Bellini_Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline --no-import-all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import os\n",
    "# os.environ['PYSYN_CDBS'] = '/Users/jlong/cdbs/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jlong/lib/python3.6/site-packages/pysynphot/locations.py:119: UserWarning: Extinction files should be moved to $PYSYN_CDBS/extinction for compatibility with future versions of pysynphot.\n",
      "  warnings.warn('Extinction files should be moved to '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WebbPSF log messages of level INFO and above will be shown.\n",
      "WebbPSF log outputs will be directed to the screen.\n"
     ]
    }
   ],
   "source": [
    "import webbpsf\n",
    "webbpsf.setup_logging()\n",
    "from webbpsf import wfirst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wfi = wfirst.WFI()\n",
    "wfi.filter = 'W149'\n",
    "wfi.detector = 'SCA17'\n",
    "src = webbpsf.specFromSpectralType('G0V', catalog='phoenix')\n",
    "# wfi.options['parity'] = 'odd' # note: not needed when specifying fov_pixels\n",
    "fov = 361\n",
    "wfi.detector_position = (4,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  poppy] Monochromatic calculation requested.\n",
      "[webbpsf] Using the masked WFI pupil shape based on wavelengths requested\n",
      "[  poppy] PSF calc using fov_pixels = 361, oversample = 4, number of wavelengths = 1\n",
      "[webbpsf] Creating optical system model:\n",
      "[  poppy] Initialized OpticalSystem: WFIRST+WFI\n",
      "[  poppy] WFIRST Entrance Pupil: Loaded amplitude transmission from /Users/jlong/software/webbpsf-data/wfc_pupil_masked_rev_mcr.fits\n",
      "[  poppy] WFIRST Entrance Pupil: Loaded OPD from /Users/jlong/software/webbpsf-data/upscaled_HST_OPD.fits\n",
      "[  poppy] Added pupil plane: WFIRST Entrance Pupil\n",
      "[  poppy] Added coordinate inversion plane: OTE exit pupil\n",
      "[  poppy] Added pupil plane: Field Dependent Aberration (SCA17)\n",
      "[  poppy] Added detector with pixelscale=0.11 and oversampling=4: WFI detector\n",
      "[  poppy] Calculating PSF with 1 wavelengths\n",
      "[  poppy]  Propagating wavelength = 1.49e-06 m\n",
      "[  poppy]   Calculation completed in 16.256 s\n",
      "[  poppy] PSF Calculation completed.\n",
      "[  poppy]  Adding extension with image downsampled to detector pixel scale.\n"
     ]
    }
   ],
   "source": [
    "psf_odd = wfi.calc_psf(monochromatic=1.49e-6, source=src, fov_pixels=fov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  poppy] Monochromatic calculation requested.\n",
      "[webbpsf] Using the masked WFI pupil shape based on wavelengths requested\n",
      "[  poppy] PSF calc using fov_pixels = 362, oversample = 4, number of wavelengths = 1\n",
      "[webbpsf] Creating optical system model:\n",
      "[  poppy] Initialized OpticalSystem: WFIRST+WFI\n",
      "[  poppy] WFIRST Entrance Pupil: Loaded amplitude transmission from /Users/jlong/software/webbpsf-data/wfc_pupil_masked_rev_mcr.fits\n",
      "[  poppy] WFIRST Entrance Pupil: Loaded OPD from /Users/jlong/software/webbpsf-data/upscaled_HST_OPD.fits\n",
      "[  poppy] Added pupil plane: WFIRST Entrance Pupil\n",
      "[  poppy] Added coordinate inversion plane: OTE exit pupil\n",
      "[  poppy] Added pupil plane: Field Dependent Aberration (SCA17)\n",
      "[  poppy] Added detector with pixelscale=0.11 and oversampling=4: WFI detector\n",
      "[  poppy] Calculating PSF with 1 wavelengths\n",
      "[  poppy]  Propagating wavelength = 1.49e-06 m\n",
      "[  poppy]   Calculation completed in 4.645 s\n",
      "[  poppy] PSF Calculation completed.\n",
      "[  poppy]  Adding extension with image downsampled to detector pixel scale.\n"
     ]
    }
   ],
   "source": [
    "psf_even = wfi.calc_psf(monochromatic=1.49e-6, source=src, fov_pixels=fov + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: (No file associated with this HDUList)\n",
      "No.    Name         Type      Cards   Dimensions   Format\n",
      "  0  OVERSAMP    PrimaryHDU      43   (1444, 1444)   float64   \n",
      "  1  DET_SAMP    ImageHDU        45   (361, 361)   float64   \n"
     ]
    }
   ],
   "source": [
    "psf_odd.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: (No file associated with this HDUList)\n",
      "No.    Name         Type      Cards   Dimensions   Format\n",
      "  0  OVERSAMP    PrimaryHDU      43   (1448, 1448)   float64   \n",
      "  1  DET_SAMP    ImageHDU        45   (362, 362)   float64   \n"
     ]
    }
   ],
   "source": [
    "psf_even.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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gmVXU2pxTl9s4In4DKCIejogvAX/V5prMrEXOr2w+RWtWUT0QfnmslDQMWCDp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\nZtZj/j9dQ0bTWM6szQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10abd67b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "plt.subplot(1, 2, 1)\n",
    "webbpsf.display_psf(psf_odd, imagecrop=1.0, ext='DET_SAMP')\n",
    "plt.subplot(1, 2, 2)\n",
    "webbpsf.display_psf(psf_even, imagecrop=1.0, ext='DET_SAMP')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}