{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7030540f",
   "metadata": {},
   "source": [
    "# Multiple Grid Cells in MUSICA\n",
    "In MUSICA, the <i>State</i> object that is present within the <i>Solver</i> object has an attribute called <i>number_of_grid_cells</i>.</br>\n",
    "This attribute dictates the number of independent sets of well-mixed air masses whose chemical system will be solved by the same numerical solver.</br>\n",
    "This tutorial will go over a simple example of solving a multi-grid-cell chemical system in MUSICA."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d9a125",
   "metadata": {},
   "source": [
    "## MUSICA: Before Getting Started\n",
    "\n",
    "It is heavily recommended to go through the tutorials in the MusicBox repository first before going through the MUSICA ones since the former explains the workflow used in the latter.</br>\n",
    "However, if you are only interested in MUSICA, here's how you can set up a virtual environment for it:\n",
    "\n",
    "```\n",
    "conda create --name musica python=3.9\n",
    "conda activate musica\n",
    "pip install musica\n",
    "conda install ipykernel scikit-learn seaborn scipy dask\n",
    "```"
   ]
  },
  {
   "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",
    "This code snippet is a MUSICA version of setting up a system, which has an identical workflow to MusicBox.</br>\n",
    "For an explanation of this code in MusicBox, please refer to the Basic Workflow tutorial in the MusicBox repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b1084ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = mc.Species(name=\"A\") # Create each of the species with their respective names\n",
    "B = mc.Species(name=\"B\")\n",
    "C = mc.Species(name=\"C\")\n",
    "species = [A, B, C] # Bundle the species into a list\n",
    "gas = mc.Phase(name=\"gas\", species=species) # Create a gas phase object containing the species\n",
    "\n",
    "r1 = mc.Arrhenius( # Create the reactions with their name, constants, reactants, products, and phase\n",
    "    name=\"A_to_B\",\n",
    "    A=4.0e-3,  # Pre-exponential factor\n",
    "    C=50,      # Activation energy (units assumed to be K)\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",
    ")\n",
    "\n",
    "mechanism = mc.Mechanism( # Define the mechanism which contains a name, the species, the phases, and reactions\n",
    "    name=\"musica_micm_example\",\n",
    "    species=species,\n",
    "    phases=[gas],\n",
    "    reactions=[r1, r2]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7861e510",
   "metadata": {},
   "source": [
    "## 3. Creating the Solver\n",
    "Something more unique to MUSICA is that you have to manually define the solver.</br>\n",
    "A solver integrates the chemical reactions that determine how atmospheric chemistry proceeds over time.</br>\n",
    "There are a handful of solvers available, but Rosenbrock Standard Order will be used here.</br>\n",
    "For more information on the types of solvers available, go [here](https://ncar.github.io/micm/user_guide/solver_configurations.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
    "For MUSICA, the state must be created manually as well, with the number of grid cells being passed into the <i>create_state()</i> function.</br>\n",
    "The state represents everything pertinent to solve chemistry. By solving, we meaning determining the concentrations at the next time step.</br>\n",
    "Feel free to change the <i>num_grid_cells</i> value to experiment yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d91a4be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_grid_cells = 2\n",
    "state = solver.create_state(num_grid_cells)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54f3e880",
   "metadata": {},
   "source": [
    "## 5. Populating the Grid Cells\n",
    "5 dimensions will be used to populate the data for each of the air masses:\n",
    "* temperature (Kelvin),\n",
    "* pressure (Pascals), and\n",
    "* the concentrations of each of the species (mol/m<sup>3</sup>).\n",
    "\n",
    "The NumPy array has the <i>reshape()</i> function called on it so that it can be split up in the next step.</br>\n",
    "The two arguments specify the number of rows and columns of the array, where -1 means any number of rows.</br>\n",
    "Do note that the ordering inside the array matters and cannot be changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1dadda9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   300. , 101253.3,      5. ,      5. ,      5. ],\n",
       "       [   100. ,  11253.3,     20. ,      3. ,      7. ]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "box_model_values = np.array([[300, 101253.3, 5, 5, 5], [100, 11253.3, 20, 3, 7]])\n",
    "box_model_values = box_model_values.reshape(-1, 5)\n",
    "display(box_model_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d6e59c",
   "metadata": {},
   "source": [
    "## 6. Splitting up the Array Output\n",
    "Next, the values from the <i>box_model_values</i> array are taken and populated into variables so that they can be passed into the solver's state.</br>\n",
    "The sample is organized into 5 columns that represent the 5 variables.</br>\n",
    "The sample's rows each represent a distinct grid cell.</br>\n",
    "These columns are populated into their respective variables and then passed into the solver's state.</br>\n",
    "Do note that <i>set_conditions()</i> and <i>set_concentrations()</i> must be called with their respective arguments for the solver to successfully run.</br>\n",
    "The concentrations must go through the extra step of being bundled into a dictionary since MUSICA explicitly requires a dictionary argument in the <i>set_concentrations</i> function.</br>\n",
    "Lastly, an empty array is initialized to represent the solved concentration array at every time step, as well as the time step length, simulation length, and the current time step (all in seconds)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e0a74a68",
   "metadata": {},
   "outputs": [],
   "source": [
    "temperatures = box_model_values[:, 0]\n",
    "pressures = box_model_values[:, 1]\n",
    "concentrations = {\n",
    "    \"A\": [],\n",
    "    \"B\": [],\n",
    "    \"C\": []\n",
    "}\n",
    "concentrations[\"A\"] = box_model_values[:, 2]\n",
    "concentrations[\"B\"] = box_model_values[:, 3]\n",
    "concentrations[\"C\"] = box_model_values[:, 4]\n",
    "\n",
    "state.set_conditions(temperatures, pressures)\n",
    "state.set_concentrations(concentrations)\n",
    "concentrations_solved = []\n",
    "time_step_length = 1\n",
    "sim_length = 60\n",
    "curr_time = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1238f02b",
   "metadata": {},
   "source": [
    "## 7. Running the Solver\n",
    "This code solves the system at every specified time step and the solved concentrations are appended to the array.</br>\n",
    "The first step will always be the initial conditions since at time = 0 seconds the reaction has not begun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "76b21308",
   "metadata": {},
   "outputs": [],
   "source": [
    "while curr_time <= sim_length:\n",
    "    solver.solve(state, curr_time)\n",
    "    concentrations_solved.append(state.get_concentrations())\n",
    "    curr_time += time_step_length"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2185c0",
   "metadata": {},
   "source": [
    "## 8. Preparing the Results (Advanced; Optional Read)\n",
    "Here, a new array is made that grabs only the first value (grid cell) for each key (species) at every time step.</br>\n",
    "The time step index is divided by the time_step_length to account for time step lengths that are greater than one for proper array indexing.</br>\n",
    "That new array is then passed into a Pandas DataFrame with the concentration columns renamed.</br>\n",
    "Next, a time column is created from a range that represents the elapsed time at each time step.</br>\n",
    "In this simulation, the temperature, pressure, and air density are all constant, so numpy's <i>repeat()</i> function is used to repeat their respective values for every time step.</br>\n",
    "Once all the attributes are added to the DataFrame, their order is changed to follow a more logical flow.</br>\n",
    "Due to the complexity of this code cell, it has been bundled into a function that takes in an argument for the grid cell you wish to convert to a DataFrame.</br>\n",
    "The function is then called twice, one for each grid cell index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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_length):\n",
    "        concentrations_solved_pd.append({species: concentration[cell_index] for species, concentration in concentrations_solved[int(i/time_step_length)].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_length)))\n",
    "    df['ENV.temperature.K'] = np.repeat(temperatures[cell_index], sim_length/time_step_length + 1.0)\n",
    "    df['ENV.pressure.Pa'] = np.repeat(pressures[cell_index], sim_length/time_step_length + 1.0)\n",
    "    df['ENV.air number density.mol m-3'] = np.repeat(state.get_conditions()['air_density'][cell_index], sim_length/time_step_length + 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 concentrations_solved_pd, df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e9dda1ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "concentrations_solved_pd_0, df_0 = convert_results_single_cell(0)\n",
    "concentrations_solved_pd_1, df_1 = convert_results_single_cell(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ada2398b",
   "metadata": {},
   "source": [
    "## 9. Viewing the Results\n",
    "With the DataFrames being fully prepared now, they are displayed and plotted to show the evolution of both of the systems over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d49f1b08",
   "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>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>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>4.976428528398802</td>\n",
       "      <td>4.999944351093875</td>\n",
       "      <td>5.0236271205073235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>4.929618429433882</td>\n",
       "      <td>4.999502304184354</td>\n",
       "      <td>5.070879266381759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>4.860227571898497</td>\n",
       "      <td>4.998027906909858</td>\n",
       "      <td>5.141744521191641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>4.769223461910271</td>\n",
       "      <td>4.994590343685047</td>\n",
       "      <td>5.236186194404681</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>56.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>0.002651107160467822</td>\n",
       "      <td>0.022649502677897896</td>\n",
       "      <td>14.974699390161621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>0.0020250213465128905</td>\n",
       "      <td>0.01784640325737679</td>\n",
       "      <td>14.980128575396096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>0.0015394935543717359</td>\n",
       "      <td>0.01398971727457048</td>\n",
       "      <td>14.984470789171041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>0.0011648552812954611</td>\n",
       "      <td>0.010910311593626484</td>\n",
       "      <td>14.987924833125062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>101253.3</td>\n",
       "      <td>40.59324282282551</td>\n",
       "      <td>0.0008772265708207769</td>\n",
       "      <td>0.008465235540415044</td>\n",
       "      <td>14.990657537888747</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      1.0              300.0         101253.3   \n",
       "2      2.0              300.0         101253.3   \n",
       "3      3.0              300.0         101253.3   \n",
       "4      4.0              300.0         101253.3   \n",
       "..     ...                ...              ...   \n",
       "56    56.0              300.0         101253.3   \n",
       "57    57.0              300.0         101253.3   \n",
       "58    58.0              300.0         101253.3   \n",
       "59    59.0              300.0         101253.3   \n",
       "60    60.0              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                   5.0                  5.0   \n",
       "1                40.59324282282551     4.976428528398802    4.999944351093875   \n",
       "2                40.59324282282551     4.929618429433882    4.999502304184354   \n",
       "3                40.59324282282551     4.860227571898497    4.998027906909858   \n",
       "4                40.59324282282551     4.769223461910271    4.994590343685047   \n",
       "..                             ...                   ...                  ...   \n",
       "56               40.59324282282551  0.002651107160467822 0.022649502677897896   \n",
       "57               40.59324282282551 0.0020250213465128905  0.01784640325737679   \n",
       "58               40.59324282282551 0.0015394935543717359  0.01398971727457048   \n",
       "59               40.59324282282551 0.0011648552812954611 0.010910311593626484   \n",
       "60               40.59324282282551 0.0008772265708207769 0.008465235540415044   \n",
       "\n",
       "       CONC.C.mol m-3  \n",
       "0                 5.0  \n",
       "1  5.0236271205073235  \n",
       "2   5.070879266381759  \n",
       "3   5.141744521191641  \n",
       "4   5.236186194404681  \n",
       "..                ...  \n",
       "56 14.974699390161621  \n",
       "57 14.980128575396096  \n",
       "58 14.984470789171041  \n",
       "59 14.987924833125062  \n",
       "60 14.990657537888747  \n",
       "\n",
       "[61 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>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>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>19.868536268722355</td>\n",
       "      <td>3.1113111545426153</td>\n",
       "      <td>7.020152576735027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>19.608195525999147</td>\n",
       "      <td>3.329170747441501</td>\n",
       "      <td>7.062633726559351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>19.22406658832665</td>\n",
       "      <td>3.6442931108015326</td>\n",
       "      <td>7.131640300871819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>18.723574920030497</td>\n",
       "      <td>4.043334633782276</td>\n",
       "      <td>7.233090446187235</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>56.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>0.0005360881626445901</td>\n",
       "      <td>0.005726115410867138</td>\n",
       "      <td>29.993737796426494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>0.00036805009976445557</td>\n",
       "      <td>0.0040698479741160445</td>\n",
       "      <td>29.995562101926122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>0.00025101920969299854</td>\n",
       "      <td>0.002871932602189201</td>\n",
       "      <td>29.996877048188125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>0.00017007318795390593</td>\n",
       "      <td>0.0020121301960677983</td>\n",
       "      <td>29.997817796615987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>11253.3</td>\n",
       "      <td>13.53460893002309</td>\n",
       "      <td>0.0001144703965387078</td>\n",
       "      <td>0.0013996875381660917</td>\n",
       "      <td>29.998485842065303</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              100.0          11253.3   \n",
       "1      1.0              100.0          11253.3   \n",
       "2      2.0              100.0          11253.3   \n",
       "3      3.0              100.0          11253.3   \n",
       "4      4.0              100.0          11253.3   \n",
       "..     ...                ...              ...   \n",
       "56    56.0              100.0          11253.3   \n",
       "57    57.0              100.0          11253.3   \n",
       "58    58.0              100.0          11253.3   \n",
       "59    59.0              100.0          11253.3   \n",
       "60    60.0              100.0          11253.3   \n",
       "\n",
       "    ENV.air number density.mol m-3         CONC.A.mol m-3  \\\n",
       "0                13.53460893002309                   20.0   \n",
       "1                13.53460893002309     19.868536268722355   \n",
       "2                13.53460893002309     19.608195525999147   \n",
       "3                13.53460893002309      19.22406658832665   \n",
       "4                13.53460893002309     18.723574920030497   \n",
       "..                             ...                    ...   \n",
       "56               13.53460893002309  0.0005360881626445901   \n",
       "57               13.53460893002309 0.00036805009976445557   \n",
       "58               13.53460893002309 0.00025101920969299854   \n",
       "59               13.53460893002309 0.00017007318795390593   \n",
       "60               13.53460893002309  0.0001144703965387078   \n",
       "\n",
       "          CONC.B.mol m-3     CONC.C.mol m-3  \n",
       "0                    3.0                7.0  \n",
       "1     3.1113111545426153  7.020152576735027  \n",
       "2      3.329170747441501  7.062633726559351  \n",
       "3     3.6442931108015326  7.131640300871819  \n",
       "4      4.043334633782276  7.233090446187235  \n",
       "..                   ...                ...  \n",
       "56  0.005726115410867138 29.993737796426494  \n",
       "57 0.0040698479741160445 29.995562101926122  \n",
       "58  0.002871932602189201 29.996877048188125  \n",
       "59 0.0020121301960677983 29.997817796615987  \n",
       "60 0.0013996875381660917 29.998485842065303  \n",
       "\n",
       "[61 rows x 7 columns]"
      ]
     },
     "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"
    },
    {
     "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', 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', ylabel='Concentration (mol m-3)', xlabel='Time (s)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "musicbox",
   "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": "3.11.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}