{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3a9ef243",
   "metadata": {},
   "source": [
    "# Latin Hypercube Sampling in MUSICA\n",
    "In the previous example, all of the grid cells had to be filled in manually with data.</br>\n",
    "This makes it not practically useful on its own since it would be tedious to make a system with many grid cells.</br>\n",
    "One solution to this is Latin Hypercube Sampling (LHS), a statistical method for generating multidimensional random samples.</br>\n",
    "It avoids the problem of clustering that can sometimes appear in pure random sampling.</br>\n",
    "This tutorial will go over a simple example of utilizing LHS to run a multi-grid-cell solver in MUSICA.</br>\n",
    "For documentation on Latin Hypercube Sampling, go [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": 1,
   "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. Rerunning Previous Code\n",
    "This is simply a copy of the first 4 steps from the [Multiple Grid Cells Tutorial](1.%20multiple_grid_cells.ipynb) since the code is identical aside from the number of grid cells being increased.</br>\n",
    "If you would like to view the explanation for this code, refer to the hyperlink on the above line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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,  # 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(\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",
    "This Latin Hypercube Sampler uses the same 5 dimensions as the [Multiple Grid Cells Tutorial](1.%20multiple_grid_cells.ipynb) to randomize each of the individual systems, being:\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.</br>\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.</br>\n",
    "Do note as before that the ordering inside the bounding arrays matters and cannot be changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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\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. Splitting up the Array Output and Running the Solver\n",
    "This code follows the same flow as the [Multiple Grid Cells Tutorial](1.%20multiple_grid_cells.ipynb) but with the new LHS values.</br>\n",
    "The only notable change is the old <i>box_model_values</i> array becoming the <i>sample_scaled</i> output array from the LHS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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_length = 1\n",
    "sim_length = 60\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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd065d8a",
   "metadata": {},
   "source": [
    "## 5. Expanding out the DataFrame (Advanced; Optional Read)\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.</br>\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.</br>\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.</br>\n",
    "You can see this pattern in the displayed DataFrame as well.</br>\n",
    "After the expansion of the DataFrame, adding the other columns is fairly similar; 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.</br>\n",
    "The time column is notably different, however, since each time step has to be repeated for every grid cell.</br>\n",
    "Due to the complexity of this code cell, it has been bundled into a function which is then called.</br>\n",
    "Once that is all done, the expanded DataFrame is displayed.</br>\n",
    "Note that the first column is the index of the DataFrame, not the time step here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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_length):\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_length)].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_length + 1.0) * num_grid_cells)\n",
    "    df['ENV.pressure.Pa'] = np.repeat(pressures[0], (sim_length/time_step_length + 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_length + 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": "code",
   "execution_count": 6,
   "id": "a338e7f6",
   "metadata": {},
   "outputs": [
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       "      time.s  ENV.temperature.K    ENV.pressure.Pa  \\\n",
       "0          0 286.00321749782404 101281.96283938194   \n",
       "1          0 286.00321749782404 101281.96283938194   \n",
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       "6099      60 286.00321749782404 101281.96283938194   \n",
       "\n",
       "      ENV.air number density.mol m-3         CONC.A.mol m-3  \\\n",
       "0                  42.59189914230601      9.603213213571069   \n",
       "1                  42.59189914230601      6.316518664406818   \n",
       "2                  42.59189914230601       7.60797163127235   \n",
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       "...                              ...                    ...   \n",
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       "6099               42.59189914230601 0.00011133657751124476   \n",
       "\n",
       "            CONC.B.mol m-3     CONC.C.mol m-3  \n",
       "0        9.101503007047965 1.3791552759048176  \n",
       "1       3.5442239182495494 7.5309574382359585  \n",
       "2         4.98172822290733   8.77528825739052  \n",
       "3        4.182414864738023  8.078019549492074  \n",
       "4        5.905763265736759  3.133282102633176  \n",
       "...                    ...                ...  \n",
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       "6097   0.00551919020208598 13.319388770527299  \n",
       "6098  0.007513964364701525 15.431405934122955  \n",
       "6099 0.0026815740270777354 12.495870560349145  \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",
    "This set of steps involves plotting the results after running the solver for the LHS data.</br>\n",
    "There will be two plots, one containing the mean concentration for each species and their respective 95% confidence interval, and 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 <i>lineplot()</i> function, which takes in an input x and y variable with the 95% confidence interval (CI) displayed.</br>\n",
    "There are additional parameters passed in as well, such as the CI value, which can be changed from 95, and setting the alpha so that the CI is more visible.</br>\n",
    "Since the DataFrame has more than one value for each time step (x-axis), the solid line represents the mean of the species' concentrations at every time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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.</br>\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.</br>\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": 8,
   "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_length):\n",
    "    min_y.append({species: np.min(concentration) for species, concentration in concentrations_solved[int(i/time_step_length)].items()})\n",
    "    max_y.append({species: np.max(concentration) for species, concentration 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()"
   ]
  }
 ],
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