App_B_wiggles_explained.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Appendix B : wiggles explained "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we insert all code needed to make Appendix  B figure.  Other experiments are kept in D_Fig_2_CRTS_sel_r_cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'CRTS_paper_modules' from '/Users/chris/GradResearch/CRTS_PROJECT/code/CRTS_paper_modules.py'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from astroML.stats import median_sigmaG\n",
    "from scipy.stats import binned_statistic\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "import CRTS_paper_modules as mod\n",
    "import imp\n",
    "imp.reload(mod)\n",
    "#reload(mod)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": [
    "\"The motivation for the additional panel is that it seems to me that the oscillation\n",
    "problem might not have anything to do with the actual sample size. It could be that\n",
    "systematic photometric calibration errors are large when comparing data obtained\n",
    "six months apart than for data obtained at the same time of year. To test this\n",
    "hypothesis, I propose to redo the middle panel (sigmaG with raw measurements)\n",
    "as the 6th panel where, instead of using all the data points when computing sigmaG,\n",
    "you *randomly subselect* 20,000 points. This number comes from the bottom panel \n",
    "where I see that counts vary from about 200,000 to about 0. Choosing a random\n",
    "sample of 20,000 points will allow you to get good results to beyond t2-t1 ~ 1500\n",
    "and thus you should see (or not) the first four wiggles in sigmaG. I think that the\n",
    "amplitude of sigmaG for the first four wiggles will remain about the same when you \n",
    "always use 20,000 random points (when you have fewer than 20,000 points, don’t\n",
    "plot any points). This will demonstrate that the wiggles are not due to varying \n",
    "sample size. If the amplitude becomes much smaller instead, then I am wrong\n",
    "about calibration problems and the oscillations are indeed caused (somehow)\n",
    "by the varying sample size.\"  (Zeljko's email   5/4/17 )  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using now only lightcurves with SDSS  17.000000< r < 18.000000\n",
      "\n",
      "Choosing stars with  SDSS   -1.00<g-i<3.00\n",
      " These cuts reduced the number of stars  in the sample from 48250 to 5790\n",
      "\n",
      "Reading in tau,xi,ei  for 5790 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",
      "\n",
      "Using now only lightcurves with SDSS  18.000000< r < 18.500000\n",
      "\n",
      "Choosing stars with  SDSS   -1.00<g-i<3.00\n",
      " These cuts reduced the number of stars  in the sample from 48250 to 3487\n",
      "\n",
      "Reading in tau,xi,ei  for 3487 objects\n",
      "\n",
      "Using structure function master files from ../data_products/sf_file_per_LC/stars/\n",
      "[######### ] 99.94%\n",
      "Finished reading all master files for the selected objects ...\n",
      "\n",
      "Using now only lightcurves with SDSS  18.500000< r < 19.000000\n",
      "\n",
      "Choosing stars with  SDSS   -1.00<g-i<3.00\n",
      " These cuts reduced the number of stars  in the sample from 48250 to 3825\n",
      "\n",
      "Reading in tau,xi,ei  for 3825 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"
     ]
    }
   ],
   "source": [
    "# Code to select a sample of xi , ei points, treating all \n",
    "# stars together ... \n",
    "# this is what makes  detail_dic{}\n",
    "\n",
    "imp.reload(mod)\n",
    "\n",
    "Min_arr = [17, 18,   18.5 ]\n",
    "Max_arr = [18, 18.5, 19   ]\n",
    "\n",
    "detail_dic = {}\n",
    "#detail_dic['qso'] = {}\n",
    "detail_dic['stars'] = {}\n",
    "\n",
    "mag = 'r'   # which magnitudes to use for cutting and reporting \n",
    "inDirStars   = '../data_products/sf_file_per_LC/stars/'\n",
    "inDirQSO = '../data_products/sf_file_per_LC/qso_detailed/'\n",
    "        \n",
    "\n",
    "for i in range(len(Min_arr)):\n",
    "    Min = Min_arr[i]\n",
    "    Max = Max_arr[i]\n",
    "\n",
    "    print('\\nUsing now only lightcurves with SDSS  %f< %s < %f' % (Min, mag, Max))\n",
    "     \n",
    "    # no need for QSO to prove our point... \n",
    "    #detail_dic['qso'][Min] = mod.faster_read_xi_ei(inDirSF = inDirQSO, \n",
    "    #                                               good_ids= mod.cut_qso(qso_cat=qso_cat, mMin = Min, \n",
    "    #                                               mMax=Max, mErrMax = 0.3, cut_mag=mag), detailed=True)\n",
    "\n",
    "    # we combine blue and red stars by choosing g-i from   -1 to 1 and 1 to 3 together \n",
    "    detail_dic['stars'][Min] = mod.faster_read_xi_ei(inDirSF =inDirStars,  \n",
    "                               good_ids = mod.cut_stars(star_cat = star_cat, mMin = Min, \n",
    "                               mMax=Max, mErrMax = 0.3, gi_Min = -1, gi_Max=3, \n",
    "                               cut_mag=mag + '_mMed'), \n",
    "                               detailed=None)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today we will save our Fig_B1 files in /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_B1_data/2017-08-15/\n"
     ]
    }
   ],
   "source": [
    "# Make a new dir ... \n",
    "# Set a directory to save the results...\n",
    "outDir = os.path.join(os.getcwd()[:-4],'data_products/'+'Fig_B1_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_B1 files in %s'%outDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#detail_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Save the  detailed_dic dictionary into files organised by object - Min - Max \n",
    "for obj in detail_dic.keys(): \n",
    "    for i in range(len(Min_arr)) : \n",
    "        Min = Min_arr[i]\n",
    "        Max = Max_arr[i]\n",
    "        np.save(outDir + obj+'_' +str(Min)+'-'+str(Max) , detail_dic[obj][Min])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "########################################################################\n",
    "# Read detail_dic  using the code above : reproduced here for clarity \n",
    "########################################################################\n",
    "\n",
    "#outDir = '../data_products/Fig_2_data/2017-04-19/'\n",
    "\n",
    "# Load the files if needed later ....\n",
    "detail_dic = {}\n",
    "for obj in ['qso', 'stars'] : \n",
    "    detail_dic[obj] = {}\n",
    "    \n",
    "    for i in range(len(Min_arr)) : \n",
    "        Min = Min_arr[i]\n",
    "        Max = Max_arr[i]\n",
    "        detail_dic[obj][Min] = np.load(outDir + obj+'_' +str(Min)+'-'+str(Max)+'.npy').item()\n",
    "        \n",
    "print('Read detailed xi, ei data from %s'%outDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "from matplotlib import rcParams\n",
    "#matplotlib.rcParams['font.size'] = 17\n",
    "#matplotlib.rc('xtick', labelsize=15) \n",
    "#matplotlib.rc('ytick', labelsize=15) \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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Do random selection and calculating mean tau ... \n",
    "# So that plotting can be done separately with just \n",
    "# a small fraction of data ... \n",
    "\n",
    "#bin_size_days = 20\n",
    "obj = 'stars'\n",
    "\n",
    "plot_dic = {}\n",
    "\n",
    "for Min , Max in zip([17,18,18.5], [18,18.5,19]):\n",
    "    plot_dic[Min] = {}\n",
    "    \n",
    "    # read in the points \n",
    "    tau = detail_dic[obj][Min]['tau']\n",
    "    xi  = detail_dic[obj][Min]['xi']\n",
    "\n",
    "    # randomly  select points...\n",
    "    # sigmaG (raw measurements ) : randomly subselect 20000 points \n",
    "    # https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.choice.html \n",
    "    indices = np.arange(len(xi))\n",
    "    size = 20000 \n",
    "\n",
    "    # first split xi, tau into 200 bins  \n",
    "    stats_temp = binned_statistic(tau, xi, statistic='count', bins=200)\n",
    "\n",
    "    # go over each bin, and calculate the sigmaG(xi) using random resample of N points.\n",
    "    sigmaG_xi = np.zeros_like(stats_temp.statistic)\n",
    "    N_pts  = 20000\n",
    "\n",
    "    sigmaG = lambda x : 0.7413 *(np.percentile(x,75) - np.percentile(x,25))\n",
    "\n",
    "    for N in np.sort(np.unique(stats_temp.binnumber)): \n",
    "        xi_per_bin = xi[stats_temp.binnumber == N] # choose points in the N-th bin\n",
    "        # randomly select 'N_pts'  from each bin. \n",
    "        # If there is less than N_pts in the bin,  then return NaN\n",
    "        if len(xi_per_bin) >= N_pts : \n",
    "            # randomly select N_pts points, and make sure that each point \n",
    "            # is drawn only once ! \n",
    "            xi_sample = np.random.choice(xi_per_bin, size = N_pts, replace = False)\n",
    "            sigmaG_xi[N-1] = sigmaG(xi_sample)\n",
    "        else :  # if the number of points per bin is less than N_pts, then set \n",
    "            # the sigmaG to nan, or 0, or some other number. \n",
    "            sigmaG_xi[N-1] = np.nan \n",
    "\n",
    "    mean_tau = (stats_temp.bin_edges[:-1] + stats_temp.bin_edges[1:])/2\n",
    "    bin_sigmaG = sigmaG_xi\n",
    "\n",
    "    plot_dic[Min]['mean_tau'] = mean_tau\n",
    "    plot_dic[Min]['bin_sigmaG'] = bin_sigmaG\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{17: {'bin_sigmaG': array([ 0.05848857,  0.05537511,  0.05915574,  0.0562832 ,  0.06189855,\n",
       "          0.05737662,  0.06325142,  0.05893335,  0.06486375,  0.0622692 ,\n",
       "          0.0681996 ,  0.0681996 ,  0.06857025,  0.06271398,  0.0659757 ,\n",
       "          0.05841444,  0.06263985,  0.05932253,  0.06199121,  0.05819205,\n",
       "          0.06100899,  0.05843297,  0.05848857,  0.05700597,  0.05745075,\n",
       "          0.0607866 ,  0.05806232,  0.06390006,  0.05917427,  0.06464136,\n",
       "          0.05961905,  0.0644931 ,  0.06115725,  0.06701352,  0.06805134,\n",
       "          0.06643901,  0.06508614,  0.06471549,  0.06175029,  0.06538266,\n",
       "          0.06182442,  0.06604983,  0.06263985,  0.06338115,  0.05871096,\n",
       "          0.05871096,  0.05895188,  0.05767314,  0.06056421,  0.05804379,\n",
       "          0.06115725,  0.05922987,  0.0630105 ,  0.06115725,  0.06391859,\n",
       "          0.06204681,  0.06560505,  0.06701352,  0.06567918,  0.06612396,\n",
       "          0.06777335,  0.0622692 ,  0.06538266,  0.06204681,  0.06488228,\n",
       "          0.06115725,  0.06404832,  0.0600453 ,  0.0607866 ,  0.05871096,\n",
       "          0.05880362,  0.0585627 ,  0.0578214 ,  0.06130551,  0.059304  ,\n",
       "          0.06251012,  0.06034182,  0.06553092,  0.06199121,  0.06560505,\n",
       "          0.06656874,  0.0674583 ,  0.06784748,  0.06604983,  0.06049008,\n",
       "          0.06271398,  0.05745075,  0.06130551,  0.06110165,  0.0630105 ,\n",
       "          0.05967465,  0.0607866 ,  0.0600453 ,  0.06086073,  0.06100899,\n",
       "          0.06041595,  0.06130551,  0.059304  ,  0.06145377,  0.06071247,\n",
       "          0.06503054,  0.05910014,  0.06516027,  0.06532706,  0.07168371,\n",
       "          0.07007138,  0.0704235 ,  0.0630105 ,  0.06441897,  0.05834031,\n",
       "          0.06115725,  0.05804379,  0.06419658,  0.06199121,  0.06338115,\n",
       "          0.06108312,  0.05976731,  0.06080513,  0.059304  ,  0.06293637,\n",
       "          0.06041595,  0.0630105 ,  0.06137964,  0.06508614,  0.0630105 ,\n",
       "          0.06352941,  0.06427071,  0.06857025,  0.07383348,  0.07198023,\n",
       "          0.06664287,  0.06686526,  0.06041595,  0.0600453 ,  0.05910014,\n",
       "          0.06362207,  0.06130551,  0.06115725,  0.06011943,  0.05843297,\n",
       "          0.059304  ,  0.05672798,  0.05952639,  0.05581989,  0.06315876,\n",
       "          0.05885922,  0.06265838,  0.059304  ,  0.06338115,  0.06716178,\n",
       "          0.06612396,  0.07101654,  0.0725918 ,  0.06486375,  0.06621662,\n",
       "          0.059304  ,  0.06167616,  0.05589402,  0.06137964,  0.0585627 ,\n",
       "          0.06182442,  0.06063834,  0.05709863,  0.06108312,  0.05715423,\n",
       "          0.06175029,  0.05670945,  0.06345528,  0.05670945,  0.06693939,\n",
       "          0.06006383,  0.06123138,  0.06465989,  0.06191708,         nan,\n",
       "          0.06093486,         nan,         nan,  0.06427071,         nan,\n",
       "          0.0585627 ,         nan,  0.05796966,  0.06345528,  0.05900748,\n",
       "          0.05418903,  0.05441142,  0.051891  ,  0.05641293,         nan,\n",
       "                 nan,         nan,         nan,         nan,         nan,\n",
       "                 nan,         nan,         nan,         nan,         nan]),\n",
       "  'mean_tau': array([    8.358968,    23.774104,    39.18924 ,    54.604376,\n",
       "            70.019512,    85.434648,   100.849784,   116.26492 ,\n",
       "           131.680056,   147.095192,   162.510328,   177.925464,\n",
       "           193.3406  ,   208.755736,   224.170872,   239.586008,\n",
       "           255.001144,   270.41628 ,   285.831416,   301.246552,\n",
       "           316.661688,   332.076824,   347.49196 ,   362.907096,\n",
       "           378.322232,   393.737368,   409.152504,   424.56764 ,\n",
       "           439.982776,   455.397912,   470.813048,   486.228184,\n",
       "           501.64332 ,   517.058456,   532.473592,   547.888728,\n",
       "           563.303864,   578.719   ,   594.134136,   609.549272,\n",
       "           624.964408,   640.379544,   655.79468 ,   671.209816,\n",
       "           686.624952,   702.040088,   717.455224,   732.87036 ,\n",
       "           748.285496,   763.700632,   779.115768,   794.530904,\n",
       "           809.94604 ,   825.361176,   840.776312,   856.191448,\n",
       "           871.606584,   887.02172 ,   902.436856,   917.851992,\n",
       "           933.267128,   948.682264,   964.0974  ,   979.512536,\n",
       "           994.927672,  1010.342808,  1025.757944,  1041.17308 ,\n",
       "          1056.588216,  1072.003352,  1087.418488,  1102.833624,\n",
       "          1118.24876 ,  1133.663896,  1149.079032,  1164.494168,\n",
       "          1179.909304,  1195.32444 ,  1210.739576,  1226.154712,\n",
       "          1241.569848,  1256.984984,  1272.40012 ,  1287.815256,\n",
       "          1303.230392,  1318.645528,  1334.060664,  1349.4758  ,\n",
       "          1364.890936,  1380.306072,  1395.721208,  1411.136344,\n",
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       "          1488.212024,  1503.62716 ,  1519.042296,  1534.457432,\n",
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       "          1611.533112,  1626.948248,  1642.363384,  1657.77852 ,\n",
       "          1673.193656,  1688.608792,  1704.023928,  1719.439064,\n",
       "          1734.8542  ,  1750.269336,  1765.684472,  1781.099608,\n",
       "          1796.514744,  1811.92988 ,  1827.345016,  1842.760152,\n",
       "          1858.175288,  1873.590424,  1889.00556 ,  1904.420696,\n",
       "          1919.835832,  1935.250968,  1950.666104,  1966.08124 ,\n",
       "          1981.496376,  1996.911512,  2012.326648,  2027.741784,\n",
       "          2043.15692 ,  2058.572056,  2073.987192,  2089.402328,\n",
       "          2104.817464,  2120.2326  ,  2135.647736,  2151.062872,\n",
       "          2166.478008,  2181.893144,  2197.30828 ,  2212.723416,\n",
       "          2228.138552,  2243.553688,  2258.968824,  2274.38396 ,\n",
       "          2289.799096,  2305.214232,  2320.629368,  2336.044504,\n",
       "          2351.45964 ,  2366.874776,  2382.289912,  2397.705048,\n",
       "          2413.120184,  2428.53532 ,  2443.950456,  2459.365592,\n",
       "          2474.780728,  2490.195864,  2505.611   ,  2521.026136,\n",
       "          2536.441272,  2551.856408,  2567.271544,  2582.68668 ,\n",
       "          2598.101816,  2613.516952,  2628.932088,  2644.347224,\n",
       "          2659.76236 ,  2675.177496,  2690.592632,  2706.007768,\n",
       "          2721.422904,  2736.83804 ,  2752.253176,  2767.668312,\n",
       "          2783.083448,  2798.498584,  2813.91372 ,  2829.328856,\n",
       "          2844.743992,  2860.159128,  2875.574264,  2890.9894  ,\n",
       "          2906.404536,  2921.819672,  2937.234808,  2952.649944,\n",
       "          2968.06508 ,  2983.480216,  2998.895352,  3014.310488,\n",
       "          3029.725624,  3045.14076 ,  3060.555896,  3075.971032])},\n",
       " 18: {'bin_sigmaG': array([ 0.0911799 ,  0.08541629,  0.09305168,  0.08665797,  0.09407097,\n",
       "          0.09021621,  0.09816665,  0.0926625 ,  0.10467156,  0.09861143,\n",
       "          0.10819273,  0.10904523,  0.10689546,  0.0985929 ,  0.10350401,\n",
       "          0.090383  ,  0.09733269,  0.09244011,  0.09681378,  0.09140229,\n",
       "          0.09407097,  0.09095751,  0.09103164,  0.08977143,  0.09006795,\n",
       "          0.09312581,  0.09021621,  0.09614661,  0.09245864,  0.10118745,\n",
       "          0.09644313,  0.09948246,  0.09874116,  0.10637655,  0.10483835,\n",
       "          0.10578351,  0.10296657,  0.10202141,  0.096369  ,  0.10120598,\n",
       "          0.09588716,  0.1000199 ,  0.09799986,  0.09496053,  0.09216212,\n",
       "          0.09319994,  0.09132816,  0.0874734 ,  0.09149495,  0.08977143,\n",
       "          0.09399684,  0.09214359,  0.09644313,  0.09370032,  0.10052028,\n",
       "          0.09733269,  0.10185462,  0.10563525,  0.10593177,  0.10467156,\n",
       "          0.10578351,  0.09733269,  0.10096506,  0.09518292,  0.10022376,\n",
       "          0.09407097,  0.09770334,  0.09429336,  0.09206946,  0.09073512,\n",
       "          0.0903274 ,  0.09110577,  0.09021621,  0.09646166,  0.09177294,\n",
       "          0.09948246,  0.09421923,  0.10291097,  0.09623927,  0.10126158,\n",
       "          0.10580204,  0.10676573,  0.10682133,  0.10333722,  0.09618367,\n",
       "          0.10037202,  0.08962317,  0.09777747,  0.09481227,  0.09733269,\n",
       "          0.09169881,  0.09355206,  0.09164321,  0.09275516,  0.09496053,\n",
       "          0.09105017,  0.09673965,  0.09088338,  0.09288489,  0.09466401,\n",
       "          0.10237353,  0.0940895 ,  0.10252179,  0.1015581 ,  0.11156565,\n",
       "          0.11104674,  0.11141739,  0.09833344,  0.10200288,  0.09312581,\n",
       "          0.09688791,  0.0919212 ,  0.1007612 ,  0.09496053,  0.09814812,\n",
       "          0.0948864 ,  0.09236598,  0.09206946,  0.09244011,  0.09622074,\n",
       "          0.09258837,  0.09764774,  0.09281076,  0.10052028,  0.09725856,\n",
       "          0.09831491,  0.10192875,  0.10667307,  0.11386368,  0.11297412,\n",
       "          0.10548699,  0.10741437,  0.09444162,  0.09236598,  0.09001235,\n",
       "          0.10103919,  0.0948864 ,  0.09273663,  0.09253277,  0.09021621,\n",
       "          0.09303315,  0.09095751,  0.09586862,  0.08658384,  0.09725856,\n",
       "          0.0918656 ,  0.09964925,  0.0926625 ,  0.09966779,  0.10424531,\n",
       "          0.10572791,         nan,         nan,         nan,         nan,\n",
       "          0.09325554,         nan,  0.08940078,  0.09675818,  0.09229185,\n",
       "          0.09918594,  0.09229185,  0.08754753,  0.09327407,  0.088956  ,\n",
       "          0.09740682,  0.0874734 ,  0.09888942,  0.08858535,  0.10222527,\n",
       "          0.0940895 ,  0.09696204,         nan,         nan,         nan,\n",
       "                 nan,         nan,         nan,         nan,         nan,\n",
       "                 nan,         nan,         nan,         nan,         nan,\n",
       "                 nan,         nan,  0.0830256 ,         nan,         nan,\n",
       "                 nan,         nan,         nan,         nan,         nan,\n",
       "                 nan,         nan,         nan,         nan,         nan]),\n",
       "  'mean_tau': array([    8.36135225,    23.77645675,    39.19156125,    54.60666575,\n",
       "            70.02177025,    85.43687475,   100.85197925,   116.26708375,\n",
       "           131.68218825,   147.09729275,   162.51239725,   177.92750175,\n",
       "           193.34260625,   208.75771075,   224.17281525,   239.58791975,\n",
       "           255.00302425,   270.41812875,   285.83323325,   301.24833775,\n",
       "           316.66344225,   332.07854675,   347.49365125,   362.90875575,\n",
       "           378.32386025,   393.73896475,   409.15406925,   424.56917375,\n",
       "           439.98427825,   455.39938275,   470.81448725,   486.22959175,\n",
       "           501.64469625,   517.05980075,   532.47490525,   547.89000975,\n",
       "           563.30511425,   578.72021875,   594.13532325,   609.55042775,\n",
       "           624.96553225,   640.38063675,   655.79574125,   671.21084575,\n",
       "           686.62595025,   702.04105475,   717.45615925,   732.87126375,\n",
       "           748.28636825,   763.70147275,   779.11657725,   794.53168175,\n",
       "           809.94678625,   825.36189075,   840.77699525,   856.19209975,\n",
       "           871.60720425,   887.02230875,   902.43741325,   917.85251775,\n",
       "           933.26762225,   948.68272675,   964.09783125,   979.51293575,\n",
       "           994.92804025,  1010.34314475,  1025.75824925,  1041.17335375,\n",
       "          1056.58845825,  1072.00356275,  1087.41866725,  1102.83377175,\n",
       "          1118.24887625,  1133.66398075,  1149.07908525,  1164.49418975,\n",
       "          1179.90929425,  1195.32439875,  1210.73950325,  1226.15460775,\n",
       "          1241.56971225,  1256.98481675,  1272.39992125,  1287.81502575,\n",
       "          1303.23013025,  1318.64523475,  1334.06033925,  1349.47544375,\n",
       "          1364.89054825,  1380.30565275,  1395.72075725,  1411.13586175,\n",
       "          1426.55096625,  1441.96607075,  1457.38117525,  1472.79627975,\n",
       "          1488.21138425,  1503.62648875,  1519.04159325,  1534.45669775,\n",
       "          1549.87180225,  1565.28690675,  1580.70201125,  1596.11711575,\n",
       "          1611.53222025,  1626.94732475,  1642.36242925,  1657.77753375,\n",
       "          1673.19263825,  1688.60774275,  1704.02284725,  1719.43795175,\n",
       "          1734.85305625,  1750.26816075,  1765.68326525,  1781.09836975,\n",
       "          1796.51347425,  1811.92857875,  1827.34368325,  1842.75878775,\n",
       "          1858.17389225,  1873.58899675,  1889.00410125,  1904.41920575,\n",
       "          1919.83431025,  1935.24941475,  1950.66451925,  1966.07962375,\n",
       "          1981.49472825,  1996.90983275,  2012.32493725,  2027.74004175,\n",
       "          2043.15514625,  2058.57025075,  2073.98535525,  2089.40045975,\n",
       "          2104.81556425,  2120.23066875,  2135.64577325,  2151.06087775,\n",
       "          2166.47598225,  2181.89108675,  2197.30619125,  2212.72129575,\n",
       "          2228.13640025,  2243.55150475,  2258.96660925,  2274.38171375,\n",
       "          2289.79681825,  2305.21192275,  2320.62702725,  2336.04213175,\n",
       "          2351.45723625,  2366.87234075,  2382.28744525,  2397.70254975,\n",
       "          2413.11765425,  2428.53275875,  2443.94786325,  2459.36296775,\n",
       "          2474.77807225,  2490.19317675,  2505.60828125,  2521.02338575,\n",
       "          2536.43849025,  2551.85359475,  2567.26869925,  2582.68380375,\n",
       "          2598.09890825,  2613.51401275,  2628.92911725,  2644.34422175,\n",
       "          2659.75932625,  2675.17443075,  2690.58953525,  2706.00463975,\n",
       "          2721.41974425,  2736.83484875,  2752.24995325,  2767.66505775,\n",
       "          2783.08016225,  2798.49526675,  2813.91037125,  2829.32547575,\n",
       "          2844.74058025,  2860.15568475,  2875.57078925,  2890.98589375,\n",
       "          2906.40099825,  2921.81610275,  2937.23120725,  2952.64631175,\n",
       "          2968.06141625,  2983.47652075,  2998.89162525,  3014.30672975,\n",
       "          3029.72183425,  3045.13693875,  3060.55204325,  3075.96714775])},\n",
       " 18.5: {'bin_sigmaG': array([ 0.13108037,  0.12023886,  0.13078385,  0.12439014,  0.13597295,\n",
       "          0.1244828 ,  0.13671425,  0.13159928,  0.14729631,  0.14173656,\n",
       "          0.15583979,  0.15404214,  0.15619191,  0.14121765,  0.14788935,\n",
       "          0.12757773,  0.13478687,  0.13024641,  0.13886402,  0.12870821,\n",
       "          0.13083945,  0.12713295,  0.12587274,  0.12461253,  0.12381563,\n",
       "          0.12843023,  0.12715148,  0.13573203,  0.12950511,  0.13780767,\n",
       "          0.13335987,  0.14168096,  0.14160683,  0.15337497,  0.15448692,\n",
       "          0.15315258,  0.15146612,  0.14753723,  0.13767794,  0.14492415,\n",
       "          0.13432356,  0.14099526,  0.13536138,  0.13499073,  0.12781865,\n",
       "          0.12861555,  0.1304688 ,  0.12483492,  0.13026494,  0.12561329,\n",
       "          0.13298922,  0.12957924,  0.13580616,  0.13070972,  0.1401057 ,\n",
       "          0.13219232,  0.14499828,  0.15011325,  0.15685908,  0.14829707,\n",
       "          0.15335644,  0.13552817,  0.1452392 ,  0.13187727,  0.13990184,\n",
       "          0.13234058,  0.13951266,  0.12839316,  0.12982016,  0.12907886,\n",
       "          0.12500171,  0.12846729,  0.12650285,  0.1356579 ,  0.12715148,\n",
       "          0.13899375,  0.13211819,  0.14188482,  0.1356023 ,  0.14314503,\n",
       "          0.14805614,  0.15159585,  0.15420893,  0.15404214,  0.13676985,\n",
       "          0.14207014,  0.12728121,  0.13693664,  0.13137689,  0.13656599,\n",
       "          0.12974603,  0.13167341,  0.12870821,  0.13143249,  0.13499073,\n",
       "          0.12937538,  0.13239618,  0.12937538,  0.1349166 ,  0.13306335,\n",
       "          0.14308943,  0.1320626 ,  0.1423296 ,  0.14255199,  0.15902738,\n",
       "          0.15804516,  0.16004667,  0.14032809,  0.14625849,  0.13083945,\n",
       "          0.13958679,  0.13024641,  0.14003157,  0.13580616,  0.13662159,\n",
       "          0.13017228,  0.13054293,  0.13211819,  0.12700322,  0.13693664,\n",
       "          0.12937538,  0.13825245,  0.12965337,  0.142274  ,  0.13135836,\n",
       "          0.13743702,  0.14262612,  0.15141052,  0.16497631,  0.16317866,\n",
       "          0.14848239,  0.15444985,  0.13306335,  0.13180314,  0.12735534,\n",
       "          0.1415883 ,  0.13552817,  0.13137689,  0.1296719 ,  0.12907886,\n",
       "          0.1304688 ,  0.12468666,  0.13462008,  0.12372297,  0.13951266,\n",
       "          0.12728121,  0.13908641,  0.12705882,  0.13676985,  0.14264465,\n",
       "          0.14507241,         nan,         nan,         nan,         nan,\n",
       "          0.1348054 ,         nan,  0.1245384 ,  0.1341753 ,  0.13139542,\n",
       "          0.14025396,  0.13069119,  0.12476079,  0.13447182,  0.12483492,\n",
       "          0.1349166 ,  0.12166586,  0.14003157,  0.12477932,  0.14627702,\n",
       "          0.12965337,  0.13137689,         nan,         nan,         nan,\n",
       "                 nan,         nan,         nan,         nan,         nan,\n",
       "                 nan,         nan,         nan,         nan,         nan,\n",
       "                 nan,         nan,  0.1149015 ,         nan,         nan,\n",
       "                 nan,         nan,         nan,         nan,         nan,\n",
       "                 nan,         nan,         nan,         nan,         nan]),\n",
       "  'mean_tau': array([    8.359562,    23.774686,    39.18981 ,    54.604934,\n",
       "            70.020058,    85.435182,   100.850306,   116.26543 ,\n",
       "           131.680554,   147.095678,   162.510802,   177.925926,\n",
       "           193.34105 ,   208.756174,   224.171298,   239.586422,\n",
       "           255.001546,   270.41667 ,   285.831794,   301.246918,\n",
       "           316.662042,   332.077166,   347.49229 ,   362.907414,\n",
       "           378.322538,   393.737662,   409.152786,   424.56791 ,\n",
       "           439.983034,   455.398158,   470.813282,   486.228406,\n",
       "           501.64353 ,   517.058654,   532.473778,   547.888902,\n",
       "           563.304026,   578.71915 ,   594.134274,   609.549398,\n",
       "           624.964522,   640.379646,   655.79477 ,   671.209894,\n",
       "           686.625018,   702.040142,   717.455266,   732.87039 ,\n",
       "           748.285514,   763.700638,   779.115762,   794.530886,\n",
       "           809.94601 ,   825.361134,   840.776258,   856.191382,\n",
       "           871.606506,   887.02163 ,   902.436754,   917.851878,\n",
       "           933.267002,   948.682126,   964.09725 ,   979.512374,\n",
       "           994.927498,  1010.342622,  1025.757746,  1041.17287 ,\n",
       "          1056.587994,  1072.003118,  1087.418242,  1102.833366,\n",
       "          1118.24849 ,  1133.663614,  1149.078738,  1164.493862,\n",
       "          1179.908986,  1195.32411 ,  1210.739234,  1226.154358,\n",
       "          1241.569482,  1256.984606,  1272.39973 ,  1287.814854,\n",
       "          1303.229978,  1318.645102,  1334.060226,  1349.47535 ,\n",
       "          1364.890474,  1380.305598,  1395.720722,  1411.135846,\n",
       "          1426.55097 ,  1441.966094,  1457.381218,  1472.796342,\n",
       "          1488.211466,  1503.62659 ,  1519.041714,  1534.456838,\n",
       "          1549.871962,  1565.287086,  1580.70221 ,  1596.117334,\n",
       "          1611.532458,  1626.947582,  1642.362706,  1657.77783 ,\n",
       "          1673.192954,  1688.608078,  1704.023202,  1719.438326,\n",
       "          1734.85345 ,  1750.268574,  1765.683698,  1781.098822,\n",
       "          1796.513946,  1811.92907 ,  1827.344194,  1842.759318,\n",
       "          1858.174442,  1873.589566,  1889.00469 ,  1904.419814,\n",
       "          1919.834938,  1935.250062,  1950.665186,  1966.08031 ,\n",
       "          1981.495434,  1996.910558,  2012.325682,  2027.740806,\n",
       "          2043.15593 ,  2058.571054,  2073.986178,  2089.401302,\n",
       "          2104.816426,  2120.23155 ,  2135.646674,  2151.061798,\n",
       "          2166.476922,  2181.892046,  2197.30717 ,  2212.722294,\n",
       "          2228.137418,  2243.552542,  2258.967666,  2274.38279 ,\n",
       "          2289.797914,  2305.213038,  2320.628162,  2336.043286,\n",
       "          2351.45841 ,  2366.873534,  2382.288658,  2397.703782,\n",
       "          2413.118906,  2428.53403 ,  2443.949154,  2459.364278,\n",
       "          2474.779402,  2490.194526,  2505.60965 ,  2521.024774,\n",
       "          2536.439898,  2551.855022,  2567.270146,  2582.68527 ,\n",
       "          2598.100394,  2613.515518,  2628.930642,  2644.345766,\n",
       "          2659.76089 ,  2675.176014,  2690.591138,  2706.006262,\n",
       "          2721.421386,  2736.83651 ,  2752.251634,  2767.666758,\n",
       "          2783.081882,  2798.497006,  2813.91213 ,  2829.327254,\n",
       "          2844.742378,  2860.157502,  2875.572626,  2890.98775 ,\n",
       "          2906.402874,  2921.817998,  2937.233122,  2952.648246,\n",
       "          2968.06337 ,  2983.478494,  2998.893618,  3014.308742,\n",
       "          3029.723866,  3045.13899 ,  3060.554114,  3075.969238])}}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Quickly save the plot points in case of any last-minute adjustments ...\n",
    "savefile = outDir + 'B1_plot_dictionary.npy'\n",
    "np.save(savefile,plot_dic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read plot data ...\n",
    "savefile = outDir + 'B1_plot_dictionary.npy'\n",
    "plot_dic = np.load(savefile)[()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#plot_dic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N bins with lt 20000 points : 16\n",
      "N bins with lt 20000 points : 32\n",
      "N bins with lt 20000 points : 32\n",
      "Saved as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_B1_data/2017-08-15/Stars_CRTS_wiggles.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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UakJ5zR1dwQ5FbE4ojhFppJSU1pSG1Nc8mhXTlvzq4/XxPLH2CRbcuCBoY6uj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SuHAHHxglSqQSXTuTqegvXmm8A//kHz7xe/sEpgp0MqXiL2JJo5ZOYxTZhAz/m//S3W2g/EPhu5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\ngiGEEEIIIYRoG/8/5UcVr/JWVlAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b6d9278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot using results of selecting equal number of points per bin ...\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax3 = plt.subplots(1,1, figsize=(12,6), sharex=True)\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",
    "pre = {'qso':'QSO', 'stars':'Stars'}\n",
    "\n",
    "facecolors = {17: 'blue', 18: 'orange', 18.5 : 'black'}\n",
    "edgecolors  = {17:'blue',  18:'black', 18.5:'green'}\n",
    "markers = {17:'o',     18: 's',      18.5: '*'}\n",
    "labels  = {17:'17-18', 18:'18-18.5', 18.5:'18.5-19'}\n",
    "\n",
    "cm = plt.cm.get_cmap('RdYlBu')\n",
    "\n",
    "for Min in [17,18,18.5] : \n",
    "    x  = plot_dic[Min]['mean_tau']\n",
    "    y  = plot_dic[Min]['bin_sigmaG']\n",
    "    print('N bins with lt 20000 points : %d'% np.sum(np.isnan(y)))\n",
    "    ax3.scatter(x,y  , label = labels[Min], alpha=0.7, marker = markers[Min],\n",
    "               s=65, facecolors=facecolors[Min], edgecolors = edgecolors[Min])\n",
    "ymin= 0.04\n",
    "dy = 0.13\n",
    "ax3.set_ylim(0.02, ymin+dy)\n",
    "#ax3.set_ylim(0.06, 0.17)\n",
    "ax3.set_xlim(-400, 3300)\n",
    "yticks = ax3.get_yticks()\n",
    "ax3.set_yticks(yticks[1:-1]) \n",
    "ax3.set_ylabel(r'$\\sigma_{G}(\\Delta m)\\,\\,  [mag]$')\n",
    "ax3.set_xlabel(r'$\\Delta t \\, \\, [days]$')\n",
    "plt.legend(loc='upper right', fontsize=20)\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "fig.subplots_adjust(hspace=0)\n",
    "\n",
    "for ax in [ax3] : \n",
    "    ax.grid(axis='x')\n",
    "    for y in [0.05,0.1, 0.15]:\n",
    "        ax.axhline(y=y, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "\n",
    "title = pre[obj] +'_CRTS_wiggles.png'\n",
    "plt.savefig(outDir+title,  bbox_inches='tight')\n",
    "print('Saved as %s'%outDir+title)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}