docs/gallery/visualization/animating_simulations.ipynb

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   "source": [
    "# Animating Simulations with Escher\n",
    "\n",
    "This example shows how simulation results can be displayed on an escher map. Using the reactive options of Escher, the results of a simulation can also be animated. \n",
    "\n",
    "For this particular example, a simple toy model is used."
   ]
  },
  {
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   "source": [
    "from os import path\n",
    "from time import sleep\n",
    "\n",
    "from matplotlib.cm import get_cmap\n",
    "import numpy as np\n",
    "\n",
    "import escher\n",
    "\n",
    "from mass import MassConfiguration, Simulation\n",
    "from mass.example_data import create_example_model, MAPS_DIR\n",
    "\n",
    "\n",
    "mass_config = MassConfiguration()\n",
    "mass_config.decimal_precision = 8\n",
    "# Turns off the warning message when leaving or refreshing this page.\n",
    "escher.rc['never_ask_before_quit'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model and Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set parameter Username\n"
     ]
    }
   ],
   "source": [
    "model = create_example_model(\"Simple_Toy\")\n",
    "\n",
    "# Set the path to the map file\n",
    "map_filepath = path.join(MAPS_DIR, \"simple_toy_map.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make color scales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metabolites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "num_scales = 6\n",
    "min_value, max_value = (0, 1)\n",
    "min_size, max_size = (10, 30)\n",
    "colors = 255 * get_cmap(\"bwr\", num_scales)(np.linspace(0, 1, num_scales))\n",
    "colors = np.flip(colors, axis=0)\n",
    "\n",
    "metabolite_data_scale = []\n",
    "for i in range(num_scales):\n",
    "    scale_stop = {}\n",
    "    scale_stop[\"type\"] = \"value\"\n",
    "    if i == 0:\n",
    "        scale_stop[\"value\"] = min_value\n",
    "        scale_stop[\"size\"] = min_size\n",
    "    elif i == num_scales - 1:\n",
    "        scale_stop[\"value\"] = max_value\n",
    "        scale_stop[\"size\"] = max_size\n",
    "    else:\n",
    "        scale_stop[\"value\"] = i*(max_value - min_value)/(num_scales - 1)\n",
    "        scale_stop[\"size\"] = min_size + i * (max_size - min_size)/(num_scales - 1)\n",
    "    scale_stop[\"color\"] = \"rgb({0}, {1}, {2})\".format(*colors[i])\n",
    "    metabolite_data_scale.append(scale_stop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "stops = [\"min\", \"Q1\", \"median\", \"Q3\", \"max\"]\n",
    "num_scales = len(stops)\n",
    "min_value, max_value = (0, 1)\n",
    "min_size, max_size = (10, 30)\n",
    "colors = 255 * get_cmap(\"Purples\", num_scales)(np.linspace(0, 1, num_scales))\n",
    "\n",
    "reaction_data_scale = []\n",
    "for i, stop_type in enumerate(stops):\n",
    "    scale_stop = {}\n",
    "    scale_stop[\"type\"] = stop_type\n",
    "    scale_stop[\"color\"] = \"rgb({0}, {1}, {2})\".format(*colors[i])\n",
    "    scale_stop[\"size\"] = min_size + i * (max_size - min_size)/(num_scales - 1)\n",
    "    reaction_data_scale.append(scale_stop)\n",
    "reaction_data_scale.append(\n",
    "    {\"type\": \"value\", \"value\": 0, \"color\": \"grey\", \"size\": 8})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Animate Simulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulate model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[93mWARNING:\u001b[0m \u001b[93mNo compartments found in model. Therefore creating compartment 'compartment' for entire model.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded MassModel 'Simple_Toy' into RoadRunner.\n"
     ]
    }
   ],
   "source": [
    "sim = Simulation(model, verbose=True)\n",
    "conc_sol, flux_sol = sim.simulate(model, time=(0, 1e6),\n",
    "                                  decimal_precision=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Render map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T18:45:04.891087Z",
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       "model_id": "b49059d35e514c47a14715e91438366f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Builder(metabolite_scale=({'type': 'value', 'value': 0, 'size': 10, 'color': 'rgb(255.0, 0.0, 0.0)'}, {'type':…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "escher_builder = escher.Builder(\n",
    "    model=model,\n",
    "    map_json=map_filepath,\n",
    "    reaction_scale=tuple(reaction_data_scale),\n",
    "    metabolite_scale=tuple(metabolite_data_scale)\n",
    ")\n",
    "escher_builder"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Animate results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-01-25T18:45:04.903441Z",
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   },
   "outputs": [],
   "source": [
    "for i in range(len(conc_sol.time)):\n",
    "    # Map metabolite data onto map per solution point\n",
    "    escher_builder.metabolite_data = {\n",
    "        met: solution[i] for met, solution in conc_sol.items()}\n",
    "    # Map reaction data onto map per solution point\n",
    "    escher_builder.reaction_data = {\n",
    "        rxn: solution[i] for rxn, solution in flux_sol.items()}\n",
    "    sleep(0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The video of the created animation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2021-01-25T18:45:25.103915Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video controls  width=\"500\" >\n",
       " <source src=\"data:None;base64,..../media/videos/escher_toy_animation.mp4\" type=\"None\">\n",
       " Your browser does not support the video tag.\n",
       " </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Video\n",
    "Video(\"../../media/videos/escher_toy_animation.mp4\", embed=True, width=500)"
   ]
  }
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