{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c0e2d1b2-9d9b-49d2-bbaf-f2a6b9d3dd44",
   "metadata": {},
   "source": [
    "# Exoplanets: direct imaging\n",
    "\n",
    "In this example, we show how archNEMESIS can be used to construct input files and calculate forward models useful for exoplanets. Here, we show an example of how to initialise all the classes from scratch, so that users can adapt the notebook according to their own needs. In this example, we are going to model a spectrum simulating the direct imaging of an exoplanet (i.e., disc-averaged spectrum). The planet is assumed not to have aerosols or surface, but this can be easily adapted to include these features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6cb5c1a-c963-438e-8edf-a9171a786418",
   "metadata": {},
   "outputs": [],
   "source": [
    "import archnemesis as ans\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012e6e10-607a-4a3d-a13e-fbe67f742cd9",
   "metadata": {},
   "source": [
    "## 1. Creating the input files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dadeacb6-0d8e-47d4-b619-b56c047d99f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "runname = \"exoplanet\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4532c906-489c-44e6-9556-11fb0731377d",
   "metadata": {},
   "source": [
    "### Defining planet characteristics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8275e64c-baf7-4a68-9e91-6e221c6793d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Astronomical constants\n",
    "AU = 1.495978707e+11      # m astronomical unit\n",
    "R_SUN = 6.95700e8         # m solar radius\n",
    "R_JUP = 6.9911e7          # m Jupiter radius\n",
    "R_JUP_E = 7.1492e7        # m nominal equatorial Jupiter radius\n",
    "M_SUN = 1.98847542e+30    # kg solar mass\n",
    "M_JUP = 1.898e27          # kg Jupiter mass\n",
    "R_EAR_E = 6.3781e6          # m Earth radius\n",
    "M_EAR = 5.9722e24         # kg Earth mass\n",
    "G = 6.6743e-11            # m3 kg-1 s-2 Gravitational constant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9a84c83c-769c-4153-a8f0-ad83495303de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# L98-59 d\n",
    "R_star = 0.303 * R_SUN # m M3V star, 80 day rotation period, >0.8 Gyr\n",
    "M_plt = 1.94 * M_EAR # kg\n",
    "R_plt = 1.521 * R_EAR_E # m\n",
    "SMA = 0.0486 * AU # m\n",
    "T_star = 3415.0 # K\n",
    "g_plt = G * M_plt / R_plt**2\n",
    "T_eq = T_star * (R_star/(2*SMA))**0.5\n",
    "T_int = 200.0 # randomly set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3991b9b-acd1-4ca3-b103-bcec537c04ad",
   "metadata": {},
   "source": [
    "### 1.1. Atmosphere"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "54af8066-cc9f-450a-b639-6828722d1f95",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Atmosphere = ans.Atmosphere_0()\n",
    "\n",
    "Atmosphere.NP = 50   #Number of points in vertical profiles\n",
    "Atmosphere.LATITUDE = 0. ; Atmosphere.LONGITUDE = 0.\n",
    "Atmosphere.IPLANET = -1  #Custom planet\n",
    "\n",
    "#Planetary parameters\n",
    "Atmosphere.PLANET_MASS = M_plt\n",
    "Atmosphere.PLANET_RADIUS = R_plt / 1.0e3  #km\n",
    "\n",
    "#Defining the pressure profile\n",
    "Atmosphere.edit_P(np.logspace(1, -7, Atmosphere.NP) * 101325.) # in Pa\n",
    "Atmosphere.edit_H(np.linspace(0.,100.,Atmosphere.NP) * 1.0e3) # dummy height profile (it will be calculated hydrostatically)\n",
    "\n",
    "#Defining the gaseous abundances\n",
    "gasID = np.array([1,2,5,11,6, 9,23,26,39,40],dtype='int32') #H2O,CO2,CO,NH3,CH4,SO2,HCN,C2H2,H2(inactive),He(inactive)\n",
    "isoID = np.zeros(len(gasID),dtype='int32')\n",
    "\n",
    "abundances_active = np.array([1.0e-3, 1.0e-4, 1.0e-3, 1.0e-4, 1.0e-2, 1.0e-3, 1.0e-8, 1.0e-6])\n",
    "abundance_inactive = 1. - np.sum(abundances_active)\n",
    "h2_frac = 0.8547\n",
    "h2_vmr = abundance_inactive * h2_frac ; he_vmr = abundance_inactive * (1. - h2_frac)\n",
    "abundances = np.zeros(len(gasID))\n",
    "abundances[0:len(abundances_active)] = abundances_active\n",
    "abundances[-2] = h2_vmr\n",
    "abundances[-1] = he_vmr\n",
    "\n",
    "Atmosphere.NVMR = len(gasID)\n",
    "Atmosphere.ID = gasID\n",
    "Atmosphere.ISO = isoID\n",
    "\n",
    "vmr = np.zeros((Atmosphere.NP,Atmosphere.NVMR))\n",
    "vmr[:,:] = abundances[None,:]\n",
    "Atmosphere.edit_VMR(vmr)\n",
    "Atmosphere.AMFORM = 2  #Molecular weight calculated internally based on gaseous abundances\n",
    "Atmosphere.calc_molwt()\n",
    "\n",
    "#Calculating the temperature profile using the parameterisation of Parmentier and Guillot (2014)\n",
    "k, g1, g2, alpha, beta = 1.0e-2, 1.0e1, 1.0e1, 0.5, 1.0\n",
    "Atmosphere, xmap = ans.Models[43].calculate(Atmosphere,alpha,beta,k,g1,g2,T_star,R_star,SMA,T_int)\n",
    "\n",
    "#Calculating altitudes based on hydrostatic equilibrium equation\n",
    "Atmosphere.adjust_hydrostatH()\n",
    "\n",
    "#Calculating the aerosols in the atmosphere (no clouds)\n",
    "Atmosphere.NDUST = 1\n",
    "dust = np.zeros((Atmosphere.NP,Atmosphere.NDUST))   #m-3\n",
    "Atmosphere.edit_DUST(dust)\n",
    "\n",
    "Atmosphere.assess()\n",
    "Atmosphere.plot_Atm()\n",
    "Atmosphere.write_hdf5(runname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e65f97-ccb0-4e95-b156-827a13eed5df",
   "metadata": {},
   "source": [
    "### 1.2. Spectroscopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f06c74bd-d718-4824-b390-e54c2ed13c3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING :: read_header :: Spectroscopy_0.py-564 :: self.NGAS=8 self.LOCATION=PathRedirectList(['/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/H2O_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/CO2_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/CO_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/NH3_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/CH4_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/SO2_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/HCN_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/C2H2_R1000.kta'], redirects = {})\n",
      "INFO :: summary_info :: Spectroscopy_0.py-233 :: Calculation type ILBL ::  (<SpectralCalculationMode.K_TABLES: 0>, ' (k-distribution)')\n",
      "INFO :: summary_info :: Spectroscopy_0.py-234 :: Number of radiatively-active gaseous species ::  8\n",
      "INFO :: summary_info :: Spectroscopy_0.py-241 :: Gaseous species ::  ['H2O', 'CO2', 'CO', 'NH3', 'CH4', 'SO2', 'HCN', 'H2O']\n",
      "INFO :: summary_info :: Spectroscopy_0.py-243 :: Number of g-ordinates ::  20\n",
      "INFO :: summary_info :: Spectroscopy_0.py-245 :: Number of spectral points ::  5119\n",
      "INFO :: summary_info :: Spectroscopy_0.py-246 :: Wavelength range ::  (0.3001500070095062, '-', 49.997337341308594)\n",
      "INFO :: summary_info :: Spectroscopy_0.py-247 :: Step size ::  0.0003001391887664795\n",
      "INFO :: summary_info :: Spectroscopy_0.py-249 :: Spectral resolution of the k-tables (FWHM) ::  0.0\n",
      "INFO :: summary_info :: Spectroscopy_0.py-251 :: Number of temperature levels ::  27\n",
      "INFO :: summary_info :: Spectroscopy_0.py-252 :: Temperature range ::  (100.0, '-', 3400.0)\n",
      "INFO :: summary_info :: Spectroscopy_0.py-254 :: Number of pressure levels ::  22\n",
      "INFO :: summary_info :: Spectroscopy_0.py-255 :: Pressure range ::  (1e-05, '-', 100.0)\n"
     ]
    }
   ],
   "source": [
    "#Initialising spectroscopy class with ILBL = 0 (k-tables)\n",
    "Spectroscopy = ans.Spectroscopy_0(ILBL=0)\n",
    "\n",
    "Spectroscopy.NGAS = 8  #Number of radiatively active gases\n",
    "specdir = '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/'\n",
    "Spectroscopy.LOCATION = [specdir+'H2O_R1000.kta',\n",
    "                         specdir+'CO2_R1000.kta',\n",
    "                         specdir+'CO_R1000.kta',\n",
    "                         specdir+'NH3_R1000.kta',\n",
    "                         specdir+'CH4_R1000.kta',\n",
    "                         specdir+'SO2_R1000.kta',\n",
    "                         specdir+'HCN_R1000.kta',\n",
    "                         specdir+'C2H2_R1000.kta']\n",
    "\n",
    "#Reading the header information\n",
    "Spectroscopy.read_header()\n",
    "\n",
    "#Printing summary information\n",
    "Spectroscopy.summary_info()\n",
    "Spectroscopy.write_hdf5(runname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda61fd1-90b9-46d2-b95a-bff9a76ed3be",
   "metadata": {},
   "source": [
    "### 1.3. Measurement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "542c5693-d779-4b06-9940-ed7bb41377dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Initialising the class\n",
    "Measurement = ans.Measurement_0()\n",
    "\n",
    "Measurement.ISPACE = 1  #Wavelength (um)\n",
    "Measurement.NGEOM = 1   #Number of geometries (just one spectrum)\n",
    "Measurement.FWHM = 0.0  #Assuming no instrumental broadening\n",
    "Measurement.IFORM = 1   #Flux ratio (F_planet/F_star)\n",
    "\n",
    "#Defining the spectral range\n",
    "vmin = 10. ; vmax = 40.\n",
    "vconvx = Spectroscopy.WAVE[ (Spectroscopy.WAVE>=vmin) & (Spectroscopy.WAVE<=vmax) ]\n",
    "nconvx = len(vconvx)\n",
    "\n",
    "vconv = np.zeros((nconvx,Measurement.NGEOM))\n",
    "vconv[:,0] = vconvx\n",
    "\n",
    "Measurement.NCONV = np.zeros(Measurement.NGEOM,dtype='int32') + nconvx\n",
    "Measurement.edit_VCONV(vconv)\n",
    "Measurement.edit_MEAS(np.ones(vconv.shape))     #Dummy arrays for the measured spectrum since we are only simulating\n",
    "Measurement.edit_ERRMEAS(np.ones(vconv.shape))  \n",
    "\n",
    "#Calculating averaging points for disc-integrated observations\n",
    "Measurement.calc_avepoints_exoplanet(phase=0.,nmu=5)\n",
    "Measurement.plot_fov()\n",
    "\n",
    "#Writing information to file\n",
    "Measurement.write_hdf5(runname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce29982c-9fd9-43a4-86af-e27c7341845b",
   "metadata": {},
   "source": [
    "### 1.4. Surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "341d560c-cdde-4e81-b0d0-216a60e9d6c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialising and creating dummy surface class\n",
    "Surface = ans.Surface_0()\n",
    "Surface.GASGIANT = True   #No surface\n",
    "Surface.TSURF = 0.0\n",
    "Surface.GALB = 0.0\n",
    "Surface.LOWBC = 0\n",
    "Surface.assess()\n",
    "Surface.write_hdf5(runname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7f87cff-fc09-41f8-8d8d-0bd9dcf90723",
   "metadata": {},
   "source": [
    "### 1.5. Scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f2f6fe0-c21d-4b2c-87ec-b6effa228e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "Scatter = ans.Scatter_0()\n",
    "Scatter.ISCAT = 0   #No scattering\n",
    "\n",
    "#Now we initialise the arrays, but they won't be used since there are no aerosols in the atmosphere\n",
    "Scatter.initialise_arrays(NDUST=1,NWAVE=2,NTHETA=5)\n",
    "Scatter.WAVE = np.linspace(Spectroscopy.WAVE.min(),Spectroscopy.WAVE.max(),Scatter.NWAVE)\n",
    "Scatter.assess()\n",
    "Scatter.write_hdf5(runname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d57ed3-feca-4388-813d-93a583e9c9eb",
   "metadata": {},
   "source": [
    "### 1.6. CIA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64aa419e-a5c2-48c9-ad82-160ef2d7cbb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialising the CIA class\n",
    "CIA = ans.CIA_0()\n",
    "\n",
    "#Indicating the name of the CIA file\n",
    "CIA.CIATABLE = \"exocia_hitran12_200-3800K.h5\"\n",
    "\n",
    "#Writing information to file\n",
    "CIA.assess()\n",
    "CIA.write_hdf5(runname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72f751e1-2143-44b5-bd6c-28cacd9137a6",
   "metadata": {},
   "source": [
    "### 1.7. Stellar spectrum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d0e1f5f-b663-458b-8e33-7ed9edcd2b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialising class\n",
    "Stellar = ans.Stellar_0()\n",
    "\n",
    "#Defining the planet-star distance\n",
    "Stellar.DIST = SMA / AU   #Star-Planet distance (AU)\n",
    "\n",
    "#Writing the information into HDF5 file\n",
    "Stellar.write_hdf5(runname,solfile='solar_noaa_wl_2024.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9519b19b-837f-4e71-983c-4f2aebf2d88a",
   "metadata": {},
   "source": [
    "### 1.8. Layering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dafca33c-0db7-4940-a071-d3dc9b9121ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialising class\n",
    "Layer = ans.Layer_0()\n",
    "\n",
    "Layer.NLAY =  31     #Number of atmospheric layers\n",
    "Layer.LAYHT = 0.0    #Altitude of lowest layer in atmosphere\n",
    "Layer.LAYINT = 1     #Curtis-Godson layer integration\n",
    "Layer.LAYTYP = 1     #Layers divided by equal changes in log(pressure)\n",
    "\n",
    "Layer.write_hdf5(runname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4557f5bb-dbfd-4175-8add-8308e2a40005",
   "metadata": {},
   "source": [
    "### 1.9. Retrieval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "910d0b59-3e22-4d89-a6bc-5c3bb29c20c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Retrieval = ans.OptimalEstimation_0(IRET=0)\n",
    "\n",
    "Retrieval.NITER = -1       #Number of iterations\n",
    "Retrieval.PHILIMIT = 0.1   #Convergence criterion\n",
    "Retrieval.NCORES = 1       #Number of available cores\n",
    "\n",
    "Retrieval.assess_input()\n",
    "Retrieval.write_input_hdf5(runname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f0a1e02-2058-454c-8519-912f05289527",
   "metadata": {},
   "source": [
    "### 1.10. Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a562dcd1-a1a2-4775-a948-a895c53b7bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Retrieving a scaling factor for the CO2 abundance\n",
    "f = open(runname+\".apr\",\"w\")\n",
    "\n",
    "NVAR = 1\n",
    "\n",
    "f.write(\"#Exoplanet example \\n\")\n",
    "f.write(str(NVAR)+\" \\n\")\n",
    "f.write(\"2 0 2 \\n\")\n",
    "f.write(\"1.0 0.5 \\n\")\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ae4520-aaa4-4c8d-8493-58e5cd930344",
   "metadata": {},
   "source": [
    "## 2. Running forward model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8dd21501-808e-437e-aabc-aad7f6c0073d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING :: read_hdf5 :: Layer_0.py-379 :: When reading file \"exoplanet.h5\", could not find element \"Layer/BASEH\" setting returned value to \"None\"\n",
      "WARNING :: read_hdf5 :: Layer_0.py-380 :: When reading file \"exoplanet.h5\", could not find element \"Layer/BASEP\" setting returned value to \"None\"\n",
      "WARNING :: read_hdf5 :: Layer_0.py-381 :: When reading file \"exoplanet.h5\", could not find element \"Layer/TAUTOT\" setting returned value to \"None\"\n",
      "WARNING :: read_header :: Spectroscopy_0.py-564 :: self.NGAS=8 self.LOCATION=PathRedirectList(['/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/H2O_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/CO2_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/CO_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/NH3_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/CH4_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/SO2_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/HCN_R1000.kta', '/exomars/retrievals/nemesis/spectroscopy/Ktables/Exoplanets/Jake/R1000/C2H2_R1000.kta'], redirects = {})\n",
      "INFO :: read_apr :: Variables_0.py-823 :: \n",
      "Variables_0 :: read_apr :: varident [2 0 2]. Constructed model \"Model2\" (id=2)\n",
      "INFO :: read_apr :: Variables_0.py-826 ::   Model2:\n",
      "  |- id : 2\n",
      "  |- parent classes: PreRTModelBase\n",
      "  |- description: In this model, the atmospheric parameters are scaled using a\n",
      "  |               single factor with  respect to the vertical profiles in the\n",
      "  |               reference atmosphere\n",
      "  |- n_state_vector_entries : 1\n",
      "  |- state_vector_slice : slice(0, 1, None)\n",
      "  |- state_vector_start : 0\n",
      "  |- target : 0\n",
      "  |- Parameters:\n",
      "  |  |- scaling_factor :\n",
      "  |  |  |- slice : slice(0, 1, None)\n",
      "  |  |  |- unit : PROFILE_TYPE\n",
      "  |  |  |- description: Scaling factor applied to the reference profile\n",
      "  |  |  |- apriori value : 1.0\n"
     ]
    }
   ],
   "source": [
    "#Reading the input files\n",
    "Atmosphere,Measurement,Spectroscopy,Scatter,Stellar,Surface,CIA,Layer,Variables,Retrieval,Telluric = ans.Files.read_input_files_hdf5(runname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0296083-f23c-4f75-ae0c-8736741cfbb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO :: __init__ :: ForwardModel_0.py-255 :: Checking atmospheric gasses have spectroscopy data.\n",
      "WARNING :: __init__ :: ForwardModel_0.py-302 :: Not all atmospheric gasses have spectroscopy data.\n",
      "# WARNING #########################################################################\n",
      "\n",
      "The following atmospheric gasses ARE NOT PRESENT in the spectroscopy data and WILL NOT CONTRIBUTE TO OPACITY:\n",
      "\n",
      "    C2H2 (id 26) isotopologue 0\n",
      "    H2 (id 39) isotopologue 0\n",
      "    He (id 40) isotopologue 0\n",
      "\n",
      "To deactivate this warning place a path to a k-table file for these gasses in one of the following locations (depending upon your input file type):\n",
      "\n",
      "    [HDF5 Input]\n",
      "        In the \"wasp121.h5\" file, add an entry to \"/Spectroscopy/LOCATION\"\n",
      "        and update \"/Spectroscopy/NGAS\" appropriately.\n",
      "\n",
      "    [LEGACY Input]\n",
      "        Add an entry to the \"wasp121.kls\" file.\n",
      "\n",
      "# END WARNING #####################################################################\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "WARNING :: __init__ :: AtmCalc_0.py-254 ::  in AtmCalc_0.py file :: THERM requires BINBB disabled - resetting\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n",
      "INFO :: calculate_vertical_cia_opacity :: ForwardModel_0.py-3713 :: Calculating self.CIAX opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3782 :: CIRSrad :: Aerosol optical depths at  (10.00189208984375, ' :: ', array([0.]))\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3800 :: Calculating TOTAL opacity\n",
      "INFO :: calculate_layer_opacity :: ForwardModel_0.py-3817 :: CIRSradg :: Calculating TOTAL line-of-sight opacity\n",
      "INFO :: CIRSrad :: ForwardModel_0.py-4149 :: CIRSrad :: IMODM = <PathCalc.THERMAL_EMISSION: 64>\n",
      "INFO :: calculate_thermal_emission_spectrum :: ForwardModel_0.py-3873 :: CIRSradg :: Calculating THERMAL_EMISSION\n"
     ]
    }
   ],
   "source": [
    "ForwardModel = ans.ForwardModel_0(Atmosphere=Atmosphere,Surface=Surface,Measurement=Measurement,Spectroscopy=Spectroscopy,Stellar=Stellar,Scatter=Scatter,CIA=CIA,Layer=Layer,Variables=Variables,Telluric=Telluric)\n",
    "ForwardModel.NCores = 10\n",
    "SPECONV = ForwardModel.nemesisdiscfm()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3db7cc36-e6a8-4622-9d05-e87184db6593",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax1 = plt.subplots(1,1,figsize=(10,4))\n",
    "\n",
    "ax1.plot(Measurement.VCONV[:,0],SPECONV,c='black',linewidth=1.)\n",
    "ax1.grid()\n",
    "ax1.set_xlabel('Wavelength ($\\mu$m)')\n",
    "ax1.set_ylabel('Flux ratio ($F_{\\mathrm{planet}}/F_{\\mathrm{star}}$)')\n",
    "ax1.set_facecolor('lightgray')\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "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": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}