PG1302-102.ipynb

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   "source": [
    "import numpy as np\n",
    "from astropy.table import Table\n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy.table import Column\n",
    "\n",
    "import matplotlib.pyplot as plt \n",
    "from matplotlib import rcParams      \n",
    "rcParams['ytick.labelsize'] = 15\n",
    "rcParams['xtick.labelsize'] = 15\n",
    "rcParams['axes.labelsize'] = 20\n",
    "rcParams['axes.linewidth'] = 2\n",
    "rcParams['font.size'] = 15\n",
    "rcParams['axes.titlesize'] = 20\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Query the CRTS DR2 http://nesssi.cacr.caltech.edu/cgi-bin/getcssconedbid_release2.cgi  , against ID , RA 196.38756\tDEC, -10.5554\trad 0.002 arcmin. Two objects are released, CSS_J130533.0-103319  (234 epochs) MLS_J130533.0-103319 (56 epochs).  "
   ]
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   "source": [
    "# read in the PG 1302 data....\n",
    "data = Table.read('../raw_CRTS_data/PG1302-102_LC.csv', format='ascii')\n",
    "data.sort('MJD')\n",
    "\n",
    "y = data['Mag']\n",
    "yerr = data['Magerr']\n",
    "t = data['MJD']\n",
    "#outDir = '../data_products/Fig_6_data/'\n",
    "#sliding_window_statistics(t,y,yerr, save_fig='No', fig_name='PG1302-102_LC.png', outDir = outDir)"
   ]
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  {
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   "execution_count": 206,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving todays work in /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/\n"
     ]
    }
   ],
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  },
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   "cell_type": "markdown",
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   "source": [
    "Calculate moving-average $\\chi^{2}_{DOF}$  :\n",
    "\n"
   ]
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  {
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    "\n",
    "def calcWeightedMean(y,yerr):\n",
    "    ''' Calculate the weighted mean '''\n",
    "    N = len(y)\n",
    "    if N == 1 : \n",
    "        return float(y)    \n",
    "    elif N == 0 : \n",
    "        return np.nan\n",
    "    else: \n",
    "        # weights = 1 / (yerr ** 2.0)  \n",
    "        # wMean = np.sum(weights * flux) / np.sum(weights)\n",
    "        return float(np.add.reduce(y / (yerr * yerr)) / np.add.reduce((1/yerr)*(1/yerr)))\n",
    "    \n",
    "#N = len(y)\n",
    "#wMean = calcWeightedMean(y,yerr)\n",
    "#chi2dof  = (1 / (N-1.0)) * np.sum(((y- wMean)/yerr)**2)\n",
    "\n",
    "def calcSigmaClippedWM(y, yerr, sigmathresh):\n",
    "    N = len(y)\n",
    "    if N ==0:\n",
    "        return np.nan\n",
    "    elif N == 1:\n",
    "        return float(y)\n",
    "    else:\n",
    "        q25, q50, q75 = np.percentile(y, (25, 50, 75))\n",
    "        iqrSig = 0.7413*(q75 - q25)\n",
    "        nsigma = np.abs(y - q50)/iqrSig\n",
    "        idx = np.where(nsigma <= sigmathresh)\n",
    "        return calcWeightedMean(y[idx], yerr[idx])\n",
    "\n",
    "#sigmaCwMean = calcSigmaClippedWM(y,yerr, 10)\n",
    "\n",
    "# Inspired by sigmaClippedWM() from aggregates.py, at \n",
    "# https://github.com/imcgreer/QLFz4/blob/30c050589d7b2dbbd58c0dac4d676bf65174fe4b/process_data/aggregates.py \n",
    "\n",
    "def SigmaClipLC(y, sigmathresh):\n",
    "    '''\n",
    "    Return only the indices of those points that are \n",
    "    within the n-sigma limit, and those that are outside \n",
    "    '''\n",
    "    q25, q50, q75 = np.percentile(y, (25, 50, 75))\n",
    "    iqrSig = 0.7413*(q75 - q25)\n",
    "    nsigma = np.abs(y - q50)/iqrSig\n",
    "    idxIn  = np.where(nsigma <= sigmathresh)\n",
    "    idxOut = np.where(nsigma >= sigmathresh)\n",
    "    return idxIn, idxOut\n",
    "\n"
   ]
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   "source": [
    "% matplotlib inline \n",
    "import matplotlib.gridspec as gridspec\n",
    "def sliding_window_statistics(t,y,yerr, window_width=500, offset_factor = 0.5, save_fig=None, fig_name=None, \n",
    "                              return_results=None, outDir = None, window_error = 'combined', sigmathresh=5):\n",
    "    '''\n",
    "    A simple function to calculate sliding window stats, and plot them. By default, no dictionary \n",
    "    with results is returned. \n",
    "    \n",
    "    Required parameters:\n",
    "    ---------------------\n",
    "    t : the obs time \n",
    "    y : the flux measurement\n",
    "    yerr : the flux error \n",
    "    \n",
    "    Optional parameters:  \n",
    "    ---------------------\n",
    "    window_width : the width of the sliding window (in days ), default is 500 days \n",
    "    offset_factor : it sets by how much shall we offset the left edge of the first window \n",
    "                    with respect to the earliest datapoint : the shift = offset_factor * window_width. \n",
    "                    Default is 0.5 \n",
    "    save_fig : shall we save the plot as png?  Default : None\n",
    "    fig_name : a name of the figure. Expect a full name, including extension. Default : None \n",
    "    return_results :  return the dictionary with sliding window statistics. Default : None \n",
    "    window_error : what error to plot per sliding window on the bottom panel? Default : combined error in \n",
    "                   the sense of combining the inverse weights.  \n",
    "                   Optional : 'median' : the median of raw photometric errors for points within the window \n",
    "    sigmathresh  : threshold for sigma clipping, defined as \n",
    "                    q25, q50, q75 are 25-th, 50-th, 75-th percentiles \n",
    "                    iqrSig = 0.7413*(q75 - q25)  # interquartile-based, robust sigma \n",
    "                    nsigma = np.abs(y - q50)/iqrSig  # how many sigmas is a given point above the median \n",
    "        \n",
    "    \n",
    "    Method: \n",
    "    --------\n",
    "    By sliding window we mean  a span of data between  t1 and t2,  such that \n",
    "    t1 < t < t2 . We calculate in the store dictionary :\n",
    "    - chi2dof_window :  chi2 per degree of freedom based on the weighted mean\n",
    "    and provided raw photometric errors,   \n",
    "    - N_pts_window : the number of points contained in the window \n",
    "    - t_center_window : the middle of the window (note : this is not  mean(t),  but (t2-t1) / 2)\n",
    "    - t_window_mean : just for comparison, the mean t of the window (data based )\n",
    "    - wMean_window : the weighted mean of the flux measurement (y)\n",
    "    \n",
    "    Returns:\n",
    "    ---------\n",
    "    store{} : (optional)  a dictionary (if return_results != None)\n",
    "        'N_pts_window' : number of points per window, \n",
    "        't_center_window': central mjd of the window  (midpoint)\n",
    "        't_window_mean':  mean of obs times in the window \n",
    "        'wMean_window':  weighted mean of flux measurements in the window \n",
    "        'chi2dof_window': chi2 per window based on \n",
    "                z_array = (flux measurements_array - weighted_mean_per_window_scalar) / raw_error_per_window_array\n",
    "                chi2dof_scalar = (1/ ( N_pts_window_scalar -1 )) *  sum(z_array^2)\n",
    "        'errMedian_window': median of raw photometric errors in the window \n",
    "        'errCombined_window': errors combined from inverse weights : \n",
    "                weights_array = 1 /  (raw_errors_array^2)\n",
    "                errCombined_window = 1 / sqrt(sum(weights_array))\n",
    "        'sigmaG_y_window': a robust interquartile standard deviation of \n",
    "                flux measurements in the window  note  : this is just based on y_window, \n",
    "                i.e. magnitdude measurements in the window.  It is not weighted by anything  \n",
    "                (an option not explored here is to use errors as weights)\n",
    "        'sigmaG_z_window': a robust interquartile standard deviation of \n",
    "                the quantity z_array , used above to calculate chi2dof, per window \n",
    "                 \n",
    "    '''\n",
    "    \n",
    "    \n",
    "    # sigma - clip the lightcurve \n",
    "    print('\\nUsing sigma threshold of %.2f sigma '%sigmathresh)\n",
    "    idxIn, idxOut= SigmaClipLC(y, sigmathresh)\n",
    "    if idxOut[0].size == 0 : \n",
    "        print('There are no outliers')\n",
    "    else : \n",
    "        print('There are %d outliers '%idxOut[0].size)\n",
    "    \n",
    "    # completely remove outlier points ... \n",
    "    #yClipped = y[idxIn]\n",
    "    #yerrClipped = y[idxIn]\n",
    "    #tClipped = t[idxIn]\n",
    "    \n",
    "    # set up window size , sliding step..... \n",
    "    window_width = 500 # days \n",
    "    offset = offset_factor * window_width\n",
    "    t0 = min(t[idxIn]) - offset\n",
    "\n",
    "    window_slide_step_size=  5 #  days \n",
    "    window_frame_start = np.arange(t0, max(t[idxIn]), window_slide_step_size)\n",
    "    window_frame_end = window_frame_start + window_width\n",
    "\n",
    "    # calculate the  sliding window statistics \n",
    "    N_windows = len(window_frame_start)\n",
    "\n",
    "    store = {'N_pts_window':np.zeros(N_windows), 't_center_window':np.zeros(N_windows), \n",
    "          't_window_mean':np.zeros(N_windows), 'wMean_window':np.zeros(N_windows),\n",
    "            'chi2dof_window':np.zeros(N_windows), 'errMedian_window':np.zeros(N_windows),\n",
    "            'errCombined_window':np.zeros(N_windows), 'sigmaG_y_window':np.zeros(N_windows),\n",
    "            'sigmaG_z_window':np.zeros(N_windows)}\n",
    "\n",
    "    for i in range(N_windows) :\n",
    "        mask_in_window = ( window_frame_start[i] < t[idxIn])  * (t[idxIn] < window_frame_end[i])\n",
    "        t_window = t[idxIn][mask_in_window]\n",
    "        y_window = y[idxIn][mask_in_window]\n",
    "        yerr_window = yerr[idxIn][mask_in_window]\n",
    "\n",
    "        N_pts_window = float(len(t_window))\n",
    "        store['N_pts_window'][i] = N_pts_window\n",
    "        store['t_center_window'][i] = (window_frame_end[i] + window_frame_start[i]) / 2.0\n",
    "        store['t_window_mean'][i] = np.mean(t_window)\n",
    "        \n",
    "        store['errMedian_window'][i] = np.median(yerr_window)\n",
    "        err_weights = 1 / (yerr_window**2.0)\n",
    "        store['errCombined_window'][i] = 1 / np.sqrt(np.sum(err_weights))\n",
    "        wMean_window = calcWeightedMean(y_window, err_weights)\n",
    "        store['wMean_window'][i] = wMean_window\n",
    "        \n",
    "        q25, q75 = np.percentile(y_window,  (25, 75))\n",
    "        store['sigmaG_y_window'][i] = 0.7413*(q75 - q25)\n",
    "    \n",
    "        if N_pts_window > 1 : \n",
    "            z = (y_window- wMean_window)/ yerr_window\n",
    "            store['chi2dof_window'][i] = (1 / (N_pts_window-1.0)) * np.sum(z**2)\n",
    "            store['sigmaG_z_window'][i] = 0.7413*(np.percentile(z,75) - np.percentile(z,25))\n",
    "        else  : \n",
    "            store['chi2dof_window'][i] = np.nan\n",
    "            store['sigmaG_z_window'][i] = np.nan \n",
    "            \n",
    "            \n",
    "    print('Using %d windows of width %d days each , with the window sliding step of  %d days'%(N_windows, \n",
    "                                                            window_width, window_slide_step_size ) )\n",
    "    # plot  : \n",
    "    # ax[0] : the lightcurve\n",
    "    # ax[1] : count of points per window \n",
    "    # ax[2] : t_center_window vs wMean_window\n",
    "    # ax[3] : chi2dof_window\n",
    "    #import matplotlib.gridspec as gridspec\n",
    "    fig = plt.figure(figsize=( 10,14 ))   # (x_size , y_size)\n",
    "    ax = dict()\n",
    "    number_of_rows = 8\n",
    "    number_of_columns = 1\n",
    "    gs = gridspec.GridSpec(number_of_rows,  number_of_columns)\n",
    "    ax[0] = plt.subplot(gs[:2, :])  # span all rows but the last one (bottom), and first 5 col\n",
    "    \n",
    "    # span the axes \n",
    "    for k in range(1,7) : \n",
    "        ax[k] = plt.subplot(gs[k+1, :]) \n",
    "        \n",
    "    # remove the xticklabels \n",
    "    for i in range(len(ax)-1):\n",
    "        plt.setp(ax[i].get_xticklabels(), visible=False)\n",
    "        \n",
    "    # make space between plots 0 \n",
    "    \n",
    "    fig.subplots_adjust(hspace=.1)\n",
    "\n",
    "\n",
    "    # lightcurve : sigma - clipped \n",
    "    ax[0].scatter(t[idxIn],y[idxIn], lw=0, s=20, c='red')\n",
    "    ax[0].errorbar(t[idxIn], y[idxIn], yerr[idxIn], ls='None', alpha=0.5) \n",
    "    \n",
    "    # lightcurve : the outliers : \n",
    "    ax[0].scatter(t[idxOut],y[idxOut], lw=0, s=25, c='orange')\n",
    "    ax[0].errorbar(t[idxOut], y[idxOut], yerr[idxOut], ls='None', alpha=0.5, c='orange') \n",
    "    \n",
    "    # set labels \n",
    "    ax[0].set_ylabel('mag')\n",
    "    \n",
    "\n",
    "    # show an example window size...\n",
    "    ax[0].axvline(window_frame_start[0], ls='--', color='magenta',lw=2)\n",
    "    ax[0].axvline(window_frame_end[0],ls='--', color='magenta', lw=2)\n",
    "\n",
    "    # overplot the mean....\n",
    "    size = 10\n",
    "    ax[0].scatter(store['t_center_window'], store['wMean_window'], s=size, lw=0, color='green')\n",
    "\n",
    "    # count of points per window\n",
    "    #ax[1].plot(store['t_center_window'],store['N_pts_window'], ls = 'steps')\n",
    "    ax[1].scatter(store['t_center_window'],store['N_pts_window'], s=size, lw=0)\n",
    "    ax[1].set_ylabel('N')\n",
    "\n",
    "    # t_center_window vs wMean_window\n",
    "    ax[2].scatter(store['t_center_window'], store['wMean_window'], s=size, lw=0, color='green')\n",
    "    if window_error == 'median' : \n",
    "        ax[2].errorbar(store['t_center_window'], store['wMean_window'], store['errMedian_window'], \n",
    "                       ls='None', alpha=0.5)\n",
    "    else :\n",
    "        ax[2].errorbar(store['t_center_window'], store['wMean_window'], store['errCombined_window'], \n",
    "                       ls='None', alpha=0.3)\n",
    "    #print('Using combined error from combined weights (see CRTS paper,  eq.4)')    \n",
    "    ax[2].set_ylabel(r'$\\langle mag \\rangle_{w}$')\n",
    "    #ax[2].get_yaxis().get_major_formatter().set_scientific(False)\n",
    "    ax[2].get_yaxis().get_major_formatter().set_useOffset(False)\n",
    "    # plot errors per window ( the combined errors... )\n",
    "    if window_error == 'median' : \n",
    "        ax[3].scatter(store['t_center_window'], store['errMedian_window'] ,  s=size, lw=0, alpha=0.5)\n",
    "    else :\n",
    "        ax[3].scatter(store['t_center_window'], store['errCombined_window'], s=size, lw=0, alpha=0.3)\n",
    "        #print('Using combined error from combined weights (see CRTS paper,  eq.4)')\n",
    "    ax[3].set_ylabel(r'$\\sigma_{\\langle mag \\rangle_{w}}$')\n",
    "    \n",
    "    # set ylims to be more in touch with the data ....\n",
    "    ax[3].set_ylim(0.8*min(store['errCombined_window']), 1.2 *max(store['errCombined_window']))\n",
    "    print('sigma min %f, max %f'%(min(store['errCombined_window']), max(store['errCombined_window'])))\n",
    "    \n",
    "    # chi2dof_window\n",
    "    ax[4].scatter(store['t_center_window'], store['chi2dof_window'], s=size, lw=0)\n",
    "    ax[4].set_ylabel(r'$\\chi^{2}_{DOF}$')\n",
    "    ax[4].set_ylim(0.8*min(store['chi2dof_window']), 1.2 *max(store['chi2dof_window']))\n",
    "    \n",
    "    # plot sigmaG_y_per_window \n",
    "    ax[5].scatter(store['t_center_window'], store['sigmaG_y_window'], s=size, lw=0)\n",
    "    ax[5].set_ylabel(r'$\\sigma_{G}(mag)$')\n",
    "    ymin,ymax = 0.8*min(store['sigmaG_y_window']), 1.2 *max(store['sigmaG_y_window'])\n",
    "    ax[5].set_ylim(ymin,ymax )\n",
    "    print('SigmaG(y) ymin=%f, ymax=%f'%(ymin, ymax))\n",
    "    \n",
    "    # plot sigmaG_z_per_window \n",
    "    ax[6].scatter(store['t_center_window'],store['sigmaG_z_window'], s=size, lw=0)\n",
    "    ax[6].set_ylabel(r'$\\sigma_{G}(z)$')\n",
    "    ax[6].set_xlabel('MJD')\n",
    "    # move xlims of each plot so that they all start in the same place...\n",
    "    xlim = ax[0].get_xlim()\n",
    "    for i in range(1,len(ax)):\n",
    "        ax[i].set_xlim(xlim)\n",
    "\n",
    "    # remove edge xticks \n",
    "    for i in range(len(ax)):\n",
    "        xticks = ax[i].get_xticks()\n",
    "        ax[i].set_xticks(xticks[1:-1])\n",
    "\n",
    "    # make N axis start at 0 \n",
    "    ax[1].set_ylim(0,)\n",
    "\n",
    "    # make all but first axis yticks more rare\n",
    "    for k in [1,2, 3,4,5, 6]:\n",
    "        yticks = ax[k].get_yticks()\n",
    "        ax[k].set_yticks(yticks[::2])\n",
    "        \n",
    "    # remove the penultimate ytick from each plot from ax[1] : \n",
    "    for k in range(1,7):\n",
    "        yticks = ax[k].yaxis.get_major_ticks()\n",
    "        yticks[-1].label.set_visible(False)\n",
    "    \n",
    "    # make last last axis yticks more rare\n",
    "    # yticks = ax[3].get_yticks()\n",
    "    # ax[3].set_yticks(yticks[::2])\n",
    "    \n",
    "    # add horizontal lines to chi2dof plot : \n",
    "    for k in range(len(ax)):\n",
    "        ax[k].grid()\n",
    "    \n",
    "\n",
    "    \n",
    "    # Save the figure...\n",
    "    if save_fig is not None  : \n",
    "        if outDir is None :\n",
    "            outDir = '../data_products/Fig_6_data/'\n",
    "        if fig_name is None : \n",
    "            fname='PG1302-102'+'_win_'+str(window_width)+'_days.png'\n",
    "        else:\n",
    "            fname = fig_name\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(outDir+fname)\n",
    "        print('Saved this plot as %s'%(outDir+fname))\n",
    "        plt.clf()\n",
    "        plt.close(fig)\n",
    "    # if desired, return the output\n",
    "    if return_results is not None  :\n",
    "        return store "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are no outliers\n",
      "Using 640 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.005911, max 0.030000\n",
      "SigmaG(y) ymin=0.001483, ymax=0.209047\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/PG1302-102_win_500_days.png\n"
     ]
    }
   ],
   "source": [
    "# read in the PG 1302 data....\n",
    "data = Table.read('../raw_CRTS_data/PG1302-102_LC.csv', format='ascii')\n",
    "data.sort('MJD')\n",
    "\n",
    "y = data['Mag']\n",
    "yerr = data['Magerr']\n",
    "t = data['MJD']\n",
    "#outDir = '../data_products/Fig_6_data/'\n",
    "#sliding_window_statistics(t,y,yerr, save_fig='No', fig_name='PG1302-102_LC.png', outDir = outDir)\n",
    "\n",
    "store = sliding_window_statistics(t,y,yerr, save_fig=True, outDir=outDir, window_error = 'else', \n",
    "                                  sigmathresh=2.5, return_results=True)"
   ]
  },
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    {
     "name": "stdout",
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     "text": [
      "\n",
      "Using sigma threshold of 3.00 sigma \n",
      "There are 25 outliers \n",
      "Using 638 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.006547, max 0.034641\n",
      "SigmaG(y) ymin=0.002965, ymax=0.017791\n"
     ]
    },
    {
     "data": {
      "image/png": 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IEUyePLmj/6fgF46mc582Mx6e9wjceSeH3nUb1vY17nvmCbYxnwEcQWLFEp4e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\nNtA504GBYvd1IN1ygD4WAhNoulJMTaTNA3DLBb6Dc7P3BX4N9MNZX40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0jvyP4OIvJDEXlJRDYGr6eDNafjbe3zvGbm8z0MwzAMwzAqjVLGMD4KfBrnhn4WmA78p6r+vIC2\nwrWk3wR+hVtL+jbcWtI/yVL3Kdxyflfj1pL+FW4t6U8F+/cHVgKTcG5zBb4HfAY4QVXnRdraB/wa\n+Eekiw9UdaGnX4thNAzDMAyjIijZSi858BxwKfBxnOJ4P24STCFcBnQFzlXVHcDzItIDuElEfqWq\ntb5KInICLpXPWFV9Jdi2GnhVRE5V1ReAOmCwqm6L1HsBWAx8F7gk1uxyVX2twO9hGIZhGIZRcZQy\nhnGnqq5X1edU9TrgWJwlrxBOB54OlMWQB4BuwKey1FsbKosAqjoHWAacEZT3RZXFYNtunEXzkALH\na2TAYkdSMZn4Mbn4Mbn4MbmkYjLxY3LJn1IqjP1F5HIR2Q9AVT8APiywrWHAougGVV0ZtDcsn3oB\nCzPVC2IvPwm849n9UxHZLSIbRGSSiPTKNnjDMAzDMIxKppQxjB2B3wPjcXGMawBV1UsLaGsX8ANV\n/W1s+0rgPlX9cZp6zwC1qnpubPvfcG7ok9PU+xlwDfBxVV0S2f4X4DFcTsljgZ8A7wKjNSZIi2E0\nDMMwDKNSaLUYRlXdC3xTRO7AxTBuAaY0p0nPNkmzveB6InImcD1wZVRZBFDVb0SKL4vIImAacDbw\naJZxGIZhGIZhVCSlnPQCuATeIrICN3lkIG4ySb5swZ+3sQewNUu9vp7tPX31ROQ4XGzk3ap6R7ZB\nqepTIlILHEMahXHChAkMGjTIddqzJyNHjiSRSACNMRTtrRxuK5fxlEM5LpvWHk+5lOfPn88VV1xR\nNuMpl7KdL3a+5FoOt5XLeMqlPHHiRLsfBySTSWpqashGKV3SVwHfAN4G/g48DnxNVf9SQFvTgfdV\n9cLItgHACuBsVX0iTb2bgUtVtX9s+1Lg4WguSBH5KDADeAU4L+5izjC27cBvVPXm2HZzSachmUw2\nnLSGw2Tix+Tix+Tix+SSisnEj8kllWwu6VIqjP+DS6NzLPAV4Hhgtqp+roC2fgT8ADgsnCktIj8A\nfgr0y5BWZwxOARyrqjODbccCrwGfVtUXg20fCY5bDXxGVetzHNfpBC7puNJqCqNhGIZhVCb19dCp\nE+zZ43+HltnXqeR+4EZaU2G8SFX/Gin3BOpzVcZibYWJu98CfolL3P0bXOLumyLHLQVeVNVvRrY9\niUvcfQ0ubvEWXKqdRLC/KzAbt1zhhcDmSNc7VXV+cNw3ccrvc8BG3CzqG3Azrk+ySS+GYRiGUfnc\nfz8kk7BqFey3H+zc2fR9//3dcTt2lHbfkCFw4YXwyU+2zPfOpjB2KGHf7weJs8MBbC1EWQzr4ibO\ndMDFCt6EUxh/Gju0A6nfaTxulZlJwL3AHCA6a/pgYAQuHvJxYGbk9c/Ice8CRwJ3AU/hVoO5Fzgt\nV/e14YjGThgOk4kfk4sfk4sfk0sqJhM/6eRSXw8LF8KWLVBTA2vXNn1ftw6WLYP33oP160u3b/16\neP9993nPnhYQSA6U0th5NnCZiMzBLbn3PDBLVQv66qq6CLdcX6Zjhni2bcdNuImv2BLuXw50zKH/\nF4AXchqsYRiGYRgVR9euMHy4U+IGDXKWvl69mr5HrYE9epRu34ABbgwt6ZbORKknvdwNfAKn6H0G\n6KuqR5ekwzLDXNKGYRiGUZlYDKNnfwkVxm+q6p9K0ngFYAqjYRiGYRiVQmvGMP4/EflSCds3KhSL\nqUnFZOLH5OLH5OLH5JKKycSPySV/SqkwXg/cJCLLReQeEfmqiBxcwv4MwzAMwzCMElDqGMbfAn1o\njGE8QlXHlqTDMsNc0oZhGIZhVAqtGcPYC/gC8JCqflCSTsoYUxgNwzAMw6gUWi2GUVW3qOo97VFZ\nNDJjsSOpmEz8mFz8mFz8mFxSMZn4icplzx73qq9v/GykUrDCKCIXiUj3AuseICIX5VlnuIg8LyI7\nRGSViNwsoTqcva97RGSziGwVkftFpHfsmM+IyGQRWSYi+0TkJ552Dgv2xV+T8/kehmEYhmGUB//+\nN9xxB1x8MXzpS+79jjvcdqMpBbukReQo4BvALuDPqvpuDnWOAL6FS5Q9SVXfzrGvcGnAN4Ff4ZYG\nvA23NGCKcher+xRuacCrcUsD/gq3NOCnIsfcCpyGWyLwS8CvVfVnsXYOA5YBV+FWgQnZqKrvefo1\nl7RhGIZhlCl79sDjj8Orr8KMGaAKIjB2LBx/PJx1VvkkzW4JsrmkCxaFqr4FXB1Y6y4NFKp/qurz\nnkH8J3Aebnm9W1R1c/yYLFwGdP3/2bvzMLnKMu/j35sESEiHNKCYEBJiQAZ01CAogiLNoiwaMEE7\nKipRwHFDHIVRZ8TAyCgwA+IAvuM4iiAoxCUKQgDBdEBQBCSoCGICIQHSgCSBdBYgcL9/PHXo06ee\n2vfu3+e66uquU2d56q5TVXc92wFmu/t64CYzm0AYhX2Ouw/ENspdmvAdwAHufmtu2WPA7WZ2cO7q\nLbj7KcApucffXaIsD7j77yssv4iIiLSR0aNhypRwWb5ddglXWRk3DnbcMSwfScliOWruw+juq939\nHOAzwPZm9m0zO8HMtjezE83sf4Fu4FPu/p9VJIsAhwPX55LFxBXANsCB8U1e2q4/SRZz5b2DUFN4\nRBXlkDpQn5p8ikmc4hKnuMQpLvkUk7gkLnvvDSedBJdcAldcEf6edFJYLkPVbdCLu7/g7j92938C\n7iY0Af/B3T+WW/5CDbvfA7g/c7yVwIbcY2Vvl3Nfie2KudjMNpvZY2Z2rpmNqXI/IiIi0mLJJfjG\njGn+5fg6SU3T6pjZ0e7+izqWp9BxngNOcff/zixfCVzi7l8usN0NwIC7z84s/wHwSnd/a2SbJ4EL\nIn0YJxImI78BeAboAb5IqPmcFdmP+jCKiIi0ufS1nEeyhvVhNLOtgJ+Z2T7ufne1+6lALPOyAsvr\nsd3Qnbj3E5rdEzeb2RPARWb2Onf/YyX7ExERkda66y547DHYaSc1Q5dSS05tuduWdSpLMWsI/SCz\nJgBrS2z3ssjy7hLblesnwLeANwDRhHHu3LlMmzYtHLS7mxkzZtDT0wMM9qEYafeTZe1Snna4n41N\nq8vTLveXLFnCZz/72bYpT7vc1/mi86Xc+8mydilPu9w///zzefWrZ7BhQw+jRsH11/exdi0cckh7\nlK+Z50dfXx/Lly+nlFqm1dka2AhcCMwgXAJwKXC5u8+vaqeFj7UYeMTdj00t2xlYAcx092sKbHcG\ncIK7T84sXwoscPdTI9tEm6QL7H8H4EngI+5+SeYxNUkX0NfX99JJK4FiEqe4xCkucYpLPsUk7tvf\n7mPDhh5Wrgwjot/2NtUwNuzSgKmEcQNwOaFf3z8CPcBvgfdUOSI6dqwvEqa92SUZKW1mpwCnAxOL\nTKvzZuBWwrQ6t+WW7QP8HjjE3RdFtqkkYfw4cBHwenf/c+YxJYwiIiJtJpl/MZmce8YMOPpo9WNs\nWB/GlM+7+7dTB3w5cBpwtZm91euTMf0PcBKwwMzOJkzcPQ84N50s5moOF7n7iQDu/rvcwJdLzexU\nQr/Fs4Cb08mimU0F3khoYt8KeI2ZHQOsd/frcuvMA8YTEtBnCNP5nAL8NJssitTbpk2DH2ZJB+10\nR+2R/kEnIlKO5LJ/kyZBdzdssQVMm6bP0HLUOkp6I/CuApN1fw5Y7e7fr754Q/a3B6H5ez9C/8Pv\nAGekE1Ize5CQMB6fWrYt8A1gFmEaoauBk9O1n2Z2HHAx+QNhHnb36bl15hCmCnoVMJbQHH458DV3\nfz5SXtUwFqAmknzFYnLZZdDXBxs3hvvr18Ozz8LWW4dJZvfZZ/g2p+hciVNc4oZjXJJrG1f7g3E4\nxqRad90FN98MK1fCypV97Ltvz0tXdZHG1zD+BjgUyEsY3f08M/sW8P0aj5Hs7/7csYqtMz2y7Bng\n+Nyt0HaXAJcUejy3zpXAlWUVVqQG6S+IgQG49154+ukwku/FF8Olq9asCb+OR42CnXeG5cvhNa8J\n22seMZHOk9R8Jf+PHg2//z385Cfw4IPhxyGMrB+M9bR5c/icfOKJkDA++WT4HF21StPqlKvWGsZ9\ngUXAh939J5HHL3T3T9dQvo6lGkapxl13weWXhy+I5Esh/eUAQ78wYo9Nnw5z5uhXs0inSNd8Pf54\neB9v3AjPPx9+NG61VfixmFzrOP2D8S1vgTe9Cd75zrCvMS24lESS7GZrP4vVjGYfa3S5b7ttaPL9\nildosEtWQ2sY3f12Mzse+IGZfQy4DFgCbCbU6C2vZf8iI0HyYbtpEyxbNvjrd/Vq2G67UIM4a1a4\npT9oN22C66+HO++EBx6A/v7wZbJ+fUgiQUmjFBer1SrnS7/VtTHllLvSx6D5z2vz5vA+Tmq+Hn44\nvI/dYe3a0M/OLHwObLtt2Gb9epgwYfAH41//Co88Aj/7GYwdCwccAMce27znkiS7d95Z+sdsocfG\njoWeHvjgB+tXrnQS+8MfwsKF8Nxz8I//CG94Q0iw2+Fc7iQ1h8rdf2RmfwHOAb5HGDgC8EPgX2rd\nvww/6lMz6Pbb4Te/gYUL++jq6mHcuFCb8PKXhy+IrbcOCePuuw/+Ak8+4Lq6QmftFSvCB/CYMbBu\nXfiC2WGH0NSyaVNrahzqRedKXD3iEqvVyn6ht1MNdtJV4557wnvm0Ufzy/3YY33stFNP2bXx6WV7\n7UXD+7OlE910c3O6HNOmDSaFkyeHMs2Zk5/oZn8wPv54SLwefjh0Xzn44PBcGvEeSl4LCMluf384\n7osvhtrPCRNCN5ru7vB5lK4ZzT4G4TPu3ntDktzVVXsSl05in346/ACfNAk2bAjHnzYNfvc7fbZU\nqi65tbvfA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wF8AbgZ+Hvudj2wT4H9qYaxQRp9ZYGkNnDy5Nr3UW6tYLb2JH2l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\nSRVGEREREcmkCqOIiIiIZPp/c1vAfYcqGDYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1218037f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in bright star for testing ...\n",
    "\n",
    "lc_dir = '../raw_LC_CRTS/stars/' \n",
    "name = '247936' # choose lightcurve from LC catalog \n",
    "address = lc_dir + name+'.dat'\n",
    "data = Table.read(address, format='ascii') # read in the lightcurve \n",
    "data.rename_column('col1', 'MJD')\n",
    "data.rename_column('col2', 'Mag')\n",
    "data.rename_column('col3', 'Magerr')\n",
    "data.sort('MJD')\n",
    "\n",
    "%matplotlib inline \n",
    "\n",
    "t = data['MJD']\n",
    "y = data['Mag']\n",
    "yerr = data['Magerr']\n",
    "sliding_window_statistics(t,y,yerr, save_fig=None, outDir=outDir, window_error = 'else', sigmathresh=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply to a few bright and faint QSO and stars : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x119bac9b0>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P7S9mam4Jvx0wFniDYZ2H8dMtb9EpuVOoQ2ySjAszOPGnEz3Ktz+0nf3T9tcv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\noQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e73128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Read in few quasars : choose one bright, well-sampled QSO, and one faint, same for stars . \n",
    "\n",
    "# Load the cross-matched CRTS-SDSS summary table : \n",
    "address = '../data_products/CRTS_SDSS_combined_QSO_catalog.dat'\n",
    "sdss_crts = Table.read(address, format='ascii')\n",
    "\n",
    "# show all colnames\n",
    "np.ravel(sdss_crts.colnames)\n",
    "\n",
    "# plot histogram of brightness distribution\n",
    "for f in 'ugriz' : \n",
    "    plt.hist(sdss_crts[f][(sdss_crts[f]>10)*(sdss_crts[f]<24)], histtype='step', lw=2, label=f, bins=40)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Choose two QSO classes : bright and faint:  \n",
    "\n",
    "bright_QSO = sdss_crts[(sdss_crts['g'] > 15)*( sdss_crts['g'] < 16)]\n",
    "# 235156.12-010913.3   has 404 points... \n",
    "\n",
    "faint_QSO = sdss_crts[(sdss_crts['g']>20)*(sdss_crts['g']<21)]\n",
    "faint_QSO.sort('raw_lc_length')\n",
    "faint_long_QSO = faint_QSO[-10:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=9&gt;\n",
       "<table id=\"table4729288240\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>raw_timespan_obs</th><th>raw_mean_err</th><th>raw_lc_length</th><th>raw_mean_mag</th><th>CRTS_name</th><th>proc_mean_N_day</th><th>proc_lc_length</th><th>proc_mean_mag</th><th>proc_mjd_span</th><th>proc_mean_err</th><th>proc_median_dt</th><th>ra_CRTS</th><th>dec_CRTS</th><th>dbID</th><th>ra</th><th>dec</th><th>SDR5ID</th><th>M_i</th><th>M_i_corr</th><th>redshift</th><th>mass_BH</th><th>Lbol</th><th>u</th><th>g</th><th>r</th><th>i</th><th>z</th><th>Au</th><th>match_angle_arcsec</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str18</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>3025.83301</td><td>0.148161209068</td><td>397.0</td><td>18.742720403</td><td>231121.09+005133.8</td><td>4.09278350515</td><td>97.0</td><td>18.6999061856</td><td>3025.8092</td><td>0.0741762886598</td><td>15.99885</td><td>347.837875</td><td>0.859388888889</td><td>475129</td><td>347.837921</td><td>0.859398</td><td>75720</td><td>-22.443</td><td>-23.133</td><td>0.4832</td><td>8.776</td><td>45.249</td><td>20.751</td><td>20.364</td><td>20.142</td><td>19.602</td><td>19.227</td><td>0.215</td><td>0.168798787984</td></tr>\n",
       "<tr><td>3002.94245</td><td>0.175125628141</td><td>398.0</td><td>19.4244974874</td><td>235249.39-000629.4</td><td>3.79047619048</td><td>105.0</td><td>19.3268761905</td><td>3002.9245</td><td>0.0990952380952</td><td>11.03495</td><td>358.205791667</td><td>-0.108166666667</td><td>3958962</td><td>358.205811</td><td>-0.108186</td><td>77157</td><td>-23.013</td><td>-23.242</td><td>0.631</td><td>0.0</td><td>45.261</td><td>20.615</td><td>20.106</td><td>20.0</td><td>19.66</td><td>19.645</td><td>0.165</td><td>0.0984291762447</td></tr>\n",
       "<tr><td>2999.89596</td><td>0.201122194514</td><td>401.0</td><td>19.6618952618</td><td>234629.98-010825.5</td><td>3.58035714286</td><td>112.0</td><td>19.6055464286</td><td>2999.8765</td><td>0.132603571429</td><td>12.0144</td><td>356.624916667</td><td>-1.14041666667</td><td>3985033</td><td>356.624908</td><td>-1.140436</td><td>76926</td><td>-24.504</td><td>-25.082</td><td>1.2051</td><td>8.151</td><td>45.899</td><td>20.279</td><td>20.181</td><td>19.805</td><td>19.741</td><td>19.689</td><td>0.179</td><td>0.0762706653404</td></tr>\n",
       "<tr><td>3002.94374</td><td>0.208756097561</td><td>410.0</td><td>19.7031707317</td><td>234648.39-010116.3</td><td>3.38842975207</td><td>121.0</td><td>19.6757710744</td><td>3002.9246</td><td>0.150010743802</td><td>12.00615</td><td>356.701625</td><td>-1.02119444444</td><td>3882125</td><td>356.70163</td><td>-1.021222</td><td>76935</td><td>-22.024</td><td>-22.715</td><td>0.4235</td><td>0.0</td><td>0.0</td><td>20.659</td><td>20.334</td><td>19.954</td><td>19.687</td><td>19.446</td><td>0.176</td><td>0.100819328951</td></tr>\n",
       "<tr><td>2920.01124</td><td>0.223547619048</td><td>420.0</td><td>19.4243809524</td><td>033931.15+011449.9</td><td>4.77272727273</td><td>88.0</td><td>19.2450477273</td><td>2919.9923</td><td>0.103611363636</td><td>13.1192</td><td>54.8797916667</td><td>1.24719444444</td><td>3748997</td><td>54.879829</td><td>1.247217</td><td>8268</td><td>-24.885</td><td>-25.527</td><td>1.409</td><td>8.741</td><td>45.983</td><td>20.609</td><td>20.456</td><td>20.199</td><td>19.923</td><td>19.728</td><td>0.651</td><td>0.15699758668</td></tr>\n",
       "<tr><td>3002.94374</td><td>0.195141509434</td><td>424.0</td><td>19.5285613208</td><td>234509.42-005506.7</td><td>3.5041322314</td><td>121.0</td><td>19.4728768595</td><td>3002.9246</td><td>0.135988429752</td><td>12.0155</td><td>356.28925</td><td>-0.918527777778</td><td>4004892</td><td>356.289276</td><td>-0.918536</td><td>76889</td><td>-24.588</td><td>-25.002</td><td>1.359</td><td>9.037</td><td>46.0</td><td>20.294</td><td>20.383</td><td>20.033</td><td>19.949</td><td>20.124</td><td>0.195</td><td>0.0981573663458</td></tr>\n",
       "<tr><td>2999.92262</td><td>0.185940366972</td><td>436.0</td><td>19.370206422</td><td>234033.70-005636.9</td><td>3.69491525424</td><td>118.0</td><td>19.2867576271</td><td>2999.9023</td><td>0.11096779661</td><td>11.9609</td><td>355.140416667</td><td>-0.943583333333</td><td>3892324</td><td>355.140442</td><td>-0.943589</td><td>76719</td><td>-27.326</td><td>-27.994</td><td>3.6502</td><td>9.528</td><td>47.078</td><td>25.436</td><td>20.682</td><td>19.571</td><td>19.448</td><td>19.522</td><td>0.178</td><td>0.0934416628874</td></tr>\n",
       "<tr><td>2997.8335</td><td>0.194336283186</td><td>452.0</td><td>19.4192477876</td><td>233155.01-011515.0</td><td>3.64516129032</td><td>124.0</td><td>19.3564524194</td><td>2997.8146</td><td>0.113992741935</td><td>10.9418</td><td>352.979208333</td><td>-1.25416666667</td><td>314358</td><td>352.979248</td><td>-1.254173</td><td>76413</td><td>-23.65</td><td>-24.173</td><td>0.8749</td><td>8.674</td><td>45.708</td><td>20.555</td><td>20.019</td><td>19.817</td><td>19.833</td><td>19.673</td><td>0.203</td><td>0.144574931634</td></tr>\n",
       "<tr><td>2890.16152</td><td>0.167833698031</td><td>457.0</td><td>19.6564332604</td><td>235225.61+002337.9</td><td>4.91397849462</td><td>93.0</td><td>19.4516086022</td><td>2890.1431</td><td>0.102159139785</td><td>12.3851</td><td>358.106708333</td><td>0.393861111111</td><td>3971396</td><td>358.10672</td><td>0.393876</td><td>77145</td><td>-25.222</td><td>-26.138</td><td>1.8437</td><td>0.0</td><td>46.094</td><td>20.824</td><td>20.781</td><td>20.534</td><td>20.019</td><td>20.188</td><td>0.167</td><td>0.0680946152988</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=9>\n",
       "raw_timespan_obs  raw_mean_err  raw_lc_length ...    Au   match_angle_arcsec\n",
       "    float64         float64        float64    ... float64      float64      \n",
       "---------------- -------------- ------------- ... ------- ------------------\n",
       "      3025.83301 0.148161209068         397.0 ...   0.215     0.168798787984\n",
       "      3002.94245 0.175125628141         398.0 ...   0.165    0.0984291762447\n",
       "      2999.89596 0.201122194514         401.0 ...   0.179    0.0762706653404\n",
       "      3002.94374 0.208756097561         410.0 ...   0.176     0.100819328951\n",
       "      2920.01124 0.223547619048         420.0 ...   0.651      0.15699758668\n",
       "      3002.94374 0.195141509434         424.0 ...   0.195    0.0981573663458\n",
       "      2999.92262 0.185940366972         436.0 ...   0.178    0.0934416628874\n",
       "       2997.8335 0.194336283186         452.0 ...   0.203     0.144574931634\n",
       "      2890.16152 0.167833698031         457.0 ...   0.167    0.0680946152988"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faint_long_QSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['raw_mjd_span' 'raw_mean_mag' 'raw_mean_err' 'CRTS_name' 'raw_lc_length'\n",
      " 'proc_mean_N_day' 'proc_mean_err' 'proc_lc_length' 'proc_mean_mag'\n",
      " 'proc_mjd_span' 'ra_1' 'dec_1' 'calib_fla' 'ra_2' 'dec_2' 'raRMS' 'decRMS'\n",
      " 'nEpochs' 'AR_val' 'u_Nobs' 'u_mMed' 'u_mMean' 'u_mErr' 'u_rms_scatt'\n",
      " 'u_chi2' 'g_Nobs' 'g_mMed' 'g_mMean' 'g_mErr' 'g_rms_scatt' 'g_chi2'\n",
      " 'r_Nobs' 'r_mMed' 'r_mMean' 'r_mErr' 'r_rms_scatt' 'r_chi2' 'i_Nobs'\n",
      " 'i_mMed' 'i_mMean' 'i_mErr' 'i_rms_scatt' 'i_chi2' 'z_Nobs' 'z_mMed'\n",
      " 'z_mMean' 'z_mErr' 'z_rms_scatt' 'z_chi2']\n"
     ]
    }
   ],
   "source": [
    "# Read in stars ...\n",
    "address = '../data_products/CRTS_SDSS_combined_stars_catalog.dat'\n",
    "stars = Table.read(address, format='ascii') \n",
    "\n",
    "# display their colnames\n",
    "print(np.ravel(stars.colnames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x118e81898>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UllyzZg4AJ/c+mTmXzwnoW/ZOTmbdccfZ0mfv2cO4VXoyQ6zQAKKUS1xKHEd9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a775cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot histogram of brightness distribution\n",
    "for l in 'ugriz' : \n",
    "    f = l+'_mMed'\n",
    "    plt.hist(stars[f][(stars[f]>10)*(stars[f]<24)], histtype='step', lw=2, label=l, bins=40)\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Choose few CRTS stars....\n",
    "\n",
    "bright_stars = stars[(stars['g_mMed'] < 16) * (stars['g_mMed'] > 15 ) ]\n",
    "bright_stars.sort('raw_lc_length')  # sorting sorts from lowest to highest \n",
    "bright_long_stars  = bright_stars[::-1][:10]  # choose the 10 longest \n",
    "\n",
    "faint_stars = stars[(stars['g_mMed'] > 20)*(stars['g_mMed'] <  21)]\n",
    "faint_stars.sort('raw_lc_length')\n",
    "faint_long_stars = faint_stars[::-1][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find stars in proximity to quasars... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Find stars around the same coordinates as a given QSO ... \n",
    "\n",
    "#  PG1302-102 has coords outside of S82 footprint, but \n",
    "# I can consider another QSO, and pick stars that are in the same region of the sky....\n",
    "\n",
    "# Erik Tollerud's tutorial about using astropy.coordinates to match catalogs. etc \n",
    "# http://www.astropy.org/astropy-tutorials/Coordinates.html\n",
    "\n",
    "# The documentation for the newest astropy : \n",
    "#http://astropy.readthedocs.io/en/latest/coordinates/matchsep.html\n",
    "\n",
    "\n",
    "#idxc, idxcatalog, d2d, d3d = catalog.search_around_sky(c, 1*u.deg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 8 CRTS stars within 10 arcmin from the chosen QSO\n",
      "Catalog of stars nearby to 031027.82-004950.7 is saved as ../data_products/qso_031027.82-004950.7_stars_within_10-arcmin.txt\n",
      "There are 9 CRTS stars within 10 arcmin from the chosen QSO\n",
      "Catalog of stars nearby to 235156.12-010913.3 is saved as ../data_products/qso_235156.12-010913.3_stars_within_10-arcmin.txt\n"
     ]
    }
   ],
   "source": [
    "qsos = bright_QSO # faint_long_QSO # bright_QSO # faint_long_QSO  # bright_QSO # faint_long_QSO  # or \n",
    "\n",
    "coo_crts_qso = SkyCoord(qsos['ra']*u.deg, qsos['dec']*u.deg)\n",
    "coo_crts_stars =  SkyCoord(stars['ra_1']*u.deg, stars['dec_1']*u.deg)\n",
    "\n",
    "for i in range(len(qsos)) : \n",
    "    scalar_single_qso_coords  =  coo_crts_qso[i]\n",
    "    d2d = scalar_single_qso_coords.separation(coo_crts_stars)  \n",
    "\n",
    "    search_radius_arcmin = 10 \n",
    "    catalogmsk = d2d < search_radius_arcmin*u.arcmin\n",
    "    print('There are %d CRTS stars within %d arcmin from the chosen QSO'%(np.sum(catalogmsk), search_radius_arcmin))\n",
    "    nearby_crts_stars_coords = coo_crts_stars[catalogmsk]\n",
    "    nearby_crts_stars = stars[catalogmsk]\n",
    "\n",
    "    save = stars[catalogmsk]\n",
    "    \n",
    "   \n",
    "    dist2d =  Column(data=d2d[catalogmsk], name='sep2d_deg' )\n",
    "\n",
    "    a = np.empty(np.sum(catalogmsk), dtype='str') \n",
    "    b = np.array([row + qsos['CRTS_name'][i]  for row in a ])\n",
    "    center_qso = Column(data = b, name  ='center_CRTS_QSO')\n",
    "\n",
    "    save.add_columns([dist2d, center_qso])\n",
    "    \n",
    "    archfile = 'qso_'+qsos['CRTS_name'][i] + '_stars_within_'+str(search_radius_arcmin)+'-arcmin.txt'\n",
    "    address = '../data_products/'+archfile\n",
    "    save.write(address, format='ascii', overwrite=True)\n",
    "    print('Catalog of stars nearby to %s is saved as %s'%(qsos['CRTS_name'][i], address) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=2&gt;\n",
       "<table id=\"table4638808608\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>raw_timespan_obs</th><th>raw_mean_err</th><th>raw_lc_length</th><th>raw_mean_mag</th><th>CRTS_name</th><th>proc_mean_N_day</th><th>proc_lc_length</th><th>proc_mean_mag</th><th>proc_mjd_span</th><th>proc_mean_err</th><th>proc_median_dt</th><th>ra_CRTS</th><th>dec_CRTS</th><th>dbID</th><th>ra</th><th>dec</th><th>SDR5ID</th><th>M_i</th><th>M_i_corr</th><th>redshift</th><th>mass_BH</th><th>Lbol</th><th>u</th><th>g</th><th>r</th><th>i</th><th>z</th><th>Au</th><th>match_angle_arcsec</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>str18</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>2937.06168</td><td>0.0619661016949</td><td>295.0</td><td>15.6900338983</td><td>031027.82-004950.7</td><td>3.98648648649</td><td>74.0</td><td>15.6925635135</td><td>2937.0424</td><td>0.0312189189189</td><td>19.0386</td><td>47.6159166667</td><td>-0.83075</td><td>3087679</td><td>47.615932</td><td>-0.830766</td><td>7232</td><td>-22.441</td><td>-22.361</td><td>0.0802</td><td>7.791</td><td>45.049</td><td>16.15</td><td>15.951</td><td>15.802</td><td>15.459</td><td>15.575</td><td>0.335</td><td>0.0797756820954</td></tr>\n",
       "<tr><td>3002.8558</td><td>0.0559405940594</td><td>404.0</td><td>15.0105693069</td><td>235156.12-010913.3</td><td>3.92233009709</td><td>103.0</td><td>15.0091097087</td><td>3002.8365</td><td>0.0296582524272</td><td>12.0164</td><td>357.983833333</td><td>-1.15369444444</td><td>3977727</td><td>357.983826</td><td>-1.153706</td><td>77125</td><td>-24.222</td><td>-24.295</td><td>0.174</td><td>9.108</td><td>45.745</td><td>15.687</td><td>15.771</td><td>15.787</td><td>15.368</td><td>15.574</td><td>0.14</td><td>0.0492670016349</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=2>\n",
       "raw_timespan_obs   raw_mean_err  raw_lc_length ...    Au   match_angle_arcsec\n",
       "    float64          float64        float64    ... float64      float64      \n",
       "---------------- --------------- ------------- ... ------- ------------------\n",
       "      2937.06168 0.0619661016949         295.0 ...   0.335    0.0797756820954\n",
       "       3002.8558 0.0559405940594         404.0 ...    0.14    0.0492670016349"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bright_QSO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving todays work in /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/\n"
     ]
    }
   ],
   "source": [
    "import os \n",
    "import datetime\n",
    "outDir = os.path.join(os.getcwd()[:-4],'data_products/'+'Fig_6_data',\n",
    "                      datetime.datetime.now().strftime('%Y-%m-%d')+ '/')\n",
    "if not os.path.exists(outDir): os.system('mkdir %s' % outDir)\n",
    "print('Saving todays work in %s'%outDir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Quasars... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Make sure that we don't end up plotting too many figures ... \n",
    "import resource\n",
    "resource.setrlimit(resource.RLIMIT_NOFILE, (500,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 13 outliers \n",
      "Using 656 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.012249, max 0.077851\n",
      "SigmaG(y) ymin=0.091921, ymax=0.462571\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_231121.09+005133.8.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 39 outliers \n",
      "Using 651 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.013189, max 0.050000\n",
      "SigmaG(y) ymin=0.065234, ymax=0.386959\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235249.39-000629.4.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 34 outliers \n",
      "Using 650 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.013639, max 0.056464\n",
      "SigmaG(y) ymin=0.047443, ymax=0.355824\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_234629.98-010825.5.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 52 outliers \n",
      "Using 651 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.015036, max 0.051121\n",
      "SigmaG(y) ymin=0.037065, ymax=0.302450\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_234648.39-010116.3.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 14 outliers \n",
      "Using 635 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.019736, max 0.126424\n",
      "SigmaG(y) ymin=0.207564, ymax=0.729439\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_033931.15+011449.9.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 46 outliers \n",
      "Using 651 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.013709, max 0.048595\n",
      "SigmaG(y) ymin=0.054856, ymax=0.338033\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_234509.42-005506.7.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 38 outliers \n",
      "Using 650 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.012603, max 0.049706\n",
      "SigmaG(y) ymin=0.029652, ymax=0.364720\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_234033.70-005636.9.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 30 outliers \n",
      "Using 650 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.012513, max 0.131630\n",
      "SigmaG(y) ymin=0.056339, ymax=0.400302\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_233155.01-011515.0.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are no outliers\n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.013222, max 0.049759\n",
      "SigmaG(y) ymin=0.145295, ymax=0.809500\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9.png\n",
      "\n",
      " DONE\n"
     ]
    }
   ],
   "source": [
    "# FAINT  QSO \n",
    "\n",
    "for i in range(len(faint_long_QSO)):\n",
    "    lc_dir = '../raw_LC_CRTS/QSO_CRTS/' \n",
    "    qso_name = faint_long_QSO['CRTS_name'][i]\n",
    "    address = lc_dir + qso_name+'.dat'\n",
    "    data = Table.read(address, format='ascii')\n",
    "    data.rename_column('col1', 'MJD')\n",
    "    data.rename_column('col2', 'Mag')\n",
    "    data.rename_column('col3', 'Magerr')\n",
    "    data.sort('MJD')\n",
    "\n",
    "    sliding_window_statistics(t = data['MJD'],y=data['Mag'],yerr=data['Magerr'], \n",
    "                              save_fig='Yes', fig_name='qso_faint_'+qso_name+'.png', \n",
    "                              outDir = outDir, sigmathresh=2.5)\n",
    "\n",
    "print('\\n DONE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 2 outliers \n",
      "Using 638 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.006726, max 0.030000\n",
      "SigmaG(y) ymin=0.007413, ymax=0.195703\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_bright_031027.82-004950.7.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 30 outliers \n",
      "Using 601 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.005419, max 0.025000\n",
      "SigmaG(y) ymin=0.013343, ymax=0.217942\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_bright_235156.12-010913.3.png\n",
      "\n",
      " DONE\n"
     ]
    }
   ],
   "source": [
    "# BRIGHT  QSO \n",
    "for i in range(len(bright_QSO)):\n",
    "    lc_dir = '../raw_LC_CRTS/QSO_CRTS/' \n",
    "    qso_name = bright_QSO['CRTS_name'][i]\n",
    "    address = lc_dir + qso_name+'.dat'\n",
    "    data = Table.read(address, format='ascii')\n",
    "    data.rename_column('col1', 'MJD')\n",
    "    data.rename_column('col2', 'Mag')\n",
    "    data.rename_column('col3', 'Magerr')\n",
    "    data.sort('MJD')\n",
    "    sliding_window_statistics(t = data['MJD'],y=data['Mag'],yerr=data['Magerr'], \n",
    "                              save_fig='Yes', fig_name='qso_bright_'+qso_name+'.png', \n",
    "                              outDir = outDir, sigmathresh=2.5)\n",
    "print('\\n DONE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) Stars... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10c49c080>"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8zn8Cd/ex/PvALXncL2rZFkXZL8rK9xeCw94vCjMnWObnJBOybxikXJQvSA9t\nP47kkN+h2B94OCK2n+MSEd0k34SmDmH7XepQZSPZFkTEtSQ9n7eSHCo9n+Rb2wNpkcdJAqF8/uNA\nkj++3wz1vXZDVtriR0PZfKjvs5t2py0OAB7aaWXEb0iGwQba1zvZ9d+79/V653rytl/Usi2GWoda\nGMm2GIph7xdFDcF3k/wjre1rpaS3kITXT/tY/UGSo6OWDvG9fge8RdL2czIljQHewgDn9EhqITka\nrK86VNNItgUAEbElIh6OiOdJvlmK9OiuiNgC/IBkyKTUh4Afxc5HU1ZbVtqi/ICp0jpkeb/4HXBI\nWbkDSeZKnxzgvW4H9pM0vWS7Q4EpwG2Qy/2iZm3RTx0acb8YVFX2i1p2nbPwINmpPkXyLf19JCd7\nbgRuTNd/gOTb+4dJDrU9g2Ry9zFg7z5e7w7g5wO832+BK0ueH0IyYf399L1mpHXaTDqOTXKy9b3A\nP5KcEPohkp5RF/D2BmqLscCX0/d5f/r7ZuCjZdsdTjI39jWSE4S/QnLkZNXmffLQFnnbL0hOoN5K\nctDPMcBHSHovvwVa+muLkjr8FpgN/F26XUde94tatkXB9ovjgBOBb5L07E5MH6+v1n5RlcbK8iP9\nh/sVycTsiyTfUM4knaMjGYq6E/gDyX9CzwBXAfv18Vr7pGU+M8D7PQFcVbbsKKADeD59/ICSMWxg\nDMkVD36X7sjrSU72PKyR2oLkChAr0zZ4CfgxcEI/2/5tWtcuYA0wt2htkdP94jTglyT/WT5F8p/k\nXw3hb2Rc+povklxE4D+AV+Z8v6hJWxRsv1hLEn7lj3nV2i98KyUzMyusos4JmpmZOQTNzKy4HIJm\nZlZYDkEzMyssh6CZmRWWQ9DMzArLIWhmZoXlEDQzs8JyCJqZWWE5BM3MrLAcgmZmVlgOQbMckzRZ\nUo+kqyW9SdJ/SvqDpG2SjpR0iKRLJf1S0guSuiT9RtK/Shpf7/qb1duowYuYWQ68keROFI8CNwCv\nILn6/2kkt+O5h+SK/3uQ3N7rk8Bxkt4ZES/VpcZmGeC7SJjlmKTJJLebCeBLEbGgbP0k4Oko+0OX\n9DGS2978U0RcPFL1Ncsah6BZjpWE4DpgckR0D3E7kdyH7qcR8d4aVtEs0zwnaNYYHuorACWNknS2\npPvSOcGtknpIbkw6DnjtiNfULEM8J2jWGNb1s3wZyZzg48CKtNzmdN3/JLlLuVlhOQTNGsMu8xqS\nWkkCcBWfF7mIAAAAvElEQVQwIyK2lawT8E8jVz2zbPJwqFnjemP685bSAEy9E2gZ4fqYZY5D0Kxx\nPZn+bCtdKOlVwGUjXRmzLPJwqFnjehBYDXxQ0mrgfuDVwPFAJ/BMHetmlgnuCZrlX9DHnGBE9AAn\nAFcAE4H5wOHAvwPHAt19bWdWJD5P0MzMCss9QTMzKyyHoJmZFZZD0MzMCsshaGZmheUQNDOzwnII\nmplZYTkEzcyssByCZmZWWA5BMzMrLIegmZkV1v8HNayk/SaJonkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1132f8a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 41 outliers \n",
      "Using 650 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.016359, max 0.175272\n",
      "SigmaG(y) ymin=0.068200, ymax=0.306898\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_233155.01-011515.0_star_19.74-mag_965796_within_4.704_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 11 outliers \n",
      "Using 648 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.033083, max 0.082768\n",
      "SigmaG(y) ymin=0.124538, ymax=0.426989\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_233155.01-011515.0_star_21.11-mag_966652_within_4.832_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 53 outliers \n",
      "Using 650 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.014201, max 0.147988\n",
      "SigmaG(y) ymin=0.066717, ymax=0.284659\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_233155.01-011515.0_star_19.57-mag_966669_within_4.518_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 13 outliers \n",
      "Using 627 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.021503, max 0.059691\n",
      "SigmaG(y) ymin=0.045961, ymax=0.275764\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_233155.01-011515.0_star_20.26-mag_966853_within_8.071_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 25 outliers \n",
      "Using 648 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.023376, max 0.068115\n",
      "SigmaG(y) ymin=0.080060, ymax=0.300226\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_233155.01-011515.0_star_20.54-mag_967265_within_9.787_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 4 outliers \n",
      "Using 633 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.026203, max 0.071317\n",
      "SigmaG(y) ymin=0.056339, ymax=0.404750\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_233155.01-011515.0_star_20.73-mag_967294_within_9.784_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 16 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.008905, max 0.035000\n",
      "SigmaG(y) ymin=0.007413, ymax=0.151225\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_17.80-mag_996061_within_7.853_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 28 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.008346, max 0.035000\n",
      "SigmaG(y) ymin=0.004448, ymax=0.071165\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_17.45-mag_996095_within_9.161_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 6 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.021609, max 0.073413\n",
      "SigmaG(y) ymin=0.053374, ymax=0.211271\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_19.98-mag_996390_within_9.413_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 16 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.029833, max 0.116752\n",
      "SigmaG(y) ymin=0.062269, ymax=0.451452\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_20.68-mag_996488_within_4.249_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 5 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.022389, max 0.079765\n",
      "SigmaG(y) ymin=0.044478, ymax=0.249077\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_20.10-mag_996564_within_0.919_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 18 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.009752, max 0.040000\n",
      "SigmaG(y) ymin=0.005930, ymax=0.115643\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_18.09-mag_996582_within_1.022_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 15 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.009732, max 0.040000\n",
      "SigmaG(y) ymin=0.020756, ymax=0.204599\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_18.21-mag_996707_within_8.738_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 18 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.008114, max 0.035000\n",
      "SigmaG(y) ymin=0.004448, ymax=0.077836\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_17.28-mag_996885_within_5.827_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 18 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.007957, max 0.035000\n",
      "SigmaG(y) ymin=0.002965, ymax=0.080060\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_17.28-mag_996968_within_5.188_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 17 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.025575, max 0.086540\n",
      "SigmaG(y) ymin=0.059304, ymax=0.302450\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_20.37-mag_997043_within_5.636_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 3 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.032938, max 0.107293\n",
      "SigmaG(y) ymin=0.065234, ymax=0.502601\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_20.86-mag_997273_within_6.682_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 26 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.022747, max 0.076894\n",
      "SigmaG(y) ymin=0.041513, ymax=0.320242\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_20.17-mag_997313_within_8.373_arcmin.png\n",
      "\n",
      "Using sigma threshold of 2.50 sigma \n",
      "There are 2 outliers \n",
      "Using 629 windows of width 500 days each , with the window sliding step of  5 days\n",
      "sigma min 0.006194, max 0.030000\n",
      "SigmaG(y) ymin=0.002965, ymax=0.053374\n",
      "Saved this plot as /Users/chris/GradResearch/CRTS_PROJECT/data_products/Fig_6_data/2017-03-15/qso_faint_235225.61+002337.9_star_15.83-mag_997387_within_8.081_arcmin.png\n",
      "\n",
      " DONE\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# PROXIMITY...\n",
    "#\n",
    "# Choose stars within a certain radius from a given QSO ...\n",
    "\n",
    "# Here we first choose quasar based on magnitude and length of lightcurve: \n",
    "# we choose the brightest quasar for bright_QSO (there are only 2 that are within the selected brightness cutoff)\n",
    "# we choose the faintest quasars for faint_long_QSO, and take only 10 that have longest CRTS raw lightcurves \n",
    "\n",
    "# for each quasar in a given set we find CRTS standard stars from SDSS S82 standard stars catalog that \n",
    "# are within 10 arcminutes. \n",
    "\n",
    "# we plot the sliding window statistic for each nearby star, which are bright and faint.  \n",
    "\n",
    "\n",
    "search_radius_arcmin = 10 \n",
    "\n",
    "# run only for two faint qso that I'm sending to Zeljko....\n",
    "qsos = faint_long_QSO[-2:] #  bright_QSO #faint_long_QSO[-2:] #  bright_QSO # faint_long_QSO[-2:] # bright_QSO # faint_long_QSO  #  bright_QSO # faint_long_QSO # bright_QSO\n",
    "brightness_class = 'faint' # 'bright'#faint' # faint' # faint' # 'bright', \n",
    "\n",
    "for j in range(len(qsos)):\n",
    "    archfile = 'qso_'+qsos['CRTS_name'][j] + '_stars_within_'+str(search_radius_arcmin)+'-arcmin.txt'\n",
    "    address = '../data_products/'+archfile\n",
    "    nearby_crts_stars = Table.read(address, format='ascii')\n",
    "    tab = nearby_crts_stars\n",
    "\n",
    "    # Show that these stars are indeed close to these quasars...\n",
    "    fig,ax = plt.subplots(1,1)\n",
    "\n",
    "    ax.scatter(qsos[j]['ra'], qsos[j]['dec'], color='red', marker='s', s=80)\n",
    "    ax.scatter(tab['ra_1'], tab['dec_1'], marker='*', s=60)\n",
    "    ax.get_xaxis().get_major_formatter().set_useOffset(False)\n",
    "    yticks = ax.yaxis.get_major_ticks()\n",
    "    yticks[0].label.set_visible(False)\n",
    "    \n",
    "    xticks = ax.get_xticks()\n",
    "    ax.set_xticks(xticks[::2])\n",
    "        \n",
    "        \n",
    "    ax.set_xlabel('ra')\n",
    "    ax.set_ylabel('dec')\n",
    "    ax.set_title(qsos['CRTS_name'][j])\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(outDir+'qso_'+brightness_class+'_'+qsos['CRTS_name'][j]+'_stars_radec.png')\n",
    "    plt.close()\n",
    "    \n",
    "    \n",
    "    for i in range(len(tab)):\n",
    "        lc_dir = '../raw_LC_CRTS/stars/' \n",
    "        name = str(tab['CRTS_name'][i]) # choose lightcurve from LC catalog \n",
    "        address = lc_dir + name+'.dat'\n",
    "        data = Table.read(address, format='ascii') # read in the lightcurve \n",
    "        data.rename_column('col1', 'MJD')\n",
    "        data.rename_column('col2', 'Mag')\n",
    "        data.rename_column('col3', 'Magerr')\n",
    "        data.sort('MJD')\n",
    "        sep2d_deg = tab['sep2d_deg'][i]\n",
    "        sep2d_arcmin = sep2d_deg * 60 \n",
    "        sliding_window_statistics(t = data['MJD'],y=data['Mag'],yerr=data['Magerr'], \n",
    "                                  save_fig='Yes', fig_name='qso_'+brightness_class+'_'+qsos['CRTS_name'][j]  +\\\n",
    "                                  '_star_'+str(np.mean(data['Mag']))[:5]+'-mag_'+name+'_within_' +\\\n",
    "                                  str(sep2d_arcmin)[:5]+'_arcmin.png', outDir = outDir, sigmathresh=2.5)\n",
    "\n",
    "print('\\n DONE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Read in stellar lightcurve...\n",
    "# BRIGHT STARS \n",
    "\n",
    "tab =  bright_long_stars\n",
    "for i in range(len(tab)):\n",
    "    lc_dir = '../raw_LC_CRTS/stars/' \n",
    "    name = str(tab['CRTS_name'][i]) # choose lightcurve from LC catalog \n",
    "    address = lc_dir + name+'.dat'\n",
    "    data = Table.read(address, format='ascii') # read in the lightcurve \n",
    "    data.rename_column('col1', 'MJD')\n",
    "    data.rename_column('col2', 'Mag')\n",
    "    data.rename_column('col3', 'Magerr')\n",
    "    data.sort('MJD')\n",
    "\n",
    "    sliding_window_statistics(t = data['MJD'],y=data['Mag'],yerr=data['Magerr'], \n",
    "                              save_fig='Yes', fig_name='star_bright_'+name+'.png')\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Read in stellar lightcurve...\n",
    "# FAINT STARS\n",
    "\n",
    "tab =  faint_long_stars\n",
    "for i in range(len(tab)):\n",
    "    lc_dir = '../raw_LC_CRTS/stars/' \n",
    "    name = str(tab['CRTS_name'][i]) # choose lightcurve from LC catalog \n",
    "    address = lc_dir + name+'.dat'\n",
    "    data = Table.read(address, format='ascii') # read in the lightcurve \n",
    "    data.rename_column('col1', 'MJD')\n",
    "    data.rename_column('col2', 'Mag')\n",
    "    data.rename_column('col3', 'Magerr')\n",
    "    data.sort('MJD')\n",
    "\n",
    "    sliding_window_statistics(t = data['MJD'],y=data['Mag'],yerr=data['Magerr'], \n",
    "                              save_fig='Yes', fig_name='star_faint_'+name+'.png', outDir = outDir)\n",
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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