{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d5e3f0f0",
   "metadata": {},
   "source": [
    "## Atomic and Ionic Cross Sections\n",
    "\n",
    "In this tutorial, we make atomic and ionic cross sections and overplot them to see the differences. 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": "fd9a4bd5",
   "metadata": {},
   "source": [
    "### VALD3 Database\n",
    "\n",
    "Unlike the ExoMol, HITRAN, and HITEMP databases, the atomic line lists from the VALD database require you to sign up on the VALD website. The line lists must be requested manually through the website and then processed using the scripts provided in Cthulhu.\n",
    "\n",
    "#### Part 1: Downloading Line Lists\n",
    "\n",
    "The steps to register and download VALD line lists are very simple and detailed below. \n",
    "\n",
    "1. Go to the [VALD Website](http://vald.astro.uu.se/~vald/php/vald.php?docpage=contact.html).\n",
    "2. If you are a new user, click on 'Contact form' on the left sidebar, and follow the instructions to register.\n",
    "3. Once you are accepted as a user, login using your credentials.\n",
    "4. On the top, click 'Extract Element'\n",
    "5. Click 'Unit Selections' and select:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43288b2c",
   "metadata": {},
   "source": [
    "* Energy level units: cm-1\n",
    "* Give wavelengths in medium: vacuum\n",
    "* Wavelength units: cm-1\n",
    "* Van der Waals syntax: Default\n",
    "* Isotopic scaling: On\n",
    "* click Save settings\n",
    "\n",
    "You should be looking at a form that allows you to set various parameters for the line list. Set the following parameters for our tutorial:\n",
    "\n",
    "* Starting wavelength: 400\n",
    "* Ending wavelength: 500000\n",
    "* Element [+ionization]: Ca\n",
    "* Extraction format: long\n",
    "* Retrieve data via: FTP\n",
    "* Do not check off the circles for any optional parameters. This includes HFS splitting, the 3 damping constants, Landé factor, and term designation.\n",
    "* Linelist configuration: Default\n",
    "    \n",
    "This will download the neutral calcium cross section from VALD. We will also need the line list for the Ca+ ion. To download that, do the same steps above, except enter 'Ca 2' for the element. In the VALD database, the first ionized state of an element starts with 2. So neutral calcium is Ca 1, Ca+ is Ca 2, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e53bcc1",
   "metadata": {},
   "source": [
    "#### Part 2: Processing Line Lists for use with Cthulhu\n",
    "\n",
    "The line list downloaded from VALD must be processed before it can be used in cross section calculations. There are two easy steps to follow:\n",
    "\n",
    "First, unzip/extract the file if you have not done so. Then, rename your downloaded file to ATOM_IONIZATION_'VALD.trans'. For example, if you downloaded neutral calcium, you would rename the file to 'Ca_I_VALD.trans', or Ca+ would be 'Ca_II_VALD.trans'. \n",
    "\n",
    "Second, call `process_VALD_file` from `VALD.py`, specifying the species, ionization state, and location of the renamed line list. We recommend adding your .trans file to a VALD data directory at the top directory level of the package. In this case, the function call would look something like:\n",
    "\n",
    "`process_VALD_file(species = 'Ca', ionization_state = 1, VALD_data_dir = './VALD_Line_Lists/)`\n",
    "\n",
    "Please note that the final forward slash in the `VALD_data_dir` argument **is necessary**.\n",
    "    \n",
    "This will create another .trans file in the same folder as the original. The name will be ATOM_IONIZATION.trans (i.e. Ca_I.trans). It will also convert this new .trans file into a HDF5 format (i.e. Ca_I.h5). This HDF5 file is what is used by Cthulhu in the cross section computation. **Make sure that this new .h5 file is in the same directory as the 'Atomic_partition_functions.pf' file that we provide.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "207bd3c5",
   "metadata": {},
   "source": [
    "### Creating Ca I and Ca II Cross Sections\n",
    "\n",
    "Let's create a cross section for the Ca I [neutral calcium] and Ca II [Ca+] ions. We start by \"downloading\" the Ca I and Ca II line lists. This was largely done in the last section, but calling the `summon` function just ensures that all the files are in the correct directories for Cthulhu to read.\n",
    "\n",
    "We require the usual parameters `species` and `database` in the `summon` function. In addition, the parameter `VALD_data_dir` must be included. This parameter specifies the local folder which contains the downloaded atomic line lists from VALD. Since our folder (called 'VALD_Line_Lists' is included at the same directory level as this python file, we simply set this value to './VALD_Line_Lists/'). Note that the final forward slash **is necessary**.\n",
    "\n",
    "For Ca II, we must additionaally specify the parameter `ionization_state = 2`, to indicate that we want the Ca II ion, and not the neutral state of the atom. If `ionization_state` is not specified, it defaults to 1. For clarity, we have specified it in both calls to the `summon` function below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5dc67760",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ***** Processing requested data from VALD. You have chosen the following parameters: ***** \n",
      "\n",
      "Atom: Ca \n",
      "Ionization State: 1\n",
      "\n",
      "Line list ready.\n",
      "\n",
      "\n",
      " ***** Processing requested data from VALD. You have chosen the following parameters: ***** \n",
      "\n",
      "Atom: Ca \n",
      "Ionization State: 2\n",
      "\n",
      "Line list ready.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from Cthulhu.core import summon\n",
    "\n",
    "species = 'Ca'\n",
    "\n",
    "database = 'VALD'\n",
    "\n",
    "# Process Ca I line list\n",
    "summon(database=database, species = species, VALD_data_dir='./VALD_Line_Lists/', ionization_state = 1)\n",
    "\n",
    "# Process Ca II line list\n",
    "summon(database=database, species = species, VALD_data_dir='./VALD_Line_Lists/', ionization_state = 2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6067106",
   "metadata": {},
   "source": [
    "Next, we compute the cross sections using both line lists, specifying the usual parameters `species`, `database`, `P`, `T`, and `input_directory`. For the Ca II cross section, we must again specify `ionization_state = 2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0abf918e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning cross-section computations...\n",
      "Loading VALD format\n",
      "Loading partition function\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 2.8847074579971377 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for Ca I at P = 1.0 bar, T = 1000.0 K\n",
      "Calculation complete!\n",
      "Completed 20493 transitions in 0.19267476599634392 s\n",
      "\n",
      "Total runtime: 4.653372884000419 s\n",
      "Beginning cross-section computations...\n",
      "Loading VALD format\n",
      "Loading partition function\n",
      "Pre-computing Voigt profiles...\n",
      "Voigt profiles computed in 0.2243308250035625 s\n",
      "Pre-computation steps complete\n",
      "Generating cross section for Ca II at P = 1.0 bar, T = 1000.0 K\n",
      "Calculation complete!\n",
      "Completed 1956 transitions in 0.014195831994584296 s\n",
      "\n",
      "Total runtime: 1.7354280369982007 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",
    "# Make Ca I cross section\n",
    "compute_cross_section(database=database, species = species, pressure = P,\n",
    "                      temperature = T, input_dir = input_directory, \n",
    "                      ionization_state = 1,\n",
    "                      )\n",
    "\n",
    "# Make Ca II cross section\n",
    "compute_cross_section(database=database, species = species, pressure = P,\n",
    "                      temperature = T, input_dir = input_directory, \n",
    "                      ionization_state = 2,\n",
    "                      )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1613cdd9",
   "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": 4,
   "id": "8adf94bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from Cthulhu.misc import read_cross_section_file, cross_section_collection\n",
    "\n",
    "# Read in cross section for Ca I\n",
    "nu, sigma = read_cross_section_file(species = species, database = database, ionization_state = 1,\n",
    "                                    filename = 'Ca_I_T1000.0K_log_P0.0_H2-He_sigma.txt')\n",
    "\n",
    "# Read in cross section for Ca II\n",
    "nu2, sigma2 = read_cross_section_file(species = species, database = database, ionization_state = 2,\n",
    "                                      filename = 'Ca_II_T1000.0K_log_P0.0_H2-He_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, making sure to specify the previous collection as a parameter\n",
    "cross_sections = cross_section_collection(new_x = nu2, new_y = sigma2, collection = cross_sections)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96757dc4",
   "metadata": {},
   "source": [
    "And finally we plot the results. In addition to the usual required parameters `collection`, `labels`, and `filename`, we have added the parameters `x_min`, `x_max`, and `y_min` to view a zoomed in section of our plot. Note that `x_min` and `x_max` are in $\\mu m$ while `y_min` is in $cm^{2}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cca405c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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 = ['Ca I', 'Ca II'], \n",
    "                   filename = 'Different_Ionization_States_of_Ca',\n",
    "                   x_min = 0.6, x_max = 1.0, \n",
    "                   y_min = 1e-40,\n",
    "                   x_axis_scale = 'linear',    # Linear wavelength axis instead of the default log axis\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
}