01.2017_PTF_data_for_CRTS_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal here is to grab the PTF lightcurves for standard stars and quasars in Stripe 82 that I used in CRTS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'CRTS_paper_modules' from '/Users/chris/GradResearch/CRTS_PROJECT/code/CRTS_paper_modules.py'>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd \n",
    "from astropy.table import Table\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, '/Users/chris/GradResearch/CRTS_PROJECT/code/')\n",
    "import CRTS_paper_modules as mod \n",
    "import imp\n",
    "imp.reload(mod)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Search source catalog on  PTF objects table and \n",
    "--> use ngoodobs > 10 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Read in the QSO in S82"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Read in the CRTS-SDSS cross-matched table :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zipping CRTS-SDSS quasars catalog from ../data_products/CRTS_SDSS_catalogs/CRTS_SDSS_cross_matched_qso_DB_QSO_catalog.txt\n",
      "Read in 7601 quasars from CRTS\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['dec_SDSS', 'N_rows', 'CRTS_id', 'dec_CRTS', 'i', 'g', 'mjd_uniq_N', 'ra_SDSS', 'ra_CRTS', 'r', 'm_ang_deg', 'CRTS_avg_m', 'z', 'redshift', 'CRTS_avg_e', 'u', 'mjd_span'])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This grabs the colnames , and the catalog as a dict from the \n",
    "# database file \n",
    "colnames, catalog  = mod.get_qso_catalog()\n",
    "catalog.keys()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the stars in S82"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zipping CRTS-SDSS stars catalog...\n",
      "Read in catalog for 48250 stars from CRTS \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['crts_id', 'g_Nobs', 'mjd_N', 'dec_SDSS', 'ra_SDSS', 'dec_CRTS', 'mjd_span', 'CRTS_Merr', 'ra_CRTS', 'CRTS_M', 'r_mMed', 'g_mMed', 'm_ang_deg', 'i_mMed', 'N_rows'])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colnames, catalog_stars  = mod.get_stars_catalog()\n",
    "catalog_stars.keys()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PTF Objects : make IPAC format S82 stars list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Here astropy ascii does not directly have an import from a dict, but we can import from a dataframe\n",
    "# thus we make  dict --> dataframe --> table  conversion \n",
    "\n",
    "# convert the dictionary to  a pandas dataframe\n",
    "df_stars = pd.DataFrame.from_dict(catalog_stars)\n",
    "\n",
    "# convert the dataframe to an astropy Table \n",
    "tab_stars = Table.from_pandas(df_stars)\n",
    "\n",
    "# initiate a Table to save the relevant cols \n",
    "save_stars = Table()\n",
    "save_stars['ra'] = tab_stars['ra_SDSS']  # use SDSS ra, dec because it's more accurate than the CRTS \n",
    "save_stars['dec'] = tab_stars['dec_SDSS']\n",
    "save_stars['major'] =  0.1 # that's a search radius  : a semimajor axis  for an ellipse with 0 eccentricity... \n",
    "save_stars.write('IRSA_stars_s82_ra_dec.tbl', format='ipac') # use that to query against PTF Objects "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PTF Objects : make IPAC format S82 QSO list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check which objects have been detected by iPTF, we will be querying against the PTF Objects catalog. To do that we upload a list of  RA and dec for the 7601 spectroscopically-confirmed quasars in S82, to positionally look for those sources in iPTF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here astropy ascii does not directly have an import from a dict, but we can import from a dataframe\n",
    "# thus we make  dict --> dataframe --> table  conversion \n",
    "\n",
    "# convert the dictionary to  a pandas dataframe\n",
    "df = pd.DataFrame.from_dict(catalog)\n",
    "\n",
    "# convert the dataframe to an astropy Table \n",
    "tab = Table.from_pandas(df)\n",
    "\n",
    "# initiate a Table to save the relevant cols \n",
    "save = Table()\n",
    "save['ra'] = tab['ra_SDSS']  # use SDSS ra, dec because it's more accurate than the CRTS \n",
    "save['dec'] = tab['dec_SDSS']\n",
    "save['major'] =  0.1 # that's a search radius  : a semimajor axis  for an ellipse with 0 eccentricity... \n",
    "save.write('IRSA_QSO_s82_ra_dec.tbl', format='ipac') # use that to query against PTF Objects \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying IRSA : NASA/IPAC Infrared Science Archive  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the main page https://irsa.ipac.caltech.edu/frontpage/ <img src=\"figures/1_main_page.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We select \"catalogs\", and then choose the 'PTF' catalog : <img src=\"figures/2_catalog_choice.png\" style=\"width: 500px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then select  'PTF Objects catalog'  : <img src=\"figures/3_subcatalog.png\" style=\"width: 500px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And run the Multi-Object Search, setting the search radius to 2 arcsec  : <img src=\"figures/4_query.png\" style=\"width: 500px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and ngoodobs > 10:  <img src=\"figures/5_ngoodobs.png\" style=\"width: 1000px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I don't need all columns : grab  ra,dec,oid,fid,  ngoodobs, nbestobs, bestmaxmag, ra, dec, obsmjd, mag_autocorr,  magerr_auto   \n",
    "press 'Run Query' on the very bottom of the page  <img src=\"figures/6_run_the_query.png\" style=\"width: 500px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Result :  6606 sources  with ngoodobs > 10 . In the result IPAC Table, I can select all rows in the upper left corner (each row corresponds to one object and contains various summary info about the aggregate quality of each lightcurve), and then save in ../IPAC_tables/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check which version I'm dealing with...\n",
    "#import astropy\n",
    "#astropy.version.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read in the qso output table \n",
    "qso_table = Table.read('../IPAC_tables/IpacTableFromSource_QSO_S82.tbl', format='ipac')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read in the stars output table \n",
    "stars_table = Table.read('../IPAC_tables/IpacTableFromSource_stars_S82.tbl', format='ipac')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### note : ngoodobs vs nbestobs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "the code below gives some idea how 'good' differs from 'best'... \n",
    "\n",
    "uint32_t bad_photo = SATELLITE | HIGH_DARK | NON_LINEAR | HOT_PIXEL | SATURATED_PIXEL | DEAD_PIXEL | NAN_PIXEL;\n",
    "#uint32_t bad_photo_strict = SATELLITE | HIGH_DARK | NON_LINEAR | HOT_PIXEL | GHOST_PIXEL | SATURATED_PIXEL | DEAD_PIXEL | NAN_PIXEL | HALO;\n",
    "\n",
    "nmag_ok = “ngoodobs” =  if( ((ipacflag & bad_photo) == 0) & ((sexflag & 506) == 0) )\n",
    "nmag_ok_strict = “nbestobs” =  if( ((ipacflag & bad_photo_strict) == 0) & ((sexflag & 507) == 0) )\n",
    "\n",
    "Thus unlike good designation,  best includes HALO,  which is a keyword for being close to a bright star (how close - email Eric B.  )\n",
    "\n",
    "Also note iPTF flags that have a good SExtractor documentation:\n",
    "\n",
    " * sexflag 506\n",
    " * sexflag 507 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select good PTF quasars in S82..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#qso_table.colnames\n",
    "\n",
    "# NOTE : since it was matched on ra, dec,  it also stores ra_01,  dec_01, which\n",
    "# represent the PTF values that were matched to SDSS-based  ra, dec "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11f4e8cc0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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eJH4NrZmZtYWTh5mZlebkYWZmpTl5mJlZaU4eZmZWmpOHmZmV5uRhZmalOXmY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\nmJlZaf8PFvl3Y0i7znwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f493940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# choose of those that are available only those with more than \n",
    "# 10 epochs ...\n",
    "require_nbestobs_gt = 10 \n",
    "select_by_nobs = qso_table['nbestobs'] > require_nbestobs_gt\n",
    "\n",
    "#  in case we want to add some more masks above., we can combine them here \n",
    "select_mask = select_by_nobs\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "plt.hist(qso_table['bestmeanmag'][select_mask])\n",
    "plt.title('PTF Quasars in S82 : %d objects with nbestobs > %d'%(np.sum(select_mask), require_nbestobs_gt))\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Best Mean Magnitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Comment string > 78 characters was automatically wrapped. [astropy.io.ascii.ipac]\n"
     ]
    }
   ],
   "source": [
    "# Save those PTF Quasars that we like to a table that we will use to query for lightcurves...\n",
    "selected = qso_table[['ra','dec']][select_by_nobs]\n",
    "\n",
    "# save those sources that we are now going to query PTF lightcurve database against in the ipac format ... \n",
    "selected.write('IPAC_get_lightcurves_QSO_ra_dec.tbl', format='ipac')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Comment string > 78 characters was automatically wrapped. [astropy.io.ascii.ipac]\n"
     ]
    }
   ],
   "source": [
    "# Test : select only a few to quickly see if we are retrieving all rows that there should be ... \n",
    "\n",
    "selected[:10].write('../IPAC_query_tables/IPAC_get_lightcurves_test_10_QSO_ra_dec.tbl', format='ipac')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select good PTF stars in S82 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x119116cc0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/1uqKig7K1zBhwoSV021tbbS1tZWI1sys76tUKlQqlbXaRo8OQyvpy8DLwBeB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\nWWlOHmZmVpqTh5mZlfb/AZuUgUAJ9GHMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a9efd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# choose of those that are available only those with more than \n",
    "# 10 epochs ...\n",
    "require_nbestobs_gt = 10 \n",
    "select_by_nobs = stars_table['nbestobs'] > require_nbestobs_gt\n",
    "\n",
    "# remove those with spurioursly low magnitude... \n",
    "select_by_meanmag  = stars_table['bestmeanmag'] > 10 \n",
    "\n",
    "# combine masks \n",
    "select_mask = select_by_nobs * select_by_meanmag\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "plt.hist(stars_table['bestmeanmag'][select_mask])\n",
    "plt.title('PTF stars in S82 : %d objects with nbestobs > %d'%(np.sum(select_mask), require_nbestobs_gt))\n",
    "plt.ylabel('Count')\n",
    "plt.xlabel('Best Mean Magnitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Comment string > 78 characters was automatically wrapped. [astropy.io.ascii.ipac]\n"
     ]
    }
   ],
   "source": [
    "# Save those PTF stars that we like to a table that we will use to query for lightcurves...\n",
    "selected_stars = stars_table[['ra','dec']][select_mask]\n",
    "\n",
    "# save those sources that we are now going to query PTF lightcurve database against in the ipac format ... \n",
    "selected_stars.write('IPAC_get_lightcurves_stars_ra_dec.tbl', format='ipac')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grab lightcurves..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use that file to query against the PTF Lightcurve Database <img src=\"figures/7_grab_lightcurves.png\" style=\"width: 500px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the query results... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# all lightcurves (may take a minute to read as the file has size of 82 MB ... )\n",
    "lcs_qso = Table.read('../IPAC_tables/Ipac_PTF_lightcurves_QSO_S82.tbl', format='ipac')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#lcs_qso.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note : there may have been sources that are on CCD edges, so that they were detected on  two CCDs  For those sources, the ra_01 and dec_01 may be slightly different on the two chips because of dithering, but not more than 1.5 arcsec (at least according to David Schupe : schupe@ipac.caltech.edu)\n",
    "\n",
    "Thus to gather all rows corresponding to a single source, we group by ra, dec (the input SDSS ra, dec, rather than the PTF ra_01, dec_01):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6472"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(lcs_qso['oid']))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6461"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(lcs_qso['dec']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6471"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many sources we have \n",
    "len(np.unique(lcs_qso['ra']))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6480"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(lcs_qso['ra_01']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# test case... querying for 10 objects ...\n",
    "lcs_test = Table.read('../IPAC_output_tables/Ipac_PTF_lightcurves_test_QSO.tbl', format='ipac')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=202&gt;\n",
       "<table id=\"table4708278344\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>cntr_01</th><th>dist_x</th><th>pang_x</th><th>ra_01</th><th>dec_01</th><th>obsmjd</th><th>mag_autocorr</th><th>magerr_auto</th><th>oid</th><th>ra</th><th>dec</th><th>fid</th><th>transient_flag</th><th>astrometricrms</th><th>nobs</th><th>ngoodobs</th><th>nbestobs</th><th>referencemag</th><th>referencemagerr</th><th>medianmag</th><th>bestmedianmag</th><th>meanmag</th><th>bestmeanmag</th><th>magrms</th><th>bestmagrms</th><th>sid</th><th>s_ra</th><th>s_dec</th><th>ptffield</th><th>ccdid</th><th>hmjd</th><th>nid</th><th>mag_auto</th><th>limitmag</th><th>absphotzp</th><th>fwhmsex</th><th>fwhm_image</th><th>qaflags</th><th>flags</th><th>photcalflag</th><th>goodflag</th></tr></thead>\n",
       "<thead><tr><th></th><th>arcsec</th><th>deg</th><th>degrees</th><th>degrees</th><th>days</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>degrees</th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>days</th><th></th><th>mag</th><th>mag</th><th>mag</th><th>arcsec</th><th>pixels</th><th></th><th></th><th>0=No, 1=Yes</th><th>0=No, 1=Yes</th></tr></thead>\n",
       "<thead><tr><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>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>int64</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>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56903.3404372</td><td>18.747</td><td>0.04</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21305664001544</td><td>6.968329</td><td>0.437729</td><td>2699</td><td>6</td><td>56903.3455472</td><td>1968</td><td>18.76</td><td>21.93</td><td>27.896</td><td>2.45</td><td>2.713</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56924.3337972</td><td>18.774</td><td>0.041</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21507653001359</td><td>6.968328</td><td>0.43773</td><td>2699</td><td>6</td><td>56924.3395172</td><td>1987</td><td>18.778</td><td>21.754</td><td>27.874</td><td>2.45</td><td>2.531</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56904.3353572</td><td>18.766</td><td>0.047</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21299870001227</td><td>6.968337</td><td>0.43772</td><td>2699</td><td>6</td><td>56904.3405072</td><td>1969</td><td>18.784</td><td>21.467</td><td>27.896</td><td>2.45</td><td>2.968</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56904.3635872</td><td>18.721</td><td>0.043</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21299891001474</td><td>6.96832</td><td>0.437738</td><td>2699</td><td>6</td><td>56904.3687372</td><td>1969</td><td>18.728</td><td>21.97</td><td>27.901</td><td>2.45</td><td>2.778</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56931.3226572</td><td>18.803</td><td>0.044</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>22270304001348</td><td>6.968356</td><td>0.437729</td><td>2699</td><td>6</td><td>56931.3284272</td><td>1994</td><td>18.802</td><td>21.732</td><td>27.858</td><td>2.45</td><td>2.63</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56931.3685672</td><td>18.799</td><td>0.044</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>22270414001278</td><td>6.968343</td><td>0.437742</td><td>2699</td><td>6</td><td>56931.3743372</td><td>1994</td><td>18.798</td><td>21.666</td><td>27.849</td><td>2.55</td><td>2.937</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56925.3380872</td><td>18.753</td><td>0.041</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21522485001368</td><td>6.96833</td><td>0.437735</td><td>2699</td><td>6</td><td>56925.3438272</td><td>1988</td><td>18.75</td><td>21.685</td><td>27.852</td><td>2.55</td><td>2.645</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56925.3921172</td><td>18.694</td><td>0.042</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21522530001285</td><td>6.968333</td><td>0.437735</td><td>2699</td><td>6</td><td>56925.3978572</td><td>1988</td><td>18.691</td><td>21.604</td><td>27.839</td><td>2.65</td><td>2.935</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56921.3020072</td><td>18.824</td><td>0.045</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21452249001270</td><td>6.968316</td><td>0.437751</td><td>2699</td><td>6</td><td>56921.3076772</td><td>1984</td><td>18.824</td><td>21.739</td><td>27.861</td><td>2.55</td><td>2.825</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>10</td><td>0.0</td><td>0.0</td><td>14.436147</td><td>1.13845</td><td>55440.4837072</td><td>19.031</td><td>0.059</td><td>27012010000113</td><td>14.436147</td><td>1.13845</td><td>2</td><td>0</td><td>9.764758e-07</td><td>31</td><td>30</td><td>25</td><td>19.07</td><td>0.023</td><td>19.064</td><td>19.06</td><td>19.073</td><td>19.057</td><td>0.075</td><td>0.065</td><td>10920411002139</td><td>14.436107</td><td>1.138411</td><td>2701</td><td>1</td><td>55440.4883672</td><td>535</td><td>19.028</td><td>21.263</td><td>27.764</td><td>2.05</td><td>2.356</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>0.0</td><td>0.0</td><td>14.436147</td><td>1.13845</td><td>55424.4650372</td><td>19.088</td><td>0.061</td><td>27012010000113</td><td>14.436147</td><td>1.13845</td><td>2</td><td>0</td><td>9.764758e-07</td><td>31</td><td>30</td><td>25</td><td>19.07</td><td>0.023</td><td>19.064</td><td>19.06</td><td>19.073</td><td>19.057</td><td>0.075</td><td>0.065</td><td>11141973002683</td><td>14.436084</td><td>1.138478</td><td>2701</td><td>1</td><td>55424.4686272</td><td>523</td><td>19.1</td><td>21.72</td><td>27.73</td><td>1.85</td><td>2.151</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>0.0</td><td>0.0</td><td>14.436147</td><td>1.13845</td><td>55424.5096972</td><td>19.049</td><td>0.078</td><td>27012010000113</td><td>14.436147</td><td>1.13845</td><td>2</td><td>0</td><td>9.764758e-07</td><td>31</td><td>30</td><td>25</td><td>19.07</td><td>0.023</td><td>19.064</td><td>19.06</td><td>19.073</td><td>19.057</td><td>0.075</td><td>0.065</td><td>11142067001834</td><td>14.436079</td><td>1.138387</td><td>2701</td><td>1</td><td>55424.5132972</td><td>523</td><td>19.071</td><td>21.042</td><td>27.73</td><td>1.85</td><td>2.617</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>0.0</td><td>0.0</td><td>14.436147</td><td>1.13845</td><td>55427.5090272</td><td>19.059</td><td>0.068</td><td>27012010000113</td><td>14.436147</td><td>1.13845</td><td>2</td><td>0</td><td>9.764758e-07</td><td>31</td><td>30</td><td>25</td><td>19.07</td><td>0.023</td><td>19.064</td><td>19.06</td><td>19.073</td><td>19.057</td><td>0.075</td><td>0.065</td><td>11195208001909</td><td>14.43605</td><td>1.13843</td><td>2701</td><td>1</td><td>55427.5128472</td><td>526</td><td>19.07</td><td>21.089</td><td>27.794</td><td>1.95</td><td>2.596</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>0.0</td><td>0.0</td><td>14.436147</td><td>1.13845</td><td>55478.2127072</td><td>19.021</td><td>0.065</td><td>27012010000113</td><td>14.436147</td><td>1.13845</td><td>2</td><td>0</td><td>9.764758e-07</td><td>31</td><td>30</td><td>25</td><td>19.07</td><td>0.023</td><td>19.064</td><td>19.06</td><td>19.073</td><td>19.057</td><td>0.075</td><td>0.065</td><td>10831272001885</td><td>14.436116</td><td>1.138389</td><td>2701</td><td>1</td><td>55478.2184572</td><td>572</td><td>19.011</td><td>20.911</td><td>27.773</td><td>2.85</td><td>4.27</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>0.0</td><td>0.0</td><td>14.436147</td><td>1.13845</td><td>55478.2572072</td><td>18.986</td><td>0.082</td><td>27012010000113</td><td>14.436147</td><td>1.13845</td><td>2</td><td>0</td><td>9.764758e-07</td><td>31</td><td>30</td><td>25</td><td>19.07</td><td>0.023</td><td>19.064</td><td>19.06</td><td>19.073</td><td>19.057</td><td>0.075</td><td>0.065</td><td>10831425001775</td><td>14.436149</td><td>1.138459</td><td>2701</td><td>1</td><td>55478.2629672</td><td>572</td><td>18.989</td><td>20.849</td><td>27.796</td><td>3.35</td><td>4.633</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>0.0</td><td>0.0</td><td>14.436147</td><td>1.13845</td><td>55056.4576472</td><td>19.068</td><td>0.068</td><td>27012010000113</td><td>14.436147</td><td>1.13845</td><td>2</td><td>0</td><td>9.764758e-07</td><td>31</td><td>30</td><td>25</td><td>19.07</td><td>0.023</td><td>19.064</td><td>19.06</td><td>19.073</td><td>19.057</td><td>0.075</td><td>0.065</td><td>11898814001945</td><td>14.436072</td><td>1.138458</td><td>2701</td><td>1</td><td>55056.4610372</td><td>170</td><td>19.084</td><td>20.74</td><td>27.385</td><td>2.35</td><td>2.959</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>0.0</td><td>0.0</td><td>14.436147</td><td>1.13845</td><td>55418.4606972</td><td>19.143</td><td>0.196</td><td>27012010000113</td><td>14.436147</td><td>1.13845</td><td>2</td><td>0</td><td>9.764758e-07</td><td>31</td><td>30</td><td>25</td><td>19.07</td><td>0.023</td><td>19.064</td><td>19.06</td><td>19.073</td><td>19.057</td><td>0.075</td><td>0.065</td><td>11128266001864</td><td>14.436048</td><td>1.138414</td><td>2701</td><td>1</td><td>55418.4638272</td><td>517</td><td>19.348</td><td>21.44</td><td>27.077</td><td>2.05</td><td>3.074</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>0.0</td><td>0.0</td><td>14.436147</td><td>1.13845</td><td>55473.2741972</td><td>18.971</td><td>0.061</td><td>27012010000113</td><td>14.436147</td><td>1.13845</td><td>2</td><td>0</td><td>9.764758e-07</td><td>31</td><td>30</td><td>25</td><td>19.07</td><td>0.023</td><td>19.064</td><td>19.06</td><td>19.073</td><td>19.057</td><td>0.075</td><td>0.065</td><td>10804860002968</td><td>14.436082</td><td>1.138461</td><td>2701</td><td>1</td><td>55473.2799372</td><td>566</td><td>18.98</td><td>21.695</td><td>27.811</td><td>2.05</td><td>2.836</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>10</td><td>0.0</td><td>0.0</td><td>14.436147</td><td>1.13845</td><td>55481.4407072</td><td>19.055</td><td>0.06</td><td>27012010000113</td><td>14.436147</td><td>1.13845</td><td>2</td><td>0</td><td>9.764758e-07</td><td>31</td><td>30</td><td>25</td><td>19.07</td><td>0.023</td><td>19.064</td><td>19.06</td><td>19.073</td><td>19.057</td><td>0.075</td><td>0.065</td><td>10899083002132</td><td>14.436087</td><td>1.138474</td><td>2701</td><td>1</td><td>55481.4464472</td><td>575</td><td>19.063</td><td>21.09</td><td>27.766</td><td>2.65</td><td>3.477</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=202>\n",
       "cntr_01  dist_x  pang_x   ra_01   ... qaflags flags  photcalflag   goodflag \n",
       "         arcsec   deg    degrees  ...                0=No, 1=Yes 0=No, 1=Yes\n",
       " int64  float64 float64  float64  ...  int64  int64     int64       int64   \n",
       "------- ------- ------- --------- ... ------- ------ ----------- -----------\n",
       "      1     0.0     0.0  6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0  6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0  6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0  6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0  6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0  6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0  6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0  6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0  6.968328 ...       0 131072           0           1\n",
       "    ...     ...     ...       ... ...     ...    ...         ...         ...\n",
       "     10     0.0     0.0 14.436147 ...       0 131072           0           1\n",
       "     10     0.0     0.0 14.436147 ...       0 131072           0           1\n",
       "     10     0.0     0.0 14.436147 ...       0 131072           0           1\n",
       "     10     0.0     0.0 14.436147 ...       0 131072           0           1\n",
       "     10     0.0     0.0 14.436147 ...       0 131072           0           1\n",
       "     10     0.0     0.0 14.436147 ...       0 131072           0           1\n",
       "     10     0.0     0.0 14.436147 ...       0 131072           0           1\n",
       "     10     0.0     0.0 14.436147 ...       0 131072           0           1\n",
       "     10     0.0     0.0 14.436147 ...       0 131072           0           1\n",
       "     10     0.0     0.0 14.436147 ...       0 131072           0           1"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lcs_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab the first object by ra \n",
    "test_obj_rows = lcs_test['ra'] == np.unique(lcs_test['ra'])[0]\n",
    "# count how many rows\n",
    "len(lcs_test[test_obj_rows])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=6&gt;\n",
       "<table id=\"table4680385984\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>cntr_01</th><th>dist_x</th><th>pang_x</th><th>ra_01</th><th>dec_01</th><th>obsmjd</th><th>mag_autocorr</th><th>magerr_auto</th><th>oid</th><th>ra</th><th>dec</th><th>fid</th><th>transient_flag</th><th>astrometricrms</th><th>nobs</th><th>ngoodobs</th><th>nbestobs</th><th>referencemag</th><th>referencemagerr</th><th>medianmag</th><th>bestmedianmag</th><th>meanmag</th><th>bestmeanmag</th><th>magrms</th><th>bestmagrms</th><th>sid</th><th>s_ra</th><th>s_dec</th><th>ptffield</th><th>ccdid</th><th>hmjd</th><th>nid</th><th>mag_auto</th><th>limitmag</th><th>absphotzp</th><th>fwhmsex</th><th>fwhm_image</th><th>qaflags</th><th>flags</th><th>photcalflag</th><th>goodflag</th></tr></thead>\n",
       "<thead><tr><th></th><th>arcsec</th><th>deg</th><th>degrees</th><th>degrees</th><th>days</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>degrees</th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>days</th><th></th><th>mag</th><th>mag</th><th>mag</th><th>arcsec</th><th>pixels</th><th></th><th></th><th>0=No, 1=Yes</th><th>0=No, 1=Yes</th></tr></thead>\n",
       "<thead><tr><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>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>int64</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>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56903.3404372</td><td>18.747</td><td>0.04</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21305664001544</td><td>6.968329</td><td>0.437729</td><td>2699</td><td>6</td><td>56903.3455472</td><td>1968</td><td>18.76</td><td>21.93</td><td>27.896</td><td>2.45</td><td>2.713</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56924.3337972</td><td>18.774</td><td>0.041</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21507653001359</td><td>6.968328</td><td>0.43773</td><td>2699</td><td>6</td><td>56924.3395172</td><td>1987</td><td>18.778</td><td>21.754</td><td>27.874</td><td>2.45</td><td>2.531</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56904.3353572</td><td>18.766</td><td>0.047</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21299870001227</td><td>6.968337</td><td>0.43772</td><td>2699</td><td>6</td><td>56904.3405072</td><td>1969</td><td>18.784</td><td>21.467</td><td>27.896</td><td>2.45</td><td>2.968</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56904.3635872</td><td>18.721</td><td>0.043</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>21299891001474</td><td>6.96832</td><td>0.437738</td><td>2699</td><td>6</td><td>56904.3687372</td><td>1969</td><td>18.728</td><td>21.97</td><td>27.901</td><td>2.45</td><td>2.778</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56931.3226572</td><td>18.803</td><td>0.044</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>22270304001348</td><td>6.968356</td><td>0.437729</td><td>2699</td><td>6</td><td>56931.3284272</td><td>1994</td><td>18.802</td><td>21.732</td><td>27.858</td><td>2.45</td><td>2.63</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1</td><td>0.0</td><td>0.0</td><td>6.968328</td><td>0.437734</td><td>56931.3685672</td><td>18.799</td><td>0.044</td><td>26991060002225</td><td>6.968328</td><td>0.437734</td><td>1</td><td>0</td><td>4.777896e-07</td><td>125</td><td>125</td><td>123</td><td>18.717</td><td>0.006</td><td>18.746</td><td>18.748</td><td>18.743</td><td>18.743</td><td>0.06</td><td>0.06</td><td>22270414001278</td><td>6.968343</td><td>0.437742</td><td>2699</td><td>6</td><td>56931.3743372</td><td>1994</td><td>18.798</td><td>21.666</td><td>27.849</td><td>2.55</td><td>2.937</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=6>\n",
       "cntr_01  dist_x  pang_x  ra_01   ... qaflags flags  photcalflag   goodflag \n",
       "         arcsec   deg   degrees  ...                0=No, 1=Yes 0=No, 1=Yes\n",
       " int64  float64 float64 float64  ...  int64  int64     int64       int64   \n",
       "------- ------- ------- -------- ... ------- ------ ----------- -----------\n",
       "      1     0.0     0.0 6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0 6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0 6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0 6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0 6.968328 ...       0 131072           0           1\n",
       "      1     0.0     0.0 6.968328 ...       0 131072           0           1"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the first few rows\n",
    "lcs_test[test_obj_rows][:6]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example qso lightcurve ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=6&gt;\n",
       "<table id=\"table4658727960\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>cntr_01</th><th>dist_x</th><th>pang_x</th><th>ra_01</th><th>dec_01</th><th>obsmjd</th><th>mag_autocorr</th><th>magerr_auto</th><th>oid</th><th>ra</th><th>dec</th><th>fid</th><th>transient_flag</th><th>astrometricrms</th><th>nobs</th><th>ngoodobs</th><th>nbestobs</th><th>referencemag</th><th>referencemagerr</th><th>medianmag</th><th>bestmedianmag</th><th>meanmag</th><th>bestmeanmag</th><th>magrms</th><th>bestmagrms</th><th>sid</th><th>s_ra</th><th>s_dec</th><th>ptffield</th><th>ccdid</th><th>hmjd</th><th>nid</th><th>mag_auto</th><th>limitmag</th><th>absphotzp</th><th>fwhmsex</th><th>fwhm_image</th><th>qaflags</th><th>flags</th><th>photcalflag</th><th>goodflag</th></tr></thead>\n",
       "<thead><tr><th></th><th>arcsec</th><th>deg</th><th>degrees</th><th>degrees</th><th>days</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>degrees</th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>days</th><th></th><th>mag</th><th>mag</th><th>mag</th><th>arcsec</th><th>pixels</th><th></th><th></th><th>0=No, 1=Yes</th><th>0=No, 1=Yes</th></tr></thead>\n",
       "<thead><tr><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>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>int64</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>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>2402</td><td>0.0</td><td>0.0</td><td>1.50903</td><td>-0.899447</td><td>56905.2801772</td><td>19.407</td><td>0.094</td><td>25921020000816</td><td>1.50903</td><td>-0.899447</td><td>1</td><td>0</td><td>8.881669e-07</td><td>204</td><td>189</td><td>174</td><td>19.686</td><td>0.015</td><td>19.64</td><td>19.632</td><td>19.656</td><td>19.652</td><td>0.136</td><td>0.127</td><td>21294506001222</td><td>1.509024</td><td>-0.899403</td><td>2592</td><td>2</td><td>56905.2856372</td><td>1970</td><td>19.406</td><td>20.78</td><td>27.757</td><td>2.35</td><td>3.429</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>2402</td><td>0.0</td><td>0.0</td><td>1.50903</td><td>-0.899447</td><td>56942.3708872</td><td>19.959</td><td>0.124</td><td>25921020000816</td><td>1.50903</td><td>-0.899447</td><td>1</td><td>0</td><td>8.881669e-07</td><td>204</td><td>189</td><td>174</td><td>19.686</td><td>0.015</td><td>19.64</td><td>19.632</td><td>19.656</td><td>19.652</td><td>0.136</td><td>0.127</td><td>21770129000931</td><td>1.509045</td><td>-0.8994</td><td>2592</td><td>2</td><td>56942.3763972</td><td>2006</td><td>19.958</td><td>20.576</td><td>27.683</td><td>2.45</td><td>3.332</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>2402</td><td>0.0</td><td>0.0</td><td>1.50903</td><td>-0.899447</td><td>56904.3177872</td><td>19.612</td><td>0.08</td><td>25921020000816</td><td>1.50903</td><td>-0.899447</td><td>1</td><td>0</td><td>8.881669e-07</td><td>204</td><td>189</td><td>174</td><td>19.686</td><td>0.015</td><td>19.64</td><td>19.632</td><td>19.656</td><td>19.652</td><td>0.136</td><td>0.127</td><td>21301786001582</td><td>1.509043</td><td>-0.89943</td><td>2592</td><td>2</td><td>56904.3232272</td><td>1969</td><td>19.616</td><td>21.253</td><td>27.794</td><td>2.25</td><td>2.442</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>2402</td><td>0.0</td><td>0.0</td><td>1.50903</td><td>-0.899447</td><td>56903.3218972</td><td>19.567</td><td>0.057</td><td>25921020000816</td><td>1.50903</td><td>-0.899447</td><td>1</td><td>0</td><td>8.881669e-07</td><td>204</td><td>189</td><td>174</td><td>19.686</td><td>0.015</td><td>19.64</td><td>19.632</td><td>19.656</td><td>19.652</td><td>0.136</td><td>0.127</td><td>21304471002246</td><td>1.509005</td><td>-0.899428</td><td>2592</td><td>2</td><td>56903.3273072</td><td>1968</td><td>19.577</td><td>21.851</td><td>27.799</td><td>2.25</td><td>2.775</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>2402</td><td>0.0</td><td>0.0</td><td>1.50903</td><td>-0.899447</td><td>56903.3515872</td><td>19.557</td><td>0.056</td><td>25921020000816</td><td>1.50903</td><td>-0.899447</td><td>1</td><td>0</td><td>8.881669e-07</td><td>204</td><td>189</td><td>174</td><td>19.686</td><td>0.015</td><td>19.64</td><td>19.632</td><td>19.656</td><td>19.652</td><td>0.136</td><td>0.127</td><td>21304490002307</td><td>1.509005</td><td>-0.899456</td><td>2592</td><td>2</td><td>56903.3569872</td><td>1968</td><td>19.567</td><td>21.946</td><td>27.811</td><td>2.15</td><td>2.641</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>2402</td><td>0.0</td><td>0.0</td><td>1.50903</td><td>-0.899447</td><td>56915.2918972</td><td>19.589</td><td>0.076</td><td>25921020000816</td><td>1.50903</td><td>-0.899447</td><td>1</td><td>0</td><td>8.881669e-07</td><td>204</td><td>189</td><td>174</td><td>19.686</td><td>0.015</td><td>19.64</td><td>19.632</td><td>19.656</td><td>19.652</td><td>0.136</td><td>0.127</td><td>21345421001547</td><td>1.508995</td><td>-0.899431</td><td>2592</td><td>2</td><td>56915.2975972</td><td>1978</td><td>19.6</td><td>21.22</td><td>27.726</td><td>2.35</td><td>2.977</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=6>\n",
       "cntr_01  dist_x  pang_x  ra_01  ... qaflags flags  photcalflag   goodflag \n",
       "         arcsec   deg   degrees ...                0=No, 1=Yes 0=No, 1=Yes\n",
       " int64  float64 float64 float64 ...  int64  int64     int64       int64   \n",
       "------- ------- ------- ------- ... ------- ------ ----------- -----------\n",
       "   2402     0.0     0.0 1.50903 ...       0 131072           0           1\n",
       "   2402     0.0     0.0 1.50903 ...       0 131072           0           1\n",
       "   2402     0.0     0.0 1.50903 ...       0 131072           0           1\n",
       "   2402     0.0     0.0 1.50903 ...       0 131072           0           1\n",
       "   2402     0.0     0.0 1.50903 ...       0 131072           0           1\n",
       "   2402     0.0     0.0 1.50903 ...       0 131072           0           1"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grab the first object by oid \n",
    "test_obj_rows = lcs_qso['oid'] == np.unique(lcs_qso['oid'])[0]\n",
    "# display first few rows... \n",
    "lcs_qso[test_obj_rows][:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again : there are 64 rows :  len(lcs_qso[test_obj_rows]) , even though nbestobs = 174... Why ?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=6&gt;\n",
       "<table id=\"table4671147368\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>cntr_01</th><th>dist_x</th><th>pang_x</th><th>ra_01</th><th>dec_01</th><th>obsmjd</th><th>mag_autocorr</th><th>magerr_auto</th><th>oid</th><th>ra</th><th>dec</th><th>fid</th><th>transient_flag</th><th>astrometricrms</th><th>nobs</th><th>ngoodobs</th><th>nbestobs</th><th>referencemag</th><th>referencemagerr</th><th>medianmag</th><th>bestmedianmag</th><th>meanmag</th><th>bestmeanmag</th><th>magrms</th><th>bestmagrms</th><th>sid</th><th>s_ra</th><th>s_dec</th><th>ptffield</th><th>ccdid</th><th>hmjd</th><th>nid</th><th>mag_auto</th><th>limitmag</th><th>absphotzp</th><th>fwhmsex</th><th>fwhm_image</th><th>qaflags</th><th>flags</th><th>photcalflag</th><th>goodflag</th></tr></thead>\n",
       "<thead><tr><th></th><th>arcsec</th><th>deg</th><th>degrees</th><th>degrees</th><th>days</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>degrees</th><th></th><th></th><th></th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th>mag</th><th></th><th>degrees</th><th>degrees</th><th></th><th></th><th>days</th><th></th><th>mag</th><th>mag</th><th>mag</th><th>arcsec</th><th>pixels</th><th></th><th></th><th>0=No, 1=Yes</th><th>0=No, 1=Yes</th></tr></thead>\n",
       "<thead><tr><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>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>int64</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>int64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>float64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>int64</th><th>int64</th><th>int64</th></tr></thead>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56920.3323372</td><td>17.249</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21434266001679</td><td>0.049834</td><td>0.040392</td><td>2697</td><td>6</td><td>56920.3380872</td><td>1983</td><td>17.249</td><td>21.592</td><td>27.843</td><td>3.05</td><td>3.56</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56952.2524972</td><td>17.237</td><td>0.034</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21923244001705</td><td>0.049843</td><td>0.040389</td><td>2697</td><td>6</td><td>56952.2576672</td><td>2014</td><td>17.239</td><td>21.624</td><td>27.871</td><td>2.65</td><td>2.912</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56914.2943372</td><td>17.258</td><td>0.037</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21364525001402</td><td>0.049841</td><td>0.040379</td><td>2697</td><td>6</td><td>56914.3000172</td><td>1979</td><td>17.248</td><td>21.014</td><td>27.801</td><td>2.45</td><td>2.836</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56914.3413172</td><td>17.244</td><td>0.037</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21364555001390</td><td>0.04984</td><td>0.040393</td><td>2697</td><td>6</td><td>56914.3469972</td><td>1979</td><td>17.228</td><td>21.027</td><td>27.816</td><td>2.45</td><td>2.618</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56927.2768872</td><td>17.239</td><td>0.036</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21547909001793</td><td>0.049844</td><td>0.040393</td><td>2697</td><td>6</td><td>56927.2826572</td><td>1990</td><td>17.223</td><td>21.705</td><td>27.853</td><td>2.55</td><td>2.977</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "<tr><td>1211</td><td>0.0</td><td>180.0</td><td>0.049846</td><td>0.040389</td><td>56927.3321972</td><td>17.282</td><td>0.037</td><td>26971060000456</td><td>0.049846</td><td>0.040389</td><td>1</td><td>0</td><td>2.035493e-07</td><td>109</td><td>109</td><td>109</td><td>17.228</td><td>0.004</td><td>17.257</td><td>17.257</td><td>17.287</td><td>17.287</td><td>0.079</td><td>0.079</td><td>21547958001377</td><td>0.049848</td><td>0.040385</td><td>2697</td><td>6</td><td>56927.3379672</td><td>1990</td><td>17.285</td><td>21.307</td><td>27.856</td><td>3.65</td><td>4.108</td><td>0</td><td>131072</td><td>0</td><td>1</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=6>\n",
       "cntr_01  dist_x  pang_x  ra_01   ... qaflags flags  photcalflag   goodflag \n",
       "         arcsec   deg   degrees  ...                0=No, 1=Yes 0=No, 1=Yes\n",
       " int64  float64 float64 float64  ...  int64  int64     int64       int64   \n",
       "------- ------- ------- -------- ... ------- ------ ----------- -----------\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1\n",
       "   1211     0.0   180.0 0.049846 ...       0 131072           0           1"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    # grab the first object  by ra \n",
    "test_obj_ra = np.unique(lcs_qso['ra'])[0]\n",
    "test_obj_rows = lcs_qso['ra'] == test_obj_ra\n",
    "# display first few rows... \n",
    "\n",
    "lcs_qso[test_obj_rows][:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why nbestobs  has nothing do do with how many rows there actually are ?? \n",
    "\n",
    "len(lcs_qso[test_obj_rows])    is 63, whereas nobs = ngoodobs = nbestobs = 109 .... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "masked_BaseColumn(data = [False False False False False False False False  True False False False\n",
       " False False False False False False False False False False False False\n",
       " False False False False False False False False False False False False\n",
       " False False False False False False False False False False False False\n",
       " False False False False False False False False False  True  True False\n",
       " False False False],\n",
       "                  mask = False,\n",
       "            fill_value = 1.0)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_outliers = yerr > 3*np.mean(yerr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1167f14a8>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RwpRMRESkMCUTEREpTMlEREQKUzIREZHClExERKQwJRMRESlMyURERApTMhER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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116fb45f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot an example lightcurve...\n",
    "x    = lcs_qso[test_obj_rows]['obsmjd']\n",
    "y    = lcs_qso[test_obj_rows]['mag_autocorr']\n",
    "yerr = lcs_qso[test_obj_rows]['magerr_auto']\n",
    "\n",
    "# Just for plotting:  remove the points that have way too high photometric error... \n",
    "mask_outliers = yerr < 2*np.mean(yerr)\n",
    "\n",
    "x_sel = x[mask_outliers]\n",
    "y_sel = y[mask_outliers]\n",
    "yerr_sel = yerr[mask_outliers]\n",
    "\n",
    "plt.scatter(x_sel,y_sel,lw=0)\n",
    "plt.errorbar(x_sel,y_sel,yerr_sel, fmt='None')\n",
    "#plt.ylim(14,23)\n",
    "plt.ylabel('mag_autocorr')\n",
    "plt.xlabel('MJD')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save in a CRTS-readable format, so that I can run these through the same analysis as CRTS data... \n",
    "\n",
    "# Or just do all day-averaging  on the IPAC table :  \n",
    "# much quicker solution, since I don't really need the raw data for anything.... \n",
    "\n",
    "# Next steps:  modify  \n",
    "# A_Fig_1_QSO_CRTS_day_averaged_stats.ipynb\n",
    "# B_sf_CRTS_SDSS_matching_NEW.py\n",
    "\n",
    "# and perhaps \n",
    "\n",
    "# C_sf_load_single_LC.py "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Querying IRSA for  stars : same steps as above for quasars... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}