{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rapid propagation of an input scene through an HLC coronagraph model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "crispy can simulate propagation of a full scene through various coronagraph models. Several ingredients are required:\n",
    "1. an input scene datacube\n",
    "2. a library of off-axis PSFs for each HLC band that is being considered\n",
    "3. a stellar PSFs (or a time-resolved sequence)\n",
    "\n",
    "In order to obtain the data files required for 2. and 3., please contact the author, maxime.j.rizzo at nasa.gov\n",
    "\n",
    "The following shows how to propagate a scene through a WFIRST HLC model using the three items above. We provide information on how to develop the items above elsewhere in the documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline --no-import-all\n",
    "plt.rc('font', family='serif', serif='Times',size=15)\n",
    "plt.rc('text', usetex=True)\n",
    "plt.rc('xtick', labelsize=20)\n",
    "plt.rc('xtick.major', size=10)\n",
    "plt.rc('ytick.major', size=10)\n",
    "plt.rc('ytick', labelsize=20)\n",
    "plt.rc('axes', labelsize=20)\n",
    "plt.rc('figure',titlesize=25)\n",
    "plt.rcParams['image.origin'] = 'lower'\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['axes.linewidth'] = 2.\n",
    "\n",
    "import logging as log\n",
    "from crispy.tools.initLogger import getLogger\n",
    "log = getLogger('crispy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from astropy.io import fits\n",
    "import astropy.units as u\n",
    "import astropy.constants as c\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define locations of data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hlc_psf_path = os.path.normpath('/Users/mrizzo/Science/Haystacks/hlc_haystacks_scene_last')\n",
    "haystacks_path = '/Users/mrizzo/IFS/crispy/crispy/Inputs/'\n",
    "exportDir = '/Users/mrizzo/Downloads/'\n",
    "cmap='viridis'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define some observation parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pixscale_as_scene = 0.0208 # arcsec/pixel, from IPAC instrument parameter db\n",
    "wavelens = np.array([506, 575, 661, 721, 883]) * u.nm # from IPAC instrument parameter db  \n",
    "bandpass_beg = np.array([480, 546, 628, 703, 860])* u.nm\n",
    "bandpass_end = np.array([532, 604, 694, 739, 906])* u.nm\n",
    "\n",
    "os5_wavelen = 550 * u.nm # OS5 HLC simulation center wavelength\n",
    "N_wav = len(wavelens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the input cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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J73Xsu7DaZcP73AbjY1LkSoxjed2LWEr7c3xRS1E3SdrXqhhjkiJZUkLOJun0OnEkTVUn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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d1c5fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# haystacks_fname = os.path.join(haystacks_path,'hay_cube_bandavg_ph.fits.gz')\n",
    "haystacks_fname = os.path.join(haystacks_path,'BertrandCube.fits.gz')\n",
    "hay_cube_bandavg_ph = fits.open(haystacks_fname)[0].data*u.photon/u.s\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(hay_cube_bandavg_ph[0].value, vmax=1,cmap=cmap)\n",
    "plt.colorbar(fraction=0.046, pad=0.04)\n",
    "plt.axis('off')\n",
    "#plt.title('Haystacks scene in ph/s, before coronagraph',fontsize=25)\n",
    "plt.savefig(exportDir+\"Input_Slice.png\",dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(71, 71)\n"
     ]
    }
   ],
   "source": [
    "print hay_cube_bandavg_ph[1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ply,plx = 36,46 # \n",
    "# ply,plx = 36,48"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Photon flux from planet before coronagraph: [ 18.43178092  20.99457165  22.09357535  10.24825766   5.62347613] ph / s\n",
      "crispy - INFO - Typically planet fluxes at the detector will be ~a few percent of this\n"
     ]
    }
   ],
   "source": [
    "log.info('Photon flux from planet before coronagraph: {:}'.format(hay_cube_bandavg_ph[:,ply,plx]))\n",
    "log.info('Typically planet fluxes at the detector will be ~a few percent of this')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scene_imw = hay_cube_bandavg_ph.shape[-1]\n",
    "cx = scene_imw // 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add optical losses & QE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Applied losses and QE\n",
      "crispy - INFO - Contrast: [  1.49927863e-08   1.70774125e-08   1.79713645e-08   6.79576767e-09\n",
      "   1.83964407e-09]\n"
     ]
    }
   ],
   "source": [
    "losses = 0.566 * 0.9 # from Bijan's spreadsheet, does not include PSF throughput (that's in the PSFs directly)\n",
    "qe = np.array([0.92, 0.92, 0.92, 0.75, 0.37])*u.count/u.photon\n",
    "hay_cube_bandavg_ph *= losses*qe[:,np.newaxis,np.newaxis]\n",
    "log.info('Applied losses and QE')\n",
    "# The user may choose to dim the planet at will by multiplying that pixel by a constant\n",
    "fudge = 1.\n",
    "hay_cube_bandavg_ph[:,ply,plx] *= fudge\n",
    "\n",
    "contrast = hay_cube_bandavg_ph[:,ply,plx]/hay_cube_bandavg_ph[1, cx, cx]\n",
    "log.info('Contrast: {:}'.format(contrast))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the sequence of stellar PSFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Pixel scale in binned OS5 PSF series: 0.0208 arcsec = 0.4743 lam/D at wavelength 506.0 nm\n"
     ]
    }
   ],
   "source": [
    "psf_series_47UMa_p13_fname = os.path.join(hlc_psf_path,\n",
    "                                          'OS5_adi_3_highres_polx_lowfc_random_47_Uma_roll_p13deg_HLC_sequence.fits')\n",
    "psf_series_47UMa_m13_fname = os.path.join(hlc_psf_path,\n",
    "                                          'OS5_adi_3_highres_polx_lowfc_random_47_Uma_roll_m13deg_HLC_sequence.fits')\n",
    "psf_series_betaUMa_fname = os.path.join(hlc_psf_path,\n",
    "                                        'OS5_adi_3_highres_polx_lowfc_random_beta_Uma_HLC_sequence.fits')\n",
    "\n",
    "# this just forms the list of filenames for all the different wavelengths\n",
    "binned_psf_series_47UMa_p13_fnames = []\n",
    "binned_psf_series_47UMa_m13_fnames = []\n",
    "binned_psf_series_betaUMa_fnames = []\n",
    "for wavelen in wavelens:\n",
    "    binned_psf_series_47UMa_p13_fnames.append(psf_series_47UMa_p13_fname[:-5] +\\\n",
    "                                              '_sceneF{:d}.fits'.format(int(round(wavelen.value))))\n",
    "    binned_psf_series_47UMa_m13_fnames.append(psf_series_47UMa_m13_fname[:-5] +\\\n",
    "                                              '_sceneF{:d}.fits'.format(int(round(wavelen.value))))\n",
    "    binned_psf_series_betaUMa_fnames.append(psf_series_betaUMa_fname[:-5] +\\\n",
    "                                            '_sceneF{:d}.fits'.format(int(round(wavelen.value))))\n",
    "\n",
    "# load the headers\n",
    "science_psf_series_header = fits.open(binned_psf_series_47UMa_p13_fnames[0])[0].header\n",
    "ref_psf_series_header = fits.open(binned_psf_series_betaUMa_fnames[0])[0].header\n",
    "\n",
    "# pixel scales\n",
    "pixscale_as_os5 = science_psf_series_header['PIX_AS']\n",
    "pixscale_ld_os5 = science_psf_series_header['PIX_LD']\n",
    "\n",
    "log.info('Pixel scale in binned OS5 PSF series: {:.4} arcsec = {:.4f} lam/D at wavelength {:}'.format(\n",
    "      pixscale_as_os5, pixscale_ld_os5, wavelens[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the offaxis PSF cube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "offax_psf_fname = os.path.join(hlc_psf_path,'hlc_offax_psf_quad.fits')\n",
    "offax_psf_header = fits.open(offax_psf_fname)[0].header\n",
    "offax_psf = fits.getdata(offax_psf_fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the angles of IWA and OWA for the various bands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pixscale_ld_scene = pixscale_ld_os5 * wavelens[0] / wavelens * pixscale_as_scene / pixscale_as_os5\n",
    "\n",
    "ida_ld_hlc = 1.5\n",
    "oda_ld_hlc = 9.\n",
    "\n",
    "ida_as_hlc = np.zeros((N_wav))\n",
    "oda_as_hlc = np.zeros((N_wav))\n",
    "mda_as_hlc = np.zeros((N_wav))\n",
    "\n",
    "for wi in range(N_wav):\n",
    "    ida_as_hlc[wi] = ida_ld_hlc / pixscale_ld_scene[wi].value * pixscale_as_scene\n",
    "    oda_as_hlc[wi] = oda_ld_hlc / pixscale_ld_scene[wi].value * pixscale_as_scene\n",
    "    mda_as_hlc[wi] = np.mean([ida_as_hlc[wi], oda_as_hlc[wi]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolve the maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from crispy.tools.cgi import xy_to_psf\n",
    "from crispy.tools.detector import readoutPhotonFluxMapWFIRST as readoutWFIRST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick test to determine photon fluence at the planet location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,u'Haystacks scene in counts per 100.0 s, \\n after coronagraph, w/o noise, w/o star')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kDdP36vylk2jTF9VZff2Vk5yXJ790MtwfY3vKOmy21PusYT23lltpl5Nx69VjeaP68hra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\nMkLnDwAAICP/Px4JhEs9bFyQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ab6c4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_scene = np.zeros((scene_imw, scene_imw)) * u.count\n",
    "# this selects a frame within the cube\n",
    "wavebin = 1 # I picked the one with the highest flux of all the bands\n",
    "exptime = 100*u.s\n",
    "# pseudo-convolution\n",
    "for x in range(scene_imw):\n",
    "    for y in range(scene_imw):\n",
    "        if x != cx and y != cx:\n",
    "            test_scene += hay_cube_bandavg_ph[wavebin, y, x] * np.nan_to_num(xy_to_psf(x, y, offax_psf[wavebin])) * exptime\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(test_scene.value)\n",
    "plt.colorbar(fraction=0.046, pad=0.04)\n",
    "plt.title('Haystacks scene in counts per %s, \\n after coronagraph, w/o noise, w/o star' % exptime,fontsize=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - Number of counts per pixel in 100 seconds at the planet location: 3.26995657558 ct\n"
     ]
    },
    {
     "data": {
      "image/png": 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xJUb7EvkDAADICJE/AACQDfr8EfkDAADICpE/AACQDfr8EfkDAADICpE/AACQ\nDfr8EfkDAADICpE/AACQjSsif0T+AAAAckLkDwAAZOOa0b5E/gAAAHJC5A/I3ca91ln2UftHhN2/\nP3iXdi/43XmvvTy6umqd5b+8G1weD7f7S7CiD94ngOWgzx+RPwAAgKwQ+QMAANm4dvr8EfkDAADI\nCJE/AACQjSviXtQAAABAToj8AQCAbNDnj8YfsDbso/bUKnb/o3jleaQkub5un7cxw02FIO2KroPj\niFLEdLBg3cEpbYL68V9/DVf1n38etk8A6IHGHwAAyMY1Pd6oAQAAgJwQ+QMAANm4os8fkT8AAICc\nEPkDAADZYLQvkT8AAICsEPkDFmzjk0+CmcHvsSi1yjxSucxTlLJlY9ivcrPF/5r3qN6Dc9mVPiac\nH1wH1z/+GG4XgHTtxL2oAQAAgIwQ+QMAANm4En3+iPwBAABkhMgfAADIBqN9ifwBAABkhcgfAADI\nBqN9afwBwwWpRTb+5m/a1xuasiVKAzOvNCfRPgMzpV2J0sBEBqaImYUFp3IWYQqZYN7Gp5+2zrv+\n6ef2bV5f9SkWgA8EjT8AAJCNa0b70ucPAAAgJ0T+AABANq4Y7UvkDwAAICdE/gAAQDYY7UvkDwAA\nICtE/gAAQDZ4wgeNP6DVvc8+ixe4as+Ndv1ze041u3cv2GkwbxZRbsF55PJbQs69mdjAmyBzOl1h\n7QX17sE1aR9FH/fxV4EH1zOA9UPjDwAAZIM8f/T5AwAAyAqRPwAAkA36/BH5AwAAyAqRPwAAkA3y\n/BH5AwAAyAqRP2Rt45NPWuf5X/+6wJIUovQp7t6+YpTKZRVFqVWiNDHXQR0swzJS2kT9lYKIRpia\np4MF75Prn34avF1gGejzR+QPAAAgK0T+AABANsjzR+QPAAAgK0T+AABANujzN2Pkz8y2xioIAAAA\nbjOzczPbaZg+MbMTMzuuvDb7bLN348/MzszMqy9Jk7EKAgAAMG/XbnN9jcnMjlVra6Xpm5LOJH3r\n7vvuvi/pXNJZn3ZXr9u+lQjf8+p0d3/VUJCv3P15mnaQCvLE3S/77AsY28ann7bO86urwduNUmeE\naTWCtCyzlCe0EaQAuTfwBkCUdqUrJctGkJrmemB5orQrM6Q5UZRiJxLVgS8hNU9wDcy02d/+tnXe\n9Y8/zmWfQA7MbFsNDb/kpSSV7a3y32Z2JOmZpMNo2337/B1J2nX3i2CZmQoCAAAwb+vQ5y8F1A7T\n66xh3o6kVw2rvpZ0oI42V+dPwRT125Z0ZGZ7TeHESkFeBwUBAABAtyNJ+y3znqa/TQG5C6l7TEaf\n+wBfpL87ko4lvU3RvFELAgBCHZxgAAAgAElEQVQAMG+r3ucvDe44C+62lu2p7xvmlV3s2m4XS+px\n29fdDyUdmtlERSt0T9KBmW2mDobTFORN1/4AAADWnZl912OxF+7+orLOpqQv3H23x7pNYynKdths\njb9SaoEemtlXKu4/75nZsbtXG3RTFcTM9lQ0JgEAAOZuUU/4cPen3Uvd8VLSlx3LlBHBplG9D9Pf\ncJDt1Eme3f3SzHZVNACfqojmDSpIau2+qE+vSillAAAAPlgpQ8ppS3aUB5V/l22uhw3LbdaWaTTo\nCR/u/qaWymLmggCz2Pjkk/aZQWqVSJiuRYpTZwzcp6JUL9H+OsoaHosNTa2yhHQlkegY790bvt3o\nnAxNA9OlK1XOEFEddF2v0XEG86I0S9d/+Uu8T2BOVni07xeStlJuv7rjNH3f3V+kz/SmW7sTSXL3\npgG47836eLeLtJM3sxYEAAAgY7u6ewd1IulERZ7lr3UTSHulIhNL3bY67qhKAxt/KfHgRa1BN1NB\nAAAA5m1VI39No3vNrLwF/G1tjMWhiodo7JUDRtJt40v1yKsc3u8xs630TLmjMr9fGvV7KOnz2uKH\naf5eZf3eBQEAAEC31FB8JOnz8nG6kn4v6VGfJ6p1Rf4uVAzo2FMxuvcbSefuXm/4yd0vzOyRpJdm\n9iRNftC3IAAAAPO2qpG/JqmR11jg1LbqkxLmjrDxN+2GZykIAAAA5m/WAR8AAABrY50if/NC4w9r\n497m71rnXf/1p9Z5nSlb2kSpVYoND193wTxIx2Fhepk5fUhG6WXmsc+u1CnzOs42Xel1osw0HqRl\nuVqftKgbv/1t67zrH39cYEmA/ND4AwAA2XAif/FoXwAAAHxYiPwBAIBsLOrZvquMyB8AAEBGiPwB\nAIBsMNqXyB8AAEBWiPwBAIBsMNqXxh9WSUc+Pv/l3YIKkvYX5b+TZPeCZGzRvMh1kMMtyNUXzuvY\nrgc5CS0oTqgrb16Uq+564A2JcLWOA4lOdVfdDtFVP115CVtEOS2jXI8rpys35zodC7CCaPwBAIBs\n0OePPn8AAABZIfIHAACyQZ8/In8AAABZIfIHAACyQZ8/In8AAABZIfKHlXHvd38Xzveffm6dF6W4\nmJsoLcsyyhOJUmMMTQMTHePAVCWSpI2oXoPfq+E+O8rTlXpl0D5nWC9MhdO+bpjOZWgaoa515+De\n3/5tOP/qj39cUEnwISJTEJE/AACArBD5AwAA2bjWit2ZWQIifwAAABkh8gcAALJBnj8ifwAAAFkh\n8gcAALJBnj8af1gw+/jj1nn+7td45SDtSJiuZE7j+qPt2tVV+4rRcayRWep1cJqYKA1MuMOOOp8l\nNU2bKF1Ll6HpXCKzXHfR+Yqu9YH81/izYCNIBXP9pz+NXRzgg0PjDwAAZIM8f/T5AwAAyAqRPwAA\nkA1G+xL5AwAAyAqRPwAAkA0if0T+AAAAskLkDwtl9+4NX3lgOpcwrcjA9DFd+wx1bHcuViy9zODz\nNTQlS1eKmK5UMENE25whDUxUP0OvSbvXcfzBfI+urSANjEcpYrrSPnXNBwLk+SPyBwAAkBUifwAA\nIBvk+SPyBwAAkBUifwAAIBuM9iXyBwAAkBUifwAAIBtE/mj8YQ7sN+2XVZTeIUzxsWYGp4EJLKV+\norQ0sxxjlK5kHmlpruKymgXzh6YnitK5dKSsWXgaoa5ra6N9fph6JtxldA3ExYnfC/fbt/vul3jD\nQCZo/AEAgGww2Jc+fwAAAFkh8gcAALJBnz8ifwAAAFkh8gcAAPJBpz8ifwAAADkh8gcAALJBnz8a\nf5gDux/l2fq1fV5XLD7INTbY0Lxondsd/75CV+6ziM3rOIeKcvkFuSAH68hjF+UWtIHlCXP1LeN8\nBOWJ8m9KkqLZczjOzpyW0fkK5pHnDyjQ+AMAANmYQw7+tUOfPwAAgIwQ+QMAANmgzx+RPwAAgKwQ\n+QMAAPkg8kfkDwAAICdE/rBYUbqWrvQoA9OnRClSopQSYaqOGcoz2Az7myVNTJvOdBzxyu3zojQw\nUeqQ6Hx1ncsg1YnPcpxtomPUjHXbYqbUM1H9BNsNj6OjDkKrlroIa4XRvkT+AAAAskLkDwAA5IPI\nH5E/AACAnBD5AwAA2SDPH5E/AACArAyK/JnZuaRDd39Vmz6RdCTph8rkQ3e/HF5EAACAkdDnb/rG\nn5kdS5o0TN+UdCbpK3d/nqYdSDozsyc0AD8wG/fa5w1NUxGlgekyNA1MmP5ihk8IH5iKwuYUjB94\nLHavvTx2/377vE//VbzhTz4eVB7/60/tM4N5HqQq6d7pwOvgXvt7JKrXYoFg/sBry6LVgrJKkgdp\nWSyq2yglywzpWjrTMLWJPpvI/4GMTPVNY2bbamj4JS8lqWz4Vf49kfRsaAEBAADG4m5zfa2D3o2/\nFNk7TK+meTuSXjes+lrSwdACAgAAYDzTRP6OJO23zHua/l40zLuQJDPbmmJfAAAA4/M5v9ZAr8af\nme1IOnP3psadJJUNu+8b5pV9/dpuFwMAAGBBOgd8pFu6X7j7bo/tNQ3qKBuETYNE9iTt9dguAADA\nCNajX9489Rnt+1LSlx3LlBHBzYZ5D9PfOw1Dd38h6UW0YTNbkyAqAADA6gsbfylVy2lLmpYHlX+X\njb+HDctt1pbBB8CC1BD+66/tK86SPmWoZexzqGWkiAlS7FiQksX+9m9b511/9jfhLq8/+ai9OD+3\nXz8bV+31E1131pFGyIPtKkplEqQOsaEpjzr2Gb+/guMIU8/EqV6i1DSD310zpHoZavDnFj4sa/SV\nMC9d3xhfSDo2My9fKnL5qTJ9z93fpGlN/fomkuTuTSOBAQAAsEBdt313dfdW7kTSiaTnkr7WTUTv\nlaTthm1sq+PWLgAAwEIQ+Ysbf02je82svAX8bSXiJxX5/85SJPBFWvZARV+/O7kBAQAAsHiDnu3b\nxN0vzOyRpJdm9iRNfiDpEY92AwAAK2FNnsIxT1M3/lI0sLHmUiOvT0oYAAAALMFokT8AAIBV5/T5\no/GHgaL0IEGKC4962nak41irlC2L1pUiJkgFE6b5+Lg91Ys+av/4sJ/ehcX5zZ9/ap/51/Z513/+\nsX29d8E+P2pPLTOTjWEpdsLUMlKcXiZK2XI/OM7g/ePR/iSZgn1G7/eh6W466jXaangs0bVOqhdk\nhMYfAADIB3GEfs/2BQAAwIeByB8AAMjHio/2NbMtSUcq8iRfSvrG3fcblpuk5X6oTD7sk2GFyB8A\nAMAKMLNtFQ26I0mfS3otac/MjmvLbap44tq37r6fGofnKvIt1x/OcQeNPwAAkA3z+b5mtOvun7v7\n6/TaVfEktfoT1F5Kkrs/Lyekf08kPevaCY0/AACAFdB0e1dF4+/9E9VSZG9HRVSw7rWkg6790PgD\nAAD58Dm/RpT69UnSl5XJT9PfO4/gLaelfoOtGPCBQcLcXlEGzYH5ASVJ9wbuk/yAsShvXHSe/9Mf\n2+cFufokyR581jrv6h8ets7b+PPftO/z3//H9nm//BKX5zcDPwqv2/P1eZSrriuvXpRf8T9rrzv/\ntH29jT/9tX2bP7wNy+Pv2nPgRXkio3lR/XR9FkS5/Do/R9q2OWgtYH7MbEfF7d1vaoM4yobd9w2r\nlctNVIkW1tH4AwAA+VjQaF8z+67HYi/c/UXDunsqBnz8oGLAx7a7P64t1jSqt2wQThrmvUfjDwAA\nYGTu/rR7qdZ1X0h6IUlmdiJpx8z20vTydm/TqN7y1kmY7oU+fwAAIB9r1OcvKfv7lZG/svHX1Edm\ns7ZMIxp/AAAAKyr197tUkcdP7l725Wu6tTtJyzSNBH6P274AACAfaza6p5K0udqge6W7uf+Upt3p\nQ1hH5A8AAGAFmNmpmR3UntLxUtKX7l69lXuYlt+rrHugIkJ42LUfIn8YxD5pTynRleZjsCg1RJhy\nI9jmvNLA2MDfVR4cR1BWC1LoSIpT7NxrL6u/e9c67/rPP7bOu/df/n1YnH/54h/a1/1v29OO/PR/\n/Oet8x7/zx+17/DfnYflsfv322cGdee/tNdPmM4luF4lSffbj+V687et8375XftxfBykQLE//iku\nz1VQ3q5rr3Wb7ddzmLppFh8F18hPc/rcwupZ7cjfpYondDwzsxcqRu9+WX9er7tfmNkjSS/N7Ema\n/EDSoz7P9qXxBwAAsALS49z6LnspqffyVTT+AABAPhaU52+V0ecPAAAgI0T+AABANmy1+/wtBJE/\nAACAjBD5AwAA+SDyR+MPA80hRUpXeoconcvKGZz+YtxilCxI8xGmpQnSlUTb/Mt/3Z6SRZIe/w/t\nTx76X/71v2md999/8j+2zvv1f/271nm/CdLZSJL99tNgZlB31+3pbnyW6/XXX1tnbfy5PSXJ/WCf\n9mOQyiRK5dJl4PUTzpuXZewTWEHc9gUAAMgIjT8AAICMcNsXAABkg9G+RP4AAACyQuQPAADkgyd8\nEPkDAADICZE/DHPv3sJ36bOko1i0KBXO0DQwM4jS6JgH9RocR3Q+Pv7//hqW53//3/6r1nn/3V/b\n07lc/tv/onXe7/7l/2qdd/3RR2F5dD+Y/6497UrEgveIb8S/u/2Xd+3b/X+/b523EaWlCdLHeMcx\nRscSCdM3RalwOuonFKxrv+ErDyLPn4j8AQAAZIWfQQAAIB9E/oj8AQAA5ITIHwAAyAZ5/oj8AQAA\nZIXIHwAAyAeRPxp/GMZ/+WXQehaloujaZxSnjlKrRGZJuzJ0n9F6FqSp2Jgh1U2UsiVI8zE0xYf9\nu4tw/n/zP/1D67zrv/tt67zNf/8v7et9/0N7eX73d2F5dHXVOsv/2p62xoP0KXYvOJc2Q6qk4Hx5\ncByRzvMcvU/CVEEDr9mh66kjvcwMnz/Ah4TGHwAAyAeRP/r8AQAA5ITIHwAAyAajfYn8AQAAZIXI\nHwAAyIcz8IfIHwAAQEaI/AEAgHzQ54/GHwaKcmmFqw1/14U5Au+1z5tln4NFuc+CXH5LEeWGC/K/\nbfz2X7XO81/ehbu8/pf/u3VedJ79/v328mz+rn2Hv4k/6vwvP7XP+3lYTstQV37J8FoP8gfqo/b1\novdBV87K4Hr2q4E5+aLcgl3v2YF5ALuuSyAXNP4AAEA2GO1Lnz8AAICsEPkDAAD5IPJH5A8AACAn\nRP4AAEA26PNH5A8AACArRP4wiL/7tX1mVxqLoTaC3yoDUz90prgYatXSuQTCVDhBGhj76OP2eX/b\nPk+SNqJzGaVlCdKcROlBolQukqR3w9K5hOmHwmsrSK8jxWlQIlF5IlFqInWkcxn63ot0HUdw/VhQ\nHqezFyT6/InIHwAAQFaI/AEAgHwQ+SPyBwAAkBMifwAAIBuM9iXyBwAAkJVejT8z2zKzUzNzM3tr\nZscty03M7MTMjiuvzXGLDAAAgKE6b/ua2bakQ0lH6bUvac/M5O77leU2JZ1J+srdn6dpB5LOzOyJ\nu1/O4wCwHP7ru9Z5dv9++7yhqSik1UvnEqW0mcc+55U+JiprkOrFfwmugY5depDOxX4N0gi9C9K5\nROsFZZXidDfhNTuvtEZBvQ8WnOcw3Y8Uv/eitD1Dt9klOicffdQ6y3/+8/B9Ah+QPu/aXXf/3N1f\np9eupAtJ27XlXkpS2fCr/Hsi6dlYBQYAABjM5/xaA52Nv2p0r+JC0pvyPynqtyPpdcOyryUdDC0g\nAAAAxjN1vN7MJumfX1YmP01/LxpWuUjrbU27LwAAgDGZz/e1DqZK9WJmOypu735T68NXNuy+b1it\nXG6iSrQwbW9P0t40ZQAAAMBwvRt/qaH2uaQfVAz42Hb3x7XFmgZ1lA3CSX2Gu7+Q9KJjv2vSjgYA\nACuPVkX/277u/sLdd1OD75WkSWoQSje3e5vSujxMfxntCwAAsGRDn/DxpYoBHmXkr2z8PWxYdrO2\nDD4EA1NjdKaUGGoeqVXmlcZjHuZU1jAFyrv29Cnelark3r32eQPTAc1ybQ1O5zKH45AUvr/Ca92j\nlCzt5bGu5DxLOCeDRdfeMsqD1cNlMOwJH6m/36Wk8/T/si/fnVu75TR3bxoJDAAAgAUaFPmrPLWj\n2qB7pbu5/5Smhf36AAAAFoGRBD0if+mxbge1x7S9lPSlu1dv5R6m5fcq6x6oiBAejlReAAAAzKBP\n5O9SxRM6npnZCxWjd7+sP67N3S/M7JGkl2b2JE1+IOkRj3YDAAArgchfd+MvPc6tl9TI6708AAAA\nFmvoaF8AAIC1Q58/Gn+Yh42gK2lXCpDB+1yxtCxReYampYlSdcySVmQZrttTknhw/UTHOThdizQ8\nZYsNSpjQQ5SyJVovOI5Z0iEFmw3Ty1wN3OcsKVnW7b0ALAGNPwAAkA8if8Py/AEAAGA9EfkDAAD5\nIPJH5A8AACAnRP4AAEA2GO1L5A8AACArRP4wOv/ll9Z5dv9++4pB+o/Ofc6SGmLRBqaliVOZdPyO\nm2XdBbN7QXmGplaJtinFqV7mYZa0K/MQpWvpMo9j6UrXEqSM8qsZjgV5WLG33zKs1qc+AAAA5orI\nHwAAyAeRPyJ/AAAAOSHyBwAAssFoXyJ/AAAAWSHyBwAA8kHkj8gfAABAToj8YXT+66+t8+w3wSU3\nQ745C3IERjkAw9x5q2aWfHzBunH+wGDeLPndou1GufzC9YJ5HXn8hl4HYX7JZeTyi/Y5Sy6/eYiu\n51lyfr5rzzMKSPT5k4j8AQAAZIXIHwAAyAeRPyJ/AAAAOSHyBwAA8kHkj8gfAABAToj8AQCAbKxR\njoe5ofGHhQrTrnSt3JGuo9XV1bD1ZjFLWpZ5iFLhRGlgoowbUdqVLvNI5xJts7M8wXajdC6RWdLk\nhMcSnJRwtRnqZ2CamMEpdFbt/QN8YHiHAQCAfPicXzMysy0zOzUzN7NzMztqWW5iZidmdlx5bfbZ\nB40/AACAFWBmO5JOJL2R9FzSA0kHZnZaW25T0pmkb9193933JZ1LOuvTAKTxBwAAsmE+39eM9t39\nsbsfptdnki4kbZvZVmW5l5Lk7s/LCenfE0nPunZC4w8AAGDJUuOu6RZvOe1pWm5T0o6k1w3LvpZ0\n0LUvBnwAAIB8rGieP3d/0zLrh/T3Iv19Wvt/1YVUNCSD7RH5AwAAWGG/l3Th7mWkr7z9+33Dspfp\n7yTaIJE/LJT//HPrPPvkk3jlKG1EkMpkqCgtjdSRxiKaF6WxWEZaGgxP5xKIrg/v+tkdpYKZSxqY\n4cff9T4Zwu7FFXT1pz+Nvk9kZEGRPzP7rsdiL9z9RccyO5J2G6ZfNkwrG4Q0/gAAABbJ3Z92LxUz\nsxNJR7VbuOXt3qZRvQ/T36aG4Xs0/gAAQDZGGJG7EGa2p+J2bz0yWDb+HuquzdoyjejzBwAAsELM\nbFvSY3c/rM+rRAGbbu1O0jJNI4Hfo/EHAADysQZP+JD0eb3hZ2ablVx/ryRtN6y+LamrDyG3fQEA\nAFZBatydSDo2s2q+voeStt39Sfr/oYqneeyVt4XT8pdpXojGHwAAyMaq9vkzs4mKR7ZJzcme30f0\n3P3CzB5JemlmZYPwgaRH7h4O9pBo/GGFXAdpYCRp4+OPF1SSQpjKZU7b9Xv32leM0tlE6WO61p2H\nMB2JpI2BaXK6ttu6ycWfy1nWC1PBRGlZorQrURqhjlQvYTqXgddllM7l6o9/DssDfIjc/UJS7w+V\n1MhrSgHTicYfAADIx4pG/haJAR8AAAAZIfIHAACysap9/haJyB8AAEBGiPwBAIB8EPkj8gcAAJAT\nIn9YHVE6ibntcj77tGC7HqXciERpM7pSjkQpZObBO1LLXAe/O++Nn5al6zyH9RfNm9f1E6UDuv61\nfcWh19YyhGmN1ug4sH6I/BH5AwAAyAmRPwAAkA1G+xL5AwAAyAqRPwAAkA8if0T+AAAAckLkDwAA\nZCPKxpALIn8AAAAZIfKHtXH900+t8+zjjxdYkh6ugzx3Qb6+wducVx6/jSDH3XXw6zlabxZd+QPb\nRHkFJXkwOzqSeeWJDOs2mhexqA468uoNvJ7tXvu8q+9/iPcJzAuBPyJ/AAAAOSHyBwAAskGePyJ/\nAAAAWSHyBwAA8kHkr1/kz8y2zOzUzNzMzs3sqGW5iZmdmNlx5bU5bpEBAAAwVGfjz8x2JJ1IeiPp\nuaQHkg7M7LS23KakM0nfuvu+u+9LOpd0RgMQAACsAvP5vtZBn9u+++7+uPL/QzM7l7RtZlvu/iZN\nfylJ7v68XNDdn6co4TNJh2MVGqjzn39unbfxySft680rVcfQdC42LEWKX3Wk6ojScUT7HJpWZBnC\nc9mRIiZIBeND7xENTUsziyidS1Cemd4HwbV39ac/Dd8ugLkJv6HMbEtS0y3ectrTtNympB1JrxuW\nfS3pYIYyAgAAjMPn/FoDYeSvEtWrK7NzXqS/T2v/r7qQioZksD0AAAAswNBUL7+XdOHuZaRvK/39\nvmHZy/R3MnBfAAAAo6DP3/BULzuSdhumXzZMKxuEdxp/ZrYnaW9gGQAAADClqRt/ZnYi6ah2C7e8\n3ds0qvdh+nunYejuLyS96NjfmrSjAQDAyqNVMd1t3xSpu0iNtqqy8fdQd23WlgEAAMCS9I78mdm2\npMfufidli7u/Sekimvr1TdIyTSOBgbm7/umn1nn28cft8+7d69jwwFQeQWoV+yh4SwZpVzpTvQRl\nDX8ED0xZYxqWsqbT0JQknevNIS3LLOlTohQ7UQqZeaXmCY7l+i9/mc8+gTnhfuIUT/iQ9Hm94Wdm\nm2meJL2StN2w+rY6bu0CAABgMTojf6lxdyLp2Myq+foeStp29yfp/4cqnuaxV94WTstfigTPAABg\nFcwruf8aCRt/ZjZR8cg2qTnZ8/uInrtfmNkjSS/NrGwQPpD0yN2bRgEDAABgwbqSPF9I/TvvpEZe\nUwoYAACApaPP3/AkzwAAAFhDQ5M8AwAArB8ifzT+kDf/+ef2mR/dj1feCFK2RGlggvQpftW+ngUp\nYuZm4HF0phzZCLb7azDPgn1GKVCi9aTF3wPpqp/gWKJrJBJdPxu//TRc99f/5z8O2ieA1UTjDwAA\nZMPmkNZz3dDnDwAAICNE/gAAQD7o80fkDwAAICdE/gAAQDbI80fkDwAAICtE/oAW/u6X4St//HH7\nvKur1llRMhePUr3M8qzKe/fa50WpXgLeVZ6rYeW1wT/Z2+tckuRB3Q5NLxOkc+msn0hwTux+e3qi\nq//0x/Zt/jGYB3xoeLYvkT8AAICcEPkDAADZoM8fkT8AAICsEPkDAAD5IPJH5A8AACAnRP4AAEA2\n6PNH5A8AACArRP6AOfCff26dt/Hpp+0rziuX38B8fVF5LCprBw9yHWqj/TdplB8vKs9seQcH1t3Q\nOpdkQe7FqKRXb98O3ieQDfL8EfkDAADICZE/AACQDfr8EfkDAADICpE/AACQDyJ/RP4AAAByQuQP\nAABkgz5/NP6Ahbv+y18GrWcff9w+L0gNInWkSBmYksSDlCydaWCCdSOD07nMkHYlFO0zKuuvv4ab\nvf7xx6ElAoBONP4AAEA+rgn90ecPAAAgI0T+AABAPgj8EfkDAADICZE/AACQDUb7EvkDAADICpE/\nYE34zz+3z5tlu0FKko0ovUyUzmUjTvViFqSmubpqn/dR+0eWRSP4OsrjPwV1G9V7R8oWACsoStGU\nCSJ/AAAAGSHyBwAAskGfPyJ/AAAAWSHyBwAA8kHkj8gfAABAToj8AQCAbBijfWn8AdkLPgivf/qp\nfb1oHgBgZdH4AwAA+bhedgGWjz5/AAAAGSHyBwAAskGfPyJ/AAAAWSHyBwAA8kHgj8YfAADAqjCz\nA0nP3P2zlvkTSUeSfqhMPnT3y7774LYvAADIh/t8XwOZ2baZHalo2G22LLMp6UzSt+6+7+77ks4l\nnaV5vdD4AwAAWDJ3f+3uh5LeBIu9TMs+r6z3XNJE0rO++6LxBwAAsmE+39fcyl1E9nYkvW6Y/VrS\nQd9t0fgDAABYfU/T34uGeReSZGZbfTZE4w8AAORjRfv89VA27L5vmFcO9pj02RCjfQEAAEZmZt/1\nWOyFu7+YctNNo3rLBiGNPwAAgCpb0LN93f1p91JTKW/3No3qfZj+9kr3wm1fAACA1Vc2/h42zNus\nLRMi8gcAAPKxps/2dfc3ZiY139qdpGWaRgLfQeQPAABgPbyStN0wfVtS776DNP4AAEA+fM6v2UVP\n6jiUJDPbKyekx8FdlvP64LYvAADAkqUcfV8o3cJNj3o7rd7KdfcLM3sk6aWZPUmTH0h6NM2zfWn8\nAQCAbNiK9vlz9zcqHu0WRvBSI293ln31avylkOIzd/+sZf5ExYOIf6hMPpymFQoAAID5C/v8mdl2\nCjseqeUedHrW3Jmkb9193933JZ1LOkvzAAAAVsP6PuFjNGHjz91fu/uhijBkm5dp2eeV9Z6ruGf9\nbIxCAgAAYBwzjfZNkb0dSU15ZV5LOphl+wAAAKO6nvNrDcya6qV8dElTRukL6f3oFQAAAKyAWUf7\nlg277xvmlYM9JopvGwMAACzEqo72XaSxUr00jeotG4RNjyGR9D5J4V7bfAAAAIxr1sZfebu3aVRv\n+eDh1nQv7v5CHY8jMTOa6AAAYBxE/mbu81c2/h42zNusLQMAAIAlmyny5+5vzExqvrU7Scs0jQQG\nAABYPCJ/M0f+JOmVpO2G6dvquKULAACAxerb+Iue1HEovR+8ofTvAxV9/cLn0wEAACwUef7i274p\nR98XSrdw06PeTqu3ct39wsweSXppZk/S5AeSHvFsXwAAgNUSNv7c/Y2KHH1hBC818nZHLBcAAMDo\nyPM3Tp8/AAAArImxkjwDAACsPiJ/RP4AAAByQuQPAADkg8gfkT8AAICcEPkDAAD5IPJH5A8AACAn\nRP4AAEA+1uQpHPNE5A8AACAjRP4AAEA2eMIHkT8AAICsEPkDAAD5IPJH5A8AACAnNP4AAAAywm1f\nAACQj2tu+xL5AwAAyFN8fvcAAAjmSURBVAiRPwAAkA8GfBD5AwAAyAmRPwAAkA8if0T+AAAAckLk\nDwAA5IPIH5E/AACAnBD5AwAA+SDPH5E/AACAnBD5AwAA+fDrZZdg6Yj8AQAAZITIHwAAyAejfYn8\nAQAA5ITIHwAAyAejfYn8AQAA5ITIHwAAyAd9/oj8AQAA5ITIHwAAyAeRPyJ/AAAAOSHyBwAA8kHk\nj8gfAABAToj8AQCAfFzzbF8ifwAAABkh8gcAAPJBnz8ifwAAADkh8gcAAPJB5I/IHwAAQE6I/AEA\ngHxcE/kj8gcAAJARIn8AACAb7uT5I/IHAACQESJ/AAAgH/T5I/IHAACQEyJ/AAAgH+T5I/IHAACQ\nEyJ/AAAgH9eM9iXyBwAAkBEifwAAIB/0+SPyBwAAkBMifwAAIBtOnz8ifwAAADkh8gcAAPJBnz8i\nfwAAADkZNfJnZhNJR5J+qEw+dPfLMfcDAAAwCM/2HS/yZ2abks4kfevu++6+L+lc0lmaBwAAgCUb\n87bvS0ly9+flhPTviaRnI+4HAABgGL+e72sNjNL4S5G9HUmvG2a/lnQwxn4AAAAwm7H6/D1Nfy8a\n5l1IkpltufubkfYHAAAwNafP32iNv6309/uGeeVgj4kkGn8AAAAtFjF4duw8f00FKxuEk/oMM9uT\ntDdyGQAAAJqtcL+8yuDZr8oxFGZ2oGLw7JOxGoBjNf7K271No3ofpr93CuzuLyS9iDZsZsRnAQBA\nDhoHz5rZkYrBs4dj7GTsxt/DhnmbtWUGee2vZlkdAABgZfv8VQbPNjV4ysGzozT+RhntWxnIcefW\nbjnN3ZtGAgMAAKDn4NkxdjRmn79XkrYbpm+r49ZuxN2t+n8z+87dn7YtnzvqJ0b9xKifGPXTjrqJ\nUT+xRdbP6+tvFrGbIRY2eHbMxt+hig6Je6kvX9lJ8VIjhSkBAADWgZl912OxF2WbqWKqwbNDjNb4\nc/cLM3sk6aWZPUmTH0h6xLN9AQDAMtXvJK6gQYNnhxg11Utq5O2OuU0AAIAMzH3wbGnMZ/sCAABg\ngEUOnqXxBwAAsBrmMni2jsYfAADAajiU3j8BTenfow+eHfvxbgAAABhgUYNnafwBAACsiEUMnl3H\n276j3fP+QFE/MeonRv3EqJ921E2M+olRPwtk7qv5jDsAAACMbx0jfwAAABiIxh8AAEBGGPABABjM\nzLYqyWmhok4kfZH++627v1pmeYC6tWj8mdlE0pGkHyqTD3N8ZnDK9/PM3T9rmZ91XaUP3SMVCTEv\nJL1y9zu5kXKtp1r9XEr6xt33G5bLsn7qzOxcxXG/qk3Psn7M7EzSVm3yrqQ3lWWyrBtJMrNNSS9V\n1NFuU6M4x/oxsxNJOy2zL9z9cWXZ7OpnGVb+tm96M52p+PW0n76oziWdpXlZMLNtMztS8aZoPO7c\n68rMdiSdqPgieq4iN9KBmZ3WlsuynsxsW8X1cyTpc0mvJe2Z2XFtuSzrpy7Vy53HLOVaP+mHg1S8\nt96/qg3jXOtGet9o+WdJE3d/3NLwy65+0nGVT6c4rL0uVDzRorpsVvWzNO6+0i8VX+ZvG6a7pKNl\nl28J9XFWnDbqquE4Txumnafj38q9niQdt9TPeW1alvVTO9ZtSafpmHeon+L9paJhEy2TZd2kYzyX\n9FbSJvVz69j2qp+/Dced/WfzUs7LsgsQFq6IcLmkk4Z5p22NoA/51db4y72uVNxm2W6YvpfqZY96\naqy302pdUD/v6+A0XVO3Gn+51k+lLk7Se+pOAyfXuknHd9TVQMm5flrqY6fa0KN+Fvta9du+T9Pf\ni4Z5F9KtWxG5y7qu3P2Nu79umFX2GynrJet6qkq3qSTpy8pk6qf4Ir/TDzLJtX7KwQs7ko4lvU3d\nUKpyrRupaBBLKvpFmpmb2Xn1+azKu36afKHbiZ2pnwVa9cZfeaK/b5hXdv680ycnU9RVs9+r6FBc\nNgypJ73vH3mmom6qHamzrp+yXty96QtIyrR+3P3Q3U3SYxV9/S5V9Ket9hfNsm7Sj6iyP9rX7v5E\n0mcq+h4fp0F6Uqb1E9iR9HXl/9TPAq1646/UNMqnvEC4GG6jrm7bUfMzErOtpxSN+EJFVHQvjWit\ny65+UofyL9y9z2OmsqsfqXjovBej5x+piMbsNURjcqub8pheeBrk4e6X7r6roi7qEdLc6ueO9CPr\n0ptTBGVfP4uw6o2/8td30yifh+kvw78L1FVNSi9wVPuAyb6e3P2Fu+96kV7hlaRJ5fZUzvXzUrdv\ngTfJuX7e89sPnq/frsu1bpqO7bX0/nZl7vVTta+7z/KlfhZoXRp/DxvmbdaWyR11VZEaMxcNURzq\n6baysVPm2cqyftKtuVNvziX2oPLvLOunSUPUJte6+S79bYpKlcf7QPnWzy2V1C9f12ZRPwu00o2/\nyodL05tqkpZp6uSfHerqRspn99gbkjtTT7elxs6lijQVOdfPFyr6Z3n5UtEnUpXpexnXT+RCyvfa\nqbyHoluS3+VaPw3+oIZbvtTPYq3DEz5eqfiVUFcmjcSN7Osq3V75vN7wS782J+kDJvt6KlUSp1Y/\nVHOsn13dvd00UZHa5LmKKEUZdcixfu5IP7Iual/IudbNV5KOzGyzFj2e6Pagqlzrp2pX7cdK/SzK\nsnPNdL1UvHneKuVpS9MO1JFM80N9KSUtpq4aj38r1c9B7XWkYgRn1vWkIlfWQfUYVTRu6kmMs6yf\nhvqaqDnJc1b1U3lfHZXHl+rgTtLn3Oqmduznup0zs7x+tmrTsqyfdKyb9Trh+lnOa+Ujf+5+YWaP\nJL00sydp8gNJjzyjZ/1VHhQ+Sf8/UtFH6f2v7pzrKqVbKG/T1UfXSZVfjRnX06WkZ5KemdkLFSPo\nvqwfc8b100uG9XOhIm3JnorRvd+oeCrM5/UFM6ybqicqon+nKuprIumJV25vZl4/Usst3xL1sziW\nWtYAAADIwEoP+AAAAMC4aPwBAABkhMYfAABARmj8AQAAZITGHwAAQEZo/AEAAGSExh8AAEBGaPwB\nAABkhMYfAABARmj8AQAAZOT/B7z2Ll+RVV0LAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a1f4490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalize the stellar PSF\n",
    "os5sci_totflux = science_psf_series_header['TOTFLUX'] * u.photon\n",
    "scene_star_totflux = hay_cube_bandavg_ph[wavebin, cx, cx] * exptime\n",
    "sci_psf_series = fits.getdata(binned_psf_series_47UMa_p13_fnames[wavebin]) * u.photon * \\\n",
    "                   scene_star_totflux / os5sci_totflux\n",
    "# this automatically includes the QE since we use hay_cube_bandavg_ph as a base\n",
    "\n",
    "# add to scene\n",
    "test_scene+= sci_psf_series[0]\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.imshow(test_scene.value)\n",
    "plt.colorbar(fraction=0.046, pad=0.04)\n",
    "plt.title('Haystacks scene in counts per %s, \\n after coronagraph, w/o noise, w/ star' % exptime,fontsize=25)\n",
    "log.info('Number of counts per pixel in 100 seconds at the planet location: {:}'.format(test_scene[ply,plx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now do this for real"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For photon-counting mode, we want exposures that yield less than 0.1 electrons per frame, otherwise we cut off too many signal electrons. We will thus do individual exposures of ~1.5 seconds. This is overkill for the bands with lower signal as we will accumulate CIC noise too much, so feel free to change that to a different exposure time for each band. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - 1000.0 s\n"
     ]
    }
   ],
   "source": [
    "exptime = 1000. * u.second # total exposure time for an element of the sequence\n",
    "inttime = 1.5 * u.second # exposure time per frame\n",
    "\n",
    "# Info for which the sequence frames were computed\n",
    "os5sci_exptime = science_psf_series_header['EXPTIME'] * u.second\n",
    "os5sci_totflux = science_psf_series_header['TOTFLUX'] * u.photon\n",
    "os5ref_exptime = ref_psf_series_header['EXPTIME'] * u.second\n",
    "os5ref_totflux = ref_psf_series_header['TOTFLUX'] * u.photon\n",
    "log.info(os5sci_exptime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Number of individual sequence frames to complete an observation \n",
    "#(multiplied by exptime that yields the total time spent on source) \n",
    "Nint_use = [10, 10, 10, 20, 30]\n",
    "\n",
    "# we will compute all of the available exposures (so we can play with Nint_use later)\n",
    "Nsci = science_psf_series_header['NAXIS3']\n",
    "Nref = ref_psf_series_header['NAXIS3']\n",
    "\n",
    "# initialize a bunch of arrays\n",
    "noiseless_sci_scene_nostar = np.zeros((N_wav, scene_imw, scene_imw)) * u.count\n",
    "\n",
    "noiseless_sci_scene = np.zeros((N_wav, Nsci, scene_imw, scene_imw)) * u.count\n",
    "noiseless_ref_scene = np.zeros((N_wav, Nref, scene_imw, scene_imw)) * u.count\n",
    "tavg_noiseless_sci_scene = np.zeros((N_wav, scene_imw, scene_imw)) * u.count/u.s\n",
    "tavg_noiseless_ref_scene = np.zeros((N_wav, scene_imw, scene_imw)) * u.count/u.s\n",
    "\n",
    "noisy_sci_scene = np.zeros((N_wav, Nsci, scene_imw, scene_imw)) * u.count\n",
    "noisy_ref_scene = np.zeros((N_wav, Nref, scene_imw, scene_imw)) * u.count\n",
    "tavg_noisy_sci_scene = np.zeros((N_wav, scene_imw, scene_imw)) * u.count/u.s\n",
    "tavg_noisy_ref_scene = np.zeros((N_wav, scene_imw, scene_imw)) * u.count/u.s\n",
    "\n",
    "tavg_noisy_resid_sci_scene = np.zeros_like(tavg_noisy_sci_scene)\n",
    "tavg_noisy_resid_sci_scene_contrast = np.zeros(tavg_noisy_sci_scene.shape)\n",
    "\n",
    "# initialize binary masks to confine region of interest to IWA/OWA\n",
    "data_mask_nan_ind = []\n",
    "xs_as = (np.arange(scene_imw) - cx) * pixscale_as_scene\n",
    "XX_as, YY_as = np.meshgrid(xs_as, xs_as)\n",
    "RR_as = np.sqrt(XX_as**2 + YY_as**2)\n",
    "for wi in range(N_wav):\n",
    "    data_mask_nan_ind.append((RR_as <= ida_as_hlc[wi]) | (RR_as >= oda_as_hlc[wi]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now the big guns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crispy - INFO - F506.0 total stellar count rate excluding coronagraph masks: 4.965e+11 photoelectrons per exposure\n",
      "crispy - INFO - F575.0 total stellar count rate excluding coronagraph masks: 5.761e+11 photoelectrons per exposure\n",
      "crispy - INFO - F661.0 total stellar count rate excluding coronagraph masks: 6.178e+11 photoelectrons per exposure\n",
      "crispy - INFO - F721.0 total stellar count rate excluding coronagraph masks: 2.718e+11 photoelectrons per exposure\n",
      "crispy - INFO - F883.0 total stellar count rate excluding coronagraph masks: 1.357e+11 photoelectrons per exposure\n"
     ]
    }
   ],
   "source": [
    "for wi, wavelen in enumerate(wavelens):\n",
    "    # this is the total amount of photons coming from the star\n",
    "    scene_star_totflux = hay_cube_bandavg_ph[wi, cx, cx] * exptime\n",
    "    log.info(\"F{:} total stellar count rate excluding coronagraph masks: {:.3e} photoelectrons per exposure\".format(\\\n",
    "          round(wavelen.value), scene_star_totflux.value))\n",
    "    \n",
    "    # select band\n",
    "    sci_psf_series_fname = binned_psf_series_47UMa_p13_fnames[wi]\n",
    "    ref_psf_series_fname = binned_psf_series_betaUMa_fnames[wi]\n",
    "\n",
    "    # scale flux to match star in Haystacks scene (the frames were generated with some arbitrary flux)\n",
    "    # this already takes QE into account\n",
    "    sci_psf_series = fits.getdata(sci_psf_series_fname) * u.photon * \\\n",
    "                       scene_star_totflux / os5sci_totflux\n",
    "    ref_psf_series = fits.getdata(ref_psf_series_fname) * u.photon * \\\n",
    "                       scene_star_totflux / os5ref_totflux\n",
    "    \n",
    "    # quick sanity check fro debugging\n",
    "    assert(int(round(wavelen.value)) == offax_psf_header['WAVE_{:d}'.format(wi+1)])\n",
    "    \n",
    "    # this is the key function, that generates an average flux map for the off-axis scene (which is time-invariant)\n",
    "    # For each pixel, go grab the corresponding off-axis PSF and multiply it by the pixel value\n",
    "    for x in range(scene_imw):\n",
    "        for y in range(scene_imw):\n",
    "            if x != cx and y != cx:\n",
    "                noiseless_sci_scene_nostar[wi, :, :] += hay_cube_bandavg_ph[wi, y, x] * \\\n",
    "                  np.nan_to_num(xy_to_psf(x, y, offax_psf[wi]))*exptime\n",
    "\n",
    "    # The on-axis PSF is time-dependent, so we have to read out \n",
    "    for tt in range(Nsci):\n",
    "        # the noiseless electron map is the off-axis PSF + on-axis PSF for this time step\n",
    "        noiseless_sci_scene[wi, tt, :, :] = sci_psf_series[tt] + noiseless_sci_scene_nostar[wi]\n",
    "        # mask out area outside WA     \n",
    "        noiseless_sci_scene[wi, tt, data_mask_nan_ind[wi]]=0.0 \n",
    "        # simulate EMCCD readout as many times as needed to observe 'exptime' in chunks of 'inttime'\n",
    "        # this is necessary because of the photon-counting, which doesn't like more than 0.1 counts per single frame\n",
    "        # by default, QE and losses are set to unity\n",
    "        noisy_sci_scene[wi, tt, :, :] = readoutWFIRST(noiseless_sci_scene[wi, tt, :, :].value/exptime.value,\n",
    "                                                        tottime=exptime.value,\n",
    "                                                        inttime=inttime.value, # only short individual exposures\n",
    "                                                        PCcorrect=False, # don't correct for photon counting bias\n",
    "                                                        normalize=False  # don't average frames\n",
    "                                                     )*u.count      \n",
    "\n",
    "    # now process the reference PSFs\n",
    "    for tt in range(Nref):\n",
    "        noisy_ref_scene[wi, tt, data_mask_nan_ind[wi]] = 0.0\n",
    "        noisy_ref_scene[wi, tt, :, :] = readoutWFIRST(ref_psf_series[tt].value/exptime.value,\n",
    "                                                      tottime=exptime.value,\n",
    "                                                      inttime=1., # only short individual exposures\n",
    "                                                      PCcorrect=False, # don't correct for photon counting bias\n",
    "                                                      normalize=False  # don't average frames\n",
    "                                                     )*u.count\n",
    "\n",
    "    # now we can make some averages, using the Nint_use values\n",
    "    tavg_noiseless_sci_scene[wi] = np.mean(noiseless_sci_scene[wi, :Nint_use[wi]], axis=0)/exptime\n",
    "\n",
    "    # this is a slight adjustment coming from photon counting mode\n",
    "    EMGain = 2500. # EM amplifier gain\n",
    "    RN = 100 # read noise in counts rms\n",
    "    threshold = 6 # photon counting threshold\n",
    "    tavg_noisy_sci_scene[wi] = np.mean(noisy_sci_scene[wi, :Nint_use[wi]], axis=0)*np.exp(100.*threshold/2500.)/exptime\n",
    "    \n",
    "    tavg_noiseless_ref_scene[wi] = np.mean(ref_psf_series[:Nint_use[wi]], axis=0)/exptime\n",
    "    tavg_noisy_ref_scene[wi] = np.mean(noisy_ref_scene[wi,:Nint_use[wi]], axis=0)*np.exp(100.*threshold/2500.)/exptime\n",
    "    \n",
    "    tavg_noisy_sci_scene[wi, data_mask_nan_ind[wi]] = np.nan\n",
    "    tavg_noisy_ref_scene[wi, data_mask_nan_ind[wi]] = np.nan\n",
    "    \n",
    "    # elementary PSF subtraction\n",
    "    tavg_noisy_resid_sci_scene[wi] = tavg_noisy_sci_scene[wi] - tavg_noisy_ref_scene[wi]\n",
    "    tavg_noisy_resid_sci_scene_contrast[wi] = \\\n",
    "    tavg_noisy_resid_sci_scene[wi] / (np.nanmax(offax_psf[wi]) * scene_star_totflux) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now let's explore what we have done..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# hdu = fits.HDUList([fits.PrimaryHDU(tavg_noisy_sci_scene.value)])\n",
    "# hdu.writeto('tavg_sciscene.fits',overwrite=True)\n",
    "# hdu = fits.HDUList([fits.PrimaryHDU(tavg_noisy_ref_scene.value)])\n",
    "# hdu.writeto('tavg_refscene.fits',overwrite=True)\n",
    "# hdu = fits.HDUList([fits.PrimaryHDU(tavg_noisy_resid_sci_scene.value)])\n",
    "# hdu.writeto('tavg_resid.fits',overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ad5c9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.visualization import (MinMaxInterval, SqrtStretch,\n",
    "                                   ImageNormalize)\n",
    "from astropy.visualization import simple_norm\n",
    "\n",
    "# dist_scene = 3.6*u.pc\n",
    "dist_scene = 8.07*u.pc\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.suptitle('OS5 HLC / Haystacks scene: F6V star at {:.2f} pc, 1 AU Jovian, 10 zodi debris\\nFlux scale in units of counts / s'.format(dist_scene))\n",
    "plt.subplots_adjust(left=0.08, right=0.98, wspace = 0.01, bottom=0.02, top=0.80)\n",
    "plt.subplot(121)\n",
    "plt.imshow(tavg_noisy_resid_sci_scene[0].value,\n",
    "           extent = (xs_as[0], xs_as[-1], xs_as[0], xs_as[-1]),\n",
    "           vmin=1e-3, vmax=6e-2,cmap=cmap)\n",
    "plt.tick_params(width=2, length=10, direction='in')\n",
    "plt.ylabel('Arcsec')\n",
    "plt.title('F506, {:.1f}'.format(Nint_use[0]*exptime.to(u.hour)),fontsize=25)\n",
    "cb = plt.colorbar(orientation='horizontal', fraction=0.046, pad=0.04)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(tavg_noisy_resid_sci_scene[1].value,\n",
    "           extent = (xs_as[0], xs_as[-1], xs_as[0], xs_as[-1]),\n",
    "           vmin=2e-3, vmax=6e-2,cmap=cmap)\n",
    "plt.tick_params(labelleft='off', width=2, length=10, direction='in')\n",
    "#plt.axes('off', which='y')\n",
    "#plt.colorbar()\n",
    "#plt.xlabel('Arcsec')\n",
    "plt.title('F575, {:.1f}'.format(Nint_use[1]*exptime.to(u.hour)),fontsize=25)\n",
    "cb = plt.colorbar(orientation='horizontal', fraction=0.046, pad=0.04)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "plt.savefig(exportDir+'os5_hlc_haystack_scene_506-575.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAqgQQXAAAAAFAJpb4PLsrn177xI0Fs4fyHgth/eOM9dY//ZGRn\nMM6WW8Kbs5/bGhaKWmvhfpxhux7EvvzGaBA7cyksKnH1av3XZtumsJDFqaWwsMB/HA7b++L5O8L5\nL4fFC7avOxfEPr75eBD70Q31hRDOpBQ9eGkpnP+9Q2HRijsqcCP6L10OYz+8LlwfHlmfXB/C955W\nJIXCEABQzL0ji0HstbO3BbGvvvaOILZ+7dW6x9+/aS7lFcJt88vXwj7C/LmtYez1bUFs+WJKl3ko\nLC72V4k+wrmrY8E4vz/0niB26VpYZOrejWERy49tCYtYUlCqff712bBfdn+iiNeH14fFKrMWlEor\ndMnRxvZjmQIAAAAAKoEEFwAAAABQCSS4AAAAAIBKIMEFAAAAAFQCRabQVb848u0g9ty9J4LYn71+\nX93j8xfCYk9pnr9yTxBbcieD2NfP3R3ETp4Oi1tcPxcWfdBgfVGJN65tCkb5zynFqYYGwsICd94a\nFo/6pfv+UxBLFo/KatPA+iC2a2043vnlsG0L18OiWJsHy1XI4oezrTaBpy+FhbhuHQgrVn2g2OwB\nYNW7fV1YBOracnjc5fK6cDucLAD5pYXvDMb51pWU4lEXwuJRJ94Kx3Onwg1lyiZcyynFhq6cqW/b\nyxfCwlZDa8NClzu3vxXEfuudXwpfFB31ydveDGJXXLIPFq6Tb1+/EMT+n7MPBrHnz4dF037jruzt\nQza5j+Ca2SYzO2Zmv5yIf97MHmpf0wAAAAAAyK7IKcqHJO1KBp1zn5J0yMzeu+JWAQAAAACQU5FT\nlEckbXbOnUl57mlJByR9dEWtAgAAAAAgpyJHcOcbJLeR8aKNAQAAAACgqLYVmTKzTZImJc23a56o\nnt84sz2I/dfX3hnEhgfrCzCsWRsWWUorPPX/vvL+ILZmICzm8OqZsDDU8kJYVMLCSeXW1ReVuH42\nLDZwPqVI0e13hfuF/uDdvx++QA9sHMhWjenM8qUg9sLVsIDGB9aF77+fHTxTX3TsqdfGgnHWDl0L\nYv8hXHUBYFV75/QTQezOd04EsTffDgs7PvCOsMDPqQthccMLZ+u3WS8Nh8WjXr8Yzv/clXA7f/Vi\nuA0fumRBTCmhtONEA1frR1xe44Jx3B1hcap/fP/vpcy/XNvSXjixFBYr2zG8sa2vsdbq15EvXw4/\nv1evhcVLryyH69bIcFjAc+fnfjWIzf3yL+VpIhKKHMGd9kWmPmJmt/nhpyU9I2mTpOn2NhEAAAAA\ngNZyH8F1zj1rZo9Lekq1hFa6uV9r2jn3uXY1DgAAAACArAqdouycOyppi5mNS3qfpEVJR51z4Q1N\nAQAAAADoghVdg+sT3aP++tvN7WkSAAAAAAD5FUpw/TW3j0tyzrnvc86dMbPHzewl59y/am8TUSW3\nDFwJYuffCIsB3Hnf6brHC6fCcQbXplSASrGcUhlicCAsEOAGwkIQdi2lgMTF+qIPA5fD+V8bCdvx\n4NaTzZqZ28D2FwtNt3zygcKvudHCAh3fMRwWnpJuKfwavTC56bW6x7PnwupR33XL691qDgCU1vse\nDE/m+4nbvxbE/sXVjwSxOzecDWJvng+3/7duqt/ujKxL2w6FNgyHBSsHhsL+QFqFmrSik2sW04pM\n1T9eCmtaSoPhzNpdnLEXfYReaHdBqSw+uC6thFHKDWY2hrFfX7w3iH34Q3/RhlYhLneCa2Y/I2nG\nP5yL4s65f2hmz5jZKefcv21XAwEAAAAAyKJIFeUpSQck7ZL0bOK5JyV9ZqWNAgAAAAAgryKnKM87\n5/6hJJlZeE6nFN5AEgAAAACADityBHc+9n/y4sO9iecBAAAAAOiKIkdwj5jZv1TtVGUnSWb2kKRD\nknaodvoykOo3X/lgENt2z2IQe/j2b9c9Xtr2ajDOmaX1Qewv37oziA2mnGhwfTll385wtsJTQ6fr\nvzaDV8IiU9evh7GPbv56+JoZFS0WkXVeWYtKDFq43LYNlqugVBb77jwSxP79+e/pQUsAoFx+5zvC\n388f+8ZPBLF3bXsziH10y/NB7L+748tB7MUr2+se/99//YFgnFOLYfGh4TXXgtjyUljcyW1IKTyV\nsl0fPpsSu5CY/3A4zsBwtiKZWXW6j/Dct8LCSN+7Zl3bXnM1eXBt2J+9dSBbkTRklzvBdc49ZWZb\n5Y/UmtmEf8okzTjnHm9j+wAAAAAAyKTQbYKccwfN7ElJD6t2ze2ipGecc8miUwAAAAAAdEWhBFeS\nnHNnJD3tBwAAAAAAeip3kSkze8gP98dinzWzF83st83stnY2EAAAAACALIocwf0j1e53OyVJZvYr\nkj6t2pHcAR//hXY1ENUyPJitsML33/ZS3eP/5tZTwTivXzsfxHa/sTeIXbiwJnyBsOaDNmwOL/K/\n/dYLQexbV+oLWQ2dDwtUpPnbty5kGq+dxSKyWknhqSo6tbw2iP3Ahu5/LgBQBSNrw+3rxWvhtvk9\nKQV40ooZPbK+frw/OxsWrPrS2+HxlmtL4W97WvGo9XeF/Yu1w2GBqvMXtoTjJepmDlwL5/9dd7wR\ntiOjXvQRHrrv20Gs3X2EVxJ9unuGwiJhVfA9a84FsQ+vTylqhhUpkuB+wTn3C5JkZjsk7ZM065z7\nUR/7fBvbBwAAAABAJkUS3PhhqGnVbhX0WIPnO8rMRlU7Ynw6Ft7vnAvvO9OB6QEAAAAA/SP3NbiS\ndprZz/t74Y5LOuCcey72/ESD6drKzEYkzUo65pzb65zbK2lO0qx/rqPTAwAAAAD6S5EEd7+kT0na\nK+lgdN9bM/u0mb0kabSN7WvmkCQ55w5EAf//qKQs9+Jd6fQAAAAAgD6S+xRl59wJ1e5/m4w/IemJ\ndjSqFX+EdULS4ZSnj6p2XfD+Tk2P4kY3vh3Ezl4JC0g8efL9dY+3Dx0Jxrl3KCzcMLo1LEY1r61B\n7OL5sNDE1o0Xg9gP3jEXxP7NyURRibdSikzdEhajQHncP3Q1iH3tKgXiAaCIXZv+Ooh95XR4POTg\nWx8KYv/zHeHdKHcO1xcgun99uO3/i5G7gtiZs7cEsesuCGn7prAQ0H0bwyvw/vjW8IQ/N1DfJ1ge\nDuf/49u+HgZXuWzlOstvg6WsEGi7Ikdw+0GUYM+nPDcvSWY21s7pzazlAAAAVh/6CADQP4rcB3eT\nmR0zs19OxD9vZg+1r2lNRclnuMtOigpENTtVeqXTAwAAAAD6TJEjuIck7UoGnXOfknTIzN674lZl\nl1btOEpasySomad3zrUcAADA6kMfAQD6R5HbBI1I2uycO5Py3NOSDkj66Ipa1Vp0anFatePogstm\nt/pZ6fQoaPNweJ3rD9/5UhB7+WL9dbM//8X/PpzZQNhhGFx7PYjtvDO87vflpfDm7KfObwhib4yE\n112ate6ouJQbx6M87hgMr9N6ZH24bgEAWrt7OOxSffyOrwaxr5zdGcQ+9l//XhAbSGz/r18Lj9ds\n2XQhiG1Oib19LuwKvnU+3AZsXhv2X1IPEyU3/yndgTeWNqVMeDIltnrcNbSx9Uh94opbCmJrM15b\nu2FgTbubgxRFjuDON0huI+NFG5OnDf5vWD3oZtKadn1tu6YHAAAAAPSZthWZMrNNkibVhcTQOXfc\n/5t2GvKoH+dop6YHAAAAAPSfIgnutC8y9REzu80PPy3pGUmbJE23t4kNHVb60eJxSQe7MD0AAAAA\noI/kTnCdc89KelzSU5IW/DAjaaekg865z7W1hY3tlyQzm4wCZrZPtWtn98diI2bmzGymyPQAAAAA\ngHIoUmQqOn13i5mNS3qfaknhUefciXY2rkUb5s1sh2qVm6Oqzlsk7XDOJasZzCtx6nTO6dEm24bO\nB7HXr4bFFiZuf6bu8VuXwuIDl66FF/TftvZyEBu//YUgdngpvE3yq69vDmIvLNwZxJJcym6igXPh\nV+sfvfVgEPvfb38+iC2ffCCc3/YXW7ZjJdJeczV76nxYXOzZi+8MYv98ezdaAwDltng9LOL4QxvC\n7dqPbAivcnv9UthHeOVMfczWhcUff2j7XBBbcoNB7HffDO9wee50WGTqr4eyFRpcTrzEwNVwnJkT\n7wtin9n2zUzzf+fBJ8K2TX4607RF0Ueol7Wg1JvXw6Jm/9+FHUHsk7e9ueI2oV7uBNfMfl7SiHPu\ncz7R7dm1qj4R3ZNhnLAsX8bpAQAAAADlUOQa3AOSdre7IQAAAAAArESRBPegpCONnvQFpwAAAAAA\n6Koi1+A+KWnSn6r8TMrzeyX9zopaBQAAAABATkUS3EOqFZYCcnvu3L1B7NjJMHb8tvrYD2wLC0+s\nG1gKYn+2cH8Q+9q5e4LY1ethoQldDU9oeHPh1iBmg/XFLJY2hsUtLKUWxb898b1BLK3IVJp2Fp6i\nWERrH1r/ehCbeevhHrQEAMrvzaWwcN+B134siH33xvC398Nb/yqI3bn9TN3jFy7dHYyzIaW605tL\nadv05SCms2ERoYWzYaEsNxxOu3Rron8RdhG0+Gq4PH7vu8P5f/yWi0HsxMfDO1kunwxj9BF6763r\nYb/y+YvvCGIfmP/hIPbn4dcDORRJcA9KmlStuNSpxHPbJD220kYBAAAAAJBX0VOU55xzT6c9aWYv\nraxJAAAAAADklzvBdc6dkZSa3HqjxZsDAAAAAEAxRaoopzKz28zs85L2tWueAAAAAABkVeQU5Tpm\ndr+k/apdlws09eKZ24PYR+/7RhDbOHil7vEPbvxmMM5Tp98fxJ47ERasWrM+LEa1feRcEFu35XIQ\nu3x+TRAbWltfQWp5ezjd8uXwq3X5cli04vcvrgtiP7EhnF8aCkF0zrbBW4LYb+/4ox60BADKZcfv\nht3B9373XwexDUNhEaglFxaA/KmNzwWxF5fq+xJ/+ubOYJwzl8Lt68iGS0Fs7Yawj3D5UrgNv34+\n3IZrKKwgdfmO+j7C4OXwWJI5C2K/dfIHgtjHdx4NXzMj+gi99+Ca9UHsM7d/OYi9/4WHutGcVaVw\ngmtmD0makjQuKfqmHpe0ow3tAgAAAAAgl9ynKJvZT5vZMUmzknarltwelLTZOfewpF9pbxMBAAAA\nAGgtc4JrZr9sZqckzUjaJelZSXtUq6j8KV98Ss65JzrSUgAAAAAAmmiZ4JrZr5jZddVOR94s6SlJ\nu5xzDzvnnup0AwEAAAAAyCLLNbjHJD0n6X2SJp1z/6qzTUKV/cTdXw9iGwbCQhNzl+sLSPz6qz8S\njPPts5uD2MDwchgbCItA7LjtVBDbsu5CEPv6tbvD+Q3Wv8a2zReDcVxKAYkrS+HX7de/Hb6vre/8\n90HsA+vCwhsAAPSbv9776SD2f74wHsT+xoaXgtji9Q1B7IILt51/eekddY9PXUiZ7ly2IlPvvfvV\nIPb80PYgdv7tsPjgwJrrQWzNXfVFMpeWwu23XQ/7CCcWtwSxF66G/YvvWhO+V5TH5sHw85v/O5/p\nQUuqreURXOfcU865XaqdlvyjZvaimf1855sGAAAAAEB2ma/Bdc4965x7VNLDkjb7RPezyfHM7LZ2\nNhAAAAAAgCxyV1F2zp1xzj3hnHtA0jOSzpjZH5rZR/woj7e1hQAAAAAAZJA7wY3zpy8/LOkJSU/4\nYlT72tIyAAAAAAByyFJkqiXn3FFJD5vZuKQvtGOeqKa0glJfvxAWcnphob7Aw/WUok2Xrg4HsU23\nhgUZhgfDwlOvX8x2Jv2mW8OCFNeX69ty54bzwTg/vPXFIHb38EIQu+zC9/Dc5fuC2NnlN4PYj25Y\nCmLonH93YWMQ++ketAMAymbTYFjE8fil+4PY3OU7gtiJC1uD2KvnN9U9Hh4Miz1tvC3cft8yHPZB\ntq87G8QWbgsLAZ24HG6vB4fC/sXdm8/UPb5rQzj/q8th4amr18Mu+b9e+IEg9otb/zSI3TcUbp+A\n1WxFR3CTfKL7WDvnCQAAAABAFm1NcKXaacvtnicAAAAAAK20PcEFAAAAAKAXSHABAAAAAJVgzrle\nt6GvmZmTJJZT5yy8dk8Qe+Lt72s53RtXwkJRtwxdCWLLKQWqLlxbG8TWD4ZFm5YVTnslUQjiPbe+\nEozzP21+OYihPF64GhYre/C+V3vQktXBrPY9cy7lywr0MfoI2SyffCCIPXF6ZxA7tnh/ELs1Zbv+\n+qX67f+Ahct/yMICUNdceFxnXcq2//L1sKDUqUth4am0n6wHt5yse/yzt38lGOeR9WFRLKxeA9vD\nwqS4qUgfgSO4AAAAAIBKIMEFAAAAAFQCCS4AAAAAoBJIcAEAAAAAlTDUehSgszYNrA9in9k2W/d4\nw8CaYJy0AhVvL90axO5beyqI7VzzZhAbHT4dxK6nXM++JlG4YtvgYDCOFL4nlMe6lOIkAIBiRmc+\nFcTm93w+iP3e2teD2HLKsZiXr25r+ZrDFhZySusjpFk3EBaeOnd9XaZpH77lRN1jCkqtXq9fOx/E\nPvbszwexr/5kN1qzunAEFwAAAABQCSS4AAAAAIBKIMEFAAAAAFSCcXPy5riJe2+k3RQ+6YuXwv0z\nXzj9fUHs9jXngtjGwctB7BdGng/HG2h9zc355XBeWaZD/+Km691V5CbuQD+gj1Dc0uthHY1By3bc\n5bkrV+oef/Hiu4Jx7k2pqzEyeCGIPTB8JohtS6n7saTwWtphhTU40mqGABH6F/kV6SNwBBcAAAAA\nUAkkuAAAAACASihlgmtmo2Y2Y2bTsWEkx/RjZnbEzJyZzZnZVCfbCwAAAADovNIluD6RnZV0zDm3\n1zm3V9KcpNksSa6ZTUiakXRc0gFJWyTtM7MjHWw2AAAAAKDDhnrdgAIOSZJz7kAUcM4d8EdhH5e0\nv8X0e51z8coG+81sTtK4mY055463vcXI7czypbrHmwbWB+N848pdQezWobDg0861bwSxV65uDWJF\nC0NRUAoAgHwOnbk3iH1q5NVM0z60dm3d4z88PxyMc2453DYvXt8QxLYOXAxi9w1RKAoos1IdwfVH\naCckHU15+qikfS2mH5OUdjpyFHu4ybQtBwAAsPrQRwCA/lG2I7hRAjqf8ty8VEtiGx2FbXJ0Nqol\nnzZfAAAAAEAJlOoIrqQx//dUynOL/u9ogfm+X9K8cy7tyLCk2j3uWg0AAGD1oY8AAP2jbAluZDEl\nFiW9RRLcCUl7ijcHAAAAANBrPTtF2czyJKKnnXOLunkKcVq15KhqUFry26wdM5KmKC7VXzbf/Urd\n489/80PBONuHzwSxuct3BLE7hs4FsXUDSytoHapmYPuLvW4CAKwqf+/dXwxi33olLB55z9DGIPbm\n9Qt1j89dDwtKfc/6bwexk0th93Fu6fYg9tDas0EMyIu+Re/0JMH1yW2ee88eU+2WPlGCG5bAvZn0\nZr6O1swmVTs1+WCOtgAAAAAA+lBPElzn3LwKnBLsnDvuKxGmHf0d9eM0vI42zszGJe10zrW6rRAA\nAAAAoATKeA3uYUnjKXuO0WgAABYTSURBVPFxSZmOxPrbBe1OJrdmNuKfAwAAAACUTNluEyRJ+yXN\nmtlkdGqxme1T7drbGwmrv2fugqTDzrk9sfiYpBlJ0366yFZJ4865XV14DwAAAACANitdguucmzez\nHZIOmVmUjG6RtMMXooqbV+yaXH/t76x/mHYNMNfi9qlPveuPg9gXX34giL14Liwy9RcL7whi921c\nCGKD+loQ+5mNFJqoGoo+AEB/uu+e14PY1751TxD7juE19dOtDe8e+d41bwexv5Ey3unltJbc0riR\nQAr6Fv2ldAmuJPlEtuk1vH6cnYnYvCTrYNMAAAAAAD1SxmtwAQAAAAAIkOACAAAAACqBBBcAAAAA\nUAmlvAYXkKQP3x9e0P+bb/4PQWzA1gWxVy9uCmIP3PlmyquE06I8KPoAAOX21vWw4NN1Xax7/Nz5\n+4Jx/uzMaBD7kc0vBLFhuxbEvnP4TJ4mAugzHMEFAAAAAFQCCS4AAAAAoBJIcAEAAAAAlUCCCwAA\nAACoBHPO9boNfc3MnCSxnMrrW6/cFcRGBsL6aueWw0ITdw1t7Eib0H4UlCovM5MkOeesx00BcqGP\n0Bv/5Os/Wff4njWng3EOn9wVxNYNLgWxsU3fDmKTm48HsW2DYbErrE70N7qrSB+BI7gAAAAAgEog\nwQUAAAAAVAIJLgAAAACgEkhwAQAAAACVQJGpFiggUU2f+YufDmLrBsLiE//rtm90oznIiQIP1UKR\nKZQVfYT+8HJKMcl/9NqPB7G71p7JNL9/fudfrLhNqA76HL1FkSkAAAAAwKpFggsAAAAAqAQSXAAA\nAABAJZDgAgAAAAAqgSJTLVBAYvX49Ff3BLHFpfVBbPqer3SjOfAo7lB9FJlCWdFH6F8/d+yTQezQ\nvV8OYmeWLwWxTQPhth+rA32O/kORKQAAAADAqkWCCwAAAACoBBJcAAAAAEAlcA1uC1xfg6Tlkw8U\nmu7E0vkgtmN440qbU1pc54II1+CirOgjlMu7nvonQezx9/zHILaccvznk7e92ZE2oXfoh5QD1+AC\nAAAAAFYtElwAAAAAQCWQ4AIAAAAAKoEEFwAAAABQCRSZaoECEijic3/50SD2woW7gtgv3vFHQeyh\ntWs70qZO+fXFe4PY//ju8H0BjVBkCmVFH6GaPvalfxDEJrbPBjEKT/UnikdVC0WmAAAAAACrFgku\nAAAAAKASSHABAAAAAJVAggsAAAAAqASKTLVAAQkA6CyKTKGs6COsHssnH2jbvJ6/eimIPbhmfdvm\nv5pQUKr6ivQRhjrWmg4ys1FJU5JOx8L7nXOLBec356c/3I72AQAAAAC6r3SnKJvZiKRZScecc3ud\nc3slzUma9c/lnd+0pNE2NxMAAAAA0GWlS3AlHZIk59yBKOD/H5X0eJ4Zmdm4SG4BAAAAoBJKleD6\nI7QTko6mPH1U0r6c89rvhyzjtxwAAMDqQx8BAPpH2a7Bfdj/nU95bl6SzGzMOXc8w7ymJO2VlPu0\nZgAAAKweWYsZPfbM3617PH3PV4JxvnxpZxA7ef2NIPaeNWeD2B2Dt2RqR9k8Ov9I3ePDP/D5HrUE\nVVCqI7iSxvzfUynPRQWmWp5ybGYTkmadc2mJcirnXMsBAACsPvQRAKB/lO0IbiStWnKU9DZNcP2p\nyZ9wzu1pe6sAAAAAAD3TswTX3+onq9P+FkDREde004q3+r+tbhV0SNJjOV4bAAAAAFACPUlwY/ex\nzeqYpAO6meBuTRknSnobnnZsZvskHWlwv9wtOdoDAAAAAOgzVrbrQszMSTqcPMXYzI5IGnfONSxV\naGazunkdbyN7nXMHE6/H9TMA0CFRhdlmv99AP6KPgE76Ly+HJzt+YN1gD1qSTdZCXEAeRfoIZbwG\n97Ck8ZT4uKSDKfG4PQpPbx6VNKPaEeIn1eQIMAAAAACgf5Uxwd0vadbMJqMjrf7U40XF7mnri0kt\nKHa0N61qsplFpysfy3h7IQAAAABAHypdguucmzezHZIOmdkuH94iaUfKtbXz4ogsAAAAAKwKpUtw\nJcknsk1v8+PHCe+kHY43L4nrvgAAAACg5EpXZKrbKCABAJ1FkSmUFX0EAOisIn2EgY61BgAAAACA\nLiLBBQAAAABUAgkuAAAAAKASSHABAAAAAJVAggsAAAAAqAQSXAAAAABAJZDgAgAAAAAqgQQXAAAA\nAFAJJLgAAAAAgEogwQUAAAAAVAIJLgAAAACgEkhwAQAAAACVQIILAAAAAKgEElwAAAAAQCWQ4AIA\nAAAAKoEEFwAAAABQCSS4AAAAAIBKIMEFAAAAAFQCCS4AAAAAoBJIcAEAAAAAlUCCCwAAAACoBBJc\nAAAAAEAlkOACAAAAACqBBBcAAAAAUAkkuAAAAACASiDBBQAAAABUAgkuAAAAAKASSHABAAAAAJVA\nggsAAAAAqAQSXAAAAABAJZDgAgAAAAAqgQQXAAAAAFAJQ71uQK+Z2ZikT/iHx5xzh3vZHgAAAABA\nMaVMcM1sVNKUpNOx8H7n3GKOeYxIOiRpTNIe59zx9rYSAAAAANBNpTtF2Sems6odbd3rnNsraU7S\nrH8uyzxGJZ2QNOqc20lyCwAAAADlZ865XrchFzObkTTunNuciDtJB5xz+zPMY07SFkk7Wh319fNV\n2ZYTAJSFmUmSnHPW46YAudBHAIDOKtJHKNURXH+EdkLS0ZSnj0ral2EeU5JGJR3MeUpzywEAAKw+\n9BEAoH+UKsGV9LD/O5/y3Lx0o2hUM5PRP2Y2a2bOzObMbLLZRAAAAACA/la2BDdKXk+lPBcdjR1t\nNLG/9ja6TvdJ59wuSZslHZc0bWYNjwA751oOAABg9aGPAAD9o2wJbiTt1OIo6W2Y4MaeOxgVlnLO\nLTrn9vh5TrWviQAAAACAburZbYL80dSsTvvrZaNTk9OqJW/1f7NcV5s2zlFJE2Y2RlVlAAAAACif\nniS4sfvYZnVM0gHdTHC3powTJb1p1+dGnvF/05LraLotOdoFAAAAAOgTPUlwnXPzkvYUmO64r0SY\nlqCO+nHSKixH0y+a2WKD6SPPNHkOAAAAANCnyngN7mFJ4ynxcUkHM0z/WUlj/pZDcaOS5vPcOggA\nAAAA0D/KmODul6T4bX189ePF6DkfG/G3AJqJT+yci051PhQbd1S1++vmPqoMAAAAAOgPPSsyVZRz\nbt7Mdkg6ZGa7fHiLpB0pR1/nlX5N7i5JU2Z2RLVbBI1K2kVxKQAAAAAoL+PebM39/+3d3XHcxpYA\n4HNIB0DbT/dRdAaUNgM6A2kdwfJmQNZG4KIykBTBWspAzuCaSmBL3Aiu5AjU+wBgNByC5PxgZoCe\n76vqItnoxkBzhOk+wADIzBIRnmEHsCXtvRWilJJ73hRYiTkCwHatM0eY4leUAQAA4B4JLgAAAFWQ\n4AIAAFAFCS4AAABVkOACAABQBQkuAAAAVZDgAgAAUAUJLgAAAFWQ4AIAAFAFCS4AAABVkOAesMyM\nzNz3ZjAwca2X2AK74vOmTuJaL7H9ToILAABAFSS4AAAAVEGCCwAAQBUkuAAAAFRBggsAAEAVJLgA\nAABUQYILAABAFSS4AAAAVCFLKfvehlHLTG8QwA6UUjyhnkkxRwDYjVXmCM7gAgAAUAVncAEAAKiC\nM7gAAABUQYILAABAFSS4AJXKzLN9bwMAMD41zxF+2PcGMH2ZeVZK+bTv7TgEmXkaEdcR8WWu+qqU\n8vcu+rMdQ8QlM28iYnGwehUR9k1gb8wRdsccoU7mCKuT4B4QO8i0ZeZJRNxExO+llNdt3WVE3GTm\n86fiuGl/tmOIuMwdhX09X19K+TD09gJ1MkeYNnOEOpkjrMddlA9Eu4P8X9zfQf4ZEavsIO8i4s/5\n+lLK1fBbzKLMfB8R56WUHxfqS0S8fioOm/ZnO4aIS2Z+jIh/llJut7SZQMXMEabPHKFO5gjrkeAe\nCDvItLWTj68R8aGU8mph2cdoYvvgA7A37c92DBGXdlJ5ExEfIuJjRPzhSDuwCnOEaTNHqJM5wvrc\nZOoAtDvIy1g4qtr6MyIul1jHWUScR8R1Zl6062R3XrQ/+yYOtxFP3ixg0/5sxxBx+a39+TIi3kTE\n18y8HmbzgNqZI1TBHKFO5ghrkuAeBjvI9HXx+XfPsu5I3OkW+7MdG8ellHLVHsH9JZrra/6OiMvM\nfDPYVgI1M0eYPnOEOpkjrEmCexjsIPXo+1pJF9dlBp9N+7MdG8ellHLbfo3wWTST0gtH3IElmCPU\nwxyhTuYIK5LgHhY7yHR1R9b7vvb1c/vzsWsqNu3Pdgwel/bamu5anRePtQWYY44wXeYIdTJHWJPH\nBE1Mexv/ZX1p/yNvZQfJzFfRXLj+IjwCYNu6GP7cs+xkoc02+rMdW4lLKeVTpvuBwKExRzhY5gh1\nMkdYkwR3QuaeUbesf0XzVSE7yMTNvdd9k5fTtk3fDUIG6c927CAuJiRwIMwRDpc5Qp3MEdYnwZ2Q\n9tb7r55seL+fHaQOH6K5S+Wi84h4u4P+bMfgccnM84i4NSGBw2GOcPDMEepkjrAG1+AeDjvI9F1F\nRGTmRVeRmZfRfHXsaq7uJDNL+1zDlfuzc2vHNTPPMvNzZl53j+Voz+JcRcSvu/oHAJNnjjB95gh1\nMkdYgzO4h+MqIm4y86KU8jbi4R0kFh4q3d4g4n00A+Dv7bU1B7GDjEkp5TYzn0XEu8x83lb/FBHP\neh7afRsLR81X7M+ObBjX22iubbuI5mYuf0TE51KK/RJYhTnCxJkj1MkcYT1ZStn3NrAj7cD0LiK+\ntFU/RcR/ze8gbZubaAavq4V+3dHdbgd5vattBwC2xxwBqIUEFwAAgCq4BhcAAIAqSHABAACoggQX\nAACAKkhwAQAAqIIEFwAAgCpIcAEAAKiCBBcAAIAqSHABAACoggQXAACAKkhwAQAAqIIEFwAAgCpI\ncAEAAKiCBBcAAIAqSHABAACoggQXAACAKvyw7w04JJlZ9r0NAEMrpeS+twGmzhwBqNE+5gjO4AIA\nAFAFZ3D34PzoPyOPMiKPZj/jqD24kRl5dBSRGdH9jIg4ysiuXX5vO2uTeadt83dP+8xZXZm1jzvr\n6KtfrCuZERmzdZdZfdenaV4yI45i1n52DGfWNmb/9u7vbh3f2y7Udes+eqp93G0/q8t7dQ+3/b78\nTn08UP9A+7v/9ofrF/v0r7s8uu6uzZ3jZbP+5c66Y66uzK07uv82i+2jtMvKrK77vWvfnYTInF9W\nvr9k26b5rzjf/m7dUXxfz6xuru1R22a+/qirb5c9Xf+tt+54VvdtVn8893ez/Nts3cfR1X+bbfdx\nfmvW1fY7jrn1d+3bdXT9mp/N6zev+W3Wfv41j7t1tH1m645vs7/vrH/2d5nbjrY+Io4z4riNUPN3\nxlFkHEfzM2Z1R019Nr8d/+N/AxjWIHOEWX0dc4QH5wN9dQPNERbH1cnPERbG/V3MEbKn/ZTnCEfZ\njcG7myMct69pjrAaZ3ABAACoggQXAACAKkhwAQAAqIIEFwAAgCpIcAEAAKiCBBcAAIAqSHABAACo\nggQXAACAKkhwAQAAqIIEFwAAgCpIcAEAAKiCBBcAAIAqZCll39twMDLTmw1Up5SS+94GmDpzBKBG\n+5gjOIMLAABAFX7Y9wYcklWOYGTmX6WUF9vcHvZDbOsmvsA6fBPicT5b6yW29crMv/bxus7gAgAA\nUAUJLgAAAFWQ4AIAAFAFCS4AAABVkOACAABQBQkuAAAAVZDgAgAAUAUJLgAAAFWQ4I7X231vAFsj\ntnUTX4Dh+Wytl9jWay+xzVLKPl4XAAAABuUMLgAAAFWQ4AIAAFAFCS5MWGaeZeZ1W17ue3sAYCoy\n82zf2wCsZpn99oddbEjtMvM0Iq4j4stc9VUp5e8h+w/djuWMLb5t25OIeBcRZxHxqpTyaZlt4b5d\nxXeu/WVE/Hcp5ccHlp+16zuPiNuI+FBKuVpmWwDGYKTj5k00Y+a8VxFh/FzBCMdMc96BjC22bZv1\n9ttSirJBiYiTiPgaEZdzdZcR8TkiTobqP3Q7ZZrxbetP27Y3+35/pl52Fd+2/jyaD/7SfPT2ru9l\n2/e6LV/b9h/3/V4piqIsU0Y6bp5FxM3cZ+t1RFzv+72aWhnhmGnOW2ls23Zr77d7f0OnXiLifUR8\n7akvywRh2f5Dt1OmGd+27nP7IeLDeyLxXVh288hgfS+RbeNdIuJs3++XoijKU2Wk4+bHiDjd93sz\n9TLCMdOct9LYtsvX3m/3/oZOuURztKJExPsHgvJg0FbpP3Q7ZZrxbf/ujnj54J5IfHuW9X6gR3Ok\n8ryn/qJ9nYt9v2eKoiiPlZGOm2dd2/bz1MHhEce2Z9lDY6Y5b6WxbZdttN+6ydRmXrQ/b3uW3UY8\neSH0sv2HbsdyxhbfiGYnj7buJjNLZn7OzIuevjxuV/FdSinlUynlz55F3bUsfa8DMCZjHDd/a3++\njIg3EfE1M68f2Qb6jWrM3ML6DtnYYhux4X4rwd1MF6x/9yzrLqg+HaD/0O1Yzqji2168f9L+/T+l\nlOcR8WM0F9q/aS/WZ3m7iu+m/iMibh9IfgHGZFTjZkREKeWqlJIR8UtEvG6XX2bmm0e2g/vGNmaa\n8w5nbLHdeL+V4A6j7+5gXZCXCeiy/Ydux3LGEt+u7dvS3jW5lPJ3KeVV29cR6fXsKr7rehnNHQMB\npmIs4+ZMKeW2NHekfxbNWaULZ/jWMrYx05x3OGOL7dr7rQR3M92p+JOeZT+3Px+7tfay/Ydux3LG\nFt/o+b3zZ4Sv46xoV/FdW2a+j+Z6a4+xAKZgrOPmTGkeWdIdNHzR14ZeYxszzXmHM7bY3rPqfus5\nuJvpAvpzz7KThTab9P8ycDuWM7b4/tX+3ncUrHutnx7ZHu7aVXzX0l5XfVtKebvuOgB2bGzjZq9S\nyqfMfGQz6DG2MXOrY/CBGVtse62y30pwNzD3RvclHN21Hw9eN7dK/6Hb8bSRxvfvB9p1/npkGXN2\nGd9VZeZ5RPzSfi0HYBLGOG4+QQK0pLGNmdscgw/N2GK7hCf3W19R3tyHaB5YvOg8IpY587Js/6Hb\nsZyxxff3iDjLzMWvgZxGc7bP13FWs6v4Lq39mvmvi8ltZp74CjowAWMbN+9pDyK6ed/qxjZmmvMO\nZ2yxvWel/Xbfz16aeokmsfgac8+ojIjLtu5krq73GVEr9B+0nTLN+Lb1n+dfp+1bIuJs3+/X1Mqu\n4tsTv/LAsrN2+eVCuY6Im32/X4qiKE+VMY2bc5+p13N1p9E8m/N03+/V1MoIx0xz3gpjO8R+6yvK\nGyql3Gbms4h4l5nP2+qfIuJZuX827TYWTqsv23/odixnbPFtPY+I68z8GM0jgk4j4nlxI6KV7Sq+\nEbMzs79F+3Wd9nluH8v3r9udRvPQ84j+O2I7Gg2M3sjGzdtoxsmLaO6++kdEfC6l/DrMv/awjGnM\nXGN7eMTIYrvxfpttVgwAAACT5hpcAAAAqiDBBQAAoAoSXAAAAKogwQUAAKAKElwAAACqIMEFAACg\nChJcAAAAqiDBBQAAoAoSXAAAAKogwQUAAKAK/w/nx/Qqef//8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117617510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.suptitle('OS5 HLC / Haystacks scene: Ideal readout and PSF subtraction')\n",
    "plt.subplots_adjust(left=0.08, right=0.98, wspace = 0.01, bottom=0.02, top=0.80)\n",
    "plt.subplot(121)\n",
    "tavg_noiseless_sci_scene[0,data_mask_nan_ind[0]] = np.NaN\n",
    "plt.imshow(tavg_noiseless_sci_scene[0].value,\n",
    "           extent = (xs_as[0], xs_as[-1], xs_as[0], xs_as[-1]),\n",
    "           vmin=-0.5e-3, vmax=1.5e-2,cmap=cmap)\n",
    "plt.tick_params(width=2, length=10, direction='in')\n",
    "plt.ylabel('Arcsec')\n",
    "plt.title('F506, {:.1f}'.format(Nint_use[0]*exptime.to(u.hour)),fontsize=25)\n",
    "cb = plt.colorbar(shrink=0.6, orientation='horizontal', pad=0.08)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "plt.subplot(122)\n",
    "tavg_noiseless_sci_scene[1,data_mask_nan_ind[1]] = np.NaN\n",
    "plt.imshow(tavg_noiseless_sci_scene[1].value,\n",
    "           extent = (xs_as[0], xs_as[-1], xs_as[0], xs_as[-1]),\n",
    "           vmin=2e-3, vmax=1.5e-2,cmap=cmap)\n",
    "plt.tick_params(labelleft='off', width=2, length=10, direction='in')\n",
    "#plt.axes('off', which='y')\n",
    "#plt.colorbar()\n",
    "#plt.xlabel('Arcsec')\n",
    "plt.title('F575, {:.1f}'.format(Nint_use[1]*exptime.to(u.hour)),fontsize=25)\n",
    "cb = plt.colorbar(shrink=0.6, orientation='horizontal', pad=0.08)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "plt.savefig(exportDir+'os5_hlc_haystack_scene_506-575_ideal.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZzXxCki/6Wtr0RUl7tcBmFvhcvXJx8eLULC4MLcUKiLeWfH9bC754bHna9+d3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\nP1STQG9yWbZ1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11afe9410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from astropy.visualization import (MinMaxInterval, SqrtStretch,\n",
    "                                   ImageNormalize)\n",
    "from astropy.visualization import simple_norm\n",
    "\n",
    "plt.figure(figsize=(14,8))\n",
    "cw = 16\n",
    "\n",
    "plt.subplot(121)\n",
    "\n",
    "# norm = simple_norm(tavg_noisy_sci_scene[1, cw:-cw, cw:-cw].value, 'sqrt')\n",
    "# plt.suptitle('OS5 HLC / Haystacks scene: F6V star at {:.2f}, 1.6 AU Jovian, 10 zodi debris\\n\\\n",
    "#               Scale unit is count / s'.format(dist_scene))\n",
    "plt.subplots_adjust(left=0.08, right=0.98, wspace = 0.01, bottom=0.02, top=0.8)\n",
    "plt.imshow(tavg_noisy_sci_scene[1, cw:-cw, cw:-cw].value,\n",
    "           extent = (xs_as[cw], xs_as[-cw-1], xs_as[cw], xs_as[-cw-1]),\n",
    "           vmin=0.0, vmax=1e-1,cmap=cmap)\n",
    "plt.tick_params(width=2, length=10, direction='in')\n",
    "plt.ylabel('Arcsec')\n",
    "# plt.title('F575, {:.1f}, raw'.format(Nint_use[0]*exptime.to(u.hour)),fontsize=25)\n",
    "cb = plt.colorbar(shrink=0.8, orientation='horizontal', pad=0.08)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "plt.subplot(122)\n",
    "\n",
    "# norm = simple_norm(tavg_noisy_resid_sci_scene[1, cw:-cw, cw:-cw].value, 'sqrt')\n",
    "#plt.suptitle('OS5 HLC / Haystacks scene: G8V star at {:.2f} pc, 1 AU Jovian, 10 zodi debris\\nFlux scale in units of photoelectron / s'.format(dist_scene))\n",
    "plt.subplots_adjust(left=0.08, right=0.98, wspace = 0.01, bottom=0.02, top=0.8)\n",
    "plt.imshow(tavg_noisy_resid_sci_scene[1, cw:-cw, cw:-cw].value,\n",
    "           extent = (xs_as[cw], xs_as[-cw-1], xs_as[cw], xs_as[-cw-1]),\n",
    "           vmin=0, vmax=6e-2,cmap=cmap)\n",
    "plt.tick_params(width=2, length=10, direction='in')\n",
    "plt.ylabel('Arcsec')\n",
    "# plt.title('F575, {:.1f}, subtracted'.format(Nint_use[0]*exptime.to(u.hour)),fontsize=25)\n",
    "cb = plt.colorbar(shrink=0.8, orientation='horizontal', pad=0.08)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "plt.savefig('os5_hlc_1Ori_Bertrand_WP.png', dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VKiW2nxRL7JeXULe6Bqyfs5+m0kE2y9SvIskX9LSWJcDnU1mGtC/MOmjaDucCszhIOxWP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\ncb9ZTep2XvtKyPxD8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1179f06d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.suptitle('OS5 HLC / Haystacks scene: G8V star at {:.2f}, 1 AU Jovian, 10 zodi debris\\nFlux scale in units of counts / s'.format(dist_scene))\n",
    "plt.subplots_adjust(left=0.08, right=0.98, wspace = 0.01, bottom=0.02, top=0.92)\n",
    "plt.subplot(131)\n",
    "plt.imshow(tavg_noisy_resid_sci_scene[2].value,\n",
    "           extent = (xs_as[0], xs_as[-1], xs_as[0], xs_as[-1]),\n",
    "           vmin=0e-2, vmax=1.5e-2,cmap=cmap)\n",
    "plt.tick_params(width=2, length=10, direction='in')\n",
    "plt.ylabel('Arcsec')\n",
    "plt.title('F661, {:.1f}'.format(Nint_use[2]*exptime.to(u.hour)),fontsize=25)\n",
    "cb = plt.colorbar(shrink=0.8, orientation='horizontal', pad=0.08)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.imshow(tavg_noisy_resid_sci_scene[3].value,\n",
    "           extent = (xs_as[0], xs_as[-1], xs_as[0], xs_as[-1]),\n",
    "           vmin=-2e-3, vmax=5e-3,cmap=cmap)\n",
    "plt.tick_params(labelleft='off', width=2, length=10, direction='in')\n",
    "#plt.axes('off', which='y')\n",
    "#plt.colorbar()\n",
    "#plt.xlabel('Arcsec')\n",
    "plt.title('F721, {:.1f}'.format(Nint_use[3]*exptime.to(u.hour)),fontsize=25)\n",
    "cb = plt.colorbar(shrink=0.8, orientation='horizontal', pad=0.08)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.imshow(tavg_noisy_resid_sci_scene[4].value,\n",
    "           extent = (xs_as[0], xs_as[-1], xs_as[0], xs_as[-1]),           \n",
    "           vmin=-3e-3,vmax = 1e-4, cmap=cmap)\n",
    "plt.tick_params(labelleft='off', width=2, length=10, direction='in')\n",
    "#plt.colorbar()e\n",
    "#plt.xlabel('Arcsec')\n",
    "plt.title('F883, {:.1f}'.format(Nint_use[4]*exptime.to(u.hour)),fontsize=25)\n",
    "cb = plt.colorbar(shrink=0.8, orientation='horizontal', pad=0.08)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "plt.savefig(exportDir+'os5_hlc_haystack_scene.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot Haystacks counts before convolution with coronagraph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnitTypeError",
     "evalue": "Cannot store quantity with dimension resulting from true_divide function in a non-Quantity instance.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m----------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnitTypeError\u001b[0m                  Traceback (most recent call last)",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/IPython/core/formatters.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/IPython/core/pylabtools.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/backend_bases.pyc\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)\u001b[0m\n\u001b[1;32m   2206\u001b[0m                     \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m                     \u001b[0mdryrun\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2208\u001b[0;31m                     **kwargs)\n\u001b[0m\u001b[1;32m   2209\u001b[0m                 \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cachedRenderer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2210\u001b[0m                 \u001b[0mbbox_inches\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_tightbbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, *args, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mprint_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename_or_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m         \u001b[0mFigureCanvasAgg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    508\u001b[0m         \u001b[0mrenderer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_renderer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m         \u001b[0moriginal_dpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/backends/backend_agg.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    428\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtoolbar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m                 \u001b[0mtoolbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_cursor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcursors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWAIT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 430\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    431\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    432\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtoolbar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/figure.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1294\u001b[0m             mimage._draw_list_compositing_images(\n\u001b[0;32m-> 1295\u001b[0;31m                 renderer, self, artists, self.suppressComposite)\n\u001b[0m\u001b[1;32m   1296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1297\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'figure'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2397\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2399\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2400\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2401\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/artist.pyc\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     53\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    546\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    547\u001b[0m             im, l, b, trans = self.make_image(\n\u001b[0;32m--> 548\u001b[0;31m                 renderer, renderer.get_image_magnification())\n\u001b[0m\u001b[1;32m    549\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mim\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36mmake_image\u001b[0;34m(self, renderer, magnification, unsampled)\u001b[0m\n\u001b[1;32m    772\u001b[0m         return self._make_image(\n\u001b[1;32m    773\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransformed_bbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagnification\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 774\u001b[0;31m             unsampled=unsampled)\n\u001b[0m\u001b[1;32m    775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_unsampled_image\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/matplotlib/image.pyc\u001b[0m in \u001b[0;36m_make_image\u001b[0;34m(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)\u001b[0m\n\u001b[1;32m    381\u001b[0m                 \u001b[0mA_scaled\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0ma_min\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0ma_min\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0ma_max\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m                     \u001b[0mA_scaled\u001b[0m \u001b[0;34m/=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_max\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0ma_min\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0.8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    384\u001b[0m                 \u001b[0mA_scaled\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m0.1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m                 A_resampled = np.zeros((out_height, out_width),\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/astropy-2.0.2-py2.7-macosx-10.6-x86_64.egg/astropy/units/quantity.pyc\u001b[0m in \u001b[0;36m__array_ufunc__\u001b[0;34m(self, function, method, *inputs, **kwargs)\u001b[0m\n\u001b[1;32m    628\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnout\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m                 \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 630\u001b[0;31m             \u001b[0mout_array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    631\u001b[0m             \u001b[0;31m# Ensure output argument remains a tuple.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'out'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mout_array\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnout\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mout_array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/mrizzo/anaconda2/lib/python2.7/site-packages/astropy-2.0.2-py2.7-macosx-10.6-x86_64.egg/astropy/units/quantity_helper.pyc\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(output, unit, inputs, function)\u001b[0m\n\u001b[1;32m    629\u001b[0m                                 .format(\"\" if function is None else\n\u001b[1;32m    630\u001b[0m                                         \u001b[0;34m\"resulting from {0} function \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m                                         .format(function.__name__)))\n\u001b[0m\u001b[1;32m    632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m     \u001b[0;31m# check we can handle the dtype (e.g., that we are not int\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mUnitTypeError\u001b[0m: Cannot store quantity with dimension resulting from true_divide function in a non-Quantity instance."
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b5fb210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8.5))\n",
    "# hdu = fits.HDUList([fits.PrimaryHDU(hay_cube_bandavg_ph.value)])\n",
    "# hdu.writeto('haystacks_ctspersec.fits',overwrite=True)\n",
    "\n",
    "plt.suptitle('Haystacks scene (before convolution and coronagraph mask loss factors)\\nG8V star at {:.2f}, 1 AU Jovian, 10 zodi debris\\nFlux scale in units of cts / s'.format(dist_scene))\n",
    "plt.subplots_adjust(left=0.08, right=0.98, wspace = 0.01, bottom=0.02, top=0.92)\n",
    "plt.subplot(131)\n",
    "plt.imshow(hay_cube_bandavg_ph[1],\n",
    "           extent = (xs_as[0], xs_as[-1], xs_as[0], xs_as[-1]),\n",
    "           vmin=0, vmax = 3e-1, cmap='gray')\n",
    "plt.tick_params(width=2, length=10, direction='in')\n",
    "plt.ylabel('Arcsec')\n",
    "plt.title('F661',fontsize=25)\n",
    "cb = plt.colorbar(shrink=0.8, orientation='horizontal', pad=0.08)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.imshow(hay_cube_bandavg_ph[2],\n",
    "           extent = (xs_as[0], xs_as[-1], xs_as[0], xs_as[-1]),\n",
    "           vmin=0, vmax = 1.3e-1, cmap='gray')\n",
    "plt.tick_params(labelleft='off', width=2, length=10, direction='in')\n",
    "#plt.axes('off', which='y')\n",
    "#plt.colorbar()\n",
    "#plt.xlabel('Arcsec')\n",
    "plt.title('F721',fontsize=25)\n",
    "cb = plt.colorbar(shrink=0.8, orientation='horizontal', pad=0.08)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.imshow(hay_cube_bandavg_ph[3],\n",
    "           extent = (xs_as[0], xs_as[-1], xs_as[0], xs_as[-1]),           \n",
    "           vmin=0, vmax = 7e-2, cmap='gray')\n",
    "plt.tick_params(labelleft='off', width=2, length=10, direction='in')\n",
    "#plt.colorbar()\n",
    "#plt.xlabel('Arcsec')\n",
    "plt.title('F883',fontsize=25)\n",
    "cb = plt.colorbar(shrink=0.8, orientation='horizontal', pad=0.08)\n",
    "cb.locator = matplotlib.ticker.MaxNLocator(nbins=3)\n",
    "cb.update_ticks()\n",
    "\n",
    "#plt.savefig('os5_hlc_haystack_scene_before_conv.png', dpi=100)"
   ]
  }
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