{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from astropy.io import fits\n",
    "sns.set_context(\"notebook\")\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background pixels CDF of 1 image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: '4-0/ufig.fits'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-832c91334295>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0msegmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'4-0/ufig_seg.fits'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mpixels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mbackground\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msegmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mbg_pixels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpixels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbackground\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/io/fits/convenience.pyc\u001b[0m in \u001b[0;36mgetdata\u001b[0;34m(filename, *args, **kwargs)\u001b[0m\n\u001b[1;32m    184\u001b[0m     \u001b[0mview\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'view'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 186\u001b[0;31m     \u001b[0mhdulist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mextidx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_getext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    187\u001b[0m     \u001b[0mhdu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhdulist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mextidx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhdu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/io/fits/convenience.pyc\u001b[0m in \u001b[0;36m_getext\u001b[0;34m(filename, mode, *args, **kwargs)\u001b[0m\n\u001b[1;32m    750\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'extver alone cannot specify an extension.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    751\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 752\u001b[0;31m     \u001b[0mhdulist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfitsopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    753\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mhdulist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mext\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/io/fits/hdu/hdulist.pyc\u001b[0m in \u001b[0;36mfitsopen\u001b[0;34m(name, mode, memmap, save_backup, cache, **kwargs)\u001b[0m\n\u001b[1;32m    127\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Empty filename: %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mHDUList\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_backup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/io/fits/hdu/hdulist.pyc\u001b[0m in \u001b[0;36mfromfile\u001b[0;34m(cls, fileobj, mode, memmap, save_backup, cache, **kwargs)\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m         return cls._readfrom(fileobj=fileobj, mode=mode, memmap=memmap,\n\u001b[0;32m--> 271\u001b[0;31m                              save_backup=save_backup, cache=cache, **kwargs)\n\u001b[0m\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/io/fits/hdu/hdulist.pyc\u001b[0m in \u001b[0;36m_readfrom\u001b[0;34m(cls, fileobj, data, mode, memmap, save_backup, cache, **kwargs)\u001b[0m\n\u001b[1;32m    786\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_File\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    787\u001b[0m                 \u001b[0;31m# instantiate a FITS file object (ffo)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 788\u001b[0;31m                 \u001b[0mffo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_File\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmemmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    789\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    790\u001b[0m                 \u001b[0mffo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfileobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/io/fits/file.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, fileobj, mode, memmap, clobber, cache)\u001b[0m\n\u001b[1;32m    138\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_fileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclobber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    139\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_filename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclobber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    141\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_filelike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclobber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/io/fits/file.pyc\u001b[0m in \u001b[0;36m_open_filename\u001b[0;34m(self, filename, mode, clobber)\u001b[0m\n\u001b[1;32m    441\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_zipfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfileobj_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPYFITS_MODES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    444\u001b[0m             \u001b[0;31m# Make certain we're back at the beginning of the file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    445\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/astropy/io/fits/util.pyc\u001b[0m in \u001b[0;36mfileobj_open\u001b[0;34m(filename, mode)\u001b[0m\n\u001b[1;32m    288\u001b[0m         \"\"\"\n\u001b[1;32m    289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 290\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    291\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    292\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: '4-0/ufig.fits'"
     ]
    }
   ],
   "source": [
    "image = '4-0/ufig.fits'\n",
    "segmap = '4-0/ufig_seg.fits'\n",
    "\n",
    "pixels = fits.getdata(image, ext=0)\n",
    "background = fits.getdata(segmap, ext=0) == 0\n",
    "bg_pixels = pixels[background].flatten()\n",
    "pixels = pixels.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bg_pixels.size / pixels.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate a subset of 5*10^4 pixels (0.05% of all background pixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bg_pixels_sub = np.random.choice(bg_pixels, 35000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maximum and minimum values of these background pixel sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-21.707711051613046, 31.67839781353382)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(bg_pixels), np.max(bg_pixels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-15.831271924352151, 19.816823581622632)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(bg_pixels_sub), np.max(bg_pixels_sub)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1104adf10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "liQpBQxsSZJS4P8DMiQdIrHh3pEAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c4927d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bg_pixels, bins=100, range=[-20, 35], normed=True, histtype='step', log=True, color='red', label='All')\n",
    "plt.hist(bg_pixels_sub, bins=100, range=[-20, 35], normed=True, histtype='step', log=True, color='blue', label='Subset')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute CDFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hist, bin_edges = np.histogram(bg_pixels, normed=True, bins=50, range=[-10, 20])\n",
    "cdf_all = np.cumsum(hist)\n",
    "cdf_all /= cdf_all[-1]\n",
    "\n",
    "hist, bin_edges = np.histogram(bg_pixels_sub, normed=True, bins=50, range=[-10, 20])\n",
    "cdf_sub = np.cumsum(hist)\n",
    "cdf_sub /= cdf_sub[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x110967890>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "QvgwYyPn6xtbTpKeAJaEELYUbt/QyGIS9jDwtRDC+C+CG5xZmNT476b/BKwLIcwBXgA2NK6kJI33\n",
       "02eA+0IIbzL2h97vNa6k5NzO2FT1F0II4+81/wHw55W+rtwrW5KkhLgIRJKkhBjMkiQlxGCWJCkh\n",
       "BrMkSQkxmCVJSojBLElSQgxmSZIS8v8BY2QiGdwbIPUAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11071fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binwidth = (bin_edges[1] - bin_edges[0]) / 2\n",
    "plt.step(bin_edges[:-1] + binwidth, cdf_all, color='red', label='All')\n",
    "plt.step(bin_edges[:-1] + binwidth, cdf_sub, color='blue', label='Subset')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms when varying the background sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels = ['4-0', '4-5', '5-0', '5-5', '6-0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bg_pixels_sub = [None for i in range(len(labels))]\n",
    "\n",
    "for i, name in enumerate(labels):\n",
    "    pixels = fits.getdata(name + '/ufig.fits', ext=0)\n",
    "    background = fits.getdata(name + '/ufig_seg.fits', ext=0) == 0\n",
    "    bg_pixels = pixels[background].flatten()\n",
    "    pixels = pixels.flatten()\n",
    "    bg_pixels_sub[i] = np.random.choice(bg_pixels, 35000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x113ed6a90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "AF+31v4y+e5x/VxN2a5sYDT5H8e1wH8AjcBD1tqnp3Asle4m4DfGmA+TmDn/TXmHU5H+ACw3xnQn\n",
       "376ynIOpYLcDdxpjnH8IrlRlwZPzb9PfA7cZY/zAC+S5X74OOc/pWuBmY8wwiV/0/rZ8Q6o43yRR\n",
       "qv6OMcZZa/4K8NNCf67UK1tERKSCaBOIiIhIBVEwi4iIVBAFs4iISAVRMIuIiFQQBbOIiEgFUTCL\n",
       "iIhUEAWziIhIBfn/FYvAZXH4p7IAAAAASUVORK5CYII=\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ed63d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, name in enumerate(labels):\n",
    "    hist, bin_edges = np.histogram(bg_pixels_sub[i], normed=True, bins=200, range=[-10, 20])\n",
    "    cdf_sub = np.cumsum(hist)\n",
    "    cdf_sub /= cdf_sub[-1]\n",
    "\n",
    "    binwidth = (bin_edges[1] - bin_edges[0]) / 2\n",
    "    plt.step(bin_edges[:-1] + binwidth, cdf_sub, label=name)\n",
    "\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Use FLUXERR_BEST to see differences in background sigma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10dab8f50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "HmPB2RhjjOkxFpyNMcaYHmPB2RhjjOkx/w8lALlQoe3gnAAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11050eb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, name in enumerate(labels):\n",
    "    cat = fits.getdata(name + '/ufig.sexcat', ext=2)\n",
    "    sns.distplot(np.log10(cat['FLUXERR_BEST']), label=name)\n",
    "plt.xlabel('Log FLUXERR_BEST')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10c492850>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "AElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11111d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, name in enumerate(labels):\n",
    "    cat = fits.getdata(name + '/ufig.sexcat', ext=2)\n",
    "    sns.distplot(cat['FLUXERR_BEST'], label=name)\n",
    "plt.xlabel('FLUXERR_BEST')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "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": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}