{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "HELPid=\"HELP_J100135.92+024116.75\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import argparse\n",
    "from itertools import product, repeat\n",
    "from collections import OrderedDict\n",
    "import sys\n",
    "\n",
    "from astropy.table import Table\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import multiprocessing as mp\n",
    "import numpy as np\n",
    "import os\n",
    "import pkg_resources\n",
    "from pcigale.data import Database\n",
    "from scipy.constants import c\n",
    "from scipy import stats\n",
    "from pcigale.utils import read_table\n",
    "import matplotlib.gridspec as gridspec\n",
    "from scipy.stats import chisquare\n",
    "from math import log10\n",
    "\n",
    "# Name of the file containing the best models information\n",
    "BEST_RESULTS = \"results.fits\"\n",
    "# Wavelength limits (restframe) when plotting the best SED.\n",
    "PLOT_L_MIN = 0.1\n",
    "PLOT_L_MAX = 5e5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " HELP_J100135.92+024116.75 at z = 0.20\n"
     ]
    },
    {
     "data": {
      "image/png": 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HAa/3pmLtFitSndrbobk5+DnXRm+pLvPgRKQnVb9brLvvMLMWYCrQDGBmln58U2/qVg+H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\nxCngEBERkcQp4BAREZHEKeAQERGRxP1/QLexX13797EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f679436b400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sed = Table.read(\"{}_best_model.fits\".format(HELPid))\n",
    "obs = Table.read(\"part_0.fits\")\n",
    "mod=Table.read(BEST_RESULTS)\n",
    "\n",
    "wavelength_spec = sed['wavelength']\n",
    "z = obs[obs['id'] == HELPid]['redshift'][0]\n",
    "DL = mod[obs['id'] == HELPid]['best.universe.luminosity_distance'][0]\n",
    "\n",
    "\n",
    "obs_fluxes, obs_fluxes_err,filters_wl,mask_ok,mod_fluxes=[],[],[],[],[]\n",
    "del obs_fluxes[:]\n",
    "del obs_fluxes_err[:]\n",
    "del filters_wl[:]\n",
    "del mask_ok[:]\n",
    "del mod_fluxes[:]\n",
    "\n",
    "filters = [item for item in obs.colnames if item not in ('id', 'redshift') and not item.endswith('_err')]\n",
    "filters_err = [item for item in obs.colnames if item not in ('id', 'redshift') and item.endswith('_err')]\n",
    "\n",
    "for filt in filters:\n",
    "    obs_fluxes.append(obs[obs['id'] == HELPid][filt][0])\n",
    "obs_fluxes=np.array(obs_fluxes)\n",
    "\n",
    "for filt in filters_err:\n",
    "    obs_fluxes_err.append(obs[obs['id'] == HELPid][filt][0])\n",
    "\n",
    "for filt in filters:\n",
    "    mod_fluxes.append(mod[mod['id'] == HELPid][\"best.\"+filt][0])\n",
    "\n",
    "with Database() as db:\n",
    "    for name in filters:\n",
    "        tmp = db.get_filter(name)\n",
    "        filters_wl.append(tmp.effective_wavelength/1000.0)\n",
    "\n",
    "xmin = PLOT_L_MIN * (1. + z)\n",
    "xmax = PLOT_L_MAX * (1. + z)\n",
    "\n",
    "k_corr_SED = 1.    \n",
    "\n",
    "for cname in sed.colnames[1:]:\n",
    "    sed[cname] *= (wavelength_spec * 1e29 /  (c / (wavelength_spec * 1e-9)) / (4. * np.pi * DL * DL))\n",
    "\n",
    "wavelength_spec /= 1000.\n",
    "\n",
    "wsed = np.where((wavelength_spec > xmin) & (wavelength_spec < xmax))\n",
    "\n",
    "obs_fluxes=np.array(obs_fluxes)\n",
    "obs_fluxes_err=np.array(obs_fluxes_err)\n",
    "filters=np.array(filters)\n",
    "filters_wl=np.array(filters_wl)\n",
    "mod_fluxes=np.array(mod_fluxes)\n",
    "\n",
    "plt.close('all')\n",
    "\n",
    "figure = plt.figure()\n",
    "gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])\n",
    "if (sed.columns[1][wsed] > 0.).any():\n",
    "    ax1 = plt.subplot(gs[0])\n",
    "    ax1.loglog(wavelength_spec[wsed], sed['L_lambda_total'][wsed],\n",
    "                       label=\"\", color='white', nonposy='clip',\n",
    "                       linestyle='-', linewidth=0)\n",
    "     \n",
    "    mask_ok = np.logical_and(obs_fluxes > 0., obs_fluxes_err > 0.)\n",
    "\n",
    "    ax1.errorbar(filters_wl[mask_ok], obs_fluxes[mask_ok],\n",
    "                         yerr=obs_fluxes_err[mask_ok]*3, ls='', marker='s',\n",
    "                         label='Observed fluxes', markerfacecolor='None',\n",
    "                         markersize=6, markeredgecolor='b', capsize=0.)\n",
    "\n",
    "    mask = np.where(obs_fluxes > 0.)\n",
    " \n",
    "    figure.subplots_adjust(hspace=0., wspace=0.)\n",
    "\n",
    "    ax1.set_xlim(xmin, xmax)\n",
    "    ymin = min(np.min(obs_fluxes[mask_ok]),\n",
    "                       np.min(mod_fluxes[mask_ok]))\n",
    "    ymax = max(np.max(obs_fluxes[mask_ok]),\n",
    "                           np.max(mod_fluxes[mask_ok]))\n",
    "    ax1.set_ylim(1e-1*ymin, 1e1*ymax)\n",
    "\n",
    "    ax1.set_xlabel(\"Observed wavelength [$\\mu$m]\")\n",
    "    ax1.set_ylabel(\"Flux [mJy]\")\n",
    "    ax1.legend(fontsize=6, loc='best', fancybox=True, framealpha=0.5)\n",
    "    plt.setp(ax1.get_xticklabels(), visible=False)\n",
    "    plt.setp(ax1.get_yticklabels()[1], visible=False)\n",
    "    \n",
    "    print(\" {} at z = {:.2f}\". format(HELPid, z))\n",
    "   \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "####################################################################################################################\n",
    "\n",
    "### MAIN BEST RESULTS FOR STELLAR PART OF THE SPECTRA AND ATTENUATION:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stellar mass: 10.73 [stellar mass]\n",
      "V-band attenuation in the birth clouds: 1.20 \n",
      "attenuation in FUV band: 2.14 [mag]\n",
      "attenuation in V band: 0.57 [mag]\n"
     ]
    }
   ],
   "source": [
    "print(\"stellar mass: {:.2f} [stellar mass]\".format(log10(mod[obs['id'] == HELPid]['best.stellar.m_star'][0])))\n",
    "print(\"V-band attenuation in the birth clouds: {:.2f} \".format((mod[obs['id'] == HELPid]['best.attenuation.Av_BC'][0])))\n",
    "print(\"attenuation in FUV band: {:.2f} [mag]\".format((mod[obs['id'] == HELPid]['best.attenuation.FUV'][0])))\n",
    "print(\"attenuation in V band: {:.2f} [mag]\".format((mod[obs['id'] == HELPid]['best.attenuation.V_B90'][0])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Below on the plot stellar components (attenuated and unattenuated) are plotted against observed fluxes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model for HELP_J100135.92+024116.75 at z = 0.20. best log(Mstar) = 10.73\n"
     ]
    },
    {
     "data": {
      "image/png": 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hInEjklktlaGrR0TyKeAIojEcIrEVyawWdfWIxJbGcJSBxnCIiJSPW29N5tAh7/2BA7Bu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\nNF6kpEC3blCzprc6a06O1/1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nq1K/EOE6Fj9zSY75Z9J8GDQdDEuvP2rtlWP+uYSgZ1KY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\nEzgAgHYCBwDQTuAAANo9AAQUdZ2oW7xaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6765060cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if (sed.columns[1][wsed] > 0.).any():\n",
    "    ax1 = plt.subplot(gs[0])\n",
    "    ax1.loglog(wavelength_spec[wsed], (sed['stellar.young'][wsed] + sed['attenuation.stellar.young'][wsed] + \n",
    "               sed['stellar.old'][wsed] + sed['attenuation.stellar.old'][wsed]), \n",
    "               label=\"Stellar attenuated \", color='orange', marker=None, nonposy='clip', linestyle='-', linewidth=0.5)\n",
    "    ax1.loglog(wavelength_spec[wsed], (sed['stellar.old'][wsed] +  sed['stellar.young'][wsed]), \n",
    "               label=\"Stellar unattenuated\", color='b', marker=None, nonposy='clip', linestyle='--', linewidth=0.5)\n",
    "    ax1.loglog(wavelength_spec[wsed], sed['L_lambda_total'][wsed], label=\" \", color='white', nonposy='clip', \n",
    "               linestyle='-', linewidth=0)\n",
    "\n",
    "    ax1.set_autoscale_on(False)\n",
    "    mask_ok = np.logical_and(obs_fluxes > 0., obs_fluxes_err > 0.)\n",
    "    ax1.errorbar(filters_wl[mask_ok], obs_fluxes[mask_ok], yerr=obs_fluxes_err[mask_ok]*3, ls='', marker='s', \n",
    "                 label='Observed fluxes', markerfacecolor='None',markersize=6, markeredgecolor='b', capsize=0.)\n",
    "\n",
    "    mask = np.where(obs_fluxes > 0.)\n",
    "\n",
    "    figure.subplots_adjust(hspace=0., wspace=0.)\n",
    "\n",
    "    ax1.set_xlim(xmin, xmax)\n",
    "    ymin = min(np.min(obs_fluxes[mask_ok]), np.min(mod_fluxes[mask_ok]))\n",
    "    ymax = max(np.max(obs_fluxes[mask_ok]), np.max(mod_fluxes[mask_ok]))\n",
    "    ax1.set_ylim(1e-1*ymin, 1e1*ymax)\n",
    "\n",
    "    ax1.set_xlabel(\"Observed wavelength [$\\mu$m]\")\n",
    "    ax1.set_ylabel(\"Flux [mJy]\")\n",
    "    ax1.legend(fontsize=6, loc='best', fancybox=True, framealpha=0.5)\n",
    "    plt.setp(ax1.get_xticklabels(), visible=False)\n",
    "    plt.setp(ax1.get_yticklabels()[1], visible=False)\n",
    "    print(\"Best model for {} at z = {:.2f}. best log(Mstar) = {:.2f}\". format(HELPid, z,log10(mod[obs['id'] == HELPid]['best.stellar.m_star'][0])))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAIN BEST RESULTS FOR DUST EMISSION:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "power law slope dU/dM (alpha) : 2.00\n",
      "fraction illuminated from Umin to Umax (gamma): 0.01 \n",
      "mass fraction of PAH: 2.50 \n",
      "minimum radiation field: 1.00 \n",
      "best dust luminosity: 10.70 [stellar luminosity]\n"
     ]
    }
   ],
   "source": [
    "print(\"power law slope dU/dM (alpha) : {:.2f}\".format((mod[obs['id'] == HELPid]['best.dust.alpha'][0])))\n",
    "print(\"fraction illuminated from Umin to Umax (gamma): {:.2f} \".format((mod[obs['id'] == HELPid]['best.dust.gamma'][0])))\n",
    "print(\"mass fraction of PAH: {:.2f} \".format((mod[obs['id'] == HELPid]['best.dust.qpah'][0])))\n",
    "print(\"minimum radiation field: {:.2f} \".format((mod[obs['id'] == HELPid]['best.dust.umin'][0])))\n",
    "print(\"best dust luminosity: {:.2f} [stellar luminosity]\".format(log10((mod[obs['id'] == HELPid]['best.dust.luminosity'][0])/(3.846*pow(10,26)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Below on the plot dust component is  plotted against observed fluxes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model for HELP_J100135.92+024116.75 at z = 0.20. best log(Ldust) = 10.70\n"
     ]
    },
    {
     "data": {
      "image/png": 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9vhjXrYO77oLbb4fnnkvRAiuSNkOHwr//7YuGRSSmZBKO8/E1EeeYWScz2z/y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\nREREJO3+H02GT3ekLtLdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f676575c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if (sed.columns[1][wsed] > 0.).any():\n",
    "    ax1 = plt.subplot(gs[0])\n",
    "\n",
    "    ax1.loglog(wavelength_spec[wsed],\n",
    "                           (sed['dust.Umin_Umin'][wsed] +\n",
    "                            sed['dust.Umin_Umax'][wsed]),\n",
    "                           label=\"Dust emission\", color='r', marker=None,\n",
    "                           nonposy='clip', linestyle='-', linewidth=0.5)\n",
    "    ax1.loglog(wavelength_spec[wsed], sed['L_lambda_total'][wsed],\n",
    "                       label=\"\", color='white', nonposy='clip',\n",
    "                       linestyle='-', linewidth=0)\n",
    "\n",
    "    mask_ok = np.logical_and(obs_fluxes > 0., obs_fluxes_err > 0.)\n",
    "    ax1.errorbar(filters_wl[mask_ok], obs_fluxes[mask_ok],\n",
    "                         yerr=obs_fluxes_err[mask_ok]*3, ls='', marker='s',\n",
    "                         label='Observed fluxes', markerfacecolor='None',\n",
    "                         markersize=6, markeredgecolor='b', capsize=0.)\n",
    "\n",
    "    mask = np.where(obs_fluxes > 0.)\n",
    "\n",
    "    figure.subplots_adjust(hspace=0., wspace=0.)\n",
    "\n",
    "    ax1.set_xlim(xmin, xmax)\n",
    "    ymin = min(np.min(obs_fluxes[mask_ok]),\n",
    "                       np.min(mod_fluxes[mask_ok]))\n",
    "    ymax = max(np.max(obs_fluxes[mask_ok]),\n",
    "                           np.max(mod_fluxes[mask_ok]))\n",
    "    ax1.set_ylim(1e-1*ymin, 1e1*ymax)\n",
    "\n",
    "\n",
    "    ax1.set_xlabel(\"Observed wavelength [$\\mu$m]\")\n",
    "    ax1.set_ylabel(\"Flux [mJy]\")\n",
    "    ax1.legend(fontsize=6, loc='best', fancybox=True, framealpha=0.5)\n",
    "    plt.setp(ax1.get_xticklabels(), visible=False)\n",
    "    plt.setp(ax1.get_yticklabels()[1], visible=False)\n",
    "    print(\"Best model for {} at z = {:.2f}. best log(Ldust) = {:.2f}\". format(HELPid, z,log10((mod[obs['id'] == HELPid]['best.dust.luminosity'][0])/(3.846*pow(10,26)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MAIN BEST RESULTS FOR AGN component:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best fraction of AGN : 0.25\n",
      "best AGN liminosity: 10.27 [stellar luminosity]\n"
     ]
    }
   ],
   "source": [
    "print(\"best fraction of AGN : {:.2f}\".format((mod[obs['id'] == HELPid]['best.agn.fracAGN'][0])))\n",
    "if mod[obs['id'] == HELPid]['best.agn.fracAGN'][0]>0:\n",
    "    print(\"best AGN liminosity: {:.2f} [stellar luminosity]\".format(log10((mod[obs['id'] == HELPid]['best.agn.luminosity'][0])/(3.846*pow(10,26)))))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model for HELP_J100135.92+024116.75 at z = 0.20. best AGNfrac) = 0.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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MeM45ty2iKEI45+ZRSiLknHsIeCjae0v5RbLbaui5itqRNZK4xt+/gj/l/okn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\n6aDw7NxUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6764f189e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if mod[obs['id'] == HELPid]['best.agn.fracAGN'][0]>0:\n",
    "\n",
    "    if (sed.columns[1][wsed] > 0.).any():\n",
    "        ax1 = plt.subplot(gs[0])\n",
    "        ax1.loglog(wavelength_spec[wsed],(sed['agn.fritz2006_therm'][wsed] + sed['agn.fritz2006_scatt'][wsed] + \n",
    "                    sed['agn.fritz2006_agn'][wsed]),label=\"AGN emission\", color='g', marker=None, nonposy='clip', linestyle='-', linewidth=0.5)\n",
    "        ax1.loglog(wavelength_spec[wsed], sed['L_lambda_total'][wsed], label=\"\", color='white', nonposy='clip',\n",
    "                       linestyle='-', linewidth=0)\n",
    "\n",
    "        mask_ok = np.logical_and(obs_fluxes > 0., obs_fluxes_err > 0.)\n",
    "        ax1.errorbar(filters_wl[mask_ok], obs_fluxes[mask_ok], yerr=obs_fluxes_err[mask_ok]*3, ls='', marker='s',\n",
    "                label='Observed fluxes', markerfacecolor='None',markersize=6, markeredgecolor='b', capsize=0.)\n",
    "\n",
    "        mask = np.where(obs_fluxes > 0.)\n",
    "\n",
    "        figure.subplots_adjust(hspace=0., wspace=0.)\n",
    "\n",
    "        ax1.set_xlim(xmin, xmax)\n",
    "        ymin = min(np.min(obs_fluxes[mask_ok]), np.min(mod_fluxes[mask_ok]))\n",
    "        ymax = max(np.max(obs_fluxes[mask_ok]), np.max(mod_fluxes[mask_ok]))\n",
    "        ax1.set_ylim(1e-1*ymin, 1e1*ymax)\n",
    "\n",
    "        ax1.set_xlabel(\"Observed wavelength [$\\mu$m]\")\n",
    "        ax1.set_ylabel(\"Flux [mJy]\")\n",
    "        ax1.legend(fontsize=6, loc='best', fancybox=True, framealpha=0.5)\n",
    "        plt.setp(ax1.get_xticklabels(), visible=False)\n",
    "        plt.setp(ax1.get_yticklabels()[1], visible=False)\n",
    "\n",
    "    print(\"Best model for {} at z = {:.2f}. best AGNfrac) = {:.2f}\". format(HELPid, z,(mod[obs['id'] == HELPid]['best.agn.fracAGN'][0])))    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In the last step all modules are merge together to computed one best model (based on the $\\chi^2$) marked as a black line in the figure below. \n",
    "\n",
    "Modeled fluxes for each filter used for SED fitting are calculated based on the best model. The relative residual fluxes are ploted in the bottom panel of the figure. \n",
    "\n",
    "Final $\\chi^2$ value as well as main physical parameters computed based on PDF analysis are listed below:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reduced $\\chi^2$ : 0.41 \n",
      "bayesian stellar mass 10.70 +/- 3.88 [M sun]:\n",
      "bayesian dust luminosity: 10.65 +/- 2.56 [L sun]\n",
      "bayesian SFR 4.40 +/- 0.44 [M sun / yr]:\n",
      "bayesian AGN fraction 0.28 +/- 0.10:\n"
     ]
    }
   ],
   "source": [
    "print(\"reduced $\\chi^2$ : {:.2f} \".format((mod[obs['id'] == HELPid]['best.reduced_chi_square'][0])))\n",
    "print(\"bayesian stellar mass {:.2f} +/- {:.2f} [M sun]:\".format(log10(mod[obs['id'] == HELPid]['bayes.stellar.m_star'][0]),0.434*(mod[obs['id'] == HELPid]['bayes.stellar.m_star'][0])/(mod[obs['id'] == HELPid]['bayes.stellar.m_star_err'][0])))\n",
    "print(\"bayesian dust luminosity: {:.2f} +/- {:.2f} [L sun]\".format(log10((mod[obs['id'] == HELPid]['bayes.dust.luminosity'][0])/(3.846*pow(10,26))),0.434*(mod[obs['id'] == HELPid]['bayes.dust.luminosity'][0])/(mod[obs['id'] == HELPid]['bayes.dust.luminosity_err'][0])))\n",
    "print(\"bayesian SFR {:.2f} +/- {:.2f} [M sun / yr]:\".format((mod[obs['id'] == HELPid]['bayes.sfh.sfr10Myrs'][0]),(mod[obs['id'] == HELPid]['bayes.sfh.sfr10Myrs_err'][0])))\n",
    "print(\"bayesian AGN fraction {:.2f} +/- {:.2f}:\".format((mod[obs['id'] == HELPid]['bayes.agn.fracAGN'][0]),(mod[obs['id'] == HELPid]['bayes.agn.fracAGN_err'][0])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model for HELP_J100135.92+024116.75 at z = 0.20, best(Mstar) = 10.73, best log(Ldust) = 10.70, best AGNfrac = 0.28\n"
     ]
    },
    {
     "data": {
      "image/png": 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8+ZJcuXKxfPly+vTpo3aYPh3GjAGzLP97JQlubm7sGTyY4osXUwZbFm38B2dn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Taz57Idb2WaNWvGkUceyZFHhnn0SktLWbJkSdlQw7NmzWLSpEll+y9btqzBPHJYuXIlZ511Fi+9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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6764e6b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if (sed.columns[1][wsed] > 0.).any():\n",
    "    ax1 = plt.subplot(gs[0])\n",
    "    ax2 = plt.subplot(gs[1])\n",
    "    # Stellar emission\n",
    "    ax1.loglog(wavelength_spec[wsed], (sed['stellar.young'][wsed] + sed['attenuation.stellar.young'][wsed] + \n",
    "               sed['stellar.old'][wsed] + sed['attenuation.stellar.old'][wsed]), label=\"Stellar attenuated \", \n",
    "               color='orange', marker=None, nonposy='clip', linestyle='-',linewidth=0.5)\n",
    "    ax1.loglog(wavelength_spec[wsed],(sed['stellar.old'][wsed] + sed['stellar.young'][wsed]), \n",
    "               label=\"Stellar unattenuated\", color='b', marker=None,nonposy='clip', linestyle='--', linewidth=0.5)\n",
    "    #Dust emission\n",
    "    ax1.loglog(wavelength_spec[wsed], (sed['dust.Umin_Umin'][wsed] + sed['dust.Umin_Umax'][wsed]), \n",
    "               label=\"Dust emission\", color='r', marker=None, nonposy='clip', linestyle='-', linewidth=0.5)\n",
    "    # AGN emission Fritz\n",
    "    if 'agn.fritz2006_therm' in sed.columns:\n",
    "        ax1.loglog(wavelength_spec[wsed], (sed['agn.fritz2006_therm'][wsed] + sed['agn.fritz2006_scatt'][wsed] + \n",
    "                sed['agn.fritz2006_agn'][wsed]), label=\"AGN emission\", color='g', marker=None, nonposy='clip', \n",
    "                   linestyle='-', linewidth=0.5)\n",
    "\n",
    "    ax1.loglog(wavelength_spec[wsed], sed['L_lambda_total'][wsed], label=\"Model spectrum\", color='k', nonposy='clip',\n",
    "                       linestyle='-', linewidth=1.5)\n",
    "\n",
    "    ax1.set_autoscale_on(False)\n",
    "    ax1.scatter(filters_wl, mod_fluxes, marker='o', color='r', s=8,zorder=3, label=\"Model fluxes\")\n",
    "    mask_ok = np.logical_and(obs_fluxes > 0., obs_fluxes_err > 0.)\n",
    "    ax1.errorbar(filters_wl[mask_ok], obs_fluxes[mask_ok], yerr=obs_fluxes_err[mask_ok]*3, ls='', marker='s', \n",
    "                 label='Observed fluxes', markerfacecolor='None', markersize=6, markeredgecolor='b', capsize=0.)\n",
    "\n",
    "    mask = np.where(obs_fluxes > 0.)\n",
    "    ax2.errorbar(filters_wl[mask],(obs_fluxes[mask]-mod_fluxes[mask])/obs_fluxes[mask],  \n",
    "                 yerr=obs_fluxes_err[mask]/obs_fluxes[mask]*3, marker='_', label=\"(Obs-Mod)/Obs\", color='k', capsize=0.)\n",
    "    ax2.plot([xmin, xmax], [0., 0.], ls='--', color='k')\n",
    "    ax2.set_xscale('log')\n",
    "    ax2.minorticks_on()\n",
    "\n",
    "    figure.subplots_adjust(hspace=0., wspace=0.)\n",
    "\n",
    "    ax1.set_xlim(xmin, xmax)\n",
    "    ymin = min(np.min(obs_fluxes[mask_ok]),np.min(mod_fluxes[mask_ok]))\n",
    "    ymax = max(np.max(obs_fluxes[mask_ok]),np.max(mod_fluxes[mask_ok]))\n",
    "    ax1.set_ylim(1e-1*ymin, 1e1*ymax)\n",
    "    ax2.set_xlim(xmin, xmax)\n",
    "    ax2.set_ylim(-1.0, 1.0)\n",
    "\n",
    "    ax2.set_xlabel(\"Observed wavelength [$\\mu$m]\")\n",
    "    ax1.set_ylabel(\"Flux [mJy]\")\n",
    "    ax2.set_ylabel(\"Relative residual flux\")\n",
    "    ax1.legend(fontsize=6, loc='best', fancybox=True, framealpha=0.5)\n",
    "    ax2.legend(fontsize=6, loc='best', fancybox=True, framealpha=0.5)\n",
    "    plt.setp(ax1.get_xticklabels(), visible=False)\n",
    "    plt.setp(ax1.get_yticklabels()[1], visible=False)\n",
    "    \n",
    "    print(\"Best model for {} at z = {:.2f}, best(Mstar) = {:.2f}, best log(Ldust) = {:.2f}, best AGNfrac = {:.2f}\". \n",
    "          format(HELPid, z,log10(mod[obs['id'] == HELPid]['best.stellar.m_star'][0]),\n",
    "                 log10((mod[obs['id'] == HELPid]['best.dust.luminosity'][0])/(3.846*pow(10,26))), \n",
    "                 mod[obs['id'] == HELPid]['bayes.agn.fracAGN'][0]))    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global redshift vs stellar mass, redshift vs dust luminosity and redshift vs SFR relations:\n",
    "\n",
    "### In three figures below red star corresponds to the analized galaxy: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## redshift vs stellar mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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l7969zJ8/n+bmZrRaLU1NTciyjE6nY/ny5Sxfvpyf//zn5ObmcvDgQZ566im2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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67651c3940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mstar,red,ldust,sfr=[],[],[],[]\n",
    "for gal in range(0,len(mod)):\n",
    "    if mod[gal]['best.reduced_chi_square']<4:\n",
    "        mstar.append(log10(mod[gal]['bayes.stellar.m_star']))\n",
    "        red.append((mod[gal]['redshift']))\n",
    "        ldust.append(log10(mod[gal]['bayes.dust.luminosity']/(3.846*pow(10,26))))\n",
    "        sfr.append(log10(mod[gal]['bayes.sfh.sfr10Myrs']))\n",
    "\n",
    "mstar=np.array(mstar)\n",
    "red=np.array(red)\n",
    "ldust=np.array(ldust)\n",
    "sfr=np.array(sfr)\n",
    "\n",
    "ax1 = plt.subplot(gs[0])\n",
    "\n",
    "ax1.plot(red, mstar,'-o',c='lightgray',linewidth=0,linestyle=' ')\n",
    "specific_mstar=log10(mod[obs['id'] == HELPid]['bayes.stellar.m_star'][0])\n",
    "ax1.plot(z,specific_mstar,'-*',c='red',markersize=15)\n",
    "\n",
    "ax1.set_xlabel(\"redshift\")\n",
    "ax1.set_ylabel(\"log(Mstar)\")\n",
    "ax1.set_ylim(7, 12)\n",
    "\n",
    "ax1.legend(fontsize=6, loc='best', fancybox=True, framealpha=0.5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## redshift vs dust luminosity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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soKCAv//97/zmN79hwYIFDsan+XAqZz3h2sj3wMBAvv76a6qqqpg/fz69e/d2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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f67651d7080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(gs[0])\n",
    "\n",
    "ax1.plot(red, ldust,'-o',c='lightgray',linewidth=0,linestyle=' ')\n",
    "specific_Ldust=log10(mod[obs['id'] == HELPid]['bayes.dust.luminosity']/(3.846*pow(10,26)))\n",
    "ax1.plot(z,specific_Ldust,'-*',c='red',markersize=15)\n",
    "\n",
    "ax1.set_xlabel(\"redshift\")\n",
    "ax1.set_ylabel(\"log(Ldust)\")\n",
    "ax1.set_ylim(8, 14)\n",
    "\n",
    "ax1.legend(fontsize=6, loc='best', fancybox=True, framealpha=0.5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## redshift vs SFR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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QamtrCQoKEkWn2rq2bt1KVVWVjXCV/fwQLcphLfqkjao/evQoiYmJbN68WbR+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6764d85080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(gs[0])\n",
    "\n",
    "ax1.plot(red, sfr,'-o',c='lightgray',linewidth=0,linestyle=' ')\n",
    "specific_sfr=log10(mod[obs['id'] == HELPid]['bayes.sfh.sfr10Myrs'])\n",
    "ax1.plot(z,specific_sfr,'-*',c='red',markersize=15)\n",
    "\n",
    "ax1.set_xlabel(\"redshift\")\n",
    "ax1.set_ylabel(\"log(SFR)\")\n",
    "ax1.set_ylim(-2, 4)\n",
    "\n",
    "ax1.legend(fontsize=6, loc='best', fancybox=True, framealpha=0.5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
