{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to crispy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This software is designed to simulate lenslet array-based Integral Field Spectrographs and their reduction process. This was developed within the context of NASA's WFIRST Coronagraph mission."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we go straight to the point and illustrate how to use the code. We require astropy, numpy. This notebook was written in a Python 3.5 kernel but should be backward compatible with Python 2.7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a polychromatic flatfield"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we need to load the various modules that we need. Since the notebook can be located anywhere, we need to add the location of the Python code to the path first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys,os\n",
    "import numpy as np\n",
    "from crispy.tools.initLogger import getLogger\n",
    "log = getLogger('crispy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the IFS parameters are contained within a Python class called 'Params'. This class is initialized using only the path the 'code' directory from the repo. In particular, the Params class stores all the relevant paths. All parameters can be changed on the fly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/Users/mrizzo/IFS/crispy/crispy/WFIRST/')\n",
    "from params import Params\n",
    "par = Params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the parameters are stored in this class. During the various steps of the software, they are appended to a header file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standard                      \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    0 / number of array dimensions                     \n",
       "EXTEND  =                    T                                                  \n",
       "COMMENT                                                                         \n",
       "COMMENT ************************************************************            \n",
       "COMMENT ********************** General parameters ******************            \n",
       "COMMENT ************************************************************            \n",
       "COMMENT                                                                         \n",
       "NLENS   =                  108 / # lenslets across array                        \n",
       "PITCH   =             0.000174 / Lenslet pitch (meters)                         \n",
       "INTERLAC=                  2.0 / Interlacing                                    \n",
       "PHILENS =    26.56505117707799 / Rotation angle of the lenslets (deg)           \n",
       "PIXSIZE =              1.3E-05 / Pixel size (meters)                            \n",
       "LENSAMP =                  0.5 / Lenslet sampling (lam/D)                       \n",
       "LSAMPWAV=                660.0 / Lenslet sampling wavelength (nm)               \n",
       "FWHM    =                  2.0 / FHWM of PSFLet at detector (pixels)            \n",
       "FWHMLAM =                660.0 / Wavelength at which FWHM is defined (nm)       \n",
       "NPIX    =                 1024 / Number of detector pixels                      \n",
       "BW      =                 0.18 / Bandwidth                                      \n",
       "PIXPRLAM=                  2.0 / Pixels per resolution element                  \n",
       "RESLSTSQ=                  2.0 / Nspec per Nyq. sample for lstsq extraction     \n",
       "R       =                   50 / Spectral resolution                            "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "par.hdr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the flatfield"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a flatfield is a function built into the unitTests module. In this case, we will construct a cube of nlam wavelength slices, each having 512x512 pixels of value 1. The standard "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function testCreateFlatfield in module crispy.unitTests:\n",
      "\n",
      "testCreateFlatfield(par, pixsize=0.1, npix=512, pixval=1.0, Nspec=45, outname='flatfield.fits', useQE=True, method='optext')\n",
      "    Creates a polychromatic flatfield\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    par :   Parameter instance\n",
      "        Contains all IFS parameters\n",
      "    pixsize:   float\n",
      "       Pixel scale (lam/D)\n",
      "    npix: int\n",
      "        Each input frame has a pixel size npix x npix\n",
      "    pixval: float\n",
      "        Each input frame has a unform value pixval in photons per second per nm of bandwidth\n",
      "    Nspec: float\n",
      "        Optional input forcing the number of wavelengths bins used\n",
      "    outname: string\n",
      "        Name of flatfield image\n",
      "    useQE: boolean \n",
      "        Whether to take into account the wavelength-dependent QE of the detector\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from crispy.unitTests import testCreateFlatfield\n",
    "help(testCreateFlatfield)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This test function will create a polychromatic flatfield at the wavelengths provided by the existing wavelength calibration. Those wavelengths can be retrieved as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function calculateWaveList in module crispy.tools.reduction:\n",
      "\n",
      "calculateWaveList(par, lam_list=None, Nspec=None, method='lstsq')\n",
      "    Computes the wavelength lists corresponding to the center and endpoints of each\n",
      "    spectral bin. Wavelengths are separated by a constant value in log space. Number of\n",
      "    wavelengths depends on spectral resolution.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    par:    Parameter instance\n",
      "            Contains all IFS parameters\n",
      "    lam_list:   list of floats\n",
      "            Usually this is left to None. If so, we use the wavelengths used for wavelength\n",
      "            calibration. Otherwise, we could decide to focus on a smaller/larger region of\n",
      "            the spectrum to retrieve. The final processed cubes will have bins centered\n",
      "            on lam_midpts\n",
      "    Nspec: int\n",
      "            If specified, forces the number of bins in the final cube (uses np.linspace)\n",
      "              \n",
      "    Returns\n",
      "    -------\n",
      "    lam_midpts: list of floats\n",
      "            Wavelengths at the midpoint of each bin\n",
      "    lam_endpts: list of floats\n",
      "            Wavelengths at the edges of each bin\n",
      "\n",
      "crispy - INFO - Reduced cube will have 19 wavelength bins\n",
      "[ 603.48591977  609.29942699  615.16893696  621.09498916  627.07812827\n",
      "  633.11890422  639.21787224  645.37559291  651.5926322   657.86956154\n",
      "  664.20695787  670.60540367  677.06548705  683.58780178  690.17294734\n",
      "  696.821529    703.53415785  710.31145087  717.15403098]\n"
     ]
    }
   ],
   "source": [
    "from crispy.tools.reduction import calculateWaveList\n",
    "help(calculateWaveList)\n",
    "lam_midpts,lam_endpts = calculateWaveList(par)\n",
    "print(lam_midpts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create our flatfield. By default it will be stored as par.unitTestsOutput/flatfield.fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Reduced cube will have 44 wavelength bins\n",
      "crispy - INFO - The number of input pixels per lenslet is 5.016181\n",
      "crispy - INFO - Using PSFlet gaussian approximation\n",
      "crispy - WARNING - Assuming endpoints wavelist is given\n",
      "crispy - INFO - Creating Gaussian PSFLet templates\n",
      "crispy - INFO - Writing data to ..//SimResults/detectorFramepoly.fits\n",
      "crispy - INFO - Done.\n",
      "crispy - INFO - Performance: 52 seconds total\n",
      "crispy - INFO - Writing data to ..//unitTestsOutputs/flatfield.fits\n"
     ]
    }
   ],
   "source": [
    "testCreateFlatfield(par,useQE=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standard                      \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    0 / number of array dimensions                     \n",
       "EXTEND  =                    T                                                  \n",
       "COMMENT                                                                         \n",
       "COMMENT ************************************************************            \n",
       "COMMENT ********************** General parameters ******************            \n",
       "COMMENT ************************************************************            \n",
       "COMMENT                                                                         \n",
       "NLENS   =                  108 / # lenslets across array                        \n",
       "PITCH   =             0.000174 / Lenslet pitch (meters)                         \n",
       "INTERLAC=                  2.0 / Interlacing                                    \n",
       "PHILENS =    26.56505117707799 / Rotation angle of the lenslets (deg)           \n",
       "PIXSIZE =              1.3E-05 / Pixel size (meters)                            \n",
       "LENSAMP =                  0.5 / Lenslet sampling (lam/D)                       \n",
       "LSAMPWAV=                660.0 / Lenslet sampling wavelength (nm)               \n",
       "FWHM    =                  2.0 / FHWM of PSFLet at detector (pixels)            \n",
       "FWHMLAM =                660.0 / Wavelength at which FWHM is defined (nm)       \n",
       "NPIX    =                 1024 / Number of detector pixels                      \n",
       "BW      =                 0.18 / Bandwidth                                      \n",
       "PIXPRLAM=                  2.0 / Pixels per resolution element                  \n",
       "RESLSTSQ=                  2.0 / Nspec per Nyq. sample for lstsq extraction     \n",
       "R       =                   50 / Spectral resolution                            \n",
       "COMMENT                                                                         \n",
       "COMMENT ************************************************************            \n",
       "COMMENT ********************** IFS Simulation ******************                \n",
       "COMMENT ************************************************************            \n",
       "COMMENT                                                                         \n",
       "SCALE   =    5.016181205573144 / Factor by which the input slice is rescaled    \n",
       "INSLICES=                   44 / Number of wavelengths in input cube            \n",
       "ADJUST  =              0.98898 / Adjustment factor for rebinning error          "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "par.hdr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Depending on your version of Python you might see a bunch of VisibleDeprecationWarning. We are working on fixing that. Let's see what we got."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline --no-import-all\n",
    "matplotlib.rcParams['image.origin'] = 'lower'\n",
    "matplotlib.rcParams['image.interpolation'] = 'nearest'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Read data from HDU 1 of ..//unitTestsOutputs/flatfield.fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x117420a50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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joaVst3U8tIbtSx0P5Pttq3iora1t1sKZ9N8Xi23ew7fV3Hig/RzbbT4s1VbZ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3nnHYntadOm+dWo2Pbnh1deeaVCbIuaYNgW/dC6dWu/bO/atQstnBmIbaxwpt1uV2q++uor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\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T4+3tDR8fH+b96f3m5OQgNTWV0fj4+MDb29twKDw7O5tZ8QHY5/nJXLVsaigzMxMZGRmM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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10adc3110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from crispy.tools.image import Image\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(Image(par.unitTestsOutputs+'/flatfield.fits').data,cmap='gray')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A zoom-in version is visible here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Read data from HDU 1 of ..//unitTestsOutputs/flatfield.fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x117491210>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8du5Bp2crYsMwjKL0S4u4Vh+xiKQ+mW8N6BZIzAec18Wax8uapZWn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\nG4ZhNMz/B2W2w0WafocxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11743f510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "img = Image(par.unitTestsOutputs+'/flatfield.fits').data\n",
    "subsize = 50\n",
    "plt.imshow(img[par.npix//2-subsize:par.npix//2+subsize,par.npix//2-subsize:par.npix//2+subsize],cmap='gray')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate detector readout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have also some routines that can add noise to the detector. Assuming that the input detector map is in photons per second, we can 'detectorify' this map by adding Poisson noise, read noise, CIC noise, and dark current noise. In the future, we will also implement Electron-multiplying noise and Traps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Read data from HDU 1 of ..//unitTestsOutputs/flatfield.fits\n"
     ]
    }
   ],
   "source": [
    "from crispy.tools.image import Image\n",
    "from crispy.tools.detector import readDetector,averageDetectorReadout\n",
    "flat = Image(filename=par.unitTestsOutputs+'/flatfield.fits')\n",
    "par.nonoise=False\n",
    "# turn off photon counting otherwise things are strange\n",
    "par.EMStats =False # turn off EM register statistics\n",
    "par.PCmode = False # turn off photon counting threshold\n",
    "read=readDetector(par,flat,inttime=100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x1179236d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vM6uak7eZWct4tImZWUvVmrw9ScfMrIXc8jazqtXa8nbyNrOqOXmbmbWQk/dpamRkJBuz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\nbzOzFnLyNjNrISdvM7MW+v8RdtsFGXA8WAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae17c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "subsize = 15\n",
    "\n",
    "plt.imshow(read[par.npix//2-subsize:par.npix//2+subsize,par.npix//2-subsize:par.npix//2+subsize],cmap='gray')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save the noisified frame to a new name. This is useful to show since writing to FITS is a very common task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Writing data to ..//unitTestsOutputs/flatfield_noise.fits\n"
     ]
    }
   ],
   "source": [
    "newImage = Image(data=read,header=par.hdr)\n",
    "newImage.write(par.unitTestsOutputs+'/flatfield_noise.fits',clobber=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reduction step is straightforward, as long as the wavelength calibration is good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Read data from HDU 1 of ..//unitTestsOutputs/flatfield_noise.fits\n",
      "crispy - INFO - Reduced cube will have 19 wavelength bins\n",
      "crispy - INFO - Elapsed time: 1.748347s\n"
     ]
    }
   ],
   "source": [
    "from crispy.IFS import reduceIFSMap\n",
    "cube = reduceIFSMap(par,par.unitTestsOutputs+'/flatfield_noise.fits')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Now we can display the cube interactively, or look it up with DS9 (it is located in par.exportDir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "200bdae0bb3c49ff95e6887355ea4ef6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets\n",
    "def plt_ifs_optext(wchan):\n",
    "    plt.imshow(cube.data[wchan-1,:,:],\n",
    "                cmap='gist_heat')\n",
    "    plt.colorbar()\n",
    "ipywidgets.interact(plt_ifs_optext, wchan=(1,cube.data.shape[0]));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look now at the header of the created file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standard                      \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    0 / number of array dimensions                     \n",
       "EXTEND  =                    T                                                  \n",
       "COMMENT                                                                         \n",
       "COMMENT ************************************************************            \n",
       "COMMENT ********************** General parameters ******************            \n",
       "COMMENT ************************************************************            \n",
       "COMMENT                                                                         \n",
       "NLENS   =                  108 / # lenslets across array                        \n",
       "PITCH   =             0.000174 / Lenslet pitch (meters)                         \n",
       "INTERLAC=                  2.0 / Interlacing                                    \n",
       "PHILENS =    26.56505117707799 / Rotation angle of the lenslets (deg)           \n",
       "PIXSIZE =              1.3E-05 / Pixel size (meters)                            \n",
       "LENSAMP =                  0.5 / Lenslet sampling (lam/D)                       \n",
       "LSAMPWAV=                660.0 / Lenslet sampling wavelength (nm)               \n",
       "FWHM    =                  2.0 / FHWM of PSFLet at detector (pixels)            \n",
       "FWHMLAM =                660.0 / Wavelength at which FWHM is defined (nm)       \n",
       "NPIX    =                 1024 / Number of detector pixels                      \n",
       "BW      =                 0.18 / Bandwidth                                      \n",
       "PIXPRLAM=                  2.0 / Pixels per resolution element                  \n",
       "RESLSTSQ=                  2.0 / Nspec per Nyq. sample for lstsq extraction     \n",
       "R       =                   50 / Spectral resolution                            \n",
       "COMMENT                                                                         \n",
       "COMMENT ************************************************************            \n",
       "COMMENT ********************** IFS Simulation ******************                \n",
       "COMMENT ************************************************************            \n",
       "COMMENT                                                                         \n",
       "SCALE   =    5.016181205573144 / Factor by which the input slice is rescaled    \n",
       "INSLICES=                   44 / Number of wavelengths in input cube            \n",
       "ADJUST  =              0.98898 / Adjustment factor for rebinning error          \n",
       "COMMENT                                                                         \n",
       "COMMENT ************************************************************            \n",
       "COMMENT ********************** Detector readout ********************            \n",
       "COMMENT ************************************************************            \n",
       "COMMENT                                                                         \n",
       "TRANS   =                  1.0 / IFS Transmission factor                        \n",
       "POL     =                  1.0 / Polarization losses                            \n",
       "PHCTEFF =                  1.0 / Photon counting efficiency                     \n",
       "NONOISE =                    F / Ignore all noises?                             \n",
       "POISSON =                    T / Poisson noise?                                 \n",
       "RN      =                100.0 / Read noise (electrons/read)                    \n",
       "CIC     =                 0.01 / Clock-induced charge                           \n",
       "DARK    =               0.0002 / Dark current                                   \n",
       "TRAPS   =                    F / Use traps? T/F                                 \n",
       "EMSTATS =                    F / EM statistics?                                 \n",
       "EMGAIN  =               2500.0 / Gain of the EM stage                           \n",
       "PCBIAS  =                  200 / To make RN zero-mean                           \n",
       "PCMODE  =                    F / Photon counting mode?                          \n",
       "LIFEFRAC=                  0.0 / Mission life fraction (changes CTE if >0)      \n",
       "INTTIME =                  100                                                  \n",
       "COMMENT                                                                         \n",
       "COMMENT ************************************************************            \n",
       "COMMENT ********************** Cube Extraction *********************            \n",
       "COMMENT ************************************************************            \n",
       "COMMENT                                                                         \n",
       "R       =                   50 / Spectral resolution of final cube              \n",
       "CALDIR  = 'wavecalR50_660'     / Directory of wavelength solution               \n",
       "CUBEMODE= 'Optimal Extraction' / Method used to extract data cube               \n",
       "LAM_MIN =    603.4859197661123 / Minimum mid wavelength of extracted cube       \n",
       "LAM_MAX =    717.1540309800992 / Maximum mid wavelength of extracted cube       \n",
       "DLOGLAM = 0.009587107513934413 / Log spacing of extracted wavelength bins       \n",
       "NLAM    =                   19 / Number of extracted wavelengths                \n",
       "CTYPE1  = 'RA---TAN'           / first parameter RA  ,  projection TANgential   \n",
       "CTYPE2  = 'DEC--TAN'           / second parameter DEC,  projection TANgential   \n",
       "CRVAL1  =                  0.0 / Reference X pixel value                        \n",
       "CRVAL2  =                  0.0 / Reference Y pixel value                        \n",
       "CRPIX1  =                   54 / Reference X pixel                              \n",
       "CRPIX2  =                   54 / Reference Y pixel                              \n",
       "EQUINOX =                 2000 / Equinox of coordinates                         \n",
       "CD1_1   = -2.4845199749997E-06 / Rotation matrix coefficient                    \n",
       "CD1_2   = 1.24225998749988E-06 / Rotation matrix coefficient                    \n",
       "CD2_1   = 1.24225998749988E-06 / Rotation matrix coefficient                    \n",
       "CD2_2   = 2.48451997499976E-06 / Rotation matrix coefficient                    \n",
       "CTYPE3  = 'WAVE-LOG'                                                            \n",
       "CUNIT3  = 'nm      '                                                            \n",
       "CRVAL3  =    603.4859197661123                                                  \n",
       "CDELT3  =    6.307066216622808                                                  \n",
       "CRPIX3  =                    1                                                  \n",
       "SMOOTHED=                    T / Cube smoothed over bad lenslets                "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cube.header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process cubes from John Krist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / conforms to FITS standard                      \n",
       "BITPIX  =                    8 / array data type                                \n",
       "NAXIS   =                    0 / number of array dimensions                     \n",
       "EXTEND  =                    T                                                  \n",
       "COMMENT                                                                         \n",
       "COMMENT ************************************************************            \n",
       "COMMENT ********************** General parameters ******************            \n",
       "COMMENT ************************************************************            \n",
       "COMMENT                                                                         \n",
       "NLENS   =                  108 / # lenslets across array                        \n",
       "PITCH   =             0.000174 / Lenslet pitch (meters)                         \n",
       "INTERLAC=                  2.0 / Interlacing                                    \n",
       "PHILENS =    26.56505117707799 / Rotation angle of the lenslets (deg)           \n",
       "PIXSIZE =              1.3E-05 / Pixel size (meters)                            \n",
       "LENSAMP =                  0.5 / Lenslet sampling (lam/D)                       \n",
       "LSAMPWAV=                660.0 / Lenslet sampling wavelength (nm)               \n",
       "FWHM    =                  2.0 / FHWM of PSFLet at detector (pixels)            \n",
       "FWHMLAM =                660.0 / Wavelength at which FWHM is defined (nm)       \n",
       "NPIX    =                 1024 / Number of detector pixels                      \n",
       "BW      =                 0.18 / Bandwidth                                      \n",
       "PIXPRLAM=                  2.0 / Pixels per resolution element                  \n",
       "RESLSTSQ=                  2.0 / Nspec per Nyq. sample for lstsq extraction     \n",
       "R       =                   50 / Spectral resolution                            "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cleans the header\n",
    "par.makeHeader()\n",
    "par.hdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Read data from HDU 0 of /Users/mrizzo/IFS/OS5/offaxis/spc_offaxis_psf.fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / Written by IDL:  Tue Feb 21 14:04:33 2017      \n",
       "BITPIX  =                  -64 / Number of bits per data pixel                  \n",
       "NAXIS   =                    3 / Number of data axes                            \n",
       "NAXIS1  =                  256 /Number of positions along axis 1                \n",
       "NAXIS2  =                  256 /Number of positions along axis 2                \n",
       "NAXIS3  =                   45 /Number of positions along axis 3                \n",
       "DATE    = '2017-02-17'         / Creation UTC (CCCC-MM-DD) date of FITS header  \n",
       "NLAM    =                   45 / Number of wavelengths                          \n",
       "LAM_C   =             0.800000 / Passband central wavelength in microns         \n",
       "LAM_MIN =       0.728000000000 / Minimum passband wavelength                    \n",
       "LAM_MAX =       0.872000000000 / Maximum passband wavelength                    \n",
       "PIXSIZE =             0.100000 / sampling in lam_c/D radians                    \n",
       "OFFSET  =              7.00000 / offset of star in lam_c/D radians              "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of John Krist cube\n",
    "offaxispsf= '/Users/mrizzo/IFS/OS5/offaxis/spc_offaxis_psf.fits'\n",
    "offaxis = Image(offaxispsf)\n",
    "offaxis.header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shift the cube to the correct wavelength range corresponding to the band. We assume that the PSF doesn't change, we just change the header keywords."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SIMPLE  =                    T / Written by IDL:  Tue Feb 21 14:04:33 2017      \n",
       "BITPIX  =                  -64 / Number of bits per data pixel                  \n",
       "NAXIS   =                    3 / Number of data axes                            \n",
       "NAXIS1  =                  256 /Number of positions along axis 1                \n",
       "NAXIS2  =                  256 /Number of positions along axis 2                \n",
       "NAXIS3  =                   45 /Number of positions along axis 3                \n",
       "DATE    = '2017-02-17'         / Creation UTC (CCCC-MM-DD) date of FITS header  \n",
       "NLAM    =                   45 / Number of wavelengths                          \n",
       "LAM_C   =                 0.66 / Passband central wavelength in microns         \n",
       "LAM_MIN =   0.6005999999999999 / Minimum passband wavelength                    \n",
       "LAM_MAX =   0.7193999999999999 / Maximum passband wavelength                    \n",
       "PIXSIZE =             0.100000 / sampling in lam_c/D radians                    \n",
       "OFFSET  =              7.00000 / offset of star in lam_c/D radians              "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from crispy.tools.inputScene import adjust_krist_header\n",
    "adjust_krist_header(offaxis,lamc=660.)\n",
    "offaxis.header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process the cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 600.6  603.3  606.   608.7  611.4  614.1  616.8  619.5  622.2  624.9\n",
      "  627.6  630.3  633.   635.7  638.4  641.1  643.8  646.5  649.2  651.9\n",
      "  654.6  657.3  660.   662.7  665.4  668.1  670.8  673.5  676.2  678.9\n",
      "  681.6  684.3  687.   689.7  692.4  695.1  697.8  700.5  703.2  705.9\n",
      "  708.6  711.3  714.   716.7  719.4]\n",
      "crispy - INFO - The number of input pixels per lenslet is 5.000000\n",
      "crispy - INFO - Using PSFlet gaussian approximation\n",
      "crispy - WARNING - Assuming slices are evenly spread in wavelengths\n",
      "crispy - INFO - Creating Gaussian PSFLet templates\n",
      "crispy - INFO - Writing data to ..//SimResults/detectorFramepoly.fits\n",
      "crispy - INFO - Done.\n",
      "crispy - INFO - Performance: 38 seconds total\n",
      "crispy - INFO - Writing data to ..//SimResults/test_krist_cube.fits\n"
     ]
    }
   ],
   "source": [
    "par.saveDetector=False\n",
    "lamlist = np.linspace(offaxis.header['LAM_MIN'],offaxis.header['LAM_MAX'],offaxis.header['NLAM'])*1000\n",
    "print(lamlist)\n",
    "\n",
    "from crispy.IFS import polychromeIFS\n",
    "detectorFrame = polychromeIFS(par,lamlist,offaxis,QE=True)\n",
    "\n",
    "# save\n",
    "Image(data=detectorFrame,header=par.hdr).write(par.exportDir+'/test_krist_cube.fits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reduce the cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Read data from HDU 1 of ..//SimResults/test_krist_cube.fits\n",
      "crispy - INFO - Reduced cube will have 19 wavelength bins\n",
      "crispy - INFO - Elapsed time: 1.641858s\n"
     ]
    }
   ],
   "source": [
    "reduced = reduceIFSMap(par,par.exportDir+'/test_krist_cube.fits')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ccccaf98bf8f49258e2bed3648f767c2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plt_ifs_optext(wchan):\n",
    "    plt.imshow(reduced.data[wchan-1,:,:],\n",
    "                cmap='gist_heat')\n",
    "    plt.colorbar()\n",
    "ipywidgets.interact(plt_ifs_optext, wchan=(1,reduced.data.shape[0]));\n"
   ]
  }
 ],
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