{
 "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_J095753.05+014429.01\"\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_J095753.05+014429.01 at z = 0.20\n"
     ]
    },
    {
     "data": {
      "image/png": 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1QUSqjwKOCA0aFemsEjdg09ogIuWnQaM9oEGjIiIi8WjQqIjUBI35EKktauEQkYpUyjEf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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ea42d33c8>"
      ]
     },
     "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: 11.05 [stellar mass]\n",
      "V-band attenuation in the birth clouds: 0.80 \n",
      "attenuation in FUV band: 1.59 [mag]\n",
      "attenuation in V band: 0.39 [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_J095753.05+014429.01 at z = 0.20. best log(Mstar) = 11.05\n"
     ]
    },
    {
     "data": {
      "image/png": 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NmOicm+Kc+xq4DtgNDC903mTgfefctOhGLCIiIodT4RKO0phZdbymlvfz9jlvMZg5QJeA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\ntoUg1goUEZFKKOQ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MSP+tDfjsrFXadSvcPByxYGZDgFdjHYeIiEgcu9yVspyI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\niIiISNQp4RAREZGoU8IhIiIiUff/AbgW+rqKy34dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e7573c8d0>"
      ]
     },
     "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): 0.02 \n",
      "mass fraction of PAH: 3.90 \n",
      "minimum radiation field: 1.00 \n",
      "best dust luminosity: 10.94 [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_J095753.05+014429.01 at z = 0.20. best log(Ldust) = 10.94\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vkmnh+CdQbGZ7Af9j00GjpakITKTeLVrkizt98EF8x69enZktHM7B0KE+cdLg\nxtQzg5tvhhtvhIcfDjoakayRTMJxf+j78Bj7NGhUsteiRbDTTr51IB6Z2qUybpxf/0MrwqbPPvv4\n//tZs/yUYxGpVcJdKs65vBq+lGxI9lq0CNq3j//41aurdqncfrufhhqksjLf3H/ZZcHG0RDcdJMv\nCJaGBTBFclEyYzhylgp/NXCJJhzhFg7nYO1auO46iHO0dlosXw7XXAP33aeulBoUFPiioQUFdTxR\n+/Z+Ube//z0lcYlkm7QU/jKzy4EHnHOr4jz+YuAJ59yKuKLIEJql0sAtXuzXymjSxCcQta0OumYN\ntG7tvy9Z4rdVVMAWW6Q/1mjr1sG558Ktt8KWW9b/9bNIQYFvmEiJIUP8NNkTTsjcAcQiaZKuWSrF\nQOsE4rgDXyBMJHuEWzi23NInH7VZvRratoVff4WffvLbli9Pb4yxOAdXXgmnnQYHHVT/12/ImjWD\nCy7wa9WISI3iHTRqwCtmti7O47UkpWSfcAtH+/Y++aitzX3dOsjP92ts/PQTdOwYTMJx550+1tNO\nq/9ri7/vxx7rW5iCaN0SyRLxJhyjEjzvc8CSBJ8jOai8HCZM8PWn6txnnirffAPz5lHe9bCqsYXH\nZHTo4BOO6dN9t0rPntWfq1WryhaOnXeu/zEcf/ubv/add9bvdaVSXp4vAnbLLX7dGhGJKa6EwzmX\naMIhAviEY9QoKCzMoITjtddg4EDKy1zs2MItHDfe6HfWlnDcdpufGXL44TW3cMyY4VtEAPbcE6hj\nQvbww/Dxxz7p0CDRYPXs6f8fvv5a05FFqqFZKpK08nI/+K68POhIElTd+ifhN+1wwvHrr5UJQnXH\nt24NTz7pa3fstVfNLRyHHOKPGVWZv4cTsoTv4RNPwFtvwb33KtnIFKNH++kvIhJTMoW/cpYWb0tM\nda0XAwZUvu+GvxcVVb535+fDpEn1G2uV2N4/HOyfVBRFxNbGwbs3kj8AJg1t79/Mt9gCli2r+YQF\nBX4sx/77w957wzvv1Hz8uefWPUF44AGYOdN/z9NnhozRtSu0aeP/b7p3DzoakbSrj8XbcpamxdYu\nnmSiogJKQwXu58yBbt18le3wrS0srN+YI1VUQOnZz8DNNzOnuKIytm0XwZ8nULjoQD+GY+FCPwNl\n6dLqT+acPxZg9mz48MPqWzjWhcZbFxbCxInJv4AxY2D+fHjwQSUbmWjECL8ez7/+pZYnyXn1sXib\nNGDhZKJ1udezAAAgAElEQVS0tHKNs+Liym1B1r2K26pVftXPSOEZKuBbLT77DLbdtuYWDjPo0aNy\nSmx+fvXH//qrX4W2X7/kqpE65/uvli6FsWOVbGSqDh3g97+H554LOhKRjJPwXy0za1bDvkwZFigp\nkLVjNGrz22+bJhyRVUbz8mDuXL+uSm3JQaNGlc8Lz26J5Zdf/ABTSDzhcA6uvhpatPCLhumTc2Yr\nKoJ77vHF40Rko2Q+Js0xs32iN5rZScCHdQ8pNczsj2b2mZnNNbPzg44nGyU9oDHTrVrlCzZFii5r\n/txzcOmliZ23eXOfzLz99qb7fv0VWrYEYMBXIynssYzCQv/eBP57YaH/GjAg4nnr18PFF0OXLnDt\ntYnFI8Fo0cL/Jz7wQNCRiGSUZBKO14B3zOxPAGbW0swmApOAW1MXWvLMrBEwBugFdAP+ZGaqyCPe\nqlX+zX9dRB278nLYZpvKx927V30crboFuz7+2DepR+9fvtzPaAEqttiB0hMn1t4tFe6GOeQQuOSS\nxF6jBGvAAHj22SzpYxSpHwkPGnXODTaz54GHzOyPQAHwC3Cgc+6jVAeYpAOBj5xzPwKE4j0KeDrQ\nqLJAbYNC58wJJq6U+u03aNeOvN9+BUIv7IcffB2NeMVqJQGfVHTu7MeERLaYRD5u3Ro+quVXZd48\nuPBCGDas5jogkpkaNfItUrff7te3EZGkB42+CEwB/gBsD/wpg5INgG2ABRGPFwAdA4olq9Q2KHRd\nvMXtM9mqVdC+PY1++6Vy2w8/xK68lZcXe8zFypUbu0iqeP99n7XNn191+6JFlYNSmzX1LSqdO5P3\na9T6hs75WAYNgv/7PyUb2ezII+HTT+G774KORCQjJNzCYWY7AU8CWwN9gJ5AqZndDdzgnKvTSCkz\nOxS4Bt8VUgD0dc6VRh1zKXB1KIYPgMucc7Prcl1Jnfz8yqmv1U2dDUp+PhROL4IWLVjxWX5lbHMH\nw7lbbBpb69awYsWmQf/6q++rj2YG223nB53utlvlMYsWwT7hoU/m6zXMm8fmrz8HnOU3f/+9Hxy6\nYhT891++zLpkt3AxsIcfDjoSkcAlU4fjfeB5oI9zbhnwspm9ADwGHAnsW8eYWoau8X/4VpQqzOw0\n/PiMi4BZQBHwkpnt4pwLL/H5A7BtxNM6AjPrGFdOSrS0duPGtScTkUW9YtXhSFds8Zg0CSi8Gbp1\n49Muf2T3s7r52EbcDKX/3PQJ4amu0QnH8uU+aYhl++3hqKPgnHMqa27MnQunnlp5zIQJcNNNbHn6\nBfQnj04jX4Ymy/2b0427gnKN3LDXXv77//5X+W+RBiqZhGOwc65KnUjn3H/NbF9gbF0Dcs5NA6YB\nmMWc/1cETHDOPRY65mLgOOA84I7QMbOAPULTdFcARwOj6xpbLkp0rZP99qu5qFfaYmu21C+6tm9d\n89mQVq1oFN2dEUu4klm0mhKO3Xbz3yO7XObP96vJhrVpA23a8H3RGPLO+pTyQSPZ8rhO8ccv2WPE\nCLjqKnjmmaAjEQlUwmM4opONiO0rnHNpnX5qZk3wXS2vRFzXAdOBgyO2rQeuws+omQP81TlXQ8nI\nhmXAAP8BfNddYfBgvy08LXPWrKhpmZli0iQ/WyMVnIPWrclb6cdw2KoYdTnCNt88dsJRUVF939C2\n28Ijj1QWBFu82FctjZE//7ZbN57gLNYUKNnIWZ06+VavN98MOhKRQCUzhuPsGna76hKSFGkHNAIW\nRm1fCOwaFci/gH8lcvLwWiqRcnFdlYoKv8Bpt27w+ON+6YdwK0VhYbAz+aqdJbPoWFizs1/rZFIK\nultataLRzz7haLLoh+qnwFZXPbSiovoWDvBrpvzjH/7fL7/sBxBGnDJTx7hImlx/vf/hfuEFFW6T\nrBZePyVSOtdSuTvqcROgBbAGWImvx5GVtJZK8Kpdh+XFp2HcOAorfK5Zp2XvzaBVK/JW+haIpgu+\nhh13jH3sllvCzz/HDrRjLROfzGDNGpg8Ge67b+PmVI1xkSzSrp1voXvuOejbN+hoRJIW60N4vGup\nJFOHY5MCWma2M3AfcGei50vQYmA9sFXU9q2AH+t68lxfLTbcejBrVmWFyzFj/PeiIt/qm5/vP5BH\ntnTU5dN3QYHvwq7zoM81a1I7a6NVKxqt/AqApvM/h567xD5u663hiy823f7TT7WPJznqKDjuODji\nCH8eadiuvNL/Yv3xj370tUiWC2S1WOfcF2Z2HfA40DUV56zmOmvNrAzoDZTCxoGlvYFxdT1/rrdw\nhFsPCgv9GinduvmxbGed5T9hjxxZub+0NDWfvgsK/HnrbO1aBiwdx6xZNSdDcS17HzWGo9m8T+Gi\nY2IfW1AQu7b7Tz9VrhRbncGDfVfKrrvWfJw0DC1bwimn+B/SgQODjkakzhJdLTaVafY6fMGtOjGz\nlkAXINzR2dnM9gaWOOe+A+4CJoYSj/C02BbAxLpeO1dbOMLjHVatSu15U9Z6EQ8zKja05sADq0+G\nElr2vlUr2uT9wogR0OaNr6rvUtl6a/gxRuPZwoW1Jxx5eUo2pKrzz4ejj4b+/WNXqhXJImlv4TCz\n6D/rhi/QNQSYkej5YtgfeBVwoa9Qoz+PAuc55yabWTv8NNet8DU7+jjnqlmmM3652sIRHu/Qo0dq\nz5uy1ot4mOF/HFJ0rlataOVWMPKLM6Fzx+oH8rVo4UuhR1u5MnbhL5GaNGniS9aPHw9DhwYdjUid\n1EcLx7NRjx2wCPgPfipqnTjnXqeW6brOufHA+LpeK1outXDEmu3x0UeVU1+HDQsutqSkamT/hg3+\nXPn58OKLcPrpta91Eau0uWYaSLJOPRX69PGtHZqSJFks7S0czrlk11/JeLnUwhFrtseee/pt4dl5\nUHXQ6Ny5PiHJ2L+BcTZw1DhlNrzoWps2vpT4+efXnjw0b161ReO3Gup2iNQmL88Pnhozxpc+F8lS\nibZw5GzyINWbNMknH+D/7oEfCxEeHxEeeFmvYzRCwjUqCgsrZ9IUFUHh5DOZtWafuJKhcBdSrLGe\nVRKHNWugS5faT9i1q8/GwubNgx12qP15ItXp08d/ElgYXVJIJHfF1cJhZnfFe0LnXNZ2TOZSl0oq\n1OsYjZBqa1SUPkXh7b9n0qSj6naByFVe4+0W2XNP+PDDymmw778Pe+9dtzikYTODG2+EW26BcXWe\nYCcSiHR1qcS7gEWKRvUFI5e6VGJp1apyJsdHH/nv0XU4MpZzVE5cqoNkBnv26gXXXOMXYwOYPRsu\nvrjusUjDdtBBPpueN6/6WVIiGSwtg0adc4fVOTJJq3hKfd90U+UU0p494Y03qtbhyOhca9068hv9\nUm1J8Dlz/ONaa3Qkk3BstZVfXn7dOl+w6ZNPYOed6/ySRBg+3I/jeOSRoCMRSbu4B42aWWdgXmix\ntJyUzV0qdSr1nQ3WrWPS1tdAaT9g0zoc4WJlsfZBRI2OX3+tuoprvI49Fp56yq8Eu+uufuCfSF3t\nsYf/WfrwQ/jd74KORiQh6Zyl8gW+3sZPAGb2NHC5cy5nRj1lW5dKtQud5Veu+hpX5c1ssH59aspB\n17TKa00uvBDOOMNXGP373+seh0jY8OFavl6yUjrrcER3oB8L/DmR4CS1ql3obD+fbNS0JkqrVv57\nu3b1PxMlKevW+aJJdbVsmV9yPlHNmsGUKX4siWpwSCp16gTbbQdvveUXeBPJUVpBKEuVl/uZmuXl\nsZOFSZNqXhNlzhxfi6N9+/qfiRKvKtNy162DRo3qftKKivimwlZHyYakw/XX+0HJzz+vnzHJWYkk\nHOFS49HbckY2jeEoL4fPP68+4ahNEDU2ElVlWm6qulSWLcvgymbSYLVvD927+08Bxx0XdDQicUnn\nGA7DL5q2OvS4GXC/mf0aeZBz7sQEzplRMn0MR01jNuL8/94oiBobdRKeIVJXFRXJdamIpNvQodCv\nHxxzjAYlS1ZI5xiOR6MeP55QZFJnNY3ZCD/OWVFdKkm10Pzwg1o4JHO1bg3HHw8lJXDmmUFHI5Jy\ncScczrmB6QxEpEZRLRzRLTThkuhQfR0OOnaEE06Atm3rJWSRhF18sW/hOOUU2GyzoKMRSSkNGpXs\nUMsYjmpLokf2kD0OfPwxNG2atjBF6qRpUzj3XHjwQbj00qCjEUkpJRwRsmnQaFj0GI5NPtWTHQNE\na1XXMRzhJea//DI18aRITvzfSGqdeaZf3O2ccyrnr4tkoEQHjVoOFw6Nm5ntB5SVlZVl9KDRWNU0\ny8qqjuEIP845J54Iq1f7aYO1iHkvKirgtNPgkkt8t4pIJist9dVHb7wx6EhEahUxaLSbc25Odcdp\nKLRkh/Xr6zZyf8kSX5JcyYZkg+OPhxkzYPHioCMRSRklHNIwLF0KW2wRdBQi8TGDP/8Zbrst6EhE\nUkZjOLJITTMxEq3D0eAsWaLZKZJdevSAsWPhu+986XORLKeEI4vUNBMj5+twgO9S2bAhua4VtXBI\nNgovX//gg0FHIlJn6lKR7NGkCaxdm9xz1cIh2Wifffxg6c8+CzoSkTpTwhGhqKiIwsJCSkpKgg4l\nYTk/vdK5uiUcauGQbDViBIwaFXQUIpsoKSmhsLCQoqKiuI5Xl0qETF9LpSZZtzZKMuJMOGImXz//\nrBYOyU477eR/dmfPhgMOCDoakY0SXUtFLRySPRJIOEaODCUc69f76qKLFkGHDmkPUSQtbrwRbr45\n6ChE6kQJh2QHM19pdN26xJ738suw556+S0WrxEq2KijwP8fTpwcdiUjScjbhMLMpZrbEzCYHHUs6\n5PyYjWjJjuH44YfK52vJb8lm11wDd97pf5ZFslAu/wUeCwwIOoi6Ki/33QPl5VW3V+k2aCiSSTiW\nLvXLfifaMiKSaTbfHHr3hn/8I+hIRJKSswmHc+4N4Jeg46ir8nI/QD064WiQkk042rWDVavSE5NI\nfRoyBMaPVwItWSlnEw7JQckkHMuW+aboyy9PT0wi9alFCzj9dHjkkaAjEUlYRkyLNbNDgWuAbkAB\n0Nc5Vxp1zKXA1cDWwAfAZc652fUda30YMKCyVHl1y85HVh1tMJJt4TjzTGjTJj0xidS3887zy9ef\neaZPQESyRKa0cLQE3gcGA5uMiDKz04AxwAhgX3zC8ZKZtYs4ZrCZvWdmc8ysaf2EnR4VFX516tJS\nX7oc/Pfwtga3bkq4nHkys1RWrPBjOERyRePGvmtl7NigIxFJSEYkHM65ac654c655wCLcUgRMME5\n95hz7jPgYmAlcF7EOcY75/Z1zu3nnFsd2mzVnE+yyerVsNlmyVcaNf0ISI7p2xfefFPL10tWyYiE\noyZm1gTf1fJKeJtzzgHTgYNreN7LwNPAMWY238y6pztWSZM1a6Bp07qVNhfJJWZwww1wyy1BRyIS\nt4wYw1GLdkAjYGHU9oXArtU9yTl3ZKIXKioqIj88UCIkXLpVAlSXFg7VLJBcdcghMG4czJsHO+4Y\ndDTSQJSUlGyy3lhFnP382ZBw1JtsXkslp61enVwLx2+/QfPm6YtLJGgjR/qvRx8NOhJpIGJ9CI93\nLZVsSDgWA+uBraK2bwX8mMoLhVs4gmzVKC+HuXP99wZV1KsmyXap/PADbLNN+uISCdruu/vfjffe\ng333DToaaWDCrR0508LhnFtrZmVAb6AUwMws9HhcKq+VCS0c5eXw+edKOKoId6kkOkvl+++hY8f0\nxSWSCUaMgEsvhWefDToSaWASXS02IxIOM2sJdKFyRklnM9sbWOKc+w64C5gYSjxm4WettAAmpjKO\nTGjhqIzF19uorg5Hg5JsC8eCBUo4JPd17OhbOl5+GY5MeOiaSNKytYVjf+BVfA0Oh6+5AfAocJ5z\nbnKo5sZofFfK+0Af59yiVAYRVAtHrEJf0YqLocEOL4kcNPrbb/E/75tvoGfPtIUlkjGuvRZOPdWv\ntaJFCqWeZGULh3PudWqZouucGw+Mr5+I6le40BfAnDnQrVtlghF+3KCFB422agWLEsgxP/zQF0gS\nyXWbbw7HHANPPQVnnBF0NCIxZUTCkSkyqUtFIoS7VLbYwmdg8aqoUElzaTgGD/ZJx0kn+d8XkTTL\n1i6VjJAJg0YlhnCXyhZb+LVR4vHNN7DddmkNSySjNG0K558P998PV1wRdDTSAGRll0qmUAtHhvrt\nN2jWzDcbx5twTJ+uAXTS8PTv7xd2O/fcBji6XOqbWjjqIBNbOAoKYJddGvgU2YoK2HJL38KxbFl8\nz3nhBXj44fTGJZJp8vLgmmvgjjtU9lzSLtEWDg1nznAFBbDrrg084Vi2zLdubLZZfNNiFyzwy3Zv\nvnn6YxPJNEcdBR995AvfiWQQJRyS+ZYt860b8br7brjkkvTFI5Lphg+H0aODjkKkCnWpRAhqDEd+\nPhQW+n+r0FcM4RYOqH0xtsWL4ZNPfJOySEPVrZtPvD/9FHbbLehoJEclOobDnFbTxMz2A8rKysoC\nH8MRrrtRVtaAC31FGzAA7r3XZ159+8Izz/gy57FcdBFccAEceGD9xiiSaebNg+uug6efDjoSyXER\nYzi6OeeqrV2gLhXJfMuXQ+vW/t877OCnvMby1lt+0JySDRG/ZH1BAcyYEXQkIoC6VKrQtNgM5Vxl\nueZddvGr23XpUvWYxYt9v/XUqfUfn0imuuEGOOcceP55MKv9eJEEaFpsHWTitFiJsvvu8M47cOyx\nldtWr4bzzoO77tKAF5FI7dvDH/7g10444YSgo5Eco2mxknsixxn9/ve+6yRs9Wo4+2w/K2Wffeo/\nNpFMd+WVMG4crFsXdCTSwCnhkMwWrjIattlmvjtl6lSYPdtP7zn3XL+GhIhsqmVLOO00eOSRoCOR\nBk4JR4YpKIARIxp4oa9I8+dDp05Vt916K8ycCf/4Bzz4oJINkdqcd56frbJyZdCRSAOmMRwZpqAA\nRo4MOooM8s03fmZKpBYt4LbbgohGJDs1bgxDhsDYsXD99UFHIw2UEo4ImqWSgb75ZtMWDhFJ3Akn\nwAMP+Bld7doFHY3kABX+SkImFf6SKIMHw5/+pKRDJBVmzPCF84qLg45EcogKf0lu+OYb2H77oKMQ\nyQ1/+INf1G3evKAjkQZICYdkrp9/9nU1VLBIJHVGjtRAMQmEEg7JXC++6JfaFpHU2W03P9X8vfeC\njkQaGCUckrkmT4aTTgo6CpHcM2IEjBoVdBTSwCjhkMz0wQfQsSO0aRN0JCK5Z5ttYK+9YNq0oCOR\nBkTTYiNoWmyGcM5/ArvnnqAjEcld114L/frBEUf4Oh0iCdK02CRoWmyGeeQRWLTI/0EUkfR58EHY\nsAEGDQo6EslimhYr2enbb30J5quuCjoSkdx33nm+Lsfy5UFHIg2AEg7JHL/9BhdeCOPHQ6NGQUcj\nkvsaNYJrrtFSAVIvcjLhMLNtzexVM/vYzN43s5ODjklq4Zxf6+Gqq6Bz56CjEWk4jjoKPvvML5Qo\nkkY5mXAA64ArnHN7AH2AsWbWPOCYpCbjx/tl5/v0CToSkYbnpptg2LCgo5Acl5MJh3PuR+fch6F/\nLwQWA22DjUqq9frr8N//wnXXBR2JSMO0xx5+FeZZs4KORHJYTiYckcysG5DnnFsQdCwSw/z5/tPV\nhAkqYS4SpJEjfTEwzVyUNMmIhMPMDjWzUjNbYGYbzKwwxjGXmtk8M/vNzN4xswPiOG9b4FHgwnTE\nLXW0ciVccIFfMrtVq6CjEWnYttrKL+42dWrQkUiOyoiEA2gJvA8MBjZJr83sNGAMMALYF/gAeMnM\n2kUcM9jM3jOzOWbW1Mw2A6YCtzrnZtbHi5AEOAeXXOJrbWiQqEhmKCqCe++FNWuCjkRyUEYkHM65\nac654c6554BY7epFwATn3GPOuc+Ai4GVwHkR5xjvnNvXObefc241vmXjFefck/XxGiRB99wDv/ud\nr3IoIpmheXM4/3z429+CjkRyUEYkHDUxsyZAN+CV8Dbny6NOBw6u5jl/AE4B+ka0euxRH/FKHD78\n0A8UHTo06EhEJFr//n6NlSVLgo5Eckw2FNBvBzQCFkZtXwjsGusJzrkZJPHawmupRNK6Kim2bp2v\ntfH44xokKpKJ8vLgxhth9GgYOzboaCTDhNdPiRTvWirZkHDUm+LiYq2lkm733w+nn+4HqIlIZjr0\nUN+t8vnnsMsuQUcjGSTWh/CItVRqlA0Jx2JgPRD9DrUV8GMqL6TVYtNs5Up47jl46aWgIxGR2tx8\nM1x/PUyeHHQkkqESXS0248dwOOfWAmVA7/A2M7PQ4/+m8lrFxcWUlpY2yGQjuoksLSZNgnPO8U22\nWaBe7kmW0T2JLSfvS5cu0KkTTJ+e1NNz8p6kQC7dl/79+1NaWkpxcXFcx2fEX34za2lme5vZPqFN\nnUOPtws9vgu40MzONrOuwP1AC2BiKuMoKiqisLAwp34g4lUvr3nqVDjllPRfJ0Ua4s9BbXRPYsvZ\n+3LjjfCXv/ixVwnK2XtSR7l0X0pKSigsLKSoqCiu4zOlS2V/4FV8DQ6Hr7kBfmrrec65yaGaG6Px\nXSnvA32cc4tSGYTGcCRg0SJo3z7+4z/5xH9iato0fTGJSGrl5/tZKw88AIMHBx2NZJjw8IN4x3Bk\nRAuHc+5151yec65R1Fd0nY0dnHPNnXMHO+feTXUcybZwpOP4mo6JtS+ebZGP65Rlv/UWbL01fP99\nzTH+9hvcfrv/lDRqlF96vga1xZToPYm1PdHHqVafPyuJbE/bz0ocsvH3J9446iJjflYGDoRnn4Wl\nSxvUPaluX6b9/iRzjVT9rU20hSMjEo5MkewYjpz+JXjhBVi7tuq20lI/gn38+OpjfOwxOPFE2Hln\nOOYY+OMfYe+9a7yUEo7kjlfCkdgxSjgS3N6o0cYPDQ3pnlS3L9N+f5K5Rqr+1iY6hiNTulSC1gzg\n008/TerJFRUVzJkzJ6XH13RMrH3xbIt8XNO+Ko47Dp55BnbcsXLbu+/CSSfBI4/41SUbR/0YVVRQ\n8c47zLnnHthhB79tjz2gDq+5tv3V7avtdSZyj1KhPn9WEtke733I9ntS3b5E70n042y/L7Vub9UK\n5s6lYvHiBnNPqtuXtr+1dZDq+5LoPYl472xW03XNaWVAzOwM4Img4xAREcliZ9a0nIgSDsDMtgT6\nAN8Aq4KNRkREJKs0A3YAXnLO/VzdQUo4REREJO00aFRERETSTgmHiIiIpJ0SDhEREUk7JRwiIiKS\ndko4REREJO2UcIiIiEjaKeEQERGRtFPCISIiImmnhENERETSTgmHiIiIpJ0SDhEREUk7JRwiaWJm\nr5rZXUHHkSrZ+HoyLeZk4jGz18xsg5mtN7PfpSu20LUeCV1rg5kVpvNa0vAo4RBJgplta2YPm9kC\nM1ttZt+Y2Vgzaxt0bBK8FCc6DngA2Br4KEXnrM7loeuIpJwSDpEEmdmOwLvATsBpoe+DgN7A22a2\neYCxNQnq2pJWK51zi5xzG9J5EefcCufcT+m8hjRcSjhEEjceWA0c6Zx7yzn3vXPuJeAIoCNwS8Sx\njc3sHjNbZmaLzGx05InM7GQz+9DMVprZYjP7t5k1D+0zM/uzmX0d2v+emZ0U9fxXQ+cvNrNFwDQz\nu9DMFkQHbWbPmdlD8ZzbzFqY2WNmtiLUijO0tptiZseZ2VIzs9DjvUNN87dGHPOQmT0W+ncfM3sz\n9JzFZvZPM+sccWydX0eM58Z7T+82s9vN7GczKzezERH7W5nZE2b2i5l9Z2aXRbZomNkjQE/gioiu\nkO0jLpFX3blTKRTTuNDPxhIz+9HMzg/93z5sZsvN7AszOzod1xeJpoRDJAFmtgVwFPA359yayH3O\nuYXAE/hWj7BzgbXAAfjm6qFmdn7oXFsDTwIPAV3xb1JTAAs993rgLOAiYHegGJhkZodGhXU2PgH6\nPXAx8HegrZkdFhV3H+DxOM/9V+BQ4PjQ6+0F7FfL7XkTaAXsG3rcE1gUem5YD+DV0L9bAmNC5z0c\nWA9MjTg2Fa8jWiL39BfgQOBaYLiZ9Q7tKwYOBv4YiqVXxGsGuAJ4G3gQ2AooAL6L2H9ODedOtbPx\n/wcHAOOA+/H3dUYo5n8Dj5lZszRdX6SSc05f+tJXnF/4N4kNQGE1+6/Ev3G2w7+xfhS1/y/hbfg/\n+OuB7WKcZzP8m1L3qO0PAo9HPH4VeDfG86cCD0Y8vgj4Lp5z4xOBVcCJEfu2AH4F7qrl/rwLDA39\newpwHfAb0ALf+rMB2Kma57YL7d89Fa8j4v7clcQ9fT3qmJnArfiEajXQL2Jfm9B574o6xyb3qqZz\n13BPqzvXDcDAiMdPAPtXdy38B8wVwMSIbVuF7vmBUeeu9mdcX/pK9kstHCLJsdoPAeCdqMdvAzuH\nuh0+AP4DfGRmk83sAqsc/9EF/yb9cqhbY4WZrQAG4MeMRCqLcd0ngJOsckzHGcBTcZy7c+j8TYBZ\n4ZM555YCc+N4va9T2aJxKD7p+BQ4BN+6scA59xWAmXUxsyfN7CszqwDm4QdIRnY/1OV1REvknn4Y\n9bgc6BA6b2NgdniHc2458d2b2s6dqH74nyfMrDFwDPBxdddyfvzHz8D/IrYtDP0zmeuLJKRx0AGI\nZJkv8W+KuwHPxdi/O7DUObc4NJShWqE3gCPN7GB8t8VlwM1m1h3/SRrgWOCHqKeujnr8a4zT/xP/\nifY4M3sX/+Z/RWhfbefessbAa/YaMNDM9gbWOOc+N7PXgcPwrSSvRxz7L3yScUEojjz8G+ZmKXod\n0RI5fm3UY0dlF3S8yWZ1ajp3XMwsH+jgnPsstOlA4BPn3G9xXCt6G4leXyQZSjhEEuCcW2JmLwOD\nzazYObfxjSo0JuMMYGLEU7pHneJg4AvnnIs459v42S03Ad/iP7k+hH8T7OSceyuJOFeb2RT8eIWd\ngc+ccx+Edn9S07nNbBmwLhT796FtWwC74BOKmryJ72IoojK5eA3ftbI5fswG5qcP7wKc75ybEdp2\nSNjjx/UAAAKwSURBVCpfRwyJHh/L11SOyQnfm/zQa4lMptYAjZK8Rjx6ApGv4TDgVTNr65xbksbr\niiRNCYdI4obgB929ZGbD8J/S9wTuwA8OvDHi2O3N7K/4OgrdQs8tAjCzA/FTaf8N/AQchB/H8Ilz\n7pfQ84rNrBH+zSUf+ANQ4ZybFEecT+BbEfYANh4fz7nN7P+AO81sCX7Q4c348SY1cs4tM7MPgTOB\nS0Ob3wAm4//ehN+Ul+Kb9y8ysx+BTvjxLY5NJf06omKr8z0NneNR4K9mthR/b0bi701k7N8A3c2s\nE/CLc+7n2s6doMOABbCxO+UkfFJ3On4WlUjGUcIhkiDn3Jdmtj8wCngaaAv8iB/gONo5tyx8KPAY\n0Bw/HmIdUOyceyi0fzl+XMMV+FaBb/EDLv8dus4wM/sJ/0bSGVgGzMEPXiTiGtX5D7AE3zLwZNRr\nqO3c1+AHj5biBxqOCcUYj9eBvQm1hjjnlprZJ0B759wXoW3OzE7Dz5z4H34MxOXEbkGpy+twCR6/\nyXNiGArch+/uWY5PNLfDD7QN+yu+pesToJmZ7eicmx/HueN1GPClmZ2FH5dSgh8nMzvimFjXineb\nSMpZRMuuiIgkyMxa4FsbhjrnHknD+V8F3nPODQ09bgvMcc7tkOprRVxzA9DXOVearmtIw6OBQiIi\nCTCzfczsdDPrbGb74VtdHLEHEafK4FChrj3ws4BmpOMiZnZfaOaOPolKyqmFQ0QkAWa2D35Q7y74\nwaFlQJFz7pM0Xa8A3y0HfozQ9fiBx09W/6ykr9WOyq6z8hizXkSSpoRDRERE0k5dKiIiIpJ2SjhE\nREQk7ZRwiIiISNop4RAREZG0U8IhIiIiaaeEQ0RERNJOCYeIiIiknRIOERERSTslHCIiIpJ2SjhE\nREQk7ZRwiIiISNr9Py1iQKrOjP/IAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e782f4f60>"
      ]
     },
     "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 $\\chi^2$ : 1.12 \n",
      "bayesian stellar mass 11.05 +/- 8.68 [M sun]:\n",
      "bayesian dust luminosity: 10.93 +/- 7.04 [L sun]\n",
      "bayesian SFR 9.64 +/- 0.48 [M sun / yr]:\n",
      "bayesian AGN fraction 0.01 +/- 0.03:\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": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best model for HELP_J095753.05+014429.01 at z = 0.20, best(Mstar) = 11.05, best log(Ldust) = 10.94, best AGNfrac = 0.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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8+fTt25dGjRqxbt26VHVu3LgxVT0xMTG0bdsWX19f/P39AS053unTp0lJSSEwMBBvb29e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\nVgtXQszMzKwW/w//IxPsyOLLrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e756858d0>"
      ]
     },
     "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 = {:.1f}\". \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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A0QNi9OjRPPLIIzz66KNERkaKhl1OTk7s37+fhQsX0tTUhJOTE42NjUiShFar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\nLS0lLy/PnJJp3xuxa9cu/vGPf5CTk+P2nH/r2Sr2XOkudfUOaPr06Q7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dYWDw448/kpiY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\nJOAQQgghhM/9f9VTRNgxEiFTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e754ece48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mstar,red,ldust,sfr=[],[],[],[]\n",
    "for gal in range(0,len(mod)):\n",
    "    if mod[gal]['best.reduced_chi_square']<4:\n",
    "        mstar.append(log10(mod[gal]['bayes.stellar.m_star']))\n",
    "        red.append((mod[gal]['redshift']))\n",
    "        ldust.append(log10(mod[gal]['bayes.dust.luminosity']/(3.846*pow(10,26))))\n",
    "        sfr.append(log10(mod[gal]['bayes.sfh.sfr10Myrs']))\n",
    "\n",
    "mstar=np.array(mstar)\n",
    "red=np.array(red)\n",
    "ldust=np.array(ldust)\n",
    "sfr=np.array(sfr)\n",
    "\n",
    "ax1 = plt.subplot(gs[0])\n",
    "\n",
    "ax1.plot(red, mstar,'-o',c='lightgray',linewidth=0,linestyle=' ')\n",
    "specific_mstar=log10(mod[obs['id'] == HELPid]['bayes.stellar.m_star'][0])\n",
    "ax1.plot(z,specific_mstar,'-*',c='red',markersize=15)\n",
    "\n",
    "ax1.set_xlabel(\"redshift\")\n",
    "ax1.set_ylabel(\"log(Mstar)\")\n",
    "ax1.set_ylim(7, 12)\n",
    "\n",
    "ax1.legend(fontsize=6, loc='best', fancybox=True, framealpha=0.5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## redshift vs dust luminosity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.5/site-packages/matplotlib/axes/_axes.py:531: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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soKCAv//97/zmN79hwYIFDsan+XAqZz3h2sj3wMBAvv76a6qqqpg/fz69e/d2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\nDUlEQVQ4hBBCCOFy/w+qLE2zpQrYEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e754e0518>"
      ]
     },
     "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+\nhoSECOOrqirJycnU1NQAiHbIpKQk/vKXv/DTTz+hqipGo5HCwkJ+/vlnwsPD6d69O8nJyej1eoKD\ng9m/fz8XLlwQw+kaGhpwd3fn4MGDxMTE0KlTJ9zc3PDx8WHKlCm4u7tjsVgoLCzEYDBQVlaGXq8X\nkZeZM2fy/vvv09DQQO/evamurubPf/4z1dXVuLi48Msvv5Camkrv3r0pLS2lqqrK4bpooX83NzeH\n9FNT814KCgps8vrWDzOz2czUqVM5deqU+BuAh4eHzbmt77PGZrq0lJiVFtFzFtV48cUXWb58OZ07\nd27SiejUqZPDva4N7mtKKMxisYjjJiYmsmHDBnEfqqpKRUVFo6Jr9pOBb1ak49H+cCai2BrIyIfE\nAWf1Gva73dLSUqKjo4mLi7vqIsL58+eLGgBnXRBPP/00H374YaO7V+v6k/79+wOXiwstFosYYx8e\nHk5OTg4TJ04UQl8BAQFiGJuGFuVIS0sjKSlJyHSXl5czffp0m924Xq9HURRhfA8fPkxdXR3u7u7A\nZadq6tSp1NTUEB8fL+TLtfoBTUAqMzOTLl26YDAYgMtO3IsvvkhqaqpwVLy9vcnKysJisVBfX4+P\njw+nT5/m7rvv5l//+hfp6enC2VJVlQ4dOlBcXExsbCwWi4Xy8nKmTp3Kpk2bGDduHKmpqcTHx5OS\nksJzzz3Hxx9/zFtvvUVDQwP9+vUTc2E0tNB/165dHQxIVFQUU6dOdZj34u/v71SDQnuY7du3T2iP\nBAUFicJWTfPCGY21Z7ZUTZGz/LZ1VGPKlCm4uro2GYHQNFasSUxMZPXq1Y12yGgRPw37+orTp0+T\nlpZGbGys0yLXN954Q3QrSSQtiX3dTmshnQ+JA84eyNbpidLSUqKiopgxY4bDg9W6iHDDhg02hWea\noNLx48eZN2+eUwemoaHBYfaHPdaGJyMjA4AzZ85w+vRp5s+fL9IHixYt4pFHHmHAgAHMnDmTefPm\n0aVLF6eG5OjRozbdHs60PrSOmqioKKZNm0ZFRQXe3t489NBDoii0S5cujBgxguDgYAIDA4WRrqmp\nEd9FS0NYLBYA6urqCAoK4s033xSD3Gpra3n66adZsWIFly5dEkautLSUhx56iD179tChQwdKS0vp\n1q0bJ06cwM/PD0W5rK7q7e3No48+SkBAALNnz8bV1ZVhw4bx1VdfsXLlSmbMmMGhQ4fo27cv69at\n49y5cw6iYSGtAAAgAElEQVTXJTAwkD179jTaBvv44487FBVqIlrWn9EeZj/99BOvvPKKzXXVoj1X\nm15oCTGr5upSPPDAA012cPzxj390eN3Hx4fbbrvNRuHW+jppg//sP6MVoA4fPpxu3bqRmZlJZmam\nKHKtrq7GaDTy29/+tlnaJxLJ1WLvCGuR3ZZGOh8SGxp7IGu796qqKlJTU+nYsaPTyIUmMnXo0CG2\nbt3KnDlzWLp0KfX19RgMBurq6ujYsSMWi4XJkycTHx9PYGCgTZeFtoueM2eOQyGrtSKmlhr47W9/\ny4QJE4iPj2f58uXMmzcPVVXFeWbNmkW3bt2IjIzk8OHDDoYkLS2N8PBwoUeRnp7uYPAURaGurk7o\nW8ybN48XXniB2267TXSumM1mGhoaHFIImpF+5plngMviWvX19Vy8eJFt27aJCEZdXR2FhYUoyuVB\ncKmpqfTt2xeAAwcO4Ovri5eXl9D/OHv2LEOGDGHnzp34+PiIcH1RUREdOnTAYrGQmJhIly5dqKys\nRFEUDhw4IJzGzMxMYmNjGTBgAFFRUQ679KioKLZv305ubq5NiD8jI4OoqCiHqENgYCCff/45Q4YM\n4bbbbqO6upqHHnpIpF6s23WtsZcSt8ZZe2ZLiVk1V5fi1VdfveoODlVV8fX1Zc6cOQ6KrUajkaSk\nJGbPnt2oY6ety5nomqqqonVXImkNrOt2Dhw4wP3339/i55DOh8SGxh7IWnoiPj4eLy8vPDw8hKKn\nM/2Gzz//nIqKCvbt20dcXJxQAE1PT2fbtm2kp6djMBjEPA/rY2gRkoULF4oH9BtvvEFubi4eHh7C\nkMLldJCiKPj6+vLII4/w0Ucf2XTjaPLkGRkZIhqhqZAGBwdjsVjIyckhLi7OZoS9lnvXakiSk5Px\n9fUlKyuLqqoqNm3aRKdOnTh79ix6vZ5Fixbx4osv2hQZ2hvpzp07k5+fz7333su+ffsYN24cS5cu\nxc/PD7hc+7Bs2TKmT59OSEgI69evp7S0lKSkJF544QVMJhNeXl5C/8NkMvG73/2OHTt24OnpidFo\nJC8vT0icp6SkUFtbi7u7OzqdTnSOaNEprUZl1qxZTJs2zWGXrtfr+e1vf8uqVatEykjr2rGfc2Kt\ntmrdbTR58mQmTpzI2LFjiYyMdGowNcfWPr3QmHFvrtPQHOPcHF2Ka2k51dao1+sdnIfmrNHZurT3\nSr0MyY2ktZxc6XzcQrRUhXJjD2SDwSB0DbS6A2c6BsHBwbzyyitERESI9Ih1igVgz549dOzYUXRZ\nONOY8PPz48EHH6S6uppXXnlF6Is8++yzomBPp9Oxb98+OnXqhE6nszFKgYGB7N69m8jISJtaj379\n+pGUlERSUhKXLl0SSpPWnTB///vfycvLY+DAgaKd0s3NjcWLF/PSSy8RHh5Oeno6dXV1Ytfu4uJi\nUxtjbaTNZjM1NTXMnTuX++67D5PJxE8//SS6XTSDsmvXLhvnQFEUvL29qa2tRafT4e/vT0FBAUaj\nkT179vDOO+/QrVs3vLy8CAsL4/nnn6dDhw4MGjSIzz77jBkzZpCZmSlqcAwGg7gOJpNJRLMeffRR\ngoKCHHbpFRUVvPPOO2RlZZGVlSUMr/19Zt9FBJe7jSZPnixeq62tdXqPatogo0ePZuPGjc0y7i0l\nZtVcXYpr6eCwX6P1Z660RqmXIbnVkd0u7ZymRr9fK/aV9/B/6Q4XFxdR+7B3716nOgZms5mSkhJ+\n+OEHMadi0qRJxMXFERoaSlRUlKh5KC4udtCYWLBgAW5ubkyePJmhQ4c6jHSvra1l+/btQklTa03V\nOku0Sm3N6XjppZeoq6sTxvaBBx5g8+bNvP/++/j7+4uaA63jQ0tfrFq1innz5hEeHi5mrRQXF3Pp\n0iUCAwMpKysTehx5eXkEBATQuXNncd2sUwNpaWnccccdTJkyhW+//ZY77riD4uJigoODiYqKYvXq\n1dxzzz34+vqiKIpo16yqqsJsNlNdXc0f/vAHfvjhB5KTkzlx4gQXL17E3d0dVVWpqqoiOzubadOm\nYTAYuP3223FzcxM6I+PGjWP16tViRgmAm5sbY8eOFQPutBC/1vGxcuVK/P39RSutpv/hTF/CvlvG\n2WuDBw8WEu72HDhwgLCwsGbrSTR2j+bl5ZGdnc306dObvsn/zbXoUjTXwb+eNUq9DMmtjox8tGNa\nS8K8qTCzXq8XE0vB8UGstVN269ZNGNK0tDR0Op3I6Ws1DhcuXMDf399BY0LbRRuNRpKTk200RLTU\ng9a9oaUnNJlva2VKrS02Ojqaw4cP8/rrr9ucR3MQ+vXrR15enk3KRFVVVqxYwQsvvMCQIUPw8fFh\n3LhxTJo0iYaGBtLS0lBVlXPnzrFu3Trefvttdu7cicVi4eeff6a6upqTJ0+KCM2uXbvo2LEjW7Zs\noVu3bphMJurr64XR16a5WsvEl5WV0bdvX15//XXc3Nw4ceIEtbW1VFVV8cQTT1BSUoKLi4t4nzaE\nbsCAAYwZM4auXbsKx0yv15OUlERcXJyo4aivrxcy69YpJusaHm0Kr3Uqy14bxTpKo+HstQkTJvDi\niy+iqqpwJjUZ9/Xr14sumeYY92tJhTR1rNbQpbjeNUq9DMmtjHQ+2jGtKWHe2INv1qxZfPLJJwQH\nBzvthFi1ahXV1dWMGDGCZcuWYTKZ2LVrFz4+PuJ9ZrOZs2fPkpCQIFIt1jUExcXFREZGMm3aNLEr\n18jMzMTFxUUUvMJlQaY777yTt956i759+1JXV0dycjJJSUnU19eLWo+xY8cK8SvtWp09e5bZs2fz\nwgsv0KFDB1RVxWQyiU4Cg8FAVVUVFRUVmEwmzp49S5cuXdi1a5dQody9ezdHjx6lf//+/Md//AeB\ngYEkJCTQr18/8vPz2bdvn0hZTZ06lYyMDKHVoV0/LeqghdYPHz7M888/z/Lly3F1daVnz56YzWYu\nXLjA7NmzCQkJ4a233qKsrIyXXnqJdevWiWNt3LiRbt26iSm41s7CkiVLiIuLs3EOtL87q79ZvHix\nQ8GpvYOnRWnsnRRnM3IUReGzzz5jyZIl4vpqehdXO6m1NYxzSxv4llpjS6xLOjCSmwmZdmnHXI2E\n+fVg/cCaPn06Op2OcePGYbFYhAiStbCXluevq6tj3rx5+Pv7YzabReg5LS0NLy8vBgwYgKqqNvNV\nNKOYmZlJZGSkSIloFBcX8+CDDwptBbPZzMGDB1m+fDleXl6MGDGCd999l+zsbDZv3oy/v78odLWu\nd9Coq6tjyZIlIl2Rm5srBtXFxcVx8eJFMjIyqK2tJSEhgU6dOmEymaipqcHT05OePXuSmppKeHg4\nZ86cITg4GIPBQH19PXFxcSxYsIBdu3aJbpagoCCMRiNms1nUYWhoA+kWLFhAYWEhP/74I/Hx8XTo\n0IHTp09jNpvx9/cXzmWXLl1wd3dn2LBhLFu2TNRUFBUVCQ0RLRqkCanp9XqWLFlCcXGxEEHT/q6l\nmKwl9LX1WEvN6/V6Ro4cydy5cxk9ejQJCQlcvHjRQRDLXowsIyOD0aNHU1ZWRkJCAps3b2b16tVs\n2rSJuLg4Hn300WtOF7YHo9oWa2yNtKxE0hJI56OdcjXths09nvX/O/sbgK+vL3q9Hr1ez4oVK1iy\nZAlffPEFcXFx9O/fHy8vL2Ecu3XrxvHjx0WXRX5+PmazmR07dqDT6XjhhRd44YUXOH36tDiHtmPW\n6gWsDZjmmDz//POUlpaiqpfnqvTs2ZPAwEBiYmJsBpOdO3eO06dPEx8fT0BAgMMcF22Nx44d45FH\nHmHRokWsXbuW3bt34+LiQnV1NX379mXPnj0EBwdTUVFBfX09ffr0wdXVFT8/P9E6bD3DRVvne++9\nh4+PDzqdjoceekikoUaOHImqqowbN044BVo9yv33388777wDwMGDBxk2bBgVFRUYDAa8vLzo2LGj\nqAkBhPqmwWBg6NCh5Ofno9frOXv2LKqqiuFvixYt4siRI0ycOJGEhAS2bt1K//79RRFqUlIS//zn\nPx0KOAsLC4mPj6ekpMRm7so//vEPUUvz5Zdf8tVXX5GdnW1T3xAZGUlqaqqYmVJcXMzx48dF6ss6\nShISEkJMTIxDa7Xk2mkpFViJpDWQaZd2irN2Q2sdAOCK7YbabIydO3dSUVFBTU0NnTt3xtXVlcGD\nB6MoCnv27HGYm/Hwww+LMH5mZiZxcXFEREQQEhJCWlqaza65Z8+e9OvXj++//54VK1Zw5513oigK\nFy9eZPr06WzcuJF+/fqxbds2vvvuO4qLi7l48SKdOnUSehfWrbFa/cLQoUPJy8sTo8cV5fJ495SU\nFFGv8M9//pOOHTuK3bymOKoZWIvFwvnz5+nZs6cw4FrLLMDkyZMxGo1ER0ej1+vp0KEDJpOJ8+fP\n4+fnR0VFBQ899BAlJSVCYVVbS1VVFXl5eUybNo20tDSef/55duzYgclkYvbs2ZjNZlGHkZmZSVJS\nEtOmTROtwICNM2M2m7nzzjttUhkDBw6ksLBQ/Lt2rSwWC66urnTp0oVRo0Zx5MgR0cECMGDAAEwm\nEzNnziQhIQFVVQkKCsLf39+hbkNVVR599FEee+wxm3tM++fly5cDl+t4nNU3DB8+nBMnThAbG0tt\nbW2bT2r9NdHak6UlkutBOh/tmNDQUHbs2ME333zD/v37MZvNYideU1MjxKWc5dG1XdFTTz0FwJQp\nU4TktbU+Q3h4uCgMLCws5Mknn2Tt2rWMHj1a5PwrKytFxMG6nVJrdR05ciRRUVH4+fnx7bffApcN\nqzaLpHfv3jz//PMkJiYSExODxWJh7NixQmRp7ty5zJ49m0WLFtHQ0EBubi5//etfGTduHD179qSu\nrg5PT08SEhJEvQLAM888g6enp3j4WjsyRqORuLg43NzcKC8vx2QykZmZSXFxMefPn8fV1VXszl1d\nXTl69CgVFRV07NgRvV5PZWUlrq6u9O3bl507d4r0kdZ2GxAQwI4dOwgMDMRkMqHX63n44YeZP38+\no0aNYtGiReK9MTExFBUVCcejqqqKwYMHs2fPHiorK6mvr6dz585CwE1zoKKiotixY4c4jhbBmDp1\nKqqqMnfuXJ5//nmmT59OdHS0+O1zc3PJz8/H29tbOD9ZWVmcP3/eoS5AKw7WsP5ni8VCQ0MDQ4cO\ntXFQP/nkE4fPAYSEhDiVIrc+dnPEwSTNoyVUYCWS1kI6H+2YmJgYgoKCmDp1KocOHWLq1KnCgbhS\n18uCBQt49tlnOXTokE0HiNlsJiEhgYkTJzJw4ECnyqNvvPGGGIGelZVlY1C0dsrQ0FBGjRrFli1b\nWLRoETNmzCAwMJApU6ZQXV2Nq6ur2AUvX76cxMRE0Q2jpRA0nY2ZM2cSGRkpRMHi4+Pp2LEj06ZN\n4+2336a2thZFUYiOjubQoUNkZGTg4eEh0kPWhkzT+LBYLHTu3JnRo0eTnJzMpEmTiI6OJiYmhmee\neUZ8zmw2U15ejr+/v2gzdnV1ZciQIfTo0YM33niDLl26kJubS11dnRDqioyMJD8/n2nTptG7d2/y\n8vKYMGECo0eP5ty5c7i6uor3BgUF4enpSWZmJuHh4WRnZxMWFsbHH3/M/Pnz0el0VFRU8Mgjj3DX\nXXcJfZKhQ4eyYsUKoqOjhUCXwWCgf//+7Nu3j65du/Luu+8ye/ZskpKS8Pb2xmQy4e/vLyIo1gqa\nycnJDtoZWq2Os66m+Ph4XnnlFZtUV1P33NChQ/n0009bRBxM0jQtpQJ7q/Jr/d43E7Lm4wbT3BqM\n5qAZ7W+//dZpHj04OJiwsDCRR7cuPtu4cSNBQUE2WgxaN8b58+fFLlzT3li2bBlpaWk8/vjjbNy4\nkb/85S/06NGDS5cuYTKZxPeaMGECWVlZ5OTk8N577/HSSy9x7NgxQkJC8Pb2FrUUFy9eFAqbubm5\nDrUGEyZMYM2aNbz22msipWOxWMjIyMBsNnP06FEGDx6Mt7c3Op0OVVXZuHEj9913H4sXL+buu++m\nsrJSGFnNWGoaH3q9nqioKLZs2cK9997LpEmTxBqsO0UyMjKIjY2ltLRUaJP4+/tz77338vnnn9Op\nUyfGjRvHmjVrcHd3Z+7cuaxZs4aIiAjKysqIiIjgv/7rv0RtB8CoUaPw9fUlKSmJI0eOMGnSJP71\nr38J3Q9Ns+PFF1+kpKREXLc777yTFStWMHnyZL7++msmTpzInDlzcHV1Ze7cuTzzzDNERESwc+dO\nDAYD+fn5+Pv7s3LlSj744AMyMzP54IMPGDNmDB4eHg6TK7UaFK24VKstcTZZNSMjg/DwcBv9FcDh\nnrMmMTGRmpqaRie1SuXOlsM6LeuMX6OjJ4tvby7anfOhKMoMRVG+UhSlQlGUM4qifKAoSu+2XldT\ntNZNr3W7OBN4AkSdRG5urk3xWXJyMj179gSw0WLQ2mS7dOlio71hXxjYsWNHsrOz6dOnj5joqhkU\ng8HAkiVL+Prrr9m5cyfDhg0TA8/MZjNlZWWi6+TcuXNMmzZN/N0aLYXw7bffipkr8fHx9OnTR7TC\nxsXFYbFYqKmpob6+XmiDJCQkcN999+Hq6ipEv5wpcGppn7KyMnGOxYsXc/LkSTp27Ehubi7FxcU8\n8sgjhIaG4unpSa9evThz5gzp6en4+/szZcoUHnvsMRYvXsz58+eZNWsW4eHhvP/+++j1epEOWbJk\nCRs2bBDnVRRFSG9rOiha14/RaGTv3r2MGDGCXr16cenSJS5dusTbb79NYmIijz76qBj//vrrr+Pn\n58d///d/8/7775ORkUGvXr1EFMa6AFRRFPLy8lizZg0+Pj4OAlh6vZ5Ro0aRnJxMTEwMCQkJKIrC\nihUrHISydu/eLWb7pKSkMH78eKZOncr48eM5dOgQO3fudLgffXx82L59O8nJyaIIVTve1YqD/Vq4\nns1KU6PRf22Oniy+vfloj2mXEGAZsJ/L638d2KooSj9VVavadGVOaC0hsIaGBlFAaO1A2ItEWSwW\nzGYzc+bMsSk+q6qqEv+v7XLz8vKIj48XtQ/28zu0CEJVVRVTpkwRKZt9+/axcuVK4P/mhtTX1+Pt\n7Y3FYhHCWRkZGTz//PMkJydTXV2NxWIhJiaGZcuWOQ2DWqdNNOfh0KFDYh2/+c1vGDFiBO+88w7n\nz58nKCiI5cuXCydj5cqVHDlyhJ9//hlFUYiJiRGD47y8vDh48CAxMTG8++67Numc/v37c+TIEVas\nWCE6VCZMmMD//u//4uXlxZIlS4So2IIFC4DLzlKnTp1Ecauqqg51D6dPn6Zbt24cPHiQQYMGiVoN\nVVXp3r07v/zyC6qqEhkZyZ49e4Rc/JAhQ8jLy7PRNtGwF2jTojPe3t4sWLCgyaFmmzZt4s0333QQ\nwNq7d68YVGcymRgxYgRffPGFOE5VVRUuLi5Nzvb58ssvqaiowNfX12a9PXr0YM+ePS0iDnarohWC\n5+bmOhR7N+f6WBeSnz59mvr6ehGh+rVKtMvi25uPdhf5UFV1hKqqq1VVPaaq6hEgCrgTGNi2K3OO\n9U3fVErEGfa7HusIyn/8x3/www8/ADikFvr06UNAQIDoDHF1dWXz5s020RFrYamCggJxruDgYO65\n5x6xC7ff2Y4dOxbAJuIyceJEXF1dWblyJU8//TRhYWHceeedQiujtraW/Px8iouLRYumn5+f0Kww\nmUxOQ/Fms1k4Ltq5ioqK0Ol0eHl5UVpaSlBQEBcuXBCG3loivaamhhkzZrBy5Uoh6hUfH0/fvn0x\nm814enqi0+koKysjPT2dsLAwvv32W2bMmIFOpyMtLY3y8nLhSNTV1eHv78+ePXtEq621c6EJmlmv\nX7uumsPj4uIi5M4zMzPJyckBLuuNaHUu3t7eQjbdaDRyzz334ObmxqVLlxwcNGdRr8DAQMrLy20i\nK0uXLiUtLY2YmBj0ej3V1dVi6mpjkuaKouDj48Nnn33GgAEDcHd3x9XVFQ8PD2pra21m+9jf2y+/\n/DJvvvmm0/taE95qrpT6r4nr3aFbf37lypWsX7+ekpISIiIiCA8PJyYm5lcp0X6jNJEkzafdOR9O\n6ACowIW2XogzrvambyxFc+rUKYeH0iOPPEJ+fr5wIDIyMggLCyM7O5uAgABRq5GVlUX37t1tDFdU\nVBTp6en89NNPvP766+Tk5Ih0SF1dHb/88ovQnrCu+3B3d6dz587A5Sms2jFdXV2ZNGkSf/zjH5k8\neTJpaWnU1NRQXV2Nn58fqampqKpKTk4Ow4YNo6GhQRzH3d1dGGLrUPy8efOAyyFiTc7dy8sLLy8v\nunTpgqenpzDwVVVVQvZdq1Wora0lODhY1IVoDoaWMjp9+jQNDQ24ubmxd+9ejh07JmaZ+Pr64u3t\nLULXqqri7+/PqVOneOutt+jdu7fo7NHWa13cqv2/5lQVFxfj7e3NwIEDKSsrE22sW7duZeLEiVRU\nVNCvXz/WrFlDXl6e+E2joqLYuHGjaOO11ymxlzDXfluz2Wzj0Fm/x1nIvancv7WzsHXrVnJycnjq\nqacane0Dl0P+zXmg/5pqDprD9WxWnH3eYDAQGxvL6tWrmTBhAg8//PCvztFraU0kScvQrp0P5fLd\ntBjIV1X1aFuvx56rvemb2vUMGzbM4aGkDQv7wx/+QFZWFrt37+b48eMOKpU6nc5BKVQ7/5///Gc2\nbNjA0aNHuXjxIiaTid27d/PHP/6R+fPn2+xsVVXF3d1dRET+9a9/iXRKZGQkISEh7N69m08++YS4\nuDh0Oh2TJ0/G09MTd3d3fv75Z/z9/Tlz5gw+Pj4iYqOJf2mGWBOy+vbbb3F3d2f16tVcuHDZt6yq\nqsJkMmGxWDh58iTp6ekiNVJQUCCOaTKZhDNlNpvR6XTs2bOH48ePExYWRvfu3TGbzRQUFKDX6wE4\ndOgQ9fX1wP9NYNXUP/Pz83F1deWee+4hPj6eTZs28fvf/14YeE3bw/oah4SEsHLlSnJzc/Hy8qK2\ntpbIyEgsFgvz589n3LhxzJs3j7S0NN599102b97MqFGjOHz4MEVFRSxcuJB9+/axaNEiXFxcHBRR\nnZ0TLqeAVqxYweLFi9m1a1eL1lZo99T06dNtxM6cvU8+0K+e692hN/X5kJCQX+UOXxbf3py0a+cD\nWA7cDTzX1gtxxtXe9E3tetzd3R0eKlpR5nfffceZM2dEesLZmHGj0WizE87IyCAqKkp0ocTExPCn\nP/1JyKFPmDCBY8eO2ZzTbDZTUVGB0Whk/vz5ohOiqKhI5E7d3d0pLS2lpKQEDw8PgoKC8Pb25p57\n7sHb25v6+nohstWnTx/y8vIoLy8nKipKGOKlS5eyatUqevToQXBwMCNHjqS6upr8/HwCAwOprq6m\ntraWfv368dVXX1FXV8fAgQOZP38+/v7+bN++nYSEBCorKzlz5gwRERH85S9/QVEUDhw4QHZ2Nvff\nfz933XUXq1evpkePHtTX1+Pl5SUiDlrLsHaNjxw5QllZGXv27BHdRTNnzhQRG5PJhE6ns3m4T5w4\nEQ8PDz799FN++uknBg8eTFFREStWrKCkpMTm2mqaG9999x0HDx6krq6O2267jQ8//JCXX35ZDLbT\numa0eyogIMBpysrf35/Jkyfz4YcfOkxF/eijj65756sp3coHestxvTt0ucNvHFl8e/PRbp0PRVGS\ngRHAw6qqnr7S++Pj43nyySdt/rdu3bpWX+egQYMaHSOem5vL4MGDbf69sa4VTVbbHi2s+rvf/Q5f\nX99G+9e1se3aTthZrcCECRM4fvy4MMQ9evSwOVZmZia1tbX06tWLw4cP06NHD7Kysrh06RIWi4WU\nlBRRf1FYWIi3tzdVVVWUlpayb98+brvtNoxGI2fOnKGqqoodO3awYMEC3N3dHQrBtF39+PHjyc7O\nxs3NTUR5tHTLjBkzALj//vs5cuQI8fHx+Pv78/e//52xY8dSXV3NhAkTmDZtGsOHD8fFxQWz2Sxa\ndy9dusSiRYvw9/fn/PnzlJWVERkZSVZWFvfcc49oGdZqJzIzM/Hw8ODgwYPi2qmqymeffcazzz7L\nyJEjWbt2rXAOtC6XkydP0r9/f+69916ysrI4evSoUHq1/y21Go0uXbpQUFDA9u3bhSCYpoiqyaS/\n+OKLFBUVsXjxYqfdI1u2bOGDDz4gJyeHTZs2ERISQk5ODiNHjmyRjitN6dYZ8oF+9VzvDr2ld/i3\nkpOSmJjo0N0lu6wcWbdunYOd1BSXW5r22O2iOR5PAUNVVT3RnM8kJSURGBjYugtzgqqqrFq1SkQw\nrCvOV61axfDhw8X7Gtu1WIfXmxJnGjx4MJs3b3b6Pk06fNSoUWRnZwthLmv0er2QQ9+5cyenTp2y\nOVZRURHdunXj3XffpUePHtTV1bF48WKee+45pk6dSvfu3amrqxNdDuXl5cTHx/O73/2OEydOUF1d\nTXh4OIWFhaLbRuuucfa9jEYjBw8eZN68eSQkJJCUlER6ejre3t507twZHx8fXF1dhVOhyYAfOXJE\nXGtfX1/REqrNZtEcB6PRKCbzenh4cObMGYqLi4XqZ0NDAykpKSxevBhfX19qamqoqqoS81QyMjIY\nN24c+/fv54knniA0NJSgoCCbDpPy8nIsFouQMn/22WcpLi7mxIkTTf6eNTU1Nn/Tdm7BwcGiC0n7\n/LZt2/jggw/Izs522j1SWVnJU0891eIdV4mJiTz55JOipfvX3E3RUlj/zvY0x6G73s9fb6fNzYqP\nj49T+X/ZZWXL6NGjGT16tM1rRUVFDBzY8v0c7c75UBRlOTAaeBIwK4rS7d9/uqiqanXbrcw5e/fu\nZcWKFWRlZTm0PK5cuZLExETA+awWawICAoRyqD35+fkMGjRIzPlo7OFTXFyMn58fOTk5DB061OFc\n2hoiIyMJDw+nX79+4lhaQaXFYqFTp040NDQQEBBAcXExtbW1VFdX8/jjj/Pdd9/h5+fHmTNnqKmp\nIVoent4AACAASURBVDw8HKPRyJgxYxg0aBBfffUVJpMJRVHw8vLivvvuE4Jezgonp06dSmVlpc21\n0eTKTSYTFy9eZP/+/fj4+AjjpxV+enh4iIhRRkYG0dHRZGRkiPOMGjWKqKgoOnbsyCuvvMLtt9/O\niy++yMsvv0x0dDSKcllWvqCggA0bNrBr1y4eeOABUTCqtSMvW7aMuLg4ABvFUK1ANCIiQqRV0tLS\n2L59O/fdd59otXX2e9obCWeGHhARji1btuDt7S1+R2taq81QPtBbnut16K7m8/b/zbWWLMDNglY4\nDVLh9Gag3TkfwGQud7fssnt9HJB1w1fTBFo0Q6up0F6zvumtJY6b2rX06dNHPBCcPVQeeOABxo4d\ny9tvv01WVpbNw0czoKtXrxbFmY2dy2g0snv3bgCxW6+qquKbb77h559/5k9/+hMlJSUMGDCAPn36\nkJ6eLgpLg4KCWL9+PS+//DITJ04Uc1UURUGn09GrVy8WL14sHANvb29mz54t5rXYG1yDwYDBYMBi\nseDi4sLkyZOZPHkyW7du5ezZs8yfP5/o6GgyMzNFS651lMi6qLW4uJjIyEhWrVol3rdx40YCAgIY\nMWIERqOR+Ph4pk2bxtdff83atWtF5KK6upodO3bg4+PDn//8ZzZv3kxeXp7owNGKcO3R0kfa2gwG\nA25ubsyYMUOcD7D5PfPy8ti4caODkWnM0D/00EM88MADPPHEE43uVFtzxod8oLcs1+vQXenzALNm\nzXIa2fg1aWHI+7TtaXfOh6qq7aZOxVk0w/qmt8/BNrVr2bJlC9u2bSM1NdXpQ+WJJ54gPDyc/fv3\nc/fdd3PgwAGSkpKAy0Zcq9W46667mDVrFtu2bePUqVMkJiYydOhQca4777yThQsXctddd+Ht7c3c\nuXOJiYlh6tSpAPTt25cdO3YwcuRIJk+ejIeHBx4eHmK9mqCZi4uLjbJpp06dSEtLIy4ujpSUFPR6\nPbW1tVRWVuLp6cmqVavQ6XQ233vbtm188803/O1vf2PRokVCZKympoaEhARSU1OZN28e7733npDt\nDgkJwWg0UlhYSG1tLYMHDyY/Px83NzcSEhLo3bu3eF9xcTGAgzjZsGHDhBjZwYMH0ev1DB8+nKee\neor4+HhycnKYN28eXbt2FfoqTaVQ6urqhKOnRUsURbEZ6qYZgvLycr766iunRsbe0JtMJrFT1QYA\n2u9Uvb29b9iMD/lAbxmu16Fr7PNXimzU1dWRmprq9JhyEJ2kpWl3zkd7w1rJEmwfBvbh9ebsepw9\nVKzrRVRV5e2336ampoaXXnrJZujX1q1bWbx4MS+99BIuLi689NJLDrv8Cxcu0L17d0pLS1HVy/NS\npk2bRnBwMAMHDiQ2NpbKykoSEhL43e9+x5///GdWrlwp1uLi4kJYWBhLly6loqICk8lEQkICo0eP\nZunSpaxbt04UjA4dOpTi4mJMJhMrV64kKyuL9PR0TCYTVVVV1NbW4uHhQUhICEuXLmXTpk2Eh4ez\nY8cOhg0bxpYtW1CUy6PlP//8c1asWAFcHuqWkJCAq6srf/jDH1i9ejW//PILr7zyiphZY33NFEWx\nUXTVxNoiIiKIjY21eUiPGTOGIUOG0KtXLzZt2iSKiRtLoeTl5eHp6cmGDRtsdEjAMUWjKAoJCQki\nfdIUiqI0e6faVDpPdqXc3LSkQ9jU/dLQ0EBWVtYNcVIlEmjH3S7tgcrKSnbv3s3y5cuZOXOmzfyL\nmTNnsmbNGocq62tRf7SOsBw+fJhLly7x8ssv2wz9UhSFw4cPM2PGDEpKSggPD2fYsGHExsayatUq\nli5dyurVq+nQoQMdO3a0USXVCjQNBgNwuf4kMjKSf/7zn2ImilZBXlFRQXFxMT4+PtTU1DB//nzC\nwsI4fvw4qqpy2223CcGs/v37U15eLuoyampqOHPmDOPHj8fNzY2ZM2fa1DFokQmDwSCkx1VVZdy4\ncTQ0NFBWVsYXX3xBfHy8mECrDcFzcXERn01KSqKkpISTJ086iJMBNnNg7Fuew8LChPOzaNEi1q5d\ny+9//3un80/y8vKE6uuWLVs4ffo0P//8s9MuAs3BuRpHoLmaELLNUAJX1gA5f/68bJ2W3DBk5KMV\nWbhwIX/5y19Yv349jz/+uENuPzk5ucnP2/+H3lQlemhoKPn5+ZjNZgwGg0MtR2lpKbm5uURHR5OU\nlMT48eNJSUkRM2CqqqoICAigY8eOmEwm/Pz8yMrKQqfTiXWUlpZy4cIFLBYL33zzDV27dhV/z83N\nJTQ0FFdXV/Ly8tDpdISGhlJYWMi5c+cICwtj69atnDp1CrgcEXrnnXeoq6sTsuddu3bl1Vdf5fDh\nwxgMBkJCQli0aBFms5mamhqCgoJQFEUIgGmaHMHBwXTr1o0xY8bw3XffUVpaik6no2PHjixZsoSM\njAybOStaxEFVVU6ePCm6b7RdnbO5NhpBQUGie0lzZNLS0jhy5Aiff/65TQrF398fX19fZs+eLZzK\npgqCr8YRuBpNB9mVImnu/dIS96ZE0hxk5KMFsd815Obmcvz4cSHmZb2LDg0NJTY29opyyRrO1E+T\nk5PFzIeYmBiys7Oprq6mQ4cODg+Zv/3tb/To0YOMjAw6d+4sJr+mpaUxe/ZsdDod27Zt41//+pcQ\n8po7d64YdgaXC1C1oslDhw5RXl6OyWSioaGBBQsWsGvXLsrKysQ5H3zwQZGGWbt2LQaDAZPJRNeu\nXUXXS0xMDOfPnyc8PJwzZ84QHBxMUVERHTp0QKfTcenSJebNm2ejc6IJgGnqo1rqY9iwYUInIzk5\nGQ8PD/R6PVFRUTZS6BpRUVGcOXOG1NRU/P39yc/PR1WdS5Zr6HQ6m0iPwWBg2rRpbNq0SUwKLisr\nw93dnQEDBvDZZ5/ZRK1aSm/g/7d35/FRVefjxz8ne2YSAiTs4FoBAYEErUgWVLB2UVxaQKokAWVf\nQlC2Cv22omJSy2YDSFASokWD2or1V7FgJYvgAgkQQKQWKxhMwEC2yT7n98fMXDMhQBImIYHn/XrN\nS3Lnzs05OTHzzLnPeU5jajo4bufl5uaeU3Csra9gEA3TkN+Xdu3aSS0M0WJk5uMSnW82Yv78+fj4\n+JCdnc2MGTPqfW1ERAQzZ840PnFf6H6q435tcHAwa9ascZqx6Ny5MytXruTdd9/lzjvvrLcmyOnT\npwkMDCQ7O5uCggJiY2MJCwsjPz+fiRMnsmDBAmM7+t69e/P++++zatUqbrnlFuPT0OnTpykvL8fH\nx4eOHTuSm5vL0qVLuemmmzhx4gTbt283NqSzWq1s2bIFpRRffvkl3bt3Jycnx9jIzc/PD29vb2P1\nSGhoKKmpqYCt3oijD8OHDyczM5OgoCCjT5MmTSImJgatNcuXLzeKn9VdNhwcHMyOHTtITU01SqHX\nzsswm82sXr2aZ555hn379rF3714WLlx40Zoqbm5u53xCdBR7S0tLIzc3l2effbbecXTl8tTG1HSQ\nVSniYr8vI0aMYP78+bJ0WrSIJgcfSqlrgGsBE3AKOKi1rnBVw9qCC2WPP/DAA1RWVp73U3RpaSlJ\nSUlG9cuKigoCAwPx8PDgzjvvPKeoT1paGvHx8fVuYZ6RkUFcXBxLlizBZDIxYMAApz8yVqsVf39/\n+vfvz5dffomnp6extfo///lPlixZYiRijhkzhjfffBNvb28jGXXu3LnU1NQYe6Uopfjhhx/w8PDg\n0KFDuLu7M3fuXMLDw5kwYQJVVVXk5+dz33338cYbb5CdnQ3YSn5XVlZSVlZGQECAMYtgtVpxc3Mz\nCo85bgFlZmYyffp09u/fT0hIiBE8OCqHJicn89prrxm1P+q+qUZFRREZGUlsbKzRP3Be2rpnzx4s\nFgs5OTlGkmZhYeEFa6qMGjWKN99807iNYbFY2LhxI7t378bNzQ2TycTixYvPW5jJVYFAU2+nSOBx\ndWrI74sEqaKlNCr4UEpdB0zDtpdKT6D2b2alUiodWA+8rbW2uqiNrdbFVhu89dZbFBQUnPM/sWM1\nxejRo9m3bx+RkZFGPkN9RX0c92uTk5ONREj48Y9DeHg4VquVuLg47rzzToKCgpxqfbi5uRmFvE6e\nPEn37t2JiYmhoqICX19fwsLCWLVqFePHjyc8PJyBAwcSHR1NYGCgURwrOTmZwsJCrr32Wq699lo+\n/PBDfH196datG3l5eYSHh2OxWDh79iyenp54enryl7/8xdh9VinFkiVLmDBhAtdffz2nTp3C39+f\npKQkoy+OHI7g4GD69Olj9MHb29tYwQIYiaPTpk0jPT2dZcuW4eXlZbw2KSnJmBmyWq3GH9qGLG19\n9tlnjT/S56up4nhTj4+PZ+rUqeTm5jJv3rxzVsY0pDDTpfxxlyJfojEa+/sigYdoTup89wDPOVGp\n1UAUsA14D/gMyAXKgI7AACAcW2BSA0zQWn/eDG1uFKVUCLBnz549Li+vHhERYcx41KW1ZurUqZw5\nc4ZZs2Y5TfcnJCQwaNAg9u3bx6BBg+qdBk1PTzdua4DtDbe6upqVK1eSnJzsdNslODiYqKgoFixY\nwObNmwkNDWXWrFkcPHjQyIcoLi6mS5cu3HrrrXz00UcMGDCAn//852zYsIG1a9fy2GOP8c477xhv\n0tu2bSMwMNCYWdBa88tf/pLAwEBuvfVWPv30U0pKSujcuTNlZWUkJiYSGxvLt99+i7u7Ox07dqS8\nvJzhw4eza9cufH19SUxM5L777qNDhw5YLBb69u3L8ePHuf322xk8eLBReGv06NFs2bKF0aNH89VX\nX7Fjxw7mzJnDkCFDjL7XTurs06cPb7/9NkFBQZSXlzN16lTjZzp79mxeeumlesfHsbR127Zt9Sb3\nxsfHk5aW5vRHev78+U5/pBcvXkzPnj0bNIbNTT6pisaQ3xfRELXKqw/RWu911XUbk3BaCtygtR6j\ntU7RWh/RWhdrrau11vla64+01n/UWt8MPAX0clUjW6OGZI+bzWb+9re/8fzzz5OWlmYUpHIsedu7\nd+8Fl0p+9NFHLF68mGHDhnHs2DG01k6Joi+99BKJiYkMGjSIuXPn4ubmRkJCAjExMWRnZ7Nt2zbm\nzJnD22+/TdeuXbFarTz44INorcnPzyckJITCwkJeffVVOnXqZBQE2759Oz169MBisRjBi1IKT09P\nSktL+fzzzwkMDKR9+/YEBwdTUlLCxo0bGT9+vHHu2bNn8fHx4b777iM/P99I6AwICDCKjjmSWR27\nte7du5fly5dz9OhRqqurWbduHdu2baNdu3bEx8ezZ88epk2bRmJiIqtWrSIyMpKioiKWLFnCdddd\nR1BQEFOnTjWSe5Wqf8t5RxsvtHywoUueL3ULdFeSNxLRGPL7Ii6nBgcfWutFWusfGnjuB1rrd5re\nrNavoasNUlJSePLJJ8nJyeHxxx9n7NixtG/fntLS0no3d3OwWCzk5ubSs2dPBg4cyKxZs/juu+8Y\nP368U/0Ji8XC559/zvfff8+3337LG2+8wdChQ9m9ezdPP/20kbfgWAEzY8YMunTpgqenJ3PmzDF2\noD116hQlJSXMmTMHLy8vCgsLMZvNvPDCC6SlpWG1WgkKCmLo0KFG34uLi42VJLt37yY0NBRPT08C\nAgKMJbvPP/888+fPN1aVlJWVsW7dOvLz81m9ejWVlZVOu7XGxsZy+PBh3N3dueuuu+jatSuff/45\nBw4c4OTJk8ZqjVmzZjmt1khJSeHw4cP1lou/1BoX5xujxix3FUII8SOXrnZRSvkAM7XWL7ryuq1V\nQ1Yb7Ny5k4SEBEaOHElCQgIDBw4kOTnZKdehdqVSi8VCUlISO3bsYN68eYSFhRnn1q3fUVpayuzZ\ns6moqGDevHkMHjzY2PrdZDIZ5yqlsFgslJWVsXDhQpKTkzl58iRz586lT58+PPHEEwwcOJBly5YR\nGRnJ+vXrOXXqFL179yY6OpqcnBxSUlI4c+YMR48exdPTk0GDBvHvf/+bvXv30qlTJ+P7tGvXjuLi\nYgIDA41luCNHjmTYsGEkJibywQcf8NVXX3H33Xezd+9eYzVLfbu1pqenGzMyF0uE69atG9dff/05\nx6Ojo42KprV/dq6ocVE7AJXqoUII0XCNrvOhlOqklLpPKfUzpZS7/ZinUioG+AZY6OI2tloXq9kw\nb948p0/GWVlZhIWFERwczKeffsrtt9/Ojh07SEhI4IknnmD69OmMGTOGgQMH0qFDB8LDw426E1lZ\nWcamcA5JSUl07drVuNWwYcMGioqK2LNnj1Otj9LSUgoKCqipqSEsLIwBAwagtSYkJIS33noLpRSL\nFi3i0KFDhIWFUV5ejpeXF3l5eQwdOhStbbuzenh44ObmhqenJ926daOsrIz4+HisViseHrY4trKy\nEnd3dwoKCvDz8zN2m3XUw7j33ntZt24d/fv357XXXqN3795GrY7aMwQXqi1Q35u5UoqqqqpzZhnM\nZjPPPvssKSkpPPDAA0RFRfHwww/z1ltv8frrr19yUqZUDxVCiMZrVPChlAoDjgJbgX8Cnyil+gEH\ngSnAH7jCcz1qu1jxpnbt2hmfjGsXr4qKigJgzJgxrFy5kt69ezN48GDy8vJYsGCBsVrGkbdgsVgw\nmUzGVu5gCyh27txJXl6ekXOQnp5Ov379qKys5LvvvjPOTUxM5MYbb8Tf3x+LxWIkbDpqegQEBGA2\nm+nevTsWi4XKykp8fHwoKytzyjFJSEjg5MmTlJeXk5iYiNaamJgYfH19+eGHH8jIyDBuvfj5+dGj\nRw9jCayDY7fbjz/+mOrqahITE8nLy+PFF1/kwQcf5PHHH2f69OlNKoBVXyBQWlrK4sWLGT9+PO++\n+y6bNm3inXfeYfTo0Tz66KMUFxdf0u+Aq4qGCSHE1aSxMx/PAv8PuAVYAdwG/A34nda6n9Z6nda6\nzMVtbNVqJyZ+8MEH5yQmOt4Qayc/KmXbZj01NZWYmBjefPNNBg0aRMeOHY1kydqBRkhICAUFBVRV\nVRkl1GfNmoWnp6cR0DgCnDlz5nD69GluvvlmMjMzKS0t5eOPPzaqkW7cuJEePXpQVFTE6NGjeeON\nNygqKqK0tJTc3FwSExOZMGGCsSlc7RyTLVu2MGDAAEpKSggKCsLX15eRI0cyYMAAZs6cSUpKCv37\n96e6utrYHM6RaOrgKOzVo0cPqqqq8Pb2pmfPnowbN47s7Gx2795Nenr6RfezqU99gYAjEbZuhVnH\nPi0NrTB7ofGX6qFCCNE4jc35uAWYrrU+pJRaAsQC87XW77q+aW1D3QqnZWVlDB8+3CgwVbuwj6Nw\n1q5du+jXrx+ffvop1dXVRu2O1NRUo2CV1Wo1ciGio6N577338PDwICUlhX/+85907dqV48ePO63m\n8PX1ZcuWLXh5eRETE8P06dO5/vrrMZvN+Pr60rlzZ3bv3o2Pjw+enp4cOXKErl270rVrV55//nn6\n9etHeno67u7uRmBTO08iKyuLlStX8sgjj9C9e3djX5ecnBxiYmIIDQ0lOTkZf39/YwO1SZMmERcX\nh9baCAAchdDee+89du/ebZQlv1T11TE4duzYeSvMumqbcCnMJIQQjdPYmY8OwGkA+wyHBchxdaPa\nCkeF06CgIPr160dRUREeHh68//773H777eTm5jp9Mj5w4ABxcXFkZGSwaNEiAHbt2mW8KZeUlDB7\n9myOHTtGdXW1Uy6E1prAwEBWrFjB119/TX5+PrfffjtdunQxZlZKSkqM+h+Ogl4HDx40Zl2eeeYZ\nampq8PHxMUqt5+XlsWjRIg4fPszChQvx8fEhKysLs9lMUFCQUzKsr68vgHE7qbKy0mlHWMeGbcnJ\nyYwcOZKZM2fy3nvvMWXKFF5//XUefvhhxo8fz0MPPcRzzz3Hjh07XBZ4ONSdiaovCdWhOVajSOAh\nhBAX15TVLv2UUl3t/1ZAH6WUufYJWuv9l9yyNiAuLo4HHniA1NRUIiMjnSpcZmRkcM8997B79278\n/f2ZP38+5eXl/P3vf8fX1xd/f388PT2BH9+wHJVIT548yeDBg7nrrrs4cOAAL774Ij4+Pnh4eODr\n60tQUBBubm5MmDCB2bNns3btWqNMuYeHB4WFhTzxxBOkpqbi4+PD0KFDOXnyJP/5z3+M4lyOyqFu\nbm6YzWa6deuGn58fFouFDh06cPr0aSPoqV0zY+PGjVgsFoYNG8bx48fZvn07+fn553zi//rrr1m4\ncKExG2K1WunZsycWi4V77rmH/fv307t372YdHzc3N1mNIoQQrVBTdrXdAWTbHybgH/Z/Z9X67xWv\nuLiYd999ly+//NK4bVI7pyA8PJzp06cTHx9Pbm4ut912Gx9++CFz5swxkjBDQkKcEjILCgrw8vJi\n0aJFPP3007zxxhucOHGCiooK/P39CQkJITMzk9OnT1NWVobJZGL16tXcdtttrFy5End3d/Ly8vD2\n9ubIkSNERkbi5+fHhAkT+P7771m7di3dunUjMDCQqqoqvv/+e8rKyvjmm284fvw4paWlVFVVcfbs\nWWpqaigrK3PK1wgODuazzz6jsrKSPn36kJeXx4oVK7jxxhudzqudXOuYDUlMTGT16tW88sorzJgx\nA7PZ3CL1L2Q1ihBCtD6NDT6uB26w/7fu44Za/72iFRcXc//999OuXTuys7PPW+EyIiKCjz76iBEj\nRtC7d2+mTZtGREQEVquV9PR0xowZY7zBOz6dl5aWOtXn+PnPf47JZKKqqoqoqChSUlKwWCx07tyZ\nzMxMzGYzkyZNMpbh9u7dG39/f2NZr6OIlyNIOXToEF988QVFRUVYLBY6duzI1KlTufnmm5k6dSr9\n+/ensLCQPn36YDabSUlJMW79OFbpmEwmtmzZQpcuXfjd737H008/7XTe+SqL1r6F01IzDrIaRQgh\nWp9G3XbRWv+vuRrSmtWdto+Li+ORRx5h48aNmEymC+YUnDlzBm9vb/Lz843N4yorK1mzZg3u7u70\n79+fdevWobXGy8vLSOJMSkoiOjqa0NBQ1q9fz6233mqUH3/00Uc5duwYa9euxWKxsGXLFtq3b091\ndTXffvstxcXFdOzYEaUUd9xxh7FD65w5c5g0aRLR0dF06tSJyspKsrOz8fHxoXv37tx333307duX\nyMhIjh49ipubG4mJiWzatMnYjK2oqIhevXoRFxfHxIkTz7tpW0FBAWlpafXOLLTkjINsviaEEK1P\nY3e13QTM0FoX278eBBzSWlc1R+Mup7qrWMrLy4mIiGDBggXs3LmTQb17ExISwu7duy+YU+BYburI\nmwDw9vZm6NChDBkyhD59+jBlyhQ++OADY0dYrTVZWVlMnz7dmA154oknmDNnDlprIyjYsGEDK1eu\nZOHChWzatAmAVatWERUVZeRrTJo0iZiYGLTW9O3bl2nTptGxY0dOnjyJUorAwEDc3NyMFStr1qzh\n6aefJiQkhJiYGPbs2eNUeXTNmjXs3r3bWD3j6JPj9orjPIvFwiOPPIKbm1ujtntvDrIaRQghWpfG\n3nZ5FPCt9XU6V2BRMccqlp49e5KQkMDy5ctJSEigZ8+e3H///bZNzzZu5O6776akpOS8m4ft3LmT\ndu3aUVZWZtyGsFqt+Pv7k5OTQ3BwMIsXL+bxxx/n9OnTeHh4UFVVRXp6upEz4ZCVlcWqVavIycnB\nZDJhsVg4dOgQHh4ehIeHM3jwYGPbepPJxNChQ43bMqtWrSIrK4vIyEhiY2MpKCjg5ptvBiAwMJDi\n4mLj+zlu1zhe9/rrrxub4jkKpBUWFpKZmXnBTdtMJhPdunVrdfUvJPAQQojLr7HBR92/3FfkX/K4\nuDjGjh17ThJpWFgYDzzwACeOHGEKkJWRwR133MH69evPySlIS0tj/fr1lJaWopQycjQcS2J9fX1J\nTk5mzJgxbN26lfHjx9OuXTvat2/Pa6+9ZhQZcyyxjYuLM2Yh3NzcmDhxIo899hjXXnstSinGjBnD\niRMn+M1vfoOvr6+xU2x6ejomkwlPT086depEWFgYnp6eLFq0CLPZTHl5OVarlTNnzjgtmwXbbMaK\nFSvIyclh8uTJzJo1i6ioKO69916ef/75cwqI1ZaRkcGIESMatDOsEEKIq4tLN5a7UqSlpZGQkHDO\nca01R44cIcBq5Y9a89j27Zz19+fll19m06ZNbNy4kZKSEqqqqujQoQO+vr7k5eVRVVVFbm4u//vf\n/7BarcabfVZWFlprIiMjCQ0NZfny5dTU1LBhwwaefPJJI2fi+++/JyYmxtjgLT8/n8WLFxMeHs6m\nTZvQWvP666/j5eXFkSNHjHLstfMwTp8+Tc+ePQHo1KkT/v7+eHl5MXjwYI4dO8bRo0edZjPqu51i\ntVqZOXMmgYGBxk69L7zwAlarlYiICOPWSnp6Olu2bHG6tSIzDkIIIRxcUeejr1LKr/YJbbnOR91t\n0ktLS0lKSjKKd3377bfc5OlJEFCdm4vXLbfg5+dHVFQU2dnZzJo1yynH4d5778XHx4dp06aRk5PD\n8uXLKSsrM25NZGdnG/VBKioqCAgIYM+ePSxdupSpU6caOR4jR47knnvuAeDBBx80VsQ4toxPS0uj\na9eu7Nu3D7DNPDiW+2qtmT17NmVltsr3jtoXISEh9OnTh6ysLLy8vHjxxRfp16+f8dq6MjMzz9mp\nd9KkSSQlJfHaa68ZuTFnz57ls88+kxkOIYQQ9WpK8LED59st/7D/V9uPa8D9Ett1XkqpcGAeMATo\nBjyotXZZ9mLtbdItFguxsbFERkYan/7HjR3LXQUFANxTUsLqo0fRWpOUlGTU+6jN19cXq9XKiBEj\nuOOOOzhw4ABubm788MMPaK2dKnD6+vri7e3Nc889R0BAABaLhW3btjntZuvIGXF8HR0dzZw5cwCo\nqanBZDLRsWNH1q1bB2AEQmVlZUZ5d0fA4thu/pFHHiEnJ4edO3eSlZVFVlYWCxcuZPjw4eckE4Qo\ndQAAIABJREFUir777rt8+umnTjMjjvLljhmTuXPn4ufnFI8KIYQQhsYGH9c3Sysax4ytmNkrwDvN\n8Q0chan27dtnbKxWWlrK+vXrKTl2jIetVgAeqq5mQ2kpaWlpxuqU2qxWKyaTycijSExMpEuXLhQW\nFmIymXBzczNyO0pLSzGZTPzhD39g0qRJeHl58fTTTxMeHs5DDz1kvLGfPn2as2fPGl878jKmTJli\nrL7x8PDAx8eHDz74wGnpq2P7+tGjRxu3a5YvX86mTZs4dOgQXbp0IT8/n1/96lccP378vEtTz1c1\n1BGoSNVQIYQQF9Lm6nxorT8APgBQzfAO98yCBbyXnIxbURE1NTX8t1s33nnhBfLz8tBac21NDTfZ\nz70J6OPhwdo5c/Dw8OCp++93utaJkhLOVFXRq29fSkpK2LlzJ4GBgRQWFrJkyRI2b95MXl4eGRkZ\n7NmzB601zz//PF26dKGkpMSYRenUqRMZGRmEhIQwdepUampqnG6N+Pn5UV1dTVRUFNu3b6dz584s\nX76c5ORko/R5+/btWb9+PZMnT+arr76iurqal156icrKSsxmM35+ftx1113Ghni1ft7nBBKO4Kzu\nLA9I1VAhhBAX19g6H0GAuXYQopTqDzyFbUbi71rrv7q2iS1r7pIl5P/vf1j/9S/iCwrw+9/54y0F\n/OPMGdsX1dVgP7cYeLJ9ewoDA8kvLKRLly78/ve/JygoCC8vL3x9fY1k0bVr1zJlyhSsVis/+9nP\n+PDDD+nVq5dTXZBnn32WiRMnMnDgQKO+RkpKCvDjbZU77riDPXv28PLLL/PEE09gMpmc6m446oUk\nJSWxY8cOrrvuOnx8fBg+fDjz5s2jXbt29fexnviu9k69l7uGhxBCiLansbddXgJygScBlFKdsdX6\nyAW+BpKUUu5a6xSXtrIFmc1m/vLGG3z04YeE/OpXrLFaGWm/zdIQ293c+EO3bkxYsoTsV16hndYc\nO3aMoqIiPD096dChg5HDERwczIEDB1BK0a1bN6Kiovjkk0+oqKigsLDQCBo6d+7Mq6++yuTJk+nV\nqxclJSW89NJL51Qe/de//sW8efO4++67jaqmgFN+xsCBAwkKCuKZZ55p8K2RurMfUjVUCCHEpWhs\n8DEUiK71dSRQAAzWWlcrpZ4CZgCtLviIjY0lICDA6di4ceMYN25cvdVMw8PDcf/JT3jRZGLj/v28\nXF3NhVIoi4Ep7u4UDBrEn9esQWtt3NIoKCjAbDbj6+tLt27dyMnJQWtNdHQ048ePx2w2U1NTg5+f\nHyUlJQwZMoT//e9/TrdWOnXqRM+ePSksLCQvL88px6SkpIS5c+cSGxvLwYMH2b9/Pzt27MBqtdab\nNLp169aLBh7FxcW88MILpKenn1Ph1d/fX6qGCiHEFWbz5s1s3rzZ6VhhYWGzfK/GBh9dgW9qfX03\n8I7Wutr+9VZgkQva5XIrVqwgJCTknOOOaqZjx44lISHBeKPOzMykqqqKMx4eHAwK4t7Tp8msrq7n\nyjY/9/Dgty++yMEjR5g1axZ5eXl06NCBwsJCvL29sVqteHp6GjvJbt++na+++oqamho6dOhA3759\n2b59OwD5+fn4+Picc2vl1KlTVFVV8dRTTzk9l5yczPjx4wkPD2fkyJGALSDZtGkTr7zyCt7e3nh7\nexMREcG77757wZmJ4uJinnnmGbZs2cL8+fPP+ZmMGjXqnNkNCTyEEKLtc3wgr23v3r0MGTLE5d+r\nsRVOi4D2tb7+KfBpra814H2pjWpJ56tmGhwcjNlsJjAwkJtuvpkb3C78o7pWKTp3705FRYUReAwd\nOhQ3NzfMZjMBAQH4+fnxwgsvoLUmLi6OEydO0K5dOywWC1FRUaxZswaTycSRI0fo1KkTK1as4MCB\nA0Z10SJ7EuzIkSOdntuxY8c5yZ9+fn5Mnz6dTZs24ebmRnh4ODt37uQ3v/kNERERLF68mOLiYqfX\nOAKxr776igULFhiFwxw/k7CwMMaMGUN8fLwLR0AIIcTVprHBx25gtlLKTSn1G8Af+KjW872B465q\nXH2UUmal1CCl1GD7oRvsXzdpj5mPP/6Y0NBQp2OlpaXExsYSHR3N0aNHCe7dmzGVlRe8ziNVVfz+\nySc5ffo0MTExeHl5ER0djb+/PyUlJVgsFsrKykhNTcXHx4cuXbrwq1/9Cg8PD86cOcOuXbsACA0N\nNTZscySNJiYmsmrVKq655hqjxoej8uj69evp0aPHeWcfLBYLJ0+erHefmlGjRjkFII5ALC8vr96V\nLGCbaTnfXjZCCCFEQzQ2+FgCjALKgDeBeK31mVrPPwLsdFHbzudWIAvYg22m5c/AXuCPjb1QUVER\nFovlnDfupKQkxo8fz8iRI+nRowc56emMqPX8h8Btnp78q9brRgKBSpGbm8tbb71FVVUVWmtKSkpw\nd3envLyczp078+mnn1JTU8PUqVMJDg6msLCQadOmsWbNGjp37szEiRM5deoUwcHBTvumuLm5UVNT\nQ0lJidNmbo4CYvVt8AawceNG5s2bV+8+NXVnMdLS0hg2bNg5m9rVppTC29v7vN9PCCGEuJhGBR/2\nsuk3A2OAYVrrJXVOeQOIc1HbzteGnVprN621e53HxMZeKz4+3ti8DWy5FpMnT2bbtm3Gm3VpaSke\nP/yACSgBJgcEMNlsxvPWW5nZoQNT2renBDABARUVFBcXExkZyZAhQ1i2bBlKKdzd3fHy8iIrK4ua\nmhq8vb0JDg4mNjYWk8nEyJEj6dixo1GhdPjw4fTu3ZuUlBSnDesGDx6M2Ww+ZzM3R8XS+uzevbve\nUungPIvhKCvv5uZ2wWBGiogJIYS4VI2d+UBrfVpr/a7W+tN6nntfa33MNU1rfmlpadx+++1kZmaS\nn59/zk6xAJ2DgrjrzBm2u7kR4uHB96GhDBoxAovFQveBA7lt2TJu9fbmQ+DuoiKKCwsJDQ2lsrKS\ngwcPMmzYMAoLC7FYLEyfPh2LxYLZbDaSRDt06ADYSqs7gohJkybx1ltvMXr0aPbv32/kfHzyySfk\n5+ezbNkyY5t7gKioKNauXcvHH398zs66bm5uDZrFqF1W/kLBjBQRE0IIcakaHHwopR5pxLm9lFKh\nFz/z8nF80ndsPR8bG2skWdb+5N+uooKt1dVM9PbmJ/fcw7fffceiRYuwWCycOXOGAYMHUxwYyPSA\nALa5udGuogKlFF988QW9evXC29ubjh07opTivffeo3///pSUlJCVlUVYWBhlZWWUlpby3XffERUV\nxaZNm9i7dy/Lly/n6NGjZGdn4+bmxjfffMMvfvEL9uzZw8MPP0xcXBwPPvggjz32GI899hjt2rXj\n66+/ZubMmcydO5eZM2dy8uRJTCZTg2cxHJVLo6Oj2bRpk9OsiyOYSU1NZf78+S0zSEIIIa5IjVlq\nO00p9X/ARuA9rfXh2k8qpQKAUOAx4B7gcZe1shk4Puk7tp7/7W9/e85OsWFhYXTs1o0DN93EopgY\nevfubWyatm7dOqKjo3n++edtpcnvvx+TlxffpqZitVrx9vamvLycAwcOUFNTg4eHB5GRkfTu3Zvx\n48fj7u5urKpZtmwZ/fr1Izs7mxUrVpCcnGwUDwMIDAwkNDSUuDjbHa2VK1eycuVK45aRW52VOLXr\nbixevLjBpdBrVy517PniWC1z6tQpHnroISkiJoQQ4pI1OPjQWg9XSo0CZgHLlFKlQB5QDnTAVgPk\nNJAEDNBa57m+ua7l+KQ/bNgwAgICnHaKjY2NRWvN+Fmz+ODRRwkODmbu3LkopSgpKWHLli14eHiQ\nk5PDNddcw4QJE5g9ezbeXbqQlpZGaWkpP/3pT8nJyaGmpgatNYMHD2bu3LnMmjWL9evXG4XGHnvs\nMVJSUpg7dy5aa6ZNm4ZSCqvVSkZGBvHx8bz++uvntL92Cfa6xx0aUwq9duXSN954A29vb7y8vAgP\nDz9nzxchhBCiqRq7sdxWYKt9j5cw4FrAF1vQkQVkaa0bXov8Mqv9xlxcXHzOTrEJCQksXbqULl26\nkJycTGRkJJ9//jmTJ09m2rRp7N27F19fX6ddXqOjo/nrX/9KdXU1ffv2ZceOHXTo0IHq6mrjGmFh\nYfznP/8hPT2d8PBwevTogZ+f3zmzHuXl5QQHB3PDDTc0eYv6xpZCl8qlQgghmltjK5wCtqRT4O8u\nbkuLq/3GXFlZ6VTOHGwrRZYsWcLy5cuNcua7du1i6tSphIWFkZqaSllZGYMHD2bZsmVER0cTFhbG\nsGHDmDlzJitWrOCWW27hxIkTxmoXR0n0SZMmERsb65To6ajdAT++8WutmTFjxiUFAU0NKCTwEEII\n0RwavdrlSuPv788zzzzDrl27iI+PN1aRJCUlYTKZCA4OBjByNL744gvCw8ON+hqDBw+mb9++HDp0\nyChWZjabGTRoEHPmzKFbt26cPn2arl27GtdwnOOoUlpQUEB6erpTuxznuXp1iQQUQgghLrcmzXwo\npc5gK/BVl8aWA/IfIElrvfES2tas6ttMbtSoUWzZsoWVK1dSU1ND9+7dmTt3Lo8//jgvv/wyVqsV\nLy8v4w08ODiYPn368Oabb9KpUyenN/acnBxiYmL42c9+xm9/+1umTJmC1Wp1mnlwzHRERUUxZ84c\ntNZGSXPZol4IIcSVqknBB7Zqok8DHwCf2Y/9FPg5kABcD6xVSnlorRMvuZUudqHN5Pbt28fnn3/O\n6NGj+eabb4iNjSU8PJzDhw+Tnp5OaWmpU35HbGwsY8aMYcOGDcbxkpISrFYrp06dYvHixZw6dQqT\nyUR+fj5paWnnzGSYzWbGjBnD3/72N1JTU2WLeiGEEFe0pgYfw4AlWut1tQ8qpaYAP9Na/1optR+Y\nDbS64KP2ZnJg28tl/fr1Rl2L4cOHU1xcjI+Pj3HO9OnTiYmJobq62sgNcdw6SUpKoqqqivT0dIYM\nGcLcuXMpLS1lwoQJLFy4kODgYJKTk/niiy/405/+ZHyP2jMcW7duNQINSfQUQghxJWtq8PFL4Hf1\nHN+Bba8VgP8HvNDE6zertLQ0EhISAFvgMXv2bCoqKoiNjTWWo65YsYLDhw87BQGOpNB169ZRXl7O\nkSNHyM7OxtfXF29vb5577jmGDBnC+PHjWbFiBQsXLiQkJITY2FgiIyONCqdJSUm88sorgG332Tvv\nvNNphkMCDyGEEFeypiacFgD313P8fvtzAGaguJ5zLitHZVPHG3xSUhJdu3Zl6tSpRiJpaWkpOTk5\n5OXlGRU+k5KSmDBhAhs2bMDT05O//OUvDBw4kJUrV9K3b198fHxwd3fnyJEjhIWFUVNTQ1hYGElJ\nScbyWscy3hkzZpCSksKkSZO48847Wbp0qdxaEUIIcdVoavCxFPiTUmqrUmqx/fEuEM+Pu8veQ/Pv\ncNtotZe2AmRlZZGXl2esVIEfA43aG69lZWURGhqK2Wymf//+PPnkkwwZMoSYmBhyc3MpLCxkwYIF\ndO/eHa01/v7+KKWM19UnPDxctqcXQghx1WlS8GFPIh0OlAIP2x8WYLjW+hX7OX/WWo91VUNdyVHZ\n1DELUnsmBH4MNDw8PEhMTCQtLc3YZr60tJRPPvmE8PBw1q9fT0VFBb/4xS/o2LGjsS8MQFFREVar\nVbanF0IIIepoas4HWutMoP6tT1u5BQsWcN9992G1WiktLeXs2bNORb0cAUNOTg7r1q1j06ZNnDhx\nAq01GzdupHPnziilSE9PJzY2ltDQUN58801jr5ZPPvmE6upqMjMzjU3q6gtAZHt6IYQQV6MmFxlT\nSrkrpX5d67bLQ0opd1c2rjlZrVa2bdtGXl4eN998s7GFvKN4WHFxMZWVlfj5+TF9+nRGjhxJRkYG\n2dnZ1NTUYLXaqsg7cjkcQYZjR9i+ffuybNkyOnfuTEZGRr1tkO3phRBCXI2aFHwopX4CHAY28eNt\nl9eAg0qpG13XvOYRFxfHo48+ynPPPUe3bt343e9+57SFfHBwMM8++6yxayxgBBXu7u7Grrdms9mY\ntRgwYADp6enG8ttrrrmGmpoa/vvf//Lcc8/x8ccfO21Pn56eLtvTCyGEuCo1deZjNfA10EtrHaK1\nDgGuAY7Zn2vV0tLSCA0NNW6xODZ1O3DgAJMnT+bAgQMcOnSIoUOHGjMiZrOZlStXcvbsWaKiokhJ\nScFisTgFFI5aISaTiTlz5vD2229z44034uXlxcqVK/n1r3/NE088wfTp08nNzZUCYkIIIa5KTc35\nGA4M1Vo7ltWitf5BKbWQVp4HorXG29vbSB797rvvKCkpITEx0dhfxWQy4e/vz4QJE4iNjTW2ozeb\nzYSHh5OVlcWKFSuIiYkxKpYePHjQyA+puyvtokWLmD9/vrFvjOR4CCGEuJo1NfioAOr7yO4HVDa9\nOc1PKcX3339vbB7Xu3dvJk2ahLu7O9OmTePIkSN88cUXnDlzBpPJdM4296Wlpfz73/9m1qxZrFy5\n0tiTxTGDUndXWgcfHx8JPIQQQgiaHnz8A1ivlHqcH/d2uR1YB7T6XdCqqqrIyMggKyuLAQMG4O7u\nzt13301qaiqRkZEA/Otf/zLKqNcNKD788EP+9Kc/cd1112EymUhISKCystIpuKhbGVVWtQghhBA2\nTc35mI0t52MXtl1sy4FPsO1mO8c1TWseWmu6d+9uJI/m5OSQn5/Pl19+aVQizc7OZvjw4bz88stG\nEqpDeno6ycnJjB07lp07d7Jjxw4OHz7MuHHjjPyQumRVixBCCPGjJs18aK3PAg/YV73cbD98WGv9\nH5e1rJkopaiurmbFihVMnDjRqNmRnZ3NjBkzjFsoU6ZMYfbs2XzwwQdOORydO3fm7NmzLF682Lge\n2GqHjBo1ysgPqb1pXGpqKlu3tvoJISGEEKJFNDj4UEotv8gpdzneiLXWcy+lUc0tIiLCmN3YvXs3\nXl5emEwmI5AoKyvDZDKxevVqkpOTyc/PN2Y/unfvzqlTp2jXrp3TNf39/dm6dSvx8fHMnDkTb29v\nKioqiIiIkFUtQgghRC2NmfkIbuB5zV4rXCk1A3gK6ArsA2ZprT9v6OsdsxSjRo1ix44d9OrVi+PH\njxs5G446HmFhYefke6SnpxMUFFTvdf39/Vm6dKnT+UIIIYRw1uDgQ2t9V3M2pKGUUmOBPwOTsSW7\nxgLblFK9tdanG3KN2rMUHTt2ZM+ePXh7exsJptHR0U5LbB1BhKMwWENuoUjgIYQQQtRPtbVNzZRS\nu4FPtdYx9q8VcBxYrbWOr+f8EGDPnj17CAkJqfeaRUVF/P73v+ftt99mwYIFDB8+HIvFQlJSErt3\n7wbAz8+PO++8k/nz58stFCGEEFeFvXv3MmTIEIAhWuu9rrpukzeWuxyUUp7AEOB5xzGttVZKbQfu\nuITr4ufnR69evVi7di2rV6+mY8eOeHp6ct999zFv3rxzcjyEEEII0TRtKvgAggB3IK/O8TygT1Mu\nWFxczKhRoxg7diwvv/zyOatUZKZDCCGEcK22Fnw0WWxsLAEBAU7Hxo0bx8GDBxk7dixhYWHGcaUU\n4eHhAMTHxxtJpEIIIcSVavPmzWzevNnpWGFhYbN8rzaV82G/7WIBfq213lrreBIQoLV+qJ7X1Jvz\nUVxcTFxcHG+++SbvvPMOtZYJO/175syZ7Ny5szm7JYQQQrRKkvMBaK2rlFJ7gBHYy7jbE05H0Ijd\ndB23WkaNGoWfn5+RXJqVlYWvry9lZWUEBwcTHR2Nt7e3LJsVQgghXKhNBR92y4EkexDiWGprApIa\neoG4uDjGjh3Lvn37qKmpITY2lsjISKZPn27kfGRmZhIbGwvIslkhhBDClZq6t8tlo7VOxVZg7Bkg\nCxgI3Ku1PtXQa6SlpREaGkpWVhZ+fn6MHz/eqZ6HUoqwsDAee+wxTCZTc3RDCCGEuGq1ueADQGu9\nRmt9ndbaV2t9h9b6i0a8Fh8fHwB8fX2pqqpySjatLTw8nLKyMtc0WgghhBBAGw0+LoVSCovFAthy\nP5RS572topSipqaGtpSUK4QQQrR2bTHn45IUFxdTUFBARkYGRUVFRnBRXwCiteaHH36QnA8hhBDC\nha66mY+4uDgmT55MSkoKlZWVAGRmZtZ7bkZGhrHaRQghhBCucdUFH2lpaYwYMYI///nPdOjQgQ4d\nOrBp0ybS09ONIENrTXp6OikpKbRr105mPoQQQggXuqpuuziSTZVS+Pv7U1JSwogRI+jbty8HDhxg\n06ZN+Pj4UF5eTnBwMA8//HCzVXcTQgghrlZXVfChlKK8vNzI8ejUqRN9+vQhNTWV8ePHM23aNOPc\n9PR0/vSnP7F///7L2GIhhBDiynNVBR8AERERZGZmEhYWxrPPPsvEiROZPXs2+/fvN2Y+zp49y6lT\np/joo49kUzkhhBDCxa664GPBggWMGjUKrTVhYWG8+uqrLFmyhLy8PPz8/Dh9+jTXXnst77//Pt27\nd7/czRVCCCGuOFdd8OHv78/WrVuJj49n5syZeHt74+Hhwbhx43jqqafO2flWCCGEEK511QUfYAtA\nli5dCiCbxgkhhBAt7KpbaluXBB5CCCFEy7rqgw8hhBBCtCwJPoQQQgjRoiT4EEIIIUSLkuBDCCGE\nEC1Kgg8hhBBCtCgJPoQQQgjRoiT4EEIIIUSLkuBDCCGEEC1Kgg8hhBBCtCgJPoQQQgjRoiT4EEII\nIUSLkuBDCCGEEC1Kgg8hhBBCtKg2FXwopX6nlMpUSpUqpQpcdV2ttasuJYQQQoiL8LjcDWgkTyAV\n2AVMvJQLFRcXExcXR1paGj4+PpSXlxMREcGCBQvw9/d3SWOFEEIIca42FXxorf8IoJSKupTrFBcX\nM2rUKMaOHUtCQgJKKbTWZGZmMmrUKLZu3SoBiBBCCNFM2tRtF1eJi4tj7NixhIWFoZQCQClFWFgY\nY8aMIT4+/jK3UAghhLhyXZXBR1paGqGhofU+FxYWRlpaWgu3SAghhLh6XPbbLkqpZcCCC5yigZu1\n1l9dyveJjY0lICAAgMOHDzN79mx+8Ytf8Mtf/rJue/D29kZrbcyKCCGEEFe6zZs3s3nzZqdjhYWF\nzfK9LnvwAbwIbLzIOf+91G+yYsUKQkJCAIiIiGD16tX1Bhdaa8rLyyXwEEIIcVUZN24c48aNczq2\nd+9ehgwZ4vLvddmDD631D8APLfk9IyIiyMzMJCws7JznMjIyGD58eEs2RwghhLiqXPbgozGUUr2A\njsC1gLtSapD9qf9orUsbep0FCxYwatQotNZG0qnWmoyMDFJTU9m6dWtzNF8IIYQQtLHgA3gGiKz1\n9V77f+8CGpwl6u/vz9atW4mPj2fmzJl4e3tTUVFBRESELLMVQgghmlmbCj601hOACa64lr+/P0uX\nLnVcV3I8hBBCiBZyVS61rUsCDyGEEKLlSPAhhBBCiBYlwYcQQgghWpQEH0IIIYRoURJ8CCGEEKJF\nSfAhhBBCiBYlwYcQQgghWpQEH0IIIYRoURJ8CCGEEKJFSfAhhBBCiBYlwYcQQgghWpQEH0IIIYRo\nURJ8CCGEEKJFSfAhhBBCiBYlwYcQQgghWpQEH0IIIYRoURJ8CCGEEKJFSfAhhBBCiBYlwYcQQggh\nWpQEH0IIIYRoURJ8CCGEEKJFSfAhhBBCiBYlwYcQQgghWpQEH0IIIYRoUW0m+FBKXauU2qCU+q9S\nyqKUOqqU+oNSyvNyt62lbd68+XI3wWWupL6A9Kc1u5L6AtKf1uxK6ktzaTPBB9AXUMAkoB8QC0wF\nnrucjbocrqRf7CupLyD9ac2upL6A9Kc1u5L60lw8LncDGkprvQ3YVuvQN0qpF7EFIPMvT6uEEEII\n0VhtaeajPu2BgsvdCCGEEEI0XJsNPpRSPwFmAusud1uEEEII0XCX/baLUmoZsOACp2jgZq31V7Ve\n0wP4J/Cm1vrVi3wLH4DDhw9falNbjcLCQvbu3Xu5m+ESV1JfQPrTml1JfQHpT2t2JfWl1nunjyuv\nq7TWrrxe4xugVCAQeJHT/qu1rraf3x34N/CJ1npCA67/W+D1S26oEEIIcfV6VGv9V1dd7LIHH41h\nn/H4CPgcGK8b0Hh7cHMv8A1Q3qwNFEIIIa4sPsB1wDat9Q+uumibCT7sMx47gWNANFDjeE5rnXeZ\nmiWEEEKIRrrsOR+NcA9wg/1x3H5MYcsJcb9cjRJCCCFE47SZmQ8hhBBCXBna7FJbIYQQQrRNEnwI\nIYQQokVdEcGHUmqGUuqYUqpMKbVbKXXbRc6/Uym1RylVrpT6SikV1VJtvZjG9EUpNVwpZa3zqFFK\ndW7JNp+PUipcKbVVKfWdvW2jGvCaVjk2je1LGxibRUqpz5RSRUqpPKXU35RSvRvwulY3Pk3pS2se\nH6XUVKXUPqVUof3xiVLq5xd5TasbF4fG9qc1j01dSqmF9vYtv8h5rXZ8amtIf1w1Pm0++FBKjQX+\nDPwfEAzsA7YppYLOc/51wD+AHcAgYBWwQSl1T0u090Ia2xc7DdwEdLU/ummt85u7rQ1kBrKB6dja\neUGteWxoZF/sWvPYhAMvAbcDIwFP4EOllO/5XtCKx6fRfbFrreNzHFvhxRBgCLbyAu8qpW6u7+RW\nPC4OjeqPXWsdG4P9g+FkbH+nL3TedbTu8QEa3h+7Sx8frXWbfgC7gVW1vlbACWD+ec6PA/bXObYZ\n+H9tsC/DsS05bne5296AvlmBURc5p9WOTRP60mbGxt7eIHu/wq6A8WlIX9ra+PwATGjL49KI/rT6\nsQH8gCPA3diKXi6/wLmtfnwa2R+XjE+bnvlQSnlii6R3OI5p209nO3DHeV421P58bdsucH6LaGJf\nwBagZCulcpVSHyqlhjVvS5tVqxybS9CWxqY9tk8zF9qosa2MT0P6Am1gfJRSbkqpRwATsOs8p7WV\ncWlof6D1j00C8J7W+qMGnNsWxqcx/QEXjE9bqvNRnyBsNT7qFhnLA/qc5zVdz3N+O6V6ovSMAAAG\nfUlEQVSUt9a6wrVNbLCm9OUkMAX4AvAGJgEfK6V+qrXObq6GNqPWOjZN0WbGRimlgJVAhtb60AVO\nbfXj04i+tOrxUUoNwPbm7AMUAw9prb88z+ltYVwa05/WPjaPAIOBWxv4klY9Pk3oj0vGp60HH1c1\nbdts76tah3YrpW4EYoFWmdB0tWhjY7MG6AeEXu6GuECD+tIGxudLbPkBAcBvgE1KqYgLvGG3dg3u\nT2seG6VUT2zB7UitddXlbIsrNKU/rhqfNn3bBTiN7d5TlzrHuwDfn+c135/n/KLLHIE2pS/1+Qz4\niasa1cJa69i4SqsbG6XUX4BfAndqrU9e5PRWPT6N7Et9Ws34aK2rtdb/1Vpnaa2fxpYEGHOe01v1\nuECj+1Of1jI2Q4BOwF6lVJVSqgpbDkSMUqrSPvNWV2sen6b0pz6NHp82HXzYI7U9wAjHMfsPawTw\nyXletqv2+XY/48L3H5tdE/tSn8HYpsXaolY5Ni7UqsbG/mb9AHCX1vrbBryk1Y5PE/pSn1Y1PnW4\nYZvirk+rHZcLuFB/6tNaxmY7cAu29gyyP74AXgMG2fP06mrN49OU/tSn8eNzubNsXZClOwawAJFA\nX+BlbJnUnezPLwOSa51/HbZ7jnHYcimmA5XYpp3aWl9igFHAjUB/bNNnVdg++bWGsTHbf5kHY1t9\nMMf+da82ODaN7UtrH5s1wBlsy1S71Hr41Drn+bYwPk3sS6sdH3tbw4FrgQH2361q4O7z/K61ynG5\nhP602rE5T/+cVoe0lf9vLqE/Lhmfy95RF/2wpgPfAGXYoslbaz23EfiozvkR2GYZyoCjwPjL3Yem\n9AWYZ29/KXAK20qZiMvdh1rtG47tjbqmzuPVtjY2je1LGxib+vpSA0Se7/ettY5PU/rSmscH2AD8\n1/4z/h74EPsbdVsal6b2pzWPzXn69xHOb9Ztanwa2x9XjY9sLCeEEEKIFtWmcz6EEEII0fZI8CGE\nEEKIFiXBhxBCCCFalAQfQgghhGhREnwIIYQQokVJ8CGEEEKIFiXBhxBCCCFalAQfQgghhGhREnwI\nIYQQokVJ8CGEaHZKqf9TSmW56lyl1L+VUstrfe2rlHpbKVWolKpRSrW71DYLIZqPx+VugBDiqtGY\nvRwudu5D2DazcogCQoGhwGmtdZFS6hiwQmu9unHNFEI0Nwk+hBANppTy1FpXXfzM5qW1Plvn0I3A\nYa314cvRHiFE48htFyHEedlvb7yklFqhlDoFfKCUClBKbVBK5dtvc2xXSg2s87qFSqnv7c9vAHzq\nPH+nUupTpVSJUuqMUipdKdWrzjmPKaWOKaXOKqU2K6XMddq13PFv4ElguP2Wy0f2Y9cCK5RSVqVU\nTfP8hIQQTSHBhxDiYiKBCmAYMBXYAgQC9wIhwF5gu1KqPYBSagzwf8BC4FbgJDDdcTGllDvwN+Df\nwABst0rW43yr5SfAA8AvgV8Bw+3Xq89DQCLwCdAVeNj+OAEssR/r1vTuCyFcTW67CCEu5qjWeiGA\nUioUuA3oXOv2y3yl1EPAb4ANQAyQqLVOsj+/RCk1EvC2f93O/nhfa/2N/diROt9TAVFaa4v9+6YA\nI7AFE0601meVUhagUmt9yriAbbajRGud3+SeCyGahcx8CCEuZk+tfw8C/IECpVSx4wFcB9xgP+dm\n4LM619jl+IfW+gyQDHyolNqqlJqtlOpa5/xvHIGH3Umg86V3RQjRGsjMhxDiYkpr/dsPyMV2G0TV\nOa9uEuh5aa0nKqVWAT8HxgLPKqVGaq0dQUvdpFaNfFgS4ooh/zMLIRpjL7Ycihqt9X/rPArs5xwG\nbq/zuqF1L6S13qe1jtNahwI5wG9d3NZKwN3F1xRCuIAEH0KIBtNab8d2C+XvSql7lFLXKqWGKaWe\nVUqF2E9bBUxUSkUrpW5SSv0R6O+4hlLqOqXU80qpoUqpa5RSPwNuAg65uLnfABFKqe5KqUAXX1sI\ncQnktosQ4kLqK/b1S+A54FWgE/A9kAbkAWitU5VSNwBx2JbYvg2swbY6BsAC9MW2iiYQWz7HS1rr\n9ZfYrrp+D6wDvga8kFkQIVoNpXVjig4KIYQQQlwaue0ihBBCiBYlwYcQQgghWpQEH0IIIYRoURJ8\nCCGEEKJFSfAhhBBCiBYlwYcQQgghWpQEH0IIIYRoURJ8CCGEEKJFSfAhhBBCiBYlwYcQQgghWpQE\nH0IIIYRoUf8f5LFYpFjSB5oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e758464a8>"
      ]
     },
     "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
}
