{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1e2e446",
   "metadata": {},
   "source": [
    "This is part 1 of a tutorial series. We recommend following them in order, starting with [Part 0: Welcome to `musica`](0.%20Welcome%20to%20MUSICA.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7030540f",
   "metadata": {},
   "source": [
    "# Multiple Grid Cells in `musica`\n",
    "\n",
    "As we saw in the last tutorial, gas-phase systems can be solved in `musica` using a `solver` and a `state`. For box models, that's all we need. For more complex applications (columns, 3D grids, etc.), we'll need to manage the state of a number of independent well-mixed air masses, or \"grid cells\".\n",
    "\n",
    ">__NOTE:__ We use \"grid cell\" throughout these tutorials to mean a well-mixed air mass. A vertical stack of \"grid cells\" would be used for a column-model. Collections of vertical stacks of grid cells would be used for a 3D grid or mesh. However, `micm` does not make assumptions about the shape, size, or arragement of the well-mixed air mass(es) a `state` represents, so the state can be used to represent air masses of any size/shape in any arrangement.\n",
    "\n",
    "_Why multiple grid cell states?_\n",
    "\n",
    "If we can create as many states as we want and use the same solver on them, maybe you're wondering why we even need multiple-grid-cell states. The answer, once again, is for performance. In HPC applications, `micm` can be used to solve hundreds of thousands of independent grid cells simultaneously. To do this efficiently, the data needs to be efficiently arranaged in memory. Multiple-grid-cell states allow us to do this."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "008e870b",
   "metadata": {},
   "source": [
    "## 1. Importing Libraries\n",
    "Below is a list of the required libraries for this tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7c921c61",
   "metadata": {},
   "outputs": [],
   "source": [
    "import musica\n",
    "import musica.mechanism_configuration as mc\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('display.float_format', str) # This is done to make the arrays more readable\n",
    "np.set_printoptions(suppress=True) # This is done to make the arrays more readable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01472a4f",
   "metadata": {},
   "source": [
    "## 2. Defining a System\n",
    "\n",
    "In the first tutorial, we used an example configuration file to define our chemical system. This time, we'll use the `musica` API to define a chemical system in code. We'll use this apporach for future tutorials as well.\n",
    "\n",
    "Let's set up a system analogous to the simple analytically solvable system we used in the first tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b1084ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = mc.Species(name=\"A\")\n",
    "B = mc.Species(name=\"B\")\n",
    "C = mc.Species(name=\"C\")\n",
    "species = [A, B, C]\n",
    "gas = mc.Phase(name=\"gas\", species=species)\n",
    "\n",
    "r1 = mc.Arrhenius(\n",
    "    name=\"A_to_B\",\n",
    "    A=4.0e-3,\n",
    "    C=50,\n",
    "    reactants=[A],\n",
    "    products=[B],\n",
    "    gas_phase=gas\n",
    ")\n",
    "\n",
    "r2 = mc.Arrhenius(\n",
    "    name=\"B_to_C\",\n",
    "    A=4.0e-3,\n",
    "    C=50,  \n",
    "    reactants=[B],\n",
    "    products=[C],\n",
    "    gas_phase=gas\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b4a60a",
   "metadata": {},
   "source": [
    "Similar to the last tutorial, we have defined a simple system with:\n",
    "* Three species (`A`, `B`, and `C`) in the gas-phase\n",
    "* Two Arrhenius reactions\n",
    "    * `A -> B`\n",
    "    * `B -> C`\n",
    "\n",
    "We try to keep the python API consistent with the format of the configuration files, and have both structured around the science.\n",
    "\n",
    "The documentation can be used to clarify important details, like the parameter names and units for rate constant types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81bcef4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mInit signature:\u001b[39m\n",
      "mc.Arrhenius(\n",
      "    name: str | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    A: float | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    B: float | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    C: float | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    Ea: float | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    D: float | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    E: float | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    reactants: List[musica.mechanism_configuration.species.Species | Tuple[float, musica.mechanism_configuration.species.Species]] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    products: List[musica.mechanism_configuration.species.Species | Tuple[float, musica.mechanism_configuration.species.Species]] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    gas_phase: musica.mechanism_configuration.phase.Phase | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    other_properties: Dict[str, Any] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      ")\n",
      "\u001b[31mDocstring:\u001b[39m     \n",
      "An Arrhenius rate constant.\n",
      "\n",
      "k = A * exp( C / T ) * ( T / D )^B * exp( 1 - E * P )\n",
      "\n",
      "where:\n",
      "    k = rate constant\n",
      "    A = pre-exponential factor [(mol m-3)^(n-1)s-1]\n",
      "    B = temperature exponent [unitless]\n",
      "    C = exponential term [K-1]\n",
      "    D = reference temperature [K]\n",
      "    E = pressure scaling term [Pa-1]\n",
      "    T = temperature [K]\n",
      "    P = pressure [Pa]\n",
      "    n = number of reactants\n",
      "\n",
      "Attributes:\n",
      "    name: The name of the Arrhenius rate constant.\n",
      "    A: Pre-exponential factor [(mol m-3)^(n-1)s-1].\n",
      "    B: Temperature exponent [unitless].\n",
      "    C: Exponential term [K-1].\n",
      "    D: Reference Temperature [K].\n",
      "    E: Pressure scaling term [Pa-1].\n",
      "    reactants: A list of reactants involved in the reaction.\n",
      "    products: A list of products formed in the reaction.\n",
      "    gas_phase: The gas phase in which the reaction occurs.\n",
      "    other_properties: A dictionary of other properties.\n",
      "\u001b[31mInit docstring:\u001b[39m\n",
      "Initialize the Arrhenius reaction.\n",
      "\n",
      "Args:\n",
      "    name: The name of the Arrhenius rate constant.\n",
      "    A: Pre-exponential factor [(mol m-3)^(n-1)s-1].\n",
      "    B: Temperature exponent [unitless].\n",
      "    C: Exponential term [K-1].\n",
      "    Ea: Activation energy [J molecule-1]. Mutually exclusive with C.\n",
      "    D: Reference Temperature [K].\n",
      "    E: Pressure scaling term [Pa-1].\n",
      "    reactants: A list of reactants involved in the reaction.\n",
      "    products: A list of products formed in the reaction.\n",
      "    gas_phase: The gas phase in which the reaction occurs.\n",
      "    other_properties: A dictionary of other properties.\n",
      "\u001b[31mFile:\u001b[39m           ~/Documents/Projects/musica/venv/lib/python3.14/site-packages/musica/mechanism_configuration/arrhenius.py\n",
      "\u001b[31mType:\u001b[39m           type\n",
      "\u001b[31mSubclasses:\u001b[39m     "
     ]
    }
   ],
   "source": [
    "mc.Arrhenius?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ccc93f7",
   "metadata": {},
   "source": [
    "In the last chapter, we used the `musica` parser to turn our configuration file into a `mechanism`. Here, we'll create the mechanism out of the chemistry objects we've defined above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "80008904-7666-449e-a527-8058e5194512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Arrhenius] A -> B\n",
      "[Arrhenius] B -> C\n"
     ]
    }
   ],
   "source": [
    "mechanism = mc.Mechanism(\n",
    "    name=\"musica_micm_example\",\n",
    "    species=species,\n",
    "    phases=[gas],\n",
    "    reactions=[r1, r2]\n",
    ")\n",
    "for reaction in mechanism.reactions:\n",
    "    print(f\"[{reaction.type.name}] {reaction.to_equation()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7861e510",
   "metadata": {},
   "source": [
    "## 3. Creating the Solver\n",
    "\n",
    "We create a solver for our `mechanism` the same way we did in the last tutorial.\n",
    "One minor difference here, is that we specifcy the `solver_type`.\n",
    "For more information on the types of solvers available, see [here](https://ncar.github.io/micm/user_guide/solver_configurations.html).\n",
    "The standard-ordered Rosenbrock solver is actually the default `solver_type`, so including it here has no real effect.\n",
    "\n",
    ">__NOTE:__ If you're wondering what \"standard order\" means, it refers to how the matrix data in the `state` in arranged in memory. `micm` is designed to be performant enough to be included in large-scale climate and weather models. To achieve optimal performance we have advanced matrix ordering strategies we can apply to encourage vectorization of the solver instructions. For most Python-based applications, the standard ordering will be sufficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18bb7d49",
   "metadata": {},
   "outputs": [],
   "source": [
    "solver = musica.MICM(mechanism = mechanism, solver_type = musica.SolverType.rosenbrock_standard_order)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2199a26f",
   "metadata": {},
   "source": [
    "## 4. Creating the State\n",
    "\n",
    "In the last tutorial, we created a single-grid-cell `state`. This time, let's double the complexity of our system and create a two-grid-cell `state`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d91a4be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_grid_cells = 2\n",
    "state = solver.create_state(num_grid_cells)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d6e59c",
   "metadata": {},
   "source": [
    "## 5. Setting Conditions\n",
    "\n",
    "Next, let's set the conditions for our two-grid-cell state. In the last tutorial, you may have wondered why the temperature, pressure, and species concentrations were in array format. Hopefully, now it's clear - one element for each grid cell!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0a74a68",
   "metadata": {},
   "outputs": [],
   "source": [
    "state.set_conditions(temperatures=[300, 100], pressures=[101253.3, 11253.3])\n",
    "state.set_concentrations({\n",
    "    'A': [5, 20],\n",
    "    'B': [5, 3],\n",
    "    'C': [5, 7]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1238f02b",
   "metadata": {},
   "source": [
    "## 7. Solving the System\n",
    "\n",
    "This time around, we'll advance the state for 60 s, collecting intermediate states each second."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76b21308",
   "metadata": {},
   "outputs": [],
   "source": [
    "concentrations_solved = []\n",
    "time_step = 10 # s\n",
    "sim_length = 600 # s\n",
    "curr_time = 0 # s\n",
    "while curr_time <= sim_length:\n",
    "    solver.solve(state, time_step)\n",
    "    concentrations_solved.append(state.get_concentrations())\n",
    "    curr_time += time_step"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2185c0",
   "metadata": {},
   "source": [
    "## 8. Preparing the Results (Advanced; Optional Read)\n",
    "\n",
    "We're going to create a short helper function to put the results into a Pandas DataFrame for easier plotting.\n",
    "The function will extract results for a single grid cell, and we call it twice, once for each of the grid cells in our `state`.\n",
    "In this simulation, the temperature, pressure, and air density are all constant, so numpy's `repeat()` function is used to repeat their respective values for every time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65de1f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_results_single_cell(cell_index):\n",
    "    concentrations_solved_pd = []\n",
    "    for i in range(0, sim_length + 1, time_step):\n",
    "        concentrations_solved_pd.append({species: concentration[cell_index] for species, concentration in concentrations_solved[int(i/time_step)].items()})\n",
    "    df = pd.DataFrame(concentrations_solved_pd)\n",
    "    df = df.rename(columns = {'A' : 'CONC.A.mol m-3', 'B' : 'CONC.B.mol m-3', 'C' : 'CONC.C.mol m-3'})\n",
    "    df['time.s'] = list(map(float, range(0, sim_length + 1, time_step)))\n",
    "    df['ENV.temperature.K'] = np.repeat(state.get_conditions()['temperature'][cell_index], sim_length/time_step + 1.0)\n",
    "    df['ENV.pressure.Pa'] = np.repeat(state.get_conditions()['pressure'][cell_index], sim_length/time_step + 1.0)\n",
    "    df['ENV.air number density.mol m-3'] = np.repeat(state.get_conditions()['air_density'][cell_index], sim_length/time_step + 1.0)\n",
    "    df = df[['time.s', 'ENV.temperature.K', 'ENV.pressure.Pa', 'ENV.air number density.mol m-3', 'CONC.A.mol m-3', 'CONC.B.mol m-3', 'CONC.C.mol m-3']]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e9dda1ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_0 = convert_results_single_cell(0)\n",
    "df_1 = convert_results_single_cell(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada2398b",
   "metadata": {},
   "source": [
    "## 9. Viewing the Results\n",
    "\n",
    "Finally, let's desplay and plot the DataFrames to show the evolution of both of the independent systems over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d49f1b08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time.s</th>\n",
       "      <th>ENV.temperature.K</th>\n",
       "      <th>ENV.pressure.Pa</th>\n",
       "      <th>ENV.air number density.mol m-3</th>\n",
       "      <th>CONC.A.mol m-3</th>\n",
       "      <th>CONC.B.mol m-3</th>\n",
       "      <th>CONC.C.mol m-3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>4.76922334303805</td>\n",
       "      <td>4.994590692532435</td>\n",
       "      <td>5.236185964429513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>4.549098259155805</td>\n",
       "      <td>4.9790291487427</td>\n",
       "      <td>5.471872592101488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>4.339133121467923</td>\n",
       "      <td>4.954264051821206</td>\n",
       "      <td>5.706602826710863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>4.138858994290876</td>\n",
       "      <td>4.921178138841699</td>\n",
       "      <td>5.939962866867419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>3.9478285858230064</td>\n",
       "      <td>4.8805922672973425</td>\n",
       "      <td>6.171579146879646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>560.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>0.3382177774161425</td>\n",
       "      <td>1.2492099030181651</td>\n",
       "      <td>13.412572319565694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>570.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>0.32260722381670276</td>\n",
       "      <td>1.2067968547753385</td>\n",
       "      <td>13.470595921407961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>580.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>0.3077171804918639</td>\n",
       "      <td>1.1656377730117506</td>\n",
       "      <td>13.526645046496386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>590.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>0.29351439205113017</td>\n",
       "      <td>1.1257072563871726</td>\n",
       "      <td>13.580778351561698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>600.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>0.27996713801757456</td>\n",
       "      <td>1.086979577032808</td>\n",
       "      <td>13.633053284949622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    time.s  ENV.temperature.K  ENV.pressure.Pa  \\\n",
       "0      0.0              300.0         101253.3   \n",
       "1     10.0              300.0         101253.3   \n",
       "2     20.0              300.0         101253.3   \n",
       "3     30.0              300.0         101253.3   \n",
       "4     40.0              300.0         101253.3   \n",
       "..     ...                ...              ...   \n",
       "56   560.0              300.0         101253.3   \n",
       "57   570.0              300.0         101253.3   \n",
       "58   580.0              300.0         101253.3   \n",
       "59   590.0              300.0         101253.3   \n",
       "60   600.0              300.0         101253.3   \n",
       "\n",
       "    ENV.air number density.mol m-3      CONC.A.mol m-3     CONC.B.mol m-3  \\\n",
       "0                40.59324282282551    4.76922334303805  4.994590692532435   \n",
       "1                40.59324282282551   4.549098259155805    4.9790291487427   \n",
       "2                40.59324282282551   4.339133121467923  4.954264051821206   \n",
       "3                40.59324282282551   4.138858994290876  4.921178138841699   \n",
       "4                40.59324282282551  3.9478285858230064 4.8805922672973425   \n",
       "..                             ...                 ...                ...   \n",
       "56               40.59324282282551  0.3382177774161425 1.2492099030181651   \n",
       "57               40.59324282282551 0.32260722381670276 1.2067968547753385   \n",
       "58               40.59324282282551  0.3077171804918639 1.1656377730117506   \n",
       "59               40.59324282282551 0.29351439205113017 1.1257072563871726   \n",
       "60               40.59324282282551 0.27996713801757456  1.086979577032808   \n",
       "\n",
       "       CONC.C.mol m-3  \n",
       "0   5.236185964429513  \n",
       "1   5.471872592101488  \n",
       "2   5.706602826710863  \n",
       "3   5.939962866867419  \n",
       "4   6.171579146879646  \n",
       "..                ...  \n",
       "56 13.412572319565694  \n",
       "57 13.470595921407961  \n",
       "58 13.526645046496386  \n",
       "59 13.580778351561698  \n",
       "60 13.633053284949622  \n",
       "\n",
       "[61 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time.s</th>\n",
       "      <th>ENV.temperature.K</th>\n",
       "      <th>ENV.pressure.Pa</th>\n",
       "      <th>ENV.air number density.mol m-3</th>\n",
       "      <th>CONC.A.mol m-3</th>\n",
       "      <th>CONC.B.mol m-3</th>\n",
       "      <th>CONC.C.mol m-3</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>18.72357316215889</td>\n",
       "      <td>4.04334124172365</td>\n",
       "      <td>7.233085596117464</td>\n",
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       "      <td>100.0</td>\n",
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       "      <td>16.40991021189361</td>\n",
       "      <td>5.708149451042962</td>\n",
       "      <td>7.881940337063443</td>\n",
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       "      <td>6.356999871717215</td>\n",
       "      <td>8.28039240644039</td>\n",
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       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
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       "      <td>8.718075461633836</td>\n",
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       "      <td>0.4660987829533735</td>\n",
       "      <td>1.822029649269642</td>\n",
       "      <td>27.711871567777017</td>\n",
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       "      <td>570.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>0.4363517331710352</td>\n",
       "      <td>1.7345223337021152</td>\n",
       "      <td>27.829125933126896</td>\n",
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       "    <tr>\n",
       "      <th>58</th>\n",
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       "      <td>0.4085031800231355</td>\n",
       "      <td>1.6507632617514378</td>\n",
       "      <td>27.940733558225475</td>\n",
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       "      <td>1.4940021374208303</td>\n",
       "      <td>28.147973224472178</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    time.s  ENV.temperature.K  ENV.pressure.Pa  \\\n",
       "0      0.0              100.0          11253.3   \n",
       "1     10.0              100.0          11253.3   \n",
       "2     20.0              100.0          11253.3   \n",
       "3     30.0              100.0          11253.3   \n",
       "4     40.0              100.0          11253.3   \n",
       "..     ...                ...              ...   \n",
       "56   560.0              100.0          11253.3   \n",
       "57   570.0              100.0          11253.3   \n",
       "58   580.0              100.0          11253.3   \n",
       "59   590.0              100.0          11253.3   \n",
       "60   600.0              100.0          11253.3   \n",
       "\n",
       "    ENV.air number density.mol m-3      CONC.A.mol m-3     CONC.B.mol m-3  \\\n",
       "0                13.53460893002309   18.72357316215889   4.04334124172365   \n",
       "1                13.53460893002309  17.528609597935834  4.941288116208338   \n",
       "2                13.53460893002309   16.40991021189361  5.708149451042962   \n",
       "3                13.53460893002309  15.362607721842412  6.356999871717215   \n",
       "4                13.53460893002309  14.382145482073177  6.899779056293003   \n",
       "..                             ...                 ...                ...   \n",
       "56               13.53460893002309  0.4660987829533735  1.822029649269642   \n",
       "57               13.53460893002309  0.4363517331710352 1.7345223337021152   \n",
       "58               13.53460893002309  0.4085031800231355 1.6507632617514378   \n",
       "59               13.53460893002309 0.38243195890688714 1.5706304291624618   \n",
       "60               13.53460893002309  0.3580246381070421 1.4940021374208303   \n",
       "\n",
       "       CONC.C.mol m-3  \n",
       "0   7.233085596117464  \n",
       "1   7.530102285855836  \n",
       "2   7.881940337063443  \n",
       "3    8.28039240644039  \n",
       "4   8.718075461633836  \n",
       "..                ...  \n",
       "56 27.711871567777017  \n",
       "57 27.829125933126896  \n",
       "58 27.940733558225475  \n",
       "59 28.046937611930705  \n",
       "60 28.147973224472178  \n",
       "\n",
       "[61 rows x 7 columns]"
      ]
     },
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_0)\n",
    "display(df_1)\n",
    "df_0.plot(x='time.s', y=['CONC.A.mol m-3', 'CONC.B.mol m-3', 'CONC.C.mol m-3'], title='Concentration over time (grid cell 0)', ylabel='Concentration (mol m-3)', xlabel='Time (s)')\n",
    "df_1.plot(x='time.s', y=['CONC.A.mol m-3', 'CONC.B.mol m-3', 'CONC.C.mol m-3'], title='Concentration over time (grid cell 1)', ylabel='Concentration (mol m-3)', xlabel='Time (s)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dbb6055",
   "metadata": {},
   "source": [
    "## 10. Going Full Circle\n",
    "\n",
    "We've now seen the two ways to configure `musica` in practice: using configuration files, and in-code using the `musica` API. What if we want to create a configuration code from our in-code mechanism?\n",
    "\n",
    "Let's give it a try!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8d8a0f5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mechanism.export(\"my_cool_musica_mechanism.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9553e185",
   "metadata": {},
   "source": [
    "Let's take a look at what it looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4fb00e50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"name\": \"musica_micm_example\",\n",
      "  \"reactions\": [\n",
      "    {\n",
      "      \"type\": \"ARRHENIUS\",\n",
      "      \"name\": \"A_to_B\",\n",
      "      \"A\": 0.004,\n",
      "      \"B\": 0.0,\n",
      "      \"C\": 50.0,\n",
      "      \"D\": 300.0,\n",
      "      \"E\": 0.0,\n",
      "      \"reactants\": [\n",
      "        {\n",
      "          \"species name\": \"A\",\n",
      "          \"coefficient\": 1.0\n",
      "        }\n",
      "      ],\n",
      "      \"products\": [\n",
      "        {\n",
      "          \"species name\": \"B\",\n",
      "          \"coefficient\": 1.0\n",
      "        }\n",
      "      ],\n",
      "      \"gas phase\": \"gas\"\n",
      "    },\n",
      "    {\n",
      "      \"type\": \"ARRHENIUS\",\n",
      "      \"name\": \"B_to_C\",\n",
      "      \"A\": 0.004,\n",
      "      \"B\": 0.0,\n",
      "      \"C\": 50.0,\n",
      "      \"D\": 300.0,\n",
      "      \"E\": 0.0,\n",
      "      \"reactants\": [\n",
      "        {\n",
      "          \"species name\": \"B\",\n",
      "          \"coefficient\": 1.0\n",
      "        }\n",
      "      ],\n",
      "      \"products\": [\n",
      "        {\n",
      "          \"species name\": \"C\",\n",
      "          \"coefficient\": 1.0\n",
      "        }\n",
      "      ],\n",
      "      \"gas phase\": \"gas\"\n",
      "    }\n",
      "  ],\n",
      "  \"species\": [\n",
      "    {\n",
      "      \"name\": \"A\",\n",
      "      \"is third body\": false\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"B\",\n",
      "      \"is third body\": false\n",
      "    },\n",
      "    {\n",
      "      \"name\": \"C\",\n",
      "      \"is third body\": false\n",
      "    }\n",
      "  ],\n",
      "  \"phases\": [\n",
      "    {\n",
      "      \"name\": \"gas\",\n",
      "      \"species\": [\n",
      "        {\n",
      "          \"name\": \"A\"\n",
      "        },\n",
      "        {\n",
      "          \"name\": \"B\"\n",
      "        },\n",
      "        {\n",
      "          \"name\": \"C\"\n",
      "        }\n",
      "      ]\n",
      "    }\n",
      "  ],\n",
      "  \"version\": \"0.0.0\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open(\"my_cool_musica_mechanism.json\", \"r\") as f:\n",
    "    config = json.load(f)\n",
    "\n",
    "print(json.dumps(config, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63507e4d",
   "metadata": {},
   "source": [
    "Why is this useful? The `musica` library than underpins the `musica` python package is also used in large 3D models. For those applications, text-based configuration files allow run-time specification of the chemistry system without the need to modify source code and recompile the models. So, you can develop and test your mechanism in Python and when you're ready, turn it into a configuration file that you can use in a model like CheMPAS-A or CAM-SIMA!"
   ]
  }
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