{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "905bcb0b",
   "metadata": {},
   "source": [
    "## Computing Cross Sections with Different Broadening Parameters\n",
    "\n",
    "In this tutorial, we create a cross section for a molecule using different broadening coefficients.\n",
    "\n",
    "We again choose the CO Li2015 line list from ExoMol. We compute the cross section using ExoMol's given $\\rm{H_2}$-$\\rm{He}$ broadening files and also using 2 custom broadening methods. One of these custom broadening methods has a constant relationship between the pressure broadening Lorentzian half-width-half-maximum and the lower state rotational angular momentum quantum number (i.e. $\\gamma_{L}(J) =$ constant) and the other has a linearly decreasing relationship. \n",
    "\n",
    "We strongly recommend reading the [Quick Start](https://cthulhu-xsec.readthedocs.io/en/latest/content/notebooks/quick_start.html) guide if you have not done so yet."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d92010d0",
   "metadata": {},
   "source": [
    "### Download CO line list and $\\rm{H_2}$-$\\rm{He}$ broadening files from ExoMol\n",
    "\n",
    "By now you should be a seasoned cultist of the Old One's family, so you know what to do. \n",
    "\n",
    "But this time, note that the `summon` function you've been using all this time has also been downloading pressure broadening files from ExoMol. In the input folder, you will see files for $\\rm{H_2}$, $\\rm{He}$, and air broadening (`H2.broad`, `He.broad`, and `air.broad`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "05ce8b91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ***** Downloading requested data from ExoMol. You have chosen the following parameters: ***** \n",
      "\n",
      "Molecule: CO \n",
      "Isotopologue: 12C-16O \n",
      "Line List: Li2015\n",
      "\n",
      "Starting by downloading the .broad, .pf, and .states files...\n",
      "Fetched the broadening coefficients, partition functions, and energy levels.\n",
      "Now downloading the Li2015 line list...\n",
      "\n",
      "Downloading .trans file 1 of 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1.40M/1.40M [00:00<00:00, 1.50MiB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Converting this .trans file to HDF to save storage space...\n",
      "This file took 0.3 seconds to reformat to HDF.\n",
      "\n",
      "Downloading .trans file 2 of 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 63.5k/63.5k [00:00<00:00, 290kiB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Converting this .trans file to HDF to save storage space...\n",
      "This file took 0.1 seconds to reformat to HDF.\n",
      "\n",
      "Line list ready.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from Cthulhu.core import summon\n",
    "\n",
    "species = 'CO'\n",
    "database = 'ExoMol'\n",
    "\n",
    "# Download CO line list\n",
    "summon(database = database, species = species)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b78788f",
   "metadata": {},
   "source": [
    "### Cthulhu's Default Pressure Broadening\n",
    "\n",
    "When you don't specify a pressure broadening prescription (as we haven't until now), `Cthulhu` will use a default treatment for pressure broadening. The default is as follows:\n",
    "\n",
    "* ExoMol: $\\rm{H_2}$-$\\rm{He}$ pressure broadening with 85% $\\rm{H_2}$ and 15% $\\rm{He}$ (via the downloaded `H2.broad` and `He.broad` files).\n",
    "* HITRAN / HITEMP: air broadening using a $J$-dependent set of broadening parameters computed by averaging over other quantum numbers in the HITRAN `.par` files.\n",
    "* VALD: a collisional approximation for $\\rm{H_2}$-$\\rm{He}$ pressure broadening with 85% $\\rm{H_2}$ and 15% $\\rm{He}$.\n",
    "\n",
    "You can of course always specify a different treatment, as we will see below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b711dfb0",
   "metadata": {},
   "source": [
    "### Adding custom broadening files\n",
    "\n",
    "Now, let's create our custom broadening file and add them to the directory. To do this, we create numpy arrays for $J$, $\\gamma_{L}(J)$, and $n_{L}$ and write them to a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c28e674d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Populate arrays\n",
    "J = np.linspace(0, 80, num = 81)    # Rotational quantum numbers from J = 0 to J = 80\n",
    "gamma_L_0 = 0.07 * np.ones(81)      # Lorentzian HWHM is 0.07 for all J\n",
    "n_L = 0.5 * np.ones(81)             # Temperature exponent is 0.5 for all J"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee5e0be",
   "metadata": {},
   "source": [
    "Now that we have defined the necessary variables, we call the `write_broadening_file` function to write these arrays to a file and place it in the correct folder. This function requires at least 5 parameters, the name of the file (`broadening_file`), the data to be written (`data`), the 'input' directory (`input_dir`), the name of the molecule (`species`), and the database (`database`).\n",
    "\n",
    "The 3 arrays we just created should be combined into 1 array prior to calling the `write_broadening_file` function. This can be done using `np.stack`, as shown below. Make sure to keep the same order of the arrays (`J` then `gamma_L_0` then `n_L`).\n",
    "\n",
    "The variable `input_dir` should be set to `./input/` unless you have edited the file structure Cthulhu automatically creates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00e94710",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Cthulhu.broadening import write_broadening_file\n",
    "\n",
    "data = np.stack((J, gamma_L_0, n_L))  # Combine arrays into 1 array to pass into write_broadening_file function\n",
    "\n",
    "broadening_file = 'constant.broad'    # Name of the file we will save data to\n",
    "\n",
    "input_dir = './input/'                # Top level input directory that contains line list data for all species\n",
    "\n",
    "# Create broadening file\n",
    "write_broadening_file(broadening_file, data, input_dir, species, database) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e6cf498",
   "metadata": {},
   "source": [
    "Great, we have added our first custom broadening file!\n",
    "\n",
    "Now let's do the same for a broadening file where $\\gamma_{L}(J)$ decreases linearly for increasing $J$ until a cutoff at $J = 20$. The steps are the same, except `gamma_L_0` will be different. For simplicity, we just redefine this variable, as well as the name of the broadening file, and keep everything else the same. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c8b969f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma_L_0_beginning = np.linspace(start = 0.07, stop = 0.06, num = 21)         # 20 evenly spaced numbers from 0.07 to 0.06\n",
    "gamma_L_0_end = 0.06 * np.ones(60)                                             # Lorentzian HWHM is 0.06 for all J after J = 20\n",
    "gamma_L_0 = np.concatenate((gamma_L_0_beginning, gamma_L_0_end), axis = None)  # Combine both arrays for full Lorenztian HWHM\n",
    "\n",
    "data = np.stack((J, gamma_L_0, n_L))  # Combine arrays\n",
    "\n",
    "broadening_file = 'linear.broad'    # Name of the file we will save data to\n",
    "\n",
    "input_dir = './input/'              # Top level input directory that contains line list data for all species\n",
    "\n",
    "# Create broadening file\n",
    "write_broadening_file(broadening_file, data, input_dir, species, database) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d3b1c47",
   "metadata": {},
   "source": [
    "### Computing and Plotting Cross Section\n",
    "Now we are ready to compute the cross section. We do this in the usual way. The only parameter that changes is `broad_type` and `broadening_file`. If we set `broad_type` to 'fixed' a constant relationship between $J$ and $\\gamma_{L}(J)$ is assumed. If we set `broad_type` to 'custom', the custom.broad file we added is used. If we assign no value to `broad_type` (or assign it to 'default'), Cthulhu defaults to using the broadening parameters already downloaded with the line list. In the case for CO, the default is H2-He broadening (as is often the case with ExoMol line lists). \n",
    "\n",
    "Since we created 2 custom broadening files, we set `broad_type` to 'custom' in both calls to `compute_cross_section`. We also need to pass in the name of the broadening file in both instances, which you will recall were 'constant.broad' and 'linear.broad'. As an aside, you can also set `broad_type` = 'fixed' for the constant $\\gamma_{L}(J)$ file we created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75f628ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning cross-section computations...\n",
      "Loading ExoMol format\n",
      "Loading partition function\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 5.0385457550000865 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for CO at P = 1.0 bar, T = 1000.0 K\n",
      "Computing transitions from E2.h5 | 0.0% complete\n",
      "Completed 6474 transitions in 1.8249874909961363 s\n",
      "Computing transitions from Li2015.h5 | 50.0% complete\n",
      "Completed 125496 transitions in 1.182199076996767 s\n",
      "Calculation complete!\n",
      "Completed 131970 transitions in 3.008382435000385 s\n",
      "\n",
      "Total runtime: 11.12400327999785 s\n",
      "Beginning cross-section computations...\n",
      "Loading ExoMol format\n",
      "Loading partition function\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 4.533660020002571 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for CO at P = 1.0 bar, T = 1000.0 K\n",
      "Computing transitions from E2.h5 | 0.0% complete\n",
      "Completed 6474 transitions in 0.07487936400139006 s\n",
      "Computing transitions from Li2015.h5 | 50.0% complete\n",
      "Completed 125496 transitions in 1.3549675960020977 s\n",
      "Calculation complete!\n",
      "Completed 131970 transitions in 1.4309075359997223 s\n",
      "\n",
      "Total runtime: 8.964980600998388 s\n",
      "Beginning cross-section computations...\n",
      "Loading ExoMol format\n",
      "Loading partition function\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 4.12634713800071 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for CO at P = 1.0 bar, T = 1000.0 K\n",
      "Computing transitions from E2.h5 | 0.0% complete\n",
      "Completed 6474 transitions in 0.06387955300306203 s\n",
      "Computing transitions from Li2015.h5 | 50.0% complete\n",
      "Completed 125496 transitions in 1.1314738929941086 s\n",
      "Calculation complete!\n",
      "Completed 131970 transitions in 1.1961962940040394 s\n",
      "\n",
      "Total runtime: 8.202235151002242 s\n"
     ]
    }
   ],
   "source": [
    "from Cthulhu.core import compute_cross_section\n",
    "\n",
    "P = 1.0                        # Pressure in bars\n",
    "T = 1000.0                     # Temperature in Kelvin\n",
    "input_directory = './input/'   # Top level directory containing line lists\n",
    "\n",
    "# Default H2-He pressure broadening\n",
    "compute_cross_section(species = species, database = database, temperature = T, \n",
    "                      pressure = P, input_dir = input_directory, \n",
    "                      broad_type = 'default',\n",
    "                      )\n",
    "\n",
    "# Constant pressure broadening\n",
    "compute_cross_section(species = species, database = database, temperature = T, \n",
    "                      pressure = P, input_dir = input_directory, \n",
    "                      broad_type = 'custom', \n",
    "                      broadening_file = 'constant.broad',\n",
    "                     )\n",
    "\n",
    "# Linear in J pressure broadening\n",
    "compute_cross_section(species = species, database = database, temperature = T,\n",
    "                      pressure = P, input_dir = input_directory, \n",
    "                      broad_type = 'custom', \n",
    "                      broadening_file = 'linear.broad',\n",
    "                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c692912",
   "metadata": {},
   "source": [
    "Now we are able to plot the cross section. We need to first read the cross section files and add the cross sections to a collection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e01e0153",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Cthulhu.misc import read_cross_section_file, cross_section_collection\n",
    "\n",
    "# Read in the cross sections we just computed\n",
    "nu, sigma = read_cross_section_file(species = species, database = database,\n",
    "                                    filename = 'CO_T1000.0K_log_P0.0_H2-He_sigma.txt')\n",
    "nu2, sigma2 = read_cross_section_file(species = species, database = database,\n",
    "                                      filename = 'CO_T1000.0K_log_P0.0_custom_constant_sigma.txt')\n",
    "nu3, sigma3 = read_cross_section_file(species = species, database = database, \n",
    "                                      filename = 'CO_T1000.0K_log_P0.0_custom_linear_sigma.txt')\n",
    "\n",
    "# Generate an empty collection object for plotting\n",
    "cross_sections = []\n",
    "\n",
    "# Add first cross section to collection\n",
    "cross_sections = cross_section_collection(new_x = nu, new_y = sigma, collection = cross_sections)  \n",
    "\n",
    "# Add second cross section to collection\n",
    "cross_sections = cross_section_collection(new_x = nu2, new_y = sigma2, collection = cross_sections) \n",
    "\n",
    "# Add third cross section to collection\n",
    "cross_sections = cross_section_collection(new_x = nu3, new_y = sigma3, collection = cross_sections) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fbd4137",
   "metadata": {},
   "source": [
    "And finally we plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2d6eb8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from Cthulhu.plot import plot_cross_section\n",
    "\n",
    "plot_cross_section(collection = cross_sections, \n",
    "                   labels = ['H$_2$-He', 'constant', 'linear'], \n",
    "                   filename = 'Different_Broadening_Types_for_CO',\n",
    "                   y_min = 1.0e-30,\n",
    "                   )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c601cf4b",
   "metadata": {},
   "source": [
    "Whilst at first glance it may look like all three cross sections are the same, pressure broadening differences are easier to see when you zoom in on a narrow region.\n",
    "\n",
    "Let's examine the region from 4.26 μm and 4.27 μm to show the differences more clearly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "150fc198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cross_section(collection = cross_sections,\n",
    "                   labels = ['H$_2$-He', 'constant', 'linear'], \n",
    "                   filename = 'Different_Broadening_Types_for_CO_Zoomed',\n",
    "                   x_min = 4.26, x_max = 4.27,\n",
    "                   y_min = 1e-31, y_max = 1.0e-26,\n",
    "                   )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}