HW2-Pt4_Thomas.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from astropy.table import Table, Column, vstack\n",
    "import collections\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read HLC and NSC Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#--------------HLC-----------------------#\n",
    "\n",
    "hlc1=Table.read('HLC.RA_00_to_01.fits.gz')\n",
    "hlc2=Table.read('HLC.RA_01_to_02.fits.gz')\n",
    "hlc3=Table.read('HLC.RA_02_to_03.fits.gz')\n",
    "hlc4=Table.read('HLC.RA_03_to_04.fits.gz')\n",
    "hlc5=Table.read('HLC.RA_20_to_21.fits.gz')\n",
    "hlc6=Table.read('HLC.RA_21_to_22.fits.gz')\n",
    "hlc7=Table.read('HLC.RA_22_to_23.fits.gz')\n",
    "hlc8=Table.read('HLC.RA_23_to_24.fits.gz')\n",
    "\n",
    "hlc=vstack(hlc1,hlc2,hlc3,hlc4,hlc5,hlc6,hlc7,hlc8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#--------------NSC----------------------#\n",
    "\n",
    "nsc1=Table.read('stripe82_315_ra_45_-1_3_dec_0.txt', format='ascii.basic')\n",
    "nsc2=Table.read('stripe82_315_ra_45_0_dec_1_3.txt', format='ascii.basic')\n",
    "\n",
    "nsc=vstack(nsc1,nsc2)\n",
    "\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "idx=Table.read('manxmatch_hlc_nsc_idx.txt', format='ascii.no_header')\n",
    "dist=Table.read('manxmatch_hlc_nsc_d2d.txt', format='ascii.no_header')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crossmatching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#-----------Read matched indices and distances tables--------------#\n",
    "\n",
    "idx=Table.read('manxmatch_hlc_nsc_idx.txt', format='ascii.no_header')\n",
    "dist=Table.read('manxmatch_hlc_nsc_d2d.txt', format='ascii.no_header')\n",
    "\n",
    "#print(len(idx), len(dist))\n",
    "#dist['col1']\n",
    "\n",
    "#----------Make list of duplicates--------------------#\n",
    "\n",
    "#  Count duplicates\n",
    "count=collections.Counter(idx['col1'])\n",
    "#  Turn list into array\n",
    "count=np.array(count.items())\n",
    "#  Make mask of multiple matches\n",
    "multimatch=count[:,1]>1\n",
    "\n",
    "#-------------Match Multiples in Indices array----------------#\n",
    "\n",
    "#  Here, I'm going to create a massive list of the indices of matches I want to throw away.\n",
    "\n",
    "match=[]\n",
    "b=[]\n",
    "\n",
    "for indice in count[multimatch][:,0]:\n",
    "    #Create mask to find multiple indicies\n",
    "    a=np.where(idx['col1']==indice)[0]\n",
    "    #Append matches to list\n",
    "    match.append([a,idx['col1'][a],dist['col1'][a]])\n",
    "    #Create mask to find the minimum distance within the matches\n",
    "    b.append(dist['col1'][a]==min(d for d in dist['col1'][a]))\n",
    "\n",
    "\n",
    "#  Apply the mask to this weird \"match\" list and make a new list of the wrong \n",
    "#  indice matches using the '~'.\n",
    "\n",
    "wrong=[]\n",
    "\n",
    "for i in range(len(match)):\n",
    "   wrong.append(match[i][0][~b[i]])\n",
    "\n",
    "#  Concatenate list of indicies into \"badmatch\"\n",
    "badmatch=np.concatenate(wrong, axis=0)\n",
    "\n",
    "#---------Check list of the right indice matches--------------#\n",
    "\n",
    "#right=[]\n",
    "\n",
    "#for i in range(len(match)):\n",
    "   #right.append(match[i][0][b[i]])\n",
    "\n",
    "#---------------------Final list of matches---------------------#\n",
    "idxnew=np.delete(idx['col1'],badmatch)\n",
    "\n",
    "#-------------Apply indicies to nsc table-------------#\n",
    "nsc[idxnew]\n",
    "\n",
    "#-------------Split nsc table---------------------------------#\n",
    "nsc1=nsc[0:len(hlc1)-1]\n",
    "nsc2=nsc[len(hlc1):len(hlc2)-1]\n",
    "nsc3=nsc[len(hlc2):len(hlc3)-1]\n",
    "nsc4=nsc[len(hlc3):len(hlc4)-1]\n",
    "nsc5=nsc[len(hlc4):len(hlc5)-1]\n",
    "nsc6=nsc[len(hlc5):len(hlc6)-1]\n",
    "nsc7=nsc[len(hlc6):len(hlc7)-1]\n",
    "nsc8=nsc[len(hlc7):len(hlc8)-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1527346"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "idxnew=np.delete(idx['col1'],badmatch)\n",
    "len(idxnew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1527346, 1527346)"
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     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proper Motion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#-----------------dPM----------------------------#\n",
    "# HLC proper motions\n",
    "HLCpmRA = 1000*hlc['RA_PM']\n",
    "HLCpmRAerr = 1000*hlc['RA_PM_ERR']\n",
    "HLCpmDec = 1000*hlc['DEC_PM']\n",
    "HLCpmDecerr = 1000*hlc['DEC_PM_ERR']\n",
    "# NSC proper motions \n",
    "NSCpmRA = nsc['pmra']\n",
    "NSCpmRAerr = nsc['pmraerr']\n",
    "NSCpmDec = nsc['pmdec']\n",
    "NSCpmDecerr = nsc['pmdecerr']\n",
    "\n",
    "dra = 3600*(hlc['RAPM_MEAN'] - nsc['ra']) \n",
    "ddec = 3600*(hlc['DEC_MEAN'] - nsc['dec']) \n",
    "\n",
    "\n",
    "dPMra = HLCpmRA - NSCpmRA\n",
    "\n",
    "N, xedges, yedges = binned_statistic_2d(r, , dPMRA, 'count', bins=150)\n",
    "\n",
    "plt.xlabel('$r$')\n",
    "plt.ylabel('$dpmRA$')\n",
    "plt.imshow(np.log10(N.T), origin='lower', extent=[xedges[0], xedges[-1], yedges[0], \n",
    "    yedges[-1]], aspect='auto', interpolation='nearest', cmap=multicolor)\n",
    "    \n",
    "cb = plt.colorbar(orientation='horizontal')\n",
    "cb.set_label('$log(count)$')"
   ]
  }
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