{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ramp Optimization Examples\n",
    "\n",
    "This notebook outlines an example to optimize the ramp settings for a few different types of observations.\n",
    "\n",
    "In these types of optimizations, we must consider observations constraints such as saturation levels, SNR requirements, and limits on acquisition time.\n",
    "\n",
    "**Note**: The reported acquisition time does not include obsevatory and instrument-level overheads, such as slew times, filter changes, script compilations, etc. It only includes detector readout times (including reset frames)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the usual libraries\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Enable inline plotting at lower left\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pynrc\n",
    "from pynrc import nrc_utils\n",
    "from pynrc.nrc_utils import S, jl_poly_fit\n",
    "from pynrc.pynrc_core import table_filter\n",
    "\n",
    "pynrc.setup_logging('WARNING', verbose=False)\n",
    "\n",
    "from astropy.table import Table\n",
    "\n",
    "# Progress bar\n",
    "from tqdm.auto import tqdm, trange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: M-Dwarf companion (imaging vs coronagraphy)\n",
    "\n",
    "We want to observe an M-Dwarf companion (K=18 mag) in the vicinity of a brighter F0V (K=13 mag) in the F430M filter. Assume the M-Dwarf flux is not significantly impacted by the brighter PSF (ie., in the background limited regime). In this scenario, the F0V star will saturate much more quickly compared to the fainter companion, so it limits which ramp settings we can use.\n",
    "\n",
    "We will test a couple different types of observations (direct imaging vs coronagraphy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get stellar spectra and normalize at K-Band\n",
    "# The stellar_spectrum convenience function creates a Pysynphot spectrum\n",
    "bp_k = S.ObsBandpass('k')\n",
    "sp_M2V = pynrc.stellar_spectrum('M2V', 18, 'vegamag', bp_k)#, catname='ck04models')\n",
    "sp_F0V = pynrc.stellar_spectrum('F0V', 13, 'vegamag', bp_k)#, catname='ck04models')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiate a NIRCam observation\n",
    "nrc = pynrc.NIRCam(filter='F430M', wind_mode='WINDOW', xpix=160, ypix=160)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRIGHT1\n",
      "BRIGHT2\n",
      "DEEP2\n",
      "DEEP8\n",
      "MEDIUM2\n",
      "MEDIUM8\n",
      "RAPID\n",
      "SHALLOW2\n",
      "SHALLOW4\n",
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "DEEP8         7    4     35.67    142.66    143.80     99.3    0.628    8.277\n",
      "DEEP8         7    5     35.67    178.33    179.75    111.0    0.628    8.277\n",
      "DEEP2         8    4     39.57    158.27    159.40     98.5    0.696    7.804\n",
      "DEEP2         8    5     39.57    197.83    199.25    110.2    0.696    7.804\n",
      "DEEP8         5    7     24.52    171.64    173.63    101.4    0.432    7.697\n",
      "MEDIUM8       8    8     21.73    173.87    176.14    100.5    0.383    7.575\n",
      "MEDIUM8       8    9     21.73    195.61    198.16    106.6    0.383    7.575\n",
      "MEDIUM2      10    7     25.63    179.44    181.43     98.6    0.451    7.322\n",
      "...         ...  ...       ...       ...       ...      ...      ...      ...\n",
      "RAPID        10  555      2.79   1546.45   1703.96    101.5    0.049    2.458\n",
      "RAPID        10  556      2.79   1549.24   1707.03    101.6    0.049    2.458\n",
      "RAPID        10  557      2.79   1552.02   1710.10    101.7    0.049    2.458\n",
      "RAPID        10  558      2.79   1554.81   1713.17    101.7    0.049    2.458\n",
      "RAPID        10  559      2.79   1557.60   1716.24    101.8    0.049    2.458\n",
      "RAPID        10  560      2.79   1560.38   1719.31    101.9    0.049    2.458\n",
      "RAPID        10  561      2.79   1563.17   1722.38    102.0    0.049    2.458\n",
      "RAPID         8 1046      2.23   2331.66   2628.51    102.0    0.039    1.990\n",
      "Length = 55 rows\n"
     ]
    }
   ],
   "source": [
    "# Set some observing constraints\n",
    "# Let's assume we want photometry on the primary to calibrate the M-Dwarf for direct imaging\n",
    "#  - Set well_frac_max=0.75\n",
    "# Want a SNR~100 in the F430M filter\n",
    "#  - Set snr_goal=100\n",
    "res = nrc.ramp_optimize(sp_M2V, sp_bright=sp_F0V, snr_goal=100, well_frac_max=0.75, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "RAPID        10  539      2.79   1501.87   1654.84    100.0    0.049    2.458\n",
      "RAPID        10  540      2.79   1504.66   1657.91    100.1    0.049    2.458\n",
      "BRIGHT1      10  145      5.29    767.65    808.80     99.8    0.093    3.509\n",
      "BRIGHT1      10  146      5.29    772.95    814.38    100.1    0.093    3.509\n",
      "BRIGHT2      10   93      5.57    518.27    544.66     99.7    0.098    4.270\n",
      "BRIGHT2      10   94      5.57    523.84    550.52    100.2    0.098    4.270\n",
      "SHALLOW2     10   20     13.10    261.92    267.60     99.6    0.230    6.089\n",
      "SHALLOW2     10   21     13.10    275.02    280.98    102.1    0.230    6.089\n",
      "SHALLOW4     10   16     13.65    218.45    222.99     99.7    0.240    6.675\n",
      "SHALLOW4     10   17     13.65    232.11    236.93    102.8    0.240    6.675\n",
      "MEDIUM2      10    7     25.63    179.44    181.43     98.6    0.451    7.322\n",
      "MEDIUM2      10    8     25.63    205.08    207.35    105.4    0.451    7.322\n",
      "MEDIUM8       8    8     21.73    173.87    176.14    100.5    0.383    7.575\n",
      "MEDIUM8       8    9     21.73    195.61    198.16    106.6    0.383    7.575\n",
      "DEEP2         8    4     39.57    158.27    159.40     98.5    0.696    7.804\n",
      "DEEP2         8    5     39.57    197.83    199.25    110.2    0.696    7.804\n",
      "DEEP8         7    4     35.67    142.66    143.80     99.3    0.628    8.277\n",
      "DEEP8         7    5     35.67    178.33    179.75    111.0    0.628    8.277\n"
     ]
    }
   ],
   "source": [
    "# Print the Top 2 settings for each readout pattern\n",
    "res2 = table_filter(res, 2)\n",
    "print(res2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "MEDIUM8      10   94    104.77   9848.00   9950.36     99.6    0.001    0.998\n",
      "MEDIUM8      10   96    104.77  10057.53  10162.07    100.7    0.001    0.998\n",
      "DEEP8        20   14    414.79   5807.03   5822.27    104.6    0.003    1.370\n",
      "DEEP8        19   14    393.41   5507.69   5522.94    101.2    0.003    1.362\n"
     ]
    }
   ],
   "source": [
    "# Do the same thing, but for coronagraphic mask instead\n",
    "nrc = pynrc.NIRCam(filter='F430M', image_mask='MASK430R', pupil_mask='CIRCLYOT', \n",
    "                   wind_mode='WINDOW', xpix=320, ypix=320)\n",
    "\n",
    "# We assume that longer ramps will give us the best SNR for time\n",
    "patterns = ['MEDIUM8', 'DEEP8']\n",
    "res = nrc.ramp_optimize(sp_M2V, sp_bright=sp_F0V, snr_goal=100, \n",
    "                        patterns=patterns, even_nints=True)\n",
    "\n",
    "# Take the Top 2 settings for each readout pattern\n",
    "res2 = table_filter(res, 2)\n",
    "print(res2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**RESULTS**\n",
    "\n",
    "Based on these two comparisons, it looks like direct imaging is much more efficient in getting to the requisite SNR. In addition, direct imaging gives us a photometric comparison source that is inaccessible when occulting the primary with the coronagraph masks. **Of course, this assumes the companion exists in the background limit as opposed to the contrast limit.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Exoplanet Coronagraphy\n",
    "\n",
    "We want to observe GJ 504 for an hour in the F444W filter using the MASK430R coronagraph. \n",
    " - What is the optimal ramp settings to maximize the SNR of GJ 504b?\n",
    " - What is the final background sensitivity limit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get stellar spectra and normalize at K-Band\n",
    "# The stellar_spectrum convenience function creates a Pysynphot spectrum\n",
    "bp_k = pynrc.bp_2mass('ks')\n",
    "sp_G0V = pynrc.stellar_spectrum('G0V', 4, 'vegamag', bp_k)\n",
    "\n",
    "# Choose a representative planet spectrum\n",
    "planet = pynrc.planets_sb12(atmo='hy3s', mass=8, age=200, entropy=8, distance=17.5)\n",
    "sp_pl = planet.export_pysynphot()\n",
    "\n",
    "# Renormalize to F360M = 18.8\n",
    "bp_l = pynrc.read_filter('F360M') # \n",
    "sp_pl = sp_pl.renorm(18.8, 'vegamag', bp_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initiate a NIRCam observation\n",
    "nrc = pynrc.NIRCam(filter='F444W', pupil_mask='CIRCLYOT', image_mask='MASK430R', \n",
    "                   wind_mode='WINDOW', xpix=320, ypix=320)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRIGHT1\n",
      "BRIGHT2\n",
      "DEEP2\n",
      "DEEP8\n",
      "MEDIUM2\n",
      "MEDIUM8\n",
      "RAPID\n",
      "SHALLOW2\n",
      "SHALLOW4\n",
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "DEEP2        17   10    344.23   3442.31   3453.20    672.8    0.777   11.448\n",
      "DEEP2        16   10    322.85   3228.50   3239.39    651.5    0.728   11.446\n",
      "DEEP2        16   12    322.85   3874.20   3887.27    713.7    0.728   11.446\n",
      "DEEP2        15   12    301.47   3617.63   3630.70    689.4    0.680   11.440\n",
      "DEEP2        14   12    280.09   3361.06   3374.13    664.0    0.632   11.431\n",
      "DEEP8        17   10    350.65   3506.45   3517.34    676.2    0.791   11.402\n",
      "DEEP8        16   10    329.26   3292.64   3303.53    655.3    0.743   11.401\n",
      "DEEP8        15   12    307.88   3694.60   3707.67    694.1    0.695   11.398\n",
      "...         ...  ...       ...       ...       ...      ...      ...      ...\n",
      "BRIGHT2      10  162     21.38   3463.69   3640.10    518.1    0.048    8.587\n",
      "BRIGHT1      10  166     20.31   3371.75   3552.52    463.1    0.046    7.768\n",
      "BRIGHT1      10  168     20.31   3412.38   3595.32    465.8    0.046    7.768\n",
      "BRIGHT1      10  170     20.31   3453.00   3638.12    468.6    0.046    7.768\n",
      "RAPID        10  304     10.69   3249.88   3580.93    358.9    0.024    5.997\n",
      "RAPID        10  306     10.69   3271.26   3604.48    360.1    0.024    5.997\n",
      "RAPID        10  308     10.69   3292.64   3628.04    361.2    0.024    5.997\n",
      "RAPID         9  334      9.62   3213.53   3577.25    331.8    0.022    5.548\n",
      "Length = 36 rows\n"
     ]
    }
   ],
   "source": [
    "# Set even_nints=True assume 2 roll angles\n",
    "res = nrc.ramp_optimize(sp_pl, sp_bright=sp_G0V, tacq_max=3600, tacq_frac=0.05, \n",
    "                        even_nints=True, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "RAPID        10  304     10.69   3249.88   3580.93    358.9    0.024    5.997\n",
      "RAPID        10  306     10.69   3271.26   3604.48    360.1    0.024    5.997\n",
      "BRIGHT1      10  166     20.31   3371.75   3552.52    463.1    0.046    7.768\n",
      "BRIGHT1      10  168     20.31   3412.38   3595.32    465.8    0.046    7.768\n",
      "BRIGHT2      10  158     21.38   3378.17   3550.22    511.7    0.048    8.587\n",
      "BRIGHT2      10  160     21.38   3420.93   3595.16    514.9    0.048    8.587\n",
      "SHALLOW2     10   68     50.24   3416.65   3490.70    606.1    0.113   10.258\n",
      "SHALLOW2     10   70     50.24   3517.14   3593.37    615.0    0.113   10.258\n",
      "SHALLOW4     10   66     52.38   3457.28   3529.15    619.9    0.118   10.434\n",
      "SHALLOW4     10   68     52.38   3562.04   3636.09    629.2    0.118   10.434\n",
      "MEDIUM2      10   34     98.35   3343.96   3380.98    638.0    0.222   10.971\n",
      "MEDIUM2      10   36     98.35   3540.66   3579.86    656.5    0.222   10.971\n",
      "MEDIUM8      10   32    104.77   3352.51   3387.36    638.0    0.236   10.962\n",
      "MEDIUM8      10   34    104.77   3562.04   3599.07    657.7    0.236   10.962\n",
      "DEEP2        17   10    344.23   3442.31   3453.20    672.8    0.777   11.448\n",
      "DEEP2        16   10    322.85   3228.50   3239.39    651.5    0.728   11.446\n",
      "DEEP8        17   10    350.65   3506.45   3517.34    676.2    0.791   11.402\n",
      "DEEP8        16   10    329.26   3292.64   3303.53    655.3    0.743   11.401\n"
     ]
    }
   ],
   "source": [
    "# Take the Top 2 settings for each readout pattern\n",
    "res2 = table_filter(res, 2)\n",
    "print(res2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_acq     :   3593.36880\n",
      "t_exp     :   3517.14160\n",
      "t_frame   :      1.06904\n",
      "t_group   :      5.34520\n",
      "t_int     :     50.24488\n",
      "t_int_tot1:     51.33384\n",
      "t_int_tot2:     51.33384\n"
     ]
    }
   ],
   "source": [
    "# The SHALLOWs, DEEPs, and MEDIUMs are very similar for SNR and efficiency.\n",
    "# Let's go with SHALLOW2 for more GROUPS & INTS \n",
    "# MEDIUM8 would be fine as well.\n",
    "nrc.update_detectors(read_mode='SHALLOW2', ngroup=10, nint=70)\n",
    "\n",
    "keys = list(nrc.multiaccum_times.keys())\n",
    "keys.sort()\n",
    "for k in keys:\n",
    "    print(\"{:<10}: {: 12.5f}\".format(k, nrc.multiaccum_times[k]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Point Source Sensitivity (5-sigma): 21.43 vegamag\n",
      "Surface Brightness Sensitivity (5-sigma): 22.74 vegamag/arcsec^2\n"
     ]
    }
   ],
   "source": [
    "# Background sensitivity (5 sigma)\n",
    "sens_dict = nrc.sensitivity(nsig=5, units='vegamag', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3: Single-Object Grism Spectroscopy\n",
    "\n",
    "Similar to the above, but instead we want to obtain a slitless grism spectrum of a K=12 mag M0V dwarf. Each grism resolution element should have SNR~100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# M0V star normalized to K=12 mags\n",
    "bp_k = S.ObsBandpass('k')\n",
    "sp_M0V = pynrc.stellar_spectrum('M0V', 12, 'vegamag', bp_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrc = pynrc.NIRCam(filter='F444W', pupil_mask='GRISMR', wind_mode='STRIPE', ypix=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRIGHT1\n",
      "BRIGHT2\n",
      "DEEP2\n",
      "DEEP8\n",
      "MEDIUM2\n",
      "MEDIUM8\n",
      "RAPID\n",
      "SHALLOW2\n",
      "SHALLOW4\n",
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "DEEP2        10   10    123.03   1230.27   1237.08    332.4    0.071    9.449\n",
      "DEEP8        10   10    127.08   1270.82   1277.64    336.0    0.074    9.399\n",
      "MEDIUM2      10   10     62.19    621.89    628.71    231.2    0.036    9.219\n",
      "MEDIUM8      10   10     66.25    662.45    669.27    236.1    0.038    9.127\n",
      "SHALLOW4     10   10     33.12    331.23    338.04    161.8    0.019    8.802\n",
      "SHALLOW2     10   10     31.77    317.71    324.52    157.6    0.018    8.746\n",
      "BRIGHT2      10   12     13.52    162.23    170.41     98.8    0.008    7.567\n",
      "BRIGHT2      10   13     13.52    175.75    184.61    102.8    0.008    7.567\n",
      "BRIGHT1      10   15     12.84    192.65    202.87    100.0    0.007    7.021\n",
      "BRIGHT1      10   16     12.84    205.49    216.40    103.3    0.007    7.021\n",
      "RAPID        10   42      6.76    283.91    312.52     99.0    0.004    5.599\n",
      "RAPID        10   43      6.76    290.67    319.96    100.2    0.004    5.599\n",
      "RAPID        10   44      6.76    297.43    327.41    101.3    0.004    5.599\n",
      "RAPID        10   45      6.76    304.19    334.85    102.5    0.004    5.599\n"
     ]
    }
   ],
   "source": [
    "# Set a minimum of 10 integrations to be robust against cosmic rays\n",
    "# Also set a minimum of 10 groups for good ramp sampling\n",
    "res = nrc.ramp_optimize(sp_M0V, snr_goal=100, nint_min=10, ng_min=10, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "RAPID        10   42      6.76    283.91    312.52     99.0    0.004    5.599\n",
      "RAPID        10   43      6.76    290.67    319.96    100.2    0.004    5.599\n",
      "BRIGHT1      10   15     12.84    192.65    202.87    100.0    0.007    7.021\n",
      "BRIGHT1      10   16     12.84    205.49    216.40    103.3    0.007    7.021\n",
      "BRIGHT2      10   12     13.52    162.23    170.41     98.8    0.008    7.567\n",
      "BRIGHT2      10   13     13.52    175.75    184.61    102.8    0.008    7.567\n",
      "SHALLOW2     10   10     31.77    317.71    324.52    157.6    0.018    8.746\n",
      "SHALLOW4     10   10     33.12    331.23    338.04    161.8    0.019    8.802\n",
      "MEDIUM2      10   10     62.19    621.89    628.71    231.2    0.036    9.219\n",
      "MEDIUM8      10   10     66.25    662.45    669.27    236.1    0.038    9.127\n",
      "DEEP2        10   10    123.03   1230.27   1237.08    332.4    0.071    9.449\n",
      "DEEP8        10   10    127.08   1270.82   1277.64    336.0    0.074    9.399\n"
     ]
    }
   ],
   "source": [
    "# Print the Top 2 settings for each readout pattern\n",
    "res2 = table_filter(res, 2)\n",
    "print(res2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t_acq     :    338.04264\n",
      "t_exp     :    331.22530\n",
      "t_frame   :      0.67597\n",
      "t_group   :      3.37985\n",
      "t_int     :     33.12253\n",
      "t_int_tot1:     33.80374\n",
      "t_int_tot2:     33.80374\n"
     ]
    }
   ],
   "source": [
    "# Let's say we choose SHALLOW4, NGRP=10, NINT=10\n",
    "# Update detector readout\n",
    "nrc.update_detectors(read_mode='SHALLOW4', ngroup=10, nint=10)\n",
    "\n",
    "keys = list(nrc.multiaccum_times.keys())\n",
    "keys.sort()\n",
    "for k in keys:\n",
    "    print(\"{:<10}: {: 12.5f}\".format(k, nrc.multiaccum_times[k]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F444W SNR for M0V source\n",
      "   Wave      SNR    Flux (mJy)\n",
      "--------- --------- ----------\n",
      "     3.80      9.52       4.54\n",
      "     3.90    230.66       4.34\n",
      "     4.00    246.18       4.15\n",
      "     4.10    235.68       3.97\n",
      "     4.20    221.71       3.73\n",
      "     4.30    197.92       3.26\n",
      "     4.40    184.55       3.19\n",
      "     4.50    169.03       2.85\n",
      "     4.60    154.66       2.79\n",
      "     4.70    139.31       2.63\n",
      "     4.80    131.21       2.70\n",
      "     4.90    115.05       2.60\n",
      "     5.00     57.52       2.38\n",
      "     5.10      1.30       2.46\n"
     ]
    }
   ],
   "source": [
    "# Print final wavelength-dependent SNR\n",
    "# For spectroscopy, the snr_goal is the median over the bandpass\n",
    "snr_dict = nrc.sensitivity(sp=sp_M0V, forwardSNR=True, units='mJy', verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mock observed spectrum**\n",
    "\n",
    "Create a series of ramp integrations based on the current NIRCam settings. The gen_exposures() function creates a series of mock observations in raw DMS format by default. By default, it's point source objects centered in the observing window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ideal spectrum and wavelength solution\n",
    "wspec, imspec = nrc.calc_psf_from_coeff(sp=sp_M0V, return_hdul=False, return_oversample=False)\n",
    "\n",
    "# Resize to detector window\n",
    "nx = nrc.det_info['xpix']\n",
    "ny = nrc.det_info['ypix']\n",
    "\n",
    "# Shrink/expand nx (fill value of 0)\n",
    "# Then shrink to a size excluding wspec=0\n",
    "# This assumes simulated spectrum is centered\n",
    "imspec = nrc_utils.pad_or_cut_to_size(imspec, (ny,nx))\n",
    "wspec = nrc_utils.pad_or_cut_to_size(wspec, nx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add simple zodiacal background\n",
    "im_slope = imspec + nrc.bg_zodi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[     pynrc:WARNING] Source RA, Dec = (0.0, 0.0) deg is not visible on 2023-01-01!\n"
     ]
    }
   ],
   "source": [
    "# Create a series of ramp integrations based on the current NIRCam settings\n",
    "# Output is a single HDUList with 10 INTs\n",
    "# Ignore detector non-linearity to return output in e-/sec\n",
    "kwargs = {\n",
    "        'apply_nonlinearity' : False,\n",
    "        'apply_flats'        : False,\n",
    "}\n",
    "res = nrc.simulate_level1b('M0V Target', 0, 0, '2023-01-01', '12:00:00', \n",
    "                           im_slope=im_slope, return_hdul=True, **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filename: (No file associated with this HDUList)\n",
      "No.    Name      Ver    Type      Cards   Dimensions   Format\n",
      "  0  PRIMARY       1 PrimaryHDU     117   ()      \n",
      "  1  SCI           1 ImageHDU        44   (2048, 128, 10, 10)   uint16   \n",
      "  2  ZEROFRAME     1 ImageHDU        12   (2048, 128, 10)   uint16   \n",
      "  3  GROUP         1 BinTableHDU     36   100R x 13C   ['I', 'I', 'I', 'J', 'I', '26A', 'I', 'I', 'I', 'I', '36A', 'D', 'D']   \n",
      "  4  INT_TIMES     1 BinTableHDU     24   10R x 7C   ['J', 'D', 'D', 'D', 'D', 'D', 'D']   \n"
     ]
    }
   ],
   "source": [
    "res.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1d40a7f981d4b95b50733c653082fc0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tvals = nrc.Detector.times_group_avg\n",
    "\n",
    "header = res['PRIMARY'].header\n",
    "data_all = res['SCI'].data\n",
    "slope_list = []\n",
    "for data in tqdm(data_all):\n",
    "    ref = pynrc.ref_pixels.NRC_refs(data, header, DMS=True, do_all=False)\n",
    "    ref.calc_avg_amps()\n",
    "    ref.correct_amp_refs()\n",
    "\n",
    "    # Linear fit to determine slope image\n",
    "    cf = jl_poly_fit(tvals, ref.data, deg=1)\n",
    "    slope_list.append(cf[1])\n",
    "    \n",
    "# Create a master averaged slope image\n",
    "slopes_all = np.array(slope_list)\n",
    "slope_sim = slopes_all.mean(axis=0) * nrc.Detector.gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ofdnAap/NMBXvt67fo1ebDqkHSwNf6LNv2CmZpxtDj7IQD3Zd9vLMBPa14srhq52hJOtGo2gxqFe4bw0M85iIrJTVAnXoUFdLWb+LQfTFUqIFKDd1Mei86XgNHVAqytGB9GslGzivBwBXdD5qzlg8emKfsX7qnNQDZ32rrnfzdlCmOZ7zrMby5GtpsxUANM0yNpF1jt5WAh59/crRq92opVHdd+4JXwzNeNrfkXEHFJRnXF29EonG2rJ3Ha6AuS4HBwm9jLkzwcU8E1gMqEMv85EUSEZP9ayGMPtM3c5rkUDLSgqozF25fG+7X9HoWi0cwETjhB4fztc4tR5btgZ0+xqvva6vSHCU0D+9D/eXKxdfI38TiBDIEgAA7T5PAzsl1MOlG/ralyuXXmukRSK5mvw6dbVsvTZHq4Y4jV6GZPube/R3PUgk/ejpIelHUCC/K/FRK64evLK9NwEz9eF2xOv/h0eBlFAOwtopRfQGwRzlCkD/2HFDipajlZORAOrhSuya6CGjvqasJPGOXD18lROEOeHjv+Uuj5IYfd+mzWQ2A+2TIDtp0v18GwhflZNy4UDsIcAItAe/41VGtti8RTJwO4r+43qaZlTOf9/J/k+gx32VnhGu5wiY1W64cvleXauByFD7oR6tHCRz/dNGPVgYQKejwEhY1Wsc45oz6qF4O0ly2JzNTqLU5dDYaqhVdCrXJdcCwWJ3g/W2XIg+7Tsn+QDU5QJXLr62BREEalxzm608cynyU8mTcmFlermulvKsXcaVC69FpR4jWa+OjnK4atcvwTj1+9C3NlApsoY55lzDy4UTTQSe8Roc7yiPGlVgMs9/lCmue5KD/D7HUsf32smH8GB9Dx6s7wGAyzin3TKAlZDwhvRr8caXvAV3H71aGCSi1y6JQUejhcz3ciF/R/ClrPLVo1fLBhLd6g0CVzZmCgwJUXr4bFY29eqF14gwRPBUwgRw0fadGEWKuOvBwr1TpxtTqFcviwFpnikCsy57SFDwNmGzdRBHBQ4YE3blrjdIH6JC1M090Tikghx6WRyl+v2BdqMGROhCaIeEd9BYS+4NoRGgwKFSQMkG9R3uXr3Sx265cCUwFwGpIcQoVQmZs6bArvbqRp5mXD18tSxQjjHZkFKctVLPTT1YuJGakzP83ND7Tq4HGGirg2/m0cgjQ1KHzlksKnp6ikrB1dUr5VkoZ9sRH/zuI1FmQ3g27ZeB3JyFge86kVEakwStMT7dNg8lC+iZ4YZD1pRKMBAHVy6LgSZeUjg4CkBSvlcNlNj1OWckNVLC1dtf7/fWdVVVEdc+m/cgnW7NsP8Pv+b3Kts2+VpVub96273C5K4GWdeAzAPDAagDqHRzNoPRWHEFj6kU3H3xtS7HXJuDhsrRC0FSIDJapTqooS6h0RgM0jMGFRvXRBamUrzWHlqbJvFkXz18lYy9AjDzzo2ze1yrki86fnm9dWZTN+80i1Fz9fbX21jZBh6fKzTzCmojgMkb8VSn7Yi7j14t/T3dWv/TWjZvC/WcZ/N2G1hV4gFdRmaUAYCrF19rcmGAV+ei6ngRmJpRVtWLqn02edR5Ysi2baiUCzVw0nYyQ6ABCzF0ROfl7tUr/TWSfNPsbCnHkgYO0BivANxQ2464ctvr3JOxHiWsbYe4iSHWiURP9GKoQZbGCVdvE0CUpnD/6JGkpyoaCRwn7gs0hLej6b+ke1863Qgg3GFtU4g6sHA6jif3HB0bemJtrEgGjRPSdrLwdtNpAYjVlRiQ/IwQViJPjddO97R0fOrGcN85SZZU31NGdc6vXH6d6DwaXvPsgHTw0HuSB+lk4wTpNKMeHYgxvBiAzVb0SyCR0kblhLqe7Dr3S71XXS5sb66rhc331dtf70D2dNsAwHqwEENSSaH89LoN/9J5K5cOmnVtuqgPa2uaBYBGLzNBgu75TdM5unrb6xyUzAUv/4cPKzk8+J62GvDQ77m9IUGa9UbCejng6qV75ZkOFk4k54R8vPboJF6XxjPQevTprek7XFV9Zc8TgCSjYRixYWMDNDJipJqu46uHr1bid9t6dLospHD0NKoOrcveCcu4BkvBva961PYP0w1qYyWGudKrxj4F0tHIBurUo9e0xDbXvYYCJ+1z2mgqjJKocS+jTkjbUeYkEg9MO7BIkQ7pZO3kv469RUYRUOo6uHv4fLFvmZITw3YBWfMkxfU5mkidWrF+2UV7/+qle/09jmG09flcMaqlVo8YC95S83CZx7i0hFSIuDA5YV+13b14Bd6Qfi3ekH4tADyJc9otA1jW6M6nq1KNO9tIyByR/aFSjsq3y+Kq5CCR3eGioXueSJobCRU73FNixlSIfTWkHza2tPFJTNvJEDRDr1CrK1CyUVTuNFg2Ghq0CkwIcxnIolNoYsiAKkt6MdKoYUsxNpXKQMPwuHjratmGE0ZGOzYqKW5iBCdhA2cIStqOAuBqlVAMCrgyRE9+wW3OLHA+yAbrnKW5uiLdjmLYqUFniwmyuZvXrHNlyBwTVAGRfEZh18OiXngYRx06M5bTRj1JDJ8LSq4OvXsI6dlTdt8UafBIcqxe8z3ivZJciWzPZqFY3NxDOGZdsZAHzIBtDPihF/f60Ivc0SuifbBwPf4sxQw1XyttmE7aTr4Ra6t9BuZqIMaMYF2ztc+iParLRdLN0xnVYmTEeOdRo5wIRGsn8m+eQQXRiQYBFLwpcUEgYqEitXooLNdi3Q3L6cS4j6Ffuu6qboqWrxM2cgOBZMYZOqXrscb1oePSMGHKgNVl7+uYa2uhBuU4mx5octSqXl83t9p1RgKk7WSApDGUSsH26pHpxXJhaWvGNlPqLgCbl11qN1MDdR4ySZ0SZcy8zIFhrAT1iWRBMUBp80oiaZzUgNV+qTza+wRVKVl4Xpo07JTGDoSEOlMgp8uuSyb9PENIqNd1rtJm656GqJu0T1xvZ8LgxkkMjBC+3YTnxcgIjsPWWX3zsB0s3GCgIWPgJBvYrMu+iVCw/gznyBUNslLw0H9w1fTT8asue58CCGDOQR16YZW5ZpcL94hYmKfqCxIQbLUCFeIN0TGytTNOsg8yHJV6F2JgWX7v5PvtGQOHzD77rYDHxmCczENHw9XYeyWwGoIScJDBf9yz9PqmK9ebYLTpdZYL7xM9yjo+Ro6w79yzdV+PxEwdQpRFkD3OzVNfdKeFiIu8Ts7Kc/0vnQzMx5sz8lHpwScp1KkepLyznwRsXXZ9pQRy2o4G0mwfV7smn6wdLJxscO/f+EyTr2KglvNGwkOfWULlF6ZTGy8s54BjxOiBHc+wERPUR9Pchnn3nfeJAIXPEuaboYQ1aWheTu59Xi7cQ0KiUj2Idm21Yyz0TeWdfXvoA7fbM6ZZx8L2Ycp9dcARCbK5GKnPkHWPQCiuk3kteqCKggkCLQIYgjb2DxCShWAXaENjafcMbj81dgngxK6Obz1cuT3Ecdaf+WStdkJt5rtZp13G6lNPO+kTCU3OAceZexbnm/JD+edYR5CsY282a0qu1yPQpM40skqfL4aW36DdMoC1gCpjhn1ttr7J0I1uaL8TAecGQuONimQuzgomddFGwVcF+aHfdbt3gMwhQUo0ZqPLUIXRjP8QPmcxtvScDIG5KdXCU0ywI6oeenmmWpGvr1tXuSpPExqGeHRdu/EEYzCyvYw7rim5ByAo73S69Zw3LSJxz21v8kVYqwOTyTeEXRd1JXis1Vy3lS51hu5NMy6/97F2E+MipzdGlUkDoPl8ykjlk7WGsDmTZMa0PoN5LFKyJPQ0znj//+UOMY67LIZdFVbK2HIqRAJRPnNkY7VflZtGhmx4uwBIFVsdKBfiRre5iW7mUjyvQb9L4/ieS18sfTG2LllIWGHsuyqXtB5F6U5zkxNkSreUJofKZKSL8lq8iMk0i0GrRigNlEQwpkZi9DaYbAWvBRV+mgqGx05cqXdd+531aGFsLFrCPCcCuzT7ODPEwUJ25tnlUBU681ak705SmMeDm8o8S4gZAR8JjBIIGwD3XPpiuQ7DJbtQQEE32pjzaPMNAAXmTbWQDAIW9odrlEArZ5FR5pzOkmvFvA567CzUQ+dx8fB10zX5+sZ1GwEn9UHfYfHwsa81bUIEJNNvibqP9jQ979rnqs9g9xgn2VWoLxUcAvBcCNWvdeGbdKXeZthOcqPYDB+SMkOPe+74NbCwb9W9Js/qCRGjqHP9FI1fehMC0YAun2Fa7Z7R2FgMvrnWqiBi8LHkPWLIyi5Io6eCsjN0TvhFgBb1YWTFKS81EFP2HDIOr/2+x2zNXXjgmhOPBDHRu8cc397HsNGN0yyEDw0/PpvOzz23v0nWhe53SUFoolGiMio5fUrMjertpGctFHSS4lMM6XFjpwnPjcBaPQ1JvW/MJRSdKPtzWm8c8I5za6RTH5DQJXGnhFealNAkIIvRJSx4UkP+J8dH968YFhbzrxNDPOM+q4D20gOP6V6THITr2NteGUMyubZSwj23v0ly1nUMm1D/QM5w37BiOFqkyD6j68BymwnguR9qCGS5uPL+6xpqdKDaTjY+kZTi9fR+Ro4UzeHRsUunm8ZIN0JG17OT0QpCOC40mGnPUc5pkMfwc91DWZQlX1/7e3x2fodyTfJe+wVAPVMz8vEpGPZ3z21vcgDIkEfKA69Ph0MNIIlAU5/XZImyQluJtinHlJ+hTbIMOirqEd6XoIHPxn/xc0HPUHbMixkiehqAqPtA3HeN0GK/GS1GcBhAuYX77npdA9i3PZCNxJWOrTkVLOSw4p47frVcbjn4XkIPM+Brg+PFiAXKVBybG7RbBrAGAizAY5S5ALjxAS78BEAEGrqI63IBTxBW5cnwHwrvLJvOa/7OoyIMnJgonMCOW1cFfejFjc37s09RoVJ5nG7x0W+6KwiWMCflgsRUf/kP/JtWQZCpPVi6AQTIhk2PGplDfo+TzQVUioXMxSYG2eybKo2WzVbzsJInYq4WePnFLxIF23fmtrY5aULcUute52ZoQhjGkuF2wZXfeM1opBojoKzpUSgnriyc5assBoxXLug91Biu1Tdj3jt4El73vU8ijTNOXnnZgOBTX3LFFNn9P/kjzgbmsJi4kIEGqESDlUahbYSAVWkSFzhBhuaE7IazMCyDMqwer5df/hKXh1DpkmAgepNQNFY7GD3m0Rr6tjAD2d34OSonMmI1eGcWgxhE8XNqfDz5RXeKZyV6N8gWElBwQwFUKSbxqM1eqMM28sA+CQAW76sxqKFATaVnKRqv8RlUvsn807NclyGsYK5+DUAq181zK6OzzPHLr/w6+cxyIWBDN1eLq2feAIkXyr2G1FUliswom8SQqAz9YQhkCDdmeIyNr84N9Qeb5XnpplY7Z3Wfev1tzTqufXZ9tZtLRi8eZcSMcf8MK1CWVW9g0AgVJacY5tjIWRyHXrwKv/QNt5uMSzWw0eYzhgNa2G0geF5++5tMHhJzB9lP1VdN9cEIVNhUn4h+CiCX67NWD++MIYLcvPvOQjiNjeU8MMQUTiiwwij1d2XUBmBetzSLrrdclpyVyClGRqVpxnxp1Yb+EaSkBOZkpdONeTgqn5UkGg1WkhmUwRhmaARn3wAyI/6UMEvjhJff9iYLB659B2h+HI26qhWAjUQM82FRJIfLwOQXz3PT/dZ0EvXYwnOR6rI3PcnvRWOJINsKShgx4Ptm7TovJkCZVeK30ltBvQv43qeyZLIeQ1AVkBlw4/sBoMi+k1vZIktPm4h7cPV1ZlEg8T0l9e65+0uDTHt4YFOogHOb3BawCBway6YDQt/pRSPhmxLyyda9U6pf60rzfcZJ1gnHiv2g0c11QO8QC5Co/mM4Wo0kB+WAuj96qHP2anCRjMvZIgeaiBzqKl6XpGat7l0jGOK1SHwDRobVnMFianW1UJn3ffbld/4aB5bRpiIJxD5y31QZpJOh0WPsKwFJlEWOTSAKTDfFKCTKJPMkzUYMRR1KMa/W9dffITJSfG8iWd3YEBHAMWqHz0bZjnYBI9ioa1mIg/KtY2whwvEZ6ZHmvalbeH/aE4EE4by9/PKbXIYInggOc/K5YyQE1ydBdPQw3qDdFGCllF6SUvprKaV/HV67I6X0vSmlP5lS+usppbvDe9+VUvqelNJ/n1L6rTe5eOvSC/GZ0QDMT53YAi+HCzHSAFiY4EIQKL0G5s5VRWQM3+jVyiykDHAGB7DFJYUnkrpkx6ZP0bgje8qF/Yq3XbPKL/aMRX7+i2//kmaDMEXXCKhsKJYnRe/FwcIMDAvlolGk3iiL0Q3eDcA3eGy2ksd2ujXPFY2pugrhHSxXTwM/gjvADTAa8REcA1ZsQFjF0ReOhjLa5hgbgVLfecilzQlMyadaMTx+6vfV7xrry5AXhg9ykyoVhx950jwilx54zGTkrfd9h288VHBZSkWbzLCogrmSw1gCDehiSfU0q4GmjHfSuO0Pf8fV9tk1aZ5zUDtNfGWybPXNwBRMVoNNPbtmNLDpxkM5kmfQ72SYYcoqnSbTWUP2uBFQ/npfj4AYQ5c+8BTmi9Ln+9/1QxLuRxAJOKsVFJeFenXZmOyGjeKmoTHjTDCWynnZ5jp6l2xdEuQoCWMV6Li21KAyoEMwY6yjzBu9ihKWJ2XJLeRQh4HhTU05fvah7zxMksUFMgToLtzAbPKo1ONsISaAXIeho1bpT0LGYvy5VSwFPCEXAKYZl97/hD8/ZUmvY/l/1MMkuorKV5KCAWmuRtYI0QTkUw+RSUULYKgRXJfqjeSapX7TNc4Q8F/19sfQeFVJipCtJUjRNVsPFhbyaeAlgpgiuoBr3CIKKI9956QHIFXUaIAxBCWCdCXhBLAWM0Zi0jSZ/Uq9Qd0WQQTDYFn1VJ81bUcBwKqzLUep77z6pK4/Mzw78WB3j5/IPsO1RQJqpYYJq68GbxOJTGHn3VhpPLHRCKNna7NtCtrYmHGfyckMS7uWzuX97/5h97hQn5D0I/m0e1wF9w3K6yj7AcGGGYL6OnMXm310O3rOma4FQPShFU0JrLgVJyJhQ4M6GoskkGr1MC2O7zR71AJ/5xhG+ybmfdNbw/0RcNC9DZVjaRiyhSgSAuTNy2+T98i+MwyQUQ2cEwI49UZw3VoUAD2WnN9oOPO+HEuG2FI+WYlxJ3JISLrR7YRwbSsoFEm5lBx00Ptn63EHxIb1JAMYSFGSAtOM6bYDH/dgmBsZsw0ySNngOmdUUYy+gegPG1MSHTHsL+TBm+zTViG45OcY0ho9JHyWzsOFjQwceg9TpQdqJ/zNCmfw/bEt8lFXWlzDQJoDY1t7SlTc/5M/ggsPPmpRSs1YR0KSz0odwfGeZgfBBF1R30R7Mx59wsJVSWwB6kBE7ygjU0x/Z79WICtsrqLOIilNXcH5IiAN9o7pVupaRs09Q7spwALw5QD+Z6A5wuu/AvCuWuufA/APAfw3IpvpNwD4qlrrnwbwRwH8hZTSbTe8cgQRbH3nG30mG7w0YcpryVuofW43S+YWVAmVi2FIxnwG9GvMUExW1/dM6VN5htd3GZO0HduiDboBGjPQh2otjB0mk7ZSxi4WaQB8EXMDCWyuheso49QaBvAFXKt4zpqQwk4MJ4bisH8M62EfKFRaWclKsQd2Ny6ApsqXjoH9y9kTpXOW+0yzl7BlGX4aRuwDldjpxpLEzUupmwqv0Ww+ujAsD4sLO8iClT3WjTQdr2Vz5jPouDK0xDYdPieNReaARK8eja254PpflLMwGDrD91/19675M1LeIUq+Gvs6GAAheCpHcgigGANtQQBkeFW5EPZgRr6dmRXyYvrOE/C5WXeds5iF8qpeicHBNO/9sbccIY0z3vrW32Gg0j7H0CmV57oYWqCwGtpNq1b3sAACYGbfWGwcVRnWLEUvqpErbpADaAoLxI39bI6YVPe0UvtzO66AGKHlULxXSWXDvLzFxzRt3DBiHgi9M5VhYVTgIe/DAPCOUVcPFsCsOYq2Gel8D268NRVFacgHY4ikg52nZiGVcHCm90ybrchJrUjH6zaMVUkOI4gAN3BrKHIRw37oPaEO0zWS1i2DGr2GxiynhLJUbz4jFMIZSGk7SvgW9X72HMO0ndxo0PPCvPpmag0Jbqw0EvhzmsEQTxojrDoZq31ZhEMfEunVcEzHayvswX6UlRBCl973mBuNIeRdqmQlN2C4fxEgdDveTQXhFg479O7V53VDPohFPXDOKRcKfMwrqd8zcLP0/LxELwCvHQGBGrFv/dpvB71HlrdMOVj2TrBQF9FrG88N414Xwq8a2WPoItMI1HiN+S82rjSkaODHMQ3AoilfHu0TnVszMkPYr+WKpuTkEZ+hVg+D4nri3rtrK/B3kkgMo6exGI3pWQiO5S89Bg/lLE0oXLmwcluBRq2Ok+l6EhIx3DTKJPc/7reBjGBFUyNLaN/0Gb/hbe83W4bh/ZWkcM5OxiqZUIeu1QncY6PnA3DdGcP1OK/0GHL95oT+iVO3Ydj3YGdIcaXigIX34LN32UPGCGAUOLK/KX6epBXHS9eA6Rbur3E+1FaxYmPUoWqrWBgyREezYh9i1EXxcTByl0Qt7V7KDW03esd5jQhedI3c99XfqiB6615oXnOc3FtK2eC64zMOfag8mNuIAZaG1/Vk0TkEPDq2aTu6l466Rr1NjRe/ej5dkx+r3m16axuvcQzxDHLCCsZpM/l+VasVjWtsz3PaTQFWrfVtAJ7eefnrAfyU/v4v9W8A+C18vdY6AngQwG+6yQ1c4Nl5nWjzUNDoJ+PKM4DGGV/+w/+7M0HVF7dtcik11fjM6KDhFUIQbFEADg7WWiaaRvnQK+scFioXQUTw0R2q5y9FxjCfrGG5DmRHGWMdXPjMjUknG9u8LfSPm7qyesam66aUnzwxptQ+sx5FUAKrYqFU+r3a86BPZQXUwLCQvJO153eR8VPBNPBC5TUJK2jnOTAMiONPsKjlQy2pOCgxAG6Y0iCIrN4YNrOwOCyniNcDHDCSPaJhwAIoFhvuzJ0VIeH3WW6ZXrai/Ryl4ARZlgv/ibunzZMS709GJcacqyzkp06dQdbny9dPZYwiAKCxXmDekAbsqgzKenHDxBRx3zlQ44YHGFAzLw2N6Mw1JV6aV/29a2AISpok1jltJ/dGaoK2saSsEMhqS5HxYogKAe5UGqKEHgosF3ZAY9J8CzmnDQ5+S/EcF+oTArKcMV8+kOvrOk3bqc0HgQCPKK9p4+WYzWMYWMVU9RBgNcDTZtuwpjEc1sI3ItsLAH0275SNpW60dv0xFH3QuTKvQmDPT159hzHaVWWgIWXCNTEzpLV4bhJkbfqBwEOb86E62aqixk2K4ce9e7QInMwQYZRBCH2xqonq0UxVqycmrw4Xi4bUAy3TSw9+JDMCW4u5OmAIYYtWKprGCcMTQ6gcn9/y0QCTm8Yoo6EXve80oNj08O20CeQNCFInN/pDuI8B65QaAG+eUt0rmb947f98xXWbrjXLqY3kUTQOaKzUivt/4m1W1j9tRvO6WGggwQk95xxrGv2LwfOPaZQHTxyjLnbPsyqHS9dv0fuUwj6pOjEWC0jrrbPQ7J/mZJ1HjDlwrk0JdbMhSmkOibVQoLl4pAfDDwkQ9fktL4jPFQkTghjKYNy/+LzaLwJxVrRtvDjcO7hf54x64cDHngYuyR9WgB18D6NnKUa38KgD855xvCiTO4Z13DMYAmmhhluxM376d36xGqmdAf+kFZINMHBPoqeGe2+tEjYKuFeZupPrl8+Sw9ly1ClRfgjkoteEv4+TeszDmuC+ebK2+1rOOeW9KPlPwo8ejehhoU6IHhJeP5L1Qa/adaL9OfSSDxYBOPdzFpdgUxnhPpKO125jbrZNxcu03vq1aA9HeetCZeTNVsLmqLdIhEYyhOQd01dWngrkwBIu95H8sSgatPPANU0dQD0cPMM1rgmVP1vnvDZgxLyFa9M7yZ8RfNVqBYnQZS9KksJ3btJSvQkCA4CU0lcC+G9qrb9O/94AuLvW+kRKqQcwAhgA/GUAD9Za/zv93N8F8JO11r+2e82DdFQHLJFYNa1UIAH33P4mvPz2X+0fVPdrVGa1z56ISUVF9il+b5fhCGXX63KwhP16uHRwoqEZNanLm8YGXdg0CiIzmFNjVMT7NMBrF8j1nZ8tQPAXlLUp0C7hqdddxuWff8RDK9QQMgaMY9CETnUOLAHxri0H5HUI+dhx8wNwzxnZwRSUG8eZoRo9lWIJrE1x45HKnuzDqPllNGaWAdwBPvZmTMq1adBbnxg+Q1e5CJwqeWVDGQYZN7Nd0A5hr8ywj/ML2CF+dTGIPNCg4HhQSZPpZOgWEIyt4vemYUjDlOMdFFNd9s72jpPI53by3K6lhlxoGNzTb7wTFz7wpMzlYrDxorelLnpPkF0GIB4AKwAx6Kez88LQO46Rne3SZVtHlhzNEE3+DWhuydw8N8PmhBVrNyMJ6VUAwJwUvYZ5ItjP1dDKT5BTS2BXUGeVn6KxZc/eyfwuBox3HKJ/au35GYE4Mc8pX2c41sHCAVsnz0lgQK+dn6En3jYLVx466WOf7fU6SKgsC6rw+3Z8RQxNjKzsdhR54Ziwj9HLrLLahOl2LvO2zmp4/kh8Rb2QkhcSieND4BbWUporyqJHvn4qwGjokK9vZOwoz/TiQoBN7TqTl1T1+5uxXavU2Vr5MtXqhw1zn6AejHqOG3B8PhrGfO5ZvBIE2uVwgXQ6ug6afB7qsndAXiX8yfScPqOFZlH/znP7WoZ431dLk0chbSbXCzQG6HHebMUQ5TiTuAihpsb40+sU9WDw+th4Ug9RL9J4CevLQhOpo9ajfOfEc7/Mg7hcyLNZBdjse2Bkptmn6sWL4rial47zVorsc7rn1C5ZCDyiAcr9WuWxHK1EjgDXd+qlN6aaezdDAPWYmErWPspS8ISktXhV69IBTGIRK6DR99TpaTOaR/VTb74TL/uxh9v9KxK/UICfg3xzXkLYo+gW171xj/3Um+/Ey979aBOlU3NGPj51Y5XfoW1D/UmDN9pfgMtELKwVCcRAFBiRTA8Mw+Y4l7U2ucf1YOlFqMJebnsRAcPpxggXuy/vyXEHTF83wCRnI8MNlFGO+To/Z9FGqo+PTz2/v4R1wp8RbAPe13BkQ9Qz1v8x5ONzrZpXsbSAjd+L65vPEeeK88fvTrOTR7SPh06AmbY0FwFXXFc3iuyKfSheWdjsNfZ5DvsR7WfaoaXCIst2965o07PFEEd9Hnte9XDZ51RvP/jH7sQX/PmHbc6sivA4YXvPbVh84onW7tU+f/yJn8cnnvh5e72qI+dpPP5LtdZXYKd9tgDr4wD+T7XWj6eU7gDwwVrrHSml7wGwrbV+j37u7QD+Wq317bvXvJTuqL8hvRnda19l7KAbvAufNCr9oKBsg99lBKqGHdGetYVfWlBCxRyYQPtMDPeK8bARRAVlbRsKF8+uAokMfFSUvA6NUW6+7C+rGHUhxLDv7Mycpq+laFldP+i2EToqALIt3HACmIkgLK237UnYUTmf06qCrLrQ0uH6HJ9+81Xc/c8fd3aE48O/lwv77u5GZUKtYWb0RplBTRd6GHczaAL7kSaNKTZmM4w/DZ/TrW8YPI+Cm+c0yzU4dmFMGmN7Ozbfs5A8gnMCKMrbrgIkCzUXU6i26QbA0BQfiXIW5yl4BIwJXAgbXZeDsOiAbNCzhOWlk415L+0MLBqrKnOWp8DSyptJw01Dxb0Yv8/13CXfOFReRM4B8L3OzwaLuRapOGix0EbKHb0VBGIqNwYOptlAFr1rHBOGGSMlAzKsMGifox4xD05vY2cbEj8bDfucvZQ69Bl5gLOu56JhlzaeOZyBBQXG0ZiMBunOcxYtztEk7Pf57LgS0HGdBrBvxm801qL+sQvXth/TjPt/4m2472u+rZETGzeNOjBvBftAMKHybKRKAPyce5Mt9rUXD0g5XCCvGZYTSAzqEBpYtbbAiZs9dXUEz5zDCPypi2LIGIkw6rIdPWnyRD1C4k1Z0poSPvAnDvC6//rYwKgVIiCJprLI6pVmbJBY4Hzx2anPYzU+7jscV5KU/ByNlcj2695kY5+SG7eRzAMag8d0R85O9kwzrv2mK7j6vz7m48YxjWQVX+P8BaOoatRIYygBbowFWWS4r5XFD3qouQ/vHff9KDtq4Nv+qK+Zp5FtF6wG9h5AC+7i2lUCxRo9meqZH69elMqrce2RoCJoJVEZvn+GQDhn72nWdCSvgl53skUP7aW9QGN/9jUpB6h3LcHN+xO8cv2H+zXkN9dQLD4RwX+s+EkgFm0KvVZaS1h+Q+6qkW2H0ZNEAHz9MBSeepHhvidr99rQzmCkRyR3adA3uUBBpiiL8Xl1rRnQIgDodsaRfQ9RAA1QCREwNh5ses1UYv52agEO9RtBZCSv4hztfpd6Q5/lI99xN175/Z9u8+Eom9S/O7UOzoQWUs9xbfE7lI9dr3bcG2lvxzUZ9Wa8zlzw5BfficsPPN7uEXFt84gS4o0YEqjzOX/gQwCAd9W3vYf4qJm63ReeZXsHgC/T33+j/g0A/4ivq2frCwD8s2e8EjfOXiuvBPeo5B+EjZOhMhw0ZTjKgVcGTJvJKpCZIFLYA2KXkBZ9PRiwUfAs3pzGPAEZP6NhSPVw6eynholJyJ8KVQ5CT5YghNBZ+Btj0SmEpQY2WN29fHaG/iyGhl0U93tnz8Wzv8qlQ51xCqN+vhRL4GfysH1WBbX2uQ0b4CLX0ECWK7cDRDXE5yU/+RmvslhVeUQFVKsw9+pCrymhXFh6qWKGarECTN+5y3YuHnag8mFKW/+uq8GTlkuxAxutalEvIMLGUAum3P8Tb3PF2HdeQSrGfxcNgzjduKKI4BrwuGCOARVHCMfhOVPWuKErOxc9bUnDi2iE1k5jhOlaJ3MD3CDMo2vAFQA8+B9f9sOkUwIKLMQsFZYkV9DATU8vwcR9GsJNwnJKKKveQAI9FtaCUW+epgzx/qjyjLl95iVVnWHnoTE/iZX04EACRQ9Inv3zBNMs/Z62E8pKmenVYP1NU6gYmbmpajnyDO9f+KwUQ4Cx/pJPIX+Xw4XcRwuLWM7V0Hnomx6mywOIaehU3fzRyTOWQ2FVaUjn7dQYRajVcyuCwV1zRjkYjAxxQwChlH2xz1pfFADVLlmOBKsd1mWP+77qm1XPBZ3FNUhwG3J+HIBR97vRmMZJz0CTgiBJ58qfazLW3kIkVT7jQbJ1NThQBlzn8Vl076h9trwaMpiW30hgp+u/OdOHre88AoG6IRjldu4e16leK80z7v3zp/Je1zW60/JQOI80Phh+3Ql5Y3qTnsJplrXKMMfI6lI2NMwmxZzTGKbad0inG9lTM1wmus7Oi5Mc0d6NYIYvqhzbUQh67ys/9biMcwgbbQwghmbzNRqqDCsnuGLoOT8TvY8MmdyM7tWZZg9DItFEQzmG5MVr03BT3WupBioLKZx/5bZDceOZ89OH8afxy2cFhNDiPs91SG8MgOHRYx376mFSzJlRo9oIkz6jHC69YIbuyzZOJP2WC19TgQygTSG20+T9h9pAi8H2TJOblOT1oZfzjjje7C9gspFONj6nXfaceD4fG/uk7yeu2a6zNRrDtxovCm0veq8isZD96AALGQZa0gTwo2toX21H9+ZxLAHNM53CAcmBlKL8FNc31pcIVHhPgivaVDF3qCgpw1DWORzkG/VPBB0AWMDIAMHQC0HMfHE+O78fIqtYGTJtxuZcTFsDtIc5v8F+eeXfv+aVPAEvBENwxfVEm4xjFUkQ2om1XevTlYu+D1An8RqRtO58Lqx4U92ZB73mpQef9GM4mPqhBfNi3qetOc4hx8wA6w4gDu2mACul9BUAfjeAl6aU/lRK6QDAfwbga1JKfwrAvw/gj8sz1f8NwE+klP4rAH8JwB+rtT7xTNdnIYVGOLjZarJ2PDTUDH4qri7LyeaAx9IzPpPCTMaqVj0LQxc+q3oRYRd4jHffmWclTbOdV1GOVm786mYj4UqzLRDzMhDUcfOPCLjvPOchACRL8o1sJVmzUlshi2xaZOVSso3dcmHIhPedJe7xsym6mmv1Mp2AenD0/T4bo2hsDgsJcKEwnjiAqLrQctSHYa7p/te5IDOW15OArqgM9V9lGBP/Ze9D7TM+/RV3uQKhccK48aE3Rto8Hyk1DCDjve/76m91ZghoFTnnXYs01MOVn5LODTslC+VhrG9VdpmbkuXtsPxnDFVgTLfmfvGQYhop0btBcNSALt4nQ8ubqwFE70tVUDBXvP4vP2UelNp7WFiTy0UGHZAS+nyfwIZehVJsjg3QxPA9yoSCEN7DKvsVaJhhDl4uD5lD8sN+LXeI81kr6tJZTZ6rJGBNQARLj4sxG+RAG6vbsWz1fHHpekONM6tWl0Iyro6N5fwBVoAgjTNOX35JnrfWtrhAgQDalOxcsTTOKCv1VLIiWCkCjJOELeenTi1PUgz05OQQdQEBUXL20wpQ6Gfqosen3nyn9cEYy+rV/iRfZ9J/s4dBKnkTmWnxqPiYipGfLVyU+W8EngTF5t07WNjhz7aOd4wHq6BIoF+K/FQ9aOTApK+r/nvkN17x8Rwn0WVKyFnOUNyIOVds6pW5/50/2IRpmy6iTgR8rXfZ1mcikUNPBXP1AqFCQECg4oK5YxRF71GYN8l/8TXF7xQ96Jfjj7m0IXnLhehnJRjqcpBiUmHtMoqAhF+aq+d6ZCc36iC5H5Y0T7nTMGJbf1w3BDy6Xuy5h94qiFoI29BrKfcAfLvsZaBrPZsPMgR5ifq+D0UwosezC1WBoxGlRmuTj0sjluAiyq3uzwZ09Tlt/fcdeO4XK69art1umDqLFPBIDK5frqdxRl5rbjZzw2L0DeBG76kevK257H4wc7L9iiBUSIrocUyt3JCAY7oAQ+RU51jZdhY3ivtpMFTtc5wz7nerhR53kdzT0WnIPtdm57JHm68eLNzQZ+gm55ogm+9Hj2rw7lqhl6H3MZxmAVVZKi3bNeOcETQyPz14mix3Wz9XtbqwFRchuIz7JY8h0rErF1Zy3divCJr4XRIAEbgEOeB8ptONH7VCgD/48Qr2XMV1RqKnLYx5Q+TUinIwoBwdtIS36hn7Hr27sd8EOBxX1Yv9Z657njHHhkCWEUUshEaArc10Xkr4/h//2553vd40BeisiisbiwxFsK42fFpv/ZlL+M5Oe1Yhgi9EsxDB171GXiiMU2/ZryZeO7pKAdtUjOGKnoQYe0nDgUxVTE5lyFxEqVSYEV3H+Fca05zkoAzt951+G7hhbkqtrjT6zkMwuOlE1hRw5iJ6ZJaLNgxnrs4EptSGuzGGPITWWYy3CinznGI8bRMGxxZjeiHMT7l4IM/FxWIHJ7dGKAB31evz1K5DPj5FOTqQk+EZs8sxjyxjZEMYvqAGUj1aeaGCGKsb507ZrpoSfvTHf0DY9yhXDZOifQ5J1U2McwC1MdSyrgaJX96Jj7bwDl5/V76r5h2QFQ0eKJMDGnSU/2VvFeaaHCWG/o2z3ddyuBi+aInJYmhu7rmMd3/fX5ey9VNx2WLOUNQVBID6LDE/brr9EN3J2IS8WTjiEMAXWSbAcsXoyclPry0MyjZ5Pr+OTZOPpF4Cy81SvVCXmj8W88lYECNJGJglvAICZBSMpVLMGIyhV7Hwh+X6MAcphLzWPp8JVWq8eIDNh1UJ1HFi2J81GqsMqWNeFwuLTOFcsTg3RfvLazFccOvEjK1H1X2WCMzcsCzXsc2o1nZdZdeTtZO1lLdTazCbXgzPzvnvaYiH/BYa1erptO/wMwTYCrDi8zXl87mmo45W3c0QVNPXuf0cZcCqaIb5aTwEBDcMY6XHSue3yatUXVIuruTsIIYbmQe5OLnD/gbg1+SkKdFiz78TVmjyoHJpwDM27qsMj4pe9hhWGXJE5MiQudljG/1ZXEbTevSQw3juXfxO7AfD12IIV9yL+RoJuKiD6UHm/blmadByLxsnz6eKocdR909eNMX0y2pw4Bpz1oJcxRA6IR+nxv6wnDnuxZxT7sPcl+O5m1xvXJu0d3L28HKGZOq6yNfXKBdWNv5CZIwu97trmS1EQDQ5TBq63qzbEN5Nr2Czf0U5mmbLJSY5+eQbLuO2X3jM97Fo++n1jNTkc8ZwrpSs+q+V8OZ+Ms1+FlYIa4vhkEaIhedPpxvx9hjpvrNedL7vf/cPi+0QyWnOFcNaty77SKlJc2mejbqENiyPg6GOpT3IednNb6eHFGjG2OyWKEPjJPmdmxA1xXW8G27M0G4jBIt795h+EYtDqNfO5lLtT9ONWoQHPEIlgtsY5gsYiWX2PWWKYJw6c9cOp81Hx0McK96L8hSJMdr4eo+aknuvcz6ri/XeL1SI4PPXOLiLwRnQGAdJkMRwlxpZ885LZQOICZJWirHU9gyfyLCSyVAgY4ebaUgXy/JayAbg3htONDeAKShEenloDGv1QalsNDkzQaZpCszTYlC3sAizhSkFAaoHC/EkRJZfN/Zy4SAwRV3DtlnYysGifR56UyZW/RLWz0ADhZPzBQTwmLVKYPFNcJp1XKuF/tn4z354I/uZNlvbVOyUcsqAbiZ28GkwQOpq4c+30KIljCNWJW5zRKZUQXUaJwNXxmwSUJXiZ0xwnJa9b7pkRMiQReDRd25U6DNLWf6QT8SKRjSaGFLad5gvLX2DMZnNsAN6A3Npssip6Trb0M3bR1kP4EVKhssh0PTI1GWPxWdODFyhSyirBYrmHdVlh7Lq8ctfdafISGT3qdhUzrqnNvIdhhdqK4veZaFLzfsWckb2dDEYOLBQNfVQlUPtVwHmw8EZ4EWv3tIArnT8CplCNQzMC8WwJ4gxbCXY9V9NSe7VSWheOVo6G81nYT+Tl0EvWhikHC3l3to3rjP+LGQKS7H1LF5HtI2geZBCCwZogocSBXK96CXoHdTTOwfAxkN0m5IOPBR2UDKAuXgAeLgwK1pRpxXNEaQXw+Q8hJcwlLPxILCF57S+hBj6JkyU/ckyHlWf2cEWwzSLh1Km4GkELKdTvEqzjx9DRLjeAIsOYDEkngFWKTdanY+HLxsIq1J9ys5uo44i07oa5BwxlUVj8AFnuYHG41Rz9nPetqN7vwIhYGGFBOsqN6BMK+gSj0PnekfDX2nAW5lzggc1NmkAk0xhCD/njde2eY+5sgR3Kdk488B0218VwJpRGRn6lDx9YJob8ur+d/6ggt/ceEalE8mqLhpBNHiYnK0TA30abRDzmwgyCmChfvTcxTVL4KIGZ1OtlyFjOdyfETD0Nmh0g6UB0MvGa7DC69KNctPDjHqZ9UiIVfAU1AqrrhnLidOD1Kns0xMRooWga5rrgcRoqhX56VMjb3kYsKUcRI+denTSegzk14zbfu4zYAVTJ2DCvkqPWK3u7WDT8FimikQgjZRQDlfigQulzWM5b4nAaEPRKj1T9OxtR/dQ8pl03zfbIUZfqX4BYJFDTV/5jFW94wQI8bmSVi6kfUXyn+GuVQ4ItwJFmgtMz1B++hQf/M67fO9jo9edxbroBaONSh0Z153t1bSBFu654nqk/Wjl9nVvJfFWRD/ZWqBuW4b5TAIE02noF6D9guuGELZnNkcE5Cn5nPI6XLucC7tuVrwQosK6LHZ1DGXMGTzjz8YRcOLgJu3WAyzA3MwNKwi4oqUQ6MaQpuIlkIPCBuBGvLpVeVAoY2qZT4EaDnZUVojXTOutA4FajYVsYrKndnEyh4eGr1XnY9txVYshEICEuilRa1uBKYbcAaIkTjaSD6Ibf0wKzE+feEjlPPsJ1IwXnzxMSEI3vJKShQPyvbjBMLeMuU/By2aMAq/DWHAFKE1p677zjVnDh6wsdCzMQRcxwR8NKsoDx19DdT7w+25vAEVab+xgX69Sk3wD0bE0gFxrC+7DAb6xpLAdXsn+cYOJLMuOaz6NUmZfvBuTFySJxpfKR8/YezO41JBnmJ4aCvGsHwuDA8yj01Rw65J6pESxVA0fdAWW3Fjn53kOEhXkRmTjJT/5mLPQBBQgaBdgIXPa2ZzHioaULQCeXwIFgLS9WTBCWca8nawoRFkO0hcFJHksDsLMa6HrLNryGe7xgIKIwHin7YRy4CFS6B2USRny4H0pAXgUqFHoYYuROZdDlcmCi8wTfFlIZs6Sn6WV9GrOYoDz+gXGTGOuSKejG8Mp2TpiGGAdOj07qnr5eZWFrEncFgJIsK5yXlOSz6g85fXWwu1srhVg8HpWhKQU5JOt50wyl4lEEcMLdT3T6Jb8iNmJIl03XAcMZ7U8OIZLqv6yYiBsuh7SZvT5qxqqzCiJEMpr3p0Q8oSU8OnffMVleKkEmepFy9NjGX0F2c3GTFZ3s3Vwq/vJmRwY/q7kmpxZNnnJf/2enW0zhHCnnM94auoqGClKMpqndDFYOCwb8ycTy/Vz7ZOUIjkJ6FqcLIQ/TWLUi34Jug9ow8LjfqnknB2FQL2rxpnJmB2bUYT4qYG8HCdg6PHWr/120ffbyeelFCe0ACcyI+Di+9xDpxk8HN501dbP8mvOi2PYrBW90Tllf/mTdk0vkSQW/hn2/Sb8mTl1tldPlo7AqsGWs21j1oI37sl2FhfXHg3O4Jk0vUFQPXRtuXt9NjOyGVrH/CRG5oTQTPNgzcV0puUrkTCeZgdWKXnERwAhtr/zKAXaMbRv2JibyDbPyCdrkUlWYlUgycOpLWIo3M/znOYzaRNmzxBUsZgP7aY1Qy8VMNIeoOcoPo+OaVpv1XssIEr+zlZN0o5+IfkOiEcoguRouyroec3ffcwPQB96z5ekPMZx49pQ4Gmky6SkGNCObQRdzDEj0NX389PHrYdN9y8DijGkkdcjuU4Qy9txT2e0VEyJIfnDI3ZikQ77/uSeXjZ+FxCbjjbhOHl6jBE2yezxJtKFRM9N2ucEwBKGc24HNxrbMbfldCuCyLyXzj0z7/8jd7pgUpCJVtU1aq4/LqxQic/OnCCzwxhcQJVUUE6MuwWcgaTbH3B3NmOt9VlYVc6MyxgKovcxhUH2ZLlw9yWRPD0fQ2+sG1mffLJuGQmOJxF/fC3+5MZBtkGLOFDwzYWfkzOdDHGLjWGXYdFZgiZg3i5RMlryWNk6i+WnLCi7sQuIm9jfnHHv9z4Cxk2n043kRhFsnW5cmQdAanHGEeRYjlURdj7r56InNSp3eiezLzg7eFGfnedepc1kzFg9WsHOs1JWG3PxnwRWOfuGX+AMkMZNWxnvUPGPYWox34aGKFs+HT1XUBWpVWRLycEIQRkBW8yPAswzJhvEaOcWMb8qq4FPxjwzDInhghq+lk8lgZugzsPU+mAEzO6tqA4emhCfWsUTlNFot7SZYee5RcMhi6yV5YB84nqBFf6M9da+p7UXQSD4JHNeDoaGLbb+qVeROVDMSREjSmQ4Ud46Z/ZpKKJzw8wAr44Fjc7EkLwCA3aUBfNeLgcH6zTgtm5MCmkS5pbeMhCQ6mapwNTWT4Z5v80Apgwpy3/y+Zd8LtRzZF4ZRgkA/nqYJzM4VZZ3mdk0Fc/DUSAogA0WgsniHgCUHZ6daCNhQN2gYbN3//PHba2nOXjEVd6NcALOhtxEnUgZJklIrwXJO665QBZakQ0aqnz+7PJJY5VlvmMhhvz02iu65uTnpqmsmREdcu5QqxfciECI5J/uo3k7WSSAAb3gVbE9XAELjXbLgeR+GA0UC/eD7EFb0U+Sc+Uh0vYd7oPFyVTLXaWnD7D8QdvrjYQI50JxDzV2PeyvKVRx5B6q4fbsTx3Ee27PBcj79FiyGtngubJmt3S59XR12XVDrX4IPEk8Prte14qjAO4xpPeFBCX3zwiiuJYoe5rDmebqaQUxX0d/Sti4eyJsHhnBE+WYeqUXUGCeBZ27ulqK/mIRDV6Hn6P9NPRuJ0XSmrqABrOCDcnn6+zZuTYNrNI7Foo+iO7N7jnimgtjZHKoRn08AsaKgTBlgQa6CWJqawMwvQFCBluuIMHZOOlZqUrKBsLW5of6gIQ078Nxi3ZDyIWy/LOoR1VnW+gvApjJ4hmyv+lNY19JNquusroKfHSuT84fx5Fhel02sJtON06Cxtw8jlkEvRzPIuGKFjXF5+J8R0Kd4JHfjTqDa4W6O4I5OiYC+X0mfPScdssA1oQtHqzvwQO//KN4+ORDmlgXBBDwRFnbsHtnSyjUDEFLCa///37GlHQ8NJALNOlGUllFDnDFlZXdC6EHaSOshIUtER1z8QWkjO0IbLbKEs4e2sbwNAMLxZUHFzOFIDKfVDJkPzSM0pguIGyyujFR8Wro3OO//m5jI2OSp20WDNOjYiE7RMFbLlAOV40iYbKmhQ6ql8dCbIJHy8ZS2U9LxCeo2KnKJaWmyfbBFy0FvqqRE3Pj+g6Pvel2ZzjIrLHRYzMwN8mZKTL2DCW1MAQFbfnpE920qoXpAGjijZskW4bbjBMs1EVDNhqGWo1ngmgxkBR8roOhqKXazYsXcoCszG/nipzV2GhcmqGaEgrz09Tz4+FH1UCGhRVScRQYwMg04Io/j7HwBbZ2qoUWdo2nJwItez0lpM1shkdTwCV40QjoyqqtnleOdg4xpGKmUaRjUrtka58y4ULHjUrAIo0q5hJZHlVxTxk9RGUZSASCxpCIXhe9fE6LB8wXFfQX6bvnm6AxlglkeY00FfEMKhCUkJ7k81/g+S0Mjwol6ZuQU3osFTAYQM7AfCRFQcrKD6al1zBvRvluyKviZ3k+VwqgmSCC3rqDXz5GLHoh4TiDelv0mQik9D6VIaUaSpkUABT9noVmdsmqVDL0qiwlf8ZAFNlYgs7DpXz2cOmecRrarLAYZNHCyRQ4GtjstWiQJvIb4GAhJtVzLLBTLh147o3OqYMnGMDggcsskmDAk9ff0Z/l4kFbRfFgEbxotX2elOS5V4PtkwyVNGOO4GDZm87luBlQWIiXVIpoACVU02288woUGBYsJamLH4CsRn9dqKfmQA4St2IqmsNhHpoM01VltTBAZaAmzJMbf9rPaPhSXkNVQ8oVsgCfshycyOrCXgJ4qD0LAmXXHUZ2HSy8QiYJE9oEgOs8BaOsNirjoWHvzDeJUR0kawloGCIIeDU0rtODBT7+W+4Cc3KM3KXRqB5ZFt2yfK9lMDoB3+M5p0qi2llCCkytCmg8EmdQOWK1Rq2IyCghkX+Vu2U4VoXyyPylaCcp2Wu/8zql4tO/+aqp+ObgdO17/Cz6DplVHS0XNbmNRFIX8AimaGMQtHHfJKCn7cjva9oBqyTXI62+yLnqlEznGogFW2g/qq3GA4JtzZM8j7UFDDSF8ESmvnDsIHYQ7dXTz7/sdldOePTXX2me34AJdQtt2QgAVQdHAGN7kILaFPqHlKRycychmnROpM3W0nREPjKe+sI7TT5NPiD2/nf+T+8MHsbSPKPNNW3WaLN1eq4uc2kjGGXj+g6k0LXNR/FgfQ8erO8BgMs4p936IhdveK0LCGATZsltMU6VTEvyc20soTzvJAvzGjwsmO/tVmCiW5H30Mktlw/lQMn1xgc6Jr8RQSvref9PvE1CFRiStpMEWA8WWL/0Ag5+6QlnGQgc4t/0yhCcAD6xvZ7LcLjy9xj2yMMY2dg3tni+AhXg4cqZ28jURJClY3X/u39YquvR5UpPDtlazmHnlcKsgEEYE1MSmqyZ1luUwxW2V4+w/MQTDiYJhFi0o3Phnm8/Qvf4MdJ2RLl85CGc2te03ho7RJm6/90/jPu+8rf7a3HxxHEPxgUA2Xw2o19PGe6GWVHg15xRZfHIQd4C42bGNTdBhu8wcZiglOF1cwj7g7CVZdWLZ2a3mAAbDTydD/ubxUxqKIShCedl2fsxAQq+CIIspCYDtcvt53ZyvlClUEN3spVrjsXzAAC9ZzLWrCndzDEhaK7wEJwuSYgEK3IWmAelHAweglMrapeRN6GMdRwjzV05c08WAtFNVvJZIPfb/R4ZuR0dWnNC3s4oiw48oDGNpT1gmZ4gBUC1y8jr0Ypw1EG+Y+Jaq4EZjl9Z+Caftx6+B8BKyJ/xOOYkYxLmsXbqWSRhPWQfX52HRHa7eqjij73j+/GWr/+d1lfzgkQZGYt7CrvOwyU5lp3Lo4AInVsCIv28zSEJBPWMcR3k09ELYgSZoIxUVmYM3jp6/eIBzlYIwealszBPO7A6EATNWkOYVxIqzFcaOjvgPZIG020rDI8cmxFvBxgzzCsWL+HYxnGht4Z9ngWoRzLCcq8AO5Yga+nspohIbouyxPG0881YFIJjU0qrTwY/ILsJDYfOCfsyVz/njEaSFuyJBAy9dWfurcSDEU0kXOlx1HLjVgRE9V9ZeXEJKwSyHh3E6ZqhvtlePcLyk0+6zgfOFBGxwjZA671MqZEnOyQ76Jx4Zherl5p3ele2OC9DqArMAiVTaWWX6xRoZMcKe4Rx3j1Y3HQI96cpFPjgayzcpHKbt1NbZATwSAMSJKW4V5XAPsy/jRuLXuhB9YmGL+AkMMfaxnFoigLZmY9Rv6fk4EfBcNQHBGks1iRjHwjH84pL0IDf3ed3i0sQMFr0UHGvq5IiD3/5Hbj6Lx+Xw54Juthvfj7Kl4JDRhnVZY/HvuR23PXPPuGgMt5jF4CxX7t6LIaSMmcujgftmuJkeZqLnzca7Sna3DnMWXCi8PtPf8GduPj+x8zW5REw9jmGJDISKZKkmeHgIRosOi9YACb2iTY3QXvsd/R6RflRD76B+bnctMjFLQdY/ate4aEmYQHU1dJjdWOymirszINz9UEtKZVoOhr6NFoBHzxuYgCsKAU3ZQ72FKoCsW9xkamg/eIfuguv+6uPGmtgm4uGfFnIBQFDzuJynYtU3wtu2TNV+CKLwven2Su1AGcXDNkVPkdULmGhmRCzbxQiemcURFlFtfW2nasIZDdbry6lY8oNLp1uUS4cyOZCeQueJIvj5gbBTWScUZeD5aaY9+dkHcKQQhVJzj2VCn+a56464wX4e3CAY7JB1iUopZqzufNtsUVZo9s8uPCtqt9pAOU0ygM4i5XpYsWyxkDRRPLGY5DgoSxqiKa5CLDXRqO1LDplF4v9bq0AZcjoNrN/fnCgbNXIBjXUSqs30ra4t2iS6+ftbMCkDNmAh413FUP6zO/6/VTECGI/utNJAEGfkCZ/PY1FAZH2YyFG1cfessLL37UJ58pJv/n9eSl9vPalh7j7p49RVp09m3nBarVnELJEH3gOr/fJwMlHv2GFV7z9FKmqXtkBvzY3+rvdLxACvF6Km7SOfc3S96wgMILNPBYUnQMDgCpHaSxAhfUna17TvOyQJ4JANdii7HQZeZxRBj2frwQDDQCSPFfeziKLnctM7eW7aTOjHA7+vFNBGTpkkmNJjKi4aRb9bll0cs2pCAhMSb47FfleVYN3M5tsss8NoFYSID5bonFBI15DGuP+cObQbXqMo+FTdF0wx4Z5OQQQRcB/XsveRiIgnY7uZYsEBfsaDffdljPsgG99RlZkzMcb8/gZiULjeNZiMYAb2XaIaXWAGgrq2B7K3N0pGOYpoRwOyCejAZOi+V+ylxIQzA5WQ3EmgoXm6IzT0cEBgeZ2Eo81Q57Yz9CaKrDqzU2nW9PPlSG8BMrc14GGjEhrjUAIQJdGd5q02qnKyMk9F3DwyWMDzfl445VKuwCYqD9DuG85kHxSK/RzHsgZndwCZP3m6xsDN+6JQRM+Hck8epZJZNU+y30BB5PxzDiO5+TjGQ9pt6gFI2C0GutiaElDlWvL3SQQZ6gi7x0AegOeaMeoPWKV+Ui40vjnOouV5UqxXOdmzURbiIY2w3vPCz8E3Gbgnh3tLH6O96ZdwLXHin271462yTjh+hvvwoX3PuKf2/GcyXxoZcT43UjMkwTga7GGAe3VXQ/XxCioAGKp/9hX/oyHZkcZi3sNiXcS7gRj+v79P/E2fN03/m7kkxH/8B9/H77pK78NluvEqKlVj/zUaUvkMxJhvW0jlNi/rLUPam0LivB5WAQoPnenecf0FFKf0y6jBxYQjyv3bj0TMJ1sMH/wIwA+hwFW97rXuJFcayu0AUUD8AEjUKFAb7bupSLKV8aqKXkJuNDFspRUoFyMEdkCtoDq0cpCuGzTiuUeIwPKvseymhQCCiA9R0zA5EYWSlxajO+i91AzRfPGLEyhBCr7EK8VSpxbeOFmcgBB8BbieBtmMYKY6D7d/T6vEfPnCBADwDNF2UlBAQIKq+THcYuKJHjxGhBE5RKAU7p+InHAXW4WoPWT5XIJ4Pks8YwPXjuCsSh7fJ3yyoWrcmoJtcETSBawpoS03sicBUZX+gb3bqmRwFaDsjWgowamGdoQ41RyoNSYZU7BkJG3oiQIMgAx6PN6Rll1yJu5uT7DX6bDDnlUI149HLVLyNtgONRq92Z/5Pmk33ktG3lZdkCF5lvJZn1mg+d+XuSaeVsM3EhIMKSvQZGWhRp96lEpiw7d2kGZXXp0w6EsMrIWcfDBrTaWqVTM9BypsTIf9gL29D41J7kGgMIQLh2jtFXZrA7GsoLYsuxsPuK9zTCi10yJgdrTQ6F943hXAcf98YhZjWdh+31jqzkJ6OBteM/qY58URJShEy+XMfEcuNQa/MHDJ2F/Os9K/BAs580kXsyqc0SPJcHRzMT46tdlThefXQ2EvJ3dSFZQEsFpWfbW96zejrLsBQD2AeSr0W55a8GbKDoUZ9hpe45RZWwuXrgjGuTK4s+HC3QRLPC5QxEXhmeZ/I7lXPBgB1avBnk+emZmN6LMUCUAUqDLMvgkb8xDRCPZ9ttixTHoHTPdXB1Ym+cmJQdOAUyaUT50XmiEfTJQnVQfzWHMk3nqd8vJR2+VkTAHQZ8GL6k9E8OftX/bOw+xePTEvXmzA8dYHj4e0WC6OYBCzrPNK4u5wNfS+qUXsPrl6zp3DmLobTJZAMyrKNeagzz0DmqDoWseaZ3/uhSiVArbzA6cl4OBfScsdd5DoatY+h9AA7rMO6X7s819AE9VK82anEZAF9IZLF9Y59P2SUYRAU78rkf3aHLfJ3DScMjGW0dPJKvkhXOzjMBd9H7oMe0Btd2sMBHtFdptQLO3GwjeNeR5lh5tPF5rJ3Koyc2knUobgeRwJLwJKpjfBTigG/SsRdp4u8CR47veCNCLa3QXzHIs+VpICQHgDgx97lSqA5PgBCFIM3kNc2T3ijY94OQ4Q1zD+FlkFu3G3TBM2v3Blm88fDFyqsjh1eVwZaGeaS5Wc6EBihGIRY8lHS96hMz84EMAbgyw+t0XXvSWkyH8erCQajIxJpUt/m6GdgXmkIim79XFIGccfc23NeGFTYU7ZPkuGV66QDnRVKRaPSaViso+kW1VwTKXcq1+vkRcDJysqommMRRN839SmfT76v2oNHDFQEsnm0Z4JKcq24KK1bcsKXZKssDRmYcoX1/DzodgCdBeN8KQWJoYi802z6jLpRh9xiwpUIku4Czjmk7WrhQ4BoBcO5RA59kIKbqyd4Fb8Gxiy4U5O7hRRg2zjEW57YJsINtRlFswWsliJDVcraoYgTg/y/5E178m4dr5Y+wrAa6ypVIQhNUmYQovVj+rPLA6qaHVV+TT0cp215JQLywcsHRikNBzI14O2VjKkFD6hG5TUIaEVIDpQIzEPFWUXgHJVFG65IZ7rUgFKH1CzfL+vFiIETpX5AkoPfQ6AuZSqRgPM/KoAK7IZ1IRkY0/Abl2niryKNcufUK/qWpcQ+8L7+uQkGZ5vfTS1zwDJZBWSECagdO7Mg4+U+Sa64JUge1RtuuXAUglgOYqz1CYGjgD3bZiPMo2Nnmq8pmOz6vTuErotgpIE9CNg31Gni3ZWORZnmVaJuQZ6oWTMU6zP+fmUsLi6WrXnZdyjTzJ/dmXeUioHdBtOaZyHaia4O95HDAvE0CcMkO9W0AZUjMOpQf6tchG2uHY0lxR+qVdP08w+QAEnFEmSydjKCAf6DYVwAK1k7ETkA3Mi2TykKqMISDjR93N+5Re5C8VefZ+I8Z8tymo3QLzMqE/LZiX2frFPkkbFPRWU6N5rJhXGVnne3FdQO68SOhPCqquAcqFeW5r1bVVUTsZuzxVzEsF/UnmRsCKq+1URL7TLKE68yrLdYPnc14m5NHXoPU3wUgHrl+pwljxE3/zr+Er/+AfVKBY5DtdwrzIqD2QN0KCVCUlyrAz3lrpj0UHum3BtOrsM5yPSJ4QwIvns6q+SOi2BeOFDt1Gnol9KosQPlwWWjYdQs4spF8khkjY1JzQn06YVqIrxRurumOrc93JeEmfdL2qFzaVqvMBvOv7/wbe/Lt/vz0PPdK1T5gvLDEddCYztiSifHdJ13jBPMh9+/WMecg2992mmKcdYWmROOq2BdsrR+JpJUGQlDDSfpcuNeOeikcXdJvZxoX6tDudUJYeeVC6jDwXzMsOx3cPuPjxTRsJMPvziAc4Yz7kOVVojGzz5tLrnoXIKn1Ct57VG10s5FnWWxFdw1yeELacSkUqvfY/mdwBQN7OmI/o8S3AoetoRlpk9RqXlYBweYYArMcZlWiNpNZKjvYgoV0PNcIpexVOgoiaElKIcpKbJ7Ehuqxl9rcWbSAykt2DFr0oEdgAXkQmibfDDgpOwfMFeOpBqQ7CeD16vVhzgMVmYjTT7GHZUm12bolkEuOAVYtOXXWbmB4ivSc9ZJLaEo4PWS4scodgry7c9ox9sqrUdDrQ5qbNbuRL7zYSstvCKSlxloFU3RHAc86ikwUwG9zOL2OhlODljHIuhD6Ld2iINMEkx4RzHNN1agBrQFuY5hnarfdgveG1YKUXlquUIgCzh6MRIesDm6suVuqLh98GFkQGavYYzN2QOcDBWgRQ0T3MvKPQkgop3ZXsk4QgOXtiQCMi61jwAnBhGSdTYA3DwM9wkVhIY0D+MfaXIUisAMhnJAMU3dC73ibG9B7snMdkjER1YeQzRcaFCoSARouI1AMpSGAx85GFYRjFens2jpfsEhVOZGrIYNBDGb1lVDJc6GTJUgi9CSxWXQwSdrpaNAA+Hl5rc7re6knuGmOuipsx9mTKpBy9Aigy3NqnomWfa58xH3QaJlUxXegwL+RzpRfDa3tBjLjNnUDeAqcvoScOqF1FvjAidRV1Tji6uEafC/pO+nv1SFjU43GBRZbvvezoSQDA6TzgJaun0KHgqekAyzxhrBlDKljmEZsy4Kjf6LQnbErLxyzzhLuG63j/8Utwx3CMnCqKWnKzbn5Pjgc46EZcW1/AYb/FQTdiW3pcXT6NqWT0Wfr5ydPbMOQZGRX3HDyOR7cXsMwjxtrhoBsxlYzTssA9y8fxC09/HjIqLg5rbEuPo26LD1+/E6+48BhO5wEvXT6JhzeXAACb0uOo3+B4WiKngmWecTwtcHFY4+HTS7h9eYLTecBRt8VYM7pUMZYOpSYc9VtsSocuVTy5XWHRzbg+LnFldd2+88unl3DH8gRPbA/Qp4LblycYS4enxhVW3Yg+FTyxPUBOFSt99rtXT+PRzSH6XLCde+RUcDwuMdWMi8MGlxan+Mz6Ai4t1rg+LpFTxXbucGmxxnbu0ecZT29XOJ0GXFhscDIuMHQzulQw14xSE0pNGOcOR8MWx+MCy37C7csTPL1dYehmbOa++fx66rHoZsxF/r7r4Bin04Bt6TCXjE7naTvL2Exzhy4X9LmgQmzMqWRcXy9VPSRcONjgZLPAop+wnXpbjote5HCuCZ2iu74r2E5yL34+pYqs/9bbAfOcUEvGvO2wPNpiGCacnIheKVPGwdEGp9eXGFYT5qlDShWlJNkC1h36gwkVwLzpgG1Gd2ESlbnpgDkBXQU2WX52SiAMBXXK8jr0QVNFPpxQxg7ptAMujKibzoCtoRlArjMlpFkM7LqcgTEjTQn1woR03KP2FegLMGWkbUJdFqRNRj0oSKcZ6ICaqt4byKdKsmUgbxPKqiBvE+aDglQFYKet3K/bJEwXC9KYkCYgzfI58QAnlAFq3QN5LUZN7WAkBipQFgpqSpLr6GvdaUJZVJRFxcWXP4XrH72MvNH7F2BeVXTrhPmgIo8J87KiP5Z75tmJmHlRMVxPKAsCbPl83sI8HnmTMB1VeU3tr26dMK8IuHR7IrBX4NKtFcR08jxlqDJWS31OKOkxyrOkWT+fqxCUAGovz7G9VNGvE9IIlGVt5jkV0c1VyY+aYfKQCjBdqOivJ1Sq0CJjMC+APKpcVf9Ze3k9klckXmon79lPfW9e6rPP+t7kxFSelDgoco+ic8zrcM5IEnUbYF7JzyjOvGfR/gkQgwH9biMkQRkSutOK2juAzKyAPQNFnzvNEDJlls9MS7Gh+o0QDwaoFSTnycG8kMkC3MXDVtGtJzz1ykNc+siJEte1ye1sKs5qPi29cPnpteT4B4+jh93rZMbDg4PXxYzvmCukKR5pG/KDGLKnNqvZi9FWZIGZmD9IYj5GaAVwZLYXidPgTWsKdsR7ElyPk9s9tXo+VQR0EQDRGRELQqgXLk305GZ3VISiYmDuPz17tSIeCs1DuBmBRg8Ti6ZI2LbeQ/uY5iL2ZYwmC95JAA6WCHg5D5F8j43PFu30xSC2MW3+acb04Y8C+Bz2YEmo1mg5NeZ+1wopKTAD4tlJMnlqZKdJ81WWC2NqMISE5pgsCMigcYCjl4Lv5wA6tqpBrLpcNcFgJUPLNYLGFQN+vZRQjw4snNFCKXio7mopHjsu2sQKS7UFQrUaqq4HC1hp5lh8IbISpcIOsAxFQupi8PGsmsTcwV3qjFFV4RblIDGvdbmQhN+Hr7fxq11u89KK7gYEOF1GHVYWruihQIE9YxhDdB0zr269QVoMflI7snoci3vcyDbw+10EnDqEh0sPsewXLbjSPLmq58skhqukYuEOFiKSM8qhsLJ5PdkBsmUQmZwuL+VZb5dCJGXIyr4C2wvCYs/LhPEwYXubDNV4qWK6PGFx2xZ3XHoCv/quT+KVB4/g6y48gC9ehIImn6vtzode3Pvd9Ysv7v32bd/27ebt19/qDvy71cY6Y0juuZhrQZfyDT//y5OQZS/tL+Dx+QTL1ON6HbFMGR0Sni4TnigZF3PBS7tD/PN1j1UacTFvMaSCD493YFZ32+d1T+K4DvjQ9ioWacbLh0fx6HwBnxpvR04Vnzc8hoc2L8Ed3XU8PF3GHd0xPra9E1eHp3BSFriY1zgpS/ziyd14yfIprMuAi90a17YXMaQZl/o1Pnp6J/pU8NHrd+Clh0/isc0RTqcBm6nHVDIW3YwnT1e4fn2FTgnBUjLmpwagq+ie5pmCCd1awDIS0J9CPMkKTPOWYAzothK2dnzPgXlZASBNDrL5GvONCc7yojNvW+axEVaAR4nZGOa67BqAxlx7ywViLo+GGpqtStCyGFBitIsW3mDBhhQjcAC3c/hZy4MOXh0rGBMOBQbQhPax0l4IO6X9lNbal/i3hhZKiPjkXr2YXjGK7Vm76nZr3yGtZ+ndOHnpfMAdHrU6+Jw9xNBs97V4rezYHQXcledGrhZiexMLqDPDnDCRaA+pOkbg63v1cOmpObv5Z+zzOGnYrcxdU0jtBu3We7Be80p3PXICQmhYmmaUC5r7RNBDhEvkGQWQQCMi7hgHGgtAxO/R88WiFvQEMbwvemZCtUIKpCF2wD1hu96k1SLk4yy9KALgVf1iYiMBD5835hyFmNLmhHs+d4w9jW5VeghP1gaOGs9RRPU6NnU1eIhiUBDGfBAQcmxVAJvqhICzKtG9bmGSBff/03+Ar//yb/TnIRiMBRXYt5g8Gec0JERGhWOfI+MRS4zGXDuOOUMoa3UmKRYSCc0SbekZU88XAKugBMDyquxAWq26hD5bcQIWiqgJmA86dJuC6UDCAMdDCfuYVslY5vGChJV164rxgrDHjIqrWZhWMpB5TihDFX2sn+u2wqJKqXZhjmsHYZtPE+aFss+rim6TUHNtGM88SZgYckV/Pcv1IYwpoEzlVvsyONuaJqAO+rP3DZAsZxmUQYfcS0LkEsYLBd2phtvM0q/+JKF2Ve61SXJvfcbayzhV9TykKRlTOx0KM82wrto7u51nnZsMpFHZ+K2Hg5VB/07td4UlV/Z48P53W32miYtBx6jo55Iy1YnjKvPSbYFpBQvXtLVUgW4NTAfChNPgmJcaGjj55wB53UIkO/2MMt0ip26A2HhluUfpYeFveStzCjhbPi+ViZ78+92mmjehdjBZp/dCctr8OvMqoVtX/Mb/8Gfw03/p12G8KH/XTvrSr10uug2wvZDQjdVY+ZqdKaes2XudGGXzcke+JpfTrLlkZUgaKptszD1kE26gqBeFDHszf1VCGxN5pBACWXQsPNwSIQQTFpLrE61eh+q/M/RSjEP5WDdWjAfJxjPP9DJU8xbVBA8X1tBEGQs1KichgfqNhPQyzJMynCfRPWwdjZ2csD3KWFwvFiIb+5enqjLleaJ5rB7iqXlUUC9+DDPm55kfVIak4Z4S2lkWIielTy6/AMoioT+e20I9Re6ZapXvDsmKxtRedGd/Kt+ZV+rxC/Odpoqy0PHbuEdRvFES9ps3BeOlHt1psfVXgx1Wu4Th6QllITqdXhnKguWvBq+bVTHVse42EmrJ8D025saa6KjXJc8V0wHD16ChftL/eSHh3gwhpr6QMOHkoeAhr1Y+J+TlmYJD/N1CRmc3eDsN89wwB1P21v54lO9osZ0c8tPkcG/9bp/RXd/gR3/sB/CW3/q7mrQIy+/TtSbHB/R+YHv08kTbIaYmhDQH2mtWwZMFzkKRMtoHPJy2Lgec8Xro9cxWG3r33sSKoTFEMNo4sXJdKL4GoMnv9qqG2W2xcQLP67LiGAx/IxkP+PuxRfAFXQddbvtBrxhBy06Br6YCY7TD+VnAAWQsyhG/H210wD2DsfhddRmx+eQYhrSXM+keCjIt0ot2fk6e98a8s3M8X6nUm3qwbhnAOkwX6h24inT5Iq5cuhdXL762STQ9U44yuGCtQAFgk1KHHvlk7YK02ZrrUCrvaOU5uiy3oyX+mdABDiIAmySWDDWvSaw+owJl3rYocEDjCrV7pyQAh8CJHrkYGheLNzDXiwmNXfZE4N1CEgRigJ6jMVpCXgNIqGx0cVtyYjxvYncs4s8I3oLAnvFqkTmIC5uLILrXI5BVpqQcrrxqH+9JABWA4yN/scOVP9oWmWiKgMTx5MLTxWmHD+4W+1B3c71w4HmB0+xzHca5qSq0HcXbFgt27I6dylmToAogFknxc6JGO4DWwzQBS8qvHgKRTyeUg96KS6DoOUS6QUuxiFnCE4N8i+LPeraO5l7oe0UT0euQzSDOGw2fUKUrpbI7JIZvJFjMfk3SVysMoBX/HvtPj3H7X7ggCd9zle+XChZxYM5ZDlUNWXZ9urCQjVvLKuepSMGIWAGxz7DqXGq85fVsOQx5M8tnAE92r6JrilYXjJUJP/JNh3jVPziWnDjNa7IE7go11AqYj1GHjO5k0kRxHfeeFdmyfT7NWoRhnJ0ZLGqQdlooYy5eTKLLOl9SLZLPmbYFeZwxXVxIjotWGSxL0RPd8Yj5wsKexyr6zSIjedJrJSEFGGLFqoBprlYZsgwZ+XQSYkBL0QOwe0qEgRhyT736CBc+dmqfN3lNcgg1AEnmZ6gtwnMrM2z5Q6q/yqK3e/IZrOBAqMpmCfisVjfOVhRBBBm2nlKtmA8H5FGjA1jEQTdTK4eu73EspPT3YMcQWMltzSE5UzYeOFMJlMVsYqU2K4JAwnrjxTIAtBXaUrIjDKzIxbLzAhC1uqxutNBB8u82uj4QSnkzohwuvCABi16Eg58thCqU3q9DJ3pI5QhTacqT8xwtS7rnXq76zwolpOS5rvqM5aAXWcqQCAJWLjyQ/JsUDVv4mEcZi0UyqGORWNhpbuZMSN5Q7CK+V2BFI6ycvYG5YkasnaVH/a6HlLPggFWR3JmL5vk51kEmYkEJ2c+rvU4byaoe8n0WhtHCGGeeLSW7jhU2CWXcTZ50nmLqRGJ4fLBL6rI/018rhsDx3U4WhlcOF9Yvs2VuVC0vthBN1ITvqb1DPWBEPb9DYprXpF0X7UH+ZPRSyG1q9nZ6RgC3fQicImhg7lck8oH2WYEWNKkeuvaVd+Pud/9yS6p3GQ/9vrvw2r/+iN4j2GbskwJES7cgeIjFNSJJH0l2tXsMzLE/gNtFXHcET/yMAqYz6R+0RdVTZSAnRmRxXAnaCDajA4Jzx+iyEG0WC2Sk7WhpKk2q0E4qiZX3J4ALHq+mgmFKuHbyIVz7+HuAWvFJfOSDtdbXYqfdeg/Wva82xcKkRFtcwSCuiwH5+okjTXWfHt97B44++LgYoqwOsnuGAFkHZQVYWWXXUAdgE2ysBPOTdtvQi5I6kRwVu18EUwxDjBMZqs6lIoquSayj0BCYxHHQazVAlN4ioAF8FOAz3qcYVsf+BJBmZ0hxXLgQgRZUESzGvpNRAMAznPL1dcvsAIiVdRLdrrbBkeKHPwd/n3wjNmYhlkxnK8U9hGw6x3UxIJ+sxSvKKpC7lXsiEKOCjn0jqOSYBAXW3I/KCjBFWhd6vkcsJU+jRhuNi7rqbWMWWVQDtcsKBtQ43k4GpGigp1mMm1mrgpVFNraTbGGqkDLdc/FNUPseARUgxvK8lORrlhE38EXDXJO05X4KUnKyinoA1LCejaVF9QIFdZG9smFkS1muO1RBTPQEZmGercoeIEBTy7wLkE0G3OSQQwGZBoj4mYKm/Dlq9T4OaoQoIG1KyJdoyAoAKkqAsCqfzKtXJjNwOs5WRCBNgV3NCfNBj7yem+c0drdoMY4+JHAX6FlZxcIXBECxrHmy5wdg+QmsGkiAW1YiU02cuv5uzxIM7N2QijJ06I43YizN7kElWMvryWShHA7ojrcKqDs7182OKtB1UbskZ10NbhzFz5vBvw5AjiBH/+Z7sWoiZdXO+dJKevl0RDx0moagLRGWdOd1Q0nuOF7xnDkbL6AFQOGcraqA+MzZcHwOrW5XloNVd6Oha6W959lJmDAvTd8hYCuW9iaoBMJ39d512Yk3QImh5po5zBcNcq2YRw8CPfYRAHMvy2pgN9EHc23OteT1fuztfxdvfevvEKN8qREGClSspD4rAc4zvvhvvx//++96nXlQ7Qyy3TGJZzlxr9Qy+HXoMN22Qv/4qQEEO94jXDde04DfaoEfe8f34743fwt4mHYkE4y0C5UmG7Abq9bpOJoO4aH0NIxzwqW/9iie+oN3SUVBlau0nRz4aJ9trGhPKOiLJC0rxxkRSO9BzLuJRHMo7EVDvh6tvPgTX4/3ocFMj0006gHfkyNZq3u0Vbbj53cq1tWjlR4SL7JUjg48HC+McSyYkEapQJqUKG1sMgJgrqkc1idBEiOOaHMGfb6bO2U2JguXkZgN9qoVg4jgjTYK3J4xmy2khEQ7ubl/tO0AATcKYNI0ew0AgsMYNhfBTMyx2q1TEAnt4N0yb5nOFQuGxXQXc2ToZ+wsSeZqRTuba04LkFhlQ85FnCPAwzdjJWiOKWVQPX8mj7GQRgT3tWJ+6MMAPgc9WAaw3vi6ttIfYELebNx8qKiEI7MBnGUDeCI0vRRA452w60c2JApxqdi+7BKGR09EmZ1X6Y6G40rdxdFTxLEN5cZNkIjs+TcXfCGDOgvijkxEDLnTxQfohkqgA0hlvNOte5EUOJWjA2dNAuBoQt/ic8XXorcpMj10nYaY2ligojmfa1eZTcraLz10ErVa2KMlfXK+ozKLzM4u6KMsaL+sNOkYjEadg9olGasIhCz/Di47XNQcaypeKn9W5NkJi2xc6iko4ciMVY3TDoZHZJ+tzGwfZJMGHJ8tsM925g2JC7LvgLOT84xyuEC+vkVlVSYatOx/rWK0H/R2fpWUFB4M4NGQi8Zv2TFQATSbu9y/Nt4PM9L4nRhaEJhXssbNwaYhcVm8dJN58FgoxyqkAc7Cw5/BDkwl+BqLGfo02OgxmI8WUoqchlV8fz1JqWuyySyRHZSyjUdSw4pn9mxE6X/z3/8n+OHf/dWtLNB4Z+P6JfuNYLzn7Gxx4bj7MRBIyc76iQfC8rt2qCyZ9+oeHHpIAMhz1ipGczTyM9oyzJFJB+z78WBXGtV11buRCYTjG0Jp86n4d/mT19NnmC4v0T+5wXj7Cotrxy3rDhg4k8O1Q3lrfS/qb5Pd4HGwnxzTgsYTAzghYoYIdsZF+5S2o5CLBAnBo5FOt2KAs+S2zpl5cIInuzm3iOsuRiQA5oVi/+wMKq4BekgoPwGAyXlfXsUunW7dExHBiz6nAa5xFoP9YOHeijjHuobFMzm7RwhofjfvY58F/Kic8pytpuz6bml4tRPKKpxtM4U1zjXFPumYGOANHs14uHEcU1t343xmz7IS9FxHAYibh4tLWw8fjh5L6kGTmXGyggxmiEZPYziCwA585us529qOZywCsDVFEtDWX+xH9LJFr6se2GxEpFYujmd7ma0S7Ta+T11E20PXHADbv+1sItokvFfcXwm2opEc936O86HmwGthL5HFMKYx9SA22ih9ZwAHAHbPZ0r01BH4xHGMRdXYP4IeEtY7NoL9jPZUBEIcS/7TIhtmG0XyXR0NFvUV7WneczcEMZYu1/uarZ5SC4AoV5yn84rThSIW7Hs9XLpDgP2JqR5Ao0+bFp0Gcb7j3rv7N/sdi5sx7JC2H4k0gsOARW52DlbefeFFb7GIhQ5AOVzJJOQgYIAvbBYxCJ6YtAmgiV6PcVJ2iLlHPE8lu1AaWs+t4lB2bPGpp4S9SUnZAL13zDHqO6STjVXCs+sGo1uSDlWYyEawGqCFwOkEd53GfyrS5uLjItDFKiejj943VW5U3ulkbePUnHQewdV608b1ptS6UOmaJaO/8f5YyXYqBfaRgMDmzVkGE16CKwDYjnjTDz4k11LPIL2WVpBjpfKg4IrnpiUuipTk7Cv2H3BwtdmeVZJFgFE62TRevfXLL7uiI4gdegGBQZFIuMHWmWyLec7O7FGuCbKCB87ie42lmSUM9XQrRkUpzsSvBvAwx3S6FbDPQifzLBtvMFjMu6eNBmJaT0ilmHciK/OeTmXuPDxXWPk0V+TthHw6qWxPugkWpM1orCcZVRozLP5hZ0cRvLE/mxmf/vLbzAtkjD2NCA3ZSXN1QLiejJHN60n7ku27+USSYbundaPUdZ7GWUJOxqJsphhd+XijwCgbqOKYdcdb2dAjOBllnGsv4XFmjPcZP/pjP+ChZEtngx/74ksyTpNvhmmcYQeP8jWCNDWofuSbv8Key7xN/L0owKFRTZ1FUA0xdNNmVoNcjKysBmHazOoVhX3WWuVhunpMw1Qs5M2BgYMUGdfZvBbpeN16PnKW0LAsslHpgakCquogoDQeZJ3WYSNVI9oATiTCIIa5nUHVJbz/P7oon+0zhs+cIJWC5aefbkkDQIrtcP7yzhZYa1hbfuYYAPOmSaJ3CJmag34LnivuJY0X2sgZD+urBwv/vs7BR7/pdunH7CF51N+pFCcJglqrjVEDAWMr0TPlaKkMte9xNqaRpCGIivktgQUWsCKycfKq22FhZYDpt3hQulWM1T2udl3Th5q1VHKtyGpYpQgWFayntRJe3GNIDowT8nqLtJlcjnK2PduIDC1glLeTyNXpFmk7CsEZDC6CYvEsi7c1bad2zyG4UrnGHDxNnI/RARjnrirr/lfe/ldVV9CDsHXZmcOzqpwS4JFkSrQ5NpPoF+4bYfw5l+izhNkqKDXAcrKRe81KjtQq+wufj4BA3zMbQ+eQBr6EA8K8aWJ/TGY/pPVGxpfjR+M42m/TrFEXs8uv2hFpvWnSOASAUVaLh7Zx7BTs2J4c11wOACWMO0O/6lLXoUYhUKaT/jMwlLOFc6ftKFEavduTic9KQFTVjmtsAF8T2I7y+RABw4p5TTn4vgt9D6kTIDAMYxJlkDZnrQ5UWAuA41er2ERcC0OvujVrX2rzPfa1rhb+e1bbiHMcjkHg+5wnAjQbd7X9LLqHzxAjfWj78jqUS86/YoO0Dgc7x/2tlxL0jUMladE8ru31Vj4z9G5/6ryaw0P/fjbt1nuwvuDe9nyBGqrdRW8VB5M5WBF0EHTlbF6VtJVD50zA6Ilho1FNQzdWoVPGkOxG4xIHGmVjxjSR9i5LQbd6dFcaeAnABvDYXOYdMawuxKXSLX7/u34I9/2mb/LNiwuErmAyCcFbZUidyZaM2Y/sANAyBlQqPHiPAIrIn3Gt2r+owJpx4NjxOhw/gk3ta3NIXxxLjkcMoaxVgA+AcjDI6d/RWxDdxlQQ2r8mvDO6vcPhzoBu4gwriEU8eG2OfcjRAtCyPRzPEPdsVWtqbTYCm/to+HEcOD50r3du+LI1h3nyYE41xMj0xpyQ5rwUspoLUa6W9zFXZ1/VYDSWnfdOzhw33htlke1aJovF1q4dSknGlc+n3goy47uHj1IWef7JbhiTgQIaP0rmmJeG5XdVz1hOjBEQvdyfbLEabMZk89DYErwS/Cxlm2FGBAIcJxo68UDF2Z8zhsLZPUv7esxtQM4iz7EKk8ovQs5TE8bF5+jcEG2qYKn3k96FzJCWeHAu2fDgBbYDaWmUR+JF9XT0VtRlJwCZQIysuT4j54py48asy4OBDRr8zCnReTBGPwk7z/xU2zeiVxmAhYrwiI24ngEL3anLBXgIuwGTaLBnMTrrKpz1oyF0aTN55APlUEEHshjY1rqM7dULWFy73r4fSUjuKToH5vXgOPEZwxpgqBUAHL/6Eo4+8hTsWAo1ZCzMkePOMaeBuRgar4J5Cgva9aD9tHXMEHfdY7mOzxxSSruAuogyu6F3dWi8aJhmlMOlyOt2lFAxrSBLMEsS0mwJGm1sDJuPxaKisatzmjaTk2tKyNFLY3Kse7+dXUmArJ5Im8cQNWL6gN6kGH4ey1FHw5nGsk1QbfQ7avW9n9PBVAnAquma3QCceeYmKiWkIZjetrlTHcpomig3i6E9XDjIpVfJS+0zGXicmv3c8mzCMzVRJAo4LLeG12QfGX3TEMxzUz7dZaIa6LGcouj1iDlTfFbqUgLGyY8fMtnj86oe4mcs5ycQIo09GT1XMQSQcw14qXh6mmIaB2WN80FbmjIV5151d9PneG+mvBDoxfGMNnqMbNJns9LnWjvBiPH1xudG5wVd8ASa9zXjQ7/zTrz67z0mtRc0/N4qT/PeKYxRjM6KlRq1r40DIJ5ltlN74GYHDd9yD5bltlCYezUCqUDo1YEsTMxF42N7R/hc7MEdKWF4DmZSjLmMXqUY3lVFENN6a/GxZ8LQ5rAp9J3F1vP797/7h4OBn+RAWRrZ7CuvQUW564qmAiDrws9rDgNSwn1f+dv9voD3g2BmLq5M9d6WvNdLDlpajxqW4sAojZP3N3qD4jgoKKpDLxt5BGNxUZIR4YLjfOmCMZYmhPttXnbBv7MYmhwrAzPsB+dvOyI/edKwL+b5YnlPZT6sZCo9V6o0rN85PLPKGsc5bUbva05ybbJDythQkdfDpRjolDmC7yJgTJh28VQW5qgB6r1Utrdn4YlOmSR9nfkMamzW5YCyWvjneI1apUCGGq4sVkB5YZK3VTSs1WP1u2Awav4DvRBlOTg4LxAjuc+YL63MEJruPADP/zKAMVcL6bHrkQFe+LOVg8GZ5MBYMk8HgOacJZSLKy38ovkdbAwR1BL7tUtWKAZdci9pn8PaAcpKlGhVcIQ+i+Ex18ZQsDChLmwmwRvBRG+CxaTGrxkwoey/hasSnIakYXqtUgA7zdlqrEQZQjDduFbWW71hZKQNzOrn5ZBu/b56wWtKfg4hFOSwzxEkMsdDw4kM+E2zPGMWQ7wsBwMZDBUyL8GJeoKnWSIB1CvL8FPzehTvi+mXIjKeNpN6HWHzw3mIHgfqgsQNM+o1MusMkTbiqvj61X2FejjNaniQ2IgsNdTo7CRfxvqxFe9vXXo4ZE3J9KWFVA29/UOtWH7sMdFJ46RgksU0dnQ0SQwd3yYnQw3GfLI2wy3pvnfhA4+LjANuWG5H9Zi7B8fCfNUw9tc781Am9T5aP9ajrCEeAB+Nao5XUYOf79PID4noNATT6VZsg1IsPDAa+PSGgd5nfp+eYIJ/zimfgXqGOkFDG60f3B/7zskY9m+eHczS5ghrPK03NhZ8tphHVUN+k4U1FphsVgJ97ovRQ7Kbq8Rx0uuk042B5jTKcSmJBCVzgtbqZYmE6DSbB8q8WtHeUsLCypIHoxdZvbe052hjEDiHiI4n33TF78HrLwaLZqBXo6l2DLi80gNxsnbyXeffbAdeB/A1wzGM3hGuu1jWm8+kAKUcHbh9E3/SZlW9kTZbt/PUZvrwn12pp3LTeOvYKoHPwdJJbTZ6D3eIYJSK+3/yR7yvmlrByC4roERbjfsDCdto20XPU7BRxS5mSXnVl7Tf1BvlRH92r9+ujqXNqcS9eQNP1jZfiUA4OAvs4Og4b/rv1X/7mnk5ywWxy8zm57oYJ8cTJOtj6hCvW/QcMMpP1p+WEz61tvEztFsOsNLGEyBlALTTfGAqDm5mtgDlvcoHjwPPgWErZLmSLwROANG0TkI9WFqinAEAskcxBGwWZiofn/qCn2bc99Xf6s9QKvJTJ37PiPxVmGtkT3plB4qGSZHZ48a467VjjC3UiA0bKABTZgaCCEAIEow9HxzExGelMuWY0r3LRVOKu3S5ie8YGByH+DsB9W4SMwCsPvKYGG5FlU6Qh2bD4mJmU+Yknt5uxlFgUayqIMeBfd1lhCJrGQEe5Y8bRByPaZbwVjLvZGpVPsTY73wORw3ZO5awBj91XcNdTrfthq3sLeZwYCJkk8nHawvrknEuGuI0G2PKwxPRCbOa15MboGxkh9ciL4V5MwwbpOFEL5GGMubTEfm6eCHydkL/6Kl4JQAr9mC5G7OEoVjoHfOU9Lv51MNNa9cJSJxCPlWXGuPND3FUgKZ9LnpWXY0seNdZCI6thz4UCJjIHs8GTjie5WjlwEbBaM3ZwXT1CmwGBA+kclOJuS9MQFfwYXkr6gEsF5ZNPgU3zUQvIFlp9S5Aw/bMS6KAt66GJoSOHjtWqCR4kfci+OxNFjhuSQt8WDGCSftv4Tew0Ll6MJzxYhP81OVg4IRGLPtj9yXzz3DDDAP1HNc6dPK8PYsk9G1+kHoIPKdPZM68r9FoJcOp82deQpIPAfyz35Z7ktu54/5QVr3dp/YZ5XCpIVnVQm5ijpCBRpU1go2kRrsZ/Fmr2tK4pRee8s1QqY0+J/Uyn5O6aEnZCMBSDaPGA0ByLkR6OEmgMn4oUQzmeaJ+i/syvTxz2E/5HgEIdXAI7zKgwiIk6n1jKL0BDYKyaD/EiAKSr9wHDRQVN1gJNoPetPMTOU4xiiEWpuqyRVNUNRI9ikTJztXSz0FiqCyN5Or7IgGd5TOpjWJgUPdZgmuXjaxz1ds4fukPvE9eo45khA7HjHuY2RPF54qk4Y5nytIguGZpQ3E9laoySGAR5imw/5GMvfS+J0RfmZyqdzWrbbAd3XNBHRHTS0g0M8WAcw64kUyZJDgkcUI7YC5G/MmaniwPzcaI0TtzWFccHwIlgiIDLPSWiPy+6rs3Pq+A60p6kXJ2cEG51bVv9yHh1akdB+C+r/pm1wW6nxnoibYMxybagzo/leMRbCY+t+UjzfMZT52RHREo6llVANQjFPaxvmtrITCVJ+oQ9kFBFtd/iv2jLcZxybkhMABIntdqKesq6jfa2bwHdVqMOANCRUh13kRv6U3aLQdYJki1et4VhTka64HdAVT4Zw0fojcLkAFg7CQ3DCpAApyIYNVwMtdlUPqs3iJ5B8ldiWSXyTIAyrIGpkavb6AnhBLWZY96sHBGl27VWu21cmElC5tKlgpQ2XUT9pDcaeAhuF8bb9GslRpDWXAbb7pf6ZljcYkQPiACnMxoMEaYgsnNg4v7YOGJ3tzAAY9X5nfItFFwM9xbs+x90yCgtj4XzdlamPGTnzyWa0yzeA+LJ/Yby8pFRFnSUvbmXQRcZnS+dp9N+pn8GVRhGrM97iQpZyUF4qZGJUOW2jqUzIiPCqem1FzTClxwzqjAuBam2QxjdKnNCznl+W1avZEeA/7LspYyc4goX/x9x1CV+cy22VhYRFEQERljDaMyo5RhVWEDTbPnAtH4zMcbV9ixkEeG516sR88r4OcA/OiP/n0PdRok9M88MgoAm8R6jlPRnIjslcoYcsZwpVh8Ip+OBoL5t+XD0JNWilT70+d/6E8ceK5VKVJKm1UINZ+lLIcQsjhb7kU+HQ1wM2fI8jAB71vy6oH07gGQgiDbyYH76J4ly7EiaFmPVvZW9Cus1DqBKeBAMG3Ea/GRP+MVzQCYp8sKTxTtZ4GEq0wFf/jtb/dyzQWWi5VONrYxpq14ryNANS/FPCOfbNrKeEpymHGk4NPA0mLw4ii6HulxYH6PhFzNNg67Sf4iPwX56bURQua56TQMdTuaDjZvCj2IKeH+d/+w9JFVzKqG6K7cOzpdPvA9IexbtvbpKQDcqNhsA6EVCgoEkG/6nYZmIIK4P8ecrDr0yOugSxY7IUgxbG3oRc/SUxH2F9sXFTQb+EuaP7XszXi3fCbAc/s4DjrPRogpEKmHSxn/Az8apS7U26Cg1b5HkJDUA8O9i0boSj1IkbQF2jwhi4jI8rledZmFHmY3UktVb76MnZFP0ZhjflAkBGYNLda5r0l1ueZQ1ZTwM9+klaN1z6N9kUz+A0Bkf7mnRCDHZywF5eKBjp9GYtBAZ1U/2jIHSyc6OefRaGfLrmfN+0S5WC48BNAIMZ37YD/YGmKOEg1kBcZPfPGd/l16qZYLn4PgcbNqriqjzCkzcAzgU19zxfVgDOknsN81wgOJg7mIByz2netB98F0uoGFDUavHkGb6iOzW82mS76X0u4MET0AxK6NRC89qJw/ktWxYmvMR+JeHkl7nf9d8GFe+NXCSKFyuPLrBOAdi6MYiZe0rPyolZInHp8SCCMS9ZEwolwz3WecxObh2GQSakGGjJSvbT+68NxRbp9Fu7XnYKW7kS5fwl2X78VL8j0+iQQGIXzAck8AX0gEMSxZCvjiy8EVHREqFck4eQlRCvgUDs/lQgesQl7aeinJ5oA0Tgj7lL3/VfPBeF/b2Bizfbj0PnCxMc9Ir2mHnnFBUNFT0Gv1spOdL1IKIPOQPHk9uEm5eBAAGp+jVq/aQ+9d5lkeUwvKQllSu080mjmnDKeZq7NkPA+sVjudu1HIgIctxM07Jcu5M/ng+AfAbLJAl3UEUZBN2uSHMhRAbe2SlJuPIJJso85nrPwUZcpitQFfxEYGVF/E0cunc9xUcIrrNLBKjaIJyjpWNrPS8Hy25GV0m/AHrid6PQqaHArLEylBKceKWpRhyrmCrThGojDHVo4o75xvC7kMbBvng0w6gX9KftYbz4gzpmxrc1RXPfLT61ammBuh+Q4c56aS2nR2vXjCvlZiYm6T5pxZZbswhmJwLMB8tiZsIniwrcrfeflmHG96InVOvNJesTVipAC9niorXj0xEAuAgweGrOXs/VQPym41sQ/83jtw71+95vpDySdj1CH3KBdXyCdblNXC87g0JJLGOWWGYxfDiAzskGRY9VawJZ4LZIVGVH5jfp2NoY1FNkIr5gM2HhBez8qah3wJXYNWGY0yb/LveWj01P/ef/iP8be+8etgOTb0/gOtTqJhy9wWrpE56ALzOKX23rbvaD5h9LIAsDxOHmeispI20zkem+prk7q8Vs+14BzFvSvmMlEma8Vn/rsOd/yZhRACNH5398/oOQG8Cm48KoTrRe9lzx3D1LkOuBfQkxaNo132O+g02yf4M1Z8o/E8F3z8t13Fy9/xaBsdEnJp7LVocywXnm9OGeNn41iwjxyTSIZynOP+wXvG5wZcdkNuabOHhudB33llYDYa3FH2apiv2KIdtBttQruCc6djZVWUSYhHGyfu5fyu2jixOmSTs69EfTrdmM1k+TS7OTQR8HHfCeuiyfUJUTsOjIP8c+75fF1WoimCn9yG6OnYWiEuOhmizcMxok0UiI1o75B4nC8fiU61kM6gAzmuIeS2OSOVc2ohu6U9XJnjpY4E8Qwt3L6mnMf5ou5eb5tIKRuzGtYm7aUI5Dh/Xcb9//Qf4L43f0twwAQgG0OOx8kJCUapUe7j91LCY7/mDtzxrx7x+d35Tqyqa6SxrsdrJx/CtY/9DAD8O3AOFuCTS2NCD6mzhPSY0E/BGXqv9T/Pdv6RJT+S0VhvJWcmsPsm3FycfdcuChoau4fdBiPWikQw7IFKObCCliAczw4A3Fhi6E4KZbnVULTcCx5gyZKu/F1LwsYkdNswuKACOGqMcgCf/Nq78Hk/eg2W7AnIe/qdEpgH5m44E158k+OYc452FJCNlbq901zkLKqNb4wGhHhdTVxvQk+oDHfllgaThexMXoQiLubIcob5evIL78DlX3jUFqhVXYwhgPGetTpwiaARzointZRftrwD9cRZ/hcNOm7KUWEHo/a8stBNCeIIbHJGU8aYnwlx9U/fexsufPgpxEIAsRQ4CyGIEg0G5jg3c0AjOZa4jmMbS0ID8AMtg+cIcMPZDttUkJIm8fSYByMWgUipBYEHi+Z6Z+6pt0QBxiuHWFw7NuOZa5CyY4w85a946Wwbdz0AWs7wCKEqAWA0RRSiDNMrHoDB7t+x7zHpP8pZvJcdeKoAJbOSEomAADoisOV5OSxJSxBlJbzN63e2EMl5hTusYMA0i9eAVSCDjrXqqbFIC3UEy0PH4wY4l2Ft19XgskQPJNAcIrxbpt5eZ8EDnjtDA2EXKESiLwKwuDfEM2Ii+cB5TqkdW4InFoaILeq1aPiTyKKRH3RFLDzBM/FMd0TjhWPD8Yrn/7FAB+VK845M32s+IXXIuYcEKyA0o1fPt4klt6OeavQe+xJBZgQL7H8kQoJcG9nH5wn7elPQgfrwZONFhtg37rsxioXzzfkkONUckXK4aklczhdtAyXt4lw3Jdl57UiQ7XgJaBzvVtAkqOTxHkbqRRBDOySA/Hhd0WMqm5FEjSGiEWwFEs7C1+I1g8wDaDwSkYiwvYhRHydrsc22wVO1SzoCDmB0nuMRMw3JPvTAZutkAcci2o6cdxL4PE8qemBIGK2WvlY5RuPUylCUnWADGsHIgjg01qkLAkli4878KtU/9//E23Df13ybe2qi10376efsTa67uPapW1lUa4ecMdkjiNfr16Fv0zg4h7w2gViS3GMjZtjiUT/xXgQvKXmRlRjCGp0ElGXiAcDJYpKjJKo4nsQFtGN39TnXKnP4Y2QSSZBaW5mlXRbDUueC+Rc/COBzuMgFuoxyISSvcSF0ucn3aA5C5cTlVnGnabbyyo3yZA7WpAo/uPuTxvZaCB3BVWTBueiM6Ui49L2PePl2IHglVFiZZMqSq6FKImiY8pRpK8u6dfdmrR5utvXYcgKqmtXQ1HjvvN66ogYMTFg5T8YZR0MhJXzej39GjZ2uFT5dVPl4LUbe6bapWiex/rM/W6yet5vbVKuVka2rAeXCgW5U9Gj1kkA58yyR1B6MmDQXiOF1HPPAHtXe81GovMrFAwv9Y1hHHTpndakIc8bln39EPqMLMkV5DOwtGEvMXIQuWxJ+TUmq2DAhv9PqaputAICUkJ8+hcVk7zC+49ULDcOVxsmZQAKKycGMAR4au8EQd6Nc1wznp1Zc+NCTqgzF2OScJj1o0/JYCH6LhGYxBCUascwfwzQ3B3haWWAr1JHcsNB78Rr2U5/FPBEpIT91qkYj7Ls08BIr9FnuAI0qD+VNWgVQwuomoEsYHjnxhPHZz+7Jm9Er9XGcGULJ+a9adn45aPiiGlQa/ldZHQ/QowWyF+dQQGcHd3KsLfdutOqEBgzhoKEu+kYPWpGQLLKQTzYSmlYkX632nuNpJaSLgFfOkRSbCJsGHMjwTCHMxQAUQyVjWWrTT50QT+VwYWceNWWbcygoEyoZmsx0UrKXnk6Gr1opb31mdNkKjKSterK4RjO82AWBGwkC7gsMZ2KeRyxyw5AWzclB33kRmiohWcztspzQWL46FslhSBjlmzlCBFvT7P0OuQs2BymZbq7L3nSNRT8wB7IUKfRzsrF9KM1BB1dN/t9skY7XDvwDoDMiREOLzoSYltqsPUZGYJRwNgyBTGGoUfQKqUEeQxFZtrweLELYe7JrWMW3GE5JMoZ7PoE7Q9qVULRS5parKawz59+M8WAkWrlp5p3W6nkhOhep6rEZDN3nmPEa3LsnTVUYXB9gnBx0MS9J+1BXg4fhc//kGE6zEjlaWj4UTUhaMMXuTQ9mIBbt/tNsusbyX7vsYXkEtAx5C+FxjScpAnfuZbStanWdYsZ8cbIyRiYpyCpHBz5++r1ytDRZbCplBttFrrlzILeOM0kfypqElnX2t+2XBF8Zvo8w18nI+Y3tJwZEwviZ54ReKDaGuClIScGbbkVHADSpFiS5CXpqxVvf8u2w3K6oqwN5whxn5vDTDkvrrR9mztBojgkbwwiVzCfQsmMJ1CNmNmzst5I9jZ7QfDMr2qI2DovgUL5tPKgDgVa+ome3y26rUO70NffaZi9Iw3XJ789hjS4G94pH756OcTwOw57JQL/PSyOTN2i3FmClBBQ5a4IIGYAb8PGjwdCs9FrpQmE+U7lwaMwkADdi6YJmXCYbQYieQ2B5OFzoZMNiDKYaLE/9gTuVKUyWdFf1fbpr8/UQktR3Hi8OmHFuQhQUHDd3M6z6wCzERiGNXhkNHaiLQQx7hu9FwKDepnIQ3NEa58sNXBihrQtPThZGYouNC0yZAzKDdq7BXLzMsXkAPR/BFyrsfDDzYM2yUJMa3Hkzmts3nW5s4zH5iOcLMSlyPTqzzkpYm+Ay1jmxhcV+aRyvnXEFSMEENRisyEOnCz2jYZJNdqkMO2EKMc1t/HEMuZpmDI8ce8gHv8uNfJwahpm5eDJ+2eKh7dwgyi8Ai/mnYcTY/XGWwg0qk+Vw0bCRFppFpWZhabMTA4OyyjlZlb02UXi2xHFj1eewvosDCKsAx/AmQGSpKtBJqfGoWaVBGoknG8+visUQhl76tBRPsBkcWqrbCoAou2z5gNpXL2fdNySE5bEpGGpCTVmlT/vMCooEJEU9m1VBCQpEjwUSIwUCowYihoUx0izrInoJOXd2/laGFSiwvIvd8eY5a6OX3ycgZWEWK+5BzwmLXSAAC+1bPm3DbcXbpM/EMLP1aAChKrlDD7z1nSQDogHRrvnaB8932TkPrgEQO/H9od+7Xgg5Z2qy++Tjte0LVrDl1HWjFUPKMN0zXr3g+o3ydiZnZDYSzTbu1Or5pOvQcv+KGO7ioXGDqR4sPCQI8H2SZAUrnnK+tSgLQ2gBuJEMtKCPBFrfeYEShqDSINkJl+KeZYYsoyCY+xhJMto+LG9ukRb5bEg4wVPMfdIzrkByYrnA8SsvhzA1NIalFC5R4o3nHWr+aOPxjx46wM5jMmKQYxBtlbAvNQfixnkm4cHxVgPY8mUVLDXAvwS7RokujktjK1FnE2DTQ3Ow8BQMBWzW1yCX9WDhBCTQFseoVYtNBC8B19Jip7BNDKMNZEUTpaTPQkLtkb/YWTpGfuLY5YhRL7RHdK2I11VzTzWSRKJEQkl7tTHtPtqnFNdkzkaUWeNaWi5a7whtQ+7RXB+7IaHR871jU4rOyw6KudZiaCKBDeDEGAmH6IGroU/j5KXFdW1W6jfaAJQvAjE2vU4MD2YF6rpatCGYgchtUjz47GpDWT5eA6CELDIbnp+PBXEYjXS0ckAXx5GeU9r3/C7tO+pQyhk/R0KdY1uqRwaw7zqONp9hX+B16nLwsYvjcU67tQCLoEI9N7EyS6K3gkpAByiWCmaRCy4wMmpkryx8kO5sDjgZCmVU6nIhAsGD+FLSymCDM9KcZMCFPCU7UwXKLKXTDXjeQD1YNn1P61HCw7hJkcXi9VPYxAB7HgCtm5Us9NCfESJhIHT8CNSWiyaEsR5IWeP85Ikol+1oYIEH3qW5St4W2VZlDHbZXoJULrQ0hlK1nOMdYMgCJZb/QRYixi9zDqM3jOzcwdLm10AQF0MT3pB9HIHWAKExNVdYVZzISuRsXhtM4h0wxRkAHEoRrxSvmcM9NY/KwD3HsldGmiFcJcwtYEYtN+dyuLRcsKjAzKBTL07NWSvuSfiEMcm9e09kDgmWqxiPyvZLYv5kRr5VoNQ1w9eEBEgtk8Z7agGCvJ7wB/7B/WeXPDc69TQIswxZw9vRDtU074x6f+zcreJ9AMRIlnwyCRMki2ceMvWCp1N6FnVt0TA4cFCZWC6eOiDomroapLIiDZjt5MBFKyHm09HXyFT0oFBu5Oot0nnIVghiMm+IeWwZXqQJ/ei8mAkP6Y2hde//j++wcOhKwMtNlJt+KKIAJXcMsKjcsnoiPVLCQKrXggUjFLhmFptYbwRMkrEm2UBAGRnTXXKLJEp1ttMKGARW3UIoaSxyXsZJxpj6j0AQbqzL+oCPAY1HAk4yrtQ3XP9kUBn6Ezda/qOnnSCQeXO1Yrh23T0SoVR4c9QE178SUJYf1/tZM7XPZoCmk7Xtb57rJTo+bSZfk2SeeY8dMgpFyQgavhktKOUUDQFA0IjZesEPC0GLAE1BhRXGCWWe3VhGY+Qa8w6YHcDiRraeOP70MlG29LpW4ERJhaOHHrP5nC4fyF5O41q9x815ePTWU5ZUvppCRdx/+qzGnIyz6UiCWhtn1Tfcf9Qu2CWTQRI02ihc+7Wa99JDjpPLPGWdhrb200gPJTJtX6YcMMSMANxK789WlKOuBt+n2H9elx4A6k6SMzpusYqi9dUI1eT3zr7WrnxXdaMdEPCkx5lgMXgus4LiNEshDd9js+kQeaYwBjE/aMeQt7UensfmhrYPCXjKYc4GBq2IBIFqBC66fmqv4O9g4cSz9tkAP+eQRMjB0gtnBd1UmfYRvYd0IhDsqN0yvvS21m6lV2no2zDGQLhYlW2WZE9JgNty0RJ0HAO10eJZfzbXgIEZ7rm21ljEh2krJFT07KpEmyuAZAOZ/A7vEaOzCMYWjFrqXQ5Up9cQKWd92M1XY8RBDI3VNKJnAlWxfQ54sEpw2YUqb1wU0fukoUrIOBtPvFy4AmCLhxd3XXtYX/iubUpzkfNBAAmlGCeP9+R3NltfpMoSAEBdLVX5dvYsMUTDjGLNL0BkSTXmlmysKV8z7EY544CNHhlVuL/02+6yBcj8LC56UZDheZUNZclxKqc0zh6PSsUyFwu5smpzpfgZYboQ5CymUOWI4wo4uNU+pfXG3meORV0JQK3LoDgY9kNZ0D5Vm8/khij7C7ThL5wDLjAuSho1BApaJIPKrhkrzh0XIpk6xr8DKEcHHuq14x1rlD3BJg2BcZJ76z8aK3aejo5Dppuf516Zux0+77qRZTX8m4NYaQAyp2bWsBxlNVMpePj/ncAcMVP6zL/p1PvKceHGkDOeesNtNv92Pp22v/6Nb/ENTL3EqRRMl8SLxzKzPhYhHEyfhZX0WM2wdp3m3ug675IY+J14kcwgodKEeh8Zxqce6XhOUpRZnhdF0NaE6epnLIQou6HHynmFoYN8Hg3VM1Z8mj3cjmVng8esYbJDRTdT6LUKQCKjWQpe/1eesDArM7a4FpnDpZuSlf0mKNJy4XY+EL9HuYqeH0CiDUw/zLJJTkVAxOm2CStlWJyEF7s32Fhkkg1adt9KrYcwNVagTFMxAG3M+MHCDdouW0XCGseMoDRsulZunUaPjr1Vx6PO0L7WIEtenKFzxpfebu5nalDKAbI0JLMbGXH/YD4CQWmMCphmz39KyRPKaYyX2pJwSpQ0YI3glbqA4I1kGu9BYzfk8FgxJqvMlZ0k0lYZkq36smF9AS3UUb1/ZLYD8WjApXPda2Xm4WRIU6wEEP1NHbXoDSw3BWJSwvDYiYeq7pJFXNNKmtK7b0Qv14QeqAzA16cBgWoAlIRIAyYY3kqDmFEQBFyUBRpxQ+/h6rw+jWKGQkWCK2c7I9T3OzTnXjaN5B3Bc61qVwXjuShBRAImrLMGpIT5SEHv1MOlh//ScKfxThmPIJMhnoDZg3W1wGe+9E5/Lo4n+8Hy/cHjRu9LKgG0A+4NYh9pGwRS19aMETN6XwLkIFN+neKAEmg9giRjlVwH1D4L4fqcXyPiYvESymvoozzLJlx/1siHrk0TUI9z//ipy+bpRu3cQLaoPKXjtYcgcmwpk0X0ezMOGkZoYZEERLwm9UCpsMJwgIcdznNj5zaeKf6tz/jor7/icxEBWUpO4ACahx2ejQ4XDWM2bEG5pxcMaJ0FdM4E77EBeOqyGM78DO3We7DC4pEk2cC6phhiUGUzX0hlJczFS7fqZjFevSCsIs+1odFMg5OhVcHAYu4VDWeyAwQMddm74mOFHTUA/LXcMuJhs2Lp1WhgCNvYudEPWHgRz3+pZJSVmX7r1367jVnVc2uolH7VP3zEgBnDbfhZPwy2OKMaGQjOA5NzyQ5rEmTVePUYmlMOV16qFpDYcD4/5wxwcBTP1rJxXRhAk7lfWsjZfKShgOpZEkBa/WRuiGFs7mwyHLNWteFCiMUjiuZPLOLCCSxLzHeiu52ggqBGjVIyTlb8I57nQGachgvnYZwkTI2Vuxa9sC4lbPS1Ohim0adyk7ajhSGmcVJPw+gGOEMLxskrEukaimEkAlKk6lCc07v/pMrErAY8GdmOoWHw6wFGdFz4yHXw4ErLOeOzRBkLRsaP/8jfls2bOYz63brom/llaGubfyOMuwGgqXh/2UU1SOrBAuiShJ5Nsz9DhobsJWOfa5b7m/zTWzhOlltlz0Fjs0C9K52FFjE/qS6Z91hszM3roc9hB/qqF87Cg2kUq8yJt0zuX+wssM7DbyljKoMxf6cJ5aOBpiDPdFgwmEwvMhxtNRiQtHUdGWOGKWuOleV06PhEsIYg53XpY53GWUCwNh4BEb1RLFHfFAbS0u9R59haips0DTwDhmpw8KD1CBj4+ThmGWbc0tNv3h2GGJGgoqySoa4Vm78SDBMaSsEjFNes6SGCSBo5/C7nPOY30VArHjacNpMBQ8kVLD6manw1hncJ630n59gME3qvYxRJgYOb+Ix6ryYXmqGB9KYy9J1hO8y3po5Zb9yLyDlh/sY4Sc41Q8B5CHjflnjnd5vw/xiyB8j+xj1bvaLu2ela4oz71dC77UDDkPtuMIZtvFOSIgoMl+U4cL4powQ8p1s/05HX5xqq1fvI8SBgI2Gh5xvWhR/aLnvS7PNNA572T4yGCXKR1m5EW98B+XzMCWLo3lIP/o6hhp0b0LVLuP7ay+7VUq/F/e/8wSb/DQDu+tePuvzos57ecxH3v+uHznjhrI+0u2jXpIRy6bCZUx6f42cHKsjk97ISrwwRtDBd1yvpdAMsBox3X8L9/+x/aoGC6sQaDfbYokyzn4DZtTaeBBOcl11bVYmt7dUjJ5J6tzXlmrORfQDcmaHEpKU6RDmm7RbAmJHo2jdLt7ComtwAYdFdc0u4HCztAGcSPADa3H0SQqpf7/yZz7iu5fNxrOjgOFg62c41SI+d5pPF0v12XATlh9eKoYpQ+1/Hv5ps7RAWz9BuvQery+KdmWbNYwjM4dC5kg2bnoVkjCGWGUD/6KkVHLBEbjJunGCNGSdTzcRsMvY8OM/C9KZiruG6WrqbmP2P5dMJaCYxGJocmR2D10JYdKNj6CCLOyQaABwqYziysYv0hBFgWgiS5nLQcDSDPxbLOI+NUPbPDrAjy8n8A15ns4WdNE9gEYCyGURAmwvEFgApx4zJw6gV3ZOnDhwGL4YBwFnSyHrqNRvAzvGm8tJNy6oK6UI2pZk1lDKlJp688QRxjil3gBcS4OJb9MqgD83801Nh7Pxm9FBHlenaZysGYmE+auCf8QySVdmO7l1h36mIaKABFu5nVfhCgQRTgAR4nXvSLASX74U5RAHy9Y1/F5C4abKTKamnT/OfNlsgA2/92m/3OOwA5NJWiQ0FPpWHs2r4iYUBsYw358jYWLgRrOuH8mzs86gFLxiexFwfHddf/GNHtinf/64f0pCAyUMmOQ+63mLBDWFTp6bqX6rVDj6mnKSZh8z2vm6NpUcbDqlFbQjS8mYMyczFck9YHfDh33iHERO8tgG4DEu8bw5op7wQFClYM9Co49VUZ+y61mglaMoZn/myK77+uF55XhQQgFM1wqIZA34mzEvSAiRerTBUls3wfxZOrHKw2baAYAr36cMYqH6zqlm2Zv1MMdHb1cPQg9w3QJJ6U19f/kccXw87tfP91IBj6ND3v+vv2JlN0evU5AAy5Jd6e7mwEGFLcs/Jje6U7OgRehituAagYfIMfR1bfcc54HruXEYMvJ1uzXA2z2itbfTHOflxli+r/2J5/2rGUSAB+LyD6JNyuLJCLZZ7o/tTk/ej65IRImcMcgPV9YxRbIRv0C8CcLa+N+paP5MXU2oTelhXckxDXQz4yLfeBRJLWC7MI0obQK4JHxfaMDFCYjng2lfc7fcNgDHm6Fp46mIQI7tWHN97h+5/YU54nRjKCzWKeydtzMhUXWJyQduEY8FxTR6SzsiVCw8+pqTjYEVn7nvzt4gdxD6Nbq8YSKkVBx99Qg7VDTaB6ZTTjXdc0y+EjFN7ZzHIAd703lqItKwjbEfJTbdCamjCxyor/JWiR98kDI9cl/7QYGe6Rsez5oqvqUj8sOCayrtFQ5xs2vLiXCNh7bg3XK6//NhjPl60f6inWC0vAHsLJef9eO3gKeUcW7/orYuFfNiSV6Z1LxHcE8QILdqRi8GJHv6k3cW1HsbLgDA97NQFtNN5bhhtCsvPKu4goUNjnPxsNV3PVrSGHkSuJRJnY4hmmYMsBELovHZrz8HCVaTbL+PK4atx9fK9oVeBIQmMkbn4ATcKdPDItJvXgEJExcTQuwwN3VDUbl4AWXiMq40l4m2yFcw05RsBr3hnzHZgqLTvQLgWvQ0EWHEOokuf7BAVPxUm0XRG2Nx26vRzcUYDgMZszn4OSjSasigUhoURUFm5Tip4Cuyu7CgLYKU36ZXgWUIxdI+LlMp5O4oXi1UbafBx3PXaddkhbdQ4Zoji4dI2UPN8qMKys3nIeHPOY1l9wOTBqmQRuEcmzDbOEqrQVF/0MdyjVpTl0ORuTVcuon/sWOZHmcU01xaARuUVZTwld0lz/AmyOA+UHc4/55z5URlmiFu5950y7zYGBEw0jAPAaVow1KxUrOY1yNzBK/Ox+iGT2WPBF6Apvc48pbgOmnPcqiZ9K0CU0As1XikHIZTAq955X5szi6hwCSy2Ez711Vfwsn/yaGtIA35sgo5LE7sf9Q7XPz3Ru7Hj1AW6LmKfTCZi2AQ/C8CSkIFGx1mLeYc7+ss2BxYJ4Vip/NnZfZS5MH6N7iPJEb1KWrLeSswzx0U9hmk9Sqgf9bTK6oN/7CLe8Bef9nWnngiGMzaekDA+rFZo+n8TjouY1QCJnj797ke/9Spe8bZHnDmOIIJrmK3vcP87fxD3feVvb43ZaFCRdQ1lugG0ZaLp7aDeZ0REuKaVLqYnUwuxNLolntWjhm2jtyhXNOA1P81zgYLHgWEysY8WtllFL3O+4ryQmZ6LlcxP0ZhB2E9Ow5lbepyCFQrK7dEczRzweXXfr4dLzEdL9I881Sb78/n4TNyHmMcWjSddOw1YIREZiFhrKRyHEmUjyFbzXTOeRb4/9Dvvwqu/7xFfi0FGdsP37PgE7duZstjBwG50BfV97DttB9pQ7BPtCfZn9xqUa8DP/aQHM4YLc0+MjTmJ22DM79o5RuQUn1uGBjNMMUae7ICKRka4j0d7K9oYHG+OL20AtmiH8B7N82SLYjB7JnsuXNqMfjYm9aaCjLTeSt4S1+nuXl6FCE+nW++jFoAwUI6wd7DF1Jjg6eb4N3lE4bwnbKVImOkX9uucfCbLDd21HfX3xlYmUX2qBaZmPZ5D932uzXS6kfGI/SUoj3aUEho8JsLuzbmdZsx3HKF7au1zxs/kLLqRgBJwvTwXOU+L64Q6l89KspbnsvF61Iu7NlapuPb0Q7j2yZ8F8Ll6Dlb+anSveaUJQTrdeJx5MA7tIVMy46c5rDYlq4rWuCnDhBkLGI0dggbGL7N1uTV6yfJGYeBrHHBuENOMNBeLf67LhbOZsdQp4M8TXdNxEw5K0RRqPFAYcCOEbG4EMmxE/Lwvn5WKQA+Aa85sqdXOkgDcUIjAk2eL2UZgTEH2uePfPBBY82Oawz23I+pygXz9RAuLqLeBxrAaWOLJSGgqv3CjjOEZcR65YVD5xMVDgEYDgq8zf2bX0IxsZlSqfNbdhQs4ExWMUQBe9YgEQixcwOegsZOcyX/qDbfj0nsfbQ9IDQaGecFi3gX7sgs4wuZD5jRtpxY0hvlsDu+OxjXz/gALTyHQAPMN59oAKANdQKNIy8GA/NSp53lFMBgPOtbNsy4HrxxEA1W9OwBk844GYWgGcClHkT0m8AhrzNbseWPM13cOCI7nZzVKeie8qzEso7G5u4HETTXKcvxeJAN4HcpIJBsANwoA1zuc+wByjJyJzxgNO44tdRZlLIQQ2Rxyo1PAb/JHWaK81no+qRTL15MkoNEZCbFopGu/7DOcB85dKM8bSRgeHD5euYD+Cd3UlYRqxk91f6PvsQOu2Se2YDym9dajN5TAMaKDz0BijkZT9KDsAvBIytCQjPeMcqT71/Fr78DRQ4+1gCTqMeZoUPbiAaQRmIXzDs0gDGH3Fj2xEzZlZ0SFHDXOi1Xo5PUoq3H9BFKjCVmKZB5JlhiiH4EI+xQBCmVcibEzQCECbLuH9otGLtDqVfYxkmQ7uqA524mgi7qHwCwAatub497LvrBvEVSSuOBzhrBK24OZ08wxydnBa9x7KZfUQZEE5JqkzUT53JknOUtzx/DneMT9e4doavY53iPKKeD2YtRVXKcRpAewZToh2g3se5HzseS+8DxilU+zqygbOySdpUtEIi2StrFFwj2CEsDll+t0E7zG0VaJ+wH1QfCexVymZh7D85quQAAiEVxRHjkXoVqvXSesRSPCON8k13eLTeyCQK7DaW7XdOif9UHJqOYs0d09gvomymaQt9prwbPVss33qxXzBz4E4HPwHKwRG3+ouYhrliWsgVY4aCCEMx8s/EBdf5bPUcJZVoxX5WYEtExozj75jCOFKPrG9U2hsYWc2vdjDsNyIYKnQuT5CUnCBhnnSQNEDYoaJ5xM+tLzrPLJ2gTQcqnmYsUVzHOj17CcLxU6Q+4qqP33Xsf97/ohi8v9+CM/o+fnhCRmrRqTpmJhLBavXqSCD2o1T1LDrgRXs4HYPhjUkS0cpHpZPZSynMzpYrleK0+9HJzxpSua1+i7Zg7JmoBudI4t495pHNIzo4mQUakzzt8ZlOASjgAshPJw3GP4piXPM4RsOynLU03xWky9usc//tjP+lLYbA2MXHrvowKM6bqOQJ9hoDRGU2ACd4z6ROM1xOR7OfbsYXWlWo6Lsc69j72Vq1YFzXL4DAPNT1z3XJcYbjVrYQWGRNqzqner61AWfl6QGfWprdRlBRQoZxkuG4MXTbC8y7jBUiYikcP5YMEBNXI//vjPOYBjBbXtaNf9xFvusmR7MYI2xnamrYSj3P+uH/I8vBgWVTQHrEv44HfeqYZSAAZczzRiQv5MXfRtCGlkB3eM0FgpzLyNuuaZ80VQZeGMERQmBzON14BrJLVHS1h1QM4V1zE9bzzUmmPOA9RDVbZUPDSTYYuA6CubwixGR4pGB+WbhAlZ+RhGA1hYChOhbfwo3xsxMPsn1tYXKdaQm7FmOA3D8iT/rfdztyhj0+yyOvnzV+YiBSKvCQ9WvV2XCzHe+qDrGObCipg0pKkXSnhezmdM+FYdcvSBR0VvxkIpcY0EI7Fy71SD9+OP/5yH3tAoV5mqQ9eE3UfAi83WQwrXo+d7UZ8DYPEJ0/cMg+T5Y7wH5Z56j4QXgb6Ob10t/XMh94i5IQ0RwsawpUhKsR8peT5xJDwiQOX8sRgKPUn0LMZ7WnK+GpWh1DX1u82Lzm/S8E8ayh9/8ud9rAEvAhZAgh1rQZspePasUI/OVdqM9ixN4Sca+DE/Jo4T/zZAuiMDABhCV6N3jdcK3v34PLameR8C376TaptRFmYJY63cp7a+RxkI5BoLxrocieDHpFjrRPYZUpxOt2bvPPifXrIxfvKL7vTzV7cjPv7Yz/nzRU/KLtlmsqP/ag3VT6sDXeoc5siRSNeQy4aIi2uHJGuMrGJ/aI9znzSiJpDVgHvutqPno0a7muufubsRpHUKvgoa2xnMaaR9rWMh0RCBINW5rovBU3asPoPrLavDUP14kHx8av3++KM/29oC3JMjWBwnoMCjq7rseVkRzN2g3TKAtcUGyGokaCywVVyL583EmM7lwuPpCYy4OGKIB8TwsMVJgVOwwzhkf79rhMeMCAIlgpXcloy1fIy5LVFpG10MlUhJqqKF2PxYGMOUGQU5Z8QzgsrhSirzKJpuXKoR6dOrwkOLqSDjidQAxj98G+5787fY/T7xxM+b0Uswi3ESJUygsw0Jrbw3gRQNby50wKsA8r40TqgoGELFceX4aXKvnblysPCxZIWnnC1H6wxrqFWvLAGSTBM3AioAM8qzGniyYZejlT1j3QVuwXCxvLGcrYCA5fasdUPSqllctGS1ZMMKIIibiyqbTzz+8/K+evfsCAGV1xq9mBxnGno0jKOiZP6HGfZ+xpCxdiqDab3V3Bv1BGW4piiaB0PDTw15O6+Kr1lYz0oP5tT5zdk36KSx1YGNZTW5tB3lgGF6O/T9xujXOW3OxdFHaXIga3Vwx3WeUjOm9HQBsHA3APj0b7oDmGaZj1IaL4cdaliAe/7xo7Dy7Fo+OK435CSFanQ8vSKZs7FpM+E13/eY5WdZ8Qayb11nh/A2QJF6gqQEZVzllLqQZynV5dAauyX0Q+egCRFNSYkjsp96dABlJxApLPrBPJ3P/LliesFKZQNeQjxDDiZmZT+WyQ/zHTdBgrJPPPULYgRuPR/KjinIyTyHNWlFs2VvmyfXbCSDjFjitTruTZ0RWTTkLdQ0yKLlpJKMoP6KRm3v5y9xTfIIjLqSIz3sKIaw1qInKKlB03hK6cklG0xjJoZ9cUy5z8RGEEovTBwbgkIasEqUGeOt+9Annvw3QdbDPkDSxQDC6ARozn7WjY6VhRXp3mJn6VCfcP8l488cRnrRqcti2Ng0y3rU3xONNn22ulzoESMhQoFzNPRtxT7mppRquWCi55ZtFAzBJXNYuI6Z003Z4Gc5N8xFof5mf8iox/wU8+QHfaP9/MTjP+9gre98Xc4hx3roxeCkHiph7FRnmjGu+cJWHIAyzblRWbHcvjhPKeHD/+URLN+G3k6COtpWi8GJnthIwKpONC8Ox432WDC8rZgHx5lh2zGUMoImEhxdtmN7Kuer9/mz8U/JCjWk4O15/X97rGGzC1x+4PEm8uETT/x8C+i4P9PbR5KHRO2O7jPSjD+LPEMleIq2snm2kq/FvpN5MbJsavdWlmHfIapNBrhXp4Ttyy43e5CtHX5en5mVg00vTOIRrKsl7DgaOhR49BBtOwJmRjUo0DU9TDKE9kYk01lFUueq9ln3z8EI4k88/nOt51bxR3yWulxokSC3HXimqa+BGwOtWwawAABltg2rBsFrihpEN+bpxj01VPyAbxp8LcOV+lycgSDYUMPNvEzKsKPLzaGYAFz5b0dgs7UEcjOm1dXL0q3xbBkrzZ1h/bMDIe0QxAxMvkCbxD0ib/4+FT9HC2hAYiPs3JD5mWiMqWFjjC03NDIaBG6AV8+hILNfTCQlYKHg6dk9KP48ltTNsefGT9ZknDzp1K7vleiEca6WZGslawEpLRrDHwLbFQGM5QQFJqgueq/4SANC5zofr60vH/o9V10OAHfB02BQwypWoTJQH0Mc+WybUWKBo0eFrXPFbY1jNsWT1MMZNyob0RiNhmxkRi22u9dwCd4vso+1SmlxDaH50O+5286msk1G16iUPe+UYQ5hIBxPQNeJeoQik2/jMTkbGRjOerBAUW+PeR0AMSa5oRK0Be8TAVC+firKfe3MK41wqwqkSprrwIpVAJZj9ZJ3Peyfp7fJAD0g1QaTfz8QDObB4bokaxdboYEeQnWCTBgQor7ZMazTdpIwORq/gG82Otdx7bKCpI0fxyx4zEwvAJ4IzTBPleu8nTDeeQQLsyIY1M2IIaBX/kQyXZx4r1DwQqqBBsOWuaxBbo1sAbxASZR36vlakfUwUhZfoAykuZpBZlEMGnKFoW8rUAEGIOLh3mmzdQ8Nw4SS62uyv1yvTZnqWmX/iB4SNjXUCPpphEhH1LCmjJPwURm7fu/tqBcO7Pw6M8Y5niF8rXadAJpx8qMMSNIcLn0v2JFNyp98MOjbaCDy+UlAsa/RKxCJLDX+JSQ5XNvAVmchyDy/T4iprREI6WTthhfXdu+frRyLWn2MNUzWAGr24k2MNDFboRQPedxsfT/hfl2Kl9+mZ56gRufcimFwndTaFCWirMszqj6jZ47V5Ag4ud/zmZj2EI1klu23z9Ko9WNhLAxzLlJdj/KlssjnMC8F7adam/wgK9JAWWO+YFgLBAyv+tOnrfd2nLygDHOaWBkXsHlNmzGcX7b1s5lsoSYHO5SrIYwb11L0EjK/cbX04hfcSzej7WVGpBgYDOBG+1EXg593Ors8GMlhhEK25z6z/klUDL1EKk1Blqb5bI4d7Z/t6EVKKLO8B+1CjkEEa1yX6rm373LfiOuQ89zlpurv4lNPNt7JcmFlHt3adV4UZbVwABk8Z2k7iq4uTnIymiUdn3rYP++vYM1Am4a2NiH+KQVniAJC7mN6JqWc9ajjXYGmyuFOtVTTGbS1aFer9+rkVbdp/4Kne6e9YAArpfTVKaW/klL67pTS/+v8u/PU9GLelwbcEE2rorIyk3xdBb8eLHDt5EOmXKkArUSqDX52ZR4MUDsbZ6F5XH2Ha08/JO9HBpIM0GpwRbxwz5VNVKlexlsNzGuPv9/QOMsQG2MZvvvkF97hwEwXrZ3DErxp155+yFlWMrJcjBQIovdlSPLusldZI8Jn6B5ZbBq7NZSEpzGTs4SzUHFxQ1EGiBWYrj39kEvXXMCEetss1aNYD5fC3nLcAjD5+H13ySnipeCRR94n15pmOzfLNih63Kh4SpE+kvFjNadS9XyNAY88+qCEpChbBQKkLlQFrBWv/nuPuTHRZT/fgzlnRbxY1576gM1j7bOfnl4q8lMnwqRbyEzvlRgJLmiIRNe4svplKfJWFwPK4VIrwwWPVDQMtdQ/AV9diGfm2vUPenUslS2WE6/B4AXg7A8gCdopgGV9/eHjD3keylJC8azs63JhlfOs0cDXs6jia8gZ144/aK+l7QjMVSrm2UG9ned8AHZukL1HOdN1cG39EfmcMtBeXU3uZ56rvmsIEDLn8QwzMzpUns0gy/oMc8VHvuWKn2GnhonlznH8WC1vKnjksQeVIevNMEynmxuHHaixZKCcTKAWD7n2+PsN5HHjs+pP69G8nhYeOJdQCMS3gaqGzLXrHwRDdwBIZS0CHX224dNPyljRq0GQzRLAgKzxg4X0T9d4moJOU/BGxtJAqK6jRv+qrDTGAiMfVN+bdxfwtUSyJyVYCCnvrWTOtacfsvVs3yWYVmO6ISJo8NM4ojwruLp2/MHmMFi7Jj23fI6QixIr6IIJ8vrs155+yHUdjeeUcOEDj5sx0hCTkU3Wvqd5FlJq6PHI47/oMq+6qCnRToOIz8ox5L5CYylUXLXn55qJeTBT2OdTcpmL3nteo8uyn0+zna1mobXU+YCElGs4EXPrLIoCcKC7GHDt+EN+UC3HnT+pe/lamGczKmNuyDjpfISwTHoxomcl6HOrPkfArLJLffXII+/z+dUjGJrzyWjzUPZLNW+nRRpoP1kN17wOQENUx/mzg3y5N+hYNl6klPDIkx9wEAq0gDuEuFkKhJKsiTrEbCiZ74e3H5XvsGBLlNkAyCufTwGckXDUC2oQXzv9sMvVOGlxDu8X7SYL6wfMK2YhtNNs+eiYZlx78hedhLf9I3uRKqY90I7SNc7Xx6sXBMSFap4xZLuZV0CJj53ctyFE6fD+6ox4ePMRj7BRsjpGFDXEIvOYuDazy2xdCbly7foHVUY6XwuUv4ZgqY1NhF7zI7kPKPFAnW/RA7EaIR0qs55jFnIR62GIIGKuJICHt7/kzxT3Bdr3lG9dV4xYiUc9GZFWK5DQ5nLRDtS1YGG12upq6TpzmnH40adws9bf9BOfRUspHQL4HwC8sda6SSn9SErpzbXWdzcfLDPmBx867wLtphr/3mXmASB3uDa/B3d+4ll0LnfPiDjZrtX34M6804/z2nn92e0zgGv1Z3Hnp1owhdwBtWUMLjy4Q3Lzs+dd75fzjcfpmfobx5KGb92cnQt9P/U9yjSdea3yNbpIy2zjca2E+QhjdOZa542dvveyBx/CnBJS1+Ha+HPyvM/0TOc9f0pAyj7O2q7V9+DOHVxx7jXj3+zrOfeR691k7J9Dq3WL8gvvt7+jxD6j9N5ABq5VHb9nIyO7Lcqp3uOR+h7cRUk9b6xSxnzOOkt9jzrPMid8P6VWXm72PM803/a8YT5SQnku6+KcVuvG5+OcNfSKjxyhHB8/87XC6+fqgxv1Z+f98+a/ud6N7vss+sXfRf8hGAPdufP5bO91pn/nrP159/s778dW6xrz+z7Q9K/R68/mWUO7Vn8Od6Zz9MuzkLXz2rW6I883eZ6bX+9Z6PsUxveZ+pw7WW85P6u98GYt9T3qqPvHcxjzM++FMTL5i8+C8Hd4lnOf4Rnkz3TQzcbyvL/j+khtn5vvsF/nyWV8Brvez+HO3J29Z3oWc3TO+q11I+vjRp8/Z098pvYw/g3uePjGoVDP1Bf7PTTZP27wXOdd4ybr76b2GvcwAIVjehNbsLERPgs9kHf2nVo3VhTh3Odjv3Z/v8He8Eh9D+5K9MSe3aNvuuZ2nt/WCOfqmfoQLxdtwXCPa+UcnfUM9z8zHrs2Fn4Od+KZjLZzrgPccJ7N5n2m+bwZ9rhJe6E8WF8G4JdqrTyU4F8C+Pobftom9AYPsCs0u++ft0huFBd5nrK7UdtV7Odd87z+nPfd3ffYl4jCn+mzNwIO5332mVr8zHnXPOezzeI577Uy+7jeaCz4vXiK+A2Ub/tePrt4b/TZ865X67+dERFl8rM0jp7Xlrtnltub9fFGBtDu35T1lG5uuKZ84zHfubbNZd2NlXsO/b3Zmjnv889mHT/bFtedXrscH99cAT+X13fX6TPEeTfvPdNaulG7kU6Ivwfy5Ew7z/h9NuN5HqDhs9Z6/vvn3ZP9i+/FOb+Znj9P/mN/4s/n2lI6d99K/XPkNp81AMg373OZb/ze7tq/2bpBXNPPQr5vsMec+/3d96LBxPZZ6PZz95Ob7aXnjenuvO7uP7u6k5+Jn4v3Pa8PZXZZ2V3n/Ht3nHbHeHfeCK6ae6dnXifPRdfe6DvnvJf6XvoXZe687zxX22a3Uebj3sSfz6Rbz+tH7OOzGbN4/V07Il432lA3s6fO0383k4Pde96AAD0zTs90LdxgPd1ovez2+dl8b/e9Z9N2965n87mbvX+eDXiTveUFKdOeUvodAL6t1vqN+vcfAPCVtdbfFT6zxvlk7CMAPvNZ3PYygCc/i+/tryftLnx2436j9rn+vJ/r19vPx+fW9fbz8bl1vf18fG5dbz8fn1vX28/H594193PyuXW9z2Y+7gJw5ZzXS6314u6LL0iIIIBrAOLNLulr1mqtK+zbvu3bvu3bvu3bvu3bvu3bvv0Kai9UiOBPAfhVKSWtHYrfCOAdL9C99m3f9m3f9m3f9m3f9m3f9m3fPifaCxIiCAAppa8B8M2QkL+x1vpnX5Ab7du+7du+7du+7du+7du+7du+fY60Fwxg3fCGKX01gH8fEjJY98DrhW8ppVcD+C8A/CyAewA8Wmv9z1NK3w3gK8NH/8ta6zv1O98FCe28HcCP11rf/qJ2+ld4Syn9NAA9ch5zrfXNKaU7APw5AB8G8FoA/1mt9WH9/H4+XsCWUnoFgHcD+Li+dAnALwD4KPZr5EVpKaWXQPTUl9Rav1Rfe85rIqX0JgB/GMBHAFwF8MdrrTeplLNvu+0G8/HfAjgBcB3AlwD4v9daP63r58cAfFq//p5a6/9Dv/Mm7Ofj37rdYD6+G89RP+3n4/lpN5iPdwA4Ch/7YgAvA/AS7NfHC9qewc69dXtIrfVF+wfgEMAHASz17x8B8OYXsw//R/wH4EsB/Lbw9/sA/FoA332Dz/8GAPfr7wOAhwDcdquf41fSv/PGHnK0wbfq798A4Pv28/GizcedAL46/P1nAXz5fo28qHPwzSr3PxNee05rAkAC8F4AL9H3/gKA33+rn+3fxX83mI//Ivz+JwD8Jf39FQB+7znX2M/HCzsf332Dz+7Xx62Zj28Lv78KwP+ov+/Xxws/Hzeyc2/ZHvJC5WDdqD238u379ry0Wuu/rrX+z+GlDOAYAFJK/8+U0h9PKf0JPb8MAH4LJI8OtdYRwIMAftOL2ef/A7Qv0jH/7pQS18DXQ8cd7drYz8cL3Gqtj9Za3wUAmjv662qt/0L/3q+RF6HVWt8G4Omdl5/rmngVgINa66fP+c6+PYd23nzUWv9U+DNDPFls35BS+q6U0veklL5AX9vPx/PUbrA+nqt+2s/H89RusD5+MPz5nwD4S+Hv/fp4Adsz2Lm3bA95oaoI3qhdRSuQT+lr+/YitZTSNwH4x7XW96eUfhjAR2utxyml/ytEGfx+yJw8GL62n6fnv/35Wuu/Sil1AP5ZSulptOvjKQC3p5R67OfjxW7fAeDv6+/7NXJr23NdE49gv8e84C2ldBuArwXw2/WlRwD8mVrrAymluwH8dErpV2O/57/Q7bnqp/36eBFaSukSgM+vtb5XX9qvjxex7di5t2wPebE9WDct375vL1xLKX0VgK8C8EcBoNb6QK31WN/+JwB+s/6+n6cXuNVa/5X+nAH8c8i8xHG/BODxKnG/+/l4cdu3APhBYL9GPgfac10T+3l5gVtK6TKAvwLgP6i1PgYAtdbjWusD+vvDAB6G5Gjt5+MFbJ+FftrPx4vTfj+Av8E/9uvjxWu7di5u4R7yYgOsffn2W9Q0DO3rAPzfALwkpfRlKaX/T/jIayH5cQDwjyDhnFCk/wUA/tmL2N1f0S2l9PqU0u8PL3Hs3wEdd7RrYz8fL1JT5fy/asgA9mvklrfnuiY+DOBUE9B3v7Nv/5YtpXQXgL8M4LtqrR9JKf12ff07U0pfpL8PkCTzj2I/Hy9o+yz0034+XuCWUsoQW+sd4bX9+ngR2nl2Lm7hHnIrqgjuy7e/yC2l9GsB/FMAP6MvHUE2yddBCo9cA/BFEBf2B/Q73wWprHI7gB+t+wppz1tLKb0MMv4/C2FHBgB/DJJg+ecB/BKAVwP4k7WtdrOfjxe4pZT+PoA/Umv9jP79X2O/Rl6UllL6CgDfCeAtAP5/kOTiAzzHNaEVoP6IfucO7KtyfVbtBvPxLyGpBY/px56utX5DSuk3A/hDAH4ewGsA/Ita69/U67wJ+/n4t243mI8/g+eon/bz8fy08+aj1nqaUvpGAPfUWv/78Nn9+niB2zPYuW/HLdpDXnSAtW/7tm/7tm/7tm/7tm/7tm/79iu1vdghgvu2b/u2b/u2b/u2b/u2b/u2b79i2x5g7du+7du+7du+7du+7du+7du+PU9tD7D2bd/2bd/2bd/2bd/2bd/2bd+ep7YHWPu2b/u2b/u2b/u2b/u2b/u2b89T2wOsfdu3fdu3fdu3fdu3fdu3fdu356ntAda+7du+7du+7du+7du+7du+7dvz1PYAa9/2bd/2bd/2bd/2bd/2bd/27Xlqe4C1b/u2b/u2b/u2b/u2b/u2b/v2PLX/Pw/wV2u20Ec6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(12,3))\n",
    "ax.imshow(slope_sim, vmin=0, vmax=10)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = wspec>0\n",
    "\n",
    "# Estimate background emission and subtract from slope_sim\n",
    "bg = np.median(slope_sim[:,~ind])\n",
    "slope_sim -= bg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc309945150>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind = wspec>0\n",
    "plt.plot(wspec[ind], slope_sim[63,ind])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract 2 spectral x 5 spatial pixels\n",
    "\n",
    "# First, cut out the central 5 pixels\n",
    "wspec_sub = wspec[ind]\n",
    "sh_new  = (5, len(wspec_sub))\n",
    "slope_sub = nrc_utils.pad_or_cut_to_size(slope_sim, sh_new)\n",
    "slope_sub_ideal = nrc_utils.pad_or_cut_to_size(imspec, sh_new)\n",
    "\n",
    "# Sum along the spatial axis\n",
    "spec = slope_sub.sum(axis=0)\n",
    "spec_ideal = slope_sub_ideal.sum(axis=0)\n",
    "spec_ideal_rebin = nrc_utils.frebin(spec_ideal, scale=0.5, total=False)\n",
    "\n",
    "# Build a quick RSRF from extracted ideal spectral slope\n",
    "sp_M0V.convert('mjy')\n",
    "rsrf = spec_ideal / sp_M0V.sample(wspec_sub*1e4)\n",
    "\n",
    "# Rebin along spectral direction\n",
    "wspec_rebin = nrc_utils.frebin(wspec_sub, scale=0.5, total=False)\n",
    "spec_rebin_cal = nrc_utils.frebin(spec/rsrf, scale=0.5, total=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expected noise per extraction element\n",
    "snr_interp = np.interp(wspec_rebin, snr_dict['wave'], snr_dict['snr'])\n",
    "_spec_rebin = spec_ideal_rebin / snr_interp\n",
    "_spec_rebin_cal = _spec_rebin / nrc_utils.frebin(rsrf, scale=0.5, total=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, figsize=(12,8))\n",
    "ax.plot(sp_M0V.wave/1e4, sp_M0V.flux, label='Input Spectrum')\n",
    "ax.plot(wspec_rebin, spec_rebin_cal, alpha=0.7, label='Extracted Observation')\n",
    "ax.errorbar(wspec_rebin, spec_rebin_cal, yerr=_spec_rebin_cal, zorder=3,\n",
    "            fmt='none', label='Expected Error Bars', alpha=0.7, color='C2')\n",
    "\n",
    "ax.set_ylim([0,10])\n",
    "ax.set_xlim([3.7,5.1])\n",
    "\n",
    "ax.set_xlabel('Wavelength ($\\mu m$)')\n",
    "ax.set_ylabel('Flux (mJy)')\n",
    "ax.set_title('Simulated Spectrum')\n",
    "\n",
    "ax.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 4: Exoplanet Transit Spectroscopy\n",
    "\n",
    "Let's say we want to observe an exoplanet transit using NIRCam grisms in the F322W2 filter. \n",
    "\n",
    "We assume a 2.1-hour transit duration for a K6V star (K=8.4 mag)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrc = pynrc.NIRCam('F322W2', pupil_mask='GRISM0', wind_mode='STRIPE', ypix=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# K6V star at K=8.4 mags\n",
    "bp_k = S.ObsBandpass('k')\n",
    "sp_K6V = pynrc.stellar_spectrum('K6V', 8.4, 'vegamag', bp_k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constraints\n",
    "well     = 0.5        # Keep well below 50% full\n",
    "tacq     = 2.1*3600.  # 2.1 hour transit duration\n",
    "ng_max   = 30         # Transit spectroscopy allows for up to 30 groups per integrations\n",
    "nint_max = int(1e6)   # Effectively no limit on number of integrations\n",
    "\n",
    "# Let's bin the spectrum to R~100\n",
    "# dw_bin is a passable parameter for specifiying spectral bin sizes\n",
    "R = 100\n",
    "dw_bin = (nrc.bandpass.avgwave() / 10000) / R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRIGHT1\n",
      "BRIGHT2\n",
      "DEEP2\n",
      "DEEP8\n",
      "MEDIUM2\n",
      "MEDIUM8\n",
      "RAPID\n",
      "SHALLOW2\n",
      "SHALLOW4\n",
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "BRIGHT1      24  460     16.01   7363.99   7523.08  30955.5    0.494  356.894\n",
      "BRIGHT1      24  461     16.01   7380.00   7539.44  30989.1    0.494  356.894\n",
      "BRIGHT1      24  462     16.01   7396.01   7555.79  31022.7    0.494  356.894\n",
      "BRIGHT1      24  463     16.01   7412.01   7572.15  31056.3    0.494  356.894\n",
      "BRIGHT1      24  464     16.01   7428.02   7588.50  31089.8    0.494  356.894\n",
      "BRIGHT2      23  470     15.67   7363.99   7526.54  30623.8    0.484  352.989\n",
      "BRIGHT2      23  471     15.67   7379.66   7542.56  30656.4    0.484  352.989\n",
      "BRIGHT2      23  472     15.67   7395.32   7558.57  30688.9    0.484  352.989\n",
      "...         ...  ...       ...       ...       ...      ...      ...      ...\n",
      "SHALLOW2     10  462     16.01   7396.01   7555.79  30678.0    0.494  352.929\n",
      "SHALLOW2     10  463     16.01   7412.01   7572.15  30711.2    0.494  352.929\n",
      "SHALLOW2     10  464     16.01   7428.02   7588.50  30744.4    0.494  352.929\n",
      "RAPID        30  713     10.22   7285.65   7532.24  30585.0    0.315  352.408\n",
      "RAPID        30  714     10.22   7295.87   7542.81  30606.5    0.315  352.408\n",
      "RAPID        30  715     10.22   7306.08   7553.37  30627.9    0.315  352.408\n",
      "RAPID        30  716     10.22   7316.30   7563.94  30649.3    0.315  352.408\n",
      "RAPID        30  717     10.22   7326.52   7574.50  30670.7    0.315  352.408\n",
      "Length = 20 rows\n"
     ]
    }
   ],
   "source": [
    "res = nrc.ramp_optimize(sp_K6V, tacq_max=tacq, nint_max=nint_max, \n",
    "                        ng_min=10, ng_max=ng_max, well_frac_max=well, \n",
    "                        dw_bin=dw_bin, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "RAPID        30  713     10.22   7285.65   7532.24  30585.0    0.315  352.408\n",
      "RAPID        30  714     10.22   7295.87   7542.81  30606.5    0.315  352.408\n",
      "BRIGHT1      24  460     16.01   7363.99   7523.08  30955.5    0.494  356.894\n",
      "BRIGHT1      24  461     16.01   7380.00   7539.44  30989.1    0.494  356.894\n",
      "BRIGHT2      23  470     15.67   7363.99   7526.54  30623.8    0.484  352.989\n",
      "BRIGHT2      23  471     15.67   7379.66   7542.56  30656.4    0.484  352.989\n",
      "SHALLOW2     10  460     16.01   7363.99   7523.08  30611.6    0.494  352.929\n",
      "SHALLOW2     10  461     16.01   7380.00   7539.44  30644.8    0.494  352.929\n"
     ]
    }
   ],
   "source": [
    "# Print the Top 2 settings for each readout pattern\n",
    "res2 = table_filter(res, 2)\n",
    "print(res2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Even though BRIGHT1 has a slight efficiency preference over RAPID \n",
    "# and BRIGHT2, we decide to choose RAPID, because we are convinced \n",
    "# that saving all data (and no coadding) is a better option. \n",
    "# If APT informs you that the data rates or total data shorage is \n",
    "# an issue, you can select one of the other options.\n",
    "\n",
    "# Update to RAPID, ngroup=30, nint=700 and plot PPM\n",
    "nrc.update_detectors(read_mode='RAPID', ngroup=30, nint=700)\n",
    "snr_dict = nrc.sensitivity(sp=sp_K6V, dw_bin=dw_bin, forwardSNR=True, units='Jy')\n",
    "wave = np.array(snr_dict['wave'])\n",
    "snr  = np.array(snr_dict['snr'])\n",
    "\n",
    "# Let assume bg subtraction of something with similar noise\n",
    "snr /= np.sqrt(2.)\n",
    "ppm = 1e6 / snr\n",
    "\n",
    "# NOTE: We have up until now neglected to include a \"noise floor\"\n",
    "# which represents the expected minimum achievable ppm from\n",
    "# unknown systematics. To first order, this can be added in \n",
    "# quadrature to the calculated PPM.\n",
    "noise_floor = 30 # in ppm\n",
    "ppm_floor = np.sqrt(ppm**2 + noise_floor**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc2f96f0590>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZEKoAjtOJrTroUQlcRERaFvDjtx4F8Gzg87ioDrrVBi4iIi0L1FGHB6/bZDQZCuDsbgO3KoGLiEgLbMCPn8yXwHNqGFkqx4HX4sUE6iAYBJe+14iISHTB+tS2gWsceCv4PC522NBwgEAduAqTfg8REckPwfo66mzqqtA1DrwVGiZyAXVkExGRZtn62qyoQlcAJ2IudFBHNhERaVa4DdyjAJ55zmpkodYElcBFRKQZNtQG7stwL/QW28CNMd2Bs4B9gSJgDfCctfbzFKctbQo8LmrDbeAqgYuISCwLn8W37gMOMLWMeOtEKLgZ9j8zI0lpNoAbY44GpgPvAwsBP1ACTDfGfGWtvaE1NzPGDAbeBNaGdhWHrnsFMBNYCYwArrfWbm7Ntdsi3AsdUAlcRESiW/gszL4UV6AWDHSs3gizL3Xey0AQjxnAjTEuYIy19pgY7x9vjBljrV3UivtVAj+z1r4RusZNwH+A24E3rLXPGmMmA3cD57Tium2yRyc2TacqIiLRvHkz+Kv33OevdvZnUwC31gaNMQ818/4brb2ZtbYMCAfvAmC8tXa6MeZp4LbQYe8Df4l2fmlpKePHj2+yf8qUKQ3jwxPhUy90ERFpScW61u1vhVmzZsUaJt0j1jkttYHfZIy5Pcr+q6y1N7cmcVGcDTwdet0Lp3QOsAPoZozxWGvrI0/o2bMn8+fPb+NtmyrwuKm1CuAiItKMTr1h56am+7v0b/OlYxVEjTFbY53TUi/063ECayWwM+JneuLJbHAG8Ezo9Ragc+h1MbCtcfBOJQ0jExGRZgXqwVPQdL+3CI6blv700HIAPwX4I3CKtdYV/gGuastNjTHHAB9Ya/2hXXOACaHXE0PbaaOJXEREpFlz/wjbV8MhF1HToS9Ba6jt2A8m/yFjvdCbDeDW2lestZcAbmPM/caYE0L7723jfacAf4rYvh44wRhzA3AqbfyC0Fp7lsDViU1ERCKUrYC3b4ORp8DJd/HBd99haO1TLP7RBxkL3hDnXOjW2jnAHGPMNGPMjdbaI9tyU2vtWY22y4GL2nLNtthzHLhK4CIiEhIMwkuXgLsATvkdGIM/YAFyYzlRY8y+xph/AucDT6Y2SelX4HWrDVxERJpa8Bisfh8m3QrFewHgDwQBsnsudGPMPsaYvwMv44zX3tta+7AxZmhaUpcmPremUhURkUa2r4X/TIehR8OBu6cmqW8ogWf3euCLgPnAHUAtcJYxxgDfwelFnhe8boPfqAQuIiIh1sLLl4MNwuTfg9ldXV7XUALP7rnQnwSeiLI/5vqkqVRRUdEwTi5yvdS2MsbgdnsJGDdulcBFRGThM7D8P3DSndBt8B5vpboKffbs2cyePTu82SXWcS0F8GnW2rWNdxpjvmxD2hLWpUuXWDPVtFmBx0W9KcCtqVRFRNq3nVvg1WthwKFwSNPJVVJdhR5ZQH344YdjFphbuns/Y8w8Y8zLxpg+4Z3W2pgzw+Qqn8eN3/jUBi4i0t79+yqoq4LvPgCupmEyXAL3ZHkv9KnANcBTodd5yymBexXARUTas69egq9ehKOvgZ57Rz0k3Abuy/JObF9aa98BMMaMDu80xnQPLUySNwq8LvwBrzqxiYi0V1XlMOdK6LM/HH5pzMOypRd6S3d3GWOKjDEdABPx+pI0pC2tnKFkqkIXEWm3XvsNVJfD9x4EtzfmYf5AEGPA7cruKvTr2b2ASeTrG1OcrrQr8LqpM15NpSoi0h4tewM+/xtMvAz22r/ZQ+sCwYyXvqHlAH6NtdYdsZCJOxmLmWSjAndoPnSVwEVE2pfaSnj5MugxEo66usXD6wM24+3f0EIbuLX2LmNMR6A2cnnPJCxmkpBUjQMHpw281vrUBi4i0t68MQMq1sGFr0dfMrQRfyCY0h7oSRkHboy5ALgfqDLGnGytnZ+8JLZeKseBN0ynqhK4iEj78c378PEjcNjFMOCQuE7xB2xKq9CTNQ78OKAEOBA4N2mpy0IFXhc1Vr3QRUTaDX+1s9JYt8Fw7A3xnxYI4s1wBzZoOYAvt9bWWmvXAeXhncaYg1KbrPQr8LidAB5QABcRaRfevh3KV8DkP4CvY9yn+QNBvJ4sbwMHhhljvh3l9Q/JsxK5z+2iWiVwEZH2Yf0C+PABOOhcGHpUq06tT3EVerxaCuATgX4R2+HZ2AamJjmZU+B1UR1UG7iISN6rr4MXL4FOfeDEW1p9el0giCcLqtBbCuBXW2v/0XinMebUFKUnY5wSuEclcBGRfPfePbDlSzjrGSiM2ck7Jn8giC8LqtBbSsG/ou201j4PYIzxJT1FGaISuIhIO7D5K3j3btjvDBh5UqtPf+HT9Xy4ooyF6yqYOPMtXvh0fQoSGZ+WSuB3GWPus9aujtwZCtzTgceAFalKXGMpHQfucVMd9EKwHoIBcLmTdm0REckCgXp48ZdOqfukO1t9+gufrue657+gtt5ZzGT99mque/4LAL5/YL/mTm2VZK0Hfgfwf8aYXsB6oB5nWFlP4FJrbdqCN6R4HLjHxS5Cc9/W14KvQ0ruIyIiGTL3j7DhEzj9MejYvdWn3/XaUqr9gT32VfsD3PXa0qQG8HjHgbc0E9sW4CRjzDBgX6AAWAt8ZK0NJi21WaDAE5pKFZxqdAVwEZH8UbYC3r4NRp4C+ybWjWvD9upW7U+1lkrgAIRK2mktbaebb48Aro5sIiJ5Ixh0JmxxF8ApvwOTWA/yvl0LWb+9aT+pvl2L2prChGS+G12WKPC4qbMRJXAREckPCx6D1e/DpFuheK+EL3PymKbnFnndTJ00si2pS1hcJfD2QCVwEZE8tH0t/Gc6DD0aDjwn4cvU1gd4Y/FmenX24XG72Li9hr5di5g6aWRS279bI64Abow5xFr7Uej1AcA4a+1jKU1Zmu3RBq7pVEVEcp+1zjKhNgiTf59w1TnAE+9/wzdlVTxx/sEcPbJX8tLYBvFWoTcMlrPWfg6MTk1yMqdAJXARkfzy+d9h+Rtw3HRnwZIEbams4f63lnPsPr2yJnhDy8uJngucBwwyxhwd3g1kpJE4lePAfZ7QeuCgNnARkVxXuRlevRYGHAqHTGnTpe5+bSm19QFuOGVUkhLXvGSNA38BeAeYAoQHYAeAjW1LXmJSOQ68wONWCVxEJF/8+ypnudDvPgCuxPtrL1y3nX8sWMdF3xrK0J6dkpjA2JI1DrwCqAB+E7nfGHMYMLftycweBR4XdeE/h0rgIiK566sXYfFLcNw06Ll3wpex1nLT7K/o3tHHr44dnsQEJkdLVei/tNY+aIyJ7LBmgP2A8SlNWZqpDVxEJA9UlcOcq6DP/nD4pW261Eufb2DB6m3cedp+FBd6k5TA5GmpCr0q9NsAT0TsT7wvfpZyhpGF28AVwEVEcsrCZ+HNm6FirbN96M/AnXjQraqr545/L2FMv2JOHzcgSYlMrpaq0B8PvbzUWlsZ3m+MWZnoDY0xI4GzgGrgKGAGsByYCawERgDXW2s3J3qPRBR43NRqIhcRkdyz8FmYfanT5h32v7uh60DY/8yELvmnd1awaUcN9599IO4sWPs7mngncrHGmB8AnUPbk4EzWnszY4wbuAeYbK0NGmP+D2eBlNuBN6y1zxpjJgN3k+ZSvqrQRURy1BvT9wze4Gy/eXNCAXxteRV/fnclkw/oy8GDS5KUyOSLN4C/DHwGbAttJ5qjg3Gq4y8xxnQAyoCHgVOA20LHvA/8JcHrJ8zXeDETERHJXgE/fP0afPYU7NgQ/ZiKdQldeuYrSzAGrjt5nzYkMPXiDeDLrbWXhTeMMSMSvN8gYAJwlrW2whjzV6AO6AWEq+h3AN2MMR5rbX3kyaWlpYwf37Tv3JQpUxrGhydqz17oKoGLiGSlzV85QXvhM7CrFDr2goLOUFvZ9Ngu/Vt9+bkry5jzxUYuP37vtC5SMmvWrFjDpHvEOifeAP6aMeZ8dq9Idg5wUeuSBzjBeUloeBrAe8DRwBac6vntQDGwrXHwBujZsyfz589P4LYt87hdGOOi3njxaCpVEZHsUb0NvvinE7g3fAouD+x9Ehz4Exh+PHz5r6Zt4N4iZxhZKwSCzrCxfl2LmHLk0CRnonmxCqLGmK2xzok3gJ8P1OIEWHCGkSViHtDdGOO21gZwSuRf48zsNgFnrfGJwJwEr98mBR439S4fHpXARUQyKxiAle84QXvxy84aFb3HwKQ7nHbtjhEF03A795s3O9XmXfo7wbuV7d/PfLyWxRt38MDZB1LkcycvLykSbwDfaq39f+ENY8yBidzMWltujLkGuM8YUwr0BG4GioA7jTF7A8OAqxK5flv5PC7qjU9t4CIimVK2Aj77G3z+NOxYD4Vd4aD/55S29zog9oIk+5+ZcI9zgIoqP3e/vpRDBpdwyn6JLzmaTvEG8M+NMcewuwp9MvBpIje01v4L+Fej3dUkViWfVAUeF358agMXEUmn2p3O7GmfPeWs221cMOxYOPFWGPlt8BamPAm/f3MZ26rqmDZ5NKYNq5alU7wB/CpgScT2QJySc14p8Lrw13tVAhcRSTVrYc2H8OlTThu2fxeUDHWqvg84C4r7pi0py7dU8n8ffsOPDh7AmH4x1w7JOvEG8OustU+EN4wxx6cmOZnlc7vw16sELiKSFA2zo0W0Sw+a6FSPf/Y3KF8Bvk4w5gcw9icw8LA2rdmdCGstN7+8mCKfmytPHJnWe7dVXAE8MniHdE9+UjKvwOOmrtarAC4i0laNZ0erWAv/+hnYoLM9aCJ860oY/T0oSM8qX9G8vXQL735dyg2njKJHp4KMpSMRLS1m8jdr7dnGmFWADe/GGer1TKoT11gq1wMHOK7+v/SvWwnLl8K9YxLqxSgi0q5VlUPpEnjl6qazo9kgFBTDz/7rVJdnWF19kFteXszQnh35fxMGZzo5DeJdD9xYa2O9hzFmb2vt18aYS6y190fs/4W19qGkpTZO48ePt6kaB87CZ6l5/lcUElH69hbB5D8oiIuINFa9DbYsgdLFod+hn50tLWNhYMb2dKSwRQ+/u5Lb/r2Yx88/mGNG9sp0cqIyxiyw1kZd/bOlxUy+Dv2ODN5n4Qz/yi9v3rxn8IY2zaUrErdo7YT6zEkqteYz1xCoQz9bFjcN1N6O0HOkM6lKz5HQc5RTfV65sen1EpgdLRVKK2v5w5vLOGZkz6wN3i2JtxNbpDeAhMaBZ7VYc+YmOJeuSFyitRPODq1hrCAuqRDrM1dXBb322R2gS5c4gXvnpt3nejs4AXrYcc6xPUc5210GgMu1531OuDkps6Olyt2vLaXaH+CG74zOdFIS1uoAbq0tNcZUtHxkjunSf/c6spFcbvj4EWdYg69j+tMl+ScYhLLlsO4j+HeUdkLV/EgqvXlT9M/cy7/evd0QqI91fvcaBT33iR6oY0nS7Gip8MW6Cp5dsJYLJw5hWM/MdaBrq5Y6sZ1orX09yluxG85z1XHTqPvXr/DZiGp0tw867wVzrnQ+hOPOg4Mvgq7Zubi7ZKnaSli/ANZ+7ATttR9Bzfbmz6lYC2/MgFHfhb4Hpn1ojeQJa53guX4BbPgE1n/SfK3i2c+GStQD4w/UzWnj7GipYK3lptlfUtLBxyXHJbouV3ZoqQR+rzHmyyj798FZwzt/7H8mz3+8hqPWPsRelO3+trjfGc4Dd+4f4YMHnJ9Rk+Gwi2HAIXqwyp6shfKVsO5jWDvPCdpbvtw9dKbnPjD6u9D/EOfz89dToz9QPQXw/h/gvXudh+moybvPS8aDVfJTVbkTpDd84gTt9Z/Ari3Oey4v9BnjjLuu29n03C4DYO9J6U1vBsxeuJH5q7cx89T96FLkzXRy2qSlAP410RcWycsnyOIeJzFz/f58Nu3EPd8YeKjzs30tfPwwLHgCvnoB+h4Eh/0CRn8fPL4MpFgyrq7KWR1p7bxQ0P4IqkKLBxUUQ79xcORUJ1j3Gw9FXfc8/7jp0dsJJ//B6RC09BVY/JLzuZv7IHTqA6O+4wT0QUeAO5FuLJIX6qpg4+cRpesFsO2b0JsGeuwNw49zPoN9D3KCt6egaRs4ZFXbdCpV1wW449+L2bdvMWeMz/2a1Jb+919trV3WeKcx5r0UpSejCrxuav3B2Ad0HeB0zDjqGmcmobl/gucvgtdvhEN+CuMugI55OcdN+xOtl+5+Z8D2NbsD9dp5sHkRBEMr33Yf7pRg+h/sBOye+zh9KJrTUjvhgT92fmp2wLLXnWD+2d+cfhlFJbDPt2HU92DoUc7DWXJbrN7hAb/TuSyyKnzLYrAB57zi/tDvIKeZr9842GssFBZHv0cWt02n2p/+u4KNFTX8/kcH4nblfu1ps+PAs82IESPsMcccA6RmIpe7X1vKH99Zzso7TonvhGAQVrzlVK+veBM8hc5/gkN/Ab1zt2djuxethGLcTifG2h3Otrej88AccAgMONQJ2h1K0pO+uirn8/bVS/D1q06aCoqd9ZFHTXZK7r4O6UlLe5GOoX7RPncuD3Qd5KzKFV6jobCrE6T7HbS7dN25d3LTkofWb6/m2Lvf4YTRvXng7IMynZxmRU7k8vDDDy+31kZtrM+pAJ7SiVyA+99cxu/+8zXLbzsZj7uVrQRblsC8P8Hnf4f6ahhylNNOPuJEtVnmkmAQfjdyd7thJG8HpwZmwCHQa9/sqL6ur4WV/4XFL8KSf0N1uZPO4cc7U1SOOHF3SUzjzRMTq8o5PMlTMOgsxFG3y1lVqy700+T1LqirjHjd6LiyFbtL1JHcPqfzbL+DnJ9uQ9T3JgG/+tsn/Oerzbx11dH061qU6eTErbmJXBTAI8x6dwW3/3sJX940iY4FCT6cq8rhk7/ARw8735pLhjol8rFnQUHn5CZYksNa2PQFLPonLHo++nBCIJtmkIoqUA+r34PFs52fnZudh/+wY52284XPOF8uw3J9psFkfSEJBqCmwpmwpHobVG+PeL0NPvhD9E5fxuV8WYr2XiyeIqcmp6AT+DpHvO7oLKcZVZZ/7nLAvJVl/HDWXH593AguP2HvTCenVRKeia3RRfbHmYFtKbDe5lLkj5MvVOquqw/SMdHmxA4lcMTlMOFXTnvl3D/BK1PhrVucRekPuchpP1VJKPO2Lg8F7edg69dOdeWwY53SUXV50+OzZAapmNweGHq083PyXc6Qta9ecj6HX7/a9Hh/tTNUbb8zcq9EF20ykpcudYLv4COcYXqRQThaYK7e5hxXk+C0FjbotDn7Ojo9u30dnS/pDa87hV53cl57OzZfa3PvmOhfHrP9c5flAkHLTbO/om+XQn5+1LBMJyep4grgxpipwLeBNcATwCXANalLVmYUeJ0OR7X1zXRki5fbC2NOc37WzYe5DzlV7B8+4LSnhqvKNPNWelWscwL2ouecHrwY54F/2C+czmAdu+dHL12Xy1maceBhMOk2uKkbUadv2LEeZg6CksFO1WzJEKfWKPy6c9/MNwEF/M6UnDs2OP9+OzbAf+9sOhlJfbXzZTka44Kibrt/OvVyxjsXdt1zf8NPaH9hF/jDgTEC6wDnb5ssx03L/c9dFnp2/lq+2riD+886kCJfC51Kc0y8JfBO1tpjjDHXWGvfNsYck9JUZUhkCTyp+o+H0x+Fipvhj4c6E3tE8lc7PdnHnJ75h2U+2lnqDPtb9Bys+dDZ128cTLod9v0BFPfd8/h866VrTOyZBgu7OiXw8pWwaSEseXl3r3oAdwF0G7Q7oHcLBfiSIdB1YPSe762p2g7U7w7OO0LBuWK988Vix3pnu3ITrZo76vTHmwbkgs6J1zKkK7Dm2+cuC1RU+7n7taUcPLgb39l/r0wnJ+niDeDhry220XZeKfA6wbO2PkpHkmTo0s/psBLNzk1w5yBn1q1+43b/FOffhy4taipg8ctOFfnK/zo1Hj1HwbE3OLUiLS1lmIUzSLVJrCD07bv2zGeg3gmk5atg2yrnd/lKZ3zxN+85nbUahL4YdBu8O7hXboJPnnA614HzpeHFXzlD77oO3LMUvWO9005vG31h9nZ0/q8U94Nho0Kv+zpDpYr7OtsPTYxdKh5zanL+ZmHpDKz59rnLsPvfXEZ5VR1/mXwIJteaieIQbwAPGGNeBToYYw4BPklhmjImXAJPShV6LLFKQkUlTmlw/QKn00y4FNS57+7hIv3GQd+xTrWeNFVX5bT1LnrOGTMdqHOG4BxxmRO0e++b6RRmTrxByO1xAnK3wUCjijZrYVdpRHBfufv1kn/vnsCmsUAtfDTLed0QnPs6C2KEA3Jx+Kev8/lu6WGb7upmBdacs6J0J0988A0/HD+AMf3y85kZVwC31k43xpwI7A98Zq19I7XJiq6iooIpU6YAqRkHntQ28FhiPXhOvnP3A8Jf4/SKXr9g98+Sl3cf32PviFL6QdB7TOxJPPJt6FDj/BxzvfPlZ9E/nSDi3+X0uB5/Iex3uvM3ysNv3glpaxAyxmk77tTLmZmwsZodMHMg0au7DVzzTXzBOR6qbpYW3PryVxR53Vx54shMJ6XVIseBAzG/fcQ1jMwYMxyoBOqBc4B/WWtXJyGdrZLqYWRzV5bxo1lz+dtFh3L4sB4pu09CQbWq3JmyMzzP8br5u8cqu33QZ789q95LhjlBrbnxq5nOUyL3aJyfsMKuzrjn/U6HQRNbngFNUiNmT+oBcPmi9KdH2qW3l2zh/Cc+5jffHsVFR7bQXJblkjGM7EZgOjAD2BJ6fUFSUpdFfJ4UdWJrLJGSUIcSZ17j4cc529Y6bYgNpfRPnCk2w1WVBV0gULO7LTLMXw2v3+DM3uTxOcE//OMpcIZStbaEFO+a1vW1TimtNvxTGdqu3L1vj+1G71esI2rprkMPuGKx5qPPBupJLRlWVx/kljlfMbRHR849fHCmk5NS8QbwRcB6YB9r7XnGmKtTmKaMKfCkoQ08WcI9i7v0d0qe4ExIsfXr3UF9/mPRz925GR4YF+vCu4N5Q2APB/mC6EF/xZvR1xd+4WJ4+/bdwThQ13K+PIVOj+GC4tDvzk4HqYLOzvzz0VSVKXhnC1VtS4a88Ol67nptKeu3O8+iKd8a0lAoy1fxBvB9gT8ArxtjioDcrpOIoSBdJfBUcbmh1yjn58CfwLL/RK/O7NAdTrrT6VxUX+uMsw3UOgG2vi702h96L/K1f/c59bVOYA74o1dpAwT9zhC6cDAuLI4IzJH7Ojs1BgWdmw/E37yniS5ygTp8SZq98Ol6rnv+C6r9u0cQPTl3DaP7duH7B/bLYMpSK94AfjtwMvAwcBjwn5SlKIMKPGnoxJZOsaozT5oJ+5+RvPs01+552iPJu4+qZ0UkirteW7pH8Aao9ge467WleR3A46pfsNZ+ba39vbW2ylr7Nq2YgjWX7K5CT9E48HTb/0ynw1qXAThjdgekpgPbcdOcQBopVRNdpCM/IpIzavyBhmrzxjbE2J8vmg3Expi/WWvPNsasYnfvIQMUA8+kOnHplrZObOmUjupMTXQhImlWWlnLX+eu5ql5sQdE9c2hVccS0VJJekbo9z3W2vvDO40xv0hZipqR8nHg+VaFnk4KrCKSBos37uCx91bx4mcbqAsEOXafXuzTpzOPv7+Kav/uZ3eR183USbk3BhySPA4cwBjTBRgOLLPW7mhzChOQ6nHggaBl2PX/5ooT9ubS46Kuny4iImkWDFreXrqFR99bxQcryijyujltXD/OnziEYT07Abt7oW/YXk3frkVMnTQyL9q/2zwO3BjzXeBBYBvQ1RhzsbX25RZOyzlul8HjMvnTBi4iksOq6up5bsE6Hn//G1Zu3UWf4kKuOWkfzjpkAF077Dli5fsH9suLgN0a8XZGOxEYZq2tM8YUAL8H8i6Ag9MOnldt4CIiOWZjRTV/+WA1T3+0hopqPwf078LvfzSWb++3F153fo/tbo14A/hqa20dgLW21hizJoVpyqgCj0tt4CIiGfDZ2u08+t4q/v3FRqy1nDSmDxdMHMK4Qd3ycjWxtoo3gA8zxlwBrASGAYMSvaExZi5QE9oMWGuPM8aUADND1x8BXG+t3ZzoPdqiwOOm1q8ALiKSDvWBIK9/tZlH31vFgtXb6Fzg4fzDB3Pu4YMZUNIh08nLavEG8KuA64Fjgc+AK9twz1ettTMa7bsdeMNa+6wxZjJwN86iKWnn87ioCyiAi4ik0o4aP898tJYnPviG9durGVjSgemTR3PG+AF0KsjLqUaSrqVx4GOstYustTtxAngy7GeMuQYoAj621s4BTgFuC73/PvCXJN2r1ZwqdHViExFpq2g9ww8c2JXH3/+Gf8xfy666AIcMKWHa5NEcP6o3bpeqyVujpa85NxtjXoj2hrX2/xK8553W2o+MMW7gXWNMJdALZ7lSgB1AN2OMx1pbH3liaWkp48c37U0/ZcqUhvHhbaVObCIibdd4fvL126u54tnPCFrwuAyTD+jLBROHsF//mMOc25VZs2Yxa9asaG/FXNu6pQBeAXyDM/saQDecRU3eAhIK4Nbaj0K/A8aY/wHH4CxR2hnYjjPL27bGwRugZ8+epHIcOKgTm4hIWwWDljteWdxkfvKghU4FHt688ih6FxdmKHXZKVZB1BizNdY5LQXwG6y160MXOQCYBdxrrb03kQQaY/YBJlprHw3tGgE8D8wBJgBrgYmh7bR74dP1LNqwg7r6IBNnvpU3EwGIiKRKIGhZWbqTRRsq+GLdDhZtqOCrDTvYWdukDAbArtp6Be8kaTaARwTvc4BbgfOttW+14X47gO8YY/rilLTXAk8DrwB3GmP2xunlflUb7pGQcHVPuPp8/fZqrnv+CwAFcRERwB8IsmzzThatr2DRhgoWra9g8cbKhpJ2odfF6L2KOfWgfrz02Qa2V/ubXCPf5ydPp5Y6sblxJm05HDjKWvtNaH+RtbbVy7xYazcAP4jyVjlwUWuvl0ztdTk6ybx8nQJSslc8n7kaf4ClmypDgXoHX26oYMnGyoZROp0KPIzuW8xZhwxkTL9ixvTrwtAeHfGEJlo5aGC3Jmt05/L85NmopSr0d4F64GwgaIwZiNMe/kvg6hSnLa1iLTuX78vRSWa98Ol6rn1+ITV+1fxIekTrXHbt8wtZtXUnJR0LQqXrHSzbXEl90Fkro0uRlzH9ijl/4mD27deF/fp1YVBJB1zN9BoPf3715TR1WgrgdcA7wJns7sgGcFCqEpQpfbsWRV1T1hj4839X8P8mDKbI585AyiTfWGtZUbqT95ZtZeYrS6hp1GlSNT+SSne9tqRJbWONP8jv31wOQPeOPsb068Kx+/RkTN8ujOnXhf7dihKaCa09zk+eTi0F8GnW2v813mmMmZii9GTM1Ekjm1T3FHhcDO7RkTteWcIj763iV8cM50eHDGhYdlQkXpsqanh/+VbeX7GV95dvZfOO2maPX7+9mic//IbvHtCPLh28aUql5KNg0LJ0cyXzVpYxb1U567fXxDx27nXH0bu4QNOW5oi4lxPNBiNGjLDHHHMMkJr1wGO1C320qpy7X1/KR6vK6de1iEuPG85pB/VvaOsRaWxHjZ95K8t5f/lW3lu+leVbdgJQ0tHH4cO6c8TwHkwc3oMfzZobtebH4zLUBy0+j4uT9u3DmeMHcPiw7s1WWYqA0yt88cYdzFtVzryVZXz0TTnbq5zOZP26FlG+q65JCTz83vvXHpvu5EoUkeuBP/zww8uttVHXt86pAJ7q9cCbY63lf8u28rvXl/L5ugqG9OjIZcePYPL+ffVQFWrrA3y6ZntDwF64roJA0FLkdXPIkBKOGN6Dw4d3Z1Sf4j0+L43bI8Hp6HPHqfsxvFcn/jF/LS98toGKaj/9uhZxxvj+nD6uP/27aY5ocQSClq827GDuyjLmrSrjo1Xl7KhxhnANLOnAoUNKOGxodw4dWkL/bh2a/cypujv7NLceuAJ4K1lr+c9Xm7nnP1+zZFMlI3t35vIT9mbSvr1V7ZRHWuqlGwxaFm/a4VSLL3cemtX+AG6XYf/+XRpK2AcO7Npik0tL96rxB3j9q838Y/5a3lvuzOkwcVgPzhjfn0n79qHQqyad9qQ+EGTRhh3MW1nG3JVlzP9mG5WhMddDenTk0CElHDq0hEOHdI85ZEsjH3KHAngKBIOWl7/YyH3/+ZqVW3exf/8uXHHC3hy1d08F8hwXq4Ry1aS96ejz8N7yrXywoozyXXUADO/VqSFgHzq0hOLC1LVZr9tWxXML1vOPBWtZt62a4kIP3z+wH2eOH8CYfpqSMtdFC6yn7L8XC9dVMG9VGfNWljP/m3J21TmfzaE9Ozql61ApWxOk5B8F8BSqDwR5/tP1/P6NZazfXs3Bg7tx1YkjOXRo90wnTRI0ceZbUdulw3oXFzBxmBOwJw7vQZ8u6X9oBoOWuSvLeGb+Wl5ZtIm6+iCj9yrmzPH9+d7YfnTr6Et7mvJZOkqszhfHhVRHLGfsMuB2GfwB5zk9olcnDh3qBOtDhpTQq7MCdr5TAE+Duvogz3y8hvvfWs6Wylq+NaIHV544krEDumY6adJKQ66dQ6z/FW9ccSTDenbKqlqWiio/Ly3cwD/mr2Xhugp8bhcn7NubM8cP4IjhPbTCUxvF22ZsraWqLkBlTT2VNX52hH4725Gvnd97vF/rZ922aqI9jjv63Nx1xgEcMqSEHp0K0pFlySIK4GlUXRfgr3NX89B/V1C+q47jR/XmyhP3ZtRexZlOmjSjPhDkzSVb+Ovc1fxvWfS1A3Khl+5XG3bwjwVreeHT9Wyr8tO3SyGnj+vP6eMG8MmabXnV7pmqUnFdfZBtVXWU7ayjfFcdlzz9Cduqmk4J6nO7GNarU0MQ3llbTyDY/PPUZaBzoZfOhZ6G38Wh1//6dH3UcwywauYpbc6X5CYF8AzYWVvP4++tYtb/VlJZU8939t+Ly0/Ym2E9O2U6aRKhtLKWZz5ew9/mrWFDRQ17dSnkwAFdeWvJlj0mWMm1Xrq19QHeXLyFZz5ey7vLSrHWCR6R8SXX8hSpNT2pwwF5685aync5QblsZx1lu2ojXof31zb04I7H8aN6hwKwp0lgDr8ujtjXweeOWXsTq+kmF744SurkTQBP9TjwVNheVcesd1fy+PvfUFsf4LSD+rPPXp157L1v8qYklGustSxYvY3/+3A1ryzaiD9gOWJ4D35y2CCOH9ULj9uVV710N2yv5qT73o0amDoWuLnu5FGM6NWJEb07U5IjbecTZ74ZdUKSDj43E4f3aAjUW3fWUhkjILtdhm4dfHTv6KN7Jx8lHcOvCxpel3T0ccnTn7KlsunEO8kOrBreJWEaB55ltu6s5aF3VvDE+6sINPqT6z9peuyqreeFz9bz5IerWbKpks6FHk4f15+fHDYo72tGmmvXj9S9o48RvTsxoldnRvTuxPBezusenXxpb/ev8QdYt62KNeVVrCmrYk15tfO6fBdfb94Z87x9+jhfREo6+ugRCsbRgnOXIm9cczikM7Dm0xdHSY68KYHncgAPO+z2N9gUZRrN7h19/O+aY+jga2l2W2mt5Vt28te5q3luwToqa+sZtVcx/2/CIL43tm+7+XvHrp4t5NmfH86yzZUs37KTZZt3smxLJcs272wYWwzQtYOXEb06MbxXZ/aOCPC9OjeddjPeIGStZevOOtaUV7G2vIrVZVUNr9eUV7Fpx54l7CKvm0HdOzCgpAMfrNjKrtr0zSamwCqZogCeRZorCXlchgMGdGXC0O5MGNadcYO6aZKOBNUHgvznq808OXc1H6wow+d28e39+nDOhMEcNLBrVvUiT4fWliKttWyprOXrzZWhoL6T5Vsq+XrzTioi1njuXOhxqt9DAX1LZQ1/+WA1tRH9Bwo8Ls6dMIh+3TqEStDhEnVVkyk9+xQXMrDECdIDSzo0BOyBJR32qAVQdbO0FwrgWSRWSah7Rx9nHjyAD1eU8cV6ZxpOn9vF2IG7A3o8s3q1d1t21PD0R2t5+qM1bNpRQ7+uRZx96EB+ePCAdj8EJxmlyHCpedkWp8QeDvDLt+ykLDSxTXMKvS4GhgLywJKODCwpYmB3Z7t/tw6t+sKqUrG0BwrgWSSekkNljZ/532zjw5VlfLiijEUbKrDWKcmMG9StIaDv378rPk/7WlAl2kP7e2P7Mm9VOU/OXc1rizZRH7QctXdPzjlsEMfs00vjoNOkbGct4299I2oNkwHm/eY4enbSSlciraEAnmVaW3KoqPbz0apyPlxRxgcrtrJkUyXgBP7xg7sxYVh3Jgztzn79ujRZIS2fSinRvvx43YbuHX1s2lFLlyIvZ47vz48PHcTgHh0zmNL2S0OhRJJLATzPlO+qY97KsoYS+rLQUpWdCjwc3BDQe7BscyW/eWFRTveeDc9uVVHt5/sPvh91OI/XbbjtB/sxef++FPnUxJBJapsWSa68CeC5OA48HUora5m3ygnmH64sY2XpLsCptoz2r9ujk48//WQcBR43BV4XBR6X89rjCm27W1XtHM9D2x8IUlHtZ3uVn4pqPxXVdXtsb6/ys6Paz/ZqP9ur6kLHOD/+xuPuGtFMVdkln2p9RDJB48Dbsc07api7soxf//2zhK/hcZlQQA8F9nCQDwX8wob9bt5asqVJb+LwNXoXF7K9qq5h9aRYOhd66FLkpWsHr/O7yEfxHttefvvqEsqjTGmp6lkRyVfNlcDbxyDYdqZ3cSHfG9uP3766NGp7ZI9OPu794Vhq/UFq64PU1gec3/7Q7/C+Ru/XhN/3B9lVW0/5Luf9aMEboD5omTCse0MA7hIKxk6g9jXs71zoadJ2H02h1x21pD910sjE/1giIjlKATyPTZ00MmrAu+GU0XxrRM+k3ae5jkt3n3FA0u4TroZV9ayIiAJ4XktXwIv1RSEVJePvH9hPAVtEBAXwvJeOgKeSsYhI+imAS1KoZCwikl7taxovERGRPKEALiIikoNyqgq9oqKCKVOmAJrIRURE8lPkRC5Al1jHaSIXERGRLNXcRC6qQhcREclBCuAiIiI5SAFcREQkB2UkgBtjiowxC40xd4e2S4wxs4wx1xpjHjXG9M5EukRERHJFpkrgtwKfRmzfDrxhrZ0JvADcnYlEiYiI5Iq0B3BjzDnA+8CqiN2nAB+GXr8f2hYREZEY0joO3BgzGhhlrb3eGLN/xFu9gMrQ6x1AN2OMx1pbH3l+aWkp48c37U0/ZcqUhvHhIiIiuWbWrFnMmjUr2ls9Yp2T1nHgxpjfAG6gDjge8AHPA1cCh1tr1xpjSoDl1tqSxudrHLiIiLQnzY0DT2sJ3Fp7W/i1MaYQ6GStvc8Ysw8wAVgLTATmpDNdIiIiuSYjU6kaY04DjgR8xpizgOuBO40xewPDgKsykS4REZFckZEAbq19Dniu0e6LMpEWERGRXKSJXERERHKQAriIiEgOUgAXERHJQVoPXEREJItoPXAREZEcp/XARURE8owCuIiISA5SABcREclBCuAiIiI5SAFcREQkBymAi4iI5CCNAxcREckiGgcuIiKS4zQOXEREJM8ogIuIiOQgBXAREZEcpAAuIiKSgxTARUREcpACuIiISA7SOHAREZEsonHgIiIiOU7jwEVERPKMAriIiEgOUgAXERHJQQrgIiIiOUgBXEREJAcpgIuIiOQgjQMXERHJIhoHLiIikuM0DlxERCTPKICLiIjkIAVwERGRHKQALiIikoPS2gvdGOMCZgPzAB8wDLgAKAJmAiuBEcD11trN6UybiIhILsnEMLIPrbW3AhhjXgROBb4FvGGtfdYYMxm4GzgnA2kTERHJCWmtQrfWBiOCtwfoDywFTgE+DB32fmhbREREYsjIRC7GmEnA5cDL1tr5xpheQGXo7R1AN2OMx1pbH3leaWkp48c3HQ43ZcqUhgleREREcs2sWbOYNWtWtLd6xDonoxO5GGP+D5gLXAccbq1da4wpAZZba0saH6+JXEREpD3JmolcjDGjjTGR1eOrgKHAHGBCaN/E0LaIiIjEkO4q9FrgQmPMgYAXGAVcCtQBdxpj9sbpmX5VmtMlIiKSU9IawK21K3B6nUdzUSLX9Pv9rFu3jpqamsQTJnmjsLCQ/v374/V6M50UEZGUyqnVyKJZt24dnTt3ZvDgwRhjMp0cySBrLWVlZaxbt44hQ4ZkOjkiIimV8zOx1dTU0L17dwVvwRhD9+7dVRsjIu1CzgdwQMFbGuizICLtRU5VoVdUVDSM9548eTKTJ0/OcIpERESSa/bs2cyePTu82SXWcTlVAu/SpUvDYPdEg/cLn65n4sy3GHLtHCbOfIsXPl3f5nRt3LiRK664gltvvZUbb7yRU089lfvvvz/m8ZWVlVx44YWcd955Cd3vj3/8I4MHD27xuPvuu69V133vvfc46KCDeOedd5q899vf/hZjDNdffz033ngjP/jBD/jwww/ZsmUL5513Hn379mXGjBlce+21nHbaaWzZsoV3332Xo48+mkGDBlFXV7fH9a655hr69u3LI4880qo0iojku8mTJ0dO7FIR80Brbc78jBs3zjb21VdfNdkXy78+WWf3ueEVO+ialxt+9rnhFfuvT9bFfY3Gampq7Pjx4+3atWsb9m3atMkeccQRzZ739ttv23PPPTfh+w4aNCgpxzR27rnn2rfffjvqe4CtrKy01lr71ltv2YMPPtha6+Ql8t9m2rRpdurUqdZaa6dPn24PPvhg++CDDza8v2XLFnv00UfbaP+eydCaz4SISDYD5tsYMTGnqtBbctPsL/lqw46Y73+6Zjt1geAe+6r9Aa7+50Ke/mhN1HNG9y1m+uR9Y17z5ZdfZvDgwfTv379hX+/evXn++ecBeOGFF3jxxRcZOXIkX3zxBQ899BDFxcV7XKOyspIrr7ySoUOHsnnzZkpKSjjjjDP4+c9/znnnncfZZ5/d0HTwxBNP7HHuzp07+eEPf8iRRx7J0qVLOfvsszn++ON59tln2b59OzNmzGCfffbhRz/6EdOmTaO+vh63203nzp25+uqrAbj00kvx+/0MHTqUdevWxcxrpI0bN9KjR/QZ/jZu3Mjw4cMbtqdNm8bFF1/MhRdeSEFBAQ888AAXX3wxd955Z1z3EhGRpvIqgLekcfBuaX88li9fTp8+fZrs79mzJwDdunXjvvvuo0uXLtxzzz08+eST/PKXv9zj2Ntvv53hw4c3BNTHHnuMffbZh6OPPhoAn8/Heeed1yR4A7hcLi6//HKOP/54ysvLmTRpEscffzxnnnkmV199NTNmzADgtddeY+7cubz++usAHH300Zx44omsX7+eZcuW8corrwDw4osvNpvf22+/HZfLxa5du/ao/t6wYQMzZsygqqqK/v37c9lllzW8N2bMGCZMmMCsWbM488wzcbvdDX8fERFJTF4F8OZKygATZ77F+u3VTfb361rEMz+bEOWMlvXv35/m5mfv1KkTN998Mz169OCTTz5h332bpnHhwoVceOGFDdsXXHBB3Pe31vLOO+/w4Ycf4vV6KS0tjXrcwoULqaqqYubMmQAMGDCA0tJSvvzyS0aMGNFw3NChQ5u93/XXX0+nTp2a7A+3gccyffp0Jk2axJo1a7jmmmtYtGhRHLkTEZFYcqoTW1tNnTSSIq97j31FXjdTJ41M+Jo/+MEP+Prrr1m/fndnuKVLl/Ld734XgJ/+9Kd873vf47rrruOEE06Ieo0DDjiAFStWAE5A/vOf/wxA586d2bHDaRJYsyZ6Ff8jjzzChg0buPHGG7nyyiv3eM/tdmOt5dNPP+WAAw6gV69eXHvttVx77bWcf/75jBw5ktGjR/P11183nLNy5coE/xLNGz16NEceeSQ+ny9m1buIiMQvr0rgLfn+gf0AuOu1pWzYXk3frkVMnTSyYX8iOnTowCuvvMI999xDcXExdXV1bNq0iYceegiACy+8kFtuuYVjjjmGBQsWsG3bNr744guefPJJFi5cyAcffMB1113HlVdeyW233cb27ds5+eSTATj11FO5+OKLqa+vp7y8vOH4zz77jIqKCp5++mkmTZrEP//5T6ZOnUpJSQkVFRU899xznHbaaZxyyilcddVVBAIB7rvvPj766COuu+46PB4PNTU1zJw5kwEDBvDKK6/w05/+lAEDBmCt5cknn2TcuHF07ty5IZ/33HMP4PRGv+SSSxqqwEtLS3nyySfZuHEj99xzD1dccUXDOfPnz+fdd99l586d3HHHHTz11FNNznnqqaf48Y9/nPDfX0SkvcrocqKtNWLECHvMMccAu8eBL168mFGjRmU4ZZJN9JkQkVwWOQ784YcfXm6tHRHtuJwK4NHWA9fDWhrTZ0JE8kXWrAcuIiIiyaEALiIikoMUwEVERHKQAriIiEgOUgAXERHJQe0vgC98Fu4dAzO6Or8XPtumy3300UccffTRHH744cyYMYNf/vKXXHLJJQSDQZ599lkGDx7M2WefzbRp0zj77LN59NFHAbj55psxxvCnP/1pj+tt3bqVTp06cfbZZ7N8+fKE07VmzRrOO+88evTowerVqxv233rrrYwdO5aXXnqp2fPHjx9PIBBI+P51dXVcdtlldOjQgRkzZjT8HHbYYSxatIhjjz026tSwIiISp1irnGTjz/Dhw+1FF11kL7roIvvSSy9Za1u58tTnz1h7a29rpxfv/rm1t7O/DaZPn26vvPLKhu0jjzzSzpkzx1pr7VFHHWVnz55trbW2urraFhUV2R07dlhrrR03bpwdOHCgrampaTh3xowZdvTo0Q3nxPL444/bxx9/vNlj3n77bXvEEUfYcePG2erq6ob98ayCFgwGWzymJatWrbLdu3ffY9+zzz5rrXX+Zi2lP1FajUxEctlLL73UEOuAZTYfViMLrwce0yvXwqYvYr+/7mMI1O65z18NL/4KFvwl+jl99oOTZ8adxvr6erZu3Rp1utCtW7dSVFREQUEB4CzyUVVVxSOPPMIvf/lLtmzZgjEmqQt9XHjhhbz//vtcfPHFPPbYY03ev+WWW/D7/QSDQXw+H9OmTeOll17i0ksv5Z133qF379786le/YsiQIWzZsoUjjzyS008/nX/961+8+uqrDB06lNWrV3PPPfdQWFjYbFp+9atf8cADDzTZv3jxYn73u9+x9957s2TJEqZOncqoUaOi7u/Tpw9nnXUW1lrGjh3Lq6++yk033cT3v//9ZP3JREQyKjxRGcDDDz8ccz3wnArgbdY4eLe0vxU++OADZsyYQVlZGb/5zW845JBDGt7729/+xkcffURZWRmvvPIKPp+v4b3p06dz8sknc+GFF/Lggw9yySWX8NZbb7U5PZEefPBBjj76aP785z/zs5/9rGH/a6+9xrx583j55ZcBOPnkk3n99df57ne/2zB16tKlS/nss8+466676NSpEwsXLmTbtm1cfPHFrFy5kqKiImbMmMGf//xnfv3rXze5965duxpWJluwYEHU9F1wwQXcf//9jB8/nnnz5nHhhRfywQcfxNx/7bXXcvXVV3PnnXdy+eWXt6mqX0QkV+VXAG+ppHzvGKhY23R/lwFw/pw23TrcBh7N2WefzXe+852o7+27774cfvjh3HTTTRQVFTW70MfWrVv5yU9+AtCweMrf//53AP7617/GPNfn8/Hcc89x+OGHM3bs2Ib9Cxcu3GP1seHDh/P5559z4oknNuwbO3Ysv/jFLzj99NPx+XzcfvvtLF++HGMMv//97wEoLy+PukIZQMeOHbnvvvsA58tANJHpCKehuf1Aw0xr0ZZyFRFpD/IrgLfkuGkw+1Kn2jzMW+Tsz6Dp06dzxBFH7LEqWDQ9evTg1VdfBWjoAHbeeefFdY+99tqLp59+mh//+MccccQRgLMK2jvvvNNwzLJlyxpWUQtbtWoVhx12GD/96U+ZM2cOM2bM4C9/+QuFhYVcddVVeDweVqxYwYYNG1pMw8iR0Vd9C6/GVlJSwrJlyxq+ZMTaD2CMiSvfIiL5qn0F8P3PdH6/eTNUrIMu/Z3gHd6fgPCKW3V1dQ2rgIU999xzrF69mmeeeYY+ffowfvzu6WzvueceFi5cyJtvvslxxx1HWVlZk3MOOOAABgwYkFC61q5dy5NPPsn27ds55JBDGD16NIcddhjXXXcd7733HgAnnngic+fO5brrrsNay4QJEzjhhBOYM2cOq1ev5k9/+hPnn38+t956KwceeCBr1qzhZz/7Gd26dePee+/l17/+Nb1792bdunXcfPPNe9zf7/fzwAMPUF1dza233srUqVMb2v4XLVrEu+++yxdffMEJJ5zAo48+ym9/+1tGjBjB0qVLeeSRRwCi7q+trW1Yya3x31tEpD3RYiaSd/SZEJF8ocVMRERE8kxOVaFXVFQwZcoUYM9u9iIiIvkicj1woEus4/KiCn2fffZRpyYBnImJlixZoip0EckLeVOFXlpa2mRfYWEhZWVl5NIXkbBo+cl1mcyTtZaysrIWJ5RpjWYnDspR+ZanfMsP5F+e8i0/kB15yqkSeMeOHe2uXbv22Of3+1m3bh01NTUZSlXiNm7cyF577ZXpZCRVpvNUWFhI//798Xq9Sbne+PHjaVzrk+vyLU/5lh/IvzzlW34gfXlqrgSeU23g0Xi9XoYMGZLpZCTknHPOybsPdT7mSUQkG6W1Ct0YM8wY87QxZqox5vfGmGmh/SXGmFnGmGuNMY8aY3on654RHQGy7pxEJHqffMtTNucn0Xtlc57yLT+J3iub85Rv+Un0XtmcpwTvE7MTW7rbwEuAv1tr77LW/hr4kTFmHHA78Ia1dibwAnB3sm6oD0Di52VznrI5P4neK5vzlG/5SfRe2ZynfMtPovfK5jwleJ+usd5IawC31n5srX2x0f13AacAH4b2vR/aFhERkRgy1onNGPMD4Ghr7a+NMbVAb2vtdmOMB/ADXmttfaNzaoBoS0+VAltj3KoLEHM5tgyf04PY6U7mfRI9L5vzlM35SfRe2ZynfMtPovfK5jzlW34SvVc256m5+/QAoq0l7bLWFkU7ISOd2IwxxwDHAJeFdm0BOgPbgWJgW+PgDWCtTd74IBERkRyW9gBujDkF+Bbwa2AvY8wgYA4wAVgLTAxti4iISAxprUIPdVj7LxAeZ9QReBB4CbgTWA0MA6611m5OW8JERERyTNZN5GKMGQbcCnwC9AfKrLU3xzj2x8Bfgc7W2p3pS2X84smPceaBvSS0ORjoaq29IJ3pbI048zQEZzTBx8BY4G/W2pfSnNS4GGNcwGxgHuDD+RJ5gbW2OuKYQpz8rAdGADOttc0v4J5BcebpGqAPsAkYB0yz1i7JQHJbFE9+Io7N+ucCxP1vlDPPhjjzkzPPhTBjTBFOnl631l7V6L3MPhestVn1AxwMfC9i+ytgXJTjRgG3ARbolOl0tyU/wDnA/4vY3j/T6U5Cnh4CLg+9PhBYlul0N5MfF3BDxPaLwI8bHXMtcHXo9X7A/zKd7iTk6RZ2f4n/ITA70+luS35C+3PiudCKf6OceTbEmZ+ceS5EpPl3wF+Au6O8l9HnQtbNhW5jDzVrYIzpAFwN3JTOtCUinvwAPwZKjDGXGmNuB7K21ABx52kzu3tU9gQWpCNtibDWBq21twKERkH0B5Y2OqxhqKO19gvgAGNMcVoT2grx5Mlae6MNPXlw/g2z9nMXT35y6bkAcX/ucubZEGd+cua5AGCMOQdnaPOqGIdk9LmQdQE8Umio2Wu2abXebcAt1tq6DCQrYc3kZxBQbK39A/AE8Koxxp3u9CWimTzdAxxqjLkHmAY8nvbEtZIxZhLwMvCytbbxfLC9gMqI7R2hfVmthTyFj/EB5wI3pDNtiWghP7n6XGguTzn3bGghPznzXDDGjAZGWWufb+awjD4XsjaARww1u7zR/gFAN+BMY8y1od1XGGOiTvaeLWLlJ2QHThsL1mk/KQYGpC91iWkhT08Aj1hrrwB+ADxjjClJY/JazVr7mrX2JGCIMebiRm+HhzqGFYf2ZbUW8hQO3g8Bv7HWrkh7AlspVn5y9bkALf4b5dyzoYX8PEHuPBd+ANSEPk9HAIcYYy5rdExGnwtZuZhJjKFmS4F6a+1a4LyIY+8A7rHZ3VmlufzsAN4EhoaOLQbcOB2LslYceRoAbAwdvg0IkqVfGEPftIdYa8PDF1cBQ0MPlnB+wkMd/2eM2Q/4PLQ/K8WTp1DnnD/itO19aYw5zVr7XKbS3Jw48pOLz4V4Pnc582yIMz8581yw1t4Wfh3qrNbJWntfNj0XsrEXeqyhZqOBcuvMl44xpifwM5yOOLcAf7bWrk9/ipsXT36MMV2A37J7GN1z1tp/ZyK98YgzT0fgTNTzCTAEWGCt/VMGktuiUK/6u3DS6sXpCHUpcAW781OE09t0IzAcuN1mdy/0ePL0PDAG2BA6raO19uBMpLcl8eQndFxOPBcg7n+jnHk2xJmfnHkuhBljTgN+idOz/kGczndZ8VzIugAuIiIiLcvKqgsRERFpngK4iIhIDlIAFxERyUEK4CIiIjlIAVxERCQHKYCLiIjkIAVwERGRHKQALiIZ1da5vbN9bnCRVFEAF8kQY8w9xphtxpifhLZHG2M2htaAxhhzjTHmdWPM4CTc68fGmG1tvU6Ma18W8foUY8yqeNJsjOlgjLkPaOtc2P2NMb8NrYAl0m4ogItkzlSgHngjtH0yUIWz3jrAEuBma+03bb2RtfYpoKKt14nhsoj7zMGZ9jMefwCet9aWtuXm1trVwFxgRluuI5JrNJWqSAYZY54C3rLWPhpaYrECcFtrp4UW5LgBKAKeAd4FRgJ/A94J/R4JnIPzReAvOEtqHoSzUFEAqLTW/jZ0r2+stYMj7n1z5HFAKXAHcC/OAhqjgO+EF2cwxvwBZ47r9ThfNpYBb+EE4vuAJdbavxtj3gH+HXGNyY0XeDDGdAqd3ze8Jrkxpi/Q21r7qTFmArAY2A84PZTfOuAk4HVCpXZr7V9C5xYBK4F+1tpgq/4RRHKUSuAimfUy8J3QohXbcFY3+k7oPWOtDeCs2HSvtfZO4CrgDmttPXAR0AX4GvgGZ4GZauAwa+311tobgW8bY8Y2vmlozeY9jgM+xSn1f2at/RnwBXBC6PhTgBHW2l9Ya28FLPCEtfb/gO3W2hnW2r9H3OJTa+3PI6/RyDCcBSEiSxA/BjqEXl8N1IbuA7AhVLrfH/gg9Hc7MHyitbYaZ6Wu3lHuJZKX1GYkklmv4izpORmnZLkA6GOMOQkn+AEY4OhQqdQP9ASw1lYYY14BfgQUAE8BxwMdItbEXhs+vpH9mzkuvJpSKbvXOt4Xp8QctrKFfC0P/d7KnuslhxXg1BpEOgi4N7RGuSsUlN8zxlxnrZ0XKmWXWWvLjDHfZvdqeGF+nNoKkXZBAVwkg6y124wxXwC/Ag631tpQUL4bOCp02E9xqpovMMZ4gZ9HXOIB4AlgvrX2z6HlNCdELK95LLuDaaTPmzkuWrvaV8CxEdtDI14HQh3vxlprP23mGpHWEtF5LdQBrY+1tj60fOM8Y8xwnKVOq0KHHQx8HHp9CjDTGHNgqMrdAJ3YvTSqSN5TFbpI5r0MLI1ou50DVFhry0LbrwEjjDF34VShdwkFOay1X+K0X78d2n4d+MQYc4cx5hactur1xpgfh877eTPHjQIGARcYY4YCRwKTQ18K5gArjDEPh0rtkdXbc3C+cJxrjDmhmWs0sNZuDKWrf2jXeGBzqKp+B7AJ5/m0L/C/0DH7hfOJ01HuRGBhaPsA4B1rbU28f3SRXKdObCISF2PM4dbaD0Kv3wTOt9auacP19gcuwalRuBp4w1r7cfNnRb1OIfAwcGMyeuyL5AoFcBGJizHmP8CbOG3aW6y1v0/CNffCaQu/A/iFtdafwDX6AdXW2vK2pkcklyiAi4iI5CC1gYuIiOQgBXAREZEcpAAuIiKSgxTARUREcpACuIiISA5SABcREclBCuAiIiI5SAFcREQkB/1/5MRvyNmopRAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(wave, ppm, marker='o', label='Calculated PPM')\n",
    "plt.plot(wave, ppm_floor, marker='o', label='PPM + Noise Floor')\n",
    "plt.xlabel('Wavelength ($\\mu m$)')\n",
    "plt.ylabel('Noise Limit (PPM)')\n",
    "plt.xlim([2.4,4.1])\n",
    "plt.ylim([20,100])\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 5: Extended Souce\n",
    "\n",
    "Expect some faint galaxies of 25 ABMag/arcsec^2 in our field. What is the best we can do with 10,000 seconds of acquisition time? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Detection bandpass is F200W\n",
    "nrc = pynrc.NIRCam(filter='F200W')\n",
    "\n",
    "# Flat spectrum (in photlam) with ABMag = 25 in the NIRCam bandpass\n",
    "sp = pynrc.stellar_spectrum('flat', 25, 'abmag', nrc.bandpass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BRIGHT1\n",
      "BRIGHT2\n",
      "DEEP2\n",
      "DEEP8\n",
      "MEDIUM2\n",
      "MEDIUM8\n",
      "RAPID\n",
      "SHALLOW2\n",
      "SHALLOW4\n",
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "MEDIUM8      10    9   1052.20   9469.83   9555.73     10.5    0.020    0.107\n",
      "MEDIUM8      10   10   1052.20  10522.03  10618.67     11.1    0.020    0.107\n",
      "DEEP8         8    5   1589.04   7945.21   7988.16      9.5    0.031    0.106\n",
      "DEEP8         7    6   1374.31   8245.84   8299.53      9.7    0.026    0.106\n",
      "MEDIUM8       8   11    837.47   9212.15   9319.52     10.2    0.016    0.106\n",
      "DEEP8         6    8   1159.57   9276.57   9351.73     10.3    0.022    0.106\n",
      "MEDIUM8       9   10    944.84   9448.36   9544.99     10.4    0.018    0.106\n",
      "DEEP8         8    6   1589.04   9534.25   9587.94     10.4    0.031    0.106\n",
      "...         ...  ...       ...       ...       ...      ...      ...      ...\n",
      "BRIGHT1      10   49    204.00   9995.93  10511.30      6.6    0.004    0.064\n",
      "BRIGHT1      10   50    204.00  10199.93  10726.04      6.7    0.004    0.064\n",
      "BRIGHT1      10   51    204.00  10403.93  10940.77      6.8    0.004    0.064\n",
      "RAPID        10   91    107.37   9770.46  10736.78      4.9    0.002    0.047\n",
      "RAPID        10   92    107.37   9877.83  10854.88      5.0    0.002    0.047\n",
      "RAPID        10   93    107.37   9985.20  10972.98      5.0    0.002    0.047\n",
      "RAPID        10   94    107.37  10092.56  11091.09      5.0    0.002    0.047\n",
      "RAPID        10   95    107.37  10199.93  11209.19      5.0    0.002    0.047\n",
      "Length = 66 rows\n"
     ]
    }
   ],
   "source": [
    "res = nrc.ramp_optimize(sp, is_extended=True, tacq_max=10000, tacq_frac=0.05, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Pattern   NGRP NINT   t_int     t_exp     t_acq     SNR      Well     eff   \n",
      "---------- ---- ---- --------- --------- --------- -------- -------- --------\n",
      "RAPID        10   91    107.37   9770.46  10736.78      4.9    0.002    0.047\n",
      "RAPID        10   92    107.37   9877.83  10854.88      5.0    0.002    0.047\n",
      "BRIGHT1      10   47    204.00   9587.94  10081.83      6.5    0.004    0.064\n",
      "BRIGHT1      10   48    204.00   9791.93  10296.57      6.6    0.004    0.064\n",
      "BRIGHT2      10   45    214.74   9663.09  10135.52      7.8    0.004    0.077\n",
      "BRIGHT2      10   46    214.74   9877.83  10360.99      7.9    0.004    0.077\n",
      "SHALLOW2     10   18    504.63   9083.31   9265.84      9.3    0.010    0.096\n",
      "SHALLOW2     10   19    504.63   9587.94   9781.20      9.6    0.010    0.096\n",
      "SHALLOW4     10   18    526.10   9469.83   9652.36     10.1    0.010    0.102\n",
      "SHALLOW4     10   19    526.10   9995.93  10189.20     10.3    0.010    0.102\n",
      "MEDIUM2      10    9    987.78   8890.05   8975.94      9.8    0.019    0.103\n",
      "MEDIUM2      10   10    987.78   9877.83   9974.46     10.4    0.019    0.103\n",
      "MEDIUM8      10    9   1052.20   9469.83   9555.73     10.5    0.020    0.107\n",
      "MEDIUM8      10   10   1052.20  10522.03  10618.67     11.1    0.020    0.107\n",
      "DEEP2         9    5   1739.36   8696.78   8739.74      9.6    0.033    0.103\n",
      "DEEP2         8    6   1524.62   9147.73   9201.42      9.9    0.029    0.103\n",
      "DEEP8         8    5   1589.04   7945.21   7988.16      9.5    0.031    0.106\n",
      "DEEP8         7    6   1374.31   8245.84   8299.53      9.7    0.026    0.106\n"
     ]
    }
   ],
   "source": [
    "# Print the Top 2 settings for each readout pattern\n",
    "res2 = table_filter(res, 2)\n",
    "print(res2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Ramp Settings\n",
      "  read_mode  :  MEDIUM8\n",
      "  nf         :        8\n",
      "  nd2        :        2\n",
      "  ngroup     :       10\n",
      "  nint       :       10\n",
      "New Detector Settings\n",
      "  wind_mode  :     FULL\n",
      "  xpix       :     2048\n",
      "  ypix       :     2048\n",
      "  x0         :        0\n",
      "  y0         :        0\n",
      "New Ramp Times\n",
      "  t_group    :  107.368\n",
      "  t_frame    :   10.737\n",
      "  t_int      : 1052.203\n",
      "  t_int_tot1 : 1052.203\n",
      "  t_int_tot2 : 1062.940\n",
      "  t_exp      : 10522.035\n",
      "  t_acq      : 10618.671\n"
     ]
    }
   ],
   "source": [
    "# MEDIUM8 10 10 looks like a good option\n",
    "nrc.update_detectors(read_mode='MEDIUM8', ngroup=10, nint=10, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate flux/mag for various nsigma detection limits\n",
    "tbl = Table(names=('Sigma', 'Point (nJy)',    'Extended (nJy/asec^2)', \n",
    "                            'Point (AB Mag)', 'Extended (AB Mag/asec^2)'))\n",
    "tbl['Sigma'].format = '.0f'\n",
    "for k in tbl.keys()[1:]:\n",
    "    tbl[k].format = '.2f'\n",
    "\n",
    "for sig in [1,3,5,10]:\n",
    "    snr_dict1 = nrc.sensitivity(nsig=sig, units='nJy', verbose=False)\n",
    "    snr_dict2 = nrc.sensitivity(nsig=sig, units='abmag', verbose=False)\n",
    "    tbl.add_row([sig, snr_dict1[0]['sensitivity'], snr_dict1[1]['sensitivity'],\n",
    "                snr_dict2[0]['sensitivity'], snr_dict2[1]['sensitivity']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<i>Table length=4</i>\n",
       "<table id=\"table140475655531152\" class=\"table-striped table-bordered table-condensed\">\n",
       "<thead><tr><th>Sigma</th><th>Point (nJy)</th><th>Extended (nJy/asec^2)</th><th>Point (AB Mag)</th><th>Extended (AB Mag/asec^2)</th></tr></thead>\n",
       "<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
       "<tr><td>1</td><td>1.10</td><td>32.05</td><td>31.30</td><td>27.64</td></tr>\n",
       "<tr><td>3</td><td>3.30</td><td>96.57</td><td>30.10</td><td>26.44</td></tr>\n",
       "<tr><td>5</td><td>5.52</td><td>161.67</td><td>29.55</td><td>25.88</td></tr>\n",
       "<tr><td>10</td><td>11.14</td><td>326.99</td><td>28.78</td><td>25.11</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Table length=4>\n",
       " Sigma  Point (nJy) Extended (nJy/asec^2) Point (AB Mag) Extended (AB Mag/asec^2)\n",
       "float64   float64          float64           float64             float64         \n",
       "------- ----------- --------------------- -------------- ------------------------\n",
       "      1        1.10                 32.05          31.30                    27.64\n",
       "      3        3.30                 96.57          30.10                    26.44\n",
       "      5        5.52                161.67          29.55                    25.88\n",
       "     10       11.14                326.99          28.78                    25.11"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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