Fig_2_CRTS_sel_r_cut.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Based on CRTS_sel_g_cut_vs_r_cut.ipynb,   taking only the code needed to make Fig. 2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A testbed for making Fig.2 :  the 4-panel plot.  The 4-panel plot shows the problem :  how we can't get anything meaningful if we plot various characteristics of uncorrected data. Thus there is no reason to do splitting into magnitude bins, correcting, etc - it's not the purpose here. \n",
    "\n",
    "I do it here with a small magnitude cut so that the entire desired range (eg. 17-19) can be run quickly on magneto machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "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",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zipping CRTS-SDSS quasars catalog from  CRTS_SDSS_cross_matched_qso_DB_QSO_catalog.txt  ...\n",
      "Read in  7601 , quasars from CRTS\n",
      "zipping CRTS-SDSS stars catalog...\n"
     ]
    }
   ],
   "source": [
    "def get_qso_catalog(catalog):\n",
    "    if catalog == 's82drw':\n",
    "        File = 'CRTS_SDSS_cross_matched_qso_s82drw_catalog.txt'\n",
    "    if catalog == 'DB_QSO':\n",
    "        File = 'CRTS_SDSS_cross_matched_qso_DB_QSO_catalog.txt'\n",
    "    colnames = open(File,'r').read().splitlines()[0][1:].split()\n",
    "    datatable = np.genfromtxt(File, dtype=str)\n",
    "    qso_catalog = {}\n",
    "    print 'Zipping CRTS-SDSS quasars catalog from ', File, ' ...'\n",
    "    for label, column in zip(colnames, datatable.T):\n",
    "        qso_catalog[label] = column\n",
    "    \n",
    "    print 'Read in ', len(qso_catalog['redshift']), ', quasars from CRTS'\n",
    "    return  colnames, qso_catalog\n",
    "    \n",
    "def get_stars_catalog():\n",
    "    File = 'CRTS_SDSS_cross_matched_stars_catalog.txt'\n",
    "    colnames = open(File,'r').read().splitlines()[0][1:].split()\n",
    "    datatable = np.genfromtxt(File)\n",
    "    stars_catalog = {}\n",
    "    print 'zipping CRTS-SDSS stars catalog...'\n",
    "    for label, column in zip(colnames, datatable.T):\n",
    "        stars_catalog[label] = column\n",
    "        \n",
    "    return  colnames, stars_catalog\n",
    "\n",
    "cols1, qso_cat = get_qso_catalog(catalog='DB_QSO') \n",
    "cols2 , star_cat= get_stars_catalog()\n",
    "\n",
    "# Perform cuts \n",
    "def cut_qso(qso_cat=qso_cat, mMin=-9, mMax=19, \n",
    "            mErrMin = -9, mErrMax = 0.3,cut_mag='r', report_mag = 'r'):\n",
    "\n",
    "    mask_mag = (qso_cat[cut_mag].astype(float) > mMin) * (qso_cat[cut_mag].astype(float) < mMax) \n",
    "    mask_err = (qso_cat['CRTS_avg_e'].astype(float) > mErrMin) * (qso_cat['CRTS_avg_e'].astype(float) < mErrMax)\n",
    "    mask = mask_mag * mask_err \n",
    "    qso_id = qso_cat['CRTS_id'][mask]\n",
    "    qso_mags = qso_cat[report_mag][mask]\n",
    "    print '\\n These cuts reduced the number of qso  in the sample from', \\\n",
    "          len(qso_cat['redshift']), ' to ', len(qso_id)\n",
    "    return  qso_id\n",
    "\n",
    "def cut_stars(star_cat=star_cat, mMin=-9, mMax=19, mErrMin = -9, \n",
    "              mErrMax = 0.3, gi_Min = -1, gi_Max=1 , cut_mag='r_mMed',\n",
    "              report_mag = 'r_mMed'):\n",
    "\n",
    "    mask_mag = (star_cat[cut_mag] > mMin) * (star_cat[cut_mag] < mMax) \n",
    "    mask_err = (star_cat['CRTS_Merr'] > mErrMin) * (star_cat['CRTS_Merr'] < mErrMax)\n",
    "    SDSS_gi = star_cat['g_mMed'] - star_cat['i_mMed']\n",
    "    mask_color = (SDSS_gi > gi_Min ) * (SDSS_gi < gi_Max)\n",
    "    mask = mask_mag * mask_err * mask_color\n",
    "    star_id_f = star_cat['crts_id'][mask]\n",
    "    star_mags = star_cat[report_mag][mask]\n",
    "    # convert floats to strings without comma and zeros\n",
    "    star_id = np.array([\"{:.0f}\".format(name) for name in star_id_f])\n",
    "    print '\\n These cuts reduced the number of stars  in the sample from', \\\n",
    "          len(star_cat['CRTS_M']), ' to ', len(star_id)\n",
    "    return  star_id\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using now only lightcurves with SDSS  18.000000< r < 18.500000\n",
      "\n",
      " Reporting SDSS r  \n",
      "\n",
      " These cuts reduced the number of stars  in the sample from 47787  to  1400\n",
      "\n",
      " These cuts reduced the number of stars  in the sample from 47787  to  2087\n",
      "\n",
      " These cuts reduced the number of qso  in the sample from 7601  to  333\n"
     ]
    }
   ],
   "source": [
    "Min = 18\n",
    "Max = 18.5\n",
    "magnitudes = ['r']  # don't need g magnitude , decided to use only r magnitude \n",
    "\n",
    "objects_in_cut = {}\n",
    "\n",
    "for mag in magnitudes : \n",
    "    cut_mag = mag\n",
    "    report_mag = mag\n",
    "    \n",
    "    print('\\nUsing now only lightcurves with SDSS  %f< %s < %f' % (Min, cut_mag, Max))\n",
    "    print('\\n Reporting SDSS %s  '% report_mag)\n",
    "\n",
    "    good_ids_S_blue = cut_stars(mMin = Min, mMax=Max, mErrMax = 0.3, gi_Min = -1,\n",
    "                                              gi_Max=1, cut_mag=cut_mag + '_mMed', \n",
    "                                              report_mag=report_mag + '_mMed')\n",
    "    \n",
    "    good_ids_S_red = cut_stars(mMin = Min, mMax=Max, mErrMax = 0.3, gi_Min = 1, \n",
    "                                           gi_Max=3, cut_mag=cut_mag + '_mMed', \n",
    "                                           report_mag=report_mag + '_mMed')\n",
    "    \n",
    "    good_ids_QSO = cut_qso(mMin = Min, mMax=Max, mErrMax = 0.3, \n",
    "                                               cut_mag=cut_mag,report_mag=report_mag)\n",
    "    objects_in_cut[mag] = {'starsB':good_ids_S_blue, 'starsR':good_ids_S_red, \n",
    "                           'qso':good_ids_QSO}\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Since I'm only using r_cut,   I won't compare it with g_cut .  Thus making r_bin is the same as r_cut  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bins = {}\n",
    "bin_types = ['r_cut']  # removed 'g_cut'\n",
    "\n",
    "objects = objects_in_cut['r'].keys()\n",
    "\n",
    "# first need to explicitly initialize the dictionaries \n",
    "for b in bin_types:\n",
    "    bins[b] = {}\n",
    "    \n",
    "for obj in objects : \n",
    "    bins['r_cut'][obj] =  objects_in_cut['r'][obj]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the xi, ei for objects in the r_cut ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# inside the main loop : get tau, delflx from a master file, either qso or star\n",
    "def add_tau_delflx(File, inDir, data, fc):\n",
    "    # read in storage arrays\n",
    "    delflx = data[0]  \n",
    "    tau = data[1]\n",
    "    err = data[2]\n",
    "    master_acc_list = data[3]   \n",
    "    \n",
    "    # grab the object name \n",
    "    master_name = File[3:-4]\n",
    "    \n",
    "    # read in the i-th master file \n",
    "    master =  np.genfromtxt(inDir+File, dtype=str)\n",
    "    \n",
    "    # read in tau,  del_mag,  del_mag_err for quasars on the list \n",
    "    delflx = np.append(delflx, master[:,0].astype(float))\n",
    "    tau = np.append(tau, master[:,1].astype(float))\n",
    "    \n",
    "    if fc is not None :  # correct new master rows only if  asked for \n",
    "        err = np.append(err, master[:,2].astype(float)*fc)\n",
    "    else:                # otherwise read in without any correction\n",
    "        err = np.append(err, master[:,2].astype(float))\n",
    "    master_names  = np.append(master_acc_list, np.array(len(master[:,0])*[master_name]))\n",
    "    \n",
    "    return delflx, tau, err, master_names\n",
    "    \n",
    "def read_xi_ei(inDirStars, good_ids_S_blue, good_ids_S_red, inDirQSO,\n",
    "                 good_ids_QSO, xi_ei_data=None, fc=None):\n",
    "                     \n",
    "    inDir_S       = inDirStars\n",
    "    good_ids_S_blue    = good_ids_S_blue\n",
    "    good_ids_S_red    = good_ids_S_red\n",
    "    inDir_Q       = inDirQSO\n",
    "      \n",
    "    \n",
    "    # Read the Stellar Master file names \n",
    "    masterFiles_S = os.listdir(inDir_S)\n",
    "    masterFilesS1 = [name[3:-4] for name in masterFiles_S]\n",
    "    \n",
    "    good_masterSB = np.array(masterFiles_S)[np.in1d(masterFilesS1, good_ids_S_blue)]\n",
    "    good_masterSR = np.array(masterFiles_S)[np.in1d(masterFilesS1, good_ids_S_red)]\n",
    "    \n",
    "    # Read the QSO Master file names \n",
    "    masterFiles_Q = os.listdir(inDir_Q)\n",
    "    masterFilesQ1 = [name[3:-4] for name in masterFiles_Q]\n",
    "    good_masterQ = np.array(masterFiles_Q)[np.in1d(masterFilesQ1, good_ids_QSO)]\n",
    "    \n",
    "\n",
    "  \n",
    "    # If no previous read-in xi, ei exists, initialize arrays    \n",
    "    if xi_ei_data is None : \n",
    "        print 'making new delflx, tau, xi arrays'\n",
    "        delflx_S      = np.empty(0,dtype=float)\n",
    "        tau_S         = np.empty(0,dtype=float)\n",
    "        err_S         = np.empty(0,dtype=float)\n",
    "        master_acc_list_S = np.empty(0, dtype=str)\n",
    "    \n",
    "        \n",
    "       \n",
    "        delflx_Q      = np.empty(0,dtype=float)\n",
    "        tau_Q         = np.empty(0,dtype=float)\n",
    "        err_Q         = np.empty(0,dtype=float)\n",
    "        master_acc_list_Q = np.empty(0, dtype=str)\n",
    "        \n",
    "        # Initialize the data structures to which more and more delta_t and delta_mag\n",
    "        # are addded from each consecutive master file \n",
    "        qso_data = [delflx_Q, tau_Q, err_Q, master_acc_list_Q] \n",
    "        star_data_blue = [delflx_S, tau_S, err_S, master_acc_list_S]\n",
    "        star_data_red  = [delflx_S, tau_S, err_S, master_acc_list_S]\n",
    "        \n",
    "    else:\n",
    "        print 'using existing xi ei arrays'\n",
    "        qso_data = xi_ei_data[0]\n",
    "        star_data_blue = xi_ei_data[1]\n",
    "        star_data_red = xi_ei_data[2]\n",
    "        \n",
    "    print('\\n')\n",
    "    c = 0\n",
    "    for File in good_masterQ: #  len(masterFiles_Q)\n",
    "        qso_data = add_tau_delflx(File,inDir_Q, qso_data, fc)\n",
    "        c += 1 \n",
    "        if c % 5 == 0:\n",
    "            pers = (100.0*c) / float(len(good_masterQ))\n",
    "            print('\\r----- Already read %d%% of qso'%pers),\n",
    "    \n",
    "    print('\\n')\n",
    "    c = 0                   \n",
    "    for File in good_masterSB:    # [:len(good_masterQ)]\n",
    "        star_data_blue = add_tau_delflx(File, inDir_S,star_data_blue, fc)\n",
    "        c += 1 \n",
    "        if c % 5 == 0:\n",
    "            pers = (100.0*c) / float(len(good_masterSB))\n",
    "            print('\\r----- Already read %d%% of Blue Stars'%pers),  \n",
    "    print('\\n')\n",
    "    c = 0                         \n",
    "    for File in good_masterSR:   # [:len(good_masterQ)]\n",
    "        star_data_red = add_tau_delflx(File, inDir_S, star_data_red, fc)      \n",
    "        c += 1               \n",
    "        if c % 5 == 0:\n",
    "            pers = (100.0*c) / float(len(good_masterSR))\n",
    "            print('\\r----- Already read %d%% of Red Stars'%pers),          \n",
    "                     \n",
    "    print('returning xi, ei for ... %d objects'%len(good_masterQ))\n",
    "                            \n",
    "    return  qso_data, star_data_blue, star_data_red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading in xi, ei for bin  r_cut\n",
      "making new delflx, tau, xi arrays\n",
      "\n",
      "\n",
      "----- Already read 98% of qso \n",
      "\n",
      "----- Already read 99% of Blue Stars \n",
      "\n",
      "----- Already read 100% of Red Stars returning xi, ei for ... 127 objects\n"
     ]
    }
   ],
   "source": [
    "inDirStars   = 'sf_file_per_LC/star/'\n",
    "inDirQSO = 'sf_file_per_LC/qso/'\n",
    "\n",
    "out_dic = {}\n",
    "\n",
    "#for b in bins.keys():\n",
    "# read in only r_cut \n",
    "\n",
    "b = 'r_cut'\n",
    "print 'Reading in xi, ei for bin ', b\n",
    "out_dic[b] = {}   # initialize the dic \n",
    "\n",
    "good_ids_S_blue = bins[b]['starsB']\n",
    "good_ids_S_red = bins[b]['starsR']\n",
    "good_ids_QSO = bins[b]['qso']\n",
    "\n",
    "qso, starB, starR = read_xi_ei(inDirStars, good_ids_S_blue, good_ids_S_red, inDirQSO,\n",
    "              good_ids_QSO,xi_ei_data=None, fc=None)\n",
    "\n",
    "# put into a dictionary : makes it more explicit \n",
    "out_dic[b] = {'starsB': starB, 'starsR': starR, 'qso':qso}\n",
    "\n",
    "\n",
    "# Straight after reading-in xi, ei,   one can proceed directly to part 9) (one bin) or 10 : all bins sigma comparison \n",
    "# or to Saving just the log(tau) samples of xi, tau, ei. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11)a)  Calculate SF, mu in approximate way "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['qso', 'starsB', 'starsR']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_dic['r_cut'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " For obj =  qso\n",
      " --- Calculating mu, sigma for bin 200 \n",
      " In this calculation, fc= 1.0\n",
      "Results saved as  r_cut_18-18.2_qso_fc-1.0_mean_tau_sig_sigG_SF_mu.txt\n",
      "\n",
      " For obj =  starsB\n",
      " --- Calculating mu, sigma for bin 200 \n",
      " In this calculation, fc= 1.0\n",
      "Results saved as  r_cut_18-18.2_starsB_fc-1.0_mean_tau_sig_sigG_SF_mu.txt\n",
      "\n",
      " For obj =  starsR\n",
      " --- Calculating mu, sigma for bin 200 \n",
      " In this calculation, fc= 1.0\n",
      "Results saved as  r_cut_18-18.2_starsR_fc-1.0_mean_tau_sig_sigG_SF_mu.txt\n"
     ]
    }
   ],
   "source": [
    "# Run the calculation over all bins : \n",
    "# - keep the option to plot a bin if one wants to compare the three methods : need to provide object N\n",
    "#   \n",
    "# - as it is, calculate three values for sigma, etc., and plot it  \n",
    "\n",
    "# Set correction factor\n",
    "fc = 1.0\n",
    "# Set the bin  : here we use only r_cut \n",
    "b = 'r_cut'\n",
    "\n",
    "\n",
    "# Loop over objects in the dictionary  \n",
    "for obj in out_dic[b].keys() : \n",
    "    print '\\n For obj = ', obj\n",
    "    # obj = 'qso'  # or starsB,  starsR \n",
    "    m_ij = out_dic[b][obj][0]\n",
    "    tau =  out_dic[b][obj][1]\n",
    "    e_ij = fc * out_dic[b][obj][2]\n",
    "\n",
    "    #n =  out_dic[b][obj][3]\n",
    "\n",
    "    #def calculate_mu_sig_single_bin(m_ij,e_ij,tau, N=1, plotLnL= True):\n",
    "\n",
    "    nbins = 200 \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",
    "    # 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",
    "    #bin_names = np.arange(1,len(binned_count[2]))\n",
    "\n",
    "     # Calculate median preprocessed photometric error per bin \n",
    "    binned_err_median = binned_statistic(tau, e_ij, statistic='median', bins=nbins) \n",
    "    err_median = binned_err_median[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",
    "\n",
    "    bin_count = bin_count[non_empty_bins]\n",
    "    mean_tau = mean_tau[non_empty_bins]\n",
    "    err_median = err_median[non_empty_bins]\n",
    "\n",
    "    # Which point belongs to which bin\n",
    "    bin_number  = binned_tau[2]\n",
    "    \n",
    "    ####\n",
    "    ####  Panel 1 : Standard Deviation \n",
    "    ####\n",
    "\n",
    "    rms_std = lambda x : np.std(x)\n",
    "    stdev_binned = binned_statistic(tau, m_ij, statistic = rms_std, \n",
    "                                              bins=nbins)\n",
    "\n",
    "\n",
    "    bin_stdev = stdev_binned[0][non_empty_bins]  \n",
    "    #bin_number = stdev_binned[2]  \n",
    "     # since each point belongs to some bin : len(bin_number) =len(delflx)\n",
    "\n",
    "\n",
    "    # error on standard deviation in the bin     \n",
    "    err_stdev = bin_stdev / np.sqrt(2.0*(bin_count - 1.0))\n",
    "\n",
    "    #####\n",
    "    ##### Panel 2  : Gaussian rms  \n",
    "    #####\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",
    "    # error on Gaussian estimate of rms in the bin \n",
    "    err_sigma_G = bin_sigma_G* 1.06 / np.sqrt(bin_count)\n",
    "\n",
    "\n",
    "    #####\n",
    "    ##### Panel 3 (SF)   and Panel 4   (mu)\n",
    "    #####\n",
    "\n",
    "\n",
    "    def approximate_mu_sigma(xi, ei, axis=None):\n",
    "        \"\"\"Estimates of mu0 and sigma0 via equations 5.67 - 5.68\"\"\"\n",
    "        if axis is not None:\n",
    "            xi = np.rollaxis(xi, axis)\n",
    "            ei = np.rollaxis(ei, axis)\n",
    "            axis = 0\n",
    "\n",
    "        mu_approx, sigmaG = median_sigmaG(xi, axis=axis)\n",
    "        e50 = np.median(ei, axis=axis)\n",
    "        var_twiddle = (sigmaG ** 2 + ei ** 2 - e50 ** 2)\n",
    "        sigma_twiddle = np.sqrt(np.maximum(0, var_twiddle))\n",
    "\n",
    "        med = np.median(sigma_twiddle, axis=axis)\n",
    "        mu = np.mean(sigma_twiddle, axis=axis)\n",
    "\n",
    "        zeta = np.ones_like(mu)\n",
    "        zeta[mu != 0] = med[mu != 0] / mu[mu != 0]\n",
    "\n",
    "        var_approx = zeta ** 2 * sigmaG ** 2 - e50 ** 2\n",
    "        sigma_approx = np.sqrt(np.maximum(0, var_approx))\n",
    "\n",
    "        return mu_approx, sigma_approx\n",
    "\n",
    "    # Loop over all bins  calculating approximate mu and sigma \n",
    "\n",
    "    mu_bins = {}\n",
    "    sig_bins = {}\n",
    "\n",
    "    sig_bins['approx'] = np.zeros(200)\n",
    "    mu_bins['approx'] = np.zeros(200)\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",
    "        # 1) Calculate in an approximate way \n",
    "        mu_approx, sig_approx = approximate_mu_sigma(xi, ei)\n",
    "        sig_bins['approx'][N-1] = sig_approx\n",
    "        mu_bins['approx'][N-1] = mu_approx\n",
    "\n",
    "    # Calculate error of points \n",
    "    sig_bins['approx_err'] = sig_bins['approx']* 1.06 / np.sqrt(bin_count)\n",
    "    mu_bins['approx_err'] = bin_stdev / np.sqrt(bin_count)\n",
    "\n",
    "\n",
    "    print '\\n In this calculation, fc=', fc \n",
    "\n",
    "    \n",
    "    # Save full results for plotting 4 panels \n",
    "    # Save the results of calculation  : Panel 1,2,3,4 \n",
    "\n",
    "    fname =  b+'_'+str(Min)+'-'+str(Max)+'_'+obj+'_fc-'+str(fc)+'_mean_tau_sig_sigG_SF_mu.txt'\n",
    "    print 'Results saved as ', fname\n",
    "\n",
    "    data = np.column_stack((mean_tau, bin_stdev, err_stdev, bin_sigma_G, err_sigma_G, \n",
    "                            sig_bins['approx'], sig_bins['approx_err'],\n",
    "                            mu_bins['approx'],mu_bins['approx_err'] ))\n",
    "\n",
    "    header = 'mean_tau   bin_stdev   err_stdev  bin_sigma_G  err_sigma_G   SF  SF_err  mu  mu_err  '\n",
    "    np.savetxt(fname, data, fmt = '%s', delimiter = ' ' , header=header )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in the calculation results "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the calculation for each bin has already been done, and only some fine tuning is needed for the figure, you can set the Min, Max mags for the cut below, and we will read in the results from the files and plot it all. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading from r_cut_18-18.5_qso_fc-1.0_mean_tau_sig_sigG_SF_mu.txt\n",
      "Reading from r_cut_18-18.5_starsB_fc-1.0_mean_tau_sig_sigG_SF_mu.txt\n",
      "Reading from r_cut_18-18.5_starsR_fc-1.0_mean_tau_sig_sigG_SF_mu.txt\n"
     ]
    }
   ],
   "source": [
    "# Read the results from the run on Magneto...\n",
    "Min = 18\n",
    "Max = 18.5\n",
    "\n",
    "# Initialize the dictionary \n",
    "\n",
    "\n",
    "plot_data = {}\n",
    "for obj in out_dic[b].keys() : \n",
    "    plot_data[obj] = {}\n",
    "    \n",
    "    fname =  b+'_'+str(Min)+'-'+str(Max)+'_'+obj+'_fc-'+str(fc)+'_mean_tau_sig_sigG_SF_mu.txt'\n",
    "    print 'Reading from', 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[obj][label] = column\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Fig.2 : four-panel plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['err_sigma_G',\n",
       " 'SF_err',\n",
       " 'mu_err',\n",
       " 'err_stdev',\n",
       " 'mean_tau',\n",
       " 'bin_sigma_G',\n",
       " 'SF',\n",
       " 'mu',\n",
       " 'bin_stdev']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_data['qso'].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib \n",
    "from matplotlib import rcParams\n",
    "matplotlib.rcParams['font.size'] = 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure is saved as  Fig_2_18-18.5_panels_fc-1.0.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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trUUIgU6nw2q1EhBgb2nU0NCAyWRq4VFojRACIUS3jZ6XX36Zhx56yGXb7r6ko+ZXSCkH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4uVrwQ6A+1FxGGMCQEigWMlko/tuNap6FZsnYqxwDvA70TkyzLOrUmFUkqpeqOspKJOdH+IyAZj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82c9cceb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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 = 10\n",
    "p_al   = 0.5 \n",
    "\n",
    "# y limits for sigma, sigma_G, 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 = 3.7\n",
    "\n",
    "# colors for quasars, blue and red stars \n",
    "colors= {}\n",
    "colors['qso'] = 'black'\n",
    "colors['starsB'] = 'blue'\n",
    "colors['starsR']   = 'red'\n",
    "\n",
    "fig,ax = plt.subplots(4,1, figsize=(8,12), sharex=True)\n",
    "fig.subplots_adjust(hspace=0)\n",
    "\n",
    "# Panel 1 \n",
    "for obj in plot_data.keys() : \n",
    "    ax[0].scatter(np.log10(plot_data[obj]['mean_tau']), plot_data[obj]['bin_stdev'], s=p_size, \n",
    "                    alpha=p_al, c = colors[obj])\n",
    "    ax[0].errorbar(np.log10(plot_data[obj]['mean_tau']), plot_data[obj]['bin_stdev'],plot_data[obj]['err_stdev'], \n",
    "                   linestyle='None', c = colors[obj]  )\n",
    "\n",
    "ax[0].set_ylabel(r'$\\sigma_{stdev}$',fontsize=20)  \n",
    "ax[0].tick_params( axis='x', which='both',  bottom='off', \n",
    "                top='off', labelbottom='off') \n",
    "ax[0].set_ylim(bottom=y_bott, top=y_top)\n",
    "ax[0].set_xlim(left=x_left, right=x_right)\n",
    "ax[0].set_yticks([0,0.1,0.2,0.3,0.4])\n",
    "ax[0].set_yticklabels(['0.0','0.1', '0.2', '0.3', '0.4'])\n",
    "ax[0].axhline(y=0.0, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "ax[0].axhline(y=0.1, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "ax[0].axhline(y=0.2, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "ax[0].grid(axis='x')\n",
    "\n",
    "\n",
    "# Panel 2\n",
    "for obj in plot_data.keys() : \n",
    "    ax[1].scatter(np.log10(plot_data[obj]['mean_tau']), plot_data[obj]['bin_sigma_G'], s=p_size, \n",
    "                    alpha=p_al, c = colors[obj])\n",
    "    ax[1].errorbar(np.log10(plot_data[obj]['mean_tau']), plot_data[obj]['bin_sigma_G'],plot_data[obj]['err_sigma_G'], \n",
    "                   linestyle='None', c = colors[obj] )\n",
    "\n",
    "\n",
    "ax[1].set_ylabel(r'$\\sigma_{G}$',fontsize=20)  \n",
    "ax[1].tick_params( axis='x', which='both',  bottom='off', \n",
    "                top='off', labelbottom='off') \n",
    "ax[1].set_ylim(bottom=y_bott, top=y_top)\n",
    "ax[1].set_xlim(left=x_left, right=x_right)\n",
    "ax[1].set_yticks([0,0.1,0.2,0.3,0.4])\n",
    "ax[1].set_yticklabels(['0.0','0.1', '0.2', '0.3', '0.4'])\n",
    "ax[1].axhline(y=0.0, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "ax[1].axhline(y=0.1, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "ax[1].axhline(y=0.2, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "ax[1].grid(axis='x')\n",
    "\n",
    "\n",
    "# Panel 3\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",
    "# Calculate the model DRW fit for QSO\n",
    "obj = 'qso'\n",
    "xdata = plot_data[obj]['mean_tau']\n",
    "sf = plot_data[obj]['SF']\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",
    "err_sig = plot_data[obj]['SF_err']\n",
    "y_fold = np.sqrt((y ** 2.0)+ (err_sig ** 2.0) )\n",
    "ax[2].plot(np.log10(xdata), y_fold , lw=3, c = 'green', ls='--')\n",
    "ax[2].plot(np.log10(xdata), y , lw=3, c = 'orange', ls='--')\n",
    "\n",
    "text = r'$ \\mathrm{Model:}\\ \\tau=%.3f \\,\\mathrm{days} , \\ SF_{\\infty}=%.3f \\,\\mathrm{mag}$'%(popt[1],popt[0])\n",
    "ax[2].text(x=0.75, y=0.3,s = text )\n",
    "\n",
    "    \n",
    "for obj in plot_data.keys() : \n",
    "    ax[2].scatter(np.log10(plot_data[obj]['mean_tau']), plot_data[obj]['SF'], s=p_size, \n",
    "                    alpha=p_al, c = colors[obj])\n",
    "    ax[2].errorbar(np.log10(plot_data[obj]['mean_tau']), plot_data[obj]['SF'],plot_data[obj]['SF_err'], \n",
    "                   linestyle='None', c = colors[obj]  )\n",
    "\n",
    "ax[2].set_ylim(bottom=y_bott, top=y_top)\n",
    "ax[2].set_xlim(left=x_left, right=x_right)\n",
    "ax[2].set_ylabel(r'$SF $',fontsize=20)\n",
    "ax[2].tick_params( axis='x', which='both',  bottom='off', \n",
    "                top='off', labelbottom='off')\n",
    "ax[2].grid(axis='x')\n",
    "ax[2].set_yticks([0,0.1,0.2,0.3,0.4])\n",
    "ax[2].set_yticklabels(['0.0','0.1', '0.2', '0.3', '0.4'])\n",
    "ax[2].axhline(y=0.0, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)    \n",
    "ax[2].axhline(y=0.1, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "ax[2].axhline(y=0.2, color='black', lw=lh_w, ls=lh_st,alpha=lh_al) \n",
    "\n",
    "\n",
    "# Panel 4 \n",
    "for obj in plot_data.keys() : \n",
    "    ax[3].scatter(np.log10(plot_data[obj]['mean_tau']), plot_data[obj]['mu'], s=p_size, \n",
    "                    alpha=p_al, c = colors[obj])\n",
    "    ax[3].errorbar(np.log10(plot_data[obj]['mean_tau']), plot_data[obj]['mu'],plot_data[obj]['mu_err'], \n",
    "                   linestyle='None', c = colors[obj]  )\n",
    "\n",
    "\n",
    "ax[3].axhline(y=0.0, color='black', lw=lh_w, ls=lh_st,alpha=lh_al)\n",
    "ax[3].set_ylim(top=y_mu_top, bottom=y_mu_bott)\n",
    "ax[3].set_xlim(left=x_left, right=x_right)\n",
    "ax[3].set_yticks([-0.05,0,0.05])\n",
    "ax[3].set_yticklabels(['-0.05','0.0', '0.05'])  \n",
    "ax[3].set_ylabel(r'$\\mu$', fontsize=20)\n",
    "ax[3].grid(axis='x')\n",
    "ax[3].set_xlabel(r'$log_{10} (\\Delta _{t})$ [days]',fontsize=20)\n",
    "\n",
    "figname = 'Fig_2_'+str(Min)+'-'+str(Max)+'_panels_fc-'+str(fc)+'.png'\n",
    "plt.savefig(figname)\n",
    "print 'Figure is saved as ', figname \n",
    "\n",
    "# Note : selecting 17-19 , err < 0.3 ,  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure is saved as  Fig2_17-19_panels_fc-1.01.png\n"
     ]
    }
   ],
   "source": [
    "figname = 'Fig2_'+str(Min)+'-'+str(Max)+'_panels_fc-'+str(fc)+'1.png'\n",
    "\n",
    "print 'Figure is saved as ', figname \n"
   ]
  },
  {
   "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": []
  }
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