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add optimize example notebook #9

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Aug 31, 2021
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add optimize example notebook #9

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add optimize example notebook
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@lkeegan
lkeegan committed Aug 31, 2021
commit 13ca6175440004d4a978b4535ed84eec6d8f7170
1 change: 1 addition & 0 deletions docs/index.rst
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Expand Up @@ -29,6 +29,7 @@ API Reference
:caption: Example jupyter notebooks

notebooks/plot
notebooks/optimize

Source code
-----------
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230 changes: 230 additions & 0 deletions docs/notebooks/optimize.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# sme_contrib.optimize"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zISJVa_IlpA7"
},
"outputs": [],
"source": [
"!pip install -q sme_contrib\n",
"import sme\n",
"import sme_contrib.optimize as smeopt\n",
"import sme_contrib.plot as smeplot\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"from IPython.display import HTML\n",
"from PIL import Image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gray-Scott model\n",
"A simple two-species model with two reaction rate parameters, that forms spatial patterns and eventually reaches a steady state"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def simulated_gray_scott(f, k):\n",
" m = sme.open_example_model(\"gray-scott\")\n",
" m.compartments[0].species[\n",
" \"V\"\n",
" ].analytic_concentration = \"exp(-((x-49.5)^2+(y-49.5)^2))\"\n",
" m.parameters[\"f\"].value = f\"{f}\"\n",
" m.parameters[\"k\"].value = f\"{k}\"\n",
" m.simulate(5000, 50, return_results=False)\n",
" return m\n",
"\n",
"\n",
"def gray_scott_anim(f, k):\n",
" gray_scott = simulated_gray_scott(f, k)\n",
" return smeplot.concentration_heatmap_animation(\n",
" gray_scott.simulation_results(), [\"V\"]\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"anim = gray_scott_anim(0.04, 0.06)\n",
"HTML(anim.to_html5_video())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"anim = gray_scott_anim(0.051, 0.061)\n",
"HTML(anim.to_html5_video())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"anim = gray_scott_anim(0.028, 0.062)\n",
"HTML(anim.to_html5_video())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Try to fit to the target steady state"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def create_target_image(f, k):\n",
" gray_scott = simulated_gray_scott(f, k)\n",
" conc = gray_scott.simulation_results()[-1].species_concentration[\"V\"]\n",
" conc = 255 * conc / np.max(conc)\n",
" Image.fromarray(conc.astype(\"uint8\")).save(\"tmp.png\")\n",
" gray_scott.export_sbml_file(\"tmp.xml\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"def apply_params(model, params):\n",
" model.parameters[\"f\"].value = f\"{params[0]}\"\n",
" model.parameters[\"k\"].value = f\"{params[1]}\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"create_target_image(0.04, 0.06)\n",
"ss = smeopt.SteadyState(\n",
" \"tmp.xml\",\n",
" \"tmp.png\",\n",
" [\"V\"],\n",
" apply_params,\n",
" [0.01, 0.05],\n",
" [0.06, 0.07],\n",
" 5000,\n",
" 2000,\n",
" 90,\n",
")\n",
"ss.find(10, 20)\n",
"ss.plot_all()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"create_target_image(0.051, 0.061)\n",
"ss = smeopt.SteadyState(\n",
" \"tmp.xml\",\n",
" \"tmp.png\",\n",
" [\"V\"],\n",
" apply_params,\n",
" [0.01, 0.05],\n",
" [0.06, 0.07],\n",
" 5000,\n",
" 2000,\n",
" 90,\n",
")\n",
"ss.find(10, 20)\n",
"ss.plot_all()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"create_target_image(0.028, 0.062)\n",
"ss = smeopt.SteadyState(\n",
" \"tmp.xml\",\n",
" \"tmp.png\",\n",
" [\"V\"],\n",
" apply_params,\n",
" [0.01, 0.05],\n",
" [0.06, 0.07],\n",
" 5000,\n",
" 2000,\n",
" 90,\n",
")\n",
"ss.find(10, 20)\n",
"ss.plot_all()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "sme_image_comparison.ipynb",
"provenance": []
},
"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": "3.9.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
1 change: 1 addition & 0 deletions requirements.txt
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matplotlib
numpy
pillow
pyswarms
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