visualize_ir_survey_locations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "import commands\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raw_frames_root = '/Volumes/abraham/xcor_data/ATLAS_mwa57694/'\n",
    "raw_frames_root2 = '/Volumes/abraham/xcor_data/ATLAS_mwa57639/'\n",
    "labels = [l.split('/')[-1].split('.')[0] for l in commands.getoutput('ls '+raw_frames_root+'*.fits.fz').split()]\n",
    "labels2 = [l.split('/')[-1].split('.')[0] for l in commands.getoutput('ls '+raw_frames_root2+'*.fits.fz').split()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: '/Volumes/abraham/xcor_data/ATLAS_mwa57694/ls:.fits'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-0dd7aa31bc2e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mdec_vals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mhdulist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mraw_frames_root\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'.fits'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhdulist\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheader\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;31m#print(h['DATE-OBS'])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/abrahamn/anaconda2/lib/python2.7/site-packages/astropy/io/fits/hdu/hdulist.pyc\u001b[0m in \u001b[0;36mfitsopen\u001b[1;34m(name, mode, memmap, save_backup, cache, **kwargs)\u001b[0m\n\u001b[0;32m    136\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Empty filename: %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mrepr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    137\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mHDUList\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msave_backup\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/abrahamn/anaconda2/lib/python2.7/site-packages/astropy/io/fits/hdu/hdulist.pyc\u001b[0m in \u001b[0;36mfromfile\u001b[1;34m(cls, fileobj, mode, memmap, save_backup, cache, **kwargs)\u001b[0m\n\u001b[0;32m    278\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    279\u001b[0m         return cls._readfrom(fileobj=fileobj, mode=mode, memmap=memmap,\n\u001b[1;32m--> 280\u001b[1;33m                              save_backup=save_backup, cache=cache, **kwargs)\n\u001b[0m\u001b[0;32m    281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    282\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/abrahamn/anaconda2/lib/python2.7/site-packages/astropy/io/fits/hdu/hdulist.pyc\u001b[0m in \u001b[0;36m_readfrom\u001b[1;34m(cls, fileobj, data, mode, memmap, save_backup, cache, **kwargs)\u001b[0m\n\u001b[0;32m    799\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_File\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    800\u001b[0m                 \u001b[1;31m# instantiate a FITS file object (ffo)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 801\u001b[1;33m                 \u001b[0mffo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_File\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmemmap\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcache\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    802\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    803\u001b[0m                 \u001b[0mffo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfileobj\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/abrahamn/anaconda2/lib/python2.7/site-packages/astropy/io/fits/file.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fileobj, mode, memmap, clobber, cache)\u001b[0m\n\u001b[0;32m    139\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_fileobj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclobber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 141\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_filename\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclobber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    142\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    143\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_filelike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclobber\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/abrahamn/anaconda2/lib/python2.7/site-packages/astropy/io/fits/file.pyc\u001b[0m in \u001b[0;36m_open_filename\u001b[1;34m(self, filename, mode, clobber)\u001b[0m\n\u001b[0;32m    491\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbz2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBZ2File\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbzip2_mode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    492\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 493\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfileobj_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mPYFITS_MODES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    494\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    495\u001b[0m         \u001b[1;31m# Make certain we're back at the beginning of the file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/abrahamn/anaconda2/lib/python2.7/site-packages/astropy/io/fits/util.pyc\u001b[0m in \u001b[0;36mfileobj_open\u001b[1;34m(filename, mode)\u001b[0m\n\u001b[0;32m    413\u001b[0m         \"\"\"\n\u001b[0;32m    414\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 415\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    416\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    417\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/Volumes/abraham/xcor_data/ATLAS_mwa57694/ls:.fits'"
     ]
    }
   ],
   "source": [
    "ra_vals = np.zeros(len(labels))\n",
    "dec_vals = np.zeros(len(labels))\n",
    "for i in range(len(labels)):\n",
    "    hdulist = fits.open(raw_frames_root+labels[i]+'.fits')\n",
    "    h = hdulist[0].header\n",
    "    #print(h['DATE-OBS'])\n",
    "    print(h['MAGZPT'])\n",
    "    ra_vals[i] = h['RA']\n",
    "    dec_vals[i] = h['DEC']\n",
    "ra_vals[ra_vals>180] -= 360\n",
    "\n",
    "ra_vals2 = np.zeros(len(labels2))\n",
    "dec_vals2 = np.zeros(len(labels2))\n",
    "for i in range(len(labels2)):\n",
    "    hdulist = fits.open(raw_frames_root2+labels2[i]+'.fits')\n",
    "    h = hdulist[0].header\n",
    "    #print(h['DATE-OBS'])\n",
    "    print(h['MAGZPT'])\n",
    "    ra_vals2[i] = h['RA']\n",
    "    dec_vals2[i] = h['DEC']\n",
    "ra_vals2[ra_vals2>180] -= 360"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def draw_square_at_point(x,y,s,c,lw,sty='-',label=None):\n",
    "    plt.plot([x-s/2,x+s/2,x+s/2,x-s/2,x-s/2],[y-s/2,y-s/2,y+s/2,y+s/2,y-s/2],c+sty,linewidth=lw,label=label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "g2=np.arange(len(dec_vals2))[(ra_vals2>-5)&(ra_vals2<0)&(dec_vals2>-25)&(dec_vals2<-22.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "g1=np.arange(len(dec_vals))[(ra_vals<-3)&(dec_vals>-24)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['02a57694o0286I', '02a57694o0290I', '02a57694o0306I',\n",
       "       '02a57694o0310I'], \n",
       "      dtype='|S14')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(labels)[g1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['02a57639o0342I', '02a57639o0343I', '02a57639o0344I',\n",
       "       '02a57639o0345I', '02a57639o0346I'], \n",
       "      dtype='|S14')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(labels2)[g2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "center9 = '''02a57694o0299I 02a57694o0303I 02a57694o0307I 02a57694o0319I 02a57694o0323I 02a57694o0327I 02a57694o0339I 02a57694o0343I 02a57694o0347I \n",
    "02a57694o0298I 02a57694o0302I 02a57694o0306I 02a57694o0318I 02a57694o0322I 02a57694o0326I 02a57694o0338I 02a57694o0342I 02a57694o0346I \n",
    "02a57694o0301I 02a57694o0305I 02a57694o0309I 02a57694o0321I 02a57694o0325I 02a57694o0329I 02a57694o0341I 02a57694o0345I 02a57694o0349I \n",
    "02a57694o0300I 02a57694o0304I 02a57694o0308I 02a57694o0320I 02a57694o0324I 02a57694o0328I 02a57694o0340I 02a57694o0344I 02a57694o0348I'''.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAJjCAYAAACBT1zRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTvw/VzO4VyEZLfoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1066a31d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "matplotlib.rcParams.update({'font.size': 16})\n",
    "\n",
    "incenter9 = np.array([labels[i] in center9 for i in range(len(ra_vals))])\n",
    "plt.plot(np.cos(dec_vals[incenter9]*np.pi/180.)*ra_vals[incenter9],dec_vals[incenter9],'ro',label='30 sec ATLAS exposure')\n",
    "\n",
    "first = True\n",
    "for infield in [infield0,infield1,infield2,infield3]:\n",
    "    g = infield&incenter9\n",
    "    l = None\n",
    "    if first:\n",
    "        l = '4 deg ATLAS stack'\n",
    "        first = False\n",
    "    draw_square_at_point(np.mean(np.cos(dec_vals[g]*np.pi/180.)*ra_vals[g]),np.mean(dec_vals[g]),4.,'r',2,label=l)\n",
    "        \n",
    "#for i in range(len(ra_vals2)):\n",
    "    #draw_square_at_point(np.cos(dec_vals2[i]*np.pi/180.)*ra_vals2[i],dec_vals2[i],5.5,'b',1)\n",
    "plt.plot(np.cos(dec_vals2*np.pi/180.)*ra_vals2,dec_vals2,'bs',label='2.5 min ATLAS exposure')\n",
    "draw_square_at_point(0,-30,20.,'b',2,label='20 deg ATLAS stack')\n",
    "\n",
    "fov_deg = 25.\n",
    "xvals = np.linspace(-fov_deg/2.,fov_deg/2.,150)\n",
    "plt.plot(np.concatenate([xvals,xvals[::-1]]),np.concatenate([np.sqrt((fov_deg/2.)**2-xvals**2)-27.,\\\n",
    "                                                             -np.sqrt((fov_deg/2.)**2-xvals[::-1]**2)-27.]),\\\n",
    "         'k--',linewidth=2,label='MWA 50% beam contour (185MHz)')\n",
    " \n",
    "plt.xlim([-14,14])\n",
    "plt.ylim([-27-14,-27+14])\n",
    "plt.xlabel('cos(DEC)*RA (J2000) (deg)')\n",
    "plt.ylabel('Dec (J2000) (deg)')\n",
    "\n",
    "plt.legend(fontsize=14)\n",
    "plt.savefig('survey_overview.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  2.82783805,  3.95728968,  4.7948327 ,  5.47608484,\n",
       "        6.05403958,  6.55607282,  6.99854212,  7.39215113,  7.74435345,\n",
       "        8.06057864,  8.34491767,  8.60053368,  8.82992149,  9.03507903,\n",
       "        9.21762432,  9.37887779,  9.51992146,  9.64164229,  9.7447644 ,\n",
       "        9.82987313,  9.89743319,  9.94780213,  9.98124021,  9.99791732,\n",
       "        9.99791732,  9.98124021,  9.94780213,  9.89743319,  9.82987313,\n",
       "        9.7447644 ,  9.64164229,  9.51992146,  9.37887779,  9.21762432,\n",
       "        9.03507903,  8.82992149,  8.60053368,  8.34491767,  8.06057864,\n",
       "        7.74435345,  7.39215113,  6.99854212,  6.55607282,  6.05403958,\n",
       "        5.47608484,  4.7948327 ,  3.95728968,  2.82783805,  0.        ])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(10.**2-xvals**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.324324324324326"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "300./185/4.*60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# analytic MWA beam\n",
    "\n",
    "# MWA beam\n",
    "\n",
    "\n",
    "\n",
    "khat = transpose(array([sin(za)*sin(az),sin(za)*cos(az)]))\n",
    "dipoles = array([[0,0],[1.1,0],[2.2,0],[3.3,0],[0,-1.1],[1.1,-1.1],[2.2,-1.1],[3.3,-1.1],[0,-2.2],[1.1,-2.2],[2.2,-2.2],[3.3,-2.2],[0,-3.3],[1.1,-3.3],[2.2,-3.3],[3.3,-3.3]])\n",
    "beam = zeros(len(za))\n",
    "freq = 185.\n",
    "lamda = 300./freq\n",
    "h=.3\n",
    "for pixi in range(len(za)):\n",
    "    dipole = sqrt(1-sin(za[pixi])**2*sin(az[pixi])**2)\n",
    "    ground_screen = (1-exp(4*pi*1j*h*cos(za[pixi])/lamda))/(1-exp(4*pi*1j*h/lamda))\n",
    "    array_factor = sum(exp(1j*2*pi/lamda*(khat[pixi,0]*dipoles[:,0]+khat[pixi,1]*dipoles[:,1])))\n",
    "    beam[pixi] = abs(dipole*ground_screen*array_factor)**2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1064438d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABJgAAAN1CAYAAAAzKUFiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1064506d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "# for i in range(len(ra_vals)):\n",
    "#     plt.text(ra_vals[i],dec_vals[i],str(i))\n",
    "\n",
    "# label_indices_for_catalog = [52,94,49,91]\n",
    "# for i in label_indices_for_catalog:\n",
    "#     print(labels[i]),\n",
    "\n",
    "for i in g1:\n",
    "    draw_square_at_point(ra_vals[i],dec_vals[i],5,'r',1)\n",
    "    plt.plot(ra_vals[i],dec_vals[i],'rs')\n",
    "\n",
    "for i in g2:\n",
    "    draw_square_at_point(ra_vals2[i],dec_vals2[i],5,'b',1)\n",
    "\n",
    "draw_square_at_point(-3.4,-23.5,3.,'k',3)\n",
    "    \n",
    "plt.plot(ra_vals,dec_vals,'r.')\n",
    "plt.plot(ra_vals2,dec_vals2,'b.')\n",
    "#plt.plot([-10,10],[-23,-23])\n",
    "plt.xlim([-10,10])\n",
    "plt.ylim([-40,-20])\n",
    "plt.xlabel('RA')\n",
    "plt.ylabel('Dec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10662e3d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "\n",
    "for i in range(len(ra_vals)):\n",
    "    plt.text(ra_vals[i],dec_vals[i],str(i))\n",
    "\n",
    "for i in range(len(ra_vals2)):\n",
    "    plt.text(ra_vals2[i],dec_vals2[i],str(i))\n",
    "\n",
    "# plt.xlim([-5,0])\n",
    "# plt.ylim([-25,-20])\n",
    "plt.xlabel('RA')\n",
    "plt.ylabel('Dec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuUAAAJqCAYAAACM3D7zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e178e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "frame_indices = np.zeros(len(ra_vals),dtype=int)\n",
    "plt.figure(figsize=(12,10))\n",
    "for i in range(len(ra_vals)):\n",
    "    c = 'r'\n",
    "    if ra_vals[i]< -3.5 or ra_vals[i] > 3.5 or \\\n",
    "    -1.5 < ra_vals[i] < 0 or 0 < ra_vals[i] < 1.5\\\n",
    "    or dec_vals[i] > -23.5 or dec_vals[i] < -30.5\\\n",
    "    or -26 < dec_vals[i] < -25.5 or -28.5 < dec_vals[i] < -27:\n",
    "        c = 'b'\n",
    "        \n",
    "    if ra_vals[i]<0:\n",
    "        frame_indices[i] = 0 if dec_vals[i]<-27 else 1\n",
    "    else:\n",
    "        frame_indices[i] = 2 if dec_vals[i]<-27 else 3\n",
    "        \n",
    "    #if c == 'b': frame_indices[i] = -1\n",
    "        \n",
    "    \n",
    "    #if c == 'r': draw_square_at_point(ra_vals[i],dec_vals[i],5,c,lw=frame_indices[i]+1)\n",
    "    draw_square_at_point(ra_vals[i],dec_vals[i],5,c,lw=frame_indices[i]+1)\n",
    "    \n",
    "    plt.plot(ra_vals[i],dec_vals[i],c+'.')\n",
    "    plt.xlim([-7,7])\n",
    "    plt.ylim([-35,-20])\n",
    "    plt.xlabel('RA')\n",
    "    plt.ylabel('Dec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for i in range(4):\n",
    "    print(' '.join(np.array(labels)[frame_indices==i])+'\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "infield0 = (ra_vals<0)&(dec_vals>-27)\n",
    "infield1 = (ra_vals>0)&(dec_vals>-27)\n",
    "infield2 = (ra_vals<0)&(dec_vals<-27)\n",
    "infield3 = (ra_vals>0)&(dec_vals<-27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "field0_labels = np.array(labels)[infield0]\n",
    "field1_labels = np.array(labels)[infield1]\n",
    "field2_labels = np.array(labels)[infield2]\n",
    "field3_labels = np.array(labels)[infield3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "len(field0_labels),len(field1_labels),len(field2_labels),len(field3_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "open('/Users/abrahamn/Documents/MIT/21cm/21cmIR/21cmIRxcor/stack_at_frame_center/field0.radec','w').write(\"%1.3f,%1.3f\"%(np.mean(ra_vals[infield0]),np.mean(dec_vals[infield0])))\n",
    "open('/Users/abrahamn/Documents/MIT/21cm/21cmIR/21cmIRxcor/stack_at_frame_center/field1.radec','w').write(\"%1.3f,%1.3f\"%(np.mean(ra_vals[infield1]),np.mean(dec_vals[infield1])))\n",
    "open('/Users/abrahamn/Documents/MIT/21cm/21cmIR/21cmIRxcor/stack_at_frame_center/field2.radec','w').write(\"%1.3f,%1.3f\"%(np.mean(ra_vals[infield2]),np.mean(dec_vals[infield2])))\n",
    "open('/Users/abrahamn/Documents/MIT/21cm/21cmIR/21cmIRxcor/stack_at_frame_center/field3.radec','w').write(\"%1.3f,%1.3f\"%(np.mean(ra_vals[infield3]),np.mean(dec_vals[infield3])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r,d=np.genfromtxt('/Users/abrahamn/Documents/MIT/21cm/21cmIR/21cmIRxcor/stack_at_frame_center/field0.radec',delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-24.538"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "open('/Users/abrahamn/Documents/MIT/21cm/21cmIR/21cmIRxcor/stack_at_frame_center/field0.labels','w').write('\\n'.join(field0_labels))\n",
    "open('/Users/abrahamn/Documents/MIT/21cm/21cmIR/21cmIRxcor/stack_at_frame_center/field1.labels','w').write('\\n'.join(field1_labels))\n",
    "open('/Users/abrahamn/Documents/MIT/21cm/21cmIR/21cmIRxcor/stack_at_frame_center/field2.labels','w').write('\\n'.join(field2_labels))\n",
    "open('/Users/abrahamn/Documents/MIT/21cm/21cmIR/21cmIRxcor/stack_at_frame_center/field3.labels','w').write('\\n'.join(field3_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}