{
"cells": [
{
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"tags": []
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"source": [
"# Some worked out use-cases for `exo_k`\n",
"\n",
"*Author: Jeremy Leconte (CNRS/LAB/Univ. Bordeaux)*"
]
},
{
"cell_type": "markdown",
"id": "1cad3809",
"metadata": {
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"source": [
"Whereas the tutorial goes through all the concepts present in the library in a very progressive manner, here we propose some more complex examples that combine many of these concepts. The idea is to show some 'real life' examples of the use of the library that can be adapted by the users to their own needs. \n",
"\n",
"Some of these examples use publicly available data: do not forget to acknowledge them if you use them in your work (see the `where to find data` section). "
]
},
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"tags": []
},
"source": [
"## General initialization"
]
},
{
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"execution_count": 1,
"id": "08ae5c0a",
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"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import astropy.units as u\n",
"import time,sys,os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "959244d2",
"metadata": {
"execution": {
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"source": [
"# Uncomment the line below if you want to enable interactive plots\n",
"#%matplotlib notebook\n",
"plt.rcParams[\"figure.figsize\"] = (7,4)\n",
"from matplotlib import cycler\n",
"font = {'color': 'dimgray', 'weight': 'bold', 'size': 10}\n",
"colors = cycler('color',[plt.cm.inferno(i) for i in np.linspace(0.1,1,5)])\n",
"plt.rc('axes', axisbelow=True, grid=True, labelcolor='dimgray', labelweight='bold', prop_cycle=colors)\n",
"plt.rc('grid', linestyle='solid')\n",
"plt.rc('xtick', direction='in', color='dimgray')\n",
"plt.rc('ytick', direction='in', color='dimgray')\n",
"plt.rc('lines', linewidth=1.5)"
]
},
{
"cell_type": "markdown",
"id": "e062738b",
"metadata": {
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"duration": 0.009905,
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"exception": false,
"start_time": "2022-09-02T07:49:05.406518",
"status": "completed"
},
"tags": []
},
"source": [
"## Setting up global options for the library"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f96741df",
"metadata": {
"execution": {
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"source": [
"import exo_k as xk\n",
"\n",
"datapath='../data/'\n",
"#change to your own path\n",
"\n",
"xk.Settings().set_search_path(datapath + 'corrk', path_type='ktable')\n",
"xk.Settings().set_search_path(datapath + 'xsec', path_type='xtable')\n",
"xk.Settings().set_search_path(datapath + 'cia', path_type='cia')\n",
"\n",
"xk.Settings().set_mks(True)\n",
"# to automatically convert to mks units"
]
},
{
"cell_type": "markdown",
"id": "c208d1f4",
"metadata": {
"papermill": {
"duration": 0.010006,
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"exception": false,
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"status": "completed"
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"tags": []
},
"source": [
"# Loading ExoMol files and changing their resolution before saving them in different formats"
]
},
{
"cell_type": "markdown",
"id": "f36f82b4",
"metadata": {
"papermill": {
"duration": 0.009432,
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},
"source": [
"For this, you will need to download the relevant x-sec of ktable files from the ExoMol website [http://exomol.com/data/data-types/opacity/](http://exomol.com/data/data-types/opacity/) into the `datapath/xsec` or `datapath/corrk`."
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" file : /builds/jleconte/exo_k/data/corrk/1H2-16O__POKAZATEL__R1000_0.3-50mu.ktable.petitRADTRANS.h5\n",
" molecule : 1H2-16O\n",
" p grid : [1.e+00 1.e+01 1.e+02 1.e+03 1.e+04 1.e+05 1.e+06 1.e+07]\n",
" p unit : Pa\n",
" t grid (K) : [100 200 300 400 500]\n",
" wn grid : [ 205.12710964 215.64420145 226.70051608 238.32370009 250.54281748\n",
" 263.38842243 276.89263562 291.08922462 306.01368831 321.70334562\n",
" 338.19742886 355.53718183 373.76596294 392.92935365 413.07527241\n",
" 434.25409451 456.51877804 479.92499631 504.53127705 530.39914878\n",
" 557.59329465 586.1817142 616.23589336 647.83098324 681.04598802\n",
" 715.96396251 752.67221983 791.26254975 831.8314482 874.48035855\n",
" 919.31592529 966.4502607 1016.00122516 1068.09272189 1122.85500677\n",
" 1180.42501404 1240.9466987 1304.57139649 1371.45820229 1441.77436795\n",
" 1515.69572052 1593.40710189 1675.10283097 1760.98718966 1851.27493358\n",
" 1946.19182912 2045.97521795 2150.87461054 2261.15230999 2377.08406799\n",
" 2498.95977434 2627.08418177 2761.77766804 2903.37703702 3052.23636091\n",
" 3208.72786553 3373.24286117 3546.192721 3728.00990977 3919.14906514\n",
" 4120.08813457 4331.3295704 4553.40158624 4786.85947781 5032.28701143\n",
" 5290.29788379 5561.53725644 5846.68336912 6146.44923561 6461.58442674\n",
" 6792.87694463 7141.15519313 7507.29004927 7892.19704091 8296.83863601\n",
" 8722.22664934 9169.42477249 9639.55123371 9940.24491055]\n",
" wn unit : cm^-1\n",
" kdata unit : m^2/molecule\n",
" weights : [0.04555284 0.10007147 0.14116799 0.1632077 0.1632077 0.14116799\n",
" 0.10007147 0.04555284 0.00506143 0.01111905 0.01568533 0.01813419\n",
" 0.01813419 0.01568533 0.01111905 0.00506143]\n",
" data oredered following p, t, wn, g\n",
" shape : [ 8 5 79 16]\n",
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" file : /builds/jleconte/exo_k/data/corrk/12C-16O2__UCL-4000.R1000_0.3-50mu.ktable.petitRADTRANS.h5\n",
" molecule : CO2\n",
" p grid : [1.e+00 1.e+01 1.e+02 1.e+03 1.e+04 1.e+05 1.e+06 1.e+07]\n",
" p unit : Pa\n",
" t grid (K) : [100 200 300 400 500]\n",
" wn grid : [ 205.12710964 215.64420145 226.70051608 238.32370009 250.54281748\n",
" 263.38842243 276.89263562 291.08922462 306.01368831 321.70334562\n",
" 338.19742886 355.53718183 373.76596294 392.92935365 413.07527241\n",
" 434.25409451 456.51877804 479.92499631 504.53127705 530.39914878\n",
" 557.59329465 586.1817142 616.23589336 647.83098324 681.04598802\n",
" 715.96396251 752.67221983 791.26254975 831.8314482 874.48035855\n",
" 919.31592529 966.4502607 1016.00122516 1068.09272189 1122.85500677\n",
" 1180.42501404 1240.9466987 1304.57139649 1371.45820229 1441.77436795\n",
" 1515.69572052 1593.40710189 1675.10283097 1760.98718966 1851.27493358\n",
" 1946.19182912 2045.97521795 2150.87461054 2261.15230999 2377.08406799\n",
" 2498.95977434 2627.08418177 2761.77766804 2903.37703702 3052.23636091\n",
" 3208.72786553 3373.24286117 3546.192721 3728.00990977 3919.14906514\n",
" 4120.08813457 4331.3295704 4553.40158624 4786.85947781 5032.28701143\n",
" 5290.29788379 5561.53725644 5846.68336912 6146.44923561 6461.58442674\n",
" 6792.87694463 7141.15519313 7507.29004927 7892.19704091 8296.83863601\n",
" 8722.22664934 9169.42477249 9639.55123371 9940.24491055]\n",
" wn unit : cm^-1\n",
" kdata unit : m^2/molecule\n",
" weights : [0.04555284 0.10007147 0.14116799 0.1632077 0.1632077 0.14116799\n",
" 0.10007147 0.04555284 0.00506143 0.01111905 0.01568533 0.01813419\n",
" 0.01813419 0.01568533 0.01111905 0.00506143]\n",
" data oredered following p, t, wn, g\n",
" shape : [ 8 5 79 16]\n",
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" file : /builds/jleconte/exo_k/data/corrk/12C-1H4__YT34to10.R1000_0.3-50mu.ktable.petitRADTRANS.h5\n",
" molecule : CH4\n",
" p grid : [1.e+00 1.e+01 1.e+02 1.e+03 1.e+04 1.e+05 1.e+06 1.e+07]\n",
" p unit : Pa\n",
" t grid (K) : [100 200 300 400 500]\n",
" wn grid : [ 205.12710964 215.64420145 226.70051608 238.32370009 250.54281748\n",
" 263.38842243 276.89263562 291.08922462 306.01368831 321.70334562\n",
" 338.19742886 355.53718183 373.76596294 392.92935365 413.07527241\n",
" 434.25409451 456.51877804 479.92499631 504.53127705 530.39914878\n",
" 557.59329465 586.1817142 616.23589336 647.83098324 681.04598802\n",
" 715.96396251 752.67221983 791.26254975 831.8314482 874.48035855\n",
" 919.31592529 966.4502607 1016.00122516 1068.09272189 1122.85500677\n",
" 1180.42501404 1240.9466987 1304.57139649 1371.45820229 1441.77436795\n",
" 1515.69572052 1593.40710189 1675.10283097 1760.98718966 1851.27493358\n",
" 1946.19182912 2045.97521795 2150.87461054 2261.15230999 2377.08406799\n",
" 2498.95977434 2627.08418177 2761.77766804 2903.37703702 3052.23636091\n",
" 3208.72786553 3373.24286117 3546.192721 3728.00990977 3919.14906514\n",
" 4120.08813457 4331.3295704 4553.40158624 4786.85947781 5032.28701143\n",
" 5290.29788379 5561.53725644 5846.68336912 6146.44923561 6461.58442674\n",
" 6792.87694463 7141.15519313 7507.29004927 7892.19704091 8296.83863601\n",
" 8722.22664934 9169.42477249 9639.55123371 9940.24491055]\n",
" wn unit : cm^-1\n",
" kdata unit : m^2/molecule\n",
" weights : [0.04555284 0.10007147 0.14116799 0.1632077 0.1632077 0.14116799\n",
" 0.10007147 0.04555284 0.00506143 0.01111905 0.01568533 0.01813419\n",
" 0.01813419 0.01568533 0.01111905 0.00506143]\n",
" data oredered following p, t, wn, g\n",
" shape : [ 8 5 79 16]\n",
" \n"
]
}
],
"source": [
"#let's decide on the spectral grid we want:\n",
"wn0=200.\n",
"wn1=10000.\n",
"Resolution=20.\n",
"new_wn_grid=xk.wavenumber_grid_R(wn0, wn1, Resolution)\n",
"# Here it is a grid a constant resolution, but any numpy array will do. \n",
"\n",
"# a more focused logP-T grid\n",
"logp_array=np.arange(8) \n",
"t_array=np.arange(100,600,100) \n",
"\n",
"dir_out=datapath + 'corrk/'\n",
"\n",
"molecules = ['1H2-16O','12C-16O2','12C-1H4']\n",
"for mol in molecules:\n",
" tmp_ktab=xk.Ktable(mol,'R1000_0.3-50mu.ktable.petitRADTRANS.h5', remove_zeros=True)\n",
" \n",
" # if for some reasons you need to limit the P-T grid (optional)\n",
" tmp_ktab.remap_logPT(logp_array=logp_array, t_array=t_array) \n",
" \n",
" # the spectral binning phase\n",
" tmp_ktab.bin_down(new_wn_grid)\n",
" print(tmp_ktab)\n",
" # choose any of the lines below for different formats\n",
" tmp_ktab.write_hdf5(dir_out+mol+'_R20.ktable') # hdf5 file with current units\n",
" #tmp_ktab.write_hdf5(dir_out+mol+'_R20.ktable', exomol_units=True) # hdf5 file with Exomol units\n",
" #tmp_ktab.write_nemesis(dir_out+mol+'_R20.ktable') # binary nemesis format\n",
" #tmp_ktab.write_arcis(dir_out+mol+'_R20.ktable') # fits ARCIS format"
]
},
{
"cell_type": "markdown",
"id": "cf36c302",
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"tags": []
},
"source": [
"# Creating k-coefficients for a new species not in ExoMol from high-resolution spectra from the petitRADTRANS database"
]
},
{
"cell_type": "markdown",
"id": "b6b039f9",
"metadata": {
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},
"source": [
"Start by downloading some high resolution cross sections from petitRADTRANS:\n",
"[https://www.dropbox.com/sh/w7sa20v8qp19b4d/AABKF0GsjghsYLJMUJXDgrHma?dl=0](https://www.dropbox.com/sh/w7sa20v8qp19b4d/AABKF0GsjghsYLJMUJXDgrHma?dl=0)"
]
},
{
"cell_type": "markdown",
"id": "1460de17",
"metadata": {
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},
"source": [
"Here I am using data for Na. For the example to work with as few data as possible,\n",
"I only consider a grid with 2 P and T points. So you will only need the following files in the Na directory:\n",
"\n",
" * 'sigma_94_1215.K_0.001000bar.dat'\n",
" * 'sigma_94_1641.K_0.001000bar.dat'\n",
" * 'sigma_94_1215.K_1.000000bar.dat'\n",
" * 'sigma_94_1641.K_1.000000bar.dat'\n",
" * wlen.dat\n",
" \n",
"Feel free to extend to the full grid once you have downloaded the whole dataset. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1cef04e0",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['sigma_94_1215.K_0.001000bar.dat' 'sigma_94_1641.K_0.001000bar.dat']\n",
" ['sigma_94_1215.K_1.000000bar.dat' 'sigma_94_1641.K_1.000000bar.dat']]\n"
]
},
{
"data": {
"text/plain": [
"\n",
" file : ../data/hires/PetitRADTRANS/Na\n",
" molecule : Na\n",
" p grid : [ 100. 100000.]\n",
" p unit : Pa\n",
" t grid (K) : [1215 1641]\n",
" wn grid : [ 357.14265128 357.14300842 357.14336556 ... 33333.23696353\n",
" 33333.27029679 33333.30363008]\n",
" wn unit : cm^-1\n",
" kdata unit : m^2/molecule\n",
" data oredered following p, t, wn\n",
" shape : [ 2 2 4536178]\n",
" "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path_to_hires_spectra=datapath + 'hires/PetitRADTRANS/Na'\n",
"\n",
"press_grid_str=['0.001000','1.000000'] # notice the use of strings\n",
"logp_grid=[np.log10(float(p)) for p in press_grid_str]\n",
"t_grid=[1215, 1641]\n",
"\n",
"file_grid=xk.create_fname_grid('sigma_94_{temp}.K_{press}bar.dat', logpgrid=press_grid_str, tgrid=t_grid,\n",
" p_kw='press', t_kw='temp')\n",
"print(file_grid)\n",
"\n",
"Hires_spectra=xk.hires_to_xtable(path=path_to_hires_spectra, filename_grid=file_grid, logpgrid=logp_grid, tgrid=t_grid,\n",
" mol='Na', grid_p_unit='bar', binary=True, mass_amu=23.)\n",
"\n",
"Hires_spectra"
]
},
{
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{
"data": {
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\n",
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