telegraphic/vizier_test.ipynb

## vizier_test.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the 1709 star sample in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 1709 star sample is indexed by Vizier, so we can retrieve it using `astroquery`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from astroquery.vizier import Vizier\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "from astropy.coordinates import SkyCoord\n",
    "Vizier.ROW_LIMIT = -1 # Return all rows of a table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to know the name of the catalog, which is `J/PASP/129/E4501` (catchy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Table masked=True length=1709&gt;\n",
       "<table id=\"table109080080\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Star</th><th>RAJ2000</th><th>DEJ2000</th><th>Ep</th><th>Vmag</th><th>SpType</th><th>Dist</th><th>pmRA</th><th>pmDE</th><th>SimbadName</th></tr></thead>\n",
       "<thead><tr><th></th><th>&quot;h:m:s&quot;</th><th>&quot;d:m:s&quot;</th><th>yr</th><th>mag</th><th></th><th>pc</th><th>arcs / yr</th><th>arcs / yr</th><th></th></tr></thead>\n",
       "<thead><tr><th>str9</th><th>str10</th><th>str9</th><th>int16</th><th>float32</th><th>str7</th><th>float32</th><th>float32</th><th>float32</th><th>str24</th></tr></thead>\n",
       "<tr><td>HIP2</td><td>00 00 01.0</td><td>-19 29 55</td><td>2000</td><td>9.27</td><td>K3V</td><td>45.60</td><td>0.18</td><td>0.00</td><td>HIP2</td></tr>\n",
       "<tr><td>HIP169</td><td>00 02 08.7</td><td>-68 16 50</td><td>2000</td><td>9.24</td><td>M0V</td><td>15.80</td><td>0.21</td><td>-0.23</td><td>HIP169</td></tr>\n",
       "<tr><td>HIP171</td><td>00 02 10.2</td><td>+27 04 56</td><td>2000</td><td>5.80</td><td>G3V</td><td>12.40</td><td>0.78</td><td>-0.92</td><td>HIP171</td></tr>\n",
       "<tr><td>HIP194</td><td>00 02 29.7</td><td>+08 29 07</td><td>2000</td><td>5.70</td><td>F0V</td><td>37.60</td><td>-0.09</td><td>-0.05</td><td>HIP194</td></tr>\n",
       "<tr><td>HIP400</td><td>00 04 56.3</td><td>+23 16 10</td><td>2000</td><td>7.82</td><td>G9V</td><td>25.60</td><td>0.38</td><td>-0.01</td><td>HIP400</td></tr>\n",
       "<tr><td>HIP428</td><td>00 05 10.9</td><td>+45 47 11</td><td>2000</td><td>9.95</td><td>M2</td><td>11.40</td><td>0.87</td><td>-0.15</td><td>HIP428</td></tr>\n",
       "<tr><td>HIP436</td><td>00 05 17.7</td><td>-67 49 57</td><td>2000</td><td>8.49</td><td>K5V</td><td>16.00</td><td>-0.12</td><td>-0.56</td><td>HIP436</td></tr>\n",
       "<tr><td>HIP443</td><td>00 05 20.1</td><td>-05 42 27</td><td>2000</td><td>4.61</td><td>K1III</td><td>39.40</td><td>-0.01</td><td>0.09</td><td>HIP443</td></tr>\n",
       "<tr><td>GJ1</td><td>00 05 24.4</td><td>-37 21 26</td><td>2000</td><td>8.54</td><td>M1.5V</td><td>4.34</td><td>5.64</td><td>-2.33</td><td>GJ1</td></tr>\n",
       "<tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr>\n",
       "<tr><td>HIP117815</td><td>23 53 39.9</td><td>-65 56 49</td><td>2000</td><td>6.64</td><td>G0V</td><td>25.80</td><td>-0.04</td><td>0.09</td><td>HIP117815</td></tr>\n",
       "<tr><td>HIP117828</td><td>23 53 50.1</td><td>-75 37 57</td><td>2000</td><td>9.99</td><td>M...</td><td>10.10</td><td>0.24</td><td>-0.38</td><td>HIP117828</td></tr>\n",
       "<tr><td>HIP117946</td><td>23 55 26.6</td><td>+22 11 35</td><td>2000</td><td>8.77</td><td>K0</td><td>25.40</td><td>0.20</td><td>-0.15</td><td>HIP117946</td></tr>\n",
       "<tr><td>HIP118008</td><td>23 56 10.7</td><td>-39 03 08</td><td>2000</td><td>8.24</td><td>K3V</td><td>22.00</td><td>0.21</td><td>-0.19</td><td>HIP118008</td></tr>\n",
       "<tr><td>HIP118121</td><td>23 57 35.1</td><td>-64 17 53</td><td>2000</td><td>5.00</td><td>A1V</td><td>48.70</td><td>0.08</td><td>-0.06</td><td>HIP118121</td></tr>\n",
       "<tr><td>HIP118162</td><td>23 58 06.8</td><td>+50 26 51</td><td>2000</td><td>6.72</td><td>G5</td><td>24.10</td><td>-0.05</td><td>0.25</td><td>HIP118162</td></tr>\n",
       "<tr><td>HIP118212</td><td>23 58 43.5</td><td>+46 43 44</td><td>2000</td><td>9.57</td><td>M0</td><td>17.30</td><td>0.67</td><td>-0.01</td><td>HIP118212</td></tr>\n",
       "<tr><td>HIP118261</td><td>23 59 13.7</td><td>-26 02 55</td><td>2000</td><td>8.69</td><td>K3/K4V</td><td>24.40</td><td>-0.23</td><td>0.01</td><td>HIP118261</td></tr>\n",
       "<tr><td>HIP118278</td><td>23 59 28.4</td><td>-20 02 04</td><td>2000</td><td>7.47</td><td>G8V</td><td>25.70</td><td>0.51</td><td>-0.28</td><td>HIP118278</td></tr>\n",
       "<tr><td>HIP118310</td><td>23 59 47.8</td><td>+06 39 50</td><td>2000</td><td>8.85</td><td>K5</td><td>25.30</td><td>-0.06</td><td>-0.17</td><td>HIP118310</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table masked=True length=1709>\n",
       "   Star    RAJ2000    DEJ2000    Ep  ...   Dist     pmRA      pmDE   SimbadName\n",
       "           \"h:m:s\"    \"d:m:s\"    yr  ...    pc   arcs / yr arcs / yr           \n",
       "   str9     str10       str9   int16 ... float32  float32   float32    str24   \n",
       "--------- ---------- --------- ----- ... ------- --------- --------- ----------\n",
       "     HIP2 00 00 01.0 -19 29 55  2000 ...   45.60      0.18      0.00       HIP2\n",
       "   HIP169 00 02 08.7 -68 16 50  2000 ...   15.80      0.21     -0.23     HIP169\n",
       "   HIP171 00 02 10.2 +27 04 56  2000 ...   12.40      0.78     -0.92     HIP171\n",
       "   HIP194 00 02 29.7 +08 29 07  2000 ...   37.60     -0.09     -0.05     HIP194\n",
       "   HIP400 00 04 56.3 +23 16 10  2000 ...   25.60      0.38     -0.01     HIP400\n",
       "   HIP428 00 05 10.9 +45 47 11  2000 ...   11.40      0.87     -0.15     HIP428\n",
       "   HIP436 00 05 17.7 -67 49 57  2000 ...   16.00     -0.12     -0.56     HIP436\n",
       "   HIP443 00 05 20.1 -05 42 27  2000 ...   39.40     -0.01      0.09     HIP443\n",
       "      GJ1 00 05 24.4 -37 21 26  2000 ...    4.34      5.64     -2.33        GJ1\n",
       "      ...        ...       ...   ... ...     ...       ...       ...        ...\n",
       "HIP117815 23 53 39.9 -65 56 49  2000 ...   25.80     -0.04      0.09  HIP117815\n",
       "HIP117828 23 53 50.1 -75 37 57  2000 ...   10.10      0.24     -0.38  HIP117828\n",
       "HIP117946 23 55 26.6 +22 11 35  2000 ...   25.40      0.20     -0.15  HIP117946\n",
       "HIP118008 23 56 10.7 -39 03 08  2000 ...   22.00      0.21     -0.19  HIP118008\n",
       "HIP118121 23 57 35.1 -64 17 53  2000 ...   48.70      0.08     -0.06  HIP118121\n",
       "HIP118162 23 58 06.8 +50 26 51  2000 ...   24.10     -0.05      0.25  HIP118162\n",
       "HIP118212 23 58 43.5 +46 43 44  2000 ...   17.30      0.67     -0.01  HIP118212\n",
       "HIP118261 23 59 13.7 -26 02 55  2000 ...   24.40     -0.23      0.01  HIP118261\n",
       "HIP118278 23 59 28.4 -20 02 04  2000 ...   25.70      0.51     -0.28  HIP118278\n",
       "HIP118310 23 59 47.8 +06 39 50  2000 ...   25.30     -0.06     -0.17  HIP118310"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = Vizier.get_catalogs('J/PASP/129/E4501')  # Returns a dictionary\n",
    "c = c['J/PASP/129/E4501/table1']             # Returns the table\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot distances as histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.array(c['Dist'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x65eead0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HAW9Kcly/VUnSbFpSAQGcCGysqoer6sfAJ4Gze65JkmbSUguIFcCjQ+ubuzZJ0oTtd3Mx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\nSWoyICRJTf8PstiN6/yfyegAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6011a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(d, 32)\n",
    "plt.xlabel(\"Distance\")\n",
    "plt.ylabel(\"counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load RA and DEC, and parse\n",
    "\n",
    "We can do this efficiently with astropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "ra_dec = SkyCoord(c['RAJ2000'], c['DEJ2000'], unit=('hourangle', 'degree'))\n",
    "ra  = ra_dec.ra.to('rad').value - np.pi\n",
    "dec = ra_dec.dec.to('rad').value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a mollweide plotting option in pylab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x9994690>]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+tDR4PGUWGtDNYnCJ1b/Q7ja1co+p1QZY6QdC6BG/NWlAiqIyWBjLlOKZhFhs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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x99b0f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.subplot(1,1,1, projection='mollweide')\n",
    "plt.plot(ra, dec, 'o', markersize=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}