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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "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),"
   ]
  },
  {
   "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": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'/astro/users/suberlak/Desktop/SDSS'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dir1 = 'stars_CRTS/0/'\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(dir1) : \n",
    "    if f.endswith('.dat'):\n",
    "        stars_list.append(f)\n",
    "        \n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "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": [
    "## Remove empty files and those with less than 10 points "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "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 = dir1 + 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": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50721"
      ]
     },
     "execution_count": 210,
     "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": 211,
   "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(dir1+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": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49385"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stars_10_pts_plus = stars_nonempty[~short_files]\n",
    "len(stars_10_pts_plus)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day-averaged lightcurves : remove those with less than 10 days "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### : 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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",
    "\n",
    "dir_in = 'stars_CRTS/0/'\n",
    "dir_err_w = 'stars_CRTS_LC_err_w/'\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",
    "        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",
    "        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": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stars_LC_proc_err_w = listdir(dir_err_w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48251"
      ]
     },
     "execution_count": 37,
     "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": 263,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def compare_star_LC(i=None, 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 = 'stars_CRTS/0/'\n",
    "    dir_err_w = 'stars_CRTS_LC_err_w/'\n",
    "    stars_LC_10_days_plus= listdir(dir_err_w)\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_err_w  + 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 days : \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",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vertical dashed lines indicate individual obs days \n"
     ]
    },
    {
     "data": {
      "image/png": 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nxH7/AXBYdUonki69G3tpmtBUCGoBGic00ruxV0GtyBih7MciIiIiMma1TW6j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fn9fmbM+D3p6VP5bVEOt88oYd68zpSXH4ye5+kojDFYa02k4xAREZHQU09tFImPj2/q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\nk8sJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0587881110>"
      ]
     },
     "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": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 97.00%\n"
     ]
    }
   ],
   "source": [
    "from collections import OrderedDict\n",
    "dir_in = 'stars_CRTS/0/'\n",
    "dir_err_w = 'stars_CRTS_LC_err_w/'\n",
    "\n",
    "stars_LC_10_days_plus = []\n",
    "for file in listdir(dir_err_w) : \n",
    "    if file.endswith('.txt'):\n",
    "        stars_LC_10_days_plus.append(file)\n",
    "\n",
    "remove_err_outliers = np.zeros_like(stars_LC_10_days_plus, dtype=bool)\n",
    "stats_proc_err_w = OrderedDict()\n",
    "keys = ['avg_mag', 'med_mag', 'avg_err', 'med_err', 'name', 'avg_err_90th_percentile']\n",
    "for key in keys: \n",
    "    stats_proc_err_w[key] = [] \n",
    "\n",
    "\n",
    "k = 0\n",
    "total = float(len(lc_files))\n",
    "\n",
    "for i in range(len(stars_LC_10_days_plus)):\n",
    "    percent = 100*(k / total)\n",
    "    if (k % 10) == 0 : # every tenth loop.. \n",
    "        update_progress(int(percent))\n",
    "    k += 1\n",
    "    \n",
    "    star_name_proc = stars_LC_10_days_plus[i]\n",
    "    orig_star_name = star_name_proc[4:-4]\n",
    "\n",
    "\n",
    "    data = np.loadtxt(dir_err_w  + 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",
    "    # Calculate stats for day-averaged stars \n",
    "    stats_proc_err_w['avg_mag'].append(np.mean(avg_mags))\n",
    "    stats_proc_err_w['med_mag'].append(np.median(avg_mags))\n",
    "    stats_proc_err_w['avg_err'].append(np.mean(avg_err_weights))\n",
    "    stats_proc_err_w['med_err'].append(np.median(avg_err_weights))\n",
    "    stats_proc_err_w['name'].append(orig_star_name)\n",
    "    stats_proc_err_w['avg_err_90th_percentile'].append(np.percentile(avg_err_weights , 90))\n",
    "    \n",
    "    if (np.median(avg_err_weights) < 0.1 and np.percentile(avg_err_weights , 90)) < 0.2 : \n",
    "        remove_err_outliers[i] = True "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the list of those stars that pass through the error-outliers-removal  filter ought be the same as the list in  stars_CRTS_analysis/good_err_LC.txt , as well as filelist in stars_CRTS_proc_err_w_good/  . If it isn't, I can't claim I understand every single step... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48250"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stars_LC_10_days_plus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47797"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_list = np.array(stars_LC_10_days_plus)[remove_err_outliers]\n",
    "len(acc_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48250"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir1_list = listdir('stars_CRTS_LC_err_w/')\n",
    "len(dir1_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48250"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.in1d(stars_LC_10_days_plus, dir1_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47787"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir_list = listdir('stars_CRTS_proc_err_w_good/') \n",
    "len(dir_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which stars in the  'stars_CRTS_LC_err_w/' dir are not in  the stars_CRTS_proc_err_w_good/' dir ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "missing_stars = np.array(dir1_list)[~np.in1d(dir1_list, dir_list)]\n",
    "len(np.array(dir1_list)[~np.in1d(dir1_list, dir_list)])\n",
    "np.savetxt('missing_stars_err_w_good_vs_err_w.txt', missing_stars, fmt='%s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       'out_991221.dat.txt', 'out_365567.dat.txt', 'out_29018.dat.txt',\n",
       "       'out_26210.dat.txt', 'out_398961.dat.txt', 'out_983663.dat.txt',\n",
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       "       'out_357654.dat.txt', 'out_361011.dat.txt', 'out_25642.dat.txt',\n",
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       "       'out_394761.dat.txt', 'out_997931.dat.txt', 'out_358574.dat.txt',\n",
       "       'out_399732.dat.txt', 'out_991463.dat.txt', 'out_384478.dat.txt',\n",
       "       'out_971939.dat.txt', 'out_977606.dat.txt', 'out_28743.dat.txt',\n",
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       "       'out_994077.dat.txt', 'out_394948.dat.txt', 'out_968264.dat.txt',\n",
       "       'out_282879.dat.txt', 'out_992287.dat.txt', 'out_995913.dat.txt',\n",
       "       'out_353168.dat.txt', 'out_13592.dat.txt', 'out_390578.dat.txt',\n",
       "       'out_372530.dat.txt', 'out_21098.dat.txt', 'out_391581.dat.txt',\n",
       "       'out_14088.dat.txt', 'out_999722.dat.txt', 'out_397449.dat.txt',\n",
       "       'out_995872.dat.txt', 'out_378186.dat.txt', 'out_29345.dat.txt',\n",
       "       'out_384824.dat.txt', 'out_989944.dat.txt', 'out_360045.dat.txt',\n",
       "       'out_999067.dat.txt', 'out_359084.dat.txt', 'out_990691.dat.txt',\n",
       "       'out_979770.dat.txt', 'out_347086.dat.txt', 'out_379552.dat.txt',\n",
       "       'out_994138.dat.txt', 'out_994250.dat.txt', 'out_967123.dat.txt',\n",
       "       'out_977357.dat.txt', 'out_13630.dat.txt', 'out_380887.dat.txt',\n",
       "       'out_14635.dat.txt', 'out_388969.dat.txt', 'out_380584.dat.txt',\n",
       "       'out_373394.dat.txt', 'out_359312.dat.txt', 'out_973455.dat.txt',\n",
       "       'out_385499.dat.txt', 'out_26584.dat.txt', 'out_990113.dat.txt',\n",
       "       'out_369505.dat.txt', 'out_377353.dat.txt', 'out_368561.dat.txt',\n",
       "       'out_389871.dat.txt', 'out_26183.dat.txt', 'out_383106.dat.txt',\n",
       "       'out_979215.dat.txt', 'out_997455.dat.txt', 'out_15587.dat.txt',\n",
       "       'out_26855.dat.txt', 'out_382920.dat.txt', 'out_372997.dat.txt',\n",
       "       'out_984648.dat.txt', 'out_364790.dat.txt', 'out_989555.dat.txt',\n",
       "       'out_392273.dat.txt', 'out_983013.dat.txt', 'out_373364.dat.txt',\n",
       "       'out_366974.dat.txt', 'out_350352.dat.txt', 'out_993534.dat.txt',\n",
       "       'out_993243.dat.txt', 'out_1000760.dat.txt', 'out_372262.dat.txt',\n",
       "       'out_350969.dat.txt', 'out_330780.dat.txt', 'out_391637.dat.txt',\n",
       "       'out_347108.dat.txt', 'out_988208.dat.txt', 'out_356526.dat.txt',\n",
       "       'out_395978.dat.txt', 'out_999989.dat.txt', 'out_985698.dat.txt',\n",
       "       'out_999903.dat.txt', 'out_999495.dat.txt', 'out_375477.dat.txt',\n",
       "       'out_844578.dat.txt', 'out_26735.dat.txt', 'out_352422.dat.txt',\n",
       "       'out_984302.dat.txt'], \n",
       "      dtype='|S19')"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.loadtxt('missing_stars_err_w_good_vs_err_w.txt', dtype=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47787"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_list = np.loadtxt('stars_CRTS_analysis/good_err_LC.txt', dtype=str)\n",
    "len(file_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47787"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.in1d(dir_list,file_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above means that the lightcurves  in 'stars_CRTS_proc_err_w_good/'  are exactly those listed in 'stars_CRTS_analysis/good_err_LC.txt'  produced by qso_CRTS_good_LC_choosing_err_based.py\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47787"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.in1d(acc_list, dir_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47787"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(np.in1d(dir_list,stars_LC_10_days_plus))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So all the LCs in the  'stars_CRTS_proc_err_w_good/' are accepted by the miswritten filter,  but there are 11 lightcurves that were not accepted by that filter before , and were now. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since it turns out that the entire filter was miswritten ( the qso_CRTS_good_LC_choosing_err_based.py has an error in brackets in line  34 which is the condition of the filter ) , it definitely did not work as expected.  \n",
    "\n",
    "Thus , I need to compare the properties of lightcurves  in  'stars_CRTS_LC_err_w/' (the day-averaged with only those longer than and including 10 days)  to those in   'stars_CRTS_proc_err_w_good/'   (the outcome of the erroneous filtration :  \n",
    "` (np.median(err) < 0.1 and np.percentile(err,90)) < 0.2  `"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://jupyter.cs.brynmawr.edu/hub/dblank/public/Jupyter%20Notebook%20Users%20Manual.ipynb#4.-Using-Markdown-Cells-for-Writing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the properties of  'stars_CRTS_proc_err_w_good/' : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[######### ] 96.00%\n"
     ]
    }
   ],
   "source": [
    "dir_err_w = 'stars_CRTS_proc_err_w_good/'\n",
    "stars_LC= listdir(dir_err_w)\n",
    "\n",
    "#remove_err_outliers = np.zeros_like(stars_LC, dtype=bool)\n",
    "\n",
    "stats_proc_err_wg = OrderedDict()\n",
    "keys = ['avg_mag', 'med_mag', 'avg_err', 'med_err', 'name', 'avg_err_90th_percentile']\n",
    "for key in keys: \n",
    "    stats_proc_err_wg[key] = [] \n",
    "\n",
    "k = 0\n",
    "total = float(len(lc_files))\n",
    "\n",
    "for i in range(len(stars_LC)):\n",
    "    # update progress...\n",
    "    percent = 100*(k / total)\n",
    "    if (k % 10) == 0 : # every tenth loop.. \n",
    "        update_progress(int(percent))\n",
    "    k += 1\n",
    "    \n",
    "    star_name_proc = stars_LC[i]\n",
    "    data = np.loadtxt(dir_err_w  + star_name_proc)\n",
    "    mjd_arr,avg_mags, avg_err_weights = data[:,0], data[:,1], data[:,2]\n",
    "\n",
    "    # Calculate stats for day-averaged stars \n",
    "    stats_proc_err_wg['avg_mag'].append(np.mean(avg_mags))\n",
    "    stats_proc_err_wg['med_mag'].append(np.median(avg_mags))\n",
    "    stats_proc_err_wg['avg_err'].append(np.mean(avg_err_weights))\n",
    "    stats_proc_err_wg['med_err'].append(np.median(avg_err_weights))\n",
    "    stats_proc_err_wg['name'].append(star_name_proc[4:-4])\n",
    "    stats_proc_err_wg['avg_err_90th_percentile'].append(np.percentile(avg_err_weights , 90))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.089423456790123465"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(stats_proc_err_wg['avg_err'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.091374368686868684"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(stats_proc_err_w['avg_err'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZMGBA4lt1MvogUl9NNT6rvrcuRx11FEuWLOHBBx+kW7dudOvWjRkzZvDpp58CcPvtt/PJ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\nhl49AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f058787a0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms comparing the two dirs  (original - unfiltered    and the incorrectly-filtered ) ... \n",
    "%matplotlib inline \n",
    "nbins = 50\n",
    "\n",
    "fig, axs = plt.subplots(2,3, figsize=(12,8))\n",
    "ax = np.ravel(axs)\n",
    "keys = stats_proc_err_w.keys()\n",
    "keys.remove('name')\n",
    "\n",
    "i = 0 \n",
    "for key  in keys:\n",
    "    hist1, bin_edges = np.histogram(stats_proc_err_w[key], bins = nbins, density=False)\n",
    "    bin_cen1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "    hist2, bin_edges = np.histogram(stats_proc_err_wg[key], bins = nbins, density=False)\n",
    "    bin_cen2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "    ax[i].plot(bin_cen1, hist1, color = 'green', alpha=0.5, ls='steps', label='old '+ key)\n",
    "    ax[i].plot(bin_cen2, hist2, color = 'red', alpha=0.5, ls='steps', label='new '+ key)\n",
    "    ax[i].legend(loc='upper right')\n",
    "    i += 1 \n",
    "ax[i].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They are pretty much the same, so no reason to worry.  If Z. thinks it better to use the error-outliers clipping,  I can always select only those that are actually fulfulling the crieria, and remove them from the stars_CRTS_proc_err_w_good/ directory, and just rerun  the code for Fig.2, Fig.3 and Fig. 4  . "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below  `old` means  stars_CRTS_processed_err_w/  which is the same as the newly-made   stars_CRTS_LC_err_w/  (before applying 'remove error outliers' filter ), \n",
    "and `new` means stars_CRTS_proc_err_w_good/  (after applying an erroneous filter, which God knows what it did )  . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of avg_mag : unfiltered vs  filtered : 18.735371 vs  18.688128 \n",
      "The mean of med_mag : unfiltered vs  filtered : 18.741062 vs  18.693179 \n",
      "The mean of avg_err : unfiltered vs  filtered : 0.133401 vs  0.132130 \n",
      "The mean of med_err : unfiltered vs  filtered : 0.124622 vs  0.123837 \n",
      "The mean of avg_err_90th_percentile : unfiltered vs  filtered : 0.186803 vs  0.184350 \n"
     ]
    }
   ],
   "source": [
    "for key in keys : \n",
    "    print('The mean of %s : unfiltered vs  filtered : %f vs  %f '%(key, np.mean(stats_proc_err_w[key]),\n",
    "                                                               np.mean(stats_proc_err_wg[key]) ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The median of avg_mag : unfiltered vs  filtered : 19.173552 vs  19.095185 \n",
      "The median of med_mag : unfiltered vs  filtered : 19.160000 vs  19.084700 \n",
      "The median of avg_err : unfiltered vs  filtered : 0.091374 vs  0.089423 \n",
      "The median of med_err : unfiltered vs  filtered : 0.084500 vs  0.083500 \n",
      "The median of avg_err_90th_percentile : unfiltered vs  filtered : 0.109295 vs  0.106750 \n"
     ]
    }
   ],
   "source": [
    "for key in keys : \n",
    "    print('The median of %s : unfiltered vs  filtered : %f vs  %f '%(key, np.median(stats_proc_err_w[key]),\n",
    "                                                               np.median(stats_proc_err_wg[key]) ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I were to apply the actual filter in a correct way , here is the comparison  : \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# require  the median error to be  < 0.1,   and the 90th percentile of the error distribution to be < 0.2 \n",
    "mask = (np.array(stats_proc_err_w['med_err']) < 0.1) * (np.array(stats_proc_err_w['avg_err_90th_percentile']) < 0.2) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len('avg_err_90th_pe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27536"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MGMBhhx0GEOU++0vgCiD8baOHu68D8NjGR/Hcml5AeBhwdVDWCwjnmK0KyiSH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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0586e38190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot histograms comparing the original (unfiltered)  vs filtered (correctly )\n",
    "%matplotlib inline \n",
    "nbins = 50\n",
    "\n",
    "fig, axs = plt.subplots(2,3, figsize=(12,8))\n",
    "ax = np.ravel(axs)\n",
    "keys = stats_proc_err_w.keys()\n",
    "keys.remove('name')\n",
    "\n",
    "i = 0 \n",
    "for key  in keys:\n",
    "    hist1, bin_edges = np.histogram(stats_proc_err_w[key], bins = nbins, density=False)\n",
    "    bin_cen1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "    hist2, bin_edges = np.histogram(np.array(stats_proc_err_w[key])[mask], bins = nbins, density=False)\n",
    "    bin_cen2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "\n",
    "    ax[i].plot(bin_cen1, hist1, color = 'green', alpha=0.5, ls='steps', label='unfilt '+ key[:15])\n",
    "    ax[i].plot(bin_cen2, hist2, color = 'red', alpha=0.5, ls='steps', label='filt '+ key[:15])\n",
    "    ax[i].legend(loc='upper right')\n",
    "    i += 1 \n",
    "# http://stackoverflow.com/questions/10035446/how-can-i-make-a-blank-subplot-in-matplotlib \n",
    "ax[i].axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think it's better not to apply that filter  - otherwise our selection later (based precisely on average CRTS  lightcurve error) would not make any sense.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think I would make an executive decision of NOT applying any filter at this stage, but instead in all analysis for Fig.2, Fig.3, Fig.4  use  stars_CRTS_processed_err_w/  or  stars_CRTS_LC_err_w/ , since these two directories contain identical lightcurves, which have gone through exactly as many selection steps as quasars did.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}