{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69a9cc05",
   "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": "3a9ef243",
   "metadata": {},
   "source": [
    "# Latin Hypercube Sampling in `musica`\n",
    "\n",
    "In previous tutorials, we created models for one- and two-grid-cell systems. This time around, we'll do 100!\n",
    "As you'll see, the code changes little in terms of creating the chemical system, solver, and state, and using them\n",
    "to advance the chemical systems.\n",
    "\n",
    "But, how to set conditions for so many grid cells? Instead of hand-writing them or using purely random values,\n",
    "we'll use a technique called Latin Hypercube Sampling (LHS), a statistical method for generating multidimensional random samples.\n",
    "LHS avoids the problem of clustering that can sometimes appear in pure random sampling, and is often used for sensitivity studies.\n",
    "\n",
    "This tutorial will go over a simple example of utilizing LHS to run a multi-grid-cell solver in MUSICA.\n",
    "\n",
    "For more information on Latin Hypercube Sampling, see [here](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.qmc.LatinHypercube.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d50c8f23",
   "metadata": {},
   "source": [
    "## 1. Importing Libraries\n",
    "Below is a list of the required libraries for this tutorial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c595ccd",
   "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": "7f80a7cf",
   "metadata": {},
   "source": [
    "## 2. System, Solver, and State Creation\n",
    "\n",
    "We'll define the same chemical system we used in the [Multiple Grid Cells Tutorial](1.%20multiple_grid_cells.ipynb), and use that to create a solver and a 100-grid-cell state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8724138e",
   "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",
    ")\n",
    "\n",
    "mechanism = mc.Mechanism(\n",
    "    name=\"musica_micm_example\",\n",
    "    species=species,\n",
    "    phases=[gas],\n",
    "    reactions=[r1, r2]\n",
    ")\n",
    "\n",
    "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99119268",
   "metadata": {},
   "source": [
    "## 3. Creating and Scaling a Latin Hypercube Sampler\n",
    "\n",
    "This Latin Hypercube Sampler (LHS) uses the same five dimensions as the [Multiple Grid Cells Tutorial](1.%20multiple_grid_cells.ipynb) to randomize each of the individual systems. Those five dimensions are:\n",
    "* temperature (Kelvin),\n",
    "* pressure (Pascals), and\n",
    "* the concentrations of each of the species (mol m<sup>-3</sup>).\n",
    "\n",
    "Next, the LHS is created with the provided number of dimensions with a randomized sample that will be scaled by the sampler.\n",
    "The upper and lower bounds for each of the five dimensions are then set, and the sample is scaled with those bounds by the LHS.\n",
    "Do note as before that the ordering inside the bounding arrays matters and cannot be changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "773d1802",
   "metadata": {},
   "outputs": [],
   "source": [
    "ndim = 5\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, and concentrations of A, B, C)\n",
    "l_bounds = [275, 100753.3, 0, 0, 0] # Lower bounds\n",
    "u_bounds = [325, 101753.3, 10, 10, 10] # Upper bounds\n",
    "\n",
    "# Scale the samples to the defined bounds\n",
    "sample_scaled = qmc.scale(sample, l_bounds, u_bounds)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82364d1b",
   "metadata": {},
   "source": [
    "## 4. Setting Conditions and Running the Solver\n",
    "\n",
    "Instead of hand-writing the initial conditions, as in previous tutorials, we'll use the output of the LHS to set our `state` values. Then, we'll solve the system over 600 s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ae18af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "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)\n",
    "concentrations_solved = []\n",
    "time_step = 10 # s\n",
    "sim_length = 600 # s\n",
    "curr_time = 0 # s\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd065d8a",
   "metadata": {},
   "source": [
    "## 5. Preparing the Results (Advanced; Optional Read)\n",
    "\n",
    "The intention of this code snippet is to split up each grid cell for each time step onto a separate row in the DataFrame so they can be averaged when plotted.\n",
    "\n",
    "This will produce a confidence interval for each time step since there will be a set of unique values at every time step for each species.\n",
    "\n",
    "As an example, if there are 2 grid cells and 5 time steps, the data table will have 10 rows, with the first being the first grid cell for the first time step, the second row being the second grid cell for the first time step, the third row being the first grid cell for the second time step, and so on.\n",
    "You can see this pattern in the displayed DataFrame as well (below).\n",
    "\n",
    "After the expansion of the DataFrame, adding the other columns is fairly straight-forward; they are simply given more rows since the number of rows is now the product of the number of grid cells and the number of time steps.\n",
    "The time column is notably different, however, since each time step has to be repeated for every grid cell.\n",
    "\n",
    "Due to the complexity of this code cell, it has been bundled into a function which is used below.\n",
    "Note that the first column is the index of the DataFrame, not the time step here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "92625aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_results_all_cells():\n",
    "    concentrations_solved_pd = []\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_pd.append({species: concentration[j] for species, concentration in concentrations_solved[int(i/time_step)].items()})\n",
    "            time.append(i)\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'] = time\n",
    "    df['ENV.temperature.K'] = np.repeat(temperatures[0], (sim_length/time_step + 1.0) * num_grid_cells)\n",
    "    df['ENV.pressure.Pa'] = np.repeat(pressures[0], (sim_length/time_step + 1.0) * num_grid_cells)\n",
    "    df['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 = 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": "markdown",
   "id": "74d6d56f",
   "metadata": {},
   "source": [
    "Now, let's use the function we've written to create the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a338e7f6",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "      time.s  ENV.temperature.K   ENV.pressure.Pa  \\\n",
       "0          0  317.0167635067038 101722.6869975267   \n",
       "1          0  317.0167635067038 101722.6869975267   \n",
       "2          0  317.0167635067038 101722.6869975267   \n",
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       "...      ...                ...               ...   \n",
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       "6096     600  317.0167635067038 101722.6869975267   \n",
       "6097     600  317.0167635067038 101722.6869975267   \n",
       "6098     600  317.0167635067038 101722.6869975267   \n",
       "6099     600  317.0167635067038 101722.6869975267   \n",
       "\n",
       "      ENV.air number density.mol m-3      CONC.A.mol m-3      CONC.B.mol m-3  \\\n",
       "0                  38.59236650811341   1.595583909208889   1.585270886031598   \n",
       "1                  38.59236650811341    8.56059970344138   9.126348120137955   \n",
       "2                  38.59236650811341   5.126079936709042  6.8445384329146775   \n",
       "3                  38.59236650811341   6.338825026981972  2.2582074122408824   \n",
       "4                  38.59236650811341  2.4635566912685825   9.055516556581432   \n",
       "...                              ...                 ...                 ...   \n",
       "6095               38.59236650811341 0.12164507170374605   0.547669866815287   \n",
       "6096               38.59236650811341 0.35817252241426667   1.200242137056683   \n",
       "6097               38.59236650811341 0.32833988053461194   1.105485210751788   \n",
       "6098               38.59236650811341 0.25341840006310173  0.9485916367880122   \n",
       "6099               38.59236650811341 0.00855493275976946 0.22565112923219416   \n",
       "\n",
       "         CONC.C.mol m-3  \n",
       "0    1.7065382493105852  \n",
       "1    4.4624016818930174  \n",
       "2     6.763337737343939  \n",
       "3     6.241628537389399  \n",
       "4     2.959459728220327  \n",
       "...                 ...  \n",
       "6095 13.327782221911818  \n",
       "6096 17.767939194001215  \n",
       "6097 15.088075933807765  \n",
       "6098 14.100160068090627  \n",
       "6099  5.396154380948566  \n",
       "\n",
       "[6100 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "concentrations_solved_pd, df = convert_results_all_cells()\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13c99266",
   "metadata": {},
   "source": [
    "## 6. Visualizing the Results\n",
    "\n",
    "We'll create two plots:\n",
    "* one containing the mean concentration for each species and their respective 95% confidence intervals\n",
    "* one for each species' concentration range."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05fb1f28",
   "metadata": {},
   "source": [
    "### 6a. Visualizing the Confidence Interval\n",
    "Here, the three species are plotted out with Seaborn's `lineplot()` function, which takes in an input `x` and `y` variable, confidence interval `ci` (95% here) and `alpha` value (opacity of shaded regions).\n",
    "\n",
    "The DataFrame has more than one value for each time step (x-axis), and the solid line represents the mean of the species' concentrations at every time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03449bba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df, 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, 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, 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(loc='center right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7758e071",
   "metadata": {},
   "source": [
    "### 6b. Visualizing the Min-Max Curve\n",
    "This figure is similar to the CI curves above, but it instead displays the range of values for each species at each time step.\n",
    "\n",
    "To build this figure, the min and max at every time step are found and populated into their respective arrays to represent the bottom curve and top curve for each species.\n",
    "\n",
    "All of the curves are then plotted for each species with a lower alpha so that the overlaps are visible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "efe08c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_y = []\n",
    "max_y = []\n",
    "for i in range(0, sim_length + 1, time_step):\n",
    "    min_y.append({species: np.min(concentration) for species, concentration in concentrations_solved[int(i/time_step)].items()})\n",
    "    max_y.append({species: np.max(concentration) for species, concentration 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
}