A_Fig_1_stars_CRTS_stats.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",
    "from CRTS_paper_modules import update_progress"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial sample of CRTS stars from Branimir ... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, find out the total number of objects to start with .. stars_CRTS/0/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/chris/GradResearch/CRTS_PROJECT/code'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dir_in = '../raw_LC_CRTS/stars/'\n",
    "\n",
    "# make sure that we only take   stellar lightcurves,  which always end with .dat \n",
    "# and ignore two other files which may be there  : \n",
    "# radec.00   and file.list\n",
    "\n",
    "stars_list  = []\n",
    "for f in listdir(dir_in) : \n",
    "    if f.endswith('.dat'):\n",
    "        stars_list.append(f)\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 52131 stars to start with \n"
     ]
    }
   ],
   "source": [
    "print('We have %d stars to start with ' % len(stars_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a directory to save stats and figures  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today we will save our Fig_1 files in /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_1_data/2017-01-24/\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "#outDir = os.path.join(os.getcwd()[:-4],'data_products/'+'Fig_1_data', \n",
    "#                      datetime.datetime.now().strftime('%Y-%m-%d')+ '/')\n",
    "#if not os.path.exists(outDir): os.system('mkdir %s' % outDir)\n",
    "\n",
    "# set in stone just for today since I'm continuing from yesterday\n",
    "outDir = os.path.join(os.getcwd()[:-4],'data_products/'+'Fig_1_data/'+'2017-01-24/')\n",
    "                      \n",
    "print('Today we will save our Fig_1 files in %s'%outDir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove empty files and those with less than 10 points "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 1410 empty files\n"
     ]
    }
   ],
   "source": [
    "c = 0 \n",
    "empty_files = np.zeros(len(stars_list), dtype='bool')\n",
    "for i in range(len(stars_list)):\n",
    "    obj = stars_list[i]\n",
    "    address = dir_in + obj\n",
    "    f = open(address, 'r')\n",
    "    if f.readline() == '' :\n",
    "        c += 1\n",
    "        empty_files[i] = True \n",
    "        \n",
    "print('We have %d empty files' % c)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50721"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_nonempty = np.array(stars_list)[~empty_files]\n",
    "len(stars_nonempty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove those files that have less than 10 points : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have 1336  files with less than 10 pts \n"
     ]
    }
   ],
   "source": [
    "c = 0 \n",
    "short_files  = np.zeros(len(stars_nonempty), dtype='bool')\n",
    "for i in range(len(stars_nonempty)):\n",
    "    obj = stars_nonempty[i]\n",
    "    num_lines = sum(1 for line in open(dir_in+obj))\n",
    "    if num_lines < 10 :\n",
    "        c += 1\n",
    "        short_files[i] = True\n",
    "        \n",
    "print('We have %d  files with less than 10 pts ' % c) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49385"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_10_pts_plus = stars_nonempty[~short_files]\n",
    "len(stars_10_pts_plus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1000037.dat', '1000057.dat', '1000088.dat', ..., '999974.dat',\n",
       "       '999989.dat', '999997.dat'], \n",
       "      dtype='<U11')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_10_pts_plus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics on the raw  LC (before day-averaging)  2016/06/11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%"
     ]
    }
   ],
   "source": [
    "dir_in = '../raw_LC_CRTS/stars/'\n",
    "files  = stars_10_pts_plus\n",
    "\n",
    "mjd_span = 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 : epochs before day-averaging\n",
    "mean_N_day = np.zeros_like(files, dtype=float)    # average nunmber of observations per day                          \n",
    "mean_mag = np.zeros_like(files, dtype=float)   # mean magnitude over all epochs \n",
    "mean_err = np.zeros_like(files, dtype=float)   # mean error over all epochs \n",
    "star_name = np.zeros_like(files)\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_in+files[i]  \n",
    "    data=np.loadtxt(address) \n",
    "    \n",
    "    star_name[i] = files[i][:-4]\n",
    "    mjd_span[i] = data[-1,0]-data[0,0]  \n",
    "    lc_length[i] = sum(1 for line in open(address))\n",
    "    mean_mag[i] = np.mean(data[:,1])\n",
    "    mean_err[i] = np.mean(data[:,2])\n",
    "    \n",
    "    \n",
    "    \n",
    "keys = ['CRTS_name', 'raw_mjd_span', 'raw_lc_length', 'raw_mean_mag', 'raw_mean_err']\n",
    "values = [star_name, mjd_span, lc_length, mean_mag, mean_err]\n",
    "\n",
    "\n",
    "stats_before={}\n",
    "for key, value in zip(keys, values):\n",
    "    stats_before[key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# save the raw CRTS LC  stats...\n",
    "outDir = '../data_products/'\n",
    "savefile = outDir+'Fig_1_raw_CRTS_stars_LC_stats'\n",
    "np.save(savefile, stats_before)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing :  Day-averaged lightcurves : remove those with less than 10 days "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### : needs be RUN ONLY ONCE  to make them! (takes about 10 mins on the workstation)"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%\n"
     ]
    }
   ],
   "source": [
    "# code based on the stars_crts_preprocessing.py \n",
    "# and QSO_CRTS_day_averaged_stats.py\n",
    "dir_in = '../raw_LC_CRTS/stars/'\n",
    "dir_out = '../proc_LC_CRTS/stars/'\n",
    "\n",
    "lc_files  = stars_10_pts_plus\n",
    "\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",
    "        w=1.0 / ( errs[condition] * errs[condition]) \n",
    "        \n",
    "        # daily error-weighted mean \n",
    "        avgmag = np.average(mags[condition], weights = w)\n",
    "        avg_mags[i] = avgmag\n",
    "        #avgmag = np.average(mags[condition],weights=errs[condition]) # works for single measurement too ! \n",
    "        \n",
    "        # error based on inverse square of sum of weights \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",
    "            \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_out + 'out_'+obj[:18]+'.txt'\n",
    "        np.savetxt(name_out, np.column_stack((mjd_arr,avg_mags, avg_err_weights, Nobs)),fmt='%11.4f')\n",
    "\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many stars with LC  longer than 10 days we have  ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dir_out = '../proc_LC_CRTS/stars/'\n",
    "stars_LC_proc_err_w = listdir(dir_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48250"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stars_LC_proc_err_w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the stats of that sample : \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def compare_star_LC(i=None, fig = True, n=None):\n",
    "    '''A short function to plot an original stellar LC from CRTS (before all averaging, etc)\n",
    "    to the day-averaged LC.\n",
    "    \n",
    "    Parameters: \n",
    "    -----------\n",
    "    i = a number of the stellar processed LC in the stars_CRTS_LC_err_w/  dir \n",
    "     or  \n",
    "    n = name of an original stellar LC , eg . '127648.dat' \n",
    "    Returns:\n",
    "    --------\n",
    "    None\n",
    "    '''\n",
    "    \n",
    "    dir_in = '../raw_LC_CRTS/stars/'\n",
    "    dir_out = '../proc_LC_CRTS/stars/'\n",
    "    stars_LC_10_days_plus= listdir(dir_out)\n",
    "    if n ==None : \n",
    "        star_name_proc = stars_LC_10_days_plus[i]\n",
    "    else :\n",
    "        star_name_proc = 'out_'+ n +'.txt'\n",
    "   \n",
    "    orig_star_name = star_name_proc[4:-4]\n",
    "\n",
    "\n",
    "    data = np.loadtxt(dir_out  + star_name_proc)\n",
    "    data_orig = np.loadtxt(dir_in + orig_star_name)\n",
    "    \n",
    "    mjd_arr,avg_mags, avg_err_weights, Nobs = data[:,0], data[:,1], data[:,2], data[:,3]\n",
    "    mjd,mags, errs = data_orig[:,0],data_orig[:,1], data_orig[:,2]\n",
    " \n",
    "\n",
    "    %matplotlib inline\n",
    "\n",
    "    fig, axs = plt.subplots(2,2, figsize=(16,10))\n",
    "    ax = np.ravel(axs)\n",
    "    ax[0].set_title('Original LC for '+orig_star_name)\n",
    "    ax[0].scatter(mjd,mags)\n",
    "    ax[0].errorbar(mjd, mags, errs, linestyle='None')\n",
    "    print 'Vertical dashed lines indicate individual obs days '\n",
    "\n",
    "    for day in mjd_arr : \n",
    "\n",
    "        ax[0].axvline(day, ymin=0, ymax=0.2, lw =1, ls = '--', color='red')\n",
    "\n",
    "\n",
    "    ax[1].set_title('Day-averaged LC for '+ orig_star_name)\n",
    "    ax[1].scatter(mjd_arr,avg_mags, color='green')\n",
    "    ax[1].errorbar(mjd_arr, avg_mags, avg_err_weights, linestyle='None', color='green')\n",
    "    \n",
    "    ax[2].set_title('Original and avg  LC for '+orig_star_name)\n",
    "    ax[2].scatter(mjd,mags, alpha=0.5)\n",
    "    ax[2].errorbar(mjd, mags, errs, linestyle='None',alpha=0.5)\n",
    "    ax[2].scatter(mjd_arr,avg_mags,alpha=0.5, color='red')\n",
    "    ax[2].errorbar(mjd_arr, avg_mags, avg_err_weights, linestyle='None', color='red')\n",
    "    ax[3].axis('off')\n",
    "    if fig:\n",
    "        plt.tight_layout()\n",
    "        fname = '../Star_LC_'+orig_star_name+'.png'\n",
    "        plt.savefig(fname)\n",
    "        print('We saved this plot in %s'%fname)\n",
    "        plt.show()\n",
    "    else:  \n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vertical dashed lines indicate individual obs days \n",
      "We saved this plot in ../Star_LC_700105.dat.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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PvHl9GHeDiStw5w//C5yC+/seJXzedCHwK1yx4996ryu6nbgXeb2A6A6eC3DnZJtwHTcX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\nzMzMrMf9DdOpUzw6TuFkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb08b2b4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "compare_star_LC(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like the day-averaging is working fine.  Now , calculate the statistics for the day-averaged stars (takes about 3 mins on the workstation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics on the day-averaged lightcurves   2017/02/01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 99.00%"
     ]
    }
   ],
   "source": [
    "dir_in = '../proc_LC_CRTS/stars/'\n",
    "files = listdir(dir_out)\n",
    "\n",
    "star_name = np.zeros_like(files)\n",
    "mjd_span = 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 : epochs before day-averaging\n",
    "mean_mag = np.zeros_like(files, dtype=float)   # mean magnitude over all epochs \n",
    "mean_err = np.zeros_like(files, dtype=float)   # mean error over all epochs \n",
    "mean_N_day = np.zeros_like(files, dtype=float)    # average nunmber of observations per day  \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_in+files[i]  \n",
    "    data=np.loadtxt(address) \n",
    "    \n",
    "    star_name[i] = files[i][4:-8]\n",
    "    mjd_span[i] = data[-1,0]-data[0,0]  \n",
    "    lc_length[i] = sum(1 for line in open(address))\n",
    "    mean_mag[i] = np.mean(data[:,1])\n",
    "    mean_err[i] = np.mean(data[:,2])\n",
    "    mean_N_day[i] = np.mean(data[:,3])\n",
    "    \n",
    "    \n",
    "    \n",
    "keys = ['CRTS_name', 'proc_mjd_span', 'proc_lc_length', 'proc_mean_mag', 'proc_mean_err', 'proc_mean_N_day']\n",
    "values = [star_name, mjd_span, lc_length, mean_mag, mean_err, mean_N_day]\n",
    "\n",
    "\n",
    "stats_after={}\n",
    "for key, value in zip(keys, values):\n",
    "    stats_after[key] = value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# save the processed CRTS LC  stats...\n",
    "outDir = '../data_products/'\n",
    "savefile = outDir+'Fig_1_proc_CRTS_stars_LC_stats'\n",
    "np.save(savefile, stats_after)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Note : in my old work (in SDSS_CRTS directory) you will find that there is an entire issue about filtered vs unfiltered lightcurves. This is because in the code I used in 2014 and mid-2015, I had in processing of stellar lightcurves a line that wwas supposed to filter out some stars with high error, but it did not work as expected. Thus I redid the processing, to figure out what went wrong, and it's not immediately obvious what the filter  actually did. Nevertheless, for the poster in January 2016 I used a re-processed version of stellar lightcurves from CRTS. If you would like to filter out any outliers from stelalr lightcurves, it can always be done at the later stage, just before taking the individual master files...  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combine before and after  day-averaging aggregate properties of CRTS QSO into one master catalog..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.table import join\n",
    "from astropy.table import Table\n",
    "\n",
    "# read the raw LC agg info , and \n",
    "# convert a dictionary to an AstroPy Table \n",
    "raw_table = Table(np.load(outDir + 'Fig_1_raw_CRTS_stars_LC_stats.npy').item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# read the processed LC agg info, and convert to AstroPy Table \n",
    "proc_table = Table(np.load(outDir + 'Fig_1_proc_CRTS_stars_LC_stats.npy').item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Join the two tables on CRTS_name:\n",
    "from astropy.table import join\n",
    "agg_combined = join(left=raw_table, right = proc_table, keys='CRTS_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=5&gt;\n",
       "<table id=\"table4616257376\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>raw_mjd_span</th><th>raw_mean_mag</th><th>raw_mean_err</th><th>CRTS_name</th><th>raw_lc_length</th><th>proc_mean_N_day</th><th>proc_mean_err</th><th>proc_lc_length</th><th>proc_mean_mag</th><th>proc_mjd_span</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>str19</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>3002.94245</td><td>15.7489583333</td><td>0.0609953703704</td><td>1000037</td><td>432.0</td><td>4.03738317757</td><td>0.0313626168224</td><td>107.0</td><td>15.7431420561</td><td>3002.9233</td></tr>\n",
       "<tr><td>2921.05988</td><td>20.2332217573</td><td>0.204267782427</td><td>1000057</td><td>239.0</td><td>2.9875</td><td>0.17498375</td><td>80.0</td><td>20.2164875</td><td>2921.0425</td></tr>\n",
       "<tr><td>2890.16152</td><td>16.0015606936</td><td>0.0656936416185</td><td>1000088</td><td>346.0</td><td>3.60416666667</td><td>0.0370708333333</td><td>96.0</td><td>16.0023635417</td><td>2890.1417</td></tr>\n",
       "<tr><td>2890.16152</td><td>20.0829166667</td><td>0.202962962963</td><td>1000114</td><td>216.0</td><td>2.88</td><td>0.170036</td><td>75.0</td><td>20.018992</td><td>2890.1417</td></tr>\n",
       "<tr><td>2890.16152</td><td>20.3421854305</td><td>0.172052980132</td><td>1000115</td><td>151.0</td><td>3.97368421053</td><td>0.0855394736842</td><td>38.0</td><td>20.3099973684</td><td>2890.1417</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=5>\n",
       "raw_mjd_span  raw_mean_mag   raw_mean_err  ... proc_mean_mag proc_mjd_span\n",
       "  float64       float64        float64     ...    float64       float64   \n",
       "------------ ------------- --------------- ... ------------- -------------\n",
       "  3002.94245 15.7489583333 0.0609953703704 ... 15.7431420561     3002.9233\n",
       "  2921.05988 20.2332217573  0.204267782427 ...    20.2164875     2921.0425\n",
       "  2890.16152 16.0015606936 0.0656936416185 ... 16.0023635417     2890.1417\n",
       "  2890.16152 20.0829166667  0.202962962963 ...     20.018992     2890.1417\n",
       "  2890.16152 20.3421854305  0.172052980132 ... 20.3099973684     2890.1417"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_combined[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save the master CRTS catalog \n",
    "\n",
    "address = '../data_products/CRTS_stars_raw_and_proc_combined_agg.dat'\n",
    "agg_combined.write(address,format='ascii', overwrite='True')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Compare stats for CRTS stars before and after day-averaging "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['avg_mag', 'med_mag', 'avg_err', 'med_err', 'name', 'avg_err_90th_percentile']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats_after.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 11089.,   8316.,   6347.,   5929.,   5455.,   3965.,   3369.,\n",
       "          3443.,   1343.,    129.]),\n",
       " array([ 0.05  ,  0.0932,  0.1364,  0.1796,  0.2228,  0.266 ,  0.3092,\n",
       "         0.3524,  0.3956,  0.4388,  0.482 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mediqMR4IlfXA6urxymp63Caawbaiams9pLWh6vuDaRivJGmalYbJTprnRW7JzGci4mTg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb1ec51a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "plt.hist(stats_after['avg_err'])\n",
    "plt.hist(stats_before['avg_err'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IliRJkiQ1iAmWJEmSJDWICZYkSZIkNYgJliRJkiQ1iItcSFKTFEoFRvPjR5SPTE+R6p6c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\n/4G410lM2Sj/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb1e301e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#m = (stats_before['mag']>15)*(stats_before['mag']<22)\n",
    "#plt.hist(mag_list[m])\n",
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "fig,ax = plt.subplots(1,2, figsize=(12,6))\n",
    "nbins=30\n",
    "# Calculate the histogram1 : new day-averages\n",
    "hist1, bin_edges = np.histogram(stats_before['avg_mag'], 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_after['avg_mag'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "ax[0].plot(bin_centres1, hist1, ls = 'steps', label=r'$\\langle m_{ij}\\rangle$')\n",
    "ax[0].plot(bin_centres2, hist2, ls = 'steps', label=r'$\\langle m_{i}\\rangle$')\n",
    "plt.legend(loc='best')\n",
    "ax[0].set_ylabel('Density (normalized)')\n",
    "ax[0].set_xlabel('White light CRTS [mag]')\n",
    "\n",
    "hist1, bin_edges = np.histogram(stats_before['avg_err'], bins=nbins, density=True)\n",
    "bin_centres1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "delta = (bin_centres1[1]-bin_centres1[0])\n",
    "value = bin_centres1[0] -delta\n",
    "bin_centres1 = np.insert(bin_centres1, [0], value)\n",
    "hist1 = np.insert(hist1, [0],0)\n",
    "# Calculate the histogram2 : old day-averages\n",
    "hist2, bin_edges = np.histogram(stats_after['avg_err'], bins=nbins, density=True)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "delta = (bin_centres2[1]-bin_centres2[0])\n",
    "value = bin_centres2[0] -delta\n",
    "bin_centres2 = np.insert(bin_centres2, [0], value)\n",
    "hist2 = np.insert(hist2, [0],0)\n",
    "\n",
    "ax[1].plot(bin_centres1, hist1, ls = 'steps', label=r'$\\langle e_{ij}\\rangle$')\n",
    "ax[1].plot(bin_centres2, hist2, ls = 'steps', label=r'$\\langle e_{i}\\rangle$')\n",
    "plt.legend(loc='best')\n",
    "ax[1].set_ylabel('Density (normalized)')\n",
    "ax[1].set_xlabel('White light CRTS err [mag]')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../Fig_1_Stars_CRTS_before_vs_after_preproc.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19053127660294869"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(stats_after['avg_err'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42864"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.array(stats_after['avg_err']) < 0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Compare CRTS white light mag and SDSS r mag  for QSO (2016/06/11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zipping CRTS-SDSS stars catalog...\n",
      "Read in catalog for  48250 , stars from CRTS\n"
     ]
    }
   ],
   "source": [
    "import CRTS_paper_modules as mod\n",
    "cols2 , star_cat= mod.get_stars_catalog()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['N_rows',\n",
       " 'dec_CRTS',\n",
       " 'CRTS_Merr',\n",
       " 'g_mMed',\n",
       " 'CRTS_M',\n",
       " 'i_mMed',\n",
       " 'g_Nobs',\n",
       " 'ra_CRTS',\n",
       " 'mjd_N',\n",
       " 'dec_SDSS',\n",
       " 'r_mMed',\n",
       " 'crts_id',\n",
       " 'm_ang_deg',\n",
       " 'ra_SDSS',\n",
       " 'mjd_span']"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "star_cat.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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atdb5piQREREJS4Evnvfa/4YP2l5KwB9Hm+yNXLzxk5D74gvzPchORCIh3JXy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\nSZIkKWUm5ZIkSVLK/j+97BZYCYmQ4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7feb1dd4e890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = star_cat['r_mMed']\n",
    "m0 = (r > 15)*(r<22)\n",
    "avg_mag = star_cat['CRTS_M']\n",
    "m1 = (avg_mag>15)*(avg_mag<22)\n",
    "m = m0*m1\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "H, xedges, yedges = np.histogram2d(r[m],avg_mag[m], bins=50)\n",
    "H = np.rot90(H)\n",
    "H = np.flipud(H)\n",
    "# Mask zeros\n",
    "Hmasked = np.ma.masked_where(H==0,H) # Mask pixels with a value of zeropltssssss\n",
    "# Plot 2D histogram using pcolor\n",
    "ax.pcolormesh(xedges,yedges,np.log10(Hmasked), cmap='jet')\n",
    "ax.plot(np.linspace(15,22), np.linspace(15,22), ls='--', lw=4)\n",
    "ax.set_xlabel('SDSS r mag')\n",
    "ax.set_ylabel('CRTS <LC> white light mag')\n",
    "plt.tight_layout()\n",
    "plt.savefig('../Fig_1_Stars_CRTS_vs_SDSS_r_magnitudes.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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