F_Fig_4_SF_three_panels.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "from astroML.stats import median_sigmaG\n",
    "from scipy.stats import binned_statistic\n",
    "import CRTS_paper_modules as mod\n",
    "import datetime\n",
    "from matplotlib import rcParams\n",
    "from scipy.optimize import curve_fit\n",
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** What happens here ** \n",
    "\n",
    "I apply correction factors per magnitude bin,  and plot the resulting SF in the observed frame, to make Fig. 4 \n",
    "\n",
    "I use a different fc for each magnitude range, as derived in E_Fig3_histogram  \n",
    "\n",
    "    mag     | fc \n",
    "    17-18   | 0.8897\n",
    "    18-18.5 | 1.1072\n",
    "    18.5-19 | 1.2876\n",
    "    \n",
    "These numbers are derived from histograms of  the quantity $\\chi$ calculated for Blue Stars, they are stored in data_products/Fig_3_data/2017-08-08/r_cut_minMag_maxMag_mean_fc.txt \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# If we were using the full linear fit to the correction factor as a function of timescale...\n",
    "# We are not, because the timescale-dependence is much smaller than the magnitude-dependence \n",
    "# Read in the linear correction coefficients....\n",
    "file = '../data_products/Fig_3_data/2017-08-08/r_cut_minMag_maxMag_mean_fc.txt'\n",
    "data = np.loadtxt(file, unpack=True)\n",
    "\n",
    "Min_arr = np.array(data[0])\n",
    "Max_arr = np.array(data[1])\n",
    "fc = np.array(data[2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 17.        ,  18.        ,  18.5       ],\n",
       "       [ 18.        ,  18.5       ,  19.        ],\n",
       "       [  0.88971443,   1.10721   ,   1.2876506 ]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today we will save our Fig_4 files in /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-08-14/\n"
     ]
    }
   ],
   "source": [
    "# Set a directory to save the results...\n",
    "# the Fig_4_data directory \n",
    "outDir = os.path.join(os.getcwd()[:-4]+'data_products/'+'Fig_4_data/')\n",
    "if not os.path.exists(outDir): os.system('mkdir %s' % outDir)\n",
    "                      \n",
    "outDir = os.path.join(os.getcwd()[:-4],'data_products/'+'Fig_4_data', \n",
    "                      datetime.datetime.now().strftime('%Y-%m-%d')+ '/')\n",
    "\n",
    "# the appropriate date subfolder... \n",
    "if not os.path.exists(outDir): os.system('mkdir %s' % outDir)\n",
    "\n",
    "print('Today we will save our Fig_4 files in %s'%outDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# override the outDir to finish calculations from yesterday ...\n",
    "#outDir = '/Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-01-10/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in xi, ei  for the objects in a given mag range ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zipping CRTS-SDSS quasars catalog from ../data_products/CRTS_SDSS_catalogs/CRTS_SDSS_cross_matched_qso_DB_QSO_catalog.txt\n",
      "Read in 7601 quasars from CRTS\n",
      "zipping CRTS-SDSS stars catalog...\n",
      "Read in catalog for 48250 stars from CRTS \n"
     ]
    }
   ],
   "source": [
    "cols1,  qso_cat = mod.get_qso_catalog() \n",
    "cols2 , star_cat= mod.get_stars_catalog()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to loop reading-in of xi, ei, and calculating SF, over the three magnitude ranges... "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE : the cell below takes ~10 mins to execute on a laptop.... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using now only lightcurves with SDSS  18.000000< r < 18.500000\n",
      "\n",
      "Choosing stars with  SDSS   -1.00<g-i<1.00\n",
      " These cuts reduced the number of stars  in the sample from 48250 to 1400\n",
      "\n",
      " Returning only QSO with  an SDSS counterpart within 2.000000 arcsec\n",
      "\n",
      " These cuts reduced the number of qso  in the sample from 7601 to 333\n",
      "\n",
      "  Also returning quasar redshifts...\n",
      "\n",
      "Reading in tau,xi,ei  for 333 objects\n",
      "\n",
      "Using structure function master files from ../data_products/sf_file_per_LC/qso/\n",
      "[######### ] 99.10%\n",
      "Finished reading all master files for the selected objects ...\n",
      "\n",
      "Reading in tau,xi,ei  for 1400 objects\n",
      "\n",
      "Using structure function master files from ../data_products/sf_file_per_LC/stars/\n",
      "[##########] 100.00%\n",
      "Finished reading all master files for the selected objects ...\n",
      "finished\n"
     ]
    }
   ],
   "source": [
    "# Set the magnitude range \n",
    "Min  = 18\n",
    "Max  = 18.5\n",
    "\n",
    "objects_in_cut = {}\n",
    "\n",
    "mag = 'r'\n",
    "cut_mag = mag\n",
    "\n",
    "print('\\nUsing now only lightcurves with SDSS  %f< %s < %f' % (Min, cut_mag, Max))\n",
    "\n",
    "# use only qso and blue stars since these are the ones that we are plotting ...\n",
    "good_ids_S_blue = mod.cut_stars(star_cat = star_cat, mMin = Min, mMax=Max, mErrMax = 0.3, gi_Min = -1,\n",
    "                                          gi_Max=1, cut_mag=cut_mag + '_mMed')\n",
    "\n",
    "good_ids_QSO, redshift = mod.cut_qso(qso_cat=qso_cat, mMin = Min, mMax=Max, mErrMax = 0.3, \n",
    "                                           cut_mag=cut_mag, redshift='Yes')\n",
    " \n",
    "## Read in the xi, ei for objects in that mag range ... \n",
    "inDirStars   = '../data_products/sf_file_per_LC/stars/'\n",
    "inDirQSO = '../data_products/sf_file_per_LC/qso/'\n",
    "\n",
    "out_dic = {}\n",
    "out_dic['qso'] = mod.faster_read_xi_ei(inDirSF = inDirQSO,\n",
    "                                       good_ids = good_ids_QSO )\n",
    "out_dic['starsB'] = mod.faster_read_xi_ei(inDirSF = inDirStars,\n",
    "                                          good_ids = good_ids_S_blue )\n",
    "\n",
    "#qso_obs, starB =  mod.read_xi_ei(inDirStars, good_ids_S_blue, inDirQSO, good_ids_QSO)\n",
    "#qso_rest, starB  =  mod.read_xi_ei(inDirStars, good_ids_S_blue, inDirQSO, \n",
    "# good_ids_QSO, redshift=redshift)\n",
    "# put into a dictionary : makes it more explicit \n",
    "#out_dic[b] = {'starsB': starB, 'qso':qso_obs}\n",
    "\n",
    "# restframe dictionaries : not needed now... \n",
    "#out_dic_rest = {}\n",
    "#out_dic_rest[b] = {} \n",
    "#out_dic_rest[b] = {'starsB': starB, 'qso':qso_rest}\n",
    "\n",
    "print('finished')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate sigma approx and sigma full, applying correction factor to errors in each bin "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# for all imports of my functions, \n",
    "# make python aware of my packages...\n",
    "import sys\n",
    "sys.path.insert(0, '../../SDSS_S82_FP_research/packages/')\n",
    "import variabilityFunctions as varF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today we will save our Fig_4 files in /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-08-14/\n",
      "\n",
      " For obj = qso\n",
      " --- Calculating mu, sigma for bin 1\n",
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      "Results saved as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-08-14/r_cut_18-18.5_qso.txt \n",
      "\n",
      " For obj = starsB\n",
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      " --- Calculating mu, sigma for bin 191\n",
      " --- Calculating mu, sigma for bin 192\n",
      " --- Calculating mu, sigma for bin 193\n",
      " --- Calculating mu, sigma for bin 194\n",
      " --- Calculating mu, sigma for bin 195\n",
      " --- Calculating mu, sigma for bin 196\n",
      " --- Calculating mu, sigma for bin 197\n",
      " --- Calculating mu, sigma for bin 198\n",
      " --- Calculating mu, sigma for bin 199\n",
      " --- Calculating mu, sigma for bin 200\n",
      "Results saved as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-08-14/r_cut_18-18.5_starsB.txt \n",
      "Done with calculating panel data \n"
     ]
    }
   ],
   "source": [
    "# set the bin number \n",
    "nbins = 200 \n",
    "\n",
    "# grab the right correction factor for the given magnitude range... \n",
    "fc_mag = fc[Min_arr == Min][0]\n",
    "          \n",
    "print('Today we will save our Fig_4 files in %s'%outDir)\n",
    "\n",
    "# Loop over objects in the dictionary  \n",
    "for obj in ['qso','starsB'] : # out_dic[b].keys()\n",
    "    print('\\n For obj = %s'% obj)\n",
    "    # obj = 'qso'  # or starsB,  starsR \n",
    "    m_ij = out_dic[obj]['xi']\n",
    "    tau =  out_dic[obj]['tau']\n",
    "    e_ij = fc_mag * out_dic[obj]['ei']  # apply the correction factor \n",
    "    \n",
    "    # Pull out some tau to plot means : common to all panels \n",
    "    binned_tau = binned_statistic(tau, tau, statistic='mean', bins=nbins)\n",
    "    mean_tau = binned_tau[0]\n",
    "    \n",
    "    # Make an array of correction coefficients per bin, using the coefficients from the file ...  \n",
    "    #a = coeffs['a'][coeffs['rMagMin'] == Min]\n",
    "    #b = coeffs['b'][coeffs['rMagMin'] == Min]\n",
    "    #binned_fc =  a* np.log10(binned_tau[0]) + b\n",
    "    \n",
    "    # Take N from each bin... 'count' function works like a regular histogram\n",
    "    binned_count = binned_statistic(tau, tau, statistic='count', bins=nbins)\n",
    "    bin_count = binned_count[0]\n",
    "    \n",
    "    # checking for empty bins : either mean or some custom function, but not\n",
    "    # count! If statistic='count', then check for 0's , and not for nan's/ \n",
    "    non_empty_bins = np.bitwise_not(np.isnan(mean_tau))\n",
    "\n",
    "    # reassign number of points in a bin and  tau position \n",
    "    bin_count = bin_count[non_empty_bins]\n",
    "    mean_tau = mean_tau[non_empty_bins]\n",
    "\n",
    "\n",
    "    # Which point belongs to which bin\n",
    "    bin_number  = binned_tau[2]\n",
    "\n",
    "    ####  Panel 1 : Standard Deviation \n",
    "    rms_std = lambda x : np.std(x)\n",
    "    stdev_binned = binned_statistic(tau, m_ij, statistic = rms_std, \n",
    "                                              bins=nbins)\n",
    "    bin_stdev = stdev_binned[0][non_empty_bins]  \n",
    "\n",
    "\n",
    "    ##### Panel 2  : Gaussian rms  \n",
    "    rms_robust = lambda x : 0.7414 *(np.percentile(x,75) - np.percentile(x,25))\n",
    "    bin_sigma_G = binned_statistic(tau, m_ij, statistic = rms_robust, \n",
    "                                      bins=nbins)[0][non_empty_bins]\n",
    "\n",
    "    # Loop over all bins  calculating approximate mu and sigma \n",
    "    mu_bins = {}\n",
    "    sig_bins = {}\n",
    "\n",
    "    sig_bins['approx'] = np.zeros(nbins)\n",
    "    mu_bins['approx'] = np.zeros(nbins)\n",
    "    sig_bins['full'] = np.zeros(nbins)\n",
    "    mu_bins['full'] = np.zeros(nbins)\n",
    "    \n",
    "    for N in np.unique(bin_number):\n",
    "        print('\\r --- Calculating mu, sigma for bin %d' % N),\n",
    "        xi = m_ij[bin_number == N]\n",
    "        ei = e_ij[bin_number == N]\n",
    "    \n",
    "        mu_approx, sig_approx = varF.approximate_mu_sigma(xi, ei)\n",
    "        mu_full, sig_full =  varF.get_mu_sigma(xi,ei)\n",
    "        \n",
    "        sig_bins['approx'][N-1] = sig_approx\n",
    "        sig_bins['full'][N-1] = sig_full\n",
    "        \n",
    "        mu_bins['approx'][N-1] = mu_approx\n",
    "        mu_bins['full'][N-1] = mu_full \n",
    "\n",
    "    # Save the results of calculation  : Panel 1,2,3,4 \n",
    "    fname = outDir+ 'r_cut'+'_'+str(Min)+'-'+str(Max)+'_'+obj+'.txt'\n",
    "    \n",
    "\n",
    "    save_data = np.column_stack((mean_tau, bin_stdev, bin_sigma_G, sig_bins['full'], \n",
    "                            sig_bins['approx'], mu_bins['full'], mu_bins['approx'], bin_count))\n",
    "\n",
    "    header = 'meanTau   stdev    sigmaG   sigmaFull  sigmaApprox   muFull  muApprox  binCount '\n",
    "    np.savetxt(fname, save_data, fmt = '%s', delimiter = ' ' , header=header )\n",
    "    print('Results saved as %s ' % fname)\n",
    "\n",
    "print('Done with calculating panel data ')    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in plotting data from  saveFiles\n",
    "\n",
    "If you have  already calculated statistics for each bin,  can simply read the file, and proceed to plotting, skipping steps above. I assume three magnitude bins (17-18, 18-18.5,  18.5-19)  , for two types of objects (blue stars, quasars). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading... /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-08-14/r_cut_17-18_qso.txt\n",
      "Loading... /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-08-14/r_cut_17-18_starsB.txt\n",
      "Loading... /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-08-14/r_cut_18-18.5_qso.txt\n",
      "Loading... /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-08-14/r_cut_18-18.5_starsB.txt\n",
      "Loading... /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-08-14/r_cut_18.5-19_qso.txt\n",
      "Loading... /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-08-14/r_cut_18.5-19_starsB.txt\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "#from scipy.optimize import curve_fit\n",
    "from collections import OrderedDict\n",
    "\n",
    "# the directory with results previously calculated ... \n",
    "#outDir = '/Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_4_data/2017-01-10/'\n",
    "\n",
    "plot_data = OrderedDict()\n",
    "\n",
    "Min_arr = [17,18,18.5]\n",
    "Max_arr = [18, 18.5, 19]\n",
    "\n",
    "for mag in Min_arr:\n",
    "    plot_data[mag] = OrderedDict()\n",
    "    \n",
    "for i in range(len(Min_arr)):\n",
    "    Min = Min_arr[i]\n",
    "    Max = Max_arr[i]\n",
    "    for obj in ['qso','starsB']:\n",
    "        plot_data[Min][obj] = {}\n",
    "        \n",
    "        fname =  outDir+ 'r_cut'+'_'+str(Min)+'-'+str(Max)+'_'+obj+'.txt'\n",
    "        print('Loading... %s'% fname) \n",
    "        \n",
    "        colnames = open(fname,'r').read().splitlines()[0][1:].split()\n",
    "        d = np.genfromtxt(fname, dtype=float)\n",
    "        \n",
    "        for label, column in zip(colnames, d.T):\n",
    "            plot_data[Min][obj][label] = column\n",
    "        \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most recent calculation, using a magnitude-specific correction factor : 2017-01-10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wbrebGTNm8PHHH3PbbbdRUlJCQUEBDz74II888ggjRoxoNvDweDwkJydTV1eH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1128f0780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "def model_sf(t, sf_inf=0.25, tau = 1.0):\n",
    "    br = 1.0-np.exp(-t/tau)\n",
    "    sf = sf_inf * np.power(br,0.5)\n",
    "    return sf\n",
    "        \n",
    "        \n",
    "rcParams['ytick.labelsize'] = 25\n",
    "rcParams['xtick.labelsize'] = 25\n",
    "rcParams['axes.labelsize'] = 35\n",
    "rcParams['axes.linewidth'] = 3\n",
    "rcParams['font.size'] = 25\n",
    "rcParams.update({'figure.autolayout': False})\n",
    "\n",
    "\n",
    "obj_arr = ['qso',  'starsB']\n",
    "labels_arr = ['Quasars', 'Blue Stars']\n",
    "colors= {'qso':'black',  'starsB':'blue'}\n",
    "\n",
    "markers = {'qso':'o',  'starsB': '>'}\n",
    "def plot_errorbar(ax, x,y,yerr, obj, linestyle='None', markersize=p_size,\n",
    "                 markerfacecolor='None',elinewidth=1, \n",
    "                  markeredgewidth=1, capsize=1, alpha=0.8, colors=colors, \n",
    "                  markers=markers ):\n",
    "    ''' A quick convenience function to use the \n",
    "    same default markers, etc. for all plots\n",
    "    '''\n",
    "    ax.errorbar(x,y,yerr,linestyle=linestyle, c = colors[obj],\n",
    "                marker=markers[obj],  markersize=p_size, markeredgecolor=colors[obj],\n",
    "                markerfacecolor='None', markeredgewidth=1,  elinewidth=1, capsize=1, \n",
    "                alpha=0.8)\n",
    "    \n",
    "# choose sigma calculation type ..\n",
    "calc = 'Approx'\n",
    "    \n",
    "# set all plot parameters\n",
    "lh_w   = 1.0  # horizontal line thickness \n",
    "lh_st  = '--' # horizontal line style \n",
    "lh_al  = 0.5  # horizontal line alpha parameter \n",
    "\n",
    "# dot size \n",
    "p_size = 7 #7\n",
    "p_al   = 0.5 \n",
    "\n",
    "# y limits for SF panels \n",
    "y_top  = 0.45\n",
    "y_bott = -0.05\n",
    "\n",
    "# y limits for mu approx \n",
    "y_mu_top = 0.1\n",
    "y_mu_bott = -0.1\n",
    "\n",
    "# x limits for ALL PANELS \n",
    "x_left = 0.5\n",
    "x_right = 3.6\n",
    "\n",
    "fig,ax = plt.subplots(3,1, figsize=(12,12), sharex=True)\n",
    "\n",
    "for i in range(len(Min_arr)):\n",
    "    Min = Min_arr[i]\n",
    "    Max = Max_arr[i]\n",
    "    for j in range(len(obj_arr)):\n",
    "        obj = obj_arr[j]\n",
    "        plot = plot_data[Min][obj]\n",
    "        x = np.log10(plot['meanTau'])\n",
    "        y = plot['sigma'+calc]\n",
    "        yerr = plot['sigmaFull']* 1.06 / np.sqrt(plot['binCount'])\n",
    "        plot_errorbar(ax[i], x,y,yerr, obj)\n",
    "        \n",
    "        if obj == 'qso' : \n",
    "            # Calculate the model DRW fit for QSO\n",
    "            xdata = plot['meanTau']\n",
    "            sf = plot['sigma'+calc]\n",
    "            popt, pcov = curve_fit(model_sf, xdata, sf)\n",
    "            y = model_sf(xdata, sf_inf=popt[0], tau = popt[1]) # tau 1 year in days \n",
    "\n",
    "            # Fold-in the error to the model SF , plot \n",
    "            # both folded and not-folded version \n",
    "            y_fold = np.sqrt((y ** 2.0)+ (err_sig ** 2.0) )\n",
    "            ax[i].plot(np.log10(xdata), y_fold , lw=3, c = 'green', ls='--')\n",
    "            ax[i].plot(np.log10(xdata), y , lw=3, c = 'orange', ls='--')\n",
    "            \n",
    "for i in range(len(Min_arr)) :\n",
    "    \n",
    "    ax[i].text(1.0, 0.25, 'mag: '+str(Min_arr[i])+'-'+str(Max_arr[i]) )\n",
    "    ax[i].set_ylabel(r'$SF$')\n",
    "    ax[i].set_ylim(bottom=y_bott, top=y_top)\n",
    "    ax[i].set_xlim(left=x_left, right=x_right)\n",
    "    ax[i].grid() \n",
    "    ax[i].hlines(y=0.1, xmin =0.5, xmax=1.7, color='green', lw = 4,\n",
    "                 linestyle = '--' , alpha = 0.8  )\n",
    "\n",
    "    ax[i].set_yticks([0,0.1,0.2,0.3,0.4])\n",
    "    ax[i].set_yticklabels(['0.0','0.1', '0.2', '0.3', '0.4'])\n",
    "    \n",
    "\n",
    "ax[-1].set_xlabel(r'$log_{10} (\\Delta {t} \\, \\mathrm{[days]})$ ') \n",
    "plt.tight_layout()\n",
    "fig.subplots_adjust(hspace=0)\n",
    "plt.savefig(outDir+'Fig_4_SF_QSO_starsB.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look at the last few bins in qso both in observed frame, and after the redshift-correction. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.2743, -0.1458,  0.1804, ..., -0.2225, -0.2875, -0.1983]),\n",
       " array([  9.02300000e-01,   9.59600000e-01,   9.75600000e-01, ...,\n",
       "          2.88712210e+03,   2.91016730e+03,   2.97669700e+03]),\n",
       " array([ 0.11183761,  0.12984976,  0.08991691, ...,  0.06020797,\n",
       "         0.06020797,  0.18515172]),\n",
       " array(['235008.88-002912.5', '235008.88-002912.5', '235008.88-002912.5',\n",
       "        ..., '002146.71-004847.9', '002146.71-004847.9',\n",
       "        '002146.71-004847.9'], \n",
       "       dtype='|S18'))"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_dic['r_cut']['qso']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nbins = 200 \n",
    "\n",
    "obj = 'qso'\n",
    "\n",
    "m_ij = out_dic['r_cut'][obj][0]\n",
    "tau =  out_dic['r_cut'][obj][1]\n",
    "tauR = out_dic_rest['r_cut'][obj][1]\n",
    "e_ij = out_dic['r_cut'][obj][2]\n",
    "n_ij  = out_dic['r_cut'][obj][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "747"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(n_ij))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Grab redshifts... one for each qso\n",
    "msk = np.in1d(qso_cat['CRTS_id'],  np.unique(n_ij) )\n",
    "names = qso_cat['CRTS_id'][msk]\n",
    "zs = qso_cat['redshift'][msk]\n",
    "zs = np.array(zs).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Express redshifts as one per delta_mag \n",
    "\n",
    "zs_cnt = np.zeros_like(e_ij, dtype=float)\n",
    "for i in range(len(e_ij)): \n",
    "    zs_cnt[i] = zs[names == n_ij[i]][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Redshift-corrected by hand- are they the same as in read_xi_ei ? \n",
    "np.sum(tau / (1.0+zs_cnt) ==  tauR) == len(tau)\n",
    "# Yes ! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tauC = tau / (1.0+zs_cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/'"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outDir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SwG9Cjh2BGjFdqKhtt9/pbft4yDkXeOWFtgsVU/4DFWeTqOD7B+ATwLEF9sMTXj0mZ9j/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fQ58Z1T38GwaxjWcStKtaVweByod9XDM+WBT/DBMenATGAGXj8xmzlwiPRHIiIsAr6ADwCQpkyD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6bCt1vjpCgdCUtw3Ao8sfnRJb1s9dz1ByiJ09OzGEwcPLHuaehfdMiSedE52ciJ6YEoRmep1Y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AdS1akoSy1Dr7cXGXEZaB9pR4G+ANXZ1ah31EMTqcF6y/og4dLiakFEWAQsGgvStenBtCQ2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\nMGPMDC6vvZy+VX2jwQevZ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63dpj4BaQWX8hVc8B+jN7Vs1thZ/PqdX7P96HZum3Zbl8yl\nVAWSc22H95haVUpEJI8BnsiDGeDTwJ3AU8BPrLWdgW1KetDT5ZhTDY6TXYQkWo0mm8F2sgBPULKV\nm1aujE0pC2b5pNueUrxgCF70QfZtTDeDI5fnSPU8fsHglXdBHwz8FOO1yCRIEQwAxPvd1BQr4hwM\neqUK3hTiwj/ec/izuyAWbPHqSDU2ut/Bc8T/72wyVPxt6KkgbLzzPRRyU88mTYI///PMglPZPncm\n50E2/Bk8wemBiTLu/DWkSuVzsKeV4t+CNOW6ilYljHU6w53dMkR+tvFnzJswLzoFKh5vpaJbL7o1\nmo0BsQBCqD0UDdx49VCg+4pNwQBFotWcUmVDgLvI7wh3cODkARrHNXbJ5Ej2mGc6z/Dj13/M9FHT\n+b9v/F9uOP8G7p15b9qZIB3hDh5e/zArdq3gL678C+ZNmJdRm0PtIVaHVjOnbk60gHS82kHxVlUK\n3ub/PxS27kzwGJtam6JBnk2HNnHhuRdG6xf5j6UQwZd0Vq/qSfk6RgWqRCQXeQ3w+B60CrgDuB14\nAnjUWhuO3FfSg54uGTypBq49NcBNFTBKJ1AT7wI4m4ulclgit1BtLMax90TgIluZnP/ZLiuf7nPl\n+tpk856HWKZfvBW7oHvx5XTaVyoXzvH6tKMjNm3Jn8GTbJ98KnTf7NjhphTecw9MnJj4uTX1qTz+\nFiSQj2XSI49TtmOdXcd2xQ16pLqg9G8XtuFu05qCy3gHi9Dm4wI802Wkg9OYvEySRQ2LosGtb770\nTWaOm8lNU26KmymU6LEfffNRWttb+e9z/jsTh09Mur2fv5+AbitbBbdNFKSKF8iCnp0OFAxoFSJT\nJ9O2FCsw4l+qPZd6Q8UOVIlIeStIgMf34H2AzwCfAh4DHgceoYQHPbazs7A1WPIpUf2TdGVzDKVw\n3MG2BLNZIP9t7OlASza1dDJ5bXr6dSz08+X6+LlcrCaaqgb5y/jqSblMtyqlz4dsrFrlMpTuv98F\n6xIp9+PMhzLug3wFeHyPV3ZjnW+t+lb0IjTbi+LVodX88NUfcu/l90aXDg9KFIjwZ7ekek6veK9X\nL6fKVCUMZiQLfHjBJi+w4+3ffKw5Ou3JC0oBcbNp/G1qaWvpMhUqk74LBp78QbFyz9godpClmPIV\n4OnNfSgiuUs2zsn5E8Va22mt/SlwPTAAeA64LNfHLahQKFZrIdkKJN43uF5goRi8FWEaG7svg+0V\nRk0mWdHUfO6Tb97xeRflTU0ug+LJJ93rUYg2equSVVX1zLGnc3559WzWro1dbKV7Tvb0+Zvpa5Lu\nOZzt4wdlu1Ka99wNDbHVsbx2+Nvkr5eT7jEVi3dutLaW5+dDLmbPhq9+NVZjKZFyP858UB9EleVY\nx8cLfHiBjXQ1jmvk3svvZdbYWdFgR1Aw+OH935vK5c9iSda+H736Ix76w0M0tTZ1ecxg24P/92/r\nrXjkLdftTel56u2nqKuuY+LwiVSZKvad2JeyP0LtIVY0r6BvVV8ahjVkfBHuX33JW9kqm8eBrsGm\nUpBNwKtS1NfUx83CylRv7kMRKaycM3i6PaAxZwH/FfhLa20WV1KFFc3gKaXpWdko49T5tAQLzcar\nR9MTK30VUjargGVSF6SUz1+ozHO4XI4pm+wxkTKS7wyeOI9f8mMd/xStXLMFsplOkkmmgz+DJ1WN\nnUyPJdiOlrYWnt/xPFNHTk1Y9NhrU7GmRAVpOo+IiPgVdIpWkicNVDMuDSXarMxV6sVXJlNHyuVi\nOleV/lpX0nFV0jH1lveXVKRCB3h8z1OSg4p8NyubAFE6+8TbJt/BjFwDRIVoU6Y0nUdERPyKEuAp\nJGPMncANwFRgJNAHCAGrge9aa7cl2bcUx2KpVdKFYzKZXFQWok9KoZ+VXVF+KvH1qMRjkl6jpwI8\nhVQKY51CBRaSLaud6DlzbUsu+xczwKLgjoiIBBW0Bk+R3AFMBppwRQ6/DzQD/wV4yxhzTfGaViDB\nWiyVKpM6KYWqw1Psukvp1uYpdjtLXaY1fnJRia9Hvt5fPfk6iFSWoo91Qu0hnt/xPGv3rM1r/Rev\njg4QrVPjSVSbJNs6Qqn2T6e+TTHrpeR63JKbUqt/JCKSSrlm8PSz1p6Jc/tHgReBzdbaqQn2Ld8M\nnnRW/pLclELWgjJ48qMnpxjp9UhMU72kCCokg6foY52wDbN2z1o2HdrEtROvzdv0pEJN+cpm/2JP\nv0pFGTzFVernh4j0ThU3RSsZY8xRYKC19uwE95dHgCfeBaMuIkVSy2UJcMk/fW5JEVRCgCeZnhjr\nZLPcebnxsjOgMpYvl/xTgE1ESlElTtGKyxgzB6gB3ip2W3IWb8qHlqwVSS2XJcCzpWlIielzSySv\nemqs400Naj3eWrHBD28p9CpTVbLHpylCxaXlzEWk3JR1Bo8x5pO44oMDcPPUrwOOA9dba9cl2Kf8\nMnhA34DnSlkEvUcxXmtNQxIpKZWUwVOssU6pZLcUMoOiHLIzNEVIRESCKjmD50+AvwEeAJYCLcC1\niQY8ZcX/rXclFnDNRTbZEurD4vO/bum8htlmxQQzRnoiuyaT4uAiIpkpyljHy2pZ0byixwr8xstW\nKWSR4XxlZ4RtmOZjzTQfa462PV+ZN3XVdd0KUZcCZRaJiJSmkszgMcZ8Ewg27KfW2rh/3Y0xg3Hf\nbn0TWADcba19NMG25ZHB46fsk66yyZZQXZbi879ukPo1zFdWjLJrRHqdcsjgKYexTk9nuMTLVimV\nTKJkWtpa+NnGnwFw27TbqK+pr/jMm0o/PhGRUlZ2RZaNMWG6D3oWWmtXpdjvLOBVYCIwyVq7L842\n9hvf+Eb0/wsWLGDBggU5t7mgFODpKpf+0MV+8WQ67TBf573ePyIVb+XKlaxcuTL6/wcffLAcAjwl\nP9bp6QBPOa90FQxClcP0r1xU+vGJiJSSTMY5JRngyYUx5iHgPuAWa+2v4txffhk8PR2UqOQL4ko+\nNhERAcojgycXPTXWyTSwUqiLfgUTREREYiq5Bk88YyO/OxJuUW4r3vR0fY9yrFeT7muqFX1ERKT8\npRzrFKP+S77q5QTru2glIxERkfSU3V9KY8xwY8xHEtx3PXATcAJ4OeGDlFsAo6eDEuVYMLbcXlMR\nEZEE8jHWyUegJdPASr4KAheysLKIiEglK7spWsaYi4HXcPPPtwJ7gRrgEqAROAPcYa39RYL9re3s\nLL9pOppalJz6R0REIsp9ilY+xjq7ju3KeUpTsaZGaUqWiIhIYmVXZDkZY0wNcD8wH1dgcATwIRAC\nVgL/Yq3dmmT/8qvBAyoOLCIikqYKCPCUxFin1Isbi4iI9EYVFeDJVdkGeJShIiIikpZyD/DkqlyX\nSRcREZHUU2JeNwAAEzRJREFUFODxyWjQo6CKiIhI2VGAp+tYR4EaERGRytHbVtHKHxXulWIqt9Xe\nRESkJKlosRNcnUtERKTSKMCTTDmuJiWVQwFGERHJg3ytblXuFOgSEZFKpylaIqVKUwRFRLKiKVoa\n68SjqWoiIlIJVIPHR4MeERGRyqYAj8Y6IiIilUo1eHoD1WsRERGRAlINGxERkdKmAE+lUL0WERER\nKSDVsBERESltmqJVKVSvRUREBNAUrUKNdVTDRkREpPg0RatS+adlVVVBfb2COyIiIpJX3tQsgPqa\negV3RERESpT+QpczTcsSERGRAtPULBERkfKgKVq5KubUKE3LEhER6UZTtFKPdTKZbqWpWSIiIqVD\nU7QKqZhZNJqWJSIiIlnIJCunylRpapaIiEgZUAZPrpRFIyIiUlKUwZPfDB4REREpHcrgSSQchuZm\n9xMOx27zChdLFytXrix2E8qa+i976rvsqe+yp77Lnvqu+DrCHawOreZM5xla2loIW41r0qFzN3vq\nu+yp77KnvsuN+i97pdp3vTfAEw7D2rXwxBPw5JOxKVbBKVfBgE/w/72o0HGpnsTlQv2XPfVd9tR3\n2VPfZU99V3y/fufXPPzqwyzfspwXdr5AS1sLLW0tdIQ7ov9W4eTudO5mT32XPfVd9tR3uVH/Za9U\n+673BnhCIdi0CebOhWXL3BQrcL8XL479PxjACf4/uL2ISDlLFdQWkbJw+NRhbjz/RpZOWcri8xYD\n8MLOF2hqbeKFnS8AsPi8xdRVa/ySTNiGaT7WTPOxZmVBiYhIyetb7AYUTV0dXHtt99o5XuFi/3b+\nAE7w/8HtRUTKmRfEXrzYfbYF/y8iZeG6SddF6+vU19QTtmEWn7eYcUPHMW7oONXeSVOoPcSTm54E\n4LZpt1FfU1/cBomIiCTRK4ssF7sNIiIiUli9vchysdsgIiIihZNonNPrAjwiIiIiIiIiIpVGubki\nIiIiIiIiImVOAR4RERERERERkTKnAI+IiIiIiIiISJlTgEdEREREREREpMz1igCPMWasMeYnxpi9\nxpjTxphdxpjvGWNqit22nmSMaTHGhBP87Euwz2xjzG+NMUeMMaeMMW8aY+4zJvHaqsaYzxpj/miM\nOWGMaTPGvGSMWVK4I8sPY8wnjTH/YoxZZYxpj/TLoyn2KXj/GGPONsY8aIzZYox53xhz0Bjz78aY\nKbkcbz5l0nfGmAlJzsOwMeZnSZ6nEvtuuDHmLmPMr4wx2yPnUZsx5hVjzOeMMfEr5Ovcy7jvdO51\nZYz5B2PMCmNMKNJ3R4wxrxtj/sYYMzzBPr3+vCtVRmMdjXPSYDTWyVomfae/NzFG45ycZNp/Ove6\nMr1wrFPxq2gZYxqAPwDnAP8BbAVmAVcDW4CrrLXHitfCnmOM2QVUA98Dgh+mJ621DwW2Xwr8Angf\n+HfgKPAJYArwc2vtp+I8x3eB+4E9kX37AcuAEcAXrbU/yOcx5ZMx5g1gOnASaMUd5+PW2s8k2L7g\n/WOM6Qf8HpgNrI/8ezxwK3AGWGitXZ/TgedBJn1njJkA7AI24N6TQZustb+Ks1+l9t09wMPAPuAl\nIASMAm4GaoBfWGtvDeyjc4/M+07nXlfGmA+A14DNwCFgENAIzAT2Ao3W2r2+7XXelSiNdRyNc1LT\nWCd7GutkR+Oc3Gisk5teOdax1lb0D/A7oBP4QuD2fwTCwA+K3cYe7ItdQHOa2w6JvAneBy713d4P\nWBPp01sD+1wZ6dOtwFDf7XXAu8ApoK7Y/ZDkmOcD5/n+HQYeLWb/AF+P7PNk4PZPRG7fWOx+y6Lv\nJkTu/0kGj1/JfbcAWBLn9pHA7si5dJPOvbz0nc69ru3pl+D2v4u081913pXHDxrreMercU7q49ZY\np2f6Tn9vYm1ZgMY5Pdl/Ove6tqfXjXWK3ukFfkEbIp20M859g4ETkZ8BxW5rD/VHJgOfzyX6cAAW\nRu57KXD7o5ET/zNx9nkwct83it0PaR5/qj/cPdI/vg/uCXH2eTly3/xi91eGfZfNH55e0Xdx2un9\nAfhnnXt56Tude+kd8/RIP/1O513p/6Cxjv94Nc7JrL801ilc3+nvTXrHrHFO/vtP5156x1yxY51K\nr8GzMPL7heAd1tqTuEjcQFyaVm/R3xhzuzHm68aYLxljFiSYT7gQsLhvBYNW4aKRs40xZwX2IcE+\nz+HSpa/Ooe2lpOD9Y4w5D5eut81auzudfcpMrTHm7si5eLcxZlqSbXtr330Y+d3hu03nXnri9Z1H\n515yN0R+v+m7Tedd6dJYpyuNc/JH7/vc6e9Nchrn5EZjnexV7Finb7Y7lonzcS/StgT3bwc+BkzG\nzWnsDUbjIo0eA+wyxtxprV3lu/38yO9ufWet7YzMc78Q983hVmPMQGAscMJaezDO826P/J6c6wGU\niJ7on4TPkWSfcvKxyI/HGGNWAp+11u7x3dgr+84Y0wf4LO4z7HnfXTr3UkjSdx6dez7GmK/i5qRX\nA5cDc3Bz9//Bt5nOu9KlsU5XGufkj973udPfmwQ0zsmNxjqZ6U1jnUrP4KmO/G5PcL93e29ZYeIn\nwEdxg59BwDTgh0A98NtAZDfTvuttfd0T/VOpfXoK+FtgBjAs8jMfV2BsAbDCGDPAt31v7bt/AC4C\nnrXWvui7Xedeaon6TudefH8B/A1wH3AV7tuja6y1R3zb6LwrXeq3GI1z8kvv++zp701qGufkRmOd\nzPSasU6lB3jEx1r7P6y1K621h621p621m621XwAewqVvf7O4LZTeIHL+fdNau8Faezzysxq4Bvgj\nMBG4q7itLC5jzJdw1fg3A3FXNpH4kvWdzr34rLVjrLV9cBfFNwPnARuMMZcUt2UimdE4R0qF/t4k\np3FObjTWyVxvGutUeoDHi4BVJ7jfu72tB9pSyn4Y+T3Pd1umfdfb+ron+qdX9am1thP4MS6dPpdz\nMdt9SoIx5ovAPwGbgKuttcE26txLII2+i0vnnhMZFC4HFuOW9vRPc9F5V7rUb6lpnJMdve/zTH9v\nNM7JlcY6uekNY51KD/BsxZ3EieawTYr8TjQHrrc4HPk9yHfb1sjvbn0XmfP5EVxBr2YAa+0pYC8w\n2BgzKs5zVFpf90T/JHyOJPuUu27nYm/qO2PMl4F/Ad7C/dE+FGcznXtxpNl3yfTqc8/PWhvCfSt4\nkTFmeORmnXelS2Od1DTOyY7e94XRa//eaJyTG4118qeSxzqVHuDxigkuDt5hjBmMm393CmjqyUaV\noCsjv5t9t/0eN2C8Ns7283GpzmustR8G9iHBPtdFfv9nDu0sJQXvH2vtTiAETDbGTEiwj/U9biWI\ndy5CL+g7Y8zXcNMIXgcWWmvfTbCpzr2ADPoumV577iVQG/ndGfmt8650aayTmsY52dH7vjB65d8b\njXNyo7FOQVTmWMeWwDr0hfzBVRXvBL4YuP0h3Fr23y92G3uoH6YAA+PcXo+r1t0JfM13+xDgEPA+\nMMN3e39gbWT7WwKPdWWkT7cBNYHnOIIbYNYVuy/S7K/5kWN5NMH9PdI/wF9G9vl3wPhuXxq5/a1i\n91UWfXep/1h8t3800p+dQGNv6jvgryNt+qP/+HTu5b3vdO7F2jIJGBrndgN8K9LOVTrvyuMHjXVA\n45xs+kxjncL1nf7edG2nxjk9138692Jt6ZVjHRN5sIpljGkA1gAjgaeBd4BGXBXxLcBV1tpjRWtg\nDzHGfANXPXwVsBs4gSsutQR30j4L3Gyt7fDtsxT4OfAB8CRwFLgBl1L2c2vtsjjP813gK7h0tV8A\n/YBPAcNxA8+HC3SIOYsc742R/47GFSNrBl6J3PautfaBwPYF7R9jTD9cBPdK4DVcBHgC8CfAaVx6\n5qu5HnuuMuk7Y8xLuA/ctUBr5P7pwNW4iPVfWWu/Hec5KrXvPgv8FJfy+a/Er6rfYq39f759dO6R\ned/p3IsxxtwHfBtYDezCDUJG4S5aGoB9wCJr7RbfPjrvSpTGOhrnpEtjnexprJMdjXNyo7FO9nrt\nWKfYkbWe+MGtT/9vkU4/HXmB/xGoLnbberAP5gGP4+YaHo2ctAeB3wG3J9nvSuA3uDfEe8CbwJeI\nExn27fMZXIT5BO5D6PfAx4vdB2n00TdwkdlEPzuL0T/A2biVP7biIsoHcR84U4rdZ9n0HXAn7gKk\nGTgeOaYW4Ge4i5Bkz9Mb+64T+L3Ovdz7Tudel/ZdhJvH/zru26ozwLHIMf41Cb4h1HlXuj/08rEO\nGuek208a6/RA36G/N5n0m8Y5eew/nXtd2tcrxzoVn8EjIiIiIiIiIlLpKr3IsoiIiIiIiIhIxVOA\nR0RERERERESkzCnAIyIiIiIiIiJS5hTgEREREREREREpcwrwiIiIiIiIiIiUOQV4RERERERERETK\nnAI8IiIiIiIiIiJlTgEeEREREREREZEypwCPiIiIiIiIiEiZU4BHRMqWMeYfjTHrjDFrjTEDi92e\nVIwx9xlj/mCMeccYU1vs9oiIiEjp0jhHRDKlAI+I9AhjzA3GmBZjzIA8Puxw4E+stbOttadSPP93\njTHbjDHtxphr8tiGtFlr/9laeyXwB6BvMdogIiIi+adxjsY5IqVAAR4R6Sl/B4wH/ls6GxtjGowx\ne40x4/Px5NbarwLfAwYCTfl4zByYIj+/iIiI5JfGOTEa54gUiQI8IlJwxphbgReBD4GvGmMGpbHb\nDcAw4GAemzITeMta257HxxQREZFeTOMcESkVCvCISEEZYwzweeDrwE+AEcB9aew6B/ijtfZMHpuz\nAFiZx8cTERGRXkzjHBEpJQrwiEihfRpYHhnAfAs4A9xvjBmSYr85wKp8NcIYUwfUo4GPiIiI5I/G\nOSJSMlT8SkQKxhhTBdwJXAdgrd1rjHkE+CLwFeBvA9vfGtl+GDASWGiMmQk8Y619OMPnvhr4M2An\nMAjYDHQSGEwFthsWuf8ea+083zYfAe4HPgI8bq19wnff/cBMa+2nfbeNB/4aeA84Hfn5jrX2/UyO\nQUREREqXxjka54iUGgV4RKSQ7gCestZ+6Lvt28BdwJeNMf/snydurX0KeMoYczdwMbAom9RlY8zn\nI88zw1q7xxgzGtgGvOl/vjjb1Ua22xp4yAeALwFfAL4GPOG77zPAO77HPBtYAVxvrd1ujGkA/gi8\nATyd6bGIiIhIydI4R+MckZKiKVoiUhDGmD7A7bj56FHW2gPAD4Fq3LdF8SwE1mc56LkYeBi4z1q7\nx/ec7fjSlhNstw84Brzk2+4qYJW1tgO4Fjcw8u6rBqbSNR36Y8C5wJ7I/9twq1q8mOmxiIiISGnS\nOEfjHJFSpACPiBTKZ4AnrLWdce77n8Ap4D5jTE2c+xcAL2f5vN8CTgBPeTcYYyYAY+k6QIm3XV2c\n7bYDv4x867UYeMx33zzcUqD+dOg2oAbYZIz5F+BCa+3fK21ZRESkomico3GOSMlRgEdE8s4Y0xf4\nFPBovPuttYeB7wNDcWnB/n0vBEaRxcAn8k3TNcDvAgOuhfjmpae7XaSthyKp15/CDZSe820/B3jX\nWvuOb/tXcOnN/YA/B1YZY/4s02MRERGR0qRxjsY5IqVKAR4RKYQ7gcesteEk23wHOAl80Rgz3Hf7\n1cCHwFpwgxRjzLg0n3ci0AdoCtw+Dzcv/XikkGCy7TZEtqsP3LcYeCkwz34ecVbAsNb+L2ttHS6t\n+Y+4AZCIiIhUBo1zNM4RKUkK8IhIXhljzgI+CTyebDtr7VHgn4HBuG+CPHNwg49Tkf/fhxsIpcMr\nLBjytac/8FHg95Gb7k+x3crITX8ReOwJ+IoSGmMGAZfh+wbOGPMrY8wG3zG+g0t1bk6z/SIiIlLC\nNM7ROEeklGkVLRHJt/+KK773iDEm1bY1uLndXzDGfDeS0twHaAEwxlwOnLLWHkznia21O4wxbwAN\nkf2rgP8NDAS2G2POBQ4l2W4Q0GyMGQEcCTz8bsD/Ddx3cJ+h/hTrGcDPvf8YY0YCt+EGbyIiIlL+\nNM6J0DhHpPQYa22x2yAiFcK4kc5uXAG/TH3HWvt1Y8x03Lz1PwAHrbX/mOT5fgp8w1rr/4aqAfgn\nXNHAvsAPgEtxS5buAv7SWnvEGHMebtWH4HafjxzD1yMDMe9xJwM/BjYAHwBX4AoLnuPbZi4wHxiA\nm3c/EHjYWvtqqnaLiIhIadM4R+MckVKnAI+IlK1iDSAiA7x9wLPW2ruy2F8DHxEREUlK4xwRyZRq\n8IiIpGCMecI/5xy4EagG/r5ITRIRERHJC41zRCqHAjwiIqktIjIH3RhTC3wX+Ky1VkUFRUREpNxp\nnCNSIVRkWUTKXcoKh3lwDzDLGPO/gNHAMmvt+h54XhEREendNM4RkbQpwCMi5ewo8GtjTAewwLfk\naF5Za38F/CrXxzHGfAW32sRQoCPXxxMREZGKpnGOiGRERZZFRERERERERMqcavCIiIiIiIiIiJQ5\nBXhERERERERERMqcAjwiIiIiIiIiImVOAR4RERERERERkTKnAI+IiIiIiIiISJlTgEdERERERERE\npMwpwCMiIiIiIiIiUuYU4BERERERERERKXP/H52b3lLyNOEGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47f43e95d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(1,2, figsize=(16,4))\n",
    "N=100000\n",
    "ax[0].scatter(tau[N:], m_ij[N:], s=3, lw=0, alpha=0.4, color='red')\n",
    "ax[1].scatter(tauC[N:], m_ij[N:], s=3, lw=0, alpha=0.4, color='green')\n",
    "ax[0].set_ylim(-4,4)\n",
    "ax[1].set_ylim(-4,4)\n",
    "ax[0].set_xlim(0,3200)\n",
    "ax[1].set_xlim(0,3200)\n",
    "ax[0].set_ylabel(r'$\\Delta(mag) \\, [mags]$', fontsize=20)\n",
    "ax[1].set_ylabel(r'$\\Delta(mag) \\, [mags]$', fontsize=20)\n",
    "ax[0].set_xlabel(r'$\\Delta t \\, [days]$', fontsize=20)\n",
    "ax[1].set_xlabel(r'$\\Delta t \\, [days]$', fontsize=20)\n",
    "ax[0].tick_params(axis='both', which='major', labelsize=20)\n",
    "ax[1].tick_params(axis='both', which='major', labelsize=20)\n",
    "ax[0].set_title('observed frame', fontsize=20)\n",
    "ax[1].set_title('rest frame', fontsize=20)\n",
    "fig.suptitle('QSO, 19>r mag>18.5', fontsize=20)\n",
    "ax[0].set_yticks([-3,-2,-1,0,1,2,3])\n",
    "ax[0].set_yticklabels(['-3','-2','-1', '0', '1','2','3'])\n",
    "ax[1].set_yticks([-3,-2,-1,0,1,2,3])\n",
    "ax[1].set_yticklabels(['-3','-2','-1', '0', '1','2','3'])\n",
    "plt.tight_layout()\n",
    "fname = outDir + '18.5-19_qso_restframe_obs_frame_tauC_m_ij_linear.png'\n",
    "plt.savefig(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today we will save our Fig_4 files in /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/\n",
      "\n",
      " For obj =  qso\n",
      "Results saved as  /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/r_cut_18.5-19_starsB_rest.txt\n",
      "Done with calculating panel data \n"
     ]
    }
   ],
   "source": [
    "ax[0].se\n",
    "\n",
    "    # Pull out some tau to plot means : common to all panels \n",
    "    binned_tau = binned_statistic(tau, tau, statistic='mean', bins=nbins)\n",
    "    mean_tau = binned_tau[0]\n",
    "    \n",
    "    # Make an array of correction coefficients per bin, using the coefficients from the file ...  \n",
    "    a = coeffs['a'][coeffs['rMagMin'] == Min]\n",
    "    b = coeffs['b'][coeffs['rMagMin'] == Min]\n",
    "    binned_fc =  a* np.log10(binned_tau[0]) + b\n",
    "    \n",
    "    # Take N from each bin... 'count' function works like a regular histogram\n",
    "    binned_count = binned_statistic(tau, tau, statistic='count', bins=nbins)\n",
    "    bin_count = binned_count[0]\n",
    "    \n",
    "    # checking for empty bins : either mean or some custom function, but not\n",
    "    # count! If statistic='count', then check for 0's , and not for nan's/ \n",
    "    non_empty_bins = np.bitwise_not(np.isnan(mean_tau))\n",
    "\n",
    "    # reassign number of points in a bin and  tau position \n",
    "    bin_count = bin_count[non_empty_bins]\n",
    "    mean_tau = mean_tau[non_empty_bins]\n",
    "\n",
    "\n",
    "    # Which point belongs to which bin\n",
    "    bin_number  = binned_tau[2]\n",
    "\n",
    "    ####  Panel 1 : Standard Deviation \n",
    "    rms_std = lambda x : np.std(x)\n",
    "    stdev_binned = binned_statistic(tau, m_ij, statistic = rms_std, \n",
    "                                              bins=nbins)\n",
    "    bin_stdev = stdev_binned[0][non_empty_bins]  \n",
    "\n",
    "\n",
    "    ##### Panel 2  : Gaussian rms  \n",
    "    rms_robust = lambda x : 0.7414 *(np.percentile(x,75) - np.percentile(x,25))\n",
    "    bin_sigma_G = binned_statistic(tau, m_ij, statistic = rms_robust, \n",
    "                                      bins=nbins)[0][non_empty_bins]\n",
    "\n",
    "    # Loop over all bins  calculating approximate mu and sigma \n",
    "    #mu_bins = {}\n",
    "    #sig_bins = {}\n",
    "\n",
    "    #sig_bins['approx'] = np.zeros(nbins)\n",
    "    #mu_bins['approx'] = np.zeros(nbins)\n",
    "    #sig_bins['full'] = np.zeros(nbins)\n",
    "    #mu_bins['full'] = np.zeros(nbins)\n",
    "    \n",
    "    #for N in np.unique(bin_number)[65:]\n",
    "        #print('\\r --- Calculating mu, sigma for bin %d' % N),\n",
    "        #xi = m_ij[bin_number == N]\n",
    "        #fc_bin = binned_fc[N-1]\n",
    "        #ei = fc_bin * e_ij[bin_number == N]\n",
    "    \n",
    "        #mu_approx, sig_approx = varF.approximate_mu_sigma(xi, ei)\n",
    "        #mu_full, sig_full =  varF.get_mu_sigma(xi,ei)\n",
    "        \n",
    "        #sig_bins['approx'][N-1] = sig_approx\n",
    "        #sig_bins['full'][N-1] = sig_full\n",
    "        \n",
    "        #mu_bins['approx'][N-1] = mu_approx\n",
    "        #mu_bins['full'][N-1] = mu_full \n",
    "\n",
    "    # Save the results of calculation  : Panel 1,2,3,4 \n",
    "    #fname = outDir+ 'r_cut'+'_'+str(Min)+'-'+str(Max)+'_'+obj+'rest.txt'\n",
    "    \n",
    "\n",
    "    #data = np.column_stack((mean_tau, bin_stdev, bin_sigma_G, sig_bins['full'], \n",
    "    #                        sig_bins['approx'], mu_bins['full'], mu_bins['approx'], bin_count, binned_fc))\n",
    "\n",
    "    #header = 'meanTau   stdev    sigmaG   sigmaFull  sigmaApprox   muFull  muApprox   binCount  binFcorr'\n",
    "    #np.savetxt(fname, data, fmt = '%s', delimiter = ' ' , header=header )\n",
    "    print 'Results saved as ', fname\n",
    "\n",
    "print 'Done with calculating panel data '    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Calculating mu, sigma for bin 200\n"
     ]
    }
   ],
   "source": [
    "# Grab points only for the final 50 bins restframe...\n",
    "\n",
    "xi_check= np.zeros(0)\n",
    "ti_check = np.zeros(0)\n",
    "\n",
    "for N in np.unique(bin_number)[150:]:\n",
    "        print('\\r --- Calculating mu, sigma for bin %d' % N),\n",
    "        xi = m_ij[bin_number == N]\n",
    "        ti = tau[bin_number == N ]\n",
    "        xi_check = np.append(xi, xi_check)\n",
    "        ti_check = np.append(ti, ti_check)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today we will save our Fig_4 files in /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/\n",
      "\n",
      " For obj =  qso\n",
      "Results saved as  /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/r_cut_18.5-19_starsB_rest.txt\n",
      "Done with calculating panel data \n"
     ]
    }
   ],
   "source": [
    "# RESTFRAME \n",
    "\n",
    "# set the bin number \n",
    "nbins = 200 \n",
    "\n",
    "          \n",
    "print('Today we will save our Fig_4 files in %s'%outDir)\n",
    "\n",
    "# Loop over objects in the dictionary  \n",
    "for obj in ['qso'] : # out_dic[b].keys()\n",
    "    print '\\n For obj = ', obj\n",
    "    # obj = 'qso'  # or starsB,  starsR \n",
    "    m_ij = out_dic_rest['r_cut'][obj][0]\n",
    "    tau =  out_dic_rest['r_cut'][obj][1]\n",
    "    e_ij = out_dic_rest['r_cut'][obj][2]\n",
    "\n",
    "\n",
    "    # Pull out some tau to plot means : common to all panels \n",
    "    binned_tau = binned_statistic(tau, tau, statistic='mean', bins=nbins)\n",
    "    mean_tau = binned_tau[0]\n",
    "    \n",
    "    # Make an array of correction coefficients per bin, using the coefficients from the file ...  \n",
    "    a = coeffs['a'][coeffs['rMagMin'] == Min]\n",
    "    b = coeffs['b'][coeffs['rMagMin'] == Min]\n",
    "    binned_fc =  a* np.log10(binned_tau[0]) + b\n",
    "    \n",
    "    # Take N from each bin... 'count' function works like a regular histogram\n",
    "    binned_count = binned_statistic(tau, tau, statistic='count', bins=nbins)\n",
    "    bin_count = binned_count[0]\n",
    "    \n",
    "    # checking for empty bins : either mean or some custom function, but not\n",
    "    # count! If statistic='count', then check for 0's , and not for nan's/ \n",
    "    non_empty_bins = np.bitwise_not(np.isnan(mean_tau))\n",
    "\n",
    "    # reassign number of points in a bin and  tau position \n",
    "    bin_count = bin_count[non_empty_bins]\n",
    "    mean_tau = mean_tau[non_empty_bins]\n",
    "\n",
    "\n",
    "    # Which point belongs to which bin\n",
    "    bin_number  = binned_tau[2]\n",
    "\n",
    "    ####  Panel 1 : Standard Deviation \n",
    "    rms_std = lambda x : np.std(x)\n",
    "    stdev_binned = binned_statistic(tau, m_ij, statistic = rms_std, \n",
    "                                              bins=nbins)\n",
    "    bin_stdev = stdev_binned[0][non_empty_bins]  \n",
    "\n",
    "\n",
    "    ##### Panel 2  : Gaussian rms  \n",
    "    rms_robust = lambda x : 0.7414 *(np.percentile(x,75) - np.percentile(x,25))\n",
    "    bin_sigma_G = binned_statistic(tau, m_ij, statistic = rms_robust, \n",
    "                                      bins=nbins)[0][non_empty_bins]\n",
    "\n",
    "    # Loop over all bins  calculating approximate mu and sigma \n",
    "    #mu_bins = {}\n",
    "    #sig_bins = {}\n",
    "\n",
    "    #sig_bins['approx'] = np.zeros(nbins)\n",
    "    #mu_bins['approx'] = np.zeros(nbins)\n",
    "    #sig_bins['full'] = np.zeros(nbins)\n",
    "    #mu_bins['full'] = np.zeros(nbins)\n",
    "    \n",
    "    #for N in np.unique(bin_number)[65:]\n",
    "        #print('\\r --- Calculating mu, sigma for bin %d' % N),\n",
    "        #xi = m_ij[bin_number == N]\n",
    "        #fc_bin = binned_fc[N-1]\n",
    "        #ei = fc_bin * e_ij[bin_number == N]\n",
    "    \n",
    "        #mu_approx, sig_approx = varF.approximate_mu_sigma(xi, ei)\n",
    "        #mu_full, sig_full =  varF.get_mu_sigma(xi,ei)\n",
    "        \n",
    "        #sig_bins['approx'][N-1] = sig_approx\n",
    "        #sig_bins['full'][N-1] = sig_full\n",
    "        \n",
    "        #mu_bins['approx'][N-1] = mu_approx\n",
    "        #mu_bins['full'][N-1] = mu_full \n",
    "\n",
    "    # Save the results of calculation  : Panel 1,2,3,4 \n",
    "    #fname = outDir+ 'r_cut'+'_'+str(Min)+'-'+str(Max)+'_'+obj+'rest.txt'\n",
    "    \n",
    "\n",
    "    #data = np.column_stack((mean_tau, bin_stdev, bin_sigma_G, sig_bins['full'], \n",
    "    #                        sig_bins['approx'], mu_bins['full'], mu_bins['approx'], bin_count, binned_fc))\n",
    "\n",
    "    #header = 'meanTau   stdev    sigmaG   sigmaFull  sigmaApprox   muFull  muApprox   binCount  binFcorr'\n",
    "    #np.savetxt(fname, data, fmt = '%s', delimiter = ' ' , header=header )\n",
    "    print 'Results saved as ', fname\n",
    "\n",
    "print 'Done with calculating panel data '    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " --- Calculating mu, sigma for bin 200\n"
     ]
    }
   ],
   "source": [
    "# Grab points only for the final 50 bins restframe...\n",
    "\n",
    "xi_check_rest = np.zeros(0)\n",
    "ti_check_rest = np.zeros(0)\n",
    "\n",
    "for N in np.unique(bin_number)[150:]:\n",
    "        print('\\r --- Calculating mu, sigma for bin %d' % N),\n",
    "        xi = m_ij[bin_number == N]\n",
    "        ti = tau[bin_number == N ]\n",
    "        xi_check_rest = np.append(xi, xi_check_rest)\n",
    "        ti_check_rest = np.append(ti, ti_check_rest)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f47fd374350>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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vNfB//8frMGgQ22+Ki+mmKi62UVzt7Tyvn/+c++3fz+/OThaYu3ez9tHVxZ7Q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\nCIKQIfx/FKJkzGGG2sgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47fd538850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ti_check_rest, xi_check_rest, s =4, lw=0, alpha=0.7)\n",
    "plt.scatter(ti_check, xi_check, s =4, lw=0, alpha=0.7)\n",
    "plt.scatter(mean_tau[150:], bin_stdev[150:], color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f47fde61bd0>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BcZgtSx6AZ51QxEZ6HXAm9fhsMm2xAnhA0qSkDxcQj9my4gF41gm5u6EKcH1EPCtpkFrS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\nf6Qf/EBDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47fde65c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.log10(mean_tau[150:]), bin_sigma_G[150:])"
   ]
  },
  {
   "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": [
    "### Look at the last few bins for qso : first in the observed frame, and check the distribution of redshifts for points in that sample .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today we will save our Fig_4 files in /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_4_data/2016-06-22/\n",
      "\n",
      " For obj =  qso\n",
      " --- Calculating mu, sigma for bin 200 Results saved as  /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_4_data/2016-06-22/r_cut_18.5-19_qso.txt\n",
      "Done with calculating panel data \n"
     ]
    }
   ],
   "source": [
    "# set the bin number \n",
    "nbins = 200 \n",
    "\n",
    "          \n",
    "print('Today we will save our Fig_4 files in %s'%outDir)\n",
    "\n",
    "# Loop over objects in the dictionary  \n",
    "for obj in ['qso'] : # out_dic[b].keys()\n",
    "    print '\\n For obj = ', obj\n",
    "    # obj = 'qso'  # or starsB,  starsR \n",
    "    m_ij = out_dic['r_cut'][obj][0]\n",
    "    tau =  out_dic['r_cut'][obj][1]\n",
    "    e_ij = out_dic['r_cut'][obj][2]\n",
    "    names  = out_dic['r_cut'][obj][3]\n",
    "\n",
    "    # Pull out some tau to plot means : common to all panels \n",
    "    binned_tau = binned_statistic(tau, tau, statistic='mean', bins=nbins)\n",
    "    mean_tau = binned_tau[0]\n",
    "    \n",
    "    # Make an array of correction coefficients per bin, using the coefficients from the file ...  \n",
    "    a = coeffs['a'][coeffs['rMagMin'] == Min]\n",
    "    b = coeffs['b'][coeffs['rMagMin'] == Min]\n",
    "    binned_fc =  a* np.log10(binned_tau[0]) + b\n",
    "    \n",
    "    # Take N from each bin... 'count' function works like a regular histogram\n",
    "    binned_count = binned_statistic(tau, tau, statistic='count', bins=nbins)\n",
    "    bin_count = binned_count[0]\n",
    "    \n",
    "    # checking for empty bins : either mean or some custom function, but not\n",
    "    # count! If statistic='count', then check for 0's , and not for nan's/ \n",
    "    non_empty_bins = np.bitwise_not(np.isnan(mean_tau))\n",
    "\n",
    "    # reassign number of points in a bin and  tau position \n",
    "    bin_count = bin_count[non_empty_bins]\n",
    "    mean_tau = mean_tau[non_empty_bins]\n",
    "\n",
    "\n",
    "    # Which point belongs to which bin\n",
    "    bin_number  = binned_tau[2]\n",
    "\n",
    "    ####  Panel 1 : Standard Deviation \n",
    "    rms_std = lambda x : np.std(x)\n",
    "    stdev_binned = binned_statistic(tau, m_ij, statistic = rms_std, \n",
    "                                              bins=nbins)\n",
    "    bin_stdev = stdev_binned[0][non_empty_bins]  \n",
    "\n",
    "\n",
    "    ##### Panel 2  : Gaussian rms  \n",
    "    rms_robust = lambda x : 0.7414 *(np.percentile(x,75) - np.percentile(x,25))\n",
    "    bin_sigma_G = binned_statistic(tau, m_ij, statistic = rms_robust, \n",
    "                                      bins=nbins)[0][non_empty_bins]\n",
    "\n",
    "    # Loop over all bins  calculating approximate mu and sigma \n",
    "    mu_bins = {}\n",
    "    sig_bins = {}\n",
    "\n",
    "    sig_bins['approx'] = np.zeros(nbins)\n",
    "    mu_bins['approx'] = np.zeros(nbins)\n",
    "    sig_bins['full'] = np.zeros(nbins)\n",
    "    mu_bins['full'] = np.zeros(nbins)\n",
    "    \n",
    "    ni_gt_3 = [] \n",
    "    \n",
    "    for N in np.unique(bin_number)[65:]:  # bin 65 is log(tau) > 3.0 \n",
    "        print('\\r --- Calculating mu, sigma for bin %d' % N),\n",
    "        xi = m_ij[bin_number == N]\n",
    "        fc_bin = binned_fc[N-1]\n",
    "        ei = fc_bin * e_ij[bin_number == N]\n",
    "        ni = names[bin_number == N]\n",
    "        \n",
    "        mu_approx, sig_approx = varF.approximate_mu_sigma(xi, ei)\n",
    "        mu_full, sig_full =  varF.get_mu_sigma(xi,ei)\n",
    "        \n",
    "        sig_bins['approx'][N-1] = sig_approx\n",
    "        sig_bins['full'][N-1] = sig_full\n",
    "        \n",
    "        mu_bins['approx'][N-1] = mu_approx\n",
    "        mu_bins['full'][N-1] = mu_full \n",
    "\n",
    "        ni_gt_3.append(ni)\n",
    "    # Save the results of calculation  : Panel 1,2,3,4 \n",
    "    fname = outDir+ 'r_cut'+'_'+str(Min)+'-'+str(Max)+'_'+obj+'.txt'\n",
    "    \n",
    "\n",
    "    data = np.column_stack((mean_tau, bin_stdev, bin_sigma_G, sig_bins['full'], \n",
    "                            sig_bins['approx'], mu_bins['full'], mu_bins['approx'], bin_count, binned_fc))\n",
    "\n",
    "    header = 'meanTau   stdev    sigmaG   sigmaFull  sigmaApprox   muFull  muApprox   binCount  binFcorr'\n",
    "    #np.savetxt(fname, data, fmt = '%s', delimiter = ' ' , header=header )\n",
    "    print 'Results saved as ', fname\n",
    "\n",
    "print 'Done with calculating panel data '    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# I figure out which bin has log(tau) > 3 ... \n",
    "# np.arange(200)[np.log10(mean_tau)>3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ni_check = np.zeros(0)\n",
    "for a in ni_gt_3 : \n",
    "    ni_check = np.append(a, ni_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1151631"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ni_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "747"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(ni_check))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# pull first only redshifts for those 747 qso in my high-observed-tau sample :\n",
    "msk = np.in1d(qso_cat['CRTS_id'],  np.unique(ni_check) )\n",
    "names = qso_cat['CRTS_id'][msk]\n",
    "zs = qso_cat['redshift'][msk]\n",
    "zs = np.array(zs).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zs_cnt = np.zeros_like(ni_check, dtype=float)\n",
    "for i in range(len(ni_check)): \n",
    "    zs_cnt[i] = zs[names == ni_check[i]][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dP//5z3/44YcfGDx4cLly/2twI7D4tPD169enU6dO/O9//0v5HpYuXcpnn33G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\nlMmJGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4800b73f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# redshift distribution of quasars that contributed to\n",
    "# log(tau)>  3 sample \n",
    "\n",
    "%matplotlib inline\n",
    "nbins=50\n",
    "fig,ax  = plt.subplots(1,1)\n",
    "\n",
    "# Not weighted by the number of points from a given quasar \n",
    "hist1, bin_edges = np.histogram(zs, bins=nbins, density=True)\n",
    "bin_centres = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "ax.plot(bin_centres, hist1, ls='steps', label='unweighted', lw=2)\n",
    "\n",
    "# and also weighted by the number of points from a given quasar...\n",
    "hist2, bin_edges = np.histogram(zs_cnt, bins=nbins, density=True)\n",
    "bin_centres = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "ax.plot(bin_centres, hist2, ls='steps', label='weighted', lw=2)\n",
    "ax.legend(fontsize=15)\n",
    "ax.set_xlabel('redshift', fontsize=20)\n",
    "ax.set_ylabel('density count', fontsize=20)\n",
    "ax.set_title(r'$log_{10}(\\tau) > 3 \\,\\, QSO \\,\\, bins$', fontsize=20)\n",
    "ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.tight_layout()\n",
    "name = outDir+ 'r_cut'+'_'+str(Min)+'-'+str(Max)+'_'+obj+'_bins_65-200_redshift_distr.png'\n",
    "plt.savefig(name)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f48005e71d0>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Nbt3gkENKs4lUinJ0T61bZy9BW7cWbuVmp/LIKBqq+izQ8r3qAmBWUJ4FTA7K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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f480059a510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mean_tau[65:], bin_count[65:])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2381082"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Check how does the redshift correction affect the distribution of delta_tau points... \n",
    "# bin_number tells each tau, m_ij, e_ij,  point which bin it belongs to ... \n",
    "# Pull only those in bins 65 onwards, i.e. log10(tau) > 3.0 \n",
    "tau_check = np.zeros(0,dtype=float)\n",
    "for N in np.unique(bin_number)[65:]:\n",
    "    t_ij = tau[bin_number == N]\n",
    "    tau_check = np.append(t_ij, tau_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tau_restframe = tau_check  / (1.0 + zs_cnt)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/r_cut_18.5-19_qso_bins_65-200_delta_t_distr.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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bbrvpeuutp2uttZZ+85vf1L///e9t2hg/frz2799fO3XqlJp5Utl+8DDNedTJ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\nRsowDMNILWakDMMwjNRiRsowDMNILf8fOyhMCSAjBUEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47fe560790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare delta_tau distribution before and after redshift correction \n",
    "\n",
    "%matplotlib inline\n",
    "nbins=135\n",
    "fig,ax  = plt.subplots(1,1)\n",
    "\n",
    "# observed frame, no redshift correction... \n",
    "hist1, bin_edges = np.histogram(np.log10(tau_check), bins=nbins, density=False)\n",
    "bin_centres1 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "ax.plot(bin_centres1, hist1, color='red', ls='steps', label='obs', lw=2)\n",
    "\n",
    "# and also weighted by the number of points from a given quasar...\n",
    "hist2, bin_edges = np.histogram(np.log10(tau_restframe), bins=nbins, density=False)\n",
    "bin_centres2 = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "ax.plot(bin_centres2, hist2, color='cyan', ls='steps', label='rest', lw=2)\n",
    "ax.legend(loc='best', fontsize=15)\n",
    "ax.set_xlabel(r'$\\log_{10}(\\tau)$', fontsize=20)\n",
    "ax.set_ylabel(' count', fontsize=20)\n",
    "ax.set_title(r'$log_{10}(\\tau_{rest}) > 3 \\,\\, QSO \\,\\, bins$', fontsize=20)\n",
    "ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.tight_layout()\n",
    "name = outDir+ 'r_cut'+'_'+str(Min)+'-'+str(Max)+'_qso_bins_65-200_delta_t_distr.png'\n",
    "print(name)\n",
    "plt.savefig(name)\n",
    "plt.show()\n",
    "\n",
    "# so what happens is that I loose a huge amount of points in high-tau bins\n",
    "# and those few leftover points may happen to have a smaller scatter simply because \n",
    "# there is less of them .... (?)\n",
    "# --> ASK ZELJKO ! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1151631"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tau_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading...  /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/r_cut_17-18_qso_rest.txt\n",
      "Loading...  /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/r_cut_17-18_starsB_rest.txt\n",
      "Loading...  /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/r_cut_18-18.5_qso_rest.txt\n",
      "Loading...  /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/r_cut_18-18.5_starsB_rest.txt\n",
      "Loading...  /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/r_cut_18.5-19_qso_rest.txt\n",
      "Loading...  /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/r_cut_18.5-19_starsB_rest.txt\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "#from scipy.optimize import curve_fit\n",
    "from collections import OrderedDict\n",
    "\n",
    "# the directory with results previously calculated ... \n",
    "outDir = '/local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/'\n",
    "\n",
    "plot_data = OrderedDict()\n",
    "\n",
    "Min_arr = [17,18,18.5]\n",
    "Max_arr = [18, 18.5, 19]\n",
    "\n",
    "for mag in Min_arr:\n",
    "    plot_data[mag] = OrderedDict()\n",
    "    \n",
    "for i in range(len(Min_arr)):\n",
    "    Min = Min_arr[i]\n",
    "    Max = Max_arr[i]\n",
    "    for obj in ['qso','starsB']:\n",
    "        plot_data[Min][obj] = {}\n",
    "        \n",
    "        fname =  outDir+ 'r_cut'+'_'+str(Min)+'-'+str(Max)+'_'+obj+'_rest.txt'\n",
    "        print 'Loading... ', fname \n",
    "        \n",
    "        colnames = open(fname,'r').read().splitlines()[0][1:].split()\n",
    "        d = np.genfromtxt(fname, dtype=float)\n",
    "        \n",
    "        for label, column in zip(colnames, d.T):\n",
    "            plot_data[Min][obj][label] = column\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sigmaApprox',\n",
       " 'muFull',\n",
       " 'muApprox',\n",
       " 'binCount',\n",
       " 'sigmaG',\n",
       " 'sigmaFull',\n",
       " 'binFcorr',\n",
       " 'stdev',\n",
       " 'meanTau']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_data[18.5]['qso'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jrUjVfVA1uM1wkRiO2/j3Ji7F/dtxXOdQ79F2fRuGYaQYKWmg1BVp+02kPm8N6gDgMQ2Z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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47fe103390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fig,ax  = plt.subplots(1,1)\n",
    "mask = np.log10(plot_data[18.5]['qso']['meanTau']) > 3\n",
    "ax.scatter(np.log10(plot_data[18.5]['qso']['meanTau'][mask]),np.log10(plot_data[18.5]['qso']['binCount'][mask]), alpha=0.7)\n",
    "ax.set_xlabel(r'$\\log_{10}(\\tau)$', fontsize=20)\n",
    "ax.set_ylabel(r'$\\log_{10}(count)$', fontsize=20)\n",
    "ax.set_title(r'$log_{10}(\\tau_{rest}) > 3 \\,\\, QSO \\,\\, bins$', fontsize=20)\n",
    "ax.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.tight_layout()\n",
    "name = '/local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_5_data/2016-06-15/r_cut_18.5-19_qso_delta_t_distr.png'\n",
    "plt.savefig(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2552.13325571,  2566.5947017 ,  2583.00406334,  2597.79069437,\n",
       "        2613.103077  ,  2628.03865051,  2644.09264881,  2658.69178317,\n",
       "        2674.31607065,  2689.64132702,  2706.40071133,  2721.79983473,\n",
       "        2736.28438102,  2752.47209451,  2768.84791084,  2783.11382517,\n",
       "        2798.11647214,  2813.19098566,  2829.78593988,  2843.96206955,\n",
       "        2860.20898872,  2874.49339498,  2891.11025152,  2907.35306685,\n",
       "        2921.67127355,  2935.9277044 ,  2952.45837738,  2968.98910974,\n",
       "        2982.92838621,  2999.35433804,  3011.99770828,  3030.72968249,\n",
       "        3044.77355927,  3059.42643104,  3076.17741074])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.plot(mean_tau[65+100:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  195.18355068,   198.92261456,   202.7333064 ,   206.61699835,\n",
       "         210.57508885,   214.60900312,   218.72019369,   222.91014091,\n",
       "         227.18035352,   231.5323691 ,   235.96775475,   240.48810755,\n",
       "         245.09505519,   249.79025654,   254.57540223,   259.45221531,\n",
       "         264.42245181,   269.48790142,   274.65038809,   279.91177075,\n",
       "         285.27394389,   290.73883833,   296.30842186,   301.98469999,\n",
       "         307.76971662,   313.66555481,   319.67433754,   325.79822844,\n",
       "         332.03943261,   338.40019737,   344.8828131 ,   351.48961407,\n",
       "         358.22297924,   365.08533316,   372.07914683,   379.20693857,\n",
       "         386.47127496,   393.87477173,   401.42009473,   409.10996088,\n",
       "         416.94713913,   424.93445151,   433.07477407,   441.37103798,\n",
       "         449.82623056,   458.44339634,   467.22563818,   476.17611841,\n",
       "         485.29805989,   494.59474725,   504.06952805,   513.72581395,\n",
       "         523.56708198,   533.59687579,   543.81880689,   554.236556  ,\n",
       "         564.85387433,   575.67458496,   586.70258421,   597.94184304,\n",
       "         609.39640846,   621.07040504,   632.96803634,   645.09358645,\n",
       "         657.45142155,   670.04599142,   682.88183112,   695.96356256,\n",
       "         709.29589622,   722.88363279,   736.73166494,   750.84497906,\n",
       "         765.22865707,   779.88787822,   794.82792102,   810.05416506,\n",
       "         825.57209299,   841.38729252,   857.50545836,   873.93239434,\n",
       "         890.67401545,   907.73635003,   925.12554185,   942.84785242,\n",
       "         960.90966318,   979.3174778 ,   998.07792458,  1017.19775875,\n",
       "        1036.68386499,  1056.54325983,  1076.78309425,  1097.41065618,\n",
       "        1118.4333732 ,  1139.85881515,  1161.69469689,  1183.94888107,\n",
       "        1206.62938098,  1229.7443634 ,  1253.30215155,  1277.31122811,\n",
       "        1301.78023827,  1326.7179928 ,  1352.13347129,  1378.03582532,\n",
       "        1404.4343818 ,  1431.33864632,  1458.75830654,  1486.70323572,\n",
       "        1515.18349627,  1544.20934333,  1573.79122852,  1603.93980367,\n",
       "        1634.66592467,  1665.98065535,  1697.89527152,  1730.42126497,\n",
       "        1763.57034765,  1797.35445585,  1831.78575456,  1866.87664176,\n",
       "        1902.63975297,  1939.08796576,  1976.23440437,  2014.09244448,\n",
       "        2052.675718  ,  2091.99811796,  2132.07380356,  2172.91720523,\n",
       "        2214.54302985,  2256.96626601,  2300.20218946,  2344.26636857,\n",
       "        2389.17466994,  2434.94326414,  2481.58863149])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.power(10,bin_centres2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7715, 7288, 6725, 6108, 5539, 5085, 4703, 4416, 3994, 3688, 3551,\n",
       "       3227, 2946, 2609, 2371, 2055, 1787, 1713, 1607, 1616, 1376, 1097,\n",
       "        882,  718,  677,  569,  471,  317,  248,  211,  140,   95,   50,\n",
       "         22,   13])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist2[100:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If just want to plot the histogram of number of counts per bin..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today we will save our Fig_4 files in /local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_4_data/2016-06-15/\n",
      "\n",
      " For obj =  qso\n",
      "\n",
      " For obj =  starsB\n"
     ]
    }
   ],
   "source": [
    "nbins = 200 \n",
    "\n",
    "          \n",
    "print('Today we will save our Fig_4 files in %s'%outDir)\n",
    "\n",
    "# store the bin count...\n",
    "binned_dict = {}\n",
    "\n",
    "# Loop over objects in the dictionary  \n",
    "for obj in ['qso','starsB'] : # out_dic[b].keys()\n",
    "    print '\\n For obj = ', obj\n",
    "    # obj = 'qso'  # or starsB,  starsR \n",
    "    m_ij = out_dic['r_cut'][obj][0]\n",
    "    tau =  out_dic['r_cut'][obj][1]\n",
    "    e_ij = out_dic['r_cut'][obj][2]\n",
    "\n",
    "\n",
    "    # Pull out some tau to plot means : common to all panels \n",
    "    binned_tau = binned_statistic(tau, tau, statistic='mean', bins=nbins)\n",
    "    mean_tau = binned_tau[0]\n",
    "    \n",
    "    # Make an array of correction coefficients per bin, using the coefficients from the file ...  \n",
    "    a = coeffs['a'][coeffs['rMagMin'] == Min]\n",
    "    b = coeffs['b'][coeffs['rMagMin'] == Min]\n",
    "    binned_fc =  a* np.log10(binned_tau[0]) + b\n",
    "    \n",
    "    # Take N from each bin... 'count' function works like a regular histogram\n",
    "    binned_count = binned_statistic(tau, tau, statistic='count', bins=nbins)\n",
    "    # save the raw bin count to see truly if there are any empty bins.... \n",
    "    binned_dict[obj] = binned_count\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PRqLYo9PI8Sv26FQrbiVYIiIiYzsZGHD37wG4e7+ZbQTeDaTNbIG77wj7LgKeDj9vAwx4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\ncxZBV6MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb41f35b750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import seaborn as sns\n",
    "sns.set_context('talk')\n",
    "fig,ax = plt.subplots(1,2, figsize=(12,6))\n",
    "colors = {'qso':'red', 'starsB':'blue'}\n",
    "#colors['qso'] = 'red'\n",
    "for i in range(len(binned_dict.keys())):\n",
    "    obj = binned_dict.keys()[i]\n",
    "    hist = binned_dict[obj][0]\n",
    "    bin_edges = binned_dict[obj][1]\n",
    "    bin_centres = (bin_edges[:-1] + bin_edges[1:])/2\n",
    "    ax[i].plot(bin_centres, hist, ls='steps', label = obj, color=colors[obj])\n",
    "    ax[i].legend()\n",
    "    ax[i].set_xlabel(r'$\\Delta t$', fontsize=20)\n",
    "    ax[i].set_ylabel('Bin count', fontsize=20)\n",
    "plt.tight_layout()\n",
    "plt.savefig(outDir+'r_cut'+'_'+str(Min)+'-'+str(Max)+'_'+'bin-count_obs.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "### obsolete code : using full linear fit correction..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# If we were using the full linear fit to the correction factor as a function of timescale...\n",
    "# We are not, because the timescale-dependence is much smaller than the magnitude-dependence \n",
    "# Read in the linear correction coefficients....\n",
    "File = '/local/tmp/suberlak/CRTS_PROJECT/data_products/Fig_3_data/2016-06-07/fc_starsB_a_b_coeff.txt'\n",
    "colnames = open(File,'r').read().splitlines()[0][1:].split()\n",
    "datatable = np.genfromtxt(File, dtype=float)\n",
    "coeffs = {}\n",
    "for label, column in zip(colnames, datatable.T):\n",
    "    coeffs[label] = column\n",
    "    \n",
    "# Set a directory to save the results...\n",
    "outDir = os.path.join(os.getcwd()[:-4]+'data_products/'+'Fig_4_data/')\n",
    "if not os.path.exists(outDir): os.system('mkdir %s' % outDir)\n",
    "                      \n",
    "outDir = os.path.join(os.getcwd()[:-4],'data_products/'+'Fig_4_data', \n",
    "                      datetime.datetime.now().strftime('%Y-%m-%d')+ '/')\n",
    "if not os.path.exists(outDir): os.system('mkdir %s' % outDir)\n",
    "\n",
    "print('Today we will save our Fig_4 files in %s'%outDir)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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