{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "634f8a78",
   "metadata": {},
   "source": [
    "# Multiple compartments\n",
    "\n",
    "Compartments can be nested within another Compartment,\n",
    "allowing some modularity in the definition of models.\n",
    "Inner compartments can be extracted,\n",
    "and simulated independently of the whole model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb77834b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from simbio.components import EmptyCompartment, Species\n",
    "from simbio.reactions.single import Conversion, Dissociation, Synthesis\n",
    "from simbio.simulator import Simulator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e5d21f",
   "metadata": {},
   "source": [
    "Here,\n",
    "we define a model consisting of two inner compartments,\n",
    "both containing hydrogen, oxygen and water.\n",
    "Water is synthesized in one compartment, and\n",
    "transported to the other compartment,\n",
    "where it is dissociated, and\n",
    "the oxygen and hydrogen transported back to the first compartment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec556d57",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Outer(EmptyCompartment):\n",
    "    class Inner1(EmptyCompartment):\n",
    "        H2: Species = 1\n",
    "        O2: Species = 1\n",
    "        H2O: Species = 0\n",
    "\n",
    "        create_water = Synthesis(A=2 * H2, B=O2, AB=2 * H2O, rate=2)\n",
    "\n",
    "    class Inner2(EmptyCompartment):\n",
    "        H2: Species = 0\n",
    "        O2: Species = 0\n",
    "        H2O: Species = 0.1\n",
    "\n",
    "        electrolysis = Dissociation(AB=2 * H2O, A=2 * H2, B=O2, rate=0.5)\n",
    "\n",
    "    # Transport between compartments\n",
    "    water_transport = Conversion(A=Inner1.H2O, B=Inner2.H2O, rate=1)\n",
    "    H2_transport = Conversion(A=Inner2.H2, B=Inner1.H2, rate=1)\n",
    "    O2_transport = Conversion(A=Inner2.O2, B=Inner1.O2, rate=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb6edb2",
   "metadata": {},
   "source": [
    "We can simulate the whole model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3a0a08dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Inner1.H2</th>\n",
       "      <th>Inner1.O2</th>\n",
       "      <th>Inner1.H2O</th>\n",
       "      <th>Inner2.H2O</th>\n",
       "      <th>Inner2.H2</th>\n",
       "      <th>Inner2.O2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00000</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10101</th>\n",
       "      <td>0.728348</td>\n",
       "      <td>0.864174</td>\n",
       "      <td>0.256748</td>\n",
       "      <td>0.113843</td>\n",
       "      <td>0.001060</td>\n",
       "      <td>0.000530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20202</th>\n",
       "      <td>0.586138</td>\n",
       "      <td>0.793069</td>\n",
       "      <td>0.366923</td>\n",
       "      <td>0.144387</td>\n",
       "      <td>0.002553</td>\n",
       "      <td>0.001276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30303</th>\n",
       "      <td>0.496147</td>\n",
       "      <td>0.748074</td>\n",
       "      <td>0.417331</td>\n",
       "      <td>0.181650</td>\n",
       "      <td>0.004872</td>\n",
       "      <td>0.002436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40404</th>\n",
       "      <td>0.433279</td>\n",
       "      <td>0.716640</td>\n",
       "      <td>0.437497</td>\n",
       "      <td>0.220904</td>\n",
       "      <td>0.008320</td>\n",
       "      <td>0.004160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Inner1.H2  Inner1.O2  Inner1.H2O  Inner2.H2O  Inner2.H2  Inner2.O2\n",
       "time                                                                       \n",
       "0.00000   1.000000   1.000000    0.000000    0.100000   0.000000   0.000000\n",
       "0.10101   0.728348   0.864174    0.256748    0.113843   0.001060   0.000530\n",
       "0.20202   0.586138   0.793069    0.366923    0.144387   0.002553   0.001276\n",
       "0.30303   0.496147   0.748074    0.417331    0.181650   0.004872   0.002436\n",
       "0.40404   0.433279   0.716640    0.437497    0.220904   0.008320   0.004160"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Simulator(Outer)\n",
    "\n",
    "t = np.linspace(0, 10, 100)\n",
    "df = simulator.run(t)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe01506b",
   "metadata": {},
   "source": [
    "and just visualize the time evolution of the `H20` species in each compartment,\n",
    "with the `DataFrame.filter` method followed by `.plot`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a5ff7386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='time'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/docs/checkouts/readthedocs.org/user_builds/simbio/checkouts/latest/docs/_build/jupyter_execute/examples/compartments_7_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.filter(like=\"H2O\").plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dcb0d14",
   "metadata": {},
   "source": [
    "But,\n",
    "we can also simulate a single compartment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1bdeee18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='time'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/home/docs/checkouts/readthedocs.org/user_builds/simbio/checkouts/latest/docs/_build/jupyter_execute/examples/compartments_9_1.png"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "simulator = Simulator(Outer.Inner1)\n",
    "\n",
    "t = np.linspace(0, 10, 100)\n",
    "df = simulator.run(t)\n",
    "df.plot()"
   ]
  }
 ],
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