{
 "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_J100232.92+020027.45\"\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_J100232.92+020027.45 at z = 0.58\n"
     ]
    },
    {
     "data": {
      "image/png": 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IR0lohENERCSeqhjhKBWNcIiIiMSjEY5+0AiHiIhIPLmOcJTrzdtERESkimiEI0JTKiIi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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb9914e630>"
      ]
     },
     "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: 3.30 \n",
      "attenuation in FUV band: 4.50 [mag]\n",
      "attenuation in V band: 1.55 [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": [
    {
     "ename": "NameError",
     "evalue": "name 'ax2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-6153ff22d30f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Flux [mJy]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'best'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfancybox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframealpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m     \u001b[0max2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'best'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfancybox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframealpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_xticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvisible\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_yticklabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvisible\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ax2' is not defined"
     ]
    },
    {
     "data": {
      "image/png": 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BJpn6hJqYqG6HSPxTwuFHk0alqouXFR7BJiY+8bhaRqSy06RRP2Y2E0gD5jrnLj5Qe00a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\nA5iZBWiSBUxwzk0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FfLvFsmER7PGOZV7wPZn7nS0iUgUl14BTJ8Dn45hxz78p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\nZmbWc/8Ge2uSnQFdogoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb96634978>"
      ]
     },
     "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",
    "    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",
    "    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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 7,
   "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model for HELP_J100232.92+020027.45 at z = 0.58. best log(Ldust) = 11.21\n"
     ]
    },
    {
     "data": {
      "image/png": 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33tpg26hRoxg1atQa237+85/z85//POG5HTp04Pvvv19j27nnnsu55567xrahQ4cydOjQ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\nmVklXAgxMzOzSrgQYmZmZpX4f7LkgFW8yxUHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb964118d0>"
      ]
     },
     "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",
    "    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",
    "    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]),\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",
    "    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",
    "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model for HELP_J100232.92+020027.45 at z = 0.58. best AGNfrac) = 0.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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uds69gDfbpbgc4GHn3HTn3MfApcBveKudBs8x2TnX3jnXISTZsFLOJwls49aN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\nCE/ZH3A3ZjkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb96368c18>"
      ]
     },
     "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model for HELP_J100232.92+020027.45 at z = 0.58, best(Mstar) = 10.73, best log(Ldust) = 11.21, best AGNfrac = 0.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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rxq8VS0C+ZWYmCxfamqgMkKWKm8rPBgyQ+/fvl+3bx0oppWzfPuW240l4XpF3ye2YEEXO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb961c49e8>"
      ]
     },
     "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]['best.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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "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": []
  }
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