S82_QSO_from_ZTF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I grab the light curves for S82 quasars from a database named `ztf` on `epyc.astro.washington.edu` machine. They're all collected in a single table named `alerts`.  Heavily based on example of \n",
    "`epyc/users/zgolkhou/uw-ztf-database-demo.ipynb` . Thank you Zachary Golkhou! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:90% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import mysql.connector as mariadb\n",
    "from astropy.time import Time\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.table import Table\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:90% !important; }</style>\"))\n",
    "\n",
    "pd.set_option('display.max_columns',100)\n",
    "pd.set_option('float_format', '{:f}'.format)\n",
    "\n",
    "\n",
    "from astropy.table import Table\n",
    "from astropy.coordinates import SkyCoord\n",
    "from astropy import units as u\n",
    "from astropy.table import  hstack\n",
    "from astropy.table import  vstack\n",
    "from astropy.table import Column\n",
    "from astropy.table import join\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Connect to the database using the user `ztf`. There's no password. You *must* do this from epyc (the database is inaccessible outside epyc).\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['alerts'],\n",
       "       ['alerts_ORG'],\n",
       "       ['alerts_limmag'],\n",
       "       ['summary']], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con = mariadb.connect(user='ztf', database='ztf')\n",
    "pd.read_sql_query('show tables;', con).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find some more info about \"summary\" table : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Field</th>\n",
       "      <th>Type</th>\n",
       "      <th>Null</th>\n",
       "      <th>Key</th>\n",
       "      <th>Default</th>\n",
       "      <th>Extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>objectId</td>\n",
       "      <td>varchar(20)</td>\n",
       "      <td>NO</td>\n",
       "      <td>PRI</td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>meanRA</td>\n",
       "      <td>double</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>meanDec</td>\n",
       "      <td>double</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nobs</td>\n",
       "      <td>int(11)</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gNobs</td>\n",
       "      <td>int(11)</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>rNobs</td>\n",
       "      <td>int(11)</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>iNobs</td>\n",
       "      <td>int(11)</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gMeanAB</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rMeanAB</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>iMeanAB</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gStdAB</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>rStdAB</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>iStdAB</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gMeanJy</td>\n",
       "      <td>double</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>rMeanJy</td>\n",
       "      <td>double</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>iMeanJy</td>\n",
       "      <td>double</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gStdJy</td>\n",
       "      <td>double</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>rStdJy</td>\n",
       "      <td>double</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>iStdJy</td>\n",
       "      <td>double</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>gRB</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>rRB</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>iRB</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>gClasstar</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>rClasstar</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>iClasstar</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Distnr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>gMagnr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>gDmagnr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>rMagnr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>rDmagnr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>iMagnr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>iDmagnr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>gChinr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>rChinr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>iChinr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>gSharpnr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>rSharpnr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>iSharpnr</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Distnrps1</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>gMagps1</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>rMagps1</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>iMagps1</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Sgscoreps1</td>\n",
       "      <td>float</td>\n",
       "      <td>YES</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Field         Type Null  Key Default Extra\n",
       "0     objectId  varchar(20)   NO  PRI    None      \n",
       "1       meanRA       double  YES         None      \n",
       "2      meanDec       double  YES         None      \n",
       "3         nobs      int(11)  YES         None      \n",
       "4        gNobs      int(11)  YES         None      \n",
       "5        rNobs      int(11)  YES         None      \n",
       "6        iNobs      int(11)  YES         None      \n",
       "7      gMeanAB        float  YES         None      \n",
       "8      rMeanAB        float  YES         None      \n",
       "9      iMeanAB        float  YES         None      \n",
       "10      gStdAB        float  YES         None      \n",
       "11      rStdAB        float  YES         None      \n",
       "12      iStdAB        float  YES         None      \n",
       "13     gMeanJy       double  YES         None      \n",
       "14     rMeanJy       double  YES         None      \n",
       "15     iMeanJy       double  YES         None      \n",
       "16      gStdJy       double  YES         None      \n",
       "17      rStdJy       double  YES         None      \n",
       "18      iStdJy       double  YES         None      \n",
       "19         gRB        float  YES         None      \n",
       "20         rRB        float  YES         None      \n",
       "21         iRB        float  YES         None      \n",
       "22   gClasstar        float  YES         None      \n",
       "23   rClasstar        float  YES         None      \n",
       "24   iClasstar        float  YES         None      \n",
       "25      Distnr        float  YES         None      \n",
       "26      gMagnr        float  YES         None      \n",
       "27     gDmagnr        float  YES         None      \n",
       "28      rMagnr        float  YES         None      \n",
       "29     rDmagnr        float  YES         None      \n",
       "30      iMagnr        float  YES         None      \n",
       "31     iDmagnr        float  YES         None      \n",
       "32      gChinr        float  YES         None      \n",
       "33      rChinr        float  YES         None      \n",
       "34      iChinr        float  YES         None      \n",
       "35    gSharpnr        float  YES         None      \n",
       "36    rSharpnr        float  YES         None      \n",
       "37    iSharpnr        float  YES         None      \n",
       "38   Distnrps1        float  YES         None      \n",
       "39     gMagps1        float  YES         None      \n",
       "40     rMagps1        float  YES         None      \n",
       "41     iMagps1        float  YES         None      \n",
       "42  Sgscoreps1        float  YES         None      "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('describe summary;', con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Table of contents : \n",
    "\n",
    "  * [simple example queries](#example_queries)\n",
    "  * [obtain ZTF data for CW Leo AGB star ](#cw_leo)\n",
    "  * [Check for ZTF data for S82 SDSS QSO ](#s82_qso)\n",
    "  * [download the median ZTF data](#download_median_ztf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"After that, it's easiest to use Pandas to execute queries. Below are some examples.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='example_queries'></a>\n",
    "    Sample first 10 rows to show the table structure : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 88.1 ms, sys: 5.57 ms, total: 93.7 ms\n",
      "Wall time: 243 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.read_sql_query('select * from alerts LIMIT 10', con)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objectId</th>\n",
       "      <th>jd</th>\n",
       "      <th>fid</th>\n",
       "      <th>pid</th>\n",
       "      <th>diffmaglim</th>\n",
       "      <th>programid</th>\n",
       "      <th>candid</th>\n",
       "      <th>isdiffpos</th>\n",
       "      <th>tblid</th>\n",
       "      <th>nid</th>\n",
       "      <th>rcid</th>\n",
       "      <th>field</th>\n",
       "      <th>xpos</th>\n",
       "      <th>ypos</th>\n",
       "      <th>ra</th>\n",
       "      <th>decl</th>\n",
       "      <th>magpsf</th>\n",
       "      <th>sigmapsf</th>\n",
       "      <th>chipsf</th>\n",
       "      <th>magap</th>\n",
       "      <th>sigmagap</th>\n",
       "      <th>distnr</th>\n",
       "      <th>magnr</th>\n",
       "      <th>sigmagnr</th>\n",
       "      <th>chinr</th>\n",
       "      <th>sharpnr</th>\n",
       "      <th>sky</th>\n",
       "      <th>magdiff</th>\n",
       "      <th>fwhm</th>\n",
       "      <th>classtar</th>\n",
       "      <th>mindtoedge</th>\n",
       "      <th>magfromlim</th>\n",
       "      <th>seeratio</th>\n",
       "      <th>aimage</th>\n",
       "      <th>bimage</th>\n",
       "      <th>aimagerat</th>\n",
       "      <th>bimagerat</th>\n",
       "      <th>elong</th>\n",
       "      <th>nneg</th>\n",
       "      <th>nbad</th>\n",
       "      <th>rb</th>\n",
       "      <th>ssdistnr</th>\n",
       "      <th>ssmagnr</th>\n",
       "      <th>ssnamenr</th>\n",
       "      <th>sumrat</th>\n",
       "      <th>magapbig</th>\n",
       "      <th>sigmagapbig</th>\n",
       "      <th>ranr</th>\n",
       "      <th>decnr</th>\n",
       "      <th>sgmag1</th>\n",
       "      <th>srmag1</th>\n",
       "      <th>simag1</th>\n",
       "      <th>szmag1</th>\n",
       "      <th>sgscore1</th>\n",
       "      <th>distpsnr1</th>\n",
       "      <th>ndethist</th>\n",
       "      <th>ncovhist</th>\n",
       "      <th>jdstarthist</th>\n",
       "      <th>jdendhist</th>\n",
       "      <th>scorr</th>\n",
       "      <th>tooflag</th>\n",
       "      <th>objectidps1</th>\n",
       "      <th>objectidps2</th>\n",
       "      <th>sgmag2</th>\n",
       "      <th>srmag2</th>\n",
       "      <th>simag2</th>\n",
       "      <th>szmag2</th>\n",
       "      <th>sgscore2</th>\n",
       "      <th>distpsnr2</th>\n",
       "      <th>objectidps3</th>\n",
       "      <th>sgmag3</th>\n",
       "      <th>srmag3</th>\n",
       "      <th>simag3</th>\n",
       "      <th>szmag3</th>\n",
       "      <th>sgscore3</th>\n",
       "      <th>distpsnr3</th>\n",
       "      <th>nmtchps</th>\n",
       "      <th>rfid</th>\n",
       "      <th>jdstartref</th>\n",
       "      <th>jdendref</th>\n",
       "      <th>nframesref</th>\n",
       "      <th>rbversion</th>\n",
       "      <th>dsnrms</th>\n",
       "      <th>ssnrms</th>\n",
       "      <th>dsdiff</th>\n",
       "      <th>magzpsci</th>\n",
       "      <th>magzpsciunc</th>\n",
       "      <th>magzpscirms</th>\n",
       "      <th>nmatches</th>\n",
       "      <th>clrcoeff</th>\n",
       "      <th>clrcounc</th>\n",
       "      <th>zpclrcov</th>\n",
       "      <th>zpmed</th>\n",
       "      <th>clrmed</th>\n",
       "      <th>clrrms</th>\n",
       "      <th>neargaia</th>\n",
       "      <th>neargaiabright</th>\n",
       "      <th>maggaia</th>\n",
       "      <th>maggaiabright</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ZTF18aahinga</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>462175923815</td>\n",
       "      <td>19.847500</td>\n",
       "      <td>1</td>\n",
       "      <td>462175923815010000</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>462</td>\n",
       "      <td>38</td>\n",
       "      <td>611</td>\n",
       "      <td>2611.640000</td>\n",
       "      <td>601.620000</td>\n",
       "      <td>83.950123</td>\n",
       "      <td>26.951459</td>\n",
       "      <td>19.584200</td>\n",
       "      <td>0.100120</td>\n",
       "      <td>0.714361</td>\n",
       "      <td>19.052400</td>\n",
       "      <td>0.085600</td>\n",
       "      <td>1.698290</td>\n",
       "      <td>19.850000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>4.807000</td>\n",
       "      <td>0.279000</td>\n",
       "      <td>0.947525</td>\n",
       "      <td>-0.531844</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>0.055000</td>\n",
       "      <td>460.862000</td>\n",
       "      <td>0.795101</td>\n",
       "      <td>0.398599</td>\n",
       "      <td>1.783000</td>\n",
       "      <td>1.182000</td>\n",
       "      <td>0.224842</td>\n",
       "      <td>0.149054</td>\n",
       "      <td>1.508460</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td></td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.809500</td>\n",
       "      <td>0.151900</td>\n",
       "      <td>83.949587</td>\n",
       "      <td>26.951482</td>\n",
       "      <td>21.349000</td>\n",
       "      <td>22.117200</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.698130</td>\n",
       "      <td>2</td>\n",
       "      <td>193</td>\n",
       "      <td>2458216.671887</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>11.262218</td>\n",
       "      <td>0</td>\n",
       "      <td>140340839508391569</td>\n",
       "      <td>140340839478855868</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.378800</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0.672562</td>\n",
       "      <td>12.729300</td>\n",
       "      <td>140330839496818027</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.716300</td>\n",
       "      <td>20.865400</td>\n",
       "      <td>20.401400</td>\n",
       "      <td>0.132577</td>\n",
       "      <td>13.034900</td>\n",
       "      <td>14</td>\n",
       "      <td>611120238</td>\n",
       "      <td>2458160.722651</td>\n",
       "      <td>2458186.665903</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZTF18aahinhb</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>462175923815</td>\n",
       "      <td>19.847500</td>\n",
       "      <td>1</td>\n",
       "      <td>462175923815010001</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>462</td>\n",
       "      <td>38</td>\n",
       "      <td>611</td>\n",
       "      <td>665.048000</td>\n",
       "      <td>675.905000</td>\n",
       "      <td>84.564446</td>\n",
       "      <td>26.929695</td>\n",
       "      <td>20.102300</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>0.469641</td>\n",
       "      <td>20.048400</td>\n",
       "      <td>0.212500</td>\n",
       "      <td>3.518630</td>\n",
       "      <td>21.447000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.983000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>6.682850</td>\n",
       "      <td>-0.053890</td>\n",
       "      <td>20.630000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>665.048000</td>\n",
       "      <td>-0.200899</td>\n",
       "      <td>0.153218</td>\n",
       "      <td>0.451000</td>\n",
       "      <td>0.339000</td>\n",
       "      <td>0.021861</td>\n",
       "      <td>0.016432</td>\n",
       "      <td>1.330380</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.203333</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td></td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.772200</td>\n",
       "      <td>0.147700</td>\n",
       "      <td>84.563636</td>\n",
       "      <td>26.929017</td>\n",
       "      <td>20.071700</td>\n",
       "      <td>19.042100</td>\n",
       "      <td>18.471700</td>\n",
       "      <td>18.145500</td>\n",
       "      <td>0.990875</td>\n",
       "      <td>7.531390</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>7.263019</td>\n",
       "      <td>0</td>\n",
       "      <td>140310845629368137</td>\n",
       "      <td>140310845609598319</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.408100</td>\n",
       "      <td>20.603400</td>\n",
       "      <td>20.129900</td>\n",
       "      <td>0.910896</td>\n",
       "      <td>12.882300</td>\n",
       "      <td>140310845684195579</td>\n",
       "      <td>20.385200</td>\n",
       "      <td>19.093700</td>\n",
       "      <td>17.977500</td>\n",
       "      <td>17.455700</td>\n",
       "      <td>0.957708</td>\n",
       "      <td>12.927300</td>\n",
       "      <td>18</td>\n",
       "      <td>611120238</td>\n",
       "      <td>2458160.722651</td>\n",
       "      <td>2458186.665903</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZTF18aahingv</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>462175923815</td>\n",
       "      <td>19.847500</td>\n",
       "      <td>1</td>\n",
       "      <td>462175923815010002</td>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>462</td>\n",
       "      <td>38</td>\n",
       "      <td>611</td>\n",
       "      <td>621.718000</td>\n",
       "      <td>813.712000</td>\n",
       "      <td>84.577934</td>\n",
       "      <td>26.890878</td>\n",
       "      <td>19.688800</td>\n",
       "      <td>0.111339</td>\n",
       "      <td>0.649423</td>\n",
       "      <td>19.772500</td>\n",
       "      <td>0.165300</td>\n",
       "      <td>0.205755</td>\n",
       "      <td>19.294000</td>\n",
       "      <td>0.036000</td>\n",
       "      <td>1.313000</td>\n",
       "      <td>0.067000</td>\n",
       "      <td>2.859200</td>\n",
       "      <td>0.083738</td>\n",
       "      <td>4.020000</td>\n",
       "      <td>0.966000</td>\n",
       "      <td>621.718000</td>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.786291</td>\n",
       "      <td>1.198000</td>\n",
       "      <td>1.029000</td>\n",
       "      <td>0.298010</td>\n",
       "      <td>0.255970</td>\n",
       "      <td>1.164240</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td></td>\n",
       "      <td>0.991097</td>\n",
       "      <td>19.360700</td>\n",
       "      <td>0.252100</td>\n",
       "      <td>84.578000</td>\n",
       "      <td>26.890871</td>\n",
       "      <td>21.081200</td>\n",
       "      <td>19.793500</td>\n",
       "      <td>19.274400</td>\n",
       "      <td>18.938900</td>\n",
       "      <td>0.960208</td>\n",
       "      <td>0.222024</td>\n",
       "      <td>2</td>\n",
       "      <td>193</td>\n",
       "      <td>2458216.671887</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>8.801106</td>\n",
       "      <td>0</td>\n",
       "      <td>140260845779919629</td>\n",
       "      <td>140270845746322645</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.122800</td>\n",
       "      <td>20.489100</td>\n",
       "      <td>0.780482</td>\n",
       "      <td>13.868900</td>\n",
       "      <td>140260845732418609</td>\n",
       "      <td>21.279100</td>\n",
       "      <td>20.033300</td>\n",
       "      <td>19.363300</td>\n",
       "      <td>18.955600</td>\n",
       "      <td>0.953958</td>\n",
       "      <td>15.353300</td>\n",
       "      <td>12</td>\n",
       "      <td>611120238</td>\n",
       "      <td>2458160.722651</td>\n",
       "      <td>2458186.665903</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ZTF17aadkdbp</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>462175923815</td>\n",
       "      <td>19.847500</td>\n",
       "      <td>1</td>\n",
       "      <td>462175923815010003</td>\n",
       "      <td>f</td>\n",
       "      <td>3</td>\n",
       "      <td>462</td>\n",
       "      <td>38</td>\n",
       "      <td>611</td>\n",
       "      <td>733.822000</td>\n",
       "      <td>929.138000</td>\n",
       "      <td>84.542418</td>\n",
       "      <td>26.858532</td>\n",
       "      <td>17.078000</td>\n",
       "      <td>0.017212</td>\n",
       "      <td>1.168850</td>\n",
       "      <td>17.088800</td>\n",
       "      <td>0.018400</td>\n",
       "      <td>0.131628</td>\n",
       "      <td>15.705000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.506000</td>\n",
       "      <td>-0.044000</td>\n",
       "      <td>0.893753</td>\n",
       "      <td>0.010753</td>\n",
       "      <td>3.160000</td>\n",
       "      <td>0.983000</td>\n",
       "      <td>733.822000</td>\n",
       "      <td>2.758700</td>\n",
       "      <td>1.000280</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>1.019000</td>\n",
       "      <td>0.329114</td>\n",
       "      <td>0.322468</td>\n",
       "      <td>1.020610</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.606667</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td></td>\n",
       "      <td>1.000000</td>\n",
       "      <td>16.963700</td>\n",
       "      <td>0.029900</td>\n",
       "      <td>84.542437</td>\n",
       "      <td>26.858563</td>\n",
       "      <td>16.771700</td>\n",
       "      <td>15.827500</td>\n",
       "      <td>15.455000</td>\n",
       "      <td>15.358100</td>\n",
       "      <td>0.969583</td>\n",
       "      <td>0.095185</td>\n",
       "      <td>83</td>\n",
       "      <td>197</td>\n",
       "      <td>2458042.000359</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>26.530439</td>\n",
       "      <td>0</td>\n",
       "      <td>140230845424180878</td>\n",
       "      <td>140220845446489414</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.490300</td>\n",
       "      <td>20.934100</td>\n",
       "      <td>0.600146</td>\n",
       "      <td>8.343180</td>\n",
       "      <td>140230845412273998</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.708600</td>\n",
       "      <td>21.553600</td>\n",
       "      <td>21.235200</td>\n",
       "      <td>0.025667</td>\n",
       "      <td>10.147700</td>\n",
       "      <td>12</td>\n",
       "      <td>611120238</td>\n",
       "      <td>2458160.722651</td>\n",
       "      <td>2458186.665903</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ZTF18aahinha</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>462175923815</td>\n",
       "      <td>19.847500</td>\n",
       "      <td>1</td>\n",
       "      <td>462175923815010004</td>\n",
       "      <td>f</td>\n",
       "      <td>4</td>\n",
       "      <td>462</td>\n",
       "      <td>38</td>\n",
       "      <td>611</td>\n",
       "      <td>2599.630000</td>\n",
       "      <td>1021.180000</td>\n",
       "      <td>83.954005</td>\n",
       "      <td>26.833443</td>\n",
       "      <td>20.341000</td>\n",
       "      <td>0.200630</td>\n",
       "      <td>0.481025</td>\n",
       "      <td>20.029200</td>\n",
       "      <td>0.207900</td>\n",
       "      <td>1.946210</td>\n",
       "      <td>21.451000</td>\n",
       "      <td>0.156000</td>\n",
       "      <td>1.165000</td>\n",
       "      <td>0.257000</td>\n",
       "      <td>5.864300</td>\n",
       "      <td>-0.311846</td>\n",
       "      <td>15.050000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>472.871000</td>\n",
       "      <td>-0.181699</td>\n",
       "      <td>0.210026</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>2.933000</td>\n",
       "      <td>0.219269</td>\n",
       "      <td>0.194884</td>\n",
       "      <td>1.125130</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.193333</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td></td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.908800</td>\n",
       "      <td>0.166600</td>\n",
       "      <td>83.954556</td>\n",
       "      <td>26.833203</td>\n",
       "      <td>22.044800</td>\n",
       "      <td>21.556700</td>\n",
       "      <td>20.710300</td>\n",
       "      <td>20.327900</td>\n",
       "      <td>0.950036</td>\n",
       "      <td>11.933400</td>\n",
       "      <td>1</td>\n",
       "      <td>193</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>6.819677</td>\n",
       "      <td>0</td>\n",
       "      <td>140190839522987197</td>\n",
       "      <td>140200839577142453</td>\n",
       "      <td>17.546000</td>\n",
       "      <td>16.779300</td>\n",
       "      <td>16.396700</td>\n",
       "      <td>16.191200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.020800</td>\n",
       "      <td>140200839534765525</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>22.324700</td>\n",
       "      <td>21.016800</td>\n",
       "      <td>20.326800</td>\n",
       "      <td>0.842208</td>\n",
       "      <td>14.532900</td>\n",
       "      <td>15</td>\n",
       "      <td>611120238</td>\n",
       "      <td>2458160.722651</td>\n",
       "      <td>2458186.665903</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ZTF17aaczquf</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>462175923815</td>\n",
       "      <td>19.847500</td>\n",
       "      <td>1</td>\n",
       "      <td>462175923815010005</td>\n",
       "      <td>f</td>\n",
       "      <td>5</td>\n",
       "      <td>462</td>\n",
       "      <td>38</td>\n",
       "      <td>611</td>\n",
       "      <td>1775.220000</td>\n",
       "      <td>1013.290000</td>\n",
       "      <td>84.213927</td>\n",
       "      <td>26.835601</td>\n",
       "      <td>17.642300</td>\n",
       "      <td>0.042425</td>\n",
       "      <td>1.707800</td>\n",
       "      <td>17.842700</td>\n",
       "      <td>0.045700</td>\n",
       "      <td>0.470053</td>\n",
       "      <td>14.810000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.609000</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>3.654570</td>\n",
       "      <td>0.200400</td>\n",
       "      <td>2.330000</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>1013.290000</td>\n",
       "      <td>2.004800</td>\n",
       "      <td>1.356610</td>\n",
       "      <td>0.945000</td>\n",
       "      <td>0.823000</td>\n",
       "      <td>0.405579</td>\n",
       "      <td>0.353219</td>\n",
       "      <td>1.148240</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.553333</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td></td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17.697900</td>\n",
       "      <td>0.063900</td>\n",
       "      <td>84.213834</td>\n",
       "      <td>26.835705</td>\n",
       "      <td>15.842500</td>\n",
       "      <td>14.850000</td>\n",
       "      <td>14.268100</td>\n",
       "      <td>13.962400</td>\n",
       "      <td>0.981952</td>\n",
       "      <td>0.492905</td>\n",
       "      <td>6</td>\n",
       "      <td>197</td>\n",
       "      <td>2458042.000359</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>8.175267</td>\n",
       "      <td>0</td>\n",
       "      <td>140200842138583477</td>\n",
       "      <td>140200842159344203</td>\n",
       "      <td>20.329700</td>\n",
       "      <td>19.315100</td>\n",
       "      <td>18.679000</td>\n",
       "      <td>18.372300</td>\n",
       "      <td>0.861333</td>\n",
       "      <td>6.929640</td>\n",
       "      <td>140200842145460477</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.705200</td>\n",
       "      <td>20.330500</td>\n",
       "      <td>19.444000</td>\n",
       "      <td>0.911786</td>\n",
       "      <td>8.750270</td>\n",
       "      <td>10</td>\n",
       "      <td>611120238</td>\n",
       "      <td>2458160.722651</td>\n",
       "      <td>2458186.665903</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ZTF17aaczqqk</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>462175923815</td>\n",
       "      <td>19.847500</td>\n",
       "      <td>1</td>\n",
       "      <td>462175923815010006</td>\n",
       "      <td>f</td>\n",
       "      <td>6</td>\n",
       "      <td>462</td>\n",
       "      <td>38</td>\n",
       "      <td>611</td>\n",
       "      <td>3036.250000</td>\n",
       "      <td>1061.070000</td>\n",
       "      <td>83.816397</td>\n",
       "      <td>26.822068</td>\n",
       "      <td>18.299600</td>\n",
       "      <td>0.084762</td>\n",
       "      <td>0.640129</td>\n",
       "      <td>18.418000</td>\n",
       "      <td>0.081400</td>\n",
       "      <td>0.519321</td>\n",
       "      <td>14.644000</td>\n",
       "      <td>0.017000</td>\n",
       "      <td>0.607000</td>\n",
       "      <td>-0.019000</td>\n",
       "      <td>1.404140</td>\n",
       "      <td>0.118444</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.974000</td>\n",
       "      <td>36.250000</td>\n",
       "      <td>1.429500</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0.766000</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>1.037940</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td></td>\n",
       "      <td>0.973453</td>\n",
       "      <td>18.447600</td>\n",
       "      <td>0.129900</td>\n",
       "      <td>83.816393</td>\n",
       "      <td>26.822218</td>\n",
       "      <td>15.302500</td>\n",
       "      <td>14.695800</td>\n",
       "      <td>14.418000</td>\n",
       "      <td>14.277500</td>\n",
       "      <td>0.966411</td>\n",
       "      <td>0.512674</td>\n",
       "      <td>5</td>\n",
       "      <td>193</td>\n",
       "      <td>2458042.000359</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>5.321662</td>\n",
       "      <td>0</td>\n",
       "      <td>140180838163877237</td>\n",
       "      <td>140180838131098181</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>22.180200</td>\n",
       "      <td>21.410300</td>\n",
       "      <td>21.023100</td>\n",
       "      <td>0.039750</td>\n",
       "      <td>11.082700</td>\n",
       "      <td>140180838123984357</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.478300</td>\n",
       "      <td>20.797800</td>\n",
       "      <td>20.474100</td>\n",
       "      <td>0.411250</td>\n",
       "      <td>15.276100</td>\n",
       "      <td>18</td>\n",
       "      <td>611120238</td>\n",
       "      <td>2458160.722651</td>\n",
       "      <td>2458186.665903</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ZTF17aadqfdv</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>462175923815</td>\n",
       "      <td>19.847500</td>\n",
       "      <td>1</td>\n",
       "      <td>462175923815010007</td>\n",
       "      <td>f</td>\n",
       "      <td>7</td>\n",
       "      <td>462</td>\n",
       "      <td>38</td>\n",
       "      <td>611</td>\n",
       "      <td>2738.710000</td>\n",
       "      <td>1397.700000</td>\n",
       "      <td>83.910289</td>\n",
       "      <td>26.727510</td>\n",
       "      <td>15.906900</td>\n",
       "      <td>0.008418</td>\n",
       "      <td>30.996700</td>\n",
       "      <td>16.027400</td>\n",
       "      <td>0.009100</td>\n",
       "      <td>0.111777</td>\n",
       "      <td>14.589000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.501000</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>1.386950</td>\n",
       "      <td>0.120506</td>\n",
       "      <td>5.520000</td>\n",
       "      <td>0.985000</td>\n",
       "      <td>333.788000</td>\n",
       "      <td>3.820100</td>\n",
       "      <td>0.572625</td>\n",
       "      <td>1.044000</td>\n",
       "      <td>0.989000</td>\n",
       "      <td>0.189130</td>\n",
       "      <td>0.179167</td>\n",
       "      <td>1.055610</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td></td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.907000</td>\n",
       "      <td>0.012600</td>\n",
       "      <td>83.910280</td>\n",
       "      <td>26.727540</td>\n",
       "      <td>15.327400</td>\n",
       "      <td>14.683800</td>\n",
       "      <td>14.377200</td>\n",
       "      <td>14.148600</td>\n",
       "      <td>0.966327</td>\n",
       "      <td>0.123954</td>\n",
       "      <td>108</td>\n",
       "      <td>193</td>\n",
       "      <td>2458042.000359</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>59.773277</td>\n",
       "      <td>0</td>\n",
       "      <td>140070839102563643</td>\n",
       "      <td>140070839123295214</td>\n",
       "      <td>22.214300</td>\n",
       "      <td>21.050100</td>\n",
       "      <td>20.369500</td>\n",
       "      <td>19.920700</td>\n",
       "      <td>0.940375</td>\n",
       "      <td>8.177330</td>\n",
       "      <td>140070839131874993</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.812300</td>\n",
       "      <td>20.890100</td>\n",
       "      <td>20.281100</td>\n",
       "      <td>0.225427</td>\n",
       "      <td>10.368200</td>\n",
       "      <td>15</td>\n",
       "      <td>611120238</td>\n",
       "      <td>2458160.722651</td>\n",
       "      <td>2458186.665903</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ZTF17aadqffe</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>462175923815</td>\n",
       "      <td>19.847500</td>\n",
       "      <td>1</td>\n",
       "      <td>462175923815010008</td>\n",
       "      <td>f</td>\n",
       "      <td>8</td>\n",
       "      <td>462</td>\n",
       "      <td>38</td>\n",
       "      <td>611</td>\n",
       "      <td>1759.450000</td>\n",
       "      <td>1392.690000</td>\n",
       "      <td>84.218729</td>\n",
       "      <td>26.728875</td>\n",
       "      <td>18.682500</td>\n",
       "      <td>0.060897</td>\n",
       "      <td>0.961517</td>\n",
       "      <td>18.541800</td>\n",
       "      <td>0.061100</td>\n",
       "      <td>0.343869</td>\n",
       "      <td>16.525000</td>\n",
       "      <td>0.023000</td>\n",
       "      <td>0.911000</td>\n",
       "      <td>-0.033000</td>\n",
       "      <td>0.441620</td>\n",
       "      <td>-0.140730</td>\n",
       "      <td>3.410000</td>\n",
       "      <td>0.976000</td>\n",
       "      <td>1313.050000</td>\n",
       "      <td>1.305700</td>\n",
       "      <td>0.926948</td>\n",
       "      <td>1.161000</td>\n",
       "      <td>1.103000</td>\n",
       "      <td>0.340469</td>\n",
       "      <td>0.323460</td>\n",
       "      <td>1.052580</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td></td>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.246400</td>\n",
       "      <td>0.093500</td>\n",
       "      <td>84.218726</td>\n",
       "      <td>26.728971</td>\n",
       "      <td>18.455800</td>\n",
       "      <td>16.964700</td>\n",
       "      <td>16.665700</td>\n",
       "      <td>16.000400</td>\n",
       "      <td>0.982083</td>\n",
       "      <td>0.345067</td>\n",
       "      <td>95</td>\n",
       "      <td>197</td>\n",
       "      <td>2458042.950301</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>16.124775</td>\n",
       "      <td>0</td>\n",
       "      <td>140070842187355371</td>\n",
       "      <td>140070842231274032</td>\n",
       "      <td>14.853200</td>\n",
       "      <td>14.425000</td>\n",
       "      <td>14.236100</td>\n",
       "      <td>14.142200</td>\n",
       "      <td>0.996250</td>\n",
       "      <td>14.545600</td>\n",
       "      <td>140070842167230583</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>22.338800</td>\n",
       "      <td>21.594000</td>\n",
       "      <td>20.796100</td>\n",
       "      <td>0.849577</td>\n",
       "      <td>15.560700</td>\n",
       "      <td>12</td>\n",
       "      <td>611120238</td>\n",
       "      <td>2458160.722651</td>\n",
       "      <td>2458186.665903</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ZTF18aahings</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>462175923815</td>\n",
       "      <td>19.847500</td>\n",
       "      <td>1</td>\n",
       "      <td>462175923815010009</td>\n",
       "      <td>f</td>\n",
       "      <td>9</td>\n",
       "      <td>462</td>\n",
       "      <td>38</td>\n",
       "      <td>611</td>\n",
       "      <td>3017.730000</td>\n",
       "      <td>1489.490000</td>\n",
       "      <td>83.822478</td>\n",
       "      <td>26.701588</td>\n",
       "      <td>19.104500</td>\n",
       "      <td>0.131375</td>\n",
       "      <td>1.436480</td>\n",
       "      <td>19.464200</td>\n",
       "      <td>0.172300</td>\n",
       "      <td>0.240529</td>\n",
       "      <td>15.450000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>-0.028000</td>\n",
       "      <td>1.432960</td>\n",
       "      <td>0.359744</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.951000</td>\n",
       "      <td>54.773900</td>\n",
       "      <td>0.383301</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>0.734000</td>\n",
       "      <td>0.609000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>1.205250</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td></td>\n",
       "      <td>0.867273</td>\n",
       "      <td>19.278900</td>\n",
       "      <td>0.257700</td>\n",
       "      <td>83.822516</td>\n",
       "      <td>26.701649</td>\n",
       "      <td>16.836900</td>\n",
       "      <td>15.573500</td>\n",
       "      <td>14.890500</td>\n",
       "      <td>14.512600</td>\n",
       "      <td>0.987577</td>\n",
       "      <td>0.232669</td>\n",
       "      <td>2</td>\n",
       "      <td>193</td>\n",
       "      <td>2458104.973935</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>5.128745</td>\n",
       "      <td>0</td>\n",
       "      <td>140040838225272544</td>\n",
       "      <td>140040838216793841</td>\n",
       "      <td>18.994000</td>\n",
       "      <td>17.829300</td>\n",
       "      <td>17.310700</td>\n",
       "      <td>17.025000</td>\n",
       "      <td>0.967548</td>\n",
       "      <td>4.755340</td>\n",
       "      <td>140030838229699789</td>\n",
       "      <td>-999.000000</td>\n",
       "      <td>21.235000</td>\n",
       "      <td>20.512600</td>\n",
       "      <td>20.135600</td>\n",
       "      <td>0.832220</td>\n",
       "      <td>8.396430</td>\n",
       "      <td>13</td>\n",
       "      <td>611120238</td>\n",
       "      <td>2458160.722651</td>\n",
       "      <td>2458186.665903</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       objectId             jd  fid           pid  diffmaglim  programid  \\\n",
       "0  ZTF18aahinga 2458216.675926    2  462175923815   19.847500          1   \n",
       "1  ZTF18aahinhb 2458216.675926    2  462175923815   19.847500          1   \n",
       "2  ZTF18aahingv 2458216.675926    2  462175923815   19.847500          1   \n",
       "3  ZTF17aadkdbp 2458216.675926    2  462175923815   19.847500          1   \n",
       "4  ZTF18aahinha 2458216.675926    2  462175923815   19.847500          1   \n",
       "5  ZTF17aaczquf 2458216.675926    2  462175923815   19.847500          1   \n",
       "6  ZTF17aaczqqk 2458216.675926    2  462175923815   19.847500          1   \n",
       "7  ZTF17aadqfdv 2458216.675926    2  462175923815   19.847500          1   \n",
       "8  ZTF17aadqffe 2458216.675926    2  462175923815   19.847500          1   \n",
       "9  ZTF18aahings 2458216.675926    2  462175923815   19.847500          1   \n",
       "\n",
       "               candid isdiffpos  tblid  nid  rcid  field        xpos  \\\n",
       "0  462175923815010000         f      0  462    38    611 2611.640000   \n",
       "1  462175923815010001         f      1  462    38    611  665.048000   \n",
       "2  462175923815010002         f      2  462    38    611  621.718000   \n",
       "3  462175923815010003         f      3  462    38    611  733.822000   \n",
       "4  462175923815010004         f      4  462    38    611 2599.630000   \n",
       "5  462175923815010005         f      5  462    38    611 1775.220000   \n",
       "6  462175923815010006         f      6  462    38    611 3036.250000   \n",
       "7  462175923815010007         f      7  462    38    611 2738.710000   \n",
       "8  462175923815010008         f      8  462    38    611 1759.450000   \n",
       "9  462175923815010009         f      9  462    38    611 3017.730000   \n",
       "\n",
       "         ypos        ra      decl    magpsf  sigmapsf    chipsf     magap  \\\n",
       "0  601.620000 83.950123 26.951459 19.584200  0.100120  0.714361 19.052400   \n",
       "1  675.905000 84.564446 26.929695 20.102300  0.161061  0.469641 20.048400   \n",
       "2  813.712000 84.577934 26.890878 19.688800  0.111339  0.649423 19.772500   \n",
       "3  929.138000 84.542418 26.858532 17.078000  0.017212  1.168850 17.088800   \n",
       "4 1021.180000 83.954005 26.833443 20.341000  0.200630  0.481025 20.029200   \n",
       "5 1013.290000 84.213927 26.835601 17.642300  0.042425  1.707800 17.842700   \n",
       "6 1061.070000 83.816397 26.822068 18.299600  0.084762  0.640129 18.418000   \n",
       "7 1397.700000 83.910289 26.727510 15.906900  0.008418 30.996700 16.027400   \n",
       "8 1392.690000 84.218729 26.728875 18.682500  0.060897  0.961517 18.541800   \n",
       "9 1489.490000 83.822478 26.701588 19.104500  0.131375  1.436480 19.464200   \n",
       "\n",
       "   sigmagap   distnr     magnr  sigmagnr    chinr   sharpnr      sky  \\\n",
       "0  0.085600 1.698290 19.850000  0.120000 4.807000  0.279000 0.947525   \n",
       "1  0.212500 3.518630 21.447000  0.146000 0.983000  0.150000 6.682850   \n",
       "2  0.165300 0.205755 19.294000  0.036000 1.313000  0.067000 2.859200   \n",
       "3  0.018400 0.131628 15.705000  0.015000 0.506000 -0.044000 0.893753   \n",
       "4  0.207900 1.946210 21.451000  0.156000 1.165000  0.257000 5.864300   \n",
       "5  0.045700 0.470053 14.810000  0.016000 0.609000 -0.001000 3.654570   \n",
       "6  0.081400 0.519321 14.644000  0.017000 0.607000 -0.019000 1.404140   \n",
       "7  0.009100 0.111777 14.589000  0.016000 0.501000 -0.001000 1.386950   \n",
       "8  0.061100 0.343869 16.525000  0.023000 0.911000 -0.033000 0.441620   \n",
       "9  0.172300 0.240529 15.450000  0.020000 0.830000 -0.028000 1.432960   \n",
       "\n",
       "    magdiff      fwhm  classtar  mindtoedge  magfromlim    seeratio   aimage  \\\n",
       "0 -0.531844  7.930000  0.055000  460.862000    0.795101    0.398599 1.783000   \n",
       "1 -0.053890 20.630000  0.538000  665.048000   -0.200899    0.153218 0.451000   \n",
       "2  0.083738  4.020000  0.966000  621.718000    0.075000    0.786291 1.198000   \n",
       "3  0.010753  3.160000  0.983000  733.822000    2.758700    1.000280 1.040000   \n",
       "4 -0.311846 15.050000  0.012000  472.871000   -0.181699    0.210026 3.300000   \n",
       "5  0.200400  2.330000  0.996000 1013.290000    2.004800    1.356610 0.945000   \n",
       "6  0.118444  0.000000  0.974000   36.250000    1.429500 -999.000000 0.766000   \n",
       "7  0.120506  5.520000  0.985000  333.788000    3.820100    0.572625 1.044000   \n",
       "8 -0.140730  3.410000  0.976000 1313.050000    1.305700    0.926948 1.161000   \n",
       "9  0.359744  0.000000  0.951000   54.773900    0.383301 -999.000000 0.734000   \n",
       "\n",
       "    bimage   aimagerat   bimagerat    elong  nneg  nbad       rb    ssdistnr  \\\n",
       "0 1.182000    0.224842    0.149054 1.508460     0     0 0.110000 -999.000000   \n",
       "1 0.339000    0.021861    0.016432 1.330380     3     0 0.203333 -999.000000   \n",
       "2 1.029000    0.298010    0.255970 1.164240     4     0 0.420000 -999.000000   \n",
       "3 1.019000    0.329114    0.322468 1.020610     0     0 0.606667 -999.000000   \n",
       "4 2.933000    0.219269    0.194884 1.125130     2     0 0.193333 -999.000000   \n",
       "5 0.823000    0.405579    0.353219 1.148240     1     0 0.553333 -999.000000   \n",
       "6 0.738000 -999.000000 -999.000000 1.037940     4     0 0.366667 -999.000000   \n",
       "7 0.989000    0.189130    0.179167 1.055610     0     0 0.583333 -999.000000   \n",
       "8 1.103000    0.340469    0.323460 1.052580     1     0 0.540000 -999.000000   \n",
       "9 0.609000 -999.000000 -999.000000 1.205250     8     0 0.330000 -999.000000   \n",
       "\n",
       "      ssmagnr ssnamenr   sumrat  magapbig  sigmagapbig      ranr     decnr  \\\n",
       "0 -999.000000          1.000000 18.809500     0.151900 83.949587 26.951482   \n",
       "1 -999.000000          1.000000 18.772200     0.147700 84.563636 26.929017   \n",
       "2 -999.000000          0.991097 19.360700     0.252100 84.578000 26.890871   \n",
       "3 -999.000000          1.000000 16.963700     0.029900 84.542437 26.858563   \n",
       "4 -999.000000          1.000000 18.908800     0.166600 83.954556 26.833203   \n",
       "5 -999.000000          1.000000 17.697900     0.063900 84.213834 26.835705   \n",
       "6 -999.000000          0.973453 18.447600     0.129900 83.816393 26.822218   \n",
       "7 -999.000000          1.000000 15.907000     0.012600 83.910280 26.727540   \n",
       "8 -999.000000          1.000000 18.246400     0.093500 84.218726 26.728971   \n",
       "9 -999.000000          0.867273 19.278900     0.257700 83.822516 26.701649   \n",
       "\n",
       "     sgmag1    srmag1      simag1      szmag1  sgscore1  distpsnr1  ndethist  \\\n",
       "0 21.349000 22.117200 -999.000000 -999.000000  0.500000   2.698130         2   \n",
       "1 20.071700 19.042100   18.471700   18.145500  0.990875   7.531390         1   \n",
       "2 21.081200 19.793500   19.274400   18.938900  0.960208   0.222024         2   \n",
       "3 16.771700 15.827500   15.455000   15.358100  0.969583   0.095185        83   \n",
       "4 22.044800 21.556700   20.710300   20.327900  0.950036  11.933400         1   \n",
       "5 15.842500 14.850000   14.268100   13.962400  0.981952   0.492905         6   \n",
       "6 15.302500 14.695800   14.418000   14.277500  0.966411   0.512674         5   \n",
       "7 15.327400 14.683800   14.377200   14.148600  0.966327   0.123954       108   \n",
       "8 18.455800 16.964700   16.665700   16.000400  0.982083   0.345067        95   \n",
       "9 16.836900 15.573500   14.890500   14.512600  0.987577   0.232669         2   \n",
       "\n",
       "   ncovhist    jdstarthist      jdendhist     scorr  tooflag  \\\n",
       "0       193 2458216.671887 2458216.675926 11.262218        0   \n",
       "1       193 2458216.675926 2458216.675926  7.263019        0   \n",
       "2       193 2458216.671887 2458216.675926  8.801106        0   \n",
       "3       197 2458042.000359 2458216.675926 26.530439        0   \n",
       "4       193 2458216.675926 2458216.675926  6.819677        0   \n",
       "5       197 2458042.000359 2458216.675926  8.175267        0   \n",
       "6       193 2458042.000359 2458216.675926  5.321662        0   \n",
       "7       193 2458042.000359 2458216.675926 59.773277        0   \n",
       "8       197 2458042.950301 2458216.675926 16.124775        0   \n",
       "9       193 2458104.973935 2458216.675926  5.128745        0   \n",
       "\n",
       "          objectidps1         objectidps2      sgmag2      srmag2    simag2  \\\n",
       "0  140340839508391569  140340839478855868 -999.000000 -999.000000 21.378800   \n",
       "1  140310845629368137  140310845609598319 -999.000000   21.408100 20.603400   \n",
       "2  140260845779919629  140270845746322645 -999.000000 -999.000000 21.122800   \n",
       "3  140230845424180878  140220845446489414 -999.000000 -999.000000 21.490300   \n",
       "4  140190839522987197  140200839577142453   17.546000   16.779300 16.396700   \n",
       "5  140200842138583477  140200842159344203   20.329700   19.315100 18.679000   \n",
       "6  140180838163877237  140180838131098181 -999.000000   22.180200 21.410300   \n",
       "7  140070839102563643  140070839123295214   22.214300   21.050100 20.369500   \n",
       "8  140070842187355371  140070842231274032   14.853200   14.425000 14.236100   \n",
       "9  140040838225272544  140040838216793841   18.994000   17.829300 17.310700   \n",
       "\n",
       "       szmag2  sgscore2  distpsnr2         objectidps3      sgmag3    srmag3  \\\n",
       "0 -999.000000  0.672562  12.729300  140330839496818027 -999.000000 21.716300   \n",
       "1   20.129900  0.910896  12.882300  140310845684195579   20.385200 19.093700   \n",
       "2   20.489100  0.780482  13.868900  140260845732418609   21.279100 20.033300   \n",
       "3   20.934100  0.600146   8.343180  140230845412273998 -999.000000 21.708600   \n",
       "4   16.191200  1.000000  13.020800  140200839534765525 -999.000000 22.324700   \n",
       "5   18.372300  0.861333   6.929640  140200842145460477 -999.000000 21.705200   \n",
       "6   21.023100  0.039750  11.082700  140180838123984357 -999.000000 21.478300   \n",
       "7   19.920700  0.940375   8.177330  140070839131874993 -999.000000 21.812300   \n",
       "8   14.142200  0.996250  14.545600  140070842167230583 -999.000000 22.338800   \n",
       "9   17.025000  0.967548   4.755340  140030838229699789 -999.000000 21.235000   \n",
       "\n",
       "     simag3    szmag3  sgscore3  distpsnr3  nmtchps       rfid     jdstartref  \\\n",
       "0 20.865400 20.401400  0.132577  13.034900       14  611120238 2458160.722651   \n",
       "1 17.977500 17.455700  0.957708  12.927300       18  611120238 2458160.722651   \n",
       "2 19.363300 18.955600  0.953958  15.353300       12  611120238 2458160.722651   \n",
       "3 21.553600 21.235200  0.025667  10.147700       12  611120238 2458160.722651   \n",
       "4 21.016800 20.326800  0.842208  14.532900       15  611120238 2458160.722651   \n",
       "5 20.330500 19.444000  0.911786   8.750270       10  611120238 2458160.722651   \n",
       "6 20.797800 20.474100  0.411250  15.276100       18  611120238 2458160.722651   \n",
       "7 20.890100 20.281100  0.225427  10.368200       15  611120238 2458160.722651   \n",
       "8 21.594000 20.796100  0.849577  15.560700       12  611120238 2458160.722651   \n",
       "9 20.512600 20.135600  0.832220   8.396430       13  611120238 2458160.722651   \n",
       "\n",
       "        jdendref  nframesref rbversion dsnrms ssnrms dsdiff magzpsci  \\\n",
       "0 2458186.665903          17      None   None   None   None     None   \n",
       "1 2458186.665903          17      None   None   None   None     None   \n",
       "2 2458186.665903          17      None   None   None   None     None   \n",
       "3 2458186.665903          17      None   None   None   None     None   \n",
       "4 2458186.665903          17      None   None   None   None     None   \n",
       "5 2458186.665903          17      None   None   None   None     None   \n",
       "6 2458186.665903          17      None   None   None   None     None   \n",
       "7 2458186.665903          17      None   None   None   None     None   \n",
       "8 2458186.665903          17      None   None   None   None     None   \n",
       "9 2458186.665903          17      None   None   None   None     None   \n",
       "\n",
       "  magzpsciunc magzpscirms nmatches clrcoeff clrcounc zpclrcov zpmed clrmed  \\\n",
       "0        None        None     None     None     None     None  None   None   \n",
       "1        None        None     None     None     None     None  None   None   \n",
       "2        None        None     None     None     None     None  None   None   \n",
       "3        None        None     None     None     None     None  None   None   \n",
       "4        None        None     None     None     None     None  None   None   \n",
       "5        None        None     None     None     None     None  None   None   \n",
       "6        None        None     None     None     None     None  None   None   \n",
       "7        None        None     None     None     None     None  None   None   \n",
       "8        None        None     None     None     None     None  None   None   \n",
       "9        None        None     None     None     None     None  None   None   \n",
       "\n",
       "  clrrms neargaia neargaiabright maggaia maggaiabright  \n",
       "0   None     None           None    None          None  \n",
       "1   None     None           None    None          None  \n",
       "2   None     None           None    None          None  \n",
       "3   None     None           None    None          None  \n",
       "4   None     None           None    None          None  \n",
       "5   None     None           None    None          None  \n",
       "6   None     None           None    None          None  \n",
       "7   None     None           None    None          None  \n",
       "8   None     None           None    None          None  \n",
       "9   None     None           None    None          None  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Useful ones (for now) :  `objectId,  jd,  fid, ra, decl, magpsf,  sigmapsf, classtar`  .   'fid' is   Filter ID (1=g; 2=r; 3=i). \n",
    "\n",
    "\"The descriptions of these columns can be found at https://zwickytransientfacility.github.io/ztf-avro-alert/schema.html.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql_query('select objectid, jd, fid, ra,decl,magpsf, sigmapsf, classtar from alerts LIMIT 10', con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objectid</th>\n",
       "      <th>jd</th>\n",
       "      <th>fid</th>\n",
       "      <th>ra</th>\n",
       "      <th>decl</th>\n",
       "      <th>magpsf</th>\n",
       "      <th>sigmapsf</th>\n",
       "      <th>classtar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ZTF18aahinga</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>83.950123</td>\n",
       "      <td>26.951459</td>\n",
       "      <td>19.584200</td>\n",
       "      <td>0.100120</td>\n",
       "      <td>0.055000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZTF18aahinhb</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>84.564446</td>\n",
       "      <td>26.929695</td>\n",
       "      <td>20.102300</td>\n",
       "      <td>0.161061</td>\n",
       "      <td>0.538000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZTF18aahingv</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>84.577934</td>\n",
       "      <td>26.890878</td>\n",
       "      <td>19.688800</td>\n",
       "      <td>0.111339</td>\n",
       "      <td>0.966000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ZTF17aadkdbp</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>84.542418</td>\n",
       "      <td>26.858532</td>\n",
       "      <td>17.078000</td>\n",
       "      <td>0.017212</td>\n",
       "      <td>0.983000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ZTF18aahinha</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>83.954005</td>\n",
       "      <td>26.833443</td>\n",
       "      <td>20.341000</td>\n",
       "      <td>0.200630</td>\n",
       "      <td>0.012000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ZTF17aaczquf</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>84.213927</td>\n",
       "      <td>26.835601</td>\n",
       "      <td>17.642300</td>\n",
       "      <td>0.042425</td>\n",
       "      <td>0.996000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ZTF17aaczqqk</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>83.816397</td>\n",
       "      <td>26.822068</td>\n",
       "      <td>18.299600</td>\n",
       "      <td>0.084762</td>\n",
       "      <td>0.974000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ZTF17aadqfdv</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>83.910289</td>\n",
       "      <td>26.727510</td>\n",
       "      <td>15.906900</td>\n",
       "      <td>0.008418</td>\n",
       "      <td>0.985000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ZTF17aadqffe</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>84.218729</td>\n",
       "      <td>26.728875</td>\n",
       "      <td>18.682500</td>\n",
       "      <td>0.060897</td>\n",
       "      <td>0.976000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ZTF18aahings</td>\n",
       "      <td>2458216.675926</td>\n",
       "      <td>2</td>\n",
       "      <td>83.822478</td>\n",
       "      <td>26.701588</td>\n",
       "      <td>19.104500</td>\n",
       "      <td>0.131375</td>\n",
       "      <td>0.951000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       objectid             jd  fid        ra      decl    magpsf  sigmapsf  \\\n",
       "0  ZTF18aahinga 2458216.675926    2 83.950123 26.951459 19.584200  0.100120   \n",
       "1  ZTF18aahinhb 2458216.675926    2 84.564446 26.929695 20.102300  0.161061   \n",
       "2  ZTF18aahingv 2458216.675926    2 84.577934 26.890878 19.688800  0.111339   \n",
       "3  ZTF17aadkdbp 2458216.675926    2 84.542418 26.858532 17.078000  0.017212   \n",
       "4  ZTF18aahinha 2458216.675926    2 83.954005 26.833443 20.341000  0.200630   \n",
       "5  ZTF17aaczquf 2458216.675926    2 84.213927 26.835601 17.642300  0.042425   \n",
       "6  ZTF17aaczqqk 2458216.675926    2 83.816397 26.822068 18.299600  0.084762   \n",
       "7  ZTF17aadqfdv 2458216.675926    2 83.910289 26.727510 15.906900  0.008418   \n",
       "8  ZTF17aadqffe 2458216.675926    2 84.218729 26.728875 18.682500  0.060897   \n",
       "9  ZTF18aahings 2458216.675926    2 83.822478 26.701588 19.104500  0.131375   \n",
       "\n",
       "   classtar  \n",
       "0  0.055000  \n",
       "1  0.538000  \n",
       "2  0.966000  \n",
       "3  0.983000  \n",
       "4  0.012000  \n",
       "5  0.996000  \n",
       "6  0.974000  \n",
       "7  0.985000  \n",
       "8  0.976000  \n",
       "9  0.951000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='cw_leo'></a>Grab the CW Leo light curve by name : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql_query('SELECT objectid, jd, fid, ra,decl,magpsf, sigmapsf \\\n",
    "FROM alerts WHERE objectid = \"ZTF18aahpgxj\" ', con)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objectid</th>\n",
       "      <th>jd</th>\n",
       "      <th>fid</th>\n",
       "      <th>ra</th>\n",
       "      <th>decl</th>\n",
       "      <th>magpsf</th>\n",
       "      <th>sigmapsf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ZTF18aahpgxj</td>\n",
       "      <td>2458245.653194</td>\n",
       "      <td>2</td>\n",
       "      <td>146.989427</td>\n",
       "      <td>13.278551</td>\n",
       "      <td>17.547800</td>\n",
       "      <td>0.020409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZTF18aahpgxj</td>\n",
       "      <td>2458257.728530</td>\n",
       "      <td>2</td>\n",
       "      <td>146.989323</td>\n",
       "      <td>13.278556</td>\n",
       "      <td>16.415100</td>\n",
       "      <td>0.012176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZTF18aahpgxj</td>\n",
       "      <td>2458263.701250</td>\n",
       "      <td>2</td>\n",
       "      <td>146.989375</td>\n",
       "      <td>13.278534</td>\n",
       "      <td>16.163200</td>\n",
       "      <td>0.006661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       objectid             jd  fid         ra      decl    magpsf  sigmapsf\n",
       "0  ZTF18aahpgxj 2458245.653194    2 146.989427 13.278551 17.547800  0.020409\n",
       "1  ZTF18aahpgxj 2458257.728530    2 146.989323 13.278556 16.415100  0.012176\n",
       "2  ZTF18aahpgxj 2458263.701250    2 146.989375 13.278534 16.163200  0.006661"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grab the CW Leo light curve by coords : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  30 arcsec  ==> 0.008333  degree (  30 / 3600 )\n",
    "df = pd.read_sql_query('SELECT objectid, jd, fid, ra,decl,magpsf, sigmapsf \\\n",
    "FROM alerts WHERE (decl > 13.278767-0.008333 ) && (decl < 13.278767+0.008333) \\\n",
    "AND (146.98921-0.008333 < ra) && ( ra < 146.98921+0.008333)  ', con)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb = Table.from_pandas(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb.write('ZTF18aahpgxj.txt', format='ascii')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/data/epyc/users/suberlak'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='s82_qso'></a>\n",
    "Grab the S82 QSO database: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check how many SDSS S82 DR9 QSOs have PS1 matches \n",
    "File = 'DB_QSO_S82.dat'\n",
    "colnames = ['dbID', 'ra', 'dec', 'SDR5ID', 'M_i', 'M_i_corr', 'redshift',\n",
    "                   'mass_BH', 'Lbol', 'u', 'g', 'r', 'i', 'z', 'Au']\n",
    "\n",
    "# 9258 QSO ....\n",
    "SDSS_DB_QSO= Table.read(File, format='ascii', names = colnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table length=9258&gt;\n",
       "<table id=\"table140557347965920\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><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></tr></thead>\n",
       "<thead><tr><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></tr></thead>\n",
       "<tr><td>70</td><td>2.169302</td><td>1.238649</td><td>301</td><td>-23.901</td><td>-24.181</td><td>1.073</td><td>0.0</td><td>0.0</td><td>20.793</td><td>20.469</td><td>20.197</td><td>20.04</td><td>20.0</td><td>0.116</td></tr>\n",
       "<tr><td>98</td><td>1.091028</td><td>0.962126</td><td>144</td><td>-23.399</td><td>-23.576</td><td>0.7867</td><td>0.0</td><td>0.0</td><td>20.79</td><td>20.183</td><td>19.849</td><td>19.818</td><td>19.43</td><td>0.183</td></tr>\n",
       "<tr><td>233</td><td>0.331289</td><td>0.17723</td><td>58</td><td>-24.735</td><td>-25.058</td><td>1.6199</td><td>0.0</td><td>0.0</td><td>20.892</td><td>20.554</td><td>20.431</td><td>20.199</td><td>20.099</td><td>0.154</td></tr>\n",
       "<tr><td>1018</td><td>1.364696</td><td>-0.098956</td><td>190</td><td>-23.121</td><td>-24.045</td><td>0.6125</td><td>0.0</td><td>45.433</td><td>20.098</td><td>19.722</td><td>19.784</td><td>19.485</td><td>19.541</td><td>0.178</td></tr>\n",
       "<tr><td>1310</td><td>0.221552</td><td>-0.292485</td><td>36</td><td>-26.451</td><td>-26.974</td><td>2.7563</td><td>9.361</td><td>46.76</td><td>20.707</td><td>19.663</td><td>19.61</td><td>19.705</td><td>19.529</td><td>0.174</td></tr>\n",
       "<tr><td>1327</td><td>0.28137</td><td>0.686984</td><td>49</td><td>-24.917</td><td>-25.288</td><td>1.3988</td><td>8.817</td><td>46.071</td><td>20.213</td><td>20.105</td><td>19.815</td><td>19.666</td><td>19.749</td><td>0.139</td></tr>\n",
       "<tr><td>1540</td><td>2.607954</td><td>0.913245</td><td>370</td><td>-27.263</td><td>-27.856</td><td>2.8473</td><td>0.0</td><td>0.0</td><td>21.346</td><td>19.15</td><td>19.093</td><td>18.944</td><td>18.758</td><td>0.122</td></tr>\n",
       "<tr><td>1580</td><td>1.669492</td><td>0.643952</td><td>229</td><td>-24.427</td><td>-24.898</td><td>1.3383</td><td>8.666</td><td>45.862</td><td>20.732</td><td>20.572</td><td>20.13</td><td>20.079</td><td>20.231</td><td>0.207</td></tr>\n",
       "<tr><td>2113</td><td>4.850273</td><td>-0.276395</td><td>699</td><td>-25.251</td><td>-25.695</td><td>1.769</td><td>0.0</td><td>0.0</td><td>19.956</td><td>20.066</td><td>20.152</td><td>19.886</td><td>19.618</td><td>0.147</td></tr>\n",
       "<tr><td>2306</td><td>4.026021</td><td>0.478127</td><td>578</td><td>-24.999</td><td>-25.581</td><td>1.8725</td><td>0.0</td><td>0.0</td><td>20.772</td><td>20.708</td><td>20.599</td><td>20.262</td><td>20.201</td><td>0.128</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></tr>\n",
       "<tr><td>1010945</td><td>22.393028</td><td>1.219312</td><td>-1</td><td>-25.24</td><td>-1.0</td><td>2.237</td><td>-1.0</td><td>-1.0</td><td>17.56</td><td>17.48</td><td>17.24</td><td>16.9</td><td>16.95</td><td>0.0</td></tr>\n",
       "<tr><td>985048</td><td>23.716694</td><td>1.255406</td><td>-1</td><td>-23.23</td><td>-1.0</td><td>0.948</td><td>-1.0</td><td>-1.0</td><td>20.32</td><td>19.92</td><td>19.52</td><td>19.43</td><td>19.17</td><td>0.0</td></tr>\n",
       "<tr><td>1707986</td><td>32.259647</td><td>1.213249</td><td>-1</td><td>-26.49</td><td>-1.0</td><td>2.77</td><td>-1.0</td><td>-1.0</td><td>20.88</td><td>20.34</td><td>20.16</td><td>19.83</td><td>19.82</td><td>0.0</td></tr>\n",
       "<tr><td>1586606</td><td>32.903904</td><td>1.231189</td><td>-1</td><td>-23.98</td><td>-1.0</td><td>1.275</td><td>-1.0</td><td>-1.0</td><td>20.5</td><td>20.79</td><td>20.19</td><td>20.0</td><td>19.83</td><td>0.0</td></tr>\n",
       "<tr><td>1600743</td><td>34.072029</td><td>1.209499</td><td>-1</td><td>-25.57</td><td>-1.0</td><td>2.187</td><td>-1.0</td><td>-1.0</td><td>20.84</td><td>20.38</td><td>19.88</td><td>19.46</td><td>19.21</td><td>0.0</td></tr>\n",
       "<tr><td>2004174</td><td>35.943745</td><td>1.257434</td><td>-1</td><td>-24.26</td><td>-1.0</td><td>2.417</td><td>-1.0</td><td>-1.0</td><td>21.14</td><td>20.43</td><td>20.3</td><td>20.26</td><td>19.91</td><td>0.0</td></tr>\n",
       "<tr><td>2020108</td><td>35.959663</td><td>1.242573</td><td>-1</td><td>-24.37</td><td>-1.0</td><td>1.801</td><td>-1.0</td><td>-1.0</td><td>20.96</td><td>20.61</td><td>20.5</td><td>20.54</td><td>20.09</td><td>0.0</td></tr>\n",
       "<tr><td>2474692</td><td>44.045433</td><td>1.223469</td><td>-1</td><td>-23.1</td><td>-1.0</td><td>0.987</td><td>-1.0</td><td>-1.0</td><td>20.94</td><td>20.73</td><td>20.54</td><td>20.67</td><td>20.24</td><td>0.0</td></tr>\n",
       "<tr><td>4952501</td><td>55.363995</td><td>1.253936</td><td>-1</td><td>-25.76</td><td>-1.0</td><td>3.104</td><td>-1.0</td><td>-1.0</td><td>24.17</td><td>21.71</td><td>20.41</td><td>20.22</td><td>20.24</td><td>0.0</td></tr>\n",
       "<tr><td>350195</td><td>352.900955</td><td>1.201872</td><td>-1</td><td>-24.63</td><td>-1.0</td><td>1.409</td><td>-1.0</td><td>-1.0</td><td>20.3</td><td>20.15</td><td>20.16</td><td>20.04</td><td>19.87</td><td>0.0</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=9258>\n",
       "  dbID      ra        dec    SDR5ID   M_i   ...    r       i       z       Au  \n",
       " int64   float64    float64  int64  float64 ... float64 float64 float64 float64\n",
       "------- ---------- --------- ------ ------- ... ------- ------- ------- -------\n",
       "     70   2.169302  1.238649    301 -23.901 ...  20.197   20.04    20.0   0.116\n",
       "     98   1.091028  0.962126    144 -23.399 ...  19.849  19.818   19.43   0.183\n",
       "    233   0.331289   0.17723     58 -24.735 ...  20.431  20.199  20.099   0.154\n",
       "   1018   1.364696 -0.098956    190 -23.121 ...  19.784  19.485  19.541   0.178\n",
       "   1310   0.221552 -0.292485     36 -26.451 ...   19.61  19.705  19.529   0.174\n",
       "   1327    0.28137  0.686984     49 -24.917 ...  19.815  19.666  19.749   0.139\n",
       "   1540   2.607954  0.913245    370 -27.263 ...  19.093  18.944  18.758   0.122\n",
       "   1580   1.669492  0.643952    229 -24.427 ...   20.13  20.079  20.231   0.207\n",
       "   2113   4.850273 -0.276395    699 -25.251 ...  20.152  19.886  19.618   0.147\n",
       "   2306   4.026021  0.478127    578 -24.999 ...  20.599  20.262  20.201   0.128\n",
       "    ...        ...       ...    ...     ... ...     ...     ...     ...     ...\n",
       "1010945  22.393028  1.219312     -1  -25.24 ...   17.24    16.9   16.95     0.0\n",
       " 985048  23.716694  1.255406     -1  -23.23 ...   19.52   19.43   19.17     0.0\n",
       "1707986  32.259647  1.213249     -1  -26.49 ...   20.16   19.83   19.82     0.0\n",
       "1586606  32.903904  1.231189     -1  -23.98 ...   20.19    20.0   19.83     0.0\n",
       "1600743  34.072029  1.209499     -1  -25.57 ...   19.88   19.46   19.21     0.0\n",
       "2004174  35.943745  1.257434     -1  -24.26 ...    20.3   20.26   19.91     0.0\n",
       "2020108  35.959663  1.242573     -1  -24.37 ...    20.5   20.54   20.09     0.0\n",
       "2474692  44.045433  1.223469     -1   -23.1 ...   20.54   20.67   20.24     0.0\n",
       "4952501  55.363995  1.253936     -1  -25.76 ...   20.41   20.22   20.24     0.0\n",
       " 350195 352.900955  1.201872     -1  -24.63 ...   20.16   20.04   19.87     0.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SDSS_DB_QSO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.027228 359.997681\n",
      "-1.265148 1.266716\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd60e4cec88>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(SDSS_DB_QSO['ra'].min(),SDSS_DB_QSO['ra'].max())\n",
    "print(SDSS_DB_QSO['dec'].min(),SDSS_DB_QSO['dec'].max())\n",
    "plt.scatter(SDSS_DB_QSO['ra'], SDSS_DB_QSO['dec'], s=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's get  all ZTF data for (  -1.3  < dec < 1.3 )  &&  ( 300 < ra < 360)  | ( 0 < ra < 55  ). (takes about a minute to run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql_query('SELECT objectid, jd, fid, ra,decl,magpsf, sigmapsf \\\n",
    "FROM alerts WHERE (decl > -1.3) && (decl < 1.3)', con)  #  AND (0 < ra) && ( ra < 55) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now (8/17/18) with more data , there is some overlap :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fd56d104da0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.scatter(df['ra'], df['decl'], s=0.1)\n",
    "plt.scatter(SDSS_DB_QSO['ra'], SDSS_DB_QSO['dec'], s=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quasars are good to illustrate coverage because there's only 9258 of them.  Check if there are any matches : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only the portion of the ztf table that looks 'by eye' \n",
    "# from the above ra,dec plot to have some ZTF alerts...\n",
    "ztf_s82 = Table.from_pandas(df[df['ra']>300])\n",
    "\n",
    "ztf_coords = SkyCoord(ra=ztf_s82['ra'].data*u.degree, \n",
    "                      dec=ztf_s82['decl'].data*u.degree)\n",
    "sdss_coords = SkyCoord(ra=SDSS_DB_QSO['ra']*u.degree, \n",
    "                       dec=SDSS_DB_QSO['dec']*u.degree)\n",
    "\n",
    "\n",
    "# match SDSS Quasars to ZTF alerts since there is many less QSO than alerts ... \n",
    "idx, sep2d, dist3d = sdss_coords.match_to_catalog_sky(ztf_coords) \n",
    "SDSS_DB_QSO['d2d_arcsec']  = sep2d.value*3600\n",
    "\n",
    "\n",
    "# Unfortunately, at this time there is none that is a good \n",
    "# match  to Schneider+2007  SDSS DB9  Quasars...\n",
    "print(len(SDSS_DB_QSO[SDSS_DB_QSO['d2d_arcsec'] < 5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are still  947  ztf objects with  1204  epochs total\n"
     ]
    }
   ],
   "source": [
    "print('There are still ', len(np.unique(ztf_s82['objectid'])), ' ztf objects with ', len(ztf_s82),\n",
    "      ' epochs total')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the SDSS S82 Stars , and see if any at all matches against the ztf objects... Although with only 947 we may be running into small number statistics...  ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Can't use \n",
    "#>> from astroML.datasets import fetch_sdss_S82standards \n",
    "# - it'll take longer to \n",
    "# figure out how to install astroML than to \n",
    "# just scp the whole catalog ...\n",
    "# I copy here the stripe82calibStars_v2.6.dat  from my computer\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames = ['calib_fla', 'ra', 'dec', 'raRMS', 'decRMS', 'nEpochs', 'AR_val', \n",
    "                'u_Nobs', 'u_mMed', 'u_mMean', 'u_mErr', 'u_rms_scatt', 'u_chi2',\n",
    "                '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',\n",
    "                'i_Nobs', 'i_mMed', 'i_mMean', 'i_mErr', 'i_rms_scatt', 'i_chi2',\n",
    "                'z_Nobs', 'z_mMed', 'z_mMean', 'z_mErr', 'z_rms_scatt', 'z_chi2']\n",
    "fname = 'stripe82calibStars_v2.6.dat'\n",
    "    \n",
    "sdss_stars = Table.read(fname, format='ascii', names = colnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1204\n",
      "947\n"
     ]
    }
   ],
   "source": [
    "from astropy.table import unique\n",
    "\n",
    "ztf_s82 = Table.from_pandas(df[df['ra']>300])\n",
    "print(len(ztf_s82))\n",
    "ztf_s82 = unique(ztf_s82, keys='objectid', keep='first')\n",
    "print(len(ztf_s82))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# crossmatch the ZTF alerts to SDSS stars ...\n",
    "\n",
    "\n",
    "ztf_coords = SkyCoord(ra=ztf_s82['ra'].data*u.degree, \n",
    "                      dec=ztf_s82['decl'].data*u.degree)\n",
    "sdss_coords = SkyCoord(ra=sdss_stars['ra']*u.degree, \n",
    "                       dec=sdss_stars['dec']*u.degree)\n",
    "\n",
    "\n",
    "# match ZTF alerts to SDSS S82 standards  since there is many less ZTF alerts ... \n",
    "idx, sep2d, dist3d = ztf_coords.match_to_catalog_sky(sdss_coords) \n",
    "ztf_s82['d2d_arcsec']  = sep2d.value*3600\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any good matches ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(ztf_s82['d2d_arcsec'] < 5 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='download_median_ztf'></a> That's not good enough.... Let's then download all unique ZTF objects, with aggregate median photometry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aside: count how many objects there are ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 5s, sys: 12.4 s, total: 1min 17s\n",
      "Wall time: 1min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "ztf_counts = pd.read_sql_query('select count(*) as nobs from alerts GROUP BY objectId', con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10670733"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ztf_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So there's 10 mln + objects, as of Aug 22 ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aside : Check those that have over 500 counts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.24 ms, sys: 1.04 ms, total: 5.28 ms\n",
      "Wall time: 20.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "interesting = pd.read_sql_query('select objectId, count(*) as nobs from alerts GROUP BY objectId HAVING nobs > 500', con)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Group by object Id**,  select only one filter  in each query  - \n",
    "that way I have essentially  g,r,i  observations \n",
    "fid : 1=g; 2=r; 3=i , \n",
    "where stellar classification is > 0.5  \n",
    "( I could put the cut even higher, it's probably based on whether the objects are resolved \n",
    "or not ... )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 541 ms, sys: 90 ms, total: 631 ms\n",
      "Wall time: 14min 47s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "ztf_fid_df = pd.read_sql_query('select objectid, fid, classtar,\\\n",
    " count(*) as nobs  ,  avg(magpsf) as avg_magpsf,\\\n",
    " avg(sigmapsf) as avg_sigmapsf,\\\n",
    " avg(ra) as avg_ra, avg(decl) as avg_decl ,\\\n",
    " avg(jd) as avg_jd \\\n",
    " from alerts WHERE fid =\"3\" and classtar > 0.5 \\\n",
    " GROUP BY objectId    LIMIT 100000', con)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26456"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#So there's only 26000 objects with observations in i filter ! \n",
    "len(ztf_fid_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ztf_fid_table = Table.from_pandas(ztf_fid_df)\n",
    "ztf_fid_table.write('ztf_alerts_i_hundred_thousand.txt', \n",
    "                    format='ascii')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download the list of ra,dec , and upload to SciServer to cross match against all of the SDSS ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Another way : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This took 10 minutes! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ztf_all = Table.from_pandas(df)\n",
    "ztf_all.write('ztf_alerts_objectId.txt', format='ascii')"
   ]
  }
 ],
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