EB_Stars_in_S82_from_Drake_2014.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I need to find out what are Eclipsing Binaries in S82, if any. Quick google search didn't bring up anything useful. I finally found the Drake 2014 catalog : perhaps I can select objects by ra, dec :\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.table import Table\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "from astropy import units as u\n",
    "from astropy.coordinates import SkyCoord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Read in the CRTS Variable Star Catalog from Drake+2014\n",
    "# http://nesssi.cacr.caltech.edu/DataRelease/CatalinaVars.tbl\n",
    "table = Table.read('Drake_2014_Table_3.txt', format='ascii.cds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Want to convert that to AstroPy SkyCoord for quick radec search / catalog matching \n",
    "radec = np.zeros(len(table['RAh'].data.data), dtype='<U30')\n",
    "\n",
    "for i in range(len(table['RAh'].data.data)):\n",
    "    radec[i] = str(table['RAh'].data.data[i]) + ' '  \\\n",
    "          + str(table['RAm'].data.data[i]) + ' '  \\\n",
    "          + str(table['RAs'].data.data[i]) + ' '  \\\n",
    "          + str(table['DE-'].data.data[i]) + str(table['DEd'].data.data[i]) + ' '  \\\n",
    "          + str(table['DEm'].data.data[i]) + ' '  \\\n",
    "          + str(table['DEs'].data.data[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "radec_astropy = SkyCoord(radec, unit=(u.hourangle, u.deg))\n",
    "\n",
    "#table[:2]\n",
    "# compare that to the first row ....\n",
    "#radec_astropy.dec\n",
    "#radec_astropy.ra.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10d822048>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WE2FNUj6BYK7woqEZLEXrkTKKFK8MLl4NfQSqhInaw7mj/SiW13H55nzMSWorhVoNgKYJ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\nbMkK65y1Y2GZgdVK6jEZMjM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pD7U0XssaRdaU5zMp8TupU9fHgLQPXOy+sbLfPImg++7z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d046080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display coverage of data....\n",
    "%matplotlib inline\n",
    "plt.scatter(radec_astropy.ra.value, radec_astropy.dec.value, lw=0, s=0.05)\n",
    "plt.xlabel('ra')\n",
    "plt.ylabel('dec')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of objects in S82 footprint is 623 \n"
     ]
    }
   ],
   "source": [
    "# choose data only within S82 footprint...\n",
    "mask_ra = ( 300 < radec_astropy.ra.value )  | (  radec_astropy.ra.value < 60 ) \n",
    "mask_dec = ( -1.3 < radec_astropy.dec.value) * (radec_astropy.dec.value < 1.3 ) \n",
    "\n",
    "mask_S82 = mask_ra * mask_dec\n",
    "N_tot = np.sum(mask_S82)\n",
    "print('Number of objects in S82 footprint is %d '%N_tot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, \"The eclipsing variables in our data were generally divided into a contact and semi-detached binary (EW/EB) group and detached systems (EA)\".   These correspond to Class 1, 2  (see Table 2 ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=389&gt;\n",
       "<table id=\"table4335551656\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>CSS</th><th>RAh</th><th>RAm</th><th>RAs</th><th>DE-</th><th>DEd</th><th>DEm</th><th>DEs</th><th>&lt;Vmag&gt;</th><th>PF</th><th>AV</th><th>Class</th><th>f_Class</th></tr></thead>\n",
       "<thead><tr><th></th><th>h</th><th>min</th><th>s</th><th></th><th>deg</th><th>arcmin</th><th>arcsec</th><th>mag</th><th>d</th><th>mag</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>str20</th><th>int64</th><th>int64</th><th>float64</th><th>str1</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>str1</th></tr></thead>\n",
       "<tr><td>CSS_J003428.3+003818</td><td>0</td><td>34</td><td>28.31</td><td>+</td><td>0</td><td>38</td><td>18.8</td><td>14.54</td><td>0.274262</td><td>0.51</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J003550.3-003145</td><td>0</td><td>35</td><td>50.32</td><td>-</td><td>0</td><td>31</td><td>45.6</td><td>17.03</td><td>0.278672</td><td>0.24</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J003604.4-010814</td><td>0</td><td>36</td><td>4.44</td><td>-</td><td>1</td><td>8</td><td>14.6</td><td>16.43</td><td>0.310506</td><td>0.56</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J003813.6+001611</td><td>0</td><td>38</td><td>13.66</td><td>+</td><td>0</td><td>16</td><td>11.5</td><td>16.69</td><td>0.23436</td><td>0.35</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J003847.0-010012</td><td>0</td><td>38</td><td>47.05</td><td>-</td><td>1</td><td>0</td><td>12.8</td><td>13.82</td><td>0.471738</td><td>0.08</td><td>1</td><td>a</td></tr>\n",
       "<tr><td>CSS_J004314.1+003520</td><td>0</td><td>43</td><td>14.14</td><td>+</td><td>0</td><td>35</td><td>20.6</td><td>14.47</td><td>0.256278</td><td>0.68</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J004340.9-003413</td><td>0</td><td>43</td><td>40.96</td><td>-</td><td>0</td><td>34</td><td>13.6</td><td>15.89</td><td>0.394827</td><td>0.15</td><td>1</td><td>a</td></tr>\n",
       "<tr><td>CSS_J004949.7-011212</td><td>0</td><td>49</td><td>49.72</td><td>-</td><td>1</td><td>12</td><td>12.2</td><td>14.55</td><td>0.365948</td><td>0.49</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J005019.2-000241</td><td>0</td><td>50</td><td>19.29</td><td>-</td><td>0</td><td>2</td><td>41.5</td><td>17.75</td><td>0.230758</td><td>0.59</td><td>1</td><td>--</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></tr>\n",
       "<tr><td>CSS_J234447.6+010010</td><td>23</td><td>44</td><td>47.62</td><td>+</td><td>1</td><td>0</td><td>10.5</td><td>14.17</td><td>0.284731</td><td>0.35</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J234508.1-004236</td><td>23</td><td>45</td><td>8.14</td><td>-</td><td>0</td><td>42</td><td>36.2</td><td>13.26</td><td>0.392834</td><td>0.42</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J234515.9-001914</td><td>23</td><td>45</td><td>15.97</td><td>-</td><td>0</td><td>19</td><td>14.7</td><td>16.31</td><td>0.319008</td><td>0.27</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J234728.8+001026</td><td>23</td><td>47</td><td>28.8</td><td>+</td><td>0</td><td>10</td><td>26.9</td><td>15.68</td><td>0.29859</td><td>0.68</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J234800.9+005359</td><td>23</td><td>48</td><td>0.93</td><td>+</td><td>0</td><td>53</td><td>59.7</td><td>17.31</td><td>0.250896</td><td>0.39</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J235102.7-005547</td><td>23</td><td>51</td><td>2.73</td><td>-</td><td>0</td><td>55</td><td>47.0</td><td>12.96</td><td>0.521584</td><td>0.44</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J235108.0-010925</td><td>23</td><td>51</td><td>8.06</td><td>-</td><td>1</td><td>9</td><td>25.0</td><td>14.63</td><td>0.311143</td><td>0.58</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J235415.5+010833</td><td>23</td><td>54</td><td>15.55</td><td>+</td><td>1</td><td>8</td><td>33.9</td><td>14.71</td><td>0.297014</td><td>0.39</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J235502.5+004807</td><td>23</td><td>55</td><td>2.5</td><td>+</td><td>0</td><td>48</td><td>7.4</td><td>15.57</td><td>0.271886</td><td>0.49</td><td>1</td><td>--</td></tr>\n",
       "<tr><td>CSS_J235740.1+005251</td><td>23</td><td>57</td><td>40.1</td><td>+</td><td>0</td><td>52</td><td>51.6</td><td>14.63</td><td>0.303798</td><td>0.31</td><td>1</td><td>--</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=389>\n",
       "        CSS           RAh   RAm    RAs   DE-  ...    PF       AV   Class f_Class\n",
       "                       h    min     s         ...    d       mag                \n",
       "       str20         int64 int64 float64 str1 ... float64  float64 int64   str1 \n",
       "-------------------- ----- ----- ------- ---- ... -------- ------- ----- -------\n",
       "CSS_J003428.3+003818     0    34   28.31    + ... 0.274262    0.51     1      --\n",
       "CSS_J003550.3-003145     0    35   50.32    - ... 0.278672    0.24     1      --\n",
       "CSS_J003604.4-010814     0    36    4.44    - ... 0.310506    0.56     1      --\n",
       "CSS_J003813.6+001611     0    38   13.66    + ...  0.23436    0.35     1      --\n",
       "CSS_J003847.0-010012     0    38   47.05    - ... 0.471738    0.08     1       a\n",
       "CSS_J004314.1+003520     0    43   14.14    + ... 0.256278    0.68     1      --\n",
       "CSS_J004340.9-003413     0    43   40.96    - ... 0.394827    0.15     1       a\n",
       "CSS_J004949.7-011212     0    49   49.72    - ... 0.365948    0.49     1      --\n",
       "CSS_J005019.2-000241     0    50   19.29    - ... 0.230758    0.59     1      --\n",
       "                 ...   ...   ...     ...  ... ...      ...     ...   ...     ...\n",
       "CSS_J234447.6+010010    23    44   47.62    + ... 0.284731    0.35     1      --\n",
       "CSS_J234508.1-004236    23    45    8.14    - ... 0.392834    0.42     1      --\n",
       "CSS_J234515.9-001914    23    45   15.97    - ... 0.319008    0.27     1      --\n",
       "CSS_J234728.8+001026    23    47    28.8    + ...  0.29859    0.68     1      --\n",
       "CSS_J234800.9+005359    23    48    0.93    + ... 0.250896    0.39     1      --\n",
       "CSS_J235102.7-005547    23    51    2.73    - ... 0.521584    0.44     1      --\n",
       "CSS_J235108.0-010925    23    51    8.06    - ... 0.311143    0.58     1      --\n",
       "CSS_J235415.5+010833    23    54   15.55    + ... 0.297014    0.39     1      --\n",
       "CSS_J235502.5+004807    23    55     2.5    + ... 0.271886    0.49     1      --\n",
       "CSS_J235740.1+005251    23    57    40.1    + ... 0.303798    0.31     1      --"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# contact and semi-detached :\n",
    "table[mask_S82][table[mask_S82]['Class'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1177b2400>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11839ccc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Detached Systems:  Class 2  :\n",
    "# plot distribution of periods \n",
    "%matplotlib inline\n",
    "plt.hist(table[mask_S82][table[mask_S82]['Class'] == 2]['PF'])\n",
    "plt.title('CRTS EA Detached Binaries in S82')\n",
    "plt.xlabel('Period [days]')\n",
    "plt.ylabel('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10d812668>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5TnJkkouSXJDk7yZV3zgMeZ5PGniONyU5Z5I1zrchfd43yVf6Pm9Ist8kaxyHIf3eJ8lZ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106680f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_in_s82 = table[mask_S82]\n",
    "select_EA = table_in_s82['Class'] == 2\n",
    "mask =  table_in_s82[select_EA]['PF'].data < 1 \n",
    "\n",
    "plt.hist(table_in_s82[select_EA][mask]['<Vmag>'])\n",
    "plt.title('EA with period < 1 day')\n",
    "plt.xlabel('< V mag > ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0]),)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Najjasniejsza z tych krotkookresowych ...\n",
    "\n",
    "table_in_s82 = table[mask_S82]\n",
    "select_EA = table_in_s82['Class'] == 2\n",
    "mask =  table_in_s82[select_EA]['PF'].data < 1 \n",
    "\n",
    "vmags = table_in_s82[select_EA][mask]['<Vmag>'].data\n",
    "np.where(vmags ==  min(vmags))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=1&gt;\n",
       "<table id=\"table4543655328\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>CSS</th><th>RAh</th><th>RAm</th><th>RAs</th><th>DE-</th><th>DEd</th><th>DEm</th><th>DEs</th><th>&lt;Vmag&gt;</th><th>PF</th><th>AV</th><th>Class</th><th>f_Class</th></tr></thead>\n",
       "<thead><tr><th></th><th>h</th><th>min</th><th>s</th><th></th><th>deg</th><th>arcmin</th><th>arcsec</th><th>mag</th><th>d</th><th>mag</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>str20</th><th>int64</th><th>int64</th><th>float64</th><th>str1</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>str1</th></tr></thead>\n",
       "<tr><td>CSS_J004746.8-005015</td><td>0</td><td>47</td><td>46.88</td><td>-</td><td>0</td><td>50</td><td>15.7</td><td>13.96</td><td>0.67508</td><td>0.64</td><td>2</td><td>--</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=1>\n",
       "        CSS           RAh   RAm    RAs   DE-  ...    PF      AV   Class f_Class\n",
       "                       h    min     s         ...    d      mag                \n",
       "       str20         int64 int64 float64 str1 ... float64 float64 int64   str1 \n",
       "-------------------- ----- ----- ------- ---- ... ------- ------- ----- -------\n",
       "CSS_J004746.8-005015     0    47   46.88    - ... 0.67508    0.64     2      --"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_in_s82[select_EA][mask][np.where(vmags ==  min(vmags))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=1&gt;\n",
       "<table id=\"table4402449432\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>CSS</th><th>RAh</th><th>RAm</th><th>RAs</th><th>DE-</th><th>DEd</th><th>DEm</th><th>DEs</th><th>&lt;Vmag&gt;</th><th>PF</th><th>AV</th><th>Class</th><th>f_Class</th></tr></thead>\n",
       "<thead><tr><th></th><th>h</th><th>min</th><th>s</th><th></th><th>deg</th><th>arcmin</th><th>arcsec</th><th>mag</th><th>d</th><th>mag</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>str20</th><th>int64</th><th>int64</th><th>float64</th><th>str1</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>str1</th></tr></thead>\n",
       "<tr><td>CSS_J211846.4-003215</td><td>21</td><td>18</td><td>46.41</td><td>-</td><td>0</td><td>32</td><td>15.0</td><td>17.58</td><td>0.249515</td><td>0.16</td><td>2</td><td>--</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=1>\n",
       "        CSS           RAh   RAm    RAs   DE-  ...    PF       AV   Class f_Class\n",
       "                       h    min     s         ...    d       mag                \n",
       "       str20         int64 int64 float64 str1 ... float64  float64 int64   str1 \n",
       "-------------------- ----- ----- ------- ---- ... -------- ------- ----- -------\n",
       "CSS_J211846.4-003215    21    18   46.41    - ... 0.249515    0.16     2      --"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What's the shortest EA variable ? \n",
    "table_in_s82 = table[mask_S82]\n",
    "select_EA = table_in_s82['Class'] == 2\n",
    "mask =table_in_s82[select_EA]['PF'].data == min(table_in_s82[select_EA]['PF'].data)\n",
    "\n",
    "table_in_s82[select_EA][mask]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Choose a single EB, that has a period of 0.23 days, extract the lightcurve ... \n",
    "# CSS_J003813.6+001611 : \n",
    "# V mag  =  16.69\n",
    "# Period = 0.23436 days \n",
    "# Amplitude  : 0.35 mag \n",
    "\n",
    "# coords in PDAC-digestible format : \n",
    "# 00 38 13.6  +00 16 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=30&gt;\n",
       "<table id=\"table4647066872\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>CSS</th><th>RAh</th><th>RAm</th><th>RAs</th><th>DE-</th><th>DEd</th><th>DEm</th><th>DEs</th><th>&lt;Vmag&gt;</th><th>PF</th><th>AV</th><th>Class</th><th>f_Class</th></tr></thead>\n",
       "<thead><tr><th></th><th>h</th><th>min</th><th>s</th><th></th><th>deg</th><th>arcmin</th><th>arcsec</th><th>mag</th><th>d</th><th>mag</th><th></th><th></th></tr></thead>\n",
       "<thead><tr><th>str20</th><th>int64</th><th>int64</th><th>float64</th><th>str1</th><th>int64</th><th>int64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>int64</th><th>str1</th></tr></thead>\n",
       "<tr><td>CSS_J010045.1-010212</td><td>1</td><td>0</td><td>45.11</td><td>-</td><td>1</td><td>2</td><td>12.0</td><td>16.33</td><td>0.5013599</td><td>0.53</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J010229.0-000334</td><td>1</td><td>2</td><td>29.07</td><td>-</td><td>0</td><td>3</td><td>34.3</td><td>15.7</td><td>0.6354628</td><td>0.46</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J013709.4+001208</td><td>1</td><td>37</td><td>9.41</td><td>+</td><td>0</td><td>12</td><td>8.9</td><td>17.57</td><td>0.641685</td><td>0.28</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J014929.7+000918</td><td>1</td><td>49</td><td>29.74</td><td>+</td><td>0</td><td>9</td><td>18.6</td><td>16.92</td><td>0.6827919</td><td>0.18</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J015128.4+011457</td><td>1</td><td>51</td><td>28.47</td><td>+</td><td>1</td><td>14</td><td>57.3</td><td>17.13</td><td>0.499791</td><td>1.09</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J015225.1-001712</td><td>1</td><td>52</td><td>25.14</td><td>-</td><td>0</td><td>17</td><td>12.3</td><td>15.42</td><td>0.797843</td><td>0.44</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J020943.4-010841</td><td>2</td><td>9</td><td>43.45</td><td>-</td><td>1</td><td>8</td><td>41.1</td><td>17.29</td><td>0.649989</td><td>0.23</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J030304.2+003029</td><td>3</td><td>3</td><td>4.24</td><td>+</td><td>0</td><td>30</td><td>29.8</td><td>17.91</td><td>0.6418724</td><td>0.34</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J031333.0+004255</td><td>3</td><td>13</td><td>33.09</td><td>+</td><td>0</td><td>42</td><td>55.1</td><td>18.1</td><td>0.4763703</td><td>0.44</td><td>4</td><td>--</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></tr>\n",
       "<tr><td>CSS_J214720.5+003503</td><td>21</td><td>47</td><td>20.57</td><td>+</td><td>0</td><td>35</td><td>3.9</td><td>19.21</td><td>0.5013094</td><td>0.87</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J215102.4-001901</td><td>21</td><td>51</td><td>2.48</td><td>-</td><td>0</td><td>19</td><td>1.3</td><td>18.2</td><td>0.675835</td><td>0.4</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J215717.4-000344</td><td>21</td><td>57</td><td>17.45</td><td>-</td><td>0</td><td>3</td><td>44.4</td><td>18.74</td><td>0.5098859</td><td>0.83</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J220345.1-003217</td><td>22</td><td>3</td><td>45.14</td><td>-</td><td>0</td><td>32</td><td>17.0</td><td>18.35</td><td>0.5854212</td><td>0.5</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J220416.7+005755</td><td>22</td><td>4</td><td>16.75</td><td>+</td><td>0</td><td>57</td><td>55.5</td><td>16.13</td><td>0.5003169</td><td>0.71</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J220822.4+010501</td><td>22</td><td>8</td><td>22.42</td><td>+</td><td>1</td><td>5</td><td>1.0</td><td>17.71</td><td>0.5020314</td><td>1.03</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J231955.2-001111</td><td>23</td><td>19</td><td>55.27</td><td>-</td><td>0</td><td>11</td><td>11.8</td><td>17.14</td><td>0.6177148</td><td>0.17</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J232028.0+000531</td><td>23</td><td>20</td><td>28.06</td><td>+</td><td>0</td><td>5</td><td>31.5</td><td>17.05</td><td>0.64275</td><td>0.18</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J233103.7+010339</td><td>23</td><td>31</td><td>3.78</td><td>+</td><td>1</td><td>3</td><td>39.1</td><td>16.67</td><td>0.4982916</td><td>1.17</td><td>4</td><td>--</td></tr>\n",
       "<tr><td>CSS_J233403.0-003400</td><td>23</td><td>34</td><td>3.09</td><td>-</td><td>0</td><td>34</td><td>0.3</td><td>17.47</td><td>0.6261693</td><td>0.27</td><td>4</td><td>--</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=30>\n",
       "        CSS           RAh   RAm    RAs   ...     PF       AV   Class f_Class\n",
       "                       h    min     s    ...     d       mag                \n",
       "       str20         int64 int64 float64 ...  float64  float64 int64   str1 \n",
       "-------------------- ----- ----- ------- ... --------- ------- ----- -------\n",
       "CSS_J010045.1-010212     1     0   45.11 ... 0.5013599    0.53     4      --\n",
       "CSS_J010229.0-000334     1     2   29.07 ... 0.6354628    0.46     4      --\n",
       "CSS_J013709.4+001208     1    37    9.41 ...  0.641685    0.28     4      --\n",
       "CSS_J014929.7+000918     1    49   29.74 ... 0.6827919    0.18     4      --\n",
       "CSS_J015128.4+011457     1    51   28.47 ...  0.499791    1.09     4      --\n",
       "CSS_J015225.1-001712     1    52   25.14 ...  0.797843    0.44     4      --\n",
       "CSS_J020943.4-010841     2     9   43.45 ...  0.649989    0.23     4      --\n",
       "CSS_J030304.2+003029     3     3    4.24 ... 0.6418724    0.34     4      --\n",
       "CSS_J031333.0+004255     3    13   33.09 ... 0.4763703    0.44     4      --\n",
       "                 ...   ...   ...     ... ...       ...     ...   ...     ...\n",
       "CSS_J214720.5+003503    21    47   20.57 ... 0.5013094    0.87     4      --\n",
       "CSS_J215102.4-001901    21    51    2.48 ...  0.675835     0.4     4      --\n",
       "CSS_J215717.4-000344    21    57   17.45 ... 0.5098859    0.83     4      --\n",
       "CSS_J220345.1-003217    22     3   45.14 ... 0.5854212     0.5     4      --\n",
       "CSS_J220416.7+005755    22     4   16.75 ... 0.5003169    0.71     4      --\n",
       "CSS_J220822.4+010501    22     8   22.42 ... 0.5020314    1.03     4      --\n",
       "CSS_J231955.2-001111    23    19   55.27 ... 0.6177148    0.17     4      --\n",
       "CSS_J232028.0+000531    23    20   28.06 ...   0.64275    0.18     4      --\n",
       "CSS_J233103.7+010339    23    31    3.78 ... 0.4982916    1.17     4      --\n",
       "CSS_J233403.0-003400    23    34    3.09 ... 0.6261693    0.27     4      --"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose a single RR Lyr ... \n",
    "\n",
    "# RRab\n",
    "table[mask_S82][table[mask_S82]['Class'] == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# RR Lyrae : \n",
    "# CSS_J013709.4+001208\n",
    "# visual mag 17.57\n",
    "# period : 0.641685 days, \n",
    "# amplidute 0.28 mag \n",
    "\n",
    "\n",
    "# 01 37 09.4 +00 12 08\n",
    "\n",
    "# It's weird, because even within 3 arcsec I easily find objects in DeepSource .... \n",
    "\n",
    "# Deep Source at 01 37 09.4 +00 12 08  ,  3 arcsec : find   ifd 2999473357723312\n",
    "\n",
    "# with coadd_filter_id = 2 .  The weird thing is that there seems to be no i-band coadd for this source,  \n",
    "# since only id for coadd_filter_id=3  was inherited by Deep Forced Source ...\n",
    "\n"
   ]
  }
 ],
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