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Crossmatching

HW2-Pt4_Thomas.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "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"
+    }
+   ],
+   "source": [
+    "idxnew=np.delete(idx['col1'],badmatch)\n",
+    "len(idxnew)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1527346, 1527346)"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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('$log(count)$')"
+   ]
+  }
+ ],
+ "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": "2.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}