FillFactor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate the fill factor for an LSST field of view.\n",
    "\n",
    "Uses the camera geometry that is intended to represent the actual focal plane (or at least, one vendor's version of the focal plane geometry). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import itertools\n",
    "\n",
    "# For the footprint generation and conversion between galactic/equatorial coordinates.\n",
    "from lsst.sims.utils import haversine, ObservationMetaData\n",
    "from lsst.obs.lsstSim import LsstSimMapper\n",
    "from lsst.sims.coordUtils import chipNameFromPupilCoords\n",
    "\n",
    "import lsst.sims.utils as sims_utils\n",
    "\n",
    "mapper = LsstSimMapper()\n",
    "camera = mapper.camera\n",
    "epoch = 2000.0\n",
    "site = sims_utils.Site()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Set up pupil coordinate points to iterate over (to calculate fill factor)\n",
    "testrange = 2.2\n",
    "spacing = 0.01\n",
    "xcen = 0\n",
    "ycen = 0\n",
    "xi = np.arange(xcen-np.radians(testrange), xcen+np.radians(testrange), np.radians(spacing))\n",
    "yi = np.arange(ycen-np.radians(testrange), ycen+np.radians(testrange), np.radians(spacing))\n",
    "x = []\n",
    "y = []\n",
    "for i in itertools.product(xi, yi):\n",
    "    x.append(i[0])\n",
    "    y.append(i[1])\n",
    "x = np.array(x)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Calculate 'visibility'\n",
    "chipNames = chipNameFromPupilCoords(x, y, camera=camera)\n",
    "vis = np.zeros(len(chipNames))\n",
    "for i, chip in enumerate(chipNames):\n",
    "    if chip is not None:\n",
    "        vis[i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.03054326] [ 0.03054326] [ 0.03055276] [ 0.03056702]\n"
     ]
    }
   ],
   "source": [
    "# Confirm pupil coordinates are basically just the same as ra/dec offsets from boresight\n",
    "import palpy \n",
    "ra = np.array([np.radians(1.75)])\n",
    "dec = np.array([np.radians(1.75)])\n",
    "xp, yp = palpy.ds2tpVector(ra, dec, 0, 0)\n",
    "print ra, dec, xp, yp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner radius matching area_degsq for circle 1.74807748895\n",
      "max distance from center used for chip calculation 3.11088748063\n",
      "fill factor 0.895371730367\n",
      "inner radius matching area_degsq for circle 1.75\n",
      "max distance from center used for chip calculation 3.11088748063\n",
      "fill factor 0.895267496128\n",
      "outer radius 2.06\n",
      "max distance from center used for chip calculation 3.11088748063\n",
      "fill factor 0.717074122139\n"
     ]
    }
   ],
   "source": [
    "# Calculate % fill factor, for a particular assumed (circular) fov\n",
    "area_degsq = 9.6\n",
    "innerradius = np.sqrt(area_degsq/np.pi)\n",
    "print 'inner radius matching area_degsq for circle', innerradius\n",
    "sep = np.degrees(sims_utils.haversine(x, y, xcen, ycen))\n",
    "print 'max distance from center used for chip calculation', sep.max()\n",
    "incircle = np.where(sep < innerradius)[0]\n",
    "onchip = np.where(vis[incircle] == 1)[0]\n",
    "fillfactor = len(vis[onchip]) / float(len(vis[incircle]))\n",
    "print 'fill factor', fillfactor\n",
    "\n",
    "area_degsq = 9.6\n",
    "innerradius2 = 1.75\n",
    "print 'inner radius matching area_degsq for circle', innerradius2\n",
    "sep = np.degrees(sims_utils.haversine(x, y, xcen, ycen))\n",
    "print 'max distance from center used for chip calculation', sep.max()\n",
    "incircle = np.where(sep < innerradius2)[0]\n",
    "onchip = np.where(vis[incircle] == 1)[0]\n",
    "fillfactor = len(vis[onchip]) / float(len(vis[incircle]))\n",
    "print 'fill factor', fillfactor\n",
    "\n",
    "outerradius = 2.06\n",
    "print 'outer radius', outerradius\n",
    "sep = np.degrees(sims_utils.haversine(x, y, xcen, ycen))\n",
    "print 'max distance from center used for chip calculation', sep.max()\n",
    "incircle = np.where(sep < outerradius)[0]\n",
    "onchip = np.where(vis[incircle] == 1)[0]\n",
    "fillfactor = len(vis[onchip]) / float(len(vis[incircle]))\n",
    "print 'fill factor', fillfactor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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Qwe8xtfOc4O62v8h7dD/l1n1DtXsxXCrTmId1nieycDOu+NdH/y8xLV0LF0793tt6T8bv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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ffc8150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot results.\n",
    "\n",
    "import matplotlib as mpl\n",
    "inline_rc = dict(mpl.rcParams)\n",
    "mpl.rcParams.update(mpl.rcParamsDefault)\n",
    "mpl.rcParams.update(inline_rc)\n",
    "plt.rcParams['figure.figsize'] = 8, 6\n",
    "plt.rcParams['font.size'] = 20\n",
    "plt.rcParams['axes.titlesize'] = 24\n",
    "plt.rcParams['axes.labelsize'] = 22\n",
    "\n",
    "plt.figure()\n",
    "plt.axis('equal')\n",
    "condition = np.where(vis == 1)[0]\n",
    "#plt.plot(np.degrees(x), np.degrees(y), 'g.', markersize=0.2)\n",
    "plt.plot(np.degrees(x[condition]), np.degrees(y[condition]), 'b.', markersize=0.3)\n",
    "plt.plot(0, 0, 'r.')\n",
    "plt.xlabel('x Pupil (deg)', fontsize='larger')\n",
    "plt.ylabel('y Pupil (deg))', fontsize='larger')\n",
    "theta = np.arange(0, 2*np.pi, 0.01)\n",
    "plt.plot(innerradius*np.cos(theta)+np.degrees(xcen), innerradius*np.sin(theta)+np.degrees(ycen), 'r-')\n",
    "plt.plot(outerradius*np.cos(theta)+np.degrees(xcen), outerradius*np.sin(theta)+np.degrees(ycen), 'r:')\n",
    "#plt.xlim(fieldra+offset-sep.max()/2.0, fieldra+offset+sep.max()/2.0)\n",
    "plt.ylim(np.degrees(xcen)-sep.max()*.7, np.degrees(ycen)+sep.max()*.7)\n",
    "xco = 0.74\n",
    "plt.figtext(xco, 0.8, 'Inner circle')\n",
    "plt.figtext(xco, 0.75, r' r=1.75$^\\circ$')\n",
    "plt.figtext(xco, 0.7, ' 90%s fill factor' %('%'))\n",
    "plt.figtext(xco, 0.6, 'Outer circle')\n",
    "plt.figtext(xco, 0.55, r' r=2.06$^\\circ$')\n",
    "#plt.figtext(xco, 0.5, ' 71%s fill factor' %('%'))\n",
    "plt.savefig('focalplane.pdf', format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# And zoom in even more, to check raft/ccd spacing. \n",
    "testrange = 1.75\n",
    "spacing = 0.00005\n",
    "xi = np.arange(xcen - np.radians(testrange),\n",
    "                xcen + np.radians(testrange), np.radians(spacing))\n",
    "yi = np.zeros(len(xi)) + ycen\n",
    "\n",
    "# Calculate 'visibility'\n",
    "chipNames = chipNameFromPupilCoords(xi, yi, camera=camera)\n",
    "xvis = np.zeros(len(chipNames))\n",
    "for i, chip in enumerate(chipNames):\n",
    "    if chip is not None:\n",
    "        xvis[i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  43.92  798.66   46.08  799.02   50.04  799.38   43.92  799.74   44.82\n",
      "  799.92   50.22  799.92   44.82  799.92   44.64  800.1    51.12  799.92\n",
      "   44.1   799.56   44.46  799.38   49.68  799.02   45.36  798.66   44.1 ]\n",
      "fill factor in x direction, @ center: 0.948628571429\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b5d6e10>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119eb7d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition = np.where(np.diff(xvis) != 0)[0]\n",
    "print np.diff(np.degrees(xi[condition])*60.*60.)\n",
    "onchip = (np.abs(xi)<1.75)\n",
    "condition = (xvis == 1)\n",
    "fill = len(xvis[condition]) / float(len(xvis[onchip]))\n",
    "print 'fill factor in x direction, @ center:', fill\n",
    "\n",
    "plt.plot(xi, xvis)\n",
    "plt.plot(xi[onchip], np.zeros(len(xi[onchip]))+0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# And zoom in even more, to check raft/ccd spacing. \n",
    "testrange = 1.75\n",
    "spacing = 0.00005\n",
    "yi = np.arange(ycen - np.radians(testrange),\n",
    "                ycen + np.radians(testrange), np.radians(spacing))\n",
    "xi = np.zeros(len(yi)) + xcen\n",
    "\n",
    "# Calculate 'visibility'\n",
    "chipNames = chipNameFromPupilCoords(xi, yi, camera=camera)\n",
    "yvis = np.zeros(len(chipNames))\n",
    "for i, chip in enumerate(chipNames):\n",
    "    if chip is not None:\n",
    "        yvis[i] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  30.42  813.06   30.24  813.42   34.74  813.78   31.86  813.96   30.96\n",
      "  814.32   33.48  814.32   32.04  814.5    29.52  814.32   36.54  814.32\n",
      "   30.78  814.14   28.8   813.78   37.26  813.42   29.52  813.06   31.32]\n",
      "fill factor in y direction, @ center: 0.96449949285\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x119fc9a10>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x105635710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "condition = np.where(np.diff(yvis) != 0)[0]\n",
    "print np.diff(np.degrees(yi[condition])*60.*60.)\n",
    "onchip = (np.abs(yi)<np.radians(1.75))\n",
    "condition = (yvis == 1)\n",
    "fill = len(yvis[condition]) / float(len(yvis[onchip]))\n",
    "print 'fill factor in y direction, @ center:', fill\n",
    "\n",
    "plt.plot(yi, yvis)\n",
    "plt.plot(yi[onchip], np.zeros(len(yi[onchip]))+0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ccd x/y 800.0 814.4\n",
      "ccd gap x/y 45 30.6\n",
      "raft x/y (w/o raft gap) 2490.0 2504.4\n",
      "raft gap x/y 50 35.6\n",
      "ccd and raft areas 651520.0 6235956.0\n",
      "fill factor, without raft gaps 94.0301695522\n",
      "total x/y (including outside gaps) 12700.0 12700.0\n",
      "square side length 12700.0\n",
      "raft xy with raft gaps 2540.0\n",
      "fill factor, with raft gaps 90.8872217744\n",
      "total square degrees for outside fov 12.4452160494\n",
      "13.8544236023\n"
     ]
    }
   ],
   "source": [
    "# From documentation:\n",
    "ccdx = 40*0.2*100\n",
    "ccdy = 40.72*0.2*100\n",
    "ccdgapx = 45\n",
    "ccdgapy = 30.6\n",
    "raftgapx = 50\n",
    "raftgapy = 35.6\n",
    "raftx = (ccdx + ccdgapx + ccdx + ccdgapx + ccdx) \n",
    "rafty = (ccdy + ccdgapy + ccdy + ccdgapy + ccdy)\n",
    "print 'ccd x/y', ccdx, ccdy\n",
    "print 'ccd gap x/y', ccdgapx, ccdgapy\n",
    "print 'raft x/y (w/o raft gap)', raftx, rafty\n",
    "print 'raft gap x/y', raftgapx, raftgapy\n",
    "ccdarea = ccdx * ccdy\n",
    "raftarea = raftx * rafty\n",
    "print 'ccd and raft areas', ccdarea, raftarea\n",
    "fillfactor_active = (189*ccdarea) / (21.*raftarea) * 100\n",
    "print 'fill factor, without raft gaps', fillfactor_active\n",
    "\n",
    "x = 5*(3*ccdx+2*ccdgapx) + 5*raftgapx\n",
    "y = 5*(3*ccdy+2*ccdgapy) + 5*raftgapy\n",
    "print 'total x/y (including outside gaps)', x, y \n",
    "\n",
    "# if we want to make our 'raft' be contiguous squares, squares over whole focal plane\n",
    "# And want raft to be square\n",
    "# 'size' side\n",
    "side = np.sqrt(x*y)\n",
    "print 'square side length', side\n",
    "raftxy = np.sqrt(x*y) / 5.0\n",
    "print 'raft xy with raft gaps', raftxy\n",
    "raftarea = raftxy**2\n",
    "fillfactor_active = (189*ccdarea) / (21.*raftarea) * 100\n",
    "print 'fill factor, with raft gaps', fillfactor_active\n",
    "\n",
    "print 'total square degrees for outside fov', (x*y)/(3600)**2\n",
    "print (2.1)**2*np.pi\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}