homeworks/HW2_PartIV_Thomas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.coordinates import SkyCoord\n",
    "from astropy import units as u\n",
    "from astropy.table import Table, hstack, vstack\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np\n",
    "from astroML.stats import binned_statistic_2d\n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "from astroML.plotting import hist\n",
    "from astropy.time import Time\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read HLC, NSC, and GAIA  \n",
    "\n",
    "I needed to split the catalogues into *several* files just to read them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "#------------------------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",
    "\n",
    "#------------------------NSC------------------------#\n",
    "\n",
    "nsc1=Table.read('stripe82_315_ra_45_-1_3_dec_0.csv', format='ascii', data_end=499999)\n",
    "nsc2=Table.read('stripe82_315_ra_45_-1_3_dec_0.csv', format='ascii', data_start=500000, data_end=999999)\n",
    "nsc3=Table.read('stripe82_315_ra_45_-1_3_dec_0.csv', format='ascii', data_start=1000000, data_end=1499999)\n",
    "nsc4=Table.read('stripe82_315_ra_45_-1_3_dec_0.csv', format='ascii', data_start=1500000, data_end=1999999)\n",
    "nsc5=Table.read('stripe82_315_ra_45_-1_3_dec_0.csv', format='ascii', data_start=2000000, data_end=2499999)\n",
    "nsc6=Table.read('stripe82_315_ra_45_-1_3_dec_0.csv', format='ascii', data_start=2500000, data_end=2999999)\n",
    "nsc7=Table.read('stripe82_315_ra_45_-1_3_dec_0.csv', format='ascii', data_start=3000000, data_end=3499999)\n",
    "nsc8=Table.read('stripe82_315_ra_45_-1_3_dec_0.csv', format='ascii', data_start=3500000, data_end=3999999)\n",
    "\n",
    "nsc9=Table.read('stripe82_315_ra_45_0_dec_1_3.csv', format='ascii', data_end=499999)\n",
    "nsc10=Table.read('stripe82_315_ra_45_0_dec_1_3.csv', format='ascii', data_start=500000, data_end=999999)\n",
    "nsc11=Table.read('stripe82_315_ra_45_0_dec_1_3.csv', format='ascii', data_start=1000000, data_end=1499999)\n",
    "nsc12=Table.read('stripe82_315_ra_45_0_dec_1_3.csv', format='ascii', data_start=1500000, data_end=1999999)\n",
    "nsc13=Table.read('stripe82_315_ra_45_0_dec_1_3.csv', format='ascii', data_start=2000000, data_end=2499999)\n",
    "nsc14=Table.read('stripe82_315_ra_45_0_dec_1_3.csv', format='ascii', data_start=2500000, data_end=2999999)\n",
    "nsc15=Table.read('stripe82_315_ra_45_0_dec_1_3.csv', format='ascii', data_start=3000000, data_end=3499999)\n",
    "\n",
    "#------------------------Gaia-----------------------#\n",
    "colnames = ['ra', 'dec', 'nObs', 'Gmag', 'flux', 'fluxErr']\n",
    "gaia = Table.read('Stripe82_GaiaDR1.dat.gz', format='ascii', names=colnames)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Crossmatch HLC and NSC with Gaia  \n",
    "\n",
    "\n",
    "There were still multiple matches with the 0.5 arcsecond distance, so I created a code that automatically finds the closest match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#--------------------------------Function to Filter Out Closest Matches---------------------------------#\n",
    "\n",
    "def close_match(idx,dist):\n",
    "    #----------Make list of duplicates--------------------#\n",
    "    \n",
    "    #  Count duplicates\n",
    "    count=Counter(idx)\n",
    "    #  Turn list into array\n",
    "    count=list(count.items())\n",
    "    count=np.array(count)\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==indice)[0]\n",
    "        #Append matches to list\n",
    "        match.append([a,idx[a],dist[a]])\n",
    "        #Create mask to find the minimum distance within the matches\n",
    "        b.append(dist[a]==min(d for d in dist[a]))\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",
    "\n",
    "    #  Concatenate list of indicies into \"badmatch\"\n",
    "    badmatch=np.concatenate(wrong, axis=0)\n",
    "    \n",
    "    #---------------------Final list of matches and indices to throw away---------------------#\n",
    "    idxnew=np.delete(idx,badmatch)\n",
    "    \n",
    "    idx_list=np.array(range(len(idx)))\n",
    "    delmulti=np.delete(idx_list, badmatch)\n",
    "    \n",
    "    return(delmulti,idxnew)\n",
    "\n",
    "\n",
    "\n",
    "#---------------------------------------------Crossmatch HLC---------------------------------------------#\n",
    "\n",
    "def crossmatch_hlc_gaia(hlc, gaia):\n",
    "\n",
    "    \n",
    "    #------------------Read RA and DEC------------------#\n",
    "    \n",
    "    hlcra   = hlc['RA_MEAN']\n",
    "    hlcdec  = hlc['DEC_MEAN']\n",
    "    gaiara  = gaia['ra']\n",
    "    gaiadec = gaia['dec']\n",
    "    \n",
    "    #----------------Create coodinates------------------#\n",
    "    \n",
    "    HLCcoords = SkyCoord(ra = hlcra*u.degree, dec= hlcdec*u.degree) \n",
    "    GAIAcoords = SkyCoord(ra = gaiara*u.degree, dec= gaiadec*u.degree)\n",
    "    \n",
    "    #-----------------Match HLC and Gaia---------------#\n",
    "    \n",
    "    hidx, hd2d, hd3d = HLCcoords.match_to_catalog_sky(GAIAcoords)\n",
    "    \n",
    "    #-----------distance filter (0.5arcseconds)----------#\n",
    "    \n",
    "    #halfarc=0.000138889  #0.5arcs\n",
    "    #hdist=np.array(hd2d)<halfarc\n",
    "    \n",
    "    #----------------New HLC and NSC tables-------------#\n",
    "    \n",
    "    #-----run multiple matches----#\n",
    "    \n",
    "    hdelmulti, hidxnew=close_match(hidx,hd2d)\n",
    "    \n",
    "    #----Delete indicies with \"not best\" matches----#\n",
    "    \n",
    "    gaia_hlc=gaia[hidxnew]\n",
    "    \n",
    "    hlc_new=hlc[hdelmulti]\n",
    "    \n",
    "    \n",
    "    #----Stack tables------#\n",
    "    hg=hstack([hlc_new,gaia_hlc])\n",
    "    \n",
    "    return(hg)\n",
    "\n",
    "\n",
    "\n",
    "#------------------------------------------------Crossmatch NSC------------------------------------------#\n",
    "\n",
    "def crossmatch_nsc_gaia(nsc, gaia):\n",
    "\n",
    "    \n",
    "    #------------------Read RA and DEC------------------#\n",
    "    \n",
    "    nscra   = nsc['ra']\n",
    "    nscdec  = nsc['dec']\n",
    "    gaiara  = gaia['ra']\n",
    "    gaiadec = gaia['dec']\n",
    "    \n",
    "    #----------------Create coodinates------------------#\n",
    "    \n",
    "    GAIAcoords = SkyCoord(ra = gaiara*u.degree, dec= gaiadec*u.degree)\n",
    "    NSCcoords = SkyCoord(ra = nscra*u.degree, dec= nscdec*u.degree)\n",
    "    \n",
    "    #-----------------Match HLC and Gaia---------------#\n",
    "    \n",
    "    nidx, nd2d, nd3d = NSCcoords.match_to_catalog_sky(GAIAcoords)\n",
    "    \n",
    "    #-----------distance filter (0.5arcseconds)----------#\n",
    "    \n",
    "    #halfarc=0.000138889  #0.5arcs\n",
    "    #ndist=np.array(nd2d)<halfarc\n",
    "    \n",
    "    \n",
    "    #----------------New HLC and NSC tables-------------#\n",
    "    \n",
    "    #-----run multiple matches----#\n",
    "    ndelmulti, nidxnew=close_match(nidx,nd2d)\n",
    "    \n",
    "    #----Delete indicies with \"not best\" matches----#\n",
    "    \n",
    "    gaia_nsc=gaia[nidxnew]\n",
    "    \n",
    "    nsc_new=nsc[ndelmulti]\n",
    "    \n",
    "    #----Stack tables------#\n",
    "    \n",
    "    ng=hstack([nsc_new,gaia_nsc])\n",
    "    \n",
    "    return(ng)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n",
      "CPU times: user 35.3 ms, sys: 7.55 ms, total: 42.8 ms\n",
      "Wall time: 41.9 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mng=vstack([ng1,ng2])\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#------List of tables-----------------#\n",
    "\n",
    "\n",
    "#------GAIA_HLC----------#\n",
    "\n",
    "hg1 = crossmatch_hlc_gaia(hlc1, gaia)    #Can't work with wrapping degrees yet\n",
    "hg2 = crossmatch_hlc_gaia(hlc2, gaia)\n",
    "hg3 = crossmatch_hlc_gaia(hlc3, gaia)\n",
    "hg4 = crossmatch_hlc_gaia(hlc4, gaia)\n",
    "hg5 = crossmatch_hlc_gaia(hlc5, gaia)    #Can't work with wrapping degrees yet\n",
    "hg6 = crossmatch_hlc_gaia(hlc6, gaia)\n",
    "hg7 = crossmatch_hlc_gaia(hlc7, gaia)\n",
    "hg8 = crossmatch_hlc_gaia(hlc8, gaia)    #Can't work with wrapping degrees yet\n",
    "\n",
    "\n",
    "table2=vstack([table1,hg6,g7])\n",
    "mhg=vstack([table2,hg8])\n",
    "\n",
    "#------GAIA_NSC----------#\n",
    "\n",
    "ng1 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng2 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng3 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng4 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng5 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng6 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng7 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng8 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng9 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng10 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng11 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng12 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng13 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng14 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "ng15 = crossmatch_nsc_gaia(nsc1, gaia)\n",
    "\n",
    "\n",
    "table3=vstack([ng1,ng2,ng3])\n",
    "table4=vstack([table3,ng4,ng5])\n",
    "table5=vstack([table4,ng6,ng7])\n",
    "table6=vstack([table5,ng8,ng9])\n",
    "table7=vstack([table6,ng10,ng11])\n",
    "table8=vstack([table7,ng12,ng13])\n",
    "mng=vstack([table8,ng14,ng15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# of GAIA_HLC Sources ==   527136 \n",
      " # of GAIA_NSC Sources ==   554895\n"
     ]
    }
   ],
   "source": [
    "print ('# of GAIA_HLC Sources ==  ',len(mhg),'\\n','# of GAIA_NSC Sources ==  ' ,len(mng))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dpos(hg,ng):\n",
    "    \n",
    "    #RA and DEC\n",
    "    \n",
    "    HLCra=hg['RA_MEAN']\n",
    "    HLCdec=hg['DEC_MEAN']\n",
    "    NSCra=ng['ra_1']\n",
    "    NSCdec=ng['dec_1']\n",
    "    GAIA_HLCra=hg['ra']\n",
    "    GAIA_HLCdec=hg['dec']\n",
    "    GAIA_NSCra=ng['ra_2']\n",
    "    GAIA_NSCdec=ng['dec_2']\n",
    "\n",
    "    # dpos\n",
    "    hdra  = (HLCra-GAIA_HLCra)\n",
    "    hddec = (HLCdec-GAIA_HLCdec)\n",
    "    ndra  = (NSCra-GAIA_NSCra)\n",
    "    nddec = (NSCdec-GAIA_NSCdec)\n",
    "    \n",
    "    #------------Plot HLC_GAIA--------------#\n",
    "    plt.style.use('ggplot')\n",
    "    fig = plt.figure(figsize=[20,6])\n",
    "    \n",
    "    #-----dra vs ddec------#\n",
    "    \n",
    "    ax = plt.subplot(131)\n",
    "    \n",
    "    plt.ylabel('$dDEC$')\n",
    "    plt.xlabel('$dRA$')\n",
    "    plt.scatter(hdra,hddec, s=3, c='k')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.axhline(y=0, c='red', linestyle='--')\n",
    "    plt.xlim(-0.1, 0.1)\n",
    "    plt.ylim(-0.05, 0.05)\n",
    "    \n",
    "    #-----dra hist------#\n",
    "    ax = plt.subplot(132)\n",
    "    \n",
    "    plt.title('HLC_GAIA')\n",
    "    plt.xlabel('$dRA$')\n",
    "    plt.ylabel('$N$')\n",
    "    hist(hdra, bins=20000000, histtype='stepfilled', ec='k', fc='orange')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.xlim(-0.0003,0.0003)\n",
    "    #-----dra hist------#\n",
    "    ax = plt.subplot(133)\n",
    "    \n",
    "    plt.xlabel('$dDEC$')\n",
    "    plt.ylabel('$N$')\n",
    "    hist(hddec, bins=5000, histtype='stepfilled', ec='k', fc='orange')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.xlim(-0.0003,0.0004)\n",
    "    \n",
    "    print ('dRA Median HLC =   ', np.median(hdra))\n",
    "    print ('dRA std HLC =   ', np.std(hdra))\n",
    "    print ('dDEC Median HLC =   ',np.median(hddec))\n",
    "    print ('dDEC std HLC =   ', np.std(hddec))\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "     #------------Plot NSC_GAIA--------------#\n",
    "    \n",
    "    fig = plt.figure(figsize=[20,6])\n",
    "    \n",
    "    #-----dra vs ddec------#\n",
    "    \n",
    "    ax = plt.subplot(131)\n",
    "    \n",
    "    plt.ylabel('$dDEC$')\n",
    "    plt.xlabel('$dRA$')\n",
    "    plt.scatter(ndra,nddec, s=3, c='k')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.axhline(y=0, c='red', linestyle='--')\n",
    "    plt.xlim(-0.1, 0.1)\n",
    "    plt.ylim(-0.04, 0.04)\n",
    "    \n",
    "    #-----dra hist------#\n",
    "    ax = plt.subplot(132)\n",
    "    \n",
    "    plt.title('NSC_GAIA')\n",
    "    plt.xlabel('$dRA$')\n",
    "    plt.ylabel('$N$')\n",
    "    hist(ndra, bins=500000, histtype='stepfilled', ec='k', fc='orange')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.xlim(-0.00003,0.00003)\n",
    "    #-----dra hist------#\n",
    "    ax = plt.subplot(133)\n",
    "    \n",
    "    plt.xlabel('$dDEC$')\n",
    "    plt.ylabel('$N$')\n",
    "    hist(nddec, bins=50000, histtype='stepfilled', ec='k', fc='orange')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.xlim(-0.00003,0.00003)\n",
    "    \n",
    "    print ('dRA Median NSC  =   ', np.median(ndra))\n",
    "    print ('dRA std NSC =   ', np.std(ndra))\n",
    "    print ('dDEC Median NSC =   ',np.median(nddec))\n",
    "    print ('dDEC std NSC =   ', np.std(nddec))\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dRA Median HLC =    -1.27415194697e-05\n",
      "dRA std HLC =    3.21320161622\n",
      "dDEC Median HLC =    2.41782830683e-05\n",
      "dDEC std HLC =    0.000700419529259\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLMAAAGICAYAAAC6B6KrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f711a610cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dRA Median NSC  =    -1.69630311575e-07\n",
      "dRA std NSC =    0.00725760832197\n",
      "dDEC Median NSC =    1.14264057316e-06\n",
      "dDEC std NSC =    0.000873377029856\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLYAAAGICAYAAABcLmnvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f711a0a76d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dpos(mhg,mng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proper Motions  \n",
    "\n",
    "(Because my computer is slow, I am only comparing (hlc2,hlc3,hlc4) and (nsc1,nsc2) here. If you have a faster one, feel free to run the entire code.)\n",
    "\n",
    "Here, we compare the proper motions between HLC and NSC. Whichever one is better will have a smaller change in position compared to the GAIA catalogue after adding the measured proper motion values. I really needed the HJD from the LMCC catalogues, but I'm just using the mean HLC date, September 15, 2002 (2452532.75054JD), the mean NSC date (2456782.4517588825JD), and the release GAIA date (2457645.75054JD)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#-----Times(HJD)--------#\n",
    "hlc_time=Time(2452532.75054, format='jd')\n",
    "nsc_time=Time(np.median(mng['mjd'])+2400000.5, format='jd')\n",
    "gaia_time=Time(2457645.75054, format='jd')\n",
    "\n",
    "mhg_dt=gaia_time.decimalyear-hlc_time.decimalyear\n",
    "mng_dt=gaia_time.decimalyear-nsc_time.decimalyear\n",
    "\n",
    "def dpm(hg,ng):\n",
    "    \n",
    "\n",
    "    #-----------------dPM----------------------------#\n",
    "    \n",
    "    #RA and DEC in mas\n",
    "    \n",
    "    HLCra=hg['RA_MEAN']*3600\n",
    "    HLCdec=hg['DEC_MEAN']*3600\n",
    "    NSCra=ng['ra_1']*3600\n",
    "    NSCdec=ng['dec_1']*3600\n",
    "    GAIA_HLCra=hg['ra']*3600\n",
    "    GAIA_HLCdec=hg['dec']*3600\n",
    "    GAIA_NSCra=ng['ra_2']*3600\n",
    "    GAIA_NSCdec=ng['dec_2']*3600\n",
    "    \n",
    "    # HLC proper motions\n",
    "    HLCpmRA = hg['RA_PM']\n",
    "    HLCpmRAerr = hg['RA_PM_ERR']\n",
    "    HLCpmDec = hg['DEC_PM']\n",
    "    HLCpmDecerr = hg['DEC_PM_ERR']\n",
    "    \n",
    "    # NSC proper motions \n",
    "    NSCpmRA = ng['pmra']\n",
    "    NSCpmRAerr = ng['pmraerr']\n",
    "    NSCpmDec = ng['pmdec']\n",
    "    NSCpmDecerr = ng['pmdecerr']\n",
    "    \n",
    "    \n",
    "    #Multiply proper motions by change in time\n",
    "    HLCra_new=HLCra+(HLCpmRA*mhg_dt)\n",
    "    HLCdec_new=HLCdec+(HLCpmDec*mhg_dt)\n",
    "    NSCra_new=NSCra+(NSCpmRA*mng_dt)\n",
    "    NSCdec_new=NSCdec+(NSCpmDec*mng_dt)\n",
    "    \n",
    "    # Difference between GAIA positions and projected HLC and NSC positions\n",
    "    hdra_new = HLCra_new - GAIA_HLCra\n",
    "    hddec_new = HLCdec_new -GAIA_HLCdec\n",
    "    ndra_new = NSCra_new - GAIA_NSCra\n",
    "    nddec_new = NSCdec_new - GAIA_NSCdec\n",
    "    \n",
    "    #----------------------------------plot--------------------------------#\n",
    "    \n",
    "    #------------Plot HLC_GAIA--------------#\n",
    "    plt.style.use('ggplot')\n",
    "    fig = plt.figure(figsize=[20,6])\n",
    "    \n",
    "    #-----dra vs ddec------#\n",
    "    \n",
    "    ax = plt.subplot(131)\n",
    "    \n",
    "    plt.ylabel('$dDEC_{new}$ mas')\n",
    "    plt.xlabel('$dRA_{new}$ mas')\n",
    "    plt.scatter(hdra_new,hddec_new, s=3, c='k')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.axhline(y=0, c='red', linestyle='--')\n",
    "    plt.xlim(-5, 5)\n",
    "    plt.ylim(-5, 5)\n",
    "    \n",
    "    #-----dra hist------#\n",
    "    ax = plt.subplot(132)\n",
    "    \n",
    "    plt.title('HLC_GAIA_new')\n",
    "    plt.xlabel('$dRA_{new}$ mas')\n",
    "    plt.ylabel('$N$')\n",
    "    hist(hdra_new, bins=5000, histtype='stepfilled', ec='k', fc='c')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.xlim(-1,1)\n",
    "    #-----dra hist------#\n",
    "    ax = plt.subplot(133)\n",
    "    \n",
    "    plt.xlabel('$dDEC_{new}$ mas')\n",
    "    plt.ylabel('$N$')\n",
    "    hist(hddec_new, bins=5000, histtype='stepfilled', ec='k', fc='c')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.xlim(-1,1)\n",
    "    \n",
    "    print ('dRA Median HLC_new =   ', np.median(hdra_new))\n",
    "    print ('dRA std HLC_new =   ', np.std(hdra_new))\n",
    "    print ('dDEC Median HLC_new =   ',np.median(hddec_new))\n",
    "    print ('dDEC std HLC_new =   ', np.std(hddec_new))\n",
    "    \n",
    "    plt.show()\n",
    "    \n",
    "    #------------Plot HLC_GAIA--------------#\n",
    "    plt.style.use('ggplot')\n",
    "    fig = plt.figure(figsize=[20,6])\n",
    "    \n",
    "    #-----dra vs ddec------#\n",
    "    \n",
    "    ax = plt.subplot(131)\n",
    "    \n",
    "    plt.ylabel('$dDEC_{new}$ mas')\n",
    "    plt.xlabel('$dRA_{new}$ mas')\n",
    "    plt.scatter(ndra_new,nddec_new, s=3, c='k')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.axhline(y=0, c='red', linestyle='--')\n",
    "    #plt.xlim(-5, 5)\n",
    "    #plt.ylim(-5, 5)\n",
    "    \n",
    "    #-----dra hist------#\n",
    "    ax = plt.subplot(132)\n",
    "    \n",
    "    plt.title('NSC_GAIA_new')\n",
    "    plt.xlabel('$dRA_{new}$ mas')\n",
    "    plt.ylabel('$N$')\n",
    "    hist(ndra_new, bins=8000000, histtype='stepfilled', ec='k', fc='c')\n",
    "    plt.axvline(x=0, c='red', linestyle='--')\n",
    "    plt.xlim(-200,200)\n",
    "    #-----dra hist------#\n",
    "    #ax = plt.subplot(133)\n",
    "    \n",
    "    #plt.xlabel('$dDEC_{new}$ mas')\n",
    "    #plt.ylabel('$N$')\n",
    "    #hist(nddec_new, bins=500000, histtype='stepfilled', ec='k', fc='c')\n",
    "    #plt.axvline(x=0, c='red', linestyle='--')\n",
    "    #plt.xlim(-1,1)\n",
    "    \n",
    "    print ('dRA Median NsC_new =   ', np.median(ndra_new))\n",
    "    print ('dRA std NSC_new =   ', np.std(ndra_new))\n",
    "    print ('dDEC Median NSC_new =   ',np.median(nddec_new))\n",
    "    print ('dDEC std NSC_new =   ', np.std(nddec_new))\n",
    "    \n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dRA Median HLC_new =    -0.00155770024867\n",
      "dRA std HLC_new =    2.49238216362\n",
      "dDEC Median HLC_new =    0.00102695790031\n",
      "dDEC std HLC_new =    3.34640369953\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJ8AAAGKCAYAAACiruD+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e726748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dRA Median NsC_new =    -0.756383314263\n",
      "dRA std NSC_new =    355796.762288\n",
      "dDEC Median NSC_new =    -0.669727251369\n",
      "dDEC std NSC_new =    nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/BriBri/miniconda3/envs/astroconda36/lib/python3.6/site-packages/numpy/core/_methods.py:112: RuntimeWarning: invalid value encountered in subtract\n",
      "  x = asanyarray(arr - arrmean)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAx0AAAGKCAYAAABpZSxBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b5349b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dpm(mhg,mng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Future Work  \n",
    "\n",
    "The obvious one is that my NSC_GAIA matches are in clusters (help!). I have the same result when I run it on all of the nsc files.  \n",
    "\n",
    "Also, my statistics are a bit off because I didn't account for wrapped degree positions (example below) and the clusters. I can figure out how to wrap them or take them out later to make the numbers more accurate. However, I just used limits to zoom in on the main objects. \n",
    "\n",
    "I also need to use the right bin sizes for the histograms.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAGICAYAAACA+N0EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e7385f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "HLCra=hg1['RA_MEAN']\n",
    "HLCdec=hg1['DEC_MEAN']\n",
    "GAIA_HLCra=hg1['ra']\n",
    "GAIA_HLCdec=hg1['dec']\n",
    "hdra  = (HLCra-GAIA_HLCra)\n",
    "hddec = (HLCdec-GAIA_HLCdec)\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "fig = plt.figure(figsize=[20,6])\n",
    "\n",
    "#-----dra vs ddec------#\n",
    "\n",
    "ax = plt.subplot(131)\n",
    "\n",
    "plt.title(\"Wrapped Positions (hlc1)\")\n",
    "plt.ylabel('$dDEC$')\n",
    "plt.xlabel('$dRA$')\n",
    "plt.scatter(hdra,hddec, s=3, c='k')\n",
    "plt.axvline(x=0, c='red', linestyle='--')\n",
    "plt.axhline(y=0, c='red', linestyle='--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}