{
 "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_J095852.73+020248.24\"\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_J095852.73+020248.24 at z = 0.60\n"
     ]
    },
    {
     "data": {
      "image/png": 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7N/lsZvXHUyoD4CkVs8HT3Azz5lW7F2ZWKk+pmJmZWc1xwGFmZmYV54DDzLZzXoWZVYpz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83a43f2400>"
      ]
     },
     "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: 10.88 [stellar mass]\n",
      "V-band attenuation in the birth clouds: 4.00 \n",
      "attenuation in FUV band: 5.25 [mag]\n",
      "attenuation in V band: 1.86 [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_J095852.73+020248.24 at z = 0.60. best log(Mstar) = 10.88\n"
     ]
    },
    {
     "data": {
      "image/png": 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HWpmMHw/XXefMKvHUpcQ7Ghrffw/dulXsNY0xxlQ8n7pUROR2YLqqHvGx/Cjg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f837503ec50>"
      ]
     },
     "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) : 2.80\n",
      "fraction illuminated from Umin to Umax (gamma): 0.01 \n",
      "mass fraction of PAH: 2.50 \n",
      "minimum radiation field: 15.00 \n",
      "best dust luminosity: 11.38 [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_J095852.73+020248.24 at z = 0.60. best log(Ldust) = 11.38\n"
     ]
    },
    {
     "data": {
      "image/png": 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lceyTB7THd6fsih9YKpJZ1q/3X6ObASsqqlZ97dkTJk6E3//eD/SMlXCsW+db\nOA49tGrb7rvDC5/D8gI/vqMm/fvDmWf6ktq33gp//KO/f8YZ9Xttkhpt2sDo0fCHP8RX5l5E6hR3\nwuGcmxbvvmb2Ir6VI6toDEcjsX69/6CPbuGI7DoZOtS3OuTlwXff+WmT0cIJR6S99oKPP4Inl/qS\n2bGODTBiBOy5p2/JmDMHHnxw2/EkErxjj/VL2L/6qgbvisSQ9rVUIpnZ9kSNA3HOrcVXIJ1Yn2MH\nQdNiG4n16/2He2TCEb12SufO/r/bqVP94NJYYiUcQ4bA7Wvh47nbbN7um6XQqVPVhubN/Qca+AXE\nJDPddhscfzw89xy0ahV0NCIZJe3TYs1sDzObbWbrgTXAD6Hb6tBXnHMbnXN3JHpskQaxYYMfHBqZ\nlS9d6pOMaG3b+haOWGIlHIWFsPfefqpsRPfIDm/9HQ47rP6xS8MqKPBja666KuhIRLJeMnU4HgVa\nA8OBQcDhodthoa8imW39ep9wRLZwLFkCe+xRfd927Wpu4diwIfaAQjP45z/h8ce3dqXs8PZ8JRzZ\n6ogj/Piel14KOhKRrJZMl8r+QG/n3MepDkakQYRbOLYucELNCUdtLRwQczZJQQEUdXgDKr9h7YWb\nyKMpZ/xrFE2Ht9v6uGSZSZPguOOgb18toieSpGQSjreATkDOJRwaNJrhZs3y/en1FR7D8eGHVduW\nLIndAlFbC0cN9TmmTwfoBrc/z2dN1lPyxheMuagFnSccVO/QJSDbb+8LgV1+Odx/f9DRiGSEhhg0\n+nvgPjPbDXgP2KYGsHPu30kcMyNo0GiGi3fRs7ps2OATiY0bq7YtXepLjkerq4WjNkOGsOvRJ3Iw\n3fnu2OnEGCEi2eTQQ/2slRde8Au9iTRyiQ4aTSbh2BnYE/jfiG0OsNDXGBWPGp6ZzQQGAvOcc6cE\nHI5kkvXr/YqwkcnL+vWxx2Nstx389FP17RUVvgR6bbp04ZOp8zljwA4sbKJCXjnhj3/0s4r69YPW\nrYOORiSrJDNo9CHgHaAf0BXYI+prprgdGBZ0EJKBNmzYdopjebmvEJqI5curqpLWonL7HfG5uOSE\nFi3gxhv94jgikpBkEo7OwJXOuTecc0udc19G3lIdYLKcc68APwYdh6RIKrpSwtav9yXEwwM+Fy6s\neSE18C0ZW7Zsu+2rr3wriTQ+Bx3kpz/PmhV0JCJZJZmE4+/4mSoiDSe84FplZf2PtX59VQvH55/7\n0uK19cm6SRU1AAAgAElEQVTvuqtf2C3SkiWxx3xI4zB+vF/KfuXKoCMRyRrJJBzPASVmNt7MTjSz\noshbMkGYWX8zKzWzZWZWGes4ZnahmS0xs41m9i8z65vMuSRLbd687df62LDBt3A0a+ZLjP/ud1Db\ngKc99oAvvth220cfQY8e9Y9FslOzZnDLLepaEUlAMgnHfUBH4DrgL8AzEbdk2xhbAYuBkcRYkdbM\nTgVuA8YBPYF3gblm1i5in5Fm9o6ZLTKzBDvkJeOFE41Nm+p/rHALxy9/Cd27w29/W/vqrAccAIsX\nb7vto4/8c6Xx6tXLJ6NPPx10JCJZIeFZKs65ZJKUuo45B5gDYBbznb8YmOqceyS0z/nAMfhqp5NC\nx5gCTIl6nqERe7khnHDEmjGSqHBJ8iuuiG//Xr3gkUe23bZmTfWy5tL4/OEPftbKgAF+qrWI1Cjl\nyUOqmVlToDewta6wc84B8/AzZWp63ovAk8DRZvZfMzsw3bFKGoUHbaYi4Yhchj4ebdvCihVV40dW\nrICdd65/HJL9mjWDm27yS9mLSK3ietc1s4uB+51z5XHufz7wmHNuXX2CC2mHr+2xPGr7cqDGNm3n\n3BGJnihcaTSSqo5miFS2cCTj4IP9MuUDBsC8ef6rCECfPn4l4FRVwhXJYOHqopHirTQabwtHCZBI\n+/EkfIGwrDR06FBKS0spLS1VslGLsjI/WD9ySZK0PT/ohOOcc/xslvXr4aGH4BTVkpMI110H99yT\nfFVakSxRn8/HeNuVDXjJzLbUuacXo2Rj0lYBFUCHqO0dgG9TeB6VNo/DsGFVq7qvWQOvvALz51ct\nSFZQEF5LJMXPjxg0WlYGU6f6CSaFhSl5WXXr1MnPZjn+eLjySo3fkG01awZ/+pPvWpk2LehoRBpE\nukqbT0gwjmeB7xN8TkzOuc1mthAYBJTC1oGlg4A7U3GOMC3eVrc1a6C01H+/aJGfTVpS4sdVAhTV\nMTE66edHtHCUlfl1tIqKkkg4KishL8mhSyee6G8isfTt638hn33Wz3wSyXFpWbzNOZdowpEQM2sF\ndKNqRklXM9sf+N459xUwGXg4lHi8iZ+10hJ4OJ1xSQbZssVX/PzpJ4hz0nPMlpDVq2GnndIWpjRy\n48f7WSv9+0ObNkFHI5JRMmWWSh/8+iwL8XU4bgMWEWpZcc49BVwOTAzt9wtgsHMupWX+SkpKNG4j\nQn3HaKT03Js3+9oZCYzhCLeEbBP/qlWavijp07y5X+DtssuCjkQk7cLjOUpKSuLaP5nVYlPOObeA\nOpKfGupsSIrVNcZi0aL0nvvNN31XSfS5F/1rP/jxQ3pd1ZQ12/n9i4vjHzsCwJ13+pLkcSy6JpK0\ngw6CmTPhuefguOOCjkYkY2REwpEpNIaj7jEW6fysXrPGF/8sLa1+7qKDf4T1H1J6zUYW7XpswmNH\nAHjpJX/wv/wlfS9CBHzT2jHHwCGHaBl7yVlpGcPRWGiWSgZzlZDfpH7TYjuEJjrtvXdqYopDYSGM\nG9eAs2kkM7RoUdW18tBDQUcjkhaJzlJJeAyHmTWv5bGsflstLi6mqKioWlETyQCVDprk1y/h+O47\nePtt2Hff1MVVh8JCPxZFCUcj1K+fHzg6e3bQkYikxYwZMygqKqI4zkUMk2nhWGRmpzvntlnNysxO\nxC/slrUFv9TCUbf8/Krui3ArWvRYitoUFNT8/DffhCNqqg/rKqtmqdQiegzKNvG9OYaC23vXPdZD\nJFWuv953rRx8sGZHSc5JVx2OSC8D/zKzcc65m0NTWu8BTgGuSeJ4EpBkCmj16lX7GI+6RH7YVxun\nUVTLwM9KF1eXSq1jUIr+SNGa0vgCFUmFFi180nH55fDgg0FHIxKoZFaLHWlms4EHzexYoBD4Efil\nc+69VAfYkBrDoNEgZ6HUi6v0XSr1WZ7eOa0dLA3v4IP9EvZ//SscfXTQ0YikTEMNGv0rMBO4ANgC\nHJftyQY0ji6VIGeh1Euli6tLpUbl5b78dEBLsUgjd8MNcOyx8Ktf1d3vKJIl0t6lYmZ7Ao8DuwCD\ngQFAqZndAVzjnNuc6DElc9R3jEZ91Da+Y9HS1rB5e4oeOJY1zyQR14oVfpbKV2kJXaR2LVvCxIlw\nxRVw//1BRyMSiGRaOBYDs/GVPlcDL5rZC8AjwBFAzxTGJw0skTEa9Z3yGf382sZ3MGXa1pU4Fx1z\nbUJjR3Z4fS7s3xJ2200JhwTnkEN8DZgXX6xldLRI7kqmtPlI59xpoWQDAOfc6/hEI1NHAMQlF6fF\nprM8eX2nfCb0/PJy2HHHpLpU9rroKJg3Dzp2TPi5Iil1441+Vdkffww6EpF6S/u0WOdczHkEzrl1\nwLmJHi+T5MoYjtoGhr75pn8866aGbtrkB5gsW5bQ0/IJ9fD99a9w881pCEwkAa1awR/+AGPH+lL7\nIlmsIcZwnFXLw66mhEQazvLlfmzaiBG+ZSN66mmcA4ozS5wtHNHjQPKo4JhWL9PkrTUw6WAKtG6b\nBO3ww+Gpp+DVV/2qsiKNRDJjOO6Iut8Uv1T8T8AGQAlHACJbNRYu9N3E8+dXPX7ttdlV8LDa+JA4\nE47ocSC/6/0JDw0ppUPvjlC8XfoCFknEzTfDCSfA88/7Wh0ijUAyXSrVViIys72Ae4FbUhGUJC5y\nuuuAAb4bJbxicO/eVV3GBQU+GYls6ahptkeQ64CEx3dsVV7ug0twDEd7VvDTLrtD8SUpjU+kXgoK\n/Dor48bBpElBRyPSIFKyeJtz7lMzuwp4FOiRimNKekyf7pONWCuyRqv2oR+kJAeN7sxKtrRpn6ag\nROrhN7/xs1befNMvkyyS41K5WuwWYNcUHq/BNYZKo1kryYSjPSvY0nqfNAUlUk+33QannOL7O5s1\nCzoakYSkvdKomRVFb8KXNx8FvJbo8TJJrsxSyUnl5bDDDgmXNu/EV2xuf2SaghKppzZtYORIv5T9\nxIlBRyOSkIZYvO2ZqPsOWAn8HbgsieOlnJl1xA9ebQ9sBm5wzv1fsFGlR3gBtvLyoCNJs/JyP6Vw\ny5aEntaFpWzadY80BSWSAiec4NdaWbwYDjgg6GhE0iaZQaPJFAtraFuAS5xz/zazDsBCM5vtnNsY\ndGCpEKvORps2VdNBP/vMf42sxfLee1WPhweGBjkoNGHOQV4eWGKrrzWnHNeseZqCEkmRyZP9H/bs\n2dC0adDRiKRFKsdwZAzn3LfAt6Hvl5vZKqANkFjVqAwVawG2fffddpbKN99sO0sl8vGwjBoUmg7O\nBR2BSHw6dICzz4ZbbvFFwURyUFwJh5lNjveAzrnRyYeTembWG8hzzuVEslGT7bevasF4L7Rub2QL\nx/bbN3xMKRVu2Uggidhu2RK+pHN2j2SWxuP00/0A0g8/hJ/9LOhoRFIu3haOeBdkS+pfSjPrD1wB\n9MYPQB3inCuN2udC4HL8KrXvAhc5596q47htgGlkecn1eFx/fdXU1lh1OK6/PrjYUiKJ1ort33mV\nf3AI/dIQjkjKmcHtt8Pvf+8LgjVpEnREIikVV8LhnDsszXG0wq9C+2dgZvSDZnYqcBvwP8CbQDEw\n18z2ds6tCu0zEjgPn/T0C32dBdzonHsjzfFLA4p37EnBP2Yzl/u4omHCEqm/3XaDk07yicdlGTEG\nXyRl4h4AamZdzRIcsRcn59wc59x1zrln8dNsoxUDU51zjzjnPgLOx5dRHx5xjCnOuZ7OuV7OuU34\nlo2XnHOPpyPmbJFVA0NrEtm60awZha3L615ldvVqrGILP9Am3dGJpNbw4fCPf8AnnwQdiUhKJTJo\n9FN8d8cKADN7ErjYObc8HYGFmVlTfFfLjeFtzjlnZvMgdmu5mR0MnAz828yOx7d2DHPOvV/bucKF\nvyJlexGwnBgYumkTNA/NNGnfHlauhE6dan/OfffxXdE5ML/23UQyjhnccQecfz4895y6ViSjhIt9\nRUpH4a/oloffAFcn8PxktQOaANGJzXKge6wnOOdeI4kZOLlS+KtZsxxo1Yi0dq2vMgqw8851Jxyr\nVsH8+ay58cqGiU8k1XbfHYYMgbvvhku0DpBkjlj/hKez8FfOypbS5tFLsMO2C7B16JADrRqR1q71\nVUbBt3Asr6NR7aqrYMKEhGt2iGSU887zSccxx0C3bkFHI1JNOkubO6rPQmmIQgergAqgQ9T2DoRq\nbTQG4YqiI0ZUX4K9tgXYcsKKFT7RANhrL/jgAzj66Nj7Pv64z7gOOggWNVyIIikX7loZNcoX0cnL\nhpqLIjVLtEvlYTMLL2bRHLjPzNZH7uScOyFVwYWOt9nMFgKDgFKA0ODVQcCdqTxXpnWpxKooOn/+\ntkvJR9bayFnLlvnR++BLPz9ewzjgl1/2q2/+5S8NFppIWnXp4ls4pkzxiYdIBknnWirTou4/mlBk\ntTCzVkA3qsaJdDWz/YHvnXNfAZPxyc5CqqbFtgQeTlUMkHldKrEqika2ZBRFL6OXq77+Gvbf33/f\nvr1v7om2YIGv0viXv0C+egolh4wYAb/9rV/OvmvXoKMR2SptXSrOuXOSjqpuffDzCcLdNreFtk8D\nhjvnnjKzdsBEfFfKYmCwc25lKoPItBYOCfn6a/9fXtg++8Dbb0OfPlBZ6f/7e+01ePJJaNkyuDhF\n0iEvz3etXHopPPOMulYkYzTEarEp55xbQB01QZxzU4Ap6Ywj01o44pETdTbqEtmlAjB6NJx5Jgwc\n6OsVDBkCjz2mN2LJXV27whFHwP33++myIhkgnYNGc142tnDkRJ2Nuqxb55emD+vY0fc1ffihn5HS\nXKvBSiNw4YW+H/Xoo6Fz56CjEUm4hUP/Ekpm27Qp9nLdO+4IBx6oZEMaj7w8X/L8kku0ErJkJSUc\nEYqLiykqKqpWRU0CtHhx1YBRkcauWzfflfjnPwcdiQgzZsygqKiI4jinS6pLJUI2dqnkvDlz4Mgj\ng45CJHNcdBEcdxwMHlx3iX+RNMrKQaMSW10VRaOWfck9FRW++Mgf/hB0JCKZo0kTPz/+kkvg6adV\nUVeyhhKOCJk0S6WsDPbc00/BLyxsJBVFoz3zDBx1lBavEonWvTsccgg89BCce27Q0UgjpVkq9ZBJ\nXSplZX45kKKiHJ/yWpN16+Cee+D554OORCQzXXKJf4M44gi/2JtIA1OXShaLLmUOVV0ocSaQuaGy\n0i9cNXGiCnmJ1KRJEz9r5eKLYdYsda1IxlPCkUFqK2Uevp/znIOxY+Gww3yTsYjUbK+94PDD/cqO\nKggmGU4JR4RMGsMRrbAQ9t47x7tXnINrroHWrf3gFRGp26hRvmtl8GDYY4+go5FGRGM46iGTxnBE\nKyz048RyNuEIJxsFBXDllUFHI5I98vLgzjt918qzz6rEvzQYVRqV7OOcL1G+005KNkSS0bWrL3l+\nzz1BRyJSIyUcEizn/MjYwkIYMyboaESy1/nnw4svwqefBh2JSExKOCQ4lZV+Qaru3f3S2yKSvLw8\nuPtuX4l0y5agoxGpRglHBK2l0oAqK/3A0D594IILgo5GJDfsvrufX//HPwYdiTQCWkulHoIeNNpo\nSpk7B6NHw0EHwfDhQUcjkltOPx3OOgveeMOvqCySJir8BZhZATAPaIJ/jXc65x4MNqq6TZ9e9X1O\nlzK/6y5o314lmUXSwczPWjnxRHjuOWjVKuiIRIDc7VJZC/R3zvUCDgTGmlnrgGMSgLfegtdeg6uv\nDjoSkdzVurWfZq6B2JJBcjLhcF556G6L0FfV/Q1aZaVf+XXKFJVhFkm3QYOgWTOYPTvoSESAHE04\nwHermNli4L/ALc6574OOqdF75hn/Jti2bdCRiDQON97o+2VXrgw6EpHMSDjMrL+ZlZrZMjOrNLOi\nGPtcaGZLzGyjmf3LzPrWdkzn3Brn3AHAHsAZZrZzuuKvj7IyGD/ef80qDzwA770X//7O+edoRopI\nw2neHG691Zc/dy7oaKSRy4iEA2gFLAZGAtX+KszsVOA2YBzQE3gXmGtm7SL2GWlm75jZIjNrFt7u\nnFsZ2r9/el9CcsLL0EcnHIWFMG5cBpQyX70alizZdtt33/kRrpdfXvub2Hffwe9+5ysgDhjgF5na\nYYe0hisiUQ44wI88f+ihoCORRi4jZqk45+YAcwDMYnbuFwNTnXOPhPY5HzgGGA5MCh1jCjAl9Hh7\nM9vgnPsxNGPl0PBjmaC2ZejBf50+3bd8BO6SS3zC8corVdveeANOPhnWrYPnn4fjjqv+vHnz4E9/\ngkmT/HSbdetg++0bLm4RqXL55X7O/cCBsOeeQUcjjVRGJBy1MbOmQG/gxvA255wzs3lAvxqe1hm4\nP5S7GHCHc+79dMcar9qWoYeqWhwZIT8fNm7cdtvChX4sxn77wQknwLHHVg0CrajwmdKKFf5Fhqfk\nqWVDJDhNmvh1Vi64wE+Vzc/4t37JQdnwW9cOX09jedT25UD3WE9wzr2F73pJSHh5+kiZuFR9g/rh\nB2jTZttt774Ll10GLVvCkUf6N7ILL4TFi/0slNNOg+uvDyZeEYmtSxcYOhRuusn/nYokIbwkfSQt\nT18PjT7JiLRlix94VlHh/0sC2LDBJxvgE48//tG3cnTrBlOnQseOwcUrIjUbNgzOOAPeftsvKyCS\noMjPx1jJR22yIeFYBVQAHaK2dwC+TeWJgi5tnrF23RW++QY6dfJdJe3bVz2WlwfXXhtcbCISPzNf\n6feUU3zXSvgfB5EkJFraPFNmqdTIObcZWAgMCm8LDSwdBLyeynNp8bYadOkCS5f67xcu9INORCQ7\ntW0LV17pbyL1kOjibRmRcJhZKzPb38wOCG3qGrrfKXR/MnCemZ1lZj2A+4CWwMOpjKOkpITS0lJ1\np4RVVPgWjM6d4csv/ba33oK+tZZAEZFMd+SR/m97zpygI5EsNnToUEpLSykpKYlr/0zpUukDzMfX\n4HD4mhsA04DhzrmnQjU3JuK7UhYDg0M1NlImPGg0nWM4ysrg44/918BrbNQlPGC0Sxc/zRX8wNAr\nrgg0LBFJgZtu8mOv+vSBdu3q3l8kSngMR1YNGnXOLaCO1pbIOhvp0hBjOMrK4JNP/ESOgoIMX4Z+\n5Ur/RtSjB9x+ux9AunEjtGhR93NFJLO1aAG33AIXXQSPP671jSRhWp6+HhqihSMsXHcjo5ehX7XK\nJxw77QRr18Krr8KvfhV0VCKSKr16wS9+AdOm+arAIgnIyhaOTJGuFo7aKovG+XMKxqpVsHNoCZrD\nD4ff/x5eeinYmEQktcaM8dUGBwyAPfYIOhrJImrhqId0tXDUVlk0fD8jrVoFu+zivx89Gk4/3U+R\nFZHc0aQJ3H23X+CttLSq3o5IHdTCUQ+qwxFl5UrYd1//fV6ekg2RXLXHHn59pEmT4Oqrg45GskTO\n1eGQAEV2qYhIbjv7bL9swaJFQUciOUotHBEactBoVggPGhWR3Gfmu1ZOPdWvAq3ZaFIHdanUQxBd\nKoWFsPfeGVqTY/XqDJqjKyJp166dX8r+6qv9VHiRWqhLJcsUFkL37hmacDinufkijc3RR/uaOy++\nGHQkkmOUcIiIyLYmTfKVSL//PuhIJIeoSyVCusZwFBT4ae6Q4ZVFo6l1Q6RxatkSbr7ZVyF99FG9\nF0hMGsNRD+kawzF9etX3GV1ZVEQkrE8f+NnPfMIxbFjQ0UgG0hgOERFJjauughkzYMmSoCORHKCE\nQ0REYsvPhylTYORI2Lw56GgkyynhEBGRmnXpAsOHw3XXBR2JZDklHCIiUruTT4YfftBUWakXDRqN\noEqjIiI1mDwZjjvOL2ffoUPQ0UgG0CyVetDibSIiNWjZ0lcfPf98ePppv6CjNGqapRLBzFqY2VIz\nmxR0LCIiWW+//eDII31rh0iCcjrhAK4B/hl0EJEKC2HcuAwtZS4iUpfzz4e334a33go6EskyOZtw\nmFk3oDvw16BjiVRYCOPHK+EQkSxl5qfKXnklrF0bdDSSRXI24QBuBa4GVJNXRCSV2rSBiRN96XPn\ngo5GskRGJBxm1t/MSs1smZlVmllRjH0uNLMlZrbRzP5lZn1rOV4R8LFz7rPwpnTFLiLSKB1yCHTr\nBtOmBR2JZImMSDiAVsBiYCRQLV02s1OB24BxQE/gXWCumbWL2Gekmb1jZouAAcBpZvYFvqXj92b2\nh/S/DBGRRmTsWD9j5cMPg45EskBGTIt1zs0B5gCYxVyWsBiY6px7JLTP+cAxwHBgUugYU4ApEc+5\nLLTv2cA+zrkb0vYCREQaoyZN4P774cwz4bnn/NRZkRpkRMJRGzNrCvQGbgxvc845M5sH9EvlucKF\nvyKpCJiISC0KC/0ib5de6pMPyWnhYl+RcqnwVzugCbA8avty/CyUWjnn4u5gVOEvEZEkHHEEvPIK\nTJ+upexzXKx/wuMt/JUNCUeDUWlzEZEkjRsHQ4ZA377Qo0fQ0UgDSLS0eaYMGq3NKqACiC7e3wH4\ntuHDERGRavLzfZfKqFGwYUPQ0UgGyvgWDufcZjNbCAwCSmHrwNJBwJ2pPJe6VERE6mHXXWHMGCgu\nhqlTg45G0iwr11Ixs1Zmtr+ZHRDa1DV0v1Po/mTgPDM7y8x6APcBLYGHUxlHcXExRUVF1QbEiIhI\nnI48EnbeGR57LOhIJM1mzJhBUVERxcXFce2fKS0cfYD5+BocDl9zA2AaMNw591So5sZEfFfKYmCw\nc25lKoNQC4eISAqMH+/Hc/TpA93rHNsvWSorWziccwucc3nOuSZRt+ER+0xxznVxzrVwzvVzzr2d\n6jhytYUj115Pqui6VKdrEpuuS3W1XpP8fN+lcuGFsHFjwwUVsMb2e5JoC0dGJByZoqSkhNLS0pyb\nodLY/gjipetSna5JbLou1dV5TXbbDa64wo/naCQa2+/J0KFDKS0tpaSkJK79lXCIiEh6DB4MbdvC\n448HHYlkACUcEWrqUkk0a41n/5r2SWR7bXGmO9NuyGtS02PxbavfzzJRqb4uiV6TWNsTvZ9qDf27\nEv0zr2n/bPr7iec5Gfu7MmGCH0D6ySd17qr32vjiqe/+qXqvVZdKPdTUpaI/gvjiqe/+SjgSe1wJ\nR42PxrV/Nv39xPOcjP1dSWA8h95r44unvvun6r020S6VTJmlErTmAB/WsOLhmjVrWLRoUdwHi2f/\nmvZJZHv0tsj7NX2fkNWroYbnNeQ1qemxurb5H+caPvyw7muUKqm+Lolek1jbE7lfn2sS/vOJ/jNq\nyN+VWD/zmvZP5Dqk+nclmeNl/e/Kb3/ry56PHZvUa0h2/6x4r61FdrzXbv2jb17bec25aqvBNzpm\n9ivgtaDjEBERyWIHO+der+lBJRyAmbUEVPxfREQkeR8552qsa6+EQ0RERNJOg0ZFREQk7ZRwiIiI\nSNop4RAREZG0U8IhIiIiaaeEQ0RERNJOCYeIiIiknRIOERERSTslHCIiIpJ2SjhEREQk7ZRwiIiI\nSNop4RAREZG0U8IhIiIiaaeEQyRNzGy+mU0OOo5UycbXk2kxJxOPmb1sZpVmVmFmv0hXbKFz/W/o\nXJVmVpTOc0njo4RDJAlm1tHMHjKzZWa2ycyWmtntZtYm6NgkeClOdBxwP7AL8F6KjlmTi0PnEUk5\nJRwiCTKzPYC3gT2BU0NfRwCDgH+a2U4BxtY0qHNLWm1wzq10zlWm8yTOuXXOuRXpPIc0Xko4RBI3\nBdgEHOGc+4dz7mvn3Fzg18BuwB8j9s03s7vMbLWZrTSziZEHMrOTzOzfZrbBzFaZ2d/MrEXoMTOz\nq83si9Dj75jZiVHPnx86fomZrQTmmNl5ZrYsOmgze9bMHozn2GbW0sweMbN1oVac0XVdFDM7xsx+\nMDML3d8/1DR/Y8Q+D5rZI6HvB5vZq6HnrDKz58ysa8S+9X4dMZ4b7zW9w8xuNrPvzKzMzMZFPL69\nmT1mZj+a2VdmdlFki4aZ/S8wALgkoitk94hT5NV07FQKxXRn6HfjezP71szODf1sHzKztWb2qZkd\nlY7zi0RTwiGSADNrDRwJ3OOc+ynyMefccuAxfKtH2O+AzUBffHP1aDM7N3SsXYDHgQeBHvgPqZmA\nhZ47FjgT+B/g50AJMN3M+keFdRY+AfoVcD7wF6CNmR0WFfdg4NE4j30r0B84LvR6BwK96rg8rwLb\nAz1D9wcAK0PPDTsUmB/6vhVwW+i4hwMVwKyIfVPxOqIlck1/BH4JjAGuM7NBocdKgH7AsaFYBka8\nZoBLgH8CDwAdgELgq4jHz67l2Kl2Fv5n0Be4E7gPf11fC8X8N+ARM2uepvOLVHHO6aabbnHe8B8S\nlUBRDY9fiv/gbIf/YH0v6vE/hbfh3/ArgE4xjrMd/kPpwKjtDwCPRtyfD7wd4/mzgAci7v8P8FU8\nx8YnAuXACRGPtQbWA5PruD5vA6ND388ErgI2Ai3xrT+VwJ41PLdd6PGfp+J1RFyfyUlc0wVR+7wB\n3IhPqDYBx0c8tmPouJOjjlHtWtV27FquaU3HugY4J+L+Y0Cfms6F/wdzHfBwxLYOoWv+y6hj1/g7\nrptuyd7UwiGSHKt7FwD+FXX/n8BeoW6Hd4G/A++Z2VNm9nurGv/RDf8h/WKoW2Odma0DhuHHjERa\nGOO8jwEnWtWYjtOBJ+I4dtfQ8ZsCb4YP5pz7Afg4jte7gKoWjf74pOND4BB868Yy59znAGbWzcwe\nN7PPzWwNsAQ/QDKy+6E+ryNaItf031H3y4D2oePmA2+FH3DOrSW+a1PXsRN1PP73CTPLB44G3q/p\nXM6P//gO+E/EtuWhb5M5v0hC8oMOQCTLfIb/UPwZ8GyMx38O/OCcWxUaylCj0AfAEWbWD99tcRFw\ng5kdiP9PGuA3wDdRT90UdX99jMM/h/+P9hgzexv/4X9J6LG6jt221sBr9zJwjpntD/zknPvEzBYA\nh+FbSRZE7Ps8Psn4fSiOPPwH5nYpeh3REtl/c9R9R1UXdLzJZk1qO3ZczKwAaO+c+yi06ZfAB865\njfKWloEAAAM0SURBVHGcK3obiZ5fJBlKOEQS4Jz73sxeBEaaWYlzbusHVWhMxunAwxFPOTDqEP2A\nT51zLuKY/8TPbrke+BL/n+uD+A/Bzs65fyQR5yYzm4kfr7AX8JFz7t3Qwx/UdmwzWw1sCcX+dWhb\na2BvfEJRm1fxXQzFVCUXL+O7VnbCj9nA/PThvYFznXOvhbYdksrXEUOi+8fyBVVjcsLXpiD0WiKT\nqZ+AJkmeIx4DgMjXcBgw38zaOOe+T+N5RZKmhEMkcaPwg+7mmtm1+P/S9wUm4QcH/iFi393N7FZ8\nHYXeoecWA5jZL/FTaf8GrAAOwo9j+MA592PoeSVm1gT/4VIAHAyscc5NjyPOx/CtCPsAW/eP59hm\n9mfgFjP7Hj/o8Ab8eJNaOedWm9m/gTOAC0ObXwGewr/fhD+Uf8A37/+PmX0LdMaPb3FUl/TriIqt\n3tc0dIxpwK1m9gP+2ozHX5vI2JcCB5pZZ+BH59x3dR07QYcBy2Brd8qJ+KTuNPwsKpGMo4RDJEHO\nuc/MrA8wAXgSaAN8ix/gONE5tzq8K/AI0AI/HmILUOKcezD0+Fr8uIZL8K0CX+IHXP4tdJ5rzWwF\n/oOkK7AaWIQfvEjEOWryd+B7fMvA41Gvoa5jX4EfPFqKH2h4WyjGeCwA9ifUGuKc+8HMPgB2ds59\nGtrmzOxU/MyJ/+DHQFxM7BaU+rwOl+D+1Z4Tw2jgXnx3z1p8otkJP9A27FZ8S9cHQHMz28M59984\njh2vw4DPzOxM/LiUGfhxMm9F7BPrXPFuE0k5i2jZFRGRBJlZS3xrw2jn3P+m4fjzgXecc6ND99sA\ni5xzXVJ9rohzVgJDnHOl6TqHND4aKCQikgAzO8DMTjOzrmbWC9/q4og9iDhVRoYKde2DnwX0WjpO\nYmb3hmbu6D9RSTm1cIiIJMDMDsAP6t0bPzh0IVDsnPsgTecrxHfLgR8jNBY/8Pjxmp+V9LnaUdV1\nVhZj1otI0pRwiIiISNqpS0VERETSTgmHiIiIpJ0SDhEREUk7JRwiIiKSdko4REREJO2UcIiIiEja\nKeEQERGRtFPCISIiImmnhENERETSTgmHiIiIpN3/A5mQjKXIiV6PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8374ed07b8>"
      ]
     },
     "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 $\\chi^2$ : 1.08 \n",
      "bayesian stellar mass 10.88 +/- 8.68 [M sun]:\n",
      "bayesian dust luminosity: 11.38 +/- 8.68 [L sun]\n",
      "bayesian SFR 17.41 +/- 0.87 [M sun / yr]:\n",
      "bayesian AGN fraction 0.00 +/- 0.00:\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": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model for HELP_J095852.73+020248.24 at z = 0.60, best(Mstar) = 10.88, best log(Ldust) = 11.38, best AGNfrac = 0.00\n"
     ]
    },
    {
     "data": {
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/4UMG9OlDYmIiACIxgXpcRCQm2FhChUKRFqWEKBRFmPPnz/Pdxo0ATHnnnWyP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\nQpxzzjmXiP8Cji+99tfgGR8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8374f7dba8>"
      ]
     },
     "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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E/TldTxV3F1SH4wbwU/an+DG7SlmWBS+hM1nuwsLCDgbDGply9erVmEwm7OzsrO7eoM1I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\n17/97W9UV1fzyCOPUF9fz29+8xsefPBBQfK7evU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773eaE5CWlsYjjzxi/r9Syuzu3rlz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\nfE4CDiGEEEL43P8HFZ0guF8EJ4UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f83751f6828>"
      ]
     },
     "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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z2YTa5D333MOzzz5LQkICubm5REZGEhUVxR/+8AfMZjN1dXV06dKF\nmpoaHnroIVEv8t577zFt2jQ2btzooP2htQfX19dz++23M3HiRCEuduedd1JQUOBUWEqv1+Pv7++Q\nnmsuOa69l5eXx6xZsxyG12m/PX9/fxYvXuw0pbdw4ULeeeedVmXMryQd0hGOwq1mkDtqgOLNjnS+\nbjydpYbDAJQC8UDzvks9EAD8LxAI/DcwAHjrei6wsxAWFsbHH3/cYux3ZmYmQ4YMoaqqCpPJ5HTb\n1vQtgoODsVgsLFiwgMjISN58802MRiN///vfmTlzJidPnqSqqgqAsrIyoYXh5ubGs88+K/QBQkND\n+fLLL/nss89EXcWePXvo2rUrhYWFeHp64uPjQ1xcHCaTSdROpKenc/78eVxcXCgtLSUgIIDy8nKh\nMXHs2DF8fHxQFIUnn3ySpUuXcuHCBeLi4jh06JBIi1gsFmw2G1u2bGHatGlkZmYyYcIEAgMDycjI\nwGQyceHCBaFVcd999zFw4EBycnL4/vvvRT5/+PDh9OzZk6qqKpKTk+nVqxfDhw9Hp9PxzTffsHDh\nQrp3747RaGTJkiVCuEursSgsLBRqqydOnBCqlOfPnxdOVu/evdHpdDz22GMsXryYzZs3M27cOAAm\nTZqEq6srf/nLXzAYDERERLBq1So+//xzevXqRWhoqJj7Aj92kbi4uNCrVy+qqqrQ6/WsWbOGBx98\nkC+//JIXX3yRyspKISx1+PBh4fBoQ+c03NzcWtRwqKrKnj17CAwMdDpyftSoUeTm5jr97fn4+PDx\nxx/z8ssvO9TltLVdtDVuZeNxte3uzvR00tPT6du3L2PGjGn13nCrYjKZWLBgAWFhYfz2t78lLCyM\nBQsW/OTOQ2ehUzgcqqpuV1X1eVVV3wKUZu9Vq6r6W1VV31BV9StVVf8JzAAeUBSl7w1Z8A0kJSWF\nzMxMMfkzIyOD2NhYkZK4++67eeGFF1ps1zwdY7FYePnll6mrqxM6E5o4lpae0WozvLy8uPfee8nP\nz8dqtRIZGcnx48dF+mPIkCHcfvvtnDt3jsTERP785z9js9nIyMgAEIWMWgHko48+SpcuXUTXyJAh\nQ8jJyaGsrAwvLy82bdpEly5dhH5GSUkJZrNZDGr7n//5H9zd3XnkkUfo2rWrg8Jpnz59cHNz49VX\nX6W+vl6oXQ4ePJi77roLq9VKz5498fDw4LnnniM3N5fw8HCR2vD29iYiIkJIjjc0NODn50dMTIyI\njoSEhPDggw/y5JNP8sUXX4jhaFarlV69epGVlcWUKVPo378/gwcPxtfXl/vuu4/GxkYHIbVu3bph\ntVqFo2cwGJg5cyZvvfUWvr6+fPDBB6iqyoIFC4iLi+OLL76gsbERRVHw9fUVRZxavYZWUKoVkmqD\n5f7xj3+wbds2oWzaXFhq5MiRYl82mw0/Pz8Hp2TmzJlMmTKlxfA6+6hIaGgos2fPZtmyZU5/t336\n9GHv3r2cPXuWGTNmkJyczIwZMzh9+rSsm/iB9jCO7VlUfrMjna/OR6dwOK6CrlyMhFy40Qu53vj4\n+NCtW7cWT5pLly4lICCAkydP8uGHHxISEuJws7JPx2hdCQEBAaLmw17YqblAk8ViITk5mWXLlokB\nbSUlJaILJTQ0lDNnzjBt2jS8vLwICQmhvr6eEydO4OnpSUBAAF5eXlRXV4sncZPJJDpTQkJC8Pb2\n5pFHHqGyspKDBw9iMpmEgffy8qK+vp4XX3wRg8HA8OHD8fX1xWg0oigKAwYM4MUXXyQ6Opqmpibh\nLPXp00cYyNDQUBoaGli3bh3Hjh1Dr9cLDYojR47Q0NDAyZMn0el05ObmMm/ePNLT00VaQ6/XC6Eu\nRVEIDw9n3bp1uLm5AYgaC7PZzMCBA4mJiSEvL4+goCCqq6sBHAS1tDVq50A79yNHjmT16tWYTCaK\ni4vx9vYmMjKSkSNHiroFVVVpampyiEIEBATg7u4u2l6XLFniIKil1+sZN24cK1asaCEsZd9VotXj\naDUcmlPyyiuvoNPpWgiA2RMWFkZBQcElf7uaquaHH37Irl27WLRokXQ2aB/jaDKZeOutt65ZHO1W\nQTpfnY+bzuFQFMUDWAq8pqqq+Uav53qj/jDvwv5JU4sUBAQEkJOTw2uvvca6deta3Kw0rQn7aEZg\nYCA7d+4UA8/Ky8uJiori/vvvF8amS5cuvPbaa/j6+qLX6wFEF4p2czOZTLi7u4v6h6amJjIzM+nV\nqxd33XUXJpOJmpoavv32W9FxsmfPHoeb45QpU7BYLLi4uODv78+ZMxdLdKxWK126dBHFm5qjUVJS\nwi9+8Qs+/vhjjhw5QnBwMG5ubvj7+xMXFyemymopI51Ox7Fjx9iwYQNms1kY3SNHjtCnTx9KS0sZ\nPnw4+/btIyQkhLvvvpvq6moCAgLYvXu3qJ1RVZW8vDzmzp2LzWYThZgxMTFYLBb27t3LiRMnxDnW\nRsVr3TMaQUFB7N27V5wDTaDMYDDg7+9PY2Mj1dXVDjU3msaF5ixpUYgjR45w+vRp9Ho9c+bM4Y03\n3qCkpIRx48bx+9//nujoaNLT0xk7dizffPONQ5ShoqKCoqIiTp8+zYwZM0RaSEO7WQcEBIghcs6w\nL1q+HLdyOuRquFbjaDKZGD16NL6+vu1yfW4F2kOZVtK+3FQOxw8FpHlcjG60nCL2E0CLVNg/aTZP\nh2ifCwkJYcyYMTz55JOEhYWxZ88eli5d6mDow8PDWb16NfX19ZjNZqZOnUpiYqKYmQEXIx4FBQVM\nnjwZq9UKIDo1tG4WNzc3dDod1dXVZGVlodPpWL9+PV27dmXt2rXodDrc3NzE7A5tgJj9zdFgMLBh\nwwbOnDnDokWLhH5GUFCQKOLUikt/9atfCYGq5557jn79+qEoCk1NTfz73/8mNDRUGEjNIYuIiMBo\nNJKQkIDBYBDzVmpra2lsbMTLy4uYmBhx/lRVpaGhgQEDBrBp0yasViu1tbXk5+eL8+/n58fEiRMx\nGo0UFxeTkZEhumGCg4NFx4t2rPY6EtHR0Q7pK00CHGD+/PlUVFQ46GbARW2UnJwcUY+hRSEyMjJ4\n7LHHHCIMR48eJTk5mTfeeIOcnBzeeustBg4cyD//+U/effddhyhDnz59RPShqKjIqax3//79OX36\ndKsG60rEtySOXKtxXLZsGc888wxNTU3y+nBlyrSS68dN43DYORv9gN+0JbqhqUra//f66693+Fo7\nCu2PIzQ01OFJs7Uwt8ViIS8vjyeeeIJBgwZhtVrp27evw/j2vLw8UlJSGDp0KC+88EKLgVya4W1q\naiIvL4+7776b/Px8GhsbqaysFNoPWppEKxTt3r073t7eGAwGFi5cCCDqJrKzs7FYLA4GVsPf359H\nH32UY8eOiU6PX/ziF6JIcsiQIeh0OkaPHs2pU6fQ6/WEhoZSW1srNCe0YWETJ06krKyMTZs2ibTE\ngAEDcHFx4Y9//COrV69mwoQJdOnShaCgINGJ4+7uLhyEHj16sHr1anr06EH37t1xdXVlw4YNuLi4\niFH19oWYCxYswGw24+XlhdVqJTk5GQB3d3cAh7kl3t7e4rs0tILTJUuW4Orq6hCJgR+1UQwGg4MR\n0nQ1Nm/eTH5+vkO6qrUn5tZuxpqs95tvvsm4ceOIiopi3LhxvP/++/zud7+7ouFkksvTHsZRc1ia\nD8Sz56d0fVoTM9T4KTlfl+P1119vYSeTkpI65Ls6RVvs5bBzNn4O/FpV1cq2bJeWlkZQUFCHru1y\n2Kt5Xg2tCXWVl5eLfTsbdqYVgmqFndpodkVRiI2NFZ/R5pNkZWVx6tQp7rrrrhZrHT58OIWFhURG\nRrJ//342bNhAt27dOH/+PB999BFfffUVcLHg0N/fXzg0mo6H1n1hMBjw9vZmyZIlQlXUXmLaYrGw\nadMmDh48yM6dO1EUBX9/fwoKCvD09GT48OHcdddd7NixgxdffBEPDw8RQg4MDBSaE/ZGWkvdaGPT\n9+7dK8bVd+vWjaCgINLS0li6dCnvvfeeiKho6xo+fDh33HEHf/3rX+nXrx9Wq5XVq1czefJkUbeh\ntXtquhW///3vRUtwVFQUn3/+OZ9++ik+Pj6Eh4dz+PBhIahVWVlJQUGB6FzJycnhgw8+ICoqir/9\n7W+cPXu2RTupNoX12WefRVVVRowYIRyf8PBwXn75ZRoaGsQxN6ctAnGtyXprtQbafq5WfEvyI/bG\n8Wp0SuwdFk3srrk4Wn5+Ptu2bftJXZ+2Stj/1Bk/fjzjx493eK24uJgHHnig3b+rUzgciqIYgF/w\nY4fKzxVFGQJUAGeAN7jYGvv/AW6KovT64XMVqqo2XO/1Xg6TycQLL7zAO++8g81mw2Aw0NjYyKhR\no1i4cOEli+TsbzqXmpvi4uIijON3332H2WwmOzvbQRtB04TQVCS1Vs3vv/+e/Px8wsLCxBTWmJgY\nVqxYIUKy9jc37ek8JCSErKwsMjMziY6OxsfHh7S0NObNm8fu3bsxm82YTCZR1PrRRx9hNpuJjY1F\nr9cLoa558+bh6enJc889R3JyMjU1NRw+fJiPP/6YlJQUoU4aHR1NTEwMISEhREVFMXHiRCIjIxk0\naBBffvklfn5+LVRFu3XrxnfffUdhYSGHDh1i2rRpZGdni3MHcPz4cSIjI8nNzSU7O5tBgwZRWlqK\nv78/OTk5hIeHYzQaUVWV6OhoEhIShDy55jSFhYU5zFLRzo8WRfjss8/Yu3cv06dPZ//+/cyYMYPX\nXnuNtLQ05s6dS1xcHHDRydKUXBsaGli1ahWTJ08mJCSEQ4cO8fXXX7N+/XqH/WuOXFNTE9u2bSMv\nL89BBGvv3r089dRT16zsqWEv6y1VOjuGazGO9g5LcwVc7SGlsrKSzz///LLX51bSqmgvZVpJ+9Ep\nHA5gKPApF2szVGDlD69nc1F/Y/QPr5f+8Lryw79/DXSqyh+TycTvfvc7qqqqSExMbPGU8bvf/Y73\n33/f4Q+/NaGe+vr6FkJdmkGrqanh5Zdf5u677+aee+5h2rRpTJs2zUEdMi4ujpKSEqZPny46UyIi\nIiguLhbCUVpNhDahtPlALlVVKS0txcfHB4vFQn19Pd7e3qiqym233UZsbCxhYWEsX74cnU5HQ0MD\njY2N9O7dmxUrVgAXhZ82bdpEVVUVL7zwAhEREWRnZ+Pt7c3ixYuJj4+nf//+zJ07V4y2nz9/vuiI\nURQFk8mEXq/H19eX8vJyunTpQnV1tYNIVVpaGjNnzhRzUgDi4+PJyMgQzoY20n369OnCQVu1ahVJ\nSUm4u7uLm3VjYyNr164VA9R8fHxwdXUVaaTw8HAmTZrEnDlzSE1NxWg0YjQa0el0fPfdd7i4uIhI\nzxdffEFiYiLBwcGkp6ezdOlSVq5ciY+PDyaTibq6Ov7v//6PqqoqDhw4IIS4YmJiSEhIoKmpie3b\ntzvIjGtj593c3Kirq+PBBx9k7ty54nfVXoPOnPFTUem8nlyrcbR3WLS2Z0Ds4/Tp0606G7eqUJh0\njjsfncLhUFV1F5euJ7lpak2WLVuGr68v48ePb1ELMWLECFRVZfHixUKv4FJRjJUrV/Lmm286/Z6R\nI0eSm5vLv//9b4YNG8bIkSM5ePAgWVlZIsJRXV0tBplphaX79+8nJiaGAQMGsHDhQk6dOsWdd96J\n1WrFYrGIsfHvv/8+5eXleHp6UlFRIfYBF29iNpuNb775RhxjY2MjLi4uuLm5UVFRwYYNG0RhqDal\n1M3NjWPHjtG7d28RfdHUK7WJrxaLhYSEBM6dOydG3W/atEmIRFmtVhISElixYgU6nQ5PT08xGl5z\nVh599FH27dsnWlbd3d3Fzbiurk7MctEm0GppHi0S01zSOzY2VnSq1NbWiujJrFmz+OKLL8jJyREt\nvIMHD+app57iP//5j4hwacW1BoOBOXPmMGfOHFH7otPpSE5OJjU1lRdeeIExY8YIp8ZgMLBmzRo2\nbtwo6jU8PDyoqKhgwYIFDoap+bTg6xVOls5G+3CtxvFqHZaOmD7dmZDOcefiih0ORVHuBZ4BQoE7\nuagEeg4oAT4E3lBVta49F3kzkZ+fz/nz551KOMPFyZh//OMfhcPhTG5cURSCg4Md5KuboyiKGO51\n8OBBvvzySzHQS7tpLFiwQMhwa7UaK1euJDY2luTkZKKioggICCAyMpLMzEzq6urYs2cPiqIwatQo\n0XL7zDPPoCgK+/btY9CgQWzfvh1vb2+xjsbGRrp06YJOpxODxoYOHcq5c+eora0FwNXVFR8fH3Q6\nHYWFhaL1UouubN26VTg1mox4XV0dSUlJREVFceDAATZv3kxdXR3BwcGkpqbi6emJTqcjNTVVSLLr\n9XomTJjAzp07cXW9+PM2GAwiRfLwww+zf/9+EfE4e/ascHzuv/9+ByOtnUete2Xfvn1iRoqnpyfx\n8fGMHDkScLyZqapKREQEv/zlLx1mmdhfS60zxT7aoBmdJ598UgzS08S7Zs2ahaqqzJ8/n/j4+BbO\nrDZ4bfny5SxatEiGk29CrnWsfVsclub7be3+0/z3dCsgnY0bT5sjB4qiBCmKspOLjkUIsA9YBSwE\nNnMxzbEEOK0oSsoPehk/KVRVxcPDA3d390s6Clq4HVpvh1MUpYVuQ/PvqqiooKamBovF0qIjAWDe\nvHnU1dWJORpZWVn07NnTQcPD29ubESNG8Omnn+Lq6sratWuJjo4Wg8H+9re/iZtVfX09O3fuZO3a\ntVRXV4sohYuLi6gZ6dq1Ky4uLpw7dw5AHIPFYhE1HjabjW7dumE0GkW3h/2AsbKyMpqamnB3dxf1\nJ0OHDiU8PFwUifr4+ODi4oLFYqFnz55s2LCBv//979TW1pKbm0tSUhIuLi4UFRUxdOhQoR566NAh\nzp07R2FhIUeOHOHXv/41BQUFlJSUMG/ePIcuEg377pWePXuSlZXV4ho3//+mpiaxP39//zZ3dvj4\n+PCPf/yDvLy8FtLihYWFHDt2rNUx7/btk5oB0rQ1pLLnzcXVGMfWhNWAVhVMpVaF5HpyJRGON4CX\ngKdUVW1V4VNRlIeAROB/gBevbXk3F1rxltbuaf/Eq+ksaLUM//Vf/yXkqFvLs7u5ubUIi2v7Kiws\nxMPDg7CwMLZs2SLErewHupnNZlxdXdm8eTNWq5UDBw5w4cIFEe1IT0+npKQEV1dXdDodTU1N6HQ6\nh66R/Px8GhoacHFxoaqqCoPBwOzZs0lLS8NqtYqprBaLBTc3NxobG/H19cXLy4sBAwbw7rvvsmPH\nDmw2myhI7dGjByaTiczMTNHtoQlaeXp6YrVaCQoKYvfu3WItWnGmzWbDZrPh6+uL2XyxM1qr4dDO\no3cBgakAACAASURBVFas+dprr2E0GgkPDxcD0uLi4sTU0x49ejBlyhRmzZqFi4uLENNqXnBnMBgo\nKChw6F7R6XStPoVqhcLa/jQlWJvNJvRNLhVtaO1pNTQ09IrGvMtw8k+XthSejx49uoXOS/N9XElx\nsURyOa7E4ejflo4QVVX3AHsURXG7+mXdvIwYMYLXX3+dnTt3cuLECQfjX11dzaxZs4iJiREFi+fP\nn3f6B63l/I1GIzU1NRw/fpzS0lK8vLxEZMPPz4+UlBTeffddIW5ln1ZZu3Yt+/fvZ/HixUybNg1F\nUbjnnnuoqKggOTmZiIgIEQFxcXGhqamJnj17Cudo1qxZeHh44OfnR9euXTl69Ciurq6EhISQmZlJ\nTU2NSA15e3sLA63NTQkPD+ett95izZo1ALi4uFBfX091dTWNjY2UlJSIWgPNodBqSUaNGkVhYaE4\nL1r1fXJyMgUFBdTU1ODq6kq3bt3IyckBLj6RhYSEUFxcjKIoDB06lAEDBnDixAlRBJqamoqfnx8e\nHh6UlZWh1+tZtWoVkyZNEnUTzWs4zGYzzzzzDImJiSI1ExAQ0GqNRFFREY2NjWJ/s2bNYsqUKWRl\nZbF582Y8PT357rvvePrpp1uNNrTmLISFhV1VMag0GD9NLpcy0QqqO6K4WCJpTptTKvbOhqIoUc5S\nJoqiuCuKEtX887cyzVMeKSkp6HQ60tLSGDx4sHi6dXNzIzExkaCgIJKTk8UMFPvBWc257bbb+M1v\nfsPq1avp378/AQEB1NTU0K1bN/R6PWVlZZjNZhobG4Xmg/1At08//RRFUZg3bx733HMPdXV1nD9/\nnlOnThEeHs7rr79OcXExycnJYtKqVtOQlZVFdHQ01dXVLFq0iEOHDuHr64vBYACga9euwEVxNW0W\nicVi4aGHHqKurg5/f3/WrFmDr6+vOEe+vr7ce++9NDQ00KtXL5YtW8Y999wjVDq1GgydTsfzzz+P\ni4tLC9Gr1NRUXn31Vfz9/amqqqK8vJzU1FQh8f3VV19RVVWF2Wymvr6etLQ07rvvPv7617+ydetW\n3nzzTaKioujRowfjxo2jqKgIg8EgHB97tBttSUkJERERong2PT2dHTt2sHjxYod5JVpha25uLqNG\njXLYn8FgYPr06WzcuJHw8HCefvrpNs8Rsb/hO1unhtQWkDTncimTxsZG+XuSXDeutktlE7AdKG/2\nus8P7xmvZVGdncu1kT3xxBP0799fdE0kJSVRU1NDaGgoGRkZoi4BcKrjoIXbz549S2ZmJrNnzyYv\nL89BvEv7zKOPPorJZGLv3r1iX1FRUcTFxZGYmCjkuEeOHMnRo0eZOHEia9as4fjx4/Tu3ZtRo0YR\nGBiIu7s7tbW1BAQEiGLOadOmoaoqX331FevXrychIUF0Y1itVjw8PBg8eDB5eXlYLBY8PT05dOgQ\nixcvZu7cuej1evr168fJkydFW63WsVFRUcFf//pXnn/+ecrKykQLrcFgoLa2loaGBn7zm9+0iCJo\nkY4///nP1NTUcP/991NaWiqiEjabjRUrVjB16lTi4uKYMmUKRqORzZs3o9PpKCsro1+/fiKNoRVW\nRkdHk5ycLJRWnaU9tKhDdXU1Y8eOZezYsRw5coRXX30VT09PLly4QF1dHTt37sTHx6fVos28vLyr\nLtqUxaCStmIvCOYMRVG47bbb2Lp1q/w9Sa4LV+twaDoYzekLVF39cjo/bWkjKy4uZvLkycDFOSea\nyJSiKBQXFwvjCD8+tWs6DrW1tfj6+jJs2DBMJhP+/v4cP35cFHlqKD+oa2qtqFqHh7+Pj/hcTU2N\nSJ8cOHAARVF4+OGH2bJlC6WlFyVNAgICSExMpGvXrly4cIHExEQmTZpE3759RZ3Cpk2bqK6upqam\nBk9PT/Lz8wkMDCQ/P5/JkyczZ84c/vd//5du3brR0NDAn//8ZxISEsjLy6OmpgZfX1+sViv9+vVj\n2bJlREVFYbPZOH78OBs2bAB+VLNUVZXZs2dTVlbGxIkTnaomFhcXc+TIESGVbt/G6+Xlxbfffkty\ncrI4D/Ypkvz8fOEAAaJWYsuWLXh6erJ27VrWr19Pr169qK+vd9qWuHz5chGmbt6lUlBQwLp161i0\naFGHaABIbQFJW7EXBGstZdLY2Mh7770nf0+S68IVORyKopTwozjXx4qiNNq97QL8jIuRj1sWZzlR\nQOREtRHu9uH4+Ph4srOzsdlsDgWk9gWeNTU1BAYG8s9//pPPPvuMhQsXivoJTagKENvt37+f8vJy\nBg8ezLfffou7uzt79uzhyOHDTPnPf7jzzju57777hHOwcuVKfH19RUeIFmnQVEaXL1+Oj48P27Zt\n4/+1d+fhUVZn48e/J/seIBAUQduKQKFqArUu2VygrbZiW8SVhKBsYU+s4BJ931YEwR9BQBaJLVlE\nFGyraF0w8kIWcCEJEBYVa9ViSEDCkj0hOb8/Zp6nM1kgCTNkQu7Pdc1VMvPMzDkc6nPnLPedmJhI\namoqYMmv0aNHD44dO8asWbNYsWIFa9euJT4+nrfeeounn37aTDZUWVlJfX09TzzxBMOHD2f9+vVc\nf/31bNmyBQ8PD06ePGmWp09ISOCVV14x15ONYCMnJ4eioiK8vb3NGiVNN3GGhYXh4eFBSEiIebzU\nOMZr5M1o6ViyUoro6Gi7tN6t7ZU420a57OxsVq5c2eyzjX8Hxuc7a9OmbAYVbdWWfCzy70lcKO2d\n4XjT+r9hWHJu2BZQqwO+wXKa5aJl3GxaChjCwsIoKiqyO85q3NiNWh9lZWVUVFSYeTCmTZtmJrfa\nuXOnOaV/8uRJ+vTpQ1VVlVm23DZb6J49e+jVqxfHjh2jrq6OESNGsPPDD5kC5G3ZQu8HHqCoqMg8\nDtu3b19zf8bVV1/Nrl278PHxMU+s1NbW2s3AfPHFF+Tm5hIaGsqBAwcICQnhuuuuw9fXl5deeomM\njAx8fX3N8utBQUGcOHECT09PwsPDSUxMZODAgQwePJh//vOfDBkyhM8//5y8vDzq6+u57bbbuPHG\nG1sMJnx9fenfv3+zGiXGfwyzs7M5dOgQNTU1rFu3zkx/blxj/J235Gw771s75mqrLdPULX2+s/4j\nLjcHcTbtXYKTf0/CmdoVcGit/wSglPoGeK27JfgybjYtnQgxllU++OADvLy8zJulkV8iPj6emTNn\n0tjYyMKFC80lEiOIsN2fUV5ezowZM8xZj08++cTcyBkXF8eePXuIjY01k2UBfPXVV9SWlvInrXlg\nyxa2ffaZWXJ+9+7daK3NPBq7d+/m6NGjjBgxgrKyMtLT0wkMDOTUqVNm4GHsBzlz5gw+Pj4EBgYy\nefJkAgICCAgIYOrUqezcuZOqqirS0tI4deoUnp6eBAQEmHk+jMDjoYceIjU1leDgYJ5//nl69uxp\nnsJpGkyAZVZo/fr15qyR7ZHX7Oxsnn/+efLy8li1ahX//Oc/7YqUKaWaJdpq+ufz2Xnflmlq2dkv\nXIUswQlX0tE9HFuBPsBhAKXUL4AHgANa67UOapvLMW42tqW/bV8zdn2np6ebRzVtj09eddVVVFdX\ns3//fubPnw9gBhG2uS8eeeQRGhsbCQsLY/DgwWzdutXcyDlt2jTS0tLMZRqAhoYGDh06xE/c3OgN\nVHz9NWOfe4633nqLqqoq3N3dueaaaygtLWX16tUMGTKEu+++m5dfftnMj1FXV2cW6TKOcy5dupTE\nxETc3Nw4cuQIgFmJ1c3NjdOnTzN79mzGjx/Pm2++iZ+fHxUVFXYVaOvr683iYsbMT1lZWaszAFpr\nPDw8zBLpcXFxvPDCCwQEBFBRUcHll19OXl4e/fr1Y+7cubz77rvNbu7h4eF89NFHfPHFF82WrAYN\nGnTeO++lCqXoSmTJRLiKjgYcrwJrgUyl1CVAFrAPeFApdYnW+s+OaqCriY6ObvZbta2YmBiWLVtG\nZmamedzzgw8+4IcffmDHjh3cfPPNFBYWNtvjYTA2me7du5fLL7+cZcuWMX78eObPn8+PfvQjwH6Z\n5vvvv+fYsWPoxkb+YF3GubO+Hm9PTyorK2loaODo0aPExsaSl5eHt7c3R48e5eabb2bjxo2Ulpaa\nSaLAUoretghUSkoKDzzwAG5ubnh4eNCzZ0+2bNnCV199RVVVlRksBQUF4eXlhZ+fH4DdkpHtNG5m\nZiYnTpw46w37lltuAVovkW4ICgoy67XY/kfUKKw2b968Zt+/ePHiVo8BtpWcFBFdlQQbojN1NOD4\nGfCp9c/3AEVa6wil1C+BNcBFG3C09lu1wVjDtz2qWVJSwoQJE7j88stxd3cH/pu/wwgejh49SnJy\nMiUlJUybNo3BgwcTGxvLjBkzSEtL45FHHuHll18GLKdPjDwTn332mWUpo6qKO2stK1xjtGb+22/j\n6enJsGHDaGhoYPfu3ZSXl+Pl5YWvry+rVq3i5MmT/PGPf2TlypU0NjYSEBDAhAkTmDVrlt2pD2PG\nAyyVX6dMmUJycjLbtm0zN2caKc4bGhooKSnhySefbDYDFBUVhdaaF154od1H8ZoGG4abb765WfCy\nadMm5s2b16zeiPGzcYqko2SaWggh2q+jAYcnYOzfGAkYd4jPgUvPt1GuzEjbfbY1/KCgINavX8/m\nzZv58ssvqampwdvbm4aGBvbt22c3i2AssSQlJfHII4+QmZmJUopNmzbRq1cv3n77bfz8/Ph63z7K\nv/qK6SNH0lhTw+SYGIKDg+l98iReXl74V1dzlbUNVwGn8vOprKjAv29fjpaWsiYrC//GRmoOH6ZE\nKQ56e3PJlVcycuRI9u/fz/vvv2+Xkt22eNvMmTPNjKjvvPOOWal07dq1dn8HWmsefvhhli9f3mq9\nj6ioKNasWeOwG3ZLsw1NZ41s2Z4iOR8yTS2EEO3T0bLv+4GpSqkoYBT/PQrbDzjuiIa5ovLych59\n9FG++eabsxbjMk5ufP/99wwdOpTHH3+c4OBgwsLCcHd3Z8KECWaRsKuuuoo5c+aQnJzMoUOHAMxc\nEw0NDcTGxtKjRw/GxMbi7uXFgLIytp4+zd66OnKOHaOovp78ykqyrcXgwJIk5a3jx9lTW8uH333H\nntpa9tbVsfPMGX7W2IiPnx9+ffqYmzcnTZqEp6cntbW1LFiwgPj4eHOjZlpaGg899BBgqfi6c+dO\nu/0mxkxNVFQUFRUVRERE0Ldv37POAPXt25eAgIAWC021d3agaZGyxMREc+Nra99vnCJxFAk2hBDi\n3DoacMwDpgDbgA1a6z3W50fz36WWi4qR8OvLL7/kj3/8I5mZmc2qeWZnZ/Piiy8C0Lt3b7NQWHR0\nNOXl5cTHx5v1O+bPn88rr7xCVlYWfn5+REZGUlhYyPXXX09ubi5+fn7U19ebsyCvvfYal117LRFL\nljDc05OsVpYYWpPl5sYIDw/yL78cHRJCUFCQuTSTlpZGUFAQ9fX17N+/3y4VcmFhIWFhYSilqKur\nA/5bWh0wA69JkyaZeT6MUxwt0Vo3CwjO94ZtWyVzy5YtBAYGnvX75RSJEEJceB0KOLTW24DeQG+t\n9UM2L60FpjqgXS7HSPhVWlrKyJEjWbp0qVm/Y+bMmUyePJmioiJ69OhBXl4epaWl3HTTTeYejT59\n+lBYWEjv3r3JysriiSeeYNy4cfj5+Zm/1fv6+jJhwgQyMzMpKyujR48e5ubQTz/9lIaGBiJvvpnA\nq68mbdQoxnl42CVCaUk5MCk4mBk9ezJw1CiUpyf9+/ensrKSoUOHMnXqVAYPHoy/vz89evTA39/f\n7sSIr68v6enpzJgxAw8PD/OUClhmNV566SVycnLM8u1G5dfOqs9gJPeS+hBCCOFaOjrDgda6QWt9\noslz32itm9ZXuShs27bNLoAwckikpqayfPlyUlNTmT59On5+flRVVeHr62tm9dRa88QTTzB//nwq\nKytZuXIlcXFxREREEBQUxKlTlmzw1dXVZnZNoyqs1pq7776bxsZGM3nY6dOnmff88xT+5CeMOsdM\nx689PNjWsyc+/fvz9NNP09DQwOHDh9FaU1payuTJk83U6atXr6a8vNwMKIycFoWFhdxwww0EBwfT\n2Nho3swnT56Mt7c377//PpMnT6a8vJzs7Gzi4+PNJaOmM0AbN25k7ty5Thwpy76O119/vdn3G4XV\nnP39QgghmutQwKGU+rdS6uvWHo5uZGczag7YBhC2miaVOnXqFFVVVXa/7W/cuJEePXrg7u5uZhN1\nc3Pj5MmT1NfXk5OT02xmYMiQIXz44Yc89dRTKKW4++67SUlJQSlFXl4eP736aq44R9t/4uHBA3Fx\n5n6NkJAQhg4dSkBAAF9++SVRUVEUFhYSERFBaGgoffr0sdufYuw7SU9PZ9KkSYSEhJjBhJ+fH8uX\nL6dfv37U1NTg7u7OwoULyc/Pt6vgOmXKFMaMGcO33357QU5xNN3XkZSUxIwZMyguLpZTJEII0Uk6\nekrlhSY/ewLhwK+B58+rRS5IKWWe0jCCAttTGEaa8507d5rLCtXV1eTm5poZOw8fPszjjz9OSkoK\nQUFBZpDS2NiIh4cHa9eu5d5772XJkiX4+fmZCblefPFFHn30UXbt2sUjjzxiZu3MyMig5vhx/mSz\nWbQlY2tq+PjECSoqKkhMTKS+vp4nn3yS+++/nyuusIQrtqnAtdZm0rLIyEgmTJjAuHHjzJMfe/bs\nYfDgwRQVFdmlJL/xxhsZOHAgJSUlHDlyhHnz5uHt7Y2XlxdRUVFmJd0LRU6RCCGEa+lQwKG1XtbS\n80qp6cDPz6tFLkhrTX19vV0AYRzDPHbsmFkK3kgy9atf/YrTp0+bN+4///nPTJkyhcjISJYvX27O\nfgAMGDCA4uJilixZwvTp0xk0aBC/+c1vWL58OQkJCaSnpxMZGUl+fj5xcXG8/vrrBAYGMmbMGNYk\nJ3ObTTuz3Nx4rmdP5h4/zi+tz40EVn30EZ7BwYwbN45Nmzbh7++Pj4+P3VKO0Z6AgACee+45uxon\nRo4N25TnsbGxJCQkmN+dnZ3NG2+8YTeD4Co3eldogxBCdHcd3sPRiveAMQ7+TKdr6USD7dq/Ugp3\nd3cyMzMpKCgwlwsefvhhYmNjmTNnDtHR0eaNzcvLi549e7J06VLy8/OZMGGCWU3Vx8cHsGxeVEpR\nX19PSEgIb7zxBnPmzOH48eNERUVRV1dHZGSkWbht37597Nu3j9raWuLj43n99dfp09iIH5YKeg+6\nu5M+ahRPbtrE01deyUP+/lQAfoD78ePU1dWZtV3AElhUVlaSm5trztoYezb8/Pzs9qesX7+eo0eP\n2qU8N5ZLZs2axeTJk1mxYkWz5Qq50QshhDB0dEmlNXcDZQ7+TKcoLy9n0aJFZGdnm8sC119/PUop\ncnNzOX36NLW1tYSEhJg32vnz5/PEE0+wYMECPDw8qK+vp0+fPnbLK0bir8rKSvz8/PD09OSRRx7h\nhRdeYM6cOZw+fZqYmBheeuklwLJPo7CwkMLCQhISEnj99dcBy5KAsWeksbERX19fcnNzSUxMJCoq\nisYzZ6jdtYstwCwvLyp69uS9559HKcWy9euZGB/PzceO8ezx49x84gSv9OljnnjJy8vj5z//OVdc\ncQXLli1j4sSJZGRkoLW2q/0CmBtkY2JiyMnJITo6ulnRtdzcXIqLi2VvhBBCiFZ1dNNooVKqwOZR\nqJQ6AiywPlyakVOjf//+rFy5kpSUFBYvXsyHH37IJZdcAsCMGTPIzMxk2LBhHDlyhMbGRh5//HFq\nampISkqib9++hISEmJsxDZWVlVRUWA6rGgXXDh48SH19PWPHjgXgmmuuwdvbm3feeYcPPviA48eP\n4+7ubrcp1Th+apxMqaysBDADgcItW3gXmOzvT59f/AI3a4VaAH9/f15OSyPsd79jir8/b585Q81/\n/mNWrc3IyGDQoEFs3ryZKVOm8K9//YszZ86wYsUKtmzZwoIFC9i+fbvdLM+wYcN4/vnnm538MNKR\ny8kPIYQQZ9PRGY43m/zcCBwDtmmtPz+/JjmfkVPDdmYiPT2dqVOnsmfPHrO0+qxZs6itraVnz574\n+/vTu3dv7rjjDnbt2kVsbCxvvPFGs1Lo6enpaK0ZOHAga9aswd/fn8LCQry8vDh06BD+/v5s3LiR\n++67j02bNhEcHExgYKC5ZBEeHk5ubq7dnpFZs2ZRUlJCr169zO8J6tOH00OH0nDqFKdPn6ZHjx52\nmz39/f2ZPXs2w4YNY9GCBQRaj6XGxMSwdOlS0tPTOXPmDKmpqdTV1REUFESvXr245ZZbmDp1KmvW\nrGmWdjwvL4/Vq1dL/RAhhBDtphyZ4rnDjbCkSH8UGIGlFsvvtNabbV7/PZaEYiOAXkCY1nrvWT5v\nOJCfn5/P8OHDm70eHR3NypUr7WYmJk6cSGpqKpMmTSI1NZVVq1ZRXFzMr3/9a9atW2dm10xNTeW+\n++7jtddeY9KkSYSFhREWFmYGLxMnTuTMmTOUlJQQGBhITU0NV1xxBWVlZfj5+REWFsaQIUPYt28f\nH330EcHBwfTo0YOBAwcyfPhwhgwZwoQJE1BKERwczNSpU9m6dSs7d+7E09OTzZs3m+2eOHEigwYN\nIjc3F09PT3OjZ2FhoblMFB4ezpgxY3jooYdQSvHoo4+a+02M3BSbNm3irbfeIigoqNnf1dlqxsge\nDSGEuPgUFBQwYsQIgBFa6wJHfW6bZziUUs3vRq3QWp9uZzv8gd3AX4C/t/J6DvA6kNrOz27aNry9\nvZsVHTM2cxpHRAsLCwHLbMGSJUvo37+/3aZQYz/E4MGDzf0PERER+Pj4cPjwYZKSkli/fj1eXl6c\nsB5LDQkJ4Z577mHKlCn4+vpy6aWX8sMPP1BfX89zzz3H7NmzqaqqYvbs2WzcuJGKigreeecdCgoK\nePrpp1m3bh25ublm1dOf/exnDB06lAMHDlBSUkJ+fr7d3gqjvdu3b+e+++4jOTmZRYsWtWuG4mw1\nSYQQQoi2as+SykmgrdMh7u1phNb6fawF4FQLdzKt9SvW167AUpuswyoqKvj222/tbsjHjh3ju+++\nAzA3afr4+NgFGKdOnTKLfhlFy8aOHUtCQgKTJk1i7969ZGRkUFpairu7O9deey0rVqygd+/enDp1\nCq01P/zwA8nJyQwaNIjf/va3pKenU1dXR319PTt27DDrlYwaNYqIiAhmzZrF4cOH8fX1JTIykr/+\n9a/mskl4eDhFRUV89tlnjB8/nt27d7Nw4UK01sTExNjNYGzcuJF33nmHwMBA5s+fb/ydnjVokBkM\nIYQQjtSeTaO3ALdaHw8BR4HFwO+tj8VAqfU1l7Vo0SJ++tOfmhk9jx49alZDNY6I7tixg5qaGqqr\nq6msrKSxsZHKykpCQ0PZsWMHAFlZWSQnJ5OQkMC//vUvdu/ejY+PD7W1tfj6+jJ37lwCAgIICAgg\nJSWFyspKTpw4QWxsLMePHycyMpLw8HDAcnN/8cUXiYuLs0udvnz5cnN/BViOshpHUuPi4hg/fjxr\n167lq6++4tChQ/z4xz9m+fLljB07locffpgZM2Zw5MgRM9iw1VIwUV5eTnJyMtHR0fzqV78iOjqa\n5ORkysvLnTYeQgghuoc2z3Borbcbf1ZKPQ0kaa032FyyWSlVBEwG0h3XRMfKzs5m8eLFJCUlmVk1\n586dy4YNG8jMzGTs2LGkpaURGhrKmTNnzOJmN9xwA+vWrePbb79l4MCBrFixgkcffZSoqChGjhwJ\nWLKGjh07loqKCmpqaggNDaWqqoo333yT0NBQampqCAsLY926dVRVVVFbW4u/vz/V1dXm8dolS5ZQ\nUVFBeno6BQUFzRJ0GTkyCgoKzBLyTZdRtNZMnz6d7du3t/r30JRxcufee+8197dorcnLy2P06NGy\nMVQIIcR56egplRtpuSrsLuDljjfHsRITEwkODrZ77sSJE+ZMQXp6OiUlJURFRZGRkcELL7zAyy+/\nzPfff09ISAi7d+8mOTmZESNGkJiYSEJCAvv27WP79u00Nja2mN68rq4OLy8vAIYPH87WrVs5duwY\nl19+OfX19TzyyCPU1NSQmJhIXFwce/bswd3dHT8/P7O0+5QpU0hISGDatGlMmjTJPLFi5NCIiIiw\nS0duMH5WSpkZQtu6LNLSyR2lFJGRkWitWbx4sZkqXAghxMVhw4YNbNiwwe4545dcR+toptH/AJNa\neH6i9TWXsHTpUjZv3mz36Nmzp5nIa+rUqXYl4Hfv3o2npydPPvkkixYtYsCAAURFRZnZNQ8dOsSB\nAwfo27evXT2UyspKEhMTufrqq+nTpw8BAQEEBQXR0NDA0KFDufTSS6msrOS7775j7Nix1NTUEBsb\nS2RkpLmB1XbviJEG3WjX0KFDWbBgAVdddRUZGRnk5ua2WETOYBSRa88ejOzsbCIiIlp8LTIykuzs\n7PYPgBBCCJd2//33N7tPLl261Cnf1dGAIxGYqZQqUkq9bH3sBWZaX3Om8zrHa+STAHBzczPLsRsJ\nsXbu3ElERARaazOtuPnFWqO15sSJE9TW1pobSBMTExk3bpx53PS6667j9OnT7Nq1i+PHj9PQ0ICn\npyc+Pj5s2rQJPz8/cyahvr6eG2+80dwfopQyT6EAxMfHc/LkSQICAsxNqf/v//0/Dh8+3GoQkJub\nS0xMTJv/ToxTOmc7kWJsmBVCCCE6okMBh9b6XeAqYDOWvBi9gLeBQdbX2kUp5a+UulYpFWZ96ifW\nnwdYX++plLoWGIbllMoQ6+t92/td8+bN4/XXXzczZhrl2P39/UlJSTGPvBqzDkZQMXv2bK655hpS\nU1MJCgpCa01WVhaJiYlUV1ebQcLw4cMZPHgw1dXVeHh40NjYSHh4OJWVlXh4eBAbG2tmJzWWPCZM\nmEBpaSmrVq0iODjY7sZvbB6NiIigqqoKpRQBAQEEBwezcuXKZpk/jVMp7cn8qZSipqbGoTMm6I6A\nSAAAIABJREFUQgghhK0OF2/TWh/WWj+ptf6D9fEkUK6UeqADH/dzoBDIxzKDsQQoAP5kfX209fW3\nra9vsL4+pb1fFBgYyObNmykuLmbGjBl4eHgwf/58srOz8ff3NwMBsOS5yMnJITU1lbi4ODOo8Pb2\npm/fvqxevZpx48aZyzKVlZXU1dWxdOlSevfuzenTpzl+/DhxcXG4ublx5swZIiMj7bKTnjhxAj8/\nP5YvX84vfvELTpw40ezG7+/vz5w5c/jb3/5G37592b17N/v37+eTTz4x+5GUlMSMGTMoLi5utsGz\nLTMTtjM/TbV3xkQIIYRoytHF264AMoFX2/Mm6wmYVoMfrXU6Djr50rRoW3V1NXfffTebNm0iJSWF\nqqoqcnNzGT58OEVFRezatYvq6mpmz54NQFVVFcXFxWYdFWPDaUVFBUlJSdxzzz1orXnvvfdwc3Mj\nICCAPXv24Onpac5s2G7+9PT0NIulzZkzB09PT7vkXrZyc3O59dZbzZmGwMBAcyNn0w2iLRWni46O\nZt68eS2eNpk2bRqjRo2ioaHBLhOpUStl8+bNzd4jhBBCtJWjAw6Xdrajn3v37uUPf/gD/fr1IzMz\nk/fee4/4+HjCw8OZMmWKeTNPS0tj6NChaK05fvy4GUAsXLiQe+65h40bNxIXF8f27dvx9vbG39+f\ndevWUVtbC2DuF0lMTDQ3rxqZSiMjI+1eM469tuXG3zTYaM8R1/Lych588EEmTZrEvn37eOWVV/Dx\n8eHkyZPU1taSlZUlR2KFEEKcF4fWUrHusyjQWrcr06ijtVZLJTk5mf79+9sd/TTk5OSwatUqXn31\nVY4dO8aUKVP4+98tWdbHjBnD3/72N5RSTJw4kRdeeIHZs2fzww8/8Oabb1JVVcW4ceO47bbbGDJk\nCAcPHuTjjz+mZ8+eXHnllXz22WdMmDCB1atXm+XlKysrSU9PJysri6lTp/LVV1+ZNVAqKyvx9PSk\nsrISHx8fvL29iY6OZu7cuW268Z+rn8XFxXZHXFu63pgxael6IYQQFy9n1VLp8B6OruhsRz8jIiLw\n8PCgqqqKJ598ktDQUJRSVFVVUVdXR25uLlprfH19CQgIYPny5fTp04fs7Gy01vTo0YP8/Hw2btxI\neHg4VVVV5kbLqVOnMmrUKNasWcOyZcvYtm2bmcArIyOD9PR0hg0bxtq1a1m+fDl/+ctfGDduHD17\n9mTLli1s376dZ555ps2zDO094trS9caMiRyJFUII4QjtWlJRSs06xyWXnUdbnOpcRz/d3NyorKxk\n3bp1jB8/nrS0NHNfxrBhw1i9ejWNjY3mhk9/f3+WLVvGrFmzWLt2LQB1dXXMnj2byMhIMjIy6N27\nN/n5+eb+j9DQUNLT00lPT2f9+vX4+Pjw/fff87vf/Y7vvvvOIWXf23PE1Vhqac/1QgghREe0dw9H\nW3JsfNeRhjib7dHP1sqtu7m58cknnzB9+nT27NnDwoULiYuLY9CgQcTHx7NlyxbKysrIyckhOjoa\nf39/rr76akaMGMHevXv54IMPzJmCJ554gkmTJtG3b99mx1yNVOQVFRVMnDiRjz/+2MwMet111/HY\nY491eM9EW/ppe8S1vdcLIYQQHdGuJRWt9Y/b8nBWY8/XuY5+3nnnnWYOjvj4eA4cOEBERASbNm3i\n8ccf59lnn2XNmjUsXrzYXErZt28fkZGRjB8/noCAAPN47IIFC5g9e7ZZWbapyspKkpKSmDZtGitX\nriQlJYWVK1cyYMAARo8efV4F09p7xFWOxAohhHC2bnVKZd68eYwePdo8EdK0hPvbb7/Nzp07zSyj\n/fr1QylFYWGhOSuxadMm5syZw759+8jIyKCurg6lFH5+fmZwkZaWRmxsLFFRUXz77bfmsVdbttcY\nHFW7pLV+tnbSpb3XCyGEEO3V5oBDKXWf1vq1Nl47ALhca93yr82dxEj6tXjxYqZOnUpZWRkVFRX4\n+PgQEBDATTfdREhIiBkgHDt2jMbGRrtCaUbwYVSInThxorkcU11dTW5uLgUFBWaAYnvM1fZmvnPn\nTvOapiIjI5kxY4ZD+tmWfSHtvV4IIYRor/bMcCQopf4HWAe8rbU+aPuiUioYiADGAaOAhx3WSgcK\nDAxk7ty5bNu2DW9vb2bNmmUXCHz44Yc899xzzJ49m/LycvLy8uwKpTWt0mok8YqMjMTX15eMjAzO\nnDljXmMUfktPTycjIwMfHx/+85//0Lt3b6du1DxbUjBHXC+EEEK0R5sDDq11jFJqNJYCbQuVUpVA\nKVAD9AQuAX4A0oCfaa1LHd9cx1i0aBG9evXiwQcftFvqqKqq4tChQwQGBpKSksJll11GZmYmoaGh\nZvZP27Tk8N8ZjMbGRvr378/ixYt54IEH7K6x3Sja2NjImDFj8PLyumAbNdv7ORJsCCGEcLT2bhrd\nrLUeBfQF4oAXgfXA/wLXA/201o+5crABlrwTJSUldrknjKqv1157La+++iqXXWY54ZuSkkJoaCjP\nPfcc27dvJywszG6DpZ+fH0uXLmXfvn0cPnwYX19fvL29W92EmZeXh7e3N1FRUbJRUwghRLfRoU2j\nWusfgDcd3BanKy8v57nnnuPEiRN4eHjY/SaflpZGXFycuWnT19eXIUOGsHv3bubMmcOkSZNIS0uj\noKCA9957j2uuuYZjx47h6+tLdXU1oaGheHp6snXrVhobG+3SldtuwszMzCQoKIjHHntMNmoKIYTo\nNrrNKRXb+iK1tbW4u7vbLWkYm0ErKytJS0vj8OHDLFy4kKSkJDMomD59OhUVFUyePJnbb7+9Wa2T\nI0eOsGTJEsLDw7nlllsoKioy923U1NQQHh7O2LFjKSsrk42aQgghupUOBRxKqRNYysQ3pbHs6fgK\nSNNarzuPtjnUokWLuPfee4mMjGTFihXccMMN5mZPY0ajqqqKxMRE4uLi0Fqze/duuw2fHh4efPvt\ntzz22GNER0ebn11VVcWuXbv4/vvveeqppxg+fDiJiYnExsaSkJBgXpeTk8OmTZvM2QvZqCmEEKK7\n6OgMx5+AJ4H3gU+tz/0C+DWwEvgxsFop5aG1Tj3vVjpAdnY2K1euRGtNnz59mDBhArNmzeLdd9/l\n6NGjlJWVsW7dOnNZZdCgQSQkJDBz5kwSEhKoqqpizpw59OrVyy53hrH3o0+fPoSGhprLI01PptTU\n1HDy5Ek+/fTTFmcvJNgQQghxMetowHET8JTWeo3tk0qpKcAvtdZjlFJ7gVlApwccTeuF2Kbyvv32\n24mMjGTVqlXs2LGD6dOnU1lZSXJyMgkJCezfv5/169dz6tQppk+fzhtvvNFs78c999zD2rVr7dKY\n255MMb4rKSmJgICAC/8XIIQQQnSyjlaLvQPIauH5j4BfWf/8LvCTDn6+Q9nWCwFL7oyFCxcSHx9v\n7sO4++67zcAgNTWV2NhYRo4cyfTp00lNTSUoKIgRI0Zw9OhRu1Tlu3bt4vXXX6dnz552+Tqafr/U\nJBFCCNGddTTgKAPubOH5O62vAfgDHS8I4mC29UJs66QY3njjDdzc3KioqGD79u12+Tm01nh6epKU\nlMTAgQPJzc01n6+srGT8+PE0NDQ0OzJrKzs7W466CiGE6LY6uqTyDJY9Grfw3z0c12GZ+Zhq/XkU\nsP38muc4tvVCIiIiuOyyy8xCa2lpaWRlZRETE8Ozzz5LaGio3UyEUorS0lISExPNDaFgSUFeX19P\nREQEe/bsYfDgwS0eh83JyWHVqlV8/PHHndV9IYQQolN1NA9HqlLqADAD+IP16S+AGK31Dus1SxzT\nRMewPYaakJBASUkJFRUVJCUlERsby+eff47WmgMHDtCnT59mp0Y8PT3NIGL+/Pk89dRTLFmyBD8/\nP7O6bGJiImPHjmXv3r3mZtGTJ09SW1tLVlaWHHUVQgjRbXV0SQWtdZ7W+n6t9XDr434j2HBVRh0V\nNzc3tNYsWLCAuLg4M2X5vn37GDBggFkfxWCcbDFmRJKTk4mNjeUf//gHvr6+aK3NmimHDh1i9+7d\n+Pj4UF1dTVVVFZ988gn9+vXrxJ4LIYQQnavDib+UUu7A74CfWp/aD2zWWjc4omHOYtRRGTNmDCtX\nruTZZ58FICwsjKKiImpqahg/frxdwi/bTae2GUkBhg8fbtZZaXoyJTc3l+LiYpnZEEII0e11aIZD\nKTUQOAhkYFlS+QPwCrBfKXWl45rneNnZ2ZSWlnLrrbea+zgAJkyYwNGjRwkLCzMTfhUVFTF58mRm\nzpxJWVkZOTk5FBYW2m02jY+PJzMzk5ycHPOEim2K8rlz53ZKP4UQQghX0tEZjuXAv4AbtNZlAEqp\nECxBx3LgN45pnmOdPn2a2tpa/Pz8cHNzs8vH4e/vT0xMDIMGDTI3fiYkJKCUorGxkaysLBYsWMAV\nV1xht7ejafn52tpaAgMDJUW5EEIIYaOjAUcMNsEGgNb6uFLqMaDlc6FnoZSKAh4FRgCXAr/TWm9u\ncs2fgYlAD+t3JGitv2rrd5SXl3PXXXdRV1dnnh4x9moYyyOTJk0668bPrVu3MmbMmGYbSo2lFK01\n06ZNY/t2lzmcI4QQQriEjm4arQVa+tU9AKjrwOf5A7uBabRQo0UpNQ/LiZjJWFKoVwIfKKW82voF\nRi2VG264gZCQEHJycoiPjycjI8NcDvH39yclJYVt27bx0UcfERAQQENDA7/97W/55JNPGDRoEHfd\ndddZy8rffPPN7e+9EEIIcZHr6AzHO8BapdTD/DcPx/XAGqDdddW11u9jqcuCajkV52zgGa31O9Zr\n4oBSLJtWN7blO4xaKuHh4Tz44IMcPnyYqVOnkpKSQkZGBhkZGbi5uVFaWsqAAQPIz88nICCgWWZQ\n23weUlZeCCGEaJuOznDMwrKHYyeW6rA1wA4sVWLnOKZpFkqpHwOXYEmbDoDW+jTwCXBjWz7DtpaK\nv78/ISEhvPTSSxQVFZGYmMjBgwcBuOaaa3jllVdobGwkMDCwxTTkRj6P4uJiZsyYQVJSEjNmzKC4\nuFj2bAghhBCt6Gjir5PAXdbTKsax2IPt2VPRDpdgWWYpbfJ8qfW1c2paS8XPz4+AgIBmxdUM3t7e\nZy0XL2XlhRBCiPZpc8ChlEo5xyW3GDderXXS+TTKURITEwkODgagpKSEBx98kHHjxplF1oz22gYM\n7S2yJsGGEEKIrmrDhg1s2LDB7rlTp0455bvaM8MR3sbrmpdLPT8lgAL6Yj/L0RcoPNsbly5dyvDh\nwwHLKZU77riD3NxcM6dGdHR0s/fk5uZKkTUhhBDdwv3338/9999v91xBQQEjRoxw+He1OeDQWt/i\n8G9v2/f+WylVAtwG7AVQSgVh2aS6sj2f5ebmxu23384TTzxBUpJlEsYoTy8bP4UQQgjn6XBqc0dS\nSvkDA7HMZAD8RCl1LVCmtf4P8AKQrJT6CvgGS7Xaw8Bbbf2ORYsWcf/995s5N4xkXZmZmbi5uZl5\nOmTjpxBCCOF4LhFwAD8H/g/LcowGjEqz6cBDWuvFSik/4CUsib9ygNu11m3O+WEcizXY1j1pbGxk\n5syZ5kZQIYQQQjiWSwQcWuvtnOOIrtb6f4H/7eDnm8diW3rNzc3tnCdThBBCCNFxLhFwOJvtsVij\nxHxaWhqFhYX4+vpSXV3NyZMnqaiokOUUIYQQwgm6RcABEB0dTV5eHuHh4SQmJhIXF8e0adPsNoyO\nHj1a9nAIIYQQTqCMZFgXE6XUcCA/Pz/f7ljs6NGjCQwM5PbbbycqKqrZ+3JyciguLpa9HEIIIbot\nm2OxI7TWBY763I6mNu9yjJTkX3/9tXlSxWAEXZGRkWzbtq0TWieEEEJc3LrNkgpAQECAWZCtpX0c\n4eHhdns9hBBCCOEY3SrgUEpx/PhxKioqSEpKanEfx9tvvy2bR4UQQggH6zZLKmBZOvHy8mLhwoXE\nxsaa5eXBEoxERUXx2GOPsWjRok5uqRBCCHFx6VYBhzGTsX///mb7OAwxMTFs3779ArdMCCGEuLh1\nq4ADoKGhgeDg4Fb3aCilqK+v52I8vSOEEEJ0lm4VcGitueyyyzhx4kSrAYXWmuPHj8umUSGEEMKB\nulXAYcxeeHt7k5eX1+I1ubm5ZppzIYQQQjhGtwo4wLJHw9PTk4yMDHJycszAQmtNTk4OmZmZBAUF\nyQyHEEII4UDd6lgswLx58/j73//O2LFjKSoqIiMjAx8fH2pqaggPD2fs2LGUlZV1djOFEEKIi0q3\nCzgCAwPJyspi1KhRTJs2jYSEBPO13NxcNm7cyObNmzuxhUIIIcTFp9sFHAD9+vVj586dPP/888yY\nMQNvb29qa2uJjo6W4m1CCCGEE3SrgKO8vJxFixaRnZ1tLqNER0czd+5cgoKCOrt5QgghxEWr2wQc\nRrXYe++9l5UrV5pJwPLy8rjrrrtkZkMIIYRwom5zSmXRokXce++9zdKZR0ZGcs8997B48eJObqEQ\nQghx8eo2AUd2djYREREtvhYZGUl2dvYFbpEQQgjRfXSLgENrjY+Pz1nTmUuyLyGEEMJ5ukXAoZSi\npqbmrOnMa2pqJNmXEEII4STdIuAAiI6OPms685iYmAvcIiGEEKL76DanVObNm8fo0aPRWpsbR7XW\nkuxLCCGEuAC6TcARGBjI5s2bWbx4sST7EkIIIS6wLhNwKKUCgPnA74BQoACYo7Xe1dbPCAwM5Jln\nngGgsbERN7dus6IkhBBCdKouE3AAfwGGAg8CR4BYIEsp9VOt9ZG2fEBrmUbnzZsnMxxCCCGEE3WJ\ngEMp5QP8AbhTa23s/PyTUupOIAF4+lyfcbZMo6NHj5ZlFSGEEMKJusqaggfgDtQ2eb4aiGzLB0im\nUSGEEKLzdImAQ2tdAewEnlJKXaqUclNKjQNuBC5ty2dIplEhhBCi83SJJRWrccBfge+BM1g2jb4K\njGjtDYmJiQQHBwNw8OBBZs2axe23384dd9xhd51tplFJ/iWEEKK72LBhAxs2bLB77tSpU075LtXV\n0nkrpXyBIK11qVLqNcBfa31nk2uGA/n5+fkMHz4csCT+MvZuNKW1Zvr06TLLIYQQotsrKChgxIgR\nACO01gWO+twusaRiS2tdbQ02egK/At5sy/sk06gQQgjRebrMkopS6peAAr4ArgIWAweAtLa8XzKN\nCiGEEJ2nywQcQDCwELgMKAPeAJK11g1tebNkGhVCCCE6T5cJOLTWm4BN5/MZtplGZYOoEEIIceF0\nuT0cjiLBhhBCCHHhdNuAQwghhBAXjgQcQgghhHA6CTiEEEII4XQScAghhBDC6STgEEIIIYTTScAh\nhBBCCKeTgEMIIYQQTicBhxBCCCGcTgIOIYQQQjidBBxCCCGEcDoJOIQQQgjhdBJwCCGEEMLpJOAQ\nQgghhNNJwCGEEEIIp5OAQwghhBBOJwGHEEIIIZxOAg4hhBBCOJ0EHEIIIYRwOgk4hBBCCOF0EnAI\nIYQQwukk4BBCCCGE00nAIYQQQgink4BDCCGEEE7XJQIOpZSbUuoZpdTXSqkqpdRXSqnkzm7XhbRh\nw4bOboJDXUz9uZj6AtIfV3Yx9QWkP91Nlwg4gMeAKcA0YAgwF5irlJrRqa26gC62f8gXU38upr6A\n9MeVXUx9AelPd+PR2Q1ooxuBt7TW71t//k4p9QDwi05skxBCCCHaqKvMcOwAblNKXQWglLoWiADe\n7dRWCSGEEKJNusoMx3NAEPC5UqoBS6D0pNb6tc5tlhBCCCHaoqsEHPcCDwD3AQeAMGCZUqpYa53Z\nwvU+AAcPHrxwLXSyU6dOUVBQ0NnNcJiLqT8XU19A+uPKLqa+gPTHVdncO30c+blKa+3Iz3MKpdR3\nwEKt9Wqb554EHtRaD23h+geA9RewiUIIIcTF5kGt9auO+rCuMsPhBzQ0ea6R1vegfAA8CHwD1Div\nWUIIIcRFxwf4EZZ7qcN0lRmOdcBtwFRgPzAceAl4WWv9RGe2TQghhBDn1lUCDn/gGeD3QChQDLwK\nPKO1PtOZbRNCCCHEuXWJgEMIIYQQXVtXycMhhBBCiC5MAg4hhBBCOF2XDTiUUtOVUv9WSlUrpT5W\nSl13jutvVkrlK6VqlFJfKqXGX6i2tkV7+qOUilFKNTZ5NCilQi9km1tpW5RSarNS6ntru0a34T0u\nOzbt7Y+Lj83jSqlPlVKnlVKlSql/KKUGteF9Ljk+HemPq46PUmqqUmqPUuqU9bFDKfXrc7zHJccF\n2t8fVx2XliilHrO2L+Uc17ns+NhqS38cNT5dMuBQSt0LLAH+BwgH9gAfKKV6t3L9j4B3gI+Aa4Fl\nwMtKqVEXor3n0t7+WGngKuAS6+NSrfVRZ7e1DfyB3VgK7Z1zg5Crjw3t7I+Vq45NFLACuB4YCXgC\nW5RSvq29wcXHp939sXLF8fkPMA/LCbwRwFbgLaXUT1u62MXHBdrZHytXHBc71l8EJ2P5b/TZrvsR\nrj0+QNv7Y3X+46O17nIP4GNgmc3PCjgMzG3l+kXA3ibPbQDe7ey+dLA/MVjykgR1dtvP0a9GYPQ5\nrnHpselAf7rE2Fjb2tvap8iLZHza0p+uND7HgQldfVza2B+XHxcgAPgCuBX4PyDlLNe6/Pi0sz8O\nGZ8uN8OhlPLEEjF/ZDynLX8jWViqyrbkBuvrtj44y/UXTAf7A5agZLdSqlgptUUpdZNzW+o0Ljs2\n56GrjE0PLL+1lJ3lmq40Pm3pD7j4+Cil3JRS92FJeLizlcu6zLi0sT/g4uMCrATe1lpvbcO1XWF8\n2tMfcMD4dJVMo7Z6A+5AaZPnS4HBrbznklauD1JKeWutax3bxHbpSH+OAFOAXYA3MAnYppT6hdZ6\nt7Ma6iSuPDYd0SXGRimlgBeAXK31gbNc2iXGpx39cdnxUUr9DMsN2QcoB36vtf68lctdflza2R+X\nHRcAa8AUBvy8jW9x6fHpQH8cMj5dMeDo9rTWXwJf2jz1sVLqSiARcMmNSd1FFxqbVcBQIKKzG+Ig\nbeqPi4/P51jW+4OBu4EMpVT0WW7Srq7N/XHlcVFK9ccSzI7UWtd3ZlscoSP9cdT4dLklFeAHLGtJ\nfZs83xcoaeU9Ja1cf7qzI0061p+WfAoMdFSjLiBXHhtHcamxUUq9CNwB3Ky1PnKOy11+fNrZn5a4\nxPhorc9orb/WWhdqrZ/EspFvdiuXu/y4tLM/LXGJccGy5N0HKFBK1Sul6rHsaZitlKqzzq415crj\n05H+tKTd49PlAg5rRJaPpbYKYE6n3gbsaOVtO22vt/olZ19PvCA62J+WhGGZ9upqXHZsHMhlxsZ6\nc74LuEVr/V0b3uLS49OB/rTEZcanCTcs09ctcelxacXZ+tMSVxmXLOBqLO251vrYBbwCXGvdc9eU\nK49PR/rTkvaPT2fvlO3g7tp7gCogDhiCpZDbcaCP9fWFQLrN9T/Csoa4CMu+iGlAHZYppa7Yn9nA\naOBKYBiW6bF6LL/hdXZf/K3/gMOwnBiYY/15QBcdm/b2x5XHZhVwAstx0r42Dx+baxZ0lfHpYH9c\ncnys7YwCrgB+Zv13dQa4tZV/Zy47Lh3sj0uOy1n6Z3eqoyv9/6aD/XHI+HR6R8/jL2galvLz1Vii\nxp/bvLYO2Nrk+mgsMwnVwCEgtrP70NH+AI9a+1AJHMNywiW6s/tgbVsMlhtzQ5PHX7vi2LS3Py4+\nNi31owGIa+3fmiuPT0f646rjA7wMfG39Oy4BtmC9OXe1celIf1x1XM7Sv63Y36C71Pi0tz+OGh8p\n3iaEEEIIp+tyeziEEEII0fVIwCGEEEIIp5OAQwghhBBOJwGHEEIIIZxOAg4hhBBCOJ0EHEIIIYRw\nOgk4hBBCCOF0EnAIIYQQwukk4BBCCCGE00nAIYRwCqXU/yilCh11rVLq/5RSKTY/+yql/qaUOqWU\nalBKBZ1vm4UQzuPR2Q0QQlzU2lM74VzX/h5LwSjDeCACuAH4QWt9Win1b2Cp1np5+5ophHA2CTiE\nEGellPLUWtef+0rn0lqfbPLUlcBBrfXBzmiPEKJ9ZElFCGHHunSxQim1VCl1DHhfKRWslHpZKXXU\nuoSRpZS6psn7HlNKlVhffxnwafL6zUqpT5RSFUqpE0qpHKXUgCbXjFNK/VspdVIptUEp5d+kXSnG\nn4FHgBjrcspW63NXAEuVUo1KqQbn/A0JITpCAg4hREvigFrgJmAqsAkIAX4FDAcKgCylVA8ApdQ9\nwP8AjwE/B44A04wPU0q5A/8A/g/4GZZlkLXYL6MMBO4C7gB+A8RYP68lvwdSgR3AJcAfrI/DwFPW\n5y7tePeFEI4mSypCiJYc0lo/BqCUigCuA0JtllbmKqV+D9wNvAzMBlK11mnW159SSo0EvK0/B1kf\n/9Raf2N97osm36mA8VrrKuv3ZgK3YQkg7GitTyqlqoA6rfUx8wMssxoVWuujHe65EMIpZIZDCNGS\nfJs/XwsEAmVKqXLjAfwI+In1mp8Cnzb5jJ3GH7TWJ4B0YItSarNSapZS6pIm139jBBtWR4DQ8++K\nEMIVyAyHEKIllTZ/DgCKsSxxqCbXNd3I2Sqt9UNKqWXAr4F7gflKqZFaayNQaboxVSO/FAlx0ZD/\nMwshzqUAy56IBq31100eZdZrDgLXN3nfDU0/SGu9R2u9SGsdAewDHnBwW+sAdwd/phDCASTgEEKc\nldY6C8vyyJtKqVFKqSuUUjcppeYrpYZbL1sGPKSUildKXaWU+hMwzPgMpdSPlFILlFI3KKUuV0r9\nErgKOODg5n4DRCul+imlQhz82UKI8yBLKkKIplpKwHUH8CzwV6APUAJkA6UAWuuNSqmfAIuwHIf9\nG7AKy6kWgCpgCJbTLyFY9mes0FqvPc92NfU0sAb4F+CFzHYI4TKU1u1JBCiEEEII0X6ypCKEEEII\np5OAQwghhBBOJwGHEEIIIZxOAg4hhBBCOJ0EHEIIIYRwOgk4hBBCCOF0EnAIIYQQwumOhNSGAAAA\nJ0lEQVQk4BBCCCGE00nAIYQQQgink4BDCCGEEE4nAYcQQgghnO7/A7KniWbfQHMpAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8374eb2940>"
      ]
     },
     "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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\niDYlwYcQQggh2pQEH0IIIYRoU/8fH3dlOxzCkGcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8374ddbb38>"
      ]
     },
     "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
}
