PTF_D_Fig_2_PTF_sel_r_cut.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making the 4-panel plot of uncorrected SF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 4-panel plot shows the problem :  how we can't get anything meaningful if we plot various characteristics of uncorrected data. Thus there is no reason to do splitting into magnitude bins, correcting, etc - it's not the purpose here. \n",
    "\n",
    "The desired magnitude range for the paper is 18.5-19 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'CRTS_paper_modules' from '/Users/chris/GradResearch/CRTS_PROJECT/code/CRTS_paper_modules.py'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from astroML.stats import median_sigmaG\n",
    "from scipy.stats import binned_statistic\n",
    "from scipy.optimize import curve_fit\n",
    "import CRTS_paper_modules as mod\n",
    "import imp\n",
    "\n",
    "from astropy.table import Table \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Grab the two PTF-SDSS catalogs ...\n",
    "\n",
    "# QSO \n",
    "address = '../data_products/PTF_SDSS_catalogs/PTF_SDSS_cross_matched_qso_DB9_catalog.dat'\n",
    "qso_cat = Table.read(address,format='ascii' )\n",
    "\n",
    "# stars \n",
    "address = '../data_products/PTF_SDSS_catalogs/PTF_SDSS_cross_matched_stars_catalog.dat'\n",
    "star_cat = Table.read(address,format='ascii' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Plot the histogram of QSO PTF population vs SDSS data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LzZo1S3aVEiZRwU16WmSbyIqkMrXgVJN69erRrl07Nm/ezLp160rsuCwi8ZWW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\nQRAEQdByhIETBEEQBEHL8X+cbAUx7cmhTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c23470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['ytick.labelsize'] = 20\n",
    "rcParams['xtick.labelsize'] = 20\n",
    "rcParams['axes.labelsize'] = 25\n",
    "rcParams['axes.linewidth'] = 2\n",
    "rcParams['font.size'] = 20\n",
    "\n",
    "%matplotlib inline\n",
    "x1 = 15.5\n",
    "x2 = 22\n",
    "\n",
    "fig,ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "nbins=np.arange(15,22.2,0.25)\n",
    "lw = 3 \n",
    "\n",
    "m = (qso_cat['r'] > x1) * (qso_cat['r'] < x2)\n",
    "#ax.hist(qso_cat['r'][m], histtype='step', label = 'SDSS r, DS9', bins=nbins)\n",
    "hist, bin_edges = np.histogram(qso_cat['r'][m], bins=nbins, density=False)\n",
    "bin_centres = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "ax.plot(bin_centres, hist, ls='steps',  label='SDSS r, DS9', lw=lw, alpha=0.4)\n",
    "\n",
    "\n",
    "\n",
    "m1 = (qso_cat['avg_mag'] > x1) * (qso_cat['avg_mag'] < x2) \n",
    "hist, bin_edges = np.histogram(qso_cat['avg_mag'][m1], bins=nbins, density=False)\n",
    "bin_centres = (bin_edges[1:] + bin_edges[1:])/2\n",
    "#ax.plot(bin_centres, hist, ls='steps',  label='PTF r, all epoch avg ', lw=lw, alpha=0.4)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "m2 = (qso_cat['avg_day_mag'] > x1 ) * ( qso_cat['avg_day_mag'] < x2 )\n",
    "hist, bin_edges = np.histogram(qso_cat['avg_day_mag'][m2], bins=nbins, density=False)\n",
    "bin_centres = nbins[:-1] #(bin_edges[:-1] )\n",
    "ax.plot(bin_centres, hist, ls='steps',  label='PTF r, avg day ', lw=lw)\n",
    "\n",
    "# shade area under the used histogram population.... \n",
    "ax.fill_between(bin_centres, 0, hist, where = (bin_centres >= 16.8)*(bin_centres<=18), step='pre', \n",
    "                color='magenta', alpha=0.5 )\n",
    "\n",
    "ax.fill_between(bin_centres, 0, hist, where = (bin_centres >= 17.9)*(bin_centres<=18.55), step='pre', \n",
    "                color='blue', alpha=0.5 )\n",
    "\n",
    "ax.fill_between(bin_centres, 0, hist, where = (bin_centres >= 18.35)*(bin_centres<=19.1), step='pre', \n",
    "                color='orange', alpha=0.5 )\n",
    "\n",
    "# the bin edges do not exactly align with sample boundaries... ignore that in my simple illustration...\n",
    "#m = ((bin_centres - 17) > 0 ) * ((bin_centres - 17) < 0.15)\n",
    "#ax.fill_between(x=[17, bin_centres[m][0]], y1=[0,0], y2 =  [hist[m][0], hist[m][0]],color='magenta', alpha=0.5 )\n",
    "\n",
    "#for x in [17,18,18.5,19] : \n",
    "#    ax.axvline(x, ymax=0.5,ls='--')\n",
    "ax.set_xlabel('PTF r magnitudes') # , fontsize=20 \n",
    "ax.set_ylabel('Counts') # , fontsize=20 \n",
    "\n",
    "ax.set_xlim(15.5,21.5)\n",
    "ticks = ax.get_xticks()\n",
    "ax.set_xticks(ticks[1:-1])\n",
    "\n",
    "plt.legend(loc='upper left', fontsize=20 )   \n",
    "#plt.title('Quasar population: magnitude selection', fontsize=15)\n",
    "plt.tight_layout()\n",
    "plt.savefig(outDir + 'Fig_2_Quasar_PTF_population_selection.png')\n",
    "#ax.set_xlim(16.5,18.5)\n",
    "#ax.set_ylim(0,50)\n",
    "#ax.fill_between?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#qso_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Read in $\\Delta m$, $\\Delta t$ PTF files , using mag cut 18.5-19, same as for Fig.2 based on CRTS data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using now only lightcurves with SDSS  18.500000< r < 19.000000\n",
      "\n",
      " Reporting SDSS r  \n",
      "\n",
      "Choosing stars with  SDSS   -1.00<g-i<1.00\n",
      " These cuts reduced the number of stars  in the sample from 15714 to 548\n",
      "\n",
      "Choosing stars with  SDSS   1.00<g-i<3.00\n",
      " These cuts reduced the number of stars  in the sample from 15714 to 913\n",
      "\n",
      " Returning only QSO with  an SDSS counterpart within 2.000000 arcsec\n",
      "\n",
      " These cuts reduced the number of qso  in the sample from 2753 to 377\n"
     ]
    }
   ],
   "source": [
    "imp.reload(mod)\n",
    "Min = 18.5\n",
    "Max = 19.0\n",
    "mag = 'r'  # use both for SDSS-based cut, as well as for reporting mag \n",
    "\n",
    "print('\\nUsing now only lightcurves with SDSS  %f< %s < %f' % (Min, mag, Max))\n",
    "print('\\n Reporting SDSS %s  '% mag)\n",
    "\n",
    "good_ids_S_blue = mod.cut_stars(star_cat = star_cat,mMin = Min, mMax=Max,\n",
    "                                mErrMax = 0.3, gi_Min = -1,\n",
    "                                gi_Max=1, cut_mag=mag + '_mMed', survey='PTF')\n",
    "\n",
    "good_ids_S_red = mod.cut_stars(star_cat = star_cat,mMin = Min, mMax=Max, \n",
    "                               mErrMax = 0.3, gi_Min = 1, \n",
    "                               gi_Max=3, cut_mag=mag + '_mMed', survey='PTF')\n",
    "\n",
    "good_ids_QSO   = mod.cut_qso(qso_cat=qso_cat, mMin = Min, mMax=Max, mErrMax = 0.3, \n",
    "                             cut_mag=mag, survey='PTF')\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the xi, ei for objects in the r_cut ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#good_ids_QSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#good_ids_S_blue.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Reading in tau,xi,ei  for 377 objects\n",
      "\n",
      "Using structure function master files from ../data_products/sf_file_per_LC_PTF/qso/\n",
      "[######### ] 99.47%\n",
      "Finished reading all master files for the selected objects ...\n",
      "\n",
      "Reading in tau,xi,ei  for 548 objects\n",
      "\n",
      "Using structure function master files from ../data_products/sf_file_per_LC_PTF/stars/\n",
      "[######### ] 99.45%\n",
      "Finished reading all master files for the selected objects ...\n",
      "\n",
      "Reading in tau,xi,ei  for 913 objects\n",
      "\n",
      "Using structure function master files from ../data_products/sf_file_per_LC_PTF/stars/\n",
      "[######### ] 99.67%\n",
      "Finished reading all master files for the selected objects ...\n"
     ]
    }
   ],
   "source": [
    "inDirStars   = '../data_products/sf_file_per_LC_PTF/stars/'\n",
    "inDirQSO = '../data_products/sf_file_per_LC_PTF/qso/'\n",
    "\n",
    "out_dic = {}\n",
    "out_dic['qso'] = mod.faster_read_xi_ei(inDirSF = inDirQSO,\n",
    "                                       good_ids = good_ids_QSO)\n",
    "out_dic['starsB'] = mod.faster_read_xi_ei(inDirSF = inDirStars,\n",
    "                                          good_ids = good_ids_S_blue )\n",
    "out_dic['starsR'] = mod.faster_read_xi_ei(inDirSF = inDirStars,\n",
    "                                          good_ids = good_ids_S_red )\n",
    "\n",
    "# Straight after reading-in xi, ei,   one can proceed directly to part 9) (one bin) or 10 : all bins sigma comparison \n",
    "# or to Saving just the log(tau) samples of xi, tau, ei. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qso': {'ei': array([ 0.06019909,  0.06090156,  0.04957953, ...,  0.07635634,\n",
       "          0.07635634,  0.0762709 ]),\n",
       "  'tau': array([  9.18100000e-01,   9.18900000e-01,   9.43000000e-01, ...,\n",
       "           1.91181780e+03,   1.92387700e+03,   1.93188700e+03]),\n",
       "  'xi': array([ 0.0621,  0.0723, -0.0443, ..., -0.114 , -0.0505, -0.0547])},\n",
       " 'starsB': {'ei': array([ 0.09134993,  0.06329423,  0.06329423, ...,  0.07680163,\n",
       "          0.0771295 ,  0.07694154]),\n",
       "  'tau': array([  8.65100000e-01,   9.18100000e-01,   9.18900000e-01, ...,\n",
       "           1.91181780e+03,   1.92387700e+03,   1.93188700e+03]),\n",
       "  'xi': array([ 0.1782,  0.1335, -0.0505, ..., -0.0464, -0.011 , -0.0213])},\n",
       " 'starsR': {'ei': array([ 0.20293092,  0.14103191,  0.08376139, ...,  0.05952655,\n",
       "          0.06060825,  0.06008236]),\n",
       "  'tau': array([  8.65100000e-01,   8.81700000e-01,   9.42900000e-01, ...,\n",
       "           1.91181780e+03,   1.92387700e+03,   1.93188700e+03]),\n",
       "  'xi': array([ 0.2328, -0.233 , -0.0209, ..., -0.0438, -0.0034, -0.0422])}}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_dic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate SF, mu in approximate and  full  ways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# for all imports of my functions, \n",
    "# make python aware of my packages...\n",
    "import sys\n",
    "sys.path.insert(0, '../../SDSS_S82_FP_research/packages/')\n",
    "import variabilityFunctions as varF\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The thing below takes a while (up to half hour...) because calculating $\\mu$, $\\sigma$ based on full posterior PDF is pretty slow ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using |dm| < 2.00\n",
      "\n",
      " For obj = qso\n",
      "Of 209946 xi points, we removed 3125 points\n",
      "\n",
      " For obj = starsB\n",
      "Of 281498 xi points, we removed 1051 points\n",
      "\n",
      " For obj = starsR\n",
      "Of 536209 xi points, we removed 4674 points\n"
     ]
    }
   ],
   "source": [
    "m_ij_cutoff = 2.0\n",
    "print('Using |dm| < %.2f'%m_ij_cutoff)\n",
    "for obj in out_dic.keys(): \n",
    "    print('\\n For obj = %s'% obj) \n",
    "    m_ij = out_dic[obj]['xi']\n",
    "    tau =  out_dic[obj]['tau']\n",
    "    e_ij = out_dic[obj]['ei']\n",
    "    \n",
    "    # do that and see what happens ... \n",
    "    mask_out = abs(m_ij)<m_ij_cutoff\n",
    "    print('Of %d xi points, we removed %d points'%(len(mask_out), np.sum(~mask_out)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  1.44537000e+05,   1.18175000e+05,   8.38550000e+04,\n",
       "          5.52770000e+04,   3.67150000e+04,   2.44630000e+04,\n",
       "          1.71480000e+04,   1.19760000e+04,   8.75000000e+03,\n",
       "          6.42500000e+03,   4.93300000e+03,   3.67400000e+03,\n",
       "          2.86500000e+03,   2.11200000e+03,   1.73900000e+03,\n",
       "          1.37600000e+03,   1.03900000e+03,   1.00400000e+03,\n",
       "          7.72000000e+02,   5.99000000e+02,   4.36000000e+02,\n",
       "          3.26000000e+02,   3.23000000e+02,   3.13000000e+02,\n",
       "          2.99000000e+02,   2.28000000e+02,   2.45000000e+02,\n",
       "          2.27000000e+02,   1.92000000e+02,   1.77000000e+02,\n",
       "          1.38000000e+02,   1.22000000e+02,   1.10000000e+02,\n",
       "          9.80000000e+01,   9.20000000e+01,   8.40000000e+01,\n",
       "          5.80000000e+01,   3.90000000e+01,   3.30000000e+01,\n",
       "          3.50000000e+01]),\n",
       " array([ 0.      ,  0.024975,  0.04995 ,  0.074925,  0.0999  ,  0.124875,\n",
       "         0.14985 ,  0.174825,  0.1998  ,  0.224775,  0.24975 ,  0.274725,\n",
       "         0.2997  ,  0.324675,  0.34965 ,  0.374625,  0.3996  ,  0.424575,\n",
       "         0.44955 ,  0.474525,  0.4995  ,  0.524475,  0.54945 ,  0.574425,\n",
       "         0.5994  ,  0.624375,  0.64935 ,  0.674325,  0.6993  ,  0.724275,\n",
       "         0.74925 ,  0.774225,  0.7992  ,  0.824175,  0.84915 ,  0.874125,\n",
       "         0.8991  ,  0.924075,  0.94905 ,  0.974025,  0.999   ]),\n",
       " <a list of 40 Patch objects>)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GucBWwJnD2vdskpkXZ+beoz4OSXqlGcVDJheW5f5tZa2e0AK6a5U/NcXtZ7KOJGkWGEXA\n/aos2588vLssf+u7rIiYB+wKvADcD5CZzwE/B14TEa/vso/dyrL9u7Oe+xhwHUnSLDCKgGs9cXh/\nW9mKsjy0y/b70zypuDozN/VZ550d20AzXu4hYFFE7NpnnZvK8pCI2OxaRcS2NAPGNwA/7NKeJGmE\nhhJwEbFHt7FhZRB2a3aRy9tWLQceA46LiL3btt8S+EL582sdzbU+6jy9fcqsso+TgU00D7oAkM0I\n61adL7YHVhnovR/NQO+b2+rcB9xA82DMyR37X0bTC72s9CglSbPIsKbqOhb4ZETcAjwIPEszDu0w\nYEvgGuCc1saZ+UxE/CeaoFsZEVfQTNV1BM2j+stppu+irc7qMpvIJ4A7ImI5zVRdxwKvA05pn8Wk\nOBc4nGZg+W0RcSPN2LhjaHpiH+wc7wb8Z5qpur4aEQcDPwX2oRkjtxY4fSoXSJI0XEOZqquMczsJ\n+Le8PEzgKeDHNOPiLssuO46IJTSB8XaaILwXuAT4ama+2GNf76fpXe0JvATcDnwpM7/XY/utgdOA\n42nC7RlgJXBGZt7Vo87OwOdpPg7dAXgEuBJY1j6LynicqkuSJm86U3UNbS5Kbc6Ak6TJq2EuSkmS\nBsqAkyRVyYCTJFXJgJMkVcmAkyRVyYCTJFXJgJMkVcmAkyRVyYCTJFXJgJMkVWlYky2rEhNNEeZU\nXpJmK3twkqQqGXCSpCoZcJKkKhlwkqQqGXCSpCoZcJKkKhlwkqQqGXCSpCoZcJKkKhlwkqQqGXCS\npCoZcJKkKhlwkqQqGXCSpCoZcJKkKhlwkqQqGXCSpCr5i96aFn/xW9JsZQ9OklQlA06SVCUDTpJU\nJQNOklQlA06SVCUDTpJUJQNOklQlA06SVCUDTpJUJWcy0VCNN9OJs5xIGiZ7cJKkKhlwkqQqGXCS\npCoZcJKkKhlwkqQqGXCSpCoZcJKkKjkOTiPjr4FLGiZ7cJKkKhlwkqQqGXCSpCr5HZxmLeexlDQd\n9uAkSVUy4CRJVTLgJElV8js4zUmOoZM0EXtwkqQq2YNTlezhSbIH16eIeGNEXBIRv4iITRGxLiLO\ni4jtR31skqTfFpk56mOY9SJiIbAa2BG4CvgZ8DZgKXA3sCQzH5+gjQSY6vWeqEeiwbF3J80eEQFA\nZsZk6/oRZX/+G024fTQzz28VRsS5wMeBs4CTRnRsGjA/3pTqYA9uAqX3di+wDliYmS+1rdsWeAQI\nYMfMfG6cduzByXCUJske3HAtLcsb2sMNIDOfjYhbgUOAfYEbZ/rgNLcM8x8qhqe0OQNuYruX5doe\n6++hCbhF9BFwrX+NSIMW/3XURyDNLj5FObEFZfl0j/Wt8td2WxkRJ0bEmoEflSRpXPbghiwzLwYu\nnm47EbEmM/cewCG9Ini9JsfrNTler8kbxTWzBzexVg9tQY/1rfKnZuBYJEl9MuAmdndZLuqxfrey\n7PUdnSRpBAy4id1UlodExGbXqwwTWAJsAH445OOY9secrzBer8nxek2O12vyZvyaOQ6uDxFxPc2T\nkr0Gel+UmQ70lqRZxIDrQ5epun4K7EMzRm4tsHiiqbokSTPLgOtTROwMfB44FNiBZgaTK4Flmfnk\nKI9NkvTbDDhJUpV8yGSIBvUTO1NpJyIWR8Q1EfFERDwfEXdExKkRscX0z2w4pnu9ImKHiPhwRFwZ\nEfeW8346Iv53RHyo8yGhUmcsInKc1xWDP9PBGMT9Ver0Ovd/GafenLu/YCD32PsnuF8yIl7sqDMn\n77GIODoizo+IVRHxTDnWy6fY1kjew+zBDUmX7+0m/RM7U20nIo4Evg1sBL4JPAG8i2baseWZecwA\nTnGgBnG9IuIk4Gs0Hx/fBDwE/Gvg39OMV/w2cEy23fQRMQY8APwE+E6XZu/MzOXTOLWhGOD9tY5m\nFp7zuqxen5nndKkz5+4vGNg99ofAUT1W7wccBFydmYe31Rljbt5jPwbeCqwHHgbeDHwjM0+YZDuj\new/LTF9DeAHXAwmc0lF+bim/cBjtANsBjwKbgL3byrcsN1kCx436+gzjetG8ubwLeFVH+U40YZfA\nf+hYN1bKLx31NRjR/bUOWDeJ/c7J+2uQ12yc9n9Q2jmikntsKc043wAOLOdw+bCv+yDvsZFfxBpf\nwMLyH+GBLm+229L8i+g5YJtBtwN8sNT5xy7tHVTW3TzqazSM6zXBPj5T9nF+R/mce/MZ5PWaQsDN\nuftrJu4xYK/S/sPAFnP9HutyflMKuFG/h/kd3HCM+xM7wK3A1jQ/sTPodg4qy+u6tHcLzaD0xREx\nf6KTmEGDul7j+X9l+UKP9b8XEX8WEZ8py7dMY1/DNujrNT8iTijn/rGIWDrO9xxz8f6C4d9jJ5bl\nP2Tmiz22mUv32KCM9D3MgBuOfn5iB3pP/zWddnrWycwXaP4lNQ940wT7nkmDul5dRcQ84D+WP7v9\nTwPwp8CFNL/OfiHwk4i4KSJ2mco+h2zQ12sn4DKacz8PWAHcExEHTGbfs/j+giHeYxGxFXAC8CLw\n9+NsOpfusUEZ6XuYATcc0/qJnWm2M6h9z6RhH/PZwB8A12Tm9R3rNgB/BfwxsH15HUDzkMqBwI0R\nsc0U9zssg7xeXwcOpgm5bWg+aruI5mO1ayPirUPc90wa5nG/p9S7LjP/ucv6uXiPDcpI38MMOFUt\nIj4KfJLmya33dq7PzEcz83OZeXtmPlVet9BMzXYb8PvAh2f0oGdQZi7LzBWZ+cvM3JCZd2Yz7dy5\nwFbAmaM9wjmh9fHkRd1WvtLvsVEy4IZjUD+xM5V25uLP+wzlmCPiI8BXgLuApZn5RL91y0chrY+b\n9p/MfmfATPw3vrAsO899Lt5fMLx77N8Ai2keLrlmMnVn+T02KCN9DzPghmNQP7EzlXZ61infRe1K\n86DF/RPseyYN/CeJIuJU4HzgTppw6zloeRy/KsvZ9vHRTPyEU69zn4v3FwzvmvXzcMl4Zus9Nigj\nfQ8z4IZjUD+xM5V2VpTloV3a25/miaXVmblpopOYQQP9SaKI+C/Al4Ef04Tbo1M8rtaTXbPtzXom\nfsKp17nPxfsLhnDNImJLmo+9XwT+YYrHNVvvsUEZ7XvYqMdX1PpiEoMbgd+hmSVg4XTaKeXb0fyr\ncE4NxB3g9fps2X4N8Lo+9vtHdIzPKeUH08yikDS/FjHyazTo6wXsQZdxXzQPmNxT2vlMDffXIO+x\ntm3eW+p9t8Z7rONYD2SccXCz9T3MqbqGpMv0ND1/YqdtKp8HM3Nsqu201TkKWE7zP88VNNPcHEGZ\n5gZ4T86y//CDuF4R8T7gUpp/UZ9P96ew1mXmpW11VtJ8TLKa5nsUgLfw8licz2bmF6Z9ggM2oOt1\nJs0DOLcADwLP0gzMPYzmzeQa4N2Z+euOfc+5+wsG9/9kW3urgH9HM3PJd8fZ70rm5j12FC9PS7YT\n8A6anuaqUvZYZn6qbDvGbHwPG/W/DGp+ATvTPIb9CPBrmjeR84DtO7Ybo/lXybrptNNRZwnNG9ST\nwPPA/6H5cdYtBnV+s+160TzxlxO8VnbU+RDwPZoZPdbT/KvxIZr57/Yb9TUZ8vU6APgfNE+YPkUz\nGP5XwPdpxg1GTffXIK5Z2/o9yvp/nuic5+o91sf/T+vatp2V72H24CRJVfIhE0lSlQw4SVKVDDhJ\nUpUMOElSlQw4SVKVDDhJUpUMOElSlQw4SVKVDDhJUpX+P/3LWCyihTtKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1151d50b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.abs(m_ij)[np.abs(m_ij) < 1 ], bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today we will save our Fig_2 files in /Users/chris/GradResearch/CRTS_PROJECT/data_products/PTF_Fig_2_data/2017-08-15/\n",
      "\n",
      " For obj = qso\n",
      " --- Calculating mu, sigma for bin 1\n",
      "N_points = 19159\n",
      " --- Calculating mu, sigma for bin 2\n",
      "N_points = 10163\n",
      " --- Calculating mu, sigma for bin 3\n",
      "N_points = 9809\n",
      " --- Calculating mu, sigma for bin 4\n",
      "N_points = 7550\n",
      " --- Calculating mu, sigma for bin 5\n",
      "N_points = 6094\n",
      " --- Calculating mu, sigma for bin 6\n",
      "N_points = 6343\n",
      " --- Calculating mu, sigma for bin 7\n",
      "N_points = 4705\n",
      " --- Calculating mu, sigma for bin 8\n",
      "N_points = 3222\n",
      " --- Calculating mu, sigma for bin 9\n",
      "N_points = 2813\n",
      " --- Calculating mu, sigma for bin 10\n",
      "N_points = 2085\n",
      " --- Calculating mu, sigma for bin 11\n",
      "N_points = 2840\n",
      " --- Calculating mu, sigma for bin 12\n",
      "N_points = 2352\n",
      " --- Calculating mu, sigma for bin 13\n",
      "N_points = 1787\n",
      " --- Calculating mu, sigma for bin 14\n",
      "N_points = 1253\n",
      " --- Calculating mu, sigma for bin 15\n",
      "N_points = 1392\n",
      " --- Calculating mu, sigma for bin 16\n",
      "N_points = 801\n",
      " --- Calculating mu, sigma for bin 17\n",
      "N_points = 1631\n",
      " --- Calculating mu, sigma for bin 18\n",
      "N_points = 1148\n",
      " --- Calculating mu, sigma for bin 19\n",
      "N_points = 696\n",
      " --- Calculating mu, sigma for bin 20\n",
      "N_points = 792\n",
      " --- Calculating mu, sigma for bin 21\n",
      "N_points = 869\n",
      " --- Calculating mu, sigma for bin 22\n",
      "N_points = 541\n",
      " --- Calculating mu, sigma for bin 23\n",
      "N_points = 1030\n",
      " --- Calculating mu, sigma for bin 24\n",
      "N_points = 858\n",
      " --- Calculating mu, sigma for bin 25\n",
      "N_points = 1187\n",
      " --- Calculating mu, sigma for bin 26\n",
      "N_points = 1373\n",
      " --- Calculating mu, sigma for bin 27\n",
      "N_points = 1309\n",
      " --- Calculating mu, sigma for bin 28\n",
      "N_points = 940\n",
      " --- Calculating mu, sigma for bin 29\n",
      "N_points = 1997\n",
      " --- Calculating mu, sigma for bin 30\n",
      "N_points = 1721\n",
      " --- Calculating mu, sigma for bin 31\n",
      "N_points = 2248\n",
      " --- Calculating mu, sigma for bin 32\n",
      "N_points = 3046\n",
      " --- Calculating mu, sigma for bin 33\n",
      "N_points = 3288\n",
      " --- Calculating mu, sigma for bin 34\n",
      "N_points = 2662\n",
      " --- Calculating mu, sigma for bin 35\n",
      "N_points = 3822\n",
      " --- Calculating mu, sigma for bin 36\n",
      "N_points = 2701\n",
      " --- Calculating mu, sigma for bin 37\n",
      "N_points = 3982\n",
      " --- Calculating mu, sigma for bin 38\n",
      "N_points = 4593\n",
      " --- Calculating mu, sigma for bin 39\n",
      "N_points = 3375\n",
      " --- Calculating mu, sigma for bin 40\n",
      "N_points = 2871\n",
      " --- Calculating mu, sigma for bin 41\n",
      "N_points = 2738\n",
      " --- Calculating mu, sigma for bin 42\n",
      "N_points = 3745\n",
      " --- Calculating mu, sigma for bin 43\n",
      "N_points = 3563\n",
      " --- Calculating mu, sigma for bin 44\n",
      "N_points = 2544\n",
      " --- Calculating mu, sigma for bin 45\n",
      "N_points = 2025\n",
      " --- Calculating mu, sigma for bin 46\n",
      "N_points = 1944\n",
      " --- Calculating mu, sigma for bin 47\n",
      "N_points = 1800\n",
      " --- Calculating mu, sigma for bin 48\n",
      "N_points = 2150\n",
      " --- Calculating mu, sigma for bin 49\n",
      "N_points = 3033\n",
      " --- Calculating mu, sigma for bin 50\n",
      "N_points = 1536\n",
      " --- Calculating mu, sigma for bin 51\n",
      "N_points = 1301\n",
      " --- Calculating mu, sigma for bin 52\n",
      "N_points = 1398\n",
      " --- Calculating mu, sigma for bin 53\n",
      "N_points = 1040\n",
      " --- Calculating mu, sigma for bin 54\n",
      "N_points = 1625\n",
      " --- Calculating mu, sigma for bin 55\n",
      "N_points = 1224\n",
      " --- Calculating mu, sigma for bin 56\n",
      "N_points = 536\n",
      " --- Calculating mu, sigma for bin 57\n",
      "N_points = 443\n",
      " --- Calculating mu, sigma for bin 58\n",
      "N_points = 466\n",
      " --- Calculating mu, sigma for bin 59\n",
      "N_points = 285\n",
      " --- Calculating mu, sigma for bin 60\n",
      "N_points = 480\n",
      " --- Calculating mu, sigma for bin 61\n",
      "N_points = 319\n",
      " --- Calculating mu, sigma for bin 62\n",
      "N_points = 304\n",
      " --- Calculating mu, sigma for bin 63\n",
      "N_points = 308\n",
      " --- Calculating mu, sigma for bin 64\n",
      "N_points = 376\n",
      " --- Calculating mu, sigma for bin 65\n",
      "N_points = 345\n",
      " --- Calculating mu, sigma for bin 66\n",
      "N_points = 574\n",
      " --- Calculating mu, sigma for bin 67\n",
      "N_points = 760\n",
      " --- Calculating mu, sigma for bin 68\n",
      "N_points = 924\n",
      " --- Calculating mu, sigma for bin 69\n",
      "N_points = 833\n",
      " --- Calculating mu, sigma for bin 70\n",
      "N_points = 1178\n",
      " --- Calculating mu, sigma for bin 71\n",
      "N_points = 1160\n",
      " --- Calculating mu, sigma for bin 72\n",
      "N_points = 1570\n",
      " --- Calculating mu, sigma for bin 73\n",
      "N_points = 930\n",
      " --- Calculating mu, sigma for bin 74\n",
      "N_points = 1237\n",
      " --- Calculating mu, sigma for bin 75\n",
      "N_points = 896\n",
      " --- Calculating mu, sigma for bin 76\n",
      "N_points = 794\n",
      " --- Calculating mu, sigma for bin 77\n",
      "N_points = 734\n",
      " --- Calculating mu, sigma for bin 78\n",
      "N_points = 749\n",
      " --- Calculating mu, sigma for bin 79\n",
      "N_points = 698\n",
      " --- Calculating mu, sigma for bin 80\n",
      "N_points = 877\n",
      " --- Calculating mu, sigma for bin 81\n",
      "N_points = 675\n",
      " --- Calculating mu, sigma for bin 82\n",
      "N_points = 466\n",
      " --- Calculating mu, sigma for bin 83\n",
      "N_points = 743\n",
      " --- Calculating mu, sigma for bin 84\n",
      "N_points = 693\n",
      " --- Calculating mu, sigma for bin 85\n",
      "N_points = 427\n",
      " --- Calculating mu, sigma for bin 86\n",
      "N_points = 667\n",
      " --- Calculating mu, sigma for bin 87\n",
      "N_points = 425\n",
      " --- Calculating mu, sigma for bin 88\n",
      "N_points = 324\n",
      " --- Calculating mu, sigma for bin 89\n",
      "N_points = 436\n",
      " --- Calculating mu, sigma for bin 90\n",
      "N_points = 240\n",
      " --- Calculating mu, sigma for bin 91\n",
      "N_points = 321\n",
      " --- Calculating mu, sigma for bin 92\n",
      "N_points = 431\n",
      " --- Calculating mu, sigma for bin 93\n",
      "N_points = 153\n",
      " --- Calculating mu, sigma for bin 94\n",
      "N_points = 163\n",
      " --- Calculating mu, sigma for bin 95\n",
      "N_points = 135\n",
      " --- Calculating mu, sigma for bin 96\n",
      "N_points = 119\n",
      " --- Calculating mu, sigma for bin 97\n",
      "N_points = 180\n",
      " --- Calculating mu, sigma for bin 98\n",
      "N_points = 149\n",
      " --- Calculating mu, sigma for bin 99\n",
      "N_points = 132\n",
      " --- Calculating mu, sigma for bin 100\n",
      "N_points = 110\n",
      " --- Calculating mu, sigma for bin 101\n",
      "N_points = 130\n",
      " --- Calculating mu, sigma for bin 102\n",
      "N_points = 196\n",
      " --- Calculating mu, sigma for bin 103\n",
      "N_points = 204\n",
      " --- Calculating mu, sigma for bin 104\n",
      "N_points = 162\n",
      " --- Calculating mu, sigma for bin 105\n",
      "N_points = 214\n",
      " --- Calculating mu, sigma for bin 106\n",
      "N_points = 301\n",
      " --- Calculating mu, sigma for bin 107\n",
      "N_points = 160\n",
      " --- Calculating mu, sigma for bin 108\n",
      "N_points = 301\n",
      " --- Calculating mu, sigma for bin 109\n",
      "N_points = 267\n",
      " --- Calculating mu, sigma for bin 110\n",
      "N_points = 177\n",
      " --- Calculating mu, sigma for bin 111\n",
      "N_points = 258\n",
      " --- Calculating mu, sigma for bin 112\n",
      "N_points = 318\n",
      " --- Calculating mu, sigma for bin 113\n",
      "N_points = 301\n",
      " --- Calculating mu, sigma for bin 114\n",
      "N_points = 192\n",
      " --- Calculating mu, sigma for bin 115\n",
      "N_points = 108\n",
      " --- Calculating mu, sigma for bin 116\n",
      "N_points = 178\n",
      " --- Calculating mu, sigma for bin 117\n",
      "N_points = 254\n",
      " --- Calculating mu, sigma for bin 118\n",
      "N_points = 296\n",
      " --- Calculating mu, sigma for bin 119\n",
      "N_points = 421\n",
      " --- Calculating mu, sigma for bin 120\n",
      "N_points = 249\n",
      " --- Calculating mu, sigma for bin 121\n",
      "N_points = 407\n",
      " --- Calculating mu, sigma for bin 122\n",
      "N_points = 197\n",
      " --- Calculating mu, sigma for bin 123\n",
      "N_points = 262\n",
      " --- Calculating mu, sigma for bin 124\n",
      "N_points = 341\n",
      " --- Calculating mu, sigma for bin 125\n",
      "N_points = 141\n",
      " --- Calculating mu, sigma for bin 126\n",
      "N_points = 59\n",
      " --- Calculating mu, sigma for bin 127\n",
      "N_points = 50\n",
      " --- Calculating mu, sigma for bin 128\n",
      "N_points = 49\n",
      " --- Calculating mu, sigma for bin 130\n",
      "N_points = 24\n",
      " --- Calculating mu, sigma for bin 131\n",
      "N_points = 7\n",
      " --- Calculating mu, sigma for bin 134\n",
      "N_points = 5\n",
      " --- Calculating mu, sigma for bin 137\n",
      "N_points = 41\n",
      " --- Calculating mu, sigma for bin 138\n",
      "N_points = 7\n",
      " --- Calculating mu, sigma for bin 139\n",
      "N_points = 101\n",
      " --- Calculating mu, sigma for bin 140\n",
      "N_points = 23\n",
      " --- Calculating mu, sigma for bin 141\n",
      "N_points = 78\n",
      " --- Calculating mu, sigma for bin 142\n",
      "N_points = 58\n",
      " --- Calculating mu, sigma for bin 143\n",
      "N_points = 295\n",
      " --- Calculating mu, sigma for bin 144\n",
      "N_points = 83\n",
      " --- Calculating mu, sigma for bin 145\n",
      "N_points = 325\n",
      " --- Calculating mu, sigma for bin 146\n",
      "N_points = 207\n",
      " --- Calculating mu, sigma for bin 147\n",
      "N_points = 69\n",
      " --- Calculating mu, sigma for bin 148\n",
      "N_points = 14\n",
      " --- Calculating mu, sigma for bin 149\n",
      "N_points = 250\n",
      " --- Calculating mu, sigma for bin 150\n",
      "N_points = 228\n",
      " --- Calculating mu, sigma for bin 151\n",
      "N_points = 207\n",
      " --- Calculating mu, sigma for bin 152\n",
      "N_points = 232\n",
      " --- Calculating mu, sigma for bin 153\n",
      "N_points = 555\n",
      " --- Calculating mu, sigma for bin 154\n",
      "N_points = 504\n",
      " --- Calculating mu, sigma for bin 155\n",
      "N_points = 805\n",
      " --- Calculating mu, sigma for bin 156\n",
      "N_points = 643\n",
      " --- Calculating mu, sigma for bin 157\n",
      "N_points = 240\n",
      " --- Calculating mu, sigma for bin 158\n",
      "N_points = 140\n",
      " --- Calculating mu, sigma for bin 159\n",
      "N_points = 351\n",
      " --- Calculating mu, sigma for bin 160\n",
      "N_points = 418\n",
      " --- Calculating mu, sigma for bin 161\n",
      "N_points = 545\n",
      " --- Calculating mu, sigma for bin 162\n",
      "N_points = 618\n",
      " --- Calculating mu, sigma for bin 163\n",
      "N_points = 310\n",
      " --- Calculating mu, sigma for bin 164\n",
      "N_points = 82\n",
      " --- Calculating mu, sigma for bin 165\n",
      "N_points = 27\n",
      " --- Calculating mu, sigma for bin 166\n",
      "N_points = 9\n",
      " --- Calculating mu, sigma for bin 167\n",
      "N_points = 6\n",
      " --- Calculating mu, sigma for bin 168\n",
      "N_points = 15\n",
      " --- Calculating mu, sigma for bin 169\n",
      "N_points = 11\n",
      " --- Calculating mu, sigma for bin 171\n",
      "N_points = 10\n",
      " --- Calculating mu, sigma for bin 172\n",
      "N_points = 10\n",
      " --- Calculating mu, sigma for bin 177\n",
      "N_points = 16\n",
      " --- Calculating mu, sigma for bin 178\n",
      "N_points = 48\n",
      " --- Calculating mu, sigma for bin 179\n",
      "N_points = 10\n",
      " --- Calculating mu, sigma for bin 180\n",
      "N_points = 79\n",
      " --- Calculating mu, sigma for bin 181\n",
      "N_points = 39\n",
      " --- Calculating mu, sigma for bin 182\n",
      "N_points = 90\n",
      " --- Calculating mu, sigma for bin 183\n",
      "N_points = 264\n",
      " --- Calculating mu, sigma for bin 184\n",
      "N_points = 66\n",
      " --- Calculating mu, sigma for bin 185\n",
      "N_points = 100\n",
      " --- Calculating mu, sigma for bin 187\n",
      "N_points = 159\n",
      " --- Calculating mu, sigma for bin 188\n",
      "N_points = 274\n",
      " --- Calculating mu, sigma for bin 189\n",
      "N_points = 180\n",
      " --- Calculating mu, sigma for bin 190\n",
      "N_points = 221\n",
      " --- Calculating mu, sigma for bin 191\n",
      "N_points = 235\n",
      " --- Calculating mu, sigma for bin 192\n",
      "N_points = 420\n",
      " --- Calculating mu, sigma for bin 193\n",
      "N_points = 884\n",
      " --- Calculating mu, sigma for bin 194\n",
      "N_points = 844\n",
      " --- Calculating mu, sigma for bin 195\n",
      "N_points = 461\n",
      " --- Calculating mu, sigma for bin 196\n",
      "N_points = 335\n",
      " --- Calculating mu, sigma for bin 197\n",
      "N_points = 308\n",
      " --- Calculating mu, sigma for bin 198\n",
      "N_points = 270\n",
      " --- Calculating mu, sigma for bin 199\n",
      "N_points = 110\n",
      " --- Calculating mu, sigma for bin 200\n",
      "N_points = 31\n",
      "Results saved as /Users/chris/GradResearch/CRTS_PROJECT/data_products/PTF_Fig_2_data/2017-08-15/18.5-19.0_qso_mij_lt_1.0.txt\n",
      "\n",
      " For obj = starsB\n",
      " --- Calculating mu, sigma for bin 1\n",
      "N_points = 34950\n",
      " --- Calculating mu, sigma for bin 2\n",
      "N_points = 19580\n",
      " --- Calculating mu, sigma for bin 3\n",
      "N_points = 13469\n",
      " --- Calculating mu, sigma for bin 4\n",
      "N_points = 9901\n",
      " --- Calculating mu, sigma for bin 5\n",
      "N_points = 7721\n",
      " --- Calculating mu, sigma for bin 6\n",
      "N_points = 7810\n",
      " --- Calculating mu, sigma for bin 7\n",
      "N_points = 6574\n",
      " --- Calculating mu, sigma for bin 8\n",
      "N_points = 4488\n",
      " --- Calculating mu, sigma for bin 9\n",
      "N_points = 2599\n",
      " --- Calculating mu, sigma for bin 10\n",
      "N_points = 2848\n",
      " --- Calculating mu, sigma for bin 11\n",
      "N_points = 3548\n",
      " --- Calculating mu, sigma for bin 12\n",
      "N_points = 2442\n",
      " --- Calculating mu, sigma for bin 13\n",
      "N_points = 1609\n",
      " --- Calculating mu, sigma for bin 14\n",
      "N_points = 706\n",
      " --- Calculating mu, sigma for bin 15\n",
      "N_points = 600\n",
      " --- Calculating mu, sigma for bin 16\n",
      "N_points = 322\n",
      " --- Calculating mu, sigma for bin 17\n",
      "N_points = 1407\n",
      " --- Calculating mu, sigma for bin 18\n",
      "N_points = 728\n",
      " --- Calculating mu, sigma for bin 19\n",
      "N_points = 266\n",
      " --- Calculating mu, sigma for bin 20\n",
      "N_points = 303\n",
      " --- Calculating mu, sigma for bin 21\n",
      "N_points = 385\n",
      " --- Calculating mu, sigma for bin 22\n",
      "N_points = 306\n",
      " --- Calculating mu, sigma for bin 23\n",
      "N_points = 660\n",
      " --- Calculating mu, sigma for bin 24\n",
      "N_points = 572\n",
      " --- Calculating mu, sigma for bin 25\n",
      "N_points = 680\n",
      " --- Calculating mu, sigma for bin 26\n",
      "N_points = 1028\n",
      " --- Calculating mu, sigma for bin 27\n",
      "N_points = 1170\n",
      " --- Calculating mu, sigma for bin 28\n",
      "N_points = 921\n",
      " --- Calculating mu, sigma for bin 29\n",
      "N_points = 2054\n",
      " --- Calculating mu, sigma for bin 30\n",
      "N_points = 1735\n",
      " --- Calculating mu, sigma for bin 31\n",
      "N_points = 2682\n",
      " --- Calculating mu, sigma for bin 32\n",
      "N_points = 3869\n",
      " --- Calculating mu, sigma for bin 33\n",
      "N_points = 4385\n",
      " --- Calculating mu, sigma for bin 34\n",
      "N_points = 3737\n",
      " --- Calculating mu, sigma for bin 35\n",
      "N_points = 5005\n",
      " --- Calculating mu, sigma for bin 36\n",
      "N_points = 4893\n",
      " --- Calculating mu, sigma for bin 37\n",
      "N_points = 5668\n",
      " --- Calculating mu, sigma for bin 38\n",
      "N_points = 5928\n",
      " --- Calculating mu, sigma for bin 39\n",
      "N_points = 5086\n",
      " --- Calculating mu, sigma for bin 40\n",
      "N_points = 3777\n",
      " --- Calculating mu, sigma for bin 41\n",
      "N_points = 3466\n",
      " --- Calculating mu, sigma for bin 42\n",
      "N_points = 3524\n",
      " --- Calculating mu, sigma for bin 43\n",
      "N_points = 3550\n",
      " --- Calculating mu, sigma for bin 44\n",
      "N_points = 2761\n",
      " --- Calculating mu, sigma for bin 45\n",
      "N_points = 2301\n",
      " --- Calculating mu, sigma for bin 46\n",
      "N_points = 1514\n",
      " --- Calculating mu, sigma for bin 47\n",
      "N_points = 1199\n",
      " --- Calculating mu, sigma for bin 48\n",
      "N_points = 1267\n",
      " --- Calculating mu, sigma for bin 49\n",
      "N_points = 1564\n",
      " --- Calculating mu, sigma for bin 50\n",
      "N_points = 786\n",
      " --- Calculating mu, sigma for bin 51\n",
      "N_points = 627\n",
      " --- Calculating mu, sigma for bin 52\n",
      "N_points = 464\n",
      " --- Calculating mu, sigma for bin 53\n",
      "N_points = 395\n",
      " --- Calculating mu, sigma for bin 54\n",
      "N_points = 602\n",
      " --- Calculating mu, sigma for bin 55\n",
      "N_points = 520\n",
      " --- Calculating mu, sigma for bin 56\n",
      "N_points = 201\n",
      " --- Calculating mu, sigma for bin 57\n",
      "N_points = 174\n",
      " --- Calculating mu, sigma for bin 58\n",
      "N_points = 220\n",
      " --- Calculating mu, sigma for bin 59\n",
      "N_points = 127\n",
      " --- Calculating mu, sigma for bin 60\n",
      "N_points = 164\n",
      " --- Calculating mu, sigma for bin 61\n",
      "N_points = 106\n",
      " --- Calculating mu, sigma for bin 62\n",
      "N_points = 85\n",
      " --- Calculating mu, sigma for bin 63\n",
      "N_points = 117\n",
      " --- Calculating mu, sigma for bin 64\n",
      "N_points = 301\n",
      " --- Calculating mu, sigma for bin 65\n",
      "N_points = 264\n",
      " --- Calculating mu, sigma for bin 66\n",
      "N_points = 314\n",
      " --- Calculating mu, sigma for bin 67\n",
      "N_points = 600\n",
      " --- Calculating mu, sigma for bin 68\n",
      "N_points = 624\n",
      " --- Calculating mu, sigma for bin 69\n",
      "N_points = 782\n",
      " --- Calculating mu, sigma for bin 70\n",
      "N_points = 1344\n",
      " --- Calculating mu, sigma for bin 71\n",
      "N_points = 1115\n",
      " --- Calculating mu, sigma for bin 72\n",
      "N_points = 1183\n",
      " --- Calculating mu, sigma for bin 73\n",
      "N_points = 1000\n",
      " --- Calculating mu, sigma for bin 74\n",
      "N_points = 1318\n",
      " --- Calculating mu, sigma for bin 75\n",
      "N_points = 1063\n",
      " --- Calculating mu, sigma for bin 76\n",
      "N_points = 1179\n",
      " --- Calculating mu, sigma for bin 77\n",
      "N_points = 819\n",
      " --- Calculating mu, sigma for bin 78\n",
      "N_points = 727\n",
      " --- Calculating mu, sigma for bin 79\n",
      "N_points = 575\n",
      " --- Calculating mu, sigma for bin 80\n",
      "N_points = 699\n",
      " --- Calculating mu, sigma for bin 81\n",
      "N_points = 663\n",
      " --- Calculating mu, sigma for bin 82\n",
      "N_points = 535\n",
      " --- Calculating mu, sigma for bin 83\n",
      "N_points = 391\n",
      " --- Calculating mu, sigma for bin 84\n",
      "N_points = 344\n",
      " --- Calculating mu, sigma for bin 85\n",
      "N_points = 528\n",
      " --- Calculating mu, sigma for bin 86\n",
      "N_points = 421\n",
      " --- Calculating mu, sigma for bin 87\n",
      "N_points = 107\n",
      " --- Calculating mu, sigma for bin 88\n",
      "N_points = 200\n",
      " --- Calculating mu, sigma for bin 89\n",
      "N_points = 289\n",
      " --- Calculating mu, sigma for bin 90\n",
      "N_points = 203\n",
      " --- Calculating mu, sigma for bin 91\n",
      "N_points = 148\n",
      " --- Calculating mu, sigma for bin 92\n",
      "N_points = 291\n",
      " --- Calculating mu, sigma for bin 93\n",
      "N_points = 79\n",
      " --- Calculating mu, sigma for bin 94\n",
      "N_points = 73\n",
      " --- Calculating mu, sigma for bin 95\n",
      "N_points = 411\n",
      " --- Calculating mu, sigma for bin 96\n",
      "N_points = 485\n",
      " --- Calculating mu, sigma for bin 97\n",
      "N_points = 203\n",
      " --- Calculating mu, sigma for bin 98\n",
      "N_points = 199\n",
      " --- Calculating mu, sigma for bin 99\n",
      "N_points = 41\n",
      " --- Calculating mu, sigma for bin 100\n",
      "N_points = 43\n",
      " --- Calculating mu, sigma for bin 101\n",
      "N_points = 44\n",
      " --- Calculating mu, sigma for bin 102\n",
      "N_points = 434\n",
      " --- Calculating mu, sigma for bin 103\n",
      "N_points = 116\n",
      " --- Calculating mu, sigma for bin 104\n",
      "N_points = 132\n",
      " --- Calculating mu, sigma for bin 105\n",
      "N_points = 170\n",
      " --- Calculating mu, sigma for bin 106\n",
      "N_points = 348\n",
      " --- Calculating mu, sigma for bin 107\n",
      "N_points = 222\n",
      " --- Calculating mu, sigma for bin 108\n",
      "N_points = 511\n",
      " --- Calculating mu, sigma for bin 109\n",
      "N_points = 181\n",
      " --- Calculating mu, sigma for bin 110\n",
      "N_points = 138\n",
      " --- Calculating mu, sigma for bin 111\n",
      "N_points = 594\n",
      " --- Calculating mu, sigma for bin 112\n",
      "N_points = 665\n",
      " --- Calculating mu, sigma for bin 113\n",
      "N_points = 578\n",
      " --- Calculating mu, sigma for bin 114\n",
      "N_points = 482\n",
      " --- Calculating mu, sigma for bin 115\n",
      "N_points = 80\n",
      " --- Calculating mu, sigma for bin 116\n",
      "N_points = 443\n",
      " --- Calculating mu, sigma for bin 117\n",
      "N_points = 797\n",
      " --- Calculating mu, sigma for bin 118\n",
      "N_points = 1097\n",
      " --- Calculating mu, sigma for bin 119\n",
      "N_points = 1239\n",
      " --- Calculating mu, sigma for bin 120\n",
      "N_points = 745\n",
      " --- Calculating mu, sigma for bin 121\n",
      "N_points = 1638\n",
      " --- Calculating mu, sigma for bin 122\n",
      "N_points = 1050\n",
      " --- Calculating mu, sigma for bin 123\n",
      "N_points = 774\n",
      " --- Calculating mu, sigma for bin 124\n",
      "N_points = 1542\n",
      " --- Calculating mu, sigma for bin 125\n",
      "N_points = 863\n",
      " --- Calculating mu, sigma for bin 126\n",
      "N_points = 286\n",
      " --- Calculating mu, sigma for bin 127\n",
      "N_points = 243\n",
      " --- Calculating mu, sigma for bin 128\n",
      "N_points = 373\n",
      " --- Calculating mu, sigma for bin 130\n",
      "N_points = 36\n",
      " --- Calculating mu, sigma for bin 131\n",
      "N_points = 198\n",
      " --- Calculating mu, sigma for bin 134\n",
      "N_points = 3\n",
      " --- Calculating mu, sigma for bin 137\n",
      "N_points = 74\n",
      " --- Calculating mu, sigma for bin 138\n",
      "N_points = 13\n",
      " --- Calculating mu, sigma for bin 139\n",
      "N_points = 180\n",
      " --- Calculating mu, sigma for bin 140\n",
      "N_points = 40\n",
      " --- Calculating mu, sigma for bin 141\n",
      "N_points = 134\n",
      " --- Calculating mu, sigma for bin 142\n",
      "N_points = 137\n",
      " --- Calculating mu, sigma for bin 143\n",
      "N_points = 704\n",
      " --- Calculating mu, sigma for bin 144\n",
      "N_points = 217\n",
      " --- Calculating mu, sigma for bin 145\n",
      "N_points = 809\n",
      " --- Calculating mu, sigma for bin 146\n",
      "N_points = 512\n",
      " --- Calculating mu, sigma for bin 147\n",
      "N_points = 175\n",
      " --- Calculating mu, sigma for bin 148\n",
      "N_points = 46\n",
      " --- Calculating mu, sigma for bin 149\n",
      "N_points = 861\n",
      " --- Calculating mu, sigma for bin 150\n",
      "N_points = 775\n",
      " --- Calculating mu, sigma for bin 151\n",
      "N_points = 562\n",
      " --- Calculating mu, sigma for bin 152\n",
      "N_points = 1052\n",
      " --- Calculating mu, sigma for bin 153\n",
      "N_points = 2353\n",
      " --- Calculating mu, sigma for bin 154\n",
      "N_points = 1902\n",
      " --- Calculating mu, sigma for bin 155\n",
      "N_points = 3098\n",
      " --- Calculating mu, sigma for bin 156\n",
      "N_points = 2899\n",
      " --- Calculating mu, sigma for bin 157\n",
      "N_points = 1096\n",
      " --- Calculating mu, sigma for bin 158\n",
      "N_points = 640\n",
      " --- Calculating mu, sigma for bin 159\n",
      "N_points = 1291\n",
      " --- Calculating mu, sigma for bin 160\n",
      "N_points = 1488\n",
      " --- Calculating mu, sigma for bin 161\n",
      "N_points = 1888\n",
      " --- Calculating mu, sigma for bin 162\n",
      "N_points = 2102\n",
      " --- Calculating mu, sigma for bin 163\n",
      "N_points = 978\n",
      " --- Calculating mu, sigma for bin 164\n",
      "N_points = 243\n",
      " --- Calculating mu, sigma for bin 165\n",
      "N_points = 66\n",
      " --- Calculating mu, sigma for bin 166\n",
      "N_points = 17\n",
      " --- Calculating mu, sigma for bin 167\n",
      "N_points = 13\n",
      " --- Calculating mu, sigma for bin 168\n",
      "N_points = 15\n",
      " --- Calculating mu, sigma for bin 169\n",
      "N_points = 8\n",
      " --- Calculating mu, sigma for bin 171\n",
      "N_points = 4\n",
      " --- Calculating mu, sigma for bin 172\n",
      "N_points = 4\n",
      " --- Calculating mu, sigma for bin 177\n",
      "N_points = 34\n",
      " --- Calculating mu, sigma for bin 178\n",
      "N_points = 91\n",
      " --- Calculating mu, sigma for bin 179\n",
      "N_points = 20\n",
      " --- Calculating mu, sigma for bin 180\n",
      "N_points = 167\n",
      " --- Calculating mu, sigma for bin 181\n",
      "N_points = 80\n",
      " --- Calculating mu, sigma for bin 182\n",
      "N_points = 194\n",
      " --- Calculating mu, sigma for bin 183\n",
      "N_points = 551\n",
      " --- Calculating mu, sigma for bin 184\n",
      "N_points = 138\n",
      " --- Calculating mu, sigma for bin 185\n",
      "N_points = 202\n",
      " --- Calculating mu, sigma for bin 187\n",
      "N_points = 412\n",
      " --- Calculating mu, sigma for bin 188\n",
      "N_points = 766\n",
      " --- Calculating mu, sigma for bin 189\n",
      "N_points = 521\n",
      " --- Calculating mu, sigma for bin 190\n",
      "N_points = 760\n",
      " --- Calculating mu, sigma for bin 191\n",
      "N_points = 762\n",
      " --- Calculating mu, sigma for bin 192\n",
      "N_points = 1396\n",
      " --- Calculating mu, sigma for bin 193\n",
      "N_points = 2892\n",
      " --- Calculating mu, sigma for bin 194\n",
      "N_points = 2571\n",
      " --- Calculating mu, sigma for bin 195\n",
      "N_points = 1362\n",
      " --- Calculating mu, sigma for bin 196\n",
      "N_points = 1016\n",
      " --- Calculating mu, sigma for bin 197\n",
      "N_points = 916\n",
      " --- Calculating mu, sigma for bin 198\n",
      "N_points = 827\n",
      " --- Calculating mu, sigma for bin 199\n",
      "N_points = 361\n",
      " --- Calculating mu, sigma for bin 200\n",
      "N_points = 71\n",
      "Results saved as /Users/chris/GradResearch/CRTS_PROJECT/data_products/PTF_Fig_2_data/2017-08-15/18.5-19.0_starsB_mij_lt_1.0.txt\n",
      "\n",
      " For obj = starsR\n",
      " --- Calculating mu, sigma for bin 1\n",
      "N_points = 52807\n",
      " --- Calculating mu, sigma for bin 2\n",
      "N_points = 29358\n",
      " --- Calculating mu, sigma for bin 3\n",
      "N_points = 24048\n",
      " --- Calculating mu, sigma for bin 4\n",
      "N_points = 18391\n",
      " --- Calculating mu, sigma for bin 5\n",
      "N_points = 15025\n",
      " --- Calculating mu, sigma for bin 6\n",
      "N_points = 15292\n",
      " --- Calculating mu, sigma for bin 7\n",
      "N_points = 11688\n",
      " --- Calculating mu, sigma for bin 8\n",
      "N_points = 8471\n",
      " --- Calculating mu, sigma for bin 9\n",
      "N_points = 6644\n",
      " --- Calculating mu, sigma for bin 10\n",
      "N_points = 5340\n",
      " --- Calculating mu, sigma for bin 11\n",
      "N_points = 6919\n",
      " --- Calculating mu, sigma for bin 12\n",
      "N_points = 5408\n",
      " --- Calculating mu, sigma for bin 13\n",
      "N_points = 3871\n",
      " --- Calculating mu, sigma for bin 14\n",
      "N_points = 2598\n",
      " --- Calculating mu, sigma for bin 15\n",
      "N_points = 2660\n",
      " --- Calculating mu, sigma for bin 16\n",
      "N_points = 1642\n",
      " --- Calculating mu, sigma for bin 17\n",
      "N_points = 3553\n",
      " --- Calculating mu, sigma for bin 18\n",
      "N_points = 2298\n",
      " --- Calculating mu, sigma for bin 19\n",
      "N_points = 1244\n",
      " --- Calculating mu, sigma for bin 20\n",
      "N_points = 1503\n",
      " --- Calculating mu, sigma for bin 21\n",
      "N_points = 1782\n",
      " --- Calculating mu, sigma for bin 22\n",
      "N_points = 1207\n",
      " --- Calculating mu, sigma for bin 23\n",
      "N_points = 2444\n",
      " --- Calculating mu, sigma for bin 24\n",
      "N_points = 2008\n",
      " --- Calculating mu, sigma for bin 25\n",
      "N_points = 2623\n",
      " --- Calculating mu, sigma for bin 26\n",
      "N_points = 3176\n",
      " --- Calculating mu, sigma for bin 27\n",
      "N_points = 3044\n",
      " --- Calculating mu, sigma for bin 28\n",
      "N_points = 2402\n",
      " --- Calculating mu, sigma for bin 29\n",
      "N_points = 4717\n",
      " --- Calculating mu, sigma for bin 30\n",
      "N_points = 4297\n",
      " --- Calculating mu, sigma for bin 31\n",
      "N_points = 5774\n",
      " --- Calculating mu, sigma for bin 32\n",
      "N_points = 7645\n",
      " --- Calculating mu, sigma for bin 33\n",
      "N_points = 8398\n",
      " --- Calculating mu, sigma for bin 34\n",
      "N_points = 7348\n",
      " --- Calculating mu, sigma for bin 35\n",
      "N_points = 9532\n",
      " --- Calculating mu, sigma for bin 36\n",
      "N_points = 7908\n",
      " --- Calculating mu, sigma for bin 37\n",
      "N_points = 10679\n",
      " --- Calculating mu, sigma for bin 38\n",
      "N_points = 11598\n",
      " --- Calculating mu, sigma for bin 39\n",
      "N_points = 9045\n",
      " --- Calculating mu, sigma for bin 40\n",
      "N_points = 7338\n",
      " --- Calculating mu, sigma for bin 41\n",
      "N_points = 6876\n",
      " --- Calculating mu, sigma for bin 42\n",
      "N_points = 7826\n",
      " --- Calculating mu, sigma for bin 43\n",
      "N_points = 7835\n",
      " --- Calculating mu, sigma for bin 44\n",
      "N_points = 5860\n",
      " --- Calculating mu, sigma for bin 45\n",
      "N_points = 4890\n",
      " --- Calculating mu, sigma for bin 46\n",
      "N_points = 4356\n",
      " --- Calculating mu, sigma for bin 47\n",
      "N_points = 3905\n",
      " --- Calculating mu, sigma for bin 48\n",
      "N_points = 4240\n",
      " --- Calculating mu, sigma for bin 49\n",
      "N_points = 5522\n",
      " --- Calculating mu, sigma for bin 50\n",
      "N_points = 2921\n",
      " --- Calculating mu, sigma for bin 51\n",
      "N_points = 2602\n",
      " --- Calculating mu, sigma for bin 52\n",
      "N_points = 2563\n",
      " --- Calculating mu, sigma for bin 53\n",
      "N_points = 1999\n",
      " --- Calculating mu, sigma for bin 54\n",
      "N_points = 3026\n",
      " --- Calculating mu, sigma for bin 55\n",
      "N_points = 2447\n",
      " --- Calculating mu, sigma for bin 56\n",
      "N_points = 1001\n",
      " --- Calculating mu, sigma for bin 57\n",
      "N_points = 838\n",
      " --- Calculating mu, sigma for bin 58\n",
      "N_points = 1058\n",
      " --- Calculating mu, sigma for bin 59\n",
      "N_points = 645\n",
      " --- Calculating mu, sigma for bin 60\n",
      "N_points = 946\n",
      " --- Calculating mu, sigma for bin 61\n",
      "N_points = 632\n",
      " --- Calculating mu, sigma for bin 62\n",
      "N_points = 486\n",
      " --- Calculating mu, sigma for bin 63\n",
      "N_points = 639\n",
      " --- Calculating mu, sigma for bin 64\n",
      "N_points = 897\n",
      " --- Calculating mu, sigma for bin 65\n",
      "N_points = 845\n",
      " --- Calculating mu, sigma for bin 66\n",
      "N_points = 1234\n",
      " --- Calculating mu, sigma for bin 67\n",
      "N_points = 1765\n",
      " --- Calculating mu, sigma for bin 68\n",
      "N_points = 2023\n",
      " --- Calculating mu, sigma for bin 69\n",
      "N_points = 2022\n",
      " --- Calculating mu, sigma for bin 70\n",
      "N_points = 2852\n",
      " --- Calculating mu, sigma for bin 71\n",
      "N_points = 2903\n",
      " --- Calculating mu, sigma for bin 72\n",
      "N_points = 3495\n",
      " --- Calculating mu, sigma for bin 73\n",
      "N_points = 2091\n",
      " --- Calculating mu, sigma for bin 74\n",
      "N_points = 2978\n",
      " --- Calculating mu, sigma for bin 75\n",
      "N_points = 2188\n",
      " --- Calculating mu, sigma for bin 76\n",
      "N_points = 2098\n",
      " --- Calculating mu, sigma for bin 77\n",
      "N_points = 1836\n",
      " --- Calculating mu, sigma for bin 78\n",
      "N_points = 1757\n",
      " --- Calculating mu, sigma for bin 79\n",
      "N_points = 1463\n",
      " --- Calculating mu, sigma for bin 80\n",
      "N_points = 1858\n",
      " --- Calculating mu, sigma for bin 81\n",
      "N_points = 1536\n",
      " --- Calculating mu, sigma for bin 82\n",
      "N_points = 1079\n",
      " --- Calculating mu, sigma for bin 83\n",
      "N_points = 1460\n",
      " --- Calculating mu, sigma for bin 84\n",
      "N_points = 1331\n",
      " --- Calculating mu, sigma for bin 85\n",
      "N_points = 925\n",
      " --- Calculating mu, sigma for bin 86\n",
      "N_points = 1216\n",
      " --- Calculating mu, sigma for bin 87\n",
      "N_points = 658\n",
      " --- Calculating mu, sigma for bin 88\n",
      "N_points = 670\n",
      " --- Calculating mu, sigma for bin 89\n",
      "N_points = 862\n",
      " --- Calculating mu, sigma for bin 90\n",
      "N_points = 509\n",
      " --- Calculating mu, sigma for bin 91\n",
      "N_points = 534\n",
      " --- Calculating mu, sigma for bin 92\n",
      "N_points = 741\n",
      " --- Calculating mu, sigma for bin 93\n",
      "N_points = 258\n",
      " --- Calculating mu, sigma for bin 94\n",
      "N_points = 380\n",
      " --- Calculating mu, sigma for bin 95\n",
      "N_points = 426\n",
      " --- Calculating mu, sigma for bin 96\n",
      "N_points = 438\n",
      " --- Calculating mu, sigma for bin 97\n",
      "N_points = 460\n",
      " --- Calculating mu, sigma for bin 98\n",
      "N_points = 347\n",
      " --- Calculating mu, sigma for bin 99\n",
      "N_points = 226\n",
      " --- Calculating mu, sigma for bin 100\n",
      "N_points = 232\n",
      " --- Calculating mu, sigma for bin 101\n",
      "N_points = 250\n",
      " --- Calculating mu, sigma for bin 102\n",
      "N_points = 618\n",
      " --- Calculating mu, sigma for bin 103\n",
      "N_points = 424\n",
      " --- Calculating mu, sigma for bin 104\n",
      "N_points = 382\n",
      " --- Calculating mu, sigma for bin 105\n",
      "N_points = 472\n",
      " --- Calculating mu, sigma for bin 106\n",
      "N_points = 819\n",
      " --- Calculating mu, sigma for bin 107\n",
      "N_points = 401\n",
      " --- Calculating mu, sigma for bin 108\n",
      "N_points = 824\n",
      " --- Calculating mu, sigma for bin 109\n",
      "N_points = 505\n",
      " --- Calculating mu, sigma for bin 110\n",
      "N_points = 350\n",
      " --- Calculating mu, sigma for bin 111\n",
      "N_points = 691\n",
      " --- Calculating mu, sigma for bin 112\n",
      "N_points = 970\n",
      " --- Calculating mu, sigma for bin 113\n",
      "N_points = 921\n",
      " --- Calculating mu, sigma for bin 114\n",
      "N_points = 643\n",
      " --- Calculating mu, sigma for bin 115\n",
      "N_points = 218\n",
      " --- Calculating mu, sigma for bin 116\n",
      "N_points = 547\n",
      " --- Calculating mu, sigma for bin 117\n",
      "N_points = 945\n",
      " --- Calculating mu, sigma for bin 118\n",
      "N_points = 1228\n",
      " --- Calculating mu, sigma for bin 119\n",
      "N_points = 1442\n",
      " --- Calculating mu, sigma for bin 120\n",
      "N_points = 870\n",
      " --- Calculating mu, sigma for bin 121\n",
      "N_points = 1644\n",
      " --- Calculating mu, sigma for bin 122\n",
      "N_points = 1040\n",
      " --- Calculating mu, sigma for bin 123\n",
      "N_points = 971\n",
      " --- Calculating mu, sigma for bin 124\n",
      "N_points = 1684\n",
      " --- Calculating mu, sigma for bin 125\n",
      "N_points = 874\n",
      " --- Calculating mu, sigma for bin 126\n",
      "N_points = 363\n",
      " --- Calculating mu, sigma for bin 127\n",
      "N_points = 251\n",
      " --- Calculating mu, sigma for bin 128\n",
      "N_points = 326\n",
      " --- Calculating mu, sigma for bin 130\n",
      "N_points = 72\n",
      " --- Calculating mu, sigma for bin 131\n",
      "N_points = 137\n",
      " --- Calculating mu, sigma for bin 134\n",
      "N_points = 14\n",
      " --- Calculating mu, sigma for bin 137\n",
      "N_points = 88\n",
      " --- Calculating mu, sigma for bin 138\n",
      "N_points = 15\n",
      " --- Calculating mu, sigma for bin 139\n",
      "N_points = 286\n",
      " --- Calculating mu, sigma for bin 140\n",
      "N_points = 82\n",
      " --- Calculating mu, sigma for bin 141\n",
      "N_points = 260\n",
      " --- Calculating mu, sigma for bin 142\n",
      "N_points = 193\n",
      " --- Calculating mu, sigma for bin 143\n",
      "N_points = 901\n",
      " --- Calculating mu, sigma for bin 144\n",
      "N_points = 305\n",
      " --- Calculating mu, sigma for bin 145\n",
      "N_points = 985\n",
      " --- Calculating mu, sigma for bin 146\n",
      "N_points = 606\n",
      " --- Calculating mu, sigma for bin 147\n",
      "N_points = 201\n",
      " --- Calculating mu, sigma for bin 148\n",
      "N_points = 55\n",
      " --- Calculating mu, sigma for bin 149\n",
      "N_points = 1034\n",
      " --- Calculating mu, sigma for bin 150\n",
      "N_points = 1054\n",
      " --- Calculating mu, sigma for bin 151\n",
      "N_points = 871\n",
      " --- Calculating mu, sigma for bin 152\n",
      "N_points = 1280\n",
      " --- Calculating mu, sigma for bin 153\n",
      "N_points = 2864\n",
      " --- Calculating mu, sigma for bin 154\n",
      "N_points = 2573\n",
      " --- Calculating mu, sigma for bin 155\n",
      "N_points = 3923\n",
      " --- Calculating mu, sigma for bin 156\n",
      "N_points = 3574\n",
      " --- Calculating mu, sigma for bin 157\n",
      "N_points = 1393\n",
      " --- Calculating mu, sigma for bin 158\n",
      "N_points = 804\n",
      " --- Calculating mu, sigma for bin 159\n",
      "N_points = 1552\n",
      " --- Calculating mu, sigma for bin 160\n",
      "N_points = 1868\n",
      " --- Calculating mu, sigma for bin 161\n",
      "N_points = 2551\n",
      " --- Calculating mu, sigma for bin 162\n",
      "N_points = 2920\n",
      " --- Calculating mu, sigma for bin 163\n",
      "N_points = 1413\n",
      " --- Calculating mu, sigma for bin 164\n",
      "N_points = 354\n",
      " --- Calculating mu, sigma for bin 165\n",
      "N_points = 116\n",
      " --- Calculating mu, sigma for bin 166\n",
      "N_points = 32\n",
      " --- Calculating mu, sigma for bin 167\n",
      "N_points = 17\n",
      " --- Calculating mu, sigma for bin 168\n",
      "N_points = 45\n",
      " --- Calculating mu, sigma for bin 169\n",
      "N_points = 33\n",
      " --- Calculating mu, sigma for bin 171\n",
      "N_points = 23\n",
      " --- Calculating mu, sigma for bin 172\n",
      "N_points = 25\n",
      " --- Calculating mu, sigma for bin 177\n",
      "N_points = 61\n",
      " --- Calculating mu, sigma for bin 178\n",
      "N_points = 159\n",
      " --- Calculating mu, sigma for bin 179\n",
      "N_points = 34\n",
      " --- Calculating mu, sigma for bin 180\n",
      "N_points = 285\n",
      " --- Calculating mu, sigma for bin 181\n",
      "N_points = 133\n",
      " --- Calculating mu, sigma for bin 182\n",
      "N_points = 328\n",
      " --- Calculating mu, sigma for bin 183\n",
      "N_points = 941\n",
      " --- Calculating mu, sigma for bin 184\n",
      "N_points = 232\n",
      " --- Calculating mu, sigma for bin 185\n",
      "N_points = 335\n",
      " --- Calculating mu, sigma for bin 187\n",
      "N_points = 609\n",
      " --- Calculating mu, sigma for bin 188\n",
      "N_points = 1071\n",
      " --- Calculating mu, sigma for bin 189\n",
      "N_points = 773\n",
      " --- Calculating mu, sigma for bin 190\n",
      "N_points = 980\n",
      " --- Calculating mu, sigma for bin 191\n",
      "N_points = 1045\n",
      " --- Calculating mu, sigma for bin 192\n",
      "N_points = 1859\n",
      " --- Calculating mu, sigma for bin 193\n",
      "N_points = 3759\n",
      " --- Calculating mu, sigma for bin 194\n",
      "N_points = 3476\n",
      " --- Calculating mu, sigma for bin 195\n",
      "N_points = 1892\n",
      " --- Calculating mu, sigma for bin 196\n",
      "N_points = 1407\n",
      " --- Calculating mu, sigma for bin 197\n",
      "N_points = 1240\n",
      " --- Calculating mu, sigma for bin 198\n",
      "N_points = 1084\n",
      " --- Calculating mu, sigma for bin 199\n",
      "N_points = 553\n",
      " --- Calculating mu, sigma for bin 200\n",
      "N_points = 130\n",
      "Results saved as /Users/chris/GradResearch/CRTS_PROJECT/data_products/PTF_Fig_2_data/2017-08-15/18.5-19.0_starsR_mij_lt_1.0.txt\n",
      "Done with calculating panel data \n"
     ]
    }
   ],
   "source": [
    "# Run the calculation over all bins : \n",
    "# - keep the option to plot a bin if one wants to compare the three methods : \n",
    "#  need to provide object N\n",
    "#   \n",
    "# - as it is, calculate three values for sigma, etc., and plot it  \n",
    "\n",
    "# set the bin number \n",
    "nbins = 200 \n",
    "\n",
    "# set the m_ij cutoff \n",
    "# Makes sense, given that it removes a tiny fraction of \n",
    "# points, most likely CCD problems, or cosmic rays - \n",
    "# the input data was not sigma-clipped in any way... \n",
    "m_ij_cutoff = 1.0\n",
    "\n",
    "# Set a directory to save the results...\n",
    "outDir = os.path.join(os.getcwd()[:-4]+'data_products/'+'PTF_Fig_2_data/')\n",
    "if not os.path.exists(outDir): os.system('mkdir %s' % outDir)\n",
    "                      \n",
    "outDir = os.path.join(os.getcwd()[:-4],'data_products/'+'PTF_Fig_2_data', \n",
    "                      datetime.datetime.now().strftime('%Y-%m-%d')+ '/')\n",
    "if not os.path.exists(outDir): os.system('mkdir %s' % outDir)\n",
    "                      \n",
    "print('Today we will save our Fig_2 files in %s'%outDir)\n",
    "\n",
    "# Loop over objects in the dictionary  \n",
    "for obj in out_dic.keys(): \n",
    "    print('\\n For obj = %s'% obj) \n",
    "    m_ij = out_dic[obj]['xi']\n",
    "    tau =  out_dic[obj]['tau']\n",
    "    e_ij = out_dic[obj]['ei']\n",
    "    \n",
    "    # do that and see what happens ... \n",
    "    mask_out = abs(m_ij)<m_ij_cutoff\n",
    "    m_ij = m_ij[mask_out]\n",
    "    tau = tau[mask_out]\n",
    "    e_ij = e_ij[mask_out]\n",
    "    \n",
    "    #n =  out_dic[b][obj][3]\n",
    "\n",
    "    # Pull out some tau to plot means : common to all panels \n",
    "    binned_tau = binned_statistic(tau, tau, statistic='mean', bins=nbins)\n",
    "    mean_tau = binned_tau[0]\n",
    "    \n",
    "    # Take N from each bin... 'count' function works like a regular histogram\n",
    "    binned_count = binned_statistic(tau, tau, statistic='count', bins=nbins)\n",
    "    bin_count = binned_count[0]\n",
    "    #bin_names = np.arange(1,len(binned_count[2]))\n",
    "\n",
    "    # Calculate median preprocessed photometric error per bin \n",
    "    binned_err_median = binned_statistic(tau, e_ij, statistic='median', bins=nbins) \n",
    "    err_median = binned_err_median[0]\n",
    "\n",
    "    # make sure that there are no empty bins : such calculation would be truly obsolete... \n",
    "    non_empty_bins = np.bitwise_not(np.isnan(mean_tau))\n",
    "\n",
    "    # reassign number of points in a bin and  tau position \n",
    "    bin_count = bin_count[non_empty_bins]\n",
    "    mean_tau = mean_tau[non_empty_bins]\n",
    "    err_median = err_median[non_empty_bins]\n",
    "\n",
    "    # A long list telling which point belongs to which bin\n",
    "    bin_number  = binned_tau[2]\n",
    "    \n",
    "    ####\n",
    "    ####  Panel 1 : Standard Deviation \n",
    "    ####\n",
    "\n",
    "    rms_std = lambda x : np.std(x)\n",
    "    stdev_binned = binned_statistic(tau, m_ij, statistic = rms_std, \n",
    "                                              bins=nbins)\n",
    "    bin_stdev = stdev_binned[0][non_empty_bins]  \n",
    "\n",
    "    #####\n",
    "    ##### Panel 2  : Gaussian rms  \n",
    "    #####\n",
    "    rms_robust = lambda x : 0.7414 *(np.percentile(x,75) - np.percentile(x,25))\n",
    "    bin_sigma_G = binned_statistic(tau, m_ij, statistic = rms_robust, \n",
    "                                      bins=nbins)[0][non_empty_bins]\n",
    "\n",
    "    #####\n",
    "    ##### Panel 3 (SF)   and Panel 4   (mu)\n",
    "    #####\n",
    "\n",
    "\n",
    "    # Loop over all bins  calculating approximate mu and sigma \n",
    "\n",
    "    mu_bins = {}\n",
    "    sig_bins = {}\n",
    "    #nbins = len(bin_count)\n",
    "\n",
    "    sig_bins['approx'] = np.zeros(nbins)\n",
    "    mu_bins['approx'] = np.zeros(nbins)\n",
    "    sig_bins['full'] = np.zeros(nbins)\n",
    "    mu_bins['full'] = np.zeros(nbins)\n",
    "    \n",
    "    for N in np.unique(bin_number):\n",
    "        print('\\r --- Calculating mu, sigma for bin %d' % N),\n",
    "        xi = m_ij[bin_number == N]\n",
    "        ei = e_ij[bin_number == N]\n",
    "        print('N_points = %d'%len(xi))\n",
    "   \n",
    "        mu_approx, sig_approx = varF.approximate_mu_sigma(xi, ei)\n",
    "        mu_full, sig_full =  varF.get_mu_sigma(xi,ei)\n",
    "        \n",
    "        sig_bins['approx'][N-1] = sig_approx\n",
    "        sig_bins['full'][N-1] = sig_full\n",
    "        \n",
    "        mu_bins['approx'][N-1] = mu_approx\n",
    "        mu_bins['full'][N-1] = mu_full \n",
    "\n",
    "    # select only those bins which are non-zero...\n",
    "    for key in ['approx','full']:\n",
    "        sig_bins[key] = sig_bins[key][non_empty_bins]\n",
    "        mu_bins[key] = mu_bins[key][non_empty_bins]\n",
    "    \n",
    "    # Save the results of calculation  : Panel 1,2,3,4 \n",
    "    fname = outDir+str(Min)+'-'+str(Max)+'_'+obj+'_mij_lt_'+str(m_ij_cutoff)+'.txt'\n",
    "    \n",
    "    data = np.column_stack((mean_tau, bin_stdev, bin_sigma_G, sig_bins['full'], \n",
    "                            sig_bins['approx'], mu_bins['full'], mu_bins['approx'], \n",
    "                            bin_count))\n",
    "\n",
    "    header = 'meanTau   stdev    sigmaG   sigmaFull  sigmaApprox   muFull  muApprox   binCount'\n",
    "    np.savetxt(fname, data, fmt = '%s', delimiter = ' ' , header=header )\n",
    "    print('Results saved as %s' % fname)\n",
    "\n",
    "print('Done with calculating panel data ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot below the raw $\\Delta m_{ij}$ for quasars, applying my mask. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chris/anaconda3/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:27: RuntimeWarning: divide by zero encountered in log10\n"
     ]
    },
    {
     "data": {
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hgM+Y2TRkC+gVyCa0SwG8s8nxdT8/DXIhXpA/ZDnpDrqf1GQ4vLghTV4Sy377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\nK9EEJoQQoiv5/wEs0TmPpGWI9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1153c1780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obj = 'qso'\n",
    "m_ij_cutoff = 1.0\n",
    "\n",
    "m_ij = out_dic[obj]['xi']\n",
    "tau =  out_dic[obj]['tau']\n",
    "e_ij = out_dic[obj]['ei']\n",
    "\n",
    "\n",
    "rms_std = lambda x : np.std(x)\n",
    "stdev_binned = binned_statistic(tau, m_ij, statistic = rms_std, \n",
    "                                      bins=nbins)\n",
    "bin_stdev = stdev_binned[0][non_empty_bins]\n",
    "\n",
    "# some m_ij seems completely unphysical... seems that there may be some 'outlier' points in lightcurves \n",
    "# that are completely off the scale, and maybe should be discarded at earlier  stage ... \n",
    "\n",
    "# makes sense that if there is one outlier point, then all magnitude differences calculated from \n",
    "# that point will be offset as much as that point \n",
    "\n",
    "m=abs(m_ij)<m_ij_cutoff\n",
    "H, xedges, yedges = np.histogram2d(np.log10(tau[m]), m_ij[m], bins=70)\n",
    "H = np.rot90(H)\n",
    "H = np.flipud(H)\n",
    "# Mask zeros\n",
    "Hmasked = np.ma.masked_where(H==0,H) # Mask pixels with a value of zeropltssssss\n",
    "# Plot 2D histogram using pcolor\n",
    "plt.pcolormesh(xedges,yedges,np.log10(Hmasked), cmap='jet')\n",
    "plt.xlabel(r'$\\log_{10}(\\tau)$', fontsize=15)\n",
    "plt.ylabel(r'$\\Delta m_{ij}$',fontsize=15)\n",
    "plt.title('PTF QSO ')\n",
    "plt.savefig(outDir+'Fig_2_'+str(Min)+'-'+str(Max)+'_raw_data.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# NUMPY SAVE  :  A FANTASTIC  MOVE ! \n",
    "savefile = outDir+str(Min)+'-'+str(Max)+'_out_dic'\n",
    "np.save(savefile, out_dic)\n",
    "\n",
    "#Demonstrate loading \n",
    "load_npy = np.load(savefile+'.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'qso': {'ei': array([ 0.06019909,  0.06090156,  0.04957953, ...,  0.07635634,\n",
       "          0.07635634,  0.0762709 ]),\n",
       "  'tau': array([  9.18100000e-01,   9.18900000e-01,   9.43000000e-01, ...,\n",
       "           1.91181780e+03,   1.92387700e+03,   1.93188700e+03]),\n",
       "  'xi': array([ 0.0621,  0.0723, -0.0443, ..., -0.114 , -0.0505, -0.0547])},\n",
       " 'starsB': {'ei': array([ 0.09134993,  0.06329423,  0.06329423, ...,  0.07680163,\n",
       "          0.0771295 ,  0.07694154]),\n",
       "  'tau': array([  8.65100000e-01,   9.18100000e-01,   9.18900000e-01, ...,\n",
       "           1.91181780e+03,   1.92387700e+03,   1.93188700e+03]),\n",
       "  'xi': array([ 0.1782,  0.1335, -0.0505, ..., -0.0464, -0.011 , -0.0213])},\n",
       " 'starsR': {'ei': array([ 0.20293092,  0.14103191,  0.08376139, ...,  0.05952655,\n",
       "          0.06060825,  0.06008236]),\n",
       "  'tau': array([  8.65100000e-01,   8.81700000e-01,   9.42900000e-01, ...,\n",
       "           1.91181780e+03,   1.92387700e+03,   1.93188700e+03]),\n",
       "  'xi': array([ 0.2328, -0.233 , -0.0209, ..., -0.0438, -0.0034, -0.0422])}}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# recover the out_dic with all xi, ei for \n",
    "# PTF 18.5-19  objects ..\n",
    "# http://stackoverflow.com/questions/30811918/saving-dictionary-of-numpy-arrays \n",
    "out_dic = load_npy[()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aside : Figure out how many points are removed by cutting off outliers... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be able to say that we have enough  magnitude-difference points to split them into 200 bins, and that removing the outliers  that are off by more than +/- 1 mag from 0 does not make much difference in the number of points used , but it does make the SF (variance measure) much more informative. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For 17.00-18.00 mag,  qso have 37870 m_ij with abs(m_ij) <1.0, and only 1.756298 % was removed by this cut\n",
      "For 17.00-18.00 mag,  starsB have 526358 m_ij with abs(m_ij) <1.0, and only 0.496047 % was removed by this cut\n",
      "For 17.00-18.00 mag,  starsR have 682627 m_ij with abs(m_ij) <1.0, and only 1.137897 % was removed by this cut\n",
      " \n",
      "For 18.00-18.50 mag,  qso have 108545 m_ij with abs(m_ij) <1.0, and only 2.057298 % was removed by this cut\n",
      "For 18.00-18.50 mag,  starsB have 287225 m_ij with abs(m_ij) <1.0, and only 0.556034 % was removed by this cut\n",
      "For 18.00-18.50 mag,  starsR have 453370 m_ij with abs(m_ij) <1.0, and only 0.995137 % was removed by this cut\n",
      " \n",
      "For 18.50-19.00 mag,  qso have 206669 m_ij with abs(m_ij) <1.0, and only 1.560878 % was removed by this cut\n",
      "For 18.50-19.00 mag,  starsB have 280235 m_ij with abs(m_ij) <1.0, and only 0.448671 % was removed by this cut\n",
      "For 18.50-19.00 mag,  starsR have 531009 m_ij with abs(m_ij) <1.0, and only 0.969771 % was removed by this cut\n",
      " \n"
     ]
    }
   ],
   "source": [
    "objects = ['qso', 'starsB', 'starsR']\n",
    "m_ij_cutoff = 1.0 \n",
    "\n",
    "stats_dic = {17:{}, 18:{}, 18.5:{}}\n",
    "stats_dic = {}\n",
    "for Min in Min_arr : \n",
    "    stats_dic[Min] = {}\n",
    "    for obj in objects :\n",
    "        stats_dic[Min][obj] = {}\n",
    "\n",
    "for i in range(3):\n",
    "    for obj in objects : \n",
    "        m_ij = out_dic[Min_arr[i]][obj][0]\n",
    "        mask=abs(m_ij)<m_ij_cutoff\n",
    "        in_bounds = np.sum(mask)\n",
    "        percent_removed = (np.sum(~mask) / float(len(mask)))*100.0\n",
    "        print('For %.2f-%.2f mag,  %s have %d m_ij with abs(m_ij) <1.0, and only %f %% was removed by this cut'\n",
    "              %(Min_arr[i], Max_arr[i], obj, in_bounds, percent_removed))\n",
    "        \n",
    "        stats_dic[Min_arr[i]][obj]['N'] = in_bounds\n",
    "        stats_dic[Min_arr[i]][obj]['perc_removed'] = percent_removed\n",
    "    print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For min Mag 17.00, the mean N is 415618.33\n",
      "For min Mag 18.00, the mean N is 283046.67\n",
      "For min Mag 18.50, the mean N is 339304.33\n",
      " \n",
      "For obj qso, the mean N is 117694.67\n",
      "For obj starsB, the mean N is 364606.00\n",
      "For obj starsR, the mean N is 555668.67\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for key in stats_dic.keys() :\n",
    "    avg_N_over_obj = []\n",
    "    for obj in objects:  \n",
    "        avg_N_over_obj.append(stats_dic[key][obj]['N'])\n",
    "    print('For min Mag %.2f, the mean N is %.2f'%(key, np.mean(avg_N_over_obj)))\n",
    "    \n",
    "print(' ')\n",
    "for obj in objects: \n",
    "    avg_N_over_mag = [] \n",
    "    for key in stats_dic.keys():\n",
    "        avg_N_over_mag.append(stats_dic[key][obj]['N'])\n",
    "    print('For obj %s, the mean N is %.2f'%(obj, np.mean(avg_N_over_mag)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[206669, 280235, 531009]"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in the calculation results "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the calculation for each bin has already been done, and only some fine tuning is needed for the figure, you can set the Min, Max mags for the cut below, and we will read in the results from the files and plot it all. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading from /Users/chris/GradResearch/CRTS_PROJECT/data_products/PTF_Fig_2_data/2017-08-15/18.5-19.0_qso_mij_lt_1.0.txt\n",
      "Reading from /Users/chris/GradResearch/CRTS_PROJECT/data_products/PTF_Fig_2_data/2017-08-15/18.5-19.0_starsB_mij_lt_1.0.txt\n",
      "Reading from /Users/chris/GradResearch/CRTS_PROJECT/data_products/PTF_Fig_2_data/2017-08-15/18.5-19.0_starsR_mij_lt_1.0.txt\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "from collections import OrderedDict\n",
    "plot_data = OrderedDict()\n",
    "\n",
    "Min = 18.5\n",
    "Max = 19.0\n",
    "\n",
    "#outDir = os.path.join(os.getcwd()[:-4],'data_products/'+'Fig_2_data', \n",
    "#                      datetime.datetime.now().strftime('%Y-%m-%d')+ '/')\n",
    "m_ij_cutoff = 1.0 \n",
    "#outDir = '../data_products/PTF_Fig_2_data/2017-01-20/'\n",
    "for obj in ['qso', 'starsB', 'starsR']:\n",
    "    plot_data[obj] = {}\n",
    "\n",
    "    fname = outDir+str(Min)+'-'+str(Max)+'_'+obj+'_mij_lt_'+str(m_ij_cutoff)+'.txt'\n",
    "    print('Reading from %s'%fname)\n",
    "\n",
    "    colnames = open(fname,'r').read().splitlines()[0][1:].split()\n",
    "    d = np.genfromtxt(fname, dtype=float)\n",
    "\n",
    "    for label, column in zip(colnames, d.T):\n",
    "        plot_data[obj][label] = column\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Fig.2 : four-panel plot (can run only cells below to make  Fig.2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: now since we are convinced that the full results are better than the approximate ones, we plot only muFull and sigmaFull. But muApprox and sigmaApprox is still there... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure is saved as /Users/chris/GradResearch/CRTS_PROJECT/data_products/PTF_Fig_2_data/2017-08-15/Fig_2_18.5-19.0_PTF_Approx.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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NNDY2kpOTQ1lZWas073Js0gqWiIiIiBx3QkNDqa+vb7Oto4LDLdOr+3w+ampqOPnkk3n5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30ckjk3iP/xDZti3Tn9xH7IC3sS/ZxvTpX1FQABER8MUX0L9/Tq21WA0lKwsul8GLL0JlJTxq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\n0zk7wcWCBZB7RUKXxyFc5WFNr6n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20Vd2s3VbLplhHQjT/65rWDJ0NvPmwXNfOQ/qeq44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jYdOOGIgfHfxFOXj6eUTu1MmzYt1PeuvF4vq1ev5sEHH+TGG29k5MiR3HTTTTgcDpqbmxkx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\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DTqoxYyDTHIMVCbzX9vX254wZc2piECeFJFhCCCGEOCkCEwkUFxcfcyIBp9PJhAkTsFjq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\nW9Ga1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11410b5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "import matplotlib.pyplot as plt \n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "rcParams['ytick.labelsize'] = 25\n",
    "rcParams['xtick.labelsize'] = 25\n",
    "rcParams['axes.labelsize'] = 35\n",
    "rcParams['axes.linewidth'] = 3\n",
    "rcParams['font.size'] = 25\n",
    "rcParams.update({'figure.autolayout': False})\n",
    "\n",
    "%matplotlib inline\n",
    "# set all plot parameters\n",
    "lh_w   = 1.0  # horizontal line thickness \n",
    "lh_st  = '--' # horizontal line style \n",
    "lh_al  = 0.5  # horizontal line alpha parameter \n",
    "\n",
    "# dot size \n",
    "p_size = 7 # 7\n",
    "p_al   = 0.5 \n",
    "\n",
    "# y limits for sigma, sigma_G, SF panels \n",
    "y_top  = 0.45\n",
    "y_bott = -0.05\n",
    "\n",
    "# y limits for mu approx \n",
    "y_mu_top = 0.1\n",
    "y_mu_bott = -0.1\n",
    "\n",
    "# x limits for ALL PANELS \n",
    "x_left = 0.5\n",
    "x_right = 3.7\n",
    "\n",
    "# choose sigma calculation type to be displayed...\n",
    "calc = 'Approx'\n",
    "\n",
    "# colors for quasars, blue and red stars \n",
    "obj_arr = ['qso',  'starsB']\n",
    "colors= {'qso':'black',  'starsB':'blue' , 'starsR':'red'}\n",
    "markers = {'qso':'o',  'starsB': '>', 'starsR': 's'}\n",
    "\n",
    "def plot_errorbar(ax, x,y,yerr, obj, linestyle='None', markersize=p_size,\n",
    "                 markerfacecolor='None',elinewidth=1, \n",
    "                  markeredgewidth=1, capsize=1, alpha=0.8, colors=colors, \n",
    "                  markers=markers ):\n",
    "    ''' A quick convenience function to use the \n",
    "    same default markers, etc. for all plots\n",
    "    '''\n",
    "    ax.errorbar(x,y,yerr,linestyle=linestyle, c = colors[obj],\n",
    "                marker=markers[obj],  markersize=p_size, markeredgecolor=colors[obj],\n",
    "                markerfacecolor='None', markeredgewidth=1,  elinewidth=1, capsize=1, \n",
    "                alpha=0.8)\n",
    "   \n",
    "\n",
    "fig,ax = plt.subplots(4,1, figsize=(12,16), sharex=True)\n",
    "\n",
    "\n",
    "# Panel 1 \n",
    "for obj in plot_data.keys() : \n",
    "    x = np.log10(plot_data[obj]['meanTau'])\n",
    "    y = plot_data[obj]['stdev']\n",
    "    yerr = plot_data[obj]['stdev'] / np.sqrt(2.0*(plot_data[obj]['binCount'] - 1.0))\n",
    "    plot_errorbar(ax[0], x,y,yerr, obj)\n",
    "        \n",
    "ax[0].set_ylabel(r'$\\sigma_{stdev}$')   \n",
    "ax[0].tick_params( axis='x', which='both',  bottom='off', \n",
    "                top='off', labelbottom='off') \n",
    "ax[0].set_ylim(bottom=y_bott, top=y_top)\n",
    "\n",
    "\n",
    "# Panel 2\n",
    "for obj in plot_data.keys() : \n",
    "    x = np.log10(plot_data[obj]['meanTau'])\n",
    "    y = plot_data[obj]['sigmaG']\n",
    "    yerr = plot_data[obj]['sigmaG']* 1.06 / np.sqrt(plot_data[obj]['binCount'])\n",
    "    plot_errorbar(ax[1], x,y,yerr, obj)\n",
    "\n",
    "ax[1].set_ylabel(r'$\\sigma_{G}$')   # ,fontsize=20 \n",
    "ax[1].tick_params( axis='x', which='both',  bottom='off', \n",
    "                top='off', labelbottom='off') \n",
    "ax[1].set_ylim(bottom=y_bott, top=y_top)\n",
    "\n",
    "# Panel 3\n",
    "def model_sf(t, sf_inf=0.25, tau = 1.0):\n",
    "    br = 1.0-np.exp(-t/tau)\n",
    "    sf = sf_inf * np.power(br,0.5)\n",
    "    return sf\n",
    "\n",
    "# Calculate the model DRW fit for QSO\n",
    "obj = 'qso'\n",
    "xdata = plot_data[obj]['meanTau']\n",
    "sf = plot_data[obj]['sigma'+calc]\n",
    "popt, pcov = curve_fit(model_sf, xdata, sf)\n",
    "y = model_sf(xdata, sf_inf=popt[0], tau = popt[1]) # tau 1 year in days \n",
    "\n",
    "# Fold-in the error to the model SF , plot \n",
    "# both folded and not-folded version \n",
    "\n",
    "err_sig = plot_data[obj]['sigma'+calc]* 1.06 / np.sqrt(plot_data[obj]['binCount'])\n",
    "ax[2].plot(np.log10(xdata), y , lw=3, c = 'orange', ls='--')\n",
    "\n",
    "# plot approximate sigma \n",
    "for obj in plot_data.keys() : \n",
    "    x = np.log10(plot_data[obj]['meanTau'])\n",
    "    y = plot_data[obj]['sigma'+calc] \n",
    "    yerr = plot_data[obj]['sigma'+calc]* 1.06 / np.sqrt(plot_data[obj]['binCount'])\n",
    "    plot_errorbar(ax[2], x,y,yerr, obj)\n",
    "    \n",
    "ax[2].set_ylim(bottom=y_bott, top=y_top)\n",
    "ax[2].set_ylabel(r'$SF $') \n",
    "ax[2].tick_params( axis='x', which='both',  bottom='off', \n",
    "                top='off', labelbottom='off')\n",
    "\n",
    "for i in [0,1,2] : \n",
    "    ax[i].grid(axis='x')\n",
    "    ax[i].set_yticks([0,0.1,0.2,0.3,0.4])\n",
    "    ax[i].set_yticklabels(['0.0','0.1', '0.2', '0.3', '0.4'])\n",
    "    ax[i].axhline(y=0.0, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)    \n",
    "    ax[i].axhline(y=0.1, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "    ax[i].axhline(y=0.2, color='black', lw=lh_w, ls=lh_st,alpha=lh_al) \n",
    "    ax[i].set_xlim(left=x_left, right=x_right)\n",
    "\n",
    "# Panel 4 \n",
    "for obj in plot_data.keys() : \n",
    "    x = np.log10(plot_data[obj]['meanTau'])\n",
    "    y = plot_data[obj]['mu'+calc] \n",
    "    yerr  = plot_data[obj]['stdev'] / np.sqrt(plot_data[obj]['binCount'])\n",
    "    plot_errorbar(ax[3], x,y,yerr, obj)\n",
    "    \n",
    "ax[3].axhline(y=0.0, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "ax[3].set_ylim(top=y_mu_top, bottom=y_mu_bott)\n",
    "ax[3].set_yticks([-0.05,0,0.05])\n",
    "ax[3].set_yticklabels(['-0.05','0.0', '0.05'])  \n",
    "ax[3].set_ylabel(r'$\\mu$') \n",
    "\n",
    "ax[3].grid(axis='x')\n",
    "ax[3].set_xlabel(r'$log_{10} (\\Delta t \\, [days] )$ ') \n",
    "\n",
    "vline_color = 'orange'\n",
    "ax[3].fill_between([2.6, 2.75], y1=-0.06, y2=0, color='orange', alpha=0.4)\n",
    "figname = outDir+'Fig_2_'+str(Min)+'-'+str(Max)+'_PTF_'+calc+'.png'\n",
    "plt.tight_layout()\n",
    "fig.subplots_adjust(hspace=0)\n",
    "plt.savefig(figname)\n",
    "print('Figure is saved as %s'% figname )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the calculation results in one $\\delta t$ bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             meanTau  1396.101935\n",
      "               stdev  0.224747\n",
      "              sigmaG  0.221085\n",
      "           sigmaFull  0.189615\n",
      "         sigmaApprox  0.191980\n",
      "              muFull  0.016377\n",
      "            muApprox  0.029300\n",
      "            binCount  325.000000\n"
     ]
    }
   ],
   "source": [
    "# Choose bin 180...\n",
    "bin_number = 140\n",
    "array_pos = bin_number - 1 \n",
    "obj = 'qso'\n",
    "\n",
    "# results for stats for each bin are stored as a dic ...\n",
    "for key in plot_data[obj].keys() : \n",
    "    value  = plot_data[obj][key][array_pos]\n",
    "    print('%20s  %f'% (key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stdev 0.172803 \n",
      "bin count 110\n",
      "mean 0.019473\n",
      "median 0.038550\n",
      "Stdev 0.204337 \n",
      "bin count 177\n",
      "mean -0.063485\n",
      "median -0.049000\n",
      "Stdev 0.273455 \n",
      "bin count 251\n",
      "mean 0.003115\n",
      "median 0.010700\n",
      "Stdev 0.324782 \n",
      "bin count 24\n",
      "mean -0.158354\n",
      "median -0.126150\n",
      "Stdev 0.201675 \n",
      "bin count 23\n",
      "mean -0.017978\n",
      "median -0.040200\n",
      "Stdev 0.327421 \n",
      "bin count 231\n",
      "mean 0.109550\n",
      "median 0.090900\n",
      "Stdev 0.276557 \n",
      "bin count 418\n",
      "mean -0.062910\n",
      "median -0.051500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/chris/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/_methods.py:82: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n",
      "/Users/chris/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/_methods.py:94: RuntimeWarning: invalid value encountered in true_divide\n",
      "  arrmean, rcount, out=arrmean, casting='unsafe', subok=False)\n",
      "/Users/chris/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/_methods.py:116: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/Users/chris/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/_methods.py:59: RuntimeWarning: Mean of empty slice.\n",
      "  warnings.warn(\"Mean of empty slice.\", RuntimeWarning)\n",
      "/Users/chris/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/_methods.py:70: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "/Users/chris/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/lib/function_base.py:655: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return n/(n*db).sum(), bins\n",
      "/Users/chris/anaconda3/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:62: RuntimeWarning: invalid value encountered in true_divide\n",
      "/Users/chris/anaconda3/envs/py36/lib/python3.6/site-packages/ipykernel/__main__.py:75: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stdev nan \n",
      "bin count 0\n",
      "mean nan\n",
      "median nan\n",
      "Stdev 0.220721 \n",
      "bin count 79\n",
      "mean 0.054313\n",
      "median -0.026200\n",
      "Stdev 0.280375 \n",
      "bin count 221\n",
      "mean -0.136123\n",
      "median -0.138400\n"
     ]
    },
    {
     "data": {
      "image/png": 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jjjv68Mzto0lb1wq8tmcCn2ANckjFGj06DLgA+EBEdKiMUkHq2Jxjeemcl1h5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fqeOK9ZFxFLgBPLzW71ksAt5ab1MsHqrE/b8GSyDWvU6mrrBm3NUn2c1sNdc82lHVPjuWjO0cy1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115eee3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RRWzdupU1a9Z4bDtnzpy25bCwMGbPns0nn3zisc2RRx7ZlrABTJkyhfT09EO2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\nS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PDfWe6+rIXjdxAMvxbRjocvpN2+Ef77gru/1sLxWUBDe01p4bh0LblsT/Xhrkl9uADl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\n9kqcmwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11556b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JiYnx9VvjF2F2BxDgKlPy94wJ4N/cSql6K6soY9l+18wHw8ocC4UHIW8fiNgT\nWC0NShrEmgPWH7/lP73I6Jxc6H0+nHK5zZGpevnmafj2r97LW7SHezf5fv+zHoSz/6/O4c6cOZNW\nrVoxduxYIiIiGDNmDB988AFPP/004vbdufHGG7n33nsBOP/88+nXrx9PP/00H3zwgXOf7Oxs0tPT\n6drVaprerVs3zjjjDObPn8/YsWOd+w0bNox//etfzvWcnBxeeOEFJk+ezOTJk53X2LNnD1OmTOHq\nq692bps0aRKTJk2ia9eu3HXXXdx7770MHz68zq/bLk2+ps1fRKQ70NGxukJEThGRGSKSKSLHRGSP\niHwoIiPtjFMp1TC/HPqF/NJ8ABJjEumeud5VmHyGTVHV3uD2g53Lq6IcHSYyvocyn7QSUcqrkpIS\nZs+ezaWXXkpEhPWzN3HiRHbu3El6enqVfS+99FLnckhICBMmTOCnn36qss/AgQOdCRvAyJEjSUxM\nPG6/cePGVVlft24dhYWFXHHFFVW2X3XVVWzevJkDBw44tz333HPExsYyfPhwOnfuzOOPP16PV24f\nTdq86+O2PBJYjtXJIR4oBjoBVwLfi8gjjR+eUsoXqtwa7TAcyVjsKmwCSduAxAHcOuBW3jz/Td4p\nirY2lhbCrqU1H6hUA82bN4/Dhw9z4YUXcvjwYQ4fPkxaWhqRkZHH3SJNTEw8bj0zM7PGfbztl5SU\nVGW9srz69sr1nJwc57YWLVpw0UUXcezYMX7/+98TGRlZm5caMPT2qHet3Zb/AuwCbgIWGmMqRKQf\n8C8gDXhcRH4xxsz2dCIRmQRMct+2aNEif8SsAlRBQYF+5vXkq/fN23nm75/vXG5zMBRytgFQHhLB\nD9sKqcioelxt42mMz7zy/KmkcjT3KAejU+mUmwHArm+ms32XtupobLX93OPj48nPzz++YNBt1qMm\n7sf5cn9P8dTgnXfeATiuhgvgo48+4i9/+QvFxcUA7Nixw1kbB7Bnzx7at2/vfA/Ky8vJzMw87j3J\nysoiISGB/Px8CgoKAKvHqPt+8fHxHq9ROQxJRESEc/8VK1bw6quv0r9/f/7yl78wfvz445I9T4qL\niwPid7gmbd6510IKcJkxZmXlBmPMehEZD2wB2gN/BjwmbcaYqcBUEXH+Bk1LS/NHzCpALVq0SD/z\nupj/mXOxQe9bLc6Tvy2f7/d8z4/7f+SaLsmw2toe2nUoZ547pt7x+O0zrymW9oXwwTwAupZsoav+\nzDW62n5g1odAAAAgAElEQVTuGzZsIC4uzv8B+cnRo0eZP38+V199NZMmVamTYNWqVdx9990sW7aM\nqChrGJovv/ySQYOsIWoqKiqYN28eQ4cOdb4HoaGhrFmzhtzcXOct0h9++IEDBw4watQo4uLiaNGi\nBQAxMTFV3rshQ4YQExPD559/7rwGwKeffkqfPn3o3r07YCVet956K+effz4fffQR/fv35+677+aT\nTz454euNioritNNOq+/b5TOatHnnnu4vcE/YKhljCkTkX1g1caeKSJIxJqvRIlRKNdj4nuMZ33M8\nxhjk0z+5CpJH2RdUPeSV5LEqIoSM+FZcd+QwZP0M+fshrr3doakgNHfuXAoLC/nTn/7E0KFVB3Qe\nOXIkTz75JDNnzuTMM88E4I033iAiIoKTTz6ZN954g61btx53C7Vdu3aMGzeOxx57jOLiYh544AEG\nDhxYpROCJwkJCdx555088cQThIWFMWjQIGbPns3nn39e5RqTJ09m//79LFiwgJiYGKZPn86ZZ57J\n9OnTuf76633zxviZJm3e7XVbrmleGLdWy3QDNGlTqgkSEdiz3LWhCbRnq1RYWshZH55FWUUZ0rol\nl+YfoWWFsSaQH/Bru8NTQWjmzJn07t37uIQNIDw8nCuvvJIZM2Y4yz/44APuuusuJk+eTJcuXfjw\nww+Pq7kaMWIEo0eP5s477+TAgQOkpaUxderUWsXz+OOPExYWxquvvkpWVha9evXivffeY+LEiYBV\na/f888/z7rvv0qFDB8BKLu+++27uvPNORo8eTefOnRvyljQKTdq8Ww+UA6En2M99PABtQKJUE7I7\nbzed4zq7hiaY9I2VuGUshk6n2xtcHcSEx9CndR/WH1qPEVjVuhNndTsH2vS2OzQVpD799NMay195\n5RVeeeUVpk+fDkC/fv344YcfTnjem2++mZtvvtljWXJyMt5G3woNDeWxxx7jscce81g+cuRIysuP\nH9b12Wef5dlnnz1hXIFCe496YYwpBr5zrKbWsGu/ykOADH/GpJTynYNFB7lwzoWM/ng0U5ZMsf4Y\nhEVC8khIe6DJTQnlPqXVsmHXw8UvQZfB3g9QSjU5mrTV7C3H87kiMrB6oYi0AG51rP5ojDlQfR+l\nVGBammkNiZFdmM3OvJ1VBgJtitzHa1uetcLGSJRS/hIUSZuItBaRtpUPXK8rxn27I8lyP26KiBjH\nI9nDqd8HfsK6BfofETlXREIcx6YCn2D1HK0AHvbPq1NK+UP6Ptfgn8M6DINvn4VN86DosI1R1d/A\npIGIo7XGhpwNFJQUQEU55GszW2Wf66+/HmOMs+enN4sWLeLjj3X6tRMJljZtq7A6AVR3n+NR6W3g\n+tqe1DEe2wRgAdZt0K+BQhEpxRpkF6AU+KMxZmE94lZK2cAY46xpAxjeshfM+ZW1EtECHtgJoU3r\n12PLiJb0TejLxpyNVJgKVs2+hlEZKyExFW6Yf+ITKKUCXlDUtPmTMWY/MBC4F1iGlaRFY7VfexMY\naIz5t20BKqXqbMeRHWQXZgMQFx5HvyPZrsJOpze5hK1SlXZtWSuh+DDs/gmKj9gYlVLKV4IiaTPG\nJBtjpBaP66sdN8WtLKOG8x8zxjxnjBlijGlljIk0xnQ3xvzeGLPO369PKeVb6ZmuW6NDOgwhbKdb\nr7buTWt8NneD2ltJW+cWnWkZ65gSyJTD9m9tjEop5StBkbQppVRduM83OqzDMGuIj0pNbFBdd8M7\nDOery79i3mXzuLH7eFfBtgX2BaWU8hlN2pRSzUqFqWDtwbXO9eFx3SF3h7USFg0dj+so3mTEhMfQ\nPtYxA0LPc10FWxeCl/GtlFJNR9NsuKGUUvUUIiHMv2w+q7JXsfbAWroe2O4q7DoUwiK8H9yUdBkC\nEXFQkg9HdsGhrdBWB9tVqinTmjalVLMTHRbNiI4juLn/zchO91ujTWfqKm+OHDvC+xve567v7uf2\nLm6d6rd+bV9QKihNmTIFETnuMXr06Dqdo23bts71RYsWISKsW6fNxT3RmjalVPMW1xESekDOdkg+\n0+5oGqzclPPXn/4KQBghFIkQbQzs/AGG3WJzdCrYxMfHM3/+/OO2Kf/QpE0p1WwcLT3K62teZ1iH\nYQxMGkhUWBSc87D1OLIHWiTZHWKDJUQl0DO+J9uObKOMCtYMvoZhJ/+2Sc2lqpqOsLAwhg0bZncY\nzYbeHlVKNRvL9i/jrV/e4g9f/4Hfzf9d1cL4zhAabk9gPlY59AfA8g59rbZ6TXTsOdU0ZWRkICL8\n73//q7L9+uuvZ9CgQV6OUiei32KlVLOQ/OBnRCZ9QkSCtb5ycwLJD35mb1B+Mqj9ID7c9CEAy7OW\n2xyNCnZlZWVV1kNDQ22KJPhpTZtSKmjFRlT94xEau9W5XHa0F3MjJvN6+D/4Xeg8ojhW5/MFKveZ\nEdYeWEtxQTZs+BS+edrGqFQwOnToEOHh4VUeCxbouID+ojVtSqmgdefoPrzw9WaOlpQjYUcIjbSm\nqzIVoSQVtqB/5Hb6s51RIT/zXvl5NZ4rNiKUO0f3aYywG6xtdFuSWyaTkZdBaUUpP782iMEFjqms\nTr8OWna0N0Dl1SurX+HVNa96LW8X3Y6FVy70+f639L+FWwfcWud44+Pj+frrqj2T+/bty6FDh+p8\nLnVimrQppYLWTWf24KYzewDwybZPeNgxusewjoN447RY+K+1HtNzBFuunWBTlP4xqP0gMvIyAFje\nrhuDCxwDCm9bCKf91r7AVFAJCwvz2EZNkzb/0KRNKdUsVJm6quMw2OY2PlsTnm/UmxEdR7A7fzeD\nkwaTdnAP7HAkbVsXaNKm/C4qKgqAkpKSKttzc3PtCCdoaNKmlAp6xhiWZlabb3TBP107NOH5Rr05\nr9t5nNfNcct3/8/w/YvW8raFUFEOIU2jfV5zc+uAW+t0m9Lf+9dXYmIi4eHhbNiwwbmtoKCAJUuW\n0K1btxqOVDXRpE0pFfSKyooY0XEE6ZnpFJcVkxoSA4d3WYXhMdDxNHsD9Lekk60x6AqyoPgw7FsF\nnXXYBeU/ISEhTJgwgeeff55u3brRqlUrnnvuOaKjo+0OrUnTpE0pFfRiwmN44ownMMZwoOgAoZvd\nGk53HRY047N5c6Qkj8PJI+i2bo61YesCTdqU37388stMmjSJW2+9ldatW/Pwww+zZMkSnaKqAXTI\nD6VUsyEiJMYkQkZwzTfqzS8Hf+HyTy5n1AejeCLErS3RNh2SQTXclClTOHjwoNfypKQk5s6dS15e\nHjt37mTSpElMnz6d5cuXez1HWloaxhhOPvlkv8beVGlNm1IqqFWYCgBCxO1/1CE3WfONZiyGHmm2\nxNUYEqIS2JS7CYA1hfsoRQhP6GFNaWUMiNgcoVKqLjRpU0oFtVXZq7h70d0MbT+UMcljGN1ttNWG\nreNpcOa9dofnVx1adKBTi07sLdhLUXkxv1w3iwHdax6PTikVuPT2qFIqqKXvSyenOId5GfNYvHfx\niQ8IMu6zIywv2GljJEqphtKkTSkV1KoM9dFxGOxbDYU5NkbUuKpMHr/fbR7SshLrFqlSqsnQpE0p\nFbTyS/JZd9DqqSYIQ9sPhQ+vgb/1gFfPgMO7bY7Q/9xr2lZlr6J05TswYyL8rTsc3GxjZEqputKk\nTSkVtJbtX0a5KQcgJSGF1kVH4MguwEDOdohrb2+AjaBTi060j7VeZ2FZIRs2zoHN86CkwBr6Q9nG\naE1nkxBIn5MmbUqpoJW+L925PLzj8KpDfXQbHvTjs4E1zMngpMHO9fVtu7oKdegP24SHh1NUVGR3\nGKoWioqKCA8PjN8V2ntUKRW0jpu66se3XYVBPD5bdZf3uZyRnUYyKGkQSSXFsGS6VZCxGEqLIFxH\nqW9siYmJ7N27l06dOhEdHY3o8CsBxxhDUVERe/fuJSkpye5wAE3alFJBav/R/WTkZQAQGRrJwMTT\nYMeNrh2CcL5RbwYmDXStxAJtesOhLVBWDDuXQK9zbYutuWrZsiUA+/bto7S01OZolDfh4eEkJSU5\nPy+7adKmlApKbaLb8Nb5b7E0cylHS48SmZcJeXuswogW0GGAvQHaqde5VtIGsP0bTdps0rJly4BJ\nBlTToEmbUioohYeEM6j9INeQFyvfdRV2HQ6hzevXX3lFOZtzN7M8azmd49twdmXB3pV2hqWUqoPm\n9VtLKdV8HdjoWm5G7dkq/Xfrf5mSPgWAtPZDXUlb5hqoqIAQ7ZemVKDTpE0pFXTKK8pZc2ANfRP6\nEhsea208/0kYfhvs/AE6Daz5BEHIvV3bipz1lJ92DaFt+0DHAUDgDGmglPJOkzalVNDJyMvguvnX\nIQiD2g/izfPftApadoBTLrc3OJskt0ymTVQbDhUfIr8kny3n30ZKQordYSml6kDrw5VSQWf9ofUA\nGAyxYbE2RxMYRITB7V3jtVWZ0kop1SRo0qaUCjobcjY4l1PbpEL6K5D+L0f7rXIbI7OX+5RWy/Yv\nsxaOHoI9K2yKSClVF3p7VCkVdDYcckvaElLg49sgb6+14aaF0Ol0myKzl/vk8SuyllPx/MmEHNlt\nDYHy4G7tjKBUgNNvqFIqqFSYCjbmuHqKpobEuhK2yJbQvr9NkdmvR3wPEqISADhSksdWc8wqKCmA\nQ1ttjEwpVRuatCmlgsre/L0UlBYAkBCVQNJ+V61bcxyfzZ2IcHrS6USHRTOi4whK2/V1FWauti8w\npVStNN/fXkqpoLQ+Z71zOSUhBdn5g6uwGY7PVt3kYZOJi4gjPCQcFj4B2763CvathlOvtDc4pVSN\nmnxNm4jEiMgFIjJZRGaLyE4RMY7HFD9d8zW3a2T44xpKqfrZkrvFuZyakGpNil5JkzYSohKshA2g\n42muAq1pUyrgBUNN2xDg88a6mIicDUxqrOsppermjwP+yCW9LmFjzka6Vgjk77MKIltC+1PtDS7Q\ndBiAAQQgc63OjKBUgAuGpA0gF1jp9ngeaO/ri4hIDPBvoAxYAwyq+QilVGMTETrHdaZzXGdYMd1V\n0G1Es27P5m519mrm7ZjH8qzlXNWmPVce2g8l+ZCzDdr2tjs8pZQXwfAv1ffGmARjzGhjzP3GmA+A\nY3661pNAT+BvwC9+uoZSyldaJEGv0RAeq7dG3aw7uI4ZG2ewOXczq+Lbugqy1tkXlFLqhJr8v53G\nmEYZKVNEhgF3AJuBJ4DXGuO6Sqna25izkbUH1tKvTT96t+5NZN8LoO8FUF4K5SV2hxcwTmp7knN5\nY3Qs/Poj6DAA4pJsjEopdSJNPmlrDCISCbyJ1fRjkjGmWERsjkopVd2CXQt4bY31/9Q1/a7h/sH3\nWwWh4dZDAdC3dV8EwWDYUZRNcY+ziAqLsjsspdQJBMPt0cbwKJAKTDPGfGt3MEopz6rOhJBqYySB\nLSY8hm4tuwFQbsqr9LhVSgUuTdpOQEROA+4HsoD7bA5HKVUD96St3/p58PpZ8Mnt1pyjqoqUhBTn\n8obs1ZDxgzU/65G9NkallKqJ3h6tgYiEYd0WDQPuMMYctjkkpZQXB4sOkl2UDUBUaBTJmeutsccy\nV8NJv7I5usCTkpDC/Iz5AGxa8Tps/9kqaJEEp1xuY2RKKW80aavZg8AA4H/GmI/qexIRmUS1sd0W\nLVrUsMhUk1JQUKCfeT3V9n37pcjVobtDaHskM925vnh7PmW7a3ceX2mMz7wh5y8pcnXMWFPh6s+1\n+8dP2HaoradDVC3od135kyZtXohIP+ARoAC4tSHnMsZMBaaKiKnclpaW1qD4VNOyaNEi/czrYv5n\nzsXavm9b1m4Bq6KNoUl9CNn0nbUS34UzzrvYxwGemN8+83q8N56cWnwqPyz8gZSEFPqXVkCGNf1X\nl9BDdNGf1XrT77ryJ03avPsXEAH8GcgVkRbVyivfO3ErO2aMKW2sAJVSLhty3DohGLdfbR362xBN\n4EuISuC9C9+zVo7sgQXPWcuZa3RmBKUClH4rvevueH4ayPfw+I2jvKvbtj82coxKKYcQCSE6LBqA\n1KNHXAWatJ1Yy04Q47glWpIPOdvtjUcp5ZHWtCmlgsLfz/o75RXl7MzfSZf/3OIq0KTthCow5Hc4\nlfhtC60N+1ZB2172BqWUOo7WtHlhjEk2xoi3B/C2Y9edbttfsDNmpZq70JBQesQlE575s2ujThLv\n1ebczVw37zpGzhzJPeF5roLM1fYFpZTyKihq2kSkNRDqtqkyGY0REfduUMXGmAK346ZgtVkD6G6M\nyfBnnEqpRlBRBuP+DplrITcD4trbHVHAig2PZWX2SgA2hJZjAIlqBTrji1IBKSiSNmAV0M3D9vuo\nOiDu28D1jRGQUqrxvL7mdXKP5ZKakMqozqNIGPBrGPBru8MKeB1jOxIXEUd+ST555UVk3vQVHTsO\n1qRNqQAVLEmbUqoZ+2zHZ+w4sgOA9y58j4SoBJsjahpEhJSEFJbtXwbAhvJ8OmrCplTACoo2bSdq\nf+b2uL7acVPcyjLqeM3rHccl+/ClKKXqqLC0kIwjGYDVg7RPSCy4DRarauY+ndXGnI02RqKUOhGt\naVNKNWmbcjdhsMat7hHfneh/nwvlpdD+FPj1hxDdyuYIA1tqQqpzeWPWKlj+JuxbDYn9YNjNNkam\nlKpOkzalVJO2/tB653Jqi65Q/I21cmAjRMXbFFXTUWXi+JyNkD7LWkkepUmbUgEmKG6PKqWarw2H\n3GZCkGhXQYf+2qC+FrrHdyciJAKArJIj5FbOhLBvtTUzglIqYGhNm1KqSXNvh5VaXOQq6KDjs9VG\nWEgYk06dRHxkPCmt+9Li7cuh8KBrZgQdZFepgKFJm1KqyTpWfoxth7c511Ny9rgKOwywIaKm6Q/9\n/+Ba6TgAtn5tLWeu1qRNqQDSKLdHRSRERH4tIm+LyHwRmSEifxCRmMa4vlIqOBWVFjGh1wT6telH\nr1a9aLF/natQp6+qH/dkd98q++JQSh2n3jVtjoTrI6zELx+YaIwxHvZLAD4HBlcrugq4X0QuNsb8\nUt84lFLNV6uoVkwZMQWAirx9sMrREzKiBST0tC+wJsgYQ1ZhFjti4xheuTFzjZ0hKaWqacjt0Qsd\nDwO85Clhc5gGDPFS1h34UkRONsbkNiAWpVQzF7Lfbb7RpJMhRPtZ1VZpRSnnzTqPQ8WHEISlIsQY\n4+qMoO+lUgGhIUnbWW7LH3naQUSGAROwEjsBNgJfAC2xatqigfZY83/e2YBYlFLNXfez4KaFVu2Q\nDvVRJ+Eh4cRHxnOo+BAGw+aELgyI6Wi1bysrgohYu0NUStGwpK2y4cMxYKmXfX7vtrwIuMAYcwxA\nRF4HvgfCgWtF5H5jTEkD4lFKNSNlFWWM/c9Yusd3J7VNKn867U+EdjodOp1ud2hNUkpCCtuPbAdg\n4+iHGJB6tc0RKaWqa0idd3esGrTNxhhvg/lc5Lb8aGXCBmCM+QmY4ViNBwY2IBalVDOz/ch2sgqz\nWJq5lHk75hEaEmp3SE1alZkRcjfZGIlSypuGJG2tHc85ngpFJBVIwkrsMo0xiz3s9pXb8kkNiEUp\n1cxUGVS3VW9Y/wnk7gSvzWtVTVLauM+M4Hpv9f1UKnA05PZohOPZ28zMZ7gtL/Syz2635YQGxKKU\nambcE4vU0Bbw0TXWSvcz4bpPbYqq6XKvaduSu4XSz+8jPHMtHN4Jd63XzghKBYCGfAvzHc9tvZS7\nd1T4thbn04F+lVK15l7T1q+kzFWgQ33US3xkPB1iOwBWb9IdG2fD7qWQnwm5O2yOTikFDUvadmH1\nCO0r4j7hH4hIGDDWbZO3pM29di3fyz5KKVVFhamoOn3V4SxXoQ6qW2/uk8fvaOeW/Oogu0oFhIbU\nbv0EnApEAjcBL7qVXY2VkBlglzFmq5dz9HNb3tWAWJRSzciuvF0UlhUC0CaqDe2y1rsKm1HSlvzg\nZw06PjYilDtH9+GmM3sAcMPJN/Db1N/SN6Ev8YtfhG0/WjvuWwWnXN7QcJVSDdSQmrYZbst/E5HH\nRGSMiNwHvOpW9l4N5xjutrzB615KKeWmSnu21r2RHGuoCkLCILGfl6OCQ2yE73rJHi0p54WvNzvX\nByQOYEiHIcRHxltjtFXSmRGUCgj1rmkzxiwSkXnABVhjrU32sFsu8Lyn40UkDjjPsZpjjNlS31iU\nUs3LSW1O4r5B97ExZyMnh7gN/NouFcKj7AusEdw5ug8vfL2ZoyXe+oDVjdfzdKiWtOnMCErZrqGN\n/68G/kfVnqKV8oErjDEehwQBfot1a9UA3zQwDqVUM9K1ZVeuPelaa2WpW8V+h1PtCagR3XRmD+ft\nzIbwdmv1UNEhNuZsZMOhDVzcoi2JBQfhWJ7VGaGNdvJQyk4NStqMMXnAmSJyKTAO6AwUA8uBacaY\nzBoOvxzY6Vj+uCFxKKWasby9ruVm1J7NXx74/gF+zLTasnVL6sl5BQetgn2rNGlTymY+GWbDGDMH\nmFPHY871xbWVUs1LYWkhhWWFtI12jDY05gk4837IWgetutkbXBBITUh1Jm0bErpwXnxf61ZplyE2\nR6aU0rHRlFJNSvq+dO5cdCeJ0YlM6DWBOwbeAVEtodsIu0MLCu7DfmyMjITRL9kYjVLKXb2TNhFx\nNChhrzFmQT3PkQZ0BTDGvFPfWJRSzcf6HGt4j+yibErKS2yOJvhUmYPUbSw8pZT9GtIVaDrwFnBP\nA87xJ8c53mzAOZRSzUiVOUfLyuHbZ2HzF3D0kI1RBY9uLbsRHWaNl36g6AAHiw5a84/m7oTiPJuj\nU6p5C4T+2+J4KKXUCVUZo23/ZvjmCZhxJax+38aogkdoSCi9W/d2rm9c+Cj8rTv881TY8qWNkSml\nAiFpU0qpWjlQ6Kj5AaLDoumW7Ta8o/Yc9Zkqt0hLcqEo11rJXG1TREopsD9pqxwFs9jWKJRSTYJ7\nLVtK6z6EZrtNpNIMxmhrLH0T+gKQGJ0ILTu6CvZp0qaUnezuPVr571yurVEopZqEKu3ZopOgsiNC\nq24Q3dqmqILP2OSxnNPlHNpEt4HDu+D7N6wCnRlBKVvZ8s0TkTgRmYzVc9QA6+yIQynVtFRpz1bh\n9j+n3hr1qbiIOCthA4jvAtEJ1nLlzAhKKVvUqqZNRLbXUHzWCcqrnAqIBtpStfPBJ7U8XinVjD06\n/FGu7HMl63PWc3rGKleBJm3+I2JNHr9tobWuMyMoZZva3h5NxqoRq06w2qXVZRjy6j1F1wHT6nC8\nUqqZSohKYESnEYzoNAJ+GuMq0KTN57Yd3sbivYvZlLOJUa3acEFlQeZqOOVyO0NTqtmqS5s2b8Ny\n1He4jmxgBjDFGHOsnudQSjVXrbpB3j44sluTNj/4Ye8P/H353wEIbTvQlbRpZwSlbFPbpO3sausC\nLMSqfVsGPFDL81QAR4H9xph9tTxGKaX45eAvHC09SkqbFFpGtITL/m0VFOZATIK9wQWh1DZuw36U\nHoHzn7LmINVeukrZplZJmzHm2+rbRJwVbDmeypVSypfe/uVt5mXMA+CpM55ifM/xVoEmbH5ROewH\nwNb8nZSOn0R4aLiNESmlGtJ79B3H42sfxaKUUl659xztEd/Dxkiah5YRLenUohMAZRVlbD281eaI\nlFL1HqfNGHO9D+NQSimvCkoKyMjLACBUQun1/cvWGG0d+sOAX0NsW3sDDFKpCansLdgLWJPHp0a2\nsdq0hYZDz+qtZpRS/qYjJCqlAt6m3E3O5Z6tehK58TNY9zF89QiUFtkYWXBLSUhxLm/Y9gX8vTfM\nuAK+f87GqJRqvpp80iYiMSJygYhMFpHZIrJTRIzjMaWB5+4kIreKyCwR2SoiRY7HDhGZKSLn+Ohl\nKKVqUGUmhBZd4NgRayU6AeI72xRV8HPvjLCp9LCrIHMtGE+jQCml/Mmn01iJSAesqalaAzHUYTgQ\nY8w79bzsEODzeh7rlYh0AXZS9TUUOtaTHY+JIvImMMkYU+7rGJRSliozITinLMa6PSr1HXVInUhK\nQgqtIluRkpDCwMTTYP2PUJRjJc0523WQXaUaWYOTNhGJAe4BfkfdBtl1Z7A6NdRXLrDS7fE80L4B\n5wMIxUrQFjhi+9oYs09EQoAU4ClgAnADsA94pIHXU0p5sf7Qeudyv6KjrgIdn82v2kW347urvnON\nFvDzV66ZETJXa9KmVCNrUNImIn2B+VhziNr17+73xpgqff5F5K8+OG8ucLoxZqX7RmNMBbBeRC7F\nquEbC9wpIk8aY4p9cF2llJuyijKOOG6HCkLfnD2uQk3a/Eqq12J2cJ/OajWcfFnjB6VUM1bvpE1E\nYoEvgS5UneIqE9iDdSvR7/x1W9IYcwSr1s5buXHcGh0LtMC6LbzK2/5KqfoJCwljwRULyCrMYvvh\n7cS8f42rUJO2RmU69Hf9d56pMyMo1dgaUtN2O66ETYBXgH8YY2o7eXwwcK9ZC7UtCqWCnIjQPrY9\n7csroPCgtTEiDlp3tzewZmB33m6mrZvGhpwNJIW35MXKgn1rrM4I2qZQqUbTkKRtgtvyZGPMUw0N\npglKczyXAJttjEOp5kFCYNQ9kLkGwqMhpMl3gA945aac/2z5DwD7oxIw0QlIUQ5EtYSjB6BFos0R\nKtV8NCRp6+N4PgI844NYmhQR6Q7c7Fj90BiTZ2c8SgWrV9e8SuvI1qS2SaVfm36En/uo3SE1K11b\ndiUmLIbCskJyinM4cMW/SUwaoAMaK2WDhiRt0Vi3Rn9ubsNdiEg0MAtrWJODwIMn2H8SMMl926JF\ni/wVngpABQUF+pnXSxlTV0+ljDIAnun8DDGhMTbHVDtN6TM/UZztQ9uzvcxq+fLRxo2cvMuno0UF\nlab0uaumpyHfvL1As5sAUETCgBnA6UAp8BtjzL6ajjHGTAWmioizw0ZaWpo/w1QBZtGiRfqZ18X8\nzwAIicx2JmydWnTiwrPOhbBIOyOrtYD/zB3vMZz499GSH5ewfaOVtIV3Cietf837N2cB/7mrJq0h\nSWswtNkAACAASURBVNtyoCfQ20exBDwRCQXeBy4ByoBfG2O+tDcqpYJXSNRe53Jqyx7wVEdolwpd\nh8I4nUqpsaQmuGZG2JizAXZ8Zw35cWAjTPiXdkZQqpE0JGmbBlwFJInIOcaYhT6KKSA5Erb3gCuB\ncuC3xpiP7Y1KqeAWGuWqxE4Ni4OKMsj6ucnUtgWLvgl9ncsbcjbCT9dZMyOA1TFEB9lVqlHUu+uV\nMeZrYCbWcB8viUgrn0UVYNxq2CbiStg+tDcqpYJfqHtNW0mJq0DHZ2tUvVr1Ikys//H3Fuwlr8Mp\nrkIdr02pRtPQ1qSTgDbAGGCJiEwyxixueFiBw5GwzaBqDdsH9kalVHNQQUhUpnMt9fB+V5EmbY0q\nIjSC8T3HExMeQ0pCCmHbV8D2b61CnRlBqUbTkBkRKvvd/4TVKD8F+FZENgFLgP1Y45fVijHm8frG\n4i9uNWxXYrVh0xo2pRpJSMRBJKQUgMToRNpmuiaN16St8T0+0u1XdIlbGzataVOq0TSkpm0KVaev\nqpwZIQXo6+mAE6h30iYirak6I0Hlbd8YEXEfTKjYGFPgdtwU4M+O1e7GmAy3sso2bFfh6nQwq74x\nKqXqKKSEsqPdiW+VTWqrXrB+uWN7OCSm1nys8q8OA1zLmTozglKNpaHDiUu1h7ftJ3o01CrggNuj\ni2P7fdW2v1yHc47EasMGVkL6kojsr+FxlQ9eh1LKoaK4M0W7/sCSq5fwVPdfuQoSU7Ujgo3KK8rZ\nExoC0a2tDcVHIHeHvUEp1Uw0pKbtbZ9FEZjcE9pwIOkE+0f7MRalmq0QCaHlwa2uDR1OtS+YZqys\noowbvriBjTkbKSkv4cf2pxK5w61dW0KzG7ZTqUZX76TNGPM7XwbSEMaY5HoeNwXrNq+nskX4phZQ\nKdVQ/SZAbDvrVly3EXZH0yyFhYRxqOgQRWVFAGxt152TstZZt0qjWtocnVLNg85FopQKOBKeQ3TH\nDygv7si8HSFc0P0COPVK66Fsk5KQwq78XQBs6DGcky54QduyKdWIGtqmTSmlfC40ai+hMbuISFjK\nnC1z7A5HOaS2cZsZ4fBWTdiUamSatCmlAk6V6aviukHWeigvszEiBVZNW6UNORtq2FMp5Q+atCml\nAk6V6auOHoZXh8PTneCbp2yMSrknbVtyt1C+8XOY+0d4dSSs/8TGyJRqHhoyuG5XXwZijNnly/Mp\npZomY0zVmra8Q9ZCWTFEaoN3O7WNbku76HYcKDpAUVkRO3d8TY9V71mF+1ZCv4vtDVCpINeQjggZ\nVB1ctyEM2ilCKQVkFWYREnYUAFMeSZfsTa5CnQnBdikJ/8/efYfHVV6JH/+eUbXkJsmWG7Zs4ybj\nboJNN5iYFpLQAgn7g5CEFrIJqctusoQku0lIAmEJZAOEZNkkQGjJEsAU25iOKe62JPdeJEuyep/z\n++OONFfGanNHujOj83meefTOvPfeObKQOPPe933PNEr2lwBQmDmEto0+Dq7zLSZj+otoJEo2E9UY\nEzWFZYVtbW0YSaDkvXDnyJnHOcP0pSunXMk5484hPzufycEkWHGP03FwvVVGMKaXeU3aIv3tbB2h\ns99uY0w77gnug+sHQdCpP0rWBBgw1KeoTKtzxp0TfhIMQuogaKyC2iNQeQCGjPEvOGMSXMQLEVQ1\n0N0HTnI4DDgHuA+oCV3mUSBdVZM6eBtjTD8ze9hsGssX0FI3lkkNrj9Rdms09gQC7StUHFrvXyzG\n9AN9snpUVYOqWqaqr6vqbcAcoBC4FnhRRGwVqzEGgNPGnEbDoUup3XUrS6rrwx2WtMUMVWVf1T6W\n717O0dzwilKb12ZM7/IlWVLVHcAFQC3O6NsdfsRhjIlt0wKuReWWtMWMW5ffyoXPXshtK29jzcAh\n4Y6DNtJmTG/ybYRLVfcCj+DMa/uGiKT7FYsxJjY0tTShGl6U/oXGH8BNb8CnfwNj5vkYmXHLG5zX\n1i5MCUDeGbDwVpjzBR+jMibx+b3NxmvA14HBwGLgBX/DMcb46bHCx3hw/YMMGDecpvIFNFXNdkbY\nbJQtprSrjFBfAtfbn25j+oLfc8lKXe3xfgVhjIkNhWWFVDVWkZy5A0mu6foE4wt30ubeosUY07v8\nTtpGudqZvkVhjIkJ7gRgTmM1Fwfeg9LtztYSJmZMHDqRlEAKAAdrDnK0/qjTEWyBunIfIzMmsfmd\ntF3tah/yLQpjjO/qm+vZWbETAFXha83v8kDqffCbebD1ZZ+jM24pgRQmDZ3U9ryw8Fn4/Xnw0zHw\n/Ld8jMyYxOZb0iYi/wpc6nrpDb9iMcb4b9vRbbRoCwDamMM8XCtHR87q4Czjl/yc/LZ2Ye0B2PcB\nNNfZXm3G9CIvBePP6uEpKUA2MAu4ApgSel2Bl1R1V6SxGGPin7sSQnpDNoOkznmSMQwGj/YpKtMR\n97y2w4EASBJoC5Rug/pKSB/sY3TGJCYvq0dXEp2C8fuBW6JwHWNMHCssDc9nG9Pg+tM0arbVs4xB\ni8ctZmrWVKZkTWFg6kBY/TwUb3I6D2+EvNP8DdCYBBSN26Pi4bEUOENV93z8ssaY/sS9COGkRquE\nEOtyM3KZN2Kek7BB+5+TbbJrTK/wMtK2h56NtDUCVcBuYA3wrKpu9vD+xpgE8r1TvsfGIxspKiti\nwY5lzsc6sKQtXoyaBesec9pWzsqYXhFx0qaq46MYhzGmn5s9fDazh88GVcpeeSTcYUlbzCqvL2fD\nkQ0UlBYwIy2Z01s7bDGCMb3C74oIxhjTXn0FR3QIQ6ihhgEMzhrvd0SmA3/f9nfu+egeAC6f+Olw\n0lZcAE31kGLVCY2JJkvajDG+21Gxg4EpAxk+YDgyYChLGn/JAOo5QY7wqi1CiFnttv2o2A5nfReG\njHVulSal+BiZMYnJkjZjjO9+uuqnrDq4iuz0bH696NcA1JHOVj3B58hMZ/Kzw0nblvItNF30p7ZK\nCcaY6Ou1zXVFZKiITBCRaSIyUkRSe+u9jDHxS1XbVo6W1ZeRm5Hrc0Smu4akDWF0prOHXlOwiR1H\nd/gckTGJLWpJm4gMEJGviMgLIlKCUwx+G7AJZy+2GhFZJyK/ERGbWWyMAeBQzSEqGioAGJQyiDEv\n38k3k5/mvMBHJNHic3SmK+5bpAVlBaAKZTtg+2s+RmVMYopK0iYi/wTsBR4ELgBy+PiebEnADOCr\nwGoReU5ERkbj/Y0x8ctdCWHqkAnIxqf4RvKz3JvyAEFsPlusc1dGKCheBz/Pg/vmwuNXQ0uzj5EZ\nk3g8J20ich/wKE6JKqDTv7LuJO5TwDoRye/keGNMgnNvqjsteUhbe7Pmof6VRzbdND1nelu7sGIH\npIU2222uhyNbfIrKmMTkaSGCiHwX+BrhTXaDwDLgeWADcARnU91BwInAAuAq4ITQOcOBV0RklqqW\ne4nFGBOf3ElbflN4ZGZjcIIf4Zgeah1pG5U5itEDRzv76lXudzoProMR0zs52xjTE14Kxo8Bfkg4\nYfsA+JKqburglDXA0yJyO3Ab8NPQ+48OXee2SGMxxsSvdiNtFSVt7Y3B8T5EY3pq+IDhvHnVmwxN\nH+q88NrPoOhFp31oPfB532IzJtF4uffwRSAj1F4FLOokYWujqi2qejfOiFvrrdIv2+pSY/qfioYK\nDtYcBCA1kMqEQ+EEboNO9Css0wMiEk7Y4JgapFbOypho8nJ79AJX+0ZVrevJyar6dxF5FrgMJ/k7\nC+fWqjGmnxiSNoQ3r3qTwvJCisu2kfLUrQDUaSo7dJTP0ZmIjJoVbh/aAMEgBGxuojHR4CVpa/0Y\nvE1VN0Z4jadxkjb39Ywx/cjQ9KEsHLUQqqvbXtusebSQ5GNUpidK60p5bvtzFJQVMCApnR9l5EBt\nKTRUQvlOyDnR7xCNSQhePv7k4Mxn2+fhGvtd7ewOjzLGJL60QTD9MzA0jw22CCGu1DbXcs9H97B0\n51KW712BjpgZ6hEo3e5rbMYkEi8jbZU4iVuOh2u4z630cB1jTBx6esvTjB44mvzsfLLGLYBxCwD4\n8e3/8Dky0xMnDDyBQSmDqGqqoqKhgkOn/hujzvk3GHFSeAsQY4xnXkba9uIsIjhJREZHeI3zXe2I\nRuxEJENELhSRH4jIsyKyW0Q09LgzwriOfY8RInK3iBSJSJ2IlInIm6EKELb7pzERqGuu4yfv/YSb\nXr2Js/96NrVNtW19QdufLa6ICNNywpvsbk5PcxJwS9iMiSovfxlbFw0EgHt6erKIzASuDz1tBlZG\nGMcpwIvAT4BLgXERXue4RGQ+TimubwFTcGIdBJwBPAwstZWvxvTclvItBDUIQN7gPDJSMro4w8Qy\nd/H4gtKCTo40xkTKS9L2Z5zNdAGuFJFHRCSzOyeKyFnAq0Aqzry451TVy+3RcmA58EucTYEOebhW\nGxEZgrNRcA5QCHxCVQcBmTibCjfhjBbeG433M6Y/KSoramvnp2bDf82GJ6+DdU/4GJWJlLucVWFZ\nIVQegKKlsPLnUF3SyZnGmO6KeE6bqm4QkUeAG3ASry8CF4ReexHYoKo1rceLyARgIfAF4CLXpeqB\n2yONA3hTVdstYhCRn3u4ntt3gJFAHXCRqu4EUNVG4AERGYyzSfCNInKvqlrNFmO6yV1zdFowAOW7\nnMeALOCTfoVlIuQuZ1VQWgBPfxn2vOO8MGoOTL2ggzONMd3ldeLI13Fua7bO6xoFfB94G6gMzf+q\nFJFmYBvO6NxFhDfVbQauUtWIlxepakvk4Xfp2tDXJ1oTtmP8BqgGkoBrejEOYxJOYamrEkL10XDH\n6Dk+RGO8Gj94POlJ6QAU1xVzZMTUcKdtsmtMVHhK2lS1AScJu9/1srsofBowMPQ+4uoH2AWcp6rP\ne4mht4jIVMLz45Ye7xhVrQbeDD1d0hdxGZMImoPNbD26te35tJId4U73jvombiQFklg4eiGnjzmd\nG2beALknhTsPrfcvMGMSiKeC8QCqWg98XUQeBm4FPgOM6ODwFuBD4A/AX3paRaGPzXC1O9s8eCNw\nIWBVkY3ppp0VO2loaQAgd8Bwsnd+5HQEUiB3OnDAv+BMxH5z7m/CTw65/mzaSJsxUeE5aWulqhuA\nm4GbRSQPmAQMxRltqwBKgPWhJC8euLcx2d/hUeG+wSIyMDT6ZozpRGl9KTnpOZTWl5I/wPUZLzcf\nktP8C8xEz/CpkJQGLQ1QsRdqyyDD9lA3xouoJW1uqrob2N0b1+5Dg1zt2g6Pat83CGeOmzGmEwtH\nLWTlVSspqS2h9v0HcdYuYfPZEkgTkDJiOhxY47xwcB2ceI6vMRkT73olaTPticiNwI3u11auXOlP\nMMYX1dXV9jPvQP7mVW3tLVUZHDjm3yle/93i6WcezTifKH2CXQ27ONR0iL/qibQuR9j+9t/Yuzfx\n9yKPp5+7iT+WtHWsytXOoOMyW+4dQauOd4CqPgQ8JCLa+tqiRYu8xmfiyMqVK+1n3pFxArtPhQNr\nmXLuF5gyaja89EJbd7z+u8X8z7yX/o1/+4/fsr/amTVSMXUmHFwGQ/M4cfJUTjw1eu8Tq2L+527i\nmqekTUTuwZm31gJ8Q1U7u4147LmfJ7wZ0+9V9R0vsfQC90zoMXSctI0Jfa20+WzGdO1wzWHuW3Mf\n+dn5zBw+k9kTz4aJZ/sdlomSadnT2vbgK8gazSn/siu0954xxquIt/wQkVOB24DrgEE9SdhC1uNs\nyHsd8C+RxtGL3CtGZ3R4VLhvcy/GYkzC2Fi6kee2P8ddH9zFfavv8zscE2X5Oa5yVpU7LGEzJoq8\n7NP2aVf7jz09WVU3Aatw9m1bIiIDPMTSG7YAe0Lt427lHSrbdWbo6St9EZQx8a6wzLWp7qBx0NjT\nz3smlrlrkLo3UDbGeOclaTst9LURp+5nJFo3rU0FPuEhlqhTVQX+N/T0ahEZf5zDbsXZPLgF+Evf\nRGZMfGtXCWHvWvjZGHhgIWxf4WNUJlqmZE1BQnuo76zcSe3+j+CtX8NTX4T1T/kbnDFxzkvSNhWn\n5mihqjZHeA33jotTOzyqCyKSJSLDWh+Ev68M9+siMvCY8+4UEQ09xh/n0r/CKT6fAbwgIvND56WK\nyC3AT0LHPWR1R43pnnY1R8v2gQahpABSMn2MykRLRkoGE4ZMACCoQbYU/R8suxM2/Q12rPQ1NmPi\nnZekrXWiQqmHa7jP9TLxYQ3O5r2tj7Gh1797zOv3H/fsDqhqBfCpUJzTgQ9FpBJnL7bf4owQvgJ8\n00PsxvQb5fXlHK49DEBaUhoTirc5HRKAkZ1NHTXxZFr2tLZ2YZprs2SrjGCMJ15Wj9bj3Br08vHY\nvV2GdniUj1T1IxE5CWexxKdwEsIanIUKjwJ/UNWgjyEaEzfc89kmZ4wiWUP1R4dNgVQbaUsU5447\nl2EDhpGfk8/JQ6fAq79wOkoKoLnBql4YEyEvSdsRnAoAkzxcw31LtCTSi6jq+AjPuxO4sxvHHQa+\nFXoYYyLUbhFCkitJsyLxCeX88edz/vjzwy9kTYDynRBshuLNMHquf8EZE8e83B7dEPqaLSKR1ia5\nwtUu6PAoY0xCyM3IZcHIBQxOHUx+fUO4Y5SVr0poo2aF2wfX+xeHMXHOy0jby4S3/bhLRE5X1abu\nniwilxLeLqMMeN9DLMaYOHDxxIu5eOLFqCrNvzs93GEjbQmpOdjMzoqdDM+dytDWnSxtXpsxEfMy\n0vYXoDzUng88LSKDu3OiiFwI/Cn0VIEHQ1tsGGP6AWluIKXYtYfXyJn+BWN6xa8/+jULH1vIZc9d\nxmvu4YFDNtJmTKQiHmlT1UoR+RFwL07i9Slgs4jcCzyrqjvcx4c2zz0DuAm4FGdTXQX2A3dFGocx\nJg4lp8HXPoADa+Dobkjv1uc9E0cyUzJpaHFugW/WOi4dONIZUR17is+RGRO/PNUeVdX7RGQuTikq\nBUbjJGB3iUgFzuKCBpz6pCOBpNCpEvpaBVysqscttG6MSRx/2/o3nix6kmk50zh//PksHLUQck70\nOyzTS9pVRqjaDd8p8jEaYxKDp6Qt5EvAbuAHhJMxwUnUhrieg5PYtbY3A59TVavZaUw/sK5kHRtL\nN7KxdCN5g/KcpM0kLHcN0qLyIlqCLSQFkjo5wxjTFS9z2gCn3FNo64w5wJ+BOle3EE7SWp9vAm4G\n5lvCZkz/0W67j+pyZxVhc6OPEZneNGzAMIYPGA5AXXMduyt3hzuDtrWlMZGIxkgbAKq6EbhWRL6E\nszAhH8gG0oCjOOWg3lPVg9F6T2NMfGgKNrG1fGvb8/xlP4XmOyEpDb63A9IGdnyyiVv5OfmU7HO2\n4CzY8wYTV9zlLEQYmAvX/p/P0RkTf6KWtLUK1SFdFXoYYww7K3bSGHRG1Ual5zCkeY/TMXiUJWwJ\nLD87nzf2vQFAQeUOLl7/hNNRsR9UQaSTs40xx/J8e9QYY7rivjU6NWVouMM21U1o7RYj1ByAAaES\n0w0VUL7Ln6CMiWNRH2kzxphjFZSGC57kN7vmM9mmugltTu4cfnTaj8jPzufEoSfCoStg5+tO56H1\nkD3B3wCNiTM20maM6XVF5eHtHqZVHgl3WNKW0HIG5HDZ5MvIz8knNSm1/c/bylkZ02M20maM6XVX\nTrmSSUMnUVhawPS1r4Y77PZo/9IuabNyVsb0lCVtxphed+GEC7lwwoVwaCO8/5zz4pCxkJnjb2Cm\n1wU1yL6qfWwu28yg5CBtFWetnJUxPWZJmzEmqh5+Ywf3LttCTWPLx/rOC3zEAykppEkTL5WN5Obb\nX/AhQtOXXt71Mt9743sAnDH6DE5PyYSmGqg+DFWHYNBInyM0Jn5Y0maMiaqPJWyBOgimAQGWBedz\nUsMjTJIDCNrltTJTbQf9eDcte1pbu7C8EOZdC0kpzq3S1EwfIzMm/ljSZoyJqmNH2AaM/itJGTto\naRhFw+FLaK4/gUId1+V1MlOTuO28Kb0VpukjeYPzGJA8gLrmOo7UHaHkU99meMZwv8MyJi5Z0maM\n6TW7fn4xi5+8h+K6RpIzdvP8bec5Wz+YfiMgAaZlT2NN8RoACsoKLGkzJkK25YcxpteU1pVSXFcM\nQFoglbz3fg/rn4LS7T5HZvqS+xZp2559LU1QXNDBGcaY47GRNmNMr3FXQpiSPozk934benIhfOEJ\nn6Iyfc1dGaGgrAAeWQIH1kBLI/zLrnClBGNMp2ykzRjTa9xJ27Sg6zPiaNufrT+ZnjO9rV1YVgjN\n9U7CBrbJrjE9YEmbMabXtEvaaivDHVYJoV+ZOHQiKYEUAhIgPSmd+pEzwp22ya4x3Wa3R40xvcad\ntOUf2R3usKStX0kJpPD4xY8zdtBYMlIy4IPfw5q/OJ22ya4x3dZp0iYi1/ZVIKr6v331XsaYPiAN\n7K50ErUAASa3jrRl5sKgUT4GZvwwNXtq+MlIK2dlTCS6Gmn7H+jGDpjeKWBJmzGJRJP500V/oqis\niOJdr5O+c5fz+qjZIOJraMZnI04CCYAG4chWaKyxjXaN6Ybu3B6N5K+rdnBeR68bYxJOErOHz2b2\n8NmwyzWaYosQ+qXGlkbe2v8WhWWFHKk7wh3DpkBJIaBOTdpxC/wO0ZiY11XS9gbdG2mbAWTTPiHb\nCZQCDcAgYDwwONTXes3VQHU3YzXGxKuBuTBihrMvl81n65eCGuRbK79Fi7YgCN8Z+QkySkJzHg+u\ns6TNmG7oNGlT1UWd9YtIAPhP4CychO114DfAy6pac5zjpwFfAL6Ok8ANBr6iqjapwZgEk5SxnYPV\nBxmZORI5/Rtw+jegqc65LWb6nfTkdCYMmcC2o9tQlKITz2DuxHNh1CwYPq3rCxhjPG/58VPge0AL\ncIuqnqOqzx4vYQNQ1UJVvQOYCnwATAZeFZGxHuMwxsSUFgaM/QNLnlnCmX89k9qmWufllAGQnOZv\naMY37k12N6elwtxrYORMp4C8MaZLESdtIrIAJ2EDuFNVH+zuuap6GLgQOAQMAx6ONA5jTOwJpBUj\nAadwfGZyprPNg+n38nPCSZt7OxhjTPd4GWm7IfS1Bvh1T09W1TLgv0NPzxORPA+xGGNiSCD9QFt7\nmibDny+H5T+Bw5t9jMr47WM1SIOh1aMbnoaKfT5GZkx88JK0nY6zoGCTqtZFeI1Voa8CnOohFmNM\nDElyJ221VbBtGbz5KzhS5GNUxm/upG370e00PvNluP9keObLsH2Fj5EZEx+8JG0nhL42erhGk6s9\nxsN1jDExJJDmStrK94c7Rtl2H/3ZoNRBjB3kTGFu1ma2Dh0Z7rRNdo3pkpcyVk04I2TTRCSgqsEI\nruEqQNcugTPGxK1gu5G2/Kpyp5E+BLLG+xOSiRn52fmoKvk5+SQNdVVJsMLxxnTJS9K2A5iHs5Dg\nKuDxnpwsIinAjcdczxgT5ySlHElqAGBocgYjWpwFCVYJwQDcddZdJAdC/+upLga+4bQPb4RgCwSS\nfIvNmFjn5fbo30NfBbg/tJq0W0L7uz0EnBR6qRpY5iEWY0yMCKSWtLWnJQ0K77htm+oaCCds4Gy6\nPDB0i7SpFkq3+ROUMXHCS9L230AJzmKELGCliPyis1WgIpIkIp8CPgRai9ErcI+q1nuIxRgTI1pq\nplFVdCe1u2/k5ibX/ls2n80cjzuZt1ukxnQq4tujqloqIl8E/gakAGnAt4Fvi0gRsBGnjFUjThmr\nCcAcwqWsWr2OU1XBExEZFHr/y0Pv1QJsAZ4AfqOqES+YEJErgOuA+Ti3g5uAfThlvh5Q1bXeojcm\nwQTTaamdwPyDW8KvWdJmQp4sepJVB1dRUFbAfcNOYtLWUMfBtTDrSl9jMyaWeZnThqouFZFLgD8B\nuaGXBafiwdTjnCK0Lxr/FHCdqjZ7iSM0urcSp74pQC1OEnly6HGNiCxW1fIeXjctFOMlrpergVRg\nSujxJRH5jqr2eK86YxJZEkE4/etwYC2UboXsiX6HZGLEm/veZOW+lQAUDF/IJHDKm9Uf9TMsY2Ke\n5yKAqvoqMA34L6Ai9LJ08GjtWwV8RlWv8npbVESSgX/gJGwHgU+qaiaQAVwNVAFzgT9HcPl/I5yw\n/RY4QVUHAQNwksG3cP4N7xaR+R6+DWMSwtH6o6TmvEZSZhHBpFqn3uiVf4Sb34KA1Rw1DndlhIJk\nga+sgH87AJ95wMeojIl9nkbaWqnqUeCbIvKvwCLgFGASzly3NKASOAysAd5U1WjusHkdMDPUvlxV\n3w3FFAT+Glr08BhwUWi0bXkPrt067+51Vb219cXQtT8Kzc/bBwwErgA+8vatGBPfNpZuJC33ZQBa\nasfhfG4ypr12lREqdsCp9pnXmO6IStLWKjRq9lLo0VeuC319rTVhO8YTOHPmJuAkYT1J2kaFvn54\nvE5VrRCRLThbnwzswXWNSUjuepLSkNvJkaY/m54zva1dWFZIUIMExEZijelKXP+WiEgGTjktgKXH\nO0ZVlXASuaSHb9G6d9xxPwaKyBCceW3QQWJnTH/iTtq+3fwm/HomPHEN1Jb5GJWJNSMyRpCVlgVA\ndVM1+0sLoWgpLLsT3v2tv8EZE8PiOmkD8gl/Dxs7Oa61b6SIZPfg+q0F7ReJyAMiMgZAHPOA53FG\n2N4lsjlzxiQUd9K2oLECKvY4NSXTh/gYlYk1ItJuXtvm7S/B41fDW7+GtY/5GJkxsS3qSZuITBGR\nq0Xkn0XkByJyR7Tfw2W0q72/w6Pa943u8KiPewD4BRAEvgrsE5EqoB5n/tok4OfAYlVt6cF1jUk4\nFQ0V7K7cDYCoMKkptMvOyJm2y735GPe8tkJpdlaPAhRvgvpKn6IyJrZFZU6biAwE/hknsTleUvTj\n45zzBDAOZwuQz6lqZ0lXRwa52rWdHOfuG9ThUcdQ1WBoccVm4H6cUTX33LV0YAiQCdR1dB0RuZH2\nJbtYuXJld8MwCaC6ujrhf+ab6ja1tXMaMkhTp70vOIxtCf69H088/cz9iDOlNoUZA2ZwQuoJKBOL\nQAAAIABJREFUDCobRnVGHgNrdoIGWffiHyjPjs99/eLp527ij+ekTUROxpnsP6H1pWMO0Q5OfRf4\nXKj//+GMWMUUERmGs0/bIuBV4Ec4t1oHAKcCdwG3AOeLyFkdJZ6q+hDwkIi0/VssWrSoV2M3sWXl\nypUJ/zPfsGYDFDvt/Ppg2+snnHwxJ8xd5E9QPor5n/lLL7Q1v/hSjadLZaYmcdt5U7jhrO7vxbeI\nRXyNr4VfaN4IHz4CwOzsBojlf7tOxPzP3cQ1T7dHRWQ2Ts3QCYT3YtuOU5f0QBen/y/Quqnu5RGG\nUOVqZ3RynLuvqsOjPu5RnITtdeB8VX1bVStU9ZCq/g04AzgCTCQGk05j+lJ6UjqjMp0F12c2uPax\nHh2fIyaJLjM1eresaxpbuHfZlq4P7MxYV/nqvau8XcuYBBVx0iYiScDjhMtSbQBOVdXJqnpZ6HmH\nQtUJXsNJ9OaKyNAIwnAnhmM6Oc7d11UyCYCI5AMXhZ7eHVqF2o6qFuMknwCXicixo4zG9Bs3zLqB\nV654heStX+PTdaGi8cnpMOx4xVGM3247b0rUE7dI1TXXUTkyvA0I+z6AYLDjE4zpp7zcHv0CTiUE\nBdYBZ6pqT8fY3wU+iZO4zcKp5dkTBTiLBALADDrY9iPUB3BIVbu794DrLwjbOzmutWpeBk4pr8Pd\nvL4xCWlmsJTM1s84I2ZAUlS3gzRRcsNZE3t0O7Mj429/oeuDOvCP7f/gDxv/wI6KHdw86yZuGTgC\nqg9DQyWUFMKI6V1fxJh+xMvt0c+62rdEkLABbHK1J/f0ZFWtBd4OPb3geMeERr/ODz19pQeXd3/M\ny+vkuBGudnUPrm9MQlobnMT1jd/lnqYrYO41fodjYpiibDu6jaAGKSgrhLGnQNYEmHV1eDWpMaaN\nl4/ArRvO7lXVSCcglLrakdweBWfe2ZnAOSKy4DixXIkz5wzCtzK7Y7WrfQvHGcUTkUzCpa7WR5i4\nGhP3fvHBLyipLWFO7hyqkgO81jyX15jLt06+2O/QTAxrV86qrAAufwGSU32MyJjY5uWjTC7OrdEd\nXR3YiUZXOyXCazyKM39OgGdEZDGAiARE5Erg4dBxS4+tOyoid4qIhh7j3X2quhunED3AJSLyJxE5\nMbSxboqInAasJJwQ3h1h/MbEveW7l/PSrpf4+fs/R5Jtjy3TPROHTCQtKQ2AQzWHKG+2z73GdMZL\n0taacEWabAEMc7XLOzyqE6raDHwa2IWz4GCZiNQANcCTOAsl1gCR3Kf5EuEi8P8EbMO5Bdp6W/bk\nUN8vVbUno3jGJIzDNYc5UOOs70lPSmNuQyUZ1PsclYkHyYFkpmRNaXteUFYQ7lS1xQjGHMNL0nYY\nZ3TrRA/XONnVjmRzXQBUdRfOQoYf4+yjpkATTsL1HWBhaLVqT697BFgIfAV4Ged7TsHZqmQHTumq\nM1X1e5HGbky8W1eyrq09Y+BYnkn7DzamfZnfptzrY1QmXrS7RVpaABufgb/+E9w9FbZ0tLbMmP7J\ny5y2D3EWD4wQkU+o6gc9OVlEAsBVoadB4C0PsaCqVcAPQ4/unnMncGcXxzQDj4QexphjrC1Z29ae\nI5kABEQ5qpl+hWTiiLsGaWFZITSkQUFoZsreVTDN5kUa08rLSNs/XO2fRnD+13FG6RR4W1WPeojF\nGOOTdcXhkbY5deE5SRvV+3YSJvHlZ4eTtoKygmM22X3fh4iMiV1eRtqewinrNAk4V0QeAW5W1aau\nThSRr+AUYm8VSdJnjPFZfXM9m8s2tz2fXbKzrb0xON6HiEy8mZw1metnXM/07OnOqFtgQLhz/2po\nbrQVpcaERJy0qWqLiNwCvAQkAV8EzhaRB4DlQNtvmYgMx9nP7FTgemABznw4Bf6qqj3ZP80YEyM2\nl26mOehUo5swaBxDdzqzHBo1iSId62doJk6kJaXxrfnfav9i1gQo3wktDXBoPZxw8vFPNqaf8bRV\nuaquEJEv48z3SsKpQfqrYw4T4NBxXgNnBeaXvMRgjPHP8Izh3Dz7ZtYWr+XEYHi2xQadSAM2OmIi\nNHaBk7SBM6/NkjZjAI8F4wFU9U/A2TirKcX1AGckTY95XXAWHjwALFZV2xvAmDg1dtBYbp1zKw8v\neZjbyWl7/YPgtE7OMubjmoPNbCrdxMu7XnYqI7Sy4vHGtIlKUUBVfVdEpgJX4OxndjqQdZxDd+Dc\nTv0vVd16nH5jTLwq3dbWfD9oReJN91U0VLDk6SXUNteSnpTOuec+HN4AdM8qZ882kc4uYUy/ELVK\nzqoaxNnM9kkAETkByAEygQrgcGjfM2NMAqhtqgUgIyXDeeELf4Wje/jmL//bRtpMjwxJG0JWeha1\n1bXUt9RTkASzpn0KRs1pP+pmTD8XtaTtWKq6D9jXW9c3xvhr2Z5l/Pvb/87UrKlcMeUKPjf1c5CV\nx9+CZ/odmolD80fMZ3+1s8f66pK1zLr6Lz5HZEzs8TynzRjTP60tXktQgxSUFVBeH1EVOmPazM2d\n29ZeXbzax0iMiV2WtBljItKuEsKBQnj/YTi0EcHqRZqemzdiXlt7TfEaghr676i+Ava851NUxsSW\nXrs9aoxJXFWNVWwrdxYeBCTAzNWPQ1MdAMN4gJLjrkMypmMTBk8gKy2L8oZyjjYcZWfZVk588stw\neKOzCOH2vZA20O8wjfFVVJI2ERkIXAacAeQDQ4EMwlt/dEVV1UvheWNMH9pQsgFFAZiaeQIZTbuc\njqwJlBy0hM30nIgwN3cuK/auAGB16XpO1CCgzurR/R/BxLP9DdIYn3lO2kTkO8AdOKtEI7oEhP76\nG2PiQrtbowHXr37eaXDQh4BMQpg3Yh4r9q4gIzmDyoZKGLcAijc5nXvft6TN9HuekjYReQj4Mt0f\nUTPGJIC1xa6krboi3DHuVLDpRyZCF064kJNHnszUrKkkB5Ih+AR8+Aen0zbZNSbypE1EPgN8hfAo\n2VGcIvKrcD5r13qOzhgTc1qCLaw/sr7t+ZyDheHOvNOAwo+fZEw35GbkkpuRG37BvUfbvvchGISA\nrZ8z/ZeXkbabXO03gMtUtcxjPMaYGBeQAM98+hnWFK9hy963GbXzt07HwBGQPRFL2kzUZE2AzOFQ\nU+KsIj1SBLn5fkdljG+8JG3zQ1+bgastYTOmfxARxgwcw5iBY6Bkf7hj3KlWash4VtFQwbsH3uWj\nwx8xauAovjR2ARQ+73TuXWVJm+nXvCRtQ3Fuja5R1UNRiscYE08G5sL4M2HfB6Fbo8Z4s7l0M999\n47sATMmawpfGnhVO2optFNf0b16StjIgF5u7Zky/8tz255iaNZVJQyeRdNKlcNKl0NwIwSa/QzMJ\nYPbw2SRJEi3awtbyrVSecgeDh/zRmd825AS/wzPGV16Stg+Bi4Hx0QnFGBPrjtQd4ftvfR+A7PRs\nVly5gqRAEiSnAqn+BmcSQkZKBtOyp7GpdBOKsra5grNmXOZ3WMbEBC/LcP439DVPRE6ORjDGmNi2\nrnhdW3v84DwnYTMmytwlrVYftjqkxrSKeKRNVZ8SkVuARcB/i8giVa2JWmTGmD718Bs7uHfZFmoa\nWzo8Ji33RVJznHZq4U7WvzuHD4LT+EvLYnbo6D6K1CS6ebnz+NPmPwFOHVJamuHwBmeD3YwcmHmF\nzxEa4w+vG95cAazBWUn6loic0sXxxpgY1VXCBpA0YHdb+9yGEmYFdvLl5KVkUfWxYzNTbRTORGZu\n7ty29oYjG2go+D94aBEs/V54s11j+iFPFRFUtUxETgd+BdwMvCsim3H2RD8MNPbgWj/2Eosxxpuu\nEjakmUD6vranSxoPA9CgKWzQie0OzUxN4rbzpkQ9RtM/5AzIYfzg8eyq3EVTsImNmYPb9phi/0fQ\n0gRJKX6GaIwvolEwfiSQDgRD1zsJmB7BdSxpMyZG7Pr5xR97bW3xWv7fUiexG5eWTU5wDwBp409h\ny/Wf7dP4TOKbP2I+KUkpzMudx5ChE2BoHhzdDc31cGg9jJnf9UWMSTBea4+eATwPDAq9pDh1SHu6\nw6YVjDcmxq0rCS9CmEN6uMP2ZzO94I5T7yAgrhk8Yxc4SRs4c9ssaTP9kJfaoxOApUCm6+UqYB1W\ne9SYhFPZWEl6Ujr1LfXMrnIVQBl3qn9BmYTVLmEDZ5+2DU867b2rYOEtfR+UMT7zMtL2LzgJmwLV\nwNeBx1W12/PYjDHx45/n/jM3z76ZooMfMuqR0O1TCbQv6m1ML2gJttA8ei5prS/sfd/PcIzxjZfV\no0tc7StV9VFL2IxJbCmBFGbUVJITDC1aGDkL0gZ1fpIxEVq2exk3vXoTpz9xOs9WFkHqQKejcj9U\n7Ov8ZGMSkJeRttE4o2w7VfWVKMVjjIl1o+fBZ38He96BnEl+R2MS2OHaw7xz4B0AVpes5fNj5sO+\nD+GEk6G+Eob4HKAxfcxL0lYBDAN2RScUY0ysenTToxyqOcSc3DksHLWQIXM+D3M+73dYJsHNy21f\nGUEvexzJyIakaGx8YEz88XJ7dBfOKtGh0QnFGBOrnt/xPH8u+DPfef07bC7d7Hc4pp+YkjWFzBRn\nrVtxXTH7abSEzfRrXpK2Z0JfZ4pIVjSCMcbEnpqmGraUbwEgQICZmgzBoM9Rmf4gKZDEnOFz2p6v\nLrY6pKZ/85K0PYJT9SAZ+GF0wjHGxJoNRzYQVCdJm5wyhIEPnwe/mADvP+xzZKY/+Fjx+K3L4MXv\nwoNnwe53fIzMmL4XcdKmqqXA1UAD8M8i8gMR6emmusaYGLe2eG1be05TaNVo/VFIG+xTRKY/cdch\nXV28Ggqeg/cfgoPrLGkz/Y6XzXXHATtxErdHgB8BV4vIH4ms9uieSGMxxvSetSXhpG122f5wR55t\nqmt638xhM0kOJNMcbGZnxU7Kpl9Cdmun7ddm+hkvMzp30b78lODUHP1FBNdSj7EYY3pBUIOsL17f\n9nxOTaXTGDIWho7zKSrTn6Qnp3Pd9OvITs9m/oj5DA66/lex731nfmXAy0wfY+JHNBIlwUm6jq0f\n2p1bpa21Sr0HITII+DZwOTABaAG2AE8Av/G68a+IjARuBS4KXX8AUAwUACuBu1W1yct7GBNrdhzd\nQVVTFQDZSQM4oTl0e9RKV5k+dNv828JPVGFANtSVQV05lG6D4VP8C86YPuT144m4vh776Mn53oIQ\nyQPW4yyImBG6bhpwMvAr4D0vK1xF5CqgCPgBMA8nYWsExgHnAz+jfQ1WYxJCciCZyydfzqShk5ir\nKeFfWLs1avwi4hSPb7V3lX+xGNPHvCxECETxkRRpHCKSDPwDGI9TqP6TqpoJZODMt6sC5gJ/jvD6\nVwKPAYOBh4CTVHWAqg4JvXYW8GvARtlMwhk/ZDx3nnYnf7vkGe4+cCDcMe40/4Iy/VZJbQkv7XqJ\nujHhFaWWtJn+JBHmkV0HzAy1L1fVdwFUNQj8VUQCOEnXRSKyWFWXd/fCIjIKeBAnuf22qt7j7lfV\nKuDN0MOYxFW5j6SmWqc9IBuGT/U3HtPvfHXZV3lzv/On9vezv0nbWJstRjD9SKIkbQCvtSZsx3gC\n+E+ceWjXAt1O2oCvA1nAGpzRNGP6jZZgCyJCQALOooPb98CBtVB9yLlFZUwfGpU5qq29OljDglGz\nYczJMG6hM8/N/ps0/YCXLT/cW1Nfrqo7oxBPT2PIAE4PPV16vGNUVUXkJeAWYEkP3+La0Nc/q+qx\nCy2MSWhv7n+T77/1fWYPn80FEy7g0yd+GsYt6PpEY3rB3BFzeXLLkwCsPrIebnrD54iM6XteFiLM\nAWYDQ/xI2ELyCX8PGzs5rrVvpIhkd3JcGxGZAIwOPf1IRGaKyGMiclBEGkRkn4j8VURO7+w6xsSr\ntcVrqWys5M39b7KlbIvf4Zh+bn7u/Lb2upJ1NAebfYzGGH94SdrKQl93RyOQCI12tfd3eFT7vtEd\nHtWeew356cCHwOeBIUA9MAb4HPCmiPx7N69pTNxwb6o7Z/8mKHgeakp9jMj0Z6MGjmq7RVrXXEdR\nWZHTEWyBMr/GDYzpW17mtB3Ame+VEaVYIjHI1a7t5Dh336AOj2rPvUXIT4A9wA3AClUNish04AFg\nEfBjEdmkqs8e70IiciNwo/u1lStXdjMMkwiqq6vj6me+/LXl7TbVnb3h77D2WcqyZrN+9o99jCx+\nxNvPPBp6+/sdo2M4yEEAnnrrMUbtfovBlUWIBnnrjMfQgP/TtPvjz930HS//ha/A2RPtJBFJ9bp5\nbQxyj0IKzry9tnl8qrpZRC4BtgIjcfaIO27SpqoPAQ+JSNu8uEWLFvVGzCZGrVy5MvZ/5i+90NYc\nMXMETXucXWzGBNIZ3uIUjM+efXHsfx8xIi5+5tHg+u+mt7/f4qJiPnzvQwAqhtSSHSyFljoAzp6W\nA6PndnZ6n+g3P3fjCy+3Rx/FqWiQAVwfnXB6rMrV7mzEz91X1eFRHV97uTtha6Wq1TijbQCzRGRE\nN69tTExrVyS+wbUFoW2qa3zkLh5f2VgJY08Jd9rWH6Yf8LK57hrgHpxRqF/6NCHftdsnYzo5zt13\noMOj2nPPgyvo5LjNrnZeN69tTExrN5+tosRpBFKcLRaM8cmJQ0/k/nPv562r3+KR8x+BsQvDnbbJ\nrukHvJaxuh24HxgIvCYi/yUiM7yH1W0FQDDU7ux9W/sOqWpZJ8e5bcapX9oV9+ZAti2ISQjtR9oa\nnMbouZDq5xRW098FJMDZY89mSNoQ5wUbaTP9jJd92la4nlbhTPD/GvA1EakAdoZeDx7n9GOpqi7u\naQyqWisibwNnAhcAvzxOnIJTHxTglR5cu15E3gDOwdlapCPTW08BdnX3+sbELuVXZ/+KtcVr2bjp\ncSY17nFetlujJtaMmAEpGdBUCxV7oWI/DOnsposx8c3LQoRFtB9Zam0LMBRnH7fuELyNUD2Kk7Sd\nIyILVPXYMfIrgYmh9v/28Np/xEnaFovIvGPntYnIQOCroaerVLWkh9c3JgYJc3LnMCd3DrzzP+GX\nrd6oiQHNwWbWlaxj9eHV7Knaw0/GzIddoUqC+96HIZf6G6Axvcjr7VE5zqOzvs6Oj9SjwIbQtZ4R\nkcUAIhIIFXt/OHTc0mPrjorInSKiocf441z7L8D77muHapkiIvnAczgrR4PA96PwvRgTO1Rh1BzI\nmQSIVUMwMSGoQW569SbuW3Mff9/2d0pGzwp37v3Av8CM6QNeRtr8WjHajqo2i8ingdeA8cAyEanF\nSUjTQ4etAa6J4NpBEfkMTr3S6cAyoFZEmnA22QVoAm5V1RUdXMaYuJKUsZ3SulJyBuTAJfc6L9aW\nwYCszk80pg+kJqUyY9gMPjr8EQCrh+dx/vk/g7ELYORMn6MzpndFnLSp6qPRDMQLVd0lIrOA7wCX\n4RSHbwI2AY8Dv4l0HzlVPSQi83Dm612FUylhAM78tRXAr1W1sxJaxsQPaWTAuEdY9OTD5A3O49lP\nP0tqUipkdKv6mzF9Yl7uvLakbU1TOeefervPERnTN/zfPjpKVLUKZ4PbH/bgnDuBO7txXANwd+hh\nTMJKGrAXEWftUEog2UnYjIkx80bMcybFAKsPf2wLTWMSltc5bcaYBJI0YE9be/ahrfD45+Ht+yDY\nnUXgxvSNOcPnEHCmF1NUXkR1YzVUHoRNf4cdK/0NzpheZEmbMaZN0oDdbe05VeVQ9CJ8+AcI2J8K\nEzsGpg5kStYUwFmYsO6D++GeafDUdfDe73yOzpjeE/XboyIyHFgATMIpup4GHAVKgNXAOlW1j+3G\nxJxgu5G2OfWhTXXzbKsPE3vm5s6lsKwQgI9ooK0kz95VzspnicbmBMbElqglbSJyFfDPQFc7cFaI\nyP8A96rqni6ONcb0kUDqESS5FoAskshrbnY6LGkzMWjeiHm8tPMl5ubOZerohc7q5rpyqCuD0u0w\nbJLfIRoTdZ6TNhEZibNpbWtFg9bNcjv6mDMU+AbwFRH5pqo+4jUGY4x3Adet0dl19eFf4HFWCcHE\nnvPGncf5eecjrSNqYxfAlpec9t5VlrSZhORpokroVuhrOAnbsZvl7gI+BN7BWedTfczpA4GHROQ2\nLzEYY6IjkHqkrT2nLvTrOnAEZE/s4Axj/JMcSA4nbHBMHVIrHm8Sk9fZxf8DTHU9fw/4PDBUVSeq\n6imqeoaqzlbVwcBM4BdALeHRuLtF5BSMMb5qLLmQ6i0/YOH+mSypqXNeHHeqzQ0ycaFpzPzwkz3v\n+heIMb3IS8H4RcCFOMmXAt9T1U73MVPVTcDtIvJb4HlgBk7i9gucWqbGGB9py0A+U3eEsUk2n83E\nvl0Vu3hk4yOsPryaiYPz+E1yOjTXw5EtULbDRolNwvEy0naVq/2LrhI2t9AChPOBstBLZ4bmxhlj\nfPZg8yWw+IcweQmMP9PvcIzpUJAgf9/2d/ZU7WF1yTqCExY5HaPnOaXXjEkwXhYinBX62gD8rKcn\nq+pBEXkQ+FfX9Z70EI8xJkIv7niRlOzXCdblsaF+LJz5Wb9DMqZLEwZPICsti/KGciobK9lx5g1M\nuuReGDzK79CM6RVeRtpG4dwW3RAqIRWJt465njHGB48XPk76iKVkjP8dyQML/A7HmG4REebmzm17\nvrqpzBI2k9C8JG0DQl9rPVzDfW6ah+sYYyJ0pO4I60rWOU9UGFg73N+AjOmBeSPmtbVXFx9Th9TK\nr5kE4yVpK8ZZROBlM5zJrvaRDo8yxvSa1/e+jqIAnFTfwtqUb8P9p0DZTp8jM6Zr80eEV42uPrwa\nao7AO/fDHy+Cp6/3MTJjos/LnLYCYCwwWkTOU9VlEVzjWld7s4dYjDERWrF3RVv7otoKp1G+CwaP\n9icgY3pgavZUBiQPoK65joM1BzlYWsSoV77vdKZkQFMdpAzo/CLGxAkvI20vuNoPikiPJhKIyPeA\nM0JPSwHbDdGYPlbTVMN7B95re35ObWjGwgmfgGSbsWBiX0oghVnDZ7U9X918FHJCN4CaamHH6z5F\nZkz0eUna/gfnFinABOB9Ebm0q5NEZISIPEJ4xakC96iqeojFGBOBt/e/TWOwEYDhDWmMbW5xOvKs\ndJWJHxdPuJjrT7qe+865jzPGnAFTLwp3Fr3Q8YnGxJmIb4+qapWIfB14HCfxGgM8LSL7gKU4patK\ngUZgEDARWIiziW4y4ZJXa4F7Io3DGBM5963Rs2oawh1Wb9TEkUsnHzNeMO1ieOc+p130krMgIeC1\nAJAx/vNUMF5VnxSRHOA+nFE7wZnndkMnp7UWlAfYCJyvqo1e4jDGRGbxuMU0tjTy1r43ubJ2FwAt\nKiSNtcpyJo6d8AnIGAa1R6CmGPZ/2L42qTFxyvNHD1X9b+B0YI3rZaF9Afljn9fhlK76hKraqlFj\nfPLJvE9yz6J7eGP+D5ne5Hx22qTjIW2Qv4EZE4HGlkbWFK9hd/U+mHphuKPQbpGaxBCV8WJVfV9V\nTwZOxUnGXgf2AzVAC85t0kLgMeBmYLSq3q6qDR1c0hjTh9JLt7Z9ovogOM3XWIyJxF8K/sKpj53K\ntUuv5Zmtzzi3SFsVvehfYMZEkafbo8dS1VXYKlBjYp6qsvXoViYPnYyIwBnf5BPP53JyoIjdOoIv\n+x2gMT00MmNk26Ka1YdXw6xbnPq5kz7ZftTNmDgW1aTNGBMfNpdt5urnr2bMwDF85sTPcMucWyhh\nKEuDC/wOzZiIzB0xF0FQlA1HNnCkuYZh1zzld1jGRJUtpzGmH1qxx1k1ur96P3ur9vocjTHeZadn\nt9UhDWqQ5buX+xyRMdFnSZsx/VBr0gZw7v4CeP0XTJQDPkZkjHdLxi9pa7+y+5Vwx9E98N7vnBJX\nxsSxqN8eFZFkYAiQQXi1aJdUdU+0YzHGfNyeyj1sO7oNgLRAKqcVvQaFK1iamsLchgepJd3nCI2J\nzCfzPsld79+Fonx4+ENK60rJeeUOWPNn54DUTJj3//wN0hgPojLSJiJnisgfRKQIqMeplLAL2NnN\nx45oxGGM6dpre19ra5+aPoKMUDGSN4KzLGEzcS03I7f9LdI9y2G4azW0rSI1cc5T0iYiGSLyFLAS\nuA6YRHiT3e48OKZtjOll7W6NHg3fLnq+xRYhmPjXeot0ZOZIZ2W0u6TV9tegsdanyIzxzuvt0X/g\nlKWKNOmyZM2YPlRaV8qaYmcf7AABzj641elISmN5cJ6PkRkTHRdNuIiZw2Yyc9hMJ2kDZ7StpBCa\n62DHa+33cDMmjkQ80iYi1wDnuF5aD3wZmApkqmqgB48kj9+HMaYbisqLSEtKA2Bu+nCyg0GnY9J5\nVJPhY2TGREdWehazhs8KJ2zQfrSt0G6RmvjlZaTtGlf7aeDzqtriMR5jTA89/MYO7l22hZrGbv76\nyb+RnLmVJUl/anvpGxvyeik6Y/rO8X8XmpkjQ/m781mF0jX/xyfeu4BgF2MWmalJ3HbeFG44a2Lv\nBWxMD3mZ0zY39LUJuNkSNmP80aOEDUBTOaEmmy80OAu2GzSl3a3RzFQb+Dbxyf27kDxoI+mjH2Pg\nlP9gfSCXwzoUgBypYr5s6fJaNY0t3Lus6+OM6UtekrYsQIF1qloWpXiMMT3Uo4QtpESH8q3Gm1ne\nMpdXg/Pbbo22ji4YE4/cvwsp2W+SMmQ9klRP0qAClrfMo1kDvNsyHUF7fD1jYoGX26MlwGigOkqx\nGGM82vXzjidY3/vRvTQHmzl33LnMHj6bpMAVTocqnxJbE2QSyw8WfZ6fv/9zAM6ed4AvLHwAktM5\nNSObU7s4d/ztL/R+gMZEwMtIWyHO6s8TohSLMaaXNAWbeHLLkzy6+VGue+k6isqLwp2WsJkEdN64\n89raHxz6gLLUdMjI9jEiY7zzkrSFtphmkohMjkYwxpje8dHhj6hqrAJgZFoW+U0K2r1bRMbEoxGZ\nIz6+0a5bU70PURnjjZek7XFgbah9TxRiMcb0knYb6lbXIL87DR44BQ5v8jEqY3rXkrySmUf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77+BiaSwHLbpcOh59UcePPP/gUjCUVJm0gMS0tOY6p1Zs5XX5DuHKR0gOOu9TsskYS0JX8LN751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\na/WxnuXl3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Zn1rXqTmQ0FVgCzgZOBHkAKsBcwBXjDzH7QjJhEJOCUtIlIQjOzIcDIsEOP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\nUNImIiIiEgD/DxrYqou08EQNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ee0ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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d7vTp02nSpAkjR44kIiKCiy++mPfff5+nn34a8Wqmv+2227j//vsBGDFiBN27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L/6lvRPNDZy464qZhx6InbFbTByaraqKiIiknEK2iSnfFD9AUs2L+HFDc9wVN9hzDnnAQBKikqY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\n8T3bokHb8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141e0470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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DmDhxIr/97W+H8Mydo0lb36b5LR8GfIJ1kUMm1tWjE4CzgHdFRC+VUSoSVXzj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\n7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x116246dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3wAMPkJqayjHHHMOAAQO46667uvDKnaNJW9uG25YnAMuxOjlkYPUe7Q/8DPhE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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1150d34a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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d1fI4JiaGWbNm8cknn/jtM2nSpJaEDWDq1KlkZ2fvs9/MmTP9nq9Zs4aamhrO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\nAxS0iYiIiMQABW0iIiIiMeD/A6QPjaLFrj/pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11504d9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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REREpqk6dOjFq1Ciuu+46hg4dSr9+/bjmmmvo2bNnuUOraOoeFRERkaK78cYb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\npHaWLnQDRiEi3gqsBVbWq2aAbYDD6unMiDg2M5/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DgE3A3cCtVBdItCwBvhgRb+6xTZIK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115eef908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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vJ+ACpJFGBx3UmaPBjHznWPz3Bawe3xCz4yajgZ73wsJCGhr6eG+nf9ebtiZ0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\n7e77u3t/YDZwLVBDojfuejM7LGIcIiKSQ2YNndWyvWjrEhiR2Nd8bdJ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EsFOzFV76Ldx+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117ef22e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WZwIfYN3kkAk0AQXAfOAtEfnZwIenlOqdGDpa8pic07k/W+Q3jbr4zIyQPdxT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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117807dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select all raw points that enter that bin..\n",
    "# It's really like making a cross-section \n",
    "# through the raw data  plot \n",
    "\n",
    "obj = 'qso' #\n",
    "m_ij = out_dic[obj]['xi']\n",
    "e_ij = out_dic[obj]['ei']\n",
    "tau =  out_dic[obj]['tau']\n",
    "\n",
    "# doesn't matter what statistic , count would suffice : \n",
    "# all I need is to bin along tau \n",
    "nbins = 200\n",
    "hist_bins = 20 \n",
    "binned_tau = binned_statistic(tau, tau, statistic = 'count', bins=nbins)\n",
    "\n",
    "max_mi = 1.0\n",
    "\n",
    "# choose the bin to analyze ...\n",
    "for Nb in np.arange(100,200,10) : \n",
    "\n",
    "    # Sanity check : \n",
    "    # whatever stdev  or  bin count I find here,\n",
    "    # ought be exactly the same as in the previously calculated \n",
    "    # stats...\n",
    "    m1 = binned_tau.binnumber == Nb\n",
    "    print('Stdev %f '% np.std(m_ij[m1]))\n",
    "    print('bin count %d'%np.sum(m1))\n",
    "    print('mean %f'%np.mean(m_ij[m1]))\n",
    "    # muApprox is a median ... \n",
    "    print('median %f'%np.median(m_ij[m1])) \n",
    "\n",
    "    fig,ax = plt.subplots(1,1,figsize=(9,9))\n",
    "    # do clipping for the sake of clean histogram..\n",
    "    m2 = np.abs(m_ij)  <  max_mi\n",
    "    m = m1 * m2 \n",
    "    mi = m_ij[m]\n",
    "    ei = e_ij[m]\n",
    "\n",
    "\n",
    "    def model(xi, SF=None, mu=None, ei=ei) : \n",
    "        sig_i_tot = np.sqrt(ei**2.0 + SF**2.0)\n",
    "        summed = (1 / (np.sqrt(2 * np.pi ) * sig_i_tot)) * \\\n",
    "             np.exp(-(xi-mu)**2.0 / (2 * sig_i_tot**2.0) )\n",
    "        return np.sum(summed)\n",
    "\n",
    "    # Plot the data histogram \n",
    "    hist, bin_edges = np.histogram(mi, bins=hist_bins, normed=True)\n",
    "    bin_centres = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "    ax.plot(bin_centres, hist, ls='steps', label='data', lw=3)\n",
    "\n",
    "    # choose model plot ls, lw \n",
    "    ls = '--'\n",
    "    lw= 3\n",
    "\n",
    "    # overplot the approx model evaluated on the bin_centres\n",
    "    choice = 'Approx'\n",
    "    SF= plot_data[obj]['sigma'+choice][array_pos]\n",
    "    mu = plot_data[obj]['mu'+choice][array_pos]\n",
    "\n",
    "    y  = np.array([model(mi, SF=SF, mu = mu) for mi in bin_centres])\n",
    "    step = (bin_centres[1] - bin_centres[0]) \n",
    "    # normalize : that way,  \n",
    "    y = y / np.sum(step*y)\n",
    "    # np.sum(y * step) = 1.0 , just as we'd have \n",
    "    # for a normed histogram \n",
    "    ax.plot(bin_centres, y, label=choice, ls=ls, lw=lw )\n",
    "\n",
    "    # overplot the full model evaluated on the bin_centres\n",
    "    choice = 'Full'\n",
    "    SF = plot_data[obj]['sigma'+choice][array_pos]\n",
    "    mu = plot_data[obj]['mu'+choice][array_pos]\n",
    "\n",
    "    y  = np.array([model(mi, SF=SF, mu = mu) for mi in bin_centres])\n",
    "    step = (bin_centres[1] - bin_centres[0]) \n",
    "    # normalize : that way,  \n",
    "    y = y / np.sum(step*y)\n",
    "    # np.sum(y * step) = 1.0 , just as we'd have \n",
    "    # for a normed histogram \n",
    "    ax.plot(bin_centres, y, label=choice, ls=ls, lw=lw )\n",
    "    plt.legend(fontsize=15)\n",
    "\n",
    "    ax.set_xlabel(r'$\\Delta m$')\n",
    "    ax.set_ylabel('normed counts')\n",
    "    ax.grid()\n",
    "\n",
    "    ax.set_title('qso, 18.5-19, bin '+str(Nb)+', '+ r'$|\\Delta m| < 1.5$', \n",
    "                 fontsize=20)\n",
    "    ax.set_xlim(-max_mi,max_mi)\n",
    "    ax.set_ylim(0,1.76)\n",
    "    plt.savefig(outDir + 'qso_18.5-19_bin_'+str(Nb)+'.png', bbox_inches=  'tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of all $\\Delta m$ points (across all bins )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the histogram of $\\Delta m_{ij}$ for points that have surprisingly low mean ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rcParams['ytick.labelsize'] = 20\n",
    "rcParams['xtick.labelsize'] = 20\n",
    "rcParams['axes.labelsize'] = 25\n",
    "rcParams['axes.linewidth'] = 2\n",
    "rcParams['font.size'] = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PPvpoVK5x1FFHBSU2AL169aJt27bMnDkzqK5y5coh7xDVrVs33/NwidfNN9+MmYU8N8D1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\nCX6znTGDP269jruJ7V7UP0p78I/SystTLOES8Br3G8hZnjz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x146764898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib \n",
    "#matplotlib.rc('xtick', labelsize=15) \n",
    "#matplotlib.rc('ytick', labelsize=15) \n",
    "\n",
    "fig,ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "xmax = 0.5\n",
    "xmin = -xmax\n",
    "\n",
    "colors = {'qso':'black', 'starsB':'blue', 'starsR':'red'}\n",
    "labels  = {'qso':'quasars', 'starsB':'blue stars', 'starsR':'red stars'}\n",
    "b = 'r_cut'\n",
    "for obj in out_dic[b].keys() : \n",
    "    m_ij = out_dic[b][obj][0]\n",
    "    tau = out_dic[b][obj][1]\n",
    "\n",
    "    # remove outliers , in the same way as for the plot above \n",
    "    m1  = abs(m_ij) < 1.0\n",
    "\n",
    "    # choose the right timescale \n",
    "    tau1 = np.power(10,2.6)\n",
    "    tau2 = np.power(10,2.75)\n",
    "    m2 =  (tau1 < tau)*(tau <tau2)\n",
    "    \n",
    "    # select only to plot those within the axis bounds\n",
    "    m3 = abs(m_ij) < 0.6 \n",
    "    \n",
    "    # combine masks together \n",
    "    m = m1*m2*m3\n",
    "    \n",
    "    # plot the histogram\n",
    "    ax.hist(m_ij[m], histtype='step', bins=80, label=labels[obj], normed=True, lw=3, color=colors[obj])\n",
    "    \n",
    "    # vertical lines\n",
    "    ax.axvline(np.mean(m_ij[m]), ls='--', color = colors[obj] , lw=2)\n",
    "\n",
    "ax.set_xlabel(r'$\\Delta m$') # , fontsize=20\n",
    "ax.set_ylabel('Normalized counts') # , fontsize=20 \n",
    "ax.set_xlim(xmin, xmax)\n",
    "#ax.set_title(r'$ %.2f< \\Delta t < %.2f $  [days] '%(tau1, tau2), ) # , fontsize = 20  \n",
    "plt.legend(fontsize=20)\n",
    "plt.tight_layout()\n",
    "plt.savefig(outDir +'Fig_2_'+str(Min)+'-'+str(Max)+'_log_2.6-2.75_hist.png' )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}