Merge pull request #13 from spatial-model-editor/add_multiple_model_fitting_example_notebook

Add initial multiple model parameter fitting example notebook

Author: lkeegan

2d-toy-model.ipynb

@@ -417,7 +417,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.0"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,

3d-model-parameter-fitting.xml

@@ -638,8 +638,50 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "id": "26",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def geometry_image(n_pixels):\n",
+    "    # generate a segmented image containing one randomly distributed, sized and deformed sphere\n",
+    "    max_radius = n_pixels / 3\n",
+    "    max_deform = 1.2\n",
+    "    voxels = np.zeros((n_pixels, n_pixels, n_pixels), dtype=np.uint16)\n",
+    "    center = np.random.randint(2, n_pixels - 2, 3)\n",
+    "    nuclear_radius = np.random.randint(1, max_radius / 2)\n",
+    "    cell_radius = np.random.randint(1.5 * nuclear_radius, max_radius)\n",
+    "    deformation = np.random.uniform(1 / max_deform, max_deform, 3)\n",
+    "    voxels[sphere_mask(voxels.shape, center, cell_radius, deformation)] = 2\n",
+    "    voxels[sphere_mask(voxels.shape, center, nuclear_radius, deformation)] = 1\n",
+    "    return voxels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "img = geometry_image(30)\n",
+    "plot3D(\n",
+    "    img, [\"Geometry image (voxel)\", \"Geometry image (surface)\"], threshold_value=[1, 0]\n",
+    ").show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "28",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -659,7 +701,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "27",
+   "id": "29",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -673,7 +715,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "28",
+   "id": "30",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -689,7 +731,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "29",
+   "id": "31",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -705,7 +747,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "30",
+   "id": "32",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -726,7 +768,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31",
+   "id": "33",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -744,7 +786,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "32",
+   "id": "34",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -759,7 +801,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33",
+   "id": "35",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -774,7 +816,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "34",
+   "id": "36",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -795,7 +837,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "35",
+   "id": "37",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -815,7 +857,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "36",
+   "id": "38",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -835,7 +877,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "37",
+   "id": "39",
    "metadata": {
     "slideshow": {
      "slide_type": "subslide"
@@ -854,7 +896,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -868,7 +910,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,

3d-toy-model.ipynb

@@ -16,7 +16,7 @@
             <options>
               <cereal_class_version>0</cereal_class_version>
               <dune>
-                <cereal_class_version>0</cereal_class_version>
+                <cereal_class_version>1</cereal_class_version>
                 <discretization>0</discretization>
                 <integrator>alexander_2</integrator>
                 <dt>0.10000000000000001</dt>
@@ -27,6 +27,7 @@
                 <writeVTKfiles>false</writeVTKfiles>
                 <newtonRelErr>1e-08</newtonRelErr>
                 <newtonAbsErr>9.9999999999999998e-13</newtonAbsErr>
+                <linearSolver>RestartedGMRes</linearSolver>
               </dune>
               <pixel>
                 <cereal_class_version>0</cereal_class_version>
@@ -172,6 +173,8 @@
       <parameter id="A_membrane_diffusionConstant" value="1" units="diffusion_constant_units" constant="true">
         <spatial:diffusionCoefficient spatial:variable="A_membrane" spatial:type="isotropic"/>
       </parameter>
+      <parameter id="k1" name="k1" value="1" constant="true"/>
+      <parameter id="k2" name="k2" value="1" constant="true"/>
     </listOfParameters>
     <listOfReactions>
       <reaction id="Outside_to_membrane" name="Outside to membrane" reversible="true" compartment="outside_membrane_membrane" spatial:isLocal="true">
@@ -186,6 +189,7 @@
             <apply>
               <times/>
               <cn type="e-notation"> 1 <sep/> -14 </cn>
+              <ci> k1 </ci>
               <apply>
                 <plus/>
                 <apply>
@@ -210,6 +214,7 @@
             <apply>
               <times/>
               <cn type="e-notation"> 1 <sep/> -14 </cn>
+              <ci> k2 </ci>
               <apply>
                 <plus/>
                 <apply>

3d-toy-model.xml

@@ -16,4 +16,12 @@ A 3d toy model starting from 3d segmented cells
 - [Jupyter notebook](https://github.com/spatial-model-editor/toy-models/blob/main/3d-toy-model.ipynb)
 - [SBML model](https://spatial-model-editor.github.io/toy-models/3d-toy-model.xml)
 
+## 3d multiple model parameter fitting
+
+Fitting parameters to multiple 3d toy models
+
+- [Online slides (generated from jupyter notebook below)](https://spatial-model-editor.github.io/toy-models/multiple-model-parameter-fitting.slides.html)
+- [Jupyter notebook](https://github.com/spatial-model-editor/toy-models/blob/main/multiple-model-parameter-fitting.ipynb)
+- [SBML model](https://spatial-model-editor.github.io/toy-models/3d-model-parameter-fitting.xml)
+
 The SBML models can be opened, modified and simulated using [Spatial Model Editor](https://spatial-model-editor.github.io/)