PTF_A_Fig_1_QSO_day_averaged.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "from os import listdir\n",
    "import pandas as pd \n",
    "from astropy.table import Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resulting PTF QSO lightcurves from  S82"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, find how many quasars we have from this query, in  ../IPAC_output_tables/Ipac_PTF_lightcurves_QSO_S82.tbl "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# all lightcurves (may take a minute to read as the file has size of 82 MB ... )\n",
    "lcs_qso = Table.read('../IPAC_output_tables/Ipac_PTF_lightcurves_QSO_S82.tbl', format='ipac')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "astropy.table.table.Table"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lcs_qso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159641"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This gives total number of epochs : \n",
    "len(lcs_qso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 6471 quasars to start with \n"
     ]
    }
   ],
   "source": [
    "# Need to group by SDSS ra to get info about how many objects we have:\n",
    "print('We have %d quasars to start with ' % len(np.unique(lcs_qso['ra'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove empty files and those with less than 10 points "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three equivalent ways of testing for the number of empty files.  Those files should be removed from the list of accepted QSO/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Since I selected the objects from PTF asking them to have at least \n",
    "# ngoodobs > 10, there are definitely no objects with less than 10 epochs : \n",
    "np.sum(lcs_qso['ngoodobs'] < 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics on the raw  LC (before day-averaging)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group by ra : the SDSS ra, since ra_01 : the PTF ra , may differ from chip to chip,  just as oid .  For these quasars there seems to be only one edge-case ( number of unique ra is 6471, and oid : 6472, ra_01: 6480)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# drop some pesky 'masked' row... \n",
    "lcs_qso_new = lcs_qso[lcs_qso['ra'] != np.unique(lcs_qso['ra'])[-1]]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# group by ra to calculate stats on each object...\n",
    "grouped_lcs = lcs_qso_new.group_by('ra')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I capitalize on what stats are **really** needed. I'm not making a Fig.1 for PTF. If needed in the future, I may worry about that, but for now, I'll get some simple-to-do stats  (per object-grouped group :  max(mjd) - min(mjd),  mean(mag), mean(err) ) .  Means are built-in as group-aggregates , and the max-min  statistic I'll try to implement as a function.  \n",
    "\n",
    "Either way,  stats are not as needed as just making the master files  (delta_t, delta_mag, error),  and the PTF-SDSS matched catalogs, to use SDSS r-mag for magnitude cuts. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=10&gt;\n",
       "<table id=\"table4947013360\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>cntr_01</th><th>dist_x</th><th>pang_x</th><th>ra_01</th><th>dec_01</th><th>obsmjd</th><th>mag_autocorr</th><th>magerr_auto</th><th>oid</th><th>ra</th><th>dec</th><th>fid</th><th>transient_flag</th><th>astrometricrms</th><th>nobs</th><th>ngoodobs</th><th>nbestobs</th><th>referencemag</th><th>referencemagerr</th><th>medianmag</th><th>bestmedianmag</th><th>meanmag</th><th>bestmeanmag</th><th>magrms</th><th>bestmagrms</th><th>sid</th><th>s_ra</th><th>s_dec</th><th>ptffield</th><th>ccdid</th><th>hmjd</th><th>nid</th><th>mag_auto</th><th>limitmag</th><th>absphotzp</th><th>fwhmsex</th><th>fwhm_image</th><th>qaflags</th><th>flags</th><th>photcalflag</th><th>goodflag</th></tr></thead>\n",
       "<thead><tr><th></th><th>arcsec</th><th>deg</th><th>degrees</th><th>degrees</th><th>days</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>degrees</th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>days</th><th></th><th>mag</th><th>mag</th><th>mag</th><th>arcsec</th><th>pixels</th><th></th><th></th><th>0=No, 1=Yes</th><th>0=No, 1=Yes</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56920.3323372</td><td>17.249</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21434266001679</td><td>0.049834</td><td>0.040392</td><td>2697</td><td>6</td><td>56920.3380872</td><td>1983</td><td>17.249</td><td>21.592</td><td>27.843</td><td>3.05</td><td>3.56</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56952.2524972</td><td>17.237</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21923244001705</td><td>0.049843</td><td>0.040389</td><td>2697</td><td>6</td><td>56952.2576672</td><td>2014</td><td>17.239</td><td>21.624</td><td>27.871</td><td>2.65</td><td>2.912</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56914.2943372</td><td>17.258</td><td>0.037</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21364525001402</td><td>0.049841</td><td>0.040379</td><td>2697</td><td>6</td><td>56914.3000172</td><td>1979</td><td>17.248</td><td>21.014</td><td>27.801</td><td>2.45</td><td>2.836</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56914.3413172</td><td>17.244</td><td>0.037</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21364555001390</td><td>0.04984</td><td>0.040393</td><td>2697</td><td>6</td><td>56914.3469972</td><td>1979</td><td>17.228</td><td>21.027</td><td>27.816</td><td>2.45</td><td>2.618</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56927.2768872</td><td>17.239</td><td>0.036</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21547909001793</td><td>0.049844</td><td>0.040393</td><td>2697</td><td>6</td><td>56927.2826572</td><td>1990</td><td>17.223</td><td>21.705</td><td>27.853</td><td>2.55</td><td>2.977</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56927.3321972</td><td>17.282</td><td>0.037</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21547958001377</td><td>0.049848</td><td>0.040385</td><td>2697</td><td>6</td><td>56927.3379672</td><td>1990</td><td>17.285</td><td>21.307</td><td>27.856</td><td>3.65</td><td>4.108</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56954.2361372</td><td>17.275</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21960345001818</td><td>0.049845</td><td>0.040391</td><td>2697</td><td>6</td><td>56954.2412072</td><td>2016</td><td>17.278</td><td>21.51</td><td>27.874</td><td>2.85</td><td>3.055</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56954.2640372</td><td>17.262</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21960367001850</td><td>0.049848</td><td>0.040389</td><td>2697</td><td>6</td><td>56954.2691172</td><td>2016</td><td>17.275</td><td>21.709</td><td>27.868</td><td>2.35</td><td>2.585</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56934.2508172</td><td>17.291</td><td>0.228</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21641917000852</td><td>0.049835</td><td>0.040359</td><td>2697</td><td>6</td><td>56934.2565272</td><td>1997</td><td>17.251</td><td>20.191</td><td>27.658</td><td>3.25</td><td>3.64</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56916.3462272</td><td>17.241</td><td>0.036</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21375820001525</td><td>0.049842</td><td>0.040372</td><td>2697</td><td>6</td><td>56916.3519372</td><td>1980</td><td>17.241</td><td>21.241</td><td>27.849</td><td>2.95</td><td>3.297</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=10>\n",
       "cntr_01  dist_x  pang_x  ra_01   ... qaflags flags  photcalflag   goodflag \n",
       "         arcsec   deg   degrees  ...                0=No, 1=Yes 0=No, 1=Yes\n",
       " int64  float64 float64 float64  ...  int64  int64     int64       int64   \n",
       "------- ------- ------- -------- ... ------- ------ ----------- -----------\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate some aggregate info...\n",
    "\n",
    "# ra = grouped_lcs.groups.keys[0]\n",
    "#http://docs.astropy.org/en/stable/table/operations.html \n",
    "\n",
    "# display first 10 rows of the first group\n",
    "grouped_lcs.groups[0][:10]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " All the quick stats below : test on few objects... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#n_groups = 2\n",
    "test_group = grouped_lcs.groups[:2]\n",
    "#test_agg_info = grouped_lcs.groups.keys[:2]\n",
    "test_agg_info = test_group[['ra','dec', 'oid', 'magerr_auto','mag_autocorr']].groups.aggregate(np.mean)\n",
    "\n",
    "def agg_func(array):\n",
    "    'return max(array)-min(array)'\n",
    "    return max(array) - min(array)\n",
    "\n",
    "test_agg_info['mjd_span'] =  test_group['obsmjd'].groups.aggregate(agg_func)\n",
    "test_agg_info['lc_length'] = test_group['ra'].groups.aggregate(np.count_nonzero)\n",
    "\n",
    "# rename columns, since aggregate leaves the columns unchanged...\n",
    "test_agg_info.rename_column('magerr_auto', 'avg_err')\n",
    "test_agg_info.rename_column('mag_autocorr', 'avg_mag')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And do on all objects.... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# And do on all objects.... \n",
    "raw_agg_info = grouped_lcs[['ra','dec', 'oid', 'magerr_auto','mag_autocorr']].groups.aggregate(np.mean)\n",
    "\n",
    "def agg_func(array):\n",
    "    'return max(array)-min(array)'\n",
    "    return max(array) - min(array)\n",
    "\n",
    "raw_agg_info['mjd_span'] =  grouped_lcs['obsmjd'].groups.aggregate(agg_func)\n",
    "raw_agg_info['lc_length'] = grouped_lcs['ra'].groups.aggregate(np.count_nonzero)\n",
    "\n",
    "# rename columns, since aggregate leaves the columns unchanged...\n",
    "raw_agg_info.rename_column('magerr_auto', 'avg_err')\n",
    "raw_agg_info.rename_column('mag_autocorr', 'avg_mag')\n",
    "raw_agg_info.rename_column('ra', 'ra_sdss')\n",
    "raw_agg_info.rename_column('dec', 'dec_sdss')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Save that info as something that could be easily accessed to make PTF - SDSS matched catalog \n",
    "\n",
    "raw_agg_info.write('../data_products/PTF_qso_raw_agg_info.dat', format='ascii', overwrite='True')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2942.,    28.,  1890.,    34.,   163.,   116.,   108.,   102.,\n",
       "          372.,   715.]),\n",
       " array([    0.      ,   193.190252,   386.380504,   579.570756,\n",
       "          772.761008,   965.95126 ,  1159.141512,  1352.331764,\n",
       "         1545.522016,  1738.712268,  1931.90252 ]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAG4CAYAAADYEiY5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABMhCEAAAAkEYYAAAAw/X+1gcr9tfN2zgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x135a37ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show some stats....\n",
    "\n",
    "plt.hist(raw_agg_info['mjd_span'], histtype='step', lw=4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11e683a90>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/+Cc6tQIMK8rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e65deb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(agg_info['ra'], agg_info['dec'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing : Day-averaged light curves : needs be RUN ONLY ONCE  to make them!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make day-averaged lightcurves, save only if more than 10 observing days (points in the day-averaged light curve). \n",
    "Save MJD, avg_mag, error, N_obs that day. For our 6471 lightcurves it takes about 1.5 mins on my workstation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test on a few lightcurves : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Input lightcurves are lcs_qso_new , and it is an AstroPy Table \n",
    "# Each lightcurve is most easily accessed as a grouped object  by 'ra' : \n",
    "# object_lc = grouped_lcs.groups[i] \n",
    "# \n",
    "object_lc = grouped_lcs.groups[0] \n",
    "mjd = np.array(object_lc['obsmjd'])\n",
    "mags = np.array(object_lc['mag_autocorr'])\n",
    "errs = np.array(object_lc['magerr_auto'])\n",
    "\n",
    "days = np.unique([int(day) for day in mjd])\n",
    "\n",
    "# store values for a given object... \n",
    "mjd_arr = np.zeros_like(days).astype(float)\n",
    "avg_mags = np.zeros_like(days).astype(float)\n",
    "err_weights = np.zeros_like(days).astype(float)  \n",
    "\n",
    "for i in range(len(days)):\n",
    "    day = days[i]\n",
    "    int_mjd = np.require(mjd,int)       # forcing mjd array -> integers\n",
    "    condition = (int_mjd == day)        # finding where int(mjd) = day\n",
    "\n",
    "    w=1.0 / ( errs[condition] * errs[condition]) \n",
    "    avgmag = np.average(mags[condition], weights = w)       \n",
    "    avg_mags[i] = avgmag\n",
    "    error_weights = 1.0 / np.sqrt(np.sum(w))\n",
    "\n",
    "    # increase error if too small \n",
    "    if error_weights < 0.02 : \n",
    "        error_weights = np.sqrt(error_weights**2.0 + 0.01**2.0)  \n",
    "    err_weights[i] = error_weights\n",
    "\n",
    "    mjd_arr[i] = np.mean(mjd[condition])\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 3 artists>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAENCAYAAADjW7WQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ddf8400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the original (blue) and the averaged lightcurve (red)...\n",
    "plt.scatter(mjd,mags)\n",
    "plt.errorbar(mjd,mags,errs, ls=\"None\")\n",
    "plt.scatter(mjd_arr, avg_mags, color='red')\n",
    "plt.errorbar(mjd_arr, avg_mags, err_weights, color='red', ls='None')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6470"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(grouped_lcs.groups.keys)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run on all 6470 lightcurves : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from CRTS_paper_modules import update_progress\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=63&gt;\n",
       "<table id=\"table4804956848\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>cntr_01</th><th>dist_x</th><th>pang_x</th><th>ra_01</th><th>dec_01</th><th>obsmjd</th><th>mag_autocorr</th><th>magerr_auto</th><th>oid</th><th>ra</th><th>dec</th><th>fid</th><th>transient_flag</th><th>astrometricrms</th><th>nobs</th><th>ngoodobs</th><th>nbestobs</th><th>referencemag</th><th>referencemagerr</th><th>medianmag</th><th>bestmedianmag</th><th>meanmag</th><th>bestmeanmag</th><th>magrms</th><th>bestmagrms</th><th>sid</th><th>s_ra</th><th>s_dec</th><th>ptffield</th><th>ccdid</th><th>hmjd</th><th>nid</th><th>mag_auto</th><th>limitmag</th><th>absphotzp</th><th>fwhmsex</th><th>fwhm_image</th><th>qaflags</th><th>flags</th><th>photcalflag</th><th>goodflag</th></tr></thead>\n",
       "<thead><tr><th></th><th>arcsec</th><th>deg</th><th>degrees</th><th>degrees</th><th>days</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>degrees</th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>days</th><th></th><th>mag</th><th>mag</th><th>mag</th><th>arcsec</th><th>pixels</th><th></th><th></th><th>0=No, 1=Yes</th><th>0=No, 1=Yes</th></tr></thead>\n",
       "<thead><tr><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56920.3323372</td><td>17.249</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21434266001679</td><td>0.049834</td><td>0.040392</td><td>2697</td><td>6</td><td>56920.3380872</td><td>1983</td><td>17.249</td><td>21.592</td><td>27.843</td><td>3.05</td><td>3.56</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56952.2524972</td><td>17.237</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21923244001705</td><td>0.049843</td><td>0.040389</td><td>2697</td><td>6</td><td>56952.2576672</td><td>2014</td><td>17.239</td><td>21.624</td><td>27.871</td><td>2.65</td><td>2.912</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56914.2943372</td><td>17.258</td><td>0.037</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21364525001402</td><td>0.049841</td><td>0.040379</td><td>2697</td><td>6</td><td>56914.3000172</td><td>1979</td><td>17.248</td><td>21.014</td><td>27.801</td><td>2.45</td><td>2.836</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56914.3413172</td><td>17.244</td><td>0.037</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21364555001390</td><td>0.04984</td><td>0.040393</td><td>2697</td><td>6</td><td>56914.3469972</td><td>1979</td><td>17.228</td><td>21.027</td><td>27.816</td><td>2.45</td><td>2.618</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56927.2768872</td><td>17.239</td><td>0.036</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21547909001793</td><td>0.049844</td><td>0.040393</td><td>2697</td><td>6</td><td>56927.2826572</td><td>1990</td><td>17.223</td><td>21.705</td><td>27.853</td><td>2.55</td><td>2.977</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56927.3321972</td><td>17.282</td><td>0.037</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21547958001377</td><td>0.049848</td><td>0.040385</td><td>2697</td><td>6</td><td>56927.3379672</td><td>1990</td><td>17.285</td><td>21.307</td><td>27.856</td><td>3.65</td><td>4.108</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56954.2361372</td><td>17.275</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21960345001818</td><td>0.049845</td><td>0.040391</td><td>2697</td><td>6</td><td>56954.2412072</td><td>2016</td><td>17.278</td><td>21.51</td><td>27.874</td><td>2.85</td><td>3.055</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56954.2640372</td><td>17.262</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21960367001850</td><td>0.049848</td><td>0.040389</td><td>2697</td><td>6</td><td>56954.2691172</td><td>2016</td><td>17.275</td><td>21.709</td><td>27.868</td><td>2.35</td><td>2.585</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56934.2508172</td><td>17.291</td><td>0.228</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21641917000852</td><td>0.049835</td><td>0.040359</td><td>2697</td><td>6</td><td>56934.2565272</td><td>1997</td><td>17.251</td><td>20.191</td><td>27.658</td><td>3.25</td><td>3.64</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56921.2836372</td><td>17.241</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21452238001865</td><td>0.049835</td><td>0.040395</td><td>2697</td><td>6</td><td>56921.2893972</td><td>1984</td><td>17.235</td><td>21.781</td><td>27.878</td><td>2.35</td><td>2.677</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56921.3307072</td><td>17.23</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21452282001892</td><td>0.049842</td><td>0.040385</td><td>2697</td><td>6</td><td>56921.3364672</td><td>1984</td><td>17.235</td><td>21.831</td><td>27.893</td><td>2.45</td><td>2.728</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56903.3206572</td><td>17.223</td><td>0.033</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21305652002080</td><td>0.049843</td><td>0.040394</td><td>2697</td><td>6</td><td>56903.3260372</td><td>1968</td><td>17.238</td><td>21.936</td><td>27.91</td><td>2.35</td><td>2.661</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56903.3503472</td><td>17.222</td><td>0.033</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21305673002141</td><td>0.049846</td><td>0.040395</td><td>2697</td><td>6</td><td>56903.3557272</td><td>1968</td><td>17.231</td><td>22.029</td><td>27.916</td><td>2.25</td><td>2.492</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56956.2194772</td><td>17.258</td><td>0.275</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21999978001751</td><td>0.049851</td><td>0.040404</td><td>2697</td><td>6</td><td>56956.2244572</td><td>2018</td><td>16.714</td><td>21.06</td><td>27.295</td><td>2.55</td><td>2.911</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56956.2470472</td><td>17.236</td><td>0.275</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>22000002001803</td><td>0.049853</td><td>0.040388</td><td>2697</td><td>6</td><td>56956.2520272</td><td>2018</td><td>16.63</td><td>21.012</td><td>27.237</td><td>2.55</td><td>2.841</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56931.3043172</td><td>17.25</td><td>0.033</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>22270358001793</td><td>0.049849</td><td>0.040392</td><td>2697</td><td>6</td><td>56931.3100572</td><td>1994</td><td>17.249</td><td>21.684</td><td>27.87</td><td>2.55</td><td>2.789</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56931.3502272</td><td>17.227</td><td>0.033</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>22270396001813</td><td>0.049856</td><td>0.040394</td><td>2697</td><td>6</td><td>56931.3559772</td><td>1994</td><td>17.227</td><td>21.715</td><td>27.861</td><td>2.45</td><td>2.8</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56952.2250972</td><td>17.267</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21923204001869</td><td>0.049842</td><td>0.04039</td><td>2697</td><td>6</td><td>56952.2302572</td><td>2014</td><td>17.266</td><td>21.705</td><td>27.868</td><td>2.45</td><td>2.787</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56920.3795372</td><td>17.249</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21434304001696</td><td>0.04985</td><td>0.040395</td><td>2697</td><td>6</td><td>56920.3852872</td><td>1983</td><td>17.254</td><td>21.602</td><td>27.83</td><td>3.05</td><td>3.316</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=63>\n",
       "cntr_01  dist_x  pang_x  ra_01   ... qaflags flags  photcalflag   goodflag \n",
       "         arcsec   deg   degrees  ...                0=No, 1=Yes 0=No, 1=Yes\n",
       " int64  float64 float64 float64  ...  int64  int64     int64       int64   \n",
       "------- ------- ------- -------- ... ------- ------ ----------- -----------\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "    ...     ...     ...      ... ...     ...    ...         ...         ...\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_lcs.groups[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%"
     ]
    }
   ],
   "source": [
    "dir_out = '../proc_LC_PTF/qso/'\n",
    "number_of_lightcurves = len(grouped_lcs.groups.keys)\n",
    "keys = np.array(grouped_lcs.groups.keys)\n",
    "\n",
    "for k in range(number_of_lightcurves):\n",
    "    \n",
    "    percent = 100*(k / number_of_lightcurves)\n",
    "    if (k % 10) == 0 : # every tenth loop.. \n",
    "        update_progress(int(percent))\n",
    "    \n",
    "    object_lc = grouped_lcs.groups[k] \n",
    "    mjd = np.array(object_lc['obsmjd'])\n",
    "    mags = np.array(object_lc['mag_autocorr'])\n",
    "    errs = np.array(object_lc['magerr_auto'])\n",
    "\n",
    "    days = np.unique([int(day) for day in mjd])\n",
    "\n",
    "    # store values for a given object... \n",
    "    mjd_arr = np.zeros_like(days).astype(float)\n",
    "    avg_mags = np.zeros_like(days).astype(float)\n",
    "    err_weights = np.zeros_like(days).astype(float)  \n",
    "    N_per_day = np.zeros_like(days).astype(float)\n",
    "    \n",
    "    for i in range(len(days)):\n",
    "        day = days[i]\n",
    "        int_mjd = np.require(mjd,int)       # forcing mjd array -> integers\n",
    "        condition = (int_mjd == day)        # finding where int(mjd) = day\n",
    "        \n",
    "        # number of obs in that night \n",
    "        N_per_day[i] = float(len(mags[condition]))\n",
    "        \n",
    "        w=1.0 / ( errs[condition] * errs[condition]) \n",
    "        avgmag = np.average(mags[condition], weights = w)       \n",
    "        avg_mags[i] = avgmag\n",
    "        error_weights = 1.0 / np.sqrt(np.sum(w))\n",
    "\n",
    "        # increase error if too small \n",
    "        if error_weights < 0.02 : \n",
    "            error_weights = np.sqrt(error_weights**2.0 + 0.01**2.0)  \n",
    "        err_weights[i] = error_weights\n",
    "\n",
    "        mjd_arr[i] = np.mean(mjd[condition])\n",
    "\n",
    "      \n",
    "    # Save the output... \n",
    "    # Once per lightcurve \n",
    "    # ONLY IF THERE IS MORE THAN 10 OBS ! \n",
    "    \n",
    "    if( len(mjd_arr) >  10 ) :\n",
    "        object_name = str(keys[k][0])\n",
    "        name_out = dir_out  + object_name +'.txt'\n",
    "        np.savetxt(name_out, np.column_stack((mjd_arr,avg_mags, err_weights, N_per_day)),fmt='%11.4f')\n",
    "        \n",
    "print('finished')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=6470&gt;\n",
       "<table id=\"table5186420464\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>ra_sdss</th><th>dec_sdss</th><th>oid</th><th>avg_err</th><th>avg_mag</th><th>mjd_span</th><th>lc_length</th></tr></thead>\n",
       "<thead><tr><th>degrees</th><th>degrees</th><th></th><th>mag</th><th>mag</th><th>days</th><th>degrees</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th></tr></thead>\n",
       "<tr><td>0.049846</td><td>0.040389</td><td>2.69710600005e+13</td><td>0.0484603174603</td><td>17.2498571429</td><td>54.92261</td><td>63</td></tr>\n",
       "<tr><td>0.061789</td><td>-1.175205</td><td>2.59220600024e+13</td><td>0.128666666667</td><td>19.9896666667</td><td>1056.22068</td><td>3</td></tr>\n",
       "<tr><td>0.074522</td><td>0.436856</td><td>2.69710600019e+13</td><td>0.108683333333</td><td>20.1102666667</td><td>54.92261</td><td>60</td></tr>\n",
       "<tr><td>0.178728</td><td>0.927669</td><td>2.25921000005e+14</td><td>0.032</td><td>18.2735</td><td>0.0390999999945</td><td>2</td></tr>\n",
       "<tr><td>0.193409</td><td>1.239113</td><td>2.69720000006e+13</td><td>0.1502</td><td>20.2062</td><td>1863.83628</td><td>5</td></tr>\n",
       "<tr><td>0.214843</td><td>0.200738</td><td>2.25922000002e+14</td><td>0.1259</td><td>20.2657</td><td>70.9601</td><td>20</td></tr>\n",
       "<tr><td>0.225083</td><td>0.917532</td><td>2.69710600037e+13</td><td>0.115596774194</td><td>20.2757741935</td><td>54.92261</td><td>62</td></tr>\n",
       "<tr><td>0.22903</td><td>1.028718</td><td>2.69720600029e+13</td><td>0.1</td><td>19.5462</td><td>1891.8302</td><td>5</td></tr>\n",
       "<tr><td>0.244902</td><td>0.586288</td><td>2.69720600019e+13</td><td>0.0928333333333</td><td>19.3371666667</td><td>1891.8302</td><td>6</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>359.820938</td><td>-1.174725</td><td>2.69621100034e+13</td><td>0.0794545454545</td><td>17.8704090909</td><td>470.86651</td><td>22</td></tr>\n",
       "<tr><td>359.822694</td><td>1.200229</td><td>2.80120500005e+13</td><td>0.120576923077</td><td>20.0369230769</td><td>1931.8716</td><td>26</td></tr>\n",
       "<tr><td>359.829693</td><td>0.174204</td><td>2.26962050001e+14</td><td>0.0891578947368</td><td>19.723</td><td>412.94923</td><td>19</td></tr>\n",
       "<tr><td>359.837189</td><td>0.98199</td><td>2.26962050008e+14</td><td>0.123210526316</td><td>20.3201578947</td><td>412.94923</td><td>19</td></tr>\n",
       "<tr><td>359.859383</td><td>-0.797318</td><td>2.26962110003e+14</td><td>0.081</td><td>19.4673684211</td><td>412.94923</td><td>19</td></tr>\n",
       "<tr><td>359.863175</td><td>-0.034679</td><td>2.801211e+13</td><td>0.0953333333333</td><td>19.7042666667</td><td>1931.90252</td><td>30</td></tr>\n",
       "<tr><td>359.876765</td><td>0.626013</td><td>2.80121100022e+13</td><td>0.0594545454545</td><td>18.7876969697</td><td>1931.90252</td><td>33</td></tr>\n",
       "<tr><td>359.906615</td><td>0.706468</td><td>2.80121100026e+13</td><td>0.12172</td><td>20.2422</td><td>469.87201</td><td>25</td></tr>\n",
       "<tr><td>359.926453</td><td>0.227288</td><td>2.80121100007e+13</td><td>0.10025</td><td>19.7826875</td><td>1931.90252</td><td>32</td></tr>\n",
       "<tr><td>359.950384</td><td>0.208232</td><td>2.80121100006e+13</td><td>0.143153846154</td><td>20.2015</td><td>1931.90252</td><td>26</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=6470>\n",
       " ra_sdss    dec_sdss        oid        ...     mjd_span    lc_length\n",
       " degrees    degrees                    ...       days       degrees \n",
       " float64    float64       float64      ...     float64       int64  \n",
       "---------- --------- ----------------- ... --------------- ---------\n",
       "  0.049846  0.040389 2.69710600005e+13 ...        54.92261        63\n",
       "  0.061789 -1.175205 2.59220600024e+13 ...      1056.22068         3\n",
       "  0.074522  0.436856 2.69710600019e+13 ...        54.92261        60\n",
       "  0.178728  0.927669 2.25921000005e+14 ... 0.0390999999945         2\n",
       "  0.193409  1.239113 2.69720000006e+13 ...      1863.83628         5\n",
       "  0.214843  0.200738 2.25922000002e+14 ...         70.9601        20\n",
       "  0.225083  0.917532 2.69710600037e+13 ...        54.92261        62\n",
       "   0.22903  1.028718 2.69720600029e+13 ...       1891.8302         5\n",
       "  0.244902  0.586288 2.69720600019e+13 ...       1891.8302         6\n",
       "       ...       ...               ... ...             ...       ...\n",
       "359.820938 -1.174725 2.69621100034e+13 ...       470.86651        22\n",
       "359.822694  1.200229 2.80120500005e+13 ...       1931.8716        26\n",
       "359.829693  0.174204 2.26962050001e+14 ...       412.94923        19\n",
       "359.837189   0.98199 2.26962050008e+14 ...       412.94923        19\n",
       "359.859383 -0.797318 2.26962110003e+14 ...       412.94923        19\n",
       "359.863175 -0.034679      2.801211e+13 ...      1931.90252        30\n",
       "359.876765  0.626013 2.80121100022e+13 ...      1931.90252        33\n",
       "359.906615  0.706468 2.80121100026e+13 ...       469.87201        25\n",
       "359.926453  0.227288 2.80121100007e+13 ...      1931.90252        32\n",
       "359.950384  0.208232 2.80121100006e+13 ...      1931.90252        26"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics on the day-averaged LC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I add to the raw LC statistics (agg_info), the statistics on day-averaged lightcurves.\n",
    "Thus necessarily I select only those rows that correspond to objects \n",
    "that had a day-averaged lightcurve longer than 10 days, so that they made it to \n",
    "proc_LC_PTF/  directory . "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "avg_err :  average of raw PTF errors over all epochs\n",
    "\n",
    "avg_mag : average of raw PTF magnitudes over all epochs\n",
    "\n",
    "mjd_span : min(mjd) - max(mjd), before day-averaging \n",
    " \n",
    "lc_length : number of epochs before day-averaging "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "\n",
    "# Read in the previously saved stats on aggregate data for all objects \n",
    "address = '../data_products/PTF_qso_raw_agg_info.dat' \n",
    "raw_agg_info = Table.read(address,format='ascii' )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ra_info = np.array(agg_info['ra_sdss']).astype(str)\n",
    "\n",
    "dir_in = '../proc_LC_PTF/qso/'\n",
    "files = np.array(listdir(dir_in))\n",
    "ra_files = np.array([name[:-4] for name in files])\n",
    "\n",
    "# Choose only those PTF aggregate info about lightcurves that \n",
    "# during day-averaging were found to have at least day-averaged 10 epochs  (10 separate days of observations)\n",
    "\n",
    "raw_agg_info_select = raw_agg_info[np.in1d(ra_info, ra_files)]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Only 2753 quasars out of 6470 have more than 10 days of obs \n"
     ]
    }
   ],
   "source": [
    "print('Only %d quasars out of %d have more than 10 days of obs '%(len(raw_agg_info_select),len(raw_agg_info)))\n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%"
     ]
    }
   ],
   "source": [
    "# the files below are only for those quasars that have\n",
    "# more than 10 observations per day... \n",
    "# agg_info.write('../data_products/PTF_qso_aggregate_info.dat', format='ascii', overwrite='True')\n",
    "\n",
    "\n",
    "\n",
    "ra_files = np.array([name[:-4] for name in files])\n",
    "# make new columns:\n",
    "#avg_day_err :  the average of processed lightcurves errors \n",
    "#avg_day_mag : the average of processed lightcurves magnitudes \n",
    "# \n",
    "# make an array for storing total timespan of obs per object, \n",
    "# as well as the total number of nights per object\n",
    "proc_mjd_span = np.zeros_like(files, dtype=float) # store what was the total  timespan of observations per object\n",
    "proc_lc_length=np.zeros_like(files, dtype=float) # number of points in the lightcurve - equal to the number of obs nights\n",
    "avg_N_per_day = np.zeros_like(files, dtype=float)\n",
    "avg_day_mag = np.zeros_like(files, dtype=float)\n",
    "avg_day_err = np.zeros_like(files, dtype=float)\n",
    "avg_mjd_diff= np.zeros_like(files, dtype=float)\n",
    "\n",
    "k = 0\n",
    "total = float(len(files))\n",
    "\n",
    "for i in range(len(files)):\n",
    "    percent = 100*(k / total)\n",
    "    if (k % 10) == 0 : # every tenth loop.. \n",
    "        update_progress(int(percent))\n",
    "    k += 1\n",
    "    # choose processed lightcurves according to \n",
    "    # order of the aggregate info \n",
    "    # then rows will match . \n",
    "    \n",
    "    ra_sdss = str(agg_info_select['ra_sdss'][i])\n",
    "    address=dir_in+files[ra_files == ra_sdss][0]\n",
    "    \n",
    "    data=np.loadtxt(address) \n",
    "    proc_lc_length[i] = sum(1 for line in open(address))\n",
    "    avg_day_mag[i] = np.mean(data[:,1])\n",
    "    avg_day_err[i] = np.mean(data[:,2])\n",
    "    avg_N_per_day[i] = np.mean(data[:,3])\n",
    "    proc_mjd_span[i] = data[-1,0]-data[0,0]  # gives number of days between first and last obs\n",
    "    \n",
    "        #mjd_diff = np.zeros(len(data[:,0]))\n",
    "    #for j in range(len(data)-1):\n",
    "    #    mjd_diff[j] = data[j+1,0] - data[j,0]\n",
    "    #avg_mjd_diff[i] = np.mean(mjd_diff)     # avg span between obs in MJD    \n",
    "    \n",
    "keys = ['ra_files', 'proc_mjd_span', 'proc_lc_length', 'avg_N_per_day', 'avg_day_mag', 'avg_day_err']\n",
    "values = [np.array(ra_files).astype(float), proc_mjd_span,proc_lc_length,avg_N_per_day,avg_mag_ttl,avg_err_ttl]\n",
    "\n",
    "\n",
    "stats_proc_lc ={}\n",
    "for key, value in zip(keys, values):\n",
    "    stats_proc_lc[key] = value\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=6&gt;\n",
       "<table id=\"table4883002088\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>avg_day_mag</th><th>proc_mjd_span</th><th>ra_files</th><th>avg_day_err</th><th>avg_N_per_day</th><th>proc_lc_length</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>17.2495</td><td>54.8939</td><td>0.049846</td><td>0.0389971428571</td><td>1.8</td><td>35.0</td></tr>\n",
       "<tr><td>20.0987352941</td><td>54.8939</td><td>0.074522</td><td>0.0838117647059</td><td>1.76470588235</td><td>34.0</td></tr>\n",
       "<tr><td>20.2590382353</td><td>54.8939</td><td>0.225083</td><td>0.0896970588235</td><td>1.82352941176</td><td>34.0</td></tr>\n",
       "<tr><td>19.4490371429</td><td>1122.9245</td><td>0.331011</td><td>0.0621857142857</td><td>1.88571428571</td><td>35.0</td></tr>\n",
       "<tr><td>20.4550266667</td><td>54.8939</td><td>0.91298</td><td>0.117276666667</td><td>1.6</td><td>30.0</td></tr>\n",
       "<tr><td>18.9295514286</td><td>1122.9245</td><td>1.148971</td><td>0.0567</td><td>1.91428571429</td><td>35.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=6>\n",
       " avg_day_mag  proc_mjd_span ra_files ... avg_N_per_day proc_lc_length\n",
       "   float64       float64    float64  ...    float64       float64    \n",
       "------------- ------------- -------- ... ------------- --------------\n",
       "      17.2495       54.8939 0.049846 ...           1.8           35.0\n",
       "20.0987352941       54.8939 0.074522 ... 1.76470588235           34.0\n",
       "20.2590382353       54.8939 0.225083 ... 1.82352941176           34.0\n",
       "19.4490371429     1122.9245 0.331011 ... 1.88571428571           35.0\n",
       "20.4550266667       54.8939  0.91298 ...           1.6           30.0\n",
       "18.9295514286     1122.9245 1.148971 ... 1.91428571429           35.0"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make an Astropy table from these stats, and add it to agg_info_selected \n",
    "\n",
    "stats = Table(stats_proc_lc)\n",
    "stats[:6]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stats.rename_column('ra_files', 'ra_sdss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#http://docs.astropy.org/en/stable/api/astropy.table.join.html\n",
    "# beautiful examples \n",
    "# https://star.herts.ac.uk/~gb/python/14-astropy-tables.html  \n",
    "from astropy.table import join\n",
    "agg_info_merged = join(left=raw_agg_info_select, right = stats, keys='ra_sdss')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#  Save the combined catalog, with both raw lightcurve aggregates, and processed lightcurve aggregates \n",
    "# this is the master catalog combining all the PTF information about quasars in one place \n",
    "\n",
    "\n",
    "agg_info_merged.write('../data_products/PTF_qso_raw_and_proc_combined_agg_info_2753.dat', format='ascii', overwrite='True')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ra_sdss',\n",
       " 'dec_sdss',\n",
       " 'oid',\n",
       " 'avg_err',\n",
       " 'avg_mag',\n",
       " 'mjd_span',\n",
       " 'lc_length',\n",
       " 'avg_day_mag',\n",
       " 'proc_mjd_span',\n",
       " 'avg_day_err',\n",
       " 'avg_N_per_day',\n",
       " 'proc_lc_length']"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_info_merged.colnames\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare before and after day-averaging: plot error and mag distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.23379035,  0.61212027,  0.40945688,  0.19647036,  0.72936668,\n",
       "        0.87329424,  0.97189702,  0.8041134 ,  0.50850535,  0.69234322])"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11d988e48>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEPCAYAAABGP2P1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcXFWd9/HPN2yBZNBuZYAkQ0IW4dERAxpZAtpMhARE\nUBEB4WEddEZRZxSFEcZ0u6ABt3kmjyOjGFxYFBGQKJDA0CIaMLLEJCQQIglkiICmx7AmIfnNH/dW\np6pSVX27U7eq0vm+X6969b23Tp36VXd1/eqcc+85igjMzMwKhjQ7ADMzay1ODGZmVsKJwczMSjgx\nmJlZCScGMzMr4cRgZmYlck8MkqZJWirpUUkXVinzfkmLJS2U9MO8YzIzs+qU53UMkoYAjwJTgKeA\n+cApEbG0qMx44EfAkRGxVtJrI+JPuQVlZmY15d1ieCuwLCJWRsQG4DrghLIy5wH/PyLWAjgpmJk1\nV96JYSTwZNH+qvRYsdcB+0m6R9JvJE3NOSYzM6thx5zrV4Vj5X1XOwLjgbcB+wC/kvSGQgvCzMwa\nK+/EsIrkw75gFMlYQ3mZeRGxCVgh6RFgAnB/cSFJntTJzGwAIqLSl/Sq8u5Kmg+MlzRa0s7AKcDP\nysrcBPwdgKTXkiSFP1SqLCJa6jZ9+vSmx7AtxNSqcTkmx7Q9xDUQuSaGiNgInA/MARYD10XEEkld\nko5Ly9wO/FnSYuBO4IKI6MkzLjMzqy7vriQi4jZgv7Jj08v2Pwl8Mu9YzMysb77yeSt0dHQ0O4Qt\ntGJM0JpxOaZsHFN2rRpXf+V6gVs9SYptJVYzs1Yhiejn4HPuXUlmZpWMGTOGlStXNjuMQWP06NGs\nWLGiLnW5xWBmTZF+k212GINGtd/nQFoMHmMwM7MSTgxmZlbCicHMzEo4MZiZWQknBjMzK+HEYGZm\nJZwYzHLW3g5S9Vt7e7MjNCvl6xjMciZBrbduX/cPVq18HcPixYu57777WLRoEUcccQSbNm3ipptu\n4qyzzmL9+vVcc801dHZ2Mm7cuGaH2svXMZjZoFerldWf20CsWrWKiRMnsmDBAt7znvdw4okncu+9\n97LvvvtyzDHHMHz4cJYsWQLAvHnzmDdvXh1fefM5MZhZS4qoz20gpk6dyty5cznttNMAWL58OePG\njWPs2LEA3H333Rx66KEAHHroob3bg4XnSjLbCu0z2ul5uY/lQzpBXdA2tI01F65pSFy29ebOnctV\nV13Vu3300UcDSQthwoQJPP300yxfvpwbb7yRSy+9FA20edKCnBjMtkLPyz3E9NpfSwtjCOoaPB8c\ng11E8MorrzBq1CgAHnzwQT75yWTJmCFDhjBixAgefvhhDjvsMNauXTuokgI4MZiZbUES3d3dvftX\nXHFF7/bBBx/MwQcfDMCKFSsYM2YMTz31FCNGjGh0mLnxGIOZ2QA9++yzDBs2zC0GMzNLTJo0iUmT\nJjU7jLpzi8GsDmpdxNbW1uzozPrHLQazOujp2T4vUrPByS0GMzMr4cRgZmYlnBjMzKyEE4OZmZVw\nYjAzsxJODGZmVsKJwczMSuSeGCRNk7RU0qOSLqxw/5mSnpH0QHo7J++YzMz6Y8aMGQN+bERw+eWX\n1zGa/OV6gZukIcBMYArwFDBf0s0RsbSs6HUR8bE8YzEzG4g77rijd9K8gZDEK6+8wrp169hll13q\nGFl+8m4xvBVYFhErI2IDcB1wQoVyg2sGKjMbNO666y46Ojq2qo6TTz6Za6+9tj4BNUDeiWEk8GTR\n/qr0WLn3SnpI0o8ljco5JjOzTJ544gn23Xffra5n7NixLF++vA4RNUbeiaFSS6B8RpmfAWMiYiJw\nJ/C9nGMyayltbdUn4Gtvb3Z027err76aD3zgA3Wpa/Lkyfz617+uS115y3sSvVXAPkX7o0jGGnpF\nRPG6iN8Gqo7ydHZ29m53dHRsdfPOrJpMS3aSLNe5tdbUWO1zkE3zv01Zt24dEcFuu+1Wl/qmTp3K\nZz/7WSZPnlyX+qrp7u4uWWRoIBQ5TgkpaQfgEZLB59XAb4FTI2JJUZm9IuKP6fZ7gE9FxGEV6oo8\nYzUrpi71uWRnSXn1Pbtqf+vMWu+2ShKt/D/9/e9/n8mTJzNu3Li61Tlz5kxOOukk9txzz7rVWVDt\n95ke79dXjFxbDBGxUdL5wBySbqsrI2KJpC5gfkTMBj4m6XhgA7AGOCvPmMzMsli2bBlnnHFG7/7i\nxYu57777WLRoEUcccQSbNm3ipptu4qyzzmL9+vVcc801dHZ21kwkxx57LLNnz+bcc89txEsYsNyv\nY4iI2yJiv4iYEBFfTo9NT5MCEfGZiPjbiDgwIqZExKN5x2Rm1pfx48fz2GOP9e6vWrWKiRMnsmDB\nAt7znvdw4okncu+997LvvvtyzDHHMHz4cJYs6e0MYd68ecybN6+kzp///Oe8853vrPh8n/vc57jx\nxhvzeTH95IV6zKwlqas+Ayz97b4rOPnkk/nKV77CJZdcAiRjBDNmzOC0004DYPny5YwbN46xY8cC\ncPfdd/OFL3yh9/GHHnroFnU+88wz7LXXXhWf76WXXuLwww8fUKz15sRgZi1poB/o9TJ06FB23HFH\nXnzxxd4B6Llz53LVVVf1bh999NFA0jqYMGECTz/9NH/5y19Ys2YNN9xwA5deeilKzyC49dZbmTZt\n2hbPM3PmTCZMmMDChQvZY489mDlzJuPHj+fmm2/m3HPP5YYbbuBLX/oSXV1dXHDBBQwbNiz31+65\nkswapG1oG+pSn7f2GT5HtVWceuqpXH311UAytcUrr7zCqFHJpVYPPvggxx13HABDhgxhxIgRPPzw\nw4wdO5YRI0awdu3a3qQAcM8992xxRtL111/P3nvvzeGHH87YsWNL9ocNG8bIkSNZu3YtAI8//nhD\nkgK4xWDWMGsurHFeapF6daHY1hs9ejQrV64EkrN7ik8DveKKK3q3Dz744JJpM9avX8+YMWN46qmn\nGDFiBI8//jgTJkzYov7u7m4+//nPM2/ePA4++GBuu+02Lr/88t79DRs28JrXvIYNGzaw88475/dC\ny7jFYGZWQ0dHB3fddVe/HvPss88ybNiw3hbDtddeyymnnLJFualTpzJnzhwWLlzI6tWrmTJlCnPm\nzGHRokWsWLGCtrY2Nm7cyOWXX86BBx5Yl9eThVsMZmY1vOMd7+Cyyy7jyCOPzPyYSZMmMWnSJCDp\ngtpll10YOnToFuWOP/74Puv64he/yMUXX8zpp5+ePeitlOsFbvXkC9yskSpdjNbeDj1VLoZua6t9\nBfNAn9sXuG3fZs2axfDhw9m0aRMnn3xyzbLbzAVuZoNJT8/g/ZC21nT22Wc35Xk9xmBmZiWcGMzM\nrIQTg5mZlXBiMDOzEk4MZmZWwonBzMxKODGYmVkJX8dgZk0xevTokknmbOuMHj26bnU5MZhZU6xY\nsaLZIVgV7koyS7W3J1NQFL7EFrYLt7a25sZn1ihODGapwpQXhWkvCtuFW73mQjJrdU4MZmZWouYY\ng6RRwCnAEcAI4CVgEfBz4NaI2JR7hGZm1lBVE4OkWcBIYDYwA3gGGAq8DpgGXCzpooi4uxGBmplZ\nY9RqMXw1IhZVOL4I+KmknYF98gnLzMyapWpiqJIUiu9fDzxW94jMzKypanUlLQSqLksSEQfkEpGZ\nmTVVra6k49KfH0l//iD9eVp+4ZiZWbPV6kpaCSDpqIg4sOiuiyQ9AFyUd3BmZtZ4Wa5jkKTDi3YO\ny/g4MzPbBmWZK+lc4LuSXkUy5vAX4JxcozIzs6bp85t/RNwfEW8CDgAmRsTEiHgg6xNImiZpqaRH\nJV1Yo9z7JG2SdFDWus3MrP76bDFI2hO4FBgREcdIej1waERcmeGxQ4CZwBTgKWC+pJsjYmlZueHA\nR4F7B/AazDJpn9FOz8s91Qt0grqSzbahnjHPtl9ZupKuAmYBF6f7jwI/AvpMDMBbgWVFA9nXAScA\nS8vKfZ7k6upPZajTbEB6Xu4hplc9Axtp8wR6ZtuzLIPIr42IHwObACLiFWBjxvpHAk8W7a9Kj/WS\nNBEYFRG/yFinmZnlKEuL4QVJryG92E3SISQD0FlUWp6p9zuZkuWbvg6c2cdjAOjs7Ozd7ujooKOj\nI2MYZmbbh+7ubrq7u7eqDkUfbWdJbwb+H/C3JPMk7QGcFBEL+qw8SSKdETEt3b8IiIiYke7vTjKt\nxvMkCWEv4M/A8eUD3JKir1jNalGXtomupOI4WyUm23ZJIiL6tYZqny2GiLhf0tuB/Ug+vB+JiA0Z\n658PjJc0GlhNMoX3qUV1rwX+urAv6S7gExHxYPaXYGZm9dTnGIOk5cDfR8TiiFgUERskzc5SeURs\nBM4H5gCLgesiYomkLknHVXoINbqSzLZW8fKdXrrTrLIsYwwbgCMlHQx8KJ1VdWQfj+kVEbeRtDaK\nj02vUvbvstZrNhCF5TvNrLosZyW9GBEnA0uAX6XdQv7XMjMbpLK0GAQQEZdJuh+4HWjPNSozM2ua\nLInhs4WNiLhT0lRKTy81M7NBpNZCPfunU1f8d4X5izINPpvZ1mlrSwbGq923Zk1j47HtQ60WwyeB\n84CvVrgvAA8Um+Ws1gd/tYRhtrVqLdRzXvrzyMaFY2ZmzVarK+m9tR4YET+tfzhmZtZstbqS3lXj\nvgCcGMzMBqFaXUlnNzIQMzNrDVlOV0XSO4E3AEMLxyLic3kFZWZmzZNlBbdvAbsBRwLfAd4H/Dbn\nuMy2W21D21BXhlOOOqF9RhtrLvQ5q1ZfWVoMh0XEAZJ+HxFdkr4K3Jp3YGbbq6wf9BL0dPqcVau/\nLHMlvZT+fFHSCJJJ9fbOLyQzM2umLC2G2ZJeDVwOPEByRtJ3co3KzMyaJstCPZ9PN29I12EYGhFZ\nl/Y0M7NtTJbB5x2AdwJjCuXTpeK+lm9oZmbWDFm6km4BXgYWApvyDcfMzJotS2IYFREH5B6JmZm1\nhCxnJd0q6ejcIzEzs5aQpcVwL3CjpCEkp6oKiIjYPdfIzMysKbIkhq8BhwILI7yMurWe9hnt9Lzc\n02e5tqFt9F3KzLIkhieBRU4K1qp6Xu4hpmd7e+qinIMxGwSyJIY/AN2SbgXWFQ76dFUzs8EpS2J4\nPL3tnN7MzGwQq5kY0ovb/ioiLmhQPGZm1mQ1T1eNiI3A5AbFYmZmLSBLV9JDkn4GXA+8UDjoNZ/N\nzAanLIlhKPBn4O+KjnnNZzOzQSrL7KpbtfazpGnAN0i6ra6MiBll938I+AiwEXgO+GBELN2a5zQz\ns4Hrc0oMSaMk3SjpGUlPS7pB0qgsladXS88EppKsGX2qpP3Lil0dEQdExIEkaz58vZ+vwczM6ijL\nXEmzgJ8BI4CRJLOtzspY/1uBZRGxMiI2ANcBJxQXiIjni3aH4xlczcyaKkti2CMiZkXEK+ntKmCP\njPWPJLlyumBVeqyEpA9Legz4MvCxjHWbmVkOsgw+/0nS6cC16f6pJIPRWVRaqXyLuQsi4pvANyWd\nAvwrcFalyjo7O3u3Ozo66OjoyBiGmdn2obu7m+7u7q2qQ31NgSRpH5JxgkNJPtR/A3w8Ilb2Wbl0\nCNAZEdPS/YtIZmadUaW8gJ6IeHWF+zxdk1WkLmWfK0kwWN5GEtCZ/bXb9ildcbPSl/SqspyV9ARw\n/ABjmg+MlzQaWA2cQtLi6CVpfEQ8lu4eBzw6wOcyM7M6yLLm8x7AeRSt+QwQEef09diI2CjpfGAO\nm09XXSKpC5gfEbOB8yW9A1gP9ABnDuSFmJlZfWQZY7gZ+BVwB8m1Bv0SEbcB+5Udm160/U/9rdPM\nzPKTJTHsFhEX5h6JWZ20t0NPlRV52toaG4vZtijL6aqzJR2beyRmddLTkwwwV7qtWdPs6MxaX5bE\n8HGS5PCSpLWSnpO0Nu/AzMysObKclfRXjQjEzMxaQ9UWg6QxtR6oRKY5k8zMbNtRq8VweToJ3s3A\n/cCzJFNwjweOBKYA00mmuTAzs0GiamKIiJMkvR44DTgH2Bt4EVgC/AL4YkS83JAozcysYWqOMUTE\nw8DFDYrFzMxaQJazkszMbDvixGBmZiWcGMzMrESWpT1vkPTO9AwlMzMb5LJ82P8H8AFgmaQvV1iz\n2cyaoDDvk1T51t7e3Phs29VnYoiIOyLiNOAgYAUwV9JvJJ0taae8AzSzygrzPlWbF6raRIJmfcnU\nPSTpNSTLbf498CDwbySJYm5ukZmZWVNkWajnp8D+wA+Ad0XE6vSuH0n6XZ7BmZlZ42VZj+E7EfGL\n4gOSdomIdRHxlpziMjOzJsnSlfSFCsfm1TsQMzNrDVVbDJL2AkYCu0o6EFB61+7Abg2IzczMmqBW\nV9JUkgHnUcDXio4/B3wmx5jMzKyJas2u+j3ge5JOjIgbGhiTWU1brOncmZy3X+B1nc22Tq2upNMj\n4ofAGEmfKL8/Ir5W4WFmuSus6VygrtJ9M9s6tbqShqU/hzciEDMzaw21upKuSH92NS4cMzNrtiyT\n6F0maXdJO0m6U9Kzkk5vRHBmZtZ4Wa5jODoi1gLHkcyVNB74VJ5BmZlZ82RJDIWJ8o4Fro+Iv+QY\nj5mZNVmWKTFukbQUeAn4sKQ9gJfzDcvMzJoly7TbFwGHAm+JiA3AC8AJWZ9A0jRJSyU9KunCCvf/\ns6TFkh6SNFfS3/TnBZiZWX1laTEA/B+S6xmKy3+/rwelq77NBKYATwHzJd0cEUuLij0AvDkiXpb0\nD8DlwCkZ4zIzszrLMu32D4BxwEPAxvRwkCExAG8FlkXEyrSu60haG72JISJ+WVT+XuC0TJGbmVku\nsrQY3gK8PmJA15aOBJ4s2l9FkiyqORe4dQDPY2ZmdZIlMSwC9gJW91WwAlU4VjHBpNdGvBl4e7XK\nOjs7e7c7Ojro6OgYQEhmZoNXd3c33d3dW1WH+moISLoLmAj8FlhXOB4Rx/dZuXQI0BkR09L9i5KH\nxoyycu8gWS70bRHx5yp1DbDRYoONVD5Xkojp2+d7o9ZrL/892fZJEhFR6Ut6VVlaDJ0DCweA+cB4\nSaNJWhynAKcWF0jXevgWMLVaUjAzs8bpMzFExC/TD/YJEXGHpN2AHbJUHhEbJZ0PzCE5NfbKiFgi\nqQuYHxGzgctIJuy7XpKAlRHx7oG+IDMz2zpZzko6D/gg0E5ydtJIkm/4U7I8QUTcBuxXdmx60fZR\n/YjXzMxylmVKjI8Ak4G1ABGxDPjrPIMyM7PmyZIY1kXE+sJOepGbh7TMzAapLInhl5I+A+wq6Sjg\neuCWfMMyM7NmyZIYLgKeBRYCHwJ+AVySZ1Bm7e3J6ZaVbl7T2SxfWc5K2iTpJuCmiHi2ATGZ0fPB\ndti1p/J9JOs8F7QNdaYwq6eqiSE9dXQ6cD7JFcyStBH494j4XIPis+3Vrj3b7UVrZs1Wq8XwTyRn\nI02KiMcBJI0F/kPSP0fE1xsRoJlV1za0DXVVuai1c3PLqm1oG2suXNOwuGzbVisxnAEcFRF/KhyI\niD+kcxrNAZwYzJqs1od98ZQYVZOHWQW1Bp93Kk4KBek4w04VypuZ2SBQKzGsH+B9Zma2DauVGN4k\naW2F23PAGxsVoA1OtU5HlXs9zJqqamKIiB0iYvcKt7+KCHcl2Vbp6Un6v6vdzKx5slzgZmbboLa2\n0hZYcYusvb25sVlry7Ieg1ldtM9op+fl9KK1ztKL1Mr5orWtt6bohCV1lS1u5O46q8GJwRqm5+XN\nF615dTGz1uWuJDMzK+HEYGZmJZwYzMyshBODmZmVcGIwM7MSTgxmZlbCicHMzEo4MZiZWQknBjMz\nK+HEYLkpn0EVNm+3ecYLs5blxGC5KZ9BFTZvr/Eqk2Yty4nBzMxKODGYmVmJ3BODpGmSlkp6VNKF\nFe4/QtL9kjZIem/e8ZiZWW25JgZJQ4CZwFTgDcCpkvYvK7YSOBO4Os9YzMwsm7zXY3grsCwiVgJI\nug44AVhaKBART6T3eXZ+M7MWkHdX0kjgyaL9VekxMzNrUXknhkoLCLplYGbWwvLuSloF7FO0Pwp4\naqCVdXZ29m53dHTQ0dEx0KqsjkrWci7WWbqus9dxNstfd3c33d3dW1WHIseFdyXtADwCTAFWA78F\nTo2IJRXKzgJmR8QNVeqKPGO1gVOXetdyLjnudZ1bRvnfyH+b7YckIqJS701VuXYlRcRG4HxgDrAY\nuC4ilkjqknQcgKS3SHoSeB/wLUkL84zJzMxqy7sriYi4Ddiv7Nj0ou3fAX+TdxxmZpaNr3w2M7MS\nTgxmZlbCicFsO9TWVjolevGtvb3Z0Vmz5T7GYGatp9a05+rX+Ss2GLnFYGZmJZwYbKsUuh0qdUl4\nlTazbZO7kmyr9KQXPPtiKbPBwy0GMzMr4cRgZmYlnBjMzKyExxjMtgNtQ9tQV8bzUC9sA2qcz2qD\nnhODVVR1Ku1ynZ5Oe1uw5sLsH/SZE4gNWk4MVlHPyz0Vp9IuJ8Ean5FkNqh4jMHMzEo4MZiZWQkn\nBjMzK+HEYGZmJZwYzMyshBOD1dTeXn3efk+UZzY4+XTV7UzW6xMK1yb09HiCPLPtjRPDdqbS9Qnt\n7ZtnSe0tB+gitwi2V7UW62lrq73Qj237nBjMrQLbQq33g1d4G/w8xtBiavXpey1eM2sEtxhaTK1v\n7/6mZmaNoNhG+hAkxbYSay2ZJ6frj5faaPvPNZn6fdWlLcYYJHcl2Wb9eY+2DW3r1wR91niSiIh+\nfa10i6HB+pqcbiAf0urSFoPHZgPV1wd98XvUM7EOTh5jMDOzEm4x1EF/m9711ja0jZ5Ooa4MhV9q\n22KswqekWn+0tRWNd3WWjn35VNbBIfcxBknTgG+QtE6ujIgZZffvDHwfeDPwJ+DkiHiiQj0tO8ZQ\nqd9+wHUNsL+/r8d5HMHyUP7e9/us9QxkjCHXriRJQ4CZwFTgDcCpkvYvK3YusCYiJpAkkMvyjKme\nuru7mx3CFloxJmjNuBxTNo4pu1aNq7/y7kp6K7AsIlYCSLoOOAFYWlTmBGB6uv0TkkSSm7qeFXQX\ncGT/uocqXWVcbKDdOpub991AR93qrZfu7m46OjqaG0QZx5SNY8quVePqr7wTw0jgyaL9VSTJomKZ\niNgo6X8ktUdEv3oqs54d0Ta0rW7dPp3RSef0zi1j6WM6gTya2oV+3c7O5GbWDCXjDxXu8/jDtiHv\nxFDpLVL+sVheRhXK9KleH/b14D5W2160DW0r/VL28epleyDTCRKD7dqI/vZStMJnWa6Dz5IOAToj\nYlq6fxEQxQPQkm5Ny9wnaQdgdUT8dYW6mv/bMjPbBrXaBW7zgfGSRgOrgVOAU8vK3AKcCdwHnAT8\nV6WK+vvCzMxsYHJNDOmYwfnAHDafrrpEUhcwPyJmA1cCP5C0DPgzSfIwM7Mm2WbmSjIzs8ZoySkx\nJF0p6WlJvy86Nl3SKkkPpLdpzY4pPf5RSUslLZT05WbHJOm6ot/R45IeaIGY3iRpnqQHJf1W0lsa\nGVONuA6Q9BtJCyTdLGl4g2MaJem/JD2cvn8+lh5vkzRH0iOSbpf0qhaI6X2SFknaKOmgRsVTJaaP\npscvk7RE0kOSbpC0ewvE9Ln0/fSgpNsk7dXEmD5Wdv8FkjZJ6nsC/4houRtwODAR+H3RsenAJ1os\npg6SbrId0/3XNjumsvu/AlzS7JiA24Gj0+1jgLta5O/3W+DwdPss4HMNjmkvYGK6PRx4BNgfmAF8\nOj1+IfDlFohpP2ACyRjgQS3ye3oHMCQ9/mXgSy0Q0/CiMh8F/qPZMaX7o4DbgMeB9r7qaskWQ0Tc\nQ3J2W7mmDUBXiekfSf5pX0nL/KkFYir2fuDaBoUDVI1pE1D41vtq4L8bGRNUjet16XGAO4ATGxzT\nHyPioXT7eWAJyT/wCcD30mLfA97d5JhGRsQjEbGMJvwP1ojpjojYlBa7l+R31+yYni8qNozkvd/U\nmNK7vw58KmtdLZkYavhI2mz8TiOb1zW8DnibpHsl3dWMLpJqJB0B/DEiljc7FuCfga9IeoJkypN/\naXI8BYskvSvdfj8N/GApJ2kMSYvmXmDPiHgakn92YI8mx3RfM56/khoxnQPc2uh4YMuYJH0hfa9/\nAPhss2NK3+NPRsTCrI/flhLDN4FxETER+CPwtSbHA8lZXa+OiEOATwM/bnI8xU6lwa2FGv4R+HhE\n7EOSJL7b5HgKzgHOlzSf5Nvd+mYEkY5t/ITkd/Q8A7jAs94qxNR01WKSdDGwISKuaYWYIuKS9L1+\nNUl3UtNiAjYCF7N52iHI0OrbZhJDRDwbaWcZ8G1gUjPjST0J/BQgIuYDmyS9prkhQXqh4HuBHzU7\nltSZEXETQET8hC2nRWmKiHg0IqZGxCTgOqDhrStJO5L8E/8gIm5ODz8tac/0/r2AZ1ogpqaqFpOk\nM4FjSb6dt0RMRa6lwd2TFWIaB4wBFkh6nKRVfL+kLS4iLtbKiUEUZbay0f33AosaHlFZTMBNwBQA\nSa8DdoqIPzc5JoCjgCUR8VSDYykoj+m/Jb0dQNIU4NGmRLXle2qP9OcQ4BLgW02I6bvAwxHxb0XH\nfkYyGA7JxZ+N/nCuFFOxZoz1bRFTembip4HjI2Jdi8Q0vuj+E0j6+ZsWU0Qsioi9ImJsROxLMl/d\ngRFR+8tGo0bM+zm6fg3wFLAOeAI4m2TNht8DD5F8IO/ZAjHtCPwAWAj8Dnh7s2NKj88CPthCf7vD\n0t/Pg8C89I3ZCnF9jOTMjaXApU2IaTJJU/+h9HfzADANaCcZDH8EmEvSXdnsmN5N0kJ+iWQWg1ub\nHNMxwDJgZbr/APDNFvg9/ST9PHiIJKHv3eyYysr8gQxnJfkCNzMzK9HKXUlmZtYETgxmZlbCicHM\nzEo4MZiZWQknBjMzK+HEYGZmJZwYBrl0muQH0ml4fyRpRDol8AOSViuZyrywv1NR+cKxfcrqGy3p\nxfS+hyTdI2lClefeW1Kf04RIeq7K8RMk7T+wV15/kt4u6dCcn+OuRk9rXSGGMyX9e4ZyK9IppnON\nN51ee7WkT+T5PLZZ3kt7WvO9EBEHAUj6IfD+iDgw3f8s8HxE9M47Jam3fA2PFdX5QZK5WM4qLiBp\nh4hYTTJXdOnJAAAGL0lEQVQ5XV+qXUzzbmA2yQVoraADeJ7kIr3BLssFTpuAjojIvtL9QAKJ+LSk\nlpivaXvhFsP25VdA8SX7laY2yDLdQXGZ3YE10PtN82ZJdwJ3pK2Lhel9u6YtlkWSfprOSFtIQEpn\npHxIycI5e6TfzI8HLktbJ2PTb9NflnSfksWRJqcPHpJ+q7wvreO89Phekn6ZPv73kianZWel+wsk\nfXyLFycdl8Z3v5IFc/ZQsm75PwD/lNY3uewxuylZDOi+9HHvSo+PlnS3pN+lt0OKHvPpNI4HJV1a\nVN37y19jhRgvULLo0UOSphc91xJJP1SyWMuPJQ1N75uSxr1AyezEO6XHJ0n6dVrPvZKGpU8xUtKt\nShYLmlHjfVA8xcjjki7V5gWZDlSyWM0ySR9KywyTdEf6u1gg6fiix/9r+prvlnRNWQvBa743UqMu\n1/atOTfgufTnjiRTiXyo6L4tFj8CXiG5lP5B4IYK9Y0GXkzLPEaytsKo9L4zSaabeFVR2d+n258k\nXbQEeAPJTKYHpfubgGPT7RnAZ9LtWcB7i577LuDydPsYYG66fV7RY3YG5qfP/QngX9LjIplB9SBg\nTlGdu1d4ja8q2j636DmrLhYFfBH4QOHxJNNZ7AoMBXZOj48nWeu8EP89wC7p/qtrvcay5zoKuKLo\ndd1CshDR6PR3eUh635Xp72CX9O8yLj3+PZLpQHYimTiw8HcYDuyQ/h0fS/d3AVaQrDVQHkfJoi/p\n/gfT7a+RTM2wG/Ba4On0+A6ki9kArwGWpdtvIXlP7Zw+76PFv+tav3vf6n9zV9Lgt6s2L+/5K5IP\ni1pejP51JZ1EMtvtMel9cyPiLxUeczjwDYCIWFxoSaTWRcQv0u37SVbmquanReVGp9tHA29MY4Gk\nFTOBJEF8N/12fHNELJD0B2BfSf8G/IJkBb5yf6NkbGRvkg/Px2vEU3A08C5JhcVQdgb2IZlXaKak\niSTz2BTGY6YAsyKd/C0i/qeP11j+XEelf9dCwptAMpfRExFxb1ruhyTTPt8B/CE2r83xPeDDJKux\nPRURD6QxPA8gCeDOov2H0ziyLLB0S/pzITAsIl4EXpT0kpKlN18EviTpbSRJbISSmT4nk/yN1gPr\nJd1SqXJrDCeGwS/LB/3WuIXkm33BC1XK1eoK2FC0vZHa78t1FcoJ+GhEzN3iSZMFi94JXCXpqxHx\nQ0lvAqYCHyIZAzm37GH/DnwlIn6uZFbY6WRzYiSrnBU//3SSBZMOUDId+ktFMVfrx6/0GkuqJVnG\n8ttlz1UpiQSVZ+ClyrHyGGrFUetxm8rq2JTWcRpJC+LAiNikZCrooX3EYg3mMYbBr7//cP0dYziC\nbOsY3AOcDCDp9cAbMzzncyTf/vuK43bgw0rmokfShLTPfx/g2Yi4EvgOcJCShdB3iIgbgX8FDqxQ\n7+4kM7FC0q2SJZ7bSbpnSGOYmG6+iqTVAHAGSVcKJC2VcyTtmpZv6+M1lj/XOYXxACVnmr02vW8f\nSQen26eStBKXAqMljU2P/1+gOz2+t6Q3p/UMT5NXHgqv41XAM2lSOJKkVQXJ++NdknZRstDMcTnF\nYRm4xTD49Xf63Czlx6bdGENIvhWWf+Ou5Jsk39oXkXwgLQIKXU7VnvM64NuSPgqcVKFcYf87JIuR\nPKCkH+QZkjOaOoBPSdpA8qF+BslCJbOUrMEQwEUVnrcL+ImkNSTdLWPS47ekx48naaH8uugxXwC+\nIen36f4KksHzbwI3SDqDZDH2FwAi4va05fI7SetIurUuqfEaNx+ImKvkNN55abfPc8DpJN/KHyFZ\nAncWsBj4VkSsk3R2GvsOJF1sV0TEBkknk3R17UrSzVOpGy/re6hWucJ9VwO3SFpAMhX70vQ1/U7S\nz4AFwNMkU+xX6pK0BvC029YQ6QfxTumH1FiSfu/XRcQrTQ5t0Ei7kmZHxBv7LFyf53sceHNErKlT\nfcMi4oU0Sd0NnBfp4vZpl9xzUXRqteXHXUnWKLsB90h6iGRw9R+cFHLRyG96zwJ3qn4XuP2npAdJ\nBt2vL0oKl5GMTVQbv7I6c4vBzMxKuMVgZmYlnBjMzKyEE4OZmZVwYjAzsxJODGZmVsKJwczMSvwv\nFH3yPU/XXCoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ee7d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = (stats_proc_lc['mag']>15)*(stats_proc_lc['mag']<22)\n",
    "#plt.hist(mag_list[m])\n",
    "\n",
    "#sns.set_context('talk')\n",
    "fig,ax = plt.subplots(1,1)\n",
    "nbins=30\n",
    "# Calculate the histogram1 : individual epoch brightness measurements \n",
    "hist1, bin_edges = np.histogram(stats_proc_lc['mag'][m], bins=nbins, density=True)\n",
    "bin_centres1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "# Calculate the histogram2 : day-averaged brightness measurements \n",
    "hist2, bin_edges = np.histogram(stats_after['avg_mag'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label=r'$m_{i}$')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label=r'$\\langle m_{i}\\rangle_{day}$')\n",
    "plt.legend(loc='best')\n",
    "ax.set_ylabel('Density (normalized)')\n",
    "ax.set_xlabel('PTF Brightness at each epoch [mag]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x134fc4588>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAoYAAAHLCAYAAABVpFlWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x135287b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import seaborn as sns\n",
    "#sns.set_context('talk')\n",
    "fig,ax = plt.subplots(1,1)\n",
    "nbins=30\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_proc_lc['err'][stats_proc_lc['err']<0.6], bins=nbins, density=True)\n",
    "bin_centres1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "# Calculate the histogram2 : old day-averages\n",
    "hist2, bin_edges = np.histogram(stats_proc_lc['avg_err'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label=r'$ e_{i} $')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label=r'$\\langle e_{i} \\rangle $')\n",
    "plt.legend(loc='best')\n",
    "ax.set_ylabel('Density (normalized)')\n",
    "ax.set_xlabel('Photometric error PTF [mag]')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare PTF r mag  with SDSS r mag  for QSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zipping CRTS-SDSS quasars catalog from ../data_products/CRTS_SDSS_cross_matched_qso_DB_QSO_catalog.txt\n",
      "Read in 7601 quasars from CRTS\n"
     ]
    }
   ],
   "source": [
    "import CRTS_paper_modules as mod\n",
    "cols1, qso_cat = mod.get_qso_catalog() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "address = '../data_products/PTF_qso_aggregate_info.dat'\n",
    "ptf_agg_info = Table.read(address,format='ascii' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6470"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ptf_agg_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x123003f60>"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAn8AAAG4CAYAAAA0S4FqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122f83128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# SDSS r-mag \n",
    "r = np.array(qso_cat['r']).astype(float)\n",
    "m = (r > 15)*(r<22)\n",
    "plt.hist(r[m], histtype='step', lw=3, label='SDSS')\n",
    "\n",
    "# CRTS white light \n",
    "avg_mag = np.array(qso_cat['CRTS_avg_m']).astype(float)\n",
    "plt.hist(avg_mag,histtype='step', lw=3, label='CRTS')\n",
    "\n",
    "#PTF r mag \n",
    "ptf_mag = np.array(ptf_agg_info['avg_mag'])\n",
    "m = (ptf_mag > 15)*(ptf_mag<22)\n",
    "\n",
    "plt.hist(ptf_mag[m],histtype='step', lw=3, label='PTF')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Fig.1 : statistical properties of those QSO I use in my analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['avg_err', 'avg_N_day', 'err', 'avg_mag', 'mag', 'timespan_obs', 'lc_length', 'avg_mjd_diff'])"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_proc_lc.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRTS Quasars: sample of 7601\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3oAAAJwCAYAAADfvxOqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFpKkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c496710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('poster')\n",
    "from matplotlib import rcParams\n",
    "rcParams['ytick.labelsize'] = 25\n",
    "rcParams['xtick.labelsize'] = 25\n",
    "rcParams['axes.labelsize'] = 25\n",
    "rcParams['axes.linewidth'] = 3\n",
    "rcParams['font.size'] = 25\n",
    "\n",
    "fig = plt.figure()\n",
    "fig.subplots_adjust(hspace=0.15)\n",
    "fig.subplots_adjust(wspace=0.1)\n",
    "print('CRTS Quasars: sample of 7601')\n",
    "# Set number of histogram bins \n",
    "nbins = 30\n",
    "\n",
    "\n",
    "# Panel 1 \n",
    "ax1 = fig.add_subplot(221)\n",
    "ax1.set_ylabel('N')\n",
    "ax1.set_xlabel('Number of days observed')\n",
    "ax1.set_xticks([0,20,40,60,80,100])\n",
    "ax1.set_xticklabels(['0','20','40','60','80','100'])\n",
    "\n",
    "ax1.xaxis.set_label_position('top')\n",
    "ax1.xaxis.tick_top()\n",
    "hist2, bin_edges = np.histogram(stats_proc_lc['lc_length'], bins=nbins, density=False)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "hist2[0]=0\n",
    "ax1.set_xlim(xmin = 10, xmax = max(bin_centres2))\n",
    "ax1.plot(bin_centres2, hist2, ls = 'steps')\n",
    "\n",
    "# Panel 2 \n",
    "ax2 = fig.add_subplot(222)\n",
    "ax2.set_ylabel('N')\n",
    "ax2.set_xlabel('<mag>')\n",
    "ax2.xaxis.set_label_position('top')\n",
    "ax2.xaxis.tick_top()\n",
    "ax2.yaxis.tick_right()\n",
    "ax2.yaxis.set_label_position(\"right\")\n",
    "a = stats_proc_lc['avg_mag']\n",
    "m = (a > 16) * (a < 21)\n",
    "hist2, bin_edges = np.histogram(a[m], bins=nbins, density=False)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "hist2[0]=0  # start at 0 \n",
    "\n",
    "ax2.plot(bin_centres2, hist2, ls = 'steps')\n",
    "\n",
    "# Panel 3 \n",
    "ax3 = fig.add_subplot(223)\n",
    "ax3.set_ylabel('N')\n",
    "ax3.set_xlabel('<error>')\n",
    "ax3.set_xticks([0,0.1,0.2,0.3,0.4])\n",
    "ax3.set_xticklabels(['0','0.1','0.2','0.3','0.4'])\n",
    "\n",
    "hist2, bin_edges = np.histogram(stats_proc_lc['avg_err'], bins=nbins, density=False)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "ax3.set_xlim(xmin = 0, xmax = max(bin_centres2))\n",
    "hist2[0]=0\n",
    "\n",
    "ax3.plot(bin_centres2, hist2, ls = 'steps')\n",
    "\n",
    "# Panel 4 \n",
    "ax4 = fig.add_subplot(224)\n",
    "ax4.set_ylabel('N')\n",
    "ax4.set_xlabel('log(<day-cadence>) [days]')  # time between consecutive observations\n",
    "ax4.yaxis.tick_right()\n",
    "ax4.yaxis.set_label_position(\"right\")\n",
    "\n",
    "hist2, bin_edges = np.histogram(np.log10(stats_proc_lc['avg_mjd_diff']), bins=nbins, density=False)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "hist2[0]=0\n",
    "\n",
    "ax4.plot(bin_centres2, hist2, ls = 'steps')\n",
    "\n",
    "\n",
    "# Save\n",
    "\n",
    "plt.savefig('../data_products/PTF_Fig_1_QSO_avg_stats_four_panels.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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
}