{
 "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_J095741.77+022142.41\"\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_J095741.77+022142.41 at z = 1.47\n"
     ]
    },
    {
     "data": {
      "image/png": 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XhoREKkdV9HCUi3o4REREosm3hyOpz1IRERGRKqIejhANqYjEq1TPXBGR8tOQShE0pCIS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\nIhI7BRwiIiISu/8POse86scKgoEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61d5a01400>"
      ]
     },
     "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: 11.41 [stellar mass]\n",
      "V-band attenuation in the birth clouds: 4.00 \n",
      "attenuation in FUV band: 5.41 [mag]\n",
      "attenuation in V band: 1.95 [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_J095741.77+022142.41 at z = 1.47. best log(Mstar) = 11.41\n"
     ]
    },
    {
     "data": {
      "image/png": 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NOhGRhSJyREQWBDoWpUqa45aEjoFQSgWDst7DMQV4E7gr0IEoFcz8WSvD32M9lFKlQ5lO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\nIiIioncpOCIiIqJ3/wJn5AFRziBeWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61d2eb8278>"
      ]
     },
     "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): 0.02 \n",
      "mass fraction of PAH: 0.47 \n",
      "minimum radiation field: 15.00 \n",
      "best dust luminosity: 12.31 [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_J095741.77+022142.41 at z = 1.47. best log(Ldust) = 12.31\n"
     ]
    },
    {
     "data": {
      "image/png": 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7VPWRSI/tNZsWa8KmCh9+CGk1vY7ExFrXrm4g6bx5XkdiTFyJdFpsSSqNPg8I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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61d2c734a8>"
      ]
     },
     "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 : 0.00\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": [],
   "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 $\\chi^2$ : 0.47 \n",
      "bayesian stellar mass 11.39 +/- 3.93 [M sun]:\n",
      "bayesian dust luminosity: 12.27 +/- 2.99 [L sun]\n",
      "bayesian SFR 178.72 +/- 56.82 [M sun / yr]:\n",
      "bayesian AGN fraction 0.04 +/- 0.08:\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_J095741.77+022142.41 at z = 1.47, best(Mstar) = 11.41, best log(Ldust) = 12.31, best AGNfrac = 0.04\n"
     ]
    },
    {
     "data": {
      "image/png": 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+mdmzZye76N+CBQvw9/cnOjqaPn36cPbsWd566y0cHR3JlSsXNWrUYNu2bQC4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\nCKF5pyvw8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61d2c73a58>"
      ]
     },
     "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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oqPjPeQN/qaiw+TeAWuANLy++f+ABDh47Jhwpa6y7lrbVIXT79u0cOXKEvXv3IssyQUFB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\nhBBC+Nz/B4pwQ2lIjyIMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61d2e7bda0>"
      ]
     },
     "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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l3TGSzoH9b7hv3z4GDx7c7t8hHQ6J4GIqiy2JKqmqSkBAgDCy06dPbzZsLD8/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61d2eac9b0>"
      ]
     },
     "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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nhAG/Vq3K+vXr+eSTTxqst6GqKkaj8ZopvPruuQsXLohj2eqo2H73hqxf6xZydE1uVklV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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f61d2ba6a58>"
      ]
     },
     "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
}
