notebooks/checkPhotometry.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Check PSF and Aperture photometry on a single visit (1 filter - 36 CCD)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Validation plots for aperture and psf photometry\n",
      "# Apterture radii in pixels are : 3.0, 4.5, 6.0, 9.0, 12.0, 17.0, 25.0, 35.0, 50.0, 70.0\n",
      "\n",
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pylab as plt\n",
      "\n",
      "from lsst.meas.astrom import LoadAstrometryNetObjectsTask as Astrometry\n",
      "import lsst.daf.persistence as dafPersist\n",
      "import lsst.afw.geom as afwGeom\n",
      "import lsst.afw.cameraGeom as cg\n",
      "import lsst.afw.table as afwTable\n",
      "import lsst.afw.image as afwImage\n",
      "import lsst.afw.display.ds9 as ds9\n",
      "import numpy\n",
      "import math\n",
      "\n",
      "\n",
      "visit = 906675\n",
      "filter = 'r'\n",
      "\n",
      "# Do not consider reference magnitude > 19 \n",
      "magMax = 19\n",
      "\n",
      "#setup astrometry\n",
      "solve = Astrometry.ConfigClass()\n",
      "astrom = Astrometry(solve)\n",
      "\n",
      "# List of flags for quality checks\n",
      "Flags = [\"base_PixelFlags_flag_saturated\", \"base_PixelFlags_flag_cr\", \"base_PixelFlags_flag_interpolated\",\n",
      "         \"base_PsfFlux_flag_edge\",\"slot_Centroid_flag\", \"base_ClassificationExtendedness_flag\"]\n",
      "\n",
      "#setup butler - It assumes that a standard LSST directory structure is setup at the upper directory level\n",
      "butler = dafPersist.Butler('../output')\n",
      "\n",
      "# setup camera\n",
      "cam = butler.get('camera')\n",
      "ccd14 = cam['ccd14']\n",
      "vId = {'visit':visit, 'ccd':14}\n",
      "calexp = butler.get(\"calexp\", vId, immediate=True)\n",
      "detCenter = ccd14.getCenter(cg.PIXELS).getPoint()\n",
      "detCenterCoord = calexp.getWcs().pixelToSky(detCenter)\n",
      "\n",
      "# Retrieve external reference catalog (SDSS DR9)\n",
      "# loadSkyCircle is replacing getReferenceSources and is returning a structure (fluxField='r_flux'; refCat)\n",
      "s = astrom.loadSkyCircle(detCenterCoord, afwGeom.Angle(1., afwGeom.degrees), filter)\n",
      "refCat = s.refCat\n",
      "\n",
      "diffpsf = []\n",
      "diffAp = []\n",
      "mags = []\n",
      "ccdNum = []\n",
      "xCoord = []\n",
      "yCoord = []\n",
      "magsPsf = []\n",
      "errsPsf = [] # Error on PSF mag\n",
      "magsAp = []\n",
      "errsAp = []\n",
      "\n",
      "for ccd in range(0,36) :\n",
      "    vId = {'visit':visit,'ccd':ccd}\n",
      "    src = butler.get('src', vId, immediate=True)\n",
      "    md = butler.get(\"calexp_md\", vId, immediate=True)\n",
      "    calib = afwImage.Calib(md)\n",
      "    #  match CFHT sources to external reference catalog\n",
      "    match = afwTable.matchRaDec(refCat, src, 1.0 * afwGeom.arcseconds)\n",
      "    count = 0\n",
      "    # Loop over matched sources\n",
      "    for cnts,m in enumerate(match):\n",
      "        # Apply quality cuts\n",
      "        rej = 0\n",
      "        for f in Flags :\n",
      "            if m.second.get(f) :\n",
      "                rej = 1\n",
      "                break\n",
      "        if rej == 1 :\n",
      "            continue\n",
      "        if not m.first.get(\"photometric\") :\n",
      "            print \"Not photometric\"\n",
      "            continue\n",
      "        signalToNoise = m.second.get(\"base_PsfFlux_flux\")/m.second.get(\"base_PsfFlux_fluxSigma\")\n",
      "        if signalToNoise < 5.0 :\n",
      "            continue\n",
      "        # Select star-like sources\n",
      "        if m.second.get('base_ClassificationExtendedness_value') > 0.5:\n",
      "            continue\n",
      "        # Correct refereence object magnitude to take into account the different filter sets in SDSS and CFHT\n",
      "        # Ref : http://www3.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/community/CFHTLS-SG/docs/extra/filters.html\n",
      "        # Another color correction is given in : \n",
      "        # http://www1.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/community/CFHTLS-SG/docs/cfhtls.pdf\n",
      "        if filter == 'u' :\n",
      "            mag = afwImage.abMagFromFlux(m.first.get('u_flux')) - 0.241*(afwImage.abMagFromFlux(m.first.get('u_flux')) - afwImage.abMagFromFlux(m.first.get('g_flux')))\n",
      "        elif filter == 'i' :\n",
      "            mag = afwImage.abMagFromFlux(m.first.get('i_flux')) - 0.085*(afwImage.abMagFromFlux(m.first.get('r_flux')) - afwImage.abMagFromFlux(m.first.get('i_flux')))\n",
      "        elif filter == 'r' :\n",
      "            mag = afwImage.abMagFromFlux(m.first.get('r_flux')) - 0.024*(afwImage.abMagFromFlux(m.first.get('g_flux')) - afwImage.abMagFromFlux(m.first.get('r_flux')))\n",
      "        elif filter == 'g' :\n",
      "            mag = afwImage.abMagFromFlux(m.first.get('g_flux')) - 0.153*(afwImage.abMagFromFlux(m.first.get('g_flux')) - afwImage.abMagFromFlux(m.first.get('r_flux')))\n",
      "\n",
      "        # only keep magnitude < magMax\n",
      "        if mag > magMax :\n",
      "            continue\n",
      "        \n",
      "        magPsf, errPsf = calib.getMagnitude(m.second.get('base_PsfFlux_flux'),m.second.get('base_PsfFlux_fluxSigma'))\n",
      "        diffpsf.append(mag - magPsf)\n",
      "        \n",
      "        mags.append(mag)\n",
      "        magsPsf.append(magPsf)\n",
      "        errsPsf.append(errPsf)\n",
      "        x = m.second.getCentroid().getX()\n",
      "        y = m.second.getCentroid().getY()\n",
      "        xCoord.append(x)\n",
      "        yCoord.append(y)\n",
      "        ccdNum.append(ccd)\n",
      "        \n",
      "        # Iterate over aperture radii\n",
      "        for ir in range(10) :\n",
      "            apBase = \"base_CircularApertureFlux_\" + str(ir)\n",
      "            apFlux = apBase + \"_flux\"\n",
      "            apFluxSig = apBase + \"_fluxSigma\"\n",
      "            apFluxFlag = apBase + \"_flag\"\n",
      "            diffAp.append([])\n",
      "            magsAp.append([])\n",
      "            errsAp.append([])\n",
      "            if m.second.get(apFluxFlag) :\n",
      "                # if aperture flux flag is set, set entry to large negative value\n",
      "                diffAp[ir].append(-100)\n",
      "                magsAp[ir].append(-100)\n",
      "                errsAp[ir].append(-100)\n",
      "                \n",
      "            else :\n",
      "                diffAp[ir].append(mag - calib.getMagnitude(m.second.get(apFlux)))\n",
      "                magAp, errAp = calib.getMagnitude(m.second.get(apFlux),m.second.get(apFluxSig))\n",
      "                magsAp[ir].append(magAp)\n",
      "                errsAp[ir].append(errAp)\n",
      "\n",
      "        count += 1\n",
      "#    print \"ccd : \",ccd,\" number of matches :\",count\n",
      "        \n",
      "print \"Total number of matches :\", len(mags)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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       "text": [
        "\n",
        "Total number of matches :"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 461\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Compare aperture flux estimated for different radii to flux in SDSS reference catalog"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metadata = src.getMetadata()\n",
      "radii = metadata.get(\"base_CircularApertureFlux_radii\")\n",
      "\n",
      "fig, (ax1, ax2) = plt.subplots(nrows= 2, ncols=5, figsize=(18, 15))\n",
      "\n",
      "for ir in range(10) :\n",
      "    idxs = numpy.where(numpy.asarray(diffAp[ir]) > -50)\n",
      "    tmp = numpy.asarray(diffAp[ir])\n",
      "    mean = numpy.mean(tmp[idxs])\n",
      "    sigma = numpy.std(tmp[idxs])\n",
      "    hr = [mean-0.4, mean+0.3]\n",
      "    if ir < 5 :\n",
      "        ax1[ir].hist(tmp[idxs], bins=100, range=hr)\n",
      "        ax1[ir].set_xlabel(\"$\\Delta$ mag - Aperture radius :%.1f pix\"%radii[ir])\n",
      "        ax1[ir].set_title(\"mag < %.1f \\n $\\sigma$ = %.0f mmag\"%(magMax,sigma*1000), x=0.25, y=0.9)\n",
      "    else :\n",
      "        ax2[ir-5].hist(tmp[idxs], bins=100, range=hr)\n",
      "        ax2[ir-5].set_xlabel(\"$\\Delta$ mag - Aperture radius :%.1f pix\"%radii[ir])\n",
      "        ax2[ir-5].set_title(\"mag < %.1f \\n$\\sigma$ = %.0f mmag\"%(magMax,sigma*1000), x=0.25, y=0.9)\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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ArzLz6cxcCywFXj+gjfkZwLxM2LyQmT8DHh5w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\niCBJkiRJklr5/8t3qCTPPstqAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3bd878c490>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Delta mag (aperture - reference) as a function of the CCD number "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, (ax1, ax2) = plt.subplots(nrows= 2, ncols=5, figsize=(18, 15))\n",
      "\n",
      "for ir in range(10) :\n",
      "    idxs = numpy.where(numpy.asarray(diffAp[ir]) > -50)\n",
      "    tmp = numpy.asarray(diffAp[ir])\n",
      "    hr = [mean-0.4, mean+0.3]\n",
      "    if ir < 5 :\n",
      "        ax1[ir].scatter(numpy.asarray(ccdNum)[idxs],tmp[idxs], s=2, color=\"blue\")\n",
      "        ax1[ir].set_xlabel(\"CCD #\")\n",
      "        ax1[ir].set_ylabel(\"$\\Delta$ mag\")\n",
      "        ax1[ir].set_title(\"radius : %.1f pix\"%radii[ir], x= 0.3, y=0.88)\n",
      "    else :\n",
      "        ax2[ir-5].scatter(numpy.asarray(ccdNum)[idxs],tmp[idxs], s=2, color=\"blue\")\n",
      "        ax2[ir-5].set_xlabel(\"CCD #\")\n",
      "        ax2[ir-5].set_ylabel(\"$\\Delta$ mag\")\n",
      "        ax2[ir-5].set_title(\"radius : %.1f pix\"%radii[ir], x= 0.3, y=0.88)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P4E1V/ZbjMcYPZUqtsVPcR5Tzz94AfgvgdFX9o89+GD+UKdXED3tcEGXflQBu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\nyIdDnRERQ5Pu10ga63vsD0g6r7UlAbXxxxsAAAAA2qNtjQvbV0qaWeW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EVVDwGAiOwCsAPAQWebNoBz12+fBuCoqh4bZiOJaDI1m9G5f3yb6ouBCyIiIiLq1ZkA\nDjv3jwC40GxzG4BviMhzAH4WwAeG1DYiIgYsxgQDFzQRGG0lIiIaiCJrMP4RgEVVbYnI2QD2isgv\nq+o/uRvNz88fv91qtdBqtapsJ1FfFhYWsLCwMOpmEE0sGcc1f7mWMdUN1wIn6l0o/Yd9h+qo3/4j\nIhcBmFfV7ev3bwDwiqre5GxzN4B/r6r71+//DYBPqOojzjbsP1QroXz3AOw/VD+99B8W56TaYrHN\n8cH3koioth4BcI6IbBaRkwFcAWCP2WYZwKUAICINAG8C8PRQW0lERLXGqSJUSyy2OT74XhIR1Zeq\nHhOR6wHcB2ADgDtU9aCIXLf++K0A/hTAF0XkO4gumv2hqv7DyBpNRES1w8AFEREREfVMVe8BcI/5\n2a3O7e8DeP+w20VEROODNS6otuKpBeNwhX7S50mO03tJwxdK/+F3D9UR+w9Rb0LpOwD7D9VPL/2H\ngQuiAPDLj6h3ofQf9h2qI/Yfot6E0ncA9h+qHxbnJCIiIiIiIqKxwsAFEREREREREQWLgQsiIpp4\nIrJdRJZF5Hsi8oku27RE5DERWRKRhSE3kYiIiGhiscYFUQA4T5Kod/32HxHZAOBJAJcCeBbAwwCu\nUtWDzjavBbAfwHtV9YiIvG59pQR3P+w7VDuhfP+w/1DdhNJ3APYfqh/WuCAiIirvAgArqnpIVV8G\nsAvADrPNBwH8taoeAY4v70hEREREQ8DABRERTbozARx27h9Z/5nrHAA/JyIPiMgjIvIvhtY6IiIi\nogl34qgbQERENGJF8mtPAvArAN4N4FQAB0TkW6r6vYG2jIiIiIgYuCAioon3LIBNzv1NiLIuXIcB\nfF9VXwTwooh8E8AvA0gFLubn54/fbrVaaLVaA2guUe8WFhawsLAw6mYQERGVwuKcRAFggSei3lVQ\nnPNERMU53w3gOQDfRmdxzq0AbgHwXgCvAvAQgCtU9QlnG/Ydqp1Qvn9C7T/tdvR/sznadlB4Quk7\nQLj9h6ibXvoPMy6IiGiiqeoxEbkewH0ANgC4Q1UPish164/fqqrLInIvgMcBvALgNjdoQUTjp90G\ntmyJbq+sMHhBRDRKzLggCgCj9kS9C6X/sO9QHbH/dMfABWUJpe8AYfYfoizMuKadZTkAACAASURB\nVCAiIiIiqkCzGQUs4ttERDQ6DFwQEREREXkwYEFEFIYTRt0AIiIiIiIiIqJuGLggIiIiIiIiomAx\ncEFEREREREREwWLggoiIiIiIiIiCxcAFEREREREREQWLgQsiIqIx1m5H/4iIiIjqioELIiKiMdVu\nA1u2RP8YvCAiIqK6YuCCiIiIiIiIiIIVVOBCRLaLyLKIfE9EPuF5/EMi8h0ReVxE9ovIuaNoJ1GI\n2H+IyGo2gZWV6F+zOerWEBGRq8C52+tE5F4RWRSRJRH58AiaSRQEUdVRtwEAICIbADwJ4FIAzwJ4\nGMBVqnrQ2WYbgCdU9QUR2Q5gXlUv8uxLQ3ldREWICFRV+vh99h+aWP32nwrbwb5DtcP+Q9SbIZ27\nzQN4lareICKvW9++oarHzL7Yf6hWeuk/IWVcXABgRVUPqerLAHYB2OFuoKoHVPWF9bsPAThryG0k\nChX7DxEREVF95J67AWgDOG399mkAjtqgBdGkCClwcSaAw879I+s/6+ZaAHcPtEVE9cH+Q0RERFQf\nRc7dbgPwVhF5DsB3AHx8SG0jCs6Jo26Ao3B+k4i8E8BHAFzSbZv5+fnjt1utFlqtVh9NI6rWwsIC\nFhYWqtwl+w9NjAH0HyIiomErcu72RwAWVbUlImcD2Csiv6yq/2Q35LkbhayKc7eQalxchGjO/fb1\n+zcAeEVVbzLbnQvgKwC2q+pKl31xnhfVSgXzJNl/aGJxjj5R79h/iHozjHM3EbkbwL9X1f3r9/8G\nwCdU9RGzryD7T7wMN4tDk1X3GhePADhHRDaLyMkArgCwx91ARH4B0aDr6m6DLqIJxf5DREREVB+5\n524AlhEV74SINAC8CcDTQ21lj9ptYMuW6F8cwCDqRzBTRVT1mIhcD+A+ABsA3KGqB0XkuvXHbwXw\nxwDOAPA5EQGAl1X1glG12WJUkUZlHPoPERER0aQoeO72pwC+KCLfQXTB+Q9V9R9G1miiEQpmqkiV\nRpEuFUcVAWBlhcELKieUVF0g3HRDom5C6T/sO1RH7D9EvQml7wDh9h9e1KVueuk/wWRcEBERERER\n0XhgwIKqxMBFRZrNKNMivk1ERERERERE/WPgokIMWBARERERERFVK6RVRYiIiIiIiIiIUhi4ICIi\nIiIiIqJgMXBBREREREREI9NuJ6uQEPkwcEFEREREPROR7SKyLCLfE5FPdNmmJSKPiciSiCwMuYlE\nFLB2G9iyJfrH4AV1w+KcRERERNQTEdkA4BYAlwJ4FsDDIrJHVQ8627wWwH8E8F5VPSIirxtNa4mI\nqK4YuCAiIiKiXl0AYEVVDwGAiOwCsAPAQWebDwL4a1U9AgCq+v1hN5KIwtVsAisryW0iH04VISIi\nIqJenQngsHP/yPrPXOcA+DkReUBEHhGRfzG01hFRLTSbDFpQNmZcEBEREVGvtMA2JwH4FQDvBnAq\ngAMi8i1V/d5AW1Yz8dx+Dt6IiDoxcEFEREREvXoWwCbn/iZEWReuwwC+r6ovAnhRRL4J4JcBpAIX\n8/Pzx2+3Wi20Wq0BNDdMcXFCIEqZZ/AiPAsLC1hYWBh1M4gmlqgWCZTXi4joOL4uGl8iAlWVUbcD\nYP+h+gml/7DvUB31239E5EQATyLKpngOwLcBXGWKc25FVMDzvQBeBeAhAFeo6hPONhPdfxi4qJ9Q\nvnsA9h+qn176DzMuiIgyMHW3E48JEcVU9ZiIXA/gPgAbANyhqgdF5Lr1x29V1WURuRfA4wBeAXCb\nG7QgFickIsrDjAuiADBqHyZeAesU4jEJpf+w71Adsf8Q9SaUvgOw/1D99NJ/uKoIEREREREREQWL\nGRdEAWDUPlycFtEptGMSSv9h36E6Yv8h6k0ofQdg/6H66aX/MHBBFAB++RH1LpT+w75DdcT+Q9Sb\nUPoOwP5D9cOpIkREREREREQ0Vhi4ICIiIiIiIqJgMXBBRERERERERMFi4IKIiPrSbicFO4mIiIiI\nqsbABRER9azdBrZsif4xeEFEREREg8DABRHRiDBTgYiIiIgo34mjbgAR0SSKMxUAYGUFaDZH255e\nNZtR++PbRERERERVY+BiAsVXeDnIIKIq8G8JEREREQ0SAxdDEkqwYFyu8lYtlPeHJgczFYiIiIiI\nimGNiyFg8bqw8f2hUWk2GbQIhYhsF5FlEfmeiHwiY7vzReSYiPzGMNtHRERENMmYcTFheJWXiChN\nRDYAuAXApQCeBfCwiOxR1YOe7W4CcC8AGXpDiYiIiCYUAxdDEFqwIIQ2hCS094eIhu4CACuqeggA\nRGQXgB0ADprtfhfAlwGcP9TWEREREU04Bi6GhAPisPH9IerdGNSIORPAYef+EQAXuhuIyJmIghnv\nQhS40KG1joiIiGjCMXBBRFQTIQYIxqTgb5EgxM0APqmqKiKCLlNF5ufnj99utVpotVpVtI+oMgsL\nC1hYWBh1M4iIiEoR1fG7aCQiOo6vi8aXiEBVg5gzz/6TbVTBg1ADBCG0q9/+IyIXAZhX1e3r928A\n8Iqq3uRs8zSSYMXrAPwIwL9S1T3ONuw7VDuhfP+w/1DdhNJ3APYfqp9e+k9Qq4rkVXUXka0ickBE\nXhKR3x9FG2k8tNvjt4II+8/gcQWaTnGNmJCCKT14BMA5IrJZRE4GcAWAPe4GqvpGVX2Dqr4BUZ2L\n33GDFkRERGUVWdFKRFoi8piILInIwpCbSBSMYKaKFKzqfhRRcbSdI2gijYkQrhBXjf1n/JUtIjvM\nzJC69yFVPSYi1wO4D8AGAHeo6kERuW798VtH2kAiIho7Rc7dROS1AP4jgPeq6hERed1oWks0esEE\nLlCgqruqrgFYE5FfH0kLicI11v0nlNoOzSawf//o2pL1nO4xGsfg3KCp6j0A7jE/8wYsVPWaoTSK\niIjGWZEVrT4I4K9V9QgAqOr3h93IOgjlPJEGK6TARW5Vd6IqjOnyp2Pbf0IahLfbwCWXhNEWlz1G\nRC6e0BERBanIuds5AE4SkQcA/CyAT6vqfx5S+2ohpPNEGqyQAheVVpRhZXfKMuo/agOo6s7+Mwaq\nGmCOaXDuOK6KUBxP6IiIglXk3O0kAL8C4N0ATgVwQES+parfsxvy3I1CVsW5WzCrihSp6u5s+ykA\n/6yqf95lX6ysS7UyjFURnG1r139CumI8qLb0O8AM6RgNWyiV3UPtOwxcVG+c+hv7D1FvhrSi1ScA\nvFpV59fv3w7gXlX9stnXRPefcfqbPCl66T8hZVwcr+oO4DlEVd2v6rLtyL9giQIz1v0npC+iMm1h\ngUwatXHPvhkFBoOIqCJFzt2+CuCW9UKer0I0leT/GGIba4F/hydDMIGLIlXdReTnATwM4DQAr4jI\nxwG8RVX/eWQNp1IYER0M9p+0ED5nZQc3HGDSoPDzREQUniLnbqq6LCL3AngcwCsAblPVJ0bXaqLR\nCWaqSJUmPV0qVLxK1V0oqbpA/ftPKJ+zUNoxCULpP3XvO4MQQhBxEMbpdbH/EPUmlL4DsP9Q/dR9\nqggRTahRDgIG9dzjnEHR7zEbp0EfdTfOwbtxei1ERER1UDhwISK/j6j6bRwZUQAvAHhUVRcH0Daq\nyDAHCVnPNc4DuTx//ud/HkcWAURRxtNPPx3nnXceZmZmRty60ap6cNNsAvv3J7eH+dy+toybdhs4\n++zo9lNP9VZEtOwxZ/8h6h37D1Fv2HeIwlIm4+I8AL8K4GuIghe/DuC7AP4XEfmyb/UCGr1hXvEq\n8lzjOJAr4tFHH8UjjzyC97///VBVfP3rX8fb3vY2/OVf/iV+67d+a9TNGyvtNnDxxdHtXgbWlG11\nFXjxxeT2MI4v+089TXKwOiTsP0S9Yd8hCkuZwMUmAL8SF/ITkT8GcDeAWQCPAmDgokJMpR4vhw8f\nxt/93d/hNa95DQDgT/7kT3DZZZdh3759OO+880bcutHyDW76+fyXGVhzYFVeowG8+tXJ7bJ6Oebs\nP/XFfjV67D9EvWHfIQpLmcDFFICfOPdfBtBQ1R+JyEvVNmuyVZklMcyBGQeB3a2treHkk08+fv+k\nk07C6uoqTj31VJxyyikjbFkY3M9Lv5//sgPrcfmsDivY2WxGmSz9PFfZ32P/Ieod+w9Rb9h3iMJS\nJnDxXwA8JCK7EU0VeT+A/1NEfgYAl+VBuFkSw2xPaK89FB/60Idw4YUXYufOnVBVfO1rX8MHP/hB\n/PCHP8Rb3vIWLC6yTEw/3L5XxcC6bsa5CCLA/lMnoX4PTjL2H6LesO8QhaXUcqgicj6ASxAV5tyv\nqo8MqmH9GMWSQFUPHOK/haz9Mz4efvhh7N+/HyKCSy65BL/6q796/DEuqZVWZvAz6EF7HQZi/RbM\n9O0P8O+n6ucqqg79J4S+M0rjHkCrM/Yfot7Uoe8A7D9UP8NYDvUpACcAOAXAqSLyDlX9Zsl9UI52\nG7jkkug2T/4GYxSD0bPPPhuvvPIKXnrpJfzoRz/CN7/5TbzjHe8YXgNqJJTP/CQOxPJecy/FOavo\nb+w/RL1j/yHqDfsOUTjKLIf6rwD8GwBnAVgEcBGAAwDeNZim1UvV9R3KBE3rcEU4JKMYjN522234\nzGc+gyNHjmBmZgbf+ta3sG3bNnzjG98Y/JOPOV/fK5OxNC79R4Z0zWdtzX/b5R7TKvob+089sM7R\n4PXy94r9h6g37DtEYTmhxLYfB3ABgP9XVd8J4O0AXhhIq2oqnl8/TPGgYMuW5ISGymu3yx2/stt/\n+tOfxre//W384i/+Ih544AE89thjOP3008s3lLzcvre4CLz97dG/vOmnef0nHoiFkm3R7XNXZTvz\n9jU15b/ttvHss6N/Vf1NYv+pj1F8D06KXr/v2X+IesO+QxSWMlNFXlLVF0UEInKKqi6LyJsG1rIJ\nN6yrp0XkzXfv9liomk1g//7kdtkrwr1cQT7llFPw6vWlLl566SVs3boVTz75ZI+vgCz3c1gkI6CM\nIp+HItv1y37uYt2et592Zf3OzAxw//3JbftcdirJzEz/V+HZf4h6x/5TX3U8xxon7DtEYSkTuDgs\nImcA2A1gr4g8D+DQQFo14cqk2w46NTdrkF7X+f/tNrBtW3T76aeH85ybNm3C888/j507d2Jubg5n\nnHEGNm/ePJwnH3P2c5iXEeAq0n/yAndlggl57HN1e+7VVeDii6Pb8Qoqth1V9k079WPnzvRzucU6\nfcvR9vv87D9EvX/fs//UU13PscYJ+w5RWEqtKnL8l0RaAE4DcK+q/qTqRvWLlXWrM8zAxbCuLOzd\nC7znPdHt++8H5ubyB4xFB5RFLCws4Ac/+AG2b99+fH3wSahMnXXMbE2KvOObVUMB6Fz1ouwqJd32\nbX/ffXz//vyiulmv0/c67HPH26+uRlNhAOCxx6IAwaACF3ntsm2ZmYn6GBD1raqF3H/43UOhG1T/\nEZHtAG4GsAHA7ap6U5ftzkdUH+0DqvoV8xj7j4OBi7CE/N0DsP9Q/QxjVREAgKou9PJ7VNwgT/zL\nyLrCU2W2x6i/oLMCMkBn2/ppX6vV6v2Xa8r3/hYdhPuCBTYwYT+HDz6Y3M/LinCDCXbfedzn7va6\n4+3i2hvdXqePPQdxj4Wb1WCnP7n7rLpYsO94u21ZXEwCg3Ego8rA3yT2H6KqDKL/iMgGALcAuBTA\nswAeFpE9qnrQs91NAO4FEMRgL2QsdhsWfvcQjV6ZVUXOB/BHADY7v6eqeu4A2jXRfBkBo5T1hVnl\nl+mwAsXT08AppyS3h+Hhhx/Gn/7pn+LQoUM4duwYgCjS+Pjjjw+nAYFxgwl33JH8fG0tmV7QTd5y\nnHY5YSD5bK2uph+zQROgsz6DDQhY7s93705+1i1g4nrllfR+bO0V2yfcAX8cWIm3tdkegzzRdfdt\n27K62tlmGwyygac87D9UZ6OuUzCE/nMBgBVVPbS+710AdgA4aLb7XQBfBnB+VU887hiwGC1+9xCF\npUzGxX8B8AcAlgC8krMtjdAgT5IGue+qAxfd2tpsAgcO+B+Lf2avcvR71eNDH/oQ/uzP/gzT09M4\n4YQyi/mMB9+gPLZ1K/CqV0W3p6f92QOu5eX07UajXJaE+zmz+9q6Nb2tLyBguSuXuJkGjUb6uWZm\nkuDIzEz0ey+9FN2PB/s2qGIfLzPgLzv9JkuZYsGNRvJ+NhqdgSYgO/DkM+n9h+pr1NmEwFD6z5kA\nDjv3jwC40N1ARM5EFMx4F6LABXPaKXj87iEKS5nAxZqq7hlYS2qg7Lz7rG2zzM0lVfuLZFvkzcmv\nKk27yAlYr/u2g7R+j2FebQ63sKHvuXzBjn5MTU3h8ssv728nNWM/lzYAEGcnNBrpgbHv/XFt3Ji+\nnbeKRbvdfeDtBiq2bvUXlszKenCnf9x1l/85XG42iV39pGymSSzOrnBfc960lDJ/F3yBPDu9xta8\n+PGPk3ba1zk93XmM80xi/yGqyhD6T5EgxM0APqmqKiICThWhGuB3D1FYygQubhSROwD83wDigpxq\niyuNqyJF+uKroQ8+mD3wKiJvCoN7xTorHd2Xpj2oqz/9BDbsgNFXx6Bf7pVxOwCs+rmsT33qU7j2\n2mtx6aWXpoo6/cZv/Eb1TxaAvFoRbh2Eu+7qzCzIuiI/Nwd89rPJ7XY7exULN4Mjb6DcbHbWxyj6\nurZuTWdU2IBJuw288Y3R7aef7lz9xJdpEk9pitsdZzIA2XVX8oIieXVcYt0yTGxQpAw7taSISes/\nND5CqFMwhP7zLIBNzv1NiLIuXOcB2BXFLPA6AO8TkZftBbH5+fnjt1utVhB1BUY91YdGx/adpaUl\nPPHEE3jzm9886qYRTaQygYvfBvCm9d9xp4pMROAij3s1dHk5PxXapnG78rIm7EoGtnCeO/hZXEy3\nxaavx88Xb58lL4Xfp0w2iDtgtO0ue8KQdQX6/vvTA11fzYSs96cXd955J5588kkcO3YslW44rgOv\nvCyIffuSbZ9/Pj1AtzUSgM4sh499LLq/bVu076yBsJvtEX9+Y3aAb7M9gHQdCvu6fNkEMfsZ3Ls3\nCdAsLaVrrcTBOlubww18uJkM7vO6rzN+rqNHk58fPZrOcIn73U9/2rmP+HnsMcgKQtnXad8/3/K0\nZfvzpPUfGi+jHvAOof88AuAcEdkM4DkAVwC4yt1AVd8Y3xaRLwL4mi+L1w1chCCEqT40Or6+c+qp\npx7/nN54440jbB3R5CkTuPhVAFsnda2dvKsmjUYyCNm6NTsV2l6x9A2O3aOcd/XaHVjZwY/bLl9b\nymQ2+NL93cGSr45BVruzlpLMa3cR3V7L1FTnQNeuimDfn34DGY888giWl5chZYoFjBn3/XCnaGzZ\nkv4M+4IJ7omjrUsxM5MMloucVLp9yw6qfYGJOFiwtJTe3jp6tNrVZywbrPizP4v+L9LX7Eof9nXN\nzSV/3/JqUszMJFPZ4v7gHn/fdBu35kUv2H+y8YowZRl0/1HVYyJyPYD7EC2HeoeqHhSR69Yfv3Ug\nT0w0YPzuIQpLmcDFgwDeAuD/GVBbaseeLMZ/1xqNdPaAb9u8fbl8V6/jAIEdgExPd2ZU2L+39ipu\n0ewQX7q5HSy5V2qz2u1Lw7ft7jdE5h5TWxjRfW6buu5mA8SD5H4DGRdffDGeeOIJvPWtb+3txdSM\nL7hg34948Hv0KPCT9cln+/algxq+QIGtS+ELNNmAmjsot9NSXDZg5r6Oo0ej/rWeaY1GI3quuLbF\n7GznZ9b9nExPp4uQ2rbk9Q/391dWkqyTM86IjoPb12Znk21nZzs/026dkFjc9+1x8U3j2rEjuh/3\nc3v8v/rVZJ+Li+maF70Mriet/5TBK8KUZxj9R1XvAXCP+Zk3YKGq1wysIRULYaoPjQ6/e4jCUiZw\nsQ3Aoog8A2D9NHS8l0PNmuYAdA7auy25COTXobCPu8EF38Ah3n+c/g10vxrs7stmRfgGby47KMya\nhgL4iwe6r9PW/uhWe2BpKR2QKVKsM29airu6QtaVcTswtoNwID9jxjpw4ABmZmbwhje8Aa9aH1FO\n0pJavs94PPj9wheS7TZu7Awe+E4cs6aWlBnI+WpBuMGH2dkkUDE7G30W4yDL0lL0/7XXRv/baSi+\nWhC2X9upInaKhg06xlneh536/YcOpQMRa2vRZ/KZZ6L7zWZnQVMbRHHZVUHypnHZY2izO3zKZghM\nev8h6gf7T3/KBCyY/ZSvTseIfYcoLGUCF9sH1ooA5aVe+9Kp46una2vp1Hcrr3Ceb6DmZgS029nZ\nCDZQYee29zolY20tfaU1b7UAO6D0HTO39kBWJl6Zuh++4FC8j1iZbA476OvFvffe29sv1pS94g+k\nj7n7WXDFwTe76pg9wXEfn5lJinXabBqgc0qSmzFhp1/YgNn0dPq5bO0I+7qy2AwLG8joZ3UUX7vy\nTgo3bOjeTjdDAkgHVGwg1Vdvw2UDIb1kCExa/ymDV4QpD/vPcDD7KV/djhH7DlFYCgcuVPXQANsR\nnLxBRFamwtGj2Sng7u/Gqwm4wQX3/1jRP+6+opTu1c9Go3tRvtjevdH/c3PpKRZAdkaFnXKRl80B\nJIO+RqPz5NteES5T98MeB7vqS5kCpbYOgp12UsTmzZuLbTgmmk3ga19Lbttgmzv4tcGgvIGY7S97\n9ybTJrZsiT633aZcLC+nMybse2unhthgQ14Qy32NthaEb/WTMtzj4k792LzZ366sz7TvGMfb+zKM\n7N/Cbv18aqozUOELBpU1af2nrNBP/mm02H+IesO+QxSWMhkXE889ObQn9+70guefT27HxQXt1BGX\nDS70u5KF206bFeErNugOzJaWkrbcf380CIwHWUVWC3DZbA5fIMMd6MWDsSuv9L+urNUdbGDJbW88\nuHJXfbF1DtwrADYw4S6jGqtqtZFx5ZsuYIN5bsDJZv3YgZhdVSRrKkLWlCRXPMjuFkxw+3SWOAti\nba3zcxUv//70052BvXY7/Zn2TR1xl091pzRdeWXyd+bKK9Of0ampzqtaWVNDgPT27vSzrVs7AxFx\n+2M2QGNrWhRZ9pWIqO6Y/ZSPx4iGpU5Tkqg4Bi66sAN+IN0Jsq68nnFGcjtrFYIybAeMB/yPPpps\nc+BAZ+FIO2g4cCC5v7KS3z43U8GtRbC21vm7RWp12EBGPNC7807ghhuSbTduTAdYgPzVHdw/Tr76\nAvEA1bfqi81C8b3/7m3+QSwnK0AAdE6BsNOAsrJr5uaSgfPcnL/2ShzIePDBzvoNbjCh2ews4uq2\n2/0MT0+nsz/sVKmlpfTyp7aWii8TwT0Ovt93s0f+4A+i/3fuzD++lu+Yxn9Tpqc7A3dZxTXb7agN\ngH/6me+Y2sASEdE44N+zfDxGNGh1m5JExfUcuBCR9wD4uKr+eoXtCVbeFcx4cA2kMy6Azgizu21c\nG8Jecc6q3+AO+E8/PdnXli2dA3Yr3k982w4yGo2kBkC88kE8CLSvy1cfI6u2B5AemLlZK9/5TnL7\n0KHslSWKBIN8q1rEBQbd2/FUA3cAabNObPCnzBKy3dx///349Kc/ja9//evlf7kGfINXd6UJW3fC\n5fu82+wat6YFkP2ZsNk2dhBu3z934G8DGwCwZ09y32Z/ZE2hsHx1KbJkHbNmMwksxu10sxrsZ3xq\nKn1MbbCjbEZRXp0Pd/9FVjLKM+79h2iQ2H+IesO+QzRapQIXInIygKsB/FsALwB42yAaFQJfarU9\nOXeL29mBh7366Z6c+6ZY2PoNNnOh28Bg27ZkOca5OWDXruQxO4Ulfi0nnZR+Xa7V1aQGQDygiV/L\ntm3pq81AZ3FBW3zQcrd3izKfempye/v2zukacd0NIAn22PR1l10ZxJ2us39/eupO1gAyXgLTZor0\nMvD6yU9+gi996Uv4i7/4C5x++un47ne/W+wXa8otaumrtZKlWz2MRqOzpsXUVPfsmvh54vfPV5ci\nK7vDvrd501Tc6UnuUqlxIMDNhsqrl+F+7uL+FgcGbU2YvXuBq65K9jU9nf6M2785vmPk9s1uS9e6\nK/PEjwOdhVezpmL1atL6D1GV2H+IesO+Uz+ckjS+CgUuRGQjgI8B+NcA/hbAR1T1YRE5nP2b48t2\nCncVhZ07k9RpX4exV6OLFK/rFrhYWwOuWV8RfXa2czDkK5T38svJc9kaF77XGV8pn5mJ5trHP89b\n3cS3L/eYnXZa8pgve8Md3LpTYh59NGq3vXKeN32jW1ttkMM9ZvGKGO4gvGxa/tGjR/HZz34Wn//8\n5/Frv/Zr+MIXvoDzzz8fmzZtyv/lmrJ1DOxny/0sFNmXmyHj1mDwTXfy1ZKI3/upqc6lhd1AhW/l\nG3fQbacNzc2lAxg22ykOJsSfMfdxXwDTrgjk9rfFxc46IUX5MmBsJombxWEDDe5UEKDzmNmAZVb/\n6DYNq5tJ7D9EVWH/IeoN+069MWAxnnIDFyLyWQA7AHwJwDZVPTLwVgWgSDDB7RTNJvCtb3X+vBt3\ngGKv+NtBnx2wuANrdwUT3yoJvqUNXXYQYQeBWYVDfQMWmwVhgwXusdm+Palr8c53AjffnDxms07c\nws6+Is95dRBcvhVM7KDOvpe2mKAd9HXzsY99DF/96ldx9dVX48CBAzjrrLOyf2FM2IK0vpVY7GoW\nLnv1393Xtm3JY9u25a/y4vbltbX0e+erh5HFlwVhsyliWQN03/LBvisERb945+bSmVdA56o6WUvM\n2oykLL7gjt2vOy3ItiVvCo1rUvsPURXYf4h6w75DFKYiGRdfAvAkgKVJCVp0Y+s32BRzd3DlSzEH\n/AOr2dlk+sbsbGfxOssNRriK1H7IWglkaip/sNXtfnzbBkncwEY8UASi4+BeBc5ruy8rImv6hi+D\nw05rAZL3yQ6U7fG3721cpyRvYHn11VfjTW96E6anpyfui88GrYpmCPiWsrX7sYEKd9826JW3+o3L\nZgv4ggluFkRWu9rtziCHLexpA2BZn6esAE27DVx7bXR7drazdodvJY9uh2AzyQAAIABJREFUf5N8\ngT1732ZUuPd9wU63Lb6+2c0k9x+ifrH/EKUVLazOvkMUptzAhao+COBBEdksIh8F8AMAu1X1JwNv\n3QjZK6s2AwPoTKd2Axt2+UY7B98u7RlP34gH1FmDcLe458aN2Ut35i1JmrX0py+wkfVH3w767HP5\njoN7FdimyWdlRQD50zfcx90pBqurne2wc/SzChfu3du5ZGw3F198MS6++GIcOnQIt99+O0477TTs\n3LkTJ8cVUAlA56oidtBtPwtlpkm4n/FGI7veQpFgQl5wwd3OBjns/bKpjDZA4+4ja9qWDazaPuBb\nTthl3w97jNypPLb4sI/N/uiG/Yeod+w/RIkyK02w7xCFqXBxTlU9BOB2ETkNwAdE5EQArxpUw0bN\nDjry0pvdaRNLS8COHdFtX8aEDYK4U0V8BTWt6en0yh/2ambWAN53tdMdDGUFMtbWsota+gY0WYEM\nOyC1V4Ttl0peYMMdPMVtjO+7V4HjFSni1wSkM0NsJsnMTLpt+/alX3cRmzdvxkc/+lH84Ac/wF/9\n1V/h2LFj+LEvbWaM2GBEN0WKKJUZ4GcFOmz2hq+/VDkvskzQo4wyBXxjeY+XPcZuW9y/C77pNPZ3\nyxbNmsT+Q1QV9h+i3rDvEIWl9HKoqvoDAF8SkQ0A/qnKxojIdgA3A9gA4HZVvcmzzWcAvA/AjwB8\nWFU9tf2r50vT7rbE6cpKdsaEu1Tn0aP+aRB2wG8LaLorf8Q/i1OybfAgb66777XG3LZMTaUHP7bd\n7nPGt21buqWI2+kZvgFN1kDYN1Wn26Boy5b0wMoGVHzLn7r7dguxxsU7izrttNNw9dVX46c//Sl+\n9md/ttwv5wip/5QdnPY7oLfZB1kBENuuovVKQpO1kkfeaykzXaOsZhM4cCC53W2bXgyy/xCNO/Yf\nmmS9BM1joz53W9/ufAAHAHxAVb9SaSOIaqJ04CKmqj8F8NdVNWQ9EHILgEsBPAvgYRHZo6oHnW0u\nA7BFVc8RkQsBfA7ARVW1weVbFtGmp7t/+NyrjNu2ZWc92IFvs9kZFMka8LsD7eXlZOWCOOXb1m/I\nS7vP0mwmmQx2SkVeUUTfc9haEvFV4927s5cY9dU9sMU4s64ouwO16el0No3v+LvLWNqr9M0m8Mwz\n/tdX1IYNG/Cbv/mbvf2yR2j9BxheIMCX/pk1pWlQWRCjVGSlGxv4s/eLzv21fH0zLwjZr6r7D9Ek\nYf+hSdXv99Eozt2c7W4CcC+AgvmsYej13ILIp+fAxQBcAGBlfUoKRGQXotVM3M57OYA7AUBVHxKR\n14pIQ1ULrAdQjq1zkNfh7NSSslkPeXP23UGGGzBwszfi23buuq+tRbnZBrt3d17VLVNrwCcONrhT\nLuKpGvaPnRuQyatD4WNrXrjc319cTC9j6Zv/H+Af4KD6D9D5/g3ry6vMPNY6s1OnsjJHfMEFV7/H\nzN2+yN9OnsgQEVERY/59UeTcDQB+F8CXAZw/1Nb1aVLOx2h4QgpcnAngsHP/CIALC2xzFoDKB15F\nrmBaNrOh22NA/rQHO3/dbu8LGOTVxujX1FT5Y2IVrXtgj4EtJmjrUDQa2fvuNnCLb2cVbIy3c2Wt\nEjMiQfUf3zEd1JdX3qB8HPkyp4oEV11F+6JPmSK9vt/liQwRUbXGcYA/Ad8XueduInImomDGuxAF\nLnIqVhGNr5ACF0U7oj3dHkgHzruC2e++y07X6La9b7pG3vSNftrazzHxDTDjwdPGjcl2RZZ1zdt3\nt22A/MKGMzPRaiHxbcuujhJI8CKo/jNs/UyHqqt+XpvvGBU9Zr5pdHbfda0bQkRURxMwwB9XRc7B\nbgbwSVVVERHUaKrIpJyP0fCEFLh4FsAm5/4mRJHHrG3OWv9Zh/n5+eO3W60WWq1W6QYNspNl7bvZ\nzF9hw2UHzlUPpLMySfrdl5vh4F6ltX/sbB2Kdrtz5YKq3q92G9i5M7o9qBOAhYUFLCwsVLnLoPpP\nPwPjKoT6BRnSFbFea30UmQqS9/et38/CAPoPEREFZgIGvkXO3c4DsCuKWeB1AN4nIi+r6h67syrG\nPlUb0/eNelDFuZto3hp5Q7K+vOqTAN4N4DkA3wZwlae44PWqepmIXATgZlXtKC4oIjqI1zXIQUfW\n1IVJ6fRlj28/70feVJG841/1VBERgar2HEWvQ//pR0gD/l6F1q97PaZ5GRej0G//Wd9HZmV3EfkQ\ngD9EdLXrnwD8jqo+brYJru8Q5ami/1TUDvafPozD92TdDOPczWz/RQBf860qwv4zGcapn/fSf4LJ\nuFDVYyJyPYD7EJ043qGqB0XkuvXHb1XVu0XkMhFZAfBDANcMsk3DCiYUqbEwCfopCNjPc/ki+jbj\nxQpkeshxIfafqoQ24B8H/RzTcZwKUrCy+9MA3qGqL6wHOT6PAa7KQ0RUxrj8PZ4kRc7dRtpACgrP\nhwPKuKhSFVHHYRYXLLuc46Qa51UpQrniBYQXta/zH+pRraySp87H1KeCq17bAHxKVbev3/8kAKjq\nf+iy/RkAvquqZ5mfB9V3iIoI5ftnVP0nlL/LVD+h9B2A3z+TgOduAWVchK7feXZZX4y9rBAwTCF8\nqY+6s4ZwDCZVXee4+j6zobR/2Me0Bv2nyKo8rmsB3D3QFhHRwI363IKIqKi6ng9XiYGLLqoMJhT5\nYgz1A9htGkuo7a2Cb/UTZsSMFo9zfdVkYFD4MpWIvBPARwBcMrjmEBEREaUFeg41NAxcZJj0D4e1\nugpcsn6qPuwByLCjjFnPUZOBGI1YyJFxfoY7FKnsDhE5F8BtALar6vO+HYVY1T0kDPqO/hhwVZ5E\nyH+nyW/U/YeIRoc1Loakzn9o47YDkzvYGXShVs6TpGEaduBi0H//hrQqzy8A+AaAq1X1W132w76T\ngQGzMI9BKN8/7D+UJ7T+E0rfAdh/qH5Y4yJgo/7j2g+37ZN6ZSJvFRLKV+fg3bgJKYMpBAUru/8x\ngDMAfE5EAOBlVb1gVG0mIiIiGpYQzuOZcUG1FUIHqsq4R+1Du0pC4yWU/sPvnnzj9He7V6EdA/Yf\nqpOQ+k8ofQdg/6HBCiXbnBkXVEscCIcvpJMLIgoD/x7wGBD1g/2HaHIxcEFElfMFlji9hoiIiIho\nOKq6iBjKeTwDF1RLoXQgKo7vExERERHR4FWdnR7CeTwDFyUw9T0sfB/CxcASERERERFVhcU5C2JN\nBRokFngi6l0o/Yd9h+qI/YeoN6H0HYD9h/xCvujO4pwVC/nNJiIiIiIiIvKxY9i6j20ZuOiCxQWJ\niIiIiIio7sZh9gADFyXU8Q0mIiIiIiIiqjPWuMhQ93Qaqg/OkyTqXSj9h31nuPgdXQ32H8rDvuYX\nSt8B2H+omJD6ci/9h4ELogDwy4+od6H0n7r2nZBOZIoah5TXULD/UBb2te5C6TvA6PpPHb8/KAy9\n9J8TBtUYIiIiCls8KNmyJTkBpeFqt3nsKR8/JxQafn/QsLHGBREREdXKuBTM5tVsKmKUn5Nx6WtE\nVH8MXBARUSamgo6vqgclw/ys8PMYDhHZDuBmABsA3K6qN5nHPwTgDwEIgH8C8Duq+vjQG0o9YV8j\nHwa1aNhY44IoAJwnSaGqwxXhUPrPpPedOnxWQjTqwGC//UdENgB4EsClAJ4F8DCAq1T1oLPNNgBP\nqOoL60GOeVW9yOxnovtPnlF/TqhTKN89APsP1U8v/YcZF0REREQOZo6UcgGAFVU9BAAisgvADgDH\nAxeqesDZ/iEAZw2zgeNgDD4nRER9YeCCiIi6YiooFTUun5VhZ46MwZX0MwEcdu4fAXBhxvbXArh7\noC0iIqKxw8AFTYwxODkkGgn2GSqKn5VyxmR6TeH8dBF5J4CPALjE9/j8/Pzx261WC61Wq8+mEVVn\nYWEBCwsLo24G0cRijQuaCKGfHHKeJFHvQuk/7Dv96Te4XGVwepCBbnffIXw3VVDj4iJENSu2r9+/\nAcArngKd5wL4CoDtqrri2Q/7D9VKKN89APsP1Q9rXBAREVGwugUE+h3AV/H7brsGFUDwtXMMptc8\nAuAcEdkM4DkAVwC4yt1ARH4BUdDial/QgoiIKA8DFzQRxuTkkIiotkaZXZCVQTHqrIe6fyep6jER\nuR7AfYiWQ71DVQ+KyHXrj98K4I8BnAHgcyICAC+r6gWjajMREdUPp4oQBYDphkS9C6X/sO9kywsQ\nDGqqSJHnneRinOw/4Qjts0HZQuk7APsP1Q+nihAREVGQ8jLf+h2s9fr7w87I46CUfEad+UNEFDoG\nLoiIiGqs7FXarO0XF6P/Z2b6b5fPKAZjoU0V5FV1IiKi8jhVhCgATDck6l0o/WcUfcd3lbbXeg6L\ni8Db3x7dfuyxwQUvqlJVAGCYV7qrLiJahUnuP6FhUKteQuk7APtPnlH2LfZrP04VISIimmCTUq9h\nEtPqJ/E1T5pQCtYSjZNRF4bm3+3qMHBBRERUU3YaRDwYKbo9kPxOowGcckpyO0/VJ2SjGkhVPZUk\n63WENm2FJtM4DaYYgCGaHAxcEBER1Zh7wl5kYOz+3B3A7N8PnHDCYNqYp+xAquoAQFWDnkEOCBn0\nIEobpwAMDc4o/3by73a1RnSK0klEfk5E9orI/xCR+0XktV22+4KIrIrId4fdRqIQse8QkavZ7O0E\nqdGITrCKDgDiE7Ks7dvt/CyQSdJuA2efHf3r5bj0+t4SuYr0XRoeEdkuIssi8j0R+YTn8Q+JyHdE\n5HER2S8i546inXU2yr+d/LtdnWACFwA+CWCvqv4SgL9Zv+/zRQDbh9YqovCx7xBRTwY5gImvhm7Z\nUnwKS9F2lNl3vP0wAih5r2N1FXjxxejf6qp/H4uLyeouAIM/Iavze1PXwZR7zMchACMiGwDcguj8\n7C0ArhKRN5vNngbwDlU9F8D/BuDzw20lURhCmipyOYDZ9dt3AliAZwCmqn8rIpuH1iqi8LHvEFHP\n3FoXZdKui2z/yivl21G1OMsBAJ56avDLwGa9jrU1/223He7KLo0GU+FDNU7TFOpSJ8J3zENvcwEX\nAFhR1UMAICK7AOwAcDDeQFUPONs/BOCsYTaQKBQhBS4aqhpff1gFUKA0GBGBfYdoothBRpWDbrua\nXt6AJmv71VXgpZei+6urnfsoO1hyt88qMmqPCxBlOLjtcI+ZbxnYQQ3kpqb8t4lGZRABmLoEQgJx\nJoDDzv0jAC7M2P5aAHcPtEVEgRpq4EJE9gL4ec9D/869o6oqIlyMmGgd+w7RZHPT0d1Bxupq+UG3\nfdzdtxuIKJKpYPfrbp+3bdnsDtuWbkVG7XG56670vmygoshz9cM93r6VW9zHZ2aS9saBqN27k8cp\nHCy658cCtaUVPmcTkXcC+AiASwbXHKJwDTVwoapz3R5bLxr486r69yLSBPD/9fNc8/Pzx2+3Wi20\nWq1+dkdUqYWFBSwsLBTefph9B2D/obCV7T915w6kH3ww/ZidepA3aGi3gTe8Ibr9zDPR//H2u3en\nMySA7EwFK67fEN+emUkCA77t87I7bPaGbYvd3u4vtnFjOliwtJQ8trYGzM0B99+ftHNxsfO5epUX\nzLHvx+oqcNVV0f2tW6P/3/Oe6P946ggwVoO2WhuH96GKYMAwMyzG4ZgbzwLY5NzfhCjrImW9IOdt\nALar6vO+HfHcjUJWxblbSFNF9gD4bQA3rf+/u5+duZ2XKDT2C+XGG2/sZ3eV9h2A/YfCVnH/CVK3\nQTuQvgLvFniMpx50G8ADwL59wI9/nNyenc3e3uWrv+DWsPBlEzQyJq65v2sDLkD+gD/r8ZmZJBgx\nPQ2IJI9NTyftnJ6O9rVzZ/Lcy8vJtsvL/U0dsQEXIB0cWl5Ovx8bNya/u7YGHD2a3D9wAPj9309e\n8xgO4GpnXKZE9Bucs8HSYWZFjMF78AiAc9ZrkD0H4AoAV7kbiMgvAPgKgKtVdaXbjnjuRiGr4twt\npFVF/gOAORH5HwDetX4fIvJ6Efl6vJGI3AXgQQC/JCKHReSakbSWKBzsO0RjxC6Z2WgAr3519G9t\nLboC/573pGs4uF55pVxRzNjUVPI8jUb6eX0BiKWlaND94x8nWQwiSZDAt/RnvCKA73ddeatv+B63\nz3355dE/+/vNZhQEOHDAP9hxgwcbN2a/DnsbSK8KkncM7XPZGhhx1gUAnHFG/ookNDy+lW0GucpI\nnVYwGVbRzLKrC4VIVY8BuB7AfQCeAPBfVfWgiFwnItetb/bHAM4A8DkReUxEvj2i5garTv2DehdM\nxoWq/gOASz0/fw7Arzv3r7LbEE0y9h2i8eKbchFnFdhpDtPTwKteFd2Pp0HEV/CXljoHD3agDCQZ\nF41G8jzx79n77nO5mQlxZoCb2eDLNnjjG6P/v/jFdLt8V2lf/erkufbtS7ZdXk4P6OPf2b8/ub13\nb5LZsLzcWbvjkvUZ4vEV4jvuSH7XBlKyXseBA+l9+WqOxNN74sGcOy0FSO7PzUXtjtn3d+vW9DGh\nsAyytkPIK5iMad2JoVLVewDcY352q3P7owA+Oux21UXI/YOqFUzggoiIiPxTFWL2ivzqahKo6HYV\nPiuV2q78EYu3dX/HPpcNgjSb6WksNtiwtpY81/PODO34NcXPHw/w3QG/L+BiX+PFF0e3n3qqc8UO\n9zU2GsCxY8lje/cmdSV8+7aWltJBEZev5ojbLgDYsSO5b9+TrPfX3QdPzEfPDtjrdrV3DKZYMGhC\nVFLd+z0DF0RERAFxB/XPP5++mrTbVLCxA2U78LVXotwr+NPT6WDF8nIygPetUGKfy62/cPRoND3C\nLSRpudu/8EL6MV/9DDeTYW4uWW3Dl5kApLMi3HobNhixezfwk58kt922PPoo8MlPprMgdu1KHreB\nirh9gL/miC9bw72/tJQcs/vv7wy42GMeq+tJZ4iyisLmbe9uM8hBdNX7rvIKddVXu8sOrNgXiHVV\nihmHzBQGLoiIiAISn1i4t4vWrIhrKsS3fdzpHC43YOJboSSPHWTPzgInnRTdn50F7rwzefy557J/\nt9HonN5x7bXJvixfYdD4dca1O+LH3EDFCy8A552X3I9vT093f53uNBU7ZcVmy9him9PT6ba4r9vH\nDfY8+ihwww3R7TiwRP3xfcbzVuXJerzsQKDMACjkQUbRAr95igysslY2KrJ/IOxjSb0Z1ns6DoP/\nOmPggoiIKsWTw/74pgvEUxOA9ADdDnybzc7pBG7th8VF/9QQIDoZc7MxgPSAxLbLtXFj9Dsnn5z8\n/uoq8PLLyXOdfnqy/etf3xlosPv+6U/TP89riw3uuLU73Gknmzcn22zenJ+lYqepuCuWNBrZ29qs\nFPv++FaFcbn7c48fhanM375RDoDqOsXCZmaVCV5wwBmecTlXqMvr6Da9LfR2uxi4ICKiyvDkcPDs\n0p5usABI14qwhSitRiMJVkxNASeYtcbcYEDetJTV1WQKhq/exs6dwMc/Ht3evh3IWglt375kX/Gy\nrVns9lu3JgGapaV0XYki3CCJDT6028n+Hnwwve3cXPdim3EQIqtP2GPsTu3ZuTPpW8y2qIZvAJ81\noM8a8Ffxt6+OAwmgexZXWXUNqFB5dT1X8A3+6/g66tpuBi6IiIgC4pv24AYX7ModbrDArmphp1zY\nqQxAUvxxbS29b7tCieVmHszMdNaCuPLK9OPtNnDiiclzdcv86MZtmx3gZxXvPHo0XVdidjY5nnFA\nxF0tJY9bt8I31WNuLrk9PZ28l77pJ41GEnjqNnUkDiatrmYX9qTe9Dvdo5/nzRsADSqQUeWgpepg\nQ9Y+fH8bs9h6JAyKUBXq+vlx+32ciVk3DFwQEVFleHJYDRuccFeW6FYMspui88+nptLTStxVQY4e\nTQ/4p6fTmQdPPdVZ+6HdjrIE3DbHbbF1J4DutTm2bu1c7tS2zQ2SzM0l8+CBKJDh7rvZBJ55JtnX\n4mJ6tZRGI338s2pa5Gk2gaefTm5bS0tJ4GlpyT8FJm7L2lo6AMP+VY2qpnf08rcva7t2Gzj77Oh2\naIEqe8yG2TabFdbt/fMdv5CO4aQbl3OFur6ORqOe7WbggoiIKlWnL8E6sAU33eNrr0A2m8mKHjMz\nnTUtsgbha2vpQINvW3fQYFfMmJlJP7ddHtLW6rC1OOx99zXbKS++trmZDu4xm57u3HfWZ9SeiDab\nyYom8THNkjeoyxoo+97PuC1u1kteUU8qppfggC0aC3T/vTKFJG1wbnFxcIGqfgZbRY5ZPwU0s9hj\nZANJcSB3Zqbz7xO/l2hQBpERNYj92v5TRwxcEHnUdZ4pEY0fX8FNl70C6Q4WfKuMdLs/NZUelPkC\nEVnTNexz28GRzQ7JStFvNtMFNdvt7LZZeccsj/s7i4vJMrFbt6aPqZ26k5eC71ue1k4lsRky8T58\n03WoP/0MbldXgYsvjm7HnzV3QG+nbdnlhYGkBsrcXGdwLm9aRNnzlKyAWt6SsG4gIu+Y9VNAs8hr\nsMc87i/u0sK+5ZgpLHWtsVCFrP42yONi/8bU8ZgzcEFkTPIfU6JJJSLbAdwMYAOA21X1Js82nwHw\nPgA/AvBhVR3Y6bEtdtft71DelVPfAL7bfZ+igQjfihi2TXnBBldeUdEi+yj6t7vsvHl7TO0V4DLs\nVJK9e9N1RdzXkFcvg8rzvfd5AYFuU3eA9H0b2LPnFu5A+/77/e9pt6KXZc9T2m3gjW+Mbj/9dOdn\n1S4JmxWA6bZ/YDDnS+6+bdCk0ehcfShWZGloCsskXDTMW4KZsjFwQUREE01ENgC4BcClAJ4F8LCI\n7FHVg842lwHYoqrniMiFAD4H4KJBtMeXzpl1Qpd3kpdXfNB9jrzVAXoNRLi/06uqVi4AyqWyZ71O\n3xWssqtSlKmHcOBAud+h8jUssqZBNJvA7t3R7enp9MDYZhT5Ak3uKj2+pXLLBMHs8r++1xLva2kp\nvcoOkAQyDhxIZ/nY4EAee8zclYp8waB+3g/LXU3o0UfTj9msLQqL/Vs4SRcNbVade7/fmhl55wp1\nrGvhYuCCyBiHjk1EpVwAYEVVDwGAiOwCsAPAQWebywHcCQCq+pCIvFZEGqpacE2M4nzZBsM4oevl\nb9+gluX0taWqubk2lR1I1wHx7d99nXmV2ftpn11O1TUOab7D5gtE2GkPdnWbvGkQ7nQEm71k65O4\n2TS23szsLHDSSdH92Vl/v+9WWNe22wYE7Ou2RV/dQIa70pDPzEy6xku73RmwsYEOt9gtkLTlwQeB\ni9bDvc88k/8Z9mVYuM/drdZLnOHiTispGzShwRvm+1Dlez/oz1Gv+y0S/Kn7Z5+BCyKPundsIirl\nTACHnftHAFxYYJuzAFQeuBilkP722Tn4gxq095NSXrYye5ETSxuwoPLi2hFTU+mBb960h7xpQ3b6\nhw3cdatP4vtdAHj55aRt9vl8wYmYXda40egsUmkL53YrMLtxY3Y2k63xMjOTDtjkZWW4bbn33iSo\nsW9ftBqQ5Q4KfYWJbbAozu4477xkH1NT/oDKpFzRH6VeB/WDvGhYZTZHFfuy/a3KbMIy8t6rEAN9\nDFwQEdGkK7hgKOzpRdHfK8V3Ajeo1NFJ5Jv6UaaQp28qzzAwG7CYvXuTrIi77koPfO3KLL7aLFnL\nB9vMBTsdo1t9Eh9fEMR+rk4+OWm7+1x5S/TaAb+vwKw7jcV93iLTQ+LjWPZzePrpye2NG6P/szJF\nikz3sMWJY74pK1S9KgtLFqnVUmS7stuWVXSJcZ+qv9/z9t1N3rS4UKfuMHBBREST7lkAm5z7mxBl\nVGRtc9b6z1Lm5+eP3261Wmi1Wj01KK8uRVGhnnyUVfWg3V4pL7PPfrI/+n0dVbz2hYUFLCws9L+j\nQLm1IwDgq1+N/o+DCy4bPFhb6ww+uAMgd/vl5fTnwAY18viCIO7UhqWlpH7Dvn3ANddEt59+2r9s\nrg16uAN+G7BpNJKrvL7VUVw2AOAGhuJpTe6+7Wt327ptW7Kv6enOwZMvSyJr5ZYsq6vpKSszMwz8\nVc1XaHJYz5U19ce3bb/vfdnCy1mq+n4vsu9u6rpcMAMXREQ06R4BcI6IbAbwHIArAFxlttkD4HoA\nu0TkIgD/6Ktv4QYuqFohnVj1e8WtjKqvHNqA2o033ljNjgN06FCSafDYY53BAnfKxdGjSRZAzA6A\n3N/fuDG9osXMTDKQjwNj7ns3PZ0etO/bl/xu3A53IOEGYB5/PF1c065A0m5HQQEgqauRF1SJi3va\n1VFsUMQGAB54INnXAw8A73wncrmFRN0MCTt4iovPAtHtbduyV25xAzCWnU4zqHo8lBhkYUnftr6g\nSbffrWp6yP79g5/a0Wsx2yqfN9QMPwYuiIhooqnqMRG5HsB9iJZDvUNVD4rIdeuP36qqd4vIZSKy\nAuCHAK4ZYZMLC/Xkg4oZl4yZUNjsATeDYuPG/JVA3O0ffTSdnQEAO3dG/8d9zl2CFOg+rcHHnQLi\nTt944IEo+ODWv1hbyw5s2GCEO63l6NGkSGiRKRXnnpu+nZdp4q784QYm4ufLqi9jgw+zs52ZJvGx\ntlkldjpNXmo8lefL9Mk6rnY1p7xpJnYg3W3VHZs1NIjvvThYXbauURHdjsP+/dXXdorfA9v3fMc/\nxD7CwAUREU08Vb0HwD3mZ7ea+9cPqz1VXmUJ8eSj7vq54saaI4PlDlg3b04/ZrMH7KC72UwvOWtX\nAnGv7D73XOdzu5k4dgnS6en047OzSRBldjb632Y6xNzsiyI1KJrNZNlWO8gD0q9j796kSOi+fdHx\nc19zVsHSuF6GDXy4AyK7aonbd2yxzZ07gd/7vej+zp3prBTf75dR19T4kJVZucWu5mQLyvr2bacF\ndVt62vfeVlkvw6rys+PLHMnK6Oun3fY9cKeTtdudz2ufK4TvLgYuiIiIAlJmLi8NXz9X88pmUDBj\npjz3SqKd+uHb1h2U+5YkdcXTMQDg/e8HPv/56LbNcPCxq4T4pjm/KOhfAAAgAElEQVS4Awe3be99\nb1KrY26uc1qKu0rI1FTnsq1A+rnPOCPZ/md+Jr/tbjttXZDl5XTg48or0wMiN5C0bVs6oOL+H98+\ndMj/u/Ftty3tdpLV8sUvptts21mk7gjlc7+L+l25xf2824yKxcXOaUFZq/aUWRmql0w293NnM0d8\n+3fb1+v3t83uqDIDb22tM0PM5QuohJD9x8AFERFRwCZ1ukDIwZphtinE1x8y9yo+0JktYAc4WcED\n3wo08bKi09PZvzs9nawK0q2mhZ3u4U49cfe/c2cSbLjyyqhvZA06rKwpGR/5SHLVPN63PWbu67TT\naWxWRFbwZ3k5XXPEDbr4BoG+5VDdgdzevckxjAMeMZtN41uOlsqx30VllpK2S/LaLCD7uSmzb5u5\nE+8v634ZNrvDzVqwn9siA/6sWhLtdmdWUlXcgKcv2DqqZVnLYOCCiIgoIL4TmUkzrsEaZlAMhztY\ncetK2AGOHST4uIMmd1nRu+5KBx6AzvtxbYfV1fwlTH21Ity2XXlldhvtIM+t5WFft51uYvdtB072\nMxvX7Gg2o2kucYAmnvJSVN70Ad+A1O037hSa009PFz8Fug98mX3RO5slYZer7fb3zbckr+17brFb\nu29fX927N/p/bi79fO028IY3RLefeSb6336uyi5pHW9na6nEz+duYzND3Do5vlorNuuoW9uKfH9k\nBWxswNM+T94yrSF8dzFwQUREFBh7IhPCCQNVg+/h4Lkn67bvZPUtOziyATT3Sv3zz5drk83eaLc7\nB9bdCk8WGcC4A/y9ezuXdXX3kXc12w4Qfc/vOvHE9GNu29yMjOef717Ho5us/uJOBTrjjOz6Gc1m\n56ovlC8rI8ZX46Ibm/Fil6e1n1mgs6aFO9C2y/LOzSX737cv2de+fZ0BtTLtdn8H6Mw48mVYuIEK\nO0UMyK610s9y2762dFs+eHW183ni497tOUP47mLggoiIKHAhnDAMU+jBmn7SjkOeAjMO+lk9Ii/b\nyR207NyZDBLiQZ2dVmLvu4O/ZjOpWxE/l111JKvtWcEEX/aGTU+3mQzufsr0vbzgkA0uuK/Rt4Rs\nmed234+NG9MDRBusabeBHTui21xVpDs3UOHLiHGDQ74aF936Xt7qM3lssMGdhrSykmTZNJvpz9zG\njfn1M+z0jfj54vs2ILBnT/oxN1DhrtiztFTstWZlbOStvJLFvj8zM0mNGVszxAaCpqfDzHpk4IKI\niIiCE8qJktXPNJZxnQIjItsB3IxoOeHbVfUmzzafAfA+AD8C8GFVfWwQbSmzekTeEoBZ0ySAdNp1\ns9l5VTrryr4toGmvQPva2u0xy1d0NCs93Sr7uczafm4uCU7MzSVXv32p63ntAjoDMPH7kZe9wVVF\n8tlAhWX7g2/aRLeAgO8z6fa96en0VB/7XDbY4BbK3bIl/fm2n7m8ortFMhXiQf7SUnbhW3f60tGj\n0fPbTCt7HOy0lvi5bFaE+5hPs9lZ/NbNrHL/5tx/fzrgYtud91yjwsAFEREREfVERDYAuAXApQCe\nBfCwiOxR1YPONpcB2KKq54jIhQA+B+CiQbTHToOoOsPFXpGtUta89bIBLzezYdCD9rxj7Kbx9/Pc\nWYEmX+CC2U3l2Okcc3Ppgpplue9XnPGQZcOG9P2s980NhACdn2+b6eAO0vPY/tJodP99e8xstgfQ\neezcrBU7rcVdjnhtrfN5s4IJvmCoWyfE7SMrK+nMEF+tGvvcIfQnBi6IiIiICupnGkvoU2B6dAGA\nFVU9BAAisgvADgAHnW0uB3AnAKjqQyLyWhFpqGqBKgfluNMggOwBf977kRUw6Pe99K1YUhVf28os\nF1lG2aCKOxWhyv7gC1j1ugIGRXwFNd1MhLyVW9zjvLaWzkyYmeksDmnvuwNlm7EBJMU8p6bSj/ky\nR+xSxHa1Gvvc7v7c6R9HjyYD/EYj3a54uoq9bwf8bhHSLEePZhcAtlNc7PFvt5PMlKefTv/NccXv\noxvs9NUJCSFbkIELIiIiohL6OWkbo4BF7EwAh537RwBcWGCbswBUHrgAymVFjPK9LBqw6GWAb4Ms\n3Wpa9CJvFYVu7IAyDl4UlXUc7Gu0733Vx2AS2UwEO1C2gQx36oLLN6gGOu/b6Ruu3buBl1+Obt97\nbzqL4cCB9O14KWEgWpJ3Zgb4whei+/Fnxa2fAaT3576uQ4eS1YL27YtW5HFX2XGfP973G9+YvK59\n+9K/n7XakFsAeG0tCoRkBefs8V9aSgIdcZFe92+OW19mcTEdFPFljoSAgQsiIiKiAQkhvXbAis6E\ntguPDmwGddaqImWElCHT7/NX1X5bL2PYitbmyCsaSp3swLfRSC+r66tp4bJZRO7UhXjKSbxvO6gG\n0venp7NX53jhheSxH/wg3Y54MB/fdgMfzz8fFaKMM0k2bozaY6eGuHyvNf7d+HcAfxDEvk43GFF2\nZaK84Nzysv82kK5hAUS/+/73R7fj2hqu6ekksySuOXLHHclzjwoDF0REREQDMK7FOI1nAWxy7m9C\nlFGRtc1Z6z9LmZ+fP3671Wqh1WqVbkzVx3xM37Oe+eb/2+VTuxnk9Bifqt+7hYUFLCwsVLvTgK2u\nJtMFfDVEbEFNoHN6SOzQIf/PYzZo4ltG1HXeecntc89NZ/3YdvkyNvJ0yyLavDm9b9+0FLvSisst\nKrptW2d2iPu4G5yxgQegMziXlSUR34+DHXYqiLt9/P7EmSGrq1HQxQ32uHVrhomBCyIiIiLq1SMA\nzhGRzQCeA3AFgKvMNnsAXA9gl4hcBOAfffUt3MAFhclXH6BMVsowAhaDYoNpN9544+gaMwD2vbUD\nb9+Spm7QyrdSSFzv4fTTk+2OHu0cWM/MpAuBxrVQurXNsjUs3EKfNgPDrjqyd2/yeDxod/fnbj89\nna4FYbMc7HQP9xjEwZ2s1+HWzHCPGZAflPWtpuI+t/v7cfYE4J8KYqcB+VYdGQUGLoiIiIgGIKSp\nBoOiqsdE5HoA9yFaDvUOVT0oItetP36rqt4tIpeJyAqAHwK4ZlDtmYRjPkq+WhFllwQu+zs0HM1m\nehUKoHOQbQMbNmBg9+cuH/x7vxf9PzubDopMTfkLgboFMm3bms0kw6FMsc04eBD/Hz+/e9sO2oEk\nw6DdTmdj2BoVvrbYGhhu/9m5E/j4x6P78dLAcTDIrc0RBxfc5/YFMuwKPrbeht2f7zX7+FYdGQUG\nLoiIiIgGZBIGaKp6D4B7zM9uNfevH1Z7JuGYj1Kvx3dCpk7Vlq3P4AtS2ft52TduoVw3C8IGJnzT\nKtzMhnYbuOSS6Hb82YkDJb7aD3ZbN3hgP4czM52FKmN2WVUbrPFNf8oL7Nn7J5rReBy42Lq1cyqO\nlVUY13cc4vfHd7xdvkwRu1ztKDBwQURERERENMFs/ZI4s8GVt1pNVjDK1kJxAxO+AIAbBLGFKG3w\noR/tdpLt0Mu+7PSnvICczTpyj4sbXLCPVcF9n2wh1Vi8IowN9lTdll4wcEFERERERANVxTQeTjUZ\nHJsFUYR9H+z7k7XCj72fVf/ETv9wp2ysrnZmitigh830yQqK+MTb5NXaKLKfrLa4/9slShuN9Eor\nQHYwIa+/ucfb97pskMpOvxmFYAIXIvJzAP4rgF8EcAjAB1T1H802mwD8JwD/E6JltD6vqp8ZclOJ\ngsK+Q0RERHXQz6DHLsU6DsELEdkO4GZE9WFuV9WbPNt8BsD7APwIwIdV9bHhtrIYXxZE1tSgrPfP\nty932oNrbS0/U8TKWjrXZiPYttgsk351248NJiwtJSuBLC1F9SzyAoFF2+jLnnH5phGNwgn5mwzN\nJwHsVdVfAvA36/etlwH8W1V9K4CLAPyvIvLmIbaRKETsO0RERDTW4qkML76YP0e/DkRkA4BbAGwH\n8BYAV9lzMxG5DMAWVT0HwL8G8LlBtSeu3/DSS8nxbbeLZSSM0tRUNMB/9av9WRBxYCKrrooNdszM\ndM8AKRIY6aZIW9xtn3oqCRT4VnWpUtbrCqXvBZNxAeByAHGd0jsBLMAMwFT17wH8/frtfxaRgwBe\nD+Dg8JpJFBz2HSIiIg9OLRgf/abpB+gCACuqeggARGQXgB1In5tdjujcDqr6kIi8VkQavuWE+2WP\nb9liqkWmgxSVty83mNJo5GdB9NP/q54mUWYfNpiSlQnSb9vq8LcypMCF2wlXAWT+SVpfL/ztAB4a\nbLOIgse+Q0REZEzyKhZ1GISUlZfOXkNnAjjs3D8C4MIC25yF6HyvUr7VOXrZR7f7ZT+TeVNLbFHL\nQfGtzjEqWXVAiuj2HuRNwwolaDjUwIWI7AXw856H/p17R1VVRLou8CIirwHwZQAfV9V/9m0zPz9/\n/Har1UKr1eqhxUSDsbCwgIWFhcLbD7PvAOw/FLay/YeIaJKMU8DGDrTq/Fo8MhazTLElGL2/V8W5\nW1btB5+iwYiqP5NVFHqtu7LHIOs98K0o4763VQQNqzh3E81aAHaIRGQZQEtV/15EmgAeUNWtnu1O\nAvDfAdyjqjd32ZeG8rqIihARqGpPCw1V2XfWt2P/oVrpp/9U3A72Haqdce8/45h5kGdcAhehv45+\n+46IXARgXlW3r9+/AcArboFOEflLAAuqumv9/jKAWTtVZBTfP2Xen9DfyzxV/h3JWnllkLKyKuxj\nwODfr176T0jFOfcA+O31278NYLfdQEQEwB0AnsgaeBFNGPYdIiIij34K6dVVmQKANFKPADhHRDaL\nyMkArkB0TufaA+BfAscDHf84iPoWg1b3z2RVf0fiAMHZZyeFT7dsif6NsgiqLQQaqpAyLn4OwF8B\n+AU4SzqKyOsB3Kaqvy4i/zOAbwJ4HEma1A2qeq/ZF696Ua30mXFRWd9Z3x/7D9XKuF8xJhok9h8K\nWcgZM1X0HRF5H5LlUO9Q1f9dRK4DAFW9dX2beOWRHwK4RlX/zrOfkfSfkN+fEC0uAm9/e3T7scei\nehFlMhv6Od5ls14G/d720n+CCVxUiV9+VDehnDgC7D9UP6H0H/YdqiP2H6LehNJ3APafuvBN1xhm\nnZCQAk299J+QVhUhIiIimmghnVgSEVF1fEUuh/m3vu7fK8y4IAoAo/ZEvQul/7DvUL9GUcCO/Yeo\nN6H0HYD9Z1KMU2CbGRdEREREREQ0McZpQJ9l3F9fHgYuiIiIiAIQV96PbxMRUba6L7VKxTFwQURE\nRBQInnQTERF1YuCCiIiIiIiIaoeZapODgQsiIiIiIhq4SalFQMPFz1Mxde9/J4y6AURERERENN7i\nWgRbtiQDqF720evvEk2yKvrfqDFwQUREREREQcsbeDGoQZQ2bn2CU0WIiIiIiGigBlmLgCtLDEbd\npxb0ahxet69P1L0WCAMXREREREQ0cP0MmMZh4FUnkxoMGufXXffXwsAFEREREREFr9vAi0ENorRx\n7BOiqqNuQ+VERMfxddH4EhGoqoy6HQD7D9VPKP2HfYfqiP2HqDeh9B1gcP1nHKZM9GJSX/cw9dJ/\nGLggCsAkfPkRDUoo/Yd9h+qI/YeoN6H0HYD9h4oJKSDTS//hqiJEREREREREY4rLoRIRERERERFR\nbdVh6VQW5yQiIiIiIiIaU1nFOuuykgozLoiIiIiIiIgoWMy4ICIiIiIiIhpTWVkVdVk6lYELIiIi\n+v/bu/tYye66juPvDy2lPIh1g+xuoVpiaWgqpRWCBAq9JdT0iVr+UWLERiOJCQJCrF1EY9WoUGMg\nBtMEKVpABQOhFqvSpXR5UMEAu32krptIArLdIk+RAAHSr3/MWXp7uXN35zdn7pyZeb+SyZ6ZOfOd\n7517P53hy5nfkSRJK2rIA4ujHFxIkiRJkrSkFuWoiq24xoUkaWUl2ZFkb5KDSW5Ncsom+5yW5PYk\n9yS5O8mr5tGrJElSq927F3doAQ4uJEmrbQ+wt6rOBG7rrm/0XeA1VXU28BzgFUnO2sYeJUmSVpqD\nC0nSKrsCuLHbvhG4cuMOVXV/VR3otr8BfBY4dds6lCRJWnEOLiRJq2xnVR3pto8AO7faOcnpwHnA\nJ2fbliRJko5ycU5J0lJLshfYtcldr19/paoqSW1R53HAe4FXd0de/IBrr732+9tra2usra01dCzN\nzr59+9i3b9+825AkaSKpGvsZbWElqWX8ubS8klBVmXcfYH60eKbJT5L7gLWquj/JbuD2qnraJvs9\nEvhH4J+r6s1japkdLZyhvP+YHy2aoWQHzI8WT0t+/KqIJGmV3Qxc1W1fBdy0cYckAW4A7h03tJAk\nSdLsOLiQJK2yNwAXJTkIvLC7TpJTk9zS7fM84BeBC5Ps7y4Xz6ddaTg8nbAkabv4VRFpADzcUGo3\nlPyYHS2iKb9qdR3wv1V1XZJrgB+pqj0b9tkF7KqqA906MZ8Grqyqz27Yz/xooQzlvQfMjxaPXxWR\nJEnSdvF0wpKkbeHgQpIkSS08nbAkaVt4OlRJkiRtytMJSyOeSliar0GscZFkB/Ae4MeBzwE/V1Vf\n27DPycBHgEcBJwH/UFWvG1PP73lpoUz5HWPzo5U2lO8Zmx0tIk8nLLWZ9r3nOD+/nQa8A3giUMBb\nq+rPN6llfrRQFnmNiz3A3qo6E7itu/4wVfVt4MKqOhc4h9Hq7udvb5vSIJkfSdI8eDphqd0xP78B\n3wVeU1VnA88BXpHkrG3sUQNy+PDosqqGMrg45uJOAFX1zW7zJOAE4Cuzb00aPPMjSZoHTycstXNx\nWx23w4fhjDNGl1UdXgxljYvjWtwpySOAzwA/AVxfVfduU3/SkJkfSdK2q6qvAC/a5PYvApd12x9n\nOP9HmTQkLm4rTWDbBhd9LO5UVQ8C5yb5YeCDSdaqal/vzUoDY34kSZIWy3Yubqvltns3HDr00PYq\n2rbBRVVdNO6+JEeS7Fq3uNMDx6j19e4QxGcB+zbbx5WpNWSTrkxtfqSHuLK7JGkR9PH5rVvc9n3A\nu6rqB9aROcrPbstvkQcWfXx2G8pZRa4DvlxVb0yyBzilqvZs2OcJwPeq6mtJHg18EPj9qrptk3qu\nrKuFMuWq7uZHK82zikjtzI/UpoezihzP57cwWv/iy1X1mi1qmR8tlJb8DGVwsQP4e+DHWHc6oCSn\nAn9ZVZclOQf4a0bfk3wE8M6q+tMx9QyvFkoPp0M1P1pZ/g8vqZ35kdr0dDrUY31+Ox/4KHAno9Oh\nAryuqv5lQy3zo4WysIOLvhleLZqhfHAE86PFM5T8mB0tIvMjtRlKdsD8aPG05MdVniVJkiRJ0mA5\nuJAkSZIkaYEdPjy6LCsHF5IkSZIkLajDh+GMM0aXZR1eOLiQJD3Msk/sJUmStFhOnHcDkqThODqx\nBzh0aLHPGS5JkrQKdu8efW47ur2MHFxIkiRJkrTAlnVgcZSDC0nS963CxF6SJEmLxcGFJOlhHFhI\nkiRpSFycU0vLBQYlSZIkafE5uNBSWoVTAkmSJEnSKnBwIUmSJGnuPFpW0jiucaGl5AKDkiRJi8PT\ncUvaioMLLS3f8CRJkiRp8Tm4kCRJ6snRw9wdnkuT8WhZSVtxcCFJktQDD3WXpmNmJI3j4pySJEmS\nJGmwPOJCkiSpBx7qLknSbDi4kCRJ6okDC0mS+udXRSRJkiRJ0mA5uJAkSZIkSYPl4EKSJEmSJA2W\ngwtJkiRJkjRYDi4kSZIkSdJgObiQJEmSJEmD5eBCkiRJkiQNloMLSZIkSZI0WA4uJEmSJEnSYDm4\nkCRJkiRJg+XgQpIkSZIkDZaDC0mSJEmSNFgOLiRJkiRJ0mA5uJAkSZIkSYPl4EKSJEmSJA3WIAYX\nSXYk2ZvkYJJbk5yyxb4nJNmf5APb2aM0VOZHamd+pHbmR2pjdqTJDWJwAewB9lbVmcBt3fVxXg3c\nC9R2NLZv375B1uq73irU6rte371NwfzMsVbf9Vah1izqTcH8LEmtvuutQq0emB9rzbzeUGtNaSWy\n03e9VajVd72h1moxlMHFFcCN3faNwJWb7ZTkycClwNuAbEdjQ/5lD7W3odbqu968w7uO+Zljrb7r\nrUKtWdSbgvlZklp911uFWj0wP9aaeb2h1prSSmSn73qrUKvvekOt1WIog4udVXWk2z4C7Byz35uA\nq4EHt6UraTGYH6md+ZHamR+pjdmRJnTidj1Rkr3Ark3uev36K1VVSX7gUKgklwMPVNX+JGuz6VIa\nJvMjtTM/UjvzI7UxO1K/UrUtX5fauonkPmCtqu5Pshu4vaqetmGfPwZeBnwPOBl4PPC+qvqlTerN\n/4eSJlRVTYcAmh9pGPkxO1pU5kdqM4TsdPuaHy2cSfMzlMHFdcCXq+qNSfYAp1TV2EVqklwA/GZV\nvXjbmpQGyvxI7cyP1M78SG3MjjS5oaxx8QbgoiQHgRd210lyapJbxjxm/hMXaRjMj9TO/EjtzI/U\nxuxIExrEEReSJEmSJEmbGcoRF71Kcm2SLyTZ310ubqxzcZL7kvxXkmum7OlzSe7s+vmPCR/79iRH\nkty17rYdSfYmOZjk1iSnTFGr6fVKclqS25Pck+TuJK+asrdx9SbuL8nJST6Z5ECSe5P8SWtvW9Rq\n/jtLckL3mA+09jUrfeSnz+x09cxPez3zs43Mz8S15p6fPrPTPc78NBpafqbJTvd48zOn955j1DM/\n42sMIj99ZmeLeuZn8lrzzU5VLd0F+D3gtVPWOAE4BJwOPBI4AJw1Rb3/BnY0Pvb5wHnAXetuuw74\nrW77GuANU9Rqer0YrZR8brf9OOA/gbOm6G1cvdb+HtP9eyLwCeD8KXrbrFbz3xnwWuBvgJun+X3O\n4jJtfvrOTlfT/LTXMz/beDE/i5efvrPT1TE/bb0NKj/TZKd7vPmZvLfesrNFPfOz+eMHk58+s7NF\nPfMzea25Zmcpj7joNK3yu86zgUNV9bmq+i7wbuBn59FTVX0M+OqGm68Abuy2bwSunKJWU29VdX9V\nHei2vwF8FnjSFL2Nq9fa3ze7zZMY/cf4q1P0tlmtpr6SPBm4FHjbusc39TVD0+RnFtlp7sn8NPdn\nftqZn+Ov1dRbn/npOztdHfPTbmj5ae7H/Mz3vWeLek29mZ/t66fP7GxRr6m/VcnPELOzzIOLVya5\nI8kNjYdtPQn4/LrrX+ChP6QWBXwoyaeSvHyKOkftrKoj3fYRYOeU9aZ6vZKczmiS+ck+eltX7xOt\n/SV5RJIDXQ+3V9U9rb2NqdXUF/Am4GrgwXW39f37nNY0fw99ZwfMT2s98zMf5mcyg8lPH9np6pif\ndkPKT9/ZAfNzrBq9ZWeLek29YX4mNfT3HjA/k9Zq6ouesrOwg4vuOzF3bXK5ArgeeApwLnAY+LOG\np6g++wWeV1XnAZcAr0jy/L4K1+gYm2n6ner1SvI44H3Aq6vq/6btrav33q7eN1r7q6oHq+pc4MnA\nC5Jc2NrbJrXWWvpKcjnwQFXtZ8zEsoff5zHNOD+z6N38TFbP/MyQ+XnIMuWnr+x0z21+xvexSPmZ\nWXbA/Gymz+yMqbfW0pv5aTLk9x4wP5PWWmvpq8/snHg8jQ9RVV10PPsleRvwgYan+B/gtHXXT2M0\neWxSVYe7f7+U5P2MDsf6WGs94EiSXVV1f5LdwANT9Pb9x076eiV5JKPQvrOqbpq2t3X13nW03jT9\ndY//ekanlnrmNL1tqPWsqtrX0NdzgSuSXAqcDDw+yTun7WtSM85Pr9kB89NQz/zMkPlZvvzMIjtd\nDfOzwSLlZwbZAfNzXPrMzoZ65me8oeen19fa/Exca+7ZWdgjLrbS/fBHvQS4a9y+W/gU8NQkpyc5\nCfh54ObGfh6T5Ie67ccCP9PY03o3A1d121cBN22x77H6a3q9kgS4Abi3qt48bW/j6rX0l+QJ6Q5f\nSvJo4CJgf0tv42ol2TVpX1X121V1WlU9BXgp8OGqellLX7PSQ356y07Xj/mZop752V7mZ+L+5p6f\nPrPTPc78NBpSfmaUHTA/W9XqLTtb1TM/Yw09P72+1uZn8lpzz041rAo69AvwDuBO4I7uRdjZWOcS\nRqu7HgJeN0U/T2G0Mu8B4O5JawF/B3wR+A6j7579MrAD+BBwELgVOKWx1q+0vl6MVpd9sPu59neX\ni6fobbN6l7T0Bzwd+ExX607g6u72iXvbotZUf2fABTy0sm7TazbU/PSVHfNjfsyP+Zl1fvrMjvlZ\nnvxMmx3zM9/3HvOz2PnpMzvmZ3nee9I9UJIkSZIkaXCW8qsikiRJkiRpOTi4kCRJkiRJg+XgQpIk\nSZIkDZaDC0mSJEmSNFgOLiRJkiRJ0mA5uJAkSZIkSYPl4GLFJNmV5N1JDiX5VJJbkjy1u+/MJP+U\n5GCSTyd5T5InJllL8vUkn0lyX5KPJLnsOJ7r37p/359k56x/NmnWzI/UzvxI7cyP1M78LIcT592A\ntk+SAO8H/qqqXtrddg6wM8nngVuA36iqW7r7LgB+FCjgo1X14u72ZwA3JflWVX14zHOdARzqnnN3\nVR2Z8Y8nzZT5kdqZH6md+ZHamZ/l4REXq+VC4DtV9dajN1TVnVX1ceAXgH89Gtruvo9U1T1A1hep\nqjuAPwB+feMTJHl0kgPAbcAacC/w1CT7u8BLi8r8SO3Mj9TO/EjtzM+S8IiL1fKTwKfH3Hf2Fvdt\nZj9w9cYbq+pbwLlJ3gLcADwdeGxVXT9hr9LQmB+pnfmR2pkfqZ35WRIecbFa6hj35xj3T7Lv0xlN\nG58B3DlBXWmozI/UzvxI7cyP1M78LAkHF6vlHuCZDfdt5jxGwXyYJL/bHSr1U8AngF8Frk/yxgl7\nlYbG/EjtzI/UzvxI7czPknBwsUK6hWQeleTlR29Lck6S84G/BZ6b5NJ1970gydkb63QL2vwO8Beb\nPMcfMgrr24GfBu6oqnOq6prefyBpG5kfqZ35kdqZH6md+VkeDi5Wz0uAF2V0OqC7gT8CDlfVt4HL\ngVdmdDqge4BfA77E6BCr56c7HRDwFuCVVXX7mOe4APgY8Nhg9A0AAABzSURBVGzg32f880jbyfxI\n7cyP1M78SO3MzxJI1bG+9iNJkiRJkjQfHnEhSZIkSZIGy8GFJEmSJEkaLAcXkiRJkiRpsBxcSJIk\nSZKkwXJwIUmSJEmSBsvBhSRJkiRJGiwHF5IkSZIkabAcXEiSJEmSpMH6fyNrYeZLDI7/AAAAAElF\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0066ea5890>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Compare PSF flux to flux in SDSS reference catalog and to aperture flux"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(18, 10))\n",
      "\n",
      "sigmaPsf = numpy.std(diffpsf)\n",
      "meanPsf = numpy.mean(diffpsf)\n",
      "ax1.hist(diffpsf, bins=100)\n",
      "ax1.set_xlabel(\"$\\Delta$ mag (PSFex)\", fontsize=20)\n",
      "ax1.set_title(\"$\\sigma$ = %.0f mmag \\nmean = %.1f mmag\"%(sigmaPsf*1000, meanPsf*1000), x=0.2, y=0.9, fontsize=20)\n",
      "ax1.tick_params(labelsize=20)\n",
      "\n",
      "ax2.scatter(ccdNum, diffpsf, s=8, color=\"blue\")\n",
      "ax2.set_ylim([-0.15,0.15])\n",
      "ax2.tick_params(labelsize=20)\n",
      "ax2.set_xlabel(\"CCD #\", fontsize=20)\n",
      "ax2.set_ylabel(\"$\\Delta$ mag (PSFex)\", fontsize=20)\n",
      "\n",
      "fig.tight_layout()\n",
      "\n",
      "fig, (ax3, ax4) = plt.subplots(ncols=2, figsize=(18, 10))\n",
      "ax3.scatter(diffAp[3],diffpsf, s=5, color=\"blue\")\n",
      "ax3.set_xlim([-0.15,0.15])\n",
      "ax3.set_ylim([-0.15,0.15])\n",
      "ax3.set_xlabel(\"$\\Delta$ mag (Ap = %.1f pix)\"%(radii[3]),fontsize=20)\n",
      "ax3.set_ylabel(\"$\\Delta$ mag (PSFex)\",fontsize=20)\n",
      "ax3.tick_params(labelsize=20)\n",
      "\n",
      "ax4.scatter(mags,diffpsf, s=5, color=\"blue\")\n",
      "#ax4.set_xlim([-0.15,0.15])\n",
      "ax4.set_ylim([-0.15,0.15])\n",
      "ax4.set_xlabel(\"Magnitude (ref)\",fontsize=20)\n",
      "ax4.set_ylabel(\"$\\Delta$ mag (PSFex)\",fontsize=20)\n",
      "ax4.tick_params(labelsize=20)\n",
      "\n",
      "fig.tight_layout()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wGvCplNKbjcpqgYeABSml7fP7+gAvAJsBo1JKf8rv3zhfdwQwIaXUYlI+h3ZI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       "text": [
        "<matplotlib.figure.Figure at 0x7f3bdff1ab50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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u7k1Ya6cn4bd/e/KlL3Ue48iR7QvfRTiqCbADtSALZx4Cqnlow6wta0DRW9/N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\nYgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3bd7318f10>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Check zero point determination"
     ]
    },
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "The zero point is determined as the weithed average of the difference between the raw flux measured in the stack and the flux in the external reference catalog (SDSS) for matched objects and after sigma clipping. \n",
      "\n",
      "In the following we are looking to the individual zero point values and their associated errors after sigma clipping rejection. These are those values / errors which are weigth-averaged in order to determine the average zero point.\n",
      "\n",
      "As we can see, there is a mismatch between the distribution of the values and their errors."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from matplotlib.ticker import MultipleLocator, FormatStrFormatter\n",
      "\n",
      "mag = [ 31.99324743,  31.99782076,  32.00215373,  32.00929753,  32.02292358,\n",
      "      32.02502762,  32.02679508,  32.02856753,  32.03033476,  32.03183832,\n",
      "      32.03442205,  32.04202186,  32.04250772,  32.04294459,  32.04420271,\n",
      "      32.04822466,  32.05138923,  32.05358324,  32.0612725 ,  32.06273691,\n",
      "      32.06462115,  32.0748725 ]\n",
      "\n",
      "err = [ 0.0011474,   0.0043829,   0.00611293,  0.00212849,  0.00233841,  0.00328785,\n",
      "      0.00236525,  0.00076485,  0.00549516,  0.00399213,  0.00363139,  0.00254406,\n",
      "      0.00504879,  0.00207226,  0.00478525,  0.00131237,  0.00241549,  0.00154332,\n",
      "      0.00532533,  0.00160126,  0.00252217,  0.00273292]\n",
      "\n",
      "ord = numpy.arange(1,23,1)\n",
      "print ord\n",
      "\n",
      "\n",
      "fig, (ax1) = plt.subplots(ncols=1, figsize=(10, 10))\n",
      "plt.gca().xaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
      "ax1.errorbar(mag, ord, xerr=err, fmt='o')\n",
      "ax1.set_xlim([31.98, 32.08])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "(31.98, 32.08)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LqWmZ/z0A4EVmsri0w8NH5wJWkkynBxmPHy+pIgC4Ocxkrbi+T0pZ1uiz//c5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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3bd87be950>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(18, 10))\n",
      "idxs = numpy.where(numpy.asarray(pixels) < 1000)\n",
      "ax1.hist(pixels, bins=100)\n",
      "ax2.scatter(pixels[idxs], numpy.sqrt(var[idxs]), s=5)\n",
      "ax1.set_yscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DAADABJuf35NkYdUxrB9KBwAAgAnW6/VMqWDdMr0CAAAAaIXSAQAAAGiF0gEAAABohdIB\nAAAAaIXSAQAAAGiF0gEAAABoRStbZpZSviPJ30nyZUl+vtb6wTbeBwAAAFi/Wikdaq3/Ocl/LqW8\nIMlPJFE6AAAAwJQ56+kVpZRfKKV8oZTy8VOuv76U8olSyp+UUv7FKS/7P5P89DiCAgAAAJPlmazp\n8ItJXr/6Qillc5ZLhdcneXmSN5VSXlaW/ZskB2qth8aWFgAAAJgYZz29otb6e6WUS065fFWSP621\nfiZJSinvTvIdSb4lyTcn+bJSyv9ca/0PY0kLAAAATIxzXdPhgiSfXXX+uSQ7a63/LMk7n+7Fg8Hg\nseN+v59+v3+OcQBgsoxGo4xGo65jANCxpmkyHC4kSebn96TX63WcCMbjXEuHei4vXl06AMA0OrV0\nv+GGG7oLA0BnhsOF7Nt3cOVsIXv33thpHhiXZ7Kmw+ncn+SiVecXZXm0AwAAAGehaZqMRh/KsWN/\nllof7ToOjNW5lg4Hk3x1KeWSUsp5Sb47yeK5xwIAAJgOw+FCDh16Xkq5OFdc8XDm5/d0HQnG5pls\nmfmuJL+f5KWllM+WUv5BrfV4krcm+UCSe5L8eq313naiAgAAbFQlMzMz6fdfbT0HNpRnsnvFm85w\n/UCSA2NLBAAAMEWWRzYsrDqGjeNcF5IEAADgHPR6PQtHsmGd65oOAAAAAKfVaekwGAzsTQ4ASUaj\nka2kAYANp9Rau3njUmpX7z0pFhcXs3v3TTlyZHlDkG3bdufo0ZuTrP7cSnyOABtHKSW11tJ1jmnh\nfgQAnmyc9yOmVwAAAACtUDoAAAAArVA6AAAAAK1QOgAAALSgaZrMzs5ldnYuTdN0HQc6MdN1AAAA\ngI1oOFzIvn0HV84WsnfvjZ3mgS4Y6QAAAAC0wkgHAACAFszP70mysOoYpo/SAQAAoAW9Xs+UCqZe\np9MrBoNBRqNRlxEAYF0YjUYZDAZdxwAAGKtSa+3mjUupXb33pFhcXMzu3TflyJHFJMm2bbtz9OjN\nSVZ/biU+R4CNo5SSWmvpOsckKaVclORXkrwwy/9I/lyt9adKKVcl+ekkW5IcT/JPa613nvJa9yMA\ncIpx3o+YXgEATLpjSX6s1nqolLItyUdLKR9M8n8lmau1fqCUct3K+Wu7DAoA00bpAABMtFrrg0ke\nXDk+Wkq5N8kFSR5I8vyVp70gyf3dJOxe0zQZDh9fzK7X63WcCIBpoXQAADaMUsolSa5IcnuSP0ny\n4VLKT2R5HatXdZesW8PhQvbtO7hytmBhOwDWjNIBANgQVqZWvCfJj66MePjNJD9Sa31fKeV/TfIL\nSb711NetXsCz3++n3++vTWAAWCdGo1FrmzxYSHIds5AkwPSxkOSzU0rZkmR/kgO11n0r147UWr9s\n5bgk+cta6/NPed1U3I+YXgHj5e8UG52FJAEAVqwUCj+f5J6ThcOKPy2lvKbWemuSa5N8qpOA60Cv\n1zOlAsbIlCU4e0oHAGDSXZPkzUnuLqXctXLtXyb5x0n+fSnlOUn+auUcAFhDSgcAYKLVWj+c5YUi\nT2fnWmYBpsP8/J4kC6uOgTNROgAAADwDpizB2TvTbwXWxGAwaG2FTACYJKPR6Am7KACwvjRNk9nZ\nuczOzqVpmq7jwMSwe8U6ZvcKgOlj94q15X4EOFuzs3OPLR75trftMNKBDW2c9yOdjnQAAAAANi5r\nOgAAADwNi0fCs6N0AAAAOEXTNBkOHy8ZLB4Jz47SAQAA4BTD4cJjazgkCwoHeJas6QAAAAC0wkgH\nAACAU1jDAcZD6QAAAHAKazjAeJheAQAAALRC6QAAAAC0QukAAAAAtKLT0mEwGGQ0GnUZAQDWhdFo\nlMFg0HUMAICxKrXWbt64lNrVe0+KxcXF7N59U44cWUySbNu2O0eP3pxk9edW4nME2DhKKam1lq5z\nTAv3IwDwZOO8HzG9AgAAAGiF0gEAAABohdIBAAAAaIXSAQAAAGiF0gEAANjwlpaWcvXVr8nVV78m\nS0tLXceBqaF0AAAANrxdu67PHXfM5I47ZrJr1/Vdx4GpoXQAAAAAWqF0AAAANrz9+9+bnTuPZ+fO\n49m//71dx4GpMdN1AAAAgLZt3749t99+a9cxYOoY6QAAAGwoTdPk7W+fzdVXvyZvf/tsmqbpOhJM\nLSMdAACADWU4XMi+fe/LiRMX5uDBj2bLloXs3Xtj17FgKhnpAAAAALTCSAcAAGBDmZ/fk2PHjuUj\nH7kt11zzqszP7+k6EkytTkuHwWCQfr+ffr/fZQwA6NxoNMpoNOo6BsDEapomw+FCkuXS4R3v2Ntx\nIiBJSq21mzcupXb13pNicXExu3fflCNHFpMk27btztGjNydZ/bmV+BwBNo5SSmqtpesc02Ia7kdO\n/UGs1+t1nAjaMTs7l337DiZJ3va2HdZwgHMwzvsR0ysAADaw5QX1Dq6cWUyPjalpmoxGH8qxYw9m\nZubFXccBVlE6AAAAE+nkSJ7R6EO5667zUsrFueKKh63hAOuI0gEAYANb/uFrYdUxbBwnR/IcP35e\naj2cLVtekn7/atOIYB1ROgAAbGC9Xs+UCjakJ06puDhXXPGi9Ps7lGuwzigdAACAiTMcLuTQoeet\nTKl4JLfccsAIB1iHlA4AAMCEKpmZmTGlAtYxpQMAADBxrFcCk0HpAAAATBzrlcBk2NR1AAAAAGBj\nUjoAAAAArVA6AAAAAK1QOgAAAACtsJDkBlBKeey41tphEgAAAHickQ4bQl35AgAAgPVD6QAAAAC0\nQukAAAAAtKLT0mEwGGQ0GnUZAQDWhdFolMFg0HUMAICxKl0tPFhKqRY9fGqLi4vZvfumHDmymCTZ\ntm13jh69OU9cv6GsOi8WkgSYcKWU1FrL0z+TcXA/AgBPNs77EdMrAACAzjVNk9nZuczOzqVpmq7j\nAGNiy0wAAKAzTdNkOFzIaPShHDr0vCyP5F3I3r03dh0NGAOlAwAA0JnhcCH79h3MsWMPppSLMzPj\nRxTYSPyNBgAAOjcz8+JcccXD6fevzvz8nq7jAGOidAAAANbcyWkVx44dyw//8Ddky5YtmZ/fk16v\n13U0YIyUDgAAwJo7Oa0iSd72th3WcIANyu4VAAAAQCuMdAAAANbMmaZVABuT0gEAAFgzplXAdDG9\nAgAAAGiFkQ4AAMCaWZ5KsbDqGNjIlA4AAMCa6fV6plTAFDG9AgAAAGiF0gEAmFillItKKb9bSvnj\nUsoflVJ+ZNVj/6yUcu/K9X/TZU6YVktLS7n66tfk6qtfk6Wlpa7jAB0wvQIAmGTHkvxYrfVQKWVb\nko+WUj6Y5EVJvj3JZbXWY6WUr+w0JUypXbuuzx13zDx2fPvtt3acCFhrRjoAABOr1vpgrfXQyvHR\nJPcmuSDJDyVZqLUeW3ns/+suJUyfpmkyOzuXw4c/23UUoGNKBwBgQyilXJLkiiR3JHlpkleXUm4v\npYxKKTu6zAbTZjhcyL59B/MXf/FVOf/8T2fnzuPZv/+9XccCOmB6BQAw8VamVrwnyY/WWv9HKWUm\nyZfXWq8upfztJL+R5CWne+1gMHjsuN/vp9/vtx8YNrCmaTIafSjHj5+XmZnNectbvsduFbDOjUaj\njEajVr53qbW28o2f9o1LqV2996RYXFzM7t035ciRxSTJtm27c/TozUlWf25l1XmJzxRgspVSUmst\nXeeYJKWULUn2JzlQa923cu1Akr211ltXzv80yc5a61+c8lr3IzBGTdPk2muvy513PpBaa6666m/l\nllsOpNfrdR0NeAbGeT9iegUAMLFKKSXJzye552ThsOI3k1y78pyXJjnv1MIBGL/hcCEHD56XEycu\nSimb0u+/WuEAU870CgBgkl2T5M1J7i6l3LVybU+SX0jyC6WUjyd5JMlbOsoHU2dmZnOSZMeOF2V+\nfk/HaYCudVo6DAYDcycBIO3OpdzIaq0fzplHbu5eyyxAVkqGhceOjXIArOmwjlnTAWD6WNNhbbkf\ngXPXNE2GQ0UDbCTjvB8xvQIAAHjWTm6PuWzBThXAE1hIEgAAAGiFkQ4AAMCzduo6DgCrKR0AAIBn\nrdfrmVIBnJHpFQAAAEArlA4AAABAK5QOAADAWWmaJrOzc5mdnUvTNF3HASaANR0AAICn1DRNhsOF\njEYfyqFDz0tSYntM4GwoHQAAgDNaWlrKZZddlQceeCil/E/ZtOnSzMz4MQI4O/7fAgAAOKNdu67P\nAw9cmiSp9ePZseOR9PtX2x4TOCtKBwAA4Kycf/623HLLgfR6va6jABPCQpIAAMAZ7d//3uzceTw7\ndx7P3Xf/gcIBeEaMdAAAAM5o+/btuf32W7uOAUwoIx0AAACAVigdAAAAgFYoHQAAAIBWKB0AAIA0\nTZPZ2bnMzs6laZqu4wAbhIUkAQCADIcL2bfv4MrZQvbuvbHTPMDGYKQDAABMuaZpMhp9KMePH09S\nu44DbCBKBwAAmHLD4ULuuus5qfVwLr/8rzI/v6frSMAGYXoFAACQUjZny5aXpN/fkV6v13UcYINQ\nOgAAwBRqmibD4UKS5Md//EeT/GSSGOUAjJXSAQAApkzTNLn22uty8OB5mZnZnOQnLRwJtMKaDgAA\nMGWGw4XceeeDefTREzl+/NGu4wAbmNIBAACm0MzMi7Np0+eyY8cjplQArTG9AgAApsxyybCQZGfm\n5/dYOBJojdIBAACmTK/Xs4YDsCaUDhtMKeUJ57XWjpIAAAAw7ZQOG87qkqGc8VkAAADQNgtJAgAA\nAK1QOgAAwAbVNE1mZ+cyOzuXpmm6jgNMoU6nVwwGg/T7/fT7/S5jAEDnRqNRRqNR1zGADWY4XMi+\nfQdXzhYsHgmsuc5LBwAgj5XwN9xwQ9dRAADGxkKSAACwQc3P70mysOoYYG0pHQAAYIPq9XqmVACd\nspAkAAAA0AqlAwAAANAKpQMAAADQCqUDAAAA0AqlAwAAANAKpQMAAADQCqUDAAAA0AqlAwAATKCm\naTI7O5fZ2bk0TdN1HIDTmuk6AAAA8MwNhwvZt+/gytlC9u69sdM8AKdjpAMAAEyYpmkyGn0ox48f\nT1K7jgNwRkY6AADABGmaJtdee13+4A8+n1JKLr/8/MzP7+k6FsBpGekAAAATZDhcyMGD56XWi5OU\n9PuvTq/X6zoWwGkZ6QAAABNmZmZzkmTHjhcZ5QCsa0oHAACYAE3TZDhcyLFjx/LDP3x5tmzZkvn5\nPUY5AOua0gEAACbA6t0q3va2HXarACaCNR0AAACAVhjpAAAAE2B57YaFVccA65/SAQAAJkCv1zOl\nApg4plcAAAAArVA6AAAAAK1QOgAAAACtUDoAAMA60jRNZmfnMjs7l6Zpuo4DcE4sJAkAAOvIcLiQ\nffsOrpwtWDwSmGhGOgAAAACtMNIBAJhYpZSLkvxKkhcmqUl+rtb6U6sef3uSf5tke631i92khGdm\nfn5PkoVVxwCTS+kAAEyyY0l+rNZ6qJSyLclHSykfrLXeu1JIfGuS+7qNCM9Mr9czpQLYMEyvAAAm\nVq31wVrroZXjo0nuTfK3Vh7+d0n+eVfZ4GxZOBLYyIx0AAA2hFLKJUmuSHJHKeU7knyu1np3KaXT\nXPBUmqbJtddel4MHz8vMzOZYOBLYaJQOAMDEW5la8Z4kP5rkRJJ/meWpFY89pYtc8HSGw4XceeeD\nOXHiwq6jALRC6QAATLRSypYk701yc631N0spX5/kkiQfWxnlcGGW13q4qtb656e+fjAYPHbc7/fT\n7/fXIDU8bmbmxTl+/L7s2PEiC0cCnRiNRhmNRq1871JrbeUbP+0bl1K7eu9Jsbi4mN27b8qRI4tJ\nkm3bdufo0ZuzvDj3SWXVeXnSYz5jgMlSSkmt1W/lz1JZbhV+Oclf1Fp/7AzP+XSSV55u9wr3I3St\naZoMh4/vVNHr9TpOBDDe+xEjHQCASXZNkjcnubuUctfKtX9Zaz2w6jlaBdaVpaWl7Np1fZJk//73\nWsMB2NCUDgDAxKq1fjhPsxtXrfUlaxQHnlbTNLnssqvywAOXJkl27bo+t99+a8epANqjdAAAgDUy\nNzfMAw98MctdmcUjgY1P6QAAAGugaZq8612/keTKJMnWrXdn//5PdBsKoGVPORwRAAA4d03T5Npr\nr8sDDzwMGvZWAAAgAElEQVSU5ERKKfnBH/z+bN++vetoAK1SOgAAQMuGw4UcPHheklckuTdXXXU8\nN94433UsgNaZXrHBrexP/hjbggEArK2maTIafSjJedm8eVN27Pja3HLLAdtjAlPBSIcNr676AgBg\nrQ2HC7nrruek1sPZseMRhQMwVYx0AACAlpWyOVu2vCT9/g6FAzBVlA4AADBmn/zkJ/PKV16TJPnw\nh3/7sevz83u6igTQCaUDAACM2StfeU0eeuiyJMk3fdO35OjRpY4TAXRD6QAAAGP28MOPJCmrjgGm\nk4UkAQBgTJaWlnL11a/Jl3/585Pcl+S+fN/3vbnrWACdMdIBAADGoGmaXHbZVXnggUuSfFXOP/9w\n3vKW77GOAzDVlA4AADAGc3PDPPDA0ZWzTbn44ouyd++NnWYC6JrSAQAAxuAjH7ktycuTHM7WrX+Z\n/fs/0XUkgM6NfU2HUsqlpZSbSin/77i/NwAArFfXXPOqbN68JZs3X5of/MHvz/bt27uOBNC5sY90\nqLV+Osn3Kx0AAJgmN944ny1bFpLEOg4AK86qdCil/EKSv5Pkz2utX7/q+uuT7EuyOclNtdZ/00pK\nAABYh5qmyXD4eNFgDQeAJzrbkQ6/mOSdSX7l5IVSyuYkP53kW5Lcn+TOUspirfXesacEAIB1aDhc\nyL59B1fOFpQOAKc4qzUdaq2/l+S/n3L5qiR/Wmv9TK31WJJ3J/mOUspXlFL+7ySXl1L+xXjjAgAA\nAJPiXNZ0uCDJZ1edfy7JzlrrF5P80Nl8g8Fg8Nhxv99Pv98/hzicjVLKGR+rta5hEgCSZDQaZTQa\ndR0DeJaW126wjgPAmZxL6XDOP6GuLh1YKyf/2Eqe+Ed45jICgPacWrrfcMMN3YUBnrFer2dKBcBT\nOJctM+9PctGq84uyPNoBAAA2pKZpMjs7l9nZuTRN03UcgHXvXEY6HEzy1aWUS5J8Psl3J3nTGDIB\nAMC60zRN+v3X5eDB87Jp06YcOzbMO96xt+tYAOvaWY10KKW8K8nvJ3lpKeWzpZR/UGs9nuStST6Q\n5J4kv27nCgAANqq5uWHuvPOB1Jo8+uij+chHbus6EsC6d1YjHWqtpx3BUGs9kOTAWBMBAMA60zRN\n3vWu30hycZL7kizlmmvOau10gKl2Lms6AADAVBgOF/Lgg5tzcgmzHTtekRtvnO82FMAEUDoAAMBZ\n2LLl0mzatDk7d16QW2/9rfR6va4jAax757KQ5DkbDAZP2ioMAKbRaDTKaDTqOgZwiqZpMhwu5Nix\nY3nrW6/Mli07Mz+/R+EAcJZKrbWbNy6ldvXek2JxcTG7d9+UI0cWkyTbtu3O0aM3J1n9uZVV5+Up\nHnv65/rzAOheKSW11tJ1jmnhfoSnMzs7l337DiZJ3va2Hdm798aOEwG0b5z3I6ZXAABMqaZpMjs7\nl9nZuTRN03UcADagTqdXAADQneFw4bHf4icLfot/GvPze5IsrDoG4JlQOgAAwBn0ej1lDMA5UDoA\nAEwpv8V/sqZpMjc3zEc+cluuueZVufHGeYtGApwDpQMAwJTyW/wnW55y8r6cOHFhDh78aLZsMe0E\n4FxYSBIAAJIsLS3lV37l13LixH/vOgrAhmGkAwAAJNm16/o88MClSY5n69aP5Qd/8PtNOwE4R0Y6\nAAAw9ZaWlvJHf/THK2czecUrvi7veMde6zkAnKNORzoMBoP0+/30+/0uYwBA50ajUUajUdcxYGrt\n2nV9HnroZUnuztatNfv3f6LrSAAbQuelAwCQx0r4G264oesoMFWapslwuJDDhz+b5OIkl+UVrzie\n7du3dx0NYEOwpgMAAFNrebeKg6n1pTn//E/l4osvyv797+06FsCGYU0HAACmUtM0GY0+lOPHj6eU\n5C1v+Z7cfvutRjkAjJGRDgAATJ3Dhw/n5S+/Mg89VFLKl+Xyyy+0UwVAC4x0AABgqjRNs1I4vCDJ\n16XW/5F+/9V2qgBogZEOAABMleFwIQ89dFmSkuS+bN16wigHgJYY6QAAwNQ577zzktRs3fqXueee\nPzTKAaAlRjoAADAVTm6PeezYsbz1rZdny5YtmZ/fr3AAaJHSAQCAqXBye8wkedvbdmTv3hs7TgSw\n8ZleAQCwwTVNk9nZuczOzqVpmq7jdOLk9pjHjv1Zan206zgAU6PTkQ6DwSD9fj/9fr/LGADQudFo\nlNFo1HUMNqjVv+FPFqbuN/xN0+Taa6/LwYPnpZSLc8UVD1s4EmCNdDrS4WTpAADTrt/vZzAYdB0D\nNpyThcMdd3w+jz56IklsjwmwhqzpAACwwS3/Vn9h1fH0mJ2dyx13fCzLt72PZseOi6buMwDoktIB\nAGCD6/V6UzelIkk++clP5p3v/Nkkr0qSbN36sdxyywGjHADWkIUkAQDYkF75ymuSbH3s/OUvf5nC\nAWCNGekAAMCG0zRNHn74kSTfkOTulNLk/e+/r+tYAFPHSAcAADaUk4tHHj9+WZK7MzPzcO699w+z\nffv2rqMBTB0jHQAA2FCGw4XceeeDSS7M5s1X5e1vvzpf8zVf03UsgKlkpAMAABvOzMyLs2nT57Jj\nxyN2qwDokJEOAABsKI9vEboz8/N7LB4J0CGlAwAAE29paSm7dl2fJNm//71TuUUowHqkdAAAYKIt\nLS3lkku+Ng89VJJ8bd7whu/KH/zB73UdC4B0XDoMBoP0+/30+/0uY3AapZQnnNdaO0oCMB1Go1FG\no1HXMWAiXXfdd+ahh16wcnZvPve5L+s0DwCP63QhyZOlA+tVXfkCoG39fj+DwaDrGBOplHJRKeV3\nSyl/XEr5o1LKj6xc/7ellHtLKR8rpfynUsrzu85KO+6///NJXrzy1eTv//3v6jgRACfZvQIAmHTH\nkvxYrfXrklyd5IdLKS9L8ltJvq7W+g1JPpXEFgYbzNLSUq666n/Jl770pSTHk9RceeXl1nMAWEeU\nDgDARKu1PlhrPbRyfDTJvUn+Vq31g7XWEytPuyPJhV1lpB2ve9235847t6RpLk9yb3bufDS/93u/\nbbcKgHVE6QAAbBillEuSXJHlkmG1f5jk/Wudh3Z98pOfWnX2Zen3X61wAFhn7F4BAGwIpZRtSd6T\n5EdXRjycvP6vkjxSa/2Pp3vd6rU0LHA9GZqmyXC4kK1bt+ahh+5OciwvfOELMj9vBg3As9Hmgtal\nq10JSinVjghPbXFxMbt335QjRxaTJNu27c7RozfniYs7llXn5Skee/rnrv7zWN69op72MQDaU0pJ\nrbU8/TNZrZSyJcn+JAdqrftWXf++JD+Q5JtrrX99mte5H5lAP/Ijb88737l8f/TCFz6SSy+9JPv3\nvzfbt2/vNBfARjHO+xEjHQCAiVaWm/KfT3LPKYXD65P8H0lec7rCgcn1nve8L8mlSZLNmz+d22+/\ntdtAAJyRNR0AgEl3TZI3J3ltKeWula/rkrwzybYkH1y59jOdpuScLS0t5corX5UHH/xCkvuSHM+F\nF17QdSwAnoKRDgDARKu1fjin/0XKV691Ftr1utd9e+66654kO5MkW7fenfe//xPdhgLgKRnpAADA\nRFjereIrs7w2VfKKV3yddRwA1jkjHQAAWNeapsns7FwefviRJBckuS+93l9m/36jHADWO6UDAADr\n2uzsXN75zruSXJnkj7N1a81nPvMJoxwAJoDpFQAArFtLS0v52Z/9f5IcXrnyfNMqACaIkQ4AAKxb\nb3jDd+X48R0rZ3ek13tu9u+/vdNMAJy9Tkc6DAaDjEajLiMAwLowGo0yGAy6jgHrzuc+d3+WF44s\nmZnZnPvu+6RRDgATpPPSod/vdxkBANaFfr+vdIBVTi4eef75L8ry1IrD+Sf/5AcUDgATxvQKAADW\nlaWlpbziFa/MF76wJUly5ZUvzLd+6zdnfn5Px8kAeKaUDgAArBtLS0u55JKvzUMP/XWSnUmSBx74\ndPbuvbHbYAA8K3avAABg3di16/o89NA3JHlRkpqk5sILL+g4FQDPltIBAIB1YWlpKX/0R3+c5O4k\nL0wpd+TKK/8673//+7qOBsCzZHoFAADrwute9+0roxySXu9Q7rvvPgtHAkw4Ix0AAOjc0tJS7rrr\nY1meUpGUUhQOABuA0gEAgE4dPnw455//ktS6I8vbYx7MS1/61V3HAmAMlA4AAHTq6qv7OX78hVme\n+Xtxtm7dkt/6rf/SdSwAxsCaDgAAdOaTn/xkHnjgwSRXJbkvMzN/ns985s9MrQDYIJQOAAB0Ymlp\nKS972ZVJekk+nuSv8t/+2ycUDgAbiNIBAIBO7Np1fWq9euXs7mzd2svFF1/caSYAxkvpAABA50pp\n8tGP/mHXMQAYM6UDAABrqmmaDIcLueqqHTlx4g+yadOm7N9/n2kVABuQ0gEAgDU1OzuXn/7pQ0mS\nt771qvzUT72j40QAtMWWmQAArJlDhw7lne/8mdRaU2vynve8r+tIALTISAcAANZE0zS58sprkpyf\n5L4kX8gFF1zecSoA2tRp6TAYDNLv99Pv97uMwYpSylk/Vms96++x+rln+31Yv57uzxh4dkajUUaj\nUdcxoFWzs3OpdWeSkuXS4UQOHPjNjlMB0KbS1Q8LpZTqB5Wntri4mN27b8qRI4tJkm3bdufo0ZuT\nrP7cyqrz8hSPjfe5T106nPm5T3z8zN+H9evp/oyBc1NKSa31zC0wY+V+ZO0cPnw4l1zyspXSIUlu\ny1133ZbLLzfSAWC9Gef9iOkVAAC06vDhw3nxi1+W5HlJ7k7yV9mx45UKB4ApoHQAAKA1TdPkZS+7\nMsnj0ypK+SvTKgCmhN0rAABozT/+x/80TfNQko8neSRJ8kM/9P3Zvn17p7kAWBtKBwAAWnH48OH8\n2q/9epILknxdkoP5yq98OD/xEwsdJwNgrZheAQDA2C0tLeWrvuoVSV6Vx3erSO655670er0uowGw\nhox0AABg7L7t2/5ujh8/tnJWk9yf3//9W0yrAJgyRjoAADBWS0tLOXTo7iTnJ/lMkj/PN3zD1+dV\nr3pVt8EAWHNGOgAAMFbXXHNtat2Z5JIkx9LrPTe//dvv7zgVAF1QOgAAMDaHDh3Kpz71J1lex6Ek\nWcp9933CtAqAKWV6BQAAY3Ho0KFcccWrsjyt4r6Vq1XhADDFlA4AAIzFK1/5TUm2ZXmEw4kkD+T9\n739ft6EA6JTpFQAAnLP3ve99OXHi0STPT3JhkgfyN/7GC3Ldddd1nAyALhnpAADAOVlaWsrf+3v/\ne5JXZXmUw31Jav7wD+/oNhgAnTPSAQCAc7Jz52uSbF45q0nuzy23HMjFF1/cYSoA1gMjHQAAeNZu\nu+22/Nmf/VkeXzzy8/nO7/y7ee1rX9txMgDWA6UDAADP2jXXfEtOnVbxa7/2Sx0mAmA9Mb0CAIBn\n5cCBA6n1RJKPJ3k4yf1517t+Kb1er+NkAKwXRjoAAPCMHT58OG94w3dleaeKJDmYN7/5TXnjG9/Y\nZSwA1hmlAwAAz9hll12V5DlJXrxy5f78h//w7ztMBMB6ZHoFAADPyIEDB/KlL30pyVdkebeKmuc8\n5zmmVQDwJEY6AABw1t797nfnTW/6vpWzLyX5aJK/zsc+dnd3oQBYtzotHQaDQfr9fvr9fpcxeBZK\nKV1HeNZOzV5r7SjJuTvdn8Mk//fANBuNRhmNRl3HgKd022235U1vekuS569c+R9JSn7pl34uX/M1\nX9NhMgDWq9LVDyillOqHo6e2uLiY3btvypEji0mSbdt25+jRm7M8jPGksuq8PMVj3T139Z/z8g/J\n9bSPrZX1kGFcnvjfkqzFf08X7wnTpJSSWuvkNrsTxv3IM1PKc7P8b/03rlz5/Xz5l395vvjFz3eY\nCoBxG+f9iDUdAAB4WrfddtvK0ZYkj64cn8ihQ7d3lAiASWBNBwAAntY3fuO1eXyEw8Ekj+Rd7/rl\nXHzxxR2mAmC9UzoAAPCUDhw4sHJ0cqTtsTz3uc/LG9/4xq4iATAhTK8AAOCM3v3ud+cNb/jOLK/n\n87Ekn45pFQCcLSMdAAA4o+XdKi5O8v+3d/9BdpXlAce/T35IiSgIOMiPxGCrCWoHgkIMrXVBrIG2\nCB2qMMLE6pQ/2qm2ZaoJHSKG6ZCxLUUrzjhVGbASZZDS0CEdomGnOCIQm2j4kUDGYsgKwRQEyQX5\n9fSPc0Ju4q7ZZM+5Z8+938/MGc55z927z3327L4vT97znjdSFB7u5Etf+hefViFJGhdnOkiSJGlU\ny5cvB6bu1jZlyjQuuuiiZgKSJLWOMx0kSZI0qiVLPg0cDfwcGAGSH/zA2yokSePnTAdJkiT9iiuv\nvJJiqDgFeDsAN920ghNOOKHJsCRJLWPRQZIkSbu58847ufjiSyhmORwDbAXgnHPOaTIsSVILeXuF\nJEmSdnPKKe8FTqF4ROZPgBHWrbuz2aAkSa3kTAdJkiS94sMf/jDFUyoo/zsCPOdtFZKk/eJMB0mS\nJL3i+utvpLitYgOwA3iJFStWNBuUJKm1nOkgSZIkAJYtW8auxSPfBiQrVlzHeeed12xgkqTWsugg\nSZJaLSJmRsTtEXFfRNwbER8v2w+NiNUR8WBE3BYRhzQd62TW6XT49Kf/HlgAzAK2AC9bcJAkTYhF\nB0mS1HYvAH+dmW8D3gX8RUQcBywGVmfmW4DvlMcaw6JFH6MYGka5PcqaNf/VbFCSpNaz6CBJklot\nMx/LzPXl/jPAAxSLEpwFXFu+7Frg7GYinPyuvPJKbrzxZuBIiqdV/IRp06Zz6qmnNhyZJKntLDpI\nkqS+ERGzgXnAXcARmbmtPLUNOKKhsCa122+/nYsvvoSiTjOrbB3h3nvvaTAqSVK/8OkVkiSpL0TE\nQcC3gE9k5i8i4pVzmZkRkaN93WWXXfbK/tDQEENDQ/UGOsmcdtpCYCZwDLAVGOHWW29izpw5zQYm\nSeqZ4eFhhoeHa3nvyBy1/61dRGRT37stVq5cyYUXfpmnn14JwEEHXcgzz/wbu56dDcU9lznK/t6O\ne/fa7p9zMQDMUc/1ymSIoSq7fxboxedp4ntKgyQiyMzY+yvVLSKmA/8JrMrMq8q2jcBQZj4WEUcC\nt2fm3D2+bqDHI5s2bWLu3OOB+cAI8DPgOTJ/2WxgkqRGVTke8fYKSZLUalFUQ78C3L+z4FBaCSwq\n9xcBN/c6tslsy5YtzJ07j6KQvBV4CniO731vuMGoJEn9xtsrJElS2/0OcAHwo4hYV7YtAZYDN0TE\nx4CHgQ82E97kNG/eu4CjgCfKloM59tjDWLBgQYNRSZL6jUUHSZLUapn5XcaevXl6L2Npi/Xr1/PE\nE08CcynWcriLAw88kLvvfrDhyCRJ/cbbKyRJkgbMSSf9HsXTKoJiOJhs2fIghx9+eLOBSZL6jjMd\nJEmSBsiqVat48cUXgJeB/wUeZc2aVRYcJEm1cKaDJEnSgNi0aRNnnnkOxSyHKcA2PvKRCzj11FMb\njkyS1K+c6SBJkjQg5s1bALwGeGPZMsLVV3+uwYgkSf3OmQ6SJEkDYNOmTTz77LMUj8h8EUhe97pD\nmDFjRsORSZL6mTMdJEmSBsCJJy4Adj4O8y7gZdav39RgRJKkQWDRQZIkqc9t2bKFTudZiqdVALzM\nxo3rmTVrVpNhSZIGgEUHSZKkPrZ9+3be9Ka3AdOBHwGv5fWvP5w5c+Y0HJkkaRBYdJAkSepjZ5xx\nNi+99CJwCsV6Dneydq23VUiSeqPRosNll13G0NAQQ0NDTYahmkXE3l+0H++RmZV+j321t5gmuz3j\nb1Ps+6LtP6eqmIfJb3h4mOHh4abDUJ/pdDo88MBGilkOxe/8CScc720VkqSeabzooEHQ/T82EykO\n7O19suvcnq+tKoZ9jWmy685ZP2v7z6kq5mEy21mE/8xnPtN0KOojl166jE7neOAl4AccccTrWL16\nbdNhSZIGiLdXSJIk9aFOp8OKFTeQOQU4miOPPIzNm+/1EZmSpJ6a0nQAkiRJqlan0+G0087g0Uen\nAkcDGzn//A9acJAk9ZxFB0mSpD6zbNkVrF37KmAW8FPmz5/L5ZcvbTosSdIAsuggSZLURzqdDsPD\n/w3A1KlTmD//KNasWeUsB0lSIyw6SJIk9ZFly65g3boDyNzCO9/5vAUHSVKjXEhSkiSpT2zfvp3r\nrvs6zz+/g+nT387Q0CkWHCRJjbLoIEmS1Ae2b9/O7Nlz2bEjgLkcdtiPWbr0lqbDkiQNOIsOkiRJ\nfeDMM89hx47jy6MfEfEaZzlIkhrnmg6SJEl9YOvWka6jHZx77jmNxSJJ0k4WHSRJkvpAUWS4F9jA\nvHnHs3z55U2HJEmSt1dIkiT1g+XLL3/ldoqlS5d4a4UkaVKw6CBJktQHZsyY4ewGSdKk4+0VkiRJ\nkiSpFhYdJEmSWqjT6bB48aUsXnwpnU6n6XAkSRqVt1dIkiS10LJlV3DVVWvLoyu8tUKSNCk500GS\nJEmSJNXCmQ6SJEkttHTpEuCKrn1JkiYfiw6SJEkt5NMqJElt4O0VkiRJkiSpFhYdJEmSJElSLSw6\nSJIkSZKkWlh0kCRJkiRJtbDoIEmSJEmSamHRQZIkSZIk1cKigyRJkiRJqoVFB0mSJEmSVAuLDpIk\nSZIkqRYWHSRJkiRJUi0sOkiSJEmSpFpYdJAkSZIkSbWw6CBJkiRJkmph0UGSJEmSJNXCooMkSZIk\nSaqFRQdJkiRJklQLiw6SJEmSJKkWFh0kSZIkSVItLDpIkiRJkqRaWHSQJEmSJEm1sOggSZIkSZJq\nYdFBkiRJkiTVwqKDJEmSJEmqhUUHSZIkSZJUC4sOkiRJkiSpFhYdJEmSJElSLSw6SJIkSZKkWlh0\nkCRJkiRJtbDoIEmSJEmSamHRQZIkSZIk1cKigyRJkiRJqsW0qt8wIl4NfBH4JTCcmddX/T0kSZIk\nSdLkV8dMhz8GbsjMi4Czanj/ATXcdAAtM9x0AOpjw8PDTYfQKuZLdYuIr0bEtojY0NV2ckTcHRHr\nIuKeiDipyRgnM39HzQGYg53MgzkAc1C1cRUdRuvMy/aFEbExIh6KiE+VzUcDj5T7L1UY64AbbjqA\nlhluOgD1MTuifWO+1APXAAv3aPsscGlmzgOWlscahb+j5gDMwU7mwRyAOajaeGc6/EpnHhFTgS+U\n7W8Fzo+I44CtwMx9fH9JkqT9kpl3AE/u0fwocHC5fwgw0tOgJEkSMM41HTLzjoiYvUfzycDmzHwY\nICK+AXwA+DzwhYj4A2BlZZFKkiSN32LguxHxjxT/CLKg4XgkSRpIkZnje2FRdLglM3+7PD4XeH9m\n/ll5fAEwPzP/cpzvN75vLEnSgMnMaDqGthllnPJt4OrM/PeI+BPgosx83yhf53hEkqRRVDUemcjT\nKybUSTugkiRJNTo5M08v928EvjzaixyPSJJUr4msuTDCrrUbKPe3TiwcSZKkSmyOiPeU+6cBDzYZ\njCRJg2oiMx3WAm8upzP+FPgQcH4FMUmSJI1bRKwA3gMcHhGPUDyt4iLg6og4AHi2PJYkST02rjUd\nujrzw4DHgaWZeU1EnAFcBUwFvpKZV9QZrCRJkiRJao9x3V6Rmedn5lGZeUBmzszMa8r2VZk5JzN/\nq7vgEBH/EBEPRMQPI+KmiDi469ySiHgoIjZGxO93tb8jIjaU5z7X1X5ARHyzbP9+RLyxmo/eHhGx\nsMzXQxHxqabjaUJEzIyI2yPivoi4NyI+XrYfGhGrI+LBiLgtIg7p+pp9utb6UURMjYh1EXFLeWy+\nxhARh0TEjeXfrvsjYr75Glv5+e8rP+v15d9q81WKiK9GxLaI2NDVVll+7Bv3XZVjk34xKOOLKscQ\nbVfFuKDtqurv26yqPrxN6u6X22KMPNTfP2Zm5RvwPmBKub8cWF7uvxVYD0wHZgOb2TXb4m6KRZ8A\nbgUWlvt/Dnyx3P8Q8I06Yp6sG8Usks1lvqaX+Tuu6bgayMMbgBPK/YOATcBxwGeBT5btn5rItdaP\nG/A3wNeBleWx+Ro7V9cCHy33pwEHm68xczUb+DFwQHn8TWCR+dotR+8G5gEbutoqyw8D3jfu58+k\nsrFJP2wM0PiCasYQU5r+HBXlYiLjgn7JwUT7+1bngWr68NblgJr75bZsY+Sh9v5xIgtJjikzV2fm\ny+XhXcAx5f4HgBWZ+UJmPlwGPj8ijgRek5l3l6+7Dji73D+L4o8DwLeA99YR8yR2MrA5Mx/OzBeA\nb1DkcaBk5mOZub7cfwZ4ADia3a+Pa9l13ezPtdZXIuIY4EyKFdt3rs5uvkZRVnTfnZlfBcjMFzPz\nKczXWJ4GXgBmRMQ0YAbF2j7mq5SZdwBP7tFcZX4GvW/cZxWPTfrBwIwvKhpDnNzToGtQwbigH3JQ\nRX/f9jxU0Ye3Lgc96JdbYbQ89KJ/rKXosIePUlQ/AI5i9ydcbKX4o79n+0jZTvnfR6D4wwA8FRGH\n1hnwJPPK5y/tzNnAimLx0nkUvxRHZOa28tQ24Ihyf3+utX7zz8DfAi93tZmv0R0L/CwiromI/4mI\nf42IV2O+RpWZTwD/BGyhGKj8PDNXY772psr8DHrfOFETHZv0g4EcX0xwDNF2VYwL2q6q/r61KuzD\n+4Hjll9VS/+430WH8v6XDaNsf9T1mr8Dns/M6/f3+4i9r/Q5QCLiIIp/1ftEZv6i+1wW83vMFxAR\nfwg8npnr2PWvGbsxX7uZBpxIMV39RGAHsLj7BeZrl4j4TeCvKKbaHQUcFBEXdL/GfP165qcejk32\nycBdfxMcQ7Q6XxWNC1qdg1IV/X2r81BRH97qHIzGfrne/nG/H5mZme/7decj4iMUU7i6p3yOADO7\njo+hqJKMsGsaR3f7zq+ZBfy0nAJ0cFmhGxR75mwmu1eWBkZETKcYLHwtM28um7dFxBsy87Fyqs/j\nZfu+XGsj9UbeiFOAsyLiTOA3gNdGxNcwX2PZCmzNzHvK4xuBJcBj5mtU7wS+l5n/BxARNwELMF97\nU+oM4QgAAAH/SURBVMXvn33jr9GDsUk/XZ8DNb6oYAzR9p99FeOCtucAqunv256HKvrwtudgJ8fF\npbr7x1pur4iIhRTTtz6Qmc91nVoJnBcRr4qIY4E3A3dn5mPA01GsHhvAhcB/dH3NonL/XOA7dcQ8\nia0F3hwRsyPiVRQLhq1sOKaeK6+LrwD3Z+ZVXae6r49FwM1d7eO91m6mz2TmJVk8aeZY4DxgTWZe\niPkaVfk5H4mIt5RNpwP3AbdgvkazEXhXRBxYfs7TgfsxX3tTxe+ffeN+qmhs0k/X58CML6oaQ/Qq\n3jpUNS7oddxVq6q/72HIdaikD+9xzHVxXEyP+sesZ1XMh4CfAOvK7Ytd5y6hWIRiI/D+rvZ3ABvK\nc5/vaj8AuKF8z+8Ds+uIeTJvwBkUKy1vBpY0HU9DOfhdinsQ13ddVwuBQ4FvAw8CtwGH7O+11q8b\n8B52rVJtvsbO0/HAPcAPgZsoVrM2X2Pn65MUA7UNFIsvTTdfu+VnBcW9ss9T3Df/p1Xmx75xv34m\nlY1N+mVjQMYXVDiG6IdtouOCtm9V9fdt3qrqw9u01d0vt2UbJQ8f7UX/uPORF5IkSZIkSZXqxdMr\nJEmSJEnSALLoIEmSJEmSamHRQZIkSZIk1cKigyRJkiRJqoVFB0mSJEmSVAuLDpIkSZIkqRYWHSRJ\nkiRJUi3+H10kYs1nUyt7AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3bd8782190>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Repeat photometry analysis but compare to a CFHT reference catalog instead of SDSS"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<H3>Create an afw catalog from a CFHT reference file (loaded from Vizier)</H3>\n",
      "\n",
      "Catalog extracted from <A HREF=http://vizier.cfa.harvard.edu/viz-bin/VizieR-3?-source=II/317/cfhtls_d&-out.max=50&-out.form=HTML%20Table&-out.add=_r&-out.add=_RAJ,_DEJ&-sort=_r&-oc.form=sexapage> page </A> - The CFHTLS Survey (T0007 release) (Hudelot+ 2012)\n",
      "\n",
      "Catalog available at : http://slac.stanford.edu/~boutigny/CFHT/Catalogs/CFHTLS_D3_stars_mag_r_20.cat\n",
      "\n",
      "More informations on this catalog are available <A HREF=http://www.cfht.hawaii.edu/Science/CFHTLS/T0007/> here </A> and a detailed discussion about the photometric comparison between CFHTLS T0007 and SDSS DR8 is accessible <A HREF=http://www.cfht.hawaii.edu/Science/CFHTLS/T0007/T0007-docsu13.html#x43-310004.4.3> here <A>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pylab as plt\n",
      "\n",
      "from lsst.meas.astrom import LoadAstrometryNetObjectsTask as Astrometry\n",
      "import lsst.daf.persistence as dafPersist\n",
      "import lsst.afw.geom as afwGeom\n",
      "import lsst.afw.cameraGeom as cg\n",
      "import lsst.afw.table as afwTable\n",
      "import lsst.afw.image as afwImage\n",
      "import lsst.afw.coord as afwCoord\n",
      "import lsst.afw.display.ds9 as ds9\n",
      "import numpy\n",
      "import math\n",
      "\n",
      "file_cat = \"../../Catalogs/CFHTLS_D3_stars_mag_r_20.cat\"\n",
      "with open(file_cat, 'r') as f:\n",
      "    lines = f.readlines()\n",
      "    \n",
      "schema  = afwTable.SourceTable.makeMinimalSchema()\n",
      "filters='ugriz'\n",
      "\n",
      "for f in filters :\n",
      "    schema.addField(f + \"_mag\", type=numpy.float32, doc=\"magnitude\" + f)\n",
      "    schema.addField(f + \"_magSigma\", type=numpy.float32, doc=\"magnitude error\" + f)\n",
      "schema.addField(\"s_flag\", type=numpy.int, doc=\"SExtractor Flag\")\n",
      "schema.addField(\"t_flag\", type=numpy.int, doc=\"Type Flag\")\n",
      "\n",
      "catalog = afwTable.SourceCatalog(schema)\n",
      "for i in range(2,len(lines)) :\n",
      "    l = lines[i].split()\n",
      "    record = catalog.addNew()\n",
      "    coord = afwCoord.Coord(afwGeom.Point2D(numpy.float64(l[9]),numpy.float64(l[10])))\n",
      "    record.set(\"coord\",coord)                  \n",
      "    for cnts,f in enumerate(filters) :\n",
      "        record.set(f + \"_mag\", float(l[11+2*cnts]))\n",
      "        record.set(f + \"_magSigma\", float(l[12+2*cnts]))\n",
      "\n",
      "        cfhtRef = catalog.copy(deep=True)\n",
      "print(\"Catalog cfhtRef has been loaded with %d CFHT reference objects\"%(len(lines)-2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Catalog cfhtRef has been loaded with 739 CFHT reference objects\n"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Validation plots for aperture and psf photometry\n",
      "# Apterture radii in pixels are : 3.0, 4.5, 6.0, 9.0, 12.0, 17.0, 25.0, 35.0, 50.0, 70.0\n",
      "\n",
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pylab as plt\n",
      "\n",
      "from lsst.meas.astrom import LoadAstrometryNetObjectsTask as Astrometry\n",
      "import lsst.daf.persistence as dafPersist\n",
      "import lsst.afw.geom as afwGeom\n",
      "import lsst.afw.cameraGeom as cg\n",
      "import lsst.afw.table as afwTable\n",
      "import lsst.afw.image as afwImage\n",
      "import lsst.afw.display.ds9 as ds9\n",
      "import numpy\n",
      "import math\n",
      "\n",
      "\n",
      "visit = 906675\n",
      "filter = 'r'\n",
      "\n",
      "# Do not consider reference magnitude > 19 \n",
      "magMax = 19\n",
      "\n",
      "#setup astrometry\n",
      "solve = Astrometry.ConfigClass()\n",
      "astrom = Astrometry(solve)\n",
      "\n",
      "# List of flags for quality checks\n",
      "Flags = [\"base_PixelFlags_flag_saturated\", \"base_PixelFlags_flag_cr\", \"base_PixelFlags_flag_interpolated\",\n",
      "         \"base_PsfFlux_flag_edge\",\"slot_Centroid_flag\", \"base_ClassificationExtendedness_flag\"]\n",
      "\n",
      "#setup butler - It assumes that a standard LSST directory structure is setup at the upper directory level\n",
      "butler = dafPersist.Butler('../output')\n",
      "\n",
      "diffpsf = []\n",
      "diffAp = []\n",
      "mags = []\n",
      "ccdNum = []\n",
      "xCoord = []\n",
      "yCoord = []\n",
      "magsPsf = []\n",
      "errsPsf = [] # Error on PSF mag\n",
      "magsAp = []\n",
      "errsAp = []\n",
      "\n",
      "for ccd in range(0,36) :\n",
      "    vId = {'visit':visit,'ccd':ccd}\n",
      "    src = butler.get('src', vId, immediate=True)\n",
      "    md = butler.get(\"calexp_md\", vId, immediate=True)\n",
      "    calib = afwImage.Calib(md)\n",
      "    #  match CFHT sources to external reference catalog\n",
      "    match = afwTable.matchRaDec(cfhtRef, src, 1.0 * afwGeom.arcseconds)\n",
      "    print(\"CCD %d : %d matches found\"%(ccd,len(match)))\n",
      "    count = 0\n",
      "    # Loop over matched sources\n",
      "    for cnts,m in enumerate(match):\n",
      "        # Apply quality cuts\n",
      "        rej = 0\n",
      "        for f in Flags :\n",
      "            if m.second.get(f) :\n",
      "                rej = 1\n",
      "                break\n",
      "        if rej == 1 :\n",
      "            continue\n",
      "        if m.first.get(\"t_flag\") != 0 :\n",
      "            print \"Not photometric\"\n",
      "            continue\n",
      "        signalToNoise = m.second.get(\"base_PsfFlux_flux\")/m.second.get(\"base_PsfFlux_fluxSigma\")\n",
      "        if signalToNoise < 5.0 :\n",
      "            continue\n",
      "        # Select star-like sources\n",
      "        if m.second.get('base_ClassificationExtendedness_value') > 0.5:\n",
      "            continue\n",
      "        mag = m.first.get(filter + \"_mag\")\n",
      "        \n",
      "        # only keep magnitude < magMax\n",
      "        if mag > magMax :\n",
      "            continue\n",
      "        \n",
      "        magPsf, errPsf = calib.getMagnitude(m.second.get('base_PsfFlux_flux'),m.second.get('base_PsfFlux_fluxSigma'))\n",
      "        diffpsf.append(mag - magPsf)\n",
      "        \n",
      "        mags.append(mag)\n",
      "        magsPsf.append(magPsf)\n",
      "        errsPsf.append(errPsf)\n",
      "#        magsPsf.append(m.second.get('base_PsfFlux_flux'))\n",
      "#        errsPsf.append(m.second.get('base_PsfFlux_fluxSigma'))\n",
      "        x = m.second.getCentroid().getX()\n",
      "        y = m.second.getCentroid().getY()\n",
      "        xCoord.append(x)\n",
      "        yCoord.append(y)\n",
      "        ccdNum.append(ccd)\n",
      "        \n",
      "        # Iterate over aperture radii\n",
      "        for ir in range(10) :\n",
      "            apBase = \"base_CircularApertureFlux_\" + str(ir)\n",
      "            apFlux = apBase + \"_flux\"\n",
      "            apFluxSig = apBase + \"_fluxSigma\"\n",
      "            apFluxFlag = apBase + \"_flag\"\n",
      "            diffAp.append([])\n",
      "            magsAp.append([])\n",
      "            errsAp.append([])\n",
      "            if m.second.get(apFluxFlag) :\n",
      "                # if aperture flux flag is set, set entry to large negative value\n",
      "                diffAp[ir].append(-100)\n",
      "                magsAp[ir].append(-100)\n",
      "                errsAp[ir].append(-100)\n",
      "                \n",
      "            else :\n",
      "                diffAp[ir].append(mag - calib.getMagnitude(m.second.get(apFlux)))\n",
      "                magAp, errAp = calib.getMagnitude(m.second.get(apFlux),m.second.get(apFluxSig))\n",
      "                magsAp[ir].append(magAp)\n",
      "                errsAp[ir].append(errAp)\n",
      "\n",
      "        count += 1\n",
      "#    print \"ccd : \",ccd,\" number of matches :\",count\n",
      "        \n",
      "print \"Total number valid of matches :\", len(mags)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "CCD 0 : 16 matches found\n",
        "CCD 1 : 18 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 2 : 13 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 3 : 21 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 4 : 15 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 5 : 15 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 6 : 22 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 7 : 19 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 8 : 14 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 9 : 21 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 10 : 20 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 11 : 27 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 12 : 18 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 13 : 18 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 14 : 25 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 15 : 13 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 16 : 17 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 17 : 18 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 18 : 11 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 19 : 28 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 20 : 11 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 21 : 18 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 22 : 15 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 23 : 21 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 24 : 19 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 25 : 16 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 26 : 15 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 27 : 12 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 28 : 23 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 29 : 27 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 30 : 14 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 31 : 13 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 32 : 27 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 33 : 26 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 34 : 22 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "CCD 35 : 18 matches found"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Total number valid of matches :"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 268\n"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metadata = src.getMetadata()\n",
      "radii = metadata.get(\"base_CircularApertureFlux_radii\")\n",
      "\n",
      "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(18, 10))\n",
      "\n",
      "sigmaPsf = numpy.std(diffpsf)\n",
      "meanPsf = numpy.mean(diffpsf)\n",
      "ax1.hist(diffpsf, bins=100, color=\"blue\", range=[-0.4,0.3])\n",
      "ax1.set_xlabel(\"$\\Delta$ mag (PSFex)\", fontsize=20)\n",
      "ax1.set_title(\"$\\sigma$ = %.0f mmag \\nmean = %.1f mmag\"%(sigmaPsf*1000, meanPsf*1000), x=0.2, y=0.9, fontsize=20)\n",
      "ax1.tick_params(labelsize=20)\n",
      "\n",
      "ax2.scatter(ccdNum, diffpsf, s=8, color=\"blue\")\n",
      "ax2.set_ylim([0,0.30])\n",
      "ax2.tick_params(labelsize=20)\n",
      "ax2.set_xlabel(\"CCD #\", fontsize=20)\n",
      "ax2.set_ylabel(\"$\\Delta$ mag (PSFex)\", fontsize=20)\n",
      "\n",
      "fig.tight_layout()\n",
      "\n",
      "fig, (ax3, ax4) = plt.subplots(ncols=2, figsize=(18, 10))\n",
      "ax3.scatter(diffAp[3],diffpsf, s=5, color=\"blue\")\n",
      "ax3.set_xlim([0,0.30])\n",
      "ax3.set_ylim([0,0.30])\n",
      "ax3.set_xlabel(\"$\\Delta$ mag (Ap = %.1f pix)\"%(radii[3]),fontsize=20)\n",
      "ax3.set_ylabel(\"$\\Delta$ mag (PSFex)\",fontsize=20)\n",
      "ax3.tick_params(labelsize=20)\n",
      "\n",
      "ax4.scatter(mags,diffpsf, s=5, color=\"blue\")\n",
      "#ax4.set_xlim([-0.15,0.15])\n",
      "ax4.set_ylim([0,0.30])\n",
      "ax4.set_xlabel(\"Magnitude (ref)\",fontsize=20)\n",
      "ax4.set_ylabel(\"$\\Delta$ mag (PSFex)\",fontsize=20)\n",
      "ax4.tick_params(labelsize=20)\n",
      "\n",
      "fig.tight_layout()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ly0mSr776aqWsqnLu3LmVn8fGxpL8OjJvbGxsZfru1NRUPvvss5w8eTJ/+tOfkmTdkWWf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\nJkAIAAAAACMmQAgAAAAAIyZACAAAAAAjJkAIAAAAACN2dN0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       "text": [
        "<matplotlib.figure.Figure at 0x7f0321daf750>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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27L0jt18fW3d0jmMAANBNakEAgDUzT0B4YynltlLKb5ZSjpVSnkhy+3BdmjPF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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0321e2ab90>"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "And now compare reference catalogs between them (SDSS Vs CFHT)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Validation plots for aperture and psf photometry\n",
      "# Apterture radii in pixels are : 3.0, 4.5, 6.0, 9.0, 12.0, 17.0, 25.0, 35.0, 50.0, 70.0\n",
      "\n",
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pylab as plt\n",
      "\n",
      "from lsst.meas.astrom import LoadAstrometryNetObjectsTask as Astrometry\n",
      "import lsst.daf.persistence as dafPersist\n",
      "import lsst.afw.geom as afwGeom\n",
      "import lsst.afw.cameraGeom as cg\n",
      "import lsst.afw.table as afwTable\n",
      "import lsst.afw.image as afwImage\n",
      "import lsst.afw.display.ds9 as ds9\n",
      "import numpy\n",
      "import math\n",
      "\n",
      "# Need to specify a visit in order to retrieve the SDSS catalog corresponding to the CFHT D3 ROI\n",
      "visit = 906675\n",
      "filter = 'r'\n",
      "\n",
      "# Do not consider reference magnitude > 19 \n",
      "magMax = 19\n",
      "\n",
      "# If stackCorr is True then use the color term correction implemented in the stack\n",
      "stackCorr = True\n",
      "\n",
      "#setup astrometry\n",
      "solve = Astrometry.ConfigClass()\n",
      "astrom = Astrometry(solve)\n",
      "\n",
      "#setup butler - It assumes that a standard LSST directory structure is setup at the upper directory level\n",
      "butler = dafPersist.Butler('../output')\n",
      "\n",
      "# setup camera\n",
      "cam = butler.get('camera')\n",
      "ccd14 = cam['ccd14']\n",
      "vId = {'visit':visit, 'ccd':14}\n",
      "calexp = butler.get(\"calexp\", vId, immediate=True)\n",
      "detCenter = ccd14.getCenter(cg.PIXELS).getPoint()\n",
      "detCenterCoord = calexp.getWcs().pixelToSky(detCenter)\n",
      "\n",
      "# Retrieve external reference catalog (SDSS DR9)\n",
      "# loadSkyCircle is replacing getReferenceSources and is returning a structure (fluxField='r_flux'; refCat)\n",
      "s = astrom.loadSkyCircle(detCenterCoord, afwGeom.Angle(1., afwGeom.degrees), filter)\n",
      "sdssRef = s.refCat\n",
      "\n",
      "magCfhtU = []\n",
      "magSdssU = []\n",
      "magCfhtG = []\n",
      "magSdssG = []\n",
      "magCfhtR = []\n",
      "magSdssR = []\n",
      "magCfhtI = []\n",
      "magSdssI = []\n",
      "magCfhtZ = []\n",
      "magSdssZ = []\n",
      "\n",
      "errSdssR =[]\n",
      "errCfhtR = []\n",
      "\n",
      "#  match CFHT and SDSS to external reference catalog\n",
      "match = afwTable.matchRaDec(sdssRef, cfhtRef, 1.0 * afwGeom.arcseconds)\n",
      "print(\"%d matches found\"%(len(match)))\n",
      "count = 0\n",
      "# Loop over matched sources\n",
      "for cnts,m in enumerate(match):\n",
      "    # Apply quality cuts\n",
      "        if m.second.get(\"t_flag\") != 0 or not m.first.get(\"photometric\") :\n",
      "            print \"Not photometric\"\n",
      "            continue\n",
      "            \n",
      "        if m.second.get(\"r_mag\") > 19 :\n",
      "            continue\n",
      "        \n",
      "        magCfhtU.append(m.second.get(\"u_mag\"))\n",
      "        magCfhtG.append(m.second.get(\"g_mag\"))\n",
      "        magCfhtR.append(m.second.get(\"r_mag\"))\n",
      "        magCfhtI.append(m.second.get(\"i_mag\"))\n",
      "        magCfhtZ.append(m.second.get(\"z_mag\"))\n",
      "        \n",
      "        errCfhtR.append(m.second.get(\"r_magSigma\"))\n",
      "        magErr = afwImage.abMagErrFromFluxErr(m.first.get(\"r_fluxSigma\"),m.first.get(\"r_flux\"))\n",
      "        errSdssR.append(magErr)\n",
      "        \n",
      "        # Determine SDSS magnitudes from flux and apply color correction\n",
      "        \n",
      "        if stackCorr :\n",
      "            magSdssU.append(afwImage.abMagFromFlux(m.first.get('u_flux')) - \n",
      "                            0.241*(afwImage.abMagFromFlux(m.first.get('u_flux')) - \n",
      "                                   afwImage.abMagFromFlux(m.first.get('g_flux'))))\n",
      "\n",
      "            magSdssI.append(afwImage.abMagFromFlux(m.first.get('i_flux')) - \n",
      "                            0.085*(afwImage.abMagFromFlux(m.first.get('r_flux')) - \n",
      "                                   afwImage.abMagFromFlux(m.first.get('i_flux'))))\n",
      "\n",
      "            magSdssR.append(afwImage.abMagFromFlux(m.first.get('r_flux')) - \n",
      "                            0.024*(afwImage.abMagFromFlux(m.first.get('g_flux')) - \n",
      "                                   afwImage.abMagFromFlux(m.first.get('r_flux'))))\n",
      "\n",
      "            magSdssG.append(afwImage.abMagFromFlux(m.first.get('g_flux')) - \n",
      "                           0.153*(afwImage.abMagFromFlux(m.first.get('g_flux')) - \n",
      "                                   afwImage.abMagFromFlux(m.first.get('r_flux'))))\n",
      "\n",
      "            magSdssZ.append(afwImage.abMagFromFlux(m.first.get('z_flux')) - \n",
      "                            0.074*(afwImage.abMagFromFlux(m.first.get('z_flux')) - \n",
      "                                   afwImage.abMagFromFlux(m.first.get('i_flux'))))\n",
      "        else :\n",
      "            magSdssU.append(afwImage.abMagFromFlux(m.first.get('u_flux')) - \n",
      "                            0.189*(afwImage.abMagFromFlux(m.first.get('u_flux')) - \n",
      "                                   afwImage.abMagFromFlux(m.first.get('g_flux'))) - 0.099)\n",
      "            magSdssI.append(afwImage.abMagFromFlux(m.first.get('i_flux')) - \n",
      "                            0.102*(afwImage.abMagFromFlux(m.first.get('r_flux')) - \n",
      "                                   afwImage.abMagFromFlux(m.first.get('i_flux'))) + 0.024)\n",
      "            magSdssR.append(afwImage.abMagFromFlux(m.first.get('r_flux')) - \n",
      "                            0.071*(afwImage.abMagFromFlux(m.first.get('r_flux')) - \n",
      "                                   afwImage.abMagFromFlux(m.first.get('i_flux'))) + 0.000)\n",
      "            magSdssG.append(afwImage.abMagFromFlux(m.first.get('g_flux')) - \n",
      "                            0.158*(afwImage.abMagFromFlux(m.first.get('g_flux')) - \n",
      "                                   afwImage.abMagFromFlux(m.first.get('r_flux'))) +0.024)\n",
      "            magSdssZ.append(afwImage.abMagFromFlux(m.first.get('z_flux')) + \n",
      "                            0.069*(afwImage.abMagFromFlux(m.first.get('i_flux')) - \n",
      "                                   afwImage.abMagFromFlux(m.first.get('z_flux'))) + 0.030)\n",
      "\n",
      "\n",
      "        count += 1\n",
      "        \n",
      "print \"Total number valid matches :\", len(magCfhtR)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "609 matches found\n",
        "Total number valid of matches :"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 261\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "font = {'family' : 'serif',\n",
      "        'serif'   : 'times',\n",
      "        'weight' : 'normal',\n",
      "        'size'   : 15}\n",
      "\n",
      "matplotlib.rc('font', **font)\n",
      "\n",
      "plt.rc('text', usetex=True)\n",
      "\n",
      "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(18, 10))\n",
      "\n",
      "diffMag = numpy.asarray(magSdssR)-numpy.asarray(magCfhtR)\n",
      "sigmaDiff = numpy.std(diffMag)\n",
      "meanDiff = numpy.mean(diffMag)\n",
      "ax1.hist(diffMag, bins=100, color=\"blue\")\n",
      "ax1.set_xlabel(\"$\\Delta$ mag (SDSS - CFHT)\", fontsize=20)\n",
      "ax1.set_title(\"$\\sigma$ = %.0f mmag \\nmean = %.1f mmag\"%(sigmaDiff*1000, meanDiff*1000), x=0.7, y=0.9, fontsize=20)\n",
      "ax1.tick_params(labelsize=20)\n",
      "ax1.tick_params(labelsize=20)\n",
      "\n",
      "ax2.scatter(magSdssR, magCfhtR, s=0.5, color=\"blue\")\n",
      "ax2.set_xlabel(\"SDSS magnitude\", fontsize=20)\n",
      "ax2.set_ylabel(\"CFHT magnitude\", fontsize=20)\n",
      "ax2.tick_params(labelsize=20)\n",
      "\n",
      "fig.tight_layout()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oWV5uPbqL9ZeXk1KS774bXL+abM9TfUspl5PcTnInrbmSt5JcS/Kg1jrf1W48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\ngOU7bI+/u0leba39eMqxj5P8qrX2ysT+x0k+aK1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nydwfjK+m+1LgZLoPx+/M2w4c6vsT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\nAAAMkMAfAAAAAAzQ/w9OOHpPm62yUQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0321f88450>"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, (ax1) = plt.subplots(ncols=1, figsize=(10, 10))\n",
      "\n",
      "ax1.scatter(magSdssR,diffMag, s=5, color=\"blue\")\n",
      "ax1.set_ylim([-0.2,0.])\n",
      "ax1.set_xlabel(\"SDSS mag color corrected\", fontsize=20)\n",
      "ax1.set_ylabel(\"SDSS - CFHT mag (r band)\", fontsize=20)\n",
      "ax1.tick_params(labelsize=20)\n",
      "\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QZa13EG4n7d7sm2+6wWHuW8b33Md46aVu23/5S/fz4GDzKiJ2wSRv+9tJvpfk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\nQi8AAM0TegEAaN7/D4+IB8k00NHsAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0321e9f850>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diffMagU = numpy.asarray(magSdssU)-numpy.asarray(magCfhtU)\n",
      "diffMagG = numpy.asarray(magSdssG)-numpy.asarray(magCfhtG)\n",
      "diffMagR = numpy.asarray(magSdssR)-numpy.asarray(magCfhtR)\n",
      "diffMagI = numpy.asarray(magSdssI)-numpy.asarray(magCfhtI)\n",
      "diffMagZ = numpy.asarray(magSdssZ)-numpy.asarray(magCfhtZ)\n",
      "\n",
      "font = {'family' : 'serif',\n",
      "        'serif'   : 'times',\n",
      "        'weight' : 'normal',\n",
      "        'size'   : 15}\n",
      "\n",
      "matplotlib.rc('font', **font)\n",
      "plt.rc('text', usetex=True)\n",
      "\n",
      "fig, (ax1, ax2) = plt.subplots(nrows= 2, ncols=3, figsize=(18, 20))\n",
      "ax1[0].scatter(magSdssU, diffMagU, s=2, color=\"blue\")\n",
      "ax1[0].set_xlim([17.,21.])\n",
      "ax1[0].set_ylim([-0.3,0.3])\n",
      "ax1[0].set_xlabel(\"SDSS mag(u)\")\n",
      "ax1[0].set_ylabel(\"SDSS-CFHT\")\n",
      "ax1[1].scatter(magSdssG, diffMagG, s=2, color=\"blue\")\n",
      "ax1[1].set_xlim([17.,21.])\n",
      "ax1[1].set_ylim([-0.3,0.3])\n",
      "ax1[1].set_xlabel(\"SDSS mag(g)\")\n",
      "ax1[1].set_ylabel(\"SDSS-CFHT\")\n",
      "ax1[2].scatter(magSdssR, diffMagR, s=2, color=\"blue\")\n",
      "ax1[2].set_xlim([17.,20.])\n",
      "ax1[2].set_ylim([-0.3,0.3])\n",
      "ax1[2].set_xlabel(\"SDSS mag(r)\")\n",
      "ax1[2].set_ylabel(\"SDSS-CFHT\")\n",
      "\n",
      "ax2[0].scatter(magSdssI, diffMagI, s=2, color=\"blue\")\n",
      "ax2[0].set_xlim([16.5,19.])\n",
      "ax2[0].set_ylim([-0.3,0.3])\n",
      "ax2[0].set_xlabel(\"SDSS mag(i)\")\n",
      "ax2[0].set_ylabel(\"SDSS-CFHT\")\n",
      "ax2[1].scatter(magSdssZ, diffMagZ, s=2, color=\"blue\")\n",
      "ax2[1].set_xlim([16.5,19.])\n",
      "ax2[1].set_ylim([-0.3,0.3])\n",
      "ax2[1].set_xlabel(\"SDSS mag(z)\")\n",
      "ax2[1].set_ylabel(\"SDSS-CFHT\")\n",
      "\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uhy/mnP94hLYWoSs6teozdHY1XOkCvjGH3G56rhqH/NbYppZ178v//b+nGTKl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\nkHPYp8Bm53RsHNPFxebP+qU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+Ldseb31y8UNC1O73u/ZP17ZuqPL6pPab7hdxE+qubve2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\n7GJWTUFOAUxnzKya65Z+yWJfR+9U1eUkzyV5Y2nLiP03Tu8Nb6I+PuD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S1tqTJJfHGAQA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\n4njONddSAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0322777110>"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "font = {'family' : 'serif',\n",
      "        'serif'   : 'times',\n",
      "        'weight' : 'normal',\n",
      "        'size'   : 15}\n",
      "\n",
      "matplotlib.rc('font', **font)\n",
      "plt.rc('text', usetex=True)\n",
      "\n",
      "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(18, 10))\n",
      "\n",
      "ax1.hist(errSdssR, bins=40, color=\"blue\", range=[0,0.012])\n",
      "ax1.set_xlabel(\"$\\sigma$ mag(r) - SDSS\")\n",
      "ax2.hist(errCfhtR, bins=40, color=\"blue\", range=[0,0.012])\n",
      "ax2.set_xlabel(\"$\\sigma$ mag(r) - CFHT\")\n",
      "\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f031a52a450>"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Finally compare astrometry in both catalogs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pylab as plt\n",
      "\n",
      "from lsst.meas.astrom import LoadAstrometryNetObjectsTask as Astrometry\n",
      "import lsst.daf.persistence as dafPersist\n",
      "import lsst.afw.geom as afwGeom\n",
      "import lsst.afw.cameraGeom as cg\n",
      "import lsst.afw.table as afwTable\n",
      "import lsst.afw.image as afwImage\n",
      "import lsst.afw.display.ds9 as ds9\n",
      "import numpy\n",
      "import math\n",
      "\n",
      "# Need to specify a visit in order to retrieve the SDSS catalog corresponding to the CFHT D3 ROI\n",
      "visit = 906675\n",
      "filter = 'r'\n",
      "\n",
      "# Do not consider reference magnitude > 19 \n",
      "magMax = 19\n",
      "\n",
      "# If stackCorr is True then use the color term correction implemented in the stack\n",
      "stackCorr = True\n",
      "\n",
      "#setup astrometry\n",
      "solve = Astrometry.ConfigClass()\n",
      "astrom = Astrometry(solve)\n",
      "\n",
      "#setup butler - It assumes that a standard LSST directory structure is setup at the upper directory level\n",
      "butler = dafPersist.Butler('../output')\n",
      "\n",
      "# setup camera\n",
      "cam = butler.get('camera')\n",
      "ccd14 = cam['ccd14']\n",
      "vId = {'visit':visit, 'ccd':14}\n",
      "calexp = butler.get(\"calexp\", vId, immediate=True)\n",
      "detCenter = ccd14.getCenter(cg.PIXELS).getPoint()\n",
      "detCenterCoord = calexp.getWcs().pixelToSky(detCenter)\n",
      "\n",
      "# Retrieve external reference catalog (SDSS DR9)\n",
      "# loadSkyCircle is replacing getReferenceSources and is returning a structure (fluxField='r_flux'; refCat)\n",
      "s = astrom.loadSkyCircle(detCenterCoord, afwGeom.Angle(1., afwGeom.degrees), filter)\n",
      "sdssRef = s.refCat\n",
      "\n",
      "ang = []\n",
      "\n",
      "#  match CFHT and SDSS to external reference catalog\n",
      "match = afwTable.matchRaDec(sdssRef, cfhtRef, 1.0 * afwGeom.arcseconds)\n",
      "print(\"%d matches found\"%(len(match)))\n",
      "count = 0\n",
      "# Loop over matched sources\n",
      "for cnts,m in enumerate(match):\n",
      "    # Apply quality cuts\n",
      "    if m.second.get(\"t_flag\") != 0 or not m.first.get(\"photometric\") :\n",
      "        print \"Not photometric\"\n",
      "        continue    \n",
      "    if m.second.get(\"r_mag\") > 19 :\n",
      "        continue\n",
      "    \n",
      "    ang.append(afwGeom.radToMas(m.distance))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "609 matches found\n"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "font = {'family' : 'serif',\n",
      "        'serif'   : 'times',\n",
      "        'weight' : 'normal',\n",
      "        'size'   : 15}\n",
      "\n",
      "matplotlib.rc('font', **font)\n",
      "plt.rc('text', usetex=True)\n",
      "\n",
      "fig, (ax1) = plt.subplots(ncols=1, figsize=(10, 10))\n",
      "\n",
      "ax1.hist(ang, bins=30, color=\"blue\")\n",
      "ax1.set_xlabel(\"Angular distance SDSS / CFHT (mas)\")\n",
      "\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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X5rZ/q6ruLrkvO9O2r1XVm0nOLdyPV+cuvzmN+c7c9i4mOZvkR3u3XTKvw34v\nO3M/d7OqLs+tuzhd/9a07qDzjm8neW162X3P6wfd/z1TTP1zZkfwl43140PWH7qtJC9ndoT1K6bH\n1dkcHMT/mOTeQYNOv/fF38vSfyiNMX57zPW/zNx9qKrL0769Ni13p/10dnrc3JmWs3M/s+/jZFq3\n32Ntz62s8TQf4IQ2fajZYrGsbsn0km9mgfIoybV9bvNZkl8n+Ua+fNl47yXlW/nyFIJrSR7N/dzi\ny8vzP/dmptMbMovTL16mT3InyYv7bP/b8+PtM8+7c2Nc2Nv+NOcP5u7rTuZe/p+/z9PPfP8x57Xf\n72V+/FeT/NvcmDfn1t3MktM8MouxR9O2Lyysey0Lpzfss+7bRxnrKOv3uf2VHHxqw972j3JKyZvT\nbd+Zlpvzc09ydbp8c+4270zz/NppD9PP7J1aceDjZWGff2PhujtJfj13+f7Cfruf5NXDHif7PNYW\nT/XYydxzxmKxbNfiSC80Mb2cfy9JxhgPk/wqyU/2uenvMnuZ+vPx5dGynem/8y+LH+vUgDHGgyQX\nxxifT3M6l1m0zm//3THGp2OMfzngvryU5MGYXt4es6OdD6Y//zbJ+3Pbu5e5l//HGD87wbwWfy/P\n1uxNdPPb++UY439OF1+Z5nttOqp6L8lY8rt5JbOjqTtJPq593qR2gAeLVxw21jG29XIOPpL7J9N/\nj/zGsDHGK9PygyT/kK8f2b06d5tXcsDpCo9j2q8ZX54Wsed+kg/nLt/J7B9O85f3fvbAx8k+j7Wv\nnOoxbScHvQICbJbohT5+nGRn7mXXs5mdY3hY7MwH1e0kfzl3+bifAvHMFAYXMgvBbz3mz38v+5zj\nOmcxoJ7P7HSO+4ecU/k489r7vVzcZ3vz2/1wjPGzafnrMcb/WjJmxuz82guZRdi7S06HWNzOZ2Ph\nPNXDxnrMbe17asPkX6f/PnuEue7nRpbvz2Q154XvLFn3OOMve5wc+FibghnYUqIXGqiqc0m+OX/k\nbDrCdi/7H+3d13TE7dx0DuzzY4y/POxn9pnLTpLbY4zXx+xNTpUlRz8P8P+SfHfZVOe2d3Y6Mnsh\ns2j7YL8fOOa8Rmbxe2VhrL14/Gyfdef2OZf68vx5wNOR+L1zcJdG77RvX8nsvNlDxzrOtqZzdt/f\n5wjp3hi/yuz38KNlcz3IGOOTaR7rdi9Jav+PXDvSY3DZ4+Swx9q0rzIOeZMlsBmiF3r4SZK397n+\nHzJ7OXZwm/2IAAACj0lEQVT+iFTlq0e95t8gdiOzczJvJ3lv7y/xyb0kP5hud3W6br8jf1cyC+ez\n089fWNje4vb388/5Mr73YuJcvjySV3Pzfraqro4xHo4x/jrJg7nouTetP3uMeZ2bLt/Il0fQd6ZA\n3AvdtzP7/b41t+76PoF3L8k/LsTws5lF8+LnzX4xh2me7yX51zHG3j9eDhvrk8fY1p4f5fCj+i9n\nuq9fmews8q/P/UPg3Nd/9Cv21n9z4frzB1w/v+6wN7/dyyzOF4/4Lu7bWtjO/Pplj5OdJY+1TNt1\ntBe21aZPKrZYLCdbkryU2RtvfpHk7Nz1ZzP7zNRHSf4tszdnXZku/zqzv8z33lT09nT765m9gez+\ndP0Xb4bL7LNe709jXc4soH46jXs3s3Nivz+Nc3dark3L/Wnd3lzfT/LcIffrxWmMO9MY70/b+/OF\n7T03jf/qdLsfzo1xfVr3w0PmddjvZW8uj5L8YmGer86tezv7vBFtut2jaXtvTcuv8+Ub5a5MY/wh\nszd47d3mZvZ589aysY6yfp/x7h8074Xb7R3hvJMv34h2PV++6eulufvxi3z9zXrPTT/7h+l3dWGf\n6389/3PTupvTuvdzyOcuT/OZfzPjlen+vT/N/2K+fBx/Z+7y+5m9uXLZ4+TAx9q0rdcWr7NYLNuz\n1BiP+6oj0NF0VOv1Mcbrc9edTfKT+etg21XVzTE7veep2C5wNE5vAPZczezl6/mXxb+XL9/EBKfF\nF5/N/KRMn95x9dAbAhvjSC+Q5Iujuu9m9nLwvWm5Psb4zUYnBscwPZ53xiFfcrGibT2X5N54Mm/W\nA45J9AIA0J7TGwAAaE/0AgDQnugFAKA90QsAQHuiFwCA9kQvAADt/X8J2aGbvEK7jgAAAABJRU5E\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0321e61d50>"
       ]
      }
     ],
     "prompt_number": 136
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Now compare SDSS DR9 catalog used in LSST to \"official\" SDSS DR9 catalog from Vizier portal"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Load an afwTable with valu read in ASCII catalog  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pylab as plt\n",
      "\n",
      "from lsst.meas.astrom import LoadAstrometryNetObjectsTask as Astrometry\n",
      "import lsst.daf.persistence as dafPersist\n",
      "import lsst.afw.geom as afwGeom\n",
      "import lsst.afw.cameraGeom as cg\n",
      "import lsst.afw.table as afwTable\n",
      "import lsst.afw.image as afwImage\n",
      "import lsst.afw.coord as afwCoord\n",
      "import lsst.afw.display.ds9 as ds9\n",
      "import numpy\n",
      "import math\n",
      "\n",
      "file_cat = \"../../Catalogs/SDSS_DR9_stars_mag_r_20.cat\"\n",
      "with open(file_cat, 'r') as f:\n",
      "    lines = f.readlines()\n",
      "    \n",
      "schema  = afwTable.SourceTable.makeMinimalSchema()\n",
      "filters='ugriz'\n",
      "\n",
      "for f in filters :\n",
      "    schema.addField(f + \"_mag\", type=numpy.float32, doc=\"magnitude\" + f)\n",
      "    schema.addField(f + \"_magSigma\", type=numpy.float32, doc=\"magnitude error\" + f)\n",
      "\n",
      "catalog = afwTable.SourceCatalog(schema)\n",
      "for i in range(8,len(lines)) :\n",
      "    l = lines[i].split()\n",
      "    record = catalog.addNew()\n",
      "    coord = afwCoord.Coord(afwGeom.Point2D(numpy.float64(l[9]),numpy.float64(l[11])))\n",
      "    record.set(\"coord\",coord)                  \n",
      "    for cnts,f in enumerate(filters) :\n",
      "        record.set(f + \"_mag\", float(l[15+2*cnts]))\n",
      "        record.set(f + \"_magSigma\", float(l[16+2*cnts]))\n",
      "\n",
      "        sdssVizier = catalog.copy(deep=True)\n",
      "print(\"Catalog has been loaded with %d SDSS reference objects\"%(len(lines)-2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Catalog has been loaded with 1317 SDSS reference objects\n"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Compare photometry from both catalog"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pylab as plt\n",
      "\n",
      "from lsst.meas.astrom import LoadAstrometryNetObjectsTask as Astrometry\n",
      "import lsst.daf.persistence as dafPersist\n",
      "import lsst.afw.geom as afwGeom\n",
      "import lsst.afw.cameraGeom as cg\n",
      "import lsst.afw.table as afwTable\n",
      "import lsst.afw.image as afwImage\n",
      "import lsst.afw.display.ds9 as ds9\n",
      "import numpy\n",
      "import math\n",
      "\n",
      "# Need to specify a visit in order to retrieve the SDSS catalog corresponding to the CFHT D3 ROI\n",
      "visit = 906675\n",
      "filter = 'r'\n",
      "\n",
      "# Do not consider reference magnitude > 19 \n",
      "magMax = 19\n",
      "\n",
      "# If stackCorr is True then use the color term correction implemented in the stack\n",
      "stackCorr = True\n",
      "\n",
      "#setup astrometry\n",
      "solve = Astrometry.ConfigClass()\n",
      "astrom = Astrometry(solve)\n",
      "\n",
      "#setup butler - It assumes that a standard LSST directory structure is setup at the upper directory level\n",
      "butler = dafPersist.Butler('../output')\n",
      "\n",
      "# setup camera\n",
      "cam = butler.get('camera')\n",
      "ccd14 = cam['ccd14']\n",
      "vId = {'visit':visit, 'ccd':14}\n",
      "calexp = butler.get(\"calexp\", vId, immediate=True)\n",
      "detCenter = ccd14.getCenter(cg.PIXELS).getPoint()\n",
      "detCenterCoord = calexp.getWcs().pixelToSky(detCenter)\n",
      "\n",
      "# Retrieve external reference catalog (SDSS DR9)\n",
      "# loadSkyCircle is replacing getReferenceSources and is returning a structure (fluxField='r_flux'; refCat)\n",
      "s = astrom.loadSkyCircle(detCenterCoord, afwGeom.Angle(1., afwGeom.degrees), filter)\n",
      "sdssLSST = s.refCat\n",
      "\n",
      "magVizU = []\n",
      "magSdssU = []\n",
      "magVizG = []\n",
      "magSdssG = []\n",
      "magVizR = []\n",
      "magSdssR = []\n",
      "magVizI = []\n",
      "magSdssI = []\n",
      "magVizZ = []\n",
      "magSdssZ = []\n",
      "\n",
      "#  match SDSS LSST reference and SDSS catalog from Vizier\n",
      "match = afwTable.matchRaDec(sdssLSST, sdssVizier, 1.0 * afwGeom.arcseconds)\n",
      "print(\"%d matches found\"%(len(match)))\n",
      "count = 0\n",
      "# Loop over matched sources\n",
      "for cnts,m in enumerate(match):\n",
      "    # Apply quality cuts\n",
      "        if not m.first.get(\"photometric\") :\n",
      "            print \"Not photometric\"\n",
      "            continue\n",
      "            \n",
      "        if m.second.get(\"r_mag\") > 19 :\n",
      "            continue\n",
      "        \n",
      "        magVizU.append(m.second.get(\"u_mag\"))\n",
      "        magVizG.append(m.second.get(\"g_mag\"))\n",
      "        magVizR.append(m.second.get(\"r_mag\"))\n",
      "        magVizI.append(m.second.get(\"i_mag\"))\n",
      "        magVizZ.append(m.second.get(\"z_mag\"))\n",
      "        \n",
      "        # Determine SDSS magnitudes from flux and apply color correction\n",
      "        \n",
      "        magSdssU.append(afwImage.abMagFromFlux(m.first.get('u_flux')))\n",
      "        magSdssI.append(afwImage.abMagFromFlux(m.first.get('i_flux')))                   \n",
      "        magSdssR.append(afwImage.abMagFromFlux(m.first.get('r_flux')))\n",
      "        magSdssG.append(afwImage.abMagFromFlux(m.first.get('g_flux')))\n",
      "        magSdssZ.append(afwImage.abMagFromFlux(m.first.get('z_flux')) )\n",
      "\n",
      "        count += 1\n",
      "        \n",
      "print \"Total number valid matches :\", len(magVizR)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1091 matches found\n",
        "Total number valid matches :"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 722\n"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "font = {'family' : 'serif',\n",
      "        'serif'   : 'times',\n",
      "        'weight' : 'normal',\n",
      "        'size'   : 15}\n",
      "\n",
      "matplotlib.rc('font', **font)\n",
      "\n",
      "plt.rc('text', usetex=True)\n",
      "\n",
      "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(18, 10))\n",
      "\n",
      "diffMag = numpy.asarray(magSdssR)-numpy.asarray(magVizR)\n",
      "sigmaDiff = numpy.std(diffMag)\n",
      "meanDiff = numpy.mean(diffMag)\n",
      "ax1.hist(diffMag, bins=100, color=\"blue\")\n",
      "ax1.set_xlabel(\"$\\Delta$ mag (SDSS LSST - SDSS Vizier)\", fontsize=20)\n",
      "ax1.set_title(\"$\\sigma$ = %.0f mmag \\nmean = %.1f mmag\"%(sigmaDiff*1000, meanDiff*1000), x=0.7, y=0.9, fontsize=20)\n",
      "ax1.tick_params(labelsize=20)\n",
      "ax1.tick_params(labelsize=20)\n",
      "\n",
      "ax2.scatter(magSdssR,diffMag, s=5, color=\"blue\")\n",
      "ax2.set_ylim([-0.02,0.02])\n",
      "ax2.set_xlabel(\"SDSS LSST\", fontsize=20)\n",
      "ax2.set_ylabel(\"SDSS LSST - SDSS Vizier mag (r band)\", fontsize=20)\n",
      "ax2.tick_params(labelsize=20)\n",
      "\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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W79hGlXVnR/rsM+n774NmjF98EUyX1RC3WjMBJUFMcsLjsbsrffxx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\nV9J//I/753f9uQ6W7403pK+/ju6/9Zb05z9Ptp1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       "text": [
        "<matplotlib.figure.Figure at 0x7f034f58eb10>"
       ]
      }
     ],
     "prompt_number": 132
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diffMagU = numpy.asarray(magSdssU)-numpy.asarray(magVizU)\n",
      "diffMagG = numpy.asarray(magSdssG)-numpy.asarray(magVizG)\n",
      "diffMagR = numpy.asarray(magSdssR)-numpy.asarray(magVizR)\n",
      "diffMagI = numpy.asarray(magSdssI)-numpy.asarray(magVizI)\n",
      "diffMagZ = numpy.asarray(magSdssZ)-numpy.asarray(magVizZ)\n",
      "\n",
      "font = {'family' : 'serif',\n",
      "        'serif'   : 'times',\n",
      "        'weight' : 'normal',\n",
      "        'size'   : 15}\n",
      "\n",
      "matplotlib.rc('font', **font)\n",
      "plt.rc('text', usetex=True)\n",
      "\n",
      "fig, (ax1, ax2) = plt.subplots(nrows= 2, ncols=3, figsize=(18, 20))\n",
      "ax1[0].scatter(magSdssU, diffMagU, s=2, color=\"blue\")\n",
      "ax1[0].set_xlim([17.,21.])\n",
      "ax1[0].set_ylim([-0.03,0.03])\n",
      "ax1[0].set_xlabel(\"SDSS LSST mag(u)\")\n",
      "ax1[0].set_ylabel(\"SDSS LSST - SDSS Vizier\")\n",
      "ax1[1].scatter(magSdssG, diffMagG, s=2, color=\"blue\")\n",
      "ax1[1].set_xlim([17.,21.])\n",
      "ax1[1].set_ylim([-0.03,0.03])\n",
      "ax1[1].set_xlabel(\"SDSS LSST mag(g)\")\n",
      "ax1[1].set_ylabel(\"SDSS LSST - SDSS Vizier\")\n",
      "ax1[2].scatter(magSdssR, diffMagR, s=2, color=\"blue\")\n",
      "ax1[2].set_xlim([17.,20.])\n",
      "ax1[2].set_ylim([-0.03,0.03])\n",
      "ax1[2].set_xlabel(\"SDSS LSST mag(r)\")\n",
      "ax1[2].set_ylabel(\"SDSS LSST - SDSS Vizier\")\n",
      "\n",
      "ax2[0].scatter(magSdssI, diffMagI, s=2, color=\"blue\")\n",
      "ax2[0].set_xlim([16.5,19.])\n",
      "ax2[0].set_ylim([-0.03,0.03])\n",
      "ax2[0].set_xlabel(\"SDSS LSST mag(i)\")\n",
      "ax2[0].set_ylabel(\"SDSS LSST - SDSS Vizier\")\n",
      "ax2[1].scatter(magSdssZ, diffMagZ, s=2, color=\"blue\")\n",
      "ax2[1].set_xlim([16.5,19.])\n",
      "ax2[1].set_ylim([-0.03,0.03])\n",
      "ax2[1].set_xlabel(\"SDSS LSST mag(z)\")\n",
      "ax2[1].set_ylabel(\"SDSS LSST - SDSS Vizier\")\n",
      "\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IVfNinS9TN7Hn511j4cGDbpW6PVzrpvH3S5VsxJVcddLcOZKyPCcnZa6+SFwo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\nOdTRuoe63AAAAIdK4++c7DhPlyYHU7CrLwh88QAAAMAmGn/AwRm7aba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AgAZp/AEAAABA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\nx85Ot/M/VhJujucdAIC5Ma9ZhcHM/qhqZ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       "text": [
        "<matplotlib.figure.Figure at 0x7f03217c9ad0>"
       ]
      }
     ],
     "prompt_number": 143
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Compare astrometric positions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pylab as plt\n",
      "\n",
      "from lsst.meas.astrom import LoadAstrometryNetObjectsTask as Astrometry\n",
      "import lsst.daf.persistence as dafPersist\n",
      "import lsst.afw.geom as afwGeom\n",
      "import lsst.afw.cameraGeom as cg\n",
      "import lsst.afw.table as afwTable\n",
      "import lsst.afw.image as afwImage\n",
      "import lsst.afw.display.ds9 as ds9\n",
      "import numpy\n",
      "import math\n",
      "\n",
      "# Need to specify a visit in order to retrieve the SDSS catalog corresponding to the CFHT D3 ROI\n",
      "visit = 906675\n",
      "filter = 'r'\n",
      "\n",
      "# Do not consider reference magnitude > 19 \n",
      "magMax = 19\n",
      "\n",
      "# If stackCorr is True then use the color term correction implemented in the stack\n",
      "stackCorr = True\n",
      "\n",
      "#setup astrometry\n",
      "solve = Astrometry.ConfigClass()\n",
      "astrom = Astrometry(solve)\n",
      "\n",
      "#setup butler - It assumes that a standard LSST directory structure is setup at the upper directory level\n",
      "butler = dafPersist.Butler('../output')\n",
      "\n",
      "# setup camera\n",
      "cam = butler.get('camera')\n",
      "ccd14 = cam['ccd14']\n",
      "vId = {'visit':visit, 'ccd':14}\n",
      "calexp = butler.get(\"calexp\", vId, immediate=True)\n",
      "detCenter = ccd14.getCenter(cg.PIXELS).getPoint()\n",
      "detCenterCoord = calexp.getWcs().pixelToSky(detCenter)\n",
      "\n",
      "# Retrieve external reference catalog (SDSS DR9)\n",
      "# loadSkyCircle is replacing getReferenceSources and is returning a structure (fluxField='r_flux'; refCat)\n",
      "s = astrom.loadSkyCircle(detCenterCoord, afwGeom.Angle(1., afwGeom.degrees), filter)\n",
      "sdssRef = s.refCat\n",
      "\n",
      "ang = []\n",
      "\n",
      "#  match CFHT and SDSS to external reference catalog\n",
      "match = afwTable.matchRaDec(sdssRef, sdssVizier, 1.0 * afwGeom.arcseconds)\n",
      "print(\"%d matches found\"%(len(match)))\n",
      "count = 0\n",
      "# Loop over matched sources\n",
      "for cnts,m in enumerate(match):\n",
      "    # Apply quality cuts\n",
      "    if not m.first.get(\"photometric\") :\n",
      "        print \"Not photometric\"\n",
      "        continue    \n",
      "    if m.second.get(\"r_mag\") > 19 :\n",
      "        continue\n",
      "    \n",
      "    ang.append(afwGeom.radToMas(m.distance))\n",
      "#    if afwGeom.radToMas(m.distance) > 0.1 :\n",
      "#        print afwGeom.radToMas(m.distance), afwGeom.radToDeg(m.distance), m.distance, afwImage.abMagFromFlux(m.first.get('r_flux')), m.second.get('r_mag')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1091 matches found\n"
       ]
      }
     ],
     "prompt_number": 152
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "font = {'family' : 'serif',\n",
      "        'serif'   : 'times',\n",
      "        'weight' : 'normal',\n",
      "        'size'   : 15}\n",
      "\n",
      "matplotlib.rc('font', **font)\n",
      "plt.rc('text', usetex=True)\n",
      "\n",
      "fig, (ax1) = plt.subplots(ncols=1, figsize=(10, 10))\n",
      "\n",
      "ax1.hist(ang, bins=100, color=\"blue\", range=[0,5])\n",
      "ax1.set_xlabel(\"Angular distance SDSS LSST / SDSS Vizier (mas)\")\n",
      "\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DJBlj3BljzMN41RgAAByolVMdxhhHquqtqvp2WvR+km+msRNJTiW5snCTG0neXjX2uHYc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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f0322735d50>"
       ]
      }
     ],
     "prompt_number": 153
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}