATapi.models.Parameter(name="Test Domain Ratio")) - test_contrasts = RATapi.ClassList(RATapi.models.ContrastWithRatio(model=test_contrast_model)) + test_domain_ratios = ratapi.ClassList(ratapi.models.Parameter(name="Test Domain Ratio")) + test_contrasts = ratapi.ClassList(ratapi.models.ContrastWithRatio(model=test_contrast_model)) with pytest.raises( pydantic.ValidationError, match=f"1 validation error for Project\n Value error, {error_message}", ): - RATapi.Project( + ratapi.Project( calculation=Calculations.Domains, model=input_model, domain_ratios=test_domain_ratios, @@ -542,8 +582,8 @@ def test_check_contrast_model_length( @pytest.mark.parametrize( ["input_layer", "model_params", "input_absorption", "new_layer_model"], [ - (RATapi.models.Layer, layer_params, False, "AbsorptionLayer"), - (RATapi.models.AbsorptionLayer, absorption_layer_params, True, "Layer"), + (ratapi.models.Layer, layer_params, False, "AbsorptionLayer"), + (ratapi.models.AbsorptionLayer, absorption_layer_params, True, "Layer"), ], ) def test_set_absorption( @@ -555,16 +595,16 @@ def test_set_absorption( """When changing the value of the absorption option, the "layers" ClassList should switch to using the appropriate Layer model. """ - project = RATapi.Project( + project = ratapi.Project( absorption=input_absorption, - parameters=RATapi.ClassList( + parameters=ratapi.ClassList( [ - RATapi.models.Parameter(name="Test Thickness"), - RATapi.models.Parameter(name="Test SLD"), - RATapi.models.Parameter(name="Test Roughness"), + ratapi.models.Parameter(name="Test Thickness"), + ratapi.models.Parameter(name="Test SLD"), + ratapi.models.Parameter(name="Test Roughness"), ], ), - layers=RATapi.ClassList(input_layer(**model_params)), + layers=ratapi.ClassList(input_layer(**model_params)), ) project.absorption = not input_absorption @@ -584,7 +624,7 @@ def test_set_absorption( ) def test_check_protected_parameters(delete_operation) -> None: """If we try to remove a protected parameter, we should raise an error.""" - project = RATapi.Project() + project = ratapi.Project() with pytest.raises( pydantic.ValidationError, @@ -617,17 +657,17 @@ def test_rename_models(test_project, model: str, fields: list[str]) -> None: in the project. """ if model == "data": - test_project.backgrounds[0] = RATapi.models.Background(type="data", source="Simulation") + test_project.backgrounds[0] = ratapi.models.Background(type="data", source="Simulation") if model == "custom_files": - test_project.backgrounds[0] = RATapi.models.Background(type="function", source="Test Custom File") + test_project.backgrounds[0] = ratapi.models.Background(type="function", source="Test Custom File") # workaround until function resolutions are added - # test_project.resolutions[0] = RATapi.models.Resolution(type="function", source="Test Custom File") - test_project.resolution_parameters.append(RATapi.models.Parameter(name="New Name")) - test_project.resolutions[0] = RATapi.models.Resolution(type="constant", source="New Name") + # test_project.resolutions[0] = ratapi.models.Resolution(type="function", source="Test Custom File") + test_project.resolution_parameters.append(ratapi.models.Parameter(name="New Name")) + test_project.resolutions[0] = ratapi.models.Resolution(type="constant", source="New Name") getattr(test_project, model).set_fields(-1, name="New Name") - model_name_lists = RATapi.project.model_names_used_in[model] - for model_name_list, field in zip(model_name_lists, fields): + model_name_lists = ratapi.project.model_names_used_in[model] + for model_name_list, field in zip(model_name_lists, fields, strict=False): attribute = model_name_list.attribute assert getattr(getattr(test_project, attribute)[-1], field) == "New Name" @@ -645,7 +685,7 @@ def test_allowed_backgrounds(background_type, expected_field) -> None: If the source field of the Background model is set to a value that is not specified in the appropriate ClassList, we should raise a ValidationError. """ - test_background = RATapi.models.Background(type=background_type, source="undefined") + test_background = ratapi.models.Background(type=background_type, source="undefined") with pytest.raises( pydantic.ValidationError, match=re.escape( @@ -654,7 +694,7 @@ def test_allowed_backgrounds(background_type, expected_field) -> None: f'before including it in "backgrounds".' ), ): - RATapi.Project(backgrounds=RATapi.ClassList(test_background)) + ratapi.Project(backgrounds=ratapi.ClassList(test_background)) @pytest.mark.parametrize( @@ -669,7 +709,7 @@ def test_allowed_layers(field: str) -> None: """If the "thickness", "SLD", or "roughness" fields of the Layer model are set to values that are not specified in the parameters, we should raise a ValidationError. """ - test_layer = RATapi.models.Layer(**{**layer_params, field: "undefined"}) + test_layer = ratapi.models.Layer(**{**layer_params, field: "undefined"}) with pytest.raises( pydantic.ValidationError, @@ -679,16 +719,16 @@ def test_allowed_layers(field: str) -> None: f'before including it in "layers".' ), ): - RATapi.Project( + ratapi.Project( absorption=False, - parameters=RATapi.ClassList( + parameters=ratapi.ClassList( [ - RATapi.models.Parameter(name="Test Thickness"), - RATapi.models.Parameter(name="Test SLD"), - RATapi.models.Parameter(name="Test Roughness"), + ratapi.models.Parameter(name="Test Thickness"), + ratapi.models.Parameter(name="Test SLD"), + ratapi.models.Parameter(name="Test Roughness"), ], ), - layers=RATapi.ClassList(test_layer), + layers=ratapi.ClassList(test_layer), ) @@ -705,7 +745,7 @@ def test_allowed_absorption_layers(field: str) -> None: """If the "thickness", "SLD_real", "SLD_imaginary", or "roughness" fields of the AbsorptionLayer model are set to values that are not specified in the parameters, we should raise a ValidationError. """ - test_layer = RATapi.models.AbsorptionLayer(**{**absorption_layer_params, field: "undefined"}) + test_layer = ratapi.models.AbsorptionLayer(**{**absorption_layer_params, field: "undefined"}) with pytest.raises( pydantic.ValidationError, @@ -715,16 +755,16 @@ def test_allowed_absorption_layers(field: str) -> None: f'in "layers".' ), ): - RATapi.Project( + ratapi.Project( absorption=True, - parameters=RATapi.ClassList( + parameters=ratapi.ClassList( [ - RATapi.models.Parameter(name="Test Thickness"), - RATapi.models.Parameter(name="Test SLD"), - RATapi.models.Parameter(name="Test Roughness"), + ratapi.models.Parameter(name="Test Thickness"), + ratapi.models.Parameter(name="Test SLD"), + ratapi.models.Parameter(name="Test Roughness"), ], ), - layers=RATapi.ClassList(test_layer), + layers=ratapi.ClassList(test_layer), ) @@ -740,7 +780,7 @@ def test_allowed_resolutions(resolution_type, expected_field) -> None: """If the "value" fields of the Resolution model are set to values that are not specified in the resolution parameters, we should raise a ValidationError. """ - test_resolution = RATapi.models.Resolution(type=resolution_type, source="undefined") + test_resolution = ratapi.models.Resolution(type=resolution_type, source="undefined") with pytest.raises( pydantic.ValidationError, match=re.escape( @@ -749,7 +789,7 @@ def test_allowed_resolutions(resolution_type, expected_field) -> None: f'before including it in "resolutions".' ), ): - RATapi.Project(resolutions=RATapi.ClassList(test_resolution)) + ratapi.Project(resolutions=ratapi.ClassList(test_resolution)) @pytest.mark.parametrize( @@ -767,7 +807,7 @@ def test_allowed_contrasts(field: str, model_name: str) -> None: """If the fields of the Contrast model are set to values not specified in the other respective models of the project, we should raise a ValidationError. """ - test_contrast = RATapi.models.Contrast(**{field: "undefined"}) + test_contrast = ratapi.models.Contrast(**{field: "undefined"}) with pytest.raises( pydantic.ValidationError, match=re.escape( @@ -776,7 +816,7 @@ def test_allowed_contrasts(field: str, model_name: str) -> None: f'including it in "contrasts".' ), ): - RATapi.Project(calculation=Calculations.Normal, contrasts=RATapi.ClassList(test_contrast)) + ratapi.Project(calculation=Calculations.Normal, contrasts=ratapi.ClassList(test_contrast)) @pytest.mark.parametrize( @@ -795,7 +835,7 @@ def test_allowed_contrasts_with_ratio(field: str, model_name: str) -> None: """If the fields of the ContrastWithRatio model are set to values not specified in the other respective models of the project, we should raise a ValidationError. """ - test_contrast = RATapi.models.ContrastWithRatio(**{field: "undefined"}) + test_contrast = ratapi.models.ContrastWithRatio(**{field: "undefined"}) with pytest.raises( pydantic.ValidationError, match=re.escape( @@ -804,7 +844,7 @@ def test_allowed_contrasts_with_ratio(field: str, model_name: str) -> None: f'including it in "contrasts".' ), ): - RATapi.Project(calculation=Calculations.Domains, contrasts=RATapi.ClassList(test_contrast)) + ratapi.Project(calculation=Calculations.Domains, contrasts=ratapi.ClassList(test_contrast)) @pytest.mark.parametrize( @@ -813,37 +853,37 @@ def test_allowed_contrasts_with_ratio(field: str, model_name: str) -> None: ( Calculations.Domains, LayerModels.StandardLayers, - RATapi.models.ContrastWithRatio(name="Test Contrast", model=["undefined", "undefined"]), + ratapi.models.ContrastWithRatio(name="Test Contrast", model=["undefined", "undefined"]), "domain_contrasts", ), ( Calculations.Domains, LayerModels.CustomLayers, - RATapi.models.ContrastWithRatio(name="Test Contrast", model=["undefined"]), + ratapi.models.ContrastWithRatio(name="Test Contrast", model=["undefined"]), "custom_files", ), ( Calculations.Domains, LayerModels.CustomXY, - RATapi.models.ContrastWithRatio(name="Test Contrast", model=["undefined"]), + ratapi.models.ContrastWithRatio(name="Test Contrast", model=["undefined"]), "custom_files", ), ( Calculations.Normal, LayerModels.StandardLayers, - RATapi.models.Contrast(name="Test Contrast", model=["undefined", "undefined", "undefined"]), + ratapi.models.Contrast(name="Test Contrast", model=["undefined", "undefined", "undefined"]), "layers", ), ( Calculations.Normal, LayerModels.CustomLayers, - RATapi.models.Contrast(name="Test Contrast", model=["undefined"]), + ratapi.models.Contrast(name="Test Contrast", model=["undefined"]), "custom_files", ), ( Calculations.Normal, LayerModels.CustomXY, - RATapi.models.Contrast(name="Test Contrast", model=["undefined"]), + ratapi.models.Contrast(name="Test Contrast", model=["undefined"]), "custom_files", ), ], @@ -851,7 +891,7 @@ def test_allowed_contrasts_with_ratio(field: str, model_name: str) -> None: def test_allowed_contrast_models( input_calc: Calculations, input_model: LayerModels, - test_contrast: "RATapi.models", + test_contrast: "ratapi.models", field_name: str, ) -> None: """If any value in the model field of the contrasts is set to a value not specified in the appropriate part of the @@ -866,14 +906,14 @@ def test_allowed_contrast_models( f'"{field_name}". Please add all required values to "{field_name}" before including them in "contrasts".' ), ): - RATapi.Project(calculation=input_calc, model=input_model, contrasts=RATapi.ClassList(test_contrast)) + ratapi.Project(calculation=input_calc, model=input_model, contrasts=ratapi.ClassList(test_contrast)) def test_allowed_domain_contrast_models() -> None: """If any value in the model field of the domain_contrasts is set to a value not specified in the "layers" field of the project, we should raise a ValidationError. """ - test_contrast = RATapi.models.DomainContrast(name="Test Domain Contrast", model=["undefined"]) + test_contrast = ratapi.models.DomainContrast(name="Test Domain Contrast", model=["undefined"]) with pytest.raises( pydantic.ValidationError, match=re.escape( @@ -882,12 +922,12 @@ def test_allowed_domain_contrast_models() -> None: 'including them in "domain_contrasts".' ), ): - RATapi.Project(calculation=Calculations.Domains, domain_contrasts=RATapi.ClassList(test_contrast)) + ratapi.Project(calculation=Calculations.Domains, domain_contrasts=ratapi.ClassList(test_contrast)) def test_str(default_project_str: str) -> None: """We should be able to print the "Project" model as a formatted list of the fields.""" - assert str(RATapi.Project()) == default_project_str + assert str(ratapi.Project()) == default_project_str def test_get_all_names(test_project) -> None: @@ -933,8 +973,8 @@ def test_get_all_protected_parameters(test_project) -> None: def test_check_allowed_values(test_value: str) -> None: """We should not raise an error if string values are defined and on the list of allowed values.""" allowed_values = ["Substrate Roughness"] - project = RATapi.Project.model_construct( - layers=RATapi.ClassList(RATapi.models.Layer(**dict(layer_params, roughness=test_value))) + project = ratapi.Project.model_construct( + layers=ratapi.ClassList(ratapi.models.Layer(**dict(layer_params, roughness=test_value))) ) assert project.check_allowed_values("layers", ["roughness"], allowed_values, allowed_values) is None @@ -947,8 +987,8 @@ def test_check_allowed_values(test_value: str) -> None: ) def test_check_allowed_values_not_on_list(test_value: str) -> None: """If string values are defined and are not included on the list of allowed values we should raise a ValueError.""" - project = RATapi.Project.model_construct( - layers=RATapi.ClassList(RATapi.models.Layer(**dict(layer_params, roughness=test_value))) + project = ratapi.Project.model_construct( + layers=ratapi.ClassList(ratapi.models.Layer(**dict(layer_params, roughness=test_value))) ) allowed_values = ["Substrate Roughness"] with pytest.raises( @@ -970,9 +1010,9 @@ def test_check_allowed_values_not_on_list(test_value: str) -> None: ) def test_check_allowed_background_resolution_values_constant(test_value: str) -> None: """We should not raise an error if string values are defined and on the appropriate list of allowed values.""" - project = RATapi.Project.model_construct( - background_parameters=RATapi.ClassList(RATapi.models.Parameter(name="Background Param 1")), - backgrounds=RATapi.ClassList(RATapi.models.Background(type=TypeOptions.Constant, source=test_value)), + project = ratapi.Project.model_construct( + background_parameters=ratapi.ClassList(ratapi.models.Parameter(name="Background Param 1")), + backgrounds=ratapi.ClassList(ratapi.models.Background(type=TypeOptions.Constant, source=test_value)), ) assert project.check_allowed_source("backgrounds") is None @@ -986,8 +1026,8 @@ def test_check_allowed_background_resolution_values_constant(test_value: str) -> ) def test_check_allowed_background_resolution_values_data(test_value: str) -> None: """We should not raise an error if string values are defined and on the appropriate list of allowed values.""" - project = RATapi.Project.model_construct( - backgrounds=RATapi.ClassList(RATapi.models.Background(type=TypeOptions.Data, source=test_value)) + project = ratapi.Project.model_construct( + backgrounds=ratapi.ClassList(ratapi.models.Background(type=TypeOptions.Data, source=test_value)) ) assert project.check_allowed_source("backgrounds") is None @@ -1000,8 +1040,8 @@ def test_check_allowed_background_resolution_values_not_on_constant_list(test_va """If string values are defined and are not included on the correct list of allowed values we should raise a ValueError. """ - project = RATapi.Project.model_construct( - backgrounds=RATapi.ClassList(RATapi.models.Background(type=TypeOptions.Constant, source=test_value)) + project = ratapi.Project.model_construct( + backgrounds=ratapi.ClassList(ratapi.models.Background(type=TypeOptions.Constant, source=test_value)) ) with pytest.raises( ValueError, @@ -1027,8 +1067,8 @@ def test_check_allowed_background_resolution_values_on_data_list(test_value: str """If string values are defined and are not included on the correct list of allowed values we should raise a ValueError. """ - project = RATapi.Project.model_construct( - backgrounds=RATapi.ClassList(RATapi.models.Background(type=TypeOptions.Data, source=test_value)) + project = ratapi.Project.model_construct( + backgrounds=ratapi.ClassList(ratapi.models.Background(type=TypeOptions.Data, source=test_value)) ) with pytest.raises( ValueError, @@ -1051,8 +1091,8 @@ def test_check_contrast_model_allowed_values(test_values: list[str]) -> None: """We should not raise an error if values are defined in a non-empty list and all are on the list of allowed values. """ - project = RATapi.Project.model_construct( - contrasts=RATapi.ClassList(RATapi.models.Contrast(name="Test Contrast", model=test_values)), + project = ratapi.Project.model_construct( + contrasts=ratapi.ClassList(ratapi.models.Contrast(name="Test Contrast", model=test_values)), ) assert project.check_contrast_model_allowed_values("contrasts", ["Test Layer"], ["Test Layer"], "layers") is None @@ -1069,8 +1109,8 @@ def test_check_allowed_contrast_model_not_on_list(test_values: list[str]) -> Non """If string values are defined in a non-empty list and any of them are not included on the list of allowed values we should raise a ValueError. """ - project = RATapi.Project.model_construct( - contrasts=RATapi.ClassList(RATapi.models.Contrast(name="Test Contrast", model=test_values)), + project = ratapi.Project.model_construct( + contrasts=ratapi.ClassList(ratapi.models.Contrast(name="Test Contrast", model=test_values)), ) allowed_values = ["Test Layer"] missing_values = list(set(test_values) - set(allowed_values)) @@ -1097,8 +1137,8 @@ def test_check_allowed_contrast_model_removed_from_list(test_values: list[str]) """If string values are defined in a non-empty list and any of them have been removed from the list of allowed values we should raise a ValueError. """ - project = RATapi.Project.model_construct( - contrasts=RATapi.ClassList(RATapi.models.Contrast(name="Test Contrast", model=test_values)), + project = ratapi.Project.model_construct( + contrasts=ratapi.ClassList(ratapi.models.Contrast(name="Test Contrast", model=test_values)), ) previous_values = ["Test Layer", "Undefined Param 1", "Undefined Param 2"] allowed_values = ["Test Layer"] @@ -1129,7 +1169,7 @@ def test_get_contrast_model_field(input_calc: Calculations, input_model: LayerMo """Each combination of calculation and model determines the field where the values of "model" field of "contrasts" are defined. """ - project = RATapi.Project(calculation=input_calc, model=input_model) + project = ratapi.Project(calculation=input_calc, model=input_model) assert project.get_contrast_model_field() == expected_field_name @@ -1158,7 +1198,7 @@ def test_write_script(project, request, temp_dir, input_filename: str) -> None: exec(script_path.read_text(), globals(), local_dict) new_project = local_dict["problem"] - for class_list in RATapi.project.class_lists: + for class_list in ratapi.project.class_lists: assert getattr(new_project, class_list) == getattr(test_project, class_list) @@ -1205,8 +1245,8 @@ def test_wrap_set(test_project, class_list: str, model_type: str, field: str) -> pydantic.ValidationError, match=re.escape( f'1 validation error for Project\n Value error, The value "undefined" used in the "{field}" field of ' - f'{class_list}[{index}] must be defined in "{RATapi.project.values_defined_in[class_list_str]}". Please ' - f'add "undefined" to "{RATapi.project.values_defined_in[class_list_str]}" before including it in ' + f'{class_list}[{index}] must be defined in "{ratapi.project.values_defined_in[class_list_str]}". Please ' + f'add "undefined" to "{ratapi.project.values_defined_in[class_list_str]}" before including it in ' f'"{class_list}".' ), ): @@ -1236,7 +1276,7 @@ def test_wrap_del(test_project, class_list: str, parameter: str, field: str) -> orig_class_list = copy.deepcopy(test_attribute) index = test_attribute.index(parameter) - sub_attribute_name = RATapi.project.model_names_used_in[class_list][0].attribute + sub_attribute_name = ratapi.project.model_names_used_in[class_list][0].attribute sub_attribute = getattr(test_project, sub_attribute_name) sub_index = [i for i, _ in enumerate(sub_attribute) if getattr(sub_attribute[i], field) == parameter][0] @@ -1283,8 +1323,8 @@ def test_wrap_iadd(test_project, class_list: str, model_type: str, field: str, m match=re.escape( f'1 validation error for Project\n Value error, The value "undefined" used in the "{field}" ' f"field of {class_list}[{len(test_attribute)}] must be defined in " - f'"{RATapi.project.values_defined_in[class_list_str]}". Please add "undefined" to ' - f'"{RATapi.project.values_defined_in[class_list_str]}" before including it in "{class_list}".' + f'"{ratapi.project.values_defined_in[class_list_str]}". Please add "undefined" to ' + f'"{ratapi.project.values_defined_in[class_list_str]}" before including it in "{class_list}".' ), ): test_attribute += [input_model(**{**model_params, field: "undefined"})] @@ -1323,8 +1363,8 @@ def test_wrap_append(test_project, class_list: str, model_type: str, field: str, match=re.escape( f'1 validation error for Project\n Value error, The value "undefined" used in the "{field}" ' f"field of {class_list}[{len(test_attribute)}] must be defined in " - f'"{RATapi.project.values_defined_in[class_list_str]}". Please add "undefined" to ' - f'"{RATapi.project.values_defined_in[class_list_str]}" before including it in "{class_list}".' + f'"{ratapi.project.values_defined_in[class_list_str]}". Please add "undefined" to ' + f'"{ratapi.project.values_defined_in[class_list_str]}" before including it in "{class_list}".' ), ): test_attribute.append(input_model(**{**model_params, field: "undefined"})) @@ -1363,8 +1403,8 @@ def test_wrap_insert(test_project, class_list: str, model_type: str, field: str, match=re.escape( f'1 validation error for Project\n Value error, The value "undefined" used in the "{field}" ' f"field of {class_list}[{index}] must be defined in " - f'"{RATapi.project.values_defined_in[class_list_str]}". Please add "undefined" to ' - f'"{RATapi.project.values_defined_in[class_list_str]}" before including it in "{class_list}".' + f'"{ratapi.project.values_defined_in[class_list_str]}". Please add "undefined" to ' + f'"{ratapi.project.values_defined_in[class_list_str]}" before including it in "{class_list}".' ), ): test_attribute.insert(index, input_model(**{**model_params, field: "undefined"})) @@ -1430,7 +1470,7 @@ def test_wrap_pop(test_project, class_list: str, parameter: str, field: str) -> orig_class_list = copy.deepcopy(test_attribute) index = test_attribute.index(parameter) - sub_attribute_name = RATapi.project.model_names_used_in[class_list][0].attribute + sub_attribute_name = ratapi.project.model_names_used_in[class_list][0].attribute sub_attribute = getattr(test_project, sub_attribute_name) sub_index = [i for i, _ in enumerate(sub_attribute) if getattr(sub_attribute[i], field) == parameter][0] @@ -1467,7 +1507,7 @@ def test_wrap_remove(test_project, class_list: str, parameter: str, field: str) test_attribute = getattr(test_project, class_list) orig_class_list = copy.deepcopy(test_attribute) - sub_attribute_name = RATapi.project.model_names_used_in[class_list][0].attribute + sub_attribute_name = ratapi.project.model_names_used_in[class_list][0].attribute sub_attribute = getattr(test_project, sub_attribute_name) sub_index = [i for i, _ in enumerate(sub_attribute) if getattr(sub_attribute[i], field) == parameter][0] @@ -1504,7 +1544,7 @@ def test_wrap_clear(test_project, class_list: str, parameter: str, field: str) - test_attribute = getattr(test_project, class_list) orig_class_list = copy.deepcopy(test_attribute) - sub_attribute_name = RATapi.project.model_names_used_in[class_list][0].attribute + sub_attribute_name = ratapi.project.model_names_used_in[class_list][0].attribute sub_attribute = getattr(test_project, sub_attribute_name) sub_index = [i for i, _ in enumerate(sub_attribute) if getattr(sub_attribute[i], field) == parameter][0] @@ -1551,8 +1591,8 @@ def test_wrap_extend(test_project, class_list: str, model_type: str, field: str, match=re.escape( f'1 validation error for Project\n Value error, The value "undefined" used in the "{field}" ' f"field of {class_list}[{len(test_attribute)}] must be defined in " - f'"{RATapi.project.values_defined_in[class_list_str]}". Please add "undefined" to ' - f'"{RATapi.project.values_defined_in[class_list_str]}" before including it in "{class_list}".' + f'"{ratapi.project.values_defined_in[class_list_str]}". Please add "undefined" to ' + f'"{ratapi.project.values_defined_in[class_list_str]}" before including it in "{class_list}".' ), ): test_attribute.extend([input_model(**{**model_params, field: "undefined"})]) @@ -1576,6 +1616,7 @@ def test_wrap_extend(test_project, class_list: str, model_type: str, field: str, "domains_custom_layers", "domains_custom_xy", "absorption", + "absorption_different_function", ], ) def test_save_load(project, request): @@ -1588,37 +1629,34 @@ def test_save_load(project, request): with warnings.catch_warnings(): warnings.simplefilter("ignore") original_project.save(path) - converted_project = RATapi.Project.load(path) - - # resolve custom files in case the original project had unresolvable relative paths - for file in original_project.custom_files: - file.path = file.path.resolve() + converted_project = ratapi.Project.load(path) - for field in RATapi.Project.model_fields: + for field in ratapi.Project.model_fields: assert getattr(converted_project, field) == getattr(original_project, field) def test_relative_paths(): """Test that ``try_relative_to`` correctly creates relative paths to subfolders.""" - with tempfile.TemporaryDirectory() as tmp: - data_path = Path(tmp, "data/myfile.dat") + cur_path = Path(".").resolve() + data_path = cur_path / "data/myfile.dat" + assert Path(ratapi.project.try_relative_to(data_path, cur_path)) == Path("data/myfile.dat") - assert Path(RATapi.project.try_relative_to(data_path, tmp)) == Path("data/myfile.dat") + # relative path will be left relative. + data_path = "data/myfile.dat" + assert Path(ratapi.project.try_relative_to(data_path, cur_path)) == Path("data/myfile.dat") def test_relative_paths_warning(): """Test that we get a warning for trying to walk up paths.""" - data_path = "/tmp/project/data/mydata.dat" - relative_path = "/tmp/project/project_path/myproj.dat" + cur_path = Path(".").resolve() + data_path = cur_path / "tmp/project/data/mydata.dat" + relative_path = cur_path / "tmp/project/project_path/myproj.dat" with pytest.warns( - match="Could not save custom file path as relative to the project directory, " + match="Could not write custom file path as relative to the project directory, " "which means that it may not work on other devices. If you would like to share your project, " "make sure your custom files are in a subfolder of the project save location.", ): - assert ( - Path(RATapi.project.try_relative_to(data_path, relative_path)) - == Path("/tmp/project/data/mydata.dat").resolve() - ) + assert Path(ratapi.project.try_relative_to(data_path, relative_path)) == data_path diff --git a/tests/test_run.py b/tests/test_run.py index a3242d45..3c85fbcd 100644 --- a/tests/test_run.py +++ b/tests/test_run.py @@ -10,12 +10,12 @@ import numpy as np import pytest -import RATapi -import RATapi.outputs -import RATapi.rat_core -from RATapi.events import EventTypes, ProgressEventData, notify -from RATapi.run import ProgressBar, TextOutput -from RATapi.utils.enums import Calculations, Geometries, LayerModels, Procedures +import ratapi +import ratapi.outputs +import ratapi.rat_core +from ratapi.events import EventTypes, ProgressEventData, notify +from ratapi.run import ProgressBar, TextOutput +from ratapi.utils.enums import Calculations, Geometries, LayerModels, Procedures from tests.utils import check_results_equal @@ -24,7 +24,7 @@ def reflectivity_calculation_problem(): """The output C++ ProblemDefinition object for a reflectivity calculation of the project set out in "DSPC_standard_layers.py". """ - problem = RATapi.rat_core.ProblemDefinition() + problem = ratapi.rat_core.ProblemDefinition() problem.TF = Calculations.Normal problem.modelType = LayerModels.StandardLayers problem.geometry = Geometries.SubstrateLiquid @@ -185,7 +185,7 @@ def dream_problem(): This optimisation used the parameters: nSamples=50000, nChains=10. """ - problem = RATapi.rat_core.ProblemDefinition() + problem = ratapi.rat_core.ProblemDefinition() problem.TF = Calculations.Normal problem.modelType = LayerModels.StandardLayers problem.geometry = Geometries.SubstrateLiquid @@ -362,12 +362,12 @@ def test_run(test_procedure, test_output_problem, test_output_results, test_baye test_results = request.getfixturevalue(test_results) with mock.patch.object( - RATapi.rat_core, + ratapi.rat_core, "RATMain", mock.MagicMock(return_value=(test_output_problem, test_output_results, test_bayes)), ): # Use default project as we patch RATMain to give the desired outputs - project, results = RATapi.run(input_project, RATapi.Controls(procedure=test_procedure)) + project, results = ratapi.run(input_project, ratapi.Controls(procedure=test_procedure)) check_results_equal(test_results, results) diff --git a/tests/test_wrappers.py b/tests/test_wrappers.py index 2ced5ad0..782e833c 100644 --- a/tests/test_wrappers.py +++ b/tests/test_wrappers.py @@ -3,21 +3,21 @@ import pytest -import RATapi.wrappers +import ratapi.wrappers def test_matlab_wrapper() -> None: with ( - mock.patch.object(RATapi.wrappers.MatlabWrapper, "loader", None), + mock.patch.object(ratapi.wrappers.MatlabWrapper, "loader", None), pytest.raises(ImportError), ): - RATapi.wrappers.MatlabWrapper("demo.m") + ratapi.wrappers.MatlabWrapper("demo.m") mocked_matlab_future = mock.MagicMock() mocked_engine = mock.MagicMock() mocked_matlab_future.result.return_value = mocked_engine - with mock.patch.object(RATapi.wrappers.MatlabWrapper, "loader", mocked_matlab_future): - wrapper = RATapi.wrappers.MatlabWrapper("demo.m") + with mock.patch.object(ratapi.wrappers.MatlabWrapper, "loader", mocked_matlab_future): + wrapper = ratapi.wrappers.MatlabWrapper("demo.m") assert wrapper.function_name == "demo" mocked_engine.cd.assert_called_once() assert pathlib.Path(mocked_engine.cd.call_args[0][0]).samefile(".") @@ -25,13 +25,13 @@ def test_matlab_wrapper() -> None: handle = wrapper.getHandle() mocked_engine.demo.return_value = ([2], 5) - result = handle([1], [2], [3], 0) + result = handle([1], [2], [3], 1) assert result == ([2], 5) assert wrapper.engine.demo.call_args[0] == ([1], [2], [3], 1) mocked_engine.demo.assert_called_once() mocked_engine.demo.return_value = ([3, 1], 7) - result = handle([4], [5], [6], 1, 1) + result = handle([4], [5], [6], 2, 2) assert result == ([3, 1], 7) assert wrapper.engine.demo.call_args[0] == ([4], [5], [6], 2, 2) assert mocked_engine.demo.call_count == 2 @@ -45,8 +45,8 @@ def test_matlab_wrapper() -> None: def test_dylib_wrapper() -> None: mocked_engine = mock.MagicMock() - with mock.patch("RATapi.wrappers.RATapi.rat_core.DylibEngine", mocked_engine): - wrapper = RATapi.wrappers.DylibWrapper("demo.dylib", "demo") + with mock.patch("ratapi.wrappers.ratapi.rat_core.DylibEngine", mocked_engine): + wrapper = ratapi.wrappers.DylibWrapper("demo.dylib", "demo") mocked_engine.assert_called_once_with("demo.dylib", "demo") wrapper.engine.invoke.return_value = ([2], 5) diff --git a/tests/utils.py b/tests/utils.py index d293d7b1..5387b30a 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -1,6 +1,6 @@ import numpy as np -import RATapi.outputs +import ratapi.outputs class InputAttributes: @@ -39,24 +39,24 @@ def check_results_equal(actual_results, expected_results) -> None: ] assert ( - isinstance(actual_results, RATapi.outputs.Results) and isinstance(expected_results, RATapi.outputs.Results) + isinstance(actual_results, ratapi.outputs.Results) and isinstance(expected_results, ratapi.outputs.Results) ) or ( - isinstance(actual_results, RATapi.outputs.BayesResults) - and isinstance(expected_results, RATapi.outputs.BayesResults) + isinstance(actual_results, ratapi.outputs.BayesResults) + and isinstance(expected_results, ratapi.outputs.BayesResults) ) # The first set of fields are either 1D or 2D python lists containing numpy arrays. # Hence, we need to compare them element-wise. - for list_field in RATapi.outputs.results_fields["list_fields"]: - for a, b in zip(getattr(actual_results, list_field), getattr(expected_results, list_field)): + for list_field in ratapi.outputs.results_fields["list_fields"]: + for a, b in zip(getattr(actual_results, list_field), getattr(expected_results, list_field), strict=False): assert (a == b).all() - for list_field in RATapi.outputs.results_fields["double_list_fields"]: + for list_field in ratapi.outputs.results_fields["double_list_fields"]: actual_list = getattr(actual_results, list_field) expected_list = getattr(expected_results, list_field) assert len(actual_list) == len(expected_list) for i in range(len(actual_list)): - for a, b in zip(actual_list[i], expected_list[i]): + for a, b in zip(actual_list[i], expected_list[i], strict=False): assert (a == b).all() # Compare the final fields @@ -70,8 +70,8 @@ def check_results_equal(actual_results, expected_results) -> None: for field in contrast_param_fields: assert (getattr(actual_results.contrastParams, field) == getattr(expected_results.contrastParams, field)).all() - if isinstance(actual_results, RATapi.outputs.BayesResults) and isinstance( - expected_results, RATapi.outputs.BayesResults + if isinstance(actual_results, ratapi.outputs.BayesResults) and isinstance( + expected_results, ratapi.outputs.BayesResults ): check_bayes_fields_equal(actual_results, expected_results) @@ -82,27 +82,27 @@ def check_bayes_fields_equal(actual_results, expected_results) -> None: We focus here on the fields and subclasses specific to the Bayesian optimisation. """ # The BayesResults object consists of a number of subclasses, each containing fields of differing formats. - for subclass in RATapi.outputs.bayes_results_subclasses: + for subclass in ratapi.outputs.bayes_results_subclasses: actual_subclass = getattr(actual_results, subclass) expected_subclass = getattr(expected_results, subclass) - for field in RATapi.outputs.bayes_results_fields["param_fields"][subclass]: + for field in ratapi.outputs.bayes_results_fields["param_fields"][subclass]: assert getattr(actual_subclass, field) == getattr(expected_subclass, field) - for field in RATapi.outputs.bayes_results_fields["list_fields"][subclass]: - for a, b in zip(getattr(actual_subclass, field), getattr(expected_subclass, field)): + for field in ratapi.outputs.bayes_results_fields["list_fields"][subclass]: + for a, b in zip(getattr(actual_subclass, field), getattr(expected_subclass, field), strict=False): assert (a == b).all() - for field in RATapi.outputs.bayes_results_fields["double_list_fields"][subclass]: + for field in ratapi.outputs.bayes_results_fields["double_list_fields"][subclass]: actual_list = getattr(actual_subclass, field) expected_list = getattr(expected_subclass, field) assert len(actual_list) == len(expected_list) for i in range(len(actual_list)): - for a, b in zip(actual_list[i], expected_list[i]): + for a, b in zip(actual_list[i], expected_list[i], strict=False): assert (a == b).all() # Need to account for the arrays that are initialised as "NaN" in the compiled code - for array in RATapi.outputs.bayes_results_fields["array_fields"][subclass]: + for array in ratapi.outputs.bayes_results_fields["array_fields"][subclass]: actual_array = getattr(actual_subclass, array) expected_array = getattr(expected_subclass, array) for i in range(len(actual_array)):