diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 8635043..504a223 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -12,7 +12,7 @@ jobs:
strategy:
matrix:
os: [ubuntu-latest, macos-latest, windows-latest]
- python-version: ["3.7", "3.8", "3.9", "3.10", "3.11"]
+ python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
steps:
- uses: actions/checkout@v4
with:
@@ -20,6 +20,7 @@ jobs:
- uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
+ - uses: pyvista/setup-headless-display-action@v3
- run: pip install -e .[tests]
- run: python -m pytest --cov=sme_contrib --cov-report=xml -v
- uses: codecov/codecov-action@v3
diff --git a/docs/conf.py b/docs/conf.py
index 2dfbbac..651b5b2 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -28,6 +28,8 @@
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
+ "pyvista.ext.plot_directive",
+ "pyvista.ext.viewer_directive",
"nbsphinx",
"sphinx_rtd_theme",
"sphinx.ext.autodoc",
diff --git a/docs/notebooks/model.xml b/docs/notebooks/model.xml
new file mode 100644
index 0000000..8786fd7
--- /dev/null
+++ b/docs/notebooks/model.xml
@@ -0,0 +1,311 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<sbml xmlns="http://www.sbml.org/sbml/level3/version2/core" xmlns:spatial="http://www.sbml.org/sbml/level3/version1/spatial/version1" level="3" version="2" spatial:required="true">
+ <model id="very_simple_model" name="Very Simple Model" substanceUnits="substance" timeUnits="unit_of_time" volumeUnits="unit_of_volume" areaUnits="area" lengthUnits="unit_of_length" extentUnits="substance">
+ <annotation>
+ <spatialModelEditor xmlns="https://github.com/spatial-model-editor">
+ <settings>
+ <cereal_class_version>2</cereal_class_version>
+ <simulationSettings>
+ <cereal_class_version>1</cereal_class_version>
+ <times size="dynamic"/>
+ <options>
+ <cereal_class_version>0</cereal_class_version>
+ <dune>
+ <cereal_class_version>0</cereal_class_version>
+ <discretization>0</discretization>
+ <integrator>alexander_2</integrator>
+ <dt>0.10000000000000001</dt>
+ <minDt>1e-10</minDt>
+ <maxDt>10000</maxDt>
+ <increase>1.5</increase>
+ <decrease>0.5</decrease>
+ <writeVTKfiles>false</writeVTKfiles>
+ <newtonRelErr>1e-08</newtonRelErr>
+ <newtonAbsErr>9.9999999999999998e-13</newtonAbsErr>
+ </dune>
+ <pixel>
+ <cereal_class_version>0</cereal_class_version>
+ <integrator>1</integrator>
+ <maxErr>
+ <cereal_class_version>0</cereal_class_version>
+ <abs>1.7976931348623157e+308</abs>
+ <rel>0.0050000000000000001</rel>
+ </maxErr>
+ <maxTimestep>1.7976931348623157e+308</maxTimestep>
+ <enableMultiThreading>false</enableMultiThreading>
+ <maxThreads>0</maxThreads>
+ <doCSE>true</doCSE>
+ <optLevel>3</optLevel>
+ </pixel>
+ </options>
+ <simulatorType>0</simulatorType>
+ </simulationSettings>
+ <displayOptions>
+ <cereal_class_version>1</cereal_class_version>
+ <showSpecies size="dynamic">
+ <value0>true</value0>
+ <value1>true</value1>
+ </showSpecies>
+ <showMinMax>true</showMinMax>
+ <normaliseOverAllTimepoints>true</normaliseOverAllTimepoints>
+ <normaliseOverAllSpecies>true</normaliseOverAllSpecies>
+ <showGeometryGrid>false</showGeometryGrid>
+ <showGeometryScale>false</showGeometryScale>
+ <invertYAxis>false</invertYAxis>
+ </displayOptions>
+ <meshParameters>
+ <cereal_class_version>1</cereal_class_version>
+ <maxPoints size="dynamic">
+ <value0>12</value0>
+ <value1>30</value1>
+ <value2>10</value2>
+ </maxPoints>
+ <maxAreas size="dynamic">
+ <value0>80</value0>
+ <value1>80</value1>
+ <value2>80</value2>
+ </maxAreas>
+ <boundarySimplifierType>0</boundarySimplifierType>
+ </meshParameters>
+ <speciesColours size="dynamic">
+ <value0>
+ <key>A_c2</key>
+ <value>4293269835</value>
+ </value0>
+ <value1>
+ <key>A_c3</key>
+ <value>4282168395</value>
+ </value1>
+ </speciesColours>
+ <optimizeOptions>
+ <cereal_class_version>0</cereal_class_version>
+ <optAlgorithm>
+ <cereal_class_version>0</cereal_class_version>
+ <optAlgorithmType>0</optAlgorithmType>
+ <islands>1</islands>
+ <population>2</population>
+ </optAlgorithm>
+ <optParams size="dynamic"/>
+ <optCosts size="dynamic"/>
+ </optimizeOptions>
+ <sampledFieldColours size="dynamic">
+ <value0>4278190592</value0>
+ <value1>4287652289</value1>
+ <value2>4291134816</value2>
+ </sampledFieldColours>
+ </settings>
+ </spatialModelEditor>
+ </annotation>
+ <listOfUnitDefinitions>
+ <unitDefinition id="area" name="m_squared">
+ <listOfUnits>
+ <unit kind="metre" exponent="2" scale="0" multiplier="1"/>
+ </listOfUnits>
+ </unitDefinition>
+ <unitDefinition id="substance" name="mol">
+ <listOfUnits>
+ <unit kind="mole" exponent="1" scale="0" multiplier="1"/>
+ </listOfUnits>
+ </unitDefinition>
+ <unitDefinition id="area_perTime">
+ <listOfUnits>
+ <unit kind="metre" exponent="2" scale="0" multiplier="1"/>
+ <unit kind="second" exponent="-1" scale="0" multiplier="1"/>
+ </listOfUnits>
+ </unitDefinition>
+ <unitDefinition id="unit_of_time" name="s">
+ <listOfUnits>
+ <unit kind="second" exponent="1" scale="0" multiplier="1"/>
+ </listOfUnits>
+ </unitDefinition>
+ <unitDefinition id="unit_of_length" name="m">
+ <listOfUnits>
+ <unit kind="metre" exponent="1" scale="0" multiplier="1"/>
+ </listOfUnits>
+ </unitDefinition>
+ <unitDefinition id="unit_of_volume" name="L">
+ <listOfUnits>
+ <unit kind="litre" exponent="1" scale="0" multiplier="1"/>
+ </listOfUnits>
+ </unitDefinition>
+ </listOfUnitDefinitions>
+ <listOfCompartments>
+ <compartment id="c1" name="Outside" spatialDimensions="3" size="5441000" constant="true">
+ <spatial:compartmentMapping spatial:id="c1_compartmentMapping" spatial:domainType="c1_domainType" spatial:unitSize="1"/>
+ </compartment>
+ <compartment id="c2" name="Cell" spatialDimensions="3" size="4034000" constant="true">
+ <spatial:compartmentMapping spatial:id="c2_compartmentMapping" spatial:domainType="c2_domainType" spatial:unitSize="1"/>
+ </compartment>
+ <compartment id="c3" name="Nucleus" spatialDimensions="3" size="525000" constant="true">
+ <spatial:compartmentMapping spatial:id="c3_compartmentMapping" spatial:domainType="c3_domainType" spatial:unitSize="1"/>
+ </compartment>
+ <compartment id="c1_c2_membrane" name="Outside <-> Cell" spatialDimensions="2" size="338" constant="true">
+ <spatial:compartmentMapping spatial:id="c1_c2_membrane_compartmentMapping" spatial:domainType="c1_c2_membrane_domainType" spatial:unitSize="1"/>
+ </compartment>
+ <compartment id="c2_c3_membrane" name="Cell <-> Nucleus" spatialDimensions="2" size="108" constant="true">
+ <spatial:compartmentMapping spatial:id="c2_c3_membrane_compartmentMapping" spatial:domainType="c2_c3_membrane_domainType" spatial:unitSize="1"/>
+ </compartment>
+ </listOfCompartments>
+ <listOfSpecies>
+ <species id="A_c2" name="A_cell" compartment="c2" initialConcentration="0" substanceUnits="substance" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false" spatial:isSpatial="true"/>
+ <species id="A_c3" name="A_nucl" compartment="c3" initialConcentration="0" substanceUnits="substance" hasOnlySubstanceUnits="false" boundaryCondition="false" constant="false" spatial:isSpatial="true"/>
+ </listOfSpecies>
+ <listOfParameters>
+ <parameter id="B_c2_diffusionConstant" value="6" units="area_perTime" constant="true">
+ <spatial:diffusionCoefficient spatial:variable="B_c2" spatial:type="isotropic"/>
+ </parameter>
+ <parameter id="A_c2_diffusionConstant" value="10" units="area_perTime" constant="true">
+ <spatial:diffusionCoefficient spatial:variable="A_c2" spatial:type="isotropic"/>
+ </parameter>
+ <parameter id="B_c1_diffusionConstant" value="5" units="area_perTime" constant="true">
+ <spatial:diffusionCoefficient spatial:variable="B_c1" spatial:type="isotropic"/>
+ </parameter>
+ <parameter id="A_c3_diffusionConstant" value="2" units="area_perTime" constant="true">
+ <spatial:diffusionCoefficient spatial:variable="A_c3" spatial:type="isotropic"/>
+ </parameter>
+ <parameter id="B_c3_diffusionConstant" value="5" units="area_perTime" constant="true">
+ <spatial:diffusionCoefficient spatial:variable="B_c3" spatial:type="isotropic"/>
+ </parameter>
+ <parameter id="A_c1_diffusionConstant" value="15" units="area_perTime" constant="true">
+ <spatial:diffusionCoefficient spatial:variable="A_c1" spatial:type="isotropic"/>
+ </parameter>
+ <parameter id="x" name="x" value="0" units="unit_of_length" constant="false">
+ <spatial:spatialSymbolReference spatial:spatialRef="xCoord"/>
+ </parameter>
+ <parameter id="y" name="y" value="0" units="unit_of_length" constant="false">
+ <spatial:spatialSymbolReference spatial:spatialRef="yCoord"/>
+ </parameter>
+ <parameter id="param" name="param" value="1" units="substance" constant="true"/>
+ <parameter id="z" name="z" value="0" units="unit_of_length" constant="true">
+ <spatial:spatialSymbolReference spatial:spatialRef="zCoord"/>
+ </parameter>
+ </listOfParameters>
+ <listOfReactions>
+ <reaction id="destroy_A" name="destroy A" reversible="true" compartment="c2" spatial:isLocal="true">
+ <listOfReactants>
+ <speciesReference species="A_c2" stoichiometry="1" constant="true"/>
+ </listOfReactants>
+ <kineticLaw>
+ <math xmlns="http://www.w3.org/1998/Math/MathML">
+ <apply>
+ <times/>
+ <ci> k </ci>
+ <ci> A_c2 </ci>
+ </apply>
+ </math>
+ <listOfLocalParameters>
+ <localParameter id="k" name="k" value="10"/>
+ </listOfLocalParameters>
+ </kineticLaw>
+ </reaction>
+ <reaction id="create_A" name="create A" reversible="true" compartment="c3" spatial:isLocal="true">
+ <listOfProducts>
+ <speciesReference species="A_c3" stoichiometry="1" constant="true"/>
+ </listOfProducts>
+ <kineticLaw>
+ <math xmlns="http://www.w3.org/1998/Math/MathML">
+ <ci> k </ci>
+ </math>
+ <listOfLocalParameters>
+ <localParameter id="k" name="k" value="0.0001"/>
+ </listOfLocalParameters>
+ </kineticLaw>
+ </reaction>
+ <reaction id="transport_A" name="transport A" reversible="true" compartment="c2_c3_membrane" spatial:isLocal="true">
+ <listOfReactants>
+ <speciesReference species="A_c3" stoichiometry="1" constant="true"/>
+ </listOfReactants>
+ <listOfProducts>
+ <speciesReference species="A_c2" stoichiometry="1" constant="true"/>
+ </listOfProducts>
+ <kineticLaw>
+ <math xmlns="http://www.w3.org/1998/Math/MathML">
+ <apply>
+ <times/>
+ <ci> k </ci>
+ <apply>
+ <plus/>
+ <apply>
+ <minus/>
+ <ci> A_c2 </ci>
+ </apply>
+ <ci> A_c3 </ci>
+ </apply>
+ </apply>
+ </math>
+ <listOfLocalParameters>
+ <localParameter id="k" name="k" value="1000"/>
+ </listOfLocalParameters>
+ </kineticLaw>
+ </reaction>
+ </listOfReactions>
+ <spatial:geometry spatial:coordinateSystem="cartesian">
+ <spatial:listOfCoordinateComponents>
+ <spatial:coordinateComponent spatial:id="xCoord" spatial:type="cartesianX">
+ <spatial:boundaryMin spatial:id="xBoundaryMin" spatial:value="0"/>
+ <spatial:boundaryMax spatial:id="xBoundaryMax" spatial:value="100"/>
+ </spatial:coordinateComponent>
+ <spatial:coordinateComponent spatial:id="yCoord" spatial:type="cartesianY">
+ <spatial:boundaryMin spatial:id="yBoundaryMin" spatial:value="0"/>
+ <spatial:boundaryMax spatial:id="yBoundaryMax" spatial:value="100"/>
+ </spatial:coordinateComponent>
+ <spatial:coordinateComponent spatial:id="zCoord" spatial:type="cartesianZ">
+ <spatial:boundaryMin spatial:id="zBoundaryMin" spatial:value="0"/>
+ <spatial:boundaryMax spatial:id="zBoundaryMax" spatial:value="1"/>
+ </spatial:coordinateComponent>
+ </spatial:listOfCoordinateComponents>
+ <spatial:listOfDomainTypes>
+ <spatial:domainType spatial:id="c1_domainType" spatial:spatialDimensions="3"/>
+ <spatial:domainType spatial:id="c2_domainType" spatial:spatialDimensions="3"/>
+ <spatial:domainType spatial:id="c3_domainType" spatial:spatialDimensions="3"/>
+ <spatial:domainType spatial:id="c1_c2_membrane_domainType" spatial:spatialDimensions="2"/>
+ <spatial:domainType spatial:id="c2_c3_membrane_domainType" spatial:spatialDimensions="2"/>
+ </spatial:listOfDomainTypes>
+ <spatial:listOfDomains>
+ <spatial:domain spatial:id="c1_domain" spatial:domainType="c1_domainType">
+ <spatial:listOfInteriorPoints>
+ <spatial:interiorPoint spatial:coord1="68.5" spatial:coord2="83.5" spatial:coord3="0.5"/>
+ </spatial:listOfInteriorPoints>
+ </spatial:domain>
+ <spatial:domain spatial:id="c2_domain" spatial:domainType="c2_domainType">
+ <spatial:listOfInteriorPoints>
+ <spatial:interiorPoint spatial:coord1="59.5" spatial:coord2="28.5" spatial:coord3="0.5"/>
+ </spatial:listOfInteriorPoints>
+ </spatial:domain>
+ <spatial:domain spatial:id="c3_domain" spatial:domainType="c3_domainType">
+ <spatial:listOfInteriorPoints>
+ <spatial:interiorPoint spatial:coord1="39.5" spatial:coord2="49.5" spatial:coord3="0.5"/>
+ </spatial:listOfInteriorPoints>
+ </spatial:domain>
+ <spatial:domain spatial:id="c1_c2_membrane_domain" spatial:domainType="c1_c2_membrane_domainType"/>
+ <spatial:domain spatial:id="c2_c3_membrane_domain" spatial:domainType="c2_c3_membrane_domainType"/>
+ </spatial:listOfDomains>
+ <spatial:listOfAdjacentDomains>
+ <spatial:adjacentDomains spatial:id="c1_c2_membrane_adjacentDomainA" spatial:domain1="c1_c2_membrane_domain" spatial:domain2="c1_domain"/>
+ <spatial:adjacentDomains spatial:id="c1_c2_membrane_adjacentDomainB" spatial:domain1="c1_c2_membrane_domain" spatial:domain2="c2_domain"/>
+ <spatial:adjacentDomains spatial:id="c2_c3_membrane_adjacentDomainA" spatial:domain1="c2_c3_membrane_domain" spatial:domain2="c2_domain"/>
+ <spatial:adjacentDomains spatial:id="c2_c3_membrane_adjacentDomainB" spatial:domain1="c2_c3_membrane_domain" spatial:domain2="c3_domain"/>
+ </spatial:listOfAdjacentDomains>
+ <spatial:listOfGeometryDefinitions>
+ <spatial:sampledFieldGeometry spatial:id="geometry" spatial:isActive="true" spatial:sampledField="geometryImage">
+ <spatial:listOfSampledVolumes>
+ <spatial:sampledVolume spatial:id="c1_sampledVolume" spatial:domainType="c1_domainType" spatial:sampledValue="0"/>
+ <spatial:sampledVolume spatial:id="c2_sampledVolume" spatial:domainType="c2_domainType" spatial:sampledValue="1"/>
+ <spatial:sampledVolume spatial:id="c3_sampledVolume" spatial:domainType="c3_domainType" spatial:sampledValue="2"/>
+ </spatial:listOfSampledVolumes>
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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</spatial:sampledField>
+ </spatial:listOfSampledFields>
+ </spatial:geometry>
+ </model>
+</sbml>
diff --git a/docs/notebooks/optimize.ipynb b/docs/notebooks/optimize.ipynb
index 0722aa8..f15b597 100644
--- a/docs/notebooks/optimize.ipynb
+++ b/docs/notebooks/optimize.ipynb
@@ -209,7 +209,7 @@
"provenance": []
},
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": ".venv39",
"language": "python",
"name": "python3"
},
@@ -223,7 +223,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.9.21"
}
},
"nbformat": 4,
diff --git a/docs/notebooks/plot.ipynb b/docs/notebooks/plot.ipynb
index 87404a3..364cb9d 100644
--- a/docs/notebooks/plot.ipynb
+++ b/docs/notebooks/plot.ipynb
@@ -15,11 +15,19 @@
},
"outputs": [],
"source": [
- "!pip install -q sme_contrib\n",
+ "# !pip install -q sme_contrib\n",
+ "import pyvista as pv\n",
+ "from pyvista import examples\n",
+ "\n",
+ "pv.set_jupyter_backend(\"static\")\n",
+ "\n",
"import sme\n",
"import sme_contrib.plot as smeplot\n",
- "from matplotlib import pyplot as plt\n",
- "from IPython.display import HTML"
+ "import numpy as np\n",
+ "import tempfile\n",
+ "from IPython.display import HTML\n",
+ "from IPython.display import Video\n",
+ "from matplotlib import pyplot as plt"
]
},
{
@@ -184,6 +192,286 @@
"HTML(anim.to_jshtml())"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Plot concentrations on 3D grid"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The API for 3D plotting has two layers: \n",
+ "- the low-level function provide a high degree of customisability, but need more code to get them to work:\n",
+ " - `facet_grid_3D`: two low-level functions that map a dictionary of name->plotfunctions over a dictionary name->data, where both have to have the same keys. \n",
+ " For each key in the data dictionary, a single plot pane will be created. \n",
+ " - `facet_grid_animate_3D` is an animated version of this function that creates an .mp4 file for and receives a dictionary of name->data for each frame of the animation over which the dictionary containing the plot functions is then mapped in each frame. \n",
+ "- `concentrations3D` and `concentrationsAnimate3D`: These high-level API directly uses `sme.SimulationResult` objects as data input, but only plots concentrations by default. These are wrappers around the low-level functions that provide default plotting functions for each pane and handle the data preparation for each pane automatically.\n",
+ "\n",
+ "- for switching between interactive and static plotting, use `pv.set_jupyter_backend('trame')` for interactive plotting, or `pv.set_jupyter_backend('static')` for static plotting. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**README:** Using `import pyvista as pv` in both a notebook and a module imported in the notebook will cause a premature initialization of `pyvista` when the imported module is initialized and in turn a configuration conflict that will prevent the notebook from working properly. To work around this, you have to make sure that the `pyvista` module is only imported in one of the two. Since notebooks often have specific requirements for how the plots can be shown (statically or interactively) it is best to only import the needed objects from pyvista when developing a module and only importing the package-level module (`import pyvista...`) within notebooks that use this module. If a complete import of pyvista in a module is unavoidable, make sure to only import the needed functions in the notebook and not the entire module, to prevent pyvista's `__init__` from running prematurely."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model_file = \"./model.xml\"\n",
+ "model = sme.open_sbml_file(model_file)\n",
+ "results = model.simulate(500, 10)\n",
+ "\n",
+ "species = list(results[0].species_concentration.keys())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def exampledata():\n",
+ " armadillo = examples.download_armadillo()\n",
+ " bloodvessel = examples.download_blood_vessels()\n",
+ " brain = examples.download_brain()\n",
+ "\n",
+ " return {\n",
+ " \"armadillo\": armadillo,\n",
+ " \"bloodvessel\": bloodvessel,\n",
+ " \"brain\": brain,\n",
+ " }\n",
+ "\n",
+ "\n",
+ "datasets = exampledata()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tempdir = tempfile.TemporaryDirectory()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Low level functions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We are plotting some example functions here to show the functionality of the `facetGrid` functions.\n",
+ "We first define the functions that are applied to each pane in the grid:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_bloodvessel(label, data, plotter, panel, **kwargs):\n",
+ " plotter.subplot(*panel)\n",
+ " plotter.add_mesh(data)\n",
+ "\n",
+ "\n",
+ "def plot_brain(label, data, plotter, panel, **kwargs):\n",
+ " plotter.subplot(*panel)\n",
+ " plotter.add_volume(\n",
+ " data,\n",
+ " cmap=\"viridis\",\n",
+ " opacity=\"sigmoid\", # Common opacity mapping for volume rendering\n",
+ " shade=True,\n",
+ " ambient=0.3,\n",
+ " diffuse=0.6,\n",
+ " specular=0.5,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def plot_armadillo(label, data, plotter, panel, **kwargs):\n",
+ " plotter.subplot(*panel)\n",
+ " plotter.add_mesh(data)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we put the it all together in the call to `facet_grid_3D`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "facetgrid = smeplot.facet_grid_3D(\n",
+ " data={\n",
+ " \"armadillo\": datasets[\"armadillo\"],\n",
+ " \"bloodvessel\": datasets[\"bloodvessel\"],\n",
+ " \"brain\": datasets[\"brain\"],\n",
+ " },\n",
+ " plotfuncs={\n",
+ " \"armadillo\": plot_armadillo,\n",
+ " \"bloodvessel\": plot_bloodvessel,\n",
+ " \"brain\": plot_brain,\n",
+ " },\n",
+ " linked_views=False,\n",
+ ")\n",
+ "\n",
+ "facetgrid.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Making an animation functions in much the same way, but we use a list of data dictionaries now, one for each frame."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We define a general plotting function that is used for each pane in each frame here. these can be different too, like shown above: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plotfunc(\n",
+ " label,\n",
+ " data,\n",
+ " plotter,\n",
+ " panel,\n",
+ " show_cmap,\n",
+ " cmap,\n",
+ " **kwargs,\n",
+ "):\n",
+ " # create a pyvista grid\n",
+ " plotter.subplot(*panel)\n",
+ " plotter.title = label\n",
+ " plotter.add_mesh(\n",
+ " data,\n",
+ " scalars=data,\n",
+ " label=label,\n",
+ " cmap=cmap,\n",
+ " show_scalar_bar=show_cmap,\n",
+ " **kwargs,\n",
+ " )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then use it in the call to `facet_grid_animate_3D` call. Note that we pass a list of data dictionaries now, while the `plotfuncs` dict stays a dictionary. You can use the plot function to customize lighting, perspective and more. See [the pyvista documentation for more information](https://docs.pyvista.org/api/plotting/_autosummary/pyvista.plotter.add_mesh)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vidpath = smeplot.facet_grid_animate_3D(\n",
+ " \"testvid.mp4\",\n",
+ " data=[\n",
+ " {species[i]: res.species_concentration[species[i]] for i in range(len(species))}\n",
+ " for res in results\n",
+ " ],\n",
+ " plotfuncs={species[i]: plotfunc for i in range(len(species))},\n",
+ " cmap=\"tab10\",\n",
+ " portrait=True,\n",
+ " linked_views=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Video(vidpath, embed=True, width=800, height=600)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### High-level functions"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The high level functions wrap the low-level functions such that we don´t have to specify the plotting functions ourselves."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "smeplot.concentrations3D(\n",
+ " simulation_result=results[10],\n",
+ " species=[\"A_nucl\"],\n",
+ " cmap=\"tab10\",\n",
+ " show_cmap=True,\n",
+ ").show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Animate concentations on a 3D grid"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "vidpath = smeplot.concentrationsAnimate3D(\n",
+ " filename=\"test.mp4\",\n",
+ " simulation_results=results,\n",
+ " species=[\"A_nucl\"],\n",
+ " cmap=\"tab10\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Video(vidpath, embed=True, width=800, height=600)"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
@@ -199,7 +487,7 @@
"provenance": []
},
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": ".venv313",
"language": "python",
"name": "python3"
},
@@ -213,7 +501,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.9"
+ "version": "3.13.1"
}
},
"nbformat": 4,
diff --git a/pyproject.toml b/pyproject.toml
index af7e255..6847118 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,13 +19,15 @@ classifiers = [
"Operating System :: Microsoft :: Windows",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 3 :: Only",
- "Programming Language :: Python :: 3.7",
- "Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
+ "Programming Language :: Python :: 3.12",
+ "Programming Language :: Python :: 3.13",
+
]
-dependencies = ["matplotlib", "numpy", "pillow", "pyswarms", "sme>=1.4.0"]
+dependencies = ["matplotlib", "numpy", "pillow", "pyswarms", "sme>=1.4.0","pyvista[all]", "imageio[ffmpeg]"]
+
dynamic = ["version"]
[project.urls]
@@ -42,7 +44,7 @@ docs = [
"nbsphinx",
"pandoc",
"sphinx>=4.5.0",
- "sphinx_rtd_theme>=1.0.0"
+ "sphinx_rtd_theme>=1.0.0",
]
[tool.setuptools.dynamic]
diff --git a/src/sme_contrib/__init__.py b/src/sme_contrib/__init__.py
index d62d967..1f658a4 100644
--- a/src/sme_contrib/__init__.py
+++ b/src/sme_contrib/__init__.py
@@ -1 +1 @@
-__version__ = "0.0.16"
+__version__ = "0.0.17"
diff --git a/src/sme_contrib/plot.py b/src/sme_contrib/plot.py
index 0807c28..0e2b114 100644
--- a/src/sme_contrib/plot.py
+++ b/src/sme_contrib/plot.py
@@ -4,6 +4,14 @@
from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap as lscmap
from matplotlib import animation
+from pyvista import Plotter, LookupTable
+from typing import Any, Callable, Union
+import sme
+from pathlib import Path
+
+from .pyvista_utils import (
+ find_layout,
+)
def colormap(color, name="my colormap"):
@@ -120,3 +128,310 @@ def concentration_heatmap_animation(
)
plt.close()
return anim
+
+
+def facet_grid_3D(
+ data: dict[str, np.ndarray],
+ plotfuncs: dict[str, Callable],
+ show_cmap: bool = False,
+ cmap: Union[str, np.ndarray, LookupTable] = "viridis",
+ portrait: bool = False,
+ linked_views: bool = True,
+ plotter_kwargs: Union[dict, None] = None,
+ plotfuncs_kwargs: Union[dict[str, dict[str, Any]], None] = None,
+) -> Plotter:
+ """
+ Create a 3D facet plot using PyVista.
+
+ This follows the seaborn.FacetGrid concept. This function creates a grid of subplots where each subplot is filled by a function in the plotfuncs argument. The keys for plotfuncs and data must be the same, such that plotfuncs can be unambiguously mapped over the data dictionary. Do not attempt to plot 2D images and 3D images into the same facet grid, as this will create odd artifacts and may not work as expected.
+
+ Args:
+ data : (dict[str, np.ndarray]) A dictionary where keys are labels and values are numpy arrays containing the data to be plotted.
+ plotfuncs : (dict[str, Callable]) A dictionary where keys are labels and values are functions with signature ``f(label:str, data:np.ndarray | pyvista.ImageData | pyvista.UniformGrid, plotter:Plotter, panel:tuple[int, int], show_cmap:bool=show_cmap, cmap=cmap, **plotfuncs_kwargs )`` -> None
+ show_cmap : bool, optional Whether to show the color map. Default is False.
+ cmap : (str | np.ndarray | LookupTable), optional The color map to use. Default is "viridis".
+ portrait : (bool), optional Whether to use a portrait layout. Default is False.
+ linked_views : (bool), optional Whether to link the views of the subplots. Default is True.
+ plotter_kwargs : (dict, optional) Additional keyword arguments to pass to the PyVista Plotter.
+ plotfuncs_kwargs : (dict[str, dict[str, Any]]), optional Additional keyword arguments to pass to each plotting function.
+
+ Returns:
+ Plotter The PyVista Plotter object with the created facet plot.
+ """
+ if data.keys() != plotfuncs.keys():
+ raise ValueError(
+ "The keys for the data and plotfuncs dictionaries must be the same."
+ )
+
+ layout = find_layout(len(data), portrait=portrait)
+
+ plotter = Plotter(
+ shape=layout, **(plotter_kwargs if plotter_kwargs is not None else {})
+ )
+
+ label = iter(plotfuncs.keys())
+
+ for i in range(layout[0]):
+ for j in range(layout[1]):
+ current_label = next(label)
+ plotfuncs[current_label](
+ current_label,
+ data[current_label],
+ plotter,
+ panel=(i, j),
+ show_cmap=show_cmap,
+ cmap=cmap,
+ **(
+ plotfuncs_kwargs.get(current_label, {})
+ if plotfuncs_kwargs is not None
+ else {}
+ ),
+ )
+
+ if linked_views:
+ plotter.link_views()
+
+ return plotter
+
+
+def facet_grid_animate_3D(
+ filename: Union[str, Path],
+ data: list[dict[str, np.ndarray]],
+ plotfuncs: dict[str, Callable],
+ show_cmap: bool = False,
+ cmap: Union[str, np.ndarray, LookupTable] = "viridis",
+ portrait: bool = False,
+ linked_views: bool = True,
+ titles: Union[list[dict[str, str]], None] = None,
+ plotter_kwargs: Union[dict, None] = None,
+ plotfuncs_kwargs: Union[dict[str, dict[str, Any]], None] = None,
+) -> str:
+ """
+ Create a 3D animation from a series of data snapshots using PyVista.
+
+ This series must be a list of dictionaries with the data for each frame keyed by a label used to title the panel it will be plotted into. The final plot will have as many subplots as there are labels in the data dictionaries. The keys for plotfuncs and data must be the same.
+
+ Args:
+ filename : (str) The name of the output movie file.
+ data : (list[dict[str, np.ndarray]]) A list of dictionaries containing the data for each timestep.
+ plotfuncs : (dict[str, Callable]) A dictionary of plotting functions keyed by data label. The keys for plotfuncs and data must be the same.
+ show_cmap : (bool), optional Whether to show the color map (default is False).
+ cmap : (str | np.ndarray | LookupTable, optional) The colormap to use (default is "viridis").
+ portrait : (bool), optional Whether to use portrait layout (default is False).
+ linked_views : (bool), optional Whether to link the views of the subplots (default is True).
+ titles : (list[dict[str, str]]), optional A list of dictionaries containing titles for each subplot (default is an empty list).
+ plotter_kwargs : (dict), optional Additional keyword arguments to pass to the PyVista Plotter (default is an empty dictionary).
+ plotfuncs_kwargs : (dict[str, dict[str, Any]]), optional Additional keyword arguments to pass to each plotting function (default is an empty dictionary).
+
+ Returns:
+ str The filename of the created movie.
+ """
+ if titles is None:
+ titles = []
+
+ if len(titles) > 0 and len(titles) != len(data):
+ raise ValueError(
+ "The number of titles must be the same as the number of data dictionaries."
+ )
+
+ if data[0].keys() != plotfuncs.keys():
+ raise ValueError(
+ "The keys for the data and plotfuncs dictionaries must be the same."
+ )
+
+ # main function, called for each frame in the movie
+ def create_frame(
+ data_dict: dict[str, np.ndarray], title: dict[str:str], layout=(1, 1)
+ ):
+ label = iter(data_dict.keys())
+ for i in range(layout[0]):
+ for j in range(layout[1]):
+ current_label = next(label)
+ plotfuncs[current_label](
+ title.get(current_label, current_label),
+ data_dict[current_label],
+ plotter,
+ panel=(i, j),
+ show_cmap=show_cmap,
+ cmap=cmap,
+ **plotfuncs_kwargs.get(current_label, {})
+ if plotfuncs_kwargs is not None
+ else {},
+ )
+
+ plotter.write_frame()
+
+ # preparations
+ layout = find_layout(len(plotfuncs), portrait=portrait)
+
+ plotter = Plotter(
+ shape=layout, **plotter_kwargs if plotter_kwargs is not None else {}
+ )
+
+ plotter.open_movie(filename)
+
+ # add first frame here to set up the plotter
+ create_frame(data[0], titles[0] if len(titles) > 0 else {}, layout)
+
+ if linked_views:
+ plotter.link_views()
+
+ for i, single_timestep_data in enumerate(data[1::]):
+ create_frame(
+ single_timestep_data, titles[i] if len(titles) > 0 else {}, layout=layout
+ )
+
+ plotter.close()
+
+ return filename
+
+
+def concentrations3D(
+ simulation_result: sme.SimulationResult,
+ species: list[str],
+ cmap: Union[str, np.ndarray, LookupTable] = "viridis",
+ show_cmap: bool = False,
+ plotter_kwargs: Union[None, dict[str, Any]] = None,
+ plotfunc_kwargs: Union[None, dict[str, Any]] = None,
+) -> Plotter:
+ """Plot a 3D facet grid of species concentrations.
+ This function creates a 3D facet grid of species concentrations. Each panel will be a 3D plot of the concentration of a single species.
+ This function is a wrapper around the facet_grid_3D function.
+
+ Args:
+ simulation_result (sme.SimulationResult): a single simulation result object, i.e., a single recorded frame of the simulations
+ species (list[str]): A list of species strings
+ cmap (str | np.ndarray | LookupTable, optional): Name of a matplotlib colorbar. Defaults to "viridis".
+ show_cmap (bool, optional): Whether or not to show the colorbar on the plot. Defaults to False.
+ plotter_kwargs (dict[str, Any], optional): Additional keyword arguments for the used pyVista.Plotter. Defaults to None.
+ plotfunc_kwargs (dict[str, Any], optional): Additional keyword arguments passed to plotter.add_mesh. Defaults to None.
+ Raises:
+ ValueError: if the data is not 3D
+ ValueError: if a given species is not found in the simulation result
+
+ Returns:
+ Plotter: pyvista.Plotter object the data has been plotted into
+ """
+ # turn the simulation result into numpy ndarray
+ datadict = {}
+ for s in species:
+ if s not in simulation_result.species_concentration:
+ raise ValueError(f"Species {s} not found in simulation result.")
+ data = simulation_result.species_concentration[s]
+ if data.ndim != 3:
+ raise ValueError("Data must be 3D.")
+ else:
+ datadict[s] = data
+
+ # create a plot function
+ def plotfunc(
+ label: str,
+ data: np.ndarray,
+ plotter: Plotter,
+ panel: tuple[int, int],
+ show_cmap: bool,
+ cmap: Union[str, np.ndarray, LookupTable],
+ **kwargs: dict[str, Any],
+ ):
+ # create a pyvista grid
+
+ plotter.subplot(*panel)
+ plotter.title = label
+ plotter.add_mesh(
+ data,
+ scalars=data,
+ label=label,
+ cmap=cmap,
+ show_scalar_bar=show_cmap,
+ clim=(
+ np.min(data),
+ np.max(data),
+ ),
+ **kwargs,
+ )
+
+ # use facetGrid3D to plot it
+ return facet_grid_3D(
+ data=datadict,
+ plotfuncs={species[i]: plotfunc for i in range(len(species))},
+ show_cmap=show_cmap,
+ cmap=cmap,
+ portrait=False,
+ linked_views=True,
+ plotter_kwargs=plotter_kwargs,
+ plotfuncs_kwargs=plotfunc_kwargs,
+ )
+
+
+def concentrationsAnimate3D(
+ filename: Union[str, Path],
+ simulation_results: sme.SimulationResultList,
+ species: list[str],
+ show_cmap: bool = False,
+ cmap: Union[str, np.ndarray, LookupTable] = "viridis",
+ portrait: bool = False,
+ titles: Union[list[dict[str, str]], None] = None,
+ linked_views: bool = True,
+ plotter_kwargs: Union[None, dict[str, Any]] = None,
+ plotfunc_kwargs: Union[None, dict[str, Any]] = None,
+) -> Union[str, Path]:
+ """Animate a list of frames from a simulation result list.
+ This function creates a 3D animation of the species concentrations over time. Each frame will be a 3D plot of the concentration of a single species.
+ This function is a wrapper around the facet_grid_animate_3D function.
+ The animation will be saved to the specified filename.
+
+ Args:
+ filename (Union[str, Path]): filename to save the animation to. Uses mp4 format.
+ simulation_results (sme.SimulationResultList): a list of `SimulationResult` objects, i.e., a list of recorded frames of the simulations
+ species (list[str]): list of species to plot
+ show_cmap (bool, optional): Whether to show the colorbar on theplots or not. Defaults to False.
+ cmap (Union[str, np.ndarray, LookupTable], optional): name of matplotlib colormap or custom colormap that maps scalar values to rbp. Defaults to "viridis".
+ portrait (bool, optional): Whether to use the smaller or larger number of plots as rows. Defaults to False.
+ titles (Union[list[dict[str, str]], None], optional): Titles of the different plots if not just the species name is desired. Defaults to None.
+ linked_views (bool, optional): link the view cameras. Defaults to True.
+ plotter_kwargs (dict[str, Any], optional): Additional keyword arguments for the used pyVista.Plotter. Defaults to None. See [here](https://docs.pyvista.org/api/plotting/_autosummary/pyvista.plotter) for more information
+ plotfunc_kwargs (dict[str, Any], optional): Additional keyword arguments passed to plotter.add_mesh. Defaults to None. See [here](https://docs.pyvista.org/api/plotting/_autosummary/pyvista.plotter.add_mesh#) for more information.
+
+ Returns:
+ Union[str, Path]: path to the saved animation .mp4 file
+ """
+
+ def plotfunc(
+ label,
+ data,
+ plotter,
+ panel,
+ show_cmap,
+ cmap,
+ **kwargs,
+ ):
+ # create a pyvista grid
+ plotter.subplot(*panel)
+ plotter.title = label
+ plotter.add_mesh(
+ data,
+ scalars=data,
+ label=label,
+ cmap=cmap,
+ show_scalar_bar=show_cmap,
+ **kwargs,
+ )
+
+ return facet_grid_animate_3D(
+ filename,
+ data=[
+ {
+ species[i]: res.species_concentration[species[i]]
+ for i in range(len(species))
+ }
+ for res in simulation_results
+ ],
+ plotfuncs={species[i]: plotfunc for i in range(len(species))},
+ cmap=cmap,
+ show_cmap=show_cmap,
+ portrait=portrait,
+ linked_views=linked_views,
+ titles=titles,
+ plotter_kwargs=plotter_kwargs,
+ plotfuncs_kwargs=plotfunc_kwargs,
+ )
diff --git a/src/sme_contrib/pyvista_utils.py b/src/sme_contrib/pyvista_utils.py
new file mode 100644
index 0000000..5248a08
--- /dev/null
+++ b/src/sme_contrib/pyvista_utils.py
@@ -0,0 +1,110 @@
+import pyvista as pv
+import numpy as np
+from itertools import cycle
+import matplotlib.colors as mcolors
+import matplotlib.pyplot as plt
+
+
+def rgb_to_scalar(img: np.ndarray) -> np.ndarray:
+ """
+ Convert an RGB 3D image represented as a 4D tensor to a 3D image tensor where each unique RGB value is assigned a unique scalar, i.e., it contracts the dimension with the RGB values into scalars in such a way that 2 different colors are mapped to 2 different scalars, too. This is needed because PyVista doesn't work with RGB values directly and expects fields defined on a grid.
+
+ Args:
+ img (np.ndarray): A 3D numpy array representing an RGB image with shape (height, width, 3).
+
+ Retruns:
+ np.ndarray: A 2D numpy array with the same height and width as the input image, where each pixel's value corresponds to a unique scalar representing the original RGB value.
+ """
+ reshaped = np.copy(img.reshape(-1, 3))
+ unique_rgb, ridx = np.unique(reshaped, axis=0, return_inverse=True)
+
+ values = np.arange(len(unique_rgb))
+ return values[ridx].reshape(img.shape[:-1])
+
+
+def make_discrete_colormap(
+ cmap: str = "tab10", values: np.ndarray = np.array([])
+) -> pv.LookupTable:
+ """
+ Create a discrete colormap for use with PyVista with as many colors as unique values in the ``values``array based on a given matplotlbit colormap. The colors will possibly repeat if there are more unique values than colors in the colormap. In this case, the outcome is intended, e.g., for separability of regions in the visualization,
+
+ Parameters:
+ cmap (str): The name of the colormap to use. Default is 'tab10'.
+ values (np.ndarray): An array of values to map to colors. Default is an empty array.
+
+ Returns:
+ pv.LookupTable: A PyVista LookupTable object with the values drawn from the specified colormap in RGBA format.
+ """
+ cm = []
+
+ if values.size == 0:
+ values = np.arange(0, 1, 1)
+ cm = [
+ mcolors.to_rgba(plt.get_cmap(cmap).colors[0]),
+ ]
+ else:
+ i = 0
+ for c in cycle(plt.get_cmap(cmap).colors):
+ cm.append(mcolors.to_rgba(c))
+ if len(cm) >= len(values):
+ break
+ i += 1
+ lt = pv.LookupTable(
+ values=np.array(cm) * 255,
+ scalar_range=(0, len(values)),
+ n_values=len(values),
+ )
+
+ return lt
+
+
+def find_layout(num_plots: int, portrait: bool = False) -> tuple[int, int]:
+ """Find a reasonable layout for a grid of subplots. This splits num_subplots into n x m subplots where n and m are as close as possible to each other. This can include a case where n x m > num_plots. Then, the superficial panels in the grid are ignored in the plotting process.
+
+ Args:
+ num_plots (int): Number of plots to arrange
+ portrait (bool, optional): Whether the min or max of (n,m) should be the column number in the resulting grid. Defaults to False.
+
+ Returns:
+ tuple[int, int]: Tuple describing (n_rows, n_cols) of the grid
+ """
+
+ # for checking approximation accuracy with ints. if root > root_int, then
+ # we need to adjust n_row, n_cols sucht that n_row * n_cols >= root^2
+ root = np.sqrt(num_plots)
+ root_int = np.rint(root)
+
+ if np.isclose(root, root_int):
+ return int(root_int), int(root_int) # perfect square because root is an integer
+ else:
+ # approximation by integer root is inexact
+
+ # find an approximation that is close to square such that n_row * n_cols - num_plots is
+ # as small as possible
+ a = int(np.floor(root))
+ b = int(np.ceil(root))
+
+ a_1 = int(a - 1)
+ b_1 = int(b + 1)
+
+ # make a couple of guesses that are close to the root and select the best one
+ guesses = [
+ (x, y)
+ for x, y in [
+ (a, b),
+ (a_1, b_1),
+ (a, b_1),
+ (a_1, b),
+ ]
+ if x * y >= num_plots
+ ]
+ best_guess = guesses[
+ np.argmin([x * y for x, y in guesses])
+ ] # smallest possible approximation
+
+ # handle orientation of the grid. min => rows for landscape, min=> cols for portrait
+ return (
+ (np.min(best_guess), np.max(best_guess))
+ if not portrait
+ else (np.max(best_guess), np.min(best_guess))
+ )
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..eda0aec
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,15 @@
+import pytest
+from pyvista import examples
+
+
+@pytest.fixture(scope="session")
+def exampledata():
+ armadillo = examples.download_armadillo()
+ bloodvessel = examples.download_blood_vessels()
+ brain = examples.download_brain()
+
+ return {
+ "armadillo": armadillo,
+ "bloodvessel": bloodvessel,
+ "brain": brain,
+ }
diff --git a/tests/test_plot.py b/tests/test_plot.py
index c9bea3d..68fe3cf 100644
--- a/tests/test_plot.py
+++ b/tests/test_plot.py
@@ -1,6 +1,7 @@
import sme_contrib.plot as smeplot
import sme
import os.path
+import pytest
def _get_abs_path(filename):
@@ -73,3 +74,167 @@ def test_concentration_heatmap_animation() -> None:
frame[1].get_text()
== f"Concentration of A_nucl, A_cell: t = {result.time_point}"
)
+
+
+def test_facet_grid_3D(exampledata):
+ def plot_bloodvessel(label, data, plotter, panel, **kwargs):
+ plotter.subplot(*panel)
+ plotter.add_mesh(data)
+
+ def plot_brain(label, data, plotter, panel, **kwargs):
+ plotter.subplot(*panel)
+ plotter.add_volume(
+ data,
+ cmap="viridis",
+ opacity="sigmoid", # Common opacity mapping for volume rendering
+ shade=True,
+ ambient=0.3,
+ diffuse=0.6,
+ specular=0.5,
+ )
+
+ def plot_armadillo(label, data, plotter, panel, **kwargs):
+ plotter.subplot(*panel)
+ plotter.add_mesh(data)
+
+ facetgrid = smeplot.facet_grid_3D(
+ data={
+ "armadillo": exampledata["armadillo"],
+ "bloodvessel": exampledata["bloodvessel"],
+ "brain": exampledata["brain"],
+ },
+ plotfuncs={
+ "armadillo": plot_armadillo,
+ "bloodvessel": plot_bloodvessel,
+ "brain": plot_brain,
+ },
+ )
+ facetgrid.show()
+
+ assert facetgrid.shape == (1, 3)
+
+ with pytest.raises(ValueError):
+ smeplot.facet_grid_3D(
+ data={
+ "armadillo": exampledata["armadillo"],
+ "bloodvessel": exampledata["bloodvessel"],
+ "brain": exampledata["brain"],
+ },
+ plotfuncs={
+ "armadillo": plot_armadillo,
+ "bloodvessel": plot_bloodvessel,
+ "wrong_key": plot_brain,
+ },
+ )
+
+
+def test_facet_grid_animation(tmp_path, exampledata):
+ def plot_bloodvessel(label, data, plotter, panel, **kwargs):
+ plotter.subplot(*panel)
+ plotter.add_mesh(data)
+
+ def plot_brain(label, data, plotter, panel, **kwargs):
+ plotter.subplot(*panel)
+ plotter.add_volume(
+ data,
+ cmap="viridis",
+ opacity="sigmoid", # Common opacity mapping for volume rendering
+ shade=True,
+ ambient=0.3,
+ diffuse=0.6,
+ specular=0.5,
+ )
+
+ def plot_armadillo(label, data, plotter, panel, **kwargs):
+ plotter.subplot(*panel)
+ plotter.add_mesh(data)
+
+ data_for_frames = [
+ {
+ "armadillo": exampledata["armadillo"],
+ "bloodvessel": exampledata["bloodvessel"],
+ "brain": exampledata["brain"],
+ },
+ {
+ "armadillo": exampledata["armadillo"],
+ "bloodvessel": exampledata["bloodvessel"],
+ "brain": exampledata["brain"],
+ },
+ {
+ "armadillo": exampledata["armadillo"],
+ "bloodvessel": exampledata["bloodvessel"],
+ "brain": exampledata["brain"],
+ },
+ ]
+
+ testanimation = smeplot.facet_grid_animate_3D(
+ tmp_path / "test.mp4",
+ data=data_for_frames,
+ plotfuncs={
+ "armadillo": plot_armadillo,
+ "bloodvessel": plot_bloodvessel,
+ "brain": plot_brain,
+ },
+ )
+
+ assert testanimation == tmp_path / "test.mp4"
+ assert testanimation.exists()
+
+ with pytest.raises(ValueError):
+ smeplot.facet_grid_animate_3D(
+ tmp_path / "test.mp4",
+ data=data_for_frames,
+ plotfuncs={
+ "armadillo": plot_armadillo,
+ "bloodvessel": plot_bloodvessel,
+ "wrong_key": plot_brain,
+ },
+ )
+
+ with pytest.raises(ValueError):
+ smeplot.facet_grid_animate_3D(
+ tmp_path / "test.mp4",
+ data=data_for_frames,
+ plotfuncs={
+ "armadillo": plot_armadillo,
+ "bloodvessel": plot_bloodvessel,
+ "brain": plot_brain,
+ },
+ titles=["title1", "title2"],
+ )
+
+
+def test_plot_3D():
+ model_file = _get_abs_path("model.xml")
+ model = sme.open_sbml_file(model_file)
+ results = model.simulate(100, 10)
+
+ # single species
+ plotter = smeplot.concentrations3D(
+ simulation_result=results[10],
+ species=["A_nucl"],
+ cmap="tab10",
+ show_cmap=True,
+ )
+
+ assert plotter.title == "A_nucl"
+ assert plotter is not None
+ assert plotter.shape == (1, 1)
+
+
+def test_plot_3D_animation(tmp_path):
+ model_file = _get_abs_path("model.xml")
+ model = sme.open_sbml_file(model_file)
+ results = model.simulate(100, 10)
+
+ vidpath = smeplot.concentrationsAnimate3D(
+ filename=tmp_path / "test.mp4",
+ simulation_results=results,
+ species=["A_nucl"],
+ cmap="tab10",
+ show_cmap=True,
+ )
+
+ assert vidpath is not None
+ assert str(vidpath).endswith(".mp4")
+ assert os.path.exists(vidpath)
diff --git a/tests/test_pyvista_utils.py b/tests/test_pyvista_utils.py
new file mode 100644
index 0000000..93e7cbe
--- /dev/null
+++ b/tests/test_pyvista_utils.py
@@ -0,0 +1,63 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.colors as mcolors
+from sme_contrib import pyvista_utils
+
+
+def test_rgb_to_scalar():
+ img = np.array(
+ [
+ [[[0, 0, 0], [255, 255, 255]], [[0, 0, 0], [255, 255, 255]]],
+ [[[0, 0, 0], [255, 255, 255]], [[0, 0, 0], [255, 255, 255]]],
+ [[[0, 0, 0], [255, 255, 255]], [[0, 0, 0], [255, 255, 255]]],
+ ]
+ )
+ scalar_img = pyvista_utils.rgb_to_scalar(img)
+ assert scalar_img.shape == (3, 2, 2)
+ assert np.all(
+ scalar_img == np.array([[[0, 1], [0, 1]], [[0, 1], [0, 1]], [[0, 1], [0, 1]]])
+ )
+
+
+def test_make_discrete_colormap():
+ lt = pyvista_utils.make_discrete_colormap()
+ cm = plt.get_cmap("tab10").colors
+ should = (np.array([mcolors.to_rgba(cm[0])]) * 255).astype(np.int32)
+ assert lt.n_values == 1
+ assert lt.scalar_range == (0, 1)
+ assert np.all(lt.values == should)
+
+ lt = pyvista_utils.make_discrete_colormap("tab20", np.array([0, 1, 2, 3]))
+ assert lt.n_values == 4
+ assert lt.scalar_range == (0, 4)
+ cm = plt.get_cmap("tab20").colors
+ should = (
+ np.array(
+ [
+ mcolors.to_rgba(cm[0]),
+ mcolors.to_rgba(cm[1]),
+ mcolors.to_rgba(cm[2]),
+ mcolors.to_rgba(cm[3]),
+ ]
+ )
+ * 255
+ ).astype(np.int32)
+ assert np.all(lt.values == should)
+
+
+def test_find_layout():
+ assert pyvista_utils.find_layout(1) == (1, 1)
+ assert pyvista_utils.find_layout(3) == (1, 3)
+ assert pyvista_utils.find_layout(5) == (2, 3)
+ assert pyvista_utils.find_layout(8) == (2, 4)
+ assert pyvista_utils.find_layout(10) == (2, 5)
+ assert pyvista_utils.find_layout(15) == (3, 5)
+ assert pyvista_utils.find_layout(15, portrait=True) == (5, 3)
+ assert pyvista_utils.find_layout(16) == (4, 4)
+ assert pyvista_utils.find_layout(19) == (4, 5)
+ assert pyvista_utils.find_layout(23) == (4, 6)
+ assert pyvista_utils.find_layout(25) == (5, 5)
+ assert pyvista_utils.find_layout(27) == (4, 7)
+ assert pyvista_utils.find_layout(29) == (5, 6)
+ assert pyvista_utils.find_layout(31) == (5, 7)
+ assert pyvista_utils.find_layout(31, portrait=True) == (7, 5)