apertures.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:123f6c319729e4f0195ecd084f6d1c580d52208efddc9b9ae55c71c62e37dff2"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "raw",
     "metadata": {},
     "source": [
      "To process an HSC image:\n",
      "\n",
      "$ setup anaconda\n",
      "$ setup hscPipe -t rhl\n",
      "$ unsetup -j matplotlib\n",
      "$ setup suprime_data\n",
      "$ setup -r ~/LSST/processFile -j\n",
      "$ \n",
      "$ visit=0908120;  ccd=056\n",
      "$ processFile.py -C ~/HSC/Cosmos/Config/hsc.py \\\n",
      "            --outputCatalog $SUPRIME_DATA_DIR/rerun/rhl/cosmos/PF-$visit-$ccd.fits \\\n",
      "            $SUPRIME_DATA_DIR/rerun/rhl/cosmos/00766/HSC-I/corr/CORR-$visit-$ccd.fits "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import os\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import lsst.daf.persistence as dafPersist\n",
      "import lsst.afw.geom as afwGeom\n",
      "import lsst.afw.image as afwImage\n",
      "import lsst.afw.table as afwTable"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def findSource(sourceSet, x, y, radius=2):\n",
      "    sch = afwTable.SourceTable.makeMinimalSchema()\n",
      "\n",
      "    centroidName = \"foo\"\n",
      "    sch.addField(afwTable.Field[\"PointD\"](centroidName, \"\"))\n",
      "    oneObjCatalog = afwTable.SourceCatalog(sch)\n",
      "    oneObjCatalog.table.defineCentroid(centroidName)\n",
      "    \n",
      "    s = oneObjCatalog.addNew()\n",
      "    s.set(\"%s.x\" % centroidName, x)\n",
      "    s.set(\"%s.y\" % centroidName, y)\n",
      "    \n",
      "    matches = [m.first for m in afwTable.matchXy(sourceSet, oneObjCatalog, radius)\n",
      "               if m.first.get(\"deblend.nchild\") == 0]\n",
      "\n",
      "    if len(matches) == 0:\n",
      "        raise RuntimeError(\"Unable to find a source within %g pixels of (%g,%g)\" % (radius, x, y))\n",
      "\n",
      "    if len(matches) == 1:\n",
      "        return matches[0]\n",
      "    else:\n",
      "        return matches"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def addCcdVisitToOid(oid, visit, ccd):\n",
      "    oid += (200*visit + ccd) << 32\n",
      "    \n",
      "    return oid\n",
      "\n",
      "def splitExposureId(oid):\n",
      "    \"\"\"Split an ObjectId (maybe an numpy array) into visit, ccd, and objId.\n",
      "See obs/subaru/python/lsst/obs/hscSim/hscMapper.py\"\"\"\n",
      "    oid = np.array(oid, dtype='int64')\n",
      "    objId = np.bitwise_and(oid, 2**32 - 1) # Should be the same value as was set by apps code\n",
      "    oid = np.right_shift(oid, 32).astype('int32')\n",
      "\n",
      "    ccd = (oid % 200).astype('int32')\n",
      "    oid //= 200\n",
      "    visit = oid.astype('int32')\n",
      "\n",
      "    if visit.size == 1:     # sqlite doesn't like numpy types\n",
      "        visit = int(visit)\n",
      "        ccd = int(ccd)\n",
      "        objId = int(objId)\n",
      "\n",
      "    return dict(visit=visit, ccd=ccd, objId=objId)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "butler = dafPersist.Butler(os.path.join(os.environ[\"SUPRIME_DATA_DIR\"], \"rerun\", \"rhl\", \"cosmos\"))\n",
      "\n",
      "inDir = os.path.join(os.environ[\"SUPRIME_DATA_DIR\"], \"rerun\", \"rhl\", \"cosmos\")\n",
      "catTemplate=\"PF-%07d-%03d.fits\"\n",
      "\n",
      "cats = []\n",
      "for visit in [908120, 908140, 908160]:\n",
      "    if visit == 908120:\n",
      "        continue\n",
      "    for ccd in range(50, 59+1):\n",
      "        cat = afwTable.SourceCatalog.readFits(os.path.join(inDir, catTemplate % (visit, ccd)))\n",
      "        addCcdVisitToOid(cat.get(\"id\"), visit, ccd)\n",
      "        cats.append(cat)\n",
      "        if False:\n",
      "            exposure = butler.get(\"calexp\", visit=visit, ccd=ccd)\n",
      "        \n",
      "pixelSize = afwImage.makeWcs(butler.get(\"calexp_md\", visit=visit, ccd=ccd)).pixelScale().asArcseconds()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Concatenate all those individual catalogues\n",
      "\n",
      "N.b. the centroids are still relative to the CCD origin"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cat = cats[0].copy(True)\n",
      "cat.clear()        # Needed so that the reserve() call will generate contigous memory\n",
      "cat.preallocate(sum([len(c) for c in cats]))\n",
      "\n",
      "for c in cats:\n",
      "    cat.extend(c, True)\n",
      "\n",
      "print \"%d objects\" % (len(cat.get(\"id\")))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "107607 objects\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "psfFlux = cat.getPsfFlux()\n",
      "nchild = cat.get(\"deblend.nchild\")\n",
      "ids = cat.get(\"id\")\n",
      "isExtended = cat.get(\"classification.extendedness\") > 0.5\n",
      "if True:      # sleazy way of ignoring saturated stars?\n",
      "    isExtended = np.logical_or(isExtended, cat.get(\"flags.pixel.saturated.center\"))\n",
      "\n",
      "plotProfiles = True\n",
      "if plotProfiles:\n",
      "    normalize = True\n",
      "\n",
      "frame = None\n",
      "if frame is not None:\n",
      "    ds9.erase(frame=frame)\n",
      "    \n",
      "radii = np.array([0.0] + list(cat.getMetadata().get(\"flux_aperture_radii\")))\n",
      "flux = np.zeros_like(radii)\n",
      "fluxErr = np.zeros_like(radii)\n",
      "\n",
      "fluxApertureKey = cat.getSchema().find(\"flux.aperture\").key\n",
      "fluxApertureErrKey = cat.getSchema().find(\"flux.aperture.err\").key\n",
      "\n",
      "nLine = 0\n",
      "fluxes = []; deltas = []\n",
      "for s in cat[np.logical_and(nchild == 0, np.logical_not(isExtended))]:\n",
      "    if False and frame is not None:\n",
      "        ds9.dot('o', *s.getCentroid(), ctype=ds9.RED, frame=frame)\n",
      "        \n",
      "    nProfile = s.get(\"flux.aperture.nProfile\")\n",
      "    if nProfile < len(radii) - 1:\n",
      "        continue\n",
      "\n",
      "    flux[1:] = s.get(fluxApertureKey)[:nProfile]\n",
      "    fluxErr[1:] = s.get(fluxApertureErrKey)[:nProfile]\n",
      "    \n",
      "    if False:\n",
      "        x, y = 1431, 3806\n",
      "        if s.getId() != findSource(cat, x, y).getId():\n",
      "            continue\n",
      "\n",
      "    if not (9.5e4 < flux[-1] < 1e10):\n",
      "        continue\n",
      "\n",
      "    if (s.getId() & 0xffffffff) == 4637:   # misclassified\n",
      "        continue\n",
      "        \n",
      "    if frame is not None:\n",
      "        #ds9.pan(*s.getCentroid(), frame=frame)\n",
      "        #ds9.dot('+', *s.getCentroid(), frame=frame)\n",
      "        for r in radii:\n",
      "            ds9.dot('o', *s.getCentroid(), size=r, ctype=ds9.YELLOW, frame=frame)\n",
      "    \n",
      "    if plotProfiles:\n",
      "        norm = 1/flux[-3] if normalize else 1.0\n",
      "        if norm*flux[-1] > 0.8:\n",
      "            continue\n",
      "        plt.errorbar(pixelSize*radii, norm*flux, yerr=norm*fluxErr,\n",
      "                     ls=':' if s.get(\"flags.pixel.saturated.center\") else \"-\",\n",
      "                     label=\"(%.1f, %.1f) %s\" % (s.getX(), s.getY(), splitExposureId(s.getId())))\n",
      "    \n",
      "        nLine += 1\n",
      "    else:\n",
      "        fluxes.append(flux[-1])\n",
      "        deltas.append(flux[-1]/flux[-3] - 1)\n",
      "del s\n",
      "\n",
      "if fluxes:\n",
      "    plt.plot(fluxes, deltas, 'o')\n",
      "    plt.axhline(0.0, ls=':')\n",
      "    plt.ylim(-0.05, 0.10)\n",
      "\n",
      "    plt.xlabel(\"Total Flux\")\n",
      "    plt.ylabel(\"$\\Delta$ flux\")\n",
      "else:\n",
      "    if 0 < nLine < 13:\n",
      "        plt.legend(loc='best', bbox_to_anchor=(1.1, 1.05))\n",
      "    plt.xlim(-0.1, None)\n",
      "    #plt.xlim(None, 25)\n",
      "    if normalize:\n",
      "        plt.ylim(-0.05, 1.05)\n",
      "        #plt.ylim(0.80, 1.05)\n",
      "    \n",
      "    plt.xlabel(\"Radius (arcsec)\")\n",
      "    plt.ylabel(\"Cumulative flux\")\n",
      "None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAyEAAAEPCAYAAABV18ucAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsgiRJEmS1K8sQiRJkiT1q/8P+PT7Jd90lAQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x11225b890>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}