Notebooks/8_SED_Plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SED plots\n",
    "Here we present the galaxy with the best SED in the models presented by Stalevski et. al 2012, 2016 [(SKIRTOR)](https://ui.adsabs.harvard.edu/abs/2016MNRAS.458.2288S/abstract) and Fritz et. al 2006 [(Fritz)](https://ui.adsabs.harvard.edu/abs/2006MNRAS.366..767F/abstract). First, we import all important packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:47.134933Z",
     "start_time": "2021-08-03T09:13:46.730483Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:47.683660Z",
     "start_time": "2021-08-03T09:13:47.135798Z"
    }
   },
   "outputs": [],
   "source": [
    "import astropy.units as u\n",
    "from astropy.table import Table, join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then read the photometry and the clean outputs from CIGALE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:47.843823Z",
     "start_time": "2021-08-03T09:13:47.684697Z"
    }
   },
   "outputs": [],
   "source": [
    "phot = Table.read('../Data/Final/CIGPhot_EnergyBal_All.tbl', format='ascii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.106850Z",
     "start_time": "2021-08-03T09:13:47.844779Z"
    }
   },
   "outputs": [],
   "source": [
    "ModelS = Table.read('../Data/Final/CIGALEOutputs/Cleanresults_SK.fits', format='fits')\n",
    "ModelF = Table.read('../Data/Final/CIGALEOutputs/Cleanresults_Fr.fits', format='fits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We count the number of photometry points for the SED fitting for each galaxy and join with the previous Tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.666087Z",
     "start_time": "2021-08-03T09:13:48.107792Z"
    }
   },
   "outputs": [],
   "source": [
    "phot['CountPoints'] = [sum(~np.isnan(list(j))) /\n",
    "                       2 for j in phot[phot.colnames[2:]].as_array()]\n",
    "PhotS = join(phot, ModelS)\n",
    "PhotF = join(phot, ModelF)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we join both tables into one to select only galaxies where we have satisfiying results in both models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.758203Z",
     "start_time": "2021-08-03T09:13:48.666919Z"
    }
   },
   "outputs": [],
   "source": [
    "PhotAll = join(PhotS, PhotF, keys=('id', 'redshift', 'CountPoints'),\n",
    "               table_names=['S', 'F'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select best and worst SED fittings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the best galaxy, we select galaxies with more than 20 photometry points and $\\chi_{\\rm{red}}^{2}$ values close to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.761502Z",
     "start_time": "2021-08-03T09:13:48.759110Z"
    }
   },
   "outputs": [],
   "source": [
    "AllS_chis = PhotAll['best.reduced_chi_square_S']\n",
    "AllF_chis = PhotAll['best.reduced_chi_square_F']\n",
    "\n",
    "SelecCut = np.logical_and.reduce([AllS_chis < 1.2, AllF_chis < 1.2,\n",
    "                                  AllS_chis > 0.8, AllF_chis > 0.8,\n",
    "                                  PhotAll['CountPoints'] >= 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.829942Z",
     "start_time": "2021-08-03T09:13:48.762259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=2</i>\n",
       "<table id=\"table140556768220928\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>id</th><th>redshift</th><th>FUV_S</th><th>FUV_err_S</th><th>NUV_S</th><th>NUV_err_S</th><th>u_prime_S</th><th>u_prime_err_S</th><th>g_prime_S</th><th>g_prime_err_S</th><th>r_prime_S</th><th>r_prime_err_S</th><th>i_prime_S</th><th>i_prime_err_S</th><th>z_prime_S</th><th>z_prime_err_S</th><th>J_2mass_S</th><th>J_2mass_err_S</th><th>H_2mass_S</th><th>H_2mass_err_S</th><th>Ks_2mass_S</th><th>Ks_2mass_err_S</th><th>WISE1_S</th><th>WISE1_err_S</th><th>IRAC1_S</th><th>IRAC1_err_S</th><th>IRAC2_S</th><th>IRAC2_err_S</th><th>WISE2_S</th><th>WISE2_err_S</th><th>IRAC3_S</th><th>IRAC3_err_S</th><th>IRAC4_S</th><th>IRAC4_err_S</th><th>IRAS1_S</th><th>IRAS1_err_S</th><th>WISE3_S</th><th>WISE3_err_S</th><th>WISE4_S</th><th>WISE4_err_S</th><th>MIPS1_S</th><th>MIPS1_err_S</th><th>IRAS2_S</th><th>IRAS2_err_S</th><th>IRAS3_S</th><th>IRAS3_err_S</th><th>PACS_blue_S</th><th>PACS_blue_err_S</th><th>MIPS2_S</th><th>MIPS2_err_S</th><th>IRAS4_S</th><th>IRAS4_err_S</th><th>PACS_green_S</th><th>PACS_green_err_S</th><th>MIPS3_S</th><th>MIPS3_err_S</th><th>PACS_red_S</th><th>PACS_red_err_S</th><th>PSW_ext_S</th><th>PSW_ext_err_S</th><th>PMW_ext_S</th><th>PMW_ext_err_S</th><th>PLW_ext_S</th><th>PLW_ext_err_S</th><th>CountPoints</th><th>bayes.agn.EBV_S</th><th>bayes.agn.EBV_err_S</th><th>bayes.agn.Mcl</th><th>bayes.agn.Mcl_err</th><th>bayes.agn.R</th><th>bayes.agn.R_err</th><th>bayes.agn.emissivity_S</th><th>bayes.agn.emissivity_err_S</th><th>bayes.agn.fracAGN_S</th><th>bayes.agn.fracAGN_err_S</th><th>bayes.agn.fracAGN_dale2014_S</th><th>bayes.agn.fracAGN_dale2014_err_S</th><th>bayes.agn.i_S</th><th>bayes.agn.i_err_S</th><th>bayes.agn.law_S</th><th>bayes.agn.law_err_S</th><th>bayes.agn.oa</th><th>bayes.agn.oa_err</th><th>bayes.agn.pl</th><th>bayes.agn.pl_err</th><th>bayes.agn.q</th><th>bayes.agn.q_err</th><th>bayes.agn.t</th><th>bayes.agn.t_err</th><th>bayes.agn.temperature_S</th><th>bayes.agn.temperature_err_S</th><th>bayes.attenuation.E_BV_factor_S</th><th>bayes.attenuation.E_BV_factor_err_S</th><th>bayes.attenuation.E_BV_lines_S</th><th>bayes.attenuation.E_BV_lines_err_S</th><th>bayes.attenuation.E_BVs_S</th><th>bayes.attenuation.E_BVs_err_S</th><th>bayes.attenuation.FUV_S</th><th>bayes.attenuation.FUV_err_S</th><th>bayes.attenuation.powerlaw_slope_S</th><th>bayes.attenuation.powerlaw_slope_err_S</th><th>bayes.attenuation.uv_bump_amplitude_S</th><th>bayes.attenuation.uv_bump_amplitude_err_S</th><th>bayes.attenuation.uv_bump_wavelength_S</th><th>bayes.attenuation.uv_bump_wavelength_err_S</th><th>bayes.attenuation.uv_bump_width_S</th><th>bayes.attenuation.uv_bump_width_err_S</th><th>bayes.dust.alpha_S</th><th>bayes.dust.alpha_err_S</th><th>bayes.nebular.f_dust_S</th><th>bayes.nebular.f_dust_err_S</th><th>bayes.nebular.f_esc_S</th><th>bayes.nebular.f_esc_err_S</th><th>bayes.nebular.lines_width_S</th><th>bayes.nebular.lines_width_err_S</th><th>bayes.nebular.logU_S</th><th>bayes.nebular.logU_err_S</th><th>bayes.sfh.age_S</th><th>bayes.sfh.age_err_S</th><th>bayes.sfh.age_burst_S</th><th>bayes.sfh.age_burst_err_S</th><th>bayes.sfh.age_main_S</th><th>bayes.sfh.age_main_err_S</th><th>bayes.sfh.f_burst_S</th><th>bayes.sfh.f_burst_err_S</th><th>bayes.sfh.tau_burst_S</th><th>bayes.sfh.tau_burst_err_S</th><th>bayes.sfh.tau_main_S</th><th>bayes.sfh.tau_main_err_S</th><th>bayes.stellar.age_m_star_S</th><th>bayes.stellar.age_m_star_err_S</th><th>bayes.stellar.imf_S</th><th>bayes.stellar.imf_err_S</th><th>bayes.stellar.metallicity_S</th><th>bayes.stellar.metallicity_err_S</th><th>bayes.stellar.old_young_separation_age_S</th><th>bayes.stellar.old_young_separation_age_err_S</th><th>bayes.universe.age_S</th><th>bayes.universe.age_err_S</th><th>bayes.universe.luminosity_distance_S</th><th>bayes.universe.luminosity_distance_err_S</th><th>bayes.universe.redshift_S</th><th>bayes.universe.redshift_err_S</th><th>bayes.agn.accretion_power_S</th><th>bayes.agn.accretion_power_err_S</th><th>bayes.agn.disk_luminosity_S</th><th>bayes.agn.disk_luminosity_err_S</th><th>bayes.agn.dust_luminosity_S</th><th>bayes.agn.dust_luminosity_err_S</th><th>bayes.agn.intrin_Lnu_2500A_S</th><th>bayes.agn.intrin_Lnu_2500A_err_S</th><th>bayes.agn.luminosity_S</th><th>bayes.agn.luminosity_err_S</th><th>bayes.dust.luminosity_S</th><th>bayes.dust.luminosity_err_S</th><th>bayes.sfh.integrated_S</th><th>bayes.sfh.integrated_err_S</th><th>bayes.sfh.sfr_S</th><th>bayes.sfh.sfr_err_S</th><th>bayes.sfh.sfr100Myrs_S</th><th>bayes.sfh.sfr100Myrs_err_S</th><th>bayes.sfh.sfr10Myrs_S</th><th>bayes.sfh.sfr10Myrs_err_S</th><th>bayes.stellar.lum_S</th><th>bayes.stellar.lum_err_S</th><th>bayes.stellar.lum_ly_S</th><th>bayes.stellar.lum_ly_err_S</th><th>bayes.stellar.lum_ly_old_S</th><th>bayes.stellar.lum_ly_old_err_S</th><th>bayes.stellar.lum_ly_young_S</th><th>bayes.stellar.lum_ly_young_err_S</th><th>bayes.stellar.lum_old_S</th><th>bayes.stellar.lum_old_err_S</th><th>bayes.stellar.lum_young_S</th><th>bayes.stellar.lum_young_err_S</th><th>bayes.stellar.m_gas_S</th><th>bayes.stellar.m_gas_err_S</th><th>bayes.stellar.m_gas_old_S</th><th>bayes.stellar.m_gas_old_err_S</th><th>bayes.stellar.m_gas_young_S</th><th>bayes.stellar.m_gas_young_err_S</th><th>bayes.stellar.m_star_S</th><th>bayes.stellar.m_star_err_S</th><th>bayes.stellar.m_star_old_S</th><th>bayes.stellar.m_star_old_err_S</th><th>bayes.stellar.m_star_young_S</th><th>bayes.stellar.m_star_young_err_S</th><th>bayes.stellar.n_ly_S</th><th>bayes.stellar.n_ly_err_S</th><th>bayes.stellar.n_ly_old_S</th><th>bayes.stellar.n_ly_old_err_S</th><th>bayes.stellar.n_ly_young_S</th><th>bayes.stellar.n_ly_young_err_S</th><th>bayes.FUV_S</th><th>bayes.FUV_err_S</th><th>bayes.H_2mass_S</th><th>bayes.H_2mass_err_S</th><th>bayes.IRAC1_S</th><th>bayes.IRAC1_err_S</th><th>bayes.IRAC2_S</th><th>bayes.IRAC2_err_S</th><th>bayes.IRAC3_S</th><th>bayes.IRAC3_err_S</th><th>bayes.IRAC4_S</th><th>bayes.IRAC4_err_S</th><th>bayes.IRAS1_S</th><th>bayes.IRAS1_err_S</th><th>bayes.IRAS2_S</th><th>bayes.IRAS2_err_S</th><th>bayes.IRAS3_S</th><th>bayes.IRAS3_err_S</th><th>bayes.IRAS4_S</th><th>bayes.IRAS4_err_S</th><th>bayes.J_2mass_S</th><th>bayes.J_2mass_err_S</th><th>bayes.Ks_2mass_S</th><th>bayes.Ks_2mass_err_S</th><th>bayes.MIPS1_S</th><th>bayes.MIPS1_err_S</th><th>bayes.MIPS2_S</th><th>bayes.MIPS2_err_S</th><th>bayes.MIPS3_S</th><th>bayes.MIPS3_err_S</th><th>bayes.NUV_S</th><th>bayes.NUV_err_S</th><th>bayes.PACS_blue_S</th><th>bayes.PACS_blue_err_S</th><th>bayes.PACS_green_S</th><th>bayes.PACS_green_err_S</th><th>bayes.PACS_red_S</th><th>bayes.PACS_red_err_S</th><th>bayes.PLW_ext_S</th><th>bayes.PLW_ext_err_S</th><th>bayes.PMW_ext_S</th><th>bayes.PMW_ext_err_S</th><th>bayes.PSW_ext_S</th><th>bayes.PSW_ext_err_S</th><th>bayes.WISE1_S</th><th>bayes.WISE1_err_S</th><th>bayes.WISE2_S</th><th>bayes.WISE2_err_S</th><th>bayes.WISE3_S</th><th>bayes.WISE3_err_S</th><th>bayes.WISE4_S</th><th>bayes.WISE4_err_S</th><th>bayes.g_prime_S</th><th>bayes.g_prime_err_S</th><th>bayes.i_prime_S</th><th>bayes.i_prime_err_S</th><th>bayes.r_prime_S</th><th>bayes.r_prime_err_S</th><th>bayes.u_prime_S</th><th>bayes.u_prime_err_S</th><th>bayes.z_prime_S</th><th>bayes.z_prime_err_S</th><th>best.chi_square_S</th><th>best.reduced_chi_square_S</th><th>best.agn.EBV_S</th><th>best.agn.Mcl</th><th>best.agn.R</th><th>best.agn.emissivity_S</th><th>best.agn.fracAGN_S</th><th>best.agn.fracAGN_dale2014_S</th><th>best.agn.i</th><th>best.agn.law_S</th><th>best.agn.oa</th><th>best.agn.pl</th><th>best.agn.q</th><th>best.agn.t</th><th>best.agn.temperature_S</th><th>best.attenuation.E_BV_factor_S</th><th>best.attenuation.E_BV_lines_S</th><th>best.attenuation.E_BVs_S</th><th>best.attenuation.FUV_S</th><th>best.attenuation.powerlaw_slope_S</th><th>best.attenuation.uv_bump_amplitude_S</th><th>best.attenuation.uv_bump_wavelength_S</th><th>best.attenuation.uv_bump_width_S</th><th>best.dust.alpha_S</th><th>best.nebular.f_dust_S</th><th>best.nebular.f_esc_S</th><th>best.nebular.lines_width_S</th><th>best.nebular.logU_S</th><th>best.sfh.age_S</th><th>best.sfh.age_burst_S</th><th>best.sfh.age_main_S</th><th>best.sfh.f_burst_S</th><th>best.sfh.tau_burst_S</th><th>best.sfh.tau_main_S</th><th>best.stellar.age_m_star_S</th><th>best.stellar.imf_S</th><th>best.stellar.metallicity_S</th><th>best.stellar.old_young_separation_age_S</th><th>best.universe.age_S</th><th>best.universe.luminosity_distance_S</th><th>best.universe.redshift_S</th><th>best.agn.accretion_power_S</th><th>best.agn.disk_luminosity_S</th><th>best.agn.dust_luminosity</th><th>best.agn.intrin_Lnu_2500A_S</th><th>best.agn.luminosity_S</th><th>best.dust.luminosity_S</th><th>best.sfh.integrated_S</th><th>best.sfh.sfr_S</th><th>best.sfh.sfr100Myrs_S</th><th>best.sfh.sfr10Myrs_S</th><th>best.stellar.lum_S</th><th>best.stellar.lum_ly_S</th><th>best.stellar.lum_ly_old_S</th><th>best.stellar.lum_ly_young_S</th><th>best.stellar.lum_old_S</th><th>best.stellar.lum_young_S</th><th>best.stellar.m_gas_S</th><th>best.stellar.m_gas_old_S</th><th>best.stellar.m_gas_young_S</th><th>best.stellar.m_star_S</th><th>best.stellar.m_star_old_S</th><th>best.stellar.m_star_young_S</th><th>best.stellar.n_ly_S</th><th>best.stellar.n_ly_old_S</th><th>best.stellar.n_ly_young_S</th><th>best.FUV_S</th><th>best.NUV_S</th><th>best.u_prime_S</th><th>best.g_prime_S</th><th>best.r_prime_S</th><th>best.i_prime_S</th><th>best.z_prime_S</th><th>best.J_2mass_S</th><th>best.H_2mass_S</th><th>best.Ks_2mass_S</th><th>best.WISE1_S</th><th>best.IRAC1_S</th><th>best.IRAC2_S</th><th>best.WISE2_S</th><th>best.IRAC3_S</th><th>best.IRAC4_S</th><th>best.WISE3_S</th><th>best.WISE4_S</th><th>best.MIPS1_S</th><th>best.IRAS3_S</th><th>best.IRAS4_S</th><th>best.PACS_green_S</th><th>best.PACS_red_S</th><th>best.PSW_ext_S</th><th>best.PMW_ext_S</th><th>best.PLW_ext_S</th><th>best.IRAS1_S</th><th>best.IRAS2_S</th><th>best.PACS_blue_S</th><th>best.MIPS2_S</th><th>best.MIPS3_S</th><th>DBIC_S</th><th>FUV_F</th><th>FUV_err_F</th><th>NUV_F</th><th>NUV_err_F</th><th>u_prime_F</th><th>u_prime_err_F</th><th>g_prime_F</th><th>g_prime_err_F</th><th>r_prime_F</th><th>r_prime_err_F</th><th>i_prime_F</th><th>i_prime_err_F</th><th>z_prime_F</th><th>z_prime_err_F</th><th>J_2mass_F</th><th>J_2mass_err_F</th><th>H_2mass_F</th><th>H_2mass_err_F</th><th>Ks_2mass_F</th><th>Ks_2mass_err_F</th><th>WISE1_F</th><th>WISE1_err_F</th><th>IRAC1_F</th><th>IRAC1_err_F</th><th>IRAC2_F</th><th>IRAC2_err_F</th><th>WISE2_F</th><th>WISE2_err_F</th><th>IRAC3_F</th><th>IRAC3_err_F</th><th>IRAC4_F</th><th>IRAC4_err_F</th><th>IRAS1_F</th><th>IRAS1_err_F</th><th>WISE3_F</th><th>WISE3_err_F</th><th>WISE4_F</th><th>WISE4_err_F</th><th>MIPS1_F</th><th>MIPS1_err_F</th><th>IRAS2_F</th><th>IRAS2_err_F</th><th>IRAS3_F</th><th>IRAS3_err_F</th><th>PACS_blue_F</th><th>PACS_blue_err_F</th><th>MIPS2_F</th><th>MIPS2_err_F</th><th>IRAS4_F</th><th>IRAS4_err_F</th><th>PACS_green_F</th><th>PACS_green_err_F</th><th>MIPS3_F</th><th>MIPS3_err_F</th><th>PACS_red_F</th><th>PACS_red_err_F</th><th>PSW_ext_F</th><th>PSW_ext_err_F</th><th>PMW_ext_F</th><th>PMW_ext_err_F</th><th>PLW_ext_F</th><th>PLW_ext_err_F</th><th>bayes.agn.EBV_F</th><th>bayes.agn.EBV_err_F</th><th>bayes.agn.beta</th><th>bayes.agn.beta_err</th><th>bayes.agn.emissivity_F</th><th>bayes.agn.emissivity_err_F</th><th>bayes.agn.fracAGN_F</th><th>bayes.agn.fracAGN_err_F</th><th>bayes.agn.fracAGN_dale2014_F</th><th>bayes.agn.fracAGN_dale2014_err_F</th><th>bayes.agn.gamma</th><th>bayes.agn.gamma_err</th><th>bayes.agn.law_F</th><th>bayes.agn.law_err_F</th><th>bayes.agn.opening_angle</th><th>bayes.agn.opening_angle_err</th><th>bayes.agn.i_F</th><th>bayes.agn.i_err_F</th><th>bayes.agn.r_ratio</th><th>bayes.agn.r_ratio_err</th><th>bayes.agn.tau</th><th>bayes.agn.tau_err</th><th>bayes.agn.temperature_F</th><th>bayes.agn.temperature_err_F</th><th>bayes.attenuation.E_BV_factor_F</th><th>bayes.attenuation.E_BV_factor_err_F</th><th>bayes.attenuation.E_BV_lines_F</th><th>bayes.attenuation.E_BV_lines_err_F</th><th>bayes.attenuation.E_BVs_F</th><th>bayes.attenuation.E_BVs_err_F</th><th>bayes.attenuation.FUV_F</th><th>bayes.attenuation.FUV_err_F</th><th>bayes.attenuation.powerlaw_slope_F</th><th>bayes.attenuation.powerlaw_slope_err_F</th><th>bayes.attenuation.uv_bump_amplitude_F</th><th>bayes.attenuation.uv_bump_amplitude_err_F</th><th>bayes.attenuation.uv_bump_wavelength_F</th><th>bayes.attenuation.uv_bump_wavelength_err_F</th><th>bayes.attenuation.uv_bump_width_F</th><th>bayes.attenuation.uv_bump_width_err_F</th><th>bayes.dust.alpha_F</th><th>bayes.dust.alpha_err_F</th><th>bayes.nebular.f_dust_F</th><th>bayes.nebular.f_dust_err_F</th><th>bayes.nebular.f_esc_F</th><th>bayes.nebular.f_esc_err_F</th><th>bayes.nebular.lines_width_F</th><th>bayes.nebular.lines_width_err_F</th><th>bayes.nebular.logU_F</th><th>bayes.nebular.logU_err_F</th><th>bayes.sfh.age_F</th><th>bayes.sfh.age_err_F</th><th>bayes.sfh.age_burst_F</th><th>bayes.sfh.age_burst_err_F</th><th>bayes.sfh.age_main_F</th><th>bayes.sfh.age_main_err_F</th><th>bayes.sfh.f_burst_F</th><th>bayes.sfh.f_burst_err_F</th><th>bayes.sfh.tau_burst_F</th><th>bayes.sfh.tau_burst_err_F</th><th>bayes.sfh.tau_main_F</th><th>bayes.sfh.tau_main_err_F</th><th>bayes.stellar.age_m_star_F</th><th>bayes.stellar.age_m_star_err_F</th><th>bayes.stellar.imf_F</th><th>bayes.stellar.imf_err_F</th><th>bayes.stellar.metallicity_F</th><th>bayes.stellar.metallicity_err_F</th><th>bayes.stellar.old_young_separation_age_F</th><th>bayes.stellar.old_young_separation_age_err_F</th><th>bayes.universe.age_F</th><th>bayes.universe.age_err_F</th><th>bayes.universe.luminosity_distance_F</th><th>bayes.universe.luminosity_distance_err_F</th><th>bayes.universe.redshift_F</th><th>bayes.universe.redshift_err_F</th><th>bayes.agn.accretion_power_F</th><th>bayes.agn.accretion_power_err_F</th><th>bayes.agn.disk_luminosity_F</th><th>bayes.agn.disk_luminosity_err_F</th><th>bayes.agn.intrin_Lnu_2500A_F</th><th>bayes.agn.intrin_Lnu_2500A_err_F</th><th>bayes.agn.luminosity_F</th><th>bayes.agn.luminosity_err_F</th><th>bayes.agn.scatt_luminosity</th><th>bayes.agn.scatt_luminosity_err</th><th>bayes.agn.dust_luminosity_F</th><th>bayes.agn.dust_luminosity_err_F</th><th>bayes.dust.luminosity_F</th><th>bayes.dust.luminosity_err_F</th><th>bayes.sfh.integrated_F</th><th>bayes.sfh.integrated_err_F</th><th>bayes.sfh.sfr_F</th><th>bayes.sfh.sfr_err_F</th><th>bayes.sfh.sfr100Myrs_F</th><th>bayes.sfh.sfr100Myrs_err_F</th><th>bayes.sfh.sfr10Myrs_F</th><th>bayes.sfh.sfr10Myrs_err_F</th><th>bayes.stellar.lum_F</th><th>bayes.stellar.lum_err_F</th><th>bayes.stellar.lum_ly_F</th><th>bayes.stellar.lum_ly_err_F</th><th>bayes.stellar.lum_ly_old_F</th><th>bayes.stellar.lum_ly_old_err_F</th><th>bayes.stellar.lum_ly_young_F</th><th>bayes.stellar.lum_ly_young_err_F</th><th>bayes.stellar.lum_old_F</th><th>bayes.stellar.lum_old_err_F</th><th>bayes.stellar.lum_young_F</th><th>bayes.stellar.lum_young_err_F</th><th>bayes.stellar.m_gas_F</th><th>bayes.stellar.m_gas_err_F</th><th>bayes.stellar.m_gas_old_F</th><th>bayes.stellar.m_gas_old_err_F</th><th>bayes.stellar.m_gas_young_F</th><th>bayes.stellar.m_gas_young_err_F</th><th>bayes.stellar.m_star_F</th><th>bayes.stellar.m_star_err_F</th><th>bayes.stellar.m_star_old_F</th><th>bayes.stellar.m_star_old_err_F</th><th>bayes.stellar.m_star_young_F</th><th>bayes.stellar.m_star_young_err_F</th><th>bayes.stellar.n_ly_F</th><th>bayes.stellar.n_ly_err_F</th><th>bayes.stellar.n_ly_old_F</th><th>bayes.stellar.n_ly_old_err_F</th><th>bayes.stellar.n_ly_young_F</th><th>bayes.stellar.n_ly_young_err_F</th><th>bayes.FUV_F</th><th>bayes.FUV_err_F</th><th>bayes.H_2mass_F</th><th>bayes.H_2mass_err_F</th><th>bayes.IRAC1_F</th><th>bayes.IRAC1_err_F</th><th>bayes.IRAC2_F</th><th>bayes.IRAC2_err_F</th><th>bayes.IRAC3_F</th><th>bayes.IRAC3_err_F</th><th>bayes.IRAC4_F</th><th>bayes.IRAC4_err_F</th><th>bayes.IRAS1_F</th><th>bayes.IRAS1_err_F</th><th>bayes.IRAS2_F</th><th>bayes.IRAS2_err_F</th><th>bayes.IRAS3_F</th><th>bayes.IRAS3_err_F</th><th>bayes.IRAS4_F</th><th>bayes.IRAS4_err_F</th><th>bayes.J_2mass_F</th><th>bayes.J_2mass_err_F</th><th>bayes.Ks_2mass_F</th><th>bayes.Ks_2mass_err_F</th><th>bayes.MIPS1_F</th><th>bayes.MIPS1_err_F</th><th>bayes.MIPS2_F</th><th>bayes.MIPS2_err_F</th><th>bayes.MIPS3_F</th><th>bayes.MIPS3_err_F</th><th>bayes.NUV_F</th><th>bayes.NUV_err_F</th><th>bayes.PACS_blue_F</th><th>bayes.PACS_blue_err_F</th><th>bayes.PACS_green_F</th><th>bayes.PACS_green_err_F</th><th>bayes.PACS_red_F</th><th>bayes.PACS_red_err_F</th><th>bayes.PLW_ext_F</th><th>bayes.PLW_ext_err_F</th><th>bayes.PMW_ext_F</th><th>bayes.PMW_ext_err_F</th><th>bayes.PSW_ext_F</th><th>bayes.PSW_ext_err_F</th><th>bayes.WISE1_F</th><th>bayes.WISE1_err_F</th><th>bayes.WISE2_F</th><th>bayes.WISE2_err_F</th><th>bayes.WISE3_F</th><th>bayes.WISE3_err_F</th><th>bayes.WISE4_F</th><th>bayes.WISE4_err_F</th><th>bayes.g_prime_F</th><th>bayes.g_prime_err_F</th><th>bayes.i_prime_F</th><th>bayes.i_prime_err_F</th><th>bayes.r_prime_F</th><th>bayes.r_prime_err_F</th><th>bayes.u_prime_F</th><th>bayes.u_prime_err_F</th><th>bayes.z_prime_F</th><th>bayes.z_prime_err_F</th><th>best.chi_square_F</th><th>best.reduced_chi_square_F</th><th>best.agn.EBV_F</th><th>best.agn.beta</th><th>best.agn.emissivity_F</th><th>best.agn.fracAGN_F</th><th>best.agn.fracAGN_dale2014_F</th><th>best.agn.gamma</th><th>best.agn.law_F</th><th>best.agn.opening_angle</th><th>best.agn.psy</th><th>best.agn.r_ratio</th><th>best.agn.tau</th><th>best.agn.temperature_F</th><th>best.attenuation.E_BV_factor_F</th><th>best.attenuation.E_BV_lines_F</th><th>best.attenuation.E_BVs_F</th><th>best.attenuation.FUV_F</th><th>best.attenuation.powerlaw_slope_F</th><th>best.attenuation.uv_bump_amplitude_F</th><th>best.attenuation.uv_bump_wavelength_F</th><th>best.attenuation.uv_bump_width_F</th><th>best.dust.alpha_F</th><th>best.nebular.f_dust_F</th><th>best.nebular.f_esc_F</th><th>best.nebular.lines_width_F</th><th>best.nebular.logU_F</th><th>best.sfh.age_F</th><th>best.sfh.age_burst_F</th><th>best.sfh.age_main_F</th><th>best.sfh.f_burst_F</th><th>best.sfh.tau_burst_F</th><th>best.sfh.tau_main_F</th><th>best.stellar.age_m_star_F</th><th>best.stellar.imf_F</th><th>best.stellar.metallicity_F</th><th>best.stellar.old_young_separation_age_F</th><th>best.universe.age_F</th><th>best.universe.luminosity_distance_F</th><th>best.universe.redshift_F</th><th>best.agn.accretion_power_F</th><th>best.agn.disk_luminosity_F</th><th>best.agn.intrin_Lnu_2500A_F</th><th>best.agn.luminosity_F</th><th>best.agn.scatt_luminosity</th><th>best.agn.therm_luminosity</th><th>best.dust.luminosity_F</th><th>best.sfh.integrated_F</th><th>best.sfh.sfr_F</th><th>best.sfh.sfr100Myrs_F</th><th>best.sfh.sfr10Myrs_F</th><th>best.stellar.lum_F</th><th>best.stellar.lum_ly_F</th><th>best.stellar.lum_ly_old_F</th><th>best.stellar.lum_ly_young_F</th><th>best.stellar.lum_old_F</th><th>best.stellar.lum_young_F</th><th>best.stellar.m_gas_F</th><th>best.stellar.m_gas_old_F</th><th>best.stellar.m_gas_young_F</th><th>best.stellar.m_star_F</th><th>best.stellar.m_star_old_F</th><th>best.stellar.m_star_young_F</th><th>best.stellar.n_ly_F</th><th>best.stellar.n_ly_old_F</th><th>best.stellar.n_ly_young_F</th><th>best.FUV_F</th><th>best.NUV_F</th><th>best.u_prime_F</th><th>best.g_prime_F</th><th>best.r_prime_F</th><th>best.i_prime_F</th><th>best.z_prime_F</th><th>best.J_2mass_F</th><th>best.H_2mass_F</th><th>best.Ks_2mass_F</th><th>best.WISE1_F</th><th>best.IRAC1_F</th><th>best.IRAC2_F</th><th>best.WISE2_F</th><th>best.IRAC3_F</th><th>best.IRAC4_F</th><th>best.WISE3_F</th><th>best.WISE4_F</th><th>best.MIPS1_F</th><th>best.IRAS3_F</th><th>best.IRAS4_F</th><th>best.PACS_green_F</th><th>best.PACS_red_F</th><th>best.PSW_ext_F</th><th>best.PMW_ext_F</th><th>best.PLW_ext_F</th><th>best.IRAS1_F</th><th>best.IRAS2_F</th><th>best.PACS_blue_F</th><th>best.MIPS2_F</th><th>best.MIPS3_F</th><th>DBIC_F</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th>K</th><th>K</th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th>nm</th><th>nm</th><th>nm</th><th>nm</th><th></th><th></th><th></th><th></th><th></th><th></th><th>km / s</th><th>km / s</th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>m</th><th>m</th><th></th><th></th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W / Hz</th><th>W / Hz</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th></th><th>deg</th><th></th><th></th><th></th><th>K</th><th></th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th>nm</th><th>nm</th><th></th><th></th><th></th><th>km / s</th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th>Myr</th><th>Myr</th><th>m</th><th></th><th>W</th><th>W</th><th>W</th><th>W / Hz</th><th>W</th><th>W</th><th>solMass</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th>K</th><th>K</th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th>nm</th><th>nm</th><th>nm</th><th>nm</th><th></th><th></th><th></th><th></th><th></th><th></th><th>km / s</th><th>km / s</th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>m</th><th>m</th><th></th><th></th><th>W</th><th>W</th><th>W</th><th>W</th><th>W / Hz</th><th>W / Hz</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th>K</th><th></th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th>nm</th><th>nm</th><th></th><th></th><th></th><th>km / s</th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th>Myr</th><th>Myr</th><th>m</th><th></th><th>W</th><th>W</th><th>W / Hz</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th></th></tr></thead>\n",
       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       "<tr><td>2MASS 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       "<tr><td>Mrk  662</td><td>0.05523</td><td>0.2586666669230908</td><td>0.030033316</td><td>0.629999985297521</td><td>0.0629999985297521</td><td>0.9679999784566462</td><td>0.019359999569132924</td><td>1.9999999785795808</td><td>0.16031220000000002</td><td>3.275000024586916</td><td>0.28017853000000004</td><td>4.32500010356307</td><td>1.2700394000000002</td><td>5.59999980032444</td><td>0.1119999960064888</td><td>5.698333300339679</td><td>0.9203260400000001</td><td>7.38249986898154</td><td>1.5777198000000001</td><td>8.106999844312668</td><td>1.2340584</td><td>8.17041653851668</td><td>0.49022499231100075</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>7.932499940320848</td><td>0.47594999641925084</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>23.916666842252013</td><td>1.4350000105351208</td><td>54.09166660209497</td><td>3.245499996125698</td><td>51.50000087022781</td><td>2.0600000348091125</td><td>nan</td><td>nan</td><td>342.6666666666667</td><td>84.95887240306335</td><td>354.99998927116394</td><td>35.499998927116394</td><td>370.0000047683716</td><td>14.800000190734863</td><td>nan</td><td>nan</td><td>512.0000243186951</td><td>51.20000243186951</td><td>375.99998712539673</td><td>21.0</td><td>495.0000047683716</td><td>49.50000047683716</td><td>223.00000488758087</td><td>15.610000342130663</td><td>71.19999825954437</td><td>8.8</td><td>nan</td><td>nan</td><td>23.0</td><td>0.1159214518595513</td><td>0.1029742198784757</td><td>0.97</td><td>1.578607074397309e-16</td><td>19.99999999999999</td><td>9.390127370913356e-15</td><td>1.5999999999999999</td><td>5.982773120622998e-16</td><td>0.35079956574293464</td><td>0.030912069653477002</td><td>0.0</td><td>0.0</td><td>17.898550611356367</td><td>13.469041506336339</td><td>0.0</td><td>0.0</td><td>39.99999999999998</td><td>1.8780254741826713e-14</td><td>1.0</td><td>1.1022570266096512e-16</td><td>1.0</td><td>1.1022570266096512e-16</td><td>6.999999999999999</td><td>2.356987078521603e-15</td><td>100.0</td><td>2.063636373123007e-14</td><td>0.43999999999999995</td><td>1.461188881671729e-16</td><td>0.39433938942917723</td><td>0.03651420749878667</td><td>0.173509331348838</td><td>0.01606625129946614</td><td>2.0739343605746043</td><td>0.21148325448178523</td><td>-0.4212629699362377</td><td>0.1287060736049313</td><td>0.0</td><td>0.0</td><td>217.4999999999999</td><td>1.304661535935008e-13</td><td>34.99999999999999</td><td>7.15675825729036e-15</td><td>1.999999993512053</td><td>5.8393358904757234e-05</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>299.9999999999999</td><td>1.1450813211664576e-13</td><td>-2.0</td><td>2.2045140532193025e-16</td><td>3345.3964249862856</td><td>1639.38405626636</td><td>19.99999999999999</td><td>9.390127370913356e-15</td><td>3345.3964249862856</td><td>1639.38405626636</td><td>0.0</td><td>0.0</td><td>50.0</td><td>1.0318181865615034e-14</td><td>1145.1438631502551</td><td>867.7415677776881</td><td>1712.9592335554958</td><td>694.4905682470235</td><td>1.0</td><td>1.1022570266096512e-16</td><td>0.019999999999999993</td><td>6.957734163432309e-18</td><td>9.999999999999995</td><td>4.695063685456678e-15</td><td>12954.283626792174</td><td>3.363956445933012e-12</td><td>8.253268733926955e+24</td><td>1096847984.6204598</td><td>0.05999999999999999</td><td>1.8360175085498625e-17</td><td>9.527632819458125e+36</td><td>9.561050541750584e+35</td><td>1.0094086009209884e+37</td><td>1.602445072728245e+36</td><td>8.929579976128169e+36</td><td>8.42060635545251e+35</td><td>4.2166007821032814e+21</td><td>4.231390310270945e+20</td><td>1.9023665985338055e+37</td><td>1.68549313204828e+36</td><td>1.6519015310753647e+37</td><td>8.44941088869575e+35</td><td>30834911228.02941</td><td>7931058142.67625</td><td>4.353764087085668</td><td>0.46283785018645895</td><td>4.567186011388124</td><td>0.41764196055398267</td><td>4.3724780541632295</td><td>0.4583277070734104</td><td>3.430348903769529e+37</td><td>1.9186439777512417e+36</td><td>2.1269522748705182e+36</td><td>2.2177390072346414e+35</td><td>2.89804267552513e+34</td><td>3.809109699843269e+33</td><td>2.0979718481152665e+36</td><td>2.2196909253788765e+35</td><td>2.349091863249304e+37</td><td>1.92839790919112e+36</td><td>1.081257040520226e+37</td><td>1.138423893939178e+36</td><td>12655542697.071701</td><td>3755568393.9088116</td><td>12652945195.973526</td><td>3755540913.1429753</td><td>2597501.0981732593</td><td>270470.25851338694</td><td>18179368517.867382</td><td>4193816466.607271</td><td>18138241251.514214</td><td>4193674692.33007</td><td>41127266.353165306</td><td>4312818.565076803</td><td>6.4264807119953756e+53</td><td>6.733818316992365e+52</td><td>7.212344788611109e+51</td><td>8.269289217620297e+50</td><td>6.354357264109261e+53</td><td>6.724345162761579e+52</td><td>0.26397510781064815</td><td>0.028919591363934766</td><td>7.417698774804554</td><td>0.5093552248171378</td><td>8.465347290703852</td><td>0.35284369004010474</td><td>8.970063500806553</td><td>0.4327007914307784</td><td>10.669791584155215</td><td>0.50109755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>21.345756834178367</td><td>0.9702616742808349</td><td>0.03</td><td>-0.5</td><td>1.6</td><td>0.35</td><td>0.0</td><td>4.0</td><td>0.0</td><td>100.0</td><td>50.1</td><td>30.0</td><td>6.0</td><td>100.0</td><td>0.44</td><td>0.6</td><td>0.264</td><td>3.2399112856540575</td><td>-0.5</td><td>0.0</td><td>217.5</td><td>35.0</td><td>2.0</td><td>0.0</td><td>0.0</td><td>300.0</td><td>-2.0</td><td>4000.0</td><td>20.0</td><td>4000.0</td><td>0.0</td><td>50.0</td><td>1000.0</td><td>2271.3277898212455</td><td>1.0</td><td>0.02</td><td>10.0</td><td>13015.4219135819</td><td>7.5706062170086e+24</td><td>0.05523</td><td>2.801595164497445e+37</td><td>1.4813104937847021e+37</td><td>5.66513321205576e+21</td><td>2.352981334902776e+37</td><td>4.205596654915991e+34</td><td>8.674652444631584e+36</td><td>1.6110068825744373e+37</td><td>42531121418.84844</td><td>3.4327692359355733</td><td>3.5630523483609626</td><td>3.4443783477058583</td><td>3.1635107751971147e+37</td><td>1.6876678258380902e+36</td><td>3.4009598283173804e+34</td><td>1.6536582275549164e+36</td><td>2.311514461188015e+37</td><td>8.519963140090994e+36</td><td>18129637336.19933</td><td>18127592070.174576</td><td>2045266.0247555883</td><td>24401484072.33662</td><td>24369085565.19686</td><td>32398507.139761653</td><td>5.089457511879127e+53</td><td>8.077782569768694e+51</td><td>5.00867968618144e+53</td><td>0.3205744495854067</td><td>0.5621114446347908</td><td>0.9854873361557703</td><td>1.9451754156366974</td><td>3.010870806623856</td><td>3.970278883834501</td><td>4.948417085601686</td><td>6.681248607939077</td><td>8.406208247998762</td><td>8.830010614563049</td><td>8.41011486021213</td><td>8.478425942601477</td><td>8.694544978390217</td><td>8.748144448861552</td><td>10.166215319459637</td><td>18.60879040142752</td><td>22.084102119668906</td><td>47.327510092419104</td><td>54.385197578819636</td><td>249.59600672961722</td><td>431.61169866065075</td><td>439.8180470040053</td><td>418.8904459352986</td><td>215.87907682343948</td><td>93.87278541656244</td><td>31.240543452242864</td><td>21.453643727577127</td><td>52.570335489243156</td><td>313.29853292732315</td><td>313.6893915840057</td><td>432.62019830189746</td><td>-103.28464683724422</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "           id           redshift ...    best.MIPS3_F           DBIC_F      \n",
       "                                 ...        mJy                            \n",
       "         str33          float64  ...      float64             float64      \n",
       "----------------------- -------- ... ------------------ -------------------\n",
       "2MASS J17103321+5844566  0.28069 ...   99.8599105276717  -52.51865960732978\n",
       "               Mrk  662  0.05523 ... 432.62019830189746 -103.28464683724422"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PhotAll[SelecCut]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We select Mrk 662 galaxy as the best SED fitting (Due to its $\\chi_{\\rm{red}}^{2}$ values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.832168Z",
     "start_time": "2021-08-03T09:13:48.830695Z"
    }
   },
   "outputs": [],
   "source": [
    "BEST_GAL = 'Mrk  662'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-21T14:25:27.976775Z",
     "start_time": "2021-01-21T14:25:27.967705Z"
    }
   },
   "source": [
    "For the worst galaxy, we select a galaxy with a $\\chi_{\\rm{red}}^{2}$ close to our selected limits in the $\\chi_{\\rm{red}}^{2}$ distribution (See FIGURE 2), with less or equal to 9 photometry points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.840143Z",
     "start_time": "2021-08-03T09:13:48.832955Z"
    }
   },
   "outputs": [],
   "source": [
    "WorstCut = np.logical_and.reduce([np.logical_xor(AllS_chis < 10**(0.49),\n",
    "                                                 AllS_chis > 10**(-0.49)),\n",
    "                                  np.logical_xor(AllF_chis < 10**(0.49),\n",
    "                                                 AllF_chis > 10**(-0.49)),\n",
    "                                  PhotAll['CountPoints'] <= 9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.906174Z",
     "start_time": "2021-08-03T09:13:48.840944Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=2</i>\n",
       "<table id=\"table140556768873632\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>id</th><th>redshift</th><th>FUV_S</th><th>FUV_err_S</th><th>NUV_S</th><th>NUV_err_S</th><th>u_prime_S</th><th>u_prime_err_S</th><th>g_prime_S</th><th>g_prime_err_S</th><th>r_prime_S</th><th>r_prime_err_S</th><th>i_prime_S</th><th>i_prime_err_S</th><th>z_prime_S</th><th>z_prime_err_S</th><th>J_2mass_S</th><th>J_2mass_err_S</th><th>H_2mass_S</th><th>H_2mass_err_S</th><th>Ks_2mass_S</th><th>Ks_2mass_err_S</th><th>WISE1_S</th><th>WISE1_err_S</th><th>IRAC1_S</th><th>IRAC1_err_S</th><th>IRAC2_S</th><th>IRAC2_err_S</th><th>WISE2_S</th><th>WISE2_err_S</th><th>IRAC3_S</th><th>IRAC3_err_S</th><th>IRAC4_S</th><th>IRAC4_err_S</th><th>IRAS1_S</th><th>IRAS1_err_S</th><th>WISE3_S</th><th>WISE3_err_S</th><th>WISE4_S</th><th>WISE4_err_S</th><th>MIPS1_S</th><th>MIPS1_err_S</th><th>IRAS2_S</th><th>IRAS2_err_S</th><th>IRAS3_S</th><th>IRAS3_err_S</th><th>PACS_blue_S</th><th>PACS_blue_err_S</th><th>MIPS2_S</th><th>MIPS2_err_S</th><th>IRAS4_S</th><th>IRAS4_err_S</th><th>PACS_green_S</th><th>PACS_green_err_S</th><th>MIPS3_S</th><th>MIPS3_err_S</th><th>PACS_red_S</th><th>PACS_red_err_S</th><th>PSW_ext_S</th><th>PSW_ext_err_S</th><th>PMW_ext_S</th><th>PMW_ext_err_S</th><th>PLW_ext_S</th><th>PLW_ext_err_S</th><th>CountPoints</th><th>bayes.agn.EBV_S</th><th>bayes.agn.EBV_err_S</th><th>bayes.agn.Mcl</th><th>bayes.agn.Mcl_err</th><th>bayes.agn.R</th><th>bayes.agn.R_err</th><th>bayes.agn.emissivity_S</th><th>bayes.agn.emissivity_err_S</th><th>bayes.agn.fracAGN_S</th><th>bayes.agn.fracAGN_err_S</th><th>bayes.agn.fracAGN_dale2014_S</th><th>bayes.agn.fracAGN_dale2014_err_S</th><th>bayes.agn.i_S</th><th>bayes.agn.i_err_S</th><th>bayes.agn.law_S</th><th>bayes.agn.law_err_S</th><th>bayes.agn.oa</th><th>bayes.agn.oa_err</th><th>bayes.agn.pl</th><th>bayes.agn.pl_err</th><th>bayes.agn.q</th><th>bayes.agn.q_err</th><th>bayes.agn.t</th><th>bayes.agn.t_err</th><th>bayes.agn.temperature_S</th><th>bayes.agn.temperature_err_S</th><th>bayes.attenuation.E_BV_factor_S</th><th>bayes.attenuation.E_BV_factor_err_S</th><th>bayes.attenuation.E_BV_lines_S</th><th>bayes.attenuation.E_BV_lines_err_S</th><th>bayes.attenuation.E_BVs_S</th><th>bayes.attenuation.E_BVs_err_S</th><th>bayes.attenuation.FUV_S</th><th>bayes.attenuation.FUV_err_S</th><th>bayes.attenuation.powerlaw_slope_S</th><th>bayes.attenuation.powerlaw_slope_err_S</th><th>bayes.attenuation.uv_bump_amplitude_S</th><th>bayes.attenuation.uv_bump_amplitude_err_S</th><th>bayes.attenuation.uv_bump_wavelength_S</th><th>bayes.attenuation.uv_bump_wavelength_err_S</th><th>bayes.attenuation.uv_bump_width_S</th><th>bayes.attenuation.uv_bump_width_err_S</th><th>bayes.dust.alpha_S</th><th>bayes.dust.alpha_err_S</th><th>bayes.nebular.f_dust_S</th><th>bayes.nebular.f_dust_err_S</th><th>bayes.nebular.f_esc_S</th><th>bayes.nebular.f_esc_err_S</th><th>bayes.nebular.lines_width_S</th><th>bayes.nebular.lines_width_err_S</th><th>bayes.nebular.logU_S</th><th>bayes.nebular.logU_err_S</th><th>bayes.sfh.age_S</th><th>bayes.sfh.age_err_S</th><th>bayes.sfh.age_burst_S</th><th>bayes.sfh.age_burst_err_S</th><th>bayes.sfh.age_main_S</th><th>bayes.sfh.age_main_err_S</th><th>bayes.sfh.f_burst_S</th><th>bayes.sfh.f_burst_err_S</th><th>bayes.sfh.tau_burst_S</th><th>bayes.sfh.tau_burst_err_S</th><th>bayes.sfh.tau_main_S</th><th>bayes.sfh.tau_main_err_S</th><th>bayes.stellar.age_m_star_S</th><th>bayes.stellar.age_m_star_err_S</th><th>bayes.stellar.imf_S</th><th>bayes.stellar.imf_err_S</th><th>bayes.stellar.metallicity_S</th><th>bayes.stellar.metallicity_err_S</th><th>bayes.stellar.old_young_separation_age_S</th><th>bayes.stellar.old_young_separation_age_err_S</th><th>bayes.universe.age_S</th><th>bayes.universe.age_err_S</th><th>bayes.universe.luminosity_distance_S</th><th>bayes.universe.luminosity_distance_err_S</th><th>bayes.universe.redshift_S</th><th>bayes.universe.redshift_err_S</th><th>bayes.agn.accretion_power_S</th><th>bayes.agn.accretion_power_err_S</th><th>bayes.agn.disk_luminosity_S</th><th>bayes.agn.disk_luminosity_err_S</th><th>bayes.agn.dust_luminosity_S</th><th>bayes.agn.dust_luminosity_err_S</th><th>bayes.agn.intrin_Lnu_2500A_S</th><th>bayes.agn.intrin_Lnu_2500A_err_S</th><th>bayes.agn.luminosity_S</th><th>bayes.agn.luminosity_err_S</th><th>bayes.dust.luminosity_S</th><th>bayes.dust.luminosity_err_S</th><th>bayes.sfh.integrated_S</th><th>bayes.sfh.integrated_err_S</th><th>bayes.sfh.sfr_S</th><th>bayes.sfh.sfr_err_S</th><th>bayes.sfh.sfr100Myrs_S</th><th>bayes.sfh.sfr100Myrs_err_S</th><th>bayes.sfh.sfr10Myrs_S</th><th>bayes.sfh.sfr10Myrs_err_S</th><th>bayes.stellar.lum_S</th><th>bayes.stellar.lum_err_S</th><th>bayes.stellar.lum_ly_S</th><th>bayes.stellar.lum_ly_err_S</th><th>bayes.stellar.lum_ly_old_S</th><th>bayes.stellar.lum_ly_old_err_S</th><th>bayes.stellar.lum_ly_young_S</th><th>bayes.stellar.lum_ly_young_err_S</th><th>bayes.stellar.lum_old_S</th><th>bayes.stellar.lum_old_err_S</th><th>bayes.stellar.lum_young_S</th><th>bayes.stellar.lum_young_err_S</th><th>bayes.stellar.m_gas_S</th><th>bayes.stellar.m_gas_err_S</th><th>bayes.stellar.m_gas_old_S</th><th>bayes.stellar.m_gas_old_err_S</th><th>bayes.stellar.m_gas_young_S</th><th>bayes.stellar.m_gas_young_err_S</th><th>bayes.stellar.m_star_S</th><th>bayes.stellar.m_star_err_S</th><th>bayes.stellar.m_star_old_S</th><th>bayes.stellar.m_star_old_err_S</th><th>bayes.stellar.m_star_young_S</th><th>bayes.stellar.m_star_young_err_S</th><th>bayes.stellar.n_ly_S</th><th>bayes.stellar.n_ly_err_S</th><th>bayes.stellar.n_ly_old_S</th><th>bayes.stellar.n_ly_old_err_S</th><th>bayes.stellar.n_ly_young_S</th><th>bayes.stellar.n_ly_young_err_S</th><th>bayes.FUV_S</th><th>bayes.FUV_err_S</th><th>bayes.H_2mass_S</th><th>bayes.H_2mass_err_S</th><th>bayes.IRAC1_S</th><th>bayes.IRAC1_err_S</th><th>bayes.IRAC2_S</th><th>bayes.IRAC2_err_S</th><th>bayes.IRAC3_S</th><th>bayes.IRAC3_err_S</th><th>bayes.IRAC4_S</th><th>bayes.IRAC4_err_S</th><th>bayes.IRAS1_S</th><th>bayes.IRAS1_err_S</th><th>bayes.IRAS2_S</th><th>bayes.IRAS2_err_S</th><th>bayes.IRAS3_S</th><th>bayes.IRAS3_err_S</th><th>bayes.IRAS4_S</th><th>bayes.IRAS4_err_S</th><th>bayes.J_2mass_S</th><th>bayes.J_2mass_err_S</th><th>bayes.Ks_2mass_S</th><th>bayes.Ks_2mass_err_S</th><th>bayes.MIPS1_S</th><th>bayes.MIPS1_err_S</th><th>bayes.MIPS2_S</th><th>bayes.MIPS2_err_S</th><th>bayes.MIPS3_S</th><th>bayes.MIPS3_err_S</th><th>bayes.NUV_S</th><th>bayes.NUV_err_S</th><th>bayes.PACS_blue_S</th><th>bayes.PACS_blue_err_S</th><th>bayes.PACS_green_S</th><th>bayes.PACS_green_err_S</th><th>bayes.PACS_red_S</th><th>bayes.PACS_red_err_S</th><th>bayes.PLW_ext_S</th><th>bayes.PLW_ext_err_S</th><th>bayes.PMW_ext_S</th><th>bayes.PMW_ext_err_S</th><th>bayes.PSW_ext_S</th><th>bayes.PSW_ext_err_S</th><th>bayes.WISE1_S</th><th>bayes.WISE1_err_S</th><th>bayes.WISE2_S</th><th>bayes.WISE2_err_S</th><th>bayes.WISE3_S</th><th>bayes.WISE3_err_S</th><th>bayes.WISE4_S</th><th>bayes.WISE4_err_S</th><th>bayes.g_prime_S</th><th>bayes.g_prime_err_S</th><th>bayes.i_prime_S</th><th>bayes.i_prime_err_S</th><th>bayes.r_prime_S</th><th>bayes.r_prime_err_S</th><th>bayes.u_prime_S</th><th>bayes.u_prime_err_S</th><th>bayes.z_prime_S</th><th>bayes.z_prime_err_S</th><th>best.chi_square_S</th><th>best.reduced_chi_square_S</th><th>best.agn.EBV_S</th><th>best.agn.Mcl</th><th>best.agn.R</th><th>best.agn.emissivity_S</th><th>best.agn.fracAGN_S</th><th>best.agn.fracAGN_dale2014_S</th><th>best.agn.i</th><th>best.agn.law_S</th><th>best.agn.oa</th><th>best.agn.pl</th><th>best.agn.q</th><th>best.agn.t</th><th>best.agn.temperature_S</th><th>best.attenuation.E_BV_factor_S</th><th>best.attenuation.E_BV_lines_S</th><th>best.attenuation.E_BVs_S</th><th>best.attenuation.FUV_S</th><th>best.attenuation.powerlaw_slope_S</th><th>best.attenuation.uv_bump_amplitude_S</th><th>best.attenuation.uv_bump_wavelength_S</th><th>best.attenuation.uv_bump_width_S</th><th>best.dust.alpha_S</th><th>best.nebular.f_dust_S</th><th>best.nebular.f_esc_S</th><th>best.nebular.lines_width_S</th><th>best.nebular.logU_S</th><th>best.sfh.age_S</th><th>best.sfh.age_burst_S</th><th>best.sfh.age_main_S</th><th>best.sfh.f_burst_S</th><th>best.sfh.tau_burst_S</th><th>best.sfh.tau_main_S</th><th>best.stellar.age_m_star_S</th><th>best.stellar.imf_S</th><th>best.stellar.metallicity_S</th><th>best.stellar.old_young_separation_age_S</th><th>best.universe.age_S</th><th>best.universe.luminosity_distance_S</th><th>best.universe.redshift_S</th><th>best.agn.accretion_power_S</th><th>best.agn.disk_luminosity_S</th><th>best.agn.dust_luminosity</th><th>best.agn.intrin_Lnu_2500A_S</th><th>best.agn.luminosity_S</th><th>best.dust.luminosity_S</th><th>best.sfh.integrated_S</th><th>best.sfh.sfr_S</th><th>best.sfh.sfr100Myrs_S</th><th>best.sfh.sfr10Myrs_S</th><th>best.stellar.lum_S</th><th>best.stellar.lum_ly_S</th><th>best.stellar.lum_ly_old_S</th><th>best.stellar.lum_ly_young_S</th><th>best.stellar.lum_old_S</th><th>best.stellar.lum_young_S</th><th>best.stellar.m_gas_S</th><th>best.stellar.m_gas_old_S</th><th>best.stellar.m_gas_young_S</th><th>best.stellar.m_star_S</th><th>best.stellar.m_star_old_S</th><th>best.stellar.m_star_young_S</th><th>best.stellar.n_ly_S</th><th>best.stellar.n_ly_old_S</th><th>best.stellar.n_ly_young_S</th><th>best.FUV_S</th><th>best.NUV_S</th><th>best.u_prime_S</th><th>best.g_prime_S</th><th>best.r_prime_S</th><th>best.i_prime_S</th><th>best.z_prime_S</th><th>best.J_2mass_S</th><th>best.H_2mass_S</th><th>best.Ks_2mass_S</th><th>best.WISE1_S</th><th>best.IRAC1_S</th><th>best.IRAC2_S</th><th>best.WISE2_S</th><th>best.IRAC3_S</th><th>best.IRAC4_S</th><th>best.WISE3_S</th><th>best.WISE4_S</th><th>best.MIPS1_S</th><th>best.IRAS3_S</th><th>best.IRAS4_S</th><th>best.PACS_green_S</th><th>best.PACS_red_S</th><th>best.PSW_ext_S</th><th>best.PMW_ext_S</th><th>best.PLW_ext_S</th><th>best.IRAS1_S</th><th>best.IRAS2_S</th><th>best.PACS_blue_S</th><th>best.MIPS2_S</th><th>best.MIPS3_S</th><th>DBIC_S</th><th>FUV_F</th><th>FUV_err_F</th><th>NUV_F</th><th>NUV_err_F</th><th>u_prime_F</th><th>u_prime_err_F</th><th>g_prime_F</th><th>g_prime_err_F</th><th>r_prime_F</th><th>r_prime_err_F</th><th>i_prime_F</th><th>i_prime_err_F</th><th>z_prime_F</th><th>z_prime_err_F</th><th>J_2mass_F</th><th>J_2mass_err_F</th><th>H_2mass_F</th><th>H_2mass_err_F</th><th>Ks_2mass_F</th><th>Ks_2mass_err_F</th><th>WISE1_F</th><th>WISE1_err_F</th><th>IRAC1_F</th><th>IRAC1_err_F</th><th>IRAC2_F</th><th>IRAC2_err_F</th><th>WISE2_F</th><th>WISE2_err_F</th><th>IRAC3_F</th><th>IRAC3_err_F</th><th>IRAC4_F</th><th>IRAC4_err_F</th><th>IRAS1_F</th><th>IRAS1_err_F</th><th>WISE3_F</th><th>WISE3_err_F</th><th>WISE4_F</th><th>WISE4_err_F</th><th>MIPS1_F</th><th>MIPS1_err_F</th><th>IRAS2_F</th><th>IRAS2_err_F</th><th>IRAS3_F</th><th>IRAS3_err_F</th><th>PACS_blue_F</th><th>PACS_blue_err_F</th><th>MIPS2_F</th><th>MIPS2_err_F</th><th>IRAS4_F</th><th>IRAS4_err_F</th><th>PACS_green_F</th><th>PACS_green_err_F</th><th>MIPS3_F</th><th>MIPS3_err_F</th><th>PACS_red_F</th><th>PACS_red_err_F</th><th>PSW_ext_F</th><th>PSW_ext_err_F</th><th>PMW_ext_F</th><th>PMW_ext_err_F</th><th>PLW_ext_F</th><th>PLW_ext_err_F</th><th>bayes.agn.EBV_F</th><th>bayes.agn.EBV_err_F</th><th>bayes.agn.beta</th><th>bayes.agn.beta_err</th><th>bayes.agn.emissivity_F</th><th>bayes.agn.emissivity_err_F</th><th>bayes.agn.fracAGN_F</th><th>bayes.agn.fracAGN_err_F</th><th>bayes.agn.fracAGN_dale2014_F</th><th>bayes.agn.fracAGN_dale2014_err_F</th><th>bayes.agn.gamma</th><th>bayes.agn.gamma_err</th><th>bayes.agn.law_F</th><th>bayes.agn.law_err_F</th><th>bayes.agn.opening_angle</th><th>bayes.agn.opening_angle_err</th><th>bayes.agn.i_F</th><th>bayes.agn.i_err_F</th><th>bayes.agn.r_ratio</th><th>bayes.agn.r_ratio_err</th><th>bayes.agn.tau</th><th>bayes.agn.tau_err</th><th>bayes.agn.temperature_F</th><th>bayes.agn.temperature_err_F</th><th>bayes.attenuation.E_BV_factor_F</th><th>bayes.attenuation.E_BV_factor_err_F</th><th>bayes.attenuation.E_BV_lines_F</th><th>bayes.attenuation.E_BV_lines_err_F</th><th>bayes.attenuation.E_BVs_F</th><th>bayes.attenuation.E_BVs_err_F</th><th>bayes.attenuation.FUV_F</th><th>bayes.attenuation.FUV_err_F</th><th>bayes.attenuation.powerlaw_slope_F</th><th>bayes.attenuation.powerlaw_slope_err_F</th><th>bayes.attenuation.uv_bump_amplitude_F</th><th>bayes.attenuation.uv_bump_amplitude_err_F</th><th>bayes.attenuation.uv_bump_wavelength_F</th><th>bayes.attenuation.uv_bump_wavelength_err_F</th><th>bayes.attenuation.uv_bump_width_F</th><th>bayes.attenuation.uv_bump_width_err_F</th><th>bayes.dust.alpha_F</th><th>bayes.dust.alpha_err_F</th><th>bayes.nebular.f_dust_F</th><th>bayes.nebular.f_dust_err_F</th><th>bayes.nebular.f_esc_F</th><th>bayes.nebular.f_esc_err_F</th><th>bayes.nebular.lines_width_F</th><th>bayes.nebular.lines_width_err_F</th><th>bayes.nebular.logU_F</th><th>bayes.nebular.logU_err_F</th><th>bayes.sfh.age_F</th><th>bayes.sfh.age_err_F</th><th>bayes.sfh.age_burst_F</th><th>bayes.sfh.age_burst_err_F</th><th>bayes.sfh.age_main_F</th><th>bayes.sfh.age_main_err_F</th><th>bayes.sfh.f_burst_F</th><th>bayes.sfh.f_burst_err_F</th><th>bayes.sfh.tau_burst_F</th><th>bayes.sfh.tau_burst_err_F</th><th>bayes.sfh.tau_main_F</th><th>bayes.sfh.tau_main_err_F</th><th>bayes.stellar.age_m_star_F</th><th>bayes.stellar.age_m_star_err_F</th><th>bayes.stellar.imf_F</th><th>bayes.stellar.imf_err_F</th><th>bayes.stellar.metallicity_F</th><th>bayes.stellar.metallicity_err_F</th><th>bayes.stellar.old_young_separation_age_F</th><th>bayes.stellar.old_young_separation_age_err_F</th><th>bayes.universe.age_F</th><th>bayes.universe.age_err_F</th><th>bayes.universe.luminosity_distance_F</th><th>bayes.universe.luminosity_distance_err_F</th><th>bayes.universe.redshift_F</th><th>bayes.universe.redshift_err_F</th><th>bayes.agn.accretion_power_F</th><th>bayes.agn.accretion_power_err_F</th><th>bayes.agn.disk_luminosity_F</th><th>bayes.agn.disk_luminosity_err_F</th><th>bayes.agn.intrin_Lnu_2500A_F</th><th>bayes.agn.intrin_Lnu_2500A_err_F</th><th>bayes.agn.luminosity_F</th><th>bayes.agn.luminosity_err_F</th><th>bayes.agn.scatt_luminosity</th><th>bayes.agn.scatt_luminosity_err</th><th>bayes.agn.dust_luminosity_F</th><th>bayes.agn.dust_luminosity_err_F</th><th>bayes.dust.luminosity_F</th><th>bayes.dust.luminosity_err_F</th><th>bayes.sfh.integrated_F</th><th>bayes.sfh.integrated_err_F</th><th>bayes.sfh.sfr_F</th><th>bayes.sfh.sfr_err_F</th><th>bayes.sfh.sfr100Myrs_F</th><th>bayes.sfh.sfr100Myrs_err_F</th><th>bayes.sfh.sfr10Myrs_F</th><th>bayes.sfh.sfr10Myrs_err_F</th><th>bayes.stellar.lum_F</th><th>bayes.stellar.lum_err_F</th><th>bayes.stellar.lum_ly_F</th><th>bayes.stellar.lum_ly_err_F</th><th>bayes.stellar.lum_ly_old_F</th><th>bayes.stellar.lum_ly_old_err_F</th><th>bayes.stellar.lum_ly_young_F</th><th>bayes.stellar.lum_ly_young_err_F</th><th>bayes.stellar.lum_old_F</th><th>bayes.stellar.lum_old_err_F</th><th>bayes.stellar.lum_young_F</th><th>bayes.stellar.lum_young_err_F</th><th>bayes.stellar.m_gas_F</th><th>bayes.stellar.m_gas_err_F</th><th>bayes.stellar.m_gas_old_F</th><th>bayes.stellar.m_gas_old_err_F</th><th>bayes.stellar.m_gas_young_F</th><th>bayes.stellar.m_gas_young_err_F</th><th>bayes.stellar.m_star_F</th><th>bayes.stellar.m_star_err_F</th><th>bayes.stellar.m_star_old_F</th><th>bayes.stellar.m_star_old_err_F</th><th>bayes.stellar.m_star_young_F</th><th>bayes.stellar.m_star_young_err_F</th><th>bayes.stellar.n_ly_F</th><th>bayes.stellar.n_ly_err_F</th><th>bayes.stellar.n_ly_old_F</th><th>bayes.stellar.n_ly_old_err_F</th><th>bayes.stellar.n_ly_young_F</th><th>bayes.stellar.n_ly_young_err_F</th><th>bayes.FUV_F</th><th>bayes.FUV_err_F</th><th>bayes.H_2mass_F</th><th>bayes.H_2mass_err_F</th><th>bayes.IRAC1_F</th><th>bayes.IRAC1_err_F</th><th>bayes.IRAC2_F</th><th>bayes.IRAC2_err_F</th><th>bayes.IRAC3_F</th><th>bayes.IRAC3_err_F</th><th>bayes.IRAC4_F</th><th>bayes.IRAC4_err_F</th><th>bayes.IRAS1_F</th><th>bayes.IRAS1_err_F</th><th>bayes.IRAS2_F</th><th>bayes.IRAS2_err_F</th><th>bayes.IRAS3_F</th><th>bayes.IRAS3_err_F</th><th>bayes.IRAS4_F</th><th>bayes.IRAS4_err_F</th><th>bayes.J_2mass_F</th><th>bayes.J_2mass_err_F</th><th>bayes.Ks_2mass_F</th><th>bayes.Ks_2mass_err_F</th><th>bayes.MIPS1_F</th><th>bayes.MIPS1_err_F</th><th>bayes.MIPS2_F</th><th>bayes.MIPS2_err_F</th><th>bayes.MIPS3_F</th><th>bayes.MIPS3_err_F</th><th>bayes.NUV_F</th><th>bayes.NUV_err_F</th><th>bayes.PACS_blue_F</th><th>bayes.PACS_blue_err_F</th><th>bayes.PACS_green_F</th><th>bayes.PACS_green_err_F</th><th>bayes.PACS_red_F</th><th>bayes.PACS_red_err_F</th><th>bayes.PLW_ext_F</th><th>bayes.PLW_ext_err_F</th><th>bayes.PMW_ext_F</th><th>bayes.PMW_ext_err_F</th><th>bayes.PSW_ext_F</th><th>bayes.PSW_ext_err_F</th><th>bayes.WISE1_F</th><th>bayes.WISE1_err_F</th><th>bayes.WISE2_F</th><th>bayes.WISE2_err_F</th><th>bayes.WISE3_F</th><th>bayes.WISE3_err_F</th><th>bayes.WISE4_F</th><th>bayes.WISE4_err_F</th><th>bayes.g_prime_F</th><th>bayes.g_prime_err_F</th><th>bayes.i_prime_F</th><th>bayes.i_prime_err_F</th><th>bayes.r_prime_F</th><th>bayes.r_prime_err_F</th><th>bayes.u_prime_F</th><th>bayes.u_prime_err_F</th><th>bayes.z_prime_F</th><th>bayes.z_prime_err_F</th><th>best.chi_square_F</th><th>best.reduced_chi_square_F</th><th>best.agn.EBV_F</th><th>best.agn.beta</th><th>best.agn.emissivity_F</th><th>best.agn.fracAGN_F</th><th>best.agn.fracAGN_dale2014_F</th><th>best.agn.gamma</th><th>best.agn.law_F</th><th>best.agn.opening_angle</th><th>best.agn.psy</th><th>best.agn.r_ratio</th><th>best.agn.tau</th><th>best.agn.temperature_F</th><th>best.attenuation.E_BV_factor_F</th><th>best.attenuation.E_BV_lines_F</th><th>best.attenuation.E_BVs_F</th><th>best.attenuation.FUV_F</th><th>best.attenuation.powerlaw_slope_F</th><th>best.attenuation.uv_bump_amplitude_F</th><th>best.attenuation.uv_bump_wavelength_F</th><th>best.attenuation.uv_bump_width_F</th><th>best.dust.alpha_F</th><th>best.nebular.f_dust_F</th><th>best.nebular.f_esc_F</th><th>best.nebular.lines_width_F</th><th>best.nebular.logU_F</th><th>best.sfh.age_F</th><th>best.sfh.age_burst_F</th><th>best.sfh.age_main_F</th><th>best.sfh.f_burst_F</th><th>best.sfh.tau_burst_F</th><th>best.sfh.tau_main_F</th><th>best.stellar.age_m_star_F</th><th>best.stellar.imf_F</th><th>best.stellar.metallicity_F</th><th>best.stellar.old_young_separation_age_F</th><th>best.universe.age_F</th><th>best.universe.luminosity_distance_F</th><th>best.universe.redshift_F</th><th>best.agn.accretion_power_F</th><th>best.agn.disk_luminosity_F</th><th>best.agn.intrin_Lnu_2500A_F</th><th>best.agn.luminosity_F</th><th>best.agn.scatt_luminosity</th><th>best.agn.therm_luminosity</th><th>best.dust.luminosity_F</th><th>best.sfh.integrated_F</th><th>best.sfh.sfr_F</th><th>best.sfh.sfr100Myrs_F</th><th>best.sfh.sfr10Myrs_F</th><th>best.stellar.lum_F</th><th>best.stellar.lum_ly_F</th><th>best.stellar.lum_ly_old_F</th><th>best.stellar.lum_ly_young_F</th><th>best.stellar.lum_old_F</th><th>best.stellar.lum_young_F</th><th>best.stellar.m_gas_F</th><th>best.stellar.m_gas_old_F</th><th>best.stellar.m_gas_young_F</th><th>best.stellar.m_star_F</th><th>best.stellar.m_star_old_F</th><th>best.stellar.m_star_young_F</th><th>best.stellar.n_ly_F</th><th>best.stellar.n_ly_old_F</th><th>best.stellar.n_ly_young_F</th><th>best.FUV_F</th><th>best.NUV_F</th><th>best.u_prime_F</th><th>best.g_prime_F</th><th>best.r_prime_F</th><th>best.i_prime_F</th><th>best.z_prime_F</th><th>best.J_2mass_F</th><th>best.H_2mass_F</th><th>best.Ks_2mass_F</th><th>best.WISE1_F</th><th>best.IRAC1_F</th><th>best.IRAC2_F</th><th>best.WISE2_F</th><th>best.IRAC3_F</th><th>best.IRAC4_F</th><th>best.WISE3_F</th><th>best.WISE4_F</th><th>best.MIPS1_F</th><th>best.IRAS3_F</th><th>best.IRAS4_F</th><th>best.PACS_green_F</th><th>best.PACS_red_F</th><th>best.PSW_ext_F</th><th>best.PMW_ext_F</th><th>best.PLW_ext_F</th><th>best.IRAS1_F</th><th>best.IRAS2_F</th><th>best.PACS_blue_F</th><th>best.MIPS2_F</th><th>best.MIPS3_F</th><th>DBIC_F</th></tr></thead>\n",
       "<thead><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th></th><th></th><th>K</th><th>K</th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th>nm</th><th>nm</th><th>nm</th><th>nm</th><th></th><th></th><th></th><th></th><th></th><th></th><th>km / s</th><th>km / s</th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>m</th><th>m</th><th></th><th></th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W / Hz</th><th>W / Hz</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th></th><th>deg</th><th></th><th></th><th></th><th>K</th><th></th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th>nm</th><th>nm</th><th></th><th></th><th></th><th>km / s</th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th>Myr</th><th>Myr</th><th>m</th><th></th><th>W</th><th>W</th><th>W</th><th>W / Hz</th><th>W</th><th>W</th><th>solMass</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th>deg</th><th>deg</th><th></th><th></th><th></th><th></th><th>K</th><th>K</th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th></th><th></th><th>nm</th><th>nm</th><th>nm</th><th>nm</th><th></th><th></th><th></th><th></th><th></th><th></th><th>km / s</th><th>km / s</th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th></th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th>Myr</th><th>m</th><th>m</th><th></th><th></th><th>W</th><th>W</th><th>W</th><th>W</th><th>W / Hz</th><th>W / Hz</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>deg</th><th>deg</th><th></th><th></th><th>K</th><th></th><th>mag</th><th>mag</th><th>mag</th><th></th><th></th><th>nm</th><th>nm</th><th></th><th></th><th></th><th>km / s</th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th>Myr</th><th>Myr</th><th>Myr</th><th></th><th></th><th>Myr</th><th>Myr</th><th>m</th><th></th><th>W</th><th>W</th><th>W / Hz</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass / yr</th><th>solMass / yr</th><th>solMass / yr</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>W</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>solMass</th><th>ph / s</th><th>ph / s</th><th>ph / s</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th>mJy</th><th></th></tr></thead>\n",
       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       "<tr><td>2dFGRS 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       "<tr><td>SDSS 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d><td>12.720594641398618</td><td>0.30229470030109756</td><td>0.024482745981241227</td><td>0.4164056808639524</td><td>0.04021790013160365</td><td>4.032018607053081</td><td>1.398685595039892</td><td>40.761143878952296</td><td>14.786652451959068</td><td>24.410383307155705</td><td>7.499193270220276</td><td>0.019755787644774356</td><td>0.0028584262721145595</td><td>41.04255273966727</td><td>14.893738849604468</td><td>38.238125476042754</td><td>12.5586748765485</td><td>23.641121842071186</td><td>7.2595661486765435</td><td>1.4536528318948871</td><td>0.55981557178402</td><td>4.136100189952727</td><td>1.5563089212874142</td><td>9.739967395913894</td><td>3.3483172427403107</td><td>0.3339329663171163</td><td>0.022159357283706042</td><td>0.3339819742925688</td><td>0.02806478248856945</td><td>0.8890070778730329</td><td>0.14566765620635122</td><td>3.282746370396569</td><td>1.100097907750182</td><td>0.07731664155693263</td><td>0.0038208258877940617</td><td>0.18207361979069894</td><td>0.00758017852279775</td><td>0.141987112815791</td><td>0.0046325224294184375</td><td>0.03841107403959006</td><td>0.00340901350177916</td><td>0.21598904158158141</td><td>0.011928635434588667</td><td>25.25326722325448</td><td>3.15665840290681</td><td>0.1</td><td>0.97</td><td>20.0</td><td>1.6</td><td>0.1</td><td>0.0</td><td>0.0</td><td>0.0</td><td>40.0</td><td>1.0</td><td>1.0</td><td>7.0</td><td>100.0</td><td>0.44</td><td>0.6</td><td>0.264</td><td>3.241447623564145</td><td>-0.5</td><td>0.0</td><td>217.5</td><td>35.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>300.0</td><td>-2.0</td><td>3000.0</td><td>20.0</td><td>3000.0</td><td>0.0</td><td>50.0</td><td>7500.0</td><td>1012.5531549290835</td><td>1.0</td><td>0.02</td><td>10.0</td><td>10685.571366310605</td><td>4.1200035959450345e+25</td><td>0.26309</td><td>7.236960963969427e+36</td><td>8.875113217972399e+36</td><td>6.822986321405039e+36</td><td>3.202828639492001e+21</td><td>1.5698099539377436e+37</td><td>6.140687689264532e+37</td><td>29055963232.74245</td><td>16.87771941168974</td><td>16.7085552296564</td><td>16.86250390387856</td><td>8.942928120247768e+37</td><td>8.163846762017121e+36</td><td>4.51302514792747e+34</td><td>8.118716510537846e+36</td><td>4.766198185297767e+37</td><td>4.1767299349500007e+37</td><td>10927684002.231705</td><td>10917691503.097692</td><td>9992499.134012422</td><td>18128279180.00648</td><td>17969646690.605965</td><td>158632489.4005182</td><td>2.4730248148521774e+54</td><td>1.3841834521785349e+52</td><td>2.459182980330392e+54</td><td>0.005994312079538455</td><td>0.018566280692608496</td><td>0.040212708100599355</td><td>0.07868267134576515</td><td>0.14037518102455393</td><td>0.180301825107759</td><td>0.21493976252948196</td><td>0.29886543077983974</td><td>0.34516544508965824</td><td>0.41228715346092687</td><td>0.3424808997638275</td><td>0.3378330006788497</td><td>0.3407746094916986</td><td>0.3436250416532976</td><td>0.3410996529387792</td><td>0.4614967513374879</td><td>0.9207099093971935</td><td>3.70980008203153</td><td>4.5831563822335255</td><td>39.624493937979054</td><td>43.72268777863886</td><td>43.288050254695534</td><td>24.950468357084336</td><td>9.619358093416796</td><td>3.9761554946149826</td><td>1.393238974599946</td><td>0.813030486417895</td><td>4.388307668417812</td><td>48.19334068621289</td><td>47.85613317666929</td><td>25.770043575356798</td><td>-9.154159693478645</td><td>nan</td><td>nan</td><td>0.012176666738620648</td><td>0.0033317563</td><td>0.07200000254670158</td><td>0.0025</td><td>0.07889999687904492</td><td>0.0015779999375808984</td><td>0.13000000035390258</td><td>0.0026000000070780516</td><td>0.17100000695791095</td><td>0.003420000139158219</td><td>0.2530000056140125</td><td>0.008</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.353944445488664</td><td>0.02123666672931984</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.3101666642416061</td><td>0.02555728850811214</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.7304285847528704</td><td>0.19108767340889787</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>nan</td><td>0.20970479704617975</td><td>0.24596407932845726</td><td>-0.49999999999999994</td><td>1.0218868913380244e-16</td><td>1.5999999999999996</td><td>7.821043134840493e-16</td><td>0.15708799444897972</td><td>0.07962208300112149</td><td>0.0</td><td>0.0</td><td>3.9999999999999996</td><td>8.175095130704195e-16</td><td>0.0</td><td>0.0</td><td>99.99999999999997</td><td>4.3369497602428314e-14</td><td>25.068618045352267</td><td>18.95308473618171</td><td>29.99999999999999</td><td>1.0251673271370717e-14</td><td>5.999999999999995</td><td>4.7844675823772985e-15</td><td>99.99999999999997</td><td>4.3369497602428314e-14</td><td>0.43999999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4</td><td>1.0</td><td>0.02</td><td>10.0</td><td>10685.571366310605</td><td>4.1200035959450345e+25</td><td>0.26309</td><td>1.9194998303848655e+37</td><td>5.809477447671432e+36</td><td>3.881439537535515e+21</td><td>1.4285442493204474e+37</td><td>2.2876037810376797e+34</td><td>8.453089007722665e+36</td><td>7.607780106950394e+37</td><td>23523737133.286755</td><td>21.48194249400355</td><td>21.087680098834436</td><td>21.446437697965862</td><td>1.0691991677502189e+38</td><td>1.0377571128757221e+37</td><td>4.669993188926293e+34</td><td>1.0330871196867957e+37</td><td>5.378586632681094e+37</td><td>5.313405044821095e+37</td><td>8309877273.222308</td><td>8297172972.5323305</td><td>12704300.68997714</td><td>15213859795.827065</td><td>15012099783.774727</td><td>201760012.0523361</td><td>3.144649748333315e+54</td><td>1.5365761689133316e+52</td><td>3.1292839866441826e+54</td><td>0.004205417195858553</td><td>0.016491205363987946</td><td>0.03945776630259594</td><td>0.08186945781721028</td><td>0.14721929705569695</td><td>0.18623597709165612</td><td>0.2198114747280033</td><td>0.30288485219724515</td><td>0.34727882976349284</td><td>0.41112973661754487</td><td>0.32623970626875914</td><td>0.3188430092399642</td><td>0.30873922325686076</td><td>0.3111477622950703</td><td>0.29879447384752456</td><td>0.42202016583480073</td><td>0.861340424717515</td><td>4.480691617074799</td><td>5.609955344654748</td><td>50.19995320145879</td><td>55.190642974467956</td><td>54.635561620317254</td><td>31.447692539944853</td><td>12.124581983873252</td><td>5.009341954474358</td><td>1.7547141715644747</td><td>0.740348535814616</td><td>5.374636947345823</td><td>60.925637142113224</td><td>60.50044773461215</td><td>32.47811580844787</td><td>-9.587226625507997</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "           id            redshift ...    best.MIPS3_F         DBIC_F      \n",
       "                                  ...        mJy                          \n",
       "         str33           float64  ...      float64           float64      \n",
       "------------------------ -------- ... ----------------- ------------------\n",
       "       2dFGRS TGN366Z095  0.14831 ... 53.77241821095423 -33.03612664365796\n",
       "SDSS J024707.59-072802.4  0.26309 ... 32.47811580844787 -9.587226625507997"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PhotAll[WorstCut]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, the worst galaxy is 2dFGRS TGN366Z095, the only one with the selection requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.908526Z",
     "start_time": "2021-08-03T09:13:48.906996Z"
    }
   },
   "outputs": [],
   "source": [
    "WORST_GAL = '2dFGRS TGN366Z095'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot 5 SEDs in one figure\n",
    "We retrieve the respective files to create the SEDs for the best and worst galaxies. These files are originally located in a [complementary repository for all the galaxies in Zenodo](https:/doi.org/10.5281/zenodo.5221764). Then, we compare the different AGN models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.916095Z",
     "start_time": "2021-08-03T09:13:48.909484Z"
    }
   },
   "outputs": [],
   "source": [
    "folders = ['FrAGN_1', 'FrAGN37', 'SKAGN0', 'SKAGN_1', 'SKAGN37']\n",
    "labels_ver = ['Fritz', 'Fritz 30/70', 'No-AGN', 'SKIRTOR', 'SKIRTOR 30/70']\n",
    "wavelengths = {\"FUV\": 0.152, \"NUV\": 0.227, \"u_prime\": 0.354, \"g_prime\": 0.477,\n",
    "               \"r_prime\": 0.623, \"i_prime\": 0.762, \"z_prime\": 0.913, \"J_2mass\": 1.25,\n",
    "               \"H_2mass\": 1.65, \"Ks_2mass\": 2.17, \"WISE1\": 3.4, \"IRAC1\": 3.6, \"IRAC2\": 4.5,\n",
    "               \"WISE2\": 4.6, \"IRAC3\": 5.8, \"IRAC4\": 8.0, \"IRAS1\": 12.0, \"WISE3\": 12.0,\n",
    "               \"WISE4\": 22.0, \"MIPS1\": 24.0, \"IRAS2\": 25.0, \"IRAS3\": 60.0, \"PACS_blue\": 70.0,\n",
    "               \"MIPS2\": 70.0, \"PACS_green\": 100.0, \"IRAS4\": 100.0, \"PACS_red\": 160.0,\n",
    "               \"MIPS3\": 160.0, \"PSW_ext\": 250.0, \"PMW_ext\": 350.0, \"PLW_ext\": 500.0}\n",
    "FOLDER_FILES = '../Data/Complementary/ExampleSEDs/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.922553Z",
     "start_time": "2021-08-03T09:13:48.917111Z"
    }
   },
   "outputs": [],
   "source": [
    "phot.remove_column('CountPoints')  # We remove the CounPhot from the Phot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### FIGURE 2\n",
    "We define a function that helps us to plot the 5 SEDs into one figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:48.943870Z",
     "start_time": "2021-08-03T09:13:48.923578Z"
    }
   },
   "outputs": [],
   "source": [
    "def five_seds(row_gal):\n",
    "    \"\"\"Create a figure with 5 SEDs for a given galaxy\"\"\"\n",
    "    fig, axs = plt.subplots(2, 3,\n",
    "                            figsize=(16, 9), sharey=True)\n",
    "    fig.delaxes(axs[-1][-1])\n",
    "    axs = axs.flatten()[:-1]\n",
    "    for i_fol, v_file in enumerate(folders):\n",
    "        file_sed = Table.read(FOLDER_FILES+row_gal+'_best_model_'+v_file+'.fits')\n",
    "        modr = Table.read('../Data/Interim/CIGALEOutputs/results_'+v_file+'.fits')\n",
    "        if i_fol > 1:\n",
    "            file_sed['agn'] = (file_sed['agn.SKIRTOR2016_dust'] +\n",
    "                               file_sed['agn.SKIRTOR2016_disk'])\n",
    "        else:\n",
    "            file_sed['agn'] = (file_sed['agn.fritz2006_agn'] +\n",
    "                               file_sed['agn.fritz2006_therm'] +\n",
    "                               file_sed['agn.fritz2006_scatt'])  # Take all the AGN emission\n",
    "        file_sed['freq'] = file_sed['wavelength'].to(u.Hz,\n",
    "                                                     equivalencies=u.spectral())\n",
    "        file_sed['stellar'] = file_sed['stellar.old'] + file_sed['stellar.young']\n",
    "        file_sed['stellar_atten'] = (file_sed['stellar.old']+file_sed['stellar.young']+\n",
    "                                     file_sed['attenuation.stellar.old'] +\n",
    "                                     file_sed['attenuation.stellar.young']+\n",
    "                                     file_sed['nebular.absorption_old'] +\n",
    "                                     file_sed['nebular.absorption_young'])\n",
    "        file_sed['nebular'] = (file_sed['nebular.lines_old'] +\n",
    "                               file_sed['nebular.lines_young'] +\n",
    "                               file_sed['nebular.continuum_old'] +\n",
    "                               file_sed['nebular.continuum_young'] +\n",
    "                               file_sed['attenuation.nebular.lines_old'] +\n",
    "                               file_sed['attenuation.nebular.lines_young'] +\n",
    "                               file_sed['attenuation.nebular.lines_old'] +\n",
    "                               file_sed['attenuation.nebular.continuum_young'])\n",
    "        # This is to define the distance\n",
    "        locat = np.where(modr['id'] == row_gal)\n",
    "        cons = (modr['best.universe.luminosity_distance'][locat])**2 * u.m\n",
    "        # Here we create the subplots\n",
    "        axs[i_fol].plot(file_sed['wavelength'].to(u.um),\n",
    "                        (((file_sed['stellar']*file_sed['wavelength'].to(u.m)).to(u.W)) /\n",
    "                         (cons*4*np.pi)/file_sed['freq']).to(u.mJy),\n",
    "                        'b', linewidth=1, linestyle='-.',\n",
    "                        label='Stellar unattenuated')\n",
    "        axs[i_fol].plot(file_sed['wavelength'].to(u.um),\n",
    "                        (((file_sed['stellar_atten']*file_sed['wavelength'].to(u.m)).to(u.W)) /\n",
    "                         (cons*4*np.pi)/file_sed['freq']).to(u.mJy),\n",
    "                        'orange', linewidth=1, linestyle='-',\n",
    "                        label='Stellar attenuated')\n",
    "        axs[i_fol].plot(file_sed['wavelength'].to(u.um),\n",
    "                        (((file_sed['nebular']*file_sed['wavelength'].to(u.m)).to(u.W)) /\n",
    "                         (cons*4*np.pi)/file_sed['freq']).to(u.mJy),\n",
    "                        'y', linewidth=1, linestyle=':',\n",
    "                        label='Nebular emission')\n",
    "        axs[i_fol].plot(file_sed['wavelength'].to(u.um),\n",
    "                        (((file_sed['dust']*file_sed['wavelength'].to(u.m)).to(u.W)) /\n",
    "                         (cons*4*np.pi)/file_sed['freq']).to(u.mJy),\n",
    "                        'r', linewidth=1, label='Dust emission')\n",
    "        axs[i_fol].plot(file_sed['wavelength'].to(u.um),\n",
    "                        (((file_sed['agn']*file_sed['wavelength'].to(u.m)).to(u.W)) /\n",
    "                         (cons*4*np.pi)/file_sed['freq']).to(u.mJy),\n",
    "                        'g', linewidth=2, linestyle='--',\n",
    "                        label='AGN emission')\n",
    "        axs[i_fol].plot(file_sed['wavelength'].to(u.um), file_sed['Fnu'],\n",
    "                        'k', linewidth=2, label='Model spectrum')\n",
    "        filters = [phot.colnames[x] for x in range(2, len(phot.colnames), 2)]\n",
    "        # It will be useful to create a dictionary for each band that you use\n",
    "        for i_filter, band in enumerate(filters):\n",
    "            axs[i_fol].errorbar(wavelengths[band], phot[str(band)][loc_gal],\n",
    "                                yerr=phot[str(band)+'_err'][loc_gal],\n",
    "                                color='b', fmt='o', elinewidth=2,\n",
    "                                markersize=5, markeredgewidth=1,\n",
    "                                markeredgecolor='b', markerfacecolor=\"None\",\n",
    "                                label='Observed flux densities' if i_filter == 1 else None)\n",
    "            axs[i_fol].plot(wavelengths[band], modr[\"best.\"+band][locat].data,\n",
    "                            'ro', markersize=5,\n",
    "                            label='Model flux densities' if i_filter == 1 else None)\n",
    "            if i_fol == 0 and i_filter == 0:\n",
    "                min_phot = np.nanmin([phot[str(band)].data[0],\n",
    "                                      modr[\"best.\"+band][locat].data[0], np.nan])\n",
    "                max_phot = np.nanmax([phot[str(band)].data[0],\n",
    "                                      modr[\"best.\"+band][locat].data[0], np.nan])\n",
    "            else:\n",
    "                min_phot = np.nanmin([phot[str(band)].data[0],\n",
    "                                      modr[\"best.\"+band][locat].data[0], min_phot])\n",
    "                max_phot = np.nanmax([phot[str(band)].data[0],\n",
    "                                      modr[\"best.\"+band][locat].data[0], max_phot])\n",
    "        axs[i_fol].set_xscale('log')\n",
    "        axs[i_fol].set_yscale('log')\n",
    "        axs[i_fol].set_xlim(0.5e-1, 2e4)\n",
    "        # To give some space at the borders\n",
    "        axs[i_fol].set_ylim(min_phot/3, max_phot*5)\n",
    "        axs[i_fol].annotate(labels_ver[i_fol]+'\\n'+r\"$\\chi^2_{\\rm{red}}$ = %1.3f\"\n",
    "                            % modr['best.reduced_chi_square'][locat].data[0],\n",
    "                            xy=(0.3, 0.8), horizontalalignment='center', fontsize=11,\n",
    "                            xycoords='axes fraction')\n",
    "        if i_fol in (0, 3):\n",
    "            axs[i_fol].set_ylabel(r'Flux Density [mJy]', fontsize=14)\n",
    "        if i_fol in (2, 3, 4):\n",
    "            axs[i_fol].set_xlabel(r'Observed wavelength [$\\mu$m]', fontsize=14)\n",
    "        if i_fol == 4:\n",
    "            axs[i_fol].legend(bbox_to_anchor=(1.25, 0.75),\n",
    "                              loc='upper left', borderaxespad=0., fontsize=10)\n",
    "            axs[i_fol].annotate(\"Galaxy ID: \"+row_gal, xy=(1.5, 0.10),\n",
    "                                horizontalalignment='center', fontsize=13,\n",
    "                                xycoords='axes fraction', weight='bold')\n",
    "    plt.subplots_adjust(wspace=0, hspace=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The galaxy with the best SED fitting is used as an example and presented as a figure in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:13:58.513376Z",
     "start_time": "2021-08-03T09:13:48.944682Z"
    }
   },
   "outputs": [],
   "source": [
    "# with plt.style.context('seaborn-talk'):\n",
    "with plt.style.context('seaborn-paper'):\n",
    "    plt.rcParams.update({'font.size': 12})\n",
    "    plt.rcParams.update({'axes.labelsize': 12})\n",
    "    plt.rcParams.update({'legend.fontsize': 10})\n",
    "    plt.rcParams.update({'ytick.labelsize': 10})\n",
    "    plt.rcParams.update({'xtick.labelsize': 10})\n",
    "    loc_gal = np.where(phot['id'] == BEST_GAL)[0]\n",
    "    five_seds(BEST_GAL)\n",
    "    plt.savefig('../Figures/F2.pdf', dpi=300, bbox_inches='tight')  # For Paper\n",
    "#     plt.savefig('../Talk_Figures/F2.png',dpi=300,bbox_inches = 'tight') # For Presentation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we show the worst result for a galaxy inside the $\\chi_{\\rm{red}}^{2}$ limits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:14:01.535832Z",
     "start_time": "2021-08-03T09:13:58.514373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "folders = ['FrAGN_2', 'FrAGN37', 'SKAGN0', 'SKAGN_2', 'SKAGN37']\n",
    "loc_gal = np.where(phot['id'] == WORST_GAL)[0]\n",
    "five_seds(WORST_GAL)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We implement a similar function to create the SEDs of all the galaxies analyzed with CIGALE. The data and the respective script is located in a complementary repository."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-22T10:41:30.883153Z",
     "start_time": "2021-01-22T10:41:30.878447Z"
    }
   },
   "source": [
    "##### Notebook info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-08-03T09:14:01.798763Z",
     "start_time": "2021-08-03T09:14:01.537565Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Andres Ramos\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.8.3\n",
      "IPython version      : 7.16.1\n",
      "\n",
      "Compiler    : GCC 7.3.0\n",
      "OS          : Linux\n",
      "Release     : 3.10.0-1160.25.1.el7.x86_64\n",
      "Machine     : x86_64\n",
      "Processor   : x86_64\n",
      "CPU cores   : 8\n",
      "Architecture: 64bit\n",
      "\n",
      "Specific Python packages\n",
      "matplotlib: 3.2.2\n",
      "json      : 2.0.9\n",
      "logging   : 0.5.1.2\n",
      "autopep8  : 1.5.7\n",
      "re        : 2.2.1\n",
      "sys       : 3.8.3 (default, Jul  2 2020, 16:21:59) \n",
      "[GCC 7.3.0]\n",
      "astropy   : 4.2\n",
      "numpy     : 1.19.5\n",
      "\n",
      "Watermark: 2.1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a \"Andres Ramos\" -d -v -m\n",
    "print('Specific Python packages')\n",
    "%watermark -iv -w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.3"
  },
  "toc": {
   "base_numbering": "8",
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}