HajimeKawahara/exojax

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing HITEMP and ExoMol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:28.880050Z",
     "iopub.status.busy": "2024-01-16T23:36:28.879445Z",
     "iopub.status.idle": "2024-01-16T23:36:31.047742Z",
     "shell.execute_reply": "2024-01-16T23:36:31.047085Z"
    }
   },
   "outputs": [],
   "source": [
    "from exojax.spec.hitran import line_strength, doppler_sigma, gamma_hitran, gamma_natural\n",
    "from exojax.spec.exomol import gamma_exomol\n",
    "from exojax.spec import api\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, set a wavenumber bin in the unit of wavenumber (cm-1).\n",
    "Here we set the wavenumber range as $1000 \\le \\nu \\le 10000$ (1/cm) with the resolution of 0.01 (1/cm). \n",
    "\n",
    "We call moldb instance with the path of par file.\n",
    "If the par file does not exist, moldb will try to download it from HITRAN website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:31.049585Z",
     "iopub.status.busy": "2024-01-16T23:36:31.049459Z",
     "iopub.status.idle": "2024-01-16T23:36:31.051818Z",
     "shell.execute_reply": "2024-01-16T23:36:31.051536Z"
    }
   },
   "outputs": [],
   "source": [
    "# Setting wavenumber bins and loading HITEMP database\n",
    "wav = np.linspace(22930.0, 23000.0, 4000, dtype=np.float64)  # AA\n",
    "nus = 1.0e8 / wav[::-1]  # cm-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:31.053272Z",
     "iopub.status.busy": "2024-01-16T23:36:31.053158Z",
     "iopub.status.idle": "2024-01-16T23:36:32.624945Z",
     "shell.execute_reply": "2024-01-16T23:36:32.624298Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "radis engine =  vaex\n",
      "Downloading 05_HITEMP2019.par.bz2 for CO (1/1).\n",
      "Download complete. Parsing CO database to /home/kawahara/exojax/documents/tutorials/CO-05_HITEMP2019.hdf5\n"
     ]
    }
   ],
   "source": [
    "mdbCO_HITEMP = api.MdbHitemp(\n",
    "    \"CO\", nus, isotope=1, gpu_transfer=True\n",
    ")  # we use istope=1 for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:32.626790Z",
     "iopub.status.busy": "2024-01-16T23:36:32.626628Z",
     "iopub.status.idle": "2024-01-16T23:36:33.569069Z",
     "shell.execute_reply": "2024-01-16T23:36:33.568614Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kawahara/exojax/src/exojax/utils/molname.py:197: FutureWarning: e2s will be replaced to exact_molname_exomol_to_simple_molname.\n",
      "  warnings.warn(\n",
      "/home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname.\n",
      "  warnings.warn(\n",
      "/home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HITRAN exact name= (12C)(16O)\n",
      "radis engine =  vaex\n",
      "Molecule:  CO\n",
      "Isotopologue:  12C-16O\n",
      "Background atmosphere:  H2\n",
      "ExoMol database:  None\n",
      "Local folder:  CO/12C-16O/Li2015\n",
      "Transition files: \n",
      "\t => File 12C-16O__Li2015.trans\n",
      "Broadening code level: a0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kawahara/exojax/src/radis/radis/api/exomolapi.py:685: AccuracyWarning: The default broadening parameter (alpha = 0.07 cm^-1 and n = 0.5) are used for J'' > 80 up to J'' = 152\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "emf = \"CO/12C-16O/Li2015\"  # this is isotope=1 12C-16O\n",
    "mdbCO_Li2015 = api.MdbExomol(emf, nus, gpu_transfer=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define molecular weight of CO ($\\sim 12+16=28$), temperature (K), and pressure (bar).\n",
    "Also, we here assume the 100 % CO atmosphere, i.e. the partial pressure = pressure.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:33.570868Z",
     "iopub.status.busy": "2024-01-16T23:36:33.570719Z",
     "iopub.status.idle": "2024-01-16T23:36:33.579479Z",
     "shell.execute_reply": "2024-01-16T23:36:33.578884Z"
    }
   },
   "outputs": [],
   "source": [
    "from exojax.spec import molinfo\n",
    "\n",
    "molecular_mass = molinfo.molmass(\"CO\")  # molecular weight\n",
    "Tfix = 1300.0  # we assume T=1300K\n",
    "Pfix = 0.99  # we compute P=1 bar=0.99+0.1\n",
    "Ppart = 0.01  # partial pressure of CO. here we assume a 1% CO atmosphere (very few)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "partition function ratio $q(T)$ is defined by \n",
    "\n",
    "$q(T) = Q(T)/Q(T_{ref})$; $T_{ref}$=296 K\n",
    "\n",
    "Here, we use the partition function from HAPI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:33.581042Z",
     "iopub.status.busy": "2024-01-16T23:36:33.580917Z",
     "iopub.status.idle": "2024-01-16T23:36:33.880949Z",
     "shell.execute_reply": "2024-01-16T23:36:33.880284Z"
    }
   },
   "outputs": [],
   "source": [
    "# mdbCO_HITEMP.ExomolQT(emf) #use Q(T) from Exomol/Li2015\n",
    "from exojax.utils.constants import Tref_original\n",
    "\n",
    "qt_HITEMP = mdbCO_HITEMP.qr_interp(1, Tfix, Tref_original)\n",
    "qt_Li2015 = mdbCO_Li2015.qr_interp(Tfix, Tref_original)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us compute the line strength S(T) at temperature of Tfix.\n",
    "\n",
    "$S (T;s_0,\\nu_0,E_l,q(T)) = S_0 \\frac{Q(T_{ref})}{Q(T)} \\frac{e^{- h c E_l /k_B T}}{e^{- h c E_l /k_B T_{ref}}} \\frac{1- e^{- h c \\nu /k_B T}}{1-e^{- h c \\nu /k_B T_{ref}}}= q_r(T)^{-1} e^{ s_0 - c_2 E_l (T^{-1} - T_{ref}^{-1})}  \\frac{1- e^{- c_2 \\nu_0/ T}}{1-e^{- c_2 \\nu_0/T_{ref}}}$\n",
    "\n",
    "$s_0=\\log_{e} S_0$ : logsij0\n",
    "\n",
    "$\\nu_0$: nu_lines\n",
    "\n",
    "$E_l$ : elower\n",
    "\n",
    "Why the input is $s_0 = \\log_{e} S_0$ instead of $S_0$ in SijT? This is because the direct value of $S_0$ is quite small and we need to use float32 for jax.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:33.882875Z",
     "iopub.status.busy": "2024-01-16T23:36:33.882739Z",
     "iopub.status.idle": "2024-01-16T23:36:33.941035Z",
     "shell.execute_reply": "2024-01-16T23:36:33.940527Z"
    }
   },
   "outputs": [],
   "source": [
    "Sij_HITEMP = line_strength(\n",
    "    Tfix,\n",
    "    mdbCO_HITEMP.logsij0,\n",
    "    mdbCO_HITEMP.nu_lines,\n",
    "    mdbCO_HITEMP.elower,\n",
    "    qt_HITEMP,\n",
    "    Tref_original,\n",
    ")\n",
    "Sij_Li2015 = line_strength(\n",
    "    Tfix,\n",
    "    mdbCO_Li2015.logsij0,\n",
    "    mdbCO_Li2015.nu_lines,\n",
    "    mdbCO_Li2015.elower,\n",
    "    qt_Li2015,\n",
    "    Tref_original,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, compute the Lorentz gamma factor (pressure+natural broadening)\n",
    "\n",
    "$\\gamma_L = \\gamma^p_L + \\gamma^n_L$\n",
    "\n",
    "where the pressure broadning (HITEMP)\n",
    "\n",
    "$\\gamma^p_L = (T/296K)^{-n_{air}} [ \\alpha_{air} ( P - P_{part})/P_{atm} + \\alpha_{self} P_{part}/P_{atm}]$\n",
    "\n",
    "$P_{atm}$: 1 atm in the unit of bar (i.e. = 1.01325)\n",
    "\n",
    "or \n",
    "\n",
    "the pressure broadning (ExoMol)\n",
    "\n",
    "$\\gamma^p_L = \\alpha_{ref}  ( T/T_{ref} )^{-n_{texp}} ( P/P_{ref}), $\n",
    "\n",
    "\n",
    "and the natural broadening\n",
    "\n",
    "$\\gamma^n_L = \\frac{A}{4 \\pi c}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:33.942835Z",
     "iopub.status.busy": "2024-01-16T23:36:33.942699Z",
     "iopub.status.idle": "2024-01-16T23:36:34.161350Z",
     "shell.execute_reply": "2024-01-16T23:36:34.160719Z"
    }
   },
   "outputs": [],
   "source": [
    "gammaL_HITEMP = gamma_hitran(\n",
    "    Pfix,\n",
    "    Tfix,\n",
    "    Ppart,\n",
    "    mdbCO_HITEMP.n_air,\n",
    "    mdbCO_HITEMP.gamma_air,\n",
    "    mdbCO_HITEMP.gamma_self,\n",
    ") + gamma_natural(mdbCO_HITEMP.A)\n",
    "\n",
    "gammaL_Li2015 = gamma_exomol(\n",
    "    Pfix, Tfix, mdbCO_Li2015.n_Texp, mdbCO_Li2015.alpha_ref\n",
    ") + gamma_natural(mdbCO_Li2015.A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thermal broadening\n",
    "\n",
    "$\\sigma_D^{t} = \\sqrt{\\frac{k_B T}{M m_u}} \\frac{\\nu_0}{c}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:34.163292Z",
     "iopub.status.busy": "2024-01-16T23:36:34.163153Z",
     "iopub.status.idle": "2024-01-16T23:36:34.209319Z",
     "shell.execute_reply": "2024-01-16T23:36:34.208664Z"
    }
   },
   "outputs": [],
   "source": [
    "# thermal doppler sigma\n",
    "sigmaD_HITEMP = doppler_sigma(mdbCO_HITEMP.nu_lines, Tfix, molecular_mass)\n",
    "sigmaD_Li2015 = doppler_sigma(mdbCO_Li2015.nu_lines, Tfix, molecular_mass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, the line center...\n",
    "\n",
    "In HITRAN database, a slight pressure shift can be included using $\\delta_{air}$:\n",
    "$\\nu_0(P) = \\nu_0 + \\delta_{air} P$. But this shift is quite a bit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:34.211118Z",
     "iopub.status.busy": "2024-01-16T23:36:34.210987Z",
     "iopub.status.idle": "2024-01-16T23:36:34.213095Z",
     "shell.execute_reply": "2024-01-16T23:36:34.212803Z"
    }
   },
   "outputs": [],
   "source": [
    "# line center\n",
    "nu0_HITEMP = mdbCO_HITEMP.nu_lines\n",
    "nu0_Li2015 = mdbCO_Li2015.nu_lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use Direct LFP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:34.214579Z",
     "iopub.status.busy": "2024-01-16T23:36:34.214457Z",
     "iopub.status.idle": "2024-01-16T23:36:34.477344Z",
     "shell.execute_reply": "2024-01-16T23:36:34.476657Z"
    }
   },
   "outputs": [],
   "source": [
    "from exojax.spec.initspec import init_lpf\n",
    "from exojax.spec.lpf import xsvector\n",
    "\n",
    "numatrix_HITEMP = init_lpf(mdbCO_HITEMP.nu_lines, nus)\n",
    "xsv_HITEMP = xsvector(numatrix_HITEMP, sigmaD_HITEMP, gammaL_HITEMP, Sij_HITEMP)\n",
    "\n",
    "numatrix_Li2015 = init_lpf(mdbCO_Li2015.nu_lines, nus)\n",
    "xsv_Li2015 = xsvector(numatrix_Li2015, sigmaD_Li2015, gammaL_Li2015, Sij_Li2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-01-16T23:36:34.479190Z",
     "iopub.status.busy": "2024-01-16T23:36:34.479058Z",
     "iopub.status.idle": "2024-01-16T23:36:35.371241Z",
     "shell.execute_reply": "2024-01-16T23:36:35.370783Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 4))\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(wav[::-1], xsv_HITEMP, lw=2, label=\"HITEMP2019\")\n",
    "plt.plot(wav[::-1], xsv_Li2015, lw=2, ls=\"dashed\", label=\"Exomol w/ .broad\")\n",
    "plt.xlim(22970, 22976)\n",
    "plt.xlabel(\"wavelength ($\\AA$)\", fontsize=14)\n",
    "plt.ylabel(\"cross section ($cm^{2}$)\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.tick_params(labelsize=12)\n",
    "plt.savefig(\"co_comparison.pdf\", bbox_inches=\"tight\", pad_inches=0.0)\n",
    "plt.savefig(\"co_comparison.png\", bbox_inches=\"tight\", pad_inches=0.0)\n",
    "plt.title(\"T=1300K,P=1bar\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.8 ('base')",
   "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": "3.10.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "72bc7f8b1808a6f5ada3c6a20601509b8b1843160436d276d47f2ba819b3753b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}