{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaia Python Tutorial: Cluster Analysis\n",
    "*Author: Deborah Baines*\n",
    "\n",
    "*This tutorial has taken the Gaia Archive tutorial (located at the Gaia Archive, https://archives.esac.esa.int/gaia -> Help -> Tutorials -> Cluster analysis ) and adapted it to python.\n",
    "The tutorial uses the Gaia TAP+ (astroquery.gaia) module, http://astroquery.readthedocs.io/en/latest/gaia/gaia.html#module-astroquery.gaia .*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*This tutorial is focused on a possible scientific exploration exercise for a known cluster, the Pleiades (M45), using data from the Gaia Archive.*\n",
    " \n",
    "*First, we import all the required python modules:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created TAP+ (v1.0) - Connection:\n",
      "\tHost: gea.esac.esa.int\n",
      "\tUse HTTPS: False\n",
      "\tPort: 80\n",
      "\tSSL Port: 443\n"
     ]
    }
   ],
   "source": [
    "import astropy.units as u\n",
    "from astropy.coordinates.sky_coordinate import SkyCoord\n",
    "from astropy.units import Quantity\n",
    "from astroquery.gaia import Gaia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Suppress warnings. Comment this out if you wish to see the warning messages\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Do the following to load and look at the available Gaia table names:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Retrieving tables...\n",
      "Parsing tables...\n",
      "Done.\n",
      "public.dual\n",
      "public.tycho2\n",
      "public.igsl_source\n",
      "public.hipparcos\n",
      "public.hipparcos_newreduction\n",
      "public.hubble_sc\n",
      "public.igsl_source_catalog_ids\n",
      "tap_schema.tables\n",
      "tap_schema.keys\n",
      "tap_schema.columns\n",
      "tap_schema.schemas\n",
      "tap_schema.key_columns\n",
      "gaiadr1.phot_variable_time_series_gfov\n",
      "gaiadr1.ppmxl_neighbourhood\n",
      "gaiadr1.gsc23_neighbourhood\n",
      "gaiadr1.ppmxl_best_neighbour\n",
      "gaiadr1.sdss_dr9_neighbourhood\n",
      "gaiadr1.rrlyrae\n",
      "gaiadr1.allwise_neighbourhood\n",
      "gaiadr1.gsc23_original_valid\n",
      "gaiadr1.tmass_original_valid\n",
      "gaiadr1.allwise_best_neighbour\n",
      "gaiadr1.cepheid\n",
      "gaiadr1.urat1_neighbourhood\n",
      "gaiadr1.ppmxl_original_valid\n",
      "gaiadr1.tmass_neighbourhood\n",
      "gaiadr1.ucac4_best_neighbour\n",
      "gaiadr1.ucac4_neighbourhood\n",
      "gaiadr1.aux_qso_icrf2_match\n",
      "gaiadr1.phot_variable_time_series_gfov_statistical_parameters\n",
      "gaiadr1.sdssdr9_original_valid\n",
      "gaiadr1.urat1_best_neighbour\n",
      "gaiadr1.variable_summary\n",
      "gaiadr1.ucac4_original_valid\n",
      "gaiadr1.tmass_best_neighbour\n",
      "gaiadr1.gsc23_best_neighbour\n",
      "gaiadr1.gaia_source\n",
      "gaiadr1.ext_phot_zero_point\n",
      "gaiadr1.sdss_dr9_best_neighbour\n",
      "gaiadr1.tgas_source\n",
      "gaiadr1.urat1_original_valid\n",
      "gaiadr1.allwise_original_valid\n"
     ]
    }
   ],
   "source": [
    "tables = Gaia.load_tables(only_names=True)\n",
    "for table in (tables):\n",
    "    print (table.get_qualified_name())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Next, we retrieve all the available data in the region of interest. *\n",
    "\n",
    "*To do this we perform an asynchronous query (asynchronous rather than synchronous queries should be performed when retrieving more than 2000 rows) centred on the Pleides (coordinates: 56.75, +24.1167) with a search radius of 2 degrees and save the results to a file.*\n",
    "\n",
    "*Note: The query to the archive is with ADQL (Astronomical Data Query Language). For a description of ADQL and more examples see the Gaia DR1 ADQL cookbook: https://gaia.ac.uk/data/gaia-data-release-1/adql-cookbook *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launched query: 'SELECT * FROM gaiadr1.gaia_source WHERE CONTAINS(POINT('ICRS',gaiadr1.gaia_source.ra,gaiadr1.gaia_source.dec),CIRCLE('ICRS',56.75,24.1167,2))=1;'\n",
      "Retrieving async. results...\n",
      "Jobid: 1495022971236O\n",
      "Phase: None\n",
      "Owner: None\n",
      "Output file: async_20170517141324.vot\n",
      "Results: None\n"
     ]
    }
   ],
   "source": [
    "job = Gaia.launch_job_async(\"SELECT * \\\n",
    "FROM gaiadr1.gaia_source \\\n",
    "WHERE CONTAINS(POINT('ICRS',gaiadr1.gaia_source.ra,gaiadr1.gaia_source.dec),CIRCLE('ICRS',56.75,24.1167,2))=1;\" \\\n",
    ", dump_to_file=True)\n",
    "\n",
    "print (job)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Inspect the output table and number of rows (around 1e5 results are found):*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    source_id    \n",
      "-----------------\n",
      "66926207631181184\n",
      "66818318054203520\n",
      "66917823855519360\n",
      "66830859358837888\n",
      "66809423175240448\n",
      "66944761890240000\n",
      "66980191076373760\n",
      "66781621852927232\n",
      "66827805636652928\n",
      "66947545031024640\n",
      "              ...\n",
      "66649989694512256\n",
      "65666785781176576\n",
      "64014803920669568\n",
      "64137880504644992\n",
      "66542306274948224\n",
      "64005909043397504\n",
      "66689881351473664\n",
      "66436615718993792\n",
      "65645757620918912\n",
      "66718434293042560\n",
      "64103559419447296\n",
      "Length = 98538 rows\n"
     ]
    }
   ],
   "source": [
    "r = job.get_results()\n",
    "print (r['source_id'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*To identify the cluster, create a proper motion plot of proper motion in RA (pmra) versus proper motion in DEC (pmdec) in the range pmra [-60,80] and pmdec [-120,30]:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Psn1DAz+z+np6dpL9fqUiiFRlVGQBiYxCRXPgM89qbsru83n4YeAnP2G4oK3N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\nKsIiQ5IkFWGRIUmSivgFb3BPDDymtpYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fe55c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(r['pmra'], r['pmdec'], color='r', alpha=0.3)\n",
    "plt.xlim(-60,80)\n",
    "plt.ylim(-120,30)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Perform another asynchronous query to filter the results by quality:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launched query: 'SELECT * FROM gaiadr1.gaia_source WHERE CONTAINS(POINT('ICRS',gaiadr1.gaia_source.ra,gaiadr1.gaia_source.dec),CIRCLE('ICRS',56.75,24.1167,2))=1 AND abs(pmra_error/pmra)<0.10 AND abs(pmdec_error/pmdec)<0.10 AND pmra IS NOT NULL AND abs(pmra)>0 AND pmdec IS NOT NULL AND abs(pmdec)>0;'\n",
      "Retrieving async. results...\n"
     ]
    }
   ],
   "source": [
    "job2 = Gaia.launch_job_async(\"SELECT * \\\n",
    "FROM gaiadr1.gaia_source \\\n",
    "WHERE CONTAINS(POINT('ICRS',gaiadr1.gaia_source.ra,gaiadr1.gaia_source.dec),CIRCLE('ICRS',56.75,24.1167,2))=1 \\\n",
    "AND abs(pmra_error/pmra)<0.10 \\\n",
    "AND abs(pmdec_error/pmdec)<0.10 \\\n",
    "AND pmra IS NOT NULL AND abs(pmra)>0 \\\n",
    "AND pmdec IS NOT NULL AND abs(pmdec)>0;\", dump_to_file=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Again, inspect the output table and number of rows:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    source_id    \n",
      "-----------------\n",
      "66623395256627712\n",
      "66581957412169728\n",
      "65614730777165824\n",
      "64053561704835584\n",
      "65828207832006400\n",
      "66858862543722112\n",
      "66863054431798272\n",
      "66588520122169728\n",
      "66657823714473856\n",
      "66592437132341248\n",
      "              ...\n",
      "66912360656318208\n",
      "66471215975411200\n",
      "66729257611496704\n",
      "64013704408496896\n",
      "64013807487711360\n",
      "66506331628024832\n",
      "66715101399291392\n",
      "66724447247218048\n",
      "66610957031353856\n",
      "66857281995760000\n",
      "66570549979009280\n",
      "Length = 218 rows\n"
     ]
    }
   ],
   "source": [
    "j = job2.get_results()\n",
    "print (j['source_id']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Plot these new filtered results on the same plot as the previous search:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KFfEsjI2JZ8LvFw9DJgNvvCGGJRAQL8boqDxmsyIy5ubEO3Hnjhik+WqRhYRQKIzjiSdk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa4acf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(r['pmra'], r['pmdec'], color='r', alpha=0.3)\n",
    "plt.scatter(j['pmra'], j['pmdec'], color='b', alpha=0.3)\n",
    "plt.xlim(-60,80)\n",
    "plt.ylim(-120,30)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Now we are going to take the candidate objects to be in the cluster. Based on the proper motion plot, we execute the same job with the following constraints on the proper motions in RA and DEC: pmra between 15 and 25, pmdec between -55 and -40:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launched query: 'SELECT * FROM gaiadr1.gaia_source WHERE CONTAINS(POINT('ICRS',gaiadr1.gaia_source.ra,gaiadr1.gaia_source.dec),CIRCLE('ICRS',56.75,24.1167,2))=1 AND abs(pmra_error/pmra)<0.10 AND abs(pmdec_error/pmdec)<0.10 AND pmra IS NOT NULL AND abs(pmra)>0 AND pmdec IS NOT NULL AND abs(pmdec)>0 AND pmra BETWEEN 15 AND 25 AND pmdec BETWEEN -55 AND -40;'\n",
      "Retrieving async. results...\n"
     ]
    }
   ],
   "source": [
    "job3 = Gaia.launch_job_async(\"SELECT * \\\n",
    "FROM gaiadr1.gaia_source \\\n",
    "WHERE CONTAINS(POINT('ICRS',gaiadr1.gaia_source.ra,gaiadr1.gaia_source.dec),CIRCLE('ICRS',56.75,24.1167,2))=1 \\\n",
    "AND abs(pmra_error/pmra)<0.10 \\\n",
    "AND abs(pmdec_error/pmdec)<0.10 \\\n",
    "AND pmra IS NOT NULL AND abs(pmra)>0 \\\n",
    "AND pmdec IS NOT NULL AND abs(pmdec)>0 \\\n",
    "AND pmra BETWEEN 15 AND 25 \\\n",
    "AND pmdec BETWEEN -55 AND -40;\", dump_to_file=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Again, inspect the output table and number of rows, and call the job 'm45cluster':*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     parallax     \n",
      "    Angle[mas]    \n",
      "------------------\n",
      "7.4545648282310184\n",
      "7.5239065408350996\n",
      "6.9301093717323772\n",
      "7.5788921365825894\n",
      " 7.328286272889863\n",
      "7.0233646883680159\n",
      "8.1389671643305235\n",
      "8.1445555234101779\n",
      "7.3648468313130566\n",
      "7.1694418609395765\n",
      "               ...\n",
      "7.2826513541337299\n",
      "7.4681735932280162\n",
      " 7.658985698496882\n",
      "7.2023395286122085\n",
      "8.2920957945484393\n",
      "7.1461398221089798\n",
      "7.8361028050064272\n",
      "7.8762955633015945\n",
      " 7.066701274479442\n",
      "7.3330438493994405\n",
      "Length = 106 rows\n"
     ]
    }
   ],
   "source": [
    "m45cluster = job3.get_results() \n",
    "print (m45cluster['parallax']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Plot these new filtered results on the same plot as the previous search:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ka1qkUguN28yMVI6EQpKY2dwss8toNF9mCgvLRjs75byZjFSLQL5XRWHSp7VC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\n3cBLwE9M03xm0X7jwGJr11Hw3LI8++y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UCkUZKMV4l8PQD384zLfO/AWBsREyZhafy8uJ\ng0d4+ss/j9e3cshFGVJFrXPmzBnOnDmzYFs4HC7b+TXTNFfeq5QTaloL0LLCbteBPwc+t2i7HcgA\n3zFN85c1TfufgSdM0zxWcP4e4Bpw1DTNop4MTdOOAW+99dZbHDt2rNguCoViG2F5KqZCM8zMhNmx\no5O3zl/gW2+8wDXbbXzZenocnXR7W5lKzvHkAyd55jd/sdLDVigqwrlz5zh+/DjAcdM0z63nXGX3\nZJimOQVMrbSfpmm/CfyPBZu6kXyL/wwpZwU4C/yWpmmtBXkZnwHCwMWyDVqhUGxJYpEYz33re7x8\n4RyDsXeZ8Uxhzzpw5pxoORtuXDRlfHhsHq5mbpEMJ9jlbefsh+/z1O3gqkInCoViaSqWk2Ga5q3C\nvzVNM5CQyTXTNEfnN/8dIiae0zTtK0AX8LvAH5qmmd7M8SoUitoieDvIH33zTxi8cYHL6RsEG25j\nT7nI2uJE9CCmK46WddIQ7saRdZKxp5myTTIRDlKv6dy8PKxEhkKxTiqd+LmYBbEb0zRzmqZ9Dqkm\neQ0wgG8B/3Lzh6ZQKKqd4O0gN6/d4uWfvM6bwx/yWuxdovYos623QMtCXQ4cSXDGwLRhaiZhlwE5\nB57ZTpxZN4YtRUxL8vJf/Uce/MRHKn1JCkVNUzUiwzTNG0hOxuLtI9ydu6FQKBR3sMIiZz98n3dm\nrxC0TdOWbSbuiGN4p0CfBFsWNLMgxTwrD7Yo5DQSrUlS0RbSziS+aAM/Gv6AX7p0HXS9pMoThUKR\np2pEhkKhUJSKldD5t3/3I1658g5+p5ckGfScl0ltmqmmG+CZAccyffs0E+wm2GPk3E5S9ixT3lle\nTAY58T/9Gl2OTuocTnbUt5VUeaJQKJTIUCgUNUih52IqMcfF5HU6tRY6vY3kyOFHZ9h3CeonwVZC\nY+C6CLmoCzIucg2TXPNPcC3upyHSRctsK7felPzzlSpPjKDqn6FQgBIZCoWiBnnuW9/j+fOv0aU3\n0eL2kUlmGTNDeOZcuDQn17lN2j8KWokrD9hy0DCZ/zvlAWeCjJZhXL+FGTXvqjwpFBSuepcsP382\njBHJylL1J/wMPN2H07v2VWE3EyWQFOVEiQyFQlFTBG8HOfvh+3TpTXQ1tmIk4vjnfMRzCSYJ4zPd\nTLVeAVcZFklzJcCRIe6dxpNqYNo9xe1oiMnxEE1NTXcJikw6y8yUSedOB909bqLhDIPPTwMBTj5z\n//rHs4GkY+maF0iK6qNaVmFVKBSKVTE5HiKSiuGv8wGge+rY6Wwlo2WYI8JUJgx15etYCDlMV5y0\nPU7MO0PGzNLW2crQcwEGn5/G7oDuHjeZVI4f/shFJGLS0uXG7ZVl6Fs77GK4///27j06zqu89/j3\n0cxYzoxk+SbZFglxQhpHSbCDjXJiG1puDYSkPsuBUgx1IGkPPkCA5bYkIQcaQmgDWQEDJxfaUJyF\nV1G5nJ4sU8JxTkghreKAEx9S4yhpICBykSUrUmRpZOsys88f+x17NNHFl3n1zuX3WWvWWPvdM9qz\n/Wrmmb2fd++edBHbVHyFrycWh/adfezZ0RF106SMKcgQkbLSuHQx9XOSDBwePFr22qWvYakthvEM\n3YkDYDWQLeJOBTVZMokRsrERVi85h3Rvmgcf+DW1848cDSiS82LMiWfo649xJH1smqbY28+HId2T\npmP3AI3LYmUZIEnpUpAhImWl6VVNrF1xAV3pfl7o72V4ZISDgy+zMBPn3UdeTfPhRhhJQbZIs8HO\nIDYCNePUDSxlTjzODV+9k/916GH++eUf86OnHuXI6ChzT6thXjLDoeEYR4azRx8+G9vPn6r0wWHS\ngxnqGib2WTkESFLaFGSISNnZ/MF3s+GidWRdls7+brLjY2xINnHt2tdxjjUST8+DbOLUAw0HmMFY\nLcsOns85o2fzWNczzJ0TZ4ktIDse4+cjHfzrs3uZWxdn4YIso2NG+lCGkeEMvS+M0NudoWVtQ0kn\nUaYak6TqYwwNTEyULYcASUqbEj9FpOwk65Ns+dhVbMxt0R6Hpp8+CM3NvPP5Pv697xn66vthPOET\nQOMnuQtBJgHD85nXfzqvqTmdURvnzPomls1fjB06wvMvzicLPJl5jhW/Ppf6+lre9qYx4onYKW8/\nP5tSTSla1jbQvrMPlx2hfkGcwf5xerszrN+wsKQDJCltCjJE5Pil0/6WSvlbxJpe1eQvJU2n4bEU\nDA2x+Q2v5cUfDXB73zBHUv0wniQbOwzJl/NW+5xGJu6DEwxG51L/8hk0ZBu4cNFyXhjqO5pw2rx8\nDjDKi71JekZfZmDkMG/b2Ezr5hZGh0bL7jJQHwj5q0vKKUCS0qYgQ0RmNjYGe/ZAR8exIKOlBVpb\nIVEClzfm2tPeTnKx4+bL30DdDxz/9NwzJBMpbE4t/7bk/8DcgWPLixdyQGYOHK7HxucSHzuNxPhc\n6sbn0cgirnnXRu74zncYODxIsraWWLyGM86ZS3beQRaM1rD5Exdx1qqzAEgkE2UTXOQkkgnWbVnJ\nqo1aJ0OKR0GGiHjTjVLs2QPt7dDYCM3NMDTkfwZYt2722zqZ1lZ/39EBXV189OLzmPfcUnYf6qN3\nZJDUoWbSrgYsC7FRmHP42GqgWSA7B8aSUJPBJUaJH5nPvJGF1FgNI26Mx3/xS9auuICdv3iErIMF\nyXr6hwd5aexlNrxu3dEAo9ylmlIKLqRoFGSIVLuZRinSaX+ssREWLfKPqa0F53z5qlUlMXVCIuED\nnlWrIJ0mmUqxJZU6mrfxvR+cy9/9+j56U12MJwf8fiauBg7Xk0gvxJwxXnuYbO0QNpbgtLE6FmUX\n8OraJhbVNrD76f189mMfBmD30/vp7O+mbs5cNly0js0ffHfEL16kNCnIEKl2M41S5EY4mpsnPq6+\nHrq6jgUmpaJgJCaXt3HWuctZdu9i7n/iUX514HlemNPFSOwwNWbUjTUQd3GGxgY5lBhh3qFlXJH6\nfRal5pGaexrDIyN09neTHhqemHCqnVlFpqUg4zhpPX+pSMc7SpHySZXU1h577OAgJJOlFWBM49gV\nKe/gqV8+w5e+9488Mv4LjsQPg4FzhhlYJkGMOAuTPsAA6B8epG7OXBqXLgbyEk5DpPccqQQKMmag\n9fyloh3PKEVT09GkSpzzxwYHobcX1q8vmyAjJxcg/PiRn/Fo5z7qR+cxWjPGkcQhMjUZmvqX42qM\nzpcPcFZNM/3Dg3QP97PhonWzMmqh9xypJFqMawZaz18qWv4oRb7CUYrWVh9QZLM++Mhm/c+5ZMsy\ndPX7ruSszOkksykWjM9n6ehSLhlbzVuSF3NW5nQSsZhf6MtlZzXvQu85Ukk0kjGNwvX8AWqTMVx2\nhI7dA6zamNYwppS3vEs/px2lKEiqLJV1Mk7F8hXLufriy/j+3p9SH5/LktRCRjLjdA/3c/XFl7Hx\nynfMet6F3nOk0ijImEZuPf/m5bUTyusXxOnqDBbb0R+8lLuCSz9JJqcepaiA4CJfbnRi99P7OTh8\naMLVIsn65Kwndeo9p/pUeu6Ngoxp5K/nX5uMHS3Xev5SUSpwlOJ4vWJ58oivFtF7TvWoltwb5WRM\nI7ee/8Euv9FROW14JHLCUimf5FklAUa+plc1ccGa8yO/HFXvOdWjWnJvFGTMoHVzC+s3LPT5bp2j\nPt9N6/mLSEj0nlP5CnNvapP+fvGSmB/Z6ElH3cSi0XTJDLSev1fp84YipULvOZWvmnJvFGQcp2pd\nz79a5g1FSk21vudUg2rKvdF0iUyrWuYNRURmSzXl3ijIkClV07yhiMhsqpbcG02XyJSqad5QRGQ2\nVUvujYIMmVI1zRuKSPiUQP5KlZ57oyBDppSbN2zf2YfLjlC/IM5g/zi93RnWb1hY0X8YIlI8SiCv\nXsrJkGlVy7yhiIRHCeTVK/KRDDO7HPgMsBI4AvzEOXdl3vEzgK8DbwIGgW8BNzjnsrPf2upTLfOG\nIhKOKDd90/RM9CINMszsXcDfAzcADwEJ4MK84zXA/cCLwCVAM7ADGAU+PdvtrWaVPm8oIuGIIoFc\n0zOlI7LpEjOLAV8B/tI5d49z7tfOuaecc9/Pq/Z24Dzg/c65fc65XfhRj4+aWeSjMCIiMr38BPJ8\nYSaQa3qmdESZk7EaPzKBme01sxfN7H4zuyCvziXAPudcb17ZLqAByK8nIiIlaLYXntL6PqUlyiDj\nbMCAm4DPAZcD/cBPzGx+UGcp0F3wuO68YyIiUuJmM4E8Nz1T1zBxsLt+QZzhoSzpg8NF/50ytaJP\nOZjZrcD101RxQAvHApzPO+fuCx57NfA88MfAPafalq1bt9LQ0DChbNOmTWzatOlUn1pERI7TbCaQ\na32fE9PW1kZbW9uEsoGBgaI9fxh5DbcD22eo8yzBVAlwdJLMOTdqZs8Crw6KDgCtBY9dkndsWtu2\nbWP16tUzNlhERMI3GwnkWt/nxEz2xXvv3r2sWbOmKM9f9CDDOfcS8NJM9czscWAEWAE8EpQlgOVA\nZ1BtN3CjmS3Oy8u4FBgAnixuy0VEpBL4aRh/dUlX5yjJuhqt7xORyK7QcM4NmtnXgZvN7Hl8YHEd\nfjrle0G1B/DBxA4zux5YBtwC3OGcG4ug2SIiUuK0vk/piPoy0L8CxvALbJ0G/Ax4i3NuAMA5lzWz\nK4C78aMdaeBefLKoiIjIlLS+T/QiDTKccxn86MV109R5Drhi1holIiIiRaG9S0RERCQUCjJEREQk\nFAoyREREJBQKMkRERCQUCjJEREQkFAoyREREJBQKMkRERCQUCjJEREQkFAoyREREJBQKMkRERCQU\nUe9dIiIiInnSPZWzsZuCDBERkRIwNjzGnh1+i/r0YIZUfYyWtQ20bm4hkUxE3byToukSERGRErBn\nRwftO/uIxaF5eS2xOLTv7GPPjo6om3bSFGSIiIhELN2TpmP3AI3LYixaVktt0t8vXhLzIxs96aib\neFIUZIiIiEQsfXCY9GCGuoaJWQz1C+IMD2VJHxyOqGWnRkGGiIhIxFKNSVL1MYYGxieUD/aPk6yr\nIdWYjKhlp0ZBhoiISMRSTSla1jZwsCtD7wsjjAz7+97uDC1rG8r2KhMFGSIiIiWgdXML6zcsJJuF\nrs5RsllYv2EhrZtbom7aSdMlrCIiIiUgkUywbstKVm3UOhkiIiISglRTquyDixxNl4iIiEgoFGSI\niIhIKBRkiIiISCgUZIiIiEgoFGSIiIhIKBRkiIiISCgUZIiIiEgotE6GiFSldE/lLHgkUqoUZIhI\nVRkbHmPPjg6/ffZghlR9jJa1DbRubiGRTETdPJGKoukSEakqe3Z00L6zj1gcmpfXEotD+84+9uzo\niLppIhUn0iDDzH7PzO4zs4NmNmBm/2Zmbyqoc4aZ/dDM0mZ2wMxuMzMFRyKlKJ2Gnh5/X4LSPWk6\ndg/QuCzGomW11Cb9/eIlMT+y0VOa7RYpV1FPl/wQeBp4E3AE2Ar8i5md7ZzrCYKJ+4EXgUuAZmAH\nMAp8OpIWi8grjY3Bnj3Q0eEDjFQKWlqgtRUSpTMFkT44THowQ/Py2gnl9QvidHWO+hwN5WdICaiU\nnKHIggwzWwScA1ztnNsflN0AfAS4EHgIeDtwHvBm51wvsM/MPgN8wcw+65wbj6b1IjLBnj3Q3g6N\njdDcDEND/meAdeuibVueVGOSVH2MoYFxapOxo+WD/eMk62pINSYjbJ1I5eUMRTbt4Jx7CXgKuMrM\nkmYWBz4MdAOPB9UuAfYFAUbOLqABuGA22ysiU0in/QhGYyMsWgS1tf5+8eJjIxslItWUomVtAwe7\nMvS+MMLIsL/v7c7QsrahrL8xSmWotJyhqHMb/hBYDQwCh4FPAO9wzg0Ex5fig4583XnHRCRq6bS/\n1dVNLK+vh+HhkgoyAFo3t7B+w0KyWejqHCWbhfUbFtK6uSXqpkmVq8ScoaJPl5jZrcD101RxQItz\n7j+Bu/BBw3p8Tsaf43MyXu+cKwwuRKQUpVL+NjTkRzFyBgchmfTHSkgimWDdlpWs2lgZc95SOSox\nZyiMnIzbge0z1HnWzN4KvBOY75zLhWfXmtmlwAeA24ADQGvBY5cE9wdmasjWrVtpaGiYULZp0yY2\nbdo000NF5Hjlkjzb28E5P4IxOAi9vbB+fckFGTmpplTZvWFLZYsiZ6itrY22trYJZQMDA1PUPnFF\nDzKCXIuXZqpnZqfhRzWyBYeyHJvG2Q3caGaL8/IyLgUGgCdn+h3btm1j9erVx9t0ETlZrcF3gY4O\n6OryIxjr1x8rF5EZ5XKG2nf24bIj1C+IM9g/Tm93hvUbFoYSFE/2xXvv3r2sWbOmKM8f5SWsu4GX\ngW+Z2S34nIwPAcvxl7YCPIAPJnaY2fXAMuAW4A7n3Nist1hEJpdI+KtIVq06dglriY5giJQynxvk\nry7p6hwlWVdT1jlDkQUZzrmXzOwdwN8APwYSwH5gg3NuX1Ana2ZXAHcDjwBp4F7gpkgaLSLTU3Ah\nckoqLWco0sW4nHN7gctmqPMccMXstEhERCR6lZIzFPUlrCIiIlKhFGSIiIhIKBRkiIiISCgUZIiI\niEgoFGSIiIhIKKLe6l1ERKSiVcq27SdDQUYJq+YTU0Sk3FXatu0nQ0FGCdKJKSJS/nLbtjcui9G8\nvJahgXHad/YBHazbsjLq5s0K5WSUoNyJGYtD8/JaYnFo39nHnh0dUTdNRESOQyVu234yFGSUGJ2Y\nIiLlL7dte13DxAmD+gVxhoeypA8OR9Sy2aUgo8RU+4mZ7knTs/+ggikRKWv527bnC3Pb9lKknIwS\nk39i1iZjR8sr/cRUHoqIVJIotm0vRRrJKDG5E/NgV4beF0YYGfb3vd0ZWtY2VOyJqTwUEak0rZtb\nWL9hIdksdHWOks1S1tu2nwyNZJQgfwL6b/VdnaMk62oq+sQszEMBqE3GcNkROnYPsGpjumKDKxGp\nXJW2bfvJUJBRgqrtxMzloTQvr51QXr8gTlfnqO+DCn79IlLZKmXb9pOhIKOEVcuJWa15KCIilU45\nGRK5as1DERGpdAoypCQoQUpEpPJoukRKQrXloYiIVAMFGVJSqiUPRUSkGmi6REREREKhIENERERC\noSBDREREQqEgQ0REREKhIENERERCoSBDREREQqEgQ0REREKhdTJERCQ06R4tsFfNFGSIiEjRjQ2P\nsWdHBx27B0gPZkjVx2hZ20Dr5hYSyUTUzZNZoukSEREpuj07Omjf2UcsDs3La4nFoX1nH3t2dETd\nNJlFCjJERKSo0j1pOnYP0LgsxqJltdQm/f3iJTE/stGTjrqJMktCCzLM7EYzazeztJn1TVHnDDP7\nYVDngJndZmY1BXVWmtnDZnbYzDrN7JNhtVlESlu6J03P/oP6kCpx6YPDpAcz1DVMnJGvXxBneChL\n+uBwRC2T2RZmTkYC+C6wG7im8GAQTNwPvAhcAjQDO4BR4NNBnXpgF/AAsAV4LbDdzPqdc98Ise0i\nUkI0v19eUo1JUvUxhgbGqU3GjpYP9o+TrKsh1ZiMsHUym0IbyXDO3eyc+yqwb4oqbwfOA97vnNvn\nnNsFfAb4qJnlgp8/xQcrf+ac63DOfRf4GvAXYbVbREqP5vfLS6opRcvaBg52Zeh9YYSRYX/f252h\nZW2DrjKpIlHmZFwC7HPO9eaV7QIagAvy6jzsnBsvqLPCzBpmp5kiEiXN75en1s0trN+wkGwWujpH\nyWZh/YaFtG5uibppMouivIR1KdBdUNadd+yJ4P7ZaeoMhNY6ESkJufn95uW1E8rrF8Tp6hz1azDo\nm3HJSSQTrNuyklUbtU5GNTuhIMPMbgWun6aKA1qcc/95Sq0qkq1bt9LQMHHAY9OmTWzatCmiFonI\nidL8fnlLNaUUXJSwtrY22traJpQNDBTv+/uJjmTcDmyfoU7hyMNUDgCtBWVL8o7l7pfMUGdK27Zt\nY/Xq1cfZHBEpRbn5/fadfbjsCPUL4gz2j9PbnWH9hoX6ABM5BZN98d67dy9r1qwpyvOfUJDhnHsJ\neKkov9lfdXKjmS3Oy8u4FD8F8mRenc+bWcw5l8mr87RzTlMlIlXCz+P7q0u6OkdJ1tVofl+kDISW\nk2FmZwALgTOBmJmtCg79yjmXxl+W+iSww8yuB5YBtwB3OOfGgrrfBv4a+KaZfRF/CevHgU+E1W4R\nKT2a3xcpT2Emfn4OuCrv573B/ZvxV4xkzewK4G7gESAN3AvclHuAc+6QmV0K3Ak8BvQCn3XO/UOI\n7RaREqX5fZHyElqQ4Zy7Grh6hjrPAVfMUOeXwB8UsWkiIiIyC7R3iYiIiIRCQYaIiIiEQkGGiIiI\nhEJBhoiIiIRCQYaIiIiEQkGGiIiIhEJBhoiIiIRCQYaIiIiEQkGGiIiIhEJBhoiIiIRCQYaIiIiE\nQkGGiIiIhEJBhoiIiIRCQYaIiIiEQkGGiIiIhEJBhoiIiIRCQYaIiIiEQkGGiIiIhEJBhoiIiIRC\nQYaIiIiEQkGGiIiIhEJBhoiIiIRCQYaIiIiEQkGGiIiIhEJBhoiIiIRCQYaIiIiEQkGGiIiI05qO\nzwAACK9JREFUhEJBhoiIiIRCQYaIiIiEQkFGhWtra4u6CSVDfeGpH45RX3jqB0/9UHyhBRlmdqOZ\ntZtZ2sz6Jjm+0sy+bWa/M7NhM9tvZh+fot7DZnbYzDrN7JNhtbkS6Y/mGPWFp344Rn3hqR889UPx\nxUN87gTwXWA3cM0kx9cA3cD7geeAdcA9ZjbunLsLwMzqgV3AA8AW4LXAdjPrd859I8S2i4iIyCkK\nLchwzt0MYGYfmOL49oKi35rZOuBK4K6g7E/xwcqfOefGgQ4zex3wF4CCDBERkRJWajkZDUD+1Mol\nwMNBgJGzC1hhZg2z2jIRERE5IWFOl5yQYBTjPcA784qXAs8WVO3OOzYwxdPNBejo6ChmE8vSwMAA\ne/fujboZJUF94akfjlFfeOoHT/3g5X12zj3lJ3POHfcNuBXITnPLAOcWPOYDQN8Mz3sh0AN8qqB8\nF3B3QVlL8HtWTPN87wOcbrrppptuuul20rf3nUiMMNntREcybge2z1CncORhWmZ2PvAg8HXn3K0F\nhw8ASwrKluQdm8oufELpb4EjJ9IeERGRKjcXWI7/LD0lJxRkOOdeAl461V+aY2YXAD8Gtjvn/nqS\nKruBz5tZzDmXCcouBZ52zk01VZJr57eL1U4REZEq80gxniTMdTLOMLNVwJlAzMxWBbdUcPxC4F/x\nkdJXzGxJcFuc9zTfBkaBb5rZ+Wb2J8DHgS+F1W4REREpDgtyGIr/xGbbgasmOfRm59zDZnYTMNno\nRadz7uy857kQuBNoBXqBrznnbg+jzSIiIlI8oQUZIiIiUt1KbZ0MERERqRAKMkRERCQUFRlkmNnl\nZvZosPFan5n9c8HxM8zsh8HmbQfM7DYzq9S+mGNmvzCzrJmtLDhW0f1gZmea2TfM7NngXHjGzD5r\nZomCehXdD/nM7KNm9ptgw8FHzaw16jaFycw+ZWY/N7NDZtZtZv/bzM6dpN7nzOzF4Dz5v2Z2ThTt\nnS1mdkPwnvDlgvKq6AczazazHWbWG7zWJ8xsdUGdiu4LM6sxs1vy3h9/ZWafnqTeKfVDxb2Rmtm7\ngG8B/4DfUG0deZezBh8e9+Mv370Ev1jYB4HPzXZbZ8ltwPP4hVWOqpJ+OA8w4L8B5wNbgf8O/E2u\nQpX0AwDB1VlfAm4CXgc8AewquKKr0rwR+J/AfwHeht8L6QEzOy1XwcyuB64FPgRcDKTx/TJn9psb\nviCw/BD+/z+/vCr6wczmA+3ACPB2/AKPfwn059Wphr64Ab/x6Efw75XXAdeZ2bW5CkXph1NdzauU\nbkAMv6PrB6epcxkwBizOK9uCP8HiUb+GIvfHZcD+4ATKAiursR8K+uSvgF9VYz8AjwJfzfvZ8AHo\ndVG3bRb7YHHwt/CGvLIXga15P88DDgPvibq9Ibz+OuBp4C34JQS+XG39AHwB+OkMdSq+L4AfAPcU\nlH0f+FYx+6HSRjJWA80AZrY3GOK5P1j0K+cSYJ9zrjevbBd+c7b8emXNzJYAf4/fyfbwJFWqoh8m\nMZ9XbsJX8f0QTBGtwS9+B4Dz7xoPAmujalcE5uNH9foAzOws/D5I+f1yCPgZldkvdwI/cM49lF9Y\nZf3wR8BjZvbdYAptr5n9ee5gFfXFI8Bbzez3AIJ1rdbjR3aL1g+VFmScjf92dhN+uPty/DfSnwRD\nZOA7rbvgcfmbrlWK7cBdzrn/N8XxaumHo4K5xGuBr+cVV0s/LMaP9E32WivpdU7JzAz4CvDvzrkn\ng+Kl+KCj4vvFzN4LXAR8apLDVdMP+M+JD+NHdC4F7ga+Zmabg+PV0hdfAL4DPGVmo8DjwFecc/8U\nHC9KP5RFkGFmtwZJSlPdMkEyV+71fN45d1/wAXs1vqP+OLIXUCTH2w9m9nH8sOgXcw+NsNlFdwLn\nQ/5jXgX8CPiOc+6b0bRcInYXPjfnvVE3ZLaZ2en4AOv9zrmxqNsTsRrgcefcZ5xzTzjn7gHuwedr\nVZM/wW8m+l58jtYHgE/mBVtFUTJbvc/geDdmaw7+fXSfWufcqJk9C7w6KDqAXz003/FsulYKjqcf\nfgO8GT+cNeK/vB31mJn9o3Puaiq/H45u1GdmzcBD+G+wWwrqlXM/nIhe/O7Fk204WEmvc1Jmdgfw\nTuCNzrmuvEMH8EH4EiZ+Y1sCTDUKWI7WAI3AXjv2phADfj9I9MslSVd6PwB0kfcZEegArgz+XS3n\nxG3Arc657wU/7zez5fiRrh0UqR/KIshwx7kxm5k9js8YXkGwuUswF70c6Ayq7QZuNLPFefPwlwID\nwJOUsBPoh48B/yOvqBmfZ/Ae4OdBWcX3AxwdwXgI2ANcM0mVsu2HE+GcGwv+Pt4K7ISj0wdvBb4W\nZdvCFgQY/xX4A+fc7/KPOed+Y2YH8P3wH0H9efirUe6c7baG6EH81Xb57sV/uH7BOfdslfQD+CtL\nVhSUrSD4jKiicyKJ/+KRL0swI1C0fog6wzWEjNltwO+APwTOBb6Bj1wbguM1+Eu3fgSsxF/C1A3c\nEnXbQ+yTM3nl1SUV3w/44OoZ4IHg30tyt2rqh7zX+h5gGL+n0HnA3+GDtcao2xbia74Ln5f1xvz/\nf2BuXp3rgn74I/wH8X3BeTMn6vaH3DeFV5dURT8Ar8d/Gf0U8Br8lMEg8N5q6gv8aPDv8CN8ZwIb\ngR7gb4vZD5G/0BA6LoYfBuoCXsZ/g28pqHMG8C/AUPCB8kWgJuq2h9gnZ+Ij1pUF5RXdD/g5xkzB\nLQtkqqkfCl7rR4Df4q842g28Puo2hfx6s5OcAxngqoJ6n8VfrjccvGecE3XbZ6FvHsoPMqqpH4IP\n1v8IXud+4JpJ6lR0XwAp4Mv4KfZ0EDzcTMGl+6faD9ogTUREREJRFleXiIiISPlRkCEiIiKhUJAh\nIiIioVCQISIiIqFQkCEiIiKhUJAhIiIioVCQISIiIqFQkCEiIiKhUJAhIiIioVCQISIiIqFQkCEi\nIiKh+P/oyDDU1koxzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f5764e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(r['pmra'], r['pmdec'], color='r', alpha=0.3)\n",
    "plt.scatter(j['pmra'], j['pmdec'], color='b', alpha=0.3)\n",
    "plt.scatter(m45cluster['pmra'], m45cluster['pmdec'], color='g', alpha=0.3)\n",
    "plt.xlim(-60,80)\n",
    "plt.ylim(-120,30)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Calculate the average parallax and standard deviation of the parallax for the M45 cluster candidates:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.4686695575 0.834822732559\n"
     ]
    }
   ],
   "source": [
    "avg_parallax = np.mean(m45cluster['parallax']) \n",
    "stddev_parallax = np.std(m45cluster['parallax']) \n",
    "print (avg_parallax, stddev_parallax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Now, we want to add information from other catalogues, in this example from 2MASS. To do this we make use of the pre-computed cross-matched tables provided in the Gaia archive.*\n",
    "\n",
    "*We obtain the 2MASS photometric data by using the Gaia - 2MASS cross-matched best neighbour table (gaiadr1.tmass_best_neighbour) to identify the sources and the 2MASS original table (gaiadr1.tmass_original_valid) to retrieve the photometry:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launched query: 'SELECT * FROM gaiadr1.gaia_source AS g, gaiadr1.tmass_best_neighbour AS tbest, gaiadr1.tmass_original_valid AS tmass WHERE g.source_id = tbest.source_id AND tbest.tmass_oid = tmass.tmass_oid AND CONTAINS(POINT('ICRS',g.ra,g.dec),CIRCLE('ICRS',56.75,24.1167,2))=1 AND abs(g.pmra_error/g.pmra)<0.10 AND abs(g.pmdec_error/g.pmdec)<0.10 AND g.pmra IS NOT NULL AND abs(g.pmra)>0 AND g.pmdec IS NOT NULL AND abs(g.pmdec)>0 AND g.pmra BETWEEN 15 AND 25 AND g.pmdec BETWEEN -55 AND -40;'\n",
      "Retrieving async. results...\n",
      "Query finished.\n"
     ]
    }
   ],
   "source": [
    "job4 = Gaia.launch_job_async(\"SELECT * \\\n",
    "FROM gaiadr1.gaia_source AS g, gaiadr1.tmass_best_neighbour AS tbest, gaiadr1.tmass_original_valid AS tmass \\\n",
    "WHERE g.source_id = tbest.source_id AND tbest.tmass_oid = tmass.tmass_oid \\\n",
    "AND CONTAINS(POINT('ICRS',g.ra,g.dec),CIRCLE('ICRS',56.75,24.1167,2))=1 \\\n",
    "AND abs(g.pmra_error/g.pmra)<0.10 \\\n",
    "AND abs(g.pmdec_error/g.pmdec)<0.10 \\\n",
    "AND g.pmra IS NOT NULL AND abs(g.pmra)>0 \\\n",
    "AND g.pmdec IS NOT NULL AND abs(g.pmdec)>0 \\\n",
    "AND g.pmra BETWEEN 15 AND 25 \\\n",
    "AND g.pmdec BETWEEN -55 AND -40;\", dump_to_file=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Finally, confirm the output table has Gaia and 2MASS photometry and check the number of rows in the table:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " phot_g_mean_mag        j_m            h_m            ks_m     \n",
      "  Magnitude[mag]   Magnitude[mag] Magnitude[mag] Magnitude[mag]\n",
      "------------------ -------------- -------------- --------------\n",
      "10.757800849005008      9.6029997      9.2019997      9.0939999\n",
      "6.0733019438444877      5.9679999      6.0510001      5.9759998\n",
      "10.643169127592188      9.5349998      9.2189999      9.1370001\n",
      "6.8275023878511796      6.6989999      6.7329998      6.6919999\n",
      " 10.15654586177056      9.1169996          8.868      8.7580004\n",
      "7.5371472521505787      7.2800002           7.29          7.257\n",
      "8.1328652600027205      7.5879998      7.5279999      7.4699998\n",
      "8.5268119167756886      7.8920002      7.7670002      7.7379999\n",
      "9.0234235063721506      8.2360001          8.033      8.0019999\n",
      "7.0246160054011426          6.848      6.9200001          6.895\n",
      "               ...            ...            ...            ...\n",
      "10.511033521483427      9.4770002      9.1400003      9.0719995\n",
      "11.597470392962894         10.261          9.835      9.7299995\n",
      "10.591447284586406      9.5310001      9.1920004      9.1230001\n",
      "10.325731863675678      9.3500004          9.092      8.9960003\n",
      "9.9103711452615997      8.9829998      8.7110004          8.632\n",
      "9.3335117945827335          8.533      8.3290005      8.2819996\n",
      "8.1126755868950937      7.6729999          7.599      7.5760002\n",
      "6.3275477760376688      6.2280002      6.2480001          6.257\n",
      "8.1878498252772722          7.526      7.3930001      7.3520002\n",
      "11.149659997470195      9.9490004      9.5640001      9.4390001\n",
      "Length = 106 rows\n"
     ]
    }
   ],
   "source": [
    "p = job4.get_results() \n",
    "print (p['phot_g_mean_mag', 'j_m', 'h_m', 'ks_m']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of the above has been performed as an anonymous user to the Gaia archive. To log in to the archive, keep and share your results, see the following instructions: http://astroquery.readthedocs.io/en/latest/gaia/gaia.html#authenticated-access"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Additional information**\n",
    "\n",
    "*The above query to obtain the 2MASS catalogue data can also be performed by using an 'INNER JOIN' in the ADQL query. For example:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Launched query: 'SELECT * FROM gaiadr1.gaia_source INNER JOIN gaiadr1.tmass_best_neighbour ON gaiadr1.gaia_source.source_id = gaiadr1.tmass_best_neighbour.source_id INNER JOIN gaiadr1.tmass_original_valid ON gaiadr1.tmass_original_valid.tmass_oid = gaiadr1.tmass_best_neighbour.tmass_oid WHERE CONTAINS(POINT('ICRS',gaiadr1.gaia_source.ra,gaiadr1.gaia_source.dec),CIRCLE('ICRS',56.75,24.1167,2))=1 AND abs(pmra_error/pmra)<0.10 AND abs(pmdec_error/pmdec)<0.10 AND pmra IS NOT NULL AND abs(pmra)>0 AND pmdec IS NOT NULL AND abs(pmdec)>0 AND pmra BETWEEN 15 AND 25 AND pmdec BETWEEN -55 AND -40;'\n",
      "Retrieving async. results...\n",
      "Query finished.\n"
     ]
    }
   ],
   "source": [
    "job5 = Gaia.launch_job_async(\"SELECT * \\\n",
    "FROM gaiadr1.gaia_source \\\n",
    "INNER JOIN gaiadr1.tmass_best_neighbour ON gaiadr1.gaia_source.source_id = gaiadr1.tmass_best_neighbour.source_id \\\n",
    "INNER JOIN gaiadr1.tmass_original_valid ON gaiadr1.tmass_original_valid.tmass_oid = gaiadr1.tmass_best_neighbour.tmass_oid \\\n",
    "WHERE CONTAINS(POINT('ICRS',gaiadr1.gaia_source.ra,gaiadr1.gaia_source.dec),CIRCLE('ICRS',56.75,24.1167,2))=1 \\\n",
    "AND abs(pmra_error/pmra)<0.10 \\\n",
    "AND abs(pmdec_error/pmdec)<0.10 \\\n",
    "AND pmra IS NOT NULL AND abs(pmra)>0 \\\n",
    "AND pmdec IS NOT NULL AND abs(pmdec)>0 \\\n",
    "AND pmra BETWEEN 15 AND 25 \\\n",
    "AND pmdec BETWEEN -55 AND -40;\", dump_to_file=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Confirm the output table has Gaia and 2MASS photometry and check the number of rows in the table is the same as above (106 rows):*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " phot_g_mean_mag        j_m            h_m            ks_m     \n",
      "  Magnitude[mag]   Magnitude[mag] Magnitude[mag] Magnitude[mag]\n",
      "------------------ -------------- -------------- --------------\n",
      "10.757800849005008      9.6029997      9.2019997      9.0939999\n",
      "6.0733019438444877      5.9679999      6.0510001      5.9759998\n",
      "10.643169127592188      9.5349998      9.2189999      9.1370001\n",
      "6.8275023878511796      6.6989999      6.7329998      6.6919999\n",
      " 10.15654586177056      9.1169996          8.868      8.7580004\n",
      "7.5371472521505787      7.2800002           7.29          7.257\n",
      "8.1328652600027205      7.5879998      7.5279999      7.4699998\n",
      "8.5268119167756886      7.8920002      7.7670002      7.7379999\n",
      "9.0234235063721506      8.2360001          8.033      8.0019999\n",
      "7.0246160054011426          6.848      6.9200001          6.895\n",
      "               ...            ...            ...            ...\n",
      "10.511033521483427      9.4770002      9.1400003      9.0719995\n",
      "11.597470392962894         10.261          9.835      9.7299995\n",
      "10.591447284586406      9.5310001      9.1920004      9.1230001\n",
      "10.325731863675678      9.3500004          9.092      8.9960003\n",
      "9.9103711452615997      8.9829998      8.7110004          8.632\n",
      "9.3335117945827335          8.533      8.3290005      8.2819996\n",
      "8.1126755868950937      7.6729999          7.599      7.5760002\n",
      "6.3275477760376688      6.2280002      6.2480001          6.257\n",
      "8.1878498252772722          7.526      7.3930001      7.3520002\n",
      "11.149659997470195      9.9490004      9.5640001      9.4390001\n",
      "Length = 106 rows\n"
     ]
    }
   ],
   "source": [
    "test = job5.get_results() \n",
    "print (test['phot_g_mean_mag', 'j_m', 'h_m', 'ks_m']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "*Visually inspect the results are the same by plotting the same as above:* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+Uda0SKUWGreZGakcmZyUxMymJpldRqP5MlNYWDba3i7nzWSkWgTyvSoKkz6t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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111f99e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(r['pmra'], r['pmdec'], color='r', alpha=0.3)\n",
    "plt.scatter(j['pmra'], j['pmdec'], color='b', alpha=0.3)\n",
    "plt.scatter(m45cluster['pmra'], m45cluster['pmdec'], color='g', alpha=0.3)\n",
    "plt.scatter(test['pmra'], test['pmdec'], color='y', alpha=0.3)\n",
    "plt.xlim(-60,80)\n",
    "plt.ylim(-120,30)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}