{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cf5a3b29",
   "metadata": {},
   "source": [
    "# User Defined Reactions in MUSICA\n",
    "There is an additional type of reaction in MUSICA called User Defined reactions, which are a more flexible type of reaction that supports defining per-grid-cell reaction rates.</br>\n",
    "This differs from the traditional reaction types where the reaction rates are calculated.</br>\n",
    "Defining the reaction rate can be useful in cases where you have observations and need to find the rate that most closely matches those observations.</br>\n",
    "There are multiple classes of User Defined reactions, with there being the default UserDefined class as well as the Emission and FirstOrderLoss classes also being User Defined under the hood."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63424343",
   "metadata": {},
   "source": [
    "## 1. Importing Libraries\n",
    "Below is a list of the required libraries for this tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f81fdb7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import musica\n",
    "import musica.mechanism_configuration as mc\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import qmc\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\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": "98b68587",
   "metadata": {},
   "source": [
    "## 2. Redefining the Original Species and Reactions\n",
    "As with the previous two tutorials, this is simply defining the three species and two reactions that have been used already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50d98bb3",
   "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,  # 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",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d06d4447",
   "metadata": {},
   "source": [
    "## 3. Defining User Defined Reactions\n",
    "This code cell defines three new reactions: one User Defined, one Emission, and one First Order Loss.</br>\n",
    "It then creates a new mechanism containing both the old reactions as well as the three new reactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6781a5a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "r3 = mc.UserDefined(\n",
    "    name=\"complex_rxn\",\n",
    "    scaling_factor=1.0,\n",
    "    reactants=[A, (2, B)],\n",
    "    products=[A, (2, C), B],\n",
    "    gas_phase=gas\n",
    ")\n",
    "\n",
    "r4 = mc.Emission(\n",
    "    name=\"B_emission\",\n",
    "    scaling_factor=1.0,\n",
    "    products=[B],\n",
    "    gas_phase=gas\n",
    ")\n",
    "\n",
    "r5 = mc.FirstOrderLoss(\n",
    "    name=\"C_loss\",\n",
    "    scaling_factor=1.0,\n",
    "    reactants=[C],\n",
    "    gas_phase=gas\n",
    ")\n",
    "\n",
    "mechanism = mc.Mechanism(\n",
    "    name=\"musica_micm_example\",\n",
    "    species=species,\n",
    "    phases=[gas],\n",
    "    reactions=[r1, r2, r3, r4, r5]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da99a490",
   "metadata": {},
   "source": [
    "## 4. Creating the Solver, State, and Latin Hypercube Sampler\n",
    "This code is a rehash that includes code similar to the previous Hypercube tutorial, just with the LHS being expanded to 8 dimensions to account for the three new User Defined reactions as the last three dimensions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3947b2c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "solver = musica.MICM(mechanism = mechanism, solver_type = musica.SolverType.rosenbrock_standard_order)\n",
    "\n",
    "num_grid_cells = 100\n",
    "state = solver.create_state(num_grid_cells)\n",
    "\n",
    "ndim = 8\n",
    "nsamples = num_grid_cells\n",
    "\n",
    "# Create a Latin Hypercube sampler in the unit hypercube\n",
    "sampler = qmc.LatinHypercube(d=ndim)\n",
    "\n",
    "# Generate samples\n",
    "sample = sampler.random(n=nsamples)\n",
    "\n",
    "# Define bounds for each dimension\n",
    "l_bounds = [275, 100753.3, 0, 0, 0, 0, 0, 0] # Lower bounds\n",
    "u_bounds = [325, 101753.3, 20, 10, 20, 1, 0.5, 0.5] # Upper bounds\n",
    "\n",
    "# Scale the samples to the defined bounds\n",
    "sample_scaled = qmc.scale(sample, l_bounds, u_bounds)\n",
    "\n",
    "temperatures = sample_scaled[:, 0]\n",
    "pressures = sample_scaled[:, 1]\n",
    "concentrations = {\n",
    "    \"A\": [],\n",
    "    \"B\": [],\n",
    "    \"C\": []\n",
    "}\n",
    "concentrations[\"A\"] = sample_scaled[:, 2]\n",
    "concentrations[\"B\"] = sample_scaled[:, 3]\n",
    "concentrations[\"C\"] = sample_scaled[:, 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af454f9",
   "metadata": {},
   "source": [
    "## 5. Creating and Setting the User Defined Parameters\n",
    "Next, each of the new reactions is given a dictionary with a name as the key and their respective LHS outputs as the value.</br>\n",
    "For these reactions, the name must match the name given to the reactions above, with an added prefix, being:\n",
    "* \"USER.\" for User Defined reactions,\n",
    "* \"EMIS.\" for Emission reactions, and\n",
    "* \"LOSS.\" for First Order Loss reactions.</br>\n",
    "\n",
    "After that, the state's <i>set_user_defined_rate_parameters()</i> function is called to add all of the reaction rates to the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49b15078",
   "metadata": {},
   "outputs": [],
   "source": [
    "complex_rates = {\"USER.complex_rxn\": sample_scaled[:, 5]}\n",
    "emission_rates = {\"EMIS.B_emission\": sample_scaled[:, 6]}\n",
    "loss_rates = {\"LOSS.C_loss\": sample_scaled[:, 7]}\n",
    "\n",
    "state.set_user_defined_rate_parameters(complex_rates)\n",
    "state.set_user_defined_rate_parameters(emission_rates)\n",
    "state.set_user_defined_rate_parameters(loss_rates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d746c84f",
   "metadata": {},
   "source": [
    "## 6. Setting the Conditions, Running the Solver, and Visualizing the Data\n",
    "As with step 4, this is a copy of some previous steps from the Hypercube tutorial, with the only change being the simulation length being extended to 100 seconds.</br>\n",
    "Note the extreme difference in the concentration curves after the three new reactions were added; B is now the dominant species instead of C after approximately 50 seconds of the simulation taking place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dda25f0a",
   "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</td>\n",
       "      <td>309.7352766001884</td>\n",
       "      <td>101389.99779492112</td>\n",
       "      <td>39.37043899688805</td>\n",
       "      <td>2.893750993982109</td>\n",
       "      <td>2.673213216234265</td>\n",
       "      <td>9.433830407714845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>309.7352766001884</td>\n",
       "      <td>101389.99779492112</td>\n",
       "      <td>39.37043899688805</td>\n",
       "      <td>7.053727382338282</td>\n",
       "      <td>2.260055721965611</td>\n",
       "      <td>4.4571713767278744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>309.7352766001884</td>\n",
       "      <td>101389.99779492112</td>\n",
       "      <td>39.37043899688805</td>\n",
       "      <td>10.711091077403818</td>\n",
       "      <td>6.697825729904961</td>\n",
       "      <td>10.843190900041073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>309.7352766001884</td>\n",
       "      <td>101389.99779492112</td>\n",
       "      <td>39.37043899688805</td>\n",
       "      <td>15.546051530784109</td>\n",
       "      <td>7.64337289065524</td>\n",
       "      <td>1.209212898549111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>309.7352766001884</td>\n",
       "      <td>101389.99779492112</td>\n",
       "      <td>39.37043899688805</td>\n",
       "      <td>14.515576668606005</td>\n",
       "      <td>6.473953665388967</td>\n",
       "      <td>9.338659341590636</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>10095</th>\n",
       "      <td>100</td>\n",
       "      <td>309.7352766001884</td>\n",
       "      <td>101389.99779492112</td>\n",
       "      <td>39.37043899688805</td>\n",
       "      <td>5.934136681910782e-10</td>\n",
       "      <td>78.38313350457263</td>\n",
       "      <td>0.9507647383147676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10096</th>\n",
       "      <td>100</td>\n",
       "      <td>309.7352766001884</td>\n",
       "      <td>101389.99779492112</td>\n",
       "      <td>39.37043899688805</td>\n",
       "      <td>3.5536302092439606e-10</td>\n",
       "      <td>93.3521115434026</td>\n",
       "      <td>0.9755165898633137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10097</th>\n",
       "      <td>100</td>\n",
       "      <td>309.7352766001884</td>\n",
       "      <td>101389.99779492112</td>\n",
       "      <td>39.37043899688805</td>\n",
       "      <td>6.772157344069222e-10</td>\n",
       "      <td>23.47690407300151</td>\n",
       "      <td>0.6340441771972303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10098</th>\n",
       "      <td>100</td>\n",
       "      <td>309.7352766001884</td>\n",
       "      <td>101389.99779492112</td>\n",
       "      <td>39.37043899688805</td>\n",
       "      <td>4.7929670861011336e-11</td>\n",
       "      <td>37.75925021640928</td>\n",
       "      <td>0.849375775534841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10099</th>\n",
       "      <td>100</td>\n",
       "      <td>309.7352766001884</td>\n",
       "      <td>101389.99779492112</td>\n",
       "      <td>39.37043899688805</td>\n",
       "      <td>3.5291165084174213e-10</td>\n",
       "      <td>101.85877228753668</td>\n",
       "      <td>3.3624463280973975</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time.s  ENV.temperature.K    ENV.pressure.Pa  \\\n",
       "0           0  309.7352766001884 101389.99779492112   \n",
       "1           0  309.7352766001884 101389.99779492112   \n",
       "2           0  309.7352766001884 101389.99779492112   \n",
       "3           0  309.7352766001884 101389.99779492112   \n",
       "4           0  309.7352766001884 101389.99779492112   \n",
       "...       ...                ...                ...   \n",
       "10095     100  309.7352766001884 101389.99779492112   \n",
       "10096     100  309.7352766001884 101389.99779492112   \n",
       "10097     100  309.7352766001884 101389.99779492112   \n",
       "10098     100  309.7352766001884 101389.99779492112   \n",
       "10099     100  309.7352766001884 101389.99779492112   \n",
       "\n",
       "       ENV.air number density.mol m-3         CONC.A.mol m-3  \\\n",
       "0                   39.37043899688805      2.893750993982109   \n",
       "1                   39.37043899688805      7.053727382338282   \n",
       "2                   39.37043899688805     10.711091077403818   \n",
       "3                   39.37043899688805     15.546051530784109   \n",
       "4                   39.37043899688805     14.515576668606005   \n",
       "...                               ...                    ...   \n",
       "10095               39.37043899688805  5.934136681910782e-10   \n",
       "10096               39.37043899688805 3.5536302092439606e-10   \n",
       "10097               39.37043899688805  6.772157344069222e-10   \n",
       "10098               39.37043899688805 4.7929670861011336e-11   \n",
       "10099               39.37043899688805 3.5291165084174213e-10   \n",
       "\n",
       "          CONC.B.mol m-3     CONC.C.mol m-3  \n",
       "0      2.673213216234265  9.433830407714845  \n",
       "1      2.260055721965611 4.4571713767278744  \n",
       "2      6.697825729904961 10.843190900041073  \n",
       "3       7.64337289065524  1.209212898549111  \n",
       "4      6.473953665388967  9.338659341590636  \n",
       "...                  ...                ...  \n",
       "10095  78.38313350457263 0.9507647383147676  \n",
       "10096   93.3521115434026 0.9755165898633137  \n",
       "10097  23.47690407300151 0.6340441771972303  \n",
       "10098  37.75925021640928  0.849375775534841  \n",
       "10099 101.85877228753668 3.3624463280973975  \n",
       "\n",
       "[10100 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": [
    "state.set_conditions(temperatures, pressures)\n",
    "state.set_concentrations(concentrations)\n",
    "concentrations_solved = []\n",
    "time_step_length = 1\n",
    "sim_length = 100\n",
    "curr_time = 0\n",
    "\n",
    "while curr_time <= sim_length:\n",
    "    solver.solve(state, curr_time)\n",
    "    concentrations_solved.append(state.get_concentrations())\n",
    "    curr_time += time_step_length\n",
    "\n",
    "concentrations_solved_expanded = []\n",
    "time = []\n",
    "for i in range(0, sim_length + 1, time_step_length):\n",
    "    for j in range(0, num_grid_cells):\n",
    "        concentrations_solved_expanded.append({key: value[j] for key, value in concentrations_solved[int(i/time_step_length)].items()})\n",
    "        time.append(i)\n",
    "df_expanded = pd.DataFrame(concentrations_solved_expanded)\n",
    "df_expanded = df_expanded.rename(columns = {'A' : 'CONC.A.mol m-3', 'B' : 'CONC.B.mol m-3', 'C' : 'CONC.C.mol m-3'})\n",
    "df_expanded['time.s'] = time\n",
    "df_expanded['ENV.temperature.K'] = np.repeat(temperatures[0], (sim_length/time_step_length + 1.0) * num_grid_cells)\n",
    "df_expanded['ENV.pressure.Pa'] = np.repeat(pressures[0], (sim_length/time_step_length + 1.0) * num_grid_cells)\n",
    "df_expanded['ENV.air number density.mol m-3'] = np.repeat(state.get_conditions()['air_density'][0], (sim_length/time_step_length + 1.0) * num_grid_cells)\n",
    "df_expanded = df_expanded[['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",
    "display(df_expanded)\n",
    "\n",
    "sns.lineplot(data=df_expanded, x='time.s', y='CONC.A.mol m-3', errorbar=('ci', 95), err_kws={'alpha' : 0.4}, label='CONC.A.mol m-3')\n",
    "sns.lineplot(data=df_expanded, x='time.s', y='CONC.B.mol m-3', errorbar=('ci', 95), err_kws={'alpha' : 0.4}, label='CONC.B.mol m-3')\n",
    "sns.lineplot(data=df_expanded, x='time.s', y='CONC.C.mol m-3', errorbar=('ci', 95), err_kws={'alpha' : 0.4}, label='CONC.C.mol m-3')\n",
    "plt.title('Average concentration with CI over time')\n",
    "plt.ylabel('Concentration (mol m-3)')\n",
    "plt.xlabel('Time (s)')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "min_y = []\n",
    "max_y = []\n",
    "for i in range(0, sim_length + 1, time_step_length):\n",
    "        min_y.append({key: np.min(value) for key, value in concentrations_solved[int(i/time_step_length)].items()})\n",
    "        max_y.append({key: np.max(value) for key, value in concentrations_solved[int(i/time_step_length)].items()})\n",
    "time_x = list(map(float, range(0, sim_length + 1, time_step_length)))\n",
    "\n",
    "plt.fill_between(time_x, [y['A'] for y in min_y], [y['A'] for y in max_y], alpha = 0.4, label='CONC.A.mol m-3')\n",
    "plt.fill_between(time_x, [y['B'] for y in min_y], [y['B'] for y in max_y], alpha = 0.4, label='CONC.B.mol m-3')\n",
    "plt.fill_between(time_x, [y['C'] for y in min_y], [y['C'] for y in max_y], alpha = 0.4, label='CONC.C.mol m-3')\n",
    "plt.title('Concentration range over time')\n",
    "plt.ylabel('Concentration (mol m-3)')\n",
    "plt.xlabel('Time (s)')\n",
    "plt.legend()\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
}