{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42082fbc",
   "metadata": {},
   "source": [
    "# Absorption by a single species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3eae4ea-0aca-4502-9043-54e2ca6fcab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyarts\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "952fa30f-9620-493b-9a69-7ea46d0ae9b7",
   "metadata": {},
   "source": [
    "## Select absorption species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1943594-5e27-4c9c-831c-c575bbc08c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "species = \"O2-66\"  # Main isotope of O2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df821a5e-11df-4000-a66f-b19ce8a431d1",
   "metadata": {},
   "source": [
    "## Activate the recipe for this species\n",
    "\n",
    "See [SingleSpeciesAbsorption](pyarts.recipe.rst#pyarts.recipe.SingleSpeciesAbsorption)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79c18bda-7f10-42cb-87b0-ba636cef37f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "absorption = pyarts.recipe.SingleSpeciesAbsorption(species=species)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da679d8d-8eef-4437-b299-7c85487df39a",
   "metadata": {},
   "source": [
    "## Select a single temperature, a VMR value, and a range of pressures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebf13038-90c1-44dc-93ad-63d4d3da105b",
   "metadata": {},
   "outputs": [],
   "source": [
    "atm = pyarts.arts.AtmPoint()\n",
    "atm.set_species_vmr(\"O2\", 0.2095)\n",
    "atm.temperature = 273\n",
    "ps = np.logspace(5, -2, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ad1d98",
   "metadata": {},
   "source": [
    "## Select frequency range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d81cde68",
   "metadata": {},
   "outputs": [],
   "source": [
    "line_f0 = 118750348044.712  # Lowest energy absorption line\n",
    "f = np.linspace(-500e6, 500e6, 1001) + line_f0  # Some range around it"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25eafbd8",
   "metadata": {},
   "source": [
    "## Use the recipe and convert the results to cross-sections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b998604b-98aa-4f46-bf78-288d65ffb78e",
   "metadata": {},
   "outputs": [],
   "source": [
    "xsec = []\n",
    "for p in ps:\n",
    "    atm.pressure = p\n",
    "    xsec.append(absorption(f, atm) / atm.number_density(species))\n",
    "xsec = np.array(xsec)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc2607ca",
   "metadata": {},
   "source": [
    "## Plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "680f3d50",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.semilogy((f - line_f0) / 1e6, xsec.T)\n",
    "plt.xlabel(\"Frequency offset [MHz]\")\n",
    "plt.ylabel(\"Cross-section [$m^2$]\")\n",
    "plt.title(\"Cross-section of O$_2$ 16-16\")\n",
    "plt.ylim(ymin=1e-3 * np.min(xsec))\n",
    "plt.legend(\n",
    "    [f\"P: $10^{'{'}{round(np.log10(x))}{'}'}$\" for x in ps],\n",
    "    ncols=4,\n",
    "    loc=\"lower center\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd66bb3c",
   "metadata": {},
   "source": [
    "## Integration test by ensuring some statistics look good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f372371b",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert np.isclose(6.792977548868407e-28 / xsec.mean(), 1)\n",
    "assert np.isclose(5.43981642113382e-24 / xsec.sum(), 1)\n",
    "assert np.isclose(1.3359834491781882e-24 / xsec.max(), 1)\n",
    "assert np.isclose(2.537911691540087e-26 / xsec.std(), 1)\n",
    "assert np.isclose(8.236637542411964e-35 / xsec.min(), 1)"
   ]
  }
 ],
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