Fig_1_QSO_CRTS_day_averaged_stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "from os import listdir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Sample of S82 CRTS QSO  from Branimir "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, find how many quasars we got from Branimir : in ~/SDSS/   I find  QSO_CRTS.tar.gz   and QSO_CRTS.tar  - these are  one and the same archives that we start with  \n",
    "I extract them to QSO_CRTS/ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'/astro/users/suberlak/Desktop/SDSS'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dir_1 = 'QSO_CRTS/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QSO_list = listdir(dir_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 7932 quasars to start with \n"
     ]
    }
   ],
   "source": [
    "print('We have %d quasars to start with ' % len(QSO_list))"
   ]
  },
  {
   "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": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 179 empty files\n"
     ]
    }
   ],
   "source": [
    "c = 0 \n",
    "empty_files = []\n",
    "for obj in QSO_list:\n",
    "    address = dir_1 + obj\n",
    "    f = open(address, 'r')\n",
    "    if f.readline() == '' :\n",
    "        c += 1\n",
    "        empty_files.append(obj)\n",
    "        \n",
    "print('We have %d empty files' % c)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#import os\n",
    "#d = 0\n",
    "#for obj in QSO_list:\n",
    "#    address = dir_1 + obj\n",
    "#    if os.stat(address).st_size == 0 : \n",
    "#        d += 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#e = 0   \n",
    "#for obj in QSO_list :\n",
    "#    address = dir_1 + obj\n",
    "#    if not (os.path.getsize(address) > 0) : \n",
    "#        e += 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7753"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QSO_nonempty = np.array(QSO_list)[~np.in1d(QSO_list, empty_files)]  \n",
    "len(QSO_nonempty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove those files that have less than 10 points : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 46  files with less than 10 pts \n"
     ]
    }
   ],
   "source": [
    "c = 0 \n",
    "short_files = []\n",
    "for obj in QSO_nonempty:\n",
    "    address = dir_1 + obj\n",
    "    f = open(address, 'r')\n",
    "    num_lines = sum(1 for line in open(dir_1+obj))\n",
    "    if num_lines < 10 :\n",
    "        c += 1\n",
    "        short_files.append(obj)\n",
    "        \n",
    "print('We have %d  files with less than 10 pts ' % c) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7707"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QSO_10_pts_plus = QSO_nonempty[~np.in1d(QSO_nonempty, short_files)]\n",
    "len(QSO_10_pts_plus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This list of  files is identical to files in QSO_CRTS_LC_longer_10/  : \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dir_2 = 'QSO_CRTS_LC_longer_10/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "QSO_2_list = listdir(dir_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], \n",
       "      dtype='|S22')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QSO_10_pts_plus[~np.in1d(QSO_10_pts_plus,QSO_2_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculating statistics for the day-averaged processed sample of Quasars . Based on qso_crts_preprocessing.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def update_progress(progress):\n",
    "    ''' A simple function updating the time progress. \n",
    "    Inspired by \n",
    "    http://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console \n",
    "    Parameters\n",
    "    -----------\n",
    "    progress : a value (float or int) between 0 and 100 indicating \n",
    "               percentage progress \n",
    "    Returns\n",
    "    ----------\n",
    "    None\n",
    "    '''\n",
    "    print '\\r[%-10s] %0.2f%%' % ('#' * int(progress/10), progress),\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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 7707 lightcurves it takes about 1.5 mins on my workstation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "dir_in = 'QSO_CRTS_LC_longer_10/'\n",
    "dir_err_w = 'QSO_CRTS_LC_day_averaged_err_w/'\n",
    "lc_files = np.array(listdir(dir_in))\n",
    "\n",
    "k = 0\n",
    "total = float(len(lc_files))\n",
    "\n",
    "for obj in lc_files:\n",
    "    percent = 100*(k / total)\n",
    "    if (k % 10) == 0 : # every tenth loop.. \n",
    "        update_progress(int(percent))\n",
    "    k += 1\n",
    "    \n",
    "    address=dir_in+obj\n",
    "    data=np.loadtxt(address)\n",
    "\n",
    "    averages=np.zeros(shape=(len(data),3))\n",
    "\n",
    "    mjd = data[:,0]\n",
    "    mags = data[:,1]\n",
    "    errs = data[:,2]\n",
    "   \n",
    "    days = np.unique([int(day) for day in data[:,0]])\n",
    "    \n",
    "    # storage arrays  for each qso (all N's)\n",
    "    mjd_arr = np.zeros_like(days).astype(float)\n",
    "    avg_mags = np.zeros_like(days).astype(float)\n",
    "    avg_err_weights = np.zeros_like(days).astype(float)  \n",
    "    Nobs = np.zeros_like(days).astype(float)\n",
    "\n",
    "    # loop through days calculating mean, etc. \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 = float(len(mags[condition]))            # number of obs in that night \n",
    "        Nobs[i] = N\n",
    "        avgmag = np.average(mags[condition],weights=errs[condition]) # works for single measurement too ! \n",
    "        \n",
    "        # weights error : only that was used in the end \n",
    "        weights=1.0 / ( errs[condition] * errs[condition]) \n",
    "        avg_mags[i] = avgmag\n",
    "        error_weights = 1.0 / np.sqrt(np.sum(weights))\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",
    "            \n",
    "        avg_err_weights[i] = error_weights\n",
    "      \n",
    "        mjd_arr[i] = np.mean(mjd[condition])\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",
    "            name_out = dir_err_w + 'out_'+obj[:18]+'.txt'\n",
    "    \n",
    "    # So now we are saving all the day-averaged LC's, even if the resulting length was less than 10 days... \n",
    "    # Easier to explain stats if they are ignored later. \n",
    "\n",
    "    # Then select only those that have more than 10 days of observations, and \n",
    "    # compare to content of the QSO_CRTS_processed_err_w/ directory, to ensure that they are the same\n",
    "    np.savetxt(name_out, np.column_stack((mjd_arr,avg_mags, avg_err_weights, Nobs)),fmt='%11.4f')\n",
    "    \n",
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistics of the day-averaged light curves (read-in previously made LCs) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find out what are the stats for the 7601 quasar light curves that are longer than 10 days. \n",
    "Each lightcurve has three columns :\n",
    "MJD, average_magnitude,  error (as calculated above), N_points_per_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%\n"
     ]
    }
   ],
   "source": [
    "dir_err_w = 'QSO_CRTS_LC_day_averaged_err_w/'\n",
    "files = np.array(listdir(dir_err_w))\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",
    "timespan_obs = np.zeros_like(files, dtype=float) # store what was the total  timespan of observations per object\n",
    "lc_length=np.zeros_like(files, dtype=float) # number of points in the lightcurve - equal to the number of obs nights\n",
    "avg_N_day = np.zeros_like(files, dtype=float)\n",
    "avg_mag_ttl= np.zeros_like(files, dtype=float)\n",
    "avg_err_ttl= 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",
    "    \n",
    "    address=dir_err_w+files[i]  \n",
    "    data=np.loadtxt(address) \n",
    "    \n",
    "    lc_length[i] = sum(1 for line in open(address))\n",
    "    avg_mag_ttl[i] = np.mean(data[:,1])\n",
    "    avg_err_ttl[i] = np.mean(data[:,2])\n",
    "    avg_N_day[i] = np.mean(data[:,3])\n",
    "    timespan_obs[i] = data[-1,0]-data[0,0]  # gives number of days between first and last obs\n",
    "    \n",
    "    \n",
    "    # the \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 = ['timespan_obs', 'lc_length', 'avg_N_day', 'avg_mag_ttl', 'avg_err_ttl', 'avg_mjd_diff']\n",
    "values = [timespan_obs,lc_length,avg_N_day,avg_mag_ttl,avg_err_ttl,avg_mjd_diff]\n",
    "\n",
    "\n",
    "stats_dict={}\n",
    "for key, value in zip(keys, values):\n",
    "    stats_dict[key] = value\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2ycOT3Hd83FlVtecOeFkAAMA22GQPSlU9ZWs31lp771YUf1KSS5M8KsklM8+9Mslzk7y0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\nJH88WVlVv5zhP9iNSR43/l2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9bEVr7zTylM5D8tpdy2PZjUr+boyINcjTugKVMR9Xxncm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5897de110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('poster')\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Number of days of observations per CRTS Quasar')\n",
    "ax.hist(stats_dict['lc_length'])\n",
    "plt.savefig('QSO_CRTS_AVG_stats_lc_length.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65.3354093681\n"
     ]
    }
   ],
   "source": [
    "# What is the mean number of days per Quasar ? \n",
    "print(np.mean(lc_length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KLF+HqkuSJEkaB7V7SC4CDgb2y8yb25Yvbu63A+5oWz4PuDczn4iIxcBmEdGXmSs7ytzV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\nJMYYY4wxxpiBYYPEGGOMMcYYMzD+B6QMt1jrWK/HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5896e9050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average magnitude: CRTS Quasars')\n",
    "ax.hist(stats_dict['avg_mag_ttl'])\n",
    "plt.savefig('QSO_CRTS_AVG_stats_avg_mag.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.5400667526\n",
      "19.708772\n"
     ]
    }
   ],
   "source": [
    "# What is the mean and median magnitude ? \n",
    "print(np.mean(avg_mag_ttl))\n",
    "print(np.median(avg_mag_ttl))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lOTPJJ0spqwbKvD/Jg5Mck+Q+Sb6V5Jw+nKSUsiTJJ5IcmOThSR7Yb3d2KWW3RTgsAABg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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5895d3090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average error: CRTS Quasars')\n",
    "ax.hist(stats_dict['avg_err_ttl'])\n",
    "plt.savefig('QSO_CRTS_AVG_stats_avg_err.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OFesDFmTm+kbNicD3qa6x8k3gUeCYzNw6Ga9NkiRJ0o5pc+J8P9VV30daPwhcUN9GqtkM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe58945a8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average timespan between consecutive observations: CRTS Quasars [days]')\n",
    "ax.hist(stats_dict['avg_mjd_diff'])\n",
    "plt.savefig('QSO_CRTS_AVG_observations_cadence.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pmTlMGWB+b59lhoCZzc9rAfesoIwkSZKkCgY5fNwKrE65E9Zi4BXACcC6wDGjLAf9B68/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\nY8ys8G+8YGbETP9aTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5892e6a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average number of observations per day: CRTS Quasars')\n",
    "ax.hist(stats_dict['avg_N_day'])\n",
    "plt.savefig('QSO_CRTS_AVG_N_obs_per_day.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "293"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many lightcurves have on average more than 4 day of obs per day ? \n",
    "np.sum(avg_N_day > 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many lightcurves have less than 10 days of obs ? \n",
    "np.sum(lc_length < 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7035"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many lightcurves have between 1 and 4 obs ? \n",
    "np.sum((avg_N_day < 4) * (avg_N_day >1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7707"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(avg_N_day)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity check : plot light curves... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For diagnostics : print the averaged LC  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QSO_CRTS_LC_day_averaged_err_w/out_235948.09+001229.5.txt\n"
     ]
    },
    {
     "data": {
      "image/png": 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pIklT4dgN6JabIpIkbaoSEZFYVDhERCQWFQ4REYlFhUNERGJR4RARkVhUOEREJBYVDhER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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4748b04550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "i=0\n",
    "address=dir_err_w+files[i]  \n",
    "data=np.loadtxt(address) \n",
    "plt.scatter(data[:,0], data[:,1])\n",
    "print address"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And print the original LC.... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f4748b3e550>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mdnu2IYqISL/Wa9nfAeypLzCznwP2Aj/m7i8E3pNRbCIikpK2yd7d7wOeaCh+HfAOd38q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\nyV5EJABK9iIiAVCyFxEJwP8DjXiG1Sdn9DYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4748ced390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "address = 'QSO_CRTS_LC_longer_10/' + files[i][4:-3]+'dat'\n",
    "data=np.loadtxt(address) \n",
    "plt.scatter(data[:,0], data[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Check whether this happens often... calculate the scatter of points...\n",
    "\n",
    "rms = np.sqrt(np.mean(np.square(data[:,1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scatt = np.zeros_like(files, dtype=float)\n",
    "\n",
    "for i in range(len(files)):\n",
    "    address=dir_err_w+files[i]  \n",
    "    data=np.loadtxt(address) \n",
    "    scatt[i] = np.mean(data[:,1]) - min(data[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of light curves affected :\n",
      "353\n"
     ]
    },
    {
     "data": {
      "image/png": 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isZ4EHJdGO5YoTY9Knz8GrOfuY3o9YBER6XdaNokCpGS4XSeftwNHpT8REZFe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\nHr2rEE84uRL4E9FUsJa731W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oOHmZaBw/nLjcYElgN3e/I03rEWKjWCp9fhiRxH6Zprsr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\nEZGSlERFRERKUhIVEREp6f8DPA9tx5Be7TgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f473a40d910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cutoff = 2 # how many mags away from the mean can the farthest point be? \n",
    "print('Number of light curves affected :')\n",
    "print(np.sum(scatt>cutoff))\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_title('Departure from the mean > '+str(cutoff)+' mag')\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Maximum departure, CRTS day-averaged quasars [mags]')\n",
    "ax.hist(scatt[scatt>cutoff])\n",
    "plt.savefig('QSO_CRTS_AVG_scatter_from_the_mean.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Sanity check : compare to stats of  QSO_CRTS_processed_err_w LC's"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the statistical info of those processed day-averaged light curves that I used before, from QSO_CRTS_processed_err_w.tar.gz . It is exactly the same number of lightcurves, so the selection works identically and predictably. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%\n"
     ]
    }
   ],
   "source": [
    "dir_err_w = 'QSO_CRTS_processed_err_w/'\n",
    "files = np.array(listdir(dir_err_w))\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",
    "timespan_obs = np.zeros_like(files, dtype=float) # store what was the total  timespan of observations per object\n",
    "lc_length=np.zeros_like(files, dtype=float) # number of points in the lightcurve - equal to the number of obs nights\n",
    "#avg_N_day = np.zeros_like(files, dtype=float)\n",
    "avg_mag_ttl= np.zeros_like(files, dtype=float)\n",
    "avg_err_ttl= 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",
    "    \n",
    "    address=dir_err_w+files[i]  \n",
    "    data=np.loadtxt(address) \n",
    "    \n",
    "    lc_length[i] = sum(1 for line in open(address))\n",
    "    avg_mag_ttl[i] = np.mean(data[:,1])\n",
    "    avg_err_ttl[i] = np.mean(data[:,2])\n",
    "    #avg_N_day[i] = np.mean(data[:,3])\n",
    "    assert data[-1,0] == max(data[:,0])\n",
    "    assert data[0,0] == min(data[:,0])\n",
    "    timespan_obs[i] = data[-1,0]-data[0,0]  # gives number of days between first and last obs\n",
    "    # we assume it's sorted! \n",
    "    \n",
    "    \n",
    "    # the \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",
    "\n",
    "stats_dict_old={'timespan_obs':timespan_obs,\n",
    "               'lc_length' : lc_length,\n",
    "               'avg_mag_ttl': avg_mag_ttl,\n",
    "               'avg_err_ttl' :avg_err_ttl,\n",
    "              'avg_mjd_diff' :  avg_mjd_diff }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66.1345875543\n"
     ]
    },
    {
     "data": {
      "image/png": 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fbffFSJIkSeo0uVzDcgUwKYqil4B/kF4Yvz8wGCCKomuAPUMIh2TK30r6XSz3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5894f3690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context('poster')\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Number of days of observations per CRTS Quasar')\n",
    "\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_dict['lc_length'], 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_dict_old['lc_length'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label='New calculations')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label='Old calculations')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('QSO_CRTS_AVG_stats_lc_length_comp.png')\n",
    "\n",
    "# What is the mean number of days per Quasar ? \n",
    "print(np.mean(lc_length))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RIYT++fXjGjjmAGBPYKsY4yf5dT8EdgfaA5XrondJkiRJqyrkZVG9yF0KVT9E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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5896e90d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average magnitude: CRTS Quasars')\n",
    "\n",
    "nbins = 30\n",
    "\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_dict['avg_mag_ttl'], 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_dict_old['avg_mag_ttl'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label='New calculations')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label='Old calculations')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('QSO_CRTS_AVG_stats_avg_mag_comp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJEmRGSAkSZIkRWaAkCRJkhSZAUKSJElSZAYISZIkSZEZICRJkiRFZoCQJEmS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\nRBAEQRAEQRAEdRMGRBAEQRAEQRAEdfMfTY58vaip5JQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5893c9690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average error: CRTS Quasars')\n",
    "\n",
    "nbins = 30\n",
    "\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_dict['avg_err_ttl'], 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_dict_old['avg_err_ttl'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label='New calculations')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label='Old calculations')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('QSO_CRTS_AVG_stats_avg_err_comp.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KomAzLZIkSZJUG4YWSZIkSYlmaJEkSZKUaIYWSZIkSYlmaJEkSZKUaIYWSZIk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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe5892baed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_ylabel('N')\n",
    "ax.set_xlabel('Average timespan between consecutive observations: CRTS Quasars [days]')\n",
    "\n",
    "nbins = 30\n",
    "\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_dict['avg_mjd_diff'], 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_dict_old['avg_mjd_diff'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax.plot(bin_centres1, hist1, ls = 'steps', label='New calculations')\n",
    "ax.plot(bin_centres2, hist2, ls = 'steps', label='Old calculations')\n",
    "ax.legend()\n",
    "\n",
    "plt.savefig('QSO_CRTS_AVG_observations_cadence_comp.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note : Nobs per day cannot be compared between the old and new preprocessing (making day-averaged error-weigted light curves), because this information was not stored anywhere when making them for the first time... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But otherwise, it looks pretty nice ! It seems that I am getting almost exactly the same with my current day-averaging... However, I am suprised that they are not exactly the same..e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a four-panel stats plot only about data that were actually used... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CRTS Quasars: sample of 7601\n"
     ]
    },
    {
     "data": {
      "image/png": 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XtUvrOp9qxyN7Fk4pkf+v3P/9SpSpZaodj+1SWmq/9BUp3bEOBaFuOU8rQ6Xj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\nujn/B+WGOYKRPeq2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2af8497050>"
      ]
     },
     "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_dict_old['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_dict_old['avg_mag_ttl']\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_dict_old['avg_err_ttl'], 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('Sampling [days]')  # time between consecutive observations\n",
    "ax4.yaxis.tick_right()\n",
    "ax4.yaxis.set_label_position(\"right\")\n",
    "\n",
    "hist2, bin_edges = np.histogram(stats_dict_old['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', label='Old calculations')\n",
    "\n",
    "\n",
    "# Save\n",
    "\n",
    "plt.savefig('QSO_CRTS_AVG_stats_USED_data_four_panels.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text(0,100,u'100')\n"
     ]
    }
   ],
   "source": [
    "a = ax1.get_yticklabels()\n",
    "print a[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text(0,0,u'0')\n"
     ]
    }
   ],
   "source": [
    "b= ax1.get_ymajorticklabels()\n",
    "print b[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 677, 2455, 1845,  698,  390,  252,  189,  187,   97,  117,   69,\n",
       "         87,   99,   70,   60,   63,   79,   39,   39,   13,    4,   14,\n",
       "         18,   12,    9,    5,    4,    7,    2,    1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "CRTS_SDSS matching : radius...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dir = 'SF_plotting/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "archive = np.load(dir+'CRTS_SDSS_stars_matched_rows_radii.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([856891, 587373, 699643, ..., 643664, 336641, 728253])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive['SDSS_matching_rows']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "archive1 = np.load(dir+'CRTS_SDSS_qso_DB_QSO_matched_rows.npz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SDSS_matching_rows']"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive1.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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