{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e6111b3f",
   "metadata": {},
   "source": [
    "# Basic\n",
    "\n",
    "We will define a simple model of\n",
    "water creation from hydrogen and oxygen.\n",
    "\n",
    "First,\n",
    "we need to import `EmptyCompartment` and `Species`,\n",
    "a `Synthesis` reaction,\n",
    "and a `Simulator` to integrate the equations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "26e276c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from simbio.components import EmptyCompartment, Species\n",
    "from simbio.reactions.single import Synthesis\n",
    "from simbio.simulator import Simulator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bd0c330",
   "metadata": {},
   "source": [
    "To define the model,\n",
    "we create 3 species,\n",
    "corresponding to\n",
    "hydrogen $H₂$, oxygen $O₂$, and water $H₂O$,\n",
    "with initial conditions of `1`, `1` and `0`,\n",
    "respectively.\n",
    "And we add a `create_water` reaction,\n",
    "corresponding to $2 H₂ + O₂ → 2 H₂ O$,\n",
    "with a rate of `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6828da32",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model(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=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504be334",
   "metadata": {},
   "source": [
    "To simulate it,\n",
    "we feed the model named `Model` into `Simulator`,\n",
    "and use the `.run()` method.\n",
    "It will compile and integrate an `ODE`-based simulation,\n",
    "returning a `pandas.DataFrame` with the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d0566f41",
   "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>H2</th>\n",
       "      <th>O2</th>\n",
       "      <th>H2O</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10101</th>\n",
       "      <td>0.838051</td>\n",
       "      <td>0.919025</td>\n",
       "      <td>0.161949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20202</th>\n",
       "      <td>0.728306</td>\n",
       "      <td>0.864153</td>\n",
       "      <td>0.271694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30303</th>\n",
       "      <td>0.647931</td>\n",
       "      <td>0.823965</td>\n",
       "      <td>0.352069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40404</th>\n",
       "      <td>0.585968</td>\n",
       "      <td>0.792984</td>\n",
       "      <td>0.414032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               H2        O2       H2O\n",
       "time                                 \n",
       "0.00000  1.000000  1.000000  0.000000\n",
       "0.10101  0.838051  0.919025  0.161949\n",
       "0.20202  0.728306  0.864153  0.271694\n",
       "0.30303  0.647931  0.823965  0.352069\n",
       "0.40404  0.585968  0.792984  0.414032"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulator = Simulator(Model)\n",
    "\n",
    "t = np.linspace(0, 10, 100)\n",
    "df = simulator.run(t)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f973d14",
   "metadata": {},
   "source": [
    "To visualize the time evolution,\n",
    "we can use the `DataFrame.plot` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74ee074d",
   "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>"
      ]
     },
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