checkPsf.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set of utilities to control the PSF determination on CFHT images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve PSF objects from calexps for a set of images and for every CCD and compute a gaussian PSF width (FWHM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import lsst.daf.persistence as dafPersist\n",
    "import lsst.afw.table as afwTable\n",
    "import lsst.afw.geom as afwGeom\n",
    "import lsst.afw.image as afwImage\n",
    "import lsst.meas.algorithms as measAlg\n",
    "import lsst.afw.display.ds9 as ds9\n",
    "import math\n",
    "\n",
    "visits = [849375, 850587, 907254, 995367, 996234, 850181, 906675, 912206, 995974, 996717]\n",
    "filter = 'r'\n",
    "\n",
    "sigmaToFwhm = 2.0*math.sqrt(2.0*math.log(2.0)) \n",
    "\n",
    "#setup butler - Replace path name by something suitable for you\n",
    "butler = dafPersist.Butler(\"/home/boutigny/CFHT/test_cfht/output\")\n",
    "\n",
    "psfHist = []\n",
    "\n",
    "for cnts, visit in enumerate(visits) :\n",
    "    tmp = []\n",
    "    for ccd in range(36) :\n",
    "        dataid = {'visit':visit, 'filter':filter, 'ccd':ccd}\n",
    "\n",
    "        # Retrieve calexp\n",
    "        calexp = butler.get('calexp', dataid)\n",
    "\n",
    "        x0 = calexp.getX0()\n",
    "        y0 = calexp.getY0()\n",
    "        width = calexp.getWidth()\n",
    "        height = calexp.getHeight()\n",
    "        wcs = calexp.getWcs()\n",
    "\n",
    "        psf = calexp.getPsf()\n",
    "\n",
    "# Display PSF\n",
    "#image = psf.computeImage()\n",
    "#print dir(image)\n",
    "#ds9.mtv(image,frame=1)\n",
    "\n",
    "        shape = psf.computeShape()\n",
    "\n",
    "        attr = measAlg.PsfAttributes(psf, x0 + width // 2, y0 + height // 2)\n",
    "        fwhm = attr.computeGaussianWidth() * wcs.pixelScale().asArcseconds() * sigmaToFwhm\n",
    "        \n",
    "        if fwhm < 0.1 :\n",
    "            print visit, ccd, fwhm\n",
    "        \n",
    "        tmp.append(fwhm)\n",
    "    psfHist.append(tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results - 1 histogram / image - 1 entry / CCD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5c522d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x89b9890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.clf()\n",
    "fig, (ax0) = plt.subplots(nrows=2, ncols=5, figsize=(20,12))\n",
    "\n",
    "rg = [0.,1.4]\n",
    "\n",
    "for i in range(2) :\n",
    "    for j in range(5) :\n",
    "        ax0[i,j].hist(psfHist[i*5+j], bins=50, range=rg)\n",
    "        ax0[i,j].set_xlim([0.3,1.4])\n",
    "        ax0[i,j].set_xlabel(\"PSF Width in arcsec\")\n",
    "        tit = \"Visit : \" + str(visits[i*5+j])\n",
    "        ax0[i,j].set_title(tit)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute PSF width for every CCD of a given image and calculate distance between the center of the CCD to the center of the focal plane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "995367 0 0.564114301899 151.590472829\n",
      "995367 1 0.554516890577 131.369309671\n",
      "995367 2 0.548671618041 114.779481999\n",
      "995367 3 0.562179298038 103.554675941\n",
      "995367 4 0.541946673569 99.501764653\n",
      "995367 5 0.547808226791 103.470931421\n",
      "995367 6 0.552435980155 114.67536063\n",
      "995367 7 0.564273283429 131.241675467\n",
      "995367 8 0.561583529918 151.469573612\n",
      "995367 9 0.570465598316 118.64773537\n",
      "995367 10 0.556214709427 91.3933521177\n",
      "995367 11 0.542057009869 65.3296179123\n",
      "995367 12 0.553393028129 42.6088942102\n",
      "995367 13 0.558234178387 31.5428540015\n",
      "995367 14 0.560766030959 42.4700586156\n",
      "995367 15 0.551095732693 65.1995456181\n",
      "995367 16 0.545647659249 91.2679561158\n",
      "995367 17 0.548213803341 118.485339529\n",
      "995367 18 0.580644198995 118.636984001\n",
      "995367 19 0.575736217411 91.3874283699\n",
      "995367 20 0.554023440379 65.3166082483\n",
      "995367 21 0.555507272568 42.5398993542\n",
      "995367 22 0.556820758251 31.4618542693\n",
      "995367 23 0.565831463504 42.4654130742\n",
      "995367 24 0.556315741183 65.1616573727\n",
      "995367 25 0.547288829577 91.2374468711\n",
      "995367 26 0.557576038362 118.454767632\n",
      "995367 27 0.606500390438 151.535576463\n",
      "995367 28 0.58333201258 131.286156413\n",
      "995367 29 0.57239280647 114.660709812\n",
      "995367 30 0.562386896216 103.467395422\n",
      "995367 31 0.567149389567 99.4072646669\n",
      "995367 32 0.548604956304 103.43205345\n",
      "995367 33 0.564679263695 114.613585791\n",
      "995367 34 0.584701894339 131.218396692\n",
      "995367 35 0.610298205526 151.452829993\n"
     ]
    }
   ],
   "source": [
    "import lsst.daf.persistence as dafPersist\n",
    "import lsst.afw.table as afwTable\n",
    "import lsst.afw.geom as afwGeom\n",
    "import lsst.afw.image as afwImage\n",
    "import lsst.meas.algorithms as measAlg\n",
    "import lsst.obs.cfht as obs_cfht\n",
    "from lsst.afw.cameraGeom import Camera\n",
    "from lsst.afw.cameraGeom import PUPIL, FOCAL_PLANE\n",
    "import lsst.afw.display.ds9 as ds9\n",
    "import math\n",
    "\n",
    "# Image identification keys\n",
    "visit = 995367\n",
    "filter = 'r'\n",
    "\n",
    "sigmaToFwhm = 2.0*math.sqrt(2.0*math.log(2.0)) \n",
    "\n",
    "#setup butler - Replace path name by something suitable for you\n",
    "butler = dafPersist.Butler(\"/home/boutigny/CFHT/test_cfht/output\")\n",
    "\n",
    "mapper = obs_cfht.MegacamMapper()\n",
    "camera = mapper.camera\n",
    "\n",
    "fwhmDist = []\n",
    "distDist = []\n",
    "for ccd in range(36) :\n",
    "\n",
    "    dataid = {'visit':visit, 'filter':filter, 'ccd':ccd}\n",
    "\n",
    "    # Retrieve calexp\n",
    "    calexp = butler.get('calexp', dataid)\n",
    "\n",
    "    x0 = calexp.getX0()\n",
    "    y0 = calexp.getY0()\n",
    "    width = calexp.getWidth()\n",
    "    height = calexp.getHeight()\n",
    "    wcs = calexp.getWcs()\n",
    "\n",
    "    psf = calexp.getPsf()\n",
    "    \n",
    "    # Display PSF\n",
    "    #image = psf.computeImage()\n",
    "    #print dir(image)\n",
    "    #ds9.mtv(image,frame=1)\n",
    "    \n",
    "    shape = psf.computeShape()\n",
    "    \n",
    "    attr = measAlg.PsfAttributes(psf, x0 + width // 2, y0 + height // 2)\n",
    "    fwhm = attr.computeGaussianWidth() * wcs.pixelScale().asArcseconds() * sigmaToFwhm\n",
    "    fwhmDist.append(fwhm)\n",
    "    \n",
    "    ccdName = 'ccd'+str(ccd).zfill(2)\n",
    "    ccdGeom = camera[ccdName]\n",
    "    x = ccdGeom.getCenter(FOCAL_PLANE).getPoint().getX()\n",
    "    y = ccdGeom.getCenter(FOCAL_PLANE).getPoint().getY()\n",
    "    dist = math.sqrt(x*x+y*y)\n",
    "    distDist.append(dist)\n",
    "    \n",
    "    print visit, ccd, fwhm, dist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results and compare to SNLS values provided by Pierre Astier for the same exposure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x73cfb10>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x89ae4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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IqGooIgAAAAAAqJoqumf3siALAAAAAACopSgiAAAAAACAaSqdrFKS8vPzlZub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\nuEmTHlNBwfMqLX1ZZ8++o+P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      "text/plain": [
       "<matplotlib.figure.Figure at 0x870bf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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AuBenPA1k9+7dio2N1Zdfflnq6tvt2rWrYLlVh+YAgLvr33+40tIG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\nag0AnmnDhg3q0eNGZWdvkXSZpHUKCemnM2eOys+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x998d490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "\n",
    "fwhmPA = [0.571016,0.555061,0.545662,0.56218,0.542048,0.543374,0.551908,0.565028,0.561476,0.568536,0.552143,0.53626,\n",
    "          0.551186,0.553445,0.553778,0.546209,0.543944,0.547457,0.578563,0.56967,0.549273,0.548339,0.551291,0.560009,\n",
    "          0.550702,0.544072,0.557894,0.614717,0.583981,0.573206,0.560824,0.564616,0.552001,0.562008,0.576915,0.598922]\n",
    "\n",
    "plt.clf()\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=2, ncols=1, figsize=(18,15))\n",
    "ax0.scatter(range(36), fwhmDist)\n",
    "ax0.set_xlim([-1.,36.])\n",
    "ax0.set_xlabel(\"CCD #\")\n",
    "ax0.set_ylabel(\"PSF width in arcsec\")\n",
    "ax0.set_title(\"LSST Stack\")\n",
    "ax1.scatter(range(36), fwhmPA)\n",
    "ax1.set_xlim([-1.,36.])\n",
    "ax1.set_xlabel(\"CCD #\")\n",
    "ax1.set_ylabel(\"PSF width in arcsec\")\n",
    "ax1.set_title(\"SNLS\")\n",
    "\n",
    "fig, (ax3, ax4) = plt.subplots(nrows=1, ncols=2, figsize=(18,10))\n",
    "ax3.scatter(distDist,fwhmDist)\n",
    "ax3.set_xlabel(\"Distance to focal plane center (mm)\")\n",
    "ax3.set_ylabel(\"PSF width (arcsec)\")\n",
    "ax3.set_title(\"LSST Stack\")\n",
    "ax4.scatter(distDist,fwhmPA)\n",
    "ax4.set_xlabel(\"Distance to focal plane center (mm)\")\n",
    "ax4.set_ylabel(\"PSF width (arcsec)\")\n",
    "ax4.set_title(\"SNLS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import lsst.daf.persistence as dafPersist\n",
    "import lsst.afw.table as afwTable\n",
    "import lsst.afw.geom as afwGeom\n",
    "import lsst.afw.image as afwImage\n",
    "import lsst.meas.algorithms as measAlg\n",
    "import math\n",
    "\n",
    "filter = 'r'\n",
    "\n",
    "# Select a chip close to the center of the focal plane\n",
    "ccd = 13\n",
    "\n",
    "sigmaToFwhm = 2.0*math.sqrt(2.0*math.log(2.0)) \n",
    "\n",
    "#setup butler - Replace path name by something suitable for you\n",
    "butler = dafPersist.Butler(\"/home/boutigny/CFHT/test_cfht/output\")\n",
    "\n",
    "filename = \"../utils/stage/obs_D3_r.list\"\n",
    "file = open(filename, \"r\")\n",
    "\n",
    "psfHist = []\n",
    "\n",
    "for visit in file :\n",
    "\n",
    "    dataid = {'visit':visit, 'filter':filter, 'ccd':ccd}\n",
    "\n",
    "    # Retrieve calexp\n",
    "    calexp = butler.get('calexp', dataid)\n",
    "\n",
    "    x0 = calexp.getX0()\n",
    "    y0 = calexp.getY0()\n",
    "    width = calexp.getWidth()\n",
    "    height = calexp.getHeight()\n",
    "    wcs = calexp.getWcs()\n",
    "\n",
    "    psf = calexp.getPsf()\n",
    "    shape = psf.computeShape()\n",
    "\n",
    "    attr = measAlg.PsfAttributes(psf, x0 + width // 2, y0 + height // 2)\n",
    "    fwhm = attr.computeGaussianWidth() * wcs.pixelScale().asArcseconds() * sigmaToFwhm\n",
    "        \n",
    "    if fwhm < 0.1 :\n",
    "        print visit, ccd, fwhm\n",
    "    \n",
    "    psfHist.append(fwhm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.clf()\n",
    "fig, (ax0, ax1) = plt.subplots(nrows=2, ncols=1, figsize=(18,15))\n",
    "ax0.hist(fwhmDist)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}