{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open an example model & simulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EtqqtL8VYdA5"
   },
   "outputs": [],
   "source": [
    "!pip install -q sme\n",
    "import sme\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import animation\n",
    "import numpy as np\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LLPnn1h1Yee7"
   },
   "outputs": [],
   "source": [
    "my_model = sme.open_example_model()\n",
    "sim_results = my_model.simulate(simulation_time=250.0, image_interval=5.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Animation of species concentrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "species = [\"B_cell\", \"B_out\"]\n",
    "\n",
    "fig, axs = plt.subplots(constrained_layout=True, ncols=len(species), figsize=(9, 4))\n",
    "\n",
    "# set normalization of each plot to maximum concentration of species over entire simulation\n",
    "norms = []\n",
    "for spec, ax in zip(species, axs):\n",
    "    c_max = np.max([np.max(r.species_concentration[spec]) for r in sim_results])\n",
    "    norms.append(plt.Normalize(vmin=0, vmax=c_max))\n",
    "\n",
    "# make a plot with correctly normalized colorbar for each species\n",
    "for ax, norm in zip(axs, norms):\n",
    "    im = ax.imshow(np.zeros((1, 1)), norm=norm)\n",
    "    fig.colorbar(im, ax=ax)\n",
    "\n",
    "# create a list of plot artists for each timepoint\n",
    "artists = []\n",
    "for sim_result in sim_results:\n",
    "    artist = []\n",
    "    for spec, ax, norm in zip(species, axs, norms):\n",
    "        artist.append(\n",
    "            ax.imshow(\n",
    "                sim_result.species_concentration[spec][0],\n",
    "                animated=True,\n",
    "                norm=norm,\n",
    "                interpolation=None,\n",
    "            )\n",
    "        )\n",
    "        artist.append(\n",
    "            ax.text(\n",
    "                0.5,\n",
    "                1.01,\n",
    "                f\"{spec}: t = {sim_result.time_point}\",\n",
    "                horizontalalignment=\"center\",\n",
    "                verticalalignment=\"bottom\",\n",
    "                transform=ax.transAxes,\n",
    "            )\n",
    "        )\n",
    "    artists.append(artist)\n",
    "\n",
    "# make an animation from the list of artists\n",
    "anim = animation.ArtistAnimation(fig, artists, interval=200, blit=True, repeat=False)\n",
    "plt.close()\n",
    "HTML(anim.to_html5_video())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gray-Scott Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# simulate for a few different reaction parameter values & store concentrations of species V\n",
    "gray_scott = sme.open_example_model(\"gray-scott\")\n",
    "fs = [\"0.03\", \"0.04\", \"0.05\"]\n",
    "vs = []\n",
    "for f in fs:\n",
    "    gray_scott.parameters[\"f\"].value = f\n",
    "    sim_results = gray_scott.simulate(\n",
    "        simulation_time=10000.0,\n",
    "        image_interval=100,\n",
    "        simulator_type=sme.SimulatorType.Pixel,\n",
    "    )\n",
    "    times = [r.time_point for r in sim_results]\n",
    "    vs.append([r.species_concentration[\"V\"][0] for r in sim_results])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(constrained_layout=True, nrows=len(fs), figsize=(9, 16))\n",
    "for ax, f in zip(axs, fs):\n",
    "    ax.set_title(f\"f = {f}\")\n",
    "\n",
    "artists = []\n",
    "for i in range(len(vs[0])):\n",
    "    artist = []\n",
    "    for ax, v in zip(axs, vs):\n",
    "        artist.append(ax.imshow(v[i], animated=True, interpolation=None))\n",
    "    artists.append(artist)\n",
    "\n",
    "anim = animation.ArtistAnimation(fig, artists, interval=200, blit=True, repeat=False)\n",
    "plt.close()\n",
    "HTML(anim.to_html5_video())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "sme_getting_started.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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}