CoaddPsf.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Analyse a Tract's CoaddPsf"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import lsst.pipe.base as pipeBase\n",
      "import lsst.daf.persistence as dafPersist\n",
      "import lsst.pex.exceptions as pexExcept\n",
      "import lsst.afw.geom as afwGeom\n",
      "import lsst.afw.image as afwImage\n",
      "import lsst.afw.table as afwTable\n",
      "import lsst.afw.display.ds9 as ds9\n",
      "\n",
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def makeKey(patch, myVisit=None, keys=[]):\n",
      "    \"\"\"Return the key used to lookup data for a patch and maybe visit\n",
      "    \n",
      "    If keys is provided then a key present in keys is returned (if it exists) with \n",
      "    the requested key as a prefix.    \n",
      "    \"\"\"\n",
      "    key = str(patch)\n",
      "    if myVisit and True:\n",
      "        key += '-%d' % myVisit\n",
      "        \n",
      "    if keys and key not in keys:\n",
      "        for k in keys:\n",
      "            if k.startswith(key):\n",
      "                return k\n",
      "        \n",
      "    return key\n",
      "\n",
      "def drawPatches(calexpInfo, patches):\n",
      "    \"\"\"Overlay a matplotlib image with labelled patch numbers\"\"\"\n",
      "    for patch in patches:\n",
      "        key = makeKey(patch, keys=calexpInfo.keys())\n",
      "        \n",
      "        bbox = calexpInfo[key].bbox\n",
      "        wcs  = calexpInfo[key].wcs\n",
      "\n",
      "        ra = []; dec = []\n",
      "        for xy in bbox.getMin(), bbox.getMax():\n",
      "            rd = [_.asDegrees() for _ in wcs.pixelToSky(*xy)]\n",
      "            ra.append(rd[0]); dec.append(rd[1])\n",
      "        ra =  np.array([ra[0],  ra[1],  ra[1],  ra[0],  ra[0]])\n",
      "        dec = np.array([dec[0], dec[0], dec[1], dec[1], dec[0]])\n",
      "\n",
      "        rabar, decbar = np.mean(ra[0:-1]), np.mean(dec[0:-1])\n",
      "        if True:\n",
      "            if rabar < plt.xlim()[0] or rabar > plt.xlim()[1] or \\\n",
      "               decbar < plt.ylim()[0] or decbar > plt.ylim()[1]:\n",
      "                continue\n",
      "\n",
      "        plt.plot(ra, dec, '-', color='black')\n",
      "        plt.text(rabar, decbar, patch,\n",
      "                 horizontalalignment='center', verticalalignment='center')\n",
      "        \n",
      "def drawCcds(calexpInfo, ccds):\n",
      "    \"\"\"Overlay a matplotlib image with labelled CCD numbers\"\"\"\n",
      "    for ccd in ccds:\n",
      "        key = makeKey(ccd, myVisit, keys=calexpInfo.keys())\n",
      "        \n",
      "        bbox = calexpInfo[key].bbox\n",
      "        wcs  = calexpInfo[key].wcs\n",
      "\n",
      "        ra = []; dec = []\n",
      "        for xy in bbox.getMin(), bbox.getMax():\n",
      "            rd = [_.asDegrees() for _ in wcs.pixelToSky(*xy)]\n",
      "            ra.append(rd[0]); dec.append(rd[1])\n",
      "        ra =  np.array([ra[0],  ra[1],  ra[1],  ra[0],  ra[0]])\n",
      "        dec = np.array([dec[0], dec[0], dec[1], dec[1], dec[0]])\n",
      "\n",
      "        rabar, decbar = np.mean(ra[0:-1]), np.mean(dec[0:-1])\n",
      "        if True:\n",
      "            if rabar < plt.xlim()[0] or rabar > plt.xlim()[1] or \\\n",
      "               decbar < plt.ylim()[0] or decbar > plt.ylim()[1]:\n",
      "                continue\n",
      "\n",
      "        plt.plot(ra, dec, '-', color='black')\n",
      "        plt.text(rabar, decbar, ccd,\n",
      "                 horizontalalignment='center', verticalalignment='center')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 136
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a butler and prepare to read data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "butler = dafPersist.Butler(os.path.join(os.environ[\"SUPRIME_DATA_DIR\"], \"rerun\", \n",
      "                                        \"rearmstr/hsc-1127/visit1266-1304\"))\n",
      "\n",
      "#ds9.setMaskTransparency(75)\n",
      "\n",
      "try:\n",
      "    calexpInfo\n",
      "except NameError:\n",
      "    calexpInfo = {}\n",
      "    srcs = {}\n",
      "\n",
      "tract = 9614\n",
      "myVisits = [1266, 1304]               # Visits in rerun (needed to avoid butler bugs)\n",
      "filter = 'HSC-I'\n",
      "display = False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Read the data.  Note that we don't keep all the pixels in the calexps"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "patches = set()\n",
      "for i in range(10):\n",
      "    for j in range(10):\n",
      "        patch = '%d,%d' % (i, j)\n",
      "        for myVisit in [None] + myVisits:\n",
      "            try:\n",
      "                if myVisit is None:\n",
      "                    calexp = butler.get('deepCoadd', immediate=True, \n",
      "                                        tract=tract, patch=patch, filter=filter)\n",
      "                else:\n",
      "                    calexp = butler.get('deepCoadd_tempExp', immediate=True, visit=myVisit,\n",
      "                                        tract=tract, patch=patch, filter=filter)\n",
      "\n",
      "                src = butler.get('deepCoadd_src', immediate=True, tract=tract, patch=patch, filter=filter)\n",
      "            except:\n",
      "                continue\n",
      "            #\n",
      "            # A butler bug looks up into _parents to find patches not present in this rerun\n",
      "            #\n",
      "            visitsInPatch = calexp.getInfo().getCoaddInputs().ccds.get('visit')\n",
      "            nVisit = 0\n",
      "            for v in myVisits:\n",
      "                if v in visitsInPatch:\n",
      "                    nVisit += 1\n",
      "            if nVisit == 0:\n",
      "                continue\n",
      "            \n",
      "            key = makeKey(patch, myVisit)\n",
      "            calexpInfo[key] = pipeBase.Struct(calexp=calexp if display else None,\n",
      "                                              bbox=calexp.getBBox(afwImage.PARENT),\n",
      "                                              psf=calexp.getPsf(),\n",
      "                                              xy0=calexp.getXY0(),\n",
      "                                              wcs=calexp.getWcs(), \n",
      "                                              )\n",
      "            srcs[key] = src\n",
      "    \n",
      "            patches.add(patch)\n",
      "            print \"\\r\", patch, myVisit if myVisit else \"coadd\",\n",
      "            sys.stdout.flush()\n",
      "\n",
      "myVisit = None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 2,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 2,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 2,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 2,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 2,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 2,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 3,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 3,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 3,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 3,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 3,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 3,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 3,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 3,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 3,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 3,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 3,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 3,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 3,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 3,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 4,1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 4,1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 4,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 4,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 4,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 4,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 4,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 4,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 4,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 4,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 4,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 4,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 4,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 4,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 4,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 4,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 5,1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 5,1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 5,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 5,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 5,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 5,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 5,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 5,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 5,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 5,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 5,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 5,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 5,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 5,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 5,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 5,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 5,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 5,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 6,1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 6,1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 6,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 6,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 6,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 6,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 6,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 6,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 6,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 6,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 6,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 6,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 6,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 6,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 6,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 6,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 6,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 6,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 6,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 6,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 6,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 6,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 7,1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 7,1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 7,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 7,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 7,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 7,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 7,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 7,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 7,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 7,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 7,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 7,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 7,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 7,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 7,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 7,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 7,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 7,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 7,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 7,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 7,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 7,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 7,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 8,1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 8,1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 8,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 8,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 8,2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 8,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 8,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 8,3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 8,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 8,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 8,4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 8,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 8,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 8,5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 8,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 8,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 8,6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 8,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 8,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 8,7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "coadd 8,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 8,8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 8,8\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ccds = set()\n",
      "for myVisit in myVisits:\n",
      "    for ccd in range(104):\n",
      "        if ccd >= 100:\n",
      "            continue          # the bounding boxes are weird for 100--103 for reasons I haven't sorted out\n",
      "\n",
      "        try:\n",
      "            calexp = butler.get('calexp', immediate=True, visit=myVisit, ccd=ccd)\n",
      "            src = butler.get('src', immediate=True, visit=myVisit, ccd=ccd)\n",
      "        except Exception as e:\n",
      "            print e\n",
      "            continue\n",
      "            \n",
      "        key = makeKey(ccd, myVisit)\n",
      "        calexpInfo[key] = pipeBase.Struct(calexp=calexp if display else None,\n",
      "                                              bbox=calexp.getBBox(afwImage.PARENT),\n",
      "                                              psf=calexp.getPsf(),\n",
      "                                              xy0=calexp.getXY0(),\n",
      "                                              wcs=calexp.getWcs(), \n",
      "                                              )\n",
      "        srcs[key] = src\n",
      "    \n",
      "        ccds.add(ccd)\n",
      "        print \"\\r\", myVisit, ccd,\n",
      "\n",
      "myVisit = None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 0 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 5 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 9 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 10 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 11 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 12 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 13 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 14 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 15 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 16 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 17 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
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      {
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       "text": [
        "\r",
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       ]
      },
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       "output_type": "stream",
       "stream": "stdout",
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        "\r",
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       ]
      },
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       "stream": "stdout",
       "text": [
        "\r",
        "1266 21 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
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       "output_type": "stream",
       "stream": "stdout",
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        "\r",
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        "\r",
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        "\r",
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        "\r",
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        "\r",
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      },
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      },
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      },
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       "stream": "stdout",
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        "\r",
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      },
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       "stream": "stdout",
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        "\r",
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       "stream": "stdout",
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       "stream": "stdout",
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      {
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       "stream": "stdout",
       "text": [
        "\r",
        "1266 92 "
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      },
      {
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       "stream": "stdout",
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        "\r",
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       "stream": "stdout",
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        "\r",
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       "stream": "stdout",
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        "\r",
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       "output_type": "stream",
       "stream": "stdout",
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       "stream": "stdout",
       "text": [
        "\r",
        "1266 99 "
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 100 "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 101 "
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 102 "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1266 103 "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 0 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 1 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 2 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 3 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 4 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 5 "
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      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 6 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 7 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 8 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 9 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 10 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 11 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 12 "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "1304 13 "
       ]
      },
      {
       "output_type": "stream",
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       "text": [
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     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Display on ds9\n",
      "frame0 = 0\n",
      "    \n",
      "for i, patch in enumerate(patches):\n",
      "    key = makeKey(patch, myVisit)\n",
      "\n",
      "    if not calexpInfo[key].calexp:\n",
      "        continue\n",
      "    xy0 = afwGeom.PointD(calexpInfo[key].xy0)\n",
      "    wcs = calexpInfo[key].wcs\n",
      "\n",
      "    ds9.mtv(calexpInfo[key].calexp, title='%d %s %s' % (tract, patch, filter), frame=frame0+i)\n",
      "    with ds9.Buffering():\n",
      "        for s in srcs[key]:\n",
      "            ds9.dot('o', *(wcs.skyToPixel(s.getCoord()) - xy0), frame=frame0+i, ctype=ds9.WHITE)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Prepare to measure the PSF models"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from lsst.meas.algorithms.detection import SourceDetectionTask\n",
      "from lsst.meas.algorithms.measurement import SourceMeasurementTask\n",
      "\n",
      "detectionConfig = SourceDetectionTask.ConfigClass()\n",
      "detectionConfig.reEstimateBackground = False\n",
      "\n",
      "measurementConfig = SourceMeasurementTask.ConfigClass()\n",
      "measurementConfig.algorithms['flux.naive'].radius = 20.0 - 2   # subtract a little so ensure that the radius fits in the stamp\n",
      "#\n",
      "# Create the tasks\n",
      "#\n",
      "schema = afwTable.SourceTable.makeMinimalSchema()\n",
      "\n",
      "sourceDetectionTask =   SourceDetectionTask(config=detectionConfig, schema=schema)\n",
      "sourceMeasurementTask = SourceMeasurementTask(config=measurementConfig,\n",
      "                                              schema=schema)\n",
      "try:\n",
      "    SourceDetectionTask.run\n",
      "except AttributeError:\n",
      "    SourceDetectionTask.run = SourceDetectionTask.makeSourceCatalog "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a grid of PSFs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gridN = [30, 50, 75, 125][2]\n",
      "myVisit = [1266, 1304, None][-1]      # None => coadd\n",
      "byPatch = True\n",
      "\n",
      "if not myVisit and not byPatch:\n",
      "    raise RuntimeError(\"You cannot plot a coadd by CCD\")\n",
      "\n",
      "dataArea = sorted(patches if byPatch else ccds)\n",
      "\n",
      "areaBBox = None\n",
      "wcs0 = None\n",
      "for key in dataArea:\n",
      "    key0 = key\n",
      "    key = makeKey(key, myVisit, keys=calexpInfo.keys())\n",
      "    if not key in calexpInfo:\n",
      "        continue\n",
      "\n",
      "    if wcs0 is None:\n",
      "        wcs0 = calexpInfo[key].wcs\n",
      "    \n",
      "    bbox = calexpInfo[key].bbox\n",
      "    bbox = type(bbox)(bbox)\n",
      "    \n",
      "    if not byPatch:\n",
      "        boxMin = bbox.getMin()\n",
      "        bbox.shift(boxMin - afwGeom.PointI(wcs0.skyToPixel(calexpInfo[key].wcs.pixelToSky(afwGeom.PointD(boxMin)))))\n",
      "\n",
      "    if not areaBBox:\n",
      "        areaBBox = bbox\n",
      "    else:\n",
      "        areaBBox.include(bbox.getMin())\n",
      "        areaBBox.include(bbox.getMax())\n",
      "\n",
      "xsize, ysize = areaBBox.getDimensions()\n",
      "if not byPatch:\n",
      "    areaXY0 = areaBBox.getMin()\n",
      "    areaBBox.shift(afwGeom.ExtentI(-areaXY0[0], -areaXY0[1]))\n",
      "xy0 = areaBBox.getMin()\n",
      "\n",
      "x = xy0[0] + np.linspace(0.5*xsize/gridN, xsize - 0.5*xsize/gridN, gridN)\n",
      "y = xy0[1] + np.linspace(0.5*ysize/gridN, ysize - 0.5*ysize/gridN, gridN)\n",
      "\n",
      "X, Y = np.meshgrid(x, y)\n",
      "X, Y = X.flatten(), Y.flatten()\n",
      "Xdeg, Ydeg = np.empty_like(X), np.empty_like(Y)\n",
      "dx   = np.nan + np.empty_like(X)\n",
      "dy   = np.nan + np.empty_like(dx)\n",
      "dmag = np.nan + np.empty_like(dx)\n",
      "\n",
      "for key in dataArea:\n",
      "    key = makeKey(key, myVisit)\n",
      "    if not key in calexpInfo:\n",
      "        continue\n",
      "    \n",
      "    bbox = calexpInfo[key].bbox\n",
      "    if not byPatch:\n",
      "        bbox = type(bbox)(bbox)\n",
      "        boxMin = bbox.getMin()\n",
      "        bbox.shift(boxMin - afwGeom.PointI(wcs0.skyToPixel(calexpInfo[key].wcs.pixelToSky(afwGeom.PointD(boxMin)))))\n",
      "        bbox.shift(afwGeom.ExtentI(-areaXY0[0], -areaXY0[1]))\n",
      "    \n",
      "    psf = calexpInfo[key].psf\n",
      "    sigma = psf.computeShape().getDeterminantRadius()\n",
      "\n",
      "    wcs = calexpInfo[key].wcs\n",
      "    pixelScale = wcs.pixelScale().asArcseconds()\n",
      "\n",
      "    for i, xy in enumerate(zip(X, Y)):\n",
      "        if not bbox.contains(afwGeom.PointI(afwGeom.PointD(*xy))):\n",
      "            continue\n",
      "\n",
      "        x, y = xy\n",
      "        xc = xy if byPatch else (x - bbox.getMinX(), y - bbox.getMinY())\n",
      "\n",
      "        Xdeg[i], Ydeg[i] = [_.asDegrees() for _ in wcs.pixelToSky(afwGeom.PointD(*xc))]\n",
      "\n",
      "        xc = afwGeom.PointD(*xc)\n",
      "        try:\n",
      "           im = psf.computeImage(xc).convertF()\n",
      "        except pexExcept.LsstCppException as e:\n",
      "            #print key, bbox, e\n",
      "            continue\n",
      "            \n",
      "        #\n",
      "        # Make a slightly-larger image so that we'll be able to\n",
      "        # measure it using our PSF\n",
      "        #\n",
      "        if True:\n",
      "            ngrow = 3\n",
      "            _bbox = im.getBBox(afwImage.PARENT)\n",
      "            #bbox = type(bbox)(bbox)\n",
      "            _bbox.grow(ngrow)\n",
      "            bim = im.Factory(_bbox)\n",
      "            bim[ngrow:-ngrow, ngrow:-ngrow] = im\n",
      "\n",
      "        exp = afwImage.makeExposure(afwImage.makeMaskedImage(bim))\n",
      "        exp.setPsf(psf)\n",
      "        tab = afwTable.SourceTable.make(schema)\n",
      "        \n",
      "        #ds9.mtv(exp, title=\"%d,%d\" % (int(xy[0]), int(xy[1])))\n",
      "        result = sourceDetectionTask.run(tab, exp, sigma=sigma)\n",
      "        sourceMeasurementTask.measure(exp, result.sources)\n",
      "\n",
      "        dc = result.sources[0].getCentroid() - xc\n",
      "        dx[i] = pixelScale*dc[0]\n",
      "        dy[i] = pixelScale*dc[1]\n",
      "    \n",
      "        dmag[i] = result.sources[0].getCalibFlux()\n",
      "\n",
      "dmag = -2.5*np.log10(dmag)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 338
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Q = plt.scatter(Xdeg, Ydeg, c=dmag, s=5, norm=pyplot.Normalize(0.0, 0.07),\n",
      "                edgecolors=\"none\")\n",
      "cb = plt.colorbar()\n",
      "cb.ax.get_yaxis().labelpad = 15\n",
      "cb.set_label('$\\Delta$ PSF Magnitude', rotation=270)\n",
      "\n",
      "plt.axes().set_aspect(\"equal\")\n",
      "plt.xlim(213.8, 215.2)\n",
      "plt.ylim(0.2, 1.6)\n",
      "\n",
      "if byPatch:\n",
      "    drawPatches(calexpInfo, patches)\n",
      "else:\n",
      "    drawCcds(calexpInfo, ccds)\n",
      "\n",
      "plt.xlabel(\"Ra (degrees)\")\n",
      "plt.ylabel(\"Dec (degrees)\")\n",
      "plt.title(\"%s%s%s %s\" % ((\"Tract %d\" % tract) if byPatch else \"\", \n",
      "                         ((\" %s\" % calexpInfo.keys()[0]) if len(calexpInfo.keys()) == 1 else \"\"), \n",
      "                         (\" visit %d\" % myVisit if myVisit else \"\"),\n",
      "                         filter[-1:].lower()))\n",
      "\n",
      "plt.savefig('psfFlux%s.pdf' % ((\"-%d\" % myVisit) if myVisit else \"\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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77dokgZbi6RkqwcHdBe6Rp5/eJuPGrRCYLvPnnxEfnw8E2khQUDsBL3FxeVmW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AcHT3nnnd0C02TevGPy+ONbRaOZLYcOXZPIyLWWDuHsPE969lwl6emXxcpqnrzwwn6ZNi1DYK58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\nwfzatg0mJMST9u1D8PNz5eDBPKKj/WnTJoDNm7N56qmnyM6+h4qKn9my5Tu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atPBn6dIT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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2ae627ce5310>"
       ]
      }
     ],
     "prompt_number": 339
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pivot='middle'\n",
      "width=None # 5e-4\n",
      "labelLen = 0.005\n",
      "scale = 5*labelLen                  # larger => smaller arrows\n",
      "\n",
      "vlen = np.hypot(dx, dy)\n",
      "good = vlen < 3*labelLen\n",
      "\n",
      "Q = plt.quiver(Xdeg[good], Ydeg[good], dx[good], dy[good], vlen[good], norm=pyplot.Normalize(0, 2*labelLen),\n",
      "               #color=plt.cm.jet, \n",
      "               units='xy', scale_units='xy',\n",
      "               width=width, pivot=pivot, scale=scale, \n",
      "               edgecolor=\"none\",\n",
      "               #headlength=1.5, headwidth=2.5, headaxislength=2.25,\n",
      "               )\n",
      "cb = plt.colorbar()\n",
      "cb.ax.get_yaxis().labelpad = 15\n",
      "cb.set_label('arcsec', rotation=270)\n",
      "plt.quiverkey(Q, 0.05, 0.05, 2*labelLen, \"%g arcsec\" % (2*labelLen), coordinates='axes',\n",
      "              color='black', labelpos='E')\n",
      "\n",
      "plt.axes().set_aspect(\"equal\")\n",
      "plt.xlim(213.8, 215.2)\n",
      "plt.ylim(0.2, 1.6)\n",
      "\n",
      "if byPatch:\n",
      "    drawPatches(calexpInfo, patches)\n",
      "else:\n",
      "    drawCcds(calexpInfo, ccds)\n",
      "\n",
      "plt.xlabel(\"Ra (degrees)\")\n",
      "plt.ylabel(\"Dec (degrees)\")\n",
      "plt.title(\"%s%s%s %s\" % ((\"Tract %d\" % tract) if byPatch else \"\",\n",
      "                             ((\" %s\" % calexpInfo.keys()[0]) if len(calexpInfo.keys()) == 1 else \"\"), \n",
      "                              (\" visit %d\" % myVisit if myVisit else \"\"),\n",
      "                              filter[-1:].lower()))\n",
      "\n",
      "plt.savefig('psfOffsets%s.pdf' % ((\"-%d\" % myVisit) if myVisit else \"\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CQTkmLwDU1NgxZcpojBplhNcbjDhlB7ATXw5HADNnjkUgICEpKXpJ5vdLwrRL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bXf/vZ8PPPMSJSXO7B7d61fBwsA/ftHY8eOSYiO1mHNmhKMGdPT7z4R4amnBuOCC2Kwa5cBI0dG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\nwYO1fPvbDzFw4ECuu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fXpmSgF15ZBBAdv/ikqNxuoADFu1BLLAcA/CCjdZ4DnUEFDSlDx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\njUaj0Wg0Go1Go9FoNBqN5vzj/wDvl97m01uAqgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2ae627de6510>"
       ]
      }
     ],
     "prompt_number": 340
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}