{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f8178839",
   "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": "cf5a3b29",
   "metadata": {},
   "source": [
    "# User-Defined Reactions in `musica`\n",
    "\n",
    "So far, we've created chemical mechanisms using only Arrhenius reactions. Here we'll introduce a few more, including the powerful \"user-defined\" reaction. These reaction types give you control over how rate constants are calculated.\n",
    "\n",
    "We'll also throw in an `Emission` and `FirstOrderLoss`, which actually use the `UserDefined` type 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": 8,
   "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. Base Chemical System\n",
    "Let's start with the chemical system we've been using in the tutorials to this point, with three species and two reactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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,\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": "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": 10,
   "id": "6781a5a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "r3 = mc.UserDefined(\n",
    "    name=\"complex_rxn\",\n",
    "    scaling_factor=1.0,\n",
    "    reactants=[A, (B, 2)],\n",
    "    products=[A, (C, 2), 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": "fc39dfd3",
   "metadata": {},
   "source": [
    "Notice that we don't specify rate constants for the User-Defined or First-Order Loss reactions, and we don't specify emission rates. This is because these values can change over time, and unlike standard rate constant types (like Arrhenius reactions), `micm` doesn't know how to calculate them based on the current temperature and pressure.\n",
    "\n",
    "So, it's our responsibility to calculate these rates and rate constants, and update them in the `micm` state, as needed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da99a490",
   "metadata": {},
   "source": [
    "## 4. Creating the Solver, State, and Latin Hypercube Sampler\n",
    "\n",
    "We'll use the Latin Hypercube Sampler to create the initial conditions, just like in the last tutorial. But, this time around we'll expand the LHS to eight dimensions: the five original dimensions (temperature, pressure, and concentrations of A, B, and C) and three new dimensions for the User-Defined, Emission, and First-Order Loss reactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 (temperature, pressure, concentrations of A, B, and C, User-defined reaction, Emission, and Loss)\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]\n",
    "\n",
    "state.set_conditions(temperatures, pressures)\n",
    "state.set_concentrations(concentrations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af454f9",
   "metadata": {},
   "source": [
    "## 5. Creating and Setting the User Defined Parameters\n",
    "\n",
    "The last step in setting the conditions, is providing initial values for our new reactions. To do that, we need to provide them to the state with their unique names. The syntax for the label is `PREFIX.reaction-name`. The `PREFIX` is based on the type of reaction:\n",
    "* `USER` for User Defined reactions,\n",
    "* `EMIS` for Emission reactions, and\n",
    "* `LOSS` for First Order Loss reactions.\n",
    "\n",
    "Then `reaction-name` is the name we gave to each reaction when it was created (see above).\n",
    "\n",
    "Once we have the labels, we can use them in a call to the `state.set_user_defined_rate_parameters()` function to set the values of the reaction rates and rate constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "49b15078",
   "metadata": {},
   "outputs": [],
   "source": [
    "state.set_user_defined_rate_parameters({\n",
    "    \"USER.complex_rxn\": sample_scaled[:, 5],\n",
    "    \"EMIS.B_emission\": sample_scaled[:, 6],\n",
    "    \"LOSS.C_loss\": sample_scaled[:, 7]\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d746c84f",
   "metadata": {},
   "source": [
    "## 6. Solving the System\n",
    "\n",
    "We'll solve the system as in previous tutorials, for 600 s in 10 s intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dda25f0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "concentrations_solved = []\n",
    "time_step = 10\n",
    "sim_length = 600\n",
    "curr_time = 0\n",
    "\n",
    "while curr_time <= sim_length:\n",
    "    solver.solve(state, time_step)\n",
    "    concentrations_solved.append(state.get_concentrations())\n",
    "    curr_time += time_step\n",
    "    state.set_user_defined_rate_parameters({\n",
    "        \"EMIS.B_emission\": sample_scaled[:, 6] * (1 + 0.5 * np.sin(curr_time / sim_length * 2 * np.pi))\n",
    "    })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ca1a3c",
   "metadata": {},
   "source": [
    "Note how we update the emission rate at each time step. The other two rate constants remain constant throughout the simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad115f60",
   "metadata": {},
   "source": [
    "## 7. Visualizing the Results\n",
    "\n",
    "Now, let's prepare and plot the results, just like we've done before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da483cf7",
   "metadata": {},
   "outputs": [
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       "      time.s  ENV.temperature.K    ENV.pressure.Pa  \\\n",
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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": [
    "concentrations_solved_expanded = []\n",
    "time = []\n",
    "for i in range(0, sim_length + 1, time_step):\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)].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 + 1.0) * num_grid_cells)\n",
    "df_expanded['ENV.pressure.Pa'] = np.repeat(pressures[0], (sim_length/time_step + 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 + 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):\n",
    "        min_y.append({key: np.min(value) for key, value in concentrations_solved[int(i/time_step)].items()})\n",
    "        max_y.append({key: np.max(value) for key, value in concentrations_solved[int(i/time_step)].items()})\n",
    "time_x = list(map(float, range(0, sim_length + 1, time_step)))\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": "venv (3.14.2)",
   "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": "3.14.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}